diff --git a/AI_Data_Storytelling_Engine.py b/AI_Data_Storytelling_Engine.py new file mode 100644 index 0000000..6d8458f --- /dev/null +++ b/AI_Data_Storytelling_Engine.py @@ -0,0 +1,355 @@ + +import dash +from dash import dcc, html, Input, Output, State +import plotly.express as px +import plotly.graph_objects as go +import numpy as np +import pandas as pd +from datetime import datetime + + +def generate_story_data(seed: int = 7) -> dict: + """Generate a compact but expressive dataset used across scenes.""" + rng = np.random.default_rng(seed) + + # Time series for 24 months with seasonality and trend + months = pd.date_range('2023-01-01', periods=24, freq='MS') + t = np.arange(len(months)) + season = 0.35 * np.sin(2 * np.pi * t / 12) + 0.15 * np.cos(2 * np.pi * t / 6) + trend = 0.02 * t + noise = rng.normal(0, 0.08, len(t)) + value = (1.0 + season + trend + noise).clip(min=0.25) + ts = pd.DataFrame({ + 'month': months, + 'month_name': months.strftime('%b %Y'), + 'value': value, + 'theta_deg': (months.month - 1) * (360 / 12), + 'radius': 0.5 + 0.5 * (value - value.min()) / (value.max() - value.min()) + }) + + # 3D clusters (four clusters in a loose spiral shell) + num_points = 220 + cluster_ids = rng.integers(0, 4, size=num_points) + centers = np.array([ + [ 2.5, 0.5, 0.0], + [-2.0, -1.0, 0.8], + [ 0.0, 2.3, -0.6], + [ 1.5, -2.2, 1.2], + ]) + pts = centers[cluster_ids] + rng.normal(0, 0.6, size=(num_points, 3)) + weights = (rng.random(num_points) ** 2) * 15 + 5 + clusters = pd.DataFrame({ + 'x': pts[:, 0], + 'y': pts[:, 1], + 'z': pts[:, 2], + 'cluster': pd.Categorical(cluster_ids), + 'weight': weights + }) + + # Flow/funnel proportions across three stages, varying by segment + segments = ['Organic', 'Paid', 'Referral', 'Social'] + stage_a = rng.dirichlet(np.ones(len(segments)) * 2.0) + # Transition matrices + a_to_b = rng.dirichlet(np.ones(len(segments)) * 1.6, size=len(segments)) + b_to_c = rng.dirichlet(np.ones(len(segments)) * 1.4, size=len(segments)) + + # Expand into Sankey nodes and links + nodes = [f"A:{s}" for s in segments] + [f"B:{s}" for s in segments] + [f"C:{s}" for s in segments] + node_index = {n: i for i, n in enumerate(nodes)} + + links_src, links_tgt, links_val, links_lbl = [], [], [], [] + total = 1000 + for i, s in enumerate(segments): + val_a = stage_a[i] * total + # A -> B + for j, s2 in enumerate(segments): + v_ab = val_a * a_to_b[i, j] + links_src.append(node_index[f"A:{s}"]) + links_tgt.append(node_index[f"B:{s2}"]) + links_val.append(v_ab) + links_lbl.append(f"{s} → {s2}") + # B -> C + # Need intermediate B totals per segment + b_totals = np.zeros(len(segments)) + for i, s in enumerate(segments): + val_a = stage_a[i] * total + b_totals += val_a * a_to_b[i] + for i, s in enumerate(segments): + val_b = b_totals[i] + for j, s3 in enumerate(segments): + v_bc = val_b * b_to_c[i, j] + links_src.append(node_index[f"B:{s}"]) + links_tgt.append(node_index[f"C:{s3}"]) + links_val.append(v_bc) + links_lbl.append(f"{s} → {s3}") + + sankey = { + 'nodes': nodes, + 'links': { + 'source': links_src, + 'target': links_tgt, + 'value': links_val, + 'label': links_lbl, + } + } + + return {'time': ts, 'clusters': clusters, 'sankey': sankey, 'segments': segments} + + +DATA = generate_story_data() + + +def scene_title(idx: int) -> str: + return [ + 'Scene 1 · Harmonic Time Spiral', + 'Scene 2 · 3D Cluster Bloom', + 'Scene 3 · Flow of Attention', + 'Scene 4 · Morph: Spiral ↔ Bars', + 'Scene 5 · Constellation Radar Glyphs' + ][idx] + + +def scene_insights(idx: int) -> list: + if idx == 0: + ts = DATA['time'] + peak_row = ts.loc[ts['value'].idxmax()] + yoy = (ts.loc[12:, 'value'].values - ts.loc[:11, 'value'].values).mean() + return [ + f"Peak month: {peak_row['month'].strftime('%b %Y')}", + f"Avg YoY monthly delta (approx): {yoy:+.2f}", + f"Seasonal amplitude: {(ts['value'].max()-ts['value'].min()):.2f}" + ] + if idx == 1: + cl = DATA['clusters'] + sizes = cl.groupby('cluster')['weight'].sum().sort_values(ascending=False) + return [ + f"Largest cluster by weight: {sizes.index[0]}", + f"Cluster spread (x,y,z std): {cl[['x','y','z']].std().mean():.2f}", + f"Points: {len(cl)}" + ] + if idx == 2: + sank = DATA['sankey'] + total = sum(sank['links']['value']) + return [ + f"Total flow value: {int(total)}", + "Two-stage transitions show segment mixing", + "Downstream distribution highlights stickiest segments" + ] + if idx == 3: + ts = DATA['time'] + return [ + "Use the slider to morph geometry", + f"Range of values: {ts['value'].min():.2f}–{ts['value'].max():.2f}", + "Spiral reveals seasonality; bars emphasize absolute deltas" + ] + # idx == 4 + ts = DATA['time'] + # Compute simple quarterly features + q = ts.copy() + q['quarter'] = q['month'].dt.to_period('Q') + agg = q.groupby('quarter')['value'].agg(['mean','std']).reset_index() + return [ + f"Quarters analyzed: {len(agg)}", + f"Median volatility (std): {agg['std'].median():.2f}", + "Radar shows balance of stability vs growth" + ] + + +def figure_scene(idx: int, morph: float = 0.0) -> go.Figure: + if idx == 0: + # Polar spiral + ts = DATA['time'] + fig = go.Figure() + fig.add_trace(go.Scatterpolar( + r=ts['radius'], + theta=ts['theta_deg'], + mode='lines+markers', + line=dict(color='#7F7EFF', width=3), + marker=dict(size=6, color=ts['radius'], colorscale='Viridis'), + name='Seasonal trend' + )) + fig.update_layout( + template='plotly_dark', + polar=dict( + radialaxis=dict(visible=False), + angularaxis=dict(direction='clockwise') + ), + margin=dict(l=40, r=40, t=40, b=40), + height=520 + ) + return fig + + if idx == 1: + # 3D clusters + cl = DATA['clusters'] + fig = go.Figure() + for key, grp in cl.groupby('cluster'): + fig.add_trace(go.Scatter3d( + x=grp['x'], y=grp['y'], z=grp['z'], + mode='markers', + marker=dict(size=np.clip(grp['weight']/6, 3, 14), color=key, colorscale='Turbo', opacity=0.85), + name=f'Cluster {key}' + )) + fig.update_layout( + template='plotly_dark', + scene=dict( + xaxis=dict(visible=False), + yaxis=dict(visible=False), + zaxis=dict(visible=False) + ), + margin=dict(l=0, r=0, t=20, b=0), + height=520 + ) + return fig + + if idx == 2: + # Sankey flow + sank = DATA['sankey'] + labels = sank['nodes'] + fig = go.Figure(data=[go.Sankey( + node=dict( + pad=15, thickness=16, + line=dict(color='rgba(255,255,255,0.25)', width=1), + label=labels, color='rgba(127,127,255,0.6)' + ), + link=dict( + source=sank['links']['source'], + target=sank['links']['target'], + value=sank['links']['value'], + label=sank['links']['label'] + ) + )]) + fig.update_layout(template='plotly_dark', margin=dict(l=20, r=20, t=20, b=20), height=520) + return fig + + if idx == 3: + # Morph between polar spiral (morph=0) and bar chart (morph=1) + ts = DATA['time'] + # Interpolate radius to cartesian y via morph + y_bar = (ts['value'] - ts['value'].min()) / (ts['value'].max() - ts['value'].min()) + r = (1 - morph) * ts['radius'] + morph * (0.5 + 0.5 * y_bar) + fig = go.Figure() + if morph < 0.999: + fig.add_trace(go.Scatterpolar( + r=r, + theta=ts['theta_deg'], + mode='lines+markers', + line=dict(color='#00E3AE', width=3), + marker=dict(size=6, color=r, colorscale='Mint'), + name='Spiral form' + )) + fig.update_layout( + template='plotly_dark', + polar=dict(radialaxis=dict(visible=False)), + margin=dict(l=40, r=40, t=40, b=40), + height=520 + ) + if morph > 0.001: + # Overlay bar chart projection + fig2 = go.Figure() + fig2.add_trace(go.Bar(x=ts['month_name'], y=y_bar, marker_color='#7F7EFF', name='Bars')) + fig2.update_layout(template='plotly_dark', xaxis_tickangle=-45) + # Merge as images for simplicity + return fig2 + return fig + + # idx == 4: Radar glyphs for quarterly features + ts = DATA['time'] + q = ts.copy() + q['quarter'] = q['month'].dt.to_period('Q') + dfq = q.groupby('quarter')['value'].agg(['mean', 'std']).reset_index() + # Derived features scaled 0-1 + dfq['growth'] = (dfq['mean'] - dfq['mean'].min()) / (dfq['mean'].max() - dfq['mean'].min() + 1e-9) + dfq['volatility'] = (dfq['std'] - dfq['std'].min()) / (dfq['std'].max() - dfq['std'].min() + 1e-9) + dfq['stability'] = 1 - dfq['volatility'] + dfq['momentum'] = dfq['growth'].rolling(2, min_periods=1).mean() + features = ['growth', 'stability', 'momentum', 'volatility'] + fig = go.Figure() + for i, row in dfq.iterrows(): + vals = [row[f] for f in features] + [row[features[0]]] + fig.add_trace(go.Scatterpolar( + r=vals, + theta=features + [features[0]], + fill='toself', + name=str(row['quarter']), + opacity=0.45 + )) + fig.update_layout(template='plotly_dark', margin=dict(l=20, r=20, t=20, b=20), height=520, polar=dict(radialaxis=dict(range=[0,1]))) + return fig + + +app = dash.Dash(__name__) +app.title = '🎭 AI Data Storytelling Engine' + + +app.layout = html.Div([ + html.Div([ + html.H1('🎭 AI Data Storytelling Engine', style={'margin': '0'}), + html.P('A narrated, multi-scene tour through your data', style={'opacity': 0.8}) + ], style={'textAlign': 'center', 'padding': '20px 10px'}), + + html.Div([ + html.Button('◀ Previous', id='prev-btn'), + html.Span(id='scene-title', style={'padding': '0 16px', 'fontWeight': 700}), + html.Button('Next ▶', id='next-btn') + ], style={'display': 'flex', 'justifyContent': 'center', 'alignItems': 'center', 'gap': '10px'}), + + dcc.Store(id='scene-idx', data=0), + + html.Div([ + html.Label('Morph (Spiral ↔ Bars)', style={'marginRight': '10px'}), + dcc.Slider(id='morph', min=0, max=1, step=0.01, value=0, tooltip={'always_visible': False}) + ], id='morph-container', style={'maxWidth': 900, 'margin': '12px auto', 'display': 'none'}), + + html.Div([ + dcc.Graph(id='scene-figure') + ], style={'padding': '10px 10px 0'}), + + html.Div([ + html.Div('Insights', style={'fontWeight': 700, 'marginBottom': '6px'}), + html.Ul(id='insight-list', style={'margin': 0}) + ], style={'maxWidth': 900, 'margin': '0 auto', 'padding': '10px 20px 30px', 'background': '#111', 'borderRadius': '12px'}), +]) + + +@app.callback( + Output('scene-idx', 'data'), + Input('prev-btn', 'n_clicks'), + Input('next-btn', 'n_clicks'), + State('scene-idx', 'data'), + prevent_initial_call=True, +) +def switch_scene(prev_clicks, next_clicks, idx): + ctx = dash.callback_context + if not ctx.triggered: + return idx + trig = ctx.triggered[0]['prop_id'].split('.')[0] + if trig == 'next-btn': + return (idx + 1) % 5 + if trig == 'prev-btn': + return (idx - 1) % 5 + return idx + + +@app.callback( + Output('scene-figure', 'figure'), + Output('scene-title', 'children'), + Output('insight-list', 'children'), + Output('morph-container', 'style'), + Input('scene-idx', 'data'), + Input('morph', 'value') +) +def render_scene(idx, morph): + fig = figure_scene(idx, morph or 0.0) + title = scene_title(idx) + insights = [html.Li(txt) for txt in scene_insights(idx)] + morph_style = {'maxWidth': 900, 'margin': '12px auto', 'display': 'block'} if idx == 3 else {'display': 'none'} + return fig, title, insights, morph_style + + +if __name__ == '__main__': + print('🚀 Starting AI Data Storytelling Engine...') + print('📍 http://127.0.0.1:8060') + app.run_server(debug=False, host='127.0.0.1', port=8060) + + diff --git a/Animated_Plot.ipynb b/Animated_Plot.ipynb index e69de29..21c2679 100644 --- a/Animated_Plot.ipynb +++ b/Animated_Plot.ipynb @@ -0,0 +1,10 @@ +{ + "cells": [], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Swarm_plot.ipynb b/Swarm_plot.ipynb new file mode 100644 index 0000000..70852fb --- /dev/null +++ b/Swarm_plot.ipynb @@ -0,0 +1,75 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "3393e76e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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zkt5oS4HOPffciINw3VXc/3bxnF+87rvvPttlP9b1RGrp6+/jH/+4+az7oYTbB0bp7tu6vEprZFKdWGhbW33O6P8jdDZPaS1VJLr5okq0jS+AuSGxADBn2pde9xuwK5LW9eXvf//7zb+1ZiB4h2UrWfjxj39slthYm8JZ39N3uK2kI7i+wioE1YJx/xoLHWBpb/tkr8t3so985CPmsy6h8v9d6Lv6+rdRuhmcP+td7OCN3GJhDZY18YuVzibpZn+6dM1aLx+J9ffVpS/+f0t9x1oL9e3Ec01aIK8JhmX//v1mTxT1oQ99KOr9dRme7gXy6KOPmuevv+9///tmkzedrdAZtbmcXzx0WaD+7f0TFl129tvf/tYk8FqPEAtddqRF8jojpLVNOkPgTx//xhtvNJvSaSxrbKZ6yZcuW9QZK63p0P1CdBbC2swynKefftr2/xkA5gfF2wASGtToZnLf+ta3zGBQ38XUwcDhw4flqaeeMoNCfbdcd9UOpi/8X/va18zaeV3e4P8usZV06Pd0F2BrQzeLboKl6/a1IFx/rn9dhs6M6IZxf/rTn8QN9J1mXWKkg8nVq1ebpUb6jr6+K6+1EDoQ043y/GmipwXyOsjXrkJW7YQuOYnmda97ndksUH/X0bpWzZUOhv/rv/7LdJLSDkO64aF2CNJdv/V6dPAYvARIl9DpLt06qNfZsBNPPNE8p/Ra9cOimxtq0qrPS/1d6XNUf1daA6RLtq666qqYuhZpMbp2JtLkWZMLfR7qxmy6UZ0uGdPZDP96nljPL176N9AEW/8eulGedl3SJYT6HNAkJ1wxtR39netslN5PZzr0fHWZoSYbumGdJq66TExnjfRvMh/092vtCq5JRvAbFMH/P9Lfr/4/I9JyKQCpw4wFgDnT7j6607AOyHQNvNY+/P73vzfvLOvgSXe31uJru7Xm+g6pNUgIXupk1VnYfc+iicO///u/m3cxdVD97LPPmg5EWvjrpsJNHZzeeeedJsHTwZTuoKzLY3RA/oMf/MD8PYILanXwrIM1HXTq4F1nBvQjFpoQ6iBa24DqAD0VdPmLzoho4qM/Q69PZ8C++tWvRqwB0HPSxENrbLSDkF5T8LI4fZ5qIqaPrc8V/X3pDvKaHFszZLHQZFjPUQvRNZHW37POEmks6ODWfxlUPOcXL02QdNCvSYBeiyZdmjzp3zVSkbMdfZ7o80gf79prrzWF0H/84x9N0qIxrDOUugROZzTmiz7XrOQo2vVobY6+GRFtuRSA1MnRzSxS+PgAgCyjg09dNqODaf/lPk6mBdXa/UgTI6uFMZxPkxx9EyKWv5sWoOvyNC3Qj9QuGEDqMGMBAIh7eYoWjWshL5BK2hlM6V44kejMj87UaZtikgogfUgsAABx0doBXZakS37+8pe/pPt0kGV0c0xtwqA1I9rZ7NWvfnXU2hctvte9bj796U/P23kCCMVSKABA1mMpVObQWhct4NdN+8477zxTKxRuh3EAzkJiAQAAACBhLIUCAAAAkDASCwAAAAAJI7EAAAAAkDASCwAAAAAJI7EAAAAAkDASCwAAAAAJI7EAAAAAkDASCwAAAAAJyxeXmZ6elra2NrOjZ05OTrpPBwAAAHAs3Ut7cHBQFi9eLLm5keckXJdYaFLR3Nyc7tMAAAAAMkZLS4s0NTVFvI3rEgudqbB+OZWVlek+HQAAAMCxPB6PeVPeGkNH4rrEwlr+pEmFmxILncYaHR2V4uJiloABRxEXgD1iAwjl9rjIieGaKd52kbGxsXSfAuA4xAVgj9gAQhEXkZFYAAAAAEgYiQUAAACAhJFYuEhhYWG6TwFwHOICsEdsAKGIi8hcV7ztVlpwU1pamu7TAByFuADsERtAKOIiOmYsXNTJYGRkxHwGMIO4AOwRG0Ao4iI6EgsXGR8fT/cpAI5DXAD2iA0gFHERGYkFAAAAgOxKLG666SbZsGHD7OZ1Z555ptx9992z39dNST70oQ9JXV2dlJeXy9VXXy1HjhxJ6zkDAAAAcFhi0dTUJP/5n/8pzz77rGzdulUuuugiufLKK+Xll1823//4xz8uf/7zn+V3v/udPPTQQ9LW1iZXXXVVuk87YxQVFaX7FADHIS4Ae8QGEIq4iCzH5/AKlNraWvnmN78pb37zm6WhoUF+/etfm3+rHTt2yLp16+SJJ56QM844I6bH83g8UlVVJQMDA2ZWBAAAAEDiY2dHzVj4m5qakttuu02Gh4fNkiidxZiYmJBLLrlk9jZr166VpUuXmsQCkWn+ODQ0RCcDwA9xAdgjNoBQxEUG7mPx4osvmkRC6ym0juKOO+6QE044QZ5//nmzKUl1dXXA7RsbG6WjoyPs442NjZkP/6xL6ZPC/4mhvYntniipPD6fP1M/a2Kmn7PlmubruJPOhWtK/jVZcZFN1+Tk4046F64p8vHg2HDiObr97+Skc3HDNc11LJXjgHNP5HhGJxZr1qwxSYROt/z+97+Xd73rXaaeYq5uvPFGueGGG0KO6+NbvzxNWHTDE6/XG9BGTNfRlZSUmFmTycnJ2eN6TL83ODgo09PTs8fLysqkoKDAJC/+f5iKigrJzc01P9OfTivp/fVx/P+oelx/nv5ci95fp5/0/PQ8Lfn5+SYB00TMP4EKviYryy4uLs6aa8rGvxPXNP/XpHFh3TZbrikb/05c0/xek56j3l+P67VkwzVl49+Ja5rfa7LGUnpN+lhu+Tv54kg2HF9joUufVq1aJW95y1vk4osvlr6+voBZi2XLlsnHPvYxU9gd64xFc3Oz9Pf3B6wTy/asXD/rE12fxPqkzYZrmq/jTjoXrim512QNnDQu/AdPmXxNTj/upHPhmiIf19dJ/9hw4jm6/e/kpHNxwzXNdSyV44BzT+S4JiGx1lg4bsYimL7wa2Jw6qmnmkxqy5Ytps2s2rlzpxw6dMgsnQpHszK7Cn795fn/z9I6ZieVx+fzZ2pG6z94StXPTcfvMdXHnXQuyTrupHNJ1vG5PIYVF8GDp/k+93DHnXQuyTrupHNJ1nEnnUsyjuvgwi42nHSOcznupHNJ1nEnnUuyjjvpXJI1lspxwLkncjxWjkosPve5z8nll19uCrI1O9IOUH/729/k3nvvNZnS9ddfL5/4xCdMpyjNmD7ykY+YpCLWjlBupk8UWqQBgYgLZJOuvhHZdahfWjoHpXlBhRy/tFoaakrn9FjEBhCKuMiwxKKzs1OuvfZaaW9vN4mEbpanScWll15qvv+d73zHTD3pjIXOYlx22WXywx/+MN2nnRH03SdN1nStXKLZKJAtiAtkU1Lx6/t2ysDQzNLfXYf65NkdR+Stm9bMKbkgNoBQxEWGJRY/+clPIn5fC49/8IMfmA/Ez79IB8AM4gLZQGcqrKTC0j80Jrta+uc8a0FsAKGIC8nMfSwAAEBsdPmTncNH7I8DQCqQWAAAkOG0psJOU6P9cQBIBRILF9EexQACERfIBlqoXV0eWFSqXx/fHLipbDyIDSAUcZFBNRZIHS0y0na9AI4hLpAttI5CC7W1pkKXP+lMhSYViXSFIjaAQMRFdCQWLupkoJsDapteOhkAM4gLZBNNIuaaSAQjNoBQxEV0LIVyEbvdFAG3Iy4Ae8QGEIq4iIzEAgAAAEDCSCwAAAAAJIzEwkV0p0gAgYgLwB6xAYQiLiIjsXAJLTLKzc2l2AjwQ1wA9ogNIBRxER2JhYuKjQYGBig6AvwQF4A9YgMIRVxER2IBAAAAIGEkFgAAAAASRmIBAAAAIGEkFi6hhUZVVVUUHAF+iAvAHrEBhCIuoiOxcAktNJqenqbgCPBDXAD2iA0gFHERHYmFiwwODqb7FADHIS4Ae8QGEIq4iIzEAgAAAEDCSCwAAAAAJIzEwkUoNgJCEReAPWIDCEVcRJYf5fvIsk4GAI4hLgB7xAYQiriIjhkLl9AOBhMTE3QyAPwQF4A9YgMIRVxER2LhIsPDw+k+BcBxiAvAHrEBhCIuImMpFAAAADJaV9+I7DrULy2dg9K8oEKOX1otDTWlSbs9YkNiAQAAgIylScKv79spA0Nj5utdh/rk2R1H5K2b1tgmC/HeHrFjKZSL5Oby5waCEReAPWIDmUJnHqwkwdI/NCa7WvqTcnt/xEVkzFi4qJNBZWVluk8DcBTiArBHbCCT6HImO4ePDCbl9hbiIjrSLpfQDgZjY2N0MgD8EBeAPWIDmURrJOw0NVYk5fYW4iI6EgsX8Xq96T4FwHGIC8AesYFMoYXX1eVFAcf06+Obq5Nye3/ERWQshQIAAEDG0oJrLbzWGgldzqQzD5okhCvEjvf2iB2JBQAAADKaJgXxJAbx3h6xYSmUi+Tnk0cCwYgLwB6xAYQiLiLjt+MS2smgvLw83acBOApxAdgjNoBQxEV0JBYuoR0MRkdHpbi42AQGAOICCIfYgNvEshM3cREdiYWLaIs0DQYAxxAXgD1iA24Rz07cxEVk1FgAAADAtRLZiRuBmLEAAACAa811J+50L81yIhILFyksLEz3KQCOQ1wA9ogNZJq5Dsb1trr8KZaduOcjLrriWJrlNCyFcgktMiotLaXYCPBDXAD2iA1kGmswvmXrITMQ18+33rfTHI8m1p245ysudmXw0ixmLFxCOxnoNvQlJSW8UABHEReAPWIDmSbSYDzau/yx7sQ9X3HR4sClWbEisXCR8fFxEwwAjiEuAHvEBjJJooPxWHfino+4aI5jaZbTsBQKAAAAGU0H48HGJ6akvLRQ7nx4rzy2rS2mZVFOcHyMS7OciBkLAAAAZDQdjGuBsy5/spKK0fEpyc3NkWd3dJljmVIA3RDj0iwnIrFwkaKiwOwXAHEBhENsIJMED8Z1pkKTim27Z5KKeGounBAXDTEuzXIaEguX0CIj1soCgYgLwB6xgUzkPxi/76kD0nJkSIoL88zsRTIKoImL6KixcAntZDA0NGQ+A5hBXAD2iA1kMq2lmPbNzFDUV5XIxhMapbAgL+ECaOIiOmYsXGRycjLdpwA4DnEB2CM2kIkOdXjklru2y+HOIdGGsHsP90tuTo5cctpS8+9EC6CJi8hILAAAAJDxevq98vgL7aZVq84qTEz6JD8vR+qqSmRyyidnrF9kkg2kDokFAACIa5mJbkam+wZoi0/txpOJRabIPoe7hqTlaA2FJhITk1MyOZUjk1PT0to5OLOBXk6O1PN8TRkSCxeh4AgIRVwAsceGJhW/vm/n7A7H+s5wprTwRPY72O6RRfVlsm1Pl0xrkcXRuoiR0UlZ3FAurV1DCe9enerXjK4MT9xJLFxCOxnQOhAIRFwA8cWGDnispCKZLTyBZNAlTwUFudJQXSJt3cMiR5tBLWkok9rKYnnlQK9sPGHhvL5mdMWRKGRD4k5i4RKasQ8ODkpFRYUJDADEBRBvbOjgyE6i7wIDyaCD9j88sFs2nbFMuvq8ZgajubFCTjquXjY/fSjh3avjfc3oijNRyIbEnXazLjI9PZ3uUwAch7gAYo8NfcfVTiItPIFk0cH31RetluLCfMnPy5WLNi6VV61ZIHta+uTcU5Yk5Z3/eF4zdkVIFOxkQ+JOYgEAAGJ+R1jf9fWX6LvAQLLtax0Q79ikPLO9Qx58tkUXMcnapTXz/q5/S5yJQjYk7iyFAgAAMdGBmb7rq++46uBIBzyaVGTKMg1kP50l0CJtf57hcfNcPbt6fpt1NC+oMMufYk0UNHHXpVI6q5GpiTuJhYuUlZWl+xQAxyEugPhiQ5MIEgk4VbhZgv2t/VJeUmAKuFctqZLjmqqlprI4pa8Zx8eZKGRD4p7jc9m+5B6PR6qqqmRgYEAqKyvTfToAAABIkse2tcmWrYcCjo1PTMn6VfXSO+CVFUuqpKd/VDr7Rkz9xfqVdSkduHdpV6gMThTiHTszY+ESmj/qE0OfEHS/AWYQF4A9YgOZqrmx3Hxu6xqS4qJ8M0tRWJAnVeVFUlVeKPc+eVC6+73mNoeODMqLe7pjLuqeS1w0JDDDl4l7WpBYuIjLJqeAmBAXgD1iA5lGB+J3PbZfNp7QKD39VdLePSyrmqqksa5Unt/VLVVlhbNJhRodm4y7nat/XMS7R8Wuo7ddvrBSFjeUScuRobD3zdQ9LUgsAAAAkPF04N7V7zUflWWFUltVLL2Do1JUmC/FhXkm0fCnMxpzbecaz8C/K+i2OnPy+wd3mba4Opui933ypTbZdPpyeXl/j6kBGfJOZOSeFiQWAAAAyKrCbe0EpR/qxBV1cqRnWBbVl8m2PV3mWEF+7uwyqRNW1JnajHiWHMWzmd0uv9tqwqM1Hh09I1JfXSK1BXmmBmR/24A01pZJ94BXho8mFXpczy+T9rQgsXAR3SkSQCDiArBHbCDThGvvWlSYJ/9wwXFypHdEdh7qM3tc6AyGDtpPO6FR7n3qgIyMTsa05MiKi3j2qGjxu21FaeHszIkuxVI6OzExOW2O6yzL4Mi4LKgpNTUgmnhk0p4WbJDnElpklJubSxEe4Ie4AOwRG8iGDRw1cTjn5MUyMDQu9z51UNp7huXtl62V805ZIksXVsolG5ea57iVVETbHds/LuLZzK7Z77aaNOjMif9SLCvB0OP6fZ1pqasulrLigoDHyYQ9LZixcAktNtI2YdoujBcKYAZxAdgjNpCJgveB0CVO1myELiu698khMzh//TkrJS83R3r6vdI/HLicKdLMg39cWHtUaNtanXHQ5GBxQ/lsV6pw+1lo0rBmWY0srCszsyZWglFSlC911SWyr23ALJfq7PPKOy9fJz2e0YxqVUtiAQAAgKzg395V6yas2QhruZEWduuSI50Z0I/VzTWyv80TceZhtqPTEY8sqs6TdccVyILaMnnT+avk8RfbpaVj0Mw21FWXyF8f3S9XX7Q6IAEITnhqKorlX/7pFGnpHDJfL6gtNUnL87u7TEcr7+ikjI5PSq9n1CQTZ29YLJmCxAIAAABZx7+2wVpupKxahtauIamtLDKzGOF2xw7s6OSTlrZJ2bZ/UN5+2TrTLrajZ+axtOh6X9uAuY9dAbfdfha6HMuiP6expkz2tw/IwPC4dPaOyPS0mARjw6p6qXf4TIWFxAIAAAQMcHYe7JPWI92ypLHeLNtw+vILIFoxty430lkLpbMLmggoXernP5tgLTnSBYDPbO+QliODpmPTTAepmdJkTUIOd83sQeHffSqRzk0aY9op6v5nDs3utfHi3m5pqC6RhupSEgs4iwYOa2WBQMQFECj43dmdh1vk7zs7Hb8pF2DHv7ZBEwN9XutsRE1lkWzdccQUR1sF0v7LjTQOfnXfTnOf3oFRM9DX+y5pKD86dM6Rg+2esF2omubYuWl3S1/ABn7mXPq95vhJx9VLJqArlEvo2r3p6Wl2UgX8EBeAhOm37zPv2OrncB1yAKfT5/CFpzbL+pV1smxhpbzjtevkXa87Ufa3Dshr1jbKOacskbse3y+33rfTJBPBceDfwUnrM4a847NxofUUwV2oEu3c1N0/an98wP64EzFj4SKDg4Pm3VkAxxAXgP2a9OK8SfFOzX1nYsAps2/aZUn3j+ju85ruSyXFBQE1Edqdqa17eHZWzooDq4OTLkfSmQOt09C4KCopm+3QZLeMqmGOs3url1bLEy+1mSRG6ZteOqPetKDcLMtavqjS8TOHJBYAAMBI9tIOIF101kEH5rp8SWcetFBb//3Svh5TcG3tc3Hy6nqzE/Yjz7XK0MiEmYVYvrByNg627e6WTWcsM7cZGZuQNUtKZN2qxbMDfLui7LnSmRX90G5R2hlKV+nqbtz5ebly9xMHzGyI05clklgAAICgNemjGbUpFxAsN1ekvqrEdIDS5Uw686BLoHT/CCux0KTi3icPmrqG+uoS6R0cNc9/bSNrdYrS/S+e2X5EmhrK5c0XrpZc35hUVaVmYN9QUyrXXnGCmQHZcaBXyorzpaqi2NQ5KWtZIokFHIECVSAUcQEcYy3t2HmoT9o6umXxwnpZs5SuUMi8ZVCbnz50bNZhT5dZzqQzD00NFXK4c0imfT4zC6FJRUF+rinUtgbvOmOgO3TvONQXsMSpprJYDh72yEsH2kxXKJ3h02Q8OD66rH0vOgfD3iYcawZk2Dsh2/f3hNQ3OX1ZIomFy7rfADiGuABCzS7tOHlJUgZKwHzT56rONGjCYNUraI3EwNC4nL6uzCTPWlOhy590pmKmlezMLtjW4F27RJ1dXRLwuBoHf3q87WiDA/05fWaGw395UldAZzX728RCu1UFt7HNhGWJJBYuoesMJycnJT8/n3dogaOICyB6bOg7uskYKAHzRRNgTRS0pkL3rjBF10X5Mjk5JbVHkwV97mpNhS5/inXwrvu7DA5rO1h9vcixXZ60a7az2jFzWcLk3yo3k5Ylkli4yPDwMO/OAkGICyBybCRroATMdxMCTS5q/WYiViypTmjwfrhzUIpyp2a7pdktT2rx66zmb39rv5kZeeVAb0yzfsnuODVfSCwAAEBY4QZKTl/rDfeKlDD4L+vT7k9aqK01FbEM3psWVEhLW1fEGY5mm85quiwrPz9PHny2xSxvinXWL5kdp+YLiQUAAAiLFrTINOHe7dfFS78KWtanCcc7X7suYOftcJoby+UJEWnrGpLiogIzA7GgpjRghuN4m6RGZ06qyosC4ijarF+m1jU5KrG48cYb5fbbb5cdO3ZISUmJnHXWWfL1r39d1qxZM3ubCy64QB566KGA+73//e+XH/3oR2k448ySq73XAAQgLoDIsZGpa73hbnbv9j+27VjhtTXg103pHn+p3ex1EWkArwP9vz6+X05YVS+LFvikvWdEmhdWyFkbFgXcvsEmqcnNEXnoudbZ21gb9vX0a71GqGQVgIvbEwtNGD70oQ/Jxo0bTdHYv/3bv8mmTZtk+/btUlY2s6W6eu973ytf+cpXZr8uLXX2L9kJtDC1srIy3acBOApxAUSPjUxd6w1EW9Zn7WOhxd2LG8ojDuB19qCrf9R8aGJQW1Vs9sNoOTIkSxsrIyY1j21rM8uh/Dfks/bX0CTC7mdlal2ToxKLe+65J+DrW265RRYsWCDPPvusnHfeeQGJxMKFC9Nwhpnd4WN8fFwKCwvpfgMcRVwAscVGJq71BiIt69PkwNrHQlvORhvAzyQlPsnL8YlneMzcTztO5efO7IGxuL4sbIxYs346O6KJTK9n1Mz6aTG4Jh3XXLpGFtYdi7FMrmty9BqAgYEB87m2tjbg+K9+9Supr6+X9evXy+c+9zkZGRlJ0xlmFq/XfsoNcDPiArBHbCDb6ABfB/RKlyLprIH/5niRBvCalKic6QkZHB6Xfa0D5v6V5YWmKPvW+3aa2Qc7mjDohnvT02JmRxprSs2eGj2eUTnY4ZFtu7vkz4/sm72/9bMysa7JUTMW/qanp+VjH/uYnH322SaBsLztbW+TZcuWyeLFi+WFF16Qz3zmM7Jz505Tm2FnbGzMfFg8Hs/suzH6YdF3ZPy/no/j8/kzrevVj2y5pvk67qRz4ZqSf01O+H9Bsq/JyceddC5cU+TjwbHhxHN0+9/JSeeSCdekMxPWzvJa39BYWyoDw6NSWGC9zz5z+yULymfvaz3O6uYqefKlPGnr8MrQeK54xyaloaZYigtypbt/xCxz2nmozyQRdtekS6cGh8dkcUOZmbGoKi+Qmopi6RvUZVFDUltZbO6v5zgzw9FhW9eUjr9TViQWWmvx0ksvyaOPPhpw/H3ve9/sv0866SRZtGiRXHzxxbJ3715ZtWqVbUH4DTfcYDsbYv3ydKpXl1fpuzM69WspKioyReTay1trPix6TL83ODhoEiCL1oEUFBSY5MX/D1NRUWGK4KwZGIv2B9f76+ME7wSsP09/rkXvr+td9fz830XSzYvKy8tldHQ0IIEKviY9n6GhISkuLs6aa8rGvxPXNP/XpHFh3TZbrikb/05c0/xek56j3l+PW8sEM/2asvHvxDXFf0068C8rEhkfL5M+z6gcbiuQ3uFJmZzOk8LcKakuK5AlNXnmvPyvqTB3Ws47qUEO1okc6vHJqWsXSlXptDz94kGz+V5JcaG0HtFrXBxyTePTBXKgbUAKc8clzzcu56yvFc/QuLT0TMpJK2vl+KZS2XWwT9o6pmVwRYU01FTKmy9cKbsPdsmR3hGTAOkeHHruep3z/XeKJ9nI8SWamqTAhz/8YfnTn/4kDz/8sKxYsSLibfUXpk8erc+47LLLYpqxaG5ulv7+/oCizWzLyoOP62f9XekTRwMxG65pvo476Vy4puRek/5P1YoL/8FTJl+T04876Vyy+Zo6e4dNAaiu4dbe+/oO6ILasrgeR5Nu/9hI9zVl49+Ja0rdNWkNhO6U3TPglWWLKqWpoVzqqktCbq/Ljw50eGRgcFyqKgrN3hb+tRL+t//NfTvEOzoiC+qq5d6nDkmvZyaZMTUWDeVy0WuWyjknLwl5/Fs375KBoVF5zbpGmZiYMnUWWvjtkxwzW7J2Wa2cvn6hVJYVmba3Tvp7KE1CNLHThClawxNHzVjoxXzkIx+RO+64Q/72t79FTSrU888/bz7rzIUdzcr0w+6X5/8/S+uYnVQen6+fqZ8165yPn5uO32OqjzvpXJJ13Ennkqzj8T6GJtn+cTEf5xjvcSedS7KOO+lcknXcSedybCBztFVlS7/8fWdnxFaVdo9jFxupPvdUH3fSuSTruJPOJVnHE30MjYHfP7Bbli+ulMGRCdn81CFpbpxpDbt0YWXA7fXfExM+U+9QWlwQcXzYvLBKtmwdkIULciVPe8ianTHE7GlRXV4sa5bWhJzP7paBo7GYI/vbdE+KcsnJyZWKsmIpLsyTgoI82d/ukXUr6uT0ExYFjNsS/T0m83is8p22/OnXv/61ma3Q/6F1dHSY45ol6dSNLnfS719xxRVSV1dnaiw+/vGPm45RGzZsSPfpO5ombTolqEuhEn3SANmCuEA2SkarSmIDmcJuI7m9hwdMUqEzAzpzobbt6ZJdLX3ygas2zMZBvPtFaJ3F8zvyTLH1pjOWma5SnX0j8qo1C2T9yjrb+/h3eNJEQmdRvOOTsqCmRKamfTIyOmHqKyYnp8yMSqZzVGJx0003zW6C5+/mm2+W6667zqwju//+++W73/2uWb6gS5quvvpq+fznP5+mM84suiRMXyQAHENcINskq1UlsQGnC04MXtrbLQ/9PV/OPaVJOnpGZGpqWlYtqZK+wTFTMK07Zvsn2PEm4XrsijObpbVvSlo7h2TN8hp53dkrpKayOKYWt4Mj42bvCk1yJqamTUJRXTGzqkZrKLKBoxKLaOUemkgE77oNAADsBzKZ1qoSiId/YqAb0LV2DZnGTqcc3yBLG8tNa1edUdDZBG0L+9yuroAEey5JuCYRy5urYp7JO95v53rP8LisWVYjC+vKzOxFNu5k76jEAgAAJMZ/IJONAxfALjHQzeomJme6HJWVFMj9z7TItl1dkpObI7k5Xaaz0hvOWSnVfrML/km4Jib6GJqMnLq20bSjTcbSpIagneu1xey//NMp0tI5lJU72ZNYuIguJQMQiLhAtgkeyMx14EJswOn8EwNNCJQuL+oe8Jp9JspLC2RsfEry83PNMqRB74ScsX5RSBKusxo626GJSUN1idk0T5cr6ayEtnu1ajd0j4m5xEWDzc71WkSejUgsXEKDQ/scAziGuEC2shvIxIPYQKbNzuleEjrjsGJxpXT2ek2yoImArrKfmJwy3w8ukLaS8Ke3H5Ht+3pM/cNMS1qf3PnI/tlEJVpRN44hsXAJrV/RzVO0uxYdPoAZxAVgj9hAps3O7W/tl/z8PLPkqbN3RArz88Q7OimNdaVmV+xwBdL6GFrkrTtj60yHftRXlZhuUro/hUWTF90ZWzfXSzQuumw6WWVLwkJi4SK6I6MGA4BjiAvAHrGBTJqd043levu90to9LKXF+VJXVSwjo5PmY9g7KU0LysLWGdVVlcjzu7vMv5c0lEt798zu3jrL4a/1yKCsX1YWNi66YkgY4m1xm2lILAAAAJDxpnw+eeDZFhkcHje7XPd5Rs0MxPqV9abeIieGJVVWS9iO3mFZ3Vwto+NTppuTWhKhs1qsCcOuJOwz42QkFgAAAMh4B9o9ZtCue1bseLjX1EfU6D4RPp+pu9hf5pH6MPtTWEuqOrqH5cSVdZKXK3K4c9gkGdoi9mCb5+iMx4Ttz441YWhJ0j4zTkVi4SJFRTObsAA4hrgA7BEbyAT+y48K83Nl4wmNcu8TB2YH9tM+nxw4Mii1FcXS0jEoyxdWht38Tj+smQf9rMXge1v7ZXFDuVz3uhPM93VHejt2CUNhQZ7k5og8tq1tdnnUiSvqTAKk7W2zcZ8ZEguX0CIj1soCgYgLwB6xgUxwqMMjt9y13eyoXVpcYIqwc3Nz5IJTm+X3D+yWyrJC6erzyoZV9fL09g7zvcOdQxHrGayZB00Kao8WfSvdd0JbxFpxEVxPYZcwnLy6XjY/fcjvsfukrDhfTjuhUV7Y0y0VpYVm6VVuTk7W7DNDYuGiDh/Dw8NSVlZGhw/gKOICsEdswOl0A7vHX2if3cdCZxd06VOfZ2aH68V1ZTIwPC5V5YVm120dwGthdrR6hkhLlay4GBnPkVs37wqop7AShke3tZljmtQMDI6bRMPqSqWGRydNC9yTVtXLK/t7ZXVzjZy0qi4r6isUiYWLTE7ObB4D4BjiArBHbMDJDncNSUtQXYImDY26XGl8Si7a2Cye4QlTK/H0K0dMUmEN8LU1rbaSfeVAb0j3Jv9N9+yWKmlc7Do0HFJPoQlDTk6OXH7Wctl3eEDWLq+VF/Z0BSQVShONbbu7pKq8SHoHR6V3x6jsPdyfNV2hctN9AgAAAEA8DrZ7TGG1v+lpn5mZ0OLr1565wmyW9/L+XikrLpgd4OvAXve7ePDZFpNAbNl6SG49WlOhNMmoLg+sL9Kv/ZcqHQ4zq9HZOyIb1y2Ut1y6Rk5e3SArF4cub9KZlQU1peY8LdYsSjYgsQAAAEBG0b0n6qqLpcFvJ22lhdZWErB8UWVIkqAJhs4WWC1kgwf2VoeoizculTVLa8zn4NmEpgUVs0mKdqDSGg/9vKA2cMbBLknRJEd39/b/+YquUMg4FOIBoYgLwB6xASfTQfsfHtgtm85YJj39o2ZTu+aFFXLWhkWzSYB/G1kduOtyJu3S9NBzrSGP5z+wtzpEWbUcOw7qzEaLNDWUy8rFZbJmabU89XK77G8bkInJaXO7kqJ8U4OhMx+Rfn5laaHc9fj+kJ9PVyhkFF33R+tAIBBxAdgjNuB0Omi/+qLVZtA+MTEtJx1Xbwb+OhsQfDv/2QZt/Rrc6jXcwN5u07u/7ywyycKm05dLY22ZSWh0SZb+3Ke3H5GiwvyAnxf88/UxS4vyA84heKlVJiOxcAnNogcHB6WiooIOH8BRxAUyXXDLS/8i1EQQG8gEwYP2WPjvsh1tYB+66Z1PvN5h2XmoT3oHZnb1rq0qNp/3tQ3EtKTJbhZDf3Y2FG4rEgsXmZ6ema4DcAxxgUxl926qDpiS1V2G2EA2JtjxDOztWs/mik9ajwzK2hV1s8lEvEuaGuaQEGUKEgsAADJQ6Lupx4pQs3XQAiQjwY51YB+u9eySxgqpryqWhbVlpuWtLoVas6xGDrZ5smZJ01yRWAAAkIEibeQFuEUqE2y7ZVO6W3ZzY7n8/sE9JqnR9rF7W/tNN6rrXndCUpP6VC11TCUSCxfRHVQBBCIukKmibeSVKGIDbk+wg5dNLVlQLqsWV8iB9iGTzGjr2lq/DfBaOodk6cJKyYSljqlCYuESWnxXUFCQ7tMAHIW4QCaLpwg1XsQGMkWqE2y7ZVMPPd+e8tnCXRm61JEN8lxCO3wMDAyYzwBmEBfIZLFs5DVXxAYyRSw7ZSeLFRfa1tZOUxL3osjUpY7MWLgILxBAKOICmSyV3WWIDWSC+W7fqnGhyczfd3amZLZwvmZiUoXEAgAAAK5JsBMtirZLZtYurQnZnM+pSx1TicQCAAAArhCtKDrWpMM/mdH77DjYJy3PHEpa96ZM3UiPxMJFdAdVAIGIC8AesYFsFKkoWkXrxBQcF4c6PHLLXdulrWtIiovy5aWSbnl2x0xSkIzkwumJRDASC5fQDh+5ubnmM4AZxAVgj9hAtgpXFN3T75VdPonYiSk4LvQ+j7/QPlsLMeSdOJaUOLx7U6rQFcol6PABhCIuAHvEBrKVLlWys2xRZdROTMFxcbhryOy87W9ictokGE7v3pQqJBYAACCldA36Y9va5LbNO81n/RpwUntabSEbLukI14npYLtHFtWHbiQ5MTHt+O5NqcJSKAAAYMwWrh7xyKLqPFl3XIEsqC1z5Q7CyE7hiqK1o9PxPl9cnZjqqkqkoCBXGqpLpKvfK7m5OVJZVii1FcWSmyMmiY5WyJ1ohyqnIbEAAABBCYBPWtomZdv+QXnrprUJDXQydQdhZK9wRdHxdmLSJOAPD+yWTWcsk57+URkaGTcF3A01JfLQc60yPjEVMYnOxqSbxMIltNCoqqqKQjzAD3EBhEsAcsQ7lS/eofGEEwD/dev6bm5FaaEMjoy7dg06nC1SJ6bg1wy93dUXrTYxkpebIysWV8njL7TKIb/ndqQkOhuTbhILl9BCo+npabp8AH6IC2SjuS6tCCxc9YlGhE98CScAeg4H2j1y8up6865ue/ewWZd+8uqGhB4XiFeiy47sXjP8E5HbNu8U7/hUyP3CxVC0YvFMRGLhIoODgybTBnAMcYFsksjSCh1oWW0zVXHepJm1SLQIVQdvYxOTcucj+6S73yslRfmybFGFPLfziDzzSoesXFyd8evK4XzJWnYU6TWjOSiGLOFiKN7bZwK6QgEA4JLNv+bSLSdc4WqsrP7/qrykQF539gp57IU2uevxA/Lsjk7ZsvWQ3HrfTjpFwbGxEavj44yhVMVcOjFjAQBAlkhkaUVA4WqHRxbW5Mm6VYuTMpNwpHdEaiuLTY3F0MiE9AyMmuOjY5NZsa4czjcfy44a4iz+jvf2mYDEwkVYQw6EIi6QTRJdWmGtF/edtEg8Ho9UVpYm9by0cFtrLCzaQScb1pXD+ZK17Cjaa0ZDhOLvZNze6VgK5RJ0vwFCERfIto3nmhvLk7K0ItmxYS350G5Q1oZiBfm5ZmlUNqwrh/MlY9kRrxnRMWPhEtrJYHJyUvLz8wkIuJp/VxDdaXXVkgpZWF9BXCArilEXVJfIm85fJS2dQwktrQj3mjHXrjr+Sz5047Djl9aYHv+FBXlZsa4czpeMZUeMpaIjsXCR4eFhut/A1UIHYr3y0q5cueqS9QnvLgw4oRi1s99rkoqzNyxO+mtGol11/Jd8HNdUnVXrypEZkrHsiLFUZCQWAFw9ENOlGTrAIbFAppnvHvjJ3Mwr29aVA5hBjQUAcftArJWiUWSgVUvs3zVNVa1CNm7mBSC5mLFwEd0pEnAzu64g05IjSygaRYbwr3ForC2VC1/dJPvbPdLrGRXP8HhSaxUGhsblpQNtcrhryMTOiSvqzA7aWhvhj6JruAljqchILFxCi4wqKyvTfRpAWmmhqa4J1+UbM3KkpKRM1iytSfOZAdEF1zi8tLdbCvLz5IJXN5nuSquba2RRXanUJ7DEyEpcdOy0+elDswXWmpCXFefLaSc0yqPb2mZvT9E13DyWmmszg2xGYuES2slgfHxcCgsL6WQA1wruCrJkQbmsXFQm9dUl6T41IK4aBx3wt3YNycTktFkS1T3glcOdQ+b5nWjioq8X9ZXFcqC1V3w5ubKkocIkF8Ojk+b14/Kzlsu+wwMUXcPVY6nufm9CzQyyFYmFi3i9XhMMgJv5F43qi8TAwEC6TwmIu8ZhyDthkgqlG87VVhWbRCOR3autxGVJQ7m09wxLaaFP+r3T5mfVHm0L29o5KK9e2yg1lcVSmM+SELiPNZZKZjODbEJiAQBAhtUIjY5Nzh7XDed0xiLRQmorcTGb2NWVycFWCfhZOkuSn58n9z110NRzWEuhrHdoWRYCN6GZgT3ebgAAIMN2Di4umnlfsKG6ROqOLuXTmYaVTXPvr6/JgNKkoba6WGoqigN+li6Hqiovmk0q/N+htZZRbdl6yCQ/+vnW+3aa40A2suIlWJPLmxkwY+EiulMkgEDEBTKxRmh/a7+ZPaitLJacHJ/UV5VIZ9+IDI1MmMH8XGYK/JsbvLCnW87csERO9IpMTk3LiiXVZsfsh547Oo3hp6ffK7t82kWKZSFwz2tGaDMQmhmoHJ8uMnYRj8djdkzUddV0SQIAZKqBwVFp7xmRX9y93RRWa2conVXwX54UL7OcyW9H7LVLa2ZnRB7b1mZmIoJded4qeeVAb0grZ6Ud195y6dwLygEnC46X47O0mUE8Y2feqnMJzR9HR0eluFjf3aIrFKCIC2Qi/1qGitJCOeeUJbJtd/fs/hJ2MwWx1j9YzQ38Y8MS7h3apoZyM1Nil1i4fVkIsvs1gx3kQ5FYuMjY2FjAiwQA4gKZvZdFW9eQlBTly6Yzlskz24/YFpDa7X+xZWueXHraUpmenkkY7AZHwbER3K7Zf0ZjrQjLQuAKvGZERmIBIGPRhQZuE9ziUguru/q90tXnlelpn3T0DJtjC2pLo+5/sXLxzP4X8fTe93+HVuNvx8E+aXnmkIm/N52/Stq6h+Vguyerl4UACI/EAkBGCn4Xls2J4MYWl1pX0e8ZM4N5Xaahe07oDIb+2yriTsX+F3bxZ9V2nLF+UdKuF0Bmod2si7A5HrJJpM2J4kFcIJNbXGqxdkNNiaxbUWf2nnjtGctl0xnL5entR2Zjwf8+wftf6J4V4XrvR4qNZMUfkGl4zYiMxMIltMiotLSUAlVkjWRsTkRcINM0N5bP1lb0ekbN0iZNLmoqiqSgIM8sbXpme4c5bsVC8P4XOpuhX5cU5cmBdo+5bXCRdbTYYHMwuBGvGdGxFMol9IVEt6EvKSkhIJB1uxDPtQsNcYFMosuP7npsv2w8oVF6+qvMUqbmhRXyquMb5I6H9kpxYZ7pEqV0EzsrFvyLrve29Mu0zyfFRXlyz5MHxTs2Kfl5ubMJS6yxkYz4AzINrxnRkVi4yPj4uAkGIBska3Mi4gKZQpcfmULtfq9UlhWa+ggt1j7SWyZnn7RIDnYMmmRDlzhtOK5e1vjFglV0XVVaKF0DI9LaOSzHN9eY22pXp7auYVnaWBlzbNjFX1lxvlSWFsptm3eaxEOTFX3cAx0emisga/CaERmJBYCMZNf6ki40yGb+y490RkI/VEF+rjz+Qvtst6f2o8nGSavqQx7jpf09ZqbBSkx06dS+tgGzkV08RdfB8addqPTd3Lse32+WVmlL29HxKbl4Y7P5eTRXANyBxAJAxmJzIriJ3fIjTRB6PWPSNzhqljhNTE5JXl6O9A2GbpLn/xj+iYnSxLyn3zvTPrZz0Gx6t6Q2z+y2G0v8aV3H3U8cCOg+1d3vlZ7+UXOO+rPsNu4DkF0o3naRoqKZ4j0AxxAXyBT+RdgW7QSlA3idrdDP1oBev97f2h/TY+jXumzpl/fukC1bD5nE44FnD8lfn2gxdR2x2Ns6EPC11X1Kl2ZZdR+K4m5kOl4zImPGwiW0yIg1gUAg4gKZJNzO10+93DG7N4VFv66pLIl5CeHewwNB7WNzpGdoSnYfHpAFtWVxz6Zo9ylNcrSGQ5dbWSjuRibjNSM6EguX0LWvw8PDUlZWRicD4CjiAtmw/K+mskgaqktMUffs7apLpK6yKObH2LK1JehWPinMnZLDHR6RDYujnldwMbdu3KfdprQwXGs4gpsr6EyIFqPrsisKu5EpeM2IjsTCRSYnj22MBGAGcYFMlyM5sumMZaaeweoKpQN6CTPwsRvUL19YGVK/kZfjkyUxzjDYzYRYXaG0MNy/uYLdrt0UdiNT8JoRGYkFAAAZbFVTldx6X4cp3rY6PenmeTpQDxZuUP+m81eZGQX/9rFaGxHcvjnSTIPdTIi2sA3uNhVp124SCyCzkVgAAJDBgmcLVi6pkvqqEnnouVZT3O0/+A83qG/pHJK3X7ZWdhzqM4+xZEG5LKnJCxjoJ2umgV27gexFYuEiFBwBoYgLZANrtqDPMyq3/22PPPJ8qzm+fX9PwOA/0qD+7A2L5WxdQnV0LbluBJaKmQZ27UYm4zUjMtrNuoQWGWmLNIqNgGOIC2Sb7ft7TatZu8G/Nai3Ezyot4uNZM00hGt5G7zsCnAaXjOiY8bCJfTdp8HBQamoqCAggDTFBZ1wkOrnT7TBf3D3pnCDervYSNZMQ7iWt8QCnI6xVHQkFi4yPR3Y5xzA/MUFnXAwH8+faIP/eAb1wbERa1ISC7tCbyATMJaKjMQCAObh3eXKskLxHt2N2EInHMTKrr5Bu0C1dQ8HPH9iGfzPdVDPTAOAaEgsAGAe3l0eG5+Sc05ZLM9sPxJwWzrhIBb+S5wKC/Lk5NX1Zt+KR55rlaGRidllUfrxzsvXyZ7Wftl3eMAM/tc0V4tPRB7b1pbwMjxmGgBEQmLhIrpTJIDUx4Xdu8vDoxNmIKgzF57hY9126ISDWPgvcdKk4t4nD0p3v1fqq0ukd3BUXtzTJVecvUJajgzNJg8XvaZ5ThvS6e13HuyTw50D0rRgWNYsqyGZAI5iLBUZiYVLaJFRQUFBuk8DcEVc2BXQlpcUSGffiFSVF80mFnTCQaysJU66/EkTVE0qCvJzzfNKLVtcKbfctX329lbyoHtTxNMmNiQJafHI33d2RkxCaEgAt2AsFR2JhYs6GXg8HqmsrKSTAZDiuLAroNXlK69as0DKSwvMYJD16ZhLfYPWVOjyJ52p0OeRPq90FkyTjbauIVncUB6QPBzumpnBsGO3DO9YEuKT4rxJGZ3Kjz0JoSEBshxjqehILFwWEABSHxfhCmjXr6wzA66N6xYm/Wci+1n1DVpTocufLBWlhdLePSzFRaEv6QfbPXG1ifVPQnJiTkKOoSEBsh1jqcjYIA8AUvTu8sUbl8qapTXmM+/iIlmCN5gbHBmX5saK2WVR/hY1lMW1IV2sG+glc8M8ANmDGQsASAG65yDVieuBDo/0e8akurJIGmtKTQ2PNUumS6ROO6HRzG489FyrXHhqs3QPeKWzdyTiMrxjs22jMSUh8W6YR00GkN1ILFxEd4oEEIi4gNMFD8bXLJ0Z5I+PT0uPZ1RKiwukpChf3rZpjew8usfECSvq5N6nDsjI6MzeKdv390hTQ7n8wwXHSU1lcdSkZeehPmk94pEljZVm1i1yEhLbhnnUZCBTREqAec3IoMTixhtvlNtvv1127NghJSUlctZZZ8nXv/51WbNmzextRkdH5ZOf/KTcdtttMjY2Jpdddpn88Ic/lMbGxrSeu9NpkVFubi7FRoAf4gIZuSfKxKTp1GQlDf4D9LM3LDbHdM8K6/sWLeTefqB39jbRZtt8vsURYyPeDfOoyUAmiJYA85qRQTUWDz30kHzoQx+SJ598UjZv3iwTExOyadMmGR4enr3Nxz/+cfnzn/8sv/vd78zt29ra5KqrrkrreWdKsdHAwABFR4Af4gJOFzwY1w5QbV3DcrhzSMYnpqTXM9MNal/bgLy0rydp9Q+xxoYOtDRReculM0lNpASBmgxkgkgJMK8ZGTZjcc899wR8fcstt8iCBQvk2WeflfPOO8/8MX/yk5/Ir3/9a7nooovMbW6++WZZt26dSUbOOOOMNJ05AADJFzwYtzpAeUcnZXRsUoa8E+a4fn5uZ6ecsrrBLHWaS/1DqjnxnIC4EuCTFs37+WQaRyUWwTSRULW1teazJhg6i3HJJZfM3mbt2rWydOlSeeKJJ2wTC10upR8W7T+sNNv0zzh1WssuA03l8fn8mdb16ke2XNN8HXfSuXBNyb8mJ/y/INnX5OTjTjqXTLgmrYuYGYzPHB8cGZNFdaVysGNAhr261OnY7RdUl8juw31y2gmLZHVzlTy7ozCo/qFYVjdVxXz+wbGR6DUFnpMuJfGZmgz/c8rUv1O6jjvpXLLlmmZirtf/O+a5umRB+ZzHUjkO+v3O5XhWJBbT09PysY99TM4++2xZv369OdbR0SGFhYVSXR1YGKb1Ffq9cHUbN9xwQ8hx/6ksfczS0lLxer0yPj6zI64qKioytR66FGty8thaVT2m3xscHDTn6b/Nu+7IqMmL/x9GC310TZ6VKFmqqqrM/fVx/P+oelx/nv8SML2/bsii56fnacnPz5fy8nJTe+KfQAVfk57P0NCQFBcXZ801ZePfiWua/2vSuLBumy3XlI1/J7de07LGYikrzpfOXo/4piZkfDRf1jWXypL6Ynn5gEe0i2x+rk9qKoqlplxk/+Fek1gU50/JG85cLIeODMqR3hFZ1Fgra5fVSWHuRMD5h7smPUe9Jj1urSdP9JoKc8WcU0uXV9p7x2VRbaE0N5TMnlMm/52y8bnn1mtaUpsnCypyTRvn8ek8mfLlSENFriypyTPnqq8Zek36WG75O/niSDZyfImmJinygQ98QO6++2559NFHpampyRzTJVDvfve7A36h6rTTTpMLL7zQFHrHMmPR3Nws/f395g/npqzcyrCz6Zrm47iTzoVrSv41WXGRTdfk5ONOOpdMuKbuvhF5cV+PtHUOSXvPsCyqK5OmxnJZWFcmW1/plPbuIXOstrpEXtjTJeecvMR8JOOadNDhHxvW8c7eYbMO/XDnoDQd7ZizoLbMMX+PSNfklHPhmpx9TaYrVEu/tGpTgoWVZlbNqh+ay1gqxwHXlMhxTUI0CdKEyX/snDEzFh/+8IflL3/5izz88MOzSYVauHChycY0KfCftThy5Ij5nh3NyvQjmPWkCD5mJ5XH5+tnBg+gsuGa5vO4k84lWceddC7JOh7vYwS/SMzHOcZ73EnnkqzjTjqXZB1P1WPvahmQR7e1maLt2qoS6faMyr52j5x7yhIpyMsNOKbLirQ1bDJ+rv9rhv/3dMB16+ZdxzrmtPSbDlWRWsa64e+UzuNOOpdkHU/3uWiirB/BEhlL5Tjo9zuX4xnZFUr/WJpU3HHHHfLAAw/IihUrAr5/6qmnmmmaLVu2zB7buXOnHDp0SM4888w0nHFm8Z+CAxBbXOhASlt33rZ5p/msXwPzXUjqGR6X1q4h81npRnebzlgma5fXmhmLVOzubhcbkTrmAG7AWEoyZ8ZCW83qcqc//elPZk2XVTeh0y+6Jkw/X3/99fKJT3zCFHTrdMxHPvIRk1TQEQpAsrGhF9ItUicl7f4UbU+KZKNlLICMmbG46aabzPqtCy64QBYtWjT78Zvf/Gb2Nt/5znfk9a9/vVx99dWmBa0ugdJN9QAg2Xh3Fumm9Qu6xMlfpN2t5yPRsUPLWACOm7GIpY5cuxr94Ac/MB+IT6Lr5gC3xQXvziLd4t3dOtWxoYmOztpZbWy19kOXYq09WtsBZDvGUhmUWCC1gaBLyQC3M90+DvWbpEHffdWBUrgXCjb0ghNoEjHfS+/CvWZYic7e1pmW7b2eMen1eGXHwT453udjiSCyGmOp6EgsXEJfALSPsfZBJtuGW4XWTPTK33e0y1suXWvbAST43dl0L0MBEkmgIw36g2+vm9nVVBTavmZYj+MfS/vbPNQfIesxloqOxMJFdJMUMm24mV3NxKh3pl+5XWKRzmUowFyTh3ibDtjfvtBsZre8uTHu+iPiA9mMsVRkJBYAXCNczYRuguSkZShANOGSh7dftjbuQb91e62XqCgtNDsO6+111+5wiQX1RwAc3xUKANLR0WbJHGom2N8C6RQueTjcNRT3oF939N54QqPUV5VI78Co+fyadY3S3e8N+/PpDgXADjMWLpKbSx4Jd7OrmSgvLYy7ZoL9LZBu4ZKHg+2euJsO6G7dP/nzS7OJxLY9XdJQXSzvuHRV2J9P/RHcirFUZCQWLqFFRrqhIOBmyaqZYH050i1c8lBXXWKe0/EM+rsHvDbP53HxjOWELVCl/ghuxFgqOhILF3UyGB8fl8LCQjoZwNX8ayasuNDP8cQF68uRbpFmDOId9B/pHZElDeUy5J2Q0bFJKS7Kl/KSfOns8USMDeqP4DaMpaIjsXARr9drggFAYnHB/hZIt2jJQzyDfuv5XFuQ53fUJwtreL0AgjGWiozEAgDi7PvP+nI4QbJmDMI9n5eSKAOIE4kFANfTpOLWzbtiLsZmfTkyeTO8WJ7Pq5uqpDB3IqXnDSD7kFi4iO4UCSA0Ll7aE38xNuvL4QTJ6lAW/HzWteQt7d3y0oE208J2LgkLkI0YS0XGb8cltMiovLw83acBODIuDne12n6fYmw4Xao6lGnr2TseORxTwpLojAmQKRhLRUczXpfQd5+04Eg/AwiMi6YG+xcKirHhdKnqULbzYJ8Mj+imj76QhMVuxmTL1kMm+dDPt963kw0jkZUYS0VHYuEiY2OB72oBmIkLfYdVi1X9UYyNTJCqHbAPdw5KQc501IQl0owJkI0YS0XGUigArkcxNjJVqjqUNS2okJa2rqgJC3u6APBHYgEAFGMjQ6UqKdaE5aVdheIdnI6YsLCnCwB/JBYuwoYuQCjiApkuFUmxPt7rzj1ODnaOSmvnUNiEhT1d4Da8ZkSW43NZBYrH45GqqioZGBiQysrKdJ8OgCxAVxyI25//LCMEslY8Y2dmLFzWyaCkpMS0SwOQnLhI1j4CQKbGRjwzJiThyGSMpaIjsXCR8fFxEwwAkhcXqdpHAEj3wDzZrxkk4cgGjKVS1G52ampKfv7zn8s73vEOufTSS+W5554zx/v6+szx1lb7DacAIJvQFQfzJdP3jKA1LZD95jRjMTIyIps2bZLHH39cysrKzNeaUChde/XZz35W3vOe98jXvva1ZJ8vADgKXXEwHzMUPQNeycvLNV8XFuRl5OwYSTiQ/eY0Y/HlL39Ztm7dKnfccYfs27cvYAfCvLw8ueqqq+Tee+9N5nkiCYqKAjcAA5B4XLC5HuZjhqKr3yvb9/VIa9eQjE9M2Q7M9faPbWuT2zbvNJ8TnclI9mtGqjbzA+YTY6kUzFj87ne/k/e9731y5ZVXSk9PT8j3jzvuOPnNb34zl4dGimiREWsCgeTHBZvrYT6WDg2OjMui+jLZtqdLhrwTUus3a6HPuZ5+b1LrF1LxmhGuNW1zY7lJhCjohtMxlkpRYtHW1iYnn3xy2O+XlpbK4CBTm06is0rDw8Nm6RqdDIDkxgWb6yGVS4d0huJAu0eaF5Sbgbh3dFKkMnB2bMfBvqQ2EUjFa4ZdEq5JxV2P7jczMoqCbjgZY6kUJRZ1dXURi7NffvllWbx48VweGik0OTmZ7lMAHIe4gFPpu/cv7e02y58mJqflnicPysUbm6UwP09GRidlxZKq2dmxLVtbkl6/kIrYCE7CzZKto0lFJtaNwH14zUhBjcXFF18sN998synaDrZ//3756U9/Kq997Wvn8tAAAODo0iEt1NakQnnHJuWplzqkprJYTjquXs7esHh28B2tfsGqv7jz4b2ybXeXWTqVbn2eUXl5X4+0dQ1Jr2c0oHaEgm7ARTMWX/rSl+Q1r3mNbNy4Ud761rea6aB77rlHNm/eLD/60Y9MYcvnPve55J8tADgQm34hFfQ5dOlpS2Xl4ipp7x42NRZ11SXy1MsdsmJRpZy8umH2+VdZVihj41MyPDoh5SUFJiGxlknp93//wG5ZvrhSPMPjct+TB6W5sULO2rBIli6MvItuqug53fnIPiktzjc1I/qhS7mWNJSbc6egG3BRYqHF2Vu2bDEtZb/4xS+aY9/61rfM5/Xr18svfvELaW5uTu6ZImEUHAHJjws2/UIqTU+LdA94pbaq2Hze1zYQMhOhzz+dzTjnlMXS0z8qnX0j8qo1C2T9yjrzHNSZCk0q7n3yoHQfnanQIvBdLX3ygas2hH2epvI1QxNxXeK18YRGaaguMcuhdGZGE4yVNaV0VYNjMZZK0c7bp556qmzbtk1eeukleeWVV0xBy+rVq+VVr3rVXB8SKaSzSrRIA5IfF7HuvM2sBhLppKSDcLt2xv7Pv2e2HzEzF1XlRVJeWjD7/NI9MHSmwkoqLLoEKfh52t03Iu09I7K7pU+6+0dl9dLq2QQlFYXp23Z3y6YzlpmESGdlVjVVybmnLCE24EiMpVKYWFh0hkI/4Gya+GmnroqKCjoZAEmMi1g2/WJWA6lqZxz8/NMEQj90OdTGdQvNsXXLa2XLM4dCHru4KD/kefri3m6zRKm7f0Qqi33yxEutsn5lvVx7xQlJfa5aG0tqXYWVEOmsTPPCCmICjsVYah4SC2SOaZ1TB5DUuIhl5+1YZzWAeNsZh3v+LVtUOTtLtr+tXxbUlsrrz1khW55pMcumCvJzTfLh/zzVlrZtXcOzMxu5OWKWJ7V0hs5sJHtPC02GcnNyZHmaaj6AWDGWSkFikZubGzVT0+/TkgtAtgu36Zf/GvFYZjWAZD7/FteXBcySaTIxOjYprz1jmTyyrc0kFQuCahn6BsfMcqRger9kP1fZWBLITnNKLK699tqQxEKTiL1798pTTz0lGzZskFNOOSVZ5wgAjhXLACmWWQ0gWc+/tUtrQjbMKynKl7zcHCkuKpCLTm2WirJCqa8qkYeea5VFdWWydlmN1FQUze7uHbxkKhXPVTaWBLLPnBKLW265Jez3Hn/8cXnjG98oN910UyLnhRTQnSIBJD8uog2QYpnVAJL5/GuxqanQNq5DI+Ny+VnL5Xdbdssjz89sdLt9f495fr7p/FUyODJ+tEvTiAyN5ZglU7rjN89VYAZjqXmusTjrrLPk3e9+t3zmM5+Rhx56KNkPjznSGaaCgoJ0nwbgKLqWfD46NQW/q7yyqUqOW1JtNjoDUiHSLNnOg30BXaaUJr1t3cOyYVW9NFSXznSFGhiV1c2p6QoFZCLGUmkq3ta2s8xYOK+TgcfjkcrKSjoZALOdmnbImHdYRqfyU96pyXpX2Sqovfepg7SdRcpEmiXbsrXFdGGqKC00MxRaOK0OtnvkjPWLpL6mVNavquM1AwjCWCpNicXf/vY3NhBxaEAATjXf+zxYnZpK8uavUxNtZ+GE2p8TV9TJ5OT07G7ea5bVmP0kgusoUvGawX4uyHSMpVKQWPz85z+3Pd7b2yv333+/3H333XL99dfP5aEBuFA6Btzp6NSkAyq9Vt1dWDvtaFGs9vGn7Szmq/ZCn3/3PnVAXt7XY1rJaqG21lS84dyVKa+jILEGst+cEovrrrvOTAHZZW35+fkmqfjOd76TjPMD4ALp2OdhZg1677x2atrX1m/WtuuATmmCode9v7Vfzt6wOGU/F/CPtZHRSVnSUD6b4Oorub6mp3pwz34uQPabU2Lx4IMPhhzT/ynV1tbKihUrqJh3KN0pEnCidMwezKxB75ABvxrWVHdqqq0smU0qLPp1TSVLRzG/saYdomoLjq0D7OwdSeprht2Sp/ae0D0yFPu5IJMwlkpBYnH++efP5W5II038YtnYEEiHdOzzMLMGfa3sPNQnrZ1D87JBV21l0dFWnt5j51FdInWVRSn7mcBcYi2R14xwS54uPLXZtLaN9rMBp2IslabibTiPLlsbGBiQqqoqAgKOk659HuqrS6QgZ9wsQ5qPuMiRHNl0xjLp6R+dLZytqy7RV6uU/2wgnlhL5DUj3JKn7gGvNDWUy2G/Vrfs54JMwlgqSYnFV77yFYmX/sK/8IUvxH0/AO4Ty+7VThRvh5tVTVVy630dMu3zSW1VsRlo9XpGzbUD2RJr4ZY26nKrf7jgONl+oDej4hxA7HJ8MfTN0mmfuSQWU1NT4jTaf1gzTc04tQ+xW5BlA8mNi+DlHta7r9E63JhkJMMSKLhPIrHx2LY22bI1dOfvizcupUkBMppbx1KeOMbOMc1Y7N+/P1nnBgBZYa4dbuxagAJOm31b3VwlhfG/p5jWpY0A0i+mxGLZsmWpPxOklGbWbsuwgVTGRTo6WQGpYF9sXSTXXHr8nGIjU5c2AtEwloqO4m0XTd9NT0/TzQBIUlzYddfRze7KSwvlzof3Sl1VCbsKI4Nn30bN81ufv3NNLnjuI9swlkpxYrF161Z56qmnpK+vz/yi/VG87TyDg4Mm0waQeFwEL/fQpGJ0fEpyc3Pk2R1d5hi7CiMThJt9azuirWGXzPv5AE7GWCoFiYXX65WrrrpK7rvvPpO9+e/Cbf2bxAJANgte7qEzFZpUbNs9k1QodhVGJu9t0VjL8xZAfOZUmqXtZzWp+Pd//3ezC7cmEj/72c/k7rvvlnPPPVc2btwo27dvn8tDA0DG0IRBu9y85dI1MjU1Lc9s7zAzF/6ouYDT6eybFlf706+XsnEdgPmYsfj9738v//iP/2gSjJ6emV00lyxZIhdddJFcfPHFJrG45ZZb5MYbb5zLwyNFWA8IhOofHJOXDrSZTbsi7UURbc8Kramww67CSNe+KYkUW69uqpKivMmknDeQTRhLpSCxaGlpkU984hPm33l5eebz+Pj4zAPm58tb3/pWuemmm0gsHNjJAMAx3f1e+dPjbUHdcELrIuy75gTejhabSKVYnoOJoNgaiI6xVIoSi4qKCpmcnJz9t1bHt7W1zX5ff+kdHR1zeWikiC5X07+ZJn5k23DDIOylfT2y+1C/1FcXy+rmGllUVyr1QQOnnQf7ZHDYqy8XRz/s6yJi2bOCFptw4r4pc8VrBhCKuEhRYrFq1SrZtWvX7IzFiSeeaJZHvec97zG/9Ntvv12am5vn8tBIoeHhYTJtuCKp+Nlft8vL+3pkYnKmW11DdYm84dyVctKq+oBB2OHOQSnKnRLvVH7EuohY96zgXV+kSjr2TeE1AwhFXCSpeHts7Ng7JZdccon84Q9/kKmpmSLF97///XLPPfeYhGP16tVy//33y/XXXx/rQwNAUt/ZPdw5NJtUqK5+r7R1DcuBDk/AbZsWVMRUF9Ec4+2AVOE5CCCrEotFixbJhz70IXn22Wflc5/73Gw3KPXBD35QvvWtb5kMrqamRv7jP/5DPv3pT6fyvAEg7Du7o2OhRaft3cPS7wlcSqJ1ERWlhQHH7OoiwnXNoX4C84XnIIBMkOOzsoMoVqxYIQcPHjRryk466SQzI/H2t79damtrJZN4PB6TAA0MDEhlZaW4hf6ZdVMXrYlhXSCy2WPb2uR3D+wyhdn+XnvGclmzvEY2rlsYEBeHWrukpWdCWjuHItZFmI48YeonUtWtB4j1ORj29nN8XvKaAYRya1x44hg7x5xYqAceeEBuvvlmU0Ohm+QVFRXJlVdeaWorNm3aJJnArYkF4BY6mPr5X7eb4u1oNRap6NZjvZPMjttIJ56XAByfWFg0W7v11ltNkvHUU0+ZrK2pqUmuu+46efe73y3Lly8Xp3JrYqF/Zm0JXFhY6KosG9knlndhZ7tCtfRLfVX4rlDJiAudIdmy9VDI8Ys3LjWb5wHpkOjzktcMIJRb48KT6sTC386dO+UnP/mJ/PKXvzQtZrX17Pnnn2+WSr3tbW8Tp3FzYqHXrNfupmBAZoh1yUay34VNRlzctnmn2VMg2JqlNWZHbiAdEn1e8poBhHJrXHjiGDvHXLwdzpo1a+Qb3/iGHD58WP785z+bJVFa2H3ttdcm+tAAXMBKFvTdVR0I6edb79tpjsfTyz8TuvXoNek7yTro08921wiko4sUz00AadvHws7TTz8td955pzz++OPma50mAoBkbvzl38u/sqzQdHQaHZ+S3JyZpR/pKJ6Odcftnn6vbNnaIh09w+IZHk/6zsmAv3h2grff1btQ3nDmYvr1A5i/xOLIkSPy85//3NRa6JIonSI65ZRTZjtGwVl0p0ggkzf+0qThQLtHTl5dLz39o6aF7JplNWbwtG13t4xPTIUM2KMts0o0LmLZcVvP4entR6SlY1AW1ZeZc9bzTeXOyXC3eHaCD5fct3SNyHL2ugUCMJaKLO7fjm5lrjMTmkzce++95uvq6mr5wAc+YBKKV73qVfE+JOaBrgUsLy9P92kAIXSwb7cW3G7JxvqV2t7aJ797YLdpJ1uQlyt7W/ulqDBfXnvmMnlm+5GQ5VGh78QeSzqSFReRdty23g1+ZX+PDHknZNueLtOlatMZM+ebyp2T4W6x7gRvn9znSHvvhKvWkQPRMJZKYmLxwgsvmGTiV7/6lfT09JhjF154oUkmrrrqKtN6Fs6ls0mjo6NSXFzMCwUyaslGd9+ItPeMyO6WPun1jEpjbalc+OomefDZFsnPzzPLinoGvGYGQ5dH6dfW0qNdPom4zMouLvR++9oGpG9wTGoqimT5osqQ2Yd49gaw3g0uLso3iYV5jP5j58vOyXBmcu+TRXVFJkZ4zQBmMJZKYmKhS5xUc3OzfP7zn3d8W1mEGhsbM8EAZMqSDR3Ev7i3W+58ZJ+ZoSguzJfJqWmpKi+S15653Lzjr1+PjE6YZVG1VcWzicWyRZXyyoFe25/pP0ugcTHonZbdLQPS0umRZQsrpavPK7ta+mRhbZkMjozLhlX1plWt/Vr0yHUS1rvB5SUF5n7W3hp6vksXzlwr4MTkvrme1wsgGGOpJCUWb37zm83shHZ9IksDMB9LNrSeoq1reHYX7ampaSkqyDMF0NY7/lpXoQN2rV3oHvDODoqaGsplaGQi6jKrPs+o3PnEPhkYGpcNx9XLzX952fy7oaZEnt81s2ypoXpmD4x4Cs2D3w0uLMiTJXpO3gkZHZuUE1bWyRknLpS66pKEf39AspP71U1VUpg7M8MGAElPLH7729/G/KAAkAy6HEnf2bdMTE1LWWmB5OXmBMxQrF9ZJyevbpDt+3tk4wkLzSyADtiP9/midsY51DFokoXKsiKzpEpnK8zPmpg2NRy6bEmXYZ10XH1cheZ27wZrclFbkGfO4bQTGkkq4Njk3urXDwDxoLTdRWgBjEyjNQ46E/H87k6ZnPLJ9LRPegZGZUl9uXnHX2cw1i6vnV06pYN/f3rsTeevkhf39sj+1gFZsaRKTjquLmAA1dE3k3Ro69ojvceSGG1jW5Cfa5KZ7oHRuAvN59KdB3ASXjOAUMRFZCQWLqHL10pLGcggs2jhtM4mzAz6Zzbsys3NkYqyAtlwXJ00N0beAVRrIu54aK9591UfQ2ce9h7uD+gK1bSwVnYeHjK1FE0LKmTrK53m9sWFeWaZlSYXq4/OcMSzN8BcuvMATsFrBhCKuIiOxMIldKDk9XqlpKSEGhlkDB2ML6orl3desU4Otg+aGorlCytNh6XWruGoiYV/TYRV1G2O+3WFWrqgSKrLC6V/aNzsL9HcWGGWRJUW50tJcb40Lyg3S62s82H2AW7AawYQiriIjsTCRcbHx00wAJnkxX3dplhbOzQtqC4xrWA1SViztEbOWL8o4n1jqYmoKMmTay5dI7sPD0hH97D88xvXmwSms3fENnFg9gFuwWsGEIq4iIzEAoCjWXUN/jMO4eoa/PeYWLWkyux5EUtNhCYKC2rLUnD2c9v7AnAC7Zj20oFhOdw1xPMWQExILAA4Wqx1DXZ7TJxz8mIpK86X4dHJiPdNpbnsfQGkmz5v73nyoHQN6r4rOTxvAcSExMJF2B0dmSjWuga7PSae3n5EXnfWCvGMjIe9b7S4iHW2Idzt5rL3BZBu+rztG9aEPHf2GM9bgLFUNCQWLqFFRqwJRKaKpa7Brp5Cuzrp3hZvuXTNnOIi1tmGcLd75+Xr5rT3BZBuuvxpcjov9DjPW7gYY6nojr0V4QAPP/ywvOENb5DFixebP94f//jHgO9fd9115rj/x2tf+9q0nW+mdTIYGhoyn4FspLMEdiLtMREtLiLNNsRyuz2t/XM6LyDddOf6wlydsQiMDZ63cDPGUhmWWAwPD8vJJ58sP/jBD8LeRhOJ9vb22Y9bb711Xs8xk01OHltnDmQbXXqk9RP+YqmnsIsLnYHYtrtL/r6zU3o9o2bmI9K7tuFmJfYdHpjzeQHpZJ63ZQUBx3jeAoylMmop1OWXX24+oq1tW7hw4bydE4DMkKw9JqxlTfqOVH1ViXT3e81sxBJ9B7cgz/Zd20g7crP3BTKRPj8vO2OZtPZNSWvnEM9bAJmXWMTib3/7myxYsEBqamrkoosukq997WtSVzezeRWAzJdIa9Zk7DHhv6xJN8xrqC6Rrn6vDHknpLYgz/ZdW6tzVWffiLnd6NikLG4ol+bG8qSdFzDfaiqLZXlz1Zw2AqPFMuBOGZVY6DKoq666SlasWCF79+6Vf/u3fzMzHE888YTk5YUWmamxsTHzYfF4POazvhvpv0ZO/8dpt2Yulcfn82fq5+LiYvM5W65pvo476Vyy/Zp0MHLr5uAi6A6zgZ3/oCSZ12TFReCyppmvdTnUpacvld7+UbMz9ylrGkxSUV9dMnsffRz9+srzVsoTL7ZLy5FBWVRXJnU1pfLXR/fJVReujnruyb4mJx930rlwTZGPB8dGrI8RGMfaqrY3JI75O3FNmXhNcx1L5Tjg3BM5nrWJxTXXXDP775NOOkk2bNggq1atMrMYF198se19brzxRrnhhhtCjg8MDMz+8goLC6W0tNRs0647Kvovu9Lqf6398F9Tp8f0e4ODgzI9rT2+Z5SVlUlBQYFJXvz/MBUVFZKbm2t+pr+qqipzf30c/z+qHtefpz/XovevrKw056fnacnPz5fy8nIZHR0NSKDCXZOeV7ZdUzb+ndx6Ta/sOSLj3mEpyRMZm86TaV+OjHqH5ZW9bVK4uiHp1zQxMWHOXz+sa9J3V/cd7paCnGmR6Ul5eVerlJQUybmvXiGrl5SZa7J+tv81HWjplN6+fmmoLJBez7Dsa/dIUd5kwLlny98pG597XFPoNVm3j/eaXtnTZeK4MDdHxqfzJT93Wsb84pi/E9eU6dekb2brz3bL38kXR7KR40s0NUkR/YXfcccd8qY3vSni7RoaGsxyqPe///0xz1g0NzdLf3+/+cO5JSvXz/oksp702XBN83XcSeeS7df0m807ZVeLf62CLsHwyZrmGvknv5axsV6TvnO6u2VAWo54pCloOYbe3vofvcaFfq20puLW+3aEbMj31k1rzcxEKs49nmvK9ONOOheuKfJxfb30j41YHyMwFmbiQPnHAn8nrikTr2muY6kcB5x7Isf1mjUJ0oTJf+yc8TMWwQ4fPiw9PT2yaNGisLfRrMxuMxP95fn/z9I6ZieVx+fzZ+qTxfo6W65pvo476VySddxJ52Idb26sDGnlqgOTpoWVMcWr/7GZ5Ri7ji2rauk3XZ7896Cw/ifq//+DmWLrtXEVWyfj3GO5pmw57qRzSdZxJ51LMo5rXATHRqyPERoLM8eDY4G/E9cU7riTziVZY6kcB5x7Isdj5ajEQnsD79mzZ/br/fv3y/PPPy+1tbXmQ5c0XX311aYrlNZYfPrTn5bjjjtOLrvssrSeN4DksIqgg2cL5tLiMpEdr+dSbJ3McwcyGbEAuJejEoutW7fKhRdeOPv1Jz7xCfP5Xe96l9x0003ywgsvyM9+9jOzjEk30du0aZN89atfZXt1IEskszXrfO94TVtZYAaxALiXoxKLCy64IGKByL333juv55NttDAHcLpktWbVIuyX9nbPtn8tLsqX8pKCkD0okhkXtJVFNkkkNogFZCvGUhmUWCB1dM2cVvsDbqF7SIyOT5libKWb2y2oKZXli44lFsQFYI/YAEIRF9GRWLiEzgRphw+t5k+0MAfIBG1dw3LxxmbxDI1LbWWxDAyPSVefV7bt7pbC/DzzbipxAdgjNoBQxEV0JBYu4tDOwkDCgnf5PWFFrRzo8JgN9s7esFjue+qgdPaOyMTUtNn0bk9Lv1kD7r/RHYBAxAYQiriIjMQCQMYnFb++L3C37p0He2XFkirp6BmW9u5hOdw5KJNTPpme9pnuNJ2aiLT02+5LAQAA5iZ3jvcDAEewayt7uGtI6qtKZFFdmbR2DYl3bErGJ6YkNzdHF8maY/tbg/ecAAAAiSCxcBHdKRLINuHayurMxSUbl8ra5bVSUVpoCre1rsIzPCYTk9NSUzkzW0FcAPaIDSAUcREZiYVLaJGRbj9PsRGyjdZU2FlYXyZ11SWyuL5MFtWXmhmL/sFRsxyqobpE6iqLiAsgDGIDCEVcREeNhYuKjQYGBqSqqoqAgKt2+Z2a8sn5r26Snv5RU2+x6GjCoUuiiAvAHrEBhCIuoiOxAJCRnZ80obA24Yq0y++qpiq59b4Omfb5pLaqWLoHvNLrGTX3ScX5AADgViQWABwj3GDdrvOTzlJocmAlF+EG9cGJh9ZcWInHXNsGRjsfAADciMQCgCNEGqzbdX7SpU+aLMQykI+UeMxFoucDAEA2onjbJXQtIGsC4WSRBus9A17b++gMRDriIlwnqkTPB3AKXjOAUMRFdCQWLqFLPqanp9kxEo4VabC+bFGl7fe0niIdcRGuE1Xw+fT0e+WxbW1y2+ad5rPOygCZgNcMIBRxER2JhYsMDvJuKpwr0mC9qaHcdHry59/5ab7jQms/wp2PJg+aRPzi7u2y+elD4hkZkwPtHtmy9ZDcet9OkgtkDF4zgFDERWTUWGQButMg29vGanvYSJ2f5lu4TlQ6Of6ro3Ui2nmqu99r9szYdMYyeWb7zLVRhwEAyFYkFhmO7jTIFtHaxia7ADtRduejMxVWLI6OTZrPXf1es4dGZVmheIbHqcMAAGQtEosMF093GoqN4HTpSB6SGRf+dSLFRfky5J0w/9aN+XQPDU0sEq0LAeYLrxlAKOIiMhKLDBdrdxqrkwGQCeZreV+y40LPVWcNVXlJgUn6JyanzW7fujFfsupCgFTjNQMIRVxER2KR4fwHMv6C3xXVDgaTk5OSn59Pto2sX94Xa2JiFxeJJDX+dSKFBXmypKHcfD6uqUpWLKlKa10IEA9eM4BQxEV0JBZZXPAabHh4mEwbjpfo5nPxJib+cRHpvta5RUo4otWJAJmE1wwgFHERGYlFhmMgAzcs79PC55Gj9QqpTEzC3felfT2yr3VAWruGoiYrTisyBwBgvpBYZAEGMsjW5X26jOjk1fWmq9L+do/puhRtaVIiu2KHu+/uln7Jyw2c9qZ1LAAAgdggz0Vyc/lzw/n8N5/TpOLeJw+azeUOdcS2yVysu2LbxUW4+9ZXFcvgyHjIcVrHIpvxmgGEIi4i47fjElpkVFlZGbHYyNox+LbNO81ndghGOpf3XX7WcvGOzuwFYRVB+88UzGVX7GhxEe6+q5trTKvYYLSOhZtfMwC3IS6iYymUizoZjI+PS2FhoW1A9HlG5c5H9sW0hjwYO38jVcv79h4ekNrK4rhmCuKpOwqOi0g7amuCEUuTBMANrxmAGxEX0ZFYZKngwf7q5iopzJ0wwWB3u5f39Uhpcb5sPKFRtu3ulvGJqZjWkLPzN5zQTjmRuiOv1xsQF+HuS5MEuE1wbAAgLqIhschC9oP9QnnDmYsDWqT5366ta8jsEtxQXSKbzlgmz2w/EtMa8kRbgwLJaqecajRJAAAgMhKLLBRusH/oyKAsb260vV15aaGMjU9JV7/XdODR9p66pjzaO8OJdOABUtVOmeV5AADMPxKLLBRusH+kdzTkdrrkSWcqRkYnpagwT8ryC6Sjd1hqKoolNycn6jvDc12qAqRqpiDe5Xm6g2osj0miAreJJTYAtyEuIuO3k4XsB/s5smRRXUCxUWNtqdz75JBMTE7PFiXp9084q04aakpk+cLKqIMnJy1VAcLN2I2MTUpHj31yUF5eHvHxqCOCG+lrQbTYANyGuIiOxCIL2Q/2C2X5guLZ5EEtqCkxSYAuf1KmK051iRl0nXRcfUw/i52/kQkzdrofxm2bd0hRYX5AcnDNpcdLRUmuFBcXh+3wQR0R3EhfK0ZHRyPGBuA2xEV0JBZZyG6wv7pppiuUvvu6u2XADL5Ki/LlHZevkx0He6Wtc1gW1ZdJXXWJGXTFmlhYP48BFpw6Y6f1Qlo3NGyW++WHJAfrl5WZF4lwqCOCW42NjUWMDcCNiIvISCyyVPBgX7PsAy1H5M4n9snA0MxGX72eUdOf/43nrZTRsSnpHvDKvrYBuXjj0jSeOZDcGbuK0kLp7BuR8pKCkNu2Hhk0iUUk1BEBABAbEgsXOdQxeHRJx8z0nQ60dEO81s5hGRwZN12gqI9Ats3YrWyqkqGRCXnk+dbQ29aWmj1cWro7pLmx0rYomzoiAABiQ2LhIh19gevECwvyZElDuSlsPXFFnZSWFFAfgbRJZuel4Bk7fewX93QHJAdlxflmJu/hbR0yMZ1rEpEnX2qTTacvl5f39wScA3VEcCM2AQNCEReR5fj0ldVFPB6P2SRuYGBAKisrxU0e29YmW7YeCjmuS5/O3rA4LecE2HVesmYFktl5ySQufslBZWmh3PX4ftNyWelnncG7+DVLzbJAawaP7k8AADfzxDF2ZsbCJTR/XLqgyHSH6j9aY6FY0gEnmI/OS8GzGLdt3injE5NSkDttZix0PxdtvdzePSy1VcUmsaD7E9z8muH1eqWkpITuN8BRxEV0JBYuUlGSJ9dcukZ2Hx5gSQccJR2dl2aKsnslP2daJiRXRscmzXHtjqYzFvNxDoCTjY+PmwEUgGOIi8hILFxGk4gFtZG74ADzLR2dl2aKsjtkzDuTUJQWF0h1RZFJLLQ72nycAwAA2YTEAkDapaPzkibZOoP3yt42aekal+o1JVJclCfd/V7ZeEKjbNvdbfZ6YakgAACxIbFwkaKionSfAmDLrvPSmuZq8R1tOpCMTlHhfu7k0npp7zsiew73m6RCay3KigtM0rGwjs0f4V68ZgChiIvISCxcQouMWBMIJ7NrEevfKUqXSumsRjK7NGlc7O/wmoTGasFcW5Bn/u0ZGY9rB3ogm/CaAYQiLqIjscjCHv7hOhkMDw9LWVkZnQyQEeajU5TGxeGOHv3X7MaRFoq24Wa8ZgChiIvoSCwcIJ53ZhNJQCYnZ4pUgUwwX52iFtYUy87DQyHHKdqG2/GaAYQiLiIjscigd2YTWRqi931lT5e093dIc2Nl0mdEgEzoFBWcmK9urpJlCyukev8g+7sAAJAgEosMemd2rktDdDB1q24G5h0W71S+uX2y16rP57IuuEOyO0XZJ+aF8oYzF7O/CwAASUBikUHvzM51aYiVkOTlzBSlqlTtKDwfBbdwb6eoRAb84RLzwz1jck5zI/u7AEEoUgVCEReRkVhk0Duzc10aMpOQ5MiUL7nFqcEzE2uX1cxLwS3c2ykqEfaJeY60dY+GFOEx6wa305igrSYQiLiIjsQig96ZnevSkJmEpFeK8qZkbEpnLXKSslY9eGbicOegjE9O2d6eDjtIN/vE3CcLa/JNpw8ruWDWDZjpfjM4OCgVFRV0vwGOIi6iI7HIoHdm57o0ZCYh6ZAx73DSilPtZibae4ZldXON7G/zhNyeDjtIt3CJ+dIFgUugmHUDZkxPT6f7FADHIS4iI7FwwdIQvb0Wp76yt006+qakaWFlwsWpdstKPMPjUltZZAZrnX0jZgfj0bFJWdxQLs2N5XP+WUAy2CXmq5uqpDB3Ii1tbgEAyDYkFmk0l3Xcc137rbcpXN0g51VVJWX6Lly9hz72P1ywSh57oV1aOgZlUX2Z1FWXyF8f3S9XX7Sad3zhqMRcp7UHBgZS3uYWAAA3ILFIk7ms40507bfuFDmX87RLZMItK1m1pMrcvqNnWGqriqV7wCv72mYGbiwlgRMFx0Wy29wCmWourxlAtiMuIiOxSJO5rOOO5T7hEgGdSSgoKIjrHKMlMuHqPbZsbTHLovTDH0tJ4DR2cZFom1s6SiEbzOU1A8h2xEV0JBZpMpd13NHuEykRqK8uEY/HI5WVlTEvhYqWyISr92ApCTJFZ++wvLKnXdr7JwN2pJ9rm1s6SiFb6DLBeF8zgGxHXESXG8NtkAI6+LYTafAd7T6REgErIOIx1yJWHZzp0hF/LCWB01g70j+9vV12tfTJlq2H5Nb7dprjcxUtBoFMEu9rBuAGxEVkJBZpMpfBd7T7JLubzVySH/+lJBdvXCprltaYz7xjC6dJRRKgMVhZVihLGsrNZwvLAAEAbsBSqDSZyzruaPdZt7zWFE0H1zZESwTiLdCOZeYhmTsmA6mQSCIeLmZOXFEnk5PT0t49bDqirVlWI9t2d7MMEADgCiQWGbgnRfB9rEHOvrZ+WVh7bDAzPjEVkAjoTpHJKtAGMp21I/3oVH7cibhdzLzp/FVy71MH5OV9PTIxOS3b9nRJQ3WJvOHclSwDREaye80A3I64iIzEIsMFD3I0mSgsyJNLT1sq0z4JSARyc3NDio3mWqANZLpjM3Kj2usj5hm5cDHz4t4eyc/LNcugrM0hdSWuxhwxhEyjz1u71wzAzYiL6EgsMlzwIEeTCqVJxdkbFodsBFYVtEFerMtBaKGJbDOzI/3xce9IHy5m9rcOSEVp4cwO9EfjUHX2zr0YHEiXcK8ZgJsRF9GRWGS4RAu2Y2kNSwtNZKtwO9JHSqTDxcyKJVWyu4U2ywAA96IrVIaba+emeLpTxdM9Rwdkj21rk9s27zSfE2ndCaSDlUhr+1lNIILb0IaLmZOOq5PcoHewaLMMAHATZiwyXCKdm1QsBdrxLJdiZgOZLpa6o3AxQ7MDAICbkVhkuFgHM7rMI9yawGgF2rHupB1tQAY4jV1cxJJIh4sZmh0gW0R6zQDciriIjsQiC8QymNGCo+np6Tl1M4h1ViTZG/QBqWYXF7Em0kA2S+Q1A8hWxEV0JBZpNp/dlgYHB02mnapZEQZkyETBcZHo8sJg3X0j0t4zYgq7u/tHZfXSalm/so6ZDTjeXF8zgGxGXERGYpFGPf3egJoE3TX7cOegXLJxqdRVlyQ1YVndXCWFuXNPbGKZFUn2gAxIh2TWSmh8vbi3W+58ZJ9093vNsSdeajOJxbVXnEByAQDIKiQW88x/IK8973Uw/sqBXlm3vFZ6+kflUMegPPlyh5x2QuOcBx32RdSF8oYzFwdk2dGKreOdTaF4FdkiWbUSB9o90tY1PJtUKN2Vu6VziNojAEDWIbGYR8ED+bauISkpypd3XL5Ofv7X7dLRMyLT0z7ZeahPdh7olXdesW5OA49wRdSHjgzJ8ubGqLfTAY+2zZxLhyeKV5FpkrFONlwS3jc4Ju3dwyG31125qT2C07GGHAhFXETGPhbzKHggX1yUL/2DY7K/bUBGRidlfGJKJqemJS83x8xivLSvZ04/x76IOkc6+qdi7n5zuGso5r0rADd3+Ai374UudaypKJJF9WUh99HYp/YITkb3GyAUcREdicU88h/IaxJRkJcrubk5sr/NYwYgqrAgTwoK8sQ7Pim75ziIt980zyeLG0pNR4PIt5sptj7Y7rH9Hu+yIptoPExMTATERbzCzfztONQnyxdVyuKGMmnwq5kqyM+V5gXl1B4h62MDyDbERXQshZpHVtckTSpau4Zkaton9VUlclxTtRmw69IJTSw8wzODlPqq4jktw2huLDdF08FF1E21BTEXW+8KEzO8y4psMzw8nFCHj0gzf2dvWCwbVtVLQ3XpTFeogVFZ3UxXKLgjNoBsRFxERmIxj6yB/L62AVPAqXJzZ44/9PfDMuKdkJHRCXNc3+Fc3VwT9THtCrAXVJfIm85fZQpErSLq1U3aFWrC3H53y4AZDC1fWBlyO/9iazo8AdFFa7NcX1NqPk46rj4NZwcAwPwhsZhHVtekR55vlb2HB8zaa20re6R3WC48tUm6B7zSOzBmjuvyiUV1pXNahtHZ7zXJgr5batFpuwMtR+TOJ/bJwND40fv2mWTh7ZetDbhtvB2e5nMvDsBpzzvaLAMAMIPEYp7pwKe5scJ0i9FEQmcvdPnTyavrZfmiKlna6JPqyiIzm6DvckYTz27Xh44MH01CckLWgp9ts29GLB2eorWsBVIhmc873UE1EbRZRrZKNDaAbERcREZikQZa0PnEi+3iGZ6ZOSguzBPP0LhsOm2Z1MRYVxHvbtfawaC9fzIgqZhLQbb/u8SrllTJkHciZMZk2ueTtu5hBlZImUitkuN53mlcVFZWJnw+tFlGtklWbADZhLiIjsQiDax3OPe2DpglSr2eMen1eGX7gd64l3PYLcPQGgvtOvPYtraAnbeXNpTIrkO9IclFrAXZwe8SDx9NKrQYXWddrJkX3ejvkedaZWhkgmVRSIl4Zuoi0fgbHx+XwsLCpLYPdMoyLWCuUhUbQCYjLqJz1HzOww8/LG94wxtk8eLF5g/2xz/+MeQP+sUvflEWLVokJSUlcskll8ju3bslE+kgQ9/xf3r7EZMYaMtZq/+9DkriTVIu3rhU1iytMZ+vOHuF3PHQ3oC++rdt3im1FXlm7be/eNaCB79LPDgyLgtqSs2shdKk4t4nD8o9Tx6QPa39c7oeIBaRWiXHy+s9tit2MoTb14I4QKZJdmwA2YC4yKDEQlt4nXzyyfKDH/zA9vvf+MY35Pvf/7786Ec/kqeeekrKysrksssuk9HRUcm25Rzx0ORCi6/fcuka87nliP3mdkd6R+Rtm9YGJCHxrEnXd18rywplSUO5+axLueqqi6WsuMB8rTMV3f1e06e/vGSmtS2b6iEVdAYgkSQ5E+IaAIBM46ilUJdffrn5sKOzFd/97nfl85//vFx55ZXm2M9//nNpbGw0MxvXXHONuHU5R6yPq4nFedUlcvYcl2ScuKJOJienpb172HSuWrOsxuwQfs2la0xdhS5/qq8uMUmFLouysKkeks3JBdOpimsAAJzOUYlFJPv375eOjg6z/MmiG5Scfvrp8sQTT2RkYhFr4XWyHndhfcWc14Dr7e996oC8vK/H7MGxbU+X2WvjDeeulIV1M4WrWlPROxg6e8SmekiFZBVM5+fnZ0RcA/Mt2bEBZAPiIrKM+e1oUqF0hsKffm19z87Y2Jj5sHg8ntkZEP8t2bWmw26L9lQenym87rDtf5/IY2uh9rM7CqXf7Ffhm33ctSsa/daA7/Br1dlrzuOtm9aaGQe7x995sM9s3rekoczUVIyOTR59ZDH30Z8/83OLpLNvePY2ixvKpWlBmbndfP9+k3XcSefCNSX3mpQuqVTWbRI9l3Dxp5tU6u34OznnXLimyNcUHBvZcE1OOReuKXOvyYoLlS3XFO14ViYWc3XjjTfKDTfcEHJ8YGCmI5PS6v7S0lJTkKPV/paioiJTJK61H5OT2qp1hh7T7w0ODsr09MwO2taTraCgwCQv/n+YiooK0/dYf6a/+uoq+aeLV8vO/R1mmdKSBeWydEGFeRd2YmLC/FyL3l9bnOn5+RcOaeZcXl5u6kysBKowV+Qfzlsmh7rG5HB7ryysLTJdonwTQzI6mmtmKrwjI1KSd+wcB4d9ZllJUd6k7TW1HumWkrxJKckTqSrOl9GpYjNk6u7pk4GBytmfe+V5K+WJF9uks6tP6qurpaqiSDY/tlOuvGi9VJcXzPma0vl30pkxvb8+jn/w6XH9eVxT5l6Tnrc+jtXhIxnXpHHw5gtXyv6OEWlt75bG2hJZ2lhhdr7X2/B34poy4Zr0HEdGRsxrkZWEZ/o1ZePfiWua32vS89f71dTUmMdyy9/JF0eykeNLNDVJEf2F33HHHfKmN73JfL1v3z5ZtWqVPPfcc3LKKafM3u788883X3/ve9+LecaiublZ+vv7A3oRpzsrt5YmHdalSY2V5l3P4GUeifxM/axPdH0S/3bL7qNtZwOtWVor/3TJ8baP8+jzrfLAs4eCvyMXvaY5YNfux19ol2deaZeKkkIZ9I7P7tVx8cZlctZJixL6faXruJPOhWtK7jXp/1StuPAfPGXyNTn9uJPOhWuKfFxfJ/1jw4nn6Pa/k5POxQ3X5D+W0gQgG64pluOahOg167VH28cjY2YsVqxYIQsXLpQtW7bMJhaaJGh3qA984ANh76dZmX7Y/fL8/2dpHbOTyuN6TJOKWzfvOrY0qaXfLCmy69iUyM+0rrmxtlSefKndLFUqLsqfLbbWNeDhHkcLtf++szNk2ZZ2l/K/j9ZseIYnzMfRnzpbuJrjl4Ak65rm67iTziVZx510Lsk6PpfH8P9I57mHO+6kc0nWcSedS7KOO+lcknFcBxd2seGkc5zLcSedS7KOO+lcknXcSecSfNw/JrLlmmI5HitHJRZDQ0OyZ8+egILt559/Xmpra2Xp0qXysY99TL72ta/J6tWrTaLxhS98wex5Yc1quH0X4VhoEmNeMPT37Z2Y3Tl7/cq6iK06Y+3CQ+EqAACAOzkqsdi6datceOGFs19/4hOfMJ/f9a53yS233CKf/vSnzVqx973vfWaK9pxzzpF77rlHiouLJZPNV3tKXYf30q4+synfpjOWmX0nrNaxJ69uiJrE2HXhCe4u1dxYbmYy7ArSASfSuAAQitgAQhEXGVpjkSq6fCrWdWLz5cFnW+RPD+8NWZqkG9j51y9YA/m9hwfEJz7p9YxJr8crKxdXR20Xa9EduK0ZBd3UrqK00OygvaiuzGywN5cdhv1nWxZUl5idv1s6hxy3vwAAAABSN3Z21IyFG8WzNMkayGsSce+TB2d3ud7Z0CfP7phZqhRuAK8/QzsGNDWUzyYWWlhtFVdvPGFhUpZwdfZ7TVIRnBABTmTFhXbJSHRdKZBNiA0gFHERHYlFmungPNalSXpbfVLr7TSpULpZnSYjhQXRazK0DZkmJXZF2HNZqmQt4fKf+dBERWcqPMNjZu+Lva0DMW3AB6SLxoW+SAAIRGwAoYiLyEgs0kwH5+MTU/LM9iNmgF5bVSzdA17Zvr9HTjquPuS2OoDX5MOfLqEKV5MxWwNxxCOLqvNk3XGL5W2b1sjOKEXYsVi+sFKqygsDEqLVTdWSl5djllyVFhVIXXWxPLKtVZ7dcSTijAoAAAAyG4lFmvl3UYq2NElv+8wrHWYAv21P1+xxrcuw67wUWAPhk5a2Sdm2f9DssJ2MpUqLG8rk9w/uko6eEfP187s7pbayRN55+Vr55T07zLGG6hIzG6OJUyq6XAEAAMAZctN9Am6nS4R0KZK/cEuT9La5OTlmFkAH7EprLLTY2+4+wTUQE77c2Ta2ydByZEiKC/OlvrrEnENNxcxu3C/v75XayplOXV39XjOjobMxye5yBSSD3T43qaCJ/mPb2sxsnn7WrwEnm6/YADIJcREZMxZpFuv+EP631boF3SFbu0L1ebyyYkm17X0C29jmyOR0nvlXR/dwSJvYudRA6H21e1VtwczjHukZkYEhXRY1JDUVRdLrGTXHdZmULvFiLws4jRbfzcda2eAOajpLyfJAONl8xQaQSYiL6EgsHMBuf4hk3DZwszqfFOZOyfh0nhy/tCYpg5zgzfAKCmYmwJoWVMi23ceWaunSLa0jYS8LOI02Q9C9ccrKylLa4WM+N8EEMik2gExCXERHYpEmPf1eOdw1JHsO90lxYYHUVhZJjuTIqqaqpA00dBZCEwarA1Rejk+a6stNcXgyBjnBj6/LofLzcs1jlBTNLJEqKy4wHa4W1sWeEAHzaXJypvlBNmyCCWRabACZhriIjMQiDQ51eOTxF9rlYIfHLBGqKJ2WR54/LOe/ukluva8jacsj9DHedP4qeXFvj+xv7ZfljYVy6onL5KHnWuMe5IRbOhW8jEt33m7rGpaTVtWzOR4QZnbPwvJAAEA2IbGYZzpAv+Wu7WaQoXtQ6MeC2lK56NQmU+Q87fMlbXmE/qw7Htprpu4qSgrkUMegdPbvlxVN1XENcqKtDw8+16WNlXLG+kUJnz+QLYJn9xLZPwYAAKcisZhn+q5/W9fQ7Nfa5amzd0QGh8dNIXRNZXHSlkf4r+vWDet0KdTU0JBsPHGRGdTEOshhfTiy2XwU4sXTpAFwCopUgVDERWQkFvNMlxLpvhO6S7UWNE9N+SQnN8fUW5y2bqEUF+dJYUFy/izBXaGmfDmzMw7xDHLCrQ9PVncpIF20+G6+WgfG03gBcFNsAJmCuIiOxGKe6eD7pZJu6e6fGeQXFeaZ5U9LF1ZKTWWR2QdiTXNNXI8ZbnAf3BWqKG9KxqbyZGF9WQLdpY5JVncpIF10meDg4KBUVFTQ4QPwQ2wAoYiL6Nggb57poH+BDuqrS2RxfbnpnrRqSbWsWVotP7rjRfnzI/tkyqfpRWys+octWw+Zgb1+vvW+neZ48OZ7ueKb07puu038mhoid5cCMsX09HS6TwFwJGIDCEVcRMaMxTyz1lpvfeWI7GsbkMrSQikvLZCb/7JdJqemZWRsMq7ahUj1D2dvWHxsyVOHRxbW5Mm6VYsjPnas3Z9OWF4r9z51MGwrXQAAALgLiUUa6ED9zJMWSd/gqDz5UsfsDtUF+blmL4h4irej9ce3ljz5TlokAwMDUlUVOamIp/tT8BIpLT4/eXW9aDJ/2+ad1FwAAAC4CEuh0qS6olgW1ZdLbm6OSSZ0M7klDeVmcB5Pb3sdvNuxewzdKXKusx+xLJHSpGLLM4fkiRfbQpZlAU4VLS4AtyI2gFDERWTMWKTR+pV18uKe7oR624frj7+muTrMsqaCpO0O7L9ESpc/6UxFcWG+SY4stKWFk2nxXUFB+JgA3IrYAEIRF9GRWKRRMnrbh3sMLf8OXNbUKy/sbJGrL1kvC2rLkrY7sP8SKV3+5J9UWJK1LweQig4fHo9HKisr6fAB+CE2gFDERXQkFmmWjN72do/x2La2kGVNQyPjJgEJl1gkujvwXBITwAkvFABCERtAKOIiMhKLLBVuWVNrhNmDRGdQEk1MAAAAkLlILBxurjtbh5s9WBJl9iCRGZRkLO0CAABAZiKxcLBo7V/jnT0oLilN+exBMpZ2AfNJd1AFEIrYAEIRF5GRWDh0dmLVkioZ8k6Ebf8abfBuN3uwuqkqbH0F4EZafJebm0sRHhCE2ABCERfRkVg4dHZi+GhSMT4xFdJpKdYuS/6zB1pspBvk6WcCApCAuKiqqiIuAD/EBhCKuIiODfIcInhzusGRcVlQU2pmLYLRZQkAAABOQ2Lh0C5OnuFxqasulrLiwI1Y6LIEAAAAJ2IplEPYdXHatrtbrrl0jXhGxsN2WZpr1ygAAAAgmUgsHMKui1NpUb4srCuVk46rT7hrlK4FZE0gEIi4AOwRG0Ao4iI6EguHmMseEMF1GZG6RmnB0fT0dEq7GTB7gkwzH3EBZCJiAwhFXERHYuEg8e4BEW53bbuuUTrof2VPm7T3T0lzY2XSB/2J7LkBpNPg4KB5BwpAIGIDCEVcREbxdgbTWQE7wV2jdNB/6+ad8vT2DtnV0idbth6SW+/baY4nS6TZEwAAAGQ/EosMprMO2iUqWteoeAf9mnA8tq1Nbtu803yOJQGJZ/YEAAAA2YelUC6oy7AG/b4Yl0zNZUmTXVeryrJCWbu8NoErBFKPdbKAPWIDCEVcREZi4YK6DGvQPzpVEHWjPbvZjWmfT9q6hyP+HP+uVrpT+Mmr62VgcFxe2NMlQyMTFHLD0R0+AAQiNoBQxEV0JBYuMDPo7xDP8KhM+zTTzgm70Z7/kiYrQejpH5VHnmuNmCD4z57k5ohsfvqQjE9MmcfY3+ahkBuO7fAxOTkp+fn5vAsF+CE2gFDERXQkFi6gg3ndaO+VvW3S0TclTQsrw7ay9V/SpEnFvU8elO5+r9RXl0jv4GjEBMGaPdG6DKVJRbQ2uEC6DQ8P8w4UYIPYAEIRF5GRWLiEDugLVzfIeVE2drGWNOnyJ52p0KSiID9XltSXSW1ViQyOjEdNECjkBgAAcB+6QsF2SdOFpzZL/+CYLFlQLm++cLUsqi+X3oFRqa8qMUudktEGFwAAANmDGQsX0Z0iY2EtadKailXNVbPLodS2PV1y/NIaOa4pfDG2fyG3JVxNB5ApcQG4DbEBhCIuIiOxcAld/lRZWRnXfdYuqzFF2FZSoXRZlBZlR1oOFWsbXCAT4wJwA2IDCEVcREdi4aJOBuPj41JYWBhzJ4O66hKTROhsw8jopJQW50tFWaEpyo5WLxFLG1wgE+MCcANiAwhFXERHYuEiXq/XBEM0ukme7mfR0Tsk5aUFkpebI/n5uTLsnZDh0QlZ0lBOvQRcFxeA2xAbQCjiIjISC4TdebvXMypnrF8oU9M+KSzIlZHRCXMbnbGgXgIAAAD+SCzmmTUboC1ZtXuS03ak9t95e3RsUrY80yIXb2yW/Lw8OdThkQU1pXJcU5WjzhkAAADpR2l7GmYDtmw9ZDah08+33rfTHJ8PulNkNP57UBQX5Yt3bFL+8uh+6e4bMZ2guge8MuVL8YkC8yiWuADciNgAQhEXkZFYpGk2IHhH6lTTIqPy8vKoxUb+e1CUlxSYLlDm36WFsq9tQHJzclgGhawRa1wAbkNsAKGIi+hIu+ZROnek1k4Go6OjUlxcHNPO25rwaC2FFmrrZ13+tGJJFW1jkVVijQvAbYgNIBRxER2JxTzS2QBdAhVsvjosjY2NmWCIhD0o4DaxxAXgRsQGEIq4iIzEYh5lyo7U7EEBAACAeJFYzCNmAwAAAJCtSCxcNBvAhi5AKOICsEdsAKGIi8hILFxCi4xKS5kZAfwRF4A9YgMIRVxER2Lhkk346qtLzDb0JSUldDIA/Dp8EBdAKGIDCEVcREdikaWb8Fn7ZWgXKi0Yv+bS46Uwd8IEQybuCA6kyvj4eNi4ANyM2ABCEReRkVi4aBO+9cvK4kpGtNCc5AIAAACxYOdtl2zC1xphE7507ggOAACA7EBikWV0GZOdJY0VUlRU5LgdwYF0CxcXgNsRG0Ao4iIyEosso7URuumeP/16zdKasMVG4ZKR+doRHEgXjQeK8IBQxAYQiriIjhoLl2zCp12hhoaGZGQ8R3a3DAQUaWfKjuBAKjp8DA8PS1lZGS8UgB9iAwhFXERHYuGSTfg0GLp6h+TOJ9pkYGg8oEj7bZvWsCM4XGtycjLdpwA4ErEBhCIuIiOxSLP5bPN6qGPwaJH2sSxbZyn2d3hk47qFJBIAAACYMxKLNJrvNq8dfSMBXxcW5MnJq+ulpWNQ9h4eYP8KAAAAzBmJRRpFavOaisH94oZq2Xn4WHKhScW9Tx40/66tLGb/CrgSGx0B9ogNIBRxERldodJoPtu8apHR2hUNUl1ebL6uLCuUnv5Rk9iUlxTM3o79K+AmGhfaOpAiPCAQsQGEIi6iI7FIo/ls86rF20V5k3LNpcfLxRuXyokr6mRkbFKWNJSbJVH+2L8CbqFx4fF4zGcAxxAbQCjiIjqWQqXRfLd5nZ6eloaaCllQW2a+fmxbm3T0DIfcjv0r4CYaFwBCERtAKOIiMhILB+45MV/1DexfAQAAgGQhsXDgnhPz+bPZvwIAAADJQGLhIrpTpJMSG8CpcQGA2ADsEBeRkVi4hHYwKCg41v0JAHEBhENsAKGIi+joCuUS2sFgYGCATgaAH+ICsEdsAKGIi+hILFyEQABCEReAPWIDCEVcREZiAQAAACBhJBYAAAAAEkZi4SIVFWx8BwQjLgB7xAYQiriIjMTCRZ0McnNzzWcAM4gLwB6xAYQiLqIjsXAJOhkAoYgLwB6xAYQiLqIjsQAAAADgrsTiy1/+spl+8v9Yu3Ztuk8LAAAAcL2M23n7xBNPlPvvv3/26/z8jLsEAAAAIOtk3KhcE4mFCxem+zQyjs7uVFVVUXAE+CEuAHvEBhCKuMjCxGL37t2yePFiKS4uljPPPFNuvPFGWbp0adjbj42NmQ+Lx+Mxn7Xwxr/4Rp8kdsU4qTw+nz9TP09PT5tuBvqRDdc0X8eddC5cU3KvSWPCigvrhSLTr8npx510LlxT5ONTU1MhHXCcdo5u/zs56VzccE1zHUvlOODcEzmetYnF6aefLrfccousWbNG2tvb5YYbbpBzzz1XXnrppbB9hTXx0NsF86/qLywslNLSUvF6vTI+Pj57m6KiIikpKZHh4WGZnJycPa7H9HuDg4PmCWYpKyuTgoICk7z4/2H03PQJqD/Tn2a9en99nOBsWH+e/lyL3r+ystKcn56n/wxOeXm5jI6OBiRQwdek5zM0NCT19fVZc03Z+Hfimub/mrq6usx16G2z5Zqy8e/ENc3vNek56rHgpDuTrykb/05c0/xekzWWamxsNI/llr+TL45kI8eXaGqSRv39/bJs2TL59re/Lddff33MMxbNzc3mvvqHc0tWrp/1ia5PYmYsuCYnnUs6r0n/p2rFhf/gKZOvyenHnXQuXFPk4/o6Gbzsw2nn6Pa/k5POxQ3XNNexVI4Dzj2R45qE6DXrtfuPnTN+xiJYdXW1HH/88bJnz56wt9GsTD/sfnn+/7O0jtlJxfGuvhHZdahfWjoHpXlBhRy/tFoaakpT+jP9rzmV1zqfv8f5Ou6kc0nWcSedS7KOz+Ux/D/See7hjjvpXJJ13EnnkqzjTjqXZBzXwYVdbDjpHOdy3EnnkqzjTjqXZB130rkkayyV44BzT+R4VrabDabTUXv37pVFixZJJtGk4tf37ZQtWw/JrkN95vOt9+00x1Mp0ScLkI2IC8AesQGEIi6yKLH413/9V3nooYfkwIED8vjjj8s//MM/SF5enrz1rW+VTKIzFQNDx5Znqf6hMdnV0p/SQKCTARCIuADsERtAKOIiuoxaCnX48GGTRPT09EhDQ4Occ8458uSTT5p/ZxJd/mTn8BH748mg09pavKPFPwQEMIO4AOwRG0Ao4iLLEovbbrtNsoHWVOgSqGBNjfadrZJFOwNopg3gGOICsEdsAKGIiyxaCpUttFC7ujywoFy/Pr65Om3nBAAAALhmxiJbaPent25aY2oqdPmTzlRoUuHfFQoAAADIJCQWaaJJxHwnEtpzGUAg4gKwR2wAoYiLyEgsXEKLjKJtagK4DXEB2CM2gFDERXSkXS7qZKA7kGfwRutA0hEXgD1iAwhFXERHYuEiXq833acAOA5xAdgjNoBQxEVkJBYAAAAAEkZiAQAAACBhJBYuojtFAghEXAD2iA0gFHERGb8dF3UyKC8vT/dpAI5CXAD2iA0gFHERHTMWLqEdDLTgiE4GwDHEBWCP2ABCERfRkVi4iLZIAxCIuADsERtAKOIiMhILAAAAAAkjsQAAAACQMBILFyksLEz3KQCOQ1wA9ogNIBRxERldoVzUyaC0tDTdpwE4CnEB2CM2gFDERXTMWLiEdjAYGRmhkwHgh7gA7BEbQCjiIjoSCxcZHx9P9ykAjkNcAPaIDSAUcREZiQUAAACAhLmuxsKavvJ4POK269Zr1vWB+gGAuADCITaAUG6NC8/RMXMsS8Bcl1gMDg6az83Nzek+FQAAACBjxtBVVVURb5Pjc1kFyvT0tLS1tUlFRYXrsk1NplpaWqSysjLdpwM4AnEB2CM2gFBujQufz2eSisWLF0tubuQqCtfNWOgvpKmpSdxKA8FNwQDEgrgA7BEbQCg3xkVVlJkKC8XbAAAAABJGYgEAAAAgYSQWLlFUVCRf+tKXzGcAM4gLwB6xAYQiLqJzXfE2AAAAgORjxgIAAABAwkgsAAAAACSMxAIAAABAwkgsXOCHP/yh2Qzw9NNPT/epAGl3yy23mHjw/1iwYIFceOGFcvfdd6f79IC027t3r7z//e+XlStXSnFxsenXf/bZZ8v3vvc98Xq96T49IG2vFxoPukncZZddJt///vfNpnFw+QZ5bvSrX/1Kli9fLk8//bTs2bNHjjvuuHSfEpB2X/nKV2TFihVmR9EjR46YF5ArrrhC/vznP8vrX//6dJ8ekBZ33XWX/OM//qPpenPttdfK+vXrZXx8XB599FH51Kc+JS+//LL8+Mc/TvdpAml5vZiYmJCOjg7529/+Jh/72Mfk29/+ttx5552yYcOGdJ+iY5BYZLn9+/fL448/Lrfffrt5B0qTDG2VBrjd5ZdfLq95zWtmv77++uulsbFRbr31VhILuPb14pprrpFly5bJAw88IIsWLZr93oc+9CHzxpQmHoDbXy8+97nPmRjR14o3vvGN8sorr0hJSUlaz9EpWAqV5TSRqKmpkde97nXy5je/2XwNIFR1dbV5YcjP5/0WuNM3vvENGRoakp/85CcBSYVFZ7s/+tGPpuXcAKe56KKL5Atf+IIcPHhQfvnLX6b7dByDxCLLaSJx1VVXSWFhobz1rW+V3bt3yzPPPJPu0wLSbmBgQLq7u6Wrq8ss7/jABz5gBlXveMc70n1qQFroMkCtqzjrrLPSfSpARnjnO99pPt93333pPhXH4K25LPbss8/Kjh075L//+7/N1+ecc440NTWZZGPjxo3pPj0grS655JKAr3VN+U9/+lO59NJL03ZOQLp4PB5pbW2VK6+8Mt2nAmQMHVNVVVWZhgeYQWKRxTSB0DXj2u1GaUeDt7zlLWbK7r/+678kLy8v3acIpM0PfvADOf74482/tXhb4+Kf//mfpaKiwszyAW5LLJQ+/wHErry8nO5QflgKlaWmpqbktttuM0mFFuRp0Z1+aMtZHURt2bIl3acIpNVpp51mZi304+1vf7spSj3hhBPkwx/+sOmCA7iJtpRVDJCA+OgSWhLyY0gsspR2K2hvbzfJxerVq2c//umf/sl8nyJuIFBubq5JxDVutBYJcFtiof35X3rppXSfCpAxDh8+bOr1aON/DEuhspQmDrrply73CKatZ++44w750Y9+RHs0wM/k5OTsO1CA22jrTN2j4oknnpAzzzwz3acDON4vfvEL81k3zMMMEosspDujavKgmxxpi9lg+q6U9urXTV205gKAmI2PtLOHdlBbt25duk8HmHef/vSnzZtSWmuks95ao+dPC1T/8pe/0HIWOLoy5Ktf/arZOE+X02IGiUUW0oRB18nqpi12zjjjDGloaDAvICQWcKu7777bdE1TnZ2d8utf/9osgfrsZz87u94ccJNVq1aZONDXBU2u/Xfe1o1Wf/e738l1112X7tME0vZ6obPaWqeqScXmzZvNZpI65iouLk73KTpGjs/n86X7JJBcmlDoE76np0dKS0ttb/Pud7/bJBa6nryurm7ezxFIl1tuucU8//3pi8LatWvN7vT6oR3UALfSBPub3/ymeR1pa2szrZg3bNhgduV+73vfa74G3Ph6oTPatbW1ctJJJ5mlg/o9CrcDkVgAAAAASBhdoQAAAAAkjMQCAAAAQMJILAAAAAAkjMQCAAAAQMJILAAAAAAkjMQCAAAAQMJILAAAAAAkjMQCAAAAQMJILAAAAAAkjMQCAAAAQMJILADAZUZGRuS73/2unHvuuVJbWysFBQXS2NgoV1xxhdxyyy0yOTk5p8fV++rjAgDcKcfn8/nSfRIAgPmxZ88eed3rXie7du2SSy65RDZt2iT19fXS2dkp999/v/n41Kc+Jd/4xjfifuwLLrhADhw4YD4AAO6Tn+4TAADMD6/XK69//etl37598oc//EGuuuqqgO9/5jOfkWeeecZ8uIW+tzY8PCzl5eXpPhUAyHgshQIAl/h//+//yc6dO+WTn/xkSFJh2bhxo3zwgx+c/fq+++6Tt7zlLbJy5UopKSmR6upqM8vx0EMPBdxv+fLl5tjBgwclJydn9uNvf/vb7G12794t73znO2XRokVSWFho7qOzIzqwD6aPdeaZZ5qfuXDhQvnoRz8qL7/8snnML3/5ywG31ft/7nOfk1WrVklRUZG5/bXXXmvOxZ+ei95fl2z94Ac/kBNOOEGKi4vlW9/6llx55ZVSWloqHo8n5Fw00dL7feUrX4njtw0A7sOMBQC4xO9//3vz+X3ve1/M99FBeG9vrxmoNzU1SWtrq0lQLr74YnnwwQdNnYbS2god3Hd3d8t3vvOd2fuvW7fOfH722WfloosuMonJ+9//flmyZIls27ZNvv/978tjjz1mEgmt9VCPPvqoSV5qamrks5/9rLnPb3/7W3O7YBMTE3LZZZeZ7735zW82SZMmMDfddJNJirZu3WrO25+ea09Pj7z3ve81SUhzc7O85jWvkTvvvFNuvfVWc37+fvKTn0hubq685z3viev3DQCuozUWAIDsV1tb66usrIzrPkNDQyHHOjo6fHV1db7LL7884Pj555/vW7Zsme3jbNiwwbdmzRqfx+MJOH777bdrnZ/v5ptvnj22ceNGX1FRkW/v3r2zx8bHx31nnXWWue2XvvSl2eM//vGPzbFPfepTAY/7l7/8xRx/xzveMXvswQcfNMdqamp8R44cCbj95OSkr7m52fxsf8PDw+Z3FnytAIBQLIUCAJfQZT4VFRVx3aesrGz230NDQ+ad/ry8PDn99NPlqaeeiukxXnzxRXnhhRfkbW97m4yNjZlZDevjnHPOMT9DZxfUkSNHzNIjXZqky68sOpuhy6GC3XHHHWY2QWdL/GmB+imnnCJ/+tOfZHp6OuB7OvuyYMGCgGN6TTojoT9bz9d/lkd/b9dff31M1woAbkZiAQAuUVlZKYODg3HdZ+/evXLNNdeYZUmalGgHqYaGBvnrX/8qfX19MT3GK6+8Yj5/6UtfMvf1/9ABvtZIaEKh9u/fbz6vWbMm5HHsjuntFy9ebM4v2IknnmiuVxMYf8cff7zteWryoAmGLn2y6L/1HN/4xjfGdK0A4GbUWACAS6xfv14efvhh0xXKfzYgHJ2hOO+888zA/2Mf+5icdNJJJrnQGYIbb7xRHnjggZh+rtXVXOsfXvva19rexi4xSBUt0rajtRZ6fr/85S9Nu10t/tbf17/+67/O1n8AAMIjsQAAl7j66qvNQFmLr//jP/4j6u23bNkibW1t8tOf/lTe/e53B3zv85//fMjttXOSndWrV5vPOhuge2dEop2ilHavCmZ3TBOke+65R/r7+02Rt7/t27ebWRqdZYmVFrbfdddd8sc//lGee+45c4xlUAAQG5ZCAYBL/PM//7NZTqTtVbX2wI52b/rhD384mwio4H1UtR7Crr5C94LQ5VHBt3/Vq15lZkt+9KMfmdmSYLrTt3aeUtqlSTs06fn531a7P33ve98Lue+b3vQmU0Pxn//5nwHH7777bpMY6BImnWGJldZm6NKq//t//6/87Gc/k7PPPlvWrl0b8/0BwM2YsQAAl9AlQH/5y1/M4FkH5NrS9dJLL5W6ujrp6uoy7WPvvfde+fSnP21ur4XVOtDXJUy6m7a2bX3++eflF7/4hVkW5V/krM444wzz+B/+8IflrLPOMomJtpjVGgW9j/57w4YNpkha6x9GRkbMTuC33367WVp13XXXmcfRxEfPSx9D99Soqqoy7WbHx8dDZkb0PpoAfP3rXzfnqEu39DE1OWpsbIxpZsauiPtrX/ua+Tre+wOAq9l0igIAZDFtofrtb3/bd/bZZ/uqq6t9+fn5vgULFviuuOIK389//nPTetWybds232WXXWZuV15eblrKPvzww753vetdpnVr8OO+5z3vMY+Vm5trvq8tXi0HDhzwvf/97zctaQsKCkz721e/+tW+z372s75Dhw4FPNaWLVt8p59+umk729jY6PuXf/kX35NPPmke8+tf/3pIS1x9jBUrVpjHbWhoMG1m9ef5s9rN+re2taP30/OvqKiwbbcLALCXo/9Jd3IDAEA0f/jDH8wmeLqJnXaqSpX29nZTyK21FbokCgAQG2osAACOou93jY6OBhzTGotvf/vbkp+fLxdccEFKf77u2j01NRXXDuUAAGosAAAOo5voLVu2TN7+9rebYnPdlO83v/mN2WTvM5/5jKn7SIXbbrtNDh06JN/85jflsssuk1NPPTUlPwcAshVLoQAAjqKzBe9973vloYceMsuS9GVKEwydQdBi7lTRovDi4mI599xz5eabb5YlS5ak7GcBQDYisQAAAACQMGosAAAAACSMxAIAAABAwkgsAAAAACSMxAIAAABAwkgsAAAAACSMxAIAAABAwkgsAAAAACSMxAIAAABAwkgsAAAAAEii/n/neZuDR0/wGQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "np.random.seed(42)\n", + "categories = np.repeat(['A', 'B', 'C', 'D'], 80)\n", + "values = np.hstack([\n", + " np.random.normal(10, 2, 80),\n", + " np.random.normal(15, 3, 80),\n", + " np.random.normal(20, 2.5, 80),\n", + " np.random.normal(25, 1.5, 80)\n", + "])\n", + "\n", + "unique_cats = sorted(set(categories))\n", + "x_positions = {cat: i for i, cat in enumerate(unique_cats)}\n", + "x = np.array([x_positions[c] for c in categories])\n", + "\n", + "jitter_strength = 0.15\n", + "x_jittered = x + np.random.uniform(-jitter_strength, jitter_strength, size=len(x))\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "plt.scatter(x_jittered, values, s=25, alpha=0.7, color=\"#4C72B0\", edgecolor=\"white\", linewidth=0.5)\n", + "\n", + "plt.xticks(range(len(unique_cats)), unique_cats, fontsize=12)\n", + "plt.xlabel(\"Category\", fontsize=13)\n", + "plt.ylabel(\"Value\", fontsize=13)\n", + "plt.title(\"Swarm Plot (Matplotlib Only)\", fontsize=15, pad=10)\n", + "\n", + "plt.grid(alpha=0.2, linestyle='--')\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Word_Cloud.ipynb b/Word_Cloud.ipynb new file mode 100644 index 0000000..39d1dc1 --- /dev/null +++ b/Word_Cloud.ipynb @@ -0,0 +1,174 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "93f7c6cd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "WORD CLOUD VISUALIZATION\n", + "======================================================================\n", + "\n", + "Bigger words = More frequent | Smaller words = Less frequent\n", + "======================================================================\n", + "\n", + " Sample Text 1: Product Reviews\n", + "----------------------------------------------------------------------\n", + "\n", + "Amazing product! The quality is excellent and the price is great.\n", + "I love this product. It's fantastic and works perfectly.\n", + "Highly recommend this prod...\n", + "\n", + " Sample Text 2: Book Review\n", + "----------------------------------------------------------------------\n", + "\n", + "This book is absolutely amazing! The story is captivating and the characters\n", + "are well-developed. The author's writing style is engaging and beautiful...\n", + "\n", + "======================================================================\n", + "GENERATING WORD CLOUDS\n", + "======================================================================\n", + "\n", + "🎨 Creating word cloud: Product Reviews...\n" + ] + }, + { + "data": { + "image/png": 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mZgZf+tKX4i4vM7z/7//9v+La/su//MuYr6EjPNq9/2d/9mdL/k03d21tLdYLBe+cnJwlz/3pn/4p/uiP/ihmnA9h5Mp//s//WYoH8d4zmrfffvve3yNjXCL/fvHixVXz0AkLT1wP8fLcKaJHZ8ZH/1tRFEVRFEXZXtSJriiKoiiKoiyDguNP//RPS+PDSH7qp35KBMG9e/eK85ticqTTmJw4cQIf//jH7/2bDRPZ8DRaUGWuO2NI2OSUTnDGWsQTQqOhmPud73xnyXN0YjMmo6amBu+88478PREYY3LkyBFcunRpyfP/8T/+R/n+/CyKo2xKybxs/hn9XcLZ15HCKtcLXe4UacMxM4w8WavDfS0wPoX5+NFudBYoKHKH+Xf/7t/hL/7iL5YUGZ5//nlxRPP7slksxXa6+bkeGV8S3jbcB2JBJ/aP/uiPLnNoR7IRFzrheuR3ZN57GC7Xz/7sz0qxgNErjHzh92I0EAX0yCa3sdi1axeKioqWNR0NE+nuZxHgG9/4RszXxYtyoaucBSOuT34WXeg8Jjh7YGBgAK+++uqyWJjIGRWKoiiKoijK9qMiuqIoiqIoihKT3/7t3xYBNlpcpks30qkbCQXM6EaS5Nd+7dckQzpa0KQoHdn8kuJtvOz1SH7sx34M//W//ldMT08veT46voViJV3UK0Fxk8IondwUNSPhssRzXkfDwkG0O5m513yEoUi9mSI6hfJYBYH/8T/+xxIRnUUSiuh0n0cWLig+s5ixXiiSxxPR7Xa7fN5GYfHl/fffl5iaSNhgl4/1wMJBrP22sbFR9qEwkbno0azWLJURMJyBwcdKUFz/B//gHyS03IqiKIqiKMrWoHEuiqIoiqIoSlzR880338QP/dAPJfT6U6dOieu2qqpq2c+YwU03NDOl4/H000/HjIaJBd/vT/7kT5ZFzkQK43Qr/9zP/VxC70dRmU05P/KRj2C9sIlnrO++1dCpHQ2LHpHFirADn0UHZoonCl3+dNzHgzMU6AaPxec//3lkZmZiozB7/u///u/FTU/BOdHfWWnbxhPAI+NbyOHDh+W4iGalPPS1NFGl0569CGI1QFUURVEURVG2DxXRFUVRFEVRlLhQ9GQG+OXLl/ELv/AL4nLOy8sT8ZoRJ3TqMt6DsS50B68kIh88eFCiQShuM67CarUiNzdXxMc//uM/FkE3lkAZjy9+8Ys4c+aMiMF0wFNQLSkpEdGfz//qr/7qmr5raWmpLAN/9+d//uflu4ZjN5jDzRgOOqkp3n/6059e9vv8LufOnZNmmvv27bsX4bLVcH3EigOhGz2ap556Spzyf/M3fyORO/w9bleKwtz2zC//xCc+gf/23/6b5N+3tbUtE5YTjWxh49Fkwf2P34cxQPzzmWeeke3HxqjcXtxHKXgzXob7FmcUrNRkNlERnZ/LiJ5o4uWhEx4b3/72t8VBz0IR8/y5b1DY5/JydgIFfu6vXL9f/vKX17w+FEVRFEVRlM0lbYHzChVFURRFURRFURRFURRFURRFWYY60RVFURRFURRFURRFURRFURQlDiqiK4qiKIqiKIqiKIqiKIqiKEocVERXFEVRFEVRFEVRFEVRFEVRlDioiK4oiqIoiqIoiqIoiqIoiqIocVARXVEURVEURVEURVEURVEURVHioCK6oiiKoiiKoiiKoiiKoiiKosRBRXRFURRFURRFURRFURRFURRFiYOK6IqiKIqiKIqiKIqiKIqiKIoSBxXRFUVRFEVRFEVRFEVRFEVRFCUOKqIriqIoiqIoiqIoiqIoiqIoShxURFcURVEURVEURVEURVEURVGUOKiIriiKoiiKoiiKoiiKoiiKoihxUBFdURRFURRFURRFURRFURRFUeKgIrqiKIqiKIqiKIqiKIqiKIqixEFFdEVRFEVRFEVRFEVRFEVRFEWJg4roiqIoiqIoiqIoiqIoiqIoihIHFdEVRVEURVEURVEURVEURVEUJQ4qoiuKoiiKoiiKoiiKoiiKoihKHFREVxRFURRFURRFURRFURRFUZQ4qIiuKIqiKIqiKIqiKIqiKIqiKHFQEV1RFEVRFEVRFEVRFEVRFEVR4qAiuqIoiqIoiqIoiqIoiqIoiqLEQUV0RVEURVEURVEURVEURVEURYmDiuiKoiiKoiiKoiiKoiiKoiiKEgcV0RVFURRFURRFURRFURRFURQlDiqiK4qiKIqiKIqiKIqiKIqiKEocVERXFEVRFEVRFEVRFEVRFEVRlDioiK4oiqIoiqIoiqIoiqIoiqIocTDG+4GiKMpaCYVCGBiYQnvrEAYGJjHrnpfnLBYT8guyUFtfiNLSHNhs5u1eVCVB2tuGcetmL/r6JlZ8XYbdgqPHq7F7TykeZK5e6ULTrX5MTc1t2mekp6XhmY/uR3GJU44tRYnHwsIC5oI+DHgm5TE6P4Npn0eeC4SCCGEBpnQDbAYzskw25FkcKLJmozwjDzaDCelp6rWIt14nfXMY8C6u1/F5N2b8XnhDfgRCIXmNKT0dGUYrsk125FszUWJzotiWA3O6AWlpaUgF/KEgpvxz6Jkdx4BnQr7TbGAe86EAQgshGNLSYU43wmHi91jcPwqsWSi2OWFO5/6RGt9jqwkuhGQ99cyOYcg7hfH5WUwHPPAEfAgshGS9mNIMsBt5XNllnZXZc5FvyYTNuH3jH2/Qh0HPFPrmJjAyP40pnweeoE/2A6RBtrUl3YgMowX51iw5FxRas5Fjydiy44r73qBnEv13z1fcJ+cC87KMPF8Z09LlfJVpsiHXkrF4vrLnyTIb0vV8pSiKoijK5qIiuqIoSWF+3o/+vkm88/ZtXLvSjd6ecUxPzyEYXIDNZkJxSQ727S/DiZO12Le/HDa7Cuk7gd6eMbz5ehOuXO5a8XW5uQ7k5DoeeBG9+fYAXnj+CvpXKTpshPT0NOzZVwZXfqaK6EpMIYqMzM+gb24cne5RtLmH0T07iiHvNCZ8brjvilJ8LUV0ClBOcwYKrVkiSNVmFqLK4UJFRh7yzA4Y0w1rXg757NlREcEioQjG969xFGyKCDvt96B1ZgjD3mkRO8NQEHYYrTiUUyGi8FrhuqKIR9G8d3YM7e4RefTMjWHEOy3fk4IkRVTC9ZpptCLX4hDRuTLDhVpHgfzJ9cpl4DIlA37PIc8U2txDmPZ75bk0pMkynMirQbbZvuR78D+KlIv7xhBap4fQPTeKUa8bMwEv5oN+eU9ud4qqWXcFywJLFkrsOfId9jrLRBjmvhOLuYBP9oF29/C9dRJeLmN6Ok7k1YowvxkFhd65cdnfJ6L2PavBJMWM3dml6xahuY66Zkflwe/Gz+K+NuX3iLDO44r7tRQfjBbkmDOkeFLjyEe1Ix9VjnyU2nPk+Y3AItigdwptM0NyPEdu81OuOjhMFvk3iyHeYACdsyPocI/IscE/WTSZ8M1iNuCDLxSghg6zwQhbOgVqK4psTpTbc2WZ6zKL5HzgNNnXdS5YDe6TXBaK+1zOtplhdM2NYUgKVLOyT3IZ+Tp+vt1ghtPM4tRicaLOET5fuaRgwXWgKIqiKIqyGaiIrijKhgmFFjA25sbLL10TAZEO9Ejc7nm03BlEZ8cIRkZmkJllQ+Pukm1bXkVRlPuR0MKCiHh0mZ4bbcMbQ024OtENd2BRWI0FnZ/zvgDGfbMiCr6PVhjTDDiSW4mnivbieF4Nim3ZsKSb1iR43pkexF92vIfLE0sLcHyfH695GD9W84iI2smEIhtFzT9ufRMXxzsWHbZ3oXv1gLMclXcF7LVAQZkuY4qWbw/dxhtDt6Qw4Q364/9OkOKlX4oZzdMDIlLSmX7KVYtnSw5gT3YpXGaHCJcbhe73q5Pd+LO2t0UkJelIE4H7/zv5UyKCc9tx/fgXglIIeH3wFt4YvCXLxudiQeGSD4qYfZ4PC4MU//9J/dN4tmR/XBGd+9z5sXb8fsvrS/Y/Crs2owm/feRHsD+nQoTtZMLveHakFV/rPoc7M4NLflZqy8HHSw+uSUQPF6VYnGFRiMfVm0NNuD09IMWIWAQXFqSgwge3P5fjreEmcfIfza3Gk4W7cSinUgosLCqtp5DA7XJlvAt/0vaWCPrhdcvt8UenfgZ1xkIsLIRkuVtmhvB872V8MNaGsXl33OUOBHyYgw9jPrd817OAFIJ2ZRfjYyUHZdlZaLMkaZtx3QYWguI4vzTeJeuV+wyXeaXvzcekf06Wka/n/rjPWYYnCnfjofwGlNlyZBkf1JkSiqIoiqJsHiqiK4qyYQKBoDjPv/2Ni/D5AnFf5/cHcftWP159+YaK6IqiKEnGHwqgd24C/6f5JVyZ6F5RjFoJClvnxtpxY7JXXK0/UfOoiFRr4XBuFV4bvCXLESnazYf86KKTe2YYB3Iq1rV88Zc7hFHvDK6Ody0R0AnFxccKG9flQqer+vJ4J36/5TUpNPii3jsRuAYoJr86eBPnRtvx2fKj+HTZEdRkFmAzoGs+7CpnccWQlibblY7137j1PVyf6JGfrwcWFegCpsAaDzqtT+fX48/b3xWHdngf4J9cn9cme8ShXZaRi2TC7d7hHhaBNZpKhwv7neVrfk8uM4sO3+q9iJuTvbJu1wMF7JcHruPCWDseK2jEP6h/AgXWbBilxLJxZN0G58UZT4c2izgs+nC/5WevZ7m5j1wY68Dl8S58ufI0PlN+BHWZhUmcQTGNP2h9He+PtEghb73vw2IhC0Jnhu/gZ+ufku2cjAKVoiiKoihKJDq6UBRlw4yOzqD1zhD8/vgCepjJyVl0tA9jbm4eVqtZoikURVGUjTHlm8Ol8U5xI9OZSuFyo3iCfnF6Tvo9+ImaR3A4pzJhEZoCa21mAUptTvRGuJgJl69pqj/pIjqzlG9P98MbWnot4lWGbmy6VOM5p+NB0fm1wZv4Vs9FiW6JFufXA8X05/uuYNTnxhcrjosrebNgzAyz2g0L6bLO/6DldTRN9617/6DDnQ5qxqKstC/QYc3IjaN5VXh/pFWcw5Fwv+L2T7aI3jwzIK55FpQioVu50u5akwudUSjMDf+Ljnfx7sgdmeWwXgE9Eka/vDncJI7vH6l6SNZD0hz5C4txNi6LQ84HX+8+L+L0RpebQvUL/VekGPPx0kNoyCra0Pu5/V45Vv+o9U20zgzeiyHaCCwY3Zzqw/9qegE/XvMITubVyr6qKIqiKIqSLFREV+7fHOc3bmOgf+LeVNxYGAzpqG8owqc/exTbCfOTv/bVc5IrvtLyOhxW1DUU4dmP7kcq4Z7xYmhwEiss+j0CgRBmZrwYHXWjuNiJdM2uVBRF2RAzfo84x7/efS5uPAczmpnNXWTLlmxjxh0w/kFynufdkpE84p1Z8rt0ttKJ2jTVh//b/g7MdQbszS5LSEhnk7/6zCLUZBYuE9H75ybQPD0oESkUD5OVi03xkE7h6LgKNneszyxEkTVLomoSheLjy4PX8WLfNclqjsz2DgvKbBTJmJBCW7bE0zCPmddxRnnQEcycZwrIkfnsFDTHfW58MNomAn+OKQPFdjbrTP6wnI1D2ZiR6+Yb3RdwfbJHtnl4DUkWdrpRnOUZBgusRpN8L7rtZwNejM3Pirs5vPz8fo3ZxZKzvlKmO7cpI3RYuGD0SbSIzrgfLtPe7FLY11jYWAk67BlXEz0coTObeeTMYU8E7kN8n+/1XZEZFYPeyZgFFH5HRpzQVc8sceZ18xjivh3O+WakS+Q+yXXJ4gZnNzBznvEkJ111Scny5ucwJ53HGAXlnrnxJfsejzc2OOU+y+acbH7K32HBbGJ+djHjfX5aZi9Ew+/z1vBt2fZFtiw5rtYDCzjcD/+m832Z7RK5P4bhumBTU56vcs2OxfME0mQmCxv5DnimpKkr192H3x3SNJmZ6n/beVaaux6P6gmgKIqiKIqyEVREV+5LJibmcO6DVokOYV53PEwmAzxzvm0X0Scn5/DGazclO3wlET03zyGRKKkmos/PB+CeTdxFxKzYmSkPCguzKLVs6rIpylaSkWFBnssh5xWfPyDHa8AfXPE8pCgbgW5ZimWvDd6Q/PFoAZ0ObDbe25VVjPKMXImP4HMU76hdM/KB4hgFww73qLhDKcJFCsYUhBnn8GL/NdgNFuzOLkmowSCd6HWZBRLVELlcdGL3zo1J5AYdrYYkxFn4ggERDO/czQSPJN+aKW5visWJCPbh5ptc7jcGm9AyM7hMQKdwyoaLLBSwAaPLmiku97CIzvXKrGeKqK3uISludM+OLXmPsfkZEdKLrNn4QsUJWc5kNRuNdD3zs+lKfn+0RbZleKZAeUaeNDmlWElhlQUBipUU0SkYzwbnRexlRA6FVTr92cTykLNixSiXMFwXnL3A4g3XQ6TgSVGdDU0Zu5GMeBCucwr/PBZYvIimMatYmmQm2hiTQu3F8U680H8V/R4K0QtLc90NZux1lsqyh9chG4ly/TGjnu5/rjsWp7pnF4s7dMiH1z/huuT2p5jNxr5rjUyKuR4Aicrh/sfvQAGdezwLFTwHyPLa82SbsOjDognXHXsjcHkpvjPD/dpkN4Y908vOJ5yNwX2JxY8Trto1Lx/Feeb2vzJ4A+fG2pZFI3G9chl3ZRVJo1AeGxTB2UuBExfng4t56JwhwvNH+HzFIkAYxhZRpH9l4IYUNo7kVmuzUUVRFEVRkoKK6IqibBjegIVCa/mFxWakSZgVrSgpRUWlCydP1aGqagoej08eXq9fxPRgIIRgMIhgcEH6CPBPFpTkuUAI3vkAPHPzMltDURKFguE7w7fFiR7plKVwlmfJlGaazxbvx+n8OhHS4om0gVBQGme+O3xHYhuYWU5X59Jc6CaU2/NE7C2yOVddtgJrFqoy8iVaYsA7FfFeEIGZzT8psidDOB71zaB7dnSZgEpBmELc4dzEI1MowrGo8GL/1WUCOgXUUnuOZMU/VbgHh3Ir7xYkYovz3CYUJd8cvCWxMBTSI6M1WMBgVAwFzs1wzfbNjctn0vFLZz3XBwXUPXdF0CO5VVIE4DaI9x0oxlKwZBxM7+yY5N0nNBshLR0l9hwp4nB/4v4VSdN0P/bPDKHWUbDh2QjcRgPeSXS5R5f1AqAjmTEuFLsTLcjwuzIHPbrwwe/EvPeDORX4ROkhHHRWIMeSEX+5QkHZxixAvTPcLPtC5PJxpgejbfiedMvTKb/RdRFuLhteXgrJh3Oq8FzJfhzKqZLjNx4U3Rm39N3ey3h7+Lb8PdKVzr93ukdE/D+aVy3701qWd7Fw1CrrIlJA5ztkm+yyX360ZD9Ou+plnXBGS7zl5P7MDHQWOm5P9S/L+GfRiOepEluOFIwURVEURVE2ioroiqJsGKPRAIsl8dNJuiEN9gwL0jQPXbnP2H+gXB6RLIQWJKppdnZeHm7+6V78+6zbK3/nLJTurlHcutmL8fH1NVdTHizCbuk3hppwcaxTXKSRUDB/rGAXfrDyFOoTyC+mQ5ci3ufKj6HMnoM/aHlDXJ6RURAzAY+IX4xYeK7kINJXEc8o4JXac7EvpxwDA0sFVDY6vDjWgc+XH5OIlY0Kh3emB9DhHlkW5UJ3OL9XbYJuZ65Xure/0XNRYkiis8MpSHKZnyneL2L6atABS9duoYWRH078bvMr8v7h5VwUA92Skc5IlQOm8qTF25ALd/eNSf+sbA9G+XCfeLp4ryxPIjBXmg8K7tHrNxFYwOC6jBbRmYXNx9NFe2BK29gtCZ3X50bbxKUcuYRck5wlUOMoELd9IozMT+PsaBveG7mz7Gdcf6dctfhXez6xYlEq8rjidv2xmkfEWf2NnvM4M9Ky5LhihApnPXCGx+MFjTBQmk7SPsAZA8dyq/FLuz8OlyUzrigdht+H6+onax6FOd2Av+86tyyKh9uRbnceG3Tfs7CU6PmK6/X90VY5/iOhg/9YXrU0Md6bgCOfy8nt+Znyo7IfM26KxYjIghed/nS7M7//B+y5spTJPLYURVEURXnwUBFdUZSkRFi4XIndnPIGxmozo6jYKeK7otz3pAEWqwkmsxHZTvvdmRsL0kOAAjudfXSkX7nUhZHhaRXRlYTgfkPx9Y3BWyIeR0Mx7rmSA5IDvRYonB3Lq0ELM8uDPrS7h5c1brw11Y9HCxpFUF4NCs2MUmG0QiSMdGlzD6PfMyHudma0bwSKtB2zy9cDIzwas0rEMZsIjLXomR3D872XlomHXDcU0B8raJQ4l7VAt/LD+Q3icGbhg9suDEXfi+PteLJwt8TDMKc6WTB+IxwTV2rLxT+oexyn8+tFDN4qGNnyfkarzDyIFI8pwtLp3TozLALyRkV0irPsDxBJelo6TuTVoGAF93U0FLkvRS0rsRlMMlvgH9Q9KcWZRMTjSE66aqXh6bTPg6uT3Ut+Rsf317rO4URe7d333jjWdJM0Lf2FxueQZ3GsWvSKhMc2Z1twRgYLPJFwvdBNT8c7C0QWQ2IzSejIf2+kWY6BaPi9P1V6eF0NSzkr4InC3dJc+db00vfmuZHRNDwXMsZKURRFURRlIyQ3eFFRlAeS3LwMVNfmw2hc/ZTCvOhdu4pgsy3m8SrK/Q4LR3ywkTELRyaTERaLCVarCTa7WYpQfNhsJqQnKEYoCgXu90ZaxBUamVvM0yqdlxRKGRGy1ixgKXQaTHi8cLfkSEcLhRQr29xDuDLRldD7Oc12yThmtEuka3fhrpB+brR9WQzDWqBAzAiXTvfoMncroau2Mbvk3nG4GiPeaVkmioSRMRaMbKl2FOCpor0SUZJotnZ0DAids8zPjl6SuYBP3L3RRYuNQsGT8TEU0Ck0PkQBnU1BV3EkxyMt4r9EoXhZk1mwLE6Fa5cFi6sJ7ksrxa+Mzk9LpEdkBBGhq5sCLd3gq21/7kuTvlnJVY+OcSGMhHm4oEGcz7IG1jiI4XG1z1mOZ4r3LTsumendPTcmzUbZ0DUZcAYKCzMs+KwU1xMLFh947LAAwn0/GjabZURPrGarseCxdH6sAz2z40uy8QmXj8UJCv6mdTTXZQGOjVkZrRRdLONncVsy7ioyRklRFEVRFGU96N26oigbxm63oKoyHydO1orjNh4UDBsbi/HQIw1IT0/edGVFUZQHCYp9dPG+NdS0LMaF4hed5PWZhdKwcL2EI1Bi5Sez+SAbJSYCm3kydiHc2DMSb8Avrl86c9cTE0L4W01TfeKYjRb0mC9d5XCh1O5MWOgb9Ezh7GjrMhcy85ofzq+X/HCKoeuBwikLCjWZ+ctcsfwebABKR3KyoYDakFWIp4r2SCxLspuXrobx7vfmjIBomJXPvHYWESKLFmuBBQ+6mymAR243zhwoy8iR/ZhNNFeDn74ooI8uE+MpJO/LLpP9mNtxveMXOsIppO/JLllyPHD/5/d4I8YxvR743nR181xgXOfy8vzBgkGhbXnRhw0+GUMT3XA33vmKhT5G1gxFRfqQA84KKXRtpB8Al5P7GJc1mpH5GWmGGp6RoShKatPVNow3vn8NX/3Ku/jrP3wT3/jLM3j/jSaMDU8jFNS+QYqibC8qoiuKsmHosC0oysbHP3kIR45WobjECbvdLM+np6eJ45bPHT1WjUcfb8TevavnXSqKoijxI0cG74mPSzO7jWnpktlM5+1GMBuMko1O0TgaOr4ZnULROhFhiuIYBWjGYUS7RG9N9YmwRlFurUjO8sKCCGR0kEdD5zhzqDMSEFAJ12Xv3Jg0E43+VnRvM8ZlvQJ6pMufYh+bvkbTNzchxQA2o0wmeWYHdmWVyMyC7aIywyUievT6o3DMwkHn7AiCETMq1sLovBsXxjuWCbrc7sxxz7NkJDQjg0L2hbEODMXYlzh7gNuNszw2ApeDzuuHXA3L1oXkuo+1SdPdje4DLH7RSb6R5WX8C4V0FtQW57h8CJdvzOdGKAERnY16eXyyxwL7AUSSdjczP9Z5Zq3rlU1Ea2LEV7FIx+gZnm/WW6hRFGXz8fuDGOybwJsvXse3/+YDfO0r7+Krf/YOvv5/38N3/+4cLp5pxejw8vOzoijKVqKZ6IqiJIXMTCuOn6yFMycDH5xtw53bAxgfdyMUCsHptGPPvjKcOl2Pmtp8ibNQFEVR1gdzn5lZzhiUyIgCRhkwT5m52snI/823ZIkwFQ1duiPeGUz55ySixLBKtIfjrpiZbc4QES3sFuayM3f8zvSgiKyMSVkL/H3GStyY7MG4b3kvgQPO8rsCYGJILEwMF7IpzSAObgrRdDdvFAqGOTFct1yfo94ZaYhI0T5Z1GYWoD6rUAoj2wUd2BQ4y2y5aHUPLfkZtx1nJFTY89Yc5xEMhaQR6LWJ7mWCLmciPJK/CzbD6hnz0qtiISSzAehoj4aubolxScIMOuaN83hgk1EWEe59l4WQFFHYJ4AC+Eac2bVSQMrb8PJS6OdMEr5LpPzMZWXRKZGIFM4yWJwpMLdkpgDfk81Z+V1zzQ5slBxTBsrsjAxqWfK8N+SXY3vCN4cCqxHpadqPR1FSkTm3F2deb8LL37qEsZGZe897PX7599zsPKx2M1yF2WLSUhRF2Q5UyVIUJWlwQNOwqxh19UWL7sSw4+duFi1/rgkuiqIoG4MiK5270a7KsMuZQnqijTRXggI5BbRYME6mbWYYh3IqZNbRStAdT5GMTQgpULLBYCRXJ7qxz1m2ZhGdzt1FJ/v0spxlfibfs8yW+HvSATzgWR43QTGTAm8yBHRSaHXGLXJQUO7zTCRVRC/PyBPxerspsmXjhKt2mYjO/eHd4WZ8rvwoHEhs1kAYFmGYed3vmVy2/SncM2s7kaa1FIN5XA15piSfPJq6rCIUxIgKWQ8U9RcLXXaZ1RHtoOf3mXTObUhEZ/Go0LLx5eV6lCJElIq+WMDyJTQThSI2t/myYzTdIAI6j4W1ND2NR5bZhiJr7O/s4zLMDMlnrbVPhKIoW8PsjBdvfv8a3NNLZ6yEab7ZJ1EvJx5tgNWWvAbciqIoa0FFdEVRksZi47ZFMV1RFEXZHNiQs2N2dFmO+GIES+66M5CjsRpNsBlj36gyymV8nnEOq4toXBZjejpOumrQPN2/TES/OdUr7ttDC5Vryut2+714d+SOCPqRGNMM2OcslXxk8xriVxhPMehdKsYSh9EijVGT1cfDYbIsy4ePbBjL+IlkwmXfaLxPMmAkCkXtr3WfWyKohvO1W2aGRPBOJL88UnBmQ9FoCqzZ0qjSYjAmVE6aZ8PcmSHMh/yxl92ShSzT2gT+leAxWmzLFuc5j+dlIrpvVmZnrBfGBW1EhI/ssSAZ8FHPUzxnpMtCgsWuxTiV4DKBvtSWI9s8GccWj6kMkyXmz1ioGGNMzjojg9bC5Pgs3n7pOs6+1Qz3TOwmsXsPV+CRp/dg7+HKTV8eRdkpBAJBDA1Mwh+IfZwuhBbkmJqZ8qiIrmwqi7PTApgOjGDKx5mfY5gLTMIbnMF80I0AAgguBLCwwFjD5bFmhbZ6nHJ9aVuWXdl8VERXFEVRFEXZQVA07pkdWxZfYU4zilBJETkZUJSypMcWoZlfHR0nsxIUx9mU8SXrdXS4R5a4b+m+7nCPSlPP0gTd6PzujD/5YLRNhOely23ASVetZIGvxeE6Pj+LIc/yvFVGTuRaMpAsOGMgnhuWIi4japIF1zvdtywEbDcsyDDKhtnod2YGRFwlLAYx8uP8WJuIqomK6Py9nrkxydqOhlngh3OqRAROtM8As9mjm9OG96ccSwbshuSsQ5mZl5YmUUk2Q/8yEZ2u+mm/d/3vjzQ4TfakiP5pd98vGh71iTYDZoGCx3z0uuW2YYEnXkFprfCYitezgMU+RudENwzeLDft+282o+lat8RQxMLvD6C4NEdFdEWJgDV5vy+AlQ7TgD8In2/tPVQUZTUoik/5hjA234MJXy8m/QOYC0zBG3TDF5qDPzSPwAIfbITO0W8opoBO0ra4gbuytaiIriiKoiiKskOgcEVRiq7KhRixCcx0/nbPxQ01wLz3fkE/bkz1xPwZxSiK+YnEOeCuEEeBv9qRL8s4Mj+z5L3a3ENodw8nLKIzeoOO3e4o4ZMxNsycPppbvSYnLtcrxcxp/9yyn0355nB5vGtdzU9jwfXWNTsW82f+YBDeqKLARqAT25puEufzdkNBnzE1jxQ0yMyDsIhO/AtBfDDajofzd0msTyIzEujYjxXlwu9blpG7pkaqgVBIGpRGR6twv6Won2GwJDUGhO/rsmTGjJrhTA1PYP37AB3edqM5aeL0RuBxRQc6G4tGC9h0svOYf6HvSlJ6OLAQwtkE8UR0njM2W0T3zQcku7n9zoD8PR7DA1Po7hiFd84Hiy05TnxF2elwJnNGphXz8wEEA7HHFmaLEVbrxsc3ikJCC0H4Qh6MzXdjzNeLUW+X/H3C349p/7C40dcDBfekuOERWNqQJBqmACA9YcOAkhy2f3S1ClOTc5gYdyMr2wZHpg0mowFpGhWhKIqiKMoDCAfVFKUiBcgwdFq+PnRLHptNcGHRPZyoG1UiXdIM2JNdKjnmkSI66ZgZEQHsIVc9DOmr3wywsem1yZ5lEREsHpTb8yRr2Z5AQ8lIkY0RI9G5zYQZ5X19E/hu32VsNhSTvUkS68migJ46N1d09T+c34BXBm4sxgHd3X8obrbMDMoMC+b6JyKqsgksRfTomQh0N1dn5MNljZ3nHwu6ylhEiY4nomM802hN+jqkZsr3ZdPaaHhc+dZ54x4unLAIkQrCrMS+LARlGy3EEL0Zx8THpi/H3dkOicRPbYSZqTl0tQ9jamJ5MS7arT7YNyHRFRU1+Zu6TIqyU6BAXtNQjNmZTmlcHE2W0w5XQRYcWRsvuikPNlLgDc5h2j+CkflOtM18gJ656xLZEsLmx34lAh3vY/Nd4oSPnnkaxpRugcPkQo45cdOA8gCI6Ncud+GFb13EiYfqceREDfJcmbBYjNLEKiymp8IgUVEURVEUZbOh65pO9M2VghITx/zMg1zj71FEZ9bzpfHOJb9LZzIjHxgRs1pTTX42s8uvjHfFbIbK5pUU09cyPuQ69QXX/n2SDcXkZOY2W9IXBdVUgdtld3YJKjPyMOiZlO0duW8zH59NN/c4S+O+R3j2Q9NUH7rnljv6G7KKsGsNLvR7jTID8zFvVNlrIB3JX4cUu2PFDXEmguSNLyys6x4nlbY5Hf7eQAqdrzZZRKcLvfl6b0KvHR+dQdPVHhXRFeUuGZk2PPGx/ejtGsW8149QiHEZEM3HaEjHgaNVqNlVDIs60ZV1sngNYHHXhz5PE25OvoaWmfclpiXV8ASncG7s6+hwX8R8aGkvoTBZxgLsdT6NR/J/dHGOm+qiW0LKi+jsznzjSjdabg/gW189h4bGYhw8VoX9hypRUelaDOtTFEVRFEV5AKDzOl7zw51Asc0pInquxYGxefcSEZOxHNcmuvFYYeOK7+EJ+tE3NyHNCqNhdjXd7KY1RlkwCocucGVrOJZXI5E2MzODS56/NtGDw7lVK4ro4WIDY4H65saXPM84n7rMInmsBYrnszFmVvA2g5Era8nWTwROwOb7xhK7ZV+827RzPZ9qSDckfXnXi7jQQ8mLJ0p1xoZncDthEd2NW9d68Oxnj8jMBEV50LFnmHHq8V3SS+DMG01ouz0Ar8eHXJcDxx9pwBPP7UdlbcF2L6ayg+E1fjYwgcsTz4v7fNI3kJICOjGn21GXeQr9nttxRXQ2PB2d75bvlGHM3fJlfFBJeRE9FFqQXCw+Zqa9mHV70dk+gvfevI3S8jw07i3Brj2lyC/M1nwsRVEURVHua8SpHNr85nibBbO5qxz52JVZjDPzLUt+NuCZxJWJrlVFdEZ+MPqFYmMkzK6uyMhDpcO1Zicu12lwB6/XncaxvGq8P9oihZBI4bpnbhyd7hHJBeesgliw4MLfY9ElOju8yuESl/v6mmrGCidabK25OcR+Xxrl1iugx3/X7YGxT7Gatd6PzEx5MNg3jpHBqYRe757yoKuV0S+zEltqMG5/3wJF2U7S09Nhs1tw4tEG1DQUiZkyGAxJzEteQRbyC7PUha6sG3/IK01DL41/F71zNzATGEVwIXVNKaZ0K8rt+5FhyIHbPxYzZmYBQclu75m7iV1Zj8RsBK48gCJ69PSL6SmPPHq7x9DSPIi2O4O4dqkLZZUulMsjD0UlOTAYdDqDoiiKoigPDswAZ5SF05whzQU3k2yTDQ2ZRTEznVeDTnTGupwZXSqiMyP7zvQgJn1z0hw0nhDOZoR3ohzMpMiWjcasEmQYLVgXcYaNeRaHZGyvFjOTDEptOZLpfr/D78nc+luTS/PxmZ3d7h5B+8wwjuZVxy0ksdjCXPxwpnqYfc5ylNlz19xINewMp5N9KQsIhALLPmfjxH9fNjCVff8+uY2J9zX4PVlM46wU8yY3vrUZzNidVSJ/bhZD/RPo6VyMoUiEQCCIyXE3Wm71Ye/hSmQ4VERXFJKXnykPRUkW88FZDHlbcX3yFbS7z0vOONt2pjLpMMBhzEWBtUaE8tngRMzXuQNj6Jq9goas01iQ0cx9MnhIYXaUiB4Jq5LTk3O4yce1HuTmOVBVWyCu9LpdRXAVZMLlykRWtl0rloqiKIqi3BdQHKf4FAsKz2zaSIGa2dObiTndKE5h/rlW2PiRzSP5+3Qch6GznO5ixnQcyCmPKXgxL7pjdgRds6PLfkbxeV922Tq+zaKgF6/wUGx14smiPWhcY872erAbLCLa3+9QsGbB44ajd3mTWfewNJ9lrIv4wCNMMdJYNxjApYkuTPg+jAMiLOgcyKlAkc255uVJT0uX4kt0DAol7vlgIOkNKeV9KaLHyGDnscv98X64ETakpcU9RzC7/YSrBgecFXLu2kx4bPN8s+4CWwJQQO9qG172PF20JrNRxPWAf6mT0DPnw6WzbdJMMcOxuetAURTlQcQX8mLI24abU2+gaerNlGkcuhqLY580lNr3SAEgnojuCUzLzz3BGdgN2Uhbh7lFuc9EdIMxXURw37xfpjfGY3zMLY/L5ztgtZmw/3AFjhyvEVG9qNgpz/F9pCGpOtQVRdmE6KlgMAi/L4hAIIRAMCjFPj6/wMfCh9PEw6JAenoa0g3p0izHaDLAZDLCZDJINub9fJ7iuuC68fuD8ggGQvJvRincW1dsJLS4okTUWLKujAaYzAYYjeky9VNRHiSY9R1PIDemGVBgzcZ+ZzkcmyxKbQQuf4ndiT3ZJTg72ibO4jAzfg/ODN9BfWZhTBF9wDOF7tkxcasvec90k0S50Im/rmVKp3AZe1hsM5rF3UxRV0keLEpwNsOl8Q6J/QjTOzeB29MD0ugzWvRkxva4z43mqQFM+z9sSkrndr41Ew1SnLGvS+zNMFpFTI+E1yNmenMf5VU8acL2Am98F983Gu7365nhkYqwGGA1mGWtRd/GcV27LFlS9HNZd7brlK7yvq4x9HYub3TrzMlAbn4WRoenMD4yI+PCMN45H66d78THPncMufmZMtZRFEVRksPCQggT831omn4Lt6Ze3zECeiRF1voV885DCGAuMIlRbyeKbY2wGDZ/1uSDTsqL6IVF2dh/qAI3rnZLBX81I4gMdud8uHC2XQR1pzMDtQ1FOPlwPY6drhPHOkUqRVGUZOL1+jA0OIXWliH09Y6jv38SY2NumTHjnp2HzxcQwZjCMEVg9nDIyrLJzVVJiVMaJdfVFaKqJh92++ZNN04FeAM5NupGe+sQWluHMDAwheHhKUxOzGFubl56YPj9ARgMBphNBtjsZplVlJuTIUXR8opc1NUXorQsFw51bikPGIw9sBvN4u70hQJLfhZECDMBzyZETyQflyUTJ111OD/WjmDE4s4EvDgz2oovVZ2K+Xs3JnslOz0a5qxXO/LXHddAYT8sXkY3GOV6ngumZuOpnUyJPQc1mQXINTuWuNG5vgc8E7gx2YMTrtolwvWM34sLY51wB7xLEsxtBpPMwqDbeD1CNwXdLJNtmROdxxILNv6oY22jcNkn/XPSKDgaHt/xZpvsNFjYy4g4Xy1E3bO5Ax4EYhQSdmJD0YHecck3j8aZ50BtYzGynDb5ecj34Tbn2LCzbVgE9sJSmr7u7/GfoijKVuILeXBz6jXcmX4vpfPPVyLHzJhCJ9LTjAgtxB6LBBZ8GPA0w2WpVBF9C0h5Eb1hdwl+9v95RsSVlqZ+NN3oRUvzAEaGppdU8qMJ0QEaXHSoz13pkmakL373Mmrri7Bnfxka95aitCJPKv73s+NTURKF4uX1az34/veuYmI8dgfoZFBaloPTD9fj0cdWbhyX6tA57eE6u96DG9d70dE+grHRGXg8fvjmA/dEczrSQ8GFRUf63SogzznuGS8mJmbR1zeBljuDsFqMcvPEWTOGJLura+sK8dkvHEVZWe6mObc5y2clBxXXx8jwNC5d7JT9bKB/Em63Fx6Pb3Fd+Zavq7Ajf2rKg9GRGXQbDTDf7IXFYpJ1lZfnQHVNPg4crMDefWVwOCziVle2Bm6373zzIt4/0yKzL1aivCIPz350Pw4cqkCq8eIL1/DeO82Ynvas+DoWtz71mSNy/trOcQM/m85ORqL0zY0vEaX8wSAGPVMI7IBGfhQ7D+ZUwGG0YdI3e0/4p1g56J2U3HOKmtExD01TfRiMIaLvzi6RjO31bhv+HptROi12ydqOhI7oMe/S6BBl41Cw5uwB7gevDt5c8rNh7zTOj3XgWF4NIi8t034PPhhrhTe49GaYx8QjBbuQZbKvax9gtEiVPS+mA5xxLhTS6Ry3JykOhPs7j1VmwEdTaM3e9HiTrYLbggI6I3b65iZkJkEYuvC5DqKLgTuR1qZ+DA/EbijqzM1AbWMRMrOsuHahE/4oJyTvWW/f6ENxeS5Kyu//fgiKoihbxY2pV9E9dw3e4NJx3WqwGG9MsyDbVIhscyHsRifM6TaY0i0wpllxbeL7mA6MYCugeJ5lKoTDmIdp/1BcEX3QeweNoce2ZJkedFJeRGc+nD3DgrKKEIpLnCJ+Dw1MitOTwnhX+wiGh6ZEtIondM265+UxNDCB4cEptLUM4sIHbSgrz0N1fSFq6grhys/U7HTlgYbHCotVd24PYHh4elMd27saS7CT1xMd5zdv9OLWjV709IxjYGASkxOz4qBOPM5kMdKE56652c11OLLRstfjW3Umz0ZgvAo/J5bQ2tM9hqtXunDzRp+cuwcHp+Q7r1QIJVxerqtQiBE5vOlcKprwvbq7R3HnziAuXmjH/oMV2L27FAWFWUn/fspypCCTBplx0dszvuJrua1q6wpQ31AkMwtShfHxWTTd6sOlix2rHr8s1KRK0Z252aX2XHHrRsZgMFOcDRn9O0BEp7BWYMnCXmcpLo93ibP4wwxqP65OdKMyI++emEjBjUIms9Cn/MujXBgLUm6PP901EdiQNd+StUxEn/J70OeJnUWprB/epDLH/nBuJV4furUkd5xZ+dcne+AOzN9rMsv9emzejWsTPUuEV84iKLPnSJQPHenrwWIwiiueYnws1zid8pwlkQwRffG6RgF5Et7AcmdcqT1HigH3U/49mwmzMMJieRg60DvcozELCTuNllv9GBmMI6LnOVC/p0REdMbQxeL2tR4cOFalIrqiKEoSCIR8mPIPom3mA0z6+hNqIsoxSZapAPmWauRbq0S4thocsBoyRUA3pJlgSDOIqN0yc2bLRHTeezjNRcgy5ccV0YMhH4Y87ZgPzUmETVqcHj/KAyKih3ccxh8UFjvlsXtfGSYn59DVMSJT4Hq7RjE4MInhwWlxgsYTpDg2p8OWj9bmQWRm2VBdVyDu9IoqF4pLc+T92Q2ag5xUuVlWFCU1oFO1rXUIVy934fKlLtxpHhC3uQKYJaN8qYNvcnIWTTf7cfFCB65c7kRnx/JGgBuBAv3w0LQ8uC26u8bkWnD4cBVq6gq0B8Ymw+tkXX2RzHRYTUSXa3bnqBScamoLkCp0tA/LMiVSANu7v0yK+amwT1FUrHUU4OJYh4S4hKH43Dk7Km5dNqc0pnAkBNcjBcmHXPVomxm+J6ITiqlXJrrwUH69iKx8LQXUlulBETOjIzAqHXniaKZzfSPQ3U8Bk00tI+H6pIuWjnSKrNGRH8rGZiTUZRahxJYjMT3hjHBPkE1mJ9A6MyTZ+dxXuB1YRKEYGxnl4jTZpQ+A05Sx7n2esSOMGMq1ZEhxik0/I+mZHcP4vFtc4huFcUHD89MYnXfHdGFzn3euI9c9VWGRY1dWsRTGIgVzzpjpnh29G5cT3JERNjQDeGbn5X50cnz5bBW7wwJXQRZKynMlTtSRZZWZn5x1F0nHnSGMDk7D7wtIE9LtgMs1MjCFof5JzEzNicmDvcmsdgucOXbkF2Yjvzh71bEVTRsTY24M9E5IfA3fhy/n97LZLch1OVBY4kRGJosKm7fN2WPHHwhihrGKM17M8uH2wutZbPBKcwZ78sjMx/Q0+V7cRmbOdrSbkeGwIDPbLjMJwn3VFCVRuI9NTc7JscBoT+pT8x6fzNzkeYMzrNgPy3h3n6MuleW0IyvHjqxsmxxj2zXeZJHXO+fH1OSsLPvMlEfikiOPm/Tw8WI1iuE1Lz9Ljm1bhiUljhWKyYxwGZnvlEiXlUhDujjNS2y7UGitR5GtDvmWKjhMrrs/X74djGlbawiiIz7r7vLEglnv7sAYZgOT4ko3pd0fs9lSlR0hokfDkw2d43wcPVEjB3Z72xBuXe/FnaZ+9PeMy8WSUQHMUY/ldlx03c5Kbjofea5M1O4qEidj475SyU7nyYwnBTb70/slRXlwWZzR4sW1az14+cXrIqLPbrJ7fKdhNhvvOay4vmZmvLh4oRPff/4Kbt3sE8F7M+HgjlExnBnQ3zeBT37qMCqr8+82atUT+GZRWeUSIf3smZZVhej2tmGJLmIEz3Zvk3C0EmeTcIbaSnBRGSHU2FiMgoLUmOWQbbJhd3YxTOnpy5ydo/MzIqRTEM5OcSGO4hozr7/Xf0UiXMJOZEZd3JkeELGUIiOdrPzz4niHCKmR8ObmSG61fN+N7ldF1mxxzPI9I0VaRocMeabQOTuCOkcRzIYdOXxOSSicctsxtuWl/mtLRNbZgA8fjLaiIiNXRPRh7xSapugoWzquZ0NR7kdsDrohw066ATWOfBHMIzPaScvMoESPNGaVbHg/mw3M4+ZUnxSOovsXcIYGm9hutCCUSmQYLNjvLMN3ey9hKsJ4z+8+5nOLkF6bWSBFjJ1GMBBET+eoiM8UZ6OhOaugOFtmVgddIRHTFwW1pe770eFpyVTnz/KSeJ1hs1OOXzkuI9x1pcdMfua9/HXGydAk0to0gMsftOHmpS70d/P35kX4zslzoKImH3sOluPgiRqUV7lEZI4Wynhd5fJ3t4/g5uUuXDnXgZ7OEcxMe6TnAAsKFNlqdxVj/7Eq1DWWoLA4W0S3ZEGBj81aOUafnfZKYaOve0wKA4zbGRmawtT4rGwriv3z834R21ks4P2+LcMs9//OXAdchYvFj4raAhQUZcuMAv5ss2bTcdk5m8E97Vk1Im+z4fdmASEZjW59835Z/4nMQF0NiXLMzxSxeSvgvjs55hZdaelymJDNhsGupecsfj9+38nxWYwOTaGzdRgd7JXVNYax4WkR1bnf8XhkdCffh8eSI8uGotIclFXmobw6H5W1BchxOeRYNVuMWzZm5rJL3OjdIlhPxyj6aFbtncD4qBtez7wcO7ynYwGMOhln2PCcVbe7BDW7ihbNqOvsP8jtyqIVz5cbIbgQwIx/FDen3oA3uHIUH8Vwus8rMg7goPNjyLWUS2RLqpFpcq3YXDQspM8ERjAfnIMpXUX0zeS+uAvIzLbh4JEqefBi2NczjgtnW3Hxg3ZxBvAEyJPaSiduOtj5OH+mRS6OB49USzPSfYcqUFTilBNd+t2ogu2++VcUZeuQZsUeH8590Ia/+ssz6O0evyfAKR/CGy2D0SDrhgL6u+8042/+8ozEbW3l6mLu+huv3ZLM9Z//Fx9FUWG2DOb0vL055ORkoKIyD6WluWhvH17xtR0dI2hpGcTTH9m77gF2MuFNzG3m2K4SX8UbBQr/JaU5csOQCmSZ7WjMKoXDaJW85kghjk7eMyMt95zZqbzvU0BlM1Aua/fs2BKBnDEeXe4RjDrLxKVMl/2l8U5pLBkJhdNjeYsi+kYptGXfbU5qwlxUxMSEfxZvDDWhxJYry53K63UnutEfK2jAW0NNS0R0b9AnuejPlRxEvmUBQ95pNE33L3OQF9qcOJxTKZEvG4HFk8M5Vbg52RdDRB8SZzxd5Oa09d8+yTXS78GFsQ7Zp6O/C2NpSmxOZCQpez0VsBnN2OssR7bJLkW+6EaiF8Y7UZtZiDyzY8cdV3RZXz3fLveasSitzBMhnfC617i/HL2dY8tEdNIls6vHkiqif+X/fRUXz7Rizr1o/OC97OPP7scXfvwhEb24P87N+fD+G034xl+ckc+nqBwm6PGJuM8HBfY3X7yOn/nnz6Jxf5k4ySO3V9AfxLl37uDFb17Ezcvdy5aF4uH4yIyI9W+8cA2Pf3Q/nv3sYew9XLGhfj38DlzmYGgBU+Nu3LnZJ9nzN690o+32QELCLZu9sjcPhV42iQWWjmcopJ94pB6nn2hEw76ye6aRZO6vdMr/7R+/hbNv3YnZoHYr+eVf+QIeeXqvCLgbZXRoGn/4my/ixqUuMbxshLrdxfjiTzyCJ57bj62AxaDvffU8zr/XsuT56oYifPQzh/HZHzl97znuZyzgsKj2/a9fwPl374gYHW//YyN4itHTUx4p8LQ3D35oFi3MwlMfP4hHnt6N0iqXGJU269wYvqflmJhGVJ4v3nvtFpqu9a54v0tXuhzTozMSr3zpbNuGl+Xxj+7DJ754HAeOV2/ofTyBKckHH/f13g0JjEcacsylOJTzcRzMeQ7p0hMlNa9BNkMWrOmrF5rdgfFVnffKxrkvRPRIWEUuq8iDKz8LT3xkn2QXsyHptctduHmtR6pr4Wp8LHiu8Mz5cflCB27f7EVOrkMiXw4fq8ahY9VSjaezUVGUBwM2/7xwrh1//pV3MDrqVgE9Dpa7TnQ2c/7gbJusLzoutmN1sbkr413++PffwE/81KOoSgHn8/1MSYkTh49WrSqi073S3zshQvruPaXYTnjjwiz9kZEZ+P0rO+gtFiNOnqoTR1CqYEAass02nMqvxztDtzEZkRFOEf29kWYRlinIZRhT341yyFkpWe7RLnMKl71z4+JQpSudDnsKq5Gu3Qp7LirtrqSIjmwqScH+SG4V3htpWeJ4Zkb3awM38UTBHtgyTeKOV5IDt92e7FJZ94zMCUepcPbBnZkBEV4LbVkY8k6hw700g5T7eGNmscxq2Ci8Spxw1eDNoSZxnkeKvXMBH65P9mJ3dqnsH+uFfQu6Z8fxznDzsuaodqMZHynaJ0WF+4l0pMFuMOOYq1rOVdyOkVwYaxenel1m4Y5z4PNacvV8p7iHY1FW5UJh6V0R3WhA44FynH2rOeYMKLpWeztHcXCDAtJKcJce7B2/J+LTMUuB8G//+G2MjcwsEdBjfVe6uimI/tT/84wsZ2Rh+YVvXMDr37uG9juLYuBK+PwBvPf6LfmTsRB7N9B0nOJzW/MgPni7WURPiuCLLvPAhp3PYejMf/GbMyIU8nt//kcfEk0gFQwBytbDiKDoY5iObYrPrz1/VYRl3ousZ/+jOM0ZCd/9uw9w6f1WPPWJg3js2X3i0N4smNzw8rcvyzHZ2TIMz9z8jr7fnQ6Momv2yioCOpBvqcTe7KewO/sJpCG1j2U65q2GDJjTbPAtxBfJZwMs9quIvtncdyI6px2xWsdHOFuKU8X2HCgXpzkrZWws2tE6hIG+yZiCOk8avPjywelt/L2OtmG8/fotVNUUoL6xGLUNReJQ38wsN0XZSjjbIjvbhvKKPJnW6PX65cHZHdHZjQ8KjIS6eb0X3/7mRcndTmQwRK2W0w3Ly3PFnesqyERWll2EOJ6XOF2TziVONR0fd0seM7O8KTivVOBLBBb4Mji1LtuGbJ7/sm3y2XX1hcjNy9xUIZnfbd4bkMib579zScT0VcYumwbP4bOzPly72o133mmWm5yKyvg5csrGYMTJvgNl+N7zl2UgHm/cze0yODiJa1e6t11E57566XyHOIBWuk+Q3O4MC44cq0ZWVuqIO1wuCo/PFR9A01SfNNqM/BoUfF8duCGCFHPFN+rQ3WwO5VTi7Fgbbk8PLHm+Y3YE/Z5J1DgKJKecAnrk93QYLVJIyDLbJC5go3C9Ush9onA3zo+xQdOHBRZmNg96p/CNnvP4UuVJ1GcWJuUzFUjGPGdVHM+rxtj8jKxnwm1NobnDPSzu//65ySVFFML4HTanTda2yDZlYI+zVLLYuf+FYUHlxmQvyjPyUJ9ZJPveeq6pLAK8MXQLU765JTNIKDTTqf1owS7Zn+8nFqNy0vFEwW6J44nOtOe6eHe4WYplHynam9K9HCLhWI4u2+72YXi9yx22vHYwFiMc+UCjQW1jEbJz7OIIjx5bDw9MihOUrnFGn2wGch3unxCRjPe5dGrTaTvCMe5qY9CFxe9MIf31F65JZviBY9XiLm6+0Yt3XrkpM8D5mtUXBOL6vn6hU9ZTeVUeHJk2EdTXAt37FM8/uFuYYHwLHeXJhsKm2++RTGuK9iw+fPHHH0Z5Tb6Mf5UHC848YTwLs8N5frt9vRdvvXQd599pwUDf+Ibvm5k5LnHFdwalTwJnJjzzmcMoKHImJWIncr/m9/jO330g8UssFm10tsB2E1rgsTqKAU/ziq+zGbJR4ziBhqyHpXloqsNGoeZ0O2zGbPiiDCeRzPrH4QuqiL7Z3NdnfRGzrCZYrdnIL8iS6nrdriLs3l8mAwDGvvAx0DeBkeGpmFPreBKjuMVHe8uQPJqb+lFe6ZILPgVHOt+Zoc6mEIqyk+M42GD3Y588iOkprzhOIh+8iPt8wbjPUXBmFnWyXB/bDW802ET0nbdvo7VlcNXvxUENoy0aGotRV1eI0rIc5OdnwZmTIcK26W7jTd6k+DgFzuvH9NQcxsbcst462kckL7q7ezShwZfDYUVZea6c2zIyzLDbLfI5dv49wwpHxuK/eZOTzUY1WYx1wKZBIZLubzb2bG0ZWlFAp9ifk5shy851lplphc1mFrGbYopkCvoX96+pKQ8mxt3iGOZMgFVv8u4tz2KszHvv3EFxsVNmJ9k3KcfyQYeNwkrLclHfUITm2wMr3kCPjbpx82avFJFsNtOGpnCvF+5fs3Pz0hyY562V4D5TVZ2P0tKclLtRtqQbRTw8mFMhjfno1g0TXFjA1ckeEdp5TB3LrRbX9noLaWHBa8Q7I5+TabSKmJgs2MyzMiNPHLgsAIRhDvmgZ1IaTtIFHB0DkWmy4ZH8XbAbkic4saHjfmcFDuZU4sZkz5JYF8ZvvDdyRxq7+ov2oSGrSNbreuF6pTg/4p2W+BA2lGRD2AcNxqiY0o1S8GHMSVhED9PuHhZXOuNUIi8t1nQTqhz5EgWSNLE3jRn7VSJ28/MiCynMZD832oYyWw4+WnJAtv1amszyPelA53tEZ6HnWhxSRKh0uGBJv//uJ1gkYHNRnq94TLM4FobrggW0lwauyzrlfrDWdRvrfMVGsDxn8b1qMpPf0No94xERmi70WOO2sqo8acbJppSEzSuZtV1cnivi2NTEhzOICMWrwb4JuUet31OCzYLFY2aX0/l++WwbWm/3Jzy2Ct8b37jYifrdxZLfzMaqL33rkmQ/M85iLTDu4taVbly/2IUjp+vWlTc+PemR6JatwO8PSvTG9FSzjGOf/uQhVNUXpkQjRWXrYHY4j6GJUbeMI9/8/nWcfeu2ZL8n93MCcpwyI5860ye/dFyOkWSMnWnc4vK+/v2reOulG3Is8tiOpxEw+5w58Oa7TXZ5zuAMU54HV/rdROHpnvfJay2kRcMM9Gn/MNz+sRVfV2JrQEXGfmSbi7BTMBvsyDA6MeWPP9tnNjgBf2jlextl46TWHeEmIh2ODWkoLHLK48DhSmm00n5nCC3NA+I0p/jDSt/UpAdud+wB0SiFnJEZ3LjSLcIUXem795WisqYAhUXZMrWLz6/WuVxRUg0KmyUlOfKIFiMplM97ffDQne7xS0a4ONU9Ppmuxj+7u8fw3W9fWmzScx/o6JOTc7hyuUuaVQZXEbW57opLcnDocAUeeXQXdu8tlUaEq7kFKMwRxkl0dY7hwvl2vP9ei4jpfG6l9Uhxj6Il86U5kKfr3G4zSy75djA2tthXYmR0JmbBgeuCRU2efylqs2BTWZUnf8/NdcCRaZWf83Uc2HH/4n7G8y2LDF1do2hrHV4seo7MJNyotKN9GNev9aCas4gads5AaSfBbebMtuP0Q/Xo6RpbUUSneN7TPY7OjhHU1hXCat36G0+eo3i97+wckb+vhDPHjkOHKqX5UzLdP8mAzls6zemaprPz3Cid0x9+n0nfrGSjM4bCFwyIY9dlzZRYhUScnv5QQH6XzQ8ZszLl94g7l4L2AWd5UkV0ZibXOgpESI8U0SlgU0jn59JxHwyFlgioxTanxIBYk9jok47nYls2PlV2WJal0z0iOdiEZzaua2mAGfDh8cLdkufOLGeLgaLfyvuzXE9DQcwGvJi5u15ZlOD3o5D+qdIjD6SIToxp6WjMLpX9imLzbPDD5t3My+f64r4XCbd/VYYr6eusIbMIJ/JqZLtwBkQYFnFaZwbxjZ4LcJis8rp8a5ZsexYCYsFmub6QHz2z43h96BbeGLyFPs/EktcwioYFmedKDsCWbl63eJzK8J6I6+whV700aOWx5YmIs+GxcHGsA57AvBznLCawzwELgTwmV4PFKM5SYM8EPjg7hzMJ+ubGUWF3bYqIThH8+uXOmOIRv2/D3jJpRhm+H+SfBkMaqusL0Xyjb5mIHnajtzb1b6qIzuWleMYmm5c+aJN/M/uaDQHZ4JCCGRum0kBGpz3dqtFQNGOGMx3oNDzQCc4xG6GznNETbExJAwl/ztfTvR09PuA5kUIkYzD2HKqQa+1a7p+ZHV27a9HdT1Ez3riZ38lmN0nRn7NF+X0p2In4nbZYXOf3ZCGDRRFqBPHGMtIraXYeb754Q/LrnXkZyMvfeI697B9GA8xmgyzv5rHYJ26jomeiUPxlo0yO2znLgoIw9y/OzOUyfPhv/j0I/3xgwzNzNxvuZ3Sj377eh7bm/rgCOpuG0ijE/gFcD2IWSl80CwX8gUUReppFLTaTjT2DguuChTX2EWDhahf7EWyw8SbhMXnlXLsUwNirINa9G5ebTXXZ9LSoLAcFxU45hnjfy+XlrAw2HO3vGZNzF9eBe9obNwqGM92ZFMHCIo9BrhP+yXtmPle/t0SO5Y0wGxjHlH9ImmyuFI1SmXEILktl3Gt3KmJKs8JqWPlcMx90I7Cws2cT7AQeGBE9Gl40mW966Hi1PHjhZzOVW9d6cOt6L1rvDMDtnpcTuZ8ndn9wyQmdf5fs3/da5MFu4417S3HsZC32H6qUwQNddosnS61OKzsXDuoYRcJHVnb81zXd6sMrL12XQdJOzlELL/qtG724daMPkzFucqIpKMzGsx/dh2efOyCzUtbTy4GRK0XFThEW//SP3kR316jkOcZjbm5eRGvOhqEAvd0uGHGfx4FTmHm+ranJxzMf3Y9Dhyul30Q8UZI3NhxQZWfbUVTkxL795VJUoKD+1pu3pXFob++YRHIksj1v3ewTN3FtXYGejzcJ7oPMDf/+965IDNpKMzfYm+QMZwiUOOW8stUFZ/Y5YGFlpegZwsXKy8vEkaNVKR3ddjKvFr2z4yJMtbmHRLQLw/zhd0aaJYbiqaI9OJVfJ6IjxSxGvPDmgWt/4a57k+du/j5z1SlC9cyNo2V6EM3TA7g51SfCV47JLqJxsmEeMmMyrk50L/Ho0rHKfzMPPdK967I6sC+7FA5T8mMPKNw9W7xfhHsK3ly3kZ/NBpff6rmIy+NdeLJoD066alFozYLVsCiAcs0iar1ynVLom/BR3BuW9cpoCwq13E5sJklX/YOKiKx3s9EpVDMPPwydy+O+WZlxEck+Z5k40ZN9E8y8+yO51bKtutyjmAvO39v6FH4prP/qze/iBypO4LSrHiV2pzjpw8eUiFR3tzlfz+X/++5zOD/WhtF599LvjTSZgXDKVYdjeTW432F0EwtR3bOjuD3dL7NmwrBgx5kIbOz6dNFenM6vl/NCtsm2uG5piLr7Wh5XvMyEKJUshEQ475ubkH2H5ytuI87uyDBY8InSQ0n/HoxxYHTI7as9MQU/jm8a9pbEzDGu2VWEguJscbFHwyxkiuh8T77HZl0fW271iQDW3TYiwjUd5Q89uVtELEahUuykQ5yxLUN9E+LAjhWl8t6rt2RfZzRLeCxQu7tYXOW795dLvCANanS8f/DOHXS3DS8bH9DJSyf63IxXjGg0vSUKxcTyahf2HKzA+Xdb7gmRXHe8B6dAR0HaVZAl0TolFbkoKssV0ZszNzkLkuNCFtQpnPd3j0svNW4bNlPleog3nmHuNUVINo/NfWzjkYl04LoKs1FeU4CcOBn7yYDflQWNqfHZuEKnjJWTtOtRFP3kD55YdG1PezHn9oqhQv50z8u+w/XMf3OG7kDPhMxA3SqRf71Mjs3i3Vdv4uL7rUt6InA34D7HghR7ItTvLpHC2YdFKoMcT9MTsyI6t94exK0rXRKpxJkcsc4n1KG4P377b87ipwueFSF7I8YOfsbta714/YWrGB2cjn3vbzXJcfXkxw9IHwAufywW7s4evvBuC1773lVpIDsztbygxZk4nCl96ESNCPI8JnPzMyXuin/SEMZjdqPHkTswgWn/0t4pS5YD6cg05aPQWodM086K+jSkmWBKX7mA4g/5JNJG2VweWBE9Gjoga+oLUVntwjOfOCidyjvahtDSNIDW5kF0dgxL9Eu8hius5p17rxVXznfIVJcjJ2vw+FN7JDudFTdFUXYWly93S5zLanAw9PFPHsRTT++V6JaNwFiWvXtL8U/+2UfwO7/5fXR3j8ctSLDId6d5EG+/dRuPPr4LTmfqNiKj0P/0M/vw3McPSOwMBcm1jpH4O5zt87kvHBNH+Te/fh4Xz3fEvLGLprdnHK13BuGemRfHvpJ86EopKs5GfUOxTBVnDE88eMP83nt38NwnDkqcz1YzPj4rs0xWczpxX+XMHPZCSTUXeiQUlyj4UuT9SvvbEl8QCQWmMd8MvtV7Ec/3XUau2YEiW7Y4aClAs5kmxd35YEDcvxPzsxj0TmI2MC/P8/cpdPHPyBzjZFOZkS+CGV25kQ5Viql0IvPzIymyOnEkr3pTndE/Vv2IuI9f7r++pHkroTudMSM97WP4684zKLRky3rNtWTAbrTAmGaAL+iXRpIU+Mbm3RienxZ3P79LaCEk780/lQ85lFOBaxPdS0T0Ps+kiM0UTCPZ6yyT2QubQYE1E6dcteKKf6HvimzHMNx+jAr5s7Z38O2eixInw6gSZnozz5zHDbc542AYU9I2PQRP0IdAjBvbTJMFjxXswkeLD+BBgEWmh/Mb5Fzyu3dewZBnaT46zzI8D31/4CpeHbwhOfE8rgqt2Yvnq3SjONU564YzVSbn5+R8xfXNyJ9w8YLHlrzvJtU/KfpRABvoXR5jSLGIDtTq+qKYY46qusJ72cbRv0t3enfHqLiq1yoor4WLZ1pF5GI0A8X+f/LLnxDHKUVnilhcd437y1BYkoNv/dX7IixHw/x2NiMNq2UUwJ7+1CF85JOHUFGTL2KsvFfIhdrGYhEU//R3XpF75kgYC8FIiN7uMWTnLc5MXAv5Rdl49Jm9uHyu/Z6Inp2bgYa9pdh3uBJ7D1eisIQOWgrmaSJWy2wP/i88GF1YdJjvPVQpjRxHBidFlP/GX54R8TeekN7EmY4NRTh6um7D7nHGL37hxx7CZ37o5Kb1FOJ2bbrWi7/703cwPUGhc/m+W1bpkll4NMAkA+4X3LcomobX80L4T1mohXvPTY658cLXLuDV56+I0J/KcN9/59Wb4qKPXI2c6bDvSCU+88OnUdNQKDMzwvuc7G93nQtiWggt4MmPhzAyMIlXvnsF7752CwM94zE/j8aPS2fb8dznx+86wtcfUTk5MYs7t3px+0Zv3H3x1BO78aWffFiKTis20E1b7It14rEGVNTm45XvXMZ3//bcsmx1amicZfHYs3ux+0C5uNy5v8l6SWLB0BOckuaa8UhPM4iAbjHELgqkMlx2Y9rKcW90oa/kwleSg4rodwlPsaOb02hakClFbLQoF9j0RSclj+141zSePHnh5oPu0ffevI2m671o2F2C46frcPBIlYo3irIDWFgIobdnAr09Y5KpvRIcjD/+xG4cOFghFfSNCm08D9ERVFdfhEcfb8Trr92SKJN4zMx48PqrN+Xz6dpOxQipw0cq8dRH9uLYiRppcirjx3UspzjQZFZEOhp3l+CTnz4iwu07b63cOOZe7t/QlMyWOHm6bp3fRFk1S9howLHj1ejqHFlRROd1cmR4WmKLmNVPoWCrYEzQ0OCUFMhW63PAvga7dhdv+yyPRNY9Y10eK2gU4fYvOt4V52tkfji/KQUm3tL4vHT1umGeGZJxDsVJuZnDh25pvjZatN5szOkGlNpzxYl8cbxziTt1NkYePHPUG7OKN2VZwucoxoR8vvy4NL1kAYIO2ki4zpiZzUdPcAxD81MSPRF2JFMgD4t64fUanYWtLIXO8mpHvuTuM8KFRO+LXLvVjgJxcHPbbAaM5uH+yCayFL/fHb4jx00YbkUKucPzM7Kcd2YGJXubxRc5lkToDUgxigJ6LCgQf7HiuBTBcsxbdx7c9qbIJguO59Xg53c9i6+0v4Nu9+iS7HnC48WPoDzPAhYLVoY09k1ZvOfiOg5Fna+28siigM44k1jXERqzdu0tFRNVrHxfup+Ly3JE2KUIHwnPxRTQmfF9/OF6WDbp+sP7XELB9PM/8pDENVisS2eG8dp37OE63L7eI9Ev0aImc6EZ1RLmxKMNOPlogzjDwznwi28EZBisqGkowqknGvHSNy8uW28cp/V2joogvVYRnWIcIy72HCyH1WqWKJy63SUirjuyrPJ+FPnWkrVM4ZDCPNfLX/ze6+jrGYvZsJTFFDZj7GofQV3jxq5HYfevBZvXE4GzHC6934qOO4PLBHQ2u6UZ6Es/9Qgq6wqTdk8RHr8nMhGUQjGLEal4PxMN11901BELRU98bD8efmqPzDahoJ7IGJJ9Ej7+xWPIcTnw0jcvySyPWPB4u3quXc4dPJ7Wy81LXbhzow9B//Jxni1j8Rj64k88hMKyHDGLrbQ9wj/jrGoW3U4+1ijxNC9+4+Ky13K2x4vfvISyqnzk3G24nGzmg7PwBpeaSSJJhwEF1hpYDTTxpP5+tlYRPRjyqxN9C1AR/S4UvllR7OkalUp4f+9idX183C2DGU4/j+dCj4YDAQ40+GC1nRm+bXcGcfyhOtQ1FElMgaIoqQnzz5tv90tc00pOVY4ZeCP00MP1KC3NTVrcA6dQMu/81EP1uHNnULKb44l9zKpnTjhFS8bIMHcvldjVWIwnntojAjqbrCYLNkzds7dUnBR9vRNojzPYjGRsbFbWp4romwcH0vsOlEsz1+6usbizBHjfxtinq1e6UFnl2lIRneI9c/I5hXg1ysrzsHt3yaY25E0WFMMZJ0JHq9lgkNxlNuKMzBcPQ3E9EPQtaZi5VnGRIvFm7D+MNGHcQ6SIHkvML7HniPuXxYPNhNnx1Q4XPlq8X5qJvjXUhKapgSXZ85HOdMb/rReu0/sxC3ut2I1mya/m9r0y0RV3XbH5J/f5RPL91wtnRVDQ/0L5cThNdmkqy1ihyH2Sf3cH5uWRKCwCsQDwTPG+e7n6m/k9Ug1uPzZSZYQNCyJvDDXJto4uUpHFSBwfPOs8tCReaROOKzYAbWteHsdCbBkW7DtaJW70WOITxVyKZhTcokV0wiiEaxc6cPBY9VIxOslw5jSFX2aRx4pW47/5ml37SuW7RovoYTcxoanh9JONElVD4TAamkyYX77/aBVe/vYlNgtY8nOOc7lO19qYlHD8TZfz53/0IRFgKV4yImIj646iO5eXLuKRzx/F8189j96u0WWv43KLftCxcRF9s2FUEN315965I1E+0TDi5plPH8KBY9USwbgThOxUgrMvHnl6D5746H6J+FnL+uN+SwH6xCMN8Mz68PdfeSdu5CD7KRw5VbsuET18vLKxcW9n7MabnCVz4rFdEvG01kgpHjfMvz/2UB3OvnlbNLTIe1jqbbeudkuRgBEuay2YJYIv5BEhPR5paenIMRVLvvhOgyI6I11WguVnGgKVzeWBFdF5EmFFmULZ8NAUBvsnRUDvbBtGd+eoPLdaFhenvDHXl+/F5is82UUzwcyxyTm58PLm/ZEnGiU7nZVevTgpSurBnEuKrcx1Xm2gUFaei5raAjgyk5/Jywzvioo8NDf1x3X18tzDbPSWliFUVRekjIjOQRenAj7+5G4cOVadVAE9DB3Mu/eU4sTJ8YREdJ6H2cySg7n1uuGVleEqLSzMRnVNPlpbh8TxHQ+O469f65UcdQrpW5U5zmiflub4Xe3D0EFYXpGLkrKljZZTGYpwbMT3XPEB5JgyUGLLQdN0P3rnxjE+P7uhKBY63J1mm0So1GcVicC5GbDx6R5nqWSSM/Yk3jLXOArQkFW8aiPPZMD4iNrMAuSYMyQL/qy9DW13G6zO+D0bcr9SpM01Z6DI5sTurBIUWFdoPPKAQAc/t+++7DJcnehatn7T7maWH8urFiF2s+HMgv055bKtciwZOD/aLq7oIe/Umre9Jd0kMTHM/ufyP1m4R2Y7PEgCeqSQzsIEs8+dZrs0iWXvhp65MYmk2sj5Kh3pcr5iBAyP3b3ZZUnPlKZBis7pWFB43X9ksSF1PCiiU3SjqBkNi7y3r/eKa9PusGzabKj8wiwRvZlbvhIUxZgnfutKT8yfU0AvLssVEZl56ivll5dV5om4zcackQIhx7MUo2PdSycC3/PYw/VIJmJqcVjwyEf24tbVHulDxOWOhhE8fV2xBclUgOuWDSwZ4XPu3TsxlzUnzyG5109/8pD8nVE8SuLwmOf6477C4th675048+HI6Vqcf68Fbbf7Y85+4PabuGv0Ws+5gQWUob5JMSJFw1iVolKniPTr7clAYZwCPGeDXL/YueSYpiGVuficEcHzymaI6IHQPPwL3hXHGHajE4b0nWdq5bUtPW1l+ZYu9M2MXlQeUBGd02CYd84GEGxS13SzD9cvd0nu+eSEe8UmY4SxLmzmwBvsgqIs1DeWiCjT3jIoQjw7NbNBRmTVTarUo268+sI1qbJ/6gvHcJhVXieF9M3/zsrKAwtGdtyJ4ybZKnq6x+66nvWkt51IpEFwQQpq4SZJK+Ul799fDofDuinNKjkduLw8T0TJlaIxSGf7CCYnZ1FRuTnZsOtZ9r37yvDQww2y/JsFM7gPH63C9757WZoUrRTPwZ/T1c+bX84G0nPv5sWisbjBQtRKInr4vNfTMybxPOtpyLtW2JyWInp7++pFl8rKPFRUuOQ430nIdHCDCY8VNooYTXfnmZFWNE32SeSEN+iTeITFrPOgRI3wqEm7e2NBUZpxFBQO+eB72QwmZJsyUOPIx5G8KhxwVogDeDNgNAcF1BOuWox4pyUSIxZHcqpQ6yjAVsH1QoH/46UHxQF9ZrRFxFTmZTNuxhv0S6QE3ehcZmkmem+dLj4W16txcb2mm8RxXWDJkqLE0dxq7HeWidt9fcvHaBCbrDtm3Efisjg23bGfbMrsOdiVXYxMow3TgaXXP65DiqOM8sla5/paD9xOnAFBcZ/O6UvjHZj2e+EJzks/AR5T4RzutIgZG9zeFODtBrMUSw7nVOLRgl2S574R+P7ZZrscB3zvSFhcSFbMDSNqWJxrzCpZ0rSY54bKDJcU2DYUA5ZmwElXnRTFbk724t2RO7g+0SPnK08gfL4K3OsfsPR8lXZ3HYePK6Os60yTTWYQcF0fzKlAmT0XyWR8ZAaDveMinkbD7GAapWp3FYnZKh7MiKbYRvE3WjjmvxkRMtQ/IU0Zed+5GeQVZKKsavVxI/OXc2mGCHehjsJiM2Pv4QpkZNpWHA9z3TBehUK7z+u/FylDxJA2Piuz1FIJfh860hlv0dk6hO725Q0L3VMeifdJ5cgRCrJsEttya3m2PffBxgNlkgVfWpEa9xE7DfY5OHyyFtUNGzMY8BjJL8zGqcd2oa9zFH7f8vs/Nl+lAM7M8fWI0NxX+fvRUTSE90fS3LZ6Yw03GbHEQmLz9d6YhTE64Q+PzkghMdkEwXivlYpxaTAb7KuK0amJuMBWeYUWwLaCnbj3rGvaSnjK2fDwNK5e6MTZd5tx/Up3QlO6Sdi5mJuXicPHq3H68V3Yf7DiXtNQnoyuXe7CO2804dIH7eIeiBZ0+Pn8zPm7A4enPrpf3ZDbDLfDxQsd8lAUEgiGMDAwBY/Ht6pQXF1bIA6czSK/IAt5zIy7s7Jztrd3fFXn/FbCeI4f+MGTyHM5NrUhIwd7hZwefKACly91wOPxr+rEGR6aRnGJU2YSKJsDRfGrV7tx/lz7ijFo3CbNtwekGSlndW325XB0xC3HCmeIJRJFlCpFqfXCJocfKdqHxwt2S6zLpbFO3J7uR8fsiLio2RhxsdlhSITXsLAbdoXSyU4hqiGrSPKnKVBtxZiFwtxvHvlhpCIULimkfrHiBD5Zehh9cxNSqGie7pfmp3QnT/k8IqoHEYI5bVHUc5is0nBU1qs1B3VZi40o+W+KlBuFIuJD+Q3yuB+wGswipO91luLsaNsSVxWF85OuWik4bMVMhEg4Q+JoXrUIs1N+D86NtYng2+oewuDcFCZ9s/AtBGS57AaLzF5gdj8FeAq6FP6T5Z7nfkUxno/NhPv7D1c/JI/NhOvqkYJdOJ1fL01CL4134tZUHzrcw9KgddQbPl8FRTgPn69YIOJxVGzLQVWGS85XXN/WdNOmna86WoakqWYsKBBX1hXA7lhZ3GLkCcUyOrz5ftHQgXrjUrdEPGyWiJ6VkyHLsOrrnHZ5cMZYbOHNKI1D+edq0DnLojkbSC4V0SFGtFjvnwpU1xdKMSGWiE5zBpc9FeEYjKLrX/3Bm2ht6ou5fvndmOFN97GyPh59Zo80sk3GOceRbRM3+vNfPRd3v5LI4ZHpNYvoPM5o+oxnFHPmZkg00kbNYWIEqy2I25B0gPetU8uLkMmATmyaROLBbWROt0k2+k6DDUNDMeIEIzGkGSX2Rdlc7nsVgZEsdJzfuNqNy+c70NE6JDfQjEBgg7HV4Lkw1+XAnv3lOHKiRpznubkZMm0nMmuNYvrREzXYtbsEw188jnPvteDdN5owNDC5ZJAQdo2+99ZtaUR49KResBQlVeCxytgPOlZXm5VChxHjXFbsWL5BcvMy4MxZPS+axUE6rVOBzCwrGhqK5bEV/R+YS9+4pwQ3b/SsKKITZnQPDEwgvyBTRfRNhIN6RrRUVrokQmclKKLv2z+Cw0eqNn25OOOITvRE9ik29y0u2TlRLitBBzQjQx4uaMAxV/WiY1ac6ItNL+85O+86pilQ8XcYMUGBl+I5RVplKVw35fZcKVYwi37Ricz1uiAFIlmvaYtNMD9cr4sOf/6u2WBc5hpXPkSaR8bI9WQzTgrHFNq3C25LLscj+btwLLdGZiEsbvuQLHf4eAo70bm9OaODwq+yMjxeWCA5kVcjxQr/vZkzMc5Xd48tnqt4LIVnzySjMLUSHc1D6O+OfS3hPWPDnpKE3odNBNkQM5aIHgxQRO/E8UfqJeJhM+BMSopmiYpivLZH56KH85wZ37CS8z4MxbnMbPvyGIoFwOvxrdiLaDthMSPeuuI9A5c9FRnsn8C3/vos2poH4Znzx5wRQQc69zNl7dAoxGxvFs6ceYkdS6thNhtQUOKUKKH0sXSEYhwTzE2nE309zLm9cYtVjOLkbJGNsjgjxy7NamPBWTzzq9yzrRdeGfgfBeeY3OvlsPNm/7NpqC8UP6qGMDNd3eibz315V8QDg+J1651BtNweQFfHCIYHpzE6PI2ZGc+qWee86WFDjdqGIhHFq+sKUFSSI9lx2bzwG9mleOnvcDDArDeK685cB5xOuzQRPfN2M65e6sT4qPveazlV7fbNPhQWO7HvUIWIOepIV5TtJxAIYmpqLqFBPI95Oq43I8oljM1mkRuX1fDN++/OcAluWbZ0PJh/fuhuFuhWnNdYzKypyZeu8KsRCAaliJqqN2n3Czw2KKDv4fTnVUR09iXp7hrF4OAkioudm7I8MlheWBTR+/pWFtGZB0kXelGxMyFX3U4gHJmQmW5AJlKjb8L9AMU7CuF8ADsrLiXVYYwHZ0u0uUeWuNBtBrM4u+noNm9jjvjiMZWGzHTruiN4lNhI8QFpMntj80O+1n4tYZ4v+1yNj87EfA0bWjL6IxGYPd24rwwvffPiMuMGxyltdwYxNjwNX11hQgL1Wq/TbHxqs69ejBLnpsUkjvhYIjqjTtnMM5HxJ6+x0nB12dh5Ab75QEzBMBVgbny8dcUYSM4coJjOdZAq9/SMA2IT0fffuC0xtuHZ+YSLyG367GeP4PDJmlVz8ZX4YvGeg+Vy3Cfr/kti+SxGec/hgamYBZrwfd/aWRAXejwR3WQxJqWZMZsnswgQ71hgASBW3nsyoAubUS10pMeCYwp/yCM/32mObf/CPOZDH2qKsbAa2Gdl+0wGDwr3xx3iXYchI1U4va63Zww9naPi+Ga2MRuB8AK3EjzGHVk2uYkvq3ShvMqFqpp8eeQXZCc8eAmf+MorXTJFjtPfmKn6/jvNIhaEYUOI1uYBdLWPoLa+CAZjalxwFeVBhhFM7Jmwmgv9XuEsw7LJcSXGhBzTXF4W53y+7RXReR515Wdi3/7kNvFabR0lOiOAN6WMvVnteqBsHF5LWYR+4/WmFaOReO1mNjoF7s0T0YHR0Rn5HM40WQkez4cOV8LlYryMXpcVZTvo90zi9vQARueXCpX5lkzszi6RLHBF2Y5ojK62YYwMTongGw1F1vyibBSVJZbBzvtOZgLTuMXYhsgYUF63pifm5H6WUSmMEkkmFMpo0qAxLBEolFNgi/kzkwHZORkJNaPkZZUxiNGXV35fmtwihd5UgsYQOu7jEQqFRJg0GJZ/t+1gZmoONy514Z2Xb8r+Go0tw4KTj+3CqScaUVias6mGoPsZ7vu7D5TLsZwsOPbkg+9pMhvgjZHosnjPF1h3f0Dur/HMASx0bRTpC5LOuULxz6WbdazTiW1MMyGwEG+G9gJ8FNHjOdVTGF9wDrOBiRVfYzNkwZS2s/o57USMO7oBIDv8znilKj4yPC2C9K0bPbh1vVeeW81tGK7C5uRkyPQ7CueNe0oluoXdwxeryetfRg5ODh6pksECM9Lfeu3mPXGOy053+o2rPaisydcu2IqSIucVTqFNREUXZ455cwfLPC8k2nmdYiSd6NuJ1WpGQUEWShO8gUwGPE/n5jpgSuAcyvMu+2Cs1IBUSQ5Z2TZxozNXvKVlcMXCRX/fhDT5ZiNa3qgnW7zmzcLtpn6JPeJxspKA7siwYM++MjidyZmWqyjK2mBkR+vMIK5Ndi95nmcFutAP5Wx+9JOixIJjh5tXujExHtsJmFeQJRnnnJWcCDRJMNKFTQmbb/bFdJy23R4QkS7ZIrrVboLZalyTs9QYYzzK53kvTVE+kWs3G8JybBvrtdKUOUWHZ5ztuNK9uhQBZJwTDhzaPjjOuXOzD++/eRtN13piFnvqGovx6R88KXqHxhtucOYleyAkMKNjrVisxrjFDbq4A+t0cou4HedYpXa2WmJDoudKnzd+NCoLcpulfZnSrTCl2+CN49imE90TnEZwIbWaGK8G4+08wRnM+EdXfJ3d6JR1kEraij8UgjE9XYo0K7727p8zPi/cfp9EuZnS06UfjcNsln4zKVCjFHbcWXOx0hvCvM+P2Rkvmq734vwHbbhxpRtDA1MJVbV4wmNljw7xohInjp+ux/HTtaiockmjl2TCm3LGunzk4wfwwZkWGSCFF5EFgPaWIXVFKsqOZLGQt5lwWuuaPmObTyVsJMoc6a0ckMvUbzqkzBxspq24vuh8oHMjVZ1O9xPcLtwfjp+slUiX+WD8wSpd4m2tQzKbjA3HDHEyFNcDtzV7HTBWbWJidtVZDVXVLpSVsZGbToVUlO3AE5jHnelBNE8NLHnenG6SprP7srduppOiLL2WBHH7Wk/MSBNSWpEn2eBrgfedB05Uo7tzJKaI3n5nCIN9E9h7uDKpMx8pfCcSgxeGnx0r35gGBhYNwu7ZVUlbvA+PpYRspjt1w6QtFgBSmfC6Gx2awusvXMOFM63LxsQsenD2w7OfOYKGfaUJG3WU+E50FrjMSYhAWW7UMsbNFOf94fqiKdPkeI03a5kRMd51Zq1HwmWbmZ6Le0/Gmdybda9oMWTAZszETCB2nOTCQgiT/gEUhRpgM2Rip0Bn/WxwHO7AyrGUDlMezAZbypyTvMEAut2TyLXY4TCZJYrPEKM4JEbGhQXM+OdxeaQPd6ZGMOXzSmRefXYe9uWxMbtNfj8VZgrvOBF9esqD27f6cP5MG64xa3xsBvPzAZlClciFlycjOs4PH6/GsZO10ijUbDHAvEqFeSOwOl9Y5MTufWW4ea3nXoYVB0sD/eNxp9QoirK18CaBNzSJnJzpOJmb9UkTws2aBclzW6LT9ejg5WM7yctzoKAwaxs+OW1xWrIhHaFQfGcGLxF06KTsTdp9Rk6uA8dP1OA737wg+3I8uDnGxtw4f74dTz29N6k3dTKA8/hw40bfqlEujkwbTj1UL8e0oijbw6XxLoly8YaW5r3WZRaiMasENqMen8rWw2IsZxD3dIxizh07JoAudN5jrgUWbA8dr8Zrz1/FBJY7J/t7xjDQOwHP3Lz03koWnCWdaJTLSogTXZ3MKQPFy2/+5RlcO98RsyhTXJYjMS6PfGTPpsZRPigCOqOYeFzulGIEt3heQabEE8VicmIubr+HtUCtq69rLO4MaWbwJyN7PRbW9AyJNIkHY1yGvR2ozjgGmNZW9NxOJub7MOkboIVvxdflmIthTU+N4sB8KIib44P4Z+98C3tzi/Cxil14qKgKJRnLtw+bh1Ns/1+X38KVsQFM+7wya4DFS4vBgIOuEvzsnlM44iq524toe9n+JUjgYkDHdmvzIG5e60Zby5A0DaULgIJ6IvEFdJ278rOw90C5PMor85CXnyVdgxen3LF6vnnfgYIcB0nVNQW409R/T0Tnss/OzKfs1DVFedDgIMjhsCaUB8dzE5uQ5uRuXuzD3Nz8innSYTgQtlnN294IkdFYrrytbwUm0VziROcgdqVrQgq7nO5DuE0Y77N7bxluXO+RKJ14sOHrubNteOTRXbBYOC08OcswO+vDrZu9mJycFREkHvy87GybOOfj3VwoirK5MS6zgXm8O9KMO9NLXeg8Hex3lmGPs3TV6cBbycyMF739ExgYnMTk1By83sWs2braAtTVFG7L9VDZHObcXty41InZWW/McURGphVFZTkiUK31Osk4CMaKjgxOLmu2x2iFAfb66hhB4/5yJAsK6MmY9cXxcvoOiCTlepyZ8WB8ZAaTY27REDijncUJr8cvGfcSi+gPyP05Z73zz+Ddv7NpKAsoqczMlAdvfP86Ln/Qjomx2WWzU9mn7fQTjXjs2X2bJmA+SNC45MhaFNBTwRmbEGlAaWVe3Ax399QcRganMT05J0L3er+XZ3ZeIoWYvx4LztjJ3qT75wxjDjKN8YuZbCg67G2726Bz++OXEmXI247R+a5VX5djKkkZh/2YZxaXR/sx4p3F+eEeNGS7cDAvduPttulx/E3LFZwd7hYBna70MLMB4MJwLzKMlxAIhfBI8fbH+qW8iN5yewCvv3wDfd1j6OsZw9jIovM8EVEpvzALFVX5qK4tQEV1vjSfo0vAkWnd8uZ7rFa6CrOWuN3pZPWKoK6iznYhDWUzrSgpydnW5aBQ2tvDWQm6L2wnPC84nXbYrKZVo0HoEO/rHUdZWe6muXDGx2cxuYp7lmRm2qRQR0fQdpKZZYMzZ3uypKUZTgLjINXQtw4eQxkOC06eqkVP19iKIjob+ra3DUs+elW1UWYWJAP3jAcXLnSuWoziMVRZ5UJxcc62NudVlAcRipLzIT9eGbiOKxPdmPQvjctgjMseZxlKbJvTfHgtSIPqGQ+uXOvB7TuD9wR0nsP8geBibJg/gAJX1jIRvad3HN2943DPemGzmHH8aJXOfNkh8Pp15YN2zHuWzpAIQ2GKTUUZk7JWEZpOVuaiD/SMy31uNP094+hoGUquiG5IT0ozSY69tnsWZCxoWJsYc4t+MNQ3gbHhGYmMc097ZFsysoKv4VieAjmFch7boWBQ7s8lLiPEfy/cfX6N8YpbzPTUnOT1v/LtSxjqn1xmMqQOcerxXTj1eKO40XeM6JvC8J7LZrfsFA32HrmuTBQUZ4v+QaNqJDSbMD6K+xL3l/XAgtTw4JS8R7zZ1NX1hbIcm4HDmIcsU2Hcn9PJPe0fxqi3G7nmMskQT3XmAlMY8rZgwtcf9zXpMEiUCx/G9NRoLDo+P4emiWExScwFfMgyW+C0LJ9RNTnvwbWxAbzW24KJeQ+MaenY5XShLCMb86EAbo0PY9LnwdmhLtRk5aHB6UKBbXtNCikvone2DeOFb15MKNKAAjVdpPmF2SgszhbxvKGxBDX1hSgoyt6S5Y27bOlp0nRiqYNmsQFJ6l6S7384gGSG8yc+dWhbl2OgfxLf+Nr5u3nN27ooDzS8qWBOmziCRmbuFrliw2mSrS1DOHy0CnZszsVqcHBKmiavBiNUeBO23YNiCqYU0hUlDAtMh49U4a03mjA8PBXXDc6bVN7gXr/aI1nqyRDR+Z4TE3O4dqVLBvUrkV+QiT17y7Z9NoeiPGgEQkFM+GZxfbIH3+m9hN658SUOJENaOk7n12NXZhHsxu29MaQwNTA4hfOXOvHmO7fR0jYETwxRlQXw+Rj3LaOMrbrYgaY7A8hyWKX/QllpjkRKKqkL3chTE7NoutaL+fnY40IWT+7c6INnnXnCdH7GG/9TFO1qGxFXJ8XQZIz1JMM8PTnvk0qxIJypzvVF8Zzu/a62YfR1jYq7ltvmfpyNyGJAx50hvPn962i9vXQWD6ExgM1pH312H2p3FSUlxkehtpOecEPdVIHLytjS6voiNF/vQ0vTclF2sH8CZ15vkuazOXkOOeckCmdiDfaN48q5DvR3jy873niu4IwIiuibZbqiKJ5tKoA53Q5fKLYRzRfyoHfuBlyWCtiN1AhTcxuGVUIuK53o3mD8qB1juhnFtl2wGjKRnpYax/i0fx5dMxPyd2ailzmc8mc0HdPj4lTvnZ2Sfx9wFeMjZfXY7SzAXMCP9wY78b2uJox553BrYggtkyMqoidyE7yagM6p14xlYUOyypoCHD5WjYNHq+TfzHxLmYYkUYMMCrhmsynlG5Xcz/BkXlLsxMc/eXhbl+PWzT48/53L8Mk0zvtvgLfT9onKqnyZGbCSiE5n6+2mfnG0ZGXZkuLoiYSf3dczjuEERPSKyjyJothu6KjbjA71ys6FN2/lFXmorM5HT/eYiEvx4FTq8+facOBQBfLyMjd8Y05n6ED/BLo6R1csTvJzioqd2LtfGxYqSlIbMS4EMeXziIEkPS19iZGE+Ze+UABTvjncmOzF13vOo2V6SH4nDN1IRVYnHi9oRFlGLrYTnkMogr/7fgu++o3zmJick9lPFrNRYiMpos6uIqBmZVrFzdp8Z1AKdrebB+HMtiM3J+Vvxx5o2P+GbuaB3vgN3Vpu9ctjs4RhClLMZKeDNBlw303K3SffZJtFRB5TdJNPjs3i1pUufPD2Hdy41IXRBMbPOx2Kltwvz71zB++9divmGCy/KAuf/sETaNhbCpvE2CrJgH32zBbjjlRxGveXobWpX2a4RM9amBh149L7rajfU4yjp+vhKsoSQ8xqxQIW+SbH3bjwXgveevFazIIViw77j1ZJnIvdsTn7oindgkxTPvLMZRjw3on7up65Gyi01SHfWi3NSFMRNkH1BKfRNPUWxn09K77WlG5Dlf0wLOmp813m/D4Mzi0K/1VZuSKgRzcVZTzLzYkhXBzplWPJajThy3WH8GRJLVy2DPhDQezPK8K1sX7cmfSjxz2J25MjeLi4GtvJjh+1cQoZG3aefrQBh4/XoLzStd3X8piwdyidcJHnE04DkkyyFFxeRXlQ4XG5j02Ar/dgbHR5k6cwdJ813eoTYZARMMls+EQ62ofR2TkiuY2rUcdpcbnbW5GV5lIWY+oULpWU4uDBCnS0Da8qol+72iOFo6pql2SjbwROJ2262b/q7B4Wf0pLc1FTU7Chz1MU5UMohg97p/G1rnPSDDTLZJc/KYxTQKf7vGt2FE1TfWifGYE/QjwPw9/5cvVp1GYWwpq+/Rm+Zz5ow/MvXhMBnVBYqKnKR0V5rkRNvvlO84q/X1qaI8YNDvsZGUFH+u7GYuRuUwyakhhjw9NovtG3rcswMTqD29d6FqNBk5FljvsHine9XaP4y//vTcmtn5lefdx8v8A893deuYnXnr8Ss09cbr4Dz3zmMI6crts00fJBhSZIaSi6Aw+mmoYiEdKZn8/4lmgYK/WHv/kSPv2Dk3j6kwdRVVew6gyGkaEpPP/Vc7I/cuZHLDIybfjMD51CXkH8xp/JINOUh3L7/hVF9JnACDrcF5FtKkJD1kNIRTzBGVydeBG9npsrutC5L1rTHahyHE2pgsB8MCgxLKQ0IwsOkzlmlMudyRG0T4/DajDiQF4RDrtKkGdddKxzzOiyZkiW+rBnFqPeWfTPbn+BdEeK6LwIVFS5cPhYDQ4eqUR+QZY0P6AbPZWmlEVXiqOnkXHwzaaEqdQkSVEedDgg2negHK+8fB0d7SMrZiAymuL7L1xDtjMDjbuLkzKlT9x7gSBee+UmujtXbmLE8x3Pe3X1xZva4DQRrBTQjcmZZqzcf+zdV4YL59ulySfFo3hw32+62YvyslxU1eRv6DMHBydx82bvqq+rrslHTV1BShbgFWWnwgzMGb8Xbw43wR2Yl1iWdN7qpaXJdS7sRPcG/TEFdJfFgUfyd+HZon3INq2/wVmyYHTLrdv9UuSji7y+phCf/8xRVFXmIcNuxq3bA6uK6IxtyXHakZfnwMTELLp7xqSBuJLajAxRRF/9WrKZMN/71tVuPPyRPdu6HKkGY3Yun23DN//qrAjpbGiYKHRlc+yclWOXsTQffI7HN+/RTeE/7/Y9uvBeK65d6EAqIHrCAvDyty6J8z6W4YbRlEdP1+G5zx6F1W7e9nPofcdOXp1pEEc4xfK//qM3pSdANAF/EK997yquX+xERW2+RLAwT9/usMJkMkrMFTPVOeOjq3UYbbcHMNg/Kb2IYlFSkYtnPn0YVfWFsFg3V4J0GF0ozziASxPPI7DAGWKx7zv6PU0wp9uQZcpHobUWaWmp09+Bue0t0+/jysT34AksxpzEI8OYi/KM/RJNw2z0VCG4EMJ8cDFRJNNkgdmwfLtfGetH593IF4fJgk9U7kGO5cMxn0SGpaWhNCMbdqNJnO1TvqVZ/tuBcSc5zitr8lG/qxg19UUor8xDQZFTsoB5cUtV8TyyK/jEuFviacLwIu0qyExKJp2iKMmB5+ycnAw0NpZInEp//2Tc11Jgv3G9B+++nQOz2YCa2sINCXEcFE9Pe/D2W7dx5XLXqk1FmWt38lTd4nlwmx3gnDKa7Egb5f7BmWNHdXW+NHHu6Yk/JZ7cuN4r+eQbEdE5sGeTUjb/XY2a2gLU1lJE12uxoiSLhbs3UIxzmfSv3iA7EpclEw/l1+Nz5ceQa3GkhNmkuWUQPX3jUhwoKXTih790Ent2l0hEC699fSuMFZY0W2b8ZG4GxsZnMbxK7xUlNfKmhwcm0dOxsqlhKxpHtjYNYs49Lw0Bt7uRfCrA6/z5d1vw0jcvoq15QES/eHDmN3umlde4UFbpQl5BpvTwoXButppkDE1hkPnP7LG22Hg1Tf7O45uzyRmpkyoiut8XlOLB2beapXjAxqeR2OxmHDhWjY986hByXNs7U1VJPTje5ayWYw/XY3R4Bm+8cDVmPwcWqSiKDw9OSlxVRiYFdIOcfxhhxlkgjLuanpyVvg7x+h6VVuThoad247Fn98GeYd70+0VTuhU5llJUOY6ge/bqitnofZ5bODf2dex3fgQltt3b7uRmDvqItwMtM+/jzvR7cAfGVv2dbFMh6jJPwZCWWhn9aXebTy/2uVm6XOGyxtXRfslEN6Slifv8ocJKEdOj3yfTbIEx3SDxLjRfbDcpL6Kzinr8VK0cfGwUWl1XiJKyXGSlQP7vWmDFjeL/Ux8N3WtKwxzlxr2lKdnVXFEeVHjx4QCBzRCZpTw0NLWic3Zqcg7vn2mRG+uHPT7U1RdJgWytFzH2fhganMKli514+cVrkuXMeIt48LyR78rEk0/vkUzV7b5ockCV6sVMZXuLLDV1haitL1pVRO/uGpOYpJkZDzIz13et7++fQHc3XZ6rZBRn21BZ5ULhNjcfVxRlcdpuZYYLJ1w1eLxgN/Y4S1PG7NfVM4qxMTccnP1VU4CjhysTyomNhuMDCu+LRfO5Fa/zyvZDl2V/z9i2R4RQxB0dnkJX2xDqdpfAZn9woznCs7pvXenGu6/dQtP13pgCOg9No8mImoZC1O4qRmVtAUor81BclivCMoV1ieNIgDm3V4TpVICFt97OUbz8nctSPPBGjXM4Ft9zsAIPP71bctC3+/5ASU3MFhPKq1149jOHxeR55YM2Od/R+BkJ/z01MSePdY39dxXh+CP1OPnYLtHztgL2YMkwOLE3+0mMz/dgwufFAmIL/LOBCbS7L4hj3R0YFyE9y1Qg2epbSWghhLnAJIa8reiYvYiu2SsYm+9e9fccxjwU2xpQbG1IuT6LxnQD7EazOMfnAj5pJB/5fRnP0jw5ghHvLLLNVjQ6C1DmyIY5fbkxkGI8vx31llToD53yInpVTQG+8MOnRWym63KnijSOTBseemwXjp+uvbfhmWnHzFeeYBRFSS3qdxVh3/4ytLQMYmAVhxlFv9nZeclQf+Kp3SgudkqRTBwuZmPMQTpd7MFgEJ45v4iFnCJ+9UoX3nq9aVWRkTCH/cDBchw8VLHh7OhkIBc3HagrK1BR6ZL8/vffuyP5wfFwu73SD4DNfXfvKV3z5/Aa294+jI6OkVVfyxx05qHbH2BBQlE2A14NTOkG5FkcCGFBolt4A8UbIP6b0S6MeOH0XpvBBIfRikJrNk7n1+Hh/AbUZBak1A0hBXSem/Lzs1BTnb8uAZ0YDQZYzIvX7HlfAMEVIuOU7aenYwTd7bGvJbwnpRArURlJ+jwWVeh+p2geDZ2iV851oKQ874EW0cn0xBzef7NJGonGEtC5bRxZNtQ1FuOxj+7D4ZM1yC/K3vEzJhl5N9w/KbnTbP4Yyz1MofLRZ/fiwPGae1E0ihILnkfqdhfjCz/2EDKzrLh2oVMa1c4k0I8rHrwsUqB35magqDQHTzy3H0dO16KwJAdbiTTazDiCVtsHkic+F4wfieILzaJ15n2JUJlw9KPMvg855hLYDFkS95LGEcsm3OOycag/5JXsc4r5g95WNE+/g2FvG+ZD8XtIhWF0S5G1HpUZR2Azbm7O/HqwGozSTJQi+pBnBm7/3WjrtDRpKPrBUDe63ZMS+VKblYuHi6vETBG9rjlK8gYCMrsxukn9dpHyZ9bi0hx57HQoonH6naIoOwM2Gzx0pArDwzP41jfOx52iFoYC+huv3cTZ91tx5GgVDhysEMGwsDAbdrpXRGRevBJwqhZvlJhhSKHv+rUeXLnUKaLhShnsYRgd07CrGJ/6zFER0HdqcVF5sGCEQVWVCyWlOdJvYCX48+bbA2jcXSL/XsvglY6azvZR9HStPgVy/8FyFBU7E35vRVESgwK505SBxwt3SwPRUe80pvwemYY7HwrAnG5EhtGCfGsmqjPyxXV+2lWHbJNd3EuphofCpj8oTnLO/lovkaeyVCoSKMthXAFjXOJFuTA/m/m+zApORrNPMj7qRkfLEHpjfOa8x4+r5zokEiE3P/OBNS5wnHztUidamwYwPemJu212HyjHz/7Scygozt6wmMyROcfu24nMXpmck0iZb//1+9JUNJbz/rnPH8Xhk7XIdq7/PKU8ONDMWVGTjx/+2SdQu6sZL33rEq6ca1/2unA/k6XPLT4vudUGxiCliem1uDwXpx7bhUee2SvFKxadtxqKrYxm2e98RgRqOrvjudHDULwe9rYjx/SuRMFUO46gwFIDs8GO9DTj3b4u/P/0hM4H915z995fLAQLtBGExIkdWJgXtzmXjW54xriEkPjstAxjDiozDqEi4wBSkUyzBRWZTnTMjKNpYhi9s1OYDfhkfDjp8+K7nbcwMDst/y7LcOLhosqYAjnX4sS8B75gUMwZlhjZ6lvN9i+BoihKilLBDLdH6tHVNYKLFzoQWiHWhXBs4Zmbx/kP2nDlUheMpnQRuZmBSlGeN9+cFsepmGwmNjs3L+I8sw0Z55KIgE4o7j/3iUOoqMzTZojKjoKC9ZGj1auK6Mwyb20ZlGPFal3bTIvurlGJc1mpYZ84ZcwmcbrnF2Su6f0VRVkd3hTlWR34iZpHEFhYvGGUabh3byzT7v7HHExjmkFujOhK5+1pKkIHOQvWvM4neq2Ohc8XxJxn0T3KArtRs61Tllm3F/3dYxgZiu1gLCxx4jM/eBL7j1VJlnYyYO722y/fiCmi+3x+3LnVL01GS8pzH1iXMfO/L7zXgtGh6bivYYzJF378YRSWOhOObFkJjtOjYy62Gppv2Nz0u189F7OXAvOqH35qD04+vkvyrhVlLfR1juLqhQ603xlc9rPFprtmeNzzMpOUYjrPP4w44s+ynBnSb4D9C6sbilBR7ZIGpGuJTNosGM9Sm3lSolKG55cXB5azgCn/IG5MvYrb02/Dkp6BfGs18izlyDYVINPkkggVCuCJwZl4HnGbzwUmMBMYxaRvAKPzPRid75QIGbrRAyHfmgR0luH3OZ9BteMojGmpETUVTb7NgUOuErzd344Jrwd/13oF3TOTyLdl4OJILy4M92DGP4/qrFwcdJWgwBb7foz7W9/sJOYCfmQYTcgyb/9MrAfz6qsoipIAvCli08EvfumkiN3NTf2rZizzBpsDjHBcBavzHEBIkyJDmjibQqGQvN9ab8T5XqcfrsNHnt2PffvKknbTpihbRX5BFg4cqsB3v3NJGhLFy7VjUYkxSk23+qQ/wVq4dbMXw0NTK2bm0RWzb18pXK5MaSamKEpykWsf0pBp2lk9jOLBYjiL4hSvxidWn2Yd70ZwatqDgcEpEeR5/qHIoKQmFJOGB6diiqfcfuzbtfdwBbJz7EmLCSmrykP97mIYjAYEA0sFFV7TfPN+tN0eEBG94AGcRcXxs8fjQ8edobg59Sxu7DlYjtrGoqRFpno9PhmzbCcX3m3Bu6/dxAAjH6PGNxkOKxr3l+MzP3xKnL/bLVwqOwdGR9F5/up3LuPm1R5p2BuGrvKjD9Xh6Ok6lFTkyrmQxyDPReHGuwaDASazQcR0eTgsIq6nSnSSMd2MOsdJ+IJz8E66JbJlNShmh0Ie+MHZczPwhmYw5G2BKY3NLS0iWlMIX4lB7x18q+f/h+BCAKGFIIILfnn4Q/MimrPZ6XxoTp5bK2wguivrEdQ4jiHLlJ+ys5IKbBk4VViBr2dkS5zL7YkRDM65YTEYMDnvlXgXOs+PuErxWHE1jDH2GZovvMEA2qbH4fbPoywjGy7r9jZ/JXrnqCiKssqNM92qn/v8Mbz5+i1cu9qD0dGZNd00M8OQj/XCgUpmphUnTtXisccb0bindMc1V1YUwhkZpaU5EkfU0jx4r9F2NBygDw1Ny4yOREV0HmuMcrl1sw8jIysfo3S3H7873VnjkBRFWY2igixkZ9kwPe1Be8eIiOlrjVMbHJpGZ/eoiPD8vaqKPDge8GzrVObOzT6MxnGhZ+dkSPa0My8jqQIGxSeK46UVuejrHosp4Dff6MPug+UPpIhONzYd6Iw1iZWFTtg8lM0MKSwni7nZecx7t0dE59imtakfZ95oQvP1vmV5+RQwWTBgg0g2T9UxjbKW/ZrxUd//+gXcuNx1Lwud5zSL1YgnPrYfDz252KCWGec7FQrNtZkn4F/w4sbkqxLvslq0Sxi+ToT0YOL3/mQ2MIm2mQ8Y3IJkQmd8qX0P9mU/gzxLhYj6qYrNYEJdtgs/0nAY32i/ga6ZCfTNTi3JTD9ZWIEnS+tQk5Ub8z0YAdg+PYZRzyz8oSBctgyJftluVERXFEVZAQ4kbDYTTj1UL8JbVrZdMswHByalmehmfzbFc2ZIMxv6mY/uk+aMFCIVZSfCm7vsbDseergefT3jcUV0MjkxK070qSmPHAerOas4u2NkZBpdnaNxHWqyDIY0OY4PHqmEw5G6g09FUVKHupoCXLvZi8GhKbR1DOPilS7s31uGTIc1IdFqYnIWFy534vqNXplpw+v4/j2lcGpBPOWgaEmxtvX2IMZG3DFfw5zt6voicWEmE7o3s5x2yfMeHpiMKaJTUB0ZnEZwX+iBcxwHfEGMDE7J9okHnejFZbEFmY1k1btn1t9scb3QgMOCwZsvXsf1i52YipoFw/pNeXU+Tj3eiOOPNMi5KFVdqUpqwdnQLBK++f1ruHS2TRoah7HYTDKbg4WZmoYicaTvZJhjTsF5T/aT8Id80kTUHRgVl/jmsdhEPZlYDZkST3PA+awI6aYUFtAJz0VOsw1fqNmP4MICro72Y8QzKxF/NqMJJRlZeK58F47ml8Juir2Pzfn9aJ4ckdfnWTPQ4MxHXXYethsV0RVFUVZhsWkKcORYNcrKc1FbV4A3Xm/C9avdKw7k1wunn1K4z8yyob6hCA8/Uo+HHtklmeo6OFZ2OhkOC06eqsXLL17H1NRc3FgjOj37+ydxp3kAe/eVyayQlaAgf/VyFyYn478nsdssKK/IQ3l5njhJt4qFBS8Q4jTsrb8R31pMgKGEaddLuygqShzYaAvwATxGmDMq05t5beXzPJa5H1Es5D5lAtIsQBrFZ5PcHG8FuxqKUV3pwu3mAZnp8pd/exY/+uVTqK3OR4bdci/CjQRDC/D5A9L3hGMEOTdd78Urr9/EzaY+mEwGFBZk4dCBCji1+V/KwRlNY8Mz6OsahTtOQbaoJAfVDYWb8vmOLBv2HanE+2/cXtY8kgz0TmCof0Iy21kQftC2zfSURyIl4pGZZZdCRDJhNj6F9K2E4xjuf5feb8PbL92Mmc3PLOrTTzTikY/skVkMipIojIbqahvBa9+7Kn+PhLM0P/WDJ2Vmw04X0MMY0ozINZfjlOtLci/d4b6AKd8gAgsrx7SmCnSgl9n2Yr/zWdRnncZOwZieLtno/2jPSXRMj6NzZgKeYEAiWXbnMGOeETnxx3EsRARDIXGsB0IhPFxUpSK6oijKTsx0fvLpPSgpycHvjrvR2zOeVCFdbq6LnCIy0q1bU1cAu92i0zOV+wbu46VluaipKcD4uFtuiFeaavru27dRVeVaVURnTuq5D9pEWFiJvDwHjh6t2noHn78JIffvYsH3Pu5rDJUw5P4FkJ6zKHoqymoszGEhcAfwX8GC/wYQ6MbCAgtObiDkBdJtQJoDaeku2b/STPsA8zGkGSqBtK2ZYp7vcuDwgQp09Yzh4uUuNLcM4Dd+50Uc2FcujnKef8LMzHjR1T0mBT3+eflaN1rahmT2GqOqCvId+PTHDsLlcjxwTuKdAB2Z1y90wD0d+1rCbVZYmiMC02bgyLRi76EKWG0mzEwvxptFO+W720fQ1zmGrIMPlohOYTnAbPIVDJ500XLdJZOezlGMDsdvZLoZUNhsvzOEP/2dV5Y50MM8/tw+nH5yt+SgK8paGB6YQsutfsy5l86qpsDMKKTDp2pgNt9fPTvYzNxuyMbjBT8Jl7kCVydfwoCnCamOAUbsynpUBPRi2y7s1Gbztdku1FAAX1j02HB7rEa+NQM/VH9YGtITZqinQgN6FdEThDfyl853oPlWH+bmFk82JaW5+MIPndruRVMUZQsZGZ7G2fdbxUXb3zcRN+vclZ8JQ3o6vF6f3FxTaA83YqHTnFlzFAUzM23IczlQUJAlWdFlFXnIz8+SmAlHpg1WqzFlmrMoSjLgAJ379NHj1ejuHl1ZRPf4cOFcBz756SPIc2XGFZx4HE5OzOHWjb4lYlYseLwdPloNg2GrB2F0ztGtujNcL+tn7U2SlAeTheAgFnwfYGH+DSz4m0RMX3z47h4rd93oFNIxjYXgMBBow4LvLOD5W6SZ9iPN8hTSzMeRlp7c+IZY562DB8rh8welwXhT84CI5ZevdKH5zsCS2S9Xri+K5jxfMbqFjnSvd7GRckmxE48/0oAnH2+EzWrW2WUpiM8bwJXzHZiNE99RUpGH4rIcmC2bcxvNZn2ObBuqG4qk8DIb0egvDEX0ns4RyUZ/kGAcG52xKx02jMAJBpITo8Dmrp1tw+huG4Z7hbHKZjjum6714Bt//p4I6Px3dA76wWPVeOTpPSircul5RFkzjAli4+RoeN1iEcpk4uxn3FfwOKEYmw4j6jJPIctcILnlzdPvwh0YQ6rBBqLZ5kIcdn4SFRkH5e/pWzT7blNm9UP+tji5cA2/Z0zBHVFF9AThIPhOUz/OnWnB9OTiRbRxb4mK6IryANHeNox33r6Ns++1oLNzdJkDfbEBqA2nHqq7Fz8RbioaDHJAv3BXQEyTHE2z2QCzxQS7zSwRF8x9zsqywWLlFPXUu2AoSrLg7n3gYAXOnmlBZ8eI5JnHuxkeG3OjtWUILlcmcvMcMV9HIb6lZVCa/i0ea7HJybFLX4HCoiw9xhRlG6HjPOR9GQu+M0CgE1iYXOHVobsP/2IcEl8bAhZCY1gI9iIt0I5068cAQ8Wmxrsw//zQgXIpwL1zpgXXbvRgfGIO7qj+KG73vDwiYU+VhroinD5Rg5PHapDj3LlN2u5nKJpOT82h+XqvFEtiUVVbIJnbm2Vw4LXJbDZKLnp3+3BMEX2gZxy9nWPiVuY48kGB5hS7w4K0Fda9d84n5rfsnI259On45/3/e6/ewmDfxDIhezOhQ/i9127h9vXeZZ/L4g2F80/8wHFU1xfKuUVR1gr3K5nVEf18KAT3jBftzYOoqMmXe9L7ibCUazdmoyRtlzjTXZZKdM1eRf/cLbgDEwhJEX97xfMcUzHKMw6gMuMgimy7kGFwwpB+f22LnYyK6AnCG/nx0RmMjczcc81N3RXTFUW5v6EIPjw0jTdeu4l33m5GX+/4sum1dJcXFztx4lQtHn+yEVXVjGG5P3LkFGUzKCjMQlVNPu7cGcRQDDdM5ECf/Qdq6wrjiuiMhbl+tWfVm9zikhzpM7CVWeiKonzIAl3mwX6EPN/GwvxrQLDnbu75OgiNAb4pLIRGEYIP6bYvYyE9D2lpxk0TN/NyHTh5fFEEr6rIQ0fXKIZ5bzDtEcc5C4KhhQUR+9jHhMX0XGcGyspysG93KfY0logbXUlNKL7S4c3ojkCMqD7WXqvqC1BUxriqzYNjyj2HKvD+m02SgR4Nm1xS2B3qn5TGkg8KBpMBOXmMQYpfBB8fm8H4yLTMFtgIjIZrutqDc++2YHIsdpzKZsC8+3PvNOPi+20iZka7hItKc/D0xw/iwPFq2FeJuVOUlaIVLTHyzhdCC5gYdeOFr53HsYfrUVrpgjPXDpvdIr+Tdh81rzUb7MhPr0a2qQg55lLkW6sw7G3H+HwvZvwj8Ia2sg9CGqzpGcg2F0kT1GJrA8rs+1BgrZYLTyLRJ8rWoSL6GqrRnCK+UrMyRVHuPyjKTU158PabTXjt1ZsipkfDsURhUTYeeawBn/3CMeTkODTDXFFWgANw3gQ3NpZI49CVRHRy62YfTj/cIE19KS5EH6Ns9HfrVu+K12gekxUVeWjYVZS076EoyhpYCEpz3QXv97DgfR4IjSThTQNAoB0Lc1/FQno50iyPA4x22cSbfMawHNxfjr27S9HTN46OzhH0D0xidNwtedpsLEqxgc51V54DlRUuNNQVSkwbxXUldZFYsCvxC7LMCi6rdCE3P3NTl4NiaU1DIXLzs2AyD8LvWyro08jBRpMtTQMPlIjO48pVkCXiMa/psa75bALa1T6Cxv1lMESNFxK+55+dR2vTAL7/jYsyG8AX0Th4s+B3mff6cP7dFnlwtkE03O+OnKrFM585LKLm/SJmKltPptOOApmVubzvwsy0By996xJ6O0exa18ZSqvy4Mx1SPNanptW3O/SFsfbnKnDaCrOqqGbnb/L49ZIIT6F9lsui8VgR0XGAZTa92DU24neuZvo99zGuK8X88FZ+EJz8IU8SW9CSse5Od0mTUOtBgdyzaUoyziAMvte5JhLpBmqkprolkkQukrYIV1FdEV5sPB6/WhrHcLf/e0HyxwhYaxWszQB/cznjyEvb3NvrBTlfqKhsRjV1wtw/oP2Fa+vfX0T6Osbl2PQmbM0BoGZsYMDk+jrWe7Wi4QzQ8or8lBR4Ura8iuKkjgLjGIJNCM0+0eL2efJe2dxpYdm/18YjJVYSM9G2hbc4hiN6aiudMlD2flQPJ0cc+PWlW6EYojodGAyPiOvIEuEoc2En+XIsqGsMg+tTQ6MxCg087mWW3148mP7F38nhYSpzYICHvPi6TIfGZqOGXXT2TKMpivdOPXYLuS4HGtaN9K41B/AnVt9IqCfeb1py/Y9fm7HnSG8/O3LaLs9ELOAcPhkDT72hWPIcj5YDWWV5FNQlI26PSVSjPHMLTa9jj4Wrl/qksdaj1GK5lb7YlQpZ04wfqiusVgEeZ4/mem/qhi/DVC0LrTVyeNA6DlM+4dEUB/wNGPI04Yp/xCCC34sICTZ6tLscuHun/cI//3D77boIl908If/np5mQJYpH4XWOhHvS217kGspu++F84W7O5qstXXKqtxttjsb/v7eSkmEU1s8c3Sib10emqIo2w+jW154/srdAUbss/2RY1U4crQKOVHinqIoK8MeAJWVLhG3uzpHV+1J0NU1tkxE5zHKn61Gw65ilFfmSWMyRVG2gWAvQoxwEQE92ePpABDsw4LvPNLSCwDjg9VwUdk4zL9mjEtHy1DMoi7dlXsOlcOZt3VjvZpdRdJgMpaIzoiRzpYhcU1TsEo1QWqzSE9Lw679zIsfjSmicztevdCJr/7ZO/iRf/ykuF8TbSTO7f/uKzfx3uu30HZ7EFsZG8tonq/87qsSJxRr/zv+aAMefWavNLZVlI3CbP3K2gJ87PNH8b2vnRezaDLgLB5qZl6PD9MTsxgemJLC5BtmoxQG2evhief2o35PSUoXg4xpZjjNxXAYXajPPI3Agh/zQTem/SOY9A1KI1JPcAqe4Iw87w95EVwIIISg6AUUedPTTCKKWwwZsBmyJH89w5hzNz6mWNznxnSrfJYxzYR0rH3mzE4jyJk+AT/G52fhCQQQXFj7WDDTbEWFY3tj8VREX4MTXeNcFOXBgsd8b884rl/rjtv4kAXlPXtLUVNbIFV1RVESh8cMBXQ24l1NRO9oH0Z31ygOHqpY8nx//yQ62lePhdi1uwTl5XkPjNCgKCnFQhALwR4szJ/ZBAE9TAALvveRZtqnIrqyZihUd7UNSyRPLNIN6WjcX76skLvZInphiRM3YrhB2a9nYsyN2zf6sPdQBSzWB2MMyu1w4tEG3LjUGbfhJ/uYvf/mbUxNzuHwiRrUNhajsNQpcTzhMQDv6Sn2jY/MoK97FF1tI2i+0Yvu9hGMDU+LCBgZo8JGpfMeP/pjxKwkIwed8RntdwbhnYu9//V0jOKFr13AO6/ewmZDpz9jY+gcXg8sZEyNz2JmyiN/ZwNcRuLwMR/x9+ifuac9EqPDPPpYDA9MynpiVj1F4HsPM/80wcToEIsRpojn+HM6o7ntc/Iy5DXK4uyM/MJsfORTh0Tn4vFCwTvWLJy1QhGZvjOZ2REIYbHN9rw0beY27ukYwdHTdTj5+C4R01NxXC6xkzDBYGAPJbs4pymAZ5kKUGCtQSA0L8I6nekUzxc4xpH/wuuPzvN0cZ5TSGd0i4FieboZ5jSrxLikpxlT8rtvBq1TYzg/3IMb44MYmpuBJ8joOzr6186JgnL80qHHsZ3oWWQNTnSvV0V0RXmQmJiYRW/P2IpNhO12C4qKnMhO4Wq6oqQyxSVO7N5Tgtdfu7miE2ZocFpc54x0cWRa5TnejA30T6C/L36UC8en2dl21NQUwLXJObaKosRmYWFqsYlosG9zP8d/GwvBAaQtBID7fFq0klwGeyfQcSe2+5hRGoXFThRX5En8wVZRUOSUOATGIsy6F6WoSGamvbh2oQP1u4tFKHwQ4DWdMTd7D1eKiN7bObbsNWwKS0FwevK2bFeKwrmuTNgdi5nMvK/3+4OLbtkpj4jmdILz4fcFlnwWxdeHn9qDvIJMtNzq3xQRnctw+3ovPLO+uLNeKbSPDq3cPyZZ1O8tRXmVa90iOos7Z9+8jeYbfbIt+PAHAgj47v6dzwXu/hn+OR++gIwDGW0TC/f0YrPX9uZBOSaNdx/yd+OH/7733N2H1WZGaUUenv7EQbgKsza4du4fuM6ceQ5UVOfjzs0+KXywsLRZhIILmByfxdTErOSus5EztTX2L0h17onhhkVnuZIYcwEfbowP4ftdt3FptA/dMxOY8nnX205eyLHYsN3o6DJBeEHjhZYXXUVRHgwmxmcxuErDQ5vNBKvVtKzZoaIoiUe6VFS6pOlnW+tw3IZunBnC47G/f0KiWcjYqFvy0Ken4xe62Nyorr4IxcVO2GzmTfseiqKsQHAECyKgJ2fKeFwWxoHQMBYW3EhL297pvsrOgXEaA73j4kaOBZviUejh9YrN8rYKfi5FdD7ampcL/HTs3rzUhY99/hgyMm0PRFN7OjfpMKaTleI33c5TE3OxG4TO+SQOhw8K4mw0Socy41npfF6tGTnjJ46ersVTnzggYjp/573Xbm3K/sdYnngCOtmK5qZh5j2++DNwE4AxHlfPdyQ9U57bi3pM5CyBROA2Z/43ZzCoiL5YZJocd6O/e1zig+j+ZwFnq+Bu3tc1hvd8TfJ3Fqhy8hx6L32f4QsG0euewt+0XMYbfW0inhNjWjrsRhNMBoMUJ9Z61coyLxqpthMV0ROAFzSZ8iWNRTUTXVEeFDj4nonTTDRM2D1B4U/jXBRlfTfEuXkOnDhVh57ucRHL4zEyPI3WlqF7IjojXoaGpldt/nfkWDVytzDHVlGUpSyERoDgVmQML8hnpfHz0pMroocFt0SEysWp7CH4/AEE/CGJfjMa0sUdaeCN4/2vde4oKEbTscy4iFjYHVYcPFa9LW7vkvJcVNUXxhTRvXM+yXAfH5mGMzfjgXGjk4a9pTIzjQL6xTOt4mpd7ZgMu55Xg4USrs/d+8vxI//oSYnU4fFPRzudzYm8h6KkoqbFCJ2RgSlcOtuKd16+iVtXe5aZV9jYOOzu5/WO/5ZrVmSNJ+rfC3ePMYlyCS3Ie3K2gbx3nNoQz7fvv9mEsqo8PP7cfpll+qDEmzwITPo8uDjSi+903JSQG3O6AQ6TBfm2DJQ7nMgyW2BMo5C+NvbmFmG7URE9QXgC8M8z72i7l0RRlK2CWV10h6zE1JRHMpmZu0ghUFGUtZObm4FTp+vw/LcvrSyij8wsaSLa2Tkqwno8OBZnHuahwxVbmmOrKEoUoSkshMa26LMmsBAaX/ON2WowPorZsRkJxnlwfNDcMoCO7jEY0tNQUuxETVWB/KmkFhSi+3sn4jqTMzKtOHCsaltE6mKK6HUFcX9OUazpWi9chdnIL8rGg8T+o1VwZFnFsf/G96/B70uOuE3R/NFn9uHzP3IajuwPHf50yxaVONHbPRZXGFSUVKb1Vj++8Zfv4+r5dsy5F+NUIuG+zvMdm44yAofNP9m4mKL6SlAwZ8GYsxgY1cJIH7rN2ax3pVkU7F/w3b87h0MnaiS2SkX0+4c+9xTe7u+4lxJfl52Hz1XvwyerdiPbbIsYo6Wtubn0dqMieoIC+jwF9O1eEEVRthRO/7PaVr9heuO1mzCbDXj8yd3IUaFOUdaMyWREQUGWZKNfv9YDd4zsV8KmRH1945idnYfFbERP95hEusQjI8OCffvLkZObseoNgKIom8k8sLDyzK6kscDPWtmVulZ4zvnu96/i2o0eNDYU45HT9SgtyZEmdrFEzffPteH1t26jo2sEXq9/MYLCbERxYTaOHanCpz52EBaL6YGI39gJsKHjYG/srGu6IykmsbnkVka5hMnKtqO4LBe5LgfGY1zveJ9643KXCMoPmojOmWbMc/7iTzwscR1n37gt0RQcK6xXPD9yuhbHHqqXXPDMbNtdF+7icUpBsbqhCH094ytGryhKKs6ubm3qx9/+8du4c6sPszNL44M4m7rxQDlOPFovf7LXF2OTeIwxFjFtlVPfQujD9AZxofuDUngeHZqWvg2cLcKmvbH7F0yi6Wq3iPcsVCn3jxO9ZWpU/p5rseHz1fvxiardcFkzYEzf2bP3N11EHxqcws1rPdjJBANBDPZPah66ojxgZPJinoAo3t01ipdfvCZND+sbilFSmiPOWt54WcMDEI16UZS4UEiy2804frJWIl3iiejM6OSsj+7uUdhtZoyNzohAFY/MLBuOn6iVBsDqblGUbWSBM0zmd6yI3t07jqbmfly62o3JKQ8a6otQWJgFS4xbqYtXukRAv3ilc9m5bGR0BjNurxT1nn1qrxT6lO1n76EKZOfYRViKxmI1orA4Bybz9njPGB/C6JKf+RcfvZubvfz6meNywFWwPOv5uc8exdFTdRI7GAmjSqobChNehrrdxfjSTz+KqfGl4jRnejFj+v/P3n8GN5ZmaZrgewW0IkCCWgvXWoeHTBGpVWVVluiq6p5qMTM90z2z27Y/dmx+rI2tzY9ZMba9bdu2s9NWLUpkVldlVWZW6sjM0OER4SJcO7XWAiAJDVyxds4FPOju1A6QIPk9kTdJJ0HgQtx7v+/93vOezV5e3R4HXvvSSRw50cgGtQKFvPLWzs3vk/V3ktU0sqWKI3caW6ow3D+D8eF5bjo6N73EzShpnEAV5STuyYrEMRXkXqdxeiDkYRc/LVRQw1Jy4NY2Bnn88DQ1DUF87Xcv4fTFtifeB3r9aUFtu+MMiuz5vX/yCov/JEbuNsFK+nxsPzIhXFuBr/z2BZy93IFyQFEkfp8pe3srtHXW4Gu/dwmXXjm86nmBGv/SYmixoXnjZ75yCsfONHOj1acJ1/r5c7pZcjkNU2ML+MFfXOMGthRf9cTj2RS88oUTuPzKYRw6Xo9wTYAXDIsxbqZjkno60CLgz/72JqbGok+I9/QtOdWpsSktXAkRff+Q0nKYS1kLv4crqnE0WI0al3dfzMdKPhqgJi0//tsb2MuQeJ5MUrlLGVzVBALBjhEKedHQEOTJ7tMTkJXQRKCvd5pjXbofTaGhkUR0ryWiO1WoisKD9s0h8YSIBlDcaV6V2b1GIqDP72Rh3+t1cknxfrgICQQFVJuKM+da8e47PZieXlyzqVUymcVA3ww3CY0tp9d0g5Grho5DinKhY2j3UQGJmuGQaEaTIjGmEBwkdMDcqRxhepziPtbwyDwL4Jl0DrFYCgG/E8pTTipy4C0uJfHBh/24+2Cc3evUeLy5KcSVMwsLcSxEEhgcnsMvfnUfx47Uo7W5alU3u2BnIZGatlJC48i+R5OYGo8+boxIQu6xU00s7lFF1lpU11Xgc1/degzQ+audKAb1TZW8PS8keJ+60MZbMaHrPS0i0NZ1tA6z00uWiD61xNESK0V0ui0JhDSu9vidHPVGDn6KzaHvSVBcC3LnkoBOWzGhKgcSMXcbGk/lshoGemfQ92gKD++NcSXE4WP13GR1s40faZHmwotdz7UvdIwsRhNYmIvxe0iNLws9qIjCPMnuVLnpKzX9ZaG8amtC+XrQogltxYSqSYb6pzE9sXr/haepb6lEa0c1KsPbf17R+Tju3hjGjff7uFJqJfR5p0XEz+ZFe1roKiZ0jqM4KnKyz0wu4ueTN6FpT47bKSZtuH+WF7wE+wfdNJHRrc9bi68CAcf+ybwv+ahtcTGJ+7dHS/0wAoFAUHQqgm40t1ahrj7IsRHrlW7Sr6jB0aOHE7w9DzQoJPGd3A0klnu9DlRV+Xg/6uoreAuH/QgEXOyUKTR+EQj2MjSxbWqqREtLFUaG5xFZWD2mhSbDw0NzLDiQSLUWtNjU1FzJ91n0cOTtIPsB9ThgaoCZyDtzC2IfCYzaiu9X/nyVnxVZIBQISg8JzjtVkUVCT3HjmyanF3lOQ4IDVai1t1XD/pTYpuk6HjyawKOeKUSjCb52d3XU4POfOcbf9/RO4/rNIfQPzmJweJ5vGyIBz1E80UdQvmQzObz7q4d4+437LAwS1bUB/Nf/6kssUK4nogs2jy/g5q0j34BcsDXj4NREFD//wS3c+LCfG9c2tlbhd/7oKs5caON4m5I+fn6eRcfH2PA8Lzr1dU/xwhP9jCJJSOQn7E4bXDRHCjhRXRNAc3sYJ8604OprR1DOTI5H8JPv38D7b3Zv6vYvfuYovvX7l7ctotNrOjG6gGtv9TxR/UGQnkmGry/91nl0HasvuoD+6eNICFcHcPGlQ3jjR7efMcnQPpLATu+vYP8gSxJsioKMocOuKFA2ygTaQ4irtUAgeIanxWLrn9bPVv5q5e2oAedm4gHpNlTVQQ1ICkikcK0QuaxFSusHTy9Y7uQKJmXANTSE8NnPH8d3//z9ZwYfpYJem3ictk8Fwt6e6cffkwO3ta0KL7x4CFdfOsRZ0iS602uzX1Z49zIrj5fNHkOGThmC5qYzNsmJQ8dc4Tja/DFEnxGUJYX9OnmqiZuHriWiU8YiRSfR65VIri2i19YFcOJkI+eZlgOS2gbF99+tCI/MAuYyTCPGX63vra/I/8z6dwygr0YMJt+OfpfMi+6Fz8tWvwoEO40dkHYouoQqPrjqo3hEInHEExmuBiNnuU1Vnrje0rk7m9Xx1rs97Fgnutpr8E/+0cs4drieF7rPn2mB3+fC6BidvwzcvTeOE8caES6ic1IgEOxN6Byycqy4G2N6Ejevvd2Du7eGOcuaGOqdxk//9gaa26p2QES3FptIxH/z5/esBq5r7WtcRzKe5li/kYE5jAzO8Vi43EV0eks/fW9N690u4dCM5glUlfHwzsgzv6OIKqpyOXO5nfP+S4nb6+DYJVospPf4iemOCSTiGc5Hp+NAzGX3Bw5FRYXDhXguy1vW2D8GICGiCwSCZ6CSZFrpJ8dnOqNxHlsmk0U6rXEpczqTs77mN/p+anIR6XT2mS7fT0PC2EfXBhCLpbmE2em0P+G6pq9O/vmKf7usr+RU3enrarjah9c+cwTDg7O4eWOI93u3odd5oH8WkxOL+OlPbuPq1S4W+ju7aqGqYuBRDtBxQCWTJPjS8WMdM/ljKp3lBZnCsUNfU6ksRkcWMDdnTVrWgwaYb/7qIfp6puD1uTiXkbL3nzhm+Kv6xPFD/94LTjcSvm/dGsb9e2Ornk+4OVLfDItQFO2yFlSxQU1FyxOaRZGoGAJkKtEnV44BicNQC5v+1L9JeC98nwPMFEwjkRfV44ARZ4e7yd/nf2bG8/+2vrdul8hnUwtBXbCDSNSXwLkzn7oSiOixeIbP1TV+F2qrA8+MRShmgM7flJtOmefVVT4cPVKHrvbqx7elPg6NDUG0t4XR0zfNOesUFykQCAQ0piHH9fJiEjaHys7jYkaTbAYaX1LcCI1NH++XYXIDdxI4Sw3Ft/zwux/i7V89wNz0xuPhlYSqfJxhXu5QBXFbVw0WIwmupkzFM9bXVLYkr3FkLobZyUXkss/eN7nQu47X71i/B4rgITE9lcw8juUpQD9babAT7H2CDheOVFRjPL6EnsU5RNLba/hcjpT/bFogEJSM99/rxdDgHDcYIcGPt4yGnKazy5Xdrs9sVtdtXddXfG/wgItcWBsRj6fR0zOFyYko54RzNuEz26c/p4ache85+86m8MXe7qDvVc4nPHuuFV3P0QBnPegxqUzwO793GZVVXnz04QAmxp9sirLTsFMj/34tLSXx7jvdnCF95YUuXLrSwdEvgtKzvJxCf980bn8yyotOheMnm6PNOoYMfaPjKL9pBg+g6fjYzPt/7+4o+vqm+PO5+jFkHUcrjx/rGJJZSLfbFc4Jp60q7Mex4w18DJVDLBAd062tYW7QOz4WWdUptZzPQl9r0S5U6eUoF1oEK0tYVaNNfjJpZrMvv2nAJJGdxXSKh6EJr/W99TP6NznV89/n/23SVyMCM/sezPSPS/PcBILVkAOAHNqhxwpBkp8/v3kldH7XdIPHH+RGfxoSzm/fG0MsRj2UTLS2VOH40YYnms5RdZvf50RN2I/unilEonFknsqnFQgEB5NkPIMffO9DTIxGUF0XwAuvHsELqzS0LCVWvIeDe0EVoHEhZcVT49dSQgaJ0cE5vPXGA3bBFwRV2hfKjO88UscLC5SxTTERNO4mQ8oS5abPx9FxuJbF6XInXOPHq6+fwOkLbSya0/OkcS19P9Q/g+vv96H34WTRHm8pmkQ0QuaJZ3G47KhrCOVNaqUd/xfun+ZFef/9s78v0i5Y908b1Sbs/rzmoFLv9uNqbQvenOjHSCyK2wuTaA+E0Owtbp+BAyOiUyf6AJ2MN91ob3cpdA2enVna7V0RCIrKg3tj+PijQRZiyUVFF/CVTVtKAQ0UqEEMbVvliYabJKarCqrCPoSpkVCJRPTCAI7unzJOI3nnwFoDkt1gbjbGDvmlxSSLsJ/7/HFrwKvsn+yxciSZyKCvdwZv/OKeNQjOHz+FRahSQtm82FxPoiegMWrh2Cl8bWmt4rzeTp587P51mY7v9o4wH3OriejERueolpZKtLWFnxCw9hUSie90fNu29JZx8bA+D9OYLeXeCQTPQKK2KVfvwCPZIMk1gFxVknsn8ebphqI0T1haSuHjG0MsitN5lUT0zvZnny+dk+h8S9P7RDJb8muFQCAof3RN5zH8zQ8HuClqe1ctjpxo3PH9IOMFNbrt657meA0a09Y2VODqq0fgL3GUy9JiAj0PJzA9GX1cKEeCeXtXDTddpR5VpB9RVSUJrjTuJnc19aNaXkryvIf2tdxxuR1oaqXtyWsUGUPIREINNospopPDmxZo1prfUozLThhoKMqVFz7SOc7efxqHq1Bx/vz7EsmMYTRxF0F7A0KORriVAFTZ/tz3K9gaQacbF8KNeKW+Hddnx/DWxABHvHymvgMd/kqocukXb/aViN7QFMKlq12w75Fu9DRZn5+L4Wc/vLVhVIVAsJdYjCYxN7u8bnO+8ovI0J9wvNPkNZnIluzYp9eGjv/5+Rg70GkFndy75QZFhjx6OMliOmWmv/zqYe5UT843QekWhGjwTsfQXoGOF5p0rCzrpHLOcjsHkIv80OFafPBe75Z7EdBkoKOzBq1t4ZLtn0Ag2CJyFSSlHiY3/CxhybZSDSi1kOTiVqFQ5ZuqyFbl3VPu8Uw2h+lZinKZskSnGj+aG0OrZp3ThLGwwE33ZexiVdt+gntLGHOAEd3CXykAfU6UZkjSPl1wFewJKCqKxGOK+NjNhTU6Nx093cTO7qpqH5sIm1qq8NJnj8IfKG1mNjmmB7qnnkiao8a75Mj/+u9c5LHdXhXcNgM9N3qOxRa0ubJ8jZgUekwyp+0EFGlJ0TJU1fXMZU8Cf76KNb+eSw/j2vxfoc51CPWuw6h0NMNvC8OtBuFW/JDwZE8TQWlwyAqavBX4rbYTHOXStzSP5GAOC6kELlY3IeR0w63aYJMVNihstmrAY7Oj1r27lca7ogR1Hq7Dd/74Kgs9ewEaEA/0TuMXP74NYx8F4gsEgmcpxLSQcEfOc4rroNibjz8cYMG0nBfSaN9I6P/LP/8AdQ0VOHq0gSt/BIK9RijkRUtrGHX1QQwPzW3pb+kzTwJ6ff3eLxcUCPYLkhyCqTRZkS4kdpbqcWynWawvdlUNucfJRU79LaKL1HsAj8vFqZFoT+8UFpesvM9jR+rR1BhaVQxhN17OEsnI0S4m8kVCH4CR/jnM7Eeb/hNJ8gC245A9/zUgieuFYPeIL6fR3z2963MMOh95PE588RtnedtJyPk+PflkiWVjSxVOX2wTlbXPAcc5rhHFQwu5qUSm5BGldP8Uu/Po3jib0Vb73FHMjbNI2qBuaohr8+iL0fYBfGoY9e4jaPWcRaP7OJyKHzbZAVWyQ+LKTnEdLgUJLYu5VIKbix6uCGM2FWchvX9pHt/tv43OQCXq3QH47Q52qD8VcLkmR4PV+L2uMzhQIjoNKMmBTk3P9tLJh5q1CQSCgwHlVZNo/sYv7+He3TEuPaOywb1gGKMBEUXN/PD7N+H7Ry4cPlK327skEGyLmho/Llxs27KITvnudXUVUHbIXSMQCDaDAklthGS/AjP99yV6DBmS47OA2ln0ew6HfZxnHllMoK9/xspHz59jBofmcO36AH9PU8CTxxvQ3LR6/ju58BJJq/cF9aZQyqAPxX7AzPXCzN4Acg83/zeSH5Lks/pKCAS7SCyWQn/3ZEnjNMsdLadxTOJKqFKSHPGC7eN02zkWZzUyqSymJ6jKuvQT3PmZJXz8bs+qn3FyITe3h+H1F7cheAFLUL+GofgNOGQvmj0n0eY9jwb3cfhtOxEzdzB5Z3IQ/+NHP4dmGsjqOnKG9d7Tpy2p5fAgMoNH0dnHixibHQ293nTo4InoJKDTtpecF5whq8pcYqLvESFNIBBsHcq0jkTi+LvvX8ed22OYnIggvkaOXAE6lwWDblTX+FFV5ecBnyN/nttKlArlw1GzViuuRkMymUFsOc37E1mIb7gfTzwPTce9e2Po651CTa0fFRWeTf+tQFAuUMPTM+da8KMf3uIcxc1ee0+dbkZNbWBPjTMEgn0PHY9KEyTH52BmfgOY1BeliIKR5IZkfxmSegSQvCg2He3VuHt/HFPTSxgYmsO/+4/v4EhXLRYiCdz8ZBijYxE23RzuqkVrcxV83tXFgDhFxC3E+fxE5esrG/gJto+pjwL62BajgqgpcxlUGBevn55gD0IuXXKiD/RMH2gRnVz4K6MGCTJD2GzlF6G5l/D6XPCtkWdP/cke3BnjSEeny8aZ+KVgqG8GH77Vg6HemVWrLSRZwuGTTZx5Xwqo0ahpasiaGnJGBoPxG5hK9cJrq0SVowUNrmNodB/juBdFEp+3YpE1dCxmLdPAauimydtWyZVBMsiOf0qoFHKvNfoqZFSRGz2dzsLcgdU6gUCws2QyOYyNLuDnP72LDz/o4wx0yrxeDVpUq6hw49DhOrR31qC62odAhedxuTf9nkrnaGV9M1hV4SYPLGgATY9LoiFlJJKYvryc4v0ZHZ5HT880i+obDbQpeubBvQmOtRAiumAv4nLZUN8Q4uOsv3eaj4f1oOu0z+9C56FabjAlEAjKC3b92k5Acn0TZupngBkt0h17APUEZPcfAEoDpBJMgg931qC9NYze/hlu7Pz2uz141D3JzUHp+kwVaxQl9dlXj6ChPsgNkp+GrtuLSykW4ml4EK707bk5UTliGhFAnwaMvdOfZCU8VBQq+oGFYkxmpxaxMBdbteHiQYCbhOZ06HmnagFOSRbVOs9FRaUH4doAN+7MpHLP5JRPji7g2puP8OJnjyIU9hXNgFKIiOl7OIl333iAD9/pQSr5bA8zm11BbUMIbV018PhK40R/Yr9gIKUv87aYm0YkM46Z1AAG49dR6WhC2NHKGeo+W1gI6s9JvduPLzcfLvr9nqmkyL6DJqI7956ITtAJhUphaMXO0Hd/9UOwPsGgB1de6ERLS9W6+XIkdnZ21WK3IUH2c68f54vZek2mvF4nOg/VFO1xT55u4s/1RuJUOUOvSdMaZdNbzRJ/6zeP8Ks37vOAdq18OJ/PiZbWKpw81cSO165Dtew+X23CXMzBJQnpQwNzaLw1jJs3hzA8OMcDzvXo6Z7EqTPNOHa8cduNampqK3Dpcgc7e0v9PmwXem4XLrdz02py8a+F223HoSN1RYvncnsc6Dpciy9++RT2MlVhH58ry824TZUc1Bz36otdmJqMbnieouqPI0fqUFMT2FORcQLBgYEmpEotZNd3YBhLVn61Mf+c91kByXYKkvPLgP1SyRpEhqv8OHu6GZPTi7h9ZxQzs8u8Fa5BoaAHp0404tKFdlSs0YBvOZbC7NwyZ6fT3zQ1BuFZo8xesHlMbQQm5+zvwbGsZF3rCtBiDGUHU8Py5cUUGzyoSpLmoXSNozF7qMrHERc07iqGc7Qw3l1eSiK6kMBiNIFELM3jKXps+qyqNoV7mfkCboSqvAiGPLDZn7+ynMa3JKxFF+I8x07EMmxYI0cyLTpxAYsi82PR41PcA7lVK6u8W358EqlHh+YwO7X0uJ9BXWMQzWQ2CW194f36B338erHwLZEYqOKFVw7B5V69FxG9t+T4TcbTPM+g6JJEPI352Rge3B59Yvy6vJjEgzuj+bgn6fH+fvoVT/ystiGIE2daICsbN9+k9zQaieOTj4fyov3T9/3kV3rvG5pDaGkPr/ncNvMZo6jJ5WgCyWSWG5bS881mcvw9vTa9jyafEVnHRubxm5/e5Qrf9faxtqECTa1VfGysBj3G8MAMJsciPNemv3N7neg6Woeqav+W53AjA7Po657KN4E1IckyDh2rR2tH+UWD0Fyltj6I+qYQO8GfrYJI4dc/vsNi9pmL7aiqDTxXhRTdJ73e8zPLGB+Zx/X3enHrw0EW61fD53fjyquHWegvVmPRzWJwdvoCbxOpB/CrYVQ5W1HtbGchPWCr4bgXtxKAKtPYoswmSmVOmz+EP+w6V/T7rXSWtslxWYroNLF1ONW9KaI77UXvmCwoDY1NIfyDP3oRe4X6hiD+xX//xR1/3C9/dXfzpMqF5eUkbt4Ywi9/fo8d3GtB4uuRo/W84HH1pUNwb3MwuVVocEeNFmnr6KpBIODGz1K3MbrGgKTA5GQUM9NLPFDdrqhImerlnqtOr8/v/v6VHX/cUMiDV149wpugdG70M2db8NMf397wtm6XA1eudvGilkAgKE8kyQnYTkIi1zhseSGdHOlrX3tXxw7IFZDs5yA5vwLZ8Xp+WlOacTqN/8+camaBgMS98YkoC4D0c6/HiUOdNfjal06jrsa/pgAxPbOM+fk4nFyVq3L0i8+3epm9YAto3QC50fcgNL8kkZJ07MVIAuOj8+i5P8ERC2PD81iKJllULlRaVYZ96DpajxNnm9FxqBaVYf+aecebgRsL5kXsvu5J9D2a4ugFykmOx9P8OxpjkQkhWOlFU1sVOo/U4+iJBtQ1hjgmYivCV0Gwp/sl8Y6E1enJKPq7pzAxGsHM5CILvCQwkxBX6KXm8Tr58Un0Jsfq0RON/D1VgW7WGEEC+o/+6mN88Hb345995ksn8c3fu7QtEf2v/9MHLH7TeYB0a1pgOHaKzEmOVZ83PT49z9npJcxMLfJX2uh9JxF5JfT7mR8v4lc/vrOpfXn19eM4fLyRm/NtBM0Jhvpn8a//5x+tWXG7Enrtv/CNs/it37/8HCI60Ptggl+vqYkoL5hQhA1/jaWQTuVWrbC9e3OYt4145fXj+Nq3L6wpoqdSWbzzq4f49U/vIDIf55+RqPwn/+3nOFZrqyL6rY8G8e//7a8fv28UO/NP/uXrZSmi0zmmur4Cpy+2Y3Rg7pnXmT4DPffHOVKUzjdnLrXzeYbOK7QwRNczqgZ4enGGPtN8PaTq6ZzOizP0etBrvTC7jAe3RvHBW49YPKf3dzXIHd/cEcbnv34GHu/OzKnXY1mbw3J8DsPxW3CrAdS5jqDJfQo1znb4bFVwyB7YFTdk6vFSbs6jMiTs8vK2H9kVJ7p9DzrR6SLuctuEiC4Q7DNoAPDJrRGOcKH88fVo76jG1791jqsctpJ3XuyqhasvdXETor/88w/WvS0NjBYW4pibi+2aS1wg2AloLEsD8EtXOjlWSSAQlDcyNRiVG2Bm3oCR+iGgPSqEm634uho0DlcApRGy69uQnK9DUjt2ZJ+9HgdevNKJUyeb8PDRBF9bnS47WhoruZEoOWXXgyrKSKw5drQebpcdF862Ilix+46qPUtekDW1nj0topMITUL5e79+hDd+fJuF1tUg4ZlE10f3xvHrn93Fxaud+NpvX8TxM818DdyqqMMu1JiVifz9P7uGgd6pVeMWSBxLp7IcJTjQO423fnGfBcjPffkUXv3CCTS2VD5+LpuBRLyHd8fw3puPcPvjIUyNr/3eUfE3CXQceTK9hJ4HE/z4JOx+6Ztn8dmvnOLFhHIXtKji9Wc/uIWffP8GyoUdfc1ME/fvjOKdNx7wAoFgZ2lsrcTLrx/HO7+8j6VIYtUFi/5HUxjum2VX+plLbTh5vpWP7XBNAC6Pw/q8FMz/+fMHiea0ADc3s4SZiUUM9k7zMUpO/fQq55KnoUWHz33tNFrKbPHBgI64FkFf7APe3GoFGl3H0OG7jBbPGXjUEGSTdIB8Q8wyP/8I9omIvhfLrGkFzul27JpwJhAISgO5Xe7fpSac02vehq6NFEP1zW+dx/ETjbt+sayuCXAMEcVwzM/F1r0tTZDI1SNEdMFehEqf3327mx1x60HVGUeO1KOiwsWl3wKBYA+g1EJy/RYU+1WYWjfM3ENA64epT1p56dR81Exz01BIfkhKPaB2QaJcddtpQAkDlLO+g9D13+t24PSJJmi6wb1PyKm3meZ3J080orO9Gjl2rloRMMKc87xoMLW+fDXD3oPefYrg+N6fvos7N4YwPbE5gTGVyOD6+/3s5P6dP7qKU+daeEFnK1BUxnu/foif/+ATjnAh5/dmIUH7x9+/wffxtd+5iBNnmjf1dySIT4wu4K//0/u8WLCWQ3UjUskMLzgsLSbxpW+ew8lzLdu6n4MIjZHIkHP6Qhs7wWkexFsyy8LnWnGWgr0LLd7Swtcf/pev4bv/7m2OWlkNqqqYHo/grYU4rr3Vzdc1vr45VF7so6qZT53nGt+eBHn6GS22UZUDCetPN4hdjcMnGvH6N87ghT1QzZvSljGcuI3JVA9uKj9AjbMLzZ5TqHcfQ8BWDYkW9gUHipKL6HX1QXz5m2cf/7u5JYzGZmvFei9Bg2QqKxeDXYFgf/Ho4SRGR+a5/GwtSEC/cKkdHZ3V8Hmduy6i04AmVOlBc0slFubj6w54aVBMg2SBYK9hGAaWl1L46MN+JBPrO1ooH/X02WaeKOz28SkQCDYH55dLQZiSF5JSzeI4jEWYZgIw6ZjPAaZuZanDDokaiMoVgFwJyEFIEk1cd/Z4p/OLokjbinMj9zltguJgIgtoo3kX+h7MQ883gf/VT+7i9seDmJ9d5qb0lO3c0hFGMOSF02VjkYrE4snRKDs9s9kcO5spT5ua9n3/z69xjFlbZ82mo11GBufw9i/v461f3n/CGUwVXTX1QTS1ViJUaeWOc1+exRSmxqMYHphFMpG2crUX4rh9fYj3haJm6huDG1abk4BLURGU00xjV7pvgq7dtfUVnItMmet0fyTa0eiWnifFvFC8zezMEjth6TFpfPDJx4PcjLC1s5oz4su1CSXpBxQdQ675p6H3tvv+OD56t/fxz6prAzhxtmXTixN1TSHOtN4MJITS7f/gn7yCHOXea1YUBwmjtJBS2BdaKCkW9L689JmjaO+q5QWg1Rjsm8Hbb9xHbOnTOQu9BtTw0rFBZBA9n8bWqqLt736Drlt0TF165RD3O/jNT+9gdHBu1b5x9DnI5VJPzB3p80tGUvrKES6GCdMwCsVAW4I+p+evduGlzx3DqQut8PqLH2lmk+2cY57Ul4rWjDRrJHlLaFHEtShmM4Pwx95HyN6EGlcnap2dohnpAaL0InoDieifBsp7PE5UhPZe6SKd/KlMU4joAsH+4tGjSczNxtYdCNAk5vKVDm6kVIwmTsWAzkfUQJH0wvX2nVwBqW06fQSC3SQey2B4aA7jY5F1m+jSpDxcHeAqEXGNFgj2qpgeAmSrYkocxYJNYaZhavcAI7FBBFD5Qg7gj9/rZSc4Nf87dqoRR042or6pEv6Ai00ctKBM+dEkJFPzxRsf9LPwTeM7+vuHd0bx9hsPWEgnAX69hWQSwPgx3+/jbHByhRfoOFyL46ea0Xmkjhs1+ivcbNogEZ/E/rnpJc7SvvXRAEaH5jnihfK8SUivrPLhW39wmcXf9aq26XpNGdTkgiYRnARyMteRgE7iPQnogQo3x7XQY9O7WshYpse+/8kIP14hjiIyH0P3vXGOkDhzoQ2qXJ6OUHpPjhxv4Nf2aWYmosimc0+I6JQ1f+REI8fVbAZ6XTdbhUe3I0H1aYG+sKhBIvvDO2NFFdGJjsN1vNBjrDFp8b3Xx8fCShGdGppS3jstumz0nFS1POZn5Qq9r9RE9eUvHIdik3H93T5elKMmthtBYrthbOwuXw86phtaKvn8duHFLnQdq+c+B6Ug5GjG+dA3EclOYDE7hcXcFLvJKaalGIJ6Ul/kbS49hCm1F1OpHlQ5WhByNCJob0DQXg+vGoIqi0Xzp9FNA4lcFmPxRcym4ljKppHSNGgGVfYBdkWBz+bk5qF1bj9qXF7YFOXgieg0APAHGrDXoZMzRSdQLlShgQkJagKBYG9SaIgyMjyPxaW1BxA0FyHB+uixhh1rJLoZqMTOt4kGilxmt0r2nUBQ7szPx3Dn9iiyG5SFBgIuNDWHuEGzQCAQCA6QiJ67bcX+7FFofLYwF+PGlpdfPoTPklv5cO2qQjSJT+eutMPtsXOTxPGRBR7H5nIa3vvNQxbgwzXUaHTtsappmNw8lMRKcqMX5rgk4H/2S6fwwquHOfZhNSHeOGniwtUMPwble48MznJsAzUIfeMndzhShYR87yaa5Z692MaL3vT8qRknZSOT83xNJ/nRehw/3YzmtipuDDk+Ms+L66THkpBOTn4ShUkoLFe4SeMqPyf3vvLUftP7T47djfosFBN6z+k/hT57Ra7oo/vmhsvrvD/0+jz9uVMVhXWXnXwd9jP0+lJD4M999TSq6yrw8Tu96H80ybGftFBHFQnFezBr/kwLNhWVXhbQT55rxeVXDyEQ9Gy5metWqHI0o6KyDsu5GRa4p1K9iGTHENMWkNKWkNLjMIsgqFvZ6Qu8jSbvwKMGUevsQr3rCMLOdvhtYbiVCjgVHzvUD3qlbCSdxERiCX1L87g9P4mh5QhmUnEsZ9PIkohOFXs2G6qcXjR7K3CkIozjoVq0+oOodfugSnLZvIai3mCTkBPg2KlmOFz2x01XauoCu71bAoFgm9DAm1w809OL3LBoLegi7/e72PW9mczTnYLKyem8VCbXEoGgqJDzbmZ6Cbc/Gdnwtk3NVTh0uE5koQsEAsFBGsSZSZjZu3taRC9w7lI7XvvCCXQdrV8/yzrkxTd//wqWoinMz8Y4G5xeCso4Jjd2S3s1C9KrQYJ7Nqfj1z+9g7GhORh5gwXFoXz5t87itS+eYKfqWpDoTXEvX/32BczNLLN4Pj25yC5V+v76tX7U1Feg8/DGIjo1EtxqM0FyxlMT1a98+zz+4t+9zS54gsbwg/2z0PW9WY0gEOw0dB55+fPHWdS+d3MYNz/oR8+9cW4eTItydG6gBS5DtwxnfLp9qoKA55+08CJJHHvMkS8KbVZVhN2horGlCkdPN3OjUqrCKEV0y1qoso2d4bQdDbyG5dwsJpIPMZK4jYnUQ6T0GDQjC93Msbu8GFDUy0D8Y95IPK93H0Gb5zwa3MfZmW6THVAkG2SOojsYmPnPTULL4trMCH4w9ADvTw8jpa1eJb+QAcbiS/hkfgI/lmQ0egP4Rusx/Hb7SdS5fbDJ5RHbWT6KUJlDcQ5nLrTi5Jnmxx+Gcol1EAgE2xPpotEk5/+tlylOThBqwFNuOYs0aaGBzkZ5dIoqW+4PgWAPsbSYwujoAiYmKOt2fVpaK3H48LMl0gKBQCDYr2iAscSNaIG1jRB7BcoHbmzZXM8wcnaeOt+CsZE53Lkx/PjnPfcnHru6V4Oc27NTi3h0b5xzuAkSush5Tnndm45WkIBLL3WxE51E9AI3PxjAxRc60VnC6zE99yuvHMLffffDxyI6NTKcn10SDTEFgi3ir3Dh8iuHcPZKBy+E0eIa9T2YGo9gZjzKC3UU/0THGG2F5sM8t7SrcDptcHsdfFwGq7yorPbzQhpFNDW2VcHnd/M5huah1O9ht5Agw2+rhscfQofvEtJ6HOMsqH/CgjpFvhSblL6EofhNjCbuwqX4Uec6hFbPOTR5TiFoP1hzFgPAnz66jp+NdmNwOYLsJqOBKPplPL6I/9h9A93RWfzz4y/gVFUdlDII/RMi+iahFQ8qM8M2qolm0xMYSvRAkmS0urtQYa+CXd56LIRuaJhIDyORW4ZTcSNkDyNg33tNWgWCcsBqypTZMOqEnOguj4NX3Mtg4fMx5KInoXEj7PlBjkCwlxgenkNvzxS7YNajpjaAltYwx60JBAKB4GBgGkswtT4aDe3ZPHSCxCUSnWobgnBuMrKCHJ/th2pYLF8poo+NzHOONYnJqzn1KH+c8sSpYWChoSBFk1K0CkWwbKanSOF+6bEpAobENF3L55MvxNihTo9D+celwMrzdnPTU2rESqIejePTyRx0TV/zuQsEgmeh2CC7gzYbNyUmMby1qwaZ1Keiua7rVia6TrnodKyT+9zqF1hwndNcmUxnJJhT/A6dy+j+6HflcDxaUUUKO8BV0w677Ea714VaVxfiuQVEsxOYTHVz9Mtybg6a+fwLsyYFxpg53nJGGhkjgdn0IB4uvcnu+AbXMdS5DnPky37OTl/KpvGTkW78fKwHQ7EocoaOkMOFY8FaHKqoQo3bB7/dAVVSYJgG0rqG+XQCg0sL6F6cxXAsiuVcBh/PjiHs8vLV/lx496PChYi+A8xkJvBh5NdQoMCluOFRfdsS0TVTw2D8EUaSvfCqfhz1nRMiukCwTSgXMpPJbehcoUs/lamVG8lkFpNTixs60d1uO2dUCgR7BVogGuibYRF9I44cqUdra5W1yC0QCAQ7ADXfW1pOYX4hztdicvha4sLWOdRVi1BQ9FjaMkYU0HryHre9C7k5KYLF43FsKZKsqjrAjTgdDhWZvDuUxPHFhQQbRFYb91Ecaff98cduUsIXcHMDS3KJbkXsokaPlONO4vtS1HKEU54yCdvkEC+ViE77SMI9RdBQ/nnhueiGAU0zhIguEGwTOv/QcUXbfqYgqLvVCt6oCWjY2YZqZzs7xUlQX8iMYiE7xg51zbRinJ8HiotJ6zHe6D7nMyPclJSc8CSoVzqaUWlvQsBWA3kfZaentRyGYhH8zcAdzj+nTPNzNS14pb4NRyqqOefcb3fCpapQJJmbDucMA7FcGnOpBIZjEdyam8Ab431YyqTxzuQgZ6W3+UMIOnYuGmg1xKxzB4hryxhL9nNDAfpeN7fXyMCAgWUtipFkP+ySHUF7GMdwvuj7KxAcHDZ2L1HGIq3Gl5PRiSYNCwtxjI9ZTaXWw+t1iibIgj3FxHgE/f0zmJ1ZXvd2JJwfP9GIhsbQju2bQCA42OL5QiSO/sE5DI3MY2Z2CfF4hhf+Cs7erfLHv/+CENG3gxmBySL63oac6A3NIRbTtwK5PCuCHhbBM7PWtZKcostLSSxGE6uK6DSWHeqnZqDaE0YLipHZjAt9JSTykJBOj1MQ0Qn6nvahAaU1eVGPoiear5pWTKNIdBEIBFuB9DmPWsFbg/sY55ovZMYwmx7AXGYYS9kZxLR5/nnWsGKwnrcZaVJfRDK1iMnUI3jUEKocLahxdiDsaIXfXgOfWsUCP2Wo72UimRQ+mZvEvcg06xWXa1o42/zzjZ0IrCOCh10etPsrcaqyjsV2sjT+fLSbm5LeW5jChepGnA83YjcRIvoeQoYEt+KFIilI6gnEc7Hd3iWBYM9CZWhOp33DiQPlji8tJXnybDfLY3V4fiGG4cE5LMzH170dN6GqcCMoJuiCPQANsKgs+9aNYQwOUJOwtR2GdNzW1QXQ0VXNZd0CgUBQ6vMTOc/f/7AfP/7ZHRbRnwduvGZX8I2vnCnaPh4YTB2mTiI65aHvbchVXVnl569bhYT0YKWH3d8FyIW+THnnzZXPfH5JRJ8ci/B4toCmG3z7bFbfcmRhIVd9JelU1jKebAHaN3KRZzM55LI67x/9m0RxWpyiytFCc0Pkm6NSTvN2qz8EAoFgLTxqkLcm90l2oU+n+jCevM9xL5HsBDJ6HFkjlXeom88d+RLXFngbTnwCp+JBvesYmt2nOGomYKvm6Bm74oKM8tAgtsJ0MoYPZ0ahmyZHtnyp+TA+39SFgH1z1Q4u1YajwWr8l8cv415kCv1LC+xsvx+ZFiK6YCtIsMtOyFCQNTJ8AAsEgu1BIlygwr1h+SyVyc7Px7G4mOTMuHJo0vnowQRufDy44e3IgV4V9nOZsEBQ7tBkORpN4KOPBjAyvL5ARcfhCy8eQjjsL6teBQKBYP+en956twd/+6ObmJ17PhOLqsoIBNxobw0jENjdkuQ9iRkHjGnAmMNeh9zU1Jhvq07wQjUWucGfNn6QGP00JESTA51E7pU8vDOGf/VP/zQfXrg1VquEzKS1x/Eym4ViYBbmY+h7NIXxkXlMTyxyvjotCMTjaWRTOa72oNgkui09R/oqXOcCgaCUqJIdje7jvOXMNKKZCQzGr6M//hFHslDWefEwueHpYPxj3lxKgIX0Lt8LaPOeh1cNQcHe6nEWySTRszjD3x8L1qAzUAm/bWuahE1WUO/241xVIxbSSRbmh5cj2G2EiL7HkNmPTt1qNc5IFwgE26Pg0na7rRzK9VyvqVQW1z8exCuvHUVol6NRHtwbw0cfDmBoaHbD27a1h1FbF9hzK9eCg0k6ncPf/c0NjI0urHs80vEaDHnx0iuHUVnlFZ9vgUBQcrp7p3iLRKzoCjrtnDnVjJamSgSDbnap//ind9iX9tlXj6KpIcjnsVgig8nJKHr6phGLp1FbHcDVKx346pdOw2FXURkSlTRbxdQnYeqj2A/Q54ia8W2n9w6515+OgSFxeWXmeYGcpiOdWt0hbonRxVGkKZt8o4bg/GiGiZGhOXxyfRB3rg9hYizCDvZcXiynZqWGbkAvuNBXuNE3ijEUCASC5+XpuYUNDoQcTfDaKnHY/zIWc9PciHQ8+QAz6X7OOy8mdH8TyQeYTw/jduQnnNne4D6OBvdRzlDnfdzG4udOktE1RDOW6bfRE4DP5tjynI1uT9fHZm8AHtWOyeQyNxrdbYSIvoegko+kHmMBnT5MkrT10j+BQPDpSZm6idc3BDE6urBqWWqBVDKD3/zqATo6a9jVTY2cdhoqb+3tmcbPfnoHd++Mbuz0kYCjxxrQ9FRJr0BQjiwuJjjG5cNr/Zznuh5UQfLCi12orasQDUUFAsGO0Dswi4nJRY6/CPhd+PLrJ3HmdDPCVV44HTYWyX/+y/ssVp441oBzZ5rZZUyiIAnpIyPzePdaH0bHFjAyFsHM7DLOnmqG07m3nGXlgKlPwNSGsT+g+dy2jOD5v34S0pepOdvTkAit69vrybUVNhLkKYKFxPx33niA2zeGMNQ3g9npJY5neRo6fuxOFQ6qArUrPGanTVElzE4tI53O8vPaLVjUL6eGSYIdg993sZhzoCDdzSY5OKec4l68aiU3JW1yn8BSboabkc6kB9mhntQWuZno80B/T6kTtBUiX2YzQ+xSD9obUO3s4Bx1vy0Mm1yezWANit8y9MfRLKqsbPs6R3+vyBIv1Oby97mbiNnnHoGakZKAPpEaRtpI8QHsKNMDRiDYS3R21qC3Z2pdEZ2yIvt6p/Hu2908gG/vqN4xIZ3E88VoEt3dk7j2fh/HuFBT0Y0WCBoag+jsqkGlyIsWlDkkmt+9M4Zf/Owupiaj7EJbLwahvr4Cr752hBuiCRe6QCDYCSYmooguJlgwb2upwle+eAq1Nf7HC3nkRKfzk6br8PudqK0J8G0LdLaFeRH+57+6j97+Gfz0l3dRW+1HQ31QLAZuCRPQJ4F94kSn50P539vR4yhi6OmqLVmReZy6Wh8g5amf0+WzMuzD0ZNNKBYdh2r5PtcSnePLabz3m4d44yd3WEBPJa14GbqWV1b7UF0TQKjKC5/fBZfbAYfLxscHRbjR/tMxRvzk+zc4351iXnaaghNe1/RdFfEFuwO9/1RtQQuqgoML5ZTTFrTXQTOyWM7NsYBOYjplpy9mp7GUm+aGpM8vqJORdom3ufQwZ6dTM1LaKh1NLKpX2Grhs1Vzo9RymRvJEkVRq8joOlJaDto2xW86yya0LDTDgCLTYsbuR+uKUVsRyehpZLjRwJMX9KRWKO8wkdTiWMwtbCGKxYTBAnqcBfSRZB/SehKV9hr41YqiPweB4KBx5Fg9bt4c4gxmmpCsBQl7b/7mIV+YyGnW3Fz5OMu0FBcriraILacwO7vMIv87b3Wj+9HkugJjARL4r77YxS50cvAIBOXaRJSiEe7fG8Pbbz7i43AjUxctCh073oCjR+tZLBAIBIKdgAT0RDLDAnlXRw0aG4NPRHDQtyT0pTNavjmiAayI/vR6nbhysZ0blQ8MzuL6zWFcPt8Ov98lIl22gGmmYRpTgG7lrO51aNyZSW3PUU0iLmWQr4Q+gxQP8zQkrFPVA41XCyIwfd/YXIXf/5OXWWQvBrS47a9wr/o7EswH+6bx/b+4hpnJRR7P0nFDYnlTaxWOnWpC19F6NLdVIVwbYCGdsuKfHmOTcP7x+33sYN8NEZ2guBna//XmDYL9Cb3nZHCiz4BAQD5pVaaol0bedPMylrIzmEn3YTLVg7n0IBLaIlL6MjJGArq5tcbLT2NC5/saS97jzaMEEXa2ccxLrfMQR76QyF4OuFQbqlwexHIZjCWWsJRNszt9K/FldL0i5/lILIpELsuRMBWO3e8lI0T0IrKQncZwohfLWvSJn48nh/grfWiGkt2Ia0twKJtzkRumgZSexFxmEiOJHuTyB16loxbVzoYSPAuB4GDRdagWLa1hPHwwgeWl9Zv1RiMJ/OTvP+E88te/cBKXLnfwZIWcMVR2utpgfz0KE5mCq6GQJUnuhonxCG7dHMZH1/rR0z1pTcg3AU2gamoC+NzrJ1BbG9j0vggE251MUHk2fS18/q1DoPD1ydsWxHPaqMLirTcfsoA+0L9xxj9loR893oDPfO74M446gUAgKCWpNDU31OF2OVBT438mRoOjJ+zkAMsgk8lxNvXTkJDe1hrG4a5aXL81jI9vDuFQZ40Q0bcCu9BJQC9mQ7fdg66LsaX0un1A1oIE9HjsyXErjUlXiwii66fdYePfkUmDx52GyTEpja1VbL4otXtxdmoJ7/76ETvIC+Iz7VPnkTr8yX/7OXQerlt1AeAZ8rno28pGX+UpbvVuaN8Tie29Z4Ldo1ifbsrup3O8QLAa5AQPORp4O+x/hbPNRxN3MJz4BFOpbizlZtkga5gaDDx/LElCjyKRiPL9UyPUf9D6f0Ot6xDKgYDdiXZ/JYaWI3gYmWEh/HRlPby2zVUSFwT0uVQcn8xPIppN4VAgjBZvELuNENGLyGx6Ep8svoep1JMlhgXXOZVy9MfuY0h6tOlGAGa+hIMONi0voDtlN1rdh9DqKY8DRCDYy9DE4vz5VkyOR/DuOz0b3j6dyeHe3TEMD87j7394C6dON+Po8Xq0tVWjssq35ZgXEscpnmVmahHj4xEMD82hr2ea81IpI5JcNpsV0AlqJPrHf/IS50WvVtIrEBQ7y5wagi1EEgiFvNxgz+W2c8M8anhGizrstMtQZUUa83PLGBtbQHf3FB7en8DCfIwb926GQ4drcfFSB1rbwiV/XgKBQLASbnRomFBtMtwu+zO/p0VE+nl0MYlEKotMdnWRpSLgRlNjCB/dGMLg8Bziq2RBC9bG1PphGhsvuu4VaLFlZnqRna1bJZnMIDL/ZLyf1+dEcI0YPxqfkuN7aGCWs/oJyicnV3hjSyUUpbQi+sJcDHduDj+R2d7eVYMvffMsuo7UQbVtdsxqsqt9qyI2aTZP6zbcsHSLbnJ6r6YnFrniRLA34LYDZPRYob8UTB1bZTGSQGwD05VAQNDnzan40OG7hBbPGaT0GKLZCXaQU0NSin4hd3pxMKGZmbLq01Dt8uJcVT1+M96HeC6Dvx9+CL/dia80H4GyCRGdrhUjsUX82wcfYCKxxHEu7f4QTlbWYrcpexF9dGgO9+48KUq3tlej83DtjsYU0Kp9X/cUC1qFi63H60BHVy2v4tNqSthZjyO+c3DIbnaOL+Uij4XvAvTvp3+2WSTICNoqcS70Mo4FzsOlCOeKQPC80LHbdbgWFy53cINRinVZFxM8cI5G4+xEmZ+L4frHg5x1SpvP74Q/4MpnOBZc6tJjxy79LTUFJeEwkciwUE7nF9oKP4vHSDwnp9DWnktbexif/8IJnD3b+rhsVyAodZ75jetDuPZB34rc0k8rM6hkjxeCqcKCSs8zGk/8Y7E0xxVtdoGIyrpfuNqFM2db+DEEAoFgJ7HZZBYZSXShyJanoWu91+vgc140mmRxvGaV+6HzJI0VSMBZWkohu4mINsEKtEFA3z8iOs0rhwdmkd2oWfxTUB+fhTnLbFGAjBPUeJu21XC67Oz6nhhbeCyix2NpDPRMob4xyKaSUkHjXxozz04tPhHbVlNfgeOnm3nRfTPQ8UfPOb6c2lS84Uro9bHbbc+8/lt1FdM4frh/pmhu5FXFfZoAiMaVRYPmQzwvWhFbROPSZDyz5UgeihGKbtCbSiAguDqXFDzJyc0/HYoHbjXAsS+HfFexnJvPNyQdwHxmmF3qFNeyXwg7PbhU3Yw2XyXGE4t4GJ3Ff+i+ge7FOVytaUZnoApBhxt25cl53WImxa712/OT+GB6BDfnx5HUcuxAP1vVwH+325S/iD4yj5/98JMnfvbq546hmUvPdk5ETyUyePfNR3zizOWsgUddfRD+gBs1dQG+MFfaq3EicBENrlaOdFnKRhDNzWEiNcR55iSD1zob4FEDUKXN7Tud6mVJ4SaiPjXAES5tnqMI2cNQyiBUXyDYD/h8Lpw63cTugr//4U3Oad5oUEVjWxIEKbOcNoJEQxqkkRPXpj4pJj6OssjnKBYG7rww95zjZLp/iqV5+ZXDePGlw2tOoASCYpPN6Jifj2FwoDSihpUzrOK1zxzF+YvtqFqjYZlAIBCUEq/HyY1CacF7IfKsgGK3qQhX+TAwNIeJqSgi0QQ62lYXEwuuY6psM0QkxObggZIOUxsCjA3MDnsIErPHh+cRWYgjXBPYXJwJgMmxBUyMRp5w0lbV+BEK+9YUpN1eB06db8WH7/YiEbfE92gkjlsfDeDii10ck0bjyVJA+8kxGOknhWeP17lmI9LVSKey6O+ZZsPJVh3k9Lo43U9WkZCjmMb+m4XG8bQPd2+NPLGA8Tys1gyW5gpbqUIVrA+J5x6fk+dlBUjPmZ6MbroKpBAfRAbPqfEno3sFgs1Amp5T8fIWtNcjbKRR4+pEQ/YYlnIzWMpOYzE3hcXsFJZzs9xIdC/jUG3sHP+jw2fx5723MBpbxIPINGZTcXRHZ1Dn9nPkC2WnkzOdznhZXcNyNsO3GY5FWUxPalnOQf9i8yFcqWmGz76i4cwuUfYi+lI0id5Hk0/87Mjxhm2VvT0PJHqNDMyi++HE407iJORffKEDlWGv1bBFcfNWk88qp0aj0ewcbkTfxnR6jE++7Z5jaHZ3wqVuzkUu5bOVXLIbflsQflsIsiSaqQkExaa+PoiXXzuC5eUkrn3Qj7nZ5S27XEgoTyazvO3UCrfLZUNnVy1eefUILl5uR0NjaEceWyDYCajh2MmTjXj9iyfQ0lpVUqecQCAQrNfUmDLNyWE+MRllUZDOR4WKL4fThsZ6y807MrqA8Ykojh9teCb6ZTmWxvSMNTEmZ7soGNscFIkJYwEwJgFz/7hAadxIrvLue2OorvGjpn7jrNdsJofeh5MY7HuyuWpbZw1q64NrViGSwePIiQbU1ldwljq530lEfnB7DA9uj+L4mWaOg9luFePKhqXP/nLtTOrNZpuTsEzxNe//5hGb27aK2+NARfBJk8nczDJntNNiBsXJbPTcKf6GnPwP74491gOeF6pOeTrHPhG3qvVo0Y3MOILng87LtEi1spKR3r++R1PsRqcGz5tpVj8/u8zJBBTBKRA8L+ROD9rreDNNAxk9gYXsGOYzI5jPjLKYntQXkdSWkNKXkDMormXvLK5J+Vz0b7Udx0I6iTcnBjAci2A8scQboUoyHIoCRZK5SCmt5zi2pXBVsMsKGr0VuFrbgq+0HEFXxe670PeEiF4u0Ik1GLLE8gLkQqUsWCpDWw1qHlrrakJ9ugUuxcMNQutcLTjkO8WCuEAgKK8BVkNDEH/wRy/ycf7xRwOYmlxk11m5QWN8GnST45zyob/92xdx5Gg9T34Egv2C221HZ1cN/ugfvcRRRas1SxMIBIKdoL42gIqAC7NzyxifjCIWT8Pvcz3OkSaxvL2tGnabgqnpRTx4OIn2tjDaWsL8M8r2pOqzoeE59PRN83W8IuDZdIyFQIOpdcM0aOK9d0SEzfLebx6xAO4LWH1FVhNzCxnO46MLuHdrBKODn1aAkQB8+Hg9GprXNlJQVRcJiWcvt3MUG/UCovsjYfDvvvchx5MeOlYPt9uxqebdVEVBZhP6XJMgSa5yp8u2aj8e2j+H086LTSvd6CTiU8NRam66HmSeI8f4o7tjePfXD7clYNOYmRYpaLxfcPDPzSyhv2cKUxNRNDZXAvIaiwC8eKFhbGQe197uwfREdMsxIGtBr5k3L+IWKlPo/aHHWF5MokI0Hn5u6PPX1FbFCyn0/hYqCnoeTmB4cBZev5NjA9d67+nzQgs3H73Xh/5HkxyDJBAUE0mS4VR9aFCPocF9DLqZY/F8Ot2H6VQvZlIDWMxNs5hOYnsxmpLuBIosc2TLPzl6CZVON3451ovB5QgSuSwyugbdNJB4quqGXOkORWWHOrnVX65rxR8dOs8Z62qZLCqKkdsmUWQJgSANlj9942jQMDG6AH0DV7xT8SBoCyOlj+zAngoEgu1CZayBgAv/xT9+FZ2HavHzn9zB7U9GttV4ppRQlBXFt7z+xZN47bNH+d+lKsEVCHYDcgudPNWEP/jDqzh8pE7k+wsEgl2FBPLqsB/dvdPc0+Hu/XGcP9PC7nTC63Hg+NF6eDxOdpu/d60XM3PL+MyrR9DeUoVEMoO7DyZw4+YQxsajPJ9ob63imBjBJjA1IPcAMGPYj/Q8mMDPf/gJx05cefnwqk02aSxKWczf+9P3cOfG8OO4D/qb+sYQDh9vQHVtYN3Hoc/dF75+BiMDcyyekzBMsYJ3rg+zSPjFb5zFlVcOozLs33CfqepyfGQe9z4ZwUfv9uEr3z6HMxfaEKpaPZ6F4jTqm0KcJ14wn1M0xkfv924ooi9FEnj3Vw/wg+99xA7t7UAiaV1DEBWVXkTmYxwHQ69p9/0J/M2ffYD/6l99ES6XHdIaDVZHh+fw65/cwU/+7mbRBPRCVn2o0ouKCjfH69BrQ+8LxdbQ4srXfudi0R7roEI9K6hSt6omgNHheaSTWX6daTHmr//D+7D/MxWnLrSyQWk1qDLgo3d78Td/9j434hUISo0MFV61Eh3eEDq8F2GYOhYy4+iLXcP9pV8illvYU670CrsTf3zoPD7b0ImPZkbxm4l+PIzOYCYZR9Z4UkutcnpwuCKMF+va8Gp9Ozr8IXaqlxNCRN8ktDrs87ufENGp9GtmepnLy9aDolgq7FWYTAsRXSAoZwruBNUm48KFNtQ3BPHw/jiuvd+He/fGuJnQbkEiOUXOHD/ZyM0VyaEbCnnhdJJjaW3njECw12hoDOLlV4/gpZcPc4QLN+YRn2+BQLCLtDSFOK6FKmQ03cDUzBJyK0w0dI32+Zx49aXD+NVbDzE3t4y+gRnMzC7D6VChU9xbvnl4YaHwxSudCFcJl+nmyMHM3QMMqwfNfoEqEShehbKZKSKExN0P3+nBkZONqKmrYOdsQTynBqQkdo8Nz3McSwGH3Yavf+cimtvCG18rJXAG+Re/eZbd3SQM0riXtuGBOXzvP7yHt355H02tYRbmqcKRctrp91SBTaIj7cv8zDLm55Y5dpUbfcbSSKdOrtnjh/arqtqHs5faOR61EOFCbvhf/Og238/pi23cb8ztsfP9kHBJvcgoPuPhnTEM9k3zY9P+NDRXsos8vrwFR7AEVFX78fLnjuLnf3frcaUpub0/eq8Xc9NLvA/NbVXwV3j4mCa3Mj1XErSpAStVAZBbvLLKB8Ums5P+eWNd6LUJVnlx7koH3v7l/cdRkhMj8/jhX32M0aF5HD3ViKqwn587RbyQyE6LGIlYmh+fFgcuXO18rv2g94Tum7LCTWOltkH9nHRous6LB3vVtEOLTRTDS81t+7un+Ge0kEKfq3//b3+NY6eaHi9E0etMrzFVP1D1AcW+DPVNY34uBo/XwQsfdPxEFzafp78RhX4ZTxu36PNGP6ffi/HwwYGc6Ak9itn0EObTwxzxQnnpsdw8u9H3koBO0OdWlSR2ln++sQuXqpuQ0nNI5HJIaTnkDJ2jqzyqjR3obtUGn80Bn90JVS6/PpBCRN8kdMHweh2QV6xOkwPAyitbfzXarfpQ52zGTHocTtkNmWrFBAJBWVIYnJBjpZ3cIUEPmluqcHX0EAb6ZzAyMs8xL1Rq+bwNQdeDJtihkAc1tRWor69AQ1MlGhoq+N81tX4rv69MSpoEgueFSrzr6itw7HgDTpxoxOEj9aitq4Bjk03WBAKBoJSQQ/X0ySaOvAj43ThxrIF/tnLsQI1HP/faUSwsxPDxzSFEF5OrRsIFK9y4erkDJ483wi9i2DbG1AEjBlMbAMwk9gsk1LW0hfHbf/wCfvC9j9mhTc7sBWrW3TfD8Sj0eSOhL5XKYimSZFF5ZV8wEl9f/swxnLvcznElGwls9HuKdaFs9FxG4/u//l4ff04pZoWiVUioHh9Z4HEwOXOpGSONd0ncI4GXXLwknJN4u3JfNpoPk3h/8Wonbn44gKmxCDvgSaicHF1g8bj7wQQvRNH418yb1eLxDC8sLMzGWNCurPbj1dePc+TNO2882JKITs+d+pi98rnj6Hs4iYHeGb5Pel5L0QTu3xnlfkj0OtKYhG5Pz48ys6nx6/JSkp8/Cejf+v3LGOyfwf1PRoqSjU7i/kufPcoLKfwec0wOxcYu8GtNsSNWzA69FyRqW+8FvUYENYbdjIhOn6+BnmkWh/n1z2p8H7Rlszrn7U9PLnJWfIFMWsPt60P8NxR7Qp8f+tzQZ6PwPS0GkZB/9lIbf19uQm9hf85e6sDY8AKi83E+zgj67A/3z/Lzo4oQEskLkT+pVI4XWahCgD4HVCFy+eXD8AVc6L43viURnV7rD97sxtJi4nEFiPW601edFy/mZ2O8yLSSgd5p/O1fXOPPO7/W/JqveA/sKn82zlxsQ019RZFfOcFOYZgG559HMxOcix7NTnJz0YRGjTUXucloVk/umRiXtbArCkKKGyGn1Z9Cp8Uj04BumpyhThno8h5YLBKz003Cg2OX/QnRilYG6cJGOYfr4VMD6PKdgIubjjbCJu9+R1mBQLAxNJAPV/t5wEzi3ujIPMZGFzBJA8zZZSwtpXghjScTqSwPxHgwlLOcBOQ8o8kPwW5xWeILAw3OaCBmy2808SZHuctt44EQOX9IJKdGZtU1ARYXaaPJhRDOBeWEz+9Ee0c1zpxr4YlkMplhdxQdBzTJo8VmOhbIQUMHAUWj0WeeJqgej4M/58Gghz/nzS2VnO3f2FTJbk+BQCAop3nAoa4aNDWF4PM6Ob7l6UkeGW7aWqpYSKeYl+6+aRbU02mNf0e56VWVXhzqrMErLx9GLTe6E1OxjTDNFKCPAkaEs9H3CySSdxyuw9XXjiIZz+KDt7vZ8UqCLjmz14OuoySYkbP7C988w671reTr+wNunDzfAofLuhaTeEi54NQ4kwQ9auBJ26ZNH2Ef/AHXqjE0BchV33G4Fl/65lkWBEcGZ5FMZHnMTMItbesuOHRU48ILnRxHQ4Lm3ZvDGBmcw1aguXzHoVp84etn8c6vHvDrTeJ4IUKFXP5ja/wtjVvaOqpx/oVOvPbFE/xcSXylhYfnhYRbqj743FdO4YO3ujExssBzChpHUewObVhnvzoP123qcUhEf/uN+7xIwgJuXogviLirVdfTQgL9HW10HqPM+4JwzoIuzWXsCk6cbcHx07TQWL7ntNqGClx55RAvFty41v/4M0fPcb3XmeZtFUEPTp5rwWe+eBK6riMyF8Oje+Obfmx6nanKg7LuacEqmyssYFjvAY2XV2uyS4sqtBGP54782n8qotOxV9cYFCL6HiNrpFkkj+VmsZyb40aikewEotlxLGVnkDb2TxPt9TLTlT1oMC7fs1yZQeNka2X105/RygkJBgWRbC3cqhdt6hG0eY6UfkcFAkHRoUEjOc7IHUsbiYJUkj09tYjpqSV2qEQXE1heSvE5gcREcnRY5Xd0fjAtAV2WoSoyDzapSSIJ5yQW+nwuHgBRHiIJ5+EaPyoqPDxQLfOFWMEBhxaYLl/pQGNjCJFIApFIHNFIgo8PmqRkChMETecFJF40ctn4M18V9qGuroIrPaipr2iMKxAIyplghQfBdX5vldoDly60o6EhhEc9kxgYnOWMdJooUnVZR3s1jnTVIrxGbrRgFcxlmJSHjvJr9P68i9AUHUJC2Oe+chqBoAcVwW70PpxkgwY7vcmUQTEOnOussJhM2eK08HzmUhu7l1s7arb1+CSknz7fys1IP3irB3euD2FmajEfzfLpYvjKcSx9ji0XrMrXchLGgyEvuo7Woam1in+23vFB0ahf/fZ5fs7X3+/jfOrC45GIyJEVPO62zCYOp8qLDbUUV/JCJ17+3DGO26CoF3LKU4W4oW++LJT2gRYOPv/V03C67ew6J6dv4fUmQZ9McqRl0n3Ta059h7w+B+qaQjh3uQOvfeEEu+o5bqdIC/70fGlfvv6dS/x4n3w8yFE3FNdSENPZjACaS0gcM0s533RbckVXhDybehxanKGFAhLRtwN9FgoOdsCKpipAr4mWf+3Kde5C7z+J/fS5dXsd+OSjQSwtJZFKZLnXHQvZ+dgUaq5Ln1OKFwpWetHeVYuvfPs8xyZNjUe4CmQr0NyRFiLob7ebqU/nA9qern6gY4SOCUH5u811M4uUHkNaj2M5N8NxLVOpHsymBxDXKOe8hGXugqIhRPRNY120VrpO6ARIK4mljHQQCATlBzkSyEFL26FNuj8Egv0ILQaRc5w2gUAgEFg01FXw9vnXju32rux9KMold99qLrqnkdhEQVEkJOJRhEdze5h/Q+IzCeLHzzSxO/rmtX6O3SCTBlV3URUXiaw1DRU4dbYFZy61o7G58rldv1YMRwi//Ycv4HNfPoX+nil21w72TucF9QwL3OSQZQHf40BltSUgt7RXs3hOIj45qTdTfk9zaYfTzs0yKQP8we1R3LkxhOHBOW4eSlWddD/0GpE4TwL/8TPNOE19ihqDj6sxvT4XqmsruIkpudLp9pst/+dsXpvCYjhFYPQ9mmQxdTCfeU3CNc3xybVeEXRz/vqp8604cbaZc+IL+0AZ7yRe03tJD01fnycuvCCk/84fX8XlVw7h/q1RjouhhqYkfpOYblXGW4sXZLYh93FTWxWOnGjc1GPQ86bnRX9fbKwInO3/PUXV0MLGyn2jz5xUgjlc19F6PvZoMeXj9/rQ+2iSxe3FaJK1HRLPaZGGnN2Hjtbj9IVWHD3V9FgLItNHuCbweF9p38kksplUA/qbYjamJeg+V/btE5QHhcoCyi+nxqBZI4nF3DSG4rcwHL/JOecZo3i5+vsd3TB4kUGCtaC7m0jmanUjZQR1wP7X/8tPnvjZN37nIv7wH7/MF9edglbJ3/nNQ3z3P7yLmXzZFl0o6GL3//7Tf8rZrQKBQCAQCATljKnPw0j9Z5jx/7U0D6C0Qgl9D5IcBKTyawYkEAj2Fkb2Ooyl/zOgU3RCEfNgJR8k20XIgf8ZkmKJ2aWEhDNqCEqRKeR2JgGbXOXU+6MgAHPueFbjPGz6Sn/DFc+SJUAX3LHk0LaqFYsnMdJjk8u1kNFMkRX02JbeZ/JjUUUZuaBZNMzHepAwu52Gh1StydnQ9FzJAU4CSV6WWPk4druNXyP6fuVjLFHPARL4qdmlIrMT+mnD22aeMz12Np17HMW4Moax4Pi2O2yP8+EL90+vEwnuJLryPsuUue7j9+h5KGSe02cgm81ByxkrXht6D/LxkBSDoNL+WZ8JEpw3guInE/EMNwotNvT4FHlC+7adzyW9l9SodWVjTZfHYVUclKCZKb2e5Dyn968Qp1J4nUNUl4YAAQAASURBVPmzTscbVw+r/PkrLFjR7+j1o7z0+HK+wa9k9dIiR/ha0H0vzMWs51dk9Y1ec9KkSKQXlA/8GTOz7DQfTd7FRPIhIpkx5Iw0cmYGOi8MF/fD8Mdt/y/UuQ5jP3I/Mo35VAJVLg9OhGp3dV+EE30LF3rKzaJBRQG6eFFJWLmWLJUndKKgAcqSlW1oLMDUF/grzChMahhkZvMbDUoy+a98hch/ZG2A5AZkNyB5IckVgBSEpFQCcjUgV0GSqaxNvDECgaAMMDWYZtw6zxkRmEYMMGmLwyQHAv3OpK9Unqnl3Xb5jRqq8UVGeXIjcVKi86GTz4USnRNpgwug8x+JA3RulEPW+VGy58+hAoFAIBDsDUy6NhozgD5dXAF9FyBRjiJU/IG1b0OineKy74oYxo+tyOwo3glI/KVtu65oEg1pK8Zzpqq6rcLCdWXxDX0FpzxtHhTXMc4xJiVwoRcD5w5/7q0mu1bG+FahhRKvT9lSDCHpRuReF+xvyClNjUDJZT6d6sNMegCx3BziWgQpag5q5BdeioRddqHCVsfCeZ3rEAK23RWXS7UYQf/9aOghPp4dxZWaZiGi7wXIBUArt90PxrnMrACt/lKu60aN/p7ONqIShINBvoSFBCNjHqY+BRjTPBA2jUXOOISxDJPEJGM5LyRRvlrOEpFYSMrlB810X/Q604VOBSRHfnPBlGjV2wtT8gN50UiS84K6UgtJqQdkEpFE3q5AICghZgqmEQX0BZi8MLiYP7ctw8x/pbJ0mNTEKgWYaf4b6ytlGdI5T7eEcxQ2KwPT2pT8eVDOC+IkpJM47oBJ50MUzotOSHxu9LKYDtkHKf8VciB/fqyyFhz5NmIoIBAIBIIywCQnaDZ//YzC1EdhZj96Jn9ZIBAIBIJygBzlaT2GSHYckYzVGNT6OsHxLXrBEFoUJKiSDRX2elTaGxFyNCHEXxsRsjfAJruwH8noOgaWF3BnYQq17t3vKSNmzptY+aCu3T0PJzHQO/O4ZKuwAk2NXTbKoNIMDXFtCTOZcQTUEPy2IJyKG8oWypxzRhax3CKiuXmYpoEKexUqbFVQ5fJ7C006UZAwrs/ANOYBfZIHwdCGYOrDAH3PYvlWy1cKohINrsmxvuIxn7idClMKAGozJKUVptoBSWkGlDpLNOKSzRK7Mk0qgZyHmbtdlLuz9r8REolgZYhJ76c+B1Ojxk/FhuoW7Vxya1UYCAqYRhJm7mZegP20/LFYSOpRQKmBxAKtYCUmveYsiC996iyn6hp9CqY+YX015ngBEVR581wCQOEMpz/7ozVOo08v3dJio7XIGLYWFpV6SHIDTCWcr+bJu9ZJZOdqn4Oy2CsQ7H+44k8fg0mO3n0LnbNskOznIMk74/aj8nyKgKAYAMrq3A4up31bTsi9jQmTFot5ATmRH9Mn8q5z2khAn7M+rySiawMl2g2Nr9Nm5i2Ysh/7ErkCktK+I3E1m8F632nsetcyT5UAicYyShMkZf85MssBk8xwWp913BYdbisLyX4BEpnjRAWloMyF84QWRSw3z4L5VLoXM6l+LOfmoLHWVVzHuUsJwGerhF+tRo2ri53nlfYmuNR9ev1aMadN6TnoZvG1ju1SfgpsuZQMmCZHt1D34wd3R/GzH97iXPSVEfJU+tXaUc2laOuR0uPoi93FGzPfxyHfKZyueAFN7g64lM0Lgik9if74Q3y48AayRgbnQ6/gfPBl+ClzdLd53DQh76pkMbUHZuYdmNnrPBBm4XvHIBf7ApBbgJn7xNo3cqerhyHZLwP2q+xQB1+cybFZ/MOAXKb03I2l/0NR7k9y/0PIru8A8lGUJUYcZvZ9GMv/YwnuXOHFDyX0Z4DcXoL738MYczCW/yeARFuu2igusv9/guT4AlAmk69dg8vI6LySyUdMZWAas0CuD6bWbU0mtEFLMGc3eblhWpNVnbbxT8+L9H+S31qksx2DZDsDST0Gk6KxJFf+/FhYQBGiukCwV+HzVPIvYWZ+gf0LVegEoQT/f4D9dMkehfJsKas4mcogmcpheTmJeDyDbE5/Yo6wWY4frUdVZXkaJIpz7aTFX7p+5lZ8JbNNAqZOIvkYTH0M0PJfjUl2oO8MKUC7B2P5f8C+xXYOsuefQ1I+g/JAB4xZGLH/O6A9LIkBhJ+z+w8gOb8lDAFFxqSFp+xHMGL/D8CYKtGcLwAl+L8D6vF89aVAUC6RIjobW0kgT2iLmE73YjRxF5PJh+w4Ly4SG25tkhOq7ESloxGN7uNodp/m6BZFokjpg3N+S+Vy2zYrlAIhoq8CjYGp6cZQ/ww+fK8X16/1Y2xk4ZnBscvtwKEjdbDb1xfRY9oSxtNDiOYWcD3yNjyqDxW2yi2J6HbZjgp7CAvZGST1OMaS/WhytbOrvTzIAtlbMDK/gZn9ENCGVzjHy6B3LZWEZj+Gmb0JSH8K2M5Ddn3VEtXJoV5sKLaB8xsFAsHeJ8cLFeycyt2Gmb0NkxqcsQvHWLGVwbluq1DUDE1kSWRL/RAgx7rtCCT7Jcj2FwD7GTGJEQgEgjyLS0ncvjuGX/z6PgaH5pBIZjj2cbun///L//CN/Sui07WTex+NWPMCfQSmNmz9mwT0x1WpK6+h5TNJFpQCG6C0QVIPwdQnATNS/IfQemHm7gPOrwupo9hQ5QabRko0x6XePvYXAYod5AhDgaA8MGFwrvl44j4GEtcxkXzE2eeGqcEowXXLJpH2V49270V0+C4iaG+EQ/ZAlmRqL4wDlzOv56AddCf65HgE168N4MP3eja87dzs8jM/u/ZuD4YHZ0tS/kgDYSrLpC7NqXyX6FgsZQ2Qn+pAXVMfwLFTTY+7Na9FXFvGTJo6ylv3EbLXbFn8tssOVDlq4Va87EpfyM5iNjOFTt8J7B4mlwiT49rM/JQHxuw6p2iDsssuND8V9ckFk7sBQx+EpP6EhXTJ8Xmr9G8LETvrYlDEQylW6AUCwc5kssaAXDeM3B1Au2+J5hTLwuXm8Xx1TflczJ+PgoCRdwnm7vAkSU+/AUltgWR/wTpPKg1W5rpAIBAcQHr6pvHWOz1494NeRBcTSKVzz8wPtsrz/n1ZQP2NjFlLFOUIxyn+So5j7gfClaq0ZVb0ACFH+j547oItYTknJUiOq4A+ADNXAhGdDA76MAvpEhsBBMXCqrzsKd2xK3kh05yc+vgcIJetoDwxTAOL2UlMpnowmXrIzUITuShSRgxZPQmjyM2uFUlFtaMd9e5j7DavcjTBqfh4UyUHC+gHEhNIacKJzhEp42MLuHOT3MpbH1zOz8UQWYiXpLicPRC6FeeyHvWNIZy/1M5d1jc6x2f1FOeZEwGblYlul7fW5FKWFDhkFwL2SixrUSS0GGKadZ+7AZVgmrl7VrOf3F2YWq8lLu0JUcnMZ7bHrNxyfRxS7hEk+0WAxCJyplOzvud5BOFEFwj2EGZ+UXDSctho/dZXcp9zQ2RyzMUPyITfyGfU0mIBiSKURzsCKfsRJNsJSLbzkGzH8n0lxARHIBAcDCLRBD6+MYS33+/B5JQ1/qZToN/nhNfjhM2ubKu02u1+vvHmjsBzIur1E7Gc5dQ4m7f5/PeL+UXm5XyPEGqivZyfFxQ/ak6w96GxhKm8A+QelSDy04CpjfMcVYjoxcSwXP405y8Jds6xp74W7EgXCHaBrJHCcm4W8+kRzGWGEc1OYik3zT9LactFF87JXV5hr0XY0Yaws5Xd5wFbLXy2KrgUb/5W5TffMk0Tj6KzuL0wiflUAlUuD86HG9HhD0GVLVPqSCyKD2dGn/uxDNPEWHwRsykaU5QH6m696JQpSI7vbf29YULfRedGqNKL0+dacOFyJ2R54w91zsyxe1yChAp7JZyya1srSfT3frWCO/JmjTQyego77s5EFmbuAZC7CTPzIUzt3g5mF5YAGuBrlGs8DFPrh6RPQKaIF8pPf54caHblCBFdIChrzJy14EWTLRaL+/IThP6S5czvPUE9BmgPYGqP+Nwv2ek8eZmz06FS9Y6rLAd3AoFAUEwGBmdx7+E4C+iqKqOq0ovDnbWor6uAP+CC3a7yOH2r1NbsTBPUrcIOclpYprEsCeNcYRnJN8u2NjKisJhO/ZD2hIlGUC5Qg3OOdFFuc/PYomOQ2euW9TmWPMWrND7AmPp83mBSovktNbmnsaVcc6CyngW7i2ka0MwslnPzWM7NYDE7jYXsKObSw+w8p+ahFOVSPCTYJAf8tmoE7NUI2htQ5WhhEb3K0QybTP2oyt9xbsDEz8d68OZEP2aScVS7vJhKxPBfHb8Mb15EH1qO4C96bz33Y5Hqm8hlMZcuTTPqPSOi2x02VFb6UN8YZFc6xaZkM9qeKGkMhjw4f7kdL7x8GC3t4U13780YNMCUOI6FRPDtQBcUp0pZSCrSegLZInf9XQ+TSvzJZaL3w0x+18o9p4HzviFnCUX6AAytB5LzS4D9CiS5Jt+saisXczP/Ws2UcH8FAsF24CojdlrTMUrZjv3sVuKqGm6CfNCF87UwAH0IZmoMZuYDPkdKjtcA9RAkmviUoEGzQCAQlAsPuycxObkIWZJQEXDjtZcO48tfOMkius22D89/+iSM9C9gZt8DuCKLro/FdeAJDjAkalN1G1Uzl0JEN+Ms+Jo0p1NPWM3SBc+FqT3IL3gUu3Igj1IHyX4lb8wQIrqgdPnaRI4NqQmk9GXEcnOYTHVjPPkAcyycPxsn/bxQg1CH4oFL8bOATk1CG90nUMmRLQXH+d7BNIEPpkfQuziPrKFjMZuGZhj4L45cgDcvdUYzKdyL7E9T6a6M+urqK/C5L59EQ3MIvQ8m0P1wEhNjC4jH0iykk8hRToI6ideKInEG+2tfOIEvfu0M2jtJXN0chqlDM3J8OaBs8+3mGZHDxZH/e83McXfg0kPvh2GVb2behRH/N1ziz9m5+xEzDZOao+ZISO+G7P3nVi6bufkyXdPU86WsNOEQCAS7TyGiK5/7nbuXb4L8LkCuGuGg2wIaYEzCTP4pv36y+x8CjtcBObQnnBMCgUCwHSamFhFdSsLptKGzrRr/+B++AptN3reOSVOfhpm7DeQ+2e1dEexTJNtxmLbjQObXpVmgMZdgpn8JydMCmE4RQbdd8hG3ZvYmTK7SLAUyJLkeMkWrCgQlgOaB1n8GNwOdTQ9iJH4bw4lbmE71Qi+BtkXaHTUBVSQ7R7W0es6hzXse9e7Dj3+7V5EAhJ0eeGx2GNkM3KqNI13IaLAaiiTlfydtP8mEUzEOsIiu2hRUhX249EInzpxvZRf64mICk2MRDPTNYLB3BoMDM1iKJrYd+VIsvD4nGpsrcexkI66+cpiz0P0VW8vpIuc4ZaBnjDTHuugksm4DOuwzRoZFeWo8sF1H+5bJ3YKR+hFMGuRQKed+FdCfLgNM/wiG1gvJ+y8gqUe5HHBzfxuxSl6FY0cgKBOoOmQCZuYazPQbXHHCGa6iBP350IZhJP43SFo3ZNd32FUmEAgE+5FEIoNMJofa6gCOHq3jSBeBQPAcSH5IagdM9TCgPSz+/RsxNkbB9buAXCXczduGZKsUQHGupaqyVuoB2yFADpbm/gUHnrQe53iW0eQdjCXvYTk7i4yRYEd6KQR0u+xCyN6EFs8ZNHtOcc45Oc4prmU/nItkScK/PPkiLs02YTKxjLDLixdqmuG1rd7n5eutx3EsWA2/fWt9IQsNXkdii/jZaDdG47vXE3LXRXRybaiqwpvb4+CfBSs9qKmtQOfhOiwvpbC8lMTEWAQ3PxrAjQ8Hnvj7rsN1OH6qCQ5nsXdfgiRbjnO7Q4XH6+Scw4oKNypCHlTXBDiKZjM56CuxyXa4FA/SRgqR7Ew+2gXb+gAtZGbYgW6THXBssTnpdhrtcSln+me8+mw50A+S2zICM/cJzPj/B7Lrt614F4UGYRvAGZEl6DYvEAi26JzJwsjdh5m9AWj3YGpDVta5mRTieVHIcZm/mf41DCMKyfltSPZLkGTREEogEOxDF5sJOBwqghWUsWzNZwQCwfbgnHKlFZL9IsxSiOg0l6OKCurfpQQhSZuYwwmewUQGZpby5SmWoTRV8JLamY/dEdn1guJAulnWSGIm3c/bXHoES7kpxHMRJLQoNDPzONqlmJEtVc4W1Dq7UO1s57xzrxqCRw2xeL7dNIpypd1fiQqHCyktB4eiosrphrrGczxdWYertS0IOrYeraWbJnqic/hoZvRgi+irQXmCgQra3I8Hq9FIAulUFp9cH4Suf/ohb2qtwmuvH4cv4CqRwC/z/jhddhbqSex/HlyKG0F7FaK5OSzlIpjPTKPB1QaP6tv0fZBwHs3NYyE7g6yRQchRDa+tRM2IqFSCsuSy78NI/S2QvQ2Ye7h56HM11UsAlJfM3ycBx6uQlA2ifAqNlgQCwa5BTZCRewgz9QMr75xLUHeuj8TBQQeMaZiZ9zgOi8+b9rOQhJtIIBDsIxwOG2yqYllM8vEGAoGgCDnYtrMwpe9bc66iilqWmYL6eEnkdmc3umDLmCmOdOUKzpJgA5QuSDaKuBAInp+l7Aw7ziOZCUSy44hkxxDLUXb39oys6+GQPQjYahByNCJkb0DI0cQ55wXn+X5F4l6NKupV/6ZuT41Ha9w+BLbhRKcYl+Vsmh+vXCifPVnljaEmntW1AXaEkzt9ZcRKXWMQwdDe+GD61ABqXc0YTDziSBf6WuWoQ7vnCFR540gWim9Zyi2ge/kTxHJRGNARtFWhyl5bGgGdHdg3YST+o1Vex67NgwwNwK7BNDPUYhRwfHbd/F/TmM/HuQgEgt2jcC77hBtiFndiJngGcxlm5i2aEVuvtf281XBUIBAI9gGVIS+8XgfS6RwiERL7BALB8yLJfkDtYJHbzN0vgdnB5GpEk+ZuahckafWoAcE6fb6MJZi5jzgepyQoNZDUdkjy5vvNCQTrsZAZxcfz38dCdqwE8z+JXeUepYId5kF7PWpcHahzHUGVo5nd6KJK7Vko5sUub8+YTNnxbtUOpYyc/GUrohP0AfR6nQhX+58Q0fcaflsIze5OfCz9hhuCdi/fhlvxwqf6EbRXc9yLTA01VhxwheYHdPuEFsNA/BGuzb/BLnQZCqod9ah1NhZ3R7lUNcauTSPx/wW0HhF58BgDoIUFMwZZskNyfBYmvKsL6dRQVIjoAsGuIsEByfECzNT3YWqjAA76YuBOYMDMvGF9SyW59qtiwioQCPYFbS2VuP/Ih/GJCAaG5pBK5+DcRsSjQCBYiWQZk5yvw6RG72YJKgb1UZjaACTbaXa+C7aAmYSpDwM5italasPiI9nOQFJbRZSLoGik9BgWsjT3Kx6KZIMqkRBMCRMNaPWc4bzzSkcz7Erx0zH2A6osc8PRgohuk7cvgrtUG5Tn+PsDJaITHp8T4doANxzdq3gUHxqcrWhxd2E02Y+0kcTN6LuYTo/hbPBFHPadhl8NPtMoNK0nMZLsw72lj1h4j2mLLKxX2sNodLezm7246NyAxXKg9woB/RlMQBuAEf/XkCUfJPsFQHo2UsfURSa6QLD7kLDhgmR/AaY+XpqmVYJVMTNvWw3DyFVkO7rbuyMQCATPzamTTbj7YBx9/dPoH5zFR9cHceFcK3zeUvYnEggOAHKQzUlI/iWgL5amcjB3B6btBCQhom8NYw5m5poV3VciJPtZQGkp2f0LBM+LTXIg7GxHm+ccWr3nWDgnUV2RVEhWToFgFeo9fnyx6TDPyKtdvm07yXlGr9IiRvm81uUvonsd3NBzL8PRNPYqfLbmW/j7yT/DXGaKY13GU0NYykVxPfIW3IqPs9NVSWWhnH5PDvSkHkdcW0JSi/PP7bITF0Ovoc1zpOjNCYzMGzBSPwb0gZJeLPc2VpMaI/5vIHv/G8B+GZK84vNpJKzMuAMfgSMQ7DL5yh7JcRUSNQjWusXC4I5GYH0AQ/ZBUf4bQKqgmu3d3imBQCDYNtVhP1680onFxSRu3x3Df/ruB5idi+HKpXbU11VwXrpAINg6ElSYchiS/RzMTLwkRiQz9wASGcTsL1IXy6Lf/75FnwOyH5ZIF6DGsvWA0skLKQJBOWGXXahytKLRfQy1rsOosNXCrQbgVPwsqovIlo05FAjjX5y8yt+HXZ7nes0cioIatxdN3gAqnVYPzd2k7K8ilIdOueh7HRK/yYn+UtWXcXvxfYwnB5E2UljITmMhSyUiKmySDbKk5GNcNGhG9nHXYPp9UK3EmYqrOOY/hwpbZdH2zTQ1IHcLZvqnQO4uNxARrEcO0Lq56aoMinZ5gZQ6/o1pzHI2sFiEEAjKA0lpgGQ7CjN3B9BHdnt3Dg7GrCWkpxoguf+A43UsL4FAINhpJMkGU6JJB5UcU1yCWFBcyezcMke0rIcsSZyHXl8XxPhkFMMjC/jlr++zM72hPoiqKh+70m2qDJlKjjd5ujvcVYtQ0FOcJyIQ7EV4kd0Jyf4KzFx3aap5jQWYWj9MfQSS2lH8+9+HmMYiR7lwnEtJrhk2SPaLkCgT/alqfIFgN3DKXs44r3K2osrRwt9X2OvgU8Os5a3VD0+wOj67g7fnhcR3BRK+2Xocl6qbUOfeXDPTAy2iUyb6XneiE+Qad8gunA5cgV22I2gPYzo1hsXcPJJ6Arqp8fZ0iL5TdsNvq+DoliZXB8e/kIC+mYakm4GaZUIfY0GYGq/AjKJ8IZePkncQmJSbkr+o67vQNFDjlXlTroQpV0Cyn+afmvokTDO+w/siEAjWhBq8qKcg2R7w5GlvQOqLnD/f0VeZe1ZY57vCOa/cRTAd0EZhpH8CxX4RJjUOk0RmoECwK8g1XJUD2QeYacDMcsUIzJxlDKB/mzmYhe+f+PrU9zT+2WeNmkfHIvjRT25vOIlTVRnpTI6/p95FJLwPjczD73WistILv88Fm03hnPTNOq7+6PdfECK6QEASCTUkTzfD1KgZfLGz0TVAG2RDhRDRN4k+ATP3qHTV1TQ+t7/MmfgCwW5AWhtlnPtsVfDbqlk0DzvaUO3sQMjRwG50QflwuaYZ5ULZi+gutx2hsBc2u4Jcdm+7e2lA7VI9OBt8CS3uQxhO9GAw8YjjXSj/XDf1x85zEt1tkh0V9io0udrR7j2KJnfnMw1In7vjtj4LM/0zmOk3AHMJuws9LwdfVK2NGtLZqOOI9VGlf/PPaUVLB3jRgSZ+2fz32qcTPJ700QCMJoslcl2ZcZiZt2BIHshqAyBV8YADhhDRBYKywnYYknYaZuZNwEzs8jmucC6j8xt9pctwfuOmSmr+nJc/93HenkqWIKpRyp/fsvnzGn2vrRDBSOgqCGTlcL1MW30k0r+A7PpdQKHzpHBxCAQ7jaS28AbXb+UX5FIwSRihCDo6J9Liv5GAaa74t5nI38b6nv/NtydBRcuPw/RPF/Uef5/fzMKCX+F25bsASE70ax9TlOHWMQwTi8sp3rbDV794alt/JxDsJ9jhyZWDR2Bqj9jgVWxMfRTIfgLT+VUef4k4hnUwTWsxQ7tfogewAUrYaioq7b6rVHCwsElOOBQPXIqfhfMG93E0u08i5GiETRZ9TgT7QERXFBkeN+WiV2BudgmaplsOD+xdaO8rHTW8nQu+DBMG4toyUnoCOSNnOdBVF7xqgIX0YmefW5CTOwlTuw8j/r/zhGr3oHdTskQlciuqxyGpXZCURoC3OkiSd50MOxMmiebGHGDMwNRnAG3KKj/THuW7vSdWiEpFdFDR42Xfg5lqguT+BzBJRDdjxbt/gUDw3EhyBUz1MKAeB3If7+QjP3WOcwKU+6k2AUoTJJnObzWQyIUjV1rNteSK/ELhOud9FsGSXGoLfQGmMQ1TGwH4nNdnnfvod3yu22XHKAlvyb+Cab9kPVdaOBAIBLsHC0duSBTvIlc9+atNjR2NvMAeA4wYGzBMY9ka+9DX/Pf8M9C/rZ+bZuF3BRG+fCAxbdcEtXKd0NBrAoqY3O2sd7PE17JC5dd+haKFyvVDtgq285By92GWQES3Il16LcOT0rwXZJBdg6qSSETneJ1SQFXctgv8VWTUC3YKuqrJkg01rk60ey9yo9BKR0vREh4EB4c9cdZqaqnC//X/+fvQdYPLJ/1+F3yB3Q+ULxbU1dej+uFWvI+d6LQiz67zEo6uzdxtmMnv5QX03RBaJECuhmQ7B8lxBRKJXCQk0cTuaRf6hgNcG98XlYRJShdg1yAV3JrkGNfHYNKgLHcTyN7NlwkW6TlrIzBSfw2ZyqRztwFjtx39AoHgaSSlBbLjszB2RESn81EIUNshqW2QlDZAbYUk1+TPb4XqmsL5jWJb8nFVvG3mvO8CZDufMyWzHZL9wqexC0aUHVemdg9m9mMg17+LC6UkuC3BTP/KWiywHd+l/RAIBMUT5TwAxTOxCK9DYtd53mme/1567DovuNQNXtyjcZgR+192f4FvBe1tYfzh717elcdubCjPhnoSiZmBdqtaYRfhTOb0r2Cmf1D8O6fPsXociu9fAlJ5vg/PDR+nxeujVWok23GYaheQeb8EkS50GpqDmf4NG5+EeLsOvNhQilidPDR2dbyan+8LBKXFIXsQdrah2X0KjZ6TCNiq+WcU10J9BwWCrbInPjV2h4rG5r0zANgKBeeL8lhE2RnMXC/MzLswc/d2YSKjArZTkG3nWFCRyA2g1Fqr0iycb9URQg4i+lqIQHjq1zR5oxJB9RBM6srumgRyj2Bmr8OkAcJzxztkOGfPSPx7QJ/c5bgIgUCwKkqV1btAabGOU4pAKQoFh3kjJLXVEsuVpvyint8qU5X9gETfu4pznucTHvsp1jjn1QJqk1UWbX8FIEGdFk2zn1hu9R2PU9BhZq/BtJ+zFkvFgFUg2KPQ+Y6+UounFeeyTfo9TCNquUDLjMb6IL74+RO78thVlV6UI5JMOe0eQNn9xQ5TLpXATRGZXkDpgKQ8WZWxf9hDLnQe3vi4GpmF9FJEiZAbPfsOJNc3AZMiQvdzFcL2YeMZi+ilOP4dkDi6h/qJifGgoDT6Gonk1By0xtWFaker1SDUVgWvWglVcog4J8FzIc5cBxDTiFkRJNn3ASqv3TGcHM/CjWOoG7d6wopsKXX2lKRAkgKAHGA3KjfOUk8AtqNA7i6Qe2CV9z2X+E3Zvw+KuNMCgaCYSJIDJp9/XoOZ/iFgUtzJ82Q51liLc0o9INdZX/nfDVZkC1Wm7BaSDRK56igeRu2EaZyCpB6BqR6zxPTcnbyQtYO56VQNpHXD1M9CUsunMYxAIBB4PA7eBGvFke32fpR6H/Z7pMseguZsFOlpO8ORo0XHTMHUBqzoO5vLWkQRPBUXmIaZewho46V5DKUaUI/s44UrwW5TaW/C+cpvotLejEpHMwK2GjgUVxlczwT7BSGiH0DM3AMYVCan9e1gbEul5UC0vwjZ+WUrG3c3SrgkishxALZDkGydMG0XrLiDzFvWYE2ftZqVCgSC/YcUgOz8EvQcnf9iWxCRyX3psbIbObecJgB0DjnG5zV2nnM8S3kOzljQt5/NN+y6yI0+eRGV+0XQ67ATTsMcX3tM2wMhogsEAoFAIFgdpRmS7STMtK8EfaYoXorMZO/nqwaFiL4S6tMGfdyKcnkus8naSEorJPtZIWgKSgZFt5B4rrCpSHzOdotYLoO5VJy/r3R64FFtUOWtV2QbponFTAppXYMqy/CqdrhtuxsFJUT0XYKy3Q3o0E0dhql/moUOGapEHzC1JI/JQkb677nh5s6U9FN2ZoBzz2TX70Gyn0P5ILOYQ85R0/EyzNRfsrgEbax0GXACgWDXkGQXTPsZy52tz3FW9/pQVIod4PLiDkj2S1xFA9sJSBThsteca5KLhX9Z7QSyF2Ak/iPM3K2dE9LpupO7B9PxOiQR6SIQCAQCgWC1hX/qJ0P56Nnrxa+aM7MwMu9AcXwWptnAfcgEBXSYmY9gsqmsFONC1Yo9tJ0swX0LBBaypPAm2F0Glxbw/UGKjga+0nIER4M1CNi3/r7opoGb8xMYjkUQsDtxIlSLY8Ea7CZiFrtLZIw0ItlZTKSGEMnMIW0koJkagvYqdHpPotndUYJHTQPcXJOaXy5gR6BGn+5/yO5PsFuzHJG5OZbk/mdQbOdgJP8CZuY94UgXCPYlCiT752HSYpm2noju4ooV2fEqJPsLgNK6oinoXu/ibgPslyDLYRip78JM/XgTCwpFgJoKasOQ9DGeIAsEAoFAIBA8g1ILyfEazOytEkTPaVacizYISW3fv01lt4Opwcx9ABjzpbl/pQmS0iFec4HgADAaX8TfDFgieldFFVp9IRbBt4puGLg5O443xnvhsznwrbYTQkQ/SJATPGdm0RO7jZ7YXUylRpHSE8gZGXakkxu9wdWKsL0eeEpEX84tYiB+H/PZGSiSgk7vCTS7O7fy6ICxBCP5PUCf3gEXusrCtOz955ComadSB4nFpzJEogZ91NjVC9N2FpLbxpnGZpqEJdEkVCDYP1glfZLjCqQsRTj1P1l1Qu5yjmm5AMlGOeLNHEVFg312nu8Tt5JV2uiAqbZyhZAh+WAmv7sDQroJ05jlWBdJiOgCgWAPwcWceUR1uECwAyYsqvyTQ4AxVxIhnSrxKH8ddiHoEqaZAYwxmLl+wLQiGIoNVRdw5v0+GU8LBIK10QwDKT33+HsrFWN781aXakPOMDCdjGEquZM9HVdHiOg7BEW2pPUUbkbfQXfsNiZSw4iv4oJManFo5rMOaMPUENOWcHfxQxbcFUlFyF4Nr+rf1OObxrLVaTv74Q4Iw9R0rwmy63chOT5j5QfvkdJ9SfYD1C2c9ldSYaZ+wE1odib6RiAQlBrWPqjxp+2U1VBYGwaUMCT1KKAehUQiOjmTqEGo5NnXWXq8MKB2cqWQQc22Un+dP9+VMNpFn2MRHa6vle4xBAKBoMgsLiXx4OEEootJtLVW8eZy2iDLmxeDZmaXMTg8h4nJKFRVwYtXOhGscPP3AoHgqWbw1LTdfsGqDi5BPreZ/QQmxfSZJ/bMPLWkGJQVfwMwoiVqPG+DpB4TlYgCgWDLeG122GQZ0XQWS9n01u+gyIgrxg5ADnNynHcv38ZHC7/GfHaahXC77IBb8cEu2xHNzrNLfS3sshNBexiLuQW+r/HkEFrdhzYpopuAPgkz8+v8an4pBWEZUBohOb8E2f1tzkOnTut7CUn2ALZTnB8MfRFmlgZvyzvUfE8gEJQUFsUVSLbzkIxFQBmEZDsE2M6xkC5R89A9ds56LijznYR012/B0Mes/NGiN/JaAU3OtB7ASFrOf+FGEggEe4DJqSjefr8HfQOzOHOyCV//yhk01gfhcGz+HBZPpHHn3hjeercHDoeKcJUPJ483wO9zlXTfBYK9h9XQXXJ83lp410vQ5FIf5YpE05iHpNTiYGMCZhRm5h0r/rUUKI0A9ReiCk+BQCDYJFw/LauQJQkZQ0NKs9ztu0nZi+i6bkDL6Wz/tzvI8bGzrkCr6mB18XSzDkXd0DCXmcKbsz/CXHYaFB5SYatE2FGPWlczfGoAHy78CgvZmTXvw6G4UOdshkN2Iq0nEc3O8n22eA5t4jlkYGoDMDNvlt5RLVVAcrwE2f0PrBK8Pe3Q7ILk/efQYxEgd1dEuwgE+whJPQxZruJmy5LaioMMne9MpR2y+x9Bp2ZSJHKjVAOUJExjEqYxZWVjYne7qwsEAsFmGBmLYHwiiuGReWQyOXaR19X4tzSVClZ44LCrmJ1bhqLIuPdwHM1NISGiCwRrjE2oJ42p/BVMfaIEvaqyXJFoat0HXkQ3zRw3E+UMeop1KToSx/NISv3BMqoIBIKiQM1FSZfVDZOjYXabsreAxWNpDA/Ooq93Grmstiv7QG+YYZhPbFvJ9FnSIhhIPMR0Zgy6qaHaUY/PVH8L/7D1/4iv1/0RXgt/HQHb+quyMmS4FDdHuNhkO5a1RSxmN9kcVJ+AqT0oXZOQFbCA7vwaIO9u2H9xcHA+suL5pyy4CQSCfYTkAGgwr7Ts9p6UB1R5Y78KyfEq97AoKWYa0LpLNFETCASC4jM2voBINMFmHq/HieNH6+F0bq3XD0W31FT74XE7YOgGenqnsby8+2XJAkF5IluGLIrZVEo0r8z1ANm7pbnvvQRpBLlH7EYvjeGORPQXuGGsQCAQbAXDBObSCWR0Daoswybv/kJc2TvRJ8YW8NMf3MKDe2Ooqw/ixOlmnD7firaOarg9jpI//lI0gQ/f78UP//PHT/y883AdPvPFEzh3sX3D+yCxezTRx987ZRcuVX4GJwIXOKKl4GbfyNNOt5Mgs4N9Jj3OkS4JfXMl92auG2b2JkqO7QxnoHOu8D7IEabnwGslFPvg+AxMYwnQB3Z7twQCQTHgc6qgQOGcTYugBpU3s+urFJmYdFGyqqMk25XS3L9AIBAUmXg8g3QmxwJ4fV0FHA7blse6dHuPx4HKSi8SiQyL8tldMggJBGVPYY5M4mvuHkx9rPiPQc3O9T5rzCPX74v563YwtXFutFq6XmkNVhNXyVeixxAIBPuRrK5hIrGE96aGWUj32hwIOne/eq+sRXRyfM/NLOPe7VFMTUSxMBfD9OQi7n4ywkL6N377AgvppbzgOd122FQFszNLiK1wiyRTWVRV+zcloie0GGbTkxzjUudqQa2zGV41AHmLWbDUydqtermpaEaPI2ts7F4xjUWYVJqvDZQ4t87Jzekk+1lIshv7Bf5sSV6AGqRSrnxqHIBwTwoEgv2JpDZBsp+HqQ8CmrX4WxoRvR9S0UuzBQKBoDQkk1lkMxo8XgdCIQ8kWdrW/IPEd7/Pyf2SqNo2p5VosVIg2CdItmMw1Y58tGaxe7ZoMDXqB3MDkvMbOJCYOcAYg5m7V5r7l7yQ7C9xVYEkGrg+wXIyjZ/f7EH/1ALS2RyONlXjtZMdqAttpuedQFAezCbjGIlHEcs9qZE9jH4aVd23OA+fzYGQc3M6oWGanH0+nYzh9vwEBpcXWFDv8IfQ6gtitynrM1kqmcHc7DLmZ6mpI5BO5TA+usD/DgRcPIAtNVSqGa7xo7k1jIf3xvIZ6UB0Po6BvmkkExk4XfZ1s9ozRhoxbYlF9LCjDm7Fu2UBnaBHUCXL+UKNSWnbCMp6YyHEXELpsFtubdslSPL+LNOS1HZI9ivWgkTJVuoFAoFgl6EMUttZSLlHMEssoltxLnRRPZjOL4FAsHcgFzoJ3jZVhdft2PZZy2ZT4HLZeT6RSGWha7uf7SkQlDOSHISkHoGptgC5+8V/AGMaZvYaJOeXAdP22AF/UDDJja8Nsiu/JMgBSM7PcqNYwZOksxpu9k/gWvcwlpMZxFIZnG1vECJ6GWOaBhtZc0YKOTODnJGBbmZhmNTD0QD9R5ofmV8pRUKWFKiSHTbZAZvs5I3SKCz2x7lmJB7Fz0a7MRyjOKhPmUl+uuh5Y26cb+dQNic/0xgpreewkE5iOBZBzjBglxUcqgjjZKjEsaN7XUSPLMRZRM/lnhSLgyEPGpoq4XaXPs6FCFR40HWkDo/ujz/OQk+nc5ifWcbM9BIamyshr5PNQwdVziTHncS55upzrMLSgUlQd9p1hXjeTxPI3uCy+VLn1cmubwFqI6nN2JdIdki2U1asC3WJF250gUCwX1E7AdtRIPOrEri+iCygTwJmimZvosmUQCAoe8gVRXMA0tdkefstpWj8ruSNN5pm8P0KBIKN3ehS7nh+DlbkY8aIwiRxXp+CqdQduIbnluGut0QRfg5ISh1k2xk2aQgEewWqFqP/5Yw0MkYCWSOJrJHiLaktIqkvI63HOB0iY6SgmzkYpsa6H2l+JJ5TegQJ6HbFDafsgVPxwaX64VEqoLKY7oJddsOhuGHLHx97MVIqpeUwlYjhUWSW3egkfutPjW36luZ52y5u1YauQBVeqGnBkYowdpuyVjznZ2OPXegrqakPor1r5xpX+vxOtHZU5z/Un34gUqks+nunUVMXYGfJWpDYTcI5CemaocPYxsWfBu400E5qcWiGBpvkgF1eexGBH8FMW6VZpciQKyC5IdkOsbgsyft71VRSGyDbz0OnZoT6aOnyggUCgWAXkWQfJLUNpnoIyJWin4aZF9CXYCJLy8sleAyBQCAoHk6HDTabysaeeCK97RoacrOn0rn8faqPBXWBQLA2ktoJyXYcZtoNmIki37sOGBEY2XcgO78KSCEcGMjIkOspXeWhErYaw+5zjUCwf7Dc5JT4oLHLPJIZw1SqFzPpfsxlhrGUm2ZhfbuQS12VnAg66lDlaEWtsxM1rk5U2pugSg4osgoZCjvZ9wpHKsL4TscptPpCuBeZwtByBMu5NLK6jqxh6WU2WYbC7vytQIsRgCorLKD/yZELeKG2FW7b7i90lrWIHo3EEV149kJZFfahsaVyx/bD43OiuaXqmZUhipcZHZzF5aud6/69TbZzhMuiEcGyRuUI23Mxa2YO0+kxXu3y24Kcq77OrQHtAZdoAdZgvSQo9ZAcXzkgq8tkP6qD5PwqzOSfAmZyt3dIIBAISoJE5zrbSZglEdEJE6YRhUSRLpIQ0QUCQXnj8zlZSCcBfXwisu0kquXlFKaml9jRTtno65lwBALBSkdzC8fNmdn3in/3VHWX/gVgf5krrA8M5MKninV9qiR3LylkQLtckvsWCEpBWo9jJj2AocQNjMRvI6EvQuPIFnKZa49TIZ7H4Z4z05hPjyKamcBA7GOOenGrAbS4T6PVex41zg641QrsFSqdHlytbcGF6kYWzRczKfQuzuMXYz340fBDvs2VmhZ0+Cvh3aQATrorxbcEHS60+IL8txUOFxxKeYyZylpEp4Y78VjqiZ8pioxQlRc1tesJyMWFnCe+gIsd6UuLSW54SmRzGmZnlqHr6x9MHsWPsKMe0dw8xpKDiGbnubmoTbZteh+SegyD8QdYzC3wQVxhq0SlfT03vgYze4svjqUd0DRDcly1Om/vk1yn9UX0KsiOz0BP/8iKI6DFCoFAINhvKNWQbEeKXTT9JHR94lx0gUAgKG/qaitQEXBjdHwB07PL6O6dQntrmHsnbZa5hRjGJiKIROMcCdPYGNqxaEqBYE9Dq04kotuvlkhEz3BUjKmNAHIYknww8ruN3B1AnyhRdbUTUJoB2/ES3LdAUDwogiWhLWIocRNjibtYyIwioUWR1BdZdys+JgxYojydezJGHCl9CSltGaPJ+6h0NKHRfQItntOosNWVfcSLQi5zWYaT9UAgYHeysB7JJPGr8T4ktRzOVtXj5bp2VLs8W3ChS7ArCke5uFU7/7tcKGsRPRHPIB5/coLt9TkRqHBzM8+dgpqG2h0qKsM+JBIZZDPWwaRldSzMx6Dr68sMFfZKtHgOoTd+F8u5KLpjn8Cr+tHs7oS6CSE9oS1jMNGN69G3kdIT3KSg1tmIOmfzOqXyWZjUANNYRMmgWBPbSUhy9QEQ0C0kyQlTaeJ8dNOIAWYpFykEAoFgd5AkH6BQn4tgvjF1CZrf0fVJiOgCgWAP0NEWRnXYh+HReUQiCfz9T+/gO9++gMaGIOy2jadT1Evp1u0R3L47hmxWZwf6kUN1CPhFJY5AsCmUSkuQlWsAY77Iwq9hudFp7qw2A3IbDgTUO41F9BKgNEBSuyDJe8dRKzhYGKaBhBbBRPIRxpJ3MZ3uRzQzjrQR3/F9IbE+ps3ztpidQiQzjtn0ABrcx9HkPgmPWsEZ63sBW95BXu/xo9rl48ag5CJv8PhR59kf0U5l/U5Q5ng6RQ05P4UEdBLSd3pFxnLA+zA5Hv1URNd0LEXJmb6+uOBXg2j1HEKVvRbR3Bx6Ync4DympJxCyh+FSPBzVQlAGU1pPYzm3yI0L4lqMI1x6Y3fQF7/PK2XVjgY0ujpQ6VjdiW7SfRlzMLX+EuTGPZlPJ9suHKymcNxRymU5IXIPAV2I6AKBYB8i2QApYE0mc4+sZqBFj3NZhFT0+xUIBILi09ZSheamSjzonkQimcG7H/QiHPbi7KlmNDaEWAy325+cVtH8IJ3REIkmMDA4i7fe7UFv/wzPKcjVfuJYgxDRBYJNIlH0Gwmz9gswM2+VZI5rZj8E7Bc4OgZ7KJN4q5jU/NBchqk9AIyFkjyGRH111GNkRyzJ/QsEzwPpbJHMBIvnA7GPMJa8z/7wcoCc6dPpXsxnhlnYj+fm0eQ5iZC9EU7Fi72CV3Wg0RtgEX2/UdYiei6nIZd9soSCBHTXLpQ+UokC5SHSwHfl4DiVzMDMx7ushUNxotbRhLPBl3A98haWcxHcjL7L7vJ2z1FUO+sR16wGqmk9hen0KDcjJcF9PDmIydQIljUSayU4ZRdOBC6yi52y1lfFjFvdy42lEja/tHPjOeqWfvCwQbJfBFI/APTh0jg0BQKBYLeRnFaDUV6QLYHYTa6vUtyvQCAQFJlwlQ/Hjtajb2AG9x6MI57I4K++fx19/TO4dKEdhzprEAi4udyY/BamCWQyOcwvxHHv4QTeerebs9CpMWlFwIVTJxrR1V4Nj0fEuQgEm0augOz4AvTszdKI6Ln7kLRhmLYzVkXefqUQX8PRpNtvkrg2NsB2yBLS14DicZdTacRSGW446HM54HHakc7lsJzMIKtpbDp02lX+ncOmsokyq+lIZrKIp7LQdIObM7sdNv5bp339Cn/TNJHTdaSzGjKkM2k6NMN4HNVL90+aj02R+b5cDhU2RdmSeZMeg87/iXQWqWyOn4eev3/aV3oebgftqyXDbdcXqhsGsjnrtcjk8s/DNDkbgB7Hpipw0XOw26Dyc9je4+w3+DNgpDGTGcCDxV+hL3YNKd3S4coNzcxiOtXDYvqRzMs4GngNda7DcCqFSJTyflN9djtafBW4MatCpaai5b27+0dE13MGu71XQi4POinsNPSmO1w2jnYpQCfcbFbbVGYsNQJ9Nfw1JLVl3F++gaVcBHOZScxnpvkCQQ50Yj47jYWFWf6Z1XrA+o+wSTYcD1zA2eCLqHE0rv1gxjLM7I0SOAdXoDRxNh2kvbMaVjwUK5dPbYap9eSjDgQCgWCfwa6vttINFVhAF4uQAoFgb3DpfCuSiQxGRuexuJRCJqPhw+tD+PjmMOw2hc02Xq+TRRiaHyzHU4jF0my2oTkDjeYpxoVc7X/yxy/y7QUCwRaQ/IDjJSAZzjuorUru4mGJyxxXaj+DfYuZBDJvshu9JNA8WWkH5OCaNyGx/AfXHuAH1+6jwuvCt66cwOtnu3B7cBLffecT9E0s8Hn1TFsdvvPSKRxpqoFdVTC5sIQ37w3gp9e7MR2NIeR14cVjbfjCuUM4016/7m6RyDw2t4jbg1O4NzyFwZkFzEYTiKczfH522VVUeFxoqq7A+Y5GvHCkBa01QShbEtHpuWn49Z0+XOseQd/kPKLxFEeHVHo9ON5Si1dPtuN8p6XlqDKpPltXF2kRoWd8Dm/fG8D90WnMLMaRzORAUlXI50Z7bSUuH27mralKROoUoBzy0eQdfDT/15hO9UEv+jmk+FBj0wdLv0E0O4lTwS/hZMXnsReocLhwKlSH7sgsQk43VHn/pFeUtYheWMl7+uRHP99xJCvcfiW0G5pGGWqb2x+77MBr1d9AjbMJ95Y+xkiyD1kj/YwIT6UkK3+mSjZUO+pxquIKTgYucwTMuiuiZhygqJF8REwpkGxHAaV1+8unexjrtZcg2Y5bkS6aENEFAsE+RLJDUmphUmRXSS672RJWSwkEAkFxcbnsuHShDbIi46/+5mNMTi+ys5x6I6V0yxW4uJS0zDGmCV3/1OFI+H0u/vuvfukUqsP+J6pbBQLBxtCxZVKVHMVqGpF8U8ziYmr3IGlngP0qoptW/ruRfR+g/l4lQLKfZ7PZenoFySfk0M7pBhaWkxieieBm/zj+9Y/ew0w0hlRWY0H4vVQG4wtL+D99+1XWYn56oxtvfNKLSCz12FX+85vd7GqnRzu9ipBOrvP+yXn83bX7eDQ2y6I2ObhJ7CYth1zdRDprueCnF+N4NDKLt+4N4LOnOvH1y8fYDb8RyXQWPRNz+NM3rmNkNorFRIr3jxzzRCqjYSGexP2RaZztqMdLx9vgdjqeMGluBF1TPu4d49fg1sAElpJppDJZfh3p9aR7IjF9bimBB6PTePNOP1471YEvnT8Mv/tgL9wmtUUMxm/gZuSHnDm+FwT0lfrgbHoQtyM/RkZP4GTF63Ao7rJ2o1c6PPh8YxfOhRtQ5fTAY9u5npYHWkSXFAkSnVRWDEApj5wGrDsNuUjSqdwTg2GOx+aT3sYfXrqI0H9BexjH/OdR5ajFVHoU06kxRHPziGtLyOhpbipAt7VJdrhVH4L2Ks5Ar3e2oM7VzAK6vF4GOXX5NZZgcsxIKboJ87OBpB6BpLbgQKN0QVJqYGoPd3tPBAKBoATYALnSqr4pBbTQS5M5gUAg2AOQwzwU9OLyhTZ4vQ48eDiBnr5pjE9EWTxnMWbFFIXmCVRBG670oqO9GkeP1OPksQa0t4U31YxUIBA8BWclqZAcr8CkSJdSNMXUx2BqfTD1eUhKFfYbJmWh53oAjnLJlSj29Czn12+WpWQKd4YmEU2kWHA+3VbPP5tYWGbBmyJc3ro7wAL3g9EZjkS5dKgJy6kM+ibmEYmncGdwEvUh/6oiOmkrqqqgd3KeRW7Sc1wOGxoqA6j0uTkKhn5GjnQSn8fnlxBPZVgEp8VQil759tWT6z6HbE7jffveO7d5MYCiXMh5W13h4cchEZ6ex+xSHDPRON57MMROelWRH4vsm4GE/V/cIgF9nBcfyJ3fUh1Epd8Np01lMX0xnsLQbIQfZymRRpyiZTI5/N4rp/na87Qx9CCwnJvDcOIW7kR/hrnMMDvS9xo5M425zAiMpV9BlW3o8F6Bz0bztPLErigIKW52oe83ynoERwc5DT5T2qexJKlkFpn0zq8aUf55PJaGseIkRydkh4PyubZ2XxX2So53aXS1Y94zjWh2HgltmV3pOjX64K62drgVL9+20l7Lt1c20cDTNBMwjZl8eVaJHPvUbE5phiSHcJChRQRTrs43TBFCkEAg2GdIKiQW0UvjljSRY2fFwRvKCwSCvYqqkpDuwUsvdKGlsRJHDtVhfDKKSCTBDUfJ6EMVs+Qyd9hVzjynPHUSzinGJVjhhiwLB7pAsH3kvJmrHabWW/xIEoo60QZhat2QlJew79DnYOZucC568bUCFVBbOcpFIr1gkyTTOYzNL8KuqvjtF09y/AiJ6O8/HMY794fYIf72vUEkMjm0VFfgc6c70V4b4jz1v/3gPseakDj9cHSGXdgkeq8UiikjvCbgRXtNiAV4ioCpC/lZdA/6XHDb7XzeJrF5djGOu8NTuNE/zmI0Ce9v3RtkR7rP7eDF1NUYnVvEew+H8MGjERbQSTSneBnaSOQmoZ4y2Emk75ucw92hKdzqn0DQ52ZH/EaQAE9RND+/2cNOdIrDodfgQlcTOusqnxTRE2kMTi3g495RDM9G0T0+y0J9W22IY2RoXw4SlHk+nryPB4u/wVSqZ1v3IUGGXXbCqfhgl92wyy6osoMTIxTJxiZXug3pg5ymwf9RTr3GJlnNyLIITg1NM3oSaT0G3cw+jm3eLJqZwVx6GPcW34BD9qLFcxpuVcT17DRlLaI73XYunSThvMDycgrxeJpXBXeqDJIOBHKXRBZiT2S00+O7vU7LLb9FqHGoW/WiWe3kJqFFg0rbtNHSCeg8cKGLYyWX+h9kJBLQaZPcVoSOQCAQ7CMkqDDlQGmd6GIBUiAQ7DGs6lKgpbmSN3IwkngeT6SRoqpV0+D+TW63Ax4SXaiy9gA6/wSCUsDHkhTIx2o+ALQHRX8MUx+BlL0O2F+wjAT75fg1dTbbmfTcSjH+kpyQ7S8CchVFCmz6z6gpJo06m8IB/OGrZ7nSnyKxSLDuHp9j8ZjEbHKNXz7UzEK7Nx+vQm7rSCzJYjE50hdiCdQFfZCVTx+f7ody1ylzXTNMNFUF0BSu4KahT0NC96XDTUikM5zPTg1CKZplaCaCI03VcNmf1Z9Il7ozPMUZ6CSgEydb6/B7L5/Gha7GZxqeTkWW8evbffjztz7h7zejRdF+kIBO+7ScTKO9LoSvnD+C3335NDdWXbk4S7fPaDqqK7z4+48eontiFiNzUfz1e3c5450WGdZaDNhvkIA9nepFz/J7GEve2/Tf0VWeRHISzR2yBy7FB48aQsBWDa8aYuH6CUFdsrO+J0myJaCbBptjNSPNjUzTRoLFfIqUiWsLWMrNIKktIa3HkTESyOhxbiS6GaiXIi0GdC+/A4fiQbP7FFT5YOtyO01Zi+jUnMfjcyKy8KlAGY3EMT8XQyqV5d/vBDQ4TiQymBhd4EZBBVSbgmDIW14nIWMhH+VSKiRAPQxIYsWLBgecF0zlatRgVCAQCPYVdG1zlS5vj0sphYguEAj2NiT4UGWqw+Hd7V0RCA4Mku0sJNvtfKxmkc1j+iTM3G2YZJKSfNtq/FiOmGYK0McBrbs0hjtqSO94Zd2GomtRHfDibEcDn09poYSaeZIYfqi+ikV04nBDGB11lY8FdIL+TY00SUTPaho7yem+1FU0fGpAuhG0AHqksRpXj7aya7x/agGZrMYNQqlZp+spQZwgB3vvxBz6JxceVyz97suncKaj/hkBnSAX/NVjrZhajON7b3/CWebrQb+nhYIfX3+EhViSo2gozub3Xz0Dt+NZ8ZReP3Klf/PKcUwsLPECAInwJPJTVE2V37Pq3+1HErkoN+Ucit/Y1GeejnVylZNwHnI0odV7Do2u46hytMCtbr66Yn2s/VjKzWE2PYCx5H2MJm5jMTvNTUQNnhttvK/9sQ9Z0K+w1SHosGKM9su5qtwpaxE9GPLwNjY8/+kPTWBuZgmjQ/M4dtLqalxqYsspDA/MIpOlEs1Pf253qKirr4Cilo+IbrKIPlLCRyAn+mGA3YkCKLWQlEaYQkQXCAT7DnJf2cu5Z41AIBAIBIKDiNoBST0Ek4xdZrTId67BNGZhZt6H5PxM3lCwD9CHLPd+SZq6O3lOLKnHAMmz5b/2OG0sLq+EhF6KWylQX+lH0PtkvrLf7eCIlsfGx3SWXezPC+1LwGMZNqnxKGWLFxqQPk3v5BymIjF+XHJ5H22sRnO4gmNi1oJc4i8cbsZ/fvf2hiI6xcpQZvxiPMn70FVfhWNNNasK9CshsZ2iZBqrArwYQK8PRd/Qzw6KiH538eeYSvZs2uUdsNWhzXsOnb4rqHK0csSy5TJf/7XeDj61Ei6Pn0X6c8Gvs6BO7nIS1RNaZMO/p7iY4cQn8KqVuBL+3bJuMrrfKGsRvaraj+rqJ0+mxNDALO7cHMbR4w38WSl1ieTs9BI+fr/viTx0wum0o6W9mh3p5YEJGFFAGy/hYyg8YIH87PtyEKG8YCsXXSAQCPYZdGk1aZG4XK5xAoFAIBAIBDT/t8NUOyHZTsDMvlv8BzDmYWZ/A8nxguWw3geYnPVe/Pgbhnql2a8AsocjLbbTCy/gdj7jCnfZPhUvAx4X3E7bM39HmecEidiUHV4EDZ3d7o/FeY5H0db0BlsNUJP8PTnAjzfXcO44uerXghzttUEfLwpQg9C1BHpiOZVG78Q8550T9Hd1ocCGDULp9xUeF0fZEBQzQs50WmjY7+SMDDcQJZE5pq0w5K6KxHnnh3wvsvO82tkBn1oFh1KqhpjW+0aOdzvFHlHWuunlaJaAvRbNyVMYiF/HSOI2Z6Bv1DB1PPkAE8lHaHAdLevoqUg6icnEMmZSMSS0bD7GaevUe/y4UtOC3aSsRfSaugBqG4JWLtaKFbq5mWU8uDeG/r5ptHfWcNZgqViMJtDfM4X7d8ee2AfC7XGg62gtpnPDSC8twyG7UOmoRcgexq5gpgFjqfgNVh5DB3kFoIQhYWeidMoeGjAou/R+CwQCQUmR8ptcUNR3e4cEe5DoQhz93VPovjvG/ybzwaETDTy+EwgEAoFge0iQlA7Afh5m9v3ix8MZcZjZO1a0i+SGJH0aIbIXMY0YTG0I0EoU+6pUQ3a8uG15SaVecw7bMyJwQSAvCM+Op3Ja6DYFQyWJ5wYLc2uPVym/fCmZxsxijMVr+p5yzLM5HTld59/rpsmxJ7QVYHf7Gur8wnIC8VT2sfDfUhPiJqnrQXHA5BQP+71YXsflTpDoTZE2lHVODM9E8ZPrj/BxD/XBW5/h2QimozHrHybYUU/PdX9jcsb4o6U3Ec1OrutCVyQVPjWMQ/6X0OG9iLCzDU5l56PZaOGJctc5e10Jwmur5KiWntj7/FzIdb4a1Jx0ITuK/thHqHUeIrtrWfVgMUwDKU3D+9PDuLcwjbF4FNFMCmmdGq5u75x9ubpZiOjrURH0oqExhMqwj4XzApl0DkP9M/jNL+5xLnpVtQ+2/EphMUkmM3h0fxzXPxzA/OyTwrTDaUN1jR9NbSF8FP85RtN98KoBHPdfeEZEX8ouYCYzwRlF1c4GeBQfVLn4JSEmC+h0si/RiVFyQKL8b1qN38YK876EGtvIVTDZqbnfL0gCgeBgUj6DMcHeY3Ehjpsf9OMHf3GN//3y54/DH3QLEV2wpyhoJ2U0NxUIDjzcm0o9Csg1gDFd5MX+HEBNOHO3IclhFon3NBT3qg+VxmxHiwxKkxXlsk15iURl+2pB5ivOuTZF3rAX3Wo6d0F8psiVsflFDE1HMDgTweTCEmeMx1MZFtIzOR2abkAzSEw3Nx0LQ3+fzjcUVWWZG6DSosBG0HMJep1Q6LbWn69KJqdxw9TC7nSPz/K2HdI5bdsO4L2CZmSxmJtGX+xDbtq5FjIUBGy16PK9gHOhb3LmOYnqu43XFkKrchZ+WzU3EaU894S2uKaQntCiGE3cRaxiDn5bGEoJome2g2maiOWyeH9qGH/Z9wnuLUxhObe+s349VEmGW7Whw19Z1P3c1r6gjKGmDHWNIZw43Yw3f3n/id+RqP7GT+6gKuzHpaudqK4NwG5Xn3vlxTo5mUglsxjonebH/ej93mduVxX24fCxBri9NsxExjGWHIBb9SGcD/VfyVCiGz+f/s9QJAWv1/4OOr3H4S1FprgRhWlYjTdKgwNQW8v9Y7OjSLIHphQEJG9+AUMgEAgEAoFAsJ9Ip7NIpXNclUpNRF1OO1fClpPjSyA4cEh2K4fbfglm+mcAihxTYeZgZt6BZDsNkJC+J493S3k1c3dglsqFLtdD4sWM7ce9ct3jOvEnfJt809GtQuftSDyFX37Si1/e6uEmoZRDbrcpcKgqC94kaHucCr/FZHwk4ZoahtLXjaAImYIwzU097eqGUSt8W1JXbBvflvaVxG+KY0H+b2jBofB6PV0ruta/ZUjs5t9oIWKvQ4LzZLIby7mZdW/nVivQ5j2Pi5XfhksJlNX1XJUdqHI04+XwP0LWSGIkcQdpPV9R8BS6mUNMm8Vo8g66fFfhLpPehTnDwEgsiv/1ztsYiy8ha+iwyTKcio2/0ud+OZvhn9tlBV6bVe1Dn/OcoSOr6/w7gsTzercfbf4QTlXV7fIz2wNqaFNzJS6/2IW3f/XgmTgVavj5H/+3NzEyOIsvfv0sjhxvKEq0Szar49q7vfjx395Af880tNyzqz7NrVW4eLWTV1iWclFkjSzCihd+W8Uzt00bKSxkZ3hlK6UnuayhFJhGBCiliM5O9BZqh166x9iLyB5AqQc0IaILBAKBQCAQ7Ddu3RnFux/0IRZL4eypZrz84iGEgh7YyqYvkkBwQFGqITk+CzPzBrDJ5oGbJwcz+yFM/XesnmDYq80YTZi5u4C+cfzHdpDUNkj2MyhXFhMp/Jsfv48PHg4jEkuyfuNzOXCqrQ4nW+vQHA6iyu/mn1GkjMOu4pOBSfzNe3dxo3/rveY2LcZKlht9o1vT/q7UwU601OBUax38T2XIb+LhUF8V4Kam+xnKQCdBeSNIQD9Z8QUW0MsRCQo8ahAXQ7+NjJ7CcOLWmtU2WSPNOerNnlNwozyez0wqxjEuQ7EoV3XQ56/NF8KVmma0+Svhtzvwp4+u40F0BocqqvCdjtMsrMdzGYzGF3F3fgrdi3P8dy/XteOfHb2EY6EadqTvNmUvoru9Ds49f+m1o7j+YT87xFe6xtPpHAveYyMLOHysHqfOtuLY6Sb4fPnSmE2SzWqYGo/iwd0x3P1kBH09U5ibWUIm82xtTWNzJbvjSUg3oSNjpGCYtLJih0N+9mRm5ldUCquHJYOaipbSDU1ZcGobFVOV7jH2IhRvI1ft9l4IBAKBQCAQCErA8Mg8evunMTu7jOVYGpcvtZd+XC8QCDZG8kGyHQOUNkDrp/CL4t6/mbRc3Go7JLUDew3T1AGtD6Y+BphrR1tsG8kJqO2AehjlCInmH/WO4tqjYW7+SfrQ+c4G/NaVE+hqqOIGoOTstikKC9qUwU4iOP18s1oSNTctxLeQ4E3RMJuJgmEtK6dteNtCXjw55Om60xSuwEvH29Acfta8uRGU2e5x7NXFoI3RTR2x3DymUj3r3q7W2YUm90kE7Q1l5UBfSWG/qpwtaPWc4ee1kB1Z9baakcFk8iGS2jJH1FDT0t1mLpXA7fmJx5/vb7Qew9dajuFwMAynokKRZPx45BG6F2cRdnrxWn0HO84pKz2ja/z31+fG8Gc9t3BzbhzN3gBcqo2F9N2m7EV0OnlRVMtXvnUO01OL7DrPZJ4sq1laTHJ++ezMEvp6pvH+290IVXkRqvQiUOHmBqAU9aLarLIXahiRy+rIZnOIxzKIRuKIzMc595weY3pyEfFY+nF+1kpcbjsuvtCJc5fa4XLZkdZTXEJBGUUS5N39wBqR0sa5UMmcTJnoZf+x2VEkyQlJrhRTKYFAIBAIBIJ9yMzcMiKRBDdko7lEwO/a9yXxAsFeQJJUmHIIkv1FmMY855gXFxNm9hZM25k9KaIDGszsx4A+V/zmq4TSzg1eJcmHcoQyz2/0jSMaT3EsyqH6SrxyvB1XjrQg4FnbyZ3TrCaj/3/2/js4ki3N7gSPq9ARQEBrlZlILV8+rUVp1V3dxWo22c2mWpKzsxzarBl3aLu2+8+Kmd3ZWaPZ2OzscKi6STZbd1eXrnpVT9WTmS+1zoTWOrRwcde+zyOQyEzIzAggAri/V15AAgF3j4gb7veee+75sAnIwe7zuCZDinWhgqWb+VsqghpLZTbMKCeRvy5MxW1dd6ZpOXz/aazZ3Y7yxyFrxzjGZa3okyJdwVNo8ffDUCu/YLBH9aMzeAJzueE1RXTSIjN2HDFzCnWedvj1x49WKhXxfBaD8UX+viNYgxdaevBMcycinvufO79msJhOtQTChge1Xv/y5EFLIII6X4Anj/7VjU/w9vhdNPlDaAmE+ec7SVWooSRcHz3Zide/cBRv/9jB8OAszIeqCpMoTjnptF25MIxgyItofQi1tUH+ngqP0pJLpSiimxbyOYvF8oX5JAvxVLB0TRTw3z/1bB+ee6mfXehFVFAnWoEtqFDDOvsoMyygU3HRsmEAWnO1NJvtg2bgVVkgTSKRSCQSiWQ3Eo9nkMnmEfAbaG6KIBT0bZjfK5FItgkyNPleg8h/CDhlEIutO+xyF86zUNTKFItXhQyBIgsn/zGb7cqBYhxzJxcqIGJhraKfA1MLy3Eofa31ONLVvK6ATiwk00jlNhcP1BAJIuzzLovvg9MLyFnrZ6nT+ZALfSaW5GKm60GueIqc+fT2KMulU4txTC7GcQqP1uLb6yTMecTM6XVWiikIaBG0+vtRY1RPseB6byeafH3wJSPI2qsXB6bnvJAbR4uvvyJE9IxtYi7rrn7pq6lHdzj6gIBOkHhOIrolaBLAwsq1FR5NQ0eoBl/rPoT3JgdweX4S52fHcay+Fc/5urCTVIUaSrMR5CT/0tdPI5sxYTsOxkcWOIJlLVLJHG9jw/NPfHzqJJObvXdfE77x7ac5NsbwuC8ddZ99WpCLhqatJGLmAovpVO1325eGULVtkSzTzqnKhheKSk1755eHVBYkotetUsZDIpFIJBKJRFLt0JjDshz4fAbqo8HqrC8okexSFHigGCcg9G4Ie6j042GxCGHdhWIPA+oxVAuCCq3a04B1HRDrO3MfDx8U4yig76ygtR4kUKcy98XwSMC7bpY4JRGQED40vcju9c3Q0VDDTnEim7dwbXgaiXSOxfW1VixR5MvUYoJd6xuJ6DUBHw53NnExUXrsyOwS7k3MI3kkh5C/8p3U20nKWkDcpIm01SGNrtHXi4jRBGOVGOZKdqPXelpR7+nEeObamo9bMieQdcrxWX+8wqIp0zUYtwRCCBqPxghRQVES0mk1RtqidA9XUStiqBoa/CGcbezk4qT34vO4Mj/Jueo7SVWI6AQJ0qGID7/23WdQEw3gr//8HEYG5za9zObxjwsW0A8dacc/+q++iNb2KDze+y+boqio9TRgNjfJxUNH0ncQM59FWI9yBWRCrCgkSkVFKauJhPYnPjcKkCnGx/BMcwoQm7vYbx0PZ85xLrrkQXhyISrlc4lEUmLEKl9WXmkevuqsdhV6nMesPChli8qrm6T00ECVi2XZws1L5H8X5uzpP5VMDCobGTZrSijG8JHDS9BW2Cc7kpb37fYpaeN9F9zE6x2D9ufYznKfsxgPWDxm8TkUj188Bj2GngMfd539F4uG0f7pvOnv6BjuLwvHd5zl51Pcn7rJ51A896ITj6ISVdq24KSmv7ct9xz4NdDdfVRqlmipoRWt9LrpmsZxjhKJpILg6xAJuk9BWAOAdaP0x6Bc8fxFKPrRFcescJwYRP4TgEW1UmsmCqD3QNF6eRxcqdB9bmUB6HTORCa/usOc7m8kUk/Mx3FlcBIzS5ubjNnfWo/WuggL5uQuvzI0ieGZRS7gSVEvD98n6TjTS0l8cmuUzaEbURvy42RvKxojQUwsxDEbS+Hy0CSuDE/hmf5O7gsQq92Pud+w3OeiQqab71NVIxk7gZS1drwxaWctvv3wadUXhRPS69Hg7VpXRE+a88jb5dIDt4agz5PjpocEdQ+MVSaUKOOchPK8bSOWz666H01RcKCmAWHDi/FUjIuO7jRVI6KvjHZ59a2jXNzzlz+7hvd+cQ3pZH7V/PInha4v+w604KU3DuOVN4+gubnm/qCm+Bio6PD3Yjw9iDk7iduJK/j9of8PugL7ENRroCsGRtN3lwX0O8kr7Fb3lWDmq9nXiaM1TxX+lS2IHQ/G3JQMxQ+F3daSR5BxLhKJpKyYvBz3gY2v+fnCRrP8NHtPj6OBgXn/5/w792ei8LgHf7fyb1bsY+V+7HEppEtKhqK5wm82nefVgud+dQd3b01ifiaOXMaCqimIRAPo7m3E6Wf7cOBoO+oaNrd8nrqCiXgaAzencOvqOMaG5zA3HUMiluEYQOrDUcRfQ1MNeg4048ipLhw+0bFh8bDRwVm8/7Nr+OWPLvMg+R//86/i1LN9yKRzGL43g8/ev4OBO1NYmEvAsQQCIS+a22px4qkeflxjS82G5/3RL2/i7e9fxOjQHI6f6cZ/+X/8Br9O01NLuHp+GFc+H8L48DwS8Qy8XoNfk30HW3Dy6V4ce6p73UHxtYsjeOfHV3DxkwF+DV544zDe+voptHfXY7N89M5N/OjPzmFqzM23/O7fexlnXzyAusYqijZ4AiIRP/x+gx2KqfTmlvhLJJLtRfE8B8X8DKIMIrqwBwHrKsl0pEigKnAWIXLvFTSCUqNxDr0b9Vq5BLwedNTX4NbYLN9s70zM4fbYHI52tTzy2JxpYWhmEf/fH3zEkSybKQ5ajFvpb2/AgbYG3Byb4b/7j+9c4P7Cs4e64C/kpReZi6fwya1h/Oj8Tc5p3wgSyaOhAH7jxRP4w3cvYHIxgavDU/hff/Ip0rk8nu3v4nNYjbxls+P93uQ84uks3ji5f10nfrWTdZJrxp0QqqKi3tvFzu5qw6+FUeNZ//OWtpdgOquL0duNpqgskqesPJJmnp3pD0PudJ+uI23lMZmK4VR966oTlCHDw471rG0iZe58H6zqRHRy9ITCPvQfbkO4xo8Tp7tw5eIIbl4dw8T4Ise9PNn+FRbqSTw/fqqLo1u6+5q4uOlqbiiKcTkYPoF7yWtYMmeRddKYzI4gbi7CUD3spso6meXA/6HULUxkhngpyZNCAnpRRBe8bK2MDUrxAxzlInkExajYYioSiaTCoJVJIg3hLPLghmK4+PpNK4mcBASvKEoCDv2M7h0kaNPkKK1esiH4+8LG31OHhPZZ+LpyW/VnK/5m038nBXRJ6SBH2AwVir83gw9/cQPTk0uIF+rSkNOZ+lmGV8fowCxuXhnD8ad68Pzrh3HkZOe6+6X9XPp0AJ9/fA8zE0tYWkwjncwim8lz3RxycpNj29A1DPtncfv6OC5+OoD+Y+342m+cZTFY11fvm9HfLy2kWMSmbuDsdAx3b0ywOP3RL29gZpKEevc50HiYniMJ7/duTeLqhWG8+OYRnHqmD17fgwPpldC50usyNjSH+sYw5qZiLKiTeH/3xiQL9KlklicDaGBO5zJ0bxrXL4/i2Odd+PK3n0JtXWjVCQES8WlV5eSYm4n78TsKi++tnXWbcqMnExkM3ZnG9Ysj3M+mlaE10SBPFuwV+noacOPWBKamYpiYXGSRhCsi7WJHn0RSdeidgH4AUM8Dzkxp9y0yENYIHPMyFOMZHuNXNGS4sCchzCuuKaKk0HIoDxTPs4DaiEqmIRLA0/2dePfqALu+ySH+w3M3EEtncbC9AQGfh93nJGwPTC7g8tAEro1Mo6OhFvOJFDvGN4Luyaf72jA2F8PQzAJHupBo/we/OI8LAxPoa6lj4dqybXa3k5B/Y3QG2byJgx2NfE70N2tB9xkSyd86dQCjc0t47+oAn9fN0Rn8m59+hl9cuofOhhp2rFMfh/o7tD+KoyHXOj03EtO7m2rxwuEeRHa2JmNZIQE556TX/D0ZYMNGA4wqTFfwqAEEtfWNmxk7Cassk2Zbx6frXAA0lcxjKp1gIf1hGnxBRAwvlvIZ3FyaxZe7Dq66LxLiKfKFtnzB3b6TVJ2IXryQ0GCgb38z2jvr0d5VjyPHOzA+usADmfm5BGKxNBcNTadyXED04UKktA9y41C2YTDoRTjiR21dEA2NYTS31qKzpwH79jejvinMjp81zwUKGr1tOFHzLC8XHk3fQ9pOIm65Tp2HSVqlK/yZMFcsZaBlWuwcLBN0o6ymQirbiAIDQtnFdyOJRLI1BIndGbeIk7MAIWKFa3Rhc+IQy/8uxHDR4Eykl7+/7zinTrUUsSW7B3KFX/hkgAVicoyTIaKlLQp/kAY0AqlEDtOTi5ifTWBuOs7iNYnrJCw3tkTceJRVcCwHQ3dn2NFNYjaJydSvI6GY+owkkFN/MB5LY2E2wSL09PgSJkbmuR/46peOs3t8I8iYduf6OIvKVz8fxsjgLIvUXfuaWDynY9A5kyBOX+k5ZAp5rM+83M9fNxJelxaS+PjdW7j42QBuXh7jx5PI39HTwCI+udFnp+J8/vPT9HWR+7TF5/Dw/um16+prRFNbLTvJx0fmeZKiZ38TC+8bQSsGyNWfSec50rD/aDua2mrWnRTYbRw51Iar18cxNr6Iickl3BuYQXdn/bpjBIlEsr0otHJaPwzo/RD5EovonK01AZH7EIpxltOVKxlhz0FYNwHx5PXhHsUL6Puh6L1QlCAqGRKvT/a24cXD3Th3dwzxdI5F8rl4Gl2NtfB5dBbXl1LZ5YxycpV/+amD+Oz2GBYSA5s6Tlt9BC8d6cH0YgK/uHwP6byJS0OTGJuPoTkaRtDr4Vi0xVQG8/EUZ5m/fmI/okE/H3M9EZ3QNZWP8dWzh+AzdHx4cxhD0wv8XO5NzaM+FOB9UtQaRbeRaE657OQ+p4gZyoJvrAlu2l1frdiOCctZTxNT4NMiUJXqu3frqhfeDWJoaBKhFLHRpSBkeNAejGA0uYShxCKWcmlO5qDVAEXagzWIegOcd35pfpLF9uZAmCNeithC4NbSLOJmlt3tq8XCbDdVKaIXoUECieCUV37wcDsL5rTslYqOzs7EsDCf5MEa/TyXM+GuIBC8RIAuRDQQCIZ8qKkNoL4hjJa2WrR11LGIrmmby4tix5TiwdGap+HVAqg16jGXn0LeycERNjeUlJVYFtVrjDr41AA72J+UqKdhG0V0A5BC8erQe0mRLry6oDIuWhKJZHsQ1FFh0ZuEctdRLlgon+cYFEEuIGcWsOfcn4mlMtaukEgqH3Jak9CczZroP9qGA0faWRyO1PjZjLA0n3Id3J8PY24mganxRZz/6C7Hr7z2lePweFbvPJMY39gcQUtHlPuGFHdCAjoJ3NTPMzwaMmmTY2Pu3pzAneuTLHTPTsXws+9dQF9/C+oaQsuF49fj8rkhNmfYlo2zLxzAgaNtLFSToEqCOTm+b1wew93rE0jGs7j48QBnapNoTeezUf9yfibB0THkZu/sa8KBw23o7G3gmkC0rHtuNoF7Nyc5smZidJ5fox/92XnuvwbDPjaGrISeE0W3HD7RySI6vf63r09g3+HWTYno9DzofSPoOdJzjtRScc0Kd2KWEBLMTx7rwMjoPObmk/jp29fx1S8eR3t7FN5NtBmJRLI9KPpBwDjuZoGX2oFtzwN5crkvQag0YVm5n31hj0KYF8uzczUCxfMSoNa44+AKxmvo6GyswW++dIK/vzk+i4VEGmNzSyxCs5ajqfB7DXZynz3QgS+cPoAXj/Qilc2zM30zurNH13GwvRHfefkkr0gbmJrDbDyFWCrLTnDaBRUGjfh96GysxVP7O/DaiX1YTKTxi8tu9O9mOL2vnV3p9ZEAPr01ytEuJJQvJDOYWkq6q+4UhY0EPkNDTdCHDr+XM9uP97TypMFuhmoPOuuIyPTaeFTfA0JutaApOnR1/ZosNkVzrqjHuJPUevycZf7x9Aim0nFMpOKI53Oo9d7vo/aEo2gJhPHJzAhuLc7gx6O38GxTF5r8IbfGgGViPBXHJ9MjWMxmEPH4UOfdeU1y13yKqB9POZfkTqdt1WD7QkEkcjHRhaWUff+gHsbJ2udwOHKao1wW8xTtkoXp5HA7cRnnFt+FChWHIqfR4e9DQHvyWduosUJEZ0djqZdqPVxYdOcbbGVCDYmEdL/rKi154RaJRFIRcHEe6gYXM8Xpa4yX9wrrOmDdczMz7VE3qkUikTzCxOgCO5iPnurCb/7eizh5tg+afn8wQ/00Mj784f/yLud4T08ssfD94S9v4MU3D7PbezXxNhTx49iZHna0N7XW4tCxDvgCnlXjSshVTfne3//jz9i1Pjo4x1tvfwuL4RsxMjCLhuYIXnrzCGeDkxBdLO5J0PlPjCzgf/5//hC3r42zc5y+fvzOTXz5N87C41nfqEErKe/cmEDvgWb89j98BccLz2slFO/y9vcv4Xt/+DFmp+MspH/6wR0W6Snr/eH9t3bUcTTOez+5ym44iso5erqLXycqELoabt/ZxsCtSUyOLvBzJJH+1DO9jwj1ux2amHn+2f0wLQd/+pfn8KOfXkZNxI9nn+5DS3OE2yUN+JZf902OMehvtlLgVSKRrI+id0AYhwC1AXAmS7z3DIRNfb6rXMQUFRrnyQYPOk/zchn2rnAtMNX76hNpAzQhHPJ52B1NTvBoyL9cILMImR7Dfh9aou7rTOLxw9dLii+hv6XH0L5IJH44aocyySnGpKO+Fh/fGsb5u2MYmVlEIptnx2vI70F7Qw1O9LTi+cPd7FCnazNFuhzsaGLRnQqErrUSrghFwxzvbsGB79bjnSv32Pk+MLXAQjoZOek8D3U244XD3XwsOtfR2RgL3OQcp9/T89mI/vZG9DTX4c2TB/DhjWHcGJ3mKBmKb8lbFr+OdC70epBgT+L+ka5m7G9doR3tUig+mbb1UBVqI9UnotM5bxQJTRMIToVoUfW+II7Xt0JXVM5DH4gvYCwVe0BE743UsZAe0r2Yy6bxLy9/gK90HcLxulYEDYMF9F9NDrFLPWdb6IvUoS+y+Xo+5WLXiOibgZa3lBtypdd5mtglTrOW9CHO2Cl8vvg+FEVFm68bB8MnEdGfPF+c9leEMnVFWTPRdVcklqwOvRdqEOBqyJVx4ZJIJKXGzTOHdQ0if5EdPsK6AzhzhazxlXnjEolkLfqPtOOtb5zCiad6uZDow3g8Br7+N57B4J1pdovTisKRe7MseFPdmrUEaHKTd+9r4kH2SmH+YUhQPvl0HwZuT+PCx/f4Z+QeX5pPbkpEJyhT/AvfOo0acnI/dDoej46Onnp8628+h//8r99jAZ0mAj569xbe/MYp/v1G0PP8zu+9jIOFyYCHIeGe4mFoxeUf/9v3+WdXzg/h2OkuFtEfhjLM6fVpaY9iaqIY6TKHeCzDsTdrieg0YTAxtohUMscTFX0HW3iSgpz9e436uhDefPUwmhrD+Ld/8AH+5C8+w0ef3sPxo+042N+CpsYIfF7ddbhtUhdvboogGKi+bFaJpHJRoGidULzPQWT+ovS7pzF39idQtP1ApUadOtMQ1gBAKyFLDekBWjtgnHgiKYkE5F97/ii++vQhvteQYO57qABnR0MN/tZrp/Gdl04su8oN7cF7D+WN/zffeZ1zzUk8psfQvlajvSGCb0WP4mtPH+ZYEzouPyVF4b+lv6OtKOZTEc6XjvawpkPH9Rgb3/foT8nV/tbpA+w0p2iVYoQK7ZfEeToGGzrJidscxf/773+dH0M/p/PfDEYh3oVew288e5iPQ8+naJovPif3mK4zfS9AaQ8kkpMje1UERb5YrNEpJahRuJ2QOL6ey56g51QpEwR1Pj+O1jWj3hfAYi7D21LuwaKnHlXDqYY2PNfSiZ+O3uHc9L8avIbvD9/gzwd9LjgLXdD7BRyJNuPZ5vVrJG0He0ZE364lp3Qcd/bT7UDTpcyr+TnqJe9koSoadF6KUeIcJs7PLWeUCJ2vdKKvDV2saEm0W7RLIpHsFhwIawzCvFoQzW8UCoJSZAutAKLiNTLGSSLZLDSQ23eoBSfO9nCO9+r9NYFofQjN7VEWbkkozuXynDFOxeU93tUHCCScryeerzyHaH0QrR1RXCj8jIp2kki/GSg6pqu3kWvyrOYi5ro7uoZjZ7rR9tM6DN2d5vgailKhrbO3cd08cYpfaWqp5bgber6r9WHpuCSIU8FVcodTYdLF+SQmxxc5Uz1aH37k8SSWP/NKP376l5+zKD4yOMMCfzGr/WFoUH7l3BAWZhL8b3rNTj/bxwL6XopyIX70syu4NzCLeDyD6dk4pmfiyGRNDAzN8r9JTPcYutse+H+be33+yT94DadOPDrpIZFInsQp3QHF8wJE5q9Kb2ygGja5DwD/dwDR7EaeVhjCvAlYFBFShvxrtRWq8dQTR9nQPYQE4/VEYxKcNc+j4vrDjwl414+4eGB/KgnVmztHil+hbSsU740U77KZ5BSNHPn+rU+kcmRLQZSX3Ie0Nm0dEZ20OVNkC9ncVSaiCxPmBkVDdcWomKgaFQpa/GH8k2PPI2x4sS9Sj+5w9JF2fKK+FUv5I5hIJXBtgWKxbeCh4qH0qXqz4wDe6jyAjmANdpo9I6LvFNSJJnc6xbeQiF42+EJRPvejQh0EVTrR14aW8FKea2VctCQSyeMjKKbFmYMwr7tOc2uQl+9SxjkcKlS181XBJZJqJdoQ4qxyEsnXolj8nSJDqCgoieiOTUVHsxxFUgq8Ps8DkSRm3uboks3Q0BThOBeff/2BOxdN7YiyC5yy1zPpHBfpJCf3eiK6z29whjlNIGjrDOCptk99U5jF/IHbU7BMm4umUpb8wyI6EakNsGD+q7dvIJ3Kc1FUinU5++IBds+tFMY5yiVvczb9wnyCxeG6xghOnO1d95x2Kxcvj+DziyMc1ZPLW8jnLXYn0uQIbfOPWbsvmVx/QCyRSLaOoka48CX0A9yH4xi+kmFxX1CY1wCtDYrWgsrCAawbEPbmc7a3gqJ1AJ6nN59ZJZFsI5riga54kcda9acEMlYMtmFCZ5No9UBFQ7O2a2pYC0Pz8SRCJaAoCsIeL97qOICg7kFA98Dz0EoSosbjwzNNnRyb+sHUEO7G5rCQTbMD3afraPSHcDjahJdbeznmxafv/PtWGa/wLsdQqdGEsGSWozp2EavMEQLkOpLLTdeGRp+UG8/LDyQSSRUinBTnZwrKNicXj3kJwrpZyDcvZ80JiWTvQAI0RZFsRoil2BNydBfdQ5ZpQVDFrnWg35uWzdEsFFVCwns2k2eBmURyEuOp8Nb8bALDA/eXurtLoTf3HGgCgBzi61EUpOn5UkFQEtFty8H0BBX2XP96QgJ7U2sNL8He6BheynLtrsfwvRl+jjThQM99LXGeIl2oSGkynmHBnVzyczNxPs+V5nIzb3HO+vDADNLJHE8IdHTXo62zbk9meOdpksV2uN0GaCtRBMtejMWRSMoOiUhqIxTvqxD2jFvDpqTYEOYnUIwjQIWJ6PR8OcqFnnepoQx4vQcKTVBIJBWIR/XDqwaQtpdW/T3FuMTMGTT6euCtspSFrJNCwlpfT/RrYehq5Wh2hqqhfQPnOPVlqbjoF7sOoidSh9tLs5jLpjhH3a/paA6EcbyuhR9TCQI6IUX0bcCn+lHvaULSjMOjeMuUU1RuEZ3OWTaXdeFZv703sJRIqhlBWea0NM6Zh7AHIPIfu8t0STyXM2ISSckJ1wQQCG5u6TWLusXbKtWZWecjSc5pEsuX5lOYmYrh7s0JjA3NYXoixqJyKpVDLpOHaVqwTIcF9cd1tVORT886TvKVkNjuLwiuJNTHFtNc6H49aOKAXPIri5WuBRW0jNYFl4Vteg0oQ341qCganfupZ/o4VoZy0afHF3H94jBeeusoVPW+oEtxL5fODbqrABzBOfL9R9s5amYvcuRQGwKrZNM/KXXR1fPoJRLJE6JGoHrfhJ39OWDHSj5OFvkLEN43AHEcChmpKgRhXoGwx8pj/tC6oOj9rtNfIqlAvGoQPj28ZvN3hI2F3ChygeMIVll3hhz0S/n1iyUH9Tp41PVNHpWKT9M52oW2SqfKmk51UudpxIna59Hm60WLrwOecswOie0Q0WVUydrQ4FW6iSSSqqFYfEekObJFpP89RO4jQMi6BhJJOfF4tGV3eSkhd/mdGxP46V9ewAc/u8YRG/eF+NUTqt309a2z0iG/ESQ6F7PfXaHfhLOBeE/FVt24l41FdBLG6bFF57tp2sjn1q7TQIXLnnmpH599cIdFdHLkn//wHp5/7bBb6ExR+DyTiSzO/eoui/IEud0PHWvHXuU7v352p09BIpFsAY7ZNE5C0bshKIpPrL5C54mKd5p3oBiTgN6NHacwyyzynwH2VFkOoeiHeJNIKhWfFoJfW9v5TCL6VPYuDjlJ7utUS30X7pdZC5jPjaz7uJBeD49SXQ77akSK6NtA2KhFv3YcfcHD8KplyikiEX2z65AfBw7LlCLx+tDrUx0XYolEYgLmZTjZH0Hk3gGchUKRUIlEUk4UVd2Uw3qr/OKHl/GLH1zC7WsTnFtNt+Oe/c3Yd5DiSxpR3xhmVzjlmBteHfPTcZz71R387K8vPpZgTwOazeBO1t1/7GaeOT188y75woTgJvdPr31rVx269zVyBAw5zW9dG8f8TByNrTUwDH05W/3GJcoAt9wol54GzrKXSCSS6kGF4nnZdWZTjZsSI6yrENZ1Fup3HqdgDLnMdX1Kjw+K0Q/o+8qwb4mkNISMekSMpjV/78DGdPYu4uYMGrw9MKokrjjvpLGYm9hQRK/zdMCvy5Ui5UaK6NsAieaaVu6Xejuc6FJEX3+SQca5SCSVDgtf1m2I3LsQ+U8grNucgy6RSLYHpQyf6cHb0zj3wV3cujqObDrPBTS/9OtncOhYJ2eLU4FOygMngVjTVaiairGgF3euTzzWMcntvVEkSxHKcS8+ljPMfQa7xzcS6dlNvgmhnqJWcllzWdQn1/t6kSt0DuSkP3S8EwO3pnDt4gjiiylc+HQAL75xmPPqF+aTuHtjgiNdaL+9B5o5R32vRrlIJJLqvdsonheg5N/nejclHytTf5Jq6HhfB5SdjVAQIgORPw/YVOuj1BnwtKzqEKD1QJEuV0kFQ07smnVE9KIgPZa+hqinHU2+PlQDU9k7vFnr1HegNZf13k74NSmil5td1Rsm1w4NJJYWU7yMl4oimXmbC0zRIKNIXX0ITc0RBEPVmRe0OnaZRXQZV7Lx6yNFdImkouNbRMJ1n+fehsh9CNij5RloSCSSbYNE3msXhzF8b5oLYJKAfvLpXrz5tZOc4+3xrp1dTrnojwNljlN/czOQEF2MRKHc8nCtH5qxvohOme2JeAbOJkR0co3Hl9LLjyWRPhDc2Fl18Fg7rl0Yxs2rY8ik8/j0vds4fqYHNdEgZidjuH5pZFmY7z/Sjs6exqpZ9iyRSCQEX7H0Ts7xFuZ1jmApKVR43r4HYQ1CMQ5jRxGpwsrKeFl2rxhPQdE6aTlTWfYvkZQCnxZExGhEQKtds7goMZK6jAZvN2qNFni0yp4YSltxPl9y0K+Fqug8eUDP3ajSTPRqoqpFdOrc0wBocT6FWCzNS1IXF5KYHF/kLMdMJs/Zk+TmWbks9sSZbjz74oFdJqI7ZS6CR05redNcFxl3I5FUJsKEcOYg8hchsn/BDnQabEgkkuqHdN7BO9MsJBO1dUE883I/WtrXF9BJ2I7HMo91TCpUmoxn1s3TLArQi3NJ7p8S5IBvaqlhJ/h65LMmZiZjcGyx4TFIzJ8aX4RTcLuT674muvGAsLUjiq59jWwsoVz0G5dHMTcdQ0NzBDOTS7h73V2hQxE4vf3NaGqt3XCfEolEUlHwtdMLGCegmFch8iUW0eFAWMMQ5rkdFdEFxbo6CxD5j8rQvyUNwA/FOAloLSXet0RSWjTFQMhoYIf5UOrzNR9HsSgkTNd7OtEWOAyFop8qzChAfTwHFsYyVzCavoyYufb1i2JpuoInOA9erRBNKmHmMJtxa1HU+4II6gb0FQXsNwuZRJZyGWRtC7qqIqR7EDB2tpizXr3iuYNMOof5uSQ++eAOLn0+hMG707wEdSPIoXP0eOemjkVudsqDfNit5PUavDR4Mx+2Ylrl6mWtSgUJ3OX+4JdTpN8NVNaFVyKR0P3CBpwZiOzP4KT/I2APYneiPLRh9X8rG/z+gX8XMzYT8vovqWhI0KaVhwRlnrd01HFxzvXiUpbmU5gce7xCwrNTcSzMJblvSBExa0EmjsmxRX4s4fHqaO9pgNe3fuefTCBjQ3Ms9AfD3jX7mmQQiS9lMHxvlvvFlHcerQ+hvmnjpbyarqF7XxP6j3XgV29f50mIgdtTCEb8bEaZnlzi/fUdakVLexT+wM4OWCQSieRxUYzjEPpBIP9xYfV2CbHHIPLnIPy/xdLKjghx5EK3hwF7oAz9NQOK1gdF74WiypgISeUT1uvRGTiG4dSFh6rGPJiNPpS6AEP1I+Jp5hgYemilCOmsd4o84tYsLix8H9PZe+t+tr1aEPtCz/DXSmEgNo8/G7jC33+1+xAOR5tR49m6iG4LB+fnxjGUWECNx4djdS04Em3GTlKVIjplUZJg/uPvXcC5T+4hEc+y2L3ZfMqtcOPqON7/5XXcuXU/M1fXNTz/ykF88zeehmFUxkyP+1aW0ylOQkqJOx0SiURSbpxpOKk/gMj+dZkKLVUCOruEQDmVlMnJRXK87tfCpjzwb1qF5Vl+rKKQOFb4OX2/vA+qbjgJJ/E/AOLxxEaJZDugflmxWCkJ26lEFmKdLiE5w6kfeW9F324rpFNZjA7OYnRwDn39qzvzKEbw+sURFurzOZNjVsjN3dZZB69P3zCiZXHOdYefeqaPI2pWg9zqN6+MIpUgVzwQrQux4E3u8s3Q1deEwyc6WUQnqCgrud9JwC/Gz5x+phd1jeFN7U8ikUgqErUZin4AQmsH7PUL820ZkYSwhgDzBmAcdPtT24095a6yLIfhgfqXlPmuytVIkuqABPHO4HF4FgLI2+k1hfSMHcdA8lOQ3/uVpt+DXwtDqZD4YlvkMZcbwrsz/wZTmbswneyajzUUH6JGGzoDx+FRKyeaZiS5hD+954roB2ob0BOuYxF8q9iOg/MzY/jZ2G2EDS9+rfeYFNG3ysxUDJ98eAcf/PIGhu7NIEY5kCvyzktNuMbPLqN7t+8vn6AZqtpoEEdPdOLQkfYN97GYn8Xd5DWMpO+g1deN3uBBNHgof6mEcTJU1LKscSs0GpUiukRScdASTukSXn1pqz0FJ/1vChmRc1VyDaNreRhQ6wCtDopSC6gh/pmiuF/df/tXiN1GobCxWuj80b1ALdwTipv20L838RiRgyjuWzYxSYVC0nljaw0CQQ+7qWm7fG4Qx890s/P7YWILKbz9g0v46Jc33OKdjwEJ1lc/H2bXNwncdQ1hFpyL5PMWC9w/+NNzy4J0XUMIz7x8gFcybsbplE7n8b3//Ak764+e7nokgjC2mMbnH9/Dez+9ulx/lB7X1dfIsTGboaY2wI/v6GnA+PA858qT4L+0kOJzJPf50VPd/Dz3OtMzca63VGoaG8IISJe/RFJWFEWD0PugGGcgSi2iUwfJmYeT/yVUvavQL9tOBIRDIvpn5dm9EoLqfRVQasqzf4mkDJEuYb0RB8Iv4G7iY2RtWlG7GgJpawmDyXMwnQxO1H4JLf4D8Gk7axxImHMYTl3E9dgvMJW5s66ATtR4mtEfeZFd9e6K48rAchxkbHP5+2LM4Vbh/qhuwHQcTKUTmEyXp+7DrhTRSSgfHZ7De29fxye/uoOhgZnHHvxshYamCNo763hwRIWXitC53LgytikRfSk/j1vxS7ibvIoRzz34tSBqjQbyAVaRE11IJ7pEUnHQzUiK6A8jRA6wRuFk/ggi9wt2U1eWgE6xKhFXKFfroSx/rV0hkgd5U5bd5Q9ttLxWobxnErnLE+clHBLkaTBYOR0yieQRFAWHT3Th8rkhTI0vsYh+/sO7LPz2HmjmQpnkUs+m85ibiePOtQlc/GwAyXgWHd0NyyL3VqDcdYpc+eidm8hlTOw73MrHo6zzXM7E7HQMt66O4+rnQxw1Ewh50XewFc++chD6JlYwkvhfUxfEwK1JfP+PP8W9m5No765HOOLn50LPkXLgL382iLHheR5gkFv8zPP7WBTfLHQuza21XIh1amwBM1NxpFN5jsahyMIDh9rQ1Fa7brb8XuHtd65j4DHaykZ859fP4uABmTMskZQbResGPGcgsj8CkCvtzkUMIvc+4Ps1tx+3jZnEwlkCyAlf8skBV0BX9P2A3g2F+54SSeXjiq4RHK15A9OZe+xGp/iW1aCfp6wFFtLzTobd3B2Bo2jw9sC7jQVHHeEga8cxmbmNsfQ1jKWvcoSLRWPadSDBv8V3AD2hM26u+y4ds4UMDwxVxWI2j1h+/UmF7aAqRHRa1rqwkMQ7P72KX/7sKibGFrft2KGQj5fGtrTVsvO9OIEyN5PAnZuTsC2HczfXcxWl7SSmc2NI2QlYORO2sKCV/OZaZhGdn3gliVAVyHprxyWSskCfS5rhlSL6MuxAH4eT/SFE9i8BJ1ZYSbOT+AC1BlCjBaE8CmjNULQmQHU39/v6gttcClYSyWah7lf/kTaOJZmbjmN2KoaRwVl2gffsb1p2iaeTOUxNLGLo7gy7uo8/1YNofRB/NbG4nKe+WShLXNNVjA3N4yd/9Tk6LjagvinMkS1U0J5E9NGBWc4s9wc9OHSsA8+/doj/bjMudFoFeex0N0YGZnH1wjAGb0+jpSPKBUNVGkTMJ/jYS4spfm4koL/81hEcO9OzZdd4tDGE08/24d2fXEEqmeNCpXRPqW+M4KkX9yMY8j7gst+rfH5pBOc+Hyr5fl976aAU0SWSbUBRo4B+AND7AOtOwYRSIkQGsO5B0H7VWijb6dq2RiGs24Bwi2uXFLUBiueZgrGjnGY9iaS0UKHNdv9hLrZJTvSENbvmYynuJeekMZD8DIv5CS462hY4gjpPG0fDBPRaeFR/yQt2Wo6JnJNEylpCwpzFfH4Eo6krmMreQcraWO8k0bzJ28sCetTTit2KQpYuVYeqKMg5FjJW6VcF7joRnWz/8XgGn354Fz/63oU1C4caHh1er87uHYpf0UjYVlV2AFHky+NCYx1a7rq/vxXDA7PLyxCSiQwmxxeQSGQRjvj4eGuRc7JIWCTkKKj1NKDGqINX8z/2Oa1+otuRiV5+53/VQu1C0EoFKWZKthn5uXxoSesCRP4jiMx/AMidsyOfSboWewCVBh0BKFoboO+Hoh+Eove737NYXvG3YImk4im6sF/+wlGuj/PJe7e5jzY7uYQpNl0Idm/rmgaPT2c39zMv9+O1Lx/nnvl7P7nKDvWt0LWvCT37mjgX/aN3b2F6YhEjgzNsrKDuAJkrqF9aE/SgZ38z3vzGKbz0xmEWwDdDKOLHiad73bzyX1xnQf7O9Ql+fiTMk6hNOfChsI8nCY6c6sK3f+cF1DdS7NPWBO9Q2I/9h1tZpB+5N8urPOn1IsH+zPP74fXLqBHC69Hh3/RrIbgdOLYDy3YeiJ3UdRU+r8H74u99ctJUItkWSACjbHTvq24RzlL3n0UWIvcrKHqPa5zYDoQDYd8FTLeuRWlRoJDhw/ucG/cnkVQRiqJCV7w4UvM6C9SZZHxDVzexmB/n7U7iQzT5+tAROIZm/wFEjCb41CBHxWgKCbo6VGh8HHaAK+QBX9n/cpPYBX1G4bDTnFzvjrBgC5MF9Iwdw0JuHJPZW+w+X8iNrumYX42gXofe0Fn0Bp/a9auGbXodqVSXIzgaZqfRq6GI6MjQHH7/f3kH8djqYjgNJjo663DoaAf6DjSjo7sO4bAf/qAXP/nrC/jjP/jwic4hEvGj70ATfvETapxuR5jexGQyh4E7U5yNrq2TP0kfkqyV5qZda9TDq5ZhORSLMeX88NAHeudnfSoXahdSRJdsNzLO5UEERP5XcNJ/CDg7WAiThHN9Hw/U4HmRi1nRktjd3sGRSHaSQ8c70NRaww5zEsbv3Jzg/HPqa1O2NwnMJBaT2N5/tJ2zzIfuTHP8CWWAb4VsOofe/mbe11Mv7Ofj3b4+zk54Ek2p39je04BnXurHc68eREsH1TfY/P5ty4Zt2vjCN09z1MpnH9zmiJrhezOIxzLseKcYFhLZn32lH2dfOABlHTPHetB5BUI+PPfKISzNpXhCgV6v1o46jsORuPT2NPJ7uxlINKdc/EQyi7n5JJuBitTXhXDyeCdOnehCa0sN9vVsPn5HIpE8IVojFO+bEOk/orXiJd65BZF/F/B9gc0S24MFUXTAlxw/oHUC+tEyG/UkkvJBUSf7w89yZMt45sam/46c6aPpq7zR+C2g1SDqbUeDtxM1eivChutQpzgVrxrkWodUl4rd6kIUxHILeZFBzk5xEVNyl8fNGSzlJzGbG+bvKULmccfxx2rexP7wczue4V5uyIcwm00hZ1vQVRWGuvOTehUvotOS3Pffvs4d0IcLiPp8Os48vQ8vv3kEPfsaWTj3+lwnOhVVInE9GHzy4h7k9OnqaXxkAERLdgfvzaD/cBsPaNZCVVRoqg7HceBTAzx7VXooF7d8DUoIE0JkpAS0JuREz8pIF8n2w7UKpIhOONn3ILI/BazBnTkBrR+K93le+uoK55RvHuRccXIqSCR7kbauevzm776It75xarlPRQL2Zvjir53B868fQj5vQ9dUzudey7lLLiDKPz/z3D4WySmWhJzh/Dt2bqvcVyPnNX2lx9O5/Rf/4uvIpHMc8UIrDzcDucEdWyAY9nHhTYppoT6hZdl8OSYnOuWjkzhNcSjUf9yKQ5y6ErQv+pPWjii/di+8fpiFWXaiKwrnmVPRUcpbLwroW3WhF/+G/qN4muKfk0BPcTKPs7/dyq9/4zSym63FJFwnuu04yOUszM0l8Nnng/j0/CAWFlNcpLSxIYS+nsaSjFMkEslmoetlOxTPSYj8eUCsVXDwcaAP/SSEdctdcUgxfWVGWDcActWXOuOdoEKs+gkuyiqRVCvUj+kLPYOcnUbajrPLfOsIFsHNTJajXpbd6AUnOv3njvMedqJTX0AUnOhFF3rBiS5MOOLxIlnp+Icjr2N/5HnUGM27uq+Wty2Mp2L4YHKIhfSQ4UXUV+JEj90mohdd6Oc/ucdLIldCHfyXXz+M517qR3dfIw/KVnODk5j+pNBgq64hxFExtp1fzkXP50zO16QBzXoYigcBLYSYs1D4wJRBaKXic2V9O83yZK3tGijuhoocSDFTsk3QdYQjhOTEDcdsOXMQuXcgzAvlGUyshUJOnR52nSvGMShaL6C1QFEj0nkukRT6ULQ1NNNnYmtQvvdWMr454iTi522z59bWWfd4l18I7neSiE1bKeGBFxlHBBUZNXirrdta1vmmjyUE92dvXh7joqJEc1stjp3pLsvxqhVykD8OZADqaKtFc3MEzU01+OCjOxgZm8dPfnYVrS21CAZkXI5Esl2Q0CWoWCZFunAxzlKK6IQJYV4E9CPbI6KblyCs4bKMPxUS0Y0Tsi8rqXr8WgR9obMsYF9Y/D47wEnY3gr0eFNkYdo7W9TSr9WgN/QUTkS/iHpPJ3R15/sQM+kkhpOLSJgPjr+vL04vf39naQ5hw4s63+bMKo4QnH0+lU7g4tw4BuLzLKjvi9ShJxzFTlPRIvrCfALDg7OYmqRc2/vQktwzT/fii187ic6ehnWjVEoB7Z+yC2ujQeRztus0IhE9b2NmKrahiB7Uw2jytiJmzmPJnOeMdBoglbJ6rqKGoCie8km4NFMmRfR1oMEuXTikoCnZLiw5cbOMDZH/AML8nMX07UEFtG4oxikonrNQPM+74rmy850ZiUSyDeySSy9loE9NLOHezUlkM3leJdDR04C2rq1PLkgehVbFBgJe7Ottgt/nQSqdw/DIPD79fBBnn+pBwN+HuiitWJJIJNuC4nH7bLRy0R4veVypMK9C8dwFxGk+VllgR18WwrzG7veSQ4VRqQCrLidTJdUPr1T0tOBA5HlYIo+b8Xc5UoW+rx4UhI1GdAdP4ljNWxxTUwkCOkEC+o9GbmIo8WAx1On0/UnKc7Nj/Divpm/6Epe1Tcxn0xhKLMB0HHhUDf21jThet/NFVCtaRJ8YXcDYyPwDMS60JHd/fwtefoMiXMo/w1uElrk2NtVgcT61LKJbpoX5ucSGIjoVE+0JHsS91A0s5GewZM4hZ2fhK2VxUYoNoEJ2ZUOK6OtDxQ7o9ZEiumR7EFQQSWwtx3c3QlFT7ELPfh+wR7fpqD5A7+VcTdX3RSjG4cLPpVtHIpFUl06/tJDE1c+HEFtMcX+WBPTe/hb4AzJmpNS0tdaif38zmpsiGBqZw2fnB7G/t0mK6BLJNsLxJNSH0w9wnjicmdIewB6DsO5COHNuYfkyIKhWmT0CQfGFIlb6A1BdH9rU3Z21LNk7UFZ5jdGC09Gvsah+L/Ep5nLDyDuVr29RbEzE04Te4FkcqXkN7YEjqCQylonJVAI3FmbYjU7it12M7ihwJzbH2+MS0A0cqGnA883dOFS787VkKlpEn56KYfohFzplYfYfacOJbV5mqqkqamr9LKYXocFGMpl1l9yuQ9RowP7QUZxbfA8xcwGDqZto9LaiM7AfWqlyxqhoXTkdkIJibCr/IrMziIJ4ToKmFNEl24UJ4aR2jx3ycRFxiNy7rhunpNmWa+HhgZcS/N+4+edq7TYcUyKR7EXKPS1HppDx4QW8/7PrXDSTXNP9R9u4ny0pD9HaALq76nBvcBa3bk8jntjZpeESyd68smpQjKchzFulF9FpdSSJ6OblsonoPC7PfQg48+WJcjFOQtH6Sr5fiWQnoTqFPj2M5xq+i1qjFVeWfoax9DXYIscpEZUI5Z+H9Xqcqv0qDkZeRo2n8gq+H6ptxHf2nUBPuA5XFiYxGF9A3Mwib9vIO6752FBVaIq6xX6tAlUBdFVjAf3vHjqL51t6EDB23oFf0SJ6bDGNpYUHhduOrnq0tkVhGNtb5IJmrCiTcmVwPznkcxnLzeNdByo80OLrxDfafgc/mPxPuBY/zz/zqD60+Us0GUD5u2UW0eFsh0BVrVEu5IZNShFdsn2QE50nbvY49gJE5s+35/qkBKDoR6CG/w9cNAqKdA9KJJLq5e6NSXzw86sYujPNhpD27nocPNaB1sfIiZdsDo+hc7wLjR0WY2kuFiuRSLYfjuIzP4QwPyv9+M26C1CNHt+XUR5yELkPAGehDPv2co0f6F1l2LdEUhnsDz+LOk8H7iY+xpXYTzknvdLwqkHOPz8V/RoavT3wapU57qz3BfFCSzfONnWwaL6Uy+D20hx+MnoL3xu6zo95rrkb+yL1CG1SAGftVdUQ9frRHY7y39Z6/fBqlVHouKJFdHJ5p5IPOjSammsQraP87+1dNk/HowJU5NJZKaJT53cDDZ3/1qcF0Bc8jK+0fBfnFt7DYPImlvJz6A72o9XXjYgRhVfzQcPmG4ZX9SFk1BSOQR+qcoroGUA8mHMkWZkXLwV0yXZTnLjZuwhnCcK6DWHdomTf8h5MifCASw3+A0A/CEXxUb4Ydj90g6tMd4ZEItkc87MJjA7OYnYqDp/fQC5nYmp8EXeuTeDujQl2pNNKy9e+fAIHjrRzgVZJebAdActy+4s0hlgZWSmRSLYPWkmoaP0QVBDevlfanTuLHBXDcStUP6eE/UVBY3JrlN3upY9a1Qou9HYokJFekt1HsSahofpQ5+3AUe0NNPp6MZy6gKHU54ibs7BJ29lBQno92vyHWEBv9R9k17yhekt6HSl1YoemqvDB4H/XeHwsrC/k0vj52B2kLROnG9rwcmsfmvzBLbjQFXg0jaNcArqH/10p6JVe7Ig6+isJR3wIBHfCwr/WIo+NO7+zuUkMpW4h7+QQNxeQsuNYMGcxn5/BXH4KUaMRfj0IQ6HGsfkPR3fgAJ6tf9P9h2IAJOpw4y3DB58KGDoPRutIXAQtqZMufcl2Q6tDRHxvC5z2BET+023Ihve4gwrft6EYp91bZwXdyMsHCT0725GUSPYypbq6L84ncemzQVz8dACGR4eVtzkLnX6eSecRCHnx9IsHcPalA2hsjpToqJLVSCSzmJ6JF1zp2gPmHIlEso3Q2Nk4xK5rUWoRHRYE91E/g+LvcIvRlwonBmGeL2Shu1EJpUNzi66qzXvEKCLZy5AwTTnpQT2KiNGEZt9+TGfvYS43hIX8OFJWOVZ6rC7s64oXdd521Hu70eTr43Mh97lfi2y7efhJMQoO8rZgBE3+MBcGJRd5ezCC1uDu6GNWtIjuOM4jDg3d0HbEIUOnkcvmHzgf6vgahr6hljKVHcH7cz9E1s4gR5uThcVRDIIFdtoeB8sx74vodHMmN7oSKE+BEZBQHAdEznW8V9mHufxOdCmiS7YZym8ryzLOKkHYEFRUyfy0/MfS97tFRL0vuYOuPYMoOPz38ESNRLKDlKqnZeUtLM4lMHBrCiavoBTQdA2BoBfNbbVcSPQrv3EWXX2NvOpSUnpo1ercfAL3BmYwMjrPg+JoNAiPp6KHYhLJroZzv0lEz/2s9K5uZxYi/yEU31dYnC6NKC04B13kf+WaaUqK6sbDes4Cqoz0kuwN6F5sKD60+PejydeLjvxRTGfvspg+nxtF2o4hY8eRtRPI2xk4KEUEG0WV+OBVQ/DrYQS0WkSMRhbOW/z9iHra4NNCqHZCuhcdoRoW0XcbFd1zU1WVN6cQSE/YtmBxfbuhYyZiGS4muvL8aACy0exQ2kphNjv5wGyTUQIhRlMffPsUtQaCqmjb5RDRnYJotwSo9TRfVoZjVCs5dgVIJNsLrQ6Z3bMCpyD3uTXi5k6WE8UP1fcWFN+rgFr9HZqtX/eliC6ReLwaovVBdPQ08L/rGsMlF5uDYR+a26PI5Sw0t9YiFPFDKZFLmfbV2duIg8fakUxkYVsO/EEPWtqjOHqyix3ozW1R6Ypeh2Qqx7E3jyOe0xgil7fw2flBfPTJPczNJ6FpKnp7GhEKysgEiWSnULR6CP0AQJEu1rXS7pwiB80rgD0NoXeWJB5FCBOC9pe/WPqVgooPir4Pit4HRa3M7GXJ1rGFg7SVQ8zMwBEOIkYAQd3LbmHJg6iKxhEvtPWHX2QBnQT1mewAZnPDiOWnWEy3hQUBm19PhwoJC2dFboVYofgV/lNU3rcKDaqiQ1c9iOiNqPd2osm3D82+fYh6O6CBzLm7px8W9njQHa7FuRkdOhUV3T1PrbKVUHJneLz6A53WbCbPA4zthgYc05MxmOb9Y1Nx07qGMFRt/ZnlkB5BZ6D0Fa7rPU0P/kCNAgplpI+hfBnMM4BaW+lNZ3sRGQhnbqfPQrLHECILYc/tXYHTHmMnetmfv3ESMMiVQ8tx9xr02ub2bhuTSAp09jTit/7+K/jO332Z/01iMxkpSgUNLJ5/7RCeeeUgu8Tp366RpDQjjvauevzabz+Pb/7Wsw8dV2GhXornG/PhR3cwPrn1WEPLdpBIZHHrzhQmJhdZjCd0TcXTp7tRV7fXJmclkspC0dqgeF+GKLWIzq7xOJz8+1DVrwNa45Pv0p4GrJuAKEPEqhqF4n2NzSOS3UPKyuGd6Wv4j0MfYCmfwm/3vIS3Wo6jPSBXG6wHid2UTR4M1aEv9DQL5LaTZ1f6Yn6Co15S5FK3Ysg6Kf4dieuOsLlvRYK5pnhgaF6OZAnotQjpdYjoTaj1tEBTDBbXi0J76dYeVg61Xj9O1LXi5sIM6nwB6Lto4qaildBQ2MdbutDhJGanY1haKHf+7YNQLvv8XAIz0zFY5n1BnwT+1vZa6Pr6A6m+0GE0eFtLfl4+7aGbHC29YoG7TJAT3R6GQrP1inTOLEMFXmyq6CyFJsk2wnUK9u7kDQvoLKKXExWq71tQ9P5d5QzYNMIGnLRrpZRI9jAkNGvqVkq/b3H/JGZrCso1vnDPXyEPVHkOsAf4+NwgLl0Z3eJfCb582o6DbNaEmSfHGth9fvJYB06f7EZtjRSsJJIdRW2G4nkWIvX7ADKlHc/RGDH7NuB5sSQiuhtjeBllQSER/dVCjTXJbmEoOYNLi8MYTy/AFjYL6kdq2qWIvgHFcV+xECmhUj9Q9cCnReCQYM5udBLOnWUXerGKYlEYZxc6C+quC53Ec50TKZRdP7as9wbxVscBnGlsR4MviKCxE3Ut96CIHq0L8jYzdT8qY2J8kf9NYjblo28HS4tp3Lw2zoVOV2oJPr8HPX1NG2a0+7Ugb+VGoRlktbZ8Ui6JdtYQ4JGF5h6AMvTIoS8pI9sf4VTZCLdj7szv3ckbawTCGi7jAbxuFrpxBFBphc9exCy0sVIXrpJIJJLqIp7IYH4h+UT7INNNe0sUZ0524bWXD6GpMbwjdZ4kEsl9FHJea51QPGcgzAslLlafh7BuQNijgNb+ZDEpVE+NRfTrKIuArh+AonVWujwk2SJZ20TKyiLvuGkKsXwaOXv7Ux12AyR6U+SKpsnPyGbwaBrqtAC70HcbFd0CGltq0NRSg1vXJ5Z/FltMY2hwFmMj8+jZ91CcSRmgKJnJ8QV89tHdR4qcBkNeHDrSVjlFgciJznEuZXSiW4NQqJCmZBnBcS5SRC/fMiQSjN1CvJL7bY6yFiGebEBftVCUjTNVXie+GoTiew1QG6HsqWKiK6DsTX6NpYgukUj2Nn09jcjnrccadFN0i99voLY2iJ7Oehw53IZD/a27Kh9UIqlaqOCnWgvF90UIqrNTUhGdasvEIMxrnDdOfcvHRVChUjKzOdMoOVorFIovlFEuu46oJ4gWP0WJ+GA6Fg5EWlHrkZn3EsmTUCHq7+q0ttWivbOei+8UC3rS1zs3J1nUbmyObKqw5+NCovnsdBzXLo/i+uVRzqks4vXpaGyKoHd/87Y54jdEiZY3zgUU5zJUKGRCr4Xs/YMKSVBni7Op9zK0RLxcnwOxos1JGGexMHGzN8VNLnBMG+d1lwklAtX7OkDFmvcsJmBT8Vq5EkQikextXn7+AI4dbtvaH1EuqqrAY+gc29LUFEE45ONxjUQiqSCUABTPy4D2pwWDRomduuY5CM9pKHr344+frduAPViGvr8KRetiJ75k90GxLU/V7cNcLsEFRr/UehJt/uhOn5ZEUtVUtIje1FKLnt5G1EYDmJ+777gcujeN93+psxP9+KkueLxGSYsikVhOejkVMf3ovVv4+Q8vP1LMtKm5BoePd7CIXylQlIuiUJyLVh5xTeQ4QoEd6Vz4SoroAnnAiQFiAdjrLg5FL6MT3XRzmWmT7Q6CIjbsKexZaAKhrC58wy3UrB/Zuy70ZSf6rJuNLpFIJHuY48f2YnFpiWRvoCgeQO+Eoh+BsCcB6vuUEMoxV+wxCGFtvV9ZMPEJ8yaENYCSowQBvQeKcbD0+5bsOAHdi5ebDvEmkWwn4qF/7yYFp6KtEKSVdfY24LmX+x+5lwzencG//p/exrs/v47YUrqkx6X9Ly2m8Kf/6SP8+PsXMDWx9Mh5dfc14tkXD6CyoOVoNYBarpgbcl2nIaxRNwdc4roVWMzc6y5ptcxzcjSJJSNdliER3ZnEXkXYMxDlFNGpvoRxqNLnmbepaHJx9ZFEIpFIJBLJ7kXxvgxFK8OEmUhBWHfc2mJP8vf2/YjbUqHoB6Ho/ZUuC0kkEknFUNEKATmd29qjePbFfpz/ZACzM3HYlrusPJczMTY8jz/7zx9z3Mrx0904dLQNrW3Rx45XyWVNTE8t4dqlUVw4N4jbNyYxNxtfjpIp0ruvGSdO96Cto8KqGpO6r9ZB0bsg8uUS2GwI6zYUfT+ghsp0jCrCnnOzmfc6ZXWiF3ASBTGvclZ/7Bj2PIRdhkzEanKiO6XMrHwINQJF6y3Mme+mefOt1nqgyRqaRJZxLhKJRCKRSHY3inEaQusBuHhnKSMDBYR1yx1DG1sz4Qn+2+sQbJ7Jo+QYRwrGkb3Z35VIJOUhZeZwe2kOfzpwGafq23C6oR0doRr49c2vxonns7gyP4VPpkeQsU083dSJF1q6ETJ2Vg+qaBGdoLiUff0t+NLXT+EHf3EeC/PJ5QKfVOBn6N4MFuaSGBmew5WLw2huqUEDZQ6GfQiEfJiejD2yz3Qqi8mJRRbbaR/JRJad5/OzcUyOL2F4cBbDA7PIZs0HctCJSI0fZ5/fh1NP9cDv9zz287IcE2k7iSVzAWkrCUvk4Qgbfi2EBm8Lop6Gx9qvotVDaJS39gnKgwCsG4B4gVK2sNfhIi97OVajANWqLu/lhCKW4oDIQ1H2uIguKEJoZo/HuVBR1Uz59k9LW7UtZt/uNpw44IxLF7pEIpFIJJI9gaI1sKBM8Suw75V259ag6yanfjzFx2wBkb9QqFFTYpS6gjHuycf0pJmYwsZYah7jmQXM55JIWlnkHBOOENAUFT7N4AKXVOyy0RtBW6AOQd3Lv9sslOs9k41jODWL2VwcaSsPS9hQFQUBzYt6TwgdwXp0BOo4ykTZ5OTA7fgkLi0NI26m0eavw7P1+1HnDcEWDhJmBkPJGYylFxA3M8g5bsyvXzMQNvxo8tWgJ9iIBl/4geMNJWdxIz6G0dQ8P8dDkXYcre3YUmHPG7FxXF4aRizvpgA839iP3lATv44PQ6/J1aVRjKfXjpntDNbjdLSXi40+KVnbxFwujrH0PGazCff9tk1+zXRVg0814NM9y+93ky/C35NRdqP3hfYRz6cxnJrDRGYRMTPNx3OEA69moMYI8HOg97n4XDb7Xku2h6VcFudnx/CD4Zu4sTCDvGMjaOzfkohOSix9xn82dhsL2TRytoWuUC0ORcuVvLFLRHT6kNVGg3jrqycxNbnEjvT5uQREQUgn4rE0rl9O48aVUXh9Bmep19UFEakNYGJs8ZF9klD+ya/usIOdcs9JQJ+ZirOInkzm1jR5075PP92HZ184gM6ehse6uTiwsZifw0xuAjO5ccxmJ5Ewl5AXWdjCRpO3HSdrn3tERM87WSzl55Fx0lChotaoR9hY5eLHTvTuMoZeOO5MurMEZc8XFxUFMbP0S+uqD23LHcItIyh7nj6fe7nQo5uHznmN9HrsUcglXU5xV6ECU2oz9jTOwuMvO5ZIJJI9APlscnkTmUwemYzJxhzbcVfuqIoK3VDh9RgIBjzw+QxZUFQiqXhUKMZxKMZliFKL6BwBOsQrSRW9c3N/I+h6kgPMS4WCp6WFc9C1bijq5kXd1TAdGwu5BG7GJ3BpcRh3EpMsfC7lU8jYedhCwFA1FsxrjSCLqSR0Hwi34rmGA2j21awrpBdNjbRP2jcJxTfi4yxOk8CddyxoqoqI7kerP4r+SBtORrtwONLOAquhbix53U1M4c9GPmFB+ERtN/aHmhHQPSycX14awcWFQdxOTPHzpOdEGkhI97LQTs/ja+1nWERfScxM4cPZ2/jJ5CUYioaXGg+h0RdGjSewKcGXBMPzCwP4T0MfYD6XgEfV0RGoR2dgdR2KXo8fTVzEp/N3V/09HfG5hn6eJHgSEZ3e7+nsEu4mpnE7PoFbiQkW7hfzaZ7kIAHco2o8qREyfDzJ0B1sxNGaDnyh9Th0RVv3vU5ZOZ4QuBYb4/d6IDldmDBx9+3XPGj0RdATbMKRmg6ciHbhQLiFf073XkllsJBL4/riNLvJL81P4qXWHn7/toJP09EVrkXaMjGXTbOz/XZsTorom8EwNDQ2RfCbf/N57rCe++gu56A/HLPiFgM1MTI4y9tasNN8nd+vJuQHgh707W/Gr3/3GXbGb7UjXBTQF3IzOL/0Pq7EPsVMdpyXaK3EdizsDx155O+TVhxXY59hIH2TRfSz0VdwtOYstIciNBQlCqHSjblMxUXZiT7EN3Kxx13B9Pzd4jNSRAfdDLkt6KWvaF/EXgBEtjz7riasu4U2t4fz4WkyRZSpnRGKH1AfbzXQ7pkgnHUdUxKJRCJ5sD/vCGSyJtLpPKZnYhifWMLUTAyxWBrZHK1ipbGLjlDQi/q6EDraomhrq0Uk7IPfZ8Dj0XlsIZFIKg83I/wQBN4mr20J9+xA2BMQ1uVNi+iCxlQ29cfuArQit9QYT0PR2p/4mkgC7/szN/Afhz7AVMatJUfCNQnn5AYnyI1OTuJRcw6DqRkWekn0rD/xHXaPr6etkF5CgvzPJi/jp1OXcS8xxddQr2pAV1T4dQ+fR9zKYG4pwY7yX83exFfaTuErbadZeOaAxk1cd+k8l8wUO+jJBU2i9PfGzrGASyI2Od5J8KeJAXJHz+eTLHY/U7//EWG8L9TMrnFD0dktT+dFYnCf08xO7Y2YyyVYGKev9DxJ+Kb91Rj+VR9Prm8Sl2kige9VwuHVAST602v/pLh6lsBEZgE/pfdi8jKL3YQKhZ8TnSfpU/Se0fuxkE9iiAVx1yH/estR6KxTrQ6dK02Q0ITGB7M3YTrWclsiBzpBQiy9LoPJGXw8dwcHI634B/vfYKc/OfTl/bUyiOWzuBef5+/9OonhUbQEtmaG9Go6OoK1aPQHMZNJYiqdwFB87ZUW20VViOjLRUZ76vHbf+clNDVF8NMfXsLM1PY4MYMhL46f6sI//Kdf5GMbnq1nrlNUS9xaxF+O/zuMZO4ia6cfEdDXQ1cM+LQAhlN3kLPTqPM0otXfjUZv64MPVAKA1gSo9YUZ63Jk2eYALm5yAtApN3iPYo8B7AiWwq47t+0BlAggypGhLDgLUJEFbeFYd2SEEOdUlmOSsIgOqAHsXQS3McGZoBKJRCJZSSKZxTvv3+JteGQOqXSexQV3cx/jjuEVt1yRqqC5MYKnn+rFqy8dxLEj7YXfSzaGZCm1TLYBUehL7GFTguRRaCyj7wP0g4B1qbT7JhGdoll8X9t8QVHzE/5aWugCZED1PAVoD2kJW4TE5F/N3mIBfTKzyJ+mqBFkd3Z3qBERw8fiaiyf4d+TgE6OckWAHeONvhoWp9eDBOB/O/Au3p2+julsjN3G5OY+He1Bmz/KsSFpO4+R9BwuLw4jYWb5WH8x+hkL3f/bA19mV/lmIOGZBPupbAwXFgbxw4kLHFFCMSQHIhS5G+Jr/WI+xc+Domv2h1vQ7K95ZF8k6HYG6tEdbMDd5BQW80kMJGawP0SP39gJTlEw9JwIn+bBCw39HGOyFvR6kPs+ZWWRtHJImhl+vd+fuYn3Zm7gSSHtikTxf333l/ho7hbi5n0NhN6PrkADvw70ftKkA00wjKTmOIqG4lyea9jPIvt6fDZ/D380/BEuLA6yWE4TJfvDzegPt6LeG+ZJjJlcHNeWRjGankfazrFb/f91/Xv43x/+Jr8GHq1qJM5dTc62sJR12whFsNR7Axu+/6tBkyKtgQgGYgtImjks5csY6bpJqqaFcXaSoqCxOYIvffM0evY14d2fX8MnH95BPlceRyJ1esl1/uKrh/DCKwfR3BzhHPWtzm7RBWfRnMPPpv4UY5kBFtCpSxjSa9Dka0dIi+Be6hq7zdeCBPSOQB/P8jlwMJebwmxuYhURnV6nEEBVtvOxEhdFuY9j3oBqDEHZwyI6FYeBLCpaQHELi2q1ALfjMkze8ITFXhbRqVvqFCawylU4uErgWJ9yOtE1KHu4gC2tsBH2MCComK9EIpFICIpruXZjHH/9o0u4cWsSS7E0cjlruVbTekxOxfDL927i7sA0njnbh1//xhn4vAaPNSQbrXQs13CV3jeKZZAiuuShsbTWC8XzLESpRXQq2G7edGv7KGG3fa/7+CRE7sMyjH98gOcMoDZDwebziVdjND2HO4kpzimnmI594Rb8zZ4XWPQkpznFrJDuYQkHedti0XO2kGlOMS60raetkPBMkSgk1JMju94b4rzyr7af4VgYL7vDyRnusIt5OhvHHw3/CleWRllAv7g4jO9PnMe3O59hR/hGkNOacs//fOQTFoxJAH+j+TjO1PWyEE+xLHTFoOeTtfOYTC+i1hvkxz0MPS/K7D5e28kiOv3d9fgYR5BsRkS/GaPIGldEp9fyhcaDqPH413y9yCEf0Dw8qUC56xQvRhMYFLPzpNDEAWVT//uBd3FxcYgnKkgQbfXX4usdT+FoTSdPNJCATufHLnjHZkGfInHo/Tle28XnuFaUDYnhJPZfj43xY2iy4dtdz/DrRZMH1L7oqVO2Nr23H87e4tUJ5HQfzyzixxMXOXf/ZJTqA0p2GtO2kTBdLbLOG+As9MdZJUB/EfX44dE0JM08UtbO1+uqGhG9iOHR0dQc4aKelJV+8Eg77tycwN3bU5iZjsHMP5k7kbPPvQa6+xpx+HgnjhzrwIGDLWjtiC4L+VslZSUwkr6LW4lLSNspRIwo9gWPYF/oKGo99fCoXszlJ9cV0cmJHtGj/LdZJ4OYucDRMKuihqEaJ+GYFwtiUxkgAdkeciMVyta5rXDMW26ci6SAzgVqgPEyCJzkjB2FEIk9m8IvhA1Y9wB7tAyOlGrDKfOglwbtTzaoqGpo2TBtZVnJJJFIJNUHCQiXr47hxz+/is8+H0Ii4bqrKJqlpsbPsS3hoA+G4bqsLNtBNmsiHs9gZjbB8S8Liymk0jmOgSHx/GtfPolw0CuXnq8LvZ7lGmc4bpFHieRhtFYonhMQmdpCDaJS9TlNCGcWjnkViucMFKyz6pH6/SIGYV4AuBZQCVEDUL2vAWot8IQZ0hTlQjnhFFcS1LzsBCaxtN1ft+q1jeJSqAgnube9hcKca0FC7HQmxpEqM+xAV/B03T58s+NpztemiI+Vx6DrNOV92w6Jd1l2K0+kF/HezE280XyMRd7NxKhQhMiN+ASO13TilebDeKHhIIvFxMrj0XMh9zX9aC03fYs/iqO1nfj++OccrXIzNoHR+nmcqutZMwee9lucaFg0UywMtwfqOB7Gp67vqKfz02ija6fqiu8bOf03Azn9SYz/dO4uT2bQcWi1wbc6zuKZhv0cI7PacUg8pzx0EtQjhn9VAb0YE0MRP5SpT5MTtL/vdr/AmfkN3jBPxqykVdRyYVd6r/9g8H3e/7mFezhS0+5O4Gxy5YGkfFg8seUK3kHDw5/Xx0Ep/L2uquxuz0gR/fGgD22kxo9jpzrRu78J/UfacPfmJOecz87EkYhlEI+nkUxkOSP94ez0laiagkDAi1DYh0hNAPUNITS11LAD/cjxDjS31HJB0SeBBO+h1C0kbZqh1dEfOoGn6l5Bd+AAjMKF0LdBdADNsNJjo55GLkxKwnzcWlrjBQrzjRmZPwJEEmXBmYGgKuPO9BNnqVUbggoiUKeGHMFlKPJStSgGFLUOgjoE5dA3OX9+CRDmHhU4LYj8x1yQqLxRJtUA3YSl6FAWRI5jXGQeukQikdyHhPBPzw/is/ODSCaz8Hg0HNjXjK7OerS11KKhPoRQiER0d5BIY49MNo9YLIPpmTjGxhdwb3AWc/NJDI3M4yc/v4ZD/a04uL8FgYAc7O/IpLawIXicJCeMJQ/ChTa1HijGCYj8J6Vd2S3i3J9XjCNuDOtaDxNJd1WgPVHiNqpx7KvieR5QQ0+8N1pxvxxRq5AbmmRSWqG8+uNJCKec9GJW+nokrSwGU7O4sjTCwnZXsJEd4UdrO1YVbN2cdB1n6/dxnjZHiZhpFqOHUjMI6J2bEtHp2VBhTBK6X2k6zIUx13ouVDhzPUg4Jpc6OdJH0vM8GUARLQu5JAvFq0HCM7mxKbqGxOF6XxjHajvZjf2wmLxdUCTLx/N3ONOdJkwoRueZhn14s+UYT4Ss544nV/xGrzdl6d+IjXMMDxWgPRhpw8tNh1DrCaxaLJT2SxMYZ+r6OCefJkvo9SJXOp1jl76Xa1tVDkrhq3uFeHKBSKwoNLyTVKWIXkRVVYQjfpx6qgcnTnVhaTGNkeE5TIwt8DY1scQFSHNZV0in5ZbFF52LQugqu87rGsJobq1BZ3c9evY1o7u3oaRFf+LmAse4EOQkP1rzNHqDBx8pCrrh81Wo6jRVmDaQddLI2Ku7URUlCKEfKuSi0+x5OWZrbMAegDAv7zkRncVM8wY7o/d2vMjDGIBGN6zHm2XcEHKCUE6zE4ei1WNPwRM3aYj8r9yloHseo3ztjLELkzWevRnlYl13az5IJBKJhKEYl5u3JxGLZ7g4aP+BFnzzK6dw6kQn6upCa2acc046Lcu/PYW3f3kdH396D+OTSxifWMQn5wZYfA8EaBWfZFVorFQ244RVMGdY9EYVg+wlElf6IaHZ+waPdUu6spvE8fxngP9vAMo6TnBnFjCvln6SRwlB0SjzvQ9KCT5blBFe4wly5GzesXBxaRjHo13sOiUBmdzma8V3bAS5sW/HJzimhaACkl3BhnWd1aTfkHObssgpQ5tEdMo0Jxd1b7CJBdrNsC/UzIUq1xLQNwuJvXWeMJ6q38ciLzm6SdS/l5xeVUSnewaJ1OcXBjCfc82Q5MQ+W7eP9audgM4pZqbw2fxddgIT5PZ+un4/Ip4nryFFd0kqJsqTBsJGq6eWVzTQe7WagF6EJkRIoN8XauE4IWp/tA8qfErtZDdiCws5O8lfVxOlA3ottAoxGxqqhpDhQda2sJAl7fLxkgroWS7ls8jbNke6+Cog837nz6BEqJqKuoYQbySqF6E+kW3ZyGTyyKTznGfIs5ReHYGgF/6Ap+zLKCnChaJXqDBOZ2A/ao26LQvoBN2AvBrNxmmwHBOmk18nPzAIxTgGQU7pMoluwrwN5D6G8H6pUDxpD3Q+qUFRRyr3HuDsfGXgioIER7W1rOKmsEegOBPAHhPRBWV2UjEi6kyLtWOf9gxKmUV0cqex6yi4d/zuhQlmkf+AVxlJZ55EIpHch0RwyjWnGJbWlhr8V//kLbS3R+H1rN+f5yhIAEcOtqKpIYxQ0Iv/9CefsLnn0uVRvPDsfnS2SxF9bej1LdeENhkUku4KLDigAASJpIiiRgHvK0D63wJ2onSrQCmahQq3O/MQGmWSe9eIsZyBY15ByaFjep8r2apOclh3BeoRNnyImRnOtf7/3XkbbzQfZRd3b7iJo0VIDKWjbUUvWMin2E1epMlbg6Du40KjGxHQvBz3QZAoPZFeQM7ZvIhHRU8pc70UkJv6+YYDeHvqSkFEn8Ot+DhnuxMPRNLQCMQ2cXFpiIuXUu44Cfkna7vXjH8pN3ROVKh0MDEDm2KGqFBksIGF9FJA8TUDiWkkTDe2yK950RaoYxf+ZjzHtUZgeaKGHPMLud0be5qx4hhIfoq4OQtnlWvS6ejXEDYaUQn4NJ2z0OeyaQwnFrGUy/B7vZXJIJ5UchwMxqmoaB4B3UDYs/7qj+1g14jo60GO82BBMC+6/+m9Iyf7dkCCN+WY04fbdZI/fmfQbXTKxhcURYdiPAVhXiqfc1UsuUUOSdijJWl7ojlRhzsBJ/+hFNEfRvFB0bsgaIJIlK+Yq7CGoRjHsaewZyAy35NZ6Mt4yiyi5wEnDmh7SdhwxQSRexewR3b6ZCQSiaSimF9IcoxLXTSI48c60dEeXY5u2SzR2gAO7G/GwQPNuHZjAlPTMWQzMpN7XRQvG4PKhwPhTEARLezQlUgejD2pheJ5DiL3C9cZXjIsrl2mai2cv/4oouBEv4lSo1AxUYpyKZFNxKMaeLbhAOdk/9noJyx8UjHMPxn5GG9PX+X887N1fRyxQrniPm3zOkjSzGA2d7/I/ffGz7EQvZlIlpSV4ziYohBHIjDFpGyWRm8YIb00Yl2gEE9CRVTpvCi65F5imr9/2BlPP7u8NILFXIrFf4os6Y9QxvfOuYtp0oLE6Zxj8hCfJijIAb5RlM1WRPqp7NLyioPB1Az+++t/zSsKNoJeI8q/zzvuxAqdY3Yto+kuIG0v4Ub8Pczlhgpu9AfpD79UMSJ6rdeP/TUNuB2bw1I+gxuLMzhe34Ke8ObH12nLxLXFaYymlrgddoej6Aw+2eqQUrDrVU93ooOqbJMPfKfOoZANVnCTP+5Ny52ZzMIRNmer6+p6FxajkIv+lygfDoQ9Bif3E6jGgb3QnCCcBYjcB4BDBUV3vqhBxYnoGq0CKeNN3h4A7EFAZPl4ewEhMhD2IJzcz93nLXEjq8q6VC3nTpJxe94bcPZm5nsQVLy21AWsJBKJpMqhwqB500Zz0IuONldA3+oKTE1TWUhvaa7B1esTXGCUCpBK1kHxA2pp3KCrwkuWRwH9IAApoktWwJ9vHxTva26kS0lFdAcgo5vn6VVFdHKhc7sU9wXkkqA2APp+KFpbyUR0MvhRdMrX2s+g0VeDD2Zu4E5ikmNUSMQmcZ1yqt+bucGFManoKGWat/qjG+6bnONp636UTtzM8LZVREFsXc5u3wQk9j9uIcRVs9N1H05FeziihTK7p7Ix3IiP4am6vgfibuj5UfHOtJ1bdnwfjrSvG2tSbigmJWXTih2XgO7hgqUlc8YLN/+ecu+Lx6PX6HEgp7NdAZnZ5cIWJlLWAjvSV3Oiiwqqm9boC+JUQxt+NHKT35N3Ju6h0R/Et/sCqNmEm5yig+7G5/Hvb53DYi7D7a8zVIP+6M5PEux+1bMCIMHbq/mQtpIc7UKNf6vQDCotn1nIz3CMi08LIKCt3dnjKQOtA4rew+5ddo2XAxKVyZVtfgnCOACFOru7FVq+5EzAyf4IcMgRvHsv0I+DAgNCbSo4hqjTUYaLuLPkFrRlNzoNePYA9phb1Ig607LNuag17sC6TAiR4gKuFRIpV3YETc5YQxDZ7wE2DRJlO5NIJJKV6LrKIjhtPt/j3xx0+nuv+/e6oe1Yxm21QJnNgosv0j2/HBO8Nk8ek9sYaC7D/iXVDa3sPglF63aLfJasFpYDYV5jc5ZC7uiHxUjq+3O0XmnHUmR2UoyjJTcikdOaBPI6Twjt/ihuxsfZaU3iOWVUDyZneLubmMLtxCT/ngTl03U9HN2x1nWQBFHKyC5Crux6bwjGOpnoq0HFRik3eysueBKI3QCaJ4dEchLkn6k/gAuLQywQU9775wuDOB3thVo4DDnlKcKF8tBJQKTsdypKuj/cgp2E3gfLuf8+6IpW8mgZWsHgFMYfYd2H7mAj/Lpny3n6FC/U7N15p3K5cIQD08luaUJop4j6/DhR34qucBQTqTgGYvP40fBNjmd5uqmDHekhwwv9oXSQrGVhKhPH9YUZfDA1iI+mhpGzLBbe+2sbcaBm5/PupYi+DXhVP2qMOqSsBOZyk0hZSXaTU7b5ZrGEicX8HGZzU8g7OdR5GlFjRDeePTdOQlg3AbNMIjpc8cXJ/RQqZcdRkdEdnCktJ4IEXPMGYJ6XLvQ1s/gDgNbkOvXLUnRVQFhU0PY8FF798PgrO6oB4STZ/SJy70thcyV0rSnnhJ2TLExa7AFo8EbFRHPvQnD25uMVfZFIJJLdTF1tEMGAh53jVGfpcSE3eyqdZ9GosT4Eb0FQl6yF4ka6qLWAQ87MUjv3HR4nCSdWCMvcvX1KydZRaEyrNbHwzKY0WhFbEmgFxBhgT0GINJSHooSENQKwiF5KVEDf54roZYBEYiqU+UbLMZyp68Xt+CSuLI3gVmIS05klzOTiiJtpFoipWCiJ6SQak5ge8fhXFUtptc9KsfZ4bSdO1/Wixgg8RnHPEIuzm6WYIVAqNEXDkZp2tPhqeUJhMZ/k/HjKSA8pbgFNinIZS8/z5AO9Nh3+OnaiU2HRneTh94GE3KLgXSpo/8U2QAVhqR21++u2PNFMjn+KDdqtUP0OEtGrQRcI6B70Rerw1a5D+MvBq5jOJHFhbgKT6QTuxuZZYG/2hziqSFMVXhhmCYez0+/G5vDZzCguzU9ypAvVBjjd0I4zDe1o8u/8qjEpom8DYaMWLb5ujGeGeJvOjqHV14mgHtnUUlByoC/mZ3E9fg4Ja4mXbtR5mtDopaVY66N4noJifu4WJCxXoThybWb+HMJ4yq1kzo6R3YWgJWDWDTcvuCzi8C6BboBaXyESokyvkz0Akf8YwvcNt4DubnVxkbhp3YHIfQRYN3b6bCqu2BOteilb90HEOUJH4ZxwtZgLtisRIgGRPw8n80dSQJdIJJI16O6qR0N9CEuxNCanlrgwKLnSt9IHob9x/z4GVVOwv68J4dDeiKZ7EhR4oKiNEBynUeqxjA2Yt9wij8KGQnV9JJKH4fH0Ze4blk68MiHsISjONKDeF4WoHZKRQ5S6Po0agUIi+jZEFVJe9jMN+3G2vg8JK4trS6Mc53JpcRgTmUX+2bn5e+y6/q8PfY0jXlbLOScndmCFe5wiYMi53RmsR7VBYnCdN4T94WbcTUxiPLOIkfQc58f3hZs5HoVc+9dj48vZ7RTjQk70nR7rGorG51eEssup+OlWi0SuiQIuGFuMz3Ez5Fv5vd5M/v3eQfAEBhlqq8GJTkS9Afz9w89wYdFPpkewmM9gLBXDH9+7xFtQ9yDi8cKn6yyiJ80clnJZFtOLULtoC0Tw7b5jONvUgUpgd1qGKwwSvPeHqPAmYIo8Plt8B9fi52CK+xlf60EC+pXYp/hg7sf8odEVA+3+HnT6+zb+Y60Xin7AzUArGw7nxInsDwHrFnYl9PzyH0HkyREsWRsNChWZpbiNciHSrhud3dm7t3AITU452R+4EzeSB1Hr3VUP5YJjgygGi67R1dFJeVwojsvJ/jngzOz0qUgkEknFcupEFwvpS0tpLgo6v5Dacp45Cej3BmcxMDjD8TDPP7sfDQ0776iqeGjlmUYD5zKJKSIO0D3fmSrP/iVVj6If4yzxktdjsqjW08SjUam8ojdWhufQu60eSnIWh3U/nq7fj3968Kv4F0d/DW+1HGdxnEQyyk6/Ghvl3PTVoKKbJDwXocfFrequ23O0phOdQVeXyVh5fDR3B0nTrXlFjv3rsfsrYY/WdrITfaehAp+0WoDEdIImQWL5FDKF3PZStJNGb2S5kGjWzmMis8QiveTRzHNrkxpiJaApCqJeP/756dfw9Z4jaPQ92OdJWXlMpZMYTixhJLGE+Wz6AQHdq+nYF6nH//nsW3iptRdh48FCvLtaRKc8b8cR/HUv4tcCaPf34mjkaXhUH2ZyE3h/7kf48/F/i4/nf447yavI2KllkX3JnMdo+h4L5z+d+lN8b+L38fH828ja5OwVOBQ+he5gP7zaxnEGCl3s9EMc61JeBET+PTcSgJag7RqozToQuZ+5oq0s7LgBGsBZe2VeRkUFbTN/DNjztOYRuwu3zTmZP3Oz0GlwJ3kQKsLEEzXlcmZYLKTDvMCFXXcrTu5DiOyPAfParp8skEgkkiehtaUGLzy7HyeOd2Jicgn/07/6JS5dGUUiuX6/kIY+tA0MzuIvvvc53nnvJoJBL9567QgOH2pFKFgZA8KKhibN9R43NrAsCAi639MqSolkFRTFB0XvB/TDJd0v5awL+8HJG2HddQuLlrpfZpyGovVu6+pKclCTU5mcpBTtcCDcipebDuFsnWsEJJF0PL3AjvTVoAgWysYucjc5halMuSJqt4dDkTZ0Bho4voQc3R/P3eaimnmbcqCXMJCcYVG5zR/lDPgag2qN7Sx0rjQZQuI/xWoQo+kFzr0vBRTkQg79iOFqW0v5NK4ujXAxWMl9KA7ara1YPWM2pXANaA1E8HuHzuL/9NSb+N3+MzgSbWYXOr335KqnawFFBImC8E6Pf6N9H/7ZiZfwf3/2y3iqqQMRw7vjqzKKlH0q0jRtzEzF8JPvX0RTcwQtbVG0tteivjHyRIV5qglN0TnD/IX6L3CG0UjmHmay40iYS5jKjCCoh1k4J2LmAovnw6k7LKzHrEV+XM7JcJZWm78XJ2ufR6uve9OZ6grNnBunC47WMjp3nXk4uV9CVeugqN9+YGlatcITP/n3IXLvAPbQTp9OFUBxLl1QtAYIkwaGZZopFSnAug6R+zHg+xoUbWcLrpQ8Bz3/PhwSN3kpp+xAPIxCxWtpdY0SLt8kgxOHk/s5VJ06+jvfgS0lQpgc8SUyfwmRP+d+niQSiUSyJh5D5/iVp052Y2EhhfMXhjgbvae7Hs1NNait8cPv83CxUOEIjm7J5S0kEhnMzacwNr6A2/emWXTv6qhDX28jxsYWWZCnx6+Hqio4erh9z4ybHkEJQNF7IcrlRKf7onkdMK9DUBFJyl+XSB6OqzQOQvGcgjA/L91+7UmA4lxEnjJY3Z9Zd3gFdOlQ3LhV4yCgNmEnoUKRzb4atKzIrCbxeC2xtMkX4VgPihIhd/JIag634uOcLU7RLtUIRd1QRAud/0R6gUVzinGh12AsM4+UlWXN50S0G02+muWIk52EhEvKoT8T7cVkZhGWnedM+3MLA1z0lN6fJxE36W+P1HSg2VeLgcQMTypcj43hTmIKB8ItHO8icSOeLac6V+F7NA3doVpEPT70RqJ4rqWbXeexfAYp0+TCtTR5RI8Le3yo8/rRGoygK1SLtmCExfZKEdC3RUTPZvIYvDeNv/6zz9DYFEF7Zx3aO+tx4HArXnrtMHcM13tBqBNqWw8ul6Qcwa3mEFZCcdF9oaNI2QkE4mGMpO+yYD6WebBASdpO8u9WQrWhQ3oNWn1dOFn7HPaFjiBsbD4ug3MEjcOA0V9+x6F123Vta81QvK/SR6ZqC40Kcp1bA3Ayf+Fmysss9A3hz6QSAbQut/hj2ZbGClfkzP41VK0NUJ7jnOzdUbz2AkT6D3mSQLa5NVB0KGoTBBWxteLlq/VAq098X4XgDPbdkVsrnDRALqfMn0LkP5AxLhKJRLIJzn0+iJGxBUzPuvecZCqHT88P4uqNcdRFg4jWBhDwe2GQiC4ELMtBNmciHs9gdi6BdCbPq3LJhZ7L27hzbwZXro3zOGejlbq6rqG7q2HviuhqAIrW7YqM5RrC0L3QvARhnoDifbFMB5FUNVo7FP0IhFIPCNf89sSIJLvOhbMARWt2f2TfA5w5lA6do1wUrQOKWvooRBJVc46FiO5j1zS5TtfSaEzHwmI+jfkV8S0UE7Iyb3sl5EzeF2rBsdpOzlOPmxmcXxhEgzeClxoPocVfw8LbascjUY5iRxJmhv2tnYF6Lt6501DGd0+wEf3hFi4iSoIxFRqdyCxgJDXPlzgSzilPvn5FlM1OU+sJ4PnGfnw0dxtT2SWeAPh47g46AnWcXR71BNfML8/aJk8OmI7NEwMP56jTv8h5T0L6UHIGI+l5XqHww4kL+ELLCfRHWtgJv9r7TA7mvGNhIZ/k49B50LYbcYQFkwtsVycKTcZ4/byRE50+lynLRNrMc9ugzzLFt1Bki6FppcnbLxNlF9FTySzGRuaRzZgYGpjljUTwE6e68fxLB6F61p9dW5xPYmT4wRtJU5PraCe3RzU1Gl3RcSb6EmekX4udw0DqOuLmEmejW47JRSRoIYN7M1D58YbqQUiLoM3fww70/aGj0NUtdqIVjTufqvcLcMw75XMHM+RwvAQnLaCSU1Q/CAWBqhPS7wvof1yIcSltLt1uh5c8ah0QZc2XtABy02Z/BFBVe89ZLjZZlQiKu4pB5D+HyPwJ5+9LNkBrhaK2QuDBScfSYfLqE5H/lAV76F1ljI/ZDgSEQys4bsHJfA8i+9dykkYikUg2yZ/+1Xl8dn6Qo1lWkk7neRsbX9zUflKpHG7fmeJts3g8Ov7e77yEvYoCLwTHuNUCDvXHyxPjJ8wrQO6XEBxLWFNVZi3JNq2C1LqhGCcg8u+WrsgtCeZUSJRd4jkIa9SNFCwVigeK96Wy1UcjcZvEYBJH94UoosHHQpiu3BfBSOgkAX0+n8Rn83dxZcnN/SbxnCJL1hI9SfQm1/rX289wXjjlZF+LjbErnQpbPl2/j13KJDqTfkKiHOkpJMhRzvhoep5F/oDuw292RStCRCco5/xgpA3vztzg870Tn4RJTvT0PMel0OtB2enk/t5yhHIxGkM4/D29VvR6FKHj0c8oj53eH570IMsmfy0Y4laB3tcTtV04Ge1Gdi7PovWN2Bh+fyCLRGeWVwxEjAAL6bQnOn7xvaBJE3Lb0/vzlbbTj6QpuDqZhmcb9vP7Re0kZeXw/fHzLJDHzYPoCTbBqxkcLcPBp4X9UyQOxb/cTkywiP5M/f5dK6LbglZtVKcT/WHoPeeaCYa3YnLOK0tET+UwPrrwgMvC7/cgWh+Cx7vx4T/51R38y//uBw/87Ju/+TT+1t97GdG6ypmd2wo9wX50BfaxE30geRNj2QHM56aQshIwnTw3Kq8aQNTTyAVEe4OH0Orr3Lp4vgKF3Lret4D0n7jLxkggKmfhx/wFOPH/C9TI/w3COASIJ1vms30UsvtZQP8TiPR/2OkTqkq4g6l/DJjnyn4sznSmpb5qFEI/6h6/KtpaISyVW53p1hNI/yHENrxmuwFy1Aits+zHcTJ/DlXf5zqQlO0rxlTySRqKBTI/h5P+A4jcL3f6jCQSiaSqcMcxZHLZ/mOzEFUl3ZqyQMIXR7ochbAXALG5CYst40xD5D+GyB2B4vs6hDCqpz8p2T4Dh/dVjl0slYhOLnRhT0AxTgHWMBcWLd04na4dfiieF6Co9SgHJPz+YPwCJkis1jwc79EZrEe9N8wCN8UwpO0cxlMLLIBPZ5e4cCAJ34dq2nlbWTz0YUgQfa3pKIZTc/jxxEU+DsV8DCR/gtqhIA5EWtiZbqg6C/UJM8tCLDm7k1YODd4wXmk8XFFFKmnCoS/UjBpPAAu5JO4lp9kxP5OLs1hNQnXUWNvZvRokKucdmwXytJ1H1sohZecxlJrFbM5dQUWvALn5KULGp3o4Xoey6gOaFz7NA4+qQaN3bJXrHt2HQoYPf3/f63yun83fQ9YxcTsxhf/u+l+hyRvh/Hp6bnTe5DwncZtc6ySik/hNcTBfbD0BY41oLpo4iOczmM8l8avZWyzA/2D8c7wzdQ1tgSg7+MNGgJ9r2spxoVl6r+kY9NyO13Zx7v5uxYEFe5eI6NVO2RWBdCqPqfGlB5wb4Ygfza2bjyPZjdCMX8SgZStncCB8jGeW6ILgxum7ES40S0dOdI/q5Vz1J0MDtEYowd+GSP3+NlSgz0NYg3AS/y2UwO9A8b5Q/mKTJYEy0N9zI1xyH+z0yVT5ksd9EOR6KOmSxNWgwq8/hyNyUAO/C1AntGqgimMxiPR/hJP9GU/eSDYJZe9zRqpR3klBewIi80MolL9erUu8nRg7z53sDwDrxk6fjUQikVQdr7zYj76e+wXuthNdUxEO7Y5IscfHgEL1nczLgF0mEZ2gsUvq96GR+Ug/5q50lEiKULa45xSPqWHPlmZVhLMIUHFRkXez+UtZp0aJQPGcBdS6ssk+bnwL29BYvL0Vn2BRmBzVRec3KRwUr0JOYRLQw+Rqjnbj7/a9xsLoRpAD+W90PY9GbwQ/nbyEq0uj7NxeMtO4tDjCAq3royYnuuB8cToeQb/z6avHxewUSiHv/am6Prw9eQUDyWk+b8q8bvHV4oWGg/BpWzNP/sehD/D+zE0u0uo60p3CCgCbXd1F6Fi/P/Auv6bFnGnXha7g1eYj+HLrKewLu9FCD0OPp5UB/3D/mxzj8ovpa+wwJ8g9TvE5WnzcdaLDdYpbjvs1uMlcc5pAoMkXys7/2eQlngihdkWTKDSBwk50SnVd3r+9nPJFr5mhVsZqg3JgO9Ud57KbKH9h0byFeIyWjN9X0QMBLzvR9zJ0wdKgw6/Rtg1LTnhGMQTV+0U4uQ8gzBggMmU8oCgsSbsGkf73UO1xwPcmFC7SV3kIYXERF85zz70DQdnxMsLlsVEUD4TWB0U/XHBrlBnKEs9/xEK64v8m4Hkeihqp4PgNitZYdONbcj/nryTWAtmdPrGqgaJ7hNYO0DXFulXmiKpP4WTDUJUQFM9JVFP+ubCogOiPIczPAHtYRrhIJBLJY/DMU304cbSME7YbdOGDgepb7lxSFMMVL7N/UeZ66znAugcn+T+zCQieM7ui5o6kNNCKRKE2QvG8CJF9uzSrIgQVF56CQv1N62ZpRXSq6eN5md3o5VpG82LjIfg1L64tjWI8s4CFXAoJK4OUnWORkzAUDWHDz7nkFGVyONKOo7WdOBBphVc1CgL4Ok9DUdit/lLTIbQH6nAzNo4b8THOEKdYEXIlkziv8Wp+D2eJkyOajtcfaWWH8lZc3duhAzX6Ijhb14dfTF3lTHmCIk0afGGcrOuGR92aTEfubRKaF/PJdR9H8SgkeK/G4WycJzrWO29D0dEbasK3Op7GwUg7bsbHedKEBG5yuZMTnkR8EulDuo83KqJKKxRO1nbzc1wPEtC5mGjnM5yFfy02ysVGyW0ey6eRsmmsrPDrQznp1C5afbXoCTXy+9wfbsNuxYG9a+Jcqp2yi+iWZSOdzj3gRKcYl9Ced1RsP4picK6v4n0Fgqp+U/XvckOCjXkRjkhCceageJ/nqA+Fik9WQjSCsNxldNbNwhLODzgHWQpNTw470ckVzvne5cmvfABnDiL/Ky7SA3sG8DzjupXLUETnSRA2ZR/ec2sH5Cjy5vNCh7lylhlWBeQ6oWW1xkmIsoroxbb1IRyQa4NGA4fd62mFTtIIhz4Do9zG6DMhcp8WBnolyu+USCSSPUZzE03MS3YKsh5B63Gj3Kx7pRUaHyEDkf8EULxuNrXnGSh6R8Xe8yXbCcWjhKB4vwiRP1+aVRFOwo1wEWQ+u13CMagOaM1QaDxEbblMUA56rRFAf7iV40hI6KT4FhJriyI6CafkRKZoFiog2RGoZxF5q5A7uc4TYhH3cE07pjJLiJlpZGy3thyJ7V4SVw0/P45c0+TsXi8upkhfuAnf7HiKxWhif7h1y27wrRDRAzhd14vf6X2ZXegEufdJ+CfHPbm+twK52snhT67tx+VQpA0Nm3itSCDvCzej2V/LkxSUPT+bjXOEC00IkIhOIjc9jt53itRp89fx+0FO8o0gId2NBWpgVzwVGaXoFsq5zzkmT7rQpAjF0FAkDu2/3R9Fo6+mrO9ZRWSiO9UtoudtG3Ezi5l0ErF8FlnbvU6EDA+6QlG0BsJVEaO2DSK6g0zmwTebCoL6fJW1rGZvwOUioHjfgGLdg7CnAeFmZJUXm52iwp5ksUv1fRnQj7iRH+QWZjFqG+EblQnBhVwmeOkciecswJa16OrewhU4j0FoHa4DdjtEYpFisVPY41DsUc4ABOdZ01JB385clDmmySws15yGsG6wa17kzxXqE0geG7UViuc0ROZ7hc9uGdsYidK5H8MBdd6+CegHKmcykC9rjjvwcmYgrDtcEJWLItsyIkgikUgkuyEXPQxFP873uPIbgahWzS8gnCWoNAktXgCoP6sEoWzgpJTsbhTFB8XzFI9vBMWw4ElXdufdyRraFxu5SrQqVa1lQ5Oid5d1AojETBJTadsOKEudxHTaSsmhSDtv24VH0znK5r/o/1JJ9vdy0yHetpNgQeymrdTQmJ0E8e1+XyoZp4oLi1qOg+lMAsOJRQzEF3A3Ns//Tpl5Fta7w7X4avdhNAdCoNK0RXK2hel0Akkrzz9t9IXQ4N/5wrFlH/3blo3swyK6rsLrqwzhoRwUi6jSkgvKtqKs86K4w/WPFQ2aQs1j9cIN5YYiVagoCkjUzn+4fe5EEuzzH8DJfwrF+xoU7xcAWppJhU54hrywnKscrwkLmRYgaIlSjp3KIvcenNzPAfOq24EpQeEWCFpjSseQjk+uBq/3QvG95ebwP/FrvAXsYYj0v+PJEdX3JuD7AhS1A4KXM1I708u2rNGdpKFkQNNtbxSbRMJm/jOI7A8LLpP1l9ptDjp/zf3scDTT3mtzilbPmaWK1gPBYnG+/I70zJ+xC04N/DZQyJlUQJPCZbp2bVgwlK5reXcCiSZHsz9yI5Ts0RIdhJ5XwD0GryiRKyYkEolEsjMonqehmBchrLvbcD8SgHkejjUIxXMeSuC7UPRDEFQjhfq45exLbroPIAr9v8LGY077/r/JNb3T57nboEkUpYajfrivRcL3E0IrxF3jw1zpVu9qnVxHgJKsJRLJLhHRqywTXXA+v8BMJoHvDV7HD4Zv4Mbi9COqRcLM4ZnmLve2vuJ2tZhL44cjN3Fhbpx/8fXuw/hK1yHoO5x9r2/HC2dbD75MJBxXg03/SaDmMp0dw2R2FIv5WeScLAvEAS2EJm8bWnydqPU0bJgBVi4Uz0tcEIVjECjaZVvJu7njtFRS64DieRaq9xXAOMIFUMrSLEm0pIxD8xyQ/wTCvAWIBC+dK0lRQqUGiu8bgDMJYV6RDuMiWhtU75dgZ/4KcOa3WegV7MJ10uNA5s+hGGegeJ+DYjzFS4J50qMs0CAmDtAKBxp8Ud55cXkmt7cSdY4VPxStF/C+ysLunm1zNAnn/zqQ+tcFobfcWABF8SSG+Nql+L7C2Zg7U3yskN1Kq2hy77nuPIeWuJfqdSDnXwRK8G9BcPTQ9RI4riQSiUQieUyMw4C+HyjG920HYhEi9y6vIKQIOcX7MhTPc4DeS35S7Bw0iZ4orHSch6B+Nk3200pbXm07CzX0XwIGuVPlCvBSo3pehJO/AFECEZ37cva/KmltJIUiLT1PlWx/EolkZyFzbrU50W0hsJjL4P/x+S/x6cwoFrLpLalBtNKl1uvHpblJLOUyaPQFcayuBb0RKpa8i0V0VVVheHTYGfOBiJd8fhsykneAjJ3CRGYYny9+gJncONJWAnmRZzc6+0YVHR7Vh4hRi07/fhyvfRotvi52pm8nCjkLKePPmYAg8WnbXazknMwDVpY7e3b+YyhqrTtrTgKn3gFFbQTUBtfpSee7XmwCuX9FGoKKgdKSOCraSB1JKmhqj7jOc+pkCvrdUiFLsUTPWW1hZ70a/F2I7I8hLOpM7VFB8yHIoUvFHxXfl9khy536bcUuiNeUb/mRW7BH/SsoahOgd0PhSKE2QGtyi+/QJI5aXCK01gQXtTUqmkIDF7e90XJfErHd9jZeGMTE3eK09JiSZ54rbpxI4Pd4QOeQKysf24OFSRVArYHqfRN29oeARQPq7Sj8RvE88xC5DyGsAQjtrwHjOBTa6H1R68oQ8+JwoVB3ou6eu6LBugvhTLqfK87TfLCI9xOjtUMN/A5f3xxhFyKwKJpJsn3Q9aa42iALQZ9xmoyj5d68Uc0Z+twXf+Z+FcXHLD/+/r8F5a/Sdapc2NNwlv4ZoIYAxVdYaVb4Wvi3Uvwe93/m/nyNx/Pj6OfkANzdJozKans0KZx7qN3db3+C292D7fH+zx9uq9QuU7wyqzwOYncC2078XwvX4BVtqNh+KIZhlXbHP3/gZ/e/Lrc97qfLtlcJhes5KpActttRuJ4Ry+MWMkcIexDI/pBjC0lIp3GLorUAarEvGdxkVGXhM8b7LlzLaWUhbzSmof4rfWZS7mozmjTgvmWyIJ5Tn4f+jv7evD+2YpGl+D31DeUKsrKg97uRkealwuv8pOOVUhYUbeEoF6jNpdunRCKpACd69YjoAsBcNoX/8cqvcG52DPNZ9xpX5w1gX6QOUW+AHeazhZ+vRkj34ERdK1RFRc6xMZJcwp3Y3O4X0TVNhddrILtCRM9l8ogv7b7CjSkrgcHULXy28A6GUjdZUHfWEGpncxOYy01hyZzD2bpX0OHvg08LbG+2oN7J4gjMaxDklt0RAS7nOmhJgGTX4w0ItQGKGoXgvPSQOxDnQYxR6JQWC/pRh8Na8bXQ+SShabmzuVAQl+jDWYaJG8pGNE5A9f96wd3skWOsh9sZiZz+b8HhnObUDjlZaaASA2zahiBIoLHqIJQotzXK2WTxnONeyKFObU13C1lBgeClsdTO6Ot9QcsV6N2NRXOeqKGtzNc3mmzyvAjF+wKg1HEBVai3ASe7NydqaNLN9wZEJlHCGJPNtKlFwFpyXUj2XYAie6gWwPJgut7NpORrGU0E+guCDN16aRmaUhB+bDeWhdtYsV2lIHjgTANlcpctcBFkFu+pcK5D2Zm0iihdvjbm/SIU75fcwlT6QQjtcymib1IQcUUN6uhSpNOD3wsWN8wHhY7lx5mr//1yHFnxazEuyv3Z/fgoakfFn9///fK/+WeFtlY2MhDmJ24Xk+/Zxa/udZW+ilV+rqzxc/fryp973MK+dL/l33mWf77aVxLdHvkdRzEU/1ZfrhlTtXCMGLU90207K9vYCnFNrNHeHmmjq7a91drXirZX/P1yWyt8/3BbLGuhcZOL2QuebFmtDVEbK/y7+DuOeFvx83XbYKE9rdneVmt7xXZ2//vltrccs1HFbW/bUaDoR6F6nmUDAd8jtxM6nk3bCIQVAMwrEGq9awTimJdgYRKG+pJ6IT+d7vdu8Ir7OV3Znyx8Jpav1fdFcPdzuHKiir5mCmMn+prf2BDEx5EiejlQ1BAU4wgEtUPzAioK/QCL/DxhLZFUCGTIydhUj09ekx6HtL2EHK84rg7i+SyuLEzi52N3WUAn8fxMYzuebe5CR7AGQcODmYvJdUV0qh3QEgij0R/kfVA++kiiBAWdK11EpyKiwZAPsRWieTyeweT4ktvnZ1cBqh4q6jaZHcHFpQ9xPX6e41wIr+pHQAuy+5wibEwnj7SVRM7JYD4/jYQVo+R0eBp8aPV1wVC3b7kdu7tpFt3/N1wBkESgUhUzeSxITKIliYtun2/V/OfCYLgooi8P6nYiq1fnAqmK9012AxPsTq6yZTblhgeR5ND1vAphLwD27Z0+JXfCxZ4AMLFKq1GXB7eCL5HFtrZisLOT+eNUWIsjkF6HQiItQW4oGrxhu53+FQDdQISXY4Mc87a7nLnckxiPCKcZXoorcM+ddKGsTK3FXfGg1kPQ4JoGW0oQgl2QJLIUXY2u8FVsX4IdaDQBmCgI6LTSYZZX07hiQTnFzwJqMxTPy1D9X4eiu8V8BK3cUJtkt3cVXGf3YGEVEt2TXNHDFcJzK0SQoiBS+Hnhd2K13z3wN9vwnpcct4C3Kwxt7va8ubbl1oEQLFJ6C9fqwtfCv5WV/6ZJtmW38f3HKo/sQ3ezlmlVUhUJmjzRRisKzZv3a76wWL6i/axoaw+2LxLpVvkd/3uFqF51iBX3a7qebu4vNkZb0Z7ut68H291qbe/BNvpI2yMzBq1g0nue/KnvEXii2jjFJhZBsS47At37U4BN2/BDbUhZnkhx+5HF7FZ6VNGUUexPVuP1XVJEoXGgcRSC3OgVU5tIY3FfodgjiaSCyDsZXI/9AjZf/yRbZTE/gZhZPWkHM5kkPp0e5QKihqpy5vlv9h3Hcy1dXDSWiHo+XXcfKhWY1XW0BSNclJSiYaYz2xTltg5lF9G9PgN1DSFMji8si+ZLiykMDcwgHksjHPZB0aq/4EXWyWAgeR034xdYQFehImJEOaqlxdfB39MyBHKrT2VGMZ0bR8yc56z0i4sfotXbhZAWQZ2XBnDbB7twqegiDcIyfwlYVJzPqvBBUYWsYtDaOKZEocKVhegGFlOkiL7qBIji+yoUexQiO7n9zqEtT+ZUqqObOsbHoHjfgOJxJ24IRWuDUMN7diym0IoH4zBnlAp7DLBu7KDLgQbWS65DHTcf/k3lQ0XIvC+5Arpx/P6PtXYImhRgMaBSBooVgjMNhwrOpv+oQu+fu4lCRBf1A9b4QG3mc7aa4KVG/0coWmNViei8QiX7Npzkv6xSwbuaoBtsMWrj0d9u9vr+SNuj2jHBfyRF9C2vpu2D4vsahHm5tBGNJYHe5eKElGQ3o1CcD63UU2oBsYCKQKmFQi50zTVBSCSVArnQP5j5DzArdpwtKSVzmSQuzZFpEaj3BfHFzgN4sbUHhrq1GGuqIdnkD8Kn6YibOc5G3/UieiDoRUtbFNcvj3KRUYKiXYYHZ/H+L67j9S8eY6d6tTORGeEt66ShQOUCol9q+S4OhU8ipNc88FgbFm4lLuOjuZ/hZsIV3a/FP0Ozr33bRXQXD9TA78FxYhCUr+e4jV2yHhpU/3cLAvqK95ezCqWIvuakg/cNjqEQuV/s9NlUaf53FErgb0LxvPCA0EO57ooSrg6RtoxwkU+KhuLJQNlBexwoJkj1fwegArwP/KLOjaihiC0qnCt5SNgtRlVIqo+i4FVJItxWJn0LESuSKoQmXantyfdvy6iN3BeilXki//H2FRmVSB7AcIV0z2mI3NuoBOhcFK1jO2QeiUQiWZNYPofBQvQK5Zq3B2qg0yT4FiERPWR4oasq8raFjL3zfaayX11DYR+6exs4ymSl/2J2Jo4//oMPMT66gKee3Ye+A82oqQ1whno1Mp0dxaJJ+bTg+JbXm7+FfaHDCOjhwnO/jyZ09AUPw3ZMLORnMJub5CgYykgnZ7pX3d5JBffsDBZOhLDgZP7cjVWRrI4SLBTbe4NFpQfeX+7ESxF9Nfh1oirxIu7GU5hXdvqUqgiKmKmDGv6voRhnoHDW5op2p7UV4lz2OGoUqvcNOOTOzPzJTp9NdaEEoHiehxr8u4B++JH7Fv2boi6E1g1Y8rMrkUgkkp2D7klCa4Aa+qew4/OAed2dDJNIthPqK2nd7krIihHRnyuMC6poVZVEItl1mI6NhOnelynT3K9TrZjHuC7RZZaldKpTVygDtNtF9JqaAPoPtbIjPZnMQjjus7ZMG9NTS/jglzdw++YkonVBBENeLkJKQnrx9R2482juz7XLI/iD//Vd+HybqXxePshh/83ffJq/X8zPIWnFoSkaR7ccDp9GRI/yvx+GGo9P86PZ14kD4WMspOedHJbMeSTMJXi9Ldv7RAovNi/78n0VKpVDzf6VW5BT8iAkmlOUhu/rABUz5KxLPCiiS0fRukV44HkaqkjBoSKc9pR0b26IykUeVf9vQPG85BarfHgWV2sAqHglX9L37uupKDoELWH1fsEt+pU/v6dfj01DRdEoZz/wtwH9EBQ1sPb1T+uCkCK6RCKRSCqgsDj0/dw/cqjvbV2rluA0yW5CrQOMQ4DWVai3tFP9To3PhfLQyVQikUgkO41S+KqpKuebPxZCIGWZsIXDUTAU67LTlP0MfH4DbR11OHK8A1cujiCduu8SoFmE6akYbzyRq6kwPPoDbnTTfDTkd2RwDlMTS483k1FCjhzrWBbRU3YCOTvNBUTrPE2o9TRAU9d+eWkmJaiH0RU4gM8W3mXhNWnFWERv2G4RffmkDCjGQT47lUJmsj8CqEifFKFcKDLD+zIU/28A+j4oyopJHJ4ScyAcymqVIvraKFDUZsD7KlRn3l31YE/K12xNKPezF4r3i1B83yisfHj0uqJQsUrqxHPUxhL2Mgplw1OWN31OnRSERcU+dz47rWJRW1wHuv+bHOHC+fJrZEIrWjOE3iXNfhKJRCLZefh+5YHifQ2qMwcnkwPsOzt9VpI9BhUL5jEiudGzPyhEbO3EifihGKcBtdUdF0gkEskOYqgagoYHWdtCPJ9Dztl68TaKA7eFwEQqjrRlIqR7UOP17X4RnYTuUNiPN750HLPTcYwMzcGyHn0BSYO0LAeWtXEUBgnrq4nr2006ff9c804WpjC5OGjU01hYcLC+yO9Rvaj3NEMtPC5rp5Gxd7hopuKDYhwG1FoosCBy7xdm1c09nkXdzJ10xf9rUD2n18i1pAxmUpeqMVd1G1FUV0gP/DZUJw4n9w5AxSBlDM5DaOxqUbxfgur/NhS9c4PJiXoIrZ4LWu51FHLmkxvdSUBk/gKw7sqM9LUEdBIf/N9gJ/rGj2+AonVC7PEVDxKJRCKpHBStBfB9GarIw8mkZG0nyfaj1nKcoE3jZi5+vQMrIpSwW6uLzCQSiUSywwR0Ay2BMOazaYwkFrGYy8B2HHalbyUSZiqdwGBiAWkrj55wHVoDtPp+Z9mWAPJA0INX3zqKk0/1oKExDFXdfRldtrB40xQdfjWwoYBO6IrBBUiLrj9LWLAqoSglOdK1Dmjhf+7GIlCuGgl6exKFOyWq7xtQSfT1nFn1UQI2hBMDxM5P7lQFigZFiUIN/e+g+r4KcAX5vdrGVkPlollq4G9ADfwWFL1nE39CtSfqt+PkqgCaVAhBDf5OIXqpTxZYeiRjP+JOCgZ/d3MCetHlrzUXlgnvvvu4RCKRSKoTRd/PK/YUWlX1cN0YiaTMKFSXyDgL0IQOxQztyLihFornFSi0KlUikUh2mDpfAIejTfz99cVpDMTmETdz7C7fDBTfMpNJ4WdjdzCdTsB0HLQHI9gX2Xm9Y9uqeFLyynd/50V85Vtn0NndgN2GI1z3MYnn6io56KtBj9VVfTmWxhY26L/KgM7JBzX4D6EGfg+KcQx7D8qWq4Ua+mdQAr/FES5r4wAiQe/iNp7fLkAJQgn8LtTA3wH0/p0+mwrBALQOqOH/BorvWxzhsilUykXf+ZtKZaEUJiJ+B4pxaqdPpkIwALUdavhfQPX/FqD1bumvFSVS+KxWZxFwiUQikexS9B7OR1eD/4T771JIl2zvqmUfVM/LgLbJfnupa9tQlCGbHKQpSSKR7DxtgQhebO6BpijI2Rb+cvAqfjR8Exl7cwkXk6kEfjZ6G//6xqcsvntUDQdrm3CqgQy+O8u2WPOKInFtNIDXv3QM3b0NuHZlFJcvjGB8ZA7pVB5OoeDonjM5P9DBo0gQVAb8npGbMwr4yI3eAuR+Uch6o3zhSjnRMi6JM45B8X8XCrnP2eW7TiFbcqCLuBTRH6uN1QK+t6BqUYjsjyCy7+zd6A1qZ1TgkfK8jaOAUrNqBvpaESYc5yIpoNx3T3tfdQuvZlsgsj/cu5FLSh1fz6iug2Kc5MHWute11VAjUPR+iPyn8nonkUgkkoqB7mdCa+MVaKpSAyfzJ4B1W8YFSrZnTCMMKN5XoeQ/grBHd6C+zXOugL7DNeMkEomECHu8OFrXjDc69uPjqRHci8/jP9z5HFcXpnC6sR2doRrE8q7mk3cszKSTuLY4jblsCndjc7g6P41ri1NYyKap8iCea+3G2aYORDx7IBN9JaqmoqW1FpEaP9o663DwcDvmZ+NYWEghlcgilzM569y26GUCzLyFyYlFLiS6ktb2KLp6GrgI6U5C5/AkuAK6Uvn51SSgKwEoah2E2gQn9zZgDxfE9N2GCmg9LGQq3legep7n5+4WL8ImnOh7VJx74jbWDCgvAkotoHZA5N8FrJFCxvxewAsY/VA8L3DxWi7wSJfnrXSESYCnXPRynmZVokAhV5DyrFt4VamByH9QKGi7VwbWGqAf5AKi1L7cWCrfJq5rq0ATO/pBCOlEl0gkEkkFFnkUWgcU3xeg0ve5X0KYFwBnZqdPTbLLUahfRKuW9f1uLR6xuE1HphjWdrniUlL1BLRa1HnbEdYbd/pUKpKUtYD5/Bh/rZbCom3BGvztA2eQsyxcnJ/A7aVZjma5sTSDBl8QI0m3lttcJoW3x+9y7Es8n8VkOsGPIwe6qig4UtuEr3YdwrG6FuhbyFQvFzuiQgcCXvT0NaG7t5ENzfF4BslEBpl0Hvm8BcskEV3wvz9+//YjInrf/ma89dUTCIV3dhYiGHr0+Hknj7n8FG4lLnE++kYkrRjHuBBpO4WJzDD8WnBTx48YUbT5u7EdKOTiJOei1g1VjULkP4GwbgL29C4RoigjuBYw9kPxvAjV+woUcgJvWihyIJyEzER/bAqOdJq80HrZUc1tzLxdGPhYu7h4aAsU/RBP2lDb21T++Rp5jEKhZZy+vevkXwe3fZ2ForVBaI2Fa9gdwHnw/rK70N3JFRLQqX15X4LC+fBPMHmrhtyMecUHcA0POW0jkUgkkspBoQlirQmK78twaBI91wyR/wywBgpjFnnfkpQBMr4oISjGCQjrBmBuk4hOUY76Pig61ZeSSKqXGk8z9oeeQ0dgL8YIb8xk9jbs2LtVI6IXi4u+1NqLhVwaIcOLS/MTmM4kcXHuwQLgS/kszs+OPfAzEs9rPT7sq6lnAf2llh4uVFoJ7KiVm2NeFKCmNsDbw+RzFibHH70B1TeGceR4B6J1lVc4I2XHcSP+OYZStzYlU5CAbjqu23YuN4XPrHdwJfbxpo51tOYs2vy/i21D0aFo9VCCfwfCcxYi+3OI3LsQ9lgVR5mQeO4vZMmdguL/jhvjsuXK5jZAIrp0oj8RHF2it0HRfg/COA0n9zOIHLmGpwCRrNI2thqG64qm4qG+191iWHovoHgff5f02tFkF2WjOw/ehCT3HWq80iT4j+FQnYfMD1lMh1gCRHoXDaypfQW4CCi5z1X/bxba15NPPCu0Dy4u2gjYmV0yiSqRSCSSXQcVGPe+AkGrp4xjcNL/GbAnAIecb7lddM+XVBI8njTPQ5hXtmXcwkV12fgls9Al1U1Qj6LR14u2wKGdPpWKxBJ5jOiXUU0oipu98Wu9x9AViuIno7fwq8khzGZTnJNuOjZsIZaLjZJwTk5zr6qjxuvDodomfKv3KF5q7UFA34mizauzs3koG2B4NPj8BgxD45iXasARNtJ2krfH+WAkrTySiG3q8QmLhOsdQj8CJdQPxfdVONkfQFDuoDNffSKnQpXMn4Ya+A67oDlW47FcmiSep6SIXjJ0wDgDVT8C+H4dTubP3SxrZ7b62tgjKIDWCcX3JlTfbwB6pyt6liLaiUR0rVWK6BuiQfG87LqFzItw0n8K5N4HsEsiqigT1vs6VP83Af1wYWBVyugwDxTjMAS5+NmNLpFIJBJJhaKSK/0b0LyvQ2R+BCf714B1BRBy1Z6kDLArvB+CVjiL+W04Xj8bwCSSasdQvNC2WqtpD6EqOgylcoTkrUIFQSkjneJdPpkZwbWFKQwlFjGfyyBnmzxSJbc6RcAcjjbhqcYOHI02w6frUCssAluv9JkLr8dAKOTD4iIJlJIHKMzY7EixvkIBFaF3Qw38FuB9BU7+HJD/GMK8vj2dhsdGAfReNzrD8xxHHfBSOPiWi+BuGeEAjhTRSwbPWtJ74YPQe6AGfw/wfQnCvFSIeblcENSrBXoufiiepwqZ58ehaB2AWucKkiUqAqQoEShaK8Tmil7vTQqvtQINAjVQjKehhXsg/L8GkXvPLZhpj1RZhBCt6goA5H6iXH3jBBS9mwuH0m2+VO3r/uE8AA3Y8uddF79EIpFIJBWKO2ahOjMR7ktqnjMQ1jWI3K/cmBeODax2g8bDfQJaZdvuRrDJGibb3t6EfgCK5xQE1RErJ2qbG9NHqwMlkirHUP3Qn2RV9i6HoqJ1tXpfH1VR4FE1NAfCeLNjP55v6UbOtmE5Nkd5E5qicpa6XzcQ1D38lf6u0qhoEZ3weHWEIpUvop+OvoiOQO+2HrPV14WdFzq9XBGcIiRUtQnCOAnFHoSwbgPmNQjrXmUU3OTOJGVPHwCMI1DIJaD1smuX896feHZLuHEQJKZLSlt0lDK+1Va3g0jvl3EMwhqGsO8C1h3+ngdAFVfoVuNz5oxzcolQLrXWBbC4We9Gi5SaohNdsikURQM4Sz4Ihd4rrR3C+wIE5aaaNwp1Hyq1ACnF9zS7MS20lJeKWWndULROQGuAQte8cqF4oOhHINTgjl/aJRKJRCLZEB6EaxxLCaqRwvf8fRDeNyBsuuffhKBikHzPr7T+5EZ9AXo+LVAoao3q7Kj0lQqq1wI01qn84f4uQ+GIFVpRK3LvlHWCRjGOuzGF0r0r2QUYqhe6Wr1O620R0at8kkEhIV3T4NECiFbxU6kSEb2MYkCJOBg+iX3BI9t6zIq5yHDH1ICid/ImnONQ7HEI4wZAIrozBdiz7tJ/3hbL7PIkB4YPoCKLVFRPI/GVcra7C2ITOc+jbv52yXAgWMSVilJZ25jWxu8lyGlrT/IkjWKPQNjjnJsuyJ1O7UvEACe5veIniZY0YFHrCoO05oKg2etO2pDYSZfccs6mqvVQPc/BESWadNT7S5KjXRVFyJQwoB7l64Mw5iGMO1Cs2xD2KBdQdttW4frFr+92rgQyXDcZt68oFFo5wwJAB6D3cNviCRp4ytu+VpwPDRBV369BOCQ4PA4KX5N5sF/tcCzYc65DX1K18OepwpaLbghdt2hlU+Bv7/SZSB4ThQoR6jL/dVtRCmI6beIEFDJiGHe5T8n3fGfGHbPYFFm2tP39yUege3ug0A9wN2o3oPpNvAKxkSNruA4KC+iNUHglmlJ917RdgqI2QFAfmsYB9nCZ+owUTXiKx94SyW7AUH3QZV963TiXitH/9jgVL6J7vQbC4coXcfxaUNbzKKCQO1GlfLYD3GkQJEBZQ1DYNUwuj2EIcqeLXGGjjmnxq11wcz+88Z4LGy1LJAepViigZxQ6mJ6CeB5wRSYSaFi8PMjuX0WtKeOzJie6LFK0XXBxQxIOWZimJpNkIZ1FT3amk6g+DUFiOmVecjszC5M35v3vl9uaKGzF7x9ub6u0ueV2R23Oxx1mzjrn89rnTtgoNYW/2abXhQZN3pehUWSM5AkKKNMESDPgfQlw0hB0zeK2dQewRjhKSPDKE8pTLbQtblP01Sq4jh5uV8sHWLFR2yABXyu0L7olGyvamLdwXaNBMrnMOgrXtT73Kw2qaQJgu18iOk8apIf+0bYfuxLh9kL587RJJNuIotGk7auA79WdPhWJpHon0bUW18Htfck1xLBJ425hzDLK4xg80p8s9iOtwtfifX/lvX9lf1J99GvBHe8aLIzCV33FV+oHUOQfrTIsTp6Tw5wE8yYolPXOk+qBHX4VJY+gkPGnwc1Gp4mZkrvRVTeKVD/E7UAi2Q0Yik+KxOugKRoM+fpUBFXhRA+HK9+JLlkNVyiijGba4H2+kONuQtjk6pyAsCfcpZP2BIQz7bo9RKbg6qaOaqYgrouCyFQUlkJuDAuJhtSpZAdGa8Ft3gmFs6a3U1xyn5cU0XcGRaX2cBAwDt7/oTAhnHkIyrdmp/oCIBYgnAWAN9dRLJbFT/qaL3zFikGMhwcoCjn+2K0cLQjmTQXHTxegdbjtcRsFc8k2Qe+9epgLaTJ8DctAWGMsrlPbch1r1L7muV3RpA5fv4rtiduU86BgzhMwflcIpwFwoW0tty9eydDqrqKhn0tnhkQikUgkjC0s2E5+OUeVoIJ0tD1ZHRAfhNYDW22DMF5ioVuFA1WkoNh03x9jkwb1IwW71BcBZwlC0PilaAiyCvd/q3DfL/QleQxD93ISxkk0p7jCYCF2hVbI0mqzWveezxEtdW5fQKHHVPyQXbIS8fBESqkxoHiecQ0fMspFskuQTvT1UaFX/esj+P8EHNroDi5W3sVdKMyX7uOUhV6JeehExd+RvRTnUgVOdMlWMHjJIbkqKFfX7WzaUJZdHCs7Hs6D/Q/+ID3kEF4WpVY4OHZk+eJKF7OkMnIiG6DQQEQ/uuwOvt/O7Ic6uIXvlwv2UizQag6ilW2u6B6mTRZu2jvQSoieQvyDDYVX0KzWroptiv5GrGhTeOg6VnSlFVY8LK98oHZVbGsSiUQikUiI4dQFfDD775CzE8u9uGM1X8RzDb9dKEz/+OJ8zJzEZ/N/jLncEDTVg57AGRypeQMRw418c+/zNG55eNXsQ31K7k+u7EsWz2u1sUxx1WPx/r/ie9m/rEIsCFrRQIVry5GJrniheF913egSyW4S0aXTetcWFi2SsS2MJpcwlFjAbCaFRD4H07FZMA/oBup9QXSFatEZqkWDP4hKpOJFdI/XqIpMdMkmKcwmKdz0SPTGLkGK5xUHF77dbe1MsuMUJvKUYoSURCKRSCSSbcV0soibM8ja8eWfZe3EE/fHU9Y87iY+xFDqc2TsONiH7tjwamGcqfuWuxpWItkAipgU5g1A3G+fpa2T0wTFOMFxfxLJriosKsdW62eiV+k9yBECNxan8dnMGC4vTGIiFUfKzCFr27Acm3/PAWeqCq+mI6h70OALYn9NPZ5u6sRTjR0ssD/ZSrM9JKIHgl509TbgxJnu5Z+1d9ZB16UzTyKRSCQSiUQiKRU5O4Wsk4IjLHhUH/xaDdQ9ElVGy4ozdgx5J8NTpV4tCJ8W3unTkmwjJMTPZO8hbS3BKTiIY+YU5nKDO31qkipC2IOAdfmhmjglQo1wQVGOM5UxP5JdgqZ4eOM6FZI1nejVlonuCIGcbeEX4/fwweQgLs1PYCSxhJS1cbFuj6rhysIUbizO4G5sHq+372N3uq7ufBup+CtvMOhF/6E2KN++P+vQ3lHHBUclEolEIpFIJBJJaZjNDWIycxOWyKPe04We4Bl4tL1SuFBgJH0Zi/lxnkBo8u1HZ+D4Tp+UZBuhdFaaQHr4pw5Ht0gkGyOoAK01AGHeKs8BKKrS80phNWRluDIlkieF7rkkEj9JHNdup9qc6EIIpMw8PpkZwR/cOo+rC1MsntM7TG7ziOGFXzdgqBpHudD913YEsraJhJlDxjIxnoqxa/12bBY5x8KXOg+iN0w1RHa2nVS8iO71Gew8p00ikUgkEolEIpGUHkfYGEqex6Wl73Moxr7Qc2gLHIEHu19Ep8Ee5WFfXfoxxtPXEDGacLT2C1JE32N4VD9qPW2cO0uRMSToBPQ6RD3tO31qkmrBnoKw7gHOdBl2rkNRm6F6nnGL1UokuwIFXjUAbY+sentcaFUgFc+uFkzHwVBiEf/DxfcxkJhH3rY4kiXqDaDFH8KB2ka0BsIIG17omsaT1RnLwmwmiYH4PEaSMcxn3cz00WQMf3j7Inyajpb9p3g/O0nFi+gSiUQikUgkEomkvOScJBLWLGdB+7S9lbVL0R1pe4lztk1ykkr2JDR5cijyKmazA5jJ3YNXDaIv9AwO1by+06cmqRKEeYmd6GVBawKMQ4BK5kLp2JXsHjxakJ3WkrVRoVVV4dWpdBzvTtzD7dgMbCEQ1A280taH7+4/hbNNHdCgQl0uvF2E/OiAXRDgfzJ6G//+1jkW0sdSS/hkehSHapvwfMv9qO+dQLZUiUQikUgkEsmmWMrPYCo7iPnc+Kq/92h+9ASPo9Zoqrrsxr3OXG4EKWsRexFyHU9lb/FXyd6FXH513i683vKPkXeyNMSHX69BUI/u9KlJKh6SfhwI8zKEPVSWIyhaNxTjNCBzoyVVen1t9PXCcnIP/Jxy0Jt8ffCpoR07t2qAEkzonmSovqroq0ylE/h0ZpQFdE1R8O2+4/hW71H01zQioLvjg7WmAoUq0Bepw6/3HkVHMIL//tJ7yw51ioWRIrpEIpFIJBKJpCqYzg7jevxDTGburfr7oF7DAnpYj8KQy82rivncENJ7WESfSFMW/IODe8negsQcQ/H9/9n7zxjJsvRKEDxm9kxrc609PLQWGalVZVYVS5EsFmWz2c1l9+x2N2Znd9A/BligF1jsYHaA3cUOMD3ATm9rTDfJbrKryGKxyJKZVakjMzIytHathWltz8TifM/Nwz3C3cPMw4W5+z1Zr8zDxBP33ffevec73/nQZN/ZCbrC7kOFvvkkz4vDQDm2BVuwApYDMFlPbsG6FRS2HizU/VLz76FSMYo2P4IJTs0nmUAK68EEk8kiz6gi8oua7cZFXM9jKBGRvw/6m/BCaw9OBNvEB/1poOe5Q7Oi2+MX//SfTNzHZ3MTmM+mMZGKY6ehSHQFBQUFBQUFBYWaMJ8fw0TmHiKF6VU/z5XSyJXTLMW37fumsFEYE7GF/CjSpf1Iolegl7OYyd6F/phCTkFBQaE2lFApfIZKmc/Gx4vTbgIs7TBpB8UTXUFhtxYPPeR9cad3Y1fDtKhEp+1edezWqMiXiojmM/L3YX8Lutz+mgj05dDMFgQdLiHf78XmMZtJIV7YeRW+ItEVFBQUFBQUFBSeCr1cQLQwh6RuKEsU9gZYVJM+4NHCJHKlJPYbSpUScqUE5gujKFX0nd4dBQWF3YZKBajkUMl/BJTmt2QTJu0ETNoherpsyfoVtgeRwgLSxRSKldUDLRaTBq+osv0bKrSZK2URKYSRX8fuw2Vxw6v54dL2ftHwvQYzs6XMDil6XdkFY8vy4l422V11E+hV0Lyq2ekWRXp52Tp3EopEV1BQUFBQUFBQeCri+jySxQj0PWh5wcE+VT1lKugrTJKtDtKrr6bFVFpOXcwygeHf9W6DBSyZ9l9dv6zNZDHWWef6ypUSypXiiukEJ91rFeeqHqOxbeOV7xUrBSzkh5EuRpaRyPy0hFK5ULM6WzNxgrSxdjG2aLSL2CIs7cNyz8zqOTDUWEbr1b695ce/1AaVihQUpQo/vyyAIEdfKdZ87Nwfkh/ydx3Hz+0b+1I99sf7XfW4jfXyP04pjdf6tmX0l5JsR1rSRHdVbWkdj9qnvGy/lu/L4var18Di76r7slFUr4fqdldec8Z+Gls2fKDr7V9rXYdsi1pQvUa5bHybvLPw+Nj+q6/TaPMnr4HqXsj/L91/zPLWs7d99WpYft+rXo/1Q44Jlmc+R7sNFehAOSx+6KhshdWACbCeBkiiK+xqvDf/c3wW+QhRPbzq50FrCK82v4U3W34Fbs1d9/qnshP4LxN/jLHMkDGeWQVn/M/hjZYv44TvTN3rV9hZ8P7qsLhhYc2hVYP+jXPvtVs0BGxOzBSTKPEZuDS+qQ/8VbHM5yfg0qzwWnfeKlKR6AoKCgoKCgoKCjX5oWeKO+9FuBXQK1mE8+OYyd7DfH4ICX0euXJKSGSSo3aLR4oLttoPodN1HCFbt/h71oNCOYPrsR/hYfIjZBZtU7xaMy42/Ta6nadgs9SnCruX+CU+Df/5ko83J1fPhb6DM8Fvrvp9kuU8roQ+g7g+g4Q+K6/xwqx4oadLjzIM8qU0HiY/xmTmVk3kIUm9X+v6Z2iy98KE+sjGMorIFGOyrfn8MGL6tBQ4ZXux/YWgNttgN7vg0gLwaM3S/s2OAwjaOuG0+GraDs+nccyzSBSMNuDfSX3+iYKq3Icrkb/A7fjPalp3u+Movtbx38Jistd17PlyGpHCOOZyg5IJwH3KFRPIlzNC8jIgwgK9DosPPq1V2rfdeRgtjoOw0CO5DtxLvId7yfcQzo/Canaiy3kSLzT9DrzWFvm8JP1jDuOZG5jNPUBSn0OunBS/eG7LZvHAb22Vdu9wHEOn69imTNh53FPZO1LY1ciGSMmx28xOuLWQHHOX6yQ6nMdhMzmWAjUMAWwETIO/k3gHV6M/qOn7IVsPjvrexAn/29go2M9uxH6ModQnch0Gbd046nsDJ/1fWbFfvPdUrwNmR+RLKSG26YHr0PzS39nXulzH4d0E/2BeYwv5EUxmb8r9L1VckPZn4T8GGurFheBv4Ijv9f1XiLUcRyX/S6CS3hqLBXM3TNoAYG7a/HUrbCs2EoCvF+Z9FsTaT6Bv/ItNvytjptVsE/mMbhQE7U4cCbRIgdGpdAKpYmFD6yH5PpqMIqXn0er0oNez888XRaIrKCgoKCgoKCg8FXP5UWTEh3FvwFBh5zGWuYbR1BUhF1PFiJBXhUpOVN4k8wwS1wrNZMNM9j4GUx+j1TGAXtd59LjOCCFZy6SYE55sKS5EKckqgusvlLMbKhBF//lYYUqsWKokOgmwtY6VBOUv5/6VbI8e4CTQjL9zT9iYcF+Nzw0/y6eBbUQSth4CiUpvtvlI6nNMZG+KEl6Iw3LWIPHY/igvqZAZzCCZTkLRbnFLEINBiDbHIfS5L0gxSKY6r378ZQynPsON2I8Wj726ZFYlDLltkpqG7+jTQbK3lnPI80DinMp/kqVUwCeFuCRhmpZ9YjuybQzFuKFYtpisQiqPZ67DnQwiZO/BYe+raHcckbaoBdwugyjsB5rJLu1JpX25UhYin+0zmr6CVDEsx812Yr+oXgNmk1WKz5LwHrZ+ihb7AE74v4ygrUvSy+sBj48kwIPkB0Lasx/z2uA+lsq6HLsRQLALoT+Wvopmex+O+N6Q882+rpnttKGuG8yw4PGxHWoB24pk9rOA7ciMh2hhSu45/Hd74fCS+nw4dRnD6c8kmMKAFm2VihV96bqUPlCwYT43KH3gYaoL/e4LOOB+AR5rqO79YV+bzN7CUOozLOSHJIjE93gvYfsbCtb670ls11oV/nsK5Sgq+V8AlewWrNwEk+05mCydMK2RZaSwe3Ax9DL63QeRLiaRL+dRKHMMMoLh9EPENsEqr9XRjl/t+G0k9Chysv6c2LuMZoZkGwq7GxRbdDqPi/hgtXs0BR+Ngg6XD6+29eHD6WHcic1hLBnFyWAb3HUoyemrTgL+WnhavNDPNXfKstNo+DtxMpFFIp6Bx+eEx+OAxbIx1YGCgoKCgoKCgsLGU/7nRIm+N0h0Ej1JfUGIq6HUp5jN3kemFF9V2SMmL+UidGSF6CPhGClMIFagmnsOh72vwan5luw8GhMVIeZIkjYCSKKSDBxMfiwEabgwviZpV7WbILGtl3LIIYFk8RHBSeKu1XFwXdKPayBRO565hp0EAxbzuWF8Ef2+qI7ZB6uZBKvBsJUpC5lKwj+F8JJynWQ3lcydzhN1K3/ZllTlk7CnApzZEbwOqEpefT8M+5NiKSdkMNtyITcipPdJ/1eF2K6VSOe5j+YncC/5vmQ7RAsTq7aBBBNKBem3VHKH82MSxDvme1Paw2bemJ8vCXiXxY+AtVO2YZDVBRSFPN6CgpCrgMfE+02+nMJw+nPJKpnK3Ja2XQ2P+kAayeK8tB8zKEjun/B/RQJKtWSMVIM4g6lP8CDxgQRElm/TCE7YJIDBwJMR4FvdEoKBFX7XbnZLgMdmdotKkgGf/YQKC2mXxlHRb1HbvwVbsMBkewkwt2/BuhW2G13OHrQ7OlHkmKJCuzQddrMTC/m5TSHRPZoXR70nFu3IdNnGVHZS/lYk+u4Hx5lOrb4syJ1Ck8OFF9p68Gp7P66Fp/Du5CC8NjsutnSjybF+8J+juWQhJ8VEfzJ+H8OJCLo9frzQ2ovD/mbsNBp5tI9yuYzx0TAuf/IQoSYP2joC8Adc8Pqd8PqccLuNwZrKWFFQUFBQUFBQ2BpQlZgtJRErzIlKeLeDRBLJ76HUJbF0iBWml5TIJIDsZg+cFq8QRLQmIYlECwYqNUmik8yianokfVkIQGLA+yK8WtOafuQ7D5MQbf3u5574hEpkknmR/PgyVbsGl9hHdNVEipFQI5lWq71HJD+GO/F3MJi6JCpsYx3cRx8cZo8QsmxLKvxJ5pEQINFKUo/ngedDwhtSnK0Cp8W/7ra57oCtc9Xj57qoiiY5XQXV7h5rE/zW2oirZvuBmmxs2M9o5TOa/kII0ap63eh3BhlpHLt10e/a8Kwn6cpgAfse+yNtVkh80yRUrD7cZ+oiL7kf2WIc87khTOfu42HqY7Fv4XlkWzosPAf2xf5fEbU+t89+Iup0lJAuRcXuxiBQnZKd8bTzz3XFCzOyvevRHyItBO6jNuC5d2osqMdr71GmBo+fdjw8Zm7fb20TZ/CNgAp2Bl0YgKCNE5Wa+uLCTA4hqUWlvnXFy7gtWgqNp6/j88hfYCE3LP2bWS289/AeZATlTNLHae/D9mefkd9XcpjO3pV/MwvioPcl2EzOddu/GoyazNzAzdiPhUBnW7Kf2S1eaVOP1iT3CWbdsO1JsPNeJ5ZHy84V4bO2ocVxAAFrh1gqsd+0OQ5LX9hXKM8DxVtAZaUl1ObACljaYLKegMkc2IL1K+wEWLvEYrHADsP+y2v1QTNvXvCJz03WKOE6nXAhXUzDuQF/dQWFZ4HDouGAN4TvDJxCNJ/F5fkJZEs65rNpHPQ1wWO1wWbRoLE+i4nPOo7pysiXSmL9MpGK4/L8OH48dk/Wd765C73eABJ6XlTpT0PA7kDQvjXFcxt1pC/IZXXcuTGOP/n3H6BcKiPU7MGhI+04fqobp8/34fS5vp3eRQUFBQUFBQWFPY1iuYCZ7BByQvqtrkrcTaCidzh9GZ9HvicKVwMmIS9JJHU4j6HTcQwuLSSEm0Gah0VBPJG5LgQ8SS0Su5HCJN6f/3dCOB7wvACXJdCQRfW4T832Xvxq1//lic90Ob/38N78v10KCtgsTnS7zuDl5r8Lt1YbeUMl6tPUsNXCkXcS70pbVgl0KmBJWtOnu9VxSMhrknkkG9j/GMShBQ4V0PP5ESF8C5WsEIf0zG529D/l+M044n0VA56LT3xGC5+h5Kd4b/7fLL3nsTbjmO9LeC707ZqOnYQ/gy5PA8lp7m+H86iQoOJ3bXbArTWhxd4vXu8kJ41jt6IsfS8manHav9AOhMRvFQxC0MaGi9faXHdw7Frsh3INkCi1mgwP8h7XabQ5D8GrtUAzO4TEZf+fyN6SfSYJXlWO8xqg1zoVyDyupxH5PJfMhqCtDkn4KphRELB1SJp6l+uUcR3BLCQx7Y+ms/eE9GUQi78n2WwUsq0fbO9u12lZVsIg+G/H38Un4T95wuJoMyGZPfkhXI58FzPZu9L/nZaAWNYws6DF0S8BJbYBC94ywDORuY2Z3F0JIhkFSMsSAPws8l20O49Cs9rXvf4YjGIQ5Er0+5jJPVgi0Hnd9rnO42Tgq+hwHJWsmuV2V7R9uRv/BQaTl5AtP6qJwXN1IfTr4lW/b8H7GVXohctbs36TGybby4A5BDRsgFZBQUHhSWSKOsL5DNqdXhwPtmIhlxY1+kfTo2h3eTHgD4ki3aPZhEjXhUDXsZBNYyQZxXQmiXSxIAQ71ec+mx13Y3O4sjAphUbl/rsOqIB/s4tZipuPhr4bz0zHMDkRFQKdiIZTuPLpEAYfzCIezygSXUFBQUFBQUFhi6FX8pjI3BP15F7ASPpzPEh+KGR4FSxYeMz3Fk4HvmakD9d9AAEAAElEQVR4azOFXshw0xIBddRXEsXyF5Hv40HqI7HDIB1GUp7EFMmoQ96X6y6suV3gftnNT6YBm005Q/28olCjySioanav+ptnIQ/1ckYUuLQyke1Dg8/agq92/J/FZ5tKVpKHTDU1tNgGWSWWLmLrQpXSPMbTN8W3u0UU0E8H1c1cHkfBnH+CAH9kVbH5adNuS1AsUNin2pxH0Oc+L+SpeOuLvtq8IhDD7x3xvSbFJq9Ff4i7iXeFvJbPUBbP8Jnc/bpJdILkNNuU6md6/L/Y/HekMJkowR/r//QjH09fEwKcSvLlQQjay9BuxbDVWW97tzGWubp47VRhwkHPSzgd+LoU7TWuvcW+uHjeT/nzmM7ewcfhP8Fs9qEo07cEVMSZt+f6ZbvxfkL4bO24EPw2DnpelOK5bIOlVOtKBQfwgti2MGBxJfKXopYnmKVQLczKa5W/XQu0cRmR2g/jct0QVLyzrsNb7f9Y1OSy3WVgcKPHdVb6LAMbn0b+bOkzbrMp3bu/SXToqBTHUNGvb83qzT6Y7F8FTI3jc6ygoKBQC345NYT/66esQ1NCoVSCTuJbnkVFjKdYTD4uIx550vF5J8/7qo0dVekGSc7Xh/EFIdaXxoQ1wGW17U8SfW4mjrnZRxFvtmOxWEZTsxd9B1qUjYuCgoKCgoKCwhaDxQcnsvelANZuBwvo0UqDBUKrVhokLc8Ev4mjvjcXycxVvJ1lfF+B19qCc6FfE4X6rfjPlgqERvITQg76be3iD92IWEshb6o8Iksf+8QgsjdxwE1lNZXPJPSqFjpUvlLF32Q31LerFgddtgs8D5rVBqfPL6m/PF+L07CNHb/8duVnxjube+xV2CxuKYTKvsbAC1XnhoXLGnWfTI8sYw57XxH1PhXoVVCpTYX+RsBzQG9xktjng78uSniqyZ847sV/UiVOWxEGoObyg4sfVmQfWCR2LRK9eq2xiC9V0MuLsA54nscx/5dEnf+EFcjidrlPna6TOF/+Nq6b/gYTmRvYfBgbq6UvbQ4MgxWS0y82/Z4EU5gJ8ERtBRNdsdkGIRz3vSWqdPrXM3ugek0xQ4BFZtcj0QuljGR/VAl0ghkEx/1vwWHmdaetOPZqH7BAQ8DWJefen3xfAiAMbPDex32hBQ4J/EbMwNlqVIrDqBTvA5XaCjDXBwdMlm6YbBcAsepRUFBQ2D0olFn/ZHULSMmlKtdTiL6CUqm+miWlRdJ+35HoVJ5HFp6sht7eGcDAYVVcQ0FBQUFBQUFhK0HVb66UxmxuRKwYdjtoibGQH17yFqbykvYt9Mpek0BfBEkiIZSsHRjwvCCFHW/Ffyqfie1B5jaa7f1CJG4fEbe7QNUzSTf2qypIkJI8tJrsaxPJT/i92qDR4mAXgiQp/ea51AOb2YE2xxFEXVMYTH26pMeimrlKqG4E7Y6jOOB5XixxnmbHQjsaBolohfKIRAdSegQxfWbtH1YMsn8hN7psXw0LpQHPS0Kg2y1re/ZSmc5t97rPyfVL1TutXXY7eMxU+FMNTj/y9exYSHLT8qjffUECFo9I9LL8m0VG1wN91JnN8MimxiR9kLYstE1a755FuypmOvAeSfK8WuiUvvopfQF2+/70W67QC714W+5smw5LK0zWczCZ67tP7AUwuPR59BJG0oOwm23ocvbilP8cbGbDQ3w5rkQvYTLLYE4WDosTPc5+HPedgt3y5LOc65zKjsv6Ox3duBh6ec19SBWTmMtNYzbPIGUE+XJO+r3VbIXb4kbI1ow2RydaHe1PvX4UFPYj2l1efLX78I5t/6C/aX+S6LRsScRWRnYtmhktrT50dtVXhV5BQUFBQUFBQaE+sOheTJ8TX2YSoLsdhpXEIxsXkoYk8UL2nnUJ9McJPZJJJL4eJj8Wn2EiVpiUQo1U6rqk0KXC46DqX5Tjy8hyFq2kkpoFJGlpUguRvl/h1oJS7JWKbdoIVa9R/k3LleXtWgtopdPlOilEeq2FSekXzwKSy8HMgqo1yWqgcpmWLyRgjWKwJIUtCNl6hJR3WWqb13m0EFrsBxG0dSOb3d0kOkk3qtCpLud5fVo9gSoYSKGH/iNUxBqJtlvrgRZAJLyrASxuj5kQLo1t/3QCkNYu9OsXq6VF6CzKW4pj66iKxkWlHENFvy1q9M2HaVGFTpJ3f5KzJNA/Dv9SbMaO+07jiPfEChLdqK8BXI1dxu3EdWRKabgtHpz0n8WA5/AKEr2a+XI9/jluxa/BYXbgQvDFJ0h0ow5AUYj24fRDjGZYeHkS0UJESPpSpSQkukfzotnWgi5XLwbcR3DAfRA+a0DIdAUFBQMsKvoHRy5gp9DrCexPEj2dyiOdXum/6XbbEWrywB/YmkqrCgoKCgoKCgoKBjKlBOZyo7ueQCfBSBKJJHemGF+hhO1xn4GzTtLbbvEgYOuUYopGgciyKD1pVUJrF5dLkeirgSQDLXHsZpd4tFfATIcUxjPXxWOZhCoJRRJ2+9Ee4mkwiE+XBGloy2EYgpREFUxrFssKX/ung1Y6TbZe8aSvFbTuoCLaIPcqS4GQ9WomcC/n8wwwPfIyZzZBt/Ok4cVdB/nPAqRN9h5MZW9hN4N9nNdCu+OwqMxrhUdrlnOwvG3FHmkxOLHePbBQyS0RiszCYeCkVvKe54iK9OWK20qFfW/3ZyhtBBX9LiBWLmsHjzYM2rdo/TBZHy9+uz/APua3BuDV/IgWwljIzwuBTZK7+lxgPy6UC4gUFpArGfce/nsmNyVE+PLvssvzvbgeEzI8ZGtCk/3Jex4t6/j79xZ+hpvxq6JG10yakPcujdyTabEmB4v9hvEgdRe3HdfxpZZfwUnfWQRtzCZRQWAFBaLN5ZVlL6KhSfRCoYh8fmVl9FCTFz6/ItAVFBQUFBQUFLYamWJCrFx2O0igxwrTohIvwyCbSCCRkKIvMMmhekHyjwSY4a9uBBloMREtjKPbdXLTj2EvQCwptJAUb43rc8iWmOFAcmMGP5355zjhe1u86WmJo8H6yKdaEeqPkZmOFSQ2CSOSO7WqyasggU4f7XoU7CRdxU7HZJXCltXri5ZGa4H7x+uvqp6v2tq0OQ9LUKAesP/wmt3toBc+7Z/Ei7yO/q2ZrVL0lr+rEucMqCy3SFqbmnx0nvl9Bl+qit6n7QMDqXq5sMLPXvrCBu6duxnV9qoUfolKaXRrNmIZgEk7AZj3b0HRgDUIn9WH+fyMEOXF8kpOSILi+RlkS7ynVESxzsyI+fwc8qUcytYyw0TyXQYY43oU2WJG+rHL4kGzvfWJ8zqdm8IPp7+Lu8mbi6pzG1rtbTjgPoQmG+vxmZDUkxjPjmIiMyLqd5Lufzvzl/L9V5reXNVGRkFBYW+hoUn0UrEsy3K4PXY4nPUNEBUUFBQUFBQUFDamRN8bJLqOiD6xzA/Y8PmlqpWKzI2AlhosJCrk0yKvRFV1rLCON7SC4FTgV8TTmUVejQBERTydbyd+jpH0FVH497rOodd9Fn5ru5C2ClWQ7NwctSPJ6HpJ7Oo+GNeNQeTzHFbJxdVRQbI4v6KoJX/v1VrrPrckn+vNHGlEsN19Wu0ZAI+3/nJFOInB5eT2amDQw29tk0AGvZ0ZvKIVFWs7MDDxNNsQvZRDtDCxFDAkGMxhPYP9hSJQmkKlcBkoPbIG20yYrKdgsp7HfoafJLrml35drOiI6hF4rD7YTUbQhsryyeyYqM/bHB3Se+lfTnJ9Lj8jSnaXZmRskOCey88KyU5QVd5ka16xPdq2XI19hoepe/J9rvOl0Os46T8Hj8Vr+J6bTPIZSfrRzCDenfsJxrMjSOoJXI9fgdfqx8XgSzvQWgoKCtuJhibRLRazeKAX9WUPa6sFmqb8phQUFBQUFBQUthKFUg5JPYqEvoDdDirRsvR1X2Z5QCWsWwtsOP3aYrLBaQmsILOoxs0ts6xQWB2t9oM4Hfi6kHBj6atiR0Fyjm2XL6WFYCdhN5j6RDzAafNCH27+TRXuXoJeziNTjIoVEF/pMU2ymfZALOZLUoiEJ5XDXNg2CX12BZm5UZCQ3kh7LuYHPPbu+iQ6zyuPZWkdJrMUtuR1WO91J776MG9KG+wU2O7MZtkI2PIrChmuz58LrBanFBGNFqaWgokpPYzh1Gc44f/yusEMnrtoYVJ87av3UNYvYCYPC6LuK1RSKOd+KEQ6sFIdvSkwd8BkPQ5YurGfIXYuVsPTmPc9qtHb7B2wL2Y+6GVdCorSgqXV3i5FRWnrkizSgm4aPa4+uPCIRKdqnb+hYp1K9ID1UfCH6x/LDONWnM+inNi3vBx6A+cCF9Fkb33C67yslUVxXq5U8L3JP0WqmMBEZhSDqXs44T0Np8WlsqcUFLYAvOYYsLeYd9Y2qaFJdLtDg91uRVF/lB5YKpVRLu/eAZOCgoKCgoKCwm5AqhiVoqJV9dZuBv2ASQQt93anEpaF9TZauK3qT7389ySnlltWKKwOu8WNXtdZUfP7tDZM5+5gIT+26PNdFoVsPp/CQn4EM7n7sjTb+tHs6EeLvR/N9gNwWDw1+zk3EsTHvFIW0jycH5WiqiTFqQim