diff --git "a/A5/A5_Classification_Ensemble.ipynb" "b/A5/A5_Classification_Ensemble.ipynb" deleted file mode 100644--- "a/A5/A5_Classification_Ensemble.ipynb" +++ /dev/null @@ -1,1014 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pickle \n", - "import warnings\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from pathlib import Path\n", - "from scipy import stats\n", - "\n", - "from sklearn.model_selection import (\n", - " train_test_split, StratifiedKFold, cross_validate\n", - ")\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.metrics import (\n", - " accuracy_score, precision_score, recall_score, f1_score,\n", - " classification_report, confusion_matrix\n", - ")\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.naive_bayes import GaussianNB \n", - "from sklearn.ensemble import (\n", - " RandomForestClassifier,\n", - " VotingClassifier,\n", - " BaggingClassifier,\n", - " StackingClassifier,\n", - ")\n", - "import xgboost as xgb\n", - "import lightgbm as lgb\n", - "warnings.filterwarnings('ignore')\n", - "np.random.seed(42)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "REPO_ROOT = os.path.abspath(os.path.join(os.getcwd(), '..'))\n", - "DATA_DIR = os.path.join(REPO_ROOT, 'Datasets_all')\n", - "OUT_DIR = Path('models')\n", - "OUT_DIR.mkdir(exist_ok=True)\n", - "\n", - "RANDOM_STATE = 42\n", - "N_SPLITS = 5\n", - "CHAMPION_F1 = 0.6110 # Score from A4 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Load Data" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Movement features shape: (2094, 43)\n", - "Weak link scores shape: (2096, 17)\n" - ] - } - ], - "source": [ - "movement_features_df = pd.read_csv(os.path.join(DATA_DIR, 'aimoscores.csv'))\n", - "weaklink_scores_df = pd.read_csv(os.path.join(DATA_DIR, 'scores_and_weaklink.csv'))\n", - "\n", - "print('Movement features shape:', movement_features_df.shape)\n", - "print('Weak link scores shape:', weaklink_scores_df.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Remove Duplicate Columns " - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape after duplicate removal: (2094, 38)\n" - ] - } - ], - "source": [ - "DUPLICATE_NASM_COLS = [\n", - " 'No_1_NASM_Deviation', \n", - " 'No_2_NASM_Deviation', \n", - " 'No_3_NASM_Deviation', \n", - " 'No_4_NASM_Deviation', \n", - " 'No_5_NASM_Deviation', \n", - "]\n", - "\n", - "movement_features_df = movement_features_df.drop(columns=DUPLICATE_NASM_COLS)\n", - "print('Shape after duplicate removal:', movement_features_df.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Build Classification Target (WeakestLink)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Weakest Link class distribution:\n", - "WeakestLink\n", - "LeftArmFallForward 616\n", - "RightArmFallForward 458\n", - "RightKneeMovesOutward 274\n", - "RightShoulderElevation 245\n", - "ExcessiveForwardLean 128\n", - "ForwardHead 109\n", - "LeftAsymmetricalWeightShift 80\n", - "LeftShoulderElevation 55\n", - "LeftKneeMovesOutward 54\n", - "RightKneeMovesInward 45\n", - "RightAsymmetricalWeightShift 20\n", - "LeftHeelRises 7\n", - "LeftKneeMovesInward 3\n", - "RightHeelRises 2\n", - "Name: count, dtype: int64\n" - ] - } - ], - "source": [ - "weaklink_categories = [\n", - " 'ExcessiveForwardLean', 'ForwardHead', 'LeftArmFallForward',\n", - " 'LeftAsymmetricalWeightShift', 'LeftHeelRises', 'LeftKneeMovesInward',\n", - " 'LeftKneeMovesOutward', 'LeftShoulderElevation', 'RightArmFallForward',\n", - " 'RightAsymmetricalWeightShift', 'RightHeelRises', 'RightKneeMovesInward',\n", - " 'RightKneeMovesOutward', 'RightShoulderElevation',\n", - "]\n", - "\n", - "weaklink_scores_df['WeakestLink'] = (\n", - " weaklink_scores_df[weaklink_categories].idxmax(axis=1)\n", - ")\n", - "print('Weakest Link class distribution:')\n", - "print(weaklink_scores_df['WeakestLink'].value_counts())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Merge Datasets & Visualise Class Imbalance" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Merged dataset shape: (2094, 39)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "target_df = weaklink_scores_df[['ID', 'WeakestLink']].copy()\n", - "merged_df = movement_features_df.merge(target_df, on='ID', how='inner')\n", - "print('Merged dataset shape:', merged_df.shape) \n", - "\n", - "plt.figure(figsize=(12, 5))\n", - "merged_df['WeakestLink'].value_counts().plot(\n", - " kind='bar', color='blue', edgecolor='black'\n", - ")\n", - "plt.title('Class Distribution – Weakest Link (14 Categories)',\n", - " fontsize=14, fontweight='bold')\n", - "plt.xlabel('Weakest Link Category')\n", - "plt.ylabel('Frequency')\n", - "plt.xticks(rotation=45, ha='right')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7. Prepare Features" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Feature matrix shape : (2094, 36)\n", - "Number of features : 36\n", - "Number of classes : 14\n" - ] - } - ], - "source": [ - "EXCLUDE_COLS = ['ID', 'WeakestLink', 'EstimatedScore']\n", - "feature_columns = [c for c in merged_df.columns if c not in EXCLUDE_COLS]\n", - "\n", - "X = merged_df[feature_columns].values\n", - "y = merged_df['WeakestLink'].values\n", - "\n", - "print(f'Feature matrix shape : {X.shape}')\n", - "print(f'Number of features : {len(feature_columns)}')\n", - "print(f'Number of classes : {len(np.unique(y))}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 8. Train and Test Split + Scaling" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training samples : 1675\n", - "Test samples : 419\n" - ] - } - ], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(\n", - " X, y, test_size=0.2, random_state=RANDOM_STATE, stratify=y\n", - ")\n", - "\n", - "scaler = StandardScaler()\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "X_test_scaled = scaler.transform(X_test)\n", - "\n", - "print(f'Training samples : {X_train.shape[0]}')\n", - "print(f'Test samples : {X_test.shape[0]}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 9. CV Evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "cv_strategy = StratifiedKFold(\n", - " n_splits=N_SPLITS, shuffle=True, random_state=RANDOM_STATE\n", - ")\n", - "\n", - "def evaluate_cv(model, X, y, cv, name='Model'):\n", - " scoring = {\n", - " 'accuracy' : 'accuracy',\n", - " 'f1' : 'f1_weighted',\n", - " 'precision': 'precision_weighted',\n", - " 'recall' : 'recall_weighted',\n", - " }\n", - " cv_res = cross_validate(model, X, y, cv=cv, scoring=scoring)\n", - " return {\n", - " 'Model' : name,\n", - " 'Accuracy_mean' : cv_res['test_accuracy'].mean(),\n", - " 'Accuracy_std' : cv_res['test_accuracy'].std(),\n", - " 'F1_mean' : cv_res['test_f1'].mean(),\n", - " 'F1_std' : cv_res['test_f1'].std(),\n", - " 'Precision_mean': cv_res['test_precision'].mean(),\n", - " 'Recall_mean' : cv_res['test_recall'].mean(),\n", - " '_f1_scores' : cv_res['test_f1'], \n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 10. A4 Champion Baseline (Random Forest)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "A4 CHAMPION (Random Forest)\n", - "CV F1: 0.6058 +/- 0.0289\n", - "Test F1: 0.6141\n" - ] - } - ], - "source": [ - "rf_champion = RandomForestClassifier(\n", - " n_estimators=200, max_depth=15,\n", - " min_samples_split=5, min_samples_leaf=2,\n", - " class_weight='balanced',\n", - " random_state=RANDOM_STATE, n_jobs=-1\n", - ")\n", - "champ_cv = evaluate_cv(\n", - " rf_champion, X_train_scaled, y_train, cv_strategy,\n", - " name='A4 Champion – Random Forest'\n", - ")\n", - "rf_champion.fit(X_train_scaled, y_train)\n", - "champ_test_f1 = f1_score(y_test, rf_champion.predict(X_test_scaled), average='weighted')\n", - "\n", - "print('A4 CHAMPION (Random Forest)')\n", - "print(f'CV F1: {champ_cv[\"F1_mean\"]:.4f} +/- {champ_cv[\"F1_std\"]:.4f}')\n", - "print(f'Test F1: {champ_test_f1:.4f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 11. Ensemble 1 – Hard Voting" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hard Voting CV F1: 0.6310 +/- 0.0148\n" - ] - } - ], - "source": [ - "hard_voting = VotingClassifier(\n", - " estimators=[\n", - " ('rf', RandomForestClassifier( n_estimators=200, max_depth=15, min_samples_split=5, min_samples_leaf=2, class_weight='balanced_subsample', \n", - " random_state=RANDOM_STATE, n_jobs=-1)),\n", - " ('lr', LogisticRegression( max_iter=1000, class_weight='balanced',random_state=RANDOM_STATE)),\n", - " ('xgb', xgb.XGBClassifier( n_estimators=200, max_depth=6, learning_rate=0.1, subsample=0.8,\n", - " colsample_bytree=0.8, random_state=RANDOM_STATE,class_weight='balanced', n_jobs=-1 )),\n", - " ('lgb', lgb.LGBMClassifier(n_estimators=200, learning_rate=0.1, subsample=0.8,\n", - " colsample_bytree=0.8, class_weight='balanced', random_state=RANDOM_STATE, n_jobs=-1)),\n", - " ('knn', KNeighborsClassifier(n_neighbors=7)),\n", - " ('lda', LinearDiscriminantAnalysis()),\n", - " ],\n", - " voting='hard',\n", - " n_jobs=-1,\n", - ")\n", - "\n", - "hv_cv = evaluate_cv(hard_voting, X_train_scaled, y_train, cv_strategy, name='Hard Voting')\n", - "print(f'Hard Voting CV F1: {hv_cv[\"F1_mean\"]:.4f} +/- {hv_cv[\"F1_std\"]:.4f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 12. Ensemble 2 – Soft Voting" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Soft Voting CV F1: 0.6365 +/- 0.0181\n" - ] - } - ], - "source": [ - "soft_voting = VotingClassifier(\n", - " estimators=[\n", - " ('rf', RandomForestClassifier(n_estimators=200, max_depth=15, min_samples_split=5, min_samples_leaf=2, class_weight='balanced_subsample', \n", - " random_state=RANDOM_STATE, n_jobs=-1)),\n", - " ('lr', LogisticRegression( max_iter=1000, class_weight='balanced',random_state=RANDOM_STATE)), \n", - " ('xgb', xgb.XGBClassifier( n_estimators=200, max_depth=6, learning_rate=0.1, subsample=0.8,\n", - " colsample_bytree=0.8, random_state=RANDOM_STATE,class_weight='balanced', n_jobs=-1 )),\n", - " ('lgb', lgb.LGBMClassifier( n_estimators=200, learning_rate=0.1, class_weight='balanced',subsample=0.8, colsample_bytree=0.8,\n", - " random_state=RANDOM_STATE, n_jobs=-1 )),\n", - " ('knn', KNeighborsClassifier(n_neighbors=7)), \n", - " ('lda', LinearDiscriminantAnalysis()),\n", - " ],\n", - " voting='soft',\n", - " n_jobs=-1,\n", - ")\n", - "\n", - "sv_cv = evaluate_cv(soft_voting, X_train_scaled, y_train, cv_strategy, name='Soft Voting')\n", - "print(f'Soft Voting CV F1: {sv_cv[\"F1_mean\"]:.4f} +/- {sv_cv[\"F1_std\"]:.4f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 13. Ensemble 3 – Bootstrap Bagging on LDA" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bagging LDA CV F1: 0.5653 +/- 0.0249\n" - ] - } - ], - "source": [ - "bagging_lda = BaggingClassifier(\n", - " estimator=LinearDiscriminantAnalysis(),\n", - " n_estimators=50, \n", - " max_samples=0.8,\n", - " max_features=0.9,\n", - " bootstrap=True,\n", - " random_state=RANDOM_STATE,\n", - " n_jobs=-1,\n", - ")\n", - "\n", - "bag_cv = evaluate_cv(bagging_lda, X_train_scaled, y_train, cv_strategy,\n", - " name='Bootstrap Bagging (LDA)')\n", - "print(f'Bagging LDA CV F1: {bag_cv[\"F1_mean\"]:.4f} +/- {bag_cv[\"F1_std\"]:.4f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 14. Ensemble 4 – Stacking" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Stacking CV F1: 0.5985 +/- 0.0159\n" - ] - } - ], - "source": [ - "stacking = StackingClassifier(\n", - " estimators=[\n", - " ('rf', RandomForestClassifier( n_estimators=100, max_depth=15,min_samples_split=5, min_samples_leaf=2,class_weight='balanced_subsample',\n", - " random_state=RANDOM_STATE, n_jobs=-1)),\n", - " ('lr', LogisticRegression( max_iter=1000, class_weight='balanced',random_state=RANDOM_STATE)), \n", - " ('knn', KNeighborsClassifier(n_neighbors=7)), \n", - " ('lda', LinearDiscriminantAnalysis()),\n", - " \n", - " ],\n", - " final_estimator=LogisticRegression(\n", - " C=1.0, max_iter=1000, class_weight='balanced', random_state=RANDOM_STATE\n", - " ),\n", - " cv=5,\n", - " passthrough=False,\n", - " n_jobs=-1,\n", - ")\n", - "\n", - "st_cv = evaluate_cv(stacking, X_train_scaled, y_train, cv_strategy,\n", - " name='Stacking (LR meta)')\n", - "print(f'Stacking CV F1: {st_cv[\"F1_mean\"]:.4f} +/- {st_cv[\"F1_std\"]:.4f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 15. CV Summary Comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5-FOLD CROSS-VALIDATION SUMMARY\n", - " Model F1_mean F1_std Accuracy_mean Precision_mean Recall_mean\n", - " Soft Voting 0.636505 0.018089 0.647164 0.641519 0.647164\n", - " Hard Voting 0.630970 0.014828 0.645373 0.631015 0.645373\n", - "A4 Champion – Random Forest 0.605816 0.028917 0.621493 0.602919 0.621493\n", - " Stacking (LR meta) 0.598453 0.015950 0.574925 0.656129 0.574925\n", - " Bootstrap Bagging (LDA) 0.565322 0.024937 0.575522 0.566827 0.575522\n" - ] - } - ], - "source": [ - "all_results = [champ_cv, hv_cv, sv_cv, bag_cv, st_cv]\n", - "results_df = (\n", - " pd.DataFrame([{k: v for k, v in r.items() if k != '_f1_scores'}\n", - " for r in all_results])\n", - " .sort_values('F1_mean', ascending=False)\n", - " .reset_index(drop=True)\n", - ")\n", - "\n", - "print('5-FOLD CROSS-VALIDATION SUMMARY')\n", - "print(results_df[['Model','F1_mean','F1_std','Accuracy_mean',\n", - " 'Precision_mean','Recall_mean']].to_string(index=False))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 16. Visualise CV Results" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(8, 6))\n", - "\n", - "# Prepare data\n", - "models = results_df['Model']\n", - "f1_mean = results_df['F1_mean']\n", - "f1_std = results_df['F1_std']\n", - "\n", - "colors = ['gold' if m == results_df.iloc[0]['Model']\n", - " else ('lightcoral' if 'Champion' in m else 'steelblue')\n", - " for m in models]\n", - "\n", - "# Plot bars\n", - "bars = ax.bar(models, f1_mean, yerr=f1_std, capsize=6,\n", - " color=colors, edgecolor='black', linewidth=1.5)\n", - "\n", - "# horizontal reference line for the previous champion\n", - "ax.axhline(CHAMPION_F1, color='red', linestyle='--', linewidth=2,\n", - " label=f'Original A4 Champion F1 = {CHAMPION_F1}')\n", - "\n", - "ax.set_ylim([0.45, 0.75]) \n", - "ax.set_title('CV F1-Score by Ensemble Strategy', fontsize=14, fontweight='bold')\n", - "ax.set_ylabel('F1-Score (weighted)', fontsize=12)\n", - "ax.set_xlabel('Model Approach', fontsize=12)\n", - "\n", - "ax.set_xticks(range(len(models)))\n", - "ax.set_xticklabels(models, rotation=30, ha='right')\n", - "\n", - "for bar, val in zip(bars, f1_mean):\n", - " ax.text(bar.get_x() + bar.get_width()/2,\n", - " bar.get_height() + 0.005, f'{val:.4f}',\n", - " ha='center', va='bottom', fontsize=10, fontweight='bold')\n", - "\n", - "ax.legend(loc='upper left')\n", - "ax.grid(axis='y', alpha=0.3)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 17. Statistical Significance Test (t-test)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "STATISTICAL SIGNIFICANCE TESTS vs A4 Champion\n", - " Hard Voting t=+1.339 p=0.2516\n", - " Soft Voting t=+1.632 p=0.1781\n", - " Bootstrap Bagging (LDA) t=-5.123 p=0.0069\n", - " Stacking (LR meta) t=-0.556 p=0.6078\n" - ] - } - ], - "source": [ - "def corrected_resampled_ttest(scores_a, scores_b, n_train, n_test):\n", - " k = len(scores_a)\n", - " diff = scores_a - scores_b\n", - " d_bar = diff.mean()\n", - " s_sq = diff.var(ddof=1)\n", - " var_corr = (1/k + n_test/n_train) * s_sq\n", - " t_stat = d_bar / np.sqrt(var_corr)\n", - " p_value = 2 * (1 - stats.t.cdf(abs(t_stat), df=k-1))\n", - " return float(t_stat), float(p_value)\n", - "\n", - "n_total = len(X_train_scaled)\n", - "n_test_fold = n_total // N_SPLITS\n", - "n_train_fold = n_total - n_test_fold\n", - "\n", - "result_map = {r['Model']: r['_f1_scores'] for r in all_results}\n", - "champ_scores = result_map['A4 Champion – Random Forest']\n", - "\n", - "print('STATISTICAL SIGNIFICANCE TESTS vs A4 Champion')\n", - "for r in all_results:\n", - " if 'Champion' in r['Model']:\n", - " continue\n", - " t, p = corrected_resampled_ttest(\n", - " r['_f1_scores'], champ_scores, n_train_fold, n_test_fold\n", - " )\n", - " print(f' {r[\"Model\"]:<35} t={t:+.3f} p={p:.4f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 18. Select Champion Ensemble & Final Test-Set Evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CHAMPION ENSEMBLE: Soft Voting\n", - "CV F1 : 0.6365 +/- 0.0181\n", - "\n", - " TEST SET RESULTS\n", - "F1-Score (weighted) : 0.6419\n", - "Accuracy : 0.6492\n", - "Precision : 0.6492\n", - "Recall : 0.6492\n", - "\n", - " A4 original champion F1 : 0.6110\n" - ] - } - ], - "source": [ - "model_objects = {\n", - " 'Hard Voting' : hard_voting,\n", - " 'Soft Voting' : soft_voting, \n", - " 'Bootstrap Bagging (LDA)' : bagging_lda,\n", - " 'Stacking (LR meta)' : stacking,\n", - " 'A4 Champion – Random Forest': rf_champion, \n", - "}\n", - "\n", - "best_name = results_df.iloc[0]['Model']\n", - "best_model = model_objects[best_name]\n", - "\n", - "print(f'CHAMPION ENSEMBLE: {best_name}')\n", - "print(f'CV F1 : {results_df.iloc[0][\"F1_mean\"]:.4f} +/- {results_df.iloc[0][\"F1_std\"]:.4f}')\n", - "\n", - "best_model.fit(X_train_scaled, y_train)\n", - "y_pred_best = best_model.predict(X_test_scaled)\n", - "\n", - "test_f1 = f1_score(y_test, y_pred_best, average='weighted')\n", - "test_acc = accuracy_score(y_test, y_pred_best)\n", - "test_prec = precision_score(y_test, y_pred_best, average='weighted', zero_division=0)\n", - "test_rec = recall_score(y_test, y_pred_best, average='weighted', zero_division=0)\n", - "improvement = (test_f1 - CHAMPION_F1) / CHAMPION_F1 * 100\n", - "\n", - "print('\\n TEST SET RESULTS')\n", - "print(f'F1-Score (weighted) : {test_f1:.4f}')\n", - "print(f'Accuracy : {test_acc:.4f}')\n", - "print(f'Precision : {test_prec:.4f}')\n", - "print(f'Recall : {test_rec:.4f}')\n", - "print(f'\\n A4 original champion F1 : {CHAMPION_F1:.4f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 19. All Models – Test Set Comparison Table" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TEST SET COMPARISON – ALL MODELS\n", - " Model Test_F1 Test_Acc Test_Prec Test_Recall\n", - " Hard Voting 0.645079 0.653938 0.654667 0.653938\n", - " Soft Voting 0.641883 0.649165 0.649217 0.649165\n", - "A4 Champion – Random Forest 0.614147 0.618138 0.644145 0.618138\n", - " Stacking (LR meta) 0.591951 0.572792 0.650306 0.572792\n", - " Bootstrap Bagging (LDA) 0.568919 0.575179 0.579848 0.575179\n" - ] - } - ], - "source": [ - "test_rows = []\n", - "for name, model in model_objects.items():\n", - " model.fit(X_train_scaled, y_train)\n", - " preds = model.predict(X_test_scaled)\n", - " test_rows.append({\n", - " 'Model' : name,\n", - " 'Test_F1' : f1_score(y_test, preds, average='weighted'),\n", - " 'Test_Acc' : accuracy_score(y_test, preds),\n", - " 'Test_Prec' : precision_score(y_test, preds, average='weighted', zero_division=0),\n", - " 'Test_Recall': recall_score(y_test, preds, average='weighted', zero_division=0),\n", - " })\n", - "\n", - "test_results_df = pd.DataFrame(test_rows).sort_values('Test_F1', ascending=False)\n", - "print('TEST SET COMPARISON – ALL MODELS')\n", - "print(test_results_df.to_string(index=False))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 20. Visualise Final Test-Set Results" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(8, 6))\n", - "t_models = test_results_df['Model']\n", - "t_f1 = test_results_df['Test_F1']\n", - "bar_colors = [\n", - " 'gold' if m == best_name else\n", - " ('lightcoral' if 'Champion' in m else 'steelblue')\n", - " for m in t_models\n", - "]\n", - "bars = ax.barh(t_models, t_f1, color=bar_colors, edgecolor='black', linewidth=1.5)\n", - "ax.axvline(CHAMPION_F1, color='red', linestyle='--', linewidth=2,\n", - " label=f'Original A4 Champion F1 = {CHAMPION_F1}')\n", - "ax.set_xlabel('F1-Score (weighted)', fontsize=12, fontweight='bold')\n", - "ax.set_title(\n", - " 'Test Set F1-Score: Ensemble Methods vs A4 Champion\\n'\n", - " '14-Class Weak Link Classification',\n", - " fontsize=13, fontweight='bold'\n", - ")\n", - "ax.legend()\n", - "ax.set_xlim([0.45, 0.72])\n", - "for bar, val in zip(bars, t_f1):\n", - " ax.text(val + 0.003, bar.get_y() + bar.get_height()/2,\n", - " f'{val:.4f}', va='center', fontsize=10, fontweight='bold')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 21. Detailed Classification Report – Champion Ensemble" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CLASSIFICATION REPORT: Soft Voting\n", - " precision recall f1-score support\n", - "\n", - " ExcessiveForwardLean 0.51 0.88 0.65 25\n", - " ForwardHead 0.87 0.59 0.70 22\n", - " LeftArmFallForward 0.69 0.68 0.69 123\n", - " LeftAsymmetricalWeightShift 0.62 0.62 0.62 16\n", - " LeftHeelRises 0.00 0.00 0.00 1\n", - " LeftKneeMovesInward 0.00 0.00 0.00 1\n", - " LeftKneeMovesOutward 0.50 0.18 0.27 11\n", - " LeftShoulderElevation 0.20 0.09 0.12 11\n", - " RightArmFallForward 0.58 0.61 0.59 92\n", - "RightAsymmetricalWeightShift 0.40 0.50 0.44 4\n", - " RightKneeMovesInward 0.50 0.33 0.40 9\n", - " RightKneeMovesOutward 0.91 0.91 0.91 55\n", - " RightShoulderElevation 0.57 0.59 0.58 49\n", - "\n", - " accuracy 0.65 419\n", - " macro avg 0.49 0.46 0.46 419\n", - " weighted avg 0.65 0.65 0.64 419\n", - "\n" - ] - } - ], - "source": [ - "print(f'CLASSIFICATION REPORT: {best_name}')\n", - "print(classification_report(y_test, y_pred_best, zero_division=0))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 22. Confusion Matrix – Champion Ensemble" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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SUXAcYNkbjC/FZECYHAnthPfW6DaNNmvRokWEHgvh0c6Iu27dug778HhBjUPu1q2b+tzj9aHd0Q44HtDDAO2PMdp4TeEZX1heB2Jr2rSpOk7xecV7g+8W8zh5jJsmIj3ZkKG6OwgiIiIiorfBz1bn8cvoeo+ZtzHcADDhE05KEZF+OOaUiIiIiLTw2WefSbFixVTFFGPE//33Xxk2bJg9MUW1t2LFiu4Ok4jeEZNTIiIiItICZpYObvZwdJ9GV290/yUiPTE5JSIiIiItYDwyxrZiaai7d++qmcIzZMggFSpUUEv8YHZlItIXx5wSERERERGR23G2XiIiIiIiInI7JqdERERERETkdkxOiYjIUsqWLauWisA2d+5cd4cTKX311Vf2Nh4wYIDLntd4TmwXLlx458fBfc2P5SntFxqbN2+2x4a1WZ1hmZWiRYuqNX7Nn7O33c9dEIsRF2IkosiNySkRUSQ2YcIEqVu3rpowxPxjPjRJ36tXr6REiRJhvl9QsMzDt99+q34UJ0yYUE1ikipVKilevLgMHz5cLl26JLp6+fKlmiXUaKM//vjD4fpx48bZr4sSJYrcv3/f4Xq8P8b1Xbt2FZ2YE5r3TTjdDZPrjBgxQsqUKSNJkiRRxyiWJSlcuLD07dtXTcCjuz179kjt2rVl586d8uDBA3eHoz4LSO6NjYiIs/USEUVi+MH3rj9C8UP9n3/+ee8YfvjhB2nXrp08ffrUYf/Vq1fVtn37drlx44ZKpHUUNWpUlXRv3LhRXf7rr7+kUqVK9utx2fD69WvVph999FGQ15cqVUoiM/NrxRqVVvHbb7/Jl19+qRJUMxyX2JDUHTlyRFUdrS5//vz2dvb29na47n//+586BgHH7NChQ1USnjVrVjUDbnD3i8jkdODAgfbLQSWoy5Ytk2fPnqm/c+fO7ZK4iMh9mJwSEUVi+DGHH56FChVSP/xu3rwZqvvt27dP/WhENQw/Xp8/f/5Oz79w4UJp1qyZ/XKWLFmkQ4cOkj17dvWDE88zf/580V3p0qXtyenWrVsdrtu2bZvDZSQARnJ66tQplfwYSpYsKZGZFV8fqr81a9aUFy9e2JPmjh07SsGCBVVV/PDhw2rtTF3Ejx8/2HZGDwYDTqCUK1fO8u8PvruIyHOwWy8RUSSGRGj27Nny9ddfS8yYMUN1HySNWEsQP9axbiC6Nr6LR48eqR/55h+ZSEZRRS1fvrx8/PHHqqvviRMnpFWrVm99PHSXrVevnvj6+kqiRIkkWrRo6of4Bx98oLrOGsmF+Yc4HjdjxoyqKoTXnyZNGqlYsaL079/f4bZr165VP9bRnROPi/F4eJ7PP/9cXfc25ornrl27xN/fX/19/PhxuX37tvo7T548gaqH5r+RsCdOnNh+Gfdt3ry5ih+VrHjx4qlu1uhaHRAQ8F5tExw8Nroe46QE2mvNmjUSnoLrAmweV4gkf8yYMfZqHrqk4zWExr1791RSaTxWlSpVAlXszVBFxGfDaB+skbl//37p0aOHWjcT9//mm2/UviFDhrz1+VFh/eKLL9RJIeNYihs3ruTLl08dc48fPw5UOezWrZt639DeeL0pU6ZUXYvxvH5+fvbb7tixQz799FOVPONxcTxkzpxZddPFSSBDUGNHjX1z5syx327QoEEO78Xbxpxu2bJFHWP4DCFOHx8f9ZkePXr0O71+jC3Hexvc8WGMLw1pzOm5c+fU+4d2wGcE3evz5s0r/fr1C9R9HifnjMfBmOG///5bJeexY8dWn5XPPvss1CfviCgCBRARkUdIly4dMhq1zZkzJ9jbdezYUd0mZ86cAc+ePQv1/Zz99NNP9vth27p1a6juV6ZMmSCfr0ePHg6P57x9+umn9tv6+/sHZMqUKdjbxogRw37bjRs3BthstmBv26pVq7fG7OfnFxAtWjT7fbZt26b2T58+XV2OEydOwKxZs9Tf0aNHD3j69Km6vnHjxvb7tGzZ0v54K1asCPD29g42poYNGwa8fv36ndrG+Xn79++v9s2fPz/Ay8vLHu+ff/751te9adMmh+c5f/58iLcP7rbmYyxLlixBvgYcTwbc13wd3L9/P6BQoUL2fTVr1gx4/vx5iPFs377d4XHQBqERVPvB999/H+L7ULBgwYAXL17Yb1+6dOkQb3/t2jV1u+PHj6tjNrjbVa5cOcj3BO0a1PvkvKE9g7qfoV+/fsHeN2/evO/0+s2f86A2xON8bBj7YPPmzeo4De7+GTJkCLh8+bL99nifzNdFjRo1xHYkIvdgt14iIrJD1WrSpEmqK++PP/6oKiTvau/evfa/Y8WKpap+79t1FpMopU2bVlVjUAG5du2aqsicOXNGjafbvXu3msDm4MGDcvbsWXvFEl2UUVXBGFdUbzds2GB/3OXLl9srkW3atJFatWqpitXFixdVe6BC9TaoeqGKhPGzRtdevF6jMlqsWDH58MMP1d+oqqK6itdj7gJsVF9v3bqlKtfGOLvWrVtLjRo11H5MzPPvv/+qShmqPk2bNg1z2wQFj4dqEiqJqCKhWoyY3QHVMMSNWFGVQ8UOJk6cKPXr1w/yPg8fPlSVb1TuoGHDhqoKjPHAoT1GwTxW+F3gWBs7dqyqduN9QBUalfNRo0ap9sfzrVixQk2Chf3G+49qJF4rqo1439CVGONgjZmIV69ebe9aj/uiqzzeK0wk5tyNPKRxqMOGDbP3BGjSpIn9+EE1NrjJrFCVR5XVgOMYPRLwuThw4ICq6L7L6//uu+/UJFP4O6ieBCGNL8VnA70ajEpskSJFpGfPnupyr169VK+J8+fPS8uWLVU7OsN16EHRvn17FZMx7vX3339XMWXLlu2tbUpEEYPJKRERKegGhwQFiRp+jKIr3ts4j6cEJEjYzN3q0AXQy+v9RpKgGyC6Vw4ePFhOnz6tug07d2/FD2UkNUiwDPjBjy6iGO+KboaY+MbMfNtMmTJJjhw57JP1tG3bNtTxIUE0klP8yMaPZOPHNhJP/GBHAokfztiP58KPZOfk9Oeff7b/6M6VK5dKtADdD9FlEpPYwMyZM+3JRVjaxhm67iJhR7KDbsVIRpDMuAsSH2NiHMSDiXuM8bnBqVy5skr4oUWLFjJt2rRQHW/OXT/RJfp9IElCW6Jb8rFjx9RkZMYEROb3AQkZTpYgecOs2OhGjuMTx54xGREmJAvqGMVnC13AkdAieUUCFtpxqEmTJnV4nNCMMcVxZkCXaZzYMdq2atWq7/z6kXwigX2XMa84RnGiCXAiDRNVGZ9ZzAb+ySefqL+RiKOrrvl1G8cVTtjgpFK1atXUZw7DC4zjjMkpkftwzCkRESmoyl2+fFklSRjvFhq4rfOG2XkBP7jNSYDzj9SwQKKFSYQQIyodqJQ5J1/GmEPAGDSMGQRUP3PmzKl+iOJHJxJwc7UHVUokfoClXDDmDz+aUTlEkuQ8g2tIbWHAjLyoRKHKaSSu5tsgOTVXiZBoYLwj4Ee9ATPEmtvWSEyN696lbZyhooUECfAj352JKWA8clDJYkjvg/F+oho2Y8aMUJ8IMR+jcOfOHXkfOFmAahzGM6K9gzrmjfcBSWjjxo3V36iUIvHDcYhxmKgQo4pnwFhTY+w3KpM4VnDbAgUKqDGrEbkUk/l4RAU/pLYNy+t/H0YiCTjJY5752Zzg4nMQ1BJA+Gybx+CH9jgjoojH5JSIiBQkpoCkyZgUB5uRYBldAbEP3fneBj+2DU+ePLFXFd8F7mt070RsqBAi6USsSEgMxo9hxLhq1SpVQcNMrEhK8aMaVZF58+apZNHoAorJaPB6UOnERDT4oYvKJRIedPdDVc5I3kKCH8XGD3dUjKZMmWKv7GBiInOSiuR106ZN77WEjFFdDWvbOMN9DEjOUXV1J1S+DG/rluv8GvC6Fy9eHOrnMh+jYO7uHVaoiC9YsMB+GZOJocKH98FcrTe/D0ik0X0eySiq5DhWcFIDlTxMxoTqHqDyh4okejTgPUXVE11bUS1HsorjBycl3OldXr8VjjHn4yyoEztE5DpMTomI6J3hh5zzZnTJxGy85tlnu3TpopJUZ0j8MDNtSDD+04DuxqgSYswlunyarzPHhcoUuohiTCkqLXhuLGMDmJ0V6ycat0WlFePxMBsougti3J8xYymSWHSVDU3XSfM4OSTGgLGoRldNIwlFAmhensScnKLLpqF48eJBtjE2IzkNa9s4w+zJxkzCO3fuVF0izbPE6mD69On2xAeV8NCuR4puqOb2xuzRwc3YevTo0RAfy1y9RCVu/PjxKpHESQvzEi5mOJmBbts//fSTqp7iGDXPfov9gPcbJ00QHxI+nDBChc8YF4zL4bEmcVDQ1TiodVKdk7l3ff1moU1ccULJgLHl169ft19G1daAk1TsokukF445JSKKxPBD1kg0zAkHqjBGl0b8eEQSiUlWMHbRGao1Rle8Bg0aqB/0GDv5NugaO2HCBDVOEjAmEJUqdPsz1jlFxRLrnKJKhNsGB+M1DYcOHZKpU6eq7o+oPAXVbQ9rh2JCIiyzgYQRP+zx+o1qKRgTDqHyhLFpSKbRXRJVFSSjmIDI+bZvg8ooJmMCI3k0J57oXozHR2JhXlbDfBssadG7d291PRKOOnXqqMlfkPziRz5eL8aJooslJg4Ka9s4w3GALqRoL0xGhAl2UG3+9ddfwzwh1siRIx3GRxpwwsJI0CMCjl10ycUSMFibFG2I+FH1DgmSI7QXJkLCCQuMAUa3ZlT98C+SJSSlmDAqderUISa95vcBsaALNk5M4CSIsQauM5wUQZdsfC7QnRwnaswTHBnH3dKlS9VyOujei+dBJRUnUcxjlkN7jIYVljMyTuTg84M2xbheTIiEhBrjzpG0vsvrx2cBCaSR4CKhxfcL3peQJlDD+4X2QhtggjEcr3jvjQmRDBgT6zzelIgszk2zBBMRkQuYl2F425INoXmMsCwlY5g9e3ZAzJgxQ4wBy9eEtJTMq1evAooXLx7ofrFjxw4oXLhwoGU9sARHSM+HZSR27typbjt8+PAQb5s/f371/KGxZMmSQPdfvXq1w22qV6/ucH3ChAkdloWB5cuXh7iUjPm1hrVtglsK5ezZswHJkye370ecWJInJG9bosTY7t27p27vvHyJIbjlQoJaMiak/d988419H465tx3bBrxHeB9CuxxPcEvJ1K9fP9D9okSJElCqVCn7ZdzXENLyMNh++eWXIJdlct5Sp04d8PDhw0DvifOSMMHF/bb79e7dO1RLyYT19UOxYsWCvM/bjo3QLCVz6dKlIJeScY4huOWriMj12K2XiIgiFCZJQSWyT58+qiqCSh1mzUU1E90SUWHBWMeQoJKC6gwmM0qWLJmaDAZLWqAbrrnboQHPgbGXqLBgfB4mP8G4MlRbsFQMxsEhFkDVFl1bMbkMqiy4HW6Px8XEUKj6hHaCHWNMqTlu5wqQ821wvbFkiAGVIIwnxEysqK6h6ojXjL/R7RZdhrHszbu0TXBQ+UIF1aioo/KIqndoxttaBZYsMWYwfvr0qZqJNTRjnVE1xzE6fPhwVcVGTwIcozgeUNVEJds8e25wZs2apaquqLLiGMJYY1S50c06KHi+6tWrqy7kxuy9mF0axyTuh2MV8DiY+AifF0yMhLGpqGrjeMBSQ3iNzjPfhid8RvE5QE8E9JpA26BCjs+MMZv0u7x+wDhVVI/DGj/Gh6PnBbru49hFm+A50VMCXdvROwRxEJFebMhQ3R0EEREREREReTZWTomIiIiIiMjtmJwSERERERGR2zE5JSIiIiIiIrdjckpERERERERux+SUiIiIiIiI3I7JKREREREREbkdk1MiIiIiIiJyu6juDoCIiIiIiIhEYuZv57JmeLp/suWanMkpkQf669Q90UWprD7y7KVoxTuqiJ9/gOgkVnSblu2sU8y6xatzzDp+/nSKmd8XrqHr50/HmMk6+HYQERERERFZgc2zR1169qsnIiIiIiIiS2DllIiIiIiIyApsNvFkrJwSERERERGR27FySkREREREZAU2z64devarJyIiIiIiIktgckpERERERERux269REREREREVmDjhEhEREREREREbsXKKRERERERkRXYPHvUpWe/eiIiIiIiIrIEVk6JiIiIiIiswMYxp0QOvvrqK6lRowZbJQRly5aVTp06sY2IiIiIiMIJu/VGcJJns9kCbVWqVBErmzhxosydOzdcHzN9+vSB2iF16tQSWXhCsrpm6TwZ0rmJtK1XTjp/UVUmD+ku1y//a7/+8aMHsmj6GOnTup58XbuMdG/yqSyaPlb8njwWq1m8aKFUrVhOCufPLQ3r15XDhw6Jle3ds1s6tmstFcuVkvy5fWXTxg2iA93aWceYdYtXt5h1/OzpGLNux4WBMbOdI2zMqc1FmwVZM6pIBInotWvXHLaffvpJrCx+/PiSIEGCcH/cQYMGObTD/v373/mxXrx4IREtICBAXr58GeHPo4uTR/bLhx/Xlt6jZ0mXwZPk1auXMq5fR3n+7Km6/sHd23L/zm2p27S9DJy8UJp0+laO7tsh8yYNFStZt3aNjBk1XFq1aSuLl66QbNl85etWzeTOnTtiVU+fPpWsWX2lV59+ogsd21m3mHWLV8eYdfzs6RizbscFMGa2M0UMJqcRLEaMGJI8eXKHzcfHRzZv3izRo0eXv/76y37bUaNGSdKkSeXGjRvq8v3796VVq1aSLFky8fb2lly5csnq1avtt9+2bZuUKlVKYsaMKWnSpJEOHTrIkydP7NdPnTpVsmTJou6Lx6hTp479umXLlknu3LnVfRMlSiQVKlSw39fcrXfGjBmSMmVKef36tcPr+vTTT6Vp06b2y//73/+kQIEC6rkyZswoAwcODJTYxY0b16EdkiRJYr/u+++/l0yZMqk2yZYtmyxYsMDhvqi04jbVq1eX2LFjy9ChQ6VQoUIyZswY+20Qc7Ro0eTx4zeVusuXL6v7nTlzRl3GY+I+Rhyff/653Lx5035/vCe4/dq1a6VgwYLqvUMbo12+/PJLiRMnjqRIkULGjh0bhiMgdO9VaGPbuHGjul2sWLGkePHicvLkSXGVzgMnSIkKn0iqdBklTYYs0rTTt3L31nX598wJdX2qdJmkTe8Rkq9IKUmaIrVkz1tIajZqLQd3bVOJrFUsmDdHatWpJzVq1pZMmTNL3/4D1XG7cvkvYlUlS5WWth06SbnyFUUXOrazbjHrFq+OMev42dMxZt2OC2DMbOcIHXNqc9FmQUxO3dwNtFGjRvLgwQNVRfz2229l1qxZKpFEMli1alX5+++/5ccff5Rjx47JiBEjJEqUKOr+Z8+eVVXZ2rVry6FDh+Tnn39WCVC7du3U9Xv27FEJEKqVSGDWrVsnpUuXVtehatmgQQOVXB4/flwlPrVq1VKVQmd169ZVZy43bdpk33f37l31eA0bNlSXkWAjeevYsaOKc/r06apbMBLI0FixYoW6b9euXeXIkSMqIW/SpInDc8KAAQOkZs2acvjwYRV7mTJlVOyA2BEHKr5oB9iyZYukSpVKMmfObK+2Dh48WA4ePCgrV66UCxcuqETcWc+ePVVbo23y5Mkj33zzjXosJOB//PGHes59+/aF8p1++3sVltj69OmjkmO8v1GjRnU4QeBqRnfd2HHjhXgb71ixJUoUa8y99sLfX44fOypFixW37/Py8pKiRYvLoYPvXskn/dtZt5h1i1fXmCni6XhcMGa2M0Uca/xijMRQ6UTFzax3795qGzJkiKxfv15atmypkrLGjRuryiBs2LBBdu3apRKkrFmzqn2oSBqGDx+ukkNjnCMqpJMmTVIJGyqMFy9eVBXGTz75RFXj0qVLJ/nz57cnp6hqIiHFfkAVNSio8iJJXrRokZQvX95edU2cOLF8+OGH6jKqpEjoEL8RJxKt7t27S//+/e2P1aNHD+nbt6/98rBhw1QCjeonErE2bdqo/V26dJEdO3ao/cZzAKqJSFrNCf7s2bPl1atXqv1Qdf3ss89U8ohkEP+iPQzmRA4xor0KFy6sKq3m9wgJfcWKb8444zo8B04QGK9/3rx5YRov+7b3CmeHQxsbEn7jNaHNP/74Y3n27Jl6DFfCyZOfZ06QzNnzqIppUB49uC+rf54jpSt/KlZx7/49dbygt4AZLp8/f85tcUU2OrazbjHrFq+uMVPE0/G4YMxs5whl8+zaoWe/ehdAcnXgwAGHrXXr1uo6JFMLFy6UX375RSUY48ePt98Pt0MCZCSmzlBhQ3USiYuxVa5cWSUN58+fV8kVEk8kOqjO4nn8/PzUffPmzasSLSSkqIzOnDlT7t27F+xrQGKFGJ8/f64u47Hq16+vzmwasSChM8fSokULlQQbzwmoQJrbAdVWQAJeokQJh+fEZew3Q3dWM3STffTokao6o7KJpA0Jq1FNxT5cNuzdu1eqVasmadOmVQm7keQhkQ/ueVD19Pf3lw8++MC+L2HChKrrcWi97b0KS2yo5BrQxRjM3X+d4T17+PChw2a8j+9j4bTRcuXiWWnZfUiQ1z/1eyKTBnWRlGnSS/XPW7z38xERERFR5MfkNIKheolupeYNyY3hn3/+sXeVxWbA2MSQoKKG7q/mZA9J0OnTp9XYTSQ46HqKyZeQxPTr108lpRjHiq7BqNhibGWOHDnku+++U8mWkSg5Q9KEbrO//fabXLp0SXWfNbr0GrGgemqOBV1vEYu5oodqq7kdwjrpEtrSDPfHa0IyaiSi6LqMZPXUqVPq+Y0kD+M7kRDGixdPJde7d+9W3YkByWdIz/O+3vZehSU2jKk1YAwqOI8Hdq7aYoIr84Z972PhtDFyaPff0m3oVEmYOGmg65/5PZEJ/TuJd8xY0rbPSNX92Cp8Evio4995kg1cxvFJntvOusWsW7y6xkwRT8fjgjGznT1tzOmrV6/U8MMMGTKoHAW/X9FL0jwkEH8j30DegdtgPhv81g0rJqduhKpc586dVeUSlTl0izUSDVTIMKEPkqygYPIhjO90TnyxoSILSApwYGCiJYx1xDjGP//8057YoDqJpBLJHO5jJETOkGCiCzASJyS7SGTx/OZYMK41qFiM6mpIsmfPrsbWmuEyEue3QfKJsalbt25VySkSfzweur/iw2FUnk+cOKH+R4expKi4+vr6hlhxNODDh4Rw586d9n2oMgf3vrzLe/WusYVGr1691Jhm84Z97wJfOkhM92/fIt2GTpYkyVMGWTHFDL5RokaVdn3HSLToMcRKokWPLtlz5JSdO7bb9+Ezt3PndsmT9023d/LMdtYtZt3i1TVming6HheMme3saUaOHKmGok2ePFn1bMRl5BcocBlwGcPSpk2bpn43o9iD4gt6h4aFdUoakRS6UF6/ft1hH5JGjOX84osv1JuGcZQYI4lutpjsBt1fkXShCohJdMaNG6cSGSQxxjqpGL9ZtGhRNalO8+bN1QGABAgVURw4GOt67tw59Rh4rjVr1qgveySWOGAw62ulSpXU7MC4fOvWLZXUBQeVUoxfPXr0qIrbDGdJcB26pGJGYCSkqAxiHCjG1b4NXm+9evXUmFgk06tWrZLly5ercbdvg4QUHwzM/IukztiHNkCXZQNiQyKI26JbNWLDGZ+3QRfcZs2aqRgx/gXthUmJgkq60YaoipohQX7be/WusYUGZhzGFth/3a1Da+H3o2Xn1j+kXZ9R4h0ztjy49+Ysd8xYsSV6DG+VmI7v10GeP38mzbsOkGdPn6gN4sZLIF7/P5mXuzVq3ES+7d1DcubMJbly55EfF8xTSy/UqFlLrMrP74lcMnXxvnLlspw8cVzixY8vKVIEPklgBTq2s24x6xavjjHr+NnTMWbdjgtgzGxnTxpz+s8//6iVOjDXCaRPn14VrDA/jlHAmDBhgppbBreD+fPnq0leMdEnhgOGFpPTCIZZbY2xgQYkiJjc599//7UvDYPbYNkWzKKLpBHdVTHOs1u3bmofun4iQUV1zaisoisrEiVU23BQoMqHCYGMLq9I8DDDLc5YYBIeHEQ5c+ZUZzxQacRBhDGIGJuKpBgTHwWnXLlyqiqJCiliN0OCjdeBcac4k4JKIxJFJGKhgSVgJk6cqCZAwqy96DIwZ84ch/GiwcFrR9JtnvgI98Pjme+P5BXjPjERFc7qoJqJ5zMmoArJ6NGjVddcdG9Gd2nMKowKpDNMGoXNDEkmPqghvVfvE5srbV67XP07uvebiasMTTr2VUvM/Hv2hJw7eVTt693yv2WLYMSs5ZI4mTV+FFWp+pHcu3tXpk6eJLdv35Jsvtll6vRZksii3cfg2NEj0qLpmwnHYOzoN98D1arXkEFD3/xtNTq2s24x6xavjjHr+NnTMWbdjgtgzGxnT1K8eHGVp6DnIHologiFlSdQQAMMDUQxDkUmA4aSoWfo9u3bw5Sc2gKCWj+EiCK1v04FPwGW1ZTK6iPPrLNMaqh4RxXx89frqzVWdJuW7axTzLrFq3PMOn7+dIqZ3xeuoevnT8eYrSRmqX4ue677G/oEmigzqF53KAShiIKuuxgjjjGoGEJnDBVDZRXDBa9evepQlEPPSPT6xDKKoWW9ujERERERERFFqNBOnLlkyRI19wx6CGLCVSyriF5++De8WexcARERERERkYeyua52iMpnly5dHPYFNVcJ5l7p2bOnvXsu5snB8EQkspjQNXny5Gr/jRs3HCqnuJwvX74wxcTKKRERERERkYeJESOGWsrQvAWVnPr5+QWaDBTde41VRjBfDBJUTLhqwLw2mHS1WLFiYYqJlVMiIiIiIiIrsFmvdohJQTHGFCtMYHJVLEOJyZCaNm2qrse40k6dOqlVOjAJK5JVrIuaMmVKNfFpWDA5JSIiIiIioiBhuUMkm23atJGbN2+qpLNVq1ZqOUlD9+7d1eoiLVu2lPv370vJkiXVqiXe3t4SFpytl8gDcbbeiKXrbKE6zrCoU8y6xatzzDp+/nSKmd8XrqHr50/HmK0kZplBLnuup1tcNzNwaFns7SAiIiIiIvJQXjbxZNbr1ExEREREREQeh5VTIiIiIiIiK7B5du3Qs189ERERERERWQIrp0RERERERFZg45hTIiIiIiIiIrdi5ZSIiIiIiMgKbJ496tKzXz0RERERERFZAiunREREREREVmDz7DGnTE6JPFCprD6iE28Nv6liRdfvfy46trNuMesWr64x6/j50y1mHY8Lxsx2JuvT8KuFiN7Xo+evtWnEuDG85NR1P9FJ1uSxxM8/QHT7YfzspWj3Q1OnmHWLFxiz69r59mN9Do7EcaLyO84F+Pnz0JMWNs8edenZr56IiIiIiIgswWrnCoiIiIiIiDyTTa8u/uGNlVMiIiIiIiJyO1ZOiYiIiIiIrMDm2bVDz371REREREREZAmsnBIREREREVmBjWNOiYiIiIiIiNyKlVMiIiIiIiIrsHn2qEvPfvVERERERERkCUxOiYiIiIiIyO3YrZeIiIiIiMgKbJwQiYiIiIiIiMit2K2XKJQGDBgg+fLls0x7XbhwQWw2mxw4cMDdoRARERFReE2IZHPRZkHWjIoija+++kolUM7bmTNnJLImq56UNM6ZNUO+bFBXShctKBXLlJCuHdvJhfPnxUqOHNwrg3p2lMa1Kkq1Mvll+1+bHK5fNGeatG5UU+pULib1Py4tfbu0kpPHDouV7N2zWzq2ay0Vy5WS/Ll9ZdPGDaKDxYsWStWK5aRw/tzSsH5dOXzokFidbjHrFi8w5og1e/oUKVEwp8PWoNYnYmX8jnMdfv7I6picUoSrUqWKXLt2zWHLkCFDmB/H399fXOHFixcueZ7IYN+e3VK3/ucy58fFMmXGbHn58oW0a91Mnvr5iVU8e/pUMmTOKq079Qry+pSp00nrjj1k8pylMnLyHEmaPKX069ZGHty/K1bx9OlTyZrVV3r16Se6WLd2jYwZNVxatWkri5eukGzZfOXrVs3kzp07YlW6xaxbvMCYXSNDpszy6++b7dv3sxeIlfE7zjX4+dOEjZVToggVI0YMSZ48ucMWJUoU2bJlixQpUkRdnyJFCunZs6e8fPnSfr+yZctKu3btpFOnTpI4cWKpXLmydOvWTT755L8zwBMmTFBVynXr1tn3Zc6cWWbNmqX+3r17t1SsWFHdP378+FKmTBnZt2+f43eAzSbff/+9VK9eXWLHji1Dhw5V+0eMGCHJkiWTuHHjSrNmzeTZs2fv3AZHjhyRqlWrSpw4cdRjNmrUSG7fvm2/HvGXLFlSEiRIIIkSJVKv8ezZsw6PsWvXLsmfP794e3tLoUKFZP/+/eJu302bKdU+rSmZMmeRrNl8ZcDg4XL92jU5fuyoWEWhoiWlUfO2Uqx0uSCvL1uxquQrVFSSp0wt6TJkkuZtu4rfk8dy4expsYqSpUpL2w6dpFz5iqKLBfPmSK069aRGzdqSKXNm6dt/oDp2Vy7/RaxKt5h1ixcYs2vg/7GJEiexbwl8fMTK+B3nGvz8kQ5YOSW3uHLlinz00UdSuHBhOXjwoEoOZ8+eLUOGDHG43bx58yR69Ojy999/y7Rp01RyuW3bNnn16pW6HgkuEs/NmzfbHxdJHRJbePTokTRu3FjdZ8eOHZIlSxb1vNjv3EW3Zs2acvjwYWnatKksWbJE7Rs2bJjs2bNHJc9Tp059p9d6//59KVeunEos8VhIRG/cuCH16tWz3+bJkyfSpUsXdf3GjRvFy8tLxfP69Wt1/ePHj1XCmiNHDtm7d6+KDYm61Tx+/KZd48WPLzpC1XzdquUSO04cSZ8pq7vD0dYLf391gqJoseL2fTimixYtLocOuv+kSmSIWbd4gTG7zuWLF6V65bJSt3plGdCnu1y/dtWFzx758VhmO0f4bL02F20WxKVkKMKtXr1aVQwNqCBmzZpV0qRJI5MnT1aVS19fX7l69ar06NFD+vXrp35kAZLJUaNG2e+LqiMSS1QNCxYsKFu3bpVvvvlGVq5cqa5HkpoqVSpVPQUkhWYzZsxQ1UkkteYK7Oeffy5NmjSxX65fv76qlmIDJM0bNmwIVD1FMmt+bRAQEOBwGa8RiSkSXcMPP/ygXv+pU6dUW9SuXdvhPrg+SZIkcuzYMcmVK5csWrRIJapI4FEZyZkzp1y+fFm+/vprsQrEN3bUcMmbv4BkzqJXYrfrn60yelBPef7smfgkSiyDxkyT+AmsXWmwsnv376kTSOgFYIbL58+fEyvSLWbd4gXG7Bo5cuWRPgOGStr06eXOrVvyw8zvpU3zL2XBkv+p3kH0/ngsu4aO7Uzvj8kpRbgPP/xQVUYN+J9j27ZtpVixYioxNZQoUUJVCJF0pU2bVu1DAmqGxDJv3rwqCUVFFVvLli2lf//+6r5IOlFdNaBC2bdvX3X7mzdvqi85Pz8/uXjxosPjopus2fHjx6V169YO+xDvpk2Ok+lky5ZNfv31V4d9qN4alVtAZRj3c05iAVVeJKenT59WSfnOnTtVd1+jYoo4kZwinjx58qjE1BzP2zx//lxtZuhGLRJNwtvIoYPk7JnTMmvuQtFNnvyFZeKsxfLwwX35Y/VyGTmgu4ydtkAS+CR0d2hERGFSrEQp+9+Zs2STHLnzSO2PK8qf69dJtRqOJ0KJyIJsnt2xlckpRTgko0Yl813u6wyJH5JNJFlIRBMmTCjZs2dXXXeRnHbt2tV+W3TpxeQgEydOlHTp0qn7IKlznlzpXc8mIzl2fm1Rozp+rJA0V6tWTUaOHBno/uguDLge8c2cOVNSpkypklMkpe87CdTw4cNl4MCBDvuQyHftFb4T64wcNli2bd0iM+YskGTJk4tuvGPGlJSp06rNN2ceafl5dVn/2wqp+8WbyjmFjU8CHzXmzXliHlxGN3wr0i1m3eIFxuwecePGkzTp0snlS44nZend8Vh2DR3bmd6fZ6fm5DZIJrdv3+7QBRbjSjH5UOrUqUO8rzHuFGMzjQol/v3pp59UN1lz1RKP2aFDBzXOFF1hkZyaJyIKKT5UMc0wZvVdFChQQI4ePSrp06dXiax5Q1KML9mTJ0+qCm/58uXVc9+7dy9QPIcOHXLoVhyaeHr16iUPHjxw2LAvvOD9Q2K6+c8N8v2sOZLqLe+dLvC6OGvzu4sWPbpkz5FTdu7Ybt+HEy47d26XPHnzixXpFrNu8QJjdg8/vydy5fIlSZw4iZsiiHx4LLOdI5SNY06JXK5NmzZqpt327durGXmRnKGih0mBjPGmwSldurQad4qxrJhRF5CQ1qlTR1Ui0U3WgDGrCxYsUN12Hz58qManxowZ863xdezYUa3Rivuhu/HChQtVgpkxY8Ywv1Z0YUZFtEGDBtK9e3dV6cU6r4sXL1azCvv4+KjxExgPi/jRlRczF5thTGyfPn2kRYsWKrnEWqpjxox563MjGX/TjdeR//M33YbDoyvvurW/ydiJkyVW7Nhy+/YttT9OnLgOXZDdCcvaXLtyyX75xrUrcu70SYkTL57Ei5dAliyYJUVKlJGEiRKrbr2/rVgid27flBJlK1rqx+UlU1f0K1cuy8kTx9XEUylSpBQratS4iXzbu4fkzJlLcuXOIz8umKeWi6hRs5ZYlW4x6xYvMOaIN3n8aClRuqwkT5FSbt+6KbOmT5EoXlGkQpWPxKr4Heca/PyRDtitl9wCkxatWbNGJYsYQ4qEDZMPoXr4NkjmcufOrcaTYiIlI2FF1cA83hQwgRDGpKJ6iQmIMClRaGa5/eyzz9R4UCSTqFZiwiJMPvT777+H+bWimy4quJjsqVKlSmoMKLrwYv1XJOIYd4tEFRVedOXFONZJkyY5VIAxXnXVqlVqHCwmV8Ksvegm7DyRkqstW7JY/duqaWOH/f0HD1NLzFjBmZPHpHenFvbLs6eMVf+Wq1JN2nbpI5cvXpCNv69SiWm8ePEli29OGTHpB7WsjFUcO3pEWpjaeOzoNydlqlWvIYOGvvnbaqpU/Uju3b0rUydPUictsvlml6nTZ0kiC3fF0i1m3eIFxhzxbt68If17f6O+0zBuPk++AjJ97iLxsfAYen7HuQY/f5qweXbHVluA89SiRBTpPQqnyqkrxI3hJaeu+4lOsiaPJX7+en21xopuk2f/LTOsBe+oolXMusULjNl17Xz7sT4HR+I4Ufkd5wL8/Lmuna0kZs1ZLnuupyuai9VY7O0gIiIiIiLyUDZrrj/qKp5dNyYiIiIiIiJLYOWUiIiIiIjIAmysnBIRERERERG5FyunREREREREFmBj5ZSIiIiIiIjIvVg5JSIiIiIisgKbeDTO1ktERERERERux+SUiIiIiIiI3I7deomIiIiIiCzAxgmRiIiIiIiIiNyLlVMiIiIiIiILsLFySkRERERERORerJwSERERERFZgI2VUyIiIiIiIiL3YuWUiIiIiIjIAmweXjllckrkgeLG0GuJ46zJY4luYkXX738u3hr+H0G3mHWLFxizaySOo9fBwe841+DnjzyNXt+ERBQubj9+qdUPtmf6hGv/MREzfzvRydP9k+XR89ei20kWnY4NHBc6xatzzH7+AaJborf++G3RRcXsiXlcuOi40PF7+Z7fK9GJT6woYik28Wh6lU+IiIiIiIjIpdKnT6+6HDtvbdu2Vdc/e/ZM/Z0oUSKJEyeO1K5dW27cuBHm52FySkREREREZAG2IBLAiNrCYvfu3XLt2jX7tn79erW/bt266t/OnTvLqlWrZOnSpbJlyxa5evWq1KpVK8yvn916iYiIiIiIKFhJkiRxuDxixAjJlCmTlClTRh48eCCzZ8+WRYsWSbly5dT1c+bMkezZs8uOHTukaNGiElqsnBIREREREVmAzaKVUzN/f3/58ccfpWnTpupx9u7dKy9evJAKFSrYb+Pr6ytp06aV7du3h+mxWTklIiIiIiLyMM+fP1ebWYwYMdQWkpUrV8r9+/flq6++UpevX78u0aNHlwQJEjjcLlmyZOq6sGDllIiIiIiIyMMqp8OHD5f48eM7bNj3NujCW7VqVUmZMmW4v35WTomIiIiIiDxMr169pEuXLg773lY1/ffff2XDhg2yfPly+77kyZOrrr6oppqrp5itF9eFBSunREREREREHlY5jREjhsSLF89he1tyiomOkiZNKh9//LF9X8GCBSVatGiyceNG+76TJ0/KxYsXpVixYmF6/aycEhERERERUYhev36tktPGjRtL1Kj/pZHoDtysWTNVhU2YMKFKctu3b68S07DM1AtMTomIiIiIiKzAJpaF7ryohmKWXmfjx48XLy8vqV27tppkqXLlyjJ16tQwPweTUyIiIiIiIgpRpUqVJCAgIMjrvL29ZcqUKWp7Hxxz6oFwULVs2VKV3dHf/MCBAxIZYDrrGjVq2C+XLVtWOnXqJJGV8+slIiIiItIZk1MPTEzWrVsnc+fOldWrV8u1a9ckV65cKknFmkVBefr0qUpkEydOHGgtpPCWPn36QIO1U6dOHS6PjWQ1qMHgL1++DJfH90Szp0+REgVzOmwNan0iVrd40UKpWrGcFM6fWxrWryuHDx0Sq/Dyskm/Nh/L8dUD5O72cXL01/7Ss0WVYG8/qU99ebp/srT7vKxYxZxZM+TLBnWldNGCUrFMCenasZ1cOH9edGDlYyMyxKtbzHv37JaO7VpLxXKlJH9uX9m0cYNYye/L5suobs2ka/0K0rPxxzJjWE+5ceXfYE9MTx3UVdrVKCEHd2wVq9HpuNDh2IhM38sw/4eZUjR/Dhk/+u3LnOjO5sIJkayIyakHOnv2rKRIkUKKFy+upnc2D2gOyi+//CI5c+YUX1/fYBNYsxcvXrxXfIMGDVJJs7Ht379fwkuLFi0cHhvb215/cDBltiu8b3tGtAyZMsuvv2+2b9/PXiBWtm7tGhkzari0atNWFi9dIdmy+crXrZrJnTt3xAq6flVRWtQpJZ1HLJV8tYZI30n/ky6NK0ibBmUC3bb6h3mkSO70cvXmfbGSfXt2S936n8ucHxfLlBmz5eXLF9KudTN56ucnVmb1Y0P3eHWMGSdns2b1lV59+okVnTl6QEpXrSXdRs2QdgMmyKtXL2XygM7y/NnTQLfdtOpnsSrdjgsdjo3I8r0Mx44elhW/LJHMWbK5OxRyASankdCRI0fUwrhx4sSRZMmSSaNGjeT27dv2iitmz8JgZpwxQaUSG9SsWdO+z3mh3S+++EJt+NsZ7vP9999L9erVJXbs2DJ06FAZMGCA5MuXT3744QdJmzatiqVNmzby6tUrGTVqlEqKMQ01bussbty46npjS5IkibofZgHLkCGDxIwZU7JlyyYTJ04Mc9vEihXL4bHNay8ZSTim0EYbjB071uG+2Dd48GD58ssv1Sxk6Bpdp04dadeunf026EaM9jhx4oQ9gUWbYAC5UbUuWbKkWgMqUaJE8sknn6iTBYYLFy6o+//8889SpkwZ1X9/4cKF6vVjBjTjft27dw+2z7+rRYkSRRIlTmLfEvj4iJUtmDdHatWpJzVq1pZMmTNL3/4DVTuvXP6LWEHRvBll9ZZDsm7bUbl47a6s2HBANu44IYVypnO4Xcok8WVcj7rSpPdcefHylVjJd9NmSrVPa0qmzFkkazZfGTB4uFy/dk2OHzsqVmb1Y0P3eHWMuWSp0tK2QycpV76iWFHb/uOkaPmPJUXajJI6Qxb5okMfuXfrhlw6e9LhdpfPnZI//7dYvmjfW6xIt+NCh2Mjsnwv+/k9kf69u0uvbwdK3HjxxBPYWDmlyASL35YrV07y588ve/bsUckQFsCtV6+euh4JHSqT6CqLquHu3bvVBpga2thnQOK0fft2dX9sf/31l1p81xmSUSS3hw8fts/ghfuuXbtWxfDTTz+pxBZrIl2+fFm2bNkiI0eOlL59+8rOnTtDNXU1Yl66dKkcO3ZM+vXrJ71795YlS5aES7vt3btXvb769eur14DX8+2336ruz2ZjxoyRvHnzqmourkcCuXnzZvv1eF3o/mzsQ1ui8okqNTx58kQlmXhvsBYUZjVDu+H1mfXs2VM6duwox48fV7OdIVFGLEj2t23bJnfv3pUVK1aIFVy+eFGqVy4rdatXlgF9usv1a1fFql74+6v/ERct9ub9ALwHRYsWl0MHw69C/z52HDwnHxbJJpnTJlWXc2dNJcXyZZQ//j7m8D+u2UO+lPHzNsrxc9fF6h4/fqT+jRc/vliVDseGzvHqGrNunvk9Uf/GivPfj3j/589k7riBUq9lV4nnk0ishseFe+jwvQxjhg+REqXKSJGi/31vUOTG2XojmcmTJ6vEdNiwYfZ9SGjSpEkjp06dkqxZs6rKJKpd5qohoCrnvA/3RRXW5/+rYUiUkMQieTP7/PPPpUmTJg77kHDh/ni+HDlyyIcffqgW5F2zZo36QYLqJxLUTZs2yQcffGC/X48ePVTSasBr6dChgwwcONC+DxVUJM1ITo3EOzQwpfWsWbPsl1u1aqUSv3Hjxkn58uVVwgloJyTBo0ePVtVmAxL/rl27OoxjRRJ569Yt1T0Y98FjIDlt3bq1+rdw4cKqYguYXtu5fVEZxv0w9tdcga1Vq5b98oQJE6RXr172fdOmTZPff/9d3C1HrjzSZ8BQSZs+vdy5dUt+mPm9tGn+pSxY8j9VMbaae/fvqSo0qs9muHz+/DmxgjFz1ku8ON5ycEVfefUqQKJEsUn/Katl8do99tt0bVJRXr56LVN++u/EiFXhe2DsqOGSN38ByZwlq1iVDseGzvHqGrNO8FlbNnuiZMyeR1Kmy2jf/8vsSZLBN5fk+aCUWBGPC9fT5Xt5/bo1cvLEMfnhx/ApROjCZtGxoK7C5DSSOXjwoEr20I3WGSqZSLpCCz8i5s2b59B9Fl17u3XrpiqXSDANhQoVCnR/dINFYmpAF2Mkxeb7Yd/Nmzcd7vfNN984JISoRAKmpkYyhy7JGOuBLrPoOhwWDRs2lD59+jgk5IAK5aeffupw2xIlSqikEO2AuIN6nUgoMVkUKqbRo0dXJwbQVdeYRhv7kcAaTp8+rdoO1WJ0tTYqpnhN5uTU/DwPHjxQFW1zAo9EGLd5W9deTGDlPIkVui2LvHk976tYif9+7GAsSI7ceaT2xxXlz/XrpFoNx0ScQqdOpQJSv2ph+ar3PDl29prkyZZKRnerI9duPZCFq3ZK/uxppG2DslL885FaNOnIoYPk7JnTMmvuQneHQhSpLZkxVq79e046D//evu/Qrr/k1OG90nPcHLfGRtaiw/fyjevXZNzo4TLp+1n//7uFPAWT00jm8ePHUq1aNVWRdIZJkMIClbkrV67IZ5995rAfyRq6pFas+N84i6CqZNGiRQt0Jiiofc5dWpGMZs6c2WHf4sWLVVKMKmexYsVU0ouqZmi6BJvFjx8/0GOHhfPrRPylS5dWFVJ8eSIRzZMnj0oIMfb3n3/+UXEb8N6kS5dOZs6cKSlTplSvHUmp8+RK4VV1HD58uEPFGfr37y/tuv1XmQ5PcePGkzTp0snlSxfFinwS+KgTDc6TbOCycRLE3YZ1qqGqp0t/36suHz1zVdKmSCjfNKmoktMS+TNJ0oRx5NSaQfb7RI0aRUZ0qSXtGn4ovh/3F6sYOWywbNu6RWbMWSDJnHplWI0Ox4bO8eoas06J6ZHd/0inYVPEJ/GbIQFw6tBeuX39inzT0HHG71mj+kim7Hml09DJ4m48LlxLl+/lE8ePyr27d+Srz+s4/P48sG+PLPt5kWzdecBeOIh0bOLRmJxGMgUKFFAT+6BqGZZZaJE04kNvhjGiGINprjQCJjHCdebkNKL9/fffatwmJlUymCcSel/Zs2dXz+H8nKg0v+3LD+NOkWwiOUXboDKMhBXJM5JUVGCNH2Do1ozblir1puKI8aOhSahxYgGJOB4XsPwNxsni/Q4JugJjjKsZ4nz0IuImLrhy+ZJU+ai6WFG06NEle46csnPHdilXvoLahxMEO3dul/oNvhAriOkdXV4HOJ6wefU6wN7jYNFvu+XPnY6Tnaya2lYW/bZL5v9vh1gBKvqjhg+RzX9ukOmz50mqcFoOytOPDZ3j1TVmq8NnbenMcWppmI5DJkviZCkdrq9Uu5EUr+j4fTysYyOp3bSD5Cr85v9N7sbjwjV0+14uVKSYLFz6P4d9Q/r3kXQZMkijr5pH3sSUmJzqDN09Dxw44LAPM8gi+WnQoIGa0RVdTs+cOaMqjxhrGdyHGcksqqFIpJC8IPlZtWqV/Prrrw7dTQGz1WISH0zKg8d3hSxZssj8+fNVNRfjTRcsWKAmG8Lf4QHjSDE2FLPxolKM8awYv4sxqm+Damnnzp1Vt17MxGvsQ8UUj2lUQTFuF2OrZsyYoZJNdOXFxEehgXGtI0aMUO2AJX0wRhaTX70N3sugusM8ehE+a7tOHj9aSpQuK8lTpJTbt27KrOlTJIpXFKlQ5SOxqkaNm8i3vXtIzpy5JFfuPPLjgnmqm3iNmv+N8XWnNVsPS49mleXStXuqW28+39TS4YsPZf7KN4nn3QdP1GaG2Xpv3H4op/917CLvzi5j69b+JmMnTpZYsWPL7du31P44ceKqGTityurHhu7x6hgzTrhduvhfT5ArVy7LyRPH1SQyKVI4JoLusGT6WNmzdb207D1CvGPGkof33lSlvWPFkegxYqgJkIKaBMkncbJAiaw76XZc6HBs6P69jN9OmFnYzDtmTIkfP0Gg/ZGNjWNOSVfoSooxjmZYbgUVP0wqVKlSJVW5QzfSKlWqOIz1dIbusqiwIbFNlSqVWm4GXwyYJMgZ9mE5lx9//FFNVOQKmLgIM+QiccSHFsk3qqiYDTg8oAKJyZUwHhQJKpJHzGpsHvsanNy5c6uxq6iyGmN9kZyiEm0eb4r2x0kCtBkSfkwINWnSJIfbhJQ8Y9xp48aN1eNgRmScIMAJCne6efOG9O/9jTx8cF8S+CSUPPkKyPS5i8THxzUnLd5Flaofyb27d2Xq5Enqf87ZfLPL1OmzJJFFuhV2GblU+rf5RCb2/kyS+MRRY01nL/tbhs0In2PdFZYtWaz+bdW0scP+/oOHqaUMrMrqx4bu8eoY87GjR6SF6TgeO3qE+rda9RoyaOibv93pr3VvZm2f2Pe/Jc0AS8ZgiRld6HZc6HBsRJbvZfI8tgCrLJZIRC5z+3H4VE5dIXGcqPJMn3AV76giMfM7/li0uqf7J8uj547dia0ubgwvrY4NHBc6xatzzH7+ev20iRXdJuuPv1mPXAcVsyfmceGi40LH7+V7ftZae/ttfGJZq4tw8hbLXPZc12f+N6bXKoIvpRERERERERG5CCdEIiIiIiIisgCbh485ZeWUiIiIiIiI3I6VUyIiIiIiIguwsXJKRERERERE5F6snBIREREREVmBTTwax5wSERERERGR27FySkREREREZAE2jjklIiIiIiIici9WTomIiIiIiCzAxsopERERERERkXtxQiQiIiIiIiJyO3brJSIiIiIisgAbu/USERERERERuRcrp0RERERERFZgE4/GMadERERERETkdraAgIAAdwdBRERERETk6dK2/9Vlz3Xxu+piNezWS+SBnr0UbXhH1StenWO+cOeZ6CR9Im+t2lnX40LHmP389TrvHiu6TauYEa+OxwVjdk073/N7JTrxiRXF3SGQCZNTIiIiIiIiC7Bxtl4iIiIiIiIi92LllIiIiIiIyAJsrJwSERERERERuRcrp0RERERERBZgY+WUiIiIiIiIyL1YOSUiIiIiIrICm3g0L3cHQERERERERMTKKRERERERkQXYOOaUiIiIiIiIyL1YOSUiIiIiIrIAGyunRERERERERO7lERMiBQQESMuWLSVhwoTqbMSBAwfcHZKW0qdPLxMmTAi3xytbtqx06tRJItLcuXMlQYIEYbrPV199JTVq1IiQeELz2M7tfP36dalYsaLEjh07zK+FiIiIiPRhs7lusyJtktP3SRjWrVunkpTVq1fLtWvXJFeuXCpJXblyZZC3f/r0qUpkEydOLM+fP5fI6sKFC2FK1nfv3q2S/IjQs2dP8fX1ddh34sQJFR/eezO8lzFixFDv09t89tlncurUKZcl6jNnzpS8efNKnDhxVCKZP39+GT58eJge27mdx48fr45bvE94LZs3b1btcv/+fbGKxYsWStWK5aRw/tzSsH5dOXzokFiZbvFaPebD+/dKv2/aS4PqFaRy8bzyz5Y/A50gnDdzijSoVl6qlS0iPTq0lCuX/hUrsnI7R4Z4dYt5757d0rFda6lYrpTkz+0rmzZucHdIkTJm3Y4LA2N2nfk/zJSi+XPI+NFh+01F+tEmOX0fZ8+elRQpUkjx4sUlefLkEjVqyENtf/nlF8mZM6dKloJLYD2Jv7+/+jdJkiQSK1asCHmODz/8UE6ePKmqhIZNmzZJmjRpVDJmhv1FixaVmDFjvvVxcZukSZOKK/zwww+qEtyhQweVSP7999/SvXt3efz4cZgex7mdcfwWLFhQsmTJ4rLXEhbr1q6RMaOGS6s2bWXx0hWSLZuvfN2qmdy5c0esSLd4dYj52bOnkjFzNmnXtVeQ1y/5cY78b+lP0v6bvjJx1o/i7R1Tenf+WvwtdvLP6u2se7w6xoyToFmz+kqvPv1EFzrGrNtxAYzZdY4dPSwrflkimbNkc+GzkrtEiuT0yJEjUrVqVVWtSpYsmTRq1Ehu376trkPVrX379nLx4kVVbULFCxvUrFnTvs9s9uzZ8sUXX6gNfztXAAYMGCBp06ZV1buUKVOqZAQGDRqkqrLO8uXLJ99++61DBXjYsGEqVlTXcL+XL1/KN998oyq2qVOnljlz5gSqcC5ZskRKlSqlEq7ChQurKhqqbIUKFVKvHW1w69Yth+eeNWuWZM+eXby9vVWyPXXqVPt1GTJkUP+iuofHRzdbc4xDhw5Vry9btmxBVgtRuWvVqpV6HXh8vHZUpwH/Q2nQoIGkSpVKJVq5c+eWn376Kdj3sGTJkhItWjSHRBR/t23bVu7evavawLwfySygst2tWzf1POj2+sEHHzg8RlDdeocMGaKSvLhx40rz5s1V1RbvkbMxY8aokxqJEiVScbx48ULtRzv9+++/0rlzZ9VuxsD1X3/9VerVqyfNmjWTzJkzqxMcaAO0Y2gf27md8TdOlsyfP99eRTZeu4+PT5CVZVdbMG+O1KpTT2rUrC2ZMmeWvv0HquNh5fJfxIp0i1eHmAsXKylftWonJcqUD3QdvjNXLlkoDb5qIcVLfygZM2eV7v2GyJ3bt+SfrY4VVnezejvrHq+OMZcsVVradugk5cpXFF3oGLNuxwUwZtfw83si/Xt3l17fDpS48eKJJ7D9/29LV2xWpH1yigSpXLlyKsHas2eP6sJ748YNlSTAxIkTVfKHhA9dI5HMYQMkgMY+c5Vq+/bt6v7Y/vrrL5WIGJAooJvl9OnT5fTp06qyisQLmjZtKsePH3d4vP3798uhQ4ekSZMm9n1//vmnXL16VbZu3Srjxo2T/v37yyeffKKSjZ07d0rr1q1V0nf58mWH14rb9e3bV/bt26eqv59//rmqzOE1Is4zZ85Iv37/nSlduHChuozkCHEhIUaSPG/ePHX9rl271L8bNmxQ7bB8+XL7fTdu3KgqmevXr7cnnGavX79WyTCqgz/++KMcO3ZMRowYIVGiRFHXP3v2TFX7fvvtN3XyAN1UcdLAeE5nSCyRcKMqakCSWb58eSlRooR9/7lz59SJBiNBa9eunXq/Fi9erNq5bt26UqVKFfXeBAVtgvYYOXKk7N27V51k+P777wPdDs+HYwH/or2Q5GIDtBOOJxxXaDdsgKr8jh07HI6XoIT02M5wLOH14FjE8+C9xjEIeH+Mfe7ywt9fjh87KkWLFbfv8/LykqJFi8uhg/vFanSLV9eYza5fvSJ379yWAoU+sO+LHSeu+ObILcePWKfbnm7trFu8usZMEU/H44Ixu86Y4UOkRKkyUqTof8cHRW7aLyUzefJklZgi8TJ3r0R3UFQWs2bNqipkSJqQPJihoua8D/dF0oVEESpXrqySWFRLAYkR7lOhQgVV6UNyU6RIEXUdEhbj9ki0AH+XKVNGMmbMaH8OVEcnTZqkvnxRlRw1apT4+flJ79691fW9evVSid62bdukfv369vuhQojHh44dO6qqHJJIJG+Aip05yUEyO3bsWKlVq5a9UookEol148aNVfdRQPXOuR2QLKLqGj169CDbHQktEk0kvWhjML9GVDIRrwHV699//11Vf432coaEc+nSpepvxIkEF+9t6dKlVaKKBB//4mwquvXivUD74l9UeI02wgkK7DcfE4bvvvtOtZNxsgDJ+x9//BGo6y3efxxbOG5Qcf74449VW7do0UK9f9iP48rcbmhvtDWqnWiTYsWKyUcffSR16tRR73VoHtsZ3iNU6FEtN54Lzw+o/rp7gqR79+/Jq1ev1DFkhsvnz58Tq9EtXl1jNrt7900vlgQJHePHZeM6K9CtnXWLV9eYKeLpeFwwZtdYv26NnDxxTH74cYl4Eps1C5ouo33l9ODBg6oChW6txmZMrIPqVFjgyxGVLHTnNeBvJHyoFAIqcxjPgUQMycSKFStUl1wD9qH7KhIrjNVctGiRqqiaobunOVlBt1ij+gpIWvClfPPmTYf75cmTx+E+YL4f9hn3efLkiXr9SMTMbYMuraFpFzxucIkpYEwlknEjMQ2qLQcPHqweB8kUnhvJKRLJ4KC7LE4ooBqIJBRdfdEWSO6Nrrr4F2OHkbAdPnxYPQ9iML/GLVu2BPsaUW10To6DSpbxHhlVYEAXXOf3wxlugyou4sLJAxwXOAmAyqdx/LzrY78rdHt++PChwxaZJ/kiIiIi/d24fk3GjR4uA4aOUr/5yHNoXzlFxatatWqqm6Yz/OgPCyRPV65cUTO8miEBQmULy3mgIosEB5VDdHlt06aNjB49WiVEqKQiFnyIkLQiucNYQlTOzHA7M/T5DmqfOaFxvp/RT9x5n3EfoxKI2WMxDtPMnBgFB5XTkLxtMiK0CbqbYuwkElQ8HiYLMiZXCgoqwGgznGzAhqQUUIXGGGJ06UVyii7PxmvEa0H3XOfXhCT1fYTm/QgOxt5iw7GBLtoYJ4zjw+iK/D6PHVaYKXjgwIEO+1Dh7dn3TU+A9+WTwEe1vfOkFbiM2a6tRrd4dY3ZLGHCNzHev3tHEiV+01vDuJzJQpNb6NbOusWra8wU8XQ8LhhzxDtx/Kjcu3tHvvq8jsPv8QP79siynxfJ1p0HQvV7Vkc2Dy+dal85LVCggBw9elR1pcQkNOYtpAQLCQIOcjNMfoRutKgKmjfsM0+MhMQMSSi65iJZMqplgLGgqJahWyk23Dc0s8qGN1RR0dUVCZ1zuxgTIRmVUed2CA1UcTEmNrhlWjAW9dNPP1WVZyytgkrz25Z0QTsZExohmTMmaMJ7hW68eA8uXbpkT/LQ5Rexo+ro/Bqduykb0I3aPCYYnC+HBtouNO2WI0cOeyU7vITlfUMX8QcPHjhs2BdeokWPLtlz5JSdO7bb9yHR3rlzu+TJm1+sRrd4dY3ZLHnKVJIwUWLZv2enfd+TJ4/lxLHDkj3Xf71B3E23dtYtXl1jpoin43HBmCNeoSLFZOHS/8n8xcvtW/YcuaTyR5+ovyNrYkqaVU7xw9p5TU5MtIPqIMZfYnIgdCHFxECYIAdjJoM7eJHMGuM1UelEF8xVq1apGVedZ9z98ssv1cy+mDUW1yMpQBKFWWgxGRCSqnTp0tlvjxlgMUOukaS5CypmmEk4fvz4qmspunNi0qh79+5Jly5d1JhFxI4xmuiii7GcuG1ooKqJsaC1a9dWkzohITTWJcVzYdmTZcuWyT///KPGWOI2mKjKSNaCg8QTE04ZJx7Mz4cZbo2JkwDdeRs2bKjeH4ytRbKK2YrxviJ5xlhOZxj7iq7XmOEY3YN//vlnNZGSebxsaOD4wYRWOPmA4wdnd7/++mt1QgATdBkTcKEbNcaNYvxpeMGxhnbGRFUY04r3MLhKMWILqjvMs/96or+3Ro2byLe9e0jOnLkkV+488uOCearre42ab8Y6W41u8eoQ81M/P7l6+b8u+9evXZGzp05I3HjxJWnyFFKjXkP5ad5MSZUmnUpW582YoqqoxUuXEyuxejvrHq+OMWOm0Eum4ShXrlyWkyeOS7z48SVFijdzHViNjjHrdlwAY45Y+L2XKXMWh33eMWNK/PgJAu2PbGyeXTjVKzlFRQ0JiBnGVCIB7NGjh1SqVEklYPjxjgTJPK7TGZIZJGhIbDF5D5IWfBAwO6wz7EMCgEQUEyBhsiLcF0kquqwiqTUP5EdihsQHyaxzl1pXQpKMBBpdbLFMDV4f4kX3WqPKi+ovZp3FxEDofuq8pmhIMGssJiDCiQFUBpGgom0AswqjaosJnBADTiJgeRqcYHhbcop48P6Z16NFcoruqHg8c7dYVKeRAHbt2lV1yUaSiCorZj8OCpJZxIW4MS4Ys+BiKZbgZhEODmJE9+JMmTKpYw7LZWCSLEyohdl/je5ISEqRLDtP9PA+cLzixAOWwMHETkjOg5vt1xWqVP1I7t29K1MnT5Lbt29JNt/sMnX6LElk0e5YusWrQ8ynThyV7u2a2y9PnzRG/Vvxo+rSre9gqfdFE7UW6sSRg+Tx40eSM09+GTpuqkS32Dgiq7ez7vHqGPOxo0ekRdPG9stjR7/5f1y16jVk0NA3f1uNjjHrdlwAYyaKGLYA/KqmcIUmRYKKMYdIYsnaMJYY3YAXLFggniI8K6cRzTuqXvHqHPOFO89EJ+kTeWvVzroeFzrG7Oev10+bWNFtWsWMeHU8Lhiza9r5nl/Yh4u5k08sa3URztH7D5c917FhlUJ9WxSBUAxcu3atWmUERSkUidAb0ch/UEhC4Q9LfaJ3Kgo2yIkibeVUB+hWii7F169fd1jblKwBH6Zp06apCiy6fGNmZWNyKyIiIiIicoQhgUg20cMRySmGrJ0+fdq+9CZgaUz0yMTKJ5jf5ttvv1W/t7E8JIYOhhaT03CGcZzozjljxgyHN4ysAWM116xZI0OHDlXdejFBErono0suEREREZE72Sw45hSromDFElRKDcYEq0bVFCt0YFgfJkSF+fPnqwlaV65cqeZoCS0mp+GMvaStDWOHUSklIiIiIvJkz58/V9vbJtPEhLCogtatW1etqIH5TzB8EZOMwvnz51WvUXOxB5OsYu4drGoSluRU+6VkiIiIiIiIIksvP5uLtuHDh6sk0rxhnzNMJmqMH/3999/VChVYEQRdeAGJKaBSaobLxnWhxcopERERERGRh+nVq1egyVuDWoIQaw9j4qNhw4apy1g95ciRI2oel8aN/5sdPDywckpERERERGSRMac2F21IROPFi+ewBZWcpkiRQnLkyOGwL3v27HLx/9dUxqoXcOPGDYfb4LJxXWgxOSUiIiIiIqIgYabekydPOuw7deqUpEuXzj45EpLQjRs32q9/+PCh7Ny5U4oVKyZhwW69REREREREFmCz4HS9nTt3luLFi6tuvfXq1ZNdu3aplUmwGTF36tRJhgwZosalGkvJpEyZUmrUqBGm52JySkREREREREEqXLiwrFixQo1RHTRokEo+sXRMw4YN7bfp3r27PHnyRFq2bCn379+XkiVLyrp168K0xinYArj2CZHHefZStOEdVa94dY75wp1nopP0iby1amddjwsdY/bzDxCdxIpu0ypmxKvjccGYXdPO9/xeiU58YkURK8nb/7+usRHt4MDyYjUcc0pERERERERux+SUiIiIiIiI3I5jTomIiIiIiCzAZr35kFyKlVMiIiIiIiJyO1ZOiYiIiIiILMDm4aVTJqdEHgiz6elEt3h1jRmz3+pGt3bWLV5dY8ZssrrRLWYdjwvG7Jmz35JeNPxqIaL3pdN0+pz+n+0cWY4N3eIFxsx25nHh3s+fTksM6bzMkJXY9DpPFe445pSIiIiIiIjczmLnCoiIiIiIiDyTzcNLp6ycEhERERERkduxckpERERERGQBNs8unLJySkRERERERO7HyikREREREZEF2Dy8dMoxp0REREREROR2rJwSERERERFZgM2zC6esnBIREREREZH7sXJKRERERERkATYPL51yzCkRERERERG5HSunREREREREFmDz7MIpK6dkTQEBAdKyZUtJmDCh6t5w4MABd4ckFy5ceKdYvvrqK6lRo0aExUVEREREFBmwWy9FmPdJytatWydz586V1atXy7Vr1yRXrlwqMVy5cqXD7XCbBAkSBPkYQd0+IpJVY0MiXaZMGfnrr78cbjdx4kQVZ2S2eNFCqVqxnBTOn1sa1q8rhw8dEivTLV5gzGxnHhf8/EXm7wzd4tUx5r17dkvHdq2lYrlSkj+3r2zauEF0oFs7vy+b6bdlRG9WxOSULOns2bOSIkUKKV68uCRPnlyiRrVuD/QNGzaoBHrr1q2SMmVK+eSTT+TGjRv26+PHjx9sAh0ZrFu7RsaMGi6t2rSVxUtXSLZsvvJ1q2Zy584dsSLd4gXGzHbmccHPX2T+ztAtXl1jfvr0qWTN6iu9+vQTXejYzvR+mJySWxw5ckSqVq0qceLEkWTJkkmjRo3k9u3b9opr+/bt5eLFi+qsTvr06dUGNWvWtO8Lq0uXLkm9evVUoogq56effqqqn2azZs2S7Nmzi7e3t/j6+srUqVPf+riJEiVSCTSqu71795aHDx/Kzp07g60gL1u2THLnzi0xY8ZU961QoYI8efIkVDH4+/tLu3btVOKO69OlSyfDhw8Xd1owb47UqlNPatSsLZkyZ5a+/Qeq2FYu/0WsSLd4gTGznXlc8PMXmb8zdItX15hLliotbTt0knLlK4oudGzn92WzuW6zIian5HL379+XcuXKSf78+WXPnj2qCy8qjUgcjW6wgwYNktSpU6uK5O7du9UGc+bMse8LixcvXkjlypUlbty4qtvt33//rRLjKlWqqIQPFi5cKP369ZOhQ4fK8ePHZdiwYfLtt9/KvHnzQn1Gcv78+erv6NGjB3kbxN6gQQNp2rSpeo7NmzdLrVq11Bjb0MQwadIk+fXXX2XJkiVy8uRJdft3SdTDywt/fzl+7KgULVbcvs/Ly0uKFi0uhw7uF6vRLV5gzGxnHhf8/EXm7wzd4tU1Zh2xnT2TdftKUqQ1efJklZgi8TL88MMPkiZNGjl16pRkzZpVJZFRokRRFUkzVD2d9z148EAlmiH5+eef5fXr16oqafSxR6KLx0OCWKlSJenfv7+MHTtWJYuQIUMGOXbsmEyfPl0aN24c7GOj6zH+p+Tn56eSzIIFC0r58uWDTU5fvnypngNVT0AV1fC2GFBNzpIli5QsWVK9DuMx3OXe/Xvy6tUrVQE2w+Xz58+J1egWLzBmtjOPC37+IvN3hm7x6hqzjtjOnonJKbncwYMHZdOmTUEmlBhriuQ0LJDI7tu3L9B+JHHm5zxz5oy6rdmzZ8/Uc6JbLf5t1qyZtGjRwn49EkmMGX1b4ovut+iq3L17dzX5UbRo0YK8bd68eVXiioQUlVwkxXXq1BEfH59QxYAuwhUrVpRs2bKpqi/Gt+IxgvP8+XO1mcWIEUMkSowQXxMRERERuZ7Nqv1tXYTJKbnc48ePpVq1ajJy5MhA12EsZVihapk5c+a3PicqmugG6yxJkiTqepg5c6Z88MEHDtejghsSVHyRCGNDIolxsUhUVRLoBI+1fv16+eeff+SPP/6Q7777Tvr06aPGqMaKFeutMRQoUEDOnz8va9euVRMxoSs0xqxiHGtQMB514MCBDvtQne3Zd4CEB58EPio254kJcDlx4sRiNbrFC4yZ7czjgp+/yPydoVu8usasI7azZ+KYU3I5JFhHjx5VYyWRVJq32LFjB3s/VCPRjeZdn/P06dOSNGnSQM+JqiQmZcJMu+fOnQt0PbrWhhaqoJhZOKSJlHBGrESJEipp3L9/vxqfumLFilDHEC9ePPnss89UEouq7S+//CJ3794N8rl69eqluj2bN+wLL9GiR5fsOXLKzh3b7fvQfXrnzu2SJ29+sRrd4gXGzHbmccHPX2T+ztAtXl1j1pGntrPNwydEYuWUIhSSoQMHDjjsa9mypUqsMDEQusFi5lx0uV28eLEaExpcpRLJ7MaNG1Vih6okusKGVsOGDWX06NFqhl5jsqV///1Xli9frmLAZSSLHTp0UMkqusyiOywmbLp375506dIlVM+DxBOPMWDAAGnVqpW9GmpAhRSvAV1xkSjj8q1bt9TsvPC2GMaNG6eqyxizi4rx0qVL1Rjc4JaqQTsFVcF99lLCTaPGTeTb3j0kZ85ckit3HvlxwTw1OVSNmm/GzVqNbvECY2Y787jg5y8yf2foFq+uMfv5PZFLFy/aL1+5cllOnjgu8eLHlxQpUooV6djO9H6YnFKEwmRDSKTMMKYSs+X26NFDJWlIwDCxD5IxJFzBwURBSNCQ2KZKlSrQMjAhQZKIdUjxnJhs6NGjR+oxMP4TlUho3ry5uh2S2G+++UZVcTE2tFOnTmF6zZi4CF11MfETEl8zPBfimDBhglpyBq8brwvL6oQmBoyZHTVqlKoCI4kvXLiwrFmzJsR2i2hVqn4k9+7elamTJ8nt27ckm292mTp9liSyaNcm3eIFxsx25nHBz19k/s7QLV5dYz529Ii0aPrfBI9jR49Q/1arXkMGDX3zt9Xo2M7vy2bVkqaL2AKMNSyIyGOEZ+U0onlH1SteYMxsZx4X/PxF5u8M3eLVOWY/f71+pseKbtOyna2k1NhtLnuuv7qWFKux2NtBRERERETkmWweXjnlhEhERERERETkdqycEhERERERWYDNswunrJwSERERERGR+7FySkREREREZAE2Dy+dcswpERERERERuR0rp0RERERERBZg8+zCKSunRERERERE5H6snBIREREREVmAzcNLpxxzSkRERERERG7HyikREREREZEF2Dy7cMrKKREREREREbkfK6dEREREREQW4OXhpVOOOSUiIiIiIiK3Y3JKREREREREbsduvUQeyFuzT75u8QJjZjvzuODnLzJ/Z+gWr64xx4quXxdPHdvZSmz6veXhiocPkQfy8w8Qnf7H/OylaPc/ZsbMduZxwc9fZP3O0C1eYMxs55CODbIOvh1EREREREQWYPPw0inHnBIREREREZHbsXJKRERERERkAV6eXThl5ZSIiIiIiIjcj5VTIiIiIiIiC7BxzCkRERERERFR0AYMGKASZ/Pm6+trv/7Zs2fStm1bSZQokcSJE0dq164tN27ckLDihEhEREREREQWYLO5bgurnDlzyrVr1+zbtm3b7Nd17txZVq1aJUuXLpUtW7bI1atXpVatWmF+DnbrJSIiIiIiohBFjRpVkidPHmj/gwcPZPbs2bJo0SIpV66c2jdnzhzJnj277NixQ4oWLSqhxcopERERERGRBdhc+N/z58/l4cOHDhv2Bef06dOSMmVKyZgxozRs2FAuXryo9u/du1devHghFSpUsN8WXX7Tpk0r27dvD9PrZ3JKRERERETkYYYPHy7x48d32LAvKB988IHMnTtX1q1bJ99//72cP39eSpUqJY8ePZLr169L9OjRJUGCBA73SZYsmbouLNitl4iIiIiIyMPWOe3Vq5d06dLFYV+MGDGCvG3VqlXtf+fJk0clq+nSpZMlS5ZIzJgxwy0mVk6JiIiIiIg8TIwYMSRevHgOW3DJqTNUSbNmzSpnzpxR41D9/f3l/v37DrfBbL1BjVENCZNTjQQEBEjLli0lYcKEavrmAwcOuDskcqMLFy7wOCAiIiKKRGxOy7VE5PY+Hj9+LGfPnpUUKVJIwYIFJVq0aLJx40b79SdPnlRjUosVKxamx2Vy6mJfffWV1KhR453uiz7e6Ou9evVqNX1zrly51IG1cuVKh9vhNs59vo8fPy5p0qSRunXrqjMbrny9iLF169aBrsNaSLgOt7GKzZs3q5icz/xQ0Pbu2S0d27WWiuVKSf7cvrJp4wYtmmrxooVStWI5KZw/tzSsX1cOHzokVseY2c48Lvj5i8zfGbrFC4yZ7exJunXrppaIQXHkn3/+kZo1a0qUKFGkQYMGaqxqs2bNVBfhTZs2qQmSmjRpohLTsMzUC0xONWKcnShevLgqkWM659DYvXu3GrBcpUoV+fnnn9WAZVdCUrx48WJ5+vSpw0K9mG4as3hRyFx5MiGs8J5mzeorvfr0E12sW7tGxowaLq3atJXFS1dItmy+8nWrZnLnzh2xKsbMduZxwc9fZP7O0C1eYMxsZ09b5/Ty5csqEc2WLZvUq1dPEiVKpJaJSZIkibp+/Pjx8sknn0jt2rWldOnSKldZvnx5mF8/k1MLOXLkiBpsHCdOHDW7VaNGjeT27dvqOlQX27dvr8rjqOylT59ebYAzF8Y+Z3/++adabwhnM2bOnCleXm/e8gEDBki+fPlkwYIF6n4441G/fn0145bh9evXasauDBkyqIHOefPmlWXLloU6ZkOBAgVUgmo+QPE3EtP8+fM73BbTV3fo0EGSJk0q3t7eUrJkSZVcG/GkTp1azRBmtn//fvW6/v33X3UZVc/mzZurDwv6zuP1Hzx40H57/P3hhx9K3Lhx1fXoirBnz54g3xOjCv3777+rtZrwOpHko3JtvH48961bt9Tlu3fvqstoS8OQIUPU64BXr16p98JoU3zAJ06cGGR1fejQoWq6btwGdu3apdoL7VKoUCH1ut2tZKnS0rZDJylXvqLoYsG8OVKrTj2pUbO2ZMqcWfr2H6jadOXyX8SqGDPbmccFP3+R+TtDt3iBMbOdPc3ixYvl6tWr6rc6ElVczpQpk/16fGanTJmifgs/efJE/dYP63hTYHJqEUiokEQh+UCihC68GESMMxOABGbQoEEqOUNihITNSNqwyK2xz2zFihXy8ccfS9++fWXkyJFBVmLRJRjdhLGhVD9ixAj79UhM58+fL9OmTZOjR49K586d5YsvvlC3C03MZk2bNlVxGn744QdV7nfWvXt3+eWXX2TevHmyb98+yZw5s1SuXNme9OGMDSquZgsXLpQSJUqoGcMAXZdv3rwpa9euVd0KkByXL19ePQZgXSa0I9oL1/fs2VP1kw+On5+fjBkzRiXyW7duVScI0LUBcubMqc4cGW3y119/OVwG/F22bFmHBHvp0qVy7Ngx6devn/Tu3VvNdGaGPvvoq79+/Xr13qBfP85G5ciRQ8WMkwtGDBR6L/z95fixo1K0WHH7PhxXRYsWl0MH3Z/sB4Uxs515XPDzF5m/M3SLFxgz2zkiedlsLtusiMmpRUyePFklecOGDVOL1uJvJHDot33q1ClV2USlD327cRYCVUGjjI7KnrHPgGQGSdo333wjPXr0CPI5kSihMoixq+j2i6qnMZAZZ0UQC2JAcojFdlHRQ3I6ffr0UMVshvtt27ZNVTex/f3332qfGc6yoCo6evRoVY1FIoZqLyqMs2fPtieWuK+x6C9eA87cYD/gOVBhRPKH6mKWLFlUYok2Mqq+uC8WCUbMuB7thKpwcLCoMBJ0PB4S3Xbt2tnbCRVrdF3AWFXAv0i60X4nTpxQ90W//DJlyqjrkQQPHDhQPRaqp4gbt3dOTmPHji2zZs1SyS82JOR4rWgHXEaiiveWwube/Xuqeo0TCGa47FzxtwrGzHbmccHPX2T+ztAtXmDMbGeKOFzn1CLQ1RRJHbqNBlXhxFTNYYGEDl1Jkdyh2oguqc7QnRcJrwHjWVFxBEwLjYphxYoVA41/NLrihiVmJM6o4iIZxqzD+Dtx4sSB7oNkDlVQA5K5IkWKqAmdAF2R8VqQrKHiiaokYkaCacSExNz5f3IYG4nHBwzWRrdfVEKRpOK+5m4JzmLFiuVwvbmdAInnjBkz1N+IB8k6knMkqqjWOr8mdHlAEo8kGXGhTfG6zHLnzu0wNhivH2tKocuEITSznyFJxmampgi3uXbcMRERERG9nc2aBU2XYXJqEUioqlWrFmT3WyRDYYUKK7rs1qpVS42vRBLpnKA6d2VFFRDVOSMe+O233yRVqlQOtzPWPwprzOjai6qjkaC9K1QbjeQU/2IMqJGMIiY8t1HJNDNmMEaX2M8//1y9NnT97d+/v6q+YuxuUIJqJyTYBnTZ7dSpk5w+fVp11cVJAVRNEcO9e/dUlRQJLuB50B137NixKrnEyQFUinfu3Bmochoe0DUblVozvN7uvfuLJ/JJ4KM+G86TbOCy88kSq2DMbGceF/z8RebvDN3iBcbMdqaIw269FoHuohjXiWomxlmat5ASFSRO6A4TFCSRGIxcuHBhlaAicQotdKnF/VHdc44Hkxu9S8xIIlElRCURXYWdoTqJaiG67RpwW4wNRTwGJJaYiAhjL9FV1+jSa8R0/fp1NZOxc0zm/8mhqosxtH/88YdK4M3jYcMKVU4fHx818REqoKgkI2FFFRUJqjHeFPDaMNtymzZtVAUacRkV3ZDgxMKhQ4fULMcGzJD2Nr169ZIHDx44bNjnqaJFjy7Zc+SUnTu22/fhhMzOndslT17HybmsgjGznXlc8PMXmb8zdIsXGDPbmSIOk1M3QIJw4MABh61ly5aqCyi64CIZQ8KCGWIxHjG45BOQGGL8IxIyVOmcIcHEBEMffPCBSlCRTIYGKnqo8CGBw+REiAcTFH333XfqsrFOaVhixplRdE9Fkoy/nSGh/frrr9VYSkyuhNu1aNFCdS/GDLfm14wED/vwPNWrV7dfh266qEhitlsknsZaTH369FGTNqEbLaq3SBqNsa+IPahuz6FljDvFxExGIoouuOhOi/fGGG8KGOOKONBO6Pr77bffBprIKihIyPE8aA+0y5o1a9RY2rfB+48Zic2bUfkOD35+T+TkieNqgytXLqu/r127KlbVqHETWb5sify6coWcO3tWhgwaoI6LGjVriVUxZrYzjwt+/iLzd4Zu8QJjZjtHFJvN5rLNitit1w2QGDkvoYJEC4kSJi+qVKmSSmww+yyqjcbyL0FB91CMocTYUnS/RTLmDNVIVBgxiy4SVCwvExqDBw9WY0XRNfTcuXOqWywqk5hdFrDMSVhjRnIUEswWjDOmmJwJy9qgSywSOVQmzVAtRfXxyy+/VONrDfigIXFDMookGUu8YLIoJI9Y6sboOoT7YWZhVFNROXXu+hpWSEDRjdpITvH68ZzoOmweb9qqVSu1BMxnn32mYkVij9eB7sUhQTV21apV0rp1a3XsoJKM7tRYS8qdjh09Ii2aNrZfHjv6zWzP1arXkEFD/5v52UqqVP1I7t29K1MnT5Lbt29JNt/sMnX6LElk0e5jwJjZzjwu+PmLzN8ZusULjJntTBHDFmAePBeMcePGhf4BbTZVbSMi6/Lzf+vH3jJiRbfJs5eiFe+owpjZzjwu+PmLtN8ZusULjJntHNKxYSV15+5z2XMt/aqAWE2o3o6wrKfI5JSIiIiIiIgiJDk1ZnAlIiIiIiKiiOFl0bGgrsIJkYiIiIiIiEjP5BTLe0ybNk1N4oOJcLC+I/z8889qNlYiIiIiIiIKG5sLNysK8xBgzNqK5Tpu376tZg3dtm2bmlUVtm7dqpYAeZ81I4mIiIiIiMjzhLly2qFDB7W8CJJUrOFonuwXy2kgQSUiIiIiIqKwsXGd07Cv0fnTTz+p9SFfvXrlcB3Wk7x27RqPQSIiIiIiIorYbr1Ro0Z1qJaa3bhxQ+LEiRPWhyQiIiIiIvJ4XlYdDGrVbr3oujt27Fg1KZK5/IyEdcaMGVK+fPnwjpGIiIiIiIgiuTBXTkeOHCnFixeXHDlySPXq1VViOmXKFDly5IiatXfXrl0REykREREREVEkZuM6p2Hj6+sre/fuVQkqxp5GiRJFVq9eLZkzZ1aJaaZMmSLorSIiIiIiIqLIKsyVU8iQIYPMmzcv/KMhIiIiIiLyUDYPH3P6Tsmp4fLly2p23pQpU0qqVKnCLyoiIiIiIiLyKGGeEAkw8VHatGklXbp08sEHH6i/06RJI9OnTw//CImIiIiIiDyAjeuchs3w4cOlT58+0qhRI6lTp44kS5ZMLSGzdOlSadOmjdy9e1d69eoVQW8XERERERERRUZh7tb73XffyTfffKNm7TWrVq2aSlRxPZNTIiIiIiKisPHimNOwefjwoVSoUCHI6ypVqiTTpk3jMUhkcbGi6/XN5/1eo+PdgzGznXlc8PMXmb8zdIsXGDPbmawvzF8tlStXlg0bNkjFihUDXbd+/XopX758eMVGRBHk2Uu9fkzoFK8Rs59/gOh2wkLHdtYpZt3iNWJ+9Py16CRuDC8t2/nBU33aOX5MPdtYx+9lHT9/OsZMmiWn+/bts//dvHlzadWqldy8eVNq1KghSZMmVX+vWLFC/vzzT06KRERERERE9I4TInkyW0BAwFtPI3l5eTk0lPku2O98+dWrVxERKxGFE53OeOtabdLxDL2O7axTzLrFC6ycuq6dWTmN+DbW8XtZxyqkjjFbSZPFh132XHPq5xYtK6ebNm2K+EiIiIiIiIg8mE08W6iS0zJlykR8JEREREREROSxNJxrjYiIiIiIKPLx8vAxp+/UyXrBggVSsmRJNRlSvHjxAm1EREREREREEZqc/vjjj9KiRQvJlSuX3L59W+rVqye1a9eW6NGjq2S1W7duYX1IIiIiIiIij2ezuW6LFMnp2LFj5dtvv5UpU6aoy23atJE5c+bI+fPnJUmSJBInTpyIiJOIiIiIiIgisTAnp6dPn5YSJUpIlChR1Pbw4UO1P27cuNKjRw+ZNGlSRMRJREREREQUqdlsNpdtkSI5jR8/vjx//lz9nSpVKjl27Jj9OqxveufOnfCNkIiIiIiIiCK9MM/WW6hQITl06JBUrlxZqlevLgMHDpTXr19LtGjRZMSIEVK0aNGIiZSIiIiIiCgSs1mzoGndymmvXr0kbdq06u9BgwapZLRTp07y9ddfqwmRpk2bFhFxRjoBAQHSsmVLSZgwoSqrHzhwwN0hkWbKli2rPntERERERB6ZnCIZ/eyzz9TfCRIkkP/973/y5MkTuX//vuzcuVMyZcoknuKrr76SGjVqvNN9161bJ3PnzpXVq1fLtWvX1OzHSFJXrlzpcDvcBu1sdvz4cUmTJo3UrVtX/P39xZWvFzG2bt060HVt27ZV1+E2VoKu5uPHj5fcuXOLt7e3+Pj4SNWqVeXvv/8O82MxGQze4kULpWrFclI4f25pWL+uHD50SKxMt3j37tktHdu1lorlSkn+3L6yaeMG0YFu7axjzDrFO2fWDPmyQV0pXbSgVCxTQrp2bCcXzp8XHejUzsuW/CSf1/1UPixRSG1Nv6wv/2zbKlanUxvr+L2s4+dPx5jDa51TLxdtkWadU2cxYsRQ65v+/vvvkjVr1vB4yEjv7NmzkiJFCilevLgkT55cokYNXQ/r3bt3S6lSpaRKlSry888/qyV8XAlJ8eLFi+Xp06f2fc+ePZNFixbZK+pWqk7Xr19fVfg7duyokvrNmzer14BE0/lEgBXhNbx8+VKsbN3aNTJm1HBp1aatLF66QrJl85WvWzWz7Phz3eIFfN6yZvWVXn36iS50bGfdYtYt3n17dkvd+p/LnB8Xy5QZs+XlyxfSrnUzeernJ1amWzsnS5Zc2nboIvMWLZO5i5ZKocJFpVundnL2zGmxKt3aWMfvZR0/fzrGTBZJTg2PHz9WSReJHDlyRFXosLROsmTJpFGjRmpdWEB1sX379nLx4kVVbUyfPr3aoGbNmvZ9zv78808pV66cNGvWTGbOnCleXm/evgEDBki+fPlkwYIF6n6YtApJ2aNHj+z3xbjg4cOHS4YMGSRmzJiSN29eWbZsWahjNhQoUEAld8uXL7fvw99ITPPnz+9wW0yc1aFDB9XdG1XLkiVLquTaiCd16tTy/fffO9xn//796nX9+++/6jIq8s2bN1fLFOEECF7/wYMH7bfH3x9++KGaLRrXFyxYUPbs2aOuW7JkiXqN8+fPV4+B147XPWPGDDVeGvtQ9Q+uCo4us0hijeu3bNkiEydOtM9wduHCBTUGe8yYMfb74DEw/hqfBbh8+bK67ZkzZ9RlvEe4D+LFSYnPP/9cbt68ab8/kmfcfu3ateq14MTPtm3bVJxffvmlem9wUgNLOlnFgnlzpFadelKjZm3JlDmz9O0/UL3fK5f/IlakW7xQslRpaduhk5QrX1F0oWM76xazbvF+N22mVPu0pmTKnEWyZvOVAYOHy/Vr1+T4saNiZbq1c6kyH0qJUmUkbbr0ki5dBmnTvpPEihVLjhz+7/+dVqNbG+v4vazj50/HmMODjeucUnhDQoUkCskaEiV04b1x44bUq1dPXY8EB9U8JGfo0ouEzUjasGassc9sxYoV8vHHH0vfvn1l5MiRgZ4TJwVQCUQ3YWxIpDBBlQGJKZI0jAk+evSodO7cWb744gt1u9DEbNa0aVMVp+GHH36QJk2aBLpd9+7d5ZdffpF58+bJvn37JHPmzGoirbt376oEtEGDBqriarZw4UK1VFG6dOnUZXRdRvKGZG3v3r0qOS5fvrx6DGjYsKFqR7QXru/Zs6dKDgGPjUp+tWrVAsXWtWtXdUZ2/fr1Ehp4z4oVKyYtWrRQ7w82JOllypRRCaVR5fzrr79UN2wklID2xazWeO3w4sULGTx4sEqq8X4hwQ2qKzReB94/VHvz5Mkj33zzjXosdKP/448/1HOiTd3thb+/+p9E0WLF7fvw3hYtWlwOHdwvVqNbvLrSsZ11i1m3eIPy+PGbE6jx4scXq9K9nTG05Y91v8nTp36SO08+sSLd21hXOnz+IkPM5ILZeuntJk+erJK8YcOGOSRwSGZOnTqlEiZUzrBOLKpnZkhsnPehCockrXfv3mot2aCgEonxqXhcQNVz48aNMnToUFXBRCwbNmxQCRZkzJhRJVDTp09XCVZoYjYgqcXEWEZ1E+M30dXXSNIAlT5URRETqrGAai+SwdmzZ6tkC4klKoCoIKPyiteAx0ECDohv165dKjlFBRFQpURSh4ooJpTCffFYvr6+6vosWbLYY0Dc2bNnD7K9jP24TWigGo0u1Dj7bH5/UFnF68EPAFSecRuMyUZboOs1/kX7mhN7A94DrAtcuHBh9R6jKmrAyYuKFd+cjcV1eI4ff/xRJeaAhB9Jubvdu39PvfZEiRI57Mfl8+fPidXoFq+udGxn3WLWLV5n+L4fO2q45M1fQDJnse5wIF3b+czpU9Lsywbi7/9cYsaMJaPGfScZM705SWo1uraxznT5/Oke87uyWXQsqKswOY0AqIpt2rTJIdkwVzjDOi4X3XDRJRbJHaqNQSVc6M5rJKaArp9Gd1F0KfXz87MnOwZMpmR0xQ1LzOhiiyouEk9UC/F34sSJA90HVUJUQQ2oaBYpUkRVAwFdkfFaUOFEpRCVQcSMRNyICYmZ8/+wMM7D6D7epUsX1T0X3WUrVKig7muelAvxRSSM/0X3aXRH/ueff1QiioTVqFrjNSF5NqC6i27YeG337t1TX7aAJDtHjhz226HrrwGvFe/VBx98YN+HWZ6zZcv21vhwYsJYl9igEv0ob5J9IiJ3GDl0kBoDOWvuQr4BESBd+vTy48/L1f9D/9zwuwzs10umzZpv2QSVXEvHz5+OMVMEJqfjxo0L1YMdPnz4HcOIXPA/A3QlDar7LZLGsEKFFdXCWrVqqfGVSCKdE1SjK6v5rIuR+BjjH3/77TfVxdTMqEiGNWZUANu1a6f+njJlirwrVE+N5BT/otpoJKOICc9trsgajBmMkehh3CZeG7r+9u/fX1VfMXYXCbWRCDsz9htJN7oQOSeySK7fBnFgHCti3L59uzoBULp0aVU9RVX29OnT9sopqsno1owN3ZeR5CMpxWXnWZdjx44t4QHdubEWsRnaqGffAeHy+D4JfNTx6TxpBS47n7CwAt3i1ZWO7axbzLrFazZy2GDZtnWLzJizQJI59RSyGl3bOVq06JIm7ZvhMdlz5JRjRw/Lz4sWSK9vHf9/YAW6trGudPr86RyzZSYEiqzJabdu3UL9gJ5eigaMi8RYS1QzQzsLr5FgomtLUJBEYuKhOnXqqAQVkyOZK20hwe1wfyRC5i6m7xMzkkgkVHi/kVw5Q/USXVzR5dcYP4pkD2NDzWtzIrFEN15UFNFV17xOLmK6fv26iieoCaIMSDCxYRwtKssYD4vkFJNC4fFXrVoVaNwpuhMjCTaqyUgU0S3XDGvPmpN+vJ6g3h+0KU4YoAsyulGjqomTB/gbybWRAJ84cUL9zxZVVXSXBmPyppCgLREHlmoyZkRG1RXJb3DvpwHdr1FdNsOxEF715GjRo6sfPjt3bJdy5SuofTgpsnPndqnf4AuxGt3i1ZWO7axbzLrFCzgBOGr4ENn85waZPnuepLLA0ITI2M5Bef06wKVLz3liG1udjp8/HWMmFyXn+JII7RZcchVZPXjwQCUx5g1jITFhDxIlJGPololldjBpUEjtgwQM40SRkCH5cIakAgkkunciQcXERqGB7r44wYDkDWMVEQ8m0/nuu+/UZWOd0rDEjLOcqD4eO3ZM/e0Mlb+vv/5adWnF5Eq4HSYTQvdizDZsfs1YTgf78DyYRdeAbroYI4sZcDEJECYPQtfZPn36qKQO3XtRvUXVEuNfkQgjdqOqjOQUSWrjxo3VmE3c/9ChQ9KqVSv59ddfZdasWfYKJSaDwmNi0ihUO1FddE5WESsSRDwOZjE2KtPoxou2QhJtjH3FPlRHzckjEkskuGj3c+fOqRgwOdLboKs12gdtiZMSiAuTKBmzNYdmmSfzZlTLw0ujxk1k+bIl8uvKFXLu7FkZMmiAem9q1KwlVqRbvODn90ROnjiuNrhy5bL6+9q1q2JVOrazbjHrFi+65a39bZUMGTFaYsWOLbdv31IbliOzMt3aecqkcbJv7265euWKGnuqLu/ZJVU++kSsSrc21vF7WcfPn44xhwfb/68K4YrNijjm9D0hMXJeQgWJBBIlTF5UqVIlNeYP1UNUG0NKKFDNQ5ULY0vR/RZJkDMkN6gwYhZdo4IaGkiCUB1EN08kRuiOisokJlmClClThjlmJDohQYUQCRwmZ8K4TIyjRBLn4+MTqGtvmzZt1FIpGF9rwIdmzZo1KhlFknzr1i01GRG6zWKpG6MbEO6HmYXR/Qddn41urLg/lpOZMGGCjB8/Xj0HpqZHwov3zTweFtXfb7/9Vs0wjC89dFvG45q7qiPBR6KLSjT+p3n+/HmVsGLcKV6nORFFcooZfo2laADtj3G6aHNMhIT2xwRP5oQ8OKNHj7Z3vcbJBsw2jBMjVlCl6kdy7+5dmTp5kvqfRjbf7DJ1+ixJZNHuWLrFC8eOHpEWTRvbL48d/WZMc7XqNWTQ0P9m5bYSHdtZt5h1i3fZksXq31amYxn6Dx6mlouwKt3a+e7dOzKwb08Va5w4cSVz1qwyaepM+aDYf//Psxrd2ljH72UdP386xkzvzxYQ0TPGEJHlPHsp2vCOqle8Rsx+/np9tcaKbtOynXWKWbd4jZgfPX/TS0QXcWN4adnOD57q087xY+rZxjp+L+v4+dMxZivpsPKEy55rUo03Pf6shJVTIiIiIiIiC/CyZm9bl7HWqQIiIiIiIiLySKycEhERERERWYAXK6dEREREREREGnbrxXqVWI8Ss9JiZlcsvQE///yzWl6EiIiIiIiIIt9SMiNGjFD379Spk30fVrvA0pSJEiVSyyDWrl1braYR4ckpliHJli2bWnLjzJkzal1OLBMCW7dulVGjRoU5CCIiIiIiIrK23bt3y/Tp0yVPnjwO+zt37iyrVq2SpUuXypYtW+Tq1atqiccIT047dOig1mtEkorE1LwSDdZ5RIJKREREREREYUzObK7bwurx48fSsGFDmTlzpvj4+Nj3P3jwQGbPni3jxo2TcuXKScGCBWXOnDnyzz//yI4dO8L2+sMa1ObNm6Vv376SOHHiQOXg5MmTy7Vr18L6kERERERERORCz58/l4cPHzps2BccdNv9+OOPpUKFCg779+7dq4Z9mvf7+vpK2rRpZfv27RGbnEaNGtWhWmqGfsXoY0xERERERERhY7O5bhs+fLjEjx/fYcO+oCxevFj27dsX5PXXr1+X6NGjS4IECRz2J0uWTF0XoUvJoOvu2LFjpWrVquLl9Sa3RQUVCeuMGTOkfPnyYX1IIiIiIiIicqFevXpJly5dHPbFiBEj0O0uXbokHTt2lPXr14u3t3eExhTm5HTkyJFSvHhxyZEjh1SvXl0lplOmTJEjR46oWXt37doVMZESERERERFFYl7vMYtuWCERDSoZdYZuuzdv3pQCBQrY97169UrNNTR58mT5/fffxd/fX+7fv+9QPUWvWgz7jNBuveg/jACRoP70008SJUoUWb16tWTOnFklppkyZQrrQxIREREREZEFoWfs4cOH5cCBA/atUKFCanIk4+9o0aKpyXINJ0+elIsXL0qxYsUitnIKGTJkkHnz5r3LXYmIiIiIiCg8KocuEDduXMmVK5fDvtixY6s1TY39zZo1U12EEyZMKPHixZP27durxLRo0aIRn5wSERERERERwfjx49V8RLVr11Yz/lauXFmmTp0qYRXm5BRr17zNn3/+GeZAiIiIiIiIPJnNdUNO3wuWFzXDREmYhwjb+whzcooyrfP6pvfu3VNTC2MALPocExEREREREUVocrpy5cog99++fVvN3lu/fv2wPiQRuZi3Zh36dYsXYkXX5NSn5u2sW8y6xQtxY1hxBFTka+f4MfVqZx3bWMfvZR0/fzrG7Kmz9VpRuH21JE6cWLp37662evXqhdfDElEEePT8tVb/k3v2UrT70fbgqT5tbPww1rGddYpZt3iNmP38A0S3BIQxs42DOi50/PzpGLNOvzGAybS1hOt5L6x3c/369fB8SCIiIiIiIo9g8+zCadiTU4wtdYZFV48fPy4DBw6UIkWKhFdsRERERERE5CHCnJxiwiPnCZECAt50+fnggw9k5syZ4RcdEREREREReYQwJ6ebNm0KtA9TB6dOnVpSpUoVXnERERERERF5FC926w29Z8+eyd69e6VSpUqSK1euCHxbiIiIiIiIyJOEqXKKCmnfvn2lYMGCERcRERERERGRB/Ly8BmRwrwQUb58+eTYsWMREw0RERERERF5pDCPOZ04caI0bNhQkiRJIh999JHEihUrYiIjIiIiIiLyIDbPLpyGLjmdP3++fPzxx5IoUSIpV66cWjrms88+U9chOTXP3ou/Hzx4EHERExERERERkWcmp02aNJHt27er5LRr166BlpIhIiIiIiKi9+Pl4WlWqJJTYx1TGDBgQETGQ0RERERERB4ozGNOiYiIiIiIKPzZxLNLp6FOTn/66SfZtm3bW2+HLr+dO3d+37gomAp2q1atZNmyZXLv3j3Zv3+/mj35XZQtW1bdd8KECRHa1qF5nvTp00unTp3UFtFwfK5YsUJq1Kgh7rR582b58MMP1fuYIEECt8ZCRERERKRVcopZekODyWnIvvrqK7l//76sXLlSwmrdunUyd+5cldhkzJhREidOHGSy9erVKxk9erS67b///isxY8aULFmySIsWLaR58+YSmdt23rx5gfZXrlxZtZ27BJWgFy9eXK5duybx48cXnc2ZNUM2bVwvF86fkxgxvCVPvvzSvlNXSZ8hg1jZ4kULZd6c2XL79i3Jms1Xevb+VnLnySNWtGzJT7J86WK5dvWKupwhU2Zp3rKNFC9ZWqxOp3bWNWbd4t27Z7fMnztbjh07Krdv3ZJxEybLh+UriFXpFi8wZtfR7fOnW8y6/sZ4X16eXTgN/TqnO3bskNevX791Q2JEEePs2bOSIkUKldgkT55cokYN+tzCwIEDZfz48TJ48GC1Ju2mTZukZcuWKimOjDB7tKFKlSoq6TNvqPpbTfTo0dV7qPvkYvv27Ja69T+XOT8ulikzZsvLly+kXetm8tTPT6xq3do1MmbUcGnVpq0sXrpCsmXzla9bNZM7d+6IFSVLllzadugi8xYtk7mLlkqhwkWlW6d2cvbMabEy3dpZx5h1ixeePn0qWbP6Sq8+/UQHusULjNk1dPz86Razjr8xyIXJKUW8I0eOSNWqVSVOnDiSLFkyadSokdy+fdteFWzfvr1cvHhRJTToCosNatasad8Hv/76q7Rp00bq1q0rGTJkkLx580qzZs2kW7duDs+Hkwndu3eXhAkTqkTJebIrPNenn36q4okXL57Uq1dPbty4Yb8eMTl3j0XXXFQKg3Pz5k2pVq2aquYitoULFwa6DZJoVHixli6eF8sXHTx40H494kQlctasWeoxvL297dfFiBFDvRbz5uPjE2w8ly5dUq8LXWvRDni9Fy5cUNf98ccf6rGdk/qOHTuqmABf6A0aNJBUqVKpZZVy587tkAyjjbZs2aJ6HuA9wobHR/Ubf5sf+5dffpGcOXOq14D3cuzYsQ7Pi33Dhg2Tpk2bSty4cSVt2rQyY8YMcafvps2Uap/WlEyZs6gzsAMGD5fr167J8WNHxaoWzJsjterUkxo1a0umzJmlb/+B6n1eufwXsaJSZT6UEqXKSNp06SVdugzSpn0ndawdOfzfZ8KKdGtnHWPWLV4oWaq0tO3QScqVryg60C1eYMyuoePnT7eYdfyNEV6VUy8XbVbE5NQikKQg4cmfP7/s2bNHdUNFIojECZDcDBo0SFKnTq2qgbt371YbzJkzx74PkJD9+eefcuvWrRCfE11gY8eOLTt37pRRo0apx1+/fr09cUWidvfuXZVcYf+5c+fs69u+KyRrSAhRzcXY2alTp6qE1QxJNfatXbtW9u7dKwUKFJDy5curWAxnzpxRydzy5cvlwIED7xTLixcvVJdfJHp//fWX/P333yoRR/UV1Vg8J5JWPI8BPQN+/vlnadiwobr87NkzKViwoPz222/q5AIq1DipsGvXLvv7VqxYMdWl2qjkpkmTJlAseJ14r+vXry+HDx9WCfi3336rumabIWEtVKiQGm+MExBff/21nDx5Uqzi8eNH6t94Fu2u/MLfX/1PrWix4vZ9Xl5eUrRocTl0cL9YHY6/P9b9Jk+f+knuPO823twVdGxn3WLWLV6iyETHz5+OMev2G4PCB2frtYjJkyerxBSVMcMPP/ygEplTp05J1qxZVRIVJUoUlXyaIYEy7xs3bpzUqVNH7UMlDt2AkWiiKmuWJ08e6d+/v/obY1IRw8aNG6VixYrqXyRJ58+ftydT8+fPV4+HJLhw4cJhfo14HUg4kbgZ9589e7Zkz57dfhtMuoXrkZyigghjxoxRY3SRzCL5AySPiAfVVbPVq1erBNOsd+/eanOGJBNJOCqwRvdaJPpoT1Q2K1WqpJLFRYsWqcozoF1wIqF27drqMiqm5oo0qtu///67LFmyRIoUKaLGlKILLypdzu+bGd4zJMNISAHvN7pkY+wwEnrDRx99pJJS6NGjh+q+jUQ/W7Zs4m5oy7Gjhkve/AUkc5asYkX37t9TCR7WbDbD5fPnz4lVnTl9Spp92UD8/Z9LzJixZNS47yRjpsxiVTq2s24x6xYvUWSi4+dPx5h1+40RXmyaD/lySXKKA4IiFrqtIslwTqyMsaZIVkIrR44cqoqHahyqgVu3blVdaZHkIBEzJ6dmGM9qVDGPHz+uklJzlQ+Pi8QN171Lcor7YZwsKo0GX19fh9lq0Q6PHz8O9OWJMTRoB0O6dOkCJaaAGXC///57h33orhsUPBcqsEj6zVANNZ4LFdKiRYvK1atXJWXKlKob8scff2yPGV/0OKGAZPTKlSsqaX7+/LlKRsPaNjiBYFaiRAk1iRKeAyclnN8zfHkh4XWuPJshFmxmb5L+aBLeRg4dpMZBzpobuKs2vZ906dPLjz8vV5+NPzf8LgP79ZJps+ZbOkElIiIKL/yN4TlYObUI/OhEAjly5MhA1yFpDCt01UACiQ3jQH/88UfV3bRPnz5qnCZEi+aYoCDZCcuJCDwHlrdx7ir7vu2A14vKpTNzEovuyEHB/syZM4f6uZAoBzXu1Uh80X6ZMmWSxYsXqy60mBnZ3NUWlU103UUSifGmeH60t3mSpvAU1vds+PDhaoIsM1TLu/YK38k9Rg4bLNu2bpEZcxZIshAqxO7mk8BHJfrOkz/gMma/tqpo0aJLmrTp1N/Zc+SUY0cPy8+LFkivbx3fW6vQsZ11i1m3eIkiEx0/fzrGrNtvjPDi5dmFU445tQqMqzx69Kia9AbJlXkLLhEzkpXQzJCMqic8efIkVPGgqy3GhmIzoJspurQaj4UEDmMozUIa/4kq6cuXL1VF14DxkuZJgdAO169fVxVW53YI7y9PPNfp06cladKkgZ7LvMQLqqdIYFetWqUSclRODahMo+L5xRdfqImnsMQPui+boVvv294jtDceywyXUTE3qqbvolevXvLgwQOHDfvCC05O4H8am//cIN/PmiOpUqcWK4sWPbpK7nbu2G7fh+R+587tkidvftHF69cBEXYCxFPbWbeYdYuXKDLR8fOnY8y6/cag8MEJkdwACQKSOPOGsZSY8Aczv2JMJ7qVYuxikyZNQkxskMxiHCQSunv37ql9GG+KsYiY6AjrnKIK2bZtW5XoIEEMjQoVKqhKIBKzffv2qXGgX375pZQpU0ZNyAOYwAmTN2HsJ5I8VOTQnTg4GBeJyYZatWqlYkOSill5MXOv+XkxgRBmAcZsuZjZ9p9//lEVXzzX26ALK9rCvBkzHjvDa0PCi+QSEyJhfC3aqkOHDnL58mWH26ENhg4dqtrWGAtrjNXFZFGIEV1z8drMMxob7xFeL14LYgmq0tm1a1f1PmL5HyS3mKwKY4CdZ1gOK8SKGY/Nmzn+8Ohms/a3VTJkxGiJFTu2WjcNG7pGW1Wjxk1k+bIl8uvKFXLu7FkZMmiA6jZeo2YtsaIpk8bJvr275eqVK2rsqbq8Z5dU+egTsTLd2lnHmHWLF/z8nsjJE8fVBleuXFZ/X7t2VaxIt3iBMbuGjp8/3WLW8TdGeLDZXLdZEbv1ugESIEx+ZIYJd1ApwyQ3mIgHSRbGVSKZQ7UuOJi9tUuXLjJz5kw1OQ8SIMxAi+VM0KUTiTDGJSKRxAywwa2N6gzdRf/3v/+pCX5Kly6tYkAs3333nf02eB5M4IPlaPBFgSVOkMBiIqXgYMIhJKRIcrFczpAhQ+yTABnPu2bNGpWMIjHHjMOIHzHg9m+DWY6du0EjKT5x4kSg22JcKMbjos1r1aoljx49Um2IiYmQxBlQScXkRkjQ0X3XrG/fvmoWY7QFHg8nGZBYo90NSDAbN26sKs74nwCS4KCquBi32q9fP5Wg4jVg9mTzZEhWtGzJYvVvq6aNHfb3HzxMTf9uRVWqfiT37t6VqZMnqf/JZfPNLlOnz5JEFu3WdPfuHRnYt6eKNU6cuJI5a1aZNHWmfFCshFiZbu2sY8y6xQvHjh6RFqbvi7GjR6h/q1WvIYOGvvnbSnSLFxiza+j4+dMtZh1/Y9D7swU4Dxokokjv0XN9JjmLG8NLnr0UrXhHFXnwVJ82hvgx9WxnnWLWLV4jZj9/vX4mxIpuY8xs4yCPCx0/fzrGrNNvDON3hpVM+CtwESOidCr1Zh4aK7HWu0FEREREREQeickpERERERERuR3HnBIREREREVmAl0UnKnIVVk6JiIiIiIjI7Vg5JSIiIiIisgAbK6dERERERERE7sXKKRERERERkQV4iWeXTjnmlIiIiIiIiNyOlVMiIiIiIiILsHl24ZSVUyIiIiIiInI/Vk6JiIiIiIgswIuVUyIiIiIiIiL3YuWUiIiIiIjIArw8fNApZ+slIiIiIiIit2PllIiIiIiIyAJsnl04ZXJK5InixtCr04S3hqfR4sfUq411bWfdYtYtXogVXb9fSoyZbRxZPn86xqzbbwyyFg0PeSJ6X7cfv9SmERPHiSp+/gGi2w/jXeceiE6KZIyvZTs/e6nXj0yd4gXG7Lp2Ljpii+hiR88yWn5f6Bizjt8Z1x+8EJ0kjx9NrMTLw0unPLVBREREREREbsfKKRERERERkQXYPLtwysopERERERERuR8rp0RERERERBbgJZ7N018/ERERERERWQCTUyIiIiIiInI7duslIiIiIiKyAJuHz4jEyikRERERERG5HSunREREREREFmATz8bKKREREREREQXr+++/lzx58ki8ePHUVqxYMVm7dq39+mfPnknbtm0lUaJEEidOHKldu7bcuHFDworJKRERERERkQV42Wwu28IiderUMmLECNm7d6/s2bNHypUrJ59++qkcPXpUXd+5c2dZtWqVLF26VLZs2SJXr16VWrVqhfn1s1svERERERERBatatWoOl4cOHaqqqTt27FCJ6+zZs2XRokUqaYU5c+ZI9uzZ1fVFixaV0GLlVDMXLlxQs3gdOHAg1PeZO3euJEiQQCK7zZs3q7a5f/++R7xu59dLRERERHqzuXB7/vy5PHz40GHDvrd59eqVLF68WJ48eaK696Ka+uLFC6lQoYL9Nr6+vpI2bVrZvn17mF4/k1OL+eqrr1TCgS1atGiSIUMG6d69u+rHDWnSpJFr165Jrly5wv15a9SoEez1rVq1kihRoqhSfUQaMGCA/fWbtw0bNrz3YyNZDeqxZ82aFS6xe6LZ06dIiYI5HbYGtT4RK9u7Z7d0bNdaKpYrJflz+8qmje9/bIWnX3+eK/06NJYWtcpKm/qVZfygbnLt8r8Ot/H3fy5zp4ySr+tVkOY1y8jEIT3kwb07YhVWb+OQLF60UKpWLCeF8+eWhvXryuFDh8TKdIsXGHP4aV4ynezoWcZhW9yisMNtcqWMJ5Mb5JFNXUrKxs4l5PuGeSVGVGv9/NPxO0PHmHX7/H32aSUpUyRXoG38qCHuDi3SGD58uMSPH99hw77gHD58WI0njREjhrRu3VpWrFghOXLkkOvXr0v06NEDFYWSJUumrgsLduu1oCpVqqhSOM5A4ExE48aNVRI1cuRIlSAmT57cpfH4+fmpsyNIkn/44QepW7duiLf39/dXB+i7ypkzZ6BkNGHChBIeMID75MmTDvvwQXwX7/s6rfY87ypDpswycep/CX6UKNb+Wnn69Klkzeorn9asLV07tRerOXF4n1SoVlcyZs2uzkwunfu9jOzTXkZM/1m8vWOq2yycPl4O7v5b2vUeLrFix5H5U0erBLXfWGucaLF6Gwdn3do1MmbUcOnbf6Dkzp1XFi6YJ1+3aib/W71OTfBgNbrFC4w5/J299UTaLz5ov/zqdYBDYjqhXm6Zt+OijF1/Rl2XJWkceR3w322sQMfvDB1j1u3zN33uYnn16rX98vlzp6VruxZStnwlicxsLpyut1evXtKlSxeHfUg8g5MtWzbVe/PBgweybNkylaNgfGl4stapM7IfFEhAUSVFNRMl8vXr1wfbrffXX3+VLFmyiLe3t3z44Ycyb968ILt7/v7776rvN854IAFGBdaoVuI+//vf/+zVRHQZNaBairMiPXv2lK1bt8qlS5eCrLqi73nKlCnVgWvEuWTJEilVqpTEjBlTChcuLKdOnZLdu3dLoUKFVBxVq1aVW7duOTxe1KhR1es3b0jOFixYoO4XN25cte/zzz+XmzdvhumoQUzOj43Y4OLFi2pgN+JCEluvXj2HWcbQTvny5VOVVlS00d6rV69WZ4mQRADeFzwH2srQvHlz+eKLL9Tfd+7ckQYNGkiqVKkkVqxYkjt3bvnpp58cYixbtqy0a9dOOnXqJIkTJ5bKlSur/WvWrJGsWbOqePE+o42tACdMEiVOYt8S+PiIlZUsVVradugk5cpXFCvqPmSSlK74iaROl0nSZcwqLbv0kzs3r8uF08fV9X5PHsuWP36Vz1t0kpz5CkuGLNmlRZd+cvrYITlz/LBYgdXbODgL5s2RWnXqSY2atSVT5szqBxw+5yuX/yJWpFu8wJjDHxLOu09e2LcHT1/ar+tUPpMs2XtFFuy4JOdv+8nFu09l44lb8uKVtZJTHb8zdIxZt89fAp+EkihxYvu2fdsWSZU6jeQr4Ng7gN4v5zBm3zW2kJJT/B7PnDmzFCxYUFVY8+bNKxMnTlS/p1FMcc498Ds6rEU1JqcWd+TIEfnnn3+CrZydP39e6tSpo5LDgwcPqu63ffr0CbL6OWbMGJXgIcFEItatWzd1Hf5FImYkrNiKFy9uvy8GOCO5QoURySS6xzrbuHGjqkgiiUbCZujfv7/07dtX9u3bp5JOJJSowOJA/uuvv+TMmTPSr1+/ULUFKsmDBw9Wr3PlypUqOUNiHB5ev36tEtO7d++qM0B4HefOnZPPPvvM4XaI95dffpHly5erRBSJ96NHj2T//v3qetwXCaU5ucc+JJyA7tn4QP/222/qvW3ZsqU0atRIdu3a5fA8OFmA9/zvv/+WadOmqRMCmPEMg9HxvEh4zQmwO12+eFGqVy4rdatXlgF9usv1a1fdHVKk8tTvsfo3dtw3Ff7zp4/Lq5cvJWf+IvbbpEyTXhIlTS6nT1gjOdXRC39/OX7sqBQt9t93n5eXlxQtWlwOHXzz+bYS3eIFxhwx0vjElFVti8ovrYvIwGq+kizemx+WPrGiSa5U8eSen7/M+CKfrGlfTKZ+nlfypo4XQZGQlen4+XP+Dbh+7WqpWq2mKgJEZrYghqBF1BYev58xRhW/bTEcEfmAAXkB8g2MSQ0La/e/81BI7lC9e/nypXrD8eUxefLkIG87ffp0VakcPXq0uoy/kfSgiun8oUaSkylTJnUZlblBgwapv/FcqMbhuZzPbpw+fVrNsoVkDJCkovyPhNN8UMeOHVtVFI0k2qjqIfE1Kn8dO3ZUVUMcuCVKlFD7mjVrFijZNfqzG1C1RfLWtGlT+76MGTPKpEmTVDX28ePHDrcPCbohmG+Lv9EXHjHheZHso2IN8+fPV12MUenF8wDOCmF/kiRJ7I+BaiqSUVR18S+m0h44cKCKC8+HhLZMmTLqtqiYGicFoH379qqijQpzkSL/JRuohI8aNcp+uXfv3uq9Gzt2rP19Rrzo6u1OOXLlkT4Dhkra9Onlzq1b8sPM76VN8y9lwZL/qWOC3v9L/8fp4yRrjrySJv2bzy7GlkaNGk1ix4nrcNv4CRLKg7vWGXeqm3v376keEM5d23D5/PlzYjW6xQuMOfwdvfpIBv92QlVEE8WJLs1KpJNpDfNJw9l7JGUCb3Wb5iXTy6Q/z8rpm0+kaq5k8l39vOr6S/eeRkBEZFU6fv7M/tq8UR4/fiRVPwl+fhSK+C7AKFJhkiMUZjAzL3734ncsClj4TY8cAUPxUIHFb1wkpmGZqReYnFoQumxiambMgDV+/HhVccRCtkHBWQkjcTKYkxwDupAaiSmkSJEiVF1iMcYUySWqgfDRRx+pg+/PP/+U8uXL22+H7qlBVXexWK95ULRxW/M+5ziQeKGrssHoXoDxt+hai8rpvXv31A93wFkZJLChgS7BqOIakPjD8ePHVVJqJKaAx0SXXVxntHG6dOkcElNA4okPZ9euXVU1GN0ckGxu27ZNVWLR1RnJJuB/DMOGDVPXX7lyRSW7OCmA98cMZ6DMEMMHH3zgsC80Z6Lw2M6zrr1pzygSHoqVKGX/O3OWbJIjdx6p/XFF+XP9OqlWI+hjlkJv3pRRcvnCOfl2zAw2GxEFsv3cXfvfZ249kaNXH8rKr4tKed8kcuGOn9q/Yv81+e3wmyEqp248lsLpEsgneZLL91vOs0VJG2t+XS5FipWUxEmSSmTnJdaE3+tffvml6mGJZBS/8ZGYVqz4pms7chb8rkbOgt+eyB+mTp0a5udhcmpBqDihP7eRHKI/N7rWIil8Vyi1m6HqGfCWCRGQSKF7KSqLSJDN+xGXOTkNrkpmfl6j0uq8z0gynfuzmyFRx0GObeHChSpBRFKKy0jwQgsfGufHDougXie67KI9kDTjtWHqbOxDwook2qiaAirc6NI8YcIElaTj8TC21Pk1hFfVEYkyqrhm6GrdrltfiQhx48aTNOnSyeVLFyPk8T3JvKmj5cCubdJn9HRJmOTNiR2I75NIXr58IU8eP3Konj64f1fiJ7TehBa68Engo8ZPY1y4GS4bJ+esRLd4gTFHvMfPX8nFe36S2iem7Pn3zdivC3eeONwGSWvy/+/6S55Dx8+fAcOF9u7eIYNHTnB3KB5t9uzZIV6P8ctTpkxRW2RMzsmUTKFLJ7rRYmY4Z6gy7tmzx2EfuqGGFRJCY1IfAybgMcZTYpyjsWECH3TzdeX6midOnFBfoCNGjFDjPJEAhnUypJBgoiiM6zRP9nTs2DH1Gt9WlTXGneKMkZGIGskpNmO8KWAMKca2ons0TjqgezImiQpNfM7jUtHdOjRdMNC12LxhX0Tx83siVy5fksSJHavLFHo4aYTEdO8/m6XXiKmSNHkqh+sxAVKUqFHl2IH/PudYagaTJmXx/a9XAoVNtOjRJXuOnLJzx3/rseHE2c6d2yVP3vyWa07d4gXGHPFiRvOSVAliyp3H/nLtwTO5+ei5pE3o2DMnTcKYcu3B29cxpMhFx8+fYe2qFWpypKIlSosnsGk05jQiMDnVAJZuwdmuoM5EYAIkJG49evRQSQ66ixpjOMNy0KVPn14OHTqkugnfvn1bjVHFGZKPP/5YJVFYV9XYMHkSuruigukq6N+OBPq7775TExWh2y8mRwovmBEZlcyGDRuqbr9IBNF1AckmxpKGxMfHR3VtQHsYiWjp0qXV4+A9MVdO0b0Xky1hkit01cX7Z54RODhYSwrjf7/55hv1HqGff1ATU73vLGxhNXn8aNm/d7dcu3pFDh/cL726dZQoXlGkQpWPxKqQQJ88cVxtcOXKZfX3NYtM5ISuvP/8uVa+7j5YvGPGkvt3b6vN//mbtY6xdEyZStVl4cwJcuzgHjVB0oxxgyRz9txqswKrt3FwGjVuIsuXLZFfV66Qc2fPypBBA9RJwRo1a4kV6RYvMObw1f7DjJI/TXxJET+G5E4VT0bWyqWWifnj2JuTtwt3XpJ6BVPJh9kSS+oE3tKyVHpJlzCWrDr0ZrZ+q9DxO0PHmHX8/CGBXrt6pVT5+FOHXnwUefFd1gA+jJjACBPkYCCyGZY0wTpDGO+I7qIYh4jZer/++uswJSEtWrSwT+qDiXywrilmlEUSFFQ1t2bNmip5bdu2rbgCuvEiGUMVGRMhFShQQM0+XL169XB5fCTyWEoHg7eRWOI1YvZiJMOhgQQUVWUjOcVgcFRckXiium1ABRzJNbojY5wpZuvFTMuoaL4tOccswZhsCTFhXDHGrponiXKHmzdvSP/e38jDB/fVWc08+QrI9LmLxMcnfNaljQjHjh6RFk0b2y+PHT1C/Vuteg0ZNPTN3+608bc3U/oP69HaYT+Wi8ESM9CwVWexeXnJpCE95cULf8lTsKg0bttdrMLqbRycKlU/knt378rUyZPk9u1bks03u0ydPkstYWBFusULjDl8JY0bQwZVzy7xY0aT+34v5ODlB9J8/n65//SFuv7nPVckelQvtaRMPO9ocvrmY+n48yG5cv/NyS6r0PE7Q8eYdfz87d21XW5cvyYfVaspnsImns0W8LaBh6QdzNRrLD9CFJTbj/9bB8/qEseJKn7+en1NxYpuk13nQj7hYDVFMsbXsp2f6XMoi3dU0SpeYMyua+eiI8J3IfuItKNnGS2/L3SMWcfvjOsP3pwc0UXy+I7zsrjb0gOuq77XzZdSrIaV00gAM2FhNllMB44xjZh0B5VWIiIiIiLSh82iY0FdhclpJICxiEOGDFHLlqD7J7r4RuSkN0REREREROGNyWkkgFlisREREREREemKySkREREREZEFeIln8/TXT0RERERERBbAyikREREREZEF2Dx8QiRWTomIiIiIiMjtWDklIiIiIiKyAJt4NlZOiYiIiIiIyO1YOSUiIiIiIrIAm4eXTlk5JSIiIiIiIrdj5ZSIiIiIiMgCvDx81Ckrp0REREREROR2rJwSERERERFZgM2zC6esnBIREREREZH7sXJKRERERERkATYPH3NqCwgICHB3EERERERERJ7utyM3XfZcH+dKKlbDyimRB/Lz1+ecVKzoNnn2UrTiHVUYM9s50hwXOn1fAL8zXHNcPHr+WnQSN4aXlp8/xuyadrYSm2cXTjnmlIiIiIiIiNzPYucKiIiIiIiIPJOXh4855TqnRERERERE5HasnBIREREREVmAzbMLp6ycEhERERERkfuxckpERERERGQBNlZOiYiIiIiIiNyLEyIRERERERGR27FbLxERERERkQXYuJQMERERERERkXuxckpERERERGQBXpwQydouXLggNptNDhw4EOr7zJ07VxIkSBChcXmiAQMGSL58+cLt8Vz1PuH4WblyZahvv3nzZnWf+/fvh3ssoXnsoNoZ+5IlSxbm10JEREREpAu3T4j01VdfqR/c2KJFiyYZMmSQ7t27y7Nnz9T1adKkkWvXrkmuXLnC/Xlr1KgR7PWtWrWSKFGiyNKlSyUyK1u2rHTq1ClUt+3WrZts3LgxQuI4ceKEOgZ27NjhsL9o0aLi7e1tPx4Af2Pf7NmzQ/XYOH6qVq3qkkT94MGDUr16dUmaNKmKMX369PLZZ5/JzZs3Q/3Yzu18/PhxGThwoEyfPt3+WvC4EyZMEHfbu2e3dGzXWiqWKyX5c/vKpo0bRAeLFy2UqhXLSeH8uaVh/bpy+NAhsTrGzHaODMcFvzNcR5djY86sGfJlg7pSumhBqVimhHTt2E4unD8vOtCljc0Ysx5jTm0u+s+K3J6cQpUqVdSP7nPnzsn48ePVj/D+/fur65AgJk+eXKJGdV0PZD8/P1m8eLFKkn/44QfxdAEBAfLy5UuJEyeOJEqUKEKew9fXV73PqCwaHj16JPv27ZMkSZI4JK3bt2+X58+fS7ly5UL12HjcGDFiSES7deuWlC9fXhImTCi///67SirnzJkjKVOmlCdPnoT6cZzb+ezZs+rfTz/91GWvJbSePn0qWbP6Sq8+/UQX69aukTGjhkurNm1l8dIVki2br3zdqpncuXNHrIoxs50jy3HB7wzX0OnY2Ldnt/wfe2cBJ2XV/u97kRKkFBW7AFFExSQUO18TC/UVRWwM7MDuwm4xeFUUTBQ7sduf3fLahaCoIKjM/3Md//e8Zx9mCWXnPLP7vfyM7M7M7p45c+Y89/eus12fney6G2+xS6+6xv7443fbf5/+NmniRMszlTTHjsYsKoFciFOMbYxuoqREM9dff317+OGHa0zrvfvuu61Dhw4hMrXOOuvY0KFDS6ZKIhCWWWaZYOy7APaoFz8zcuTIYtQ2FkVES5dddlk76qij7Mknn7TPP/+82u/luauttpo1b948pKX27NnTPv300zDWBg0a2Msvv1zt+US4FltsMZs6dWoxrZOxde3a1eacc84gsois3X///WG8LVu2tJ122imI5DjCecABB4QoZ5s2bUKK59VXXx1ET79+/axFixbWvn378Dti3nrrrRBpYw74mV122cXGjh1bjB6PHj3aLrzwwuI88Bp8jPyulVdeObw/Tz/9dMloIeK9c+fO4TkLLLCA7b///sXHzjvvPOvSpUuYJ97b/fbbz3755Zca1wHvZfw+8Dc7duxom2++ebX7+Zr5JMoOvI8rrbRSWA9LLrlkiDIipp1sKuyzzz4bXgfPX2WVVcJjpVLHX3nllfB4s2bNrEePHvb+++8X05H5G0RJfd6475lnnrGffvrJhgwZEt5bxsdrwuHiY53R74Z4nvma1w+sLf4Wa4H1dvDBBxf/firWWLOXDThwoK273gZWKdww9Drrve32ttXW29hS7dvbsSecFNbCXXfcbnlFY9Y815V1oT2jPFTS2rj4iqtt8y23tqXad7COS3eyE085w775+mt79523Lc9U0hw7GnNlUFVVvlseyYU4zYopxEPjxo1LPj5mzBjbdtttg4hFHJB+O2jQoGmeh7A799xz7YYbbggC87PPPgvpksC/22+/fVGwckMgOKSL/vvf/7ZWrVoFYYfwcBA9/O211lrL3njjjRDF22uvvYJAINUSYU20LIbvEYKICwfRcckll4TXivhlPIjYYcOG2b333msPPfSQXXzxxdV+D4K6bdu29uKLLwahuu+++9p2220Xxk6EccMNNwzi00UtYh3hi1BCMD/wwAP27bffhr8FiNLu3bvbnnvuWZwHRKSDOD/zzDNDBHD55ZefZo4vv/xyGzBgQHj9b775ZnAaIJAdXu9FF11kb7/9dhj7Y489FqLRNYGQQ5C6sHz88ceDEGOu+drha54LTz31lPXt29cOOugge+edd0LUnffrtNNOK/k3JkyYEMQeopk5O+WUU+zII48s+VzW1eDBg8PcEbnffffdw/2k6R566KFBlPu8cR8OFsZ+5513hmjz9Kjpd2dhrfp68r91xx132MILL2wnn3xy8T4xc/w+ZUoweLp171FtnXbr1sPeeP21XE6jxqx5rivrohKpxHmuxDHH/PLLz+Hflq1aWV6pxDnWmEWlkAtxOmrUqBDZw+OEaCCKePjhh5d8LuJj6aWXtnPOOSf826dPnyD8svz+++92xRVXhOgUUTUiel7Hx98iYukRW24uhj/88MOQQorYAEQq4sDFBuKG6Nhmm21mSy21VIh07rrrrrbooouGx/fYYw+7+eabQ9opIIAQbkQ3Y0499dQQcUU49u/fP0QwEXt8v+aaawYBHgsyWGGFFezYY48NUeOjjz46zBdiFXHJfccff3xIJ0E0A+KX33f66aeHtFm+JtLJ7/3ggw+C+OZ1E73zeSCN2kH8bLDBBuF1kqqahdeASEMYEuFcddVVq9Wv8jUiEtGOSOb5I0aMqHEd8FwiwS+99FIxQoow7dWrl73wwguh1pSUMMS5i1MimIho3gOipowXwck6KQXiH0cCUWei4zgfalprCFz+vkfRcSQwBtYOawhR6fPGfdTHHnPMMSHqzfvC72ad4hCY2d+dhb/jTaP8b/Fe8D4RLff7xMwx/sfx9ueff06Tns73nlGQNzRmzXNdWReVSCXOcyWO2SHDbPDZZ9gKXVey9h06Wl6pxDnWmCuHKtWcpgehQUolAgSRgZDbZpttSj6X9EdEUAwptlkQXIgqh5TTmWlKg3jbaKONgriATTfdNIhRon6AMEAM8xwicEQf48gVUVWEA9EzIIrnAi0mjkSSbst4EVfxfdnxxj/D32ATRMzHPwP+c0SWEaIIHL8hUuM6xumBsK8J/sZXX30Vaixr4pFHHgmPL7TQQkFIEdVFPMfpyjFEXYkIIkpxArz22mtBwPHeIf6JUnu9qYtTXiMiOn6NHgku9XdYP8wjwn566yc734zBX/f0QHR+8803wTFCZJV/mXMcFP/0d/8dmCvmMr6540QIIYTIE2eddrJ9/NGHdvpZg1MPRQhRnyOn1CQiTIgMIg4RqTPbibUm6PwbQ7RsRqmWeMFIPyWtlqgYN0TjuHHjqjVGIpKKSCKddvjw4SFq6A17iESSZspzpkyZEiJ1pVI24/F5p+LsePEgzug1ZX8P+M9R34mARvjHN6LDRCNn5n2pCSKF04PaVaLLiLDbb7891Fheeuml4THmpSZI40VQk65LNJiut+CpvdxYK55+zGskehq/PoQgrzEWoH+H6c3t9MBpQLo1aeWkRNMQia9nx++eVc4444wQIY9v3FdfadO6TXDsZBtW8L07pPKGxqx5rivrohKpxHmuxDHDWaefYk8/OdquGDLU5s95RlAlzrHGXFnnnDYo0y2P5EKcxpCzT2ok6aukcGYhlTfbcMjTQGcFRCRiNOa+++4LHWKJ2MVihzRd6vzihkukyJJaSzomx9wgQh1Se4kaXnbZZaEGsXfv3pYC0pmp9yRqi6CLby48S83DzEAklN9b09EyiNGQnjN4cEh3RcATaZ0RRESZUxpiIVQdxDQRVW4eNfXXSDQ0+/q4xTW+8fpBvMbRw9m1fmp6HhH8WenWOzv/PmuUyH984776SqPGjW2ZZTvbC88/V7yPdfrCC8/Z8it0tTyiMWue68q6qEQqcZ4rbcwEDhCmTzz2iF0+5DpbaOGFLe9U2hyDxiwqhdyJUyDqhEfKI20xNEDiTEya2FA3SQ2jNyyala6lCCtqMxE21AdQo0q09l//+leI4CI4/UYDIer+brrpptCQCeOeyCkdU2lcRJSO2lOHrxFkjHHHHXecYZSxtqBZEVFfxoAAI5WXLsGkTbuwYR6IVBPpZB5mJXpHUyfEJ02PmAPqa72JE+KQOeV7jgiiMRUprjPC606JVBMtdfiaccb1pkCd7X/+858QPUWIE6nkGCCcG6WgHpTXSBMnnst8eFRzVtcPawHnBfOG2KV2mhpl/mVtsrb43Tg9OAZmdsLfp9HXl19+Od36Fuqq6f4c32bnUTQTJ/5q77/3brjBl19+Eb7++usZOyJSscuu/eyO20bY3XfdaZ98/LGdevKJwRG21dZpnEgzg8asea4r60J7RnmopLVBKu/9995jp555jjVr3tzGjv0+3Er1YcgTlTTHjsZcGVTV85rT8h0eOguQTksDo7PPPjs0lYnhSI7bbrstNOLxbrN0PqVz7awY3dQlEoWjrpLUUAQN6bxxBNQhArf11lsH8UqjIsQx6b+kb1AviAhENMfQ5IgIYE1dWMsB6aQcb4JIppMvAoojWOhS7FFFusFS50tjHjZVBNfMws9x8eCoFH4PqSzMDyDwOUrmrLPOCmKeyCfppKQ8Tw/eX8aI8I/FKTWnvB5EdBxRpfYXMUjdKX+LdFlqPIlelwJxds8994T1wnEt1OwicBGts5IGTE000XSEMhF10rh5jaSBszbpwMx6JDWZo2Wot52d8HpZc0RleV9nlLJeW7zz9lu25+67Fr8ffM6Z4d/Nt9jKTj7tr6/zxsabbGrjx42zyy65KBhAS3daxi67cojNk9NULNCYNc91ZV1ozygPlbQ2bhtxS/h37+haAieccno4YiavVNIcOxqzqASqCqms2tkITWiIymXPI00JHWM5L9U754r8QkScaDIpr6mi3OVm4pTK+dg3a1xlv/3v2NqKoGlD05g1z3VmXVTSfgHaM8qzLn6ePPv7JNQmLZo0qMjPn8ZcnnnOE09/OL5sf2uNDm0sb+Ts7Zg5qOWkYy+NZ4gMclwHkdY8QBSW6B7HuHB0isgfpAHTGZkuwnT7JbJM6nZ9EaZCCCGEEELkkYoUp9Q3IvyopyTdkzTKvDR5QSTTQIkjZVKm9Iqa4agXUnn5l7RsapyJvgshhBBCCJGSqno+/XUirVcIMWtUUpqeUvTKg9LHNMc1rYtK2i9Ae0bto7Te8qB9uXzznCeeKWNab0+l9QohhBBCCCGEKEWDWTg9oi6Sy6NkhBBCCCGEEELULyROhRBCCCGEEEIkJ2dZ1kIIIYQQQghRP6my+o0ip0IIIYQQQgghSnLGGWeEYzxbtGhh8803XziV5P3336/2nN9++80GDBgQjvqca665bJtttrFvv/3WZhWJUyGEEEIIIYTIS+i0qky3mWT06NFBeD7//PP28MMP2++//24bbrih/frrr8XnHHzwwXbPPffYrbfeGp7/1VdfWe/evWf95esoGSHqH5V0NISOhSgPOrJAc1zTuqik/QK0Z9Q+OkqmPGhfLt8854nnP/6xbH+r21Kt/9bPff/99yGCigjt1auX/fTTTzbvvPPasGHDbNtttw3Pee+992yZZZax5557zrp16zbTv1uRUyGEEEIIIYTIAVVl/O/vghiFueeeO/z7yiuvhGjq+uuvX3xOp06dbNFFFw3idFbIma9ACCGEEEIIIURtM3ny5HCLadKkSbjVxNSpU23gwIHWs2dPW2655cJ933zzjTVu3Nhat64eiZ1//vnDY7OCIqdCCCGEEEIIkQOqqsp3o9FRq1atqt24b3pQe/rWW2/ZLbfcUiuvX5FTIYQQQgghhKhnHH300XbIIYdUu296UdP999/fRo0aZU8++aQtvPDCxfvbtWtnU6ZMsR9//LFa9JRuvTw2KyhyKoQQQgghhBD1rFlvkyZNrGXLltVupcRpoVAIwvTOO++0xx57zJZYYolqj6+88srWqFEje/TRR4v3cdTMZ599Zt27d5+l16/IqRBCCCGEEEKIGlN56cQ7cuTIcNap15GSBjznnHOGf/v37x+isDRJQuQecMABQZjOSqde0FEyQgghhBBCCJEDXhrzVyfccrDqEq1m6nlVFKiW4LrrrrPddtstfP3bb7/ZoYceajfffHNosrTRRhvZZZddNstpvRKnQtRDfvvDKoZKPeftp0lTrZJoNWcDnWdZy1TqWtaYNc91ZV1U4r5cifP88+TKmucWTfJV5fhSDsVpOVFarxBCCCGEEELkgKp/cP5oXSBfrgIhhBBCCCGEEPUSRU6FEEIIIYQQIgdU1e/AqSKnQgghhBBCCCHSo8ipEEIIIYQQQuSAeh44VeRUCCGEEEIIIUR6FDkVQgghhBBCiDxQZfUadesVQgghhBBCCJEcRU6FEEIIIYQQIgdU1fPQqSKnQgghhBBCCCGSI3EqhBBCCCGEECI5Eqdl4L///a9VVVXZ//3f/830z1x//fXWunXrWh2XqGzWXnttGzhwYOphCCGEEEKI2URVVflueUTidDaw2267BfHJrVGjRrbEEkvYEUccYb/99lt4fJFFFrGvv/7alltuudn+d7faaqtp7mccd911V/H733//3XbccUdbaKGF7K233rJy8cQTT4SxtGnTpjgXzksvvVScszyx+OKL2wUXXJB6GBXHLcNusk02WNdW7drFdu6znb35xhuWZyppvLeNuNl22m5LW6fnKuG2e98+9uzTT1qeeeXll+yg/fexDdZd07p26WSPP/qIVQqVtDYqcbygMWueK31dVOK+XInzfN2Qq6zvjttZr24r2wZr9bRDD9rf/jtmTOphiVpG4nQ2sfHGGwcB+sknn9j5559vV155pZ1wwgnhsTnmmMPatWtnDRuWv//UxIkTbYsttghi8Omnn57tAnlmaNGihd15553V7rvmmmts0UUXLftYKo1CoWB//PGH5ZkH7r/Pzj37DNt7vwF2y6132tJLd7J99+5vP/zwg+WRShvv/PO3swEHHmJDh91m1w+71VZZtZsdNnB/+/ijDy2vTJo0yTp27GRHDzreKolKWxuVNl7QmDXPdWFdVOK+XInz/OrLL9l2fXay6268xS696hr744/fbf99+tukiROtLlNVxlsekTidTTRp0iQIUKKkRDPXX399e/jhh2tM67377rutQ4cO1rRpU1tnnXVs6NCh4Tk//vhjtd/74IMP2jLLLGNzzTVXUQDDiSeeGH5m5MiRxQgkkcoYftcGG2xgX331VRCmRHTj8dxxxx3hbzdr1sxWWGEFe+6556r9PD+z5ppr2pxzzhle14EHHmi//vpr8fHJkyfbYYcdFiKyzZs3t9VXX32aMcCuu+5q1157bTXD9ZZbbgn3Z7n99tutc+fOYT6JYg4ePLj42DHHHBP+RhbGfvLJJxe/HzJkSJgz5rZTp0522WWXFR+bMmWK7b///rbAAguExxdbbDE744wzrCaYJ37f1ltvHeaJ94z3zllllVXs3HPPLX7Pe0/0/Jdffgnff/HFF+F3fPTRR+H7G264IfwMgp31stNOO9l33303TbT5/vvvt5VXXjnMA+8D8963b9+wDhh7PC+puWHoddZ72+1tq623saXat7djTzgpzO1dd9xueaTSxrvmWutYzzXXskUXW9wWW2wJ2++AgWEtvvXm65ZX1lizlw04cKCtu94GVklU2tqotPGCxqx5rgvrohL35Uqc54uvuNo233JrW6p9B+u4dCc78ZQz7Juvv7Z333k79dBELSJxWguQOvvss89a48aNSz4+ZswY23bbbYOQef31123vvfe2QYMGlYx6InwQNE8++aR99tlnQQwC/26//fZFwcqtR48exZ/95ptvbK211gpfjx49OgihLPxNfg+iuWPHjiH116N0H3/8cfjd22yzjb3xxhs2fPjwIJIQdg5fI2gRmjxnu+22Cz/z4YfVPYe77LKLPfXUU2H8LkARniuttFK1573yyivhNfXp08fefPPNIMCPO+64UH8LO++8s7344othbM7bb78d/jYiD2666SY7/vjj7bTTTrN3333XTj/99PA7EPJw0UUXBXE5YsQIe//998PzGcv0OOmkk8K4+DubbrppGMe4cePCY8yxC3KinLxOaoWZK597xHv79u2LKdannHJKeN9JvcZRQHp2lqOOOsrOPPPM8BqWX355O/zww8Pvwhnx0EMPhb/56quvWmp+nzIlXCS6df/f2mvQoIF169bD3nj9NcsblTbeLH/++ac99MC9NmnSROuy/Iqph1OnqLS1UWnjBY1Z81xX1kUl7suVPs/wyy8/h39btmpldZqq+h061Tmns4lRo0aFqBbijogiH/hLLrmk5HNJ+V166aXtnHPOCd/zNYIWQRWDkLniiitsqaWWKopBjxDyt4ho8rdKCc+DDjrIllxyyRC9xZtXCoTpv/71r6IAI2JJhI9oI9FERJg33CFiiLBDjF1++eUh2nfdddcFwbngggsWf98DDzwQ7kcUOvPNN59tsskmQWQiHImi7r777tOM57zzzrP11lsviElAML/zzjthnhBwjI8o6bBhw4rPQVwSTXXxRyo1UcXevXuH74kW8zuYcyK1jJfXssYaa4QIJZHTGcHfRrgDr4t5QCQjxGlKRIoyFyfeQxwSO+ywQxCPPM6/7iSA+HXz/vC7Vl111RBp5T11eJ+JegOP8TduvPHGMD+A2F544YUtNeN/HB9e+zzzzFPtfr4fM+YTyxuVNl7now8/sP59d7QpUybbnHM2s7PPu9iWXOqvNS/q59qotPGCxqx5rivrohL35UqdZ2fq1Kk2+OwzbIWuK1n7Dh1TD0fUIoqcziZIjyUC+cILLwQR1K9fvxB1LAUROwRJzGqrrTbN8xCVLkyBdM44BXR6bLbZZvbBBx8EUVYTROTi3w3++4nsISYRTH7baKONwuZA5JfIJpscAjJ+DtG9OLIZizJ+HzW5RFsRvlmIEvbs2bPafXxPJJa/Bfwc4tQjlTfffHPxd5H6yt/u379/tTGdeuqpxTEhNHmfcAiQpkwUckbE80T6csuWLYvzRNrzzz//bK+99lp47QhRBKtHU7mP7+Po8Oabbx7qbUntdeHqUWWH1F+HsZOOHKc0zz333OE1zAicFxMmTKh24z5RWSy2+OJ24/A77Nobhts22/exk44/2j75+K9UcSGEENqX6zpnnXZyqOk9/az8lDXVFlVl/C+PKHI6m0C0ePSOyCARPqJdCKW/C7WLMUT6EGQzA6m0NEJCFPIzhxxyyHR/v3fNRXx6tI50YwRcFoQVKa40ekJs8W9MHAF0iJzutddeYT4QZ1nP3cxCBPPII48MKa3Urn7++echUuljhquvvnqa2lQfI6nEiGtqOh955JGQrkt98G233TZL74PPEym8vNeIUUQ30c5evXqFMeEcQFi7AEU8I/C5EfGdd955gyjle8Rndj3NDoiAExWPIbp81LEnzpbf36Z1mzC32WYKfN+2bVvLG5U2XqdRo8a2yKJ/RfmXWbazvfP2mzZ82A129HHV31tRf9ZGpY0XNGbNc11ZF5W4L1fqPMNZp59iTz852q667gabv0S2oKhbKHJaG5PaoEFo3nPssccGAZWFiNfLL79c7T666c4qpJB6RLEURHCJVnKsTdy0Z2ZAxJEOi+DO3vi7Xbt2DX+bCGL28VJpxnQqpqEPIq5USi/QxOiZZ56pdh/fE511cUkqK2IPcccNMUjaMMw///whxZjobHZM3gwKiHwiHhGx1NJSA+s1pH8HxvP444+HumCipEQ1eS2kaRORZvzw3nvvhYsAtaREXEmfnplIONFzBDJReWf8+PFB/M6Io48+2n766adqN+6bXTRq3DhclF94/n/NtBDuL7zwnC2/QlfLG5U23pqYOrUwjUND1K+1UWnjBY1Z81xX1kUl7suVOM8EVxCmTzz2iF0+5DpbKAflTOWgqp6fc6rIaS1BcyCa2Fx66aWh+VEMEUnqK4kAEkkkzdSb/szKuZ808qGbL2nCRCJbtWo1TZSPCCpiGaHKh5wxzQyMrVu3bqHOdY899giRPMQqNazU0iK4SKdFcFLjiVj9/vvv7dFHHw1psF7LGkMjIP5+TVHTQw89NKQ78zzEI5FI/lbcbRf4u0T/uAhwbE8MUUKivcwFNZ+ksOIIQMwRPWbeEYyMl3m59dZbg5gmAvp3QZBefPHFIRKK4PT7GDvrII44I+x57j777BNqVHmtM4JINOvE5w4xTjMrxj8j6PbLLctvs/F0ml127WfHHXOkde68nC3XZXm78YahwSmz1dZ/1f3mjUob76UXnWfde65p7dotaBMn/moP3j/KXn35RbvosqstrzDOz6NU9S+//MLef+/d0MRigQX+qlHPI5W2NiptvKAxa57rwrqoxH25EueZVN4H7r/XBl94iTVr3tzGjv0+3D/XXC1Cl2FRN5E4ra2JbdgwCLuzzz47pLTGEMUjjRQxduGFF1r37t2D2Nh3331LComa2HPPPUMkkvpEUlqJ3sX1jbGYQ8ggVPGSeRrs9EBgUi/JuIjyIWyJ4MU/S+Mj6jl5HV9++WVIC0HQUu9aCoTZ9FJHiNbSRZemSYg2RCSNgbLdbBH7zC3RVDoexyCkqdWliRJiDlHdpUuXYmMn6jx5T0i35ecRw/fdd99MCb2aYH6Y17jxEe8D7238fiBecUIQVacREq+XiDbp1zOC18N7TEo0r4E5JwqaBzbeZFMbP26cXXbJReHCsXSnZeyyK4fYPDlNE6q08Y4b94OddOxRYaxckNt37BgMoNW7V6/PzhPvvP2W7bn7/46KGnzOmeHfzbfYyk4+7a+v80ilrY1KGy9ozJrnurAuKnFfrsR5vm3ELeHfvaPrCZxwyunhiJm6SpXVb6oKM1vEKGoVUkDpzEsNpRC1zeyMnNY2TRtW1nh9zD9N+qsuuVJoNWcDmzilsi4HzRpXVdTaqNS1rDFrnuvKuqjEfbkS5/nnyZU1zy2a5KvK8a0v/uqhUg6WW3jaPjGpUeQ0EaSqErUjTZO6SiJj8RmiQgghhBBCiHpGldVrJE4TQVopKbE04qEWkTTN2dmoRgghhBBCCCEqCaX1ClEPqaQ0IaWPlQel9dY+lbqWNWbNc11ZF0rrLc88K633n/H2l79auei80Ow5unB2kq8kayGEEEIIIYQQ9RKl9QohhBBCCCFEDqiq5zWnipwKIYQQQgghhEiOIqdCCCGEEEIIkQOqrH6jyKkQQgghhBBCiORInAohhBBCCCGESI7SeoUQQgghhBAiD1RZvUaRUyGEEEIIIYQQyVHkVAghhBBCCCFyQFU9D50qciqEEEIIIYQQIjmKnAohhBBCCCFEDqiq34FTRU6FEEIIIYQQQqRHkVMhhBBCCCGEyAFVVr+pKhQKhdSDEEIIIYQQQoj6zgffTCzb3+rYrpnlDUVOhaiH/PaHVQxNG1bWeH3MP02aapVEqzkbVOQ8T5xSOf7VZo2rKnKONWbNs9ZFus/fZle+ZJXEqL1Xrah92ffmXFFl9Rp16xVCCCGEEEIIkRxFToUQQgghhBAiB1TV89CpIqdCCCGEEEIIIZKjyKkQQgghhBBC5ICq+h04VeRUCCGEEEIIIUR6FDkVQgghhBBCiBxQZfUb1ZwKIYQQQgghhEiOIqdCCCGEEEIIkQeqrF6jyKkQQgghhBBCiOQociqEEEIIIYQQOaCqnodOFTkVQgghhBBCCJEcRU6FEEIIIYQQIgdU1e/AqSKnf4f//ve/VlVVZf/3f/830z9z/fXXW+vWrf/W3xNCa0oIIYQQQqTiySeftM0339wWXHDBoIPuuuuuao8XCgU7/vjjbYEFFrA555zT1l9/ffvwww9n+e8orbcEu+22W5h0bo0aNbIllljCjjjiCPvtt9/C44sssoh9/fXXttxyy9ns/rtbbbXVNPdnF8Dvv/9uO+64oy200EL21ltvWbl44oknwljatGlTnAvnpZdeKs5Z3nj22Wdt0003DeNu2rSpdenSxc477zz7888/Z+n3yMFQM7cMu8k22WBdW7VrF9u5z3b25htvWJ6ppPHeNuJm22m7LW2dnquE2+59+9izTz9plUAlzfMrL79kB+2/j22w7prWtUsne/zRR6wSqKQ5djRmzbPWRe2z08oL2qi9V612u3z7/9mtjeaosn3WWNSG7drVbt19JTt6g6Ws9Zz5Sqis1H25rvLrr7/aCiusYJdeemnJx88++2y76KKL7IorrrAXXnjBmjdvbhtttNE0mmFGSJzWwMYbbxwE6CeffGLnn3++XXnllXbCCSeEx+aYYw5r166dNWxY/g/xxIkTbYsttghi8Omnn57tAnlmaNGihd15553V7rvmmmts0UUXtbzBONdaay1beOGF7fHHH7f33nvPDjroIDv11FOtT58+wcuTd6ZMmWJ55oH777Nzzz7D9t5vgN1y65229NKdbN+9+9sPP/xgeaTSxjv//O1swIGH2NBht9n1w261VVbtZocN3N8+/mjWvZHlpNLmedKkSdaxYyc7etDxVilU2hyDxqx51rooH5+Om2j//s9rxduRd79XfGzP7ovaaou2tjMf/siOuvs9m6d5Yztmw/aWJypxX54dVJXxNitssskmwX7eeuutp3kMe/qCCy6wY4891rbccktbfvnl7T//+Y999dVX00RYZ4TEaQ00adIkCFCipEQzCU0//PDDNab13n333dahQ4cQmVtnnXVs6NCh4Tk//vhjtd/74IMP2jLLLGNzzTVXUQDDiSeeGH5m5MiRxQgkkcoYftcGG2wQ3miEKRHdeDx33HFH+NvNmjULno3nnnuu2s/zM2uuuWYItfO6DjzwwOAFcSZPnmyHHXZYiMji7Vh99dWnGQPsuuuudu2111bbPG655ZZwf5bbb7/dOnfuHOZz8cUXt8GDBxcfO+aYY8LfyMLYTz755OL3Q4YMCXPG3Hbq1Mkuu+yyasJt//33DykEPL7YYovZGWecER7jte25555BzF911VW24oorhjHsscceYa5vu+02GzFiRLWocPx+8f5yH/PL4/369bOffvqp+P7wnl1yySXVHAR8AHkMr5HD2uHDCh9//HH40M4///xhDay66qr2yCPVPYGM8ZRTTrG+fftay5Ytba+99ipGbnEA8P6yMeTFAL1h6HXWe9vtbautt7Gl2re3Y084KbwXd91xu+WRShvvmmutYz3XXMsWXWxxW2yxJWy/AwaGNfDWm69bnqm0eV5jzV424MCBtu56G1ilUGlzDBqz5lnronz8OdXsx0l/FG8Tfvsj3N+s8Ry2Qae2ds1zn9sbX/1sH4+daBc8McaWbdfClp6vueWFStyX6ytjxoyxb775Jti8TqtWrYKdn9UjM0LidCYgdZbU0MaNG9f4hmy77bZBxL7++uu2995726BBg0pGPc8991y74YYbQt72Z599FsQg8O/2229fFKzcevToUfxZ3nAigDB69OggnLPwN/k9iKqOHTuG1N8//vijKIr43dtss4298cYbNnz48CBWEXYOX7OAEJo8Z7vttgs/k80X32WXXeypp54K43cBiqBaaaWVqj3vlVdeCa+JCOWbb74ZxNxxxx0XRBbsvPPO9uKLL4axOW+//Xb42zvttFP4/qabbgr566eddpq9++67dvrpp4ffgbgE0gdwDCAy33///fB8xgIPPfRQEHA+xzHkzDNHN998s80MvBd4hBCL/v7we3lP3nnnHfv++++L703btm2Lop4UbOZ07bXXDt//8ssvIcX40Ucftddeey3ML2PxuXRYJ4h0nsPrJT2if//+4T3i/cUJgfcqNb9PmWLvvvO2dev+v7XaoEED69ath73x+muWNyptvFlIRX/ogXtt0qSJ1mX5FS2vVPo8VwKVOMcas+ZZ66K8LNiqiQ399wo2ZMcudti6S9q8c/1lx7Zv28wazdHA/u/LCcXnfvHjb/bdz5Ot0/xzlXmUImXodPLkyTZhwoRqN+6bVdApQPAlhu/9sZlF4rQGRo0aFSJbXqP43Xff2eGHH17yuaT8Lr300nbOOeeEfxFj1I9mQagQUVtllVWCkENoIFKAv0VE0yO23GIxTCoqUUKitzU1VkIs/etf/wqi66STTrJPP/3UPvroo/AY0UTE4MCBA0OEF7GFsCPkTi444ui6666zW2+9NURXl1pqqfD71lhjjXB/zHzzzRdC+y4yiaLuvvvu04yHus711lsviCvGxJzwmpknIKKKABs2bFjxZxCXeFnat/8rtYRUaqKtvXv3DpFi/j344IPDnAPj5vUwTqKm/Isohw8++CD8S9S1FERh/TkzgvcCDxBRUX9/eM+Ims4999xBlAKi9NBDDy1+j/jmfXdHA68X5wU/x7iJkDLXCOyYddddN/weHuN24YUXBiFL7TNzSdSbPP7UjP9xfBBM88wzT7X7+X7s2LGWNyptvM5HH35ga3Vf2dZYbQU789ST7OzzLrYll8pX+lVdmOdKohLnWGPWPGtdlI/3v/vVzn9ijJ1w3wd22VOf2vwtmthZW3SyORs1sDbNGtnvf061X6dU773x46Tfw2Oi/nDGGWcE+za+eQZiKiROa4DIFBEqIlakq5LSSdSxFETsSM+MWW211aZ5Hql4CA2HVFRE78yw2WabBSHloqwU5HfHvxv89xPRRUwiqPyGuJk6dWqI/BLZxNBB+MTPQWTFkU0HMcrvoyaXyCDCNwuRzp49e1a7j++JxHozIn7OxSn56kQy/XeRlsvfJmIYj4mIoY8Jwcv7hFMAwUa0NEtt1pUiVnv16hVEKSnBRFH322+/4HWivpX5Y23w3nvkFNGPYMbJwOthnrKRUxwYMTwnmwLdvXv3GY5vdnnERFoWW3xxu3H4HXbtDcNtm+372EnHH22ffPyX40kIIYTI8srnP9kzn4y3/46bZK9+McFOvP8Da954Dltjybk1WTmnqoz/HX300aFkLb5x36ziGZ3ffvtttfv5vlS25/TIV1uuHEHNpUfviAwS8aLpD0Lp70Ln36ywmVnhRCottZOIQn7mkEMOme7v9665iE8XRUTsEHBZqGMklZZGT6Ti8m8MAioLkVNqIZkP0lKz3vuZhSjnkUceaa+++mqoXf38889thx12KI4Zrr766mmEmY+RCDTi+v777w+1m6QRk+9OPSlC24VdnCLtcP+yyy5bTIeD+P0g4jkzkLJLTSupzl27dg2pvy5YEaeejg0IU6LfpO2yvoiWkxKebXrE+psd4P0iih5DNPqoY0+cLb+/Tes24b3I1r/yPenNeaPSxus0atTYFll0sfD1Mst2tnfeftOGD7vBjj6u+nubFyp1niuJSpxjjVnzrHWRDqKkX/402RZs1dRe++KnkNaLWI2jp63nbGTjJ86c7SPqBk2aNAm3fwrZjYhQMkLp8QIERAjy7bvvvrP0uxQ5nZlJatAgNO+hqQ0CKgtRu5dffrnafXTTnVVIHZ3e8SZEcIlWktqJuJkVEHFE9RBE2Rt/F1HF3ybSmn28lMeDTsU07EGAlUrpBaKDzzzzTLX7+B7R6OKSLrqIN9J5udHwibRhz1PnLCWis9kxeTMoQAwiaBGx1NJSAztu3DjbcMMNQ8pt3ITJIY2WCK6nAM8777zhX29QBdlzbGt6f7zulJRory3lX8Qyr9fv89dPtJeGRqSLM7c0XJoRzCUf8Jjnn39+hj83uzxiNdGoceMgll54/n/F7jhEXnjhOVt+ha6WNyptvDUxdWoh112c68o855lKnGONWfOsdZGOpg0b2AItm9i4iVPso7ETQ1rvCgu1LD6+UKumNl+LJvbet38FBkQ6qqrKd5sVCBphG7t9THCIr8n+IyhG6SDZjdjYZGSiE7DjSx2TOT0UOZ1JaA5EzSln+xDpiiEiSX0lEUAiibxRXo85K+d+0siHbr6kCROJJO87G20lgopYRqgS5aupDjYLY+vWrVuo+aRbLZE5BBVRPDrOIhhJp2UhIeYQqzT5wQNCujC1rFmol+Tv1xQ1pWaSlFaeh3gk/Ze/FXfbBf4u0TyMbY7tiSHqR7SXuaDmkpRUHAHjx48P0WPmnRRmxsu8IBARfKTM8j1p0NQAE+XltSNkeU2Mm/eRSCsgeOlgTNMmmi+RQp0Vtbw/fDD5eSLppOpyY344Q5X0ZGqVAUFKlJT3P05tps6UrspEm3mMelyPbk8P5oDfg1OCbr+skwceeOBve8T+f8O+2cIuu/az44450jp3Xs6W67K83XjD0ODE2Wrr3pZHKm28l150nnXvuaa1a7egTZz4qz14/yh79eUX7aLLrrY8U2nzzNx+HqXXf/nlF/b+e+9ay1atbIEFFrQ8UmlzDBqz5lnrojzs3m0Re/HTH0OTo7mbN7adV1nQphYKNvqjcTZxyp/28HtjbY/ui9jPk/8I3+/TczF795tfQq1qXqjEfbku8/LLL4eyR8ezOOPgGSV52NyUutEHBluV/j2zgsTpzE5Uw4ZB3HDALCmtMUTxSCNFjNG4hlpAOucSxp6VUDnHnhCJpN4QEcS5nHHULRZzCC+EKsLG02CnBwKKFFPGRcMjhC31r/HP0vgIjwev48svvwypYQha6l1LQSRxeuljRGvpoku3XQQqIpIjYrLNohCJzC3R1Kx3BSGNAKSJEoISUU3EEe+Mn7nKe0IUlJ9HDN93333FNF1+N/OI4OR10/wJgcg88DvceYATgHpX3jPmit/DXOCUcEgN3meffcKckTqHoEbM8jv43ffee2/4IPp8I4SJqscpuohpIs38LuYOpwFpDzOC94HIMH+T+fTjaZjX1Gy8yaY2ftw4u+ySi2zs2O9t6U7L2GVXDrF5cppaWGnjHTfuBzvp2KPCWOeaq4W179gxCNPVu1ev584blTbP77z9lu25+/+Owxp8zpnh38232MpOPu2vr/NGpc0xaMyaZ62L8tC2eSM7fL0lrWXThvbTpD/snW9+tkPverd4nMzVz31mU20RO2aD9tZojqpQl3rZUzPO5Conlbgvzw6qLJ+gSaZXjog9jJ0fHwf5d6gq1Ga3mHoMYojOvNRQCpE3ZmfktLZp2rCyxutj/mnSjCPieaLVnA0qcp4nTqmcS1izxlUVOccas+ZZ6yLd52+zK2e9TCwlo/ZetaL2Zd+b88Tn48rXuHKRuf95vensRpHT2QSpqkTbSHGlrpBIX3yGqBBCCCGEEEJMj6p8aeWyI3E6myCtlDRQGvHQ/ZbU2NnZeEYIIYQQQggh6jISp7MJGvlkm/kIIYQQQgghxMxTVa8nS0fJCCGEEEIIIYRIjiKnQgghhBBCCJEDqup34FSRUyGEEEIIIYQQ6VHkVAghhBBCCCFyQJXVb1RzKoQQQgghhBAiOYqcCiGEEEIIIUQOqKrnoVNFToUQQgghhBBCJEfiVAghhBBCCCFEcpTWK4QQQgghhBA5oKqet0RS5FQIIYQQQgghRHIUORVCCCGEEEKIPFBl9RpFToUQQgghhBBCJKeqUCgUUg9CCCGEEEIIIeo73074vWx/a/6WjSxvKK1XiHrI2F/+sEqh7VwNbeKUyvKhNWtcVVFz7PP88+SpVkm0aNLAfqugaW7a0CpqvJU85krcM777uXwG6T9lvhaNKnJdaMzlmefhr31plcQOXRdKPQQRIXEqhBBCCCGEEDmgSjWnQgghhBBCCCFEWhQ5FUIIIYQQQogcUFXP2/WqW68QQgghhBBCiOQociqEEEIIIYQQeaDK6jWKnAohhBBCCCGESI4ip0IIIYQQQgiRA6qsfqPIqRBCCCGEEEKI5ChyKoQQQgghhBA5oKqeh04VORVCCCGEEEIIkRxFToUQQgghhBAiB1TV86pTRU6FEEIIIYQQQiRH4rQM/Pe//7Wqqir7v//7v5n+meuvv95at25dK+Opzd89q3/nxBNPtBVXXNHKwW677WZbbbWV5YHFF1/cLrjggtTDEEIIIYQQOas5rSrTLY9InM4m0YP45NaoUSNbYokl7IgjjrDffvstPL7IIovY119/bcstt1xZxNbo0aNt3XXXtbnnntuaNWtmHTp0sF133dWmTJlidZUnnnii+B5kb998802ycdUk0F966SXba6+9rNK55spLrefKnavdduy9meWZV15+yQ7afx/bYN01rWuXTvb4o49YnqnEOb5uyFXWd8ftrFe3lW2DtXraoQftb/8dM8YqgVuG3WSbbLCurdq1i+3cZzt78403LM9U2ngrbcyVtl8433/3rZ183JH2r/V62no9V7Zdd9ja3nvnLcszlbQuHI159vHkXcPsimP2tVN3+5edtVdvG3bucTb2q8+qPWfcN1/azYOPszP33NpO67eZDb/gJPvlx3GzcRQiD0icziY23njjIEA/+eQTO//88+3KK6+0E044ITw2xxxzWLt27axhw9ov8X3nnXfCWFZZZRV78skn7c0337SLL77YGjdubH/++afVRX7//ffi1++//354H+LbfPPNZ3lj3nnnDY6DusASS7W3ux98oni7/JobLM9MmjTJOnbsZEcPOt4qhUqb41dffsm267OTXXfjLXbpVdfYH3/8bvvv098mTZxoeeaB+++zc88+w/beb4DdcuudtvTSnWzfvfvbDz/8YHmk0sZbiWOuxP3i5wk/2X79d7GGDRvZORdeYTeMGGkDDj7MWrRsaXml0tYFaMyzl/+++7qtvuGWttcpl9iug86xP//8w4aefoRN+W1SeJx/+Z6KzH7HDbY9TrrI/vzjD7vpnEE2derU2TwakRKJ09lEkyZNggAlSko0c/3117eHH364xrTeu+++O0Q0mzZtauuss44NHTo0POfHH3+s9nsffPBBW2aZZWyuueYqCmBPh+VnRo4cWYwQEj186KGHwjjOPvvsEKldaqmlws9dffXVNuecc87U7wY+6CeffLItvPDC4bWRevvAAw9ME6mMx8vr4z5eb02ceeaZNv/881uLFi2sf//+xehyzJAhQ8K4mJtOnTrZZZddVnzM53L48OG21lprhefcdNNNxccRorz++NagQellzms844wzQqSbuVlhhRXstttuKz7Ga7/88sur/cxrr70Wft+nn34avj/vvPOsS5cu1rx58/De77fffvbLL78U56hfv372008/Fd8j3rdSab2fffaZbbnlluG9aNmypW2//fb27bffTpP+fMMNN4SfbdWqlfXp08d+/vlnSw3Ol3nazlu8tW7TxvLMGmv2sgEHDrR119vAKoVKm+OLr7jaNt9ya1uqfQfruHQnO/GUM+ybr7+2d9952/LMDUOvs97bbm9bbb2NLdW+vR17wklhj7nrjtstj1TaeCtxzJW4X9w09Fqbb/52dswJp9qyy3WxBRda2Fbr1tMWWnhRyyuVti5AY5699D36LOu69sY23yJLWLvFlrLe+x5pP439zr4a80F4/LP337Ifv//Wtt73SJt/0SXDrfd+R9pXn3xgY95+bTaPRqRE4rQWeOutt+zZZ58N0cpSjBkzxrbddtsgYl9//XXbe++9bdCgQdM8b+LEiXbuuecGQUIUFAFz2GGHhcf4FwHjopJbjx49ghjja54/Pab3u+HCCy+0wYMHh+e88cYbttFGG9kWW2xhH3744d+elxEjRgSRdfrpp9vLL79sCyywQDXhCQjN448/3k477TR79913w3OPO+64IMRjjjrqKDvooIPCcxjb3wFh+p///MeuuOIKe/vtt+3ggw+2f//73yEtGgG644472rBhw6YZX8+ePW2xxRYL3/O8iy66KPw8Y3zsscdCSjfwfiBAEZv+HsVz7CCEEabjxo0LfxunBhH4HXbYodrzPv74Y7vrrrts1KhR4cZzEfup+eKzz2yLjda27bbYyE4cdIR98/VXqYdU56j0Of7ll7+cKC1btbK88vuUKUE8d+veo3gfn+9u3XrYG6/nz/CptPFW6pgrkaeffNyWXqazHXfkIbb5Br1s9522tbvv/MvxmkcqcV1ozLXPbxN/Df/OOddfEX8ycKiRbNioUfE5DRs1Do7/T997swwjEuVCR8nMJhALRL3++OMPmzx5cthYL7nkkpLPJeV36aWXtnPOOSd8z9cIWgRZNl0V4UT0E/bff/8QzQT+FtE+/haC1Nluu+1CRJSoIvd369bN1ltvPevbt28QSTPzuwFReuSRR4boHJx11ln2+OOPB7F16aWX/q054meJlnKDU0891R555JFq0VNSoRHFvXv3Dt8T1SRVmTmjbtYZOHBg8TmezgtEO2MQkQjHLMwbwpe/371793DfkksuaU8//XT4W8zfzjvvHMaCcF900UWDiLzlllvs2GOPrTYOh4gmr2mfffYJohvnBBFONs74Pcry6KOPhvRrnBZEXwHR3Llz51Cbuuqqq4b7+PvUsBJ1hl122SX8bHbdlJNll1veBp14mi26+OL2w/ff27VXX2777dE3pJERTRaaY9bt4LPPsBW6rmTtO3TM7ZIY/+P4UPowzzzzVLuf78eM+cTyRqWNt1LHXIl8/eUXNvL24bb9zn1tl357hlrTC889I/TE2GSzLS1vVOK60Jhr/7px/9BLbdGll7P5F1ki3LdIh2WtUZM57aFhV9n6ffYwKxTs4ZuvDs+ta3WnVTltVFQuJE5nE6TmkgL666+/hppT6ku32Wabks9FSLngcFZbbbVpnkdNootHINL43XffzTD977rrrgsiiSjeCy+8EEQY4vLFF18Mv2NGv3vChAn21VdfhQhhDN8T6f27EOVEuMUgDBG9wNwRHUS87rnnnsXnIPgReTHU1JbiqaeeKoo34GJcio8++ihEjzfYoHqqFk2junbtGr4mjZb0YqKnRGqJVDJHOAAcxC0R2Pfeey/MG2NFbPO7Z7amlHlBlLowhWWXXTY0UuIxXyuI3/i1zcx6QIRziyFN22wOmx1077lm8ev2HZa2Zbssb9v8awN77OEHbPOtSq9/Ub/m+KzTTraPP/rQhlz/v/R7IUTtgbHeadnOtveAv5ynHTstY598/KGNvH1ELsWpEFnuvfZC++7zMdb/pIuK9zVv2dp2GHi83XPNBfbCA3cGx3+XHuvaAkt0CF+LuoPSemcTRInat28f6havvfbaIAqvueaaf/Q7s8KKD1+hUJipn11ooYVCZI3oLZFDBBOR0tnxu8HrOOOfiRsT/R28VpP6WOpX/UZU+fnnn6/23JqickRaeR/85um3Nf2te++9t9rfIkrrdadA9NRTe/mXNGr37lL/utlmm9nyyy9vt99+u73yyivFqHJtdEYu9Z7NqAkAwhlhH9+4r7Zo0aKlLbLYYvbF59U77In6OcdnnX6KPf3kaLtiyFCbfzrZA3mgTes2wbmXbcDC923btrW8UWnjrdQxVyLUpS+2xP+cz7DYEkvat9/8r69EnqjEdaEx1x6jrr3Q3n/1eet3/HnWap55qz3WfoVV7eCLbrIjrrzDjrz6Lttm/2Ps53Fjrc38fwVe6gpVZfwvj0ic1sakNmhgxxxzTEj/pNNfFtJ4qbmMIX1zVpnZDrxt2rQJUTYikzMD6b8LLrigPfPMM9Xu53siet5tFuImSjM6x5UoJKI9JhadNEri71JvGQtMbojO2QmvgwgiKbvZvxVHMHfaaacgjhGeiFbEqsN9IWVx8OCQPt2xY8cQcZ7V94h5+fzzz8PNQSTTbMrn++9y9NFHh4ZM8Y37aouJE3+1L7/43Nq2rX5BEfVrjnFaIUyfeOwRu3zIdbZQJt0+jzRq3NiWWbazvfD8c8X7+Hy/8MJztvwKf2VT5IlKG2+ljrkS6bJCV/v80+qNCT//9FNr9/8zp/JGJa4Ljbl2rhsI03dfejp0420zX83rtXnLVjZn87nsk7detV8n/GidVv5fvbKofJTWW0uQ+nn44YeHSBrNj2JogESXV2o6SWFF1FFLCLOSmkCaJ/WlpAkTzSMqRtSW37f11luHtF0iptQvEj3lSJmZhbFT/8nvIL2VVGF+r3fGdRFHgyNqHj/44IMg0qYHDYw4m5WUXFKE+V2Mi1pP56STTrIDDzwwvBailKSkIuTHjx9vhxxyyAzHTZprtgMwc5ONOpIeS3MimiBxEVxjjTWCcEOAI869vpU5prER7xMik6ZQDnNAtJh53XzzzcPPxtFp/3mitNSGElUn1Teb7ktnZzr+InypyyU1mK6/1L3WlL48syDA/0rjrc7Pv/9hs4NLzj/HevZa29otsKCN/f47G3LlpTZHgzls/Y03tTyLu88/+1/U8csvv7D333s3NOtZYIEFLW9U4hyTyvvA/ffa4AsvsWbNm9vYsd+H++eaq0XowJlXdtm1nx13zJHWufNytlyX5e3GG4YGB+NWW/+vvj1PVNp4K3HMlbZfwPY77WL77r6L/efaq2zdDTa2d99+0+658zY7fNBfx9vlkUpbF6Axz14Qpm8+86jteNip1njOZvbz/68jbdqsuTVq/Jcd8+oT99u8Cy1mzVu0ss8/fMfuG3qpdd90W2u7YH47Uf8dqvIZ0CwbEqe1NbENG4YmQxzpsskmm1R7jCggUbhDDz00dMWl7pJuvfvuu29JIVET1GVyXAkCBgFE7Sa1qzT1obaTKB6Nk2isQ5dXxM7MgkBErDFGBB8RPD/+BhB7N998cxgzaa3URVLnGtdjZqH7LDWldLNFQFKTy88jsJ099tgjiDeaRSGQSd9FuMWNh6YHUekszz33XIhsZjnllFNCBJg0V6K11HiutNJKIeodg2hELNJUKj6OB7GJk4F6XqKRvXr1Cr+L5zkIW94LXjspSgh+P07GwSHBkUAHHHBA+B1E3hHms+JMSMV3331rJxxzuE346Udr3WZuW37FlezK64dZmzZzW1555+23bM/d/9dca/A5f3U83nyLrezk09J3P64Lc3zbiFvCv3tH8wwnnHJ6OGImr2y8yaY2ftw4u+ySi4KgXrrTMnbZlUNsnpymFlbaeCtxzJW2X8AynbvYaedeYFddcqENHXKFLbDgQnbAoUfahptsZnml0tYFaMyzl5cevjv8e93JB1e7f+t9jghHzMDYrz63R24eYpN++dlaz9vOem29s/XYtHoASFQ+VYVZKTQUtQbRR6JucWqnELXF2F9mT+S0HLSdq6FNnFJZ21SzxlUVNcc+zz9PrqyDzFs0aWC/VdA0N21oFTXeSh5zJe4Z3/38z/o2lJP5WjSqyHWhMZdnnoe/9qVVEjt0XcjyxM+/le9a3KJp/io8FTlNBEeNEG0k5ZR0UCKFRFqFEEIIIYQQoj4icZqIDz/8MKTBjhs3LpyhSfpsbTaqEUIIIYQQQuScKqvXSJwmgrNQuQkhhBBCCCGEkDgVQgghhBBCiFxQVc9Dp/mrghVCCCGEEEIIUe9QWq8QQgghhBBC5ICq+h04VeRUCCGEEEIIIUR6FDkVQgghhBBCiBxQZfUb1ZwKIYQQQgghhEiOIqdCCCGEEEIIkQeqrF6jyKkQQgghhBBCiORInAohhBBCCCFETs45rSrTf7PKpZdeaosvvrg1bdrUVl99dXvxxRdn++uXOBVCCCGEEEIIUSPDhw+3Qw45xE444QR79dVXbYUVVrCNNtrIvvvuO5udSJwKIYQQQgghhKiR8847z/bcc0/r16+fLbvssnbFFVdYs2bN7Nprr7XZicSpEEIIIYQQQuSAqqry3WaWKVOm2CuvvGLrr79+8b4GDRqE75977rnZ+vrVrVcIIYQQQggh6hmTJ08Ot5gmTZqEW8zYsWPtzz//tPnnn7/a/Xz/3nvvzd5BFYQQ4h/y22+/FU444YTwb6WgMWuOtS70+dN+kS+0L2uO68q6qBROOOGEAnIwvnFfli+//DI89uyzz1a7//DDDy+sttpqs3VMVfxv9spdIUR9Y8KECdaqVSv76aefrGXLllYJaMyaY60Lff60X+QL7cua47qyLupa5HTKlCmhvvS2226zrbbaqnj/rrvuaj/++KONHDlyto1JNadCCCGEEEIIUc9o0qRJEPzxLStMoXHjxrbyyivbo48+Wrxv6tSp4fvu3bvP1jGp5lQIIYQQQgghRI1wjAyR0lVWWcVWW201u+CCC+zXX38N3XtnJxKnQgghhBBCCCFqZIcddrDvv//ejj/+ePvmm29sxRVXtAceeGCaJkn/FIlTIcQ/hhQQDmUulQqSVzRmzbHWhT5/2i/yhfZlzXFdWRd1lf333z/cahM1RBJCCCGEEEIIkRw1RBJCCCGEEEIIkRyJUyGEEEIIIYQQyZE4FUIIIYQQQgiRHIlTIYQQQogKoVAopB5CvaHS5rrSxitEKSROhRAiB8ioEELMiLFjx1pVVVXF7ReVNt7Ro0eHfyttrhnv1KlTUw9DiH+ExKkQos7w4IMP2m+//WaVwvXXX2/HHntsRRlBjPHPP/9MPYw6zRlnnBHOkauE9eCMGDHC7rrrrtTDqNNwlMaCCy5on332WcXsF8A4GW+lcOaZZ9qAAQPshhtuCN9Xwlzvt99+1rNnz/B1gwYNJFBFRSNxKoSoE3z88ce2ySab2MCBA23y5MmWdyZOnGhPPfVUOMD6rLPOqhgj6Ouvv7Y55pgjfH3ttdfa66+/bnnH53TMmDH27bffWt6Zc8457dRTT7Vzzjkn9+vB18Tpp59uV155ZVjPonb497//bWuuuab16tXLPv3004rYL1544QV79dVXw9d77bWXDR482PLODjvsYB06dAj729ChQ8N9eZ7rP/74I6yL7777zrbaaqtwnwSqqGQkToUQ0+AX4ffffz+Ij3feeSf3s7TUUkvZ3XffbTfddJMdfPDBNmnSJMszzZo1s9NOOy0YFffcc4+dcsopuTaCXnrppSDuFl544WBsHn744TZo0CBr3bq1VULUhqhenz59bPjw4fbzzz9bnseLg+WKK66wo48+2s4++2z7/fffLc8ssMACQZjiFLrsssts1KhRVgn452z8+PH2ww8/WN5BMJFtseyyy9raa69tX3zxRW73i5NOOsmef/556927t5177rnWt29fGzZsmK2//vqWZ6ZMmWJLLLFEWM9t2rQJY77xxhvDY3md64YNG9q2224b5vmjjz6yLbfcMvcCtaZx5XF+RQIKQghRgttuu60w99xzF5ZYYolCy5YtC1dccUVu52nq1KmFP//8M3x9//33F5o0aVI46qijCr/88kshr+P9/fffw9f/93//V9hjjz0Kyy67bOG8886r9py8sPrqqxe6d+9e+P777wv77LNPoVmzZmFN/Pe//83dWEsxcuTIQtOmTQsXXnhh4csvv6z2WJ7GHq/j8ePHF0488cRCVVVV4eKLLy7enzcY1x9//BG+fvTRRwtrrbVWYbPNNis8/PDDhUrgjjvuCGt78cUXLxxxxBGF1157rZA34vd+2LBhhXPOOSesC/aMTz/9NHfreKONNip06NAhfP3mm28W5p133kKDBg0KN998c/E5eRpvqXl+4IEHCgMHDiy0adOm0Llz59yOPR7zvffeWxg0aFBYGzvttFPJ5+SBeP7uvPPOwo033hjmO6/jFeVHkVMhROysKjbdOOaYY4Inlrobvt53331DLVxewUt877332muvvRaie6TKEtnLaw0q3u5bb73VjjjiiBCR/PLLL0PKG/VOefLSX3XVVSEV9tlnn7W2bdvaPPPME6LSzCvpnHmHsZNyyno48MADbe6557bvv/8+RFCJBudlnoGxsI5vv/12W3311UOqOnPOuPOa4suYSfO+88477Y477rAff/wxpPYSOaMGPM+8/PLLIdWUaF7//v3tlltuCenU3gwnL7AmgL3iyCOPDF/vueeexXTOPNWgvvnmmyGiO3LkyPD9I488EiK91MqyP7/44ou5bdzj80zGwi677BKuI0cddVSIpl544YW5jKD6mA899NCQzTJhwgTr0aNHyMbJYwQ1rj9mzHz+mO9DDjkkfAbzNl6RiASCWAiRY/Bgnn322YWDDjqoGN2Dyy+/PHi/zzjjjEIeue+++0J07IILLihce+21IerUuHHjwn777VeYNGlSIW+88sorhebNmxeuvvrqEM37/PPPg7d71VVXLZx55pm58dLffvvthaWXXjqM99RTTy3su+++hWeffbYwYMCAEKF+5JFHwvPitZInfv7558JKK61UuOiiiwoTJ04sHHPMMYWePXsW2rVrV5hjjjlCVDVPEGlq0aJF4corrwyRf9YF64HP3llnnZXLqMLo0aMLjRo1CmNmbRDBIdq0ySabFB588MFCHvn4449DBPKUU04p3vfCCy+EtbL11lsXnnjiiUKeePfddwsLL7xw4e677y7e9/rrrxfWWGONEPX97LPPcrFfEPFnnLvttlv4rJFl8d133xVeffXVwlJLLVXYfvvtCy+++GIhT8Rz9tFHH4X5vOuuu4r3jRkzprDhhhsWVlxxxcItt9xS8udSf/7atm0b/oXffvutMHTo0PA6ttpqq+Lz8rR38Plbf/31C2+88UZYu+wd7BmsjzyOV5QXiVMhRLWLAQYFaUErrLDCNIIDgYoAPO6443I3a7vvvnthhx12mCZlD4FKelbqFN+sITNixIhgrP3444/F+xAiXJwXXHDBILLzAMbDFltsUejUqVNYF4wRvv3228Kee+4ZBGpsyJ922mmFt99+u5AnccraYD1jKG+55ZaFSy65JBjMm2++eWGXXXZJPcRqa4O5bN++/TTpx6effnqY/8suuyx3joCTTjqp0KNHj2r3PfPMM8GpgXhyB0YeYK557xdaaKHgHMIJF/Pcc88VunbtWthuu+2SpibHhjlOlU8++SSsXwRp/BycAa1atQpjRkSl4tdffy1+zZhwVjBeT/33+9nzcMLxNZAGzprOwzz/9NNPhW+++aaw5JJLFkWop6x/8cUXQQBS4sB1MCVZ0UYpC6nTP/zwQ7V9jzIG9oy+ffsW8gTO47XXXrvQp0+fwuTJk4vr5z//+U9IVY+v43lxAIjyInEqhKh2AeACR6SGi9p11103zeycf/75hXnmmacwduzY3MwcBgRRmtjr6kbFkUceGV5L6giqzzHebSI0GBQYakTKYoPjww8/LLRu3bqwyCKLhAh2ChDzcXQDEYcIxZDEeHeoQUWgEoFElPJ4ly5dinOfao4Rx0SYyAL4+uuvCxMmTCjcc889heuvvz4Y+s4222wTnDF5gAg1UUZqkFmvRJrA5/KDDz4IIoTH+AymxOeZsWLQE4Ek4u8CxcfMa5pzzjkLvXr1qlZTlod9jujuoosuGtYsryOGzydRJxwX8XpJMc7DDz881KQzDiJ3hxxySLXHWdvdunULkXUcLylgDzjwwAOL31966aVBmHJj7DHsH8sss0xYL8stt1yhY8eORYGSkv333z9khbD/Mr6DDz64uC/73kykD8dh9j1IxcknnxyE/XvvvRcE9fDhw6eJTuKEYc9gzHmAa/AJJ5wQrn04VGJcoHINYa5F/UXiVIh6jF9gs2KCaJ5HUGlWUCp1K2+QHjv//PMXU5scmslwsSONE6FSbmIjhoYxzCmGOmJjvvnmC0ZRHHV4//33w4X5sMMOKzY7KSdE7Lbddttq0TnGiMFJoxOa3cSRMIxjjI3VVlstOAemTJmSNCWLRl4YZKusskqIcmD8jho1qtpziJwdffTRIRJCumRqEKJE+InoYqgjMtZbb73CO++8U81pRASYaEh8fypIh2Ytk+6N0OPrOOURcMCsvPLK4fV4xD0F/hn0Nenf47DACdS/f/+ik8h56aWXQrQy1ViBz9nyyy8foox8Ho899tiQkk6KuoNzACcLz0nxmePawTz6557vcQ5xY12Qor7rrrtOk41BZgjZAL7PlDsbIJ5n9mL2iSeffLLYpAexP3jw4OJzeH3//ve/w2PZdVQu4veXBk2kT7/88sthP9t0003D5yzOYuH6QQYATq9UDsNSa5Ksm3PPPTc4uQ844IBqj+GEofnizjvvrLTeeozEqRD1FL+wYgCR9oMgocOtQxosBjxGJx0i8zZuLmLU1jgY7NSKbbDBBkUjwyMPGEKxAEwBaWFXXXVViDDGdbJEHYnqPv/88yGlDKcAkco4RSuVIYEX29PvfLyIpqxABcbr702qtFOivXTX9DRBjDLWL0Z9nOqNsUxkzKOTKWHdUst7/PHHF+/DAKbGjage6bEIaD6L1GThDEgNziuMS24O4yPl/6abbgpZFawFOofy+YtT18uNr8nHHnssZAQgRIn0+jwy1y5Q33rrrUJeYFz9+vWrlnbMZ4xxIvjJFCGzgnRqHEMuPsopQrLi7Jprrgn7A4LZU0tvvfXWkgI1JpVwAgTy3nvvHaKm8f6HU5O9A8GHU2jNNdcMkV5/Tsp6SNYykdA4g4Iu09RLc/2j7AaHHO/FxhtvXBxruec5niMi0tTzIqSBNcL6JY2Xz2VMfF1X3Wn9ROJUiHoMBhCpglx8MZD5Gu8wnk0XqBj2XKSpkUyNG0NEZDDOSGfr3bt3MSpDZJLv8chyUebizBYTbnYAAJCQSURBVJEn2ahIucGDzRxipMXNjoAoKsbxYostFgQT0TyiUannmeg441pnnXXCvMbjZV6pQ8VIKvWzqUD846DwOSdtE+EfG/c4CYYMGZIkKpaFMTK/1IvFAtojkziMWDccy0HkPw9imppHUrxJfSRt18H5w2vA2YLBSY0vNZ3ZlNkU4JBAONOkB8HP2IiUkZbu+yBpkUT+8xCVRsyTCk1KNBGxGD6XpKdzP3V7RMZSZyv4557PHxkLNOFxgco1BIHKPozYzhPM26GHHho+Y4jPLDg5Se1mT+EamXqemWP2La4jjJmSlRiuc0QiSZklW4i9JdWY4+sATir2MCK97HVkf+AcwnmBQMXpxvswvd8h6hcSp0LUU0itovEK6ZpA1A4DmHQmvK/u4eQCQm1LHow2N9q5OOM5xuik6QqGnEf4aApC9AaRTd1QXqIhdCMkdRMD2aO4fvFFMBEhIz3Ou26Wm1KGAOlu1AURxcsKVO4japNaSMcQ7aDZCrVWGEJ77bVX0SijuQ2Ne/DK58noIfpBsynSN7/66qtpHiciwq3UYylgn0BkYByTSp81fEmr536yFYiWpCB+f3G0EfGKo7ysWRo1IbDZ3wChzfNSzHOp9YjDDeGJ0wrRV4q4hr7c2QqlxA4p6WRb4DQk+8Mj5ghU0u1ZMzhBU1Fqnonye7MxX888r6ZIdB6akSGacaZwPi8ZNzGMl+sL5RmpM1mADuM4i4nkPvXUU+H9x1FBlhbjwmGIQHXRKgRInApRT3nooYeCR9MNoSWWWCKkNtEMhIsHNR9eo5kXYx6xhBHvNVdEEYiOIVbxFiPw/EKcx3QgamkwgjiOx+c0D3MbNyQhmocx6Ua7z3lWoHLUAk1QUkYRIBbzGMAY89TykqoXw9omfT111+aa1gWpmkRpvEtvHtevgzOFNE0a3vCZS52a6eCMyh4NhEDG6RbXojNWUsBZ1wg/X0sp1kb8PmcbA9HpFpFHhPSGG24o3p8VG+XeQ+K/j9OHawnXDSBSh0Cl3puxxym+ZFqkEkrxPCPq4/e6VI8F5pRb/HMpa0yzsBez17FnxNkJ2c9hOfcRHN4xrGeuGzi3Y3AC4Kglou4OL+Y9D3uIyAcSp0LUU4ggkSbIxYu0JS5yGBZ4XUnN4kJNelaejGQufni5GTtGPIKUZj0YGkSBiaBy0U55kXMDhrEyFqK7MTS9YW7xKKcWpkQa40Y11CqRYkVEiVpBmjPFApWGSLFAdVKljNGRl7HGhjvdQZlfUr+JiuCZJ/0Nz3zKI27iDrcY70Tq4mNBiBrQ7AYBnReB6mMmlRCRF4+XiOSOO+5YmGuuuYodnFN+7tgTqHslyhzDfkaKY9ZAZq+j+y2Nx/IgPog04xAkFZOjNvxzydwj8ri/VHO6ckLjJcbmMHc4MonikTbtWTgIUNY4kT2uIdkGeimjvOedd14QTHzWuHZkeyyQlk7mTWriMZPGjSOZ2kyi/h4xp6YegcqekTp9nr0g29yI1F2yEbjWZZ0vOLdIsc86ZCRQBUicClEPcKOLGisuGHHqGt/T1t8vyFwUiTLR8IYGBnmCsXl3VT/X1FNkEdiIEl5LiuMf4nlGkJJWilFMgx7SpBGrbnAgUKnbo8ttKoFKJ03q7kjRHDduXBB6RBw5joCIKMYwdb0u6DzFlwgfHSJTG2zUCRK5Iy02m7rNWqB2l669pG9iwKWs1/T3GEFKR2ka2GC0EREj2hsLVAw2xp+is3SpMRMhxwFAzZjXxroBSeo/RilrPG5ClnrMNO7yeUW0ctQJNYWs8Rjqpl20pnQU4TxhveJ4IxWdxjbsb35uKQKVxjysmVRH8iCIuC40bNgwdGUmes542AuodSRtmpIQskJcgOI0woF4xBFHFPIA4nOBBRYITekQ+uzB2R4LiECuIwi/PMDc8bljn2bvJWUap6Jf94ha4xygSV2qNHrAVnChGTs8+eyRleVz7DWwOD/5/AlRColTIeo4cZTJm4FgVLgHnIscQgpPJkYGFw0a8/jFJPW4Sdvklo160ZTnlFNOqZbShwCJD31PAWluGOs03QEiORg7iD283v666Bg699xzJz0v1qN1RBsxkONzbTFAOdKGaKnPPXXHeOnLHdHLCh9EEYYac+iGMOsYweqOF5wrrHGESspjTJzHH388iH/vJOzHbGBYDh06tPg8ogw08/LoaUqYO8bImEn39rR0IjjeUROHF4Yxe0i5zxGOBaULZu5jfIzTBSprYt111w1rHWOf1F+iPET9OCMyJTgFEXDu8EFseFMpIql+nBTOIcaeMrKEI5NaQUQo+4WfBergdOMxb/qGECGDIQ/RMK5/1HY//fTT4XtEPg2ncHBleyywzvNQW8o4cKx5XT89CVjXOAmJALsTltdGVDtVpoULTh8zDkE/0gbHIY4hsprcpmA9cP2m87QQpZA4FaIegCFM2hVihAYKGDlc5GhQ4BdqjFA8nBiZqbuCutGJ2CCdFDGNYY8x5DWGXAAxNjFAOW4BoZfaoEcg0aiJiKhHPBAfNEHCAKUOi/MT3YggYpmC+GgPIo9E8JhfUvFiiKIiUOkMGqd0QrkMIUQywsI7q7qXHgcKhiYGM9EvDCA36hGCeYF5IqLAGiUiDaxhPmsYlET/MUC9/irluohhvule6yl5jJm1jLOCvYQuyB69wcFCJC1VNogbvTRdIb0RgcEeh1Dys1eJRPMeEJWkBICU02wKcDnHHX/GSKf3KDWOrcsvvzxEIhHPOA0RpjHlFns4/NwZwdx6dJH02CwI1EaNGhX7GTgpjzHxawmp0+50YZ6pN2Y/RqTGPRaclGev8reJQvtZq2RdtG7dOuzX7BtkhfB6vDeAk7IUgOsKzh6yLIj0e7MmotDsz5wGgIOcdHrKR1zUpi5vEflD4lSIOg5GAalLJ554YvgeTzxCifSxGJoScKHm3zxABBKvNp5YvNpElzCIMDIAIYonnNRUOvamEtR+YfWUJgxkLtDUWZFiTFQSaAbC+DGOU9YHEclA5McCjsgYa4LobrZbKaIJYwLRncL4wTB2h0QcFSfagaAmTY+6Now2hCrrodSxBKnwxis4KnAGYUxS0+1RAww3xB6GW1w7m4LYSGScrAvmHPFHirqvZdIiWct8H59JWG4QpqxZ9jbO2IyjpawF1kEsUDGGuSFCskZ9ufFsAD5P7LnscewXdC71dYMzgOOc3NmVwojnCDG6ciOWPTLO3sacM7el6mBxaOA8zIPocAcsYydNmgZNOCa8azD7HWKKtePOo9S4M4I9g7VB93HEHNFSj0ayX+Bk8XKcFHNN+QrRXOD64DYFGViUAuDUxKYAav9xjnOeM4LbhX8eItQif0icClHHIfUHEceFBM8mHlcuIn4xw0uf8jiQrNjxcWFYurHAxRkDgvqVrPBGbKeONJFC6uLNvcGkWiFCvA4IUYg3GXHK60kBaa6IOaJKiP8YmplQz0RtUzaCQBOkFB752ODC4KFW04131jXGDp0fMZbdyOEoGSKpeTCMicyRvhZ3FGatkJLs9YSkcxKBoltoqnURQxaFR3G90yrRMCIe7rhirZBauOCCCyY/4oYaWBwrRM0ZJ/h77wKVx+La3tQgTNkHYgcRDiuEqHc/phlZnz59Qqp9qmgYpQmIIMRE3O3Y1wapvQjUm2++eZqfzUM3csoQEJ1xUzqyLriWuHMA0YSThZ4AeUg/Zp1S64/jxecOAYg49T2D/Zi1QVlLqrXBZwsHG1FyIrlEn+NMBBeo//rXv4rHvGXJw3yLfCJxKkQ9AOGEsEOY7rPPPsWLAtEDjHk8sikvFBjvcY0rY8EYZlxEZrKCGlGSqjFIKYiWZptoMEaMTU93RHyQvpdqnjEgSd3mX0/HzMIRPUQVSglUKMfYazK2MCpJvSOVm4YgWXC8ML+kd6euI3QwKknl9sidOylI1/T1S5MWMhuyHU1TwXiaN29eTVAPGDAgRMIc6tIRqOWuMS21ThB1NBPis4ax7vtILFC9jCF7zEy5x+og6FkXcc0dmR8IfuaWdHWMeox+fx3lFiHUvtK8i3TSmmA/oGMv4p/05CzlFqal9ieyRCgLcCchnzPO3aR2nvReMjDiKG/q9GOcE+zT8dm27B9kBzHH1M/TvdmdoSnG7CCgGRcOCt+TcRK6oxCBSpkFTlmcckLMLBKnQlQ4fnGLmxJkufLKK4OhwUXYa/f4OQQTqWOkD6WCCxlRBBo1xSnFpGmSskfqJvVtfgHm+RjzGEXTe821SamoAPVsRMk8+oW4Y+x46RFURCBS1Li5QYY33s+HdTDkiSAgruNOwtQHYVAQVSgnPgYMMOrviIoyBn/vWadEOYjwxge2I/RI7aUONQ9deeOaXrquYqD5a6DbNKKDCDaRYAzRbD1vijHHwsmNX09JRlAjQBg364K1nO2QnApENGeWkmaKuKPWMdvMzbuwEklLCWeBeqSZr+kWyxp32I+prycizZ6RsiaP7AOcQfFRH8wznzs+g4zbU6OJoCL+8yJAYscldY9c9ygP8Xnkca6HfC7Z6/JQ+0j6vDt7qCVFQPvZsezfiGhq01kbXC9Tjtn/Jp+z7bbbLnQXpx7WnT9eZw/sE7yW+MgmIWaExKkQdQAubHjcp2d8ERGhRgWPPGKPZic0hUjd/MhT2BBxREs9YkfqEjVYNETy10UUFQOOKEm2SUi5wcDhokxNE0YFwpPaLIw2F1kYzqQmk3qY0jBG4C266KIhhdtBqNLKn8PQuWH8eOdgagqJsJczWuN/C6FGmiZ1rt6Z0jtsZgWqe+tJ7UZUpzxKwSEaw7qNO/BSr8k5wrGRT2SEWkI/Szb1mGm+4tFbDHnm3Z0pOISI2pB+TKZFHsQ0n7lsBgCdZBk3dW0uUFnnKcfr0GyM9UxDKT8XlqNjcMCxHhycW4zXPw8pavKYYxwR7AkOWQk0TsMBxJ6MU5NrCuMkOs3ekof6QXoSMM+IpmHDhoUxkZaMozBuqoYDib0k5Tw7CH7EMg4USlQYC58zju5xhy3jRfzjSIwdteWkpusB1zkym3BaZY9r8jPJlcIrZgWJUyHqAAgijAaOR8iKtviCQtMQDh6nAyueTD8ztNyU8vZilJEixDEafkFmvNRtUjPLcRU8hpGROjrGBXfvvfcORhBGBNExarAw8BHOXquXGjcIMMqIhtEZlnpCoozUMBEdQ1xzVAzNpzDqnXKmE8bClNolDGEEJymbeOQZd4wLVKJL3s0yL7AGWBekumFcIqxJpcYpVCr1MSW8xwhSBJ0fEYMwBdYIUaUYoiGpshWy55hSc0eUkVReP7YCEEw4BxAnzD+vi/TCcuNr2sfMWsbhQr0xRjyCBOHMvoaTpZTxXk6DnnHGn3XmmMguTW1wrnB94bPmZQo4rxCp2b0uZYdb4NqA05VMHK5xfO5w3uIUwJlY6tpT7pTpbFdeMps4y5RMCrp4k81CUylSv72hU5aU6ceMCecxTf4c7A6uicy91/iyh+A0TzVmUblInApRoWDoxvVA3qQEgywWqFmjAy93XlKQuVjFFyyMCYxJaoQ8iofhiVFEjRAdI1NHTN2wIOUKgwKBh0GMKMVr3K5du3D8Q2riqBLpdxgTHvnACMLAiOscMd6y3SrLmTJGsw/e++zZiQgNGmtkRRHrAIFCKnXKhljZOcIRwDyS6kj0iWOEiFATdWR95yG6lIUoL2m7iFPWLhEzmvWwpv34jbxAujeOFOaXyCNrmlscseH8W9YGmRipo6aeGk9qMe8/Y6YWGacWtd0Y82S0pCytAG+240156LLKv5RQcE1BlMadmXFikCmSl1ppsiY8LZYmUqTs0mmYdUJUEucc65n78oLvBYyd/ReRSgSaNGQchdR+cy1MTbzHkblElhMims7o3sHbXwcOcvZxHBc8L6UzS1QuEqdCVCCkvuKFx+CNjTI88KUEKmBY4MUk9Y0ISKr6GuoJiYB6zZqPg4gTYg/DgrTOuD42LxD9IOXVBT6GBO8DdWQYaxgYXLC5OGfTm8oJxu+ZZ54ZvibdihQ8QPCXanSEgUkjpLiOs9wQxaf+EkHnYpPOvMwlxiWRGqJhGJdu8HP8RupusUAjLIw2X684ijDqWeuIPAQqr4Pb9BrMlBNSdqkljWumiVCTwYBAJSqJMU8EKi8NptgzcFRgxLsDhno2jGD2C85zjve7mhp/1SaxIxCHEO85RwQxFrIBqDVmDTM+9ggEHs+h6VQqSCvGacW+gYAmsju9M6PplE0Ne9zQKSU0DGIfRhiR/oogwtHinb15vHfv3mGeWeepiB2xOJfjrt2k6+J0wUlB93wip+zbjNnXe2pwrJC5RPdd1gB13p495BBJp5kWZQup0o9F5SNxKkSFgkGBV5W0sLhVfimBiiFEOi8XkpRnbHqtJvWCGJNu9HLRo8uqd7ulMysXZuqxPI0sFS6eaazBRZhxYwgRkcGQIHLq58+5UYGhlzISQndSBB2RIzqZckxCqfQ1jAcEFal7pJCVO+2KplfxERWMk8gzIpTIAcKDGjKi1NTpYWAyTk9BzYvRg9ggBZIUQm9wg6MirjMl6k8UuNy1x6VSFomkE23kc4jA4DmkEhLJ8zRCUpFJy2P95MEBAKSf877jHELosUdQP092BY1iEHp5OTKG6Pn1118fztOk2zjNhUjx5sb+7PswEVVEVMq1TNoz4pj3mjR65hayTXcQ2ETHcC6yB/qYy+3ozP495hBnHFkUOFQ4JgtnJ59HF9mIQBr2pJrn+O+SDsv5pMwjexzXDK5zpHnvuOOOwVnIZ5LoNXtIHvY5bAkyQNwBhJjGiYFDgH/jvS5Gqbzi7yBxKkSFwUXLN3yiMhiZiIs4auACFcONaAPecGr5UtVqElWMj//gwsaFGfGEJxtvbBzFASI4GEoYGKkvcMwt0RkiHQiobbfdNkR5aWiDQU/EyaNmGE4pIjZZevXqVUyTLXXkB9FJjCFSOEnzjVOty7WOca6Q1ugNYgDRj7ivKcrIPLOeUtVL12SM0xQEBxDREDraIroRo3GdqXe/LTeMjXRYwCgm4kFUl4ge9YSkQFKLjKOLiK9DUyFPr08J68MdWS6Uie4yVp9TDGcEKoLau8imWhMeBfOzhHHIkf6ISGXvoBkdGRjZz2VKEYJA4jPHZw+HUPZ1ESnjzFgyLBCB5d4vSjlb6E3gRx4xTvYGMkXIXsGJMddcc1WL6qWaZ65t7LHAuJhD/1whqnHC4ihi7DiL4mt5qjFn4f0mgkvWCg6WhRdeuNhgD0cMawc7RIjZgcSpEBWGGwsY7kRHSbtr2LBhiBqQluUgBjGOqQ1BmBLVSwEXM9IaSdWNj01grAhULmp+plvW0MHbnaoDq88zBhAC1KOjDt1hifAhRngN2aY9qcZLlByjF0MHgxhjE8OTFNjYuCP1ikZOpHm74VNuA4j0ctJ4EfrxQe04VIiAUCfmRlyqw+ZrmmeidTRuIurlkWlSCony4nThNfEaeH2pBB5j9TnGWUFafzZNkMeJeuCkQJzyeJ6aTDF3GO9xsy7WKRF0RLavDYx6xl0qbb2ckEJKinf22CY+f3zmPO2fGl8/dioF/nnyf4nSIaJwsNCYjk63WWcWTZLoOpyHdE3mmGsfnzPWdXwuL04XHLM4K5jr+LpYbphfnBOkoxPpx+Gafd/JysFZgUPD6zXzAg3HGBv4+05XYUS/O2G5vrDH4BjKyz4tKhuJUyEqEFLwGjVqFEQdFzYuftSIIfbiCzFpk1zA3XhOBamMpP9wgY6PM3GBStdVT3dMHSWNQTAR3UOAevpp3OCBeR0yZEgwNIn+phIh8VmEcdMST/HlGJlYoEJ2rKnmnbHTRTMrUImgkuJL5Lzc563ObFMeIiBEHRlntqsmjYToiEzaXuroI4LNnSh8Dp1YXCCqiLLzHIRfqihvKRgzc+ngBMAYJgpJ+jENyVjjqdOPSTumuzjOQHdmZRvCMHYikDheUn7mYgdgLOjJrsGxhUBlTThca2JHYcqIKXsukTucncwzEVLSYePrHGKVbACuL6nmOY6m4wDis8XnsNT74HseEVaiqHkReYhTbAsvU+E1IULJ2PKIOt/HDq+8jF1ULhKnQlQgXDAwjGMQqRy5QgQ1TpHFGEoFFym/QJMKhFeeaBKphVmByuvxVM28XNww2jjwPBtNyho7GG3UyZabUoKIlFLSrOLUPAQqEfSTTz451LohuDGinZSHz7sBT1pmKYHKeqHON2VH3niOaPhBHSHHHLkgwfDkrNj4PFaPAE+vsUw5xuxHH/H5wsikkU3csTTr2KBmM+WZvDEunqkxJeLokVJgfWMgL7bYYkFIpcgMyX5u+J69jZIKxuRHYvl+4f+y3v1nyymcmL9s51Xmj6wW9mBfC4g8jgVhz+BnWDN0FM7DvvzMM8+E618c2SXtG+cgHZq90V7e0mL5zCHwcRazn/n77qndPrc4ELNR7ZSwH5P6H/e1IIKOcw4hzethX0lVfyzqJhKnQlQAvuHTYROxiVBCXHh0wy90RHTw2nM+YcpUpuy4MSQxcDhflYgvXm+83w5jJaWQyGrqo2KyUHeH4Ge+Y9Gf2oBgDWAAEzkCIqPUb9LVFuMYMUITHufYY48NBj6GKJGxrNe+3GuCaChzG0dFWQNZgYro4HXGaXvloNT7ylhoNMX8YSQ71G+6QI3HngdwRvhZlKQTkr7L5zB7pEYeDGHHj3/xtYIBz5Eg1LTFzjaayLAuWEflJjtf/nlizOx3lFQw1z4236Nj472chvz7778f9l7GBIyRtFeio0S92HtJJ/UoKo5C9gzEB5ExjwCXe53wHvvfZkze9ZqyingOXaCSbpr6+KAYmv3FUUWuIYi57BnCXLvjM2NTn70aQ1kLe7CvcWq6aV6IA4M14s/PU9aTqGwkToWoEGjGw0X55ZdfDmm8fB1HIIELhjdkwWDOA9TgYbRfdtll4TB0avXwcCOUYoFKDS1pevF5eykuzkSNaEhBFMxrg4h+YWxSt8fcZ38m1bySyoYXHo88jTb8UHRSsEiFRKRy5mMcXSfamqpmzOcLQ4w6QqI2pDeSlumPe4pv3CQplZDGoPd6K6/LY7ysZ1LrYyOSz5sfGcN7kwdYt6QIEhXz/YDOrBxBRTdhb9ZEDVn2jNtUIOZoHEQWCIa9pxPSEIlzH+O69VSfv1g4cPwS9eYIfjrE+rmf7Gd8NplnF6ipHQB8pqhBZ0zUZcb7L/PMfoFwitN8qSv0eS73fkEpBencrFPfA9h/EdnU+3tWQtxRnc9fXJ+cGrJWGJNnWiC0uU4TjeSagsPZHUZ5iDqynskMiQU+WUHYFfF6yVtkWtQtJE6FqAAweKitiSNhNGPh+AoaVNClEMMHI5N6ppSpvFnwGmP0xF5V0saoGcMI9cgfpOiyCW4UkNJI/SApbNSw0cDCI9B48DEmSIlNeYZpNiqGQMULjxEXH1+DsEZwkJZcqsFNKi83KWFNmzYNKcgYk6xZjDfvUOkNfBo0aBCOS0rJjTfeGMbGZy2OniLuiNJ4GrqvH47gIHKdl7RY//zhCED0xwKVKDUGMhF2avZ4L/IA+xg1uhzRRFdYGsiw9zHXGM6k8pY7il4TRHM5fgVHEEexkOqIY9Dff6LTjBfDPmVaeix6EKhkT7CuY8cV4BhkvyBamj3CK4WwJmLO3kamAvWjngLrjlrKRDx12l8jab2p9rZSc8R9pCGzn7ljBYHq3by5zvAas8f2lBv/u9Qbs1+QhYNjzvcFOmNTehG/LiFqC4lTIXIOQg5PMWk18Rl+NCLgKBOa8XhKFkYmntiUxDWmeF9JHyON14+CcNwD3rJly8K1115b1jHGF1Y3ZIhIMxaOKaHuByMOoUEnZI6GABquEPGlSU+qhjFZo4AoHUdTkM6dPawdsUpEFSdA3NwkFdQ09u3bNxhrwDyzNuImPf4aOReSyGVKMBivu+66sAY8uutNY1gD1MNmBWrK1LaaxsDnCwMYgeqig/R5mpPhHMjDsTzsDwj/Rx55pPgYgo50dQQT52oSTcV5FD+nnMSfefZZhAWZIPFeTTYAUT1/bawfOsemMuZLiR1Sz4mQ4piI05FdoLJf8DlNSVyTSVQRgR8LVLIvXKCWSutO+Tn07J/483jSSScFgRpHH9lfWEe+NsodfYzXZPw1Nd7sDTiv+NxxDcF5iK2RLQcQojaQOBUi53Dh5ZgELsQIp+yFhDRNUoY4uiDVsStZODIBoUyUDM8rZ4SS8hbXF+LhJp0J47icRyr43CF8uAg7RKBJw4sNBCLSe+65Z4hKerSG9yN16jFgqOGgAAwcBCopnNkjIBAhRCnzUA/E3NOZmZRChD5GMPPrkL7nZ0OmJJ5nxoxBmRWoCCkEKk1jamrCkgI+bxwxlc2eQCRh4JM672mbqdMI42OxmEcyLIiUUp8eiz7EM2IEcUrUPcU+h8DHqeLdsEnxxrHiDhSPfOHkYq3EDcmccgvU+O9xnma2iRDimui0vw/+L+sjD9FH73LNPse1IitQR44cGRycRPVSRaa96ZiDiONazTXQHweuK6T4Mt5SjsKUawMhSuS/d+/eYZ/wucS5yRzzucP5zevi2B4hahuJUyEqAGprOJOS6JjXuuVBbJSCaBg1P4hRh+gHUUlqWUhFJQKBoU8kp5zHhPgFmTHETTXc+GSMWcOdKA2NQ/iZlMTGBJE7UvNo/hEby9TyIrDjVOmYFGsmNtwYK+uYdGPESCxMMYh47NJLL81F9BHBz3uOUcnc4wAqFUFlvnFexB1YyznO7H0IKMbDHMcNVuCwww4L4o605NTHrsTOtVatWhWzJ2g0xWfz+uuvn8Zgp/7R0zjLCTXd2fMycfpQe+x1u6xZbqxxopLU2Kcknjsca5QjIPzjkgQEKtH/muodUx4Xg0MNMecN8hCkrNusQEXo8RnMS5opDs9dd9011Ee7o83Hxtom+pj67NUY5pgaf67NNDjCkcx1JD6SjLXB5zE+E1uI2kTiVIgc4QYCHks88nGKLqKPjqDUNfmRFXm5IPvYOVYDg43UNm8A4VDfhNHMhZB/EYLlTEH2ueJvModEbGOIfuEdRlTH51LSxIka1OyxLeUkNhypvyPljjnGC49AdUONGk1qUNdaa61p5j/VmBGdRPE8fRCjzM/7iyPXHGtBjW85o+g1jZljE+jKe/bZZxedFYy/VAQVAZuqkzA12og1ahxZp4DxSG06xzyQThpHUBFSzDsNfFLUbVKTm+1ay77AmgX2PD5re+yxR/FnfN2kcliQrYKgiI/ScHAA8DmMm6QR5ePsWyJQeYBIFzWb1MKSqcDeF5eHeJMk6kzzAk4Uarpp+Ofr2ucWIU3ndF6DZ4445b4ekh5NxhJONRyyRPsZAzc63NITwiOowOeUFFlq2VOJvPhawnj5vHkDNxwXOK+GDh063d8hgSpqG4lTIXJC3MmUGlI8mAi52FtJmikpTEQaHn/88UIeOfjgg4P44CKcbXCEAKSVPpG9FKmxiGeiz5z3GeNRUQw5jDQeZ3wY9jQ9IcoXd7AsF9loBg0qEPUYEYhlml9RF0SNmwtUIqhEGEjtTIWPm3FS18acsp49GoZwZo0grFnP3IjqEIlMDeuTqAf1u6VSBYmisYZSdbd1A5y1TKojUTqMYMZM/R0pr8w/og9RwjrwDrI4ZGju5N+XE1K5SfVn34pFBEY8ewVjRjzRkMXXDwJvRoZybUK6Mes0e34t88xejNhg/DSPof7//PPPD589PpN5yGxBBDHn7Ak4gmiIRbYCAoTX5tCchyyWPDg7WSesg/jcWubSz5FGkDLHpCR7d/IU6ek4qhgnadHUc88999yhHhqh6lkUfM1nk2wQoqg0FKL8IoXIw+nKOsim01PPDUSjW7RoEVJ8gewmnC6eri5EOZE4FSJHUKPJBYKUMAwJuvthHNEwxsUHUT284KSaZj3HeRKoRJgQIz7u1JA+jNFIVCMWmqQzEWVyIYJAxTNPVJJ/8eCnEE3ZOkaEMl0U4w6bvP8YPohnouqeioXRnNrQROQRpSH6SDQUQw2j2J0qOGEwlKnbJGqasilPbCwimIk4+vzSqIeoCEabR3VJB8dxRDZDOXGjkm67OKgQSdS40bAL4cn84gxgvfL+s1aoQ2YNU8eJqE7ZSRixTDo6ERsXb0QkiZjjnOD1xEKD9UG9fYo9hJRoznCkiQ2CyaFml1RY30NYE8wzESiOmkJ8uEFfToGKQy3bGIhx0S04hrGxV+Dk8tTSeM5Ti+oTTzwxHH0EfPaITOKA4XpHeYind+PQSDVWhByin0wExgLMPXsc+wKZAF4OgDOIfZBGeqz/FF15SScmK4jzauMjYqiL5uxg9mK3Oxz2FT6PeTmSTtQvJE6FyAl44km3o3EFkHaH0caFGqOSiIJ3i0Ro+RlvqfCLK6l4pAVlz3bEwPcUoZQCNY7QEnlEiGLYMCbqmjCKEVIxeOlJG8PoJxWx3CCG3Kh0kcl8U1sVpzw6GB04MehwG9cKlUOgZo0s/iaGGUY6UWegvpHIKWs4JpvimRrmjiNWmGMa29CtmTRCojQYlqRT8xxuqY5rwonSo0ePcNRKlkcffTTsFewZvAfciPohsngvyu0A8PUX15Uj4EiFxUnB+8/nixo3HCyc/whEdnFYkFpd7jHjTHEor0BcYLgjUHn/EUrxkU0Oe3MslsoZFaNLMOna2b9Jejci1Pdff5z9jv2CvS9bF1lOSn3uyUxgr2AvY65ZG+yHXBdxeGadK+UWqIhRnMN0382+DvYEHMnzzDNPtbRu6mbJaEjVlRfIVmIvY+yeKYSjGzHNWojPD2a94MziPcjL3izqFxKnQiQk3vgxbojOIKaIyBDlcyHChZkLCClkeYhExrV5dKykhhTDgRRCjPpYoGLYYXCkGDcdeBF5foamG58YcnSNxXDDq+ykjjY6GL9uwLgTgto7ItLUk8bHD8Dpp58eRBWvNZuyXA5Yr3GdLtEB5pcIGemECyywQDVhiqHvaXopKWV4Ee0nRY/IJOnS3lwKgUcabWpI56V7JrWCWeeFfybZK7LpsKm6gWIIE72NMwEQqAh+0kmBfzGaEUukGSK+2VfKnbFADSFpmJyz6yCeEcpEykjfdedPdj7jtVROgz77/hMFc4ccY2evo0FPnKLOHk1aOpFpxEmKkoV4/vj7RB7JVEDgESElXZZ6X+/MzD7NukjdyItxkr4bR9Pj18N+R5SUzrelKPfn0FOfgUgvjbH4rPl1mj0aMY1jnLXD/sE+h/3h1yAJVFFuJE6FSAx1Hd5a3rtrkl6DCHGjAdGKkYHBljpi6hDlwFjz422INJICh7c1jqISbeBiniLSRFQAw5h6qjg6SlMhojfcn9rYmR4uNFxAk0LIuBGi3IfxgEHH67j44ouDMwMDrlzp3hgt1BVj3NDpkei/g7HDOBkvTgpvbIMThtor6vNSGj3+t+l+zVgw1BEnbtT74fNuTBJtx6jzNL5UUMeLU8Wjd3HkiK/5nHE24UEHHZTM4RILUxqkefOxOGJEnR77mQtU0gdJM+W5NMJJUZOOsCBlkygunymHsZBuiqMtPpM5L84sHwtzyH5BWin7GmucawmOIrIrcGzg2CKVk9Re0sNJ+UaUlJP4c8+8cq2jfpMxujMoPn+V/Yy9ZKONNko+5zQgI8WYrBuIx+Mpu0RVOZec63nKNGmuzayH2GEcC1RP8UWg4mAmU4vMIpxyKVLThXAkToVIDGljpOLFxhipQdSOxe3eER95iJq6EUctGGMHDGVqrjB68MQjPmKBWm7PPKLUU6AxfkmHZUzU9DqDBw8O5ypixKc6t3RGYDxQ44a4927B1GHRMIsIEwYFQoQ0OMCoxygq95l/RBGo0aVBkx/1wfl4jIVapxiiUNQdpuzK69AQhGgdqYMYvxjI2Q63GPB050UQkj6ZGiKQzDW1xk5W5LN34BRKQdwVm32N9zsmdgYhShCocQ1qChBzHmFi3xgxYkTReeXQNZbX4pkgTh4cLPEY2O+ocaR2mnRq5pUoOs4AhArCm72DnyHjgX2bdPAUkK5LlgLlE+xv1J9TCuJ7A+8FZ7MSRWWfc8GUUqAyn6QbU/9f0xrAkUVabEoQpjiGSp23y3UCgco10VN8mWtKiXAwxmezCpECiVMhykz2QoZw4yIRd7dFXHF8AZ5kHiPFMNsgJ9W43aONQUFqJkIIw4FmEIDnGwMDw84FajkNOCJhiCIEvke5MH5LCVRSfBGo1O+lTjOtyeBiXHiy27ZtWxSoX3zxRZhnokxE/dyI4D3AQVDORln+3hJxwvhFyGEU40ghKkIDKkQIKckIbaLteejKSy0jhrpHwkjXJPJP11WH+jaMOFLcUp9z63PN540IEvOa7diN4YyRyeOe0ZBqbkmPpcY7WwPJOoi7BbM2iNiQiZFCoBLd4jOD6PQMhZoEKhF1PnM4NPjc5WW/YE2w13nmDfsdwgSBGqfbU39M9NR/luZvOLhSZI+QAYRg9jpjMohwAHntI2uB18O5vVwbfY8rt2DKzrPvdTiIsjX0wL5HbSdd9lNBp3HKbOKOzD7ubAQVG6PUfqxUXpESiVMhEoBA4qLrjUK4IHPchrfP5wLMhYXGJhyzkYeIDSCOvFusG0J45Umd9XRjRBOCjxsiqtwgzIjoksqGUTMjgUoElXP+EFWpPMWxAUSdLGuDiC7GMkYaHm3SZBGofqxFbDwQpUJgI/xqc61kBbQ33XEQ0RhFnFOI2GPuiaDSHInIJO9HHrry+pmwpLABY6IDa9xsysfJ81J2rPT3mffYBTLp3kTTMe7jtQyIa9IOU0Wm+fxRs8la9TRpP8qCSOojjzwSvo/XDeKfW6ru436GNMfx+OdregKVLqYIkFQGfPw5pB8BNYKUffAZ81RN9moEKteP7Jm21CsPGDAgiOxynTWd3TsYE1Fzsm5I56am148xQeDREZvPXZwtVG7nRTxm0njJYGLc3rALZxZ7HpFn7mevIPpLZDVVvSb9FXASxmfvAjWw2BhxVg0ClbXDdTG1c1aIGIlTIcoIFypSBjmihAsITSm8fTtRpTiVF0hjytM5YzTewWMcd7ClfhMh6qmxXLRp/+9ptSmMCQwajHQ6rNYkUOMaVMZbqgNnOYiNFyIZdCgltRSvNhFgb3BExIP0Q0SJ1+n5a2b8vAfxMQG1BaKH+sz47/vaYOxEDPycW9K/84LPs48X0YwRSeYCzglekz/GOqFTb+pjFOImR8wtQsTTpukGSkomRj1pyawN/kUUxmdEpgCxsfnmmwcBR1o065P0TY+SlSJ1aj0ClXr5mgRq3OCG96BUSm25Ya/l/Sb1lTlH4LM/eO03zi1SfBGt8bFH1FPzsymOFSKzBccmEV3SdXFkIZJdmAJZQkT04vWScp4ZI7W5nBvr65R9AwHN5xKnC0J11VVXDWIvVb0m+xd7BU4JMocc1i5rg9T07LhwLvPc1LW8QsRInAqRAGo+SNtFnNKBl/oVUvToYEkkLy+Uqj3B0GG8HuVg3BjIdIrlftLjUkZ6/cLL+KYnULlgI1DyAk1JMIK9eQW1QkQh8W47dK7E+ME7nzXY4uM6ahOMH69pc0jfRHy4tx6DHoGK2I5TBlOnihHh8E6sGGMIesaZTc8jQsJ6jhs8pQInCoKJVL1sLTFRL5wXZAmQykvKKUc7pf7sMbcIDOrucLCQ5p/tLuzR1GxX4XJRSlwiOksJVNIhST0mBbnU7ygXOIZ830VoIIa8XpRGWYg8r0V2gUSkmnFnxYeXZ5QT6nqp63bnJtkqfP78XGHAoUWaNftcHprxUE/PmGOHD9dDdxIxXkQ0+3WcMp0q/Zg547rGdZgIP04rhKk7X2MnXfaaIYEq8oLEqRBlAsHmh54DtVekO2JwUCtIFAdxyoUkL6mPgOBAROPxBlL1SCckFdmNBwxoIk0cJZO6NjYGQUrTjaxAJfLIfCOwUkR4vbsxKXcORqU30aB7M/VXHlWnFtlTOjFKYyOi3AYyxhjik+NhaIpF7R3RG08vzdagcgRLXowe3nciHR415z0g1Y1IOpEQ1jbngeal+REGLmvUjXeECQ2xmFMiTR4JyR4nkhLS0qmRJ60bYbThhhuGlFMXew6fSyI8KWp5s5+fWKhxVAhznhWoiGgcWqnWMmsz7j2AE4su2exp1P/HabHcR7Q6jpZCHj6H1EqTQu3Q5Rtn12677Raugwhp9uY8ND8C6uYprwE+e8wrdbqkzsdn4sakHDP7Lhk0iGX2aBycvg5iwYyDNnYKCJEnJE6FqEXcWMTQpS4TUUeklIsEhg9fe4omETKv1crLcTEYw0QSEBlcoBHUvCZSIPFue92pk8o49r9LmhoRBOqt3HDHsKQGNStQmf9UqYQYL4hRUvCIRAIRczqsMi4MzbgbK+lkCBJvmOW/oxzjhDi13NcmApquu6wNd1ywrnkv4jMXiSbkBQQoqYREdIEUeyIjOISINHB+KNGoctXhzQgEHpkK1MIiRnAAkSqLE4v1zHrBWZD6PEL/u6RqIuzi7A+auxHpZ9zeEZfPI9HU+IiLchFH4+iATs0gexvlCQ4Rc14Hn0NvkhTXPqYSH0Sh2Xd9b+Zr5pJxEll3ECfUyXoDtRTrIjtHfj4s+wbXwribO4IPYUqUj3r7VM2PSs0TnZnphs51ENFM6jyOFVLsEX5+Dmsq4nk+9dRTQwYL2ROsVyKoONpwGsfP5/PIa0oRPRdiZpA4FaKWobER0QO824gMOttiZBB9wvDkguzg4Yy7K+YBBAa1bRg/1EFyYaPeDVHi9ZApcEPCjU3mmaY2HLOCEYHh5g1ZXKCS/oihn6rxSgwRc7zXvP8YDNTmEUliXuNUXsaK8UyEIYWRiaHjnSdZs0Q5aP7BnNL8io63GG5OVqCmwv9+tmabyB7z7B0qmXtuCGycFSk/f6Xm7Prrrw8GJ1EzhJSfA4mzgn0lLyA2EKDsEcxjLCweeuihsG+w3/EaqNFLIUxjWNNEv6gnPOuss4qdpt15hUAlwsf9qaPoLiLY40gv9owKmqQxPtLQHcZPBgb7X+qoI2TfZ/Y9rieI0Omt/ZTNj3BiEUFn78WBxfrAqUUHbBejOC04Uzov52QzLtZvXKrCHBJBxXmBYwv4HHbs2LG4L+q4GJFHJE6FqAX8QouBQ+3MhRdeWHyMiwEpTBhyNA3BuKgpPSgVRI7iSALpPwg74AKIwKNBBGOnhjOFERFHbUkfxID3aCNOAC7InLHp0RqMNs7hZN69XqjcZIUPad400kAwAcYP40Z4YBAjmKgl5HWUOzrGe06TIyLRvM8YNdRJI5Yc5hSxhFOAiIKTh1oxj9rxntPBMl4/fO7ohkx0IS9j9feV95xUabIT3LlCjR5NbOL1TxbAlltuWRRTqcdOhAmnEJ9DPyomjszwXtDwLVXDJmozP/jgg6KThai/18EinlnbfBZj5xX7BGmdqQz4l156qdr3CCbec2r7WbvMLynTpJkSHTvppJNCWiyZAHlIiyV9nusEzjWuE14zTUkIWSOpHRROvKfyfjOH1JnSlMmdQb6WeS7rA0cLe3MeHADUvLJHU3OMEzyG8SFQcW7xHLJDJExF3pE4FaIWLxh+hIa3aY8NYQwkjv/ggkEqYarax+wFGoMYg57UQTzwREGI6FIfSfqVR0mIOJAK6emz5cANAcQzRg/1V8wb6Y0e3SPtlMZCCBAiSxhqbuRjyGfrsMoFRg5jidPvXAQSwaGeidQ3BOq8884b6oWIshMBKXf3x2uuuSa8t24ck8bGOkXYx+mNgKFGqh4RndQHz2cZMmRISKVnflm/3jyG94C0NjeWU0d5HSJjGJgIffYNDGTWB9Ebhwg7DqK81MXG6wAnC2NGjLjAiyPXpJnGnb7LBbWYrF9fzzhYSOkFHBcY7qwVjHgEKnXH2f243AIVJwVjpos7tdFE8wBhT+QfEQV8HrmOIJRYM3ydl7RYul3jAEDs0aGedF4+gxy5QtqxO23zIPB8n8O5gsBjreKQI/3cj2ViTdAZmSgqe3MeHAAODk13dGfHw/dk45C1IGEqKgGJUyFqCdJ+uFhwo6OtExsMeGNJm03R0r8UXJQRGdRBIvzoZspZkAg/0mIRrXEaU2w01zZ+waWBCs1K4kPOMToxJhAbGA1+1Anpx0REOE8vfg9SQOdg1gLGDpFzxoMYRZQS/cAg9rosBDQCnA6LKbo/EpXDAAMip6SBMT7GTwfIbOQZ0Y8wIaU6xdm2TimRSRSPqA01mswzRjwpvTg3qKHOCzRBI0X62muvDd+zFhBKrBuHfYK0WZwcKRoJZeeZSCRzGTc7ooZ3pZVWCoawOzJS1rbh7EHMeW23ZzAgkhF8jNUzV0iNxDHEOidrICXsv6Q/sx/jCKRpl9dCk9XCOOOzLNkn4vWfMi0WByF7WHyUFA3TaKDGuHEm0swpT3WPjBlHlh9fw9ziACLK6/NJtg7pyOyPqRwA02uGRzYIa52mSFliJ5FSeUXekTgVohYhbQkxQnpQHDHIg6c1e4HDy41o4uy2GFLF6FLJhZoGEHhosz9b2/h8ESnCYEMox3iUBgOU9GNv2kNKL157BJV7v8tJPD8Ym9SNUi9GLRvGO0eYIOxorkHEqaZOx+VaLz5eBCnijQgexg6RG8CR4gI1jkC7cyXluaY+dpwUNORhjDiI3JlC1IP0byLq3sgJwRo3mUoJ0SQcQS5USZUmxdTxDt48L+X5q/HZq2QoEBHzelicABjxRN7JBmH9ZCPt5QTnFAKflOIYb0JHFJr6fxd9zCuRasR2SgPe55goHanQvA6ieqT8kz7tzfPYTzyimpJ4n/Pju0jbpaQlmylCpJr9mzXD+kl1Lcz+XerocWLiEKTcIu5+zBrmusj6iNdzSgcA64D3f9dddy1ccMEFxfvprN+kSZPini1EJSJxKsRsFngIKAwGr4nEWMbAJ60wpVE5PUjTxbjEOPZGMbFHm46reIwx6DmKg9dW7lRI0ouJmMbNd4D6PM7ZBKJ3pEXSrRKIrtIIopwR3hiPhDpEH0kVQxBhAGNgIpSIKpDGS1peXqBRE+83NW4xNNzA4OfgdqJ3rAvmPHsGZwowyFgjpJUSxXWBhwjJZgiQGpsyYyE+b9DnlZpN9o7FF188RP/9MUQf6zgPotT3NMSFCw/GzlrBYPZ1T+osaxuHVwpwRhGZQzzH4GijERkig8gvmRU44Ej9Jy2d1PVS5zuXAyK38Z5BMzIcWOxxwHE2fB7pbMv65liWctf8Tw/mkYgoJSsIOyJ5jRo1qta/wCH7wsVdyrpv0qe5nhFNJ1uEKDX7mQtTwGmIg9mjqqlLAeg2jvMQRzFp3FyT43OnieyyD5LFIEQlInEqxD/EL1LUi2EMITJIg8SI8AYcXAARqHg5y1mjObMQYSRVEEMibniTNc5IdfL62XJD5IiUMKIx3hzmnHPOCV5ib3pEPRZ1pqRHYmjQWTYrTMrZDIQUY1KOY483qdKkt8WRBoxi1g4GPvWbqSE1l7Ejnqk9jlNLgcgCBhxnhGIkpWxs4p8/1iViiDQ8n29SOkmDxcCnmUz8PuShERL1d8wxMDbWBmuA8cbQjXXttdcODdbKDYLJ58rnj5RXnFk+73Rf9VR6d2xxwzgmEpUCMhKoK8Xhxr7rDheaB8V7MIKajBD2bJ7r6Y/lFh9Eb3nvyaqIz8OmyRtj9gwAnscezWfPn58Sv0bgnOrVq1cxLR1wCiJMEUrxa8rL55DrBhkrntWEw4o5jc//JBuEun+iwKnGGv9dbAk+b97MC7uD+XXHkEPqNHuGEJWIxKkQs0ipuh7SxEg3pcEDxgNGMRczjAoXqF6DSipOHgzjLIyPhhV0gozT4LK1TClwY4aoLkYkXmIMCJpXeJMbh86bRPNIb0oRGXOhTHMVjDUEP0aPnwXqAjo+LgZDg1S3lAZQlh9++CEYl0SlSXfzBixxNJ3XSjpcirWQPQ6IaDmNj+LzE4E0asbvzYNSr+UY9g2iHjgyPPKL6KC+kAgq0Tyi7amaHyEu6e5JZDQWFETNWa/MJcY9YtrnlXXt3adT4WNhjbAXU1fKPLMfl4rwI1ZxYqWq7/Yu3swbezARUbrv+l7Afke5Qrx2WR/sK6nSj+OOy4yLNP82bdpU60wPvDbEHWP15+YJ5joW+FyfcWoS8SdCTVkIGQ0pmh/xGfNyFF8LRKVxYnlqPY3rWONARk7crTdP5UNCzAoSp0LMAr7ZZ9PrqAeiq2JW7BG9w1vvnR+pF0udSujGASljpJYijDyFlxQxzm4jhSkWfakNinjcROmoE0PoZ4/oyf5MuaF2FGM+jn5S/4gBRCSSKAJrAeOedKxYYMXjT9VlE4OTrsKktHnTI6J1CFRSZLMCNRU0kULAxUfEEKGOG1/FaelEf1OeyVsTCAz2CNL0AGcAgpAsBoxO1hK1m14TWW4QcuwHCCPqkN1AJjqGYMY5hLiKHSr9+vULYjVFrWkpY5zPG+uZ45jiFPWajmYqp0FPNghRMMbmUXGcPtQQkpHA/TfddFPY88i28A7DWQdWufcLHBDsv0TnHMZE6QqZLdnsIM5j5ZaS7Pvq6dPs1TgEYqcWXekRply7cXSmaH7EeHj/cRbH/SpwHJNt4celuTD1x0j/j+dfAlVUIhKnQvyNbrHU3BEZc+hiG59FGQsTDEw/9y81bogRoaEZBcY8/yL2MPiBaBjGKGLbIzqpx5ut3SRKRgSVi7SnN82ok2E5IIpITRsRU5qYODgBqH8liorxhtgjLZaUrCypHAGsCaJgNGDhhjjCSw8IVVI5SZcmxTQlpGsi6DCOmes4XdCjHPHnDQcAAit+P1Lg72vcNdOjZaT8e603a5gbkXbqrLNn45YLHyfzx7xyY89DhJDqSLSJNHs36rmP6BgRSt9Lykn82UfMkY7snaOJKBFBJ4JKzXoeah19TbA3sN8S1XWBynWEzxw16Di1aDDEsWTUT6dsOuZQA0s5BevW09KBGmRqYSkDcIHE3LMfxt3VU5ItQSAyiYMAETq9fTjFWqEBHWUJzJ/PJ5lY3lHaj3YDnEHU2uMgSO1MFuKfInEqxCwKU1J+jjnmmGqP490mOkbDhDhiQ9oeRwGkqrvycWPQO6QK4nGlNg/PPd+TZkp3RQw6F6gYS1zo4p8tJ36BpVkQaW0YlYzLmxsRhaZOk/PyvAY1JT7XdAom8oxBQdfS2HggAoaxScdYjLu2bdsmq+HNGmyMxevFSNHD+CES7O8DNW804ED8pah9jCHyjNBHnBINI6rnY2R8fBa5j+68fFZp0pKHeSaygSMrjviyblgPNI9hjaQWTI6/7xjFrAucFYgkd1jhFPJ1TOQJgYVzI073TAFp0BjvON1ofETzJkDUIVA5ToiIX+p59vnlX8aIA4W1m3VGEElHAPqxZHlocsN1DWcsmSA4rOKINE4sXgepp+zNrA2uJXk4voS1S0QaEUeWkKd4cy3k+peydj4mdmBhW1A7SsmHl1CQHYJjgOgumS446GjkRcZFTRkBQlQSEqdCzADf5KlJQphmm8NgyGPoIEboDInh6YKO1E0u0ikipy6WuODSKMY9r6QBEXmKjTMMN+6j3s0vjKQlE7lJCdEjanmpA0JwMD6ijm7AkcrJ/Yydr/MoUOMGIYBBROSBKA4pm3lIu6J2CUPSPfOkwcZNeTxyTRpqqkgeuOHFZ44mUgg6zi3l5qKJx3C24BRCoGBEe1QyNUOGDAlnKVIbi+PHU+eJOOFocWM5L4YlkVLEJxFR0nVJ7UZ4+PmaOIowntkTadKTYr+I5wpHG5E7opGkPTLHOF28Dpx9jv2PjBE6y6bGP/u8BvY6F6jUe8cQvaaEgX0upciLrxlEconsUuPIHLPfOdxHszqcAJQCpDoTNPs5ohwHpwpZAFyrEc58BnF0sv95mUjKPTkeM+fv0niMNY1jgiiqO7qxM3B08pkk04VMJ792p3a8CPFPkTgVYiag0ydHJ+C9jC9cCCWvpcGA97RCjoLAQCZ1M4VhHEd6iXhQF+ZgLOA9dtxgIOqLF7ymszZTQOQxTrfidWBUUD/oIgmDlPtSHbNRkyHDeFygZlNKMR4Qev6zqY0JDEkMHMaEoEOY+tioL6M5SzYdtRz4GOL6Re5DFCGWSJunhpuoAQKVKLtDVgApeymjvKVEJo4qojZEIYniMW72CD6TBx98cCEVcboo4+bzxV526qmnFu9HPPNZIxKGMyD1us1+9hB4cdo56cUIpLhxGseGeHpynvYLxkNEks8hETAXqKXGWW6Rh+OPvxn/XcZK3TdOW7IUuD66g6sU5R5zPM84C3FaxWucvYI6XqLsrBGyK3AQxZlPKfcL9jYynHAEUYeMUEVMY2O4E4g1QrYIn9VUxx8JURtInAoxk3DhJXLnR61w8UD4eRTBLwx0tkRQ4YX1NNkUF2XqMkulIOM55siEs846q5rgIMKKMPEzQlPgF1hENdEO0kizh7gjUDHqcQy48EjRfCVrAOF9J72K99wjYKRhuUCNj+ip6XfU9jhrmieMeFIgMYZIFYt/hjRU0vZSnRXL2JhD1ivj9yguzhTGiwhl7eI4Ipoap8ymxNcyKZvsFfvvv3/IRvAjQWjUQ9oxqbFkNhAZQbBSo5ciops9R5XsD0SSf/7caGdtE/llTeO4yEPkn/RzmtcQWYzPe/T1w1EyiJDYeQHlFqjxXLEfEPnHIeFRf681RqAS8XeBmsIx5FAXz9pkbuke66mvCD3WK80AAYGKE8CPGEpJLPKI6jNO0nbZI7LXE/YLXhfimutfivWM0I/XJHscJQveKM2heRM9LHgdpZyxecm4EOKfInEqxAyIDRia2WCwcQHmQpy9qOQFRBGpVqRexdBAgfMIaaxBF8Azzjgj3I9BPGjQoJA+RJQ4JbfeemsQ/ZwXi1HEhdi7HTsclE7aJt7kVEfdxH+TZh+MBwOING6EiKdf8V7g2GC+qXlLBc1hWLee4hiDoYnoJ3JAzRiQBu71mqmi6axLIgVeb8f4OSLIG+6QNo9oBSJjHFlBaiSiLw/QZIozCKlx47NFCh6fvezZu6RBkjqbqpM3TiD2NaKObvQiTnHGxendLpJYz3PMMUfhX//6VxIxHQsI9jCEJym8fMYYV1aE0pkcJwDvQx6MeOaZtcB65sgS6geHDRtWTaDiLKK5FJHeVHBNY8/is4cDhf2AMgv+JfpPMyxKE9jreC7OOZ7LesoDpG6zf9HYjY7NOAOoVadWNgsNqFI0ysJJwZyRhh5DRhZrNgslLjyfzyuZLkLURSROhZhFMIK4OCCQ/CKW2tjJQtoPRgNpV9RfAaIDQ5mmPKQWDhgwIHhhORKCdCHEbOraPNKTuCiTzouowyvvYi9rpGEgpWo0FYNxjJD2I0wwHnBcEMnxJjy8FmoNOZs11VohxZWIDILC1wT4Gibtjagpa4J1QvomBmnqNUHNLrVtRE+JMiFIiXKQ2o1w5TV5NBWBSsSh3Gevxvj7y3vP/NFsxcUU9Y7UjSH4cALFIiu1c4voI5819jWvTycLpGHDhsEJFIORj/DOHhlSbqiPRtQjlABjnbmlTpbzjmNYE3mI8rK3EaEj0wIQTu58cecV46R5Vh7OxMb5wLplHSDwKKPg2kG3dD/yiJp1j66zt6Qcs6e1EuHP1vyT/YEwZX+Lu3yn/BzyGeIzx54WN7vC6YZzCGdiHDm/9NJLQykADoDUa0OI2kLiVIgZGJmkjiLm/FgCQHjQiAevpzc/yptAxXAjUoBAJVpKTRupkLHRQQoq0VQMpNRCjy6fRD6IflEj5EYDohrxjLBLGUUoBXNGuhvRXiA9D2ON9UGkjA7DPq+xGEm1VnxNYNzEAtWNH9YEIhXDiDonfx9SEM8RBj3rAuFJhAPDHeeEp8OSLuvEZ8eWA39Ps3+X9HjSX+PzEwEBQkOeN954Ixf7RmzgUo7gAtX3O4Qp0Ujq8hCwpP+STh3vh+UiFhF06Oa9x6kWC1FSYRGoTZs2Detker+j3HCtoOYfkQcIJJyDlICQfRFH0OJ1kVqEkLlCajrjIwXcHQHUouN0K1UKUu7ax7hLNHPHPtamTZtqZ2G7A5S9BKeGPzc1ZCtQPsG1Y+jQoeE+HG6U31DCgpOIcZPhgpOOOnAfd+q1IURtIHEqRAl84+eigGAimhSnwYILEM4pzKad5gXS2UiLJRXr3HPPLd6flwuazzPpjBg/dLBFRMepghg5zDtpbhjIKc/5K2XYkpKJIUQtLxFUNzxp2IPhSWOs+BD11NGbmgQqa4LIB0dx0PkxZWOQUiBQWQOMzSOjzKun8KY0MkkzxgkU17vSRAYR6hH1eD7phkzkNy/EEVAEKl1AEaie4s/r8vNvOTKG7IvU6ZrUPuK48vMe4/cfgYpw4jGPUKYg/qy7WCOizg2nFdeVCy64INzPOvEIKlHpPIw5hvnFUcH4PJLOc/3al/LzR+0z4+L6EO9nZDlRipON8JOd440M8zLP7GUuUD3aS409dgf1x6R406CMM8l1XIyo60icClEDGGR43/G8UgNEGg0XQO/6CETGuGhQL5QHD2wpMIQQSDSL8TP/IC/jpXMm0RpS8/Aa0zGY1NNY8HMxpmkFQjtVnU1sTBCR8cYgLvQxLHbZZZdiFBIDGjFFRCS1IJ0ZgYp4In26QYMGSVN5fV0yRhpjxZ83ji0hUsO5j163yfNTrmWiYUTP2RuI7Mbpgt69Oz5KiggrdbHZDs6pIEJO+mAcYSIyg0BlTXsNKpEc1noKR1z8/pL6iNPH6/2p36RmE4ESQ8YLIipV99L4M08qJteP2OlGFgvOOBxbwH5CxJemN3kYM6m8vP/UF9PsyJvPeQQ1dnamvpZwNBep3KwD9gaH5kc4kGmK5AKV94B0X/blPED2lTebw+mWFahcT3DA4fQkau1rIy8OZiFqA4lTIUrABQEvrB+jQHojx8OQ0pa9iFOn57WFeaWmaFkq3JghwkGakkcPuPBysSadCUPfU6b9YpzqjM3Y+MIYJp0UcRGfR0jqNIele6dejCSiff6zeRaoRG14XUTY8yBM6RDqNW2kxuKUIAsAyFRgnol85OXYowMOOCA0WmHN0tkYYx7ozIs4pQSA+zAyvclUXvYMshZwqvCZ86h/LFBZF6lT/h3Sd6l3zQp7DPpSAtVJebwGHVdZw4j/eB7p8o7IwwnK9YW66rjTcMox856TwYJDEAcs+50fJ0U0j7pN1nteov84KoguMi6cm3wGHaLrfAZxgCK0KRHhKKQ8HLnC9Yz+BHz2vGQljqDW5MCSMBV1HYlTIUrgxyjcfPPNwWO80EILBfHhIKA8Xa9SQIxgAHFxJgU1NRjqnE+J8PD6O4/gcVHmgk234VigpoaoAUYbAj9bY0gtIfVBNKLiX1L28p5+5WuC2iyM+7huKxVE0DHMEPYYw8w1Rnx8FA8CFeGEAynlMRv+/hL9IjMB4YTY5+bHg/AYmQt0c6YRDkZ0HhwAMRzHQ9QOAz4WqBj2OOUQKakNYtYBc4cxT3QR4jGRhszRWTUd2VQuYicUThZS/eOaaId9DWcna7t9+/bhtflaTrlf4ABgrXpmCN/TDMm7Cfv4yAwhxTvlWOP3n2sFexllFtQh4/R0uI+znNkvqPn1z225BWqpuSJzgWsF2RSxQGU9c0569ugbIeoDEqdCRNDYwRt9YGjSGZSW/xhubnSQokc94cUXXxwubnkVHqXACKVmL66BLAel5ojusaTnYZxR2xuDQCXFl6NN4jqichLPEeNnTIgOP2qllIFEKhaREtZNpaRfeb1kXqKQNN3huBWgYRdRm/g4EwdjudzdYkudF8t9pOWxJ+C8IBKJ04W1Eh9pwmePNe/pkSmhDvOuu+6qdh/jpjssIgnHgEP6ZsrIqe8dfJ5YG9Tx4tDyVOn488W6IZU6BaTBZiH67MfXONkMCpwxODL8daSO6NHcjXIEwAmAo8i7CJMS691442tfua+B1HPz9+O5InrKPka6P1kKdL8lUloTqec5njcEKg6KWKCS4st6Zq0LUd+QOBXi/0NtFcdneLdEIgikLnGhdoMSw4K0PDzLeUl1m1XK2eimJmHmY+ACTM0udYR+dmX8HGoMU8wzaWGnnXZatfswGhBK7smOXxtRVKKQeTSAZoaU0ccsnP1I7SsCECGCMHUjjtq9q666Kun4WKdEZehozBj9GBtqCOliiwgl6oRRSTQ1bpKUBxDSrG/2urg+1gUq92MoX3LJJcnGSMozKdFZQcdnjggYmQlx9+74s5jCWUimB2siu99xdjSZKtnsD/Y2jl/JNndL6cjyv42DDecKtd4IUz5zDo2aOGc4Pgu73PNNNBqHJnXeRPQ9wstcrr766uH4MUCgEmVnP8kT1MfGgtPnD+cgmRU85jWozHMlOb+FmF1InAoRQVoQ3su4cyUGJx3/+vfvH2qCSIFM3a2yksCTzWHiNJWKBZwbnIhPavCIMJQSeClAaLiAjutKqXXknMrsa+A10mCD82XFPzc+MegxLL3G2w00Inuk15f7uBiHyBGROe+qiuGLse6OFSLmiFbAuOfICvYT7yicktjI5exExs5ryWYtcIYl3YQxkqmfLrdxTBTRG9vQRIgGTLFow+GD44g1wlhdoMYRyXKPmT3Cx8jcxqKVMzURSvH4GDOOC3eEpqCmGnjec2pk42NtAEcM65lMllSCiTkmisvYcBTiKKZOnn+JQHNsE2UVXFN4Ls4Xnss6ygtkUxDVJQ05+15QM8t4KVmIG4/lrV+BELWNxKmol2TPm3QhQtMVWrXHaW0YGERy8NQiQIguiBnjc4sw9Tb/tMUnTTDbcZdICWKELr1EnlISGwJ4uWkI4p1hMSwwinbbbbfiayQqgqFJGqeMiFlfH6TmkqaHY4KIAc1hEEbUYfn5ldxPFIr6vWyEvdwQWcKJRaTs4IMPDoIUY5PmMIg9jlvxaCoClSZJfvRNynlmncZZE9Sd8xpwuHgDJ6AZC0ZyqvRjMhOoGWTuqPsnykvKNOUW7pQg0k96PXONwzDlUV5xdgTd0FmjpPY7ODZZH9TCUmtMCid7BSIqVaQ03qdYz4h8nK9+xiYOC14HAorXRAo4Y+b9SF1Hj4OIFGrqYMluevbZZ8P4vYEa0V6i0sB69zTkFJS6HnAfa5vrXdxZ2DtR8z5wBE7ey0GEqE0kTkW9wjt+OtlIHR5wLhguPmKUXvP3wKONMKUpBVExUpeoBWKOSd3z7rbUF/pxHKnSTOP3+JtvvgkdNWmAhHMCbzzjuvrqq4NAxRgi/Y00yLw0M6kU4q68pJDiEMKwRPSxDmgggyODZjw06UH0Ec3JSyMhnFdEkVirrBOOFiJ6w7pgDccNcFJFeeMx89lD8BNtJPLvHbtpgEUNOt1MEYBEpRGG5a5Jz4JwwymIyMdg33XXXUNTOkSIp0lj5FPTiTGfB6cQgo7IHQ13WMucF+wQ8Wctk4XDsT00EvL9IqUIQUSTOk+Ggh+VxpnS1PPiiKOrLY+TRs11MQ9jBpwRfqQNx6sADk86ClMiQu+I1CUW8ZpkP+DINMblRwexZyBQ2UNw0DF+5tjPkM3DPAuRColTUW+gVo0onp81h9jA4KVOhSYQXkdDYwW8stl6LPH3HQLU7frB8kRkSPHFsMDowfDkeyBqlnUgpBAfRMQQTTBixIhgGGPAudGOgOK4BQxQauBSdX+sZHjPiXLQWMwjZqyJiy66qFiDhUFHJI/uvHmr8Uagsq4Rdx4ZZX14Cm9enBQIU2rnyfog5ZgIGA4XN+qZZwQJghDnAGfLpsKNcY5dIXoUzyFnTnMuKCm/RCPJaoBURzXFf4/yD8bF/oXDje85XzMWqMwrUcjnn3+++LMp9wuidzgK3ZGCI44zjuOsIcaHA5c9O25MVU5qel8ZD0KOPcMFHc/1KHpePn9cJ4hCM9eUBFG+4hkhOIn8fu/krWuIEBKnoh7B0S90ywTv9Ei6JlE86sKI3iCg8GJiEBPZ83omMWtgGMRGBQKOOjb3GmN4ch4dtWN8jYHBfOfhwkw9MV0f4/NgY4Fak3iWl3vmcKORxiVE64CUTYwzmtw4efns+Xgx0hEYGPUODbuI1BDx8NRvnp8Hw5gxELXlGBuOpYghwjTvvPOGdGqHz15ejm3iaCmijF6/SbSReWb/IB2Z18S+nS3PSAH7AaI/dmYSTS8lUGNSR3tZu96Yh+se8+0dh7k+ki6bJaUDgLFxXeZ9Jw3d0849gkq5iJOHzx8g9Pmc4fDGKU40mvOZOSaNjCJATFPby3vg1xBdS0R9R5FTUe/AIKPOCk+xg8FDtIxIKtGDZZddNnTkpRZSzLwREafjxpEuxAeCj2jZLrvsEjr0ujEPXLTzEDHFYGNtkK5LA5C4Rg+ByrFCpBaWShsTswaCiRRI0tkQ/nFXXqJ91MJ57WYe0o+9po09AqPe1ytRXdYMRmdejuNxWL9ERN1wj+eTtGk+kymFEvWXcWYL9eY+50RzqUEnVZo02LgGFvGU6hiTWDgQAUMYEfny64mPB4FK2jEp05QF5A3ENOuWCDoZDPH5tjxGvWncMTl19JFoP9156QHAmsDBwvWGfZoaabIDqPvOE4yR8cYg+nGu8Fipz52EqRCFQgMTop4xbtw4q6qqsvPPP98efPDBcF+3bt3svPPOs1tuucXWXXdd++GHH2zMmDHheWLGNGjQwD744AM7/vjjw/e33nqrLb/88vbRRx+F7xdaaCFr3bq1rbXWWvb000/bI488Ysstt1x4DCfZJptsYh07diz7VE+dOrXae/z111+H2zvvvGOTJ0+2xo0b2++//x4e22677cIaufLKK+3hhx8u+1jrCrzf0KJFCxs6dKitttpqtuWWW4Z55b1gvm+77TZ7++23Uw81jGf06NG222672ZFHHmmvvPKKjRgxIqzf5557Ljxnl112sd13393ef/99O/vss4vrJQ/zzPqde+65bdSoUeG+Jk2ahHUNq6yyik2ZMqX4+S03r776qu28885hHz7kkENs7733tkaNGhU/jyuttJI99dRT1qlTJ3vsscesbdu2xdfFXsLz+Lqce/SkSZNsjjnmCF+zT/To0cMGDRpkP//8s3344YfF8XGbf/75bc8997RNN900XHN83afY40qxyCKLhNczYMAAO+qoo2yfffYJ93PfFVdcYc2bN7d27dpZathr2Q/uu+8+O/nkk23XXXe1Tz/91NZcc82wXpo2bWoDBw60Y489NlzPU81zjI+BsX333Xf222+/FR/r3r27bbjhhjZ8+HCbMGHCND/r60uIeo0UuqjrxGl57pUkNY8IKREyGhNkwTOfssNmJUEqJp0zSbUiikC0Y4455ih2fvRUXaKn1JgSTcgDpFp5evfRRx8dvO/AUQV45nfcccdiHXKcbkyqobzbs/75IwuBWt34uB2idy1btgyfNeaYyB7vBdHJ1F15nTPPPLOwxx57hK8ZP2uDKG+WYcOGhZKAVPiaJPWVde1ZDKxz6qdpdhRDrTfRMZ6XIg2SzxbZKu3atQtrwDMp4uwLUv7jo71SpmtyvBRNr4CoF43QWK80dCPdlD2PGmkgIuZjjY/jKff4479HIzdS6clW8LVCnTfzz+shWkoGC1kBce1j6hRZ+kFQ2w233HJLiPKyRwOpst6Nl/GmmOdsCUsMqch0auZ4pHhMZIaQ0ZCqI7YQeUfiVNRp/IJAK3xqHGm04RdmGpcgUDkiJj6HMFWn2EqEmhqMBa9dw/hBoHparMPFG0MCYxPRl9rowWAkFY/mMHTSxDiOU3VptoJRjChxAyK7LiRQZ83AJCWaxiCkaF566aXhfo5loi6PtF6MNdLdSPlO2ZU3C+eBkpbJeqZzaZx+zOsgHTVlh1hKEhzSj5lH6rsRTHSPBcZImQJrmsZIfA45fzNOrS8nbswjmNg/SH1151CcfowjCBGIMZ+HNHREG+uXc5njI8U46mjgwIFBoHKtgaxgKfd+F/99jmFinmnG07p16zCnOFqAayL7IGmxiECO7slDV17/26T3cz3Bocxr8L0DqNPknGF3Iqa+rvD5ozTEj7IB0v251gwfPjw43Lie8Hp4L1ILfyHyisSpqPNgwDVr1qxwySWXTHN0DMYPR0JwccZrLGYNDDLEPRA5pS6PBiAIVAx66q6y9TY8xnuSGgwa1gWNQLw5RWyMYSxjiCJGsq9DzBg3vOhgSrSR2jaMN9YMQjUWIzQ1IaKDiMpbV17GzBEsHPtAQ6z4teHYICKZ4rgYDF2P8NNAiBpYjoGhVvOEE04IIpUsBu+ETZdYmsmw12EwpxCmWcHGGF5++eVwVixNYsjAiJkwYUKoNaypqVC54Rge9i/e92w9NA28iAQ3adKkcPPNNxfyAnsX59ni8GF/I7JO7TQOAb8e8loQq3Etb1668uJIJJOCeadxkIOziGs3x5SlEHlkHuy+++7F79nX2CPIUsDZRm+F+Lnch1ODued4LHcApG6MJUQekTgVdRoutpyR6EYPDUI4yxRvLAYdkBZEJ0gu4HnpVpl33BhAkGI8crQDRyn4cTFED1ygeoden2sMzVTNjxwMLwxjogUYFBwx5OOMjQUiqEScMPjF9PF5i5tI4YwgUsc68Mc///zzEGHH4KTLad7WNKm5ZAJguBMRQ1yT6tipU6fiERDcTzSKSHDK9GPSHDkKiwg/3WERpQ4OFyLRRGjizBBeZ4qu2LGYoyM2ndP9M0da9wEHHBAEarwmOGYI50CqLt6+JljTiM+DDjoonKuK8OdcUM+q8LXNc+g6zvUkFWSAeNbKBRdcUOjQoUMQ1axjh/WLSCISHEeA8xDl5dpM0znm2UtDcGbyWSMNneN4uL7gZOE1pEg/xhlFB3o68ZKhQFYQdgbXFPYPoqRESzlmysFJRAYA10x3guahO70QeUTiVNRp6DZIdAGPK154aoYwHIiYcXQFFxGPoKrG9O9B5AYhSjpYDAYF5+ZhdHIEB7Wm1DelmudSHmoMGmogSSXdZJNNStYA8TqUwjtzYOgSycBYwzFEpJGaK4+uOy5QF1988cJJJ51U7f1I3ZWXyAfHSmH4kvZPRImzIKmPZbxEPTBEEdflTj/2NRxHal2gknKc7QrL8VkIa5wvXg9ZblgDccYKzgr2AeaSGvRXXnmluCZI0STllD2FqBjGfypDPt4vsnsHddErrrhiNYEKCBMiYqmiYVzHcBZ6mitiCacKZQveGTkW0rwG1nFcB56Sww8/PKxj1gxzy3UFxyB7CZlNnTt3Do+zbji+KWX6MU5uOhyTrcDni6687hTAmYGAzgrUGF1ThKgZiVNRZ5uv+Nc0e5hzzjnDRYToqDdTwMDMtnkXswZNjjBwMBSoc4sjN0CTDWqcOKeQ+SeFLwWxsYiRc9FFFxVuuOGG4niIouOZR4xwtAmGA9Hg+HgFGRMzBnGEqIijOBiaTZs2DUeuZNcOUQeObcLQSw2RDWraiNZ5sxiMY9YKcEwMAo8x81rKnX7sa5i9jagtDZhiBwq1uxjtL774YrWfoyESEUmiTpypWE4Y60orrRQchJ9++mlwUiEuqB/EYciYcBT6mInunX322cHRRepxqtTH+O9Rk8n+1qtXr3CkiUeAcXQSQUVQ4ZQhOunnhqYYM1DvyDqI9ysi++zNOIg8Uu3XRkQf6ad52NtYEziMcQQBR/Pg3KSvgYODAkcHDoFU6cccd4Sj2+ePawTj5hoXg0Dlc8l1jxpTIcTMI3Eq6gx+sXJDjTo2wJigQQF1QEQc/GJGvQhNLvJwYa5kEBakiZ122mnB8KR2MAaPfV4i0xiSGBI4JqjHIzJz7733FsUHUQTS4IiOET1Tc6xZo1+/fkFUxGAcU7dLBCeuGXMxkrpjpe8bRHIZvwtn1gmdqB0iTalwoYMThXEhKGgyFYNwRpzymDcoi9PpEYcpwAFECibp8ezJpEPGkUYEKs5DF6gu/lKJjxiivDitSJnm+oGzgmZSPiaccewV1FATvY5T2lOAIwXBnAXxzN6MeM6mIjupr4MIaxf3lIfQC4Buty4CKRHIUm4HAFkKvN843Dx7gZpYBCoOONK+Y7h+8DOsf9WWCjHzSJyKOgWpNBg6dPTDw1lTqi8pQ0T0StXbiJpxg5FUPIw1LsreLAijB4GKoZYVqHkAYYSh6d1NWSMYmxhFztixY4NBSiqZG6CqCypNKWOLRjEu6GJhj/DnMQRqHPHLEziqWLdEzmleEnflpVaMWrhsE5xyQp02DVVYnxjEpcAQRqAioDxdNhWx2EF4kpVAJIyU2GyNLw4NotZZAZKymynzR4M3b5ZGZJ1mRzT2iqF+luh0qvRjHCm+LhH/RKpLfT651rE3U76QxwZvZNmsvfbaYX5ZC3HWCo/hxODanbozPYLZo7sO4pmMLNJ4ae4VE68HCVQhZg6JU1FnQBzRXdW7gOLF5qKB+KBujAs4EQQ6VpJmhlEhZh282hjv3bt3Dzcu2CNGjAiPYfTQfIpje0iByxOk4ZHiDUTS8cz7MSDUSGY7OYOE6fShqU18fiafLdJes2c9ev0bwpWaOK/1zgM+RmpfWbcY8HHEFJFNkxteVwpx6g2MSJn281bjtExEB045b+ZGRJXuwqTG5mmPwylEyjcCm3Tf+HUQ1SVqRmpsXiDN1IWe7xdxJK9Ud/dyRh+ZO44KatiwYXBKxEdg+eNZcc9aoXEdtb2pqEmgkfJN9BkHAE5OhwglWS5kNaRyVlC7TZ20N/wr9XrYGxCorG8cXUKIv09DE6KO8Mcff9gXX3xhiyyyiP3+++926qmn2qOPPmpvvPGGNWnSxEaMGGGdO3e27bbbzgYPHmxLLLFE6iFXHK+88orts88+dvbZZ1u/fv3s+++/t/nnn98++eQTHF3h6912280mTpxod911lx1++OHWtm1bywNzzDGHzTPPPHb33Xdb37597ZxzzrE999wzjPuee+6xTz/91Pbff3+ba665ij/TsKG2yOnx+uuv20033RTe7xtvvNGmTJlSfKxBgwbVnrvccsuF+W7atKmtvPLKlgre76qqKvv444/D17zHiy++uB1//PH2xBNPhDV+1FFHhf3kzz//tJNOOskeeughe+yxx8I+Um4YK2P873//awsssEDxPtbxyJEjw77G46ztBx54wLbddtvwfgwdOtTmm2++so936tSpxff+rLPOsp9++slOP/1069atm5188slhbtdff3175JFHbKmllgrvwaKLLmo33HCDzTvvvJYX2Mu4jvAaGD/7xd57711c99zPNaRTp07V9phywRpYbbXVbOuttw57MmtzzJgxxTXy66+/hnXB+8GNr1u1amUfffSRLbjggpYC3mtfG0OGDLHPP/88fH/sscfaCiusYLvvvrudcMIJ4To+atSoMJ/nn3++ffvtt+F6wmv2z285eeutt2yNNdawLbfcsnjf6NGj7emnnw7reLPNNgvvw1577RVez7777hvW9AEHHFDWcQpRZ/gHwlaI3EEdKak1eC+JHNDMAogk0Jpe/DOIIBAdAyKNNNog/dHxyBKpkaTI5skzzzm3rA0asMQpY9TLcuSGpxuKmYfGIETNiSrQYZV0UpqMkRbNjXnmM0inZmo6qYtMmRrrEF0kQkqaN9kWpHh7ZIl6dTIDqN1jXdDJudxdebPROKJH7G1EHklPpyyBzx7RJBp7kRlCOm/ceMWbtqT67FFrSio3qfPxmbak+FKDR5S6VFOplM2P4sgnzYPotEqZSJwFQkdWurNut912SdM046wO6oypn2fNMt90nCZDgbRk/5ob522mqjGN54qGXmTccMwR5TV0aGYNA/sF64NjvtZYY41wHU/ZlRc4v5aaaYfj0OhbQPdg0qTZR6g3JbJLDwb2udQ1vEJUMlX8L7VAFmJWce8pHs3PPvssRAo22WST4B0mooDXmAjCnHPOGbyv//73v4OX/sQTTyy717Uuce6559odd9wRbkRCNtpoI7v88suDt5gIDpGnCy+80Bo1apRkfLFXnWgotGjRwtZee+3wNVHdm2++OUScllxyyfD8gw46yMaOHWvPP/+8IqUzEREjmuTv71dffRWiMMOHDw+frffffz9EQPjcffPNN+Fffz/4bBId69Kli6VcG4y5V69edvTRR1ubNm3sqaeesttvv90OPvjgcIPzzjvPfv755/DaiPKVM8ui1Dz/+OOPxT1twoQJIRpJ5G6ttdayhRdeODyHLIWXXnopRHJSR/yPPPLIEMVdZZVVwufq3XffDRHo4447LjzOOImQsV+kjOTFEKFjnKyTU045xdq1axfmknXCHkJkjOjkbbfdZl9//bW99tprxchkNkugNnnnnXds2WWXDV+z9/7rX/8KUTqip1dddVVYI6xZxtasWbPwvEmTJoWvt9lmm+RrgygoYyVTYfnll7dx48bZeuutF+ad60qHDh1s8uTJIapK1g3RXj63ZDKUe+y+Zzz33HMhas4Y+P6XX36xww47zDbffPOQdcF6Yc/AHllooYWKP59izELUCVKrYyH+LtR/4H2nhTseYzzH2YYanPd23HHHFeaee+4aGySJab3bfl5bFjqv4s2m/opITvwz1OTh5SYSmRoiHURJiY4RPaBLr7PFFluEzpV47qlx4tzb1J75SoGmPDTkASKmRKHpwkzNLg2yll9++VAf5sRNkeKzOcuBr8u4gyr7A+OnftMf9zNX6dR81llnFfI2z9TnEr2jWzB19exppaKiRMWo/01dJ3333XeH/cGbxpBFwfEwNEOi661D/T8RqTx85s4888xwHi/ZNZy/yo06Qz/ShAgw1xci08yzz3G5x06TJo7uIhLN3BElZU92GD+RSO9AXopyrw+Ok/LryQUXXBC6oVNbTKduh2sG5wqzf5RqUljuWlM/ExZohEbtNuuAiCn1pETV43kkE4P+C3lsNCVEJSJxKioSmmtwEb766quLRyxwocYIch5++OGQaoihkafGIHmH7o+krHH8SxaM4oEDBwYDg8ZH3m2TZkOkUtOVNQVuvPAv46frI2uC8XC8AoblAQccUHw+hvNDDz0UjD0XKamN+jxDgyDeb4QHnzME6BxzzFG4/vrri8/hDE0EKilu8XEy2aNBygmGbt++fYN4pokNR0AgQjjzMcYFKnsFjZGcco+5pnm+7rrrqo0nHhfzzuePNV5uBxwNmrIGOefDIjJimH+chLwmBIrjn72UKaaA4HjwwQeL37M+SFWP90DSNeN5T7Ff4JhAlDK2li1bhiZjWWciafU8dvvtt5fdIZSF+SOdmHED46VjN+NzAejvBc4XhDdOojFjxiQbcykHwPSOQWN/43O60047Je0uLURdQuJUVCRDhw4NdTZA3RIRMu/E6oYDFxSMOjqKipmH+cILzAWX6IbjBiReY6KmGBnNmzcP9UJ0P05VlxcbmggQuj4ijjCI/T66bHIYOhGzGf0OUfoIBT87ExGHwUYUKRthxximcyj1WdRHpob3Ox4HURwEKmcS/uc//6n2XJwaRP+XXXbZIETyPM/xzyAQyQQo9+cPwRHXAzr3339/2BeoOY0hak2nWF5THKEut0Eff9Y5HoYoI9HQ7NmwzDtCia692fNLU4gQHzcOWdbJcsstV62WN67l5vUwz34MTirolE8Nd3z9INJLvTQOAK4l2e7TXNdTRtNrcgD4GvD3AacQTm9qTnHGuLNCAlWIf47EqahIaKhA0wQMSm/K4xcF0g1pbpOHdLFKhWZHNKtgjmOB6oYowg/DAiMTD3icopUKmsSQGkaDGzzfsbFG2hgRHZrbcCyImHmIlHukkYgeadKkt2H8Iv6ykTOONEH4YZTy+UwJDYPiKK4bx+wXOFdoLhTDOiZ1Nu/zzF7H8StEa0jljFM7y0HWAEcke7QLpyDHwhDBw1Hk0PAGIc1ZnER5s2dFlnvcOCLIvkHYM8dErP04Hof9j8h1VriWk6zjDLGE8Oc8zdVWW62YwQLxNY8mZKmzQchaoeSmVEYD88468c9b9nWmuH7PyAHgj+OEw3lEiQviVGUhQsxeJE5F7nGDAuOHFFJ46qmnCuuss06oJc3WPtI1j66hHjkTs1egYjTgRcZwpptlNqpQLmJjBmOCDqukdfP+UwsZdxF2gYphTCRNkdKZ/9whlEjN69OnT4h8+Vl/nK3pwskjIOA1Y+XuFlvqPaVW0M8sjSN8pHvzGAJ12LBhhUqcZ98Ls4Kq3PA+k9LPmaDujGBOMdz5rBFJJ2rKXrLNNtuE9UHqdzZyXU5hSsSUWnOiomTe9O7du+isyKbCsp+kcnTGDjaidGS1+BrACUCpAgI1jkSTPk2aulNugcoa8HGz3/pZsdnPJ+uAjCfEXepazVlxAACZDKSBjxw5srg2UjsChKhLSJyKXOMGBUeY4MXkyAdS7hAaREQQJNS98TwucERM8crHDQ3E7BWoiFEMZRqcpDxiw6F2lDo2FxkYDjSoILWQFM4YUrF8TUmgzjzUbiOQSOOMwThjHWAkEyWj4Q3RqHHjxhVSgPFOUyCHY4+IkPn7HQsUDFCEK4KQpkOVNM8nn3xymOcU6cfUEVKXe8IJJ4S6fhdKpEOT4v/VV18V92ychEQel1566WDkY8DzPpDZQIZLChDLRHXjMhDA0cbRIBzNU6pWs5wClX2LvdehORZpptREc2QQdZGACD3wwAPDvLN2cAaQHZJCTPPZeuGFFwoNGzYMcwznn39+oUePHsXHsxF3BCqOGF5DKv6OA4AGWn70DehaIsTsReJU5B5qmOhWyflndH50SAdCNCFa8dzjCefinQfBVFcFKh0s6YTL+5GHeSa1EWM+22gFI5jIE11DPXIWo7qgWYuEICaIepFCjyiJueeee4JQoms2Qi9ba1hOEEREzREfQN20i9NSYIwSHfnoo48KqamEeSZDwbvWMkYiX5zp6EKJ6CPiyQUqYOzzOfXPHM2HqEmOo3vlgjGQCk3KJmdfZ0UFjeCoH6QEINV5vKxFoo3U8TNvOCNIgSXKS2R3++23D2uc82I9FZ2MERwaOGw9QyCVYGIOWaesC0Qn6xnIZMJxSKTfv2bsCMAUYvqfOAD4/KlsSIjaQ+ecitzC+XFTpkyxPn36WMeOHe3ss88uPsb9jRs3Dmf+cbYi55Bx9tvSSy9tiyyySNJx10U+/PBDO+SQQ+yZZ54J5y0y3yuttFLSc0ydp59+2rbffntbddVVbejQoda6deviGXOcecu5f6wdzqUTfw/OIuS84EsvvTScrbjHHnuEcyrjsxc5v7B9+/ZJP3+cTcr5mgceeKCts846YcysmeWWWy48ztpg7+B5/Nu1a9dwPjLnV+aBPM/zkCFDbMCAAXbTTTeFz9Tjjz8ezv7kds0114SzPr/44otwZiVnU3JmpZ/BCqNHjw7n3HLG8EMPPRTmvrYpdQYp+8IRRxwRxtG/f/+wVuaaa67i44yfc1cZaypeeeUVGzRoUNh3mXNeh+9fn376aRg/5zgzp+x7nAvKGvb9McX5mvHf7Nu3b/gccgboyy+/HM4Y51rN54/XwtfAZ+/6668PX//5559h7ZeDjz/+OFwzOC/4scces/Hjx9tmm20Wrh+cYct1Y9SoUeEcXuaXs5FZ91z/mjZtGtYGZxCX+4xbIeoNtSh8hfjH4J0kmnDuueeW9AbHkVRRu9B0hTNCUx0XE7/32XpiIrpESelSGT9GBJXHVA8083iEi8gBR8NcfvnlxZowshVOO+20EDE78cQTCynx9RDXknpjLtIKieIRUWf/oPs0ZyKTekrqJjcikBw3lIpKmWfgM8RcxsfsAN1sqS3lGBBPhfUIMJFVP0LE3xvSvjnDtdz7BXNMBNejtawZatI9XTNbt5sq6hhH44iMbrbZZiGdm3KVeM1Qb0yUlAhw9mzvlGeCXnbZZSHaC9R0s2aIpBKJpmkW65wbZ4fyGU25L5N5QDYQUXxqY88555ziY8wvEWoyhDxCnT0SS9cUIWoPiVORS2hS4WDoIDqyhgMNkkjlTN0RtD6RPTYiBRjtpGuSWkhjLG+8Qy0cApVuvNSWZpExMfOQEk09N6KOG0aw1wgioGgQgtgjxTslCB3S8YDxke5ImiAOCoxgUjRZK6XWb+ozICtpnkl/pGwC55R3rqWet1GjRmF+qSsk3RHxyVmr3OjM62IrZZ03c0cDJmoxSX297777phGoCJOswysPdYSc5828Urbiqec+l4hAut2uu+66uToTNO4cTSdpUnw5rqcmyr0vV6IDQIj6hsSpyB0YnHTh9QgdZ6VhWGQ75h1++OHBsEjRFESk4ZJLLgnnlRJNwpinFgtvvTfgQaC2atUqGNF5EB+VCBEF5pjoBtAcBKOTJiBulFFTyNE91HunOHqFOmL2AzrcMjYEEo13aI7m4KBAoCJM4uNkshGQVFTCPJeqPWeuiZZSF8m5sTRI43PHWcII6TZt2lQ7T7jctXnx+8pxNRxpxL9E7qg3ZS69ThaBSu0hdYZcZ1IRC2HefxdKvk5wxDHGrEDFgZFSRNd0Jmh8Li/13zx2++2353JPzrsDQIj6iMSpyB00KeFCQRqWG2g0B0GgkiLEESZ0gUSE8FxRd8kaXjT+8E6QLlJIy0K0ukB94IEHgjGXh8hHJUJTIaJiLkj8HOGsuCOlfuzYsWUfHyKDaIZH8DhvEFHHex4bxYAxzHphjWCA5om8z3MpGCfzzN5bqsMxXdSfeOKJZM1isp95xsJRMA5rpm/fviGtmyN6AHFNam8exowQ9XTY+HxNInykoCL+46yiUr8jL2eCxg2lyHziNfF+pKJSHQBC1EckTkUS4s2+VASDerChQ4cWvydqQLdKzjblIoI3NlXtoygP8brgKI3rrrsuOCWyKWIDBgwInS05ZigbXZJRMeuQ4khUGlG02GKLBcHk84gg4QiOlOndAwcOLKy33nrhayKnHTt2DA4rPws0e2Yi9YScqUkELU8lAHmf55rAeEcocT4lafU1pWem7GaK2KRmkDRePwc7K1C7dOkSIut5GTPpx6ShM15qo1nPHBkUC1TmnDpIr63O+5mg8XyeeuqpyUorKtUBIER9ReJUlB3f5PHC0x4fRo0aVRg0aFA4Lmb06NHFJgU1kUejTdSOMOXoCTzzCFAMClLzsgKEs+ioNyR1TMzc5y8bZXSoGSNtk/pdN+z9ZziWBYOfCFmqNYEg5SiHPn36hDMSqdsEImEuUP2cQj9LEbw2uVxU6jzPSoovNz//OCWxcEAEkXmDA3P11VcPa8KjpLFA2XzzzYtHDqVO8WZNsw5IPwYcFmSJUAtJHa/DXJNGm0pE/50zQY877rhqxwalrP2vFAeAEPUdiVORxIjgwsbFmGgX99EFkroORKkbFNzwcGJsIl5Jg8tLvZgoD5ylutVWW4W6IBqW4MAgqk4dXtwJFHBm6Oy5mYMIIiny1ApmQcQRnezQoUMxCkJzkGOOOSYY/XnIWOC8QfYHBFwMEXYMeoxkzofEsKchi6d8l5tKn+cZCVTqTzn/kdrTvPQrQNR5+uiHH34YItKkISMAs86BFNEwGkVlnWt0s0U0xbDfIeyyZzj7mMu51/2TM0Epx8nDvlwpDgAhhMSpKCN+UcVoJMrlXTaz4InFQOMYBTzbiFZS8jDYdHRM/WHYsGGFtdZaKwiQOFKOQO3atWsQqHGEzJFRMWOIeJBSiriII18+d8wr0QWOY2nevHkwNolc4yxIjR9Vss0224Q6TerRY0j/R5DixCDCSpQsFZU8zzMDUWnSOVOlPMZ/96GHHgpCjveco2/i9wBxxZpgbUzvd9Q2ONSy+xncf//94f3PrlW6xJIdwOuKo5HldM6Sxk3zK9YlDYK4ftOIjqynG2+8MaRPcz33I1eIOCL6eJ00IvPXWs55rkQHgBDifyhyKsqCb/Z42LmQIT5jaGITn5dG100iqJ4CxDl6eU1xE7OHrMFFTd7SSy8d6vGy7z3CFEOeCNn48eP1FvyD1EzqrGLh5MYkhhviCaOYz2ae0tzo0M2a4FghDOXsWaCMl2glqYapqeR5nhVS1uR56jSOK463QYjEUENI8zRESKo05Oz+RmMvjkMD1ildYXHGIv4cIr8ILbJC5p133mLUr9xU0pmglegAEEJUR+JUlA0uwBydwIUshpQaoqScjeeQHsT5f3lJFxPlg6M1HnnkkfA1x8QQNd9ll12m8YSTPtavXz8ZEbUgnIgY0MWURkPbbrtt+DoVbiSSOkgDm8svv7y4FmiAhUBl/8gK1DxRCfNcqdA4r1u3bmHucGJyxBjHCnktcvweIKrycN4xKd1kAhGR9CZdZIpQg0wqLB2mEU2sGTIEiFBzJBKNvcpJpZ0JWskOACHE/5A4FWWDi8Sqq64azqB0A+2MM84IghWvZjYyQn3I9A7vFnULDAuO/qD7KsaCc/755xd69uwZhGi2ztSNEXm5Z69wwtCn1htDNA8ppggNnFWkyHLDCB4xYkR4DKFKzSZdNml4klcqYZ4rDT73iFNSuD1KTiYFTdSY01tvvbXkz5VboBLFp68CKegPP/xwuI/x0pSHDBCOSwP6KtCRHHFN1gjNhRgrUWlS2X3Np6KSzgStFAeAEGJaJE5FEgMNgbrnnnsGT+WDDz44zfNeeOGFkIYVN2EQdRtPC7zzzjtDqmZ8TAX1QAhURKsbco6E6ewVTtTrIfJI08uDYCINDwcWEXUgBZYUPM4q9PeeNUGqN2ctZo8TyhN5nudKoNRnnRRS0k05esfxCCrpmqnFBueAcp3jCDRqpInys8d5hhD1xjQVivc16mQRe/56Edu8xrjrbW1SiWeC1hUHgBBC4lQk6qiIhxWj7Nxzzy1e3PwCR4MCmi/koV5M1B41iUoMM7zb2aOEOGaIqGp8jp6YvcKJtL02bdoEo947cKYGY3LrrbcujhEDnw6sjte30Sxt7NixhbyT13muZIiIZbsGEzljnbCXpBSmvMcewUVA0Tl4t912K4o4BCf7GllFWfFJ1+H+/fsH50y5HBiVeCZoJToAhBA1o8ipSAIe1w033DCcKfbkk08W70eYcjFP2WFTlJchQ4YUbrrppmr1TdQPYmyQ3h0zfPhwdVCsRTheg6yGPB1jQp0gqbyIT5pjITjc+GU9EDGrtHOP8zjPlQKRPEQI9YSenkvdIELpqquuqvbcX3/9NVlmBZFxRB3RvJgFF1wwCGaiu5QxxB2oEVZx6QLNsejJgEO33FTKmaCV6AAQQkwfiVORixQ3Lgp0ymvatKmEaR0n9qojOPr06RPqw6j7cUMOA421gbce4zIrPtTiv/Yot9Dz9eAdV0sJOYx5atAxlOOfOfTQQ0Nnzkrs5F1pgjoV2SgcdY9E0hEaZNjg3MKJRYdenBelhFIKgcr1bc011wxOiJdeeincx7jpJszRQj169Ai1j4hPmgFyo2zB9zYfc4o02Uo5E7TSHQBCiNJInIpcpLhxNh0XbUVM6zaxoTV69OhwjAa8+eabIa2KzrydO3cOHno6cO60007F56u2tO6C4bjddtuFqEcW0jMHDhxY6NChQzGlm+6gHEdFwxNFH+vHfkFU1FM1EUNEwDhjFQFIjTrdjknLvOuuu4rPyYsDFjGKWKI5D6nHNMJirV9xxRUh4kuKN42xnHKOvZLPBK1kB4AQomYkTkVylOJWP4jFJcKiS5cu4VgQ92yToofRdtRRR4UIGUYQN868FXUb6r9I3cWgzB614k2QWBPUjnFWIfWFRM2Uhlc/9gtSTNu1axfqzlkL2bVDejdrokmTJoW11167kCcQUKQhk2rKOLMQ9Se1NIW4qwtnglaCA0AIMWtInIpcoBS3+gNdVYl4UWvMsQ+loFbsvvvuCx16qR0CRU7rNjWdBep7A5EbhAlG8dtvv520zk2UD46Sov48PpuSSFf2OJhx48aFqCn1kR49zVOPBdY1NZpxF/LsayinYKpLZ4Lm2QEghJh1GpgQOaBRo0aphyDKwAcffGD33nuvjRgxwtZcc037/fff7f/+7//s+OOPtyeeeKL4vDnnnNM22WQTGzRokN100032zjvvWFVVld6jOkyHDh3soosuCu/zKaecYs8880xxb/jzzz+tcePGNnjwYHvppZesffv2tuCCC6Yesqhlpk6dam+99Zbts88+tsIKK9gnn3xiw4cPD3vHfvvtZyNHjgzPw9Hepk0b69Wrl7Vo0cLefvvtXL03Sy21lF188cVhnKeddlpxbTds2LDa8+aYY46yjSneT3/++Wc74ogjbJtttrEvv/zSFllkEevXr599+umndswxx4Q5f+655+yAAw6w8ePHh72ZsX/88ceWl73jiiuusG7dutn1119vTz/9dPGxP/74w1q1amVrrbVWmF/2EiFEvpE4FULUChhiWZo3b27fffddMHoQpUcddZT9+9//tjvvvNPWXXdde+SRR4o/y437unTpYl999ZXepXosUDEoDz30UDvnnHOCsYxQFXV/72AdIJZGjRpl11xzje2xxx523XXXBecEovWGG26w3377rZpAxWmBEwxhW2oPSr22EUgDBw60N954I9lYHn/8cTv55JPtxBNPDHsugv61114Lc7nVVlvZ119/bTvuuKMdcsghNvfcc9vOO+8cxOq4cePslltusaWXXtrmn39+a9q0qeWFPDoAhBB/k78RbRVCiFlOGyMVj/SqAw88MHRMpD6Mr0eOHBkeX2uttUK9afbYCOqbOI9O1M8UXzpyUnPIkRWqMa27kPoan0PpcB/N0WiIdeqpp4YjTPwIEfYMr1kH1gr1hfF5p3njnXfeCY2cUjXhqetngrJ3UH+aPfdWCFE5VPG/vytshRCiFNdee61dffXVIS13scUWC9FP54svvrBvv/02RDZWXXXVcB/pvWuvvXbw1u+///7F57755psharbiiitqousZH374YYjcEAH59ddfQ1rhSiutlHpYohZ44IEHbMstt7TNN9/clllmmZBRQVTOo1zsAUTt5p133mKqJs/l+6FDhxZTVCdOnGg//fSTLbDAAhXxPrEHNmhQvgS2IUOG2IABA0KpxLbbbhsiqFtvvXW4EZlmLOzP6623XkiFveOOO2zhhRcu/vzo0aNDtJp06oceesi6du1qeeTdd98Nr5VMi3LOrxBi9iBxKoSYbbivq3v37vb6669b37597eWXXw7GT+/evW3ZZZet9nyMSerIMEZJ36Oe0NOwMEiVglW/ef/990Mt3Omnn26dO3dOPRxRS1x11VXBkUVtIymvSy65ZEgnJbWbf6lBhwkTJoQU32HDhoXSgFdffTXUJHsZgIRIzVDTT5kEqbzU+DsLLbRQmG96ATCXzDV78WabbRYcAuzJ8803X3gu5RU4Hrfffnvr2LFjRXweyu0AEEL8cyROhRCzDQxEohh41u+//37ba6+9QpT0yCOPtCWWWCLUClIP1LZt22B04oW/7bbbQpONRx99tNj8RqJUOETV1TCt7rPaaquFJkdkT9x6662hFpIbNZBESTfaaKMgSM8999wgnmiqhiOLKGq2rlCUzkTo379/qMs97rjjbJVVVgkOQ8T+hhtuGPbg1q1bB8ciUVWgARmNhtiPfW+X2BNC1DYSp0KI2Y5HTc844wzbdNNNg+DE0KTLI0YRTUsOP/zwYOxMmjQppPRiAMnQFKJ+4c4omgURpfvPf/5TTNMlike2Bc2PdthhhxCtO/DAA0MDH54jR9asC1Tmj/km/ZnMFRpMderUKaTP00jqrLPOsh9//DE0QaLBUPweCSFEOZA4FUL8Y9yb7t51ICWPCAipd0Q2OAqCWjCMTAwh0sNILyPNLP4dQoj6B/XlPXr0sLvvvtvWWWedUGfesmVLu/322+2jjz4K+wSilIhpdq8RsyZQiVDjCCCdmhTdGEQrndTXWGMNCVIhRBIkToUQ/wia1XBEDLz44oshPQ84a5B0Xs7Go24Qw5K6JhptwLPPPmurr766DCAh6hGcWey15zRN48xSjiZBaJ555pnhjMr33nvP2rVrZ3fddVcoAQCieewdPE/C9J/B+aQ0RkLkczQTQhSymSuKmAohUqAwhRDib0NkFOEJNDKh8RFNNIAGNk2aNAmpvERMY2GKcUmUxFN5hRB1H7IoSBc9//zzQyfmvffeO9QTewSUbsxPPfVUSDN97LHHisKU/YJ6SAnT2YPOBBVC5BlFToUQswx1o6SG0Vxjm222CUYl3ngMy+WWW67YxIaW/tSe8lwOcRdC1F++++67EB29+eabQ70j6f3xfgG77LJL6ODNY6Aoae2m+B588MGhaR1HySy//PK1+NeEEGLmUORUCDFL0MiIJkY77bRTiJQSCSEiQoQUjzy4oUlqHjdS9UDHKgtRP6GmnCNJSOmlFGDRRRe1hx9+uLhfTJ48OXy9++6725QpU0KmBaiutPbo0KFDOAu0V69ewUkghBB5QOJUCDFLfP7558HAbNq0aWhSQn3YJZdcYrfccktI8SU64kKUyCrpvnSEJKoqQ1OI+idKwZuddevWzR5//PFwfAl7Bh29gRIAz8r47LPPwn4hap9lllkmHBnD++PvlRBCpESHgwkhZgpPr+NsPFLBaGSCMCUqSkMTDnMnkgp04Z133nmLB7fjnafGVAhRfyAa6qKTDrB0351//vnD3kAUlXRemh5Re+6160OHDrXLL7/ctthii8Sjr3+oW7oQIg+o5lQIMcv07NnTnnvuudDQBEPS4RgIxOuee+4Zjoy54IIL7Msvv7QXXnghGD46x1SIus++++4bGh6RNgpHH320XX/99SHbYp555glHmFCn/sUXXwTHFZ27eS7HmHDEyddff61zj4UQop4icSqEmCVeeeWVcJD7yiuvbCNHjgyNjvysUqBWDHFKdISoCY1NqCnTOaZC1H1ojMbZmQhNOu6OHz/eNttssxARRXTiwBo1apQ98cQTtuqqq4bMiptuuinsE4jXG264QfuFEELUYyROhRDTpSZRybmDl156qV1xxRW2xx572AknnFB87L///a9NmDAhNNlQxFSI+ufAGjRoUOgGy3ma7CGHHXZYeOzTTz8NKbz33HOPjR49OghUT//10gFlWAghRP1FNadCiJkSpiNGjAiNSoiEkM5Lt03+5XFSezEqqTWFxRdfvNrviA92F0LUTf7888+QjktWxSmnnGInn3xy6O595JFHhscRn4sttpidffbZYb9Yb7317MEHH7Tu3buHx/0cU+0XQghRf1HkVAgxQ4h0DB8+3FZYYYXQxIR0Pc4qpL6UM/LoxnvllVeGM0/PPfdczagQwp5//vkgUqk558ZRUx4dxdFFxgWC9tFHH9VsCSGECOgoGSHEdOG4hxtvvDHUl1IvhlBFoDp03+zbt6/tuOOO9sknn+gsUyHqGfERJGeddZYdc8wxxWNjiJ527drV1l9//VCP6tFRMi+oL/WzToUQQgiQOBVCTBeamGy55Za24oorhugpX1922WUhakrTk0mTJtmCCy4YunPefvvtReNTCFG/Uv+pNR0zZoydeeaZdv7554f7SPE9/fTTw3FTG2ywQXjczzvGsaXzNYUQQsRInAohpgvHPZC6S4SDLrxERvbZZ5/w2LXXXmtHHXVUSM1r27ZtUZi68SmEqNu4MKWudPfddw9ZFcsss4wdeuihIaUXaHrE1506dbLOnTuHDr2lfocQQgihLiVCiMAPP/wQziDMQoR04MCBtummm4ZzSznDEH755Rd7/PHHQ/Oj2LiUMBWifkHnXbIpaG7Uo0cP++abb0LKLo4r9oNjjz02CFQ6+CJQiZgKIYQQpZC7UghhTz31lG277bb25JNPFmfDU3M5DobaMdLySOP97rvvQnOTHXbYIURVzzvvPKXyClFPIHuCTIps6v+SSy4ZhCm0a9cuOLEQo3TwvvDCC8P9PXv2DA3T6OhLtoUQQgiRReJUCGHzzTdfEKMc8fDMM8+EGSHigQHZqlUrO+6442yNNdYIzZEWWWQR22+//ey3334LIpVjH3ieIqZC1G1wTH3//fc299xzV7uf5kY0O6Lm1Jlrrrlsk002sUaNGtnBBx8c9hYgy4K9BoEqhBBCZNFRMkKIwIcffmgHHnhgMBwRo0Q5gBoyDMwpU6bY5MmTwxEyNEWad955g6H5xx9/6FxCIeo42Vpy6s3XXXfdkNb/+eefW//+/YOTi3NNOXIKPvroo1CjTg0qTZLuuuuuYnRVCCGEKIUip0KIQIcOHeyiiy4KBijNS55++ulwP8IUw3Ts2LG2/fbb22uvvVatyyaRUyFE3SYWpj///HM4Uopzjb/88suQTdGvXz/79NNPwzEydPV+7rnn7IADDrDx48eHCCr7BNFVIYQQYnoociqEmGEElRozhCmG6LvvvhsEqxCifkDjM+rScUaR3s+ZpURLN954Y2vWrFk4/3iBBRawO++80+64446QXdG+fftQEkCZAI4sjpRBuG633XapX44QQogcI3EqhKhRoBItobHJxRdfHJofvf7660GYKpVXiPrBkCFDgqgkVfeDDz4ITisyLLbaaquwJ3B2afPmzUPHXgQqfPLJJyFSSkSVPYRUX0Tr6NGjbeGFF079koQQQuQYpfUKIaab4kt9qYSpEPVTmA4YMCAcE8M5x9dff71NmDDBRo4cGaKoCE3uJ83X9wmgcy9Nkuj+vccee4Sfu+222yRMhRBCzBCJUyFEjQJ18ODBtv/++9sbb7yhiKkQ9YgnnnjC9tprr3AcDMdMwTrrrBOipDQ64pzjSZMmBcH52GOPhcZplADQ0TfeQ2iYRGpv165dE74aIYQQlYLSeoUQM4VSeYWoX6n9dOBt06ZNqD1fZZVVrHfv3jZq1CjbcMMNQ6Oj1q1bW/fu3YviFWfWFVdcEY6J8e6+RFipORVCCCFmBolTIYQQQtRYe/7/2ruz2JreLo7jq2qqqnmIseaQNohZLxpRUxBjhOhFDRekhlSEGCo1Jfw13BBVF8oFQpCK0BpKYiy9caEhhCYIoUXbUFTZ/6z15pzXOR3eo4Pdl+8n2Rf7nH32Xufc8OuznucJDg6WoqIiKSkpkbS0NOnfv7+NhuocVN0qprCwUGJjY21uutJ9j9nHFABQHYRTAABQaUCNj4+XnJwcOXjwoK3a/TMNrffv37dVfAmkAICaIpwCAIBK6f6kujCStufqyr0aRCtq9WfEFABQU4RTAAAQUIuvSkxMtMWPAACobaxSAAAAAtpeSlt3ExISbAVvAABqG+EUAAAEFFCTk5MlOjpaIiMj+cUAALWOtl4AAPDL2CYGAFDbCKcAAAAAANfR1gsAAAAAcB3hFAAAAADgOsIpAAAAAMB1hFMAAAAAgOsIpwAAAAAA1xFOAQAAAACuI5wCAFBPbN68WYKCgrxH+/btZezYsXLjxo06fW5CQoL06NHDe3748GF7fkFBQcD3SE9Pl/3799dpXRWpTq0VWbBggURGRtboHrVdEwD8bQinAADUIyEhIXLnzh07UlJS5N27dxITEyMPHjz4bTVMmTLFnt+qVStXwykA4O/S0O0CAADAfzVo0EBGjRrlPR8xYoSNHh44cED27dtX7qdyHEdKS0ulSZMmtfYz6oitHgAA/E6MnAIAUI91797dgmJeXp5P++mFCxdk0KBBFkrPnTtn7+lop7YBh4aGSsuWLWX+/Pny9u1bn/u9evVKpk2bJs2aNZMuXbrIrl27AmpL/fr1qyQmJkqvXr3smV27drVaPDUdOXJEcnNzvS3Jnvdqs67q2r17twwfPtye3aFDB5k6dao8fvy4wmszMjLs923atKkMHTpUsrOzK/x9Bg4caNdorRs3bpTv37/XWr0A8Ldi5BQAgHqsuLjYWns7d+7sE+RWrlxpYVHDqx4aAMeMGSOTJ0+WEydOyKdPn+z96dOn23seev7y5UtrGda23Z07d8qLFy+kYcOq/0swe/ZsuXr1qmzYsMFGdvPz8+XMmTP23qZNm+z80aNHcvToUXvNM/Ja13UFQu+7fPlyCQ8Pt99TR6GjoqIsoLZp08Z73evXryU+Pt7m/rZu3dpqmDhxojx58sRCrdqzZ4+sXbtWVq1aZaH34cOH3nCq1wMAasABAAD1QlJSkhMaGup8+/bNjry8PGfWrFmO/nOdmZlp18TFxdl5dna2z2ejo6OdqKgo58ePH97XcnNznaCgIOf8+fN2npGRYZ/NysryXlNYWOiEhYU54eHh3tfS0tLsuvz8fDu/dOmSnR87dqzS2rWuiIiIcq/XZl0V8a/1fykrK3NKSkqc5s2bO6mpqT71V1bDunXr7Ly4uNg+t379ep97pqSkOCEhIU5BQUG1agIA/AdtvQAA1CM6stioUSM7evbsKdeuXbO5pjqC59G2bVsZOXKk97ykpERu3bolc+bMsRG8srIyO/r16yfdunWTnJwcu+7u3bvW2qotth56Pm7cuCprysrKsnbbefPm/dJ3qeu6AqWtuePHj7ffTUdi9bt8/PixXGtvZTVofer27dv2Of0+nu+ih17z+fPn37poFQD8iWjrBQCgnq3We/36dZu32a5dOwtxukjSzzp27Ohz/uHDBwt/2mqqhz9tj/W0rVa00JH//fxpW3GnTp2spl9R13UF4vnz5zJhwgQZNmyYpKamWnt048aNbUXiL1+++FxbWQ3auqs8c3CHDBlS4bM83wcAUD2EUwAA6hENohqkquIfEnWOpr6m80FnzJhR7noNuUoDps4N9ffmzZsqn6cjjhogdWXgXwmodV1XIDIzM220U+fHerbG0dHO9+/fl7u2shq0PuWZn6r30j8a+NORbgBA9RFOAQD4P6er4I4ePdpG+LZv317pdbotTVFRkS1s5Glf1fMrV674LAzkT9tW//nnHzl58qTMnTu3wmt0NNJ/JLKu6wqEtttqQNY2aQ/9HhpQ/VVWw7Jly+xcv4u2BOsCSzNnzqxRXQCA8ginAAD8AZKTky1UaXjUuaG62qyGqMuXL8vChQttxdxJkyZZS2psbKyFTR1J3LFjh7Ro0aLKe2s41dV2Fy1aJE+fPrX5rjryeOrUKVuBVw0YMEAOHTokx48fl759+9qoqO7PWpd1/Uy30wkLC/N5TbeE8QRNfdaSJUtsuxtdZdczivozDcKLFy+WLVu2eFcM1tHihIQEe19f27p1q63Wq99Baw8ODpZnz57J2bNn5fTp0xZeAQDVQzgFAOAPoFuj3Lx5U5KSkiyIlZaW2l6kMTEx0qdPH7tGRxA1RC1dutSCmgbFFStWWOtqenp6lffX4KWhTedt6lYrOhdT53J6aKi7d++e3U/nqMbFxdl+oHVdl4cGZ3/btm2zbWu0Dq1Z9zcdPHiwhWpd1Miftu9qOF6zZo2F8IiICLl48aLP3NfVq1fb3qa6pczevXttRLZ37952bx09BgBUX5Au2VuDzwMAAAAAUGNsJQMAAAAAcB3hFAAAAADgOsIpAAAAAMB1hFMAAAAAgOsIpwAAAAAA1xFOAQAAAACuI5wCAAAAAFxHOAUAAAAAuI5wCgAAAABwHeEUAAAAAOA6wikAAAAAwHWEUwAAAACAuO1fWXFd5eUfSpUAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "class_labels = sorted(np.unique(y_test))\n", - "cm = confusion_matrix(y_test, y_pred_best, labels=class_labels)\n", - "\n", - "plt.figure(figsize=(10, 8))\n", - "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n", - " xticklabels=class_labels, yticklabels=class_labels, linewidths=0.5)\n", - "plt.title(f'Confusion Matrix – {best_name}\\n14-Class Weak Link Classification',\n", - " fontsize=13, fontweight='bold')\n", - "plt.xlabel('Predicted Label', fontsize=11)\n", - "plt.ylabel('True Label', fontsize=11)\n", - "plt.xticks(rotation=45, ha='right')\n", - "plt.yticks(rotation=0)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 24. Save Champion Ensemble" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved: models\\ensemble_classification_champion.pkl\n" - ] - } - ], - "source": [ - "artifact = {\n", - " 'model' : best_model,\n", - " 'model_name' : best_name,\n", - " 'scaler' : scaler,\n", - " 'feature_columns' : feature_columns,\n", - " 'cv_metrics': {\n", - " 'f1_mean' : float(results_df.iloc[0]['F1_mean']),\n", - " 'f1_std' : float(results_df.iloc[0]['F1_std']),\n", - " 'accuracy_mean': float(results_df.iloc[0]['Accuracy_mean']),\n", - " },\n", - " 'test_metrics': {\n", - " 'f1' : float(test_f1),\n", - " 'accuracy' : float(test_acc),\n", - " 'precision': float(test_prec),\n", - " 'recall' : float(test_rec),\n", - " },\n", - " 'a4_champion_f1' : CHAMPION_F1,\n", - " 'improvement_pct': float(improvement),\n", - "}\n", - "\n", - "out_path = OUT_DIR / 'ensemble_classification_champion.pkl'\n", - "with open(out_path, 'wb') as f:\n", - " pickle.dump(artifact, f)\n", - "\n", - "print(f'Saved: {out_path}')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.11" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}