"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Correlation heatmap\n",
+ "plt.figure(figsize=(17, 13))\n",
+ "cmap = sns.diverging_palette(220, 20, as_cmap=True)\n",
+ "sns.heatmap(df.corr(), annot=True, cmap=cmap, fmt=\".2f\", linewidths=.5,)\n",
+ "plt.title(\"Correlation Matrix\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Processing for modelling\n",
+ "Afterwards, we split the dataset between train and test and display their sizes and target distribution."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training set size: 14968 rows\n",
+ "Test set size: 6416 rows\n",
+ "\n",
+ "Training target distribution:\n",
+ "has_resolution_incident\n",
+ "False 0.98744\n",
+ "True 0.01256\n",
+ "Name: proportion, dtype: float64\n",
+ "\n",
+ "Test target distribution:\n",
+ "has_resolution_incident\n",
+ "False 0.989246\n",
+ "True 0.010754\n",
+ "Name: proportion, dtype: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Split the data\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=123)\n",
+ "\n",
+ "print(f\"Training set size: {X_train.shape[0]} rows\")\n",
+ "print(f\"Test set size: {X_test.shape[0]} rows\")\n",
+ "\n",
+ "print(\"\\nTraining target distribution:\")\n",
+ "print(y_train.value_counts(normalize=True))\n",
+ "\n",
+ "print(\"\\nTest target distribution:\")\n",
+ "print(y_test.value_counts(normalize=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d36c9276",
+ "metadata": {},
+ "source": [
+ "## Classification Model with Random Forest\n",
+ "\n",
+ "We define a machine learning pipeline that includes:\n",
+ "- **Scaling numeric features** with `StandardScaler`\n",
+ "- **Training a Random Forest classifier** with balanced class weights to handle the imbalanced dataset\n",
+ "\n",
+ "We then use `GridSearchCV` to perform a **grid search with cross-validation** over a range of key hyperparameters (e.g., number of trees, max depth, etc.). \n",
+ "The model is evaluated using **Average Precision**, which is better suited for imbalanced classification tasks.\n",
+ "\n",
+ "The best combination of parameters is selected, and the resulting model is used to make predictions on the test set.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "943ef7d6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fitting 4 folds for each of 72 candidates, totalling 288 fits\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
+ "\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.3s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.4s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.1s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.3s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.7s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.4s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.5s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.2s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.5s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n",
+ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.6s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n",
+ "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.0s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.8s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.9s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
+ "\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n",
+ "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.4s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.8s[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.3s\n",
+ "\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.4s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.4s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.0s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n",
+ "\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.3s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.3s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.3s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.5s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.5s\n",
+ "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.1s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.3s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.4s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.5s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n",
+ "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.8s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.0s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.9s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.2s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.4s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.4s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.7s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.4s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.2s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.3s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.2s\n",
+ "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.2s\n",
+ "Best hyperparameters: {'model__max_depth': 10, 'model__max_features': 'sqrt', 'model__min_samples_leaf': 2, 'model__min_samples_split': 5, 'model__n_estimators': 300}\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "# Define pipeline (scaling numeric features only)\n",
+ "pipeline = Pipeline([\n",
+ " ('scaler', StandardScaler()),\n",
+ " ('model', RandomForestClassifier(class_weight='balanced', # We have an imbalanced dataset\n",
+ " random_state=123))\n",
+ "])\n",
+ "\n",
+ "# Define parameter grid\n",
+ "param_grid = {\n",
+ " 'model__n_estimators': [100, 200, 300],\n",
+ " 'model__max_depth': [None, 10, 20],\n",
+ " 'model__min_samples_split': [2, 5],\n",
+ " 'model__min_samples_leaf': [1, 2],\n",
+ " 'model__max_features': ['sqrt', 'log2']\n",
+ "}\n",
+ "\n",
+ "# GridSearchCV\n",
+ "grid_search = GridSearchCV(\n",
+ " estimator=pipeline,\n",
+ " param_grid=param_grid,\n",
+ " scoring='average_precision', # For imbalanced classification\n",
+ " cv=4, # 4-fold cross-validation\n",
+ " n_jobs=-1, # Use all available cores\n",
+ " verbose=2, # Verbose output for progress tracking,\n",
+ " refit=True # Refit the best model on the entire training set - it's already true by default\n",
+ ")\n",
+ "\n",
+ "# Fit the grid search on training data\n",
+ "grid_search.fit(X_train, y_train)\n",
+ "\n",
+ "# Best model\n",
+ "best_pipeline = grid_search.best_estimator_\n",
+ "print(\"Best hyperparameters:\", grid_search.best_params_)\n",
+ "\n",
+ "# Predict on test set\n",
+ "y_pred_proba = best_pipeline.predict_proba(X_test)[:, 1]\n",
+ "y_pred = best_pipeline.predict(X_test)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
mean_fit_time
\n",
+ "
std_fit_time
\n",
+ "
mean_score_time
\n",
+ "
std_score_time
\n",
+ "
param_model__max_depth
\n",
+ "
param_model__max_features
\n",
+ "
param_model__min_samples_leaf
\n",
+ "
param_model__min_samples_split
\n",
+ "
param_model__n_estimators
\n",
+ "
params
\n",
+ "
split0_test_score
\n",
+ "
split1_test_score
\n",
+ "
split2_test_score
\n",
+ "
split3_test_score
\n",
+ "
mean_test_score
\n",
+ "
std_test_score
\n",
+ "
rank_test_score
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
35
\n",
+ "
5.492363
\n",
+ "
0.074103
\n",
+ "
0.193978
\n",
+ "
0.016560
\n",
+ "
10
\n",
+ "
sqrt
\n",
+ "
2
\n",
+ "
5
\n",
+ "
300
\n",
+ "
{'model__max_depth': 10, 'model__max_features'...
\n",
+ "
0.041262
\n",
+ "
0.021222
\n",
+ "
0.028958
\n",
+ "
0.058779
\n",
+ "
0.037555
\n",
+ "
0.014185
\n",
+ "
1
\n",
+ "
\n",
+ "
\n",
+ "
22
\n",
+ "
3.078427
\n",
+ "
0.037090
\n",
+ "
0.129033
\n",
+ "
0.003915
\n",
+ "
None
\n",
+ "
log2
\n",
+ "
2
\n",
+ "
5
\n",
+ "
200
\n",
+ "
{'model__max_depth': None, 'model__max_feature...
\n",
+ "
0.046899
\n",
+ "
0.023721
\n",
+ "
0.029079
\n",
+ "
0.049230
\n",
+ "
0.037232
\n",
+ "
0.011028
\n",
+ "
2
\n",
+ "
\n",
+ "
\n",
+ "
54
\n",
+ "
1.725934
\n",
+ "
0.030368
\n",
+ "
0.065814
\n",
+ "
0.002268
\n",
+ "
20
\n",
+ "
sqrt
\n",
+ "
2
\n",
+ "
2
\n",
+ "
100
\n",
+ "
{'model__max_depth': 20, 'model__max_features'...
\n",
+ "
0.046455
\n",
+ "
0.021084
\n",
+ "
0.030397
\n",
+ "
0.050986
\n",
+ "
0.037230
\n",
+ "
0.012059
\n",
+ "
3
\n",
+ "
\n",
+ "
\n",
+ "
23
\n",
+ "
4.754896
\n",
+ "
0.284760
\n",
+ "
0.197159
\n",
+ "
0.010598
\n",
+ "
None
\n",
+ "
log2
\n",
+ "
2
\n",
+ "
5
\n",
+ "
300
\n",
+ "
{'model__max_depth': None, 'model__max_feature...
\n",
+ "
0.045281
\n",
+ "
0.024624
\n",
+ "
0.028884
\n",
+ "
0.049424
\n",
+ "
0.037053
\n",
+ "
0.010511
\n",
+ "
4
\n",
+ "
\n",
+ "
\n",
+ "
64
\n",
+ "
3.150147
\n",
+ "
0.123393
\n",
+ "
0.133204
\n",
+ "
0.010875
\n",
+ "
20
\n",
+ "
log2
\n",
+ "
1
\n",
+ "
5
\n",
+ "
200
\n",
+ "
{'model__max_depth': 20, 'model__max_features'...
\n",
+ "
0.048786
\n",
+ "
0.021536
\n",
+ "
0.031982
\n",
+ "
0.045861
\n",
+ "
0.037041
\n",
+ "
0.010974
\n",
+ "
5
\n",
+ "
\n",
+ "
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
\n",
+ "
\n",
+ "
1
\n",
+ "
3.655133
\n",
+ "
0.052994
\n",
+ "
0.141072
\n",
+ "
0.002776
\n",
+ "
None
\n",
+ "
sqrt
\n",
+ "
1
\n",
+ "
2
\n",
+ "
200
\n",
+ "
{'model__max_depth': None, 'model__max_feature...
\n",
+ "
0.044698
\n",
+ "
0.019424
\n",
+ "
0.026336
\n",
+ "
0.041751
\n",
+ "
0.033052
\n",
+ "
0.010513
\n",
+ "
68
\n",
+ "
\n",
+ "
\n",
+ "
49
\n",
+ "
3.499403
\n",
+ "
0.044126
\n",
+ "
0.146713
\n",
+ "
0.003312
\n",
+ "
20
\n",
+ "
sqrt
\n",
+ "
1
\n",
+ "
2
\n",
+ "
200
\n",
+ "
{'model__max_depth': 20, 'model__max_features'...
\n",
+ "
0.043488
\n",
+ "
0.019535
\n",
+ "
0.026128
\n",
+ "
0.041667
\n",
+ "
0.032705
\n",
+ "
0.010165
\n",
+ "
69
\n",
+ "
\n",
+ "
\n",
+ "
48
\n",
+ "
2.029998
\n",
+ "
0.085049
\n",
+ "
0.118226
\n",
+ "
0.019632
\n",
+ "
20
\n",
+ "
sqrt
\n",
+ "
1
\n",
+ "
2
\n",
+ "
100
\n",
+ "
{'model__max_depth': 20, 'model__max_features'...
\n",
+ "
0.040683
\n",
+ "
0.018370
\n",
+ "
0.026502
\n",
+ "
0.038585
\n",
+ "
0.031035
\n",
+ "
0.009097
\n",
+ "
70
\n",
+ "
\n",
+ "
\n",
+ "
12
\n",
+ "
2.102099
\n",
+ "
0.029990
\n",
+ "
0.092719
\n",
+ "
0.007638
\n",
+ "
None
\n",
+ "
log2
\n",
+ "
1
\n",
+ "
2
\n",
+ "
100
\n",
+ "
{'model__max_depth': None, 'model__max_feature...
\n",
+ "
0.035229
\n",
+ "
0.020518
\n",
+ "
0.024970
\n",
+ "
0.039950
\n",
+ "
0.030167
\n",
+ "
0.007769
\n",
+ "
71
\n",
+ "
\n",
+ "
\n",
+ "
0
\n",
+ "
1.983677
\n",
+ "
0.277025
\n",
+ "
0.091703
\n",
+ "
0.020498
\n",
+ "
None
\n",
+ "
sqrt
\n",
+ "
1
\n",
+ "
2
\n",
+ "
100
\n",
+ "
{'model__max_depth': None, 'model__max_feature...
\n",
+ "
0.037104
\n",
+ "
0.016652
\n",
+ "
0.023631
\n",
+ "
0.034512
\n",
+ "
0.027975
\n",
+ "
0.008264
\n",
+ "
72
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
72 rows × 17 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " mean_fit_time std_fit_time mean_score_time std_score_time \\\n",
+ "35 5.492363 0.074103 0.193978 0.016560 \n",
+ "22 3.078427 0.037090 0.129033 0.003915 \n",
+ "54 1.725934 0.030368 0.065814 0.002268 \n",
+ "23 4.754896 0.284760 0.197159 0.010598 \n",
+ "64 3.150147 0.123393 0.133204 0.010875 \n",
+ ".. ... ... ... ... \n",
+ "1 3.655133 0.052994 0.141072 0.002776 \n",
+ "49 3.499403 0.044126 0.146713 0.003312 \n",
+ "48 2.029998 0.085049 0.118226 0.019632 \n",
+ "12 2.102099 0.029990 0.092719 0.007638 \n",
+ "0 1.983677 0.277025 0.091703 0.020498 \n",
+ "\n",
+ " param_model__max_depth param_model__max_features \\\n",
+ "35 10 sqrt \n",
+ "22 None log2 \n",
+ "54 20 sqrt \n",
+ "23 None log2 \n",
+ "64 20 log2 \n",
+ ".. ... ... \n",
+ "1 None sqrt \n",
+ "49 20 sqrt \n",
+ "48 20 sqrt \n",
+ "12 None log2 \n",
+ "0 None sqrt \n",
+ "\n",
+ " param_model__min_samples_leaf param_model__min_samples_split \\\n",
+ "35 2 5 \n",
+ "22 2 5 \n",
+ "54 2 2 \n",
+ "23 2 5 \n",
+ "64 1 5 \n",
+ ".. ... ... \n",
+ "1 1 2 \n",
+ "49 1 2 \n",
+ "48 1 2 \n",
+ "12 1 2 \n",
+ "0 1 2 \n",
+ "\n",
+ " param_model__n_estimators \\\n",
+ "35 300 \n",
+ "22 200 \n",
+ "54 100 \n",
+ "23 300 \n",
+ "64 200 \n",
+ ".. ... \n",
+ "1 200 \n",
+ "49 200 \n",
+ "48 100 \n",
+ "12 100 \n",
+ "0 100 \n",
+ "\n",
+ " params split0_test_score \\\n",
+ "35 {'model__max_depth': 10, 'model__max_features'... 0.041262 \n",
+ "22 {'model__max_depth': None, 'model__max_feature... 0.046899 \n",
+ "54 {'model__max_depth': 20, 'model__max_features'... 0.046455 \n",
+ "23 {'model__max_depth': None, 'model__max_feature... 0.045281 \n",
+ "64 {'model__max_depth': 20, 'model__max_features'... 0.048786 \n",
+ ".. ... ... \n",
+ "1 {'model__max_depth': None, 'model__max_feature... 0.044698 \n",
+ "49 {'model__max_depth': 20, 'model__max_features'... 0.043488 \n",
+ "48 {'model__max_depth': 20, 'model__max_features'... 0.040683 \n",
+ "12 {'model__max_depth': None, 'model__max_feature... 0.035229 \n",
+ "0 {'model__max_depth': None, 'model__max_feature... 0.037104 \n",
+ "\n",
+ " split1_test_score split2_test_score split3_test_score mean_test_score \\\n",
+ "35 0.021222 0.028958 0.058779 0.037555 \n",
+ "22 0.023721 0.029079 0.049230 0.037232 \n",
+ "54 0.021084 0.030397 0.050986 0.037230 \n",
+ "23 0.024624 0.028884 0.049424 0.037053 \n",
+ "64 0.021536 0.031982 0.045861 0.037041 \n",
+ ".. ... ... ... ... \n",
+ "1 0.019424 0.026336 0.041751 0.033052 \n",
+ "49 0.019535 0.026128 0.041667 0.032705 \n",
+ "48 0.018370 0.026502 0.038585 0.031035 \n",
+ "12 0.020518 0.024970 0.039950 0.030167 \n",
+ "0 0.016652 0.023631 0.034512 0.027975 \n",
+ "\n",
+ " std_test_score rank_test_score \n",
+ "35 0.014185 1 \n",
+ "22 0.011028 2 \n",
+ "54 0.012059 3 \n",
+ "23 0.010511 4 \n",
+ "64 0.010974 5 \n",
+ ".. ... ... \n",
+ "1 0.010513 68 \n",
+ "49 0.010165 69 \n",
+ "48 0.009097 70 \n",
+ "12 0.007769 71 \n",
+ "0 0.008264 72 \n",
+ "\n",
+ "[72 rows x 17 columns]"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Retrieve cv results\n",
+ "pd.DataFrame(grid_search.cv_results_).sort_values(by='mean_test_score', ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We apply a threshold selector to find a proper value for F2 optimisation, rather than defaulting to 0.5."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Find the best threshold for F2 score\n",
+ "\n",
+ "def find_best_threshold(y_true, y_proba, beta=2.0):\n",
+ " thresholds = np.linspace(0, 1, 200)\n",
+ " f2_scores = []\n",
+ "\n",
+ " for t in thresholds:\n",
+ " preds = (y_proba >= t).astype(int)\n",
+ " score = fbeta_score(y_true, preds, beta=beta)\n",
+ " f2_scores.append(score)\n",
+ "\n",
+ " best_index = np.argmax(f2_scores)\n",
+ " return thresholds[best_index], f2_scores[best_index]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best threshold: 38.2% — F2 score: 15.31%\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Predict probabilities\n",
+ "y_pred_proba = best_pipeline.predict_proba(X_test)[:, 1]\n",
+ "\n",
+ "# Find best threshold for F2\n",
+ "best_thresh, best_f2 = find_best_threshold(y_test, y_pred_proba, beta=2.0)\n",
+ "print(f\"Best threshold: {100*best_thresh:.1f}% — F2 score: {100*best_f2:.2f}%\")\n",
+ "\n",
+ "# Use that threshold for final classification\n",
+ "y_pred_opt = (y_pred_proba >= best_thresh).astype(int)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fc2fcc89",
+ "metadata": {},
+ "source": [
+ "## Evaluation\n",
+ "This section aims to evaluate how good the new model is vs. the actual Resolution Incidents.\n",
+ "\n",
+ "We start by computing and displaying the classification report, ROC Curve, PR Curve and the respective Area Under the Curve (AUC)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "30786f7c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " No Incident 0.99 0.89 0.94 6347\n",
+ " Incident 0.04 0.43 0.08 69\n",
+ "\n",
+ " accuracy 0.89 6416\n",
+ " macro avg 0.52 0.66 0.51 6416\n",
+ "weighted avg 0.98 0.89 0.93 6416\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Print classification report\n",
+ "print(classification_report(y_test, y_pred_opt, target_names=['No Incident', 'Incident']))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Interpreting the Classification Report\n",
+ "\n",
+ "The **Classification Report** provides key metrics to evaluate how well the model performed on each class.\n",
+ "\n",
+ "It includes the following metrics for each class (0 and 1):\n",
+ "* Precision: Out of all predicted positives, how many were actually positive?\n",
+ "* Recall: Out of all actual positives, how many did we correctly identify?\n",
+ "* F1-score: Harmonic mean of precision and recall (balances both)\n",
+ "* Support: Number of true samples of that class in the test data\n",
+ "\n",
+ "Interpretation:\n",
+ "* Class 0 = No incident\n",
+ "* Class 1 = Has resolution incident (rare, but important!)\n",
+ "\n",
+ "A few explanatory cases:\n",
+ "* A high recall for class 1 means we're catching most incidents.\n",
+ "* A high precision for class 1 means when we predict an incident, we're often correct.\n",
+ "* The F1-score gives a single balanced measure (good for imbalanced data).\n",
+ "\n",
+ "Special note for imbalanced data:\n",
+ "Since class 1 (or just True) is rare (1% in our case), metrics for that class are more critical.\n",
+ "We want to maximize recall to catch as many real incidents as possible — without letting precision drop too low (to avoid too many false alarms)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "4b4da914",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# ROC Curve\n",
+ "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
+ "roc_auc = auc(fpr, tpr)\n",
+ "\n",
+ "plt.figure(figsize=(6, 5))\n",
+ "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (AUC = {roc_auc:.4f})')\n",
+ "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
+ "plt.xlabel('False Positive Rate')\n",
+ "plt.ylabel('True Positive Rate')\n",
+ "plt.title('ROC Curve')\n",
+ "plt.legend(loc='lower right')\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Interpreting the ROC Curve\n",
+ "\n",
+ "The **Receiver Operating Characteristic (ROC) curve** shows how well the model distinguishes between the positive and negative classes across all decision thresholds.\n",
+ "\n",
+ "A quick reminder of the definitions:\n",
+ "* True Positive Rate (TPR) = Recall\n",
+ "* False Positive Rate (FPR) = Proportion of negatives wrongly classified as positives\n",
+ "\n",
+ "What we display in this plot is:\n",
+ "* The x-axis is False Positive Rate\n",
+ "* The y-axis is True Positive Rate\n",
+ "\n",
+ "The curve shows how TPR and FPR change as the threshold varies\n",
+ "\n",
+ "It's important to note that:\n",
+ "* A model with no skill will produce a diagonal line (AUC = 0.5)\n",
+ "* A model with perfect discrimination will hug the top-left corner (AUC = 1.0)\n",
+ "\n",
+ "The Area Under the Curve (ROC AUC) gives a single performance score:\n",
+ "* Closer to 1 means better at ranking positive cases higher than negative ones\n",
+ "\n",
+ "**Important!**\n",
+ "\n",
+ "While useful, the ROC curve can sometimes overestimate performance when the dataset is imbalanced, because it includes negatives (which dominate in our case, around 99%!). That’s why we also MUST check the Precision-Recall curve."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "6790d41d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# PR Curve\n",
+ "precision, recall, _ = precision_recall_curve(y_test, y_pred_proba)\n",
+ "pr_auc = average_precision_score(y_test, y_pred_proba)\n",
+ "\n",
+ "plt.figure(figsize=(6, 5))\n",
+ "plt.plot(recall, precision, color='green', lw=2, label=f'PR curve (AUC = {pr_auc:.4f})')\n",
+ "plt.xlabel('Recall')\n",
+ "plt.ylabel('Precision')\n",
+ "plt.title('Precision-Recall Curve')\n",
+ "plt.legend(loc='lower left')\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Interpreting the Precision-Recall (PR) Curve\n",
+ "\n",
+ "The **Precision-Recall (PR) curve** helps evaluate model performance, especially on imbalanced datasets like ours (where positive cases are rare).\n",
+ "\n",
+ "A quick reminder of the definitions:\n",
+ "* Precision = How many of the predicted positives are actually positive\n",
+ "* Recall = How many of the actual positives the model correctly identifies\n",
+ "\n",
+ "What we display in this plot is:\n",
+ "* The x-axis is Recall \n",
+ "* The y-axis is Precision \n",
+ "\n",
+ "The curve shows the trade-off between them at different model thresholds\n",
+ "\n",
+ "In imbalanced datasets, accuracy can be misleading — the PR curve focuses only on the positive class, making it much more meaningful:\n",
+ "* A higher curve means better performance\n",
+ "* The area under the curve (PR AUC) summarizes this: closer to 1 is better"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "# Compute confusion matrix: [ [TN, FP], [FN, TP] ]\n",
+ "tn, fp, fn, tp = confusion_matrix(y_test, y_pred_opt).ravel()\n",
+ "\n",
+ "# Total predictions\n",
+ "total = tp + tn + fp + fn\n",
+ "\n",
+ "# Compute all requested metrics\n",
+ "recall = recall_score(y_test, y_pred_opt)\n",
+ "precision = precision_score(y_test, y_pred_opt)\n",
+ "f1 = fbeta_score(y_test, y_pred_opt, beta=1)\n",
+ "f2 = fbeta_score(y_test, y_pred_opt, beta=2)\n",
+ "f3 = fbeta_score(y_test, y_pred_opt, beta=3)\n",
+ "fpr = fp / (fp + tn) if (fp + tn) != 0 else 0\n",
+ "\n",
+ "# Scores relative to total\n",
+ "tp_score = tp / total\n",
+ "tn_score = tn / total\n",
+ "fp_score = fp / total\n",
+ "fn_score = fn / total\n",
+ "\n",
+ "# Create DataFrame\n",
+ "summary_df = pd.DataFrame([{\n",
+ " \"flagging_analysis_type\": \"RISK_VS_CLAIM\",\n",
+ " \"count_total\": total,\n",
+ " \"count_true_positive\": tp,\n",
+ " \"count_true_negative\": tn,\n",
+ " \"count_false_positive\": fp,\n",
+ " \"count_false_negative\": fn,\n",
+ " \"true_positive_score\": tp_score,\n",
+ " \"true_negative_score\": tn_score,\n",
+ " \"false_positive_score\": fp_score,\n",
+ " \"false_negative_score\": fn_score,\n",
+ " \"recall_score\": recall,\n",
+ " \"precision_score\": precision,\n",
+ " \"false_positive_rate_score\": fpr,\n",
+ " \"f1_score\": f1,\n",
+ " \"f2_score\": f2,\n",
+ " \"f3_score\": f3,\n",
+ " \"roc_auc_score\": roc_auc,\n",
+ " \"pr_auc_score\": pr_auc\n",
+ "}])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_confusion_matrix_from_df(df, flagging_analysis_type, name_of_the_experiment=\"\"):\n",
+ "\n",
+ " # Subset - just retrieve one row depending on the flagging_analysis_type\n",
+ " row = df[df['flagging_analysis_type'] == flagging_analysis_type].iloc[0]\n",
+ "\n",
+ " # Define custom x-axis labels and wording\n",
+ " if flagging_analysis_type == 'RISK_VS_CLAIM':\n",
+ " x_labels = ['With Submitted Claim', 'Without Submitted Claim']\n",
+ " outcome_label = \"submitted claim\"\n",
+ " elif flagging_analysis_type == 'RISK_VS_SUBMITTED_PAYOUT':\n",
+ " x_labels = ['With Submitted Payout', 'Without Submitted Payout']\n",
+ " outcome_label = \"submitted payout\"\n",
+ " else:\n",
+ " x_labels = ['Actual Positive', 'Actual Negative'] \n",
+ " outcome_label = \"outcome\"\n",
+ "\n",
+ " # Confusion matrix structure\n",
+ " cm = np.array([\n",
+ " [row['count_true_positive'], row['count_false_positive']],\n",
+ " [row['count_false_negative'], row['count_true_negative']]\n",
+ " ])\n",
+ "\n",
+ " # Create annotations for the confusion matrix\n",
+ " labels = [['True Positives', 'False Positives'], ['False Negatives', 'True Negatives']]\n",
+ " counts = [[f\"{v:,}\" for v in [row['count_true_positive'], row['count_false_positive']]],\n",
+ " [f\"{v:,}\" for v in [row['count_false_negative'], row['count_true_negative']]]]\n",
+ " percentages = [[f\"{round(100*v,2):,}\" for v in [row['true_positive_score'], row['false_positive_score']]],\n",
+ " [f\"{round(100*v,2):,}\" for v in [row['false_negative_score'], row['true_negative_score']]]]\n",
+ " annot = [[f\"{labels[i][j]}\\n{counts[i][j]} ({percentages[i][j]}%)\" for j in range(2)] for i in range(2)]\n",
+ "\n",
+ " # Scores formatted as percentages\n",
+ " recall = row['recall_score'] * 100\n",
+ " precision = row['precision_score'] * 100\n",
+ " f1 = row['f1_score'] * 100\n",
+ " f2 = row['f2_score'] * 100\n",
+ " f3 = row['f3_score'] * 100\n",
+ " roc_auc = row['roc_auc_score'] * 100\n",
+ " pr_auc = row['pr_auc_score'] * 100\n",
+ "\n",
+ " # Set up figure and axes manually for precise control\n",
+ " fig = plt.figure(figsize=(9, 8))\n",
+ " grid = fig.add_gridspec(nrows=3, height_ratios=[1, 15, 2])\n",
+ "\n",
+ " \n",
+ " ax_main_title = fig.add_subplot(grid[0])\n",
+ " ax_main_title.axis('off')\n",
+ " ax_main_title.set_title(f\"{name_of_the_experiment} - Flagged as Risk vs. {outcome_label.title()}\", fontsize=14, weight='bold')\n",
+ "\n",
+ " # Heatmap\n",
+ " ax_heatmap = fig.add_subplot(grid[1])\n",
+ " ax_heatmap.set_title(f\"Confusion Matrix – Risk vs. {outcome_label.title()}\", fontsize=12, weight='bold', ha='center', va='center', wrap=False)\n",
+ "\n",
+ " cmap = sns.light_palette(\"#A73A52\", as_cmap=True)\n",
+ "\n",
+ " sns.heatmap(cm, annot=annot, fmt='', cmap=cmap, cbar=False,\n",
+ " xticklabels=x_labels,\n",
+ " yticklabels=['Flagged as Risk', 'Flagged as No Risk'],\n",
+ " ax=ax_heatmap,\n",
+ " linewidths=1.0,\n",
+ " annot_kws={'fontsize': 10, 'linespacing': 1.2})\n",
+ " ax_heatmap.set_xlabel(\"Resolution Outcome (Actual)\", fontsize=11, labelpad=10)\n",
+ " ax_heatmap.set_ylabel(\"Flagging (Prediction)\", fontsize=11, labelpad=10)\n",
+ " \n",
+ " # Make borders visible\n",
+ " for _, spine in ax_heatmap.spines.items():\n",
+ " spine.set_visible(True)\n",
+ "\n",
+ " # Footer with metrics and date\n",
+ " ax_footer = fig.add_subplot(grid[2])\n",
+ " ax_footer.axis('off')\n",
+ " metrics_text = f\"Total Booking Count: {row['count_total']} | Recall: {recall:.2f}% | Precision: {precision:.2f}% | F1 Score: {f1:.2f}% | F2 Score: {f2:.2f}% | ROC AUC: {roc_auc:.2f}% | PR AUC: {pr_auc:.2f}%\"\n",
+ " date_text = f\"Generated on {date.today().strftime('%B %d, %Y')}\"\n",
+ " ax_footer.text(0.5, 0.7, metrics_text, ha='center', fontsize=9)\n",
+ " ax_footer.text(0.5, 0.1, date_text, ha='center', fontsize=8, color='gray')\n",
+ "\n",
+ " plt.tight_layout()\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot confusion matrix for claim scenario\n",
+ "plot_confusion_matrix_from_df(summary_df, 'RISK_VS_CLAIM', 'Contactless')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Feature Importance\n",
+ "Understanding what drives the prediction is useful for future experiments and business knowledge. Here we track both the native feature importances of the trees, as well as a more heavy SHAP values analysis.\n",
+ "\n",
+ "Important! Be aware that SHAP analysis might take quite a bit of time."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "d66ffe2c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "## BUILT-IN\n",
+ "\n",
+ "# Get feature importances from the model\n",
+ "importances = best_pipeline.named_steps['model'].feature_importances_\n",
+ "features = X.columns\n",
+ "\n",
+ "# Create a Series and sort\n",
+ "feat_series = pd.Series(importances, index=features).sort_values(ascending=True) # ascending=True for horizontal plot\n",
+ "\n",
+ "# Plot Feature Importances\n",
+ "plt.figure(figsize=(8, 8))\n",
+ "feat_series.plot(kind='barh', color='skyblue')\n",
+ "plt.title('Feature Importances')\n",
+ "plt.xlabel('Importance')\n",
+ "plt.grid(axis='x')\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Interpreting the Feature Importance Plot\n",
+ "The **feature importance plot** shows how much each feature contributes to the model’s overall decision-making.\n",
+ "\n",
+ "For tree-based models like Random Forest, importance is based on how often and how effectively a feature is used to split the data across all trees.\n",
+ "A higher score means the feature plays a bigger role in improving prediction accuracy.\n",
+ "\n",
+ "In the graph you will see that:\n",
+ "* Features are ranked from most to least important.\n",
+ "* The values are relative and model-specific — not directly interpretable as weights or probabilities.\n",
+ "\n",
+ "This helps us identify which features the model relies on most when making predictions.\n",
+ "\n",
+ "**Important!**\n",
+ "Unlike SHAP values, native importance doesn't show how a feature affects predictions — only how useful it is to the model overall. For deeper interpretability (e.g., direction and context), SHAP is better (but it takes more time to run)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "e2197cea",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "PermutationExplainer explainer: 6417it [45:34, 2.34it/s] \n"
+ ]
+ }
+ ],
+ "source": [
+ "## SHAP VALUES\n",
+ "\n",
+ "# SHAP requires that all features passed to Explainer be numeric (floats/ints)\n",
+ "X_test_shap = X_test.copy()\n",
+ "X_test_shap = X_test_shap.astype(float)\n",
+ "\n",
+ "# Function that returns the probability of the positive class\n",
+ "def model_predict(data):\n",
+ " return best_pipeline.predict_proba(data)[:, 1]\n",
+ "\n",
+ "# Ensure input to SHAP is numeric\n",
+ "X_test_shap = X_test.astype(float)\n",
+ "\n",
+ "# Create SHAP explainer\n",
+ "explainer = shap.Explainer(model_predict, X_test_shap)\n",
+ "\n",
+ "# Compute SHAP values\n",
+ "shap_values = explainer(X_test_shap)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "9cae1a51",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_881/3711913411.py:2: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n",
+ " shap.summary_plot(shap_values.values, X_test_shap)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzoAAAOsCAYAAACCjsPqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3gU1dfA8e9sS28kEEJCr1L8AQbpRbr0LoKCoAjSbKDYQfFVFBGpIiIdCdXQFdHQlC4WpPcEAiQhvW2Z949ll2w2gSQkNM/nefLATu7M3Jmd3dwz99w7iqqqKkIIIYQQQgjxENHc6woIIYQQQgghRGGTQEcIIYQQQgjx0JFARwghhBBCCPHQkUBHCCGEEEII8dCRQEcIIYQQQgjx0JFARwghhBBCCPHQkUBHCCGEEEII8dCRQEcIIYQQQgjx0JFARwghhBBCCPHQkUBHCCGEEEKIh9z48ePx9PS87e/OnTuHoiisWrUqX9sv6HpFSXevKyCEEEIIIYS4PwQFBfH7779TpUqVe12VOyaBjhBCCCGEEAIAFxcXGjRocK+rUSgkdU0IIYQQQggB5JyClpmZyejRoylWrBi+vr4MHTqUZcuWoSgK586dc1g/PT2dkSNH4ufnR1BQEGPGjMFkMt3lo7CSQEcIIYQQQoj/CJPJ5PRjsVhuuc64ceOYM2cOb775JmFhYVgsFsaNG5dj2XfeeQeNRsOKFSsYNmwYX3zxBd9++21RHMptSeqaEEIIIYQQ/wEpKSno9focf+fh4ZHj8ri4OGbPns27777Lm2++CUC7du1o3bo1Fy9edCpfv359pk2bBkCbNm349ddfWbVqFcOGDSuko8g7CXSEEEIIIQRGo5H58+cDMGjQoFwbxOIeUHrkvay6Jtdfubm5sWPHDqfl33zzDcuWLctxnb///pv09HS6dOnisLxr165s27bNqXzbtm0dXlevXp1ffvklLzUvdBLoCCGEEEII8R+g0WgIDQ11Wr5hw4Zc17l8+TIAxYsXd1heokSJHMv7+vo6vDYYDKSnp+ezpoVDxugIIYQQQgghchQUFATAtWvXHJZfvXr1XlQnXyTQEUIIIYQQ4r6m5OOncNWsWRNXV1fCw8Mdlv/www+Fvq/CJqlrQgghhBBCiBz5+/vz0ksv8fHHH+Pq6krt2rVZuXIlJ06cAKzpcPer+7dmQgghhBBCiHvu008/5cUXX+STTz6hd+/eGI1G+/TSPj4+97h2uVNUVVXvdSWEEEIIIcS9JbOu3ceUnnkvq64uunpk8eyzz7Jr1y7Onj17V/ZXEJK6JoQQQgghxH2t8Mfe5Mf27dvZvXs3jz32GBaLhQ0bNrB06VKmTJlyT+t1OxLoCCGEEEIIIXLl6enJhg0bmDRpEmlpaZQvX54pU6bwyiuv3Ouq3ZIEOkIIIYQQQohcPfbYY/z222/3uhr5JoGOEEIIIYQQ97V7m7r2oJJZ14QQQgghhBAPHQl0hBBCCCGEEA8dCXSEEEIIIYQQDx0ZoyOEEEIIIcR9TcboFIT06AghhBBCCCEeOhLoCCGEEEIIIR46EugIIYQQQgghHjoS6AghhBBCCCEeOhLoCCGEEEIIIR46MuuaEEIIIYQQ9zWZda0gpEdHCCGEEEII8dCRQEcIIYQQQgjx0JFARwghhBBCFJ7YJEjPdFz293nY/o/zciGKkIzREUIIIYQQd+5MNPzvNUhOv3W54e1h5ot3p04PDRmjUxDSoyOEEEIIIe5MQgpUHH77IAdg1hb48Y+ir5P4z5NARwghhBDiIWCyqNRfbEI32YT3Vya+/9d893beYWL+yr/8bdHUQ4gsJHVNCCGEEOIh4DHVTKbF+v8kI/TbpFLK00zzMtqi3fGPf8Bvx/O3zvHLcC0BivsUTZ0eOpK6VhDSoyOEEEII8YDbdNJkD3Ky6hmuFu2Odx2F9h8VbN2G4wq3LkJkI4GOEEIIIcQDyKKqTD1gImC6iY7hOZeJzSiCHadnwtV4mLAcmr5T8O2cvgKvfVdo1RIiO0ldE0IIIYR4AHVcbWbLuduXm3bQxOjHCqHJZzZDmRfh0vU735bNlxsgxB9e61p423woSepaQUiPjhBCCCHEAyY5w5KnIAfg5V8LaaflhhVukGPz+kJ4dV7hb1f850mgI4QQQgjxgHhuk3VWNa/pOQzIuYVz8fkrn6PI2DvfRm6mboSd/xbd9sV/kqSuCSGEEELchz7fa+KD3yDTDOV94EwCFDRceXeHhSVd7uD+9vA5BV83r5q9C49Xgr2fFf2+xH+C9Og8JC5dukRoaChz5tyFL6K7YOXKlfTs2ZOGDRsSGhrKpUuX7nWV8uXAgQOEhoayfv16+7KifI/mzJlT6OcpNDSU8ePHF9r27rYHvf5C5Nf69esJDQ3lwIED97oqohAETjfxxk5IM4MZOHUHQQ7AxrN3WKHZP97hBvJo3yl4Ycbd2dcDRcnHj7CRHh1x3zlw4ACTJk2iefPmDBw4EJ1Oh5+f372ulngIzJkzh6pVq9KiRYt7XRVxj1y6dIn169fTokULqlateq+rI4STt3eY+GRf4W833ngHK2feycoFMO8XOBoFuz+5u/sVDx0JdMR9Z+/evQC8//77+Pg8PA8SCwoKYvfu3Wi1RfzgNgGQ47meO3cunTp1kkDnP+zSpUvMnTuXUqVKPXSBTocOHWjbti16vf5eV0XkweVklV8vWHDVqSw8AmcT4EjMnfXa3E7bFSZ+6lOApp93v8KvzO38dhzafAAb3gUXuaZFwUigIwrEZDJhNptxcXEp9G3HxMQAPFRBDoCiKEVyvkTO5FyLB1l6ejo6nQ6dLu9/prVardxIuU9dT1fZddHCoSsq607DH9egiB/jmaOtF8BkUdFp8pHeVGwAZJiLrlK38vPf4PoUtKgBvxbwoaQPDUlJKwgJdPJh/fr1TJgwgdmzZ3Ps2DFWrVrF1atXCQoKYvDgwXTq1Amw3jHs0qULQ4YMYejQoQ7bmDNnDnPnzmXdunWUKlUKgPHjx7NhwwZ+/vlnpk6dys6dOzEajdSrV4+33nqLgIAA1qxZw7Jly7h06RJBQUGMGjUq17vSW7ZsYcGCBVy4cAE/Pz+6dOnC888/7/QHMyYmhrlz57Jr1y5iY2Px9fWladOmvPTSSxQrVsypzmFhYYSHh/Pzzz8TExPDrFmzCA0NzfP5i4iIYNGiRZw4cQJFUahcuTIDBgywH4ftvNnYtl23bl2++eabPO3j2rVrLFmyhP3793P58mUyMjIIDg6mY8eOPPvssw6NANv7OXPmTA4fPsz69euJjY2lbNmyDBo0iHbt2jlsu3PnzgQFBfHaa68xdepUjhw5gl6vp2nTprz88ssO5ywnt7oufvrpJ8LCwjh58iRms5lKlSrx7LPP0rp1a4dyFouFhQsXsnbtWmJiYggJCWHQoEF5Oje5OX36NFOnTuWPP/7AYDDQqFEjXnvttVzL57WuoaGhdOrUiSeffJLZs2dz8uRJPD09adOmDcOHD8fd3d3p/MyePZu9e/eSlJREiRIlaNu2Lc8//zyurq72cgkJCXz77bfs2LGDa9eu4ebmRlBQEG3btmXAgAFO+x8/frzDtbVhwwY2bNhgL5ef8Qy2a2DMmDFMnTqVv//+G1dXVzp06MCoUaMwm83Mnj2bH3/8kYSEBGrUqMHbb79N+fLl7dtISUlh4cKF7N27l8jISFJTUwkMDKRVq1YMGTLE4VgPHDjAsGHD+OCDD1BVlSVLlnDx4kX8/f3p3bs3AwcOdKjfnj17CA8P599//yUmJga9Xk+NGjUYPHgwjz32mNPxbNu2jW+//Zbz58/j5+dH165d+d///seIESP44IMP6Ny5s71sZmYmS5YsYcuWLURGRmIwGKhTpw5Dhw6lWrVqOdY5PT2d77//nujoaEqXLs3IkSNp2rQpp06d4quvvuKvv/5Cp9PRvn17Xn31VafvqAsXLjB37lz27dtHQkICxYsXp3Xr1rz44ou4ubnZy9m+QyMiIpg+fTq//PILKSkpVKtWjddee42aNWsCNz/zABMmTLD/Pz/fMWC9hlasWMGFCxcwmUz4+/tTq1YtXn/9dYc02/zWf+vWrUybNo3du3dz/fp1lixZwqBBg2jcuDGff/65Uz1mzJjBggULWLp0KVWrVrUf39dff+3w3Ww0Glm2bBk//vgj58+fR6fTUaZMGTp16sRTTz1lL5ecnMx3333HL7/8wpUrV/Dw8ODxxx9n+PDhhISE2MtlZGSwYMECfvzxR65cuYJerycwMJBGjRrx8ssv5/k8Pmyup6vMOGQh/JTKhUSISb83wczt6Kc4Bi0eOuhTVeHTZhpMJghPr8tpU3GOzTjHyIlfUOF68j2qaRYRR0DpAeWKQ69G8GIbqFzqXtdKPAAk0CmAmTNnkpGRQY8ePTAYDKxatYrx48cTEhJC7dq1C7zd0aNHU6JECYYNG8bFixcJCwtj7NixPPHEE6xdu5auXbtiMBgICwvjzTffZM2aNQQHBztsY8eOHURFRdG7d2/8/f3ZsWMHc+fOJTo6mg8++MBeLjo6mkGDBmE0GunatSshISFcvHiR1atXc+DAARYvXoynp6fDtt977z1cXFzo378/iqIQEBCQ52NbuXIlkyZNoly5crzwwguAtbEwZswY3n77bXr06IGfnx8ffvgha9eu5Y8//uDDDz8EuG0AkdXJkyf59ddfadGiBSEhIZhMJn7//XdmzJhBVFQU77zj/ATn6dOnk5aWRq9evQBrY+idd94hMzPToaEHcPXqVV566SVatmxJq1atOHbsGOvWrePo0aMsWrTIoZGaV7NmzeK7776jUaNGDBs2DI1Gw6+//sq4ceN444036NOnj73sl19+yffff0/dunXp168fcXFxTJo0yek6yKuoqCiGDBlCZmYmffr0ITAwkJ07dzJq1Kg7rivAsWPH2LZtG926daNjx44cOHCA5cuXc/r0aWbOnIlGY50P5fLlywwcOJDk5GR69epFmTJlOHjwIPPnz+fPP/9k1qxZ9kbwuHHjOHToED179qRy5cpkZGRw9uxZDh486BDoZGW7tt5//33q1KlD9+7dC3S+wHoNjBgxgjZt2tCyZUv27t3L0qVL0Wq1nDlzhoyMDAYOHEhCQgKLFy/m9ddfZ9WqVfZjvXbtGuHh4bRs2ZL27duj1Wo5dOgQixYt4vjx48yY4TwId/Xq1cTFxdGlSxe8vLzYvHkz06dPJzAwkPbt29vLrV+/noSEBDp06EBgYCBXr14lPDyc4cOH8/XXX1OnTh172Z9++ol33nmHkJAQhgwZglarZcOGDezcudNp/yaTiVGjRvHXX3/RoUMH+vTpQ3JyMmvXruX5559n7ty5VK9e3WGdlStXkpiYSLdu3ezfW2PGjGHSpElMnDiRdu3a0bx5c/bu3UtYWBh+fn727waAo0ePMmzYMLy8vOjRowclSpTgxIkTLF++nD///JNvvvnGKTAaOXKkfTsJCQksXbqUl19+mXXr1uHh4UGdOnUYNGgQ8+fPp3v37vbzkZ/vmI0bNzJ+/Hjq1KnDsGHDcHFx4cqVK+zevZu4uDh7oFOQ+o8YMQJ/f3+ef/550tLSCAwMpFmzZmzfvp2EhASHXm6LxcLmzZupXLnyLVPwjEYjI0eO5ODBgzRo0IAnn3wSg8HAqVOn+PXXX+2BTnJyMoMHDyY6OpouXbpQoUIFYmJiWLVqFc899xyLFy8mKCgIgEmTJrFu3To6duxI//79MZvNXLx4kf379+f5PD5sYtNUai80E3kfxAT5lWKC+UdUVp80o1XgemZtAI4Dc16fzPbZ4wmNPHNP62h37hpMDocZm2DreGjyyL2ukbjPSaBTAJmZmSxatMieB92qVSu6du3KihUr7ijQqVGjBm+++abDsmXLlnH16lXCwsLsgUe9evV4+umnWbt2LSNHjnQof/LkSRYtWmS/w/rUU08xduxY1q9fT48ePahVqxYAn332GSaTiaVLlxIYGGhfv3Xr1gwaNIilS5c69Tp4eno6NDjzKjExkWnTphESEsKCBQvsx9GrVy/69+/P1KlTadOmDV5eXnTo0IF9+/bxxx9/0KFDh3ztB6x3ZsPDw1GUm128/fr147333iM8PJyhQ4c6BWjx8fEsX77coV59+/blyy+/pE2bNg7BS2RkJK+99hr9+t3MV65QoQJffvkly5cv57nnnstXfY8dO8Z3333HoEGDGDFihH153759ef3115k5cyYdO3bEw8ODc+fOsXz5curVq8eMGTPsvVMtW7bk2Wefzdd+bWbNmkViYqLDHeA+ffowduxYjh8/XuC62pw6dYrJkyfbe+169+7N5MmTWb58OVu3brX3ms2cOZPr168zdepUmjRpYi/71VdfsXjxYjZs2EC3bt1ITk5m//799OrVizfeeCPPx+nm5kaHDh14//33CQ4OLtC1ZRMZGcmnn35q78Hq1asXzzzzDIsXL6Zp06bMmjXLfv35+PgwefJk9u7dS8OGDQEIDg5m48aNDp+jPn36MHv2bObNm8c///xj74GwiY6OZtWqVfZrtGvXrnTq1ImwsDCHQOfdd9916CkA6NmzJ3369GH+/Pn2hr3JZOLLL7/Ez8+PhQsX4u3tbT+Wp59+2umYw8LCOHjwINOnT7cfh638U089xdSpU516RK5du8bKlSudvrfGjh3LpEmTaNmypcP5W7lypUOg8+GHHxIQEMCiRYscrqnHH3+csWPHsnnzZqcbEdWqVWPcuHH21xUqVGDcuHFs2bKFnj17EhISQv369Zk/fz6PPvpoga6DiIgIPDw8mD17tsN7OGzYMIdyBal/xYoV+egjx/ScTp068fPPP/PTTz/Ru3dv+/IDBw5w5cqVHN+vrJYtW8bBgwedPrdgDZZsvv76a6Kiopg/fz5VqlSxL+/cuTN9+/Zlzpw59lkMIyIiaNSokb1HTMD8f9QHMsjJKjETsqdHpRpcmVev5f0T6NikG+H/VsOmd+91TcR9TqaXLoDevXs7DPYsUaIEZcqU4eLFi3e03ex/sGyNko4dOzr0rlSuXBkPDw8uXLjgtI369es7pJEoimK/y/3rr9ZHIycnJ7Nr1y6aNWuGi4sL8fHx9p9SpUoREhJinxAgq379+uU7yAHr5AJpaWn07dvX4Tg8PT3p27cvqampOe6vIFxdXe2NTKPRSEJCAvHx8TRs2BCLxcK//zo/jKxXr15O9erZsyeJiYkcPHjQoayHh4dDYwOs14OHh4f9/ObH5s2bURSFjh07OrwP8fHxNGvWjJSUFP7++28Atm/fjqqq9O/f3yEFr1q1atSvXz/f+7ZYLOzcuZPq1as7pLlkvWYKWlebsmXLOqVY2oLBiIgIez127NhB1apV7UFO1rIajcZe1sXFBYPBwD///HPPphwvUaKEU5pe7dq1UVWVp556yiHItt34yPpZ1ev19s+RyWQiMTGR+Ph4Hn/8cQD++ecfp3127tzZ4Rp1dXWlVq1aTt8BWYOc1NRU4uPj0Wq11KxZkyNHjth/d+zYMa5du0anTp3sQQ6Au7s7PXr0cNr/5s2bKVeuHI888ojD+24ymahfvz5//vkn6enpDut06tQpx++t4sWL24OcrOcpNjaW1NRUwBognzx5kvbt22M0Gh32Wbt2bdzc3NizZ49TPbPegICb6a93+t2claenJ+np6ezatQtVzTkxqaD1f+aZZ5yWNWjQAH9/fzZu3OiwfOPGjWi1Wp588slb1nfLli14e3s7BJE2tl5GVVXZvHkzderUoUSJEg71dXNzo2bNmg719fT05MyZM5w6deqW+76X4uLiyMjIsL9OTk4mKSnJ/jozM5PYWMeHX16+fPmWr6Ojox3e86z7uJx8PyapFY6q1+7PxzsYL17N9f2Aon/PC2sfeaWi5PlH3CQ9OgWQU5qQj48P0dHRhbpdLy8vAPtYnqy8vb1JSEhwWl6uXDmnZRUqVACsaUoA586dw2KxEB4eTnh4eJ7qAlCmTJlbH0AubPu11eNWdbtTJpOJBQsWsGnTJi5evOjUEElMTHRaJ6dzZhtTkb1ewcHBTjMaGQwGgoODC3QMZ8+eRVVVe9pcTmxfmrbt51bfnBpOtxIXF0dqaiply5Z1+l1O71V+6pq1XtkFBATg5eVlP57r16+Tmpqa4z59fHwICAiwl9Xr9bz22mt88cUX9vSa0NBQWrRoYQ8UilpOn0fbZzX758YWRGT/rK5cuZLVq1dz5swZh7vqgMMfTZvcvnOybzcyMpKZM2eyZ88ep+1kDcBs5zOn9z6nZWfPniUjI8MpwMsqPj6ekiVL3rLO3t7eDj3INrbzl5CQgLu7O2fPWh/4MWfOnFyfOxUXF+e0LPs+fX197dstLIMGDeLQoUOMGTMGHx8f6tatS+PGjWnTpo2956ag9c/p3NvGMC1dupTz589TtmxZ0tLS+PXXX+1B0K1cuHCBqlWr3nJyjuvXr5OQkMCePXtyfY9tQRHAa6+9xgcffEDfvn0JDg4mNDSUpk2b0qxZM4dy91L2dMTsqdgGg8Hp3NlS83J7nfX6zr6P7pU1TDl4jwbsF7HK92mgo+/TBLJ8r93t97yw9iGKlgQ6BZDbF7mtUZ21QZGd2Zz7F2Fus+Xktjy3u4l59eSTT9onUMgupz+KBRl/crd9+eWXhIWF0aZNGwYPHoyfnx86nY5jx44xffr0Oz5nRUFRFKZNm5brdVWxYsW7XKPc3Q917dWrFy1atGDXrl0cPHiQbdu2sWLFCtq0acMnnxT9Mxdu1ZC73XcDwJIlS5g6dSoNGjSgb9++BAQEoNfruXbtGuPHj3cKfCD374CsUlNTGTJkCGlpaTz99NNUqlQJDw8PFEVhwYIFdzx+olKlSrz66qu5/j77s65yq/Otzp/tPNn+feaZZxxS5bLK2hN1u30W5ue+TJkyrFy5kn379rF//34OHTrExIkT7ZO2hISEFLj+uX3HduzYkaVLl7Jx40aGDx/OL7/8Qmpqaq7f3/llq+/jjz/uNMFFTlq0aMG6devYvXs3hw4dYt++fYSHh1OnTh1mzZr1n5zeukmIwty2GsZEWEjIvNe1yT+9Bj5pqqCoFt7eYcRk0eKZkcGLe7fS/vjhe109RwYdvNQOxjn3PguRnQQ6RcD2Byyn3oPC6rnIzblz55yWnTljza213e0MCQlBURR72klRs83Wc+bMGae77rY7nwUdTJ/dpk2bqFu3rlOD91apKzmds9zqFRUVhdFodPhDnpmZSVRUVI49LbdTunRpfvvtN0qWLJlj70dWtrqcO3fOYQakrPXNDz8/P9zd3Tl//rzT72zXTEHreqt6xcTEkJSUZD8ePz8/PDw8ctxnYmIiMTExDmMGwNor1K1bN7p164bZbOb999/nxx9/5JlnnqFGjRp5qtu9smnTJkqVKuUUMP722293tN19+/Zx7do13n//fYfZCwFmz57t8NrWK5XTe5/TstKlS3P9+nXq1at3V+7Y23qPNRpNoX9H3epGVF4ZDAaaNGliT7XctWsXr7zyCkuXLuXNN98s9PpXqVKFKlWqsHnzZl566SU2btyIl5cXzZo1u+26ZcuW5dy5c2RmZmIwGHIs4+fnh5eXFykpKXmur4+PDx06dKBDhw6oqsr06dNZtGgR27dvv2XP38PshUc1vPDozc9HVJLKrxdUTKqF5Udh23kw3cP62ZwdoqWMN5gtEJUMIV7Yp5s2Gi14Hl4CwKBnB6CfpoGNt9raXbRtPLR89F7XQjxg7o8+5oeMh4cH/v7+7N+/3+FOYmRkpH2sQVHZu3cvx44ds79WVZVFixYB2MdK+Pr60rhxY3755RenMRW2da5fv15odapfvz5ubm6EhYWRkpJiX56SkkJYWBju7u40aNCgUPal0Wic7t6mpaWxbNmyXNdZtWoVyck3R5EmJyezevVqvLy8nKbkTUlJYeXKlQ7LVq5cSUpKSoEeQmkbDD1z5swce/uypoI1b94cRVFYunSpQ9ljx46xb1/+H6Ot1Wpp0qQJ//77r8MUy1mvmYLW1eb8+fNO1/zChQvtxwPW96xp06YcP37cqbG/YMECLBaL/dymp6c7jQXRarVUrlwZyPnmQlbu7u6FmsZUEFqtFkVRHK5TW8rlnW4XnHsv9uzZ4zTu55FHHiEgIIANGzY4nLPU1FTWrFnjtO2OHTsSGxvL0qVLc9x3Tu/9nahatSoVK1Zk9erVREZGOv3eZDIV+H20TWte0PXj4+OdltnGRdq2WRT179ixI5cvX2bLli0cOHCANm3a5OlZUe3btycxMZF58+Y5/c52rWg0Gtq3b8+RI0f4+eefc9yOLdXObDbnmBZpm/ntXn++7ifBXgrP1NDwXE0dW3rrMI7Rod74SX9Fy5v1oKwneGjv3hNSynpCOR8FjaKg1yqU81Fyf6aOTgtvdoeouXepdrloVh1MKyXIEQUiPTpFxDaL0ujRo2nevDkxMTGsXr2aihUr5jggvrBUrlyZYcOG0bt3bwICAti+fTv79u2jQ4cOPProzS+JcePG8cILLzBkyBA6duxI1apVsVgsREVFsWPHDjp06OA061pBeXl5MXr0aCZNmsRzzz1nT7fYsGEDFy9e5O2333bKcy2oVq1asWbNGt566y0ef/xxYmNjWb9+/S0fPurr68vAgQPtMyCtX7+e6Oho3n33XadUkpCQEObOncvp06d55JFHOHr0KOvWraNcuXL07ds33/WtUaMGL774It988w39+vWjdevWFC9enJiYGI4ePcru3bvtY2/KlStH7969WbFihX2K67i4OFasWEHlypWdZknLi+HDh/Pbb7/xyiuv8NRTT1GiRAl27tyZY6Cbn7raVKpUiffee49u3bpRpkwZDhw4wLZt26hbty5t27a1lxsxYgR79+5lzJgx9OrVi9KlS3Po0CG2bt1K3bp17dfM+fPnefHFF3niiSeoWLEiXl5enDt3jlWrVhEcHOwwfXJOatasyb59+1iwYAElS5ZEURSn5yUVtVatWjFjxgxGjx7NE088QUpKCj/++GOBJvrIqnbt2vj7+zN16lQuX75sn85406ZNVKpUyWHguE6n45VXXuHdd99l4MCBdO3aFa1Wa/+sREVFOfR8PP300+zdu5evvvqK/fv3U69ePTw8PIiOjmb//v0YDIZcx6IUhKIofPjhh7z00ks8/fTT9vFY6enpREZG8ssvvzBy5EinWcvyonz58nh4eLBq1SpcXV3x8vKiWLFi1KtXL0/rjxgxAi8vL+rUqUNgYCBJSUmsX78eRVHsNwOKov5PPvkk06ZN49NPP8ViseQ5be3pp59m586dzJs3j3///Zf69evj4uLCmTNnOH/+PLNmzbIf159//slbb73Ftm3bqFWrFnq9nsuXL7N7924eeeQRxo8fT2pqKu3bt6dZs2ZUrVoVPz8/Ll26xKpVq/D29s5TL5MAF53Cp811fNrccfmPZ0y0d77XUGiOPV+A+9ul/GFsV/g85zG9RUotwpMh/hMk0CkitmeCbNq0iYMHD1K+fHnee+89jh49WqSBTrNmzShbtiwLFizg/PnzFCtWjBdeeMFpxp2SJUuyZMkSFi5cyPbt29m8eTMGg4HAwECaNm1KmzZtCrVetsBr8eLFzJ1rvTtUpUoVh6mHC8Nrr72Gh4cHW7duZfv27QQGBtK9e3eqV6/O8OHDc1xn1KhRHD58mJUrVxIXF0eZMmWYOHGiw7S9NiVKlODTTz9l6tSp/Pjjj+j1etq3b88rr7ziNK1vXr344otUr16d5cuX8/3335OWlkaxYsWoWLEiY8aMcSg7ZswY/P39Wbt2LV999RWlS5fmzTff5MKFCwUKdEJCQvj222/tY5tsDwz98MMPHQKRgtQVrHe6X331VWbNmsWaNWvw8PCgT58+jBgxwiEFKigoiAULFvD111+zefNmkpKSCAwMZNCgQQ4Puw0MDKRLly4cPHiQiIgIjEYjxYsXp3v37gwcOPC248jGjRvHpEmTmD9/vr138W4HOs8++yyqqhIeHs4XX3yBv78/bdq0oUuXLk4z+uWHl5cXM2bMYNq0aYSFhWE2m6lWrRpfffUV4eHhTjNktW/fHp1Ox7fffsucOXMoVqwYXbt2pXLlyowdO9aht0Cn0zF16lRWrVrFpk2b7EFN8eLFqVGjRqGNFcmqatWqLF26lPnz57Njxw5Wr16Nh4cHQUFBdO7cOc+BSXaurq58/PHHzJ49mylTppCZmUndunXzvL1evXqxdetW1qxZY3+2TdWqVXnjjTccZi8s7PoXK1aMRo0asXPnTsqUKeNw4+pW9Ho9M2bMYMmSJfz444/MmjULg8FAmTJlHAItT09PvvvuO5YsWcLWrVvZsWMHWq2WEiVKULt2bbp16wZYz9/TTz/Nvn372LdvH6mpqQQEBNCsWTMGDRpE8eLF83VcwlG7CjrUMTBwk4lFhdxUUABXfQETeT4beHcDHQU4+/Xd298DQWZTKwhFvR9HZwtxF+T2FPHcdO7cmaCgoHw9Qf2/LDQ0lE6dOtmfvSEeDLbJEubPn29/7pYQ4u7r/oOJHwpxBm8fA8SPvvX9baPRyPz58wHrDIMOE0u49oGMuzDKSKuBjDDIwyQs/yWqkvMDsXOiqM7p5/9VMkZHCCH+g4xGo9NYq9TUVFauXImPj4/D87iEEHff2m7W8Tzxo7RcGqZFHaPj7TuYRb+u88zu+bP+rTvcQB7ULA2XvpUgRxQaSV0TBZaQkIDRaLxlGVdX1zsef5Oenu4wWUBuAgIC7mg/Dws5X/lzt67j+01UVBSjR4+mbdu2lCpVipiYGDZu3EhUVBTjxo37z00RHBMTc9synp6eD8Q0++Lh4uOi4HMjk/TjZjo+bgYn4iz8cNLCmzvzvp21Xe8w9anNrcdA3rEW1eHXiUW7D/GfI4GOKLCxY8dy6NChW5YpjNSlrVu3MmHChNuWyzpz2H+ZnK/8uVvX8f3G19eXmjVrsnnzZq5fv45Wq6VSpUqMHDmy0MfoPQhyGpOX3QcffFCgCRCEKGxViml4o76GNSdM7L1y+/LlvcHHtRB6SV7tBF9uuPPtZFe1FPxwF3qMHmBqPsboyGiem2SMjiiwo0eP3nY63+LFi+f4xPv8iImJ4fTp07ctdzeeCfQgkPOVP3frOhb3t7179962TMWKFaUnVNx3Hpln4thtnghxaahCkNftA51bjtGx2fonvLsUouMhKgZyfw563vi6wfWcp64XN1mU2z/M10ajLizCmjxYJNARQgghhHiAGc0qbl+ac4w53BRIfT1vCTx5CnSyW7ANBs3MR22zufIdlPAt+Pr/ERLoFIxMRiCEEEII8QDTaxVW5TIG5+eniziR6blWsPL1gq3771cS5OSZko8fYSOBjhBCCCHEA65bZS2DazouezMUGpW6CzOY9Wqc/xalhys8UrpIqiOEjUxGIIQQQgjxEJjXXsc3bVUuJUOQJ+g0d/Hu/of94N1leS+/7NWiq4sQN0igI4QQQgjxkNBqFEp734Mdv9MLVBXe+/72ZcsVhy71ir5ODxVJSSsICXSEEEIIIcSde7c3vN0Tpm2AM1fA0xVMKvRrCp//ACcvwUvtYFDre11T8R8hgY4QQgghhCgcGg280sV5+VJJVRN3n0xGIIQQQgghhHjoSI+OEEIIIYQQ9zFVxugUiPToCCGEEEIIIR46EugIIYQQQgghHjqSuiaEEEIIIfItw6Si1dzl5/X8Z8k5LggJdIQQQgghRJ5tO2em0yoz6Sbraxcd7OqvJTRIe28rJkQ2kromhBBCCCFuSVVV3v4lE7/P02m91Eh6hgoWAIUMk0K9hWbGRxjvdTWFcCA9OkIIIYQQIlfJGRb8/i8NkynLQlctGBTrLXNFARUm7FGZ8VcmX7bU8mxN6d0R95706AghhBBCiFz1W5XpGOQAGC2QqUK6BTLN1mVahdhUGLDexMvbzHe9ng8zNR8/4iYJdIQQQggh/oMuXzEybc41Zs29SkxczmlnRqOFjUezRzlYe3FsVKyBz41UNhSFaQck0BH3nqSuCSGEEEL8x3y9MJa9W2JRTCYyLBZ+2RqLxc1Ag1BXfF3S7eW+/e4qFsXLsatAAXQa678uGmuA49CdoICq8n+/m3m7oaSwiXtHAh0hhBBCiP8Qs9nC3i2xuKWkUvp6AnrVGqHEuhjYY2zEvz4enPBIZmuiK4kndOCvgQxbjw2gu9GbY7iRGJRTvpSi8E6EibKeKv1rSXPzzsn00gWhqKoq6XxCCCGEEP8R7354kbSDcehS0yielu7wuxgfL657eRHlYiBFq+WytwvHg3ytqWkaxfoDYFKtvTmqCkYVLOrN3yvKzXa50UIVf4Vjw/QoijTWC8qkvJDnsjr12yKsyYNFxugIIYQQQvxHbFsVjS7iPBqLimem87gcvcmMAgRnZKJTVa4pWriecTOQ4caP7kYTMtMCZtXaq2NWwWSxljWr1iDIoHAiVqXjcpl6Wtx90pcohBBCCPEQyTxznZMDfiLmryRw0RLUozyVZzfn2uU0tn4XSaqPNxaNFoubK27JKQ7rprq6ANZw5pinG3F6/Y0gR4NT+pSFm+lsNirWAEdRbv5Or7D5PPhMNRI9QoebXnp28kuV1LUCkR4dIYQQQogHlHr8EurJaDBZZzlLWP0vK1v8ys44Hy74BECchZRv/uZvw5fM6/MbGouK3mQmxcXAVX9fEtxcATArCrFenqS4uQFgUuCaQX9zR9ocGtpZBz+oKpgt1h4d26gIDdaeH60GdBoSjfDYtxlFcBaEyJkEOkII8QBav349oaGhHDhw4J7VoXPnzrz44ou3LXc/1DW7+6VO48ePJzQ0tFC3OWfOHEJDQ7l06VKhblfcB4wmTN/v5kypDzjg+zGH3CdwssY3/PvIdxzx+YA9ARP57vNLnK8USEwpH85XK8mxR0OwoMNoduOJfacpcT0Bg8mMUavBrNNxulQgf5YrzV/lQoj09wMgQ1HYXsyHDG2WZqIxh+miFdXayaOqkGG2Pk/HaIYUozXoyR4cKQpHExSUielo/y+DPiszSDfKUHFRdCR1TQghhHjAREREcPz4cYYOHXqvqyIKk8VC2vvhpC7Yx6VkX867BKLoLJRPv4BLXDpJiifJ+mL4G70ophrRK0bOl/DnjHcptGYolplO2dgYzivFMep0ZLoYuBbgQ6KnG97JaehNFgJik7ju5UHWNDTzjYAmRqtlp7cn0a4GMnTZpoVONYIhS7NRwZqeZtBYx+l46Kwpbulm678ZJnDLoZmpWqeetmgUVp6E1Z9n8PtzeuoFaWSyAlHoJNARQghRpDp06EDbtm3R6/W3LyzyJCIigg0bNuQY6Dz//PM899xzGAyGe1AzkRvVomKKTces16BNTEU9coXYqb9h/OU47qZ4TBoNV5VgNBYd6W7FuO7mTfmkyxRPT+SazpcjPpXIMGgwWMyoaRqKpaayo1oNklz1VLgczaPRUfbQ5ZGLF0l0d8ei0XCsdGmM+ptBi8FiwqTV5jgjtBaI12lRtdZgxGC2YLBYUFHI0GkwZZjA5ca2LKo1JU1Rbi4D0GshMcP6O/sEBlkoWGds01h7gywmqP+t8cazeRTQaCjlaiHEC6a00tK4rDRVrSQILAi5eoQQQhQprVaLVisPDbxbdDodOp38ec+R2Qzb/7X+v3l1sF2XxyLh+CVoXA08XOD7XXDoNPRqBK56iEuGJ2qCmwskpMD738O5a3DuKmpkjHWsiqseU0w6mUY3TLihIwkDaagoaIBMPEiiOG5cR4MJBZVAkjCj5zqBxFiK4UEyfsSRkepKjL46//qWJSQ1Go3JQumUK6gJ1vE0eiWTdBcLpdPPkpGsocq1FIdmsFdGBl4Z1rEwIbGxXMcbI3pQLGR6wJViPpg0CmZFsT9DJ1NjDVoqZ2binZ7JMRc9KRpb6pqKzmgmOSUTc/qNZZ76m6lrWXtiNAq46iB7j5CNRbUOnLCltblqrOuoqn18z6U0DZfSoMkyC1gybk5woAGtRsFTD60raHiprpaWZRV2RaqkmuCJMgqGnMYSif8s+SYUQogHmNlsZs6cOaxfv57Y2FjKli3LoEGDaNeunUO5iIgIFi1axIkTJ1AUhcqVKzNgwABatGjhtM38lM0uKiqKUaNGkZqayrRp06hSpQrr169nwoQJfP311/bxKLZls2fP5tixY6xatYqrV68SFBTE4MGD6dSpk9Nxzp8/nx9++IG4uDjKlCnD4MGDOXv2LHPnzmXdunWUKlWqwOfRJjMzkyVLlrBlyxYiIyMxGAzUqVOHoUOHUq1aNXs5i8XC/Pnz2bNnDxcuXCAhIQF/f3+aNGnCSy+9hK+vr8N2MzIy+Prrr9m8eTNJSUlUrFiR4cOHF6iOL774IocOHQJwGN/zwQcf0LlzZ+bMmeN0TmzLVqxYwdq1a/npp59ITk7m0Ucf5c0336RcuXL88ssvzJs3j3PnzlGsWDEGDRpEjx49nPa/d+9eFi1axJEjR8jMzKRMmTL06tWLXr16Feh47pro69DyAzgaaX39SAj8MgE+WQPTNlqXuerBzQDXb8xENnPLzfVL+MBbPeC1BTcH23NjiMqNf/WAjnRUElCwOAQfOuLRYiKeMvhwDldSAdCQQQkuUIILDtUtnhBNbEJl0jRuKJZUYiht/12m6krpjH957GwUKhBFHXK7468AbqRx3eDG5QAv9lWqilmrRW+xkOLiQrqqogIJWi0pGoUSJhOeFtUxTe0Gg8lCmm3cTprRmobmnUPPoa2MOVsgZFGtaW5u2pvP2rH9TlGsvUGZ5htBTZbn8dhOMtZYNcEEq49YWH3Egp+3hus35jco4w3bntJRyU+CHWElgY4QQjzApk+fTlpamr2RuX79et555x0yMzPp3LkzACtXrmTSpEmUK1eOF16wPnRuw4YNjBkzhrffftuhMZufstkdO3aMl19+GS8vL+bPn09QUNBt6z9z5kwyMjLo0aMHBoOBVatWMX78eEJCQqhdu7a93Geffcbq1asJDQ3lmWeeIT4+nkmTJhVKcGNjMpkYNWoUf/31Fx06dKBPnz4kJyezdu1ann/+eebOnUv16tUBMBqNLF68mJYtW9K8eXNcXV35999/CQ8P5/DhwyxZssQhVe+dd94hIiKCpk2b0rBhQyIjIxk7dmyB6j948GBUVeWPP/7gww8/tC9/9NFHb7vu+PHjcXNzY9CgQcTHx7NkyRJGjRrFsGHDmDZtGr169cLb25vw8HD+7//+jwoVKji8D2vWrOGTTz6hVq1aDB48GDc3N/bu3cunn35KVFQUL7/8cr6P5675eNXNIAes/395Hqz47eaydKP1JydXE2DsIocgx0bJ9n/Fac5lKxeSMaFFT+Ztq6vDhAcxWCyluEag0+8TKEEJrOlqLiSSgU+u21IAk07Pmapl7NNHu5jNVIu+imdGButKB3PGzbr8rIuByukZ5NQfoypYA5Abs6jhpsUpB07lZvCiYn2YqEa1TjWdYb653KJa/6PROE5aoFFuPoBUR5ZI0hYQ3Vj3xn6vp6jWlDfgQiK8s8NMWNeHr3kr00sXzMN3JQghxH9IfHw8y5cvx9PTE4BevXrRt29fvvzyS9q0aUNmZibTpk0jJCSEBQsWOJTr378/U6dOpU2bNnh5eZGYmJjnstnt2bOHN954g0qVKjFlyhSnHo3cZGZmsmjRIntQ0KpVK7p27cqKFSvsDezTp0+zevVqGjZsyFdffYXmRjpN69at6dev352cPgdhYWEcPHiQ6dOn07BhQ/vyXr168dRTTzF16lS++eYbAAwGA1u2bMHV1dVhG48++igTJ04kIiKCNm3aANZzExERQadOnRg/fry9bN26dRkzZky+69mgQQO2bNnCH3/8QYcOHfK1rr+/P1OmTLEP+vb19WXy5Ml89tlnhIWFUbJkSQDatm1Lx44dHd6HmJgYJk+eTNu2bfn444/t2+zduzeTJ09m6dKl9OzZk5CQkHwfU2GLi4vDw8MDFxdr4z05ORnXQ6edGz1/nM3fhk05zDyWDyqQiR4TerSYblvenRhSKI5FUZwCiqzBki8XOUkgXqRjQoM+W6CVjBuRIf5YtBpcjSZStFqC4hLwzMjgorsbZ7w8HMqfdHXBrAVtlsNVgUxvV3DRWVPPbGNv1BtBjJKlYJYeGMDas5NusgYrGuXGQ0Xtv7QGKgbb2J9s62Uf40O27VtsO7Q6fNX6i+TkZFRVtX9fZWZmkpSUhL+/v73s5cuXHW7IZH8dHR1NYGCg/fOS03V1p/sQRUumlxZCiAdYr1697AEJgKenJz179iQxMZGDBw+yd+9e0tLS6Nu3r1O5vn37kpqayt69ewHyVTarTZs28corrxAaGsqsWbPyHOSAtZGcteejRIkSlClThosXL9qX7dy5E4C+ffvagxyASpUq0aBBgzzv63Y2b95MuXLleOSRR4iPj7f/mEwm6tevz59//kl6ejoAiqLYgxyz2UxSUhLx8fHUq1cPgH/++ce+3YiICACeffZZh/21aNGCsmXLFlr98+Kpp55ymNnKFsQ0a9bMHuQA+Pn5UbZsWYf34eeffyYzM5OuXbs6nJ/4+HiaNm2KxWJh3759d+1YbqVYsWL2xihYr2Fdk+rOBZvlsOxWXO9sQo00/FDREkfpHCcDyEoFNKi4EUeyh86hvAYzgZy3v9aTSZqi4YimNMe1IZzXBJCMC+nouI4nabjgmZBO8cvxFI9PwpCZSYZWw/lifsR4eaDJoZfKywzFzBbcLCruFpUAswX7cz6zBh9ZAxzbZmwpZ+qNACfFeCNtzWRteWbv8DKpNwIm1Rrc3I7jyXDQKNhaIU9PT4ebMgaDwSEAAZwCjuyvS5Ys6fB5yem6utN9iKIlPTpCCPEAK1eunNOy8uXLA9bxMqmp1nEAFSpUcCpnWxYVFeXwb17K2hw7dowPPviAhg0b8vnnn+d70oHg4GCnZT4+PkRHR9tf254Hk1NQULZsWX777Ten5QVx9uxZMjIyaN26da5l4uPj7QHB1q1bWbJkCcePH8dkcrw7n5iYaP9/VFQUGo0mx/qXL1+e8+fPOy0vKtl7W7y9vQFyTKHz8vJyeB/OnTsHcMuxRXFxcYVQyyLydk/YexK2H7G+bl4DvngOKgXBB8utjfAAb6hZGiKOOK9fLRg+ehr6T7WWzcaxXwEsaFEwY0GDBT2ZeGBGRzEucI2KXMQVT2LRkY4n19FguRknZNmWggVPJZ6jwRUpkZCEi8lMsruGwHQP3FKTMWp0/FH8UdJUA0HXYojFByM60nDFFeONXiQdAVeSCLiSROlzsaxrX5szgcXtda2dms4hd1d7epjeYsHrxpwBLlmCIK9UIxkGbbYjzcZe+RvBjl4DXnrrts2Wm+lr2RktkP1XavazemPZjSpptVAzSMOf16yv6wbC/zWTiU/ETRLoCCGEKLDSpUuj0+k4cOAAv//+O02aNMnX+ll7aLJSc7jDfDdUqlSJV199Ndff+/lZH6j4yy+/8NZbb1GjRg3GjBlDYGAgBoMBi8XCqFGj7ln9bye3852X98H2/wkTJhAQEJBj+ZwC1/uGjwdEfGSdYQ2g2o2gb1wPeKE1nL0Cj5YDFz3EJ8PBM1CjNGQYrZMT/K+ctbHevT6E77c+GPPiVdTtR8HVgFrKD/MPf2C5eB0VC2bVhXgC0QDuxKJXUojTF+eEvhouJiiVkUYyARgwYsYbAykYSEBPhr3KKuBKEtWT/+Ssd0kiA3wBMOq1nHHrhKspDZNGj0mjQ2820efqdtxUC2Z7804hEcdn5ihmCzFeN3tsAfwsFkpmGol2MeBjNNEwPpGDxXyd0n7SDNqbvTmWG70wWo11DI4tzSxrXKK5MfW0rVdEqwGDCpk5fT5u9OhkHYsD9sBGp1Ep5goh3hrqBqn0qKalTXktOo3C6evWWddqFX+Yx7E8zMdWdCTQEUKIB5jtLntWZ89axx0EBweTlpYGwJkzZ3j88cdzLQc37/bnpayNh4cHU6ZMYfTo0YwdO5ZPPvkkT7Oz5Yett+H8+fNOPRKF2RtSunRprl+/Tr169XJt+Nts2rQJFxcX5syZ4zBOJ6f3Izg4GIvFwvnz56lYsaLD72znNb/uxYMVS5e2zvrl6+tL/fr17/r+C021HMYQBXhbf2x8PaFVlskdsnbGabXQ42bKpDKmu/VfQPOV42bds+2m9I0fgKQziZz67G+0f13EcOginhnXyVACCVTP40EStm4LMwrXXIMpZbqMpykVJUPPdQ9vrrkZSNe52bdtVjQY0WcJcm4szxauZBj0qDlcP43jE1FQcbXc2K8CWvVm8zpNryHFy3AzENEo1hQ0oxnSzNZAx0NnnaTAfnKcdmOddlq1WHtwbLQ3xiBpoaS3QrsyUMlfw9gGGlz0t++hqSizrIlcyBgdIYR4gK1atYrk5GT76+TkZFavXo2XlxePPfYY9evXx83NjbCwMFJSUuzlUlJSCAsLw93d3T7OJT9ls/L09GTGjBnUrFmTcePGsW3btkI9xqZNmwKwfPlyLJabjaNTp06xZ8+eQttPx44diY2NZenSpTn+PjY21v5/WyCUtT6qqjJv3jyn9Zo3bw7A4sWLHZZHREQUOFBzc7M2cBMSEgq0fkG0adMGg8HAnDlz7GOVskpOTiYz8/aziQkrrwre1Pm6MY/+1pdq6WMJUf+PipYJeKoLSNowifPNuhHT9Aky2tfFr2NlDMF+XPfyI9XdhXJxkWgtjnlepROvocdE9gEwLtkmPfBLTMUlw3FmOUVVcbFY7EGOCchUwE0144aFRHc95/09HJ+XA9ZgJ9Nyc1yNKUtPjW3q6Ozr3HgeDgbFGuBoFZLH6lHfcUF915XLo11Y0M2Fd5vq8xTkCHEr0qMjhBAPMF9fXwYOHGifSnr9+vVER0fz7rvv4urqiqurK6NHj2bSpEk899xz9ufTbNiwgYsXL/L222/bJx7w8vLKc9ns3N3dmTZtGq+++ipvv/02H330EW3bti2UY6xYsSLdu3dn7dq1DB8+nBYtWhAfH8/KlSupWrUqR48eLZQejqeffpq9e/fy1VdfsX//furVq4eHhwfR0dHs37/f3sgH6+xwv/zyC8OGDaNjx46YTCa2b9+eYwDQsGFDmjZtyoYNG0hISKBRo0ZERkayZs0aKlasyOnTp/Nd11q1arFixQo+/fRTmjRpgk6no2bNmkWaOhYYGMi4ceOYOHEivXv3pkOHDgQFBXH9+nVOnTpFREQEK1euLNQpv/+rvDtWwrtjJYdldW78oKqkvLqC2K3xXDJ7kKnRg0UlSatlT906cC6d0nGJKDe6U5L9DVzXeFH8WiImnZbTlQLRqBZcTCbSdDoybvTMmBUFjapiVBT2+3pRNUsw5J2QynU3PWmu2ZqNarbJAzLM1jE5uT20015exVsHLSpq+L6rFneD3He/HZleumAk0BFCiAfYqFGjOHz4MCtXrrQ/SHPixIm0b9/eXqZ3794EBASwePFi5s6dC0CVKlWYPHmyU5pZfspm5+bmxtSpUxkzZgzvvfceZrOZJ598slCOc9y4cRQvXpzw8HC++uorypYty7hx4zhy5AhHjx51mAmpoHQ6HVOnTmXVqlVs2rTJHtQUL16cGjVqODzEtF27dqSmprJs2TK++uorvLy8aNasGSNHjqRVq1ZO2/7kk0+YPXs2W7ZsYd++fVSsWJHPP/+cLVu2FCjQadeuHcePH+enn35i27ZtWCwWPvjggyIfI9OlSxfKlCnDkiVLWLNmDUlJSfj6+lK2bFleeuklpxmnRBFQFDymPoXtE56ZakLnokVzI7iI++cap5t/S3DCVdwtyXiluoNFy65qoUR5BxDn5UXZ+AQSPdxZEVicTI1CMYvKRVcXtBYz5/V6qhpNDs8L0gLBSemcctM7pqaZbozLsQU7twtYLNC6rMLWp3N4yKgQRUBR79cRk0IIIcRtvPrqq+zfv5/t27fne8Y3IR5WqqpybvlJTn57gkQXd6o/XY5HnilPaprKi0PP4JZhJCAllXkVymDMNh7N3WSivNmCa7bm4UUPF076etx4YCg3H+KpKJBptr52yfIZVGyTE9x83k7HciobekuQUxAZykt5Luuizi7CmjxYpEdHCCHEfS89Pd3p4ZwnT57kt99+o1GjRhLkCJGFoiiUf7oK5Z+u4rDcw11h0YKK9Bx8jpOe7s7Pz1FVGsYncSjAhwrpN1PXLEC0+41eU4ttprUsqVSGHD5/qnJjumjrmKHJzVVeb3jnPa9C5IcEOkIIIe57GzZsYNOmTTRu3Bg/Pz/OnTvH2rVr0el0DB06FLAGQ1knZshNblMj32upqan25x7lRqvV2qe4FqIgtFqF4S8VZ9SCRLwsKjrVhEVRcDNbqJaSSrJBh3u6kYs6Df5mlVSDlotebiQZdFmmk84yjXT2h386UMGo8kRpJMgR94QEOkIIIe571apVIyIigrCwMBISEvDw8CA0NJQXX3yRatWqAdYHeE6YMOG22zpw4EBRV7dAso6Lyk1QUBDr16+/SzUSD6vmdT0o+U0s5931eGGddc1dVUl0MVAiI5OSrskseacMC76/zpQLOq7rNNYUNJ0GdIpjb46iWntuFOwP8rRS0VjMHB9uoJK/9LiKe0PG6AghhHgoxMTE5Glg//36DJjIyEiioqJuWcbFxYXatWvfnQqJh9q2fSl8+HUMlw16dKpCoNFIGYOZ+rV+Qac3M2jQIPR6PU+vSGP5Cc3NSQi0OE8ZfWNaalTsD/jErJIwRo+3q8yoVhhkjE7BSKAjhBBCCPEflJZh4cffUlE08GQjDxRMzJ8/H8Ae6AAoH6bfHIeTY6BjuRHkYE1lU1Uq+8OJYZKuVljSleF5LuuqzirCmjxYJHVNCCGEEOI/yM1FQ7cnbj4by2jMudxzjyosOGrrtckyPgduTEN9I2/NooIFtFoJcsT9QfoThRBCCCFErr7pbMBXMQMqmCw30tPUm2lqNhaVtb01mN6SIEfcHyTQEUIIIYQQudJrFWLfcGVjDy3ty6lgtFgfEmrhZsqaClGjDXSrqr/HtX1YKfn4ETYS6AghhBBCiFvSaBQ6VNWxuZ8rl0brqVncNs2aShVfuDxSRylvaVaK+4uM0RFCCCGEEHkW5Knh7+clqBH3Pwl0hBBCCCGEuI+pkpJWIBKOCyGEEEIIIR46EugIIYQQQgghHjoS6AghhBBCCCEeOjJGRwghhBBCiPuajNEpCAl0hBBCCCFErk7EWegxPw0lKg10Gl7u4cELDeShoOL+J6lrQgghhBAiRxkmlYZfpnAmQ8uRkj6c9XZjzvfXeeyj61xMtNzr6glxSxLoCCGEEEKIHE3dlUmctytpBh2qRiHFRceBEH8OuXnR8PNk4tMl2LkbVJQ8/4ibJNARQgghhBA5+mC3BRQFVBXSzZBmsf4YLUT5uPPS0pR7XUUhciWBjhBCCCGEcBIVZ0JnUa1BToYFsnbeGFXKxKbwy3mVp6fH3bM6CnErEugIIYQQQggnfZekERyTAkmZjkEOgALu6UZC4tI4fMxEqbHxJEoam7jPSKAjhBBCCCGcHIhTOK/Xg5ploRZw04CbluNBPiR5GPDUQYWEFB6blEi6Sc1tc0LcdRLoCCGEEEI8hFSTGTU6oUDrzlsaS5tT0WS4GUCntW0R9Ip1zA6gahROFvfiQOliHCznj9vVdLwnpmJRJdgR9wd5jo4QQgghxENEvRxHapUPUZNVQCUVPReeeIwaK3vg5u+ap22E7c4gyccD9NosSxVrClsOt8nTDTpOlPLFmGrmrW0ZTGqdt/0IUZSkR0cIIYQQ4mFx9gpJweMhGRQUFDS4Y8bl13McDvyWhH1Rt1z90IUqLDnUFnezygUvd+cCt0hNyzBoKWdM5/MIIwNXpxOXKmN2CotML10wEugIIYQQQjwEMiKOs/GxtWhUx+adApTlGgmqF7uab2Vh+VX8MuYAZpM1ELGYVY5ExNDj+Yukng2hfJKJFItKvD6HxJ9btaMVhWhfDxrHJvPD72n4T8nkr2hz4R2gEPkkgc5dcODAAUJDQ1m/fj0Aly5dIjQ0lDlz5hTqfsaPH09oaGihbvNOvfjii3Tu3PleV0OIe6Zz5868+OKL97oaD4w7+X78r5/r0NBQxo8ff6+rIe6i6weucWTgL0QtPA7AXwM30uj6kVzLu7imE1vSk0wXPafXR7Gq7CqMqSYmDv6b979LxBWFC26uRLkYOOTrjdGMdWrprPS3bjqmG3T8EeJL5fRM0Gloucx0p4cpRIHJGJ0HzPr160lKSqJfv373uipCiAfApUuXWL9+PS1atKBq1apFuq+kpCSWLVvGY489dt/ddCks586d49tvv+XYsWNcu3YNk8lEyZIlady4MQMGDCAgICDHdaZPn86hQ4cwGo1Uq1aNoUOHUq9evXtwBOKBoaokfrCVpM1nce9TA5+R9bn23i6Sj8VjqeLPpS+P4046xZVY1KXpnH7RheKYMSlaUvSe+GQm2+9mq4AZwNX6wiXdiEmnIc7PnSXV1+Hh7km32GROlPBBrVWOJFcDjRNTsCSlsj3QF1eLik5ViQ1wt09EcCspLno8zCZcLBZijRr+umLi0UBpct4ZSUkrCLnq7oGgoCB2796NVqu9feFs1q9fz+XLl3MMdN59913eeuutwqiiEKKQrF69GiUPDYOicunSJebOnUupUqXuSqAzd+5cgAIHOnfy/Xg3zvXVq1eJiYnhiSeeoESJEmi1Wk6dOsXatWv56aefWLZsGcWKFbOXj4yM5Pnnn0er1TJgwAA8PT1Zu3YtI0eOZNq0adSvX79I6yseAFfjyYxKQVetBMbVf5E+fx9qaDkiP/sHd9LxIgYO/MmJN34hjuIEEUXgxmjKk4qCkUT82etdjxStdTyNRQPX/dxws6TTOOofAlJTsaAFzMQaPK37VFXcUi2oOoVSV1KokB4PQJmr8cR4uhFTowxn3N1INOjI0GpxMRkJSM0k0dcVoz5vn82dFUoQnJBGpI8b/1tgBlT+GaRQo4Q0PcXdI1fbPaAoCi4uLoW+XZ1Oh04nb6kQ95rJZMJsNuPi4oLBYCj07aekpODh4VHo270f3Mn3Y1Gc6+wef/xxHn/8cafldevWZdy4caxfv56BAwfal8+YMYOkpCQWL15sDzQ7duxInz59mDRp0j0PhMUdSE6zdpV4udkXqRlGSMlEKWb9fKrJGWQeikKjWrAE+5Ox4wLKpVgsv50ibe8VXOMu4UUsekwkU4Jk/FGw4P7LP5RBwYOraLGmfvlwhav6YEoar2WphJYL7kH2IAdAYwH3FCPJ3u7sDq5Jp5P7AA2n3EqS6mLAooDRoCcmyBuTQYc+00ypyDj0RgvpOi0//a8CZ73dURUFdNZrM9FFjwYIiE3laoAHZt3tRz6oikKkj5u1B+jGjYuaC1VQrMfziA+MrQ9NQ6C4hxYfF/kciMInY3Tugdxy0Dds2MCAAQNo0aIFTZo0oWvXrrz77rtcv34dsOafHzp0iMuXLxMaGmr/OXDgAJDzGB3bsuTkZD755BPatGlDo0aNGDx4MP/8849T3eLj45kwYQKtWrWiadOmDBs2jGPHjhXqWJv4+HgGDRpE8+bN2bdvH2DtqQoNDWX//v0sXryYrl270rBhQ3r06MGGDRty3M4PP/xA//79ady4Mc2bN2fEiBEcPnzYoczQoUOd6r1lyxZCQ0N5+umnHZavWrWK0NBQ+3nJOrZq3bp19OnTh4YNG9KpUycWLlxY4OM/dOgQw4cPp3nz5jRu3Jj+/fvzww8/5Fj24sWLTJgwgQ4dOtCgQQPat2/Pa6+9xtGjRx3KHTt2jDfffJO2bdvSsGFDOnbsyNtvv01kZCRw63EPc+bMITQ0lEuXLtmX2a6b69ev8/7779OqVSuaNGnCSy+9xLFjx5y2sXLlSkaMGMGTTz5JgwYNaNeuHe+9957DNm1s4wj++usvXnzxRZo0aUKrVq346KOPSE1NdSofExPD559/br8m2rRpw/Dhw9mzZw8Ar732Go0bNyY5Odlp3SNHjhAaGmrvZcgrWx337t3Lc889R+PGjWnXrh2TJ092qqPt/J0+fZopU6bQoUMHGjVqxN9//w3kPm4kIiKCwYMH06RJE5o2bcrgwYOJiIhwKmdb/9ixY4wcOZLmzZs7Xbu5Wb9+PcOGDQNgwoQJ9u+MrPVJS0tjxowZ9vPbrl073n//fS5fvpzX0wVYPy9dunQBYO7cufZ9Zf/8bdu2jRdffJEWLVrQuHFjevToweeff47RaAQKf4yObdm5c+d4+eWXadasGc2bN+eNN94gJiYm3/vITcmSJQFrr5ZNWloaO3bs4LHHHnPoTXN3d6dbt25cuHCBI0dyH0+Rm9OnTzNq1CiaNGlCy5Yteffdd4mLi8uxbF4+m0ajkdatWzN48OAct7Fo0SJCQ0M5dOgQABkZGcyZM4cePXrQuHFjWrRowVNPPcVXX32V72N5IBlN8PxM8BsAfs/Cs19Beiamz34is/gbZPqPIbPpZBI7f8c/XjP5q/lP/N1iCzGVv8T0/DKS39vGtc2xuMddwIcraDGRTBBplECLFg16THjhSYw9yAHQoBLoEOQAKCRqfZyqqLsxyUCK3p0Mg0qUmw9/BZcHFDQquGSa8I+OQ2M2c6lsMY78rzQW4N+yJbjq7mINcgDcbt48TdNpqZicjn9qZt7PVfYgPsvrowkw+Ceo/B34TjfTcoWJxAx5/o4oXHL7/z6xceNGxo8fT506dRg2bBguLi5cuXKF3bt3ExcXh5+fH6+//jozZswgPj6e1157zb5u+fLlb7v9kSNH4ufnxwsvvEBCQgJLly7l5ZdfZt26dfY7w5mZmQwfPpwTJ07QuXNnatSowcmTJxkxYgTe3t6FcpxRUVGMGjWK1NRUvvnmG6dUmpkzZ5KRkUGPHj0wGAysWrWK8ePHExISQu3ate3lpk2bxqJFi6hRowbDhw8nNTWVtWvXMnToUL744guaNGkCQL169fj666+JjIwkJCQEgP3796PRaDh16hTx8fH4+vral3t4ePDII4841Gn16tXExcXRpUsXvLy82Lx5M9OnTycwMJD27dvn6/h37NjB2LFj8ff355lnnsHd3Z2ffvqJiRMnEhUVxYgRI+xl//33X1566SVMJhNdu3alYsWKJCYmcujQIf788097PXfu3Mkbb7yBm5sbXbt2pXTp0sTGxvL7779z6tQp+3EXxKhRo/D29mbIkCHExsayYsUKXnzxRb777jsqVapkL7dkyRJq1qzJU089hY+PD6dPn+aHH35g//79LF++3H6ObU6cOMGrr75K586dadeuHQcPHiQ8PByNRsM777xjL3fp0iWef/554uLi6NChA9WrVyctLY2///6bffv20aBBA7p3786OHTv48ccf6dmzp8N+bNu0NcDz49ixY2zbto1u3brRsWNHDhw4wPLlyzl9+jQzZ85Eo3G8T/Tee+/h4uJC//79URQlx7EaNitXrmTSpEmUK1eOF154AbDe6BgzZgxvv/02PXr0cCh/5coVXnrpJVq3bk3Lli1zDAhzUqdOHQYNGsT8+fPp3r07derUAbCnVplMJkaOHMmff/5Jq1ateOaZZ7hw4QKrV69m7969LFq0iMDAwDztq3z58rz22mtMmTKFJ554gieeeAKwNuptZs6cyfz586lQoQL9+vUjICCAyMhIfvnlF4YNG4Zer8/TvvLr2rVrDB06lBYtWjB69GhOnjzJmjVrSElJYebMmQXaZkZGBmlpaWRkZHD27FmmTZsGQOPGje1lTp48SWZmJrVq1XJav2bNmoD1c277f15ERUUxZMgQMjMz6dOnD4GBgezcuZNRo0blWD4vn029Xk+nTp1YsmQJ586do1y5cg7bWLduHWXKlKFu3boATJo0iXXr1tGxY0f69++P2Wzm4sWL7N+/P8/H8UCbugG+23bz9ZLtWHSumBccti9Sd53GxGXSqQyAFjNuZKAAqXgRwAW8iQXAgpY0/Bx2YcJAEr74cPW21XEzpzktMxqsPSh6s5EkrRu6dAMuGSYsWb63FMAtOZ2kYnrSPF3Y3qIGl0r4Yka1TkCgUcBVB4oZMsyUTs3A1aKSZshHWqmq5mlMD8CvF+DtnRZmtM5/2up/gUwbXTAS6NwnIiIi8PDwYPbs2Q7pZ7a7sQAtWrRg2bJlZGRk0KFDh3xtv1q1aowbN87+ukKFCowbN44tW7bYG4fh4eGcOHGCl156ieeff95etlKlSkyaNImgoKCCHh5gbTi+/PLLeHp68t1331GqVCmnMpmZmSxatMje4GnVqhVdu3ZlxYoV9kDn3LlzLF68mP/97398/fXX9rLdunWjd+/eTJo0iYYNG6LVau09XPv377c3+A8cOEC7du3YvHkz+/fvp02bNqiqysGDB6lbt67T2IDo6GhWrVqFp6c1t7lr16506tSJsLCwfAU6ZrOZzz77DDc3NxYuXEjx4sUB6NOnD0OHDmXhwoV07tyZMmXKoKoq48ePx2g0snDhQipXrmzfzqBBg7BYrHfr0tPTmTBhAp6enixdupQSJUrYyw0ZMsRerqCCgoL47LPP7Kk1LVu2ZMCAAXz11VdMnz7dXm758uW4ubk5rNusWTOGDx9OeHi4QyoPWBuA8+fPtzfwevbsSUpKCuvWrePVV1+1N44//fRTrl27xvTp02nYsKHDNmzH1qhRIwIDAwkPD3cIdNLT0/nxxx9p0KBBnhvrWZ06dYrJkyfTokULAHr37s3kyZNZvnw5W7dupV27dg7lPT09mTVr1m3TRxMTE5k2bRohISEsWLDAfl316tWL/v37M3XqVNq0aYOXl5d9naioKN599126deuWr2MICQmhfv36zJ8/n0cffdTpe2P9+vX8+eefPPvss7z88sv25fXr1+eVV15hxowZfPTRR3nal7+/Py1atGDKlClUqlTJaV///PMP8+fPJzQ0lK+++sohPS23RnphuXjxor1H20aj0bBy5cocG/Z58cMPP/D555/bX5cqVYqPPvrIHkyCNcACHD6XNrZltjJ5NWvWLBITE/n666/t3299+vRh7NixHD9+3Kl8Xj+b3bt3Z8mSJYSHhztcC4cPH+bcuXMO71FERASNGjViwoQJ+ar7Q+PHw06LLFuPOi1z5+YNCQ9SUbBmulnQ4MHNHjgVDTkl2MRTwinQidd64We+2WuoAnGqP4YMM5ku1r9dmQYtqe56FNXC41f/5KhvRYLjUtBaLA6BDoCape2s6FS8U1Mpn5TKv14e1khIVcFFCwYNXgnWXnP/1EySXHO4KWFRwWgBg8Ya3OQjyLHZeEZlRr7WEOLWJHXtPuHp6Ul6ejq7du1CzT6VYyHIPnmB7Q/kxYsX7ct27tyJVqt1Sovp1q2bvTFWUHv37mXo0KGUKlWKefPm5RjkgLUxmfWubokSJShTpoxDPbdv346qqgwYMMChbPHixencuTOXL1+2/8GvWbMm7u7u9juNly9fJioqinbt2lGxYkV76pytdyenAdSdO3d2OH5XV1dq1arFhQsX8nUOjh49SnR0NF26dLEHOQB6vZ4BAwZgsVjYvn07AMePH+fMmTN07tzZIcixsfUm/P7778THx9O/f/8cG1PZex3ya8CAAQ7jBx555BHq16/Pvn37HHoVbA0pi8VCcnIy8fHxVKlSBU9PzxxTJGvVquV0F7tevXqYzWZ7Sk1CQgK///47jRo1cgpysh6bVqulS5cu/Pvvv5w6dcr++59//pmUlBS6du1aoGMvW7asPcixee655wByTDHr169fnsbI7d27l7S0NPr27etwXXl6etK3b19SU1PZu3evwzo+Pj5FMk37r7/+ikajYdCgQQ7LmzRpQpUqVdixY8cdB8s2W7ZsAay9y9nH4CiKUqTjVIoXL+4Q5EDO34H50aJFC2bOnMnkyZMZMmQInp6exMfHO5RJT08HyLGnyjaeyFYmLywWCzt37qR69eoO31WKojBgwIAc18nrZ7Ns2bLUrVuXTZs2YTLdTJcKDw9Hq9XSqVMn+zJPT0/OnDnj8Hm738TFxZGRkWF/nZyc7JBWmJmZSWxsrMM62dM1s7+Ojo62/n2uVNJpf0ppP6dlmdwcM5aOKyrW2MGFdMzcvCa0GNHh2EtrQSGGkkRSCSN6zGiJ1JYlwqcp/7hXI07rQ4Zi4KrOH6PihndSJn5xafjFpVEl7jyPX/6bFhf3EWUoRbzBhwQXN5Rsj7NRFUjxvjnWz8Vkwj85hU5XYmh/NZa2l6/x/N7jdDx6Aa8MI5XiEgEoH5uCT2qG47TTZhVSjJBuhiQjpBZsSunKWU5jnt+PG4r0PS/CfYiiJT0694lBgwZx6NAhxowZg4+PD3Xr1qVx48a0adOmUAYdBwcHO7y2pRIlJCTYl0VFRREQEOCQagLWP9KlSpVy+DDnR1xcHC+//DIVKlRg9uzZuLq65rmeYG3kRUdH21/bGsIVK1Z0KmtbFhUVRfXq1dHpdNSuXZuDBw8CsG/fPrRaLXXr1qVevXrs3r0bwB4I5TTda251ynru8sJW7woVKtyy3nCz8XW7WbJswVa1atXyVZe8yiktsnz58uzZs4fLly/b671//37mzp3LkSNHHP4IADleN7mdU7h5TV68eBFVVfM0U1jXrl357rvvCA8P5/XXXwes6TbFihWjefPmt10/Jzkde0BAAF5eXvb3KasyZcrkabu2dXO6DmzLsm8/ODi4QLOQ3c6lS5coXrx4jqmpFStW5MSJE8THxzvMIlZQFy5cQFGUHAP3opaX6y2/AgMD7T2FLVq0sPd2pqen2wNH23edbfxRVpmZmQ5l8iIuLo7U1FTKli3r9LucrifI32ezR48evPvuu+zatYsWLVqQkpLCzz//TNOmTfH397eXe+211/jggw/o27cvwcHBhIaG0rRpU5o1a3bHN1cKS/ZrNvvNOoPB4HBMgFPWQvbXtnFYvNkdwvdBdLz1dYA3mtkDUF4MQ91/3rpMryVD7w2p1vAmDVfSccGVDDxJIoniuJBiT0byIpIYKqGgRcHMNfyxoOEyFbiM9b3VWIyUSb9GquJDimLCRU1Cb9aRaVCwpIL2xj2J5HR/UjQ6Yorf+FuuqqQrevQmFUU1YdJpUFSwaDSoGscbDArgkmnksbh4tDca+MEpaZSLS8I/PZN/AxSueHqQ4qJ37K3JNFl7dFy0YNAWaCZkFy1MbHLzey7P78cNRfqeF+E+8kpS1wpGAp37RJkyZVi5ciX79u1j//79HDp0iIkTJzJnzhzmzp17R+MsgFwbSUXRe5Sdt7c31apVY9euXWzevJnu3bvnWja3P5J3Us/Q0FB+++03Tp8+zYEDB6hRowbu7u6EhoayfPlyoqOj2b9/P76+vjk2woqigXm33epuudl8Z0+tPnLkCCNHjiQkJISRI0dSqlQpXFxcUBSFt99+O8cegVud04K81yVLlqRhw4Zs2rSJ0aNHc/nyZQ4dOsSzzz5712YizE+D9X7a9t1U1D03ublV47uwvgMrV65M1apVWbVqlT3QsfXcXr3qPM7Ctixr725hy+9ns2XLlvj4+BAeHk6LFi3YunUraWlpTimTLVq0YN26dezevZtDhw6xb98+wsPDqVOnDrNmzSqysVb3jfKBcHwGrNkDZgv0aIDi54l+91gsG/6GS/FoOtYiwN8Dlxl7SFxxCr2PFp1POYzbI9Ekp6E3Z5BCcVxJIRMXrhOMGT0KCr5E40emPcCxUtGrKj6pKQRws8fAoJqokfYPx7yr45ZqQWNRSXfVEVfM+p2hWMzUjD/DJZ21ca0zq+hufOdrM4zojGZMhuzfkSrabB8L//RMEnVa3Exmrni5YNJm+0y56sBitP6bG9tnLct3gKsWqhaDrpUURtTWUMJDGvOicEmgcx8xGAw0adLEPpB+165dvPLKKyxdupQ333wTuHWD9U6VKlXKnpKUtVfHZDJx6dIlhzED+aHT6fj888956623+L//+z9MJhO9e/cucD1td2dPnz7tFACeOXPGoQzc7KXZv38/+/fvt6cyhYaGotVq2bNnD3/88Qf169cv0vNrq5Otjreqt6134MSJE7fcpu3O7vHjx2nQoEGu5Wx37BMTE51+l1PvhM3Zs2edBlKfPXsWrVZrvyu1ZcsWzGYz06ZNczjvaWlpBe4FBChdujSKouQ47iAn3bt3Z9euXURERNjXKWjaGliPM7uYmBiSkpJy7CHIK9s1e+bMGadpim37vJPtZ3erazo4OJjff/+dpKQkp8/3mTNn8PDwcJpIoqD7Klu2LL/99hsnTpzI1+D7B0lGRoZDD1GlSpUwGAz2GfiysqWNVa9ePc/b9/Pzw93dnfPnzzv9Lqfvlfx+Ng0GAx07diQsLIxr164RHh5OiRIlckwd9fHxoUOHDnTo0AFVVZk+fTqLFi1i+/bttG7dOs/H9MDydofnWjosUvRatN1rOxZ7qwXeb7XIdTMWsxn1xTBctpxCLeOP5p9IUpJ98CIOLUbiKU4GbiioKChk4kIinhjIxIieOPwIST9G2YxNJOh8SNMbuGwIwj8FSqVEEZR5Db1qQuP+P065V3HYt85ooXhUIpfL3+ylsCiQodfjanJOPTtZzJsySWkoud0b8Lj19O7FkjNw8dTyf231PFNTi04jQY0oevdHH7Nwyu2Gm+lIWf9wuru7k5iYWCQ9MU2bNsVsNvP99987LF+7dm2OU/fmh06n45NPPqFVq1ZMmjTJaR/50axZMxRFYfHixQ655DExMaxfv56goCCHdKeqVavi7e3N6tWriYmJsQc+np6eVK1alWXLlpGcnFzkTymvVq0aJUuWZP369Q7T2ppMJhYvXoyiKPY0qypVqlChQgXWrVvH6dOnnbZle/8bNGiAr68vS5cuzXGqXFs5Dw8P/P392b9/v8O1ExkZmeN4E5tFixY5lD927Bj79u2jXr169mDY1juT/Zr87rvv7mh8h4+PD40aNeK3335zGrOS0/6aNGlC8eLFWbNmDRs2bOB///tfgQaZ25w/f97p3NimFS9oOhxYB/q7ubkRFhZGSkqKfXlKSgphYWG4u7vfMmjNL9v7lFOKVosWLbBYLCxYsMBh+e7duzl+/Hi+U5Fs40FyCqhtkzfMmjUrx1Suu9G7XBhym5L6wIEDnD592uHGgLu7O02bNuXgwYMONy1SU1P54YcfKFOmDDVq1MjzvrVaLU2aNOHff/+1P1YArOdu0aJFOZa3/T6rW302u3fvbg+O/v77bzp16uTQA2s2m52CJEVR7N+5BU0F/K/SaLV4zOtHQNT7FP99OP5J/0dJ9TN81G8JWDWQQI9UgrlCoDYOzxquuJOAGQUz1pncUj1gZ/HHydC4UMJ4jbLpUTx6/QR14v6iTMZl9Kr1b2TN1L8JSY9EUS1oLBb0aWZ0RhX/6ATcE1NI1elIcTGQ4OlJpl5HYrZeuUythigPDzaWCcI7JR29Kdv1c6ubhKpKHW06f77mxqWxbjz3qE6CHHHXSI/OfWLEiBF4eXlRp04dAgMDSUpKYv369SiK4jB7Uc2aNdm5cyefffYZjz76KBqNhnr16hVKDn23bt1Ys2YNs2fPJjIy0j699M8//0zp0qXvOMVJp9Px8ccfo9Pp+OKLLzCbzTzzzDP53k65cuV49tlnWbRoEUOGDKFNmzb26aVTU1P56KOPHP4wazQa6tatS0REBC4uLjz66KP239WrV8/eeC3ok9zzSqvV8sYbbzB27FgGDhxI9+7dcXd3Z+vWrfz9998MGjTI3pOjKAoffPABw4cPZ+DAgfbppZOSkjh06BANGzakb9++uLq68t577/Hmm2/y1FNP2aeXvn79Onv27KFfv372AfV9+vRh9uzZjB49mubNmxMTE8Pq1aupWLEi//77b451vnz5MiNHjqRZs2bExMSwYsUKXFxcHGZlss0G+PLLL9O9e3f0ej179+7l1KlT+eoNyMkbb7zB4MGDGT16NJ06deKRRx4hPT2dI0eOEBQUxOjRox3Ob5cuXZg3bx6Aw1TdBVGpUiXee+89unXrRpkyZThw4ADbtm2jbt26tG3btsDb9fLyYvTo0UyaNInnnnvOPsh7w4YNXLx4kbfffvuOJ//Iqnz58nh4eLBq1SpcXV3x8vKiWLFi1KtXj86dO7NhwwYWLlzIpUuXqFu3LhcvXmTVqlX4+/vn+xz6+vpSunRpfvrpJ0JCQihWrBhubm40a9aMmjVrMnDgQBYuXEj//v1p27Yt/v7+XLp0iW3btrFw4cIC9xrfTZ9++qn9hknJkiXJzMzk6NGj/PTTT7i7u/PKK684lB85ciT79+9n5MiR9OvXDw8PD9auXcu1a9eYOnVqvnuRhw8fzm+//cYrr7zCU089RYkSJdi5c6f9eWtZFeSzWb58eWrXrs3mzZtRFMVpavbU1FTat29Ps2bNqFq1Kn5+fly6dIlVq1bh7e1Ns2bN8nU8Inf6nrUp1rP2rQt9sAPdV0c4XawR+moGMv++gHeyQhnTCeDm8260WKib/AfByWlc0fpxzssfd1MG/moqLZZ04OV3LxPv7k6yQY9RoyFJgXrX4vAwmYj1cGVLmZJcdnUBrcIFjQZNhgWNyYJFc+OhojndELkx61opT4VDLxXed5oQ+SGBzn2iV69ebN26lTVr1pCQkICPjw9Vq1bljTfecGiA9+/fn6ioKLZt28bq1auxWCx8/fXXhRLoGAwGZs+ezVdffcX27dvZunUrNWvWZNasWUycODFfswPlRqvV8uGHH6LT6Zg6dSpGo9Fpxqe8GD16NKVLl2blypXMmDEDvV5PjRo1mDhxosP0rjahoaFERETw6KOPOjw93RbolChR4o7u/udVs2bNmDVrFvPmzWPx4sUYjUbKlSuX49TBNWrUYOHChcybN4+ff/6Z1atX4+vrS40aNRyeKdS8eXO+/fZb5s+fT3h4OKmpqRQrVow6deo4POtm4MCBJCcns2nTJg4ePEj58uV57733OHr0aK6BzvTp05kyZQrffPMN6enp1KpVi5dfftlhLFPt2rX57LPP+Pbbb/n6669xcXHh8ccf55tvvmHIkCF3dL6Cg4NZvHgx3377Lbt372bjxo14e3tTuXLlHMd6devWjfnz5+Pm5nbH6TPVqlXj1VdfZdasWaxZswYPDw/69OnDiBEj7njAde/evQkICGDx4sX2h5lWqVLFYTrrwuLq6srHH3/M7NmzmTJlCpmZmfbJOHQ6HTNmzGDevHls3bqVX3/9FS8vL1q1asXw4cOdBuLmxUcffcSUKVOYOXMm6enpBAUF2Ru/o0aNonLlyqxYsYJFixZhsVgIDAykcePGD8w4pHbt2rFx40Y2bdrE9evXURSFkiVL0qNHDwYMGOB0zkqXLs28efOYPn06CxYswGg0Uq1aNaZNm0b9+vXzvf+QkBC+/fZbvvzyS8LCwjAYDDRq1IgPP/zQKQAv6Geze/fuHD58mNDQUKf0YFdXV55++mn27dtnT3UOCAigWbNmDBo0qEjHHAlnxSc0o/iEm8GlKc3EvoAvCDB7Y1AdZ3LLwJMMDCiKGU9jOqZKXtTc8QxuxVyYstCXZ965hDbexFU3F6IMeq6WCSKylDcW23icLNNFW0wWyMzyvJ1sX4kuGSZMGjDrtex/9sEf5yoeXIr6oOQLiHvGbDbTunVratas6fDsFPHwGj9+PBs2bHBIj3kQxMTE0LFjR7p06eLw4NH8Cg0NpVOnTowfP77wKifEA2Lr1q289dZbTJw4Md8PRRb3XnJUMqeqTCLEeA1/o3XSi1T8uEwlVBQyh9Wnxuwnclw3LTWDp4aeISDFxNYqwUQGeKOoKhVjEzGYLZzy9yZTdyNwsQU+2Z6X45ppolhyBsmB7vzWX0eNAElTKwxJymu3L3SDlzqlCGvyYJEeHeEgPT3d6c7q6tWrSUpKKtDdRyHuplWrVmE2m+nRo8e9rooQD6yVK1fi6+tLy5Ytb19Y3Hc8gz2pfW4MyxutR2NU0RnNBCYlUywliZIfNMDv/Ra5rqvTa+jUdCcAP1zuh4vJzICDJwlKTgMgVadlfmhlYj3cbgY3WYIcxWIhHZX2jd2Z1/Ehn33vLpPppQtGAh3h4OOPPyYjI8Oe4vX333+zZcsWSpcubU8VSk5Ovm0am16vtz+n4mEn5+Pe+/HHH4mOjmbx4sU0bNiQRx55xKnM9evXbzvOzN3d3ek5Uvcjs9mc45iM7Hx8fAplqt/cBuBn5enpWSTpZ3fjfTMajXkaRO/n51ck082np6fnacKXgICAQt+3TVxcHPv27ePw4cMcOnSIkSNHOqT5igdMcR/6nnyG6PB/uXwslUrP1cQrMH+fz751tPz10zV7kAPgbjJTIS7ZGujkQGe2YLAgQY64b0igIxzUr1+flStXMm/ePFJTU/H396dbt24MGzbM/uDSyZMns2HDhltup27dunzzzTd3o8r3nJyPe++dd97BxcWF2rVr89577+VYZsCAAbd9IvWQIUMYOnRoUVSxUF25csVpkHhOvv7660KZZCMv6UsffPABnTt3vuN9ZXc33rc///yTYcOG3bbcunXrKFWqVIH3k5utW7cyYcKE25YrylTSM2fO8O677+Ll5UXPnj0LNFGMuP+U7FqdkgWcZX9yWx2DVqQ4LS93PYn9pXMYi6WqWFSIf8ulYDsUogjIGB2Rb2fOnOHatWu3LOPt7Z3jXfWHkZyPB8Phw4edngyfXXBw8B0/nPduyMjI4PDhw7ct98gjj9ifoXQncpreO7uKFSsWSY/D3XjfEhMTOXr06G3L1a5dGxeXwm/ExcTE5DiNfHaSPiyKmtFoZP78+QAMGjSIZ144T7Wr1t7jZIOOc36exLi7sqNCkOOKqgoqHB2soVqATD5QFBKVMXku661OLsKaPFgk0BFCCCGEEE6Bzta/TKycdJZENz3hNcpizmnGyUwTGuDoMANVisnjGYuKBDoFI6lrQgghhBDCSbs6rnR8rKL1OTk5PPNJsVgo5aFyZqQrBq0Mlhf3Hwm9hRBCCCGEE61GoaaPJccgB1Xl8XPXWN3HRYIccd+SQEcIIYQQQuQo4lmDdQxONo9GxtF3YAnqB0lT8m5QUfL8I26Sq1MIIYQQQuTI30PLuDrcDHZUlUqeFv6YWoJX6ss00uL+JmN0hBBCCCFErj5prWdiS5X9l1Uq+UGAu9wnFw8GCXSEEEIIIcQtaTUKDYIlLepekZS0gpGQXAghhBBCCPHQkUBHCCGEEEII8dCR1DUhhBBCCCHua5K6VhDSoyOEEEIIIYR46EigI4QQQggh8szy53n+bDKfQzXmErfk8L2ujhC5ktQ1IYQQQgiRJ4k/HeVqhx/wNVufq5P47EYufX+cmhufusc1E8KZBDpCCCGEEOK2rh+P5UyP9Zg8vdhS+1GuentRPeoSbX/8G9VsQdFKolBRUe91BR5QEugIIYQQQojbOtRsEYFGDZ93bU+ymysA50sU56q3NwED9jJqacN7XEMhHEmgI4QQQgghnJyf/Tcx3/yJW2VffNuVwJDuwp/l/e1Bjs2h8mV57OQR/vrtOo828rtHtRXCmQQ6QgghhBAPAbNFpdMaM9vOg/FGrpOLBt5vCG83zF+Tb1flxZQ7H00AwOELpKzW4KPXE1nM26msqigEZVxj+dyLEugUEVWmly4QSaYUQgghhHgI6KaY2XLuZpADkGGBd3ZD33WmPG9Hv8aAITYZjT4Rve4qV720/FMyiDhPDdUvn8I9I82hvHtGJqf8ylNh778cqDMV0jML54CEuEPSoyOEEEII8YALnnXrQCbsBCzP47bK7k3nseT9uJICQKmkC+i0tTgQ8Cjtrv1M/R8Os7pWay55B+CZkU73HUc4X8qVy7qSHE0ribbap9Q59/4dHpEQd04CHSGEEEKIB5jZonIptfC2VyXxnD3IsakV/y9nigVRJuEKAC/9vhILChlaPYd9GlM98ip1DX/xl28NritupKeacHWXZqa4tyR1TQghhBDiARYyy5yncv/G5K2cXnVOPdNhxjc92f76H59qLCvXi2Vle3PWO4A0jRv+mfE8cXU3gemx/Fj7K5IPRebtAEQeKPn4ETYSagshhBBCPKCWHjERnZ63snUXqaS/dusyxbabOOVdlhIJ51GyPL0lSevBmYBgGlxzJUHnw96AUPvv0nQuHAkMJNqlNhsfaYpZo8E12YR/l3k0ifygIIclRKGQHh0h7pFLly4RGhrKnDlz7nVVCsXKlSvp2bMnDRs2JDQ0lEuXLt3rKuXLgQMHCA0NZf369fZlRfkezZkz54E8T3dTUZ7/8ePHExoaevuCD5DOnTvz4osv3utqiLsoMcPCM5vzXj7Dcvsy7r9oSNW4cUFXlUxcUbE+rNLLnMKQv1ZyrER5Dhav6bTe0eAyTGr5HH+Xqsy/JStyqFJVltVrT9rpuLxXUIhCJj06Qog7duDAASZNmkTz5s0ZOHAgOp0OPz+ZYlQIIYrKsiMm+ucjyMmrkLgkfDKNgCeXqU4wf6LjZsrbY1FH2dqwCcWv3px5TW/OpPWl7Yw5OIdUvStRPiWI9A3k50qPs6HjGtp//BhePesUfmX/Q2R66YKRQEcIccf27t0LwPvvv4+Pj889rk3hCQoKYvfu3Wi12ntdFSFua/Xq1SiKNIb+C3ZcKHiQU2KGiasjc27+xa05diPIsdJicghybLwt10l098M71RrsNL/yO2XTLlp/l5mK97VzPHLtHK1O7uPLxs9wYMQvPCGBjrgHJNAR4j/CZDJhNptxcXEp9G3HxMQAPFRBDoCiKEVyvh5WqqqSlpaGu7v7va7KAy8lJQUPD498rWMwGIqoNuJ+EJtqZstZlZd+gqS8zSmQo2vpoJ9s4soIhWJu1ps4ZovKX+fT8XomDD03P79m9BhxRY/jIKC/gitz2bsEwdfiqH/qDKXTck7B1aDy7OENuKWa+aPEO3gE+1NhYiuUepXQFHeXwFwUOQl0xENt/fr1TJgwgdmzZ3Ps2DFWrVrF1atXCQoKYvDgwXTq1AmwjgXo0qULQ4YMYejQoQ7bmDNnDnPnzmXdunWUKlUKsOb3b9iwgZ9//pmpU6eyc+dOjEYj9erV46233iIgIIA1a9awbNkyLl26RFBQEKNGjaJFixY51nPLli0sWLCACxcu4OfnR5cuXXj++efR6Rw/ojExMcydO5ddu3YRGxuLr68vTZs25aWXXqJYsWJOdQ4LCyM8PJyff/6ZmJgYZs2ala9xCRERESxatIgTJ06gKAqVK1dmwIAB9uOwnTcb27br1q3LN998k6d9XLt2jSVLlrB//34uX75MRkYGwcHBdOzYkWeffdahN8X2fs6cOZPDhw+zfv16YmNjKVu2LIMGDaJdu3YO2+7cuTNBQUG89tprTJ06lSNHjqDX62natCkvv/yywznLya2ui59++omwsDBOnjyJ2WymUqVKPPvss7Ru3dqhnMViYeHChaxdu5aYmBhCQkIYNGhQns5NTmzX3oEDB5x+FxoaSqdOnRg/frxT/cuWLZuna+zgwYPMmDGDEydO4OnpSZs2bejevTtPPfWUw3k4cOAAw4YN44MPPiAtLY2VK1cSGRnJc889x9ChQ/nnn39YtWoVf/31F1euXEGr1drP0RNPPOFU98OHDzNt2jSOHz+Oh4cHrVq1omfPnjmeA1VVWb16NT/88ANnz55Fo9FQvXp1hgwZ4nR9Z2Rk8PXXX7N582aSkpKoWLEiw4cPL8ipByA6Opo5c+awf/9+YmNj8fT0pHTp0vTo0cP+fZKfOmZ9j8qXL8+iRYs4e/Ysbdq0oVixYixatIjvv/+eypUrO9QjOTmZdu3a0aBBA7744gvg5vWe/bN37Ngx5s+fzx9//EFSUhLFihXjf//7H8OHDyckJMRebu/evSxatIgjR46QmZlJmTJl6NWrF7169XLY3p9//sm8efM4fvw4SUlJ+Pj4ULlyZYYMGUKtWrUKfG5Fzhb9Y2LglsLdpgnwn2FBl5lB6YQkIov5Uvd8FDNcfQlIzsySJKUQQ3kCOI2eTFQUFFTi3bwxa7VcDAxgxM+/YEKPIYeeH4CAlHjSdXpqXztG9LVqXOq0jgBO40Yi6biT2aMVXg2LoSz8FYvehev+1Ug/l46ucjF8P2qGoW5JEifvJWXhXygeBrxfexyPp6oX7gl5AEjqWsFIoCP+E2bOnElGRgY9evTAYDCwatUqxo8fT0hICLVr1y7wdkePHk2JEiUYNmwYFy9eJCwsjLFjx/LEE0+wdu1aunbtisFgICwsjDfffJM1a9YQHBzssI0dO3YQFRVF79698ff3Z8eOHcydO5fo6Gg++ODmbDXR0dEMGjQIo9FI165dCQkJ4eLFi6xevZoDBw6wePFiPD09Hbb93nvv4eLiQv/+/VEUhYCAgDwf28qVK5k0aRLlypXjhRdeAGDDhg2MGTOGt99+mx49euDn58eHH37I2rVr+eOPP/jwww8BbhtAZHXy5El+/fVXWrRoQUhICCaTid9//50ZM2YQFRXFO++847TO9OnTSUtLszfA1q9fzzvvvENmZiadO3d2KHv16lVeeuklWrZsSatWrTh27Bjr1q3j6NGjLFq0CFdX1zzX1WbWrFl89913NGrUiGHDhqHRaPj1118ZN24cb7zxBn369LGX/fLLL/n++++pW7cu/fr1Iy4ujkmTJjldB0Upr9fY4cOHGTlyJN7e3gwcOBAvLy+2bt3Kn3/+meu2v//+exISEujWrRv+/v4EBgYC1iD53LlztG7dmqCgIBISEtiwYQNjx45l4sSJtG/f3r6Nf/75h+HDh+Pu7s6AAQPw8vLip59+cqhbVu+//z4//vgjrVq1onPnzhiNRjZv3syIESP47LPPaN68ub3sO++8Q0REBE2bNqVhw4ZERkYyduxY+02L/DCZTIwYMYJr167Rq1cvypQpQ3JyMqdOneKPP/5wCHTyU0eA7du3ExYWRs+ePenZsyceHh5UqlSJRYsWsXHjRl555RWH8lu3biUjI8NhnznZuXMnb7zxBm5ubnTt2pXSpUsTGxvL77//zqlTp+yBzpo1a/jkk0+oVasWgwcPxs3Njb179/Lpp58SFRXFyy+/DMC5c+cYMWIE/v7+9O3bl2LFihEXF8fhw4c5ceKEBDqFLNVoKfQgx05R8MvI4GwJf1wzjUxZvpniyc7TSmfiQRRVcTVcwz8zBj0mNBbrrAZaswX3jAziCaY4p3Npiqu4m6zb9SSGOMpynbK48TeupKJZs5WENcXw5RIxVCKNywCYTl3nym+ReL9Sj4QJu+xbi+n7Axp/N9xaly/sMyIeQhLoiP+EzMxMFi1ahF6vB6BVq1Z07dqVFStW3FGgU6NGDd58802HZcuWLePq1auEhYXZA4969erx9NNPs3btWkaOHOlQ/uTJkyxatIhq1aoB8NRTTzF27FjWr19Pjx497A2Hzz77DJPJxNKlS+2NSYDWrVszaNAgli5d6tTr4OnpyaxZs5zu2t9OYmIi06ZNIyQkhAULFtiPo1evXvTv35+pU6fSpk0bvLy86NChA/v27eOPP/6gQ4cO+doPWHt/wsPDHVIY+vXrx3vvvUd4eDhDhw51CtDi4+NZvny5Q7369u3Ll19+SZs2bRyCl8jISF577TX69etnX1ahQgW+/PJLli9fznPPPZev+h47dozvvvuOQYMGMWLECPvyvn378vrrrzNz5kw6duyIh4cH586dY/ny5dSrV48ZM2bYe6datmzJs88+m6/93om8XmNTpkxBURTmzZtnbwD37t37ljN5RUdHs2rVKqfg9vnnn3e61vv27Uu/fv2YN2+eQ6AzZcoULBYL8+bNo2zZsvb9Pv/88077+/XXX9m8ebM92M667UGDBvHFF1/QrFkzFEVhz549REREOPRygfWaGzNmTF5OnYOzZ89y/vx5Ro0axcCBA3Mtl5862pw+fZrly5dTvrxj46169eps2bKFUaNGOfRubty4ER8fH5o0aZJrPdLT05kwYQKenp4sXbqUEiVK2H83ZMgQLDcaqzExMUyePJm2bdvy8ccf28v07t2byZMns3TpUnr27ElISAh79uwhPT2djz/+mJo1nWfeEoXr27/U2xe6A9e8vQBodfQ0wfFJOZbRYKIUR9Fmmkg2uHHFrRjv//wNY7q8jkmn5UjpEGpdhCSK48U1p2DHcXpf6zVnwgUVLQpmDKQThydmtKThmP6sJmSQ/K3zjZaUxf9IoCPyRKaXFv8JvXv3tgc5ACVKlKBMmTJcvHjxjrb79NNPO7yuU8c62LJjx44OvSuVK1fGw8ODCxcuOG2jfv369gYoWMeFDBgwALA2mMCaprJr1y6aNWuGi4sL8fHx9p9SpUoREhJinxAgq379+uU7yAFrCktaWhp9+/Z1OA5PT0/69u1LampqjvsrCFdXV3uDz2g0kpCQQHx8PA0bNsRisfDvv/86rdOrVy+nevXs2ZPExEQOHjzoUNbDw4PevXs7LOvduzceHh7285sfmzdvRlEUOnbs6PA+xMfH06xZM1JSUvj7778B6116VVXp37+/QyO1WrVq1K9fP9/7Lqi8XGOxsbH8+++/NG/e3CGdSafTOV3nWXXs2DHHHjw3Nzf7/9PT04mPjyc9PZ169epx9uxZkpOtDx6Mi4vjr7/+onnz5vYgB0Cv1zsEpzabNm3Cw8ODFi1aOJz75ORkmjZtyqVLl+yfs4iICACnoLJFixYO+8or2zV38OBB4uJynzI3P3W0adKkiVOQA9bzGxMT4/B5i4qK4s8//6Rdu3YO32vZ/f7778THx9O/f3+HIMdGo7E2AX7++WcyMzPp2rWr0zXdtGlTLBYL+/btczgH27dvJyMjI9d932txcXEO9UtOTiYp6WZDPjMzk9jYWId1Ll++fMvX0dHRqOrNwONu7MOTlJwPsJBozNZ0M0XNPaByJw4tJhbX7cAz/T5mWK93mdqsP73/+BGv1BSWNa7PmSAvvHMIcnLYIwB60tDcSHWzTV+t2P/nSHFz/humeOjvyftRFPsQRUt6dMR/Qk5pQj4+PkRHRxfqdr28rHfHckqL8fb2JiEhwWl5uXLlnJZVqFABsDZowJouYrFYCA8PJzw8PE91AShTpsytDyAXtv3a6nGrut0pk8nEggUL2LRpExcvXnT4owLW3qXscjpntkZi9noFBwc7NQYNBgPBwcEFOoazZ8+iqqrTuIWsbH/obNvPrb579uzJ9/4LIi/XmO15PjkFALcKCnK7xuLi4pg9ezbbt2/PMShITk7G09Pzlucop+vv3LlzpKSk0LZt21zrFBcXR9myZYmKikKj0eRY//Lly3P+/Plct5ET29i+BQsW0L59e6pUqUK9evVo3bo1NWrUKFAdbXI7j+3atWPq1Kls3LiRRo0aAdbeHFVV6dix4y3rawumsga5OTl37hzALccu2d7Dtm3bsmnTJubPn8+yZcuoVasWDRo0oF27dgQFBd1yP3dT9uA7e1qvwWDA39/fYVn2+md/XbJkybu+j+fqejEswoyxiDp2Gp46z+6qFdhWvSLR3p6UTLTegDAqGvTqzYfu/FWyEmF1bvbCRvqWZPWjxQGoEBnNpeKukIf2u4IZLZn4c86+LJFAvIhFgwUvrpHEzYwFXSU/fMc3JeaZdTe34abDa3hdDCUdg/cH9T0XRUsCHfGfYLtzmZ2tUX2rmV/M5tynt8lt2uHclmdvxOfXk08+mWtOfk6zgxVk/Mnd9uWXXxIWFkabNm0YPHgwfn5+6HQ6jh07xvTp0+/4nBUFRVGYNm1artdVxYoVi3TfOTGZTEW2z1vJ6RpTVZWRI0dy9uxZ+vbtS/Xq1fH09ESj0bB+/Xq2bNliT5vKL1VV8fPzY+LEibmWKcrzP3z4cLp06cKuXbs4fPgw4eHhLF68mAEDBjB69OgC1zG3z6qvry+NGzcmIiLCPhPbpk2bKF++vENwdSdsn7EJEybkOo7PdiPFYDAwa9Ys/vnnH/bs2cOhQ4fsk59MnDgxx4kmRMFpFIWrIzQ8Ms9CdNrty+eLqvJnSBBP7f0Ts1bDuv9VZeDug1x38+J4QAmqX4vGOz2NdFcfDgc7B+KWG3/nTpUphbc2Ef7K9ntFQZPt+9uFZErxNyoqKRp3UvxK4/VFL9zKusDCX/Fz1WMoWZm0Y0noK/vhNSoUbXEPNCU9SFn8D4qHHq+X6mKo6dxDKUROJNARAmtvC+Tce1BYPRe5sd1NzerMmTPAzcZFSEgIiqJgMpnuSsqTLXXpzJkzPP744w6/O3v2rEPd7tSmTZuoW7cun3zyicPyW6UV5nTOcqtXVFQURqPRoVcnMzOTqKioHHsRbqd06dL89ttvlCxZMsdUo6xsdTl37pxDOljW+uaX7VpNSEhwmM77VtdpXq4x213GnHo58tvzcfLkSU6cOJHjbHU//PCDw2tb7+et6phV6dKluXDhArVq1brtNNbBwcFYLBbOnz/vFFgU9PyD9fPRt29f+vbtS0ZGBqNGjWLRokU888wzFCtWLF91zItOnToRERHBzz//TNmyZYmMjHQa/5QTW4/R8ePHadCgQa7lSpcuDViDqrx+v9SsWdM+Ric6Opr+/fsze/ZsCXSKgK+rhssjbt5Uef0XE1MO3dk2q/vB4ed06LVewGP25TtKniVJdaF8fBzFU61pc5+26UGai/MNFsViwT8+kZhivhwKrsb2CnVpfsZasUytjmmNn6blqX3UvXQcCwoWjRZl10T4X1m07no8AIcJ1FvURAE8b/xk5daqPG6tZEyOyD8ZoyME1nEc/v7+7N+/36EHITIy0p7nX1T27t3LsWPH7K9VVWXRokUA9mmcbXd1f/nlF/v4j6xUVeX69euFVqf69evj5uZGWFgYKSk3c8RTUlIICwvD3d39lg2n/NBoNE69NmlpaSxbtizXdVatWmUf4wHWNKjVq1fj5eXFY4895lA2JSWFlStXOixbuXIlKSkpuU73fSu2CRdmzpyZY29f1vzs5s2boygKS5cudSh77Ngx+5iH/LKlOGVff8mSJbmuk5drLCAggOrVq7N9+3YiIyPtZU0mE99//32+6mjr6cr+vp46dcrp8+Tv70+tWrXYvn27Q0BlNBpzvAY6duyIxWJhxowZOe47+/kHWLx4sUOZiIiIfAdvYL3Osvecubi42ANm242S/NQxL5o0aYKvry8bN25k48aNaDSaPE380aBBA3x9fVm6dKn9WVdZ2d6fNm3aYDAYmDNnDunp6U7lkpOTycy0zpoVHx/v9PvAwED8/PxyTM0Vhe+LljqMrxa8+da6NBx5Xode6xy8PLqlGy7GTEISrH9Prvh4czHAnxgvx5QtxaLS4J+TuBpvfK8pCp8/8RyvdnmdSU88x6CnJhBRuR7vPzmCBdV6sLNaI3TmFegaVkLjnvu4MpE7FSXPP+Im6dER4oY+ffowe/ZsRo8eTfP/Z+++w6OovgaOf2c3m94LIRAgEHoTMEgv0qV3UZCmdGwIitjgJ76KItKriDSlIyQUBQRElK6ItNAhCQFCet027x8xS5YkkISEUM7nefbRvXt35k7ZMGfuvWeaNSMqKor169cTGBiY7YT4glKhQgWGDx9Or1698Pb2Zu/evRw6dIj27dtTs2ZNS73x48fz2muvMWTIEDp06EClSpUwm82Eh4fz22+/0b59+yx3z/PLxcWFN954gylTpjBw4EDLcLmQkBCuXbvGhAkTsoxNzq+WLVuyYcMG3n//fZ577jlu375NcHDwPR8+6u7uzoABAyyppIODg4mMjOTDDz/MMgTI39+fRYsWceHCBapUqcLp06fZvHkzAQEB9OnTJ8/trVatGkOHDmXhwoW8/PLLtGrVCh8fH6Kiojh9+jT79++3zL0JCAigV69erFmzxpLiOjo6mjVr1lChQgXOnj2b5/W3bduWuXPn8tlnn3H58mVcXV0tk85zkttz7M0332TUqFG8+uqrloQPO3bssFzc5/bhfmXLlqVcuXIsW7aM1NRUypQpw9WrV9mwYQPly5fn9OnTVvXffvtthg0bxquvvkqvXr0s6aWzCyRbtWpFp06dWLNmDWfOnKFJkya4u7tz8+ZN/vnnH8LCwizz2Bo0aECTJk0ICQkhLi6Ohg0bEhYWxoYNGwgMDOTChQu52p4MR44c4bPPPqNFixaUKVMGR0dHTp8+zaZNm6hevbol4MlLG3PDxsaGtm3bWpb33HPPZZtc4G729vZ89NFHvPfee7z44ouW9NIxMTEcOHCAl19+mebNm+Pr68v48eOZPHkyvXr1on379vj5+RETE2MJTteuXUuJEiVYvHgxBw4coHHjxpQsWRJVVdm3bx+XL1+2JLgQhc9Gq8H0joLbNyYS8zgK9Jfe2Q+tBnCq5oVTaqrlLri93oBiNqNmGqZrl6anxZF/sTcYiXGxfqjtOZ8ynPMuDf/9rXgm7AyuJNLs37fy1kghCogEOkL8Z8CAASQmJrJ161aOHj1K2bJl+eijjzh9+nShBjpNmza1PMzxypUreHp68tprr1meXZOhePHirFixgqVLl7J37162bduGra0tvr6+NGnShNatWxdouzIuipcvX86iRYsAqFixIlOnTs1XT0hOxowZg5OTEzt27GDv3r34+vrSrVs3qlatmuPk6Ndff52///6btWvXEh0dTenSpbM8myVDsWLF+OKLL5g+fTo///wzOp2Odu3a8dZbb1llBsuLoUOHUrVqVVatWsWPP/5ISkoKnp6eBAYGZklbPHbsWLy8vNi4cSMzZsygVKlSvPfee1y9ejVfgY6zszMzZsxg2rRpLFmyBAcHB1q0aMGnn36a47Ch3J5jzz77LLNmzWLOnDksWbIEFxcXWrduTbt27Rg4cGC288Cyo9VqmTFjBtOnTyckJISUlBQCAwOZOHEioaGhWQKdmjVrMmfOHGbPns3SpUtxdna2PDA0u2D0k08+ISgoiI0bN/L9999jMBjw8vKicuXKVim/AT7//HPmzZvH9u3bOXToEIGBgXz11Vds3749z4FOhQoVeP755zl69Cjbt2/HZDJRvHhxBg0aRL9+/fLdxtzo2LEjq1evJjk5+b5JCDJr1qwZ3377LUuWLGHTpk0kJyfj6elJ7dq1KV++vKVe586dKV26NCtWrGDDhg0kJCTg7u5OmTJlGDFihGWCdcZNoJ07dxIdHY2dnR2lSpXiww8/pEuXLnneLpF/GkUhYYwNtRYbOZ6HDv373bAweikk3LTDRZ+GW0oK9c9d4M9Kdx5Y++zFc9gb0m9+BIbfILyYJwlO6UM07fR6qoVeIsBwhZ9qtiBRa0f308PzvnFCFBBFfRRn+gohRDaCg4OZNGkS8+fPt3q6fE5yelL80yIiIoLOnTtnO1cmL3bt2sV7773HZ599Rtu2bQuwhUKIgqBMzV0yklre8NfAnO9xGwwGVkxfTMnPNZRKiMNGNYOqcrRcAKElilMxJpTaV0L53aeh5TtmReGGpxtnSvvR68BuiqXEsrf8sxwLLMfLYfvounfwA2+fgJvKR7muW0z9tBBb8niROTpCCCGA9Dkbdz8bJeMhtVqtNsv8JyHEo6Fm9snystj/8v0v+wxuGtyN6XO6It3duFZMR5JqS8nwaMI0pVlbqwV63Z3laFQVj/hEdIoe77RESiWHY5Om0uvvHRLkiCInQ9eEeIrExcVhMBjuWcfe3v6B59+kpqZaJQvISU6pbJ82j8r+0uv1dOrUiXbt2lGmTBni4uLYsWMH586dY8CAAU/s8UpOTiY5OfmedbRaLR4eHg+pRULkzeZuGgIW3X+yjqNt7u5v+6TGEG9TgtK3o0l1TOGyoxlV0eCRmAyJoDUZiXF2xqxRcEjTY28w0vGvgxRPvUmyjSPOaSn02DfwAbdKiAcngY4QT5Fx48Zx7Ni985J27NiRiRMnPtB6duzYwaRJk+5b78iRIw+0nifFo7K/bGxsaNSoEXv37rVk6SpTpgzvvfcevXr1KtR1F6XM89By4ufnR3Bw8ENqkRB5U8ZNQ/MSZvZEFMzyouy9CHf24pnrYdgnO1D75lnOeJUhRWuPZ1ISW+vVIsInfd6Wxmym7qkLlLl8FWdjHPu8G9LQIQqN14OnVhfiQckcHSGeIqdPn872WUGZ+fj4ZPtE+ryIiorK1UTvh/FMoMeB7K+iFRYWdt/nZdnZ2VGrVq2H0yAh8qncQiOXcvgTv6A1DH3m3ve3DQYDS5YswSZCxf47ZzSqgm9iAhpVJcbRnrBi3pSOjWFhK+vEJ46paYz+ZR16xZbSQ+pR4X8NUDSS5rgg3VQ+znXdYur/CrEljxfp0RHiKVKlSpWHsh5vb+8ndphTYZD9VbT8/f2zPNBViMfRxaE2nI8xseC4irMOjt8CGw1887yGki65n5ZtLKFQeUB5Tqy4wkVPb3QmE0ZbDduffYYKEdez1E+2t6N63EkOVnieipMbZrNEIYqGBDpCCCGEEE+I8h5avmr+4Mup8XEdao57houzTuAS4IxNw1IcfuMsV7yyuSmjqvxcoTGNFt3/QbZCPEwS6AghhBBCiCxsXO2p+EFdy/tuqaEEGypwyb94lrrFgvwp3cD3YTbvqSLzTPJH0ksLIYQQQoj7arPjJdpePYx7vHWWSI+4BFp+L7054tEjgY4QQgghhMiVtocG8+LfOylxIwqXxCSCTp/l684JRd0sIbIlQ9eEEEIIIUSu6Jzt6BQ6mib7L6CGRePRqTE42hV1s4TIlgQ6QgghhBAiT9wbBQKBRd2Mp4aKpOvODxm6JoQQQgghhHjiSKAjhBBCCCGEeOLI0DUhhBBCCCEeYTJ0LX+kR0cIIYQQQgjxxJFARwghhBDiKRObqjJwq5GmPxi5HGMs6uYIUShk6JoQQgghxFPkYJiR+qvuvC+7GLoHGlnVsejaJO5Hhq7lh/ToCCGEEEI8RTIHORk2XHj47RCisEmgI4QQQgjxlOi2PudhaiduPsSGCPEQSKAjhBBCCPGU+OlSzp8l6R9eO4R4GCTQEUIIIYQQzD9e1C0QOVFRcv0Sd0igI4QQQgjxFEgxmO/5+Q/nH1JDhHhIJNARQgghhHgKfHPk3oGOEE8aSS8thBBCCPEU2HW5qFsg8kst6gY8pqRHRwghhBDiKfBreAEuLCoe/r4IZuklEo8u6dERQgghhBCAgkHVoFPuEbw0eBcO3DWZx9sZbi0r3KYJkQ/SoyOEEI+p4OBggoKCOHLkSFE3JVtHjhwhKCiI4ODgXNUfOnQonTp1KuRWFYyc9n14eDjvvPMOrVq1IigoiIkTJwJY/X9R6dSpE0OHDi3SNoiis/9azs/PySxLzq4bseDQG5Tu6a+7gxyAqMT0zxKSH7SZQhQo6dERQgghCsikSZM4d+4cgwcPxsvLC39//4e6/gULFlCpUiWaN2/+UNcrHm3TDhl557fc1b1tsMPXNiX9zeFz8Nx7uV+Raz9QN+S9geK+JG10/kigI4QQolDUqVOH/fv3Y2Pz5P1T0759e9q0aYNOp7OU6fV6/vrrL3r37s0rr7xiVX///v1otdpCb9eiRYvo2LFjtoHO+vXrURS5WHqapBlV7Keb8vSdz1M7Mt12bfqbvAQ5GZTucHgKBFXI+3eFKGBP3r8+QgghHgkajQY7O7uibkaBSkpKwsnJCa1WmyVwiY6ORlVVXF1ds3zvUdgPtra2Rd0EUcjMZjNNfzTzx/X8Z+lKwSn9f37IZRdQdur+FyA9Ww72fQYORX/+i6eTBDpCCPGYU1WV5cuXs27dOm7evImfnx+DBw+mY8eOljq//PIL27ZtIzQ0lOjoaBwdHalVqxbDhw+nQgXrO6/Hjx9n8eLFnD17loSEBNzc3KhQoQJDhgyhRo0auW7XkSNHGD58OJ988onV3Jv4+HhmzpzJ7t27SUtLo2rVqrz99tv52vb333+f3bt3s337dtzd3a0+u3z5Mj179uSll17inXfesdoXq1ev5ty5c5hMJsqXL88rr7xCq1atrL4fFBREx44dad++PQsWLCA0NJQqVaqwcOFCgoODmTRpEvPnz7fMvwkJCQHSe1UWLVoEYPk8Y1l3z9M5cuQIy5cv599//yUlJQUfHx+effZZ3njjDcv2rF27lj179nDx4kViYmJwc3PjueeeY8SIEZQoUQKAiIgIOnfuDEBISIilLRnrgPQ5On5+fixcuNCqDXv27GHZsmWEhoaiKAoVKlSgf//+WXqFMr4/YcIEvvnmG/766y8URaFevXq8++67eHt75/KoiQelN6ksPqHy03kzodFwNR4KLPeZqjD7TAtu/bWfCWQzZycvjl4Ex5esy+x1UMUfuteHke3A0+VB1vAUkd7Y/JBARwghHnNz5swhLS2N7t27Y2try7p165g4cSL+/v7UqlULgDVr1uDm5ka3bt3w9vYmLCyMjRs38uqrr7JixQpKly4NpAcHo0aNwsvLiz59+uDp6Ul0dDR///03oaGheQp0smM0Ghk9ejSnTp2iffv21KhRg9DQUEaOHImbm1uel9ehQwd27NjBzz//zIsvvmj12ZYtWyx1MsydO5fvvvuOhg0bMnz4cDQaDbt372b8+PG8++679O7d22oZp06d4tdff6Vr165WgePdunfvTsWKFZk2bRrPP/88zz//PABly5bN8Tvr16/niy++oFixYvTo0QM/Pz8iIyPZt28fN27csAQ6K1asoHr16rz44ou4ublx4cIFfvrpJw4fPsyqVatwd3fHw8OD//3vf3z88cfUrl2bbt265Wr/rV27lilTphAQEMBrr70GpAdKY8eOZcKECXTv3t2q/q1btxg2bBjNmzfnjTfe4Ny5c2zYsIGkpCTmzJmTq3WKB/dyiJn15wrpySqKwiXPYrz+58+Fc2mdaoC/LqW/ftgHf30Ndrr7f0+IfJBARwghHnN6vZ5ly5ZZ5ou0bNmSLl26sGbNGkugM2vWLBwcHKy+16FDB15++WV++OEHxo8fD8CBAwdITU3ls88+o3r16gXe1s2bN3Pq1CmGDBnCsGHDLOVly5Zl2rRp+Pn55Wl5DRo0wMvLiy1btlgFOqqqsm3bNsqXL0/lypUBOHPmDN999x2DBg1i1KhRlrp9+vThnXfeYc6cOXTo0AEnJyfLZxcvXmTOnDnUq1fvnu2oWbMm3t7eTJs2jfLly9O+fft71r9x4wZTp04lICCA7777DheXO3e1R4wYgTnTs0lWrVqV5dg1bdqUkSNHsmnTJgYMGICDgwPt27fn448/pmTJkvddP9zpWfP39+f777/H2dkZgJ49e9K3b1+mT59O69atrdp27do1Pv/8c1q3bm0p02g0rF27lsuXLxMQEHDf9YoHcyFWLbwg5z8NL4fimpZaqOsA4HQYhByBHg0Kf13iqSTppYUQ4jHXq1cvq0nxxYoVo3Tp0ly7ds1SlnGhrKoqiYmJxMbG4uHhQZkyZfj3338t9TIudvfu3UtaWlqBt3XPnj1otVr69u1rVd6zZ0+rACO3tFotL7zwAqdOneLy5cuW8qNHjxIZGWnVC7Nt2zYURaFDhw7ExsZavZo2bUpSUhInTpywWn7FihXvG+Tkx86dOzEYDAwZMsQqkMig0dz55znj2JnNZsuxq1ixIs7OzlbHLq8OHjxISkoKffr0sRx3SD8H+vTpQ3JyMgcPHrT6jo+Pj1WQA+lD/ACr862oRUdHW52/iYmJJCQkWN7r9Xpu375t9Z3r16/f831kZCSqeifAKKp1RMYkUdiinB7icLK45Mf6eDzoOnJLRcn1S9whPTpCCPGYK1myZJYyNzc3IiMjLe/PnDnD/PnzOXr0KCkpKTl+v02bNmzdupUlS5bwww8/UKNGDerXr0/btm3z3NuSnfDwcLy9va0urCF9onzJkiWtLhpyq2PHjqxYsYItW7ZYemq2bNmCVqulXbt2lnqXLl1CVVV69uyZ47LuvkjJGNJX0DKCgkqVKt237uHDh1m0aBEnT57MEnzmZ39lCA8PB6BcuXJZPssoy6iTIadzDSAuLi7fbSlonp6eVu+zO9+8vLysyu4+v+9+X7x48UdiHQ2dVKp5mThpfaoWqIcW6Lg4QOe6j/XxeNB1iMIlgY4QQjzmMt/9zyzjTmRkZCRDhw7FycmJV199lYCAAOzt7VEUha+//toq8LG1tWXu3Ln8+++/HDhwgGPHjrFgwQIWLVrE5MmTLXNPHiXly5enYsWKbNu2jZEjR5KWlsavv/5KvXr1skyQVxSFmTNn5rjPAgMDrd7b29sXWrtz4+TJk4wePRp/f39Gjx5NiRIlsLOzQ1EUJkyYYDXE7WHIab8BVne+ReFRFIWtPbSM22Pi58uQaABTQe56VeW6qweLg5ox6Mjewhn6Y2cDjarA5/3AO2uWQiEKigQ6QgjxhNu9ezfJyclMmzbNMswoQ1xcXLZph6tXr26ZoxMZGUnfvn2ZN2/eAwc6JUuW5ODBgyQmJlrdDdXr9YSHh2ebmjk3OnbsyLRp0zhy5AhRUVEkJSVlSR5QqlQp/vjjD4oXL37PJAEPQ0ZPUWhoKGXKlMmx3vbt2zGZTMycOdOqNyUlJeWBenMAy8NML168yHPPPWf12aVLl4Dse3BE0SvtqrC6c9ZLuB0XjLTZ+IALV1RmeyxjQJlkNEcecFkA/ZvAZ6+Av2TlEw+fzNERQognXMZd+LvvuG/cuDHLUK3Y2Ngs3/f19cXDw6NAhiY1a9YMk8nEypUrrcrXrVtHUlL+5x60a9cOrVbLli1b2LJlC87OzjRr1syqTsYE/Tlz5mAyZX2I4t37ojC1bNkSnU7HokWLSExMzPJ5xrHKeFbP3cfuu+++y7Y3x9HRMdfHqV69ejg4OLB69WqrfZ+UlMTq1atxdHSkfv36ud4mUfRaB9qgjr3z+qJR3pehwQiKgvrj2Pw3JGQCqBvSX0vfliCnAKh5eIk7pEdHCCGecI0aNWLWrFl8/PHH9O7dGxcXF44fP84ff/yBv7+/1UX/4sWLOXDgAI0bN6ZkyZKoqsq+ffu4fPky/fv3f+C2dO7cmY0bN7Jo0SLCw8OpWbMmZ8+eZefOnVnakheenp40bNiQXbt2odfr6dy5c5aHdFarVo2hQ4eycOFCXn75ZVq1aoWPjw9RUVGcPn2a/fv3c+DAgQfextzw9fXlnXfeYcqUKfTp04cOHTrg5+fHzZs32bt3Lx9//DGVKlWiefPm/PDDD7z55pt069YNnU7HwYMHOX/+fJbnBkF6T9yhQ4f4/vvvKV68OIqi0LZt22zb4OLiwhtvvMGUKVMYOHCgpQcsJCSEa9euMWHChCxzEMTj5b0GNrzXAFr+aOTX8PvXB5UPdZm6hD7uDv/bkLeVRiwCP6/71xPiIZBARwghnnD+/v7MnDmTOXPmsGTJEjQaDc888wwLFizgyy+/tMoC1KxZM6Kioti5cyfR0dHY2dlRqlQpPvzwQ7p06fLAbdHpdMyZM4cZM2awd+9efv31V6pWrcqcOXOYPn16vjMSQfrwtX379gHWz87JbOjQoVStWpVVq1bx448/kpKSgqenJ4GBgYwd+wB3sPOhZ8+e+Pv7s2zZMlatWoXBYMDHx4e6devi6+sLQK1atfjyyy/59ttvmT9/PnZ2djz33HMsXLiQIUOGZFnm+PHjmTJlCkuWLLH00uQU6EB6xj5vb2+WL19uechpxYoVmTp1apYHhorH166XbPjkNyP/O3T/uiUdMvWsTuoHNjr4eHXuVnRxngQ54pGiqDJ7UAghhBDiiaY3mrCbfr9LPpUFrksAGDRokFXaemIS4dINCBqX/fiopB/AsWiTdzzJrij/l+u6ZdQJhdiSx4v06AghhBBCPOFsbbSAMf8L8HBOf5k3gKrCVz/B4fMwYzCUkF4c8WiSQEcIIUSuGQyGXE129/DwsEykz4/ExERSU+/9ZHadTmd5hosQ4v587eHGvX9WuaMo8G63AliQEIVLAh0hhBC5dvz4cYYPH37feps3b6ZEiRL5Xs/UqVMJCQm5Z506deqwcOHCfK9DiKfN9Bbw0taiboXIDxWlqJvwWJJARwghRK5VrFiROXPm3Lfe3U8Hz6v+/fvzwgsv3LNOfp+5I8TTqmdlLS9tzV9mQyEeRxLoCCGEyDVXV1fq1atX6OspV64c5cqVK/T1CPE0sdHcu1egnT8Q/3DaIsTDIA8MFUIIIYQQTMrHA0aFeJRJoCOEEEII8ZRo6HePD2UayCNMycNLZJBARwghhBDiKbG/b86zFsp7PMSGCPEQSKAjhBBCCPEUGVYta5m/M7jYPvy2CFGYJNARQgghhHiKzH/BhsVtwF4LNgqMfAauDZf8VI8yNQ8vcYec1UIIIYQQT5nBNW0YXLOoWyFE4ZIeHSGEEEIIIcQTRwIdIYQQQgghxBNHhq4JIYQQQgjxCFMlbXS+SI+OEEIIIYQQ4okjgY4QQgghhLin+DSV2FTJ6SUeLzJ0TQghhBBCZOtavJnqCw3Ep6igUfBygKuv2+Kok6FUD5MMXcsf6dERQgghhBDZqjTfQLxBSX/ojp2W2yYN7tMNRd0sIXJFAh0hhBBCCJFFWLyZFD2g04DyX4+ComBA4d09xiJtmxC5IUPXhBBCCCGeYht/T+TEdTMvNdBZlQ/YmAa2NmAyg94MNgrYpAc9Xx2Bi7FG1nWVS8mHQYau5Y+cnUIIIYQQTzBVVUlZdQLVwQ7HjhVQbNIH9Nw+F8Nno04R7uCIjQpjVmuwr1qN1v4nAfj1ihlszJBsBGddepCTyfrzKgaTGZ1WBgiJR5MEOkIIIYQQT6jUtf+Q3Hsx8TbOmBQFb+NtYr9/kz8+Pc2xYl4sqVWJZF365WDV6DjKntKi99MyMsQAigZMKjjkcLmoQv8tZn7sLIGOeDRJoCOEEEII8QQy3kgkpvdSnEmkjPEaJrRE4cuZN/YQ61eG9dUCqZWYjI/BwE1bHcfcXKiQlMw3pzty8ZoR3Owho7fGZM66AkVh1VmVAReNtCsnl5Ti0SNnpRBCCCHEEygqaCpOxBPp6s6/nvVRFQ1VYkLxMN7mbJXGNIxPxNuQnlSgRJoBvzQDf7u54JWcRs2EZHY4FScpI9DJbniaqoKi8MJ6lYP9VJ7zk3kkhUWeYJQ/EugIIYQQQjyJwm4T5eLELv9mlqJIBx9wMqLXavA0WmdO89UbMLg58a+dHapJj51JxT4pjduOtneyrmWWKRPbC2uN3H5Dl7WOEEVIAh0hhBBCiCdE2omb3Hp7F6nnY/BAz1n3GpbPzIrCqQoBJDk5pBeoKo56IzrznWFpaYoGDeCWptL08m0A4m1t+KOMJ6k6bY7rjU4rlM0R4oHI7DEhhBBCiCfAteZL+OOZEE7/msbNqyqOJGJjNlk+v+3heifIAVAUq+Dlhq2Om3a2BKQZcMk0J8dVb6TZpSgCoxJzXrmi0HC5kVvJMsiqcCh5eIkMEuiIh+bIkSMEBQURHBwMQEREBEFBQSxYsKBA1zNx4kSCgoIKdJlC5FWnTp0YOnRoUTfjqbJgwQKCgoKIiIgo6qYUCfnb9/QyRcRyy3k8p39T0Cs2mBUNCYoLJ5Q6VI85jaKmBy2pdrZZvmtWFFTADNzQ6UBVccsm8YCD0UyxZP092/HnDSg218Rv1+RhouLRIEPXxGMpODiYhIQEXn755aJuihCPpIiICIKDg2nevDmVKlUq1HUlJCTwww8/8Oyzzz4VF9qzZs3ir7/+4tq1ayQmJuLp6UmFChXo169fttuv1+v57rvv2Lp1K7du3aJYsWJ06tSJgQMHYmMj/wyLB3Oj2VJSfgsnjmKomjv3r+3UNIqpiTgl29P+7FH+8i6PGS0RxX2svm9jNqMACVot1+1t0akqF+x0OKXqcTJb9874JKbhpDeSZHvv87bFGjC+U2CbKES+yV9YUWT8/PzYv38/Wm3OY35zEhwczPXr17MNdD788EPef//9gmiiEPm2fv16lOwm7z4kERERLFq0iBIlSjyUQGfRokUARRrovPrqqwwcOBBb26x3rQvSiRMnKF++PC1atMDFxYXbt2+zbds2hg8fzqRJk+jQoYNV/ffff5+9e/fSuXNnatasyT///MP8+fMJCwtj4sSJhdpW8eRKOx9LRMVZoGqxJxXnzB+qKlXViziQBmhwNyfR8OYpztwsT7KDI6crlsSs0aA1mXDQG1GBn4p5kPRf4B2jgT8d7WmRmGI1GEoDNL58m31lPEm2yznxgMmkonxpwNNBYXFbha4V8/7vvLCmypC0fJFARxQZRVGws7Mr8OXa2NjIXVJRJIxGIyaTCTs7u0K52E5KSsLJyanAl/uoUlWVlJQUHB0dc1X/Yf32Fy5cmKWsT58+dO3ale+//94q0Pn999/Zu3cvffv25e233waga9euuLi4sHLlSrp168YzzzxT6G0Wj6G/LsKBUKhTjvg/bhP/3i5UgxkbUrDHgBYDWjwxYUMqjmgx42ZOIE7jgpcazRXFF1sM+Km3cMCIHQacSMbvdhz6SFvMikKckyMqCiG+XpYgJ4Neo7DTzYlAs4nAhFRLuc6kZp+BLTNN+ufRadBtswoYsQHq+sHAaqDTauhcXsHLQS7eReGSOTqiyOQ0RyckJIT+/fvTvHlzGjduTJcuXfjwww+JiYkB0uc+HDt2jOvXrxMUFGR5HTlyBMh+nHpGWWJiIp9//jmtW7emYcOGDB48mH///TdL22JjY5k0aRItW7akSZMmDB8+nDNnzjB06FA6deqUr+09duwYI0eOpFmzZjRq1Ii+ffvy008/ZVv32rVrTJo0ifbt21O/fn3atWvHmDFjOH36tFW9M2fO8N5779GmTRsaNGhAhw4dmDBhAmFhYffcx5D9fIaM/RQTE8PHH39My5Ytady4MSNGjODMmTNZlrF27VpGjRrFCy+8QP369Wnbti0fffRRtnMkgoKCmDhxIv/88w9Dhw6lcePGtGzZkk8//ZTk5OQs9aOiovjqq6/o0qULDRo0oHXr1owcOZIDBw4AMGbMGBo1akRiYtbJsSdPniQoKMjSy5BbGW08ePAgAwcOpFGjRrRt25apU6dmaWPG/rtw4QLTpk2jffv2NGzYkBMnTgA5z9HZs2cPgwcPpnHjxjRp0oTBgwezZ8+eLPUyvn/mzBlGjx5Ns2bNeOmll3K1HcHBwQwfPhyASZMmWX4jmduTkpLC7NmzLfu3bdu2fPzxx1y/fj23uwtIn3vXuXNnABYtWmRZ192/k127djF06FCaN29Oo0aN6N69O1999RUGg8GynIw5fGvWrKFXr140bNiQ5cuX57ot2Z3TGWWXL19mzpw5tG/fngYNGvDSSy/x+++/52lb78XR0RE3Nzfi4+Otyn/++WeALMcu4/22bdvyvK60tDRmzJhBu3btaNSoEf3797f8Lu7277//MnHiRLp3706jRo1o2rQpgwcPZvfu3Vb1pk6dSlBQEFevXs2yjKioKOrVq8ekSZMsZb///jtDhw6lZcuWNGrUiA4dOjBu3DiuXLmS5+0R2Zi4CuqMhZELia8/jcQxv6A1mLBBBexJxYUEvDBhR0Z/i4oGX+IpZr7JbY0X0YonkYov/yiVSSO9R8WMBrfYZBRAo6rYG4xctbflUuYEBZmkeDpwwt+df31duO2gI9zFnn0BXiTfZ+hadozAn9dh2E4Y/LOZcotMHImUxAWicMltb/FI2bJlCxMnTqR27doMHz4cOzs7bty4wf79+4mOjsbDw4N33nmH2bNnExsby5gxYyzfLVu27H2XP3r0aDw8PHjttdeIi4tj5cqVvPnmm2zevNlyp1yv1zNy5EhCQ0Pp1KkT1apV49y5c4waNQpXV9d8bddvv/3GuHHj8PLyol+/fjg6OvLLL78wefJkwsPDGTVqlKXuqVOnGDFiBEajkS5duhAYGEh8fDzHjh3j+PHjVKlSBYB9+/bx7rvv4uDgQJcuXShVqhS3b9/mzz//5Pz58/j7++errQCvv/46rq6uDBkyhNu3b7NmzRqGDh3Kd999R/ny5S31VqxYQfXq1XnxxRdxc3PjwoUL/PTTTxw+fJhVq1bh7u5utdzQ0FDefvttOnXqRNu2bTl69CibNm1Co9HwwQcfWOpFRETw6quvEh0dTfv27alatSopKSmcOHGCQ4cOUb9+fbp168Zvv/3Gzz//TI8ePazWk7HMjAvwvDhz5gy7du2ia9eudOjQgSNHjrBq1SouXLjAnDlz0Gis7w999NFH2NnZ0bdvXxRFwdvbO8dlr127lilTphAQEMBrr70GpAf2Y8eOZcKECXTv3t2q/o0bNxgxYgStWrWiRYsW2QaE2alduzaDBg1iyZIldOvWjdq1awPg6ekJpPc8jR49muPHj9OyZUv69evH1atXWb9+PQcPHmTZsmX4+vrmal1ly5ZlzJgxTJs2jeeff57nn38ewKoXZs6cOSxZsoRy5crx8ssv4+3tTVhYGL/++ivDhw9Hp7szBObHH38kLi6Orl274uXllet23M/EiROxsbGhX79+GAwGfvzxR8aOHcuGDRsoUaJEvpYZGxuL2WwmKiqKjRs3cunSpSzn3MmTJylWrBjFixe3Ki9evDg+Pj6cOnUqz+v94IMP2LNnD02aNKFBgwaEhYUxbty4bLdjz549XL58mVatWuHn50dcXBwhISGMGzeOyZMn065dOyC9l2nVqlVs3ryZ0aNHWy0jJCQEk8lE165dATh69ChjxowhMDCQQYMG4ezsTFRUFIcOHeLatWuUKVMmz9skMomMgc/WW94mUDzbu9LmbIcyKSTgYlViUmyIUt1wI5UU7PFOjEWJ98Pkaoeq0XArmwQFAGgVVIf03+Z5L2fOezlnXy+f4vXwwe9mfu4pw9pE4ZFARzxS9uzZg5OTE/PmzbMagpJxdxqgefPm/PDDD6SlpdG+ffs8Lb9y5cqMHz/e8r5cuXKMHz+e7du3Wy6WN23aRGhoKCNGjODVV1+11C1fvjxTpkzBz88vT+s0mUx8+eWXODg4sHTpUnx80ieC9u7dm2HDhrF06VI6depE6dKlUVWViRMnYjAYWLp0KRUqVLAsZ9CgQZj/e9ZBamoqkyZNwtnZmZUrV1KsWDFLvSFDhljq5Zefnx9ffvmlZY5JixYt6N+/PzNmzGDWrFmWeqtWrcLBwfpOYNOmTRk5ciSbNm1iwIABVp+dO3eOJUuWUL16dQB69OhBUlISmzdv5u2337ZcHH/xxRfcunWLWbNm0aBBA6tlZGxbw4YN8fX1ZdOmTVaBTmpqKj///DP169fP10Xy+fPnmTp1Ks2bNwegV69eTJ06lVWrVrFjxw7atm1rVd/Z2Zm5c+fed8hUfHw8M2fOxN/fn++//x5n5/SLhp49e9K3b1+mT59O69atcXG5c5ESHh7Ohx9+aLnAzC1/f3/q1avHkiVLqFmzZpbfSXBwMMePH+eVV17hzTfftJTXq1ePt956i9mzZ/Ppp5/mal1eXl40b96cadOmUb58+Szr+vfff1myZAlBQUHMmDHDarjq66+/nmV5kZGRrFu3zhKUFRR3d3e++eYbyzkdFBTEgAED2LBhQ5YL+9xITk6mVatWlvd2dnZ069bN6uYLpPeG5HQTxsfHh5s3b+ZpvQcOHGDPnj107NjRan5PnTp1GDt2bJb6r776apbt69OnDy+//DKLFy+2BDrly5enZs2ahISEMGLECKu5k5s3b6Zs2bKWIXZ79+7FbDYzZ84cq+OUEbw/CqKjo3FycrKcb4mJiaiqavl96fV6EhIS8PLysnzn+vXrVn/f734fGRmJr6+v5RwqtHVcjEUx3kkJndPcDA3Z9Yao6Llz48BLjSZADcOBFHQk40okF20roVOMJNql37jzT8n6ABwXg5HbxdyzXW9BCo1O34ZH+ngU8jpyS+bo5I8MXROPFGdnZ1JTU/n9999R1YLv0r47eUHGELdr165Zyvbt24dWq80y1KRr166Wi9O8OH36NJGRkXTu3NkS5ADodDr69++P2Wxm7969AJw9e5aLFy/SqVMnqyAnQ0Zvwp9//klsbCx9+/a1CnLurpdf/fv3t5pIX6VKFerVq8ehQ4esehUyghyz2UxiYiKxsbFUrFgRZ2fnbIcE1qhRwxLkZKhbty4mk8ky3CguLo4///yThg0bZglyMm+bVqulc+fOnDp1ivPnz1s+37lzJ0lJSXTp0iVf216mTBlLkJNh4MCBANkOMXv55ZdzNS/k4MGDpKSk0KdPH6vzyNnZmT59+pCcnMzBgwetvuPm5pbvoZL3snv3bjQaDYMGDbIqb9y4MRUrVuS333574GA5w/bt24H03tS75+QpipIlYUOHDh0KPMiB9Iv7zOuqVq0ajo6O2Q7Vyg07OzvmzJnDzJkzef/99y29jqmpqVb1UlNTc5yvZWdnl6X+/WScg6+88opVefPmzbPtScl8IyI1NZXY2FhSU1OpW7culy5dshr62a1bN6Kioti/f7+l7NixY1y9etXq95Rx/v76668YjY9mGmFPT0+r883Z2dnqJoKtra3VxSiQ5eLz7vfFixe3OocKbR11yoHXneXYkH1PrgYVLZn3v4oOI55K+jF1UFOoqp7DmWS0qJhxQK+153DxQGJ8PC3zaMqkpFIvOg7Nf//meqTp6XU1HI2p8IeVtQ5Ib8MjfTwKeR2icEmPjnikDBo0iGPHjjF27Fjc3NyoU6cOjRo1onXr1gUyCbtkyZJW7zOGVsXFxVnKwsPD8fb2zjIBWqfTUaJECRISEvK0zowL+HLlymX5LDAw0LJOuBNw3S9LVsbFWeXKlfPUltzK7g502bJlOXDgANevX7e0+/DhwyxatIiTJ0+SlmZ9VzC7/XT3/of0i3m4cwyuXbuGqqq5yhTWpUsXvvvuOzZt2sQ776TnMt28eTOenp40a9bsvt/PTnbb7u3tjYuLi+U4ZVa6dOlcLTfju9mdBxlldy+/ZMmS+cpKeD8RERH4+PhkOxQzMDCQ0NBQYmNjCyTguHr1KoqiZBu4Zye3+zOvshvK6ebmZvXbzwutVku9evUs77t27cqwYcMYPnw4K1eutAS/9vb26PXZP3skLS0Ne3v7PK03PDwcjUaTbVBTtmzZLHNkoqOjmTdvHnv37iU6OjrLdxITEy2BS+vWrfn666/ZtGkTTZs2BdJ7uHU6nVWChd69e7N3716++OILZs2axTPPPEPDhg1p27YtHh4eedoekQ17W1gzFgbPhiu38PG+RWRUKRQ0pD/xRgU0KJiwIxUjDqgoaP4bzFZcjcGkavDiVpZeHzuTSpq9rVUyAcVspvmtaOpHRaNXVXzS9CiA1mjGnGoCnQJaDai5SEJwP5mW0S5AYUpTud8uCpcEOuKRUrp0adauXcuhQ4c4fPgwx44dY/LkySxYsIBFixY90LwTIMeLxsLoPXoU3Cu9sclkyvGz3Dh58iSjR4/G39+f0aNHU6JECezs7FAUhQkTJmTbI3Cvi/b8HIPixYvToEEDtm7dyhtvvMH169c5duwYr7zyykPLvJfXC9VHZdkPU3Y9NzkprG3OqZezoH77Wq2Wdu3a8cUXX3Ds2DGee+45ID1IvnXrVrbfyXimTmFRVZXRo0dz6dIl+vTpQ9WqVXF2dkaj0RAcHMz27dutfqf29va88MILbNiwgdu3b2NnZ8euXbto2rSpVQDj7u7OsmXL+Ouvvzh48CB//fUX06ZNY8GCBcyYMYOaNWsW2jY9NVrUgIvzICIabXEPSmg1JCw8RNJX+9Arttg66+DvywBoMEOmYU0qCj7cxp9LGK2TTqPX2GCbqs8StNinpZF5EPJZT1cMWg2YVUhTwRawyX9Q0j4A2gZAfT8Nlb01pJnAx1GGYuXFk3mVUvgk0BGPHFtbWxo3bkzjxo2B9Ow+b731FitXruS9994D7n0B/6BKlChhGaKVuVfHaDQSERFh1U2dGxm9GBcvXszyWUZZRp2Mu9mhoaH3XGbG3dyzZ89Sv379HOtl3LG/OxMUZO09yOzSpUvUqFEjS5lWq7V0u2/fvh2TycTMmTOtempSUlLy3OuVWalSpVAUhbNnz+aqfrdu3fj999/Zs2eP5Tv5HbYG6dt5t6ioKBISErLtkcqtjCD94sWLlovgu9f5IMu/271+IyVLluTPP/8kISEhy/l88eJFnJycsiSSyO+6ypQpwx9//EFoaGiWYYtPmoxezcy/t2rVqrFt2zYiIyOtEhJERkZy69YtS89JbpUsWRKz2cyVK1csPasZ7j53z507R2hoKEOGDGHYsGFWn+WU8bF79+6sXbuWkJAQy1Di7H5PWq3WkmEvY139+vVj8eLFzJgxI0/bJHKg0YB/enITBXAdVg/XYfWsqsQtPETSsI2AHQoKJhTSsMGf82gxYsLIJc9SXHfzpkTsLaJxQWcy43Mjmpu+niiKgllRiLe1xdZsxtZk4pS7C5srBzxY201mSrlp2NxNoZavJBsQRUf6DMUjJTY2NktZxvCszENMHB0diY+PL5SemCZNmmAymfjxxx+tyjdu3JhtKuP7qVy5MsWLFyc4OJioqChLudFoZPny5SiKYhlmVbFiRcqVK8fmzZu5cOFClmVlbG/9+vVxd3dn5cqVVsu8u56TkxNeXl4cPnzYal+FhYVlO98kw7Jly6zqnzlzhkOHDlG3bl1L8JfRO3P3Mfjuu+8eaH6Hm5sbDRs25I8//sgyZyW79TVu3BgfHx82bNhASEgIzzzzDAEBAfle/5UrV7Lsm6VLlwLkezgcpE/0d3BwYPXq1SQlJVnKk5KSWL16NY6OjvcMWvMq4zhlNzSrefPmmM1mvv/+e6vy/fv3c/bsWZo2bZqneV4Z80CyC6gzkjfMnTvXkko6s8etNzU+Pj7b7UhJSbFk+6tWrZqlPGP77/57kvH+hRdeyNP6M87Bu9Nu79mzJ8uwtYxjePc+Pn/+fI6//woVKlCtWjU2b97Mpk2bKF68eJbzMru/0wEBAdjb22d7DojC4zb0Odx2j8SkUXHgBu6EUY5/sSMVBdhWrSEr6ndgV5V6LG/Qkf0VawHgGhePU2w8rvHxeMXEUjw2Dtu0NFZUCGB1xTIk2d71MNDcztdRVTCa8XfVcHW4jQQ5oshJj454pIwaNQoXFxdq166Nr68vCQkJBAcHoyiKVTan6tWrs2/fPr788ktq1qyJRqOhbt26BTKnoGvXrmzYsIF58+YRFhZmSS+9c+dOSpUqlechX1qtlnfffZdx48YxYMAAunXrhqOjIzt27ODEiRMMGjTI0pOjKAqffPIJI0eOZMCAAZb00gkJCRw7dowGDRrQp08f7O3t+eijj3jvvfd48cUXLemlY2JiOHDgAC+//LJlQn3v3r2ZN28eb7zxBs2aNSMqKor169cTGBiYY2rb69evM3r0aJo2bUpUVBRr1qzBzs7OKkNXRva7N998k27duqHT6Th48CDnz5/PU29Adt59910GDx7MG2+8QceOHalSpQqpqamcPHkSPz8/3njjDav927lzZxYvXgxglao7P8qXL89HH31E165dKV26NEeOHGHXrl3UqVOHNm3a5Hu5Li4uvPHGG0yZMoWBAwfSsWNHID1177Vr15gwYUK+kl3kpGzZsjg5ObFu3Trs7e1xcXHB09OTunXr0qlTJ0JCQli6dCkRERHUqVOHa9eusW7dOry8vPK8D93d3SlVqhS//PIL/v7+eHp64uDgQNOmTalevToDBgxg6dKl9O3blzZt2uDl5UVERAS7du1i6dKlee4lLUrHjh3j//7v/2jRogX+/v44OTkRERHB1q1buXHjBkOGDLGabJzxvKSVK1eSmJhIjRo1OHHiBJs2beKFF16gVq1aeVp/gwYNaNKkCSEhIcTFxdGwYUPCwsLYsGEDgYGBVjdIypYtS7ly5Vi2bBmpqamUKVOGq1evsmHDBsqXL5/luVwZunXrxuTJk4H0LI53B72TJ0/m5s2b1KtXDz8/P9LS0tixYwdJSUlWc3nEw+HUvBROpkkYwuJILPUe6n9zdaIc3TlS2rpnPtbTldLXIygTf4NYbzeUTPeknNP0OCanoHF2wHz3Y3XMuQx0FIXirgqnXpUARzwaJNARj5SePXuyY8cONmzYQFxcHG5ublSqVIl3333X6iGgffv2JTw8nF27drF+/XrMZjPz588vkEDH1taWefPmMWPGDPbu3cuOHTuoXr06c+fOZfLkyXnOkgTpKZfnzp3L4sWLWb58OQaDgYCAgGxTB1erVo2lS5eyePFidu7cyfr163F3d6datWpWF0XNmjXj22+/ZcmSJWzatInk5GQ8PT2pXbu21bNuBgwYQGJiIlu3buXo0aOULVuWjz76iNOnT+cY6MyaNYtp06axcOFCUlNTqVGjBm+++abVhPJatWrx5Zdf8u233zJ//nzs7Ox47rnnWLhwIUOGDMnzPsqsZMmSLF++nG+//Zb9+/ezZcsWXF1dqVChAt26dctSv2vXrixZsgQHBwerlL/5UblyZd5++23mzp3Lhg0bcHJyonfv3owaNeqBs9n16tULb29vli9fbnmYacWKFa3SWRcUe3t7PvvsM+bNm8e0adPQ6/XUqVOHunXrYmNjw+zZs1m8eDE7duxg9+7duLi40LJlS0aOHJnlmS+58emnnzJt2jTmzJlDamoqfn5+lmFZr7/+OhUqVGDNmjUsW7YMs9mMr68vjRo1euzmIZUvX54mTZpw9OhRtm3bRmpqKu7u7lStWpX333/fMuQ2sy+++ILFixezbds2tm7dSrFixRg+fLglm19eff7558ybN4/t27dz6NAhAgMD+eqrr9i+fbtVoKPVapkxYwbTp08nJCSElJQUAgMDmThxIqGhoTkGOm3btuWbb74hJSUl22dRtW/fnuDgYLZs2UJMTAxOTk6UK1eOKVOm0LJly3xtk3hwOn83nC5/QVzAJGxI5bDDs1mTBygK1RKu4ZyanG2yYkejCY3RjCY5DbPDf9kCFcA+F5eLqsrpwQqVveTSsjBIeun8UdTHbdyAEEXEZDLRqlUrqlevbvUsmSfJxIkTCQkJ4ciRI0XdlDyJioqiQ4cOdO7c2erBo3kVFBSU5fkkQjxt9Ho97dq1o2rVqsyePbuomyPyIe3odaJPxPLtD3Gk6e6kN3fQpzLy1w3o0bGqTgtU7Z2L5zSNhim1q5Kk02HSaDDZaNMTELjoQHefHhpVBbOK+l4ODx8VD+y08k2u61ZR3y7EljxeZI6OENnIrtdm/fr1JCQkWKWUFY+GdevWYTKZ6N69e1E3RYjH3rZt24iPj8+291Q8Huye9cNvYBXaHj2Ie1L6vCnPxHi6Hf0NG7OZa05e+ETE4BSfisZkxikhFYcbibzy5xmG/H2OprGxYK9Nf9bOvaZc/hfgAPTIXQZ5IR4q6V8UIhufffYZaWlp1KxZE1tbW06cOMH27dspVaqU5R//xMTE+w5j0+l0lufEiIL3888/ExkZyfLly2nQoAFVqlTJUicmJua+86ocHR2zPDfpUWQymYiJiblvPTc3N3Q63X3r3U92iS7u5uzsXKjDz5KTk60eUpsdrVb7wM9vKerz5FHY17/99hvXr19n4cKFlCtXrsCHU4qHr8wzbvTYvR+tjQFnYxKgEKnz5JxjSRJdbAjzcCa0uA+lb8bQ4fgR9LbF+b/6zbF1tgMbbXogYzKDWWN5wKgVRQFUdIqZdd3tsn4uCowMXcsfCXSEyEa9evVYu3YtixcvJjk5GS8vL7p27crw4cMtDy6dOnUqISEh91xOnTp1WLhw4cNo8lPpgw8+wM7Ojlq1avHRRx9lW6d///5cv379nsvJLv3uo+jGjRvZzpm42/z5863mtOVXu3bt7lvnk08+oVOnTg+8rpxkns+UEz8/P4KDgx9oPUV9njwK+/qrr77i1q1bVKlShQ8//LBQHlYrHi6fkL6EOU3GZHQgDnvMmNEaYyieFo0S70n95JO0uphEYMIlTvqX50BQEB3iE4kyRrPdtRyY/7u4TjaAk856zs9/z+JRAP1YCXLEo0nm6AiRTxcvXszxQYAZXF1ds+1lEA/P33//bXm+SU5Kliz5wA+jfRjS0tL4+++/71uvSpUqlmcoPYjs0nvfLTAwEG9v7wdeV07CwsLu+cwnwBLsPoiiPk8ehX0tnkxpt9O42uw73E+fxsbOhHZqH1xHNuHA24c4ERLJtZLuRPp6Y/hvLo+d3kCdVvuo12kQtZap6amlk41gp0lPSqAo6b08JhVstazqAC9Wkfvmhe2UMj3XdauqbxVaOx43EugIIYQQQjylJrx3iRvXDJg1GuzMRipW+wtnnwQGDRqE6wyVVFWBOH3WLzrZoChgHi+9OQ/DyTwEOtUk0LGQEFwIIYQQ4in1f1PKYjSqpKSYsbc3s2TJb5bPQnootFpHemKC1Exz2BTAbObiaMmyJh5tEugIIYQQQjzFbGwUXFy0GAzWKdZaltWBYkgfsmajAaM5PSmBjcLfA2wIcJPkveLRJmeoEEIIIYTI1heNlfTEAzb/zdHRaajto/CMr1xCikef9OgIIYQQQohsvVffhrp+Job+rJJmgm9aKPSsJJePD5ukl84fOVOFEEIIIUSOWpTRcn5oUbdCiLyTfkchhBBCCCHEE0d6dIQQQgghhHiEydC1/JEeHSGEEEIIIcQTRwIdIYQQQgghxBNHAh0hhBBCCCHEE0fm6AghhBBCPOW+6bSf0ruuUibVnkR7HcGa63R/rXRRN0v8Ry3qBjympEdHCCGEEOIpNmLwv1QLOY9bih6tquKWosdp2B4uh6UWddOEeCAS6AghhBBCPMWCNp/NckGoNausG/13UTRHiAIjQ9eEEEIIIZ5SqlnFKzEt289uR0iPzqNC0kvnj/ToCCGEEEI8pQa8dgYbReVGSTduF3PGrKRfUKfqtGyqX76IWyfEg5EeHSGEEEKIp4zJrFJnTCQ+RluONygLmvQAxyEhjapHr6E1moh1dCjiVgrxYKRHRwghhBDiKbHjaDKdXz5L7RERJJlsqB4dZwlyAFJc7LhVwhUbFWpdulGELRWZqSi5fok7pEdHCCGEEOIpsPdQPK/+ZOJW5VKUvJVEhfDbeKZmnZ+ztWEFQl1daH34HIbYQHTudkXQWiEenAQ6QgghhBBPMINJZd+6cHqccceo0+B/I4GRW45S48otzlUvTrSvq1X9v0oX42wxd06X9iJg2F8MX12/iFouxIORQEcIIYQQ4gn10vvXcD4dTbSTPbG1fPGPiKfWxRvUuHILgICztzDaaIn3ciJNq2F/QHHOFnMHIMXeli1mV4YXYfuFeBAS6AghhBBCPIH+b3k0qZeS0NvbcahUMQDCSrrxm4MN3Q6cpVhsMjqDiSp/h5PgaEvfCV1JsdNZLcM9OglTkgGtky67VYiHRC3qBjymJBmBEKJAHTlyhKCgIIKDgwGIiIggKCiIBQsWFOh6Jk6cSFBQUIEu80m3YMECgoKCiIiIKOqmPLDY2Fg+/vhj2rVrR1BQEEOHDs31dzt16pSn+gVh6NChdOrU6aGuUzzdYqP1xK+8QFBUDLZaCPN2gf9SR9/0dGJ2V+u/n2ZFoc3hi1Zljil6fBJSmVh3F+Pq7maFxyp2KwvZ2W4jqlkuvcWjT3p0hBCPrODgYBISEnj55ZeLuiniEfPNN9+wY8cOBg8eTMmSJfH09CzqJgnxSNgUEsWkTamUjU/EwcWJBBsbFJ1C+9NXuelsz98lvDFqNRysXIJ3hrWi8rUoOh86z/GaJTnv74FfVDxmjYZEex1J9rb83LgslS9GUS4snq31KuEXHUfPn0/wl/ZrbEjDq5UvzmNa4tqmDIpW7p+LR4sEOkKIQuXn58f+/fvRarV5/m5wcDDXr1/PNtD58MMPef/99wuiieIxdPDgQerXr8+QIUOKuilCPBLSUg30HnieU16eqHa2HPfxBEWhw6VrFLt1J7Na7fDbLKpXGbNWyz/lffmnvC87ngskzUZLcqZhaz7xCZz8+FOMaVouUdFSboOBapzAnkT06DDtvIh+51GiUFCxxwYFBTNa0tARj/a1ZiitaqBpUBFsbVCi42HtH9DuWTT1Ah/qPnqcSdro/JFARwhRqBRFwc6u4FOT2tjYYGMjf8IeZUlJSTg5ORXKsm/fvo2bm1uhLLsoFeY+E48/o0nlfKQJVTVz4JyBw+f0XLtt4Pb5FJzSDFzw8baak2BrMuGVZp0+2qjBMoQtQ4yTvdV7+zQDrc/+Q8m0C+yjnfX30XGGKlTnGM4kWMpNaEimGFoM6HHAjA4T9iR/ewbTt1cABTDjQjgOxGOauI4EPFDRoCURFQ22pJBm64Va1gedPhFDpBGTkzNGjQOqjS2KnQmbhAS0zxTHGKfBeDUOja8dtk3LY9e0FPq/ozEn6HGs4wE2GkyqBv2282iLO6N7pji6V+qm9zpdjEyf9BJY3GrbVLMZwx9Xwaiia1waxSYPN+hMJlj3JxR3h2bV09+fvAYlPMHb9b5fF4VDrhKEEIUqIiKCzp07M2TIEIYNG2YpDwkJYc2aNVy9ehWj0YiXlxc1atTgnXfewcPDg06dOnH9+nUAq7k48+fPJygoiIkTJxISEsKRI0csn2WU7dmzh1mzZvHrr7+SlJRE5cqVGTNmDNWrV7dqW2xsLDNmzOC3335Dr9dTrVo13nrrLaZNm8b169ct84xya+jQoVy/fp3vvvuOb775hj///BO9Xk/t2rUZN24cZcqUsdRdsGABixYtYvPmzZQoUcJqOZ06dcLPz4+FCxdayoKCgujYsSMdOnRg7ty5hIaG4ubmRu/evRk4cCDx8fFMnz6dffv2kZycTN26dfnggw/w8fHJ0s6UlBS++uordu7cSWJiIuXLl2fUqFE899xzWeoePHiQZcuWcfLkSfR6PaVLl6Znz5707Nkz2zaPGTOG2bNnc+LECdzc3Ni8eXOu919KSgqLFy9mx44d3Lx5E1dXV+rVq8eIESPw8/Oz2m+Qfg6FhIQA8Mknn+R5DsyZM2eYPn06J0+eRKfT0aRJE958880sw+D0ej0rVqxg+/bthIWFYWtrS+3atRk2bBiVK1e2qhsfH8/MmTPZvXs3aWlpVK1albfffjvb9d9vnx07doxvv/2WkydPYjQaCQgIoFevXnTt2jXLsnJbN+McXbBgAdOmTePIkSMoikKzZs149913sbe35/vvv+enn34iKiqKsmXLMm7cOGrVqmVZhtlsZtWqVWzevJmIiAgURcHLy4tatWoxYcIEuQFRSP6+bGD04ngiY82WMo3ZjG9yCjedHDE7OqJRVasgxtZkRnvXVJpLHtlfdOv0JjSqmS++/ZWqV6J4Rj2IAVtM2VwqxuPJH7TCnSie4SA6jGgx40IkAA5AMp6YsENHGiYcMlpMAiVRMGJPMs7cwog9CfjhQBTxlMWst0U5a0RHLM4kkJhSHANa7LmJM5FoMGHYdYV4SgL2mG+mkXriGKlzjmVqYRwKelLxhkw9Icrgn/B61ozmyLn0glY1Yf274OqI4fh1Yp//DjUmNf0zZ1s8dgxEV7/U/Q/OH2egxSeQZkh/7+sGdjq4GgU6G3i3K0yWIdhFQf4aCSEeui1btjBx4kRq167N8OHDsbOz48aNG+zfv5/o6Gg8PDx45513mD17NrGxsYwZM8by3bJly953+aNHj8bDw4PXXnuNuLg4Vq5cyZtvvsnmzZstd8v1ej0jR44kNDSUTp06Ua1aNc6dO8eoUaNwdc3/3beUlBSGDBlCjRo1GDVqFOHh4axatYp33nmH1atX52sIX4azZ8+yb98+unXrRocOHdixYwezZ8/Gzs6OkJAQSpQowdChQ7l27RqrV6/mk08+Ye7cuVmW88knn6DRaOjfvz/Jycls2LCB119/nZkzZ1KvXj1LvQ0bNvD5559To0YNBg8ejIODAwcPHuSLL74gPDycN99802q5N27cYMSIEbRq1YoWLVqQnJyc620zGo2MHj2a48eP07JlS/r168fVq1dZv369Jdjy9fWlRYsWlCpVio8//pjatWvTrVs3AGrWrJmnfXnz5k1GjBhBixYtaNmyJWfOnGHz5s2cPn2aZcuWYW9vb2nX66+/zj///EP79u3p3bs3iYmJbNy4kVdffZVFixZRtWpVq204deoU7du3p0aNGoSGhjJy5Mgce59y2me//fYb48aNw8vLi379+uHo6Mgvv/zC5MmTCQ8PZ9SoUZZl5KUupJ+jI0aMoE6dOpb2bt68mbS0NNzd3fn333/p3bs3RqORFStWMGbMGIKDgy2/ne+++4758+fTpEkTevTogUajISIiwnLDQAKdgqeqKu8uT7AKcgBc9QauO6cfFw1k6alJtNURZW+Hd6aHgppM2a4Ap6Q0vvr2VwJuxgGgoOBACk7Ek0T2fxNj8eYilanEv1blCZQkFfcctkbBBuN//wc6UnHjGrcJJOOyVEVLAiWw5RwuhGNChwvhlpBFRyquhBFD+WzXkIYb6V021vvDVk1CcyTsTsHOf+DzDfB5P+L7rLkT5AAk6onruQqva2NRlPsMG3vx6ztBDsCNuDv/bzDCZ+ugbS1oUvXey7knGbqWH/LXSAjx0O3ZswcnJyfmzZtndVE0fPidpzU0b96cH374gbS0NNq3b5+n5VeuXJnx48db3pcrV47x48ezfft2evToAcCmTZsIDQ1lxIgRvPrqq5a65cuXZ8qUKZYehLyKjY3llVdeYcCAAZYyDw8PZs6cyaFDh2jQoEG+lgtw/vx5lixZYumZ6tKlCx07dmTatGn07t2bcePGWdX/4YcfuHz5MgEBAVblWq2Wb7/9Fp0ufTx+586d6dmzJ1999RXr1q0DICoqiqlTp9KmTRs+++wzy3d79erF1KlTWblyJT169MDf39/yWXh4OB9++GG2PQ73ExwczPHjx3nllVesAqh69erx1ltvMXv2bD799FMqVKhAhQoV+PjjjylZsmSez40MYWFhjBkzxmr+V7ly5fjmm29YtWoVAwcOBGD16tUcPXqUWbNmWR27nj178uKLLzJ9+nRLz9vmzZs5depUlt7LsmXLMm3atGzPqez2mclk4ssvv8TBwYGlS5daeuV69+7NsGHDWLp0KZ06daJ06dJ5qpshNjaW/v37079/f0tZQkICO3fupHLlyixZssTyuyxbtizvvPOO1W9n9+7dlC1blm+++cZqW15//fXcHwCRJ9GJKhdvZo1QUnMxtOpX/+LUj7yFX1IK8bY6buvscEjWk+Joa6njEZvK6z8dtgQ5AGEE4MN1anCEk9QhAXc0mDBjvc4YvK3em/4LXXJiQyo26K3KtBjRkYoB50ylCnoccSAOe2KzXObrSEODATM5pb3OGhio2SUb/v00aooB05moLB+Zw+NRbyah+Dpn/V5mEdH3/hxg36kHDHREfkh6DCHEQ+fs7Exqaiq///47qlrwKUrvTl6QMfTt2rVrlrJ9+/ah1Wp56aWXrOp27doVZ+f7/KN2DxqNhj59+liV1a1bF4CrV6/me7kANWrUsBp+p9PpqFatGqqqZlln7dq1AettzvDyyy9bghwAX19f2rVrx+XLl7l06RIAO3fuRK/X06VLF2JjY61eTZo0wWw2c+jQIavlurm55TuF8u7du9FoNAwaNMiqvHHjxlSsWJHffvsNs9mcw7fzzsnJiV69elmV9erVCycnJ3bv3m0p27ZtGwEBAVSpUsVqHxiNRurVq8fx48dJTU2/C7xnzx60Wi19+/a1Wm7Pnj1znHeT3T47ffo0kZGRdO7c2WrooU6no3///pjNZvbu3Zvnuhm0Wi0vvviiVVmtWrVQVZUePXpY3XzI7jxydnbm5s2b/P3339lu06MgOjqatExzUxITE0lIuDOfRK/Xc/v2bavvZAyVzel9ZGSk1d+rh7kOdycFX7f8XbIl2diwv3gxbjg68EvpEqTZaPG7lYTfjQS8opMpFRFHlctRNDkVZvW9aIrxFw0Id/bCwTMMLyKzBDkAdqRiwNbynJf0YCLn3odsgw2yn2yfERAZLUPf7jCjybY9mZeYq7IapVEcdGhKZu21Ut3twPPO/KUcj3lu5uDUDACyHnNRuKRHRwjx0A0aNIhjx44xduxY3NzcqFOnDo0aNaJ169YFMhG7ZMmSVu/d3d0BiIu7c7cyPDwcb29vHB0drerqdDpKlChhdcGSFz4+PlmSL2QMW8q8/vy4e7sAyzC7u+f5uLi45LjO7Ib/lStXDkjfL2XLluXy5csAjBw5Msf2REdb38UsWbJkvofmRURE4OPjk+2wwcDAQEJDQ4mNjS2wNNIlS5a0CvYAbG1tKVmyJOHh4ZayS5cukZaWRqtWrXJcVmxsLMWLF7ecU3cHyhnLze6cym6fZTznKOOYZBYYmJ6lKqONeambwdvbO8s5mtN5lFGe+TwaNWoUY8eO5bXXXsPHx4dnn32Wxo0b07Jlyyz7tKjcfZ5kd0y8vLysyu7ucbv7ffHi1hPXH/Y6JvZy5s3v49Eb73yeqtWiqCrqvYZWKQpNrt/EPymZEkkpXHRzQQEcU404phpRVJWg8xEYNKC7615ClFKMbsMHEeHlwrS5v1DlmvVvXsGMDynEUwZPzmHEDg16NOgxY5up5p1hZCZ0mNCi5U4PlREd9sSRiKOlnj0x6EghBXdScMeWBOxItCwtCV9yul+vIxENaaThSeagy4A9qp8nyvX/tqO8H3yQPt/QZXFX4jquAON/O0Gj4LqoKxrdnUvlHI/5opHQ7QvIeLaQox2YzZD633C2Xg2hfR0g6zEXhUsCHSHEQ1e6dGnWrl3LoUOHOHz4MMeOHWPy5MmWieaZh0PlR04X24XRe3Q3jSbnu66Z13+vMd+mbAfR57xd9/osv9uc8b1Jkybh7e2dbZ27A6+MeS1PmvLly+eYUADShybmV1Hss3udozl9lvk8qlmzJj/99BN//vknR44c4ejRo2zfvp3Fixfz7bffPpHZ8B4FrZ+xY9//vDh03kBymomfDqVy8pqJNKOKKcWAycYmPeC5KyGBvd5AyaT0uV/P3Ygi2caGSCcHFLNK6YRE/FJT2Ni0Ip0Onc+yznWNq3DV1x2AfwKL/xfoqGgxYY+eUtzAFhMmbImjFE7c5DYVUS2XlxkBjgIYyAhMognAkWi06NGS3kOiIQ0PzpOGC2ZXF2zVVBITvDFgC6jEUQJbEtEpaRhc3DAorpBsQNGB1s8BXXVv9OeSwWjCoW5ptG46dAkqaQdvgVaDrl4JnL7piuJkC7+eSN9PLWumJwsA7NpWwPvGe6R8dyx9Ga8GofHJ5Y23znUhZhnM3Aa+rvBqK4hNgt3/QhkfCMp+LlFeSHrp/JFARwhRJGxtbWncuDGNGzcG4Pfff+ett95i5cqVvPfee8C9g4EHVaJECQ4dOkRycrJVr47RaCQiIsLSI1JYMu6Ux8fHW91FT0tLIyoq6oGDvXu5dOkSFStWtCq7eDH9iegZwUupUumZhtzd3a0SFBSWkiVL8ueff5KQkJBl31+8eBEnJydLz1xBCA8Px2AwWPVA6PV6wsPDreY0lSpVipiYGOrWrXvPACFjGw4ePEhiYqLV3f2M5eY2yUXGMcg4JpndfZzyUrcgOTo60rJlS1q2bAnA2rVrmTJlCps2bbKa+yMKlpeLhhdqp/fG9ahv3RsdcSONbuMjcDSrRDg7YdJocDQYSLbRkqrV4GAy42Ay8cLVcJJttNiYVWzNZmLtdMyd8XO26/ujRvrfIffEVFofvUhG4KJBxZs4tKiYSQ9f0nDGgC0KqahosEFFSxJq4+pojGlofB3RtaiMtlE5UibvQFPMAbuve6A4pw9Ly/glFmSfoA5wzO6DtrWzra/xdMRpbOP8rczVCT7MlI3S0wV65H9OpigYMkdHCPHQxcbGZinLSNObeYiMo6Mj8fHxhdIT06RJE0wmEz/++KNV+caNG0lMTCzw9d0tI9X0wYMHrcp/+OGHAp2Lkp0ffvgBg+FOhqAbN27w888/U6ZMGcuwttatW2Nra8uCBQssc1AyS0xMRK/XZynPr+bNm2M2m/n++++tyvfv38/Zs2dp2rTpfQONvEhKSmLt2rVWZWvXriUpKYnmzZtbyjp06MDt27dZuXJltsvJPAejWbNmmEymLHXXrVtHUlJSrttWuXJlihcvTnBwMFFRdyZIG41Gli9fbkkHnde6BeVev9/4+PgCXZfIvRK+dhxcUpa3Bnrik5RCqbh4KsXE4WA2Z+kNcDSasP3v74zL7WTsDVl7kW+4OeIbk0TPvaeYPWM7PgmJ2JOKghkTGm7hSiQOHK7iQdRnz+NrnoyP+hXe6jcUU7/GU52Gm7oA932v4/rnWJx/GondGy2weTYAl41DcFjQzxLkCFFYpEdHCPHQjRo1ChcXF2rXro2vry8JCQkEBwejKIpVFq3q1auzb98+vvzyS2rWrIlGo6Fu3boFMk+ja9eubNiwgXnz5hEWFmZJL71z505KlSqV4/CxgvLcc89RpkwZFixYQFxcHCVKlOD48eOcOHGiQHsusmMymXjttddo27YtycnJrF+/nrS0NKusbb6+vowfP57JkyfTq1cv2rdvj5+fHzExMZw/f549e/awdu3aLHM68qtTp06EhISwdOlSIiIiqFOnDteuXWPdunV4eXllSZH8oPz9/Vm0aBEXLlygSpUqnD59ms2bNxMQEGCV2OGll17i4MGDzJgxg8OHD1O3bl2cnJyIjIzk8OHDlmAQ0rPXbdy4kUWLFhEeHk7NmjU5e/YsO3fuxN/fP9fnlFar5d1332XcuHEMGDCAbt264ejoyI4dOzhx4gSDBg2yZFHLS92C0rNnT2rUqEG1atXw8fEhKiqKjRs3otPpaNOmTYGuS+Rdl+fd6fK8OwajmbYDzmPQaNBrNThmc/4l2mgpEXE7m6WAb1wy7//4BwCpWoWLPu54JKXhYErD3xhOldix2Dg7kH3fiChohT/w+skkgY4Q4qHr2bMnO3bsYMOGDcTFxeHm5kalSpV49913rR4O2rdvX8LDw9m1axfr16/HbDYzf/78Agl0bG1tmTdvHjNmzGDv3r3s2LGD6tWrM3fuXCZPnpxtL0ZB0mq1TJs2jalTp7J69Wp0Oh3169dn4cKFVumuC8OkSZNYv349S5cuJSEhgfLly/PJJ59Qv359q3qdO3emdOnSrFixgg0bNpCQkIC7uztlypRhxIgRWSZaPwgbGxtmz55teWDo7t27cXFxoWXLlowcOTLLJO0HVaxYMb744gumT5/Ozz//jE6no127drz11ls4ONy5y2xjY8P06dNZt24dW7dutQQ1Pj4+VKtWjY4dO1rq6nQ65syZYzmnfv31V6pWrcqcOXOYPn16nrItNW3alLlz57J48WKWL1+OwWAgICAg2/TdealbEPr168f+/ftZvXo1iYmJeHp6Ur16dQYNGpRlSKQoOjobDX26efLNLgO/+xWjWfgNXIx3MhlE29uyvnIAdbT2VLmWfbADkKLT0GLP8zjWL4lqNKOxzf+zwIR42BT1YczOFUKIx4TJZKJVq1ZUr16dWbNmFXVzhBDigbR8/zrhcVDjVgxuBj17KpXCrFG47Oliyda2etI6PBPTsv3+3mr+fPpvy4fZZJGNI8q8XNcNUkcUYkseLzJHRwjx1Mqu12b9+vUkJCQ8lAn4QghR2HZ97seWycWIfdaHcFcnLvi4ccnL1Sol9YWSOfeS64tnO51fiMeCDF0TQjy1PvvsM9LS0qhZsya2tracOHGC7du3U6pUKbp16wakT7q/3zA2nU4nKXWzYTKZiImJuW89Nze3B37+SlxcnFWChezY29s/0MNghXhcBXpq2THehyO/Kfy5J404xzvPUbIxmnCLz/o3TgUOVPLj1QW1Hl5DRY4kvXT+SKAjhHhq1atXj7Vr17J48WKSk5Px8vKia9euDB8+3PLg0qlTpxISEnLP5dSpU4eFCxc+jCY/Vm7cuEHnzp3vW2/+/PlWc7PyY9y4cRw7duyedTp27MjEiRMfaD1CPM6Cmnqz7MYN3juQxGVPV2wMJjxjU1jQ/lk6HD5P2cgYolwdSXTQ0fzEVf6qVJyPAiUzmnh8yRwdIYS4h4sXL3Lr1q171nF1daVKlSoPqUWPj7S0NP7+++/71qtSpUqunzGTk9OnT983tbGPjw/lypV7oPUI8SQwGczE3kxj4Lir3HRw4LajA4mOOuKd7VBUlc77Q6l+5RYmrcLHJ1sVdXMFcFiZn+u6ddXhhdiSx4sEOkIIIYQQTyGzSaXra+f4s7gvUV5Odz5QVRxTDTT65wq/rJWbOI+CQ3kIdJ6TQMdCkhEIIYQQQjyFNFqF1d+UJsrzroQDioJZo+CgkXvh4vEmgY4QQgghxFPKpGixMZmzlKfqtNRNTSiCFglRcCTQEUIIIYR4Sjm76Sh++66ARlXRGs28/k21ommUyMKMkuuXuEMCHSGEEEKIp9jPA+zxuxmHxmRGMavYpxpY6RSBWzlJxy4eb5JeWgghhBDiKVa1ujMRXzoTFq1nyaq1+Lgl0n3woKJulhAPTAIdIYQQQgiBr4uCr01iUTdDiAIjgY4QQgghhBCPMFXm3uSLzNERQgghhBBCPHEk0BFCCCGEEEI8cWTomhBCCCGEEI8weXRr/kiPjhBCCCGEyGLcpmRcPkzA/aMEZuxNKermCJFn0qMjhBBCCCGsNPgmiYQwI5WNJswKfBpsx+UoM9/0cCrqpgmRa9KjI4QQQgghLFQVTFfS8DCa0AI6FQKT01jxR1pRN02IPJEeHSGEEEIIYZFosMPuv0khZiDBQYcZKJli4HayipejpDp+2CS9dP5Ij44QQgghxNMoIYXLYzZxoP0Kjv9+3VIck+aECug1CsdLuXPGz5VQP1dOlXan14K4omuvEHkkPTpCCCGEEE8ZU1g058rMJdnsiS1g3raOVW3KQA84GlsGowZCi7ug12kt3zHYaDmQYEtiihlnB7lXLh59EugIIYQQQjxlDrRcS6RfJc6WLYFdSioYTNhdSSMy0osLcaVRbLUk2WhBb8JVb6R4YhpmReG2g46G79wixc+e0A9dURQZUvUwyNC1/JFARwghhBDiKZKabOCYUzkuVi1Orb8v4JpwJ3X0uT0BOFSFcBsbSDQAEK+quJvBL1WPZ5IeBdBFplJ2gpnLn3sU0VYIcX/S7yiEEEII8ZQwGVX+r91Bbni7UuFimFWQA9Dg1DVUs5nbukz3whWFaw62GBTF0q+gU8E7Xo/zhwkYTeaHtwFC5IEEOkIIIYQQTwGzycwXXQ7imZqCa3IKOoMpSx07owmDAtw1JE1VFFK11mU2KpSLSab+wtTCbLYA1Dy8xB0S6AghhBBCPOEiTsYxo8Euapz+B72tDp3RyNVibhi01peCUe5OxNvZZfm+1qyiM6lcdbAl1NmeG3Y2qIDBRsOpK8aHtBVC5I0EOkII8ZQIDg4mKCiII0eOFFkbOnXqxNChQ+9b71Foa14EBQUxceLEom7GfT0u7RQF6+bf0SwffIgwbzv+qFIHk70KqpGZLevwfz0bccXHFaNG4WRpH85XKoUBQFXRmtP7B2zMKgFJqZxxdeC6gy0xtjZcdrLnkqMdDkYVAyqfbk8q0m0UIjsS6AghhBBPiISEBBYsWPDYBIii8J2efZxrTRdQMvY2ZjsP2pzby7hd8ygbeYkXjp7jmrcr4wa25HBdHY3j9tDhzFZaXT6NAtSMTaJGbBK1Y5NQgLS7en9u2dkQbq/DbKNh1s9pJKTKXB3xaJGsa0IIIR457du3p02bNuh0uqJuymMlISGBRYsWAem9N3fbv38/Wq02S7l48oSGhHHszX3UuX0UG1tHTpUtj2/iLRqdO8a/NMAl1oGhu/9i+M9HiKwQz4snfsesaNCqZoKunmB/qRloAdv/enWUHCZ/xNnaoJgVbjna4vpJPM+W1rHnNQec7eReekGS9NL5I4GOEEKIR45Wq33qL8iTkpJwcnIq0GXaZTP3Qjym0gxw6io4OWDycSVi4n7i/76I08GL/O1diQh3PyrfjsEmVYuXKYlSN2/gZo7lkm0FDlauRIqDLQBOyalUSIrmk/bvkmDnRNXIUJqH7sVPr1olJPAwmtCaVUyaO2XuBhO+qQbOutin19XpOHodXP6XjINiZMoL9tjbauhRVYudRsFBn4rG2Q6e8t+2eHgk0BFCiKeMyWRiwYIFBAcHc/v2bcqUKcOgQYNo27atVb09e/awbNkyQkNDURSFChUq0L9/f5o3b55lmXmpe7fw8HBef/11kpOTmTlzJhUrViQ4OJhJkyYxf/58S89ERtm8efM4c+YM69at4+bNm/j5+TF48GA6duyYZTuXLFnCTz/9RHR0NKVLl2bw4MFcunSJRYsWsXnzZkqUKJGnfXfhwgWmT5/OX3/9ha2tLQ0bNmTMmDFZ6h05coThw4fzySef0KlTJ6vPJk6cSEhIiNXwsqFDh3L9+nXmzZvHzJkzOXLkCPHx8Rw5cgSz2cySJUs4cOAAV69eJS4uDi8vLxo3bsyIESNwd3e3WifAokWLLD07fn5+BAcHA+m9PB07dswyT+enn35i7dq1XL58GRsbG6pXr86QIUOoVauWVb2M73fv3p3Zs2dz6tQp7OzsaN68Oe+88w6Ojo552p8iB9EJMPpb2HQItAqkGsFWCyl6MKuWzFoKkIgrcXjjzU1MlCaaMpQO11Pq+kWqmg9gRscFnqHeX1dQgX/KVrEEOQDRbi78VKGD5f1Jv8qc9SyWJeuaApRJSiHGzpZUjYKr0YR/sh4boGZcMmdcHNBnDG1TFFLQ8cY2I6Dw6bLrLAtZQPPLZzBoNJwrVprgaRN5s4cL9rbSUyEKjwQ6QgjxlJk1axYpKSn07NkTSA8gPvjgA/R6veWifO3atUyZMoWAgABee+01AEJCQhg7diwTJkyge/fuluXlpe7dzpw5w5tvvomLiwtLlizBz8/vvu2fM2cOaWlpdO/eHVtbW9atW8fEiRPx9/e3ujD/8ssvWb9+PUFBQfTr14/Y2FimTJmS5+AmQ3h4OEOGDEGv19O7d298fX3Zt28fr7/+er6Wd7fk5GSGDRtGzZo1GTlyJNHR0QAYDAaWL19OixYtaNasGfb29pw6dYpNmzbx999/s2LFCnQ6HWXLlmXMmDFMmzaN559/nueffx7gvsHHzJkzWbZsGdWqVWPkyJEkJyezceNGhg0bxtdff03jxo2t6oeGhvL222/TqVMn2rZty9GjR9m0aRMajYYPPvigQPbFU++1ubDxoHWZ4U5ms4zQQAXi8aYkF4nFh2junNuKWctu/5Y4xZhxSdJbvhfvaN1LmGyftZcvxsknS5kKqLZaKiamkmBnQ6yjjhiNHV5JaTiYVUql6LngbH/Xt9JbuiRkEc0vnwFAZzZTNfIy//zfUuZoB/BOb9f77g4hQ9fySwIdIYR4ysTGxrJq1SqcnZ0B6NmzJ3369OGbb76hdevW6PV6Zs6cib+/P99//71Vvb59+zJ9+nRat26Ni4sL8fHxua57twMHDvDuu+9Svnx5pk2bZumZuB+9Xs+yZcss83datmxJly5dWLNmjSXQuXDhAuvXr6dBgwbMmDEDjSb9TnOrVq14+eWX87Xf5s6dS3x8vFUvU+/evRk3bhxnz57N1zIzi4uLo0ePHowcOdKq3NbWlu3bt2Nvb30RWbNmTSZPnsyePXto3bo1Xl5eNG/enGnTplG+fHnat29/33VevnyZ5cuX88wzzzB//nzLPu3atSu9evViypQpNGjQwGoY4blz51iyZAnVq1cHoEePHiQlJbF582befvtt6dV5UEYTbDqcq6oG7HDlNgCJeFh9Funtxs46Nei3+XdLWVhxD5Iy9eYA2JiyPkvH3mhEBatLawWId7AlyV7HTTcHS/mNVDuqRsTjZMy6HAA7o56Wl09lKW9z/jD9/+rDO73vs5FCPACZKSaEEE+Znj17WgISAGdnZ3r06EF8fDxHjx7l4MGDpKSk0KdPnyz1+vTpQ3JyMgcPpt9tzkvdzLZu3cpbb71FUFAQc+fOzXWQA9CrVy+rJAXFihWjdOnSXLt2zVK2b98+APr06WMJcgDKly9P/fr1c72uDGazmX379lG1alWrSf6KotC/f/88Ly8nr7zySpYyRVEsQY7JZCIhIYHY2Fjq1q0LwL///pvv9e3duxdVVenfv7/VPvXx8aFTp05cv349SxBXo0YNS5CToW7duphMJiIiIvLdloIUHR1NWlqa5X1iYiIJCQmW93q9ntu3b1t95/r16/d8HxkZiaremZFfaOvQKODlTG7YoMf836WcPYlWn132L4bZRkuMa3rgGe3myMUyPqh3ZU4rG3kde/2d7VCBX/28s12frclsFeQAJNnriHGyJckm+3k3aVod4c4eWcrj7J3wcElvyyN9PAp5HaJwSY+OEEI8ZQICArKUlS1bFkgfnpWcnAxAuXLlstTLKAsPD7f6b27qZjhz5gyffPIJDRo04Kuvvspz0oGSJUtmKXNzcyMyMtLyPuOCu0yZMlnqlilThj/++CNP64yOjiY5OTnb5WW37fnh4eGRbc8XwI4dO1ixYgVnz57FaLR+OGN8fHy+15mxnwIDA7N8llEWHh5O1apVLeU57X9I75V6FHh6elq9zxyEQ3ovmZeXl1XZ3cMm735fvHjxh7eOT16E0Yu4H81/s3VUwJNIovGz9OzYGtKHqx2oXZHn//yXaPc7Q9ZUIE2nw6yBuhFn6Bz6G//4l+fn8lWZX+tZouxtqRyfglumXhoViLHP/rIx2UZDlE0Ol5SKwvgWvfk+eCFa9U57P3v+FQa9kN6mR/54FOI6ciuHpHfiPiTQEUII8VCVKlUKGxsbjhw5wp9//pllDsj9ZO6hySzzndeipig5j6c3ZTNUCMgyNC3Dr7/+yvvvv0+1atUYO3Ysvr6+2NraYjabef311x/6dt8rMH2UjsFjbdQLUL00BB+G5LT0xABpBrgVD5dvYr56CyU2/YaEMzGkYE+K4oyfehYj9qQqjlS6nsqZcv7c8nJj/Qv1KX4zGp05PUiO8nAnydEBRVX5tmlH3JPjeOnITrSpcUTZpw9tu+BsR2BiGm5GE3qtQpiHI6kaLRjMoMv0G1RVImxssr0StzEZMepsWFenMY7+7gzavZVy+nh+HT6Isb2rULm0pI8XhUsCHSGEeMpcvnw5S9mlS5eA9Lv1KSkpAFy8eJHnnnsux3oA/v7+ua6bwcnJiWnTpvHGG28wbtw4Pv/881xlZ8uLjIQDV65csbQxw5UrV/K8PA8PDxwdHbP97sWLF7OU3auH4+4ervvZunUrdnZ2LFiwwCoYyu443ivAyk7Gsblw4UKW/ZSxXdn14IiHoFm19Fc2Mof6CuD43wvAbDATPukAKfP/5vn9x7lVypXbbs6E+/ridzMKs0ZDkqMDDimpeMfEolVVzIrCsrov0PGfXXzeuBV6GxsMGg1nXP8bpmarAQcbSDWlZ4Bz1IGNAmbS32cEOWYzdmYVO7OZfW85UbNE5sQH9f57QZ+C2kdC3IfM0RFCiKfMunXrSEy8M54/MTGR9evX4+LiwrPPPku9evVwcHBg9erVJCUlWeolJSWxevVqHB0dLfNc8lI3M2dnZ2bPnk316tUZP348u3btKtBtbNKkCQCrVq3CbL7ztPbz589z4MCBPC9Pq9XSuHFjTp06ZZUWWlVVli1blqV+iRIl0Gq1HDp0yKr8+PHjnDhxIk/rzujByrwdqqqyePHiLHUdHNIvTHM7nK1p06YoisLy5cuthsRFRUURHByMn58flSpVylN7RdHS6DRUn9yQNlEjab+rCc434mgY+g/t/v0Do52GBEcHFLMZn/+CHACNquKWkEySgztLf1qJTaZzDQC9GZKNYDClBzVJBojTQ4I+vYdHVUFVaVtOQ8rnLsR96U7NEtJbI4qe9OgIIcRTxt3dnQEDBlhSSQcHBxMZGcmHH36Ivb099vb2vPHGG0yZMoWBAwdank8TEhLCtWvXmDBhgmVsuouLS67r3s3R0ZGZM2fy9ttvM2HCBD799FPatGlTINsYGBhIt27d2LhxIyNHjqR58+bExsaydu1aKlWqxOnTp/Pc+zFy5Ej++OMP3nrrLV588UWKFSvGvn37iImJyXbbOnXqxE8//cSECRN49tlnuXbtGsHBwVSoUIHQ0NBcr7dly5b8+uuvDB8+nA4dOmA0Gtm7dy+pqalZ6rq7u1OqVCl++eUX/P398fT0xMHBgaZNm2a77ICAAF555RWWLVvGkCFDaN26tSW9dHJyMp9++ulT/+DWx5lSqyw9w0qzocIPHA8MJNXWDv/IW4TpiqG5a5ihApzz9GVO7WdwNRiJtrPOzobhruAnE0ejiRSdhu2v5S6Jgsg7SS+dPxLoCCHEU+b111/n77//Zu3atZYHaU6ePJl27dpZ6vTq1Qtvb2+WL19uefBkxYoVmTp1apZhZnmpezcHBwemT5/O2LFj+eijjzCZTLzwwgsFsp3jx4/Hx8eHTZs2MWPGDMqUKcP48eM5efIkp0+fxs4u6/ND7sXf359vv/2Wb775htWrV1seGPq///0v2wBtzJgxqKrKnj172Lt3L1WqVGHatGls3LgxT4FO27ZtSU5O5ocffmDGjBm4uLjQtGlTRo8eTcuWLbPU//TTT5k2bRpz5swhNTUVPz+/HAMdgDfeeINSpUqxdu1aZs+ejU6no1q1akyePJnatWvnup3iEaXV0vVsXxKa78Q+9ixhrqVwTUzKkj5aBd5v1gKDzgZH7gQ1zgYTpVLSsDepxOm0XHW0w6jJ9E1Vxdtg4uLn8jwc8ehRVJk5KIQQ4iny9ttvc/jwYfbu3Su9FeKpcnTWWQ4tOkeSswMac3ofgUJ6kHPLTstfJUtQMSGFddUCiNDp0JnMPBObTOZfSaxOy1mXOymm7UwmSpiMXPzSE1F4ditLcl33eXVQIbbk8SI9OkIIIZ5IqampWTKZnTt3jj/++IOGDRtKkCOeOs++XonSnf15771rlL96iWbnLqE1q3gkJ2JOtaEnp9n/bAXUiqVAa4O73sTdvxJ3gwmtWcWkgM6kEpCqZ/4o6c0pbNIrkT8S6AghhHgihYSEsHXrVho1aoSHhweXL19m48aN2NjYMGzYMCA9GMqcmCEn3t7ZP0BRiMeNTxknmgeY+bBcfZoeu4pLWjIpOGJGixm44emGjUEFBwVTNtNCVMBTryfJyQ4bjcoHL7nSvIJt1opCPAIk0BFCCPFEqly5Mnv27GH16tXExcXh5OREUFAQQ4cOpXLlykD6gzgnTZp032VlzrQmxOOu/xdVOdH6T+a1q8ew7YdxTjNg1Cj8VbUsabYKXiYDYao9MbY2pKTocTDf6U+ItNNxy8kOrcFMt8oKrzwjQY54dMkcHSGEEE+tqKgoLly4cN969erVewitEeLh2bviCjuW38AhJRX7NCO3PN3Q67RUfOEQm0Pr8S/+XHO0Q6tCsTQD9qb0NNKuJjPXPe1Y19eBJpUkyHlYfs3DHJ0WMkfHQnp0hBBCPLW8vb1lWJp4KjXrV4aLJ5P557Q9qqJgq9fTb6gXRyKgnE841yL8cI1PIcJeR5pGwcWoYgBMisqN/7kVdfOfOpJeOn8k0BFCCCGEeAoN+rwKqqqSlmjE3kWHwWDgyBLwdYwHRcHZZKZiUpqlfqyNhu9GS5AjHh+aom6AEEIIIYQoGoqiYO+isypzcUhFZ846s0EB6layz1IuxKNKAh0hhBBCCGHhqOiJscuafj3WRebkFBUVJdcvcYcEOkIIIYQQwsq6MU5EOOrQKwrJWoWLLnYcGOtS1M0SIk9kjo4QQgghhLBS01fL1ameHA4zojcqNAqQB+yKx48EOkIIIYQQIlt1/eVS8VFgLuoGPKZk6JoQQgghhBDiiSOBjhBCCCGEEOKJI4GOEEIIIYQQ4okjAy+FEEIIIYR4hKkaSRudH9KjI4QQQgghLNYk1sZ2BmimGll3xljUzREi3yTQEUIIIYQQALwV35td5lqAggr0CoHnV0qwIx5PMnRNCCGEEEIAkIITYD1Mas/1ommLuEOVkWv5IoGOEEIIIYQgxQhVIsOY/PNqqt4IY2+5qkx44SWiHZ1RVRVFkatt8XiRQEcIIYQQQtB+WSq7F/wP38Q4ACrfiqBi1HVaDPu4iFsmRP5IoCOEEEIIISi+/28cDWl0HvguuypUxyU1hcnbV1E65hbgV9TNe6pJ1rX8kUBHCCGEEEKQrLPjhVffZ3/ZKunvbe0Z0ms47smJMmxNPJYk0BFCCCGEEPxcsSZmjda6UFGIdXTGrKpoJNgRjxlJLy2EEEIIITBrtTl/pqoPsSVCFAzp0RFCCCGEEOly6LXRSm9OkVKlayJfZLcJIYQQQoicKQoxqeaiboUQeSaBjhBCPIDg4GCCgoI4cuRIUTdFCCEKjdccGbomHj8S6AghhHgkJCQksGDBAgkaHxNyvJ4yqorylYGj141F3ZKnkqpVcv0Sd0igI4QQ4pGQkJDAokWLOHr0aFE3ReSCHK/HVEoqtPwIqr4Oz74Dq3+3fKTo9Tl/T1FAUQhaoTL+Vwl2xONBkhEIIYQQ+WA0GjGZTNjZ2RV1U4S4Y8kOGDYfDLkcatZnGvSZhgo4/G8JydznfFYUphyDKceM3BgOxZzlUlI8uhRVlXyBQgiRX8HBwUyaNIl58+Zx5swZ1q1bx82bN/Hz82Pw4MF07NjRUveXX35h27ZthIaGEh0djaOjI7Vq1WL48OFUqFDBarnHjx9n8eLFnD17loSEBNzc3KhQoQJDhgyhRo0aeWpjREQE33zzDYcOHQLg2Wef5Z133mH48OH4+fmxcOFCS73OnTszZMgQhg0bZrWMBQsWsGjRIjZv3kyJEiUs5VFRUSxatIjff/+d27dv4+7uTpMmTRgxYgSenp6WenFxcXz77bf89ttv3Lp1CwcHB/z8/GjTpg39+/fnyJEjDB8+PEvb/fz8CA4OzvW25mUfA6xbt44ffviB69evU7x4cfr06YOjoyOTJk1i/vz5BAUFWW3/6tWr2bRpEzt37iQqKoq5c+cSFBSEXq9nxYoVbN++nbCwMGxtbalduzbDhg2jcuXKVutUVZX169fz008/cenSJTQaDVWrVmXIkCGW9d19PMqVK8eSJUu4cuUKPj4+DB48mM6dOxMZGcm0adM4cuQIRqORZs2aMX78eJycnKzWmdvjlLGd69atY8uWLWzZsoWYmBgCAgIYNWoUjRs3Biiw4yUyiUmEL3+CQ+egbnko5wvTNsOFSDA+nEQAUQ5O+Ez6LsfMa7kxuhbMaiXBT0Hb4rQi13U7JPUrxJY8XuRMFEKIAjBnzhzS0tLo3r07tra2rFu3jokTJ+Lv70+tWrUAWLNmDW5ubnTr1g1vb2/CwsLYuHEjr776KitWrKB06dIAXL58mVGjRuHl5UWfPn3w9PQkOjqav//+m9DQ0DwFOrGxsQwZMoTbt2/To0cPypYty19//cXw4cNJSUl5oG2OjIxk0KBBGAwGunTpgr+/P9euXWP9+vUcOXKE5cuX4+zsDMD48eM5duwYPXr0oEKFCqSlpXHp0iWOHj1K//79KVu2LGPGjGHatGk8//zzPP/88wA4OjrmqU253ccA33//PbNnz6Zy5cqMGjWK1NRUli9fjoeHR47L/+ijj7Czs6Nv374oioK3tzdGo5HXX3+df/75h/bt29O7d28SExMt6120aBFVq1a1LOPjjz/m559/pmXLlnTq1AmDwcC2bdsYNWoUX375Jc2aNbNa5++//86GDRvo2bMnrq6ubNq0if/973/odDrmzJlD3bp1GTlyJKdOnWLz5s3Y2try0Ucf5es4ZZg4cSI2Njb069cPg8HAjz/+yNixY9mwYQMlSpQosOMlMnnhUzh4Lv3/fz1RJE04WKbiAwU5ALP/Bq3GxPQWOT+TR+SdWSNzb/JDAh0hhCgAer2eZcuWodPpAGjZsiVdunRhzZo1lkBn1qxZODg4WH2vQ4cOvPzyy/zwww+MHz8egAMHDpCamspnn31G9erVH6hdS5cu5caNG3z66ae88MILAPTs2ZMZM2awfPnyB1r2l19+idFoZOXKlfj6+lrKW7VqxaBBg1i5ciXDhg0jMTGRw4cP07NnT959991sl+Xl5UXz5s2ZNm0a5cuXp3379vlqU273cVxcHIsWLaJ8+fIsXrzYMvysa9eu9OjRI8flOzs7M3fuXGxs7vzzuXLlSo4ePcqsWbNo0KCBpbxnz568+OKLTJ8+3dJrtnv3brZt28aECRPo3r27pW6fPn0YNGgQX3/9NU2bNkXJdLF56dIl1q5di5+fHwBt2rShQ4cOfPzxx7z55pv063fn7m1CQgJbtmzhnXfesQQduT1Ombm7u/PNN99Y2hEUFMSAAQPYsGEDo0ePLrDjJf5z9MKdIKcInS1W4v6VcmHRPyrTWxTIooR4IJKMQAghCkCvXr0sQQ5AsWLFKF26NNeuXbOUZVyAq6pKYmIisbGxeHh4UKZMGf79919LvYy763v37iUtLe2B2rVv3z68vb1p27atVfkrr7zyQMtNTEzk999/p2nTptjZ2REbG2t5lShRAn9/fw4ePAiAnZ0dtra2/Pvvv0RERDzQeu8nt/v44MGDpKWl0bNnT6s5Nt7e3paAMDsvv/yyVZADsG3bNgICAqhSpYrVfjAajdSrV4/jx4+TmpoKwNatW3FycqJ58+ZWdRMTE2nSpAkRERFcvXrVavnNmze3BDmAZXs0Gg29e/e2qlurVi2MRqNlP+flOGXWp08fq2CrWrVqODo6Zmnboyo6Otrqt5OYmEhCQoLlvV6v5/bt21bfuX79+j3fR0ZGknm0f4Guw/RoPKOmxfl/718pF4wmtfD21X8K9Xg8xHWIwiU9OkIIUQBKliyZpczNzY3IyEjL+zNnzjB//nyOHj2aZdhY5u+3adOGrVu3smTJEn744Qdq1KhB/fr1adu2rdUFb25ERERQrVo1NBrr+1qenp64uLjkaVmZXb58GbPZzKZNm9i0aVO2dTK2SafTMWbMGL7++ms6d+5MuXLlCAoKonnz5jz33HP5bkN2cruPMwKBMmXKZFlGdmUZMg99y3Dp0iXS0tJo1apVjt+LjY2lePHiXL58maSkJNq0aZNj3ejoaKs2ZHduubi44O3tja2trVW5q6srkN5jBXk7Tpn5+/tnKXNzc7Ms91GXed4RkGVonq2tLV5eXlZld/+27n5fvHjxwltH3fJQuyz8denuTXmoakVc4avgZYzr+MoDDWEbWENjFSg/dsfjIa4jt1TpmsgXCXSEEKIA3B1IZMi4GxgZGcnQoUNxcnLi1VdfJSAgAHt7exRF4euvv7a6KLe1tWXu3Ln8+++/HDhwgGPHjlkmiU+ePNkyH6KgKfe4sDGZTNmWv/DCC1YJFzLL3FPSs2dPmjdvzu+//87Ro0fZtWsXa9asoXXr1nz++ecP1vD/5GUf55e9vX225eXLl+ftt9/O8XsZ835UVcXDw4PJkyfnWDcwMNDqfU7nVk7lGevJLLfH6X7LlvxFhURRYPtH8OlaOHwegspDaS+Ysx3CboP54ez3NK0NB0oFPlCQ07cSzG8tV+Xi0SCBjhBCPAS7d+8mOTmZadOmWWXWgvS773ffmQeoXr26ZY5OZGQkffv2Zd68eXkKdPz8/Lh27Rpms9nq4jU6OtpqyAXc6Q2Ij4/Pspzw8HCrsNDXPgABAABJREFU9/7+/iiKYhmelRve3t507dqVrl27YjKZLJPy+/XrR7Vq1e4ZaOVGXvZxxl3VK1euULduXau6V65cydN6S5UqRUxMDHXr1r1n8JFR9+rVq9SoUeOhTNzPz3HKrQc9XuIuxdxh1hDrsne7Z1s1RyYTjF4I83fkqwnl3p1BhKdPrusf7gMVvDW42UtgIx5NcmYKIcRDkHEBfPcd8Y0bN2YZ0x0bG5vl+76+vnh4eOR56FDTpk2Jiori559/tirPLhGBk5MTXl5eHD582KqdYWFh7Nmzx6quu7s7jRo14tdff+XEiawZolRVJSYmBoDU1FTLHJUMWq3Wku45I7DKmF+TXaCVG3nZx/Xq1bNkx8s85j4qKopt27blab0dOnTg9u3brFy5MtvPM6+7Q4cOmM1mZs+efd+6BSEvxymvHvR4iUKg1cK8EaBuyP5lXg9HvoLS3lDZF6qUgEV36ke4e91/HcCLFUAda0OQv40EOQ+JqlFy/RJ3SI+OEEI8BI0aNWLWrFl8/PHH9O7dGxcXF44fP84ff/yBv7+/1dCwxYsXc+DAARo3bkzJkiVRVZV9+/Zx+fJl+vfvn6f1DhgwgO3btzNp0iROnjxJQEAAf/31F//88w/u7u5Z7sr37t2befPm8cYbb9CsWTOioqJYv349gYGBnDp1yqru+PHjee211xgyZAgdOnSgUqVKmM1mwsPD+e2332jfvj3Dhg3jypUrDB06lOeff57AwEBcXFy4fPky69ato2TJktSuXRtIvygvVaoUv/zyC/7+/nh6euLg4EDTpk0LfB+7u7szZMgQ5syZw6uvvsoLL7xAamoqGzdupEyZMpw6dSrXPRYvvfQSBw8eZMaMGRw+fJi6devi5OREZGQkhw8fxtbWlgULFgDpmc46derEmjVrOHPmDE2aNMHd3Z2bN2/yzz//EBYWluNcmvzK7XHKqwc9XqIIKAo8GwhXFmb/+b0uklUVFIWjL0GdknL5KB4PcqYKIcRD4O/vz8yZM5kzZw5LlixBo9HwzDPPsGDBAr788kurTDwZAcbOnTuJjo7Gzs6OUqVK8eGHH9KlS5c8rdfd3Z1vv/2W6dOns3nzZhRF4dlnn2X+/Pn0798/y/yMAQMGkJiYyNatWzl69Chly5blo48+4vTp01kCneLFi7NixQqWLl3K3r172bZtG7a2tvj6+tKkSRNat24NpPdGde7cmaNHj7Jnzx4MBgM+Pj5069aNAQMGWM17+fTTT5k2bRpz5swhNTUVPz+/XF8452UfAwwaNAgnJydWrVrF7NmzKV68OK+88gqqqnLq1Kls565kx8bGhunTp7Nu3Tq2bt1qCWp8fHyoVq1alrkxn3zyCUFBQWzcuJHvv/8eg8GAl5eX5Xk+BS23xyk/HuR4iceIqqIz6NFPcLp/XSEeIYoqMwuFEOKpExsbS6tWrejevTsTJkwo6uY8Ur788kvWrFnD9u3b8fb2LurmCPHQKFMNQDa9OqrKwb4Kz5WQ++NFZZPHD7mu2yXm5UJsSeHI6GG+efMmPXr0sPTCx8XF4ebmhlabvwfQysBKIYR4wt09PwbSHyQKFPgE9cdJds8oioqKYsuWLQQGBkqQI0QmEuQULVXJ/etxoqoqY8aMoWzZsvTt25cxY8YQGhoKpD+nKCAggFmzZuV7+XLWCiHEY8ZgMOQqKYGHhwdarZY333wTPz8/KleujNls5vDhw+zbt4+aNWvSvHnzwm/wA4qJickxvXUGR0fHPGcxO3r0KDNmzKBFixYUK1aMiIgIfvrpJ1JSUnj99dcfpMlCPHGMJjM2Wrk/LgrWV199xYwZM3jvvfdo2bKl1VBaNzc3unfvzvr163nrrbfytXwJdIQQ4jFz/Phxhg8fft96mzdvpkSJEjRp0oQtW7awe/du0tLS8PX1pV+/fgwZMiTfwwEepv79+9/3aeJDhgzJ84T6UqVK4e/vz8aNGy3pp6tWrcrAgQOf6p4uIbJQFAlyRKFYtGgR/fv35//+7/+yzTpZs2bNPGfCzEwCHSGEeMxUrFiROXPm3LdexhO6+/XrR79+/Qq7WYXm008/zXaYWWYlS5bM83JLlSrF119/nd9mCfFUMZhUdNrHbFzUE+RJTRt97do1GjZsmOPnTk5OD5TCXgIdIYR4zLi6uj5VPQ61atUq6iYI8dSTIEcUhmLFinHt2rUcPz969CilS5fO9/KlH1IIIYQQQtCtbM6fmc3mh9cQ8dTo3r078+fP5+LFi5ayjGeY/fLLL3z//ff06tUr38uX9NJCCCGEEAKDwYDtDMguxbQ6VgYBFaWN3j/mum63qJcKsSUFKy4ujqZNm3Lp0iWaNGnC9u3bad26NYmJifz555/Url2b3377Lc/JZjJIj44QQgghhBD/z959x9d0/w8cf93c7D1EJCIEMUPRGG2t1iwSu9RWtSm1qq0arf5a2m+pHWqX2krMUltrj9qx0kQGksje997fH+m9XDchU5D38/G4D+65n/P5fM64ued9PuO8xNSKnL9eJXZ2dpw4cYIJEyYQGhqKubk5hw8fJiYmhilTpnD06NE8BzkgLTpCCCGEEILMFh23nyOJpARPtupYKiBxrLToFKXNzjlv0en88NVp0Sls0qIjhBBCCCEA+NY2AF+jk0DmffCuFSXIEa8uOXOFEEIIIYROO+urbO5fHxMTuUx8Wbyu00t/9NFHz02jUChYunRpnvKXM1gIIYQQQgjxwh04cEA3y5qWSqUiPDwclUqFs7MzVlZWec5fAh0hhBBCCCHECxcUFJTl8vT0dPz9/Zk9ezb79u3Lc/4yRkcIIYQQQoiXmEaR89frwMTEhBEjRtCyZUtGjBiR53wk0BFCCCGEEEK8dN544w2OHDmS5/Wl65oQQgghRDEWEpLM4lVRVKpqUdRVEULPvn378vUcHQl0hBBCCCGKqQFDAolIt0ChUHD+bjIazfu0qLW/qKsliomvv/46y+UxMTEcOXKEc+fOMXHixDznL4GOEEIIIUQxdPHEA+7/F+RoKRQKDp5tzPAirJcwpFG8JoNvnjJ16tQslzs4OFChQgUWLVrEwIED85y/BDpCCCGEEMXQN78kACYGy1VK0xdfGVEsqdXqQs1fJiMQQgghhCiGKofcgixaCl6XmbuEkBYdIYQQQohiJvjsQ1JMTDFSqVArlbrlFimppGkyCDoSglez8kVYQ/Ek9WsSfAYHB+dpPQ8PjzytJ4GOEEIIIUQxs3bBbe46eugFOQApJsb0OvI3gyze5mCzIqqceG2VK1dOb0xYTqlUqjyVJ4GOEEIIIUQx80+SDWmWhuNzNEolkXYOeDyMAqRFRxSsZcuW5SnQySsJdIQQQgghipGw++nEWdpmOT4HINzBHoeUDC5/eoTyo97AspzdC66heJrG6PXou9avX78XWp4EOkIIIYQQxcjhPx4+8/NUpRFlwqI5fDedP/44gklGOh9taoBVDbcXVEMhCoYEOkIIIYQQxci+DeFoXFzIro0gxcgEu+RUFIB5aub0v791PIGpo4I+pzq+sHqK4uP48eOcO3eO2NhYgymnFQoFX331VZ7ylUBHCCGEEKIYeeDkmG2Q4xIVQ61bhjNjKYDwVHPC/wrH9W3XQq2fKD6io6Np27Ytp06dQqPRoFAo0Gg0ALr/5yfQkefoCCFy7cyZM/j4+BAQEABAWFgYPj4++Pv7F2g5U6dOxcfHp0DzfN35+/vj4+NDWFhYUVcl32JiYpg8eTKtW7fGx8eHQYMG5XhdX1/fXKUvCE9/L4R4GU2eFQZG2V/+uT2MzvYztVJBj8WxRP4TVRhVE8+gUeT89SoZP348//zzD2vXruXOnTtoNBr27t1LYGAgQ4YMoVatWvn6PZNARwhRpAICAli7dm1RV0O8hGbNmsW+ffvo3LkzX3/9NR999FFRV4mwsDD8/f25ceNGUVdFiDxxXbcf5X93zLOiesaMWCXjklCqFXw4M5y71+ILo3qimNm1axeDBw+mW7du2NjYAGBkZETFihWZP38+5cqVY/To0XnOXwIdIUS+ubq6cvz4cQYMGJDrdQMCAvjtt9+y/GzSpEkcP348v9UTr6iTJ0/SoEEDBg4cSJs2bWjQoEFRV4mwsDCWLFlCYGBgUVdFiFw71mc9bW78jeYZwcx1T3cyntHiU/d2GH/WrEy931S8MyCQCqOC6Tf0Hy4dvl8YVRavuZiYGKpXrw6AtbU1AAkJCbrPW7Zsyd69e/Ocv4zREULkm0KhwMzMrMDzNTY2xthY/ky9zBITE7GysiqUvKOiorCzK17T2hbm/hQvofsx8OthuBgEVmZgZgK2llDfC5YfgH0XISEF1BpQKqCEbebn3h4wtRuUtIMxy+HMbchQQVwSmBqTWNKR1OthxJhbsbLuu5RMiKHdtfNUTUvj65aDURsps62SRqHgbqkSeIXex0ijQm2k/zc4xdQYjZGCKGsrbrk5E2VlwR1jN1ae0lB61wOqR0RS4WE0Z8q6kmiu5t8SpUgyMUOjUGCVmsLHpw7w7u3LhNo6sbphM/5x8yRZlTkGyNkSnMwhKiXzTvwbzjDtHSMSM+BgsIYqTgq6VlJgotQP1O4nalhzTUOaCrpXUVDO7hXrv5UDzwpOX2Vubm5EREQAYGZmRsmSJbl48SLt27cHIDQ0NF/P3ZErCCFEvoWFheHn58fAgQMZPHiwbvmOHTvYsGEDwcHBZGRk4OTkRI0aNRg7diwODg74+voSHh4OoDcWZ9GiRfj4+DB16lR27NjBmTNndJ9plx06dIi5c+dy4MABEhMTqVKlCmPGjMHb21uvbjExMfz8888cOXKEtLQ0qlevzujRo/npp58IDw/P9XiKQYMGER4ezrJly5g1axZ///03aWlp1K5dm/Hjx1O2bFldWn9/f5YsWcL27dtxc9OfltXX1xdXV1cWL16sW+bj40O7du1o27YtCxYsIDAwEDs7Oz744AP69etHXFwcs2fP5ujRoyQlJVG3bl2+/PJLnJ2dDeqZnJzMDz/8wP79+0lISKBixYoMHz6cevXqGaQ9efIkq1at4sqVK6SlpeHh4UGXLl3o0qVLlnUeM2YM8+bN49KlS9jZ2bF9+/Yc77/k5GSWLl3Kvn37ePDgAba2ttSvX5+hQ4fi6uqqt98g8xzasWMHAFOmTMHX1zfHZQFcv36d2bNnc+XKFUxMTGjUqBGjRo3C0dFRlyYxMZGVK1dy8uRJ7t27R1JSEi4uLjRr1oyBAwdibm4OZLY+Tps2DYBp06bp/l+nTh294wiwfft2fv31V0JCQnBycqJr16707dtXL83z9ue5c+f45ZdfuHLlChkZGZQrV46uXbvSoUMHg+3MaVrt+evv789PP/3EmTNnUCgUNGnShAkTJmBubs6KFSv4/fffiYyMxNPTk/Hjx1OrVi1dHmq1mnXr1rF9+3bCwsJQKBQ4OTlRq1YtvvjiC7k5kVO3I6DBRIiMy1l6lQbux2a+bobD1pNgooR0wyfGW4XHEG9tx7vDv6bPmcMMP7EfgD1eDbhdwiPr7AFTlQrQEGNnTVrEQ7ziggmxcketyAyMNGjY7+2Z+X8jBQ/sbDDJyEBbg+nbD/Du9Tu6PMd3ac3V0p6698mmprQKvMj7Ny6gAULtHDjh8l9+wP2kzJdW+L+w598nZ+DSsOySgn1djXQXv7djNDRYoyIyOTPFN3/Dnx8oaeD2egYGr5vGjRuzb98+vvzySwC6devGzJkzUSqVqNVqZs+eTatWrfKcv/w1EkIUip07dzJ16lRq167NkCFDMDMz4/79+xw/fpzo6GgcHBwYO3Ys8+bNIyYmhjFjxujW9fT0fEbOmUaMGIGDgwMff/wxsbGxrFmzhlGjRrF9+3bdHfG0tDSGDRtGYGAgvr6+VK9enZs3bzJ8+HBsbW3zvG3JyckMHDiQGjVqMHz4cEJDQ1m3bh1jx45l/fr1KJXZ3y19nhs3bnD06FE6duxI27Zt2bdvH/PmzcPMzIwdO3bg5ubGoEGDCAkJYf369UyZMoUFCxYY5DNlyhSMjIzo06cPSUlJbNmyhZEjRzJnzhzq16+vS7dlyxa+++47atSowUcffYSFhQUnT57k+++/JzQ0lFGjRunle//+fYYOHUrz5s157733SEpKerrobGVkZDBixAguXrxIs2bN6NWrF8HBwWzevFkXbLm4uPDee+9RpkwZJk+eTO3atenYMXM625o1a+ZqXz548IChQ4fy3nvv0axZM65fv8727du5du0aq1at0gUwDx8+ZNu2bbz33nu0bt0apVLJuXPnWLVqFTdu3GDevHkA1K5dm/79+7N8+XI6duxI7dq1AfSCJoDNmzcTHR2Nn58fNjY27N69m7lz5+Li4kLr1q1ztD+PHDnC+PHjcXJyolevXlhaWvLHH38wffp0QkNDGT58uC6P3KSFzPN36NCh1KlThxEjRnD16lW2b99Oamoq9vb2XL58mQ8++ICMjAx+/fVXxowZQ0BAgO57tWzZMhYtWkSjRo3o3LkzRkZGhIWF6W4mSKCTQ7MDch7kZCeLIEdrcYPmRFtY8/nB3zOTGhmz6O2uWaZNNQIzNaiUSlRKJfdcShDhZE/iRQt8A49wza4yMZYW/FO2JJfLltJbV/Pf/G1v3Q7WC3IAjDX60wSrjZR8/24H3r9xAQXQ48Jx/q9551xt8p/BGg4Ea2hWNrPcn86odUEOQFIGfHtCTUCnvP8dFi/OmDFj2LdvH6mpqZiZmTF16lSuXLmim2WtcePGzJ07N8/5y18jIUShOHToEFZWVixcuFDvwmfIkCG6/zdt2pS1a9eSmppKmzZtcpV/lSpVmDhxou59+fLlmThxInv27KFz58wfzm3bthEYGMjQoUP1xg9VrFiRGTNm6FoQcismJobevXvr3aF3cHBgzpw5nDp1irfeeitP+QLcunWL5cuX61qm2rdvT7t27fjpp5/44IMPGD9+vF76tWvXEhQURLly5fSWK5VKfvnlF0xMTADw8/OjS5cu/PDDD2zatAmAyMhIfvzxR1q2bMm3336rW7dr1678+OOPrFmzhs6dO+Pu7q77LDQ0lEmTJmXZqvA8AQEBXLx4kd69e+sFUPXr12f06NHMmzePb775Bi8vL7y8vJg8eTKlS5fO9bmhde/ePcaMGUOPHj10y8qXL8+sWbNYt26d7gndpUuXZufOnXrn6QcffMDChQtZunQply9fxtvbG3d3d+rXr8/y5cupWbNmtvWKiIhg06ZNuv7m2mO4fv16g0Anq/2pUqmYOXMmFhYWrFy5Utdi98EHHzB48GBWrlyJr68vHh4euUqrFRMTQ58+fejTp49uWXx8PPv376dKlSosX75cty88PT0ZO3as3vfq4MGDeHp6MmvWLL1tGTly5PMPygsSHR2NlZWVrkttQkICGo1GN9g5LS2N+Ph4nJycdOuEh4fr/U14+n1ERAQuLi66loR8l3GvcGcuu2fnhGNyApbpaQD84+pFhrGJYUKNhkQzU8yS0/UWZxgbc9GrHPXv3sZMk8rlch4sbmI4C6a23cQ9Otbgs0QzU4NlUVbWuv+Xjs1+hrdnCU14fDxCEww/v5egeTmPeRbvi7saNWpQo0YN3XsHBwf2799PTEwMSqVSt2/zSiYjEEIUCmtra1JSUjh27JhuTvyC9OTFKzzu+hYSEqJbdvToUZRKJR9++KFe2g4dOuguQvPCyMiI7t276y2rW7cuAMHBhs+fyI0aNWrodb8zMTGhevXqaDQagzK1LQpPbrNWjx49dEEOoGtNCAoK4u7duwDs37+ftLQ02rdvT0xMjN6rUaNGqNVqTp06pZevnZ1drruPaR08eBAjIyP69++vt7xhw4ZUqlSJI0eOGDwoLj+srKzo2lX/DnbXrl2xsrLi4MGDumUmJia6C/uMjAzi4uKIiYnRdfO7fPlyrsr19fXVO7/Mzc2pUaNGludGVvvz2rVrRERE4Ofnp9ct0cTEhD59+qBWqzl8+HCu02oplUq6deumt6xWrVpoNBo6d+6sF/BldY5ZW1vz4MEDLly4kNNd8sI5OjrqjRu0trbWu2AyNTXVuxgFDC4+n35fqlQpvbEC+S6jvWE30oLU/sppgh2cOe9WDgDL9OSsEyoUJBtn3frxyNYa56QkqkVEMvLPv/nm9/3w1N/z9P/WPVapHGlK/cvK2v8aTgvc/cJfuv/vqFonp5ujY24MrcopdMejfQXDLmrtKyhezmOexfucUity/nqVXL16Ncvl9vb2+Q5yQFp0hBCFpH///pw7d45x48ZhZ2dHnTp1eOedd2jRokWBDLYuXbq03nt7e3sAYmMf31UMDQ2lRIkSWFpa6qU1MTHBzc2N+Pi8TY/q7OxsMPmCdtD8k+XnxdPbBei62T09zkf7I5BVmVl1/ytfvjyQuV88PT0JCgoCYNiwYdnWJzpa/45r6dKl89w1LywsDGdn5yy7DVaoUIHAwEBiYmIMuoLlVenSpfWCPci8ECldujShoaF6yzdu3MjmzZu5c+eOQbCV2/Mkq2NoZ2eX5XHKan9qnxmhPV5PqlChAoCu/rlJq1WiRAmD8ze7c0y7/Mm6Dx8+nHHjxvHxxx/j7OzMm2++ScOGDWnWrJnB/hbP0PdduPsAftoOSamZEw6YKiFdDWVLwL+RBkGFnuY1oWIpWLIfVPrnrAZodeMCMwNWMbjzQGbuXEPTO1epHn6TK65eTyTUgEJBupGCDIUC46fKKxEXj3n645Ye3wvX+bJzS96++S83SjkTbm+D6r/zN9zelk96+PJVwAFKx8RzoYwrvzaopVdOnXu3GfrXXjT/1fGmUymM1GrUz5jlrZF7ZqvRkXtQ2RFmNTXCxerx1Xw/bwVBcQp+Ppc5GUF/bwVfNJD7+K8Kb29vvL296d69Ox988AEVK1Ys0Pwl0BFCFAoPDw82btzIqVOnOH36NOfOnWP69Om6geZPdofKi+wutguj9ehpRs/4UX6y/GfNFKNSZd23/llBREFvs3a9adOmUaJEiSzTPH3Rrh3X8jr59ddfmT17Ng0aNKB79+6UKFECExMTHj58yNSpU3PdypSbQLAo9uezzt/sPnvyHKtZsya///47f//9N2fOnOHs2bPs2bOHpUuX8ssvvxS7mfLyTKGAad0zXxpN5svICNTqxw/0VKszX1HxYG8JpqaPgx9tmoVDMtNoaTQolEqMk1IYfyWYQSWcuOn8Dqe2GlMmKhC32Ifsq1QfjJSZdQA8YpO5VsISj7hUbNIy/zaZpqfT7a+TPPlXTG2kwDRdxcfHjvBrvbdJMjUh2toys04ZKo6Vd6fV6D5YGUP/t634UKnByx6MjRRogCRVBdTfriDNREN8GnxlacRkID5VjdLICCtT/b+ZRk/8DVVrNHrvH+9GBdPeUTLtnczzND8zdIkXb+HChWzYsIHJkyfz1VdfUatWLV3Q8+TkPnklgY4QotCYmprSsGFDGjZsCMCxY8cYPXo0a9as4bPPPgOeHQzkl5ubG6dOnSIpKUmvVScjI4OwsLACaRZ/Fu3d8Li4OL075ampqURGRuY72HuWu3fvUqlSJb1ld+5kDhTWBi9lypQBMlvDnpygoLCULl2av//+m/j4eIN9f+fOHaysrHQtcwUhNDSU9PR0vVaGtLQ0QkND9cY07dq1Czc3N+bMmaN3of/XX3/xtBdxEaU9Ptrj9aSnj2Fu0hYkS0tLmjVrRrNmzYDMFrEZM2awbds2vbE/IocUCl3QwZPBppFR5svFQT/t07IKUC3NoW4l7ACfr96Hr96nHtDuo6DMIOfJ1YHqkUmkGSkoHfaA6mHhtLh2Fcs0/XE7G328mbp7C6UiVajTVRwebIW325OXkhY53uQnh+/YWzy/BSarIOdpr3OQ87pOLz148GAGDx7M/fv32bhxIxs2bGDixIlMnDiRevXq0b17d7p27WrQ2pxT0rYnhCgUMTExBsuqVKkC6HeDsbS0JC4urlBaYho1aoRKpTJ4IOnWrVv1HkhWWLR3o06ePKm3fO3atQU6FiUra9euJf2JLif3799n7969lC1bVtetrUWLFpiamuLv709KSopBHgkJCaSlpRVYnZo2bYparWbFihV6y48fP86NGzdo3LjxM1sbcisxMZGNGzfqLdu4cSOJiYk0bdpUt0ypVKJQKPTOwYyMDIN6ArqAOb9dFJ+lSpUqlCpVioCAACIjI/XqtHr1at100LlNW1Ce9d2Oi8vnLGKi0DnFZ98lzlSt4X5JR8wTU4m0sEb7VypDYcSdEtZ8dGELoZaluF/Cml0baj0V5AiRdy4uLowYMYIjR44QHBzM//73PxQKBWPHjs1Xy46coUKIQjF8+HBsbGyoXbs2Li4uxMfHExAQgEKh0Jutytvbm6NHjzJz5kxq1qyJkZERdevWLZBxGh06dGDLli0sXLiQe/fu6aaX3r9/P2XKlMm2+1hBqVevHmXLlsXf35/Y2Fjc3Ny4ePEily5dKtCWi6yoVCo+/vhjWrVqRVJSEps3byY1NVVv1jYXFxcmTpzI9OnT6dq1K23atMHV1ZVHjx5x69YtDh06xMaNG/N8J+1pvr6+7Nixg5UrVxIWFkadOnUICQlh06ZNODk5GUyDnF/u7u4sWbKE27dvU7VqVa5du8b27dspV66c3sQOzZo1Y968eXzyySe8++67JCYmsnfv3iynSfb09MTKyopNmzZhbm6OjY0Njo6OuskoCoJSqWTChAmMHz+evn370rFjRywtLdm3bx+XLl2if//+ulnUcpO2oHTp0oUaNWpQvXp1nJ2diYyMZOvWrZiYmNCyZcsCLUsUvLGfVWTS/FjU2XSxzDA2Jt3EhMASLtx1cMJEpSbJxIS6oYEccn4Xi5gMut2UVjtReFxdXalevTpVq1bl8uXLJCYm5jkvCXSEEIWiS5cu7Nu3jy1bthAbG4udnR2VK1dmwoQJeg8H7dmzJ6Ghofz5559s3rwZtVrNokWLCiTQMTU1ZeHChfz8888cPnyYffv24e3tzYIFC5g+fXqWrRgFSalU8tNPP/Hjjz+yfv16TExMaNCgAYsXL9ab7rowTJs2jc2bN7Ny5Uri4+OpWLEiU6ZMoUGDBnrp/Pz88PDw4Ndff2XLli3Ex8djb29P2bJlGTp0qMEMQvlhbGzMvHnzdA8MPXjwIDY2NjRr1oxhw4ZRqlSp52eSCyVLluT7779n9uzZ7N27FxMTE1q3bs3o0aOxsHjcxaZ3795oNBq2bdvG//73P5ycnGjRogV+fn4Gs7aZm5vz7bffsnDhQn766SfS0tKoU6dOgQY6kPnsiAULFrB06VJWr15Neno65cqVy3Jq79ykLQi9evXi+PHjrF+/noSEBBwdHfH29qZ///4G3SXFy6emjz0l4u/ywK5Elt3g7OITKRMdxUNrW9KVxqQbaagUeZ9YMzus01MZu79FEdRaaF7Pnms6Go2GQ4cOsX79erZu3UpkZCQODg50797dYJbI3FBoXsTIXSGEeImoVCqaN2+Ot7d3vh5EJoQQr6IBff7hvokDtonJGKnVJJqbkW5qglNMPG/cuEucgyUdT50jydQM++QklBo1x8t6UOV/DXmrQ5mirn6xtNJzU47T9r3bpRBrUrCOHj3Khg0b2LRpEw8ePMDW1pYOHTrQrVs3mjdvnu8HEEuLjhDitZaSkmIws9XmzZuJj49/IQPwhRDiZfPNzKoM+jKCVBNjWpy6hLFKhdpIgVKt4a6rMxcqeWIVn8QbIaHcKeHAG/7v0r+FPORSFLwmTZpgbW2Nr68v3bp1o3Xr1piaGj5oNq8k0BFCvNa+/fZbUlNTqVmzJqamply6dIk9e/ZQpkwZOnbsCGQOun9eNzYTExOZNjcLKpWKR48ePTednZ1dvp+xEhsbqzfBQlbMzc3z9TBYIYoDt1ImWKUkkmhhzdFaVagcHIZFShoRTvYEemQGNA+cbGl5vs1zchIifzZu3Ejbtm0Lbap9CXSEEK+1+vXrs3HjRpYuXUpSUhJOTk506NCBIUOG6B5c+uOPP7Jjx45n5lOnTh0WL178Iqr8Srl//z5+fn7PTbdo0SK9sVl5MX78eM6dO/fMNO3atWPq1Kn5KkeI4kCpyBy5EGtjxanqXgafl79//0VXSTyD+jWdXrpz586Fmr+M0RFCFHt37tzh4cOHz0xja2tL1apVX1CNXh2pqalcuHDhuemqVq2qe65QXl27du250xc7OztTvnz5fJUjRHGwZW8syzbEZTkhgWNsPK0sH9BzZcFOTS7ybnn5zTlO2/9O4QYPrxIJdIQQQgghipmMDA0dBt/LMtBpfPYKo0++W6BjJUT+SKCTN9J1TQghhBCimDE2VoBCAxgGOqmWxihe065Sr6rXfXrpwlJwj6AWQgghhBCvDCcbI8iiY09aPicOEeJlIYGOEEIIIUQx5P+9GxqF4nGwo9FQPuI+aW/FFm3FhCggEugIIYQQQhRD5mZGzP7SERSgAVQKBUaV46jgGV7UVRNP0SgUOX69auLi4vj+++9p1aoVtWvX5tSpUwBER0fz008/cevWrTznLWN0hBBCCCGKKS9PS3b8YglAeno6y5fvKuIaieLk3r17NGnShJCQELy8vLh+/ToJCQkAODo64u/vz7///svPP/+cp/wl0BFCCCGEEEK8cOPHjyc+Pp4LFy5QsmRJSpYsqfd5hw4dnvucu2eRrmtCCCGEEEKIF+6PP/7gk08+oVq1alnO9Fe+fHlCQkLynL+06AghhBBCCPESexXH3uREcnIyzs7O2X4eHx+fr/ylRUcIIYQQQgjxwlWrVo0jR45k+/nvv/9O7dq185y/BDpCCCGEEEJPaDyExKqKuhriNTd69GjWrVvHjBkziI3NnNZcrVZz69Ytevfuzd9//82nn36a5/yl65oQQgghhAAgMs2CL1O6wzLInHQ6gxMfQv3ScslYlDSvZ881evXqxb///sukSZP48ssvAWjdujUajQYjIyP+7//+jw4dOuQ5f4VGk8UjcYUQQgghRLGSnp6O6c8AhlfVmnES6BSlJZW25jjtwMCOhViTwhEcHMzmzZu5desWarWaChUq0KlTJ8qXL5+vfOWsFUIIIYQQz6TWaDB6TQfEi6KRlJREo0aNGDhwIEOGDMlXF7XsyBgdIYQQQgjxTLEpMl5HFCxLS0vu3r2b5bTSBUUCHSGEEEIIwbMGM2y49uLqIQxpjBQ5fr1KWrduzd69ewstfwl0hBBCCCEEKRnZfzbkwIurhyg+vvrqKwIDA+nduzfHjh0jNDSU6Ohog1deyRgdIYQQQgjBe+shq4kIhCgs1atXB+Dq1ausXbs223QqVd66TkqgI4QQQgghOBv1jA81GjQaTaGOpxDZ07ym+33y5MmFek5JoCOEEEIIIZ7rdpSKiiXk0lEUnKlTpxZq/jJGRwghhBBCPJtCQbXFqUVdCyFyRcJyIYQQQgjxXOkmJkVdhWLrVZtNLae+/vrr56ZRKBR89dVXecpfodE8azJBIYQQL4uAgACmTZvGokWL8PHxKerqiCxMnTqVHTt2cObMmaKuihC5pvghHZ41XkKjIW2MEhOldAh60RZV357jtEOu+BViTQqWkVH255JCodCNC8vrZARypgohhHilBQQEPHO2nle9PCFeGgoFJX6WB4eKgqNWqw1eGRkZ3L59m08//RQfHx8ePHiQ5/wl0BFCCPFKCwgI4Lfffnspyps0aRLHjx9/YXUR4rnUagg4BWcDocowUHQyfG06CqlpOcouTuIcUciMjIzw9PTkxx9/xMvLi5EjR+Y5LxmjI4QQ4qWRmJiIlZVVUVcjz4yNjTE2fr1+WlNSUl7L7SoyKhXsPAe3wqFTffj7Buz/B5JS4V4U2FqBjTmcuwX34yAlDdwdwcUBUjPAzBhuh0NsEliZQUwSqPNZp66zSFMYUWnsjwS6uD83ueLHrJ8surwV9Ksh50mheE2nl36exo0b89lnn+V5fTkbhRDiFaPRaFi9ejWbNm3iwYMHuLq68tFHH9GuXTtdmj/++IPdu3cTGBhIdHQ0lpaW1KpViyFDhuDl5aWX38WLF1m6dCk3btwgPj4eOzs7vLy8GDhwIDVq1MhxvbRjiObPn8+FCxcICAggKiqKsmXL0r9/f1q1aqWX3tfXF1dXV8aMGcO8efO4dOkSdnZ2bN+e2Rf93Llz/PLLL1y5coWMjAzKlStH165d6dChg14e4eHhAHrjlp4cxxQcHMySJUs4deoUsbGxODs707x5cwYNGoSFhYVenSIjI1m+fDnHjh3jwYMHWFtb4+XlRZ8+fWjQoMFzy8tujM7Nmzfx9/fn/PnzJCcnU7p0adq1a0evXr1QKpW6dNr1Dx06xNy5czlw4ACJiYlUqVKFMWPG4O3tnePjARAWFoafnx8DBw6kbNmyrFixguDgYBwcHPDz82PAgAF6AYy2/H379jFnzhyOHz/Oo0eP2LZtG25uboSFhbFw4UJOnjxJfHw8JUuWpGXLlgwYMABzc/Nc1a1YComEehMgIibz/dgVOVvvzsPM19NSkwqqZphq1BxdOBXXr/xRP3FOGnjGBXf/vTD+cAYPR8jlpSgYZ86ceeY4nueRM1EIIV4x8+fPJzU1lU6dOmFqasqmTZuYOnUq7u7u1KpVC4ANGzZgZ2dHx44dKVGiBPfu3WPr1q0MGDCAX3/9FQ8PDwCCgoIYPnw4Tk5OdO/eHUdHR6Kjo7lw4QKBgYG5CnS05s6dS3JyMl26dAEyA6Avv/yStLQ0fH199dLev3+foUOH0rx5c9577z2SkjIv3I4cOcL48eNxcnKiV69eWFpa8scffzB9+nRCQ0MZPnw4AGPHjmXevHnExMQwZswYXb6enp4AXLt2jSFDhmBjY0OnTp0oWbIkgYGBrFu3josXL7J48WLdhX5YWBgDBgwgOjqaNm3aUK1aNZKTk7l06RKnTp2iQYMGzy0vK1evXmXQoEEYGxvTtWtXnJycOHr0KHPnzuXmzZtMnz7dYJ0RI0bg4ODAxx9/TGxsLGvWrGHUqFFs3749Ty1eR44cITQ0VFf+kSNHWLJkCREREUyZMsUgvfacGDBgAMnJyVhaWhIeHk7fvn1JSEigS5cueHh4cPbsWZYvX87FixdZsGCBtPo8z5drHgc5LyGnpHis0lOJV1rmOY/IFNgXpKZFORkdIZ5v1apVWS6PiYnhyJEjbNmyhY8//jjP+ctfJCGEeMWkpaWxatUqTP6b6rVZs2a0b9+eDRs26AKduXPnGrRWtG3blh49erB27VomTpwIwIkTJ0hJSeHbb7/NdWtBdmJiYli3bh3W1tYAdOnShe7duzNr1ixatGihd+c/NDSUSZMm6bXSqFQqZs6ciYWFBStXrsTZ2RmADz74gMGDB7Ny5Up8fX3x8PCgadOmrF27ltTUVNq0aWNQl6+//poSJUqwatUqvQChXr16jB8/nt27d+uCr++//56HDx8yd+5c3nrrLb181OrMvkHPKy8rP/74I+np6SxfvlzXmtatWzc+//xz9uzZg5+fH/Xq1dNbp0qVKrpjBFC+fHkmTpzInj176Ny5c47KfdLNmzdZtWoVVapU0ZU/fvx4AgIC6NSpk0FAW6FCBb755huD7Xj06BGzZ8+mYcOGAHTt2pWff/6Z1atXs2PHDr3jWJSio6OxsrLCzMwMgISEBDQaDTY2NkDmdyg+Ph4nJyfdOuHh4bi6umb7PiIiAhcXF91T3PNSRvrxa7zMEzSnGpuQrsz/peGy84k0L2udr331Io7Hy1BGTr2u00v369cv289KlCjBxIkTmTx5cp7zl3BbCCFeMV27dtUFOQAlS5bEw8ODkJAQ3TJtkKPRaEhISCAmJgYHBwfKli3L5cuXdem0wcjhw4dJTS2YhwF26dJFl6+2jM6dOxMXF8fZs2f10trZ2Rm08ly7do2IiAj8/Px0QQ6AiYkJffr0Qa1Wc/jw4efW49atW9y8eZPWrVuTnp5OTEyM7lWrVi0sLCw4ceIEALGxsfz999+8/fbbBkEOPHsK1GeJjo7mn3/+oXHjxnpdBhUKBR999BEABw8eNFivR48eeu+13eSePMa5Ub9+fV2Qoy2/T58+2Zbfq1cvvfdqtZojR45QuXJlXZCj1a9fP4yMjDh06FCe6lYYHB0ddRejkHkOai9GAUxNTfUuRgGDi8+n35cqVUp3wZvXMkyaFszNhMKy4K2WpJiY5jufT+pZ5ntfvYjj8TKUUdzdvXvX4BUUFERsbCwPHjzg//7v//LVLVZadIQQ4hVTunRpg2V2dnZERETo3l+/fp1FixZx9uxZkpOTs12/ZcuW7Nq1i+XLl7N27Vpq1KhBgwYNaNWqVZ5/kMuVK2ewTNu1KzQ01KAuyqfGA4SFhQGZrRhPq1ChQpb5ZOXu3bsA+Pv74+/vn2Wa6OhoIDOA0Gg0VK5c+bn55saztsXT0xMjI6Mst+XpY2xvbw9kBmR5kdUx0dYpq/LLli2r9/7Ro0ckJSVluR12dnaUKFEiR8ek2Pu/XnDwMtzN+3S5heWerQPj2/XOdz5lbOCt0s8Y4yPEExQKBc7OzgY9ELSSk5N5+PChrrt1bkmgI4QQr5jsWhe0z3+OiIhg0KBBWFlZMWDAAMqVK4e5uTkKhYL//e9/eoGPqakpCxYs4PLly5w4cYJz587h7+/PkiVLmD59Ou+++26hbkthDmDX7o9evXpl2UoDYGtrW2jl58fTwZ/Wi3rGt0wsUEhc7OHOIjh+De7ch9a14XYE/H0dElMh6CHYWICtJfx1De5FwsMEqOMJpRwgJR1MlHApCMJjMmdjexAHEY/yNfOaBrBMSaZkQiwPbOyfk1iT5YQESuDPLtCknFxaipzz9PRk9erVBq3YWtu3b6dHjx55fmConI1CCPGaOXjwIElJSfz00096M4NBZouAqalh1xRvb2/dGJ2IiAh69uzJwoUL8xToBAUFGSzTtq5k1Rr1NG2aO3fuGHymXfZkPopsZoHS3gE0MjKifv36zyyzTJkyKBQKbty48dz6ZVdeVtzc3ICstyUoKAi1Wp2jfZJfWR2TrPZldhwcHLCysspyO+Li4oiMjKRSpUr5rmex8U7VzBeAsx00KNiWRAOPEsDCBPz3wOiVBh8rbs3HpIQ9sXOynjb6aZpxcvn4omle0+mln3fzJj09PV+zrskYHSGEeM1ofxSe/gHZunUrUVFRestiYmIM1ndxccHBwSHP3aQ2bdpEQkKC7n1CQgKbN2/GxsaGN99887nrV6lShVKlShEQEEBkZKRueUZGBqtXr0ahUNCkSRPdcktLS+Li4gy2t3LlylSoUIHNmzdz7949g3IyMjJ022hnZ8fbb7/NX3/9xcmTJw3SPpl3duVlxdHRkZo1a3LkyBFu3bqll9/y5csBCr3VDODkyZNcv35dr3ztbEdNmzZ97vpGRkY0atSIGzdu8Ndff+l9tmLFCtRqdY7yEUXEwRrMzWBUe9BsMXxVcCUJE5rcvfrcrOqXfD0vuMWLExcXR3BwMMHBwQBERUXp3j/5+ueff1i3bl2+xjVJSC6EEK+Zd955h7lz5zJ58mQ++OADbGxsuHjxIn/99Rfu7u56XQCWLl3KiRMnaNiwIaVLl0aj0XD06FGCgoJ0g9Vzy97enr59++omGQgICCAiIoJJkyblqEuUUqlkwoQJjB8/nr59+9KxY0csLS3Zt28fly5don///nr9tb29vTl69CgzZ86kZs2aGBkZUbduXRwdHfn6668ZOnQoH374IX5+fpQvX56UlBTu3bvHgQMHGDFihK6eEyZM4KOPPuKTTz6hXbt2VK1alZSUFK5cuYKrqyuffPLJc8vLyrhx4xg0aBADBw7UTe987Ngx/v77b1q3bm0w41ph8PLyYsiQIXTt2pUSJUpw+PBhTp06RZs2bahZs2aO8hg+fDgnT55k3LhxdOnShTJlynDu3Dn27dtHnTp19J7jJF49JW2V3M9Bt7UTfV/meePEq2DWrFl8/fXXQGYL+ejRoxk9enSWaTUaTZZT8OeUBDpCCPGacXd3Z86cOcyfP5/ly5djZGTEG2+8gb+/PzNnztQ98BKgSZMmREZGsn//fqKjozEzM6NMmTJMmjSJ9u3b56n8kSNHcuHCBTZu3Eh0dDQeHh5Mnz6d1q1b5ziPxo0bs2DBApYuXcrq1atJT0+nXLlyBlNRA/Ts2ZPQ0FD+/PNPNm/ejFqtZtGiRTg6OlK5cmXWrFnD8uXLOXLkCJs3b8bKygpXV1d8fX2pW7euLp/SpUuzevVqfvnlF44fP87OnTuxtbXFy8uLjh075qi8rFSrVo1ly5bh7+/Ppk2bdA8MHTlypMHsZoWlcePGugeG/vvvvzg6OvLxxx/n6vkUrq6urFixgkWLFrF7927i4+NxcXGhf//+Bg8eFa8ehULBxTKGk03oeUFjxIQhjeL16YTVsmVLrK2t0Wg0TJgwgQ8//JA6deropVEoFFhZWfHmm28adMHODYXmRY1sFEII8VoLCAhg2rRpLFq0KF8/TKLghIWF4efnx8CBAxk8eHBRV0e85BQ/pgPP7pom43OKxvxau3OcdviF9wuxJgVr2rRpdO7cucCe4/Y0OVuFEEIIIQSoNfCMB1OuavkC6yKKhSlTphRq/hLoCCGEyFZ6enqOJiVwcHB4AbURWk9O0pCdJx/aKkSOqFWQ3QxXGg3dqstlY1HRPCMAfR0cP36cc+fOERsbi1qtP1e6QqHgq6++ylO+csYKIYTI1sWLFxkyZMhz023fvv0F1EZo5WS805QpU3I0y50QWv0qw4pbWT8nB8BU+XpfbIsXLzo6mrZt23Lq1Ck0Gg0KhUI3o6X2//kJdGSMjhBCiGzFxcVx7dq156arVasWZmZmL6BGAshyCuynVahQgRIlSryA2ojXRXp6OqazyTbQkfE5RWdenT05TjviXM4nfilqAwYMYN26dSxbtoz69etTvnx59u7di6enJ7NmzeLvv/9m9+7duLi45Cl/CXSEEEIIIUT2gY5Gw9r3FXzoLYFOUXldAx1XV1c+/PBDfvrpJ6KionB2dmbfvn00a9YMgE6dOmFmZsZvv/2Wp/xfn7nqhBBCCCFEoZAgp2hpFIocv14lMTExVK9eHXg8rvDJB063bNmSvXv35jl/CXSEEEIIIQQAJhnpBsuMNOosUgqRf25ubkRERABgZmZGyZIluXjxou7z0NBQFPkI3iQ8F0IIIYQQADT4N5BjnlXRKJWZCzQa3r5zDahVlNUSr6nGjRuzb98+vvzySwC6devGzJkzUSqVqNVqZs+eTatWrfKcvwQ6QgghhBACgPY1z2N+Lp03Q++SrlQSWMKNVYvqF3W1xKvVIy3HxowZw759+0hNTcXMzIypU6dy5coV3SxrjRs3Zu7cuXnOXwIdIYQQQggBgI1xOl3qnadfv/6YmpoUdXXEa65GjRrUqFFD997BwYH9+/cTExODUqnExsYmX/lLoCOEEEIIIfS8YmPaxWvG3t6+QPKRyQiEEEIIIYR4ib2us64BBAcHM2TIECpXroyjoyNHjhwBIDIykk8++YTz58/nOW9p0RFCCCGEEEK8cFevXqVRo0ao1Wrq16/PrVu3yMjIAKBEiRIcO3aMxMREli5dmqf8JdARQgghhBBCvHATJkzA3t6eEydOoFAoKFmypN7nbdu2Zf369XnOX7quCSGEEEIIIV64I0eOMHToUJydnbN8Xo6HhwehoaF5zl8CHSGEEEIIQWySmmWnm/Dn3jdoMfA2i7c+KOoqif9ojBQ5fr1K1Go1lpaW2X7+8OFDzMzM8py/dF0TQgghhCjmUtPVDO17gu37llEyKZ5EpQn+NzuwN6UOrT6sXNTVE6+pOnXqsHPnToYNG2bwWUZGBuvWraNBgwZ5zl9adIQQQgghirkWUyLocekwvn4jKTF4FoNa9KXn+d2opu3m3IC8j5EQ4lk+//xz9uzZw9ChQ7l8+TIA9+/fZ//+/bRs2ZJr164xceLEPOev0Gg0moKqrBBCCCGEePU07HaasyXLkGJiBqZKUBpRK+QW3508ToyFNV3P9kaplPvjRWV2g/05Tjv6RPNCrEnBW716NaNGjSI2NhaNRoNCoUCj0WBra8vChQv58MMP85y3dF0TQgghhCjm0oyMSDExw9zOjBRLE6zSM7hbwYtfE+KpHgvnfZfgs2twUVdTvIZ69+5Np06d+OOPP7h16xZqtZoKFSrQqlUrbGxs8pW3BDpCCCGEEMVc1agwrpbzIsXCmPdvhVP+USIA9+zdSUu4T/CNJHyKuI7i9fDFF1/QvXt3atasqVtmZWVFx44dC7wsaYMUQgghhCjGgm7HMeL0SWyMNNQKj9EFOQDuCSkkmhmj0aiLsIbidfL999/rxuMAREVFoVQqOXDgQIGXJYGOEEIIIUQxdrLHCirFBrN4y0KOzB/Fl3tmU+ve4wtRJWqMrGw4Wv6bIqxl8aZRKHL8ehUV1pQBEugIIYQQQhRjiY9MOFy+Ir7XT2GZnopr/EP6n1iPW2wEAFZpybS//Af2SQkkhSc+JzchXh4S6AghhBBCFDNnwzNQfJ2C4usUjnhWod3ti3qfKzVqaoZexTQjjYr373CWJrhFRhEyTKaaFq8OmYxACCGEEKIYUanV+CxOB6UC9/g40mwsSDA1xzYtWS9dgpkVXhF3qBAVTGWCyFCZwsFzqNL7oTSRe+Uv0qvaJS07QUFBnDt3DoDY2FgAbt68ib29fZbp69Spk6dycn2WnjlzBh8fHwICAvJUYEHw9/fHx8eHsLCwAsszICAAHx8fzpw5U2B5FpapU6fi4yNzn7zsXoZzatCgQfj6+hZZ+a8qX19fBg0aVGTlF/TfuKLenpfRy/BbVtDk+y5yYs6pNIy/TwcTIzA35p6zI7818GFWozZ66R5YO1HmUShD/15N3XsXsSUWRx5SOTaMWyXHF1Htxeviq6++om7dutStW5fmzTOf+zNs2DDdMu3Lx8eHunXr5rmcYtWic+bMGc6ePUuPHj3yPS/368rf35/KlSvTtGnToq6KEOIVcuPGDQ4dOoSvry9ubm6FWlZYWBgBAQE0bdqUypUrF2pZT8vIyGDmzJlcvXqV8PBwkpKScHZ2pnr16vTt25cqVaoYrJOQkMCCBQs4ePAgsbGxuLu788EHH9C5c2cUr9ldWvFyC47NYNRBDag1mQ8F/Y9n1H0+PbZLL+1Ftyo0C/wry3zKx4Xwd6mveCtCJicQubd8+fIXVlaxCnTOnj3LkiVL8PX1NQh02rRpQ8uWLTExMSmi2r0clixZQrt27STQKQByTomXxebNmwv9gjowMJAlS5bw5ptvvpBAZ8mSJbi5ueU50KlTpw7Hjx/H2Dh3P4Pp6elcu3aNN954gzZt2mBpacn9+/fZvn07/fr1Y+7cuXp3H9PT0xk2bBg3btygW7dueHp68tdff/H9998TFRXF4MHyAEZR+DQaDaP3pDLnggLztDTeDL7N8SrVdZ/3P30Q21T9bmtNb53AiKxnwjJWZ5Biactt28FUmNQSJnQu1PqL16vrWt++fV9YWcUq0HkWpVKJUql8fkIhniMxMRErK6tCOadUKhXp6emYm5sXaL7i9WZqalrUVXjpGBkZYWZmluv1LCwsWL16tcHyzp0707ZtW1avXq0X6Pz+++9cvXqVcePG0b17dwA6duzI+PHjWb58OX5+fri6uuZ9Q8RrJyVDg0YDRgrIUIOVqYJHyWo0QHQygJqQeFCoNVyKAoUabseoOXFPw70kuBevyGyxQZGZiUaDNsOqD+9RNjqSO44uWKemkGBuAYB5RrpBPUzUqmzrmGhsyrt3/+aBhT0ZE9ei/GwNaiDdwgKjHk0xbVkN3q4MpiZQ0q4wdpMQOVIggY5Go2Hz5s38/vvv3L17FyMjI6pVq8bAgQMNxpJs3LiRQ4cOcefOHR49eoSdnR316tVj6NChBncB1Wo1K1euZOvWrURGRuLu7k7//v3zVMepU6eyY8cOAPz8/HTLBw4cyODBgwkICGDatGksWrRIV2ftsgULFnDx4kW2bdvGo0ePqFixIuPGjaNGjRqcPXuWBQsWcOPGDaysrOjatSsff/yxQflXr15l2bJlnD9/nqSkJFxdXWnbti19+/bN9R1FrUePHjFr1iyOHz9OamoqNWrUYNSoUVl2nfjjjz9Yv349N2/eRKVSUbFiRXr37q3rFxkWFqbbLzt27NDtK8js8jd48GBddxGtPXv2MGnSJLy8vPjtt990yzdt2sT333/PihUr8Pb2BnJ3juSkvlo+Pj60a9eOTp06MW/ePK5evYqZmRlNmzZl7NixWFpa5mqf+vv7s2TJEtavX8+WLVvYv38/CQkJVKxYkeHDh1OvXr0sy2/Tpg3+/v4EBgZStWpVFi9enOU5BRATE4O/vz9HjhwhKioKJycnGjduzODBg/UG4WnXnz9/PpcuXSIgIICIiAgmTZqUr374MTExfPrpp9y5c4cffviBevXq6cpauHAh169fZ9OmTTx48ABXV1c++ugj2rVrZ5DP77//zsaNGwkKCsLY2Bhvb28GDhxIrVq1dGnyc96cOXOGIUOGMGXKFDQaDb/++ishISE4OTnRtWvXPN8RCgkJYdmyZZw8eZLo6Gjs7e1152LVqlX10gYFBTFr1izOnz+PQqGgfv36TJgwgRIlSuilS0hIYNmyZRw4cID79+9jZWVFvXr1GDZsGO7u7npp09PTWbt2LXv37uXff//F2NgYDw8P2rVrR7du3bKtt0qlYsaMGWzdupURI0bkavt9fX1xdXVl8eLFBsu++OKLHG3js2i/NwBDhgzRLW/Xrh1Tp04Fcn7eP4/2XAWYNm2a7v916tTRbZ9Go+H333/n999/586dOwC4ubnx7rvv6ur35PlVEONaHBwcMDMzIz4+Xm/5nj17MDc3N3jid48ePTh48CB//PFHrs/luLg45syZw8GDB0lNTaVatWp8+umnWaY9ceIE27Zt4+rVq0RGRmJiYkL16tX56KOPePPNN3XpxowZw8mTJ9m7dy/W1tZ6eVy5coW+ffsyePBgBg4cCGT+TmzYsIHg4GAyMjJwcnKiRo0ajB07FgcHh1xtj8iUodbwyZ9qll3WkK4GBaDSgLUJJKRpQPFfAKPSZI6wVpC5TKUBI6PH703IDGwy1JnptVGTkYJrzmW45lzmcaFqDSjgt5pvM+roLkyfEdwApCiVnCldhbKRITywtKV8XCTpCgU/NPyA06Ur4/kogiFbAii3dDcajFCiBhc7OPg1VC3zzLyFKAwFEuhMnjyZvXv30qxZM3x9fUlPT2f37t0MHz6cmTNn0qRJE13aX3/9FW9vb7p164adnR23b9/m999/5/Tp06xbt07vB2/WrFn89ttv1KlThx49ehAdHc2MGTMoXbp0ruvYqVMnEhMTOXjwIGPGjNGV4+Xl9dx1582bh0qlonv37mRkZPDrr78yYsQIpk2bxjfffEPHjh15//332bdvH4sWLcLNzY02bR4P6jt27Bjjx4+nTJky9OrVC1tbWy5duqS7MJ4xY0autwdg5MiR2NraMnDgQKKiotiwYQODBg1i2bJlVKxYUZduwYIFLFu2jLfffpshQ4ZgZGTEwYMHmThxIhMmTOCDDz7AwcGBr7/+msmTJ1O7dm2DH+W6deuyaNEi7t27p7twO336NEZGRty6dYuYmBjdPj19+jRWVlZ6F425OUdyUt8nBQYG8umnn+Lr60urVq04e/Ys27Ztw8jIiC+//DJP+3bKlCkYGRnRp08fkpKS2LJlCyNHjmTOnDnUr19fL+3Vq1c5cOAAHTp0yDIgeFJCQgIfffQRISEh+Pn5UaVKFW7cuMGmTZs4ffo0K1euxMrKSm+dn3/+mYyMDDp27IiVlRVly5bN0zYBhIaGMnLkSJKSkli8eLFBt5/58+eTmppKp06dMDU1ZdOmTUydOhV3d3e9AGbOnDmsWrWK6tWrM2zYMJKSkti6dSuDBw/mf//7Hw0bNgTyf95AZrer6Oho/Pz8sLGxYffu3cydOxcXFxdat26dq+2/evUqQ4cOJSMjg/bt21OhQgXi4uI4d+4cFy9e1Cv74cOHDB48mKZNm/LJJ59w8+ZNtmzZQmJiIvPnz9el0x7TiIgI/Pz8KF++PJGRkWzatIl+/fqxevVq3R379PR0RowYwdmzZ2nQoAHvv/8+pqam3Lp1i4MHD2Yb6KSkpPDll19y/Phxpk2bpvf3JT9yuo3P89577xEZGcnWrVvp378/np6eALpjnpfzPju1a9emf//+LF++nI4dO1K7dm0AHB0ddWkmT57M7t278fb25qOPPsLGxoagoCD+/PNPvUAsP1QqFfHx8WRkZHD//n1+/fVXkpKSeOedd3Rp1Go1169fp0qVKgatR9WrV0ehUHD16tVclZuRkcGIESO4evUqbdq0oUaNGgQGBjJs2DDs7AzvnAcEBBAbG0ubNm1wcXHhwYMHbNu2jWHDhrFo0SLd/uvYsSNHjhxh7969dO6s3w1J+/dUezNs586dTJ06ldq1azNkyBDMzMy4f/8+x48fJzo6WgKdPJp7TsPCi4ZdxRLSyQxgIDNggf+CGwVoAKXi8edaCkVm8JP+X+Ci/m9cjlJ/Dir7hHjWrpvL+zcvkqEwQo0i2+5qAKtqtSfU1pMSro9oGPQXxEViotEQYufMLafS3HIqzVk3LwJ+/QqLjNTMle7HQquvIXhJHvaKEPmT70Dn4MGD7N69my+++IJOnTrplnfv3p3+/fvzv//9j8aNG+v6h69btw4LCwu9PBo3bsywYcPYtm2b7s5WUFAQ69ato27dusybN0/XBei9996jd+/eua5nzZo1qVixIgcPHqRp06a56kOuUqlYsWKFbqyFp6cnY8eO5bPPPmP58uVUq1YNgPbt29OuXTs2btyouxBJTU3lm2++wdvbm4ULF+pabzp37oyXlxezZs3Szf6TW66ursycOVO3b9977z369OnDzz//zNy5cwG4fv06y5Yto3///gwfPly3bvfu3Rk7dizz58+nbdu2WFlZ0aZNGyZPnkzp0qUNLqS09Tt9+rTu4uXMmTO0atWK3bt3c/r0aVq0aIFGo+Hs2bPUqVNHd8xyc47kpr5aN2/eZPny5brWo86dO5OYmMj27dv59NNPc92qA5ldGX/55RfdMffz86NLly788MMPbNq0SS/tnTt3mD9/vkEAlJWVK1cSHBzMZ599RteuXXXLK1WqxMyZM1m1ahVDhw7VWyclJYW1a9fmu7va9evXGTVqFNbW1ixbtizL70BaWhqrVq3SbXezZs1o3749GzZs0AU6QUFBrF69mjfeeINFixbp0nbo0IGuXbsyY8YM3nrrLZRKZb7OG62IiAg2bdqku8us/Z6tX78+V4GORqNh6tSppKens3LlSr2bHP3790etVuulDwkJ4bvvvqNFixa6ZUZGRrpWrHLlygGwaNEiQkNDWb58OZUqVdKl9fX1pXv37vj7++taNdauXcvZs2cNzm/AoHyt2NhYPv30U27dusXs2bNp0KBBjrf5eXK6jc/j5eVFzZo12bp1K/Xr1zf4e5aX8z477u7u1K9fn+XLl1OzZk2Dv1X79u1j9+7dvP/++0ybNg0jo8cXdtnt47y4e/eurisagLW1Nf3796dfv366ZXFxcaSmplKyZEmD9U1NTbG3t+fhw4e5Knf79u1cvXpV1xtBy9PTk59++smgG9ykSZMMfnM7d+7MBx98wPLly3WBzttvv42Liwvbtm3TC3RSUlLYu3cvDRo0wMXFBYBDhw5hZWWl95sGFFgQWVztvpvDJ8NrYxqN5r9WnGwm0H16cRbZf7d3He/fzHx+jrHm2d+PDIURGoUdNmnppBpbc6BCc1wToiiV+JC3/73CsbI1AIi2tOVoOW9a3Tr7eOWQKAiPBlfHbHIXz/M6jdF5kfI9CfquXbuwsrKiadOmxMTE6F4JCQk0atSIsLAwgoODdem1f3DVajUJCQnExMRQqVIlrK2tuXz5si7d4cOH0Wg09OzZU+/Cp0qVKjm6oCxIXbp00RtQrv1h8Pb21gU5gK5LwJPbe/LkSaKiovD19dVtr/alvfN38uTJPNWrT58+egOMq1atSv369Tl16hRJSUkA7N69G4VCQdu2bfXKjomJoXHjxiQmJnLp0qXnluXt7Y2lpSWnT58GIDw8nNDQUFq1akWFChU4deoUgO4u/ZMXOrk5R/JS3xo1auiCHK26deuiUqnyPD1vjx499I65tvUgKCiIu3fv6qWtVKlSjs/JQ4cO4eDgYNBi1qlTJxwcHDh48KDBOl26dMl3kHPy5EkGDx6Mm5sbS5cuzTbQ79q1q952lyxZEg8PD0JCQnTLtN/NPn366KV1dnbG19eX8PBwbty4AeTvvNHy9fXV60pjbm5OjRo19L5nOXHjxg3u3LmDr69vli25T14Qa7fnyQAAHgf82v2h0WjYvXs3tWvXpmTJknrnq4WFBd7e3pw4cUK3/p49e7C1tc2ye+vT5UPm/howYAChoaEsXry4QIMcyNk2FoS8nPd5tXv3bgBGjx5tsE+z2sd5Vbp0aebPn8/s2bMZN24cHh4eJCQkkJ7+eKxDSkoKQLYTkpiamurS5NShQ4dQKpX07NlTb3mXLl2ybBV7MshJSkoiJiYGpVKJt7c3V65c0X2mVCrx8/Pj6tWr3Lp1S7d8//79JCYm0r59e90ya2trUlJSOHbsGBpNDi/OX7Do6GhSU1N17xMSEvS6FaalpREVFaW3Tnh4+DPfR0RE6G1vQZfhmduhLM+77lU/dWyyOFTNbxn+/icZG45dSzQxY2flFsSbPZ7ISaMw4rpzZq8A84w0vfSmqgz9ok2NSbV8/D14FY7HiypDFK58t+gEBQWRmJhIy5Yts00THR2t625z+vRplixZwpUrV/ROFkDvZAkNDQXI8o6ip6en3sVDYXu6q5ytrS1AlheLtra2ugcfAbqL4q+//jrb/J/+UuSUtnvI08tOnDhBeHg4FSpU4