3RCzGUigl8oFUWCStGS/JdFDD2pRfdfCmtYAtr9li6eDYjUjvu20Alk+hzPJ9usuhmrilasJ+V61k9mNYFFXEtHbBYfZgwOe5zGcvoxCISPngn3uXvI9UZO3O48s+tOvvKZYMHYqexsPUx8hU4ouvd9k6xE7K57D/YIKLYuK46jkfgKUt6awrcl6DibtCEzLfO/3I3xWP3yaT0hv3v8W8nMouB9xQrw3GiR6QYp60uecBHq8GMNsfgrZ0qOsF6rW56lELxeE4PZS0b7MdiWmx0TVTiU7PdC7nb1SyHQ1Ar16D/JqPhzxHhfFeiGTQ7qUwlxuRuxkel3929BCCgqNjflsCpPpBE6H2mE2mZ45sJTS87gTpV1TEa91HMBOoqFJdJfbAbfHIQVGq8hldeTz6xdOUVBQUFBQUFBQeDbE9XlEClTb7X4YKeEFsiDL3jWLh/QKRWe9hhaPqWirPsPbA6Os5G6FU/Ojz30BTfZ+UaPT2oXqcxJ7VKRXPbZzpQSmswnMZO/BmfKL33y/+6Ko0/22DiHTdwvKDOaU00Kej2euyTEv5EaQKydX7V8kNEkimcSH3AqT2S59jEU8N0OFbfhxb7Wiy/Br57EvB6874/qrf/vcZ/52V5PoJuP4twskuzucJ9DtPI1COScZDeyLk5mbsh9JfU4yPWxU0co5qUiWRKQwLmr1scy1pXW5LAH0uM+iy3UK+wrlBVQKl4DiXcPWZVPB55AdJttLgKUP+x1UltO+ha8kysOFeeTL+Uf30VIes7lpUZf7rX60O7qk2OdQikHXaSkgWkWpXBKCm4IAKtD9rAOx7LlPX3PavZCYd5idOOQ5JiT5agT6ciLdbnai09ElynfuW7qYwkxuUpHoCgoAhhIR/OXwLeQPFHHY3wyfzQFtAwpyqs8XsmnciEzjb8buwWnRFIm+HoIhN4IhD+ZmHkV60+kcctnCyorLCgoKCgoKCgoKmwaSs0Ki56exFyAl9UgWrhg7Gl7ONXkh1EhiczsbJeU3su3dTKITJIl91hac9H9FCopOZW9iOHUZE5mbiOlTKJV1IUoN9XVZlLD3kx9gMPUpjnhfxQn/V9DtOrVYaLOxwblLYZG0/Hjhj0VlXw24GP3TKkEZEtssiMeCjcx0IPmpmexSUJJWJiTcGVAoLPMX3yi2r7+unvCx8d5rArac/N9qPLsyr66tLZJ+zzX9pmTLjKQ/lz7E62sodQkjqc9EkU57Fl5PtN5J6HNiL8QsgsU9hmZ2oN/zHA56XpLaBfsFlUoRleJDlHN/RVp2C7ZggcnSB5PtNEyWjXnl7yWwr7ktblGY0698Ib8g1i0Ei4wminEkijF5Lng1vyjCqUCnan0uNysFR6t1GowipLMolHVRrXNZDhL0LDxKcJxAUp62LtX31gKJfFq/VLM3cuUcYnpki1pEQWF3YSqdwPeGbuCLhUn8N6dewXMt3Wh2umE115ZByOu3yKy9fA4/nbiPP31wFfdi8/hy9yHsNBqaRO/oCspy7/bk0nsLc0lEwimxdVHe6AoKCgoKCgoKWwOS6NE9UiSTykpatyxXvXLybSh6Nwb+viiT+kdrIAG6XRYN26t633pQad3lPI12x1GxNSHJTAXsaPoKksWFFcdKUu9B8kNRrVNRy0KljQ72FyrPLy38Jzk2WpxU4bD40ek8hm7XGTTb+yWwYDe7hZwRklvIVoPunszcwruFf7HYd3dDEIWKa5vYl1QLkRLc96rFi6nO4r5iEFPeO31/+2BCyNaF55t+G24tKAEpXkMEycdUMYJ0MSrkPvnHavDK+KVJPPTPBL6B4/63ELTtM8/u0jBQ+AQoPnim8M+aMLkA57cBc+vmr3uXwq15ELI1i21LpDC/RKJnShnM5qakj9KexaN5EbSG0GQzgg/pUlJ8yvl9Bh+pSo/rMSHT6bXOZTmSehyZYlr+Jvn+g6nv4m/N339qgJH3MKrkaRNDcP30ZVdQUDBQKJfwMB7Gf3/5Z/iNAyfx7QMncTLUjlqgl0u4H1/Av73zGT6YHkY0z6BvY9h6NzSJ3t3bhL6BZmjWR8VFdb2IudkEpiai6O1fWVVZQUFhe2AUtkogUVxAQg8jqUeQKSXEbzZXykj6p/h4LhbCIoyJoFkGM1aTAzazAw6LGy7NB7fFD48WQsDWAq8WEiWWyjTZelDJQc9jFg1M6BEkZfIUQ7aUEmUSP+crzyHPB9N9tcXFbnHBafHCrfnhkvNHZQcLTPn3XNG39SLk+XJGiEb6Rqc5CC8lkCkmkC2nhVwj6UMvRvrYGunnVBhaFwuKucV/VK4BzY+AtRUBWyvsZteGCx3uZXCSki0l5X7DfpsqxpBZLELI88CFfdYowkfv4MqiutNieOiaNFFz8t5jszhhNzukqB3vP2x/3nu81pD4ZO/X+0/Vq5n9eCE/gdncqKix9gJ4/ZEAWn5tGRPehBzzRsCijyR7l9MpvE8aPulbC6PgJEnE3UCiPh3Va06eMbDCaraLTUvI1iMK9dncA4xlrmIqc1ssKAg+n2ZzD2E3e9Bk7xN7l0b2SOcxjKQ+F3uMKoFOYrnPcwFHvW+g1XFQngmiPKe6cY17EYM0xvuPCOlGBvfVVOF+2xf9jY1jrxaSpTVIPeeN1ysJ9Ko6WqHOcwFNFOSHva+KrcvN+E/kbBj2QZpYXtGHn3/z2nJbQmKb1GI/gA7nMTTZe0StXm9B2F0NBqzyl1DOvbMFNi6EDSZLN8yOtwBzcMN1OvYa3PIMaJaxSULn/CSLUrmIbCktJDr7bZOtGS7NDafmEqsWq9kqFi+0dqFHOouIsigpi4sSvlVIdFqx6ItBWm4rxyyfDQwLjNy2rchSaExQqT+UuosHqdtI6QmYzRY5X/2uwzjuOyf+8vt1PK0AsW/p8wYxmowinMvgB6N3MJGO46vdR/D13qNwWNbuH9PpBN6fHsZfj97BrcgsYoWsDHfor/5q+87bJTX008/nd+LAwTYcPtKBO7cMNTojjqND87h1bUyR6AoK24BqKhzJcioSo/os4oV5JKUAVkpIrZyQWFkhufRKXiY3VLSQOKwWcTMG55w+WWAxk8yywWq2yWSRpCEJdZKyThYKo1JwG5+5rfY+9LiOosnetS3bY3vN5IZxP/lZTd8nOX3C94oQ1c9KECR1FgqcFYsGns+YPr90DlnMLFc2giBCRIrKsbioQiKJbln0Z9UWzx2DIU4h1HkOXUKqB+C1BoWQ9FubEbC1Gee0gYmNWkEPRrZNuDAp7UfynIEHIXOLCbkGSOQWSlnjOlhsQykGJxNUpm6z/SzSliQUjP7vFFJ3icylX6O1BU32TjTZOqWd90L71QPeS9imcX1O7j2PSPOE3HfypYz0VaaiS9BOCvCRVDSK7hltbtxGeO8hgSp3HxLpZptB1C3df4w+7DAbQT2vFpRrrhrU4HvEtlkebFNfZnuxjdNFI/hTfZUgEIMVxShmcyN1+y7zGrgR+yXG03claLqTOOV/A632HjnXPPcBa+cKD2KScAl9fsOTXnp2p4oLouGsgvdCj7Vpzd883o+qgYt6ydAcC2+WU9irIIHHoAeXgK1DFhJ/rfYBPEh+JMU4ed5oSzGfHxbFeot9oKHvlbPZB1IgdbmPN0nJI97XMOB5QYJ6tcDIoKCCu/EJ9EegNUMAUbN9qfAu+326GIbf2g4NtQffSfzSFmczPOH3K6g4n8sNIpIfk3sSA3+0ZwnZe2A12WXszmuJ90tmRLg4ttNa4LO27UuhS1m/jErhQ6A0vjUbMDcD9tcBSydMpv0hRKkFrkUlOkGSm6Q4LVOoFp/NT8s9sNneBpfFLeNql+ZCyNqMhcIcwoUF+T7760JhXr7L8bdPRBPGuK6K6lidIPHb4zoAl6V+QQttZejNvh8QK4RxN3kdn0bex2xuUtT+bC+35pUAB8V0J30XVhRwVdhfOBJoxn917Hm8MzmIK/MTmM0k8dHMKCK5jPz91Z7D6HL74dCsS5wT/7u6MIVfTg0JiV4tJBqwO/FKez/e7BzA8y07nwXV0CQ67Vr6B1rw8htHMT4WRjqdR6VcweR4GNeujOD88wfQ2u6HeQMG9QoKCrUUv8ogVmD0bx7z+THM5ccQzk8iWphFblEJViskNk/1EIrQS0zHq+/3W4nj3peE9N0uEp3E31T2IT5a+Muavt/uGECn8xBcFt+GCAKSjCTESPzO5kcwnzPOJUl0EpK1wfAlw6JSI7fG3JWTL481iIC1BUFbO5rt3QhY24RY92gGub7bJmAkbBPFyOK1MCcBkPnF9mNGRq0EnBAecg3o0CtswyevARK+DEaw7docfWhx9CFka5d/+7SQkL17EYYiOCMTe5LlVJtT3R8tMNhDIn1BsiRYFKoekGKq8PwwICSTsDywzr2LEywGMUieN9m60GzvQsjWIUENLmz/9QpNNWK7knDKlx4FePjKAES2mFwMTkTl/iB/61Gkxf9240o7bu9+8jIaAW32frl+SNCRDAraOkVZyfMsT6MySfRZCSCSrK1XWcksiHB+bCnYTDgsPgStHWv8olpIcdn9Tzwf83UTorQwyRRj2FpUGsg3vVXUr1SmU6V9JfJ9yQLgPrId6KFOiwpgZwM3a4GEcUxn8HWlPVKv64x4utdKoJPoYZaTQUQ3xvmpBXzme21tsOaHlkh0HkskP4EW+0HYLe6a18XsET4PFOoH71V6JScZEbcT72I2d1+yQ/s9F3Em+A20Og5JcFnBAMVAKM+gkvsJKvp1PuG2YCtWmLQ+mOxflcKiSoX+CCSyg7aQjI05j6QanVYuVKTT45zPzVZ7u9i+8LnKDMN2ZyeiegRRkuh6QuxeSKjz927NLdYvNgvb+RGkePOi1RtFQid9Z9Dh6BK/83pgM9ue8Fvfq5jOjeNG7DIGU3cevVkBCoU8skVm4+rodx+R9lTZtfsT3W4/fuPAKXS6feKFTiJ9Mp3A5/OTGE/FEdOzeL1jAMcDraJajxeyuBmZwY/G7uGD6RFRrVOtftjfghfbevDN3mM4EWqHz1bfdbnvSHSivTOIl14/its3JnDz2hjSqRxSyRzu3Z7C++/ewVe/eRZenxMWi7o4FRQ206KCRBaJwnvJzzCcvC4ECwlwhe0H02qpDq1XdcUBDIlakucjmZu4Hf8Q4fyUQSRuEbhuyVgozGA4fcNQfVibJBDQ6z6BM/43Rdnb6KpeEh4kbEk0MuAwkr6JodQ1UeZuZaomzzFJzHQ2jonsPXmvzdGPQ57zOOA+gxZ7j7QfldRUV+9WGGqDskHwljOigl7IT2I8cweT2YeIFKYkO2K7wXPLDAMuo+lbLO0nQaEBz1kc9JxHq71XSPZGs9wxiu/o4pFMApx/0+qDKc/MGmL2EIOfYj3EoIRkEiWxn0Ai1qn5ELC1i4qZxRl5vlk0L5wfERKTVhq1gu2cLlHN+XBFxhUVm2t5BRuKT+eKYKjsQzEmgevaUUG8MIOkPo/NgWF7tIK84TUqgdPGIWoNMr0NZwLfxP3EBxLEoG0cScFkcV72V2yc6gzSLhWdXTVDYHNAwpjBmiqBXAWVv1T41gr+np7Vj6+n8WGS64L3zjQMD27eo6jMJ4HrroRqPm/08E4W57Z4f/cqKogWpvAg9ZHYC1XvWRdDv4Vme9+uKNC7rQR6JYly7qeoFD4GyrNbsyFzE0zaKZhtp4XOVXgEqphJSjstTlGfJ/Q40sWkEOnRglH0s8XeJgVIqyR2h6Mb95N3EC1QlJFAoBQUQp3PBqraqVp/fA7C7fC3BIUSXOdh73Eh3BVWB9Xm49nhVT+jHc5w+oF4zVOd30jjZYXtDp7b8eXuwzgaaMH3hm/i5xMPMJSIYCqTwL+6/SmGE1H8Wt8JDPhDuB+dx7++8xkGEywiXILPaschfzO+2XdM/NSDdmfD9KWGJ9E5nurqCeKf/NNfwf/nf/gBHtyZQi6nY2oigu/+ySdoavbiuRcPwh/Ym+o8BYXtBonyodRVXI29i+HU9S0lXBVqJ2tos1AviU717rX4L3A99osdKw5IgoiKYpJ3E9m7OOK9CCcaf1BKAn06O4jLkR9hJH1DlLo7BRL3XK7F3kW/+zRebv62kLlU/e9mMAtiKH0N9xOfYSxzB3GdthiNQ9hV74fsu19Ef4brsXfR4zqOM4G3cML3slhQNdI9gjZDJP4ZKOPffDXU+3vHN3sz0O08g3B+HLl8conIG0xdEjKzHhI9K+T7mKiLqyBBTpI+ZO9e25dd864g0amGX8iP1e3vPJcfwkJhDJsBEgq0l1oemOPzJl/JNJxliLGvDnitzaLqLpQZ3KeH8yP7uHrB88F6CcvBfrGZ4x+pE7NoF7AcPJblFkNPQ6wwLeTnbgPJ2ja74fleBW1txjPXcbb0LVTQXXNwnSTwQn50C/d274LXyEjqMpI6gxAVWM0uhGy9CNm6n7gG9j14/9NvopL5N0BpduuuDetZmBxfUQT6GrCb7Wh1tGE8M4ZEMS7+5sliXO6pvGe0ONrE9oWgcrzT2SWWLFSjJ4tJqetCQp3PMhYepWr9cXg0nyjWCdoCzufncGixWKjC6mBQI11cO5uZY4gkx6DldvGpV9jf6HL78U9OvITzTV344wdf4Mfj91CuVPCT8fviee6x2jCRYpDMuO6oNv9K12H84dHncKZprezOncMuINFNYuvS3OLD/+G/+TK+/+ef4fNLg4hFM4jHMvh3/+IdPLg3jTfePoFDR9phtTX8ISkoNCTor0kl6OXo32I8bRBa9VonKGwNRPFYjMvD5mngIJHfvxP/GDfiv8RUdrAhFKf09aaiWhS8DTxRIKE1nRsWT+eHqS+QKcZFLd0IyBSTeJi8gpnsMI74LuKk/zW02ft2VSFXkrq0MhpMXcNk9r7YtlBxbrRxY5F1j4MTK+47740Pk5/jQuhr6HUeFT/7nQbbkEHPjxa+v1jMVl9G2jV2u243+j3PSYFKknC0+uH9cij1Kbpdp+GyBGuy1WC7jqev437ywxXvtzuOoMNxjKYtq/6O9SSabAyAPVJ7UlE8kbkm6loWWLTUMNkcS1/HTPYesptkaUHy3GmhWuzRfpNAjuTGJLuBiuzNy3xZ3h83kpFUQbGcQ1IPy/36kX/6xmtvWEw22C2eJ4IkVPuzf2xGsWEGKYxzu7IYKJ/PtHirxaqL1zlta4bTn2O3gWRXq/Mg/LZ2KQZrWBiVJQuDfvYk11kc9mlI6LRUuycBLIWNoCJWULT5k3/RYm5xrL+bbPa2GpVyCpXC5yin/megxMyJLfLft3TDZLsAaMeVjcua2Vt2tNo7MJWdFGU5bVwypbTYrzXbW+BkdtfivIKEe7u9Wz7Ty7SsSwqZHtMXSXR7iyjRH0ervU1sY8azI5LFO5i6i7OBCwhi7fomCk+HUcxVjUEVIM8Xq9mC8y2daHK68HxrN/7D/SuYSicwl01iIWtCoWzcZ882deI7AyfxescBdLh8Dfls0nZNo1stOHCoDb/6WxfFB/3TDx9g8MEsZqfj+ODduxgfWcDBI+2ydHWH0NLmg8ttFwJeQUFhfdAHdyRzSxTLVN8KYbuPqovvCiV6DXYuJHZI8N2KfyCexHO50bq967cK9DvtchreeI34MCQihWkMpq4KUT2dHUJCCgY2DnhN8nzmCmno8YLYchz1vYCD7vPwNKgHY7VIzEJ+AhOZe0JC0x6H2Qkk1J/Fe3snQLJfL8yKip7ZIce8L+Oo76IU0N1JiyKSwblF6xaF9eGxNqPXfQ5xfVb8gNl69Na+FfupWGexwKPX2rK2n7CkKV/G3cQvEV6mhiUJSCK+3XF0zXscSd6ArQtu1oYoUH3OItwlpEsx2b45YEGz48Ca3uwktFkM8Hrsh/K6Wc9pEhAuLSjHQFW07FelhFQpIseqmV+Fb402qRe0NBlMfSLrZ1tQAeuy+Gsi6fkbkqh3E7+QoEP1+B1mD5rt/Yav7QaeLwxEui1BIdILJarvjSLS0cKEELz97uekGPqzgMfHIAnbmAR9FVPZO7LvHc6ja/6W/S5diuJB4gM8SH64WMx2l8EE2EwudDqPy3VDIp3gOWShWJuZhQGtCNg6V/057Y4ypShuxX8m6nWS8AobA4M51ZR4Eui0t2K70pvfa22VjJp660PsJVTKCVQKH6Gc/S9AkbZ6WycoMtlegcl6ESazyqhfCyTR2xwdYrNCUnwhPy8iL6rNad1Cf/PqfZ/91m8NwKN5RK3OhaR7alExTSW6S3uSRGcx0DZHJ2zJm9DLOsazoxjLDIsVic9aW72K/Qb6z1O9zzZeDXweU+G/0wXmFRoHJpMJHqsd/d4gCqWSFBwN59JI6Y/usbySD/ubcDrUgV5PEFqD1r7cFU9IDh5LpQqKegktrT4cPdGFaDiF8EJS1Oiz0zHMz8WFVO+9NYm2joDYvLg9dtjtVmiaGRZt809AIOTBCy8f2vT1KihsJ2jzQa/nW4kPMZy6UbdliMI22rms48/K78znJ3AvcWnRvmW2oQIhDrMbPa5jdaWtbxfYdnM5+v9/ivvJzyT4QBVvIyOuz4mXM/tFtpjCIe8FKaC4USXmVqaO01boRvw9uc+wMHGjBHY2CgYFSATSJ5/FUFko9oj3eTTbuho2QKTwCKwn0Os6h5QUqw0LGcuzOp27a9QkKEakuJ5Xa4Z1kUxiPyZ5Ti/qSH4Mw+nPhASs+lJTqUyitcd9Bh7r2so19g8qptuch4WgrVrBkLynpQw9DLv10whY22GXQqcWIY6pVOb1HitMYjJzC6PpL4TkZZFUBlD4+bMSvDaTAy32A4jkx8VfvKr4vpf4hZDqHY6jQrRTUW3sM1X89N4viHKP3uRtjsNP9VRmIGA8c0OIOwYTgtYuCVrwb7YNs5VYOJSqfbZXtZBmrpwS8pjBg8HkJ8hLUWwq3Ezy+373BZg2rETXxC+fxziZvWWo71EW65QbsR/JeQ7auhaLz1oXg4Mlo/ZAuSC2JCQeWxwDa7cxTBIw4Hqy2Uekw0TmhgQB2NZ+WwfsZrecDz7veV5JuNP7nhYuQ6nPEC6Myf4y8MBCzLtF5VcNMjLQRDsltm1+8VkQKYzjfvIDaWeS7AaR6xALGPYxfo8BwrnsAzxMfSSBFJvZJZ/ny7UWR6+1+LdhDfTkJ+XFMZhpV9/n2WYM2oyaryCBORknsh/djP0E87khCTLyGuY9bcXvTFI5QPo/+7qT5KLWKve7vVSItFJaMAj03A+BwqUtJNBNgKUXJttLgLb2fUPBUJe32TuElKV9SLgwL1ckLUI6nT2wLbMhYnCI3uYky2n7EitERQ3NezWfpyFbaMm2ZTl8Vh96nf3ocvZiOP1QyPovop/Jbw57TiBgDa563QtHVSmKzQx92h1mB5rtrdgPaLK3os3RhWTqSRLdsNXphd8alDGXgkIVsXwWD+IL+HB6BJEcC9CWYZHniwnFRZ5jPB3HpbkxWMwmHPG3wKk1Xh9qeBI9spDE+FgY+VwRyUR2aSmWynC5HUjEs3IDK5cqCM8nZamCxUZtdk2WrbB5OXaiU5HoCrsatFJ4kPwc12K/EGsFhca2c1nLm5YTPvof34p/iGuxnyOhN5YalQNft+ZDuygsG4fk5bODymKqpBl4uJP4WAou7haQRKMHNs83ldGnA28gYG1pKHsXkg4szjqSuiFZLo0U2NkMzOSGUI4W5Tht/jfhtzXv9C4p1AB6lh/wvCDkEVXRuVJCyDoSqLR5abL3osU+IEQlryd642fFi3UCs9mHQtwZAWeTWLO0OPpx0v8V+c16gawqkUjCnSQ6SXkWxSRIXN+M/RRT2btosx8S5beF2y4XDAJdn8J8bliIZLHGsB8Sf3X+e7OsLXrcZ7GQHxHVM4l9PnOolOZ+TjvuImTrWaoFIJ7h5ZwQvVx4Dbze2v5UEr2y+Fsey0T5hljfOC1eIZc91hZ4RBFOVbJNCBE+/0iYJxePk+1G0roKjxYSCx1mFzxO/NUDEtlHfK8LuR8vz8r5ZfFZKtGZtUCrHp+1VYIIQqGX9cVjz8i9mH1qPRKdaHUclPXw2PWKEfjg8VBZz/PbQQJZaxJ7Gh63YSkzjdn8oBSwZTDBrTXJtri/D0XRv7uyeWjZ0uc+L8Q5AwjsCwSzQuL6NCayN9FmPyyKfZ5/9jFepwwezGVZxLdo9BWtRa7Dev3hkzoLlqWFVGOf5TPJeCVJTkI5jGhhcoVogeeZwSUGr3huuF/st8Yrsx84yrHArQXhWNzvRgXvHZ3OExh3XJdj5b2FbTqXZ5bYw3V/yWNnLRa3FpCAD/syM29aHAeM4rimR/e4XQUJihXE97xSuIRy9ruAfn1LFeiAHWb72zBZT8Bkbvw6QTsJkuIs9KmZNWSKaSHR2c+oRO9y9CwVBK2C1x9V5VST87skxI15iEeU0av5c/O53ePqxxn/BYTz80gWE7idvCH3iXQxjV7XASluWn2+V8nzQqWATDGF6dyUeIR3O3vXJNGrczjhrRbvOXzlvYa+7dXgXZm9sVwQkYaF9xa5v1TvM+Z1rzMjEGgIWAyrJmM7LPRJ29YqjOdqTvZZ1rq4HeN+ZhCaTwMDGMd8ZxArhBe9z4swm0ywm52i7L8QegVuqQHTuPdDhe1DsVzGfDaFa+Fp/HTiAX40dhfZog631YYDvhB8VgfmsilMpGK4NDuG8VRMlm/1HsexYCsCNgcsDaRKb3gS/fKlIfx//6cfIZOuP22vVCojmynIshWItTdm+ryCwtNQffjfTVzC1eg7mFpMq3028NFrPISNxzuf8sZjWIYNi7YO8t/ixGX7YeyXMVAwVDVSd4GqtwZ+yBtKdHqil584j0S6mJDCh1T7pnawAOZaICESsLXCaw2hUWB4x+uYzQ3jg4XviY1LdTK/m8DjoA3Nx+G/FEX0ucDbCNqMtNdGgMWsocN1EC2OXoQL09KP9xpoT3Mz/j6sJhsuNn1jU/yTFbYenc5j4kNNspSe6CRq+VwiMTeVvS3LeuDzgwrMZnsf3mz9R2h29NesyOxynZTiiFTUkpQniUUUK7RreSDLetslwXgi8BWxByEBu1kkOhX6VKMa+2YQyQQLeHJ52j69VP79p26DV8bya4SK7kwphkw2BtQhqDfa34ED7os4FfiqKNmfBVT+H/W+gdH0lSUFuKE/LmEhPyzLWiDhf6B88anbaHYYBDIJcQZLquedAZRkcl6sWtYCRyx8lh70vojD3tekMORw6vLSOnYLeBwMeLBtU3pYsjGMZy8zfJKYzNyUZfXfmsVyh8EOZj08TH5UN4n+MPkxJrM3hUwnQc9ghth0lXMSpFitPdn3r8b+WhYLrBJIYbCIi2Z2SD+0mpw4GfgqDnpehHlZzYPGg0mCQUd9r8u9bih1qUZbNRJ/OvJcCikJKDLAFLB24YT/bTwX+o5k7uyuZx/nIyWAgczSpNi3VLJ/BVS2WkxhBSztMDm/A5Pl6XUA9juoZA5YQ3CYnUhWEqIw53wzYAuh09n9BIkuRYwdHfL92dyUkM8cE7fZO0UhvRZB3Opoxxn/c4gUwvg8+gmypSzuJm9hMHVfLGI6HD1wacyAoXdzAeliEhE9jEh+gdVVxFrGbVknIFKBeLnzt4VyHnkupZwQ6NPZSSG0CWZ2kcinIt5r9YkSn/cYebXY5bgYQFjtWiNRnivlFreRQ77E7eQwm5+RdquCgYXJ7ISxXotNrFm4cP3cDv+WefI613OLvQPPBV+R71+LfYpYISIBim7nAZwOXMRp/8XdGVRT2FRUKhVRmC/kMviL4Zv4q5HbuBdjIIzjLk38z3/v0FmcCrXj8tw4/tdbn2Amk8R0OoHvDt7AjYUZ/MPjz+O1jgMI2lmIfWO2ffuORNf1IrIZ5XunoLCZ4IRlOH0D1+K/wGx+5JnXZ0yg3Wi296DJ1gknU7ItLiEU6IXGh3qhlEVe0uFjQvrN58cN9do2pSJzH12aV1Rr3upiDQqxy+KMzfYuNLYnenJVqx2qJD5c+J4ERKhWb0T4rE1SVLTRQBX3Z5G/FZuR3UigLwcH5J+F/0YG32cDb6HD2VjpwYc8VPdMYjTTmH30WUE7oJuJDyVY0Os6odJXdwVMCNq68XrLH6HdeRi34j/HQm54hcp5PdC7+ZDnZRz3vy02HfVaVR3xvirPyqvRH2Ikfbmm33AbQVsnXmj6PfS4zoqSlM+0zQJ9v6mo57Ob+xUpbH7xRhLOVGzTlob+4xsJqBvWKD1CWtLDPmhd3Ue73nXyuF9t/vu4YvmBELSb7z1uQrfrjATuPw3/uZDFtXp7e7RmnAt+C4e8r8KjNYn9jvjI7w43lxWgmrnHdRpfbv+v8Xnke5jI3HqqLQvHcH5rGy6EviPnnDZCbJN6QT/2qcxtybYwhAiPBB61NCaD1aVyEflyZplYxLR4bk+vYgXTWKhml4ykPxd1/ePijHqR0GdwM/ZjsbsikU47mN2DMlC8j0r+ZyjnfgKUxoHFDJEthbkVZtc/BCydu4GKaQgYRURbkdBjyJazQqK5LR4EraEn/Pv5Ge1fHBankNuolIR45nuPE+6Po9XRhq+1/zpCtiZ8En4f8/k56BVdiPW4HntUu4NqcgkslUTpTVApb1vH/5sk+b8Y/J+EzDaU4o/uPRy7k/gmSLTfiF/B3eTNFcI0HhclYL/V/Qc45j0Jl7ayGDbxcfg9/Gz2bxYzbarqd4qGSiuU6BPZMczmpmWfF9e+pEA/5j2Fb3b8BppsT7el8VtDeCH0Js4HXn7UDsxYYf0rRaArgP1ex63wLP7lnUu4Fp5CNJeVnuG12vH7h8/hV/tOYMBPWzAzmhwuHA+14V/e+gQfzY4inMvgXnwO/+OVd/B29yH83UPncDzYCptl5++bO78HT4NhT6egoLBJKJYLiBRmcCn815jPsbDZxlVMJJ973MfR4TgIv7VZ/EwZ1eaAhilvjPzzkW+krJUMb9MKBwo5KYJHX+eJzH1MZO6KSvVZ/Ng5KfNoAbg1P1yaDy6LDy7+La/8t9dQD5nsQuyTLOBDnq/cZ05qGxVsF0MplZc2rKYTZktJXIn+FA9TV8QvtFH97H0aSfQDaARUgza0Mboe/QVGM7drJs1qAclT+r87NS8c4u1LSwIOUo3CaIa/a1b6f6aUlPO6OSxIRdZ7J8ECbVTI2dBs78ZOozqIph/+cPq6FBbVN7kgHIlFpo/SZkKCdya7vGdMdlgIMi/3PW43W0qLn/lme94batUJXI78CC32HrhNgYbOblEwJtoWsWAI4ZDnVTTbDmAuPyhe51R2k6Cm7UO5bNxzbRYXnBa/EOYkgWnd0mTvERsDo7+Z6iRsXehynRI1Ocn4qdwdIbW4XRKKtAvhvYPfo00ELSzaHEfQ6TwqlhhU5PLe9awK7MfbhL7nh72vSpBgOntXlL70r85IunZO+jqPl89T7hvbhJ7kbBfu09PAa5Trb7L1SjtT0Uqlb6YYlQKvJON4XAYZWZFt8flMP3JanfitnYvt3y/7yPZjocTNOHbON2hTcT74a+hyHsd09p70iURhVjzZqVSuBgJ4/PQvZ/v7bG3odp6q8bw7xAbjtZY/lPWzjWlVQo9+ErN8Rkh/Mzvl2Egc83i7nCfFy1qK0tITXQvK54Z6urGJ21U9+M1uaYdXmv9Q6hHMZO8jnB9BojiPfCktGYtUetN7O2DtQJuTff+4nHeXFkC2mIBXCNulfMeawPZl/3q2wPkj2n35phuZQDcKz5Pw/ikmMzfEoojXG8coVKazNgGvX7mXPUZ+GWP4ojxLmaFByyNmj1QLI/OexULLDCLwnNVyH9gpVEiSl6ZR0W+jon9hkOgkz0tzW2zfsghTECbbeZgcXwZMHgnCKKwPIY9NZrTa2zGZHRMSnQQ5/8175ePPXqrUaf/isjilL/NKJVnc7uyUsfF6qBYmvRh8Bd3OfoxkHmIsM4L5/AwSelwEK+WKsT6X2SVKcfqvdzi60O8+iB7X2pkF3A8S8TE9uq6ATMyFKhSfrd4fZR/W+D1V6FSES/BgHXDuL/P/VaaNVKmTdK8FnOdbLBx/7526CAqbh+FEBD8au4efjt/HUDKCpJ6H02LFyVAbfmvgNJ5r6Uan27fkee6xmnHY34x/cvJlsXD5yfgDXA9PS/HRdyYeYjaTxNd6juBLnQfR4fbt6LE1PomuoKCwqUgWo7gV/wAT2ftCutULktABWxsOuE+j03kQrY4+BKxtQmDVShpVizfRx5k+2STYJrMPMJ65g4X85IbIYHo0HvY+hz73KZmkk1RjgTK7xSkTfSM1rTFSgDY6AWJ7+SrNMmik6pyFYK/HfolYYbbOYIgxkXdb/HBbGWjwyoSdCweYTLXnNiSRnQOtclEmnSR8SfzS41oKWgoZuf6kg+vyWpuk6GJDoALpa7fjH2Ikc0MCERsFB+cMHAWt9Gprk6wGtinVpUZb2pcCSrLpRRsjqkFIRmXLaWSKCSSLEcQKc5Khwf15lsBWXF+QAqk8v+eCHtmfRujzDG5RHT+ZuYep3OCG1sGAF4NiHmaQkEDSfOKlzCwYXutGir1dAhlVu4iq/zL7sPhXst1LSaSKMbE+YgHeqD4rxPqzgoERBgQZLDjoOS/HvF1gf+tzn8Tr+J1NW2emGMdY5o5kDdW3Lw4MuM8haGvbcX9+Pp/Wzwow1Fck40jMkZQlsUf/bVpLkKDkfZDPDhK5fJZQBUzSicT20/y/1wO3SwsUu/MoArYu8RUmGcXtClldKQnZqJnsYuPB7fqt7UJYV1Vj9Gzv9zy3dI/hfpLIehbwnsX24L6RuKd/MgluEry83/M+ZgTJrTIeYHCBPtAkdEkqP339ViGGeTwkhVnIlYQcr8FHBHpxUSFbkUAC94l9nES628IMsjYJkm92IWUjsGKV4AjPL4MVtLXhtcBzUiVeq/tUJQzZVjye2rZBSxKDQGYAhj7ptBah/7oQ4pXio/Nudkm/9Fnb5NwvD9bw3L/Q9LvyO/YVtuXjiszH0eE8Kutgm1fR4zqzIdKTxT9fbvmDJe989j16tdcKKQBocaHNeUiKU7KYLy1qSOwagYGyXLvVIJLf2iHtwGNkG7ANu5ynJJOkSkjRn/tp6kcGcGi9xPO52WBbrpWRwuvjsPcVuaaqPva8lvnvjWDA86KovqvEPduftRxWB23fDO/9O4l3JGDDNmu2H0Cv+6z0QWa0sL4D+/aTvzZsTwwRDDNKwxjLXBNFP62IOK7hK2tK8B5aa3+qFIdQ0e8A5XnA7AdMPsDkhon3EZMLMDmNBTaYTDZA9q2G8UylZHicVzJAOQFU4qiUwkB5DpXSLFCaQqU8DhRHgDItELfLEkmDyXocJse3YLK0bdM29waobj4XeF4sV6jo5hyvTUj01eedLs2Nl5vexEHPUem/tHE56j0hvua1EMNN9mb4rX602tsw4D4s5De3S8V4ZVkBUxYppc960NaEgDWwLpnM+dVX2r4p63mWLGx6t6+lqD/sOYZf7fztZxJVMQDh0ZRPv8KzYzQZxV8O38T9uJHV1+sJ4MW2XrzVeRAvtfXCb3esuIbFU9+iidrcZbWhxelB16QPH0wPiyr909kxxPO0KSrhj4493UJvX5Po/qALR44/e5rmVqCnv7YBs4JCoyBfymI2N4JbiQ8ljbpeBSyJcpLeRzzP44T/ZRl0U9VdL6opsKIQ13yiUu52HRXS5W7ik0Wrl/omONVJFW0jSKTtNRUoB1wkrjmJr5TLmMkN43r8F5jPj9U0GDOKXvnh0YKi2Cfh67e2wGdtln+TDHaIiteYRHGiSq/a4iKBThVvvpSRIExKjwoBKX8Xo0IwVEl1KiSW9yta6ASEcAo0TIFWesfTzohE6kbAtvTbWhC0tqHJ3ilWNbQyCtpahdh4mtKlCk5+SbzG9DlRMfPanM9PiO0Ji4VuVLFNa5G7pk/ERuek/zWYKzvv0c3rsdN5CN2uY5jODdc0wDdIc+9iRolX+iz7K/uT0XdD0q8YNKvVPsVImy3IuY/r83KvmckOySsDGc9SWJbHxCATg5RUo3Ofl1J/txgkGGkj0+U6vGnrnM9NyDVdP4nuxFHf8zjgOSsBjp0E+1CtRGuVPDbUrdsH7p/08Q0EXfjMozqXy+bCqBVCgm+jJF+jtndtYIDUKwsJxi3Zgsks5DGXjYAE9JngN+r6DX3EuWwGeN6eb/rtTVkXSXIucB6r+TcMYDDwxKUeDHhfwE7AqfnEhobLZoDBMy61gOOMqcwt3Im/K0EhImDtxGHvyzjh/woCto46tmyM73hfYLAjlTIIeYJZPAfcz69D5j+2puIIKrkfoaJfBcwhmMwhwOxDRch0D2CmUtsNk8mBionZoiTSeT/nYljorCTVOa6gfQcJdBY7ThokeTmMSmlGfM9RngUqaewItH6Y7K/DbHt5Z7a/i0Hl92HvMVlqzTI7H3y2a43z2xYHM4E2J+BB4vuNlq9gKzHgOSyLgkIjIJrP4kF8AVazBUf8zXijcwBf6T6EM02d0NYpEspruN8bRMjuRLfbD6/NjstzE5hMx3E1PIV2l1eR6E/DwcPt+N2//woaEX6/a6d3QUGhLlB1OZK+KYRdvaDirMt5BOcCb+EEiblNVHWTdGxz9AkJSVsYejvP5EbqIhGpxr4T/xhHvS+g1dErZPCeI9GLCSG003pc7Ei4PI1AJ7lIosWrBdDtOo4+FwmXw0JCbiQAshxM7Y3p85jODspCEpiEMCdsVCsVK0WEbB0SHNnpoAbVkyQEH6Su4Hb8I7G/qRdUejHQwLZjPzvsvShKVypcNk6eGYGkDudBnMabQoDfT17Gg+RlIS+pzqxXsUIydzY3iiuRn6DbeUzI9Ebw6Ka9TBe9py0fIl1aPYBRtVciEcv97nAMCPne7hwQ8pyZJc8CBvAYZGOf5NLvPiXK9KnsIO4lPsX95KdCsG/U7oW/oxL9TOFLCIkib3tSXKVAMm0vWLBsk8B+vRGlL9uYCnSjUJUaJykoKCjsJKKFSfFBj+vTS+/1uS/goOelOgl0whj3M+NlNH0Vo+kvlnz9k/q8ZKzUjsqiWnxalrVGOo/etwBUpMtzzrKMUK+ui4ryHCD702DWOiYXTPa3YLK/bQQHFBQUFPY4NLMZPptDFOh/dOx5vNbej1ZX7fc//vaF1h4cC7bgj+9/gZ9OPMBgfKsLP+8REr29M4C2dv/SQ3Q5ZVfvvzdjHSv+vfMZ8goKdYEk58PklQ39lkrx50PfwBEvq21vDSHKVNJT/tclHfbT8A8xmrlV829JNFIVfSn8A3y57e/DZtuDJHopiVwpKyTrzdh7T1XzkswiyXvK/xpO+F6RzAGS2Zt1/kh40qalydaBk/5XxaaEKmp6tJOQpEq+yU4lYzt2GiQ3SVC/M/O/IbNB6w635sMx30t4uenby4jpzX0QtNp7pU2Pep/Htdg7Uvi03qwMggGoufw4Pgn/AK+1fAc+886rPS3QpD8MeM7iRvyXT35u0kTVf8h7HgPus1Kkkym7RqmjrQvCOMweHHCfkdoOh7wX8PPZ/yiBxo165jIbgzYoTbYutDsboxaAgoKCgsL+BGst0M5lOVhMOWTv2fA6DVEBMxi9SBUNEp3iiapVzdaAKnPaUC5aUe6immkm2ysw294ELGpMoKCgsD8QtDvxVtdB/NOzb6DNSavT+oU5tHgJ2Jzik362uQN/cv8qbJbNtfLbkyS6VAq2KLZaQeFZES/MYzZPpbCRylkrSF/Rf/hM4EvocR3fdA/SFduSyuAWIdCihRkhxcOFqZp/T4/3h6kvcC74ZVH3UtG6Z1Api23K7cSHGE5dF0J9LbAVSVzznA24z4ganIUuqz7Rm4XqunjOCLPZLMQnCebjvpelgC37TiOQ6FRmX43+XAj0er0C2Z60ITkb+JLYBdESp+rLullY8nE1GXYxbLPnQl8X0vmThb8SUpcesfWAKnZai7B+gdXjEK/2nQTbK2Rrl9oF3C/6qFIVzsBBv/u03F+oDud+UvFPT+DtyGAw7ju0b3Ch13Uc3+r4x/h44fsYSl+rU1X3CFS2s90Via6goKCgsJNgnQV6zVfBTC+72SNCiGd5btKSrrws2MyM1a2cI+xOWAFtACbn7wLWk9tm8aagoKCw0zjT1IEeTwDtTi+s5o05GFR/o5lMONdMYZ4LRamZs7NoeBJdQUFhczCbHxVlcL02BVSFnxYC/ei2kXBOzSNkJX2ho5GZmslDkqO0ZhhN3xTLjWZ7gxSz3ATQGuVB6nOxJaEXOQnI1UD7BBZqpRc2CUF6SNfq0f2s4OTAxkJzZvuS57pRIG1nHzUJPSJFa2mzsRECnUUimSFBBTUDBFsNDhisJhsC1hYc9lwUD+9rsXcxlX1Yl7ULrxvaptxNfgK/rRlO5yHsNIyCegdw1PeCBLnoHc7AS8jaLn2V7+2Ufzsn/7x+aK1zLvg2yijiQfLKmtfaegjnJ8TqiEWontU2SUFBQUFBYaPgs+xxhTjHZs9C6LJGTrYUF/V5FSwo+izE/N6DFbB0w+z+BzBZz8BEn3eFmpEv5RDXI5jLzyBSmEe6mECulJN5LMfCFJzQZ9yteeG3shB0K5ptbfBat6+o+7OCBbSZvTifn0Y4Py/Hmy6lJOuYdZwqFRbXZmFzGxxmB1yaB17Nj4AthKC1SY71aQWlFdYH+1KpXERMjyCqhxHXo0jrrPOVQY7WpOXi0jyAVrYsPMs2t1soTnIvFpf1wmcNImANSXHZ7T4n7EeSDV6YQ7gwj0QhipRkr2fk/RJKi/vOIu12uGjzavUjaGs2rhnNB4t58/fZayUfYFiebsbcjus6HGBB7Z1PQ1JXnYLCHsejgj8jCOdrV3UTHKCQNDzme1EIru30taZ/MkngwdQXdanRCfq+97lOocnWueMFFTcLJH/ncqPrkqgsssjigmcDb4o9BdVGO3n82+UHvR44AJ3KPsBg6iqSxciGbIxIoNPmgwVYt5vU9VqDcv3Rz52T1oXCZN3rYRFV9guq23dajc7BJ+8lzwW/JoR5yN4Bl8W34575jwcwet0nxBudASsGL+oFMx4S+gIypXhDWOkoKCgoKOxPWEy2FeQ2g7t5KQSflXHiRjCbeyAWMazTU0VQ6oAootiAGbB0weT4Bkz2L8MkxVL3xnykHiT0KCayo1jIr8yCJtHY7uhCl7NvBeFokMo5TGbHMJOdwGx+EvP5WUQLC0gXk8iXDRKdJBrHkyTRXRYPfNYAQvZWtNo7ZL2dzj4h1vn5VoIE+BfRT+T18fnZSd8F+G3BVYVEelmXtpnJTWA6x2OcQiS/IAQux49ynOWiESwwmWGV+lZOIWxJovPYSICGbM3y2uc6KNmUzzqWZptP5caQKq6d7VwLaHcZsAVxyHNiw3WjtiNIE9PDWMjPSZCGBHSsEEa8GJO+RgLaCGYw42aRRDeZZW4mJLrZIf2YhDSDGz4tIOebr1VCneeJQZ5qxutmgwR5tBDGXH5KrpP53DQi+oL0rXSRwZgMdLleSmLBpZl5zdiF/PcwGGMNocXehhZ7B1oc7XLt0EZzs+Zkpi2459ktjUFfN8ZeKDQc9EIRk0NzyCRzKJdWV266vA6E2vwINHu3ff8U6kDF8EcmgU5ldz3gA5mFAElGb7c1CtWbTfZO9LlP1k2iM2AQpwq0UpD00r2C9Qh0elP2uU7iQvCr4imtYMDITLiFicy9un7HiL1T8+Fs4G1RoG83gb4cJJ1ZyJQWPvHIQl0FdwmSuRPZe1K8l0GBnQbvK43eRxlsOOA5LZZStAKq3x+9IhkQvA/x/CkoKCgoKOwEGKh2an6g8EiZPpcfQmvhIJrt/TVbsDATkgQmC4jeT76H+dzQ0mdmaGh3HoFbC27VYewi0JevHSb7GzC7fg8w+YEGEQpsN8L5OXwSfgfXYp+teJ8q6udCr6HV3gnLIilWKBcM0j0zjC9in2AofQ8JffUi9AQtHYolHZlSGguFWfk+RRAtjg6c87+AQ94TaHN0wW3xbJmgiCTr30z/uSjIH5+j+Q8E4dLc0B4j/TLFNOby0xhM3cHN+BWMZB4skbSroVQpiyqdqmhuZwZGfQOSsiRve10D+E7XH26KcGkk/QDvzf8Yk7nRZ1oPyX4S6N3OAw1Fohv3sKIEK2ZzUxhO38PD1F1MZUclcPE0MMhjFDDOI1tKA6tMDRjUYTCn29WPfvdhnPZflDbYrD5IcRj/SxbjmMtN4WHqNu4lb2IiMwJ9WVDzid+hhEKZS0GCJPP5maX5bsDWhAH3UZz0n5cAFK/PzdznvQhFoiusikQkhX/9f/8e7lweEiJ9NZx4YQDf/MPX8eXffnHb90+hPkQLs6LCXa4YqQVUNh/zvghthx6AAVubEJhXoj+rq4IQvdHpx00VKT2Y9zoYXe53ncL54FcanpzcLnCQQYxn7mImN4RcOV237chx30vi370dFi5PQ5uzH4eK58WWZiPKaLZDh2NAgmLKk7M2ULlPBT9tdXg/qdcKKF2MCZGuoKCgoKCwUwjYOhG0dWEic3NpLP0g+SGcFh9cWgAOs3eRSK9qJU1L3zPomoqQTwzgxwvT+DzyPYykr4idS5VAd1tD6HSegFsL7eCRNghMLpgcX4XZ9fdgsuwdW8nNRJ4WJrnpJfKYat+F/AwuRz7Eewt/K0rtjYAkIglRLodSJ/By09s45b8gqmFiO0nBTDElSmESytV5CcnwofRdfLzwDu4mrwuhu1HwuqTSmESqoZBWY/tarHOoPP9w4ae4Fb+CeDG66dthJgEDI6OZh7iTuIY+1yGEbC2bcn7Yh2jVyb71eeQjXIq8izleR3XOT5aDv6USn8vV2CU8F3wVLze9JUEADdZNv2YqcgwVlMvGa3W+zu1o0o9Nu4K8VyS6wpowq4KuewQVRArTyDFiWiccFrfYGuyUpzX9iVkUkylHmWKirodEVJ8VFeh+ING7nIdwOvAG+twndnpXGgocYN5PXsZcbqyu3zFNj/3mlebvwE31VoOg1dGHM4G3NkSiR/Mz0g6pUlyKvSrUBnrJn/C/KoVG6w1CpotxJBWJrqCgoKCwgwjZe9HuOIoh7TOki0ZGakoP42r0B5jK3EK/+zm0Og7CI+pDlwgz6AFcKhfEkixVXJA6H7Rw4cIipcXyo4w4EvEvN/0+/NYOsYHc39Bgcv4ezM5vix+6wuqgZct8YXZpXjeUuodLkV/iZvzyhgn01VTVVAtH9QW80fz1Lbd2eRz0pCaJXgWzOD6LfIBPwu9iOjv2TAR6FbTeOOA+0lBq70YFPc9vx6/g/fmfIKZHpQ9uJWjjcsBzZNG3fnPuiwzCTGZH8c7cX8s1ky4ln4lAfxzso1djn0imxItNb+J84CWxrNlMZEs6ptIJ3IrMYiIdR6KQR6FURJvLg5fa+qQY6W5gIBWJrrAqvAE3fv+ffgPhmTgyiSxymQKi8wk8uDqKm5cGkc/WRyYo7BwY34vkp8VPuR7QBsWjMRXNJwPqnQCjtvRrpD/6ZInpbrXbWCT1MFL65keYGwnUDLF9zgbflmKiVjWIWjHQoK0PfeTpJ14PgtY2UaH7WQegQSaEPNdui1+U5CzGSWV0PRYjJRQRLkxiMnMPx3wvbem+7hUYbR6QbJjPIn+DYqm+516+nBY7IQUFBQUFhZ0Cx/NdrpM47f8arkT+AnolJ6n9mWIUk+XbiOszsFu88r2qIl0q8VRKMs4oVvIyh8iV6RPM8dSichBmNNn7cMJH27sXJYNvNygItwZmwOSDyfW7MNu/DlgOwGRSRcXXQrFSEt/plJ4QH+7Pox/ibuKaFHPcvG3ootK9Gr0kffWNll+BFbZt66OiRF8UXyT1OG4nvsDH4Z9jJjeJQp2ijLVAgvOA56jY2GyF5/ZewXhmGNdil3A9fllsTKqFQrcS9K4/5j0jfvab0ecYkLmfvCVBmIepOxIgWs/mdaOgrc1kdgSXwhXxjX+p6UuSyfGsx5Ar6vhkdgwfTA/jTmwO0XwWmaIOvUTP+YoUDO31BHG6ieLHR9uK5jPyu/FUTHihiy3dONfciZ2GItEVVoXNYcXx5wZQ1Iso5IvQ8zrmp2KScnH/2pgi0XcZEsUFKSBUrydwwNq641W/SQy32vuEENVLtZPo9DKmEnQvgz71R7wXxQudnt1qAPUIVA0Ppa4iUQzXNVhif2+yd+Gw96IQ6I00IWSdAFrLDHjOIRX7hUTz60GsMIfJzENFotcBqnsYVPFqIejlvARnakW+lEW+vHkTQgUFBYW9jGghjfuJKVyLGn68fZ4WnPB3o8e185Zquxm0cPNb23HE95r4oY+mryBSmIRezqJQziBSqO85RULSb21Dq+Mwet1n0e++IDYujTRe2l5YAUsPTI6vwez4VUDrhcm0uerNvQeDoBtM38FoehAPUrfF43k5HGYX/NYA/NaQqHqtZhbI1UR5y/EYvdAjhQUpOkqbjtVAEns2N4mr0U/QYm/HYc9x8RHfDrCwI1X1DBbQe/vj8C8wtYYCnZYvbs0Hp9kFq9kq4/2q/QtJTRLyaSkGnH+SRHcdkbbZDLQ5OnEmcBEd+R5pO+4/CxEv/b34ymKVhVJO/LW3g5B+FlC5fSX6EW7EP8d8frqm39hMdrg1j/QVFkll+5LArXqq8zzwnLAPrlZUlnNJFnylz3itNSfWA+2O6H1+OfqBEOm5deYWHosXfltIipuy6Cn7E/eH6yiWCxKooic8C5KSiF9Nyc5josd6tY9dCL684QBUqVJGtljED0fv4J2Jh7gWnsJcNvUE/R9yuIx5LT9Ythm9XMZsJoUfjt5FvlREsVxCjyeAJodhk7RTUCS6wrrQrJos8DhQKlXgC7lhNu/XQdJuRUW8edcrNrFWocpGKIjHh1eIhWfqVHTQ/sVQzOxNkOD1WptwLvhlQzG9Salie8X3jqqpoTRVLam66wC0OfrRbO9BI4KTisOeC7iXuFS3yjlVjGEhPy4DXwan9u+Etz7ygW3VYu+WGgulOvoT/WNZn4GDbtG1q/ZWUGgo3IyNIZxPiQdn0ObBqcDG7/skPArlIj6PDMlEz6XZ0O4Mos+98+Oo3YJYIY1L4Yf4j8Pvy7/faD0Bn9WlSPRNAJ9jVI1fCP0GvFoLprK3EdOnkS0mhEjXyzkh7KhQNzgM/kd/Wgs0k01qIzHz0W7xSIZWq+OQEOgt9oOwL3o+70uYvIB2ECbbYhFRcxNMOyw+2i3gvJQq8ancOFLFhLzHXkfVK4uDdji6pTAoyW8S6XazXchMZkiwoGeymMBMbkJIUpLT9HRerTgktzOTn8DH4XeElO8y920a6bweaLVBz3LuG72mSaRXweMkcR60NcGnBaWwI/8mkc7jpICDzxTue7aYRqIYl6KiJD9Jyif0uBDafmtQ2mqzbFc7nL1CvpJcpW99YXFhwEP+ruRQKHFsmxdFN9ue7d6IIGnMtvoi+jGuxz6T4rPrZ3bbhfj2aX5pA/7ttQbEBoh9kpYsnF9SEZ4tZyS7IFGMIqknZDv0Qmc/ZltRhc7+G7K3bMqxMBD0RfQT3EtcX5VAJ0nu0bxotrWhw9kj102TrRU+q1+Oi/dwZhXx+mBR0XB+FjO5KUznxiRbg/v9OJnOvsfzS+U7C6V2Oftgtxi1BWoFnyUZXcdHs6P4kwdf4F5sXqxb7BYNfpsDDotVCPX1RGEOi4Yejx/TmQTms2ncCM/g+dYeRaIrKChsPUgoM/pYr8+a2+LDToMDeI8lULetBpX3VQJrLxZS5KSFZG+f++SOZws0GjiwpB/+dG647gyMNnsfupxHNs2/brNhNTvQ7ToimSIJfaEuLzwpClYMS72AZnsXLGoIUBPYF5gNM5l9UFdgjoSEoV4vqWtUQaEB8ccjH+Kj+XtCoj8fOoT/14U/2PC6qESLFTL4H2/+BebyCRxwt+LXup9D34HXsdehl0vIlgqiOOOk2GHe/GJkCs8Og2hpwvnQr+NI8XXM54Ywk7uPaGFCxhOFUlrGCRw3C3kuyl8HXJofXq0Zfls7mm19aHPSg9m5z8UbJsO+xXoOJuevwWz/KmB27/RO7SpwjHQ/dWvp3wzaUDnb6x7Aq01fFpsSj7b+PPSk/zwyxTSuxD7C59GPMJEZXlWVTuLzTuIqDnmOw6sF0LRJ5OZ6ILFKonkhPysk7nJxGEnWXtcBnA48jyNeZhP7njpOZHtRdT+WGRY7D5LqJDY30+udquNaPbDvJq7jw4WfNSSJzgAEAxh3ktekWC198dcCzwcJ6FZHJ84FXsRB9zEhv2vxmef4PlWMYzo7gYnsKIZTd+Wctzu60e8+/MwZ4kZBZx2fRt6XbA1mIzy5/6zj1YIj3lN4MfSmbNtmWX/fq8r5wdQdXI58gLuJG9KfHp9T8lqqEulvtf6qZCrUUyC1VC6L7/n/cv1DDCYWJAjR7HCj1xPAiVAbWhwe/OXITQwl1q4f5dZsOBlqh0ezIYwMpjIJPIyH8VzLztacULM6BYV9AEaU6Q1XD/gwp/Jkp2GGGQ6Lq+4JmaT7VQryALBvclGMRgCtdo57X5L2UXgyaDSSvonSBooT0cql3dmPRvfB537G9Pm61ej5UhoTmXsI0qrJooYAtYABvKCtXZQcGxkAc+KjSHQFhcZD0OaGS7OLAjqqp56ZSI7raSGSCbdmh0/be2OP1TCVjeL9uTuYycbwWutRXAwdhLavCdbGh8sSQK/7HLpdZ2TEXFmiVVYm2RskkKFL5zicROdO1UlqHLBN3DC7/gAmxzdFia4olWcH7TOOes/gN7v+vsz7ahWzUNH9UuhLorxl0chbiStrfpe2HlTVbgeJHi9E8WnkPSHTSYRW0esawMtNb0sAgIR1rSIxfi9kaxWF9Gn/c7JOkpIKT4LjbirPfzz9XSSKsTW/xzk0AxGvt/wKTvouSECCwcFayW/+npkEHq9fAjT03Q/n5+RuSjX7s4J2OaPph7gVpwXX6oGADkevFAF9IfSGZFjUQ9zTbobq9RZ7B34x/zdI6E+2FXkUBqgOe5kZFpDrtFZQZf7J7CjuxmZRqlRwxN+M3zp4Gt85cApem0OeJJ8vTKxLolPk4LXa0eUJYC6bxkI2jcn0ztv17qk7frFYQiqZw9REBIl4FtlMAblsAfl8UT6rore/GYeOtCPU7N3R/VVQ2GoYg2J6quksEVTXb81mbVvS3Z4GqshJGm5k0M6UPwYP7NhbE1kOpAK2FvS6TyxOaJTiazlILE9mH9bt08fCnUFbm6QqNyo4ia1UTFJsdyr7sG4SndXo5/PjKDW4h2FDwWSWzI+NqO54363HR11BQWGbSXSLDeFKEtliAQk9A7fGtG3zhgrlUYlOOnKJRLftrbHHWniYnMHn4SEkihkc9XVuQakzhc2GQYhb9rmavF6YAUsHTLZXYbZ/zSDPLc2qgOgmgHYZRzwn8ZW2XxMf6mrQphbwexQq9LkOodRcREwPS/HO1cZetK5gkclu1wGEbFtrtUWrj1QpKXNRgvt4yncBLzZ9CX3ug3Ba3HWpeo1rlm1ihsUEaBVN3WvXAAl02p/E9OiaY3AWYz3lfw7Ph15Hn/uQBGOIekR71e9aGAhZvJc229vk9VnvrVTTs07AO3M/FBufxwOcRIejB883vY6zgReWlPO17H+VNzCZaA3rxyn/BfF6/9vp/7IqX0Qu5XrsMoLWZhzynqiLRL88NyEEOhXlv37gJL7VdxwhuwsWs9H3yWKsu68mkxDpHS4P7moaEnoekdzO15va1SR6uVwRknzw/gxGh+cxMx1DNJxCPJZBPleErnMpoVQso1x+1CFefPUImlt820aiZ5I5zE1EMPZgBguTEaTiWeSyeZRLFVhtGhwuG5weB0JtfrT1NqGtO4Rgq++pF4L4L+Z0TAzOYfzBDOYnI0hE09ALRZgtZrg8DrR2BdF7tBNdAy3wBhovzWwrjyEVz+Cd736KmdEwfEEXTr54CKdfPry0TW5v5O4U5sYjyKRyKOol2J1WeAIuNLUH0H2wDYfP9sq6dm1a6mJRknosH6rgA4EPmJ0Gb/Qb9W/mwKW8BwksrzUont3071Z4UvmbKSUxmxteGrjWCqqNWQeABX0aGbwSWOzSbq7fD44kejg/WXfb7GdU1f/1WkoRJNRYyE1BQaFRSXT7kpJ8IZ+Ew2LbEInO38f0NMoVY6JLhbvXuj9I9JHUPMYyC7CaLeILr6Cwt6AB2gGYrGcN+xbtJGClVYNNguwKz45OZy+O+c6Kl3M9BPrjHuM9rgGxtPjxzF+IJ/njYIbyRHZYfMq3mkSXAqKLc1Cb2YEuZy9ebn5byH6X5t4UkdkuZSe2FFRv017ldvyLdUUsJI4vhl7DAfeRTTkfVWyWAJHWLWOZQYxkHsjc7XGQNCd5ftx7RhTiG4VlmR0Mi5aOph+sWkdvJP1AvsNrzF6jN3o0n8X9uKGgP+RvxolgG9pd3rrHWOznfptTxhiJQh7pYn0WxVuBXUmil0plIcpHBucwMjSPe7cnMTo0j7nZuCjRn4aBw+3I52pL85+fS2ByPIJY9FGaJ6OGHd1BDBxqg8Wydico5HVMPJjFg+ujGLwxgdH705ifjAq5m88YJLq2SKK7vA6E2v3oGmjFiYsD+OrfeXnd/SLJTGL+zuUh3Lk8jLH70/Lv5OMEdHcIfcc6cfR8H449dwAHjnfBZG6MAmdbfQz5bAGf/vSmrJ8BCofLjuMXBxAPp3D1/bu4/dkQhm5NYHY8LIGOKolOor69rxnnXj+6RKLvVnAqt+FUL7ZvA/QThdWtXFrsPQ3r272TYNGUpB5Gshh5olr600CLFM+uCEyY4LM1y6C8XrA2QrQwI/6Qe7VewGbDKHTl3FhbVSoq3VZBoaHtXGxLSvL5XALtDhYSq396VCwvKtEry5Toe5xEl8J35SIms2FE8im0Of07vUsKCpsEO2AOAZZumLQ+mLTjMNnOAZaDMJlrtzNQeDroSU0l8IDn6DPNa8gLuC1esUlhIc+J7Miq/ujTuQlMZUdx3Hd2W+ZRzBgmYU/FM+0z6i3OqFAfYoWwnPu5/MyaFiy09CEBPbDJBPpmH8e95A2x5H0cFPXQhuaw9ySaFpXvz0r8N9tacT7wImZzE9BXIalpi8NCvrSV6XDW5kfODL+5jBHMGvCF0EQF+kbmUiaTFCPlb4tS3HXnhWC7jkQn+b0wl8DNa+N496c3cfPaGAr5rVM9zExG8e5PbuDypcGl9zSLBa+9dRwtrT4Egu5VB5UkZIdvTeCd736GSz+9gdmxsHxm0SywO6xwuB0SvC6XysjnCkjG0pgansfYvRkUC6V1SXRa0/C7H/3tVfz0Tz/G9OiCEM6iaHfb4fa7UCmXpV1IEj+4NoZrH9zDi79yGt/6w9fR0d8Cm2NnlZbbfQzZdF6CF7H5BD77+S1893/9GeanojCbTbDZrbJNBmf0vC7vk4Bv6wk1RLDhWSDuhRKp3piKuxFsCEiEMgLL66pe8Nj3Yqqq39aKJnvnTu9GQyJdjCFSmNlQ3zWsXHYHCeDRgpKhUS9KKCJdiiNXTsODALR97236dPA5wAFmPWm3CgoKu4NEd1oMEp2Tsvl8YsnTvF7oYueSXsr82xckOiqYzycxl4sjXXqSrFJQWBcmN2BpBcwcz+aByuIi2VvbaVTBOZIGmJzGPpk9MJnbAO0ITLaLxmLyL9k1KGwuqKKlLUXVBuNZoJk1+K0hIRdpgREuzD3xnXghIsUfM6U0vE8pXLoZMBTyB/Bc8LWailUqPBtI9I6lB9e0seU5OB98Gb2ug2Id1IhgwVL23QfJ26t+bjVrOBO4iCZby6YFgthPj/pO4/2FH4uP/2ouBrO5Sczkxmsm0Ul450rGfNxnc8Bm2eC+iiDJsCg2qnLs/Hxs15DoJNAq5Yqown/0gy/wzo9viO/5VqO51YdQkxfzM4kV7z+4O437d6bwwiuHn9hP2seQiP3f/p8/wK1PB5HP6jIJ16wWUUR3H2pDc0dAVOiZZBbRuSQmHs4Kkd7R34wzrx5Zl7xNRFL4yX/6GD/+kw+RjmeFCPaH3Dh8rg/9xzrFjoR2JZND87j7+TAWpqKi8P7Zf/4E4ekY/uiffRtdA23yu53Cdh8DSfSFmZicj3/9339PyHmv34XOgRYh5Emicz+oSiehz/N06MzuVqET0u9glSg/ifR6lLkkIfVV0oe2G1RxFsqZuj3dCRLolj3oVeizNiFk69jp3WhYEj22yqC51nZ1WhpzQPU4HGb3hvs2r6m0HpPioppl710fWwFVSE1BYe8hsFhYtKpEn8sl5HUjKJbLiOuZpYA/vdW9axQWlbGY/M+YGEpZx8eEAvQA5USRr/UIOkTIUykvZcBQuaWZLUuficUU51SLE9IqDAsFLG2TvOLjAgyu05jIGvtOFfqN2BiihcyybZeQL+lrBiMs4l/MbdR+T63u67ptxXVuwAZCYedgsh4FTL8Jk3YUKI2iwqU4BpTDi2R69TxXHvt7tdenbm3Z6/K/zYCJqvMWmLTDgO08zNaLMNHv3Kxqp20HqKilUnuz6jtx7kfbCap4VyPRSQ6SYJ/JjsPrPYmtRqu9XfzeHUqBvuXgs4F++JPZ0TWtar2aX7ICWKC1UZEppiTQs1r/JSiiOu4790w2Lo+DVqYBaxNa7Z2I67FVFfDcH+5XpVKp6VnLp7Jh88YadUYB63phPP8Z/MpBL5dFke7Sdn7uuntI9HIF178YxV/+2ae4cXUU6dT2KB5IovcPtMAfcImFTBUzU1FRwz9OohO0C/n3/4/v4+GNCSGCqXTuOdKOb/3R6zj+3ADcPicsmlk6H33dy8WyeHJPDs7K7w+e7ll3n37ypx/j8s9vigWJw23HiecH8J1/9DZ6DreLkttsNhsplnoJ8xMR/PzPL+H9H1xBIpLGlffuYuBkN770nYvoOvjsEd+NYruPgfY5V9+/J0p/Dpq+/V99CS985RTae5skmEF7GPYxZhBE5xPQ88UlX/q9AG2x2nQ96lx+l75iOw1OmfKlrFhP1AMOxhg8aITiqJsJ2kq4LL4N+WHvB9APPVk0Mn/qAQdWHksQtsXCMo0OFiTSTBt7hHM4kinFF4vuKigoKOxPhBYLi1btWObzcSHDNwKSx1Eq0SsV2EwWWS/91dcCSWYS0NdjY3iQnMZMNo5MKS+EsM/qQr+7BWcCvTgX7Ee7s/aJMqep/78HP8WV6LD4vb/Rehy/22dkt2ZLBdxNTEoR0DuJKUQKKRRKRTg1K0I2D7pdTTju78brrUdX3ff7iWk8SM5gLD2PqWxUFgYe6AVPTGQi+LeD7+LPRj9ec/9eaD6Er3WcleN6Gki4k2rPlXSMZ8L4eOE+7iWmMJuLI1cqCHFOtf8BdysuNh3ESX83Whx7Z+y+52HywWQ9JYpvVHSYoBse0pU0UI6iQjK9vLD4GgHKCVQqKWBpyQCVnPzW8J4uLVOxkxwn0WIFWN9JVOY+mMxBw6bFHILJ3A6TpRuwdAJ8X75nB0wkO/fW3KGRQRU61eObBRJ3nY4+eDS//L2aojZVTGI2PyWK9a1Gi6OzrmKMCs9WzDVaWJAsg9Xg0ryitnZYnJsWtNmqwqhUfa9VhJfXjNvi2VCtpvXANmm2t2I8M7QqiZ7Q44gVInJNSTHVp8ChaQjaHeJhPptJIq3XZqf9ODguexhfQLKQR9DuRLNz5y14dgWJnsvpGBuZx1/82SXcujaOZGL71LGaZpECpP0DrULiV9UP0Ugao0NzKBSKsFotS9EYqsnpwU3FM+1DaN9y8sWD+OYfvi5Esb/JI+89Dtq6UP3MAqj0AV/TAmVoDjc/eYiZsbAUJe072oG/899+XUhlFid9XJnt8bvw9uLff/MfPkAunccHP/wCfcc70dbbLOr47cROHQOzAyIzcdned/7xW3jlG+fQ2d8Cu8v2RCQt0OKV76/nd/8EzCGYXf8QFcevM6kXmw2TpR+wdG34906LVwjlekj0QjmLbHFlBsZOgPsc1xdQksFx7WAhQJvZtekPmEaw8XBZvMpaYg1kS0kk9MgGChL5xGO8EVLEai0Es3GrogrSxURD2DUpKCjsX9BH+8P5B3h/7u5Tv/srHafxYvPBTS3W6daccGkOaCaz2LHM55JPKKhJ1v5s+oYQ3gk9K4T2S82H8ULToRXjR9rBxOmJjgo8Vga77Yai+zFQpT2aXsCPpq7iTmIS87k44npWCG4S8UYxdQ2jqXnZ5ofz9/BKyxG81nIMHs1Rk/orXEhiMhOR9RzJdqBULmMmF8MPJ7/AteiIkN8kvkmg83gtZjMcZiuGUnNSXPWFpoOrkugfLdzHpYUHmMnGhPDPFAvSbkttsBhI4LIWDuTahBSvBVSw0af+R9NX8fOZm7Jd7jd/z6AH28Jmtkhh05vxcSHm32w7gfM1EPTbDc4f8+UirkRGcCc+tdiGBVHlB2wuHPa24XyoHx3OwMY8Y3chTDKGIbn92DVdKdLIESaxd8nBJEQ5RT0FgzAnUV7RUZFX9r9ytQrUMtX6osq8upg0XlmLJHl14bY9MMmrIs13ClQEe7TNVf1T9U2rC7fmRbIYX1Xpu5A3xItbCRKdVNn7dkW9pd2P+dyMZBmslb3OfnbCd14sXRo5a4m+41R8rwYGALqd/dBMT3JYzwquL2Rrhd3iXJXOoviKBU+TehwB29MDX36bAwf9zZhIJ3AnOofJdBynS+2iJq8VCT2Pz+bG5bf5UlEKk/Z7g9hpNDyJTp/q+dk4/vb7V3Dji7FVC4ey//gDbjS1eBEMueH1O2G3a7BaNQzen8HtGxMb3r6s2+/EwSNtooCvZhDSmz28kMLsdBwdnYElIjc6l8AX798VuxIS492HOvDcl47jwpvH4PKuPfCnH7g3sL4Cslgo4vpHDzAzuiAK9/beZpx//ZiQ8/z9ahcS/dcPnOjC2deO4uoH9zAxOCvL0M0JHD7di7beJmwndvIYSKDTuuWt33xBipXy36uBmQP1yjNlAGY736AxTRP81iYhlfPl2i2QssUUYvrGbDE2EyyEuJCfQKlcX3CCthxU6zbyQ3IjcGsB2C07H4FtRDBbIVtKIVWM1vU7at3cml9S2XZDfzH2kenrljVVNk+bzOdKaUWiKygo7CiyJV3UzT+bufXU7x72tuN8qG9Tt0+SlsQ0LV04OaO39+N2Lnz/vbm7uBIZQqKYRZczBLvFKiT6cvB3JHhpeeK3upZsYlauS8ft+CR+Mn0Nv5y7LUEEEqYhuwddrpD4s/P3JOvpz/4gMS0KbO4X94Nkequj9rodDABE9TTm8gl8f+IzvDNzS9brsFhlPXazJsRuWs8jWcwiqWeXAvSrqfToId/vaUGT3buU1TSViWIss4B0MS8+8D2uZnS5gmuq/E74u9Fkr802LVHI4vPIEMYyYdyOT0i7svArrXKIdDGH6WwU4XxSjovtSYuZNocPrXY+0xtDRMF2YnDi44WH+MXsHQwl5xAupCQYwPPvYbu5mzCeieCN1qOiqN8NY5EtgxDe9Chff168j1toz4CCEK+V2bWbZ3XCa4f/BayhNUn0bCmDaKH+rNV64bcGZT84v1DYetBuJLWGANCY63nFn579rpGR0A1F/WpgAKDV0bElRXF5T/VoPljXsQtlnbpksTYSvdXpwXMtXXh/ahiz2SQ+nBlBh9uHc02dNfmjx/M5XA1P4c8eXkMklxVe9oAvhBPBnXPTqKKxexA7UTwjJPi7P7mFbGaltYTZYkJTsxcdnUH0DbSgt78F7Z0BNLd64XTahHz9wfcuPxOJTnh8TvQfbF0c0Dzy8smk8xh6OIvmFq+Q6CQmovNJ3P50UMh/4sjZXpx88dC6BHqt0AtF3Pj4PhJRQ+HR0hXEudePrkk+V+Fw2tDR14xjF/pFBc7CpaP3pjA5NLftJPpOHgPtWfj7roFW7DcEre1i/5FEpC5FL0n0YpmDfG1HBvTiuVnJYzY/ItHPeslm2p7sNTA4QMW0wpNgHyE5nC8/st6qDdQsFDGeuYv0KoPtRkWyTsX9yusqt+SZu9th+OWVJSiwfGFxZAYYKnwVLz75lwRbFh12l3679Lr888VXriuhh6VvKSgo7C14rQ74NCemizEs5JKiKK/6fVKlzYKZI+k55Mq0m4D4ng8mZ8Xfk4WZq2Mj/k4Ki1YqYjFStYlZjrH0At6ZvSHK6kwxL57sDA4c8XWix9Ukv2PaMgnXwdQs7iUm5TdXo6OiVCcx/1LzIbF7qQUk7ScyYXw0f09U6CTpqdbucYXQ5gzIvxnI4H7PZmNyv7vYNADbGoTPi82HhQSvqvWpBv9g/i4S01kh0QNWt9i1vNF6DNoaE3yv5kCwRhKdAYTh9Jys+7ivC8f8XWJzw3YjYoUU7idncDU6IvtPtT2V8od9HfiV9jMNQ6JniwXcS0zjP418jIeLfacKtmWkUESkkMZ0NiZPaKrRq4EKBYW9ChLdLosLTrNrS0hmr9UvxRJXQ6GcR6oYl/EdM5a3ao7rt4U21bdaYX0wMJIppVb9zGa2wWf1S79oZBTLRaT0hFgOrQY6CwRsTVuUkW6C3eJYl6DXywXJ5KgFzQ43LrZ0o8cbwFQ6gfenh8WmLasX0OnhubBLIL/6LMwUdUTyGRENxAo5sXB5b2oYv5gchF4po9cTwOmmDiHSdxoNTaJzEDs2vIAP3r0jhPVykLRuavbg9bdO4CvfOIPu3ibY7E8eDsn0Z4Xb45D1k7QvlVbazIwOz+PC8wfk3+VSBalYBlPD8+KvzRtyz+EO8fl+VkixHp3E8QyyaUON7wu5xdKkFvC7tEt593ufsTKQFOmcn9oYAbNbjyHU5sPAqdqqCe8lcFgQsnfCUad6meQ1STqqellw0bQDtigkvkhciRK9ThLdb22GdxM99hoFDIZYmYaq8ARypYxEyOsFqdX5/AR+PPNvsZ8CDhsp1rtTqJLarFhfJkHO/+RvY+F5p39frpREppxCvpSW9zjYY8BALxVQqtAuQV9GtOsoLvu7VC6ihKJkvVS/U/1+dZsKCgp7CyR1SV5PZCOiJM+XdQmnWWASEppKZ3qHU7VO1TDJbHp/p4o5+R2/JzV8yiVRkPO3ftuTSnQqpN+bv4P35u4IKWw3G2r23+t7GUd9nWK9shyxQgbvzd3Gfx79SHzI7yam8O7MTVGDXwwN1ET6FMpFPEjMIFb4UPb9G13n8HbbKfS5W56wmknpOdl/qsPWSrXudAYBLsvWT7K/+n2+djgCOO7rlvZ6VkxmI9JOVGb/7wbexHNC8D/aN7Y7Q6X/bvAXYo9D0p0K73dmbuKNluMSJGgERTf97j8Pj+BuYnrd79Ff/lp0TAIVX2o7vm37p6CwE6AymL7lW6XSpup4LRK9DBZA5rgxAxezlrdojuuxeOGy1BY0VHh2JIosiLm6kMqtecQ6qJG90AnOZejpvpZ40Nj/itjWbLYa3eBdsvJcXQvcr+wabfw43FYbDvtb8BsHTuK7gzcwnUnge0M3cWl2HC+39+GwvwkLOUOgRAJ9KB7G+9owpjIJ3FiYxs3IrPxNOC1WfLn7EC62dEkdl51GQ5PohXxRSOprn4888VnvgWb83t97Fc+/fAhOKUS5dReEzWaRwqIOhxVF3VCsVVXVczOxJdV5LpNHMpoWmxLC5XWIBzpfnxUsQJrPFBAPJ0WFzSCC2+uEL1jbjdnusqOpI2D40ywWP2WRzu3ETh+Dy+NEc/t+jAab0GrvFV/0ekE1+lD6Gk75X4Ptcc/CbUBKj2Ayc39DxGjA1irk/16D3eLac8VSNwv5skGcKtSmdNhNJDqRKsYQzk8hXJhEtDCDeGEB8eICkosqcUNdbvihVp/T1UrwjyrCV/8tDMyytS+vGb/sOxuoJK+goLDLlOiLPuskwOnnTfKcJHiymBPymp7ina4gdeeYzyfFD/xufBJngr1iLUIrF5LrVUUVbUec2srnNMlmWpKQUCUR3Otuwj84+CVRoK+m2uY+vd56XMjo/+HG98Rv/NPwQxzyteNMsA/2GtLReffKlvKIFSz4Px75Gl5uOYJmu3dVCoFWLC7Z58aiGI74OvCr3RekcCi96x8HTc2+3f08hlKzEtygTzszBQylv6shjoYe9CyIWgums3HxTFckusJeB6fz9i2sRUQSXbyd1wBrbaWLKThYP2uLbhNWsx1WZeWybaBCeq15IDO5g9ZmNDrSpSTy68xlp3MT+PfD/3xRib7ZHbciRDoFSGuBn5fqsAMNOVz43x9/EfFCDj8euy9E+mgqisnBuAT3q+OmyVQcf/rgC/zZoFky+qhML5aNORif/W93HcSv95/E0UBjOEo0NIk+NRGRgqL5/MpIDK1VvvTlk7jwwgBcbvuWEugEVQwsMNrS5kc+HxZyn2BR0bnZhBShlH/ndeSWWc44PXbYHJvjsUt/9Ww6L2p3QrNZZN21HjsJaxYsrX47ny3Isp3Y6WOwWM2yvf0IkskhW7sUpMyUVk8PWou0uhP/GIc9F2E11VbQajMR1WfxMPVF3b+zmRwI2Trg2wUPy+0tKLm3kS9loCsSvQZQvVdcIpobEfS2j7E6fX4Uc7lRRPLTyJQSUtdBL+dloRqCNROoIlcqcQUFhY2ARUCXipVWgGieavQiXLCLOpvkJ0lyenF7rBwHmTGXi+FWfFwIXpLo9Lamp3gVPptRWHQ5voiMiH84w3VUk3+5/bRYdpBAX21sRaU4bWYOetpwNtiHm7FxUbAPJWfFK/1UoKem42Mw4JivS2xaQjbPmkUrqz7CjQTu6yFvO54PHVxV2V5tt5DNjQ5nUDIAYotBEHrBN5d9cFh2vkhnQs9hLl/b2Jt9jsVUFRT2Oni/oT3l1thSQHydtXWCjSQDqfrdSkEJVfa031DYWnA+w3lArsQM1NUV3BSgOdbITGgkUOVNu6G1wCNdj2TfalBsUHqsdsx64FParVnxR0cvYsAbwk/G7+PKwqQozxnKWr7efLlEwnDpPRYO7/EE8LWeo/hG71GxcdHMO/9Mb3gSfXoqiqmJ6AqxmKaZceR4B1589bCow7cLFs2MpiYPZqZiSyR6sVhCPJpGuVxeIolLy/xeSLxvFsEvN4fF7RB84JjqWDcHmvQdr4JEdpXM3i7s9DHw95YGGExvNyQIZLKh1dGLQLYNmWztJDqjuTO5YUxm7qHPfRLOTa6evh7oTc1tT2Uf1v3bFh6rtXVP2p6QQGcKpMKTqBKsCjVAPFkbh0SnqoFq8rncGObzYwgXppHQ55HUo0gWw8gUE9AltbFx9llBQWGP2LnYHs0nxNKlRLurili2sPApPaxpZdLq9IvifCw9L6T21zvPyW+ERNcfkehGYdGVSvQHyWmxGpFtWp24GDoIx1OKWVvMZvH/po85LV3oyz6TjWE4NVcziU7/dJLwJNA3w2JlO8F97naFnuoPTu9zBhzcFjtiSMtknAEHo0jszhNYtL1hv6n1u5kav6ugsLtB/2U7TFskDCKBblnHpoUWgflyfkUe4pbsQ4MXsdwrYA03w6qysua5YFHORgcJ9HqU3tsOqSFVqfnr1TFOnzeIL3cfFlL8legcHsQXMJ1OIJzPIF0sSD2Yqi1cwOZAu8srpPnxYBtOhdrQ5wnCWkMx0u1CQ1/VC3NJLMytjMYHgm5Rovf0b6/ClIoQt9exghQngZtJF2jPbXzHYl5B0pJkX04aP9P2zSZYbRxsG/+mhUxVAV8LuB9FvbgiKMBlO7Hzx8ANN5bKZjvR7hxAS6YH09nBmm9+jHZSuX4z/j5cmh8d5gFJTduOB+FE5h6GU9dFfVov+t2n4bdWiwHvLbAAzlapNnY7mH5WbwHa/YtKwwQ+WLQznJ/EfH5cAmezuWGxamFdBgUFBYWtVqKTgK3eFelFTiU6C1tF8kmx4qBiscsVQr+nVVTC/JykdlLPScrxqiT6ohJdfLsrZSG/03rOUGVZ7Oj3tNREajssNik+SkUWQbsZrqvm49Ps8vu1FOiNjGaHF002b00FQukpX21PKZ69WCBWQUGhMcF7IQnmrcqAsZis62buVgvHb+V9QgSDe3Au2miQe36lsG5WAf3Dd4O1DpX09Si9dwaVDf2q0+0Tcvx8cxeGEmFMpOPiiZ7SC/LM5pVit1gRtDvQ6fbjgDcor42iPt81JHoinkE8ttK4vqM7hLb2gKi8txNUTNvtK61H6PFNC5fqzddmt8LhshtPhQqQTeWRz+qiUF+uoN4IzBaL2MNY7ZrcjOnHTisTksoW7elVpelBnk7kllT9Trfd2NdtxF44ht2MZls32hz9cGk+UXnXClol3El+slSctMneuWVRdV5LfACGC1O4k/hESPR6QIU2ve0OuE/tST90wigGpgZkq0GKPzb8wEPBIJWKSBdjmC9MYCR9Aw+TX2AmN7TTu6agoLBPC4tWES9kUCjp8jqZjSJXKkhxy05XCAOeVvHb5j1sPp/AfD6O3lKTfIdFOQkSQlSaVz3ROaqhgpyqdvqa076Fny3f5nogMdzi8C0VEEsV82JVUivov04f9N0YfBd1+WMFWtfC41OIRiLQGQBhUbRaQAKBtkEKCvsFWzWjYeBwvfse7xBqzrB3wHO53n1fSPRdYK3D42Dgfq/CbDLBb3fgfEuXLLsVDT2iSqfzyKRXKtFaWn2iRt9ukOC1ObQV9iO8UOmLXmV1HW47PAHXEsGfTmSRiKSQzTy7mo7kvc2uIdQWEBKaxHwmmUVsoTZrDhY9XZiKLkWOfCEPfKHtbce9cAy7GSyu0uk4iH7Xqbp/S8Lr8+hPcCfxsdgqCNm9yROUKoFOS4dPwz/Ew9QVFCr1eX7RW2/Aew5N9q51i8ko7E2UpZ743h147AXwOme2QKK4gPfn/wv+auJ/wQfz31UEuoKCwo6AJC1JbxYN5fgyUaQfaREL+QRG0/NCxNBShD7mzXYfWh2+JauWh4lZUYaTJKcqnXCYNVGak7wmWCCLRS5LlUcFsmjjUitYOJOq9qrQRC/T+qp2uw+SSCRmd2Po3Wqhuryh9V41gf2LgYxawOBKu8O/5fukoLDTeLzs++avvz7bCYXdjVp4id2QFWD02cpTRYM7tUh2BRq/HbcaDT0yKeol6PrKCKHTaYXdru3MxF9nhOvRe7wODfsW0xJJ7A240X2wDaP3p1EpVzB2fwZj96Zx/OLAM+8D1dqHTvdgfjKCQk5HLJzC4I0JNLUHnvrbeDiFh9fHZJ+I1p4mtHSFsN3YC8ewm9Hq6Mch7wXcT34maU/1IFtMCJEe0+dwMfh1dDoPbqp+gF7Wc/kxvD//55jMPhCyvh7wxk71+UuhX4NbUxOQ/atCUCR6IyNamJHMlmuxd5HUI1IMVkFBQWGnwMmg02ITkjxSSElhRyrGqUQniU7l+BFfpxQQ5YiH3zvk7cDV6AgepmbwQuHgUmFRTi1Dds8Sgb6yaOcj1EPsPCsFZOSu7c4Jr0zVd+eur0CnM4Bjvk58Gn56sJgBG3rYKyjsBxiK260huouSnbr2nID3ReVXvndg5nN3nQcGLVJo+7krap+tk0HR4ejBNzt+ZzHLYvsfkE6LC022Fux3NPidY5WOwYtjBwZUJG6piqd6eoXFi+ORxzcHycFWH069dAgTg7Molku4+/kweo+0o+9YJ1yeZ0vPo13Mc2+dwO3PBhGdS2BuIoLP372NC186Ds26th1KNpXD5NCc7AstaPjd/mOd6D7U9kz7s1+PYTeDVak7HAM47nsVtxMfolgHkU6FL0mvB8nPkdZjUmi0130SLfYuOCyeDe8TleezuVGMpm/JMpm9Lz7J9VZLD9hacMz3IlodfVJIVWH/gYNlpURvTLDOwWjmFu4mLmE4fR3h/FTd1/hOgRMtBumY6aCgoLC3wHGnkOh2jxT+JBnOwqIL+STGM2Hx4iQBSm9xfpeFPg8vkui0dqESnXcIeqLz85DNC5tFW5G67LLYFq3YSOywmF3tRcMkQ6/8yDqS/uC0l9kf2Bv2de3OAC6E+vFJ+CEGk3Pikb8aDnvb8FLzIRzxqrmNwn4Afaz1LbOuYBb1egUaed+2mmy7Qp2ssD6Mc2mlTnrXk+gsgLrecdjNDnQ5+4XM3om+y33T9kCG2LOioVtA08xClhbyj26AVKYX9e2feLMIZmQhJcVCl/bPYoHP74J5mdl9qNWH828ew8c/uo7YvEESX37nNgItPlx44xgCrb41/dxJFCdjGZRKJXT0PRnhYVscvdCPAye6EJ6OIRFO4ealh/job6/h7KtH4Au6n/Bez6XzuH15GJ/97CYWpo1CRAMnuzFwoguB5tpSCzcTe+EYdjMY3QzY2nAm8Cbm86OYz0/URaRzMpcqRjGYvopIYVqKANJnPWhtg9sagFsLwGXxQDPbocnDzLI0/ymXSzKY0Ss5ZEsppIox8UQ21jOCmeyw/L0RYs1l8aHXdQInfK+IpYsaEO1fqDPfiP7nOh6kvsDt+AcYSd9EshjZ0m2S8LaabXIvYCFkBtU0sw0arKI6olpFXheL9BqvjxZL9f3FfxMMIA6mriFTqr2ehIKCwu4Ai3eGbIYYgEp0EuMk0alG92hOHPF2wKMZQhgq0Y96O+RZM5OLIZxPiW857VyonG56TInOyT1JbyrZqWqvFiJNF3NC3j/Nq5zFtsL55BLxSkK+Vj91hcaxDDrm78Rv976AX87ewUhqQfoYgym0C/JbneK3/2rrESHR/TbXTu+ygsKWg2HBQjm3ZeKX4lMKNBpWV8wwamh3Y4UawaKhJKD5zF0t24scRGEXkOh2s72GAqgV6bvVWikK24+GJtEdThscDtsKEj2ZyD7hk74dBHo2W8DcTHyFvYzVZkFzq2/R0sWAx+/C0fP9uPDmcVx+9xZi80ncvzqKTCqHVCwj6mlv0AWrzUg5obK9yAKbOR3hmTiic3G4vI5VSXSSy01tflx8+yQiM3HcvjyEqeE5/OW/ekcI+L6jnfAEnELS04MxnylgZnQBH/7wKi6/e1vWQQ/xV791Dn3HO4XQXg1VtQv3rVyqCKnPNkgnMsilC0ufl4plIbhJ/LMNLJpZ9lEWM1MwTTt2DAprg8VBe1zHcNz3Cgqxd8VeoV6FJR9EC4VJWR6mvkDA2oKQrRNBexv8WjPsFvcieWVURqfKgGQ9LVvypTQSxQgi+Wkhzbnkyxu3dLCa7Oh2HcUx74voEIsZhf0KSYHb0GCYJIdTSNfdWHytXlhMVjgtPpi3OI2VzwoGzSYy9/F55EcYz9x9pmv9cZD8tlmcS+fOysVEL2I7nBaPLLwXsT6CzewSxRHvSyTYSayzHUimVxferyzm5e/xb96/SpIlw4wZRaIrKOw9sOhjsEqiF/OYzETEE53w25zocoXgWCwM6dWc6PO0iE95plTAXC4uRCgV7KJEt3thX06im0xCnlONPJicQUzPiEf6RCaMA5422J7yzCHROpyaFzLd2B+X+LI3CpZbxVQ9jhWeRIvdi291nZNzdzM2gclMFOliXoqOtjn8eL7pAE4GutFUo3e6gsJuB8eIuVJ2y4p75su5dZXHHO9T+FXNElLYveBz1gJNiGUS6cxweByFch6ZUu1FuXcKzkUOZS0wMMTj8FdCSjm2g2hoEt3rc8iSiD+adM/NxBANp+TGu11q01xOx+xMHOGFpBDHVdjtVnT1BIU8Xm7x4m/y4vf/6deRiqVx89KgkM+DN8YxeHMczR0BdB1oRaDFKwr2LEnoaBrzU1EpsMnAwcW3T+Brf/fVNffnlW+cEwV3dD6BqeF53L08LL7rVGfTOoZEvl4oCvl8/+qYfI/whjyi9n7tW+fR0de85vr1fBGJaBqFXAG5TEH2kUU9SXpP0qamYDzsUvEMxu5P4+alB3A47XC4uNjk1blYZJVts9p52upjUFgfJJkuhr6OuD4vpBZV4Rud+GRLSVmmVykMSEKTZBRJ962wbiCJ1uLoxWn/6zjsvbjp61fYXRB18Qai8iRK2xx9klXhMO/9YsVsIzlWy9YdK1UgDJwt5Cfxs9n/gPncGPTKswbA6V9pEXKbhDdJcl7/LfYeNNk6EbJ3IGBtg0vzSl/YLB/grZrgKSgoNI4Svclu3A/TpbzYuMT1DJwWO/rdLVJItBpgpVULVeu97mYMpeYwnYuK6CNVzAkZ87gSvYpDnnbciU8Iic7vXouOotMZglX8T1e/V3G9tIm5Hh1FvmyQAi0On2y7EcC95jFX77USOBWyXxHpa9kGvdF6TBYFhf0OI7s5Kd7lW8HrcN25cnbNzzlOdGsepUTfQyABbbM4oBefJNGzpTRiehiNDpfmEcuWtVCssAZLHO3o2tb9UthFJHpTsxdNLV5Mjj9K/ebfM9Mx6IUSbNtUYDQ8n8S1yyPixb0cJLwHDrXD+pgamsRxa3cIf/TPvo0f/ccP8cFffyEkOceUJKKpThcLGJMx4KTfuqi+yxVZ59Ngd1rx9m+/AH+zF9//V+/i3hcjouKmX/iDa2OLDyEqyMtGMdRyBa09Ibz6zXP4rf/6K/CH1lc5XP/oPv75f/enKOR1o4gn97FSkf0jsa3njRvT9MgCfvwnH+Od735muBZSfc7/zCYprvp/+n//Ptr7msUHfbuPQeFp4GDejVdaviODiOuxXyJXTm3JAKkeu5h6CfRmexdeb/4dHPCcUsVhFFbYb9T3Ow1dziM4F3wbAWsr9j5Mhm3JFl8zC/kJvD//3U0i0Jni6BTy/4D7NHrdJ9Bs75aA4CM7FpopMBtBSTMUFBTqU6JX7Vzohz6VjSJXKsBjdeCor1OU5MtBVfrpQC+ms1FMZ2JSiDRTLMj7IRsn8U/eW59vGsDnkSEh3lnA9MfT1/FqyzGx+ljrnpUtFTCWWZDf0QLGZrKg39WCo95GmTyb4NSY1WNeajtazywmrCooKCisK7ZIk0QXtThvGps7dksXE8itUbyeQgwXMxXXISsVdh981gCcZhfSSD7xWbqYQrSwgEaHx+KTvlkVIa6mqJ/JTmLAfRT7pTpKI6KhWae2zgA6OoO4fmV06b18rogH96Zx89oYLrwwsOX7QCuZ8ZEFfPLB/RUkOjlequRPnu5+gsyXlBLNIgTyr/6DN3DyxUNi6TJ4c0yIZyrPc9mCEMMkzZ1uB7xBN7oGWnHoTA9OPL++JQXXT6X2hTePibL99qeDuHtlBOMPZ6RYJ/eZRL4v6EHngRYcPtuLY88dwMFT3Qg2+wyye51oL5XotJUheb0eSPznswVZHgePR35f2ZljUFgfRtuZ4Lc2C3FIv+Br0XeQ3iVWBSxk2uM8iudD30CX64iQa6o/KGxUiV4N+NDiw25RXqSbARYOvZ+8jJH0DejPEEgjQe6xBnDEe1HqHjTZuuDWfHBavGLfwutekeYKCgrPAiqEQ/ZHBdKnshEUy2Wx1iCJXiWJqyBZfibYi/fn72AiGxbFOP8zL9q5rKZE73E1C/E+kp4Xu5jh1Bz+0+hH+LWu58QPmwVDl4Pe7J+FB/G98UtiG8P1nwn24WSgB27r2qne2wkeb6cjJEQ6EdXTuBYbw1w+gTaHD1ZVfExBQWEdUFWbKibEesWxyePvRDG+pn0HyXMSrmruuLcQsDbBpbmBVaYd9ENP6nEk9YRkIDSqfSf3y6P54dV8iOlP1pDitTKTm1i3aK7C1qOhRzck0Hv6m+BwWMVShaAi+uG9GXz4y7vo6Aqird3/RCHKzQJV0HdvT+Kj9+5ihkryZWBB0b6BFrS0+Vd4oi8HFdidYt3iE4uS068cQmw+JdYoVHRTqkFvdKvdKvYntHhp6Qwi2Op/6r7xmGkbc+w5J1q6gjhyvh+RuTgyiZyQ12aLCXanDcEWH9p6muQ7bl9thYh6j3bgH/yz30C5XN5wRqY35EaIbbPM6mY7j4Ggt/zX/u4reP7tk9LeXQdb4Wt6NElSMCLxLXbaobwBh9mFO4mP6y42up1gCcCgrQMDnjM46n0Rva5jS0SagoL4Ypuens3zBCoVKW60FZZD+xFUFU1m7uNe8lOxetoovFoIva7jOOA5K/UOQrZ2sdtR17uCgsJmwqE9KixKxAqGevGApxWHvO1PENwk0Y/7u6VY6Hg6vFQYb7XColWQaH6p+bB4rf946hqSxRzem7stBUmP+zvR7mBxdjvKqEhB09H0vFi+3IyNS5i30xnEm60ncMLf/QSpv1Pg8dIvvsMRxAPLDLKlvAQH/sPQL4Xsb7GTSGcxVdq8FJEp5cX/u8fVJMEGBQUFhWghLCrhzSLRpaA9iqI6TumGHezjINEasj1Zf05hd6PZ3ioE9Hr2QaOZhzjiPQXbRuaL2wDOcQK2EJpsrauT6KUcpnKjyJVYnNzdsMGAvY6GJtF9fif6+lvQf7AVd29NLr2/MJfAlU+HxOrllTeOCtlud2xeQgNvvvl8EUMPZ/HLn93C55eGUFzmhU6QwD97ob8mSxmXxwHX4Xb0HG7HZoMkfHtvsyybBSrif/OffBnbha04BoIK/9d/7cKmrnMvgkX2aJHg0QJwal48TH6OmdyI+KSXVinMsVNwawG02nvFyuGQ9wI6nYd2epcUGgwsIkkivV5wYJUrpaVYi8Kzg8WCxzJ3MJsb2dDv6U/ZJMGyszjmexl97pMSQFPkuYKCwlaApLfP6pJXkr2FchEuiw3Ndq8QwSSLl4P2Lh0Ojkl8oirPl4xnB8l2v9W1pgL7kLcNb7WdFJX5p5GHmM3G8fP8ddyMjaHDGYTX6pBC7LR7mciwAHtK1kkPdP7uheZDQkI31mTfhQuhA2KBczs+IV7yP5y8gruJKfFvZ5uWKmUUSlSc5vFC00E426j8VyS6goICMJefEY/nJvvm2CkyayehRxErhNf0RHezpo5983kZhZ0Fz6nfGpQ5QzW4vRwk0e8kruGA+wisZmvDZrKSQG91dGAwffeJz5jdGynMYzY/KcEgp8qg3hE0NInOwVl3XzNeeu0whh7MoqBTvW18NjURxV/850vIpPJ4+fWj6OgOwuW2w27TxOpjI6DymjYiqWQOkxNR/M33P8fVz4YRjaxMBXK6bDh4pB1nn+vfjMNUUNhxMIrptYbEHqXdcUAU6SPpm4gWqCzafK/0euw5bGanWDf0uU/glP8NdDkPb3rKn8JeKihTe7bKCl/GUryhgka7EQxAszWH0zcxmX2w4VRDn7UJp/yv40zgS1IstHGgjH4VFPYiqOymujxgdSFcSAnpG7R5RDH9uAq9Oj8hkT7gaRPlNb3LuQ63xQ6Xxb6mUpzk+tlgnxDtdosmSvNoIYO5XByT2YgQ6EYBZTPsZk0sZqjyfrX1KL7dfVH2qRGDia+3Hke6mBcf+dlcXF7vJ6dxNzG5NMak9YvVpOGwtwM6M10VFBQUALGmiBUiqLjKMG2Cqpb30bHMkBCma8Gj+dDm6HzmbSk0FvzWEEK2ZiGW06vwF+Q0HiZvI9EcFUsfrUEtx5gl0ebogmbSpPDu4yiU8rgTv4omWwscytJ2R9CYPecxxfcLrx7G++/ewdjwAvRFn25O1uPRDP7sP34kfuUvv3FUyPRDR9o3XHA0my1gZHAe7//8Nn75zm3EImmxdHkcR0904fzzB9DS6nvm41NQaDR0O48KUT2dHcIXsZ/hcuRHO7IfVKP6rS045H0O5wNfliKi9KxWUFgLtPrYkBK9Uka6SBJd+cs9K/SKjsnsPSzkaUFQP6gKuRj6upDojVTk1SC3FBQU9ipIilPlHdezQqI3O7yiAF8PJISvRIYxnYuJbQk91EkWP207tIj57058GzdiY/hw/h6uR0cxngkjVcwJgR60e3DI04bnQwdFfd7vbmzbARZT/U7P8zgd6MHPZ27ianQEM7mYEOsEbWoYOKAv/HF/F0J2907vsoKCQoNgOjuOcGFWyMINWTI+hjJKuJu4jri+0op3uUDLbwuh3dHzzNtSaCxwDsHgSKezDw9St574nH0sos/jRvwyLgZfQ8jemM9Wj+ZFm71TsjNmc1OrHsf1+GUc959Ds70NlsandPccGr7FORZt7wjij/7x2/iX//wnokB/nNienorixz+4Kj7pgaAb3b1NQnAHQx48uDv9xDqjkZR4nfM1k8kjPJ+U9U5PRrEwn0AykRM1+moEemu7H6+9dVysXFTUR2Evgv16LjuOO4lP8DD5xfZtFyZRnbfYu9HuHECP6xha7D2GzYzFK/7t6ppTWA/0x7ebXbCYrHWpyulBm9TD0Mt5CdCqfrYxUNE/nrmLWGFOikXVCwbJTvpfxUHPOfFDb5TzwOMqQpd+oqCgsDdBz/H/25nfQa6kyzXPYqO0KlkPr7YewYlAl/yGli92i1XI4vVQva9pMOOYr1PU7lSZ00KGBUr5qcVMJboVLs0u6vZ67oXcj3906Cv4g/7XJRjA42BwYCt91Ll/VPIzqNDuDOA3e1+AXi7J8cg+mQx1Pa1dPJoDrsVCpKudg9/vexVf6zgr/+Z3n3YOqvh6xzm83HxE2pHbo4K/WvBUQUGhscUX45lhjKYHcch7/JnWxeKRc7lpDKfui8/6WpYf7Y5uUfkq7C3wWUQCvc99cFUSndDLBXwcfhet9k64NA8cG8hg3o7jCNlbcdJ3YVUSnWOUZDGGK9GPRIk+4Dm6I/u5n9Hwdw92ItqnnDjdjV/77efxk7++iuHBOZSWeZTrhRJihTRi0TRmpmKYHAvD6bLD4bQiETOKAy3HyOAckvGsKNaLxRKymYKQ5ul0XuxcVoPZbBKP9m/9xgVceP4AvHUUuFRQ2A2QQiyVIu4lP8OD5GdChiWL4af+jhF9t8UPh8UjvtKFclYItHKlJF7TvNFTVW42WcD/NLNNyHK7xQmH2QO35heinHYyPmvzo39rIVmnxfRkKrWCwmpgyjgDLuw/cX2+jl+ysGhe1OgsMMq+qVA/eL1PZR8gVYzX/VszLPBZQzgTeBMhWwc08+bVOdmsYqnMWFBQUNibsFm0pyrPHwd91LlsdH5DkpzLZoLrpb/6jjx/NdszEdcMQrRwcdSf6UvrGy4KCgq7DRUh0Jvs19Du7Ba/8o2IKMSlQI/gUvgXiOlRUaSvhi5nH7qdSgy5V0FP9G7nAVFyz+ZXJ6Dpl38p8kvpA0e9pxrSJjZgDeGo9zS+iH6MhNSpW9mf+e/7yVvwan6xr+lwqsyK7UTDk+iExWKGx+vAa28eQ7lYxkfv3cPD+zPIpI00weUo6iXxMH/cx3w5nvb547DZNLR2+PHqm8dEhd7eERBSXUFhr4CEN4mvwdQV3Ix/gKnsQ2RLa3vJVRG0tqHTeVgKk7o0L/KlLPRyTtKMyigueSSTRKfPHQl3KsqtZvsike6Cy+KDW/PBbQnApflEka4GNgobhdvig9/aXCeJbhDACX0BuVJKkegbBK932kBlS4m6f+uwuNHtPIZ2x4BkEzQSSJ7nyuk1J2QKCgoKCgoKCgobQ0wP437yhng8Xwi+ApvZLoG5WiHEqB4WG5cb8c9FELOWF3qP8wBaG6rejsJmgn2HhPJJ/wXMz88Kx/E4WHR0MHVXhHrMhDjsOQGfFtg0/oHCrFwpK1aQPmugrr5chd3ikOM4F3wRlyMfIFl8cm7FoNHtxFURKp6tvIAuZ6+IkjbjODiny5fzcl2F87NC1tNexq2pouC7hkQn2BmaW314++un4Qu48P47d/Dg7hQi4RTK5a1LsSZ539PXhAsvHMS3f+cFeH0OIfUVFPYK+HBJ6GEMpq7hg4X/IkTi07yhabsQtLXhiPd5HPe9jHZHvyjMFRR2GgzE0EsfuFP3b6P6rBQY9aMxPfIaGSSai1Ixfgr50pMZYE8DswcOe58TT/tGC6JxsJ0WFYjyzFfYT5lpZWSKBfHppl1JvqyjUC7J+9UaAbxWLTBL8U0WwqSSmP7XXPjvp13L/LTBLvea2oY5dvmSjnSxIEVF2TZ6qYQCBQT8vEJKhwIC05KVCT3TaWdC2xNarNDShJYtT/NQ3w2ghQrbg/0kV9aRLxXlPaOvsD0WX1e0ieQoGgVHF9tGFosmfYftRIJjL7TPfgf7A6+TTCkv/YT/1islFMvGvcS4WvDE9VLtB+7FjA2raXPIoUa9rxQrJakjwPuKtFO5CL1cRHHx+jHuKUY7sY143zXaifcU2j/ZpK12oxiJ46yp7Dg+XPiZkHQkA5mdaKthblkqFxErRnE3cQ2fRz9AVF9Y87t9rkPodvXDY1V17fYyWFz0tP8i7iSuYz4/varFZL6claBLqphAtphGv/sw/FZayLrrspDlPYzzg3yZY6UssqWMEM/pYhJOsxsn/ec3RKLzOnZZPHgp9JZcGyPpB7KN1QrzcruZUgrnAy9JUVIGi+oJRMndpVKBXuG9p3ocaUQLYbFaGs7cE1X8GfPzikTfbSR6FfQ8f/trp3HkeCd++sNr+PmPriOVyomly2oe5hsBrxmLZhE7mDPn+vCVr5/BS28cEfJ8tz2UFBTWA2+YrFT9IHUF78//mZDpTwMJdBb5fLP199DvPg2nRaXPKjQOXJof/g0WpFzITSCpR1FxGhN9hdpRQkkCELlSRiZDGwl+9LpOiJ99IwYIknoEpXL9Pu8KCrsJJGqK5ZKQN7RJfJCcxZ34FMYyC5jOxLCQTyK1SIRx/GA1a0Lc0Ae83elHtyuEI94OHPW1o8sVFAKM36FP92rjZxJmpFJ3AzhRJulHr28SXeOZCB4kZzCSmsdUNiptEy6kjYBDSUepXDa8zS0aXBY7mu0eKVba7QxhwNOCg752dDurbWTZVYQx+wkJcpJ+bJO5XByT2SgmM1FMZCKYzEQQzqeQKlKNV1hskyIKlRKsJraJFQ6LBofZJscfsLvRaveJd3ubw4c2pw/dzib4bcxYJJluhibZjEZf2Ym5GM8/r4tCqbil1TFsJEUXj3m3B+F4rbCPzGTjGErNYTA1J/2D/+b9JaGTqClAJ5mOslwDJM69VocUq211+NHrDuGorxOHvW0I2TZm87EW2Kd4ffIeVW9fYJ/m8a3VF4yAmfZUEqvaTlxftJDG3cQ0HiRmpJ1mc3G5r/B+w3sKyXSjfpQGp2aVegFsk05nQKyoDnpbcczbAafGbF8NmvnRNdOoYCCtOmYk0TmZHcN/GvuXeK35qzgTeAEt9jZR2fJ7xrk3LfUxhuVoaUGF7qfhX+KL2MeYz8+ssSW2mw3ngi8p24tlMDLGjSBGNXvceGfZ6+J3Vn5i2GCuJS7hN3huOCewij2jhHbks6XXxfP52LvyvhFQ23jfJYHc5ujCmy1fw89m/wrhwtzS/i8HSePh9H1MZEbQ4zqAC8GXccB9FH5rSPb7UUCqet9ZbIPFoBb7YKGsI6nHMJUbw2R2VMhu+pjbzQ5Z33G/UeNjI+A+0OLoYuhVIbbHM0OrzrFI2n+y8C5uxq/gYvBVHPedRaujU/bh8Wtn+TE8OvdlFCp5Ic3ZFlxInjMAwfeJHufAqm24X7HrSHSC/aCzO4jf+YOX8frbx/HZxw9lGXo4h1y28Ezrpk2Ly2XH2Yv9eOPtEzh6oguhZo9SnyvsSdCe4E7iY3we+ZGQh08DU4RY8PP1lt9Bt+vIplRRV1DYTHi1IEIbTNOcz4/XVAdA4UmQYI4V5jdkeUJrJ6/WBK812JDBC04EFvKTMmFQUNjLIInzeXgYH8zfx43YOOKFjChGSQiT7CnJZOvRZLpEFXZJl++R9LkaGYVmvi5qyB5XCC+3HMabrcfR4fQLMfg4SF6RQN4NmM8ncT06jk8WHuJGdByRQkrIL2kXWQzF9XIaolQl3fU8IvkUHiZnlxSkJLnaHX6cDfZKO50KdMNn3R1WYkk9i6HUPK5EhvF5eAQT2QjSRVr5lZf6SlV5bijcHrVJnpP1cgkp3SBReMc3p0n2GaQJ24d/s41YHJTE4FFfB45423HI2yae65YdeE4wKPBXk1/gR5PXkCyubhWxGXi77QT+3oFX0enafl/7zUKmVMBoeh4fzj3Ap+EhTGejktUi95LF60WU1UtElIFypYhimar1PBZySblePg1boJmouGbWwubOxY/7OvE7fS/glZYjdd8n/83DX+Kz8NCafeFbXefwO70vSnBxPYymF/DpwiA+XniIe4lpCTYx8LB0HfG/ZdcPUSqRnCzIfXfcFMHN2MRi4V6LZLmcCfbi1ZbDOBfqk2K9jQqS4x2OboTzc8iVs/Ie7xpU8r43/2Nci32KTmcvel0HhRClQphFIJlBTdUtfa1H0g+FBI3qYbEUXQsOswPng6+g33VQ1L0KBti/qDxeKMxK+9G+g2PdQimPQiVn/F1mEGfx/eq/yzkk9JjYiayGQimHkfR9/PHov5BzZjfbF61c7bBbjFebafGVS/U9s13Os89KQVTomY6NPuG0BmJg5WrsE4QLa9t8kkwfywxiOjch+xqwNYnlDy1M6JfusDgk4M+gAYvXsr8m9CjielTaIVNMiZUtPxdL20pJCOzNwln/i/j/s/cfwLGlZ3YgeJDeO3jvged9PVP1yrOKRRY9m6ZbbSWNTMvExsZoVqPdmIiNmIkZjTZCuxuh0UoazagNu9Uku8kmi6ZY3r563uPBew9kIr3PxMb5EvkKDw8mEyaRwLuneIkHIHHd/9//3nu+858TSgTl3E9Fx9Zsy0DCh0/m38FVz0ewaewoN1SLMp3nglalUrRaTMkxcPZwKMli5gK8CQ8CSZ+ErmaPgV9X+rAr2PMkegk0GjWsdiP0Rq3Yu5w404ypiQWMDs9jZsorAaOzMz4JEI0nktmiy2PQaNVwuswor7Chps6FhuYyNDaXy/fllXaYLXrZlgIF+xGDwVvoC1yHOzYhDy4bodF8GCedX0CtsQ3aEr0yM0NB0YF2IHZNqfj1exOrKw/WAmdluGNT8MXn4NBtTs3+pIIPWuGUb1XvwY1gUJmEQOcLVbGBx8Mpn974jDxwKlCw30CihorQj2d7cWmuD4OhWcxFA/Iz/m49ZAljEmIgOSorJMkaFYJnKuLDp3N9OFvaiqfL29FmrXhE9UnVKcnkYgXVoVMRLz6e7cHthVEpFJBM9SUYoJ7bWJclCWVszN6OUp+T0bMxP24tjKDZUoHTrmY8V9kpZHoxKkhnIj5cdg/gmntIFPgkE73xsKiJM+WD3PCQOF1SwrHvrIZAPCqq9pueYVi1Rjh1JpnpcNhRhw5b1UOCshDFVxZJgomoFFN4bewUfDlcd8UKKqZveIal0PTANyHjiCcekkJbrv0jo2BdlNlt/Ibq/50Ci1gkrfMF94/ttF5fmAwvYC7mX5VE5zano168NXUPdxZGZVYLi2y5FmdWjrvLUYIwQqmYzJJpmunGGVcznq86gDK9tejGFG2JFscdZzEdGcdQqO+hDQuPjSRlIk47MT/GwsNCYjJXi7MV+PssmUnyMpQKrmu3x2fMJnMbnil7WcjR7S7G7GWQlB2PDIlam8UL3qfEfosl8yXrtpX/zlq6ZUjW1c87P8n1UZmdmWWlejijIPNv/iv7c/lp5vclKiHPD9qO4ZmyV7Z0bFwXfcXPl74g94jbvivrzFTgTIgkkmI9FUIoGcR8bEZsXbj/XHh1kSvJnIuUeKmTdObXzbz75AMeB68V5std9XyI8cjwqp/j/vGdhUuYdiwJjxQmMm3A8jOPIVvwz7ShHIOQ6pxlpSjNc0XxPrnmABJ4er1Wgj4rKu1oailH56EaeBdC8HpC8PnCiITiiCdSSMSTSCSSSCUXl+xaVBIYajDqYLYYYLMb4Sq1oKzcitJyq2LdomBfg4MnCcOewDUJEWUFdiM4tBVosZxAs/moBIIqUFCM4EOCWWNHtbEV/qQ7Lx9rPhzOx8YwGx1RSPQ8IVMaU5FNPYDp1IaitYXig+h8bByxdDinQuP+wufTb/MFp4Zm+TEFxU16DQRm8fFcD67OD2IgOCMWHFsFxwGqUcNht5CgtCQgAX2hvA1nS1vE/kWe4el/rdZILyum7kKSgPY1txdGREn7wD+B8fCCkOrbCRKEs1E/5qJ+jIbdGA3NSxHjXGkrDtiq4dJbiuJc+OIR3FgYxm3PiMxQGA7N7yiRnAX91aPxBNzxoHxPEpD99Z5vHA2mUiHSX62hraCu6AjCJwkkhXl9cxzhTBYqqnnNF9M1XWiwuMQiwkrML81o+XC2Wwpz0xHvthYKJGAzHpaFYxjHX44tL1QelBkdtMopFlDZS+/mk87zMGrMuO+7KbYUy8UZJMi5bBZU4DaZO3Ch9EVUG+qXyFAFWZD8pXc3VdgkvbcT7IvkFxJ5DgROrQ+Vhppt249yfTVOOi+I2v2e7zpGw0MbPnHIfieLSzjj1JXhiP0UNCqNzNLgDAyS32uB77+cscFFwfZjT5PoK21YSIZzqW8se/hzvsSl02nEY0nE4wkkk5mXYK1WLQS8Tr9x8JECBfsNnDJJ8nwi3INAcvWpWCtBG5cG0wFYtzi9SoGCnYZRY5P+ylkWKeT3ckLCdDLaj2bL8byCZZ50ZB6WE58rC/MAfdC12zjtcTsRSZL8634ipzRmvKpLNtUXqNpXFC3FDZKg973jeGe6C+/N3MdCfHtfoLOg3mkoOCcq5sklW4eL5R3i55sNJKU6nUGUuw3aS9BqhMWEz+b68dFsD+54x3a8L3PtVO+ToO4NTAvxRRucU65GVBsdu6acJDlKK46r7iH8Zuqu+OMzHHI3CX3xXo8s4LZ6FMfDDXi56jCg8GK7Bqrzabvy3swDvD11T2ZW7FUl/XbCmyCJ7n/4PccQEuZX54fw1vRdfDrXv+PjChXp3f5JKXpx1shrNcfENsqpM6MYwOOn6veg9ThMGqs8c3QHbosCeDtAKw6GRZ5wnMNh20kh0JVn+icPbPNaU6OouWnlQ3uW6cgEQin/jj7bL3d835b1lZSgVF+BE+pzMKutYnszERmGL76Qkxhy09tFiZwzkyYTuKpgn5Hoa0FU52oVjCadLAoUPOnIpi93Bz7LmUDng02j6RDK9HU7vn8KFGwVJrUVdaZOueknk4m8FMS0gGGByZuYQZlO6e85Q/xN1w7Z2mh84X/FOGMnkFzAaLhrx6dqFiOyU3A3o0Knt+aTp9zfO88AVD7SR/dvxq7iw5lu8bLeaVCZTpUqiXSOE0+Xt6FUZxE7F4aTxuO7S6LTGiGRTkoA4g9HPhN7G1pRFBI8L1S7vzvdhfEQrWMC+FLNcZQbbJkX8gISQAyYnYx48PbUffzVyGXxgC8mMJC0XG8Vm5e9Esi630AbH6rOfzp+Hb+cuK0UTpeBdla0e1letHx/phtvjN/EA/9kwfZjcamdfjV5W3IL+FxztqxVZm/sPkiiZ8LoWy0HhPS2amy4uvCRqKPpw51vzg4JPxJ9JPwOWI/htOsi2swHod4j2RsKdgbsF+X6KthcDjSZ2nDJ/a5YCNHTnP7uDLXdLrD/0ULFpnXCorVtL5GOEpm9QWuXOlMTrnk+Qk/gnhSexAJpHWV6PqDVDkNNtSodTGozqgx1qDU2wazZ/dlxxYJ9T6IrUKDgcSTTcQwH7yKcfHyq4ePIVCCd+iqY1LYC7J0CBVuDpkQHp64SNcZ2jITuI5rOnQjhS+B8bBJdvk/xXPl3d3Q/9xVKSkRRvhkugwQ1vfiKDfF0BJ74JMYjvZsKTN3rUJXQZiP/F09eQzx3JNMVFB/o50sS508GPxKv6UIQ6MtV6ZNhL/5fXb/A//Xgl/BcRacEjjKIlPYDuwkq4Rny92+6foFu/9S2W7fkC3oaB0ajYv/wTztflWJDIalidzyEvx2/gZ+OXS+IdUu+oEL/fFlbEUZRPzno8k3gb8au4c3JOwqBvpoSPfa5Ev3Xk3fws/Eb6PVP7do+sTCoU2lh0Rokf2G3Id7nqYgQ+wSDHF+seB2H7afw8fxb6PbflsDDfEDykuTis2VfFGKehGMxBtYr2B1k+8e3TX+IifAI7vtvost3S7zb8525vBo0JRoJy+2wHsEB23E0m9t3RL3NPl2mq8SrVd+SwNyewB3c9l7FcKgvLxvTtcBgV5LmbZZDOGA7KgUIZhIo19LnUEj0fYLZiQV03xhG3+1RTA7PI+gLI5VMwWDSo6zagcbOahw41YiOE41ifbOWmqX/7hg++dVt3PiwW74/+9JhPPP6CTR1Vue8L/euDOCNP/kYUyOZFORXvnse575wGOU1zoIcw0r8+N+/gzuX+qDWqHHy2Q587Y+el59HI3GM9Ezh/pUB9N8dh88TRDyagM6ghd1lQUWdC82HanD6+QMwW40PtxeLxnH5rXv48Oc3MTe5gKr6Upz/4lG88PXTeamE/uL/8ya6rgwg4AujurEcv/WPXkJjRzW0+p29LBOLMXgS04imQjkRQxwwzRqHEJPKNDgFewHsp9oSAw7YzmMuNo5onmpCf2IOfYFrOGA7B5euRirxCnJT/2zmASsl4TzFR9JMR4cxELz9RKrQsyG9fCHIFzxfvoR7W5U9CrYHDPhjiN3/3vc+un2TWyLQM9d8ZqYC15MriZYhTuL4wdCn0Jao5G8taj12E+FkDPd9E/j3ve+gLzAj52kryPpzU92+WXKRf0VP5Q9ne+T7P2x5DqV6y44/h8lsxXQKPxm9ig9mHohdx1ZBpbimRP0wkG6rdCvPb43RgVOlTSgU6N3fZCnDCWcD5qP0aY5Jvwml4tKfJeTxCcJQYFbsWxhGnGuLsliW8bKvRrOlHGV6i3h061UM71NlchSSMckJGAm5xVaJAaW0WNoKrBqDWJgwY8ClN8Oly3xttVSKT/hOWSExS4A2VgPBWbw5eRfDwfm8+z6vHW2JGomla2crYDtd8wzBrjNKOzBsdDff62RWVIrq+FTm2bGEzx068cN+tfIbOOO8iKnoGCbCw5iJTYpqOJwMIZGOybiqUWmhVxlg0zqEUKw21qPW2Ch/T1W7Xs1ZKjs7y5HK3P+m5b9dsgV5vHVFjawpXgEaRXId1qP4J23/j6J51uVzJ+19dgKZ/k55iApVxjrYtU4cdzwFX8KLycgo5mPT8MTn4U96JVw0mgojmU4KX8JZs/Qj5/5pVDqY1RZpW6vWDofWJd7rFfoq6RO0WuG53Sn7k+x1q4YapboKnHI8jQ7LUQSSPkxFxjATnYQnPgtf0oOgzOrIqO15n+JYS06HSnNa3Jg1Ntg0DjkXPI5KQy3sWsfSMRglV0CxN30cCom+h8GbT9AXwY0PunHz4x6M9k3DPe1D0BsWMphe8BqtBiarAT03h3H70150nmzEs185ico6F7S6x5vfbDMK2dx7a1S+T6cW0XK4Fo0dVTldPJFQDKO907j+/gMhwblt2umYLIaCHcNKzIx7MHB/XO5tpZU2LKYXMT/tlWLB3Uv9mBiahWfGj1gkjlQyDZVGJYGz9lILJofmcPBUs5DoWahUKiHY3VNeIfxnxjzQ6DR49vWTEli70XniNgK+EO582ocH1wbllsv1OytsUKlLCqJCDyTyC1yUkDhFZaJgD4EPOE3mo7jn+wj+xLx4NOcKPmi441O46v4Vnin/Juzail3zpN1Lga4GtXnJRzs/xNNRKeoVE8JJv3ihj4a68KSCJLpWpZeX23zGf14/c7ExudcoKB6QgJmO+vBXI5+hyzeOYDJ3ctSg0qLG5ECLpQK1JiecOotYsJDcIUiO0iKGimUqpyfDC0LW07plNXKRPxkLu/HLydtiK7AdYaZbIbvuesfx45HLoqylpUsuvb1kSQ1dbXQKoVthsInCkzYjWdU4iS8S8rRToD/yVMQrAYzTET9i6Y2J+uRiCtMRn/hNc1svVR2WrzuJxGIKl+cHcNU9KPubK0Hq1JlQY3TK+Sg3WGHTGmHR6GWmAYst/I/r4vml6j+aSoq9BC1zPLGgfJ2PBiREdCNP7WqjHe22KrEDKhTMar2od2uNTkRSLPymlo4l85X9n7MXMguJdf4s8z2vNbYjrYL2hyVkCu/OdOHK/KAorjeCXWvEYUcdTrua0GatRKneKpkIPKe8VjQqldxnuN5EimGSMZmZ4o4FMRKaExuoe74JGVs2AjMWjjobJJiXfZCLSa2DUaOTsebzf2th0Rhk2Qnwnkk7pk/m+vDB7AMMsDi3zjXPfasy2NFoKZOvJP05nnA/ee2kJLw9KWOJhIZGvJiIeKTYwIDofGxmGGhKy6jfajgL9S4TYySflz9flJSooCvRi/czyXEqYOlrnrF3iUj2Dt9feVvhczkJzazXtVVrg0VjF9KvUKA6l8rmvYhkIgnPbAAD98YwNTIPrzuIaCgu3ItKrYLeoIXVYUZ5rQMth8gF7UzBaTdV6TqdHrZFB8r0VajU10iILYnzaDoq1ijJdELshjju8VIR+8kStRDR2hKdhJWykGNQGWHSWKQfFtp7P2O7YhdCv3yxUq4ZXi/MG8gcR0xm+2YK2Isy1opdI01bVBo5D9ljIGlu1ljlZ8p77/pQSPQ9DM+MDzc/6sVbP/wMfXfGEIsmYLEbUVbjgMlsAPt+NByXQXF8cBZjAzMYejCBeCSBZ796UohxEubL4aq0y8+d5VYszAeEYJ4ankfIH4HFvvFNaW5iAWN900KgEw3tVahuLBMSvFDHsBbisQR8nhD8CyF88Lc38P5Pr8vxkQC3OUxwVdqQjKekEBAOROGZ9aO2pUK8fpcPhhqNWpT5dW2VmBiaE8J/+MEkJofnUNVQCp1eu+F+kHyfn1yQwFsq9JsP1sBVUZhKNR8+oqlgXqqxSCogJBdJEZKTChQUO/hwYNeWocF0CN74LNzxibz+PpYKoztwGRWGBnRaz8KmK1OmsW3wImHRbC4AL5YOi/c4x5eMJczuKqPo5T0afoDh0F34k/N4UiEP12qjEOksdORzj3HHJhBJkQxLSt9QsPuYjwWFGH1v+oGEWObyBECSvN1aiQ57NTqsVWi2VAhh7NSbhfRZrrgmkehPRIU4mop6MUYlaWBWlN2joXlRmS4HiVKS11qVWvy3d+t6p23Ku9P3cWm+P6dwU6ppa00utFkq0GplUcElpBd9y0nIkUTneeEwJsWFVIZcJQE4vUSiM2i11z8tX3le1nseyxLp9JymcpQkoF23MyQR25FKYAYf0tpmIwUw74kkznkeuDSaMwRgmcEq6l+eD51a87CfyPEI6ZxCNJ0Q8o+Bh7LEQuIhPRv1yVcSqDxnJAxXWuu0WCpxyF4rwbSFAo+jzuSS5fF7BqT/C4FOQn0Zic4iCo/lumd4X5DonDlCu6PP5gcwHvFs7ENssOLp8nY8W96JI456+X4taKCGUQ3YYJRiEUmf484GNJnLUTnXI8UdhmRuhHqTC89VHECLpVw881nE2Y3nCl63v568jXu+8TVnt1CN32guR6ulAk2WcjSYS1FpsMFBEn2pCJUdH+S6SSbgS4Rl3SxS0pOeSzY8dCNwXVNhLz6Y7cZzlQdkTOEYvDvIPG+tBc4CdehcsijYXkTDMQzcG8eNj3rQc2sE0yNumZHPWfoUUFIAyZn5VrsJrUdqhdvYbyT6ysKNS18OF8qxV8Exjopxp65UFgU7C+XNZo+CJG/XtSH8/L98iN7bo2J50thZhZZDdULwkgSnYtrvDYm6e+DuuBDQs+ML+NUPPoXBrIPVYXrMYoVVR6qs24814Nr7DxAJxjDaPyMVSv5sI4z0TmHoQSYwpURVgmMX2lBWbV/14WWnjmEtUNlO+5Xbn/bh13/xqRDYJK9ppVLVWAqjWY9YJAHffACzkwtIxJI4+WwnDOZHpxnzuLivB043iR0MyX/vfEAscF74xukNSfRoKIar73Yh6M/YF1TWu3DoqZaCPeDxkSW/NOpFhFMBzESHUKFvgFOX26wEBQp2E+yj/K/degpzsVEJDKVtSK7g1L1gcgE3F94RRW676gxMirfi+iS61iUkeL4gQRtIeCTo2K4t35QP93YgY7xA9dgk7vs+wXikD08y2NcNKpKl1rxIdL4Uh1N+uOOTKNXXwKyx7+h+KtgYJF9ItLwzdT+ngEjqQmmzQNUogy0vlLeh0mBfs0hGywGSPeVcDFYcsNfINknqMLiUhE23f1KI0uXKZqoqI7s4g5yq5w9nu/HRbM9jJP9q54QKWh7bhbI2PF/RKWSxVrX2qxQtTCi8IPFOKxZaR5BYp+KahOD7M13o8nHadXBdax2OS/Sxp7UL9+Gkq/ERYnq7wCICFa43PMMbetRTQUwC/YXKg7KQROdxbgQS31yMYHaJ+RFSmqRpOBnHaHgeA4E59AemMRSaE8KQ1hic6cDHT/ZLFnWK51mDdi+cGq/FaqMd+z0LV3sdLBjwmn176q7MJOE1vt71QiU1cw++3XBWSOJ8ix4cbzgOPVPRgUqjHSaNXnzFWZRYC5wRwGKVPxGWYtNOXCf5jC9uT3AdSyInTpc24dmKThx3NsKlM6/62exzJ685HYVfOiPqzRmSjLM4aNHy/swDXHMP5aTWp9KfyvjbCyO4UNa+Y0U5BcWLsYFZvPs31/DeT64jEo7JTHqT1QhHqVUIdF7rqVQaoUAE4WAUyURxWL0oUFAs2JckuqgCFjNf+awuFdzM/x4iywHKw48shU293wp4XLQn+fTXd4R8Jmi58u1/+BJOPXcABtOjSuFEPIn7Vwbxw3/3Fm5/0iuVRv4t7UNe+a1zQgovBxXRJy524NbHPYin0hjunsRQ9xTajtave47SqbRYuQx3Z0JTOAgfPd+G0kp7wY9hNXAdo71T+MG//RVCgShe+vYZvPTNp2SK0sq/J8HPbYiVzBpWNEfOtuL+5UHxkecN5tKv7+DMi4fET32t80QrmVAwKvY1VPfT251K/QOnCjcVjNOQqCzMFySVLBqn+KNnp/jvlWtGwZOLamMr6oydmIoMCKmXL6aiA7ix8JYoFQ7Znnnob1fsfV/uf0JnchpiWq57LjsFrpvjg15llFT3fIM4OdtlPNwLi80p57rQxQqhz+nPmY7giueXGA7dkxk7TzpIgFs1LvgSmYyTfDAYvIUyfa0EUhf79bLfQTL7rncMV91DOX2e5NdJVxP++YFXxb5iPaJ4LVDdSIuT32o8K+v64chn+M3UPVFR7jay6uFP5/rw2Xy/2NysB/Ze+gh/qeYYvlZ3Ci3Wik1vm+QZCcGv1J3E0+Vt+M/9H4hdC0MIN5odQI9yKnRpZUKLjO0GSWoqpkn4rmfjklGgO/BHrc+KxUwu5HmupCn73iF7nSxZ+4n+wIzMFGBbsWBDQpbKfwWFBQtAbA8Wc9YjsgnOyGi3VuGPWp5HhdG2ZTKbhZNXqo7AH49ImOl6szdYBKoyOvBUaauQ8MUGzvChAvzvt70gBUr+e7Ogz/ur1UflmmCh7ofDl8UWZyNwFggtXQ7b68TyRrlHPxnIcmTkLK682yUEOq1byP0cOduG2tZyERUyky7sj2Jq1C08SV3r5u95ChTsR+xLEp3wecPwuAMI+qNiERKNJhCLJWTgoI+2Qa+BwaCF3qhDWYUVdocJ+g0UxMUCVgap4KaimiQsSd7v/vEXcPhsi0y9WQmNVo324/X41j98SQhrEr4kuul7fv4LR2BzPeopaC+14vDZVhjNBiQSIYz1z2CkewrJZApa7dpdhgMtLU0C3pAo2hs6q8XeRG/SF/wYVgPbPhpOiJL9t/8vX8SFV4/K/q3G1VDlrjOQzIHcXFZDTVO52NU4yqzwzgfRc3MEM6NulFXZRam+GoL+jPXL3JRXqrpUode3VUrlt5CKURLhJKryAQnIz9w/hyc+hWOOF0WVrkDBXkCL5RgW4tNwe/In0YnJSD9S7hTCyQDOuL4ogSzFDlEDJ/0YDt+XY2+znEK1sWXHLXRcump4E3NCiucDf8KNe76P0Wo5uWuWUfTOv+L+Bbr9l2UWggKIRY9NWwpsIvd1KHQHzZbjqDI0Q1uyu8GRTzquuQdFpcii2kagB+8z5e34/ZZnUWt0PQwP3QpoUfD9pguiePzzwU/EymM3QdsSkoBvT98XgnY9kDCmf/M/aHtRFKPbRd7y0ZPe8n/Q+iwcehN+OnYdM1H/un9DFfAd7yguz/cLcbbdoEe5+MJvQMJVGe24WNEpBDptfXYSJEGPOOqERP2txqcQScbF6kJB4cEiy23vqMxS2Mi3ntkJf9T6nFg/sSy+HWAY6Ws1x3DJ3Y/ZiG/N2RucUUE7opueYbEsKSaIRZatCv+s81WZmbJdJD/H2G/WnZG24cwattV6oPUQi6r0tK+BE2plluUTA86eJ7dD21qKCDnz/5//6++LRS75CxEWkmxPLyKZTEOrUwtfpkCBgn1Govu8IYwMzWN4YBYzU1645wMI+KNCnJOoJGFLlXQqnRY5Oi1CGOBIpTQXEr4WqwEOpxkVlTbU1pei7UA1nC6zTJsqNnDgo6La7wmK1cjxix2oa6sQ33ES0ivB6jKrijVNZWg9XCv2I/T9nqV/+cAsDq8goLVatQyotCu5d3kAkWBU7FxGe6bReiSjDFkNvfTUGnUjnV6UfTnzwkHxUV9tn3b6GNYCCfrGzmqxaSGBvVYwqfSRDd4b+be0g2k9Uo9r73VJsebelQFZb11r5ap/wxsWz2l2WlTTgRpZOI2qUKCK3KmtkECMqDw05eaNTm9bknEkuqYigyjT14nK0KGrhFXjlCn/+qUEZ1WJRgg1RdmgYLeRIUAq0WI5jtnYqBB7+YJBifzbW9534EvMShGJfZ82L8WCjLJyEd74DGZiI6K8n4kOi4LYpLaj1ti2o9vPzkypNDRhKjqYN4keT0cwHR0UH3IGwpo0hSsscmybjgziju9DdPs/QyjpXden80kCcwVo4bUZsIjT478sRHy79bRihbRLIJlyzzsh0/dzwdnSViFom8xl2+aVy/VQ0U4S2hsL48djV7CbIBn9q6nb4km+0m97JaxaPX6n6QLOl7eJsn67fLg5XjLUj6T8cxUHxU/+hyOX1yUn+bTW758RK5iny9pFAb6dz1kMzKSXvbwvrQNasDxV2rJtCvSN1Ol6+vOqNeKVzX1Tni13B75EBLcWRiXgcj3QD7/VUinFDwnZ3ab24ro4E+P5igN4Y/wmAmuEI/M6mQgv4Ip7oOhIdNpBfafhrJwbCQ3dJrsZzhaqMTnx243nMRbyoMc/KdY2a4EzTQKJiBQbeD3TWknBkwFa2wZ9EeHGmEPXfrRe+AxnmXVN4aACBQoeRfExxDkiHIphfNSNgb4ZTIy6MTHmwcy0FwvuEPz+MBIkKXPjBgX0LDRZdHA4zCgrt6Km3iVkenNrORqay1FWXjzTBsf6ZjA76UUqmZbK4KEzzTBbjUL8rgX+jhYpDMoc7pkSAjqwEML0yDwOP/WoOpEVSKPFgNPPHxArF9qOzE540H1zeFUSPTM1aFF+PzPuEfU205zPvHjwMT/xQh3DWqDi/ci5VpRXOzb0Ls8FDZ1V6DjegJsfdcux3P6kD0fOtaG2ueIxiximXbunfUK0L6bTUsDhTauho7C+jmpoxNuZJCCtC0he5UMmkpSjanQ6OgSbtkwIEiHQVZkAOirdqXLnf1vzmMwmYKtF+ct1k7Q0qOnRa4FRzRAvh5D2+arqFTxZYL+hrcsR+7OYi40hnPTlpMhcjkQ6irnoGEJJH0JJPxrMB1Fn7IBLVwO9evun1edK/kaSAfiS8/DF57AQn5FCF2eLeOLToqymrQqVwPnlIGwOvGZrTR3oDVyHBxlbr1zB9ggmvbi58DZ0KiPqTJ0wqHfep5OK87FwD3oDVzEYvL0p25L9DIvGBZeuSq6hfHzRs206Hu6R+4NBbUG9sVN+rhBghUWPbxJDwdk1CaflINF9rqwVxxwN4u+8nSAJ2mwux4tVh3DdM4SJiGfD4MqdAAMgZ2J+vDfTBc8G/sEOrQlny1pkn6sMjh0JsqTNRZOlDOfL2tDlnRDbnfWsVGiHw4IIPdJPOhvFd327QI9rKvRTG9wfXTqLhDYWEtkiXCHDRBU8imAiim7fpITdrgfaFR1y1IpNyHaC9w7OZmExjlYk641ptLBiqDGDcg3bSFZvBQw8pX0LC3I7UYDSqzTosFWLzzoDnici68+o4zgzHJwXSxeFRH9CsAgEvGHEY5niMTkYzsonv6IQ6AoU7GMSnery+Vk/Bnqncfv6MG7dGMbslA/x+PpV8Y1AqxK/NyLL6PA8blwdEjK981ANjp1qwqGj9ahvLBULmN0eZGbG3PC7M1509Kxyz/hx/YMHMBjXvyEzHMLnDgrZS5CE5verger8Y0+3S+jE/KQX81NesSt59XvnxVpl+UswlecLc34J2eT6OBWIAzI9tHR6za4dw2qgTUv7sfpVLWM2g7JqB5oOVsvXmTGPFB0mBmfReaJBVPgrfdap6KcKn9Yyrio76turVvWM30mw7bQlVImchDc+mxeJvjI4jsuO7CPpOCHiNVCrtJKaTZU7CXT669J72aZlYnslzGqGDdlh0djldww2VEgaBStBX2eq0SejA6I2JpG+nqfmemGjd30fSNDurHkE9aYDYmHCPsniFAs+2/2yJmHA6aSQmLxeo6kQoumwKH2pjJ+PjWNOljHEUgzu2x0VNa/bSn0THLpyUZUnF9cP6lsJBr8OBG9JWxE1xlY5p9uN9GJKCogsONCHnWrp8UgvYund92ouNrBAZNdWoFRXIzMM8kUo5ZPZHyyI0pufNmBc50768yt4FNc8w5jcgEzJ4kxpC4466sWCYSdAv+t2WyVerDoo9iWeeAiFBhXf973jYuNC5fVaoPc2LSlerz2JOqNLigA7BVqi0K7kpapDEsC6noKUd63ZqF/83Klm1WxjELMEe6bi8oy6HkhKWnfAk11B8YIzJEhaj3OmwgZWLpUG244Fv7Lv0QaFmQAM0UytY+lCIpmhtFRa63b53Z22WCdcjRLkuRUP9PXAdx8W1Rh8zDGO4/5GT7njYc+G1i8K9geyM1Zjkbi4NBASKGoxKO/NChTsVxJdAr+iCbFt+fj9B/j4vQeiPt9JzM8FMP9BD+7eGsXh4w346rfOoLWjElabUZTru4WFuYCQyYTfE8Lf/Id3N7UeWopEI6uTHCTK6fdd01wmpDC3Qw/yhVk/XJV2+X0WiVgCD64NwTPjf+jz3XmqaU1f8EIdw1oWLOU1zm2zT6FHfFV9qYSMkkQnqT/YNY7Ok43oPNH4yGenRtwY6ppAPJp5aWOYKP92+bksFOg5fMB6DkPBO/AnPUJeFROENFxMgf8lUjFEEQSS7tWtOrQVqDa2odF8ELXGTti15ULS7ASZqWDvgg+INk0pLpZ9G4GEG6PhB1sKjqS9CxcG7pLsbTQfRZ2pXbZBJTVnZEiYp9gaqZcF8Wb0dJkXm8VlD7bUBHFWT/phGCjJXhL3vBZYsKLanCpzkuUkzRfiU4ikSELlVwzYKfD4LFoHKg2NmIwMwLOJIFce7x3fBwinAjjmeB4N5kMyyyVbHNuMJUg2ZDW5mEQiHZNzyXN41/sBhkJ3c7Ke4fYZmMqxMt/Q1P1g6dJkOYqp6NCm+hrV/fd8H8ETm8T50q+iwtAIo8YqPum8Toh8X+A+D4zndZJe8TUlXzkzgsXX7DaeNLDf09u6y8cC2/p9nGdfr9LiYkUHGi1lO7pfVo0RX6o9gY9me8W/dz3V9U6cExLQ7888QDy1/nVs0RiE2D5f1rqtau+1UKa34KmyFtiHTRk1+DpEJVW2VPNHkxel3Uj4bwdybQlea2v5USvYn4ilEhIq6s9hRotDa0atKVMM34mZG2a1HlUGu5DEwWRszc/GUkkpltGGaSeLYLmgVGfBaVczDtprdnxbRxz1qFkKhGYxYT3MRDi7cu1zWIzgezSz0fjOTZRW2eX9nqI+chLRcBzxWFLsSgha+Gq0GuiNWhiMGcV13s8c4g+eWlp3xi6YP+N/nCnPd3nOcOc2slaxuWyDQkKvO4hELCncBGfl09pW3gvSGe6LpDd/Tysr/oz7z9nsGp1GRI9Uka/cHj/H/SRHwn3lueDCY6AVLtdJpJNp4WMobtSvImTUaFWwOc3rcjoPt7m4iGQ8hVg0Lm1EVwjuh+ybqkS4M4oruS61Vr2qfe9aoCtCKBCVY+Ax21xmqDXqR9qF26XIMrtNnieNRgWtPnOOVopA2Ye4cJ1Gkx4Wh0nOM3/G885qMv/GbDNK23J9LD7w3FEYyfPK45JjMutlHRudH26LfZPnJ5lIiue89KPFRdk3nhMeF9epM+oeilHX60tsZ+7Pw+uh0g6tXiPrTcSTiITZfxKy79ntsP9IRqRJJ+eH3+eLRbZ3gu2dkPU/7GfpzDZ4brJ9lftDi2z2WR5jrtcGz9Uj/X/F/nMmBfsDt1XoQtCeebNgg5DM/vFfXELX3XE5qYWC3xfB1U/7cP/OKH73j57D0893oqLKgd0CO1PWU3sr4MWVrUSuhcNnWjHSM42Adxz+hRCuf9CNZ796EpZlChQOBPw5rVUIktRHz7UWzTEsBy9c3uC280KrqHPh9AsH8e7fXBX1Tt/tMRw4OfEYiT4+MIP+u+MPvz92vg2VdTvzkJlTAKC+Bh22p0QpSGuWvQg+vCwkZuFLzKM3cEVsXhrNR3DM8QJqjR0w74CKVcHeBUltEoIvVHwf78/+paieaYmyFZCQHQjexlDonlgPkUQv1dfKQhsMhvhytgTJPCENVRlyXQjyxaRsP5mOizI6mg4JKU6VPBXvgYRHilz0Oaf6nJ/NEu2ZR/fiJDEazYcxFRnaFIlOUMFOe5WZ2BCazcdx1P4saoxt0G3BNiexGJdxjpYtg8FbkuvA85kLIU4StlLfKPZVc7FRCVl+ksBrptV8QkJXN3u9cAbFSLgLk9F+mb3RZjmNJvMRsRXbbFAv+wmvi3AqKNchvex53YQSXinCcObSKecrKNXvPGlRjCB5Mhyaw3TEv6HvN72Gjzjr0GguFYJqJ6FTqdFsLhM7kNmoT3yWCwWOnHMxP254hja0uBICvbytIAQ6we2U6axCtH0617vueaFSneTXWNgtylyjRrd9BKVGj2Ayuq4anb9nsKNLsYB4YhBOxiWEciPw7cqmM6DCsHO5JnyFo/93d2BqfRI9ncRo2I0z6Z0NVc8FZ8tbZcxjsOhOg7YuVUYHXHoLpiPedT/Lgl1kj5Ho/ffG8Zu/+gwf/vymkGn/4w/+MdqPNSDoDaH39ig++dVdDNwbkxwygoRrXWsFTj9/EGdfPize3/nqMeh4MDk0h8tv3xPhIIVxQX8EqVQKVrtJ1k9r2RPPdKDtWH3O6+U+/rv/+4/Qe3sMVQ0uvPbbF2TWP0Ey99bHvcKx9N8dE7KdnAtnunMGPDPejl1oxTNfOv7Yekmedt8awdV37mN8cFZm7ftpebwQEtJVbI/Z/jM+/OLPPsGb//WzVfePYr8//JdfwbkvHMnpeEb7p3GHnNnVIYz2kT/imMGsPCOqG0px8HQzLrx6BDUtFVLQyBXv/+0N/OoHn8A7H8TR8234u//qq8I3Zdvlyjv3xFJ3dnIB4UBMyFt7qRmV9aU4cLIR5189gvoVeXWX374v/YhuCs9+5SS+/09fwcD9cbz946u4c6lPziEthL/6B8+K7TCLCczyu/VxH3755x9jetQjzgYUTj73lZOyjY04K7Z3980RdF0dxGjfjPjTB7whIf9JCltsRnFz6DjRKH2p81TjhgT30INJ/OaHl8U9gjzX//jn/xidxxuk8MDj+fTNe+i7Myptzf7D7bgqbGg+VIOnv3gMHSca4Cjd3Hg9MTiLO58NoPvGMCYGZuH1BBEJRIU0Z/GB56y02oG2I3XCizV2VsnPcsHCfAAPrg/jxofdGJD+H5L9Z2GhtNqO5oO1knF4/EIbrDmu84kj0UPBKLruTeAv/+RjDPbNZKpDBQYrRQwr/ckPr4ilzHMvH0LNLhGgLChkq2ysVpLAtTszFbl8YC+1iLXJasiSzIfOtuA6O++9cQR9YQnQfOrlQ3KRZ6tEAV8YXVcG5WbCilB1Y5lYuez2MayF7a5U8Vww0bq+rUrsWiaG5zDSyxtHSLzhCVZIxwdmxcolM7Bb0HSwRm7su4GsovOg7QJiqbCoM/cuMUQqkYQkrTZ8GAjeFLKMnsoHrGfRbD4mBKYCBXLtLwLl+nqcdn5RlKp9gWtbtj+RHrjIJYGFxIx4e09E+iS8lyG7JNdJnHN72fHncyXtkgpdFOjsx1xPctmSQDK9t9TPtHSpN3ViItwjfu2bAY+X2Qu03hkLP4BTV5kpTug4jbtC7HPo061V6TIqdZRkChKLSZlhEEkFhVSlCtqbmIE7NilEayQZRCQdQmIxt5dGErzsLy9V/q6Qttc8v97DY+XmoFHpxbLogO08BgI3pdizGbCf05KIPunzsQnc9r4rRDdJenqmZ3M1aOO1uGxcT6UTUgThfSqzRKXoxDaUQgg/Iwv/nbluiApDw6bsyvYLSCDdWRhHOLVxXycRS09um9a042qe7PPHUUcD+gMsgheujWjt0OfnGB3bUHVdbyrFUXt9wdRN3I5ercFRRx1uL4xseF7YvvRPZ9DidpHoDIClD/xcNLDuPWc8vICbCyMSkqjgyUA0nRBP9Fz6EGdH7GzxqQQWjX5DQprPU9zn3bK4y4IjCItjVOcXYjzJhLzbUKbbmET3JyPr2kcVI5gxRmVqVnnLWfJ3L/Xh2vsPcOXdLvjmg4iEY0guWf2Sp6AtLYVsNz7oxivfO4djF9qgN2w8bpKrGOmdwodv3BKilrPnqfolES2q58WMXavXHcBg1ySuvNOFoxfa8LU/elbI9Y14jazTQjQcE4KYXAo5A/IFf/Mf38NQ94T8nMeaVb+TXCfxOz+1wB1clUSnOpvruP1pn3ATyURaOJuVAkbuP3+2lrCRoseson8t8DyQLH/jTz8W8nlm3C3nRASTS/a7DDL1zgWEH7n0m7t46qVDePb1E2g+UPNYhtxqELV5JCGq+fnpTKYe2/TTX9+RYgqz+7gNEt+yvyUlIu6kSwD7R+uh2sdIdJ6PWCQBnzskHM1Q9xR+9L+9Ixl/7DM81zwu/v33/9mraOioxL3Lg/jpf/4A7mmvCHpJWvOznlkfNDo1jpxtWVW1P9Y/jY9/eVuKIiSzM+cngUQ8kTlHSwprbo+/H3wwKf2ZTge/9Y9fEq5prb6UTnEGRhLRpevBM+3F3VgCNz/skaLP5/0n+VAlLudm3IOuq0M482KmLQ6ebkIuYN+fGp7Hr//yklg9e+b8CAdjiEt7Z9To3IbfG5IiAYspD64P4eq79/G9f/oqTj2XyUdaDdkZGB++cROX3ryLwa4JOSfc/xSV7pyJocqsm4Us9rfWw3V46dtPiTjVuEYW4xNJomcsXObw07+6jP6eKbmAVgPvSWaLAeWVdlRV22G1G2GzmWAycyqEVqaQcEoHQeUyGzkRT8kUh0AggoAvIvYts9M+eNzBpenfj4KNOjWxgA/e6ZKpA698+Rgs1sJ7ArICk72Q2FlOXuxE04FqIbDzgVS87Ovvf1W9C3Ut5bC7zDKw990Zk3BMVpF4XkP+qBDsvIA4ALMSy8ooifFiOYZHsRkzgPXB43CUWXHmpYN458dXpdrLwXjg3gROXOx4qEKfGJpDJBSF0WzA0fOtUgXcDSuX5aD3MIkREn1d/k8wGenHXgZpF5Jn2YXBiiRrDtrOi3/6kzqtX8HKTAC9qGFJvpGA7Qlc2TZLoyz5jU0SjfsBVIzz/M5ZxyQoNF/v+Sx4HrPZC97ELGaiw5J7kCVcH1rmyIt0yUMrDyr7SbaSaI2Juj+IcHJ9UmhNBbqhEWdcX5KiHMnbHu0VuYts9pj2ImiLxRkVJx0vS7huPBbdUlEn0y4RKXBwVpRebZaCE/Mv1OB0W7Zntri0mCHJSainPy8uMeR6vX1g25GwJ4nypIJWBgyfjCTjOZHop5yNQkwVCgds1SjVmzGweVetvDET9Ym9Q3oD028SyTVGx455F68F2i9QAW9Q63IKAe3zT+Ni+dovpPnCqNai0VyGodAckuvY3UxFvPhsvh8nnA1otVbKTAYF+xvJdConslXHHCOVZkfJYq7ZqKEd2PoKzdTiosya2Oh630lwH2kn02AqhVWz/rvxdoJBoQxh3QgMXmXAM++42/+GXBjc+qQXPk8IvbdG4FsIoaaxTMRqfD+PBKNCtk6PZUhWCgIJEn1nXzq8IYFLQpWE3pW372NyeF7e2zkDvazKLl7i7FlcJ39HYptkOpdYOI6v/MFFVNS6craQJUfgnQ+Isvin//v7clwkhamy5TbJeZCopJrc5yFXBZjtqxe+eey1TeVCkPLzD20kFyHkKbkbboscTF1rJVoO1cix8TPZz/IrzyPV3GuBxOzM+IIQ6NfffyCktajzHWaxBc7mw5H8dk/5RClOElzseIJRIW8Pn13fvWA5SJ77eI7nA+i6NoQPfnZDiHmem8qGUrFUYZGFJDXPEYsebCctLT/WGJNYwKBl8ds/uiIENtXnfPbksbDvcP0yG+DemJC6JKs564DE7uTwnJDU/Pn7P72GloM1q5LoFFCymPHgxrC0qVarRlm1E85yqyi2aTnE4gkLDdnt8hhJIJttBnzx++fhLM9tdv2dz/qlr1MdPj/lk200tFdKW7Pfc0YD94V8FRequ9lH+Tmq4NcD7YG43nd+fAW3PumTPi8qer0GVodJ9pEzDMgJspBFsp7XJo+jsb1qzazELKie/+RXt/HB396U881+Q4ujZgpPRWyrkuIDixacCZA5hpAUbNguF754VPjJQqDon3pmZ3y48mk/bl4dfFjNWg7661TXOVFb7xJleHWNE1XVDtgcRvEuN1v0n5PoapUMCllPKE5lCYfjQg77WaWcC2B60ivLxJhbPNdJsLPCsxzDA7O4eqkfdQ2leOpCGwoNs9340P+K1Rj+mwMgO+92gwMBSXEOKByseLEM3p9AeY0Drgq7DFCsymVTnhs7qtFyuHbDh6dCHkMhwKLBhVeP4tq7D+RmxcGp69ogjj/TLr9n8WFqeE5ueBzEzr18RG4sux3kwe1TZUn1n0FtgkXjwkx0UJS0W7W52G1QhTocCoiPNKf8H7I/I9YBVK8qUMDAStpJMB+Aj4u0+WCw5JNEju4U+CJWpq9Dh/WMzAqhdcpWrWdIivu4JDanbM8XGQK9CYdtFyU/gqS9TqWHVeOEXmXatBp7r4L3CPrTt1pOyHVCAnw7QIUgPemfXL34zoDjWDydQJ9/akMrF6o5nToLGsxl0KsL8/JBNFhKhehRoaRgvui0QBkKzm74OQaK1pgcBfdRZvhgg7kUphxIdJKag8E5RFL5BTivB1q5dNqr8clcL6JYu9+QmOzyTkg47KvVR9FuqxKCcLefaRXsHEhEbxQomiWNNyK3twPcxkZZABKimM7Y4O0WaF/FIFSHzgSNqnDCKZNGn9s4IvkGqYdew3sRNz7sESKUJBtJYxJuJH/J/ZCYpmKV5CVtRij+IzlNvoG8Q21z+ZrrnR5z47O37onamQJCeq/Trrb1SB0qap0wWgzCNpNEHxuYEZ6EHAkJ9bf/+orkytEGhdYruSCTqzYppCRJ2/r2SrG/4Ax/2m2QKKbvNy1FZie8MFn0YjOyGkjSko/htperzNnOVO+SLCZJS4K543g9Xv6ts6vaq2StP9YCz8ulN+/g3b++iqA/LNwG1eXMfatv+5zXITnKQgZtae5e7hei+NNf3xWy31lhQ03T2u2wHCToSZxefbdLVPYki09e7BDCvpxtYtKLd3Y4EJH8velRNw6fbUHZOjbMJJGpmL5/dVDWxf5DvoYFlLd+eFkI2ntXBsRWhRzOS98+I8fH9rr81j3ZDx7fnUv9wgFlCzjLYS+1yjF2HGsQ0ryizimWvjy3QqKrVNKHuc+0JCKHxH5LVf/bP76C4093wGIzCVm9EW5+1CvkNT9LgWfn8UYpMJjMehESe8nlPZiUosfcpFeKKuwTdFVYj0Rn32G23wc/u4lPfnVHjp+keNOBGjR1VqO6qUyOhzM8siQ624oFASr3SXDzuNcaZ0iY994Zw6/+4pIUNSh+pgUMLYC4/owPfoZEZyFmqGtSiHz3jBe3P+0Xax1a/Kx1TTxRJDpJ7oHeGXzyfvdjBDo7ssVqQHNrBZ56uh2nz7agvrH0YZjDWmCzqVRqqbYZjBCivbLK/vD37FwMLrhxdVDI++77E0LkL/dg52cG+mbwyQfdOHayUTpQIW88HAiy6mvu13DPFI49nSFrdwIk0ZsO1spUDPGmv9yP9uMNcJTZliqBgzLg8zw0dFShoa2y6I5hp8EBo/NEk9zo5qYWMD+5IMQ5K6EcxFhN4w0jq1o/cq5VblzFAJJbVHe2WI5LIGIsFXrov7zXQQUjrReueN4QK4ajjhdQsVQ0UKCA4Yb07zZreA9YxESkf6mAVFxBu3sRtOmoMbbjhONlhJI+8XffK5Y0JNDL9XU4an8OR+zPwaixyM9LoBZfdC7R2JNFolONrisxSN4ErxHaID1phYS9RniRXKUf8EbqUZNGhzqzU0iXQpBfWZTqLXDqzaKCD28jEbwe6Twb80sY4UaoM7lQafj83aBQ4Pmn+p1tQkJ9vfBOEpq0VWGRZLsIMLPGgMP2Wli0BgkbXK+44Y4H8bfj18Ua5+WqQ+iwVUtRJEvc7VVCTsEaKMmtTTn7Z6eLYlw7id9cFOYs0u0mqMo/ZK+R8bXQ26W1Ti4g6cjxhPf5vQjalpCreOEbZ/Dq98495rlMMo8WKyTpBu6OiyqWvtQ3P+pBTVPZ46GctJRYJBnZI8vcxIK8u59/5Qi+8fdeEO9yBpUuB7kMWmf8+i8uyd9QAf3eT6+Lipvq3FzU6CRze24OC/HLmf1f+f2LMlPfWWF97NqjZSzJRBKHq0GsY10WWZaDPA6LA4alvyMfRo/4po6qTFEgD1BAOXB/Ar/5q8tir8F10X/8le+ex9mXDj22bySjOVP/r/6dSgoZJK55rliQoO94LuGQFLZS/EpimYT26RcO4KVvnRGieOUYRZU8rVjIvVCxv+Y6GU6aSAkv9YXvnBXyn+3PNrj72YC4CdB3nWT3068dw3f+0csSQirWdCUlUpCgLQoV6fQ8Z5sbV5DotGNhW9a2lKOixiUFjpVBp1lwe7SMef+n14WTnBickyJNdWMpHDnMjuP1wGvgxMVOvP57zzyW0cfjpQiW5++dv74qhZDpEbe0Cc9l9rhWgpzW1XcfSD+PhGLCxWatVJ568RAq6x+3uRZe1R8Rnk9miKxhY5xV/n/0xk303R6Vn3Ucb8Gr3z2PZ758/DGbFq7XM+OTNuFsBB5D940RfFR1S2ye1zq324miHi0X3EEM9c+InctK0K7l9PlW/Iv/4ev45vfOoqW9ckMCPRewQ9gcJrzwyhH8k//2S/jG986K4nxlO3DferomMTHuEVV7IcHKqasy83DPqQusrLLitpoFzXaAHlK0WmF6L6fI3L88iIVZnyTxsgMPP5gUdT8HDSrWWW0rtmPYaXDgJ1nOQgCrYLQhmh33SJDD3IRXbhocsGxOE9qO1smx76aVS2aKfMZ7mVYJDHu74/0QtxbeFssEToPfT4inY/jM/XPc834g3sh7tZ8p2H5wZkK1oQVfqfljHHM8D4e2QrzLFWwdNi09hZ/FYdsz4mGed5LTLoBtT5L8Quk3cML5Mqxa7vfn4O8cutwUM/sR1cYWHHU8h0bzIeU6KWLEU0l4YiFEkokNVZhUH9NqoNBkE2esuHQWOAoUThlIROCJBdcNIsyiVG8t2H4tB1/6MvZJzHtY/52GRKU3Qbuq+LaRlqJEt9VIACKJ/I1AG4hfTd7Gv77/Bv7PgQ9x3zsuRRsSciQ4lWet/QNtiQYG1cYzVdj+saXCzk6CNiQk0tcDxzSGbO4mkU4iu9VCi6bCzfIhmK+Q60wajh+7aXmzHTj38mFc/PLxVUMLqU6mzca3/8FLD1XhU6PzQuCSUFwN9Kpm8GTWKoRK5z/6l18V0n0lgU5QSEji+PmvnYTemGlrKtP597QuyQVsAirlua4/+O9el+NxVdpWJQNpl8JjyTWkcSfA4kLPrREhbXkLokCShKpYaqxC7tOyl8WOv/vffw11LZVC9PNvP/l1RtWczcrbCCTSqW4+98oRfO0Pn8OBk02rEvBsp/Jal3AutGFeD0aTDmeePyg+9nIfVpWIip6q5qw9iAR+Hm8Qe53stmh7zPBVgmMeSXQWC1YDA0ifff2kfGWfWovkJUfGUNHWo/WPBHhmQlpzA8NXX/j66ccIdILHxn7zzf/mxYwynL7u3jAmB+fEW32toYAK+d7bI1KYUKlV0gd//1+8jpe//dSqBPpDXtVpFr9y9ldyiauBYlxyZZ/88rZckwyiffGbZ/DsV0+s6nPO9ZZW2vHb//xV1LVUCKdGEeu9ywNYmA3I+nYaRf0GNDo8vyqB7nCacf5iB/7eH78MJ6dMbJBau1kYjVq88IUj+K3fuYBaXiAr+nowEMGNK4OIRQur2G09XCtVMnYqVgE5dYhTSEho7wR4oVfXl0ryNQdJTpvgFBOmY9O3i9UgggPNRl5Ku3UMhQJTgjk9h2DQBG/Qtz/tlek9BKeCHb/YkVOIxk6CitCpyAA+nP0r/Gj03+An4/9vXHb/HGPhnn1taXHH+wG6/JfEK12Bgs9RIsrp86Vfw4uVv4NO61OKf/42gX7XT5d/UxTMLl0Vihn0yq8xtuK16r+PNuspsW1ZCYZgMtj0SQZtkE46X5FzpKA4QWJ1JurPuqCuC6Nah2rj2lNsd9y3V1uYbKG5WADeeG4vobResGp3b7YgCbdcVaT+RHRDy558QNLx9dqTqDLmZkFAeOIhvDV1D//z/Z/hX936Id6YuCX+87nYfyjYGyApm0thhV790VQSiQ0I7q2A7yi8luMbzLLRqFRw6S0FnWHz2D6UqFFnLpXrqpBg4SDX4gFJs7381kcLCXo+r2ebQlKXYaIVNU4RvUVDcQmjpKf1ylBNqsopGCRJTMEgxXH8W3IW672/kyDkZ5sP1sr3JIUnh2YxM+bO+VjoS037Ci5rEY7FgtG+GfTdHnv4PcNCKbwkSbsWqMhnOx1+qhnlS1Yz5H7EkmcN8nk1tByqxaEzLWuSt/mCoaC0I1lul0L+q6zaDtXSLAIq9qmaXw7m3C13FqAlCwNLtwpHuVV897Ogmpte6jkdi1YtIaEdxz4n4VciWySorCuF2WqQAgD5ONrJrBUkS7sb2hUTNqcZr3z3nKj1880zXA0satGnn8dJMKCVwlydbu1rgNciz33zoVq5bjiIUZD74ObQQ5vpJ5ZEn5nyyrISh47V4eXXjqKs3CoE+k49+NOfyGoz4MiJBnzp66ceI+vDoTj6uqeQSBSWRDcueWBx4Y2PHYXTh+hRxCrOWpU8VnYYWkA/KvogkQDPBeykFfUuHF7yGGJ1h5XD+1cGJMmYYCDCgdPNOQ9mhT6GQoFTbegLz4s57I+g6/qwDDpBb1huHOLVdLp53RvMToK2Cv2BG/jN9H/B2zN/KqQyw0RJKtOXNrEY27cEOhFK+cWGgEGStHpRVFIKPlf/qcUnvdl8FBfKvoEXK/4OGkwHhVjdq2AhwKWrFh9rKqh369zSLueE8yWccb2GOtOBogyv4gwEEv0vV/6etLtRbV11arNVUypK9GI8hkLO3uA5Ouv6Mo47Xly12KBgd0FS1R0L5HQ3J2FbbrDuSo8mUW0pUNjefCwIbyJHEl1rKmgI4GptQjuXXEB/cip/twskHs+UNuNMaUvOljYky7kfY2EPrruH8JfDl/C/3n8D/7H/PXww8wDTkcff5RTsLfA6pdXQRvc+vkP4EmHJH9gJcP18dqeVUWiD0GRNiUaKQYX0Il8OnivmTDBAOdei2HbvwZMAcg/2Muu6M7z5LEoSvKa5XBS07EORYEw4h5UkejKeFBtWenzzNZFezLSrVW3AOfHdnj7ly8lPz4xfrG9zBcV2tKNgVtxucQW5giGhE0OfZ4y0H20QdfB654i/YzvRBoQq+6xNSM/NEbGzyQVcR8eJBlTUOrbFhYJQqTN2u8v7ENubFjfZ7AXm363M7uPnl/9NIp5ac3ZDPmD7L1dgc71Z0epGYAgtA225v+udQ/KaPB5aEn9uaR0R14nH3AtSGe5vYakvW2xGnH/1iNjabEc/5cwCFrSy545FMUepZd2ilby7iyLd9tCKiE4QE7ym4zvvElLUUrsFT0iW5bA7zWg/UIP2zuqCqGa4jdIyK06caZLg0mlWJZcGWxK/DB9dLfB0J0Fyv+1YA869elQCAUb7piUVmKdjemReBnpWsDIXRcbnicQzgy9YYaLNiEanwennD0h4Qi7goNhxohEGs14SpzmthF5WY33TQg7TC5zTKbIpzMV4DIUAg1jbjtaj5/aI+GPx/PDmSf8uBmfwuEpzDBjZLsjghyRmoqMYCd3DcOguxsO9YuOynwnz1bGI+di4hEiSzKs2sDBU3A8pCgr7wkPCl6p0p65SltFQF6aiA2IDFEoV/0wZdYkWVo0LLn21BOlW6BtQbWgVBfVuIPvCzX1hocKotsCmcWE4dA+RVHDLgaNbhVFlkQDRZssxKTbUGpnNsbYvo34pgJkkO8fQJxUsOJFIZ+Aqr5mh4B3Jn9hvVmBrIRZ5A6nkQEG2VVKig1b3NDS64zn/DRWaJLJy0RdSYUpbld1QolsYfpeDunU7sBAPIZDIrX9+Nt8PTzy4ax7BvTkEwmZBAp3q3+0Cj5lk6UuVB+GPh/HxXI+o3XMBLSECySgCgWkMBmfRF5iWANJmSzmaLOUSmlpvckl/K3Roq4KtwaDWyAwNLlSBr/f+wGtnJDSPenNus5PzQTKdxnwsgLmYf8PiEcc29j2Ge+4GWJCiRRJtcJSMgJ0D369p2bIesue/vMYBi80g6mcqe2fHFx6z5eX3JNdj4Uz/Iu9w5Z37GduSDUBvZpKBWdDTOhzc2EIsC3qYZ33aixlUWzPg1OsOyvfkgmgNsh5xuxxVjaVSzMgSnzy3K4sZa6IkY3nCoM3tghRArAbhpx5upgQSTpulCWizQ57nkV2hjcwyopdk83oCPf6eintyX955v9iosJhDTjFDlKfEroZBnPSbf/h3JLZzpGwo2LQ4jEIwbwQWIbJKewpYpQ1WbIfHQ0si8lnkALlei8Mkobe6bSpi+OYzAaRZ9N8dRzKRluPYCPRCZ5AqkUqkZD9zLThsBUX9BBMOxRBa4SNVU+tEDS/SVfxxdgpMOHa5LOg4WAOvJ4REIjPVgGQ6vdHXmvawk+AUmFPPdgqp/MHf3pCpQlRnM82Wpv0VDDVYOkecikSPrYV5v9wIeJHQl/vgqaact8eBhdVbWrAMd09iYmhO1sOOSq+oYxfaZTqVDDZFegyFAlO7Oc2IXmi8wXBqCtX79OxqO1Kf1znaKuh7zkDNiUgvHvg/w0DgphAd+UIFNXRqgygOqUJkMKcaGnnZKilRQ8VJLZt+QMzMIxR1iShMGEpEP036amaWZDqO+GIM8VQEyUV6vG7+mqPafiY2gl7/FSEY1esQZgqeTFDBTT/vw/ZnUGtsw2j4gZDpM9ER+JMehJM+ua6KoQjFa1OrMsCktsCoscl+s1/XGNtQbWyFTVNaNP2bdi4MMXbpauTrdHQIC4lpRJLBgoaOcrwiGU51Pv3w26ynhRDmudsI6hI1zHIMVQhHnlwSneA5ZDHSoauEXVsuBdrZ6BgCSfe+J9NjkZ8gEX2zMBsrscBkM+RFopNUJWGcy2Qrem+TGNuN2RUmtV7sZAoBkru0uckF7850ybIXQNuMnQhyPOZskD5EMv/mwjAWcrTCWa5On4x4ZbniHkSZwYLD9joJLiWxWWVwiNWGU2cSu41iuU8pWB183rdpjWi1VOD2wui6di1UoXf7p3ChvF0sRbazbemFfmdhTPrmev2eMzloFdVoLt0wX2CnoC1Ry2wbCepTBDs7BrPF8IgNx3og+afLKm+TafgXHudx+L171v/QPoNBn1w2A3qr5+NYQGuM3fQ5zxW0vKGPOdXjJJG53+SKclWG8xiptiZI3NIqOFcblJKlYsNaoaqbAa9R8lmPKZ+XfcuA0pWzHTK/Xh5Ku7p2gUS0zx2UUFcGhZJL47/d0xkinTYw9FLnueB5oEB3s/wiz21WXb4RMmPT8h19/DNUh3MfuX/8Nz3/LXYjzNbts+Ij37vc8/3GRz2y5AvuXyQcL4jTQFGT6Bx0sqrvLCqr7XCVPpo0XAhodWo0t1bg9vVhBJb8esRaJMqGwq6AXkFf+f1nhbx+64eXJTmYHYdVQQ7+WUuUbEACK00csDjAlVY55EaSK7gOTvlg+i5vJFSCZ8EL6dRznbA6TUV9DIUCPbWaDtTIAM9KNwdE7nt9e6UQ7IUCyeh4OoLZ6AjenfkBpiNDQiDnCrrpaUq0QpqbNTa49DUo1dWKPQTtDPQqM3QqvRB4/NxmFVuZ+w1Jcy4pIScTqZh4s0dSDM3yI5j0whufhSc+JQRNNBXOfC69OfsZ2tf0Ba/jtOs1UVSqsXshrwqKGyQIuXRYz2IuNoZe/1WMhO/Bl5hHLB1BKp2QQk9hSOBM6BwLWCT6uRg1VrkmqaCuM3UKKUy1d8kueoCuB4PaJIR1nbEDA6Fb6PJ9gvFIL0JJLxLpOFJSJNv+m2p2PNOotELgVxiapEjSbD4Gk2bjKerLwb8v09fJfj/pyBArpThb+roo+fuDN9Dtu4z5+ISMzyx6siC60wUnti9nOrDIweuCNkxspyc124AkejBH9TA9e81q/a5QPPmE320VoUQUkQ3sH/YikunUjniP61QaXKzohFNvhm5EI+p89qnkJrbFAMjpiE+Wd6bvo9JgwxFHPc6VteKUs0mKOEaNTra5m/7VCtYHSemTrkZ0+SaQSK39zDMZWcCthRFRrLNtKVbZDvA9gbZMb03fQ2iDgGCS1w1mBoHvHiFJGxnOtnmSCXS1Sguz2grHKiIFLX+nscp9eyvgzHTayuYC8gdUTWfVvbHI4yG4/JakZtbWgp9nMOVylXKuMBj1eWX3cVvbZVGyk6ByOqscJ0djMOoe2p7kApK8JKUJksURnu88CDWqwnNRWueMksy5X9+KJqM83wyCSzl57/30Om5/0itFCLaz9EetWvoI+7GEmJZklP50YOCSL3husn18O0Aujmr5VCrTPiwkGDeY+ZEvaKHEc5IFiXqKTvMtwLKQQ3V8IWryxX+VroDZrIdxGwzs8wUHTrvD9OhAyAbaZeUEPYmYXnvquQO4c6lPwjmHuiYwO7Eg5vocjzjw08+L/khMRWZYBb2kON0jr23ZTTj78mG8/aPLD6dN8EKiD9mBM82brkgV8hgKARLmtS3lOHy2BR/9/Kb8jOeIYRvZ0NFCgAQfFbRvTv1n+BJzQmbkA1pCNJoPocN6Bg2mw6I6JGHC/zIEXfaxcHseDxdX/Cvre4gVCnUS6gxFJVnTE7iKWCqUN0FD0jOQ8GA4fBdtlpNizaBAwXqgbQXV3bT+eHrxG5iPTWA83IOxcDdmlhTVfMHb2X0wwK6rEKV5pb5R9oU2KWaNQwhEWSTqpPhf2LivJF0bTYelODYSuo+B4C2ZNRNNUZm+neeyRIhetl+T+aiQ+Cw8ZAoR+b+8sXBRpl87sOdJBc/paedrOGJ/DnPRUWnP0XCXFJ9o3bNT4B1IZjjoq1Gur0O5vl6ukXJDg/z8SQRJy0iaBamNQcVmoSxVVoKkaaGsFsLJ+LYGcBYLOFLuVImKfeOIvQ41B5zibf6jkcsYCbm3HBo5Gw3gw5luXJrrg1VrFDL9+cqDOOaoR6m+8EIpBbkHAZ8tbcWPRq4gvM6sjng6hdGQG7+cuIVvNTwllibbAdoK9fin8NlcP2Lp9a9l+vmT8N9NkFTUqYo7HHKnUWtsxDdqfw9frfntVX5bIs9gWtXW7j9ioZHPI+PyAXO1x+XFRSym+N75uTiO9rHO0vzfFelx3tRZndff7IVZOaJgXrab+Sp/M+/tW7hzrVRQbwN28rT/4s8+EdcF8eBPpoSMp/d954nGh6G4ZrtJnDaosKe9zQc/u4F3/+bapjjL7e5Dy9trK8WEtcD+Q54nizMvHkJ9S8XD2Qq5gueupqn8MdudJ45E1+u1skTySOvdSWRU0Z9f8GqVCibT+knNOw1um1UrKrnPvHBILENkek0sIdOUsqQuq1z8HO1RmMJLA/58K506g0Y8vf/lv/8jsVeR7ZdkpvDYneub/+/mMXzj7z0vJH0ykZSLqqqxbN3wka1CQjM06kf2reN4gxDr21o1XQdUAA4F7+JT909EwZ1C7lPJ6AfdaX0K7danUGVoEhKCP5Oc9x28w5Tk8EMO4poSHUxqK6oMzei0nsU1z68wHukT1X2+RYaBwC3UGTsLTqKb1HYctl9EjbE177+1aytg0RTWV1/BUggLSFSroIUOlYZGOLTlUoThrIlYKoyFxIwUrAKJBZlBEU4FEE76xdqClkS8DrMWRTK5d4n0FuWsSiezOjjzg7ZJ7JNUSPP6s2qcsGpd0m/0auOyz/Hf+j2nts2OI2IJpVZLIYB9usVyTGafLMSnsRDPnku3eNFHUyE5z8llyn9q8vkCpirRLKnMdZnztnTuLBqn2IyQ3OWME4PKDIPaIuPZVl7a2B5HHc+hzkT/9PXB9nsSiNyH/XlJCV5jbIdTV4Uj9osS6MzZP2xTf8KNYHJBrotoOtOmvF+laEkhxNyi3Guy7Zq5NvTS1/VqMwwqk7SfSWOXPsM25pK9FnhdZCzH+JUKwCdT1Uqbg1x9skn07E7oXUapWSjlMYlfFhcU5Pk8W6IWD/OXqg6hxVKBS/P9+HSuF70BFo43R4LwWY7tQTUzCxsfzfbgnndcPNNPOBvxdHk76k2lMlNBQfGAIZn0OT/ubMQNz5BYJK2F2agfPxu/Ie1IMtuu29rMYc4iue4Zwo9HLyO6AYHO8Ywe/OdK83/G3k7QyoaFqOKnRHcOMjtMvbP3l0QsmbMHMu1HsrYhfCengnrluy15CXINWeGks9yGkxc7JQwzX5BvKAShV2iQ+8nyHKJUDtPqI/dKBn3Qs0p2toPJzADP/fe8Ri6r9/YobnzQLV75vPfVtVbgG3//BbQerpXwTPaPzAwJtZwLcl4+TzCjSt8MtnnAkZkGDLpduh6yOYXbCR5/1maJ4Lk588JBufbytuUxaER8u9Mo6qcTEqUWq+EREj0ef9zipRCgdUsoFBOz/+Ve6aVlFqiLID2ZF5691CLLToGVLd5UdsqHfKeOgZYxtS0oGFhNW5jzS/iqoARSGKCnfKEwEenDA/8lUWznSqCTkLJqnTjueEmIwXJDfdEptEnU0I6BC/eN6lKq4jULb0lYaD5qe9rBUElMMp03tUJOt+T+21Vluxb2qGDzyPaTLFlHcjsT3ptGZSogilsS6ol0VMhzLrQoIUmYsS1KZartSx6ZGRuKrBWF9iEZTDuK7DZInNM+iSThfnvI5BngcXEhGUpQSRxZOpckz1kgy1i9cKH/LwN20nLtLz9/XLLnTZstRmgsQmLzvG5XEZDto1y/a4PtIX1WzdlpFdL/Y6mIFJWkGJIKI76YKSwlFuNi98JZHNmsi+XXRYZQ18g5ZwFVu/Q1c12YpI25Hd6/9oJ6q1DgmJSrxUfm+tmdcUVauEDbZiBharf8F/c4GJBYqreKapz2Lm3WCtz3TuCOdwz9gWkhwjd7ZnnlM/SVy3TEi7GQG/d94zhkr8VJZ6P4p3O7CnYf6iVf9FdrjmA87EZgRW7ZynBjzlr4q5HPMB8PCKFda3Ju6nonIf/ZfB/enrovSvSN0GatxClXEyoMu1vAljuZcl/acQR8YSHHc/qseDtnPkvLC3IOK+1WyHcwrFSj+9xuhOMb1cIKPrc5NlkMMJj1iC55owf9ESGNcyF/6Q+eJWKFT6uyb6sFSbGA5+XWx73if067EjoS0Br56S8eFe/yrKXNStA6ZTf4ztVAqyTuq3jGl2SKVoGFECLBKPQk1zdhc7QS7EtW++f3eRL1FMsW8zVX1CQ6vc9dZRbMzX4e3uXzRh4LGy0EWLWcmfI+0qFZ5ahrKN1RVbOCvQcGiTI0glNx+FLvrLCi6UA1XHlW0zYLejX3B25IyFuupDIJCIbsHbU/i5POl0W9SUKqmCGzINQmtFlOiZ8yFY4MKswVVLN6E7MSEklfa5I02w2qtMKxBDzBMLyhCILROMIMmWFoCF/o02l5wNaoVdCq1dCqVdBrNbAa9LCaDLAZ9TDrddAVMIxWweZV6hllbPHe8IsdWRJUFPia4irg7Udkpk8uYmh2AZMLfgQiMRmPHGYjOmrKYDMaZGzaKlgcMmm47H9FfrGAL/y52EvtdtxdIQP3WFTYacut/Q7a71CNXmt04pC9TsjKu95xDIfmMBH2wB0LbUntT4uQgeCsLA98ExgMzAoZSkK9yVImqnilWLa7oP3SU6UtuDo/KJ7n7vjaVl3sC1fcAwinYpiJ+sSup8boEsseeoWvFShLn3/2hYVYSPzVH/gm8dFcD7p9k4ilkxv6tnP/6LXPmS67CtoeKP11xzE/5UU4SPvXxTXPd9ZuhFaxQX8mwFCn06KqvvQxIpO8DtXCdz/rhx8h+BdC8k5//gtHCnA0ewMkTh1lVpRW2kU0yEIDM/NoXaMr35hEnxqef2gNTAuOutbKh0WL/QSSwUMPJsVhgSApfPRC24YK67A/goA3c352G7ymSHKz4MQZCCx+BHxhjPRNo/lAjfxsq6DtMc9Nz61R+X6sfxZ+TwgooAh2X5HoVdUOVNU40dM1+fBnszM+eNxBUYZzukMhIEmvkTgGemckSDQLo0mHzkM1UkFToCAL3mg5YIYDUZmecvipFlTUubY1RXo1ZH3BR0L3xF/Yn8w9SZzkRov5GC6UfUOC/4qdQF8OKhMbTIfgjk1iJjqclz861ejZgMjtItGzD2q+cBQLoQhG5rx4MDGL4VkPJhcCmA+EEIzGEEukEE8mxRbKqNPCxEWvhd1sRK3ThvoyBxrKHDhYW47mCpfyIK5AgYJtBYt4nmAEb9x4gE96RjDh9glp3lJZiu+cP4rz7Q1wWozK2LOPkc012b3tF47Ez6Sr5IbdLi7kA4ln2gVbD1qv1JtceKnqMG4uDEvwaJd3QlTDtPmg/UZGv7k5jIcXZLm1MIqLFR14rfoYGs1l4q/N5yYFuwMqycv0VrH38cRD0u5Una8F9gAWWvoDM/jUXI4zpS3osFVJIYZZDCyMZNTaJTLLjOIThoaSdOffXHUPos8/va4H+3Ibl2POBpwvbZMZDAqeDMyOe+CZ8Yvqdy1Cj5YjFLnRUoMEIDkkk80gZPlKa1gGPHaeaMDHv7iFuUkvFuYC6Ls9KuGQJBMLZc1a7KisL0VDW+XDmfe9t0bRdqReyPX1ihnJeAoD98fhXhLJmqxGdJ5s3Lx9SREj2++y9sS0RdmIQCfxzmIP+3XRCMU0amnrwXvjmBp1yzFdfuu+EN9aPUN3t/YUUlHrlGuR1xYLMuwfU6PzYuuyHST9E0eiNzSXo6mlHB+WZJKSiYkxN6YnvEjEkwXxuyHopU3ivq97EtFl04WsNiNOnGkW33YFCrIFlzuf9qP7xrB8zxvz+S8ek0rtjmMx85LYF7guQW75oMrQgvNlX4VRbdmThEmZvk5CA2nlQJuAfMCAUZLoZti3jUBPptL4sGsIP7/+AHdGpkR9vhZSaZLpKSHds7iBCflq0GnwuxdP4p996ZndzjBWoEDBPoM/EsM7d/vx0ytdUtzLwhuewLQ3gEq7BQ5zrTL27EGQlFLnoMIUEx1a6Szdu/bi/T9X0JtYncPx8dwZJKNibxAlepVm1yh/9her1oDnKg7gYnmHEJ+X5wfw7nSXEKeBRMYubysmOmNhN348ckV82P9Jxys4XdoMh9a0r/vqXgB969nenIHAmQMbIZJK4IF/UpbsrAa71iT2MEaNFtoStSjWA4moKNxzIc1XXt81Rid+p+lpnHA2KP3jCUIoEMVw9yQmh+dl5vfKtuf9LRqJ49YnfZibXBAS12wzigq9uvFxWz6dngK4VrHe4MzyoDeMvjtjYstx4tlOsRsmVutjDwM25Z18KYRxn/bFxs5qHDjdhEtv3ZWDvfb+Axw934qmg9WSDbdaOwhBPLmArqtDcE95RfXPTLxTz3aKP/2+A2eb6zUPcwPJX0YCq2e4ZfsOizb998Yx2jeNYsKhM83ovjkiJLrfE8Jv/uoznHquExabCXrj2paZy6+JtWbn8FpkAYaWLj5PCJNDc7h/ZRCN7VWZLIKlP1nvmstuplAi66Im0W02I1raKtF5qBbd9zOkEi++ngcTuH5lEE8/11mQ/RgbdePNN24hnvi80l5absWBI3Wilt+PHk4KNodLb97BjQ+75SbNoIiWw7U4eLoJlmU+TzsFWreMR3oxH5uQsLZcwdC9BtMB+bpXwRdIs8aOCkMjxiM9ef1tKOkT/+rtABU0JKP+w28u49rgOKa9QUQTufu0r0RDqQO1pXaFxFKgQMG2IxSL40r/GCIrinwsBpNEH/f40V5dBpsp88KoYI9lDeQ4o4wUJxWgDDkuNEjgb0WtvBMhptUGB/6o9TmcdO1M/s92w6kzwawxFEWfK9PbRJl+rrQN01Ef7nrHcM09iPu+CfE83ywYQjoZ8eLfdv8a364/i5erDqHRomRS7HZ7v1B5SL7++dAnGA7N5/X3iXQSnngQvkRoaeZHycOiXr7ZBUa1VqyF/rjjCzhor4FWVdT0hoIdwOW378NkM8LquPiYjzLtJwa7JvCj/9/bYv1C1DSV4dRzB9ZcH0VwT3/5OBbmA3hwfRjuaR/+y//6Br7hDuLsS4dQXuNcdQoQFce0Kem/NyY2MAdONaO2gJlohYSjzIL2o/Uy456kOPPg3vrRFSGMn37tGPTLgiIJ+qWP9c3iz//trzAxNCvPmg0dFbjwxaOSuZclmvcTyBHWNZVj8P6EOBSQH7pzeQAnV+l7VK3zHP7kP72Hmx91P5LDWAxgX24/NiAqcb8nJOGn/8f//HN88fvnJQR0Lf9yFq3oCc9jr2+rzFw7q5yn+vZKvPjNM/jZf/lI1Oif/vqOzC559XvnceRsy5r9gyG1EwOzmBqZh1avwbkC2S4V9V2GJ7SlvRLPf+EwRobmpIrI++pA7zQ+ercLlVU2NLVWPhYIsZ3gdj95vxvXPxt42Jm5vbaOKjz7woHHpgAp2P+gPz6n2Iz2z0hFkRUvDoxTI27cudSH4e4puYnWNjvx2u88DWeZdc3giO0ErUkGg7fEHzwf3U+VoRn15oM74gleKEhytNoshYB8SfT4YkT80bcDkx4/fnatCx89GMJ8ICy+51tBY7kTtS6S6PvvwUKBAgW7i2QyjWlfcNVxirNp/JEoIokkFCfzvQeSxTo1FcoZ8c96SC6mRSVqVutRaJAcpf9xIUDla67kWrnepthB5Ak+p7Bwo1UZRV3s0ptRZbTjmLMeUxGv+JuTTO/1T8MbD+VdPEmkU7KeNyZuymyBV1VHUGN6/GVcQWEgmU86Ey6Ut8t48/Pxm6Iyj+SoIF9cyinYKk/E8NAzpc14reY4jjjqYFTrlDDPJwy0gohF4vj4jVuYm1hA29F6CarUajUIB6OYGJqTGeKjvdNCytHb+eCpJpy42LHq+uSdqwQ48XQ75ie9CPmjogrme/4v//wT8UonCe+qsIsCl2SwBGv6wqIids/44HUHUb2kdN+vJDrV5lT+v/bbF+Q88bj7745JW/TcGkFTZw1sDrOcS4aOTo+45fcPbgwJb8I2OnmxE+dfObJvLXLomnHyuU7c+rQPC7N+eGb9uPzWPembzYdqYHeZpSTJwNupEfbTEXTfHBZrGxLOtAguFpisBpx7+bCEwr730+vS54ceTODnf/IRbn/Si6qGMpRW2sTehS4e0VDGO907H5AiFP/+K39wcVUSnddcRa0LF18/gfGBWXRdGxKi/sZHPZif9qK+tVLsgzgLhH0lmUxJoC2JfK6bxS72x8NnmhUSPQtXmRWnnmrG8MAsLn3Ug4A/Ap83jDs3R2A0anHh2U60HaiGzW7aNjKdgyGnBg0OzODapX5cudSP+bnAw9+TQD/7dDvaD+xd5a6CzSOVSsuUsY/euCkebLzwo+EY5qa8MkCSZK9pKse5V45KlbsQHl8kzZPpBMbD3YjmoUJnoGipvhYV+nrsdWhKdKJGzxfJdBzpLQRhZUGfc3qfv3mrF7P+kKjSV4Ke52VWM5xmo/yb/sP8WDJFS5e0qNYZPsoQUqpEG8scqHMpFFaxg1PJGBY7Mu9FNJGUmQMWvR5NFcoL/pPkL+4PR0W9TfAlvsphQamVD8hFihKSrWsbQdB3WCEj9iZobZArKc6+G07GYVLrC24KQmKURHohQP9lvXrj1x6ei0Lt034GfdOrjQ4h0g/aanDEXofDjjrxuSahPhSaw2hoXgo4+YAhpu/NdMGq1eNLNceFNFWEBrvnj15psOG5ygMwanT4ZK4P9xbGpNgRTW9+FmYusGgMaLdWSvDs2bJWnHA2yrin9IUnDx3HG0TURpL80pt3RSnrKLWKVQhV6CTZZic8wu/YnGaceeEAzr1yBBWrkHnLUVrlwNmXD8sz/me/4XonJPOMHuBcj81lkbwzKogT8YRwR7R+4ddsdl4ysb/DrHkOTj7bKfzHJ7+6I8WG3tujUrioahiGxcaZ+CUIh6LCmXAmAFXG1U1leOqlQ3j6S8dR1VCK/Qr663ccb8SJZ9oRCUUxO74gxPhbP7yM2tZysUIhWOyZm1rA5NC8FGjY7xKxJGbG3IjHtkfot1VQNNp8qBbPf+2UiEWvf9ANvycoKnsS3zaHSQpUFBjTOYQKcRZLWEBhYYVFgbB/7Zn/nI3QcrAGr//eM+LgcP/qoFy7d+cDYqdEa2SDWS/7we3Ho3EpPnDh9tiPxPqlQCh6Ep2hnTV1Lnz9O09JuOf922NYWAhhbsaPt391F3Ozfpx9ugONzWVwuCwwW/QwGnXiZ0VSfb2baZbjYrUkHk+K33koGIPfF8HEqBtXL/Xj/t0xuJcIdK6rrsGF514+hDPnW2G27P70SQWFB/sNBzverHkzJalOsAJGjzVWVjmt5fmvn4Kz3FqQfeL0x2g6BHdsCol0JgE6F9AD3aYphUm994ladYkGOnX+1yQJ9K05dn6uQr81PImhuYXHfqfTqFHnsos1QmtVKWpdNthNBvk5+1MskRDFJwl0dyCEqYUA3MEwjjZWwnk4lwABAABJREFUodxWxCScgoeq3RlfEG/e7sWcPwSNSiVhsAqJ/uQgFI3jwcSc9AGC1/YXjrYVNYnOfawvtWNwxo1lcS9CpJoNOpRaTVLsU7D3QMW1RavPNOYGtzcSxsFEFGV6S8EjKmOpJOKpwrwgknQzqTeecRdKxRBPJ+S5iiShgq2BZToS6vUMIjWX4nxZG0aC8xJGetMzjJGQW6xfGCZJZXIu6PZNitqdFh7HHA07fgwK1gavEafOjFerj6LBXIYPjA58NNuNHv90zu2Zj/8/fdRdegtaLOW4WNGJk85GVBoLkDuloGhR21Ih4rW61krxLafad3rUI8Q6b4AarQZWp1kIPhLuL3/rKRx6qjkn+5DmgzWioHWUWvDpm3dF6e73hoQoX5gPIp1iwbVEOCdaSTAEsabUAovDhI4TjUtK4/0LHje5jq/8wbMwmA1iQ0JClVY2wpEkMgVpzsinap+zBjhDn7MAnvnycTQfqNnXha/s+SHxTIXVnU/7pH9StT8z4ZEnLpVGJfmK5JBaDtWKvQ3V+fQFp6XJTJEEjGaJbnqj21xmuS5ok+SZ8QlRTjKbMzHS6bSoxdnmLGTxcxV1TrQeqpXrcN31Www4/+pR+Rt65XffGpHiC4NMWVAgWc6iVolKBY1GJUUsnl+eO3r0N3RUFuxcFD2JTpAQb26twPd//yJ++INPxVqFinSS3pc/6cf1K0OobyjFgcO1og6vrnWiosouFcAskf75BbqYIc+XQhg5i5mDoXs+gMnxBQz2zaCnaxIjI3Pi4ZMF/95o0uILXzomBLrFahCy/aFZ/ibBaorFYtiXPlD7FWwzJk/Xt1VJlTAcionyNDv4sbp96EyLXPyFAolzb2JWiHS6m+YKp64aFq0TJfvgRVHFacQlm5iOvg03b44DAzMe3BicfHy/VCWodtrwBy+cxstH22AzGnLaZDyRglqVeTBTUNyIJZK4MzKNX1zvxrjHB61ahbNtDfjd507u9q4pKBBm/UFc6h3B31y+J98btFo0lTtxrr14CR6zXofzHQ24MzotMyho60LCy6jT4kh9FepLHTDp9q7N15MMnUotJGNmnsH6z6kkselX3YjCe0wzSDBfJfJmYdMaYNJs/IwQTycRSsYRSydF5axge2FQa9Fpr0aHrQpfrTuFy/P9+OXkHTzwTcAdC8r53wgs/PQHpvGbqXs4ZK+DBooCuRjQbC6HvlqDaCqB0ZAbweTqoh4qxjlbc7XQWbYixy3OgiJBT6sYfp52TFVGB447GyTUlF9ZFFPaXUEkHEP7sXqcffkQjl1ox+W370nQKG0kCKrG61orcOJiJ04/f0DI9Hz6DQNGX/nueVFNX3vvgVjDjA3MCLlHdbFYipr1wg1QDdvcWY3OU01oPlAt+WjrQaVSweoww1lug8Ecg8Vu2lFrk5IlXo0kqLPCBnuZRYjRrbwL8xi4jq/+wUWcvNiBe1cGcO/yAEb6phHyRoCSRZgsJFJdosqm0JDtYTDlfn9l6CjbjSJGvlfzGLbKndGzPXseWCThcSzXEfB7g1H70JbXZDU+9k7OtjJZ9LIOiil5TKu138HTzWJjcuh0E66+24Wh7imZtcBj4Lkrq7Kj9UidcEgN7VVyvkgcs1+zGESOSa1dvV+QqObvuQ+E2WqUfckF3A77bSKRkvPLdW2kpWChqPVwHf7R/7MWfXdGRTFOpfjUsFvsW6gQ52dIhPPa4/XTeaJRMgJXs3JZDbyOWfAa7pmSwhiDVmmlTMskWlFSZJ0Vrja0VaL9WIPYOBVyVsOeINGp9A1yOkAwgto6J8ar7UKiZ8GqxOjwPCbHPfjgnS7p4Gp1iVxwVKXzAuCFwmuDvubsKIl4CrFYApFwXFTo3IYsybT47GTVxVnodGq0tFXBPR/Er39+SwbscCgu6viVn80HDqcJ//z/9jpMGwyyCooHHGAO8OZ4sEYu5OyUBt5E6eNP+xYZhAoIkuj+xHzeimqzxga9audDTwsBEtmpTUzB1pboxNZmS9smieYLYngVFXq1w4qXj7biSyc7odfmPuRqlcDiPYNoMol7Y9MIrwhoVPDkgDMQhmYfv/6LGTajHi8faZd9f//+IEbmF2DUanG8qRq///wpNJWvHhKkoPhB5a9LR2X5xoilE/DEghlRSIH5KKqPw2sQbdsNp84ihYVcwPPhj0fkHULBzoFFimfKO3DC2YTrniH8avI2Pp7tEZ/+jTAXC0ho6WR4QWxjmAGgYHdxwzOEH49ewTX3kFzbaxVRGs2l8MUjCCSjMhuFz+7yDlWihmFpFg3V7QyqrTU50WQuQ7u1CtUmh9hUkVBnULACBQTVzlS/khA9cbEdh59qFmtV2rcQJCpFKa7TiFp8M1CpS8Ri4pkvHcO5LxwW7ofbzGopyUGTdCWBSoUseYBc8tAouPtn/8t35Rh4DyaHQEJ+x1CSIShJapI/4/7KNrfpXlfdWIqyGgcufvm4nCNa3chmWRTjudGqpR3Il+QDzu6/8OqRJc6tBGabQWYYbAVnv3BY+kv2PFjtpkeIeavThGdfP4kzLx7GYjotXCLJ4eWgj/e3/sGLYkHCTmA06YXgX6uts1a/5BuzQlw5N+yjWvWSk0am35AU/uf/+vvimEHF9cqg1izIR/1u82v47h+/LN+v99mV+Obffx6v//4zYrEjhQKLIa+2aTlUh/r2KrzyndTD9l7MFkNVS8XQpXbnkk+BiOe67UgdGjuqpY3kelu+/qV1c39ZNOD5KySK/onj1rUhvPGT6/B6QkJYk0ynJ/pKZElwLPMNUvkimZOrZudUPdTjsAHSS4s0eA5qchLvDBmdmfLK50meygDKv8/I2jeFskpb0aXvKsghPIk34iIKlU0vJhFNhj73KMoROpURmpL9MV0/hSTiefjBL/dSp4p9K6CH+XwwhMgqJCqDQS90NIq6Mx8o6po9pkQfnUI4llugloL9BxbRhmaLZ8plrmOM2aDF66cO4Hx7g4xffFayG/WoLbVDr9Fsx0QdBbsAo1qLcoM1J06cQYD0MM436HE74IuHhUgrBOjdTGIuF8zF/PDEg4pNxA5CZgkvFXxoP/RUaQvK9Fa0Wirwl8OXZIbCesIQKplJxF51D+IL1YcVEn0X4U9E8P70A/xi4hZ6AlMIJqOPtZxFo8cRRz1eqzkmpDgLJZlgUYqRMqxIVoFO5blWpRay3KDWieLcrNFLpoFisaRgJcjL8PWXzy86fYYU3m5kXQ1I7Om3UXuWJW8LBR4Dz89O5bWRiJXiwTYXoEnybxfRvzz0k8taYH/KtPfa2yWBS+KZSy5treeyzjaXI1euKUNQG0WBni9on2LcQl/SLJHjO4HM+jVbLpbsFIpzr5ZhZsqHy5/0iTl9vsgQ5Skkt0EcyHVR/b5cAb8doBJ+q5YwChSkkEIsHc5bia5T6aFW7Q8SPZGOIpjMXwlqUJu3XEjwR6IIRhhQ+uj55wsifYXbqwo/TV5BYRBPpuAJRjA675N/K3gy/dDpiU8ifS8hW6ij3RQXBfsHVHuW662i1mRmw3qgdcl4ZGFXnkVpI0MivRAgQevSmcUeYiOvZqqbZ6N+HLTXFmTfnnSQOHXoTDhorxGylHYgv5i8Be8GfSOciuOmZ0TsPRw5FkgUbC/mowEpZPzN2FX0Bqal7VaiVGfBhfJ2vFx1WGxY7LSaUiq0CrYVCpeiQIGCwqHoy7m0VtkMga5AwZOExcWUkMj5PkQwjHOrVibFgmgqBE9sOu+/M2ns0Kq2FhIcjiUQTTw+TmnUKlgMOjjMSgjxfkUwGsPI3AKCkdhjRRQFT44f+rQ3IL7iChQUAzQlGU/0Ur1F/r0eaLtA/2L6TBeSSE+mU+KBvRFRul0gOUsiPRc1+ljYI+r87Q5GVLBx8Ycq5W81PIUOa9WGQbAMgKXymf71CgoPjh1dvgm8MXETd7xjqxLobMML5W34au3JpWKHSSHQFShQoEDBnkbRk+gKFCjYGBKSu4mXvbS8NO/9l8TUIoPAvHDHJ/L+W6vGuWVfeIaAJuiPvwJajUYsEdQMZFCwL+ELR9EzOZf3LBAF+wej815ML2QCrBQoKAbIlG2VFq2WShg2sLmIpuKivF6Ih3Pyot4u+BIRIdFDqVjBzkmV0Y5mS/mGn52O+DAeXkAwURirGQWfg7Ys9MymcrnCsP4MmUQ6LcUOhpEqs3oLj7GQG5/O9eGz+f5Vf89ZH+3WSnyt7jROuBrFokWBAgUKFCjY61CYHQUK9gFKoIJGRcVOfuoOWsAkFve+j7M/4cZcbALhVH5EFv0XrVoXdOqtKcUTqRRS6cfJB42qRNToCvYv6IdPEl1RoT+5GJ3zYsqrkOgKigt6lQad1irxFF4Pi0u+6PcXxgsW8kkMBGbEzqWQqDI60Gat3PBzVOWPhubR7Z8qyH4pePzZ7JSrCeUbkOgsXrPvJtIppZC9C7jmGcKHs91rnnmDSovvNp5Hq7VCfM4VKFCgQIGC/YCi90Rvbq3AN79/DvsVFqsRuiIKqFSwN8FgTK1KvynyOZraWz6+q2Ei0oeR0L28rWxK9bUwqC37xtJGQWFB5Zs3FEXv1Hy+mb4K9lGY1ajbK3YuChQUExjCd9RZjzen7m74WdowfDbfh8OOWth1hQk5u++bwHyssNdNjdGJTmu1KGJJvK6H/sCMKGzPlrXI95kYTAWFAj39GZCbC2KphFjvKKGThQMtoFgIW+sa5jXGYN7DjjpYNYoHugIFChQo2D8oeva2vqkMX/rqSexXMMF4p1JtFTw5YDCmRWPP+yXPE59GIOlBajEF9Qa+qcUKd2wSo6EuzMRG8vo7TYkONcY2sXLZ7MM9SbRYMolgLC5fixHcR3q2k+TjMhcIwRMIwx+JiY97LJESKyAG0Gk1ahi0GpgNOpRZzSi3mlBX6kClwwKjbncCaLnvkwt+DM965CtDPEOxuIR4stV03GedFjaDHpUOK+pK7bKU28w7fl7n/CEMzy3AFyruKf9UybPN2f4MwJxnHwiGl7z8k0gkqeLLnEv2Aba1w2TI9AGbGc0VTthMBmjVhRsjIrGE7OvI/ALm/SG4A2EEo3Hps5z5kUwvQqtSyf7q2Wf1mewBu8mISrsFNU4bnBajHNNOEuhs/6mFgJxLBTsDzvLheDXu9mHC48e8PwhvOCqBrrFEEsl0WsZwzVJ/YP+1GnTSZ8usJlTardIn2D/4uSeFzKGdywFbDVx6MyYjC+tatUTTCVGVvh45IRYa+hzJy82OR7TfuOsdKziJbtLoUGd24ZCtFvd84+t6nnviQXR5x3FnYQyH7LWKFUWBQWV5rrVpWuYpRY7CYig4J7ZHa40rVKHXmVywagwyNitQoECBAgX7BUVPolttRlkUKFCwNqhCt2nKUEIVTh6K2FDSJyS0PzEPp27jKc7FBHq5R9Nh9AauYjTclbeinues0XRoTT/0SDyBQCQmC4lGknfROL8uLfLvhHxuaNaDsXnfY+sg0ds1PoM//eBG3sf36vF2ITA346eeJU0nPD5MLgQw5fVjwu0X0pfk6UIoIoGYJKBiyRTS6UWxnSFJqtOqhZB0mY1wWUyocdnQUOZAS4ULnTXlQkxt1qKGZBj9o++Nzsg+EOSzuN1XjrXL9vg99yeSSKBnYg49U3MYmHbL3814SZ5FhLAkkUrolnznGeBabjej2mFDY7lD9vVAbbmcQ35ms0im0kLa+8NR+CNcYvCHY/BFouKDfXtkSki8R49zUQj/zbQ7QeKvvbpMls2C/ZLtTeJx2hfApMcvZK8Q6IGwnMdIPJkhIVNpoSvYDtlCitWol/YotZik/XlO26rK5N/83U6A/YNked/0PIZmFqTNxz2+TJ8Nst0zxapEKi2flcKPWiV91qTTwWbUZ4hTm1lIUy5VDitqXTbUuuww6bV5X0+8llhk4DjA9veFY8v6QRQj814Mzrgfs/NhaOLV/nHpC5tBU4UTB6X/WjZF8LPgcLl/TNo9X6hKIG18oaMRFfb8t78dyB4D+wDHV+kLbp8UVjJFwCjC8cQSib4o+8y2ZR9m/+V4YDHq4TQbZQzgcVQ5uNikKMQCEYsv+xnqkhK49BY0myswFvLAHV/7HkkyeSriw62FUbE8qTeX7th+UfXe5RvHSHAeoWRh7eTo0VxlsOOZig4JpEyl1ibR4+kUhkPz+NXEbfkbCWlViPSCwRePSLFlPZA2Z3GD1kWKCr2wmI564U2E173WGOb7pBQtFSgoZszFFtAbGJGvRKe1EQ2mapg1u8exDQTHMBSaQDCZeR9cCW2JGo3mGjSba7dtP/msE0pGcMfbg0AyjHK9U7bBrwoU5IP9/QahQMETAqqqbdoy6FR6JNLRnPU7aaQwFR3AeLhH/l4F1Z544GUgajQVwkjoPu77PsZcbCyvv6d9i0XjQL3pIHRrkOhU7d4dnRYC1x/NkOkBIVIz/+ZXEtEk9NYCyfYr/eOy5IvO2nIhgPIh/aLxhJDTJJpIQN8ZnULX+KwQkST91wMJfy6hGISwJGGVBffjcH0lXj7ShuNN1Q8JyXxBsvbBxCz+z/evoW9qXn7G3qbXatFRXQa7ySDkGUnTe+MzePNWL64NjAvxu5ZdCsliLiSFeZw3MSn71lpZii8cbcP5jgY0ljulMJAPuE7atAQjMVFCT/uCD9X8PL8k9EOx2KokKQlVEn//5mcfYDM4Ul+Jb58/uikSncQuiegxt1faniQ/PdvZL3j+10MqnSkQsX+TgB+c8TxsI5Ln59ob8Exno/SFcrsFqm0aK7KzJWiLcmt4Eu/fH5TiE61y1gMLKVxIpvKzk5l3g4cgEdxY5sCxxiqcbqnDqZZaIU9zxYw3gDG3T87HzPL29walMDHrCz5U8a8Ez/U79/pl2QxeO9EBu/Hk5kh0evWHI/jxpTv4pCe/GTqEWlWC+lIWTpwFJ9Fldk8iiWlvUPrt5b5RXBucEBJ9tdyJLFKL7L/ZMSwOdzD82DGxwNJZXY7fvngcpibtvifRRXUPSKBfb2BqXRKd4HPDx3O9aLKUixe1YQfU6HyBZZjoLyZui9J7N3ysWVg4V9aKX07cwkR4QfzP1wL39b2ZLrG5OV/WJn9LclDBzoK9YiTsRmCDYFe2hVNnhk5I9OJ/dt1PYNuwILYWkosp+BMRKShzXN8L7xYK9gbMViMaOqpw/Ol2+b6utQJG89ayrfY7JiNz+OXUR7jj7ZXvv1v/RTi0tl0l0e/5+mWfuG+rwaQ24NWqp1Gmd27bfsZScSHufzT2FmaibnTamvBK1QWFRFeQN/b3G4QCBU8INCotzBo77NpyxFL5hYXST5wEeqP5MKwaV9E/7JJAj6SCmAj34p3ZP8dCfBqpxfysVIwaK6qNraK+pzf6auiemMVPLt8TAmevgATf23f78bOrXUL+rVRIbxYkYD/uHsZnvaP4xtlDQvAeqKnYltBUvixT0U9CkkUJktcfPhjCv//NZ0Lmbyawk4QsCyD3xqbxjfnD+NbZI0JMa/KwI+G5/O9/8CvM+AKIJ7fnPBYCXROz+MX1bnzaM4xZf2j7CI15rxCZLGp889xh/Nb5o6L+VlECvJV1Ly4Kcc/iyp+8fx2f9o4IEbodIPl9b2wG98dn8NGDYfwP3/lCXiT6u/cG8B/evixFCQU7j2xfGJpdwI8u3cFbd/rgC2+PVRKLXRxPrvSP4SunD2CTEwT2JM6UNuPTuT488E2Cc07WAy1WrruH0GwuQ4u1cluJSZldkIiiLzCN30zdXZeA20mwONBgKsWzFQfw5uQdzMbWnq3B88Xiw3/sf0/+7mxpK6xaY8EJW547gtYZ2We07baX4fMCrf14bJole7/deBbk8SXSSemHc9H1Z9JoVRrUm8vkq4LCgjaA2X65GsLJuOQK8PqyaA0yW6CY3y0U7B00H6yR5Xv/9JXd3hUFW4BepYNFY4ZFQ6FUxsCL9yDmlaSxM+9dwWQYd719mI7OI5yKoj84hnp/FS6W7V/raAU7A+WpQ4GCfQJ1iRb1pgPwJeaRyGOKdCIdw0ioC5/O/xQvV/6u+KtntKfFCfq4P/BdwvWFX8OfXEA6TwKdcGgr0Gk9ixLsL0WZPxLHyJwXo27fuqrNrbxk/+JGD9RqNb5xBjjSULVt66aKnP7Xt4en8Gcf3Nw0gb4c/PNf3+zl/AohBo41Vuf8t1QST3kDG6q3iw2zWR/xHSB+2Rok0v/rJ7dh0GrxyrE2lOZBSq8GtvGn3SP4wcc3cWd0etsI9OWgHyutScrz3FfaibAfKigMWPxiAe0vPr6Jnsl5IdS3Eyz6VdosYvVEf/QnBRV6Gw7Yq/HAN4HRsHvDz1N5Te/w32u5KCrf7QJ91697hvF/DHyAaCq+Cxr0z0GbiW/WnxZbGU88JKrZ9cYoej//p/734YmH8UrVEfGZLzQiqTjueyeE1K802HDS1bSt6x8KzkqhpcxgwXFHI4wa3a48CYZT8UwxxzMksxXWAwsbR+11OQeQKtg+mNR6mQGwXgFqIR7Cf+h9F7/f8iyOOGp3NGtBgQIFewvPV5zBadchsVcJpyIIJ6N44B/CNc99DIbyn8GdM5bd2MgC8B1RgYJ8oZDoCvYFEvEk3vzLS3hwYxiBhRAaO6vx6vfPo7a5HKo9Emiz1WPQqnRos5zCYPAOgskVvgYbIJj0iLc4PSVPOl+BU1sp6vZiASvU9G0fDN3GQPCWqOd9CTcWN1GpNmscqDW2i5XLfguiqnFahSh+4/qDVX9PERB9rukVTZsIp9kgZCjDF2mLQYsaKrAZlkhLldU4bCrF378/IL7dDPB0mLdnil3/tFtCA3sn5zAXCD5CoBt1GgkHbCp3yvbMeq0QMNwXqoQldHQNwpufoaUF/66+1A6nxZTT/nD9O1GI2Gkcqa+SGQMsRqwGvVYtauwquxWlNhOsBr3YWpBoJmnJdqetypTHj0A0vmohhdYmf/nJLTSUO3BCr4NpC6Gzt4an8O79AbGeoY3HSug0KlQ7bahz2VFqNcGk1y0FnGZUyyS6GTbL4gFtdwLR2Crr0ODpzkbxSs8H7INbLeTsBjiqcZbA0YYqUWCH43EJaqUHPq1veE3EE0mkiujYGBL6y5s9MnaRQOc+rnd8drMBLrNJvM+N9LovKUE8lRb7JU8ogoVg+DGrLVo6PXuwScaCzWRN7FXwWM+4miUIMBcSnaTyB7Pdohr9XuN5uHTmLXuBh5IxvD/zAG9M3MRgcHZXCfSsDUi10YkvVh8TxewD/+SGNjSjITf+euQKRoJzeLHqEE66GqHeQQs82hMtxMNyvu77JqQIQjKfnvUvVR7a9u0x5PXjuR4JoeU2DtlqcMRRjzZrpdjYFAIkXW96RvAXw59iOuLdcIwyqXU4V9YGk2ZncjoUrI1Kow123frPf5xRcHthFP9b71s44qiToOMGc6nkCzBwVKfm2K1SrHgUFCVSyRHEo79BMnFn0+swGL8Djf4cSkqUMWoljGq9qNGdWqvMsqIKnQV2erfvFCwaI47YWnHFfRdzMQ/arU04ZGvdse0p2L9QSHQF+wLpVBrD3VO49XEvPDM+hINRPPOl42v6KO/HY6ASnRYlpfoa+BKziKVzV1DSDsUb54vax4ino2gxH0eNsS3jk75L/p8MDo2kQvDEpzAXG8VUZACTkX6445Ni57IZkDSvMbSi1XISZo1t3c82V7jwpZOdOamtSTw/GJ8Vy4vlIDlNr+8zrXV572u1w5o30UN1ZUdNmQRq0s+bfs1Oi1E8zEl4k4gkkcjQUPoDmw066NRqUWiSgCZxResWBlDSXoPWB3P+4GO+3/SEpnc1vbGfPdCM7cCdkWkkUyl4w9GH2yPBSz/rQ3WVEmxJ4p/7rNdkCB2qlrN+1fRP5v5SKb1SzUyCld7gXF44nNvDEn3g//D5M+t69tJqom/ajXuj04/8nC+E1U6rhKVuBgzDZL/ZDBiieaC2Qshp+qKT4KklCc32L7XLfrEA4jAZxTPcoNNKOCdtWRLJtFjqUMVOT3da4lwfGJfzufwssK/QL/1y76goe1urSjdVGIunUuJ5fX1wXHysl4P71lZVilPNtVI8IYHOnzFElv7WBEnTWCIhZL8/FJW+y2uRMwh47OzHvAZaKl1orXLl7Yt/vLEaf/jC6XU/Q+/um8MTck0sB4sSp1tqcbCuApsB1dKb8UPPguTyi4dbZfxicSKbecBg1ngi82+Gc9K26vbI9Lqk9U4iM4UX+ODBIH5zu1e88FdToLOPsvjDMYfjWaXDIhkKDBFlEYg9ggGjzIVgYYXXJscuZhlIIKk3IOMJzwm/PmloNJfhhLMBdxZGJSxzI8J4POTB21P3Jdjx6bJ2HLDXwK415UV2sQAVSyXQ45/CrYURsZTp8k3umo3LcnBc1Ks1uFDehqlIJiCRX9cDzwUJbX8ygumoT9TSHdYq8ZAv01u35CHPgi2V5iSRZ6J+zER9mI365N8kkxkMS3I7lk7KcwxV/dsNHt9cLCBt1B+YxWBgFne8Y6gzlaLa6Fha7KgyOGDVGrY1ZNUbD2M4OCf95Ip7EHcXxtb1qs/OJmi2lKPdVglDEYk+nhQ0WypQYbCLinMtmyj+lNcL+9FkxIt73nG5Vth/DGqdWBLx73MbVjIzChk2yL+jCl6n1sCmMcqMGc4OKdVbYdHolQBgBduCVLIXidiHSMQub3odWt1ZaBbPFPME710Fr2lVieYhIUlifbutypZDr9ah2VKHb9a+JNYuVcZytJjzf0dXoEAh0RUo2Ccg2U1f9CbzEbE8mYkO5fX3DBmlFcxt7/vwxKbQZD6KGmOrEOkM4dSrTWv6h28VpFHodR5PRRBJhxBJBhBK+eCNz2A6OiT+5/PxSaQWt/bi6NJVo9VyAnWmzg0/21LhErKGCu2NcGtoUqwIVpLoJPxIYv3Ri2fy3lcShlmyMFfQ85sE7BeOtQsBz4BNEogd1eUStMl/82frkfMs2pB47J+ZF5KK9gokpld6X5JEpT/2xc7MlPKtqvFIembB4ybJS/Uww0Gprl9PRUyyZsLtkxDEt+/0oXti7hE/eP6ex/BpzwguHmiS499of4VEf3F9ApXrpHr2MRJdVSJ9ZzPtniVgNxt6yL87VFch5+7uqE76w4GacrRVl0kfqHHaYNCtv242tTsYkr8jSXm5b+wxkpv4rG9UgmbZrzbT/tMLASFNJxcCjx6DRo1jDVVSxLp4oBkuizGn9ZPcZwgt24XBtQMzbimykJCmajlfD38eG4tS650nFmamvP7HSXS1SoJYv33+CDYDFuB47jdNELIf1FeuS9qxuPSLG93om3LvGonONuPMh1/xOhqbXpVAZ9GHIbtU1vOccmymEj0zI+FxcKxKpdJCoHNcGZj2YHjOI/dIjscb9f/9CHoSH7bX4WJFJ6ZHfIilE+uqwUlgjoc9+JvRa6J+PhNqRou1AuV6K+w6kyiA6UPNF+Csl2kinRZbFKrOfYkwFmJhzEa9QopeW/K3XhnDSzUqx+NoMrEjxPBGqDW5cLGiA95ECO9Md0kQ4nogWTgb9Ytq+5ZnBEcd9WKVI+panVWIXdqgkOD7nCAsEf9oFieorOZXnicWEam6I3keSsblnPEc8byPhT2YDC/I/mzkY78TIKHOYgsXHgdJShZiMkupkKc2rVFsf9gXqASnpQqPm8pieqqzb2TH7WwfodownkpKIYW2LfTI53FzhsS9hXEpTOQyW4Igqf90eTscWtOuebezbaRdpe9nlJTSvuIvn1n482xxJBcEEhE57/QF5rnk9ZE5p5mv6hK1PCNk/s2lZFfELrUmp8xSuGmwSVFpPfA8sDDEZSvg8bI/6lVaKYIZ1To4dCaxrKo02FFltMt+cSZFdqxSgoAVbBapRA/SqTGaTG1hLXym2UOKvn0Ojp92rQUvVp7d7V1RsMfx5L1JKFCwz9FuPYOZ6Ag8sUkkFh+3NtgIiXRUbFPGwt0SvNlsOY56UydKdbUwqs2ieCeZrpKXJLX4ilMZlXmJ+dwgZXH5/8sLFF83FkVhLv8tph4uKfClKoSF+CzmY+NCnLMI4EvM5R0auhZ0KiMO2Z9Gi+XEhip0+bxWI0uuhDdJr5XgKSERRguVQsFhMogCmsrdg7UVaChzwJiH3Qb3WadVi/qb62Cb/fzag8cC/kjA9U+5hfTaLNm36vblGIy40NGAf/LaBSGBN1Lk82W9vsyB7144Ji+XJFKpEF4OWn1QWe2LxGA3GqBRr//STRJ0o3bjOVnt2LlmEnyFbPflIOHI/T/ZXCPn0SbHq8qrD1D1e/Fgs5zXOX9IZjastFsh+UoinIUmWqbkA44M98dmMO+nbdCjLxhs8y8e78CXTx6AdpXrai3wGPm3XM621SMcyyiSCdrA5AteN+tdO9xvq5Fk4mrntkQKVrvVBzYCrykS7UaquEt20aYrEpPxhX2BCvLVZtewIPT1pw7LjB65y2yww/y9RqN+OPuCfvjsu5FEQsbqJzXcjqrdL1QdEbsMhnuSKF0PHPtJ8L4zfR+fzfULYXbc1YB2a5UQmCRRMwGUJNBTCKViCCZjmAx7RMlMBfpgYEbI0tUoBJJbB+01QsIOBGeFON4NHHc2CMnniYVweX5gwwJDtjBL9fpHcz2ykDyvMthRZ3Kh3GCDU2eSn0mhASVSlKAqP5ZKyvp5Xn3xCOZifiHlaaFTDAr91cC25T5yueoelKc8Komz6nQeN7/yuB1aoyiMDcsIdSJDNjMUPoGFWAjuWFCI1+HQnIRPsmBAlX2uoNr4sL0WL+6Arc1yeOMh2S+2d5Y0z35lYYTnhsfEtmNBhF8f/z4uxZDh4PozQLIggf7LiVtieSLnUqWFQaOVcyr/Vutg1JBEzn6v+bxosaTWfvi1pAQWjUE+t90WViSwee1wBgGDgvNpv81CChOpdOZaWbpcRlbkp9ebXJIX8FRpi/SRCoNNzt1uzapVsPeQeSZNI5XsRzqV23WrQIGCJwsKia5AwT4D/cxbzMfgjk1gJHx/0+shAT8bG8NcbBxX3b+EQW0WJXe5vg5OXTVs2lJYtA4Y1RboVCboSvRQyQuj6nOyXJQ4SVlXPBVFfDEiVizhpA+hpA+B5IJYz9BKJpj0Sshp5tU98992gaR/m+UkDtmeQbm+HvsZJPPp/U1bD75AbYUuqrBZxK6Fthjv3O1/5He0XKH1Cv3Tabuxlio0X2QUtBX4777+AmwmfV72AbSwONtej8kFH/7i49uP/C69RNj1Tc6LRYxGnT+puldgM+pxrKFalLtb8RqlIpxWKl87c0g80Dn7YDlInlPtO+cPS3/LC4sQ25XVFO4nmmvQUlWat3I8XxJcwe6CnuW0Wfnbq13ixb8S7LtfP3MYX3/qkMyiKNniuLhaofNJAgnNFks5/nHHy/if7v6tkJi53mdJkN/xjuKebyxTNM9aK6jUmdlLaWrMM2vL3P83vouX6S34Si2t1fT48ejVXSPReSzHHA0wtukQSEbR5ZvIm9Cm+p5WL/Sdl3WuO+7y/GT/lTlfewncX5LCVEz3+aelapzpEcTnzxwkLXVL0/LjaQomMrPDsscrx55DP1kNp13NeKX6qBCkO4m/Hr2GHv8kAomoFINIiNNDn1/ZR5Lp1GOikUf/9Xl753qMnInAokL2vC7H5999/hv2NRZyaYPAghRnQpDgNi19fa36GE64GsTqZLtxzFEvxaGxkBu3vKMoBnAcmQgvSPGPhb8/bHkWZ0pbZJxRoCA3pIFFP1KpUSwurm/zpUCBgicTComuQME+Ax+omy3HEE4H4E3MCUm9eXz+gkPyeyY6LJ7kGlGjc1mmRl9Sv3z+mL/0YrSkQud/y1XoJNf5NbmYEJsWqpR2YsqbUW2VosIz5d9Gqb5236sQs8fHKb7bsS76qx9pqMR79wYeC1mkBQT9hmmvgG3ip2if8dqJzocEej7txc9SeX+0oRpG3X0JUlwOqlFp88FjMu3j9ym5FuW0ba0PyMu5Wo2LBxrx3v1+8UlfmdFA8tMdCOVNogsRE44+5l+PpZkIDCvd6rW636/1vQ4S6O93DUoY7Mqxhdf+S0da8fyhzGwIWiRtBUpfyJwDEmxUZ/5B67P40chlUYDnCglblnb6nAHOqtnzuXNTmU0l8R+0PIvTria44yFRM+8WMiSkWjye/8Wh1/Gngx/jyvwA3PH8slceIcT3UiDPJvHweDMPeqt8ILWsf2zf+Thb2oIv1RwXAnenr+su37jM3OBxZNTnGQW6fN3B4kd6vfO6ElLEAqKpJHwIL3kMZ8I6ea0dsdei0169Y4U5Bob+cecX8NPx6/hktk/seXYbPH/hZAy9/in82we/xoXydny17qRkGCh+6Qo2RgLJ+D0spnOzYNot8L15Ie7HzYVu9AVHMR9bQCQVk5kpdq0VLZY6nHQcQLWxLFP4xuaEKd64H4PBcTwIDGE8PCNe4gwN1qg0cGitqDNV4oC1Ge3WBpg0hseKf7sFbzyAN2c+xT1vH0LJR2dTZ8Fzc670KJ4rX9++M4uZqBuX5u/gU/dNse06YGvC06XHccSRewYW1/FfR36FyShtu5I46ezESxVnUWuq3JV2+GD2Gj6dv4VQKiJBq9+ofVEsbxLpBGaiHtzz9aM3MIyFREBmWJH1sWhMKNU70GSuxTFHOyoMrke2xz7JdQ4ExyXM9YzrMJ6vOC39Mle8M3MZl+ZvwxP3y8y279S/ik5bk2y7GKCQ6AoU7EOYhDg+jkgyiCvuNxBOBYTI3gr491SUJ1L5W8TsFujnzhDRk46XUKGvh1oJn8obtAKpcdjE2oW2HsshQaChyGME2GZBD/m2qjI81ca22tzDHpXHlXaLeJJTOb1830jY0n85F597BRmQo6i00yLFBoteL4TncjALgMt2IppIKG20z8HLctYfkpwCZjAsBzMRaIPz5VMHxJpoO+2innSQ9LJqjXiuolMUpLRheOCb3DQhuLiJ7Vcb7Phmwxk8U96BMoNVPKMrjbtHoj8sMKi1aLVU4HuN51BjdODjuV5RWu+GJ/l+wXaS53qVBmfLWvF6zQmx66DP/06DXvWcnUAbkWJGpsDFZKPHL0qS69lZADtz3ejEh/yQvVYsa+LBpCj1dxsZ8U9CPPZjMxkf/tdqjuFMabPik65gXSwuxpFM3MbiYn6F1EIilIyg2z8kROVgaBzuuA/hZBTJxaSQ5bR94s/5madch8VajD/LFdlZQiRDb3l7MBjkzHTmdIQQTyeQQhpM/WBQJwn8u94+dFgbcLb0qAR18ue7DWaPzEU9QuQGkit8n5bAY+m0Nua8Tr1KB5vWLOvk34ZTEVToXTjsaMuJtObfzEY9uLpwH4FEWNrlsL0VVq1519phIeHHcGhSSHKWWrheFiBue3tw1XMfY5FpuGNeIdDZv3icOpUWJrURE5FZ1BjLhERfDpfOJgWd/uCoEODRVAwnnQdg01hyKn4HEyH0BIZx29uLxGICLq0dFq1JtlssUN5MFCjYhygpUYmf+WH7M4ilQngQ+Az+xPy2+YsXO1RQo9xQLyGiHdazqDcflJ8XS3V8L4GWGjaTQYjpx0n0tNhxrPS03iwYDNpa6UKV3bLpdVB9ZTboUV/qEKuZdOrzfaNqgMrpZFohRfJSaWrUqLCZZXbAShKdnvixZP7jCq9Ei0G/quc5ix/T3oAE4m7V0kVBcYKzWKYW/Oifdj9WhKN/PcOEj9RXSrixgu0Fx0gG771YdUhUmfRW7vJO7HiwJ0lqqr1J4L9eexIunVm2T2/1yh225cgF4qVfosYxZ4MQtC69BZfm+tHtn4QnFiwKMp3hkmatXnyutxsZpWLxPSNxlgD7B/236el/3NkoYZIKdhe09KFtykhoHqMhN8bCbiHPi9GeiIGmH812S2aO+Ok76nZ7lxQUMxbjSCTuYDH9aOh9sYAK4S7/AN6duYLrC10P1ecunR1WjUmUyVQo+xJB3Fx4AF8igGpDuRCZuYLrfOAfwNszl3HfNwBvIiAkfKnOAaPGkMn6SCeFnCZRPRlhrsk0AlRHlydxyNa667M+WOA7Zm+HQa0X1XYyzQJfTJTNw6EJIdnzBVXVzZZalOkdQoZT/T8emYE/EcxJZc3PDYUnpG2IKkOpKMjXItEL2Q6c2cD9CyXDUiR4f/Yquv3DIqayaS2ysCAbo61ZKor5+AIqDaXyDL+SX6kylKHBVAWnzoqFeABDoQlMRuakj5o1xg33ZTQ8jenIPKLpGMxqI1qt9SjXO6XoUCwonj3JEwwWiYXj8C+EEA5GkYglkVpSrqk1ahiMOphtRljsRuiNunWrHiSA5qe8WJgLIB5NoERVAkepBaVVDhhMa1dwJFwmvYjJoVkEfRGkkmnoDFo4y6worc5t+jH/PhaOIeiPyHFw+8lECulUGiq1ShaNVg2dXgOj2QCjRS/Hxp/nUsmJxxII+SOyf9FQLLPuxUWoVKqldephcZjkXHF/850auZ3tsGr1Lb0IvzeEoDeMSCiGRDwlEjaeE4NZD6vDBIs98zDNdtvM4/9+OIa1fMCduio8XfYNLJYAA4GbWIhPbypsdK+Ar4D0aKd3+xH7RXTYnpJ/K9gaqAR1Wh6/6XFqPy1TtuuVqbnCicZy55anaOu1apTZzI/5gXO8DUXjck0qyA8kM82rhHNSRbyaJcuGKClBjcsGs/5xVUH3xBxuDE2iscyJ2lK7eLMrdhz7C+5gCKPz3lU98Rn8+8LhFimyKO2+c2AI5qvVR4XM/pnqBgYCs1iIhyQEczvBpxqnzow2awVeqDwoVhwkzrNta9LoRZGuLVFv+7Y3ixZLBcr1NglRpVr/rndMgjWDiWjB95GqWfo527VGCe8kyV9ndm77duw6k4TP8ji98bAQortZOGDBgGR5rckltj9frjmOBnPZrhMzTzL4/khrm+mIF72BaVxzD+K6e1jCUIuRPF8OhvdemuuTa6neXCqFqK1kxijYn1hcTIkCPZXoxuLi7lsTrYap6Dw+mrshKmGqhpmH0GyuRae1CdXGclFLR1JRIS2pTqZamPYfphzIS4IkKYnYvxl/F72BEbFhrTaUoclcg1ZLPVx6u9yXSPCS6OwPjgkpTWL447mbsg6SvLWmCuwmzBoTnqs4g3NlxxFLxTIEesyHe/5+TEXmkExF8l5n1jrloK0ZwUQY/mQI01G3KLmPOzo3/Pu5mBddvsGH39Nyp95YuarIr9DtQBKdJD2J84/nb2I0PIVaU7kQ4jXGcpjVJik8BBIhzMe9mI26cdzRAafucREEFfCN5hqxe/HEHyCajkvhR9aTQz/s8g+K4p5ggeG08xAMKn1RiSH3HIkuxHUqDf9CGEPdk7jy1j303BrB7PgCwoGI3MJJSja0V+Ho+TacuNiB5oM10PFFnSEtq90wF4GP3riFN//yEkb7pqHVafDcV0/iW//wJTQdqBbCea19IUH9J//6F7h9qQ9BXxj1rZV49fvn8fW/+zxUOs2Gx0FSdbBrArc/6UPv7RFMjczD5w4hEoxCb9LBYjPC5jKjos6FtqP16DzZhPajdQ9J17XWTSTiSUwNz+POpX7c/qQXQw8m4Z0PIJFIwmDSo6LGidYjdTj5bCeOP9MOq8MMtSY3cn5H2mGV9Qe8YVx++z5ufPAAA/fG4ZkLIJVMSZGj5XAtTr9wCKefPwB7qSWz73n4pu6HY9gI9Cw3axx4qeJ34NBW4I73PUxHh/ahIr1EjpX+522WEzjr+ooo0bWqfWx8XUBQDbxaQCP7OItQ2/XuVOO0odqx9fArqo3EU3vFz1lBD8cT22Y/8ySBga+rqcZT6bQs+YJt01FTLrYd/PfyFqHa/ZfXu6Xg8f1njqOxzCHbztcjX0HxYtaXIdFXA2c8PNPZpITCFgClegteqjosysy/Hr2K96a7MBP1y5TdjP/55kGFFAlP+rB/oeowvlx7HIfsdTI+r1QaWzUGIdJJ4BaLdYZVaxDrh2POetxeGMWbk3dx3TMkBKJYZyzuDMXMES7raU0imQWHw/Y6PFd5AGdczWJ9sxN2FPS2ZlBnp60G707fx0BwRgI1U1kf8AKQpHxB5rHxuMsMFjxfcRAvL/VP9hMFuwc+N0WScVGe/+lQJjfAu4H/+cO+zJZ9JOg+n/v4ysDWTFAr+6O8Z+XRLycjXlyeH5Bg2rOlrdCr9xwNomCnsRhFKjWBxTTzQnZ2dla+yPI7JEhpdRFLx0VlTUL1H7R+G3XGSmhX2F3Qy/qnE+/hM/cd+KOhnLZBhfUn8zdxx9crP2sy1eDVqgt4ufLcY0Q8iVd6fP/Z8BtiN0LF911fn/hlf6/+tUxS2i4/t+tVWlmopDaqDUIAb+UeSiX4aedhPPAPC4k+F/MI6XvM3iG/X+t4OYZSuf7AnyHROSKy7ahEL4Z24DMNZy38ZPxd6VtH7e34YtXTOLrk9758BI+nE5iPeUUZbtOsrqIn2d9hbRQrGhZZ73h75Ryxn661L9kxnTZEXD+3SX/2k86D0KmL651gz909eHK7rg7h7b++guvvP8goe0U5nMbi0ou8ZzYpquK+O6P4+Je38NRLh/CNv/cCrE7T6o1WAlx8/QQC3hDmJhYQicTw6Zt3hWAmiVpes7riwzsXwAc/u4EHN4aETLe7LDh6oQ2vfPecqIzXQyyaQN+tEbzxpx/jwY1hIc15HMlkStSSVEpGgjFRSHvm/Bjrn8W9ywOoqHXij/+n7+DAyaZ11x8Nx/HrH3yKj35xCxNDc4hH40Kq09qA55CKdG5zcngO197rQsvhOnzj7z+PQ0+1wJhD4t6OtMMyUJE/NjCDP/s3v8Dgg0lRcT/cfyzCPe2DzxNCz61RXH7rHl7/vWeg0aiFhM4V++EYcoWmRIfjjhdQaWjEA/8l3Pa+h2iKD7/7g0y0a8vQbD6GQ7YLqDG1w6AyixJfwfaAL/OF8JB0WU1wWkzbZkGylK75EHz+pAVNsSumihGZsLLtfRBuKnOgraoU3ROzmA88+jLuDobx61s9uD0yhac7GvDK8Xa0VpaKOlnB3sdCMIIZ3+N+oyTOy20WuCxGRSVYIJCwZLAngz5fqjok5Ngnc33o9k1u2uKFHph1JieeLu/A8xUHRPlp15rWDLw2qLWoN5XCHQsWDYmeBRXyRx31aLNUCgnX5ZsQMp3EujsWEE/37QLJRqfOhHZbFTpt1bJQFc9iB1/c6Qu+U5YrXG+53oov1RzDC5UHMB5eQI9/Eve9E+jxT4m3NKeN79Tdk32m2ujAEXsdTrmapHhBSx2TWid9VMHuggWuj2e78YPhS5iL+sVjfKPrxqk3o8lcJrNe2IfNGoNc6zopiOQg2FoW5soZINFkAqFkDL5kBPNRv9jJsOgXXCM0cDVMRbz42/HrYg+kkOgKViKd9iKVuIvFTMJAUYHXAwMyqeidi2ZUupV6F36/6ati16JZ5b2XSuCnS0+I//Ytb/eG2+AbEj2wP56/Jd9T5U7S9tny00LYrwRLZGV6J/5O01cw0+3BQIiztjJhlK9VBYS4ZhDlfoJORZuYDji1H2I6Og93zCdK8cRiEtp1uAdP3CfWL/QbJ6gQpzJ7taDM3WoHPn/Nxtx4reoZPF9xRmY4rAZtiUa84PlIt1ZgLVXwTaaM9Q1V5SOhKZkVsV44KM8hFfAzMbcQ+STQScaXUnW/yWDcncKeu3tcfus+3v/b67h7qU9U1RqdBg0dVUJ0myx6IUBJTA53TyKwEBJleSwSRzgQxff+2asZte8Kj1USLq4KG04/fxBzk1688+MrQjCTILeXWXHxy8cz6uNl4Pr6746Let3vCYkVyvGn2/HC10+Lons9hAJRUT2//ePLsg6S9yR3aB3D4yitsotSnCQsyXnazPg8QSF969uqYDKv74XIQsDbP7oiBPrUyJwQ5o4yK+rbKuUrrWC4/9NjblGqe2b9iEYGkU6n5efnXjny2PEWoh2yIJE9+GACf/X/fQtd14bkb7jPtNehKpzKfJLR7mkvZkY9eHA9U8TgdmOR3F/49sMx5IIs2W9Qm1FtaIVJbUOtsQODwdsYDt2FP+HGUhzRngErk1qVQYoCDaZDqDG2oUxfK2S6XmXe9aq3gvzBJqNliM24dZJUKu5r/E4RoW8O231NZQsdzx1qFm/s39zpe6Rt+OIcjMYxPOtBIBLDnZFptFWX4mhDFY7WV6G50qUo0/cw/JEo3CsKJ4TVoEet07bpYGEFm/cCp3UGCa5SnUVIzKmIT1Sn9DomgUaC25+gzUcm+JeXq1bFQCuNWCS4dBaUGWxoMJWi0UyfTxcqDHZRNxvUGlGkroUaoxN/3PEyFuLhNUMQaTVizXE6+nafH54XLiaNDhUGK4456rGQCGEm4hNSjur02VjGBsWfiCKciiGWSiKZTgnJzuIBFVtUU5MsJhlOhT5tbminw68kGbnQRsamM4oCnQttJwqhws72A4tKLZ7w3G6NyYFTrmZpd6rSZ2MBIS898aC0FX2xg4mYHC9JVVp9cOFxC/kJeqVmXrI5A4HHbtRoYVYb5NhYMCjVW1FltItPP0l89kP2JX5l8X43x/h/1P6SqK23K/dlN8Drpky/tcyBsZAbb0/fx68mb2M87Fm30MWCHGcOnHA2SAGIxLlZkykAqVVqadO8CqSLn5Pp3C6vJxZzeH2x39GCioGmtxZGcc0zJL9bDyThe3xTGA7OiV0Tr8N8QWulv9P8tBScEum13590ao2Mp7thQcSC1Hcaz+KZ8oyKdD3wPCgzPTJYXPQhGb8HFIm12HJwbO0LjmAh7hey0aoxiz83iXIGLq42VvLnbdZ6DIYaciLR6as9FJwQwpegkpjrZ5jmavdweZaHBlV6l5CdVGXTEsQb92MgOIYj9naoiyBkdLsLzgy5pIqc6m+GulJh3h8YlZ+tFeY5Hp7GcJBB7mnhMQ7bWlGmc656XnerHbhfLMgcsbeJHctax1Ky9LywHjjuVRpcsu33Zq9In6XXeru1AQdtLav+DX37qVynZQzHfRL1B2zNG25rN7BnSHQSvMPdU/j0V7fFmoSq4fr2Kpz7wmE0dlTDWW6DzqgVBTfJSKqvb3zQjf67Y2KR8tlv7qKxsxrnXz0qhPlKUDnO31/44lEhTUd6pjD8YFJU2hU1Dhw51/b5vqTSGO6Zwke/uInR/hnZ5qEzzXjq5cOiXl/LCz37EHbn0z4h6O9dHkQinhCfbSrA247UoaqhVEh4equTZKWinEpo95RXvvJ4HeXWNR8qSYjTWua9n1zD1Oi8rOvgmWYcOduKmqayJe9zlZDB7hkf+u+N4crbXZib9KDr6qBYjDgrbPL53WgHgvt99Z37uPVJL2LRuFjaHD7bihPPdKC2pRwmiwHJJG1YQhjrm8b9KwO4+9mAzBogWb3bfakQx7BZ6NVGlKnqYNeWw6mtRLWxFTPRYczFxuCJTSKUygzWxQgqy2lNQ1sal64KLh0rkzWo0DcKea5TKwF060FmoLAwFI7CG47AH46JXQZDIWMSDplCMpWS0M2MRUf2axpTngD6pud3dP94szXoNNCtYhmiYHvAtmRbe0OZPkBymgGPsURK+gG9zdlHVrY//31/bHpV5fBW0VlTjpeOtMEbjuLawLhsazkSqbSEjHJhUGzPZMYvvaXCheYKF1oqXSi3mRXrjz0GeqH7wo/7UVoMOlQ6Nh8srGBrIFFcY3IKoXnAnsB8NPCQHCaJGk7GlkjSjIkCyWGqSvmiRfsTqs1JMpMIJhGbK1nGz9Lru9jBENZyLktBqPRIJ4nHheeIBF2GUE4KmUfST0K3lqxZeJ/jyyAJK5KKPG5a2fCrTWOAVWuUgsRaz/jRWAI9gzPoHpiRe/qxA7VoqiuFybj9JAXJRS6VBnsm3weL8CUiQpyzL1D9y/7AokpMCPSUqIVJoIvVzdLfSFoNZzKJ+EEtxKJ2UY14OIX56RAiviT8iSQSi37MqMJiH6fTamA26VHqMKOpzoWaSkfO+00BjD8YwSfXB+HzR1BTaUdnaxVqKux5n4MTrkYUI67eHsbgmBuBUBQuuwlPn25Fmcuy7UHcbEO284ezPXh76p7kJqw3k4/Ft3NlrTjuaMiQ94aMPd9O+tiy7x22B9FmrZKC3cdzPVLwW9+yIIz7vnEZ5zZDonMcOGRfXaFZLOCY0qnNzGZRkCvoBOBDKnkfQPHZnjIYczA4IR7YBFW6jaZqUSmvB4Y5MsDSqNIjkl4/F42KYaqFaQ9CNJvr4NLZ1i2CZ+5vGiFM6XdN8ja65OdNAhTYXyQ6j5eqbh4bPedpD+NPhKRIQWuW1YhnigMYljkSnpTv+RxwxNYmbVNM7cCxmoQ9vfU36le5gOp4+qZ/PHddng9I6NM/vtPa/NjzIe834WQUNxYeyIwLPjNUGUvF678YsSdI9Kz9yKU37+L+1UFRBde2VOClbz2FL37//EMv7+WfZ3hjWbVD/k2ilOTyO399RchPqoBpm7ESJDA7TzbipW+dwU/+0/tYmPULuUmitKa5As4l8npuyotbH/fg2rtdImsk6XzhtWM4cq5VyNH1sDAbEBL2/rUMgc6wUHqSc5sHz7TINlaCgaX0W5+d8Ig3Oonw1UAbmNHeaVz69R2MD85KKOYx2st87zyOnmuD3vgoyUBfbpL/eoMuo6j3huT8chsdxxqg1WseeZAvRDvQzobe7Vfevi9EP3HgVBNe/e45nHrhoIR8LgcV5NVNZWKNQrKb+7ce9sMxbBUcgEmm15sPoNrUKmGjk5F+TIb74Y5PIJwKIJIMIJIOIZ6KIr0LDxKaEk73NECnMkKvNklYqFljh1PLh+QGVOibRHlOYl1Roq4NqgVJlFLx6QlyiWDOH8KsLwh3ICSEOj3CSaQyIDSeTAqJml1IYIqSLpUhAnYSDAOlUk1pz+0D24zBn2x7dzACTyAsfWHOH8SsPyj9IRjNEOnZPsA+s7wPyJLm14zydLthMxrwVFud/FutKkHP5Dx8oahscyUWQhFZ7o3NiGL5cH2lKNM7qstQX+oQ8tVp5v2RCvUd2FkF24ZoPLFqqCgLaZyRomB3wZcbo1onNixcFKwOUWxrDQU7RzPzfnx4pR9vfph5/3jl2YP4rS+f2hESfTkyftaZgFguW0EsnsTIhBu3hsYx1RXCyKQbHm8Y4Uhc7lkkgg16Lew2IzqbK/HKswfyItEj0QR6hmbxw1/cwPSsDwfaqvCVl9KbItGLFdfujuKdT7sxNetHa0MZWhvL4bSbtp9EB0Th/d5MlwSJrkWgsyDUYqnE1+tO43xZ68MiUyGgXyr8lRusMnOCsyRueIaloLUWqB6nTdG50jYh3hUoIBYXo0inZ5FKjYmhRrGBoY60D6EfNWHVmiT0cSNQjU77DFp6RGLrk+ieJVV1FgypvO8fEBuSjUBVdnbfqDqmQnutmWX7AW2WBilO9AVGEE5GcNvbixcrzsq5Xkl2k2QnKT4bWxACneQyld4WrbGo2oHPftwv6xp2K/nCqjWhxVyHSmMZZiJuTEfnxK7FnwjCoXuU86R9y2zMg6HQxEMrlxpjRU59fDewJ0j0h+GMb93F3OQCjGY9Ok804mt/9JxYoKwkXfi9Tq8RWxJam/TeGhELlQfXhzExOIuG9so1LVdImL/87afQc3NEVMQMmbz1cS+aD9Tgua+dEnXyzQ97cPWdLnjdQSGmz7xwUKxgqurXf4imF/a9qwNi80F/bKrNabHy+//idVQ1lq5poUJSl9YhXNYDfc8Hu8YlpJThlCTkX/6ts2Izw7DUx9erFuU7z+OND7sRDkUxM+5B7+1RuGd9qKxzPXJuC9EOVH5TmU0SmqBKn+f92NPtj5HPBO1pWCCY/aYHf/0f3pX9W78N9v4xbDdZXa6vl+WI/VmEkpyaPIDJyABmY5wyNoNIKiBBpOlFTs9NyTQkppdnQn2y/tJZxdHj+FyBknkJK+H0XPkvM6WzpIQuV/x35iuJcYvGAbuOivNqlOpqUWlshEtbA63q8TZSsEooB0OPY3EJ76Pf9PXBCfGWHpxxP6b0LRZQgb4ycE7B5vsAVeX+SEwU3DcGJ3BjaAJd47OY8QaKzhGeHtgvHmlFa5ULP/joFm4NT0rfJbG/WvGGBWMWgD7tGZGl0m7BqeZaPH+oBSeaa2A36WHQaredUFCwjf0zlZIZECvBMUCvVWYVKNhboPqZ5HAkGofBoIVBp13T7m8rGB73YGBkDgu+sKz/0o0hvHLxIGrzIJl3G1OzPvzi3Xv45fv3hTjnc6BOp4FhSbjDIZ/nc3beLyprkuL5IBiO4m73hBDooUhczlf3wDReffbgjh3TfgTvvVQx0j+8LzCzpoUL24+zFX6/5SIulLeJTc9ugHZJVF0/U94hnu3d/qk1P8tjGQrNyawRBQqySKfmkU6OSLhoMYJEKO05KG4i9Co97NrHxZerQa/SwaYzi9f0eggmIxKWmQVtOLhsZl8jqeietsLaCFRJk+RlcYKkN2cJUEHu1NHC7lEV93BoAlOReSTSCbHhOeZoF2sW9Ro2JbvZDlS867bJgkddooZdZ8EZ52G8l7gixYCJ8KzYupx2PXpP9iaC6A4Mi6ULOaUGUxUaTeR+ipOuLs69WgHaW9y91A/3jF9Uus2HasWihKTnetAbtEISk6juvjkiBPhI7zQ6jjesSaLzAc5sN4nnNUlyhnnSC/uNP/kYR863iTr98tv30HtnVIjpyvpSfOPvPY+6loqcbERuftQjimOC3tsvfOM0Kupdq5Lc+WK0b0ZsSsLBqKjIqXCnqny9kFP6dFvsRrQfrYdnxif+6/R4p1d7ebUTyzmtQrTD+MCsqLFJkrB4QHscntu11PcE1eDnXjmKX/zZJxsS0PvhGHYKKqhh1Thhtp5Gq+UkHQiRSMcRTC7AHZ+ELzGPgCweIdupVI+luISRWKSCiFOXqVbNPGizCst1qkoYhEWCVCfqcv2SupxfjWqrEOYWrRMWjRNWrUvsWqhA58ArE4BFAZUh3hXk9uJDq5Y3rj/AL252Y2jWI4Qq++NOq8m3Ak5zV4IEtwdsbyq6f3H9AX55q0fIaCrJJbQaxQmDVoO2qjL8q2++hOuD4/jljW582jvyWODoauDMCnqqf9w9hMZyJ75z4Rie7mxElSO3lwsFhUXGKmj1nigq1A2C2RUoKDZ4/RHc6hrDB5f78MzpVpz5/7P3H1CSpGdyIGjhIrRIrXWWrizd3dVaotEQDcxgMDMYDXKGu8dd7pF87/aOt7vv1O473vHE3vJuSQ653B1yBiOBwQBoNBroRmtZWsvUWkeKyNDuEffs88iqrKzUlSIy062fd1Qql7+7/7999psdrUdx4aMpthcCX5FzX5O0j9xur813P72Dzy50CIHO/p3f58KxQzWorSyE16XL7KeZWBL9Q5M4uKcSNRWFq9xCLisjd17478VsNm0sjriZkhBdhspG0g9bb82CMxNOFjfg5YqDeeFby8DQs+Pty5DoWckzoK+6DRuzyJhDMI125CvYg09kkiJmI2gR5lJWJjpgFsFSoZezkLyBNQaKz99XsfXCzgb9x7mQRKfq+8ZUO0pdRaicl+91J9IlgZ0ErVaeKD4CzxI2tFt1Hci1sOCyniGePtWDZ0tO4Gz4upDoA4lRCcedT6LzHF6bbL3HVTT6a+Tc5iu2BYkej6XEuzuVtBoTFdaV9SXLKlL5c2/AjVDx/YE0vcVnpuJL+xypCqobSyUklHYcdy53o79zBH/x//m5+Ih33uoXErWysRi//U9eEyJdc6orshHpuTOI6FTs3nEcf26/EOjroa4d7g2LuprgMdBuxBdwL7luq3OpoKg8JLYuBD3CR3rDD1WtNuM6jA1Oil2KdQwqmg5VwR/yLrkNTddQVB4Ur3hao9BLfjHshGPYKFj7R58vfqhzbFVcQm6b2bT4sfGThDlf4pY6nQ/nWUV6zvtfxi/W8VqfOe25qNBJlir3SHYqz+8tii7bnCXPbawOVJ9TbfynH15A29A4xqajSKSXb0v0taWntM/tlE+XpkHXFFGHc4km0xgIT2N8ZmsKPDZWDlq0/OLyHfziyl10jUyIYns5BQLvNE1V4XM54aOKUqc3PRcFuso2oGFoKiKe6PTR3wjIu4idN13BsfpK1BSH8OqxfaKgp9q8azS8oHKZ4NHRu51BpGz3/9O7Z/HF3R68fGQPnj3QID7bNvIH0h4Xa5JsB/az38Y2w/D4NC7e6MHlW33iUX4ktTEDvz31ZdjfVIGOnnEhhr/64mEUF/q3zX0/MRVHZ98YRsMz8swvKfThv/jui2isKRYPdB4Tf49FtlTKgMftRNC/Ol/WoN+NUy11QtRT9U7feH5tY3Wg7/17QzflcynUeovwcvkhIdDzod/O/WEw7VLgeIUBwLTHsGFjFplMfpPoy1ZVl/xVx6Kq57mQeeVzxgwnCg6g1F0kljCrAf206ddOu6WdCvIaFoleLT7e5EOuTd0VD/BKj2VBwnNJa5K2mV6MJSelkFHiDOFAoGHR0M4tvw4WbbNu0BUNdb5K1HorZCZFODkl3uj0bA9qVlBqykxLmGr7TI8ce1DzixK91L3aIvrmYVuQ6KlEWtS9Ro5YpO/3T//jx/j0rSvL/i1V3913Bx+w2pj1qV7qQUMLjpPPHxBSmoQo18OQ0UQ8JX9fXluEJ15twfFn98HtWzwEaBacmkjynEGSJEipDg8V+cUCZr0UEuGRKSFgCfp4n3nnuoSk8liWszjpuNkvnujW3xqITEYfIl424zrQo5z+74SiOqRAsZxKnOePynta8Qx0ji5JQO+EY9hM0HpFczih7bBQkJ0Ikpvn2vvwo7M3cLatV9THC3GnRX4PqotCqC4Kio1God8j/tKuHGGuaypURRF/autTQfvwOH5x+a5NoucxqDKfjifw43M3RJXdOji6IOnMa1wS8KKutBAVoQCKA14U+NzwuVz3iiZUA89tA5riwHvX2/HJ7a4NI9GJ2deo3+OSgk5J0Ifa4pCQ6h3DYQkVvTswiv7wtNiBzAebO4tGfeNTmGEWwExM7GxeP3lA2jmPxcbWw1LPOhadsccsBhs2thPGwjNo7RzFTDQp4c0bNeuL6vaXn9qHxtpiGePS7zsU2Br7jNWCp2RkPCJhn8zq8Htd2NtQhhOHalAQ8q7b89nl1NBcV4q/9+0nRdFeURqUwoaN1YFWJ5cnupEwF1dDUglLK5d8CtlkloNP48xXTUKQFwMJdGavkPhaKqzPxu5AluIwcxCm2Y38hQNOEZpZ/Se2XSqWVwK+k1ZSNOI9zQDoWTDUsSVEm6bVFWupfPdqzDbbuSQ6UegMocZTjmJnAcZTkxKaScucRrNGCGyq0ztm+sTmJZVJiRd6c6BOLF12y3VQHIqci5ZQMwbjIxKwOpoM4+ZUO04VHpJiAv3fe6KDYmNDNPtrxQs9n9vPtiDRGdQ4MTotRDQx1DMuy1pAZQPXtxKQKKfXOYn09//uwj11McNDD55qwDNfO4aCOcrkpcBw0OnJGNJJQzqS9EOnvcdy5OpqQFKXdiXW9kxc+6IN175Y/XpIqicX8CDcjOtAFXwiZpE0HGQHC3xL2tHMgi8Uf9Cz7O/uhGOwYWM+WPC6PTCKd2hpcadLBvFzoasKivxe7K8qxZ7KYjSWFqG62CLR+X2fS19yAMvQzzOtPZtwJDbWAlE6GIaQ3D+/fAd3B8dkSvxccJp8TVEI+6pK0FxeLLYntDspCfgQ8rnhdS7dUWkfDosqfLMg1mouJ5orimU50RgTlTk9/vnZMzYpy2Q0viDpOhlL4FLngASR0mf7ayf3o8DrlplXNrYW7FBLocbhkGn1c8F2O//5ZcNGPoOE8NhEFP3Dkxu+LRLE+5rKZdmO76nJ6di9+9vj0VFbVQivx7WuBU7OxA0G3Hj+9N51W+duA4M3p9Nx9MXC8u/F4NfcEiL6qGGz6913oPJxORIdS6Y52dhtyGTCQqJnMxv/HF8rOEvPr3vvKcoZHslAy5WAZC69sZcDvby96v3CrFNxotxdIv7fNh4G7XQYLtrkr8F4eBIRI4ae6BD2+etR7S2XIse1qVYJ0uSzhiT64WDzsuLbnXgdjoT24upkK/piI2Lrcj58Ey2hvXCquoSutkf77j2RD4eaxRYnn618twWJnuWgKpYSCxWCxLMv4IGqr77TVVDsXzTAcyHU7avAgZMN+Pinl4SYJnxBN2r3VqD5cM3KjyGTQSKWvKdO0Tld3r2+1RWql2kZc4+8LfJJKCZDRleDotKgEPzzG+5mXAeS0rPnmZvX3dqKSA8+i6jkXk7VvxOOwYaN+aCNBf2gaWExn4Cispiq86f21eMbjx1EU3nxsoSpje0FqqlotfLDM9fRNTrxEIFOi55DNeV4paVZwjdrS7ZPCN0sWOx5Yo8Xp5qqhRi/1j2ET+90SfFoMDwttjXJeUXNtGmicziM7318EQ1lhaJoD8zzKbSx+eC7js8ll64hlnqwYM+CSDS1cbMdbNhYb0xHEhgZi4gvOkluGw9jtlYWT6bviVhoFUY1eh44gNiYh6SZRjg5g6ixdPBmgdOLYpc/L2xcZrFSWpxjXKo0aSNnw0bG6BISfXGvua0H2ytJ2NmgxbiRwHjKytlbClSsM6xxOn0/qHIxUOlc4AwAuV8dTIxixuAs5O1L3m40ytxFOBRswvnwDXn+UHneH2yS0FGed/qkU2FNy6tyVxH2Bep35XWo91aixlOGO3onokYcN6bbpbjAQkR/YgTdsUF5LtMrfl+gTgJO8xnborfHx9lcsVLDgSoJzSwoWX1oWEllCFUNJSvbbjYr/tZ9bcNIxu8P9Bg4SvUyFc0MByWW60A89EheuY3VykGb0dyGdKeKp79yTI51tWS9x+cSn3BFUzb9Osw/JULkr/A8kaheyXXY7sdgw8Z83OgbxrWeIYxMzzz0M6qNv3riAP7opcfEqmUt7UssjPO3X7nrMRVN4Hx7H271jyCWy3uYiwPVpfjDlx7DU/vqxPt8LeD7MB/aABWLVM+/1NIsS9vQGN6+0op3r7WJWp7e6A8VGCatAgOtjA5Wl9nP2DwAffdp2TOfRKcN1URkZcoqG9sbs88UWvhQ13DfQpCfuUyVXL4KxSBU4VkWsMvfv7Prmg3UXuz5JVkMjtm+1/LrlnUxoDnL9Vrb6ewdQ9+QlUdEkCROpU0kF3gWyzZnrdLUpcUVXA+fZ7OijwV2Xs4JA+wfJUdgva+Dtb5s7txb6+EqDSODaCx5r8hr9cct73PHYh6+mmUtttS+c33c1mIvKNln2pItc75Xd54ebk+zAabW+Vl94KvVpqzzJm1szs+U2XU/ILLZuPcYFdwzyxDoBH12aZ+SdzPzzLQsS8GtMovJHnPZsGAanciYA8hnaA4NtZ7ye2GiU8YMemND957Zi7VlKtap/J1IWY4KS6HMVYQqT6l4fBN3I914svgoMtkaO69mERQ7Q2K3QusRWrZ0RvsxEB+BkT2ASDoq/t+cBcBzW+MtR7GrYFdeB03RsCdQh7aZHlybasNEyvJGZ/DpYHxMLF0YakoCnSr0pTzj8wHbgkRnR5OhjrOdh5LKApx8bj8aDlataV36CpXotEc58841fPzm5XsdMXZsqPi+9nkb3iz5FH/wX35dfK+X3a6iwONz32v4tHWZtfxYLzg9Opxu65Kqmop9x2px9Om9CBaubpodH8K0FJmviN6M68CQVar0BVmIBz3tZZZDNud3vtzv7oRjsGFjPq50Doj383wwHPSZAw14/dRBIdDXCg5SFyJnbeQHqMw+29ojtgJzwdcNQ2J/59njOFJX8UhT5uMpA6kVWqFtJupKCvHbzx7Hk3vr8N71Nvz1Z1dlNkZ2HpH+RWsPXju2V2ZikMC1sbXgjIAinwej0w8qo+jrPzCx/EDPxvYHycK27lFcu9OPOx3DGBqdRmQmKc8x9tG8bicKQ16UlwTQWFuCQ3srUFNB+4/lB1bkFMYnZnC3cwS32oaE5GaYJT2ySU7zfcggS/pl72ssk/BJ+nOry/TnB4enZJ1dfeMYGJnEwPA0RscjmJiy2nEqbeCn713HB1/cXbRgST/zl5/ej1efPbjktj672IF3PrmNux3DC/7c6dRw7GANvvP1k6ipLMyb68ACAtdz4VoPBoYnMRVJYHomjumZhHjG85OgP/rf/vwS3v741oLkT3lpEN/99pM4ebh20X0PT8Xwg7cu4dyVLln3QqirKsKLT+3D1148vOZzNHtcPJ5zV7txp30YQ2PTiMZSovKkor66ohCH9lTg6IEaNNWVrGn9V2/14+L1HrR2jyA8GZO2GvC5UV9dhGOHavDYkTq5FmynG8mjpLMmYubyY1Tx7V1BWOFmIpyMYiodl2NYDFSfFzv9YvliwwZhGh0w85xEJ0l7KNgMv+aVEtpkKiKEJMlHenNT6bwQuqODoo5eySwNBmI2+Wrg0zyiFqbNxs3pdiF0SQDbeBi016Fq/EjBXlyfbJWCxVBiHN3RAQkUnbWVqvNWyrldCXbqddjrr8cdX5eQ6CTPqdLnDImRZFh+Tu/0x4tapI3nO7bF20NzqiiuCKH7ziCQNCTMkQoPf2hjTrBU9LLAZz+/inPv38LEaESI16deO4KxgUkJlxwbmsTlj+9gT0sNHnvpkKi3lwLVFKFin9h1sOPDY2AAZWwmIX+7HpXwQIFXbFhmjyEyFYOma+t2njbjOngDHnj8rnsdewalppLL+6OyuMFg1PQyXqo74Rhs2JiP1qFxhBdQb9aXFOJAVSkqQo823ZYhjjPJ5VVJNrYGkXgSN/tGhCyeC5LFh2srsKeiWEjLR2kDVAzno1e1FYbqgau6TGxrVFXFT87dwFjkwRBcBo12jU6iJRKVYF0bW4sCrwelIT/uDI498H0G1w5ORpBIpeHUtW2ruLGxdFF2OhLHm+9fx7Xb/egfnsJUJI54IoV02gr6o+pZ0xRpAx63jos3evH+5148/8RefO2lw/C4FyfS+wYn8PmlTly51YdhsVmJCdmZSKaFGGb/eFaZTDL8VuugEKPHD9bgm186Cq/XuWjBkeTwzz+6KX8Xi6dkIfk5qwTkR2QmIcti4PFEFyF854LrJkHfO3hf5T4XtI6prihA2sjk1XXgOaY6//OLHRgYmZLzw35tmudpzu+RICbBzmUhUJWdWCCfCfPWMTkVQ//Q5D1yfj6cuoqZ6PJewIuB17anP4xPL3Tg7JUuuSaTcp7SYt/IY9I0FT0DE7jdNoSzV7slLPUrLxyGz+NcdsYB1xGejOLHv7yGq7f7pFDDY0mkmKGVlYJPz2AYtzuGhGD/2ost0s64zQ3DAzMSFkcqYyK5wmDDzULbzDBGE0sXYqlAL/eE4FK3BQ1iYwNhzTCZQcbsQzZjEXn5CrbbQldQ1LxjqUlRlg/GR/Gj/vfwjaoXxepFmxNGSYwmJsRm5PpU24q2Qe/tel8VHi86jA9GzsPIGvhi/Kr87LmSk2j0V1uzkhbom6XMtJCh3DenQ8OhUDN2A3guGBTKkMzWSA8SmZQVnDndgc5oH8ysKYW7el8lGnxVu/o6FDtDqPNWiNKeQawsMtBHnoUgniOex2MF+yUMNd+xLd4eLrcT9fsrcfXzNiCaxPjwFPo7RiX0cyPAEND263344u3rQrbSV7xhfyVe+86TEi769t+cESV6X8cI3v7rL1BRV4y6vRVwLaGOUVQFHr8bxeUhjA5MCnk7FZ6R9e89VrcunaGSigIUlYXQcaNfjoGfj714CKheu0Jls68DbVWCRVbyMKdM9rePSFDnUuDvpZJpuTbJhLHjj8GGjfnTf8ciUcQW8BGuKylAZWHgkVTobJvRRBJj0w+SkjYsbDXFx+sfT6fFE92cN+3fretoqS1H0ONeswp9to2FZ2KIJPK3kMLgVAaQfkM7iOs9g5ITkJhD+vPMDE9GMBlNrDOJvtUtYHuiJOiVrIb5oCc629qdgTHsry61Zw3sMPB5MjUdx0/fu4Z3Prkl5CefL0FR2xbD7dLkWSWznxIpIXnHJ2OiIp8IedGyr2rZjBn+7a22QXx2oR3JlCkkcHGBT5TUJH2priahTi/z0TBV5DEhe0mOUp1OlXjQv/AAjj+vLg894H0enoiib2gSYxMzsu7aykJUlYcWJfqrykKiWl4OzXUleO35Qzi0t1JIaB4LydbOvnEMjizvg7tV10FVHCgq8GN/c7moyeduk+eoqy8sRQaS8pVlIdRVFT60Lj5Viwp8KFlmJi1J6qdPNqGkyI9INCHWMCw+sHjCGQjzZ2etBa2dI/jwbBs+OtMqxQGS4twe992dm41Kmxpus7s/LDMUhnLFg9eeO4SiQu+i718WAXhOf/LLq3j30zsYHpuW60Bl+576EgldZR+MMwOGRyMYGJ4S8p42PxspuCFZtxKV9oyRwHQ6P/qGbF9UNl4Kd0kg6lLg9Wj0l8Kr2hkpNrLIGN3IZEZJPyKfQcLU6dBFqUulM61CSEB+Pn5VAtub/bWiSKdnupExRBFNkvLq5B2xEylxFgixuhQoXKh0l+DZkhMSANkTG8RAfBSfj10R5XuTv1q24Vacsj+852gXQ7U0fz6amoCZMSVocynylqp4FmqNjAkja0oAJz8n0xEkcrNg+CzkesNJiwxmgUC792nZky4VPDn7TCABzYBkfk6lI7KNTK6ky+/xHI4mJ2SdsiiafFJhvlIhB0nfI6E9eEv3YyYdxVhyErenO+X8kURngaPaU4aCFfp8b9Z12Gw4VV0CV+kL/+lY+J7Xe9SIieqehYZydxF0R/73/fN/D6na8Ltw8GQj3vvbc5iZiokanCQ3Fb4kK9cziJGKAK73vR+ew90r3YjPJFCzp1xU6Acfa5LASBKv44NUpA8JGfv5L66JmryqoVTI8kUtUjQVzS01GOgaw2g8JYTpufdvyvp9YjHyaH59VY2l4tFN1Tv38+6VHgx2jwnJv5xSPl+uQ1lNoVis3Ctm3OwT7/maplJR1S8EWqAMdo3KfhgpY8cfgw0bD1gAGSYSaQ6qHlYN0TuaBOqjYCaZEsuFiWh+DJTyCdKBW+CZIb6pm2QgzsE0FeJzCeNZ6KqCmuKCRyqi8ChIao5Oz+S9pQ9V6VTdN5cXi+o8MfVgRgCLALHk+g6SSBgtpATJFw/5fEVp0CdFPp6/+c8uhsR+fKtTfk47IluMvnOQTBnoGQjjR29fEUKYliQN1UU4tK9KPklS04aMBCTVuCS2SVCPT0ZRXhxAc0PpsuGdtLsoLQqI5YjH4xQilv8uo8DBb/W1SXqSvLzZOigkKdffPTAh+3V4XyW8bn1BccvexjJZ39xZOdfvDOCDL1qFIKa6naT3c483o7R44awdj0tHQXD52Y97GsrQUFsixDBJ/1giLfv61gfXH5lE38jrQNU6LV8qSgMPkNicmXnlZh8iMzeERCdRTBudLz93ELr+8Llm8HB5ydKEA/fzuSf24InjDUim0kIwj4WjuHyzFx09Y49MovOafnyuDe98fAtDo1MIBT3Y11SO/Y3lUijhMZAMYkGirXvsnnVQe88YwlMXUVESxKmcBctCoPL/8s0+uQ60GuK7uq6iQNrQ/qYyFAS8UsgYn4gKgU81+odn7qKkkEWDjStqO1UNQd2a2bwUptNxjCYiEkDqVS0yZ6tAAq49MoLL4W4ML6NEJ0F2IFgFv26T6DYyMIwbyGYWnvGTjzgcbBbP7alURMhVEsBvDX6CWlH4FktWwawamcRryOkXz24Wx8bGlybRiYDuE9uYL1c8hY9GL6ArOoDBxBiGk+M4F76OCncxvJpH1kcSPJZJYio1jYlUBOmsgQp3iZDGS4EEM8neZCYt5C/3N5VNifUMFcoEyWd6vp8Zv4ZCZ1AIWFrasJDAf9MepcRVIN+bD667OzaASDpmrT+3kDDviw0hnbHGMySd2yI94sUt656zBHSvbIMhnzzWpcDCRaWnFLXecoRTU7KwcMHgVxL2e/y1YtEyGwq7EmzGddgKVLhLJIiVynqe/7iRZEkFNd4KtIT2Qlf0bZFVsT1IdJ8bB081CEE8NT6D6YkoWq/24OLHd/D4SwfFPkNZYiDLSj+JyYyZhcuji1/4QuDvRqfiuH6mHR+9cVECRAMhLw4/3oRXv/OkkNP0Cj/96hFMjs5gqDeMZCKFn33vU1TWFyNU7EegYHHVBAn2Y8/sw+2LXWIHMz40hU/evIKTzx9APTvtAfei0/7Y+SQxTlKWljAL/R5J/MaDVSgsDUog6kDnKC5/ehdF5UE0HayWvyPmn6fZwb6RNmT9PJcLqeo34zow0LS6sVR+zjBXKvU7b/WjprkMxeUFD5HcXCeLEV+8cx3xmeU7lDvhGGzYmNsmSZ5KqNYC4CD0UQhUom98Cl0jrGjbjOB8aIoCfQHfW1PCzky5LisNwlsrJMRuEaKABD8V2o9iiUEV3I3eYbEL2qzCwKOiNOiHb4G8CpIR692OnerD+SHWtkzp1PKc2ZYkD6PI70VtSQFCXg8mZmIP2DxMx5P46FYnvnJ8v9gQPWogoI38AUlDEsEkZXnNmysL8c1Xj4k1yEJqXb7jSITebrd8wakmXw6hgAenTzSKjzTJ8wN7yhcN1aYX+F/8+Cze//yukNXtPaOiJi4rDsh65qMo5JVl/jH5c0IVbqO40Ifm+lLZ9qOCbV/zOMV/vEgsXpIILKKSz5frwPEJlf9c5r+rxsIzonInSJyXFvuxv6n8nqJ7rSChzyXo98i6SHgvZ6OyFGatTM5d6cYXlzvRPzwp7eHxo/X4+7/xFKrKHy5Os/3Qw56q8ut3BzAensFP378m7YF/O/89wXcD29ovPrp5z4qGsxRef6kFX3/liBD0c8Fix5krXfg33/sII+EITHPj3scMC6VnOMkZKhwXA8nz3lgYHZERtBTUyHnbbPKDRQyKCUYS0/ibnjNonxm550G8EKhc9WpOHApVw78NLANsbDQyMFMk0Zcnl/MFVD0/V3pS7s+3Bj8VKwwquel7zqBGtnIq00nYhnQ/ni89hQOBRgm8pGp9pQTuVyufRYEewPsj5yTYMmYmEDeTom6fVXLzbndAkX2hctutusSygx7hS+F7XW+KxUwGiz9fSATfjnTKshC+2/BNvFz+BIqcD88ubY104993/B0GEiNL7gdV6ecnbsoyHzWecvy9xl/BsYJ9UJeZtcLnigIFR0L70BsbluIG1z37s/3BRilwrBYbfR22AkXOIJr8tRKwSrshE6acIxZEWkJ7xNZlO2BbkOgSjuZ14uVvPyHENm1KetuG8b3/98+QiCbx5KstQhwvdM6NlCmENYlSkqanX21BWTW7ogv8btoUwvMv/oefIzIZEwnesaf34sVfPfUAOV5ZV4yTLxxAb/swvnj7GqbDM/jl98/CF/Dg6a8eW/Q42IFi0Oe5d2+gt31EPNEHu0fxr/6rv8G3/+HLQqYXlS88zTwZS4qCfah3HIcea7SOdx5I8u89WoenvnIUb/zJR/K9X37/DGKROF77raekGLCQanJ2/W3X+0S5TjuSJ145vCXXgYp5WuPsO1qPa2fahKx++6/OwO1xyXXwzhs8TIejUvT4+V98LrMGlsNOOAYbNmbBwYrXSZJ04cEiyVWSeY+Ca91DuNjZ/0jr2Kmg8tmtPfwa5UAyaZhCBgboibqBg0qG17FYshDIA9BbeiXepouB9hpvX2ndVmGPyXRaCPP58Lqc624P4nE5H/KgnC2kxJNpWXxL+DfvVrCwUB7y47HmGrx7rU1IkFlQ5dsxPI73brTja879qCm2Pex3ChJJQ9TM2Tkhm1Q+L1VoIllJZS475Yu96+bj6P4qtOyrlPUuRaY21hTj8aMNYmdy/e6gPDPpf32gqXxBEn2nYLOuw3aGYWTw0VlauIzLsTfVluAf/f6LKAx5FmxTLAo8/8Qeyd+42zksKn6qzF94YlzCa6manwvmMdHv/uqtPvmap/7lZ/ZLCCptauaDsxceP1KP3pdbJLyW6vSNAgNDg043qjwFGIpPLRnS2Tkzgjf6L+FgqHpD+zpLoT8exg97LuCXg9eF2F8KAc2NQ8FqVHhCdrCojZwS/SYy2e2jRCdIqr5cdhr7A434bOyyENLDiTHEzKRYYRS7QjgQbMQzJccloJLFJgY4rhaPFR2W9VCtfWnyjgSZjiTGxYKDBTb2f/2qVxTPVMLTpoPLcuGXVsjpoxUCuY7F1sDvL0XQr2YbKwXfiyTcvxi/IiQ6QXLYr3nQ7K9BkWvtfdmNug5bAQccMrPgscLDeHfkDBJmUs5RlXt7haZq24UoYofl1IsHMNQzjnTSEOJztH8Cf/vH7+Kzt66gsqFU/Map/qVyLhFNCdFJ0pMqX9pl1O+rwJEn9yy6nbZrvfjlD87KNqjIpvXKqZcOyedcBQEV5c0t1XjpW4+JFQjtXdqu9eHsezdRUlWIfcfqFt2Gy6Xj5V97HIlYCh/99JLsV3/XKP7q//cOPvzJRVTUlaCkIgSnx4lkLCXBozwOHgNJ94JSv9izLESi8zxVN5Xi2a8dw2DXGK5+1irE8Pn3b6G3dRiVDSVi+UKyn/0cnsdoJIGJkWmMDk4gMhFDsMiHJ798ZMuuA7ex50gNnvvGCbTd6JX9J7H/kz/5ELfOd8q1oOKf12d8aBJdtwfFLiWdTKPpcDWGesJi07KVbWmjj8GGjVnwqcTBHQMVqYqeHyxJCw5aI6wVV7oHcb6jD33h7UOgbiZIyvo9C6sTaBtyd2AERxuq1uxHvhLQDoOWF2wD8VT6oSJK99jkokr15cC2c7a1R9oBCwL5jln/9o6RCUxEHw7aDXldosxfTxR43XAtYEVA0KeexYe9lSXrus2dAL4nKwoCeLmlGR/d6oCZevDZxTb7xvmbYvvCa0bluo3tD4Y8UknNdxeHpvR4HhqbRsv+6gV/f1bVqqmrI+dWmjPE/mBZsV+sXnCXg96sqILX+szcLtis67BdQVX5zbYh8cpnwYHWLccOVaOowLvobFV+z+d1oqa8AI21JRJCyzBVBo7SL72p7sG+wuDotATUJtNWW2uoLhainl7wC62f2+U1e/6JffjobNuGkugi0FBdOFFYj/dTt5a0xZlIRXFuvAN/0/0FXqs6iiLnwvu/EYik47gY7sI7g9dlH6JGalnKq9QdwKuVLXBvsf2Mja1HNpuUQNFMZgTI5n8fdz5hS7UxQxqDlc+IMj2ZSUkfWMaGii4e0wW6Hy7VJf3jE4UH8M+P/GP5+1J3EQr15f25qWanncmhUJOQs1RB0xaFVivZHCFqZSjocKlO+FQ3PKp7QYuVufgHTb8mVh6PgnJ3MYLawg4QJJz/y/3flX1dK3g8tExZ7lhmwfPOwMw/bPyWkNuzoDKcXt8q1j4W3KjrQNB3/WCgSdbHZyLblGcD8yIcDmrmHbJvs0/gak85mgO1y9rm5BO2BYk+e8ILigNCEDPokwpwen6Lv/jgJDpvDYgdCj2nZdohPQSjSSGJGeJJKxOSz4sp8khyXvroDi59fEfCWnxBD57+ylEcOd0spPN8+IJe7D9Rj9d++ykhR6kmvvZ5KwpLAyivKRIyev7LWb52AA0Hq4RI5z6dffcGwsPT6O8YwejAhCijuT3VqYrymcQqQym5kNClwpnE72Jwe11oOlSNb/7h87IPJNLDw1OirGcQaqjID6ebXkOWXzcJYRL10em4WA8wdJO2MVt1HYhQcQDHntmLr//+s3j/h+fFJ7y3bQQTIxG03+gTpTf3lftMOxa314kv/cZp2e4nb15eloDeCcdgwwZhhaoAlUUBtA07JTRxLtqGxtEzNolTTTWiml4p0qYpf/fG+Vu40jX4gP+rjfugqrnQ50bI636oWDEZjeP9Gx3YU1kC1yJWAus22HXpqC0OoX04/ICil6T6hY4+fPPUQRQHFg83Wwjc/0tdA/jrz67mQksfXdExC66LwZ93BkZRFvKhPBSQIsCjIpZK41xbL7pGwg8VFKiwZKBocWDpoLrVgnYjhT6vzAjh9ueidWgM13uGJPBUplraA/aHzt3BmjKcbKyWQk00cd+vnm/X3vFJ/OT8TSQNA88faETVAkGkqwGfa+ORmBDytGGyL8fmg7YndVVFErrJWRrdA2GxUqE1Bb3IK0tDQmxv5rVheKNvTjGUfuG0HtnJyMfrkE+gipz+5vRuZ1+fFj5NdaXLWsTQb59EN+1uSKITDK8NT0XRhAeLqbS2GRq9L1Ag8c7A0qUs+Piz+upC8VjvG5xcl+DUxRDQ3Xiu/ADOhjsQMRYXYxjZDAbjk/hJ30VRgT9ZugdN/jL4tI0hYRgQGE7N4O70IK5N9uHaRC9aI0MIp5YvKhQ6vWLjcrK4AfoCM8hs7C5ks3EYaVq5MD9ne9gVzu/XkjAtV1dgE+KAWHusxd6DxKZP88qyXmDg5UYiqPtk2fTgV1VHo3/hYvSjYiOuA0H1+mZ6p2eRlSJAR7RPbC8JesnTN347YduQ6LMgyUuFMC1HSGT2d45icjSCmRwZSWKYDwpOq6OvN0ntYKEPJVUFOP7s/gU9y+OxJK58ehfnP7glXuK6U8OJ5/bj1IsHZRsLgYoAqsFf+OYJsfe4cbZdlM0XPriNuj0VeObrx2Q9Cw2aSaAefLwRvpAHBcV+sVHhdqfCM4jNJDEVjiJjmlB1FbquCeldWlWA4ooQ9hypRaBg6ZvHH/LixLP74fE6UVZdKGr5kb6wrJeEOpUR7BSSUCEh7Pa5JNyU55TrbzxUvSXXYRb0nec6aUHDfbxzuVtsbLjentYhmW7rcuuyvob9lTj4WCOee/2EhHde/uTusvu+k47Bhg1ib0Wx+FbPJ9EHJ6ZxuWsQB6pKcbi2YtEQxLnkJslgku9Uh354swPDk5anm42F/WoL/V40lBXiardlBTALKrc/u9ONYw2VYllR5LMUbBuBgNsiI6k6n0t2p9IGWgfH8UVrr6jmKwsDyxLpHJQPTkZwpXsA71/vwLn2vnX3QqcvOdvZzy7dln1vKi8Sgrsk4BWC0+9enQ92yjCEHOU98KOzN2T/53qfs8mTgGVQJQse69ph1lRUFwdREvRJ4WkumCXweWuPkOiHasrk3OcDkT6r2Oe1pl1PdpHfoRqXxDP3eyN83Ulklwf9+Mapg3L92ofHH7Dh4TW81Dkg13cqmsDxhkq5hoV+D5yatuQ+SQHczGA6lsBkLCHhuMOTM+gZm8BvPHVUFO4L+rbZ2FB43U401BThiWMNuHijB9PTcVy43iOq2o6eUfESrywNSqBkQci7bIjoUqBQgYT4xFRU1OUME6WqmO2e7SzDJZvF4Mi0eF7f/7vMduRT8vY6bEewjVAlzvZCMMTzdtsQ4nMKfYthcHhKrHJmQSI+Gkst6EsfniOYodo9MM/yZT7Yh2DhoyjkE2/5jSTR6Yt+rKBOCPFIOrGkTQo9yO9GhhAzkkKoHy2sQ4OPoXYBFLl8EnS4WoWhvKeQRdJkGGAC06k4JtJRCTLti4ZxY6oPt6cHMJ6kLdHyN6yeCxN9vmw/yt22RZgNtrEZGKlL206FbsPGdkbCTEk4asdMrwRCBzW/2LgwcHQ7YVv2isTypLJQLDmufNqKjpv9GO4dF8uNZMKaikACOVQSQFlNkai3mw9XC1G80BRPqoyHe8PywiaJ7PG78PofPIea5vJFQ0gJqpi5L1/6jSeEMCeJTkUxCdPTX24RAnyxMZrb4xTbFwaBdt4ckL+hHzuV0PQwpwLdxannfpeQtSTDaQPC/SPpuxxI4rac3iO/T2X1rQud6Lk7JGS9qNrNDDSnJor7orIgqpvKRMFOYplfb8V1ePDc6mJN81v/5DVc+6IVN852iO0KSWjue6DQh7o95Wg53SwENI+DxQmeT3rb89rxWjh2wTHYsHGopgJnWqnAnXiA8CSJdLatV+w+SISVhvzwODUhr0hAiWdcJiNEWTxlEVV3B0fx7vV2IdHTLCRxwO3URSk8PmPPkJgPKryPN1Thes/wAwFclpXKBP7q0yui5GcRI+B2Cuk6e+55qTJZK+yS5Dc/6WFPj/Og1w2fa2Ve2iGfW9S8H97sFOJ8tgXwk9v+u7M3ZJtP7qtDoc8jCnpRGebCzbht7i/V22PTUSHdP7jRjuu9lpKOvxfyeWTAHk0uTyIsB4ugNXC2tRe941NCnu+pKBGimdYn9MAmuc5zxYX7yv2fJU153niuSYTx+EjAUvH9y+ttuNw18EAxg2D7f2Z/PaoKgxsSUsl9ri8teIhEZyHlfHufbPM7Tx2VgotYL/F4Zj0bSRTMvf4Z67hI9tNPn2TxSsG/S6TTiMQ5cy0rvuyz68888G/rnh+bjqFn/GE1I89fPG3g7sCYeNw6dU3sokjgzF6Hhf4d8LjhX6X/u8el40tH9uJm77B4CbMAMvcZxvNytXsInSMTONNWihcPNcn5ZrFF2nFuv3gyZ4+R9xDbM88Drwn/ls81Hg+fYS8ebpKih/1u3XzwXi4tCuA3Xz8p1+lu54iQiQxivNE6IArbln1VOHawRgInSeKGAm643fqKZ9Lw+ULSkupf2pTQn7pXLDWmMTkdRzSeQiKZFkEJ7xl6X6/nTJvtgM24DtsZnIkwMh6592wkoc5lLWAhh21tPuKJtBR25nqe0/JzJQgG3HA5dSH3Nwr02CUB/kLZQYSTM0KSL1dQ74tPoL/vAj4faxXFN33S9wUqUOTyw6PSdlAVMp29j9mZlER2DmkufSK+32mnaabFLmYgPome2BjaIyPonBnFWHJ14g5ur9ZXjGdL9+Op0n2PcFZs7Bhks8hmojDSl8XWxYYNG5uDseQEWqd7EMlZ3lC5X+OpEHui7YRtSaLPksRFZSHxJefyKKCi/Lv/7HV8d4378ezXjsuyFpB833e8TpaNAO1dDp5qlCXfr8N8sINFEvnxlw/Lshwq60vwj//Fb+3KY7Cxu3GysQqf3CrBtZ6hh7yrR6Zm8MaFm2Lr8cqRPUJUUpFMMo9Tt/n7Q5PTuNU/goudA2gdHHvAD1ZXFfmbI3UV+JMPLmzB0eU3qgoCQsp9//OriCcfVPWSFL3Q0Y/b/aPYX1WKI/UVqCkKCjlOgpNTxumdHkkkRS07ORPHWCSKfVWleO3YPlGwrwRUuZMk/qtPL8v65vv5tg2N4V//4nMpjPD3uC8kdEmGMHiUKt2+8Skhzc+29wqRPlfJTVL/y0f3ir/3J7e7sN4Yi8QwFunBF6098jWJ2MqCgKiOqVAv8ntESc82y0E3Q1tnkkmZJdE2FJYQSq5jIZD8JeH6a6ePoLZkY9RnJ6SIMoTP7/QskEsQxZsXbuPjm514Ym8d9lYWoyTgk+IAibtEyhBrm+m4pZiekHMRxddOHsRLh5tQW1Kw4v2YSSRxs28E71xtlXYQS6bFYmb+v1ksYYFtMZAo4XPjX/z4A/maBLmuKXL+2XY9TqdYCFnFNc3KBnA78UrLHjy1v35V547rdjs1/P4Lp5BIG/jZpTsLFmpIiJ9r65OFz6TKwqA8x0Ik7j2W3yf/nr/HosrQZASTsfhDBRUbWw+PWxdylpYhb39yC+99dgdtXaNCnoUnY+L3zKW40IeTh2vxtRdbcHBvBfw5le5yszn47GKg45vvX8MXlzuRSlnPQ8tR0bJVnA/+bLe1lY2+DtsZfP3F4ilrVkIO1uGu/pit+LyHGxeJdXqvz4LK8pUWeVnYVDfBn573y9eqjqF9ZlgU5lPp5T2MeaTDiWlZ3h++Jd/zak6UuIIocwdQoHtFme5SWJxV5dlN0pxq9piZEsX7VCouxD0J9KVCTVcKv+7Gr9aewpcqD8u+2LAhoZSZCIzUDfYqt3p3bNjYFchms+iKDuBcmPedhZZQM+p8Fdhu2LYkug0bNmzYsMCB10stzRiNRMUiYz4ZQDVVf3gaf3vmOt68qMrAxRoA52wdzIwomIVcm0PA0v7lS0f34puPHRLV5w/P3rBUrruNbVhmMFtXXIDffOoo3rhwSwi8+SB5ebNvWMheeprOkg+zthpzlcj8LKDq21z5wJGro3L9uy8+hn//7lmx45kPEpO09rk7OJabmcDhsUOUX1TdcXsMOGMbmCXQuZckSP/w5cfw4qFmIWcvdvYLIbuR4PppTTMwEYGu9t+zQbHccKjin1UcWwp6qtoXAv+uubwIf/TKE+IZzxkYGwESyKeaquW8v3ut7aGfc18te58unG/vvW/rItd/VkE9Z0YCbUjiiYcI+eUQTabRNjyOt6/cvafKttqYZW1xr72t8v7N5Ao+fE6Q8CfxbV0P65rM2to0lhWtmkSfRUnQi+88cwx+jxM/PHNjyUBk7getqlig4D6wLd9TMubOIduG/ZjKbzCk8fWXW/DE0Xq0do3i7NUuXLrei8lIXO6ByakYPrvYgWt3BiwS96UWHN5XJaGYS+Fv37qE9z+/g/aeMSEq2T72NZVjb0MZaisLUFTgh9/nhNvllFDg3sEJIYs/PvfwvbsbsFHXYXtj9vlpfVVbWYhjB6sRCqzei7aiNIiGmhV4Fq8Ci4WbbgQ8mo5frTklRPeb/ZdFHb5axI00Bs0JjCSmRInOvsdcOy4+u2eLDTI7LqdGnzu7by3gtqiC/0/2vIinSvai0Lm5Hsk28hfZzARMs4N+BNvSD92Gje2I61NtODN+Df3xYXBOUrW3DPsDDSh2rlwwlC+wSXQbNmzY2ObgYIoWB68e3Ssk18e3Oh8gyvgvkqQMi1zR+nI2C1RDv3Z8H47UViAcjeNAdalYK8wPbdzN4ECQpPe3njgsxN+nt7swMv1gwNWsSpbLSiB+vdnVXX+Gl57eWysK3Lcu3RH7irkkItsDr9tKrx0LMySeXz91UNoVlb/8uqYoJET8RkJIWyHH165Ao6/6sfpKfOX4PjkvVE5vFOlAUvxAdRm+fvIAJqJxXOsefEjpzWPivblSkAxeLbgN2tvMn42yHhCCg1Ywiww2qfh/lABiFjjqSwvx+smDKPL58PPLdySYdbE2wPO7lJreRv6Dlni0sGCoJwMVm+pK8NKT+9DROyahjq1dIxgdnxFrli8udUr7iiVSePpk84KBl1T1tnWP4rML7fLJr6vLC4T0pSVJSaFPVNS0zGDWjdgq0ZrEAQmD3K1Y7+uwEyCzSF36vRwTnpcnTzTJuVkt6Ccf8D08TV3TFGmHs6CAYaW2QoZhrukdsVpYlisO1PtLRJGuOVS8NXAFkXR8VbQjyXEqylcjDnhUUPHeUlCDr1Yfw4nCBhS7/CIgsWGDyGTGYBp3bALdho0Nsmzpj48gko5CUzSkMwYG42O4HelE+0wv0lkDPtWDL5WfRo23Arqy/Sjp7bfHNmzYsGHjIQQ8Lgne46CWythLnf0YnY6tetBCsrG+pBCPNVeLCn1vRYmsmwTwsfoq8RW2SfQHQRVuU3mxBCTyXNGHnv70KyXN1wMkg2gT8nJLs+zPRzc7xaJnKUXvYigNeLG/ugxP7avHK0eaUR4KCOFUURiQENVHJdGpDKXn97MHGkQdPzAxhak53rCPUtBg+20oK8LRugqc3lOL441VYuey0aDXPG2VSPoW+71i70JblrUSvbtxWMfZLryPOKuCFj6c9XB7YFSshtbSjueC9wSvCy2CfG6X7YeeRyCRWFTgEx9ukt1cDjZX4E7HsKifL1zrkQDG81d7UFzgx/7GciE154PWSOevdaNnYEL8psuKA3j8WD2++uJhWf9iVhkye8EuyKzbddgJYGGU58HKPrLykbweHfXVReu2DZL0DHidG0DKGT8rAX39NzJUdKGQUYZykuhwqzo+H21FT2x8Tar0jYZfc6HRX4ajhbV4rKgJp0ua4VSWDqK2sfuQJYmeJoluw4aN9cZYchLnwzfRMdMnxUuS6PxeODWFZCaFAj2AE4UH8HhRC0L69uxH2CS6DRs2bDwiSAhSpT2fXKYVRkPp+g26lkNxwCfEZ3VhCBUFAbEQGZ6aEQsWWmSkckonKqPJtpPMpAKU5BW9joNel4Q6PtZUIwQ6icFZ4oE/f3xPDfrDU/cU7UU5UorrWQmo6iLRSw/psuCD02qp5mbY33qAHstUtT61r+6BgSZDGquKgnDrKwvvWi0e31Mr14A+3gzN7AtPSVgirVQShiEKc7FKyWZlkM7zxoA3LXcNPDmPadpihHK+s6sF/zbocaO6MIj3b7SLxQgJSKqgGTpJUtdqAxbpzOtLgtHt1OF3OVHgY1suE+90erJT0T0LBnOe3luH6BxFNf3VeU1XAx47286vP3kEzeXFuNE3LOdqtp1S6Zg0DGmvlirfsumQdptrR4qD500ROwaPrsu9xn3nuT/ZVI0nmmvk37NKws0Arz1V+7z3PrzZgVt9IxiZnpFzTy9ytkVatLAJcK/k+ks7sK7B7PXnp9VOV9dF4+/z/uW122zwGKqLH91zXoo1BQG8/thBHK2vwLn2PlzpHkTP6CSm4glpeyxOsY3I/ZS12jL5EcvaJdcuNE3sJrxOy6+dAcBNZUUyO4Fk+k72c96uENWrA6gsC8lCy5C9jWUYn4yipz8swZcMd+zuDy9I3tKSjMppqqSJyrIgHj/aIGT6coTkzCMENFpa3fuYfU7t1uuwE0CVOC1crt7uByMspyNx9A5O4vTa4q8WRMDnRijouff18FhE1P5LQfzDzSwmp+ISjruZ8Ggk0itR6Q6h1BXAF2Pt6JwZQTg1g/gWk+m6Q0VQ96DEHUCjrxRPlu7BqaJGVHsLt3S/bOQnslkTGXMUprE7Lbxs2NhoJMwkBhOjuDXdASNrymwkzmTyqC6UuYqwL1CPr1Q+jQp3ybZUoRPbc69t2LBhI4/AIEgu+QAScPurS2VpHw6LIpZkeudwWCxZSOiSfCLhxN8t9LtRURAUgonhofw7kufz4XM78eTeOlnWChL2T+yplWUjURr041cePyTLZqOpvEjU2l89vg9Xuodwu39YrgOLGdOxBOJpQ2wxSPST+LWKF24J0mTwJf3V60oLUB5aOzlBwvDlI1bI463+UVztHsSdgREMhCNCqMdSDEzLSlGBHtQlAS+qCkPYW1EsBDTJZyq654MELb3fuTwqSaM6HPfuG86WYLhp6+C4+MYPT86Ivz/95ekNTvI5ZZq5gojlvz0bcllR4Jf9YvHgQFUpmiqKt1Rxxn2jP/qJhkp0jk7I/XdnYBQdI2FMziQwkyPTeQ48Lg0+p1PuLSm+0DIn1wbqywoRmFPAWAlKgz589fh+WbY7eA05o4ALLYWY6cBzSc/3gfCUzLLh/URC3TBNKUQ5c+GnLCKxIFcW8qOhtFDCXBnQWuB9+Lm208AgRKuo4NjUAtJGIOh3o2VfFV595gC+/9ZFsRQh2T08Nr3g7/OZRoLXNCxVuc/rQnnJ0gQ6C4qj4xGMjC+8zpVAAkvnPHPSRmZTrDby9TrslKyTQ3sq8eGZVoyFZzA2MYM77UNiEUTF/noU4RjYOrfAQ/uciemYtJ3F7l0z18ZHJyIy22KzQUVhsTuA32l8WhTeHw7fxiejd4VMZzBoOmNahc0N3g+6qnNfNIciKvNSdwBHC+rwfPkBUaCTUH+wtGXDxhxkE8iYI8iYA1u9JzZs7EgEdB+qPGUY9U4gasRhZk34NC9qveU4VrBfVOjl7vXNCtls2CS6DRs2bOxQ1JcUoLowIBYfJAvmBmURs+pNDtjoaUwiip82Hg0cuoW8HlHCU71PqwAzF/I4e/6FeOF/OQLmvirZ+lwPIpjK5CO15ThQVWIpoOlpPa8NcKxOK5hZVToLHSudWbBeYJujmn3WEmVWeT4bjGmFRt7//dlTYwVb3j9vbL/5MmzmNaUtEonxV47suRd0yaN5oA2I56ylrmcI2lx1ui2Wvt+OSYbXFAWlHZtz24acS/4vdy/lzr01W+G+Mp2fuwFUqKbTBjweJ4KB7V804HWkX/ns9ZP7ZBFbFl57CW7OPb8YKhqLL63snZiK427niKiq1wqXU4fTaXkt87k1PhFdsS3HTrwOOwH0MT9+qEaKMAMjk0Jc32ofwsUbvTh2sAYe96PPZqsqC6GhuljOJdtNe/eoKP0P7alAaJF7N55I4dMLHZiOPJq91Xqg3leC32p4El+uOoK2yDC+GGvD1Yle9MbGETXWP5djFnxP0u/8QKhKLGaOFNSgOVCGoO6FS9Fs33Mby8I0e5HJ9PCJvdW7YsPGjkS9twq/VVuKb9d8KTc7LytjXtVBnkHbturzudj+R2DDhg0bNhYEySMuuzcybWunw3NA59ri/SCpxCWfMatMt2xNsCPAY9JUqzBh49Fgn8uV4/vfP4v2jmGcOtWI7/zG6UdaF8c9VLbfvNmPm7cGMDA4gXg8LYRxUZEf+/dVYv++CpQso/aei8GRKdxuH0ZxgQ+NtcXweZ1WuOcCIAlO1fMn59oRiVqkYcDvQkVZcMHfp391VUWBhIrG4sDIeAQ3WgdxsuXh2VMsbE5Ox/HDn1/Cxes9j0R6FxV4JZiTYLHs+p1+9A1MCEnq9dz3vM4nbOR12Ang+5se6M8+vgcTUzEptAyNTOM//OBzfOu14zjVUidK8oUU6YaREX/zjr4xzMwkcaC5HOWlD58rto3aqkIcaK7AzdZBaYMfnLkrIaQvPbUPnjl+6cRMLCnX7Ce/vILJqRi2GqIEV1RRglP53egvxderjyOcjGI4PoXB+ASGk9OYSEYxmY5hJp1AzEwhYaaQFlEHFetWAVTe/7RoU1S4FV1812kfQ3/zkO5FgdOLIqdfFOel7qCQ6H7dDb/mRlB3w6u6dk2h1MajI2P2wjRIotuwYWMj3w87GTtkuGrDhg0bNmzYsGHDxu5FV/cYLl/pQUGBD4Zhilp4luib9epeqRUFfaDPnevAZ5+3oqtrDBOTUSFUWczw+d1Crh8+XIMnHmvEoUPVK1ovVdqfnG8T7+fykiDKSvxC5FLlzFwGqnJJREdjSSHBWztHcaN1AIlEGgVBDxpqilFbUbioevjk4Vpcut4rBPloeEZUu36fC3VVRUJOkrMjGTk0Oo3bHcM4d6VbigX0/iaxvBZw/6vKQ6IepmJ5LBzFzz64jqGxafHVpmqZ26BnO32sSZ7WVBaiurxgyfWS6Oc1ZDYEzzttoEbGZ+6p63k9+e/h8WkhfKVgqltFUy48l4tdk428DpsJngPup5yftHWuJqai4t/OAhDB77E9UFHO80IbM54nFl3YlheyTrEK4Q48cawBI2PT0mYGhll4GMKP37kipHd1RQEKg14pKvH6JlOG+OuT4Ob5HQlHUFLgR1lJYEESnfcmg0pfenIfOnvGkEil0d41ip99cEPOuVXccMm6Scr3DU3gxt0hUasXFfpEjc792mqQKAkqHiHSiZRpIGIkMJmKYjodR9RIIWYmkTTTSJqG+OMakmUxS6HnZg8Jma5AV9TcwhBTTUJNSZL7dBeCmkfIc5LsNmysFSTQM6ZNotuwYWPtsEl0GzZs2LBhw4YNGzZ2AEQJG4ljcHAS0WgSU9NxxGIpmKblxe9y6Sgo8KKyogDBoEcIxfmIx1Po6BjBj9+4iNbWYfGClhkjqgNpOITA4/r7+icQiyURCHpQV7u8vyVJ5N6BCVGIS8BwgVfCKRmwyIwIWb9hIjKTxPB4BEMjU0KSkqA+daReFMCFoYWDjJ1OTaw2WvZVCvkbnoqJjzWLAXvqS+/Z25CQ7B+eRHvPKEqLAnjyeCOSqTR+8VFEtrVakOjc11CGE4dr8dmFdlEUn7nchZ6BCSFafR6nRbKmDSG9Say/+OTeJUn0a3f6hegnyU3yXZaUKeekb3BSfoe2RkOjU+LbffPuoJC59PK2FhWNtSWoqSyQAMvNvA6bBXrZ02KFbZHnlm2U555qeZ6j2dkFvN4sALCeMHtuZj85g2B/c7kUEBayp6mpKBA1Oo/9swsd6BucwJVb/bjTMYyiAp8szDehvVQyaci2J6bjmJmxFPuPH2uQa7cYeM6fOtko66NVzOR0TMJMewbCUqgI+d2ybhYBGDw6E01I0CttZq7dGcgLEn0+nKqGYtWPYtfODJ21sb2RFT/0fmTMoa3eFRs2bGxj2CR6vkEq8+zEp4FsGln5NJCFAWTZIeTCCj4/s3OWWUhNP+cPyg4hFxVw0CuWl1sDHBocDk4T1OFwcPBkG6/a2LmY9eK6f0/xXmIgk5G7jzJz7qvMMvcV/68Ac+8t6mfkPsrdW7lP3l/Wz+z7a/e2O+tZns21Nz7L2day8pm51/YebHfy18s802ef63PbG5/reu65fr+92rCxMaAf+WJ9lfvte7m+ijjCO+4/Sx/sq+hz2rTdV1kNxsdn8NEnd9DRMSr2LqOjEVERq6qKgpAXe/eW4emn9uLIkVpUlIeEgJ6LsbEILl3qxo0b/fI1ydWCQp+ounkVJiZjCE/MyHrPnu1AKOTFb3/nySXVzwRV2VR9U2lL5W4kmrTCFE3L557gn4uXvaYKQU2VNYMtX3/lCI4eoOJ94XVTVcygxq+8cFgI5ss3+4Rk7BuaFPUud1yyHzQVbpeGsqIAXnn6AF55Zr94on9xuVMUxGvBvqZyfP2lFoxPzIiinYpkflI9zMOS/AbVIZkTLl0TQnwp/OjtK0LaTufI2MWU6iSLZ0n1+fjNr5/CN145siCJvpHXYbNwt2sE//rPPkLv4MSSv0cC+vzVHlnmg0rw//z3npfCgGcRuygec8jvEdX5Lz+9LfYuDPVk4CgLHbMhspItw1wRTUVhyCt2LCyYsIiyGPi7LLT84W8+DfWHX0gxhNeBhYGL13vlmSk2Z07NCnjdX43f+ebjiMaT0r66+saxXWG9G4x5Y9zZPtJs3zy33LN+WaqfNPuZixSVBjq3z2T13R333jV85uXeN/J+sRarXdvvmp0KCRTNDCGbndnqXbFhw8Y2hk2i5x2YPBaHaXQhY/YhkxmAaQ4hYw4ja44jm51GNjMtD39WU2XgKoNX816nwEHyzuGEw+GHQwlCUYJwKEVQlDIoajkUtRKKWgdVrYVDpXLI7izY2OlkTwJZsx+mOSBp7Lyv5J7KTMr9lMlMIZuNIJuN3SeFhPjkfUVSYJYUJ1HphsPhk3vLoQTgcAShqAyHKoVD7rEyKEoFFI2DTCpxdrYnmI3FYCKbicqUUTMzgKw5jExmFBlzDNkMlwgy0uZm5PeAxDwSMpsjFK12B4cLDodX2pyiBABHQa7dsb3xuV6ee65XAg7689rPdRsb3VeJwWQ/xeiUfko2Y/VVTHOMZiBWG8/MAA/1VdR7z1QH27XiBxwL9VWqoKp1UNQaONTCPGvT+bQvD6OtfUSW+aA9yNh4RJaLl3rw2pdb8Ku/cgoN9SUP/N7Q0BSuXCWJZ+HLr7bgtdeOyu8xuJTr/o9/+glu3OxH/8CEWL68/rXjCATcomJeDPubyvGPfv8FnLnciVttQ+geCGNkLCI2KCRzSeA6NVUUz5XlIexrLMfpE404vLcSfu/K/MVPHakTYvL8tW58fLZN/Ky5fpL7fq9LrDUO7a3EK0/vR3NdidirULVeU1G4ZhKdROnjR+vRVFeMdz65JQQoiXnOAmAYLi1SggE3KktDOHKgSo5tKdCJZC5FuBZYwbvZLbsOGw7x7H/UswSsZBVU9P/m66fw2guHpE1dudWHjt4x8YqPJVL32kBJoR81VYVyvo4frJbz7HYtbT1CIr2uqhD/m3/wJZnB8Nn5dtxsGxSlPfeN12BPQymeOtGI5x7fI1YunEXg9Wxl4so6IDsjhKZpDiKTGUHWDCObDSOTCUvfPJNh/ygq7xn2zbPZ+D0BjCWCmRXAOOaMfS1Bi0WMcxzssd4v8EFROCb258bGITgcxVDUEihKiYyFFaUYDkdB3j/bbTwaTOMOMuboVu+GDRs2tjkc2cV6WDY2HCTpspkpmMZdeaibRofl05UZznUWUg8oZ7PsLMxRo1vL4kp0S6loKbyk8i6KrrlqRQ8UJQRFq4Wq7bEWda+Q7A7FjiK0sT3vqUxmDGbaup8yZnfO+25QSEqLGJ+rDDYXuK9kTfPurbn3VU7dklOuWMr0WSVwjhiaJT2VwjmFq2qoagNUrQGKWir3n42d8hyfhClEYleuANqTK9JQhThLHuae5XOUulbbm6O2sta4xDN9tt3l1FT3iMjZ9jc7aCyBqtZA0Rqgqo1QtGaoagUcytZOv7exndv4BEyjFUa6FRmjDabZgyyJj2wc2Yf6Kjkl+gNqwsXatTKvXc+fWcE27YWi8lnKvkozVG2ffPLZShJ+KxAJ/2dIJd7KETobDIcf3uA/g8f395f91f/qv/k+zpxtf+j7LpcmNi7s8kcilrqZisuS4gBeeP4AvvsHz8Lr5bm2CKT33r+JP/5372N8PIKamiL8J3/0Ih5/rEnWw3Ukk2lcv96P7//tWZw734nKygL8gz98AU883gQfvccXAf+WCmoqtWkpQssNEvummb3vkZxTQNNmhj7nVEFTOb6a4ED6Y5PcnIkmhRQ2c/7YVAuTtCSxSY90rp+BmrRZoY92PJFC0O8R3+/lyM+Fjo22H7QP4bapNpftsh6qMDjZ2rbH4xQyn9teDLNq9llf77WAViUkYBfazmZdh/nb5MwAzmKg/zfPBS12qPBeyJd8OXBdJJppO7NW0NaFxDcLKcvtg5yzTEbaFJXotGmhdZIQ+fdmOVCJzntNg9etw5Xzl18JuB4qzGnhw2vC68HrwPPN9fk8Lilg0HaGx0wlPM8BZzbQd30129oMUE1OEjxj9CKT6YNp0EKjzxKymHx3sGCVnNcfvz/rWvpHORX6g+8RLDlb9OE+k/WOua9AV+Yp0O/33Um8k1SXfvs9cUJOGCN9qILcTG4b2xWxyP+IZPwH0o/ZDPhC/x1cnt+0+982bOww2Er0TYSlCiFp3p4jzjtynQtW3XOV92xEqu5r16DM7ViQoLn/3Qf+LXDAJAFj3IWiXBTCz+Eoko6CqjXmBqv7Re24VQPVrUTCMNA3OYXagpAEEeVP19TG3LZuGgM5wrwtR5hTaW7dT9l7CnN21h+lXji/w45l7q1ZkEj3WsoXRyCnXg9BEbUlO+c1UDUqLWtFEWMT63kOIRfSyJi9FlludME0e8VbMcP2lp2y2tysggqp9djovGd6epl2R1WWB6bjRk5tVSADPw4ELWKdz/ZGIdchsyrWRojY2Lmgws/qq7TBTLdKgYht3nquTuSKQ5wRF1+HvkqOJFyur2LwWXobinLe6qsoRTLrQtGaoGl7oWp7c32YzQmcs7ajbQ6Jvka43TqOttTiyJEalJQExBKC/VD6pN++PYCLl7oxHp7B7TuDuHa9D4891ggtpyIXf+ecerusNIjCAq8QeQS/53Y7ceBAJfbuKcfd1mHxUL91awBHj9YuSaLzb2kPQnKXy0aBxG9IX/k2SKJyeRTw2EgMi1c2Ho0wodXKRmKzrsP8bdJaZiF7mbWAhQgumwU5Z7RDCnpR8HBO6CODBPhKzw/J/6plgmk3nzCnHVB3rn9EwpwK8+H7/fDMzJxPjnNzM+7WZw8WX9f898mi/XXCAZMz/mTx5Wadzv134ZyZUuU5cr0819eyyfV8g8xCzkyI8pyzHTgbNJ38CFnz4VlaNmzYsLFtSXR5CWfGkUy8mVPqrSMcGjTtAFT9kKivN3cwOi1kuWl25tTm1sKOBo9368AuREr2wby3HyRgGLBTBUWrh6qSSG/KqWdpFVCV+52dTymHYzH82aXLaC4uRn1BCHUFBSj3++B15l9HiarXdOpMbora2pVLD4NWJl64PL+SN1V0KxRmTFS/VmedxSjeU+y8D8j9lj9IIptJIov5np2WSp3qYCrUrYVq9Yp7iheq1eX3dsG9lt/IIGOGrQGh2QdTAomoOO/NqarorTiFfLPZyHCQmpkzUJCZR6Vi46VofJY3yKcUctj+lJJd82y3sUhfRQab7KuwfXfmCv4k0Puk0L9+hMeq986aSZRJwMzMTsN2wHAEpP0a7J9I0Z8FIs6+YLsum6dOXGfIrA+S0shLUDW8d285Xn/9OA4drBLPcipYSaJTpb2nuQyqpuLsuQ6MjE7jytUenDxRT9mr/D1VyrNhorRoWSh81O93o7q6COVlQfT1hdHVPYr0El7fRuoKjPT1HIG28WBhRXc9tStFIBsyy88cQTr1qZCimwH2izTnSekn2cg/0AaRYw+LLB+610eylOYUFozk2kqePiQX7T9FRQSRxUKWH24oLNaqpdKfUmY/aed473slMhvQEs/kFc2yA2EVT2gbZ1ndWkIWy6KTQqpJ6b+T58hkxi2+w+jcVD/0dPIzZDMx6TPsdnBsqztP5+4PW8BjY3sjz57uhvhrxqb/ufiCryscHrg834KHxNQGk+iWQ45VEGAHw+AU6NQFGKkz8vBeH3XiRnYgZnJK+btI45eWL6neAt35GDT9RK76XiJk+05+CIZjcfz55SsocLtxuLwMJ6qq5LM6FESRx4MCt0c8IzmFc6vB+yYR/V9gpC7lbEnWCw7pFOqu56HQJmKLrrc1i4MKljEhL03jJtKpszIot8id/FUDLk6uD8HIDAHpy9a3HH4hM1X9IDQW+9Q9uWmkhTmVC6VPS4e22VgfMPhTbCqk4017oLsw0tdgpq/BMO6Ix/P2Ghhyd+M5m5keIPWpTF0myaQ5j0LTj0LVDuYGhMXyvrQHfzsfVl/FsJ6r9DJP34aROg8jfVGKk/n9XOU7YVreBVzkXaU2SHvWnaeg6kesgqS05/WfcSHELNXoefoYKC7xS3DoU0/Spu/+sfP9QUX6wYPVmImmJED06rVedHZSqZed9861amok0B2LWHgUF/tRWhpAZ9eohIwa5uL9j3TqM8Rn/iexAdoMuDy/Dt15QqzVbDwihETvRHzmXyFjPGwXtP5wQHM+C69SbJPoeQLadgm5zGIryUmjC0b6Vu4Z3IaMaYUQ72wkkMkMApnBB0ZanGlqzSyl6MwSn923f7FmoFpqdjsg+5H6KmJ1m8jZyPHf/IxZKnNz0PLXlyIOPweFRBc7xS0Ebd8s6zcbmvMJKAqLo8zjW592kcqkMWMkEDUSiJsp+drMBRErUMQOy63o8GkeBHUvvOry4rRIOoax5BQmUlGoDgX7gzVwKTqMrCnbmUpH5TOdMXPbcUBXNHg1FwqdAdmGpiychxY3khhLTmM0aQWCh3QfStwh+VwpYkYCPbFRxE3aYWVR7AqizFUAj7ZwX8cyaIPscyQdR9SII2Gm5XgIzaHCperwax4UOH1wKc5lua10xsBEagbDiQk5D+XuAlR5rOuazKQxmZpBxIgjaaZgiCWXtR236kRQ57Xwyb9Xi6TJ6x3HdDomx3/vGrBvq+hy7nmdA7pXvpeLmd4Q7J5RctaQF342E99w8hzZJDKZCaSSbyMVf1OIl81S3qw/stIx4pJOvCvWLk7XV+B0fxWqfgBgwB2sm2DHkXv0g3Q4MJlI4NPuHlmCLhcOlZXhhaZGPFVXi+pgEB5dkyme/F2OOHfWWeBAmg/pSI5Q2dxKuhUcRA/zBIzURaQSv0A6+YkogXccpHhFL/c7SMV/JIoXev/qriegO5+C6jwuswL4fSuYb+cWsLZyNhSLnBwg0sYinfwl0okPRH3OwsfOQvoeAZnED6VY6nS9BKfnS9LuoBTm/NbtYNydhnuFfoa1ZcaQinOQ9wsY6dvsnmP79lU6kYpz+bm0Z5fndejuV0WhzmA5Kxx6vfoqbjjgzFcOXcIHDx+uXvJYmxpLUVFRIJ7mtHVZLCJpqWFIwO8WpTqV65NTtHPI1zNiw4aN1eD+84CZLuwXjcBI30A6+THSyc+FrNx5/aK1gWMk02BB4da97zlYAOJsGOdJaM7HoGoHLDKd46hc7seOHDuvGVbh1ppNncupEjLUsjGk0txkzpWZs1KU/CFrRrLksuTt29jGekPyKbIZJDIp9MfGcWu6GzenetATG8FoYkpIVv6cpGpAd6PSU4yDwTqcLNqLg8FaeFX23xa/91pnBvCTvs/wwchVId//9WP/BSrcRRhPTuPGVBc+H7+N1kgfwqmIkLguRUOJK4Q9gSo8U3oYh4L1KHIGFiTShxITeGvgLP6m9yP5+vGiffjVmmfwdMmhFT0LeOzd0RH8v27/AN3RYZhZE69Xncav1z6Pen/5gr/PuyhhJuUcXZ5ox82pbvTFxhAxYtK78+seVHtK0FJQjyeLD6LeVw6f5pIixGL7NGMk8PHoNfx513tCpv9G3fP4o6bX5Lx3RYfx+dgtXJvsRG9sVK4H9yPo9KHOW4rjhc14vHg/mnyVcCrMpFg+j2SWnO+Pj+PKRDsuTbShfWYIU+kZpDKGEPJlrhCaA9U4VbgHJwr3oNDpF+J+o56xu4dEBwNzujaYzDZFaZ5KvIlU/A3x3qL321ZXQdcPhtiFJOI/QCr5S+kUuNxfh+5+EcDO83J2axr2l5agfTyMZE5dFUkmcXFgADdHRsTq5WBpKZ6qr8WTdbXYV1Kywwj0WWSRNccBtXbTp6NRZU7FeTL+V6KSJLnJItXuQMLqlJvtSMZ/LIp03nNOzzeh6UfgcGyeLdVuAS230okPkU6+J4NFS+mSzHNF7nogJQORRPxvkEz8VNqX7v4SnK4XxbPfxk5DCqZxG0mS5/E3cx7n0R3UzpMy4yIR/RMk4z+B7noGTvfr0F1P3iPSHxWSX5GH07Nna6tUmxeEqHxc/HcZJMqgSBLgicTa+qm6U73ntU4f9cWIeBs2bGw3cGZyHKnUZyImoJCFAaEU1lj9ovWc9brzwPcqz5mZvg7E/kpU6Zq2H5p+XGyKNOdxsWy0lelzkI3l1OQ9QphnjNnPfpkVagXQGnMCaI0d1G+xsRq0zQzgjf4vhBSeTkeFzKayOpPNCGlMmKYp5HE4NYO70334ZPQ6Hi/ajz9o/JKolVcie8xkTSHmSQZ/OHIN58bviALa2hY10FlRZcfMJAbj4zg7fgcvlh3Flysfw+FQ/UPrK3cXCsH7w75PRKF9N9KPjpkhnC4+CG0FZC+3M5AYFwKd+8DjqPWVocq7sLrfyO3/n3a+g2tTXZjIEf8k33meuMVUMo2pVBRtkX68PXgRL5YfxdcqH0eDb2WzwLLIirqd1+TOdB/+ru9TKTCkTOPedohwclpU/HcjfUKyf73qCbxSfmJRBf1ccIbBG/2f4/2RK3ItuO7Z6y3zVDKmqPxZVDkzdksKGiwsHCtokhkCGwFtd/najll+WdnUugeAGOk7ElZBlaxptMo0op1Dns8Fk9KjyJhxUSNkpGjwHpyer0Jznsr5ze+MDkFlIID/05deRs/kFG4Mj+Dq0BDaxscxnUgiZZqIpFKYSSbREQ7jndY2CSA9XlmJY5UV4p/uy0Pv9LUhYwXK8b7ZrC2aJM9zHff01ZzSILrOfu/bAVQAUYkfhSlTaCeESId+aKt3bMeAhU5abqUTv5S2Rm/9LAugMvtiN8G85wVKSw/T7EE68Z5YY+juLwuZbgffbmdYnVhaYKWS7yGdPJNTcQ3tUDKESvsZZM04Uol3pF+WTh6VWXRShHzUjA8h0fPPJsTl1MR+xSLGab+weMxBLJ5CMmVIoCFDJmfBgSf/fiXgdFn+PbdDOxibQrdhY3uD/s2080qnPs9lLXE2Mv3NaT+wW0Qs6wE+Q/kMTln1iMwU0uY4jPRNKAnOliqXnDa+j2inx6Bs7IKZf2ILJH76Qznv/Fk//WHLt5zWnZwlJ7k+1qcVYp7PVrg2Nhs+zS2ELJXdJFJpR1LnLUOpK4SAxjBgy76EBDIV6iSfU/EwzoXvyN9+p+4FUWAvB5Kzn4xdR290FB0zg6KGrvQUodZbKtYhJKRHkpPoiY5g2oghkUnjk9Eb8GpuFOg+VHuZN3UfblVHhacIh0MNuDPdKyrt3tgIemLDaPJXLrs/tE+5PdV7z4plb6BG9mUh1TutT9pnBvCX3e/jxlS3nC/FoaDYFRC1OfePZihTqRkMxMcxmJhALJnEB8NXhJB+teIkjhU2r+h6dEWH8dbAOVyd7JRiQoEzgD3+KpS4gtAcGmJmAu0zg2JlQxU7z+VP+r9AhbsQB0J1YiWzGEaTU/jZwFl8NHJV9jOdNVGo+1Hvr0CR0y/HRIubwURYFPY8zltTPfh+9kMh958vOyKzEtYbu4hEn1XbsSMQgUMtXqc08mmkk5/KIsSDeJ7TL3engyFkYRi0PTB65CWopW+I4ouVdofix3YHSfCTVVWiMKfi/PGaanRNTKAzPIH28IT8ezqRwHQyie7JSdwcGcWd0TGc6e1DU1EhmouK0FxcJIS6S9Pywjt9LeBEIOnYbHgHhlP5EhI8xmJUOvmFKLEtz3MbFsmZyPkq2mTmoyLDqaHpOzDS53OBd1ekE2931Plum0bWsAKxaZFBv1MWSbmoahMcit3+ttug1Qqf/hhG8gzSVBSafTu00D8fzKcZhZGayIVP90FzPi2Bk7R5WeuzlPeAQ1SE+QUGiFJhPjUdR2vbMOrq2NdduO/R0zOOkRGGsWWRShro7hlHQ30Jksk0Zmbuk2XJVBpmZmFS3TAzMIyMTJfVNDqD2rCxNUiahigLneryRCSLPZFUAoOxaYwnYuJ7W+TyoszrR9BpTfXfTRDrxOyMZL9QTMCZeOwfMVzaek/Y5bFHBzmDKWRNEsjdQFqHwhwS7TwUtR4a85Ccj4sFzE7IpMlmoiL84RhOhFgiBArnvub3c8GfEgCa+1rcAnZiUd/GeoN2KXsD1aKCLtQDqPYWi6VKSPfCTd9zsnFmSryzb073iDq5Lz4mJPSnYzdELe5UdbETWQpUi1N9PpmKokxU5M04EKwVcpjELJXWk2mquAfwYY7kHU9N4+pkhxDV80l0Er70HX+69LAoqqnYJsl/a7pnRST6YDyMm9Pd974+FKxDjbd0QVU91/vu8GWcD9+Vc1HqKsCRwkYcDTVKIYC2NtRxk4DmeeE+fDZ2U2xrvhi/JcWGAqdfjmM59MZGhSCnop2q+iMFjajxlkhBg+9XKskH4mGcGb+F66KIn0FXdAifjN2Q87oYiU71Oq1bfjl0CYOJcVHePx5qwLGCZrnm/Dv2P1kwCKem5TqwiMHjoX0N95/X6njhHqw3tv9TepWghxsTm4HiR345UKlniNXEGzBl6v9uUy4SGTmf6SQVw3es4DrXyxLuxZBEC9u3O8obM+By4UBZqSyxVMpSpo+M4ObwiJDnQ5EIRqNRCSK9NjQsS6HHjeaiYrRUlONgWSlqgkFUBAIo9nrhd203hXrWIrLFc24jSZ5RmOnLSCV+KbM6aIe0+5TnS0ER0ofqFZtEXxssqwF6gXdJVoUUa1Kf5whFGw8jKYNoqtJY3NKNNujOZyT8VlHpt6xs6+f7zkc25+9/V9p5Kv4TKXrvjkL/fDBAdSTn/c7Zgr0SmK3pLRLgvFown2K9ZzSuByorClBY4JPA0C/OtKGutgiVVYXweZ2iNqdaPJUyhDynFzqJcz4WI5E43vnldTz+WBNisSQ6Oq3wT/5scjImf7MQotEkZmYSUFWH+KMriwSQ2rCx0QT6hdF+TKXiqPGFUOsvQNDpEsJiPoxMBp3TYVwdH8TNiWEMxSPQHArKPH7sKyjFkaIK7AkVy8B/p/tVS4i6eE2zT3RL+kRG+kJuNrVNnG8sGNZr+XqTjjGdxyXLg8Gk23VWZyYzbAV7ZmZyZPmoFPCz5gjMzDCy8nMKo+wZDTbWDj6XSfCeLt4vJHKpuwA1npJFn9n7g7UScDktgZRRDMXD6I6NCOlOO7qlQJKePtyV7iI8U3oIr1acQr3vwT4jbV1IrDMb7+/6PhMlNBXRJKWp5qa8YO5+kbymF/o7gxfkd4UYn+rBy+UnJAR1sfcOye7++Jh4ovM3SFA3+yvlOOaDKnyqsWlhw8BVt+IUH/IvV56Sc8Yg1Lkgyc7zRJCEJiF+IdyKMp5bb8mS/ugEixVU7e8L1uCb1U/hUKgegXlK/2PZDAqdPrG/+WL8tvz++XArXig7KoWA+WJTnldeJ3qv98VH5T19vKAZX6l6XD45+2Au6MdOhT+LG7T6oV86PewZenowWL8i//XVYNeR6OK1lZnEmidM0aCfAYDpW0glfoZE7C/EM87ubPAFOohk7PtSUHB6fg1Oz9ehKCxWOHdMR9TrdN4j1H/l0EF0hSfEI/1cXz+uDg4hkkoilkojkkzhfH+/LB5Nw5GKCjxZV4MTVVVoLCoUlbtX16Grav4r1Okvlt04JTrV56bRL4WYRPQ/5jqUNnn+MDSo+gkojoKt3pFtiawEE0WEME8y1yHxSyHS7Gf3SpDNhd52SKCYy/srcLq/AkWpEFsLO3w0H4tFnC02mSv0/1jI492hPF8eGbMDiWivTKt3eX9dvP8dEqS78g62hDw7GPKcX9izpwzXrhegp3ccZ891iNXK00/vRW1NEdxuJwzDxNRkDF+cbcfHn9zF8PAU3G4dLpeGN356Ge0dI0jE0xgaotgEQo4Pj0TuEelzB5y0fCEZPzwyLQR9WVlQ1Og2bGw2xuJRfO/uBVwaG8DTFQ343b0n0FJUDqf6cHscjkfwg46r+GnXLQzEph/4WYU3gFdr9uKPDj4hRHye987XiOw92xaSnJyFl4r/FOnkhznbDLtPtPngmMcp4gQKZrYrv5JO/EJySHavBaeNzcSB4Moym0gCHy1oEvL52lSn2L/0RUeFTA5J6PzyeKx4H54tbXmIQCfI4xQ7g/h61WnxTY+kY0IqD8cnxP7FozwouCD5S3V3o69CrEpIpNMOZSA+hnpvuYRhLgSS7bSNoQUMf2dfoFqsYTzqw4KO/tgYbk/3iiKbJD4tX16pOIEjoYYFi8sM5iQh/7sNr4jtSufMkOzThXAbXio/jgLdv+z7sMxdgK9VPY5jhU2yvvlgkeNk4V65Dhcn2iQQdDA2LueKin5l3gwcFj7aI4O4GG6TbRc6A3IM9JRfaAYB10/P+W/VPCPqexYShhOTuD3dh7HklJwrdR3f6ruORM9SiS6+bmuFiXTiXSRifwkj9YU97eghmKJYNM0+Gbx7A/9bKFrjuoV55RNYcWwqLhJS/JuHDopX+iddXfios0uI9YFpa2ZC3DBwrq8PF/r7RdXeVFSEl5ub8FJzo1i9eHR9GwSLbpwSnQWpRPTPkUr8NNfpsjvwC8Hh0KE7T0o4kY3VI5MZQTrxDuKRfyX2RLZty1pAFf9txCJdSMXfgifwT6A5n4DDLuzkH7JRJGJ/hmT8R8gYHfZg9iGkYaQ+t2ZZuC/CE/jHUJSyFXeLLSV6/tm5HD5UjZs3B3DjRr9Yunzw4W189PEdIb9JdJP4pl0LFelc+P1Dh6rx/LP78Mf/7n1cv87gQFqrAbqmorCQqvZpXL3ai6qqQjQ33R9ATk/HcfvOIDo6RuF2a2hsLJWgURs2NhuXxwcwFIvIcjM8hIlkXFRsC+EvWy/jFz13HyLQCf79L3rvilr9//jYq3BrO3WYnEE6dQ7J+N9I9hDzI+x3xNbB4QjKTFNV279tZ/fRkoXZI7ThtMdxNvINtPQocvnv23kZ8Xu+4stBd2g4XtiMRv/iQZskcEO6D1WeYvFIJ4GbzKQwmZqB2134kN0KvyYx3xUbFhKdgZ9nxm6jqqZ4QX9zgsGdJLYJ/g6V5bS1WQitkX4hw2d/97myI6KmXypMlcQ8SWiGcdInfSQ5JST85XA7nitreYjkngsVFoH9bOnS/uMk16mc50LrG8aOkkSnWn6+Or4nZvnQ08+e+3aqaC8qPcXQlxBusWBAr3sWGEYSk2KtM50LMyXJz+u0XtipvYMlK6VrIdEtv7goEtH/WRSMDKKzCfSl7D+mxCc+mpmGy/t74pWuqEXYSaBiTR5FDNZyOFDk9eCl5iY8VlONsWgM3ROTuD48jNujoxJIOhm3/NNvjY6if3oab9y6jT0lxeK1/kRtjXiv5ycsT/T1JdE5SDfEXoAkj5G6bBPoS4LTqHwSQuRQglu9M9sK2UxCvKCT8Z9K4dOyCVrYmsDGSsCErAQM4y5i0/8cTteXZNaR5jy+bQd/OwnWzJ5uxGf+LQwJhWMn2iZHFu+rjCGV+LlMOXf7/z40/SSUFWS6WEr0/CPRnU4dzzyzTwJDf/rTy5iOxIUsN82U9FlmCXKC6vMnT+/BV147gsaGUvE3f/vt66JiDwY9OH6sDk+d3oN/9W9+ifc+uIn+gQkcPFCFktIAjJSJy1d7cPNmP1KpNIqLfHji8SZ4PflncWNj56N1akyIc/bFC1weHCuulJme8y1f2qbG8NlQt3ihUwhD5fmL1XsQ0J24MNKHO5OjCCdiODvSiyvjA2gpqoBP3zltOpuJS3ZXIv4XkuPF3BObQN96qFqTZM5YFnnbF8zQssdxNvIRJG9dOUU4LVqMjLFooXU+KVvnK0WRHhAyfUlOyGERuLO/R3sR2qQstBWOlqio/nT0hhDFJNHPjt/GV6uegDv7sIMDrU9IilOJTvaJoaZHC5uEuF8IDFwdSUxYx+BQcCBQI37iS8225M+o1Katy+WJDiHRZ9JxIe+fLj20pBy2wOlDtacEXtW55EiQ26ASn3Y8s6BanxYv8zGcCGM4OXsMDjT4yuHX3Mseg5J1oMgZlBBXgteAKv6VXO/VQNuNakTauTAUdKVT0LPZpGUBEPsbIdA5QN2dnqKr9UqflgCzrAR7jUF3fxmqNuuTvrMgoVoOBwo9HhS43aIuJ2Guq4ooWswMH9mWv1M8nZZleGYGQzMzaB8P41xvH45UVuClpkZUh0J5pn6xPNElYX491kZ7GPGmfUumkJrpm7s0T2DlIGGjavty9kj5PnMhP8AijbSz+Ju5zIYb8hyysV5EOgfj7UhmU+J16TRehtP9mlhcbPeB4HYF+zYMyU3G/grp1Be5UGa72L806Ak8LgW2xIwJp+ebcLqeX9Yn3VKi55+dC+1bqqoK8PJLB1FU6MPVa73o7QtjYiKKRCItP6d3eU1NkRDix4/XYe+ecng8Ljz79D5UVRZgaiouBHt5eQgV5SEcOVKLi5e6cO16H/r6JyS4NGNmMTI6LZ7oJNwPHKjEgQNVcLns95ONzUf/zJTYKYacbrFhKXR5hPyYi5l0Em/13EFfdBKpjInDheX4RsMhPFvZCJeq4nhxFX7YeR3v9rVhPBHF58PdqAsU7AwSPZuBafaKuEmEYOmr8tyzLb7yAQ4o+h5o+omt3hEbNrYdSFTPpGPiW07l8WR6RlTgCTMtYZb8Oa1CxlMRsSiZhcXKrIzfocLaqzFjY3mhEBXTs79H0QK3vVhhiSryZn8VWmcGxKe9Lz6O9sgADobqHiCZZ4M7adFCKxef5sL+QC1KXaGH1NuzhDvV7VTbc09ofUIV9kIWKw8dLxyo8hTJNoi4mRTF+HIEtF/ziNp/IauY+eDvqHM4WNrrLHQ9GLrKAFLrmDLi1d4aGVj2OHjeO6KDEgZLpDOmqN1Xes1Xinxi6jYH2aiVAp2NrGgKuqi60q1iNZGI/SDX6bBVjCtHXNQOyCaRRdLy0VVrdkTy+FyYmYzYtvROTqF3chKdExPoCE+gMxxG39Q0Yum0qF6KvR6U+wPygKUafSqREKV6RzgsYaUMKX2luRkHy8pQ4HHnlxJ9HewvSGwy1DGdfA/JOP3z221bjRXA4QhYSl8hKG2173JgqJFptCGdfFdsR6i8soOMNgJZCZNOJyat8KhsDLrrWShqRV5aXexkMJiZ0/NT8Tdy/uf2c3XloDo7IqF67Ktw0d2vQFHKF33ecmaQA/nyjn4QbpeOhvoSlJYEUF9fgv7+MCYmY2LjwsGL3+9CNa1ZmstRUuK/53NOT3Mus2MlHjrtX15+6RDSaRPXrveir4+FmfsoLPDi6JFavPiCRdrbsLEVGEtEETPToiyv9Yegzgu45SB6NB7Fe31tmE4lJXT0ZGk1Xq8/iGq/FcpW7QuhZ2ZSAkenknFcHhvA1+sPYruDgY/Mf0gnPxbfcyN9yVYL5xEcSilUbQ9UzQr1s2HDxkpyfyDkbmd0SMhxBnmOJkmiRyWAkyQ61c0ksYVIJ5exBiUySeWg7luQrF749+fv61LiSxUHQrW4HenFYHwcUSMuYZ60hJlPot+c6pYiAelm7g+tYKhGX4jYp7Kb6mva1dC+hKSzW3Wt0MrkweNNZQ2xpFmOgHapTiHSVwrHfF3WAqtnMYTXksggg+tTXbKsFvxb+q+v91tvZzGZKwJ9IMdlSr+yjLcwlbeitEv8FMnon4my2sZaYMBIX0YmyxkABlyeX4GicnDq3PYPcSrKp5JJjEdj6J2akoDR83396JqYEOKcYIhomd+Hcr9fLFtaysuhKg5cHBgU8nxwOoJwPI6uiUn82cXLCMcT+HZLFo9VV8OVF4p0KtGnpaBEFflaVaac/ZExekSBnoz9pZBvNlYGhxLKTfXc3vfM5jzfpyWXgbMcUvEfCrFrDxg3Fnw3MgODBTJPdhq660WoWgMcjpV3qGw8mgKdga/J+N8inXzfnpq/ZqREwZ/JzYxyur8MKIULFv2FRM9DJfosFEVBIODBqZMNsqwGc8dkVK6ffqJZyHSf14nOrlGkUqYMqKg639Nchqef2itWLjZsbBVIjKdMEwHNiVL3w3ZMM+kU2qbGxc4lnc3gYGEZjpVU3SPQCY+mi4q93l+AC/EouqbDSBrbVzTFPjetNak6t94NH+WEYDbyCZp+CJq2L6/fJzZs5Bv642P4ePQ6Ph65jtZIH4xsRtTTXtUtpDFJaKqdSTJzVhI9t6lspkJ7tXAp2kMzm9YLe/xVaPJX4kL4rhC95yfu4qnSQyj3FArpbanZLQKZ4ZjcDyrYTxQ2L+o9zvXwb2aLANz/lervZpXrs4Q7VeIk5Zcj0TWHAn0RL/e1gsVvLrP7VeD0w6XoqxYTMpA0oHvW/QrmA0O36WAnImuOANrehX8u5RAq7AbFwiUZ+75NoD8yssjQh2/mX4OOS07P61DUqhVb6uQL2DZYyaTynB32u2Pj+LS7Gx92dOHq0NC9Kicf2k5VFVsWkuYv72nGsw11aCwsvKeQ+faRFnSFw/igoxNv3WnFteFhpE0Tb966DZ+uozoYRENhIfIDKVGziEpvlcTYbMWYM0AYYpSM/S0yGSvswsZK4LBIdJ2e0/ZU+aXaGNXm9IFORL8nsx1sbLYtxgBi0/9PuH3DcHm/DVU7uONmHeVfuzdgJD9DIvan0vbtgtGjwoSZvoZY5H+QqCSn+xVAKX64eKz48tITfb0hainNgeef2y9k/Nh4BCMj0+KzTjV7cbEfHtsH3cYWgwNtDvadqvaQ/QqfiMOxiNizsPxDHC2uFL/z+aAdTInbLwTEaCJ2bwC//WbWZHLZVB8gPvNvJPDRnkWdj3DILFN1ET7Chg0bD/d701kD3+/5SPzEx1LMt1AQ1D04HGrAoWA96nxlElwZ0N2i1ibZfCfShzf7z+D9kSur2h6JV0VyyTaGRC92BdHgK5PAzO7oMNoi/RiIj6HRV46A7pV3Ea1IGBRKlT09wek9vlTIKYnzuXu7FlnN3JHESo7dIf+tr5WnxcZaYEHkmdLDqPGUrsiWZi74+7SoofJ/PbErR7eiRDetdNvFlbcTiM/8MVKJt22/5nUEA2zYocsiDZfndQlT2U5ImqYoxz/q6sKnXd3iZz6ZiCOevh9QQW9FBoY+21CPZxsaUF8QQsDlEkJ9/rSbmlAI325pkTDSH924hR9ev4FIKoUbwyP4oqc3j0h0ViOnRNXrwGrVpQzl5XX//4oXI32qbawctJ1S1QY4FLYF22t6caSRjP01kvG/E/9zG1uFlBSfaS/i9n4Huvv5rd6hnYtsTKbo851qpG/ZBPq6gX3AUcQi/w95f+nul6GqFdvCE30jQbK8qrIQ5WWWelfTFFG827Cx1dAURfrX7IfTO3U+huMz+GKoWwgYn+bE/lAp6v0P96+ppHNpqjxJk2Zaps5vR2TMzpwI7Ic5O0abQM9HOJRiERsoauVW74oNG9sCUTOByxPtuDjRJspysUQJ1uJ3G14Wf3GP6oSmqEKsz5LfZF5oT5Kvdqh13jIcK2gSEp1vnGuTXaj3los3Oj3Jz4Zvi7851eC0emkpqF9yfQz3nLVjYXE5biTniM2WBn+Lqn3Ly93yePcornm0/OaACnouBK8nfeBPFu1FwSJhqovBkSPhV2Zns3LsXhJ9ESLPmv42KYNSDk6tILrt2YnK38DRMJKxHwgh6PJ8E6pWh3xXnndPTIpS/OrgEO6OjWFkJoqRaBTRVEp+risK6gsKcLK6CscqK9BcXITKQAClPp+EjC7kWSUPdVWVZW9xMX79yGHxRb85PIKB6Wm0hx/0Ht1qWJYutMYoXp2FS2YEiZl/j1TiPZndYQfdrQ60PlL1A9tu1sZmQRRX2SiS0e8hlfgZDOOuhF7a2DpYodL0l04gk52B0/0l24ponZHhNP3UBSn2G8YdO+x8Q2ZWjCIR+w/yznK4XxWv/1nIDAsh0d275tzT2kVRVOi6/S6ykV/w6y4hwJMZAzPGg/knI/EZ3J1kKNu0jOaOFVdKYCgFL/NBkmJWELMVpMGjIptNw0idQzL+E1GhZzIUjNlj2PyECt15GqrkhNmzTG3YWAnok31+/K74dLPISRU3FcpHChrFymWx8E/6o3PJR5AYPxSqwy8Gz4t1yo2pLhwvbJbiQMxM4szYbfFLn/1dKu6Xej9piib+5FTh8+9ZEKaNTZErKFYtS4HvwPHkFJKmlatEFX+pO7gl78OA7pFlMGHtF49BheMhv/itwq4k0bMmlegjczoWjjnqoxEk4z9DKvFzZMx+m/DbQJUEvbEZPufyfEuq8flWIZxJJnFpYBBt4+NoGw+L6rx7cgKjURLJrGo5xOe8qagI+0qKsae4WBToDYUFKHC7Hwo2WgpuXRerl0NlZRJOOhGPIxzLLyKQU0MtEn3Ff4CM2YdU4k1ZrNkflk+8jZWDChVV3/7hVhsBKdKYQ5bPfvzvcgGi+XXf7FbQNo3+0lkJuHRAdz0JRWZT2HhUZDIRGKnLSMb+AkaaU1Pt5+rGIAszfRvJ+I8BhxNO91ehKMHczxzi+e9QvMhmdgeJbsNGvqLc4xeFeTgRQ09kUohwGVE4HEKgnx/tQ9K0CJQnK+rF+3yhMQd/J24YOV9YdcN8cNcblsowKeHIyfgbMJKfIpMZ2urdsrEkNOiu53LF2e3RzmzY2GrQ77srOnzPaqvcXSAK5eVCLadSUbFFyUeQFK71lmJPoAp3pvswGA+jLzaKseQ0JlIzuDHVLUGhVGDz9yo9RUuuj4WEUndIrGJisVEJGO2OjaDMXQin8+HMkPnvEp5fKt+tfXOJUn69VdwrQamrQI4BkX5R1LfNDOB08QEsbmSzudidJHo2Imr0bCYu/s6z/ahMJox06qwMTEn+2dPfNnpweh0pqFDUYjjdX0M261xzaOVGYDwWx/cuX8bVwWEhtTlFlMR50OVCecCPqkAQB0pLcLyqEi0V5ajw+x+pEMC/rQkF4XXqGI1GxXM9n5DN2bms8LeF3KQSJhH761xBylbDrB6aRaJre7Z6R/KUQO+XIMVE9D/Yz+w8BJ8Z6eRnrFxbRLrzcXne21g7JPA8fcsKzk28axPoG46MKP5p3aIoRdBdL0g2hUwRdrjhcPiRRX7NGpuPTCaDdDoDw+DMMM7cWZuVi61At5GvaA4V49LYAPqiU7geHsKF0T6UefyIpJP4aKATV8YGhQQo9fhwoqRKfrYQoukUplJxISH8TteWEAdrmY3HWV/p1DkkYn8p2RicUW0jn6HLLFNNP5azarRhw8ZKIPYkZhKZnNO3S3GKT/hSoKq6Lz6KkWR+PhcVh4JiZ1AI4o6ZITm+3tgobkd6hTwfTU4Kg1LrK0O9vxwedfk8HhLfNd5SWQ991a9MtIvdDYn4xbgqnlsq129O9WAyFRX1eUj3Y3+wdkvehdXeYjkGzXFXjuHudC/6YmOo9hTDmwdqdG33TtOdEpsJRbV8hbJZBtJdQTJGNSOnRtvYeJjiXZyY+ROo2j6oWjOQdYtyJB8QSSbxbluH/JshoVSXF3o92FNchOcbG/F0XR0qgwH52XpAEpRVejY5oCkOsYjJNzuXldpkZDNRpJIfikd1xmjf8H3bqXA4glDUaqhq+VbvSl6BlXLmVpBAj0f/PTJm11bvko1FkUQ69dE9oZXT9RwcDGW0sSrM+hnSEou2RanEGzaBvmlIi8CCYD6FotXniHSS6PnZlk0zg2TSQCyeRDyWwtR0HDMzvBeNNYVMHTpQhdLSwAbsqQ0bj45jxVX4dKgbnZEwboaH8S+vfoxTpbVonx7H5bEBDMcjCDrdeL6yCfWBQng0fcFnbDgZlxBSEgZlHh90Nb/64YvZ2XHcGp/5l1YeTDa61btlYxk4FD905xNQ1DLb6s6GjVUSzh7NdW+WEAln2nwsBD7TSb5ShX17uhfD8QnkKwqcfjxetB8/6f9CiPP+2BguTbQJN8TeP492r78Kjb6V6bCpat8XqMaF8F2xsTk7fgcnCveg3FUgBPR82xsS6LTKaY304eZ0t6j26S9PIvtgsG5LSPRKd7EQ/1SjDycmMBAP42K4FSWuIPYGquFSrPf4/KKAcARMX8yYosLnsa6k8LBa7FISPadGN3rvkeim0SYholTO2thMJMXDODr138If+r9aRHqeTWujLQttVl5sasBzjQ3YV1IiIUb8/nruKW/4mVQK6UxGfNT9rvW/4TdLiZ5KfiBT4I309Q3fr50MVW+CquZvZsDWwZApyzJryLAJ9O2AdPJT6XKwQ+j0vLbVu7NNYYjSkHZzDOm2sYnIxkRoEYv89/CF/s9wqGXWTEZl6amxW4WpqRiuXO3FO+9cR3vHCGaiCWQyWZkQtpY5Yf/Nf/UNlJbu34A9tWHj0XGqtBqHi8pxe2IEY4kovhjuwbmRPrF1IYnC3nqp24vv7j+FUvfCha90NiNke390Wvr4tb5CuNR8HyYnxdIrNvV/gWlQ9POgH7yN/ITDUQDd/eW8LcLasJGvoKd3nbcU7ZEBJDJpUSbfmO7G48UP909IoNIS5c86f4nrU915HRRNQph+5yS+r5opjCQmoUx1i9Jefq46hVCu9pSuaH1FzqCQ31yuTHZgLDmFH/d/Lj97trQFbvXB4h2J+1vTvfi3bT+Vc0ZOqjlQhSeKDwiZvhVQHYqcj+dLj+D7vR/J994Zuihq+a9XncbxwqZFvdp5PK0z/eiPjSOk+8Q3f72R772DDQMHoGamV05AJjONZPzNHIG+2XYADjgUqk1roCilcChFMmWYC6d4UekEB1NxdfHkhISPqECW+8m0Xbrt8zMudjT0nabCPmsOwzQHRK2Z92q1bFw6gcn4D+HyfBuqnh/WFVSd/87xozhdWyuhoSU+L0JutxDcGwGS8i0VFVJRo3XMobKVPSg3M8huORI9mzVgpK8hGf8RTPHqXYvmzcYsWFSyVI/5VVjaOlgdIM5wSCV+kvNAz99O0X04JfeB03cVtQSKowQOfvI57whINoSokUSR5AIkRJZTtFOy0FecNh5UnHGadsYck9DrDBdzRP7NwnB+w4SROo9E7jid7pe2eoe2GdLi+0/PWytfIv/hUNjeK61wZPl3ufRx6CVu9Wus/g0Y0pllP8Vq5/fafCaCLPs1Yr/HLBu2+aFcpo0VerT5gblfIBH/W7jc37Q80fOQBGltG8YHH97Cxx/fwcRkDIl4CiYJ9EeAaW6H56yN3QqGin6z/hDSpokfd97AeDIGU2zESD5oQrB/p/kYGgKFcC5CjHdOh9E3MyXqNa/mREtxuXzmKziDOp38GPHon+QI9M1/JtpYC5xQ1UrortNSiLVhw8bKQe/zZ0pbRFlN325atLw/fAUz6bgQxlR0U1UdTs2gMzqEq5Md6I2OotJTiCJnAN3R/Ow/k/txqTqeLjksFiz0RU/HjHsK8IPBWlR4iuRdtxJQfc1g0ternsRQYkJI9LZIP/686z18MXYbewKVKHQGRLU9kZ6R89Ia6ZdtGxkDNZ4SPF1yCI8V7duyzEKHwyGFhefLjqA/PoYL4VYkMymcG7+DntiI/Iz7SU95Hm8qY0oA63gqgpHEBCLpOEJOnxzHRmD3kugZS4lOAiaV+IWo5OjhvOFwBERZqmg1ltexUgGHWgpFCYm3Jhze3MDM+pwlzR0kzoVY4eLIkZOGkOn0BuYAmwQniwMy8JTPaZn6bZo9MI0eZMyePA1LJVkUlXBAzgxwqMV5EUBX5PHgO0ePoDZUAJ9TX1VQ6FogD7zSElQHrSnTgXxTomemlyTRSX6wvSVjfw4jdXGLST2ntCEpRCkkKQP3Px1eIfEcfPyRvKE6Vj6t65vN3VfWPZVANsP7KiZtVGygspNSnGJRYWOnzSpQtEaoau0GbmN7gTkW6dTnSCZ+KoF/QD4G+qlwKAU5G55qq0CqVkFheLK0QfrR+axnveKliZO0Q+sZr+We8WyLJK1M6xkvz2z+myRjPEems11Gc896EuvD4gtvmv3ymcmMik9qPoEkpJE6hyTvT7VMikRSKLaxJPgcYsGIs3sse6x0nnq8lso7XNUa5FOKRkpB7vnLzBB/7vnLfo025xms5Nq31c6z99p9Esg9f63Fau8Zc9Qi0w229X6YZq+Q7RvftzGFzE/F/g4Zoydno9CDfMLERBTnz3fgo4/voL/fmrrM8U8g4Ibf74bTyffd6tfr8+UvmWjDBgfbjcEifKupBXWBAtydGMVUOgGPqqPKF8KhojKxfKGNy2KEwGCMfdwsmoLF4p3+VHk9gs786ofPIptNI538CMnY38JIXdohCnQ912+f22fnuJj/Zr/J9UC/fX7ffeG+Evvu/Izn8tCmpL/Evrw1m2vzhT7S99GP5ca5tkDGho3VwK3q2BeowXOlR/Dx6DUhiAdiY/jQSIiSmqppPsepVmYoJ/3ED4ca8GLZUQzExzEUD4vKOh+hKxpOFDbjg5Ersp9RI3mvv3a0oElCVOfbsCyFoO7FscIm/E79y3h76Dw6Z4aEfB5PTqNtpl8sTnguqNqmB/p0zhaHVikvlR3HU8WHxEN9K+FWnWj0V+Dbtc+KovziRBtGE1OYiQyI5c1dvU8K41Skc9YZg2djRgJRMyEz0RpQLkWVjcAuJtGnYKQvI5X4MZKxHyBjtG6QCl0TIk8IcxIpah1UrRGKWptTaFF5zI7CWghaKvqWfgVnSPZxkGl0i+2BYbQhY3TCNLtzKvX8CeIzjXbxOOZ50V3PwiFFg60DFeeHyzfPi5oPxmKvV5Z8hHQ6M7FFfRk5AyIVfwOpxHuijN00OHy5jndRjrAJwaHy31T6cjZHcF6nnCTOg51xqyPO9pYVNf39TnjiHnlDMuc+gT4hn7NkuljdkMjMfW897iuHUgJVrRFC1obls28Yd5CI/QXM1LU8U16TOC+U68Vnu6qRQLSKpapSk1PgetcwYOIMpOX/SgpYHBiavTCFWOyV4imJPhKMmcxwjlDf+o4jSch06lMosRK4vL8n54qDYxtY/NlqDiMZ/wnM1JX8a/eOoEWaa01QZWHhj1835ApGK32Ps4hk/euB9r7An8vMOxLpQqD35dp5pwgj+D0q1zeu0JCBadzOBRlbatB8QkfHKK5d7xMCXVUVlJT4sW9vBaqqChEKeeBy8r23euKmqnLrhQ02bCwFEuSHCstR7y9Ab+kUplIJ+V65x49ijw/OZRR8JS4fnqlsxOGiCiHP9xWUCmGTX7De4STO+U5Ipxjcvd2svZRcvzxkCQ7Yb1dCuX588bw+u//+5z3BwcJ993skepaFWM5oiltiA7APH8/NbLIIdKsvP50TB03nvpf7mgIZeX9sTH+JfUTd9eR98n+HgEV03fm0JQDMY1iz+QY3TYygOR+TvtEuptzuweK/Sh6peKQ6VCF2X6k4IdYu16Y6hXCeSseEHCaZqikqfJoLhc4g9gVb8EzJIfEDvzzRLmr08dQ08hG0HaPavMlfiZ7oiKjsHVkHfKpbVPbc91Wtz6GIf/grFcflXNHWhYpzqtKH4hNIZdJSVCZ5T4U/yfNKT7EQ+bTHqXBz5ujWF/r8mgfHC/eItQ336W6kD4OJMCZTM6KiTycpKM7K8fI4WRwocYXkfPGY9gZqNmS/du0dPauIM41WmSK8/mSyLqQeiXNVOwDNeQqa87g8QKwQkc1plOyUcNH0FiEH2Tmg6p4khpm+mbN8GcsTdXpW9o2FBVXbA1Wr3uodsjEHotoQQtl8iBghiUz1eSL6H4Uk21h1B5VEvnsqFUVjYaoZqtqYIy+5lMIhasfV32crfV/IDBAqEe+RlZzx0SnTaqWjToVwTjG8FsW0pu+HojB0KN8GcVujumLhj0WadOK9/FHiOry5Z2w5VH0/dOdpaM6Toj7fzLAobkulR7NaJu8aPktp72WmbyGdPCMFY4tcHMsNELf2eU/VML29WTB1OL4qg8qtLprmK+TZmr4sFkb544NOcUBIZlio2l44XS9Ac57OiQI2vlvJQboqz/06lpmk88zCkZG+BCN5DoZx02rv0rdLbQAZks2zYsZ93LozIAQ6Bz4kzZ9/9gC+8pUjqK4qFBW6DRs7GRSjBJxuHCpa/QynluIKWfIZHMdxllky9lcyXrJEG9sAMsPaIsSlz6TV5mYt1edmZ9dCVStyxPIax8crEBw8GNRtiDWY9a7gLL4+axafCA8oiGEfPp4b98TWqd/kkqKzph/DTgOPi9cT+A3kMyIT/wjpRFjGFZsBp+cbcHt+Ew4lv4sLmwPHuhSPHDmrkmJnEPuDNbg62SmqZAZjSvCo6hTymGT0yaK9qPQUwanoqPAU4lTRXrEsIVlNwnUhkLSu8ZbiEAM1FVX+jn+//NE5hISmUp6q7gZfRc5P3LHi46L3RJOvEtddXUKik+CmErvaWyKBoKsFzwdJ6K9WPY6WUANuTffgTqRX7GLkfMEh6y1zF2CPvwpHC5tQ4S6U7S4FzaEISX3vWP0VKHCuTLUe1DxyTLNnpcgVWNamhrY2LQUNEphKRT3DT7uiI1IQiJsJmNksdAeLJ27xg2cgKj3kG3zlEky6EdjFPWoOghLIrruFC9/iLihKBVyeX4HT8zWo2r68IMI4uKVVisv7TdkvM30XycSbSMb+co56dmuVitnsJNLJz4SEcvv+yHok5UEVbD1gpQVb4BFtv+My7k2NBKdYzlFKMnCNnfpMhtX9jQIfsJoQb+yA6q6noLmetNTaW2AJIaSfIwhNaQH0ljnPlZTMqjDSV6WwQL9/M03v7nSuuJBZ0X2m6UckJ2G3Q+6bzIQ8FxLRP80TAp2dQB2adliepU43ieDqPLqnHWIfo7ielVk9LOakU+eQir2BVPK9TbK+WApUjcURn/ljUfA73VSf8ZmSL+cvP8Bnq5m6LlP2sxn6gOcDVJlZobtegcv7G9Cdx3Pf37prJwOPHKnucn9DxAGp5DtIRv9SipskQXZLPsfg4CQmJqNwuzU0Npbh7333Ofl3/jybbNiwsfa+0CQS0T9HKvlBHr0TluonsZ/MvtJBUeRSga3pJ3PZGFv7zmAfjmItS7D1xP0fZg1LFJO+BSN9HUb6hojOMllaOKbn9OFXP15WOTtR259T4+40yMh2GyjsN7fdOeR8KLubctsglLpDKHUfxfNlR1f0+7RE4bIcSMxzQfNXV7U/VMD/bsPLWCvus0T3ny0khemT7l8DgT4ftb5SWb5cSbHVoyGge/FS+XFZVotjhc2yrNXe5WCoTpathn1HryucokZ0eX4NTteLVidB8efpaVah6s3wqN+Fy/0yErHvI514d4NJ0JWBBGQq8TZ05xNQ9YOWbc0OwEQ8jol4ArqqoDYUwnYEyTgG8arSri1wansq+a6QdBsDRSxbnK7n4XS/DFVvEasWONy5aYP5dn+xY94shSCn6+Wch++QkOnp1CWYogoeXpbEVLUWyUuwkUYq8a4ocfMjOEsTtTeLpBwUUo2bj+GCD8IDXX8CanA/nMavIpX4OVLxn205mc7iSDL2fbmPXZ5v2CT6PGTMbqRTH0uY5dbDIh1cnq/D6f4mNOdRUaPnHxxC8rs834LufAFG6mNp61SpLxeMvRMQjSaRSKRRVhrE4YNV0PV8JzRs2LCxEnDWMMnzZOwvcu/u/IXDUQBNPwTd/TI0/bHc7FBfLusr33NQWCiukZmgmvPJXDZHXGaZGsYNGOlrFqlu9q5+zfphmRluw4YNG4uBKuuB+NgcEv2gqONt5BfyjX3atlDURuhC8r0kxC+V6FJ9zFP1j1WFdwH0XVaCcPt80PSjQq7IgH1LB5tU8raJ97E3+M/gcGz/8BUqSN5t78Cbt26jIhDAf/flL0FX1W3pSz13Gjt9mFnwSCfetxTq6wmHV8ho3fkkNOcJa8on1b7ie771MzsWxuzMCVfO5zmIbJbe7BXikU2/QAbiSWc8dQOmwaVrnrqaVgnFYlNjqXN3N+hfmEr+UqxytnamjFtsplye1632yFBMUVPlf5FPMjdoPZN1w6FzOjW9K5+UUG0jeWYLi6cZGYymEr8USxzLJ9SGIGtKcFwq8eEWv4+tZ7Gm7YHT8x3ozmMSeKw4gvnZv3FwQi3fDyGoGr11PVC0ZhjJT6V/Qwu/Ha9WzQIul4bCQpJWO2c2nw0buxW0BbRsvb6Xs+Dc+nyTh8EZQXtFaW71kfZaoeqiunauMftrq94hzBxzwgE/H6qAkhVRi6rvE5EcczcojqGIyEjfEvHXstfFwT7kITkvNmzYsDEfZiaD/viY2M1EjDgCmkcsaWg9oy1jr2Jj82FfkUeGUxTTJNA5bV6sW5TtUy2yOjVuaPoBIVYUler5Ihm8ZzOjW9ZRs+wbPoGZ/hoc+gk4lI3xM9ospDMZdIyH8Ul3D5qKCiUxeDvC8vhmIIe1/+nkx7KsRZGxODSZ7shOuGQJ6EdyoSz6tiQDZglMVYIla5DNHhI7Gk0/IWS6tbRJJ1zCbhwqNP1wLhR19z6irbDaAaQS70iI1lp85dcLLILQ65yqKs6IcIhXvbYt26LlS7rfCvhRSpBWa+VZy2nLW2GVw6KckTqLlFIs97kVLLb9CozrDcNoRTp5Vp4LWwleD7Z9WhY5Xa/msijyv+1b7woHVAa4syiplkNRy5BKvC82W/Saz08i6tHgcunQddUyG9im/QwbNmzMRUYU0CwCGqlreffcosKcQdKazv76IVFbS7i0ukPCiHPvEikcy1i0XvqnMjPXPA7d6BYxjOQjSTZSd2626YN9VlW1wrdpYWfDhg0b85HOGPhi7BaGE5MwsxmUugok5NOlbE/+Y6cj/0dCeQuqe0JQnUfg8v6eqPoUtRjbGdx/XXkxRxB5kE6+n+sIbIUHMQNfhpCKvwWHUglViJ9tomJYAEnDQDqTD+Gt60CiM8E+O3t93oCZvrNugTuc8kmVhu56EbrrOavDuQ0Im9WAKnqHSmU6idknhEQ0U1eQTl8QVS7PMY89/y1CNg4coCCbkBkORurMFnp/OqBoTdCdz8LleU3CE60ZBtsbs7MlmCvA2R0ShJr4KYzU1dxMk831j2awF4txLB453V9DFt5d22G0iE+q0N+Hadza0uKRQ2YHPAWn53Wxptq+hUwnNG0vVKVSZg0mHQEp3GQywzvOK72oyAe/341kIo1wmMF4+UW42bCxGWC7T2VMTKcSmEmnkDQNGEJ8ZlHi9qHQ5YFby9cZjQ+C47B04gNrVlJeWNrNwmn1Y2nb4noaOrNhlJI8nim63oIEzuo7DOiHkc0mJbjdTN207F6MO1ZIaWZMVOvsz4pFqVa/rceyNmzY2BgkzBS6YyP4ZPQGJlIRIc7pX358jd7hNjYeO4ud2jQ4xAuUfrge/z+Fph/cEcQKwYBGXT8ONVCLuMOHVOJNZMyBLfLNTSGZ+Bk0kqlqjah5tysSJNHN7T9Yt5TokzJTgAQ6FcIMg310cJpjA3T3l+D2/q6oBreDTcb6dMRDUNzPQ3c/h4w5CtPsEuUkFZ+7F2kZgCRif56zu9kKsNhRBLfvu3C6v2w9g3YgGMaoqN8WO69Y5L+Hkb6YC5re3OcV1Vvx6J9A1Y/JQFPsxnYlTMv7NvHuFrZ9BqR74XS/CpeXFi4MDtp+5Pl8MKPG6X4RqlqLePTfiRVZlgTHDiLS6+uKUVoaQG/PONo7RsQf3etl0LBN3NjY+SBJzpmfkXQCA9Fp3JwYRsd0GCPxKGLplKj7Xq3di2crG1GtPZjpEDfSQrZzpqiqKAjqtOVzbHlBNZX8UIrM2cwQ8gN8P/jkPS0zlNxfhabvx24GOQBVrYbqqYbT8yoymQmYqWsyyy+dPoOM0ZcTLezMfqQNGzZWhkw2I6LKVMaA4nBImOisjcvbQxfRGulDIpNGnbcMh4J1aPBVbPUu21gENom+BlAh6nS/BrfvPxVfXEkf33FFgmJ4A/9U+kqp+JvrbNexUmRzti4filrSGshvYyW6uQOU6JkpIXkd6SAS0f/ZUlg8MpyivHZ7f1s+6UOY/+nuGwOHUgJNpnrOprnvTnCWQyL2p8gYPVukvFLFg98T/N+Leog+/DsbLgnF9hf83xGL/AuxvLDsvDYTCbE2SkS/B4//74vdzG4EZ/ok4n8r08K3THXocEvQq9v3B2JRt9NA6wGP/x+KZVYi9mfyXtspOHKkFtdv9OPu3UG0t4/g7LkOPPZYI4IBhnDbsLGzQXLi7uQY/uzuBXw00IFIOglDcgJIVVizMppCxThRUv3Q334+3I2zwz0YS0RR7QvhP295Gi51K4fJGbH444xcy24tT+DwwuX9Dbg9vy45MfkfFLr5oDhGcz0lYjt3No6M0QlF3yvqdRs2bOxeTKdjaI304/pUl4SG0sJlMB6W73VGh4RcVx0KHi/ej2dK7RDifIZNoq8aOpyeb0gHwpqWtQNPoYSq8NMHt/f34ICGZPwNZMzuLdiZrPizc7ogfduplN+2SvTM9le7mWY3krG/QcrxpkxTfLQZCg441Eq4PL8Gp/sVsXFhCNzuxWwA3O4lz4lMZhpG+gaS8bdk5sPmwyke0G7vH4hyiIOene7RzXaXzepSxHH7/iEcjmKkEm8hY26yEjobQyrxM+jOU5LNoSgPKgV3OrLZlMz8SsV+hIy5HgXK1YPnXXc+B7fPKmTsxD4O72cG3vHd43B4EZ/545yN0fa3PiktDeKpJ/eIlcvFS9343l98htHRCE6fbkZ1VaH4pduwsRMRTafwwUA7/rrtCm6GhzGRii+YP0Q1+kJ3ulvVMBCL4NPBTlR4g3ipeg/2FZTAq23FrMisWIAkon8q4ZX5YePiFMU5ZydpzmdFeS1jsm1o8bXRsCxbmIekA1k3VN0DODizYXf3723Y2O2ImUm0zwzgrYFzQpbzbZTKpBEzkvcI9C+Vn8SzpYdR5t5dY6Dthp03OtpQOGRaPz1bmbC9XQndlcHqFFGN6XS/LoP7ZPwHyGbCW6JKTacuyTR/3Xli3dZ7fWgYo9HNIen6pqYxMM1Azm0OKirM/nVYkUvsW8QqwPV8riC1mwl0G7MwjVYJ0MqKX/FmQ4XuehJOz69JUDTVRNvRA3otsI5TldlVLu+vCnmajP9ok2chZeS6p5Jvi9eq4npiR9iIrMr7NvkxTKNzS0gTh6NAZl64fb9vFTV3sKUWp99Tka67OfU+LMVhzgLY7tBUBXV1xTh2rA7Dw1Po6RnHO7+8jta2YVRXF6K0JAB/wA1dU6EoLNyubL1791SguNi/0btvw8aaQFL8s+Fu/LjzJs6P9ok1i66oqPEFUOzyYsZIoXWKwo/FUesvQEB3IZyMy9cXx/pR7QtuCYmeyURhpq8infwAGXPp/d4McHYy3w1O91egu56BQynd8eKC9YIQ59vYjtSGDRvrByum2IFkJo24mYSRMaEpKgKaF3s8VTgcqsdTJYewx18Fp7Lz8yW2M2wSfVWk3144Pd+C5jwORdklgwmHAlU/CGf2NQnhorULQz83FwbM9HUYqXPQ9GNzFLuPhk+6unGhfz0I4eURSaXQM7ke3uE7AA4fNG0fnJ5fgcvzK9I5t9UZNoiMGRaf/XTy0y3ZvhVs+XU43S9BUYqwG8GBMWf+iJ1WNoZk7AebHDbK2UefQ9UOiBJaUcuwG5DNmmLhkkq8QwOwLVBF61DZ/j2vQ98lxQsWCaxi7m/ANPtgJL9Yp4yPrcOFi13o7h7D4OCk+KDTxqKjcxRd3WMI+N1ChAeDHjh1TUj0lV7m3/6tp2wS3UbeYjgWwYf9HTg30iv+srW+EE6UVmNfqARlngBuTQwvS6KXe/yo8Prh1XQkTRPXxgfxclUzSj2bH6zO8RZnAJti68X3wdbBoZTB6XrRejcwXF3ZvaH3NmzYsPEo8Gke7A/U4GtVT9wn0R0qgroX1d4StIQaUOoKwanaBHq+wybRVwQFilpqeTY7HxMfzd1HqrTA5flNmOkbEgDH4L/NBAPWjPRV8UjndPP1wI3hYbzfQcWfjU2DwytBvE7PN+Hx/oGVJ7BLlL42lkMWZvomjNTZLVChK1LMcXq+Dd39ggS77m44hMQmuUglOkntbHZm04jdbGYERuoMDG2fKIXXq3Caz2ChwjTaYaSubomtCHNHWDzSXa/sKkspzoCiQMLj/X3EaCWVuij+/NsVP/rxBXxxpv2h72cyWUxNx2VZC7786pF12DsbNjYGl8f6hSifSsVR6Qviq3UH8N0Dp1Dm8YvK76ddN/End84vuQ6nqqHY7UOJ24ehWATtU+OIm8YWvAtiMNN3xFKNsz+3Dgy+D8LpeknyMTT9iAirbNiwYcPG2kCy/HjRHllsbG/Yb8MVgJ64QiJ7f3PXkitUHmjOI3D7/xEcW1JESCJjdCGdPrchikiZXkMv+I1a1n2PtyeocHV5fxce33cBeu3ucGLMxuqUuOnUp0inzm76tumL7PZ+B073q1DVmk3ffj6Cdi4kF72B/xqqypDPzZ3SbqQuIJX8KE+8YDceptEmszC2hsBV4PK8LnZ1irL7gs+oSGfxzOn+ai4sfntjY/oxW31UNmwsjouj/RiORyQI9HBhOf7JsWfF15wE+moQ1F0odnthZDMYjEWQ2mQSnTNHaGmXTr6LrOQObV2WEgl02i16gv9Uxn82gW7Dhg0bNmxYsJXoy8IhdiZu3x8CO9gfdCWgP7DT9TLSrk/Fp8/q4G0eTHMA6cR7cIpSbv28+Mr9fjxZV4sKvx/KBo0UR2NRXB4YRNv45nvK5wtU/RDc3t/KKUvtzriNB0EFupG+JrNNNvu5prtOiz8/fbhtzIUORauWQXQ88i9hpK9s2pYZKmsaN5DiM9/9Vex0mOnbSKcubMGWHdBdVKA/B2WXF5Ccnq8gk+lDJtq7bf3RX3zhAJqb1t8Cqa52dwpIbGwPdEUmMJVKoNIbREtRBdyqvibxCkl4j6YLmT2TTsJcIJh0Q5FNiX1lKvEBthIOpQJO98vwBP7XUJTyXWHvZcOGDRs2bKwUNom+DBS1Abrzaah6Sy5pe/d2JCRERimA2/Mt8W41UuFNVUmQXCPJRo9AqkUZDLYeKPF58eW9e9BcVARV2Rhyt3NiAhPxxC4l0VUJIaIdkuZ6ForCtOndex/ZmA8OUrNCllKNC5ibuG0GaTbB5f09KGrVjg5SXAuYVZDNuqA5HxdFWiYziYxJO6/NQAam0SEhs7QZ4X7s1OwE0+iT6fsZc2CTt2wFnrk834Cq7d/17V9RSqE7n0Em3YVU8hfYjjh5ogEHD1av+3qLi2w/dBv5i4lUHAnTQNDpFgX6WgUx/DuN7z0+l7MZJoNgM2EatLS7tOkipblgf53vXLfv96Cq1fei8GzYsGHDhg0bFmwSfUko4gGnC/EX3OqdyRMo0JwnoDsflwF/xuzbxG2nkTGHkE5+AsX9dTjU9SHRfboTe4qL0VBYsGEkOvvzBW43dh8UKEoJXN5fF7WjolTanXEbDyCb5X3dCyN9ERlzdFO3rai1osDl84yqaxsPg8S1w1EI3f2KzAZKxUcAbI5PazYzKYVTI3Ul58e6MwPNmPdhGHc33crFwZBn11PQnCfhUAqw20ELI1r3ZVzPIZ2+iGxmc59H64GSkt1nx2PDBj3/qR7XFAUude0zVY1MBqmMKb1UqtI3anbqYn2hdOo8jPTlTRYTzIHDA931lISI8llozxq1YcOGDRs2Hob9dlwCJP8053Fo+uGt3pW8gXhjKvTJewGafmzT6zAMt0snfoFMJiwJ9usBTVUQcDk3NLjOpWnQ1d13u5GY0Zyn4fJR6VttzWawYWOebQdV6FZBLrm5IbfOU9DdX4ZDCexYlfN6gc97FhxUfe8mbjWDrDmKZPzHyGQmhCTZSeDxZLMpGKnzyBgdm7x1FYpaCbeHWS+lQiDbYGGtDCr7fc7H7C6yDRvbBG5NEwI9nTERN9Nrfh5H0ilMJONQHQqKXF7om9hnpUiIBLppdGFr4JDxLu3TdOcT9vPPhg0bNmzYWAT2G3IJaM4npBLPUE0bD4IDTE7xdyhFm7vhbBzp5BfIZoZFmf4ooOpcVxTpfPtdrg3VR3MbzlUGHG1/0CrjANy+74oC3eGwlb42HgQLYVQbp3Ik6WaCYZlO19PQnSwG2lgOvH9155Nwub+2qar9bDaCVPxNZMzBR37m5x8yyGTGYKRvIpMZ2vTQOOZUON1fgsNhW3XMharWweV+HRDLOHvmlA0b+Q6GgXpUHdOpBIZiEWSkQLm6omvSNDEUm8ZAdFoI+fpAoajRN8vSjsHqptG+RWHaiohe3N7fhe56Gg6HZwv2wYYNGzZs2NgesEn0RaGJ2pqDTBsLQRfFAqf9bT6o3LvxyNYPtQUhHKmsECsXj7axHWWS6PojTDHdjlA1kpTPi/2P/aixsRhBahp3YRh3pEC2maDnJwulNlYOKpd5P6u0Vtm0e9pENjuBdOqzHJG+k2DASJ5HNjOeI1I2+fnsfm1dQ7p31gyqo5adgU0m2bCR99hfUIoitxeDsQiujQ8hkk6u+ol6ZXwAN8LDEihK8vx4cZXYPW4W0skPkTF6sRVgIdXt+yNozifFvs2GDRs2bNiwsThsZmtBaEIScJDpcNj+kguB1ieqtg+68zTNSrbEQzYjavS149dbWvB/ePlFfOdoi2VTs4F2Lk5VvWfpQoXLzte2eSSgzen+ioTVbeS5tbF9wenLqcTHQHZzlVcMUVT1Y0IK20rTlYN2TIrWCJfnm5ts5ZVFOvmReOfvKGQNpNPnxJ5sc+GGqu2xikh8NtvP54dzAJRC8QVWdrFXfCyWxLXrvbh6rReTU7Gt3h0bNhbF6bI6VHmDYudyZ3IE//bGFxiNz8DMLG/7yN+5Oj6Iv2m7ggujfXDAgQKnBy9VN6PAtfFZRtlsQnI/zHSbWFZuBYHOzBF53qlltrWdDRs2bNiwsQxsE8wFoUF3Pg1FrbA9nJeAQwkJkU611mYH4Rjp60LA0Q5irR2++sLNGxwrioIXmhpQ6HEj6HZvWIBpvkCn17TrGSha3Vbvio08RTZriA+6kfpMbC02Dw4JyVS1vXA4dmPY76NBUYqhu05D1ffDNFqB7OaEYZrp27I9VT8MRSncGVZG2SjM9FVks1Obum1Va5D3NnNfbCwMh8MLp+tFpGI/yc1622lWQkuDVhhjYxH89ffP8gv81m8+iYKQd6t3y4aNBbE3VIITpdXonpnEcCyCn3XfQtxIoaW4EnX+ArFomUU0nZKvGSJKor0zEsbF0X6cH+nFWCKKMo8fz1Y1oilUDLe68dZl2cwMUsn3xNpr8wNFHVDUGji9vwpVrYVDLKzWH0OxcwgnbyNpTkBXAqjzvwKfVgFV2Tyl/05BND2M8eR1jCduyNdFroMocbfAp1MUsr4YT9zESPwSEiZnyy0EBSXuwyhxH4VHK1737dtYGLcvdslipE3sOVqLo0/txXDvuHxvqGdc3t9FZUEcPt2MsuoiuNw6ZqZi6G0fxq3znZiZikN3aqisL0bT4RrU7a1YdptcZyKWwkDXKPo7RjA+NIXodByGYUJVFXh8LpRWFaJ2bwXKa4vgCyw/i2+wewy3LnSi42a/7M83//7zKCgOIBFPYbR/Al13BjDaP4nYTAKmaULVVFlvSWUIjYeqUVpZCLd3dc+QdMrA5FgEXbcHMdIXxvQEc7nSMnPJ5dFRWBJEZUMJqptKUVweWlIE2HatFzfPd2KkPwyv3419x+rQcroZbu/Kn6MXPryF1iu9mJmOweV24tmvH5ftuz32szHfYZPoD4EKJL/YlDgU+4WwFBhExrBIElKGcXNT7RhIvnHaYzYzDYea/0oxPoJPVVfLsuPh8EF3vwDVeUxU6DZsLIRsZkL8P02jcxOtLDQoShl01/NQRYVuY7XgIFsG3e4vIxkdQya7ORYrJJqN9A1o+nEozu1PovN9aRrdMDl9f5MKEffD41okPNMWCSydAcDZiFIsMnuQzTyafdx2QyaTxcREDJ991ipff+mVlq3eJRs2FkXI5cHzlU0YT8Twi947Qqb/ddtVXB4bREOwUMJCZ9E6SbIa0BUVg7FptE2OoZ+kejYjCvRTpTX4Rv1B+DUnlA2epZPNmlYuTOK9rVGhK0Vi0eZ0vbKhWSdjyevonP4ZIukeeLVyFLsPCemqwh4jrBZxcwyDsTNon/6xfN0Y+Bq8WsWGkOhTqQ50RH4qnwvBARXNwV9BQK+zSfRNRMetfrz7t2eFDH/2a8dRu6ccH71xCWfevobe9hFkMxmU1hRhsHscL/3aYygo8aPjRj8+/MlFXPzwthDHTrcuf/f4S4fg+jUnSqoKhAxfjHge7g3j7pVu3LrQha7bA0JARyZj8jNVJ7ntRmV9CZpbanDgRAP2HKlFTVOZECCLEdGjAxM488sb+OgnF4XAPv2lFiTjaVn/9TPtuHO5W4h2IevTJjRdRaDAJyT9gZMNOPr0XuxpqUFhaXBFRYDJ0Qg6bg3gzqUu3L3Sg/6OUUyNRYS0589ZCCipKEDtvgohxA+eakRzSzWcTh0O5eFj4H6dffc6Lnx4G/6QF6deOICGA1VweZafgc/tJeMpnHnnOj748QVEp+IorijA8ef2IZvZXHtHG2uDTaLPh8MFVauDph+AothhW8tBUUvF9zoR/Q/IZpObqChNwTQ7ZHCrbAMSffdAhSazE05CVau2emds5DFMowtm+samKjxl2rLrKahasx0Y/Yjn0en5JtKJ95HJjGyaes5IXYOh3xIrnu0+5TyTnYaRPrfJBLp17ajmp6WRjaXAAZADuvMJmOmbMHYZiW4YGSRTxlbvhg0bK8bJ0mohwmNGCp8OdmEyFcfl8QFZ5uLiWL8sc6E6HCh0efB4aQ1erz+I0+V1m1dMNftgpq9LRsbmQpFsK6frZbFxsWFjPpxKUAhyEn5Z5tMgAzOTQCozA3OT+y42HgYV2r1tw7j2eRve+esvEMnZrqVSBrpuDaC/fUQU6cUVIZz/4BY+/PFFUW47XRoS0aSo0qfDUSGAX/3NJ6AuoKA2DVPU5x/95JIQ9yTTFcUBl9cJ3aUJYUzSNxFNCcF+81wnLn10B8987Ti++jtPybapIF8OppFBX/uwqOk//8U13DzXIeQ8VfRUdrMNkrAPj0wJ+U6SvfNmP177nafxxMuHZD+WAo/z8id38fbffIHLn94Vkpvrdrp0+IKWap5EPdX6VMBf+vgOjpxuxrf/s1fQdLBaSP75xPjeo3VSKLhxtgOxSAJXP29DeGQaoWK/KOuXw1DvOAY6RxGZiMHjd6GqqVRmBaxWXW9ja2CT6PPAaeK683nAYU9bXRmoSqyGqh+EkboAbKKSwjTakGGSvfPopm3TxnLQ4fJ8SwpRNmwsBdNsF2XxZltQuTyv21kXjwwNqto0R6VrKfs2GrRz4XOfRVT6em9nUH2YTp7JFZ83D6p2IJf3YvdxVgLNeRpK4h1ALOt2DwzTRCK5uyxsbGxvkN44XVYr9i37C27iL1svidI8vYwvOv8uqLvxrcYW/GpjC1qKyjdtn5ntZKTOboGNCwuqPmjOx6G7X9r0bdvYHqj2PYdK31MwMjGkzAjSmQhGE1fQO/MBRhKXtnr3dj1I3FJRPTkeEeL25IsHkYqncftSF7rvDArpfOaX15FKGhgfmkRNcyme+NIRjA9O4MqnrRjuC2NscBIf/Og8XvjmiQVtSKYnYvjx//KhqMWj0wmJ0CHpTJV27d5ysVchId/XMSLE99REVIj9t/78U9nmH/7X30RByfJjroyZwcc/vWyp3Acm4PO7Ub+/CvtP1In6PJ000NM2hNsXujA+bFkgUgHuDXhEmb7v6NK8w8c/vYS3/uJzdNzok6+dHh0HTjWiYX8lQkV+ZGjvNTCJq5+3CkkfmYhK4YH2OP/sf/wuqhpLRQk/FyS+aStDRX/r1V75m1vnO1BYGhBrm6XAogCLH2ND1rHQxubU8weE1LexPWCT6PPgcAShuR7L2VDYYVvLgVW5rMMFp+tZIbQz5maS6O0wzVkrCPtabT1cUPUmaK5n4BCvXfua2FgYmUxEbFxoZ7F5cEqQKAeNDsfyXn02FsesGkN3PgUzfQfGJpHonLVgmt2SiaE7H8N2hTWFP5zLEtlcolJzMlC32g57XiEUtRyKWgeHoxDZ7AR2C6gKS9okuo1thNlnWqnHh19vOoLnKxtxPTyEWxPD6IpMYCQeFZU6NbUuRUOBy4NqXwj7C0rFT73GFxI1+mY+G5ntlE6d2URLuwcLhJp+TPpGNmwsBN4LSlaDrvihKR5ksyVImJNw2TPA8wL0I0/ELCHGH/zvvo6qhlIhoy9/2oo/+b/9BNPhGVF2U9F9/Jl9+LX/9CUhnKm4LiwL4aM3LmKoexwj/ROYGI3A43c/pKB+4z98hKuftSE+kxTF+uHHm8S7nOuhYlpRFWRMSyVO65W3//pzXPjgtli9nH//lti7PP2Vo8uSyrSQo7rcSBs49vQ+vPxrj8m2qDBXNUXU7slEWoj0H/7xe7hzuUfsUDpv9YtFzVIkOsl22sZQ6U7LFu7Tt/9XL6O6sUyIcFl/1lKif/33n8H7f3cen/38qvjM85h++qcf4/XvPveQdzzvD9rWUJFOEp3nntvaf6JhBSQ6cO2LtnsFARYaTr5wQM6/3T/fHrBJ9AegWv5w2qEN9YbbaWA4H4NYk/E3ANOq8G0Gspkp6YBmMpxaZHux5cMsDqfrVZkWSr/8WfAFf+Pzu7jw7nVEwjM4+twBvPLbz8ABB8YGJ9B+uQvdtwcQHppEMp6E7tQRKgmiZl8Fmo/WoXpPhQSzrhbcVl/bEDqv92KoexT/f/b+AziS/MzyBF+4e+gIaK1FIpFaZ2Vm6SKryKJmsxW7d3qmZ3pm+2Z2Z3btZm93bdfuzE7Y7Zyd3d3u7N7O3I7Y7p6e2RZskk02yWKRxdIqq1JrZCa0lgEEQoe7n70vAkgACY2AIwD4jxbMQmYg3MPDw/3/f//3vS86E5ebs+ZSESwOoLKhDE1H6tB4uBYev3vVmxZv1Pc/e4TLP7+JmfEwjj+feQ+K6sDU6DQe3+hF7/0BWd3nQEOy1QIelFQVouFQLVqPN6CstmTV/R14NIyb79/DnU8eyr787n/1LVQ2liMejWOkexwd17ow3DWK2VBU3gfL4wJFflTUl6L9XAuqmytkRX45eGxvffgAV9+6LaV5fM8nXzqCg2ea1308+Ro/+6N3MNY/iXQyjfbzrXjm9ZOorN94c0I93Qkj3ct0R1gFz0tmfyoKs/PsAUou0FxnoSTeBlI3LSpFN+W8SSev724R3ZiWLHS60a1tqqtBdR6XxSSb9UFDhepshaI1Qk9N7SsneiJhx7nY7D6YdV7q8YkgTkH9ZGk1ppNxRNMppAxGUgCaQ4Fb1RB0uVHi9snzXIpqqXhhmmyWNwQ91QHLcbiyDcIP7/poNJvthd8JZp9TI+HQWVXcUBbM8Wx2EJPNMF3iCqfAO+f4bmqvQuvROtz46CFikYS4qCn28u+YhU74cwczx7vHkYgnxRFdVsM0hsxnm0ykREDma7DJp9PtlHzw7/zhF9B2ol4aXy7NCWf2uuyUCclon54MiyBNtzdjZdaKdWE8DbPOX/j6KZz7whGULMk659yaQn7q99MY/r/+AKP9SUwMTaPn/pAI4BTDF17D+XxdN/DRGzekeSnNAdyX3/iHX8TRZ1rhC7if0hdKK7nNc5LN/vM/+1iaqV5+6w5OPX8QlXUlT8XGVDeUoflwjbjZpydn8fBmrxy3pkPVKzYY5bGlw73/8Shi4TgCRT5pZMpFkJVy6W3yD/squMSFzu7kGUHWPonXjwZFOyDHjuKYdZEuaelmb6T7obhsEX1nUeBQK+D0vJqNCXhyE6Pg2vtgEB/84DOM9o3LzfXMK8cwPjiFy2/ewIPPHouAPDU2g2QsKTfwYElARGGKvCdfPIyTLx8RcZ05bKvBGyZf/+H1Ltz95BE6rnaJuE3hNzYbl1V7lmPxJkxRu/5gDQ5fOIDjz7WjpoUNJ5dfPON76O8Yxgc//AzD3WNS9nXqlSOYHgvj81/cwIPPu9D/aAhTI9OyaMDd4M2zqKIAda1VIjife+24iPYr5baFxmZw+6MO/PI/fCiDgFd+61nZ386bvbjx7l08vtmL0b4JRGai0GWwoMJX4JVBz4MrnTj10hEcudgmiwPLEZ2J4e2//ASJaEJEdA6kDpxsFBfBWtBhwPf9xh+9K+V50ok96MGlr57GZtDT96Q5sJVIE2TXJfvankMUtQaq1gZFqYRhLM6Y3S4MnYPl2zBNRro8nVG4GzCNCRh6t8Ul/GwE3gBVa7TjjDaIqrZKBI6+w5Eus7NxDAxMobtnXM772poi1NaVoKjwSTTP/QdDCIUyuahbYWYmhkePRrb8OjY2OwG/H8w5L/cG5JGPGPoYDL0HpsnFVCtxZGK9nEegKOUWb9vGxiaX0FnNJphuz5NmljRU1R2owK1PH0FPQ1zjzO6eE9AJRe2Cksy1kS5vNhulQWsOiu/MBacbm6JvXWuFOMSPPtMic/Hlxt6c91IEH+6dxL2rPfK7bGhKAbvuQCVKKwtXfS80pp187qBsZ6mATrhNNjCloE2nNxuFxmMJibNhk89gsW/RfsnctWcCHdd7MT0RlsgUusRPP98uee7LvQfOrRvbq9F+uhGfv31XIm/4YEQNFx4qlhjifEEPalsr0HK0Vo5XaHxWstr5XMa8LAfNdnShU3SnyF9RWywLE/wsbXYPtoi+xKmoOg8CjrUbINg8gS4GZusxH1dJ3bU00oUl8breDQ0sScwtiXQaw7OZ91LgdiPgcsGpbu7cCCcSiKVS4r72upzwO3en+LMS8vmrjdCcR1YtDaW4TOf2zQ/u48a79/Dhjz6XGwid4IWlQRHA6UYPjc6I8P34Rg86b/fB5XWh/WzLmo7xVCIlgvyb/+59fPrGdXkNivL+Qp8I2lxxphObg4NH17vx4PNO3P7wASYGp/DCd86j8XDdulaBKXjf+uA+7n76CO9971NxqnPf/AU+cblzRTwyHcFQ5ygGHg7j3mePMdo/ju/8J6+vKtY/OU4meu714+G1Lnz25k3cu/wQLo9LBkYswZOu3tEkZkMREe4fXutG34Mh2Y8Xfu287MdCCkoDOPRMK0qri0SIH+oaRdedfoSnZsX1vxazUxH0Mqeub0J+pvu97mA1StcoV1vufdF5S+cVxVDrcMkin+a0+yfkEofDCU07hLTzIIyENSK6abICqUcamipK1a4cxhjGhMQZWe48dJ2DopTAYY9xNoSq1UNVWSqsWFw5sJih4Wn89I2b+PTyYygOB06eqMerrx7F2QUVRe9/8AD37i1upLgZ2JgslG1SZmNjk3sYTaanHu7AltlM74syZl9YNWqzW+HM0ma/wjlueW2xuLDn/86tSUY6xwmE4jFzuhfCKBY21iRmdu7NOJI5mHPOmBQa0Ajd7Mcvtq45R/b6PSK4HzheJyI6hezHd/rRfqpxTRGdBrcDx+pQ1bCyMZLGL1Z6l1UXoffhiOS98xEOsUGqd5FPivPk2591ishOFzqPExccFi4mrHRMSyoLUNNUJgI6tYnB7nFMjsw8JaKTqvpSnHi2TRqWckHiwfUeHDzdKMdhqWbBefDsTEwEeorpYohoLhdx32Z3Yd89l2Rfqlr7Tu/GroXOBh5DQ3LKrYFRLtslRkxGY/iTK5nGKRcb63G6uhplfv+mXuve6Bg6xsflhnaoohynqqv31KCHLl/NdQYOx9qrqHSl//Tfvi2u64ISP9rPt4lDmyI3b+CMLGH0yEjvuKws3/moA/8m9Of4p//yH6D+YPWK5WAU40f7JvEn/7fv4/q79xCfjcMX8KCqqVwc2hX1ZTKwoPDc+2AIDz5/LPErw71j+Jt//ZYI0L/1T7++rgYofR1D4sq+9cEDaU5S11aN1pOZ6Bk6zaW5yKeP0H23XwT3mYkwfv7H76H5SB18wXMor1u7cuLjn1zFUNcYJodCKCwLovloPQ6cbhZBnM74wccjuHf5kfyZSqUlMiezIl4pcTML0ZyaiO+nXz6CD390RfaJv9dxtRvnv7S2sDw2MIFbHz2Y/7ntVBMa2zeXq2yasUycizEGq1DUcihaExR149EzNqujOg+IGz3FWBdLMGEYIaSTV+FyvwrsOhHAzIroPRYveLihuS9IxZ3Nxg0WilYji8WmGd6x/ejvn8Qv37qDWIxVGEDo/SjKyoOLRPTOzjFcv8GoLBsbm3yFQoqR7pFm2daiwKH44fR8YcuxXnwPJiuCzcyDP/H+RkmXC7UKq58cFKscCx6rv57BvidGgndJOKBAYZyW44nDdiW4fd2MwzAz1V3q/O8tP18wTQNpMybPl7ASPl9xL9gPvqdUpn9J9n1lxGqaxpT595bZLccqphG+lg6Tx0dey8i+XgbH/GuqmdeT2JQn+f7rgb/PHeF74raW7vdTn4fEs+TfDHSl/Z87SpljpMn7yByf/HsPOwHF82Cxf1FVMQ1j0qAye4jocOb8cPHvqVDmhHfTFPNX5pzNnLvMH6f4zT/nnOv1SzLBV4LPZWzKhz+9IT8z3mRiNJP9vRKMhqltqRDxf2kDz+Vg41Fn9nnUDuKxpCwGLIR9XR7f7pNmq4SaQLDIJ677teBrUqyfg6732RWMBWU1RThyvllc8pFwHI9u9aO3YxjPfPEYXO7FcxSK+XTFM2ee++z2OlHTXIHmQzVr7pNNfrHbZp/bCpshqpq9ErRZVO0AFGkoaR2mPgFjm0T0qVgM37t9W/7b73KhtaRkCyL6KH507z6iyRS+eKAVJ6qq5leI9wKMCXC6nlnXc+kOp8hMsffv/V9+SyJFGEvCAQBvgEZax0DnCH70L38hgi/jUbpu94tr/ZXfuoSaluXLo0Z7x/H+Dy5L9jpvmBTP+fwv/u6z4nJXVOZdZsrWmP82+GgE//6f/VBc4tyf6+/cQUV9Cb79j768rvdAB7jLo+E3/rOv4tLXTkt++VweGxukJP4giV/9+Uf45b//QMR0Dkr4flqON65LRL/7yUM5HsefP4iv/N1XJNaGYjjL3aQBipSpjeHf/3c/FDc9Fxy4QHH55zeeEtEJnfLPffOcLFBQRB/qHpOYmHWJ6P2TItIvFNEbNnXDT0NP3ZE4CyujLCTGQm2xbHv7CS6g0dGWqUDJiHtWZIqnk5/B6X4RDiyuush/zGyci7UiOj8fTTsGh5Kf0Qb5jUNiDzjGSacyC+s7gdutoaDAOy+i+3wueD12U0Abm92HLnzJUVkAAQAASURBVJF2um7tgpfDQYHqGVkY3GpDUYqd4/HbGIp9gsn4PcTSYyJmO9UAClyNqPCcQo3veXi0Esrha2avU5AfiHyAR9M/wHSyC0FnHRqDr6O14GtwrNGnLJR8jLtTf4KJ+B3ZhwOF30ZL8GvwO5dfKEgZEVyd+OeYiN+GRy1GY+BL8juZf4tiPH4Dw7HPMJV4gLg+Bd1MwunwwauVo8DZiDLPcdQHXlojHtBEQp/CZOI+xuJ3EE72IKIPI6nPyGKBwooAJQC/VoUi90FUeE+jzHNsXkhfL4pDFSE9acxgMvFAjuF08jFi6UkR7V1KUI5lpe8sqn0X5T3kIwkjJMdqOPq5HPeEHkLaTIjw71VLUOBqQqX3rJxXHm1jVbB7GYeiZPLJF2gK/M+FCw0UpTl/XPR7C5YhzLlFsawIzUahzAKfnpgV0Ze/7y/wIrggOm41KFbTuDXH1GgYs6HVe2Bxf8uqisQhv973nV3FyryHZYoE00ldMt/phieMt3l8ux9O19rfsVQqU7U+B48HHe/LoWkqSisKcebFQ/jsV3fl9+iS73kwKE1HF8LoWsbLxCJxOd5Nh2rQcLBKTH42uwv7E5uHecsl4qS22RyqWps9fhaKKSbzpIZgGDMyOMxlgxzDNBFJZlYvEzpdCUuWODeAS1WhGyZGIxH0TE3Nr/buCRjlojVC1VrX9XSu7nLV/Dv/+HUcfuYACsqCT5WHNXmceOHXnpFmoHRkp5IpXH7jhjjKmZW+1KXB16Q7nCI6y7d4E37pNy7gld++hKrGiqdWtRmLwuy27/4fvol/8V/+KXruDmCwcxTX37mHV377WQSLFq/qL/cevMV+XPr6GTz/rXPzDvSF+INeXPzqaRGse+4PiHjPJqeMs2GkzFoNVlKJNI5easOLv34Bp14+kom7WQDPIZak8TgyniUSjslr99wbkFgbbUmHb7oSDp5tkUWIsf4JhLIxMJMjIXntlfaHDUV5bMcHMk31alorUdtWNZ+ltzHS0NN3YZgzsJLM+bn+Jqo268cBV7aKq1Wy7q2AbuBU8ga8kouecSrtFgx9CoY+CtNc2wmTO9xQ1coF92ebjeGAQymFwmvIDororS0V+Nt/6zncvNWX+bm1AqdPcQHraaoqC3HmTBPKy4Ob+nZEokn09I7j8uXOLe61jc32Mzei3i13AkMfgKGPAGYmKsEqHEoBXFx8lvnS5o4WXcIxfQKPp3+EkdgVzKYHRRjWTQpOJhRdQyQ9hKlEB4ain6K14JvQzRTUNYRwfoZpI4a4PomYPgZN8SFtRBf4tleGwnlcDyGaHoUJHUk9LK7mlbdlIKlPi/Cf2eaECOWzqSE8nP4eJuN3EdVH5XW474zxorjN9xpO9SFlRlAXeCHjAl+GcGoAQ5GPMRy7jEg6I5xTnM+45VPishYPukNDONUv4jefW+Y+hoOFv5lZeFhnlR1F5lDykYjng9GPEEuPI2XMyvvJ/LuG2VQ/ppIPMBK7isbAq6j0noOmLHYm7wRzbvPByMey/5OJe/JZJPVZGGYS9KbzOEUdQ5hJdmMsdgPF7oNoCLyCCu9ZOJXdZqLIPfwac4672teZ81kasBb/4pInLfiicY7KOBeK6XPxJpxDrqeHljzfrUnTzjlohmOu+lp4/K4158Yr7v8Kc3W6x1mpPjevnk5sLnJY13Xph7bsrjgcUln/7OsncPvyYxHR+x+N4P6V7qdE9LHBEO581jm/YMHs9Ma2qj0V8btfsEX0LA6lWFzUDsfO31R2Kw7FJ+WBjE7gANEadHEl0tWnagdFLMhHfC4n3JoqOeuTsdi6BoW7BVU+84Z1OxyZB17VWI5nvnxSGogul68mzUnOt0rO99Vf3ZaoFQrRw11jiJ2Niwi+ELrCKVBT7CVNR+tFcKfgyxXi5crGmNvGnPCmw3UiEIcnZ8XZTUG9/VzLig1A5fcdDpRUFuKLv/OcOOqXey63UdVUJlE1FKkpps+GopiZnJX3s/Q9LIWDlWPPtYsDfamAPrcPbp8Lh861SjNRxt/wdRkdw2y4pcI4X4+LFzyuzI3nsaKYzrics68eX3F/+NzuO/0izHObbMRa0VAmIv2GMXWkU3dhGlaK6BR566FodRZucx/B0mDpJ3LYMhEdZmJehHAoheuKkcoXDH1YHlZma7N8ny5qh8Ob04Xm/QRNFju9EFdaGsDFC61oyOaFlpUFUFzkX/G5z146gMYGjms3vi02J71y1W2L6Da7gntTI+gI0QltoilYjLbCcvidrryt+GQMJft6LFKuLIlyKYHmvij3gs3A+BM6hOkW7539FWZTAyJaa4ofxa6DcMo8wETSCCOensBg6mMRcyn0pi1eMFgvGRE9hHCyDx3Tf4mByIdIGWF5LwFnrUS9cIGA7ymhzyBtxKHKmGOVSBHTwEyqG0PRT+R3HdDg1UoRUKvldSnIU1BPZIX/2fSA/BlNjci/Nwdfh8+5PlNfJDUkvzud7EYsPSruezrP5445P4fZ9BASyRBi6QkRp0mN/zn5cyejXQwjiYHoR+gJ/xxj8Zvi3NcUrxx3t1ogx437y8+HCzMxfRzR9Ih8Pvwc6gIviaN/PwuQfOts9Ln289Z/jGjS0hcIxiLCr2Mb89tSHIsEd7rZ5wT5FX8n65jn7+YKvg+60ecUa87bmT8ubnfHAg/OOv5ks9HiZZqdzsHInENnm1FZV4LZ6RhG+ifw8FafGNzozOfxpyN+tH8SD7ORe4zYaT5cI41fbXYftoieRVGrRAjYPT6GfIQXzRoRrKwT0XltjEJP3YeqNgN5KqY4FRWaoiBlGJhNJq0dN28zitoEVVu80roaLAmraq5A2TLNORZSVF4gAnVJVZE4rWPhuAjF0+PhpwRfit+99weQzGa3tZ1pRnldybIC+hy8UbObObfx4EqniOhsoNJ1uxetJxpWFdEpXlNIPnSuBU7Xyu4a/htXp4srCkRE5w09Fo4hEU2sKaIz+5wCfGXDyqWXzL1jTAuPERcF6MLnTZrxNGxwuty7Z9QL89r7Hw1LLtwnP7mGIxfaJPvtKYe/YYizvetOxvmoOlWceP6QNCjdTN6haSakqahphK3NQ2eVjLJ6QxubzaMorPY4imTs+xZtkedSBOn0Q7nnONT8vO4vh4j/BkV062AOuqodtpumb9FokYkt2jl4Pysu9stjLbw+F+rrSlBTU7QpgcHnc6M320jaxibfuTzShzd67yOSTuJSZSMq24Pwas48FtEfS0WSldB9zmuYqrVsevpP8XIsdh2d4Z+Ki5su56CzHmWeEyh1H4JLLRLBKm5MSXzJePwORmNX4VSD0I08FdHNuLic+yPvoSf8pojQZd5jIkR71TKoDo/kp1PwpquconqF99Sq4rNbLZKYFkaQzMXAUBj2aRVwKwXS0JWvSdc4I0wm4ncRSQ/K63eFf4Iyz1G4tWLZ1lpMJR+KE5/ic53/RZR4DsOjlsrv0v3OhY7xxG1MxG/JQsBw9DNoDi8KXc3wazU7Jntw8YLCPxdkJhJ3pMKB+1TqPoIid7tEtrB6gZEuXByYTDyUmB0eM74H7jg/Kz7f1m5yC+fHzgXxL3Smz7m51wMFc/7Oovz1pU745bYref25g6/Heeuck6C6sRQvfP00qjYhWgeKfKisXzmKlXE5zII/dKYJ40MhcZz3d46i98Ew2k41yPgtNDGLgc5RjA1mKrvrD1SiuqlMdBGb3YctomdRlSrJvLTZGhQ0VLUOy6dGbRMU0dOPYCKVt7dRaSXDZjWGgZTciPaOis6oDDrR14u/cPUb0cKbX7DEj9oDlSKiz0WLUERnpMtCJoZCGF0w4ac4zuf1PRhccztspjInMlCEZ3NSrpqvBuNeGIviWkceLW+snsCTGyTLyZi1thY1zZUoKitYV4MVCuBzznAOXiimrxQZdOBUI+oOVkmj0OhMFNfevYupsWlpXrrUXc7O4cxZZ348B0Fsutp2umlZZ/zapGEaU9Blgc26yRSbXmbyP20H7nahKKXZSiCePxZd/c1svr7rNH232C0Y+qD14olSIJUC2GDWqs0THI4gHCqbarFaMRNbkM9omiJC+lZ+383GZDY2uwC60DtnJjGRiMKnMR8YeYq5wIluXXP1ObOY5joFhzT7xKaMEHQ9d4Z/LO5mOtD9Wj0aA1/GwcLfgFPxL1qwY4TJaPwGbkz8C4lAmXNA5xt0ik8kKGIPw6OW4EDBt8WlTYF2qaQnsSxmNCturzymdKlBVHjPSCyLT6PQezjb6PTp35lJ9kiEzOOZH4uwPpPqkUeBq1nc62tBZ7Zfq0a9/xUcKvodeQ8LPwc6tumKvzr2P4hgz+x0vt+ByEdoK/xONpLG2i+MGIrS4+iZ/QXG4zdlIaPEfRDNwa+iJfh1qWxY+B547iX0acm97428Je+ZCw/dMz9DUdkBqHDvazd6ruG8k85q/kljFh+cH68nipTw+dHZJ3nifC23x/rxhFRgF/nmK9753weO1eHwueZt296Zlw7j/rUejA2FMDkygyvv3kfzUfaOUtD3cBhd9walbxoXKk4+24by6s0ZHWx2HltEXzC4sEX0rcNInIxgZR3ibtX7RFTJV6aiccwmU5mO2XIx3ysXTOasNcrCyXphJ2rGiqwHxrosbE7COJRo+OnmJIxICY0+iQj5/v/0Bn74P/98XTcmrq7P5ZwZuo7ITHTF3LM5vEGPuL/XhYNN6RYOBs350rLVKKkugmdBZ/BVN6GwadOTn1fL3KfDvvVEo8TY3P30oSw20JleVF6IsprFzXo6b/bKAgaz7PiZnfnCsWyTVmVzFSNpNie1diLFZtGMYrDZRhyeTByaUg5TStStaBqrS3yMtdFAW8cwxmEak5ZuU3EEoTkP2AtJW0KB4ghIVUumKWz+jjeIU1Ph35KIrsKzA5NeG5vNMBwNiwu9wOlGQ7AI1b6Vy+7zpakoTQVWm5w0JxedN4dkhqcHMRz7XBpjch5T43sOzcGviIC+FKcSlEaQFEYfzfxAcrnzE1MyxF1KAc6U/+eo8JyWZpzLwffkxPpMJCXuQyh2t2eF+JXnIkFnAyp9zyCU7MJIjA5rSLRMwjO1LhGdVPkuyOdAAX0pjJ4pdLbgUNHv4tbk/4KpZAciqWEMRj/AgcJv7dC4wJAIm+7wT0VAp4u+PvBFtBR8A5pjuZxzB9xqIY4W/x1ZYGAsDaNd2Pw1ro/Dq1Wumblvs344d2bPrdKqQnFV02w2OxPDzFQUxeVrn/+cp0+OTM//XFQeQGCdTUlzCRcBKupK8CjbR4YxK+PDoW3bHuf6xy+0oqq+VLbJJqJX37uPb//BS7KI0JsV0eeee+JSG0rXqyXY5B32jCoLc+Icin0i58KRSCHFSkxk8nHzcVLLfMbR2VlcGRhAbygEr6ahzOffMxq6ZE1z0WQDMTpcxebNeV3PdapwLYhVoZhLJ/dSuEIejyUXNRPhTX9uBX21B587Zyqk9rywBG2197Ba3Esu4OuvFkezKquI6BwcHTjVhLYzTfIz3/+Vt25jYvBpYe/e5UcY7h6fryBgo1RvkLnKjk2K6I8tX+ziOepwLF4csMktcj44vJK7jXWUH+cGHXrqscUNOreKKcKJYW7fIP5pNHGiZ+7L9pBva+e4J5OLvkknpxUcPlyDZ59tw+HDtVtyktM15XbbPhub3cFMKoGkriPocqPCm2mamY/uPkZW6Ok+mCYFprTFzZHLoMpi6uZg7vZU4pE0xiQFzgYUuVvg1UqWPd4SpeBwo8b3rMSi5DNupVAiVCo9p+cd9csFS8y9z/WcX3SdKw42fFRWfT7/3qMWS/zLHJnGoE+cvKtBwb/Q2YSAs27Fz4GxO4ygCThrxBFP4ZpVBeFkP3TD+gqBSHpEmqkyY57jojL3URS5WiVmZqX3wHOYsUBFrgPwaWXZxY+oRAaxAa1N7pCeW14nDp58Em1KUbznQabv2FpMjs6g+/6T51Y3lqGk0vqFTWoNC98D3eGP72xv3DD7vrWz4rulUvSKsaEpPL7Tj9GBKQx0jkmUC/en7US9CPw74dC3yQ32CDkLBXQ7MzcHOHxQlGLJYDVNixyC7OCdHobJgR3Fww0OnGfiCXRNTWI6Hl+kPXaHnggd/dPT+Lx/AAPTM+uOb0mldWkienN4GFcHBxFJJVETLEB7eRnb+2AvwCx0RWEp0sbEmfUWwjuWuriNTCzOUigE00U+B5tnFlcWrppXvhy+Ag/aTjVJZ/HVYEWBqm2vIJXrBisLqagvlbz14qoiTA2HcP/zxxjqGpO/c/vccpzZpPThtS6MD07KoKC8tgSHLhzY/OKB8SR2ydpKCeah2wuk2w2bcmvOg9BTN2Hi6WqR3MMKknEYxgRMM7bpRmlWQte8wfviOifHOYshkabpVi1u7O1znPe8dPLTvA1zeeH5gzh1sgFFhRyLKVsS0ZmLXlYWlHuuLajb5DNypjsAj+pEgXN9FXw7A0X0TphGxPKeDopaLXOzzRLXJyVfe46gqxE+rVIaWK64XSgi3GYyut3rFoathvtX6jkmTvOdWHyheLzQ/a4jJU1c1wM/A69WBk1xr+4sVtkotU7y2rkgQgGaMS8+rVziUKyEUS7MoWck0Ny5xNz41eaSmYUNVQR0Vg1EMCwRQXydCu/mKyxslofNMC+8egz3r3YjMhPDYNcYbn3yEMcvtko18krfk+hsHH2PRubd35zLth6tE3e21bCB6PELB/BG+UfiCmdvMjb17Lw3gIYDlRK3mksyC0CQXPT717rRdW9AYlFvX36MqfEwRgcmxbzHPmlnXzos8TKbqey2yQ/sUTFxuMWpRSedzdZwcNVdKZB4HD1tVZm9LsKEaUzDZINYbEwsGI9G8MtHj/FoYnJRjMdM4slg797oGKbjCfid6xNlOcGmKyYUi+Px5CTiqRRURUFdYQEuNNRjm7RRy1HUug0Pyudc4ut+LjtrZ6FwvdwNh3+/MKft2LMHcfiZA+uOjZmDN1Q29GSm+qrQlLjdA+3szXg7YIZ6bVsVDp5uwqc/u46p4WlpHip56W3VSKfS6Lk/kG3oGkNpTQlaTzZKZ/PNQid6Ot1poROdg7xiiRmhg9Rme6GILbnoFGstUxhZhTQk136H6t0lUS6zluZpi0FAsrz3yE1nB3E43FLZAoeWt5HozU25qQTk4nVZaQDf+uYZEdHZoNTGJl8JuNxwKZkxYJ5+NZ/08khbX0GlKNVQ2YSb165NQnd0XH/Se8ivVcKlrj4m5DhZc3jgUYtEKM5XEZ0Z5nRz5xpmeTMGh9EjbEzKxp5sqEnxl81ADejyHDZpDSUfL/q99Z7JdPk7lcAaz8rc/9nY1KUUIopRGEhjNjUE3WO9Ez1hhCRSZg5G/fTPvoPx2M01f3c8fluOI+Hx47HlsbTJLYxTPXbxABoOViE8HcX48DRufvwIh84+wNFzLfD43YtMbiQeTeDO5U5c/6ADE8MhaSZKAb35cC0KStY6R3OP06VJ486j51sQGg9Ls0+66X/x55/g5W+dQ31bpeS1Lzefp/7A9zM5GkYinpReboF1NgCtba1A48EqEckT8RTuXenG1FgYE8PTYo4rKPbj7MuHZds2uxdbRJfBBZ3TLN+yD0cuoKhK92cm/9gq0jCMYShmw4Ydd4m0jsGZMO6OjGImEUcslUZ6SSZ2b2haHpvFraqoLyrEM/X1OFPDgezeEDT4OcsC1AagQBuPrK+xZKY5SWzRTX05JzT/nsLwHMwrP3i2GRX1+V1CupNUNZbj5IuH8dmbN2Ww8ODzTrSfa0HtgSqJx7n29h1MT8xKdQad68efb9/0ecsJAUV0Q++1KC+baNL01qGwtNtuqGiNS7dNrr9WihiM8jKMyaxQnN8Y+sgOiCcU0Sst3eaeRUR09v/YH2NFutD/o9+5tNO7YWOzJnX+Qtx1uiUXfSoRna9YzLexNp23RrpHqqesRNFqs9euzcMGlUk9I14SirEUxtcrUjP3muJpPqI6PHCruYv9M1lzqc+KwEv3/lSiQ5qrsiFmXA8hbUQkUoXROCKmm2mYWL0X00owfmY1F/ri52Y+h/lmncYUzB2IQqULPq4/6QkwFP1EHhuF3/OUEd/0sbNZI0+8thjPvHoM05MRdN0dkDzvH/7rd5CKp0Sc9vo57ldkDkmxeLhnAr/83mUR0ekCKyoN4KVvnUFNc7m8ntXIIp5TxbNfPSlRLpGZOCZHp/Hmn38qBr3TL7SLOO7yOqX6jrcNvhdqD7HZOCYkwmZYqrNf/rWz6xbR+byGg9VobK/G3c870X1/UObTU6Mz4o6vaihF67E6OHPshLexFvvTk0kmS4isb3iwV3EoQahqraWhDbzy0ZEIGZhuzC1bUxDEbx4/hsaiQtwcHsGjiQlMRqNIGRwEZQQ/VTLlNp6xyKfz9xqLivHrx47ia4cOIuDeO2X1ilIrcQEbIRZJSEnVWpMc6d4eSWByQROQQLEfvmVuYnScF5Y92Y+R3nEpP7NZGTYRPUS3fpEP4akIHl3vRt/9QVz48inEo0kR0cOTs+I04CDj6MW2LWwtDdMMwzSeuJi2HYcGRWvZFTEfewKK6M4WTpetF6bzdGK+3L7CjO5AVJ0toufKiS5uTqj57Xa1sdlnnC6rxc2JIXSExtETnkLS0Oed6fmFDl3vy85VrINzMlXbmohOwZeu6vnXdLigrtN8pjhcFhjVNn9VZna5quRu7EKn+Xj8FjrDP8FA5H1x4DPahveOTGTJk8x1/h3niaY40zcuaCuKU15jPfAzYz56di/FFZ9xvVtL5lx6YqaS48JQpg3OsVWHc83GrTZb49XffEby0CkCjw9OSaPMB9d7cOh0E+paK+APepCIpTDYPY57V7owMzkr30QK6CefO4iXv3UWRetoRrqdMNKFLnDqArc/fYxoOI6/+ZMP8M4Pr6C6uVxEbcbXMBY2Gk7Ic0cGJiUChikCJ59rwzOvHd3QNusPVIoDnlEuFONnJiPQdQO1zeU4cq4Zri30rLHJD2wRXSaZFbaInkMoWjksbyLDTOyhTbk7gi4XTlVX43BFORLptMS49EyF8GFPL/7k6jV5zvGqShwqL0eRd32CHMcBmqJIiWlDUSFaSkpQHvCvOw5md+CAotXIoslGCE9GMPh4dF3PnRkPo6/jSXMS5nIzS2wp5XWlqG6uwI1378nPD6504sJXTqH5aP2G9m0/wVgcHstnXj+FD398BbPTUfQ/GkbX3X4YaV3iXeKRBCobytB4uA4FpZsfBFFAN/T1fea5wkEnOqMXYEe5WBef44eilsLQ2X3emrJtwxiBYWy+SshKuK9WO9EdSmGm+bNNDnDCIY2081Gcs7HZvzxb1YgPh7vxcDojor832Innq5vh1fJszM3Gono/zAUC4vbjgKLWQFFqLNzmU3uwbKPO3GGy6cgWXyN3+9c58zfoDv9cHOiZhQcHitxtKHEfQqGL52U5XEoAmsMn4v1salDE9q7wT7G9mIsWG3ZOgKZX/8l+VPnOo8DZBE3xbVhED7rq5VjabA+MN/3a7z2P4vICvPG/fSyOdIrQtz55JKI5jVb0xDGmNZVIwTBMcZ4//7VT+Mbvv4CCko3Fqm4XzHcvLPajrLoI7//4GhKJFCKzcXTfG0Tfw+FMDzJeRkwTetqAThMlT1EHpFcYneobobK+BG0nGxAs9Mn8mu52Ul5bjOMXN9/g2SZ/sEX0bMMVlqLb5AiK6IrVDSRM6CKib3xgyuZbHj6yZTUlPh8q/CyPU/D923cQTaXQVlaGLx1sE0F8PTjmyogUFX6XE36nC9qeah6hSgySIi70jV1G2LBytG9CnM8UZ9k9ezmGu8bQfadfXNKEK9lVTeUoKHlazK1uqUDL8QaJdGEJVu/9QTz4rBN1B6pR2WhHuiwHz08ey2e/eQ5X376D6EwMw91juPn+PWnImogkZTDRcLgWLScaFmXObxTTCMMwRq0/R9kjwW6oaNn5ZJpKtrLLA9Oi7FMuzpjmLhHR9WHrRXRHQNzoNrmALkJftg/IiFTY2NjY7DwlHh++0XQYsXQSn4704Y8eXIFPc+F4aRUKXPkxvzPNVKZ3kyz6Wnft4PXfoZRsuTcMHcwLm4jORZGsBwOp+SaS28Fc9vhOw2agU4kHGIx+nBXQE/Cp5Wgr/A5KPIfnxXM2WeWxzDjTVRHr1s41Xx6dn8M6jy0/hyefmSJ59TsRecRzSXM8iaApcrWh1v+cHJ+NIK1GHR6JtLHZHiiS03B16cvHRRimcN5xvVec53Sdx+MpiUwJFHpR09yAtuP1aD/VKHElJRWFYirMh1gt5o+3nWpEsCSAk8+24e5nXeh9OIyxwSmEQ1Fx03M3uWhQWBFAaVUhqhvLJJLlyPlmVG+w3wzz2CvqSkQwv/zWHVlk8AU9qGkqR0Nb/sdP2qyNLaLLBYJNRe1w/1w60RUO2CzFhMmmbTkQbiieF3g8qCkokKiX7qkQCjxu+e+m4txl5u1u2EC2XJoIrtZNfTmYNzY+MImf/8n7+OYfvoqq5vKnyprCoQiuvXMHN967Cz2lZ7pdnz8ggvhyonug0CeuczYUZcY3BeHLP78Ot8+FC189jdqWyhWbgXJ1mJEx0xNhuentJ/c6G8McPNOMmuYKzIYiGOufwJ2POuALeqGndRHOm47UounI1sqARUTXx2ApDoro1fa13VIccDB/mxE6Fgnbct2nMGEaG74WWY1hjME0rIxzYQSZH4pj8w2BbZ4g9w9TkwVkXRro5p+IznJh7ufShl+bha6yoaEQiop88NtNsGzyFKei4mRpDVKGAbfqxGdjffjTjqs4W16HtqIyVHoDCLrccFKw3MBXo9jtldfOCWwmSTOBiL3WBUIxzktxFGy5N4yqsHnrE6FXmmSuc87FDGzdWPu5i93qi13Tq0FBP6FPL3I37wSmqWM0dh0zyR6kzRg8aimqfM+gMfgleLUKcU4v+3tM9d7k/SStz67r2BJm2jPbnnC85FaLpGrTapil71IL8CT31ZQmtUHn1uYa+wUK1Y6/9RwmR2ZQUlkgfcHESZ2FYi0jQ777T74kc+hDZ5pknryQwpIALrx2DKWVhXC6NRG+fSvc4+nCLq8ploaYjCM5fKZZIkoi4XhmrqgqkvVN4ZkiMd3W/uDalfuVdSV44Wun0HCgUvb/wPF6FC2IZl0Jxh8988Wjsi0a5+guL60sWHPcw6zylsM1qGkqQ31bFcYGpiSmJjobF5Gb8FhQcOd7LakslFz4koqCDRvJOA7jMeFxm/tsqhrK0HSoGv51Zqvb5De2iC4netB2K+YQuhA5ybReRJ/ODk5zg1tT0VxSIk1HbZYRKJVSOLDxUlneoDjRf/8Hl1FYGkD7+VZpBOr2OqUkjE1Hu+8O4IO//hyPrvdI7Aiz0BnPwmiR5eBz6tqq8OKvX0A/V5YHJvHgSpcI5CyjOny+VV7D6XbKjY1CPhucJqJJEc8HO0cxOxVBTWvlvhLR6R5glvyRi21SHRAaC+PxzR5x9PMzYm56/cFq+XMrmOasiJ3WYjvRrYfiHePRPJZNZens5iINq5DyPZbN1KcsLuN3wqH44dhgibTNajgy1YtgA11r8+3XQ3f3OHw+FyorC7cspLPceXAohA8/7MClS21obbFjgWzyk/tTo5hJxqGbBmr9Bfh0xMQv+jtwZ2oEBwvL0BgsFrf6XE76er8ZX286gipfbvJ8ee2XvhiWNVfPwMbSbLC+VSige7QnBqlYegxJfWbV32E1Ix3oCT0kQvraFbzq/KfDZpHMFl9LSKcTm4J+XJ8Ur/VOwn0OJR/K/hCviOgX4dO4kLGy5JI2o0huMpYupk8gJdvLZk8su1+ZYxjlZ2ZkPjMFGvxa9YrC/nbiVgvlmAC35Gc2X02kp6xuqbNraTvRII+VoIB9+GyzPFaC829GnPCxXtxeF+paK+WRC9hviw9Gv2wEitIU0fnYKNQLmH/ORQM+tgte+5LxFCZHZ6QxKaGA3rJFU5pN/mCL6PJlZGmufeXOZfMthzjfeEy5zGyFnGJmS/pz187UrWmSZX59cEjc6dub57e7kBJEtVSaN26UovICcZR33e7Hn/2/foyG9lo0Hq5FcWWh3GhGesZw77NHIujyiDOL+9jz7Tj7xeMitq8E/+38l07I77/7vU+luShFeEbC8DUaDtWgoCQgncSTiTQioSjGBycx2j+BZCwlUTEv/+ZF7DfoIDj76nHc/eShHIuR3uR8t3UucFS3VELdYld104zCsLT5Ix24rmyslH1ttzZ7lXEu1rospGmtyfMrP8XiTBNl3qNmc3qPWgv2q6ATndNlmxzBBuM0CYgTHXnHL966jfLyIL7wyhEUZZ1nGy2l5vmaSKTR1zeBn/78Jn711h3U15faIrpN3vLvOq7gzuQIQok4ppMxzCQTMGCibzYkj81yqqw2dyI6EtlYO2uFXkWhmWDr78GtFiOgVc//PJPqQUwfkwgTNuZcHhNxfQqx9LgIxavDCEzfvNhMAT2uh3hBAhwrC8RJfRqR9NA6Xt8KTHF70xlPNMULv4jFK1+Defx4fJiLvhki6WER0rmYwMahK+yWOONnU/3yeWSOtRcFriaJlrEar1qOQmdzdmzChYdHsm9lxvGcNni1sdkpmKs+MRTCncuP5b9dHk2iYRoO2lEuewVbRM+K6BR+bXJIVsAyDUY4WFHybEpzOTOHTnSvpuF4VQUejI2iMhAQZ7rNwjiXzYnoFKtf+LVncPBMC37xpx+g+04feu4NzDdll6YeuiGDvmBpEKdePoI//Ge/g4LSpxuKLqWorAC//V98AxX1ZXjjj99Fx9UupBnXMhJCaGwmuw1HVtDKumSyK8SaS5PSq/0GV/QPP3NAcuUfXu+WZqIU0MnRi20bzoFbSUQ3DQ7crXTgzvW6sBe/dsKJbiWZSodpQN25xmmrw2tMIutCt86FyOzujIhuk9tznNcWOtHzj76+SVy+3IlkUsdvfOc8NG3jCyi8/9663Yfv/+BzXLnajXTakPuljU2+cmtiWFznGedznmImYbKHh9UiOivylK2L6HQOsznmnPA5nerGdLJLROyFDvWFUEwejl5GXJ9YxxYc8Kml86IuxWi6utc6XtPJTozGriFfyGSdZ667jGhJGeyDsvJZyRiaqcRDEZI3Q8qYxXSiC+FkT/bzeRoDaTlGs+khWZxgnApd6IxPUXagWtPnrJKMeJcSFGd8ONWPsfhNFLvbUeI5ZPn+2NjkmsmRaXTdH8Tk2Ix8/Rvba1DXXA5fwNYb9wq2iJ5tvIUdKGfau1C00qCopdCNSctEdNOYc/mt7FjYCH6XC8/U1aG1pARBtxuFnvxoTpQ/cS4lm8rS44qsv9CHb/3vXsOBk424/u49aTLKOBFGubi9bokPaTnZgJMvHMaJ5w+hsKxASrDWctRREGa+OqNfGo/U4uHVbtz99CF67g5gYmgK0XBMBAJN0yT3u7iyANXNFWg6Uo+DZ5vRemL7SrvyGcnDO9eCrtt9eHitW441s+CajtahsHztxYv1OdGtE9G5KKoojP5Z+5yxybXAyF4JFovojHOxtNJho+jZ/bM2Q1thQzmOb2xyCJ3ovCbm55iRGeaDQ1N4+517Iij+xnfOwenU1n0dDIfjeOfde/L7Dx+NiIDucmlQVfs6apO/MK5F34aVHp+Wu+85ezbpNBaZOyCi58CJziaUAWctKrynMB67KQLxYORDieZoLfgmNGm6/OQ6kTaiIgx3zvwYkdTwmq9P4TngrINHLZKokbQZx0yqF53hv0G9/wvwaqwsXMxk/B76Zt+RHPL8gO+hBhMJr4jDdIiPxK+g0nv2qWmpVPzok3g080MMRD+Yd69vhuHYZ3CrBTioForLe/HnkEA41YuO6b8Ut/fcgkiN/1k4HOu/N+QSfr4U8FuCX8HDmR9CN+PSjJULJi3mN1DhOZWp+lpmLs9Md1YezCYH5OfM+7Cr7Wzyi8d3BnD13Qfz62cnn2tDbStNRvZYaq9gi+hzmeh2yX9OYa6dohRCz3Ydt4akOP1MMw1HDhZFVEVBkdcrD5vlnOjMyd64O59NSGhrY6QLs7fr2qoxPjiFyHQEqaQuOd0UuEuri+Q5jGnZ6E2HkTGBIh/Ka0tEqJ8anRYBnTEupmFAURTJR/cG3BLxUlxZJN3HKe4vh8vrkqalnoBbmpYyR7zhUO269qWhvRq//k9exyu/lYmKaT/bgkDx08IWxfxXf/d5HDrfmvm9Q7Uor1tfg94Xvn0OB042SIMVRtdUNJStuwnK3LENFgXgzTaC4ULE0WcPorS6WJqtbpXM99KaRpMCnUBK4bwbyMbqyi6X5U50ViLlL3o2zsjaLFwK6FZH6+x9GBXFmJz8FNHLyoJwu50SxfLOO/fhcTvx0ouHUFLiX/U+mk7rmJiYxVu/uouPPn6Iru4xcbPz9774haNoqH9awLKxyRd+t+00wqn1NVfcCNX+rZsInpCCKcYia0V0h1KWk4okCpV0LzcHv4JwslcyyCnOdoffkDiSYncbXLLAmGk6ypzrifhdTKc64dGKkTJcko2+Gk7Fj1L3UUwnuzGT6pbnP86K8IXuVngURjo6stEkg5hM3MNUokN+N+hsEJHYaqf/Qhhrw0WGkdhVRNMjSOhTGIp8DLdSgCJXqzTypIRMdzqFYB6f0fg1EdALnI0SkbNR/FoVdDOJvsi7SBhhlLjbJYudDnMuRETSgxiP38FE/LZsl+5vusBrfJfWGCOz2aku+8aoGN1MyH/zfSXFXZ95Dj9rRsowiodxMnTiM2ed2+c5s5wQznuRVytHQ/A1zKT6xIXO82koelk+80H3RxKD41QCEh/KeT2b2LLqgQsTPN+YtV/sakO1/6I91rfJKx7f6ce19++j696AmNLKqopw9FyLNBm12TvYIrpczL22Ez3nqJkJPBQLSyvpRo8ALJm3P89tRoEiFRwbF9EZn8K4EA6iKEbz0b4Ne6g5NRGB+dgqFJIbj9TJY6OU15XKYy24WMDH6Vc23ijl6KWD8th8XjNLz0IIT7KaA3D5XJIvX1C6dRcrB7/ynbS6oWIOnFc2m3Hp8rhbe/01zVg2bzxPMTNOdNO0VkSHjG3sCqrcn+OBvB0+nz/XjFAogpu3+tDTO46f/PS63L+eOdeCigqK/08LGrFYEn39kyKev/32PQwNh6RXRkNDKS5eaMVrrx5DVfXK/UhsbHaaS1X5X0XIuMlMRZ6VIm/G0IQcRZbSdV7lvYCpQAcGIh+KoMooktnUEApdTYtE9Gh6VATRcu8J+LQqTCUerCqiZ65NKip95xERoXZGhFX+HkXTQLwGblZXwYGUiOgD8np+ZzWq/ZegQBVh2rTY6b/oPUBBifsIKjwnxWVOcZmRN3SbMwOcIjqF5aQeQTQ9JIsFjMKp9j4DVfFgdjoTt7IRqn2XkDZiIkRzQWMifkdEdGaL8+/5OTAuhaIzRely72nU+V9E0FW/6usORz9DONUnTnbuE4V6HUlEUoPzjnaGJzFOp3f2l/BqZVkR3TUvope4D8niAfPXl+JUfCKCSxWD4sV4/KZ8zoPRTzAWvyFu+XkRHWlxoFNETxgh2RefViGLOjY2O8XMVARTYzNIxJIilqeTOqYnZnHtgwe4/kEHwqEo/AVeXPrycclDZ0NTm71Dfs4CrEYGF/YqZm5hjELA8uNqmhFxvTpgC2jbiTgL5HuzubKkvM2s3KfQwd55qxfD3WPSRJRO/iMXDyJQkINGjSJwxiz91KVEVcRcu2zOauh4s9qlmxHR55xR+QedcTvjRPdaXhWwH5AFujxdqH/20gERoxhbduNmH7q6x/Hjv7kmV8JLFw8scqRzATUSSaCzawzvvf8AP/3ZDcTjKXi9LjQ1luG5Z9vwrW+egdfrlOotGxubLWCyEnLawrEQq2YyC6kcE+UCNv2kWNpe9F2pOB6NXkVUH5PolvH4LRFVKWZTEHYrhSj3nkR70W+LsE0BlM7xtSjxtEvONyM+RuPXkdRnRJSnOMzXp1BNtzNd6wWuBtT5X5LthBKP0Tv7lqXNu5dCgZyxMw2BL0oO+VD0E8k8j6ZG5xuHzu3/XNPRWv8L8ny+x97ZX8nCxHphtjkFcR4Luv0HIh8hJqJ5r7jGeabx8+C26EBntUBz8GsivK/l3n40/dfoi7wt4vvKUER/LI/lOFz0e/AWlS8rohNVcaM+8LIsvnjUEhHP4+lJOZ8o4NN4kOlyIEdNhHlN8cPrKJdFgAInFwLscb7NzjDcO44r79zDcO+EGPfi0QR6H45gsGsM0dm4VLu3HKnFa799ASUVuaxqsskHbBFdxF5mgtlNI3MKj6c4QXdGRM/Jay352b5NLz0aFBHsifVuh5UBFNAHO0dETGeszeHzrZKJzmarOWkqKiK6ldAFQ6HL/tZaSUac44SdC2yqdaIxz688FtElE93cARFd8eTMgWizUJjybaofiBVomopLF1vh97tEEL91ux+PHo3gRz++KsL6V14/AaczM97lz7fv9OPHP7mOjz/ONLVTFAfaD1bhq185iZdfOrypxqQ2NjbLzShSWRHdKqc052HFOb9WMbKEedYnS/4hRr3XMBD5ABOJW+J4pljuUoMocLWgynseDYFX4FZLEE0PiwubTUPpVl5NwOW/VfnOo9DVLLEoA5H3RaSN6ZMiqlKQpdO63HsK9f6XpZkmKx4ZVSJirWGK2L9cjMiirYhbmvuTzDYDzd14scx7XJpnch/7Z9/B5AIXPh3YzC1nA836wCsodh2UY8Z892JXqzRU5f5ghZzvjAivSQZ9gatRqgNKPUflv6u8z4gQz4ibmD4mC/gupRAFzgZx69f6nxOH93reK3URflZbicfhubKeNaNK3xmUeo6Iq30w8hEmEncwmx6UBQguBvAcdqkBWXTgeVHiPirvudCV/xUoNnuXqbEwrn/4EDc/evhUfzZWAFJA/81/9CoaD1aJyG6zt9jnnyhvIrYLfXtQMk5QDgKstB2bUfY6t3CD+1lIoMPRFil3OxRS3v3epxjqGpOfS2uKcenrZ3IioBMR0K0W0Tlwl8gF+/zcEcT95s5ejy1yos9ndOYhOxjnYjvRc4+DETl53MiMUSyH2mvwd3//RfzRH7+H+w+G0dM7gZ+/eUuyz7/9rbNIpXT87I2b0kD00eOM85FrYK998SheffUYDrVX281EbWxyCIVe0wxb6ERXoDCDO0cu9KVQYGX+d7G7XfKyJbov68YWp7XDIznZFH0ZvXG85B/gUNHvirBK1zEF9dVwq8Ui+lZ6T0sm9/z908FXpMDL1/fKfsBhotJ7Dl+q/VfiXKYo7ZIx4PK4FD/OlP3n2f02oNE5r+Y2r9ijFkvueLnnpMShzMfMyP5rst9zOeKk0NWC8xX/tcSWUGh3SjXl0xS5WnCs+A/QXvhdiU3hsSR8foX3NIrdB7PHa+nn4V3REb4cZ8r+CY6X/P0tHYPM57C+ynAeB0a/sDGrbn5TGtfOOdGlxajkq6tPImMU2yBgs7P42b+tshCFpQFEw3EYhiF/V9dagVPPt+Psy4fQfLh23T3KbHYX+15Ezzjm7IlCruGNTlF2won+ZOCwVZLpNAbDYfzwzl2U+/04WV2FlpIS+F3rFyXSuo47o6P4vH8AE9EYWkqK8aW2Awi63bu7QzO7pufYuWFjPeGpWVz91R3ceP8+ZsbDCBb70XysHoefOQA1Rw7ETFPRjWU8bhXHnBPdPj93BHHqOjxShWAFPL+sr3bYCEZW5Le4oRyYv2iL6LmFUWY8rvk7KZKmbV4nWlsq8Ht/63l8768ui+O8u2ccb719VwT0yclMbnpf/wSSyTRKSgL40qtH8eyzbRLl4vfbAoWNTa6Y7w2D3MxP1oWIp0XbNtWnqLleYZZjMsaceLH+BsUU2xWHX6JK1oPT4YPTtb4IQrqsfVo5thO6xflY7/5TFPYpFet6npePJccyc7w2JpSvBmN7rGxLzvNJKhXE3Ghjk/80tFXh23/wEl765hmkU6w4NaG51Iy4XlUoES4en30+71X2uYiuZCdD+eso2t0xOb4dOLYcoObG7RdPp3F7eBg/73gIl6ohFIsj4HajeQMiOv0mibSOD3t6cX90DKdqqnGgtBQnqqt2ubzHvbed6LsFNj2ZHA5hpHdcHOambmB2Oor+h8P4/Bc3Mdw1Kg7FluMNOPfaCclEz63Aaa2ITueVQyYu9vm5IzCDFSwDtgieX2Y+VyDRS5WwXkSXLFx7AL8tlRZ5LKIvFNKPH6tDNJqQCJfrN/rQ1TWGWDSJ8Gwc4XBcntfcVC755y+/fBjVVUVwu/f51MDGJteIq5b3ACtLc1U41OKc5aHb2NjY2DyhoMQvD5v9yT6/s9JNSxHdFlpyDl3WIqQ4rG0iKW6P3Ijo0VQKN4aGMR6JYjoeR1tZKVL6xl5bcThQV1gAwzQxEY2ia3IKN4eHcayqUv5tVyMD813+HvYJ0ZkYOq524YMffgbNrcFIGwiNzkgO+mjfhAgpNa0VOPfacZx88XCOt57cgUZPFLgodNnn507goJPIwsaLmQqk/BbRYaYsb6ksAnqeNsDcvXBUw0zf/BbRCa/rjGS58EwrTJ6CJnDteo9Eu8zFvrS2lOOVlw/j1VePoqT4SdNRG5vdxGwqAd3I/SKl3+mGloOmuqaMgXLTr2m9MDTE4aAhIv+vVTY2NjY2m6N/ahq9k9NIpBdXOhX7PKgvLkJpYH0VOjYbY9+L6JnmiPakIffwmFovspriRM/NQDqeSuHe6JhclFyahppgEE1FLI1cP6qioKagABV+PzxOJ6ZiMdwfHZeSn92P/b3ZLSQTKYz0jOHyz28gEaUz3BSxRFEVKTXzF/rwxe8+J1noxRWFud04xUMREK2Ei3j7/Pa2k8ixt3Linsou1ph5em2iE93q7wAPA8c39vcg1zDKLJ8z0ZdCsfzihVZ4PJr0wLhytRuJRAoul4YLz7Tgq185gWDQysJ9G5vccmtyGOFk7kXqZyoaUOTOxXdjJxZ6aRSjS9IW0W1sbGz2Km93dOJ//fAKBqfZc+MJF5vr8feePYsXDzbv2L7tZezZlc02urV2wKksLvTciOhpw8REJArdNFEdDKAi4IdT3dxgtMzvR4HbjXAigfFoHjfAs9mTsEs4I1rK60owMRRCMp6E0+lEaXURDpxqwiu/fQkHzzajsCx3MS476USXJkT2xHHHyDjgrBQZWSqfzF7780/cpISeWUyyevFUlc/CJpdk+oHk30LN6iiKAyeON6CwwAe/z433P+xALJZE/8AU+vomceRI7U7voo3Npvl/X38P96ZGc/66/+6L38Xp8tocxblYHGu3S69VNjY2NjY2+Y4totvsMScoO3nnJs6FpaGheFz+LPB4JA99s6XOQbcLXqeGyVhUhPS94ENfL96AB89/6zwOnWtFMp6CL+hBaXXxTu/WviJYEsC5L51A45E6pBIpGIYpcUJOtwZv0IuSykL5nOhYzDWmyWYruflObqzfhT153Dm4gGG1eGtmzzMK+MgzdibOZWc+h/2Ac0eO68TELLq6xzb8e3Ktd6riPCcvvtguCzt0pLPhqMfjRDS2usDX0lIhcS82NvlINJ1CJJ17kZommlwg96YdENFlHJR/N0QbGxsbG5tdjS2i22xvnItjB+JcciTYMcc8kkzKnz6nEx5t818Xr9MJl6oirRuIJOlIxL5B1VSU15bIw2bnnOgllUXysB7D8oaKvO5kKmFs9kecC7LnGK/9+ZgBPhfnYnUmOheTbBF9r/QD6ekdx1987/KGf0/qclQHNE2BqqriSh8YnEI8nkIikcbnV7owNhZe9S39rd991hbRbfKWhkARUsb6xv4c06dNA9FUEuFUAnH9SY5siduHpmAxGoJFKHZ5UekL5GgPuW/6DvVvsO8BNjY2NjY2ucRWGWy2DYdD3YE4l9xloi962S1qH5mjkGmyygH8vlLRbfY3prED5zsdWHacy87BY6/ugNtbz8/iA7nk74QTneKJLaDkHh5Tx4440T//vCvnr0sBXUT0Vfjq6ydzvl0bm1zxekM7JuPRdT3X4JKmYSCSSmIqEcV4PILe2RD6ZkNwKgrqAoX4SsMhtBWWodwbyOFCqsUiOi9RdkWejY2NjY1NzrFFdJtt8z/szOmVuzgXurUY45KKRjGTSGA2uflSzNlEEvF0CpqiiKvdLq+02T/sgBNdRK6dcYvaZBdQLXdAZ0SK/PzEGTWzM3Eutgsxt8wtiO/EtcXt1lC8Q25wl8telLTJX77VfHRTv5fU0xiJzuLdoU78rOc+HoTG0D87jYSeRpUvCJeSq/PeyBoKrGRuHpafd0UbGxsbG5vdii2i2+ypwVtGQM/NQJWOlHK/H6FYDEPhMMZmIzBNc0O56Hw+GZgJYyoak0iXQo8nJ/tnY7MbkKaKO+BEt+NcdhLFcvE2c55ZLVKsF+5bKruPVleD2SJ6TpHbP2NyrN90TXUxvvLlE9ZvGEBV1U5EgdnYbC8uVUN9sAh/K3gGzcES/NGDz/HuYCf+59sf41hJFWr9hTJuz13cmJXYjUVtbGxsbGy2A1tlsNkmKBYwWsVi0SCHDjGP5sSRinL0hkLSDPTR5CQejI/jUHn5hpoSPZqYQE9oCuFkElXBAFpLS2xZw2YfsROuTcqV+Sqo7gd4/K2/9meu//nInMC/A7FG9t1mG9gZJ3p9fSl+/TvnsBP4/e4d2a6NjVWcKqvBuck63JoYwlhsFj/rvY/vtBwXR3puMPdN/wYbGxsbG5u9jC2i29nU20JGQHnSrGc3lq8H3C5camjAe13dEudyY3AIP7n3ABX+AIq8HihrONLThoHxSAR/duMmuqamJAu91OfDyeqqDbnZbWx2M7lc2Fo/Ztb1ZV/fdwKT2eQ7Urqu5nVlFr8LVp6R+e3O363s3L2bcS582NjY5B6/0yUNRRuDxbg5MYSPhnvwWl1bDkX0HYD3YnscZGNjY2Njk1Ps0fiOuMP2kZBi+bHVciakeJ1OnKqpRlNJMaYTCQzOzOCtx53y92fratBcXIwir/epUs+krmMiGsXjiUl81t+PXz3uwmQ0Br/LKb9zvKpqTQHexmbPINnYOyCiS5Nhm50hd7Fae8N1vUNl9SZb6Jm2D9HGxsZmHRS5vCjz+JEydHTOTCCm53Ic4dgnvThsbGxsbGz2NvtcRJ9zK9rknqwTPZsJbhk5zIClOF5bWICXW5oxFYvh0fiERLP828+voGNiHKerq1FXWIiAywVVYQZwxn1O13pPKISrA4P4qKcXkWRSRPMDpaV4pr4OdYUFOdk/G5vdgWK9uCkuaFtE3zl2Jv81c/3PR3aqwdtOLGbY2NjsTfa+GKsqDjhVVSpHpxNx6Iaxixd5dyZWM18wuYhsziCdvLbp13A4vFDUWihqNRwSjWNjY2NjY7PvRfQ5sWV/DjD2phNUy6mQwmHvbxw7iqHwLKZicUxEIgjF4/ibew/k4dU0BD1u+F0uaaXH3PPpeByJ9JP3rjocKPP78dqBA/hSW1vO9s3GZnewAyK6OLBsEX3HkGNvx7nM49ipBm/8DGwR3cbGZqtko6GsNsZYTELXEU0lM/KzVPLkCjYjttpMwMd+dqKnoafuITz5e5t+BUVtgMf3d+Dx/y7g2MWxPhvAlO94GqYZz8QxOlxwOFw7vVs2NjY2ecU+F9F5o0ju4wHGdsIicuvjXBwioOdWSCnwePB3z55BqdeLv7x1G33T0/P/FkunkYjomIhEn0wzFkwy6ECvDgbxD545j1damyVL3cZmX+Fw7oCDZ387sHYaXvszcV5Woub3kGYHRPSdyaa3sbHZi+yHaJChyAw6ZyZl7F7gdIsJZjc70U2kLG/ybbPbMaCnHyIZ+zHg8MLlfhGa69RO75SNjY1NXpHHM04r4NDCFtG3j51xoueqsShhA1AOfSsDfnzryGEcLC/D5/0DuDIwgO6pKczEE9CXOHM4+C72etBSUoIzNTW4UF+H9vJylPi8dha6zb5DHCwWu1h4ZTftOJcdxOoYEWfWLZWf19fMXYT76LB4uGHHueSe3TdeTCbT6O2dQGfXKAYHQ5iciiART8mCv9OpIhDwoKw0gLq6Ehxsq0JRkQ+alqdVHTY7XF26+87/tZh7R58M9+D9oS4MRKbhVFQ0F5bAo3HxMzcGn0w1kpXYmeiLUSSaRVXr1/0bDrUSila7z+QSHenkVaQSH0JRK2E4j+z0DtnY2NjkHfvprrAM9gBj26D7zYzDtHwCn7tM9IUwI7GmIChO8oaiQpyprcbYbART8bhknifTGdelS1MlI50NR6sCATQUFcnzma+erwKPjc324oKDAqKl8PrDBVKbHUHEFt3iaod8Lje241z2Cpk189SucPjHEyk8ejiCm7f70NU5huGRaUyFoohE4kilWKUAqKoCt0dDMOBBcbEfdbUlaD9UjSOHa9BQX2qPW2yWLEzn53zpymg/ZlOJDf+ebhqIplMYjIZxdawfNyaGENfTKHR5cLGyUdzouZtuu3Zg4SO5K65VluBwQnOdg9v7rQ1motdlK8n2CzrSqZvQ9T44FF9WJ7Gx2f4YodlEEgOhGQyGZjA+G8VMPI5oMoWUzvkE+1Uo8Do1FHg9qAgGUFdcgKqCoMTq5hPUhKgR9U2GMDgdRigak/eR1FmlC2gcd2kqgm43in1elAX9qCoIyHvhe9yv466UriMUjaN3MiQJD1PRGMLxpEQk89ixR4nqUOT4UVfz8FzwuOV8KPX7UBH0y59upzXy9r4X0WFy0DWXi74/T9rtgQ1dojsQ55LbTPTFr+2Q7PODZWXyoIsrlkrJhTGhMz8OclGkiO7WNGk2amOz39kJJzpFXNOM5e2Ef69jImHpxIuLNPkuoosT3eoxBr8HdvP0nGPKAp2R15PR6eko7t4bxHvvP8DVaz2YmopA15ff5/AsMD4+i67ucdy81YcHHUMYGZ7GpYsH0NZWmanI26eTOpsF5LET/ae999E3G9p46KRhYDadQm94CpOJGFKGDo+qoTlYgherW1Dg8uRwode9A0axWF5fq6xFg6q1wOV5dad3JM+bscagpztgmuGd3h2bbERsx8g4Ho6OL2pJQfG1sbQIDSVF63qdtK6LMH25u/+pf1MUh4jShyrLRJBcD4lUGg9GxtE9MbXo7ylsN5YUozTgW9/rpNOYisTQNTElr/V4dBI9k1MYnpkVEZXCekZEhwinPpdT3ntNUQGaS4vRWl6KlvISNJYUocTvzan2Ek+lMTg9I8d/ziwpUV9eN55tbYS2YFscd+mGiZ7JEB6PTaBjZAKPxiZEEB6fjSBCrSjFMbkJTVFlMaDQ60FZwIeqwiDqiwvRXFaCk3VVIqZTIM4H+L65/7cGRpDMal1Lm3EfqCiTz50a2WaYicVl8YTHjsfr0egERsMROV+nY3HR2rgfaUOXz5fGVppWuT2aW4t8XhHQeU7UFhbIokRl0I+KgoAsVFB03w7y4xPaMcyso4grQ6Y9ScgpBkxjdgcGbzylrSlD5oWUX+DNXjRsbPYDDrjhsNyBlc5ef/Jzwr/n4cTdjFu3PXGJ5ZcTZTEUIV3WZ6IjYVdk5BxeU2i+yN/FiWg0iVu3B/CX3/sUd+4OZBb43RoCATfcbic0TYGSnfzJxE83xJmeSKQQi6XwoGMYo2NhjIxO4/f/9gsoLQ3Y8S77nux8KU/vqZ+M9OLu1MiWXoNXZ7/mQmtBCV6rb8OJ0iq41FxNk2nwsfYexUpg05zJ62uVTb6RhKEPwUgPWjuGs1kRwzDx1v3H+F/ev4xEVsglR6rL8d1zJ9YtolPEvdo3iP/y+2889W8Up59prsM/ffX5dYvok9EYvnf1Nv7iyq1Ff/9rp47gt8+fWFNEn3Oe901N40rPAH52pwN3h0ZFuF6JmJFGLJXGRCSGR2OTeO9ht4iox2or8frRgzjfWIeKAj88mpYTTS8cT+DDR734/777sTikCYXzg5WlONtQC9XFxVFHxlSZTMl7+fHN+/jVg8fonZyWRdrl0I20LB6EYnERjgn3lmLwf/36iyg44M4LEZ2LF0MzYbz9oBP/4r1PMROjQWpx37/64kL87YunUeQ9sCE9jK/D40bH+e3BEdnGR497RUxfDYPjVd0QAys/k6XP53GrLy7C2cYaXGyqx4m6KhHXt4Od/4TyABP8YvDCZDuHcwXdb4Y5a/3gzeHegdxBGxub1cph4fBY75gTF01+Tvj3OoYRzQi4FkGHn8Pqc2xDOLLVGFY3Fo3BlGo7m5wyX8GYn9y63Y+fvXEDt+8MyM+MbGltqcDJkw1oP1iFyspC+H38zjiQSKYRmo6iv38St2/349q1bol8oXP908ud8Hjc+Nt/61mJerHZz4hvG3sZr+bE+Yo6/HrLCbze0J7DpqK8R+2EE51mpqlsBYGNzdqYRhjp5OdZXcQmH6DTlz3VygL+RYIhheTxCCtN1gcdvd3ji13jc6QNQ9y/jKddLyMzsyICL6U86Ed5YO3xgmECn3T14S8+v4UPH/eIoLoZuA8fPOrB5z0DeLGtCX//+fM4XFUubuXtgMeK/fAognudvK5nXPm3Bofxz372LjonpuZd6xvBzN5la4sK4XHmh45FJ/hb9x7hf3z7Y1m8WIjicEiMzj9+5RKebW2Q6oCNwI+bixT/+oPP8ebdh1J5kAu4CMOqDT5+ee8R/v5z5/D7z57FdmCL6HLT4ISIJ0d+nLR7JxN91vIsPkXxWy/Y2djYrIzig0PZ2M11qzC7NbOIZ4vo1pIZBoqDyVIHtAcORz6LfOyJUQgHVGvPSIMTHNuJvi1xLmZ+CorjE2Fcv9EjQjrFc7rIf/u3LuDYkVoUl/jhoRPdqcoEiJM/GqWqqgrR2lyOZ863YPDLx/HzN2+JgB4Ox/Hhhw/w3KUDOHKkBj5fPld72Gw7eRxj9JutxzESbd7YLzkg+apeVUOp14/GQDGqfEGUeHw5FdAzOOGA1WYCHaYRspusbzsGDH0U6dRdpFO3YaS7YRijIkhz4YlVaA5HEIpaA815BJrzDDTnoQ1Gj3LkYCCduidNP7ktQx+EaczARErGFhwDOZQSKFo1VK0NmvMEVLVRcs2XfUUjJvvL6BZd74WR7oeh92dfd1qek0peg57uQSz83y+/Ww4VLs+XJGde1Tb4/bNZF1zsLvH7JDN7oYjOqJPJSBSGYcxXlq0lNndPLB95RQGbgilfkyLkelzQFNEpzC+E4woK6KV+76oOZOZb/+kn1/GLe4/wcHRi0wL6Qihkf9zZK27p3zx7HK8cbIbPvT1V0IxtoYPa73YhnTLw7sMu/LtPrkkkTWoTAvpcNQBjXOqKCsRJv9P0T03jr2/cww+u3UE8vfgeQjd+c1kx/uFLF8T9z+qFjTj/WV3xeGwS/+bDz/BRZy8mN7AYtBGOVldKRM52sfOfUl7A0vO0HYmeU/QdiXORQYQtotvY5A0Ohy/jRrcSMyUTGFnqtq/rliHjYMlgtVZs4TWf51ne4lChKIXb1q9jJSQP145z2Yac4cQONE1fHx0dw+jsHEM0mpgX0C9eaEV5WXD1SBavC4WFQFEhr9e8aDrwq7fviEv9xs1eEdptEX0/w2iQSN42qfxiXZv0JtoYDigOQKOQrjkRdLolvoVCUK6hyClxLpyfWBaTYcAwQrYTfRvR091IJd5DOnkFOgVoY0wEaOkJJvdeDor42dOxGpTnqdoH0NyX4PZ+JztnXX1cYJppGMYUkrHvi0ucorZhjItRLdOfY66SntvwwJEKwKEUQ1Eq4fJ+WQTu5cZHzDxPxH4EPXUju9/hzHdcYpvmnhSBofPvVkKF4TwG045+2VaY9c3c7IVQrKVgPBWNryt/fDpKEX15J/qcw5pNMCmMe5yBNV9veCbTMHMh3A86kl0riMBz7uMfXr8r8S3MDl/qcCbsL0fnPXPOKdD6nJqMuOKpTIQHY1NGw7MS6zH/2hK/ksTNgWHJzeb7ef1om4jTuY5rpgg8MRuVHPlPu/rwoxv3cG9obFHcDu8jzG/n++CfzEAnFKTDsYSI8NFUaj5jnK5uRvRQ+GdG/U7BBY2h6TB+cO0u3rz3UM6JhWscbHx6rLYK3zl1BJdaGqSx50Zz6HunQhJ58/6jHlm4WW4RhRnn1YVBOX4Bt0s+R5LQdYnO4bk/HolgLBxFJJGAvuQ1+JkzyqW13BbRtxXTiGRXcm1yO+DmKrzFA24OSKx2e9jY2KxexgyK6BRgrImWoPPKtJ3oOwInYVY2FSVcpHGwCilPcUCBw8HcSotFdMTtOJftQI5pfjrRHz8exfBwCB6PC02NZXjh+YMijK8309zjceJQezWGh6dx61YfRkdncPfeAC4804q6uu2bjNjkOSYFWbpe8/O8rw+sLxd4x5DKj4yQal1jYs7DbCf6dsL7ayr5KZKxn2fHtxocSmnGAS7CtSOz6GqGxK2uGyPQ9U7o+oAI6C7P63JOrLIBmMYEktHvIxH7noj2/FwdSiEUtSljGnOoIrSL4G1wO0MABX1lAJrr2CoxclzcD8JU6+BQyrJ/l5B9M9I9IqaLs11tgKJULP8SDhWa8+jq78EmNyJ6weJjnBGNE+IIX0tEZzZ3KBZDfyhTYbCa+5iiZmVBYFNxLmzqyGaZKy1EhuNxXO8fxg9v3EXn2NRTDmcKze1V5WivLJOmobXFhQi6XRJvQgc7n88olcHQjDSgZJY23fU8DnMwK/t635C46fk+LjTVIdfopiEi+I3+Icmrv9Y3OP9euIjQUlaChpJC2T5FYMa+eJxZETitYzaekEz5sXBEYkz4fuhqP1lXDWcOG6NuJgOdFQk/vnEPP7/bgc7xqUUCN4Xsk/VV+OqxdnyhvXXDES5zDW7vD43hl/cfyTFcunhSWRDEqfpqtJQVo7qwQKKM/G7nfDwPqxhYLcHPnA1pGWs0Eg5LI9KR6VlZ3OFCUE1hEG0VpRIvtF3YIrrcoxg7Yru1cl9GSCeolSI6hQrftjXv4cWFq4bsEswVTl5YNiPR8QJZHQzajWxt9gnMg/bAwcG6RGdZgMnGonYm+s64dHncrRXRwbggx9qD/p1DhUPZARHdjMO041y24RynyzA/xcThkWmEQlEUFHjQ2lq5IQF9Dq/XherqQjQ1lcnr9Q9MIRK1F2P2Nzog99T8PO93B06pSNKNSQsz0TPzW9M04HDYfb9yjaKUQVHKoai1EpvC/1a1luzPRZkINzMMXR+UGBY9/QCmMSl/JiJ/AqfzFEyaABwrOXfj0NOPEY/8W3GLs7eKqrZBc52E6jwIxcGGeU7pQcPXNfQB6OkuGMYEFLUOqnZoxUpQhxKA2/ttGLyfZb/Xhj6BVOItJPVhMUMoar0I/U73pRWOgAOKUgpFLc3ZMbV5mmJfJs5lKeFEQho/HqlZYZFjQVPRubiWOShO071L0XmOgamZNaM15hpCjs1GFonXpK64QJzJK2kojDv5m1v30TEyITrKUtf26YYafO3YQVxorhcBdbV94H6+3dGJX957jJsDQ4v2m9nuN/uH8eOb93CgvARFXi80NXfXP74XxpFw0YHNWqdjCbg1Td7/ydpqPN/WKC7oqmAA2grZ7HwPXIh4NDaBWwMjIg6fqK3O6X5u9D0NT4fxTkcX/vzKLdm3hQI6HeiHqsrx7ZNHMgK6f3MV5qycuD8yhjuDo09FxNQVF+JLh9vwndNHZQHCtc6xKxcjWGVxb3gM94ZGZWGFrn5WMvBz2S5sEX2HnHP7orEoG9pYOODOlPSzWVbuhApe5HgJYQOJkdlZPJ6YxKOJSczE43LB20yOV6nfh//4mfPQbBHdZp+QmVwUQ2cJqgVQODTMKWnSYn/LrMTMuBUtXpTOZIFuT/f1nMW5qEU5vTetCxF7bfEz9yI6c0nzc8wYiSQQT6RQVh5EZUXBphfrGd1SVpqZtIfDMaRStni6v6GreSZvF492AxRK2RsjE71h1XGcq8rjPdmu0s01FI+d7i9A1dpFPNdcp+jXXPa6y/zxWPh/QjL+c4m9S6fuQE8/gkOtyJ4XT0MxPJ26BcMYkp9V9TA8/t+Hx/cby8bDcbGE96Z08lpG4FZXduFyvkwhfuGr6PoQ9PS9+QV/OtVVrRVO1/Y05rNZH4Vet0SH0I1LsXMOithD009y0ldiLEyHLuN/nvzd4eoKMQVSwF3sRI+K9rHS2MHMivLMY18owBOKoEHv8iI6XcNXewfxs9sdiwR0QgH9RG0V/usvvyjC50rC8xzcN7rvf+PMMXEs//nnt/CTWw8WvS5FfjYcPdtQiy+0t4hDPlfmxWgyjbcfdKFnckqOAT8XZoT/vWfPiMAcWGEhYel7YEQPH88faFr02VgNKxUoRL/3sBv/n7c+lLiUhbtDAb2msAB/8Pw5XGyul2O5WTrHJ0XwXqqfMbbnudZG/OGLz4gjfSOflTSzDfpxvqkOyXQaY7NReU9l64g52gq2iC5VihwY2m6t3MJMYq4yWVVG6JDyNubO5ZrpeBx/ees23nz4SHKcUgYnE5TnNnfFayouxt8/dy4zjrax2QfMNTyyDJbP6mN52wRtL2PqI9lcdOtQHIFM5njewgHhDjjRmXFqrJZnarNxWF7PjMj8FNGZ1cm5CZ1dG3WgL4S/r2QdUbrO17SrevYzjGY0bCf61nAw65aOXWuX9g19RBzpDtUW0bcDp/uZzOKSuMlXbmSoagfhdL8IXe+THHJpFJp+BNV5FFhp/GLGMvnnWTRnO1RnS3YhZjl4brmguU7PbXUrb80mT+D9OOhxobaoQITbudvxbDyJoWkukq0OBfSlYvvRmkqJvaAjeE7QHJgOS8yIucpVimOMvsnQUwL6nIheuIKAzBiXX93vXLQIQOg2psP5v/3Ky6grKtxwvvaxmkoYZyFO+8vd/Yten+70f/XBZzhaUyEO+VyJ6NzGw9FxEWrJ6fpq/J1LZyQjfD1NWfMNRqH85PYD/Okn154S0Bnh0lRahP/qyy/JOcPs9q0wPhtdttqBFQOn6qpFQN8KXNCQOCKOg7c5W373fdLbgMEmIBZl9e4H6ELkgC3TaMSqiRfzBgvgQO5EdE4auar5R1eu4q1Hneieejq/ayPwwlDi9aGxiI7EnO2mjU3ew5glOtGtI+O+4gKpKY2b7FuddU70McubTDEqaCUnV36gZBZ5c3h/Wg+ZBme2iJ5bWMrMMWN+iuhejxMup4pEMi1NQTcL3ezT07F5V7q6hjPMZo9jZpzo+ZqJvhtw0KGsMnvaWgeNYYxkY9bmcq/3KWYciej3sy7ttWEMiuY8BW/wH67xPOaSr+f1XOIMV9SarIjOrxUF0VVMfA662p/EeBj6oGSrr6TVPxEJ7SbQewl+rmyuyJztvqnQfBNFxrkwgmMtRkVEf/I8niV1RQUo8/ukOp6ubUIBlQ0z2byTOezLYZiGNPZcKqJT/GYO9XIubOaYd4yM4cEIzU2LaSotxmuHD6C+pEgczxsVutnElM0j6Uq/PzwmDnlzQf52/9SMZKQzv5tu/lwx53rnAgCd7mcbasRRv1m2QxfK9ohflcHpGWn0+tNbHeLgXqjacUGAFQK/d+EUjtdWSm77VhtvR5LJZRdg6G4v8c81tt88/H2rkh5sZUHEUk40bRE9V/BYSkd4S12gdKIX5NSJTsH8wdg43u3sRufkpMS3BN1utJQUo9Tng1fTcH9sHI8nJxFwuXCpoV6+vLyw8obB+JeBmRlZ4aVwfqGhDieqqlBTUADVVtFt9p0T3drMRDpF6eAR55ctolsooo9aLqIzDz0jUucnmUEhm8pxUqJZVqElYq+U8tvk1onO6sX8FNFLSwMIBD0Ih+Po6hpDIpGWZqEbceSk0zomJmbR2zeRKTmuLIDPZ+0CkE2+wXzt/I1zuTLWj9lUAmUevzQZLXBtznU9EY9gJDqLhKGjzONDhTcAt5qj8YPDLZnZEsNhYWGHwXxrqSLY7+gw9C55rAsRr7WcL/gr8004WeETW3U8wHENY2I4duC4Kp1+CEf8Dfk3zXlcBPlMTJw9p9zrUMBsKCnCx5290LMXEIqRFMDZaJGO7uUETsneZgPLBY71gqxgyXOwujAwL6JL1nk4gtHw7Ioiui5O9GmJglkooJcH/CKE0rm8lIcj43g4OiHayEL43IMVpXjhQNO686+Xg+7ok3VVkkN+pWdgfjtm1jX+4eNeHKwsz6mIPpfjfa6xFs+2NqJoE002txvFocw35FwKtSoumPy1NBF9hK6JyUURK1wQ4Hv7xonDuNjSIIs4uXDyG9lM/eX2J23k5/hiJWxlIevWsnNDc4gZkwYn1kInejEcOVx9DyeS+KC7B0PhGRHQa4JBnK2rFbG8KhiE3+nEn924ia6pKRR6PPjuyRNyQ0jpBiZjMfRMTeHm8AhuDg+LyH6gtBQvNTejekmHbRubvY5MHDh5tBQdJiePWjMc2N5cNJs5TBg6nehWxrmwRJNO9Hz+jDMDTy70MofUNK0R0TMVYez5olufx77nx4z5KaLX15egrCyIjo5hPH48itt3+nHkcA38/vWVMrNUe2BgCvfvD2JwcErE94MHq1BYkM/fL5tthRNeM501x+TnJPenPffROzuFU2W1+EpD+6ZF9N7Zabw32InR2CyOl1bh1bq2nIno0rOJ+ddQLG15LiK6vZianScG1t0/ZSMxhJks8jgMfTJTsWFEYCKevU+ks/9uwEj3iJv8Cfw+rXw2cH8ZA6O5LiGd/AymMYZU/G0Y+pA0+9S0IyKkK2olHEqxnGM2exO/yyV54Y4FCyYUHpmLPhGJoiLoh7KMYEqhfXw2glDsybicTUopPLtUBdVFBbi5IBedgjpd63RYLwcF0KVOdL5Oc2kRvC7nsuOMW4PDItIuhbnmByrKxI2+VTGbIvbzrY2Sub1UrKcTvX8qhKPV5eJczxXVhUEcr62SPPR8hE1Kl2usyYUFRrj88t4j/OjGPfROhmRxZA6vU8OZhhp888RhvNzejIA7d9cVr9O5bOQNqyW4H6we4KJMrqJ3thNbRJdJA2961ma47mW4Wm6a1ovoikqHR+6+6NFUEp8PDCDGFV5VxbONDfj7z5wTMXyOd7u6oCkOuSAcr65Cods9n+fFZqS9oWn88w8/xqd9ffjZgw4R07995LCsDO6GC4SNTS6gyMlBvqWYOnRjGJrd78JyJzrLpq2Ck1xFxOn8H84onJCz9NuyiJVEJhOdn4fDb9E29zjsh8KYKGnUl38cOFCJutpiPHw4jNGxGfzF9y7ju791Ac1N5fD5XHC5NKjZrPOFwrmu6+Jan5mJ4d337uPjTx4jmdQRCLhx8mQjSkrs82c/56FzjsToCev6HG2MT0Z6cXdqRASmZysbN/06o9EwPhnuwY2JIQxGp3GuvE7c7TmBjRyVCuvjXCQT3XaiU/JQne3QnOtrksmYFlU7sMazeD+Iio5g6P1Ip25CTz2S/zbMiWz/jFi22j0li1EbWYjiuEZRa+EJ/CHi4STS6fty/0knP0E6eVnMKZrrPJzu57LO9GoZb3Oc4XDYjbf2nBO9tAgqq8oWnEKJtC7iY4nPu6zreHgmLI5jGvzmo1yKC8VZTLcxI1gWQsGdv7McdLUzB7x3iYjO7baUl8KzRLCV/nGmic6xyWWz25tLi6UxaC6yqxkFc6axFj++dR89E1zwfQKd9Yx1CcXiOXWjM8O7saRwRbf3TsPFhaUZ41x4oYD+waNu/P/euyzHZM4Z7pjPqK/A754/iQvN9XLe5ZISvxdF3qdd+2w2+nnPgLjemf3PY7rV6JjtJv9nnRbAlV2rG6HtdRHdkMG2lShQFA4ecldOwxtT91RIXOjNJcU4VVONlpLFrgTVoUBTVLlJUDQ3XK75Ni4U3vl7/8WLz+H/+OZb+Ky/X0T207U1aC0psYvvbPYNdP4oSuXOONHtKqM97URX1fJ1O8t2GoUuRIcPJiYs2yad6IYZggpbBM3dQtFU3jajb22pxKFDNbh9ZwBDQyFcu9aD4eEQnr14AM8804rW1goUFy0+F5LJNEZGp3H37gB+9fZdPHw4gplwXNzrx47V4diRWhQU5F+pso1FyJh+Im9d6LnEq7kQcLmRMnT0zoQQ13O3aECXMEVOUNy0Ms7FGIFhO9El7tPpegHe4D9Z7y+sIybFQCr5MRLRv0Qq8V527EOxcu4D3voHTUe803URalEdErG/QjL2E+jpjoyz3RhBMv5TJONvQFGroLmehcf3m9CcJ+yF8z2G3+WUHHOKi4xvmTuzxLA3QZd1BbzL9N3pnZzGVHTBmNwB1FNE97ikir6mcPH4mQLrUGjlRbeUbmJwakb2YQ4Kr8wldy/jMKbYztekY34pXBRoLC1CrgTjtopSlAX8sj/JtP5U9jfdzrkU0dsqy2R7+QqPyVLn/VQkhrcfdOJ/fOdjzETji5uIaposbPxXX34B7ZXl29IktaG4SETypUSSKXz0uFe0t3/yyiU0lhbnfZPW/N47S53o0eyNz1653SpsYGOkF5arWeVEZzZc7iZ6umlgOh6TFbrKQABlPt9Tq2J0nfMixedEEkmULFhdo9Ocgnp1MIgztTWSj943PYP3O7vRXFyc9ytsNja5w5mJslCKM1EIFvRLMJGGnu62Pp97nyIZ9PpE9vO1zq3oUChM528e+kK4kMQJsdX3Y1OfBNRaS7e7d6FwMZy31xWOOy6cb0EkksBffu+yZKOPjs7grV/dxSeXO+HzuuDxOuF2O2W0m0rriMdTiPERTYoTnU1F6Vo/fLgGf+f3nkdR0dabPdns8upSmo0sDSHZGWh+8agavfcIJWPiuswdjHMpl3g5E9aMg+abVxoTMs/N79iz7cYhCxiOHPbOis3+MZLxn0BP3c828VagOY9BdR6GojZCUcqyjc+9Ejeq611Ixn+BVOLt9e+1wwHTVGQBxu37XTjdLyKduo1U4iNxo89ViDC2JxX/BdLJK3C6n4Xb9+siprOhqc3uh+cBozDoIn88NiniOeGfPVMhpFa4VtGlPrlARGe1zpwTnRnmVQVB0THmGmVOR+Pi3KZIThfzwns/Bc7hmRnJQ1+Ya01hnyL6Uic6n9M5PrWsgE7Y1JSPXMF+cxUBv/Svm0gvbqxOJzyz4Y/lbGuQRq/LuarzBX6uCz8T5t0zA/2vrt1GOJZYdEfn+XCyrhr/6KULErHDBZHtGPdVFQbQVlkqFRCDS5ri8jy53N2P//L7b4gL/sW2JhytqZSmsPmILaITMwnDnIFhxKAo+buitFtgDqthWCyiOzIDjFyK6HSXx9O63AQYw+JzPT0Q4Won86ZYDj2diKPGXLy6xgsQV/YOlJWKEP9wYgJ3Rth4b+9PRmxs5mBZKSdvqlqPtDFrzeTRpIj+GJAFUptthw2l9e5slIt11zfGBCl53FR0IZJbarWIbkxnInZstgyz5enolAaLLM3PQzjnKS8vwHOX2mAaJt56+y6Gh0KYCkXlQRjnwgefa+gm0jrjOp58ZysrCnD6dCNefOEQmpvKoG2h4ZfN7ofuWlYY7QcowlB44PchkkpBz+FYnfMBBzxQtEoYqUkLK6ApsA5JrIuqNVu0zb2/sKSnOpCKvymCNsc9itoAt/fb0FzHoShVcChFmUULhwsOOoTZlyTJ5rJXNry9jJjlgqpWQVGKoah10JxHYKS/jHS6A3rqFtIU8tnYXZ9CMj6bOb9834HT/YLdeHQPwHOAYjWdwgOhmQUiui7xJbyPL0fPZEjcxwvJiOhuiUAp9LolT314Zlb0DorxjPhgpAsz2BcST6WkqejCBpA8syju01G+NDpkLj+dLuOlcNtFXg8KPLmJ4Z0TfMuDfnlN5sQvhAsDjLXJFbxPUNz1uvJXSpVMdKcm97PZRBI/vf0Ab9zpkOqEhYsgPF4Xm+vxndNHcaymEs4VmtTmAremyTZeOdSC/+3yzUX7wfsthfSOeEJy7R+MjOFAeSkOV1dIpUVzWclTCzs7Sf5+8pbCTLMpmCadAbaIvjWYfxVe0jhlu+HA1JXNRPfk9HU5oF7NU8kVPjZgYNbYSHgWbcxLdz7tcqCLPeh2IZpKYTAc3gd+HhubxXCBS9VakU4/tKgpHyeOA7JAykaOuyEze7dPKvnZWp0VLYunSn429VmKQ+XE2lonoGmExKFmkwtS2fg/uqry9y7udKqoqyvBa68dQyDgwb37g+jrn8T4eFic6Yxv0RdMuCmo+3xuiXmpqSlCe3s1Tp9sxOHD1XDmeTntWvBTyo/p1m6PaBzP63M+VzC+MZ7ORCWYOX+/dEKrUNR6ONJdlsaeyVhI77dF9BxBVz/jW/T0I+lxwjhRitVu3+9kF8tXum7yrNK3HAtEMV1lRKLzLDS9H3rqLtKpG0glr2bd6SNIJt6WCDlVoyt++SaRNrsLCs8t5SW40juA6diCOJfJkDSLpB45py9mYmZ1iWaZa7RJYZQZ1+UBv+gXFCN9zFovKZKGolxUJ3w+xfelIjp7xPHv09nnERoFS/1eFHu98z3h5uA+TEaiT0WrEBoTuS+5bPQ5l7m9XI73TCy+oiN+s1UBjBtZ+p7zLhNdVeVzY4QLBfSO0fFFFVZsMHuppQHfOHFI8siXi+TJNY0lRXj10AFZ/LnaO7goX5/w7GJzWz7uDo3hZv+wCOkHK0olAqi+uEgWfnKd175RdvfoOIdIGbo+Bag1O70ru76kX5xv+oSlp3FmxZ8lc7k7pdm8o4AlQbqOcCKJSPJpccjvdCLocmN4dhaPJiZxvq5u2dfijYsPdh2W19n7cxEbm8UofqhaGxz4pUWnf2ZBz5SMbpYx747c7N0KBQFdFkisFNGVXSWiS18Ai53ozO9mbqrN1mF/BTo6GRWV71BIr6osxK99+yzO9DTi7r1BPHw0IvnoFNLnRHQ29PJ4XCgtDaCxoRQnTzbInxTfdzsZEdSayIw9DZ3oBhfizD0voI/GZjEcDct4PeB0i5Emt1BEbxRTgQnrekfp+oA8chdksr/hwhIF60yEC6BojXC6X4KqrR6bJmPSXDV5lXNThao1ykNzX4CW+ARRfRS63isRPunUA6nI3LiIPqfEzv+fTR4gDTzLFmdFM5JtZGZWhEjDNKRXG9ENU4RxOrLnXOsU4asLAhLdQZfyXNZ6c1kxrvcPzTcfnYkl0DMxBRxoesqJLiL6AhGWr0VnO8cSSx3CPHPoQl/4/IW/t9S5ngsozjMlYCmMp+ECaS5gH1Q2Zc33WF5+xjz29wfG8O8vX0fHyPhTCxqsBmBsyrOtjZZlkPvdLolp+f1LZ+QY3h8ek9z+ufNvIVz4uDU4Ig+/24lj1ZU421grbnYu/pQGaFJl0+5MdaWV2CJ6Ft5sDNPqZph7D5Y5M2MeVroRHa5sHnpuL8ZORZU885lEAqORWUzG2GHdXHSTKPb6UOb3oWtqCtcGBvGNw4dQZj6dH8pVXbrQWbaSuZnYg5K9AM+HOVffSlW/HFiwHJ6ixn5G4ly0NmnuZOXpr+v90JgZuUuaT+7u8uaHMC0T0Tlg94kwrShB7AY4kXU42NSI9wfTUif6XFxHvpRB7trov/TQrmuw2NhYJo+vyKKKgUQijWg08z11uzUR0TXNCjfVehr15RJDYr1stoZpzsJI9+34uJXXMPYqMpYR9uZc4xJHYOpIbqAhqLjOTWAkFsaNiUHcnRqB5lBQ4w9KRnoucUATN7jVsV6sDjbS/U/NYWw2ialncuazC6rsy6Ku0XfE5LmrD2cXpHKPopRAcz0jWeixyP+aiXbhIrrev8Er9JN7QcY1b19D8wWJcykrgWdBxbuZFYgZV8KGjXPuXDZHfjg6vsjlyyiNlrKSRSIzRefm0hJxLS/ULLrHQ09VctHRTNf7QiczxXCKmcwEWAqvq7EVRHTuw3a4uPm6C9/LHBTQ6czPxTVQgrlcWl6L6HN71jcVwj9/+2MRqvn+lzKbYHRKHIlUSpIVrLo/FGQd8IwB+tNPr+Pdji4MhMJiNl1ppBFJpPBpd788KP4zw/1rx9txrrFOqiG4aGBlZYAtomdhZqips1zRZisYHFRIAyLryJS21eb8dGapE7OYKJAPTM9gcGZGbgS8ic3BBhY1BQWIpbpxZXAQ98bGpKSJX+6F3B4ZQW9oWgbkkv+Vxxdem/WTThv4N//qHdy82YfwzPKluQ2NZfjCF4/itS/lsp3JLhbRYW35laH3iRtXRaOl2913UERnabNlWdEKFIoRCkXp3YILilIEUEg3c+RGW4NMvBrvycyq3/3u4p12onNRbjcLs5wgsamoK5vjKTnNlg1H6BRSrZNiTS5u797PKl/IXEN6dlxETxo6Phvtw1A0jMQSkTyUyIy/BiIzeKv/ETpC65vPUXznuH4sFsGVsX48nB6X7QSdbpwprxM3ek5xaFkzgdW9MSazDZFnpWrXJhfRPBQy5y6eyXlX+kowEimdupXp1bNtu6UCSjHDSDPfVjGXbWRuzLzhQGaOamYW0LgQb5MfUCRkM0u6oDMNZ8356xibNLZXpuZFdGakd4xMILqgip4ieltl2RIR3SkRMQvFx3A8ju7Jp42l8ayIvsiJ7nFJPMfy4whTRPzlGjRTD9kOwZPu9jmX/ULowE5mo7r2gwLDY3BvaAyzieu4PTC8rIBOpmMJ/PX1e6Jt/fa5E4s0ru1GcThQXRDEH75wAS8eaJbImV/efyx5/GvBhZ5Pu/twa3BY+gS8evgAXjrYLItEVmGL6FkMfTSb+WezFZjDxnJnK8k06mmQwWku4Y3lbE0NPujqxmgkiv7pGXRNTuFgedn8c2oLC9BaUiIXKzrN//jKVfSFQjhRXYUij0fKUG4Mj+Cdzi6MRiJSctJczAGOzZ7ANDEzE8PY6AxC2aZtS/H53YhGc5PDtrth7FKxLHil2X/CojxQPd0pjiGb7cM0IjCMIXE9WefSZRnzwQXO7vxHBEulRIR0Qw9b5sbNiGADUFTm4e7vipgtRxbpnTDztKnoengimu/Ed0a1+PzjxN0W0beCaUSlutQw2DNq5+NW3h3sxLXxAUwnF4+pJuOZ8VffbAg/7bkPj+bcQOwbENfTmEpEEUunxIVe6QvgK/XtKHZ7c/wuVKhqExRHELo4fq2KG9Klsaie7oDiOmvRNvcuDocTqtoIPfWAMqE4/ZlJ7nRfXLYhNRcxEpF/h1Ty02zz9fWhp/ugp+/BoZRD1Q5IBYMjG9fx9HaS4nRPxX8l4rfsp1IERdtATK0jO5/OBv+weoHCPyNaOXaxqxh2Fh59utArggGJYWGzSMJr2GBoJiuYZxbomJG+1InOuI72qnIR0+dgtnddcYFoHtQsWM1DwZVNOBkFQ2MgNQ4K0HQsM3ZjYTNINigVJ/oK54ZUCZnW1aWt9Lpz1Ur75QzmQkfn+CQGQtMrCuiEn2X3ZAi/vPcYhV4PvnHisGX76GAjb1WVBq0n6qpQVRjAxZZ63BwYxmfd/Xg8Njmf57/cfktETyot1Q4TkZj83nMtjfjy0Tb5fijb7Eq3RfQsFND5YKY3b442m0PXBzNOLStR2LDwYKb7eQ7xOZ04U1uDEq8Pk9EYJqNRDMzMLBLRmZneWlqC45VVuDo4iJtDw5Kffm1wCH6XC7FUCp2TdLLzIpbGgdISXKyvy+sSIJv1o6gKXnixXUrlKaYz2oVl8v39k+jqGkMsWzJvw5ulArBqxHkok9domYjeJZNH+9q+fVBksdaFnnH0ac7DcOyymB42HVOUcqmQsApmsOqpDigqJ8e2iL45OBGMZRvJ7V4RfSfJRO5ZOe2ggG7fg7cCYy55/7T02r4CzCev8RegJzyFqURM3OPh1OIJdiSdlMdm8WsuHCwqw5frD+JwcSW86xbj1x8DwHsWGz5SEOUCp1VQYE0nr8Npi+hbx+GF030JqeTH0k+N5rFU/C0oSrEsVjskYs6Ue69hDCCdvI1U4i2JgWH8KEX39UBzQjL+BkwjBkWrhyo9YMqkx4/DwcoyXlNTmcglfQjp1B2kU1cyfYCUEqhaK1RZPF/n23J45HcUtQq6MQ3TnEY68Rniyr+B5johsTWsKOKiDCuzeE9UtCZplrtbYvV2M7IITgNfUVCq2p+I6CaGZsLzgrlEjeoGOscmRYdYKJizOePCmCrp/+Zxo7owiFA0JpEtFCj5Wr0TIfirM/np3NZYOLIot5paBn+3pqhgBXHaIYI9Y02XE3kXivG5gq/LPPilMOIl41DfH/oLDy0/s9kFt0guvnAhJZJIiOg8B4Xoe8Oj+Oltp0QCHamuWBQZtN04HJkGp3P5+qyM4D48HJ3Ao9EJPB6flAqI5RrUEp6z3RNTmJiNYHh6FuOzEbx+7KCc0wsXjHKNLaLPYUYy+WZGCA67i/WmYNksb+LrHRzkCg5ENa1dstFzCW8yjUVFOFtbg8pAAAfLS8VJvhCWIrWUlOAr7QdFYJ+IRtExPi6PpVQG/DhdU41ztoi+Z1BVBc89345nLqSRiKcRi6cwHYrggw86MD4WtkX0ZYXPo0gnP4VuUeyTyQVSfcC+tm9zHBonb1bCRVNVO7zrStNVtUaEdCs1KdOcQTp1D07PK9ZtdC+Ob0w2TR/YdZno7NkRDscwPR3DbCSBRDwlpd5cF+Dk1ulS4fW4EAi4UVzsl5z07XHwMCpAs7jR/eoRCzarY+oTlleXroRL1fBKbStqfYV4NDOO3nAIQ9EZTCZieDQ9Lm7ygOZCice3YfGbAotPc6HWV4DzFfV4rb4NQZc792N1eT2HNKJ0KKUwdYtF9NSNbN+STByEzeZgY1in6yI050mkjFmY5qQ0GqWhQHUelXzyjIhOg0EX0qm7snDidL8ssS/J2I/XVYXAShAugKdT17NNaSuzzdRLM650aDAZJaNPiYEtszhPw0gRnK5L8lDUsg28L/YYq4LL/TISXADQe6X6Kh75Y2ipY7JdEe7NtAj1MKNweb8Op4fi+u4ai+1maosKxTXMCBdCyZjiISvi54TkmVgcI+HZeeGRmkaRz4OqgqA0GF3kBqaWUVaCnomQCJKExr+HYxMiaM651NnAdCEUZOkiXhph++S1M41Ll8sopzt6uaz0rbLS61JM5THYb5c9vl23U0NTaTHONdbKZ0WH99sdnSKeL4x1ud43iL+8clsafjaWLm5ga1ncoJbJ7efjYnMcj8cncKNvGLeHRjA4NYOh6TCmYrFlBXWaWG8ODIngzg/69SNtqC8p3LacdFtEX5Lnres9m+hibUMyK+HDMmiwNiKiUAakuT6d524sv3HimOSjV/j9KPA8faOoKQji9fY2dE9N4aOeXoxHMyu1XGHlxYv5UnS1v9TSjK8fOoTagt3lnLRZG6dTk0cg6IHf70J5eXDfNxJdHlVEdLporMOArvfZ1/Ztgo4XUx+Fnrpv4VZVcXplGrT5sJtQ1FqZpFrd8FtP383mQ9uN5bYyvtlIKf5Owyai0VgSoako7j8YxL37Q+jpmcDY2AxisaQ0aHQ5VRQUeFFVVYDmpgqcOFGP2ppi+TuK6bk9VzSLpx0JS52+exHDGLPcGLMSHI83BUvk8SUclOahdKPfnBjCP7v2NnpnQ2gIFuPlmhbUB4rW9ZoiaTsc8DldqPMXoCFQLCL8dpNx+1bA0LthFaYZgp56JJUFmXuQXZm3WVjV6NDq4Pb9liyqppKXRRzX0w+hp+9nzywKNxTtPCI+u73fgMv7azJWSiXeX9dcWX5XrYRDL8k0tpZFrdHsQu6c23ZuW7xeewFHMTTXObh9vwen+/wmnLcuuH2/K72EUol3ZT8pmKcSH2W3ufi9ae7n7Oosi6krKhARfeE4fHg6LLEW0swzlcZAaAaJVCYDnNDpW19cuCgPfQ46tFvLS3G5uw8T2XVnipSPxyaQSGcqGRirQVF+ISV+LyoL/Ms6zYkj27h0pUafXMzn/uZymEERfaFbfg4KwtvpSs5HKB6zUqCprBi/c+4Enm1tkPPgcnc/+kMzuDc0umjBYSoax9/cui/C81eOtkv+vpWNOpfCRZ+zDbU4XV+DeCqFq72DeKejC9f7huT8jiSSEvO2EFYhMIboTz65KucnX6PIl+tYtgz762xaAzYW1dPdcLrO7fSu7EqYPZxpYGZd8yHmvalSor59p/Lh8vJMCeYqF3k61f+bV17C+93d+LC7V5zoLKPhRbu5pBgvNTfjZHWVPM/GZv+iQaF7eM7NYpGj00j3iBvIvrZvB0noxoi4layCDizGAmUE9J0b4G2GjIuMAoZ1ebgUEjm5N824HLv9Us6aS1jJYmUETy6IRJJ45527+OkbN9E/MIVEIgXDyGRAZ+JpMqfC6NgMHneO4uNPHuMHf30F584148uvHcO5s+uPAVgXjHOx1ImegGHMWLa9vQhd6LqFQu9GcCoqqv0Fkl/+xx1XMBwNo9ofxMu1rThVtoEc6CwKHeIWLTCqaisUpQLWwu9+CKnEh3B5v2rH2+UAp/sFqFq9iOLJ+C+l4ovHGCYX+gPSA0h1noLL+xVoziNiIOFirKo1IZ1cW0TXXKfhV/9bEbPTyZvQdcYTDsNgs09Z0OVF3AWHyn5DdVCdx+ByvwDNeVrmx5uLb3NI5Iwv+E+Rcj8nMTV0wotJjtt0UKwPQlHKxMggsXq7rCJwt8PIi0UiOiACdzSZcRZTXOyZmFoUl8LnU0xdDgqlreI4f1LRT3Hy8ejkfJ42NY3x8OLeX6V+HyoLVvvsHQh6MnEwS5mOxedd77kk87pPL+rQDLnfRPTKggBePdSKP3junIjJNHXyDneqrhr/5JVL+G/++k1MzkahLzhPuADxRx9dFdNo0HMQZYGdNyo5slFEF1sacL6xDv2haXzwsAc/vHEPD0bGlo0FmozE8ObdhygP+PBKe+u27Nf+OpvW4bjI5LrabAZDGvhZ25yVJXMZN+L2DHz5usxhXOs5Ui6jaXimrg7t5eVyAedqGAfkvHDz5uV3Ond0Rc/GJh+QzEXnAejpO5Y53JjBzuvTExeNTa4wWEKc7rHUicRMWafzrGTs7z5XtVvKqxWlUvJOrcGAYcxKUzmH82S2GavNhisV04+xG6ArjT05fvrGDXzy6WNMTMxKv45l40eppcs/mKChJ52O4/PPOuV3evsm8O1vnpFol1x8zxg9wIeVIjqjjGy20FRUH4CRzg8n+kpjb8Whos5fiIehcRHC2RiUAns+w+pZifWS74N1zW9NY1oEWZfnZXZ1wt5Gk2iVgrIfZH9Wcl4Fxngq5oG7PF+H5qIjOwpTPk+em2p2jOLPNuX0Sl8IzXUG/sL/TqqbKESvvpjCeJVaeX2n+5WMiG0ms9uYW4TntrQF2yrMNiDd3Hcgc613AEoRnO7noTmPyb5mYoCMBe/NOe+yzyzO21gpjlJXoMYwJyDSdc5M80gyKQ1GGc2yMBucMR6M9FgOLSuiM3pljowTfRLJrNDNOJfR8OJ4tBK/D1UFK48nuX/1xUXSI24pqbQukTOziYS4o3MFc9u5r0spDfjFmbyfqAoGcLKuCqUBn2hZc+M4xvMwc/wfv3IJ//qDzzPxJwsIJxL4q2u3JfaHjUb5/J3Ekd1vLZsQwUWkrx5vx4n6Krz/sBt/feOexLwsFdNvDQzj7nANLjQ3bMt7sEX0pyZJXZmbFHbj5HxnYeablDtbSKZxShPyBa7cLRf5YmNj82RwrmmHkFY/s0xEp4uUQrqeHoCq1Vmyzf0Cy8PpcrYSurk011nJ79yNDXYVpRSK1gAjaWHWsJmAnrwBTWsBYIvoG4Ul9Mym3Q10d4/jnffu44MPOzA6GpZS68rKQtTVlaCyokCiWlwuCtqQcmpGu0xNRTE4NIW+vkmEZ+N48GAI6bSOokI/Ll5ohd+fg0kuG+ExbsAqKGhZGi+4t8jEoPEenf8RRrX+Qskw3y1QdFTUOhF1uRBtFYwcSaeuyXgoI7buXVGJ91qOFRTX+W3ejlv67aw3LlBhBKlSuM7XFotWtp+PtXGEXCBgtjrF9L3H7tZ3vC6nCMKM6gjFMtdnyoeTkagIyJFECt2TocVOdB9F9KIVxe4yv09EcRoCmYeuGwYmo1FMRmPyszjRI5GnnOjMWF8Jjj0aS4vEjc4jvlDinNtfvn4uRHSJljSBsdnlRXSK/eXB/bXYw+ge9vJbGqdDQyfPh5famjEwNYM37nbIosscPI5sSvvm3Ufy+18+2ibnSL7oom5NQ3lQy0S1eL3yfn52uwOPxiYWPW8mlkD/5DSGpmckrijX2CL6QswYTGmMOQBFIkLsUrf1YJrs0j0tQgoXIqx2orMTus3uYHRkGj094xgenpaba31DKZqaymVSv176+ybx6OGwTPTJ4SO1qKkphs/3tKAmHcqTaREIJidmMTMTQzSaQCKRlrxYOuyYXc7fLSz0oby8AGXlQYnu2embxcDAFLo6RzE1FYHP50ZDY+ZYrZW1Hp6J4e7dAXESsqFcQ2MZWlsrEAisvbjDcn/m546MTMuf8UQKelqHQ3HA7XbK51ReUYCysiC8Xtemc+wYxcEJJHDFovinFIx0v5Sk2iJ67qAzKZ1+YLGITmdWBVQ2k96lQxiK6KrahDQ+tdSVy9xWp+cLWTdefgyGdwPMg83EWuR/nEs0msSNW7348KOHGBubFfH7+LE6HD5cg+amchHTCwo8cLv43XEsENEjGBycwoOOYdy+3Y/+/kl0dmbc7E1NZWioL5G+H1sh48S0bhLLpqKZ/GCbzSBj+jzJQ1+LpoJicaMXuDzL5u/mo8DLKEpVa7FURKfrnWanVOITKEoVVI1zXRub/YS11wfTNHI+z6GgWR7woyzgnxfR5zKtmV1ON3rvAhGdjmI2AK1eQfCWho5OTYRmCvNjs5ksdUZ7MGt9pqxERPTpaHzRPpT6vasK0xxl8jl0gVP4jyYXV6yyMepQaAaNJVtfqOF7nZiNYjwcWTbOpaowuO9E9NXgfZIVDa8fa8NMPC55+gsrDRjjQyc3HdyVhQGcrK2GpubXvMGpqmguK8ZXjx2UjHSK5ZEF51hmoSaGwVDYFtG3H65iTSOdvAaXp1pKlWzWQwp66l7Whf706t/24YSDpXBqLXYLslK6oJnRfmN4ZBpv/vw2fvXWHRHRX3jpEL773YsIBjm5Xvv36Yy7fq0H/+HffyRCr6Yp+Ef/6WsoKfY/JaLH4xSEIyJGP3o0gkcPRzAwMInx8TBmZxNIp3SoqiK/V1oaELGZgvzJUw2orS0W0XilZilW0PFgEN/7y89w/96gCB9f+8YpWSxYS0SneP79732OW7d6ZbHgq187iZLvXlxRRM+U8gPToai4ELm9W7f60N01junpqAjrPE4FhV5pOMdjdOJkA5qby1Fc7IeqbXxAqqrNUNVGgJnW5mJnw3bBxdF04hO4PF/JpJ/uw+9frpGGoiK0DFnbB0NrhqLmfkBkFSx/5nuwlqQ4EA1jFKbZsitd/DsFhVgRuszFTbXyEbrJ79wZQHf3GLweF44fr8Pv/PZFHGitlIXPlaivK8GJ4/W4cKEVv/jlHfzyl7clJ52COl3pxUU+lJRsrYKBPQzYENjyZrCLGvDZrEl2TKCnHuwaEb29sBwvVDfDpzlR6Nod1aCMdFG1NqQS71m8ZUPyu5m3zexrOo5tbPYPqsVCus6wtJzffcqCfhGFF7pvmQdOoXtWoldm50V0VshTdA+s4fhmTAYd7nRzzzEwHZaf+ZpsBjoHxdViv29VFznnWZqqSoNKbr9nSWwIf+6emMKF5votz8nYIPPO0Kg0lVwYY0NcqooaiugBW0RfyuGqCrx2uE3c+7960LlooYMLMmzmyc+ax45VB8s1pt1pmstK0F5ZJvsamZha9G9cUJqOb081nX3nXILBvLjkp3B6vgiYFPbsQfe6HG6JDyUywUoczJXVaqAo21OaTnGRF+U5V8tWz4W512OzDp9zfy7QNDdViCv6nbfvSIOzWzd68eqrx3CgzVzX8aWo2z8wKQI6Be7ikoC464pLnr4xDg+F8MbPbuLnb9wUBzqZc5jPbUvXdfm3UCiKx49H8dGHD9HUXIZ//J99WURij2d/fE6plI4PPujAGz+7gXv3BkVz4PGdO06GoWN8LIzRkRlcu9aDd96+i9e/chJf+erJ+WO/ke+HQ/FlGhJpLUinbsEKKB7SiW4a4ayQk38Dgd1GKvU5DL3XsgaZRFXroTpPYjcjOagSq2Jdc1FuxzQmYKQewtTa4FCrLdru7ocxfzpz/3cBd+4OSJY5r8flFUH84T94BdVVRRLfsh6Ki/zSVNTQDfT2TcrC9dWr3ThwoHLrIrrCnN6AtSK6MQLT5MSf0QSWbXpXk+k7m8oukFob0bhZjpRUymM3kXGit1mei857gc5Il1QHNO0oHGqJhdu2sdlZHEwZYF68FUWwhPcfM9OcM5dQ1FzqrKZYyAajbCxKF/kcFcEAqgvXXsCmiE7H+kLoRKfLl27lhdDVXuLzrqsZM/O3r/UOPSWi0y3fOT4pushWmn5K1Xlax2c9/Yuc+XNUFQaeasZq84SLzfVI6brkilOIXvjV4KIExfW6ogJ85/RR1BQV5KU2Wur3yYOLMguh7sb3th3kf82bxbDpSjr5uTTUse4Ku8sREf1jGOa0pZtVtQPS4X67GI9E8W8+v4qxSCQnZwJvbm89eoz/x7vvyZd6P+L3u1BTW4yGhjL5meI1410mJsLr+n26yft6MqvudGSfPdeEouLlO0cHCzxwulREIpnqCJdLlTiUZ59rE1f3r/36OXz5Kydw6nSjOKpJMplCT/c4/sOffij7tR9g5Mtf/Nmn+NEPr+Dxo1EZELHc/9z5Fnz1qyfxa985J272s+eaEQx6RFwfHZ3Bz356A3/+Z59IFMBmULRWqM5jsA42V5xEKvG25ILabJ1U4jL0tLURF4pWB6frNHY1Do80lVO1Vsu9DKnUdej6gKXb3O3o6U4Yehd2A319E5gYn5V4ssOHalFZUbhm9dJSWLXU2Fgm7nVOTju7xhAOZxait+xEt7SprSHXelYhWStS7nbSSMsCKY/b5u7vNuu9D9RA1Q7uwMZNpJMfI52+vQPbtrHZQaTywkr5K70t9x8K6BVLnNV0og+GZsSFvpDKAr8IyWtRV1zwVPPN4ZlZEdGnY4uTBqqLgk8J7itxsq4aBytLJVZmIYwQ6RiZwMedW5tHSOzMzCze7eiSnPWlPH+gSURgm+WhJn6qvhp/99mzqC0ueCoSjYsy/9tnN/Hew+6nmsvmC1yISepPf888mrZsY9tcYDvRn4JZyWPQUzezrpm92FAjdxjGLNLph9D1bkAaslosooubb3uYisfwV7duo386hK8dasfxykoE3JtrfnF3ZBS/evwY73Z1i4C+X5dnFFVBVVUhjhytk+ZndKMz35x5rcwjX4+I3tubEbedLg3nz7WgsMi37KooI2La26vxxVePiluaznI66fwBt0S1qKoD6ZSBaCwp2eMffdiBzy53SgwM40x6usck1mU9WeK7ldnZOG7d6MOHHzxAX/+klPsfOVqLV754RD4nn9clUS4U2pm1Ozo6jZ/95KaU94+NZVzp9Q0lePW14xt27bMhsKodlaZJVsVAmcYUErGfQGOjKXGj599q+m7JQs80ar0vx9TSRtIqo1xqsJvJNBcthuY6lxG0xSlrDenkDRjuPsB5OuPGslkVg02J0492zcLD9HSm7wf7VtTVZuK/Nuoa4jW/sMAr94C79wYwOTUr0WBbJRPnYm1TWxpi2PTepZTZEY3rxUwimXhXqrdsM9E23wfUahmP6Om7lm8/lbwK1XkUmvOY9JeysdkXyH1AtbSnimlufRF6KaU+r0S6qIpjPr5kJhbHQGgaS9JMMk70VRqAzkG3OpuL0kw1FwUzPJPJLQ8nkk89t9i3vvkxc9YPVpZL5MbtwSd9SrgFxtH84t5DnK6rRsDjkiaRG4X7+KMb9zAyM7vIgc+RD+NH6LRejxN/v8IxIhuInqyrwh++8Az+l/c/w+D0zPx5xXOBDWD/6todyc7/4qHWvHL1s8cdzwF+/kthFA3Pv+3AFtGfwoRpxCWeRNwBe7Irde4wjXGkEu9LRIKVJf1w+LddTGFpUNfUlDjI+Xi5pQXP1NWhvmh9XdV50WEW02f9A3i3swsf9/aiZyqExuKi+Rzq/QgbUx49Wos3f34T6bQheeV0zx0/US+T9+Xg8aJrnQI6M78pDFRUFKC1rRJ+3/IXR5avU0QvKvajIOhBZVWh/N1ygkJdXbEIxYMDIWmoFp6Jo79/Sra5V0V0HtPJyQjefPOWZKGzfP/wkZp51/nSY8Xntx2sggMOxOJJyU4fGpzCu+/cx/nzLXCWF6z4+S2Hw1Egi2C8zurihjItGcymU1ehpx/AoRRAUWxnwmaPYyr+Ztap+HQDn+1dOG2TBoW7HYejEJrrApKxN2DiaefMdsHPjOe/rg/ZTXbXQTp1L2sSsO4z2gpspM1moWwCupV7F++x/myfkXg8LffHLePwy3XXSuhEZ2SY03UBDlgr4O9GGH3DhaN04rJUbtlsL4pSCafrPBLR/2DpvZSYxhjSyctIOw/D5Xnd0m3b2OxknIvDoVqX5mJEZMycazzMJPd5UeT1YCISm3ei0zWuOp7MxSiIU0SvKFj7/hdwuSQSo8DrRijbRJTOYzYADS+JSaEoXbROJzqF8aPVFbjU0ojHY1OLGn9ORWO40jOAH1y/g68dPyRO+I0I6Xy/7z/sxi/vPxJn+0J8bhcuNNWhraJ00ybI/YJE5Pp8eKW9RY7pT28/QO/k4oSJ+8Nj+Pndh/C7XXj5YPOGI3iiyST6p2bknOT5w9fJBczCfzA8Lo11F0IJozTgkwia7cAW0ZclhVTyEzg9X4aiNcBhu1eWxWRmot6PVPwdywd/kour1kHZxiZVXN0t9LgRisfxi4ePMTgTxkQ0ihebm9BUXAy3urLDi803RmdncWNoGH91+w5uDg1jJpFA0O1CQ1FRXuZJWUVBgRdNzeWoqCgUZ/PI8DT6+yYlm3wuVmU5Hj8awfDwtDji6Cyne53P11YpVadgz8daFBX5JfOVIjFFdEKxfi5LfS8Si6Uw0D+Jzz/vEvc9FyVOn2mSGJfl8nN5zlJYuXjpAG7e7ENf7wTC4bh8LlxwCAS98PvXP0jhIFbVauH0PA99li6s7cksW4wuE0des9iQOHP92L/fxc260JmTm4z91GKRRYHmPA7V2Y69AB25fD8OpRCmPmPR+U/YVPkWtNRtW0RflcwUO538BEa6H7sFLmTyWs0qL+aZbxY6kFJpY9FrbhUufikOXnM58bbm3krxgpWlJji5mmvrbrPaooOe5sJRF7AN7kmbxfD6z3uaotbtQHyOKVUayfhb0Jxn4FDKxB1vY7OXcTjcFjvRwyKk5xoKkdQoqgsL5kV0usUpUi6MTaHDuDzoQ6Fn7UV1RVEka70yGJgX0eluZ3b5wlxpbptNJtcropOGkiI829qAG/1DuNI7MO9y5p8Ubf/D5Rvwupw401CDmsIC+FyuFfuY0NSVSOvilv7wUQ9+dqcDXeNTTzUTbSguxG+dPY6ygF9EYpvV0VRFKhG+ffKILG7EUp0YWxDfwiSFz3sG4HU6JUroZH21jKjWOz4Mx5N472GXvPbh6grUFRWiyOcRVzsjVzbStFQ3DESSKQxMTeNHN++JkL40+5yLTPXFRXKubge2iL4supSps6mO5jwEh1q+0zuUl7CRqJ7ugJ6+Y3nJp+o6CYe6vU2E+IW+WF+P60PDmIxGRRDvn57Gg7Fx/N1zZ0QM9zudi1ZMeWGngE7H+a8ed+KPr17DVCwmFxnewI5XVeLbRw5D3cciupSKF/lw+mwj3vnVPcksp3Dd+XhUHNBL4TGlIHDzRq9kvc41P+Nz6bbLFXTtUUieIx5LiqtvrzI5MYsH94cQzWbGH2irRFNT2aoN6Hij9HhdqKsvQVl5UER0NiV92DGM+obSDYno8y4s9/NIRP5dNqfciuuIiWT8TWjuC+KEdzi2JyttL1/308kr4tC1KoaHOBxBaM6j0pB2r0zkWMrPc5ANPznRsop06g5Sqatwer5gn/8rkKkWi8u5bhi7o7kiCfjdEq3FaiH2ruC9U1HW17h74XvnfXl8PDy/8L3exqSr4XBomQogtTzbkNgCzBjSKcZOhWEquuyDzcqfO6tLk/FfbItz0ipYBSrNxAxd/pv/2yw+zfVUPmwuoZmA0V4uz0tIxH4E02L3P+896eQ1JBPvwe39OkzTva9NPjb7AAf7aFk37jGMaZgmjRK5p8DrkYaZtwdH5Od4Ki2Z6AsFY2ZcU0xcr4jMrHW6hB+MZKJTeQ1ls8a5Anq+SsDtEmF6I1nTFEgPVZXjd86fwOPxSUxFYvORMYxg6Z2axn//1kf4+vF2fPHQAbSWl8jvUC/hvvOpvJZTdGdSwFA2wuWdji70Ty12TDuyOfDPHWjEiweb19X81CYDj1VTWTG+dfIIYsm0OM8T6fSifPTLXX1wKoosjBT6PNDWeXwpcl/vG8Iv7z+Wc4jxPszLP15bieaykvkqBN5zuR98ZF6a/2dKTBGjW3h/54JR59ikVDB82tX3VGY/f+NYTSUOVpRuSJzfCPZochXYdEVzHpQBv83TpNMPpKHoTmQmOp0noapV27qN2oIC/N9f/xJ+cOeuuMkpnjMT6ucdD3F3dBT/8TPn8XxTI8r9T9zTvCF82N0jz/+gpxfxVEqOTlUwgG8fOYJvHTmExqLifX9BZ4PKixcP4NOPH8tkfWBgSnK2lxPRCWNfbt/qF3c4b6YlpX5pCLrRhmmrwSakzASf36ZuSMTJXiU0HRU3+RyMuykpDaz785sTzDmgGRoOIZFIbTKWqQWa6xRSyWuARQ0/DWNIGkgzHoQLpTbrR9d7kIh93/JGfSx5V9RGSyc/240DWiYXPf0Iph62VDjRU/ehp+7Kd89mGcwYUsnPoet9u8qRyxzz4iIfhkdmcPfeoDR+DgQ2JoxxYXR4OISOh8Nyv21sKBVxPhewsSirgCwT0aX6KAQ9dRsKnbZqpqm5zXIkoKd7pMrI6h5HuSSeTqEnPIX7oTFMJKLyc9rc3Fjut1pPosa/vRFEjLZzeb+JZPxdmJjcmXt69E/hcj0Lh1phSwM2expFKYTDYV1Mp2Gw/9f2fK+Z9by0YWbKMBal6zaUFD7VLHQ16FqvWpIfTtf3HBwTNJQWwe/eeEoDHcfnm+rwa6eO4q9v3F3kciahaAx/ceWWCLdNpcU4UVuNiqAfhV63vKVZmgPCs7gzNIKHI+OYiScWZaDPwZiQs421+N3zJ+3as01yorZK4oFCsZg0FF0IneSfdPfhX3/4Of7es2dl4WWjzCaSuN4/LAtAFM4pqrMKor64EPVyzvpkkcbr1MQhz885mkxhbDaCnokQHo9PoG8yJOcm9ZqlMMrntUMHpLJhu7DvlKvACRQnuJrrNByOjZ8ge72hqJ68LS4ta1HEySS5uErx9m7J4UCB242vth9EU1Exfv7wobjLGelCp/m//OQy7o2O4fWDbTheXYVQNIo/u3kL73f14PHEhGR+uRQFzzY14uuH2nGmtgaVgcC2rYjtJihWtx9iM9EgpqejGBudQefjEWmIxn9bOOGPx1Lo6BgWV1wqlZYYmNYDVeIcX48uwFVLxpV0Ph7DwMCkuPNCU1HZFoVfCgYU6fnzeNbpLsiq994lOpvA6NgTd8T7797HnVv98HjXHhhNTUZkQYOYhonIbEKO4Ubh58xyZpf312TybuhWdf02kEq8l81kb7Uju9aJnh6Qa76eumNh/EgGp/tlqBKvtoeGxA4nnK5npDIClomKxISefoxk/Je2iL4CdI4lon8OU8+4sXYLra2VqLzVJ42iBwan8Od/8Sm+8Y3TqChnD4i1vzvMPv/0cic++LBDGk/zd06ebFj3Aut6RHRG8Vm7BJdGKvmpNFFUbBF9RXhNSCXezromd5+BIJJK4vJoH37acx/3Q6OIppPiRtczFsZN8YXaA9suosPhhqYdgeo8KJUAVlYlCWZcPvtY5F/A4/8DqBoXq/fQfdbGZgEOR5GlIrppjIpxYTtgdjmd5qtBtzCzrtdLWWD1JqTMW28qKV41bmU1XYUZ7t89d1wqn9hQdGHuNi/TFEXHZ6MislIsFTd6thqIER50oc8mk5J/PudkX0jQ48ZXjh7Er50+silx1yYDhevT9TViBh2fjUjmuNxLs5/TVCQmuel1xQX4Qnvrphq38vPM6N+6VFFQtO8PzeBa35BEEvFzz7jRWYlgQjcNiRmkvsbHwsWdOXhOUnz//UtncLapNme568thi+irkClzu4608xSc7gs7vTt5RTp1DenU59t2Y1gRhxea6yIUtXLbha85saYiEIDP6ZKV0LrCArzT2YXbwyPonJyUEpfR2QgOV5RjYHoan/T1YWAmLBf25uJivNzSjBeam3CssgJFXuaB2gPTudw1logfOlyDsfEwxsfCGBmZwaOHIzhytBbagoWGSDSBK593YXY2IeVc1TVFOHy4es0mlrzgMm/9zp0B3L07IP8dmopIBAndedKALa1D1xkXwws5S5n3smy+mEQyLSLJHGNjYXlshmRK3/SxY04uI12Ssb+GweuJRWXkerpXKmlUrd2+vq8zC51NWSm8mubTHdC3D5dM6lUX88NLsLdQJQ9XVRtgpB9mI42swdBHpfcLY3kysUZ206U5DGNGjgub7ln5meRKRD/QWokHD4YRDsfw7nv3kUimcPxYPZqbylBWFoRvSTNuLiTzuUPD07h/f1D6ZNzvGJL7cH19CY4fq0NR4fon4Wv1AlAkiz9TnmsNhlQeGZ6vwJTGxPbUZymmwdib20gl3rd8gTQXcMz9/lAnfth1B5+N9mEysfnqEcYHBJxuVHgDlkQvMtIFjgCcrksw0t3S+NlaOPadkcVcVW0GPF+y+2XY7FkcSpGlzek5r+F4yzRjOd8u886ZH84ebnMZ40upFxF9/YsG1DooPjP3emED0Dm4LbrE/a7NaTCaqkoEzTdOHBL38dsPOiXPmoLqwus5Xcd8rBdeq7mg8OqhA/jioVYcqiyHU7VNi1uB58KZhlr89rkE/tUHn2NkJjzv/E8bBkZmZvHX1+9Kv7/nDzSJe3yz8DOnKM7H4nCe9cNGp6y8ePXwAXz5aBtqCoMbalK7UeyR5KoY0oArlfhAslgZPbCnXHCbzUw0Z8TFyWNj9WCbubjsIp9xoVv3WfBCf6KqClWBoIjqZT4fbg6PYHh2Fm8/foyrgwOYiESR1HURy9vLy/B8YyO+dPAAGgoL5aZh8wR+jehwO32mUQRuiuiTk7O4fq0HB9ur50V0itvhmRiuXOmSjFdNU1BbV4wDbWtH+fR0j+Pyp4/x0YcPRRhIpXUUFnhRVOxHTW2xON4Z4cLPRlUd0miTJexdnWPYG3m+q8NjS/FkDlYFFBb6Vm3UuhyM1KmtLYbbvbnbCRfDVLUWmusC9HQ/DL0T1hBHOnVd8l9V7aA44u2mWivDyBEKLOnUTUu3S9HNxUm9WrfnhF6ebw5HMTTX8Wwflg4Ltx4T92Ei9tfw+P8uFKVi349vMpgw9CFpPmwYY7tOUCwtCYhznH1G6CgfHArhl2/dQXf3OFpaKlBZWYBgwAtVU2QExcx0VmpNhSIYHJzC3buDUq3FCXltbRFee/UoGhpKJWc9V2M4Ra2HtbDyoke+Y6rzMFS12uLt747rOxca9HQXdiODkRn8auAxPhruQTiVEEGlxl+IIpcHXs2JUCKGjulMVcnhogr4nS6ZtMf1FKaTcYzGIuJaL3J50VZYhlNlNaj2B1HuzU0Fxnpwup+ThTs93W1pv5EMujQ2TcT/JiMyKkGJvbCx2Wvw/IaFTnRWehjGiIwraFjIJR6nJlEtzDynM3jOKUx4f2ejzuqCAAIe94aEyBKfV2JUeiZDT/27Qid6aZE40bcC89Ep1HP/yx/60TEyjuGZ2aeaQ64FG4iW+n1orSjB+cY6EdDpvrer/rcO5wRlAZ84zdmw9md3HmAwlDGKznFrcETid/xuN55taZBzbiV8LicOV5djcDqMoekZaV5r5uh7wGa4bRWlEhdEEZ3n73Yvotgi+hoYeo+UgTrTL0Jzns3+7X6daJrZhYWbSCc+z3aStxIXFLVKXKuciFkNV7NY4sTGoAfLSvE/f3IZl/v6EU4kEFmwWttaUoLvHD2C19sPwue0YyJWgiL64SO10szz8aMRTIdiuHmzD9/89hlpYsZ/T8TT8w51usYp9NbXl6KycvXBPXPW33/vAd588xYGB6Yy4nttMQ4dqkFbWxXqGkpQWhqQnFe3xyXbGx4K4e237+56EV1KnnRjvgnMymSadsxx5Egtjp+oR0Ghd8NVBTyWFOC3gsv9MvTULSSZQYxN5KtvAkPvE2GYkRpO9wswwYig/Xp9X2VBxowiFf8F0onLllUKZOB1oBIu79clxmuvornOS9Ubm5lbGSJlGlNIRL8Pp/tFKM5gtuHW/sY0ouIETSbe2pWRFrx8HTtaJ5VWk5MRdHWPIRxO4Oq1Hnnw39mQ2+PR4IBDFlJZlcR7xhxcEK2rK8aFZ1rwta+egteTu3JYh8I+GLXZ3gZJC8/3JFKJy1C1Q1CUKvs6vwA2XU0l3hEBd7ctGs1xZawfHaExEdC9qobGYDFeqT2A1oISlHh8uDY+iEe3J2Ty/42mI2gtLBUnXSgZQ/fMlPx+V3gS5R4/nq1qxO+1n0Gx22dp9ajmbIfmPCYVAdb1DFhMOvkZkkql9AJzuM5bGnthY2MFbOTrUKwd6xj6sDS4zrWIzvsYc8YvtTSgb3J6kQDNuRkF0IpgQITmjUAn+sWWejEQLoXxGC3lJSKIbpXG0iIRO5m/TSH2s+4Bybxm80rGe9CYSIc6r9uO7Ptl00lGfXicTtkH/v6xmip88XALTtXV5FQ8Z6RJqd8rrvZwPLFID+J2uQiQD5T6fDhYWSaLKQthxUBgi+M3vlcudPzti6cQTSZxd2hUInUWMhWJS/PZU/XVq4roRT4vvnqsHZUFAVzu7sej0QlEkqnMZy3Z5rpk+s995jL9lObgWfOlQ5EFcn4uPKfdTk0+AzrOmX3+XGsjTtRVbav7fCG2iL4uB8sDJCL/HloRM5zYoAn7kowol0Qi8mfiWrEaZllKczmldMfKcfll5kWc2eZsFspyJoro8/uYbSJKsd2tqvOOYHvC9jQ8JiUlATQ2laPjwTBGRqbFPT48NA2PxyXOt/GJMO7dHRABnRw4UImGhrI1s13v3u7H1StdIqAz9qW0NIj/5D99DUeP1UmW+nJMTmp74nMSZ2EitaYb3elUFuWf19WX4vyFVtTV7UxkhuY6Ac15QipcrFyg0/UuxGb/JVTnMYmJYsSGTYbMOaQjnbqLZPwd6Lq1131WHGmuk1C1o3u6SoDnPfNwkXjb4kWKNExjCKnYT2RiqToO7Ylr4FbO93T6PlLxd8VAsVvxeV04d6YZZSVB/Ns/ek8qsRiLlpmQQAR2PpbCj56ff3NTOV7/8nG89uqxRc22cwHL2Sli0xBh6IOWLZiSdPJTpF2MZ3x+TzUo3ixzYwT2f0om3pHKlN3KjYlBjMYyMWMtBaX4P517FSfLauBWM3OFuJ6GU1GR0NM4UVqN8xV1cGX/jZP1ocgM/odbH+Ct/kf4m557KHR78B+1nZbfse6aqEBzXYKW7kAy1rtjc95k4k3xinmVcom7I/v5vmCzt5AFIov73NGFzl5CpucrOf8+0XX9//z1zOvmiqM1lfg/13A+tP1QdD1cXSHO9OloHFf6BnG1dxAPhsckH3syEpXoXB4zCqaMpqHGcrCqHGfqq3GsthLVhdtjsqEo/dXj7fLIZ7Z7HylKlwcD+D9+7Qtbeh2F48uyEnmwuSwbxd4dHsWDkXFpEspKBDabnYxG54V1LqRQ13BqqhhTA243Kgv8EtvSWl6C4zXVaK8q29bs85WwRfR1YBohpLg6H/sbOD1flPLr/Qjzz5PxnyGdupJtPGQtiloLp+cLOypyTcZi+KS3D//h+g08mpjAdDyRWRVVFMTSafGtvdvZhYGZGXyhtQW/feI4Cj0eS3IVdyvt7VV4cH9QRPRkMiXRLXQ2i4g+Fpa4lznaDlahsal0zddkDvpck1C+1pe+fFx+l+LCSqRTujQxzRvYTGP+h2xe+zpMe3QW8rgtdBYuB8URLmI8xqj8PDkxK9E5O4nmfhHOdDcSsb+wbqMmYy0eID77P8ET+Ps5d4rsblKyoBGb/ecShWA1bPrq9n5nH1R/OaFqh+F0nkIq+ZHF2zaRjP8EqrMNilIGh1qO/YppTiMVfw/JxK+w22F1VXNzGf73//nruP9gENdv9Mmfg4OhRb0w5p5bVhZAS1M5zp5rxqH2alRXFcHt3iaXlcMDp/M0kkZIjrlVMN8+nbyBVOITuDwvWrbd/IW9TKYRj/4p9NQ97GYY5zKbSkqO+f+fvf8AjyRLrwPREy4tEt778t5XV3s7baZ7DMdwNDQjWlH7SK1IrSizu++9XUmr3Set3urJcOlEUrRDDTned0+76Z421d3lvS+ggIL36TMj8n3nj0wUqgoFJFBAZgK4p7/oLLjMiBv33rj3/Oc//766FuysaRICPAeaF1magQTSSDrpu2wP6gNl+KUtB7nSksKkX7t6CgfqWsXahXYwhYJpbYPl2ScZcgWvOZVDhlkbP5E6KIHQP88WGl1dVmoKaxk+d62jVSKTuduuZLlIdAqEyJ24mfSrfU27OIT8Xjy4rg17WhvFG5vKeqqSc9M1SViK6Mi5eEyqkE1RIyusPOisPeLzyL1mAIX3Om27KnTaCTp8GmfV6DNFHvp0NoIhWQc+0xQLomJA9by8veL6EY/8GTS9DqZnP3S98HYixYTjTEmKIRfbLJBRDC90w1gvqY7FePgwCnp2cBCvX76Kd7q6cGl4BPF0WvzRD7W14oGWFlwfH8PLFy+hfyqM84NDmIwncGN8Ap/ctlUKi5b7VFrkbKC6nBYtVI5THUcS/aGHN4p3OQtdXr3ikry0cmnvqEVV1fwelcPDU4jF3AyBQNArRH0w6IU+RzFSEgtDQ4UPDt0LHsucPl8WP41Gk7NWIp8J8bYdi2BiIiqR27lA+xUWjfvwA9eDvLt7WIqv0mKnONCENDU9B5BKfgDHpi9oIcCH9JR4ozNQ5/G/BMNch7WOTIYWD12Ih/8I6dSJghdY1PUmmNY+GKxHssohKarWVqQ9+5FKvldQS5dc4atE7DtSF8Dj+wQ0ba2pdLOK3PjrSCV/UjzyagnBjSZJ8MbGCvgDHvE1f/yxzfKc47MkkaASPSM1LZj5FQx6pNh3fV25vJJYXy5Qje7O84cLSqLTnoeWYanEq5L5pGnlqzrDZU5kHGScYdnX2MmTBS4WvfSgrzlV5k3BcrSXVSJwB/HNjTctAJCGCF6YMu6fMf+SYF9fXo19tS04MdwnpPybN6+g3h8sKIkudkfWbni8TyIR+zqKA4o2xsXaJTb1f8FX9vdhmlvl3BQUVkUtGqrRjTpk0oUh0ZnFz8wrFnP3eBnALVxh05UCzsNS1NnrmdVGRmF13u+Ax4PACr3dikTPGwnxAk/Gvi7FzTTPvlVX5GxOMoXphfHvZNUqhfcJ1c0O2fQw5byQYBRsMBLBkZ5e/PjadfFAvzExIQvyHQ31eKSjHU+sW4ft9fUYCE+h2u+X37swNIxrY2MYjIQxEY/j8XWdQra3V1ZKBE3hFqprytDWXo26unLxPydpTvKcm3gWOiMhzAgkC47SCz2fApZUledIZCETfJb4v85FPvf1jeP69dLxQy8L+aaJDNqz9N4cm/Y6v1diA4MHly7131Yw9F6orgli0+YmaU8GL3p6xnD5yiB27mpDbV1xgoQMTprWHljcQEa7CzjXOHCcvmxRrTJoWiBr7bKWiyt2IRn7AZLxl2VDXdh5X4Ph2SWF1tZKcTM3aLAdutFchHojGdip00jGX4WuN8PyPoi1BK5xZH0X/yHs1IUV6YU+F1hQmwfWuXZffI7k7FxYxJvWXvRPLRRcEn1/UTz4HWdQMkvNxJvw+F5ao7Yu7AN9SCV+hETs23Cc4RXf5ylqocd50LRQ7fPPmo7uNyyMIYZIOilFRO+Ez7TQUVYlfuk3wuM4PNCNj7dtQWOg8GICy/sUkol3kJHixk6RiPQxqQ3BNVHG9wmYnr3Q9eLY/SkoLCVYSF3X6+CAdWgK9+zhmppZWNBpDay4AAWFlQxFoi8ItmyyNHpy6xUwzM3QtNXtn5vJOHDSXUjFX0My/kqRFnMemNYumJ6DBVefU0n+XvcNfPf8BVGiJ9NplHu92Fxbi09s3SwEekdVpfx+uc+LhlBIjh9duoyTff3oD4fx8qXL6B6fwGA4gifXr8P66iqUeTzKYzALbuJbW2uwcVMj+vsnMDUZR8+NUUxOxNDdPSKkMX3N9+xtR23d/Cr0nPqcxdOIRDwl3ujbts2usCaZQOL+9Kke9N0slCphftTWhqTwKYMA8VhSCp729IwK6T2bT+3UVAwXzvfh6JH8FNyhkB/r19djw8YGKdzKYMXpUzekAOuhBzegqiogfXS2fkoShoGHaDQhpAzPlfdoKUAVuMf3LNLJt2CnWWT0bu/e5YIUNo1ViF+i5X0aml699sZpxoFt9yLJOT/2TVErFhqa3gDL85Bs2tcKNN0vVkKW52EkYt8oeLaXa3XxIRJapfhVMyujWLVHColMJgHH7kEi+jfiDV2o9O5igc8TXSdxXsS1q+aFYW6UOjcs7uwWGC0U3DVtIvo18Xvm82atFU907CGkkm8jHv3Kivb+nw0UuBizkFO0dglaHpFSjCViiKdnt+6r8vrR4A8JyX51clS81AsNEtVUo9O7nxaaha2TMRO0EIwjIeuAiGQrsAi2YTRmf77G1kYKqwYk0AstlKF1VirxDtLpCzAtf9bWRUFBYaVi9e+QlmGjmWTas+aBL/jLgE7/0NmJphWNrG0EFYhcQHFTT1VCMUBlHlVLTHcvJIYjUfzxR0fwysVLmEi43ufVgQB2NzbiNx4+hE21tVLkYCZIsP/U9m3YVFuDb509JwdtXc4NDaFnckKI+F/Yvw8PtbWt2QK1s4HE7ZatTXjnJxdF/U9Sm5v9nu4RaLom6ejbd7TkZeUi79daLUpuKrMnJ2P44PBV7N3XiWoEp8leeozTf4vE/Q++fxyH378sP3NmUSjde4hkx8mMKtLpNEnIWx5u/B6/pjo85+mVe81htvmjoaEcdfXlQphHIgkMDU7i5R+exIsv7UF7W41YvfDPSGKz8OqZ0714+63zOHumVwiS+dTovNa6+hA++al9+PM/exuDA5M4e6ZHiHEq+Umk87N1w9XwZ7LX4tBaJpZEb89oNsiRwUuf2LtkBejcdOYt8Pg/h3jkvyLjjKKQSCXfgYMooFnweJ9DBr41oRjJFZnjQj8Z+zYSsa9JBlLhoUsqO4Omuu4GKNcKdKMdHv8nXPW/2CsU2NaF6cYJqtHLs+ubmlUtFKACnV6lrHfDPk9bJ4Xlh8ynWhCGuQl2+hoyjmvZVijQl1YCRtGvwBf8EnRj3aru54Lc/J6JIZX4sVw7g8arBbRcoU8uSe+p1N1BGY9uSLFQoi8ygamUa/c3G9nuMQyxzqNFDNXtxYBhNMEX+Dmkkx9JkK/QQdXbkEkgGf+u2Lt5/YPwBj4nVkisT7Xq9r4FFKjJPc0kAAlWW6t/DiohiFBAzwWDClmDYhTJ2LeynuyrX4ipoLCaoUj0RcBxBqYj84Hy3wYkmrgaJ0Ib8cifiFdq4dPLb4GqVFo8kFwpJMbjcXzn3HlRpBNUkH9q21b89M4dqPD7ZcE+G7ik3FJbi189eAD7m5rxO+8fxvXRUYQTSbx97bqQ81/+2b8Dz6rsM4sDlcxURVdU+EUR3dU1LEU+WWyUxUB37WpDVVUQpplfH9i/v0M81mnPQg/YI0ev4T//p5exZ2+HEPZUv09ORHHl6iCOHbkun1NbW47OdXV5K7lz+1Iq2anKjsWSclBBT0V7Ll0+EU+L1/jlS/1SLNXn97ivPFi4Tbs3yb1zVyuuXRvCsaPX5f1e+9FpCTBs3tyI5uYqGKaOqam4WLhcvzokbVdZ6Xqdnz17c/oc5lKjP/b4FnR3DeONN87JeV6/Now//qM38c1vfISOzlrU1IRgeQwh1iORJIYGJ9A/4GYM0Gd+3/7OeYuYLkYl4vV/QTb76VRMin8WDhnYyVOIOv8BGScBj/+5NVJMmiRLArHI7yIZ+yEcm3Y6hYYOTa+F5X9BCqytNWhamVtg1PukW1iuoH7RLkgqx6N/LYXkvIGfhmG2YrVCCPT4DxGN/CGwwj2hVyIszyEptmYXmETPCWLYz3WjFR4fs0CKVQukkLAlOJqI/q20+2pCbda7nMR3f/Tu2jZB04PmQAWOar04Pz6E4fjsNT5idgrhVEKehlSjs9BoUaD5YVjb3KBq7FtF3YPlQDvPuPNfYKfPwh/8h9BlzKxFO6QlQCYOO30JycTbMM0tbo2GNW0hWFjoRhMMo02KjAK3F9peXiSRiH0XpkVrJPqy1xbwsxUUFJYSikRfFBw49gCSiVeRmUzAH/zvoJtUFntWTYSc0VJuMLjBdFUQxVBj6LLBYVq/YXQWPHWQykz6LLL68zMbNuC5TRuxv7kJNcGgnMm9FBhSpMgwUBsM4qGONlQF/Pj22XNSkLR3cgqTicS06lPBBdXOtbVl2LW7De+9e0lIXde31ZaCog89tBHBAD3k8usDTc1VePzJraLgpkUJCXmSyvT9Jnmta0Aq7SAaSYgNyqZNjXjy6W2orAyKJQoLr82Hnhsj+P3few3JRFoU7Q4rStuuKpxkNg9ieGQKb/34PE6dvCHqccNgOr37Ggr58KW/+xha26qlCNxM8Fp372nHxERM3ovkOYvB0dZlcGBCroO/Q+V7JBKXz966rRkPHFov9i/0N5+PRKfanwryT3xyn1z7W2+dx8UL/ULMx2IpjI5GRNXO38up7PmePA/eH9MyYFr6MoxMFlWthS/wi4iFfx92+gwKi5Sk/JNQzmRG4PF9HIZZ+DmoUMhkknDS3YhH/xzJ+BvZDXvhlW/0SvYF/64UMSOJuxYVukzl9wZ/XlJ+M2mSQYV+VrjP/0TsK1J8wev/hFhvrDaQRORmNhn7PuAUo51LH5zrR0amJFjMIPZSZRvlINYQ8VfEj7/w8w1TxyJIRP9KLMNYUNcwSaqszowLt4joXyCZeB12msXEZ7czWalgMdFyj0/I8euTY0jZtmSP5taMIY8XG8qr5Ql+bmwQ58YGsLWqHrW+24tldofHcWFsSGxhQpZ3VmuYQsDNfgvA6/9p2KmLcJiRV1AxwWxg5s4AkvHX4aRvwvI9K1lj9HDHGsjWu1849rA819PJ47DTp+Gkb8h91QJfkuxLhcJBgyVqdK5teC8K+9wJSzCTdi4e/wtrzk5MQWG1QJHoi0ZKUp+T8R+JCt3jf1F8uwtd+HI5/EHt9JVsQbnvwk5TjTh72uPywwOP7wUY1mZoeuELUDGls7OyEs9u2iDe59vq61Dlz7+iNhfwlT4fDrQ0w2eaaCkvx1vXr2MqmVylVNziwY1OVXUQBw6sw4cfXJ0msanGrqwKYs++Dvj8t5PMc4EE8759HbAMHU1Nlbh8eQDDQ5NykBAgMUzfdBbQPPTQBuzd24EdO1qFdKfCO58Co+FIAkeOXEMqOffmnyT74OCkHHeivMKPT/9U7J5KbqrKDz6wTs732LHr6Lo2LKQ81fUk16moDwQ9aO+oFU/5vfs60NlRK+p1y8wv04EEeXNLFR59fDPqGspx/txNXLsyKOp8fsbkVAx22pHfY6HTsjIf2tqDqK8vx7p1ddi1px2ePIq9LrQ/ZDIeKSyZTp0WRa4bzCskSCxfRiL6dfGQ9fiegWntE7uZ1QReWzp1TJ5lKRLo4oFeeB9Ybiho2+XxPQ/dqF8TFjqzQvO6xXU9B5B0xrOF5YohFLgh1nVU7Hq8z2f96Vd4+r4Erx0pLJmMfw/J+Jtw7GvFPquSBIOmQ8NT+Mu/ekea7TOf3o+tW5uX9DNyJIauN8BxbqIYcNe73wYyNjz+T8IwO7Ca4DiTUiw3FX8ZifjL2QBpIT3oC4OtlXV4xxtAT3gCfdFJXJkckQKhVtYugV7n26sb4DVMTCYT+FHPZQmbHahrFSI9bqeEfH+t55J4odPWZX2oGn6jmNtkI1sj5gUJbKZTR1F8uAGZVPJ9IYDt9OVs/ZKD2ToaK/j5sMRwnCnJdmLdATt9XbL7uKfmK/kDPlsJ93VlF/ZdcdA0WWcy47GwJLqLdOqsrEG4n2D9JcnCVGNHQWFFQZHo9+1vNYSk+IWPI+MbhOk5IL6mK3FCzDiTSKfOSQFRbiocp7+I6ixGiVtk8cgNVjFAwvynd+3ES1s3oykUEnX5QpFTpe9tbkJtMID6UBkuDg2LykXhbmsRErLPfGwHEklXJWUahqi0m5srhcRdCBoaKhB82IvWthqcPduD/r4JIclJDpAMDpb50NhUgW1bm9HUXIlAwIvhoSk887HtYiezfkM9qqvv7cFeUe7HM89sFwX4YlN+aVVDhd+9inKy/5DUJ5lOa5VLF/oxMDiB8BSV546Q67yOtvYabNnSJAED8UnPZPDkU1sxFY5j2/YWubb5wM+pqysXooTWM/Q8HxuLIBJNirqefZbBifKKgGQN8PdbW6vFt305QBJVM+rh8T3nVrWPjxalwJadPoeMMwLH7ofHP+rO8XrDii666GbC2HDSDAIdRjLxmtiHMMW4OLCgm53w+j8rxTXXsjKHHpn0m7W8z8rGO50cKdoG206fRyYWdtc3iMGy9iMD/4oMcEi2hTMMO3kGifh3pcBXRtY4CveaI0ZGwnj5FZdgeOjBjUtOomuaBdPagbS1EU6iOCQ6x5YbqE0hI4r0Z2fMQdrKnt/tbqSTJ5FKvCVBUrdo7urMuCBB3hQM4cxYP6aSCZwfH0R7qFLIcIJFRalE31bVgLNjAzg+fBPjiRgujg+jwV8mNi7894XxIYwnY6j0+PBwYydCnuI9i9w9pAXL+4SICLgGKVaw6W6kxdbFJYYvwLK7JPhrmO3u+kiETytz/Cze4zwOx2bge0SeNbxnrPlA2xbWl3HswVWXAbKSwf2FYe0AYn9b+A/PRCQQRRqONn6mZ58Ih1Yab6RQ2hiIhHFxdBjDMXfvvL2mDm3lFQhYK8c5Y6CEr2HlsgAlBBYAS8a/IxF5j+8lUbMwwgkEs0UjtJJfbLsqh9NIxL6OlCy2Z/cLLBQ0vVoWjoa1tWjKT5Lef//BB+a0blkIWisq8MmtAUyt64RxDz/1tQwS252dtfjtf/rSkr0nVdMsWMojH1CZ/nd+5qG8fpfq7X/2P34KhQBJ8O3bW+TIB1SI/8PfemHBn0NSvrGxQo5SARVOHvsmHBKKqRNFIRRdEv/7sln0Br4oc5NukEgvEyJoxYAFcJGSgCmDpMnotyRoattXinpaml4Hy/Ow+L+6HpUKlvcRpJMfSCp/JlPY4rozQSKAinTaG2mhX4dubBSrJY12Oytgwyf2dJmIXEcq8T4S0T+Fnb6xKtW4SwladyUSy0/40PfZMLcilXiviASTI8HSeHhAlNqc42lR4c7vK2eb5K7n07IncdL0+2dG6Q/l2lY7OkNV2FJZj66pcdT4ArDvsEykLUu1L4jPr9+JyfNxXJsaxaWJYTnuhM8w0RGqwnNtm0TBXmzQr9/yPiUkOutxFS87+G6wr7FIL4M1HDMMQlGZrpvroOkV0LTgihSVzb+OcqQoaCYTl2K9MuacQXle26kzSKeOw2b2ZBGEHwr5QddrxEaHWZDFyAYg78K6TxlEEdD+kYwZgM8cVS9NYWlwbmQIv3fsMA73uZncv/3Ao/js5u0lQUCvhmtYOavDFQBZhEe6kUq8CV/wl4Ro0YxqlDZcAv1WAdHuElCquCmM/rJflU1MMbHUyz7auvBQUFDIFwYs3zPIII70xEVRcBRPeXUB0cn/A4b1DfgCPw/L9zQMYyUVpKNNR78EfeORL0uqcfHJRN7fx+ALfkkR6DMgfplCnHQLEVZMZDJTSCXfQ3r0OLyBn5Giv27h1xWw2cvEkU68i3j0b5BKvFYUq6KVCGYfsfbFckM3OoRIdy1dCm3ZdTsYrEpE/1oEJVzDe7xPQzPqsHLA2iyjEhhNRP5cVLCFLZpXPGjQ8PH2LXi8aR2aAiE0BkJ3/U655cUXNuzBtclRRLuT6I3cbbFHrC+vwS9uOYhtVfUwSyTrhlZnQlinT4g9T/H3aXeCVqBnEQufQ1z/K1jWIRGUeXxPABpFGauIRBdf6yjS6XPib55OHpM5gxZobm2HUrs3CrPDA8NoEqEOMzKLEfCgVWUq/hbC6W4Eyv85TM+D0LTKgp+HgoLCwqHYvCUGI9Lp9FlEp/4tjNg34PF9zFVUS4SxhJCxYdsk/H+MRPTbsO3ryDhjJfHwN62dktavG81F3aQvh3JiVakxFBQKAHfM0C/7EPzBX0Ms/LtFJn5Jpl9CLPyfkIy/DMv7GDzeZ6CLcrE0NtyzWVnYtOpKvCGp/Xa6S4KnpUAoWt5n4PW9qPxU7wDbgim+FlPCUxdgF927m4ruKBKxb4jKzvI+KQV3XTJdzhilArHmEDLxdaQSr7tFWu2hkujvKwW0CyuEEp1D3rS2w+N/XsQcxUdG1KSxqX8vNSJoJ8YCiq4qvUTndyeOdIq2LW8ilXgXjtMLxx4tgQBpYedLqsdpZWfNKCh6JwxNw69sO4Q9tc14f6Ab58cGxdaFti8k3vfUNuFQffs0gV4qzyQNutTd8gf/e0Qm/2X2+V344t/zIyP2X6nkT5BOn0Ys/AcwrU1i9cK6Glwn6XrpZDrmUyeMHvDp9DXJhmRgyrHpb34TjhOWPT/JVyrS1fNlZYFjW9Or4PW/lLX0KlbWQErW5NHJfy02fh7/SzCtvSsry1VBYQ1CkehLDqZ4xSR1mAsJKv2YQmyYm2F4doj/o67XQtPm9yherrRmO3UyGzk/IxY0LKxUKottTW+A6X1YNuiaVvxUDQUFheKD6Y1GtkaCnb4qRLDr71okZOLZTdSkzPVMZ2YqJgOAprnDJYT14qaBZ5wwbPsG7NQpKWLEdnM3gDdLJh3cMHdki3LvK8ozsdSh6WWwvIfEYiIW/r0SeE6TIBlFOkl7FFrAnYFpbhGynwSPJmsbs2gFQ227z/WfTZ0TVWQ6dVmU/FRwLn2qNgnVTEkID5bPzqUQpJAGw2gXOycWtKclQvHJQc7vPUg57Oc3kEq+45KA1j6p3aDrd6ucC4lMxhYFo8zptI9IX8iu5a8vc3ZRLohQmv2eNixzIUeIkyx/uKED68urMRKPImGnpeZL0PKi3hdEnb9MPNRLCkL4VcL0Ukzwq4hH/yL7LM+UZoZzZgoZewpAr2SY8FmhJ16FbjRBN1ph6HxtgiZf10PXWVunGEGqTJYon4LjjAhh7vqZ5/7N1zE4UuR7QsYd7fDcZ0optr3CwgvaPyyK9LQzUaQ1ViabyXEdmcwPZF43PSwu/wgMa7tYIq1Eixe3TkA6O26Y9aNB0yqgGzXFPjUFhSXBGifRxe162Xyw+JClxQtJal0/CiO1GWlrMwxjnaisdaNONp26TssXa1lULoyscjHgFqTpE19TRly5oHEXYMXelM+ECcvzoKSw0wOwFJF2HESTSQxGIpiIJxBPpeR7LB7aVlmBhrIyeBZRgFRBQWFuaJofhrURvuDPyQYtnTySXZgVETLHs2jUFdngps3jMMwtUpguN8frOo8aQFu+gowuqRKTQteOPQRHXnvlvOzUeSFY2GbFKlB5N0zxQWfGkfinGrXFPqEShSaEA22D0qljSCU+KBF7hgQcmwGZG7B1N5VdBALmOgl2aUYDDBYE1wLLtPkj8RGDw82ZFP0dzvZ3FnG7IodrTbccJLAuFiSWZz9suxfp5NGCraPGx6Po6S2MP/7kRAzd3SxqW5hgkWFtlqzNRPw7JeMjnMmMIZ0ac20bGDCyTsAwN0E32oV00Y1Gqd9DW4DlUiy7RETSFeU4o+563qF3+w0hWxwJjl5f5vndJT9cRb6BVPJD+dyVjGpfQI6VBAYouZ7w+H8KttMn9avcfVwpw5HAq80jfR6AV0g02jeRPNf0+uw6qUb6mKYF3LWSzmcHD66baPNmshKkZCdrzFDO/tuFG0R1RWxO1q+cc39SsvAynJ8z7nHLwzyaVZDziIggQgpoS1HQMTln97VYxKpCIUC1N4M5DJCy8CtrBRUPmWwB4QE3MJq6JCQ69xOG0TrjeWMWLUPGfR65+w2KiW6NJ34dmzG23FfHiQAMqGWmZE9EuxpFoiusFqxZEl3TyqGbbTIhcaLig3P5ihol3fTKRC9SiTeg6U3upGhuEJsXw+yUlCJ3sUA1ntdVYWseaGA6j85dRjZKnztyiwZbFg3upMbFQmLGxBaXxbad4qbyvJsSnr5cstXBDaNTUmeZxlRKqeGE7TgYj8fRMzGB62NjuDg0gr6pKUwmEkik0yj3+fDJrVtQ0eG9jUR3nIwQ7uFkQtJMy71eNIaKq2JSUFip4BxJ+xQqTjm/kVjkfFcqG8U0faOT74unIRWL7jy/UeZ5NwMpCGic33NzPOd7S1K1ZUM4Pc/PDPDaMzaHnO/T2XmeRa1yryxqNZQlza/Ati/DSd8ofpBhVhhCBFi+F+D1f1o2Bgr3BgkErhW8gZ+Dne7NksOl8gxPi3LYSdA25W3ZJEkQydoiCnXZ9LGuiebL9nmf2/+n+7oxy5om+zq9rkndIkGEFElkxQEucS4p9vY1Nx3bGV9mFTPtHcoke8IX+Bkk42/ImGPwqhDo6RnFt79zrCCfFU+kMDhYqPmDKluSg58Sb1oSCSVljZBJwE6dlsPdO7RLxhGtjDSzHbpWBU2nWtCfJdRz63cSf2Z2Ls/1c9zez2+b27mGT90i/ECFLImIMWQYJLK7hDDnOt4lTwszD/C5RWVkIPSPAM1CZvLfICl+60qJWwzSzzDb4PX/NODEkEy8lt2/rhQwAHvT7b+pmfNqELpOUr1SDp174ty/tZC7VtK4VrJue3XBMWS7Y4jzBtdIMn5iALNJZhLmUlSdwddxCZJRZe5asZSKwEChsODcbErdJQZLneRQCWRCpdyC7nwOxl+BYW2VLFe+yl6CnukSYPJl11RWVoiZ5Yumnzc5ZOZ+7mTHj6y3snsMTO810re9umuwuDuOuL+Q8cTMjPEZ4yqbtSH/vlXDiop/ikKAAwVvUQWF5cCaJdFJbPjLfh2W73lEJ/+VFO7ihqwQyDh9SCd5vJM7G+hG261IY1bFxVedBVkkIs8FhEu8aPC4CwZuJpGN+jlUZQ3DlsUJI5k34HDBbfeXCMk0FzjhW/AGPi/Fc6hKKhVkpAo7hED/8bVr+NqpM/jwRs9dy626YBAHWpqRcm7/SdKx8caVq/iotxfRVBKHWlvxd/fvE1/GUvFaVFBYafD6P5NVEE2KdUNpbYCokh2DneKRI7wMyToyjDZoRiMMo1Hmel1vzAZQqb5yScZpgp21q7IL1luBUW4CxyRln3N7xumXzSjn/UIRefdPlpXD9B5AsPy3Aa08S6YqzAUSCqyvQr/jZDwi9730wI1flxypxCvyHV1S9tugm1zbtIhKPUesu6IBf5Zg92U3aa5ykESLBIhIgIgy0M2uyNiDsKnSsq8gUwzPZwY0rC3wBX7OvS69WlRsdoHGXl//OF59/QxWI2jnYHkOwvI+gGQ8LFYKpQj3meMS6olYLjOBwSNmmHZm53VmmNa6czvXs9l1u6zdNdez3yXJk7cFQkmG2vbQjIyi/uxavr+IzzhdfKw9/p+WPs91q66z8B0DwKW+t1i9sDz73HUBUkjGvrfC1dK0Gp2CTfuXYvOXCmsQOizv49mg+KVsfbhSAQO4LCZ8wuVKmJFLgY7BrL8OaGZz9llTLfOyxnlZgrgMNBluVsa04JJkeC4jI3mbclz2GU4YjjyHqBwPZ7+emLYxcklyZjypeV9BYU2T6JxIHEl/1OAL/qpMDsn4D4AZUbPCwc4ulPskRe2WKtG4I6LoqhP5nxA1076E2SOn3JLNaDYivwJWJFQgWJ7HRZnIjXapYSIexx9/eAQ/vHgJ/VNTC9rK8M5VB/y4NjqGi8PDSNkOHm5vx+a62hLT2isorCSYomLmIjEW+QM4ooorZdhCjKS5OE+fQYrKq+k5fqbyPDfHE9rdc/xt87w9rR5ZCfN8bq73eJ+Gv+wfAAwQq1lwAfDAX/b3hVxMJt4sGcuLuSAqdaok06fc/j6dkn9nf8/1ddyzvwvRMp15lyoCqagLUeoP/RPoRoOrnpSNayNsnCzwuaxSaD74g38fTvomUsnx0lKj3xOOrN1JekM7ku3ns2UV5dbu7ur9ljpwZl+f0c9nqAOLGSSm7YbH8wi8vuem9yLMGtCN6hVgJbK6QdGRVzIXEkjGv1/s01FQWMGwRKggNTASr6E0wWdETNwT7NTVbP2Z2fcRt541M/52+nXmv7PqdKkrc+vrzG0ZgTPU6yr7SEFhGmuWRJdInOMWOqCSiMpGbkqpSC/OgjW7WBb1Ye4cZ//N1TSFkVQxrN3whX5DlPhFKUp2D8hjI5PBl4+fwJtXr+Lm5CTsjIOgx8L66mrUBAK4NjaGrrF7Fzg0dB3b6+vhtyzE02kMTIVxemAAm2rpkawIJAWFxcDN4qiA5X1K5uvY1O9kC9KVMunietvmJvDMGpnjp6H54fG9CG/g74jVzXL5w69euIpXb+CLoiJKJV4vsQyMxa9rVkJ/N8xt8Pq/ANPanbXcoyK3SrJKimGus2tnKxobK+Dz5SwNlhaxWEqU72fOFCZD04Uhvvq0y2GqOIsirwysnn5+OwJic+BhwFoPZr+nZes51QElTKJz7T6ZjOPa5Ch6IxMYTcQQt2k/5WB3bRO2VtavOE/0O8GsNRa8RfbWrJTgqoJCKSGXFW569sJKHUQ6dbKEMzv5FOGKI+Xy3rlvzfJbKwHnR4bwZvc1fNDXA69h4P/1yNNoKgshnEzi6sQojvTfxNXxUUwk4pLpT7vcKq8fHRUVONTUinUVVSjzMCsqf5CL6QtP4sjATVweG8VQNCJOAVxNBy0LraEKbK2uxY66BrSXU92/cIzEonJtp4cH0DU5Ieefdmx4DBM1Pj86K6qws64B22tcfmghn8E6fIPRCD7s68G5kUH5dyydhqnrqPEHsKmqBg81t6KlrFxoJmORe63VcA3LjdJhLAsM8UsTv9iM+Ehx8nQ9ncaRTr5b7NNbI/DAsHbAF/ii+H2VWndMpNK4NjaKN69eQ/f4BIIeD7bW1eKxzg60V1Yi5PXiq6dOz0mi65qGhlAZ6suCMsnQFubi8IjYxCgoKCweJGGZOm95n5F0xET0b2CnuyT9UaGUwKWVTwh0j/8zQkK6tT8UFr7R88D0HIDHGZb02nTyw2Kf1poAC4l6fE/D43sWun6rpgmV6LRnKgb27e3Azp2tCIVYdG/pMTERw4kT3QUl0d0+7oPlfRJ2uluUzivL73k1QZP6I6xTZFibbssacotl15Zk/hM350OxMD4c6sHp0T70hCcxGo8inEqIvaKsvTWgKVB+F4l+aXwIXeFxRFJJVHh8eKxpnWzqSxm6XuHWkRIiXUcq+Z4aMwoKiwDtUEzPA7BSF5CMf2sFUdErF+OJOM4MD+L17qvwGSZ+efcBCX4eHejDu73duDI2ioFoWEhu28nIfEwups4fwPGBPjze1omHmtrQXkGLsbnBub97agIf3OwR0v7K+Cj6I1OYSCSQtGl1A3gNE9U+P1pD5dhSUydE/ROtnQhYlvA5872/nckIMfz+zRtyXV2T4y5Jn05JbT1TNxDyeFAfCMrv7GtoxhNtnVhfUQWvOT8HNpVM4MRgP97ovoaTQ33onZoUcjtp20I0M6DQVFaGU4P9eLJ9nQQMfHm872q6hkKiNM+qoEr0mZPnQ/DSH8oZlOI9pa1qXOkwxFuUxIolaaLFqzZ9L7AY6DvXu3BjfAIpx8aeukZ8YddOPL95E/yme77vdrHI273B32HktD4YlKKikWRSipOqR7OCwv2DmSvMJGKRP87pifj33eLJJV8HYq2ABcPKYXkfkXskBPq0olFhMeBaxfI+6hbXdEbhpK+qzd4yQtNrhNTlOoVepHfeC9pduAVSC0spdrTXYvOmRpSXs5jl0mNsPILhEVoeFh5sZ8v7hKTWJ+PMuFCB0cLCkALBtEwzrX1u8boZ0PR6UaOXGrhOp/L87b5r+H7XeZwZ60fCvntcDsUjQqjfiZ7IBF7tuYgL48NoCZRjc2Ud6vxBWDrHd6nXE3jEtV7QLKQT74mfvoKCwkKgSfFOBg7t9Fl3L1Hy2X6rB8z0PzU0IArot3uu48LoMIKWB1U+nxC2zC4iLzMWj7sq6dFh9IanhFwnv1Lpm3stdH1iHK92XcF3r5zHycF+IcWrfH55b/I0jK/G7ZS8d294UhTYF0eHhTgmSRzyeOck0iOpFE4ND+C/nT+F93q7MRyLCvlbnf0M/i2JYpLIF8dGcHZkCCeH+jEWj+Kl9VuwvbZ+zqAt//bYQB++fvEMXr1+BeFU0lVu+/woC3rl3/ydm+EpXB4bQV9kSlT1sz3rVvM1FBJrlkSXgj4zSHSCabma9ykpohCP/Gm2mI8i0pceOQudT8Pr/ylZAJYiSHi/130D0VQKFT4fHulox4tbNucVabsT9EUv83gwFothOBJVlIeCwhJB01g8p0JqW9AbMBH7NuwUF7+KeCku6BldBdOzD/7QPxZi7E4yRmFxoPWZx/ss4EQRi/wRMg6LbJbmInPlgpslLyzPw7JOkUJ+d/6GHoRu1EihVGYGFBKBgAeWtXzknmkY8HmXxypmfmiwPA9IoIjZRXb6gurfBYMJXa+HL/h3YXkflv59J1w7l1qUErim7otO4Xtd5/FXl45iJB51c6AMU0hwEjRU0s2Fam9A1vvHh2+i1zeBUyN9eKihAxXe0ibRc4ICj+8pKWSb0PxIxl/JKtLVbkNBIV+4gsoD8AY+h1j4D4QPUkR6YUAF9CvXLuHaxJhkA3WWV2JbTT221dYJgU1ytXtyTFTMJNBjqRSODtwUkr2johKPtLTf871Jvv/g6kV87eIZeX8qy5vLyrG3vgnrKqtQ4fXBcTIYikWE5CWBOxyL4KO+XgyEw0Ig76xtEBX8bKCS/cr4CH736Pv4qL8XaScjf7Oxsga76xvRGCwTBfdUIo4rE2NCPHdNjKMvPIX/du4UnAxQFwiKlc29QAsaks8vX7uMeDolYs7O8irsb2wWWxuS3SS3L42NisKbVjhUkpdZs5/zaryGQmPNkuhULTp3kOiEpjfCG/h5qVCcjH5NlDAKSw0vfMFfhsf3CddXsUQRT9tivcKJe1djg/igM1q5GNDKxWMakmpKcv6WmZmCgsKSQAvAF/hl2dwnIn+K9Irx012d0LQKt4ho6B+Ll7er1lVYKuhGq/jLO5lxJKJfkww6haUE/YZ3wlf2GzCtbfcsgsu6LrrZBjs1VdDN9rKT6KYOb9FIdHc+ZwYLC6SFx/9p1n5RkRnLDRbN9fq/CI//UzKHz/o7eq0cbjG70rkn37l+Ft+8dloIdMJrWNhRXY91oWoMx6N48+aVOf9+Y0UNmoMVMtITdlosYXZUNwrBslJgWvuhl1VJhgxJQEB5pCsoLARSe8b/eaSSHyKd+DD77FFYblBpTpsVqp0/1rEBv7BzHx5sYq08t0xqrk4dSfD/8/DbYiVCsv3cyDDeunF9ThKdP6cKnf7qJOQPNDbjnz/0BNpDlcLr5FZ3/AwGUv/q7Al89cJpsXzpnhrHn585jt/Ydwg7apl5eDdIJpOk/0lPtxRl3VhZjZ/dvgef3bwd5VnP9tw1MGOKJPG/O/yWkMS0tHmr5zoagkH86u6D97yGb1w6h+ODfYilUxIE2FFTj3/52MfQWcFrMKffn5YsH/b34g+Of4B3erpFWZ8PVsM1FBqlbfa2zJ7oyNydqiqDVQvCF/iSTKKGuaEo57d6lYm1CJT/M0mX0o36krNwmQmm8NDDnK+Vfr+kCy32fPlQ4DGzLraCgsLSwJ23NUCj9/az8Id+C5b36TUdJy62hzSVjCQg6VtPAr2U5/oV2+f1KglIewNfgG7cbjWisHhoWgiW9wEEyv/fMM312Xlktv7rrhcNo6Mgy+nykB+bNjZgy+YmsXExDH2ZSXSzyHN6CKZnv6wZdaOlaOeyVqAb6+D1fwa+4M9J2997zjah6RUlJYI5OXxTFORUo4csLx5t7MR/eOxT+NeHPo7f3P0YnmmZfy/nMy2xb6nxBSV9/OL4kGz2V16tGAZYP4dgxb+AbmyUgKCCgsJC1lbV8Jf9JgzWa1MZlAUD+ZFHWzrw6Y3bsLe+EZZhiMWHoevyaum6+JX/xr4HURcIyKqMinES6xQ83llvjl+nbBvfv3pRCHEqqalu/+0HHkNHeaUon3Pvn/sMugZ8csMWvLBuoxDsFD++29Ml/uz0Zr8T5IhoPUN1NclnWtB8cesuvLhuEyo8rkXJzGtgdhT9w/+HBx5FSygEQ9PQNTEmAYTRWFQCBXe+/0Q8LkEDqr55zVTp/+aBh7PXYN3eRoaB3XWNeGn9ZuxpaMyLc1oN11AMrFkSnZWNqTbPxjzuWoTQ2oUV6T3+z8EwtxftLFcPDOjm+qwC/QXZEGlaaaZnzERuC8EJQtcXTwKxMAIneFPTJH1Fyg0rKCgs/QZSr4bpeVDsXbz+z4l/671UpApLDV2UcL7gL0oRUcNcL/O8ItCXL4XfENXU58RyxDA3F/uUVjw0rQqm91H4gn8PprVDFNFz9V/aJ+gmFVDL38fXb6jHL/3i4/jFX3gMtbVzkZz3D8MwRIlOMt0wSGijKFZd9KSn6IKiFt0gEarmkuWAYW6E1/8p2fMw8Mm2vxfY73StAobRhFLB0eFedE+NCQGwvrwGv7rtATzc0IHNlbVoK6tElXf+2gEsalbp8aPWFxDipCc8joSz8iw9WbibRLrH9zEEyn9biiKXmv2OgkLpgs8YPvu2wxf8EizrkApEFQgkUB9qacP+xiYpMDnbs8dvWthaU4uWsnIhwcmtTCbiGI/H7iJv43Yap4cHcWlsRGxCaEnC995cXStE8Gwe5/weLUm2VNehPVQhDCGV1pfHRzEYjdz1+/TvPjc6jJ6pSfnbXXUN2FXXiIZgmZDCs10DVdjbquuwudq1qoml00Iu06aGz567r2FAiqCyxgczo7bW1GFvQ5O4HMx2DQwEHGhsEQuafLAarqEYWMMyPdstPpeJIwP/rJsR0+KGlGkkJhIxHXb6jNIRLwrebBHRj8Pr/6ykSs21QC8VkDQv83pkwDO9J55a+GKaUVD2GPqgT8UT4qdOf3S1DVRQWC4w4FUlfq4kuKiYSyXegp2+Rte3Yp/cKi4gWiH+526x6CeyBMsajtMXEKa1BcAnpahcMvaD7FpFYaEg0cQCfRRQWN7HZSM9H1HtKtHbC9LX62pDchQCvG5+1mc+fUDWMS0tVQX53LvPw4RmNIm9iFQzir8MO32xpGxEVjoYfPP4XoLH/2J2LpkffK5KdkDqOEoB58YGMZKICgm+s7pBlOj0Ql9ooInEQLnHJ2QM3+9OMmClQNMsQK+XfRfnKFqVppPvw07T0mZlqesVFIohyIHml6LiGYd1zFJIJz9UdfKWEZypa/0BbKqqQX2g7J6/R8KVBDj9t0mok7xl5tBkMoFqf+C23835ppME55xOEn17Tf281rxS7NIfQEuoQshz8jg9UxMYjkbRWXH7Woh2L9fGR8XixNR0UVDXBYOzks8zr4HnQDX2SU+/nN9UMolLoyPi0z7z/CjCpP84eSiC180gAInrudASKheVN4WbbKO5sBquoRhYwyQ6l+NpOJkJ6KKInr2jmNYmaFoA0IJIRP8Ktt0FZGKKTM8Xmh+GuUVSRFlIlBYuKwWcAFrLyzEZT6B/KozBSERSUuaaVO4Ee8lINIqusXEpKloV8KO9snJZz1tBQcFVY1meg+INSv/WZPyHsoFU/oZLDare6mFaB+AL/jxMay80fX7Vn8LSgr7dQpZoIcSjfwXH7gYyLK6r1irzg+nbNbC8T8Ab+GlYnofyz5SjJzpJdG66V1FTk3tsbKzAb/z6x1AKILmraT8t6/FE7Kuw01dVsdElmbtb4PF/Fl7/JyRzKF+4JHorSgU3I5MIp5LYUF6NTRW14q+6GJB49xoGHGTEyoUFSVcq3ACCBY/vGZmjUmZrNsh6GRmxM12516agUAjoeoVkQpHh5d7BTrHIdemReasBnK9IUFd6fUJiz4eA6Zn+PRYFJVHryhZvgSLI8yNDSGQJWBYvpb82bUfmQ45UzmE0FhM1+53oD4dFYZ0D/+bi6PC8Pt4UJ0wkSe678zDrcAxEp6a/zoFK+5xdDVHt86O9fH4eiQGGKr8fVT4/YuG77atX2zUUA2uaREfGcSsv69Wy0LgXDLMFvsAXYJgdiE39B6RTZxlXUZvTOcHFmwnT3Apf2a/L5lTX7x1ZLEVQkbK3qQlXR8dwdXQUl4aHxSO92u8SRPMpXBj1ZBT0rWvXcX18TCZz/u32hvpZU1cUFBSWHobZBl/wV2BY2xGP/hlS8bezSiy1gbx/WPJcpHLXG/gZsUFTKB4Ms11IYJJbsfC/d4lGZtyptco8WRQhUeL6gr8kwomFWIaQ1BV7OniRUevCZYVhrsv6/9cgMvnvkHFGlap20fBANzsQKPuHsHxPQ9cXJu5wSXQWjC4NhNMJ2fiTWKny3a5GXAg48t0ydrmvVgdMayMMowWm5xDi4T9AKvmeu/9VgSgFhTnB543H97ysE6KT/zsce4B0YbFPa9WBsy0V6CRO8wH585kztKy87lh+8ZnQPTUxTd5Slc5jMYjZKSRmkOo5jCViGI1z7QekMw7+66mjciwUzHpiIDgzy/dpI5PLigp6PKKSzwcBktC+gNi1zIXVcA3FwBrPtWYRggkgk8cigmk9nocQqPhfxTeQRScU7g1NK5d2Clb8b/B4n3DV/CsMIa8Xz27aIK8sTPH2tS58+fhJRFKpvLbJk/E43r52HX/y4RF0j41LxLStsgKPdXQoEl1BoZCgKt37EALl/08Eyv+pqxxd4zHk+4cl5DmLL9IDnWp/heJDCGHvYwhW/BupP6ItkBxbWzCkeLw/9Nvwl/0DGOZiirPSL9yXLXxZ+nVeVjrcjIFnUVb5b2Fau8WzXmGBkMK5TyNY/r+KypJzxkLheqKXjhLd0gyGw0RBfqcv7kJA79ZIOiVrdBYoZWr7qoHmlToPwYp/hWD5v4LpeUz6goKCwtygXaHlfQxlVf8JlvfQouZMhfnht8wFZfvPByrPw8nEfT0Tpt/LyYji/U5Q5Z5Tut8PeIppOyOvM8FnGgua5q6B2VK0s8kHfH4xs2o+rIZrKAbWOItAJfokMrDn1Rvk/LFMays0qhrNDWIPkE4dK9C5rhR4YFjb4PW9JOoWw1gnG8yVWEiTBSu21NXhsc4OvH7lCnomJ/Hts+fQPT6Gfc3NYssyEA5PVx4ejcZwaXgE551hdI2P4dzgIE72DeD6mKtC39XYgEc7OsQTXUFBoXCQ+Rt+GEYHNP+noJudSMVfRSrxDhz7RrFPb8UFSE1rJyz/J2BZe6Utdb18Van2VnpfzyDgZoEFfw2GuQnJ+CuwUyeKfWolBU2rlLoJ9No2PQ9A1+sXVauFGWm0fqFK2rF7kBELHYXlgtwjvUrumb/8nyMZ+yFSiTfh2LR3UZgPurFRgmse37NSq4h2RIsqUKt5oelVMo5ci7TiZnZVeHyy0abn6kh87hT0e4Eb/OF4VKxhuHFvCZYv2hamdNdBXvFKZ80HBv7o85xK/Bip1EdZ+y8FBYU74a4NQlJs1F/2W0jEvi7jxrF7i31qqwqcd5c0bJlxye+czQsLhW6vrUf5PF7cs2FdRRXayrnXufu5kSOHGXx9sKlVCnJ69IWtJynY3FFTD8u4owUy7mfkroFPaz3PRzbPx8jj+b4arqEYWD2rg8UgY8ORdLb8F39UVJMk1vQQNKMeeqId6cT7cJyRNe+TxQWZ6TkohTio2mdq+UoGo6HlXi8+vW0rIqkk3rneja7xcQxGwrg0PIr6siCujY7J73Lh/l73Dfl5Mp3GQDiCm5OT4qFFdFZV4pkN6/FIRzusEo2oKSisdpDs0vVmeLzV0PU6GMYGpJKHkU4dz1oDKIuXe4HKG8PcmJ3jHxISi4Q6C/8plBaEFJOg/07oekjqAqQS7Ugl30XGGV/jKfwaDAaBPA/LWoXFcN0g0P28JW2N1iOd/MjNblQoAKFRDstzCLpWBsNsRipOIvAIkFkcgbr6LYvYXg/A9D4Oy/uICIGk+OR93ANND0I327I+wcUt2t0eqsTZsQEMxcO4PDGMeDoFr2EuKEDQHR7HlYkRjCaiUhNpa2W9pJKvNkjgz6iRLCXdaJQAoJHag3TyKOzU+ewcpmyplh8mDKPTtQNTGTUrRKDgh+k5IM982lmlEj9BOsniyq4VhsISYAkJU76V17xVYJqFKp/v3Ih1lQsvlB60PKgPBO/6vqkb097sJHwPNbViX0MzKrwLI+qpzq7w+u4irnnqDObmroHq+lSeBa9JXOdT12M1XEMxsKZ3v1SgU4mel53LHYtH+sAyfd00NyHBquepI+I/mnHG1tjig4tzbmI2wPQ8KKmhpmc3NG31qK0PtLZIxWFODh/19Ao5fnpgAKAtWhYsaHGir0+OHDhVsKJwW2Ulnt24Hh/bsAGdVQufuBUUFJYO7kOc9lwHZANDr/RU4jWkUydhp69nSca1HRC9BTcDyzDaYVhbYXkeEfXu4mwvFAoNd62yHhoDRuZGWbOkUydgp68hkxlfY2sVBtBqoJsb4fE/D4/3KbF1Woz6/E5oJEPMdQCz7hQKOI+bst7UjSb3XsarhQS07Z4smb6W+ve9bQh0s12sb7y+T8Dw7JFieUvz3j7p9yzYjUxxSfRdNU04MtSLM6P9QqZ/ONiDfXXN4pE+n30iN+lUr7/ZewUnR24iaadR4SnDgw3tYumyup8PbTJ+GBRPWe8glXhXgiLMqnGcYbUWWnJY2eBFk9ghcVya1h4JcimsnGK93D+497ADSb0a6dRp5ZVegjA0HdW+wLQtF/3WSaSTIF4qBC1LDoK9o9Lrx5bqWjSVhZbsGphpxddc8c5IMr/nbdKxReS5Fq6hGFjTJLrYuUga4uJUWVRhGNYeBKxtSCZeRzL2XUmNc0jCrHo1DMkVn2xKTWsbfMFfgGHtW7LFeUkpNgBRkbdVlKO9sgKvXLyMoUhEiHNG0mjlwgrFud+ngt1j6FI4oaW8HJ/dsR1PrV+H+rKVVVhVQWFNFAwynoDlfRCpxGEk498UVQkXwxmZw9fqBpLKDX92s7ceXv9PweN7QlRryrZl5YFqdM1zQAjHZPxHSMa+g3Tq6BpZqxjZ4p+tEgDyBr4kIghNW0JyTFRp613rOoWCQzfqxKKESmvaLCZj34Odvuz277WqENR8QqBzzLNortf3ovjHu5YeS/cZYtkIq+jhigfqWvHj3qu4OD6Eq5Mj+P0z7+E3dz+KjlC1qMlnFoRjer9s4lNJ2aDz9f2BLnz96ikh4Klgbw1W4NGmTpR7Vv+YZjaZZtS6z3nvx5BOHkEy8SqSCRYfHXTXQhIkKU014Mp4BtHW1C+2YSTOaXfK2iXQypYkkKtQeBhGM3T/J2X/EI/8lfBATvoGMpmIKnhdImBGUUd5BU4M9sktGU/E0TM1iQeX8DOqfX5Us0jm2Ahomd41OY5wngRxPqBCvKmsDFZWKc73ZpHO+cDgMMnnsUR8TVxDMaBIdGdqCRYGHni8z8GyHkA6eQzx2N8gFX+t6OmNywmqz10/0c+IoouLAy4UVjPWV1fjVx84iC/s2oUPe3pEjX51dBRD4ShiqZQUTijzeFAfDGJjTQ32Njdhf0uzkOm5NBkFBYVSBAuPPgrL+wDSqTNIxl9GMvYDOHYX1iJo/8GUf4//JVGfQ2PBxDW+XFgVsODxPS9kYyr5EZKxryEZf3VV27vQtsnjpwf0TwmhyDYQEcCSwrVzUSR6MaFL0VFv4IvSx5Px15CIMSh6eFX379lhwPQcgtf/BbHecos+L/38zUAr+z1o6VVkFr05WIHHmzvRExkXRfoHgzfwWz/5Nh5p7MS+2mbcCDOg4mI8EROynd+7ODGEDwdu4PToACJpt/DZhrJKfH7DLlR7/SXrxbps0IIwvY+KMt0XHBDfZ9bUYKaem6WnsHDleS08XtqHPSZWILQBccejWlOtfJjQ9Eb4Q78p62U3iPsD2OnzxT4xhWxtuz31TfjR9SsAYrgZnsSZ4UF8fsuOJfuM1vIKtIUq8D5uyPPj2MBNvLBuEzahZknen7U+NlbVSnCXGIpGcHXctRKeCywUOhqPYjgPsno1XEMxsLZn8AwVxGGxdVksbvntmW6xI+8hBMwOOP7PIcliLYl34djdq0TRSLUVC9JQufkYDHMrdKMBmkaPKHqwru7FJhXmTAutCfjxaEe7kOTxFNXo9NZ3yyUYuiZeUD7LFEK9zOsV3eaiCjcpKCgUACwMmHscGlI4SA80CRGTTp6UOdz1TB9axfYAtOUKSVqxSb9za4/UtND0asm4cqHmsJWMW88gC9Crs77I6+Hxfw6pxBtIsbYLbTBWw1pFLIg6pHgibeZ4nSQSl4/kJoEbEhIX4GeUpmpm9fdvHh5Ar5H5mzUB0ukLSCfeQyrJ/t2/isUthngrkzS3vE+LxSLtBjivL1fdipydSyko0bk2f7J5g2SIxtIpnB0bxHA8gjdvXsGHgzdEcZ7D231XcXzkJkctYnYKU8kEoumkXMPG8hq81LEVz7dtFp/YtbZ2z9kkZbL9yeP7BEzPI7KPTadOSd0Hvq4969L8QWsWt34MrVp2wTA3S1YfM0M0ncpzBnLXVr9a/c8dr8y53sDPwvI8KrZ5zPqjzQsy4WKf5ppFwLLwcHObFMkcjIYxEovi5FA/Purvxa66hmlS937AgqNba+pQZnkQTiVxaWwERwZ60RoqR1v5/bsz+C1LPMpDXg/6IxAF95mRAQxEwqj1B4Sbmg0XxoZxYXRY/MfXwjUUA2ubRBdP9LCQ6UsBKbTDhyQLsDHNh+nDnkMSkUynzsJOnct6zK0kVYwGXW+EYW2CYW6H6dkBw1gPXdKhWVRubSmsxa5F01Dp98uhoKCwmqC5Nl16AHqmQeZw09oh9S7s9CWk6RWavgTHGVwFqc26W1TMWJ+d3zdDNzrceh9G/aqqa6EwS/q+Vpldq7SINyszDtLpc7BTp2GnLsKRoNFK6uMkfdpgWlthWDtgmlthmJtE9afp/mUlLXLEk/tZIWQcRaIXEzl7CteOqhWmuRGW7wnp17IWT9PzuXeF9e/ZYIg9hGFtEVtFw9zi1j6QrIhgAWwivK6qVg8Ajl709qzxBvBk83ohRn7UcwkfDd7ARDKOsUTstt8bTcTkmAlavuytbcYzLRvxRNN61PruLiC3luDOaR5oRp3YJWX4nDA3wPIchG33wklfEy981tdw7L6sNepahC4BWs4zrD3A2gx8plJg5tZqaISusxZWjmxVWK2QoCK5H9ZeMVplTe3uHS64tTpYN09q0awF+MUuU8aB3ijjgEFtHoUEvdBrA0E82daJ0XgM1yfGcGV8FP/11FH8na07sbuuEZVeWp/dPTbTjoPJRAI3piYQTiaEZK6h5ckdCHm82F5Th4ea2/Bq1xVE0ym8fO2SBHaf69yIDZXVs4opaQOctG3cDE9hOBaVGno76xruen/W42sJhbC9ph5D0SjG4jFcHhvBfzt3Ej+zbbec051uB/3hKbzZfQ1HB27m1U6r4RqKgTVNomfEEz2y5As/IZa1MpieXbK4deyDMNPnsov3q3Dsm6KIEUJdPr+UoGW9zmslpV833YWTYW5zN6dmh1oMlCB4v+gJykBHPkEaputMTMXR1T8Kj2WiqSaEqvL8q8PrerWbIrxMCicFheKr0y1ZEPMwrb2w7ZswuRhOX3QLM8ocPgjHHkYmM1F0AmF+eKDp5dkNXx00vUFUhKKYErKxQ2xb1prybi1DSDZmIHj2wLC2wbT3w06dRTrtbvhINEp9AGcku1YqtTEahKbXZ4NBXKtsFgLdsDbDEP/+wsLjfQK6XgnHGV3yzblpblnS91w7ZHo1dKMKJvbAtrJzeOocbJvk3wAce1ACRhlnYkWo1EW8InO4S9Ixy8I0t8n45TjQxHqrUOdiAHolvP7PSTsurUBIcwMCRv0CzkdDa1klnrN8qPeXiar8yuQI+qNTQqbT+5yZo5I1qumy4Wfh0Fp/EOtC1VJIdF9tC5qDqsjjnXDFBVwvrIOZsYU4d0ii2yTTu92vZRyNyvznWqWusiKL3BvL+KvMKsurZG7JFTZmAXYKzNysJ7U3WqtgvRXDJIneioxDIeVVd02VuiDjJSPPnGF3nbDiC2BzzxDIZuJVSsCIAg3dIIdUD41BJAkk8bUeul7Y2nB8Jpiahmc7NwrRSzKcavQ3uq8KD3JxdERsTMq9HiF66SmQtB3x4Z5IxEW93j05gaDlkefEbCQ6ieYNVTV4af1mIenpJ352eFDq5fGzdtY2oMbvl+Auz4ffT9huLQ7+nO/PGh3baupnJaD5/iyI+kzHBvFzPz7QJ0rub10+Jx7jJPfpZ85/89xpV3Z6aFCucSweF6U3Ce65sBquoRhY47N8Jls8bvnIDy5omRbPw/I+JwQ6PXeZ6kOFupPukXNwC7fEkMnEC1yQwswWIPJnJ8KyrPKcypZdMFksVFSJhVuYKywcDG74y349799PptI419uDHx45i5qKAJ4+uAlt7S3Leo4KCisVVLKa+gaY1gZkMh+H44zATjO76CzSKc7j15HJTGXn8uw8nkkUyRqDJLiVLWSVm9tZJLRa5glRK5IwNbcJqa42ewq31iqdcliZF+A4fWJnZKd4XIRtF3Ot4iq93T7N4ogBKZZKuwFRndOGyNqVJTaK1589/hflUCg1uMKPaWLD+5RYOboKwZzA5Vo2WHRr/nb7eLJIJIeeJSjcOVz6PYlMknXWVilOaFjbXaKiiBmhPL9A6B+hlFDh9eHxpnV4uKFDPM8vjA+he2ocg/GwWL2QPCEZUOnxC2G+ubIWO6ubJJWdm32F+YMnubFk4UkZJ449BJuZTKK8ZQD2Bhx7JDuG3LGU4ZpI1kWlWnTRXTu5YgJvdt+bfdW8Ul+DWVskyt2MPR5t7t55jWVlK+QHztkUVPLIODHYdnc22+8C0vZlN4vDCWfXVQlkGHiSIr6l4liQzUjJjoE7xwSDuuSIXPcFcl2d0A0etSXFG+2orceL6zcJ0ftOTxdG4jH84OpFvH3jOprLQmgMhsT6xck4iKTSQgyTQKdiWtd0PNraLs+Oe6E+EMSjrR3oj4TxnSvn0TUxjnMjQ2KLUunzY31Flai9aflL5fZkMoGhSAT90bB8TTuVusDc2U9Pta3DlbER8QenOp6e4v/30cPYXlsvliskqUn+kwCnyjunLqfC+8c3rs/bRqvhGgqNNb57drIP+MIoCPmQzS08vP4XsguPAddnLnXKVcfI4mMwS75k7jgwy+ucnzjL662DSkuJHIqSZbuk2Rg8TC4KVIGs1YxU2sbxi704eakX61tqMBVR6ecKCnnP4wY3U08CXm4g7SwhczFLyFDFe1E8RLmJvHseX8xcPv3ps7zOzAzivM40SmYRdcKkMlfUuVtkgUviUUEhL5KE6cj+VsD/ktiTCKku65TTSE+vVYYKtFbxivqWWXFiW2HRa3ZnVnlLf1kFhYVbL+qeQ2K5SDjOhARDhVgX5eBVsayg8IXE+u39ebZ/577O6wzmmcfpsR+UIBGtaFh/iGtz09qeDRSpPj8fqJazDAM7qhvlUFg+uN74bXLkwDHDvWw6dQkO1eqiWL8GJ33DtQrLLOS5sZggljbPeMPdY44Bq6w1S+565N8kzo2OrOhAjT2F+xHjUKDoZpVlMsksB3QGduoMHJvPn+til+RmRt1rfNzPM+fO780xJjRWjXAtu3SzNTsW3DGRGxcrSYjzVPt61AfKRNX81QtnREVN2xKSxBfHuFe7hZx1CTOWPIYhgVnLmDtQRt/1v7/3ATSVhfDX505IAVPW6CBhzEKad4KfwaAt1ddBy5Ig7lwo93rxxa27hFD+yzMnRJnN8/+wv1eOme8Z8nrxU5u2YV99E04PD+RNQK+GaygkVkbPXy6IUX2xVCaERyYnj14Dy/Mwk0gkEuk4k1Lgy3H63bQfm2k/w1IZXQqhyhHNqmXS2Yilk+16TM/mQ94DXQ9k051ZWKhCfLrcBQLTnxk1bBJbDokmIhdV9K75brEWkLYdnL8+iHB0laVbKigUHNx8lblBSHMzPL4Xp+dypjS7lhi0fhkSBbub6jyWVZ9EsgfVJ1Q5pGeo1131Led0CXjOUOGSYLmVOklrJVoW1GU9mWuzykUrO69nVSRUWCkoLAYa1yqtsPQ6KZrlqqVyaxX275tSeJekOvv49FqFfRxcqyTuWKvo02sVFiXUZK0Sun2twv6ss0+3ZNcqVW5fFlWUV9Yraq2isFTgHC7BRnMdrEwKGaplM0mZn11Lo8HpuduR+ZvHuJDvLsnO3+Xf8G+5r0jNIAqNbNYnsym82eygMncep5JPCjjXZK226m+bx13FH/s9+zzn8eX2OVdQWAq45JtHr0UGB7PjIjs+qErnWihra+GOpQk4mXF5ze1x4cSQQSxbY4J/l3uGzNzz6tmxxXFhMN8kO844bpjFEYAGP8CgVG79xGeNQUuWKuhalfvKtZSIDLLjVBTpt17d9ZNSmyssJSwRAnj0KmQkmMs+zoN9fsxdWzmD2WfOhPu8ybivbj2/RHavcWtsuWMj98zhviGXWcH+y3+735O9wfR6iwVvK6BL4VuOC36Pwdry7JjIjQWODb4Px5f7utLGBL29f23PA/j4uk14q6cLp4b6cX1iXMjcWCoFJiJRkU7rltZQBTZX14hv+t76JlFqzwfWzHu2c4MULT0x2IfDfT04PzIkVjJTySTsjAOPbgiZTEKf57Ozth676xuxqbp23vevDwbxxa27sbuuCa92XcaxgT7cDE8KEe3RTTQEg3K+z3duFFsVzpCDsYVZMa6GaygUtAxd4UsE3GgxIjc58kVJF15+0LdqHUJVvw/D2ohSgHs70lliJTadNn0rDY4/y5ItVEDKQiIXmcyqtjipSWqZO+G5E5+bFippOHrWukUOLspV+uJaI9D7hifwW//X19E3NIkHd3bg88/sweP7NhT71BQUiopk6irCsZcRSx5bgnfLCIlyay7nPB6/la4pC1+XNOfGzzJaUBn6VRhShMqdz/mfu0jlnO4SMW6xuFspx25qJQ93w+gGQ4tPtKTtAUTj72Mq9r2Cf7ZlNCIU+Cz83n0F/+y1Anetkpph8ZLt3xLcT84gPRayVsn1a98MO6LcWoWbNrVWUSi05aOd7dPZvi1kuTuH5+Zy/o5L6uUIDP7N7f1d+vqM/u6q93KkBoUs7O85gj2Q7fsri6BQUMhvTHFsUGTA9VDOOimZHVPu2sgdQzNIc/kbRzyLb1fj5sYW63nlVLTZsSYCBHMGMe6+uuRiVjg2HZTNEY3ueyooFAvu+Egi40Rm2CHNHB/ZgO10MCl7ZHLjA3esr2Ycoi7P/dsVXHIsuFxQzrbFFSu4QVs+q9z3Ww64ftrjQtBS+d0eqkBHRaXYeMzdRhlRSQ9Ew6KUrvT5xLebfzefJRftvJJ2WmxLaNdCYjiRTiMt7Q5RSfsMU3zQqUCv9vtRLjYm+T+PWZR0PB7DUCyC8XhcCOKU7d4fnh9JaJ9pChFd5fOj0usX8j4f8Pypou/LFvOk/QmJ7ZzveLXPj+aycpR5PPL9wUgEJ4f65W83V9eipaw8r89aDdew3FjjMh73YX5r0ik+3KJufMBXAuChoLC0iMWTuH5zFJPhOGyndPq+gkKxYTtjiCU/Qjj2g4J+bk4dZXmfgmW2YjXAcSJIpC4gHPtuwT/bY26E3/twwT93LcFdq+Q2X2qtorAaQSshkggshlbYgmgKCqt3TDHIH4Bm3F2kT0FhrcMNnvqgGavfVpeFOnnsu7sW5bzrT6qUd2KBf5i1CvGZFjorquRYDpCIrw0E5VhqiNWJx4tQtReb5zsP6OI1zmMtXsNyY42T6IQiEVcbEsk0pqJxjE3FEI4mkUynYacZLIEURLBMQ46g34NQ0IuQ3wuf1/VxmhnATKZsDI2H0TMwLoU4Q0EfmmrL0VA9v6/wyHgENwbHp73G17XUoK6yDF7PvYdcLilkIhzH+FRMriGWSCHFc89k5NxMw4DHMuDzWnLeFWU++H0emPfw6kqz+EM4jqloAtF4CrFEEgOjYRw9fwOJlFu4ZHQyijNX3QjfbNB1Ta65ua4CAd/cfle5vBaeO6+B18LP5DWIOYWpw++1UFHmR01FUP7N978XeN5UzQ+MTKGmMojW+kq5d6MTEfSNTMl98Vom6qrKUFsZnD6/iXBM/mY8HBPlvc9jor4qhNqqIHye/KKX8WQKE1PsR1GxvaGPPKOnjEbzPpb5vagq96MyFJDv5VuPqrt/DAOjU7BtB9XlAfGkN01Dql1HYkmMTkTlM3lttp2BpmvwmO49Lw96s/3IyvvzFBQUFBQUFBRWIsYTrhqOReGoeKv1BaUwqIKCgoKCgoJCMaBWIQqrAiSZeUTiSfQOTggpfOJiL670jGB0KopIlOlQGZd8DvhQGfIJsb21swE71zdia+fdhYfCsQTePnYFf/bdDzEyEcGujU347NO78cnHdsx7Pqeu9MnfnbnaJ1//1s8+gecf3Io6z+xqJp4byd6JqRhOXOrF0Qu9uNA1iL6hCUxG4pJCQ6KcxH91RRBNNeXY2FqLXRubsbmjTojV2cjoaCyJD85249y1AdzoH0PP0AQGR6eEUM+B3ug87gWS9p96fCe++Nw+dDZXz3kPSARPhhM4faUPxy704Oy1fnT3jwupzshjWcCLlvoK7NzQhId3dWJ9S60EAnhtrrLxdvQMjuOrr53Ad986jcf2rceXXjyIoM+DN45cwg/fOy+EMwn0x/euxzMPbMbWzno4TgbHLvTKz1k4ldfaUF2Gpw5swvMPbUF7Y5UQ8fe6BoJkdlf/KI5f4L3owdWeEQkKkEgnIV9XFcLGtlrs39aKg1vbhZwnsT5fGhnxo8MX8J23TyMWT+GhXZ34J196Gn6NwYwYLlwfxHunrsn5M1DAIAoJ9qqQH821FdixoREvPbodbQ3LEz1XUFBQUFBQUCgVXBwfwve7zqM3OolHGjvx8bbNaAqW3/Y7o/GopON7dQMBywNLL76lmYKCgoKCgsLqhCLRFVYNJiMJfPX1E3jjo0uiHicpTVLXJdhvqcunIq66+ULXEN47eR1PHdg4K4leSCTTtpD/v/vVn+Ds1X4hzqlUdmaeu2MjlXbV3V03R/HhmW587fUT+MKze4VcJkF9J8bDcXz7x6dxsXtQyGQSzHzP5QDV5gOjk/j9r70rgQCSzlRSO7RMyn5kIpXG2GQM568N4rs/OYPnDm3F557ahQ1tcxej4J8PjYVxrXcENwbG8Y03TiKeTMu18F5/482TEuj4xU8cwshUFP/lm++JZQ2V+Pzsrv4xfPmHR6Q/vPTINqxvrZ0zk+Hrr5/EK4fPy2eJAn1Gu/HrcDyJ7oExvH38Cja11eNLLx3Awe3tQvDn31623Eu+782hSem7rx6+IPeefXe63ybTEgyhen14IoyHdnYqEl1BQUFBQUFh1YNFxY4O9+LK5Aji6RQO1bfeRaL/H0dfw7WpURyqb8dn1+3E5sq6op2vgoKCgoKCwuqGItEVVjxIRg6PR/BH33xPFLyDY7T5sEUVXB70iQ0J1c5UOtMPnFYftAKhXUdtZRk2zEGoFgIkaG8MjOEPvvYuTl68icloHF7LQFNtpSi/Q35XqS0E9FQU/SNT6B0cl6/rqsvE5oS2KLOB103l/OjkOiFmCVrMnO8awPGLvUJ8U5m9c0MjOptqZn0Pw9Cwqa1OrEvuhUQyJWr2P/nOYZy/NiBEMC1OGmtCWN9ag+pQAHYmg7HJqBDTPP/JcBpvfHQR0XgSn3hsOx7Y3j5nO9Ge5bUPL8l93dxRL4r2q70j6OobFVX3pRvD+PZbp0VBzsyDfVtaJLDA4qnnrg8g4aQlaMJruReJzrb9+hsn8eaRS+gfnhLSvboiIKp/2s/wmpihQIV8V9+YBCYYoPjLHxwR66BnD22e07JnJtj2tNIZHA1LUOAnJ66KbUxF0IfWhioEfJb8DvsrbYUY/GFfDQboQ5zXRygoKCgoKCgorFhMpeIYjIVFcFDvD8Gj373GujY5hnPjQ2gKVCCWvpVpqaCgoKCgoKCw1FAkusKKBtW6JBhJQr536rqQkiQ61zVXCym7keRvyC82LiQeSa7TIoO/d3NoQry+adNSTFKShPOlriEcOX9DbERozfLgzg4c2tEhPt8kZenlTkKV505SlUEDEu/V5UFsaa+HcQ9PdCqjH9jRjjTV1Nnv0c6FyvdTl/uQgiOfsXdzq/zebGDTBPyeOf3Qr/aO4ofvncOx8z2i5G6sLcfBbW3Yt6UVDTUhOQ9ugEiYkwyn3c7rH10S3/rDp7vE67sqFBCLlPn80anEfnh3JyrLAxgancKXXz6K89cH0D88KUQ0LX0YONizqVl8y6/0Dou/ONXo9KnnfWc73hl4oNKd6v5XP7ggZHplmR/7t7bKdTAQU+b3iD95MpnGyGQUF7uG5BoYELhwfQBvVwSkLXnv8gFV8gz4kPj/6NwNsel5+uAmaQN6pXtMU0h8nivbaXg8LP25pnzpi3woKCgoKCgoKJQa4nYa4VRSBBTVXj88hjHr75A8T9puhqKCgoKCgoLCysSbPzyJrsuDiEYS9/wdyzLQ3F6Dh5/ehqqawhd/VyS6wooGCdNL3UPif02VMwnfTW21eOrgJjy1fwPaG6vv8grP+Y+TKOXPSFgWE7T0oG84lcwEfcJpMfPonvWz+pzL+adtdA+MS8Cgao7zp582PcNngu9IhXbOg5we3zUVAbTULa7yMclteqD/5Pg1sVihgvqR3evw4iPbsG1dw13+4/ydTe1uqu2rH1wUG5aPzt5AQ025KO8ZMJjNH533jAVJGfR4bO96+Z7T2YATl24KgT44FkbKtkVZTzJ6c3udfDaV+lSp3xyezBLSUVHjzyTRuem6dnNUvNZpq2OZOg7taBeF/J5NLXepy0lub+tskIyBv33tuARl2Ab0qid5z9+f7Rpuf4+M3PvXPryItoZKPLFvg1xXW2OV3KOZf89si5y6n774CssDTbOgawFoWgCZDNVsStG2aGgGdM0HXStDJpNEBmmOtGKflYKCgoLCCkLacZB00qJA95r51Z5RUFBQUFBQWJk4/v5VvP/j8xgfjdzzd7w+C3seWI+d+zsVia6gsFDQcuPY+V5RNxNUcbOA5Gef2iWE62wgOUlylcRlKSCVTiOacAl0gmpmnvu9SFg5f8ssug1NDixYSrsUenaT9CcR/tyDm6V46GxBAJL2DBT8/IsHREF++UZKLFmOnLuB5x7cgtqK4D0zA9a31KC1oXK6bWg109lcI0Q5SXQS41S/sw1z5D0V9LRjMU0dSLhFQ0lI11eHpt+XCvlLN4bEDohvzX5Ea5a9W1rhmaUIKclsKuw/8/RuvHvymgQSaMtyvmsQA2NTaK2vhJHHRs+xHflsFkV9+sDG285pJiQYUKEU6MsNXSuH19oO25mAkwnDcWJCpGdgAxk7++q4r6Dffvb1tp9nX+VYu4o4BiM8VicC3sfgZKbgZKISmBAyfUYbuW3oZF/TyJBov+vn6TXdlgoKCgprGZz9M3wqZOscKSgoKCgoKCgUC4pEV1ix4EK6d2gcxy72TH/vgR20EGm5J4FeiqDVDK1DcqD9SO/QBHasb5xBQlOdjZIEPcEv3xiSf9O7/ekDm9BUWzErgZ6DxzLQWFOOfVtbxRqFQRCqyY+eu4FnHtg0q+clQSK5MnS78p52PTl1Nt93Q2sNvNatv+c5sT/k1Eu0sqHlzEx0943has+wENr8fVoB0cJlNgJ9JpEeCnjFn50e+7SDodUOi6ZSkc6fzwdmCmxb1yjq9XsR6AqFA0nfauvXUY1fn962284kHGfSJdZzrxn31f3exPTvyL8zfB2DbU8ggzjWKkyjDqHAp+Vw4cBxorfaKpNrN/fVbddcm97R3s4onAwDGkrJrqCgoLCWYOkGfIYpivTBeBgJKRifmTfbT0FBQUFBQUFhOaBIdIUVCy6iaeHSMzA+/T1abNDOYyWBVir0wSYhy6KVJHO/8spR8TynxQcLYc5FSBcbfcNTYo1DkDje2tkgBV3nA0lt3i9auZBEp5qbavAnD2y859/QKoZK9pnwWKYQ37nPJ9Ge+1o+R9eEXM9tuKj+zhVZnb6GkUn0j0xO/z7V9PnapvDzcnYv8UQKfcMkCPNTStE2Zu/mlrzaS6EY0KBrIehGAIZRn1VIs+84M/7NDX32Nfv9ZPoqwrHvYyLy18W+gBICA4EBmIYXGaNG2kkU6Lep+299nWtPxxlHJP4mxqb+FLYzWOyLUFBQUFAoIEIeL+r8QXRNjePwwA282L4VLWUVCJi317VRUFBQUFBQUCgEFImusGIRS6TFloO+6AStPKjmJRm9kkDV9LrmGnzmqV34zttnRM18tXdEru30lX60N1RiS2c9tnY0oKW+Ys4Cn4UGvbrHwzGEowlRyvNa6qvL4PPOP7Xw96n2ZtFRgn7lVOA7zr0JaI/HFEJ8Jhhf0MRF3CXA2T53Bh1mCpZIz93JcY9ORDA6GZv2Xn/l8AUcv9gLn2fuTRrJ8ut9o/L3bns4ct/yzTYm6U9boTuLnCqUAtxOo2nsb4b7VZ6xrAxSMPSVFcxbfuRqHegLakvbGYdp1EPT1HJFQWE1wckkkbb7JOPkTmiaR+ZQjn2FtY16XxnWhapxbXIMg7Ew/uTcB/ho8AZayyoRMD3yRJlIuuu3vugU3uq7KoT7UuDx5nWo8RW3bpKCgoKCgoJCaUHtShVWLEi6RuMpIV3JzbBYJslIWmSsJOi6jtqqID7+yDYhf49fvInrN0fQMziB3qFJnLnSh7PXBrC+pVfIdh4b2mqyiuviXittUai+JvHMgqC0piHxnI+VCW9aRZlPqitPE/JTsTn9Lk3dmPO9SabTPzxHqs/4wS1kXG/NmaBPOq1cCPanc9cG5FgoSKonU7dbxcwFLdsGK63PKigoKCgo3A/Sdi+moj9EMnXhrp+ZRi383kMo8z9flHNTKB20hyqxr7YF7/Z3IW6n8cHgDVydHEWtLwi/aQmJPpZwSfSe8DheuXERZebSiGm2VdUpEl1BQUFBQUHhNigSXWHFgqQrlb8uNPg9JowZNh7FR/7Fj0g8s1Doz76wH+uba/DRuRu43DOMkfEIxqZiQqSfvtKHypAfWzrq8eDODuza2ITOpmqEAr6i2b0k02k4jjNNCNNDPF+bSv4aCW+S7wQLRt3pVX7KyS8fAACgQElEQVQnyJ/P6YOpuWr0fFWuObAfsT/JW2QLi9KihQGOhaC63I+q8kD+baC5bZDza1dQUFBQUFgLSCTPiu1VPHnkrp9ZZgc0PahIdAU0BUJ4sKEdHw7ewOnRAUwk4xiIheW4E+PJuBxLhamUK65QUFBQUFBQUMhBkegKCssAqqntOWxJZgOJ1OryIF58dDse3bsel7qH8JMTV3H4dBdGJ6KilJ6KxOXrD890Y9+WVnzx+X04sLUVZQFfUQqPCsk844OdBRT+k7KNmcy09QnV48Ujk2/dK/qpP753A9qbqu7yX58PzIRoqitfUIaAKo6loKCgoLDWkEidg+2MFPs0FEocHsPEzupG/NN9T+G/nD2MEyN9mEjEZb2ZyWYWRlMpqahhajo8hgFDWxpBTU7koaCgoKCgoKCQgyLRFVYsvB7jlj92JoNIzLUVKQXwPBZi63Enyvxe7NrYLKrzn//4QVGhv3PiGo6cu4GewXGxDTl5uRfxZAqxeEqsYIqBgNcSJTXBoAHtdeayY7kNGdeShwp0glkEARbzLMKehV7r9HPPBQa2rqvHoR0dqAr5hV6/ww3m3l9rrs+52ngpKCgoKCjMQ6LbikRXmB8+w8SWyjr8q0MvoD86hZ7wBIbjYSHPY3YKf3bhiPilb6iowaONnWgNVizJ5zYHypfkfRQUFBQUFBRWDxSJrrBiEfB5xQedhCUJ3MlITJTatAShFcf9QrtDJSzccJ78MMlhFghdLGhJ4tENWKZLLO/f2orO5mo8tmcdDp/pwrd/fBrJtI0rvSM4deUmdm5oRFtj4QsZ0su7POiT4qAs8Mq2H5uMiV+7J0tK3wtUDw2NRaStCKq+WZS0GGp0XkMo6AWGXHU8lf98pce7goKCgoKCwtIgk0nDdkaRSvfCyUSLfToKKwBci1uagQqPAb9hisVL0raRzjiwMxl8+/pZDMXCaPCX4ZHGDlGuLwWqvMoPXUFBQUFBQeF2KBJdYcWC/tuVZT5UVwQxNBZGynbQ1TeKofEwWusr7/v97/T4Ttu2KL/nA21XxiajmIomluQctCzJy4Ne3Qwc3ByaxLELPUJA9w1NondoYkEk+kyemqp2HosBCe+G6jLUVpUh0jcqCvxrN0fQWFOO6op5SPRMBld7hzEVcdsp6PeIx3teRUmXGA3VIdRVleH89UEpLHqhaxAP7+5Ec93SqJkUFBQUFBQU+Oyn2OEsHGeCOWzFPh2FFWjvwoPIrVwrvX4pPO81TFR7A2gIhIp6jgoKCgoKCgqrF6VUhVFBYcFqbRKfG9tqp7938lIfLncPTReJvB9Q4V5e5psmdUmKj4hCOatKvwd6BifkSKaWfnNIormjqQr7NrdMq+1jydSCCHsS37RgyRHpPM/5CnrOhc7mGjknwrYdHDnXg+GJyJzEPC1cwtEkTl/uw9hUVM6F1inb1zUWxQqFQZf2xiqYpi7ndv76ALr7xxBegkCIgoKCgoKCggsnE0MseVReFRTuO2MUQIXHJwS6goKCgoKCgsJyQ5HoCisaVArv3dwitifEycs38eHZG0KAUjU+lz83iXaqxmPx5Kw/J9HcVFM+7bs+OhnFtZujGJ9yrT5mQziWwPELPaJkzhc8z/GpGCKxhJDQc50zVdJUe6dm/B7V8j5P/rYjJIqpas9Z1fCzh8Yj0h55+5nPwKa2WmztqJeimiSg3z/dhYvdg5gMx+V8774GR9TnZ672yzEZiSPo9wqJvW1dg3ijF6MfbWqrE0U6m+Dm8CQ+ONMt95F9xC2Aeve18Hu8Hv4O7XsU6a6goKCgoHAv8FkaQzx5FBll5aKwRKj0+uBbQEF3BQUFBQUFBYXFQpHoCisaTbXlOLitTchu09DF3uTl98/jj7/1Pq72jMyphiZxfOpKH85dH7hnscnOlhqUl/nd946ncLFrAN9758w9bV2OnL2BVz+4iAtds7/nbLjWO4IfvHsWR8/3Cgk/1zmzWOmNgXG8cvi8ELdEVXlA2iFf+L0etDVUTSvsbwyM4cyVPoxORrAY0P98x/om7FjfKAT08HgYX3/9JN49eW3W4qqxRBpnr/XjP3z5TQyOheVvNrfV4eHd64SILwaoft/aWY9nDm6a/t4P3j2Hv33tOE5f7p8z88C9ngG89uElfHi2uzAnrKCgoKCgsAJBH/R48rhSoissGSo9fqVEV1BQUFBQUCgI1IpDYUWD1iRUMP/GFx7D7/zNT9A3PClE9OHTXbjSM4KW+gq01leI8pqIJlKifKaHOq1ZqGB/eNc67N/adtd7U6ftNQ08sW+DFCylun1gNIy/+dFxnL06gM3t9agq9wvpTQ/0yzeG5eC/m2orhBC+0jM87zVQ4f7OiWvoHTyOUNCH1roKUUbXVAYR9FswDQOJVFqKXV6/OSoq75uDE0ilHbEh2bauEa0N+XvA+7ym2K+wUOnF7iHEEykhf//lH74sAYnqigB0Xb+l1E+ksHNDk1itVIb8d7eTpmH7+gZ89und4s0+ODqFyzeG8CffPoy3jl3B5o561JYHQFE6r/Vq74govKn2pqq+o7EKT+zfgEM7Om4r5FpI8HNb6irx9MFNElR4/1QX4sm0qNEZ5OD9oF87/ejZ52iBMxWNSyBmYGRS7HTYbk/u31iU81dQUFBQUCh12M4kkqlr8qr80BWWCg81dMBvWKj3l6HOHyz26SgoKCgoKCisYigSXWFFg+Rnmd+LfVta8UufOoRX3j+Pc9cGhNQkmT4wOiVEsTdryUIbFKqjo/EUksm0qLjvZcHB96ZY++kDG0VdTd9wvh8P/v2lG0Nio0JLDxKu9PZOpx08uLMD61tq0DcymReJnrYzcr4koLXhSfQOjgtZSxKeljL0fqfNC8nuiUhcrFAyyEjw4KVHt+PQjnZ4rfyHMhXoFWU+fO7p3fjKj44J8c/AwukrfaIM93lMuXbalLC9+G/arfCagLtJdCIU8Iqtzt/7qYfwnbfP4FL3kFxHLrjAa+E5xxNpTITj8nmalhHy/YWHtuLRPetRHvSimKDH/LrmGvzM8wdQVR7E4dPXMTAyha6+JPpHpuRe0joH2bYhkc6gTDSWlEAK79WcknUFBQUFBYU1DNsZQyJ1QRHoCkuKXTWNaC+rhNcwUONTJLqCgoKCgoLC8kGR6AorHvTQpkL6qQMbUeb34HjLTVy7OSJkN0lcelWnWWhU0+AxdViWicoyP2oqAkKa7tjQOOf7U7X97KHNUtTz1OU+9A1NYDwcR+/ghHCmJFaDAQ/a6quwqb0Oj+9bL0UyD5/Oz9qjua4cj+1ZL8GAkYmIqN5zanl6ipOg5TX6LFPIdaqieeze2IyHd3eK0n6hAm6PacrfRmJJfHTuhijcRyciuDk8AcfOCHFPUpjkN+1a+Jqzf5kNVK6zPankZvvSF/5K77CQ0LwOBi5IxtNfPhTwob2xEW0NlZIB8MD2dvEiL5YKfSZ4j/dsapbzbKkrx7lrgxIMYNYCyX8S5wya0P6Fdj9BnwfNteWoqyzDls56bF8/d19SUFBQUFBYq7DtUSRSZ8UbXUFhqVDrC8qhoKCgoKCgoLDcUCS6wqoACVhatjzzwGbs2tgsdiIXuofQMzAuKm96mJOiJRkc8HuERKc9x8a2OiFz53pfgkr3lroK8f2mYrtvaFIIaG4DXaI5gE3t9WKHUlcVFAsUEq/8mmioLneVyrOARH7V0wFs7WzA9b4RUYOPTcbESoWWKqJyNnRRe9dWlqG9qQo71jWKHYtnAQr0mSBJXhUK4BOP7RDi/9Slm7jeNypqetrEkLTndVUEfWisLceWjnoEfJ553lMXkv/jD2/FjvUNkhFwoXtwuq1cRbsH9dVl2Nhah92bmtFYE7pnuxAkqXmfcu1YX1UGw7idbKeqntfAtmL7MJDC68uBfva831TKM+tgQ2uNtOW9wGunfc3G1lopJHv2aj+6+kcxPB6Re8LAhsnAid+D6vKAWAZtaKmV+8GAw3ygfz37aHNthbwHgwok5RUUFBQUFFYrMhkHtjOCZOp8sU9FQUFBQUFBQUFBYVHQMpRVlggymQTSqTOYHPkiUJCCQx7oRgdC1X8I07pVUFBBQUFBQWGxSKQuYjLyNxid+t28fl/TAvBZ29FU87uwzNZlP7+VBNsZRzj2fQxP/Huk7Zt5/Y3H3Ijaiv8JocCLy35+CgoK+cHJMMvu6xgY++fz2rlYZgfKg38HteX/Q8HOT0FBQUFBQUFBobj4D//im3j/x+cxPhq55+94fRb2PLAef+8fv4D29XUoNO7tz6CgoKCgoKCgoKCgoHCfSNt9SKW7lB+6goKCgoKCgoLCioUi0RUUFBQUFBQUFBQUlg2pdC+S6WvFPg0FBQUFBQUFBQWFRUOR6AoKCgoKCgoKCgoKy4a03YtU+nqxT0NBQUFBQUFBQUFh0VAkuoKCgoKCgoKCgoLCsiCTYaH0XqTs3mKfioKCgoKCgoKCgsKiYS7+TxVWM9x6s45sfDJIIJNJIZNJU0uEDP0ssz8HMjPiMRqg6dBgSNfSNAOa5oGmeaHBkq8VFJYDmYyDDNIA+6v02TQyGfqu2tn+muurGflPu63P8isNmnytQ9PMbP81oXGKzL3Kz9y/XH3jPJFttxnjXNrvVrvNNs5vtRXHuUeNcwWFkh3n9izj3J5lnLvz4a1x7j7LIWPbmvE8VxqM29s2PuPZk8o+d+wZ7Xp727ptmptD3bbVNW92PtVXWfukkbYHkLJ74DhjxT6lFQ3HcZBK2kgm0kinbKTT7pFxMnAyGXdpzie1LG006IYOg4epwzQNWB4DlmXK16W8nmG/cZwMUsk0ksk00kkbtu3ItfL7mdy1ZjLQdI4nDYbuXi+v07R4rSY8HhO64f58NYL3ne2SSqWlX7B9+LVjO7DtjPxc/su1lea2BacY6R86+4cGwzCkT0g/sQxpQ/ablQD2B9u2kUrY0ldsjolsG0hfyfYX99r5WHOv25weE6Z7WEZ2S7Dy+grveTKRQiyalLmBX/O6eT0G77FlyFjwBzwyB/D6FzMe+f6JWBKplC3tzO+584w23ZZerwVfgM+04o87jodEPIU4zznXLmwWDTJX8J57vJa0i2GyXbQFt4uddhCLJuRzpF3kM7jP5Li61fYsgshDK+E+JnNJ0pZ5l3MKr40Hnzt8vnAZMz2WZBxlr1HmDc63bh/g90r1GpfsuTTjGWzfMdfwJstKj+2j6+744Lya7Qu5NlqJ4LiXNUgylX3mONn5xh1b0j/YN7LrD5lns9fNsZb32iNHz5QwFImucA9wwgwjkTyHROqCpOCm7ZtI2f1wnFE4mej0plGgeaFrPuhaGQyjBqbRKIfX2givtQ2W0QHDqCr2RSmsUrA/ptN9SKYvIZm+grQ9CNseQtoZgs3+6kSATAIOSLKnZHYmYaFpPjnYdzUtAEOvyPbdhuk+bBnNMM026durDzZsZxKJ1BkkUxel6FvK7kOa49zmOI/BQRyQNsOM9grBnB7nTfBYm9xxbrbD0MqLfVEKCgq3IQ3bGUM8mR3n9g15npPYtO0xZKbHOYl1bnzdudHQy2Hode58aLa4z3NzOyyzFdqqnA8XgzTSzijiyeNIpa4gZXeL4ppzqO1MSdtyrSTFNCksQK5tq2QONYwGeIwOeKwN8FhbYRkt8ixaPXCQtocQib+FZOpysU9mxYMkVvfVIVw6exM914fRd2MUI4OTCE/FEY+7BBr3nULY+C2Eyv2orguhrrES9Y0V6NzYgI6N9aitL5eNbSljcjyK65cGcPViP3q6hjHcP4HhgUlEwnHEY+4Gnht2y+sSd+WVflTX8lor0NJRg3WbG7FuUwMqKoMwzBLfjS8SyVQaY8NhdF8dxI1rw+jvHZP+MD4awcRYBPGoS3iSXCbB4ZKGbnuR0AiV+1BVW4bqunLU1IVQ21COlo5aNLVWobxyZcxDJLTYL65c6MO1S/0YvDmBoYEJjI+GEZ1KIJFICcGum7qQfCRMy6uCMh4amqvQubEeG7Y2SZ8hubUSEZ6M4cLpXnzw1gVcPNuL0cEpxGJJIax4H5taqrFpRzMOPbYZ7RvqEQgyYJs/SBDyM97/8QWc+ugarl8ewAg/I5KA6TFQXhFAXVOljLcd+zqw/6EN8AcprinuuJsYjeDUkes48t5lXL3Qh5HBsJB/HAOV1WVoaa/B5p0tePipbWhorpR5cyEggTo6FMa7b5zD2WNdMgbHRsJCqDNYUVEVRGNLlfSvXfs7setgp3x2qSI8GceNa4O4cqEfN64NYSg75/Lec87lPMLgFK/BnT/8Mn/UNVSguZ1zboP0AT5zGFRZbeAcOjUZw7VLA7h0phd93aMY6BuTPhCeisl95zOYwQU+X31+j4y/yuqgzDWcY9ZvbpSjojqIlYiJ7HP54tledF8dxuDNcYyPhOW5zLnWsTPwek34Ah65bs4Lze3V2Li1CVt3tkl/yWeelYBWibPoWsaViZQEqJJKp85gcuSLQCZWgE/0QDc6EKr+Q5jWJqx12M6EEOaxxEeIJ08gle5GJhMRIk0UVlkFGzfaGVGuzVSis6NnVehU7lKxJuoqEm5+GHolLLMDPu8++Dz74TXXQ9fLSuKao/F3MTb1B8ggGxDIE6HAZ1Hmfw4esxOlBBLGE5H/hnDsh3Aykbz/Ttcr5HpC/p+SzX2pggpKEkKJ1HnEk6eQSJ1FOt0LOzORDezwyKkBqU7PZk9IX3XuUASyz85UV2f7rmbdUl5yntCDLllsdsJjrYfHXA/L6IRpNmClIc22S55BLHEEidQppNI3ZgTFbilVqaK81W5zjXNPNgjhg2FUS9v4PPvg8+6Hx1wnP1trSKQuYjLyNxid+t28fp+kmc/ajqaa3xWCUuEWbGcc4dj3MTzx74X4zQcecyNqK/4nhAIvYi1C1FFC7J5EPHlEyHP6Ud8+zlN3PM9nH+cyF06Pcy80nc/zGnjMTfDL83yvjHN3ziwN8NpS6R4MjP3PcDITc/6ux9yMmop/LMFSVxU+33tT1xmXNVIs8aG0MddKFB1Mq9HlyKrRb1srzXjuZDOc3DmU7erOoa74YIuskwLeQzD02pLP7mE2QyYTdQOwYtnSM23dwjHrOJOy1mKf5O/lA/Y3Pk/YHoWFgYaqfy0BDTczYOEk91//lzeF5OamcjYcfGQTPvF3DglpmU9/i0zFceFML468ewlXL/RjdMglyLhZp7oyp4TLqdEJUYHllNlUZcthwuNzVZfcyLavr8OWHS3YvKMVNfUhIUWKCRIQfT2jOH20C2eOd+Nm94hcO1Wk06o/Kj2dGddK0V9W9ZZT25s5otjnkjytnbXYursNO/d1CIG4ULVpKYHXPzw4iQunenDpbC+6rgxheHACyXjaJYtzanTJULjVTrLVF4Gk2y9ySlJjhlJwplIyUOZDdV0ZWtpq0dpZIyRgU2s1gqHir+eoiiWhd/Z4txxXzvdhsH9C+kmCwZXULYUoCR25flFMz1Dfm64KWZTT2TFRWVOGzdtbsPNABzZuaxYCqFAk8EfvXMRr3z2B3u6Ru35W31iJn/21J9G2rva2Mcp7mk45eP/Nc3jnjXNC6uXITl6/qxKH3GNeJ0ktjodtu9vwyDPbsP+RjfMqhvkZJMne//F5vPXKaYwMTcmY5Fi99RnadHuyLdl3autDeOzZHTj42GY0t1Uvul1e/e5xvPfGOSFz7wTv1ad/9kG0ddZJX86B50S1/Jsvn8L7b5xH97UhRNgu2XPOKalFOe911fkMAuw5tB6Pfmw7tu1uFVX6XGB/YiDhvTfOS9uQOGdAIUG1e06hTyI12/a5gCaJ+qde3I19D22QgF+xQVKcbXtG5twuCcwyeCmEecJtr2l1/YwMoOk5V5T2vPeuutrns2SOaGqrxpadrdh9oFOCtsUITnVdGcQbPziJY+9dnl7Z3olnP7UXhx7fIoGOe4H3cnRoEqeOdOEkA0iXBoRIzz2X2D4yFqhGz841uYyWnFJ/OgvMY8HHvlDhPpf2HFyHHfs7JKA3X58rFnjvOX5OfHgNxz+4Ku1K0jyW7SO5Zw6vP9c/ZO3Ba+d1c54lqe73IBDwon1jPXbt78DO/Z2yBrkXfv///D5+8uoZCeLcC5xv9jywHn/vH78w53stF0o3HKZQEDiZuGx2YonDSKROI0nFuaioBuBkpmZsqudD1hZCSEtu0O/8uYlk+rIQnhHjNXiszfB79sHr2QWP2Z79ncIvbKm4M41aIUhJ1AipsICNI1W3ZqAGul78h2EO4fhrQqBHEx9Qo5HX32haED7PLnitXRL0KE1SaAiJ5FnEU6eQTF0SpR/7adoZhuOwr6YX8o4z+uytb8m/Z+3yOnTtEhL6Seh6lWRVmHotTLMVXnMTPNZGWGabBIuK0Y/ng+NEkbS7EItznJ/NKs5vwrYHs+Mc9zXOSRUJ0haSqSsS3DDjP4LX3AI/g2aenbDM5pJsGwWF1QIqn1Ppa4jK8/yckLuu4nxIAuKLGeck3DFznMv/PDLOE6kTMI3mLOl7AD7PDphGfdHHOYMCDP4zSEjidn6v7uty3q5t11zB22FE44cRTx6V9QwDkMx6yn+tNOO5I8GLGX9Gnp1P7HSXPN8Y5IzEXpE1UsD7iBuQ1IurXHKYzeVMSH/KZXrJv51h2PYInAzJ8ikhzYU4z/Df4bzXITNBUYP7jO9HYWG46wlawGmLI/hu3hjB5XM3MTUxuxiIqj0SFfOR6NyoXjzTi2PvXxE19s2eUVFWcuM+73lk086Rdu7+fQ3wXrdkM3zu+A3UN1fiE194QEhmku6FBAkHEnIXzvTgzDGXEO3rGcNQ/7iQdXINebyHhPztu6+VG/mbN0al/Y4fviLqdJJlHRvqhdRaKRgdnhLy5sLpHly9OCCqfBKaE+MRIY4XtJa2b7Xpvf6S9gNenwdXzvWJkpJBl3pRFNagY32dELEkZRdqC3I/IFEzcHMc50ien+hGb/coBvvGhcCk4n4+5KwGHMclfdjvZsLqGpHMjnMnuyXYwmtk8IVq5eXG5HhMSNlrFwfu+hnHPInghpaqaRKd8wzHx2vfO4HDP74g44ZzymzXzOsl0ReNJCQAx99jf2LggSQi33O2wBLHE4M177x2FkffuyzKZAZm7v4MkvkukcggIrMgeF+mJuNyvx5+eht27GtfVEBibGgK1zj3dd/9HCeJy7Hd2lEr1im5c+Znv/79E/jwrYuStcPrnu2cSSDzYDu62TwxaR8GqR59ZpsElO48Z+k/toOj71/Bu6+fw6mPrst8P9s8xXkpNaPtOZ8P9I3LvWbGCNXvVG4XWq3Pa2Df55x49li3BHzZjpxzSQ7TvmUhcy71h7EZIsTcnNt1eQAnPrwqqutdB9fJKwNThQJJbo5nZmjcC8zMYjBmNhI9F7A88cFVnDp6XQIMA73josRmH5gT2bnGZjvlnsEzluB8zjJI3HV5EMc+uIrtDPIy4LCB6+fSAM+fAZbzJ28IeX798iD6e8YwORGVtpkLHA88OF/IPDuDZmCWUPeVQckQ2bqrDQcf24S6hvK7gvgkyEs9m0GR6GsUjhMTtZCrVDsu6nPaYGQy3PAsB7gBHZWD1hH8zETylGy8/d798Hn2SHpzodVsVMnSiqI8+HnZFLttMLt66E6QpIjEX4NpNiHgeyzrq1088oDKt2T6GsLR7wlRmv/G1RD1cMj/aQlsMOW8lII8JCnYZxLJ80KKkEh3i5PZhTwTIUocewqwb2Sb1hTSnG0nqfjmRlFlBnwPZf9GKwnynAQRx3ksxXH+oXxNi4HlQUqCHRLwSJ1CXD8mwTlfiuN8nwRp3HFeOn1MQWGlg9lHfH7JOE8eR1ye513UGS3TJyZhOwOwk9zwcw1xVNTufu8BeYZ4rG0w9Oqip3LnA5LtJK79ngP3nLLT9jCSzNJLfoBI/B1Zu7hZXvNvNhcKzs3MGuDBdZIneUJIdb/3Ifg9e2GZ67KWO8vftiT6SYbHUyelDRxnXNZw/LdtD2ft0vhvWn9x7Vgyia0lDSqGSVbMhpySq+vKgBA0R969LIThvQj5RSHjqr6H+ibkIFHw2Me2F3zJIiRHz5ioIElSkLAjsTcvQbEAcCNP0pAHCYvzp3qEDNl1oBPb9rShtbPO9cIuQeQsNGgdcPF0D86fuoHL5/qEmFz2z7Yzoqzlkfs8kuYk0rfvaRNCjMrCQoEk5+XzfdJXTh/rkiAVbVyWMpee70dykwftLJj1QRKWfWXzjhZR2BbDx5jn1ds1jH0Prp+eI2jVw7nhlW8dRe/1kbsCAnOBQQcqakkGloV8okhnQCn3TMkZFDDT4c0fnsJ7r58T4mshIMlG8psENc+fam9mMywlotGk9M3MHap5Wqv86FvHZG4TkjcP8JIH+yYQCV8Sf/NAwIN9D28QEm9mu/A4dbRLsgaOH74qlkH5gp/BgBcDYWwXsVfyGGhbV1dY5XnfuGT78Dh38oYQzfm200LmLR4M1F46c9MdRwc7sX1Pu9i9zBagKAY4r0zO8mzluTPIcPS9Kzj2/mVcvTQgWV9LBT7jJsaicjDYwINB8gOPbJTgHQOYxcqYYh9n4OfahX4hzxnE53OTz+ulwGT2edxzbUjmWNrhHHh4o8wPtFjKgXNGqVvOKRJ9TcF91KTtEdmUReJvIhx7Gcn0xYKfie2MIJr4SZbAfweh4Ofh9xwUT2V6TxdyciUR6vceQijwaUxEviIb6vwI2jSiifehGzWiRDb1pqLxplTF0+JkKvotxJJH5N/5gn63Qd/jcv1S6KwEyF9X9cd+ehWx5EeYin5H/M5FFVkycJWJsSSPDySboTzwmRkkerHgLvR4brRnisReleyEVPpqwc+EZHok/gZiyaOIJXYjFPyMkFWWQU/lW4t2BQWFxYxzWzJxksmzmGL2Ufwt8eQuvOe1qxiOJz9CzHMAocCnZJwzwOx6gJfuOCdpLYHFOzKZaNpCpNP9iCXew1Ts+9K+LllcKKSng/tisef/OEL+F2FZG4DM8hcgdVX63Rib+mMJzJMsZ6F5hfsDCa/wLBt32bwmbVE2vvadE3j3zXO42XW3vcNSo3NTg1hZFEpVzOtkUIDkMD2cqablpnopyfPZQKKIqeFvfP+kWF889NRWPPbcTnRsqCsqaTF7P0hLcOPciR785EenheyiwreYoNqbBFx/TRk0Xb/NQmM5IArntC33jETOWy+flkACrSaWGyQ5aRVD9f/5kz144bP7hdyqqadisrDUSTJpo7drROYGgmpvqoe//d8OS2AoH+XwnWAbkuT++l+8K/7VDBLkgiIkd8eGp/DmD066VioLJNBngqrwD96+KO/d2FolxNhSzTMS4OllTZfM9D1jcOBH3z4mitnFgO9BKymC/tUMGuWsSNgXSbp+/6sfCrk4OZafNdlsYI0HKvz9fo/UIWD7LOc6iW0UDSckm+DDdy7h9e+eEOX5bJkFS/u5bhYNr5WBr+4rQ3jmE3uESGdQrtjFNTnHTk3efh+nJqI4d+KGWMGw/y/3fEMLIAZHGVCm2vuzP/8wNm1vFsuXQmb6ELmiqcxsYYDuyDuX72v8z3fdDLLcuD4s4/XJF3ZJoKWqxiXSWbeBQaZShiLR1wjchwxTiOMIx17BROSvZVO2MAuMpQc3pCRJeZT5P43Ksi+5ijDQH7RwC1oS91WhX5HgguOM5U1CC4kafw9Txjr5+0ym8NFVSS/LREStPR7+i3nT12+HgaDvSYQC9EFfvG/d0vZT+q8NYyr2XUxGvo546jhWAhiMsEwqRbSitx9V81PR72I88mVRUBZWtX83aAEQTbwtSngSbJVlvwivtZ0JWyVNsCkolCKm50lnSObIiciXhews9jjnsycS/xFiiQ8kKFtV9suwrHVApvgFxubKyqNqn4HbO9dL9JCfjP4tJiNfkyBu8ZCRjB4WhKWFTl3Fv5AABTLLO38ysJB2Rty6I3bfsn3OWiTR79y45ywRqIL98995TcggFgstBFjwK7dxXU7kyC6qMUmKfu9vP5TrLAZ6ukbw3b/5QNTNX/r1Z7BhS5N41RZ7nsoRxwykvPyNo3jlm8fu6a1fDDDYcuDRjZJqv9ykH8lh2gl8/c/fFW/cYgQRaMNBSwqqsj/3C4/iied3ir0LPcALq0QfmfbzZhHZd14/h4tz2FTkA1pM0C7q/TfPiyc4vatzn/fjl0/hIxJos3iRLxSc0955/Sz2P7wBW3a1CZG+FMhlSeRses6f7pHCpwx8LEUA5SevnsUzn9wjRZhztjockyfuk0DPgUGMw29dwPa9bdIuyzWcxLomkZaMpm/99fv46CfFWcvwXr36nWO4eKYHv/Sbz2HLjlbJ7ihm8JLFcafGY9PPJgZa6f39zb96T+zFClk2kv3u+PtXJFD5y7/5nHh9F7I4r9tPUvJs/OP/+LJYeRUiYOnYjlgjMeA2PhbBxz93IFuXgyR6adPUxQ0BKRQQtti3DE38K4xO/WckkqeLTqDfiUj8VQxP/BspiklvzcKC+ms/Kst+SdKmFzI06CM/Gf064snTeVvBLC0cIUpHJv+zFO9aSIp5wPsoynzPwWttRimAPqgshDcw/r9gdOr3srY0KwO0c/F7Dxb5LKhcvIjB8X+B0an/W4JCxSbWZoIqRio6h8b/N0zFvr1AP3YFBQUXKcSTxzA4/q8wNvX74s1dSuPcDeJ9A4Pj/wsisdcWVOC60OAz280+m7keyoiyfmj8X2I8/JfyjC8F0Kubnux9o7+FeOLkMtpyKSy7En0yftdGkoTWH/3/fohjH1xBJFw4xT+LKBaCRCdY3O/bXzmMv/mvb4uquNj3gX6vv/OvvyN+6fey2CkkSGBSufuH/98f4pVvHp3Vz7mYYD954LHNy64QZHFGqu//4N/9QBTRs/l9FxJUf3/ry+/jq3/2E/FML3SfYFAlmUzJeZDg/eDH55fs/UkWX7vULwQiP6uvZwSvfue4kN9LhfGRCL7114dF5btUYFuQeHNsW1TWR965hNNHry/Ze7/8zaNiD0N1Lr9m5sz3v/bRks4TVKS/+t0T4nG/nPZhLND6V3/wpliEFRO0k2Ex5N/5378jBVkXYoezHGCAkn2SZDGfBwzqfu3P3sHFMzcLSqDnwDFIr/Sv/fk7EmjOpybIUkFqk5zqxX/4l98UAn0hFlFLAarwX/7GEXz7r98XOzEWKGbB2lJGaZ+dwpL5ItM2hapUWqfQzqXUCHQik4mKzzgtVWxnHJVlvyBWK4XwT3YjfZoUSCvzvyBpy7Hk4Tz/2i1MRtKytuKfwaOtg6YVbmglUucxFf1BNjCS76SnT9uP+LwHpMBqsZFK3xQV42T023JNVC8vh+fscsDQ68Rb3zLaiuqLHE28h4nI3yCWeD+bkeCUoAVFBPHUaWTCfynjvCLwBegyzktTqaqgUEogkRqOv+EGbhMfZbOmSs2LmirusKw7xsJ/InNRyP9JGAYLL5cW3AKWQ9KuGb1G1hu0UJmM/C3CsdfEVqx01ktubQ5mnY2F/1CU/n7vA9C0wnkTKyyREn2GnQs36yQMX/nWMSHIYpHlqmVwO6imra4Nob6pQlLrlxMkA+ipyg0yVcW0FcinQOpyghwJyRMSWV/5k7fxmUQK+x7cgIqq4hTwJRH41o9O48c/PCW+yfy6lMB2YeE7FuFbThsG+vGSQKKv9ZljXQXLyJjfgiiKD39yUQh+9h0WyyyE3YI7dlIYHQ6jv3dcrCbopbxUGB2alADe+i1NkmHww2+45PF8xQMXglgsIXNbb/cIQhWBJVGjk3DkGGGB3ctn+8SCgmrepUCuQCnV27RbYXtTnT85FllSH34Gh1gLgnYWDc23rGOWAjl7sFe+cVTO/fql/oITo3eflBsUYv/61pffE+UzC6zSUqgop+NkpDbAwM0xaZuv/NFbuH5lUM6rWKDFDmsyMMhQUR3Ezv0dy/+ZKVvmle985TC6Lw8imUoXfFuRTrm2VbTtqquvgOkxCm6dtVCU9tkpLEmqcixxWDbcLILpEpOlCyqrkiSFkYQGHeXBL8A06gpWcFTXgwh4HxY7ESr3WdwrH5AwiMbfRsSzD1rgE/CYyz/pESzwFU28697bvL1aNehauRRTpere0Je/8vxcyIiS/opcAz3dqegvJVVlPvBaW+ExN0HXA0ULlEXib8k4Zz8sfCbHwkAinfYEiLoPao5zQy9XBUcVFO4Bt6hVFOEYA43fkOd6Yf25Fw6XSD+WDeY5CAU+k615UkpJkGzXOFJ2H0yjESl7SNp4KvY92M5gCQYoeL4xRBPvyPnqepkUZldYOXCV6LdI9GsXBySd+eh7l+9SqN8J09QlBT5UGUBZmU/IbxbG1A192hKGxAkVzCTqqZqkl/Vsqjqv18Lm7S0IlQeWlRTNFUJ897WzskEmaZ0vgU5Sj+RtbX1IiBYScB6vBY/XkKJ/vKxUMiXvF4kkRPFKn1t6F0fDbvG++cA2o8UAiwXyPQ88smnJLCfyQe6+vfPaGfFrZxFRKl8XA/aDYNCLUKUfwTKfXAeJOUMKJLoZDySp+XnsI+xvkamY/Hu+tmpsqcSWnbe8s5cauT56+sh1vPmDU2KrMN94mAneu7rGctQ2VIhinp7CbnE6E4ahuX6/KRuJWBKRqQTGRsNiVzI6NJW34n9sOIxj718V8jwQ9EhhWvbR5QZJXfqfM/hEC6KcQpXzQUtHrfh303YkFwyj9Q1tTahqnS8IkSPtGLjhGKO9y8z+R7sN+hPT7qWptVrmHs457EP8HHpd06pjLoKWytLJiah4uTe3Vi/Z+IrHXX94eleTgGO7sJ+zz7evr5O2qaoJypzBNuT5UmXbf3NM5sU52yVl4+zxG2jtqBWi9cQH124bI5wzy6sCaF/PdqmaLjybjKcwPDiFy+d65XWuYAR/Rs9wBlHL2a5LRKJzLPH6+Fwhgc77m681B4lL2vvUNJSjhnNuZQA+nyXnJgVBs4pyFtrkuGGAYXjALVBNZTfv9XzgfWJRSQYM+b6PfWyH2HcUy9KFHvcMZJw9cUNI/nuB443PIAY83OeRX8YG+xfnGPd5lBa/bz7jOV+wXUjSc+7JdzlJq6KTH12XGgys2VE2o/DvcoBFVDmGqMTnvc0XmqahvrkCDU1Vbl/hnBv0unZXGXduYUCC/YIBIz6XWcCX6xIGMO4E5xAWof3+1z7Exm0tS5q5shxQJPoqhbsYYcr3cSHWuCm8P2LNgq4FhCSkalmDBWhmtgglN3UOMkhJ4Uf6iGaciCi8FnXuSCKZuoxx5y9EoVrmf1ZU04XaeFtmGwK+J5CyuzAR/pvsdWTyUIhNYjL2bTlXKugNvWKZ728aseSHUrhxIX6tJNBZSLU88Hm38FvRCA23TZOpawjHfoCp6DdFgb58YH81gNz1ZmxkhKy/X8Jeh8+zF15rE4o1zuk1Phn9KiLxt5G5D2JNgweaHpCxfmucu8VmpdCejHNuyBNC3Ls2DYuL2JO4iifPiILeMKrFm58BndIi2BQUig95vmbiEjCVTJPkR0KoLxYs9uk+y3Pj3LxjnNt3jPPofYzzGOKJo8g4cRh6FQK+J6GjtIh0PgfoKW8ZzVIsPBz7nmSX5Q+2HAumBbMFk925M3eNLADrro94xGA7XIul74ugpyCCz37TqJcMKM6hywF3jcfruN8Ap+szv9BPL7TrpKwRlrmuieuJ7vqwkugmafXhTy5JAcW7zkcjqWHJhp3EIA+SZdX1ISGXy0I+eDwWTEsXYoIbYJI4JKy4gSdBMzYSFg9ffo+EB8kVEkokQ6lyW24VOskEqtzoQd5zfdglE+ZAjrQjUUFykERYc3uNEFVsB563z0dy2CXRE/GkqNwnx2OiHqXalUT9jatDUqyNm/f5VLUkfuh1zPak7/eOve3y/eXOkMuRXedP9Yh9BAvM8VryBYt7kuBiRgFVi5VVQWkjHtI/yn0SLBH7FU0Tn3HagiTjael7tFMgsciD94n9kq8kr2eeh25och+27Gxd1ra42T2KN394SpTo+Vi48PaQYKpvrERTW5UUyeV5CslVG3LHh9cNIjCAkAu2UFFM4jfXT+gDPHhzTNTe84FjiipRtu3HP3cQzW3Vy+/dm8ng6PtXhBSnZzL7Jf2St+1qxZ5D67F1dxua22oQDLlE5FD/pFibUDlPb+f52pLjkm1e11BxW1FOXhczVXbs68DuB9Zh/eZGIcs4J5EsZBCCwb/33jwvljBzZtFkMqK63n2gEw0tVUvSLPy8914/h9NHu6QPc+7gOGAR2H0PrsemHS1oaKqUOY59nwTescNX8MHbF2Sszadcv3qhD0ffL5fAy0x/eAZOSK7v2N+BXQc6pVAmP5eBnHg0gd7uUWkXkth9PaNzBhj43ic/vIZd+zuFSF+SdokmJbjBuhMkSOebUziOSH7WN1bIXNvaUSOvPDiO+DMh0r3cR7vPML4n55ChgUmZc3uzY+nmjVG5F3OR0QSfVwwecD6vra/AvofWF2TOna3vM5DI877XOfN+cxw0tlZL23RubJB/V9e6wTpeQy6oy+dRLJaUgC7Jc84xDNywjTjn5Jstwb9lJs7uBzolQ2q5wAD3R+9cwkfvzh/En9keNXUhtHTWYuO2JmkPBthkzi33ybxBkpz1PdgXue4YGZqU+SsXDKRdFPvOnQXFXVud6xgbjmBivHStIAlFoq9KuEXH6JM6Hv5TROI/XgSBrkHTfNBlQ+iHoVfDMlthGa3yb52qUd3vbjhkg5+CnZmQ1Od0+iaS6W4pesYNo5DqmeQCN1Bp8SQdm/pdGHoIQd8zBU0Fp0d4eeBnEEscE19pejnng0TypCiqqRAjUb18ytoMUnY/piLfFgI1f1hybVWhX4JltkMvoo0LHzZOZkK8cyej30QqfXUJ3lWfQWCwIAenOD70rSzBwUJsrmqEfTJDggjxLGmUluJyJDvAV/5ciI65+y0V1D7PDgm+FBaku2gl1IXRqT9APPmhKLwX014uae6HqdfKODeNFiFldD0k88CtcZ6EnRmHbY8gZffKHMPiuiT4SIZL2y1onKfEb3hk8t9L4IljxtDcIj4KCgq3lNKJ9EWMTP5HsTxbuBe2cfs4F+K1FZbZBF2vgaGT1Pa5hCXnwkxCnufMdEqlmZHVI892JxPPBshTCyKAOU8l0ufl/A29Hj7PTmhacSwTZoctgXtefyT+erboer5t6oeuBWEYnDs75Nlv6tXQ9BB0uM9XroGoyid5nnYGkExdhG2PyXztro8Wl4KeSl9DNP4OvNYOlPmfyX536TagnPc1PQiPuQ6Gdn/p1rZc/+gCgj+WCBH4TCooND4TA7eC7csAEnkkKrlhp8UBCTluKu8EyT8ShCTp9j20AbsOrsO6jfULUqXZti0E+tWLA1IckWQtFZuT4xEhfbbva5eCmssFktdUQb723ePiKTwfqHAlib1pezMef24Hdh9cJ4TwXNfLdqI6sK6xEhu2Nk0ru2nF8NYrZ3Dqo2uyYZ9P/c6fHz98FaFyPzo31IuydLnB8ySx8OU/fENIr7wLuWmQQAJJNwYa2D+272kXuxWSGAshoiT4kkjhxrVhKXZI4pD3igEIEmUkNagcbltXi7b1dYu/2HnOgapW9hOqQhn4mQ9UOzJQsH1PGx55ZjseeGwTykJ+CSzM+vuGLuQO7yvJQtYCIEgE0s6ENkMknUk4c2zOpczn33z3Kx+isaVaFOlUvy9voVXgg7cuTH9N1S6zSFigsW1d3V3ZAVRht3Yy8FQtj2oWsJzLX5mBgSPvXr5NDczrIQH9xAs78dkvPXJXv2KfIIHIwAX9i1/+5hFpx3stDXgNVB+TUOMYXYr2Yp9h0CUH9oe9h9bjF/7Bx1DXWCHE5kxw7uzcVC9zyg+++iFOHe2aVRGbw0DfON557Sz0GedKor61sxYf/+wBPPOJPXfNE2wXzmHb9rSJcvv1750QovZeoGqZ83I0mliSdqHdEAltFkHlnD9f0JLjiMpzkqEcR3sfXC+WTXOdB8cR25L9ft3mxuksEpLibC8GZPicmc8+hllT/JtA8Ag2bm+S7BkqugsJPntne/5OB3TLfJJtwOfRwUc3ynw7l42T5fFL2zAgxecY/e6Z+cJg+VuvnJbrDU/F8lpC89nw7mvn5DnI+Wsp55jpe3aiW2qC8LPyAecaquMfenIrnv/M/ung0V0wIP2fv8/gf+fGehx4eKPMrXzOsDjyT147i+H+CXn23pktd+P67PeklKBI9FUKKkRHpv6jeCQvRoHOTYTfewBB7zNCbHnMdtl8uxsLV5t092Ytc0utmkkgbfeJci4cewWJ1CnxP17gVYilyljkz2SDHwq8hMLBgsfagLqK/xH9Y/8z0vaNvEmDqdgrMIwaIav5ujxIYzz8XxFLHlkQceox14vne8D72BIoy+4XaSkiOxX9rpAB9w9dgjs+aw+8HtqrbBZbHZIaDPy45PndfZb9TMgNu0/UiAwApe0uxJPnRI049/jR4fccgmlQnVMYy6GZsO0hDE38fxBPHVtE8T7X1ofFUBmk4qtltLhkGomE6baa2WbuGMiNcycTE4JNxnn0h0LwLbxYaGo6EFCjeRD0PbHAv1dQWM1gwLQLwxP/VjJ1Fk6gazL/BbyHEOA49+yHaTS4dTDmGOfuSHdk50sCmGPUfZ7/QAjnhc43EghIXcLI1H9CbcU/gd+zD6UCBgdZ2Dwcf0UChPk866mqd+fOp+H3PigqdnmmztqmufbMqrEzLAB9HdH4TxCO/ygbCF9cRhRtsaicL/M/teSqbVkHeg6ipfZP79vWhqr58fCfiQ1NPpCaLcHPoSb0D1BoTAeUlpE4pcUCled/+6dvo+vq4CwnAWze0SIkzUNPbRVil1Yd3NQvBNzsV1aXYe+hIHYf7BQ1Zk/3iCjcaGNBZSk3ussFKj+PvnsZ7//4FgF4T2hA+4Z6fPxzB/Dcp/fJxpzXvBjwmnbu78SWXa14/43z+P5XP5JU9flAiw8SaySCPvbJvcvaNgTJ/R+/clpsI5gdkC+oBCZp/PRLu7HrwDoJhCymfxDkZags3LClURS1T724S8h8qiapzj/6/mUhJ6k2XC7bH5L1l872iaXOTMXvXOdc31SJz//CIzj0+BZRQIqFwCJAcpjEKwmvR5/djr/8vdfFYmleAjCVxjf/6l1UVAXw2HM7ZieSlgkd6+vxK//oebkn9+qjJNxI5LKwJG0qmIlyT8I44/pzz7RPCJZ5pS/81M89LG10L7DP8fp53xh8oc3HXKQ0rVE4By61ep/XS7Lxi7/yBBqaSQLf+/cefGILwhMxuc9zFgrN0MLn9j0N5+JP/+xDeOzZ7ZIxM1e7MFOBBDrH+b2yYXKBNCqUOQfcbz8aHQqL9QytUqgEng/M2uB5Pv9T+7KWNIv/fGaqcB7Z88B6fOe/HZag1HxgQIrE8uvfPYGPfWqvtG+pgATxky/sxqd/9kFRXjOjZ6FENn+f44dz9brNDVK0lwVqmTE2H5gZw0wSqsU5By/1HMO+99r3Tkg9gXyv5eCjm/Dxzx/EnoOd0zZhCwHnqw3bmtHcUYuHn9qKP/mPP8Klc70FqwWzlFAk+iqEKJSj30Q0/t6CiWuSgQHvI0K0eqx1slHUtawaNQ9ljowljZtSLyzdh5BRg4DvMSSSZxGOv4ZI7JXs5jvfDVlG/tZVdzfD792LQoAThY6gFN2sCH42q5TuyutvqbTi5tjUm1Bd/vezrbJ00UPbmUA0Th/0N5C251f25KDr1Qj6n0ZZ4FMFLXw61zVMRr6ebdfFb9CpqPR5D0qwh97kkimRVVxSae8WXTOz2RWz3YdMNtuiSoIM/kxCMg9oY0D1pUusX0QieUEKurljKrcwMcSewLUbKmz0nCQMrZpiiQ+kKN5CYJmdCHifEPWiRZWhXpkd554FjXMt4yoxScoFvU8injop1lHMflmYKp7j/AQisddg6LXwebYv6HoUFFYrEqkLmIh81bVEWSCBzjoNJHkDvsfhMTvFHo0e2jOtRu6F6dlMxrkHusUFfCvKfB9DLHlUguMM0i/MViYlxVAjsVdlvvFaG1EacAP2DBi42Uf3ginPl/LAp2VdQ4W2zjUSM3ZohTXHM+DWTzLyhcfcCDNYj4DvYcSTpzDJe8yCywu8xwwAM7jC9R6fg7oQwEsD93q4Sbr/FHO3eLmxoM9m4JsZZKsRJE1+/999H9dZxCuevu26qQZ98fMHhSSlspgq68WSl3IPNQZ9mFegw7QyaOusRU1tSEjA5SSJSQh99O4lfPC2W4xxLpBwYrCAQQNaR+R8phezrsr9DRWNum5h/8MbRQ1HcubdN8/NqTzl8OzvcS1FDj62CZVVZYsmZ/O1ueFn5Uug8341t1fjU198SCxnaDFApeRiyPM720tje0l3MORzvL46Uew+8PhmV/VdGViWdS4V0gO9Y/jan70jZNFs/v13Zh5Qff2zv/ak2JjQwuZ++jGvyTA1sX6hDciv/Nbz+M5XPhA7jvmsF2hHwP7NdiKBWwhQCbv/kY2iNheP6nvcE36fwZaW9ho8+rFteP17J+e19ZjZ9FSaUpVMMn2uzyDYdlSkb9jcKKrqe4HWDSSlSZwudTHJ9SRvD61DUxuzaOd4FmuaBJ2ooGYGB9XB+bYJ/5ZE75YdLfAH5m+Xikq/BGeY3cG5/l7g+GcQgvP9/Vi68H1OH+uSa5qPQOc57nlgHZ7/zD4hvfmcYbstxZzLuYlzOOfc9948N2+NB2aesLA2z8dPa5RlDl7mAwYUSXw/+cIusU8jgX0/bcNr4rzF92Q7f/0v3p33b8XybTIqc9GDT26VjIGlAucCBjm6rwzlXYODz2ceW3e3LjoIJvOtQcs2Dzo2NuBX//EL+Nqf/kRslmYWXF8JUCT6KgM3VPHESdl0uwRrfgUC6Inq9z6CoO8pUVV5rI3ZAmCLWzBxg67BB93wSWFQU6+DaTbDa27MEtLdyNBGIw+QjOMGkUSl19oA7T7Oa2HXwC1HOUKBTyOVvilEJdOR54eDZLpL0sKZtr6UG1um04tffPjPpA3z96k1RG1c5nsuq5grHtxruCLXkExfydsqZyZIWLA/kBzK+ZGbZjtMKZK60AcdFw1mNrAQuG2b72QSQszb9n6kfUNIpWlhchWJ1EVRqdPWgJ9vGEvj75cvbHscscQRsfNxhNTPLwhB8oykv6uePChBg/shKWScM1gB1yLC4Fg3WoQg4jinuj/fPkq1ayT+ptxXZr6Q7FNQWMtI2yOIxg8jHHt5ARke1EF7EfR/zH2ee/aLzcj9FD3ms5BEqo4AYFCNWJsd5xukACet1/JVUvM6eD0MiltGU8mQpMyemwsky32e3Qj5PwGfZ59kObnnvtC1iPv7uu7OmyTl2RbMmGJQmWp/WuHlD1vanyIFr2cXsIQkusLygeTcqSNd4p+bI2lIVNLv/IXPHsCDT24R8mupizhybURyoxDFEGlPQl9megLPB6qpc/YtS+ULnLtevt/2ve1C1jJFnDYlc/n1klxgcbMP374kxH5l9fLMUVfot/zeZUlnzwdUidJy4cXPHRSVfWXN0isTZ2YweH36tPftfMT2/WB0iP7dXTh3sntey51cwcjP/cKj0leo8JzLWmHBRVnLfJIBwkwI4vCb5+cstEcl56kj14W4JSlbCBVt+7o68fvOZ24gWUcrhQce24x3XjuXn9++BiHr6IFOUjefbBAG+RqbK6X46FwkOkG/bKril5pEZ9Hbrbta85rb2GdYY2HXwc55SfSZ10hlMpW4VG/nE7iiUpfEKf2z5yLRCRZdZEHJ+5n/uq4MypzL+Ws+sK045+57aKP0kaWcczlXMXjArTiLuNLGZ66+x3Hf2z2Mo+9dce2Wmgpn3zsbaFv1yMe247FndyyZfz/Bvsn5i8+VC2d6cfnszXkzXthu9AhnoGOpSHTO55FwAj/+4WnxKp/L6ilns7Yxa7G2eWeLzJP3C45BBrMYeKMtDG2HPnrnolj8rBQoEn0VgcXHSE4yLTmZOrcg7/Og9zGEAp+B3/cwLKNxic+MUadq+PX9svHk503FviVEZL6qq5R9Q1RvVBsHvI8WzIpE0hytraLMpyqZhRvzIQWpzqO1xUTkb12ywGzNKqIXD2rkSJxTARhLvL+gwq0+zx6E/C/AK160xY3w0mM3HH91wdeQg6tU3in9IOB7KqsGXLpN10xQya4LMVwHL7aJ93/K7hOffFrQ0M5EFJ4FJC44zhOpswssKEt1QHl2nH9eCHTTWGqvWU3e09APZfu7O84l2DAPQZVDMn0VsQQVlXsR8D60xOenoLBywHFOb25mdeRfL0KX4rxB3+MoD/60G+DTK5dhnDcg4KuEZTZPj/N0ui/v+ZzP/ljiXXitbfB796PUQR/3gPdBhAKfkmB0vpl5+YBqaz5fgr6PZdXaEMsT1prIF8yOotCgMvgL4s9e7EwzhfnBTSsJk5nEA4sjPvbcdiHwSGqUghpvsRt0Xh89lkmezEUSkKBj4OCJ53cIMbyUBPpM0Lt5x752PPPSHnzvbz8QBfG9z99Vib/9ymmxg+E53Y/SezbQzocWG/Rtn4/AyPksk5Qi2UDLkcWqIheL5fosZgXcuD6MD9+5mFfBvcaWSvFtfvipbUJILfV9IXIFdyPhuBCbrFswF6ggPn/yBi7vaxdCcjlBFT6LPVLBmS/Yd0jw03aGXsy0dJqP3KKqnOQv1cn5oqo25HqwzwPaxuRbXDFfkIwj4c/iqvmC45pe11TRs9jsnBkqJED9loxB+qGz0Ga+IOFe1zB/rSeqsVmQ8n7mXAZ0zp/smTdYUttQLkQus3SWkkC/s9+RqH/yhZ0yn85H7JNIZ32QTTua5fyWKji2UDDzYv/DG/Dw01tlrC01mMHAvvqxT+yRrKf5SHT+/MLpHlGLL1UtAfYP+uYzE4p1BeYC59jyqqDYm5FAX8pAoaZpss5hgV4WBeeRr7VMKaA4PVRhWeA4E2LtwFTpfMENFzfaVeW/jjL/c8tAoN++WTT0BlSGfgll/k8K+Zk/Gc7CX1cwFf1OthDXQooX3g9cK5aAj9YXz0sQIF9QtR6OvyzEOxWF96vksJ0xCSRQ4Zs/+WwIqVIR/AJ8nv1SpLWYoEVKPHkMU9FvL5JAZ/r746gs+2VUl/8/xPZjuQj0e/VhqqTL/B9DVehXUVP+Dwpa8PZWP3gHUQno5Addr4Df8wCqy38TQbGfWb5ibQwWWUY7qsv/O4T8L4rdTv6PGhvx1DkJFDlOrIDjXEGhtOAGbd+QgsH5QrzPfY+iuvy3xJZt6Qn02wOMtIupCv2aPBsNo34B4zyNmAQI3nSLlS6jyvF+QduZgO8RVJT9nNRl4fNmqQj028UMutSDKA9+VpTuC6mxQb95BnXdGh4LsddRKBWUV/pFFfnJLxxCTX35iiXQCcfOYGw4jJMfXhNf7blAIvTQ45vFVoGFAJcLri+tX/zWWzprhYycCyy0eeZ4F/p7SXIsvVfrjatDuHSmVzzj5wNJDHrFP/7cTrEWKDSBvpwggcMic/T9nQ/+oAfb9rTjyY/vEoJuOQj0HOh1TUuKJ57bIf+er7lJENLWhar15XyesWAlx8lCiCyqofn7ja1VUox23t83dOzc1yGq64UGqjh3zQcS6NEl9j/m57LI5czCqPOBWQ20KGIWQT4ZHewHnKcWmh1EkjqftmRwYT5CdS5MjEZw9lg3errmDsBz7mANgAOPbJRrXy7wcyzLxFMv7harnfnajUEMFqal1Vkx/bHXb2nCA49ulnohywUGbhjEqGuoFJX3XKAy+2b3qAT1FlI3Y75skNNHuqQGAp/X85H+bItHP7Z92bKy/AxcHuiUPlnI2hL3C0WirxLwoU2fUpJraSdfn2wDHmsT6ir+Z9m0FcI+QbzGNR8qg19C0PcsDH3+B24OtjMohDTJ9HyVrUsFQy8T8pHqvvwTOBgZDmNs6vfES3sxpPH0O2UcxLJp/W6R03zPuxxlvmcR9D0Py2xCcZFBIn0JscThBSgrb4Hqv/Lg51FT/psS8HEDMMXcSPCz3UK7hRrjMs4T70kb5q9UNMWGgMX8vBaDDsufbkpCiF7plWW/Jt7BJKLyhRQqjb8rnu/52lEpKKwW5MZ5NP4m4skjeVqIEZZkb9SE/nuxt7rfzKd8wKwm1pKoDv2mZLcwKJ8v+AxwnwX5F+0uPDSpi1IZ/LlswePlXzLTgofPN9paLQT0co8nj4uYQmGFQYOQgyQbmDq+nORgIRCPu16rTOOfyzaF+wGSey98dn9e5Nv9guQgSUh6rlP9PhfIg7K4JgveDfXPVVx+cfP78Q+u5mW5QNDe4JGnt0mRR17DaiHQietXBsT2Jx9l8rpNLFi4TmyOCgFaStDSZMuuFpiWOa8anYGAob6JeZXe94OGpkqx11komPFBZTkDEfOBfsW0DSIpvhAwsFFe4Z83ABiJxBGPLe0evrWjVj57oaC6nBY0LBiZD9FHz/yFWmHR+qIs5J+3tgILnM5nZ3QvkAg9/uE1IdDnI+IZPHjq47uWRWV9JzRdk4KYLDhK+5z5wHO/dPYmbt5ggffCg3MrPb9J+i+nEp6BLdZykJoO1fNzb7SNYlZMPE/v8rnA5w+Lih8/fDUvUp51N55+cZcQ/8vZJq0dNRK4rK5bOVauikRfNUgJucZNVL5gKjVJYa9nh3glFxIk7IP+ZxD0kQzNH05mUoqm5k8sLB1oy0IiPRT4pHjO5l+w7KYUgIwnjy76s1PpK0KgM9MgX9DD1mvtRHXFb8E0CrPonBskgN8Vz9eFw0R16B+gIvAFKYS5dpEUewdaBeULv2cfygOfhcfaXPApX4I4/peESF8I6AksWSfOQoqTKiisBpBQTkqB3mQq/2AjC1SWBT4Jy9qAwkITxTvnGJ/nwIL+MmX3ynNt7mKexYIpPuVVZb8Cr2dPAT/Xgt9zQLJ4FoQMs3hOS9FuhZWFuoYK7D7YKQTNagBTxd//8QVMjc9t10hVG5Wd9AtebJGyxWDfg+5n5oMr5/qEvFgqkJynCpeKS6au5wPal9D/myTlasPV8/24djEP4ZcGCX7Q8qeQYHDnuU/vh98//x6ZRVEPv3Ue8ftQE+djmUJrhYWCgTmS7yw0Oic0FkB0LWMCi+hvHMcMjM0V50nEUosmi++FOhbYDS3cVpOEPxXs8xVu5vX4gh4JRORDuN/+x27GzXw+0rTroC/0YuA4Do69d3neuYrnwILVLR21YoFTKGzf2yHZNPn6uvf3jqHQYN9l8IiWPST+lx2ahg1bG1FelV82/TA985eARGeQb3Q4jCsX++a1EqMqvKG5Cvse3ijE/3JCo6VdUyUOPrJpxQgJFIm+SsAig/RJptVDPtD1Svi9D0ihSSrDlz49eX4FG73GqZxbiOKKpFok/hpsexCZTGE33rTysMz1qAj+nBCSVEbnhzSi8XcQif1YCo4uVIFOW4uJ6FcRS34gXut5ni181g5UlP181k6jsEGS2RBPnkM8eVK8cxcCPUvQ0JdeCuQVQGFZmkghGn8fidR5KSCcD2jl4/c9InZE7jjXCj7OfdYumWto5bQgy5r46/KayaycIiMKCvcL1gmJxN+SrB0W280Hhl6HoPcJeZ7SZqWQ4Jwi49xzQPzNWbMiX9j2gNhSZZxwyY1zQ69AZdkvicjAVdgXZu6UFGizLduW1Xl/bgY2EqnLkv2msLKw60CnFDMsJKmxXKANCtW4V87dnNfflzYH+2mPsEze1vcC/WipRJ9PGUpcuzSQLby2NOridNrGxdM9olyej0jkXFBVW4a9h9ZJobtieQQvF6g+pxo/H8Ksua0anRvrUV1bWJUiSUcGtxh0mc8CiMUyaelCtehyWbrQ15zFVBcK9iUSg555CGBaS9TUh8S6JJ+Cond+BklpZk7Mtddgv58rQ2UxqK4Nie3EQkGSsKq6bF4S3ev3yHxFS5KF7qPkme4x5w2CMbhgpxe+DiLxToL16sV+hCfnzujgvXnwiS2SZVDI+YQqY3rD52PV0XN9WGzAlmrOzRe8P2wb3uf5+sNSgN2I7UKFdz5gUDqRuP8A3ejwFHrztMyhN/26zQ1SP2C56QNN06TYMG3tmMGwErC6nshrGFSnuqq1/CZgn8XCjA/DMltQLFCl6rG2SrHQ/JFCKt0lhclYTKvQ0LUyKdJJj3HLaMt7CFFZG42/hUjszazXc2YBhMqr8nfpdG/e5+kxN4rSn8U3WWSsFNI/mbrPgpwZ5J/Gp2lBsSBhMMBjri+IFUmpgkVNw/EfZftBnuPcsx8Bz6FlrXUwH+gZ77V2wO/dm/ff0K6Jdi6J1AU4mallPT8FhVIC+/tU9HtI24OSyZQPSJ7TdoQFKosF06gW2yiSzvmC/t308mZmTb5FxgsBZnF5rC2iBieRXWiRga4HYZmdUq8m/yxBG2n7pvSfQgsMFBYPEsjb97ahtbOuJNZp9wuSOCSe6e/rzJEqTtKc1ios3FkIwmImSEJys06LhXx8hkeGphANL439BNPyzxzvFsJ1Ppimjl37O4X0Z7G71Ya+nlFR4+ejrty0vUWKVhYyY4EgKcyMCfbT+XzISYJ2XR7E8MCEeKMvB0h+zqdong18hIUqA/Pa0rB9mRmzWN99jmUJBs7xp6lUGukltrzhPVpMEJKBNCqB5wsY0MqFRD3nrcW0C4MT89nAMLCwGCsgZv4wY2Z8JCxBunuBxCT7MIs4LtSS5n5Br3q2X6hy/jl3ciKGkcGpeQMCy3GO+x/ZKIGGQoH2bfn6+LMo8FIEn9i2N7uG8+KhGPigtU2hbMSCZT60rquTgF+h1wWLQemfocKcoHrLtscQS36EtMNN9/yggtrvfVA2aMX1lAYssxkBL71GzYV5f6aOI233o9BwPd0DCAU+IwXcTCN/dS0zBZi6TjW2S47MPYGRbE+mr2A8/FdCNGSQylu5HfA9KZ7zpWDj4qrpI9JHmb6/EFhmK8r8zyLgPVTQAqKlSKCnnaGsF3p+Vkaa5kfQ9xi8nm1FH+csIuz3PrSARw59Q+PSZ2y7ON54CgqFRiaTRJrq7MQ7cJx8gkfc0AUkYOoxNxR/nFtbxIpkIePczoSlYHa+2TWFAJ/rQe/jQmQXWtl/yyKnWubMhXjbZzIRERdw7lQofZDU4CaaBDoVpqsBJM8vnumBPY+KkErX+sYKKZJYaNWZq3grE5X3fKBnLP1jx0buP8ODpAVJELZPPh7gtN449MRmIfxXQ4DlTtDGJd923bKzRVSRxYBhGZItMp/tAq0RWCj1+pXBvO7vYkmmxdj6aNAQCHjnzb5gIciauvIFq9BzIMnMfsvPuxdIFC9VgcQcQhUBeOezqpkFhq5LgEqfZ3zROz0f7+p7fo6hzxsAoqJ8PnuN2RCLJCQwN59KmQHbusZyNLZUFrxwtWRCVAclQDMfGHwdHw1jeKBwa0JmmXCeZcByXsujJQTJYi+L/eYxvTPDhUHYpVCi9/3/23sPIMvO687v3PRyv849OecEYAYZBIhAgiQAxiUVKWml9W7trrxa2+WyJVsuV9lVKtuqtbdqqyTLq7V2rbBaaSmKlEgwgAQIggSR0yBMzqFnpvPLN7r+576eAMz0+273C/e9Pj/WYw9m3uu+/b3v++77/uec/zmv5lqB+/P6zWo2PM0A8xLNftcpNP+OAyKidznI3qrab3J2qurBCRnUyO7tZBb69XYTqcTeeoNR9U29ah/kTL1WdkG/FShdN40Ryme+TOnEPaSR2gcaNBatOQdppvRn7O0OcfmWzw18ziorlr9LldpLymX9WNLpxAEWnlMRMgJbCcR/2z1JtnMkUtMzBHuQ9Y+AxXIHglrNfpvnhFqDWp2tb5ABHiXQ0ypMYyxsasoNRtXtCdBHAEGDTqxzQWg3sC8K723wiG2ccYIsZVTooPdFHAKmlrGarc6iNBjlYFntlXoGdRzWuU4JcwPl0p/q6Edk9I1JWjuItGgHCc+fJE8pACN0Gggru25HY7FsT4ikWL+FuTIdPzxOQQMxaGgsT2s3jnSsUebQcB8NDqntUxB6ITwsFQhkKKE/ffQyC18LAREzm09x47lGGdDdyqljl1gsawSyoiHkYJ10AsxR+CTn+xsHuiAOozFiYbY1lVUQ0BeVRcxe542zy00LzXezpBuLW5OaDrHYWPBjPtZBo/0hKtm+JFmLEN0QwOPs/Aa/LgT6vEIW9ULruZFwDfuSqJ+B8HxUciAw18geCoLthq0ryDAWV2WwVCCOqjaQRjAWFR3tAtnnGzaP8fvcLmsxvAdhcMXg96QRiw2yfHi+4H52+cKM0pxFYAGe6O3ESpic/d7uaonFICJ6l+P5RW5AFsXyAPYpyPCNBzrpej8lrX0RPMaJbPhCs9DQXs+s64HAm0s/HqnpGARBNICdK39zQf96iOYV+3WaKf2pcgY6xtLUR6k/92v1bMB4EFrSPB+54VnC3EnpxN1k6u2LgsYVzJUirIACtZJijUzOpIR4HQ8MMvRBFtKj2BPU7HfJ86Y6us4FoV1wFnr1p8rzHdUmudRj3NgzHphk6iOcka4cLAtq3BDd83B/6LyIjkouy9xKCWtbRzP7EYhIWFuUg/TzwBNdMtG7AxzWt+5a3VMiaWGuSqePXWl42M/3p5UFlVaQziYopeihDEG0GaIo7D5gYVIu1xqODzL1IdzCcmaxWcFx5/zpSZpr0HwWmdNjqwe4UqPdVi7Xr9MVqwY4k7ZRdiSE0FNHx1uTiY7mlunEojJlcSeDWNdIIISYF/plL1JE13Dv0huO0VLFwBt/KIILSbZMifxSTaNEorHlKebeYmx0bhyXhX+G7y0uuFAp1+j4oXFy7IWzlHH9sOfoFBBFVa1LYJ81M12idoF9du2mkY4EFxIJq2GvAoAs9CWvmyAMUMCirBEIWsIfPplq775rWQYH2NtZEbBYevPOvGyA5QFsMl5iuwxV4JNsskdy5zNfQnsUZBzvI11TP0j4QYUFB2RddQp4jcPSJZ/5fH08VQjI9SZopvDvqQqB8KZNwHyq1F6kudLX677vapsmRMqh/L9gv3uIK3GBRRJkGkbMjksn93PAp91+tPEj4HlQtV9juwclNIPHLmzyF5d1jn4Cu7lBbyTPZO9CR/ofCEJ7CcjzJqjqvK2856NHRDr1IAei47LOIehzBrXy9QS8zl3vPFdodZoEe5HvikEvEYP3TFQSaaRuKYMsdHw+EuIPGrut2TCsLCzEHQiH0xMFbi6qkvnXqczieVEM4pkK5WK1KZ7o8C6+eHZKycoCIvrWnatY+OyFKoUPi6ilYpWz0BtZUEBQWb9pdFENHZv6+dXQ2YoCtiELAREU7zFsXZpNMmGxT/6ihqGe9drotXgO9qNGgm+DH7Uw/PGmOSI6flYqlVi0fzJeb5iwxaOGGfqLsdG5/gc1mr/IEo46KshCh91Ureo0zGJPZUJf907uuaqWO/i9inPt+xyD/RY9FzqxxWBuqTS5xtu71GrNQqHKXvMLeefPg7kSNhRt76CYlkGr1g2LnYvQWuCZ7XoXyXbPKls84LCNQ2J46I4H8PxEI0wtkvcoxOhxcr0r1EkMfZSbd/ZlvoBbhOKrYG9ynArlr5PtHP6IrQvE9WLl2bqY4itfRzb1CX7AaiYuwjManHneDNWcQyyUqGLoKzib0TLiUjHRORAgwzoP/eRd5axvZDGiQV1cgOBnmZsj9T8I1/kFZR94QehWIH4iYIS1rnbAtNgOLWFt5UB0XIANiWWujyjq++R4ZyNXK7UCWLkkTAQBOgsOLqgoQlAiSuAxzEJXrV4TOvn+wqMW5dLdkHGlArK1IeioHPThQ4zM3k4BAZ1tJxQFHWR7LpVq1aYL56Y447QREOw2bl3BVia9BppKXhmfpUrFbph5a5gQVIZiIahgrTaqGsHUR3Z9YbbMwmYzSaZNHo9Fi1r8soVfCxsXiNJLEs5UXtukRHTYsaCh6GIbfobfpPHrsA4TS7WXULq8aAODAB/Wksqeiz1FpQ9EK8VR1XWMtVNqUjNnFVIZi0bRc6EDKrqmOHebYXc4N12iUrGiNM0Q5M51oErOMHTuf9EN977O35WERYMDZ805ynYZKisi9E/dQIYxRHqEZlWtBuXKOHRHaaAF0EjV67CIDrEa3tN96c+y+I2ydFQHNManUvU5ssxNZBgj/L5ATEfGLex5YPmi6h+Osm/4yvdnv8I+91EO3K0mzCQ+Q64/oSgAh6AZJsYEwutyBwKy7Z5QLtGHoIZAWVTxpdXArgk2UsjwjPI50fEuiogu9Dyed5kc9zRX7qiARsu4bxpaf7imYgIanZrGqsiZ8Y57LlLPjNaAfidr+N4TDzQOSqCySH3PREba0ptPCa0FmWe5/jSl04vPoowbhbkKTU+qVcVevjhLr/3kCB19L1qz+WZx6eIMXTyn1lzNtt2GfsMq1KouC17IxG4EhMHV64d7Zm5cj+d67MnrNrCfAPj90QwRDS87DawN4L3dCGR5zk6X2fe+mb6+VsJatFe5KhDzOFu4S6of+HpTrfexRtWQauVKO0HFw8RltSpv9HV442fH6dRRWOG2H1iInD2pptk4jtv0INRCYA5hfXfJtF9SoLtcUqtoRxY6gt3txjB0GhzKLcqeqd3Eb0cQIvkk19zDypHLMON7VyTblHYAAcA0V0XwSg7xvMlYiGsQetFMbajvH9HE7P/JgQ2VTDBY0ZSqP2TfajMzygfliv0q/x1EUzUMzuLPpj7J1jJxw/fnuKEoRTzUp6zbyTI63/g2DqCBru0ciyRihZ7E8RHQASpNwianemRx0Rc7F6HHQbDIdk8pPx9NehPmFqSxUJzA54uwF0O00wgqy6JafjUbCNa4diQaxAK2wcpyRnqU6i80ZRbiDWwZ0Gitl/yuIeggA1eFd984zY9uwHN9zp5eKo7tsoVJI19bCDkQdZCN10vz4/rxnJ4oKlkKQFCBrUCjpoztAD7+8N5WFqvKNg00sd83xqCR3/hSgRhtsoUQdY2IjsagrbacQPAijpmx1YpDs4re4ScOj/OjG4DlFTzA2+lL3siqqRcolapUq6qJ6LBcU93vmp2Zn+tLcaNg7EdN7Z/QZERE72IgUDrOSeXns4hubY5om9IODDL1QdKQbRUBz5/jRxww9D7KpZ+gSu1NbuyF7GsVqvYb3IgNHrKmuYamC/831Rz1wIiu91E2/QnKZ74cC0/cD4OxsN2TcL2N9DqMh8GCq4BgmeOeVX5+mIm+KVZZ6OCqNUFUEd1H5lb7GswIQidAVZXrXlB+PqyauIIrZq58+JxhaHm+G0X56IuAeBTLr1YA+zDDGI7RvVQnXcPBLspnIxw8pRFz3IE4iEPqUryH40a1bLemoWIMMqfxWCoQheamyw0tTCDWwaN3KY0M4wxEETQNVAlMQERBRiSCTp0mk0spZ5YXCxVeD83EsvSWZ6Lj1ocqmW7ZlnCdaMoI4a21P0cj3YzfoCBbuxlNj+MZuGyPiI49BtZezawaiSvYk+yqWlUV7kGdGBMNi5p7HVhsX+XbS68CaxUioncxflCIJK4h4xvZvbBPiRe4YcMb0Yj8+/vBzRpzdo6B3K+wdYlXnubrU6Fiv0ETs/8He1iH9jyqhxCN8pkvUS79qVh5X18P3h9YFKjLKTqLGKa5si4eCLD4gS94tIzv1aRFXE+tB9ktmciZs7B48JUskgShe4GI7PrjETO+49Eg/EZM0nTs3VGDZTMR7n2tAeOpa4MUJ7jBaYT3uIk924QWAtEHjdZit3yXKOi0oqFip/HR8K8JfrQQheZmy/z9FgLeyxBsexXf81n4w1eVdcLBphhk5KMxo6qnMxrRwgO/2ZYirRa3ca8JA3vdsjGh6WvrrxVD0uoqgMUAq6lSofdEdG6y2qYMZG54ugwE9KsiuqIoDVsn/ozSIVLpBAdPneZuo01FRPQuhRs2+nPk+lG8rUwyjLH4ZajyDdsIfT8j5K8hOzXocObah4F4mU9/loW/YuV7ShlhyDSu2m+zhQuqC9R+f50yyQe5kShK+qNm8bcLiJ+Od045Mw7ZygljE1sVxPV3aidB4IQiuq/u/Y/1zXYKMRu/sFEeriniOg+K5HPfB0HoTYLAZhHd86Yi9Rgw9JHYHXbDda5H9PEOK+v8Dovo6E+CqrLYEeUtFgG9K8A6CTM+47V+lwIO5+30sW0bQXOWFSwKYL/A3ScXAF6waALYqyCIAIFZpUwfWaIQU+IgokNAV7WVwVpoth1FuFe0Yb/osj0p/MyxPMEc4z2lBwnaWBXWi70nbobjeMoZ/rBT6aQveSIZNlKOMyKidykoe4ZPcNhUVPE1fpGK5aepUnstdkI6iNokFMKDr9iErZ1ZY6nEfsp649woreYcVHiVFwqFnmpWvUWWsYry2S9zQ9G4ZqGjUarvV8jl91XxdohqCXMjW5IIYRACAZkoGZquN0Vz5b8js/bT2G3x2K/C6hH1j0cQ1hBMEIRexfOLbFsUkPr9zHUvUqH8DSrXXoic9d1q0O+DgmglmAiUITmgkxj6UNjIUxDaQC8J6PMl+DikCzcHmZXwRW+U1A47lzg2MWwW+P3DcVD5HKixXUccbI8goKuKbRA3ETRpJjEYgtihXbV/WJ6Dgzmmmlks3LraJQ5BunYAWzKVCiCALHC9gzZahmm0vGHwUundu3SPgwZcOHRHek0wSzOl/0C9QkBu5IN6OzCMQcok7yPPv0JO4RyLoM3zKNXJNEYol/4MZVMPk2mMUlwJyKEgiFYtgEx0y1zHWZbCvB94tGZ7nn+JZor/jnoFFtAjNqYVhG7C96cjWxY53mmaLv5b6hk4UNbZdW7o/WIjJgiLxPd9FtKFjwLBGJnXKtnJmhFWKfQsQTgOKiI622hA4IqBlmKwnYqmLG76XtD8jOsYjEOsYP1cW9aBuWb0a1jOQKiNu1jbLHhfUrTJwb7byeClbsR/v+vhu/QyyFBV9NzuXdxQSI9h/XLC2kS59Kcpk3ygqYKwrmUpae2jofx/yVlzsfhkeQsCv0xeRAEYFgCmsSqGzW87A6pHxA88vutcEJqBF8xR4C9vy6Iw6Op1dJ3j/ir3HkFYHDicQ0gXFhbSGwHhopczIzECPA5BBC9wikfGqmoDS/x+zfDRF4SGgcsmVzwIvQtuz6pe82HQTuuKoGWn6N27dI+D7F4IbMsb/ijWxCzv5mKZG2g4/1tkmeub1sw1lbidBvt+oy6gG/G3HArg8a4OvHQNfYAz0oV5P/DlLqLPr3M5kAi9SegHHq/+HstxnaPxsYjogrAE4n3m7Sih7US8mup1Au6CBa9bxbkCkTAOejRsEFTfF9i+LJfsVqFzwA0+7kKjEB84u1t1X+JNNwYbb4wRpapLgde0iGshWOJaLG9tSbLMTTTY949peu6PyXYPL+k7Jq29lEt/ilKJO7qi6WYQ1HieRgMi+iB7ywth89zoY9ibBBTEcJ0LwtIJm2R3tqlmfFZ559a5riVIo/j1ixGEbgDCIfy8VZ73yBP76LGn7qBuAM3VRlbml/Q95rP6MD6NLF2QxdzL2aUYB4ypivgHHQcWQbgvxEFEV620YD/hHq4mEOIjiqrsuZiLdz2wlb7wy/dTN2BaOg0OS3+aZmNEsK4JK8s6v+/GGVGqupSAbBYpBRDPRa5pOumUoWzqUbLto+RXyuR6Zxf1vQx9hLKpxyiTeph9W7sBWHBgnkYD2Rt93GBUCJvnRh/DXiSea1wQmgEEdGmeGwPQcF0T4UMQFgPEcRUvb2Rar1g9SAfu37KssighXqA5pQsP4wU+0kA07uVmgciETKZMJTEHc6VWc2KRmW/bHrmKnv94nyGkC0IrgdUR5prKOhoc6Vt2e65wk2adqs2R3eY3R46CyxVInd/3F0J2+C4F3qFBDJtqCjeCjHHLWEW5zBOUTt5FmpZezHehTPJjLMYnzC3UNWB+RhSGcHPXtRTbugggns1zBUFodsBRmkN1Go0MfgiCEB2IOYlE40oOZLc5jqvUZLPXskZTKYvrVBcCY1Mt927yBMTzbC6lJOZAPK+UauQ1uUnnYqhVHXIUgxupVIKshCQDCa0FQctEylRaRxBFsbcIyxfsSSqVC8CpdfYe7dpuLPb9hRClqmvBRiibYbeQTtxGKWsX6VrfIl6tUTb1MCWsrZzd3i1AFAobQkaEs9AlUg4QKBNxTRB6mzAgLuu88+D+KvceQVgMyZRFmaxaTwGIkdVK7wrFt8oCzPalG3rS2jWXSqUaC19xz8RbbPZsfjDLlQsqAZfiXDUW9jaVco3fGxUyuSSvB0FoJYmEyQEpVVGyUlpee65wI6m0xXNGhWrV6WhFlF1zyUPVVozpHkVOuEkmerwnl3ANx71AtnuSPH9yEa/2qWK/Qa53nrpvjka1KIBvJD54ipBxNUNV1rkg9H6wLOi8SCAIgrBY0pkEZfvUBJ1q2aFiYXn1gYAP+MBghnRNQUQvVMnp0Ux9w9BocCinlBEJD/LZ6VIsxBTM15pi4AfrIJVOtPyahOUNAjX5/ozSc7GvFGalx9ZyJp1JUiKpFtxDkNuudk5EL5drsb8HioguCC0EWSS+X6bZ0n+kUvUni842LFa+S6XKj1iM7y6ii+EiJt04ehJOEITeRpOmuYIg9MABvS+vZllYKlVZHF1uInr/YLZhJvq8dcjl8ZlYZGC3IiN/ZEVeycsZv//k5Tly7M6LKYXpMpUVM3lz+TSlFasyBGGxJNMJyg+oieiVik3TE4WWX5MQXzLZBCXTaiJ6ca7CQnYndDPX9ahacWIRPF0IMezqYq9tPCK8gnR9gH21e8nzM5U4EOtYUEA1mit/kwV01xtf9PdBBnux8j0yjCHKZ7/CvuFdMUcjbzFB3Ue990pYF4dJFGmd62QYY5RNPkS9RNLaI+EEoXeBhVUkqy6dLGMtpZP3Ui8ByzJZ54LQnfT1p2lwJKf03OJshaauLC9BBxmAIyvzSg01IaJfODtFoyv6lT1suwX8PivXDnJz0Uagyer4helYNFqdnipSqVhVDJZkKJORTHShtcA+a2Qsr/TccqlGV8ZnW35NQnxBwEXVcm1upkxlhf2u2fiezz8blm9xdzOLmYiOrrFjlMr+BlHQDt8mdAcfZHG5+8BbF8VvTSNDH6CB7C/X7TJ6AwiGjZr0dArfL1HN+YAK5a+T456AqcsSvlvA36tY+QFZ5gbKph7qjiZt7G8egQAekDYF5Mf0XW0vGL+ogQjTWEEDuV+hXsI0Vsd2nQvCUtH4Xh5lnWt8H+i1dW6Z62WdC0KXkh9I0/CYWt+f2ekyXb44u+z8aFeuGVLyAkdj0dPHLtPeO9ZHPOt1R4NViDn5gSwlkuaCPuPISDx/epLs2lLOT0tj3pd+4tIcFWbLCz4XTj0DwznK9aWVMu0FYSlk+5I0tqpf6bnoLXDx3FTLr0mIL+hFketTE9Fnpkodsf9xPZ/3WtUmzp3EjJtgZBhrKZv/Hzt9Kd0hrkUSKAMWz5G5rWlJ0hp48glLA17gjneW5sp/SxX7LQqCpZetBlSlqv0mzZW+TklrKxn6aHSRup3wHI324T+AfB6UieADLlM0PDxFXOe6lqFU4s66v7wMoiDEHeyT0SrEfNL1vKxzQRBiAxrcDY30USqTYBF4IaYniyzoIOsM9ibLYQ+DR/aa9cNKIjoyAI99cIE9YSHi9tL44HdBNvqK1QN08kiK7Frxls91HY/On5mkUrHG1i4qY9cK4A8MWxl41S+Epuu0et0QNxbtpfdMiK+F1vBYntJZ7LnOgo2IEQA6d2qS1xGqYWR+Lj+QhQ6rqWTSolqDwCQqxZARjubOKtVTzcJzfLp0YTr2fugUax8MofGhW4tSKoYMX5cCzoaOeX1El4ObmOdPUbn2Ms0U/6IpAvo8nn+FyrWf0Fz578jzZ2PtHx59jgKfvGCWG2oK+EBuRQxEwA4HJVCdy9oRBCEaYWDbjL7OZZ8UBCFGXtewdFm1dqih7/fMdIkunp2iSgc8Vzspoq9eP0RWEgkmCz+3VKrR0ffOswVD4PfmmW3D5jH2iF8ICDjTE0WaujxHtUpnPtci0HPhzBTNzpTJdRc+c0Fs2rh1BQeUBKHVIKiEJrZrN440rHxAJvq50xO8p2BdCctzvqAKaEDBdg32VQh2O3Z7913HcensyYmOVh+pIiJ6l6JrWdI1tbLJa/jk+9OLbm4pqOJSufoizRb/FP2wm//dvUs0Xfi3VHMOkt9Egb7Z6JRe3Bz1plkgErDO+3itRwHCmh/MyDoXhC4BWeWaptYcah4ExH0fdghyGBIEIR7AomPH3jUNM9cgDOOA/t5bZxoKk72CYeocZFi7YZgF9UZe4BBtj7x3nooNsp+7lc07Vyp76J84Mk6THfLQx/w8dPCckrUBRKptu1dTvl+twa4gNCO7eO8dGxr2GECCX2GuQm+/coIrK4TlyeBwH61c09jG2vcCmrxcoPFz0A3bh2N7dOroJe4LEndERO9SdC1Hhq7WTGKeIPDI9SYkS7XFlKs/pWLlO2S7JyO8CgcO1UxEl1zvCk0X/oRqzvtcYRBHND1Duh5NRA/II9e/TEELgg/diKFDRFc7ZMyD9e16kxyQEAQh/hh6P+l6RBE9QHk7/C1FRBcEIR6goeKOfWvJ0BsfL6cmCvT6i8fIc5dHwB/2CVbCpO171nD2aCOQiffyjw51TDxuNes3j3FTRJXGqR+8c5aujCM5pP3Al/2d107S7PTCSUsIHGWyCdqwdYwyCu+vIDRNRL9zI1t0NKI0V6WfPfcBVUrLpwJIuJGhkRytXDOo9NzLF2fo1LHL1C4CP+AAz7FDF6jaocqjKIiI3qXoeo6z16IB8XW8bukitALbOUHF6jNUrr1KQaCWPYLmtvCq78t8ljTlrGOXKrVXqFj5PtnOEYojupaqZ6JH2GYQ6HHPkx/IDR7oWp7XehTQmNXzLnHQTBCE+IOm31ErTvygSq5/RUR0QRBiQ64vRZu3r6TB0T7OvF4IZPa+/9YZtnXphtLtZmBZBu09sJEz0lUyoA++cZrOHL9MpWLvZaNjDNZtGqXRlY0bIyIzEVYU7R4HZEMiE/PkkXH2qV8I9ALYsnM1DQ7n+H0WhHaAqpYtO1fR8Io8WYmF5x3ss9594zQ366006Fsh9CYjK/pp7YYRpeeOn5+m44cusqXVQn77zQJWQ5ib8GNH8DLuiIjepYSZ6CjHMKJlqLrnWGQTmgu8yX2/xKI2rFw8/5LS6zSyKGntpP7sL9BA7lcpae0gTVMrA/SDOSpVfkCl2gtcYRA34OWt69lI2eiwIrHd0xQE7e8IHUcQKEOWarR1XiMH61z8kgWhK9AhovM+GWWdV8j1LoqILghCbEgkLRZFd+5byxmSC2HXXBbQX/7xYZqdLrflkN5pkHUNu4/RlQOUSDawX/ADujI+S2+/eoLOnZroOW90WJ9s3rGSNm4da/jcmakSHfvgIgss7QTNGN/42TGuBmhkO9SXT9NdH9vGzR6laaPQLuCFPjzaxxUu+f6FKxoxhy9fnOUKoIlL6KvWW3uKoFYttmrdEHujN2Jmskinjl+iy+Ozbbn/TE8W6f23TpNte11xtBERvUvR9TQZ+mCkbHSI6LZ7QjlDWlADNyEIGjXnXZorf41s97jya01jFWWTj1A+8+UwGz39FFnGOuWlabtHqVT5IVVqL/P7G68boka6liHTWK38+8CaxnFPke8vjwNVIww9W1/nuUgZqmwlJLZNgtAVGDqC4oO8X6qCoK3jnqFAbJsEQYgR6XSCHnhsFw0M5Ro20EQ25DPffJNOH7/cFR6oS0U3dBoey9OWHStpaEQtweSVF47QO6+eZD/jXmPTthW0eecqSiQa21keeucsHXz9FNXaVLXg2C5dPDdNz3/vYMOmpggIDI320f77tlAi1dhWQxCava8ggDO2aqBhAAdn6x9//106+v4FyUZfhsBSbHg0z9ULurHwXHEcj8bPTtPrLx7lP7dSl/Fcjy5dmKa3XjlBgd8d5xoR0bsYCOgJY73y8+EzXXOOssgmNJOAHO8iXZ75PbLdU2y1ooZOucyT1Jd5kjQtRRolqD/3q5RO3sU2HqpUaq/SbOmvyfHGY+eDrek5sswNdc93FZCJfpI8H96HvX+gUgEVJ5axRvn5COjY7jGxbRKELsLQhzmoqoofFMjhYFm89nxBEJY3yXSC7nloO/uuIjN9ITzPp/NnJunZp9/m5pHLhdvu3kTrNo8qPXfi0hy9+NwH9NKPDlGvAeF5y45V7CPeiDMnr3AwAdYu7eDShRl686XjbGfQyFpgYChL23atpvWbRslsYGMkCM0Gfvx33LOZ1m4aoVTaUrLpeOGZ9+j9t8+05fqEeDE0muP5otK75NLFGXr222+zj34rcxtnpkt0/PA4N9PulhxK2em7FmT59pFlboxk81Bz3yffnxO/5CaCzPPZ0l/Vm3yqZ4pkU5+kbOphMs21VyPHyETMZ3+OsqmH1LO3OThykKYLf0B+UIpVBrehZSlhbIy41Xg8pnG0qGk/Gnvmh9UJamAO1JwPyPMxF2SdC0L80cjURyKJ6J5foJpzhIPisBMTBEGIA/g4m0xZ9MBju2m9olD86gtH6PnvHKSjH1yg5cD23Wto++7VLL6qAE9uCBnIIIU/ba+As8+m7SvogU/sbvzkgFhg+fZ/fpVmZ8ocgGkV8OuHgP6j776jJOggCHDfo7tI0zWxchE6AgKWdz2wjbbtUUu6QrPcHz39DttnCMsLVInddtcm6uvPcACmke0abLS+9403aPLKXEuux/d9evvVk/TiD6GjUdcgInoXg/LvhLUtQpYvfLvn2NIlzPQVlorrXaJK9UUqVp6mICgr+tOaZBnrKZ/5AiWtPaRroW8kPnhpmk5JazdlU49RKrFf8SoCcr1JKlVfoEL5W+T5E/GqlrC2QAqO9LqafbDu9yuYxihZ1uYIr/DI92fIcY+RHxRbeGWCIDQL01xJlqleWYZKHc+fIts9Ij0kBEGIDfgsq+s67b9vM3ujqzTRhGj58guH6Xtff50tO+C/2u6EEN8PuKHZWy+foB9+6y1ucoa/awWZXJJ2799Au/er7fnVikPHPrhAT3/tVXrm79+iqYlCy65tIaHDcVy23WnmewNbmz13bKCdt61r2BhxbqbM2eh/++cvsqdzK5rPoXnpi8++Ty888y5nozcCPQB23baOtu5UD4ILQvP3XI33k737NygF50qFKltnfPtrr7HvP5o7d2LPnZ0ucbNT7G3wxG5lcEwIQT+OsVX9dMd9mymXX/j+jDkBKzEEcRF4wXvUbA6+dopefv4wnT5xhbqJxiZkQmwxjEFKWru4OSWykdUIqGq/zeKtaQy3+Ap7G2QAVmqvcTNRLqtXQuPgRz7zRUon7yHjJu+BofdROnkvuf5l9rwNRfFGNzY0jT1Pc+W/ItNYSenk3fx94tAAN2FtJU3PUOBjjqrdoKv2Qf7dU4nbSdOW9zZl6qOUNBEsM5WtguCPj7mZMLfVG5MKghBn0DsiYW6OtM79oEKV2ksclEUTZ0EQhLiwYvUA7b9/K1sHvPbisYaNycbPTdNLzx+iSsWmuekSbd29hgZHckp+2YsB4gB8ryHMXrk0R5fOT3MDT/izwysY/tbIqFdPVIomem3evpIO3LeVjh8a55/diGKhSu+/dYavDcITxLK1G0eUGsQtdnw4sDBRoInxObo8PsP/DfuVNeuHyDDVG2EvBMZ43aYR+sRn76DJy3P8uFWAAAIb7G0g6CSTJt394HZas2GE0pnEkq8DgvzcdJlee/EoPff0O3TkvQvkNWgmapg67T2wgW6/e3PL3gdBUGVkLE+33b2Rzp66Qj/9AZwHFt5zsZZe+8lRqlVsbiK5fd9aGl3RX9/3WrOnYE3NzZb5Z8MDG1nOp49dZluv3XesVwq6Cku//2T7UvTwp/exPVaxUCHfu/VcwXuG++KPnj5IGml0+z2b2Fe9GfdfePNjP3/vzdNsGdNNLG91qsuBnQsO3YYxxhnRqh7SVft1tgsJgh2kac35ELS8CDca2znGAnq59rNI71kqcQf1575KpjHGm9HNSFgbKBs8QjXnEBXL30HoQ+GqbPZHL5rPsFCfSuzruACNBrjw87bMNWQ7FeWmtghKwNIFmZYYp+WMDk90cyOL6a6Pdd44Sh+QR+XaS5RNPcrZrbLOBSH+nujoH2HoQ+T5l5Vegwz0UvXHlE19gsxgTNa5EGOiCpEBJniLrkVo10F934ENND1RpLOnJmn83FTD10BYeeH777J9yWNP3U7b96zhZnnIlstkEou2y4CADwHWtl2qlm2qlGtUKtZYjIZ4c/S9C9y48vL4LB/sEQDwFhAVmsHgcI727F9PZ45vp+/+7ev8cxtNeZTWHz54jkX308cu0Z0f20abtq2k/ECasn1pSiQMrgJYrGCOLPNqxeYxQiY+slVPHr3ENirIhM9kk/SL/+QR9rs3mni8gAD94OO76d3XT9Fbrxyn2WlU9t5a7L58YYa++ZcvcQUDhHT4y/cPZtmPPOr8wPfD74rviTnwrf/8Kp0/PUGO3TjLfc36Ybr7wW20fa963yJBaBWY+9t2raGHHq/QySOX6MKZyYZCOvbAnz13iM6cuEIPfWov70lY3335DFfM6IvdcyGYez45NZcrabDnYp1BQL9wZor3k0PvnKMLZ6c4Cx7BUuxvirl2whJJJi32RX9x12quwJqZKjV8zWs/Pcr7Jd6n2+tCumUZfF+OMi/wer73np6k7379da6IwH93GyKidzGw/tD1HGcdl6rPku/PKr2uar9HNecEpRMFMoyBll9nrxF+yK3RXOmvqFx7QUngDoFVy04ayP0aZx0uLHhobNUzmPvHVLXfJMc9rxwkKZT/hixjBVnmWjKNkZZk0aiDm2+KUokD5Lrj5CmK6AgIoGKiar9F2dTjy9pjEOscVSep5H5e52qBCI/HDo1uk4k9ZERoVCsIQmfWOaybUonbeJ0rBcvq1VCOe5YDbYaWa8u1CsJi5jfhoUgQoBpDmmN3O/BcvePezTQzVaS//OPnyXUaC5M4YJ84PM6PjVtX0B33bKID92+lrbtXUzqdIN0IhVL+WIivV1NbEHip/5ljMKElTJjx5lFxrsL2HMi8g4Bz7NBFFnAgXneKdRtH6VNfOkDvvnmKzp2eJLuqdi0QPH74rbfp5R8f4Yz2ez++g/bduZHGVvdTKp2oj0+jMcLYhJYKsGqplW06e3qSTh4dp5MY/yPjnH14vbi/ecfKloyDYeiU78/Ql3/9Y2wdANuARnNlZrJE3/iLn7H9z4OP76EHP7GHG+YhQx7Cn14fgw8fgXhO+KE9DdtJzJTog7fPcvb5Kz8+wn/fEI3Iskz69JfupD37N/CYC0IcQIbx7tvX0+d+8V76sz98lkoFJLAt/BqI3RDR/+KPnuPA0G13beQA3a7b1lMqY5FpGryXhOtpgT0X/2MrrnpwqlDlxpTIND926AId++AinT15JRTLhY4C4RsVBx//1F66cmmW3nz5RMNqMeyd6BWBIOPxQxfoqZ+/l0ZW5NkehvfcmwRcMBfm78PIdvdcjwPXL//4MH3/m2/SlfHZm/b5QJUP9udG19RJRETvciCi51KPcwayT2oiOkrFK7WXKWntoFz6sRZfYe+B7L+58tepVPspuZ66f5NlbqZs+lHKJO9XakegUYIS1iYayv8WTc39G3Lc08qNJQuVb7Mf+WDfP8RWRJ1E19KUST5I5epPIvm1QwSu1HZQJvVx0ihFyxlkp2ZTj1C59qJyNj/WOYI8CXMjZVKYc4IgxBnTWEGZ5ANUqv5ISUQPcahY/QGZ5mpKJ25v8RUKwmKxYGan/OwgsNkyT+h+VqwaoIce38PCL8q2kd2sCuxVYCPy42fe4yzoVWsHacWaQc6AQ9k/RHX4aOOwjSxzu+pQrRZ+LZdrnGE3eblA01NFKhdrXJbuOi45jsfCeis8taMAoWDNumH6zd/5LP3R7z9Np45dVgo0zIOxPHTwLAvfX//zF2lwKEtjqwdobNUgDQ5nKT+YYYHDNAzy6xmAyPpExjnGA+8JmsVNXilQYabM44KfD+Gcv2J82qRhQIBB0ORTX0TSjcfe543A+46gCCyDvv+NN2jb7tW0Zecq2rB5jFasHqT+oSylswmyTIOFQvzeyF6fuDJH509NcjDl+KGL/PrQA1/tvpvLpejhz+yjex7aTsNjkqQixAtUuTzw2C62Rnr2W2/TxGX1hpDjF6Zp6ntFeun5w5TKJGjVmkFasXaQrWIQ6EpnkrzncnDS8ep7rsN7C9YQbGEmsOdOFNiCCuuO9xLsuzHYc4Ub2XvnRq42unxxlu+3KuCe+sNvv02vvHCUdt22lnbsW8t7Nyq44MefqNsBuXZY5YMgOsTy0ycu0+GD5zlgA2912Ld8WECHCA97rv33b+Hgi+o1dQIR0bscXcuwQGaV1pDnX6EgqEUQKLeztYhpDLX8OnsF3y9S1XmfZkt/xZ7dyPhVtXHJpR6mXPoJ0vWMeqUBIUjyGFVrb1MpeEax2WbAzWNL1ec4O7HTgRJNS1ImeTdZxmpy3XPK/v1oflux36RS5TnqyzxByxn4msMnH373jl+iQDFDLwyW7aZkYhcZulSdCEKcMYwRSifv5Iz0MECrlq1Trr7A/SMS5iYydDnQC/EDnwO0CEcOJCv4fveV9wofxbQMFjSf/MrdLGK/8uPDyqIOBBe36IWC72SR/bmRoY5DOsrIkcEM8RU6L2dUez4/PNi3uB7VqmEjTIjCcWxYB8EgmbZY+P3SrzxA3/mb1+jQwXPKQvp8aXyY2VnhkniIYKn0eRbPkS2tG9eyA5EJCKGYhS03tFqAAIYxiiLetwJcIywdbr97E9vJ4H1Ew8FGcEBktsIBBQjk8NjN5lKcHY4xwBxB1mWYCYnmqN7VIAKqE5D5HiUzFgIlBJ6nfv4ethpClq4gxAkE5zBPP/3FO3nNo1Euqm5UwB5dcWsscGpTqPgo0qljl8I9N4GA3E323PrXeasPrC8ENRv1FBA6D7LR7/n4DirMlunpv3mN99BG4P5RLtb4AZseVHXl+q7tuagWwwTxA5/nAOYEbMJgoYYeJJhbt7ofIzj+1M/dzVVP3/vb10VEF1oHPK/hGZ1OHGDLD9c7p/Q6ZARX7FcpWdtN+czn65Yfy9cyQ7VZI+wxZkv/iWrO+3zIU0OjTOpjlE09xiJH9Pd3BeUzn+MgSak6o/Rz8Zyq/Q4Vyt+kpLWNxVdNa02jkEbAtgaiUDJxG4+f6hxFgMJ2jlKh8i1KJfbUf4flWTKJ986qN4z1vCllz2T0SqjUfsZzIJd+XNa5IMQYXUuSaa6ldPI+DoL6/ozS61zvAgvpCXMD9ztRqXQShHaia1kW0lVBFjosCj1/lnQtv6wt3XrloL5hyyg9/vn9fMh+9SdH2A81ChBq5g/uvQQ8zJHdeecDW1lwwFhBPIYQFRUI4UU85rq3ioNF6vu2XLVbQVDhZuX+HwbPhUCDR6uAdQGu7ZOfu4M2bhmL5AUsCO0EwZ01G4bpkSf2cSATTZtRtREFWHGgkTEeQu+yat0Q3fvwTg7CPvv0O0r9OeZp5p6Lvf/AA1vpkSduo1w+RS8/fzgMgMbU0kVE9K4nvIFnUo9Q1XmPXG9cMXvNp5pzmArlb7GtS8LcsmwFSjUCcryLVKo+T8Xy0xEsNXSyjI3Ul36KMwUXK2SnkndTlgXoi+wVrlJfCdG9XPsJFSo7KZ/5B2ToIx1qPBcKt7ApqNnvsuCjalXAwZ7qSzRn/R3lM1/igEKnm6V2BmQSJSmX+hTV7PfIs3H4VMka8uvBlO+wnRAaEUvzQUGIK/Bx7aO+zGd5ndv+nOJe6bOlG/ZHy1hHCWtzG65VEKJVU2l6OsIrPPL8aa74S1p7JPjbI2LxrtvXsaCDcm0ckM+euqLUwHE5MDCUo/sf3cmZ6RgjNBCdnSnHVkBoJbAFgM87MmqR9YomieVStWO9hhHDg40QvPlhTXTbXWFClAT3hLizdddqrkjBnvvTxPucVR5FJBV6H1R1obfG4184QDPTZXrvzTPspd+oKW0zgQ3MbXdv4kD7uk3o50fUN5ChVMqKbRBH0pV6hHTyHkpZ+8jQB5Vfgyw3HLyni39KjneBM63jTXDdo734foX9qOfKf0t+MKd4DRBE8pTPfolFcGMJtjm6luBM9tAOpl/5da53mabm/h+q2gfZK72Tba9h6ZK0drFIFAXXv0zThT+icu0lcv1JCoLleuCyKJN6kO1ZIEio4vmTPHdhQYQ/hw3b4kxn1rggxMWiLZt6lIPbUfZKVKcgyDtX/gZn78Z/n5R1vpyAnZjOjW/Vjx2eP8WfXWSe9Bbwrf7sL9xLX/zq/eyjmsun2XIjLkC0tVCSrnWmEev9j+yir/7TR+nuB7fTaL1p23LUauE1/shnbuOxQPAlP5BlUb2dYNwx/iMr+umxp26nz//ivbT/XiSdfbSBniDElQ1bx+gz/+BO+oX/4uNsk5EfzJLZ5rXU0Mqp3pxS6Azwv8e9+Tf+5eO0Z/966h/MtuW+rGnEWeeo8Pn0lw7QHfds5oA7HoNDOb6OuBKfFSQsCTShzKYeZjE9Csj0nSt9jWaLf0GOe7Zl19ftlGsvU7HyPbKdQ5HKl1OJfZTP/jxZxpomNZ17iIX0aNlcEzRd+GOq2e9TJ9G0HNsUpJIHIr4yzEibmP3fqFT5AQtEy3md59KfYmucKLjeeZor/TXNFP88UjNcQRDajUYaJakv8zlKWtsjvdJxT/A6ny39JflBoWVXKAhR0bQsJ3mEQroasCgsVZ9V7j0jdA9Dwzn6+Gf20X/3e1/hzLPBkb7YCMUQ9TdtW0GJpNURoRRN++CR/s/++yfp1//l47Rj71pKppZnpTCENWQn/ubvPEVPfPkublLbTuDxC2Hpt/6nz9EXfuk+Wr95tK0/XxCaRX4gw4G53/1Xv0hPfeUeWrl2KDaBIGTJb9m1itLZpFgkdRB43q/dOEL//LefZBug4bFoSY+LAffZJ798F/3cbzxEt991YxUtmmMPDMdXRF+Ovgg9CTenSeyjTPJ+qtnvkOOpCuIBBUGZ5spf40N3X+aLlE7cFSvLDMeFhclbZLvHOAO3P/vLbbs+7j7tnaFi5VtUrb2ibEOCpZUwt9Fw/l+wHzhpS49XodEovK3zmS9QzX6bbOeYYpNOWHq8ScXKd0nX+9hfvBNwx+XkAX4fq7XXIoo8AWfVIxjgeOcon/kiZ2rGBc+b4Tmp6+oCQVTmP+yg8WDNeY9s57Bio1kQcCBitvQXXEkBaxyUyMdrnZ+jSu11Xm/wv+/P/lynL0kQ2s78Ose9HPu2457mahz1ffIizRT+AzdmxjpPmFtjY+EUBD4H6yv2K3ydsJeCdY2wPOY1+vdgb7ddtaaSflCkmnOIytWfcfDd0Ft/oBPaA4SSZNKklWsH6YtfvY/uuHczvf3KCXrrlRN0+tjltjcBhXC+Y+8a2ntgA9sfrFo7RBkIOlpn1ophaCwo3fXANhZu33/rDL3x4jF6/+2zLfX8vhXIFETVwN0PbuMAQ7sywufHYnRlPz35lbv4PXr9xWPs7zw9UWzZPEFjUojndz+0nfbft5nGVg2y0IfsSEHoRrCWEKCD7/QTX76T9hxYTwdfP0Vv/uwYN4ZsdxPQbF+Ks+L37t9IO/et4XsBKm8kG72zcwRb3PBonr7wy/dzMPfFZz+gN146zs2em0k2l6Tte9bSJz53B+26bS1X+3z4vjI43Mc2Z3ElPgqKsGQMPc+ZvjikzhT/BC7eEQ7el6iILF9vmpzUSUqnPkaWsZaF23bj+2W+Hts9SjXnCNnuCXKcU/z7QOBvV2kvDvzwPkemPjLRIUqokrC2Ul/mSUol9nP2MHILm4GuZ9jOYyD36zQ592+U/cVxGC1Vf0iGMcoZ7aYR+k21G0MfokzyHrLTj9Nc+esRX+2S7R6nQvnvyHXHKZv6ONubILut3WIwmp553gQHBGrOUXLck+z53g5BCGXxqEhAlh4sWkh5nXssXBXL3yHPm2TLiHTyXrLMpVdJLAbfL3KfATSPrblHyHFOcuNZjXR+XwVhOQP7r2zqk+R441Qo/71irxPu3sFBdOyT2KOyqUcolbyLGxN3As8vkOueo5p7lNc638/dU6RradJS6o0mhe7HNNaQZW4k2z0S4Z51iaaLf0LDep6SCdjBpVp8lUJbS/gTJq1YPcg2JmOr+mnnbevo9PHL3ADv3MkrdHl8tul+qLqh0cBglkZXDdDK1QPsdQ3RfOWaQX4MjuQ4A7nTQExC9ijEJlzvhi1jdPdDE3Ty6CU6e3KCzp+eoJmpEjcTbTZodDo82kejq/ppzfphWrd5lFavC8cIIly7hS5kSEJIz+RS/HXnbWvp2AcX6MThcR6Lmani0jyetbD5LeYBggVbdq6kTdtWsjfvWD37PS5Zu4KwpGC2afAa6utP09jKftq+ezWdOXGlvq9coUsXZqhUaG5jYuwXvMev7KcVa+p7Lvbb+r4L6yYEqYSYzBHL4L4UydQ2niu7b19Phw6epeOHLtLklQLZtcXZwmILHV05wP7rO/at4SorCPW5vvRNA7O4F8PSJa6IiN5TaJQwN7HdQ815lyq11ygg9S72EGRL1TnO9LW9M5RO3MHNCHH4DjNsm/8BAv7MflBm8R5+za5/hVz3IjeTwkELzU897wpnXJvGKmUhoRlAeEYGVLHyHRYBVMV7NPBEFmE2/akWZCZrLKLm0p/mBqMc+PDVum1DvChVn+PgCAR+LP92fyhERmTC2ka59FPc8NJ2T0cQgYHHgjXEIQgxjneagwqWuYkz3FqRqQZv4SCosT8rHq43wQ18kSE6H+iBOA2v+j5qR1alxln4ufTjZDtHqGK/GimwBIHNq8yG69w9yYGehLmRgysI0rRmnTv1dY4xnGRLGew3GEMI6BhDvKdY35axgSj2vu2C0GrC6rKc90kOIledNyO8NuD9sYCguHuOMu4Jbmxtza9zLdm6de4Xw30S+6V3mfutYM/mgLhzlNc/Ar+o1BKbjuWFZa7n+3+p+ozyPQuVkuXaC5Qob+LPgeirgoSRuILD54H7tvJBsVa5tfgLkXb95jFKJhfXbL7XQNY3i5YbR2nv/g3c/A6CzsWz0zR5ZY4KsxUqzFWoXKpRreJQreqQ47iciex7YX8F9lE1dPZxxYEcX9GUDF6v+P4QhtPZBIs5IyvyLBJAwIGgMzzSx6+Jo1CK3wNZehCaEGS4cGaKx+bc6Qm6fGGGZqdKVChUqTRXoUrF5vGByIHx8T2fvPr4cJahgTHCOSIcI5TSz48R5iTGCOLa0EiOfyYCG2s3jNDY6gEew06C68/1pVh02bR9BW3fs4YbjiLocvHcFAcUkKVfLtaoUq7V54jHY+AHAemc1a6TYRlcBYHfF79Trj/NvrtDI30smm/ctoLWbxrledKuYMHq9UPcsHTXbetu+Ry8/3hPFgOmdV8+Tfc/spM2bLm1Jc3QaJ5GxvI8TxbbHPCeh7ZzwMX3b57gBcFs03a1oD4yleGLjyDJzTBMg0bG+riSZDFgPuCaH33ydirM3rrCA3s1gkeLBXsNxn7j1rFbPgee/yvXLL53WlQw/9dtCoNjew9sDPfcE1fowtkpmrg0R3OzZd53K6UaVbGvVB2y7XA9hRUgAWnYR+b3XOwrJtZWuJ9gr83M77n5NM9fBKQ4WLl2kIN0CI61as/F+4oKJ2Q8L0QynaCde9dSp4BAjb0JY7wQW3aubqugjPcFcx6BXIjem3euouMfXKDzZyZ5fsxMlaiIe045nBsI5npY89hrDZ2DNbDjQkUV9m18H7znazeO0tZd2MNX8l6zELCT2X//lluaOZhWaD+DSqlOoAXwqxB6CtebonLtJzQ5+6/5IB1FSJ8H2dNJaydlUh/nMtqEsY40vS/MHuMHsqux+d36RhtOLTwgQrqcIRcEdv1Ro4CqfODmrHPnBNXcY2Q7H7CAfjOrD4jofeknaXTgfyZNs1qeaQwP8Suz/ztV7Tf4IKeGRbnUo9Sf+xXKpT/ZsuuDsItSf2Sjl2s/pSCoKPuSZpIfo9H+3+Fs+U7ZeUCEhm/vTPEv65YkixVTTBaHkAUOMRiHdF3P1udpijQtWZ+nRgNLIx/1GGFzXcxPssnneYo5WiLPv0K2c5wz4eczz+E1fz1D+f+KRvt/m9pFWD3yLE3O/Wv+c7RgRIimZShl7aJM6mFKcdBsDXvWYo0j4w/rnCKtc6e+zufXejUMQARzXD2AQA5nnjvvk+Odp4Cb3d4IRHTY9YwMtG8smw0EQ3hTTxX+MML7sJtWDf8hWWbnPszFEVQAFStP08Ts/1WvvGkMLExG+v8H6stE6R8RT1BZVqh8l6bm/qAuQEffK9HgOpXYG97PE7fXhfQPr3NDcZ27N7mf19e5j+DcBXL4fh6uc+zvN7s/QUTvz/4CDeV/k5qNH9R4vz535edZ0Fdl9fD/y8HJuNhcef4cTc7+PhUq367v8Y3pS3+JBnK/RpnUvRQ3UGWIqorLM/8L+YF6ZR9Ak11UeqEnTCikowItteRqSQ6SIzmD713u1Z8VFwuk5Q4O51NXCizsjJ+b4iy42ekSizvlco2cmseH94ACPrTDrgDCDA7vEHNwcIcQgIy2odE+FkpHV/RTKm0tWiiMC9iTkTF6ZXyWxs9P06WLMzQzGQrJEDcwPq7tsZCM50IQRpAH42NZEDgsFjf6BzNcMg/RCeODTFH8N57bDeD9n50pc+XC2dMTNHmpQNMTBRYBIfBwsMX1uZGiZZmUTFssug7i9x3Nc/b56g3D7LeO+RPHQIogtAsE4BC0uHh2ivdd7L8coJoNA1SO7V7dUyCez++3+IrgFIJw2D/m99uh0RyNjKGKJBmrBtJCdPCeI2gLIf3MCVQtTNPURJEDUNhrsRcjqI19FJU9CETi3ovgCQcot67g+YF7dS8Qj5OC0FRMY4hyqcf58Dpb/HMWrqJaoEBErDrv8EMrpjlTNZW4k4V1ZBKhUSbKzTWaj/7Mf+i48efggO37c/Xs00v8gA2F44XWDcjwhldznGAfdBc+6N+hSu0nkV4Lz8++zOcpk2ytHQUOeOnkXSzUQ+BFVroKEC0hvk8V/x2N9f8u6fpARz4wGvoYDeb+CdnOSSpVf/QRQVodl6r26/yAIIQse4xLwtrOIg0ESUMfZmH9GtqN8zRAlkqV5yHWDJpwuu4FFnkxR5Gt7Sl7j7cPCGGoSPC8SzRTQsPQi9HXeVCmiv06PyDkwnMfwQhe5+ZWMs3VoVhByVuv8wDHV5sFH2ST43o4U5/H7yQHHCCs+TFuCPvRWPL8f1//9zf787W/wyhc/RMHCaOInXg17KMQvKleN87XG0Fdv061Bf7u2p+vLW2tg+O58Ljd7M/XxhLfq8aibbS5je/gfGgsrx/NhcftVv8ejmeby9jNddSXfoLXFta5v4j7JfoglGsv8gMNrxPWLg6ahffzrRygRr8RBM9vvc4RaHQ4qIHqMPSoCNf5OV7jWOvYN1HBJQg3A5VOlrWJ0sn9XBUXBSRWwL6sUnuVsqlPUD7zebKQCEAIlN9s3d58f5nfYa7uNX6pXjVx5WrABUFlQxP/9TjAFhvrhvgh3CQzO58OG6EqZvb2IhD7keGIx+333NiYThCEaEAQh+iJh6wn4cP3nIHhHD/27N9Ayx0R0XsUZOAiy8v3C1Qof5MzaBcLssyQ1QXxu6h997os9MTVzHTiv0OEERkh9YzUoMwZYcSZPl49gw1/RrZvPVu1jfYsquAgVa6+QLOlv474Sp0Gc/+IRdwws6/1ICsL1hi2e458zlJU/f2eo2LyAGVTj4eNT9uOxvNmOP9f82G2WP3BkkVWzFNUXkDY4Tlan6ekWTxP9evmaShYVq9mUPL8vJqNjjnp3pBVHVcgfCHr0AtmqVh+OkJD4Y+CbNGafYgDG9ePH/YSXud6ui6yadetc/sm63y+8mQ+sz+e6/yjeCy6YhwCH79ThXzCnytsRYP/xr8hm5K/zv83/s2vUEDl+nMrdaFRLXMUYB6igd6FyX/GVgVhhvB81U+adP3af1//Vdcy/L7c8Nz5v69XYrRb9A3B+qpdHaP58fKvH1v++/rY8Vhf9+egPsY8ngW2BQntftSw3bM0Mfv7NFP89x8ZNwSL5veD68fuhr8jjCvGLxzL+coW7PGdCpgN5P4h791RLLxuBsYWdm+oCLm2zpF9l6r//qkPrXOsYzuc23SrdW530ToXOgkSMlBRWKo+H6FR+zw+W8C5pf9EhcrT/NklYW7hYLmuD9bXajKUyuufNcN9CHtLqb6PF+qJHbP8mQP7UDh3PZ73CCohwCRNTAVBEARBEISbISJ6j4ISV0MboHzmS3xIRpMxlFYvjjDTNDwo3/BT6iXgKMu4vjTDvypIRj8kdRaIfuXq81w+HZbOq4Hy30zqEcqkHiLDWNG2hqxo1IkGkcjwny39R8XxRrOuKzRT/DMy9BWUSd7bAu/2xtHMINA5yxLWNxD7ipXvRRrzW2WfhsLth//NqAvq+rVM9OvEoG4Faw8VIf3ZXySdklSofId92hc/fsj6hVB2s3WO28X8vK5n8nXpOgfw5C/XXiLbORza9mB/qwf6rllWILgSfr0WXKn/+83+7obXRBEUkb0OcfMwjzNETeKv5tWxv/Z3176G78n8v1/78/V/FwqlSW46jbWONdcKy5Fy7WecJRoGpuz6Wrw+eArLhA+N6Uf+bqGxjLJOba4mgv3LzcbyZmMa7g8LjTPGEgG5JFe5ZFMPUzKxm9oBfi6E9MHcr7NQWKw8Q453apHfDXOtynZqH13n89ZXH17n83tl961zIV6gp0sycQdbsFXsN29q6bUwqDCZJd+b5UAl+mqguoKt2+pre37eIlh+de7WgzzXguP1z7TX7SsImhn+SMQqIkEQBEEQBGE5ISJ6j5OwNlMfPcEZNoXK39dtP5p1EL7mj9or4FBXrD5DNftd5XFCBh8sbgZyX+Usq7BpW3uA4AELDti62M4hvn41sQk2KO9RsfwtzriCVU+7PUBDGxmLUtbtRBmf52ix8l1yOcuy2a0a5g/S1JMkre1Emc+zkAAhHZmmzaP31jlAw0VUnMDiIqw2iMPvN1+h06wloNXF4TDjGHuFRc0X0WGFBEugQvkbdQE9qv1KK5gX4pt5KaGQnk7ew5ZRSWqPiA4QmIVonw8QGE9RsfJ9st3DTfwJGKR4V94I3Q/2IQ6eZ79Krj/BVY6L6ecBQhuxSfJoKcF3QRAEQRAEQVBHRPSeR2OBtw9lrnqWhcqae5jLWSWr7BrIfsRhDBn7ldprEXxddfbhhgc9Mqs60YxKZxH8Nspnv8w+1MgyVxMEXSpWf0imuYpMYyU35ewEup5iCxz8HqQZLGw63hnOlhRU0VhgY7sKPUeFcoJs5wiXr8s6vzlh89jQNqR3Ca6zz4JVSmsCBSxWs01L1KzSbqMuzMPSh5DF2k5CW55U8o7QckbPUqH8NFeehHtlp4MWgqAGmtqigWvNOUiFoMZVI91cESYIgiAIgiAsH6RN7jLBMtdQPvMVGh34Xcok76v7YFvUfTTf3zfgxpJFKlV+yM2u0FhSFQi/6eS91J/7pXoZsdbBJpNPUjp5P/tkq4KGnvDYxSP0g+6MEIPsTojAI/3/LQ3kfoUS5g7SNFjMyBYVhYS1ia1dRvp/m1KJA2z3032xUq1j60gQuoGktYMGsr9KI/n/hpLWbrbIkHUudAuoqkCT0YHcb1A29Qg3/5Z7vSAIgiAIgtANyKfWZQRKwNEwaWzw92iw759S0tpJ3QU8apvfsBMe0Cgpniz8AVs8RCGTfJD6Ml8kQx+hTgMhBY06YTMQJUBScz5gP/JK7XXqLBrpWp490lcM/q+Uz3y+frgWomb5wft65dC/ooHcr1PC2kJdBao59PY05hWEbkXX+ymTfphWD/8B5bO/1BKv+1bC/tVaNwbyhWZhGqu4GXt/9hd4PguCIAiCIAhC3Om21CVhyR7UJpn6KAu/SWsPVWovUaH8bbLdk4v2pWw1lrmRs2qzqQc56/vGJqZLz0KH7cVU4Y+4CV2UMUhaeymXeoxSiX1tayR6a8L3NvQa/XkODFTtNxRf61HVeZdmiv8fJc2t3KgSmeHtBu7NpOH/U5xdOdT3zymT+hiVKs9RsfqDWFsQmcYYGfogxYGwaSsaEa5kcQKBs3LtpxwocXidx9P2wTK3UDp5d7jOE3d1+nIEIdbwPSdI1oXIX6NM8m4qVX9Mpeqz3GwxnhiUsLbyfTybfJBSif2dviChw3PYNNdSPvtzXE03W/orqjmHYvtZVBAEQRAEQRBERF+GIAPMMlaSofWTZaxmz/Sa/T5V7Xeo5hwmz7/cUaENmbQmX9cWzqpOmJsoYW5mz27TGGlqCbjtnmCBtsLNBWuKr9LYxqUv/RSlk/eRoecpLuKpRknKJB8i2z3F3uiud1bptb4/S1X7dZop/RlnguNAy6J2h4Dfb0LfzIK+Zaxjz/SqfZCq9rvkuKfID2aps5gsmiesHVzRkbS2UDpxN8UtYAYbJ1Qo4GvS2kU15z1e5wgcef5Uh9d5P19Xwpxf5xvJsjbz+20aUoEgCKrNmXFv1PVBMo01HNRF02g0EbedY/W9soPrXB/kviEQz7mxLd/PN3HAN7ShEZYzaMSeMDeQnv4MGcYoVao/pbL9CgeCertfhSAIgiAIgtCNiIi+jNH1NCX0LXyoReYnDt0QKm33GLneOLneJRbaWtu0zCJdz7EgCf9mUx/hRpe4pqS1nYU/HLRbkRnteVMsniNDNxQU1W1xsslHKJt+NJYl9Ja5mrKpR/n9K5S/qXgQ9cn1Jmi29DUOqsCmpvNCpsbzIpW4k7OpU4lDPEdhP4MDtutd5ocfzEQIgCzuOjQtQYY2QLoxxNeEzHMIV6jmSFl7Oesb6ymOICCR1HdwIMrx7qaq/Savc8c5Sa5fX+feVL1RYmvWORoaI/AUrvNBFks4UGZuvrrO8e9s8SAIQmQMrK/EXt6/08l72J6rZr9LNu+V4+T58+vcbd0615Kka/V1znvlKFnGGkpYm+sBxx38752v3BLiBD7f4XML7qu4JyRq28Jgr3uCKwTx2aR12elm+BlUG+Cfn0zcRrqWatHPEgRBEARBELodUSwEPtBa5lp+ZNOfJNe7yAcYiG3IWEVGsx+UWKgMApuCwKEAB5rAo4C8usVGcN3BfL5hmI6cbfY41tjP3CKNTBYkw0eSm2Aiew7ZqMhSg5gGca0dgiSEcwiIuM6ktS+CsNvPDbFw2Iur6JdO3EG+X6gLpDiAqmM7R+vvQ6dF9BuzqlOJvfzw/AI57pkw6OO8Q45zgt9LPyhfN0fdUCzir159bs7PU/6u9ZYQWn2O4r+N+vy0wgd85TWLdC3NglDCgBC0jTMqMV9Nc2U4r7sE/E7I+MMjl/4M+/9zUMJ+i2r1zPSPrvP5MfQV1zl8jk0eu3Ack/V1jmz4tZQwt/IYpsxdZBgrSNc7J1bgmrCGfb9IywHTWMvN/FoBvi8Ciur7aHeDKilUcsU3s3dzOLfTn+WqpJrzdj0AeYR8f4b8oMLBcV7jN9zPG61z4+r9PAxs33g/N/Vhngfz6zy8j4zwv7UT7EcQQmEL5vnqVUthU+74NDvFOOPzUZhMMKb0Gow/AqfdCD5PpRJ7KJXYRY57nqr2W1SuvUI1+z3y/GkKgjL5uD8R7lELfQYN7+3h/X1+zpr1z2vXz1kEx/Nkmqvr1REI6u4Uf3ZBEARBEAThlmhBEMTTIFeICT453iVuvGm7R8MMYPcSuf5l8rxJ8oMCH8b9uujGWiTVRTQ9xdlxaBZ5Lft0BVvIwOfcMjdw5o9k/QhLAYI5Mi1hXYAqCsc7W7eyuUKeP8le6teEYfuqoBwKvCmef/yVM6WH2WMYdkf4ylURBgJMq647mPcaofjguBd4/MJxPFOvRAmzV/2g+KF1XhfUKPGhdT5EJq/zlZxZOL/O0XgX4p4gCJ1d57Z7lgOltnucHBd7JdY47unTFPhFFihZqAzc+joPG4DqWoYzdmHDhKBiuM6xP4brHME5rH8Ik4LQLAKetx7V7CNkO4fCe9R8dQV/Bp0l369wJVUQeKSxYG7VRXLcm3Kk66iKwJwd4uAt39ON9Rxw6ESQRxAEQRAEQeheREQXGhCEWb11ARIZa/hvIvxdmAEU3DJzTbsxE52z1+qZvvVM1TArSEq7hcUTbmHzcxRZau5N5uj8/Lx+u4NAVJ+rPAdDYZjnKGehm9dlps9XHPSqiB4GI66NYbjOOcuPx9C7yRjOr/Ewix9jGGb9zY8bvibq6xzjKutcEDq/zp2P3M/DipOb7ZXz+139Xs7382v7ZHj/nr+fQzzH3/fiHil0iuDqvJ0PhIdZ6Nfm7IfvTdd//qxXnF2tnsDn0A9/BpV7kyAIgiAIgqCOiOiCIAiCIAiCIAiCIAiCIAiCcAsk/UIQBEEQBEEQBEEQBEEQBEEQboGI6IIgCIIgCIIgCIIgCIIgCIJwC0REFwRBEARBEARBEARBEARBEIRbICK6IAiCIAiCIAiCIAiCIAiCINwCEdEFQRAEQRAEQRAEQRAEQRAE4RaIiC4IgiAIgiAIgiAIgiAIgiAIt0BEdEEQBEEQBEEQBEEQBEEQBEG4BSKiC4IgCIIgCIIgCIIgCIIgCMItEBFdEARBEARBEARBEARBEARBEG6BiOiCIAiCIAiCIAiCIAiCIAiCcAtERBcEQRAEQRAEQRAEQRAEQRCEWyAiuiAIgiAIgiAIgiAIgiAIgiDcAhHRBUEQBEEQBEEQBEEQBEEQBIFuzv8PgPOuDfR7h6UAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Word cloud created!\n", + "\n", + "🎨 Creating word cloud: Book Review...\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Word cloud created!\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from wordcloud import WordCloud\n", + "\n", + "print(\"=\" * 70)\n", + "print(\"WORD CLOUD VISUALIZATION\")\n", + "print(\"=\" * 70)\n", + "print(\"\\nBigger words = More frequent | Smaller words = Less frequent\")\n", + "print(\"=\" * 70)\n", + "\n", + "# Product Reviews\n", + "sample_text_1 = \"\"\"\n", + "Amazing product! The quality is excellent and the price is great.\n", + "I love this product. It's fantastic and works perfectly.\n", + "Highly recommend this product to everyone. Quality is outstanding.\n", + "The design is beautiful and the performance is excellent.\n", + "Great value for money. The product exceeded my expectations.\n", + "Fast delivery and great customer service. Very satisfied with my purchase.\n", + "The product is durable and well-made. I'm very happy with it.\n", + "Excellent quality and great features. Worth every penny.\n", + "Love the color and the design. It's exactly what I needed.\n", + "Perfect product. Highly recommend to anyone looking for quality.\n", + "\"\"\"\n", + "\n", + "# Sample text - Book Review\n", + "sample_text_2 = \"\"\"\n", + "This book is absolutely amazing! The story is captivating and the characters\n", + "are well-developed. The author's writing style is engaging and beautiful.\n", + "The plot twists kept me reading until the very end. I couldn't put it down.\n", + "The character development is excellent and the dialogue feels natural.\n", + "This is one of the best books I've read this year. Highly recommend it!\n", + "The storytelling is masterful and the pacing is perfect throughout.\n", + "Every chapter brings something new and exciting. A must-read for everyone!\n", + "The emotional depth of the characters is remarkable. Truly a wonderful book.\n", + "\"\"\"\n", + "\n", + "print(\"\\n Sample Text 1: Product Reviews\")\n", + "print(\"-\" * 70)\n", + "print(sample_text_1[:150] + \"...\")\n", + "\n", + "print(\"\\n Sample Text 2: Book Review\")\n", + "print(\"-\" * 70)\n", + "print(sample_text_2[:150] + \"...\")\n", + "\n", + "def create_wordcloud(text, title=\"Word Cloud\", colormap='viridis', background='white'):\n", + " print(f\"\\n🎨 Creating word cloud: {title}...\")\n", + " \n", + " wordcloud = WordCloud(width=1200, height=600, background_color=background,\n", + " colormap=colormap, max_words=100).generate(text)\n", + " \n", + " fig, ax = plt.subplots(figsize=(15, 8), facecolor=background)\n", + " ax.imshow(wordcloud, interpolation='bilinear')\n", + " ax.axis('off')\n", + " \n", + " title_color = 'white' if background == '#1a1d24' else 'black'\n", + " ax.set_title(title, fontsize=20, fontweight='bold', pad=20, color=title_color)\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + " print(f\"✓ Word cloud created!\")\n", + "\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"GENERATING WORD CLOUDS\")\n", + "print(\"=\" * 70)\n", + "\n", + "create_wordcloud(sample_text_1, title=\"Product Reviews\", colormap='viridis')\n", + "\n", + "create_wordcloud(sample_text_2, title=\"Book Review\", colormap='cool', background='#1a1d24')\n", + "\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/__pycache__/dreamjournal.cpython-314.pyc b/__pycache__/dreamjournal.cpython-314.pyc new file mode 100644 index 0000000..48e32ba Binary files /dev/null and b/__pycache__/dreamjournal.cpython-314.pyc differ diff --git a/atlas.py b/atlas.py new file mode 100644 index 0000000..17eb778 --- /dev/null +++ b/atlas.py @@ -0,0 +1,261 @@ +import sqlite3 +import datetime +import random +import json +from pathlib import Path + +from fastapi import FastAPI, HTTPException +from fastapi.responses import HTMLResponse +from fastapi.middleware.cors import CORSMiddleware +import uvicorn +import pandas as pd +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.manifold import TSNE +import networkx as nx + +DB_FILE = "cognitive_atlas.db" + +def init_db(): + """Initialize the SQLite database and create the table if it doesn't exist.""" + if Path(DB_FILE).exists(): + print(f"Database '{DB_FILE}' already exists.") + return + print("Initializing new database...") + conn = sqlite3.connect(DB_FILE) + cursor = conn.cursor() + cursor.execute(""" + CREATE TABLE knowledge ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + title TEXT NOT NULL, + source TEXT, + entry_type TEXT, + date_added TEXT, + tags TEXT, + notes TEXT + ) + """) + conn.commit() + conn.close() + print(f"Database '{DB_FILE}' created successfully.") + +def add_entry_to_db(title: str, source: str, entry_type: str, tags: list, notes: str = ""): + """Adds a new knowledge entry to the database.""" + if not title or not source: + raise ValueError("Title and source are required fields.") + + tags_json = json.dumps(sorted(list(set(tag.lower() for tag in tags)))) + conn = sqlite3.connect(DB_FILE) + cursor = conn.cursor() + try: + cursor.execute( + "INSERT INTO knowledge (title, source, entry_type, date_added, tags, notes) VALUES (?, ?, ?, ?, ?, ?)", + (title, source, entry_type, datetime.date.today().isoformat(), tags_json, notes) + ) + conn.commit() + print(f"✓ Entry added: {title}") + except sqlite3.Error as e: + print(f"Database error: {e}") + raise + finally: + conn.close() + +def get_all_entries(): + """Retrieves all entries from the database.""" + conn = sqlite3.connect(DB_FILE) + cursor = conn.cursor() + cursor.execute("SELECT * FROM knowledge") + entries = cursor.fetchall() + conn.close() + return entries + +def delete_entry_from_db(entry_id: int): + """Deletes an entry from the database by ID.""" + conn = sqlite3.connect(DB_FILE) + cursor = conn.cursor() + cursor.execute("DELETE FROM knowledge WHERE id = ?", (entry_id,)) + conn.commit() + deleted = cursor.rowcount + conn.close() + return deleted > 0 + +def load_sample_data_to_db(): + """Loads sample data into the database if it's empty.""" + conn = sqlite3.connect(DB_FILE) + cursor = conn.cursor() + cursor.execute("SELECT COUNT(*) FROM knowledge") + if cursor.fetchone()[0] > 0: + conn.close() + print("Database already contains data. Skipping sample data load.") + return + + print("Loading sample data into database...") + sample_data = [ + {"title": "Chapter 3: The Bias-Variance Tradeoff", "source": "Introduction to Statistical Learning", "type": "Book", "tags": ["machine-learning", "bias", "variance", "overfitting", "regularization"], "notes": "Key concept for model complexity."}, + {"title": "Understanding t-SNE", "source": "Distill.pub", "type": "Article", "tags": ["data-visualization", "machine-learning", "dimensionality-reduction", "t-sne"], "notes": "Interactive explanation of t-SNE."}, + {"title": "Creating 3D Scatter Plots", "source": "Plotly Documentation", "type": "Documentation", "tags": ["data-visualization", "python", "plotly", "3d-plotting"], "notes": "Using go.Scatter3d."}, + {"title": "Introduction to NetworkX", "source": "NetworkX Tutorial", "type": "Tutorial", "tags": ["python", "network-analysis", "graph-theory"], "notes": "For creating and analyzing graphs."}, + {"title": "L1 vs L2 Regularization", "source": "Andrew Ng's ML Course", "type": "Video", "tags": ["machine-learning", "regularization", "lasso", "ridge"], "notes": "Lasso (L1) vs. Ridge (L2)."}, + {"title": "What Are Word Embeddings?", "source": "Towards Data Science", "type": "Article", "tags": ["nlp", "machine-learning", "word2vec", "embeddings"], "notes": "Representing words as dense vectors."} + ] + for item in sample_data: + add_entry_to_db(item["title"], item["source"], item["type"], item["tags"], item["notes"]) + print("✓ Sample data loaded successfully.") + conn.close() + +# --- FastAPI Application --- +app = FastAPI( + title="Cognitive Atlas API", + description="An API to serve personal knowledge graph data.", + version="1.0.0" +) + +# Add CORS middleware to allow cross-origin requests +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +@app.get("/", response_class=HTMLResponse) +async def read_root(): + """Serves the main HTML frontend.""" + try: + with open("atlas_frontend.html", "r", encoding="utf-8") as f: + return HTMLResponse(content=f.read()) + except FileNotFoundError: + return HTMLResponse( + content="

Error: atlas_frontend.html not found.

Please create the frontend file.

", + status_code=500 + ) + +@app.get("/api/graph-data") +async def get_graph_data(): + """ + Processes the knowledge entries and returns a graph structure (nodes and edges) + for visualization. + """ + try: + conn = sqlite3.connect(DB_FILE) + df = pd.read_sql_query("SELECT * FROM knowledge", conn) + conn.close() + + if df.empty: + return {"nodes": [], "edges": [], "message": "No data available"} + + # Use tags to create a document for TF-IDF + df['tags_str'] = df['tags'].apply(lambda x: ' '.join(json.loads(x))) + + # --- NLP and Dimensionality Reduction --- + vectorizer = TfidfVectorizer(max_features=100) + tfidf_matrix = vectorizer.fit_transform(df['tags_str']) + + # Use t-SNE to get 2D coordinates for the graph + perplexity = max(min(len(df) - 1, 5), 1) + tsne = TSNE(n_components=2, perplexity=perplexity, random_state=42, n_iter=500) + coords = tsne.fit_transform(tfidf_matrix.toarray()) + + # --- Build Graph with NetworkX --- + G = nx.Graph() + nodes_for_vis = [] + + for idx, row in df.iterrows(): + G.add_node(row['id'], label=row['title'], group=row['entry_type'], notes=row['notes']) + nodes_for_vis.append({ + "id": int(row['id']), + "label": row['title'], + "group": row['entry_type'], + "x": float(coords[idx, 0] * 100), + "y": float(coords[idx, 1] * 100), + "value": len(json.loads(row['tags'])), + "title": f"{row['title']}
Source: {row['source']}
Type: {row['entry_type']}
Notes: {row['notes']}" + }) + + # Create edges between nodes that share tags + tag_map = {} + for idx, row in df.iterrows(): + tags = json.loads(row['tags']) + for tag in tags: + if tag not in tag_map: + tag_map[tag] = [] + tag_map[tag].append(row['id']) + + for tag, nodes in tag_map.items(): + if len(nodes) > 1: + for i in range(len(nodes)): + for j in range(i + 1, len(nodes)): + if not G.has_edge(nodes[i], nodes[j]): + G.add_edge(nodes[i], nodes[j], weight=0) + G[nodes[i]][nodes[j]]['weight'] += 1 + + edges_for_vis = [ + {"from": u, "to": v, "value": data['weight']} + for u, v, data in G.edges(data=True) + ] + + return { + "nodes": nodes_for_vis, + "edges": edges_for_vis, + "stats": { + "total_nodes": len(nodes_for_vis), + "total_edges": len(edges_for_vis), + "total_entries": len(df) + } + } + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error generating graph: {str(e)}") + +@app.post("/api/add-entry") +async def add_entry(title: str, source: str, entry_type: str, tags: list, notes: str = ""): + """API endpoint to add a new knowledge entry.""" + try: + add_entry_to_db(title, source, entry_type, tags, notes) + return {"success": True, "message": "Entry added successfully"} + except Exception as e: + raise HTTPException(status_code=400, detail=f"Error adding entry: {str(e)}") + +@app.get("/api/entries") +async def list_entries(): + """API endpoint to retrieve all entries.""" + try: + entries = get_all_entries() + return {"entries": entries, "count": len(entries)} + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error retrieving entries: {str(e)}") + +@app.delete("/api/entries/{entry_id}") +async def delete_entry(entry_id: int): + """API endpoint to delete an entry by ID.""" + try: + success = delete_entry_from_db(entry_id) + if success: + return {"success": True, "message": f"Entry {entry_id} deleted successfully"} + else: + raise HTTPException(status_code=404, detail=f"Entry {entry_id} not found") + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error deleting entry: {str(e)}") + +@app.get("/api/health") +async def health_check(): + """Health check endpoint.""" + return {"status": "healthy", "database": DB_FILE} + +if __name__ == "__main__": + print("🚀 Starting Cognitive Atlas Server...") + print("=" * 50) + + # 1. Initialize the database + init_db() + + # 2. Load sample data if the DB is empty + load_sample_data_to_db() + + # 3. Start the FastAPI server + print("=" * 50) + print("✅ Server is running!") + print("📖 Open your browser to http://127.0.0.1:8000") + print("📊 API Documentation: http://127.0.0.1:8000/docs") + print("=" * 50) + + uvicorn.run(app, host="127.0.0.1", port=8000, log_level="info") \ No newline at end of file diff --git a/atlas_frontend.html b/atlas_frontend.html new file mode 100644 index 0000000..d94c3e2 --- /dev/null +++ b/atlas_frontend.html @@ -0,0 +1,123 @@ + + + + + + Cognitive Atlas + + + + + +
+

🌌 Cognitive Atlas

+
+ +
+
Loading Knowledge Galaxy...
+ + + + + diff --git a/bias_variance_playground.py b/bias_variance_playground.py new file mode 100644 index 0000000..79e5a52 --- /dev/null +++ b/bias_variance_playground.py @@ -0,0 +1,569 @@ +import dash +from dash import dcc, html, Input, Output, State, callback_context +import plotly.graph_objects as go +import numpy as np +from sklearn.preprocessing import PolynomialFeatures +from sklearn.linear_model import LinearRegression, Ridge, Lasso +from sklearn.pipeline import Pipeline +from sklearn.metrics import mean_squared_error +from functools import lru_cache +import os + + +def generate_data(n_samples: int, noise_sd: float, seed: int, function_type: str = "sine"): + """Generate synthetic data with different underlying functions.""" + rng = np.random.default_rng(seed) + x = np.sort(rng.uniform(-3.0, 3.0, size=n_samples)) + + # Multiple ground truth functions to choose from + if function_type == "sine": + y_true = np.sin(x) + 0.3 * np.cos(2 * x) + 0.1 * x + elif function_type == "polynomial": + y_true = 0.1 * x**3 - 0.5 * x**2 + 0.3 * x + 0.5 + elif function_type == "step": + y_true = np.where(x < -1, -1, np.where(x < 1, 0.5 * x, 1)) + elif function_type == "exponential": + y_true = np.exp(-0.5 * x**2) * np.sin(2 * x) + else: + y_true = np.sin(x) + 0.3 * np.cos(2 * x) + 0.1 * x + + y = y_true + rng.normal(0, noise_sd, size=n_samples) + return x.reshape(-1, 1), y, y_true + + +def fit_poly(x_train, y_train, degree: int, model_type: str = "OLS", alpha: float = 1.0): + """Fit polynomial regression with different regularization methods.""" + degree = int(max(1, min(20, degree))) + + if model_type == "OLS": + reg = LinearRegression() + elif model_type == "Ridge": + reg = Ridge(alpha=float(max(0.01, alpha))) + elif model_type == "Lasso": + reg = Lasso(alpha=float(max(0.01, alpha)), max_iter=2000) + else: + reg = LinearRegression() + + model = Pipeline([ + ("poly", PolynomialFeatures(degree=degree, include_bias=False)), + ("lin", reg) + ]) + model.fit(x_train, y_train) + return model + + +def compute_curves(n_train, n_test, noise_sd, degree, seed, model_type="OLS", alpha=1.0, function_type="sine"): + """Compute model predictions and errors.""" + x_train, y_train, y_true_train = generate_data(n_train, noise_sd, seed, function_type) + x_test, y_test, y_true_test = generate_data(n_test, noise_sd, seed + 1, function_type) + + model = fit_poly(x_train, y_train, degree, model_type=model_type, alpha=alpha) + yhat_train = model.predict(x_train) + yhat_test = model.predict(x_test) + + train_mse = mean_squared_error(y_train, yhat_train) + test_mse = mean_squared_error(y_test, yhat_test) + + # For smooth curve visualization + xs = np.linspace(-3, 3, 300).reshape(-1, 1) + _, _, ys_true = generate_data(300, 0.0, seed, function_type) + ys_hat = model.predict(xs) + + # Calculate residuals + train_residuals = y_train - yhat_train + test_residuals = y_test - yhat_test + + return { + "x_train": x_train[:, 0], + "y_train": y_train, + "x_test": x_test[:, 0], + "y_test": y_test, + "xs": xs[:, 0], + "ys_true": ys_true, + "ys_hat": ys_hat, + "train_mse": train_mse, + "test_mse": test_mse, + "train_residuals": train_residuals, + "test_residuals": test_residuals, + "yhat_train": yhat_train, + "yhat_test": yhat_test, + } + + +@lru_cache(maxsize=512) +def monte_carlo_error(degree: int, noise_sd: float, n_train: int, n_test: int, seed: int, + runs: int, model_type: str, alpha: float, function_type: str): + """Run Monte Carlo simulation to estimate bias and variance.""" + rng = np.random.default_rng(seed) + train_mses, test_mses = [], [] + for r in range(runs): + s = int(rng.integers(0, 10_000)) + res = compute_curves(n_train, n_test, noise_sd, degree, s, model_type=model_type, + alpha=alpha, function_type=function_type) + train_mses.append(res["train_mse"]) + test_mses.append(res["test_mse"]) + return np.array(train_mses), np.array(test_mses) + + +def figure_fit(res, show_residuals=False): + """Create the main fit visualization.""" + fig = go.Figure() + + # True function + fig.add_trace(go.Scatter( + x=res["xs"], y=res["ys_true"], mode="lines", name="True function", + line=dict(color="#00E3AE", width=3) + )) + + # Fitted curve + fig.add_trace(go.Scatter( + x=res["xs"], y=res["ys_hat"], mode="lines", name=f"Fitted model", + line=dict(color="#7F7EFF", width=3, dash="dash") + )) + + # Training points + fig.add_trace(go.Scatter( + x=res["x_train"], y=res["y_train"], mode="markers", name="Train data", + marker=dict(size=8, color="#FFD166", line=dict(color="#444", width=0.5)) + )) + + # Test points + fig.add_trace(go.Scatter( + x=res["x_test"], y=res["y_test"], mode="markers", name="Test data", + marker=dict(size=7, color="#EF476F", opacity=0.7) + )) + + # Optional: Show residuals as vertical lines + if show_residuals: + for i in range(len(res["x_train"])): + fig.add_trace(go.Scatter( + x=[res["x_train"][i], res["x_train"][i]], + y=[res["y_train"][i], res["yhat_train"][i]], + mode="lines", + line=dict(color="#FFD166", width=1, dash="dot"), + showlegend=False, + hoverinfo="skip" + )) + + fig.update_layout( + template="plotly_dark", + height=500, + margin=dict(l=40, r=20, t=40, b=40), + legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1), + xaxis_title="Input (x)", + yaxis_title="Output (y)", + hovermode="closest" + ) + + return fig + + +def figure_error_curve(noise_sd: float, n_train: int, n_test: int, seed: int, + model_type: str, alpha: float, function_type: str): + """Create the train/test error vs complexity curve.""" + degrees = list(range(1, 16)) + train_means, test_means, test_stds = [], [], [] + + for d in degrees: + tr, te = monte_carlo_error( + d, noise_sd, n_train, n_test, seed, runs=25, + model_type=model_type, alpha=alpha, function_type=function_type + ) + train_means.append(np.mean(tr)) + test_means.append(np.mean(te)) + test_stds.append(np.std(te)) + + fig = go.Figure() + + # Train error + fig.add_trace(go.Scatter( + x=degrees, y=train_means, mode="lines+markers", name="Train MSE", + line=dict(color="#FFD166", width=3), + marker=dict(size=8) + )) + + # Test error + fig.add_trace(go.Scatter( + x=degrees, y=test_means, mode="lines+markers", name="Test MSE", + line=dict(color="#EF476F", width=3), + marker=dict(size=8) + )) + + # Error band for test variance + upper = (np.array(test_means) + np.array(test_stds)).tolist() + lower = (np.array(test_means) - np.array(test_stds)).tolist() + + fig.add_trace(go.Scatter( + x=degrees + degrees[::-1], + y=upper + lower[::-1], + fill="toself", + fillcolor="rgba(239,71,111,0.15)", + line=dict(color="rgba(0,0,0,0)"), + hoverinfo="skip", + showlegend=True, + name="Test ±1σ" + )) + + # Mark optimal point + min_idx = np.argmin(test_means) + fig.add_trace(go.Scatter( + x=[degrees[min_idx]], + y=[test_means[min_idx]], + mode="markers", + marker=dict(size=15, color="#00E3AE", symbol="star"), + name="Optimal", + showlegend=True + )) + + fig.update_layout( + template="plotly_dark", + height=380, + margin=dict(l=40, r=20, t=40, b=40), + xaxis_title="Polynomial Degree (Model Complexity)", + yaxis_title="Mean Squared Error", + hovermode="x unified" + ) + + return fig + + +def figure_error_histograms(degree: int, noise_sd: float, n_train: int, n_test: int, + seed: int, model_type: str, alpha: float, function_type: str): + """Create histograms of train/test errors across multiple runs.""" + tr, te = monte_carlo_error( + degree, noise_sd, n_train, n_test, seed, runs=80, + model_type=model_type, alpha=alpha, function_type=function_type + ) + + fig = go.Figure() + + fig.add_trace(go.Histogram( + x=tr, name="Train MSE", opacity=0.75, + marker_color="#FFD166", nbinsx=25 + )) + + fig.add_trace(go.Histogram( + x=te, name="Test MSE", opacity=0.6, + marker_color="#EF476F", nbinsx=25 + )) + + fig.update_layout( + template="plotly_dark", + barmode="overlay", + height=320, + margin=dict(l=40, r=20, t=40, b=40), + xaxis_title="MSE Value", + yaxis_title="Frequency", + hovermode="x" + ) + + return fig + + +def figure_residuals(res): + """Create residual plot to diagnose model fit.""" + fig = go.Figure() + + # Train residuals + fig.add_trace(go.Scatter( + x=res["yhat_train"], + y=res["train_residuals"], + mode="markers", + name="Train", + marker=dict(size=8, color="#FFD166", opacity=0.7) + )) + + # Test residuals + fig.add_trace(go.Scatter( + x=res["yhat_test"], + y=res["test_residuals"], + mode="markers", + name="Test", + marker=dict(size=7, color="#EF476F", opacity=0.7) + )) + + # Zero line + x_range = [min(res["yhat_train"].min(), res["yhat_test"].min()), + max(res["yhat_train"].max(), res["yhat_test"].max())] + fig.add_trace(go.Scatter( + x=x_range, + y=[0, 0], + mode="lines", + line=dict(color="white", width=1, dash="dash"), + showlegend=False + )) + + fig.update_layout( + template="plotly_dark", + height=320, + margin=dict(l=40, r=20, t=40, b=40), + xaxis_title="Predicted Value", + yaxis_title="Residual", + hovermode="closest" + ) + + return fig + + +# Initialize Dash app +app = dash.Dash(__name__) +app.title = "🎯 Bias-Variance Playground Pro" + +app.layout = html.Div([ + # Header + html.Div([ + html.H1("🎯 Bias-Variance Playground Pro", style={"margin": "0", "fontSize": "2.2em"}), + html.P("Interactive exploration of the bias-variance tradeoff in machine learning", + style={"opacity": 0.85, "fontSize": "1.1em", "marginTop": "8px"}) + ], style={"textAlign": "center", "padding": "20px 8px", "background": "linear-gradient(135deg, #667eea 0%, #764ba2 100%)", "borderRadius": "8px", "margin": "12px"}), + + # Controls Section + html.Div([ + html.H3("⚙️ Configuration", style={"marginBottom": "16px"}), + + # Row 1: Data generation + html.Div([ + html.Div([ + html.Label("🎲 Random Seed", style={"fontWeight": "bold", "marginBottom": "4px"}), + dcc.Slider(id="seed", min=0, max=999, step=1, value=42, + tooltip={"always_visible": False}, marks={0: "0", 500: "500", 999: "999"}) + ], style={"flex": 1, "minWidth": 180}), + + html.Div([ + html.Label("📊 Train Size", style={"fontWeight": "bold", "marginBottom": "4px"}), + dcc.Slider(id="n_train", min=20, max=400, step=10, value=100, + marks={20: "20", 200: "200", 400: "400"}) + ], style={"flex": 1, "minWidth": 180}), + + html.Div([ + html.Label("📈 Test Size", style={"fontWeight": "bold", "marginBottom": "4px"}), + dcc.Slider(id="n_test", min=50, max=600, step=10, value=200, + marks={50: "50", 300: "300", 600: "600"}) + ], style={"flex": 1, "minWidth": 180}), + + html.Div([ + html.Label("🔊 Noise Level (σ)", style={"fontWeight": "bold", "marginBottom": "4px"}), + dcc.Slider(id="noise", min=0.0, max=1.5, step=0.05, value=0.25, + marks={0: "0", 0.75: "0.75", 1.5: "1.5"}) + ], style={"flex": 1, "minWidth": 180}), + ], style={"display": "flex", "gap": "20px", "marginBottom": "20px", "flexWrap": "wrap"}), + + # Row 2: Model configuration + html.Div([ + html.Div([ + html.Label("📐 Function Type", style={"fontWeight": "bold", "marginBottom": "8px"}), + dcc.RadioItems( + id="function_type", + options=[ + {"label": "Sine Wave", "value": "sine"}, + {"label": "Polynomial", "value": "polynomial"}, + {"label": "Step", "value": "step"}, + {"label": "Exponential", "value": "exponential"} + ], + value="sine", + inline=False, + style={"display": "flex", "flexDirection": "column", "gap": "6px"} + ) + ], style={"flex": 1, "minWidth": 200}), + + html.Div([ + html.Label("🤖 Model Type", style={"fontWeight": "bold", "marginBottom": "8px"}), + dcc.RadioItems( + id="model_type", + options=[ + {"label": "OLS (No Regularization)", "value": "OLS"}, + {"label": "Ridge (L2)", "value": "Ridge"}, + {"label": "Lasso (L1)", "value": "Lasso"} + ], + value="OLS", + inline=False, + style={"display": "flex", "flexDirection": "column", "gap": "6px"} + ) + ], style={"flex": 1, "minWidth": 200}), + + html.Div([ + html.Label("📏 Polynomial Degree", style={"fontWeight": "bold", "marginBottom": "4px"}), + dcc.Slider( + id="degree", + min=1, + max=15, + step=1, + value=5, + marks={i: str(i) for i in range(1, 16, 2)} + ), + html.Div(id="degree-display", style={"textAlign": "center", "marginTop": "4px", "fontSize": "1.1em", "color": "#00E3AE"}) + ], style={"flex": 2, "minWidth": 280}), + + html.Div([ + html.Label("⚖️ Regularization (α)", style={"fontWeight": "bold", "marginBottom": "4px"}), + dcc.Slider( + id="alpha", + min=0.0, + max=10.0, + step=0.1, + value=1.0, + marks={0: "0", 5: "5", 10: "10"} + ), + html.Div(id="alpha-display", style={"textAlign": "center", "marginTop": "4px", "fontSize": "1.0em"}) + ], style={"flex": 1, "minWidth": 200}), + ], style={"display": "flex", "gap": "20px", "flexWrap": "wrap"}), + + ], style={"padding": "20px", "background": "rgba(255,255,255,0.05)", "borderRadius": "8px", "margin": "12px"}), + + # Main visualization + html.Div([ + html.H3("📊 Model Fit", style={"marginBottom": "12px"}), + dcc.Graph(id="fit-graph"), + html.Div([ + dcc.Checklist( + id="show-residuals", + options=[{"label": " Show residual lines", "value": "show"}], + value=[], + inline=True, + style={"fontSize": "0.95em"} + ) + ], style={"textAlign": "center", "marginTop": "8px"}) + ], style={"padding": "12px", "margin": "12px"}), + + # Error analysis + html.Div([ + html.Div([ + html.H3("📉 Error vs Complexity", style={"marginBottom": "12px"}), + dcc.Graph(id="error-curve"), + ], style={"flex": 1.2, "minWidth": 320}), + + html.Div([ + html.H3("📊 Error Distribution", style={"marginBottom": "12px"}), + dcc.Graph(id="error-hist"), + ], style={"flex": 1, "minWidth": 320}), + ], style={"display": "flex", "gap": "16px", "padding": "12px", "flexWrap": "wrap", "margin": "12px"}), + + # Residual plot + html.Div([ + html.H3("🔍 Residual Analysis", style={"marginBottom": "12px"}), + dcc.Graph(id="residual-plot"), + html.P("Well-fitted models show randomly scattered residuals around zero with no patterns.", + style={"textAlign": "center", "opacity": 0.7, "fontSize": "0.9em", "marginTop": "8px"}) + ], style={"padding": "12px", "margin": "12px"}), + + # Statistics and controls + html.Div([ + html.Button("🔄 Recompute Bias-Variance", id="mc-btn", n_clicks=0, + style={"padding": "12px 24px", "fontSize": "1.1em", "cursor": "pointer", + "background": "#667eea", "border": "none", "borderRadius": "6px", + "color": "white", "fontWeight": "bold"}), + html.Div(id="bv-stats", style={"marginTop": "12px", "fontSize": "1.1em", "fontWeight": "bold"}) + ], style={"display": "flex", "flexDirection": "column", "alignItems": "center", + "justifyContent": "center", "padding": "20px", "margin": "12px", + "background": "rgba(255,255,255,0.05)", "borderRadius": "8px"}), + + # Footer + html.Div([ + html.P(id="line-count", style={"opacity": 0.6, "fontSize": "0.85em"}), + html.P("💡 Tip: Try increasing polynomial degree to see overfitting. Use Ridge/Lasso to reduce it!", + style={"opacity": 0.7, "fontSize": "0.9em", "marginTop": "8px"}) + ], style={"textAlign": "center", "padding": "16px"}), +]) + + +@app.callback( + Output("fit-graph", "figure"), + Output("error-curve", "figure"), + Output("error-hist", "figure"), + Output("residual-plot", "figure"), + Output("bv-stats", "children"), + Output("line-count", "children"), + Output("degree-display", "children"), + Output("alpha-display", "children"), + Input("n_train", "value"), + Input("n_test", "value"), + Input("noise", "value"), + Input("degree", "value"), + Input("seed", "value"), + Input("model_type", "value"), + Input("alpha", "value"), + Input("function_type", "value"), + Input("show-residuals", "value"), + Input("mc-btn", "n_clicks"), +) +def update_all(n_train, n_test, noise_sd, degree, seed, model_type, alpha, + function_type, show_residuals, mc_clicks): + # Validate and clamp inputs + n_train = int(max(10, min(1000, n_train or 100))) + n_test = int(max(20, min(2000, n_test or 200))) + noise_sd = float(max(0.0, min(2.0, noise_sd or 0.25))) + degree = int(max(1, min(20, degree or 5))) + alpha = float(max(0.0, min(100.0, alpha or 1.0))) + seed = int(seed or 42) + + # Compute results + res = compute_curves(n_train, n_test, noise_sd, degree, seed, + model_type=model_type, alpha=alpha, function_type=function_type) + + # Create figures + show_res_lines = "show" in (show_residuals or []) + fig_fit = figure_fit(res, show_residuals=show_res_lines) + fig_err_curve = figure_error_curve(noise_sd, n_train, n_test, seed, + model_type, alpha, function_type) + fig_hist = figure_error_histograms(degree, noise_sd, n_train, n_test, seed, + model_type, alpha, function_type) + fig_residual = figure_residuals(res) + + # Bias-variance decomposition + ctx = callback_context + if mc_clicks and mc_clicks > 0: + runs = 50 + xs = res["xs"].reshape(-1, 1) + preds = [] + rng = np.random.default_rng(seed) + + for _ in range(runs): + s = int(rng.integers(0, 10_000)) + x_tr, y_tr, _ = generate_data(n_train, noise_sd, s, function_type) + m = fit_poly(x_tr, y_tr, degree, model_type=model_type, alpha=alpha) + preds.append(m.predict(xs)) + + P = np.stack(preds, axis=0) + mean_pred = P.mean(axis=0) + var_pred = P.var(axis=0) + bias_sq = (mean_pred - res["ys_true"]) ** 2 + noise_var = noise_sd ** 2 + + bv_text = html.Div([ + html.Span(f"Bias² = {bias_sq.mean():.4f}", style={"color": "#00E3AE", "marginRight": "20px"}), + html.Span(f"Variance = {var_pred.mean():.4f}", style={"color": "#7F7EFF", "marginRight": "20px"}), + html.Span(f"Noise ≈ {noise_var:.4f}", style={"color": "#FFD166", "marginRight": "20px"}), + html.Span(f"Total ≈ {bias_sq.mean() + var_pred.mean() + noise_var:.4f}", + style={"color": "#EF476F"}) + ]) + else: + bv_text = "Click the button above to compute bias-variance decomposition" + + # Line count + try: + with open(__file__, "r", encoding="utf-8") as f: + num_lines = sum(1 for _ in f) + line_text = f"📝 This application contains {num_lines} lines of Python code" + except Exception: + line_text = "📝 Enhanced Bias-Variance Playground" + + # Display values + degree_display = f"Degree: {degree} {'(High Complexity ⚠️)' if degree > 10 else ''}" + alpha_display = f"α = {alpha:.1f} {'(Disabled for OLS)' if model_type == 'OLS' else ''}" + + return fig_fit, fig_err_curve, fig_hist, fig_residual, bv_text, line_text, degree_display, alpha_display + + +@app.callback( + Output("alpha", "disabled"), + Input("model_type", "value") +) +def toggle_alpha(model_type): + return model_type == "OLS" + + +if __name__ == "__main__": + print("🚀 Starting Enhanced Bias-Variance Playground...") + print("📍 Open your browser to: http://127.0.0.1:8070") + print("💡 Explore bias-variance tradeoff interactively!") + app.run_server(debug=False, host="127.0.0.1", port=8070) \ No newline at end of file diff --git a/cognitive_atlas.py b/cognitive_atlas.py new file mode 100644 index 0000000..1d9ef50 --- /dev/null +++ b/cognitive_atlas.py @@ -0,0 +1,105 @@ +import datetime +import random +import json + +class CognitiveAtlas: + """ + Manages the collection and structure of personal knowledge entries. + """ + def __init__(self): + """Initializes the Cognitive Atlas with an empty list of entries.""" + self.knowledge_entries = [] + + def add_entry(self, title: str, source: str, entry_type: str, tags: list, notes: str = ""): + """ + Adds a new knowledge entry to the atlas. + + Args: + title (str): The title of the knowledge piece (e.g., chapter, article name). + source (str): The origin of the knowledge (e.g., book title, website). + entry_type (str): The type of content (e.g., 'Book', 'Article', 'Video', 'Podcast'). + tags (list): A list of keywords or concepts associated with the entry. + notes (str, optional): Personal notes or summary. Defaults to "". + """ + entry = { + "id": f"entry_{len(self.knowledge_entries) + 1}", + "title": title, + "source": source, + "type": entry_type, + "date_added": datetime.date.today().isoformat(), + "tags": sorted(list(set(tag.lower() for tag in tags))), # Standardize tags + "notes": notes + } + self.knowledge_entries.append(entry) + print(f"Added entry: '{title}'") + + def load_sample_data(self): + """Loads a predefined set of sample data into the atlas.""" + print("Loading sample knowledge entries...") + sample_data = [ + { + "title": "Chapter 3: The Bias-Variance Tradeoff", + "source": "Introduction to Statistical Learning", + "type": "Book", + "tags": ["machine-learning", "bias", "variance", "overfitting", "regularization", "supervised-learning"], + "notes": "Key concept for model complexity. High bias (underfit) vs. High variance (overfit)." + }, + { + "title": "Understanding t-SNE", + "source": "Distill.pub", + "type": "Article", + "tags": ["data-visualization", "machine-learning", "dimensionality-reduction", "t-sne", "nlp"], + "notes": "Great interactive explanation of how t-SNE creates clusters for high-dimensional data." + }, + { + "title": "Creating 3D Scatter Plots", + "source": "Plotly Documentation", + "type": "Documentation", + "tags": ["data-visualization", "python", "plotly", "3d-plotting"], + "notes": "Scatter3d is the go.Figure object to use. Can customize markers, colors, and hover text." + }, + { + "title": "Introduction to NetworkX", + "source": "NetworkX Tutorial", + "type": "Tutorial", + "tags": ["python", "network-analysis", "graph-theory", "data-visualization"], + "notes": "Useful for creating and analyzing graph structures. Can be used to find paths between nodes." + }, + { + "title": "L1 vs L2 Regularization", + "source": "Andrew Ng's ML Course", + "type": "Video", + "tags": ["machine-learning", "regularization", "lasso", "ridge", "supervised-learning"], + "notes": "Lasso (L1) can shrink coefficients to zero, performing feature selection. Ridge (L2) shrinks them but rarely to zero." + } + ] + + for item in sample_data: + # Simulate adding entries over a few days + entry_date = datetime.date.today() - datetime.timedelta(days=random.randint(1, 10)) + item["date_added"] = entry_date.isoformat() + self.knowledge_entries.append(item) + + print(f"Loaded {len(sample_data)} sample entries.") + + +if __name__ == "__main__": + # Create a new Cognitive Atlas + my_atlas = CognitiveAtlas() + + # Load it with some sample data + my_atlas.load_sample_data() + + # You can also add a new entry manually + my_atlas.add_entry( + title="What Are Word Embeddings?", + source="Towards Data Science", + entry_type="Article", + tags=["nlp", "machine-learning", "word2vec", "embeddings"], + notes="Representing words as dense vectors to capture semantic meaning." + ) + + # Print the first entry to see the structure + print("\n--- Sample of First Entry ---") + print(json.dumps(my_atlas.knowledge_entries[0], indent=2)) + print(f"\nTotal entries in the atlas: {len(my_atlas.knowledge_entries)}") \ No newline at end of file diff --git a/cognitive_atlas_server.py b/cognitive_atlas_server.py new file mode 100644 index 0000000..17eb778 --- /dev/null +++ b/cognitive_atlas_server.py @@ -0,0 +1,261 @@ +import sqlite3 +import datetime +import random +import json +from pathlib import Path + +from fastapi import FastAPI, HTTPException +from fastapi.responses import HTMLResponse +from fastapi.middleware.cors import CORSMiddleware +import uvicorn +import pandas as pd +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.manifold import TSNE +import networkx as nx + +DB_FILE = "cognitive_atlas.db" + +def init_db(): + """Initialize the SQLite database and create the table if it doesn't exist.""" + if Path(DB_FILE).exists(): + print(f"Database '{DB_FILE}' already exists.") + return + print("Initializing new database...") + conn = sqlite3.connect(DB_FILE) + cursor = conn.cursor() + cursor.execute(""" + CREATE TABLE knowledge ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + title TEXT NOT NULL, + source TEXT, + entry_type TEXT, + date_added TEXT, + tags TEXT, + notes TEXT + ) + """) + conn.commit() + conn.close() + print(f"Database '{DB_FILE}' created successfully.") + +def add_entry_to_db(title: str, source: str, entry_type: str, tags: list, notes: str = ""): + """Adds a new knowledge entry to the database.""" + if not title or not source: + raise ValueError("Title and source are required fields.") + + tags_json = json.dumps(sorted(list(set(tag.lower() for tag in tags)))) + conn = sqlite3.connect(DB_FILE) + cursor = conn.cursor() + try: + cursor.execute( + "INSERT INTO knowledge (title, source, entry_type, date_added, tags, notes) VALUES (?, ?, ?, ?, ?, ?)", + (title, source, entry_type, datetime.date.today().isoformat(), tags_json, notes) + ) + conn.commit() + print(f"✓ Entry added: {title}") + except sqlite3.Error as e: + print(f"Database error: {e}") + raise + finally: + conn.close() + +def get_all_entries(): + """Retrieves all entries from the database.""" + conn = sqlite3.connect(DB_FILE) + cursor = conn.cursor() + cursor.execute("SELECT * FROM knowledge") + entries = cursor.fetchall() + conn.close() + return entries + +def delete_entry_from_db(entry_id: int): + """Deletes an entry from the database by ID.""" + conn = sqlite3.connect(DB_FILE) + cursor = conn.cursor() + cursor.execute("DELETE FROM knowledge WHERE id = ?", (entry_id,)) + conn.commit() + deleted = cursor.rowcount + conn.close() + return deleted > 0 + +def load_sample_data_to_db(): + """Loads sample data into the database if it's empty.""" + conn = sqlite3.connect(DB_FILE) + cursor = conn.cursor() + cursor.execute("SELECT COUNT(*) FROM knowledge") + if cursor.fetchone()[0] > 0: + conn.close() + print("Database already contains data. Skipping sample data load.") + return + + print("Loading sample data into database...") + sample_data = [ + {"title": "Chapter 3: The Bias-Variance Tradeoff", "source": "Introduction to Statistical Learning", "type": "Book", "tags": ["machine-learning", "bias", "variance", "overfitting", "regularization"], "notes": "Key concept for model complexity."}, + {"title": "Understanding t-SNE", "source": "Distill.pub", "type": "Article", "tags": ["data-visualization", "machine-learning", "dimensionality-reduction", "t-sne"], "notes": "Interactive explanation of t-SNE."}, + {"title": "Creating 3D Scatter Plots", "source": "Plotly Documentation", "type": "Documentation", "tags": ["data-visualization", "python", "plotly", "3d-plotting"], "notes": "Using go.Scatter3d."}, + {"title": "Introduction to NetworkX", "source": "NetworkX Tutorial", "type": "Tutorial", "tags": ["python", "network-analysis", "graph-theory"], "notes": "For creating and analyzing graphs."}, + {"title": "L1 vs L2 Regularization", "source": "Andrew Ng's ML Course", "type": "Video", "tags": ["machine-learning", "regularization", "lasso", "ridge"], "notes": "Lasso (L1) vs. Ridge (L2)."}, + {"title": "What Are Word Embeddings?", "source": "Towards Data Science", "type": "Article", "tags": ["nlp", "machine-learning", "word2vec", "embeddings"], "notes": "Representing words as dense vectors."} + ] + for item in sample_data: + add_entry_to_db(item["title"], item["source"], item["type"], item["tags"], item["notes"]) + print("✓ Sample data loaded successfully.") + conn.close() + +# --- FastAPI Application --- +app = FastAPI( + title="Cognitive Atlas API", + description="An API to serve personal knowledge graph data.", + version="1.0.0" +) + +# Add CORS middleware to allow cross-origin requests +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +@app.get("/", response_class=HTMLResponse) +async def read_root(): + """Serves the main HTML frontend.""" + try: + with open("atlas_frontend.html", "r", encoding="utf-8") as f: + return HTMLResponse(content=f.read()) + except FileNotFoundError: + return HTMLResponse( + content="

Error: atlas_frontend.html not found.

Please create the frontend file.

", + status_code=500 + ) + +@app.get("/api/graph-data") +async def get_graph_data(): + """ + Processes the knowledge entries and returns a graph structure (nodes and edges) + for visualization. + """ + try: + conn = sqlite3.connect(DB_FILE) + df = pd.read_sql_query("SELECT * FROM knowledge", conn) + conn.close() + + if df.empty: + return {"nodes": [], "edges": [], "message": "No data available"} + + # Use tags to create a document for TF-IDF + df['tags_str'] = df['tags'].apply(lambda x: ' '.join(json.loads(x))) + + # --- NLP and Dimensionality Reduction --- + vectorizer = TfidfVectorizer(max_features=100) + tfidf_matrix = vectorizer.fit_transform(df['tags_str']) + + # Use t-SNE to get 2D coordinates for the graph + perplexity = max(min(len(df) - 1, 5), 1) + tsne = TSNE(n_components=2, perplexity=perplexity, random_state=42, n_iter=500) + coords = tsne.fit_transform(tfidf_matrix.toarray()) + + # --- Build Graph with NetworkX --- + G = nx.Graph() + nodes_for_vis = [] + + for idx, row in df.iterrows(): + G.add_node(row['id'], label=row['title'], group=row['entry_type'], notes=row['notes']) + nodes_for_vis.append({ + "id": int(row['id']), + "label": row['title'], + "group": row['entry_type'], + "x": float(coords[idx, 0] * 100), + "y": float(coords[idx, 1] * 100), + "value": len(json.loads(row['tags'])), + "title": f"{row['title']}
Source: {row['source']}
Type: {row['entry_type']}
Notes: {row['notes']}" + }) + + # Create edges between nodes that share tags + tag_map = {} + for idx, row in df.iterrows(): + tags = json.loads(row['tags']) + for tag in tags: + if tag not in tag_map: + tag_map[tag] = [] + tag_map[tag].append(row['id']) + + for tag, nodes in tag_map.items(): + if len(nodes) > 1: + for i in range(len(nodes)): + for j in range(i + 1, len(nodes)): + if not G.has_edge(nodes[i], nodes[j]): + G.add_edge(nodes[i], nodes[j], weight=0) + G[nodes[i]][nodes[j]]['weight'] += 1 + + edges_for_vis = [ + {"from": u, "to": v, "value": data['weight']} + for u, v, data in G.edges(data=True) + ] + + return { + "nodes": nodes_for_vis, + "edges": edges_for_vis, + "stats": { + "total_nodes": len(nodes_for_vis), + "total_edges": len(edges_for_vis), + "total_entries": len(df) + } + } + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error generating graph: {str(e)}") + +@app.post("/api/add-entry") +async def add_entry(title: str, source: str, entry_type: str, tags: list, notes: str = ""): + """API endpoint to add a new knowledge entry.""" + try: + add_entry_to_db(title, source, entry_type, tags, notes) + return {"success": True, "message": "Entry added successfully"} + except Exception as e: + raise HTTPException(status_code=400, detail=f"Error adding entry: {str(e)}") + +@app.get("/api/entries") +async def list_entries(): + """API endpoint to retrieve all entries.""" + try: + entries = get_all_entries() + return {"entries": entries, "count": len(entries)} + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error retrieving entries: {str(e)}") + +@app.delete("/api/entries/{entry_id}") +async def delete_entry(entry_id: int): + """API endpoint to delete an entry by ID.""" + try: + success = delete_entry_from_db(entry_id) + if success: + return {"success": True, "message": f"Entry {entry_id} deleted successfully"} + else: + raise HTTPException(status_code=404, detail=f"Entry {entry_id} not found") + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error deleting entry: {str(e)}") + +@app.get("/api/health") +async def health_check(): + """Health check endpoint.""" + return {"status": "healthy", "database": DB_FILE} + +if __name__ == "__main__": + print("🚀 Starting Cognitive Atlas Server...") + print("=" * 50) + + # 1. Initialize the database + init_db() + + # 2. Load sample data if the DB is empty + load_sample_data_to_db() + + # 3. Start the FastAPI server + print("=" * 50) + print("✅ Server is running!") + print("📖 Open your browser to http://127.0.0.1:8000") + print("📊 API Documentation: http://127.0.0.1:8000/docs") + print("=" * 50) + + uvicorn.run(app, host="127.0.0.1", port=8000, log_level="info") \ No newline at end of file diff --git a/dream_constellation_map.py b/dream_constellation_map.py new file mode 100644 index 0000000..7149e86 --- /dev/null +++ b/dream_constellation_map.py @@ -0,0 +1,216 @@ + +import plotly.graph_objects as go +import numpy as np +import pandas as pd +from sklearn.manifold import MDS +from sklearn.preprocessing import StandardScaler +import networkx as nx +from datetime import datetime +import colorsys +import random + +class DreamConstellationMap: + def __init__(self): + # Sample dream data (you can replace this with your own data) + self.dreams = [ + { + "theme": "Flying", + "emotions": ["freedom", "joy", "excitement"], + "symbols": ["wings", "clouds", "birds"], + "intensity": 8, + "date": "2024-10-15", + "color_tone": "bright", + "dreamscape": "sky" + }, + { + "theme": "Ocean Depths", + "emotions": ["mystery", "peace", "wonder"], + "symbols": ["fish", "coral", "waves"], + "intensity": 7, + "date": "2024-10-16", + "color_tone": "deep", + "dreamscape": "underwater" + }, + { + "theme": "Ancient Temple", + "emotions": ["awe", "curiosity", "reverence"], + "symbols": ["statues", "inscriptions", "torch"], + "intensity": 9, + "date": "2024-10-17", + "color_tone": "warm", + "dreamscape": "indoor" + } + ] + + def _create_star_coordinates(self, n_stars, spread=1): + """Generate star coordinates in a spiral galaxy pattern""" + theta = np.random.uniform(0, 2*np.pi, n_stars) + radius = np.random.normal(loc=0.5, scale=0.2, size=n_stars) * spread + x = radius * np.cos(theta) + y = radius * np.sin(theta) + z = np.random.normal(0, 0.1, n_stars) + return x, y, z + + def _generate_star_colors(self, n_stars): + """Generate realistic star colors based on temperature""" + # Simulate star temperatures (2000K to 12000K) + temperatures = np.random.uniform(2000, 12000, n_stars) + colors = [] + + for temp in temperatures: + # Approximate star color based on temperature + if temp < 3500: + # Red stars + hue = 0.0 + elif temp < 5000: + # Orange/Yellow stars + hue = 0.08 + elif temp < 6000: + # Yellow stars + hue = 0.16 + elif temp < 7500: + # White stars + hue = 0.6 + else: + # Blue stars + hue = 0.7 + + # Convert HSV to RGB + rgb = colorsys.hsv_to_rgb(hue, 0.3, 1.0) + colors.append(f'rgb({int(rgb[0]*255)},{int(rgb[1]*255)},{int(rgb[2]*255)})') + + return colors + + def _create_nebula_effect(self, n_points=1000): + """Create a colorful nebula effect in the background""" + x = np.random.normal(0, 1, n_points) + y = np.random.normal(0, 1, n_points) + z = np.random.normal(0, 0.1, n_points) + + # Create different colored nebula regions + colors = [] + for _ in range(n_points): + hue = random.choice([0.7, 0.1, 0.9]) # Blue, Red, Purple + sat = random.uniform(0.5, 1.0) + val = random.uniform(0.1, 0.3) + rgb = colorsys.hsv_to_rgb(hue, sat, val) + colors.append(f'rgba({int(rgb[0]*255)},{int(rgb[1]*255)},{int(rgb[2]*255)},0.1)') + + return x, y, z, colors + + def create_constellation_map(self): + """Create the main constellation map visualization""" + # Create figure + fig = go.Figure() + + # Add nebula background + neb_x, neb_y, neb_z, neb_colors = self._create_nebula_effect(2000) + fig.add_trace(go.Scatter3d( + x=neb_x, y=neb_y, z=neb_z, + mode='markers', + marker=dict( + size=2, + color=neb_colors, + opacity=0.1 + ), + hoverinfo='none', + showlegend=False + )) + + # Create star field + n_stars = 500 + star_x, star_y, star_z = self._create_star_coordinates(n_stars) + star_colors = self._generate_star_colors(n_stars) + + # Add background stars + fig.add_trace(go.Scatter3d( + x=star_x, y=star_y, z=star_z, + mode='markers', + marker=dict( + size=2, + color=star_colors, + opacity=0.8 + ), + hoverinfo='none', + showlegend=False + )) + + # Create dream theme nodes + themes = [dream['theme'] for dream in self.dreams] + theme_x, theme_y, theme_z = self._create_star_coordinates(len(themes), spread=0.5) + + # Add dream theme "constellations" + fig.add_trace(go.Scatter3d( + x=theme_x, y=theme_y, z=theme_z, + mode='markers+text', + marker=dict( + size=10, + color=['#FFD700', '#7EB0D5', '#B793F5'], + symbol='star', + opacity=0.8 + ), + text=themes, + textposition='top center', + hovertemplate=( + "Theme: %{text}
" + + "Emotions: %{customdata[0]}
" + + "Symbols: %{customdata[1]}
" + + "Intensity: %{customdata[2]}" + ), + customdata=[ + [ + ', '.join(dream['emotions']), + ', '.join(dream['symbols']), + dream['intensity'] + ] for dream in self.dreams + ], + name='Dream Themes' + )) + + # Connect related themes with constellation lines + for i in range(len(themes)-1): + fig.add_trace(go.Scatter3d( + x=[theme_x[i], theme_x[i+1]], + y=[theme_y[i], theme_y[i+1]], + z=[theme_z[i], theme_z[i+1]], + mode='lines', + line=dict( + color='rgba(255, 255, 255, 0.3)', + width=2 + ), + hoverinfo='none', + showlegend=False + )) + + # Update layout for cosmic theme + fig.update_layout( + template='plotly_dark', + scene=dict( + xaxis=dict(showgrid=False, showticklabels=False, visible=False), + yaxis=dict(showgrid=False, showticklabels=False, visible=False), + zaxis=dict(showgrid=False, showticklabels=False, visible=False), + bgcolor='rgba(0,0,0,0.95)', + camera=dict( + up=dict(x=0, y=0, z=1), + center=dict(x=0, y=0, z=0), + eye=dict(x=1.5, y=1.5, z=1.5) + ), + ), + title=dict( + text='🌌 Dream Constellation Map', + font=dict(size=24, color='#E3E3E3'), + y=0.95 + ), + showlegend=False, + margin=dict(l=0, r=0, t=50, b=0) + ) + + return fig + +if __name__ == "__main__": + # Create visualization + constellation_map = DreamConstellationMap() + fig = constellation_map.create_constellation_map() + + # Show the interactive visualization + fig.show() \ No newline at end of file diff --git a/dreamjournal.py b/dreamjournal.py new file mode 100644 index 0000000..0421a30 --- /dev/null +++ b/dreamjournal.py @@ -0,0 +1,749 @@ +""" +Dream Journal Visualizer - An Interactive Dream Analytics Dashboard +A sophisticated application for tracking, analyzing, and visualizing dreams +with ML-powered insights and surreal 3D visualizations. +""" + +import dash +from dash import dcc, html, Input, Output +import plotly.graph_objects as go +import numpy as np +import pandas as pd +from datetime import datetime +import hashlib +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.decomposition import PCA +from sklearn.manifold import TSNE, MDS +from sklearn.cluster import KMeans +from sklearn.metrics.pairwise import cosine_similarity +import networkx as nx +from collections import Counter +import re + +# ============================================================================ +# MIDDLEWARE LAYER - Data Management & Processing +# ============================================================================ + +class DreamDataMiddleware: + """Middleware for handling dream data operations and caching""" + + def __init__(self): + self.dreams_db = [] + self.emotion_colors = { + 'happy': '#FFD700', 'sad': '#4169E1', 'fearful': '#8B0000', + 'anxious': '#FF6347', 'peaceful': '#90EE90', 'excited': '#FF69B4', + 'confused': '#9370DB', 'angry': '#DC143C', 'nostalgic': '#DDA0DD', + 'neutral': '#808080' + } + self.lucidity_levels = ['not_lucid', 'semi_lucid', 'fully_lucid'] + self._initialize_sample_data() + + def _initialize_sample_data(self): + """Generate rich sample dream data""" + sample_dreams = [ + { + 'date': '2024-10-15', 'time': '03:30', 'title': 'Flying Over Ocean', + 'content': 'I was soaring above a vast ocean with bioluminescent waves. Dolphins were jumping alongside me, and I could breathe underwater. The sky was purple with three moons.', + 'emotions': ['peaceful', 'excited', 'happy'], 'intensity': 8, + 'lucidity': 'semi_lucid', 'characters': ['dolphins', 'myself'], + 'locations': ['ocean', 'sky'], 'symbols': ['water', 'flying', 'moon'], + 'colors': ['purple', 'blue', 'silver'], 'duration': 45 + }, + { + 'date': '2024-10-16', 'time': '04:15', 'title': 'Lost in Library', + 'content': 'Endless library with floating books. Each book contained memories of people I never met. The librarian was a shadowy figure who spoke in whispers. Time moved backwards.', + 'emotions': ['confused', 'curious', 'anxious'], 'intensity': 6, + 'lucidity': 'not_lucid', 'characters': ['librarian', 'shadow figures'], + 'locations': ['library', 'maze'], 'symbols': ['books', 'knowledge', 'time'], + 'colors': ['brown', 'gold', 'black'], 'duration': 30 + }, + { + 'date': '2024-10-18', 'time': '02:45', 'title': 'Childhood Home Transformed', + 'content': 'My childhood home but everything was giant-sized. I was tiny like an ant. Found my grandmother baking cookies in the kitchen, she had passed away years ago. She smiled and waved.', + 'emotions': ['nostalgic', 'sad', 'peaceful'], 'intensity': 9, + 'lucidity': 'not_lucid', 'characters': ['grandmother', 'family'], + 'locations': ['home', 'kitchen'], 'symbols': ['family', 'food', 'childhood'], + 'colors': ['warm', 'yellow', 'brown'], 'duration': 35 + }, + { + 'date': '2024-10-20', 'time': '05:00', 'title': 'Concert in the Void', + 'content': 'Playing piano in an empty void surrounded by stars. The music created colors and shapes that danced around me. Audience was made of pure light beings.', + 'emotions': ['excited', 'happy', 'peaceful'], 'intensity': 10, + 'lucidity': 'fully_lucid', 'characters': ['light beings', 'myself'], + 'locations': ['void', 'space'], 'symbols': ['music', 'stars', 'creation'], + 'colors': ['black', 'gold', 'rainbow'], 'duration': 60 + }, + { + 'date': '2024-10-22', 'time': '03:00', 'title': 'Chase Through City', + 'content': 'Being chased through a neon-lit city that kept shifting and changing. Buildings would appear and disappear. Finally hid in a cafe where everyone was frozen in time.', + 'emotions': ['fearful', 'anxious', 'confused'], 'intensity': 7, + 'lucidity': 'not_lucid', 'characters': ['pursuers', 'frozen people'], + 'locations': ['city', 'cafe'], 'symbols': ['chase', 'urban', 'time'], + 'colors': ['neon', 'blue', 'red'], 'duration': 25 + }, + { + 'date': '2024-10-24', 'time': '04:30', 'title': 'Garden of Memories', + 'content': 'Walking through a garden where each flower represented a memory. Some were wilting, others blooming. Found a fountain that showed reflections of past dreams.', + 'emotions': ['nostalgic', 'peaceful', 'happy'], 'intensity': 8, + 'lucidity': 'semi_lucid', 'characters': ['gardener', 'myself'], + 'locations': ['garden', 'fountain'], 'symbols': ['flowers', 'memory', 'water'], + 'colors': ['green', 'pink', 'gold'], 'duration': 50 + }, + { + 'date': '2024-10-25', 'time': '02:30', 'title': 'Mirror World', + 'content': 'Everything was reflected and reversed. Met my mirror self who had lived a completely different life. We exchanged stories through telepathy.', + 'emotions': ['confused', 'curious', 'excited'], 'intensity': 9, + 'lucidity': 'fully_lucid', 'characters': ['mirror self', 'reflections'], + 'locations': ['mirror world', 'reflection'], 'symbols': ['mirror', 'duality', 'self'], + 'colors': ['silver', 'blue', 'white'], 'duration': 40 + }, + { + 'date': '2024-10-26', 'time': '03:45', 'title': 'Underwater Temple', + 'content': 'Exploring ancient underwater ruins with glowing hieroglyphics. Fish swam through walls. Found a crystal that showed visions of the future.', + 'emotions': ['curious', 'excited', 'peaceful'], 'intensity': 8, + 'lucidity': 'semi_lucid', 'characters': ['ancient priests', 'sea creatures'], + 'locations': ['underwater', 'temple', 'ruins'], 'symbols': ['water', 'ancient', 'crystal'], + 'colors': ['blue', 'turquoise', 'gold'], 'duration': 55 + }, + { + 'date': '2024-10-27', 'time': '05:15', 'title': 'Storm of Emotions', + 'content': 'Standing in a field during a storm where each raindrop was an emotion. Could taste feelings. Lightning struck and created portals to other dreams.', + 'emotions': ['intense', 'confused', 'anxious'], 'intensity': 10, + 'lucidity': 'not_lucid', 'characters': ['storm entity', 'myself'], + 'locations': ['field', 'storm'], 'symbols': ['weather', 'emotion', 'portal'], + 'colors': ['grey', 'electric', 'dark'], 'duration': 30 + }, + { + 'date': '2024-10-28', 'time': '04:00', 'title': 'Quantum Market', + 'content': 'Bizarre marketplace where vendors sold impossible things: bottled time, crystallized thoughts, caged laughter. Paid with memories instead of money.', + 'emotions': ['curious', 'excited', 'confused'], 'intensity': 7, + 'lucidity': 'semi_lucid', 'characters': ['vendors', 'shoppers', 'myself'], + 'locations': ['market', 'bazaar'], 'symbols': ['trade', 'abstract', 'exchange'], + 'colors': ['vibrant', 'purple', 'gold'], 'duration': 45 + } + ] + + # assign stable ids and coerce numeric fields + for d in sample_dreams: + d['id'] = hashlib.md5(f"{d['date']}{d['time']}{d.get('title','')}".encode()).hexdigest()[:8] + # Ensure numeric types + d['intensity'] = int(d.get('intensity', 0)) + d['duration'] = int(d.get('duration', 0)) + # ensure lists exist + for k in ['emotions', 'characters', 'symbols']: + d[k] = d.get(k, []) if isinstance(d.get(k, []), list) else [d.get(k)] + self.dreams_db = sample_dreams + + def add_dream(self, dream_data): + """Add new dream entry""" + dream_data = dict(dream_data) # copy + dream_data.setdefault('date', datetime.now().strftime('%Y-%m-%d')) + dream_data.setdefault('time', datetime.now().strftime('%H:%M')) + dream_data['id'] = hashlib.md5( + f"{dream_data['date']}{dream_data['time']}{dream_data.get('title','')}".encode() + ).hexdigest()[:8] + # coerce numeric + try: + dream_data['intensity'] = int(dream_data.get('intensity', 0)) + except Exception: + dream_data['intensity'] = 0 + try: + dream_data['duration'] = int(dream_data.get('duration', 0)) + except Exception: + dream_data['duration'] = 0 + self.dreams_db.append(dream_data) + return True + + def get_all_dreams(self): + """Retrieve all dreams as DataFrame with correct types""" + if not self.dreams_db: + return pd.DataFrame() + df = pd.DataFrame(self.dreams_db) + # Ensure columns exist and types + if 'intensity' in df.columns: + df['intensity'] = pd.to_numeric(df['intensity'], errors='coerce').fillna(0).astype(int) + else: + df['intensity'] = 0 + if 'duration' in df.columns: + df['duration'] = pd.to_numeric(df['duration'], errors='coerce').fillna(0).astype(int) + else: + df['duration'] = 0 + df['date'] = df['date'].astype(str) + df['time'] = df['time'].astype(str) + df['lucidity'] = df.get('lucidity', pd.Series(['not_lucid'] * len(df))).fillna('not_lucid') + return df + + def get_dream_by_id(self, dream_id): + """Get specific dream""" + for dream in self.dreams_db: + if dream.get('id') == dream_id: + return dream + return None + + def compute_emotion_stats(self): + """Calculate emotion statistics""" + if not self.dreams_db: + return {} + emotion_counts = Counter() + for dream in self.dreams_db: + emotion_counts.update(dream.get('emotions', [])) + return dict(emotion_counts) + + def analyze_text_patterns(self): + """NLP analysis of dream content""" + if not self.dreams_db: + return None + + texts = [d.get('content', '') for d in self.dreams_db] + # TF-IDF vectorization + vectorizer = TfidfVectorizer(max_features=50, stop_words='english') + tfidf_matrix = vectorizer.fit_transform(texts) + feature_names = vectorizer.get_feature_names_out() + return { + 'tfidf_matrix': tfidf_matrix, + 'feature_names': feature_names, + 'vectorizer': vectorizer + } + + def cluster_dreams(self, n_clusters=3): + """Cluster dreams using K-Means""" + analysis = self.analyze_text_patterns() + if analysis is None: + return None + kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10) + clusters = kmeans.fit_predict(analysis['tfidf_matrix']) + return clusters + + def reduce_dimensions(self, method='tsne'): + """Dimensionality reduction for visualization""" + analysis = self.analyze_text_patterns() + if analysis is None: + return None + + tfidf_dense = analysis['tfidf_matrix'].toarray() + n_samples = tfidf_dense.shape[0] + + # If too few samples for TSNE, fallback to PCA or return zeros + if n_samples < 2: + return np.zeros((n_samples, 3)) + + if method == 'pca' or n_samples < 4: + reducer = PCA(n_components=min(3, n_samples), random_state=42) + coords = reducer.fit_transform(tfidf_dense) + # If result is lower-dim, pad to 3 cols + if coords.shape[1] < 3: + coords = np.pad(coords, ((0,0),(0,3-coords.shape[1])), mode='constant') + return coords + elif method == 'tsne': + # TSNE perplexity must be < n_samples, choose safe value + perplexity = min(30, max(2, n_samples - 1)) + reducer = TSNE(n_components=3, random_state=42, perplexity=perplexity, init='random') + else: + reducer = MDS(n_components=3, random_state=42) + coords = reducer.fit_transform(tfidf_dense) + # ensure shape (n,3) + if coords.shape[1] < 3: + coords = np.pad(coords, ((0,0),(0,3-coords.shape[1])), mode='constant') + return coords + + def build_character_network(self): + """Build network graph of dream characters""" + G = nx.Graph() + for dream in self.dreams_db: + chars = dream.get('characters', []) or [] + # normalize char names to strings + chars = [str(c).strip() for c in chars if c] + for char in chars: + if not G.has_node(char): + G.add_node(char, count=1) + else: + G.nodes[char]['count'] += 1 + for i, char1 in enumerate(chars): + for char2 in chars[i+1:]: + if G.has_edge(char1, char2): + G[char1][char2]['weight'] += 1 + else: + G.add_edge(char1, char2, weight=1) + return G + + def analyze_temporal_patterns(self): + """Analyze patterns over time""" + df = self.get_all_dreams() + if df.empty: + return None + # Combine date and time safely + df['datetime'] = pd.to_datetime(df['date'] + ' ' + df['time'], errors='coerce') + df = df.sort_values('datetime') + df['intensity_ma'] = df['intensity'].rolling(window=3, min_periods=1).mean() + df['duration_ma'] = df['duration'].rolling(window=3, min_periods=1).mean() + return df + +# ============================================================================ +# VISUALIZATION GENERATORS +# ============================================================================ + +class DreamVisualizer: + """Generate all visualization figures""" + + def __init__(self, middleware): + self.mw = middleware + + def create_dreamscape_3d(self): + """Create 3D dreamscape visualization using dimensionality reduction""" + coords = self.mw.reduce_dimensions(method='tsne') + if coords is None or coords.size == 0: + return go.Figure() + df = self.mw.get_all_dreams() + # Ensure coords align with df rows + if coords.shape[0] != len(df): + # try PCA fallback + coords = self.mw.reduce_dimensions(method='pca') + + # Create color mapping + colors = [] + for emotions in df['emotions']: + primary_emotion = emotions[0] if isinstance(emotions, (list, tuple)) and emotions else 'neutral' + colors.append(self.mw.emotion_colors.get(primary_emotion, '#808080')) + + # Size based on intensity + sizes = (df['intensity'].astype(int).values * 5).tolist() + # customdata for hover (date, intensity) + customdata = np.stack([df['date'].astype(str).values, df['intensity'].astype(str).values], axis=1) + + fig = go.Figure(data=[go.Scatter3d( + x=coords[:, 0], + y=coords[:, 1], + z=coords[:, 2], + mode='markers+text', + marker=dict( + size=sizes, + color=colors, + opacity=0.8, + line=dict(color='white', width=1) + ), + text=df['title'].values, + textposition='top center', + textfont=dict(size=8, color='white'), + customdata=customdata, + hovertemplate='%{text}
Date: %{customdata[0]}
Intensity: %{customdata[1]}', + name='Dreams' + )]) + + # Add connecting lines for similar dreams + analysis = self.mw.analyze_text_patterns() + if analysis is not None: + try: + similarity_matrix = cosine_similarity(analysis['tfidf_matrix']) + threshold = 0.3 + for i in range(len(coords)): + for j in range(i+1, len(coords)): + if similarity_matrix[i, j] > threshold: + fig.add_trace(go.Scatter3d( + x=[coords[i, 0], coords[j, 0]], + y=[coords[i, 1], coords[j, 1]], + z=[coords[i, 2], coords[j, 2]], + mode='lines', + line=dict(color='rgba(255,255,255,0.08)', width=1), + showlegend=False, + hoverinfo='skip' + )) + except Exception: + # similarity might fail for edge cases; ignore gracefully + pass + + fig.update_layout( + template='plotly_dark', + scene=dict( + xaxis=dict(showgrid=False, showticklabels=False, title=''), + yaxis=dict(showgrid=False, showticklabels=False, title=''), + zaxis=dict(showgrid=False, showticklabels=False, title=''), + bgcolor='rgba(0,0,0,0.9)', + camera=dict(eye=dict(x=1.5, y=1.5, z=1.5)) + ), + height=600, + margin=dict(l=0, r=0, t=30, b=0), + title=dict( + text='🌌 Dreamscape: 3D Dream Space', + font=dict(size=20, color='#00E3AE') + ) + ) + return fig + + def create_emotion_wheel(self): + """Create circular emotion distribution chart""" + emotion_stats = self.mw.compute_emotion_stats() + if not emotion_stats: + return go.Figure() + emotions = list(emotion_stats.keys()) + counts = list(emotion_stats.values()) + colors = [self.mw.emotion_colors.get(e, '#808080') for e in emotions] + fig = go.Figure(data=[go.Pie( + labels=emotions, + values=counts, + hole=0.4, + marker=dict(colors=colors, line=dict(color='white', width=2)), + textfont=dict(size=14, color='white'), + hovertemplate='%{label}
Count: %{value}
%{percent}' + )]) + fig.update_layout( + template='plotly_dark', + height=400, + margin=dict(l=20, r=20, t=60, b=20), + title=dict(text='😊 Emotion Wheel', font=dict(size=18, color='#FFD700')), + showlegend=True, + legend=dict(orientation='v', yanchor='middle', y=0.5) + ) + return fig + + def create_character_network(self): + """Create network graph of dream characters""" + G = self.mw.build_character_network() + if len(G.nodes()) == 0: + return go.Figure() + + pos = nx.spring_layout(G, k=0.5, iterations=50, seed=42) + + edge_traces = [] + for edge in G.edges(): + x0, y0 = pos[edge[0]] + x1, y1 = pos[edge[1]] + weight = G[edge[0]][edge[1]].get('weight', 1) + edge_traces.append(go.Scatter( + x=[x0, x1, None], + y=[y0, y1, None], + mode='lines', + line=dict(width=max(1, weight * 1.5), color='rgba(255,255,255,0.3)'), + hoverinfo='none', + showlegend=False + )) + + node_x, node_y, node_text, node_size = [], [], [], [] + for node in G.nodes(): + x, y = pos[node] + node_x.append(x) + node_y.append(y) + node_text.append(node) + node_size.append(G.nodes[node].get('count', 1) * 8 + 8) + + node_trace = go.Scatter( + x=node_x, y=node_y, + mode='markers+text', + marker=dict( + size=node_size, + color='#7F7EFF', + line=dict(color='white', width=2) + ), + text=node_text, + textposition='top center', + textfont=dict(size=10, color='white'), + hovertemplate='%{text}
Appearances: %{marker.size}', + showlegend=False + ) + + fig = go.Figure(data=edge_traces + [node_trace]) + fig.update_layout( + template='plotly_dark', + height=450, + showlegend=False, + hovermode='closest', + margin=dict(l=0, r=0, t=60, b=0), + xaxis=dict(showgrid=False, zeroline=False, showticklabels=False), + yaxis=dict(showgrid=False, zeroline=False, showticklabels=False), + title=dict(text='👥 Character Network', font=dict(size=18, color='#7F7EFF')) + ) + return fig + + def create_temporal_heatmap(self): + """Create calendar heatmap / timeline of dream intensity""" + df = self.mw.analyze_temporal_patterns() + if df is None or df.empty: + return go.Figure() + # Use scatter timeline with markers sized by intensity + fig = go.Figure(data=go.Scatter( + x=df['datetime'], + y=df['intensity'], + mode='markers+lines', + marker=dict( + size=(df['intensity'] * 5).tolist(), + color=df['intensity'], + colorscale='Viridis', + showscale=True, + colorbar=dict(title='Intensity') + ), + line=dict(color='rgba(127,126,255,0.3)', width=2), + text=df['title'], + hovertemplate='%{text}
Date: %{x}
Intensity: %{y}' + )) + fig.update_layout( + template='plotly_dark', + height=350, + margin=dict(l=40, r=20, t=60, b=40), + xaxis_title='Date', + yaxis_title='Intensity', + title=dict(text='📅 Dream Intensity Timeline', font=dict(size=18, color='#00E3AE')) + ) + return fig + + def create_lucidity_gauge(self): + """Create gauge chart for lucidity levels""" + df = self.mw.get_all_dreams() + if df.empty: + return go.Figure() + lucidity_counts = df['lucidity'].value_counts() + total = len(df) + fully_lucid_pct = (lucidity_counts.get('fully_lucid', 0) / total) * 100 + fig = go.Figure(go.Indicator( + mode='gauge+number+delta', + value=fully_lucid_pct, + domain={'x': [0, 1], 'y': [0, 1]}, + title={'text': 'Lucidity Rate (%)', 'font': {'size': 20, 'color': 'white'}}, + delta={'reference': 20, 'increasing': {'color': '#00E3AE'}}, + gauge={ + 'axis': {'range': [None, 100], 'tickcolor': 'white'}, + 'bar': {'color': '#7F7EFF'}, + 'bgcolor': 'rgba(0,0,0,0.3)', + 'borderwidth': 2, + 'bordercolor': 'white', + 'steps': [ + {'range': [0, 33], 'color': 'rgba(127,126,255,0.2)'}, + {'range': [33, 66], 'color': 'rgba(127,126,255,0.4)'}, + {'range': [66, 100], 'color': 'rgba(127,126,255,0.6)'} + ], + 'threshold': { + 'line': {'color': '#00E3AE', 'width': 4}, + 'thickness': 0.75, + 'value': 50 + } + } + )) + fig.update_layout(template='plotly_dark', height=300, margin=dict(l=20, r=20, t=60, b=20)) + return fig + + def create_word_cloud_chart(self): + """Create word frequency bar chart (TF-IDF based)""" + analysis = self.mw.analyze_text_patterns() + if analysis is None: + return go.Figure() + feature_names = analysis['feature_names'] + tfidf_matrix = analysis['tfidf_matrix'] + scores = np.array(tfidf_matrix.sum(axis=0)).flatten() + if len(scores) == 0: + return go.Figure() + top_n = min(20, len(scores)) + top_indices = scores.argsort()[-top_n:][::-1] + top_words = [feature_names[i] for i in top_indices] + top_scores = scores[top_indices] + fig = go.Figure(data=[go.Bar( + x=top_scores, + y=top_words, + orientation='h', + marker=dict( + color=top_scores, + colorscale='Plasma', + showscale=False + ), + hovertemplate='%{y}
Score: %{x:.3f}' + )]) + fig.update_layout( + template='plotly_dark', + height=500, + margin=dict(l=100, r=20, t=60, b=40), + xaxis_title='TF-IDF Score', + yaxis_title='', + title=dict(text='💬 Top Dream Keywords', font=dict(size=18, color='#FFD700')) + ) + return fig + + def create_symbol_sunburst(self): + """Create sunburst chart of dream symbols""" + df = self.mw.get_all_dreams() + if df.empty: + return go.Figure() + all_symbols = [] + for symbols in df['symbols']: + if isinstance(symbols, (list, tuple)): + all_symbols.extend(symbols) + elif symbols: + all_symbols.append(symbols) + symbol_counts = Counter(all_symbols) + if not symbol_counts: + return go.Figure() + labels = ['All Symbols'] + list(symbol_counts.keys()) + parents = [''] + ['All Symbols'] * len(symbol_counts) + values = [sum(symbol_counts.values())] + list(symbol_counts.values()) + fig = go.Figure(go.Sunburst( + labels=labels, + parents=parents, + values=values, + branchvalues='total', + marker=dict(colorscale='Viridis', line=dict(color='white', width=2)), + hovertemplate='%{label}
Count: %{value}' + )) + fig.update_layout( + template='plotly_dark', + height=450, + margin=dict(l=0, r=0, t=60, b=0), + title=dict(text='🔮 Dream Symbols Hierarchy', font=dict(size=18, color='#9370DB')) + ) + return fig + +# ============================================================================ +# DASH APPLICATION +# ============================================================================ + +# Initialize middleware and visualizer +middleware = DreamDataMiddleware() +visualizer = DreamVisualizer(middleware) + +# Create Dash app +app = dash.Dash(__name__, suppress_callback_exceptions=True) +app.title = "💭 Dream Journal Visualizer" + +# App layout +app.layout = html.Div([ + # Header + html.Div([ + html.Div([ + html.H1('💭 Dream Journal Visualizer', + style={'margin': 0, 'fontSize': '3em', 'fontWeight': 'bold'}), + html.P('Explore the landscape of your subconscious mind', + style={'fontSize': '1.3em', 'opacity': 0.9, 'marginTop': '10px'}) + ], style={'textAlign': 'center'}) + ], style={ + 'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 50%, #f093fb 100%)', + 'padding': '40px 20px', + 'borderRadius': '12px', + 'margin': '20px', + 'boxShadow': '0 8px 32px rgba(0,0,0,0.3)' + }), + + # Stats Cards + html.Div([ + html.Div([ + html.H3('📊', style={'fontSize': '2.5em', 'margin': 0}), + html.H2(id='total-dreams', style={'margin': '10px 0'}), + html.P('Total Dreams', style={'opacity': 0.8}) + ], style={ + 'background': 'rgba(102,126,234,0.2)', + 'padding': '20px', + 'borderRadius': '10px', + 'textAlign': 'center', + 'flex': 1, + 'minWidth': '150px' + }), + + html.Div([ + html.H3('⭐', style={'fontSize': '2.5em', 'margin': 0}), + html.H2(id='avg-intensity', style={'margin': '10px 0'}), + html.P('Avg Intensity', style={'opacity': 0.8}) + ], style={ + 'background': 'rgba(255,215,0,0.2)', + 'padding': '20px', + 'borderRadius': '10px', + 'textAlign': 'center', + 'flex': 1, + 'minWidth': '150px' + }), + + html.Div([ + html.H3('🌟', style={'fontSize': '2.5em', 'margin': 0}), + html.H2(id='lucid-count', style={'margin': '10px 0'}), + html.P('Lucid Dreams', style={'opacity': 0.8}) + ], style={ + 'background': 'rgba(127,126,255,0.2)', + 'padding': '20px', + 'borderRadius': '10px', + 'textAlign': 'center', + 'flex': 1, + 'minWidth': '150px' + }), + + html.Div([ + html.H3('⏱️', style={'fontSize': '2.5em', 'margin': 0}), + html.H2(id='avg-duration', style={'margin': '10px 0'}), + html.P('Avg Duration (min)', style={'opacity': 0.8}) + ], style={ + 'background': 'rgba(0,227,174,0.2)', + 'padding': '20px', + 'borderRadius': '10px', + 'textAlign': 'center', + 'flex': 1, + 'minWidth': '150px' + }) + ], style={'display': 'flex', 'gap': '16px', 'justifyContent': 'space-between', 'margin': '20px'}), + + # Main visualization grid + html.Div([ + html.Div([ + dcc.Graph(id='dreamscape-3d'), + dcc.Graph(id='emotion-wheel') + ], style={'flex': '1', 'minWidth': '420px', 'padding': '10px'}), + + html.Div([ + dcc.Graph(id='word-cloud'), + dcc.Graph(id='symbol-sunburst') + ], style={'flex': '1', 'minWidth': '420px', 'padding': '10px'}) + ], style={'display': 'flex', 'gap': '16px', 'margin': '10px 20px'}), + + html.Div([ + html.Div([ + dcc.Graph(id='character-network') + ], style={'flex': '1', 'minWidth': '420px', 'padding': '10px'}), + + html.Div([ + dcc.Graph(id='temporal-plot'), + dcc.Graph(id='lucidity-gauge') + ], style={'flex': '1', 'minWidth': '420px', 'padding': '10px'}) + ], style={'display': 'flex', 'gap': '16px', 'margin': '10px 20px 40px 20px'}), + + dcc.Interval(id='update-interval', interval=60*1000, n_intervals=0) +]) + +@app.callback( + Output('total-dreams', 'children'), + Output('avg-intensity', 'children'), + Output('lucid-count', 'children'), + Output('avg-duration', 'children'), + Output('dreamscape-3d', 'figure'), + Output('emotion-wheel', 'figure'), + Output('word-cloud', 'figure'), + Output('symbol-sunburst', 'figure'), + Output('character-network', 'figure'), + Output('temporal-plot', 'figure'), + Output('lucidity-gauge', 'figure'), + Input('update-interval', 'n_intervals') +) +def update_dashboard(n_intervals): + """Update all dashboard elements periodically.""" + df = middleware.get_all_dreams() + total = len(df) + avg_int = round(df['intensity'].mean(), 2) if total > 0 else 0 + lucid_count = int(df[df['lucidity'] == 'fully_lucid'].shape[0]) if total > 0 else 0 + avg_dur = round(df['duration'].mean(), 1) if total > 0 else 0 + + # Generate figures using visualizer + dreamscape_fig = visualizer.create_dreamscape_3d() + emotion_fig = visualizer.create_emotion_wheel() + word_fig = visualizer.create_word_cloud_chart() + symbol_fig = visualizer.create_symbol_sunburst() + char_net_fig = visualizer.create_character_network() + temporal_fig = visualizer.create_temporal_heatmap() + lucidity_fig = visualizer.create_lucidity_gauge() + + return ( + str(total), + str(avg_int), + str(lucid_count), + str(avg_dur), + dreamscape_fig, + emotion_fig, + word_fig, + symbol_fig, + char_net_fig, + temporal_fig, + lucidity_fig + ) + +if __name__ == '__main__': + # When run directly, start the Dash development server + app.run_server(debug=True, port=8050) diff --git a/genai.py b/genai.py new file mode 100644 index 0000000..e69de29 diff --git a/genomevisu.py b/genomevisu.py new file mode 100644 index 0000000..6936045 --- /dev/null +++ b/genomevisu.py @@ -0,0 +1,177 @@ +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.patches import Rectangle +import random + +class DNAVisualizer: + def __init__(self, sequence=None): + """Initialize DNA visualizer with optional sequence""" + if sequence is None: + self.sequence = self.generate_random_sequence(100) + else: + self.sequence = sequence.upper() + + self.color_map = {'A': '#FF6B6B', 'T': '#4ECDC4', 'G': '#45B7D1', 'C': '#FFA07A'} + + def generate_random_sequence(self, length): + """Generate a random DNA sequence""" + bases = ['A', 'T', 'G', 'C'] + return ''.join(random.choice(bases) for _ in range(length)) + + def validate_sequence(self, seq): + """Validate if sequence contains only valid DNA bases""" + valid_bases = set('ATGC') + return all(base in valid_bases for base in seq) + + def plot_linear_sequence(self): + """Visualize DNA sequence as a linear pattern""" + fig, ax = plt.subplots(figsize=(14, 4)) + + for i, base in enumerate(self.sequence): + color = self.color_map[base] + ax.add_patch(Rectangle((i, 0), 1, 1, facecolor=color, edgecolor='black', linewidth=0.5)) + + ax.set_xlim(0, len(self.sequence)) + ax.set_ylim(-0.5, 1.5) + ax.set_aspect('equal') + ax.set_title('DNA Sequence Linear Pattern', fontsize=14, fontweight='bold') + ax.set_xlabel('Position', fontsize=12) + ax.axis('off') + + # Add legend + legend_elements = [plt.Rectangle((0, 0), 1, 1, facecolor=self.color_map[base], label=base) + for base in 'ATGC'] + ax.legend(handles=legend_elements, loc='upper right', bbox_to_anchor=(1.15, 1)) + + plt.tight_layout() + plt.show() + + def plot_helix_pattern(self): + """Visualize DNA as a double helix pattern""" + fig, ax = plt.subplots(figsize=(12, 8)) + + t = np.linspace(0, 4*np.pi, len(self.sequence)) + x1 = np.cos(t) + y1 = np.sin(t) + x2 = np.cos(t + np.pi) + y2 = np.sin(t + np.pi) + z = np.linspace(0, len(self.sequence), len(self.sequence)) + + # Plot bases on helix + for i, base in enumerate(self.sequence): + color = self.color_map[base] + # Strand 1 + ax.scatter(x1[i], z[i], color=color, s=100, edgecolors='black', linewidth=0.5, zorder=3) + # Strand 2 + ax.scatter(x2[i], z[i], color=color, s=100, edgecolors='black', linewidth=0.5, zorder=3) + + # Connect strands with lines + if i % 5 == 0: + ax.plot([x1[i], x2[i]], [z[i], z[i]], 'gray', linewidth=1, alpha=0.5, zorder=1) + + # Draw helix backbone + ax.plot(x1, z, 'k-', alpha=0.3, linewidth=2, zorder=0) + ax.plot(x2, z, 'k-', alpha=0.3, linewidth=2, zorder=0) + + ax.set_xlabel('X Position', fontsize=12) + ax.set_ylabel('Sequence Position', fontsize=12) + ax.set_title('DNA Double Helix Pattern', fontsize=14, fontweight='bold') + + # Add legend + legend_elements = [plt.scatter([], [], color=self.color_map[base], s=100, label=base, edgecolors='black') + for base in 'ATGC'] + ax.legend(handles=legend_elements, loc='upper right') + + plt.tight_layout() + plt.show() + + def plot_base_composition(self): + """Plot nucleotide composition""" + bases = ['A', 'T', 'G', 'C'] + counts = [self.sequence.count(base) for base in bases] + + fig, ax = plt.subplots(figsize=(10, 6)) + bars = ax.bar(bases, counts, color=[self.color_map[base] for base in bases], + edgecolor='black', linewidth=2) + + ax.set_ylabel('Count', fontsize=12) + ax.set_xlabel('Nucleotide Base', fontsize=12) + ax.set_title('DNA Base Composition', fontsize=14, fontweight='bold') + + # Add count labels on bars + for bar, count in zip(bars, counts): + height = bar.get_height() + ax.text(bar.get_x() + bar.get_width()/2., height, + f'{count}', ha='center', va='bottom', fontsize=11, fontweight='bold') + + plt.tight_layout() + plt.show() + + def plot_gc_content_sliding_window(self, window_size=20): + """Plot GC content using sliding window""" + gc_content = [] + positions = [] + + for i in range(len(self.sequence) - window_size): + window = self.sequence[i:i+window_size] + gc_count = window.count('G') + window.count('C') + gc_percentage = (gc_count / window_size) * 100 + gc_content.append(gc_percentage) + positions.append(i) + + fig, ax = plt.subplots(figsize=(12, 5)) + ax.plot(positions, gc_content, linewidth=2, color='#45B7D1', marker='o', markersize=4) + ax.fill_between(positions, gc_content, alpha=0.3, color='#45B7D1') + + ax.set_xlabel('Position', fontsize=12) + ax.set_ylabel('GC Content (%)', fontsize=12) + ax.set_title(f'GC Content Distribution (Window: {window_size})', fontsize=14, fontweight='bold') + ax.grid(True, alpha=0.3) + + plt.tight_layout() + plt.show() + + def get_statistics(self): + """Get and print sequence statistics""" + print("\n=== DNA Sequence Statistics ===") + print(f"Sequence Length: {len(self.sequence)}") + print(f"Sequence: {self.sequence[:50]}{'...' if len(self.sequence) > 50 else ''}") + print("\nNucleotide Counts:") + for base in 'ATGC': + count = self.sequence.count(base) + percentage = (count / len(self.sequence)) * 100 + print(f" {base}: {count} ({percentage:.2f}%)") + + gc_content = (self.sequence.count('G') + self.sequence.count('C')) / len(self.sequence) * 100 + print(f"\nGC Content: {gc_content:.2f}%") + print("=" * 30 + "\n") + + +def main(): + # Example usage + print("DNA Genome Pattern Visualization Tool") + print("=" * 40) + + # Create visualizer with random sequence + visualizer = DNAVisualizer() + + # Display statistics + visualizer.get_statistics() + + # Create visualizations + print("Generating visualizations...\n") + + visualizer.plot_linear_sequence() + visualizer.plot_helix_pattern() + visualizer.plot_base_composition() + visualizer.plot_gc_content_sliding_window(window_size=20) + + # Example with custom sequence + print("\nExample with custom sequence:") + custom_seq = "ATGCATGCATGCATGCATGCATGC" + custom_viz = DNAVisualizer(custom_seq) + custom_viz.get_statistics() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/hey.py b/hey.py new file mode 100644 index 0000000..e69de29 diff --git a/interactive_dashboard.py b/interactive_dashboard.py new file mode 100644 index 0000000..d94e5db --- /dev/null +++ b/interactive_dashboard.py @@ -0,0 +1,1161 @@ +#!/usr/bin/env python3 +""" +🎨 Interactive Data Visualization Dashboard +========================================== + +A comprehensive, interactive dashboard showcasing various data visualization techniques +using Plotly Dash. This dashboard combines multiple data sources and visualization types +to create an engaging, educational experience. + +Features: +- Multi-tab interface with different visualization categories +- Interactive controls and filters +- Real-time data updates +- Responsive design +- Custom themes and styling +- Educational tooltips and explanations + +Author: Data Visualization Collection +Date: 2024 +""" + +import dash +from dash import dcc, html, Input, Output, callback, dash_table, State, clientside_callback +import plotly.express as px +import plotly.graph_objects as go +from plotly.subplots import make_subplots +import pandas as pd +import numpy as np +import random +from datetime import datetime, timedelta +import json +import base64 +import io +import csv +from dash.exceptions import PreventUpdate + +# Set random seed for reproducible data +np.random.seed(42) +random.seed(42) + +# Initialize the Dash app +app = dash.Dash(__name__) +app.title = "🎨 Interactive Data Visualization Dashboard" + +# Disable dev tools to avoid compatibility issues with newer Python versions +app.enable_dev_tools(dev_tools_ui=False, dev_tools_hot_reload=False) + +# Global state for theme and filters +current_theme = "light" +global_filters = { + 'date_range': None, + 'category_filter': 'All', + 'region_filter': 'All' +} + +# Enhanced CSS styling with theme support +app.index_string = ''' + + + + {%metas%} + {%title%} + {%favicon%} + {%css%} + + + + {%app_entry%} +
+ {%config%} + {%scripts%} + {%renderer%} +
+ + +''' + +# ============================================================================= +# DATA GENERATION FUNCTIONS +# ============================================================================= + +def generate_sales_data(): + """Generate realistic sales data for e-commerce dashboard""" + np.random.seed(42) + dates = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D') + + # Generate seasonal sales pattern + base_sales = 1000 + seasonal_factor = 1 + 0.3 * np.sin(2 * np.pi * np.arange(len(dates)) / 365.25) + trend = np.linspace(1, 1.2, len(dates)) + noise = np.random.normal(0, 0.1, len(dates)) + + sales = base_sales * seasonal_factor * trend * (1 + noise) + sales = np.maximum(sales, 0) # Ensure non-negative sales + + return pd.DataFrame({ + 'date': dates, + 'sales': sales, + 'month': dates.month_name(), + 'quarter': dates.quarter, + 'day_of_week': dates.day_name() + }) + +def generate_customer_data(): + """Generate customer demographics and behavior data""" + np.random.seed(42) + n_customers = 1000 + + # Age distribution (skewed towards younger adults) + ages = np.random.gamma(2, 15, n_customers) + 18 + ages = np.clip(ages, 18, 80) + + # Gender distribution + genders = np.random.choice(['Male', 'Female', 'Other'], n_customers, p=[0.45, 0.50, 0.05]) + + # Income (correlated with age) + income = ages * 1000 + np.random.normal(0, 5000, n_customers) + income = np.maximum(income, 20000) + + # Purchase frequency (inversely correlated with income) + purchase_freq = np.random.poisson(lam=20 - (income - 30000) / 10000, size=n_customers) + purchase_freq = np.maximum(purchase_freq, 1) + + # Customer satisfaction (0-10 scale) + satisfaction = np.random.beta(2, 1, n_customers) * 10 + + return pd.DataFrame({ + 'customer_id': range(1, n_customers + 1), + 'age': ages, + 'gender': genders, + 'income': income, + 'purchase_frequency': purchase_freq, + 'satisfaction': satisfaction, + 'lifetime_value': income * 0.1 + np.random.normal(0, 1000, n_customers) + }) + +def generate_product_data(): + """Generate product performance data""" + np.random.seed(42) + categories = ['Electronics', 'Clothing', 'Books', 'Home & Garden', 'Sports', 'Beauty'] + n_products = 50 + + products = [] + for i in range(n_products): + category = np.random.choice(categories) + + # Price based on category + base_prices = {'Electronics': 200, 'Clothing': 50, 'Books': 20, + 'Home & Garden': 80, 'Sports': 100, 'Beauty': 30} + price = base_prices[category] * np.random.uniform(0.5, 2.0) + + # Sales volume (inversely related to price) + sales_volume = int(1000 / (price / 50) * np.random.uniform(0.5, 1.5)) + + # Rating (slightly correlated with price) + rating = min(5, max(1, 3 + (price / 100) * 0.5 + np.random.normal(0, 0.5))) + + products.append({ + 'product_id': i + 1, + 'name': f'Product {i+1}', + 'category': category, + 'price': round(price, 2), + 'sales_volume': sales_volume, + 'rating': round(rating, 1), + 'revenue': round(price * sales_volume, 2) + }) + + return pd.DataFrame(products) + +def generate_geographic_data(): + """Generate geographic sales data""" + np.random.seed(42) + countries = ['USA', 'Canada', 'UK', 'Germany', 'France', 'Japan', 'Australia', 'Brazil', 'India', 'China'] + + # GDP per capita (rough estimates) + gdp_per_capita = [65000, 45000, 42000, 48000, 40000, 39000, 55000, 15000, 2000, 10000] + + # Population (in millions) + population = [330, 38, 67, 83, 67, 125, 25, 213, 1380, 1400] + + # Sales correlated with GDP and population + sales = [] + for i, country in enumerate(countries): + base_sales = (gdp_per_capita[i] / 1000) * (population[i] / 100) * np.random.uniform(0.8, 1.2) + sales.append(max(0, base_sales)) + + return pd.DataFrame({ + 'country': countries, + 'sales': sales, + 'gdp_per_capita': gdp_per_capita, + 'population': population, + 'region': ['North America', 'North America', 'Europe', 'Europe', 'Europe', + 'Asia', 'Oceania', 'South America', 'Asia', 'Asia'] + }) + +# Generate all datasets +sales_df = generate_sales_data() +customer_df = generate_customer_data() +product_df = generate_product_data() +geo_df = generate_geographic_data() + +# ============================================================================= +# DASHBOARD LAYOUT +# ============================================================================= + +app.layout = html.Div([ + # Header with theme toggle + html.Div([ + html.H1("🎨 Interactive Data Visualization Dashboard", className="header"), + html.P("Explore data through interactive visualizations and gain insights", + style={'margin': '10px 0 0 0', 'fontSize': '1.2em', 'opacity': '0.9'}), + html.Button("🌙 Dark Mode", id="theme-toggle", className="theme-toggle") + ], className="header"), + + # Global Filters Bar + html.Div([ + html.Div([ + html.Label("📅 Date Range"), + dcc.DatePickerRange( + id='date-range-picker', + start_date=sales_df['date'].min(), + end_date=sales_df['date'].max(), + display_format='YYYY-MM-DD' + ) + ], className="filter-item"), + + html.Div([ + html.Label("🏷️ Category"), + dcc.Dropdown( + id='category-filter', + options=[{'label': 'All Categories', 'value': 'All'}] + + [{'label': cat, 'value': cat} for cat in product_df['category'].unique()], + value='All', + clearable=False + ) + ], className="filter-item"), + + html.Div([ + html.Label("🌍 Region"), + dcc.Dropdown( + id='region-filter', + options=[{'label': 'All Regions', 'value': 'All'}] + + [{'label': region, 'value': region} for region in geo_df['region'].unique()], + value='All', + clearable=False + ) + ], className="filter-item"), + + html.Div([ + html.Label("🔄 Live Updates"), + html.Div([ + dcc.Interval( + id='interval-component', + interval=5000, # Update every 5 seconds + n_intervals=0 + ), + html.Span("ON", id="live-status", style={'color': '#27ae60', 'fontWeight': 'bold'}) + ]) + ], className="filter-item") + ], className="filters-bar"), + + # KPI Cards Row + html.Div([ + html.Div([ + html.H3(id="kpi-revenue", className="kpi-value"), + html.P("Total Revenue", className="kpi-label"), + html.P(id="kpi-revenue-change", className="kpi-change") + ], className="kpi-card"), + + html.Div([ + html.H3(id="kpi-growth", className="kpi-value"), + html.P("Growth Rate", className="kpi-label"), + html.P(id="kpi-growth-change", className="kpi-change") + ], className="kpi-card"), + + html.Div([ + html.H3(id="kpi-customers", className="kpi-value"), + html.P("Active Customers", className="kpi-label"), + html.P(id="kpi-customers-change", className="kpi-change") + ], className="kpi-card"), + + html.Div([ + html.H3(id="kpi-retention", className="kpi-value"), + html.P("Retention Rate", className="kpi-label"), + html.P(id="kpi-retention-change", className="kpi-change") + ], className="kpi-card") + ], className="kpi-row"), + + # Main content + html.Div([ + dcc.Tabs(id="main-tabs", value="overview", children=[ + # Overview Tab + dcc.Tab(label="📊 Overview", value="overview", className="custom-tab"), + dcc.Tab(label="📈 Sales Analytics", value="sales", className="custom-tab"), + dcc.Tab(label="👥 Customer Insights", value="customers", className="custom-tab"), + dcc.Tab(label="🛍️ Product Performance", value="products", className="custom-tab"), + dcc.Tab(label="🌍 Geographic Analysis", value="geographic", className="custom-tab"), + dcc.Tab(label="🔮 Predictive Analytics", value="predictive", className="custom-tab") + ]), + + html.Div(id="tab-content", className="tab-content"), + + # Drilldown Table + html.Div(id="drilldown-table", className="drilldown-table", style={'display': 'none'}) + ], className="main-container"), + + # Export Buttons + html.Div([ + html.Button("📊 Export CSV", id="export-csv-btn", className="export-btn"), + html.Button("📈 Export Charts", id="export-charts-btn", className="export-btn"), + html.Button("📋 Export Report", id="export-report-btn", className="export-btn") + ], className="export-buttons"), + + # Notification Container + html.Div(id="notification-container"), + + # Hidden divs for storing data + dcc.Store(id='filtered-data-store'), + dcc.Store(id='theme-store', data='light'), + dcc.Store(id='drilldown-data-store') +]) + +# ============================================================================= +# CALLBACK FUNCTIONS +# ============================================================================= + +# Theme toggle callback +@app.callback( + [Output("theme-store", "data"), + Output("theme-toggle", "children")], + [Input("theme-toggle", "n_clicks")], + [State("theme-store", "data")] +) +def toggle_theme(n_clicks, current_theme): + if n_clicks is None: + return "light", "🌙 Dark Mode" + + new_theme = "dark" if current_theme == "light" else "light" + button_text = "☀️ Light Mode" if new_theme == "dark" else "🌙 Dark Mode" + + return new_theme, button_text + +# Global filters callback +@app.callback( + Output("filtered-data-store", "data"), + [Input("date-range-picker", "start_date"), + Input("date-range-picker", "end_date"), + Input("category-filter", "value"), + Input("region-filter", "value"), + Input("interval-component", "n_intervals")] +) +def update_filtered_data(start_date, end_date, category, region, n_intervals): + """Update filtered data based on global filters""" + + # Apply date filter + filtered_sales = sales_df.copy() + if start_date and end_date: + filtered_sales = filtered_sales[ + (filtered_sales['date'] >= start_date) & + (filtered_sales['date'] <= end_date) + ] + + # Apply category filter + filtered_products = product_df.copy() + if category != 'All': + filtered_products = filtered_products[filtered_products['category'] == category] + + # Apply region filter + filtered_geo = geo_df.copy() + if region != 'All': + filtered_geo = filtered_geo[filtered_geo['region'] == region] + + # Simulate live data updates + if n_intervals > 0: + # Add some random variation to simulate real-time updates + noise_factor = 1 + np.random.normal(0, 0.02, len(filtered_sales)) + filtered_sales['sales'] = filtered_sales['sales'] * noise_factor + + return { + 'sales': filtered_sales.to_dict('records'), + 'products': filtered_products.to_dict('records'), + 'customers': customer_df.to_dict('records'), + 'geo': filtered_geo.to_dict('records') + } + +# KPI cards callback +@app.callback( + [Output("kpi-revenue", "children"), + Output("kpi-revenue-change", "children"), + Output("kpi-revenue-change", "className"), + Output("kpi-growth", "children"), + Output("kpi-growth-change", "children"), + Output("kpi-growth-change", "className"), + Output("kpi-customers", "children"), + Output("kpi-customers-change", "children"), + Output("kpi-customers-change", "className"), + Output("kpi-retention", "children"), + Output("kpi-retention-change", "children"), + Output("kpi-retention-change", "className")], + [Input("filtered-data-store", "data")] +) +def update_kpi_cards(filtered_data): + """Update KPI cards with filtered data""" + + if not filtered_data: + return ["$0", "0%", "kpi-change", "0%", "0%", "kpi-change", + "0", "0", "kpi-change", "0%", "0%", "kpi-change"] + + sales_data = pd.DataFrame(filtered_data['sales']) + products_data = pd.DataFrame(filtered_data['products']) + customers_data = pd.DataFrame(filtered_data['customers']) + + # Calculate KPIs + total_revenue = sales_data['sales'].sum() if not sales_data.empty else 0 + revenue_change = np.random.uniform(5, 15) # Simulated growth + + growth_rate = np.random.uniform(8, 20) # Simulated growth rate + growth_change = np.random.uniform(-2, 5) + + active_customers = len(customers_data) + customers_change = np.random.uniform(2, 8) + + retention_rate = np.random.uniform(75, 95) + retention_change = np.random.uniform(-1, 3) + + return [ + f"${total_revenue:,.0f}", + f"+{revenue_change:.1f}%", + "kpi-change positive", + f"{growth_rate:.1f}%", + f"{'+' if growth_change > 0 else ''}{growth_change:.1f}%", + f"kpi-change {'positive' if growth_change > 0 else 'negative'}", + f"{active_customers:,}", + f"+{customers_change:.1f}%", + "kpi-change positive", + f"{retention_rate:.1f}%", + f"{'+' if retention_change > 0 else ''}{retention_change:.1f}%", + f"kpi-change {'positive' if retention_change > 0 else 'negative'}" + ] + +# Main tab content callback +@app.callback( + Output("tab-content", "children"), + [Input("main-tabs", "value"), + Input("filtered-data-store", "data")] +) +def render_tab_content(active_tab, filtered_data): + """Render content based on selected tab and filtered data""" + + if not filtered_data: + return html.Div("Loading...", style={'textAlign': 'center', 'padding': '50px'}) + + if active_tab == "overview": + return create_overview_tab(filtered_data) + elif active_tab == "sales": + return create_sales_tab(filtered_data) + elif active_tab == "customers": + return create_customers_tab(filtered_data) + elif active_tab == "products": + return create_products_tab(filtered_data) + elif active_tab == "geographic": + return create_geographic_tab(filtered_data) + elif active_tab == "predictive": + return create_predictive_tab(filtered_data) + +# Export functionality +@app.callback( + Output("notification-container", "children"), + [Input("export-csv-btn", "n_clicks"), + Input("export-charts-btn", "n_clicks"), + Input("export-report-btn", "n_clicks")], + [State("filtered-data-store", "data")] +) +def handle_exports(csv_clicks, charts_clicks, report_clicks, filtered_data): + """Handle export functionality""" + + ctx = dash.callback_context + if not ctx.triggered: + return "" + + button_id = ctx.triggered[0]['prop_id'].split('.')[0] + + if button_id == "export-csv-btn" and csv_clicks: + return html.Div("📊 CSV data exported successfully!", className="notification") + elif button_id == "export-charts-btn" and charts_clicks: + return html.Div("📈 Charts exported as images!", className="notification") + elif button_id == "export-report-btn" and report_clicks: + return html.Div("📋 Report generated and downloaded!", className="notification") + + return "" + +def generate_ai_insights(sales_data, products_data, customers_data): + """Generate AI-powered insights based on the data""" + insights = [] + + if not sales_data.empty: + # Sales insights + peak_month = sales_data.groupby('month')['sales'].sum().idxmax() + total_sales = sales_data['sales'].sum() + avg_daily = sales_data['sales'].mean() + + insights.append(f"📈 Sales peaked in {peak_month} with ${total_sales:,.0f} total revenue") + insights.append(f"💰 Average daily sales: ${avg_daily:,.0f}") + + # Growth trend + if len(sales_data) > 30: + recent_avg = sales_data.tail(30)['sales'].mean() + earlier_avg = sales_data.head(30)['sales'].mean() + growth = ((recent_avg - earlier_avg) / earlier_avg) * 100 + insights.append(f"📊 Sales trend: {'+' if growth > 0 else ''}{growth:.1f}% change over time") + + if not customers_data.empty: + # Customer insights + avg_satisfaction = customers_data['satisfaction'].mean() + high_satisfaction = len(customers_data[customers_data['satisfaction'] >= 8]) + insights.append(f"😊 Customer satisfaction: {avg_satisfaction:.1f}/10 ({high_satisfaction} highly satisfied customers)") + + # Demographics + avg_age = customers_data['age'].mean() + insights.append(f"👥 Average customer age: {avg_age:.0f} years") + + if not products_data.empty: + # Product insights + top_category = products_data.groupby('category')['revenue'].sum().idxmax() + avg_rating = products_data['rating'].mean() + insights.append(f"🏆 Top category: {top_category} (avg rating: {avg_rating:.1f}/5)") + + # Pricing insights + avg_price = products_data['price'].mean() + insights.append(f"💵 Average product price: ${avg_price:.0f}") + + return insights[:5] # Return top 5 insights + +def create_overview_tab(filtered_data): + """Create the overview dashboard with key metrics and AI insights""" + + sales_data = pd.DataFrame(filtered_data['sales']) + products_data = pd.DataFrame(filtered_data['products']) + customers_data = pd.DataFrame(filtered_data['customers']) + + # Calculate key metrics + total_sales = sales_data['sales'].sum() if not sales_data.empty else 0 + avg_daily_sales = sales_data['sales'].mean() if not sales_data.empty else 0 + total_customers = len(customers_data) + avg_satisfaction = customers_data['satisfaction'].mean() if not customers_data.empty else 0 + total_products = len(products_data) + top_product = products_data.loc[products_data['revenue'].idxmax(), 'name'] if not products_data.empty else "N/A" + + # AI-powered insights + ai_insights = generate_ai_insights(sales_data, products_data, customers_data) + + # Sales trend chart + if not sales_data.empty: + sales_trend = px.line(sales_data, x='date', y='sales', + title="Sales Trend Over Time", + labels={'sales': 'Sales ($)', 'date': 'Date'}) + sales_trend.update_layout(height=400, showlegend=False) + else: + sales_trend = go.Figure() + sales_trend.add_annotation(text="No data available for selected filters", + xref="paper", yref="paper", x=0.5, y=0.5, showarrow=False) + sales_trend.update_layout(height=400, title="Sales Trend Over Time") + + # Customer satisfaction distribution + if not customers_data.empty: + satisfaction_hist = px.histogram(customers_data, x='satisfaction', nbins=20, + title="Customer Satisfaction Distribution", + labels={'satisfaction': 'Satisfaction Score', 'count': 'Number of Customers'}) + satisfaction_hist.update_layout(height=400) + else: + satisfaction_hist = go.Figure() + satisfaction_hist.add_annotation(text="No data available", + xref="paper", yref="paper", x=0.5, y=0.5, showarrow=False) + satisfaction_hist.update_layout(height=400, title="Customer Satisfaction Distribution") + + # Product revenue pie chart + if not products_data.empty: + category_revenue = products_data.groupby('category')['revenue'].sum().reset_index() + revenue_pie = px.pie(category_revenue, values='revenue', names='category', + title="Revenue by Product Category") + revenue_pie.update_layout(height=400) + else: + revenue_pie = go.Figure() + revenue_pie.add_annotation(text="No data available", + xref="paper", yref="paper", x=0.5, y=0.5, showarrow=False) + revenue_pie.update_layout(height=400, title="Revenue by Product Category") + + return html.Div([ + # AI Insights Panel + html.Div([ + html.H4("🤖 AI-Powered Insights"), + html.Ul([ + html.Li(insight) for insight in ai_insights + ]) + ], className="ai-insights"), + + html.Div([ + html.Div([dcc.Graph(figure=sales_trend)], style={'width': '50%', 'display': 'inline-block'}), + html.Div([dcc.Graph(figure=satisfaction_hist)], style={'width': '50%', 'display': 'inline-block'}) + ]), + html.Div([ + html.Div([dcc.Graph(figure=revenue_pie)], style={'width': '50%', 'display': 'inline-block'}), + html.Div([ + html.H4("🎯 Key Metrics Summary"), + html.Ul([ + html.Li(f"Total Revenue: ${total_sales:,.0f}"), + html.Li(f"Average Daily Sales: ${avg_daily_sales:,.0f}"), + html.Li(f"Total Customers: {total_customers:,}"), + html.Li(f"Average Satisfaction: {avg_satisfaction:.1f}/10"), + html.Li(f"Active Products: {total_products}"), + html.Li(f"Top Product: {top_product}") + ]) + ], style={'width': '50%', 'display': 'inline-block', 'padding': '20px'}) + ]) + ], className="fade-in") + +def create_sales_tab(filtered_data): + """Create the sales analytics tab with drilldown functionality""" + + sales_data = pd.DataFrame(filtered_data['sales']) + + if sales_data.empty: + return html.Div("No sales data available for selected filters", + style={'textAlign': 'center', 'padding': '50px'}) + + # Sales by month + monthly_sales = sales_data.groupby('month')['sales'].sum().reset_index() + month_order = ['January', 'February', 'March', 'April', 'May', 'June', + 'July', 'August', 'September', 'October', 'November', 'December'] + monthly_sales['month'] = pd.Categorical(monthly_sales['month'], categories=month_order, ordered=True) + monthly_sales = monthly_sales.sort_values('month') + + monthly_bar = px.bar(monthly_sales, x='month', y='sales', + title="Monthly Sales Performance (Click to drill down)", + labels={'sales': 'Sales ($)', 'month': 'Month'}) + monthly_bar.update_layout(height=400, xaxis_tickangle=-45) + + # Sales by day of week + daily_sales = sales_data.groupby('day_of_week')['sales'].mean().reset_index() + day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'] + daily_sales['day_of_week'] = pd.Categorical(daily_sales['day_of_week'], categories=day_order, ordered=True) + daily_sales = daily_sales.sort_values('day_of_week') + + daily_bar = px.bar(daily_sales, x='day_of_week', y='sales', + title="Average Sales by Day of Week", + labels={'sales': 'Avg Sales ($)', 'day_of_week': 'Day'}) + daily_bar.update_layout(height=400, xaxis_tickangle=-45) + + # Sales trend with moving average + sales_data['ma_7'] = sales_data['sales'].rolling(window=7).mean() + sales_data['ma_30'] = sales_data['sales'].rolling(window=30).mean() + + trend_fig = go.Figure() + trend_fig.add_trace(go.Scatter(x=sales_data['date'], y=sales_data['sales'], + name='Daily Sales', opacity=0.6, line=dict(color='blue'))) + trend_fig.add_trace(go.Scatter(x=sales_data['date'], y=sales_data['ma_7'], + name='7-Day Moving Average', line=dict(color='red'))) + trend_fig.add_trace(go.Scatter(x=sales_data['date'], y=sales_data['ma_30'], + name='30-Day Moving Average', line=dict(color='green'))) + trend_fig.update_layout(title="Sales Trend with Moving Averages", + xaxis_title="Date", yaxis_title="Sales ($)", height=400) + + # Quarterly comparison + quarterly_sales = sales_data.groupby('quarter')['sales'].sum().reset_index() + quarterly_pie = px.pie(quarterly_sales, values='sales', names='quarter', + title="Sales Distribution by Quarter") + quarterly_pie.update_layout(height=400) + + return html.Div([ + html.Div([ + html.Div([dcc.Graph(figure=monthly_bar)], style={'width': '50%', 'display': 'inline-block'}), + html.Div([dcc.Graph(figure=daily_bar)], style={'width': '50%', 'display': 'inline-block'}) + ]), + html.Div([ + html.Div([dcc.Graph(figure=trend_fig)], style={'width': '70%', 'display': 'inline-block'}), + html.Div([dcc.Graph(figure=quarterly_pie)], style={'width': '30%', 'display': 'inline-block'}) + ]) + ], className="fade-in") + +def create_customers_tab(): + """Create the customer insights tab""" + + # Age distribution + age_hist = px.histogram(customer_df, x='age', nbins=20, + title="Customer Age Distribution", + labels={'age': 'Age', 'count': 'Number of Customers'}) + age_hist.update_layout(height=400) + + # Income vs Satisfaction scatter + income_satisfaction = px.scatter(customer_df, x='income', y='satisfaction', + color='gender', size='purchase_frequency', + title="Income vs Satisfaction by Gender", + labels={'income': 'Income ($)', 'satisfaction': 'Satisfaction Score'}) + income_satisfaction.update_layout(height=400) + + # Gender distribution + gender_counts = customer_df['gender'].value_counts().reset_index() + gender_counts.columns = ['gender', 'count'] + gender_pie = px.pie(gender_counts, values='count', names='gender', + title="Customer Gender Distribution") + gender_pie.update_layout(height=400) + + # Purchase frequency by age group + customer_df['age_group'] = pd.cut(customer_df['age'], + bins=[0, 25, 35, 45, 55, 100], + labels=['18-25', '26-35', '36-45', '46-55', '55+']) + age_group_purchases = customer_df.groupby('age_group')['purchase_frequency'].mean().reset_index() + + age_group_bar = px.bar(age_group_purchases, x='age_group', y='purchase_frequency', + title="Average Purchase Frequency by Age Group", + labels={'purchase_frequency': 'Avg Purchases', 'age_group': 'Age Group'}) + age_group_bar.update_layout(height=400) + + return html.Div([ + html.Div([ + html.Div([dcc.Graph(figure=age_hist)], style={'width': '50%', 'display': 'inline-block'}), + html.Div([dcc.Graph(figure=gender_pie)], style={'width': '50%', 'display': 'inline-block'}) + ]), + html.Div([ + html.Div([dcc.Graph(figure=income_satisfaction)], style={'width': '60%', 'display': 'inline-block'}), + html.Div([dcc.Graph(figure=age_group_bar)], style={'width': '40%', 'display': 'inline-block'}) + ]) + ]) + +def create_products_tab(): + """Create the product performance tab""" + + # Price vs Sales Volume scatter + price_volume = px.scatter(product_df, x='price', y='sales_volume', + color='category', size='revenue', + title="Price vs Sales Volume by Category", + labels={'price': 'Price ($)', 'sales_volume': 'Sales Volume'}) + price_volume.update_layout(height=400) + + # Revenue by category + category_revenue = product_df.groupby('category')['revenue'].sum().reset_index() + category_bar = px.bar(category_revenue, x='category', y='revenue', + title="Total Revenue by Category", + labels={'revenue': 'Revenue ($)', 'category': 'Category'}) + category_bar.update_layout(height=400, xaxis_tickangle=-45) + + # Rating distribution + rating_hist = px.histogram(product_df, x='rating', nbins=10, + title="Product Rating Distribution", + labels={'rating': 'Rating', 'count': 'Number of Products'}) + rating_hist.update_layout(height=400) + + # Top 10 products by revenue + top_products = product_df.nlargest(10, 'revenue') + top_products_bar = px.bar(top_products, x='name', y='revenue', + title="Top 10 Products by Revenue", + labels={'revenue': 'Revenue ($)', 'name': 'Product'}) + top_products_bar.update_layout(height=400, xaxis_tickangle=-45) + + return html.Div([ + html.Div([ + html.Div([dcc.Graph(figure=price_volume)], style={'width': '50%', 'display': 'inline-block'}), + html.Div([dcc.Graph(figure=category_bar)], style={'width': '50%', 'display': 'inline-block'}) + ]), + html.Div([ + html.Div([dcc.Graph(figure=rating_hist)], style={'width': '50%', 'display': 'inline-block'}), + html.Div([dcc.Graph(figure=top_products_bar)], style={'width': '50%', 'display': 'inline-block'}) + ]) + ]) + +def create_geographic_tab(): + """Create the geographic analysis tab""" + + # World map visualization + world_map = px.choropleth(geo_df, + locations='country', + locationmode='country names', + color='sales', + title="Sales by Country", + color_continuous_scale='Viridis') + world_map.update_layout(height=500) + + # Sales by region + region_sales = geo_df.groupby('region')['sales'].sum().reset_index() + region_pie = px.pie(region_sales, values='sales', names='region', + title="Sales Distribution by Region") + region_pie.update_layout(height=400) + + # GDP vs Sales scatter + gdp_sales = px.scatter(geo_df, x='gdp_per_capita', y='sales', + size='population', color='region', + title="GDP per Capita vs Sales", + labels={'gdp_per_capita': 'GDP per Capita ($)', 'sales': 'Sales ($)'}) + gdp_sales.update_layout(height=400) + + # Top countries bar chart + top_countries = geo_df.nlargest(8, 'sales') + countries_bar = px.bar(top_countries, x='country', y='sales', + title="Top Countries by Sales", + labels={'sales': 'Sales ($)', 'country': 'Country'}) + countries_bar.update_layout(height=400, xaxis_tickangle=-45) + + return html.Div([ + html.Div([dcc.Graph(figure=world_map)], style={'width': '100%'}), + html.Div([ + html.Div([dcc.Graph(figure=region_pie)], style={'width': '33%', 'display': 'inline-block'}), + html.Div([dcc.Graph(figure=gdp_sales)], style={'width': '33%', 'display': 'inline-block'}), + html.Div([dcc.Graph(figure=countries_bar)], style={'width': '33%', 'display': 'inline-block'}) + ]) + ]) + +def create_predictive_tab(): + """Create the predictive analytics tab""" + + # Simple linear trend prediction + sales_df['day_number'] = (sales_df['date'] - sales_df['date'].min()).dt.days + z = np.polyfit(sales_df['day_number'], sales_df['sales'], 1) + p = np.poly1d(z) + + # Generate future dates + future_days = np.arange(sales_df['day_number'].max() + 1, sales_df['day_number'].max() + 31) + future_dates = [sales_df['date'].max() + timedelta(days=int(d)) for d in future_days] + future_sales = p(future_days) + + # Create prediction plot + pred_fig = go.Figure() + pred_fig.add_trace(go.Scatter(x=sales_df['date'], y=sales_df['sales'], + name='Historical Sales', mode='lines', line=dict(color='blue'))) + pred_fig.add_trace(go.Scatter(x=future_dates, y=future_sales, + name='Predicted Sales', mode='lines', line=dict(color='red', dash='dash'))) + pred_fig.update_layout(title="Sales Prediction (Next 30 Days)", + xaxis_title="Date", yaxis_title="Sales ($)", height=400) + + # Customer lifetime value prediction + customer_df['predicted_ltv'] = customer_df['income'] * 0.15 + customer_df['satisfaction'] * 100 + ltv_hist = px.histogram(customer_df, x='predicted_ltv', nbins=20, + title="Predicted Customer Lifetime Value Distribution", + labels={'predicted_ltv': 'Predicted LTV ($)', 'count': 'Number of Customers'}) + ltv_hist.update_layout(height=400) + + # Seasonal decomposition (simplified) + monthly_avg = sales_df.groupby('month')['sales'].mean() + seasonal_fig = px.bar(x=monthly_avg.index, y=monthly_avg.values, + title="Seasonal Sales Pattern", + labels={'x': 'Month', 'y': 'Average Sales ($)'}) + seasonal_fig.update_layout(height=400) + + # Correlation heatmap + numeric_cols = customer_df.select_dtypes(include=[np.number]).columns + corr_matrix = customer_df[numeric_cols].corr() + + corr_heatmap = px.imshow(corr_matrix, + title="Customer Data Correlation Matrix", + color_continuous_scale='RdBu_r') + corr_heatmap.update_layout(height=400) + + return html.Div([ + html.Div([ + html.Div([dcc.Graph(figure=pred_fig)], style={'width': '50%', 'display': 'inline-block'}), + html.Div([dcc.Graph(figure=ltv_hist)], style={'width': '50%', 'display': 'inline-block'}) + ]), + html.Div([ + html.Div([dcc.Graph(figure=seasonal_fig)], style={'width': '50%', 'display': 'inline-block'}), + html.Div([dcc.Graph(figure=corr_heatmap)], style={'width': '50%', 'display': 'inline-block'}) + ]), + html.Div([ + html.H4("🔮 Predictive Insights"), + html.Ul([ + html.Li(f"Predicted next month sales: ${p(sales_df['day_number'].max() + 30):,.0f}"), + html.Li(f"Average predicted customer LTV: ${customer_df['predicted_ltv'].mean():,.0f}"), + html.Li(f"Peak seasonal month: {monthly_avg.idxmax()}"), + html.Li(f"Strongest correlation: Income vs Lifetime Value ({corr_matrix.loc['income', 'lifetime_value']:.2f})") + ]) + ], style={'marginTop': '20px', 'padding': '20px', 'backgroundColor': '#f8f9fa', 'borderRadius': '10px'}) + ]) + +# ============================================================================= +# RUN THE APPLICATION +# ============================================================================= + +if __name__ == '__main__': + print("🚀 Starting Interactive Data Visualization Dashboard...") + print("📊 Dashboard will be available at: http://127.0.0.1:8050") + print("🎨 Features:") + print(" • Multi-tab interface with 6 different visualization categories") + print(" • Interactive charts and real-time data updates") + print(" • Responsive design with custom styling") + print(" • Educational tooltips and insights") + print(" • Predictive analytics and forecasting") + print("\n💡 Use Ctrl+C to stop the server") + + app.run(debug=False, host='127.0.0.1', port=8050) diff --git a/quantumanalyser.py b/quantumanalyser.py new file mode 100644 index 0000000..92d6458 --- /dev/null +++ b/quantumanalyser.py @@ -0,0 +1,463 @@ + +import matplotlib.pyplot as plt +import seaborn as sns +import pandas as pd +import numpy as np +import plotly.express as px +import plotly.graph_objects as go +from plotly.subplots import make_subplots +import plotly.figure_factory as ff +from sklearn.cluster import KMeans +from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler +import networkx as nx +from matplotlib.animation import FuncAnimation +from matplotlib.patches import Circle, Wedge, Rectangle +import matplotlib.patches as mpatches +from mpl_toolkits.mplot3d import Axes3D +import subprocess +import os +import sys +import warnings +warnings.filterwarnings('ignore') + +plt.style.use('dark_background') +sns.set_palette("viridis") + +def create_sample_data(): + np.random.seed(42) + + time_points = pd.date_range('2023-01-01', periods=365, freq='D') + crypto_prices = 50000 + np.cumsum(np.random.normal(0, 1000, 365)) + social_sentiment = np.random.normal(0.5, 0.2, 365) + trading_volume = np.random.lognormal(10, 1, 365) + + crypto_data = pd.DataFrame({ + 'Date': time_points, + 'Price': crypto_prices, + 'Sentiment': social_sentiment, + 'Volume': trading_volume, + 'Volatility': np.abs(np.random.normal(0, 0.05, 365)) + }) + + cities = ['New York', 'London', 'Tokyo', 'Paris', 'Sydney', 'Dubai', 'Singapore', 'Toronto'] + city_data = pd.DataFrame({ + 'City': cities, + 'Population': np.random.randint(500000, 15000000, 8), + 'GDP': np.random.uniform(50, 2000, 8), + 'Tech_Score': np.random.uniform(0, 100, 8), + 'Innovation_Index': np.random.uniform(0, 10, 8), + 'Latitude': [40.7128, 51.5074, 35.6762, 48.8566, -33.8688, 25.2048, 1.3521, 43.6532], + 'Longitude': [-74.0060, -0.1278, 139.6503, 2.3522, 151.2093, 55.2708, 103.8198, -79.3832] + }) + + n_points = 200 + cluster_data = pd.DataFrame({ + 'X': np.random.normal(0, 1, n_points), + 'Y': np.random.normal(0, 1, n_points), + 'Z': np.random.normal(0, 1, n_points), + 'Cluster': np.random.choice(['Tech', 'Finance', 'Healthcare', 'Education'], n_points), + 'Size': np.random.uniform(10, 100, n_points), + 'Growth': np.random.uniform(-0.2, 0.5, n_points) + }) + + network_nodes = 20 + network_data = { + 'nodes': [{'id': i, 'label': f'Node_{i}', 'value': np.random.uniform(1, 10)} for i in range(network_nodes)], + 'edges': [(i, j) for i in range(network_nodes) for j in range(i+1, network_nodes) if np.random.random() < 0.3] + } + + return crypto_data, city_data, cluster_data, network_data + +def setup_git_branch(branch_name="quantum-data-viz"): + """Setup git branch for the quantum data visualization project.""" + try: + print(f"🔧 Setting up git branch: {branch_name}") + + # Check if git is initialized + if not os.path.exists('.git'): + print(" 📁 Initializing git repository...") + subprocess.run(['git', 'init'], check=True) + + # Create and checkout new branch + print(f" 🌿 Creating branch: {branch_name}") + subprocess.run(['git', 'checkout', '-b', branch_name], check=True) + + # Add all files + print(" 📦 Adding files to staging...") + subprocess.run(['git', 'add', '.'], check=True) + + # Create initial commit + commit_message = f"🚀 Initial commit: {branch_name} - Next-Gen Data Intelligence Suite" + print(f" 💾 Creating commit: {commit_message}") + subprocess.run(['git', 'commit', '-m', commit_message], check=True) + + print(f" ✅ Git branch '{branch_name}' created successfully!") + print(f" 📋 Branch status:") + subprocess.run(['git', 'status'], check=True) + + return True + + except subprocess.CalledProcessError as e: + print(f" ❌ Git error: {e}") + return False + except FileNotFoundError: + print(" ❌ Git not found. Please install Git first.") + return False + except Exception as e: + print(f" ❌ Unexpected error: {e}") + return False + +def create_crypto_radar_chart(df): + fig = go.Figure() + + for i in range(0, len(df), 30): + subset = df.iloc[i:i+30] + avg_sentiment = subset['Sentiment'].mean() + avg_volatility = subset['Volatility'].mean() + avg_volume = np.log(subset['Volume']).mean() + price_change = (subset['Price'].iloc[-1] - subset['Price'].iloc[0]) / subset['Price'].iloc[0] + + fig.add_trace(go.Scatterpolar( + r=[avg_sentiment, avg_volatility*100, avg_volume, abs(price_change)*100, 1-avg_volatility], + theta=['Sentiment', 'Volatility', 'Volume', 'Price Change', 'Stability'], + fill='toself', + name=f'Period {i//30 + 1}', + opacity=0.6 + )) + + fig.update_layout( + polar=dict( + radialaxis=dict(visible=True, range=[0, 1]) + ), + title="Crypto Market Radar Analysis", + font=dict(size=12, color='white'), + paper_bgcolor='black', + plot_bgcolor='black' + ) + return fig + +def create_3d_city_bubble_chart(df): + fig = go.Figure() + + fig.add_trace(go.Scatter3d( + x=df['Longitude'], + y=df['Latitude'], + z=df['Tech_Score'], + mode='markers', + marker=dict( + size=df['Population']/100000, + color=df['Innovation_Index'], + colorscale='Viridis', + opacity=0.8, + line=dict(width=2, color='white') + ), + text=df['City'], + hovertemplate='%{text}
' + + 'Population: %{marker.size:,.0f}
' + + 'Tech Score: %{z:.1f}
' + + 'Innovation: %{marker.color:.1f}' + )) + + fig.update_layout( + title="Global Tech Cities 3D Bubble Map", + scene=dict( + xaxis_title="Longitude", + yaxis_title="Latitude", + zaxis_title="Tech Score", + bgcolor='black' + ), + font=dict(color='white'), + paper_bgcolor='black' + ) + return fig + +def create_ai_cluster_analysis(df): + fig = plt.figure(figsize=(15, 10)) + + scaler = StandardScaler() + features = scaler.fit_transform(df[['X', 'Y', 'Z']]) + + kmeans = KMeans(n_clusters=4, random_state=42) + clusters = kmeans.fit_predict(features) + + ax1 = fig.add_subplot(221, projection='3d') + scatter = ax1.scatter(df['X'], df['Y'], df['Z'], c=clusters, cmap='viridis', s=df['Size'], alpha=0.7) + ax1.set_title('AI-Powered 3D Clustering', color='white', fontsize=14, fontweight='bold') + ax1.set_xlabel('Feature X', color='white') + ax1.set_ylabel('Feature Y', color='white') + ax1.set_zlabel('Feature Z', color='white') + + ax2 = fig.add_subplot(222) + pca = PCA(n_components=2) + pca_features = pca.fit_transform(features) + scatter2 = ax2.scatter(pca_features[:, 0], pca_features[:, 1], c=clusters, cmap='viridis', s=df['Size'], alpha=0.7) + ax2.set_title('PCA Dimensionality Reduction', color='white', fontsize=14, fontweight='bold') + ax2.set_xlabel(f'PC1 ({pca.explained_variance_ratio_[0]:.1%} variance)', color='white') + ax2.set_ylabel(f'PC2 ({pca.explained_variance_ratio_[1]:.1%} variance)', color='white') + + ax3 = fig.add_subplot(223) + growth_by_cluster = df.groupby(clusters)['Growth'].mean() + bars = ax3.bar(range(len(growth_by_cluster)), growth_by_cluster, color=plt.cm.viridis(np.linspace(0, 1, len(growth_by_cluster)))) + ax3.set_title('Average Growth by Cluster', color='white', fontsize=14, fontweight='bold') + ax3.set_xlabel('Cluster', color='white') + ax3.set_ylabel('Growth Rate', color='white') + ax3.set_xticks(range(len(growth_by_cluster))) + + ax4 = fig.add_subplot(224) + cluster_sizes = np.bincount(clusters) + wedges, texts, autotexts = ax4.pie(cluster_sizes, labels=[f'Cluster {i}' for i in range(len(cluster_sizes))], + autopct='%1.1f%%', colors=plt.cm.viridis(np.linspace(0, 1, len(cluster_sizes)))) + ax4.set_title('Cluster Distribution', color='white', fontsize=14, fontweight='bold') + + plt.tight_layout() + return fig + +def create_network_visualization(network_data): + G = nx.Graph() + + for node in network_data['nodes']: + G.add_node(node['id'], label=node['label'], value=node['value']) + + for edge in network_data['edges']: + G.add_edge(edge[0], edge[1]) + + pos = nx.spring_layout(G, k=3, iterations=50) + + fig, ax = plt.subplots(figsize=(12, 10)) + + node_sizes = [network_data['nodes'][i]['value'] * 100 for i in range(len(network_data['nodes']))] + node_colors = [network_data['nodes'][i]['value'] for i in range(len(network_data['nodes']))] + + nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color=node_colors, + cmap='plasma', alpha=0.8, ax=ax) + + nx.draw_networkx_edges(G, pos, alpha=0.5, edge_color='white', width=1, ax=ax) + + nx.draw_networkx_labels(G, pos, {i: f'Node {i}' for i in range(len(network_data['nodes']))}, + font_size=8, font_color='white', ax=ax) + + ax.set_title('Interactive Network Graph', color='white', fontsize=16, fontweight='bold') + ax.set_facecolor('black') + + plt.tight_layout() + return fig + +def create_animated_crypto_candlestick(df): + fig = go.Figure() + + fig.add_trace(go.Candlestick( + x=df['Date'], + open=df['Price'] * (1 - df['Volatility']), + high=df['Price'] * (1 + df['Volatility']), + low=df['Price'] * (1 - df['Volatility']), + close=df['Price'], + name="Crypto Price", + increasing_line_color='#00ff88', + decreasing_line_color='#ff4444' + )) + + fig.add_trace(go.Scatter( + x=df['Date'], + y=df['Price'], + mode='lines', + name='Price Trend', + line=dict(color='white', width=2), + opacity=0.8 + )) + + fig.update_layout( + title="Real-time Crypto Candlestick Chart", + xaxis_title="Date", + yaxis_title="Price ($)", + font=dict(color='white'), + paper_bgcolor='black', + plot_bgcolor='black', + xaxis=dict( + rangeslider=dict(visible=False), + type='date' + ), + yaxis=dict(fixedrange=False) + ) + + return fig + +def create_quantum_style_visualization(df): + fig = plt.figure(figsize=(16, 12)) + + ax1 = fig.add_subplot(221) + ax1.scatter(df['X'], df['Y'], c=df['Z'], s=df['Size']*10, + cmap='plasma', alpha=0.7, edgecolors='white', linewidth=0.5) + ax1.set_title('Quantum Scatter Field', color='white', fontsize=14, fontweight='bold') + ax1.set_facecolor('black') + ax1.grid(True, alpha=0.3, color='cyan') + + ax2 = fig.add_subplot(222, projection='3d') + ax2.scatter(df['X'], df['Y'], df['Z'], c=df['Growth'], s=df['Size']*5, + cmap='viridis', alpha=0.8) + ax2.set_title('3D Quantum Field', color='white', fontsize=14, fontweight='bold') + ax2.set_facecolor('black') + + ax3 = fig.add_subplot(223) + hexbin = ax3.hexbin(df['X'], df['Y'], C=df['Z'], gridsize=20, cmap='plasma', alpha=0.8) + ax3.set_title('Hexagonal Density Map', color='white', fontsize=14, fontweight='bold') + ax3.set_facecolor('black') + plt.colorbar(hexbin, ax=ax3, label='Z Value') + + ax4 = fig.add_subplot(224) + contour = ax4.tricontourf(df['X'], df['Y'], df['Z'], levels=20, cmap='inferno', alpha=0.8) + ax4.set_title('Contour Field Visualization', color='white', fontsize=14, fontweight='bold') + ax4.set_facecolor('black') + plt.colorbar(contour, ax=ax4, label='Field Strength') + + plt.tight_layout() + return fig + +def create_holistic_dashboard(crypto_df, city_df, cluster_df, network_data): + fig = make_subplots( + rows=3, cols=2, + subplot_titles=('Crypto Price Evolution', 'Global Tech Cities', + 'AI Cluster Analysis', 'Network Connections', + 'Market Volatility', 'Innovation Heatmap'), + specs=[[{"type": "scatter"}, {"type": "scatter3d"}], + [{"type": "scatter"}, {"type": "scatter"}], + [{"type": "scatter"}, {"type": "heatmap"}]] + ) + + fig.add_trace( + go.Scatter(x=crypto_df['Date'], y=crypto_df['Price'], + name='Crypto Price', line=dict(color='#00ff88', width=3), + hovertemplate='Date: %{x}
Price: $%{y:,.0f}'), + row=1, col=1 + ) + + fig.add_trace( + go.Scatter3d(x=city_df['Longitude'], y=city_df['Latitude'], z=city_df['Tech_Score'], + mode='markers', name='Cities', + marker=dict(size=city_df['Population']/200000, color=city_df['Innovation_Index'], + colorscale='Viridis', opacity=0.8), + text=city_df['City'], hovertemplate='%{text}
Tech Score: %{z}'), + row=1, col=2 + ) + + fig.add_trace( + go.Scatter(x=cluster_df['X'], y=cluster_df['Y'], + mode='markers', name='Clusters', + marker=dict(size=cluster_df['Size'], color=cluster_df['Growth'], + colorscale='Plasma', opacity=0.7), + hovertemplate='Growth: %{marker.color:.2f}'), + row=2, col=1 + ) + + G = nx.Graph() + for node in network_data['nodes']: + G.add_node(node['id']) + for edge in network_data['edges']: + G.add_edge(edge[0], edge[1]) + pos = nx.spring_layout(G) + + edge_x, edge_y = [], [] + for edge in G.edges(): + x0, y0 = pos[edge[0]] + x1, y1 = pos[edge[1]] + edge_x.extend([x0, x1, None]) + edge_y.extend([y0, y1, None]) + + fig.add_trace( + go.Scatter(x=edge_x, y=edge_y, mode='lines', name='Network', + line=dict(color='white', width=1), opacity=0.5), + row=2, col=2 + ) + + fig.add_trace( + go.Scatter(x=crypto_df['Date'], y=crypto_df['Volatility'], + mode='lines+markers', name='Volatility', + line=dict(color='#ff4444', width=2), + hovertemplate='Volatility: %{y:.3f}'), + row=3, col=1 + ) + + heatmap_data = np.random.rand(10, 10) + fig.add_trace( + go.Heatmap(z=heatmap_data, colorscale='Hot', name='Innovation'), + row=3, col=2 + ) + + fig.update_layout( + title_text="🚀 Next-Gen Data Intelligence Dashboard", + title_x=0.5, + showlegend=False, + height=1200, + font=dict(color='white'), + paper_bgcolor='black', + plot_bgcolor='black' + ) + + return fig + +def main(): + print("🚀 Initializing Next-Gen Data Intelligence Suite...") + + # Setup git branch + print("\n🔧 Git Configuration:") + git_success = setup_git_branch("quantum-data-viz") + + if git_success: + print(" ✅ Git setup completed successfully!") + else: + print(" ⚠️ Git setup failed, continuing with visualizations...") + + print("\n📊 Generating advanced datasets...") + crypto_df, city_df, cluster_df, network_data = create_sample_data() + + print("📊 Generated advanced datasets:") + print(f"Crypto data: {crypto_df.shape[0]} days of market data") + print(f"Global cities: {city_df.shape[0]} tech hubs analyzed") + print(f"AI clusters: {cluster_df.shape[0]} data points for ML analysis") + print(f"Network nodes: {len(network_data['nodes'])} interconnected entities") + + print("\n🎨 Creating cutting-edge visualizations...") + + print(" 🌟 Crypto Market Radar Analysis...") + radar_fig = create_crypto_radar_chart(crypto_df) + radar_fig.show() + + print(" 🌍 3D Global Tech Cities Map...") + city_fig = create_3d_city_bubble_chart(city_df) + city_fig.show() + + print(" 🤖 AI-Powered Cluster Analysis...") + cluster_fig = create_ai_cluster_analysis(cluster_df) + cluster_fig.show() + + print(" 🕸️ Interactive Network Visualization...") + network_fig = create_network_visualization(network_data) + network_fig.show() + + print(" 📈 Real-time Crypto Candlestick...") + candlestick_fig = create_animated_crypto_candlestick(crypto_df) + candlestick_fig.show() + + print(" ⚛️ Quantum-Style Field Visualization...") + quantum_fig = create_quantum_style_visualization(cluster_df) + quantum_fig.show() + + print("\n🎯 Creating holistic intelligence dashboard...") + dashboard = create_holistic_dashboard(crypto_df, city_df, cluster_df, network_data) + dashboard.show() + + print("\n✅ Next-Gen Data Intelligence Suite Complete!") + print("\n🚀 Revolutionary Features:") + print(" • AI-powered clustering with PCA dimensionality reduction") + print(" • 3D interactive global tech city mapping") + print(" • Real-time crypto market radar analysis") + print(" • Network graph theory visualization") + print(" • Quantum-inspired field visualizations") + print(" • Multi-dimensional data intelligence dashboard") + print(" • Dark theme with neon aesthetics") + print(" • Machine learning integration") + print(" • Advanced statistical analysis") + print(" • Interactive hover effects and animations") + +if __name__ == "__main__": + main() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..8c32985 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +dash +plotly +pandas +numpy +scikit-learn +networkx diff --git a/tools_check_balance.py b/tools_check_balance.py new file mode 100644 index 0000000..a3516a6 --- /dev/null +++ b/tools_check_balance.py @@ -0,0 +1,22 @@ +import pathlib +p=pathlib.Path(r'c:\Users\abhra\Data-Visualization\dreamjournal.py') +s=p.read_text(encoding='utf-8').splitlines() +pb=0;qb=0 +maxpb=(0,0);maxqb=(0,0) +for i,l in enumerate(s,1): + pb+=l.count('(')-l.count(')') + qb+=l.count('[')-l.count(']') + if pb>maxpb[0]: maxpb=(pb,i) + if qb>maxqb[0]: maxqb=(qb,i) +print('parens max',maxpb) +print('brackets max',maxqb) +print('\nline for parens max:') +print(maxpb[1], s[maxpb[1]-1]) +print('\nline for brackets max:') +print(maxqb[1], s[maxqb[1]-1]) +print('\ncontext parens:') +for ln in range(maxpb[1]-3,maxpb[1]+3): + if 1<=ln<=len(s): print(ln, s[ln-1]) +print('\ncontext brackets:') +for ln in range(maxqb[1]-3,maxqb[1]+3): + if 1<=ln<=len(s): print(ln, s[ln-1])