O7du2g0Grp06ZKv8o2NjalVqxZnz2beoTl16hRKpZI6depQt25djh8/DqC7oH1yMG5uzpG81DerrozaLhxPHovcyGrfli9fHsg8N5/83MPDI8f5hoWFUbVqVYNxWdpxGtevXzdYJzf5ZyU6OppRo0ZRvnx5Fi5c+MygKbt9GRERoXuvDR4rVKhgkFa7LDQ0lGrVquXrvHlenXJ7bLUX7jmdpSsn59WjR4+IjY3lxIkTBuPHtJ68uA4ODqZy5co5HgQ/ZswYMjIyWLduHWXKFHz/9sL47mQlL+d9XoWEhFCiRAmcnJwKLM+sWFhY6N3g8PPzo1evXkyYMEHXoq79rj0Z/DwpLS0t1zcxQkNDKVGihME4GlNTU0qXLm0wRujevXvMnz+fEydOGHz29Ex87du3Z9myZWzbto2xY8cCmS1Ijo6Oet2L+/fvz7lz5xg3bhx2dnbUqVOHd955hxYtWuS4C2Jhe7IrI5Dl/nr6HHm6Nezp96VKlSrUMibUM+L3WyoeJJE9zX9jdOBxNza15vH/n0yX/lQLjQJQkhnwpKlBpeGWowsVo+/rJdN1OXtCoJM750t5Y/3UuWyqygxwzrk9vnlU9lEEDf+9jJrHd9MVX3bBzO5xkPQqHI8XVYYoXPkOdDQaDQ4ODkyfPj3bNNqLnytXrjBixAjc3d0ZMWIEbm5umJmZoVAo+OKLLwq0W0FByu4uYE5m1NLeCRg1apRet5YnOTs7571yOaBQKJgzZ06225HVBWtWfHx8+Ouvv7h9+zZnzpyhevXqWFpa4uPjw7p164iIiOD06dPY29vr3THPzTmSl/o+6zi8iLuNhT0DWn7zt7W1pUqVKhw7dozdu3cb3FV/Unb7PD/7Ma/njVZRzYb4rLv/2v2h/bdevXqFMl1mq1at2LJlC0uXLmXy5MkF2iIBOdtGkTOWlpa8++67rFy5UjcmzdbWFjMzMx48eGCQPi0tjZiYmDw/7TsnkpKSGDhwIMnJyXz44YdUrFgRKysrFAoFK1as0N1g0CpVqhRvvfUWu3bt4pNPPiE8PJxz587Ru3dvvSDVw8ODjRs3curUKU6fPs25c+eYPn26blKKpyfgEDlTwV7B9Y+UbLyhISldg0KR+XtYs4SGpZcgLlWDWgW3YhVEJGky32sANf91Y1Oga7bRAMYKyNBk/l/xX7o0dWYA9N/QnV9qNaX1rX/06pFVQ1GovSttA/dz2PNxwGuenkyN+1fRAN4Pgog3s8TzUTgfXvwTY2NQeLpBtTIwxhcaVssiV5Eb0nUtb/Id6JQpU4bg4GBq1Kjx3LEQe/bsQaVSMWfOHL07icnJyQZ3mrSfBwUFGfzRfLrrUE4VxYPZtHfjn777VxDu3r1LjRo1DJYplUrdHYMyZcrw119/UapUqSxbKXJDe7f99OnTnD59WteVwcfHB6VSyYkTJzh//jz169fX29e5OUcKsr75cffuXYPAVDt7U14mw9AqXbo0//77LxkZGXoXDhkZGQQHB+cr7+wYGxvzww8/8Pnnn/N///d/ZGRk6I2TyC1tHW/fvm3w3cxqH+X1vClo2u9iYGBggeXp4OCAjY0NiYmJOfp+ly1blqCgINLS0nI05XO/fv1wd3dnzpw5qFQqpk6d+tJOg/+sY1fQ5/2zyvLw8ODw4cO6md1eJG0vBe1DQY2MjHQTLzx9zK9cuYJGozGYfON5SpcuzcmTJ0lISNC7m5yWlkZoaKiuxwFktqA+fPiQyZMn6802CrBw4cIs8+/YsSPHjh3j0KFDui6oT3Zb0zI1NaVhw4a6iUeOHTvG6NGjWbNmDZ999lmutkk85mCuYNAbhud301w27Kep1PwVqiE1LYNFZzTcjIYrj8gMeiyMIVUF6Rp2lX+DOBMzbNMNW3G0HpnbsLlmc0YcX0enK9u45lwJq/QkaoZfwjItgTAza7r5OdJtaB1wdwL65K6yQhSifN8ebNu2LWq1mnnz5mX5+ZPdjLQ/0E/fKVy2bJlBa06TJk1QKBSsWbMGlerxdIfXr1/X9evPLe1FdlxcXJ7Wz4u33noLR0dHVqxYkWVXkJSUFBITE/OU96pVq/T2pXbf1K1bV7et2oG68+fP19uPWk93A7O0tMy2y0rlypWxtbVl8+bNREZG6i5gra2tqVy5MmvXriUhIcGg+1FuzpHc1rewrF27Vq+7yf3799m7dy9ly5bNVwDWpEkTHj16xO+//663/Pfff+fRo0e8++67ec77WYyNjfnuu+9o1qwZM2bM0JvWObe0E0esXr2ajIzH/bAjIyMJCAjA1dVVr3tYXs+bglapUiXKly/P9u3buX37tsHneWnBMDIyonXr1ly5coX9+/dnmSY6Olr3/9atWxMXF8fSpUtzXH6fPn0YM2YMu3fvZtKkSXr7/GWiHQuS1d/Xgj7vtX/fsvpb9f777wOZMwM+/btSEK1Ujx49yrL3QWRkJPv378fS0lKv1blVq1akpKSwZcsWvfRr165FqVQ+s0tvVpo0aYJKpWLNmjV6yzdt2mTwW5Ldb+6JEyf0xsQ+qWHDhjg7O7NlyxZ27NjBG2+8YdCFPCYmxmA97WMNCrLLo8g7U6URTT2UtKpoxtbu5lweZo7mS3M6VQLSVGCiAAUkm5gxz6cl2jNae6ZEWtiy6s22LK7fkeGdJhJpbU/puIeUj/mXtjf3UTf0HMfdq/Bw0WhKp6yCb3v/F+QI8XLJd4tO8+bN8fX1ZcOGDVy/fp1GjRphb2/PgwcP+Oeff7h37x7btm0DoGnTpqxdu5ZRo0bRsWNHTExMOHnyJLdu3TJ4jkK5cuXo2rUrGzZsYOjQobz33ntER0ezYcMGvLy8dHeackM7YH3OnDm6aV0rVKigNxVzQbOwsGDatGmMGzeOzp074+fnR5kyZYiPjycoKIiDBw/yww8/5GnWtfDwcEaMGEHjxo2JjIxkw4YNmJmZMWrUKF2a6tWrM2jQIBYvXkyPHj1o3rw5zs7OREZGcu3aNY4fP6433snb25tTp06xYsUKSpUqhUKhoFWrVkDmRV2dOnU4dOgQZmZm1KxZU7de3bp1WblyJYDBtuTmHMltfQuLSqXi448/plWrViQlJbF582ZSU1MZP358vvLt27cvf/75JzNnzuTGjRtUrlyZGzdusG3bNsqWLUufPoV3J8zY2Jhvv/0WY2Nj/ve//6FSqejVq1eu8ylXrhy9e/dm1apVDBw4kBYtWuiml05KSuKbb77Ra3XI63lT0BQKBVOmTGHYsGH07dtXN710fHw8586d46233tKbRSunhg8fzsWLF/n888/5888/qVGjBiYmJoSHh3P8+HGqVq2qm3Xtww8/5OjRoyxdupSrV69Sv359zMzMuHPnDv/++y8LFizIsgzt5BgzZ84kIyOD7777Ls/P3yos1atXx8jIiGXLlhEXF4eFhQWlS5fG29u7wM97T09PrKys2LRpE+bm5tjY2ODo6EjdunVp3rw5LVq0YOfOnYSEhNC4cWNsbGwIDg7m77//ZsOGDfnazt27d/Pbb7/RtGlTSpcujbGxMcHBwezcuZO4uDgmTZqk1920Y8eOBAQEMGvWLMLDw/H09OT48eMcPHiQAQMG5GoGUMgcC7R161aWLFlCaGgoNWvW5MaNG+zfvx93d3e9G0S1atXCycmJ2bNnEx4eTsmSJQkMDGTXrl1UrFhRb9IBLe2kBNpg/OnZAbXLbGxsqF27Ni4uLsTHxxMQEIBCoSiwqc9F4dj8gTkec1IISQCP2IcEOzjzZctulI97QPcrJ1EAScamTGrWG2tM6HF+D+9fP06MhQ2l4qPQ9pSLNrOmxY4+mFfJ3fkr8k7z9DgskSMF8ks5ZcoUfHx82Lp1KytWrCA9PR0nJyeqVKmi90eyVq1azJw5k19++YVFixZhZmZGvXr1WLx4se4hZE8aN24cTk5ObN26lZ9//pkyZcrw2WefERwcnKdAp1atWowcOZItW7Ywffp0VCoVAwcOLNRABzJbdVauXMnKlSvZvXs3jx49wtbWFnd3d3r27JmjZ/lkZe7cufz0008sXryYlJQU3QNDn85v0KBBVKtWjXXr1vHbb7+RnJyMo6MjFSpUYNy4cXppJ06cyIwZM1i+fLnu7qA20IHMi9FDhw5Rs2ZNvW4Y2gvWkiVLZjmBRE7PkdzWt7BMmzaNzZs3s3LlSuLj46lYsSJTpkzJ96xX1tbWLF26VPfgxO3bt+Pk5ETnzp0ZPHhwoQ/kVSqVfP311xgbGzN79mzS09Pz9BDeTz75hDJlyrBx40bmzZunm3Fw+vTpulkJn5TX86agVa9enZUrV7J06VL279/P5s2bsbe3p3r16nrPCcoN7XTdv/76K/v27ePIkSMolUpKlixJrVq16NChgy6tiYkJ8+bN49dff2Xv3r0sWLAAU1NTPDw8nvvQyq5du2JsbMz//d//MWHCBGbMmJHtbF5FoVSpUkyePJmVK1fy/fffk5GRQbt27fD29i7w897c3Jxvv/2WhQsX8tNPP5GWlqab4ALg22+/pXbt2mzbto0lS5agVCpxc3PLdsKI3KhduzbXrl3j2LFjREZG6v6W1atXj+7du/PGG2/opTcxMWHBggUsWLCAvXv36rq1jR8/3uCZYDlhYmLC/Pnz+fnnnzl8+DAHDhygWrVquhngnpzRycbGhnnz5jFnzhzWr1+PSqWiSpUq/Pzzz2zbti3LQAcyp4pfvnw5FhYWWe6zLl26sG/fPrZs2UJsbCx2dnZUrlyZCRMmFPoNC5F/wZ+Ys+1qCh+stc+MWpQKenQeyeI33sU+NZnDHlWwzEjj+qLxWKVnTjZQJvYByUozTFXphFrbU/rG/6F0kxYc8fJTaGTEqRA62sG027dvz/WdViGEeB1ERkbStm1b/Pz88vzQZfHye+eXJP76F4xUatTA1k2z6dN+KPFmFnx6chc/7V9rsE6smRUPujbDa3W/F17f4m5mkyM5TjvhcONCrMmr5eXq+yCEEEKIIrVp0yZUKpXeA57F6+f4x5bEJKbzwRYVJ089otWdf7g35xOOelSmTrjhpE8a4IRHLd6ZkfuWSCGKyisf6KSkpJCQkPDcdCVKlHgBtcm7pKQk3UM+s6NUKnFwcHhBNXp9vC7niFZCQsJzHzJoYmKiex7K66647o9Hjx5lOWHHkywtLZ870+GzpKen52hwuYODQ75ng1OpVDx69Oi56ezs7Aqly15kZORz01hbWxfKdPJFve1ae/fuJSIigtWrV/PWW2/lekY48eqxtzLhj94mDF1yluuObtR+GEzbWxeyTHu+VHV+d/OmlVve/6aIvJPppfPmlQ909u3bx7Rp056b7syZMy+gNnm3evVqlixZ8sw0rq6uBAQEvKAavT5el3NE68cff2THjh3PTFOnTh0WL178gmpUtIrr/ujTp89zn7A9cOBABg8enOcyLl68yJAhQ56briC6et6/f99gCuSsLFq0qFDGgbRu3fq5aaZMmfLcsVR5UdTbrvXll19iZmZGrVq1+OqrrwqtHPHyeb+8KeeivajxMBRj9G+gqIF1tVpzulRVpi9+s2gqKEQevfJjdCIjI7OcKvZpBf0Mm4J27949QkNDn5lG+wMkcud1OUe07ty5w8OHD5+ZxtbWttjcjS2u++PChQu657Zkp3Tp0vl6eGNcXBzXrl17brpatWphZmaW53Ig8xk0Fy5ceG66qlWr6j0rpqCcPHnyuWkqVKhQKC2/Rb3tQqSnqfiz9HxqRIZQGsPfy269vsfjYTA/7Mn/hB4ib2Y0PZrjtJ8dalSINXm1vPKBjhBCCCGEyJ/d7nOoEhqFJ5f0lsebWtKz57e0ufo3Q07kfgp+UTC+f/dYjtNOPNiwEGvyannlu64JIYQQQoj8OefpRayiJCXuhWBDDJA5AcEKH18q3XtAs8HVi7R+QuSFBDpCCCGEEMXcIzNT9jR6k7uBTrx/5RTOKVHcsitHiXAlqlKP8OrfsqirKESuSaAjhBBCCFHMfbf5HSZ9cIrASuUI9CqLe2QUZmnplFTHMGhbk6KunhB5YlTUFRBCCCGEEEXLxM6cpm0dMIsOwSk6mEjTBFyV9xh0sC2WjvmbbETkn0ahyPFLPCYtOkIIIYQQguZDqxBi9jdW91Po+nEPTN1ejefLCZEdadERQgghhBCZFJBYyhyF8+v1kGVRPEmLjhBCCCGEEC8x6ZKWN9KiI4QQQgghhHjtSKAjhBBCCCGEeO1I1zUhhBBCCCFeYtJ1LW8k0BFCCCGEKKYWH05m7DE1GmMjFjdUF3V1hChQEugIIYQQQhRDFX+Mw/FeEt0jYlBoYN49WzQlG9Lf5VhRV02IAiFjdIQQQgghipmYZBXm99N4OyQKy3QVFhkq6oU9gvsyrbR4fUiLjhBCCCFEMTNwcxoVohM4XqYE/5SyR61QUP1BLF4PYwmMci3q6omnyBidvJFARwghhBCimDkfkYGJkw3XSz5uwbno6kCKsREnYxvwXRHWTYiCIl3XhBBCCCGKmVi1gluO1gbL7zpY45yUUQQ1EqLgSYuOEEIIIUQxE60wRq3M+n63VYbqBddGPI9Geq7libToCCGEEEIUM+oMQKHA5KmgJs1YSZy5KQlpRVMvIQqSBDpCCCGEEMWMkUKBW1wSTsmGEc2/DlZM+z2xCGolRMGSQEcIIYQQopgxyVDxTvBDMrLoEhVjZsqa69JXSrz6ZIyOEEIIIUQxY6yBWFMTIq3MDT5zTkwhwdSY239FYmWjpFQNhyKooXiSTC+dN9KiI8RrJCwsDB8fH/z9/Yu6KgVi48aNdO7cmbfeegsfHx/CwsKKukq5cubMGXx8fAgICNAtK8xj5O/vn6f9lNv1fH19GTRokN6yQYMG4evrq7ds6tSp+Pj45KouBV2v4qS4b7/IHfP0DP4sXwqyuIB+614Ug08FsnzqHTb3Oc1uh9Vsee+PIqilEPkjLTpCiJfSmTNnmDFjBk2aNKFv374YGxvj4CB3FYUQIr8iElTEWJiiMja8322sUuMWn4xFhhrXyDhul3UlwdaExkcD+b3UKtpe7oJJCcsiqLUQuSeBjhDipXTy5EkAJk+ejJ2d3XNSvzpcXV05fvw4SqWyqKuSZ5s3b0Yh3SheOnJcRE51+C0DlfHjv0EmGSrSjZVYpWXw3p0IzFRqMoyMeONaEOXuPWBfozewdDiIa1RJfm53lAYXb5BsakOUoxUujUvQdMW7cu4VMum6ljcS6Agh8iwjIwOVSoWZmVmB5x0ZGQnwWgU5AAqFolD214tkampa1FV47SUmJmJlZZWrdeS4iJy6GJ4Glha69+nGSoxVahrfvU/5R5mzraWZm7KleV3qXAvCIimFbTVaUSUomEanbmOiMcYyJRmnuCRMgu4SuOoYatQoLC3w2N0F45QUlG9VQGljOP5HiBdJAh0hcikgIIBp06axcOFCrl+/zqZNm3jw4AGurq589NFHtGvXDsgci+Hn58fAgQMZPHiwXh7+/v4sWbKE7du34+bmBmSOZ9ixYwf79+9n9uzZHD16lPT0dOrWrcvnn39OiRIl2LJlC2vXriUsLAxXV1dGjhxJ06ZNs6znnj17WLFiBcHBwTg4OODn58eAAQMwNtb/2kdGRrJkyRKOHTtGVFQU9vb2NGrUiKFDh+Lo6GhQ5/Xr17Nt2zb2799PZGQkCxYsyNU4jEOHDrFq1SoCAwNRKBR4eXnRp08f3XZo95uWNu86deqwePHiHJXx8OFDfv31V06fPk14eDipqamULl2atm3b0rt3b73WFO3xnD9/PhcuXCAgIICoqCjKli1L//79adWqlV7evr6+uLq6MmbMGGbPns2VK1cwMTGhUaNGjBo1Sm+fZeVZ58Uff/zB+vXruXnzJiqViooVK9K7d2+aN2+ul06tVrNy5Uq2bt1KZGQk7u7u9O/fP0f75lmSk5P54Ycf2L9/PwkJCVSsWJHhw4dTr169LPdBTo/Hk4KCgli3bh3nzp0jIiIClUqFp6cnXbp0oUOHDvmqV3auXr3KsmXLOH/+PElJSbi6utK2bVtdl8jciIiIwN/fn9OnTxMVFYW1tTVlypShU6dOuu8+gEajYfPmzfz+++/cvXsXIyMjqlWrxsCBA/W+L0+eD56enqxatYq7d+/SokULHB0dWbVqFb/99hteXl569UhISKBVq1Y0aNCA//3vf0D2x+X69essX76c8+fPEx8fj6OjI2+88QbDhg3D3d1dl+7kyZOsWrWKK1eukJaWhoeHB126dKFLly56+V28eJGlS5dy48YN4uPjsbOzw8vLi4EDB1KjRo1c7U9ReNJUGhquVXH6/hMLNZrMfy0MA5AMpREHy7tQ/lEixv+lU5sac+aNigAkW5rzVy17zlZNo9PuE5ioNYCCDCxQoiEFW0jScKvJNmx5gAv3MCeJJGwIpzypRlbYNCyF0c17xD0yx7S8La5L22DVoFQh7wlRnEmgI0QezZ8/n9TUVDp16oSpqSmbNm1i6tSpuLu7U6tWrTzn+8knn1CyZEmGDBlCSEgI69evZ/z48bz77rts3bqV9u3bY2pqyvr16/nss8/YsmULpUuX1svjyJEjhIaG0rVrV5ycnDhy5AhLliwhIiKCKVOm6NJFRETQv39/0tPTad++Pe7u7oSEhLB582bOnDnD6tWrsba21sv7q6++wszMjJ49e6JQKChRokSOt23jxo3MmDGDcuXK8fHHHwOwY8cOxo0bxxdffEGnTp1wcHDg66+/ZuvWrZw/f56vv/4a4LkBxJNu3rzJwYMHadq0Ke7u7mRkZPD3338zb948QkND+fLLLw3WmTt3LsnJybqLuoCAAL788kvS0tIMBtk/ePCAoUOH8t5779GsWTOuX7/O9u3buXbtGqtWrcLcPPd3MRcsWMCyZct4++23GTJkCEZGRhw8eJCJEycyYcIEPvjgA13aWbNm8dtvv1GnTh169OhBdHQ0M2bMMDgPcmvKlCkYGRnRp08fkpKS2LJlCyNHjmTOnDnUr18/X3lrnTlzhnPnztGwYUPc3NxISUlh//79TJ8+nUePHmUZsOWnXseOHWP8+PGUKVOGXr16YWtry6VLl/D39ycwMJAZM2bkuO4ZGRkMHz6chw8f0qVLFzw8PEhISODWrVucP39eL9CZPHkye/fupVmzZvj6+pKens7u3bsZPnw4M2fOpEmTJnp5Hz58mPXr19O5c2c6d+6MlZUVFStWZNWqVezcuZPRo0frpd+3bx+pqal6ZWbl6NGjTJgwAQsLC9q3b0+ZMmWIiori77//5tatW7pAZ8uWLXz33XfUqFGDjz76CAsLC06ePMn3339PaGgoo0aNAjID1eHDh+Pk5ET37t1xdHQkOjqaCxcuEBgYKIHOS+T9TU8FOZDlxANPSjExJs7MBMeU7J8Wmmpmyjnv8tT/5zYAGowzg5zMAsjAnGjcKcMtMjDlJm+ixhjUkHokDv5Lm3I1hYQmW6l2qxemZWzyuJXFh1q6ruWJBDpC5FFaWhqrVq3CxMQEgGbNmtG+fXs2bNiQr0CnevXqfPbZZ3rL1q5dy4MHD1i/fr0u8Khbty4ffvghW7duZcSIEXrpb968yapVq6hSpQoA3bp1Y/z48QQEBNCpUyfdxcjMmTPJyMhgzZo1uLi46NZv3rw5/fv3Z82aNQatDtbW1ixYsCDXd8Lj4uKYM2cO7u7urFixQrcdXbp0oWfPnsyePZsWLVpgY2NDmzZtOHXqFOfPn6dNmza5KgcyW3+2bdum12e8R48efPXVV2zbto3BgwcbBGgxMTGsW7dOr17du3dn1qxZtGjRQi94uXfvHmPGjKFHjx66ZeXLl2fWrFmsW7eOfv365aq+169fZ9myZfTv35/hw4frlnfv3p2xY8cyf/582rZti5WVla5FpG7dusybN0/XOvXee+/Ru3fvXJX7NKVSyS+//KI7p/38/OjSpQs//PADmzZtylfeWm3btjVoIejRowdDhgxhxYoV9O7d2+Dcymu9UlNT+eabb/D29mbhwoW6fDt37oyXlxezZs3SzYyXE3fv3uXff/9l5MiR9O3bN9t0Bw8eZPfu3brgXat79+7079+f//3vfzRu3Fjv/Lx9+zbr1q3D09NTL69q1aqxZ88eRo4cqdcSuXPnTuzs7GjYsGG29UhJSWHatGlYW1uzZs0aSpYsqfts4MCBqNVqILNV98cff6Rly5Z8++23ujRdu3blxx9/ZM2aNXTu3Bl3d3dOnDhBSkoK3377Ld7e3jnYa6KoHAzJ/ToW6RnYpqbr3me22RiKtrfOYumTjHhIGZSoMoOcbKjTNDzacBuXsbVyX1khckCmlxYij7p27aq78AIoWbIkHh4ehITk4dflCR9++KHe+9q1awOZF4hPtq54eXlhZWVFcHCwQR7169fXBTmQOS6kT58+QOZFGGR2fTl27BiNGzfGzMyMmJgY3cvNzQ13d3fdhABP6tGjR66DHMjsFpOcnEz37t31tsPa2pru3buTlJSUZXl5YW5urruITE9PJzY2lpiYGN566y3UajVXr141WKdLly4G9ercuTNxcXGcPXtWL62VlRVdu3bVW9a1a1esrKx0+zc3du/ejUKhoG3btnrHISYmhsaNG5OYmMilS5eAzDv/Go2Gnj176l34VqlSJd+tLj169NA7p11cXGjdujVBQUHcvXs3X3lrWVg8HheQmppKTEwMcXFxNGjQgMTERIKCggqsXidPniQqKgpfX18SEhL09us777yjS5NT2vPj7NmzREdHZ5tu165dWFlZ0bRpU70yExISaNSoEWFhYQbf24YNGxoEOZD5vY+MjNSrZ2hoKBcvXqRVq1Z6++Vpf//9NzExMfTs2VMvyNEyMsq8BNi/fz9paWm0b9/e4Pxr1KgRarWaU6dO6e2Dw4cPk5qamm3ZRS06OlqvfgkJCcTHx+vep6WlERUVpbdOeHj4M99HRESg0Xb9egXKMMpDA4BndALK/8pPNlZysKwz1S/dpsmJK5SIitWlKxP+ZJkasqJBgQL1c8s0sjAu8n1VlGWIwiUtOkLkUVbdhOzs7IiIiCjQfG1sMpv0tWN5nmRra0tsbKzB8nLlyhksK1++PJB5kQSZXVDUajXbtm1j27ZtOaoLgIeHx7M3IBvacrX1eFbd8isjI4MVK1awa9cuQkJC9H6oILN16WlZ7TPthefT9SpdurTBBaapqSmlS5fO0zbcvXsXjUZj0NLxJO2Ppzb/7Op74sSJXJf/5PpPe/LYZPV5biUlJbF48WL27dvH/ftP96vJ+tjktV7aIEjb/TErT1+UPIt2HN6KFSto3bo1lSpVom7dujRv3pzq1avr0gUFBZGYmEjLli2zzSs6OpqyZcvq3mf3vWrVqhWzZ89m586dvP3220Bma45Go6Ft27bPrK82mHrypkdWtMHlsGHDnllfgJYtW7Jr1y6WL1/O2rVrqVGjBg0aNKBVq1a4uro+s5wX6emurk93wTU1NcXJyUlv2dP1f/p9qVL6Y0le9jI+8oYll8iVqy72hNhZYZuaToS1OSqlEf7NarF40Q7cI6LY8d6bqBQKqt+8p1tHSToqnp4IQ0NJMm/6hVOedLLuzmtsb4xD94oYF4Pjkd17Ubgk0BEij7R3Q5+mvah+1lSbKpUq28+ym3Y4u+VPX8Tn1vvvv59tP/+sZgfLy/iTF23WrFmsX7+eFi1a8NFHH+Hg4ICxsTHXr19n7ty5+d5nhUGhUDBnzpxsz6sKFSq84BoVji+//JJjx47RsWNH6tSpg52dHUZGRhw/fpy1a9fqulMVBO1xHjVqFJUqVcoyjbOzc67yHDZsGH5+fhw7dowLFy6wbds2Vq9eTZ8+ffjkk0905To4ODB9+vRs83n6eGb3vbK3t+edd97h0KFDupnYdu3ahaenp15wlR/a/TRt2rRsx9xpb3qYmpqyYMECLl++zIkTJzh37pxuopLp06fz7rvvFkidRP4tbmWMrWkGc85D+pNfK40GNOARk0iIg5XetMVWqenEm5sQb/74Rs45z8wLc6VaQ4V/Iygf8kCvO5uSDJSkkYZ2lkANZsQThQuORFCJ0zzEg2TrEtgNq4PRX9eJ+1eNaV13Ss5qjrHjy/+b8jLQZNmJUDyPBDpCFBJb28wBl1ndoS6olovsZNX9586dO8DjCxZ3d3cUCgUZGRkFNtD8WbSDnu/cuWMwW5b2znt+B9Nr7dq1izp16vDdd9/pLX9Wt8Ks9ll29QoNDSU9PV2vVSctLY3Q0NAsW1qep0yZMvz111+UKlXqua0m2roEBQXpzZj1ZH3z6u7duwYBwdPnTX7Ex8dz7Ngx2rRpwxdffKH3mbZrVEHWS9tKYmFhUaDnuLu7O927d6d79+6kpqYycuRIVq1aRa9evXB0dKRMmTIEBwdTo0YNLC3z/2DFdu3acejQIfbv30/ZsmW5d++ewbi8rGhbjG7cuEGDBg2yTVemTBkgM6jK6X7y9vbWjdGJiIigZ8+eLFy4UAKdl8yP7xrzYxaHxGlqHLUiHnHf2pxUU2NMVGq8IuNwSUjhYAX9Vozy9x/p/u8Yk4BFSpqus5qCFEyIx8gkjQwrFeXXdcW2VUW99Y0B/b9Uzcj5FDZC5I+M0RGikFhZWeHk5MTp06f1WhDu3bvHoUOHCrXskydPcv36dd17jUbDqlWrAHTTOGvvFB84cEA3/uNJGo2GR48eGSzPq/r162NhYcH69etJTEzULU9MTGT9+vVYWlo+82IsN4yMjAxabZKTk1m7dm2262zatImEhATd+4SEBDZv3oyNjQ1vvvmmXtrExEQ2btyot2zjxo0kJiZmO933s2gnXJg/f36WrX1Pdq9q0qQJCoWCNWvW6KW9fv36M4OFnFi7di3p6Y8HIt+/f5+9e/dStmzZAum2pm2tevrYREZG8vvvvxd4vd566y0cHR1ZsWJFll08U1JS9M7F50lISCAjI0NvmZmZmS641d7UaNu2LWq1mnnz5mWZT266y0Hm+B17e3t27tzJzp07MTIyytEkHQ0aNMDe3p41a9bonkv1JO1xaNGiBaampvj7+5OSkmKQLiEhgbS0zFm4YmJiDD53cXHBwcEhy30sXk7t3jBjZyVXVMr/xjIqjbBKz6BcTALusY+/Exap6Yzb9pcuzS2PUsRaWaBARYYClJaplDnUF6+076j16HODIEeIoiYtOkIUog8++ICFCxfyySef0KRJEyIjI9m8eTMVKlTIckB8QfHy8mLIkCF07dqVEiVKcPjwYU6dOkWbNm2oWbOmLt3EiRP5+OOPGThwIG3btqVy5cqo1WpCQ0M5cuQIbdq0MZh1La9sbGz45JNPmDFjBv369dN1l9uxYwchISF88cUXBv2d86pZs2Zs2bKFzz//nHr16hEVFUVAQMAzHz5qb29P3759dVNJBwQEEBERwaRJkwy6Fbm7u7NkyRJu375N1apVuXbtGtu3b6dcuXJ079491/WtXr06gwYNYvHixfTo0YPmzZvj7OxMZGQk165d4/jx47qxN+XKlaNr165s2LBBN8V1dHQ0GzZswMvLixs3buS6fC2VSsXHH39Mq1atSEpKYvPmzaSmpjJ+/Pg85/kkKysrGjRowO7duzEzM6N69eqEh4frpkjP7kI5r/WysLBg2rRpjBs3js6dO+Pn50eZMmWIj48nKCiIgwcP8sMPP+R41rUzZ87w7bff8t5771G2bFksLS25du0a27Ztw9vbWxfwNG/eHF9fXzZs2MD169dp1KgR9vb2PHjwgH/++Yd79+5lOy4uK8bGxrRq1UqXX7169bKcXOBp5ubmfPXVV3z22Wd069ZNN730o0ePOHHiBD169KBp06a4uLgwceJEpk+fTteuXWnTpg2urq48evSIW7ducejQITZu3IibmxtLly7lxIkTNGzYkNKlS6PRaDh69ChBQUG6CU/Ey2+Zrymrbj0O2ss9SuDtkMwAvPPVe4TaWBBuacy4gD+wiVdy38mWVBMlLg/DeDe+3zO7ZovCoZF9nicS6AhRiPr27UtCQgK7du3i7NmzeHp68tVXX3Ht2rVCDXQaN25M2bJlWbFiBf/++y+Ojo58/PHHumfXaJUqVYpff/2VlStXcvjwYXbv3o2pqSkuLi40atSIFi1aFGi9tIHX6tWrWbJkCQCVKlXixx9/zFNLSHbGjBmDlZUV+/bt4/Dhw7i4uNCxY0eqVauW7YDrkSNHcuHCBTZu3Eh0dDQeHh5Mnz6d1q1bG6QtWbIk33//PbNnz2bv3r2YmJjQunVrRo8erTerWG4MGjSIatWqsW7dOn777TeSk5NxdHSkQoUKjBs3Ti/tuHHjcHJyYuvWrfz888+UKVOGzz77jODg4HwFOtOmTWPz5s2sXLmS+Ph4KlasyJQpUwqspQ3gm2++Ye7cuRw9epSdO3dSpkwZhg0bhrGxMdOmTSvwer311lusXLmSlStXsnv3bh49eoStrS3u7u707NnT4EGcz+Ll5cW7777L2bNn2bNnDyqVilKlStG/f3969eqll3bKlCn4+PiwdetWVqxYQXp6Ok5OTlSpUkVvCvGcateuHevXrycpKem5kxA8qUmTJvzyyy8sX76cbdu2kZSUhKOjI7Vr16Zixcd33/38/PDw8ODXX39ly5YtxMfHY29vT9myZRk6dKhugLX2hs3+/fuJjo7GzMyMMmXKMGnSJNq3b5/r7RJFQ2n830XzfxfP1R/o32QoHZ+McwKcrF4Lm/gkXO9HUmve29RuLQ/3FK8WheZlHJUrhBAvSEBAANOmTWPRokU5urOf3dPnhRDiVaL4IV0X6LQODKNyVLze53Gmxiz7rXJRVE1k4as2Z5+f6D/f7Hrz+YmKCRmjI4QQQghR3Dwxw+E/pewNnnhzyy5vrdOicGgUihy/xGPSdU0IkS+xsbF6A8WzYm5unu/xNykpKXqTBWQnu+lxixvZXzmXlJREUlLSM9MolUocHBxeUI2EKHwKzeNHfYbZWrK5ehlqRsRgrFajUGuoV9/w8QJCvGok0BFC5Mv48eM5d+7cM9O0a9eOqVOn5qucffv2ZTuG40lnzpzJVzmvC9lfOffkmLHsuLq6EhAQ8IJqJEThs0hJJcn68fTnYbaWhNla4hKfzHu3wpn+oQT24tUnY3SEEPly7dq1LJ8V9CRnZ2fdk+zzKjIyktu3bz833Yt4JtCrQPZXzt27d++5z7YyMzOjVq1aL6ZCQrwAzjMSiDQy043T0TJNV+EWl0Tg19Z6zwoTRWtS22ffUHzS9J11CrEmrxZp0RFC5EvVqlVfSDklSpSQbla5IPsr59zd3Q0evirE6y5JaQIYjufQKCBFKeM8XjYy9iZvZDICIYQQQohixtxIA1l06nFMTsMtIT6LNYR49UigI4QQQghRzHxQ2QiFRj/YUWg0NA66T7syJ4qwZkIUHOm6JoQQQghRzMxpbYL/P6m4J6RgrlJjkaGiTmgUV5xtaW7zqKirJ56ikZ5reSItOkIIIYQQxYyJUsEA1zQeWprxr60lkeam7KhYigqut4q6akIUGAl0hBBCCCGKoSX9bAkaZkInTRzvWKdzbJgF7zldL+pqCVFgpOuaEEIIIUQxVcrBmHVflgQgPT2dI0VcHyEKkgQ6QgghhBBCvMTUMr10nkjXNSGEEEIIIcRrRwIdIYQQQgghxGtHuq4JIYQQQgjxEtNI17U8kRYdIYQQQohiLiVZzZLVUfx925s0lbKoqyNEgZAWHSGEEEKIYuzqxQTaLUnkrrUVGNVkTZA3UVvimNLNqairJkS+SIuOEEIIIUQxNmzWA+7aWGGjUlE7NoHqCcn8eFpJa/+4oq6a+I9GocjxSzwmLTpCCCGEEMVYoK0Nrimp1I5PRnuZ7JaajuveG7Q9Z8uORT4o5AJavIKkRUcIIYQQophSPYynfHwi1RNSeDKUUQBXS3tzqEwVqo27i1qjKaoqCpFnEugIIYQQQhRDl0fs57c6AXzy1zFMsghkFBpIMrfguqsHJjPSiqCGQuSPBDpCCCGEEMVMyqNU/l0dgue9eEoFpvDO+VsGacJtzTP/o9GgNlby0Y6MF1xLoaVWKHL8Eo9JoCOEEEIIUcwcfncbtnEq1ChRAI3P3aLpqesYZahIMTYi0MmKuw6WmYkVCtBoWH5Nuq+JV4sEOkIIIYQQxYzZtSgANEC0ozVhbg7UvhFM2wPn+bNiSW4622QGOE/SwLj96S++skLkkcy6JoQQQghRjKTM3odVRhpJmHDVuwzh7pnPyzFSqSkRFIl5ahopZqb6K6k1oFDwvwsafmxeBJUu5jTSIy1PpEVHCCGEEKK40GhQf7octdIIBeB9OYQGx69jmpTKXSd79jSqwbt3o6kZHotCO0FBhjrzXwWght+uyVgd8WqQQEe8VM6cOYOPjw8BAQEAhIWF4ePjg7+/f4GWM3XqVHx8fAo0zxfF19eXQYMGFVn5/v7++Pj4EBYWViD5FfX2FJbXdbteZgV9br5qXuW/a+LFebThIpd4h7R0C9100sYZau7b2RBlZQkKBUZAmdhkykclQmI6xKVBXDqkqUGhoMfOotwCIXJOuq6J11ZAQADx8fH06NGjqKsixAsXFhZGQEAATZs2pXLlyoVaVnx8PGvXruXNN98sFhfac+fO5fz584SEhJCQkICjoyNeXl706tUry+1PS0tj2bJl7Nq1i4cPH1KyZEl8fX3p168fxsbyMyxerODu+0nDXm/ZP2+U5aGdlUHakvEpdDm1h5Gn92GsVrGkVlMmt+mOxlTJn3czaOYp5694uckZKl5qrq6uHD9+HKVSmet1AwICCA8PzzLQmTRpEp9//nlBVFHk0+bNm1/LJ24X9XaFhYWxZMkS3NzcXkigs2TJEoAiDXQGDBhAv379MDU1fX7ifLh06RIVK1bkvffew8bGhqioKHbv3s2QIUOYNm0abdu21Uv/+eefc/jwYfz8/KhZsyb//PMPixYt4t69e0ydOrVQ6yrEk4JtppOMnd6yVFNj4uytMMtQkWCmn77ZrVN8c3CD7v2kv7YTbW3DrBbtab4ZUkZrMDN+/f5+v4w0yH7OCwl0xEtNoVBgZmb2/IS5ZGxsLHdSXxKFfVH6ImVkZKBSqTAzMyuU7UpMTMTKyvCu6+tKo9GQnJyMpaVljtK/qO/14sWLDZZ1796dDh06sGLFCr1A59ixYxw+fJiePXvy6aefAtChQwdsbGxYs2YNHTt25I033ij0OoviS6PRENNpPpa/H8WUkmieas0xzlChzFDhEp9IrLkZGf/dWExVKnj734sG+XW9epJZLdqDRoP5/9LwraBkexf5PRUvJzkzxUstLCwMPz8/Bg4cyODBg3XLd+zYwYYNGwgODiYjIwMnJydq1KjB2LFjcXBwwNfXl/DwcED/DvOiRYvw8fFh6tSp7NixgzNnzug+0y47dOgQc+fO5cCBAyQmJlKlShXGjBmDt7e3Xt1iYmL4+eefOXLkCGlpaVSvXp3Ro0fz008/ER4erhtnlBshISEsW7aMkydPEh0djb29PdWqVWPgwIFUrVpVL21QUBCzZs3i/PnzKBQK6tevz4QJEyhRooReuoSEBJYtW8aBAwe4f/8+VlZW1KtXj2HDhuHu7q6XNj09nbVr17J3717+/fdfjI2N8fDwoF27dnTr1i3beqtUKmbMmMHWrVsZMWIEffv2zfE2+/r64urqqnfxqF32xRdfPHcbY2Nj+eWXXzhy5AgPHz7EwsICV1dXWrZsSZ8+fXJcD8g8V9q1a8f777/PwoULuXnzJtbW1rRo0YJhw4bpXXD7+/uzZMkS1q9fz7Zt29i/fz+RkZEsWLAAHx+fLLcL4NChQ6xatYrAwEAUCgVeXl706dOHpk2bZrlfxowZw7x587h06RJ2dnZs3779udsREBDAtGnTAJg2bZru/3Xq1NHVJzk5maVLl7Jv3z4ePHiAra0t9evXZ+jQobi6uuZ4n505c4YhQ4YAsGTJEl3Ljqurq9534M8//2T9+vUEBgaSnp6Oi4sLb731FqNHj8bExESXz5QpU0hOTmbjxo3cu3ePfv366X33n0V7TLZv346bm5vesk2bNrFz50527tzJo0ePKFeuHMOHD6dhw4Y53tZnsbS0xM7Ojri4OL3le/fuBeDDDz/UW/7hhx+yZs0adu/enetAJzU1lUWLFrF7927i4+OpUKECw4YNyzLt5cuX2bRpE//88w/3799HqVRSsWJFevfuzbvvvqtL9+OPP7Ju3Tq2bNmCh4eHXh6RkZG0bduWNm3aMGXKFCAzgFu1ahW3b98mJSVF97dqxIgRlC1bNlfbIwqGZuNfZEzagOJ2OMkqO9Ixw5wYLEjEDkjDHBdCuW/iQmK6EypMAHBQP6Tp7WP8WbkJVR5EcqBiaSJsLAm3MafHOTuDch7Y2mf+R6EApZKAuxoUM9MopUri2MO9VAi8BXfvw8M4KOsMP/WDRtUfZxASCYv2QnQCfPA2vFtDv4AjV2DdMbCxAEdrCAyH2p4woBlYFPyNT/F6k0BHvHJ27tzJ1KlTqV27NkOGDMHMzIz79+9z/PhxoqOjcXBwYOzYscybN4+YmBjGjBmjW9fT0/O5+Y8YMQIHBwc+/vhjYmNjWbNmDaNGjWL79u26u+lpaWkMGzaMwMBAfH19qV69Ojdv3mT48OHY2trmabuuXr3K0KFDycjIoH379lSoUIG4uDjOnTvHxYsX9QKdhw8fMnjwYJo2bconn3zCzZs32bJlC4mJicyfP1+XLiEhgY8++oiIiAj8/PwoX748kZGRbNq0iX79+rF69WrdRW16ejojRozg7NmzNGjQgPfffx9TU1Nu3brFwYMHsw10UlJS+PLLLzl+/DjTpk2jTZs2edr+p+V0GydOnMi5c+fo3LkzXl5epKamcvfuXc6ePZvrQAfg+vXr/Pnnn3To0IG2bdty5swZ1q1bx+3bt5k/fz5GRvpzuHz11VeYmZnRs2dPFAqFQaD5pI0bNzJjxgzKlSvHxx9/DGQG7ePGjeOLL76gU6dOeunv37/P0KFDad68Oe+99x5JSUk52obatWvTv39/li9fTseOHalduzYAjo6OQGbL04gRI7h48SLNmjWjV69eBAcHs3nzZk6ePMmqVatwcXHJUVmenp6MGTOGn376iXfffVd38fxkUDh//nyWL19O+fLl6dGjByVKlODevXscOHCAIUOGYGJiokv722+/ERsbS4cOHXBycspxPZ5n6tSpGBsb06tXL9LT0/ntt98YN24cW7Zs0QVFuRUTE4NarSYyMpKtW7dy9+5d/Pz89NJcuXKFkiVLUqpUKb3lpUqVwtnZmatXr+a63C+//JJDhw7RqFEj3nrrLe7du8f48eOz3I5Dhw4RFBRE8+bNcXV1JTY2lh07djB+/HimT59O69atgcxWpnXr1rF9+3ZGjBihl8eOHTtQqVR06NABgLNnzzJmzBgqVKhA//79sba2JjIyklOnThESEiKBTlGYth7F1PUYoyCCKqSR+VtlTfQTHZ4yW2reSD+HCiXXqUU0LsQoSpAak4zfuV2srdeQ02UeB94/NW5Hl0snsElNASDF2ISZTfXPcRQKUCiIMLJm1WUV0/adffzZwzho/BUcmJYZ0NyLhDrjIPK/GwKL9sKKkdD3v6B77RHoOTvrbdx2CvZNzfMuetWpX8Mu3i+CBDrilXPo0CGsrKxYuHChXjcV7V1lgKZNm7J27VpSU1NzfeFdpUoVJk6cqHtfvnx5Jk6cyJ49e+jcuTMA27ZtIzAwkKFDhzJgwABd2ooVKzJjxoxc3RGHzK4FU6dOJT09nZUrV+Ll5aX7rH///qjVar30ISEhfPfdd7Ro0UK3zMjIiI0bNxIUFES5cuWAzBas0NBQli9fTqVKlXRpfX196d69O/7+/roxAmvXruXs2bP079+f4cOH65X3dPlasbGxfPrpp9y6dYvZs2fToEGDXG33s+RkGxMSEjh9+jRdunRhwoQJBVLurVu3+PHHH3UtLF27dtXd7d63bx+tWrXSS29tbc2CBQue22UqLi6OOXPm4O7uzooVK7C2tgagS5cu9OzZk9mzZ9OiRQtsbGx064SGhjJp0iTdBWZOubu7U79+fZYvX07NmjUNvgMBAQFcvHiR3r17M2rUKN3y+vXrM3r0aObNm8c333yTo7KcnJxo2rQpP/30ExUrVjQo6/LlyyxfvhwfHx9+/vlnva6oI0eONMgvIiKCTZs26YKygmJvb8+sWbN046Z8fHzo27cvW7ZsMbiwz4mkpCSaN3/8MBEzMzM6duyod2MFMltDsrvB4uzszIMHD3JV7okTJzh06BDt2rXTG99Tp04dxo0bZ5B+wIABBtvXvXt3evTowdKlS3WBTsWKFalZsyY7duxg6NCheuMit2/fjqenp67l6fDhw6jVaubPn693nLTBu3jB0jPQ/LANBZCEnS7IMSEBE5IBSMQRa6J1qyhR4cVlTuGMkcaIJOyp+/AOj8z1u9xedvWg5pgf6Xf6EMZqFavfbMyNkqWzrUqDf29m/cGEVXD6B/hl/+MgR+u7LY8Dne+3Zr+d+/+B0zehrlf2aYR4ikwvLV451tbWpKSkcOzYMTTaOf4L0NOTF2i7voWEhOiWHT16FKVSadAdpUOHDroL2Ny4ceMGd+7cwdfXVy/I0Xq6FcHZ2VkvAMiqnhqNht27d1O7dm1KlixJTEyM7mVhYYG3tzcnTpzQrb9nzx5sbW2zvFh5unyA8PBwBgwYQGhoKIsXLy7QIAdyto3asTCXL18usCmFy5Yta9CNrF+/fkBmkP20Hj165GhcyMmTJ0lOTqZ79+5654i1tTXdu3cnKSmJkydP6q1jZ2eHr69vrrfheQ4ePIiRkRH9+/fXW96wYUMqVarEkSNHsg1uc2vPnj1AZkvp0+PtFAqFwYQNbdu2LfAgBzIv7p8sq3r16lhaWhIcHJyn/MzMzJg/fz5z5szh888/p1q1aiQnJ5OSkqKXLiUlJdvxWmZmZgbpn0d7Dvbu3VtvedOmTbNsSbGwsNCrS0xMDCkpKdStW5e7d++SkJCg+7xjx45ERkZy/Phx3bJz584RHBxM+/btdcu05++BAwfIyHg5n6cSHR1Namqq7n1CQgLx8fG692lpaURFRemto+3unN37iIgIvd+cl6aM8AeQnJlGxeNzzZRkXWvOk8sff56KKdq8FfjXb8HWmnX0E2k0BDk4M7XVB0x6/8NnBjkAaqOsWx1UjxIytyMm0fDDmMTH+yqrz59K+//s3Xd8Tff/wPHXHdlbQkQiRuxVI2oTtcWIWUUpahTVVlul1ZZWf6pFrZi1R20lVo3aikTwtWOLRBDZO7n3/v5Ic+u6CUkkgryfj8d9cM/9nHPeZ9yb8z6fcTK8sscjh+sQ+UtqdMRrZ8CAAQQGBvLFF19gZ2dH7dq1adSoEa1atcqTjtquroY/5Pb29kB67UWGkJAQnJycjDpJm5iYUKJECYMfvuzIuHDP7uhYT8cI6RfFT8YZGRlJdHQ0J06cMLjz/KQnE5i7d+9SsWLFbA/+MHr0aNLS0li7di0lS5bM1jw5kZ1tNDExYfTo0UybNk3fNM/T0xMvLy/efvvtXK03s7vvTk5O2NjYEBISYvTZ0/0ZspIxb9myZY0+y5j29PJdXV1zNeLg84SGhlK0aNFMm1l6eHgQFBREVFRUniQcd+/e1fdFyo7s7s+cero/GqSfT09+r3NCpVJRr149/XsfHx+GDh3KsGHDWL16tT75NTc3JyUlJdNlJCcnY25unqP1hoSEoFQqM01qypQpw507dwymRUREMG/ePA4dOkRERITRPHFxcfrEpVWrVkybNo2tW7fStGlTIL322sTExGCAhZ49e3Lo0CF+/vlnZs+ezVtvvUXDhg1p06YNDg4OOdqe/PL0ufv0DShTU1McHR0Npj1dE//0+6ebH74y63Avga5jXdh6CguiiMQNUJKAAzruoADSMrncS8acZMyJsjHlsb0F1tEa3j92mdX1KxFpZQpxGc/NASzU6a/nuGfrmOl01dA2qMzM4N3GMGsnPHmT8r3G/+2r9xrDL39mvvASRaDZf319XtnjkcN1ZJdOmq7liiQ64rXj7u7Ohg0bOHXqFP7+/gQGBjJp0iR9p+PMLmhyIqsLy/yoPcqtzGpYMmTEmfHv22+/naPBAbKrTZs2bN68mcWLF/Pdd989M6bcyM42QnrTLy8vL44ePcrp06fZv38/69evp1WrVkyePDlPY8pMTi9UX5Vlv0yZ1dxkJb+2OavzKa++1yqVirZt2/Lzzz8TGBioT7SdnJx49OhRpvNkPFMnv+h0OkaOHMmtW7fo1asXVapUwdraGqVSiZ+fH7t37zaouTM3N6ddu3Zs3ryZx48fY2Zmxv79+2natKlBAmNvb8+KFSs4c+YMJ0+e5MyZM0yfPp0FCxYwc+ZMatSokW/bJDKnWPYx2iELUG06jpP2BlGUJA0zYiiKLY+wJpIoimPHAxToSMGEC+qahNtYElEkvdbPIiWVmtfvYJeczJK3KnDf4t/vog5ISAOVAkwz+fv473fILiEed9NUsDKDxBTQ6sBEBR+2hC990ss2qAjrPoefNsLjWHi3EfzU579l/fheep+fNUfA0jR9QILgx+mDEUztD6YmxusX4hkk0RGvJVNTUxo3bqwfMeno0aN8+umnrF69mq+++gogX59hUqJECU6dOkVCQoJBrU5aWhqhoaEG/SyyI+MudlBQUJ7F6ODggI2NDfHx8QZ3nrNSqlQpbt++TUpKSraGRv7ggw9wc3Nj1qxZaDQaJkyYkC+1D9nh5OSEj48PPj4+aDQavvvuO/766y/69u1L1apVn7+AJ9y6dctoWnh4OLGxsZnWMmVXRgJ+8+ZNo9qmjHW+yPKf9qzz39XVlX/++YfY2Fijc/XmzZtYWVnpazJfdF2lSpXi+PHjBAUFGY1c+KbJaNLy5MhrVatWZdeuXYSFhRncHQ4LC+PRo0f6mpPscnV1RavVcufOHTw8PAw+e/rcvXbtGkFBQUajVgL8+eefmS6/a9eubNiwge3bt+ubCT/ZbC2DSqXC09NT35z02rVr9O3bl8WLFzNz5swcbZPIA/ZWKNePBkZjBWS0bdCExZD8xTqiV/8PBWncpxIWxJGgtOCGoxuxVmaYaVIoHZueAN21LobLo0hi1Zn8lqdojRMdnQ51chLxg8HU2R6+//T5sfZomP7KjKkJ/Px++kuIPCB9dMRrJyoqymhapUqVAMPmZZaWlsTExORLTUyTJk3QaDT88ccfBtO3bNli0OY9uypUqEDZsmXZtm0bN27cMPo8N9ugVCpp27YtFy9eZN++fZmWebIpS9u2bYmJiWHx4sXZXn+/fv0YPXo0u3btYvz48S+9vX5SUpJRHweVSqVvJvX0UL/ZcefOHaO+OMuXLwegWbNmuQuU9I7+FhYWrFu3jvj4/9qZx8fHs27dOiwtLfO0n1NGAp5Z0ywvLy+0Wi3Lli0zmH7s2DGuXr1K06ZNc1RDl9EPJLP9nTF4w9y5c0lNTTX6/FWqKc2OmJiYTLcjMTGRrVu3olQqDZLrjO1/+rci4327du1ytP6Mc3DlypUG0w8ePGjUbC3jGD69j69fv55pfzOA8uXLU7VqVbZt28bWrVspXry40XmZ2W9w6dKlMTc3z9V3TuQfVXFbzFcNxlk3G8fE2SSSRhSORGiL4vIgAeeoGLzvnqLO4+vUfnwD77uncEyKxjYlk9/yf/vfuEc8pOa9mwAkfqoidbwNps45u7knxMsiNTritTNixAhsbGyoVasWzs7OxMbG4ufnh0KhMBjxqVq1ahw5coRffvmFGjVqoFQqqVu3bp70O/Dx8WHz5s3MmzePe/fu6YeX3rdvHyVLlkSj0eRoeQqFgu+//57hw4fTv39//fDSsbGxBAYG0qBBA3r16pXjOEeMGMG5c+cYN24c+/fvp3r16piYmHD//n2OHTtG5cqV9SM3vffeexw5coTFixdz6dIl6tWrh5mZGTdv3uTOnTvMnTs303X07t0bExMTfvnlF9LS0pg8efJLexjrnTt3GDJkCM2bN8fDwwMbGxtu377Nxo0bcXV11Q+rnBPlypXj22+/xcfHB3d3dwICAti/fz+1a9emdevWuY7VxsaGUaNGMWXKFD744AM6dOgApA/dGxwczNdff52rgSyyUqZMGaysrNi4cSPm5ubY2NhQpEgR6tatS8eOHdm+fTvLly8nNDSU2rVrExwczMaNG3F0dDQade957O3tKVmyJHv27MHNzY0iRYpgYWFB06ZNqVatGv3792f58uX06dOH1q1b4+joSGhoKPv372f58uU5rgEtSIGBgfzf//0f77zzDm5ublhZWREaGsrOnTt58OABgwcPNmiD37hxY5o0acLq1auJi4ujevXqnD9/nq1bt9KuXTtq1qyZo/U3aNCAJk2asH37dqKjo2nYsCH37t1j8+bNeHh4GNwoKVOmDGXLlmXFihUkJSVRqlQp7t69y+bNmylXrhyXL1/OdB1dunRh0qRJAAwePNgo6Z00aRIPHz6kXr16uLi4kJyczN69e4mPjzfoyyNeLSpzNWVT/4+tzqtwjEjvN1Y56i7m2lQ0CiUx5pbYJcVTLfIOXmHhrC3j9t/gAgrAPL02Z8a25ewtX52G7ctibiL3y18W6aOTO5LoiNdO9+7d2bt3L5s3byY6Oho7OzsqVqzImDFjDB4O2qdPH0JCQti/fz+bNm1Cq9Uyf/78PEl0TE1NmTdvHjNnzuTQoUPs3buXatWqMXfuXCZNmpTjkZQgvYnL8uXLWbx4Mfv27WPTpk3Y29tTtWrVHF8MZbC2tmbJkiWsWrWKvXv3cvjwYVQqFcWKFaNmzZoGwxabmJgwZ84cVq1axV9//cXcuXMxNTXF3d39uSN/9ejRA7Vazf/93/8xZswYpkyZYvBslPzi7OxMp06dOH36NAcPHiQ1NZWiRYvSpUsX+vfvn6v+HpUqVeKzzz5j7ty5bN68GSsrK3r27MmIESNeuB9Sjx49cHJyYuXKlfoHa1aoUMFgOOu8Ym5uzk8//cS8efOYPn06KSkp1K5dm7p166JWq5kzZ47+gaEHDhzAxsaGFi1aMHz4cKMOuNnx448/Mn36dHx9fUlKSsLFxUXfLOvjjz+mfPnyrF+/nhUrVqDVanF2dqZRo0avXT+kcuXK0aRJE06fPs2uXbsMHpY5bty4TB9A+vPPP7N48WJ27drFzp07KVasGMOGDdOP5pdTkydPZt68eezevZtTp07h4eHBr7/+yu7duw0SHZVKxcyZM5kxYwbbt28nMTERDw8PJkyYQFBQUJaJTps2bfjtt99ITEw0ei4QQPv27fHz89M/fNXKyoqyZcsyZcoUWrRokattEi+HQq2ial9XYmedJx4bLLUpXC3mzu6q9Ukws8A6KYGGN/6HR2w8w67c5LSjPSedHdP76SigRNRjulz05xevTvzTWi4hxatPoXvd2g0I8QrTaDS0bNmSatWqMXv27IIOR+SQp6en0fNJhChsUlJSaNu2LVWqVGHOnDkFHY7IYzqdjivKn9GiJVVtxrbmjUlTpSct9yzNOersSLSpCRWi42jy4DHzKpUhyswUiphR895NBpw+RLGFA+lVRRKdl2lUj8xvTGRm1obKzy9USMhZKkQuJSUlGd2N3rRpE7Gxsdnq/C+EEK+iXbt2ERMTQ5cuXQo6FJEPFAoFqc3KYnfoAneLmuiTnChTE1Z5uJOqSq+5fmhhToJahZlGCyZKUCi46OLOJ50HoJMk56XTSsu1XJEzVYhc+umnn0hOTqZGjRqYmppy/vx5du/eTcmSJfUXCHFxcc9txmZiYqJ/PsybIjIy8rn9lCwtLY2eQ/Smx/KiNBoNkZGRzy1nZ2eXJ00Hw8PDn1vG2to6X5ufJSQkkJCQ8MwyKpXqhZ/fUtDnyauwrw8fPsz9+/dZuHAhZcuWzfPmlOLVUePguwSZBJKssUwfHlqh4KK9jT7JyXCuiB0aEyVYp/+epKrUtColV9zi9SGJjhC5VK9ePTZs2MDixYtJSEjA0dERHx8fhg0bpn9w6dSpU9m+ffszl1O7dm0WLlz4MkJ+afr16/fcpz9nNuTtmx7Li3rw4EGmfSaeNn/+fIP+arnVtm3b55b5/vvvn9uH60U82Z8pKy4uLvj5+b3Qegr6PHkV9vWvv/7Ko0ePqFy5MuPHjy+w4eLFy/HAphRaRSqlQsO44+qCOpOeDBqlEuzMQKFAodGiU8C2rnJeiNeH9NERIh/dvHkzy4cFZrC1taVy5TerPe3Zs2f1zxTJiqur6ws/3PV1i+VFJScnc/bs2eeWq1y5Mra2ti+8vpMnTz63jIeHB05OTi+8rqzcu3ePkJCQZ5YxMzPL9YAdGQr6PHkV9rUoXOL9Q7nWchGni1bnn+oVOFfEltNFHQxH97JQgaUJpGogQUNJF1PuDpd75AVhxLtXsl3Wd12lfIzk9SKJjhBCCCFEIXTSYxFmj8KZ4PMuQdaWXLa3BjMVaHRgqkz/v0IBMSlgpkL3jVlBh1xoSaKTOzIAuhBCCCFEIVTvxmDsFr5LmlpNtag4rFPS0gcesDUFc3V6kqPVgZmCix9KkzXx+pH6RyGEEEKIQqpMr7KwN5hUMxMGX73FRXtbrjvbkWqmpkhiMufsbAn5zJQStpLoiNePJDpCCCGEEIWYQqMhVaUi2MGeCrFxVIuOI8bclItF7Uj+xhxTtYy0VtC0yDHIDWm6JoQQQghRiP3QOQ2ABBM1t4rYc7VoEe4Wt+G4bwlJcsRrTRIdIYQQQohCrFaXcsz/woqy6hhsNVHUdD3L9mnOBR2WEC9Mmq4JIYQQQhRybpWLMH2ODUuXLi3oUEQmDIb9FtkmNTpCCCGEEEKIN44kOkIIIYQQQog3jiQ6QgghhBBCiDeO9NERQgghhBDiFaaVLjq5IjU6QgghhBCChwkQrTEv6DCEyDNSoyOEEEIIUYhFJGpw8U0lRacC3kOpSKNrEjiZFHRkQrwYqdERQgghhCjEHGelkoIaFApQKNBiQtnFBR2VeJJWocj2S/xHEh0hhBBCiELq+J00UKqMpsemFkAwQuQxSXSEEEIIIQqpJhs06TU5QryBpI+OEEIIIUQhpZV73q8FnSSjuSJntxBCCCFEYaXL+qOUNM3Li0OIfCCJjhBCCCFEIZSU9owsB5j6R8hLikSI/CGJjhBCCCFEIfT5gaz75yi1WtSL9oJ5T/h1M0TEvuTohHhxkugIIYQQQhRC265n/dnm5b8y5sgOSE6DMavAsT9UHvHyghMGtIrsv8R/JNERQgghhCiEtHcegM64+Zpak0bx2CjjGa7chzk78j8wIfKIJDpCCCGEEIXQQzuHTJuupanUBDsUzXymjxfDuOX5HJkQeUMSHSFegJ+fH56engQEBBR0KALQarUsWLCAzp07U69ePTw9PfNtXRMmTMjX5b/KhgwZQseOHQs6jNdKQEAAnp6e+Pn5FXQoufK6xy+M6XQ60tQmWX5+09E565l/3go3QvMhKpEVHYpsv8R/5Dk6QohXQmxsLGvWrKFOnTq5TiC2b9/OokWL6NSpE7Vr10aplHs5QuSX0NBQ/Pz88PLyomLFigUdjsiBgPtp1F1NprU5lR7co/6dID46vufZCyk3EuZ8CCPa50+QQuQBSXSEEK+E2NhYFi1aBJDrROfkyZNYW1vz7bffosjnh6uNHz+ecePG5es6XlW+vr7oMmnXL95ctWvX5tixY6jV/102hIaGsmjRIkqUKCGJzmtiun8aXx+C5Cw+H7d/M/+3e232Fzjyd/h2DUSsypP4hMhrkugIId4Yjx8/xsbGJt+THAC1Wm1w0VeYmJhk3dzlZYiPj8fKyuqlrEuj0ZCamoq5uflLWd+rSqlUYmZmVtBhiBzSaHWMOaRh+unnl3WOiWLing05X0lkAii6pv+/ein4sjN0qQfWFjlflsiS9iX8XXsTSbsOIfKATqdj5cqVdO7cmQYNGtC1a1e2b99uUGbPnj189tlneHt706BBA1q0aMHnn3/OtWvXjJZ37tw5Ro0aRZs2bWjYsCHt2rVj1KhRnD9/PsexhYaG8uWXX9KsWTOaNWvG6NGjCQkJoWPHjgwZMsSgnKenJwsWLDBaxoIFC/D09CQ01LBNdnh4OJMnT8bb25v69evTtm1bfvrpJyIiIgzKRUdHM23aNDp37kzDhg1p0aIFffv2ZcWKFUB6+/9OnToBsGjRIjw9PfH09Mx2P5CM/gMBAQHcv39fP/+ECRMAuHDhAhMmTKBr1640atSIpk2bMnDgQA4cOJDp8sLDw/n111/1x7NVq1YMHz6cEydO6Mtk1kcnY1pcXByTJ0+mVatWNGzYkIEDB3LhwgWj9URFRTFx4kRatGhBkyZNGDZsGFeuXMlVH5iFCxfi6elJSMh/D/gLDw/H09OTunXrEh0drZ9+69YtPD09WbZsmX5aTs7Pp+MbN24c9evXJyoqyqjs7du38fT0ZNq0aQbT9+zZw6BBg2jatCmNGjWif//+7Nu3z2j+jON46tQpBg0aRJMmTfjss89ysmuIiopiypQp+vPU29ubKVOmGMWb0efu5MmT/P777/rzde/evTla38GDB+nduzcNGzbE29ubefPmkZaWlmnZlJQUlixZQs+ePWnYsCFeXl589tlnXLlyxaDck31k1q5dS9euXWnYsCFdu3Zl7drM78AHBgYyfPhwmjVrRqNGjejTpw9//vmnUbkbN27w1Vdf0a5dOxo0aECbNm0YOnQoR48ezXT9Gftq2LBhAEycOFH/nXvyN0UUrMRUHUV9s5fkAJR7HIaJVvNiKz1/B/rNgrLD4Ko8bFQUvMJ5O1KIPObr60tycjJdu3bF1NSUjRs3MmHCBNzc3KhZsyYA69evx87Oji5duuDk5MS9e/fYsmULgwYNYtWqVbi7uwPpF4YjRozA0dGRXr16UaRIESIiIjh79ixBQUFUr14923FFRUUxePBgHj9+TLdu3ShTpgxnzpxh2LBhJCYmvtA2h4WFMWDAAFJTU+ncuTNubm4EBwezadMmAgICWLlyJdbW1gCMHTuWwMBAunXrRvny5UlOTubWrVucPn2afv36UaZMGUaPHs306dNp3rw5zZs3B8DS0jJbsZQpU4YffviBJUuWEBUVxejRowFwc3MD0i88b9++TcuWLXFxcSE6Oprt27fz5ZdfMmnSJNq2batfVmhoKIMGDSIiIoL27dtTpUoVEhMTOX/+PKdOnaJ+/frPjWfkyJE4ODjw4YcfEh0dzerVq/nkk0/Ytm2bviYiJSWF4cOHExQURMeOHalatSrXrl1jxIgR2NraZv9A/Ktu3bosXLgQf39/XF1dATh16hRKpRKtVktAQAAtWrQAwN/fXz9Phuyen5nx9vZm7969/PXXX7z77rsGn+3YsUNfJsPcuXNZsmQJDRs2ZNiwYSiVSg4cOMDYsWMZM2YMPXv2NFjGpUuX+Pvvv/Hx8aFDhw452i9xcXEMHDiQ4OBgOnXqRKVKlbh69SobN27E39+f5cuXG9UOzZw5k7S0NLp06YKVlRWlSpXK9voOHDjAmDFjKFGiBB9++CEqlQo/Pz+DpCFDWloaH3/8Mf/73/9o3749PXv2JC4uTr/fFy1aRJUqVQzmWbduHY8fP6Zr165YWlry119/MXXqVGJiYgySjMOHD/Pll1/i6OhI3759sbS0ZM+ePUyaNImQkBBGjEh/HkpUVBQfffQRAN26daN48eJERUVx+fJlLly4QOPGjTPdzlq1ajFgwACWLl1Kly5dqFWrFgBFihTJ9r4S+WvJBS2RWbVRy8QZ19LEmppjk5L04it/FAsT18OanN2UECKvSaIjRB5ISUlhxYoV+iY9LVq0oHPnzqxfv16f6MyePRsLC8OqfG9vb3r37s2aNWsYO3YsACdOnCApKYmffvqJatWqvVBcy5cv58GDB/z444+0a9cOgO7duzNz5kxWrlz5Qsv+5ZdfSEtLY/Xq1Tg7/zc6T8uWLRkwYACrV69m6NChxMXF4e/vT/fu3RkzZkymy3J0dMTLy4vp06dTrlw52rfPWedWR0dH2rdvz59//klycrLR/IMGDWLkyJEG03r16kXv3r1ZvHixQaLz888/8+jRI2bPnk2DBg0M5tFqtdmKp1KlSvrjCVC2bFnGjh3L7t276datGwBbt24lKCiIjz76iEGDBunLlitXjilTpuDi4pK9jf9X9erVMTc3JyAgAB8fHyA9oalQoQLJycn4+/sbJDrW1tZUqlRJP392z8/MNGjQAEdHR3bs2GGQ6Oh0Onbt2kW5cuX067py5QpLlixhwIAB+ottSD8en3/+Ob6+vnh7exskHzdv3sTX15d69erlaJ9A+nfg7t27fPXVV/To0UM/vUKFCvzyyy+sWLFCf6GfISkpiTVr1uS4uZpGo2Hq1KnY2tqyfPly7O3tgfQEolevXkbl161bx+nTp43Ote7du/Puu+8yY8YMFi5caDDP3bt32bBhg/4717NnTwYNGsTixYvp3Lkzzs7OaDQafvnlFywsLFi+fDlFixbVlx06dCjLly+nY8eOuLu7c+7cOSIiIvQ1kNnl5uZGvXr1WLp0KTVq1Mjxdza/RUREYGVlpW9uFxcXh06nw8bGBkj/zY6NjcXR0VE/z/379w2+d0+/DwsLw9nZWd809lVfx/lHOetHl2BqTq8+o9i27FdUedAHT3f5nn78r1d9XxXkOkT+kqZrQuSBHj16GPRbKFasGO7u7gQHB+unZVxE6nQ64uLiiIqKwsHBgVKlShk0a8qoBTl06BDJyTm4HZeJI0eO4OTkRJs2bQymv//++y+03Li4OI4ePUrTpk0xMzMjKipK/ypRogRubm6cPHkSADMzM0xNTblw4YJR07eX5ckL+KSkJKKiokhKSqJu3brcunWLuLg4IL2J3T///EPDhg2Nkhwg26O49e7d2+B9RhO3J8+HI0eOoFKpeO+99wzK+vj46M+BnFCr1dSsWdNgqPPTp09Tt25d6taty6lTp4D08y8wMJDatWujUqn0ZbN7fmZGpVLRrl07Ll26xO3btw3WHxYWZlALs2vXLhQKBd7e3gbnTVRUFE2bNiU+Pt6oiWaFChVyleRAem2eg4MDXbp0MZjetWtXHBwcMm2+2L1791z1ybl8+TIPHjygU6dO+iQH0r/TGQnuk3bt2kXp0qWpXLmywX5IS0ujXr16nDt3jqQkw7vrbdu2NbixYGJiQu/evdFoNBw5ckQfR1hYGJ06ddInORll+/Xrh1ar5dChQ/rYAI4fP67/HrwJihQpYtCnyNraWn8xCmBqampwMQoYXXw+/b548eIG/f9e9XV4e+T8Em9nFU/e+fAbXrABGwCKd/67Ufeq76uCXEd2aRWKbL/Ef6RGR4g8kNFU6El2dnaEhYXp31+5coX58+dz+vRpo2ZjT87funVrdu7cydKlS1mzZg3Vq1enfv36tGnTJsc/kKGhoVStWtXoAr1IkSIGP845dfv2bbRaLVu3bmXr1q2ZlsnYJhMTE0aPHs20adPo1KkTZcuWxdPTEy8vL95+++1cx5ATERERzJs3j0OHDhn1H4L0xM3a2prg4GB0Ot0LjyD19PmQcdH7ZD+ZkJAQnJycjJrnmZiYUKJECWJjY3O8Xk9PT06cOMGtW7cwMTEhNDSUunXrkpyczPr163n48CGRkZFER0cbNFuD7J+fWenQoQOrVq1ix44d+pqaHTt2oFKpDGrMbt26hU6no3v37lku6/Hjxwbvn9Vs7nlCQ0OpXLmy0cARarUad3d3o74wL7K+jP5RmTV1K1OmjNG0W7dukZycTMuWLbNcZlRUFMWLF3/mcsqWLWuw/owbChnTn+Th4WFQtk6dOnh7e+Pn58euXbuoUqUK9erVo1WrVpnOL14fHT2UdC+vZaNxN7tn+ubgVlTPL/Zs7WvDBONaTCFeNkl0hMgDWd3pzxiCNywsjCFDhmBlZcWgQYMoXbo05ubmKBQKpk2bZnBhaWpqyty5c7lw4QInTpwgMDCQBQsWsGjRIiZNmqTvv5LXnjVSmUaT+f29du3aZdln4sm7Xt27d8fLy4ujR49y+vRp9u/fz/r162nVqhWTJ09+scCfQ6fTMXLkSG7dukWvXr2oUqUK1tbWKJVK/Pz82L17d7abpGXXkzUlT8eSnzKSF39/f0xNTVGr1dSqVYvU1FSUSiWnTp3Sd8B/MtHJyfmZlXLlylGhQgV27drF8OHDSU5O5u+//6ZevXo4OTkZlFUoFMyaNSvL703GxXiGlz3i2ctcX7ly5Z45uIKDg0O+xzBx4kTef/99jh8/zpkzZ1i1ahVLlixh9OjRRn2uxOtlQ2c10ck6Zgdo+Paf7M0TYZHzGmUAOtdNT26K20Px/D9vhcgOSXSEeAkOHDhAQkIC06dPNxqpKzo6GlNTU6N5qlWrpu+jExYWRp8+fZg3b16OEh0XFxeCg4PRarUGF5URERFGNQYZHeBjYmKMlvPkSF6Q3j5foVDom9hkh5OTEz4+Pvj4+KDRaPjuu+/466+/6Nu3L1WrVs23IaGvXbtGUFAQgwcPZujQoQafPT0CVcmSJVEoFFy9ejVfYnlSiRIlOHXqFAkJCQa1OmlpaYSGhuaqxq1SpUpYW1vj7++PiYkJ1apVw8LCAgsLCypWrIi/vz8xMTEUKVLEIJnIzfmZmQ4dOjB9+nQCAgIIDw8nPj7eKBEuWbIkx48fp3jx4pnWTuQ1V1dX7ty5Q1pamkGtTlpaGnfv3s1WbVVO1gVw584do89u3bplNK1kyZJERkZSt27dbDeLzGw5N2/eNFh/xr8Z059VNkO5cuUoV64c/fr1IzY2lv79+zNnzhx69uyZ5XfzZQzjLl6cnZmC8Y3UjG8EEYlaAsK0tNmUdflRXQZiotPS7fzJ7K1g7/fQ8q28CVZkSStft1yRPjpCvAQZFzFP39HfsmWLUTOdzIbodXZ2xsHBwaDpU3Y0bdqU8PBw/vrrL4PpmQ1EYGVlhaOjI/7+/gZx3rt3j4MHDxqUtbe3p1GjRvz999+ZDnmt0+mIjIwE0vvEPN3PQKVSUb58eeC/xCqjj0hmidaLyGrfX79+3Wi77OzsaNiwIcePH9f3MXpSXtbINGnSBI1Gwx9//GEwfcuWLbnuK6FSqahduzaBgYH6/jkZPD098ff3JzAwkDp16hhcpObk/HyWtm3bolKp2LFjBzt27MDa2ppmzZoZlMnotO7r65tpTWFO1pcdzZo1IzIy0iip/fPPP4mMjMzTGtLKlSvj7OzMtm3bDL7HcXFxbNpkfGXp7e3N48ePWb16dabLy2xf7N69mwcPHujfp6amsmbNGlQqlX6EtEqVKlG8eHH8/PwIDw/Xl01LS2PlypUoFAr9cYmOjjaq0bSxscHV1ZWkpKRn9hPMSNBz+rskCk4RCyWty6jRfaGmmj2QyW/aI2s7+r73MeGW2bjZkrBGkhzxSpMaHSFegkaNGjF79my+++47evbsiY2NDefOneP48eO4ubkZXPAtXryYEydO0LhxY1xdXdHpdBw5coTbt2/Tr1+/HK23f//+7N69m4kTJ3Lx4kVKly7NmTNn+N///oe9vb3RHdmePXsyb948Ro0aRbNmzQgPD2fTpk14eHhw6dIlg7Jjx47lww8/ZPDgwXh7e1OxYkW0Wi0hISEcPnyY9u3bM3ToUO7cucOQIUNo3rw5Hh4e2NjYcPv2bTZu3Iirq6t+WFp7e3tKlizJnj17cHNzo0iRIlhYWNC0adNc7vV0ZcqUoWzZsqxYsYKkpCRKlSrF3bt32bx5M+XKlePy5csG5ceMGcPAgQMZNWoUHTp0oHLlyiQlJXHx4kVcXFwYNWrUC8WTwcfHh82bNzNv3jzu3bunH1563759lCxZMsvmgs9Tt25dDh8+DGBQO1O3bl19gvt0rU1Ozs9nKVKkCA0bNmT//v2kpKTQqVMno4dMVq1alSFDhrBw4UJ69+5Ny5YtKVq0KOHh4Vy+fJljx44ZPK/oRfXv35/9+/fzyy+/cPXqVSpWrMjVq1fZunUrpUqVyvF36llUKhWfffYZ48aNo3///vj4+KBSqdi2bZtRnz2A9957j5MnTzJz5kz8/f2pW7cuVlZWhIWF6ZsfPv1cK3d3dz744AO6deuGpaUlu3fv5tKlS3z44Yf6vjwqlYoxY8bw5Zdf0r9/f7p06YKlpSV79+7l/PnzDBgwQN8PaceOHaxZs4bmzZvj5uaGWq0mMDCQf/75h1atWj2zGV+ZMmWwsrJi48aNmJubY2NjQ5EiRYz6f4lX0/kP1Sh+Tc30syQTU46WqYTPRf+sF6DdBFKrJ15xkugI8RK4ubkxa9YsfH19Wbp0KUqlkrfeeosFCxbwyy+/cP/+fX3ZjARj3759REREYGZmRsmSJRk/fjydO3fO0Xrt7e35/fffmTFjBtu2bUOhUFCnTh3mz59Pv379jC5C+/fvT1xcHDt37uT06dOUKVOGb7/9lsuXLxslOsWLF2fVqlUsX76cQ4cOsWvXLkxNTXF2dqZJkyb6oWqdnZ3p1KkTp0+f5uDBg6SmplK0aFG6dOlC//79DS6kfvzxR6ZPn46vry9JSUm4uLi8cKKjUqmYOXMmM2bMYPv27SQmJuLh4cGECRMICgoySnRcXV1ZuXIlv//+O8eOHWPHjh3Y2tpSvnx5o5G7XoSpqSnz5s1j5syZHDp0iL1791KtWjXmzp3LpEmTjGrBsivjItPMzIwaNWrop9eqVQu1Wk1aWprRhWhOzs/n6dChg370ryefnfOkIUOGUKVKFdauXcsff/xBYmKivjndF198kdNNfiZra2sWL17MggULOHz4MNu2bcPR0ZFu3boxdOhQo2fovKiWLVuiVCr5/fffWbhwIUWKFKFDhw7UqlXLaIhztVrNjBkz2LhxIzt37tQnNUWLFqVq1aqZ9n979913iY+PZ926dYSFhVG8eHE+//xzo9H7mjZtyty5c1m8eDErV64kNTWV0qVLM378eP3w45A+GMHVq1c5cuQI4eHhqFQqSpQowaeffmr0PKOnmZub89NPPzFv3jymT59OSkoKtWvXlkTndaJNA5VJes3OU0lLmfBnfO8fLZUkR7wWFLr87h0rhHjlREVF0bJlS7p27crXX39d0OGIJ2g0Glq2bEm1atWYPXt2QYcjXhEBAQEMGzaM77//no4dOxZ0OOINYTU1jYRMkhyAHb//RPur54xnuj0fShV7CdGJJ/XqfzvbZdcuL51vcbxupEZHiDdcUlKSUfOT5cuXA+T62SQib2R2bDZt2kRsbKwcGyFEvnO0goT49CSn+9njNLh7jUa3r2KWlsode0cuFC1BtUdPPP9syxhJcsRrRRIdIV4zqamp2er86+DggEql4pNPPsHFxYVKlSqh1Wrx9/fnyJEj1KhRAy8vr/wP+AVFRkY+t4+IpaWl0fNoXgc//fQTycnJ1KhRA1NTU86fP8/u3bspWbKkvplcXFzcc5uxmZiYYGdn9zJCfiXk9DvwouQYiDfVpEbQ/y8d1e/fYd2amSifaORjkZrC6npNqdbOGkZk3gxViFedJDpCvGbOnTvHsGHDnltu27ZtlChRgiZNmrBjxw4OHDhAcnIyzs7O9O3bl8GDB+fJRWB+69ev33P7iGQ2dPTroF69emzYsIHFixeTkJCAo6MjPj4+DBs2TN93ZOrUqWzfvv2Zy6lduzYLFy58GSG/EnL6HXhRcgzEm6pfDTX9/0ql+Y1LBkkOQMXw+wya3ByqSw3Oq0AnfaJyRfroCPGaiYmJMepAn5maNWsaDTbwOjp79uwzh7iF9AEE3NzcXlJEL9fNmzd59OjRM8vY2tpSuXLllxRRwXvZ3wE5BuJNpvg1lXZXzrBzyc8G0x9a2aIMXYSTrUkBRSae9O4Hxs/nysq6ZaXyMZLXiyQ6QgghhBCFlPLnRFCq2L50Cu2vnAFAo1AwsMdHLF/XsoCjExkk0ckdabomhBBCCFFI1YkMJaBoKToM+IpW1/6Hx+MH7C1fg+tOxVle0MEJPa20XMsVSXSEEEIIIQqpfWOKY79Eh06pZE/Fmk98Ig1+xOtPWdABCCGEEEKIgmHnaIVbSlz6Q0Mz6HQ0fzO7PYpCRhIdIYQQQohC7O7X9tR0VpBei6OlhvI2f3Ur6KiEeHHSdE0IIYQQohBTKBSc6acmNTWVpUuX/Tt1QEGGJJ6ileGlc0VqdIQQQgghhBBvHEl0hBBCCCGEEG8cabomhBBCCCHEK0yLNF3LDanREUIIIYQQQrxxJNERQgghhBBCvHGk6ZoQQgghhOBBjI6vbnUnQalm9DexJKnU/NjKlHHvmBd0aELkiiQ6QgghhBCFXKsvQomLSqKqiTk6nYJktZKbdiZ8fVjLklOxXBtrU9AhFmoa6aKTK9J0TQghhBCiEBsw/h6Ot4KJNbXEPlWLQ5qG4kmp1H8QjY1awXWNKUmpmoIOU4gck0RHCCGEEKIQOx+WxroKVSmaZpjMKIFSEfFgpsL2h4SCCU6IFyBN14QQQgghCqnNPwdx19Qej4eR6NQqQGfwuX1cImBPqlJVIPGJdFqFtF3LDanREUIIIYQopA4fT+Dzw2eJsDbnrrmpwWcaoNrDSMo+jkFpoWba1qgCiVGI3JJERwghhBCikLJMSCK+ZBHMgWLhkVyxNidJBbGmSk6WdOB0qaK0vXoPrVrJ5H+0BR2uEDkiTdeEEEIIIQopE0V6Y7U29x7gEp8EkTGodenN18qHR7KiTjkqP4xCrdGSaGH67IWJfKOVlmu5IjU6QgghhBCFlOm/AxCodDq0KqU+yQEoEZtI9fuRXClqR5pKiUt4LJ/uSS2oUIXIMUl0hBBCCCEKoYfbrlP+biipCgV3HexR6XRGZYokJhPkYA1xKVgmJDHHP7kAIhUidyTREUIIIYQohP4aG4h5chKK6ERS1Cr2lyjGiaIOxKv+G2GtVPAjmp65iV1UAudLu6DQSRsq8fqQROcVERAQgKenJ35+fgCEhobi6enJggUL8nQ9EyZMwNPTM0+X+aKGDBlCx44dCzqM107Hjh0ZMmRIga1/wYIFeHp6EhoamifLK+jtyS9v6nbllxf57Svs+9rT05MJEyYUdBjiNRAz7SjXFN9RNDiKOI0FJlEJHHaw4WRRB/a6OrOwUhmiTdTYPY6iQdBdOl26ybf7TmAbl0STu1cZuD6uoDeh0NGiyPZL/EcGI3gD+fn5ERsbS+/evQs6FCFEAQsNDcXPzw8vLy8qVqyYr+uKjY1lzZo11KlT55W7oZJXbt++ze+//86VK1d49OgRaWlpFC9enEaNGtGvXz+cnJwynWf27NkEBgaSmppKpUqVGDp0KHXr1i2ALRCFXfz2y1z94iTJuBJYvjwNz97ky25N0T3xnJY4EzXnzUypERGjn2aflELjW/eIcDIl/k9/AqvUpXY164LYBCGyTRKdV5SLiwvHjh1Dpcr5A7r8/Py4f/9+ponO+PHjGTduXF6EKESe2rRpE4o38IFoBb1doaGhLFq0iBIlSryURGfRokUAuU50XuS372Xs64cPHxIeHk7z5s0pVqwYKpWK69evs2XLFvbs2cOaNWsoUqSIvvy9e/cYNGgQKpWKfv36YW1tzZYtWxg5ciSzZs2iXr16+RqvEOh0aCLiiX5rGqYhj9CiQ01pzImj7bVA7C1jiLI0M5otztTEaJrXzauMqduJk7oKaH/6h3VrmqJ8A3+3xZtDEp1XlEKhwMzM+IfnRanVatRqOezi1WNq+uYMW5qWloZGo8HMzCxftis+Ph4rK6s8X+6r4EV++17GOfT222/z9ttvG02vXbs2Y8eOxc/Pj/79++unz5kzh9jYWFauXKlPNL29venZsydTpkwp8ERYvJ50Wi0pI1ajPBWE8rOW6E7eQXMoCO39GNISdRCfghotiVgTjx0qNNgQhRIFKhRU5C4KQBcHCSamuCSlcMPKwmAdxRMNBx3QAuGWQGIqOlM1G2s1QDU1DbRaLDRpVNQk0KiCKWPUN3EvaQUmarCxgHIu/y3kWig4WIOTbeYbFhoBKWlQulie7q83gUZ+J3JF+ui8orJqp759+3b69euHl5cXjRs3pnPnzowfP57IyEggvY16YGAg9+/fx9PTU/8KCAgAMu+jkzEtLi6OyZMn06pVKxo2bMjAgQO5cOGCUWxRUVFMnDiRFi1a0KRJE4YNG8aVK1fytK9NVFQUAwYMoFmzZpw6dQpIr6ny9PTE39+flStX0rlzZxo0aEDXrl3Zvn17psv5888/6dOnD40aNaJZs2aMGDGCs2fPGpQZOnSoUdy7d+/G09OT9957z2D6xo0b8fT01O+XJ/tWbdu2jZ49e9KgQQM6dOjA8uXLc739wcHBTJw4kfbt21O/fn3atm3L6NGjuXz5slHZ27dv88knn9C0aVOaNWvGmDFjCA8PNyoXFxfHrFmz8PHxoUGDBrRs2ZKvv/6ae/fuGZVNTU1l+fLl9O7dW7/v3n//fdatW/fMuDUaDf/3f/9H3bp1c7z9mfWvyJiWnW2Mjo5m2rRpdO7cmYYNG9KiRQv69u3LihUrchQH/NfX4eTJk3zwwQc0atSINm3aMHXqVBISEgzKZvRVunHjBtOnT6d9+/Y0bNiQ8+fPZ7ldAAcPHmTgwIE0btyYJk2aMHDgQA4ePJjlfrly5QojR46kWbNmRudlVvz8/Bg2bBgAEydO1P8ePBlPYmIic+bM0X+f2rRpw3fffcf9+/ezu7uA9O9Cp06dAFi0aJF+XU9/t/bv38+QIUPw8vKiUaNGdO3alV9//ZXU1PQha/O6j05OzqEXUbx4cSC9VitDYmIihw8fpk6dOga1aZaWlvj4+HD37l0uXryY43XduHGDjz/+mMaNG/POO+8wfvx4IiIiMi27YcMGRowYQbt27ahfvz5t2rTh22+/Nehbl5qaSsuWLRk4cGCmy1ixYgWenp4EBgYCkJyczIIFC+jatSuNGjXCy8uLd999l5kzZ+Z4W0TOpR0MQqvqg9n8LZgEXkT5/kyUc7ajOB9KQrgOdXwiakwBcyxIowgR2BGFGvS9N5781yo1hcV/rqJkYjJKnQ5TrZZKcQlgZ8sNV2dizM24UNyJ+U3e4hfvTmBpCmolKBTpL5WKRFMzzprbs/1cPG49JqGrNxZqfwHlR8Bbn8HRy1BzNFQYCS6D4LMl8OQIb8mp0GsauA2GMsPgne8gIhYhXpTc2n+N7NixgwkTJlCrVi2GDRuGmZkZDx484NixY0RERODg4MDnn3/OnDlziIqKYvTo0fp5y5Qp89zljxw5EgcHBz788EOio6NZvXo1n3zyCdu2bdPfPU5JSWH48OEEBQXRsWNHqlatyrVr1xgxYgS2tlncocmhkJAQPv74YxISEli4cKFRcxtfX1+Sk5Pp2rUrpqambNy4kQkTJuDm5kbNmjX15WbNmsWKFSuoWrUqw4cPJyEhgS1btjB06FCmTZtG48aNAahbty7z58/n3r17uLm5AeDv749SqeT69etERUVhb2+vn25lZUXlypUNYtq0aRMRERF06tQJGxsbdu3axezZs3F2dqZt27Y52v5Lly7x0UcfkZaWRufOnfHw8CAmJobAwEDOnTtnsO5Hjx4xdOhQvLy8GDVqFNeuXWPz5s3Ex8fj6+urLxcXF8fAgQMJCwujU6dOlC1blvDwcDZu3MgHH3zAypUrcXFJv+uWmprKyJEjOX36NPXr16ddu3aYmppy/fp1Dhw4wLvvvptp3ElJSXzzzTccO3ZMn6Tlhexu49ixYwkMDKRbt26UL1+e5ORkbt26xenTp+nXr1+O13vlyhX279+Pj48P3t7eBAQEsHbtWm7cuIGvry9KpeF9om+//RYzMzP69OmDQqHItK9Ghg0bNjBlyhRKly7Nhx9+CKTfxPjiiy/4+uuv6dq1q0H5Bw8e8NFHH9GyZUveeecdo2QrK7Vq1WLAgAEsXbqULl26UKtWLQB906q0tDRGjhzJuXPn9Inh3bt32bRpEydPnmTFihU4Oztna11lypRh9OjRTJ8+nebNm9O8eXMg/aI+g6+vL0uXLqVs2bL07t0bJycn7t27x99//82wYcMwMTFuKpMXsnsO5URycjKJiYn682zWrFkANGrUSF/m2rVrpKSkUL16daP5q1WrBqR/3zP+nx0hISEMHjyYlJQUevbsibOzM0eOHOHjjz/OtPyqVauoVq0a7777LnZ2dty4cYM///wTf39/1q5di729PSYmJnTo0IFVq1Zx+/ZtSpcubbCMbdu24e7uTu3atQGYMmUK27Ztw9vbmz59+qDRaAgODsbf3z/b2yFyT9t6Kqb8V9uiABSkAYkosAYMazaV/JdQpKAmCluKYZgY1w2+SY/rQVwtXlI/TaNSsrJBVUJsLNBpdZCcBhotZDQrfbqGQaHgTpFi/NCyGxP2bvxv+v/uQLdf4GF0+vs0DczYDl7VoPO/taNzd8O6Y//Nc+ACfLcW5gzOya4RwogkOq+RgwcPYmVlxbx58wyan2XcsQXw8vJizZo1JCcn5/hCs1KlSowdO1b/vmzZsowdO5bdu3fTrVs3ALZu3UpQUBAfffQRgwYN0pctV64cU6ZM0V8s59aVK1f45JNPsLa2ZsmSJZQoUcKoTEpKCitWrNBfFLVo0YLOnTuzfv16faJz+/ZtVq5cyVtvvcX8+fP1ZX18fOjRowdTpkyhQYMGqFQqfQ2Xv7+/PtEJCAigTZs27Nq1C39/f1q1aoVOp+P06dPUrl3bqP9AWFgYGzduxNo6vWNm586d6dChA+vWrctRoqPT6ZgwYYK+RqV8+fL6zwYMGIBWqzUoHxwcrK+Fy6BUKtmwYYPBBcv8+fMJCQlh6dKlVKhQQV+2Y8eO9OrViwULFuhHa1qzZg2nT59mwIABjBgxwmB9T68/Q3R0NJ999hnXr19nxowZ1K9fP9vb/DzZ2ca4uDj8/f3p3r07Y8aMyZP1Xr9+nalTp+Ll5QVAjx49mDp1KmvXrmXv3r20adPGoLy1tTVz5859btPQmJgYZs2ahZubG8uWLdOfM927d6dPnz7MmDGDVq1aYWNjo58nJCSE8ePH4+Pjk6NtcHNzo169eixdupQaNWoY/Sb4+flx7tw53n//fT755BP99Hr16vHpp58yZ84cfvzxx2yty9HRES8vL6ZPn065cuWM1nXhwgWWLl2Kp6cnM2fONGieltVFel7J7vckJ/78809+/fVX/fsSJUrw448/6pNJSE+wAIoVM26GkzEto0x2zZ07l5iYGObPn6//7erZsydffvklV69eNSq/du1aLCwMmyQ1bdqU4cOHs3XrVn0zuy5durBq1Sq2bt1qcC6cPXuW27dvGxyjgwcP0rBhQyZOnJij2EXeUKYmZjo9DXNMSQSybvp5k1KYkwwY1wDedjS8qXGhmA33ijzRRNZMBdHJYKI0TnKesKNSbcNEB/5Lcp506OJ/ic6hTGo2M5tWiGml5VquSNO114i1tTVJSUkcPXoUXSYP9XpRTw9ekPFHNDg4WD/tyJEjqFQqo6YzPj4++gu23Dp58iRDhw6lRIkSLF68ONMkB9IvOJ+881usWDHc3d0N4jx06BA6nY5+/foZlC1atCgdO3bk/v37+ouCatWqYWlpqb8bef/+fUJCQmjTpg0eHh76pnMZtTuZdbLu2LGjwfabm5tTvXp17t69m6N9cPXqVW7evEnHjh0NkpwMT9ciFC1a1ODiDYyPm06nY9euXdSqVYtixYoRFRWlf1lYWFCtWjVOnDihn3/37t3Y2trqaxqetX5I31+DBg0iJCSEhQsX5mmSA9nbxoy+MBcuXMiz4a5LlSqlT3IyfPDBBwCZNjHr3bt3tvq/nTx5ksTERHr16mVwzlhbW9OrVy8SEhI4efKkwTx2dnb5MgT7gQMHUCqVDBgwwGB648aNqVChAocPH84yuc2p3bt3A+k1x0/3wVEoFPnaTyU751BOeXl54evry9SpUxk8eDDW1tZERUUZlElKSgLItKYqoz9RRpns0Gq1HDlyhCpVqhj8DikUiixrLTOSHK1WS1xcHFFRUVSoUAFra2uDpsmlSpWidu3a7Ny5k7S0NP30rVu3olKp6NChg36atbU1N2/e5Pr169mO/WWLiIggOfm/Wo+4uDiDZoUpKSk8fvzYYJ6nm2s+/T4sLMzgb29BrUOjzDyRMSWeZKxQkvk5lYKaOKyJxJ7Up+5zXyruTrLJfzVBGgUE21saLkCpABMVpD37+qP+3WtG03RWxjEneRT9b32VjP/eJ5YtavD+VT0eL7oOkb+kRuc1MmDAAAIDA/niiy+ws7Ojdu3aNGrUiFatWuVJx2RXV1eD9xnNtaKj/7sTExISgpOTk0FzFEj/Q16iRAmDL3xORERE8Mknn1C2bFnmzZuHubl5tuOE9AvBsLAw/fuMi10PDw+jshnTQkJCqFKlCmq1mpo1a3L69GkATp06hUqlonbt2tStW5djx9Kr0zMSocyGhM0qpif3XXZkXHRld3SsrNYL/x23yMhIoqOjOXHiBC1btsx0OU8mMHfv3qVixYrZ7hA+evRo0tLSWLt2LSVLlnz+DDmUnW00MTFh9OjRTJs2Td80z9PTEy8vr0w7jmdHZs09nZycsLGxISQkxOgzd3f3bC03Y96yZcsafZYx7enlu7q65moUsucJDQ2laNGimTY79fDwICgoiKioKINRxHLr7t27KBSKTBP4/JadcyinnJ2d9c36vLy8eOedd+jXrx9JSUn6xDHjdyyj/9GTUlJSDMpkR0REBAkJCZQqVcros8zOJ0j/3Vq0aBEXL140uEADjH6vu3btyvjx4zl69CheXl7Ex8ezb98+mjRpgqOjo77c6NGj+f777+nVqxeurq54enrSpEkTmjZtmunNkILw9Dn79I04U1NTg20CjFokPP0+ox9WQa8jdeb7aD6ei4r/zqv0S3ETzIkhDVCTgBYLQEsy5piRiurfp6xoUHGFchTnIWakcLmEO3tr1EWh0xkMMZ3lc0FVWd+UsEuM5+edawwn2lig+LUffLkCYv+tjWpWFfNBrf9b5Bc+4HcaLv1748HFAYtfDW/AvKrH40XXIfKXJDqvEXd3dzZs2MCpU6fw9/cnMDCQSZMmsWDBAhYtWqRvdpVbWV1I5Uft0dNsbW2pVKkSR48eZdeuXXTp0iXLsln9IX2ROD09PTl+/Dg3btwgICCAqlWrYmlpiaenJ2vXriUsLAx/f3/s7e0zvVDLj4vQ7HjWRUXG/sj49+233zYYDSqvtGnThs2bN7N48WK+++67PL/Qyc42QnrTLy8vL44ePcrp06fZv38/69evp1WrVkyePDlPY8pMTi5YX6Vlv0z5XXOTleyeQy+ifPnyVKxYkY0bN+oTnaJF0+9IP3z40Kh8xrSMMvnh4sWLjBw5Ejc3N0aOHEmJEiUwMzNDoVDw9ddfG9XWvfPOO9jZ2bF161a8vLzYu3cviYmJRk0mvby82LZtG8eOHSMwMJBTp06xdetWatWqxdy5c/Otr5VIZzKyGRrvKiS1+Q11yD2oVBzNwyQIiUGl06EgGQ2mKEgBzNCi5TGOqNFgQzwx2JCMGXcoSaKlKceqVUYFlIqM5p6dLWkqJQ8tMxnBUKtLT3KUT3yH//3+KNM0WKSm0irqNre7NaeqixpsLKG8C3T0BDsr6NkI9pyFYnbp/XOe/C1wsoVz09M/T0qFtrUgkyGvhcgpSXReM6ampjRu3Fjfkf7o0aN8+umnrF69mq+++gogXy8kSpQowalTp0hISDCo1UlLSyM0NNSgX0FOqNVqfv31V8aNG8f//d//kZaWRo8ePXIdZ8Yd3Bs3bhglgDdv3jQoA//V0vj7++Pv70/nzp2B9ARIpVJx4sQJzpw5Q7169fJ1/2bUCgQFBeXZMh0cHLCxsSE+Pj5bz+woVaoUt2/fJiUlJVvD9X7wwQe4ubkxa9YsNBoNEyZMKLDEz8nJCR8fH3x8fNBoNHz33Xf89ddf9O3bl6pVq+ZoWbdu3TKaFh4eTmxsbKY1BNmVcT7evHnTqLYpY50vsvynPet8dXV15Z9//iE2Ntbou3vz5k2srKz0Nbsvuq5SpUpx/PhxgoKCctT5/nWSnJxsUENUrlw5TE1N9SPwPSmj2ViVKlWyvXwHBwcsLS25c+eO0WcZv2tP2r17NxqNhlmzZhmcU4mJiZnWvpuamuLt7c26det49OgRW7dupVixYjRo0MCorJ2dHe3bt6d9+/bodDpmz57NihUrOHToUJY1xyLvqMoURRX0f/r3mV3M6VJS0R26iml0EmaxGiIH7qA4YdgSTQy2qIBz1aqj+zdxKZKYhH1iEpeK2fM/l6KZ9MPRpffTAcwTkykVEcbpn0thZaYGTABz4K1/X5lwsIZ3G2e9UWoVtK+TvR1QCGl4+TeJ3gSvRh2zyJan239D+gACYNj8wtLSkpiYmHypiWnSpAkajYY//vjDYPqWLVuIi4t7oWWr1WomT55MixYtmDJlitE6cqJp06YoFApWrlxp0N48PDwcPz8/XFxcDJqHVaxYEVtbWzZt2kR4eLg+8bG2tqZixYqsWbOGuLi4fH+SeYUKFShbtizbtm3jxo0bRp/n5pgqlUratm3LxYsX2bdvX6Zlnhyatm3btsTExLB48eJsr79fv36MHj2aXbt2MX78eIN9/jIkJSUZ9XVQqVT62reYmJjMZnumO3fuGPXFyRgyu1mzZrkLlPSO/hYWFqxbt474+Hj99Pj4eNatW4elpWWe9nPKuCGRWRMtLy8vtFoty5YtM5h+7Ngxrl69muOmSBn9QTLb3xmDN8ydOzfTplwvo+Y4L2Q1JHVAQAA3btwwGGHN0tKSJk2acPr0aYObFwkJCfz555+4u7vnKAFXqVQ0btyYS5cu6R8ZAOn7LrNh1DNuODy9b5csWZJl36suXbrok6Pz58/ToUMHgxsXGo3GKElSKBT639PcNgUUeU9haoKyVTVU3T2xGlAPN90POOumYNPSidLcwYp4yjx4YDCPUquj0o17qDM7P5RKUCpQ6HQ0vXKRZV2t/01yhHh1yRn6GhkxYgQ2NjbUqlULZ2dnYmNj8fPzQ6FQGIxwVK1aNY4cOcIvv/xCjRo1UCqV1K1bN0/a2fv4+LB582bmzZvHvXv39MNL79u3j5IlS6LRaF5o+Wq1mp9++gm1Ws20adPQaDT07ds3x8spXbo077//PitWrGDw4MG0atVKP7x0QkICP/74o8Efb6VSSe3atTl48CBmZmbUqFFD/9mTz4TJ7dPes0uhUPD9998zfPhw+vfvrx9eOjY2lsDAQBo0aECvXr1yvNwRI0Zw7tw5xo0bx/79+6levTomJibcv3+fY8eOUblyZf2oa++99x5Hjhxh8eLFXLp0iXr16mFmZsbNmze5c+cOc+fOzXQdvXv3xsTEhF9++YW0tDQmT5780h5Oe+fOHYYMGULz5s3x8PDAxsaG27dvs3HjRlxdXQ1GwsqucuXK8e233+Lj44O7uzsBAQHs37+f2rVr07p16+cvIAs2NjaMGjWKKVOm8MEHH+g7eW/fvp3g4GC+/vrrFx7Y40llypTBysqKjRs3Ym5ujo2NDUWKFKFu3bp07NiR7du3s3z5ckJDQ6lduzbBwcFs3LgRR0dHo1H3nsfe3p6SJUuyZ88e3NzcKFKkCBYWFjRt2pRq1arRv39/li9fTp8+fWjdujWOjo6Ehoayf/9+li9fnusa4Zfp559/1t8MKV68OCkpKVy+fJk9e/ZgaWnJp59+alB+5MiR+Pv7M3LkSHr37o2VlRVbtmzh0aNHzJgxI8c1xMOHD+f48eN8+umnvPvuuxQrVowjR47on6X2pIxROD/55BO6dOmCiYkJJ0+e5Pr161nW1JUpU4aaNWuya9cuFAqF/tlIGRISEmjbti1NmzalYsWKODg4EBoaysaNG7G1taVp06Y52h7x8jntHUlEzZ+wPxdG+TtK0hRKrpQqSbyZGRFKBbXCIih/P4LzpZ4aWl6rpWrwI+oH3eNgfQ/qN8l6CH0hXhWS6LxGunfvzt69e9m8eTPR0dHY2dlRsWJFxowZY3AB3qdPH0JCQti/fz+bNm1Cq9Uyf/78PEl0TE1NmTdvHjNnzuTQoUPs3buXatWqMXfuXCZNmpSjEYSyolKp+OGHH1Cr1cyYMYPU1FSjUaGyY9SoUZQsWZINGzYwZ84cTExMqFq1KpMmTcr0wtfT05ODBw9So0YNgyZbGYlOsWLFcjUMbU5VrVqV5cuXs3jxYvbt28emTZuwt7enatWqBs8JyomM4bpXrVrF3r17OXz4MCqVimLFilGzZk2DNvgmJibMmTOHVatW8ddffzF37lxMTU1xd3d/7shfPXr0QK1W83//93+MGTOGKVOmvJT2+s7OznTq1InTp09z8OBBUlNTKVq0KF26dKF///656uNSqVIlPvvsM+bOncvmzZuxsrKiZ8+ejBgx4oX7IfXo0QMnJydWrlzJokWLgPTavCeHs84r5ubm/PTTT8ybN4/p06eTkpKiH2hDrVYzZ84cFi9ezN69ezlw4AA2Nja0aNGC4cOHG3XEzY4ff/yR6dOn4+vrS1JSEi4uLvqL348//pjy5cuzfv16VqxYgVarxdnZmUaNGr02/ZDatGnDjh072LlzJ5GRkSgUCooXL07Xrl3p16+f0T4rWbIkixcvZvbs2SxbtozU1FQqVarErFmzstWU9Glubm78/vvv/Pbbb6xbtw5TU1MaNmzIDz/8YJSA16xZk19++YXff/+d+fPnY2Zmxttvv83ChQsZPDjr55N06dKFs2fP4unpadT019zcnPfee49Tp07pmzE7OTnRtGlTBgwYkK99jkTeKXL2G9BqCVd9Q1qMjmNlS6FVKql7MX0kvd5HzzPH2oIQR1uUWi1apRKUSpTAA3MzAsc5PnsFIs9ppOVarih0r0t7AfFK02g0tGzZkmrVqjF79uyCDkeIF+Lp6UmHDh30tVxCFCZ79+5l3LhxTJo0KccPPBavl7Bhm5lyoyTX3YtTJCGRMsFhlAz7r3nmfXtrrhV3YIVX+s1BkzQNqclp6L5/8ZFeRc40GZb9YamPzJeR3TJIjY7IsaSkJKO7r5s2bSI2NjZXdyiFEEK8OjZs2IC9vT3vvPNOQYci8lnx+V1J6Pg/ykREYZOcwrHKZbjWqAp3newoGR5Nl1NX+KvmfyONarTQt5p07xavD0l0RI799NNPJCcn65t4nT9/nt27d1OyZEn9sNBxcXHPbcZmYmKif5bFm66w7o/IyMjn9tuytLQ0ei7Tmx7Li9JoNJn2yXianZ1dnjQdzKoD/pOsra3zpfnZyzhuqamp2epE7+DgkC8jCiYlJWVrMBcnp/zrExEREcGpU6c4e/YsgYGBjBw5MlujLorXn0fII+Kc7Xhkac6xssVINEk/x+8WtWdum7fRZjSZ0ulQpqSyslve9SEUIr9JoiNyrF69emzYsIHFixeTkJCAo6MjPj4+DBs2TP/g0qlTp7J9+/ZnLqd27dosXLjwZYRc4Arr/ujXr99znwI9ePBghg4dWqhieVEPHjww6iSemfnz5+fJABrZab70/fffP7cPV268jON27tw5hg0b9txy27Zto0QJ4ye4v6i9e/cyceLE55Z7cqS1vHbz5k3Gjx+PjY0N3bp1y9UgMOL1VDI8gsvOdjyyMtcnORlS1Uo8HsVw38aCBLUS84QkQBKdgqAtgGeQvQmkj47IFzdv3uTRo0fPLGNra0vlypVfUkQFq7Duj7Nnzxo9jf1prq6uL/yw29ctlheVnJzM2bNnn1uucuXK2NravvD6Tp48+dwyHh4e+VLj8DKOW0xMDJcvX35uuZo1a2JmlvcPMQwPD890OPmnSdNgkR++aHUCExM1sWam+DasbPT8nHKPYokzVfPQypRJdbWMa2FRQJEWbo0+Cst22WPzcj6QzJtKEh0hhBBCiEJq1vggfBPtqPUgmlMli3HL8b8am+IxSVilpnHLwQqXB4+4Ny3vazRF9kiikzvSo0wIIYQQopAaNt4DtUbHupplibIwpfa9SDzC46gdEkmF8Fju2luiVUDRx4kFHWqhplEosv0S/5E+OkIIIYQQhZSpuQqv8EfcKmpHpKUpCWolLnHJhFmbE+ZijlapAJ2OXl1f/Fl8QrxskugIIYQQQhRis5ZXw7LDSU4Wc+SfMq7cdrA07Kuj1fFVJ4eCC1CIXJJERwghhBCiEFOpFPy6qz4XdwUzenUIwY52PHCwRKtUYmep4+aYV3/Y/TddWkEH8JqSREcIIYQQQlChZXG6BS+FZHhvQH9srPN+lEEhXiYZjEAIIYQQQvzHDMzN5BJRvP7kLBZCCCGEEEK8caTpmhBCCCGEEK8wGTY6d6RGRwghhBBCCPHGkURHCCGEEEII8caRpmtCCCGEEIWYRqvDdLoGLQADcOERAwo4JmEoTVqu5YrU6AghhBBCFGJqfZKjABTcpyhFZhZsTELkBUl0hBBCCCEKqdjkzB5FqSBOB8vPy2MqxetNEh0hhBBCiEJq/JEsPlAo+OCvlxqKeIY0FNl+if9IoiOEEEIIUUitvPTsz38/K7U64vUliY4QQgghRCEVmfLsz3feejlxCJEfJNERQgghhBDGdDqsVQUdhBC5J8NLCyGEEEIUVjodKLLu15GsfYmxiCylStebXJEaHSGEEEIIkSlVUmpBhyBErkmiI4QQQghRCKVonlNdo1Cw52LsywlGiHwgTdeEEEIIIQqh1uu1/zVby6QJm01CPCZaTQFEJp6W+ozmhSJrUqMjhBAiW/z8/PD09CQgIKCgQylQsh/Em+JQSPq/Cp1hwtPuciAqTRrV79/l7Xsy7Jp4fUmiI4QQQghRiOkUT1wOKhSYadKYvGMNxz0qE+hausDiEuJFSaIjhBBC5ED79u05duwYtWvXLuhQhMg1rTa9f45Ca9xPJ97UjM+P7KDjxQCSTcyYtiPiZYcnRJ6QPjpCCCFEDqhUKlSqgnu4SFJSEmq1GrX65fwJT0tLQ6PRYGZm9lLWJ16Oaou1oNOhUxrf8253+QxKdEzZuZoqX0zn3OI9sOAsOFrDVn9Q6KC8K4RGgGsRiEsBlRIGNYO4ZDh4GRpXhOqlwcIUHkZDjVJgbgaWpqBSgKMtqP/9HqVpwMz0pW7/60bGvssdSXSEEELkiE6nY+XKlWzcuJGHDx/i4uLCwIED6dChg77Mnj172LVrF0FBQURERGBpaUnNmjUZNmwY5cuXN1jeuXPnWLx4MVevXiU2NhY7OzvKly/P4MGDqV69erZiun//Ph07dmTw4MEMHTpUP33kyJGcOHGCzz77jD59+uin9+/fn/j4eDZu3AjA7du3Wbt2LYGBgYSFhaHRaChTpgzdu3fHx8fHYF1+fn5MnDiR+fPn4+npybFjx/jkk0/44osv6NWrl1FsAwYMIDg4mN27d+uTk7t377Jo0SJOnTpFdHQ0RYsWpWXLlgwZMgQLCwv9vBMmTGD79u3s3buXWbNmcezYMSIjI9m6dSslSpTI1r7x9PSkQ4cOtGvXjnnz5nHt2jWsra1p1aoVw4cPx9LSUl92wYIFLFq0iHXr1rF161b27dtHeHg4c+fOxdPTk6ioKBYsWMDhw4d5/Pgxjo6ONG3alKFDh2Jvb5+teEQ+uRQMny6Bk9fSE4joeMhkVDUdoFEo+LliLToP/CrTRa2q3YTrRV0YfWgbPf73D1/8sxdd2F0MusOfvJ7+b/ATtT2jbv/3/z3nXnSLnq10MVAq4NaD9I2yNIVxXWF8z/xdr3itSKIjhBAiR3x9fUlOTqZr166YmpqyceNGJkyYgJubGzVr1gRg/fr12NnZ0aVLF5ycnLh37x5btmxh0KBBrFq1Cnd3dyA9wRgxYgSOjo706tWLIkWKEBERwdmzZwkKCsp2ouPi4oKrqyv+/v76RCc1NZWzZ8+iVCoJCAjQJzpxcXFcuXKFrl276ucPCAggMDCQxo0bU6JECZKSkti3bx+TJk0iMjKSAQMGZLnu+vXr4+joyI4dO4wSnbt373L+/Hl69eqlT3IuX77MsGHDsLGxoWvXrhQrVoygoCDWrl3LuXPnWLhwoVFtTcY+GjRoEImJiQbJSXZcuXKF/fv34+Pjg7e3NwEBAaxdu5YbN27g6+uL8qm7+t9++y1mZmb06dMHhUKBk5MTcXFxDBw4kODgYDp16kSlSpW4evUqGzduxN/fn+XLl2NlZZWjuEQeSdNAux/hbvhziyr+ffXq+2mWDwoNLOlBYEkP1tZowJ3JI7BOSc7TcPPE7YeG7xNS4Nu14OwAg1sVTEzilSOJjhBCiBxJSUlhxYoVmJiYANCiRQs6d+7M+vXr9YnO7NmzDWomALy9venduzdr1qxh7NixAJw4cYKkpCR++uknqlWr9kJx1a1bl+3bt5OUlIS5uTnnz58nKSmJdu3acfjwYdLS0lCr1QQGBqLRaPD09DSIrXv37gbL6927N8OGDWPZsmW8//77WTYVU6lUtG/fnpUrV3Lz5k3Kli2r/2zHjh0ABrVdP/zwA05OTqxYscIgMXj77bf58ssv2bVrFx07djRYh4eHBz/++GOu983169eZOnUqXl5eAPTo0YOpU6eydu1a9u7dS5s2bQzKW1tbM3fuXINt9vX15e7du3z11Vf06NFDP71ChQr88ssvrFixgo8++ijXMYoXcCIoW0lOhj+repJoZm78wVNDTEdY29J+0NccmD8BlU6XF5Hmv/XH3shEJ0GGl84VGYxACCFEjvTo0UOf5AAUK1YMd3d3goOD9dMykhydTkdcXBxRUVE4ODhQqlQpLly4oC9nbW0NwKFDh0hOfrG7xp6enqSlpXHmzBkA/P39KVKkCO+99x7x8fFcunQJSK+9USgUBonOk0lZcnIyUVFRxMTEUL9+feLj47l9+/Yz1+3t7Q38l9hkbPuuXbvw8PCgUqVKQHrCce3aNdq2bUtqaipRUVH6V82aNbGwsODEiRNGy+/bt2/udsq/SpUqpU9yMnzwwQcAHDx40Kh87969jRK7gwcP4uDgQJcuXQymd+3aFQcHBw4cOPBCMealiIgIg/MpLi6O2Nj/HnyZkpLC48ePDea5f//+M9+HhYWhe+Ji/5Vah5MNORHklEWzx0wupo+Urczm6vVytPwC5Zi+L16XYy7yl9ToCCGEyBFXV1ejaXZ2doSFhenfX7lyhfnz53P69GkSExOznL9169bs3LmTpUuXsmbNGqpXr079+vVp06YNLi4uOYqrbt26QHqC06BBAwICAqhTpw6VKlXC1tYWf39/atSoQUBAAOXLl8fOzk4/b0JCAgsXLmTv3r08ePDAaNkxMTHPXHe5cuWoVKkSu3fvZsSIESiVSgIDAwkNDWXUqFH6crdupT+TZMGCBSxYsCDTZUVEGI9wVapUqefvgGcoU6aM0TQnJydsbGwICQkx+iyjaeGTQkNDqVy5slECpFarcXd358qVKy8UY14qUqSIwfuMhDqDqakpjo6OBtOePt+efl+8ePFXdx2OQPcGsPEfsmPwqf183b53lk3XnnaqZDl6/M84AX/lmKrhi87A63PMRf6SREcIIUSOPN2fI0PGnc+wsDCGDBmClZUVgwYNonTp0pibm6NQKJg2bZpB4mNqasrcuXO5cOECJ06cIDAwUN8hftKkSTRv3jzbcTk6OlK2bFkCAgJISkriwoULfPnllyiVSmrXro2/vz/dunXj2rVr9O7d22Deb775hqNHj9KlSxdq166NnZ0dSqWSY8eOsWbNGv1QvM/i7e3NtGnT8Pf3p169euzYsUPfrO3pfdS3b18aNGiQ6XJsbW2NppmbZ9LMKB+97PWJPPDHaOhwGE79OxjB/27DnUcQFgmJhmN22SYm4BV0noMVqmcr2SnzOAwdUCCNpxSAtTkkp6YPrqBQQOmiMKxt+ufLDkBsIjSpDON7QEXjGzFvgkRpuZYrkugIIYTIUwcOHCAhIYHp06cbNA8DiI6OxtTUeBjZatWq6fvohIWF0adPH+bNm5ejRAfSm69t3LiRw4cPk5qayttvvw2k1/bMnDmT48ePo9Pp9LU/ALGxsRw9epT27dvz9ddfGyzv1KlT2V5327ZtmTlzJjt27OCtt95i//791KtXDycnJ32ZjJoSpVJJvXovrzlQRk3Sk8LDw4mNjc20hi4zrq6u3LlzR9/XKUNaWhp3797N9nJEPlGroH/z9NdzmAJDzqdxcHf2+t2M7PQBw/8Z/oIB5pPPOxd0BOIVJn10hBBC5KmMGh/dU52Xt2zZYtR+PSoqymh+Z2dnHBwciI6OzvG669ati1arZdGiRRQvXhw3Nzf99JSUFJYtW4ZKpaJWrVrPjTc8PJw///wz2+t2cHCgYcOGHDhwgN27dxMfH6/vu5OhYsWKeHh4sGnTJu7du2e0jLS0tFxt9/PcuXPHqC/O8uXLAWjWrFm2ltGsWTMiIyON9smff/5JZGRkjpNSUbDeq67OXtM1nY4vGsgzbsTrSWp0hBBC5KlGjRoxe/ZsvvvuO3r27ImNjQ3nzp3j+PHjuLm5odFo9GUXL17MiRMnaNy4Ma6uruh0Oo4cOcLt27fp169fjtddp04dlEolt27dMhi5rGzZsjg6OnLz5k2qV69uMNqZlZUV9evXZ9euXZiZmVG1alXu37/P5s2bcXV1zVHi0aFDBw4fPsxvv/2GtbW10QAACoWCH374gY8++oj33nuPTp06UbZsWZKSkrh37x5///03I0eONBp17UWVK1eOb7/9Fh8fH9zd3QkICGD//v3Url2b1q1bZ2sZ/fv3Z//+/fzyyy9cvXqVihUrcvXqVbZu3UqpUqVydbzE6+EXL7lcFK8nOXOFEELkKTc3N2bNmoWvry9Lly5FqVTy1ltvsWDBAn755ReDUYeaNWtGeHg4+/btIyIiAjMzM0qWLMn48ePp3DnnTVJsbW2pUKECV65cMWo2V7duXXbv3m00HeDHH39k9uzZHDlyhB07dlCyZEmGDx+OWq1m4sSJ2V5/kyZNsLOzIzo6Gh8fH8zMzIzKVKxYkdWrV7N06VIOHz7Mpk2bsLKywsXFhY4dOxo0q8srlSpV4rPPPmPu3Lls3rwZKysrevbsqR84ITusra1ZvHix/oGh27Ztw9HRkW7dujF06FB5hs5ryEoF8ZrnlxMFL6Vgeki99hS6p+vqhRBCCPHG8PT0pEOHDkyYMKGgQxGvmG3XNHTe+pzLQJ0O3Zcmzy4j8p3iU+PRGLOim1Hk+YUKCemjI4QQQghRCHUqr0p/SOizyIMqxWtMmq4JIYR4ZaWmpmarj4yDgwMqleolRPRqiIyMNOjrlBlLS0ssLS1fUkTitfWcRObr2i8pDvFskm/miiQ6QgghXlnnzp1j2LBhzy23bds2SpTI4mnvb6B+/fo99wnrgwcPZujQoS8pIvGmup73gwAK8dJIoiOEEOKVVaFCBXx9fZ9b7umnkb/pfvzxR5KTk59ZJuO5NgEBAS8jJPGGKiaVguI1JomOEEKIV5atre1LfbDm66JmzZoFHYIoJIqYF3QEQuSeDEYghBBCCFFIdSn77M+/kPsMrwaFIvsvoSeJjhBCCCFEIWVl+uzPbcyl8Y94fUmiI4QQQghRSM1pmVUNgI7kT6V2QLzeJNERQgghhCik7MxVWBuNzK5jnCeYqgvPkO3izSSJjhBCCCFEIRb7mZq/uoGVSocNMfxqtZqJjQo6KiFenCQ6QgghhBCFXOsyaiJHwlTbjdiqUgo6HCHyhPQwE0IIIYQQ4lUmo6nlitToCCGEEEIIId44kugIIYQQQggh3jiS6AghhBBCCCHeOJLoCCGEEEIUctNOpuHkC1NiWpOklS7crxxFDl5CT85kIYQQQohCTPVrCloUoFAQo3Plk5i+dEsAV7uCjkyIFyM1OkIIIYQQhdS0E6n6JAdI/1ehwH2RpmADEyIPSI2OEEIIIUQhNfagBtRPXQ4qFOhSdAUTkMiCtEnLDanREUIIIYQopNKyuID2eBT1cgMRIh9IoiOEEEIIUUgpsngQZaSVxUuORIi8J4mOEEIIIUQhpdBqjaaZpWqIsTDj0N6IAohIZEpGXcsVSXSEEEIIIQqhsNg0ykfEUfN+JEptep8cp/gk2l4LRatU8sXmmAKOUIgXI4MRCCGEEEIUQjO2xlEsIZnaYVHUvfeYZLUSh6RUANqev0m4ox3bWm2n4x7vLJu4CfEqk0RHCCGEEKIQOn4zjTSdjjt2lhwuXYxIC1PcohOo/iCKtwODeOv+/1AnJHLYYhHNkoYUdLhC5Jg0XRNCCCGEKISOmNmgUCjYWsmNCAtTKoTHUP5xLKE2FtQPCsb9/mOKxcVhlZLGUbP53Ps7tKBDLrykj06uSKIjRAEIDQ3F09OTBQsWFHQoeWLDhg1069aNBg0a4OnpSWjo6/XHMCAgAE9PT/z8/PTT8vMYLViw4LXcTy+bn58fnp6eBAQEFHQor4UhQ4bQsWPHgg5DvGZMdKBTKnjn5gPaXg+j+sNoGgaHs6JjI3SAUqPETJeERYqWBy3+5OY/Dws6ZCGyTZquCSFeSEBAAFOmTKFZs2b0798ftVqNg4NDQYclhBDiOcxTNNikpOIaHU+1h9Ekq5Ts8yjOjSLWqLU6gq3a89vvO1CTSCrmANxvtpmyKcMKOHIhskcSHSHECzl58iQA3333HXZ2dgUcTd5xcXHh2LFjqFSqgg6l0Grfvj2tW7fGxMSkoEN5Lfj6+qLTydPsRfYpdFqSVEq6X7oHwDH3olx3tAEgVaXgSEU39tYoS+v/3dLPY56qIepkGPb1ihdIzIWXtEnLDWm6JkQhkJaWRnJycr4sOzw8HOCNSnIg/SF6ZmZmqNWF536QTqcjISGhoMPQU6lUmJmZoVQWvj9V8fHxOZ7HxMQEU1PTfIhGvKmsU9Oo+DhO//62g5VRmQuVLTAhSf9egYLb9deQlpjyUmIU4kUUnr/gotDx8/Nj4sSJzJs3jytXrrBx40YePnyIi4sLAwcOpEOHDkB6X4xOnToxePBghg4darCMBQsWsGjRIrZt20aJEiUAmDBhAtu3b2ffvn3MmDGDI0eOkJqaSt26dRk3bhxOTk5s3ryZNWvWEBoaiouLCx9//DFeXl6Zxrl7926WLVvG3bt3cXBwoFOnTgwaNMjoAjs8PJxFixZx9OhRHj9+jL29PU2aNOGjjz6iSJEiRjGvW7eOrVu3sm/fPsLDw5k7dy6enp7Z3n8HDx5kxYoVBAUFoVAoKF++PP369dNvR8Z+y5Cx7Nq1a7Nw4cJsrePRo0esWrUKf39/7t+/T3JyMq6urnh7e/P+++8b1KZkHE9fX1/Onj2Ln58fjx8/plSpUgwYMIA2bdoYLLtjx464uLgwevRoZsyYwcWLFzExMaFJkyZ88sknBvssM886L/bs2cO6deu4du0aGo2GcuXK8f7779OyZUuDclqtluXLl7NlyxbCw8Nxc3NjwIAB2do3mYmOjub333/n8OHDPHr0CAsLC1xcXGjdujX9+vUzKLt//37WrVtHUFAQqampODs706BBAz799FNMTEwICAhg2LBhfP/99yQmJrJhwwbu3bvHBx98oN/e7G4npNfsrVixgosXL5KSkoK7uzvdu3ene/fuBuUyjsvXX3/Nb7/9xpkzZ1AoFNSrV48xY8bg5OSkL5txzOfPn68/v7L7vc6g0WhYunQpf/75JxEREbi7uzNw4EBu3bpl9N3O62OQ3f3n6elJhw4daN++PQsWLCAoKIjKlSvTunVrfv75Z6ZNm0azZs0M5tFqtXTo0AF7e3vWrFkDpPfRuX//vkFfM4Dg4GCWLFnCyZMniYiIwN7enipVqjB48GAqV66sL3fp0iWWLFnCmTNnSEhIwMXFBW9vb32T1Aw3btxg4cKF/O9//yMqKgpbW1tKly7N+++/T+PGjbO9L8VLkJQCM7fDoUtQtSR82Zm7fkHEfL2JtTXb0t7eDLXOjjt2loRZm2OZkkasmWENavmIUEpylZu89cRUJY8sv8SOCJRoMCOJRKyIwhlbdRRmpawwmdMf2tZ+udsrxFMk0RFvPF9fX5KTk+natSumpqZs3LiRCRMm4ObmRs2aNXO93FGjRlGsWDGGDRtGcHAw69at48svv6R58+Zs2bKFzp07Y2pqyrp16/jqq6/YvHkzrq6uBss4fPgwISEh9OjRA0dHRw4fPsyiRYsICwvj+++/15cLCwtjwIABpKam0rlzZ9zc3AgODmbTpk0EBASwcuVKrK2tDZb97bffYmZmRp8+fVAoFAYXkM+zYcMGpkyZQunSpfnwww8B2L59O1988QVff/01Xbt2xcHBgR9++IEtW7Zw5swZfvjhB4DnJhBPunbtGgcOHMDLyws3NzfS0tL4559/mDNnDiEhIXzzzTdG88yePZvExET9BbSfnx/ffPMNKSkpRh2xHz58yEcffcQ777xDixYtuHLlCtu2bePy5cusWLECc3PzbMeaYe7cuSxZsoSGDRsybNgwlEolBw4cYOzYsYwZM4aePXvqy/7222/88ccf1K5dm969exMREcGUKVOMzoPsGjt2LIGBgXTr1o3y5cuTnJzMrVu3OH36tMFFtq+vL0uXLqVs2bL07t0bJycn7t27x99//82wYcMMmoL98ccfREdH4+Pjg6OjI87Ozjnezs2bNzN58mSqV6/OwIEDsbCw4OTJk/z888+EhITwySefGGzHo0ePGDp0KF5eXowaNYpr166xefNm4uPj8fX1zda+yO73+pdffmHTpk14enrSt29foqKimDJlSo6Smydl9xjkZP9BepLx999/4+Pjo0/WWrRowfTp09mxY4dRonPq1CkePnxInz59nhnvpUuX+Oijj0hLS6Nz5854eHgQExNDYGAg586d0yc6R48e5csvv6RkyZL07dsXW1tbzp8/r0+8pkyZAkBUVBQfffQRAN26daN48eJERUVx+fJlLly4IInOq+b9mbDxn/T/7wokbcMJzMNT2VC3G9/un8/bI3/ERGHHpWJ2OCSmEK9WYpaaSvK/vxElH0cy9Pg+rDCsYdRiSgSupGBJKa6SjBmWxGPBTRRpwI0IaDcJtnwFPvVe8kYL8R9JdMQbLyUlhRUrVugv7lq0aEHnzp1Zv379CyU6VatW5auvvjKYtmbNGh4+fMi6dev0iUfdunV577332LJlCyNHjjQof+3aNVasWEGlSpUAePfdd/nyyy/x8/Oja9euVK9eHUi/WEtLS2P16tX6C1GAli1bMmDAAFavXm1U62Btbc3cuXNz3PQqJiaGWbNm4ebmxrJly/Tb0b17d/r06cOMGTNo1aoVNjY2tG/fnlOnTnHmzBnat2+fo/VAeu3P1q1bDR5E17t3b7799lu2bt3K0KFDjRK0qKgo1q5daxBXr169+O2332jVqpVB8nLv3j1Gjx5N79699dPKli3Lb7/9xtq1a/nggw9yFO+VK1dYsmQJAwYMYMSIEfrpvXr14vPPP8fX1xdvb2+srKy4ffs2a9eupW7dusyZM0dfO/XOO+/w/vvv52i9AHFxcfj7+9O9e3fGjBmTZbkLFy6wdOlSPD09mTlzJmZmZvrPPv74Y6PyYWFhbNy40SBBzcl2hoeHM3XqVFq3bs1PP/2kL9ujRw+mTp3K6tWr6datG25ubvrPgoODmTx5Mq1atdJPUyqVbNiwgdu3b1O6dOnn7o/sfK9v3LjBpk2baNCgATNnztQ3gWvZsqXBOZFd2T0GOdl/GW7evImvry/16hleFDZp0oQjR44QExODra2tfvqOHTtQqVS0a9cuyzh0Oh0TJkwgNTWV5cuXU758ef1nAwYMQKvVApCcnMyPP/5ItWrVmDdvnv43IyOZ++233/QjE547d46IiAij4ydeQaERsOmEwST1nQdcKteAd64dR6XT8D/X0tS8H8XA0zdR63TodDpqXLzF5WJ2WCel0PJ/N9GmFCEpk/4hyViRhppSXEWDCZBsXGr2Tkl08op00cmVwtfwWRQ6PXr0MLiDXaxYMdzd3QkODn6h5b733nsG72vVqgWAt7e3Qe1K+fLlsbKy4u7du0bLqFevnj7JgfR+IRl3hQ8cOACkX1wdPXqUpk2bYmZmRlRUlP5VokQJ3Nzc9AMCPKl379656l9y8uRJEhMT6dWrl8F2WFtb06tXLxISEjJdX26Ym5vrk5zU1FSio6OJioqiQYMGaLVaLl26ZDRP9+7djeLq1q0bMTExnD592qCslZUVPXr0MJjWo0cPrKys9Ps3J3bt2oVCocDb29vgOERFRdG0aVPi4+M5f/48AIcOHUKn09GnTx+DJniVKlUyupjNDjMzM0xNTblw4cIzh6XevXs3ACNHjjRIciD9/Hr66ebe3t5GtXA52c59+/aRkpJC586djco2adIErVbLqVOnDJZftGhRo4vkjKZp2f1eZud7feTIESA9wXiyn0+5cuWoX79+ttbzpOweg5zsvwwVKlTI9Lzo0KEDKSkp7NmzRz8tISGBgwcP0rBhw2fWoF69epWbN2/SsWNHgyQnQ8Y+OXnyJI8fP6Zjx47ExcUZxNuoUSN9GUD/3Tt+/DhxcXFGy3xVREREGPRLjIuLIzY2Vv8+JSWFx48fG8xz//79Z74PCwszGOzhlV9HmgYyGZxCo1BhkxzPtiqeWKdoaHLnEep/yykUCq5UdKfHP5foFBCEZUoaMThyD+PzB3RoMP33f1lchaemvfh2ZPH+tTseWbwX+UtqdMQbL7NmQnZ2doSFheXpcm1s0keqyaxJjK2tLdHR0UbTM7tzXbZsWQBCQkIAuH37Nlqtlq1bt7J169ZsxQLg7u7+7A3IQsZ6M+J4VmwvKi0tjWXLlrFz506Cg4ONRoyKiYkxmiezfVamTJlM43J1dTUascvU1BRXV9dcbcOtW7fQ6XRG/U6elPFHLmP5WcV74sQJo+nPYmJiwujRo5k2bRqdOnWibNmyeHp64uXlxdtvv60vd/fuXX2fquzI7DzJyXbevn0bgOHDh2dZNiIiwuB9Vt9JINPvSWay873OSEZKlSplVLZUqVIcP348W+vKkN1jkJP9lyGr72uDBg0oUqQIO3fu1C/v77//JjExEW9v72fGm5H0VaxY8Znlbt1KH1Ero/nps+KtU6cO3t7e+Pn5sWvXLqpUqUK9evVo1apVpr8ZBeXpBPDppr2mpqY4OjoaTHNxcXnm++LFDUcZe+XX4V4UWtaAff/Tf6Ypake5Rzc5XbIGD6w1FItPMrrjnWqiZlGbugzYexqL1DQSsCGz++I2PAYU6FCgRGP0OQAftnzx7cji/Wt3PLJ4L/KXJDrijZfViE26J+5gZUWjyeLHG7Icdjir6S867Gu7du2MOlpnePrOPZCr/icv22+//ca6deto1aoVAwcOxMHBAbVazZUrV5g9e/YrOVSuQqFg1qxZWZ5XHh4e+bbu7t274+XlxdGjRzl9+jT79+9n/fr1tGrVismTJxvE+Kzz+klZnSfZ3c6MYzRx4sQs+4E9nZQ8axS17B7z532v80tOjkFOzpOsjoNaraZNmzb88ccfBAcHU7JkSXbs2IGtrS1NmzbNk23K2GeffPIJFSpUyLRM0aJF9f+fOHEi77//PsePH+fMmTOsWrWKJUuWMHr0aN599908iUnkkY1fwoR1cOgiVHVHNeFdzHdexnH6CdKwI8LCBC2GaUyKUsHKJtW47WjDLyv3kVmSY0UkalIpQijxWGNFLAlYEkVx7BThmDqoMfmlF/Rr/rK2tBCQtmu5IYmOKPQy2r1nVnuQVzUXWcm4G/6kmzdvAv9dHLq5uaFQKEhLS8tVk6ecyuhLcfPmTYO71PDfnd/cdqZ/2s6dO6ldu7bBBSI8u/lSZvssq7hCQkJITU01qNVJSUkhJCQkW/1AnlayZEmOHz9O8eLF9bVIWcmI5fbt2wb9U56MNzecnJzw8fHBx8cHjUbDd999x19//UXfvn2pWrWqvqYiKCiIatWq5WodOdnOkiVLAmBvb/9Szs+cyKhdvXPnjtExuHPnTq6X+7xjkJP9lx0dOnTgjz/+YMeOHfj4+HD69Gm6dOny3KGkM2qJgoKCslXOwsIi28ewXLlylCtXjn79+hEbG0v//v2ZM2cOPXv2zHaSLV4COyv4baDBJOePi+P8cXO0Oh0fTozjXHF73gqLQgmkKRQcLONMilrF/upl+LNuBXz8/zt/tEodxbV30KHB6cY3WJT9r4bD8t+XEK8S6aMjCj0rKyscHR3x9/c3uBt87949Dh48mK/rPnnyJFeuXNG/1+l0rFixAkA/jLO9vT2NGjXi77//NmrXnzFPZGRknsVUr149LCwsWLduncGzPOLj41m3bh2Wlpa56t+QGaVSaXQHPjExUT9cbmY2btxo0DcgLi6OTZs2YWNjQ506dQzKxsfHs2HDBoNpGzZsID4+Psvhvp8lY8AFX1/fTGv7nmyO1KxZMxQKBatXrzYoe+XKFaM+K9mRlJREUlKSwTSVSvOjHfsAAFFUSURBVKVvopaRqGcMsz137lxSU1ONlpOdGo+cbGerVq0wNTVlwYIFRvFB+vFJSSmY5200adIEgLVr1+o73gNcv349x00HIfvHICf7LzsqVqxI+fLl2bVrFzt37tQPLf08FSpUoGzZsmzbto0bN24YfZ5xLmQ0j1u2bFmmTQeTkpL0vwXR0dEG+xLSm+26urqSlJSUb8/rEnlPqVCQYKpGqdPxj5sjWyq7sbiOB5eL/ftMNIWCH3t6sbJp+qA4GoUChSKZUEphPutdgyRHiFeV1OgIAfTs2ZN58+YxatQomjVrRnh4OJs2bcLDwyPTDvF5pXz58gwbNowePXrg5OTEoUOHOHXqFO3bt6dGjRr6cmPHjuXDDz9k8ODBeHt7U7FiRbRaLSEhIRw+fJj27dsbjbqWWzY2NowaNYopU6bwwQcf6C+otm/fTnBwMF9//bVRu+TcatGiBZs3b2bcuHG8/fbbPH78GD8/v2c+fNTe3p7+/fvrh5L28/MjLCyM8ePHGzX/cXNzY9GiRdy4cYPKlStz+fJltm3bRunSpenVq1eO461atSpDhgxh4cKF9O7dm5YtW1K0aFHCw8O5fPkyx44d019Aly5dmh49erB+/Xr9ENcRERGsX7+e8uXLc/Xq1Ryt+86dOwwZMoTmzZvj4eGBjY0Nt2/fZuPGjbi6uuoHw6hWrRr9+/dn+fLl9OnTh9atW+Po6EhoaCj79+9n+fLl+v5kebGdzs7OjB07lkmTJtGjRw/at2+Pi4sLkZGRXL9+nYMHD7Jhw4ZcD+f8Ijw8POjSpQtbtmxh+PDheHl5ERUVxYYNG6hYsSKXL1/OUe1Ddo9BTvZfdnl7ezNjxgyWL1+Ou7u7fkTGZ1EoFHz//fcMHz6c/v3764eXjo2NJTAwkAYNGtCrVy8sLCyYOHEiX3zxBd26daNTp06ULFmS2NhYbt++zYEDB/j111/x9PRkx44drFmzhubNm+Pm5oZarSYwMJB//vnHaNRD8erzeBiOQqegVlgky1wcSFUbN71e3bQ6FUIfc9/JjE4nbmBNNEU/fjuTpYl8JRWluSKJjhBA//79iYuLY+fOnZw+fZoyZcrw7bffcvny5XxNdJo2bUqpUqVYtmwZd+7coUiRInz44Yf6Z9dkKF68OKtWrWL58uUcOnSIXbt2YWpqirOzM02aNMnzYV4zEq+VK1eyaNEiIP3u8NSpU3NVE5KV0aNHY2Vlxd69ezl06BDOzs506dKFKlWqZNm5/eOPP+bs2bNs2LBB/wDISZMm0bZtW6OyxYoV4+eff2bGjBn89ddfmJiY0LZtWz799FMsLCxyFfOQIUOoUqUKa9eu5Y8//iAxMZEiRYrg4eHBF198YVD2iy++wNHRkS1btjBz5kxKlizJV199xd27d3Oc6Dg7O9OpUydOnz7NwYMHSU1NpWjRonTp0oX+/fsbXGB+/PHHlC9fnvXr17NixQq0Wi3Ozs40atQo2xeiOdnOTp064e7uzqpVq9i8eTOxsbHY29tTqlQpPvroI6POuS/T2LFjKVq0KFu3bmXmzJmUKlWKsWPHcvHiRS5fvpxp/7as5OQY5GT/ZUe7du2YPXs28fHxRg8mfZaqVauyfPlyFi9ezL59+9i0aRP29vZUrVrVYHj9Bg0asHz5cpYvX86uXbuIjIzE1tYWNzc3+vTpo6+1qlOnDlevXuXIkSOEh4ejUqkoUaIEn376qdGzgcSrL9HMigv2JpwpUSTTJAcgRa3mrqMtfU+cBFQUXdop03JCvIoUulext68QQjzFz8+PiRMnMn/+fP1QxM/SsWNHXFxcWLhw4UuITrxuPvvsM/z9/Tl06FCWA4gI8aar/PlDrjjbwzMGCOn+zyW+2nwUnTqO0lO8KDpaanMKgmJs7PML/Uv387Nr7QsTqdERQgjxxkpKSjKqxbp27RrHjx+nYcOGkuSIQi3NXIFSB9qnpiu1OszSNHQ/foHS4bE8KmpKu4dZPyRXiFeVJDpCFBLR0dGZdk5/krm5+Qv3v0lKSsrWgwSzGoq4sJH9lb+2b9/Ozp07adSoEQ4ODty+fZstW7agVqv1/drkGIjCyrysPdoI4+n2Scm0v3CHWHsbKiY+oN3DD40LiZdL+ujkiiQ6QhQSX375JYGBgc8s06FDByZMmPBC69m7dy8TJ058brmAgIAXWs+bQvZX/qpUqRIHDx5k3bp1REdHY2VlhaenJ0OGDKFSpUqAHANReK3xVtB0YTJR5iYoFAp0gG1yKj4Xg7HWwIO0NPrsf/aDaYV4lUkfHSEKicuXL2f6rKAnFS1a9IWfbh4eHp7pULZPe9WeuVJQZH8VPDkGojBzHxdOsKO9wbS3g8NJNFEx+30rmr0lT8d5FSjG5aCPzmTpo5NBEh0hhBBCiEKq2PePeWTz1JD+Oh1lHkdwY3IxeQDsK0Ix7vnNazPoJufNIyDeBPLAUCGEEEKIQirSPJMaG4WCVIVakhzx2pNERwghhBCikHKISch0+kMbabImXn+S6AghhBBCFFILO6jh6V4MOh0usdlvKiXEq0oSHSGEEEKIQsqnkR1F4xINkh3zlDQCv7YtwKiEEUUOXkJPEh0hhBBCiELs4URbfvTUUC4xnvrxN5jqtAYbc7lEFK8/OYuFEEIIIQq58c3NuTTWkgEuhzBRaAo6HCHyhDwwVAghhBBCiFeZjICXK1KjI4QQQgghhHjjSKIjhBBCCCGEeONIoiOEEEIIIYR440iiI4QQQggh9D6P6YblTPjfg7SCDkWIFyKDEQghhBBCCA7fgqExA8h4GMtbK0FNGqlfyOWieD1JjY4QQgghhKDlNnj6iZNSpyNeZ5LoCCGEEEKILF24L+lOgVPk4CX0JNERQgghhBBZOvOooCMQInck0RFCCCGEEFm6HlHQEQiRO9K7TAghhBBCZMndpqAjENImLXekRkcIIYQQQmSpqFVBRyBE7kiiI4QQQgghslTcsqAjECJ3JNERQgghhBBZarq+oCMQMupa7kiiI4QQQgghspRc0AEIkUuS6AghhBBCCCHeOJLoCCEKHT8/Pzw9PQkICCjoUAqFq1ev8tFHH9G8eXM8PT1ZsGABAFFRUXz33Xe0bdsWT09PhgwZUmAxenp6MmHChAJbf3YtWLAAT09PQkNDC2T9oaGhBsdQCCFeZTK8tBBCiHyTlpbGmDFjSEtLY9iwYdjY2FC+fHkAfvvtN/bu3cvAgQNxdXWlSJEiBRytEEK8oqTvTa5IoiOEECLfhISEEBISwqeffsq7775r8NnJkyepX78+gwcPLqDohBDZpZyahvYLuWwUrxc5Y4UQQuSbx48fA2BnZ5fpZ5lNf1PEx8djZSUPIBEv6Phl+HUrnL8DNx7k66pUk1ehUZtm+pkuX9csRP6QREcIUWjpdDpWrlzJxo0befjwIS4uLgwcOJAOHTroy+zZs4ddu3YRFBREREQElpaW1KxZk2HDhumbYGU4d+4cixcv5urVq8TGxmJnZ0f58uUZPHgw1atXz3ZcycnJLFu2jL/++osHDx5gYmKCs7MzDRs25JNPPjEoe/LkSVasWMHFixdJSUnB3d2d7t270717d32ZcePGsX//fubOnYunp6d++j///MOoUaNo164dP/zwQ472XWhoKPPmzePkyZPExsZSrFgxWrduzaBBgzA3NwdgyJAhBAYGAjBx4kQmTpwIQIcOHdi+fTsA27dv1///+++/p2PHjtneLoCOHTvi4uLC119/zW+//caZM2dQKBTUq1ePMWPG4OTkZFD+xo0bzJgxgzNnzmBqakrDhg0ZPXp0ltu5Z88e1q1bx7Vr19BoNJQrV47333+fli1bGpTz9PSkQ4cOtG/fngULFhAUFETlypVZuHBhtvdpamoqa9as4a+//uLOnTuo1Wrc3d3p0KGDUW1YSkoKvr6+7Nixg8jISEqXLs2IESNo3LhxrrcBICAggJUrV3LhwgUSExMpWrQoderUYdSoUdjb22cZ+z///MNXX31FhQoVmD59Ora2ttnebvEMc3bCx7+/tNW9H3CIZfVbZfm5TqdDoZA2VAVD9ntuSKIjhCi0fH19SU5OpmvXrpiamrJx40YmTJiAm5sbNWvWBGD9+vXY2dnRpUsXnJycuHfvHlu2bGHQoEGsWrUKd3d3AG7fvs2IESNwdHSkV69eFClShIiICM6ePUtQUFCOEp0pU6awbds2vL296dOnDxqNhuDgYPz9/Q3Kbd68mcmTJ1O9enUGDhyIhYUFJ0+e5OeffyYkJESfFH3zzTdcunSJ7777jjVr1mBvb094eDjff/89JUuWZOzYsTnab/fv36d///7ExcXRvXt33N3dOX36NEuXLuXcuXPMnTsXtVrNwIEDeeutt1i6dCldunShVq1aAJQrV463336b7777jlq1atGlSxcAatSokaPtyvDo0SOGDh2Kl5cXo0aN4tq1a2zevJn4+Hh8fX315UJCQhg8eDApKSn07NkTZ2dnjhw5wscff5zpds6dO5clS5bQsGFDhg0bhlKp5MCBA4wdO5YxY8bQs2dPg/KXLl3i77//xsfHxyBZzo7U1FRGjhzJ6dOnqV+/Pu3atcPU1JTr169z4MABo0RnwoQJqNVq+vbtS2pqKn/88QdffPEFmzdvpkSJErnahk2bNvHzzz9TrFgxunXrhouLC2FhYRw5coQHDx5kmehs376dH3/8kaZNmzJp0iTMzMxytO0iC1otfL36pa7ynFvZZ37ex0/Dmk5y6SheIzohhChktm3bpqtTp47uvffe06WkpOinP3jwQFe/fn3duHHj9NMSEhKM5r9586aufv36usmTJ+un/fHHH7o6derozp8//8LxNW/eXPfxxx8/s8yjR490DRo00H399ddGn/3666+6unXr6oKDg/XTzp8/r6tXr57u008/1Wk0Gt1HH32kq1+/vu7y5cs5ju+bb77R1alTR3fkyBGD6TNmzNDVqVNHt2XLFv00f39/XZ06dXTbtm0zWk6dOnV033///QttV4cOHXR16tTR7dmzx6Dszz//rKtTp47u1q1b+mlff/21rk6dOjp/f3/9NK1Wq/v888+NYrl8+bKuTp06ujlz5hjFMXr0aF3Tpk11cXFxBttSp04d3YkTJ4zKZ8eyZcuyXJ9Go9H/f/78+bo6deroPvnkE51Wq9VPv3Dhgq5OnTq62bNn52obwsLCdPXr19d1795dFxMTk2UMISEhujp16ujmz5+v0+l0uiVLlujq1Kmjmzx5skGcBe3x48e6pKQk/fvY2FiD7UpOTtaFh4cbzBMaGvrM9/fv3zfY5/m+jsRknY4uL/VVYvwDHb+mZvkqtSD11dxXr/E6sotvE7L9Ev+R4aWFEIVWjx49MDEx0b8vVqwY7u7uBAcH66dZWFgA6U024uLiiIqKwsHBgVKlSnHhwgV9OWtrawAOHTpEcvKLPV7P2tqamzdvcv369SzL7Nu3j5SUFDp37kxUVJTBq0mTJmi1Wk6dOqUvX61aNT766COOHDnC4MGDOXXqFCNHjqRSpUo5ik2r1XL48GEqVqxo1Ezqgw8+QKlUcvDgwRwt80W2C6Bo0aK0amXY3CajiV7GsdRqtRw5coQqVaoYNN9TKBT069fPKI5du3ahUCjw9vY2iqNp06bEx8dz/vx5g3kqVKhAvXr1crXdu3fvxtbWlg8//NDoM6XS+E91r169DJoQVa1aFUtLS+7evZurbdi3bx+pqakMHjwYGxub58ag0+mYMmUKvr6+DBs2jLFjx2YaZ0EpUqSIQc2StbW1wXaZmpri6OhoMI+Li8sz3xcvXtxgn+f7OsxNofaza1jyWoyZxTM/35DesvTV21ev8TpE/pL6RyFEoeXq6mo0zc7OjrCwMP37K1euMH/+fE6fPk1iYmKW87du3ZqdO3eydOlS1qxZQ/Xq1alfvz5t2rTJ8R+20aNH8/3339OrVy9cXV3x9PSkSZMmNG3aVH8xefv2bQCGDx+e5XIiIiIM3vfr14+jR49y5swZ6tevT+/evXMUF0BkZCQJCQmULWt8AWZnZ4eTkxMhISE5Xm6G3GxXVscRIDo6Wj9PQkICpUqVMiqb2bbcunULnU5n1CfoSRkDLWTIaMaYG3fv3qVixYrZbvbl5uZmNM3Ozk6/vZCzbchICCtWrJit9f/xxx/Ex8czfPhwBg4cmK15RC5sGwfvfA9BL+e5SSkq1TM/r+sil40FRrro5IqcsUKIQiurO9A6Xfr4QmFhYQwZMgQrKysGDRpE6dKlMTc3R6FQMG3aNIPEx9TUlLlz53LhwgVOnDhBYGAgCxYsYNGiRUyaNInmzZtnOy4vLy+2bdvGsWPHCAwM5NSpU2zdupVatWoxd+5cTExM9DFOnDjRqMN9hqcTgNDQUK5duwakX9gmJCS8cqOC5Wa7nlWTkLG83FAoFMyaNSvL5Xt4eBi8zxiE4WV43rmbIafbkF1vv/02Z86cYcuWLbRu3TrTxEvkAVdHuDoHEpNBqYCoOHgcC4+j4VYYPIqF+9Gg0EBkEjyKBCszQAVJyRCfDEolRMaBpRqcHODBY7gaBompYKpIf3R8MtCkMilqk+cEJMTrRRIdIYTIwoEDB0hISGD69OkGzZ0gvabA1NR4GNZq1apRrVo1ID1R6tOnD/PmzctRogPpd+fbt29P+/bt+f/27jsqqmttA/gz9C5VIKKABVusYEERNDYiiqhoYgOJgu2LJpbYcm3xxpjEaDRiR0SjErFhFw2CvWCJiTUqRkFURHpn9veHdyaOM1QREJ/fWqxk9uxzznvKjPOeXY4QAsuXL0dISAiioqLQrVs31K5dGwBgbGxcou5S+fn5mDVrFgoKCjBlyhQsXrwY3333Hb755ptSxWViYgJ9fX3cu3dP6b3U1FQkJibCwcGhVOt8VWn3q6RMTEygp6eHBw8eKL2nal9q166N06dPw8rKCvb29uUWR2FsbW0RGxuL3NxclddVWZRmH2StUbdv31bZ6vW6+vXrY8yYMRg7dixGjx6NlStXvlGLFhVD938tfZamL/8AoFPz8t/Oj3mFvtW7dvlvjuhtqzodaomIqhjZXfDX75Lv2rVLqdtScnKy0vKWlpYwMTFR6E5UnIKCAqSlpSmUSSQSeZci2bq6d+8OLS0trF69GtnZ2UrrSU9PR25urvz1ypUr8eeff+Krr77Cp59+imHDhuHgwYPyqZ1LSk1NDZ06dcKtW7dw+vRphfeCg4MhlUrRuXPnUq3zVaXdr5JSV1eHi4sLrl+/josXL8rLhRAICQlRqt+rVy8AL2fmKygoUHr/9fP/ptzd3ZGamor169crvVfWVqnS7EPXrl2hqamJtWvXIj09vUQx1KtXD2vWrEFBQQECAgLk3Q6petr7Ce+N07uHVy0RUSE6duyI5cuXY/bs2Rg0aBAMDQ1x9epVnD59GjY2Ngo/HtevX4+zZ8/CxcUFtWrVghACJ06cQGxsrMrB7oXJzMyEu7s7XF1d0bBhQ5iYmCA+Ph5hYWEwMjKCq6srgJdJ1PTp07FgwQIMHDgQvXr1grW1NV68eIG///4bx48fx/bt2/HBBx/g7NmzCAkJgbu7u/w5NePHj0dMTAy+//57NG/evFR348ePH49z585hypQp8Pb2Ru3atXHp0iVERESgdevWpZ5a+VWl2a/SGjduHE6fPo0vvvgCn3zyCWrWrIkTJ07gxYsXSnWbNm2KgIAArFmzBkOGDEG3bt1gYWGBxMRE3LhxA6dOncLZs2fLvJ+vGzx4ME6cOIH169fj+vXraNeuHbS1tXHv3j08ePAAgYGBpV5nafbB0tISkydPxqJFi/Dpp5/Cw8MD1tbWePr0KaKiojB79myV43fs7OywZs0aectOYGBgmbvDERGVNyY6RESFsLGxwbJly7BixQps2LABampqaNGiBVavXo3vv/8ejx8/ltd1c3NDYmIijh49iqSkJGhra6N27dr4+uuv0bdv3xJvU0dHB4MHD8b58+dx/vx5ZGZmwtzcHK6urvDz84OFhYW8rqenJ+rUqYPNmzdj586dSEtLg7GxMWxtbTF27FiYmZkhKSkJc+bMQa1atTBjxgz5shoaGvj2228xdOhQzJo1C0FBQQoz0BXF2toawcHBWLVqFQ4ePIi0tDRYWlrCz88PI0eOhIbGm/3TUpL9KgsbGxusW7cOS5YsQWhoqPyBofPnz0ePHj2U6gcEBKBJkybYtm0btm7diqysLJiamqJevXqYMmXKG+3j6zQ1NfHLL79g8+bNOHz4MAIDA6GlpYU6derIk9OyKM0+eHt7w8bGBiEhIdi2bRvy8vJgYWGBNm3awNLSstBt1KlTB6tXr8bYsWMxZswYBAYGKj1Ml4ioMkjEm4zUJCIiIqJqQfJjHgqb3ktM4b3xyiSZm1V8pf8Rc4ueJvx9wjE6RERERFSogNI9bouoymB6TkRUAfLy8ko0KYGJiQnUi3mWxduQnZ2tchD66wqb8pmUVfVzTlRSLfmxp3cUEx0iogpw9epVjBkzpth64eHhZRpo/6YiIiIwb968Yuu9OmMZFa2qn3OiklKes4/o3cBEh4ioAjg4OGDFihXF1ivrQPs35ezsXKL4qOSq+jknKqmU/MqOgCBRPXaKisZEh4ioAhgZGZXrAzDLm7m5ObullbOqfs6JSqq1RfF1iKoiTkZARERERIWScH5eekcx0SEiIiKiQrWpXdkREJUNEx0iIiIi+h/l5hszfY50oHcTEx0iIiIiQu5E4OUcawKyhOcf/0oMiOgNMUUnIiIiIgDAaqMQAICfnx80NfkzscrgpGtlwhYdIiIiIiKqdpjoEBERERFRtcNEh4iIiIiIqh12viQiIiIiqtI4SKcsmOgQERER0UsFUuik51V2FETlQiKE4PNuiYiIiN5zBXqDoJaVDwleTi4t8XEFNn5RyVERAEjm55S4rpit/RYjebdwjA4RERHR+673N/IkB3jZUUqERANSaWVGRTKSUvyRHBMdIiIiovdcwf7LSr+RJQDSnadVRjhE5YKJDhEREdF7rrCGgJyrjyo0DqLyxESHiIiIiFTKL2DXNXp3MdEhIiIiIpUM8jkDG727mOgQERERvecKm4I3R1urQuMgKk9MdIiIiIjec4X9IHyuo1ehcRCVJz4wlIiIiIhU0sjNr+wQCOC00WXEFh0iIiIiUpKhqY2HNa0rOwyiMmOiQ0RERPSeK1BRppeXg+da7LpG7y4mOkQVID4+Hk5OTli9enVlh1Iutm/fjgEDBsDZ2RlOTk6Ij4+v7JBK5eLFi3BycsLevXvlZW/zHK1evfqdPE5VQUBAAPr06VPZYbwTeJ3RmyhQU1cqkwCwS00CClSlQURVH8foEFGpXLx4EYsWLYKbmxt8fX2hoaEBExOTyg6LytGWLVtgaGhYYQlGRW+PiJRpSlUnM9bpKcjXGIi0X0bBZHyvCo6K6M0w0SGiUjl37hwAYPbs2ahRo0YlR1N+rK2tcerUKairK9/VfN9s3boV1tbWFZZ4FLW9FStWQIjCJr6lV40cORIjRoyAlhanA6aSSc3OR8ABYP/fBfjZqTMMc7Ph+ddFaBf8OwFBaMsO6Bh7C47/tw45oz+GtgZHxdO7g4kOUTWUn5+PgoICaGtrl/u6ExMTAaBaJTkAIJFI3srxqmwZGRnQ19ev7DDKTFNTs7JDqBRlOW8aGhrQ0OA/6+8zIQQuJLz8fycrYN6JAsw/X4IFJWoY+ck4AEDDp3E4teI/MMtMBwB4/3EWhxxawjHuPpK0PsXhMxvQuZEu7Gow4aGqj9+IVG3s3bsX8+bNw8qVK3Hz5k2EhYXh6dOnsLa2xmeffYbevXsDeDkWw9PTE/7+/hg9erTCOlavXo21a9ciPDwcH3zwAQBg7ty52LdvH44ePYqlS5fixIkTyMvLQ5s2bTBjxgyYm5tj586d2LJlC+Lj42FtbY3PP/8cnTt3VhnnoUOHEBwcjH/++QcmJibw9PTEyJEjlX6gJCYmYu3atTh58iSeP38OY2NjdOrUCWPHjoWpqalSzKGhodizZw+OHj2KxMREBAYGwsnJqcTH7/jx4wgJCcHt27chkUjQoEED+Pj4yPdDdtxkZOtu3bo11qxZU6JtPHv2DJs3b8aFCxfw+PFj5OTkoFatWvDw8MDw4cMVWlNk53PFihW4cuUK9u7di+fPn8PW1hZ+fn7o2bOnwrr79OkDa2trTJo0CUuXLsVff/0FTU1NdOrUCRMnTlQ4ZqoUdV0cOXIEoaGhuHPnDgoKClC/fn0MHz4c3bp1U6gnlUqxceNG7Nq1C4mJibCxsYGfn1+Jjo0qr57bnTt34ujRo0hPT0f9+vUxfvx4tG3bVqG+k5MTevfujV69emH16tW4ffs2GjduLD8/xZ1j2ToA4PHjxwrXz6ufievXryMoKAiXL19GZmYmrK2t4eHhIe/K+KqHDx8iKCgI586dQ1JSEoyNjdGkSRP4+/ujcePGxW4vICAAjx8/VhhPBQCXLl3CunXr8NdffyE/Px92dnYYOHAgvLy8FOrJlg8KCsKSJUtw5swZ5ObmolWrVpg6dSpsbW1LdU6kUim2bduG8PBwxMfHQyKRwMzMDC1btsTMmTMV9r+kx0kW48qVK7Fs2TJcvHgRqamp2L59OwYOHIghQ4Zg0qRJSrHMnDkTv//+Ow4ePAgTExOV318AkJ6ejo0bNyIyMhLx8fHQ1dWFnZ0dBg0apPA5Kul3TkpKCtatW4fo6Gg8e/YMurq6sLa2Ro8ePeDj41Oq40nlJzFTwH1HAWKelGFhyb9Jy62atTCvmzeWhQcDAGqlJWNkzHEAgJXIw7X5B+Hn1gdftFHHki5sAa8wEiaWZcFEh6qdFStWICcnB/3794eWlhbCwsIwd+5c2NjYoGXLlmVe74QJE1CzZk2MGTMGDx8+RGhoKKZOnYouXbpg165d6Nu3L7S0tBAaGopp06Zh586dqFWrlsI6oqOjERcXh4EDB8LMzAzR0dFYu3YtEhISMGfOHHm9hIQE+Pn5IS8vD3379oWNjQ0ePnyIHTt24OLFi9i0aRMMDAwU1v2f//wH2traGDp0KCQSCczNzUu8b9u3b8eiRYtgZ2eHUaNGAQD27duHKVOmYObMmejfvz9MTEwwf/587Nq1C5cvX8b8+fMBoNgE4lV37txBZGQkOnfuDBsbG+Tn5+PMmTP45ZdfEBcXh1mzZikts3z5cmRlZcHb2xvAywRo1qxZyM3NVerq9PTpU4wdOxYfffQRunbtips3byI8PBw3btxASEgIdHR0ShyrTGBgIIKCgtChQweMGTMGampqiIyMxPTp0/HVV19h0KBB8rpLlizB1q1b0bp1awwZMgRJSUlYtGiR0nVQWnPmzIGamhp8fHyQmZmJnTt34vPPP8eyZcvQrl07hbrXr1/H77//Di8vL3lyD5TsHAPA/Pnz8dNPP8HY2BifffaZfHnZOKyTJ09i6tSpqF27NoYNGwYjIyNcu3ZNnlgtWrRIIZaxY8ciPz8fffv2Rb169ZCamopLly7h6tWraNy4cbHbUyU6OhpTp06FmZkZhg0bBj09PRw5cgQLFixAXFwcxo8fr1A/KysL/v7+aNasGcaPH4+4uDhs27YNkydPRmhoaKm6KwYFBWHVqlXo1KkTBgwYADU1NcTHxyM6Ohq5ubnyBKY0xwkAMjMzMXr0aDRv3hzjxo1DUlIS7O3t0aRJExw+fBgTJ05UiDM9PR1RUVHo0KFDkccqLS0NI0eOxL1799C1a1d4e3ujoKAAt27dwsmTJ+WJTmm+c6ZPn45Lly5hwIABaNCgAXJycnD//n3ExMQw0alE356Tli3JUeFwwxYqyyUAfjjwK0LadMHSGCMMdBDoUIs/wKkKE0TVRHh4uHB0dBSDBw8Wubm58vInT56I9u3bixkzZgghhIiLixOOjo5i1apVSutYtWqVcHR0FHFxcfKyOXPmCEdHR/Hdd98p1F28eLFwdHQUvXr1EmlpafLy27dvC0dHR7F8+XJ5mWybbdq0ETdu3JCXS6VSMXnyZOHo6Cj++OMPefmXX34punXrJhISEhS2+ddff4m2bdsqxC6L2d/fX+Tl5ZX4eMmkpKQIFxcX0bdvX4X9SEtLE56enqJTp04iNTVV6XiURVZWlpBKpUrlX3/9tWjTpo149uyZvEx2Pj08PJTi8vDwEF26dBFZWVny8t69ewtHR0fx66+/Kqx78+bNwtHRUWzYsEFeduHCBeHo6CjCw8PlZaquixs3bghHR0fxyy+/KMU8adIk4erqKtLT04UQQty/f184OTmJMWPGiPz8fIV1ODk5KV1XJSE7tz4+PgrXdEJCgnBxcREDBgxQqO/o6CgcHR3F2bNnFcpLe4579+4t/P39leLJzs4WPXr0EKNGjVK61mTH+cKFC0KIl9f2wIEDhbOzs7h9+7bSugoKCordnhBC+Pv7i969e8tf5+fnCw8PD+Hm5iaePn0qL8/NzRV+fn6iTZs24sGDBwrLOzo6iuDgYIX1bty4UTg6OorTp0+r3G5hhgwZIry9vYusU5rj9GqMK1asUFpXaGiocHR0FCdOnFAo37Vrl3B0dBTHjh2Tl6n6/lq4cKFwdHQUO3bsUFr3q+egpN85aWlpwtHRUSxcuLDIY1DZnj9/LrKzs+Wv09LSFK7xnJwckZiYqLBMfHx8ka8fP36s8P1V1bbhsiVP4Ify+dvgtFSsaveLeKLvKwT6Kf19FHBJ4Ic8sfRiwTt5rKrSNkoKC3JK/Ef/4vTSVO0MHDhQoV9/zZo1UadOHTx8+PCN1jt48GCF161atQIAeHh4KLSuNGjQAPr6+vjnn3+U1tGuXTs0atRI/loikcjvgEZGRgJ4eaf25MmTcHV1hba2NpKTk+V/H3zwAWxsbOQTArxqyJAhZeqff+7cOWRlZeHTTz9V2A8DAwN8+umnyMzMVLm9stDR0YHkf83veXl5SElJQXJyMpydnSGVSnH9+nWlZby9vZXiGjBgAFJTUxETE6NQV19fHwMHDlQoGzhwIPT19eXHtzQOHjwIiUQCDw8PhfOQnJwMV1dXZGRk4Nq1awCAqKgoCCEwdOhQhTvvjRo1Ump1Ka0hQ4YoXNOWlpZwd3dHbGws7t+/r1DXwcFBaXvldY7PnTuH58+fo0+fPkhPT1c4Hh07dpTXAYBbt27h3r176NOnDxo0aKC0LjW1sv3zc+PGDSQkJMDT0xMWFhbyck1NTfj4+EAqlSIqKkppW59++qlCWZs2bQBA5ee0KAYGBnj69CmuXLlSaJ3SHKdXDR8+XKmsZ8+e0NTUxP79+xXKDxw4gBo1aqBTp06FxiGVSnHkyBHY29vLW+xeJTsHpfnO0dbWhpaWFv78888qPY21qampwpg7AwMDGBoayl9raWnBzMxMYRlra+siX1tZWcm/v6riNtpalb1lRTsv9+X68vPw+cmDOFunAcZ4j8YRh+ZKdaUArn7wsstnm/9t8107VlVpGyUmKcUfybHrGlU7qroJ1ahRAwkJCeW6XtmX26t94WWMjIyQkpKiVG5nZ6dUVrduXQBAXFwcACA2NhZSqRR79uzBnj17ShQLANSpU6foHSiEbLuyOIqK7U3l5+cjODgYBw4cwMOHD5Vm00pNTVVaRtUxs7e3VxlXrVq1lAava2lpoVatWmXah/v370MIIe82p8rz588VYiks3rNnz5Z6+68u/7pXz82r76u6DsrrHMuSKlm3RVVkx0N2Y6Fhw4bFrrc0ZD+uVe1LvXr1ACjvi4WFhdJEE7LJNFR9Tosyfvx4TJkyBaNGjYKFhQUcHR3h4uKCrl27yq+90hwnGRMTE4UfTK/G6eLigujoaKSnp8PAwADx8fG4fPkyvL29i5ysITk5GampqXB2di5yn0rznaOpqYlJkyZh8eLF8PT0RN26deHk5ITOnTsrjRmjijWzvRqiHxXgYhm6r7WKi0V48CJo5+fjD+s66DT+GwDA7J6foNP9m7BNfjkJjQDwVa+heK5niEmOEnZboyqPiQ5VO4XdKZb9qJYUMaCvoIiHohXWj7+w8td/xJfWxx9/rDDG4lWqZgcry/iTirZkyRKEhoaie/fu+Oyzz2BiYgINDQ3cvHkTy5cvr5LTCEskEixbtqzQ60r247qqeJvXgez8TJw4EQ4ODirrvNrKUlUU1XpU2muuefPm2L17N86cOYOLFy8iJiYGhw4dwvr167Fu3TrUqFGjTMepqPPm4eGByMhIHD16FF5eXjhw4ACEEPDw8ChV7MUp6XeOt7c3OnfujJMnTyImJgbHjh3Db7/9hu7du2PhwoXlGhOVnJmuBBeGa+Biwsvrr7UlsOBMAeafBop73OcfVjaQCAGjnCxcqvXvTYT7ZpZoMG0Zut35A8HbVqAgNxct536MWAcN2HLWNXoHMNGh946RkREA1a0H5dVyUZjY2Filsnv37gH4946pjY0NJBIJ8vPz37jLU0nY2NjI43j9jqzszvSbDqaXOXDgAFq3bq30Y6ioboWqjllhccXFxSEvL0/hLndubi7i4uJUtrQUp3bt2jh9+jSsrKxUtqq8ShZLbGys/Ji+Hm9Z3b9/X+kH8+vXTVFKe44Luxkgay3S1dUt9tqU1b19+3ax8RV18+F1sjhl+/+q0hyTN6Gnp4euXbuia9euAP6d6GHPnj3w8fEp1XEqCRcXFxgbG2P//v3yRMfOzg4ffvhhkcsZGxvDyMgId+7cKbJeWb5zzM3N4eXlBS8vLxQUFGD27Nk4fPgwhg0bhqZNm5Z436j8Ob3ShW12Bw3M7lB43cw8Kb4/K0XwZR3o5OYiSVcfHR7cUqiTp6GBa9Z1YJ6ZBjWxE8PeVuBEbwHH6NB7R19fH2ZmZrhw4YLC3dxHjx7h+PHjb3Xb586dw82bN+WvhRAICQkBAPkUv8bGxujYsSN+//13+fiPVwkh8OLFi3KLqV27dtDV1UVoaCgyMjLk5RkZGQgNDYWenh7at29fLttSU1NTuoOelZWFLVu2FLpMWFgY0tPT5a/T09OxY8cOGBoawtHRUaFuRkYGtm/frlC2fft2ZGRkFDrdd1F69Xr5FPAVK1aobO17tfuRm5sbJBIJfv31V4W6N2/exPnzJXmQReG2bNmCvLw8+esnT57g8OHDsLW1LTYBA0p/jnV1dVXeCHB2doapqSmCg4NVdvnKzs6Wr9/BwQF169ZFeHg47t69q1T31eugsO2p0qhRI1hZWWHv3r3yZzoBL7tFbtq0CRKJBG5ubiVaV1kkJyerjAn49+ZJaY5TSWhoaMDd3R1XrlzBoUOH8M8//xTa8vIqNTU19OzZE/fu3cPu3buV3pedg9J852RnZyM7O1vhfXV1dfk4rJKeR6oa9DTVMLeTBmInaEE/PxemWRl4YGyOhk8eQU0qBQDUTEvG2m0rIA4pz4pJVNWxRYfeS4MGDcLKlSsxYcIEuLm5ITExETt27EC9evVUDogvLw0aNMCYMWMwcOBAmJubIyoqCufPn0evXr3QvPm/gz6nT5+OUaNGwd/fHx4eHmjYsCGkUini4uIQHR2NXr16KT3rpawMDQ0xYcIELFq0CCNGjJD/gNq3bx8ePnyImTNnKk1lXVZdu3bFzp07MWPGDLRt2xbPnz/H3r17i3z4qLGxMXx9feVTSe/duxcJCQn4+uuvlbr72NjYYO3atbh79y4aN26MGzduIDw8HHZ2dkqD0UuiadOmCAgIwJo1azBkyBB069YNFhYWSExMxI0bN3Dq1Cn52BvZc1x+++03+RTXSUlJ+O2339CgQQPcunWrmK0VrqCgAKNGjULPnj2RmZmJHTt2ICcnB1OnTi3R8qU9x82aNcOePXuwcuVK2NvbQyKRwNXVFbq6upg3bx6mTJmCAQMGwNPTE7Vr10ZaWhpiY2MRGRmJH374AU5OTpBIJJgzZw7GjRsHX19f+fTSaWlpuHTpEpydneXnpKjtvU5dXR1fffUVpk6dCl9fX/Tr1w96enqIiIjAtWvX4OfnV+bxaiXh7e2NZs2aoWnTpvJrYdeuXdDU1ESPHj0AoFTHqaR69+6Nbdu2YeHChVBTU8PHH39couXGjh2LCxcuYMGCBTh37hxatHg5bfCtW7eQn5+Pb755ORajpN85Dx48QEBAALp06YJ69erB0NAQsbGxCAsLQ61ateSTtNC7a8Cf5zHgz/N4YlADD0zMUTfhEcxzt1Z2WERlwkSH3ku+vr5IT0/HgQMHEBMTA3t7e/znP//BjRs33mqi4+rqCltbWwQHB+PBgwcwNTXFqFGj5M81kbGyssLmzZuxceNGREVF4eDBg9DS0oKlpSU6deqE7t27l2tcssRr06ZNWLt2LYCXd+R//PHHMrWEFGbSpEnQ19dHREQEoqKiYGlpiX79+qFJkyYYN26cymU+//xzXLlyBdu3b0dSUhLq1KmDBQsWwN3dXaluzZo18d1332Hp0qU4fPgwNDU14e7uji+++ELlj+aSCAgIQJMmTbBt2zZs3boVWVlZMDU1Rb169TBlyhSFulOmTIGZmRl27dqFn3/+GbVr18a0adPwzz//vFGiM2/ePOzYsQMbN25EWloa6tevjzlz5pSqpa0053jcuHFISUnB9u3bkZaWBiEEwsPDoaurC2dnZ2zcuBEbN27EwYMH8eLFCxgZGcHGxgZDhw5VmGGtadOm2LhxI9avX4+jR49ix44dMDY2RtOmTRWeaVXU9lRxdXVFYGAg1q9fj02bNiEvLw92dnb4+uuvlR4YWt6GDRuGU6dOITQ0FOnp6TA1NcWHH34IPz8/he6FpTlOJdGoUSPUq1cPd+/eRdu2bWFpaVmi5YyMjLBhwwYEBQUhMjISkZGR0NfXh729PT755BN5vZJ+51haWsLT0xMxMTE4fvw48vLyYGFhgX79+sHX1/edGCtIqmWpqUNP+m9rtGV6CizTU3DX2BwlfyobUdUiEVVx9C8Rvff27t2LefPmYdWqVSW6892nTx9YW1tjzZo1FRBdxSjsSfdEROXtqb4PamamK5VfsbRBy4RllRARvUqyMLfEdcUMrbcYybuFY3SIiIiI3nPqKsYhCgANnr7dSXqI3iZ2XSOqplJSUhQGsKuio6PzxuNvsrOzFSYLKIy5OTs/ADxeVVFBQUGJJvioUaNGkc+tIXqXaUtVJzoSdvyhdxgTHaJqaurUqbh06VKRdXr37o25c+e+0XYiIiIwb968YutdvHjxjbZTXfB4VT1PnjyBp6dnsfVK2o2S6F2kn6fcNUoNQNlGN1L543OLyoJjdIiqqRs3bhQ71auFhYXKJ8yXRmJiosrpg19XEc8EehfweFU9OTk5uHLlSrH1GjduLH8OF1F1I5X0L3w8g9hZkaGQCpKFRffQeJWYwZZnGSY6RERERO+5whKdHADaTHQqHROdsuFkBERERESkUjbHpdE7jGN0iIiIiN5zBVB991v/o8YVHQqpwiE6ZcIWHSIiIqL3nHRAO7w+lkEA0Dg0txKiISofTHSIiIiI3nPaYdOQY6IPKV4mOPmQQPK1d2WHRfRG2HWNiIiIiKD+JAjrNmyApECKEaNGAhyfQ+84tugQERERkZxQ589Dqh54JRMRERERUbXDrmtERERERFUZZ10rE7boEBERERFRtcNEh4iIiIiIqh0mOkREREREVO0w0SEiIiIiomqHiQ4REREREVU7THSIiIiIiKja4fTSRERERERVGaeXLhO26BARERERUbXDRIeIiIiIiKodJjpERERERFTtMNEhIiIiIqJqh4kOERERERFVO0x0iIiIiIio2uH00kREREREVZmE80uXBVt0iIiIiIiqublz58LAwKCyw6hQTHSIiIiIiKjaYdc1IiIiIqKqjD3XyoQtOkRERERE77lr166hZ8+e0NfXR40aNeDt7Y1//vlH/v7IkSPRqVMn+evExESoqamhTZs28rL09HRoampi+/btFRp7YZjoEBERERG9xx4+fAhXV1c8f/4cmzdvxqpVq3Dp0iW4ubkhLS0NAODq6ooLFy4gOzsbABAdHQ1tbW1cvnxZXuf06dPIz8+Hq6trpe3Lq9h1jYiIiKoNIYT8RxeVTl5eHrKysgAAqamp0NTUrOSIqj9DQ0NIqsCMakuWLEFeXh6OHDkCU1NTAECrVq3QpEkTBAcH4/PPP4erqytycnJw7tw5uLm5ITo6Gv369cORI0dw6tQpuLu7Izo6Gg4ODrC0tKzkPXqJiQ4RERFVG2lpaahRo0Zlh/HO++KLLyo7hPdCSkoKjIyMiq0nprzdn+wnTpzARx99JE9yAKBRo0Zo0aIFTp48ic8//xz29vawsbFBdHS0PNEZM2YMsrKyEBUVJU90qkprDsBEh4iIiKoRQ0NDpKSkVPh209PT4eHhgf3797/TU/hWl/0A3o19MTQ0rOwQAAAvXrxAy5YtlcotLS2RlJQkfy1LcFJTU3H16lW4uroiIyMDYWFhyMnJwfnz5+Hv71+BkReNiQ4RERFVGxKJpER3yMubmpoa1NXVYWRkVGV/VJdEddkPoHrty9tmamqKp0+fKpU/efIEDg4O8teurq6YNGkSjh8/DnNzczRq1AgZGRmYNm0aIiMjkZOTozBhQWXjZARERERERO8xFxcXHDt2DC9evJCX3bp1C3/88QdcXFzkZbIWnJ9++kneRa1ly5bQ1dXFd999h9q1a8POzq6iwy8UW3SIiIiIiN4DBQUFCAsLUyqfOHEiNmzYgB49emDWrFnIzs7G119/jTp16mDEiBHyeo0aNULNmjURFRWFZcuWAQDU1dXRsWNHHDx4EEOHDq2oXSkRJjpEREREb0hLSwv+/v7Q0tKq7FDeSHXZD6B67Ut5yc7OxsCBA5XKN23ahKioKEyZMgVDhw6Furo6unfvjp9++klpHJGrqyvCwsIUJh1wc3PDwYMHq9REBAAgEUKIyg6CiIiIiIioPHGMDhERERERVTtMdIiIiIiIqNphokNERERURtHR0Rg8eDA6dOiA/v37Izw8vNhl8vLy8PPPP8Pf3x8uLi5wcnJCcnLyW481NjYW48aNg4uLC3r27Imff/4ZeXl5xS4nhEBwcDA8PDzQsWNH+Pn54dq1a2893sKUdT+2b9+OL774At26dYOTkxOOHj1aAdFSZWKiQ0RERFQGV65cwdSpU9GsWTMsW7YM3bt3xzfffFPsD+js7Gzs3r0bWlpaaNWqVYXEmpqaijFjxiA/Px8//PADxo0bh127duGnn34qdtmNGzdi9erVGDJkCJYsWQJzc3P83//9Hx49elQBkSt6k/3Yv38/kpOT0bFjxwqIlKoCzrpGREREVAbr1q1D06ZNMXPmTACAk5MTHj16hNWrV6Nbt26FLmdoaIjff/8dEokEe/fuxZkzZ956rDt27EBGRgZ++OEH1KhRA8DLqYYXLVqEzz77DBYWFiqXy8nJwYYNGzBs2DD51MGtWrVC//79sXnzZkyfPv2tx/6qsu4HAAQFBUFNTQ3x8fHYv39/RYVMlYgtOkRERESllJubi4sXLyolND169MD9+/cRHx9f5PISieRthqfk9OnTaNu2rTw5AIDu3btDKpXi7NmzhS73xx9/ICMjQ2E/NTU10aVLF5w6deqtxqxKWfcDANTU+LP3fcMzTkRERFRKjx49Qn5+vtJT4O3t7QG8HEdSlcTGxirFamhoCHNz8yJjlb2naj8TEhKQnZ1dvoEWo6z7Qe8nJjpEREREpZSamgoASg9TNDIyUni/qkhNTVWKFXgZf1GxpqamQktLC9ra2krLCSGQlpZW7rEWpaz7Qe8njtEhIiIiApCeno7ExMRi69WqVasCoiGiN8VEh4iIiAjA0aNHsWDBgmLrhYWFyVtu0tPTFd6TtSrI3q8qjIyMlGIFgLS0tCJjNTIyQm5uLnJychRaddLS0iCRSFS2rrxNZd0Pej8x0SEiIiIC4OXlBS8vrxLVzc3NhYaGBmJjY+Hs7CwvL2xMS2Wzs7NTGsMia8EqKlbZew8ePICDg4O8PDY2FlZWVtDR0XkL0RaurPtB7yeO0SEiIiIqJS0tLTg5OeHYsWMK5REREbC3t8cHH3xQSZGp1qFDB5w/f15hTM3Ro0ehpqaG9u3bF7pc8+bNoa+vr/BsoPz8fERGRlbK82jKuh/0fmKLDhEREVEZjBo1CqNHj8Z3332Hbt26ISYmBocOHcLChQsV6rVr1w4eHh6YPXu2vOzUqVPIysrC9evXAQDR0dHQ09ND3bp1Ubdu3XKPdcCAAQgNDcXkyZPx2Wef4enTp/j555/Rv39/hWfPjB07Fo8fP8bu3bsBANra2vDz88OaNWtgYmKC+vXrY/v27UhJScGwYcPKPc63tR8AcP36dcTHxyM5ORkA8OeffwIATExM4OjoWJG7QRVEIoQQlR0EERER0bsoKioKK1euxIMHD2BlZYURI0agb9++CnWcnJzQu3dvzJ07V17Wp08fPH78WGl9/v7+GD169FuJ9f79+/jhhx9w9epV6Ovrw8PDA+PGjYOmpqa8TkBAAB4/foy9e/fKy4QQCA4ORlhYGF68eAEHBwdMmjQJzZs3fytxFqes+zF37lzs27dPaX2tW7fGmjVrKiR2qlhMdIiIiIiIqNrhGB0iIiIiIqp2mOgQEREREVG1w0SHiIiIiIiqHSY6RERERERU7TDRISIiIiKiaoeJDhERERERVTtMdIiIiIiIqNphokNERERERNUOEx0iIiJ6p4wYMQISiaSywwAA/Pnnn9DQ0EBERIS87Pjx45BIJAgODq68wKhKCA4OhkQiwfHjx8u0PK8l1a5cuQI1NTVERUUVWY+JDhERURVw7949BAQEoFGjRtDT04OJiQkaN24MX19fREZGKtS1s7PDhx9+WOi6ZIlAYmKiyvdv3LgBiUQCiUSCEydOFLoeWR3Zn46ODho0aIBJkyYhKSmpbDtazUyaNAkdO3ZE9+7dKzuUChEbG4u5c+fiypUrlR0KVZDk5GTMnTu3zMlaWRV1rbVs2RJeXl6YPHkyhBCFrkPjLcZHREREJXDx4kW4ublBU1MTPj4+aNq0KbKysnDnzh0cOXIEhoaG6NKlS7ltb/369TA0NISuri6CgoLQqVOnQuu2bNkSkydPBgAkJSXhwIEDWLJkCSIiIhATEwMtLa1yi+tdc+bMGURERGD37t0K5a6ursjKyoKmpmblBPYWxcbGYt68ebCzs0PLli0rOxyqAMnJyZg3bx4AoHPnzhW23eKutS+++AJubm44cOAAPDw8VK6DiQ4REVElmzdvHjIzM3HlyhW0aNFC6f2EhIRy21ZeXh42bdqEgQMHokaNGlizZg2WLVsGQ0NDlfVr1aqFYcOGyV9PmDABffr0wb59+7Bnzx4MHDiw3GJ71wQGBsLc3By9evVSKFdTU4OOjk4lRUX0fujUqRPs7OywatWqQhMddl0jIiKqZHfu3IGZmZnKJAcArKysym1be/fuxdOnT+Hr64sRI0YgIyMDoaGhpVpHz549AQB///13oXVWrlwJiUSC8PBwpfekUilsbGwU7tIeOXIEn3zyCerWrQtdXV0YGxujR48exfbBl+ncuTPs7OyUymNjYyGRSDB37lyFciEEVq5cCUdHR+jp6cHAwABdunRR6iZYmPz8fOzevRvdunVTarlRNa7i1bLAwEA0bNgQOjo6aNasGfbt2wcAuHbtGtzd3WFkZAQzMzNMmDABeXl5Kvfz3r176Nu3L2rUqAEjIyP069cP9+7dU6grlUrx3//+F66urrCysoKWlhbq1KmDsWPH4vnz5yr3a8eOHejcuTOMjY2hp6eHhg0bYsKECcjNzUVwcLC8ZdHPz0/epbEkd/ljY2MxfPhwWFpaQltbG/Xq1cPMmTORmZmpUG/u3LmQSCS4desWZs6cCRsbG2hra6NFixY4cOBAsdsB/h0Xc+zYMcyfPx+2trbQ1dVFu3btcPbsWQBAVFQUXFxcoK+vD2tra3zzzTcq17V792507NgR+vr6MDAwQMeOHbFnzx6VddeuXYtGjRpBW1sb9evXx9KlSwvtVpWSkoJp06ahfv360NbWhoWFBQYPHqx0DkurpMe5qHFuEokEI0aMAPDyurW3twfw8oaM7JzLPmuvfr62bt2K5s2bQ0dHB3Xq1MHcuXORn5+vsO6Sfk5Lcq1JJBL07NkThw4dQnp6usp9YYsOERFRJatXrx5u3bqFnTt3on///iVapqCgoNAxODk5OYUut379etjb26NTp06QSCRo1aoVgoKCMGrUqBLHe+fOHQCAubl5oXU+/fRTfPnllwgJCYGnp6fCe8eOHUNcXJy8Sxzw8odNUlISfHx8YGNjg7i4OKxbtw5du3ZFZGRkkd3rymL48OHYunUrvL294efnh5ycHPz666/o3r07du7cqRTz62JiYpCeno62bduWarsrVqzAixcvMGrUKOjo6GDZsmXo168ftm/fDn9/fwwePBheXl44cuQIli9fjpo1a+Lrr79WWEdGRgY6d+6Mdu3aYeHChbhz5w4CAwNx9uxZXL58WZ4Y5+bm4ocffsCAAQPQt29f6Ovr48KFC1i/fj1Onjyp1PVw1qxZ+Pbbb9GkSRN8+eWXsLa2xt27d7Fjxw7Mnz8frq6umDlzJr799lsEBATIz4mlpWWR+/zgwQO0bdsWKSkpGDduHBo0aIDjx49j4cKFOHXqFI4dOwYNDcWfpL6+vtDU1MSUKVOQm5uLpUuXwsvLC7dv31b5Q1mV6dOno6CgABMnTkRubi4WL16MHj16ICQkBCNHjkRAQACGDh2K3377DbNnz4a9vb1C62VgYCDGjx+PRo0aYfbs2QBeXqdeXl5YvXo1AgIC5HWXLl2KL7/8Ei1atMC3336LzMxM/Pjjj6hZs6ZSXCkpKejQoQP++ecffPbZZ2jatCkeP36MwMBAtGvXDhcvXoStrW2J9vFNj3NxGjdujCVLluDLL79Ev3795N9PBgYGCvXCw8Nx7949jB8/HlZWVggPD8e8efPw4MEDbNiwodT7UtJrzdnZGatXr8bJkyfh7u6uvCJBREREler06dNCU1NTABANGjQQfn5+IjAwUFy/fl1lfVtbWwGg2L9nz54pLBcXFyfU1dXFnDlz5GVLly4VAFRuC4Do0aOHePbsmXj27Jm4ffu2+Omnn4SmpqaoUaOGePLkSZH75e3tLbS1tUVSUpJC+bBhw4SGhobC8unp6UrLJyQkCDMzM/Hxxx8rlPv6+orXf8K4ubkJW1tbpXXcv39fAFDY5507dwoAYvXq1Qp18/LyhKOjo7CzsxNSqbTIfQsKChIAxJ49e5Tei4yMFADEhg0blMo++OADkZycLC+/evWqACAkEonYsWOHwnpat24trKyslPYTgJg4caJCuWyfRo8eLS+TSqUiMzNTKb5169YJACI0NFRedu7cOQFAdOnSRWRlZSnUl0ql8uOhat+KM2TIEAFA7N+/X6F8ypQpAoBYt26dvGzOnDkCgPDw8FA4B+fPnxcAxPTp04vd3oYNGwQA0apVK5GTkyMv37NnjwAgNDQ0xIULF+TlOTk5wsrKSrRv315elpSUJPT19UW9evVESkqKvDwlJUXUrVtXGBgYiBcvXgghhHjx4oXQ09MTjRs3FhkZGfK6Dx8+FPr6+gKAiIyMlJdPmDBB6OjoiCtXrijEHRsbKwwNDYWvr6+8rDTHuzTHWdVnSAaAQgyqPkOvv6empiZiYmLk5VKpVHh5eQkA4syZM/Ly0nxOS7LvJ06cEADEjz/+qPJ9dl0jIiKqZM7OzoiJiYGvry9SUlKwYcMGjBs3Dk2aNIGrq6vK7ix2dnaIiIhQ+dejRw+V2wkODoZUKoWPj4+8bOjQodDU1ERQUJDKZY4cOQILCwtYWFjAwcEBkyZNQpMmTXDkyBGVd6tf5evri5ycHIWucenp6di1axfc3d0VltfX11eo8/z5c6irq6Ndu3Y4d+5ckdsprc2bN8PQ0BBeXl5ITEyU/yUnJ6NPnz6IjY2Vt1oV5tmzZwAAU1PTUm17xIgRqFGjhvx18+bNYWRkhA8++ECpNc/FxQUJCQkqu+VMnz5d4XW/fv3QsGFDhYkRJBIJdHV1AbxsAUxOTkZiYiI++ugjAFA4rr/++isAYOHChUrji2TdhspCKpUiPDwcrVq1UhrLNGPGDKipqWHXrl1Ky02cOFFhm23atIGBgUGx5+VVY8eOVWixkrUKtGvXDk5OTvJyLS0ttG3bVmHdERERyMjIwIQJE2BkZCQvNzIywoQJE5Ceno6jR48CePkZyczMxPjx46Gnpyeva2Njg6FDhyrEJITAr7/+CldXV9SqVUvh+tPX10f79u1x5MiREu+jTFmPc3np3r07WrduLX8tkUjw1VdfAcBb3a6ZmRkA4OnTpyrfZ9c1IiKiKqBZs2byMR0PHjxAVFQU1q1bhxMnTqBv375K3Yz09fXRrVs3levavHmzUpkQAkFBQWjevDmkUqnC+JqOHTti06ZNWLhwoVLXlnbt2mHBggUAAG1tbdja2qJOnTol2idZMhMSEoIxY8YAeDkGJCMjQyHZAoC7d+9i1qxZOHz4MJKTkxXeK+9n5ty4cQNpaWlFdrl68uQJHBwcCn1fFpMoYmpbVerWratUZmJigtq1a6ssB4Dnz58rdBUyNjZWOW6rcePG2L17NzIyMuSJ42+//YbFixfj8uXLSuN9Xrx4If//O3fuQCKRFDpOrKyePXuG9PR0NG3aVOk9U1NTWFtbq0zkVR0nMzOzQscWqfL6OmTHUzbm5PX3Xl33/fv3AUBl3LIyWdyy/zZq1EipbpMmTRReP3v2DM+fP5ffQFBFTa307RBlPc7lpXHjxkplsn1/m9uVff4K+45gokNERFTF2NrawsfHB8OHD0enTp1w6tQpnD9/Hi4uLmVeZ1RUFO7evQsAaNCggco6+/btg5eXl0KZubl5oQlVcTQ0NDBkyBAsXboUf//9N+rXr4+QkBCYmJgojIFJT0+Hq6srMjIy8MUXX6BZs2YwNDSEmpoaFi5ciN9//73YbRX2Q+f1wdDAyx9HFhYW2LJlS6HrK+o5RQDkP1JL+zwhdXX1UpUDpU+mZHbu3IlPPvkEbdu2xc8//4zatWtDR0cHBQUFcHd3h1QqVaj/Ji035a2w41GaY1GWY/22yeLv1q0bpk2bVmlxlObzUpW3K/v8FZY0MtEhIiKqoiQSCdq1a4dTp04hLi7ujdYVFBQEbW1thISEqLxjPHr0aKxfv14p0XlTvr6+WLp0KUJCQuDv74/jx48jICAA2tra8jrHjh1DfHw8goKC4Ofnp7D86wPxC2NqaoqYmBilclV3kxs0aIDbt2+jffv2SoOqS0qWCJWmK1V5SU5ORkJCglKrzo0bN1CzZk15a86mTZugo6ODyMhIhS5VN2/eVFqng4MDDh48iKtXrxY5wUJpEyELCwsYGhrir7/+UnrvxYsXePz4cZV8Ho+sNeivv/5C165dFd67fv26Qh3Zf2/evFloXRkLCwsYGxsjNTW1zDcQVCntcZZ1uUxKSlLofqnq81KSc37jxg2lstePk2y7Jf2clmS7spbpwm5McIwOERFRJYuIiFB5RzMrK0veX//1LjClkZKSgrCwMPTo0QODBg2Ct7e30p+npycOHjyIx48fl3k7qrRs2RLNmzfH5s2bsWnTJkilUvj6+irUkd1hf/1u/ZEjR0o8PsfBwQFpaWk4f/68vEwqlWLJkiVKdX18fCCVSjFjxgyV63ry5Emx22vVqhWMjIzk0xVXtO+++07h9a5du3Dr1i2FRFVdXR0SiUSh5UYIIe+K+KohQ4YAAGbOnInc3Fyl92XnRpYYlrQlS01NDX369MHly5dx6NAhpX2QSqXo169fidZVkbp37w59fX0sX74caWlp8vK0tDQsX74cBgYG6N69u7yurq4uVqxYoTCN86NHj5RaDdXU1DB06FCcP38eYWFhKrdd2HiTopT2OMu6ZcrGGcksXrxYad0lOecRERG4dOmS/LUQAt9//z0AKFyTpfmclmS7Z8+ehYaGBjp27KjyfbboEBERVbIvv/wSz58/h6enJ5o1awY9PT08fPgQW7Zswe3bt+Hj44NmzZqVef1bt25FVlYWBgwYUGidAQMGIDg4GBs3blQa6P6mfH19MXnyZCxatAgODg5o3769wvsuLi6wsrLC5MmTERsbCxsbG1y5cgWbNm1Cs2bNcO3atWK3ERAQgMWLF6Nfv36YOHEitLS0EBYWpjKBlE0p/csvv+DSpUvo3bs3zM3N8ejRI5w5cwZ///13seMK1NXV0b9/f+zevRs5OTkKLVRvm7m5OXbu3In4+Hh07txZPr20paWlwvOCvL29sWPHDnz00Ufw8fFBXl4edu/erfRMFQBo27Ytpk2bhkWLFqF169b45JNPYGVlhfv37yMsLAznz5+HsbExmjRpAkNDQwQGBkJPTw/GxsaoWbOmfIIDVb799ltERETAy8sL48aNQ/369REdHY3Q0FC4uroqJb5VgbGxMb7//nuMHz8e7dq1kz9XJjg4GH///TdWr14tn1TCxMQE33zzDaZMmYIOHTrAx8cHmZmZWLVqFRo0aIDLly8rrPu///0vTp06hUGDBmHQoEFo3749tLS08ODBAxw4cACOjo4Kz2AqqdIc58GDB2PmzJkICAjAzZs3YWpqikOHDqmcst7MzAz169fHtm3bUK9ePVhaWkJfXx99+vSR12nRogU++ugjjB8/HtbW1tizZw+OHj2K4cOHw9nZWV6vNJ/T4q41IQQOHToEd3f3wltmC52vjYiIiCrE4cOHxbhx40Tz5s2FmZmZUFdXF6ampqJz585i/fr1oqCgQKG+ra2taNq0aaHrk00dK5te2snJSWhoaChN8/yq7OxsYWhoKBwcHORl+N80v28qISFBaGhoCABiwYIFKutcvXpV9OzZUxgbGwsDAwPh5uYmoqOjVU6DW9jUuPv37xctWrQQWlpawtraWnz11Vfi5s2bhU6NGxISIlxcXIShoaHQ1tYWtra2ol+/fmLbtm0l2i/ZlMxhYWEK5UVNL61qqlxbW1vh5uamVC6bavn+/fvyMtn0vHfv3hWenp7C0NBQGBgYCE9PT3Hnzh2ldaxZs0Y0btxYaGtrCysrK+Hv7y+eP3+uNIWwzJYtW0SHDh2EgYGB0NPTEw0bNhQTJ05UmKZ5//79olWrVkJbW1sAUBn76+7duyeGDRsmLCwshKamprC3txczZsxQmI65sH0u7ji9Tja99KtTOssUtt+FXVM7d+4Uzs7OQk9PT+jp6QlnZ2exa9culdtdtWqVcHBwEFpaWqJevXpiyZIl8mnIX48lIyNDzJ8/X3z44YdCR0dHGBgYiEaNGolRo0aJs2fPyuuVdjrvkh5nIYQ4e/as6NChg9DW1hZmZmbC399fvHjxQuUxOnfunOjQoYPQ09MTAORTRL86LfSWLVtEs2bNhJaWlrCxsRH/+c9/RG5urtJ2S/M5LepaO378uAAg9u3bV+jxkAhRxhFuRERERO85d3d3ZGRk4MSJExWyvc6dOyM2NhaxsbEVsj2iosTGxsLe3h5z5sxRaE2sCP369cPDhw9x4cKFQsfzcIwOERERURktXrwYZ86cKdOzT4iobC5fvow9e/Zg8eLFRU5awDE6RERERGXUtGnTtz4lLxEpatWqldL06KqwRYeIiIiIiKodjtEhIiIiIqJqhy06RERERERU7TDRISIiIiKiaoeJDhERERERVTtMdIiIiIiIqNphokNERERERNUOEx0iIiIiIqp2mOgQEREREVG1w0SHiIiIiIiqHSY6RERERERU7fw/xv46VIYtuJIAAAAASUVORK5CYII=",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot summary\n",
+ "shap.summary_plot(shap_values.values, X_test_shap)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Interpreting the SHAP Summary Plot\n",
+ "\n",
+ "Each point on a row represents a SHAP value for a single prediction (row = feature).\n",
+ "The x-axis shows how much the feature contributed to increasing or decreasing the prediction.\n",
+ "* Right (positive SHAP value): pushes prediction toward the positive class (i.e., higher chance of incident).\n",
+ "* Left (negative SHAP value): pushes prediction toward the negative class (i.e., lower chance of incident).\n",
+ "\n",
+ "Color shows the actual feature value for that point:\n",
+ "* Red = high value\n",
+ "* Blue = low value\n",
+ "\n",
+ "In other words:\n",
+ "* The position tells you impact.\n",
+ "* The color tells you feature value.\n",
+ "* The density (thickness) of dots shows how often a value occurs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Show the individual prediction for the highest predicted instance\n",
+ "highest_pred_index = np.argmax(shap_values.values[:, 0]) \n",
+ "\n",
+ "# Use waterfall plot for a single instance\n",
+ "shap.plots.waterfall(shap_values[highest_pred_index], max_display=20)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAABNsAAAPZCAYAAAAoeixUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1hT59sH8O8JhLBlykZwATLELe69Zyu4W7cdjlfraK3Wra22ttVOrauuqqh1r+KsWkFREReKExVERBmyc94/+CUSEzAgEMDv57q4NM95znnuc3ISyJ1nCKIoiiAiIiIiIiIiIqK3JtF1AERERERERERERBUFk21ERERERERERETFhMk2IiIiIiIiIiKiYsJkGxERERERERERUTFhso2IiIiIiIiIiKiYMNlGRERERERERERUTJhsIyIiIiIiIiIiKiZMthERERERERERERUTJtuIiIiIiIiIiIiKCZNtRERERERERERExYTJNiIiIiIiIiIiomLCZBsRERERFRu5XI558+ahWrVqkEqlqFatGhYtWgRPT0/I5fJCH++3336Dq6srMjIySiBaIiIiouIniKIo6joIIiIiIqoYfvrpJ4wbNw6fffYZ/Pz8UKlSJQwdOhTffvsthg4dWujjpaenw83NDdOmTcO4ceNKIGJ1OTk5OHfuHG7evImMjAxYWVmhQYMGcHZ2LrH9w8PDce7cOVhaWiIwMFBt+4sXLxAWFoa4uDikp6fD1NQU1atXR+3ataGvr1/kcyUiIqLix55tRERERFRsVq9ejfbt22Px4sUYPHgwbt++jezsbPTv379IxzM0NMSHH36IJUuWoLS+Iz527BgiIiJQvXp1NGnSBBKJBPv370dsbGyJ7J+SkoKLFy/mmzRLSUnBjh078OTJE3h7e6NJkyaws7PD+fPnERISUuTzJCIiopLBZBsRERERFYv09HRcunQJLVq0UJatXr0aPXr0gKGhYZGPGxQUhHv37uHo0aPFEWaBnjx5gujoaDRs2BCNGzeGl5cXunbtCjMzM5w9e7ZE9v/vv/9QuXJl2Nraatx+8+ZNZGZmolOnTvD394eXlxdatWqFGjVq4N69exxiS0REVMYw2UZEREREb2348OEwMjJCTk4Opk+fDkEQ4ODggIiICLRr106t/sOHD2FoaIhhw4aplP/zzz+QSqWYMGGCsqxevXqwsrLCzp07S/w8bt++DUEQ4OXlpSzT19eHh4cH4uLikJKSUqz7P378GHfu3EGTJk3yPWZmZiYAwNjYWKXc2NgYgiBAIuGf9ERERGUJfzMTERER0VsbOHAgRo8eDQD48ccfsW7dOnz00UcAgLp166rVd3JywogRI7B+/Xrcu3cPAHD9+nUEBgaic+fO+O6771Tq161bF6dOnSowBrlcjvT0dK1+8huSmpCQgEqVKsHAwEClvHLlysrtBSnM/nK5HKdOnYKnpyesrKzyPaajoyMA4Pjx43j69ClSUlIQHR2Nq1evwtvbG1KptMCYiIiIqHRxNlUiIiIiemtt2rRBSEgITExMMGbMGEgkEsyYMQMA4O7urnGfL774An/88Qe++eYbzJ07F926dYObmxs2bdqk1luratWqWLduXYExxMbGYs+ePVrF279/f5iZmamVv3z5Uq0HGfCqV1lqamqBxy3M/teuXUNKSgq6du1a4DFdXFxQv359XLhwQZmYBIA6deqgQYMGBe5LREREpY/JNiIiIiIqFhEREfD29lYmyhISEqCvrw9TU1ON9Z2cnDBy5EisWLEC4eHhSEtLw/Hjx2FiYqJW19LSEmlpafkmswDA2toaXbp00SpWIyMjjeXZ2dnQ09NTK1eU5eTkFHhcbfdPT0/HuXPnULdu3XxjycvMzAwODg5wd3eHoaEh7t+/jwsXLsDIyAg+Pj5v3J+IiIhKD5NtRERERFQsLl26hI4dOxZqn0mTJuGnn35CREQETp48CScnJ431FMM+BUHI91gymQzOzs6Fav91+vr6GhNqijJNibSi7B8WFgaZTAZvb+83xnTr1i2cOHECffv2VSYu3d3dIYoiQkNDUb169bdagIKIiIiKF5NtRERERPTWnj9/jgcPHsDX11dZZm1tjezsbCQnJ2scsgkA8+fPB5DbI6ygecsSExNhbGxcYC+wnJwcrVfmNDQ01LiwgLGxscahoi9fvgQAjb3uCrv/ixcvcP36dQQEBCjLFfHL5XIkJydDKpUqE2hXr16FjY2NWg/BKlWqICoqCk+fPn3rJCMREREVHybbiIiIiOitRUREAAD8/PyUZZ6engCAO3fuqJQrLF68GH/88Qd++uknTJ48GfPnz8cff/yh8fh37txRWeFTk7i4uLees83a2hqPHj1CZmamyiIHT548UW4viDb7JyUlQRRFnD59GqdPn1Y7xqZNm+Dj46NcoTQtLQ0ymUytnlwuB4B8F3sgIiIi3WCyjYiIiIje2qVLlwCoJtsCAgIAAOfOnVNLtv3999/4/PPPMXfuXHz66ae4efMmfvnlF3z55ZcaF1QIDw/HwIEDC4yhOOZsq1q1KiIiInDt2jXUrl0bQG6Psxs3bqBy5crK3mXZ2dlISUmBoaGhyhBObfbX19dHhw4d1NoOCwtDVlYWmjRpAnNzc2V5pUqVEBMTg+fPn8PCwkJZHh0dDUEQCuwRSERERKWPyTYiIiIiemsRERFwcnJSSfxUrVoVPj4++OeffzBs2DBl+fnz5zFw4EAMHDgQX375JQBgypQp+O233zT2bjt//jyePXuGnj17FhhDcczZVrlyZVStWhWhoaFIS0tDpUqVEBUVheTkZLRs2VJZ78mTJ9izZw/q1q2L+vXrF2p/Q0NDuLm5qbV9+fJlAFDbVrt2bTx48AC7d++Gt7c3ZDIZ7t+/jwcPHsDT0/ONQ1uJiIiodKlPVEFEREREVEgREREah4oOGzYMu3fvRlpaGgAgJiYG3bt3R506dbBixQplPUdHRwwbNgx//vkn7ty5o3KMrVu3wtXVFW3atCnZk/ifVq1awdfXFzdv3sTp06chl8vRqVMnODg4lMr+r3NwcEDPnj1hY2ODK1eu4MyZM0hKSkKDBg3QrFmzIh2TiIiISo4gcpIHIiIiIiohL168QNWqVbFo0SIMHz680PtnZGTAzc0Nn3/+OcaPH18CERIREREVL/ZsIyIiIqISU6lSJUyZMgWLFy9WTuhfGKtXr4ZUKsVHH31UAtERERERFT/2bCMiIiIiIiIiIiom7NlGRERERERERERUTJhsIyIiIiIiIiIiKiZMthERERERERERERUTJtuIiIiIiIiIiIiKCZNtRERERERERERExYTJNiIiIiIqVUlJSWjVqhWSkpJ0HQoRERFRsWOyjYiIiIhKVVJSEo4fP85kGxEREVVITLYREREREREREREVEybbiIiIiIiIiIiIigmTbURERERERERERMWEyTYiIiIiKlXm5uZo0qQJzM3NdR0KERERUbETRFEUdR0EEREREb1bLl68CH9/f12HQURERFTs2LONiIiIiIiIiIiomLBnGxERERGVuvT0dBgaGuo6DCIiIqJix55tRERERFTqHj58qOsQiIiIiEoEk21EREREVOqSk5N1HQIRERFRiWCyjYiIiIhKnUwm03UIRERERCWCc7YRERERUanLycmBnp6ersMgIiIiKnbs2UZEREREpe7y5cu6DoGIiIioRDDZRkREREREREREVEyYbCMiIiKiUle5cmVdh0BERERUIphsIyIiIqJSZ2hoqOsQiIiIiEoEk21EREREVOru37+v6xCIiIiISgSTbURERERERERERMVEEEVR1HUQRERERPRuefnyJYyNjXUdBhEREVGxY882IiIiIip1cXFxug6BiIiIqEQw2UZEREREpe7Fixe6DoGIiIioRDDZRkRERESlTiqV6joEIiIiohLBOduIiIiIiIiIiIiKCXu2EREREVGpu3jxoq5DICIiIioRTLYREREREREREREVEybbiIiIiKjU2djY6DoEIiIiohLBZBsRERERlTpTU1Ndh0BERERUIphsIyIiIqJSd/fuXV2HQERERFQimGwjIiIiIiIiIiIqJoIoiqKugyAiIiKid0tKSgqHkhIREVGFxJ5tRERERFTqEhISdB0CERERUYlgso2IiIiISl1iYqKuQyAiIiIqEUy2EREREVGp09PT03UIRERERCWCc7YREREREREREREVE/ZsIyIiIqJSFxERoesQiIiIiEoEk21EREREVOrkcrmuQyAiIiIqEUy2EREREVGps7Ky0nUIRERERCWCyTYiIiIiKnUWFha6DoGIiIioRDDZRkRERESl7vbt27oOgYiIiKhEMNlGRERERERERERUTARRFEVdB0FERERE75YXL16gUqVKug6DiIiIqNixZxsRERERlbqkpCRdh0BERERUIphsIyIiIqJSl5CQoOsQiIiIiEqEvq4DICIiIqJ3jyAI+W47/VBEZg5nOiGiXBJBQDPn3H+JiMoDztlGRERERGVG2GMRDTfk6DoMIipj5jRIw4yWZroOg4hIKxxGSkRERESlLjIyUmP57DM50GfnFSLKQ18CJInGug6DiEhrTLYRERERUanLzs5WK7sQJ2LvbSCb4y6IKA8BwJP4J7oOg4hIa0y2EREREVGps7CwUCubfZq92oiIiKj84wIJRERERFTqbGxsVB5HxIvYGa2jYIiozDMy4jBSIio/2LONiIiIiErdrVu3VB7POS1nrzYiypdUKtV1CEREWmOyjYiIiIh06spTEdtvipyrjYjylZT0QtchEBFpjck2IiIiIip1bm5uyv/POyOHHnu1ERERUQXBZBsRERERlbrU1FQAwPUEEZtvsFcbERXMwsJS1yEQEWmNCyQQERERUamLj4+Hk5MT5v+X26uNybaKr5IMWNRCgt41BBhLgdDHIj47JseFJ9rt72kFfN9agmZOAjJzgL23RUw8JsfTNNV69ibA7CYStHcTYG8MPEoFdt4SMf8/OZ6lF/95UelIT0sDYKjrMIiItMJkGxERERHpxK1EERuuiWCereITAOx9Tw+1bYHFYbkJsk/8JTjWVw/11uXg1vOC93cyBU7008OLDGDaSTlMDYBJ9SXwtdVDw/U5yJLn1jORAmcG6MFECvxyUcSDZBG1bQWMqSOgtUtuW7zfyqf0DGZKiaj84DBSKlMWLlwIQRAQHBys61DeWlJSEgIDA2FjYwOJRAJbW1tdh1Roo0aNgiAIiIyMVJaV5HPk6+tbrNcpODgYgiBg4cKFxXbM0lTe4ycqik6dOkEQOHnXu6B27drKXm1U8fXxENDUScCQA3LMOSPil4siWm3OQY4IzG765o8k0xpLYCIF2mzJwbILIhaeFRG0Ww7/ygKG+Ly6iXpUE+BWKbedWaflWHlZxLgjciw8K6KOnYDalUvyLKkkCQI/uhJR+cGebSVIFEVcvnwZ165dQ0pKCgwNDVG1alXUr1+fS1e/Az777DMEBwdjwIABqF27NiwsLHQdElUAkZGRWLp0Kfr374/WrVvrOhzSkeDgYBw6dAhz586FnZ2drsMhKpLD52/iz6tVIWc3o3dCn5oCYlNFbI969YQ/TQO23BAxqJYAAz0gMyf//d+vIWDPbREPkl+VhdwXceOZiCAPASsico9rLsvdFpeqemM9/t/jtOziOR8qfbY2NroOgYhIa0y2laAzZ84gMjISbm5u8PPzw/PnzxEZGYmEhAR07dqV39xXcCdPnoSrqys2bNig61CK1aRJkzB27FgYGRnpOpQKr3fv3khOToZMJlOWXb9+HStWrIC7uzuTbe+wQ4cOYcWKFRg3blyFS7bt3LkTWVlZug6DSsFvURbgX0LvjjqVBYTHqQ8ZDn0sYnRtCWpaApFPNe/raArYmQg4FytX2xb6WESXqq/upBMxInLkIn5so4fPjuUgJhnwsxXwZSMJdtyU48azYjwpKlXx8fEA7HUdBhGRVphsKyHPnj1TJto6dOigLDczM8Pp06cRHR2N6tWr6zBCAoCEhARYW1uXyLGfPXtW4T4EA4BUKmXPzFKip6cHU1NTXYdBVGRFeY+VyWQqCWaqmO4nidj12BI57NX2znAwAU7EqJc/zl2UFo6mAiKfar4hHExU676+v7XRq55x1xKAUYfk+LaVBP8NfPVRZ02kHCMOqifrqPzg7I5EVJ5w4HsJiY6OBpA7B1Venp6e0NfXx82bN3URlnIunPj4ePTq1Qvm5uaQSqXw8vLC/v37VeoWNDeXprm1bG1t4evri+PHj8Pf3x+GhoYwNzdH//79kZmZiZSUFAwYMABWVlaQSqXw9vbGf//9pzHOrKwsjBo1CjY2NpBKpahSpQp++OEHjXUPHz6Mpk2bwszMDFKpFI6Ojvj000+RmZmpMeZLly6hefPmMDU1hU0hu6NnZmZi/PjxcHZ2hlQqhampKZo2bYp///1X7brFx8cjMjISgiBAEASMGjVK63YOHDiATp06wcHBATKZDIaGhvDy8sJvv/2mVlfxnN6/fx8dOnSAqakpZDIZateujZCQEJW6inhGjRqF77//HlWqVIFUKoWNjQ1GjRqlds00ye++ePnyJcaMGQMXFxdIpVIYGxujUaNGOHLkiNoxYmNj0a1bN5iZmUEmk8HX1xf//POP1tdHk+XLl8Pd3R1SqRTW1tYYPnx4vuejbax550z74osv4ODgAKlUCgcHB3zxxRcaj71jxw7UqVMHxsbGMDAwQNWqVTFv3jy1eqdPn0aLFi2UrwcLCwv4+flh9erVGtsHcq99YGAgAGDatGnKe+v195mC5L0Hli5dCnd3dxgYGMDe3h4LFiwAAFy7dk35GjE0NES7du2QkJCgcpzQ0FD07t0bLi4uMDQ0hEwmQ7Vq1TB37ly1NhVz/4WFhWHIkCHKc3Zzc8OaNWvU6s+YMQN169aFlZUV9PX1YWFhgXbt2qnMHaiQlZWFjz/+WOW9YunSpRrnGwRy35t79eoFGxsb6Ovrw9LSEt27d8e9e/c0xnzmzBkEBQXBwsJC+boKCwsDAPz666+oWrUqDAwMYGtri9mzZ2u85ps2bULdunVhbGysjHHmzJlq9RTvoaGhoWjYsCEMDQ1hbGyM5s2b4/bt28p6nTp1wooVKwDkvq8V5T0mJSUFo0aNgqOjI2QyGYyNjeHq6oqBAwe+dfxHjhxBvXr1YGxsDC8vL6xduxaCIOT7mvHw8IC5uTkyMjKU56ep5/ft27cRGBiIypUrQyqVwtzcHHXq1MHGjRtV6oWHh6N9+/awsLCAvr4+bG1tMXDgQDx//lyl3vXr19G5c2flvWNubg5PT09888032l5Gegtfn2XSozwTAMj0tPtRMNIHMjQME03PfrU9P4ptGvfPUd//YUpuj7fxR3LQ6+8cfHdOjoFeAr5uwY8+5ZmhIUdVEFH5wZ5tJSQ+Ph6CIKByZdVZWPX19WFtbf2/btAFE0VR+eFDGzKZTOuhqc2aNYOFhQXGjBmDhIQErFu3DkFBQbh79+5b9fSKj49H9+7d0aFDB/Ts2RMhISH466+/oK+vj6ioKKSnp2P06NF4+vQp/vzzT7z33nt48OAB9PT0VI4zffp0pKenKz/4BQcHY8KECUhLS1P5wLZ69WqMGjUK9vb2GDJkCKytrfHff//h119/xeXLl3HixAmV46anp6NNmzbw8/PDxIkTERcXV6jz69ChA44fP446depg8ODBiI2NxebNm9GuXTvs27cPbdq0QZcuXSCVSjFnzhyYmZlhwoQJAICGDRtq3c6mTZtw584ddO7cGW5ubnj69Cm2bduGjz/+GC9fvsTEiRPV9mndujXMzc0xduxYZVxdu3ZFSEgImjZtqlL3n3/+wfr169G3b1/Y29vjwIEDWLFiBR48eKCWdNVGRkYGGjdujGvXrqFDhw4YOnQonj9/js2bN6Nz587Ys2cP2rdvr6zbvHlz3Lp1C+3bt0ejRo1w8eJF9OzZE2ZmZoVuGwB+++03fPLJJ7CxscHo0aOhr6+PrVu34ujRo28Vq8Iff/yBxMREBAUFwczMDH///Te+/vprPHv2DL///ruy3sqVKzFq1ChUqlQJH3zwAczMzLBz507MmDED0dHRykTaw4cP0blzZwBAUFAQ3NzcEB8fjwsXLuDUqVMYOnSoxvPs0qULoqKisGbNGnTv3h0tWrQAADg6Ohb6mh05cgRbtmxBv379YGVlhU2bNuHLL7+ETCbDwoUL0aBBA3z22Wc4d+4c9u3bhw8++AB79+5V7r93716cP38erVq1QtWqVZGSkoI9e/bgq6++wpMnT7Bs2TK1NgcNGgR9fX2MGDECmZmZWLt2LUaMGIH69evDx8dHWe/333+Hj48PBg8eDGtra0RGRmLnzp1o3rw5IiMj4eTkpKwbGBiInTt3ws/PDyNHjkR8fDymTZsGe3v1YSbXrl1D06ZNkZ2djV69eqF69eq4efMmgoOD0bhxY0RERKh9iTB48GAYGxvj448/Rnx8PNatW4cuXbpg8uTJWLBggcr1mzVrFvz9/dGzZ0/l/rNnz8bs2bNRs2ZNjB49Gqampjhy5AjmzJmDW7duqQ0zf/r0KTp06IBWrVqhc+fOuHTpEnbt2oU+ffogPDwcADB27FikpKTg1KlTmDx5svL3TGHeY/r27Yt9+/ahQ4cOCAgIQHZ2NqKiotS+ACls/HFxcejevTvatGmDHj16IDk5GQMGDMD48eOxdetWtcU+wsPDERUVhcDAwAJ7s0VGRqJFixZ48eIFOnTogPr16yMlJQVhYWHYv38/BgwYACD3va1Hjx4wNjZGUFAQnJ2dcenSJWzZsgXnz5/HpUuXIJPJkJmZibZt2yIhIQGBgYHw8PBQTvVw4sQJTJ06VetrSYX3PF3EH5dF5IgcRFpetXARcKyv3psrAvBclY0bz3LnSpNp2MXwf59GCppLTbFN4/56qnWaOAJ73pOg8YYcnP/fn3k7b4lIyhAws4kEqyJze79R+cNez0RUnjDZVkJSU1NhaGiolkQCABMTE8TFxSEnJ0fjdoWUlBRs2rRJ6zb79++vdbKiVq1a2LFjh/Kxr68vxo4di2XLlmHWrFlat/m6uLg4LFu2DGPGjAGQ+0GtWrVq2LBhAxo1aoQLFy5AIsn9VtHGxgZff/01Nm3ahEGDBqkc58WLF7h27Zryg+/06dPh5eWF+fPnY8yYMTAzM0NKSgomTJiAmjVr4sKFCzAwMFDuP3nyZHz77bcIDg5Gnz59lOUpKSkYOnQoVq1aVehz27hxI44fP46WLVviyJEjyvMYMmQI2rRpg7Fjx+LKlSuoXbs2ateujW+++QZWVlaYNGlSodv68ccf1RZUmDdvHjw9PbFkyRKNyTYHBwecOHFCGdfAgQPRoUMHjBs3DufPn1epe/fuXRw+fBht27YFAMyfPx/NmzfHgQMHsG/fPnTp0qVQ8U6fPh2XL1/GunXrVJ7Lr776Cp6enpg4cSIuX74MAPj6669x69YtjBgxQtk7BwCmTJmCxYsXF7q3YVZWFqZPnw4TExOEh4fD2dkZAPDll1/Cy8vrrWJVePjwIS5dugQPDw8AwJw5c+Dn54c//vgDEydOhIeHB7KysjB16lTIZDKEhoYqh4nPmTMHdevWxdq1azF27FjUrVsX+/btQ1JSEn788UeMGzdO63OtXbs2unbtijVr1iAgIKBI95bCgwcPEB4eDm9vbwDARx99hGrVqmHy5MmYOHEivv32W2XdZs2a4eDBg0hMTISlpSUAYMKECWo9uRYtWgR/f3+sXLkS3377rdofxRYWFjhz5ozyHu3cuTM6deqExYsXY+3atcp6UVFRavf/5s2b0a9fPyxevFjZy/X06dPYuXMn6tati9DQUOX76ZAhQ5SJyLyGDRuG7OxshIWFKZ9LAPjggw/QqVMnfPnll1i+fLnKPtbW1iox29ra4uuvv8bMmTNx4cIFeHp6qly/H3/8UZlsi46Oxrx589CqVSuVXpNz585FUFAQNm3ahClTpqB27drKbbGxsWr3xXvvvYcdO3YgLCwMDRo0QNeuXbFz506cOnUKH3zwgUqiUlvHjx9H/fr1cfDgwXzrFCX++Ph4zJkzBzNmzFA5VteuXbFx40acOXMGAQEByvKff/4ZADB69OgC4x06dCgSExPVXrcAkJPzqqvLiBEjYGlpiYiICJUvjn777Td8/PHHWLJkCb744gv8999/ePToEcaPH59vr2ltPXv2DCYmJsr7PSUlBaIoKn8fZ2ZmIjk5WSWex48fw8HBId/HsbGxsLOzU36BVtHaqCQzgI8NcOmJCDlnbSuXrieIGLK/gNUM8nic8r9/U18NB81LUfYoJf8hgorho/ntn5AmKhdXGF1bgrhUKBNtCruiRcxuKqCJo4BrCRyOWB69ePEcgFGFe09kG2yDbZROG6WNfalLSHZ2tvLD2esUHwizswteDsnIyAhdunTR+qcwE9a//kGod+/eAHI/5L4NS0tLZaJNoV69ehBFEePGjVO5JoreQ1evXlU7zoABA1R6mNja2qJ///5ITU3F9u3bAeR++H7x4gUGDhyIJ0+eICYmRvkTFBQEANizZ4/asV/vWaGtrVu3AshNnuQ9j5YtW6Jx48a4evUq7t+/X6Rjvy5voiEpKQkPHz5EUlISGjZsiIcPH+LpU/UZhKdNm6YSV7t27VCnTh1cvHgRiYmJKnXr1KmjTLQBgEQiwbRp0wCgUAlehe3bt8PR0RGtWrVSeR7S09PRqFEjXL16FcnJucuH7dmzBxKJRO15mDVrVpEWXQgJCUFCQgJ69OihTLQBr+6Zt4lVoWPHjirJGSMjI3zyySeQy+XKnj1548g7H6ORkREmTJgAURSxfv16AICVlRUAYP/+/Rqfy9LQvHlzZaINAFxdXeHk5ARBEDBnzhyVugEBAcjJyVF5rea9R1NSUvDw4UM8fvwYLVu2RFpamnKoZV7jx49XuUc7duwImUymMkQy77FzcnIQHx+PmJgYNG3aFEZGRjh37pyy3ubNmwEA48aNU/niolmzZqhXr57KMePj43H27Fk0b94cJiYmKs+9l5cX7O3t1XrCaopZ8b7VtGlTZaIt7/XL+x7wxx9/IDs7G6NHj1ZpLyYmBr1794Yoivj7779V2rO0tFRLwLZr1w4A1JLAb8PY2Bh37tzBqVOn8q1TlPhNTEzw+eefqx3rk08+AZA79FZBLpdj9+7dcHFxUXk/et2jR49w/vx51KtXTy3RBrz6nfrvv//i3r176N69O9LS0lTi7dKlC2QyGQ4fPgwAyj/MFPu8DSsrK5XEsqmpqcoXXwYGBmo9xl//w+/1x/b29io91StaG4IgYE5TCRNt5VjcS2DtFVGrn6T/zehw8YmIunaC2rPeyEFAapaIqES1ZpQepQBPXoqob69+zzR0EHDxyavkmZ0xoKfhT3Dp/8r0+emn3Kto74lsg22wjdJpo7SxZ1sJ0dfXR3p6usZtim/h9fULvvz6+voqyYPi9PocT4phWa8nZQpL0w2t+OD8ei8jxdCn1+eCAqCxp4YiZsV8d4oPnl9++SW+/PJLjfG8PlzXzMysyIsWPHjwAIIgoFGjRmrbPDw8cPr0aVy9ehWurq5FOn5e9+7dw5gxY3DixAkkJSWpbY+Pj1frAdagQQO1etWrV0d4eDiuXbuGJk2aqJS/TjEErSgfPGNiYpCZmQkXF5d86zx8+BCenp549OgRLCws1OI3NjaGnZ0dUlJSCtX29evXAajfX4D6fV7YWBXy/l+hbt26AF7Nz6hIVGu6dxXPzZ07dwAA77//Pjp06IADBw7A3t4eNWrUQLNmzTBs2DCVXj8lqUqVKmplZmZmsLCwgLGxsUq5IjkYGxurLEtMTMSnn36KQ4cOaXwNaxoqX6tWLY1tvnjxQqVsy5YtmDt3Lm7cuKG2KmXeROjdu3cBAP7+/mrHrVq1qkpi7vz58xBFEfv27cv3uX992L+mmBV18rt+eYemK+7Nfv36aWwPUL2mgOb3UEWb2kw/oK158+Zh/PjxaNasGSpXroz69eujZ8+eGD58uDJ5VdT4NS2g0rRpU1StWhV79+5V9uresWMH4uPj8X//938FxhoREQFRFN/Yg08xzPb3339XGd6dl+Je9fb2xtChQ7FmzRq4u7vD3d0dAQEBGDx4MDp27FhgO1Q8ulYV4GWRjRsv9CFnJ6N3QnCUiEAPCd6rKWBbVO6Tbm0EBHoI2B39qmcaAFStlPvv7Ty/HrZFifjQW4CzGRDzv18FbVwFeFgJ+P78qzkAoxKBju4CWroIOP7g1c3V3zM3y3YhjjdceVWpUiVdh0BEpDUm20qIiYkJnj9/rnGoaEFDTPOSy+X5Juw0MTQ0zLc33evyW01SFF/9AVLQsfIO28mroH3ySy7mbbMwFPtNmDBBY6IJANzc3FQel4e5HuRyubLXVd++fdGwYUNYWVlBT08PK1euREhISL7XX1dEUYSrqyu+/vrrfOuUVOK4sMpKrAcPHsSpU6cQHByMM2fOYN26dVi1ahWmTp2qXKigJOX3/lPQazjva7Vz584IDQ1F9+7d0bJlS1SuXBl6enrYuXMnNm/erPEe1eY94ODBg+jfvz/s7e0xYcIEVK9eHSYmJsoFAOTyok2qrmijXbt2GDZsmMY6Jibq45Pyizm/65f3XBT//+abb/JN8L2ezCvo+hf13DUZNWoUunXrho0bN+LYsWMIDQ3Fvn378OOPP+LcuXMwMjIqUvyGhob5ttmnTx8sWrQIW7ZsQf/+/bFq1SpIJBK13tBvq3///ujevbvGbXl7TK9atQrjx4/HX3/9hdOnT+Pvv//Ghg0bMGjQIKxbt65YYyJ1giBgpOtDTIxQT1xTxRQcJeLMIxGrO0lQy1qOp2nAJ/4S6AnAzFOq728hQbnvse4rXv0uWXBWjsCaejgapIcfw+UwlQKTG0gQES9ideSr996fLsgx1EfA7t4SLAsXcS9JREsXAQO8JDh0V45Q1e8IqBzJzMgEYPzGekREZQGTbSXE1tYWMTExePLkiUpPhezsbCQkJGjVpTE1NbXE5mzThqLnkabeFLGxsW9MFr4NTasOKnqy1ahRA8Cr3kampqYahwsWN1dXV5w/fx5nz55Vmw9K0atJU8+dwjp58iTu3r2rNqcZALX5pPIKCwtTm2vt1q1bkEgkar2+bt26pbZ/aGgoAM09dt7EwcEBL168QFBQ0BvvC0dHR4SHh+Pp06cqvdtevnyJuLg4jQmPgijug2vXrqlt0zTsrjCxKih6+OSl6EVTrVo1AFAOM9V07yp6WLm7u6uUN23aVLl4RVxcHOrUqYMlS5Zg3rx5+SZdtF0EpSTFxcUhNDQU7du3x86dO1W2FWWBjbxWrVoFuVyO/fv3w8/PT1n+/PlzpKamqtRV3KsXL15UmTcMgNrQVD8/PwiCgKysrFJ5vwBe9SC1s7Mr9jaL4z5wdHTEpEmTMGnSJMjlcnz44YdYv349Vq5ciTFjxhR7/J9++im+++47rF69Gt26dcPRo0dRp04d5WsoP4rn7sqVKwXWUwyL1tPT0zpexRybQO5w6MaNG2P9+vWYN29ekd4LqXBa2iajljVw/RnYu+0dIBeBLttysLilBOPqSGAkBcJigSH7cwocQqoQkwy03JyDJa0k+LqFBJk5wN7bIj47JlfpFReVCNRbl4N5zSQYVEuAvYmARynA4jC5WlKPype09DQAFroOg4hIK5y1oIQoPjy8/mH/+vXryM7O1jiM73UlOWebNhQfdA8dOqRS/v3337/1cNM32bhxo0qSLz4+Hps2bYKxsbFyfrn+/fvD3Nwcv/zyCx49eqR2jOTkZI3D24pKsdDCrFmzVHqYnDp1CmfOnEGtWrWKZQipoifN6z3+Tp06hX///Tff/RYsWKASV0hICC5cuAB/f3/lpPYKFy5cQEhIiPKxXC5X9qYqaMhYft5//328ePECkydP1rhdMXwSyJ0oXS6Xq6wqC+Re17S0tEK33bZtW1hZWWHXrl2IiYlRlj99+lRjsrowsSocPHgQN27cUD5OS0vDL7/8AolEolwFsU2bNrC2tsbu3btVEj0ZGRn44YcfIAiCcr6pR48eqfX8srOzg4ODAzIzM9WSSnkphlAU571dWPndo9HR0SoLrxSFIgH6+rEV897lpbhXly5dqnI9//33X7VFQZycnNCgQQOcPHkS+/btU2tXLpcX25yLCiNHjoS+vj7mzZunNg8gkPu+9vLlyyId29TUFAAKvaIykLuoyOv7SSQS5Tx3inkEizt+V1dXNGjQACdOnMAPP/yAtLQ0jXOwvc7R0RH169fHuXPnNL6mFe97rVu3houLC7Zt24ZLly6p1cvMzFT+roiPj1db7dvU1FT5u/vJkydanxcVXW0/X8xuImGi7R3yPAMYeUgO219yYPpjDlpvzlFbyADI7dGWt1ebwtUEoNM2OUx/zIHVTzkYvE+OJxrehqISgaDdclRZngPZ97nHmnJcXuCKp1Qe6P4LRyIibbFnWwmxsrKCt7c3rly5gkOHDsHV1RWJiYmIjIyEg4ODVsm2kpyzTRsNGjSAv78/du7ciV69esHf3x8XL17EsWPHYG9v/8YFHt5GpUqV4O/vr0xwBQcHIyEhAXPnzoW5uTmA3Lngfv75ZwwbNgyenp7o2bMnatSogcTERERFReHYsWNYu3atymqkb2PAgAFYvnw5jh49igYNGqBjx46IjY3F5s2bIZVKsWzZsmJpp2HDhnBxccHatWvx8uVLeHp64saNG9i+fTuqVKminCPsdY8fP0b9+vXRqVMnPH78GJs3b4aBgQG+//57tbpubm7o3r07+vbtCwcHB+zfvx8XL15E+/bt0bVr10LHvHDhQhw/fhzff/89Tp48iebNm6NSpUq4d+8eTp06BZlMhoiICADA1KlTsW7dOvzxxx+4d+8eGjdujAsXLuDIkSOws7Mr9BBZqVSKOXPmKFf67Nu3L/T19bFlyxaYm5urJaUKE6uCk5MTAgIC0LdvX5iZmWHHjh24desWhg8fruxZJ5VK8c0332DUqFFo0KABgoKCYGZmhp07dyIqKgpDhgxRzvP2ww8/YNWqVWjTpg2qV68OAwMDnDhxAuHh4WjZsmWBPVQbNWoEQ0NDrF+/HiYmJrC0tISjo6NyUZDSYG1tjTp16uCff/5Bjx490KBBA9y9exdbt24t0rx7efXr1w9//fUXunTpgv79+8PAwABHjx5FdHS02nVp0qQJunfvjt27d6Nu3bro0qUL4uPj8ddff6Fq1aqIjo5W6QG2du1aNGvWDD169EDHjh1Rp04d5OTk4Pbt2zh69Ch69epVYO/RwvLw8MDs2bMxffp0VK1aFT169ICbmxuePHmCK1eu4NSpUzh//nyRVhNt2bIllixZgkmTJiEoKAhGRkZo0KCBsqdkQZ49ewYXFxc0adIEfn5+sLOzw+3bt7FlyxaYmJgoE2AlEf8HH3yA//77DwsXLoSRkRFGjBih1X5//PEHWrRogUGDBmH9+vWoV68eXr58iXPnzsHFxQXr1q2DRCLB2rVr0b17dzRq1Ajdu3eHt7c3UlNTER0djSNHjmDq1Kn44osvsGPHDkyaNAlt2rRBzZo1YWZmhvPnz2PPnj2oWbNmvlMTUPG6efMm3vPwQE1L4FYiwD5HRFSQynmmAiAiKuuYbCtBAQEBMDMzw7Vr13D//n0YGhrCx8cH9evXLxNDwbSxbds2DBkyBAcPHsSBAwfg4+ODAwcOYOTIkWoTYxenefPm4ciRI9iwYQNevHgBBwcHfPfdd5g4caJKvUGDBsHd3R0zZ87E3r17kZSUBBMTEzg4OOCDDz5QWRSgOBw6dAiTJ09GcHAwFi9eDJlMhtq1a2PRokVo1qxZsbQhlUqxf/9+fPzxx9izZw+2bdsGV1dXfP/99wgPD8832Xb06FEMHz4cS5cuRVZWFjw9PfHdd9+pDXkFcuet8vLywg8//IBHjx7B3Nwcw4cPx88//1ykmGUyGc6cOYOZM2di69atyuMoks5Dhw5V1jUyMsLJkycxcuRIHD9+HMePH0fNmjXx999/Y+LEiUW6rz799FNIJBIsWrQIv/32G8zNzdGrVy+0bdsWAwcOLHKsCiNGjEBSUhLWrFmjHP46ZcoUfPPNNyr1hg8fDgsLC8ybNw9r1qxBdnY2nJ2dMXfuXEyfPl1Zr3Pnzrh06RJOnDiBnTt3QiKRwM7ODhMnTsTcuXMLPFczMzP8+uuvmDNnDhYsWIDs7Gz4+PiUarINAHbu3InRo0fj5MmT2L9/PxwcHDBp0iRIpVLlyrZF0aNHD/z222/45ptvsGzZMhgYGKB+/fo4fvy4xnt527ZtGDNmDLZt24Zvv/0Wjo6OWLBgAf777z9ER0cre4ABuUOOL168iKlTp+LIkSM4dOgQpFIpbGxs0KpVK43P/duaNm0afHx88M0332Dr1q14+fIlzMzM4OLigjFjxqjNK6mtHj16YPz48di4cSOmT58OuVyOkSNHapVsMzc3R//+/XH69GmcO3cOGRkZsLCwQLNmzTB//nyVL4OKO/4hQ4Zg8uTJSE1NRdeuXVWen4L4+fkhLCwMkyZNwqlTp3Dw4EGYmJigWrVq6Natm7Je69atcfbsWUybNg3Hjh3Djh07YGhoCDs7O/Ts2VPZM7px48Zo3bo1wsLCcPDgQcjlctjY2GDo0KFFXrGaCi89PR0SQcDsphL038NUGxEVLLfnddEWOiMiKm2CWNTZ6YmoTOjUqRMOHjyo1UITkZGR8PX1xciRI4u1B09FFRwcjMDAQCxYsEBt2CuVbY0aNcLFixeRkpKS74IwRKRb0dHRqFatGnLkIjxW5eD2c4B/lBKRJlIJ0N8lHmsD3zzvNRFRWcA524iIqNzSNJfYiRMncO7cOfj7+zPRRlSGOTk5AQD0JAJmNZEw0UZEBZLJ8l/xmoiorOEwUnrnvb5qoSa2trZvvdJrfHy8xsRAXoaGhnB0dHyrdioKXq/CKa37uKz59ttvsXnzZrRt2xaVK1fGtWvX8Pfff0NfX/+dGw6YlpaGx48fv7Gei4sLk5BUJly/fh3+/v4AgH6eAmb8C9xLYu82ItKsuBeDIyIqSUy20TtPsfpcQYpjGOHgwYNx8ODBAuv4+PiorWD7ruL1KpzSuo/LmoCAAOzZswfr169HSkoKjIyM4O/vj3nz5qFNmza6Dq9U7d27F4GBgW+sd/ny5SItykBUkvQlAmY2kWDoAc7dRkSaPX+eCIDDSImofOCcbfTO27Rp0xvrNGzYUKtkRkHOnDmDu3fvFljH1tYW7dq1e6t2Kgper8IprfuYyq7Y2FgcPXr0jfW6d++u9cIERCVJseCMQlaOiGp/5CAmmb3biEgV52wjovKGyTYiIiIiKnVxcXGws1NdWfCPCDlGHmLvNiJSJZUAo70zsayjsa5DISLSChdIICIiIqJSp2mOwQ+8BTia6CAYIirzsrKydB0CEZHWOGcbEREREZUJBnoCZgRI8PE/cgi6DoaIyoxsOZCW9hJAJV2HQkSkFSbbiIiIiKjUeXt7aywf6iPg1CMBGdmlHBARlVkSAQi0fgYukEBE5QXnbCMiIiKiUhcVFYWaNWvqOgwiKidEUYQgsM8rEZUPnLONiIiIiErdy5cvdR0CEZUj165d03UIRERaY7KNiIiIiEqdiQlXQiAi7WVmZuo6BCIirTHZRkRERESlrkqVKroOgYjKEXNzc12HQESkNSbbiIiIiKjUXb16VdchEFE5Ym9vr+sQiIi0xmQbERERERERlWlRUVG6DoGISGv6ug6AiIiIiN49jo6Oug6BiCqIH8/Lce2ZqOswiEqMjREwM0ACqR5X5C0vmGwjIiIiolInCPzAQETac3V11Vh+K1HExGNyCAAkfFuhCkgEkC0HmjqK6FyVN3l5wWQbEREREZW6hw8fwtbWVtdhEFE5kZGRobF8wdncRFuOmPtDVFHdun0bqFpd12GQljhnGxEREREREZVpcXFxamV3nov484rIJBsRlTlMthERERFRqfPy8tJ1CERUzi0Mze3VRvQuqGRurusQqBCYbCMiIiKiUvfgwQNdh0BE5Yivr6/K4/tJIlZfFpHNXm30jjA0MtJ1CFQITLYRERERUalLSUnRdQhEVI7cvHlT5fHXZ+Vgno3eJZqGUlPZxWQbEREREZU6Q0NDXYdAROVIenq68v8Pk0WsuMy52oio7GKyjYiIiIhKXfXqXFGNiLRnZmam/P83oXKITLTRO8bRwVHXIVAhMNlGRERERKUuMjJS1yEQUTni5OQEAHicIuL3S+zVRu+e5OQkXYdAhaCv6wCIiIiIiIiICnL9+nX4+/tjcZicibZiVkkGLGohQe8aAoylQOhjEZ8dk+PCE+3297QCvm8tQTMnAZk5wN7bIiYek+Np2qs6VcyBu6M0px/67c7B5ht8Ut8kmXOdlivs2UZEb+X777+HIAj4888/dR3KW0tNTUW/fv1gY2MDiUQCGxsbXYdUaGPGjIEgCDh79qyyrCSfI39//3J5nUpTSV5/Ozs71KpVq9iPqyt3796FIAgYMmSIrkOhUmBvb6/rEIionHmSKuKXi+zVVpwEAHvf08MALwE/XZBjynE5KhsLONZXD9Ut3ry/kylwop8eqlsImHZSjm/PydG1qoDDgXqQasg2bLwmx6C9OSo/Zx7zCdWGnoTpm/KEPduI8rhw4QKePn2Kp0+fIjk5GaamphgwYICuw6JSMmXKFGzevBl9+/ZF3bp1YWpqquuQiIgqLKlUqusQiKgccXZ2xqJzcmTLdR1JxdLHQ0BTJwF9duVgW1Ru0mvLjRxEDdfD7KYSDNxb8AWf1lgCEylQb10OHiTnloU+luOfID0M8RGwIkI1kRYeJ2LDNSbXisLNzU3XIVAhMNlGlEdYWBhkMhlsbGyQmZmp63ColB07dgzOzs7YtGkTBEHQdTjFZuzYsRg5ciRX/qMyr0qVKkhOToaBgYGuQ6FS8ODBA1hbW+s6DCIqJ56kyrHsAnu1Fbc+NQXEporYHvXqwj5NA7bcEDGolgADPSAzJ//9368hYM9tUZloA4CQ+yJuPBMR5KGebAMAYymQlQNkMXFaKHfu3AWqVtV1GKQl9kMkyqNfv3748MMP0bVrVxgbG+s6HNIgMTERcnnJ/GZOSEiAmZlZhUq0AYC+vj5MTU2hr8/vV7SRkZGBFM6J8days7Px4sWLQu0jCAJMTU2ZbCMiIjVLwrKRVUDSh4qmTmUB4XEiXk+JhT4WYSIVUNMy/30dTQE7EwHnYtUTaqGPRdSprP439cwmEqSO10f6BD2EDtJD+yoV6+/ukiQXmZ0sT5hsI8rD3Nxc1yGo6datGwRBwL179/D++++jUqVKkEql8PT0xM6dO1XqFjQ3lKa5tRTzPZ0+fRp16tSBoaEhzM3NERQUhLS0NKSlpeGDDz6AlZUVpFIpatWqhRMnTmiMMycnB5988glsbW0hlUpRpUoVLF68WGPdY8eOoVmzZjAzM4NUKoWjoyM++ugjpKWlqdRTxHz16lW0bNkSpqamsLKyQmpqqtbXLzs7G5999hlcXFwglUphamqKgIAAHD16VO26xcXF4dq1axAEodDzRoWEhKBz585wdHSETCaDoaEhPD09sWzZMrW6iuc0KioKnTp1gqmpKWQyGfz8/HDgwAGVunnnsPrpp5/g5uYGqVQKa2trDB8+XO2aaZLffZGeno7/+7//U14bY2NjNGzYEIcOHVI7Rnx8PHr27AkzMzPIZDL4+Phg3759Wl+f1+U319i+ffsgCAJmzpypFv/q1au1vsfmzp0LR0dHSKVS2NvbY8qUKViyZInadVDMcRcSEoJBgwbB2toaRkZG2L59OwDgl19+QUBAAGxsbCCVSmFmZoaAgAD8888/GttduHAhnJycIJVKYWdnh4kTJ+abHH727BmGDBkCe3t75bFbtWqFc+fOqdW9desWWrduDWNjYxgZGaFhw4YIDQ3N/wK/wYEDB9CgQQNYWFhAKpXCysoK9evXx99//12kGPM+R2PHjoW9vT0MDQ2xZMkSeHl5wdzcXGMCc9u2bRAEAePHjwdQ8JxtK1asgJ+fH4yNjSGTyeDo6Ij3338fSUmvVuYSRRFz5sxBtWrVlK9DX19fbNiwQe1433//PWrWrKk8XuXKldGmTRvcunWrCFeUisLDw0PXIRBROfEsTcSG+zbs1VYCHEyAxxr+tFaUOZrmnwxzMFGt+/r+1ka5PeMAQC4CB+/IMfm4HN2352DCUTkqGwP735egS1Um3LRhZmam6xCoENjNgaiYpaena13XwMAAEi0nuuzQoQMqVaqETz/9FImJiVi7di0GDRqEGzduwNHRsajhIiEhAZ07d0bbtm3Ro0cPHD16FFu3boUgCHjw4AFSUlIwatQoJCYmYs2aNejbty/u3LmjNiRx3rx5SEtLQ79+/aCnp4fg4GBMmTIFqampmDVrlrLexo0b8eGHH8LOzg4ffPABbG1tcfbsWSxfvhyXL1/GyZMnVa5Jeno6WrRoAW9vb0yYMAFPnz4t1Dw/nTp1QkhICGrXro2BAwfiyZMn2Lx5Mzp37ozt27ejS5cu6NChAxYvXow5c+bA1NQUEydOBADUq1dP63b++usvREdHo2PHjnBzc8OzZ8+wbds2jBs3DsnJyZg2bZraPp07d4apqSnGjBmDJ0+e4K+//kKvXr2wZ88etGvXTqXuiRMnsGXLFgQGBsLJyQkHDhzAqlWrcO/ePRw+fLjQvfGysrLQuHFjXLlyBe3atcOQIUOQlJSEzZs3o1u3bggODkaPHj2UdZs1a4aoqCi0bdsWAQEBiIiIQGBgYKn+0tf2HpsyZQoWL14Md3d3jBs3DhkZGVi9ejUqVaqU77FHjBgBqVSKoUOHQl9fH9WqVQMA/PrrrzA1NUVgYCAcHR1x+/ZtBAcHo1u3bggJCUHTpk2Vx5gxYwbmzZuHKlWqYOzYsUhPT8fatWs1tpuYmAh/f388efIE3bp1g4+PD+Li4rBx40a0bt0ax48fR926dQHkJryaNm2Kp0+fonv37vD19cWpU6fQuXNnZGVlFfo6RkREoGfPnrCwsMDAgQPh6OiI2NhYhIaGIjQ0FL169Sp0jApz5sxBVlYWAgMDYWlpCX9/f0ilUsyYMQNr167Fp59+qlJ/xYoVkEgk+OijjwqMefTo0Vi+fDmcnJwwaNAgODk5ITo6GocPH0Z8fLzyi5IuXbrg4MGDaNasGfr27YusrCxs374dH3zwARITEzFmzBgAwNKlSzFx4kR4eXlh7NixMDExwYMHD3Ds2DHcvXsX1atXL/R1pcJ7/PgxqnI4DBFp4cdwOTJFJmTeRACUya03yfhfL0Ej/Vf/zys9+9X2/Ci2adw/z/Ezc4AHyUCnbapfQK67moOrQ/XwXUsJ9t1mt8U3MTUx0XUIVAhMthEVs8KsONitWzetE2U1atTA7t27lUmVevXqYeTIkVi6dCm+/vrrIsUKAE+ePMGiRYswefJkAMDs2bNRo0YNbN26FfXr18eFCxegp5f7W9ve3h5z5szBn3/+iVGjRqkc58WLF7h06RKcnJwAAF999RU8PDywePFifPrpp7C1tUV6ejo++eQTVK9eHefPn1cZqjt9+nTMnz8f69evxwcffKAsT01NRa9evbB+/fpCn9uOHTuUCZFjx44ph1GOGjUKTZs2xcSJE9GpUyd4e3vD29sbixcvhoWFBSZNmlTothT75rVgwQJ4eHjgp59+wueff66WWLW1tcW///6rjGvo0KFo2bIlJk2ahIsXL6rUvXv3Lnbt2oVu3boBAObPn49WrVohJCQEwcHBCAwMLFS8s2fPxqVLl7B8+XKMHDlSpbxGjRr4/PPP0b17dwiCgCVLliAqKgoffPAB1q5dq6z71VdfYe7cuaU255I299jTp0/xww8/wMXFBRcuXFAmuqZOnVpgLxojIyOEh4erJZFPnjyp9ryOHTsWjRo1wsKFC7Fnzx4AuQmxxYsXw9HREeHh4bCysgIATJo0SWMPvjFjxiA2Nha7d+9Gx44dleUTJ06En58fJk+ejJCQEADA559/jidPnmDGjBmYM2eOsu6gQYM09th6k23btiEzMxO//vor3nvvvXzrFSZGhfT0dERGRqrcE82bN8fs2bOxceNGlWRbUlISjh07hjp16sDLyyvfOI4dO4bly5fD29sbp06dUkleiqIIUczt5rB27VocOHAAn3/+ORYuXKiss3DhQtSqVQuzZ8/GyJEjIZPJsH37dhgaGuLs2bNvnTB+9uwZTExMIJPJAAApKSkQRVF53MzMTCQnJ6tck8ePH8PBwSHfx7GxsbCzs1O+31fUNhITE5GcnFzuz4NtsA22UfJt7I62gchebW/UwiV3FVFteK7Kxo1nQFo2INOwi+H/MgVp2fkfQ7FN4/56b94/MR1YHSnii0YSOJkCDzmLR4Eex8bisbVJhX2dl3QbpY3DSImKWZcuXbT+KUySYurUqSq9l3r37g0AiI6Ofqt4LSwsMGHCBJWyhg0bQhRFfPTRR8pEm+LcAODGjRtqx3n//feVSRAAsLKywocffoiXL18qh+Rt374dL168QFBQEJ49e4aYmBjljyJZ9PowSiA3AVQUmzZtAgB8+eWXKvOVNW7cGM2aNcONGzc0nktR5E3IJCUl4eHDh0hMTERAQAAeP36M+/fvq+3z2WefqcTVvHlz1K9fH5cvX8aDBw9U6vr5+SkTbUDu3FaK3lzBwcGFjnfz5s1wcHBAp06dVJ6HlJQUNGnSBDdu3EBsbCwA4O+//4ZEIlF7Hr788ksYGRkVuu2i0vYey8rKQr9+/VSSMi4uLirX73WjR4/WuICE4nkVRREJCQmIiYlB5cqV4ezsjMjISGW9bdu2ISMjA/3791cm2gCgatWq6Nq1q8oxRVHE7t274eXlBW9vb5Xrb2RkBB8fH5w/fx7Z2bl/nR44cADm5ub44osvVI5T1CS7pWXu5Cvbt29XGYL5NjEqBAUFqb2v2djYoHnz5ggLC8O9e/eU5StXrkRGRsYbV3xesWIFgNz3gdd7CQqCoExir1mzBoaGhhg2bJhKvLGxsejYsSOePn2Ks2fPAsidMiAzMxNr165FTs7bfZNuZWWl/EMQAExNTVUSeAYGBmrX5PU//F5/bG9vr/J+X1HbMDY2rhDnwTbYBtso+Tb+r54EItiz7U2uJ4gYsj9Hq5/H/0tsPU59NRw0L0XZo5T8s5yK4aP57Z+QJha4uAIAPEjOPb4V1/HSSkV+nZd0G6WNPduIipmzs3OJHNfHx0flseLNJTEx8a2OW7lyZbWJ8xUfxl/vCWRnZwcg95uG13l7e6uV1a5dGwBw8+ZNAMClS5cA5A41y9tDJ6+nT5+qPDYzM0OVKlXeeB6a3L9/H4IgoGHDhmrbvLy8cOLECVy7dq3AXjXaevjwIT799FMcPXpUYwIjPj5ebbnu+vXrq9Xz8PBAWFgYbty4ARcXF2W5pqFWiiF8ryfmtHH//n1kZmbC1dU13zoPHz6Eg4MDYmJiUKlSJbW6inmznj9/Xuj2i0Kbe0zxr6beZAU9z5qODeT2bJs8eTIuXryIjIwMlW1550CMiorK9zg+Pj7KZCCQey8kJycjIiJC5TnOSxAEpKenw9TUFLGxsXB3d1dLbDo7O8OkCMMJPv74Y6xfvx4bNmzA1q1b4eHhgZYtW2LkyJHw8/MrUowKnp6eGuuOGDECR48exW+//absdfbnn3/C2NgYw4YNKzDe27dvQxAElSG7mkRHRyM9PR01a9bMt05MTAyA3F6noaGhGDt2LL744gv4+fmhQ4cO+Pjjj1G5cuUC26HiwznbiEhbA7wEfHEsG4/T9NUm8qdX4l4Ca68U7gpdfCKiubMAAVC5to0cBKRmiYgq4KPGoxTgyUsR9e1f3xto6CDg4pM3x1K1Um6CJf7N0xC/8+zt7HUdAhUCk21Exezly5da15XJZCo9xwqS30qSYp4+9QXN25Vf742C2temzcJQ7DdmzBg0adJEY53XP9gbGBiU+VU0RVFEixYtcP/+fQQGBqJx48awsrKCnp4e1qxZg0OHDpXYCqpFJYoinJ2dsWjRonzr1KhRoxQjylWUOciKg6ahhNHR0ejQoQMMDQ0xcuRIeHt7w9zcHIIg4PPPPy/UQh15KV4Hvr6+ar3V8tLU0644yGQynDt3DocPH8bff/+NM2fO4LfffsPvv/+OxYsXY/z48UWOMb/kX1BQED7++GNs374dCxYswJ07d3Dp0iV06dJFbZhufrSZl9DMzAy//fZbvnVbtGgBIDcBeufOHQQHB+PAgQM4e/YsZs2ahR9++AEHDhxAo0aNtIqJ3k5ERAT8/f11HQYRlQP6EgEzm0gwOuTNdalwgqNEBHpI8F5NAduicn//WxsBgR4Cdker9kyr+r8O5rfzLDa+LUrEh94CnM2AmOTcsjauAjysBHx//tXfvzZGwNPXEmqOpsAwHwGXnoiILdqfVe+U3M+Zpm+sR2VD2f4ES1QOFWZuscLM2aYNRY+M13uHAcCjR48KPYl+YVy5ckWtTNGTTZG0UfQ2MjIyQv/+/UssFoUqVarg7NmzCA0NRefOnVW2Xb9+HUDBvZ20de7cOdy+fVttTjMAWLVqVYH7ubu7q5TduHEDEolErcfH7du31fYPDw8HoJ6g1IaTkxOePXuG9957T6WLtibOzs4IDQ3F/fv3VXrnZWRk4NGjRypz72nL3NwcycnJauWK50UTbe4xxcT2V69eVat77dq1QsX4559/Ij09Hb/99hs+/PBDlW2ffvqpyhx8it5UmmLMO9wUyJ2rz8TEBKmpqejXr98bX5f29vaIjY1FWlqaSu+2mJiYIif8AKB9+/Zo3749gNzVTuvWrYtFixZh3LhxhY7xTfT19dG9e3ds2LABZ86cQXBwMERRxNChQ9+4b7Vq1fDff//h1KlTBc4x5+rqipiYGDRv3lyr14SRkREGDx6MwYMHA8hd5KR///6YO3euci4+IiIqO+qIV+Fk6oNHKa/3oaK3ERwl4swjEas7SVDLWo6nacAn/hLoCcDMU6pfFocE5X5J777iVQZuwVk5Amvq4WiQHn4Ml8NUCkxuIEFEvIjVka+eqUUtJahWSUDIfTkepQBulQSM9hNgIgXGHy1bX0qXVUnJSQDYA7+84JxtRMWspOZs04ZiCNiRI0dUyn/99VeNQz+L07Zt2/Dw4UPl42fPnmHt2rUwNjZWzi/Xp08fVKpUCatWrcLdu3fVjpGamor4+Phii6lfv34AcidIz9uzLywsDCdPnoSHh0exDGNS9Lx7vcdfWFgYjh07lu9+3333ncqcV//++y/OnTsHHx8ftWRBRESESgJAFEXlnG19+vQpdMz9+vVDUlKScuXV1925c0f5/x49ekAul2PmzJkqdebPn4+0tKL1+Xd3d8fjx4+VyTIgd3L933//Pd99tLnH3nvvPUilUvz111948eLV164PHjwodAJF0evz9ed10aJFasO333vvPRgYGGDTpk0qr7Xbt29j3759KnUlEgm6deuG27dv47vvvtPYdt7XR8eOHZGUlKQy6T+Qu3BCUcTExKidU7Vq1VCpUiWkpKQgJyen0DFqQ7E4wvLly7F161bY29ujZ8+eb9xvxIgRAIBZs2YhJUV95mRFr9GhQ4dCFEWMGTNGY0/SvPe0YjhpXs2bNwcAlfuGShaH7BJRYUglwFcBEibaiplcBLpsy8Hm6yLG1ZFgcUsJnqYBbbbkFDiEVCEmGWi5OQfRz0V83UKCKQ0l2HdHRPutOSq94g7dFSEC+LSOBL+0k2CUn4ATMSICNubg+AM+q9oQOG9hucKebUR5REVFKT/MpaenIycnR9l7yNTUtMC5gBRKas42bfj4+KBevXrYt28funfvjnr16iEiIgIhISGwt7cv0SF6lSpVQoMGDdCnTx/o6elh69atePr0KaZPn678QGVqaorly5dj0KBB8PX1RY8ePVCzZk28ePECUVFROHr0KH799VeV1UjfRu/evdG2bVuEhISgQYMG6NSpE548eYJNmzZBKpViyZIlaiuEFoWfnx+qVKmCTZs2ITMzE7Vq1cLNmzcRHByMKlWq5LuIRXx8POrXr48uXbogLi4OmzZtgoGBAb799lu1um5ubggKCkJgYCCcnZ2xf/9+XLhwAa1bty5Ssm3OnDn4559/8Msvv+C///5DixYtYGFhgfv37+PUqVPQ09NT9tL67LPPsGrVKvz55594+PAhAgICEBERgX/++Qd2dnZqk+RrY9KkSTh8+DC6dOmCgQMHIjs7G9u3by+wl50295itrS3GjRuH7777DnXq1MF7772H9PR0bN68GY6OjoiOjtb6Oe/Tpw/mz5+PCRMmICwsDDY2NsoeVq+ft7W1NT777DMsXLgQdevWxfvvv4+MjAxs2rQJDg4OKokeIDcBHhYWhilTpmD37t1o2LAhjIyMcPfuXRw/fhzVq1dXrvS5cOFC/P3335g/fz4iIiLg5+eHf//9F5cuXSrSSpqff/45jh49ilatWinnAjx48CBiYmIQFBSkTB4XJkZtBAQEwM3NDVu2bEFaWhpGjhyp1RDxVq1aYfjw4Vi5ciW8vLzQtWtXODo64u7duzhw4ABOnjyJatWqYfjw4di5cyd27dqFWrVqoV27dqhcuTJiYmJw/vx53L9/X5nMb926NQwNDdGoUSNUqVIFz58/R3BwMARBwMCBAwt9TaloSnOBFSIq/xwdHfGhlYCZp8Ehh8XseQYw8pAcIw8VXC9vj7a8riYAnbYV3Dvtr+si/rr+dosSveuqVnV/cyUqM5hsI8rjxo0bePz4sUrZuXPnAOSuZqJNsk3XduzYgcGDB+Off/7B4cOH4e3tjd27d2PcuHEae3MUl+nTpyM0NBQbN27Eixcv4ODggIULF6r1vgkKCkKVKlUwY8YM7N+/H5s3b4aJSe4S1oMGDUKrVq2KNa4DBw5g6tSp2Lx5MxYvXgyZTAY/Pz/Mnz8fbdq0KZY29PT0cPDgQYwePRr79u3Djh074OLigkWLFuHGjRv4+eefNe63f/9+jBs3DkuXLkVWVhZq1qyJb775Rjm0L68WLVqgfv36+Pbbb/Hw4UOYm5tjyJAh+Omnn4o0xE8qleLMmTOYNWsWNm/ejF9++QVA7uIYtWrVUkl4GhgY4NSpUxg+fDiOHj2KkydPokaNGtiyZQu+/PLLIt1XHTp0wHfffYfvvvsOS5YsgaWlJfr27YsOHTrk29tJ23vs22+/hZmZGX7//Xf8+OOPsLa2xtChQ2FsbIzZs2drPey1Vq1aCA4Oxueff45Vq1ZBEATl62nChAlq571gwQKYmJjgl19+wdKlS5WrpTo5OWHSpEkqdS0tLXHp0iV8/vnn2L17N86cOQOJRAJra2v4+/vjo48+Uta1sbHBqVOnMHLkSBw8eBAHDx6Er68v9u/fj+7du2t1LnkFBgbi4cOHOHToEF68eAEDAwM4ODhg5syZmD59epFi1Fb//v2xcOFCCIKA0aNHa73fH3/8AX9/f/zyyy9Ys2YNRFGEtbU1mjZtCltbW2W9Xbt24YcffsAff/yBlStXIjs7GxYWFqhZs6bKfTJs2DBs3boVwcHBSElJgampKapXr44FCxYw2VaK7t27p1yQh4joTQRBgExfwPTGEowNkbOHG71z7t67BzDhVm4IYlFnOicioiLp1q0b9u7di6SkpDf2TLp79y7c3d3x4YcfYs2aNaUTYBnz/fffY+LEiVi7du1b9Xrs378//vrrL0RERMDX17cYIySiorh48SIXSCAirSneM9KzRbj+nsPVK+mds9T/Lsa2q67rMEhLnLONiIgqlNTUVLU5u+7fv4+dO3fC2dlZuVAHEemWLlY8JqLyz1BfwJeNJZy9it45piZcibQ84TBSIip37ty5ozbB++tsbGxgbm7+Vu08ffoUSUlJBdaRyWRwcnJ6q3YqirJyvfbv34+JEyeiQ4cOcHV1xd27d7Ft2zakp6dj2rRpyoUPKprY2Nj/LQmfPxMTE9jZ2ZVSREQFi4+Ph4mJia7DIKJyIu8K8iP9BMw9AySk6zAgolL2tp9tqHQx2UZE5Y5iUveCfPXVV5g9e/ZbtTNkyBDs3bu3wDpeXl64evXqW7VTUZSV6+Xl5QUXFxds27YNKSkpkEqlqF69OiZNmlRsi2+URZ06dVJZ3VWTNm3aFGpRA6KS9Pz5c12HQETlyIMHD1C9eu4QOmOpgC8aSTD5OOduo3fHo8ePAG8OIy0vOGcbEZU7W7ZsQU5OwasZ1atX760XtAgNDc13JVEFKysrdOzY8a3aqSh4vXTr6NGjiI2NLbCOi4sLmjVrVkoRERXsypUr8Pb21nUYRFROvD7PY2qmCOffc/A8Q3cxEZUmztlWvjDZRkRERERERGXa9evX4enpqVL2zVk5vjjJ3m30btjcMQ1BvgUvrkZlBxdIICIiIqJS96Zhz0REeSmGkOb1SR0B5gY6CIZIB9LTOUlhecJkGxERERGVOg6uIKLCiIyMVCszMxAwuaEEEi5NSu+AFy9e6DoEKgQm24iIiIio1NnY2Og6BCKqAMbUEWAi1XUURCXPXL/gOaupbOGcbURERERU6l68eIFKlSrpOgwiKidiY2Nhb2+vcdvZxyIeJvNjLVVcNkYCmjsDgsBunOUFk21EREREVOpeX1mQiIiI8nf16lXUqlVL12GQljiMlIiIiIiIiIioDMvMzNR1CFQITLYRERERUamrVq2arkMgIiIqN8zNzXUdAhUCk21EREREVOoSExN1HQIREVG5kd+chVQ2MdlGRERERKXu2bNnug6BiIio3IiKitJ1CFQITLYRERERUanT09PTdQhEREREJYKrkRIRERERERFR+bMqBBi/UtdRvD1BAPZMA1p451vl2bNnsLKyKsWg6G3o6zoAIiIiInr3XL58Gb6+vroOg4iIyrMDF4DUdKC8dyHSkwAz/wKOzs23SkZGRikGRG+Lw0iJiIiIqNTl5OToOgQiIqoAynueDQCQIweOXQFOXcu3SlxcXCkGRG+LyTYiIiIiKnUcCkNERJSHngSYtVnXUVAxYbKNiIiIiEqdpaWlrkMgIqIKQdB1AMUjRw78EwGE3tS4mVMvlC9MthERERFRqYuOjtZ1CERERGVLAb3bbt7UnISjsonJNiIiIiIiIiIqpyrErG25cuTA/nAgXP0LqfT0dB0EREXFZBsRERERlTp3d3ddh0BERFT26EmA2VvUis3MzHQQDBUVk21EREREVOqSk5N1HQIREVUIFWTONoUcObArDLh0R6XYyclJRwFRUTDZRkRERESl7unTp7oOgYiIqGzSlwBzVHu3Xb9+XUfBUFEw2UZEREREpU4QKlhPBCIi0pFinLOtkjHw+0fAk9VAykbgyGygTlXt9/d0AvbPAJI3AAlrgT/HATbmqnU8nIBvBgMXvgOSNgCPVgJ7vgTqVXtVJ1sObD8LRN4rnvOiUsdkG2ll1KhREAQBkZGRyrKFCxdCEAQEBwcXa1u2trZlblljQRDQqVMnXYdBpBORkZEQBAGjRo3SdSjlRlHfH9/1ax0cHAxBELBw4UJdh0KloHbt2roOgYiIKoRi+vJGEIC904EBzYGf9gNT/gQqVwKOzQGqO7x5fydr4MQ8oLo9MG0D8O0uoGs94PBMQKr/qt6IdsDI9sC5aOCzNcCSXYCHI/Df10Bbv1f19CXA3K3Kh87OzsVznlQqmGwrg0RRREREBDZv3oyVK1diw4YNOHPmDLKysnQdWrEZNWoUli9fruswiKicCA4OxqhRoxAXF1fibR09ehSjRo1S+XKholm9ejUaN24MW1tbyGQymJiYoGrVqpgyZQpSUlI07rNmzRp4enpCJpPB1NQULVq0QERERClHThXJlStXdB0CERFVAFqn2o7OAVaPyX97nwCgqScw5KfcIZy/HABafZU7h9rsvm8+/rT3ARNDoM1MYNk+YOE2IOg7wN8dGNL6Vb1NJwGXUcDIX4AVh4FvdwKNpgLPUoBZedrJlgNbTwPXYwAAOTk52p4plQFMtpVBZ86cwX///QdLS0s0adIEVatWRWRkJA4ePAhRLDvLGk+aNAnJycno3bt3ofddsWIFtm/frnHbvXv3EBoa+rbhEVExqVWrFpKTk/HTTz/pLIZDhw5hxYoViI+PL/G2/vvvP6xYseKt5sUo6vtjaV3rCxcuQE9PD3369MH06dMxbtw4uLi4YPHixWjevDnkcrlK/V9//RXDhg1DRkYGpkyZgqFDh+LSpUto2bIlbt26VaKxUsVVkb5EJCIi3RGLaxhpnwAgNhHY/t+rsqdJwJbTQM+GgIF+/vsCwPuNgT3ngAd55iQNiQBuPASCmrwqC78NpKar7vssBTh5FfB6rfea3qvebY8fPy7CSZGuvOFuodL27NkzREZGws3NDR06dFCWm5mZ4fTp04iOjkb16tV1GOErUqkUUqm02I9rbGxc7MckosJLSEiAtbU1JBIJTE1NS+z4FVFR3x9L6lq/bunSpRrL33vvPezYsQOHDh1SDp3PyMjAl19+CSsrK4SHh8PS0lJZt23btpgwYQJ2795d4jFTxWNhYaHrEIiIiF6p456bCHu9g0voTWB0B6CmIxB5X/O+jlaAnUXu0NDXhd4EutR7c/v2lrnJvbyy5cCmf1V7vFG5wJ5tZUx0dO6L8/U5yzw9PaGvr4+bN2/qIiyNNM1JlJKSglGjRsHR0REymQzGxsZwdXXFwIEDAbyajwgADh48CEEQlD8KmuZsU5SFhoaiYcOGMDQ0hLGxMZo3b47bt2+rxXbixAnUrVtXOdypY8eOiImJKda510JCQmBhYQFnZ2dcu3YNANCpUycIgoD4+Hj06tUL5ubmkEql8PLywv79+9WO8fz5c3z44Yews7ODVCqFhYUFOnTooDK05uXLlzAwMEDHjh1V9u3RowcEQUBQUJBKeatWrWBkZITMzEwAr+bbCwsLw5AhQ2BlZQWpVAo3NzesWbOmyOc/d+5cVK1aFQYGBjA2NkbdunWxc+dOjXW3bt2Khg0bwtTUFFKpFJUrV0bXrl0RExOjUu+XX36Br68vjI2NIZPJ4OjoiMDAQKSlpQEoeB4sX19f2NraqpQp7puQkBDUrl0bhoaGyvvh3j3VyUYTEhIwbNgw1KxZE2ZmZpBKpbC3t8cHH3yApCTVX3p555VasGABXFxcIJVKYWNjg//7v//TeA2OHDmCFi1aoFKlSpBKpbC2tkabNm1w8eJFpKWlwdzcHF5eXhr3nTBhAgRBwI4dOzRu1yRvjF988QUcHBwglUrh4OCAL774It/rd+nSJTRv3hympqawsbEBkP88YpmZmRg/fjycnZ0hlUphamqKpk2b4t9//1Wpl3f/pUuXolq1ajAwMFC+L7xJp06dsGLFCmWciveMvPFERkaiffv2yutrZ2eHIUOGqD13bzJq1ChMmzYNABAYGKhsK+/7RlpaGv7v//4P7u7uyve56tWrK/cDinfOtrxlq1atQvXq1ZXvFwMHDlS+1ouDq6srANVVIrdv347ExEQEBgYqE20A0Lp1a+XrKyMjo9BtLV++HO7u7srXw/DhwzWei7avzSNHjkAQBAwdOlRjew0bNoSRkRGeP38OIHdFr86dO8PGxgZSqRTm5ubw9PTEN998U+hzoaJ5/T2biIioaIppzjYHS+Bxonq5oszRquB989Z9fX9rs4J7xjXzAgJqAptPqW/TE4D5wfD29s5/fypz2LOtjImPj4cgCKhcubJKub6+PqytrbUaQiWKYqE++MhksmJbEaxv377Yt28fOnTogICAAGRnZyMqKgr//ZfbFdfZ2RmLFy/G5MmTUatWrXw/FGny9OlTdOjQAa1atULnzp1x6dIl7Nq1C3369EF4eLiyXnh4ODp16gS5XI5+/frByckJhw8fRqtWrYrlHAFg/fr1GDFiBNzd3RESEgJHR0eV7c2aNYOFhQXGjBmDhIQErFu3DkFBQbh7966yJ09GRgaaNGmCa9euoXnz5mjZsiWioqKwfft2NG3aFGfPnoWHhweMjY3h5eWF8+fPq7QRGhoKQRCU1xYA5HI5Lly4AF9fXxgYGKjUHzRoEPT19TFixAhkZmZi7dq1GDFiBOrXrw8fH59Cnf/gwYOxfv161KhRA+PHj0dycjK2bNmC9957DytXrsSQIUOUdWfNmoU5c+bA0tISQUFBcHNzw71793DkyBHcvHlTOdHn0KFDsWbNGjg7O2Pw4MFwdHTErVu3cPjwYSQlJcHIyKhQMSrEx8ejR48eaNWqFXr06IHw8HDs378fzZo1w5UrV2Bunrs6UHR0NLZv3442bdqgd+/ekEqlOHnyJNavX48rV66oXX8gdw6rxMRE9OnTBxYWFti2bRt+/PFHuLq6YuLEicp6q1atwujRoyGTydCrVy/UqFEDjx8/xvHjxxEaGgp/f39069YNmzZtQlhYGBo0aKDSzpYtW+Dg4FCkIdt//PEHEhMTERQUBDMzM/z999/4+uuv8ezZM/z+++8qddPT09GmTRv4+flh4sSJb5wfrUOHDjh+/Djq1KmDwYMHIzY2Fps3b0a7du2wb98+tGnTRqX+4cOHsXHjRgQGBsLd3V3rXi1jx45FSkoKTp06hcmTJyvfHxs2bAggd96nJk2aIC0tDe+99x5q1qyJEydOYO3atTh37hzCw8PVXg/5GTRoEGJjY7F7924MGTJE+UeNIhGalpaGRo0a4fLly6hbty569+4NQ0NDREZGYu/evViwYIFW7RTF0aNH8ddffyEoKAiOjo7Yu3cvNm7cCEtLyyIPO01ISEBaWhoSEhJw8OBB/PHHHzA1NUX79u2VdU6fPg0AaNGihdr+devWRUREBM6fP48mTZqobc/Pb7/9hk8++QQ2NjYYPXo09PX1sXXrVhw9elStrravzTZt2qBatWrYuXMnsrKyVHoW3rx5E+fPn0eXLl1gYWGBzMxMtG3bFgkJCQgMDISHhweeP3+OyMhInDhxAlOnTtX6XKjobt68CX9/f12HQUREFZG+Xu7KonlJ9QGZNDfxldezlNzebEYGQEa2+rHS//dloFEBf08qtmVomCIhPetVnUwNx7etBGycANx5Aiz6W317thxYdxwxgxvAvW1A/jFQ2SJSmbJlyxbxzz//1Ljt8OHD4u+//y5mZ2cXeIykpCTx999/1/onKSnpjXGNHDlSBCBevnxZWbZgwQIRgLh161ZlmYmJiVi/fv03Hg+A2LFjR43bbGxsRB8fH7UyAOKPP/6oUt67d28RgBgaGqosa9GihQhA3Llzp0rdli1bFtiutvF+8803op6enhgQEKB27Tp27CgCEHv16qVSvmzZMhGAOHPmTGXZ7NmzRQDiwIEDVequWrVKBCC2a9dOWfbJJ5+IAMTz58+LoiiKV69eVdYBIN6+fVsURVE8efKkCECcOHGicl/Fc9ewYUMxJydHWX7gwAERgPjBBx8U6lqEhoaKgiCIXl5e4suXL5XlN2/eFI2MjEQbGxsxMzNTFEVRvH79uqivry86OTmJsbGxasdS3Mv79+8XAYh+fn5icnKySp2cnBxl3JruOQUfHx/RxsZGpUxx30yePFmlfNKkSSIAcezYscqyly9fiunp6WrHHTZsmAhA3L9/v7Js69atIgDRwsJCfPLkibL8xYsXopmZmVizZk21MjMzMzEqKirfaxAWFqbxfti5c6cIQBw/frzavgVRxCiTycTr16+rnGf16tVFiUSiUu7j4yMCEIcOHap2rMuXL4sAxJEjRyrLNmzYIAIQW7ZsqXJfHTt2TJRIJGKtWrXU9pdIJOKZM2cKdR4Kmt6DFNq2bSsCEFevXq1SPmDAABGAOHfu3EK1VdB9Nn78eBGA+OGHH6pty/veXNAxCqLpWivKDAwMVM4/JydHdHFxEStVqlSoNvJq1qyZCED5U7NmTfHw4cMqdd5//30RgMbnbvr06SIAcf369Vq3mZmZKVpbW4umpqbigwcPlOVPnjwRra2tRQDiggULlOWFeW3OmjVL472geA9V1D1+/HiRXleaJCQkqMSXnJys8rshIyNDfPr0qco+jx49KvDx48ePRblcXuHbOHfuXIU4D7bBNtgG22AbOmwjcLEoF3qLIl77aTld1FqVUbn7JL8UxT8Oqx+r8//+luwwW32b4qfepNw6g35Q3/bN9txtBoHq24z7ieLZKFFMTBFF7/H5H194T7z69z9l//kow22UNibbypiNGzfm+6HlyJEj4u+//67xQ0deWVlZ4oMHD7T+ycrKemNc2ibbbG1tRWtra/Hff/8t8HhFSbZZWlqq1f35559FAOLKlStFUcz9ECeTycQaNWqo1VUkdN4m2TZ27FhREASxR48eyoRSXopkmyIpphATEyMCEPv3768sq1+/vigIgvj48WO147i5uYmGhobKD+979uxRSdYtWLBAlEgk4oULF0RBEMQlS5aIoiiKn3/+uQhADAkJUR5L8dxt2LBBrR2ZTCY2a9asUNdCkWxYvny52ragoCARgHjo0CFRFF8ltV5Pkr4uMDBQBCDu27evwHpFSbYZGRmpJAVFURRTU1NFIyMjjfeJKOa+eT9+/Fh88OCBGBwcLAIQP//8c+V2RSJryJAhavs2bNhQNDU1VT7+9ddfRQDiuHHjCjw3xTlUqlRJzMjIUJZ16tRJlEgkyoSqthQx9ujRQ23bkiVLRADijBkzVNoGoDEpqikB1KtXLxGAePz4cbX6TZo0EQGI9+7dU9k/ICCgUOeQV37JtuzsbFEmk4nu7u5q+zx8+FAUBEFs2LBhodoq6D6rUqWKaGJiopYULswxClJQsq1t27Zq9RWJsGfPnhWqHYWTJ0+KGzduFJcsWSJ269ZNrFmzptrvoC5duogAxIsXL6rtP2/ePBGA+Pvvv2vdpuK9eMCAAWrbxowZo5Zsy+tNr81nz56JMplMbNq0qbIsJydHrFy5sujq6qosi4yMFAGI9erVE+/evat17FS8inrfEhERKeWXbLMYJIptZ6r+XLwjigfC1ctlQbn7RD0Uxb3n1I817KfctnzG558McxyeW2fyWvVtfx4VxadJ6uXSwNx40jJEscWX+R9b/31R7L9E4xf3VHZxzrYyRl9fX20VOAXFUr/6+gWP/tXX14ezs7PWP286XmHMmzcPqampaNasGezs7NC1a1csX768WJYpdnBwUCtTDCdTDK998OABMjIy4Obmpla3Xj0tJqUswPHjx7Fs2TJ07doVO3fuLHDy89fnnHNycgIAJCa+GsP/6NEjWFpawt7eXm3/atWqIT09XTmnWfv27WFoaIhjx44ByB1OVr16dfj7+8PV1RUhISHKGE1MTDQOma1Vq5ZamZmZGV68eFHwib/m7t27AKA21BGAcjiqYg47xSqFbxpedufOHQiCgObNmxcqFm3Y2dmpDUE1NjaGnZ0dYmNjVcpnzJiBKlWqwNDQEA4ODnBxcUGfPn0AqD53CtWqVVMrs7S0REpKivKxYkVLxZDHggwdOhQvXrxQzqWXkJCAo0ePonHjxnB3d3/j/pp4enqqldWtWxfAqzkiFczMzGBnZ6fVcR88eABBENCoUSO1bR4eHgCAq1evqpRXrVpVq2MXxv3795GRkaHxuXB0dISFhQUePnxYbO09fvwYzs7OpbKIweuqVKmiVmZllTt3yKNHj4p0zGbNmqF///7KRQ769++PwYMHY9euXco6itePYu7EvNLTc1fSMjEx0bpNxWtC0xyFr793Kmj72rS0tET79u3x33//4f793AmMt23bhidPnqBfv37Ket7e3hg6dCjCw8Ph7u6OatWqYdCgQTh48KDW50FvT9M9RUREVHgapkR6npq7Emjen8SU3PnTXi9XDP28eBeoWxV4fYqlRjVyVw+NKuDvrUfPgCcvgPrqf5OiYQ3g4p3XQhaAP8cBbf2AAd8DJ66q76eQIwdmBGr8W5DKLibbyhgTExOkp6drTE6lpqbC0NAQenp6BR5DLpfj5cuXWv/kl9wrilGjRiE6OhqLFy9GgwYNEBYWhtGjR8PPz++t/6iWSPK/XYvzHPLj4eEBOzs7HD16FIcPHy6wbn6JOPH1lW20ZGBgAF9fX4SHh0Mul+PcuXMICMgdr9+oUSOEhoYiJycHly5dgr+/v8ZrlV9StagxlYSCnuM3bX/bhO6kSZMwb9482NjYYPbs2Vi9ejU2btyIr7/+GoDme+xNr8XC+uijj2BqaoqVK1cCyF0wIiMjAyNGjCjWdvIjk8lK9PhcafjtFHS/FdfrWLHAx7Jly5Rlii867ty5o1Zf8YVAUZPB2ijsa3PcuHHIycnBjz/+CAD4/fffoa+vj/Hjx6vUW7VqFS5cuICpU6fC2dkZf//9Nzp16oTBgweX2LmQqidPnug6BCIioleCz+SuCPpe41dl1mZAYBNg9znV+daq2uX+5LXtDNCtPuBs/aqsjS/g4QRsPa1ad9kIoF8z4JPlwI6z+cekJwH6NAG8nNW+yKayjQsklDG2traIiYnBkydPVHpyZWdnIyEhQWPvrtelpqZi06ZNWrfZv39/mJmZvbmilhwdHTFp0iRMmjQJcrkcH374IdavX4+VK1dizJgxxdaOJi4uLpDJZMreV3lpmuC+MOzt7bF161a0bNkSPXv2RHBwMLp06VLk4zk5OeHcuXOIi4tT6010+/ZtGBkZKRcPAIDmzZsjLCwMv/zyCxITE9G5c2cAQMeOHbFlyxb89ttvePnyZbEuBKGJ4kN1WFiY2sTWilVUFT1WatSoASB3gvX69esXeMxz587hxIkTBa4Wq1gdU9NCIbGxsRqTEXFxcUhLS1Pp3fby5UvExcWpXN9t27bB1tYWoaGhKsdZt25dvvFoQ9GjMCws7I2rbxobG6Nr167YunUrbt26hfXr18PS0hKDBg0qcvuKXkR5KRYU0dQbTFuurq44f/48zp49qzZxflRUFADNvSmLKr9FXFxdXWFoaKjWSw/IvSeeP3+uvA+1VVBS19HRETExMXj58mWFTB6mp6dDFEWVHq9NmjTBTz/9hBMnTqB///4q9cPDw2FkZFSonsOK3paKHrB5Xb58Wa2ssK/N9u3bw93dHZs3b8aUKVNw4sQJNGvWTG0hGwCoXbs2ateuDSB3Ne3GjRtj/fr1mDdvHr89JiIiKjeKqfNA8BngzA1g9RigljPwNBn4pFNuwmvmX6p1Q2bn/uv+0auyBdtyE3NH5wA/7gVMDYHJPYGIu8DqI6/qje8GfNoZOH0deJkBDHxtEaodZ3PLgdxebV8FFs/5Ualiz7YyRvHh9/UPHNevX0d2djaqV6/+xmMYGRmhS5cuWv8UdZXH12VlZamtXiiRSJQfwp4+faosl8lkeP78ebG0m5dUKkXDhg1x8+ZNlWFQAJS9IN5GjRo18O+//8LKygrvv/++WhuF0a1bN4iiiMmTJ6uUr127Fnfu3EHTpk1VPlh27doVQO556OvrKx/36NEDEolEeX7dunUrckzaGDBgAARBwA8//KCy6m10dDR2794NGxsbZcJv+PDh0NfXx+LFi1WefwVFj5Rhw4YBAD7//HONPSAV9fz8/AAAhw4dUtn+/fffaxzmCeQOU5o5c6ZK2cyZM5GWloaOHTsqyxTXOm8vmczMTCxatEjjcbXVr18/mJmZYfXq1RoTQq/3yvm///s/yOVyfPzxx4iKikKvXr0KHLL8JgcPHsSNGzeUj9PS0vDLL79AIpFgwIABRT6uYgjfrFmzVM7h1KlTOHPmDGrVqgVXV9ciH/91imGbr7/H6OnpoWnTprhz545a8mXy5MkQRRHdu3cvVFuKLx80JXV79eqF1NRUjB07Vm1bafSwLS63b9/WWK54reRNnr333nuwsLDA1q1bVV5nx48fx+XLl9G6detC9Yps27YtrKyssGvXLmXPOCD3d4SmL4qK8tocNGgQHj58iEGDBiErKwujRo1S2R4fH6+2arepqanydzB7XJUOxXs6ERFRmSCXA13mAZtPAeO6Aos/AJ4mAW1mFjyEVCEmAWg5A4iOBb4eBEzpBewLB9rPVu0V5++W+28TT2D9/6n/2JrnbteTAL0bAT65XwBq+uKQyi72bCtjrKys4O3tjStXruDQoUNwdXVFYmIiIiMj4eDgoFWyTTFnW2l79uwZXFxc0KRJE/j5+cHOzg63b9/Gli1bYGJiotI7x8vLCxcuXMD48ePh5uYGQRCUw5fe1nfffYcWLVogKCgIffv2hYuLCw4ePKj8kJhfDxltVa1aFadOnVK2sWHDBrz//vuFPs7UqVPx119/Yd26dXjw4AGaNWuGmzdvYtu2bahUqZLKMC4AaNWqFUxMTPDw4UP4+voqkw82NjaoXr06oqKiYGFhgcaNG2tqrtg0aNAAAwcOxPr16+Hn54eePXsiOTkZW7ZsQXp6On7++WdlcsjDwwNffPEF5s2bB09PT/Ts2RNubm6IiYnB4cOHsXLlSrRu3Vo5dGvdunXw8PBA165d4eDggNu3b+PgwYO4ePEi7Ozs0KBBA/j7+2Pnzp3o1asX/P39cfHiRRw7dgz29vbIzlZfStvOzg4///wzrly5gnr16uH8+fPYv38/HB0dMXfuXGW9Ll26YNmyZWjUqBG6d++OFy9eYOfOnW89VNTc3BzfffcdPv74Y9SuXRu9e/dGjRo18OTJExw7dgzjxo1TSQQ0btwYtWrVwj///ANBENSGvhWWk5MTAgIC0LdvX5iZmWHHjh24desWhg8frnE+N20NGDAAy5cvx9GjR9GgQQN07NgRsbGx2Lx5M6RSqdr9+7ZatmyJJUuWYNKkSQgKCoKRkREaNGiApk2b4scff0RAQACGDRuGvXv3KpPix44dg7e3N6ZMmVKotlq3bg1BEPDtt98iISEBpqam8PDwQOfOnbFgwQIcOnQIq1atwsWLF9G6dWsYGRnhypUruH37Ni5evFis511SateuDV9fX/j5+cHZ2Rnx8fE4fvw4Ll26BFdXV8ybN09ZVyaTYe7cuRg3bhzq1q2LQYMG4cWLF1i7di3Mzc3xww8/FKptqVSKOXPmYOzYsahbty769u0LfX19bNmyBebm5khISFCpX5TX5rhx47Bo0SL8888/sLW1Rd++fVW279ixA5MmTUKbNm1Qs2ZNmJmZ4fz589izZw9q1qypcU5KKn43btzQOHcfERFRYQgQoFXvttZfvbnO81Rg5C+5PwXJ26Mtr6sPgE5zNW9TGPpT7s+b5MiBr4KUD9/2cyyVLibbyqCAgACYmZnh2rVruH//PgwNDeHj44P69euX6ReYubk5+vfvj9OnT+PcuXPIyMiAhYUFmjVrhvnz56skCleuXIkRI0Zg+fLlygm2iyvZ1qBBA+zbtw8TJkzAX3/9BalUiiZNmmDlypWoXbs2DA0N37qNKlWq4PTp02jWrBkGDBiAP//8U+3D3JvIZDKcPn0a48ePx/79+3Hy5EkYGxujVatW+OGHH9QSIRKJBP7+/jh16hSaNm2qsq1JkyaIiop660UgtLVu3TrUqFEDq1evxvfffw99fX14enpi5cqV6NWrl0rdOXPmwMPDA99++y02bdqErKwsWFpaomHDhqhZs6ay3p9//ok6depg+fLlWL16NURRhI2NDZo3bw5zc3NlvW3btmHIkCE4ePAgDhw4AB8fHxw4cAAjR45UW/AAyB2avWHDBkycOBH//PMP9PX10bZtWyxfvhwWFhbKet999x1EUcTWrVuxYMECVKpUCZ06dcKYMWOU8+MV1ciRI+Hi4oI5c+bg77//Rnp6OipVqgR/f3+NCwx8+OGHmDp1Knx9fZVD3IpqxIgRSEpKwpo1a/D06VPY2NhgypQp+Oabb97quEBuD8PJkycjODgYixcvhkwmQ+3atbFo0SI0a9bsrY+fV48ePTB+/Hhs3LgR06dPh1wux8iRI9G0aVN4e3vj33//xWeffYZ9+/bh5cuXsLKywocffoilS5fCwMCgUG15eXlh4cKFWLZsGWbNmoWcnBx07NgRnTt3hpGREc6ePYupU6di165d+PHHHyGVSuHo6Kg2xLIsGzRoEE6cOIFNmzYhJSUFBgYGcHJywpgxYzB37lyV1wYAjBkzBsbGxvjmm2+waNEi6Ovro27duli2bFmhh+kCwKeffgqJRIJFixbht99+g7m5OXr16oW2bduqDbcuymvTxsYGbdu2xb59+/D++++rDQ1u3LgxWrdujbCwMBw8eBByuRw2NjYYOnQoFi5cWOjzoaJ5vXchERFRUYjQuERC+aYnAbrUBfxfzYv78OFD2Nra6jAoKgxBLEuzoxOVoH/++Qft27fH2LFjsXTpUl2HQ6XA1tYW9vb2GueBKsuWLl2K8ePHY8mSJZgwYUKRjhEcHIzAwEAsWLAAX3zxRTFHSFT29e7dG7t27cLVq1eVK+RS2XL79u0SWaWYiIjeIUHfQgw+DaEiZjXOLQbqvZpj+eLFi2pzZlPZxTnbqEJKTk5WeSyXy5XDonr06KGLkIi0tnz5cpiZmWH06NG6DoWoXIqPj8eBAwdQv359JtrKMG0WfSIiInqzCtavTU8CdKqjkmgDwKkXyhkOI6UKycPDAw0bNoSvry9SUlJw6NAhXL16Fa1atUK7du0AAPfv39c4x1delpaWsLS0LI2QdY7XQ7fu3buH7du348SJE7hy5QrGjh2rttplVlYWHjx48MZjlZcPsMnJyRoXIXhdcfR8SUtLw+PHj99Yz8XF5a0WpNCkMM/b2yxYk5iYmO9CIQr6+vrFunBFXvHx8WpfdLzO0NCwRCf3PXXqFE6dOoWNGzciIyMDX32lxdwspDM3btzgN/RERESvy5EDs9SnKHrw4IFWc7hT2cBkG1VIbdq0wdGjR3HgwAHk5OSgcuXK+Pjjj/H9998r69SrV0/jCpl5jRw5EsuXLy/pcMsEXg/dCgsLw8SJE2FsbIyePXti8eLFanVu3LgBX1/fNx5r69atJRFisfvpp58wbdq0N9YrjtkO9u7di8DANy+bfvnyZfj4+Lx1e3kV5nlTrPJaFFOnTsWKFSsKrGNjY6NVgrMoBg8ejIMHDxZYx8fHp0SHda9duxYrVqyApaUlZsyYoVy1mYiIiCqyCjSGVE8CtPYBGtVU25SSkqKDgKioOGcbvbN27dqF1NTUAuvUqlXrrSeoLy94Pcq+lJQU7N69+431WrduDXt7+1KI6O1ER0cjNDT0jfWKY+GB2NhYHD169I31unfvrlzpt7iU1vN26dIlXL16tcA6JiYmJTaU/syZM7h7926BdWxtbZW9i4kSEhJgbW2t6zCIiKg8q4hztp1aADTxVCu+fv262iJ6VHYx2UZEREREpS42NrZcfDFARERlWNC3EIPPQKgIaQ09CdDcCzg6V+Pm7Oxs6OtzcGJ5wQUSiIiIiKjUxcbG6joEIiKisiNHDszul+/myMjIUgyG3hbTokRERERERERUPlWEXm0SCRBQE2jhretIqJgw2UZEREREpa64FyMhIqJ3UGsfYPt/gKDrQN6WUGCvNgCceqGc4ZxtRERERFTqONEzEREVB1EUIQjlPttGFQznbCMiIiKiUpeenq7rEIiIqAK4du2arkMgUsNkGxERERGVOlNTU12HQEREFUBmZqauQyBSw2QbEREREZU6FxcXXYdAREQVgLm5ua5DIFLDZBsRERERlToO+yEiouLAhQOoLGKyjYiIiIiIiIjKpaioKF2HQKRGX9cBEBEREdG7x8nJSdchEBFRBRf9XMTNRFHXYby1Zk4CTA244mp5wmQbEREREZU6USz/H36IiEj3XF1dNZaLoojAXTm48KSUAyoBfapmYut7xroOgwqBw0iJiIiIqNQ9evRI1yEQEVEFkJGRobH84F2xQiTaAOBFjoGuQ6BCYrKNiIiIiIiIiMqluLg4tTJRFPHVv3LoVZCRl8nJyboOgQqJw0iJiIiIqNTVqlVL1yEQEVEFFXJfRJh6Do6o1LBnGxERERGVunv37uk6BCIiqgB8fX3Vymaeqji92gBAKuUw0vKGyTYiIiIiKnWpqam6DoGIiCqAmzdvqjw+/kDE6UdATgVah0dfX0/XIVAhMdlGRERERKXO2JirqhER0dtLT09XeTzzVE6F6tUGAGlpaboOgQqJc7YRERERUalzd3fXdQhERFQBmJmZKf//b4yI4zE6DIbof9izjYiIiIhK3ZUrV3QdAhERVQBOTk7K/886XfF6tQHsDV4esWcbEREREREREZVL169fh7+/P/57JCLk/tsdq5IMWNRCgt41BBhLgdDHIj47JseFJ9rt72kFfN9agmZOAjJzgL23RUw8JsfTPKNAPayAYT4SdHATUM0CSMkEwp+ImHlKjvP5rKCalZUFQPZ2J0elij3biIhIZzp16gRBKNtfP44aNQqCICAyMlKr+oIgoFOnTiUcVfHI7/pv3boVHh4eMDQ0hCAIWLhwIYKDg5X/15XIyEgIgoBRo0bpLAYqPg4ODroOgYiIKpC37dUmANj7nh4GeAn46YIcU47LUdlYwLG+eqhu8eb9nUyBE/30UN1CwLSTcnx7To6uVQUcDtSDNE/mZYSvBCP9BJyLzU3kLTkvh4elgP8G6qGtq+YTyE22UXnCnm1ERO8IURRx+fJlXLt2DSkpKTA0NETVqlVRv359SKVSXYdHZURsbCyGDBkCa2trfPHFFzAxMUH79u3VVvoqKZGRkVi6dCn69++P1q1bl0qbpBt6elxZjYiI3p6zszPOxYo4ePftjtPHQ0BTJwF9duVgW1TuUqZbbuQgargeZjeVYOBeeYH7T2ssgYkUqLcuBw+Sc8tCH8vxT5AehvgIWBGRe8xN1+WYdRpIzZM/W3U5B9eG6WFWEwlC7ueoHbusfzlN6phsIyJ6R5w5cwaRkZFwc3ODn58fnj9/jsjISCQkJKBr1678JZ6Pn376CUuWLKmQc2Xs3LlT7ZvSkJAQvHz5Et9++y0+/vhjZbmPjw+Sk5Mhk5XsEIbr169jxYoVcHd3V0u21apVC8nJyTAwMCjRGKh0xMTEwMbGRtdhEBFROZeTk4PZ/+vVliMW/Th9agqITRWxPerVQZ6mAVtuiBhUS4CBHpCpngdTer+GgD23RWWiDQBC7ou48UxEkMerZFu4hqGiz9KBkzEiWrlo/nvc1NS0SOdEusNhpERE74Bnz54pE20dOnSAl5cXAgICEBAQgEePHiE6OlrXIZZZBgYGMDU1hURSMX5lyuVyJCYmAgBkMpnaH28PHz4EANja2qqU6+npwdTUVKe9ICUSCUxNTZlsIyIiIqWjNxKx5/bbJdoAoE5lAeFxIl4/TOhjESZSATUt89/X0RSwM8kdGvq60Mci6lR+85fa9iaCytxueaWkpLxxfypbKsYnByIiKpAimebr66tS7unpCX19/VIbIpif+Ph49OrVC+bm5pBKpfDy8sL+/ftV6uTk5ODTTz+Ft7c3LCwsoK+vDxsbG/Tq1UuZIMpr0aJFqFGjBoyNjSGTyVC5cmW0a9cO9+8Xbubc/OZsO336NOrVq6dMWLVr1w737t0r9LlnZWXB0tISVatW1bh91qxZEAQBy5cvV5a9fPkSY8aMgYuLC6RSKYyNjdGoUSMcOXJEZd+886xNnz4dzs7OkMlkmDp1KgD1OdtsbW2V2wIDAyEIgnJ7fnO2yeVyzJkzBzVr1oShoSEMDQ3h6uqKESNGKOskJCRg2LBhqFmzJszMzCCVSmFvb48PPvgASUlJynoLFy5EYGAgAGDatGnK9hX3bX5ztmVmZmL8+PFwdnaGVCqFqakpmjZtin///VelXt79V61aherVq0MqlcLCwgIDBw5EZmZmfk8TlQBPT09dh0BERBXA8juVi2UFUgcT4HGqermizNE0/0YcTFTrvr6/tVFuz7j8NHMCAhyBzTc0ZwxF8S0ziVTqOIyUiOgdEB8fD0EQULlyZZVyfX19WFtbIz4+/o3HEEURGRkZWrcpk8m0HprarFkzWFhYYMyYMUhISMC6desQFBSEu3fvwtraGgCQlpaG1atXo3Xr1ujUqRNMTU1x7tw57NmzB+Hh4bhx4waMjIwA5Cbapk6dilq1amH8+PEwMjLC/fv3cezYMcTExMDV1VXr89AkIiICHTp0QFZWFoKCguDi4oKDBw8WaY4xqVSKrl27YsOGDTh9+jSaNGmisn3z5s0wMzPDhx9+CADIyMhA48aNce3aNXTo0AFDhw7F8+fPsXnzZnTu3Bl79uxB+/btVY6xfPlyJCUlITAwEA4ODnBzc9MYy/z587Fnzx7s3r0bQ4YMgbe39xvj79ChA0JCQlCzZk2MHDkSlpaWuH79Og4cOKCsEx0dje3bt6NNmzbo3bs3pFIpTp48ifXr1+PKlSs4f/48AKBLly6IiorCmjVr0L17d7Ro0QIA4Ojo+MYYjh8/jjp16mDw4MGIjY3F5s2b0a5dO+zbtw9t2rRRqX/06FH89ddfCAoKgqOjI/bu3YuNGzfC0tISP/300xvPmYrHw4cPUa1aNV2HQURE5djleBFH483VygWgwORWXhn/GxpqpP/q/3mlZ7/anh/FNo375zm+pmGotsbAxq56uPMCWBSqeV44zq9c/jDZRkT0DkhNTYWhoaHGCclNTEwQFxeHnJycAicsT0lJwaZNm7Rus3///jAzM9Oqbq1atbBjxw7lY19fX4wdOxbLli3DrFmzAADGxsaIi4tTO+a8efMwY8YMrFixAuPGjQMA7Nq1C4aGhrhw4UKJDDn8v//7P6SmpmLLli3Knljz5s1D8+bNcefOnUIf75NPPsGGDRvwyy+/qCTbLl68iOvXr6NPnz7KudKmT5+Oy5cvY926dRg0aJCy7ldffQVPT09MnDgRly9fVjl+fHw8rly5gipVqhQYx6hRo5CQkIDdu3eja9eu6NOnT4H1ly5dipCQELRr1w4HDhxQuX9ycl79Nenr64u4uDi1+d6GDx+OVatW4cCBA+jUqRNq166Nrl27Ys2aNQgICMCkSZMKbB8ANm7ciOPHj6Nly5Y4cuSIcrjvkCFD0KZNG4wdOxZXrlxR2ef+/fs4f/48fHx8AOT2HnRzc8P69euZbCtFycnJb65ERERUgKXhckggQg7VL3hbuOSuIqoNz1XZuPEMSMsGZBp2Mfxf1iQtO/9jKLZp3F8v//2NpcCe3nowMwCa/ZWjsmhCXvr6TLaVNxxGSkT0DsjOzs53zjFFgiQ7u4C/IAAYGRmhS5cuWv8oeplpY8aMGSqPe/fuDQCIiopSlkkkEmWiLSsrC3FxcYiJiUGPHj0A5C4AoWBmZobMzEysWbMGcnnBK0cVVk5ODk6fPo1q1aopE22K+KZNm1akYzZp0gTVqlXDvn37VJJUP//8MwBg9OjRyrLt27fD0dERrVq1QkxMjPInPT0djRo1wtWrV9WSGF26dHljoq0oFMnX5cuXqyVq8z42MjJSJtoyMzMRGxuLmJgYdOnSBQBw/PjxIsewdetWAMCcOXNU7vGWLVuicePGuHr1qtrQ4ebNmysTbUDuc9ewYUO8ePFCOZ+dtp49e6bS4zMlJUXl+mdmZiIhIUFln8ePHxf4ODY2VmW4SEVtQ09Pr0KcB9tgG2yDbbAN3bXRroqglmgDgOsJIobsz9Hq5/H/pkN7nPpqOGheirJHKfkP5VQMH81v/4Q0Ua1Xm1QCbO8hgZ8t0PPvHFx5mu/hkZb2MredMv58lOU2Sht7thERvQP09fWRnp6ucZsiuaOvX/CvBH19fTg7Oxd7bID6XHJOTk4AoJb4WLp0KX766Sfcvn1bJSkFAM+fP1f+f/78+QgPD8fo0aPx2WefoXbt2ujYsSM++eQT5bDUorp37x4yMjLg7u6utq1hw4ZFPm5gYCC+/vpr/PXXXxg4cCDkcjl27doFFxcXtGvXTlkvJiYGmZmZcHFxyfdYDx8+VJkPy8PDo8hxFeTBgwewsLDQeC1eN2PGDPz555948OCB2rwjhU1wvR6DIAho1KiR2jYPDw+cPn0aV69eVRk6rCnxaGVlBQB49OgRLC0LmAE5n/0UXl9wwsDAQO2ec3BwKPCxvb39O9FGrVq1VJKy5fU82AbbYBtsg23oro0+NQW4mmThQapUZWGDuJfA2iuFm+fs4hMRzZ0FCIDKsRo5CEjNEhFVwJ8rj1KAJy9F1Ld/fW+goYOAi09UywQAf3aRoG0VAUG75TgRo12MZf35KMttlDYm24iI3gEmJiZ4/vy5xqGiBQ0xzUsul+ebsNPE0NBQ6xU885uHIm9S5tdff8X48eNRo0YNTJs2DW5ubjAyMkJOTg4GDx6sUrdu3bq4c+cOtmzZgoMHDyIsLAxfffUVvv/++/9n777DmrreOIB/byCEFYaAbMSBA3HvPau4tRXcq9ZVrbb9dagdVmttq7V22OGqtm5BrbZuq9aF4lZcuAUnICIgMzm/P2giMQECYgL6/TxPHs25557z3kuA8OYM7Nq1C7Vr1zb6Okxl7NixmDVrFhYvXowBAwZg48aNuH//PiZMmKBTTwgBPz8/fPXVV3m29XRS1NbW9rnEbKz33nsPs2fPRt26dfHGG2/A19cXCoUCN2/exMSJE4t99GFB8nutcwFi0zlz5kyJ/F4kIqLSw0ImYVozCwzd/uxthUcLhFSR4dXKEtZG57wfcLEBQqpI+OuK7si0Co45/15NelK2NlpgSHUJPkog9r9BWG39JFQpI2HOMd33Oj+2k6FvVRlGbldh/aWC33sUZsYIlQxMthERvQTc3NwQGxuL+/fv63zKk52djYSEBKM++UlNTX1ua7YZY9myZZDL5Th69CgcHJ4shBsZGWmwvq2tLYYOHYqhQ4cCAJYsWYJhw4Zh2rRpWLduXZHj0CSKDK3NllcsxvDx8UHDhg2xb98+JCYmYuHChZAkCePGjdOp5+npiaSkJISGhhaYIH3e/Pz8EBERgWvXruU7um3t2rVwc3NDZGSkTsxLly7Vq2vsphq5Yzh27BgOHz6s3VBBQzMNOTAwsFBtEhERUelRPfscyjsG4XrS02PKCic8WiDitsDiYBkCXdSITwPerC2DhQRMOaCbLPsnNOf9TPkFTzJwMw6rEVLZArtDLfD9cTXs5cD7DWQ4HSewOOpJZBPqShhbR4aDtwQeZwEDqum+91l/Oac8t4KWe6GSh8k2IqKXQMWKFXHixAmcOXNGJ7F24cIFZGdno1KlSgW2oVmzzVjF/QmcTCaDJEk600fVarXBddJiY2P1RndpEjG5p5sWhVwuR9OmTbF7926EhYVp121Tq9WYMWPGM7U9ePBgRERE4LvvvsOuXbtQp04dva/Na6+9hjlz5uD999/Ht99+q9dGQYmv4tS/f39ERERg9OjR2Lx5s04iTa1Wa0c2asrVarX2/5mZmZg5c6Zem46OOR8VP70OR1569+6N9evX47PPPsPOnTu1fR44cAAREREIDAx85t1n6flwd3c3dwhERPQCsJQBU5rIMHTrs42UVwug81oVZrWSYXwdGWzkwJG7wNAtqnynkGrEJgOtVqvwbWsZvmopQ6YK2HRV4H971Dqj4mqXzUmuNfWW0NRb/4NT//nZuPFUsi0rK4+dE6jEYrKNiOglUKZMGVSvXh1nz57F9u3b4efnh8TERERFRcHT09OoZNvzXLPNGK+99hr279+Phg0bIiQkBFlZWdiyZYvBqa0tW7aEUqlEw4YNtdcaHh4OSZIwePDgZ47l22+/RdOmTTFw4ED89ddf8PHxwbZt255p7TEAGDJkCN5//33MmjULaWlpOruNanz55Zf4999/MWfOHOzbtw8tWrSAo6Mjbty4gQMHDkChUOD06dPPFIexxo0bh7Vr12L79u2oXr06OnTogDJlyuDixYs4cOCAdmOCzp0748cff0SjRo3QrVs3JCUlYcOGDQZH5jVq1AjW1tZYtmwZ7Ozs4OzsDC8vL4SGhhqMoX///pg/fz52796NBg0aoGPHjrh79y5Wr14NuVyOH3/88bneAyq6p3enJSIiKgovLy9Ud5Hw6QEgJvnZRrc9zABGbFdjRAHTUnOPaMvtXAIQvDb/pN+wrWoMK3RisHAj/8n8mGwjInpJNGnSBEqlEufPn8fNmzdhbW2NoKAg1K9fv9BT98zh7bffxqNHjzB//nzMmjULdnZ2aNGiBebNm6fdUEFj6NChWL9+PcLCwpCSkgJ7e3sEBARg9uzZOjuIFlXt2rWxfft2jB8/XpvUady4MdatWwd/f/8it2tjY4OOHTti7dq1sLGxwRtvvKFXR6FQICIiAlOmTEFYWJh2x1JNQnXYsGFF7r8odu7ciSlTpmDFihWYN28eJEmCu7s7OnXqpK0ze/ZsCCEQFhaGGTNmwNHREcHBwRg3bhyaNGmi055SqcQvv/yCadOmYcaMGcjOzkZQUFCeyTYA2L59O95//32Eh4dj1qxZUCgUqFWrFmbOnInmzZs/t2unZ3Pz5k29BZCJiIgKS5IkyC0kfNpUhje2mXYdWFNRKu0LrkQliiS4EjARERERmdjJkye5QQIRET0zze+TTJVAhQUq3E55ttFtJVETlxQcHOZk7jCoEIzbJo6IiIiIqBhVrlzZ3CEQEdELxMpCwseNZS9cog3gbumlEaeREhHRSycxMbHA9dUsLS2feWH9mzdvFrh7lLOzM5ydnZ+pH6LS6O7du6hQoYK5wyAiolKuWrVq2v8PC5Lw2UHg3mMzBvQcWFrKzR0CFRKTbURE9NL58MMPsWDBgnzruLq6Ii4u7pn6qVevHuLj4/OtM2LECMyfP/+Z+iEqjR49emTuEIiI6AUQExOj3exLYZkzum38LvULNcLNyorJttKGyTYiInrpjB07Fm3atMm3jp2d3TP3s2jRIqSmpuZbJzAw8Jn7ISqNrKyszB0CERG9AFJSUnSev1FTwrQIIC7NTAE9B48fPwbAXbxLE26QQEREREQmJ4QoFTshExFRyXbhwgVUrVpVp+y7Y2q8u/vFGd3WuEwyIl7nsiOlCTdIICIiIiKTO3XqlLlDICKiF4BmCmluI2tKcLY2QzDPiY2NjblDoEJiso2IiIiIiIiISqWoqCi9Mlu5hEmNZHhRxk+rVCpzh0CFxGQbEREREZmcm5ubuUMgIqIX2OhaEhwVOUkPC6n0PiQAmZmZ5r6dVEjcIIGIiIiITK44NiEhIiLy8PAwWG5vJWFuOxm2XS/9K7e1t7sPgGu2lSbcIIGIiIiITO7kyZOoXbu2ucMgIiIq8bipUOnDaaRERERERERERCXU+fPnzR0CFRKTbURERERkcoZ2jyMiIiJ9XLOt9GGyjYiIiIhMLj4+3twhEBERlQoODg7mDoEKick2IiIiIjK5hw8fmjsEIiKiUiGvTSCo5GKyjYiIiIhMztLS0twhEBERlQrR0dHmDoEKie9yiIiIiMjkgoKCzB0CERG9aIQAZv0JnIs1dySF0zIQeL2duaOgYiQJIYS5gyAiIiKil8upU6dQq1Ytc4dBREQvkhv3Af/RgEwGSOYOxkhCANZWQOwCwNneYJUHDx6gTJkyJg6MngWnkRIRERGRyfHzXiIiem7UakBVSh5qAaRnAt//neflZGRkmPDmUXFgso2IiIiITM7FxcXcIRAREZUMagHM3ggkpRo8fO/ePRMHRM+KyTYiIiIiMjkHBwdzh0BERFRyPM4Aftxs7iiomDDZRkREREQmd+3aNXOHQEREVHKo/9vcITlN71CNGjVMHw89EybbiIiIiIiIiIjMLSUd+GmLXvGlS5fMEAw9CybbiIiIiMjkKlSoYO4QiIiISha1AL5eD6Tojm5LT083U0BUVEy2EREREZHJPXz40NwhEBERlTxJj4Fft+kUKZVKMwVDRcVkGxERERGZ3IMHD8wdAhERUckjBPDV+pwNE/7j7e1txoCoKJhsIyIiIiKTk8n4NpSIiEooR1tg3mjg/mIgZQWwaypQpxDLH1T1BrZ8AiQvBxJ+B/4YD7ga2IV78mvAhknA3d8AsQ6Y0ien/EEysGCHttqFCxee8YLI1Pguh4iISoVevXpBkiTcvn3bbDFcv34dkiShX79+BdYtCfEaa8+ePZAkCRMnTjR3KPkqLXGScWrWrGnuEIiIiPRJErDpY6B/C2DuFuCDP4CyjsCeaUAlz4LP93YB9k4HKnkAk5cD32wEutQDdkwB5Ja6db8YADSoBJx4aoduAeCLcCA9s9gui0zLsuAqRET0Mnn48CEuX76M2NhYPHr0CCqVCg4ODihfvjxq1KgBuVxu7hDpBRYZGYmFCxdi+PDhaNSokbnDoefozJkzqFGjhrnDICKil83uacD1+8CwuYaP924CNKsK9J4FrI3IKVtzEIieC0ztAwz4Lv/2J78G2FkD9d4HYuJzyiIvATs/A4a20RmxBv9RwI04wEUJxP+u2078I2DRP8DYTvDx8SnKlZIZcWQbERHpuHjxIs6cOQMHBwfUrVsXjRo1gqOjI44ePYoNGzYgOzvb3CGWCqtWrUJycjI8PY34BJS0jh8/jgULFuDkyZN6x1q2bInk5GR8/vnnpg+Mip1KpTJ3CERERPp6NwHuJgLrDj0pi3+Uk3Dr0RCwKmDM0muNgb+PPkm0AcA/p4GLt4DQprp1b8Tl3Y4AMD0MyMji78xSiCPbiIhIR4UKFVCnTh1YWVlpywIDA3HkyBGcOHECFy5cQFBQkBkjLB0UCgUUCoW5wzCrhIQEuLi4FFt7MpkM9vb2xdYemZezs7O5QyAiItJXpzxw/GrORgW5RV4CRnUAKnsBUTcNn+tVBnB3Ao5e0T8WeQnoXK9wsdx9CCzZhTuN3OHu7l64c8msOLKNiIh0uLm56STaNCpWrAgASExMNHVIOuLi4tClSxcolUooFArUqFEDGzZs0KuXnZ2NiRMnwtfXF3K5HPb29mjcuDG2bt36THUNWbduHWxsbFClShVcuZLz5srQmm2asvPnzyM0NBSOjo6Qy+WoUqUKwsLCDF5rjx49oFQqYW1tjaCgIGzcuBH16tWDq6srkpOTjb1tWsuWLUPFihUhl8vh4uKCQYMGISUlRa/e//73P0iShH/++UfvmIeHB6pUqaJTJkkSWrZsiY0bNyIoKAjW1tZo2jTn09tbt25h4MCBqFChAuzs7CCXy+Ht7Y3Ro0cjNTVVp88xY8YAAEaPHg1JkrTtAnmv2ZaamoqRI0fCw8MDcrkcjo6OaNu2LY4cOaJTL/f53333Hfz8/CCXy+Hq6orRo0cjKyur0PeTiq44E7FERETFxtMZuGPg/a6mzKtM/ufmrvv0+S7KgkfG5SYBmLYGUhZHtpU2HNlGRERG0SRkbGxsjKqfkZEB8fQngnmQy+WwsLAwqu6rr74KOzs7vPnmm0hISMCKFSvQp08fhIWFoVu3btp6Xbt2xbZt21CzZk30798fCQkJWLlyJXr27Inly5fjtddeK1Ldp82dOxdvv/026tati02bNsHNza3Aa+jZsyeUSiXGjBmDR48eYcmSJRg2bBjq1KmDSpUqAci5f82aNcOlS5fQrl07NGnSBOfPn0f//v2hVCqNuldPW758OQYPHgwXFxe88cYbsLGxQVhYGA4cOFCk9p529epV9OnTB126dEFISIg2eXXy5Els27YNbdq0QaVKlaBWq7Fr1y7MmzcPV65cwdatW2FhYYHQ0FDcvHkT4eHh6Nu3L+rVy/n0t0KFvHf/ysrKQtOmTXH69Gk0adIEw4cPx7Vr1xAeHo727dtj+/btemu/rVu3DomJiXj11Vfh6uqKdevWYd68eXBzc+MUVRO6fPkyateube4wiIjoRWZpkbOzaG5yS0Ahz0l85fYgJWc0m40VkGFg2RTNZgU2+h9Ka2mOZRj4AC8960mdTCOXZREAbiei+ol4oIFxp1AJIYiIiAqgUqnEn3/+KebPny8SExONOmf58uVi3rx5Rj0uXLhQYHs9e/YUAET9+vVFenq6tjwiIkJIkiSCgoJEVlaWEEKIv//+WwAQjRs31ql75MgRIZPJREBAgLa8MHWvXbsmAIi+ffsKIYT48MMPBQARHBwsUlNTDcZ769YtvbKOHTuK7Oxsbfkff/whAIi3335bWzZz5kwBQAwaNEio1Wq9chcXF/Ho0aMC75tGdna2cHNzE3Z2duLMmTPa8sTERFG2bFkBQHz44Yfa8nfffVcAEDt37tRry93dXVSuXFmnDDlvB8XcuXP16qemporMzEy98m7duglJksTBgwe1Zb/88osAIH799Ve9+rt379aL87vvvhMARM+ePXXuaXh4uAAgmjVrJlQqlc75jo6O4tKlSzrxOTg4iAoVKhiMMz8JCQk6r5vk5GSdr0tGRoaIj4/XOef27dv5Pr9z547O1/xF7ePo0aMvxHWwD/bBPtgH+yhBfVy/JwR6PXm0+lgYrdzInHOSHwuxcIduO+glRKfPc+p1mKp/TPOo915OnYHf6R/7el3OMasQ/WMug3OOTVmlf8ziNfGo06fFf6/+U+q/5kb2YWpMthERUYH2798v5s2bJ44fP270OXfu3BExMTFGPZ5OVBmiSVT9/vvvescaN24sZDKZOHfunBBCiP79+wsAIjw8XK9u27ZtBQARGRlZ6LqaZFufPn3EgAEDBAAxePBgbZLPULyGkm2bN2/WqRsfHy8AiO7du2vLmjZtqnNNGhkZGcLOzq7QybZDhw4JAKJHjx56xyZPnlwsyTYfHx+dN0aGpKWlidu3b4uYmBgxf/58AUDMnDlTe7ywybZmzZoJSZLEyZMn9eoHBAQIa2trcffuXZ3ze/furVe3ZcuWws7OTiQkJOQbPxUfYxP3RERERns62eY0UIh2U3QfJ68JsfW4frkiNOec6FtCbDqqn/R6/b8PFIMm5J1s8xqeU+f93/WP/bFbiPhHhs/LL9mGXuLiko0mvIlUHDiNlIiI8nXkyBGcPXsWVatWRZ06dYw+z8PD47nEY2jaWWBgIA4dOoTo6GhUq1YN169fhyRJ2mmIuVWvXh27du3CxYsX0aBBg0LV1diwYQPS09MxZMgQ/Pbbb5DJCrcEavXq1XWea9auevjwobYsNjYWjo6O8Pb21qlrZWUFLy8vPHjwoFB9XrhwAQC001RzK8zXNT+enp4GN4XIzs7GBx98gFWrVuHu3bt604sLey25xcTEwMnJCeXLl9c7VrVqVVy6dAk3btzQWVS4XLlyenXLlCmD1NRUpKenFzkWKpyUlBQ4OTmZOwwiInqRPUzN2Qk0t8SUnPXTni7XOHkdaFENkCTdTRIaBQCp6UD0bcPnAcDtB8D9JKB+Rf1jDQOAk9cKF7+FDGgZCKlp1cKdR2bHDRKIiChPR48exYkTJ1C5cmW0aNGiUOempaXh8ePHRj2ys41ct6KEqFWrFhwcHPD333/j4MGDhT7f0tLwZ11PJ6HMSZKkPI/ltf28tbW1wfI33ngDc+bMQYUKFTB9+nQsXrwYK1aswKRJkwCY/rrzuv9kWvHx8eYOgYiISF94BODhDLza+EmZixIIaQr8dVR3vbUK7jmP3NZGAF3rAz65NgJqWwOo4g2EFfJ9o0oNTO1r8INCKtn4bpOIiAw6evQojh8/jsqVK6NVq1b5Jl8MWb9+vcFdLg1p1aqV3u6WeTl58iRq1qypU3bu3DnIZDJUrlwZQM6C+gcPHsSxY8fg7++vVxeAtr/C1NUoX748fv31V7Ru3Rrdu3fH2rVr0aZNG6PiN5aPjw8OHTqEW7duwcHBQVuelZWF27dv55nYyovmGi5fvqx37MSJE3plrq6uAPQTIunp6Xjw4AHKlMlnJ66nrF27FpUrV8aePXt0El137twxuo28+Pn54cCBA7h+/bre6+LixYuwtrbmG1QiIiIyXngEEHERWDwOCPQB4pOBN4NzRplNWaVb95+pOf+WH/2kbMbanMTc7mnA95sAe2vg/R7A6evA4l265w9sBZRzA2z/mxnQMhD4qHfO/1fszUnYtQjEuZMnualQKcORbUREpOfYsWM4fvw4AgICipRoA4C2bduic+fORj18fX2NbvfHH39ERkaG9vnhw4dx+PBhVKtWDQEBAQCAvn37AgBmz56tU/fEiRPYvXs3AgICUKtWrULXza127drYt28fAKBXr17YsWOH0ddgjB49ekCtVuPLL7/UGfn13XffITU1tdDtNWjQAG5ubti5cyeioqK05UlJSVi4cKFe/Ro1agAAtm3bplM+bdo0qNXqQvUtk8kgctaJ1ZalpKRgzpw5enUdHR0BAAkJCUa13bt3bwghMHXqVJ241q9fj+joaNSrV8+oHWLJ9PhHAxERlUhqNdB5OrD6ADC+CzBrMBD/CGg7Jf8ppBqxCUCrT4Ard4GvBgIf9AQ2Hwdemaq/C+nwdsD0/sDk13Ket62R83x6f8DPDZjat9gvj0yDI9uIiEjH2bNncezYMdjb28Pb21tvJJSNjQ18fHwKbOd5rdn24MEDNGjQAJ07d0Z8fDxWrFgBKysrfPHFF9pRU126dEHHjh2xbds2NGrUCMHBwUhISMDKlSshl8vx5ZdfwsrKqtB1n1ajRg3s378frVq1Qu/evbFq1Sp06tSpWK5zwoQJmDdvHpYuXYrbt2+jSZMmOH/+PLZu3QoPDw9kZRnYUj4fFhYW+OabbzB06FC0adMGISEhsLGxwZo1a2BnZ6dXPzg4GH5+fli6dCkeP36MKlWq4PDhwzhx4oTOSDtjdO7cGatWrULr1q3xyiuvID4+HuHh4bC3t9er26xZM0iShPnz5yMrKwuOjo7w9/dHz549Dbb95ptv4rfffsO6devQqlUrtGzZEjdu3MCaNWvg4OCAb775ptBr6pFpnD17Vm/9QiIioueuzacF13mYCoz4OeeRn9wj2nI7FwMEf170WCxkQMNKQOsgAICXl1fBbVGJwmQbERHpiIuLA5Az8mjPnj16xz09PY1Ktj0v69atw6RJk/DTTz8hMzMTAQEBmD59Onr06KFT7++//8ZHH32EFStWYPbs2bCyskJQUBCmTp2K4ODgItd9WmBgIA4cOIAWLVqgT58+WL58Obp16/bM16lQKHDw4EG88cYb2L17N/bt24eAgACsXLkSH3zwAZKSkgq99tjgwYO1o8AWLFgABwcHdO7cGX369NGL2cLCAps3b8awYcOwfv16WFhYoG7duti6dWuhE4qLFi2CjY0N/vrrL0RGRsLFxQU9evRAz5490blzZ526fn5++P777zFz5kx8/vnnUKlUaNGiRZ7JNrlcjoMHD+Ltt9/Gxo0bERERAVtbWzRv3hxff/21zsYWVLIUNmFMRET00vhvrTb8N7ukKLNMyLwkUZJWYyYiIqJ8qVQqODk5wd/fH6dOneKoLSq1rl27ZnAXWSIioiK7cR/wz2O0WWlhIQPqlAciZ2qTbSe5Zlupw3foREREJVRycrJe2ezZs5GSkoLGjRsz0Ualmru7e8GViIiIXjYqNTCtnzbRRqUTp5ESERGVUP3790dCQgIaN24MOzs7REZGYseOHShbtiw++ugjADkbCSQlJeXbjpWVlVmn/hIZEh0dzU/piYiIcpNJQE1/ILiOTnG1atXMEw8VGZNtREREJVTHjh3x448/Yt68eUhPT4ejoyM6duyIb775Bv7+/gCAN954A3/++We+7VSuXBkXL158/gETERERUdGpBTCtr96otpiYGFSqVMlMQVFRcM02IiKiUuzYsWOIjo7Ot46jo6PeRgRE5vbgwQOUKVPG3GEQEdGLpDSv2SaTgEBf4PQcvWQb12wrfTiyjYiIqBSrV68e6tWrZ+4wiAotPT3d3CEQERGVHGqhswNpbtbW1mYIiJ4FV1YmIiIiIpO7f/++uUMgIiIqGWQSUNUb6NnQ4GFOIS19mGwjIiIiIiIiIjIXzai2PHaaj4qKMnFA9KyYbCMiIiIik6tRo4a5QyAioheNrQJQyM0dReEFeAKvNTZ3FFSMuGYbEREREZlcdHQ0qlWrZu4wiIjoReLmCBybBXH9PiQDa5+VWBXcAQuLPA97eHiYMBgqDky2EREREZHJZWRkmDsEIiJ6EVX3w3kpBYGBgeaOpNgw2Vb6cBopEREREZmcUqk0dwhERPSCyszMNHcI9JJjso2IiIiITM7b29vcIRAR0QvKwcHB3CHQS47JNiIiIiIyuQsXLpg7BCIiekFx2iWZG5NtRERERERERPTCiI6ONncI9JJjso2IiIiITM7Hx8fcIRARERE9F9yNlIiIiIhMLjs729whEBERAMQ/Ahp+AMQmmDsS40gSsPMzoEXeu436+fmZLh4iA5hsIyIiIiKTu3v3LtfUISIqCW7EAdfumzsK41nIgM9WA/9MzbNKRkaGCQMi0sdppERERERERERUOqjUwK4zQMTFPKvcu3fPhAER6WOyjYiIiIhMLigoyNwhEBFRaaUZ3UZUQjHZRkREREQmd+XKFXOHQEREpZVKDWw/CRy5ZPBwjRo1TBsP0VOYbCMiIiIik0tLSzN3CEREVJpZyICpawweunTJcBKOyFSYbCMiIiIik7OzszN3CEREVJqp1MCmY8CJq3qH0tPTzRAQ0RNMthERERGRyfn5+Zk7BCIiKu0sZMBU/bXblEqlGYIheoLJNiIiIiIyufPnz5s7BCIiKu1UamDDEeD0dZ1ib29v88RD9B8m24iIiIiIiIiodLKUAdN01267cOGCmYIhysFkGxERERGZnJeXl7lDICKi4uJoC8wbDdxfDKSsAHZNBepUMP78qt7Alk+A5OVAwu/AH+MBVwf9epNfAzZMAu7+Boh1wJQ+QLYaWHcIOHuz+K6H6Bkx2UZkpJEjR0KSJERFRWnLvvzyS0iShPDw8GLty83NjdtVk1lFRUVBkiSMHDnS3KG8VGrUqAE3Nzdzh2E2/NlHRERUCkkSsOljoH8LYO4W4IM/gLKOwJ5pQCXPgs/3dgH2TgcqeQCTlwPfbAS61AN2TAHklrp1vxgANKgEnLimW24hA6aHaZ/6+PgUw4URFR2TbVQiCSFw+vRprF69GosWLcLy5csRERGBrKwsc4dWbEaOHIn58+ebOwyiEis8PBwjR47EvXv3nntfu3fvxsiRI3WS6S+qGzduoFOnTihXrhzs7e0hl8vh4eGBLl264MSJEwbPuXz5Mjp06AAHBwdYWVmhUqVKmDt3rokjpxfN7du3zR0CEREZY/c0YPG4vI/3bgI0qwoMnZsznfPnrUDrT3PWU5vap+D2J78G2FkDbacAP24GvlwLhM4GapcHhrbRres/CvAaDgz8Trc8Ww2sPghcvAUAUKlUhbtGomLGZBuVSBERETh06BCcnZ3RtGlTVKhQAVFRUdi2bRuEEOYOT+u9995DcnIyevXqVehzFyxYgHXr1hk8duPGDURGRj5reERFFhgYiOTkZLMmVLZv344FCxYgLi7uufd16NAhLFiwwOzre0RGRuLGjRvPtY/79+/j+vXraNasGd566y18+umneOWVV7Bv3z40bdoUEREROvVv376Npk2bYu/evQgNDcVHH30EGxsbvPXWW5gxY8ZzjZWIiIhKgd5NgLuJOVM5NeIfAWsOAj0aAlaWeZ8LAK81Bv4+CsTEPyn753RO4iy0qW7dG/m8L7SQgC9yZhzduXOnkBdBVLwKeNUTmd6DBw8QFRUFf39/dOjQQVuuVCpx8OBBXLlyBZUqVTJjhE/I5XLI5fJib9fW1rbY2yQyRkJCAlxcXCCTyWBvb//c2n9ZZGRkICsry+h7aWNj85wjAho0aGBwF8ht27YhODgYM2fOxPr167XlH3zwAeLi4rBw4UIMHz4cADB58mQEBgZixowZGDNmDJydnZ973PTiqVatmrlDICKi4lCnPHD8KvD0oIjIS8CoDkBlLyAqj/XUvMoA7k7A0Sv6xyIvAZ3rGR9HthpYvhf4NNT4c4ieE45soxLnypWcH7RPr9tTtWpVWFpa4tKlS+YIyyBDa7alpKRg5MiR8PLygkKhgK2tLfz8/DBgwAAAT9bCAnL+uJUkSfvQMLRukaYsMjISDRs2hLW1NWxtbdGiRQtcvXpVL7a9e/eibt26UCgUsLe3R8eOHREbGwtJkhAcHFyk6/38889RoUIFWFlZwdbWFnXr1sWGDRsM1g0LC0PDhg2109TKli2LLl26IDY2Vqfezz//jBo1asDW1hYKhQJeXl4ICQlBWloagPzXxTO0vpXmPv3zzz+oVasWrK2ttdf/9IihhIQEvP7666hcuTKUSqV2Ot3gwYPx6NEjnbrh4eGQJAlffvklZsyYAV9fX8jlcri6uuLtt982eA927dqFli1bwtHREXK5HC4uLmjbti1OnjyJtLQ0ODg45PnH5jvvvANJknSSHgXJHeOkSZPg6ekJuVwOT09PTJo0Kc/7d+rUKbRo0QL29vZwdXUFkPeabZmZmZgwYQJ8fHwgl8thb2+PZs2aYf/+/Tr1cp//ww8/oGLFirCystJ+HxQkODgYCxYs0Map+R7JHU9UVBReeeUV7f11d3fH0KFD9b52BRk5ciQmT54MAAgJCdH2lfv7JC0tDW+//TbKly+v/b6uVKmS9jxNO5Ik4eDBg+jbty/KlCkDGxsbbN261ehYDL2mNWWXL19G27ZtYWdnB4VCgXr16uHo0aOFutb8VK1aFQCQlJSkU75582a4u7trE21AzgcNI0aMQGpqKpYuXVrovi5cuIBWrVrB1tYWNjY2aNSoEU6ePGmw7g8//IDGjRvD1dUVcrkcSqVSO9IuN39/f7i6uhqcNjJ37lxIkoSZM2cCyJla8sEHH6BcuXKwtraGjY0NvLy80KVLF2RkZBT6eqhobt7kQtZERC8ET2fgTqJ+uabMq0z+5+au+/T5LsqCR8blJknAjLWoXr268ecQPQcc2UYlTlxcHCRJQtmyZXXKLS0t4eLiYtSUMiFEof5gUigUOsmuZ9GnTx9s3rwZHTp0QJMmTZCdnY3o6GgcOpQzrNrHxwezZs3C+++/j8DAQAwbNszotuPj49GhQwe0bt0anTp1wqlTp7Bx40b07t0bx48f19Y7fvw4goODoVar0bdvX3h7e2PHjh1o3bp1ka9r0KBBWLZsGQICAjBhwgQkJydjzZo1ePXVV7Fo0SIMHTpUW/ezzz7DtGnT4OzsjNDQUPj7++PGjRvYtWsXLl26pF2wdNiwYViyZAl8fHwwaNAgeHl54fLly9ixYwcePXpU5FE+cXFx6N69O1q3bo3u3bvj+PHj2LJlC5o3b46zZ8/CwSFnZ6MrV65g3bp1aNu2LXr16gW5XI59+/Zh2bJlOHv2LI4dO6bX9pIlS5CYmIjevXvDyckJa9euxffffw8/Pz+8++672nq//fYbRo0aBYVCgZ49eyIgIAB37tzBv//+i8jISNSuXRtdu3bFypUrceTIETRo0ECnnzVr1sDT07NIU5QXLlyIxMREhIaGQqlU4s8//8RXX32FBw8eYN68eTp109PT0bZtW9SsWRPvvvtugeujdejQAf/++y/q1KmDQYMG4e7du1i9ejXat2+PzZs3o23btjr1d+zYgRUrViAkJATly5eHk5OTUdfw1ltvISUlBQcOHMD777+v/XnQsGFDAMDZs2fRtGlTpKWl4dVXX0XlypWxd+9e/P777zh69CiOHz8OKysro/oaOHAg7t69i7/++gtDhw7VvjnTJELT0tLQqFEjnDlzBnXr1kWvXr1gbW2NqKgobNq0SW8q5YABA2BlZYXhw4dDkiSUK1fOqDjyk5GRgebNmyMoKAj/+9//cPXqVaxevRo9evTA9evXizTCNiMjA3FxccjIyMCZM2cwZcoUANBJMl65cgWJiYlo166d3vnt27cHABw+fLhQ/d67dw8tW7ZEQkICevbsicDAQOzbtw/t27dHZmamXv158+bB0dERoaGh8PT0xOXLl7F27Vp06NABBw8eRN26dQEA/fv3x5dffomVK1di4MCBOm0sXrwYNjY2GDNmDABg3Lhx+PXXX9GoUSMMHjwYFhYWuHr1Knbt2oXHjx9DoVAU6pqoaFJTU80dAhERPc3SImdn0dzkloBCnpP4yu1BSs5oNhsrICNbv630/36v2+TznkxzLMPA2tzpWU/qZBpo3xCVGvhjD2IG1EeFdo2NO4foeRBEJcyaNWvEH3/8YfDYjh07xLx580R2dna+bTx69EjMmzfP6MejR48KjGvEiBECgDhz5oy2bMaMGQKACAsL05bZ2dmJ+vXrF9geANGxY0eDx1xdXUVQUJBeGQDx/fff65T36tVLABCRkZHaspYtWwoAYsOGDTp1W7VqlW+/eYmMjBSSJIlq1aqJx48fa8svXbokbGxshKurq8jMzBRCCHHhwgVhaWkpvL29xd27d/Xa0nzttmzZIgCImjVriuTkZJ06KpVKqFQqIYThe6wRFBQkXF1ddco09+n999/XKX/vvfcEAPHWW29pyx4/fizS09P12n399dcFALFlyxZtWVhYmAAgnJycxP3797XlSUlJQqlUisqVK+uVKZVKER0dnec9OHLkiAAgBgwYoHN8w4YNAoCYMGGC3rn50cSoUCjEhQsXdK6zUqVKQiaT6ZQHBQUJAGLYsGF6bZ05c0YAECNGjNCWLV++XAAQrVq10n59hBBiz549QiaTicDAQL3zZTKZiIiIKNR1aBj6ntNo166dACAWL16sU96/f38BQHz++eeF6iu/19mECRMEADFkyBC9Y7l/FmniDQoKEhkZGYXqX8PQa1rzdXr69fD2228LAGLp0qVF6mvRokUCgPbh6Ogo/ve//+nU2bFjhwAgBg4cqHd+UlKSACDatGlTqH6HDBkiAIgvvvhCpzwkJER7/3JLTEzUayMiIkJYWlqKnj17asvu3r0rrKysRMuWLXXqnj9/XkiSpFO3fPnywtvbu1Bx5yUhIUHn50hycrLO75SMjAwRHx+vc87t27fzfX7nzh2hVqtf+D7OnDnzQlwH+2Af7IN9lPo+jl4WAr1yHq0+FkYrNzLnnOTHQizc8aQNzaPTf+/HOkzVP6Z51Hsvp87A7/SPfb0u55hViP4xl8E5x6as0j9m+Zo4/feu53Ov8mmTfZTsPkyNyTYqcVasWCGWLVtm8NiuXbvEvHnzDCZIcsvKyhIxMTFGP7KysgqMy9hkm5ubm3BxcRH79+/Pt72iJNucnZ316v70008CgFi0aJEQQojMzEyhUChEQECAXl1NgquwyTZNsmH+/Pl6x0JDQwUAsX37diHEk6TW00nBp2n+sN68eXO+9YqSbLOxsdFJCgohRGpqqrCxsTF4X4TI+YF9584dERMTI8LDwwUAMXHiRO1xTSJr6NCheuc2bNhQ2Nvba5//8ssvAoAYP358vtemuQZHR0ed5ExwcLCQyWTi6tWrBZ6fmybG7t276x379ttvBQDxySef6PQNwGBS1FCyrWfPngKA+Pfff/XqN23aVAAQN27c0Dm/SZMmhbqG3PJKtmVnZwuFQiHKly+vd86tW7eEJEmiYcOGheorv9dZuXLlhJ2dnV5SOK94582bV6i+c8sr2SZJkl7/mkTYpEmTitRXTEyMWLFihVi4cKEYP3688Pf3F6+//rrOa3H9+vUCgBg+fLje+dnZ2UX6Gnt7ewtHR0dtgl7j8uXLBpNtGiqVSsTFxWl/bvv5+YkKFSro1OnYsaOwtLQUsbGx2rJRo0YJAGLHjh3asho1aggbGxvx559/Fip2Kl7G/O4lIiITyJ1scxooRLspuo+T14TYely/XBGac070LSE2HdVPer0+N6f9oAl5J9u8/nuP8f7v+sf+2C1E/CPD5+WVbLN4TYg3fjL4gTeRKXHNNipxLC0toVarDR7TrMVjaZn/DGhLS0v4+PgY/SiovcKYPn06UlNT0bx5c7i7u6NLly6YP39+sWw/7enpqVemmV6nmV4bExODjIwM+Pv769WtV68QC4zmcv36dQDQm+oIAEFBQQCgXXD98uXLAICmTZvq1c3t2rVrkCQJLVq0KFJM+XF3d9ebgmprawt3d3fcvXtXp/yTTz7Rrtvk6ekJX19f9O7dGwCQmKi/dkTFihX1ypydnZGSkqJ9rtnRUjPlMT/Dhg1DUlISlixZAiBnHbndu3ejcePGKF++fIHnG6JZeys3zVQ7zZqIGkqlEu7u7ka1GxMTA0mS0KhRI71jVapUAQCcO3dOp7xChQpGtV0YN2/eREZGhsGvhZeXF5ycnHDr1q1i6+/OnTvw8fExepODp9dbLA7Ozs56/Xt4eADIec0UhY+PD/r164fhw4fj+++/x44dO7BmzRrt6x+Atk9D0/I1UwCtra0L1e+9e/fg7e2tN/W1YsWKBjeH2bVrFxo1agRbW1u4ubnB19cXvr6+uHnzJpKTk3Xqjh07FtnZ2fj+++8BAGq1GuvWrYO/v7922iuQsxakXC5Hz549UaZMGbRv3x7ffvutdq1IMo2oqChzh0BERE97mJqzE2juR2JKzvppT5drpn6evA7UrZCzXlpujQKA1HQg+nbe/d1+ANxPAurrv69DwwDg5LXCxS8EMPm1YlnGg+hZMNlGJY6dnR3S09MNJqdSU1NhbW0NCwuLfNtQq9V4/Pix0Y+8kntFMXLkSFy5cgWzZs1CgwYNcOTIEYwaNQo1a9Z85j/kZLK8v2WL8xpMKb9rKuj4syYw33vvPUyfPh2urq6YOnUqFi9ejBUrVuCrr74CYPieFvTaK6zRo0fD3t4eixYtApCzYURGRgbeeOONYu0nL897baqXcWddpVJZcKVCyu/7QDy981cRVapUCfXr18emTZvw+PFjANAmfO/cuaNXX5NYN/QhQHE5f/48OnfujEuXLmHUqFGYN28eli9fjhUrVsDX11fv2rt16wZfX1+sWrUKALB69WrExcXpbczRpUsXXL9+HT/99BPatGmDixcv4n//+x8CAgJw+3Y+fxAQERGRvvAIwMMZeDXXGmkuSiCkKfDXUd311iq45zxyWxsBdK0P+OTasb5tDaCKNxB20Pg4LGTA4NZAeXe9D4CJTI0bJFCJ4+bmhtjYWNy/f1/nj7js7GwkJCQY9YddamoqVq5caXSf/fr1K9Y/kL28vPDee+/hvffeg1qtxpAhQ7Bs2TIsWrQI48aNK7Z+DPH19YVCodCORsvN0IL/xtD8wX3kyBHUrl1b59jZs2cBPFlMPiAgAABw8OBB1K9fP982jx49ir179+a7O6pmd0xDG2PcvXvXYPLr3r17SEtL0xnd9vjxY9y7d0+7OQMArF27Fm5uboiMjNRppyi7K+YWGBgIIOd+FbT7pq2tLbp06YKwsDBcvnwZy5Ytg7Ozs94C74WhGVmXm2YDDUOjwYzl5+eHY8eO4fDhw2jZsqXOsejoaABPrr045LVpiZ+fH6ytrfVG6QE5r4mHDx9qX4fGyi+Z5eXlhdjYWDx+/PiFTx6mp6dDrVYjPj4efn5+qFixIpydnQ2OQNq5cycAGBzpmB93d3fcunULWVlZOqPbrly5ok3yaSxZsgQZGRlYunQpQkJCdI6NGTPG4MYQAwYMwFdffYWtW7diwYIFkMvleOutt/TqOTs7480338Sbb74JIGeU6/Tp0zFr1izMmTOnUNdERaMZnUlERKVceAQQcRFYPA4I9AHik4E3g3OSX1NW6db9Z2rOv+VHPymbsTYnMbd7GvD9JsDeGni/B3D6OrB4l+75A1sB5dwA2/8+MG4ZCHz036j8ZXuf/J/IzDiyjUocTTLgzJkzOuUXLlxAdnY2KlWqVGAbNjY26Ny5s9GPou56+bSsrCy93RxlMpl2+mZ8fLy2XKFQ4OHDh8XSb25yuRwNGzbEpUuXsHHjRp1jmhFbhdW/f39IkoTvvvtOZzrZlStX8Ndff8HV1VW70+nw4cNhaWmJWbNm6Vyvhma02Ouvvw4AmDhxosERf5p6NWvWBABs375d5/icOXMMTvMEcnaP1OysqDFlyhSkpaWhY8eO2jJNgi33CLbMzEzMnDnTYLvG6tu3L5RKJRYvXmwwIfT0iLm3334barUaY8aMQXR0NHr27Fmk3SU1tm3bhosXL2qfp6Wl4eeff4ZMJkP//v2L3K5meuFnn32mcw0HDhxAREQEAgMD4efnV+T2n6aZwvj095SFhQWaNWuGa9eu6SVG33//fQgh0K1bt0L1pUm2G0rq9uzZE6mpqQYTNqVxROm1a4anY0RERODEiRNwd3fX+Tp26tQJ9+7d046+BHJ+1i1YsAC2trYFJpSf1q5dOyQlJWHWrFk65ZMmTdKrq/kefXoE27Rp05CUlGSw/QkTJkAul2PGjBnYt28fWrZsqTdVOjY2Vu+8Jk2aAAAePHhg/MXQMynOJRyIiMiM1Gqg83Rg9QFgfBdg1mAg/hHQdkr+U0g1YhOAVp8AV+4CXw0EPugJbD4OvDJVfxfS4e2A6f2Bya/lPG9bI+f59P7AiHZApZyBGV5eXsV7jUSFxHc5VOKUKVMG1atXx9mzZ7F9+3b4+fkhMTERUVFR8PT0NCrZplmzzdQePHgAX19fNG3aFDVr1oS7uzuuXr2KNWvWwM7OTme0UrVq1XDixAlMmDAB/v7+kCQJb7/9drHEMXv2bLRs2RKhoaHo06cPfH19sW3bNm1yKq8RQ3lp0KABBgwYgGXLlqFmzZro0aMHkpOTsWbNGqSnp+Onn37SJoeqVKmCSZMmYfr06ahatSp69OgBf39/xMbGYseOHVi0aBHatGmD4OBgDBo0CEuXLkWVKlXQpUsXeHp64urVq9i2bRtOnjwJd3d3NGjQALVr18aGDRvQs2dP1K5dGydPnsSePXvg4eGB7Gz9bcDd3d3x008/4ezZs6hXrx6OHTuGLVu2wMvLC59//rm2XufOnfHjjz+iUaNG6NatG5KSkrBhw4Znnirq4OCA2bNnY8yYMahVqxZ69eqFgIAA3L9/H3v27MH48eMxcuRIbf3GjRsjMDAQO3fuhCRJmDBhwjP17+3tjSZNmqBPnz5QKpVYv349Ll++jOHDhxtcz81Y/fv3x/z587F79240aNAAHTt2xN27d7F69WrI5XL8+OOPzxT301q1aoVvv/0W7733HkJDQ2FjY4MGDRqgWbNm+P7779GkSRO8/vrr2LRpEwICArB//37s2bMH1atXxwcffFCovtq0aQNJkvDNN98gISEB9vb2qFKlCjp16oQZM2Zg+/bt+O2333Dy5Em0adMGNjY2OHv2LK5evYqTJ08W63U/bx9++CEiIiLQqlUrlC9fHkIIREVFYevWrVCpVPjmm2906s+cORPbt2/H2LFjcejQIfj4+GDt2rW4fPkyPv/8c7i4uOTRk2Fff/01Nm3ahE8++QTHjh1D9erVsXfvXkRFRemNMA4NDcXs2bMxevRo7Nu3D2XKlMHBgwcREREBd3d3g1PJPTw80KpVK+3Iu9GjR+vVCQwMRLVq1VCvXj14e3vj9u3bWL16NSwtLbUfBNDzFxsbqx29TEREJVibTwuu8zAVGPFzziM/5fV/LwMAzsUAwZ8bPmZMLBKAcz88eVrIv3eIip1Zt2cgyoNKpRKnTp0Sq1atEgsWLBBLly4VBw8e1Nu9zpSM2Y308ePHYvDgwaJSpUrCzs5OWFpaCldXVxEcHCyOHTum096xY8dEnTp1hLW1tQAgcn875rUbqaFd+jQ7UM6YMUOnfNeuXaJWrVrCyspK2NnZiVdeeUWcOnVKABA9e/Ys0j2YOnWq8Pf3F5aWlsLa2lrUrl1brF+/3mDdZcuWidq1awsbGxthaWkp3NzcRJcuXXR2CRQiZ5fMqlWrCoVCIaysrISXl5cICQnR2U30ypUrokWLFsLa2looFApRr149ERERkedupEFBQWLnzp2iZs2awsrKStja2or27dvr7e6ZmZkpxo0bJ9zd3YWlpaVwcXERAwYMEBEREXo7ceZ1n4XI2QHR0I/TLVu2iCZNmgh7e3tt++3atRMnT57Uq/v1118LAKJmzZoG76cxcsc4ceJE4eHhISwtLYWHh4f44IMP9Oobun8ahnYjFSJn19bx48cLLy8vYWlpKezs7ESTJk3Evn37jDq/sCZMmCDc3NyETCbTa+/UqVOiffv2QqlUCgsLC+Hm5iaGDBkikpKSitTXV199Jby9vYWFhYXerr3JycnizTffFD4+PsLS0lLY2NiIihUrio8//lhbJ6/dUwsjr91IDX2dinqPV6xYIZo3by5cXV2FlZWV9vszODhYHDhwwOA50dHRon379trXcvny5QvccTg/586dE82bNxfW1tbC2tpaNGzYUJw4ccLgz7l169aJatWqCYVCIWxsbESDBg3E/v378339anZR9fDwECqVSu/4W2+9JQIDA4VSqRSWlpaiTJkyokWLFmLnzp1FviYqvBMnTpg7BCIiEkJ3N9LS+LB8TYg+3+hcEn/HkLlJQhTTyspEVOLt3LkTr7zyCt566y388MMPBZ9QCrm5ucHDw0NvGnJJ98MPP2DChAn49ttv8c477xSpjfDwcISEhGDGjBkGp+QRvSy2bt2KTp06Ydy4ccU+4pKKT3p6eqF3syUioufg2BWg/vvmjuLZRH0HVH+yDMbJkyf11pomMiWu2Ub0gkpOTtZ5rlarMX36dABA9+7dzRES5WP+/PlQKpUYNWqUuUMhKvW+/fZbWFhYFDlxTaZx69Ytc4dARESlnaUMeLWRTqINeLJ5G5G5cM02ohdUlSpV0LBhQ9SoUQMpKSnYvn07zp07h9atW6N9+/YAgJs3bxpc8yw3Z2dnODs7myLkl86NGzewbt067N27F2fPnsVbb72lt9tlVlYWYmJiCmzLmF16S4Lk5GSDmxA8rUKFCs/cV1paGu7cuVNgPV9f32fakKIgt2/fRnp6er51lEol3NzcitxHYV4nxbUhTG4l4V4/fPgQS5YswZkzZ7Bz505069atWF5H9Pw8/aEQERFRoWWrgU9D9YpjYmKMWuub6Hlhso3oBdW2bVvs3r1bu+h52bJlMWbMGMyZM0dbp169egZ3DM1txIgRmD9//vMO96V05MgRvPvuu7C1tUWPHj30dmcEgIsXL6JGjRoFthUWFvY8Qix2c+fOxeTJkwusVxwrHGzatAkhISEF1jtz5gyCgoKeub+8dOzYEVFRUQXW2bp1a5H7KMzrRLOrbHEqCfc6NjYW77zzDhQKBVq0aKGzeyqVTAqFwtwhEBFRaWYpAzrXA2qV1zuUkpJihoCInuCabUQvsY0bNyI1NTXfOoGBgahVq5aJIqKnpaSk4K+//iqwXps2beDh4WGCiJ7NlStXEBkZWWC9fv36PXNfd+/exe7duwus161bN9jb2z9zf3nZuXNngaP5/P390aRJkyL3Ye7XSUm511S6qFSqZ979mYiIikFpXrPt2CygbkW94gsXLqBq1apmCIgoB5NtRERERGRyXLyaiKiEKI3JNgsZ0LE2sOljg4ezs7NhacmJfGQ+3CCBiIiIiIiIiEoPlRqY0ifPwwUt4UH0vDHVS0REREQmV7ZsWXOHQEREpZGFDGgTBDQMMHckRHliso2IiIiITM7a2trcIRAREQCUL4v0ci6wjislmwpIAD7Le1QbgFKxljG92LhmGxERERGZHNdsIyIqOYQQkCTJ3GEQvTC4ZhsRERERERHRS+z8+fPmDoHohcJkGxERERGZXOXKlc0dAhER/SczM9PcIRC9UJhsIyIiIiKTu3fvnrlDICKi/zg4OJg7BKIXCpNtRERERGRySUlJ5g6BiIj+ww0FiIoXk21EREREZHJyudzcIRAR0X+io6PNHQLRC8XS3AEQERER0cunevXq5g6BiIiMoFIL/H1VQAjT9OejlFDfgzujUunGZBsRERERmdzJkydRu3Ztc4dBREQA/Pz88jwWHi3Q92+1yWKxkASih1uighMTblR6cRopERERERER0UssIyPDYLlaCEw5oIbMhHkvlZCQzM1RqZRjso2IiIiITM7V1dXcIRAR0X/y2iF6/SWBi4mA2kRTSIleFEy2EREREZHJ2dvbmzsEIiLKhzDDqDaiFwWTbURERERkctevXzd3CERE9J8aNWrolf11ReBsAke1ERUFk21EREREREREL7FLly7pPBdC4FOOaiMqMu5GSkREREQmV6lSJXOHQERE/0lPT9d5vuWawKk4MwVD9ALgyDYiIiIiMrmEhARzh0BERP9RKpXa/3NUG9GzY7KNiIiIiEwuMTHR3CEQEdF/vL29tf/fcUPg2D2u1Ub0LJhsIyIiIiKTs7CwMHcIREQm8TBdYOR2Fdx+yobdd9los1qF4/eMz2SdTxAIDlfB/vtslJmbjUGbVYh7rH/+F4fU6L5eBfefsyF9k43PDqiM7uPChQsAnoxqs+CoNqJnwmQbERG9NIKDgyFJfPcI8F6Q+Rna+Y6I6EWjFgJd1qmw4rzAuDoyzGwlw/3HAq1Xq3ApseCEW2yyQMtVKlx+KDCjhQzv1Zdh01WBV8JUyFTpnv/xfjWO3BWoU7bov993xwgcvgOoOKqN6JlwgwQiInouhBA4c+YMzp8/j5SUFFhbW6NChQqoX78+5HK5ucMjIjM7ffo0atasae4wiIieq/CLAgdvA2HdZOhdJWesS2gVCZUXqTDlgBoruuY/ynfGITVSs4Bjgyzg55CTRGvoCbwSpsaSKIGRtZ4k1q6NsIC/o4T4xwJuPxs/qg0AfHx8AABT/hvVxmQb0bPhyDYiInouIiIicOjQITg7O6Np06aoUKECoqKisG3bNgjBd3DmtmHDBiQnJ5s7DHqJqdVqc4dARPTchUcLuNsCr1Z+khRzs5UQWkXChssCGdn5vydae0mgawVJm2gDgPblZKjsDKy5qHuuv2PRR7SpVCr8GyOw/xYTbUTFgck2IiIqdg8ePEBUVBT8/f3RoUMHVKtWDU2aNEGTJk1w+/ZtXLlyxdwhvvQUCgXs7e3N1r+pd6LkzpclT5kyZcwdAhHRc3fivkBddwmyp5ZuaOgp4XE2EJ3PXjG3kgXuPwbqe+gn0Rp6Sjhxv/iyYnfu3MFnB1Vcq42omDDZRkRExU6TTHt6TaaqVavC0tISly5dMkdYWnFxcejZsyccHBwgl8tRrVo1bNmyRaeOSqXC2LFjUb16dTg5OcHS0hKurq7o2bMnbt26pdfmzJkzERAQAFtbWygUCpQtWxbt27fHzZs3jY7r66+/hiRJCAsL05ZlZGTA2toakiRh165d2vKEhARYWFigZ8+e2rLly5ejdevWKFu2LKysrGBra4u6deti7dq1en09vWbboEGDIEkS9u7da/B+WVlZoWnTpjrlK1euRN26dWFrawu5XI5y5cphypQpeue7ubmhRo0a2LVrF+rVqwdbW1tUq1bN6PsSHh4OSZLw5ZdfYtKkSfD09IRcLoenpycmTZqkV79GjRpwc3PDqVOn0KJFC9jb28PV1VV7fO/evWjWrBns7e0hl8vh4+ODt99+G1lZWUbHRM/OycnJ3CEQET13d1IBTzv9ck3Z7ZS8E2Z3UnXrPn3+g3QUODLOWCcf2mBPDEe1ERUXrtlGRETFLi4uDpIkoWzZsjrllpaWcHFxQVxcXIFtCCGQkZFhdJ8KhcLoBf+bN28OJycnjBs3DgkJCVi6dClCQ0Nx/fp1uLi4AADS0tKwePFitGnTBsHBwbC3t8fRo0fx999/4/jx47h48SJsbGwA5CTaPvzwQwQGBmLChAmwsbHBzZs3sWfPHsTGxsLPz8+ouHr06IGJEydi69atCAkJAQBs3boVGRkZkCQJmzZtQtu2bQEAGzduhFqtRvv27bXnL1q0CElJSejZsyd8fX0RGxuL8PBwhIaGYu3atTqJuae9+eabWLZsGX799Ve0bNlS59gvv/yCrKwsDB48WFs2depUTJ06FZUrV8aoUaNgb2+PXbt2Ydq0abh8+TKWL1+u08a9e/fQrVs3tG3bFt27dy/SFNaFCxciMTERoaGhUCqV+PPPP/HVV1/hwYMHmDdvnk7d9PR0tG3bFjVr1sS7776Le/fuAQB27NiBrl27wsLCAn369IGHhwe2bduG77//HlFRUdi5c2eh46KiuXr1KmrXrm3uMIiIjKYWAplGLoWmsAAkSUJads7/n2b931/iadl5t6E5ZvB8iyd1FMXwV/3y+xW4VhtRMWKyjYiIil1qaiqsra1hYaH/7tDOzg737t2DSqUyeFwjJSUFK1euNLrPfv36QalUGlU3MDAQ69ev1z6vUaMG3nrrLfz444/47LPPAAC2tra4d++eXpvTp0/HJ598ggULFmD8+PEAchJf1tbWOHHiBKysrIyO+WlVq1aFh4cHDh48qC3bsmULlEolKleurDPqbPv27ZAkSSeBtm7dOr3RQhMnTkTNmjXxxRdf5Jtsa9KkCSpWrIgtW7YgKytLZxOL1atXw97eHkOGDAGQM3Jx+vTpaN26tc5ou88//xyhoaFYuXIlPvjgA9SqVUt7LC4uDtOmTcMnn3xS2NuidevWLZw6dQpVqlQBAEybNg01a9bEwoUL8e6772rLgZzXz7Bhw/Dbb7/ptDF+/HhkZ2djx44d2qTiF198gTZt2uCff/7B6tWr0adPnyLHSEREL669MQJt1hi33uT5YRao6gLYWAIZBhJ06f8l0mzy+Ytcc8zg+aqCzzdW9AOBnTc5f5SoOHEaKRERFbvs7GzIZIZ/xWgSbNnZ+XyUC8DGxgadO3c2+qEZZWaMpxM+vXr1AgBER0dry2QymTbRlpWVhXv37iE2Nhbdu3cHkLMBhIZSqURmZiaWLFnyzIu+N2jQAJcuXUJiYs4iLgcOHECdOnXQqlUrnDlzRjva7/Dhw/Dz89PuHgboTstLTEzErVu3tNNkz58/X2DfoaGhePjwIVatWqUtO336NM6fP4+OHTtq7/HChQuRnZ2NUaNGITY2VufRq1cvCCHw559/6rRtZ2eHiRMnFvW2AAA6duyok1CzsbHBm2++CbVarTeSDgC+/PJLnec3btzAhQsX0KRJE53RezKZDNOmTQMArFmzplAxPXjwQGcEZkpKis6ovczMTL314u7cuZPv87t37+psIvKi9uHh4fFCXAf7YB/s4+Xpo6qLhMXBMixor8aPzR9jcbBM+5jT+JHOcyklZ0S1p13OdNCn+7h8P2eOqJe9lOd1WGfmvBfQTCfNHfedVMDZSg2F5ZMk2dN9pKWlG3WvKjoB3jaZKEnptpLyNWcfL04fpiYJbglHRETFLCwsDOnp6Rg0aJDesZ07d+Lq1asYPnx4viPbnofg4GBs27YNmZmZOiO3gJypHsHBwTprt/3www+YO3curl69CpVKpdeWpu7x48fRqVMn3L9/H/b29qhVqxY6duyIN998Uzst1Vg//PADJkyYgCVLluDVV19FmTJlMHnyZLRq1Qrt2rXDhg0bUL9+ffj4+CAkJASrV6/Wnnvy5Em88847iIyMxOPHj/WuL3ciUHMvcr8NuHXrFvz9/dGiRQvtiLVRo0Zh/vz52Lp1Kzp27AggJzn5dDLtaaNHj8Yvv/wCIGfNNicnpyKv1RceHo6QkBB88MEH+Prrr3WO/fvvv2jdujX69++vTbjVqFEDN27cwKNHj3TqbtmyBZ07d8brr7+ORYsW6RxLS0uDnZ0d6tWrhyNHjhQpTiqcmJgY+Pr6mjsMIqLnKmSjCvtiBW6PsdDZJGHkdhWWnxN4MM5CJ2H2tLI/ZaO1r4Q13XXfM1VZlA0fpYR/QvXfS8U/FnD7WYUpTSR81sy491oz/7mDD0+4GXlVz9/JwRaoVbYkpf+ICofTSImIqNjZ2dnh4cOHBqeK5jfFNDe1Wo309HSj+7S2ts5zNN3Tnk60aeROPP3yyy+YMGECAgICMHnyZPj7+8PGxgYqlQqDBg3SqVu3bl1cu3YNa9aswbZt23DkyBF8+umnmDNnDnbt2lWodal69OiBt99+G9u3b4eVlRWys7PRrVs31K1bF3Z2dti8eTNu3boFIQQ6dOigPS8xMRFt27ZFeno6Bg4ciNq1a8PR0REymQxfffUVTp8+XWDf3t7eaNiwIQ4cOICEhAS4uLhgw4YN8Pb21ibact+nr7/+Os9kSWBgoM5za2tro+9BcVAoFCbtjwovISGByTYieuH1riwhPFpgXbRA7yo5yaP4xwJhFwW6VZR0Em1XHub8fq3o9KTstcoSfj8rEPNIwNchp/yfG2pEJwLv1Cu+ZNS4Zi746TIQkwxwNA7Rs2OyjYiIip2bmxtiY2Nx//59eHp6asuzs7ORkJCgU5aX1NTU57ZmmzGWLVsGuVyOo0ePwsHBQVseGRlpsL6trS2GDh2KoUOHAgCWLFmCYcOGYdq0aVi3bp3R/ZYrVw4+Pj6IiIiApaUlypQpg/r16wMA6tSpg/379yM+Ph4ymQw9evTQnhcWFobExER88cUXmDx5sk6bmnXojDFkyBAcPHgQv/76K2rUqIF79+5p16bTqFSpEgDA3d0d/fr1M7rtZ3XhwgW9suPHjwMAKlasWOD5mgTgxYsX9Y4dOXIEQgijN7OgZ2fshiZERKVZ78oSGnsCw7aqcS4BcLUBfj6phkoAU5vpfkjYbk3OKPrrI5/8mT65kQxh0Sq0WaPChLoypGQBs46oUcMVGBak+3N06Vk1bjwCHv+3Q+neWGB6RM6o9kGBEso55v1zN/rCOUxpWhPDtz3bchhElIPJNiIiKnYVK1bEiRMncObMGZ3E2oULF5Cdna1N1uRHs2absQqzZpsxZDIZJEnSmT6qVqv1ElkAEBsbq7N2GgDtmmAPHz4sdN+NGzdGeHg4MjIytIk2AGjdujW++uorxMfHo2LFinB1ddUes7TM+ZX+9OoQy5cv11mLriBDhgzBe++9h1WrVuHw4cOQJAnjxo3TqTNixAh8//33mD59Ol599VW9JGdcXBzs7Oxga2trdL/G2LZtGy5evKhdty0tLQ0///wzZDIZ+vfvX+D55cqVQ9WqVREREYEDBw6gWbNmAHK+rpqEZGhoaLHGTHnLvYEGEdGLykImYfNrFnj/XzV+OKFGWhbQwANY0skCVcoU/KGDr4OEf/tY4N09akzcq4aVBdClgoTZrWV6008XnVHj39gnz3fHCOyOyXlf0Nxblm+yDchJyH16ALidwtFtRM+KyTYiIip2ZcqUQfXq1XH27Fls374dfn5+SExMRFRUFDw9PY1KtllaWuolsEzptddew/79+9GwYUOEhIQgKysLW7ZsMTi1tWXLllAqlWjYsKH2WsPDwyFJEgYPHlzovtu3b4+wsDDcvn1bZ1RZt27dMH36dNy7d09nWicAdOnSBY6OjpgxYwauXbsGX19fnDx5Elu2bIGfnx9u3rxpVN8KhQIdO3bE2rVrcenSJdSpUwcBAQE6dapUqYKpU6fi448/RoUKFdC9e3f4+/vj/v37OHv2LA4cOIBjx44hKCio0NeeH29vbzRp0gR9+vSBUqnE+vXrcfnyZQwfPhxVq1Y1qo0ffvgBXbt2RYcOHdCnTx94eHhg27ZtOH78ONq1a8edSE0oKiqq2F8jREQlkbO1hIUdLbCwY/71co9oy626q4RtvQtee21P36L/ee/l5QW5hYRPm8gwagdHtxE9KybbiIjouWjSpAmUSiXOnz+PmzdvwtraGkFBQahfv36pmD729ttv49GjR5g/fz5mzZoFOzs7tGjRAvPmzYO3t7dO3aFDh2L9+vUICwtDSkoK7O3tERAQgNmzZyMkJKTQfffo0QNjxoyBWq3WmSrasGFDODs7IzExUS/Z5u7ujo0bN+Kdd97BypUroVKpEBAQgFWrVuHXX381OtkG5GyKoBlZl9eIscmTJyMoKAhff/01wsLC8PjxYyiVSvj6+mLcuHHw9/cv9HUX5I033sCjR4+wZMkSxMfHw9XV1eCmCfl55ZVXsH37dkyaNAlr1qxBRkYGypYtiwkTJmDWrFnFHjPlraAdiYmIyHQ0782GVJfw2cEnO6ASUdFwN1IiIiIq0TS7kc6YMQOTJk0ydzhUTK5fv/5ckrJERFR4J0+e1G7o9PMJNcb9ozbrVFLuRkqlnXHbthERERERFaPcaw4SEVHJ8XoNCa7FuxQu0UuH00iJiIies8TERCQmJuZbx9LS8qXaCTMrKwsxMTEF1jNm51oqnS5fvqwdRUFEROZVrVo17f+tLSV81FiGd3abd3QbUWnGZBsREdFz9uGHH2LBggX51nF1dUVcXJyJIjK/ixcvokaNGgXWCwsLM0E0REREL7eYmBidDaxG1JTweQSQoL8vFBEZgck2IiKi52zs2LFo06ZNvnXs7OxMFE3J4O/vjxUrVhRYr3nz5vDw8ACXmH3xcL02IqKSIyUlRee5rVzCpEYyvP8vR7cRFQU3SCAiIiIik7t165bezr5ERGQeFy5cQNWqVXXKUjMFfOap8DDD9PFwgwQq7bhBAhERERGZ3Ms0bZqIqKTLPYVUw85KwsSGMjDlRVR4TLYRERERERERvcSioqIMlr9ZR4KDlYmDIXoBMNlGRERERCZXq1Ytc4dAREQFUFpJeP+/0W2Wsuf/sPhvGJ3E4XRUynGDBCIiIiIyufPnzyMwMNDcYRAREQAPD488j42rI+HmI8lkGyUoUu8gyJVrelLpxg0SiIiIiMjkTp48idq1a5s7DCIiKmGEEJA4tI1KOU4jJSIiIiKTc3BwMHcIRERUAp0/f97cIRA9MybbiIiIiMjk8puyREREL6/MzExzh0D0zJhsIyIiIiKTi46ONncIRERUAnHkM70ImGwjIiIiIiIiohKBI5/pRcBkGxERERGZnJ+fn7lDICKiEogjn+lFYGnuAIiIiIjo5ZORkWHuEIiIqJRJSBMYu1Ntkr5kEjC9uQwVnLgzKhUek21EREREZHL37t2Dp6enucMgIqISJr+Rz98dUyMsWpgkDiEATzs1ZrexMEl/9GJhso2IiIiIiIiISoS8Rj4npgvMOSagNk2uDXIZcD8uDgDXkKPC45ptRERERGRyNWrUMHcIRERUAt27d89g+Q/HBdKyTRwMUREx2UZEREREJnfp0iVzh0BERKVEUobA7KNqk41q07CxsTFth/TCYLKNiIiIiEwuPT3d3CEQEVEJZGjk89wTAqlZpo/FSi43faf0QmCyjYiIiIhMTqlUmjsEIiIqgZ4e+ZycKTAr0vSj2gAg6dEj03dKLwQm24iIiIjI5Ly9vc0dAhERlUBPj3z++YTAo0wzBUNUREy2EREREZHJXbhwwdwhEBFRCZR75HNqpsBXkWqYYVAbAMDJyclMPVNpx2QbEREREREREZUIuUc+/3pKICnDfLGkp6eZr3Mq1ZhsIyIiIiKT8/HxMXcIREQvnYfpAiO3q+D2UzbsvstGm9UqHL9n/Lix8wkCweEq2H+fjTJzszFoswpxj/XP/+KQGt3Xq+D+czakb7Lx2QGV0X1oRj4/zhL48rD5RrUBQHq6GTN9VKox2UZEJc6cOXMgSRL++OMPc4fyzFJTU9G3b1+4urpCJpPB1dXV3CEV2rhx4yBJEg4fPqwte55fo9q1axfpPhXmvOvXr0OSJAwdOlSnXJIktGvXTqfM3d0dgYGBhY6nOON6Wbzs1/+yUamM/8OLiIienVoIdFmnworzAuPqyDCzlQz3Hwu0Xq3CpcSCU1qxyQItV6lw+aHAjBYyvFdfhk1XBV4JUyFTpXv+x/vVOHJXoE5ZqcjxLjgt8MDMG1fLpKLHTy83S3MHQPSiO3HiBOLj4xEfH4/k5GTY29ujf//+5g6LTOSDDz7A6tWr0adPH9StWxf29vbmDomIqES4c+cO3N3dzR0GEdFLI/yiwMHbQFg3GXpXyRl3E1pFQuVFKkw5oMaKrhb5nj/jkBqpWcCxQRbwc8hJQjX0BF4JU2NJlMDIWk8SU9dGWMDfUUL8YwG3nwv34YqPjw/SswW+OGTeUW0ASuUH5VQyMNlG9JwdOXIECoUCrq6uyMzkNjovmz179sDHxwcrV66E9AJ9MvbWW29hxIgRsLa2NncoRVKuXDkkJyfDysrK3KFQLvy6EBERPT/h0QLutsCrlZ+8J3WzlRBaRcKycwIZ2QIKy7zfr669JNC1gqRNtAFA+3IyVHZWY81FgZG1ntT1dyz6+16VSoVFZwTiS8ByaXHx8QD4wRAVHqeREj1nffv2xZAhQ9ClSxfY2tqaOxwyIDExEWq1+rm0nZCQAKVS+UIl2gDA0tIS9vb2sLQsnZ/ZSJIEe3t7JnWeo+zsbCQlJRXqHH5dXi7Vq1c3dwhERC+VE/cF6rpLelMjG3pKeJwNRCfmfe6tZIH7j4H6HvrvaRt6Sjhxv/jGoN24dRfTS8CoNgAQoiREQaURk21Ez5mDg4O5Q9DTtWtXSJKEGzdu4LXXXoOjoyPkcjmqVq2KDRs26NTNb20uQ2tRada3OnjwIOrUqQNra2s4ODggNDQUaWlpSEtLw+DBg1GmTBnI5XIEBgZi7969BuNUqVR488034ebmBrlcjnLlymHWrFkG6+7ZswfNmzeHUqmEXC6Hl5cXRo8ejbQ03Y/ENDGfO3cOrVq1gr29PcqUKYPU1FSj7192djb+97//wdfXF3K5HPb29mjSpAl2796td9/u3buH8+fPQ5KkQq9F9c8//6BTp07w8vKCQqGAtbU1qlatih9//FGvruZrGh0djeDgYNjb20OhUKBmzZrYunWrTt3c62LNnTsX/v7+kMvlcHFxwfDhw/XumSF5vS7S09Px9ttva++Nra0tGjZsiO3bt+u1ERcXhx49ekCpVEKhUCAoKAibN282+v7k5dy5c2jWrBlsbW1hY2ODJk2a4OjRozp1nnVtsDVr1qBNmzZwd3eHlZUVbG1tUbt2baxateqZ4srPzz//jOrVq8PGxgYKhQIBAQGYM2dOkeLfunUrGjRoACcnJ8jlcpQpUwb169fHn3/+qVPvwYMHGDp0KDw8PCCXy6FUKtG6dWu9uDWvh8WLF+Ott96Ch4cHrK2t8e2336JatWpwcHBASkqKXhxr166FJEmYMGECgPy/LgsWLEDNmjVha2sLhUIBLy8vvPbaa3j06JG2jhAC06ZNQ8WKFbXfMzVq1MDy5cv12pszZw4qV66sba9s2bJo27YtLl++XIQ7SkVx7do1c4dARPRSuZMKeNrpl2vKbqfknVi6k6pb9+nzH6QDGdnFk5jaeNsJd41/a/5cldZZHGR+pXNIAtFLJj3d+JVBraysIJMZl0fv0KEDHB0dMXbsWCQmJuL333/HwIEDcfHiRXh5eRU1XCQkJKBTp05o164dunfvjt27dyMsLAySJCEmJgYpKSkYOXIkEhMTsWTJEvTp0wfXrl3T+2U2ffp0pKWloW/fvrCwsEB4eDg++OADpKam4rPPPtPWW7FiBYYMGQJ3d3cMHjwYbm5uOHz4MObPn48zZ85g3759OvckPT0dLVu2RPXq1fHOO+8gPj4ecrnc6OsLDg7GP//8g1q1amHAgAG4f/8+Vq9ejU6dOmHdunXo3LkzOnTogFmzZmHatGmwt7fHu+++CwCoV6+e0f2sWrUKV65cQceOHeHv748HDx5g7dq1GD9+PJKTkzF58mS9czp16gR7e3uMGzcO9+/fx6pVq9CzZ0/8/fffaN++vU7dvXv3Ys2aNQgJCYG3tze2bt2K3377DTdu3MCOHTsKPRovKysLjRs3xtmzZ9G+fXsMHToUjx49wurVq9G1a1eEh4eje/fu2rrNmzdHdHQ02rVrhyZNmuD06dMICQmBUqksVL+5ZWRkoFWrVqhSpQreffddXLlyBeHh4Wjfvj0iIiJQrVq1Ired26JFi5CYmIju3bvDz88Pt2/fRlhYGAYMGAAhBPr161escY0ePRrz5s1D3bp1MW7cOFhaWmLz5s149913cevWLXzzzTdGx3769Gn06NEDTk5OGDBgALy8vHD37l1ERkYiMjISPXv2BJAz4rN27dq4f/8+unbtiqCgINy7dw8rVqxAmzZt8O+//6Ju3bo6bU+bNg1ZWVkICQmBs7MzateuDblcjk8++QS///47xo4dq1N/wYIFkMlkGD16dL4xjxo1CvPnz4e3tzcGDhwIb29vXLlyBTt27EBcXJz2Q43OnTtj27ZtaN68Ofr06YOsrCysW7cOgwcPRmJiIsaNGwcA+OGHH/Duu++iWrVqeOutt2BnZ4eYmBjs2bMH169fR6VKlYy+n1R0jx8/NncIRESllloIZBq5FJrCImf0eFp2zv+fZv1fViAtO+82NMcMnm/xpI7iGTMMmSqB32K8IAElYmSbtUJh7hCotBJEZDJr1qwRy5cvL/R58+bNM/px69atAtvr0qWLACC6dOki1Gq1tnzBggUCgPjwww+1Zd9++60AIH7//Xe9dmrVqiVcXFx0ysqWLSsAiJkzZ+qUV6pUSUiSJBo0aCCys7O15Z9++qkAIObNm6fXp4uLi4iNjdWWJyQkCFdXV2Frayvu378vhBAiLS1NODo6iqpVq4rU1FSdPj/66CO92GvVqiUAiAEDBhR4nwxZt26dACCaNWsmsrKytOURERFCJpOJKlWqCJVKpXM/qlWrVqS+EhMT9cpSUlKEt7e38PT01OlH8zVt1KiRTlx79+4VkiSJWrVqacuuXbsmAAhJksRff/2lLVer1aJly5YCgFizZo22fOzYsQKAOHTokLbM0OtCc7/nz5+vdx2urq6iWrVq2tfbV199JQCIwYMH69T95JNPtF/7wtJ8bXv27Knzul68eLEAIDp16qR3D4YMGaLTBgDRtm1bnTJDX0NDX5vY2FihVCpF7dq1izWu/fv3CwCiX79+en22aNFCWFtb63yfFETzPbd27dp86/Xv31/I5XKxdetWnfLo6GhhbW2tc580rwcPDw8RHx+vUz8uLk5YWlqKpk2b6pQnJSUJhUIh6tWrpy0zdP27d+8WAET16tXFw4cPddpQq9Xa74MlS5YIAGLixIk6dbKyskRAQIBwdXUV6enpQgghWrVqJaytrcWjR4/yvQfGSEhI0LYrhBDJyck67WZkZOjdk9u3b+f7/M6dOzqvlRe1j6ioqBfiOtgH+2Af7MMcfey+oRKYlWXU43x8Tr92czLFoI1pen1supLT1tarqjyv48gdtcCsLPHDgQd61/HuPxkCs7JEepZa7zpuPcw5NmV/tl6bhp5fTFAbfV3P+yGfnSkGrr5j1NfD0PPS+Lp6kfswNY5sIyoFOnfubHRdFxcXo+t++OGHOqOXevXqhREjRuDKlSuFiu9pTk5OeOedd3TKGjZsiMuXL2P06NGwsHjykVjnzp0xbdo0XLx4Ua+d1157Dd7e3trnZcqUwZAhQzB79mysW7cOo0aNwrp165CUlIQJEybgwYMHePDggbZ+SEgIvvjiC2zduhWDBw/WaXvq1KlFuraVK1cCAD766COd9coaN26M5s2bY+/evbh48WKxjKBycnLS/v/Ro0dITk6GEAJNmjRBeHg4bt68CX9/f51z/ve//+nE1aJFC9SvXx/Hjh1DTEwMfH19tcdq1qyJrl27ap9LkoTPPvsMbdu2RXh4OEJCQgoV7+rVq+Hp6Yng4GDExsbqHGvatCn+/vtv3L17F56envjzzz8hk8n0vg4fffRRoUZpGTJt2jSd1/XQoUPxySef4MCBA8jKyirUKMa85P7aPHz4EKmpqRBCoHr16jhz5gwyMzP11h0ralzz58+HJEkYPXq03n3t3r079u3bhy1btuCNN94wKnZnZ2cAwLp169C+fXuDU92FEPjrr79QrVo1VK9eXadfGxsbBAUF4dixY8jOztZ5vYWGhur9DHJ1dUWLFi2wf/9+3LhxA+XKlQOQMzowIyOjwN2ZFyxYACDne9bR0VHnmGZ6NgAsWbIE1tbWeP311/XuU8eOHTF37lwcPnwYLVu2hIODAzIzM/H7779jzJgxOj+TCqtMmTI6z5/ecdjKykrvnnh6eub73MPD46XoIyAgQOf7pLReB/tgH+yDfZijj6ouEhYHGzebxdNe86+E+Mwn7zc0fdy5nbN2sZe9BE83w9ehmT6aZumo836mTJkyuJ+uQhnrnM0VFE9fh1wOQHcIXn73qnIZCc1dHiHigQNUZh/a9uQ6S8LXnH08Wx+mxmQbUSng4+PzXNoNCgrSea75gZWYmM/qqEYoW7as3sL5mj/wq1SpolPu7p6zu0/uJJmGocWza9XK2ebo0qVLAIBTp04ByElkTJs2zWA88fHxOs+VSqX2D/7CunnzJiRJQsOGDfWOVatWDXv37sX58+eLJdl269YtjB07Frt379ZZl0ojLi5OL9lWv359vXpVqlTBkSNHcPHiRZ1kW4UKFfTqaqYFxsTEFDremzdvIjMzE35+fnnWuXXrFjw9PREbGwtHR0e9upq1uB4+fFjo/gHA1tZW7zUG5Fzr3r17cffuXZ17UFRnz57F+PHjcejQIb2pcJIk6SXbniWu6OhoCCHQqlWrPOO5c+eO0bGPGTMGy5Ytw/LlyxEWFoYqVaqgVatWGDFiBGrWrAkg57WVnJyM06dP5xmXJElIT0/XefNTtWpVg3XfeOMN7N69G7/++iu+/PJLAMAff/wBW1tbvP766/nGe/XqVUiShGbNmuVb78qVK0hPT0flypXzrKNJws2YMQORkZF46623MGnSJNSsWRMdOnTAmDFjULZs2Xz7oeJz7tw51K5d29xhEBGVSh52EoYGFW7Jj9plJeyLFVALobNJwuE7AraWQGXnvM/1VkpwswGO3tXPgEXeEahdtvg2A3u7ejL27y0Z614//UEfkbGYbCMqBQqzro1CoTB6lEZeO0mKXLvu5Ldul0pleKGI/Po3ps/C0Jw3btw4NG3a1GCdp5MFVlZWJX4XTSEEWrZsiZs3byIkJASNGzdGmTJlYGFhgSVLlmD79u3PbQfVohJCwMfHBzNnzsyzTkBAgAkjej6Sk5PRsmVLpKWloX///qhTpw6cnJwgk8kwa9YsnDhxolh3rhJCQJIkLFiwIM8djQuzFqBCocDRo0exY8cO/Pnnn4iIiMCvv/6KefPmYdasWZgwYYI2/ho1amDSpEl5tvX0Oot2dgZWTUbOiLcxY8Zg3bp1mDFjBq5du4ZTp06hc+fOOqME82PMGoJKpRK//vprnnVbtmwJIOeDhmvXriE8PBxbt27F4cOH8dlnn+G7777D1q1b0ahRI6NiIiIiKk16V5YQHi2wLlqgd5Wc35XxjwXCLgp0qyhBYfnk9+eVhznvBSo6PSl7rbKE388KxDwS8HXIKf/nhhrRicA79Yov2dattideuQHsugmzj27LzMgAYGPeIKhUKtl/bRIRAGDZsmVG1+3ateszbW7wNM0oj6dHhwHA7du3C72IfmGcPXtWr0wzkk2TtAkMDASQM7Xt6UXpn4dy5crh8OHDiIyMRKdOnXSOXbhwAQCKZVTb0aNHcfXqVQwePBi///67zrHffvst3/PKly+vU3bx4kXIZDK9kVVXr17VO//48eMA9BOUxvD29saDBw/w6quvQlHAYrI+Pj6IjIzUmwqbkZGB27dv55lUKsjjx49x8eJF1KhRQ6f86tWrUCqVekPUi+LPP//EgwcP8Mknn+iNpsxrdOWzxFWhQgUcPnwY/v7+aNeu3TPHr/HKK6/glVdeAQBcvnwZdevWxcyZMzF+/Hi4ubnBzs4Oqamp6Nu37zN/n1taWqJbt25Yvnw5IiIiEB4eDiEEhg0bVuC5FStWxKFDh3DgwAG8+uqredbz8/NDbGwsWrRoYdTr18bGBoMGDcKgQYMA5GxI0q9fP3z++ef4+++/jb84KrLi/F1FREQF611ZQmNPYNhWNc4lAK42wM8n1VAJYGoz3Smp7dbkfKh+feSTlMHkRjKERavQZo0KE+rKkJIFzDqiRg1XYNhTo+yWnlXjxiPg8X87lO6NBaZH5HxQPChQQjnHvN9bnDt3DlOb1cKOG0buAPEcpRViozqi3Iyb5E1EZtW5c2ejH4VZs80Ymmllu3bt0in/5ZdfDE79LE5r167FrVu3tM8fPHiA33//Hba2tujVqxcAoHfv3nB0dMRvv/2G69ev67WRmpqKuLi4Youpb9++AIAvv/xSZ2TfkSNHsG/fPlSpUsXgdMHC0oy8e3qE1JEjR7Bnz548z5s9ezays59sJbV//34cPXoUQUFBegmI06dP6yQVhBDaXV579+5d6Jj79u2LR48eaXdefdq1a9e0/+/evTvUajWmTJmiU+eLL75AWlpaofvO7dNPP9W5b7///jtiY2PRrFmzYlmvLa9RkWvXrtUmXIszrlGjRgEAJk+ejIyMDL3jMTExeY4yNSQ2NlbvdVWxYkU4OjoiJSUFKpUKMpkMXbt2xdWrVzF79myD7Rj6fsuPZifS+fPnIywsDB4eHujRo0eB52nWovvss8+QkpKid1wzwnPYsGEQQmDcuHEGR33mfv09vaYbkLO+IQAkJSUZcTVUHJ7nhzVERKTPQiZh82sW6FNVwg8n1Hj/XzVcbYBdoRaoUqbgn8m+DhL+7WOBik4SJu5VY2akGp3LS9gRYqEzKg4AFp1R45MDanx5OOc9x+4YgU8O5JRdSyp4uFoTLwltfAELM/+q4G8qKiqObCN6zqKjo7V/IKanp0OlUmlHD9nb2+e7vpDG81qzzRhBQUGoV68eNm/ejG7duqFevXo4ffo0/vnnH3h4eCArK+u59e3o6IgGDRqgd+/esLCwQFhYGOLj4/Hxxx9rR9zZ29tj/vz5GDhwIGrUqIHu3bujcuXKSEpKQnR0NHbv3o1ffvlFb4OEourVqxfatWuHf/75Bw0aNEBwcDDu37+PlStXQi6X49tvv4VM9uyfY9SsWRPlypXDypUrkZmZicDAQFy6dAnh4eEoV65cnptYxMXFoX79+ujcuTPu3buHlStXwsrKyuCmA/7+/ggNDUVISAh8fHywZcsWnDhxAm3atClSsm3atGnYuXMnfv75Zxw6dAgtW7aEk5MTbt68iQMHDsDCwkI7WvF///sffvvtN/zxxx+4desWmjRpgtOnT2Pnzp1wd3fXSRgWhr29Pfbv34+WLVuiVatWuHLlCsLDw+Hg4PDMGy9odOzYEU5OTpg9ezZiY2NRrlw5nD59Gps2bYKfnx9u3rxZrHG1atUK48aNw9y5c1GpUiV07twZPj4+uHPnDk6dOoXDhw8jMTERSqXSqPgnTpyI3bt3o3Xr1tp1+7Zt24bY2FiEhoZqk4m//PILjhw5gg8++AB//fUXGjZsCBsbG1y/fh3//vsvKlWqhH/++cfo+9akSRP4+/tjzZo1SEtLw4gRI4yazt26dWsMHz4cixYtQrVq1dClSxd4eXnh+vXr2Lp1K/bt24eKFSti+PDh2LBhAzZu3IjAwEC0b98eZcuWRWxsLI4dO4abN29qE+9t2rSBtbU1GjVqhHLlyuHhw4cIDw+HJEkYMGCA0ddEz+bWrVtwc3MzdxhERC8VZ2sJCztaYGHH/OvlHtGWW3VXCdt6F7xkzZ6+RU81aEY+T21mgZarzDu6jb+nqKiYbCN6zi5evKi3ePnRo0cB5OyQYkyyzdzWr1+PQYMGYefOndixYweqV6+Ov/76C+PHjzc4QqS4fPzxx4iMjMSKFSuQlJQET09PfPnll5g4caJOvdDQUJQrVw6ffPIJtmzZgtWrV8POzg6enp4YOHAgWrduXaxxbd26FR9++CFWr16NWbNmQaFQoGbNmvjiiy/Qtm3bYunDwsIC27Ztw6hRo7B582asX78evr6+mDlzJi5evIiffvrJ4HlbtmzB+PHj8cMPPyArKwuVK1fG119/rZ0umFvLli1Rv359fPPNN7h16xYcHBwwdOhQzJ07t0gjTuRyOSIiIvDZZ59h9erV+PnnnwHkbI4RGBiok/C0srLCgQMHMHz4cOzevRv79u1DQEAA1qxZg48++qjIryuFQoF///0XI0aMwOzZsyGEQL169fD9998b3HCjKMqUKYPNmzdj/PjxWL16NVQqFSpVqoTff/8dS5cuNZhse9a4fvzxRzRq1Ajfffcdli9fjoyMDDg4OMDf3x8TJ06EjY3xa4mEhITg1q1b2L59O5KSkmBlZQVPT09MmTIFH3/8sbaes7MzTp06hYkTJ+Kvv/5CREQEZDIZXFxcULt2bYwePdroPjX69euHL7/8EpIkaUfsGWPhwoWoXbs2fv75ZyxZsgRCCLi4uKBZs2Y6b4I3btyI7777DgsXLsSiRYuQnZ0NJycnVK5cWefnxuuvv46wsDCEh4cjJSUF9vb2qFSpEmbMmMFkGxERkZlp3oe28JHQwhs4eNt8a7fFJyQA4OZJVHiSKM5VnImIyCy6du2KTZs24dGjRwWOcLp+/TrKly+PIUOGYMmSJaYJkIjoKRkZGQWu70hERC+fkydPaner3n1TjbZrzLMpmFwG9PGJx9LQZ1/zl14+XLONiIiIiEwuJibG3CEQEVEJ19pXQhNP863dxg+FqKg4jZSICDmLpxc00NfV1RUODg7P1E98fDwePXqUbx2FQgFvb+9n6udFwftlvLt37+Lx48f51rGzs4O7u7uJIiLKn6ENL4iIiKpVq6b9vyRJmNpMhg7h5hndVpilOohyY7KNiAjQLhSfn08//RRTp059pn6GDh2KTZs25VunWrVqOHfu3DP186Lg/TJecHAwTp06lW+dtm3bFmpTA6Lnydra2twhEBFRCRQTE4NKlSppn7cvJ6GBO3D8vunXbnv48CEATiOlwuOabUREANasWQOVKv/djurVq/fMG1pERkbmuZOoRpkyZdCxYwFbRL0keL+Mt3v3bty9ezffOr6+vmjevLmJIiLKX3Z2tlE70hIR0csl95ptGluuqtF5nWlHt3HNNnoWTLYRERERkckZ+mOKiIjowoULqFq1qk6ZEAJ1l6pwOg5QmyiDIZcBIwMzMTfY1jQd0guFGyQQERERERERUYmQewqphiRJmNZMZrJEm0ZWVpZpO6QXBpNtRERERGRyHh6clkNERPqioqIMlnetICHIFZCZaGdSAeBxWpppOqMXDpNtRERERGRycrnc3CEQEVEpYurRbdlqwNUq2zSd0QuHq9ISERERkcnFxMTAxcXF3GEQEVEJk9/I5x6VJPwTapoxQzIALXy8TNIXvXiYbCMiIiIiIiKiEiG/ZJtMktDWz0TzSAGcO3cOgYGBJuuPXhycRkpEREREJlelShVzh0BERJSvzMxMc4dApRSTbURERERkcnfu3DF3CERERPlycHAwdwhUSjHZRkREREQm9+jRI3OHQERElC/unE1FxWQbEREREZmcQqEwdwhERET5io6ONncIVEox2UZEREREJsc124iIiOhFxd1IiYiIiMjkTp8+jdq1a5s7DCIiojz5+fkZLFcLgZpLVDib8Hz7L+8InBxsAQeF6XZgpeLBkW1ERERERERERE/JyMgwWL7xsnjuiTYAuJYExKU9/36o+DHZRkREREQmV7ZsWXOHQERElK979+7plQkh8OkBNWQcbEb54DRSIiIiIjI5Gxsbc4dARERUaJuuCpyJN3cUVNJxZBsRERERmdyNGzfMHQIREVG+atSoofOco9rIWEy2ERERERERERE95dKlSzrPt10XOHEfUAszBUSlBpNtRERERGRyAQEB5g6BiIgoX+np6dr/CyHw6X41LDiqjYzAZBsRERERmVxcXJy5QyAiIsqXUqnU/v+fmwJH7gEqjmojIzDZRkREREQm9/DhQ3OHQERElC9vb28AHNVGhcdkGxERERGZnFwuN3cIRERkJg/TBUZuV8Htp2zYfZeNNqtVOH7P+CFj5xMEgsNVsP8+G2XmZmPQZhXiHuuf/8UhNbqvV8H952xI32TjswOqQsV54cIFAMC/MQIRdziqjYzHZBsRERERmVz16tXNHQIREZmBWgh0WafCivMC4+rIMLOVDPcfC7RercKlxIKzWbHJAi1XqXD5ocCMFjK8V1+GTVcFXglTIfOpbNjH+9U4clegTtlnG5I25SBHtVHhMNlG9JILDg6GJPE3R0kSGxuLDh06wNnZGZIk6W05Xpzc3Nyea/slmSRJCA4ONncYpcrIkSMhSRKioqLMHUqRlPb4XzSnTp0ydwhERGQG4RcFDt4GlgTLMKWpDGPryLCnjwUsJGDKAXWB5884pEZqFrAr1ALj68owubEMa7rJcCoOWBKlm2y7NsICd8ZYYlnnoqU+fHx8sC9WYG8sR7VR4ViaOwCil4kQAmfOnMH58+eRkpICa2trVKhQAfXr1+d0mhfE7t27sXLlSowfPx5BQUFFamP48OHYvXs3hg8fjkqVKsHLy6uYoyQijfDwcGzfvh2ff/453N3dzR3OS0UI/tVCRPQyCo8WcLcFXq385AN/N1sJoVUkLDsnkJEtoLDMezDA2ksCXStI8HN4Uqd9ORkqO6ux5qLAyFpP6vo7PtugApVKhc8OqmAhMdlGhcORbUQmFBERgUOHDsHZ2RlNmzZFhQoVEBUVhW3btvGPjhfEoUOHsGDBAu36DkURGRmJevXq4ddff8V7772H/v37F2OEum7cuIHIyMjn1n5JlpycjA0bNpg7DDKhuXPnIjk5GYGBgdqy7du3Y8GCBdwZ0wxcXV3NHQIREZnBifsCdd0lyJ6aXdPQU8LjbCA6Me9zbyUL3H8M1PfQT6I19JRw4n7x/k219fxD7LrJRBsVHke2EZnIgwcPEBUVBX9/f3To0EFbrlQqcfDgQVy5cgWVKlUyY4RUUiQlJcHJyckkfdna2pqkn5LI3t7ebH2r1WokJSXB2dnZJP0lJydDoVDAysrKJP2VVFZWVi/9PShJlEqluUMgIiIzuJMKtPTRL/e0y/n3dopADTfDI9LupOrWffr8B+kocGRcYcy7WhaWEpDNZBsVEke2EZnIlStXAEBvfayqVavC0tISly5dMkdYWnFxcejZsyccHBwgl8tRrVo1bNmyRaeOSqXC2LFjUb16dTg5OcHS0hKurq7o2bMnbt26pdfmzJkzERAQAFtbWygUCpQtWxbt27fHzZs3Cx3f2rVrUaVKFVhZWcHJyQmhoaE4ePAgJEnCyJEjtfW+/PJLSJKE8PBwvTZq1KgBNzc3vfIdO3agWbNmUCqVkMvl8PLywtixY5GZmalT7+DBg2jZsiXKlCkDuVwOJycn1KxZE4sXLwaQsx7U5MmTAQAhISGQJKlQ64Jp1pMSQmDbtm3a87/88ksAwA8//IDGjRvD1dUVcrkcSqUSTZs2xd69ew22t2vXLrRs2RKOjo6Qy+VwcXFB27ZtcfLkSW0dQ2u2acoiIyPRsGFDWFtbw9bWFi1atMDVq1f1+tm7dy/q1q0LhUIBe3t7dOzYEbGxsUVaE61mzZp6X6M5c+ZAkiT4+/vrlH/66aeQJEn7Oi3s6zN3fFlZWXB2dkaFChUMxvXZZ59BkiTMnz9fW/b48WOMGzcOvr6+kMvlsLW1RaNGjbBr1y6dc8PDw7Vfx48//hg+Pj5QKBT48MMPC3Vv9u7di2bNmsHe3h5yuRw+Pj54++23kZWVpVNPsw7jzZs30alTJzg6OsLR0RHR0dFG96VSqfDWW2+hbNmykMvl8PPzw+zZs/Osf+XKFfTs2ROurq6wtLSEs7MzunXrhhs3bujU07zGDx48iJCQEDg5OcHKygqVK1fG6tWrDbb9hYjiCAABAABJREFU+eefo0KFCrCysoKtrS3q1q1rcETi4sWLERQUBKVSCSsrK7i6uqJ58+Y4evSoXv+aNduCg4OxYMECADk/HzTfc7l/ptDzc+3aNXOHQEREz0gtBNKzjXtoZvKkZQMKC/22rP8bCpSWnXd/mmMGz7co+PzCOHJHIOKBkok2KhKObCMykbi4OEiShLJly+qUW1pawsXFxagpTEIIZGRkGN2nQqEwevOD5s2bw8nJCePGjUNCQgKWLl2K0NBQXL9+HS4uLgCAtLQ0LF68GG3atEFwcDDs7e1x9OhR/P333zh+/DguXrwIGxsbADmJtg8//BCBgYGYMGECbGxscPPmTezZswexsbHw8/Mz+jo2bNiAvn37ws7ODsOGDYOTkxM2btyIwYMHG91GXhYvXoyRI0fCw8MDQ4cOhYuLCw4dOoRffvkFZ86c0Saybt26hU6dOgEAQkND4e/vj7i4OJw4cQIHDhzAsGHDMHDgQNy9exd//fUXhg4dqt1pr1q1akbFMnDgQFSuXBnvv/8+AgMDMWzYMADAK6+8AgCYN28eHB0dERoaCk9PT1y+fBlr165Fhw4dcPDgQdStW1fb1m+//YZRo0ZBoVCgZ8+eCAgIwJ07d/Dvv/8iMjIStWvXzjeW+Ph4dOjQAa1bt0anTp1w6tQpbNy4Eb1798bx48e19Y4fP47g4GCo1Wr07dsX3t7e2LFjB1q3bm3UNT+tWbNm+PXXX3Hy5EltjDt37tQmj2JjY+Hjk/NR6J49e2BjY4P27dsDKNzr82lyuRxdunTB8uXLcfDgQTRt2lTn+OrVq6FUKjFkyBAAQEZGBho3bozz58+jQ4cOGDZsGB4+fIjVq1ejU6dO+Pvvv7VfN4358+fj0aNHCAkJgaenp17yMD87duxA165dYWFhgT59+sDDwwPbtm3D999/j6ioKOzcuVPvnFatWqFMmTIYO3YsUlNTCzVasl+/fggLC0P16tUxfPhw3Lt3D5988gk8PDz06p4/fx7NmjVDdnY2evbsiUqVKuHSpUsIDw9H48aNcfr0ab0E6qBBgyCTyTBixAgkJydjzZo16N+/P9RqNfr166dTb9myZQgICMCECRO0dV999VUsWrQIQ4cOBZCTjB8+fDj8/PwwcuRIODs749atW9i3bx+ioqJQv359g9f51ltvISUlBQcOHMD777+v/fncsGFDo+8VERHRy2xvjECbNQVvagAA54dZoKoLYGMJZKj0j6f/lySzySdLoTlm8HxVwecXxtQIFSwkAZXgZnJUBIKITGLNmjXijz/+MHhsx44dYt68eSI7OzvfNh49eiTmzZtn9OPRo0cFxtWxY0cBQPTs2VOn/McffxQAxJQpU7RlKpXKYJuff/65ACC+//57bVmzZs2EtbW1yMjIKDCGglSqVElYWlqKkydPasvS09NF1apVBQAxYsQIbfmMGTMEABEWFqbXTlBQkHB1ddU+T05OFo6OjiIwMFAvzvfee0+nnfnz5+tdoyH59W8sAKJjx4565YmJiXplERERwtLSUufrl5SUJJRKpVAqlSI6OlrvnNyvM1dXVxEUFKRz3NXV1eC19urVSwAQkZGR2rKWLVsKAGLDhg06dVu1apXndeRnw4YNAoCYOnWqtszNzU20b99eJyaVSiWUSqVo2LChtl5hXp9C6N/nAwcOCABiwIABOvVOnDghAIjevXtryzSvj6VLl+rUjYuLEy4uLjr3NCwsTAAQdnZ24vr164W5HVpVq1YVMplM/PvvvzrXq7n/q1at0pZrvqfbtWtXpL4iIyOFJEmiZs2aIjMzU1u+c+dOIUmSACDOnDmjLW/cuLFQKpXiwoULOu1s375dyGQyne/PESNGCAAiICBAPH78WFt+4cIFoVAohJeXl14c1apV06l76dIlYWNjI1xdXbXx9enTRwAo8P5q+s8dv6GywkhISBDp6ena58nJyTqvw4yMDBEfH69zzu3bt/N9fufOHaFWq1/4Pu7evftCXAf7YB/sg328zH2ci3kg5p/IEIvPqMTiMyrxy5HH4pcjqdrnC05kiB8jksTiMyrxMD3nvPK/polO4dl6fSw8rRKYlSV2nbuf53XEPlILzMoS0/Y+eW+guY6Bm7JFmR+zDMZ95uodgVlZYsr+bKPv1avr0oTFN5kCs7LM+ric+CTOkvA1L619mBqTbUQmsmLFCrFs2TKDx3bt2iXmzZun8wPFkKysLBETE2P0Iysrq8C4NH+YHzt2TKc8NjZWABD9+vUzeF5mZqa4e/euiImJEadOnRIARN++fbXHg4ODhUwmE/PmzRMqlarAOPJy9epVAUC0atVK79j333//TMm2hQsXCgDiiy++0Lt3kZGRAoAYMmSIEEKI8PBwAUAEBweLuLi4PON9nsk2DZVKJeLi4rSx+vn5iQoVKmiP//LLLwKAGD9+fIF95ZVsc3Z21qv7008/CQBi0aJFQoic14BCoRABAQF6dbds2VKkZFt6erpQKBSiTZs2QgghoqKiBACxePFi4evrK7p16yaEEOLff/8VAMR7771nsJ2CXp9CGL7PFStWFM7OzjoJyTfeeEMAEDt27NCWVahQQXh5eRn8vuvcubOQyWTaNwSaZFtISEih7oXG9evXBQDRrFkzvWN79uwRAMSrr76qLdN8T+/atatI/U2YMEEAEL/99pvesbp16+okpu7fvy8kSRKdO3c2eC+8vLxElSpVtOdrEltz5szRa7tbt24CgIiIiNCJY/78+Xp1Q0NDBQCxfft2IYQQI0eOFADEJ598km+C/3kk26jobty4Ye4QiIjIDHpvyBbuP2UJVa7kihBCjNiWLWznZIn0LHUeZ+Zwm5slQjboD1KovDBLtF1tePBCXKpaJ9lmjKg4tdkTbU8n26j04JptRCZiaWkJtdrwEGuVSqWtU1AbPj4+Rj8Kai+3p9ft8vb2BgAkJupuB/TDDz+gcuXKsLGxgYeHB3x9fVGrVs7+2g8fPtTW++KLL+Dq6opRo0bB0dERzZs3x+eff46EhASjYwJypqgBMLh5hKbfojpz5gwA4KOPPoKvr6/OQzONTDO997XXXkOHDh2wdetWeHh4oFq1ahgxYgQiIiKeKYbC2LVrFxo1agRbW1u4ublpY7158yaSk5O19TQ7oT7LVDhPT0+9Ms0UO809iYmJQUZGhsHpkPXq1StSvwqFAkFBQdqpqn/++SdkMhl69OiBxo0b48iRIwCAv//+GwDQtWtXnfONfX3mJSQkBImJiVi1ahWAnI0MNm7cCF9fX+10VQCIjY3F7du39V43vr6+2Lx5M9Rqtd46cVWqVCnSPTl37lye5zds2FA7xfZpRf0aaNbRqlOnjt6xp78Pjx07BiEENm/ebPBe3L59W+9nSF5ta6Zba77nr1+/DgBo0KCBXt2goCCdup9++inKly+Pzz//HI6OjmjQoAEmTZpUpPUhyXQePHhg7hCIiMgMeleWcO8xsC76yWJo8Y8Fwi4KdKso6WxucOWhwJWHuoumvVZZwt9XBWIePSn/54Ya0YlASOXim/JZ3VVCz4oCxbTXAr1kuGYbkYnY2dnh4cOHUKlUsLDQXdEzNTUV1tbWeuVPU6vVSE9PN7pPa2tryGTG5dTlcrnBciGe/BL75ZdfMGHCBAQEBGDy5Mnw9/eHjY0NVCoVBg0apFO3bt26uHbtGtasWYNt27bhyJEj+PTTTzFnzhzs2rWrwDXDiiq/69UkNTU08b7zzjsG/6AHoJNI2rZtGw4cOIDw8HBERERg6dKl+O233/Dhhx9ixowZzx58Ps6fP4/OnTvD1tYWo0aNQvXq1WFvbw9JkvDhhx8iLS2tWPvL7z7mlTQuLi1atMCxY8dw8OBB7N69GwEBAXB2dkb79u0RFhaG8+fPY+/evVAqlWjRooX2vMK8PvMyduxYzJo1C4sXL8aAAQOwceNG3L9/HxMmTNCpJ4SAn58fvvrqqzzb0qwtp2HqnV8dHByeex+ae9q+fXu8/vrrBuvY2RnYLqyYeXt74+LFi9iwYQM2bdqEyMhIfP311/jhhx+wZs0adOnS5bnHQIVX0O88IiJ6MfWuLKGxJzBsqxrnEgBXG+Dnk2qoBDC1me570HZrct6/Xx/5JHUxuZEMYdEqtFmjwoS6MqRkAbOOqFHDFRgWpJsZW3pWjRuPgMf/7XKwNxaYHpHzXnZQoIRyjvln0vq6XsGfV/Q/9CcqCJNtRCbi5uaG2NhY3L9/X2fUUHZ2NhISEgyOJHpaamoqVq5caXSf/fr1g1KpLFK8hixbtgxyuRxHjx7V+UM+MjLSYH1bW1sMHTpUu4j5kiVLMGzYMEybNg3r1q0zqs+qVasCAC5fvqx37NSpU3plrq6uAGBww4m7d+/q/HGnadve3l5nUfb8NGvWDM2aNQMA3Lt3D3Xq1MG3336L6dOnQyaTGZ3cLKwlS5YgIyMDS5cuRUhIiM6xMWPG6CRLAwMDAQBHjhzBgAEDnks8AODr6wuFQqEdgZTbsWPHitxuly5d8N133+Gvv/7CsWPH8OqrrwIAevXqhTFjxiA8PBynT59G/fr1de53YV+fhvj4+KBhw4bYt28fEhMTsXDhQkiShHHjxunU8/T0RFJSEkJDQ597wkDz9bx48aLesSNHjmgTf8WlfPnyAIATJ07oJcWf/j6sWbMmJElCVlaW0d9DmrZbtWqlU6YZpaYZ4aaJ48iRI3pxnD17VqcukPOBQe/evdG7d28AObu3tmnTBlOnTs032WbsJjJU/J4eUU1ERC8HC5mEza9Z4P1/1fjhhBppWUADD2BJJwtUKVPw72VfBwn/9rHAu3vUmLhXDSsLoEsFCbNby3RGxQHAojNq/Bv75PnuGIHdMTmJt+besgKTbVWU6eheEdh8FdyVlAqF00iJTKRixYoAnkxd1Lhw4QKys7MNTpN8mo2NDTp37mz0I6+dF4tKJpNBkiSdEWJqtRqTJ0/WqxsbG6tX1rJlSwDGTefTqFChAipWrIgDBw7oJNcyMjLwyy+/6NWvWbMmAGD79u065XPmzNGbztavXz84ODjg559/xu3bt/XaSk5O1k57vX37tt7IOHd3d3h6eiIzMxOpqakAoE1uGrO7bGFoEjpPj86aNm0akpKSdMr+z959x0Vd/3EAfx3csYdsAUFwT1yIojlw417gKtMsGmpmpZVZapqlppn1q1xpaU7UzNwzF4pbUXHgQBAUFGSvu8/vD7qT8w44EO4QX8/H4x7F5/v5fj/v7+A83vcZQ4cOhbW1NVasWIGoqCiNY5VVzzSZTAY/Pz/cuHEDf//9t9q2onp8FScgIACWlpZYtWoVkpOT0aNHDwD5CesaNWpgyZIlyMzM1FjxtCTPZ1FGjhyJnJwcLFy4EAcOHECzZs00fj8HDRqEJ0+eYNKkSVqPoRyKWRaqV6+OevXqISwsDMeOHVOVKxQKTJ8+HUD+CrllZfjw4ZBIJFi4cCFyc3NV5fv378e5c+fU6rq7u6Nly5Y4cuQIduzYoXEshUKhdSjnzz//rNYb89q1a9izZw/c3NzQunVrjTgKrsIcFRWFbdu2wdHRUfUMaHu/admyJUxMTDR+P55lZWUFID95Tvr17L+HRET08rAzk2BZd2MkjpUi/QMpDg2VwreqZuLrTohUrVebUkNHCXYPNkb6B1IkjZdidS9juFhq7n9oqBTiY+2vjp7Fp0Pc3NwwrY0xE21UYuzZRqQn9vb2aNiwIS5fvow9e/bA09MTSUlJiIiIgKurq07JNuWcbYYyaNAgHD16FH5+fggKCkJubi527typdWhr+/btYW1tDT8/P9W5hoaGQiKRYOTIkSVqd86cORgyZAg6dOiAoUOHwtbWFn///bcqEVCwZ0rLli3RtGlTbN26Ff3790fTpk1x/vx5HDp0CFWrVkVeXp6qbpUqVfC///0Pb7zxBurVq4d+/fqhdu3aSEpKwvXr13Ho0CH8/vvvGDx4MBYuXIjffvsNnTp1Qq1atWBiYoLDhw/j7Nmz6NChgyrJFhAQAIlEgu+++w6PHj2ClZUV6tati8DAwNJccpXg4GDMnz8f77zzDo4cOQJ7e3scP34cYWFhcHFxUUsw2djYYP78+Xj33XfRpEkTDBgwALVr18bDhw9x6NAhvP/++wgJCXmueJTmz5+P9u3bIzg4GEOGDIGHhwd2796tSmyWpteQsbExmjRpguPHj0Mmk6n1SvL398eqVasAAH369FHbryTPZ1Fef/11TJo0CfPmzUNmZiZeffVVjTrffPMN/v33X3z//fc4cuQI2rVrB1tbW9y9exfHjh2DqakpLl68WOJzL8yiRYvQu3dvdOvWDUOGDEHVqlWxe/dunD17Fp07d8aQIUPKrC0/Pz8MHDgQmzZtQrNmzdCnTx88ePAA69atg5eXl0Yi8ffff8crr7yCvn37onv37mjWrBnkcjlu3bqFgwcPon///liyZInaPnK5HE2aNEH//v2RmpqK9evXIzc3F3PnzlXVadmyJUaMGIHVq1fDx8cH/fr1Q2pqKjZs2ICsrCz873//U/XoHDJkCB48eIB27drBy8sLGRkZ2Lp1K7Kysoq9Nh06dMCCBQvw8ccfIzg4GObm5mjZsqWqByuVn2e/wCAiIqpoJBIJmjtL0NMb2H0HkDPpRroy2NIMRC8huVwuLly4INatWyeWLl0qVq1aJY4fPy5ycnIMFpNy5UJtoGW1xhkzZgh3d3chlUqFra2t6N27t4iNjdWoO2PGDNG0aVNha2srjI2Nha2trfD19RUbNmwoVZzr168XderUUbUbFBQkduzYIQCICRMmqNWNiooS7dq1E2ZmZsLU1FS0aNFChIWFaaxGqnT06FHRuXNnYWdnJ4yNjYWNjY2oW7eueOedd0RsbKwQIn/F2G7dugkXFxdhYmIizMzMRPXq1cWHH34o0tPT1Y737bffCnd3d2FsbFyqFTkL22fz5s2ifv36wtTUVJibm4uWLVuKo0ePFnpeO3fuFP7+/sLKykpIpVLh4OAgOnfuLM6fP6+qU9hqpM+WCfF0Vc3Zs2erlR84cEA0adJEmJiYCEtLS9G1a1fVCqD9+/cv0bkrffrppwKA8PHxUSv/888/BQCtq6UKofvzKUTRq74OGjRIABDm5uZqy4wXlJ2dLT799FNRs2ZNYWJiIkxMTETVqlVF586d1VYeLuy6ldShQ4eEv7+/sLS0FFKpVLi5uYkJEyZovH8U9Tutq7y8PPHee+8JBwcHIZVKhYeHh/juu+8KXbnz3r17Yvjw4aJq1apCKpUKc3Nz4eHhIYKCgsTx48dV9ZT7Hzt2TAwaNEjY2toKqVQqatWqJf7880+tscyYMUN4eXkJqVQqzMzMRNOmTcWWLVvU6vz888+iVatWws7OTkilUmFtbS0aNmwo/ve//6nVKyz+CRMmCCcnJ2FkZKSxwjGVH65GSkREFd25c+eEEEKcvG+4lUm5GumLSSKEDjNGExFVQD///DPGjh2L77//Hh988IGhw6EC9u3bh65du2L8+PFYtGiRocOhCiIkJARLly7FpUuXVCuK0ssrNTW1TOcVJSIiKmvnz59XzR3bbWMeDkTrv3fbzTeNUbMK55h90XDONiKq8BQKBdLS0tTKsrOz8eOPP8LIyAh9+/Y1UGQE5P/BXJBCocCsWbMAgPeGiAqlbU5JIiKiiqTgYkwz2hpzGCnpjHO2EZFBJCUlaSxY8CypVApPT09kZGTA3d0dgYGBqFevHhITE/HPP//g7t27eO2111CjRg09RV06ubm5uHfvXrH1XF1dy3xRC32oW7cu/Pz80LhxY6SlpWHPnj24cuUKOnbsiC5dugAAoqOj1ebL08bOzg52dnb6CLlCKMnvQFngPSAiIiIqmXv37qnm1vZ3kyDAAzgcw7nbqHhMthGRQXzyySdYunRpkXUcHR2RkJAAU1NTtGnTBgcPHsSWLVsghIC7uzumTp2KmTNn6ini0rt27RoaN25cbL2NGzdi8ODBeoiobHXq1AkHDx7Erl27IJfL4ezsjHfffRfff/+9qk6LFi2QmJhY5HHeeustjYn0K7OS/A6UBd4Dqmi8vb0NHQIREVGRnh1dM72NMTqs5wI/VDzO2UZEBnHhwgVcuXKlyDqWlpaVYhhiWloatm3bVmy9gIAAVK1aVQ8R6d/ff/+N9PT0Ius0aNAATZo00VNEhqfv3wHeA6poYmJiDLrCNhERUXEiIyNRr149tbJ2a/MQdl9/vds4Z9uLick2IiIiItK7gpNOExERVUR5eXmQStUHBB6IVqDzBoXeYmCy7cXEBRKIiIiISO8kEv7hQEREFVtERIRGWYCHBK1dAWP+M0ZFYLKNiIiIiPSOQ5aJiOhFJJFIMKOtERdJoCJxgQQiIiIi0rvLly+jYcOGhg6DiIioUIXNp9y1ugQdPYDzD8u3fXfTDFS1sC7fRqhcMNlGRERERHqXm5tr6BCIiIiKVFiyTSKR4OCQ8k+nCGHNaRdeUBxGSkRERER6V6VKFUOHQEREVKFdvXrV0CFQKTHZRkRERER65+TkZOgQiIiIKrScnBxDh0ClxGQbEREREendjRs3DB0CERFRhWZjY2PoEKiUmGwjIiIiIiIiIqpgCpszjio+JtuIiIiISO+qV69u6BCIiIgqtOvXrxs6BColrkZKRERERHqXmZkJOzs7Q4dBRERUvH8vA3FJZX9cqREwyB/giqOVDpNtRERERKR3Dx8+hJubm6HDICIiKtqF20DHL8rv+Ns/B3q20LrJ09Oz/NqlcsVhpERERERERERE2qRklt+xjY2AL9cBQmjdnJ2dXX5tU7liso2IiIiI9M7Hx8fQIRARERmWXAGciQL2XtC6+cGDB3oOiMoKk21EREREpHfXrl0zdAhERESGZ2wETCu8dxu9mJhsIyIiIiK949AYIiIi5PduO3EdOBShsalx48YGCIjKApNtRERERKR3NjY2hg6BiIioYlD2bnvGjRs3DBAMlQUm24iIiIhI71xdXQ0dAhERUcUgVwBHrgKHL6sVZ2VlGSggel5MthERERGR3nHONiIiogKMjYDp69WKrK2tDRQMPS8m24iIiIiIiIiIDEmuAA5GAMcjVUXu7u4GDIieB5NtRERERKR3Hh4ehg6BiIioYnmmd1tkZGQRlZ9TcjoQ8gvgNAqwHAYEfAmcjdJ9/6sxQI+vAKvhgP1I4LUfgIQnmvW+DgX6zgZcRgOSgcB0zbnpKiMm24jKUEhICCQSCSIinq4k880330AikSA0NLRM23JycnohV6eJiIiARCJBSEiIwWJo3LgxnJycyuRYFeF8ykNlPa+KriyfzRfRi/q+RqWTm5tr6BCIiIgqFrkC2HsBCC/nhREUCqDXLGDNEWBcIDB3JPDwCdDxS+DG/eL3j0kE2k8FbsYDs0cAH/cFtp8Bus4Acp75933qGuDUTaCZd/mcSwXFZBu9sIQQuHjxItavX4/ly5fjzz//RFhYWKX68B4SEoIlS5YYOgwigwgNDUVISAgePHhQ7m0dPHgQISEhaonyyuru3bsIDAxE9erVYWVlBZlMhqpVq6JXr144d+6c1n1u3ryJbt26wcbGBiYmJqhVqxZ++uknPUdOlU18fLyhQyAiIqp4jI2AGfm926pVq1a6Y3T8Ahj1Y+HbQ8OA49eAleOAaUOAsYHAoa/+WxV1feH7Kc3eBKRnAQdmAO/3AqYMBjZ8BFy4A6w8qF739q9A3G/A6g9Kdy4vKKmhAyAqrbCwMERERMDLyws+Pj5ITk5GREQEHj16hF69ekEikRg6RADAxx9/jPHjx8Pc3LzE+y5duhTR0dFaexfdvXu3wpzjy6xBgwZITU2FiYmJoUMpUxXhvPbs2YOlS5fi/fffh4uLS7m2deLECSxduhTdunVDo0aNyrWtooSHh0MIUa5tPHz4EHfu3EHbtm1RvXp1WFhY4Pr169i6dSvatGmDAwcOwN/fX1X//v37aNOmDVJSUvDqq6/Cw8MDoaGhGD9+PFJSUjBlypRyjZeIiIjopSJXADvOAmejIHe3Kp82QsMAlyrAwNZPy5xsgeA2wOrDQHYuYCorfP9NJ4DevoBngREZXZoAddyADceBkG5Py72cyzz8FwGTbfRCevz4sSrR1q3b019ka2trHD9+HFFRUahVq5YBI3xKJpNBJivijaqULCwsyvyYVHJGRkawsiqnfwQN4NGjR3BwcCi381Ie/2WRnZ2N3Nxcna9laZLyJdWyZUtcvXpVo3z37t3o0aMH5s6diy1btqjKJ0+ejISEBCxbtgxjxowBAEyZMgUNGjTA7Nmz8e6778LOzq7c46bKx5CJbSIiogpNagTM2IC4GYHl86XzudtA8xqA0TODHf1qA0v2AtfvA42ra9839lH+kFPfmprb/GoDO86UfbwvIA4jpRdSVFT+xI3Pzu1Tr149SKVS3LhRzmPcS0DbnG1paWkICQmBm5sbTE1NYWFhAU9PT4wYMQLA0/mygPw/gCUSieqlpG1uI2VZeHg4/Pz8YGZmBgsLC7Rr1w63bt3SiO3w4cNo3rw5TE1NYWVlhe7duyMmJgYSiQQ9evQo1flu3LgRfn5+quFpzs7O6NWrF2JiYjTq/vbbb6hVqxZkMhmqVKmCESNGICcnR6Pe2bNn0bVrV1SpUgVSqRROTk4YMWIEkpOTNereunULQUFBcHZ2hkwmg42NDZo1a4Y1a9YUGXdsbCzq1q0LCwsLrF+vQ9fp/2ib26xgmS7nePz4cbRv3x729vaqej4+PlixYoXOcQD5wy4lEgm++eYbfPbZZ3B1dYVMJoOrqys+++wzjfrK+cEuXLiAdu3awcrKCo6OjoWeFwDk5ORgwoQJqFatGmQyGaysrNC2bVscPXq00OuyaNEi1KxZEyYmJqpnvDg9evTA0qVLVXEqn/9nr3PXrl1ha2sLmUwGFxcXjBo1CikpKSW6biEhIareWUFBQaq2Cv4OZGZm4oMPPoC3t7fqd7ZWrVpqvbqUczYeP34cQ4cOhb29PczNzbFr1y6dY9E2Z5uy7ObNm+jUqRMsLS1hamqKFi1a4PTp0yU616LUq1cPAPDkifrEtjt27ICLi4sq0Qbkf4nw1ltvIT09HatWrSpxW5GRkejQoQMsLCxgbm6OVq1a4fz581rrLlq0CK1bt4ajoyNkMhmsra3Rpk0bHD58WK2el5cXHB0dIZfLNY7x008/QSKRYO7cuQAAuVyOyZMno3r16jAzM4O5uTnc3NzQq1cvZGdnl/h8qHRu3rxp6BCIiIgqpjwF8PcpmF0vp+lU4pIAVy1flirL7j8uet+CdZ/d/3Fafs+4lxx7ttELKSEhARKJBM7O6l1SpVIpHBwckJCQUOwxhBAl+qPK1NS0zIZtDhkyBDt27EC3bt3g7++PvLw8XL9+HSdOnACQPzZ/3rx5mDRpEho0aIDRo0frfOzExER069YNHTt2RGBgIC5cuIC///4bgwcPxtmzZ1X1zp49ix49ekChUGDo0KFwd3fH3r170bFjx1Kf1/Tp0/HVV1/Bzs4OwcHB8PLywt27d3HgwAHcuHFDbc6BgwcPYt26dQgODoabmxu2b9+ONWvWwM7OTm0uqH379qFv376wsLBAcHAwqlWrhgsXLmDDhg04c+YMLly4AFNTUwD5yZf27dvjyZMn6NatG3x9fZGWloZTp05h586dGD58uNa4L168iB49eiAzMxPbt29HQEBAqa9BQbqcY2xsLAIDAwFAdc0SEhJw7tw5HDt2rET3XmnZsmVISkpCcHAwrK2t8ddff+Hbb7/F48ePsXjxYrW6WVlZ6NSpE3x8fPDhhx8WOz9at27d8O+//6JZs2Z47bXXEB8fj/Xr16NLly7YsWMHOnXqpFZ/7969WLNmDYKCguDt7Y0qVarodA7jx49HWloajh07hkmTJql+1/38/AAAly9fRps2bZCZmYmBAweiTp06OHz4MH7//XecPn0aZ8+e1XkI7Kuvvor4+Hhs27YNo0aNQsOGDQEA9evXB5CfaGvVqhUuXbqE5s2bY8CAATAzM0NERAS2b9+O2bNnqx1vxIgRMDExwZgxYyCRSFC9eiHfCpZAdnY2XnnlFTRq1AgfffQRbt26hfXr16Nfv364c+dOqXrPZmdnIyEhAdnZ2bh06RKmTZsGAGpJxqioKCQlJaFz584a+3fp0gUAcPLkyRK1++DBA7Rv3x6PHj1C//790aBBAxw5cgRdunTRmmxfvHgxbG1tERwcDFdXV9y8eRObNm1Ct27dcPz4cTRv3hwAMHz4cHzzzTdYu3YtXn31VbVjrFixAubm5nj33XcBAOPGjcOvv/6KVq1aYeTIkTA2NsatW7dw4MABZGRkqN5TqHxlZWUZOgQiIqKKS2qEOuvPA8Hdi66Xmwc8ydAsy84FEp/5EtreKr83W2YOYKolHWT23+fnTM3PZCrKbdqGmZrJntYpahjqy0AQvYA2bNgg/vjjD63b9u7dKxYvXizy8vKKPEZKSopYvHixzq+UlJRi43rrrbcEAHHp0iVV2ezZswUAsXHjRlWZpaWl8PX1LfZ4AET37t21bnN0dBSNGjXSKAMgfvjhB7XyAQMGCAAiPDxcVda+fXsBQGzdulWtbocOHYpstzCRkZFCKpUKd3d3ER8fr7FdeT8uXbokAAgTExO16ySXy4WHh4ewtbVV26969erCzc1NJCYmqpX/8ssvAoCYPXu2qszX11cAEKtWrSq0fSGEaNSokXB0dBRCCLF//35ha2sr3Nzc1OLRlfJ83nrrLY0yXc5xyZIlWu9ZaWzcuFEAEKampiIyMlJVnpGRIWrVqiWMjIzUyhs1aiQAiNGjR+t0Xn/++acAIDp06CDkcrmq/NChQ8LIyEg0aNBAY38jIyMRFhZWqvPR9vuk1LlzZwFArFixQq18+PDhAoCYOXNmidrS9nuqNGHCBAFAvP766xrbCj5XyngbNWoksrOzS9S+UsFns2AZADFhwgS18g8++KDQ510Xy5cvFwBUL1tbW/HRRx+p1dm7d68AIF599VWN/Z88eSIAiICAgBK1+/rrrwsA4uuvv1YrDwoKUl2/gpKSkjSOERYWJqRSqejfv7+qLD4+XpiYmIj27dur1b169aqQSCRqdb29vYW7u3uJ4qayd+PGDUOHQEREVLzDl4XAAIO8FJIBQsQ9Ljq+g5d0P+btB/n7WA4T4o2fNI+1/XR+vV1nC2/v1I38On8c1Nw26ff8bVk5mtsSnuRvm7a26POpJDiMlF5IeXl5MHp2fPl/jI2NVXWKYm5ujp49e+r8Ksu5lCwsLHD79m0cO3aszI6pZGdnh/fff1+tTNkD5dKlSwCA3NxcnDx5ErVr10bfvn3V6n766aelanfZsmXIy8vD5MmTtc4roLwvSu3atVObr8fIyAh+fn548uQJkpLyuyYfPXoUd+/eRZ8+fZCZmYmYmBjVq2fPnjA1NcXevXsB5E/ifubMGbRo0UKjV4u29gFg7dq16NWrF1xcXBAeHl7m8wfpco729vYAgJ07dyIxMbFM2u3evTvq1q2r+tnc3BzvvfceFAoF/vzzT43633zzjU7H3bhxIwDgq6++Uvv969ChA1q3bo0rV64gOjpabZ9WrVqhdevWKEtyuRxHjx6Ft7c3Ro0apbZt3rx5kEgk2LZtW5m199dff8HS0lLr6pvanqvx48eX+cISEokEs2bNUivr1asXAODKlSulOma3bt2wZs0aLFu2DO+//z7s7OyQlJSk1rssLS0NALT29LK0tARQ8t5J+/btg62tLSZNmqRWXthzqOwNqVAokJiYiJiYGFSrVg1ubm64ePGiqp6LiwsCAgJw/PhxxMbGqsoXLlwIIQTGjh2rKrOyssLjx4+xdevWEsWuzePHj9V6SaelpSE1NVX1c05ODh49eqS2T1xcXJE/x8fHqy2UUVnbsLe3rxTnwTbYBttgG2yj8rdhEMZGyKjvmr+QwX+0nodPdWDvNGDvNKRs+hA526cAPtWBbk2R+fdkZGydrNqeY2+Rf61c7VTDQdWOqRwi6mav3kaBa5VkJlGrq3at4pIg7K3wKE29R11FuOf6xmGk9EKSSqWF/oGnnK9HKi368ZZKpaVfSvk5zZo1CxMmTMArr7wCZ2dn+Pr6ol+/fhgzZozWP95LwtXVVaNMOQRPObz23r17yM7OhpeXl0bdFi1alKpd5dw7bdq00am+tqF1ysTT/fv3YWdnpxr2unjxYo3hj0rKN9mLFy9CCKFzwuzJkyd47bXX4OHhgVOnTsHGxkan/UpCl3McNGgQunXrhl27dqFq1aqoXbs2XnnlFbzxxhtqK0KWhHLurYKUQ+2U8x0qWVtb6zzp6r179yCRSNCqVSuNbXXr1sXx48dx5coVeHp6qspr1KhRktB1Eh0djezsbNSsqTkpq5ubG6pUqaKWbHlecXFx8Pb21nmRg2fnUiwLdnZ2Gu1XrVoVADQ+aOiqWrVqGDZsmOrn8ePHo1mzZkhISMDff/8NAKo2tQ25T09PBwCYmZmVqN0HDx6gTp06GkNfa9asqXXhlwMHDuCzzz7DhQsXNOJ4dn67sWPHYvfu3fjhhx8wd+5cKBQKbN68GV5eXqovHYD8xN7w4cPRv39/2NnZoXnz5ujZsyfefffdEn+xovydVnr2PpmYmGgsCvLs+/SzPyvvbWVvIzo6Gk2bNi3XNirLtWIbbINtsA22Ydg2DEKuQPLEHrAsMJVRoefRpQkAQPUXzdytgKsdzPuof+ltAsABVkBTL+DIVUChUD/myRuAhWn+qqLPtvEfu0Y1AScb4HT+3xVq1yr8BiRNvbXfjwJDWg1xz/WNyTZ6IVlaWiI5ORlyuVwjOZWeng4zM7Nik1YKhaJEPTLMzMwK7U1XUiEhIejduzfWrFmDQ4cOITw8HDt27MAPP/yA06dPP1cvuqJiVCgUpT5uWSvq/hT8VgMAhg0bhj59+mit++wf27qysrJC7dq1cerUKSxcuBBffvllqY5TFF3Pcffu3Th27BhCQ0MRFhaGVatW4bfffsMnn3yiMR9YWSvvualexlVzra2ty/yYRf1eP/v7Ulq1atWCr68vtm/fjoyMDFhYWMDb2xuA9m8GlQn28vwgc/XqVfTs2RMWFhZ4++230bBhQ1hZWUEikeCTTz5BZmamWv0+ffrAw8MD69atw9y5c7F+/XokJCRoLPTRq1cv3LlzB2vXrsX+/fsRHh6O/fv3Y8GCBQgPD4ebmxuIiIiIDMZIAvh4wem1buVz/MH+QGgYsPkEMPi/zhKJKcDG40AfX/X51qLi8/9bs0BCbJA/8PtB4F4i4JG/wBr2X8xfxXSi9r/bXjZMttELycnJCTExMXj48KHaH3p5eXl49OiRTn/8paenY+3atTq3OWzYsDL9I9rNzQ0ff/wxPv74YygUCrz++utYvXo1li9fjnHjxpVZO9p4eHjA1NQUd+7c0dh25kzplmquXbs2gPyVNX19fZ8nPBXlRPXGxsZqPXC08fHxgUQiweXLl3U6trGxMQ4fPozOnTtj+vTpyM3NxcyZM5875tJq27Yt2rZtCyC/50+zZs2wYMECzJo1q8RJ3sjISI0yZS9Bbb3BdOXp6YkzZ87g5MmTaN++vdq269evAwAaNGhQ6uM/q7AFSTw9PWFmZqbRSw/I74KenJyseh51VdQ1dnNzQ0xMjCoBVZllZWWphmt6enqiZs2asLOzQ0REhEbdffv2AYDWno5FcXFxQWxsLHJzc9V6t0VFRSEjQ31y35UrVyI7OxurVq1CUFCQ2rZ3331X68IQI0aMwLfffotdu3Zh6dKlkMlkGD9+vEY9Ozs7vPfee3jvvfcAAF988QVmzZqFefPm4fvvvy/ROVHpuLu7GzoEIiKiikkhgK+G4srVq2q9wMvMYH+gdR1g9E/AlRjA0Rr4eRcgVwAzhqrX7Zy/iBbuFBhpNGVQfmIu4EtgQi8gLQuYtxVoXB0Yrb5gGlYdAu4mABn/jVA4fAWYlT89DV7rAFRXX/SwsuCcbfRCUiYMlHOQKUVGRiIvLw+1atUq9hiGmrMtNzdXY8VHIyMj1fDNgvN2mZqaIjk5uUzaLUgmk8HPzw83btxQDRdT+vbbb0t1zDFjxkAqlWLevHla5x4rTa+6gIAAeHh4YNOmTbhw4YLG9pycHNy/fx9AfkLE19cXp0+f1ppE1da+qakpDhw4gHbt2mHWrFn45JNPShzj87p//75q6LOSi4sLXF1dkZOToxqqVxK7d+/GtWvXVD9nZmbi559/hpGRUaErsupi8ODBAPJXnS14PY8dO4awsDA0aNBAbQjp81J2F3/298XY2Bht27bF7du3sWrVKrVtkyZNghCi0J6QhVEm0rWtZNy/f3+kp6drTdhUpN6iurp9+7bW8rCwMJw7dw4uLi5q9zEwMBAPHjzA8uXLVWW5ublYunQpLCwsMGLEiBK137lzZzx58gTz5s1TK//ss8806ip7hz7be++rr77CkydPtB5/woQJkMlkmD17No4cOYL27dtrDJWOiYnR2E85bPvx4yKWuqcyVVa9MomIiCoVIwnQyBPoXTYdGLQyNgZ2TAWGtAUWbQcm/QE42gAHZgB1dfgyzMMR+Hdmfm+3T1cDc/8CejbPnxvu2VVIl+8HvlgLfLM5/+eDEfk/f7EWuP2wzE+tomDPNnoh2dvbo2HDhrh8+TL27NkDT09PJCUlISIiAq6urjol2ww1Z9vjx4/h4eGBNm3awMfHBy4uLrh16xY2bNgAS0tLtcn969evj3PnzmHChAnw8vKCRCLBBx98UCZxzJ8/H+3bt0dwcDCGDBkCDw8P7N69WzVxf2G9igpTt25dfPbZZ5g1axbq1auHfv36wcvLCzExMdi7dy+WL1+OgICAEh3TyMgIv//+O/r06YNWrVqhT58+aNiwIdLT0xEVFYUDBw7gk08+Uf2RvmzZMrRv3x6vvvoqVq9ejRYtWiAjIwOnT5+Gh4eHRmIGyB/vv2/fPgQGBmLu3LnIy8vD/PnzSxTn81i4cCF+++03dOrUCbVq1YKJiQkOHz6Ms2fPokOHDqXqTenu7g5/f38MGTIE1tbW2LJlC27evIkxY8Zonc9NV8OHD8eSJUtw8OBBtGzZEt27d0d8fDzWr18PmUyGH3/8sdTH1qZDhw5YsGABPv74YwQHB8Pc3BwtW7ZE27Zt8cMPP8Df3x9vvPEGtm/fjtq1a+Po0aM4dOgQGjZsiMmTJ5eorYCAAEgkEnz33Xd49OgRrKysULduXQQGBmL27NnYs2cPfvvtN5w/fx4BAQEwNzfH5cuXcevWLZw/f75Mz7u8ffLJJwgLC0OHDh3g7e0NIQQiIiKwa9cuyOVyfPfdd2r1586diz179mDs2LE4ceIEqlWrhk2bNuHmzZuYOXOmxnwZxZkzZw62b9+OL774AmfOnEHDhg1x+PBhREREaDzvwcHBmD9/Pt555x0cOXIE9vb2OH78OMLCwuDi4qKRqAby5/zo0KGDqufdO++8o1GnQYMGqF+/Plq0aAF3d3fcv38f69evh1QqxRtvvFGi86HSu3//vmpOUSIiIvrPf73aIJGUfmqLQzqM2LGzApaNzX8VpWCPtoIaegK7dZiKR5dYKiEm2+iF5e/vD2tra1y9ehXR0dEwMzNDo0aN4OvrW+JEkT7Z2Nhg2LBhOH78OE6fPo3s7GxUqVIFr7zyCr7++mu1ROHy5cvx5ptvYsmSJar55coq2dayZUvs2LEDEydOxLp16yCTydCmTRssX74cTZo0KfGk50B+b5O6deviu+++w9q1a5Gbmws7Ozv4+fmhTp06pYozICAAJ0+exJQpU3Do0CFs2bIFZmZmcHFxQb9+/TBgwABVXR8fH5w6dQoff/wxjh07ht27d8PS0hI1a9ZE7969C21DJpNh9+7d6NWrFxYsWICcnJwyTxwVJjAwEBcuXMDhw4exdetWGBkZwcXFBR9++GGph7W++eabSElJwcqVK5GYmAhHR0dMnjwZc+bMee549+zZg0mTJiE0NBTz5s2DqakpmjRpgrlz5+KVV1557uMX1LdvX0yYMAFr1qzB1KlToVAo8NZbb6Ft27Zo2LAhjh49io8++gg7duxARkYG7O3t8frrr2PRokUlXg20fv36+Oabb/Djjz9i+vTpkMvl6N69OwIDA2Fubo6TJ0/ik08+wd9//40ffvgBMpkMbm5uxQ5vrogGDBiAuLg47N69GykpKVAoFLCzs0NAQAC++OILjUVO3N3dcfz4cbz33ntYt24dsrKy4OHhgR9++EFj5WNdVK1aFUeOHEFISAh27NiBHTt2wMfHB/v27UPXrl3V6jZt2hTr1q3D559/jqVLl8LIyAiNGjXC7t278c477yA+Pl5rG2PHjsW+fftQtWpVDBw4UGP7qFGjsH//fqxevRqZmZmwsbFBw4YNMW3aNHTo0KHE50RERERUJowk+T3L+vkBKHkHCKo4JIJ9+ImoAOUfvOPHj8eiRYsMHQ6VQGhoKIKCgjB79mytQ/KIXha7du1CYGAgxo0bp7fEOZVcTk5OiRPjREREenfkCtB+qv7aW/8REJw/l/P58+fLZ842Knecs43oJZaamqr2s0KhwKxZswDk9yoiInoRLViwAMbGxpg4caKhQ6Ei3L1719AhEBERVRxGEqC2KzCotaEjoTLAYaREL7G6devCz88PjRs3RlpaGvbs2YMrV66gY8eO6NKlCwAgOjoaeXl5RR7Hzs4OdnZ2+ghZL3Jzc3Hv3r1i67m6upbZwhllEcuLIDU1VesiBM+qUaPGc7eVmZmJuLi4Yut5eHhoXdWyrNy/f181DLww1tbWcHJyKnUbhn5mK8K1Tk5OxsqVK3Hp0iXs27cPffr0KZPniMpPaRaAISIiqrQUApg+JH/xgv/Ur1/fgAHR82Cyjegl1qlTJxw8eFA1MbqzszPeffddfP/996o6LVq00Lq6aEFvvfUWlixZUt7h6s21a9fQuHHjYutt3LhRtUJnRYjlRfDTTz9hypQpxdYrixkOtm/fjqCgoGLrXbp0CY0aNXru9grTvXt3REREFFtn165dpW7D0M9sRbjWMTExmDhxIkxNTdGuXTu11VOpYrKwsDB0CERERBWDRAJ4O+evDlrAvXv3dFr8jyoeJtuIXmKrV68uts7y5cuL7X3QoEGDsgqpQvDy8sKaNWuKrVfWCwI8byxVq1YtkyRVeQoODoaXl5de2nrllVd0unblHc/3339fbG++543B0M9sRbjWjRo1qvDPP6nz9vY2dAhEREQVgxDAtGC1Xm0AkJaWZqCA6HlxgQQiIiIi0jtO+kxERC+E8l4gQQLAwxGI+gWQqifbIiMjUa9evfJrm8oNF0ggIiIiIiIiIjIEAWDaEI1EGwAOIX2BMdlGRERERHr3oizsQkREVG4kANztgdc6aN1c3Ly/VHFxzjYiIiIi0jtjY81v8ImIiCociST/v1Lj/ORYWcqVA18GAzKmZiob3lEiIiIi0ruYmBg4OjoaOgwiIqKitamL++M7wQ1mZX9sqTHwekChm6tWrVr2bZJecIEEIiIiItI7LpBAREQvCiEEJJKy7tZGlRnnbCMiIiIivePqakRE9KK4evWqoUOgFwyTbURERESkd7GxsYYOgYiISCc5OTmGDoFeMEy2EREREZHepaamGjoEIiIindjY2Bg6BHrBMNlGRERERHpnZlYOE00TERGVAy5UQCXFZBsRERER6V3t2rUNHQIREZFOrl+/bugQ6AUjNXQARERERPTyuXTpElcjJSKiF979NIGx+xSl3r9XDQne9GE/qMqGyTYiIiIiIiIiokJ4enoWum1OuAJ/RwkoROmOfTBaMNlWCfGOEhEREZHeubi4GDoEIiIinWRnZ2stj08X+PV86RNtACBXyEu/M1VYTLYRERERkd6ZmpoaOgQiIiKdPHjwQGv5d6cUkD9Hoo0qLybbiIiIiEjvoqOjDR0CERFRqSVkCPzvnHjuZJtEIimbgKhCYbKNiIiIiIiIiKgQjRs31ihbcFqB3NKvi6AiAZNtlRGTbURERESkd3Xq1DF0CERERDq5ceOG2s+PMgV+OPv8vdoAQCHKIGNHFQ6TbURERESkd/Hx8YYOgYiISCdZWVlqPy88o0A21zWgIjDZRkRERER6l5KSYugQiIiIdGJtba36/6Qsge/PPN8KpAUZGTEtUxnxrhIRERGR3pmYmBg6BCIiIp24u7ur/n/RWYHMvLI7thBczrQyYrKNiIiIiPSufv36hg6BiIgqgOQsgZA9cjj9Lw+WC/MQsF6Osw90T0BdfSTQI1QOqx/yYP9THl7bIUdChub+X59QoO8WOVx+zoPkuzxMP6b7ONDIyEgAwJNsgfmnFGXWqw1gsq2yYrKNiKiSGzduHCQSCU6ePGnoUCq93r17QyKRIDU11dCh6KQixHvnzh1IJBKMGjWq2LoVIV4qOxcuXDB0CEREZGAKIdBrsxxrrgqMa2aEuR2M8DBDoON6OW4kFZ+EikkVaL9OjpvJArPbGeFjXyNsvyXQdaMcOc+sXjD1qAKn4gWaOZd+9c+fzgmkl2GvNqq8pIYOgIioMjl37hwSExORmJiI1NRUWFlZYfjw4YYOq1L77rvvcP/+fSxYsKBStkdERERUWYVeEzh+H9jYxwiD6+b3BQquK0Gd5XJMO6bAmt7GRe4/+4QC6bnAmdeM4WmTn0TzcwW6blRgZYRASJOnibXbbxnDy1aCxAwBp59LtrpBtWrVkJojMC+8bHu1AYCxUdHnSC8m9mwjIipDp06dwv3792FjYwNTU1NDhwMAWLBgAVJTU9GyZUtDh1IuVq9ejT/++KPStkdPbdq0SZXEphefk5OToUMgIiIDC70u4GIBDKzzNCnmZCFBcF0Jtt4UyM4rOrO16YZA7xoSVaINALpUN0IdO2DDNfV9vWxL36NNLpfjl/MCKTmlPkThx1ZwWdPKiD3biIjK0NChQ2FjYwMA2LhxI3Jzcw0cUf4k5JyInCoDU1PTCpPEpudnaWlp6BCIiMjAzj0UaO4igZFEPRHm5yrBkosC15OAxoV8NxObKvAwA/CtqplE83OVYMetsuuCdismHt+GO4Czq5Gu2LONiKgMKRNtFYm2OdsePnyI4cOHw8XFBTKZDFZWVqhRowbGjRtXqjb++usvtGrVClZWVpDJZHB2dkZgYCBu376tqpOXl4ePPvoIHh4eqjb9/f1x8OBBtWMVnMNr1apVqF27NmQyGapUqYKhQ4ciMzNTVdfFxQUXLlzAo0ePIJFIVC9lz7P9+/cjMDAQbm5uMDU1hZmZGerVq4cff/xR63lER0djyJAhqutiY2ODpk2bYuXKlTq1V5i7d+9i4MCBcHR0hFQqhZ2dHXr16oWoqCi1errel++//x516tSBhYUFTE1N4ezsjE6dOuHmzZtF36hCxMXFoUePHrCysoKpqSl8fHywa9cujXq63sOS1tVmw4YNMDc3R/369XHv3j0A2udsU5bdvXsXgwYNgq2tLWQyGerVq4etW7dqHPfBgwfo3bs3rKysYGZmhsaNG2P37t1o2rQpHB0ddb1kVAbu3Llj6BCIiMjA4tIBVy3fvSjL7qcVnt6KS1ev++z+j7NQbM84XYXG2CM5q0wOpUEiKX2PO6q42LONiKiCycrS/V9yExMTGBmV/HuTwMBAnD9/Hn379kXTpk2RlZWFyMhIhIWFlfhYs2fPxtSpU2FnZ4egoCB4e3sjOjoa+/fvx9WrV+Ht7Q0A6NGjB/bv348mTZpgxIgRePjwIdavX4/AwEBs3rwZPXv2VDvusWPHsHnzZgwePBjDhg3Dzp07sX79elhbW2Pp0qUAgK+//hozZsxAcnIypk2bptq3TZs2AIB169YhKioK3bt3h5eXFx4/foxNmzbh/fffR2pqKqZMmaLa59q1a/D398eTJ0/QtWtX+Pr6IiMjA6dPn8auXbswatSoYtvT5ubNm/Dz80Nubi769++P2rVrIyoqChs2bEDbtm1x+vRpVKtWTef7smjRInz44YeoX78+xo8fD0tLS9y7dw+HDh3CnTt3UKtWrRLfw8DAQFhZWWHcuHF4+PAh1q1bh/79++Off/5Bly5dVPVKcg9Ler8LWrBgASZPnoyWLVti586dqFKlSrHn0K1bN9ja2mLs2LFISkrC77//jldffRXXrl2Dm5sbACA7Oxtt2rTBrVu30LVrV7Rq1QqXL1/GoEGDYG1tXeLrRkRERE8phECOjiMiTY3zk0yZefn//yyz/zIVmUUsRqDcpnV/46d1TJ8z65GZK/D7varl1quNybZKShARUbnYsGGD+PPPP0u83+LFi3V+xcbGFnu8sWPHCgDixIkTQgghkpOTBQDRq1evEsf2rNu3bwupVCrc3NxETEyMxva8vDwhhBCbN28WAETbtm1Fbm6uantYWJgwMjISdevWFXK5XHVMAMLExEScOnVKVVehUIjq1asLGxsbkZOToypv0qSJcHBw0BpfUlKSRllaWppwd3cXrq6uqjaFEKJ169YCgFiyZEmh51Fce7169RIAREpKiqrslVdeEdbW1uLixYtqdQ8dOiSMjIzE66+/LoTQ/b506NBBmJmZqbVRWsp4W7VqpXZfDh8+LCQSiWjSpImqrCT3sDT3W3kdPvzwQwFA9O7dW2RnZ2uNt+C5K8t69eolFAqFqnzp0qUCgPjkk09UZTNnzhQAxJtvvql2XGV5Yfe1KI8ePRJZWVmqn1NTU9Xiy87OFomJiWr73L9/v8if4+Li1M6lsrbx4MGDSnEebINtsA22wTby2zh4Vy4wL1en19XE/GNYLswVQzenaLTxz838Y+26JS/0PE7FKQTm5Yrfzmuex7v/PBGYlyuychUa55GQnr/fl0effr4r6lpdeqjQ+bxK87L6Pqtc7sez5/Gyt6FvTLYREZWT0ibb7t27p/Or4D9ChXk22ZadnS1kMpnw8vIS586dK3F8BX3++ecCgPj222+LrBcUFCQAiB07dmhsa9++vQAgrly5IoR4mnxp3769Rt2hQ4cKAOLu3buqsqKSXwU9efJExMTEiHv37onBgwcLAOL27dtCCCESExOFRCIRTZs2LfY4JUm2JSUlCYlEIrp166b1/lWrVk3UqlVLCKH7fenTp48wMjISP/74o1oSsDSU8W7YsEFjW8uWLYWRkZGIjo4WQpTsHpbmfr/22mtiyJAhAoAYM2aMWiL02Xi1JdsOHz6sVjcxMVEAEIMHD1aVtWrVSu2clLKzs4WFhUWpkm1UesrfPyIiqhzi0hRixSW5Tq/krPzESq2luSIwVPPzzLKL+cm2iw8VGtuUYlLyk2BzTmp+Znh1e56w/zFXy15Pk23Tjur+Oar18sfC+LvyTbZR5cJhpEREFYxySGF5MTExwbRp0zB9+nQ0a9YM7u7u8PPzw6BBgzB8+PASdWW/fv06AMDf37/IetHR0ZBIJPDz89PYVr9+fRw+fBhXr15F/fr1VeWenp4adR0cHAAA8fHxWrc/KzY2FmPHjsXBgweRkpKisT0hIQFeXl64fPkyhBBq7ZeFixcvQgiBPXv2wMPDQ2sd5Txhut6X2bNnIzw8HOPHj8dnn30GHx8fdOvWDe+++y6cnZ1LFaevr69GWd26dXHq1Clcu3YNHh4eJbqHpbnfGzduRFZWFt58803VMOGSaNSokdrPymclKSlJVRYbGwtbW1u4u7ur1TUxMYGrqyuSk5NL3C6VHq83EVHlUtVSglGNSjYksqmzBEdiBBRCqC2ScDJOwEIK1LErfF93awmczIHT8ZoDPMPjBJo6l93wzIkNUzHkSPlMOVGaKWGo4mOyjYiogsnIyNC5rqmpKYyNtUxUUYzPP/8cw4cPx5o1a/Dvv//i4MGD2LJlC3799VccOnSoVMcsa0XFIETxs2YIIdC+fXtER0cjKCgIrVu3hr29PYyNjbFy5Urs2bMHCoWiLEMuNM6AgAC89dZbWuuYmZmp/l+X+9KoUSPcvn0boaGh2LVrF06ePInp06dj4cKF2LVrF1q1alWu51RemjdvjkuXLmHLli0ICQlBy5YtS7S/VKr9I40uzwoZRmH3jIiIXh6D60gQel1g83WBwXXzk2OJGQIbrwn0qSmBqfRpwiwqOf/f9JpVnpYNqiPB75cF7qUIeNjkl++/q8D1JGBii7JLtvVvVhUBd4DDMYC8jD9a8LNK5cRPOUREFczq1at1rtu7d2/V5O8l5e3tjc8//xyff/458vLy0KtXL+zZswfbtm1D//79dTpGvXr1AAAnTpxA+/btC61XvXp1nDx5EuHh4QgMDFTbFhkZCQCl7lVWWE+806dP49atWxg5ciR+//13tW2//fab2s8NGzaERCJRxVKa9rRRHjc7OxvDhg3TaR9d7ou5uTlee+01vPbaawDyF4IYNmwYZs6ciX/++Ufn+JROnz6tWshC6dq1azAyMkLdunUBlOweluZ+165dGwsXLkTXrl3RvXt3bN++vdgekyXl5uaG06dPIzY2Vq2nYW5uLuLi4mBubl6m7VHRnu2NSEREL5/BdSRo7QqM3qXAlUeAoznw83kF5AKY0Va9x1fnDfmrL9wJeZrGmNLKCBuvyxGwQY4JzY2QlgvMO6VAY0dg9DO97FZdVuBuCpDx3wqlh2OAWWH5X7y+1kCC6raFf8a7cuUKZrRtgvbrdFwBogSYbKuc2F+RiKiC6dmzp84v5VC5kkhLS1MbWgfk9zDx8fEBADx8+FDnY73xxhuQSqVYtGgR4uPjNbYre44NHToUAPDNN99ALn/6IeXUqVM4cuQI6tatq0rqlJSFhQXS09ORmZmpVq7sNfPsB5hTp07h0KFDamUODg5o1aoVzp07p5GIK3geRbWnjaOjI1q3bo0TJ05g06ZNGtuFEIiOjgag+32JiYnROE67du0AAE+ePCk2Jm3mz5+PvLyny30dPXoUp0+fRqNGjVRJqZLcw9Le75YtW+LgwYMA8ldIPXLkSKnOpzC9e/eGQqHAV199pVY+Z86cEvUopbJx4cIFQ4dAREQGZmwkwY5BxhhST4JF5xSY9K8CjubAgWBj1LUv/gtODxsJ/h1ijJpVJPj0sAJzwxXo6S3B3iBjtV5xALD8kgJfHFPgm5P5nw0P3hP44lh+2e0nxSe82lWToJ07YMzFQ0kH7NlGRFSGrl+/jrS0NABAVlYW5HI5zp49CwCwsrJCnTp1ij1Gec/ZduXKFXTs2BGvvPIKGjVqBHt7e0RGRmLjxo1wcnLCgAEDdD6Wl5cXpk2bhi+//BKNGjVC37594e3tjdjYWOzduxc//vgjevbsiQEDBqBz587Yv38/WrZsiR49euDhw4dYu3YtZDIZFixYUOr5Klq1aoXjx49jyJAhaN++PaRSKfr06QMfHx9Ur14da9euRU5ODho0aIAbN24gNDQU1atXR1RUlNpxVqxYAX9/f7z11lvYuHEjfH19kZWVhdOnT8PFxQXr1q0rsr2aNWtqjW/VqlVo3bo1hgwZgq5du6J58+YQQuD27ds4cOAAAgMDsXLlSp3vS0BAAMzMzNCqVStUr14dycnJCA0NhUQiwYgRI0p1DRMSEuDr64uePXviwYMHWLt2LUxMTPDdd9+p6pTkHj7P/W7WrBn+/fdfBAQEoHfv3vjrr78QEBBQqvN61qRJk/Dbb79h2bJluHv3Llq1aoXLly9jz549qFq1KnJzc8ukHdINv8knIiIAsDOTYFl3YyzrXnS9gj3aCmroKMHuwcVPgXJoaOnTH8qRJNPbGqHzhrKdhsTYyPDTt1DZY7KNiKgMXbt2DXFxcWplp0+fBgC4urrqlGwrb15eXujXrx9OnDiBo0ePIi8vD/b29ujTpw++/vprODk5leh4U6dORZ06dfDtt99i3bp1yM3NhZ2dHVq2bIkGDRqo6u3atQuffPIJ1q9fj3nz5sHU1BQ+Pj74+uuv0alTp1Kfz8yZM3Hjxg38+++/+OeffyCEgL29PWrWrIndu3fj7bffxo4dO7BlyxZ4eHhg7ty5uHbtGv73v/+pHadevXo4c+YMPvroIxw5cgR79+6FpaUlatSogTfeeEOn9rSpWbMmLl26hMmTJ2Pfvn3Yt28fZDIZHB0d0a5dO4wZMwaA7vfljTfewMaNGxEaGoq0tDRYWVmhVq1amD17dqmTbTt37sT777+PRYsWITc3F3Xq1MGcOXPQtWtXtXoluYfPc78bN26MI0eOoEOHDujXrx82bdqkEUtpmJmZ4dixY3jzzTdx+PBh/Pvvv6hTpw42b96McePGqRLlpB+l6ZlLRERkCMppRAI88oe9noovu7nb5Ao5mJqpfCSCXysSERHRS0wul8PW1ha1atXC+fPnDR3OS+PJkyewtbU1dBhERETFOn/+PJo2bQoA2HNHge6hZde7zUoqR+oHpmV2PKoYOGcbERERvTRSU1M1ymbPno309HS88sorBojo5XX79m1Dh0BERFRiXatL0MIFMCqjudsk4CRwlRH7KhIRkYZ79+4VO39VlSpVYG9vr6eISFfJycl4/PhxkXWMjY1RvXp1PUVUsQQHByM1NRWtWrWCubk5wsPDsW/fPri4uODzzz83dHhERERUARVcRV0ikeCrtkbotblserdJyiprRxUKh5ESEZEGFxeXYlclff3117Fy5Ur9BEQ6GzdunMZ8dM9ycHBAYmKiniKqWL7//nv88ssviI2NRVZWFmxtbeHv74+FCxeidu3ahg7vpZKSkgIbGxtDh0FERFSsmzdvolatWqqfhRBo9occlxIBxXNmVDiMtHJiso2IiDT8888/WofbFVSvXj00a9ZMTxGRri5fvoyLFy8WWcfMzKxEq84SlYfo6Gh4enoaOgwiIqJiFZyzTenvmwr0++v5e7cx2VY5MdlGRERERHqn7Q8XIiKiiigyMhL16tVTKxNCoPFKOa4+fr7ebTYygScTZM8ZIVU0XCCBiIiIiPTOyIgfQ4mI6MVQcAipknLutucdRirA/k+VET/lEBEREZHe+fj4GDoEIiIinURERGgt719bgnr2z7cyKQcbVk5MthERERGR3l26dMnQIRARET0XI4kE09s8X+82J5PcsguIKgypoQMgIiIiopePXC43dAhEREQ6qVq1aqHbBteR4Ogw41Ifu3YVi1LvSxUXF0ggIiIiIr27e/cuqlevbugwiIiIDOrKlSto0KCBocOgMsZhpERERESkdw4ODoYOgYiIyOBycnIMHQKVAybbiIiIiEjvbt68aegQiIiIDM7GxsbQIVA5YLKNiIiIiIiIiMgAipoPjl5cTLYRERERkd55eXkZOgQiIiKDu379uqFDoHLA1UiJiIiISO/S0tJQpUoVQ4dBRESkG4UCyC3FStpGEkDG1MvLhneciIiIiPQuMTER1apVM3QYREREuukyHTgYUfL9XKoAd34FzEy0bvb09HyusKhi4jBSIiIiIiIiIqKinCrlwj4PkoEVBwrdnJ2dXbrjUoXGZBsRERER6V3Tpk0NHQIREZF+zNwI5ORq3fTgwQM9B0P6wGQbEREREend5cuXDR0CERGRfsQlAb8fMnQUpEdMthERERGR3uXmav+Gn4iIqNKRAPhqA5Cbp7GpcePG+o+Hyh2TbURERESkd7a2toYOgYiISD8EgJhHwJ+HNTbduHFD//FQuWOyjYiIiIj0zsXFxdAhEBER6Y9EAsxYD+TJ1YqzsrIMFBCVJybbiIiIiEjvrl+/bugQiIiI9EcI4E4CsO6oWrG1tbWBAqLyxGQbEREREREREVF5k0iA6esA+dPebe7u7gYMiMoLk21EREREpHeenp6GDoGIiEi/hACiHgAbw1RFkZGR5ddecjoQ8gvgNAqwHAYEfAmcjdJ9/6sxQI+vAKvhgP1I4LUfgIQnmvW+DgX6zgZcRgOSgfkJxZcck21EREREpHeco4aIiF5KRhJg2jpAoSjfdhQKoNcsYM0RYFwgMHck8PAJ0PFL4Mb94vePSQTaTwVuxgOzRwAf9wW2nwG6zgBynllRfOoa4NRNoJl3+ZzLC4jJNiIi0psePXpAIpEYOgwqhpOTE5ehp3L38OFDQ4dARESkfwoBXL8PbD4BAKhWrVrpjtPxC2DUj4VvDw0Djl8DVo4Dpg0BxgYCh74CjI2AaeuLP/7sTUB6FnBgBvB+L2DKYGDDR8CFO8DKg+p1b/8KxP0GrP6gdOdSCTHZRkRUSQkhcPHiRaxfvx7Lly/Hn3/+ibCwMOTm5ha/M+ldSEgIlixZUmnbIyIiIqL/FOjdJpfLi69fGqFhgEsVYGDrp2VOtkBwG2BrOJBdzN8Em04AvX0BT6enZV2aAHXcgA3H1et6OZdZ2JUFk21ERJVUWFgYTpw4ATs7O7Rp0wY1atRAREQEdu/eDSGEocOjZyxduhSbN2+uEO3dvXsX4eHheouFXk7sPUlERC8thQCuxAB/n0JcXFz5tHHuNtC8BmD0TNrHrzaQkZ3fu64wsY/yh5z61tTc5lcbOHerbGOthJhsIyKqhB4/foyIiAh4eXmhW7duqF+/Pvz9/eHv74/79+8jKqoEE6NSiT169MjQITwXCwsLmJubGzqMMvWi35PK6Pr164YOgYiIyHCMJMCXa/MXTSgPcUmAq51mubLs/uOi9y1Y99n9H6cV3zPuJcdkGxFRJaRMpj3bc6RevXqQSqW4ceOGIcJSSUhIQP/+/WFjYwOZTIb69etj586danXkcjnGjh2Lhg0bokqVKpBKpXB0dET//v0RGxurccy5c+eidu3asLCwgKmpKZydndGlSxdER0eXKDblvHLR0dHo1q0brKysYGpqiiZNmmD//v1qdSMiIiCRSBASEoJFixahZs2aMDExwYgRI1R1Zs6ciRo1asDExAQWFhZo3rw5tm7dqnEMANi9ezckEonqVdDatWvRvHlzWFhYQCaToXr16pg2bZrWczhw4ADat28PW1tbyGQyODg4oFOnTjh//rxO7RU2Z9uSJUtQv359mJqawszMDPXr19c6FFW5f3h4OPz8/GBmZgYLCwu0a9cOt26V/JvQb775BhKJBGvXrkVISAgcHR1V12DhwoWFtn/gwAG0aNECFhYWqF+/vmr7li1b0KxZM1hYWMDExAQ1atTArFmzShwXPZ/s7GxDh0BERGQ4CgFcikajuzr8e5ibBySmqL9y8/ITXs+WKxdeyMwBTKWaxzIzebq9MMptpjIt+8uK35+g5coTEdGLLiEhARKJBM7O6vMnSKVSODg4ICEhodhjCCFK9MewqampzosfvPLKK6hSpQrGjRuHR48eYdWqVQgODsadO3fg4OAAAMjMzMSKFSsQEBCAHj16wMrKCqdPn8Y///yDs2fP4tq1a6reV3PnzsUnn3yCBg0aYMKECTA3N0d0dDQOHTqEmJgYeHp66nweSgEBAbCxscH48eMRHx+P9evXo1evXti/fz/atm2rVnfv3r1Ys2YNgoKC4O3tjSpVqgAAXnvtNaxevRq1a9fGhAkTkJqaig0bNmDgwIFYvnw5Ro0ahWrVqmHevHmYNGkSGjRogNGjR2vEMmPGDMyYMQN16tTB22+/DSsrKxw4cABfffUVbt68iT///FNV97fffsPbb78NU1NT9O/fH7Vr10ZcXBz+/fdfhIeHIzg4uNj2tPniiy8wa9YsuLm54Z133gEAhIaG4u2330ZcXJxG4i8xMRHdunVDx44dERgYiAsXLuDvv//G4MGDcfbs2ZLcCpWpU6ciKytLlcwMDQ3FxIkTkZmZic8++0yt7oMHD9CnTx906tQJffv2RWpqKgBg+fLlCAkJga2tLUaOHAlra2ts3boVX3zxBaKiorBixYpSxUYlZ21tbegQiIiIDMvYCHlTV0Pav3XR9Y5FAgFfapYfvwasO6pedvvX/DnUzE2A7DzNfbL+S5KZmxTennKbtt5rWbnF70+AICKiSmfDhg3ijz/+0Lpt7969YvHixSIvL6/IY6SkpIjFixfr/EpJSSk2ru7duwsAon///mrlP/74owAgpk2bpiqTy+Vajzlz5kwBQPzwww+qsrZt2wozMzORnZ1dbAy6xti2bVshl8tV5Xv37hUSiUQ0b95cVXbp0iUBQBgZGYmwsDC144SHhwuJRCLq168vMjIyVOU3btwQ5ubmwtHRUeTk5KjKAYju3btrxHPz5k0hlUpFQECAxragoCAhkUjE+fPnhRBCPHnyRFhbWwtra2tx/fp1jfoF73lh7QkhhKOjo2jUqJHq57i4OGFqaipcXFxEQkKCqjwhIUE4OzsLU1NTER8fr7b/s/dICCEGDBggAIjw8HCt7RZm9uzZAoBwcHAQDx8+VJU/fPhQODg4CEtLS7VnRdn+V199pXacnJwc4eDgIMzNzcWNGzdU5RkZGaJevXpCIpGIM2fOlCi2R48eiaysLNXPqamparFkZ2eLxMREtX3u379f5M9xcXFCoVBU+jYSExMrxXmwDbbBNtgG23g52lBYDhUCA8r0pTAeJJ60+rD483icKsTe8yJx3QEh9p7Pf/l8ILI6TBGKPedUZU82HRFZyf/FXes9kdttmsZ5pC7ckt/+xTuFX6uYxPw6czZrXKusoDlCYf+a9muV8EQIDBDpk34r9/tR0nuub0y2ERFVQmvWrBGrV6/Wuu3AgQNi8eLFav+AaZObmyvu3bun8ys3N7fYuJSJrGcTGjExMQKAGDZsmNb9cnJyRHx8vLh37564cOGCACCGDh2q2t6jRw9hZGQkFi9erJYgKw1ljNu3b9fY1rx5c2FkZCQeP34shHiabPP399eoO2HCBAFALFmyRGNbcHCwACD27NmjKiss+fXpp58KAGLdunUa13zNmjUCgJg+fboQQohffvlFABDvv/9+sedZkmTbDz/8IACITz/9VKPu5MmTBQDx008/qe1vZ2enUfd///ufACCWL19ebHwFKZNt48eP19g2btw4AUCsXLlSrX1LS0u1ZKYQQuzcuVMAEEOGDNE4zuLFiwUAMXHixBLFRqV37tw5Q4dARESkO6thZZ5sExggotftKb5tbTpMFeL1RYVvHzxXCJfRQjz72fitn4WwGCpEVo72/ZScXhciaJ5meZ2xQnT6Uvs+/yXbxLS1RR/7JcA524iIKiGpVAqFcr6GZyiXF5dKi55JQCqVolq1ajq/ijteQc/OB+bu7g4ASEpKUitftGgR6tSpA3Nzc1StWhUeHh5o0qQJACA5OVlV7+uvv4ajoyPefvtt2Nra4pVXXsHMmTOfa1L8li1bapTVqlULCoUCV69eVSuvUaOGRt07d+4UepxGjRoBgMZxtImMjAQADB06FB4eHmqv4cOHAwDi4+PV6vr5+RV73JJQzgHYtGlTjW3KsmfnAXR1ddWoqxzWrMswZm2U160g5bOkrX2ZTH2eEeWE/NqOo7xPt2/fLlVsRERERCVibAR0bAiXAR3K5/iD/YEHycDmE0/LElOAjceBPr7q87FFxee/ChrkD/xzGriX+LRs/8X8VUyD2pRPzJUI52wjIqqELC0tkZycDLlcDmNjY7Vt6enpMDMz0yh/lkKhQFZWls5tmpmZwejZpcUL8WwSREkUWI3pl19+wYQJE1C7dm1MmTIFXl5eMDc3h1wux2uvvaZWt3nz5rh9+zY2bNiA3bt349SpU/jyyy/x/fff48CBA1qTRGXJwsKi3I6tPM85c+bAw8NDa50GDRqUW/ulVdSzUFgiuCyZmZmVexv0fKpVq2boEIiIiAxHrgBmDMWVK1fK57PqYH+gdR1g9E/AlRjA0Rr4eZeqXTWd/5t7987ip2VTBuUn5gK+BCb0AtKygHlbgcbVgdGd1PdfdQi4mwBk/Dff8+ErwKyN+f//Wgeguvo80i8DJtuIiCohJycnxMTE4OHDh2o9jPLy8vDo0SOtvY6elZ6ejrVr1+rc5rBhw8p0wvPVq1dDJpPh9OnTsLGxUZWHh4drrW9hYYFRo0Zh1KhRAICVK1di9OjR+Oqrr7B58+YSt3/q1Cn07NlTrezmzZswMjJSW9myMN7e3qrjPPsB6vLlywCg03Fq1aoFAHBxccGwYcOKrKtMup06dUptRdTnVbt2bQDA+fPnMWTIELVtFy9eVKtTniIiIjTKLl26pHP7devWLfQ4p0+fBvD0vlH5y8vTMmkzERHRy8DYCPCvC7RvCJw/X05tGAM7pgKTfgcWbc9fPbRlLWDleKCue/H7ezgC/84EPlwJfLoaMJECvVoA80dprlK6fD/w7+WnPx+MyH8BwCv1mWwjIqLKoWbNmjh37hwuXbqklliLjIxEXl6eKoFTFHNzc41kU3H1y5KRkREkEolq2CuQ3yNqypQpGnVjYmI0esm0b98egPpw05KYPXs2evTooeqhtX//fpw7dw7NmjWDnZ1dsfsPHz4cP/zwAxYuXIiRI0fC1NQUQP6QzG3btsHR0REdO3ZU1Tc1NdUa61tvvYUffvgBs2bNwsCBAzUSmgkJCbC0tISFhQWGDh2Kjz/+GCtWrMD48eNRs2ZNtboKhUJ1PoW1p01QUBA+/vhjrFy5Eh9//LFqxdhHjx5h5cqVMDU1RXBwsE7Heh5r1qzBF198AScnJwD557527VpYWFhgwIABxe7fqVMnODg4YNu2bbh165Zq+G92djYWLlwIiUSCV199tVzPgZ6Kj49H1apVDR0GERGR/hXoXebm5la6YxyaWXwdOytg2dj8V1EK9mgrqKEnsFvLKqilieUlw2QbEVElZG9vj4YNG+Ly5cvYs2cPPD09kZSUhIiICLi6uuqUbFPO2WYogwYNwtGjR+Hn54egoCDk5uZi586dWoe2tm/fHtbW1vDz81Oda2hoKCQSCUaOHFmq9uPi4uDr64sePXogLi4O69evh4mJCb7//nud9m/ZsiVGjBiB1atXw8fHB/369UNqaio2bNiArKws/O9//1MbTlu/fn2cO3cOEyZMgJeXFyQSCT744APUrVsXM2bMwNSpU1GjRg307dsXXl5eePjwIS5fvoxjx47hzJkzaNSoEWxsbDB//ny8++67aNKkCQYMGIDatWvj4cOHOHToEN5//32EhIQU2Z42Li4umDRpEmbNmoUmTZpg8ODBAIDQ0FDEx8dj+vTpqgRYebK1tUXTpk3V2n/06BFmzpyp1vuxMDKZDHPmzEFISAhatmyJ4OBgWFtbY+vWrbh+/TpGjRqF5s2bl/dpEBER0cvM2Ci/h1lA/hyyEonEwAFReWCyjYiokvL394e1tTWuXr2K6OhomJmZoVGjRvD19X0h/lH/4IMPkJKSgiVLlmDevHmwtLREu3btsHjxYtWCCkqjRo3Cli1bsHHjRqSlpcHKygq1a9fG/PnzERQUVKr2Dx48iDFjxmDRokXIzc1FvXr1MH/+fFWPOV2sWrUKtWvXxooVK/D9999DKpWiXr16WL58Ofr3769Wd/ny5XjzzTexZMkSVUJRmfyaMmUKGjVqhDlz5mDjxo3IyMiAtbU1PDw8MG7cOHh5eamO89Zbb8HDwwNfffUV/vrrL2RlZamSVK1atdKpPW1mzpwJd3d3LFy4EL/88guA/CGXixcvViXwytusWbNw4MAB/Pnnn3jy5AlcXV0xf/58fPjhhzofY8yYMahSpQpmzZqFlStXIi8vD9WqVcPMmTMxderUcoyenqVtoQoiIqJKT9mr7b/P47GxsXr50pL0SyIKzjBNRET0kuvRowd2794N/vNYcXzzzTeYMmUKNm7cqOrVRi++a9euqebRIyIiqvCsh+cvEvA8jIyAZt7AqbmqZNv58+fLfTEv0j/dlo0jIiIiIipDmZmZhg6BiIhIvxQKYMYQVaIN0G3BLHrxcBgpERFVeklJSUhKSiqyjlQqhaenp54ioszMTMTFxRVbz8PDQw/RkCFYWloaOgQiIiL9MZIAjTyBni3Uiu/du6fTfMr0YmGyjYiIKr1PPvkES5cuLbKOo6MjEhIS9BQRbd++Xaf59C5duqSHaMgQmNwmIqKXikIAXw1T69UGAGlpaQYKiMoT52wjIqJK78KFC7hy5UqRdSwtLdG3b189RUTx8fE4ePBgsfX69OkDKysrPURE+sY5aoiI6IXyPHO2GUmA+tWASws1km2RkZGoV6/e88dHFQqTbURERESkd0y2ERHRC+V5F0gInQQM8tcozsvLg1TKQYeVDRdIICIiIiK9c3NzM3QIRERE5c9IAtR1Awa00ro5IiJCzwGRPjDZRkRERERERERUHhQCmDEUMGL65WXCvopEREREpHf379+Hs7OzocMgIiLSzZC2yN1yAjJZCdMoXs7AYM3ho0pVq1Z9zsCoIuKcbURERESkd5yzjYiIXjRCCEieWeCASBv2YyQiIiIivatfv76hQyAiIiqRq1evGjoEekEw2UZEREREehcdHW3oEIiIiEokJyfH0CHQC4LJNiIiIiLSu/T0dEOHQEREVCI2NjaGDoFeEEy2EREREZHemZubGzoEIiKiEuFiBqQrJtuIiIiISO9q1qxp6BCIiIhK5Pr164YOgV4QJVyzloiIiIjo+UVERHA1UiIiKj+nbwKX75Vsny4+gLtD+cRDLxUm24iIiIiIiIio8sjMBtpOAXLySrbf6ADgt/GFbvb09HzOwOhlwWGkRERERKR3nPeGiIjKjVxR8kQbAPzxL3D7QaGbs7OznyMoepkw2UZEREREeieVcoAFERFVQN9sLnTTgweFJ+KICmKyjYiIiIj0LiYmxtAhEBERqZMrgBX7gegEQ0dCLzgm24iIiIiIiIiIAEAA+HaL1k2NGzfWbyz0wmKyjYiIiIj0rl69eoYOgYiISJNcASzdC8Q+0th048YNAwRELyIm24iIiIhI72JjYw0dAhERkXZCAHM0e7dlZWUZIBh6ETHZRkRERER6l5qaaugQiIiItJMrgMV7gLjHasXW1tYGCoheNEy2EREREZHemZqaGjoEIiKiwskVwLytakXu7u4GCoZeNEy2EREREZHe1alTx9AhEBERFU6uAH7eCTxMVhVFRkaWT1vJ6UDIL4DTKMByGBDwJXA2Svf9r8YAPb4CrIYD9iOB134AEp5o1vs6FOg7G3AZDUgGAtPXldkpkLqXMtnWp08fSCQS3Llzx2Ax3L9/H3Xr1i3TzHhqairatGkDiURSZscsL9euXYOjoyPatGlj6FCoCBXhmTp9+jQkEglGjBhhsBheROvWrYNEIsH06dMN0n5Zv8cZ+nwqqorw71lZ4u/7y+XSpUuGDoGIiKhouQrgu7/Ltw2FAug1C1hzBBgXCMwdCTx8AnT8Erhxv/j9YxKB9lOBm/HA7BHAx32B7WeArjOAnFz1ulPXAKduAs28y+dcSEVaksrJyck4e/YsEhMTkZ6eDoVCASsrK3h6eqJJkyawsLDQus/JkycRFxcHhUIBR0dHtGjRgt0vSyk1NRXfffcdZDIZpk6dauhwKqT9+/cjNDQUb7/9Npo2bWrocIjoBZGdnY0ff/wRcXFxmD9/frm3t3jxYly9ehXTp09HlSpVyr29gv7++28sWLAA169fR3JyMuRyOZycnNC9e3fMmjULrq6uavWFENi2bRtmzpyJK1euQC6Xo169epg7dy66deum19iJiIiI9EahAH7cAUzuDzjaoFq1aiU/RscvAC9nYOV47dtDw4Dj14CNHwOD/+uMEtwGqDMOmLYeWDOx6OPP3gSkZwFn5gGeTvllfrXzk20rDwIhBT6r3f41P5bElPxedFRuStSzLT09HRkZGfDy8oKfnx/atGmDatWq4erVq9i8eTMyMzPV6qekpGDr1q14+PAhmjRpglatWiE3Nxc7duxATExMmZ7IyyI1NRXr1q3DL7/8orHNysoK+/fvR3p6ugEiqzhOnjyJX3/9FefPnzd0KC88PlNUUQQFBSE1NbVcv2TIycnB5s2bsWDBgnJro6B//vkHP/zwA5KTk0t9jI0bNyI9PR3Vq1cv0X6RkZFISUlBYGAgPv74Y0yePBk+Pj74888/4ePjg4cPH2q0M3z4cNy8eRMhISEYN24cHj9+jJ49e2Lv3r2ljp9ebs7OzoYOgYiIqHg5ecD32wAAcrm87I8fGga4VAEGtn5a5mSbn3DbGg5k5xa6KwBg0wmgt+/TRBsAdGkC1HEDNhxXr+vFf3v1pUQ929zd3bX2SHN1dcW+fftw7do1tZ5E4eHhyMnJwYABA+Do6Aggf36OjRs34tixYwgODn4hhjy+KCQSCczNzQ0dBlUC2dnZyM3NhZWVVbk8U0+ePIGlpSWk0hK9BdFLzNjYGFZWVoYOo8IxMzMr1X6TJ0/G5MmT1coUCgUmTZqEBQsWYMWKFfjkk08AAI8fP8bcuXORmZmJo0ePqv6dDw4ORmBgIN5++21ERUXx33MqsdI+v0RERHqlUAAL/wE+6ou4uDi4uLiU7fHP3Qaa1wCMnukL5VcbWLIXuH4faFzIF6uxj/KHnPrW1NzmVxvYcaZsYyWdlcmcbco/gHJyclRlubm5uHv3LlxdXVWJNgCQyWSoV68enjx5goSEhLJovsxcuXIFgwcPhouLC2QyGWxsbNCzZ0/cunVLrV5kZCRGjBiBWrVqwdraGjKZDG5ubhg3bhwyMjI0jnvhwgV06dIF1tbWMDMzQ4sWLUrV6+ratWvw8fHB9evXcf/+fUgkEtXr0KFDWufXKlh2+vRpBAQEwNraGubm5ujSpQvi4+ORlpaGDz74AG5ubpDJZPD09ERoaKhG+xkZGZg+fTrq1q0LMzMzmJqaokGDBlizZk2Jz0VJCIHVq1ejYcOGMDMzg5WVFQYOHKjRqwIAYmJiMHr0aLi7u0Mmk8HS0hLt27fHmTNP30DefvttfP755wCA0aNHq65PmzZtEBYWBhMTEwwfPlztnNq3bw+JRIKxY8eqypOSkuDr6wsrKyvk5eWpynV9RnSNFwBmz54NiUSCxYsXY9KkSar6zs7OmDFjRomvacG5snbu3AlfX19YWFjAwsICnTt31ohV2f4vv/yC8ePHw93dHVZWVvjuu+8KnbNNoVBgw4YN8PPzg5WVFWQyGapXr47p06erfdtTcP/w8HB07twZNjY2sLOze67erQqFAsuWLYOtrS1q1KiB6OhotbYuXbqEHj16wMbGBiYmJvDx8UFYWJjGcaKiohAUFARnZ2fIZDLY2dmhX79+uHv3rqrO8z43yjm1jh07hqCgINjb20Mmk6FWrVr4++/Szf+Qk5ODn3/+Gc2bN1dd/6pVqyIoKAiJiYlqdeVyOebMmYMaNWpAJpPB3t4eY8eOVXuugfzfxZ07d6JDhw6oUqUKpFIpXFxc8O6772rt2Xjy5En06dNHde1sbW3RsmVL7Nq1q8jYT548iZo1a8LS0hJ79uzR+Zy1zdmmLPviiy90Osei3L9/H76+vqrnpOD768qVKwHo/twXR/ms/vPPPwAAb29vVVsFz+/atWsYOnSo6r3Z2toajRs3xh9//KGqU5ZzthkZGcHDwwNAfoJNKSIiAhcuXECbNm3UvlBr3rw52rdvj9u3b+PUqVMlaksIgdDQUDRp0gSmpqawsrJC37591dpVSk5OxkcffYQmTZrAzs4OUqkUdnZ26Nu3r9p5nzlzBtbW1mjZsqXGMeRyOYYMGQKJRILDhw8DAO7cuYMRI0bA1dUVpqamsLS0hJeXF957770SnQuVXnR0tKFDICIi0k1WDvDD9vI5dlwS4GqnWa4su6/5+Uht34J1n93/cVrxPeOoXJSqW0leXp7qpZyTDYDqQzqQ/0FdLpdrzfoqhw0kJCQUO4QgNzdX5z9ijI2NIZPJdD0NNeHh4ejfvz+SkpIwYMAA1KlTB7du3cJff/2lSo4ph+mEhYVhx44d6NKlC2rXrg25XI5Dhw7h559/RkREBA4dOqQ67vXr19G/f3/cuXMHvXr1go+PD8LDwxEcHAwLC4sSxevq6opPPvkECxYsQGZmpmo4lYmJCerXr1/s/sHBwahevTomTJiACxcuYMeOHQgMDET9+vVx+PBhDBkyBLm5uVi/fj2GDh2KGzduwNs7f+LEtLQ0jBgxAn///Tdat26NgQMHIjs7Gzt37sSIESOQkJCACRMmlOCK57tz5w7eeecddO3aFf369cOJEyfw119/4dKlS7hw4YJqHsCoqCj06tULUVFR6N27Nxo3boz4+Hhs2bIFbdu2xdGjR+Hr66uKZcuWLRgyZAh8fX0BAHXr1oWXlxdq1Kihdn9iY2MRGRkJiUSCffv2qZVfv34d/v7+qt5XJXlGdI23oO+++w5paWkICgqCTCbDli1bMH36dNSsWROvvvpqia9tWloaXnvtNTRt2hQTJ07ExYsXsWvXLvj5+SEiIgJVq1ZVq//tt98iMzMTwcHBsLW1RatWrQo99i+//IIPP/wQMpkMw4cPh42NDXbt2oUZM2YgIiJCa7J28ODBcHd3x/vvv4+cnJxS91LKy8vDnDlzMHPmTDRs2BB79+6Fvb09UlNTVXUGDBgAV1dXjB8/Hnfv3sWmTZvQs2dPREdHw9raGgBw8+ZN9OjRA1FRUejcuTNatWqFixcvYseOHTh69CguXLiAatWqPfdzozRy5EhYW1sjJCQEjx8/xoYNGzBo0CDcuHEDXl5eOp9/dnY2xo8fj2XLlsHBwQHDhw+Hq6srbt26hbCwMMTExKh9wbFu3TokJCRg6NChsLOzwz///IOff/4ZVatWxRdffKGqt3jxYkycOBE2NjYYPnw4nJycEB4ejmXLliE8PBwnTpxQvV/99ddfGD16NFJSUtCrVy80bdoUSUlJCA8Px759+9CjRw+tsf/zzz948803kZeXh6NHj6JZs2Y6n3dRdD3HolSpUgUfffQR5s2bh5s3b2LevHkA8v9dad++PYDSPffamJmZ4YMPPkBmZibOnz+PKVOmwM4u/wOScv6zEydOYNCgQbh//z46deqEUaNGITMzE2fPnsW2bdswcuTIkl4mrZKTk5GamoonT57g33//VQ2h7d27t6rOqVOnkJeXh3bt2qntK5VK0bRpU/z11184deoU/Pz8dG5306ZNeP311yGXyzFixAg4ODhg586deOeddzTq3r9/H6tXr0bTpk3RuXNnWFtb48yZM9i9ezdatmyJyMhIODg4oGHDhmjTpg327NmDa9euoW7duqpj3L59G4cPH4a3tzfat2+P5ORkvPbaazh69Ch69+4NX19fZGZmIjIyEv/++29JLyMRERFVdgoBLPgbDcdp/5yrkpsHPMnQLMvOzZ8nrSB7q/zebJk5gKmW1IyZSf5/M3M0tykpt5lqySuYyZ7W0badypcohUuXLonFixerXmvWrBHXr19XqxMVFSUWL14sLl++rLH/48ePxeLFi8XJkyeLbevgwYNqbRX1OnjwoE7x9+7dWwAQt2/fFkIIkZ6eLjp37ixMTEzE+fPn1epu2rRJmJmZieHDh6vK0tLSRE5Ojlq99PR00adPHwFAdV5yuVy88cYbAoCYMmWKqm52drZ49dVXBQDh5uamU8xKsbGxok6dOlr3S0lJEf7+/qLgbS1Y9uabb6rKMzIyRNeuXQUAUbVqVfHkyRPVtl9++UUAEJMmTVKV/fjjjwKA+Oijj9TafPjwoWjSpImwtLQUKSkpOp9HZGSkcHBwEADEvHnzVOUFr83XX38thBAiNzdXDB8+XAAQO3bsUDvOsWPHhJ2dnfD391eVff311wKAWLFihVpduVwuBgwYIAContd169YJAKJdu3YCgIiPjxdCCPHrr7+qxVaSZ6S08To6Oork5GRVeVhYmLCyshLNmzfX+boK8fQZASDee+89VXleXp6YPHmyACDeeOMNjfZtbGzEgwcP1I6l7Zm6d++eqFmzpjAyMlK7FgkJCaJFixYCgNi3b5/G/kFBQSU6D6VTp04JAGL48OEiMzNTTJgwQRgbG4tu3bqJjIwMrbGOGTNGVa5QKMTEiRMFAPHrr78KIfKfhTFjxggA4oMPPlC7RlOnThUARHBwsKpuaZ8bIZ6+33Tp0kUoFApV+cKFCzV+z3SxadMmIZVKRY0aNURSUpLaNoVCIeRyuRBCiLVr1woAwszMTPVeJ4QQN2/eFG5ubsLR0VFVduvWLVGtWjXh7e2t9nssl8tVz8xvv/0mhBDiwYMHolGjRgKA2L59u0b7eXl5Qgj19yqFQiGWL18ubGxshJeXl1o8ulKez7Rp0zTKdDlHXWh73pVK8tzr6tl/i5RSU1NFx44dBQDx008/aeynvMZFHUNXyvdb5cvFxUV1r5U+/PBDAUD8/PPPGvsvW7ZMABCffPKJzm0mJSWJli1balyzhw8fiqZNm6p+35VycnJEWlqa2jEUCoWYOXOmACC+/fZbVfnKlSsFADFx4kS1+nPnzhUAxDfffCOEyP8cY2FhITp37qxz3FT20tPTDR0CERFVVqkZQmBAmb/ip/9edLsHL+l+vNv//e1lOUyINzQ/84ntp/Pr7TpbeHunbuTX+eOg5rZJv+dvy8rR3JbwJH/btLVFnw+VWqmGkXp5eaFnz57o1q0bmjdvDhMTE2RlZanVUQ7fMXp23DHyewoUrFOUJk2aoGfPnjq9mjRpUprTQVRUFE6cOAF/f384ODggJiZG9apVqxa8vb3VJoC2tLRU9fDIzMxEXFwcHj9+jFdeeQUAVD39EhMTER4eDmNjY9XcN0B+T7Rx48bpfa6SgvPzmJubq67X6NGjYWNjo9rWpk0bmJmZITIyEkD+kLW///4bEokEI0eOVLs+2dnZaN26NdLT07UO0yuOlZUV3n//fdXPBa/Nxo0bAeT3Fjp69Chq1KiBxo0bq7Xv4uKCBg0aIDw8XGOBjmcZGRmhbdu2AIADBw4AAA4ePAhjY2OMGzcOEokEBw4cgBACR44cAQB07twZQMmekdLG++abb8LW1lb1c82aNeHt7a11iKquCvbqMTY2xltvvQUXFxds3bpVo+7IkSN1mqz63LlziIqKQmBgoNrvnKOjI8aMGQMAqntX0JQpU0pzCiq5ubl44403sGjRIrz66qvYsWNHofPJFfx9k0gk6NKlC4D8YXlA/u/myZMnYWRkhC+//FJV19jYGGPGjIGHhwe2b98OhULxXM9NQZMnT1YbjhsQEABTU1PV75kuxH9D7/Ly8jB79myNFSwlEonGe26fPn3Ues65u7ujTp06SExMRFpaGgBgz549iImJwYgRI/DkyRPV83r//n107doVpqam2L49v9v82bNncfnyZXTq1Ak9e/bUaF/5/l7QnDlzMHbsWNSoUQOnT58uUU8+Xehyjs+rtM99ady8eRPh4eGoWbOm2jBlJW3XuLQ++OADrFmzBj/99BOGDx8OS0tLjWH8yvcqU1NTjf2V/46VZBGVW7du4eLFi/Dx8VH7XXFyctLai1c5DB/I//fowYMHiI2NRZs2bWBubo6jR4+q6nbt2hV16tTBH3/8ofqMkZqair/++gvGxsYYPXo0gPx/a8zNzXH58uXnXkzn8ePHyM7OVv2clpam1tM2JycHjx49UtsnLi6uyJ/j4+MhhKj0bdy7d69SnAfbYBtsg22wjYrXRnx8PMqakBohsbWX6met59HEC9g7Ddg7DY/Xvw+x50vApzrQrSlSNn2InO1TVNvTrKT55+FqB8QlaZ6Hcoiom32hbT40zlOrq3at4pIg7CyRmvM0V1OZ73lxbehbqYaRWllZqYaAeXl5wdvbG1u2bEFeXp5qWJByCJVCodDYXzksVJfJ0e3s7FTDa8rLnTt3kJ6ejn///VdtKGxBBf+ATUlJwdSpU7FlyxbExsaqPRRA/rxNynoJCQlwcnJSS2YBgIuLC5ycnMpnNZNC1KhRQ+1n5XWtWVN9MkXl3DXKuZ+Sk5MRFxcHIUSRCc0HDx6UOCZPT0+YmJiolSmvjXLerKSkJMTFxSE3N7fQ+wPkJ1CK2g4AHTp0gJmZGXbt2oVhw4bhwoUL8PHxQcuWLeHu7o49e/aga9euiIiIgK2trWp+opI8I6WNt1atWmrblXPYlXalQmtra42honZ2dnB1dcX58+eRnp6u+gMWgE5DkYH8RAAAtbmblJo0aQKJRKKqU1CdOnVKEL2mbdu2ISsrC6+//rpqDq3CPPusK5OIyiRCamoqEhMT4ezsrPH+orxG9+7dU9Up7XNTVEzKeaqenWOtKFlZWbh//z4A6DwE89nrrnyuAODRo0ewsrJSJfxmzZqFWbNmaT2O8vc7MjISQgit82Jpk5CQgKlTp6J27do4duyYamh4WdLlHJ9XaZ/70rh37x4yMjK0tlXWWrRogRYtWgAAxo4di6VLl+K9995DTk6OKlmvTGoX/MCjpPyireB7SXGio6ORnZ2t9T2ncePGGmW5ublYvHgx/ve//yEqKgq5uerzjij/zQUANzc3dOvWDT/99BP++ecf9O/fH2fPnsWZM2fQvXt31dQWNWvWxLvvvovvvvsOzZo1Q9WqVdG6dWsMGTIEwcHBWr8oLIy9vb3az88+byYmJnBwcFArc3V1LfLnZ9+7K2sbmZmZqqH9L/J5sA22wTbYBtuoeG08e4znZmwEyZtdYVLjaayFnkeX/L+ZVVHbbQJc7WAz8BW1+qqzaOoFHLkKE6lU/TxO3gAsTPNXFS2kTedm9QAnG+B0VH6bBa9V+A1ImtXQ/m9tgSGtleWeF9eGvpXJUoAODg5wdHTElStXVH8AKj94a/u2W1mmy4fznJwcnSe5lkqlGokbXSgTgm3atMG4ceO01lH+gZibm4t3330Xa9asQevWrfHee++pJrQPDw/HwoULtSYYK4LCekPo0ktCCAGJRII//vhDa31jY2ON+XzKWu3atQtdMMDMzAxOTk5atxXk6emJWrVq4fDhw6p5t0JCQuDm5oa6deti3759iImJwY0bN9CpUydVT6SSPCOljbcse6uURnkkQcry+A0bNkR0dDQ2b96MsWPHFpnsKexaPpsY11VpnxtdYipvRX2pobweyv+GhISgY8eOWuuWtjeajY0NqlWrhosXL2LlypXlMvm8LudIuunRowc8PDzw888/q5Jtbm75H/Du3bunUV+50ElxX3Q8j59//hkffvghqlWrhsmTJ8Pb2xvm5uZITEzElClTNP7NHTlyJFasWIFffvkF/fr1w+rVq5Gdna327BkbG2PmzJl49dVXsXHjRhw5cgRHjhzBX3/9hQULFuDo0aOl+jxBJVPaeXaJiIj0TgLgs4GoXtW22KolNtgfCA0DNp8ABrfJL0tMATYeB/r4qs+3FvVfj72aBZJTg/yB3w8C9xIBj//mbt5/MX8V04l9yj5e0kmZJNuA/CGhBb/1tre3h7GxsdbeTsreJbokR44fP47r16/rFEOdOnUK/UOxKNWrV4eFhQUyMjIwbNiwIuvGxsbiyJEjcHd3x7Fjx9S+/b569apaXRsbGzg5OeHq1atISUlR69324MEDJCQkaGRsKyIbGxs4Ozvj8uXLaNGihc49oHQRHR2NnJwctT9qlNemXr16AABbW1s4OzsjKSkJQ4YMKbbHgbZEh5KjoyMaNmyIiIgIrF27FsnJyejWrRtMTU3h5+eH/fv3Y+PGjcjIyED37t1V+5XkGSlpvOUlNTUV8fHxat8SKHvdOTg4lKgnSkHKHnjahl5dvHgRQgiNXnploW7dupg9ezZGjhyJgIAA7NmzB23atCnVsaytrVVfECQnJ6sNx1ReIysrK9VCA6V9bsqamZmZKvFx/vz55+4tqKS8X9bW1sU+3/Xq1VOtbqwLU1NTbN26FQMHDsS4ceOQk5ODDz744HlD1jt9Pveenp6wsLDAhQsXyuR4JZGXl4fc3Fy1HrW+vr6QSqWqYdIF6yqvx7MLvhTFw8MDpqamGv9mAsClS5fUfs7Ozsa2bdugUChw6NAh1aI9AHDo0CGtK4A3btwY/v7+2L9/P06fPo39+/fDxcVF68IddevWVS02lJqaiqFDh2LHjh3YunUrgoKCdD4nKp2GDRsaOgQiIqLiGRsBozoBnk64cv582Y8+GOwPtK4DjP4JuBIDOFoDP+8C5ApgxlD1up2n5f/3zuKnZVMG5SfmAr4EJvQC0rKAeVuBxtWB0Z3U9191CLibAGT8l7s5fAWY9d9UKK91AKoXP60Q6aZEWQBtH2qB/JXCkpKS1OZ7kslk8PT0RFxcnNpY2tzcXERGRsLW1lanZJs+5myrVasWWrVqhfPnz2P9+vUa23NyclTjfSUSiepVsMfEnTt3NPZ1dHREy5YtIZfLMWfOHLXj/fTTTxrz3OlCJpPB1NQUKSkpeuuxYWZmhn79+gEAPvroI42hr0II3Llzp1THTktLw6JFi1Q/F7w2gwcPBgBUq1YN7dq1Q2Jiotp1VMrLy1PrcVFw+NizjIyMVD3wli9fDhMTE9V8XJ07d4aRkZFqiGLBuYRK8oyUNN7yNHPmTNX/y+VyLF26FA8ePEDfvn1LfcxmzZqhZs2a2LVrl9ofxo8ePcJvv/0GAOX2R2q3bt2wdu1a2NjYoGvXrhp//OvK0dERrVq1gkKhULtGCoUCK1aswL1799CzZ09VorS0z01Zk0gkGDx4MKRSKaZMmYKUFPUVjYQQpXpfCAwMhJubG3799VetcwSmpKTg8eP8JcebN2+Ohg0bYv/+/di9e7dGXW09e6tXr46tW7eiRYsWmDhxomqlz4pG+f4KQHW+SuXx3Ct7ez7bVs2aNeHn54ebN29i8eLFGvs9b+/p3Nxcre9BCoUC69atQ2xsrFryrHHjxvDx8cHx48fVEoBnz57F4cOH4eXlVaKVSGvWrAkfHx9cvHgR+/fvV5UnJCRg9erVanULzkNY8NnOyMjA/PnztT7vZmZmGDp0KORyOT799FPcvn0bo0ePVutdmp6erjFE38rKSpUwffaeUPl43vnyiIiI9EII4LOB5Xd8Y2Ngx1RgSFtg0XZg0h+Aow1wYAZQ1734/T0cgX9n5vd2+3Q1MPcvoGfz/Lnhnl2FdPl+4Iu1wDeb838+GJH/8xdrgdsPNQ5NpVeinm1Hjx5FRkYG3NzcYGVlBblcjsTERERFRUEmk6F169Zq9f38/HD//n3s2LEDjRs3hkwmQ2RkJNLT09GjR48ieyAp6WPONisrK8ydOxcDBw7EsGHDsHz5ctUcNrdv38bRo0fRoUMH/Pnnn6hatSpatWqFjRs3om3btujevTsePnyI7du3aww5MTIywuTJk7F//37Mnj0bFy5cQJMmTXDy5EmcOHFCp2Tjs+zs7FCrVi1cunQJw4cPh6+vL8zNzTF48OBCJ4svC2PGjMHhw4exefNm1K5dGz179oSTkxNiYmJw7tw5XLhwQWMeHV1UrVoV06dPx7Fjx1C/fn2cOHEChw4dQo0aNVS9X2QyGb7++mtcunQJU6ZMwbZt29C2bVuYmZnhzp07CAsLg7OzM44fPw4gfw4iMzMzfP/990hPT4eNjQ3q1q2LwMBAAEC7du1gaWmJ+/fvw9/fXzXBd506dVC9enXcvn0bTk5Oaj34SvKMlDTe8mJjY4P169fj+vXr8PPzw8WLF7Fr1y7Y29vj66+/LvVxq1WrhokTJ+LDDz+Ev78/hg8fDltbW+zatQsREREYNGhQuSacAgICsG7dOgwfPhzdu3fH9u3bERAQUKJjGBkZ4dNPP8WhQ4ewYMECREREwNfXF5cuXcLOnTthb2+P7777Tm2f0jw35aFXr154/fXX8dtvv6F27dro378/3NzccOfOHRw9ehQbN24s8bdtNWvWxIwZMzBhwgQ0bNgQ/fv3R926dZGSkoLr16/j2LFj+P777zFq1Cg4Ozvjq6++wqhRo9CrVy/07t0bTZo0wZMnT3Dy5Em0bdtW49oB+c/N1q1bMWjQIEyePBl5eXn47LPPyuiqlA0zMzM0bNgQhw4dwogRI9C5c2eYmpqid+/e8Pb2LvPn3tfXFxs2bMB7772HAQMGwNTUFF26dEGjRo0we/ZsDBw4EO+88w5CQ0PRqlUrZGdn48yZM3B0dMSGDRtKfZ5PnjxBv379kJWVBX9/f3h6eiI1NRXh4eE4fvw4LCws8OOPP6rq29vbY/LkyXjjjTfQsWNHvPHGG5BIJNiwYQOSk5Px559/6vTvuVKVKlXw8ccfq56hESNGwN7eHjt37tT4Us/ExATdunXDvn37EBAQgGHDhkEul2P//v2IjY0tdD6+Hj16oFatWjhw4AAkEgneeustte1Hjx5Fnz590L59ezRu3Bj29va4cuUKtm3bBhsbG/TpwyEXREREhPxeba+2B2rkjxZSjjIpkUMzi69jZwUsG5v/KsodzS9iAQANPYHdX2rfVtJYqGyUZOnSmzdvip07d4rVq1eLZcuWiWXLlol169aJo0ePitTUVK37PH78WOzatUusWLFCLFu2TPz111/i3r17JWm2zPXu3VsAELdv31Yrj4qKEiNHjhTVqlUTMplMmJmZiWrVqomBAweKo0ePquo9ePBAvP7668LZ2VlIpVLh5OQk3n33XbFy5UoBQEybNk3tuGfPnhWdOnUSlpaWwtTUVDRv3lz8888/ok6dOsLNza3E8UdGRopOnToJa2trIZFIBABx8OBBkZKSIvz9/UXB26qtTOnrr78WAMSKFSs0ju/g4CD8/f3VyjMzM8V3330nfHx8hIWFhZBKpcLBwUG0bt1azJ8/v8TnoNx31apVokGDBsLExERYWlqKfv36ifj4eI19Hjx4IMaOHSu8vb2FiYmJMDExEa6urqJHjx5i27Ztqnq5ubni22+/FdWrVxdSqVQAUDuXR48eiWbNmgkAYvr06arynJwc0bNnTwFABAUFaY1b12ekJPEWdh+KundFiY2NVT1bO3bsEC1atBBmZmbCzMxMdOzYUdy4cUOtfmHtFxWDXC4X69atE76+vqpnwcPDQ0ybNk3k5eU99zkUdOrUKQFADB8+XK38+PHjwsPDQ5iamordu3cX2VZhx7hx44YYPHiwcHR0FMbGxsLGxkb06dNH3LlzR+MYpX1uCnu/Kez3TBdZWVli4cKFolGjRsLc3FxIpVLh4uIigoODRWJiohBCiLVr12p9PyoqpgMHDoju3bsLBwcHYWxsLKysrEStWrXE22+/LaKjo9XqHjlyRHTv3l3Y29sLY2NjYWtrK1q2bCl2794thFB/DguKj48Xbdu2LTS2wmg7n9KcY3Hi4uJEv379hJ2dner9Vfm7oetzr6vk5GQxatQo4ejoKIyMjDTOJSIiQgwcOFA4Ozur7kfjxo3FqlWrnus8MzMzxYwZM0Tz5s2Fvb29kEqlQiaTiWrVqolXX31V6/Mvl8vFli1bRIsWLYS5ubkwMTERPj4+YufOnSU+byGEUCgUYt26daJRo0aq9/4+ffqI3bt3a/yuZmZmipkzZwovLy9hYmIirK2tRWBgoDhw4EChv0MKhUK89957AoB45ZVXNLbfvn1bDB06VNSoUUNYWlqq/i0fOHCguHbtWqnOiUrO0J8HiYioEkvNEAIDnv8lGSjE9VjVYR8+fGjAk6IXiUQIzh5NVJncv38fAQEBSEtLQ2xsrKHDISIyiIkTJ2LhwoVYs2ZNsXMRkmE8O2cmERFRmUnLBKxHPN8xpEZAcFvgz4mqovPlMWcbVUqGmbmdiIiIqJzExsZi+/btsLOzw8CB5TjHCj2X0s73SkREpBdyBfAFF0yi0imz1Uip9BQKBR48eIDMzMwi61lbW5dqnjd9kcvliIuLQ05OTpH19DEPX2VTkmfkRZCXl4fY2FiNxTae5ezsXOicTJXJy3o9MjMzVQuLFMbIyAhubm4ac2KWxMOHD5GWllZkHXNzc7i6upa6DaXHjx9rTLz/LJlMBg8Pj+du61kpKSlITEwsso5UKoW7u7vaYgFlxZDnrnThwgXs3bsXu3fvxo0bNzBr1izVohdEREREOpMaAQNbA/WqqRWX9/zMVHkw2VYBpKenY9CgQQgLCyuynr+/f7lPqv88Hjx4gM6dO+P69etF1hs+fDj+/PNPPUVVOZTkGQkNDdVTVKUXFRWFtm3bal0xtqBp06Zh+vTp+gnKgF7W67F161adhvcdPHgQHTt2LHU7Y8aMwT///FNkHTc3tzIZdv3ZZ59hyZIlxdYrjxkcfvrpJ3z++efF1rt9+za8vLzKvH1DnrvS9u3b8fnnn8PS0hIjR47E5MmTy60ten7K1V+JiIgqnDwF8GWwRvG9e/f47xfphHO2VQB5eXk4fPgwHjx4UGQ9Ly8v+Pv76ymqksvOzsb+/fvx5MmTIus1bNgQPj4+eoqqcqgsz4hSRkYGdu/ejaysrCLrtWrVCjVq1NBTVIbzsl6PuLg4HDp0qMg6MpkMnTt3fq7esMeOHUN0dHSRdZydnctkFd0LFy7gypUrRdaxsLBAv379nrutZ924cQOnT58uso6ZmRkCAwNVq+mWJUOeO72Y7t69i+rVqxs6DCIiqoyeZ842qRHQtyWw6RONTZyzjXTFZBsRERER6R3/YCEionLzvAsknJ8PNPHWKI6MjES9evWeIzB6WXAYKRERERHpXXnMHUhERPRcpEZAzxZaE20Ap0Ag3XE1UiIiIiLSu8aNGxs6BCIiInV5CmCa5lxtShEREXoMhl5kTLYRERERkd5dvHjR0CEQEVFlJZEAAISxBJAZ6/YykgA9mwPNaxo4eKoMOIyUiIiIiPROoVAYOgQiIqqsLM2AtR8iYWcYnJ2ddd9vZMciN1etWvX54qKXBhdIICIiIiK9i46Ohqenp6HDICKiSkwIAcl/vdyI9InDSImIiIhI76pUqWLoEIiIqJK7evWqoUOglxSTbURERESkd7du3TJ0CEREVMnl5OQYOgR6STHZRkRERERERESVjo2NjaFDoJcU52wjIiIiIr178uQJbG1tDR0GERFVYhkZGbCwsDB0GPQSYs82IiIiItK7lJQUQ4dARESV3PXr1w0dAr2kpIYOgIiIiIhePo8ePYKHh4ehwyAioopkywlg8R7d6895DWjiXX7xEJUSk21EREREpHcSicTQIRARUUXzzWbg1E3d6hpJAHMTYMunhVbx9PQso8CISobDSImIiIhI75o0aWLoEIiI6EWmEMBf4cClu4VWyc7O1mNARE8x2UZEREREehcREWHoEIiI6EUnNQJmbix084MHD/QYDNFTTLYRERERkd7l5eUZOgQiInrR5SmA0OPA1RhDR0Kkhsk2IiIiItK7KlWqGDoEIiKqDIwL793WuHFjPQdDlI/JNiIiIiLSO0dHR0OHQERElUGeAlh3FLh+X2PTjRs3DBAQEZNtRERERGQAN2/quNocERFRcYwlwNeavduysrIMEAwRk21ERERERERE9CLLUwCrDwNR8WrF1tbWBgqIXnZMthERERGR3nl5eRk6BCIiqkyMJMDsTWpF7u7uBgqGXnZMthERERGR3qWnpxs6BCIiqkzyFMDvB4E7D1VFkZGR5dNWcjoQ8gvgNAqwHAYEfAmcjdJ9/6sxQI+vAKvhgP1I4LUfgIQnmvW+DgX6zgZcRgOSgcD0dWV2ClS+mGyjMhMSEgKJRIKIiAhV2TfffAOJRILQ0NAybcvJyanCrSwjkUjQo0cPQ4fxQomIiIBEIkFISIjBYmjcuDGcnJzK5FgV4XzKQ2U9r/JU2ve+l/1ah4aGQiKR4JtvvjF0KKQHCQkJhg6BiIgqo282FV/neSgUQK9ZwJojwLhAYO5I4OEToOOXwA3NRRo0xCQC7acCN+OB2SOAj/sC288AXWcAObnqdaeuAU7dBJp5l8+5ULlhsu0FJYTAxYsXsX79eixfvhx//vknwsLCkJubW/zOL4iQkBAsWbLE0GEQUQUQGhqKkJAQPHjwoNzbOnjwIEJCQtS+OKhsVqxYgdatW8PJyQmmpqawtLREjRo1MHnyZKSlpWndZ+XKlahXrx5MTU1hZWWF9u3b4+LFi3qOnIiIiKgIcgXw237gXiIAoFq1aiU/RscvgFE/Fr49NAw4fg1YOQ6YNgQYGwgc+gowNgKmrS/++LM3AelZwIEZwPu9gCmDgQ0fARfuACsPqte9/SsQ9xuw+oOSnwcZFJNtL6iwsDCcOHECdnZ2aNOmDWrUqIGIiAjs3r0bQghDh6fy8ccfIzU1FQMGDCjxvkuXLsXmzZu1brt79y7Cw8OfNzyiMtWgQQOkpqbip59+MnQoZaoinNeePXuwdOlSvfSEOXHiBJYuXfpcww5K+96nr2t97tw5GBsbY/DgwZg6dSref/99eHh4YN68eWjXrh0UCoVa/V9++QVvvPEGsrOzMXnyZIwePRoXLlxAhw4duKIklVqTJk0MHQIREVVGAsC3+X9HyuXysj9+aBjgUgUY2PppmZMtENwG2BoOZBfTAWbTCaC3L+BZYHRNlyZAHTdgw3H1ul7OZRY26ZfU0AFQyT1+/BgRERHw8vJCt27dVOXW1tY4fvw4oqKiUKtWLQNG+JRMJoNMJivz41pYWJT5MYmel5GREaysrAwdRpl59OgRHBwcyu28lMevjEr73qevZ2jRokVaywcOHIgtW7Zgz549qmHx2dnZ+Pzzz2Fvb4+zZ8/Czs5OVbdz586YOHEitm3bVu4xU+Vz9epVNGjQwNBhEBFRZSNXAEv2AlMGIS4hDi4uLmV7/HO3geY1AKNn+i751c5v9/p9oHF17fvGPsofcupbU3ObX21gx5myjZUMhj3bXkBRUfkTLz47Z1m9evUglUpx48YNQ4SllbZ5i9LS0hASEgI3NzeYmprCwsICnp6eGDFiBICncxYBwO7duyGRSFQvJW1ztinLwsPD4efnBzMzM1hYWKBdu3a4deuWRmyHDx9G8+bNVUOiunfvjpiYmDKde23//v2oUqUKqlWrhqtXrwIAevToAYlEgoSEBPTv3x82NjaQyWSoX78+du7cqXGM5ORkvP7663BxcYFMJkOVKlXQrVs3XL58WVUnIyMDJiYm6N69u9q+ffv2hUQiQXBwsFp5x44dYW5ujpycHABP59s7deoURo0aBXt7e8hkMnh5eWHlypWlPv+NGzfCz88PVlZWkMlkcHZ2Rq9evRATE6NR97fffkOtWrVU5zhixAhVfAWdPXsWXbt2RZUqVSCVSuHk5IQRI0YgOTlZo+6tW7cQFBQEZ2dnyGQy2NjYoFmzZlizZk2RccfGxqJu3bqwsLDA+vU6dAX/j7b5tgqW6XKOx48fR/v27VX3oEqVKvDx8cGKFSt0jgNQn/vqs88+g6urK2QyGVxdXfHZZ59p1FfOXXfhwgW0a9cOVlZWcHR0LPS8ACAnJwcTJkxAtWrVIJPJYGVlhbZt2+Lo0aOFXpdFixahZs2aMDExUf3OF6dHjx5YunSpKk7l+8Gz17lr166wtbWFTCaDi4sLRo0ahZSUlBJdt5CQEEyZMgUAEBQUpGqr4HtCZmYmPvjgA3h7e6vew2rVqqXaDyjbOdtK+gw9D09PTwBAYmKiqmzz5s1ISkpCUFCQKtEGAAEBAWjcuDH279+P7OzsEre1ZMkSeHt7QyaTwcHBAWPGjNF6Lo8ePcIbb7yBOnXqwNraGjKZDFWrVsXIkSPV7u+BAwcgkUgwevRore35+fnB3Nxc9V4RGRmJwMBAODo6qt4f6tWrhzlz5pT4XKh0yvLZJSIiUiMEMPev8jl2XBLgaqdZriy7/7jofQvWfXb/x2nF94yjFwJ7tr2AEhISIJFI4Oys3qVUKpXCwcFBp2FWQogS/XFkamqqlux6HkOGDMGOHTvQrVs3+Pv7Iy8vD9evX8f/2bvvsCavNgzgd8IIeyiIgKA4GIq4cODGrVWrdW/aWjtcraPaatVaP9va2qW1jtZRrXVbrXuixVFxgCIi1gkoCojKHsn5/qBJDQmQsIJ4/64rl+a8533P8w5GHs44e/YsgLxx9V999RWmT5+O+vXrF/jBSZvExER069YNHTt2RM+ePREeHo7du3dj4MCBuHjxoqrexYsX0aNHDygUCgwdOhSurq44fPgwOnbsWCrnCAAbNmzA2LFj4eHhgaNHj8LFxUVte9u2bWFnZ4cJEyYgKSkJ69evx+DBg3Hnzh1Vb5+srCy0bt0a165dQ7t27dChQwdER0djx44daNOmDf7++29VUsjHxwcXLqj/JeTcuXOQSCSqawsACoUCly5dQsOGDWFqaqpWf+TIkTA2NsbYsWORnZ2NdevWYezYsfD394evr69e5z9v3jzMnz8f9vb2GDx4MGrVqoW7d+/i2LFjuHHjhtr8CcePH8emTZswePBguLi4YO/evdi4cSPs7e3VhtMdOXIEffv2hYWFBQYPHowaNWogPDwcW7ZswYULFxAeHg6ZTAYgL0HRvn17PH36FN26dYO/vz9SU1MRGhqK/fv3Y/jw4Vrjvnz5Mnr06IGMjAzs3bsXgYGBep13QXQ5x7i4OPTs2RMAVNcsISEBly5dwqlTp/T6WlD6+eefkZycjMGDB8Pa2hp//PEHvvjiCzx+/BgrVqxQq5uZmYlOnTrBz88PU6ZMKXJ+tG7duuHEiRNo0qQJRo0ahfj4eGzevBldunTBvn370KlTJ7X6hw8fxsaNGzFo0CB4eHjAzs5Op3OYOHEiUlNTcerUKUyfPl31va9FixYAgKtXr6J169bIyMjAa6+9Bk9PT5w8eRLr1q3D+fPncfHiRY1nvSAjR45EfHw8/vzzTwQFBaFBgwYAAB8fHwB5ibaWLVviypUraNq0Kfr37w8zMzNERERg7969WLhwoU7tFIeuXyf6SEpKQkZGBpKSknDw4EH8/PPPsLKyQteuXVV1Tp/OG87Qvn17jf2bNm2Ky5cv48KFC2jdurXO7S5fvhzvvfceHBwc8Pbbb8PY2Bhbt27F8ePHNerevHkTO3bsQKdOndC/f3+YmJjgr7/+woYNG3D16lXV971OnTqhTp062LVrF3JyctR6Ft64cQMXLlxAr169YGdnh+zsbHTu3BlJSUkYNGgQvLy88OTJE0RERODkyZOYMWOGzudCxWdjY2PoEIiIqLKSK4DlB9FgWt/C6+XkAk/TNcuycoDEfH+0rWKV15stIxuQaUmlmP37+2ZGIX9MUm6TaRkBYWbyXx1t2+nFIuiFs2XLFvHrr79q3Xb48GGxYsUKkZubW+gxnj17JlasWKHz69mzZ0XG9dZbbwkA4sqVK6qyhQsXCgBi69atqjJLS0vh7+9f5PEAiO7du2vd5uDgIHx9fTXKAIjvv/9erbx///4CgDh37pyqrH379gKA2LVrl1rdDh06FNqurvF++eWXwsjISAQEBGhcu+7duwsAol+/fmrlS5YsEQDE3LlzVWWffvqpACBGjBihVnf16tUCgOjSpYuq7L333hMAxIULF4QQQkRGRqrqABC3bt0SQgjx119/CQBiypQpqn2V965FixZCLperyg8cOCAAiNGjR+t1LaKiooSxsbFwdXUV8fHxGtuVz+eVK1cEAGFqaqr23MjlcuHm5iZsbW3V9qtZs6ZwcXERiYmJauU//fSTACAWLlyoKvP39xcAxPr16wtsXwghfH19hYODgxBCiKNHjwpbW1vh4uKiFo+ulOfz1ltvaZTpco4rV67U+gwXx9atWwUAIZPJRFRUlKo8PT1d1K1bV0ilUrVyX19fAUC8/vrrOp3Xb7/9JgCIDh06qD0zwcHBQiqVivr162vsL5VKxZkzZ4p1Ptq+vyh17txZABBr1qxRKx8+fLgAID777DO92tL2fUtp8uTJAoAYM2aMxrbnn6vCjlGYkj5D+mrbtq1A3swmAoDw9PQUhw8fVqszYMAAAUDrvZs9e7YAIDZs2KBzm9nZ2aJq1arCyspKxMTEqMofPXokqlatqvG1nJ6eLjIzMzWO88YbbwgAYv/+/aqyefPmaX0WlN8flXVPnDghAIjJkyfrHHdBkpKS1OJLSUlR+76flZWl8T3r/v37hb5/8OCBUCgUlb6NhISESnEebINtsA22wTZKsY3m04VA/1J7xazZU/h5HL+i87EeX/z3d2fLYUK8sVTjmEnrD+XVPXCxwGuVdepqXp1fj2tcq9wpq/O2ZWZrXpuEp0Kgv3g2ZVX53o9K1EZ5Y7LtBbRx48YCP9gcO3ZMrFixQusHk+fl5OSImJgYnV85OTlFxqVrss3R0VFUrVpVhISEFHq84iTb7O3tNer++OOPAoD45ZdfhBB5H/RkMpmoV6+eRt39+/eXONk2ceJEIZFIRN++fUV2drZGPWWyTZkUU4qNjRUAxLBhw1Rl/v7+QiKRiAcPHmgcp1atWsLMzEz1AX/Pnj1qybqFCxcKqVQqLl26JCQSifjmm2+EEELMnDlTABBHjx5VHUt573777TeNdmQymWjbtq1e12LatGk6JY2USYTOnTtrbFN+wH/8+LEQ4r8k4dtvv63xfN69e1fIZDIRGBgohBAiLi5OSCQS0axZsyJjVSbbNm7cKMzMzISnp6eIjY3V63zzn4+2RIku57ht2zYBQPTo0UMkJCQUKwYlZbKtb9++Gtu++eYbAUB88sknqjJlsk1bclTbefXr108AECdOnNCo37p1awFA3L17V23/gICAYp9PQcm23NxcIZPJhIeHh8Y+yuegRYsWerVVWKKsZs2awtLSUqSkpBT7GIUp6TOkr7/++kts3LhRfPPNN6J3797C09NT4+dLr169BAARFhamsf+CBQsEALFixQqd21R+nx0+fLjGtgkTJmgk256XlZUlHjx4IGJiYlRfLzNnzlRtf/z4sZDJZKJNmzaqMrlcLqpVqybc3d1VZREREQKAaNasmbhz547OsVPpunTpkqFDICKiiqa0km3S14Twe19cunix8PYepwhxOEz95fe+EN0+1SzPyMrbp+57QvTU8sfcnw/ntX25kN8tYhPz6ny5Q3PbyO+EqDJK+37/JtvE3N8LPx+qMDiM9AVkbGyMzMxMrduUq60YGxd+a42NjYu3DHIpWLBgASZPnoy2bduiWrVq8Pf3x6uvvoo333wTRkZGJTq2s7OzRplyyJlyeG1MTAyysrJQq1YtjbrNmjUrUfsnTpzAwYMH0bt3b+zatavQuvnnnHN1dQUAJCcnq8ru378Pe3t7VK9eXWP/OnXq4M6dO4iNjUXNmjXRtWtXmJmZITg4GEDekLO6deuicePGcHd3x9GjR/HBBx/gxIkTsLS01DpkVttE1dbW1nj69GlRp65GuTqhrsPKatbUnEC0SpUqAP67BsphwCtWrNAY/qiUlJQEIG8oqBBC56GvT58+xahRo+Dm5obQ0NAyGdqkyzkOGDAA3bp1w4EDB1C9enXUq1cPbdu2xRtvvIGAgIBitevt7a1R1rRpUwD/zf+oZG1trfMEsjExMZBIJGjZsqXGNi8vL5w+fRqRkZGqOcAAoHbt2vqErpN79+4hKysLdepoTjLr4uICOzs7xMXFlVp7Dx48gIeHh0EWwtDlGdJX27ZtVf//4IMPMG/ePIwaNQrW1tbo2zdv2IW5uTmAvCG0+Sl/FllaWurcpnKVV+XQ3Ofl/76o9Mknn+DXX39FTEyMxorbz3/PtLe3R9euXbF//37cu3cP7u7u2L59Ox49eoQPP/xQVa9BgwZ4/fXXsXbtWnh4eMDDwwMBAQEYNWqUxtyXRERE9AJSCGD+MFgW9TubvVXeSqD5y5ztNcuVGtcC/roGKBTqiyT8fQOwkOWtKloQ16qAow1w/qbmtnM3gMYehcdLLwwukPACsrS0RGZmptZljNPS0mBmZlZk0kqhUCA9PV3nl0KhKLX4x40bh5s3b+Krr75C8+bNERoairfffht+fn5aP8zpQ5p/RZjnlOY5FMTLywtOTk44fvw4Dh8+XGjdglYqzP9BUlempqZo2LAhLl68CIVCgfPnz6sSNC1btsS5c+cgl8sRHh6Oxo0ba71WBSVpixuTrgp7XvO3PWzYMGzcuFHra/HixcVq38rKCs2aNcPdu3fx3XffFesYRdH1HA8ePIiQkBBMnDgRtra2WL9+Pdq2bas2+X5ZUc53V1a4inDJ6PN1Ulzvv/8+AGDJkiWqMuUfMW7fvq1RX7nYiYdH2f1iOG3aNCxYsAAODg749NNPsWbNGmzcuBFffPEFAM3v7ZMmTYJcLsf3338PIC9Bb2xsjMmTJ6vVW716NS5duoQZM2agRo0a+OOPP9CjRw+MGjWqzM6F1D2fjCciIio1UgnQwA3o21zrHytLbGAA8PAJsOO/ebGR+AzYehro468+39rN+LzX8wYEAHvOAzH/LUiFo5fzVjEdpPscuFSxsWfbC8jR0RGxsbF49OiRWk+u3NxcJCUlae3dlV9aWhp+//13ndscNmwYrK2tixWvNi4uLpg2bRqmTZsGhUKBMWPGYMOGDfjll18wYcKEUmtHGzc3N8hkMty5c0djW/4FBvRVvXp1bN26FR06dMCrr76Kbdu2oVevXsU+nqurK86fP4+HDx9q9Di6desWzM3N1XootmvXDqGhoVi2bBmSk5NVk+13794dW7ZswfLly5Genl6qC0FoU69ePQB5E6v7+/uXyjGVE9UbGRlh2LBhhdb18/ODRCJRW7G1MEZGRjh58iQ6d+6MefPmIScnB5999lmJYy6uNm3aoE2bNgCAhw8fokmTJvjmm2+wYMGCQhPK2ih7ET1P2UtQW28wXbm7u+PChQv4+++/NSbOj46OBqC9p2RxFbRAi7u7O8zMzDR66QFAfHw8njx5onoedVXYNXZxcUFsbCzS09MrZfIwMzMTQgi13qytW7fG0qVLcfLkSY2vvYsXL8Lc3FyvXsHK3pbKFZqfd+XKFY2y7du3w9HREefOnVNLOK5fv17r8bt27QoPDw9s3rwZH374IU6ePIm2bdtqLFIDAI0aNUKjRnl/tU5NTUWrVq2wYcMGLFiwoGx+OSc1xVnFloiIqEgKAXw6FJBIEBkZicaNG5fu8QcGAK08gdeXApGxgIM1sOxA3qIMnw5Vr9t5bt6/d54bmfPxgLzEXOAcYPIrQGom8NUuoGFN4HX1BcawPhi4mwCk//sz82QksGBr3v9HdQBqqi+aSBUHe7a9gJQfkPN/KImKikJubi7q1q1b5DHMzc3Rq1cvnV/KYUQllZOTo7HCoVQqVX1QS0z8L7svk8nw5MmTUmn3eSYmJmjRogVu3LiB3bt3q21T9pQoiXr16iEkJARVqlTBgAEDNNrQR+/evSGEwPTp09XK161bh9u3b6NNmzZqHz5feeUVAHnnYWxsrHrft29fSKVS1fn17t272DHp4s0334SxsTG++uortXuqVJxehoGBgXBzc8P27dsRHh6usT07Oxv3798HkJcQ8ff3x/nz57UmlbW1L5PJcOzYMbRr1w4LFiwwyGqE9+/f1+ix6uTkBGdnZ2RnZyMtLU3vYx48eBDXr19Xvc/IyMCyZcsglUoLXJFVFwMHDgSQt+rs89fz1KlTOHPmDOrXr1+qvVaUwzbzf/8wMjJCmzZtcPv2bY3ky/Tp0yGEQJ8+ffRqS/mHBW0rO/fr1w9paWmYOHGixrby6D1bWm7duqW1fO7cvF8In0+evfbaa7Czs8PWrVvVhmyeOHECV65cQWBgoF69Ijt37owqVapg9+7dqp5xQN73f21fr8rvcc9f3+zsbCxatKjANkaOHIm4uDiMHDkSOTk5GDdunNr2hIQEjUSPlZWV6ufro0ePdD4fKr6iVjwmIiLSm1QCeLkA/TWnOik1RkbAvtnAkDbAD3uB6b8CDjbAsU8BL9ei93dzAE58BtSpDszcACz6A+jVFDg8V3MV0l+OAp/8Dny+I+/98Yi895/8Dtzm7ysVGXu2vYCqVKmCBg0a4OrVqzh06BDc3d2RnJyMiIgIODs765RsM9ScbY8fP4abmxtat24NPz8/ODk54datW9iyZQssLS0xcuRIVV0fHx9cunQJkydPRq1atSCRSFRDnEpq8eLFaN++PQYPHowhQ4bAzc0NBw8eVH2QLKgXja5q166NU6dOqdr47bffMGDAAL2PM2PGDGzatAnr169HTEwM2rZtixs3bmD79u2wtbVVG+oFAB07doSlpSXi4uLQsGFDVYLCwcEBdevWRXR0NOzs7NCqVasSnV9RvLy88NFHH2HBggXw9vbGq6++ilq1aiE2NhaHDx/GL7/8gsDAQL2OKZVKsW7dOvTp0wctW7ZEnz590KBBA6SlpeHmzZs4duwYZsyYgY8++ggA8PPPP6N9+/YYOXIkNmzYgGbNmiE9PR3nz5+Hm5ub1l4xpqamOHLkCHr27IlFixYhNze32ENTi+O7777D6tWr0alTJ9StWxempqY4efIkLl68iA4dOhSrd6mrqysCAgIwZMgQWFtbY+fOnfjnn3/w5ptvap3PTVfDhw/HypUrcfz4cTRv3hzdu3dHfHw8Nm/eDBMTE41ns6Q6dOiAb775BtOmTcPgwYNhbm6O5s2bo02bNvj+++8REBCAN954A3v37lUlvIODg9GgQQO1ubp0ERgYCIlEgq+//hpJSUmwsrKCl5cXevbsiYULF+LQoUNYvXo1wsLCEBgYCHNzc1y9ehW3bt1CWFhYqZ53WWnUqBEaNmwIPz8/1KhRAwkJCThx4gTCw8Ph7u6OBQsWqOrKZDJ89tlnmDRpEpo2bYqRI0fi6dOnWLduHWxsbPQeem1iYoL58+dj4sSJaNq0KYYMGQJjY2Ns2bIFNjY2qrkXlXr16oUlS5aovu6fPn2KXbt2FTqsdtKkSVi0aBGOHDkCR0dHDBkyRG37zp07MW3aNHTq1Amenp6wtrbGhQsXsGfPHnh6eqJ58+Z6nRMRERFVEMpebf+OVNDWs71IwTqMcLG3An4en/cqzB3tc02jgTtwcE7pxEIVEpNtL6iAgABYW1vj2rVruHfvHszMzODr6wt/f/8SJ4rKko2NDYYNG4bTp0/j/PnzyMrKgp2dHdq2bYv//e9/aonCX375BWPHjsXKlStVk3CXVrKtefPm2LdvHz744ANs2rQJJiYmaN26NX755Rc0atQIZmZmJW6jZs2aOH36NNq2bYvhw4fj119/1fjAVxSZTIbTp09j8uTJ2L9/P/766y9YWFigY8eO+O677zSSJVKpFI0bN8apU6dUwxCVWrdujejo6BIvAqGr+fPnw8vLC19//TV+//135OTkwN7eHi1atICnp2exjhkYGIi///4bH3/8MYKDg7Fz506YmZnByckJr776Kvr376+q6+fnh9DQUEybNg2nTp3CwYMHYWlpiTp16hTas8/ExAQHDx7EK6+8gm+++QbZ2dmlnjgqSM+ePREeHo6TJ09i165dkEqlcHJywpQpU4o9rHXs2LF49uwZ1q5di8TERDg4OODDDz/El19+WeJ4Dx06hOnTp2Pbtm346quvIJPJ0KhRIyxatEht4v3S0LdvX0yePBkbN27E7NmzoVAo8NZbb6FNmzZo0KABQkJCMHXqVOzbtw/p6emoUqUKxowZgx9++AGmpqZ6teXj44PPP/8cS5Yswbx58yCXy9G9e3f07NkT5ubm+PvvvzFjxgzs3r0b33//PUxMTODi4lLk8OaKZOTIkTh58iR+//13pKamwtTUFK6urpgwYQI+++wz2NnZqdWfMGECLCws8OWXX2LRokUwNjZG06ZNsWTJEr2H6QLA+PHjIZVKsWjRIixfvhw2Njbo168fOnfujBEjRqjVXbx4MYQQ2Lp1KxYuXAhbW1v06NEDEyZMKHDhEAcHB3Tu3Bn79u3DgAEDNIYGt2rVCoGBgQgNDcXBgwehUCjg4OCA119/HZ9//rne50PFU9CCGERERMUilQC1nfKGef6rIn82pspNIsp65nOiF8iRI0fQtWtXTJw4ET/88IOhwyEqtm3btmHQoEFYuHChqrcf0cukf//+2L17NyIjI+Hl5WXocEiLqKioEvWwJSKiSqjFh0DoP8Xff8NkYEQH1duwsLDSn7ONSAecs41eWikpKWrvFQqFauhU3759DRESERGVgoSEBBw4cAD+/v5MtFVgyl7rREREJSaRAB7VgCGlO7qCqLg4jJReWl5eXmjRogUaNmyI1NRUHDp0CJGRkejYsSO6dOkCALh37x5yc3MLPY69vT3s7e3LI2SDexmvR05ODmJiYoqs5+zsXGoLiZRGLC+ClJQUrYsQ5Fe7du0St5WRkYEHDx4UWc/NzQ0mJiZF1tNHeT1DycnJagsYaGNsbFyqC1c8LyEhQeOPGPmZmZkVb+4UHZ06dQqnTp3Cxo0bkZWVhTlzdJgLhQymNFc5JyKil5wQwNwhgLH6nK4+Pj4GCohedky20UurU6dOOH78OA4cOAC5XI5q1arh3Xffxbfffquq06xZM62raT7vrbfewsqVK8s63ArhZbwe169f12leoa1bt6pW6KwIsbwIli5dio8//rjIeqUx28HevXsxaNCgIutduXIFvr6+JW7veeX1DM2YMQOrVq0qtI6Dg4NOCc7iGDVqFA4ePFhoHV9fX42VtEvTunXrsGrVKtjb2+OTTz5RrchMFZOrqw4rthERERVFgrwVPoe309gUExOj0wKCRKWNc7YRFWL37t1IS0srtE79+vXRqFGjcorIsF7G65Gamoo///yzyHqBgYGoXr36SxNLabh58ybOnTtXZL3SWHggPj4ex48fL7Jenz59VKv4lpbyum/h4eGIjIwstI6lpWWZDZM/c+YM7ty5U2gdR0dHVc9hIs6jQ0REGoo7Z9vP7wFvav6OwZ81ZChMthERERFRueMHICIi0qBvsk0CwKUKcOsnwFRzKhAuxkOGwgUSiIiIiKjc1ahRw9AhEBHRi04AmD1Ia6INAIeQksEw2UZERERE5U4ulxs6BCIietE52QGvdypwc0RERPnFQvQcJtuIiIiIqNzpskIwERG9ZGo66ld/9kBAVroryROVBq5GSkRERERERESGt24SxIz+kEgkutX3dS9084uwaBhVTlwggYiIiIjKXU5ODkxM2BuBiIjURUZGon79+oYOg6hEOIyUiIiIiMrd7du3DR0CERFVQNnZ2YYOgajEmGwjIiIionKXnp5u6BCIiKgCsrGxMXQIRCXGZBsRERERlTtLS0tDh0BERBUQ51mjyoDJNiIiIiIqdzVr1jR0CEREVAFFR0cbOgSiEuNqpERERERU7iIjI9G4cWNDh0FERC+QzFyBLHnR9WxMofuKpkRlgMk2IiIiIiIiIqoQ3N3dtZYLIdBmoxwXHxV9jM/bSTGzJZNtZDgcRkpERERE5c7FxcXQIRARUQWUlZWltfzgHaFTok0C4MJDUbpBEemJyTYiIiIiKncc3kNERNo8fPhQo0wIgTkhChjp+KPjyZMnpRsUkZ6YbCMiIiKichcXF2foEIiI6AVx9J5A6ENAzg5r9IJgso2IiIiIiIiIKoSGDRtqlM09pXuvNgCQyWSlGBGR/phsIyIiIqJy5+PjY+gQiIioArpx44ba+xMxAqfv69erzdiYa0GSYTHZRkRERETlLiYmxtAhEBFRBZSZman2fu4puV692gAgLS2tFCMi0h/TvURERERU7lJTUw0dAhERVUDW1taq/4fECpyINWAwRMXEnm1EREREVO7MzMwMHQIREVVArq6uqv/PO61/rzYAsLKyKsWIiPTHZBsRERERlbu6desaOgQiIioDTzIFxh2Sw/HHXFh+l4vAzXJcfKj7hGtRUVEAgLP3BY7eK94KpNnZ2frvRFSKmGwjIiIionIXERFh6BCIiKiUKYTAKzvk2HhNYEITKRZ1kOJRukDHzXLcSNYvazbvtBzGxejVBjDZRobHZBsR0XPu37+PPn36QCKR4M6dOwCAlJQUBAUFQSKRIDg4uNTaCg4OhoODAzp27Fhqx6zszp8/Dy8vL9SsWdPQoZRYZmYmVq1ahQYNGsDS0hISiQTz5s3Tad/vv/9er/ql4c8//4REIkFQUFC5tUlEREQvlm3X81YOXdtDirmtpRjfRIrgIUYwkgBzTyl0OkaNGjVwPl7g4B0gtxi92gBAIilmlo6olHCBBCIyiCdPnuDixYtITExEWloaFAoFrKys4O7ujkaNGsHCwsLQIZaK69ev47vvvkP37t3Rr18/Q4dDFUhISAhmzZoFqVSKqVOnMvFKL53q1asbOgQiIipl26IFnCyA1zz/S3Y5Wkgw2EuCDZECWbkCsiK6q8nlcnz671xtxRlCCgC2trbF25GolDDZRkQGkZaWhvT0dNSqVQuWlpaQSqV4/Pgxrl27hps3b2LAgAEwNzc3dJgA8iZYXbFiBZYtW6b3hN7R0dFYvnw5MjIyNJJt7du3x927d2FiYlKK0dKL4tKlS0hISMDixYsxZcoUQ4dDVO74vY+IqPK59EigqZME0nw9y1o4S7DyskB0MtDQsfBjHL+ejD23qpYojqdPnwKoUqJjEJUEk21EZBCurq5qKw0pOTs748iRI7h+/ToaN25c/oFpIZFIIJPJSv24UqkUlpaWpX5cKj3Z2dnIyspSW4K+tDx8+BAAUKVK5fpFsCyvGVUuMTExqFq1ZB+miIioYnmQBrSvoVnu/O+vvPdTBRo6Ft6zbeXtajCWFH8IKQAIUYKdiUoB52wjogpFuUx3RZrUtKA5265cuYIhQ4bA2dkZMpkMlpaWqF27Nj744AMAefNq9e3bFwCwbt06SCQS1QvQPmebsqxDhw7YsGEDGjduDDMzM1haWqJPnz6Ij49Xiy0zMxO//PILfH19IZPJYGNjgyFDhuC3334r1vxaynm5Ro4ciSVLlsDb21t13NGjRyM1NVVVt7D507TNKTZ16lRIJBJ8++23ePvtt1G9enWYmpqiXr162LNnD7KysrBmzRr4+fnB3NwcNjY2ePfdd5GTk6NxfCEEtmzZgqZNm8LCwgIWFhbo0aMHbt26pVE3JiYG48ePR61atSCTyWBmZoYWLVrg+PHjWmP+8MMPMXPmTNSqVQtWVlaYOHGiztcvJycHf/zxB9q1awcbGxuYmJjAxcUF06ZNQ3p6utp1W7x4MQDg9ddfVz0XynkCdZWeno7p06fDzc0NJiYmcHJywqxZs5Cbm6tWTy6X49ChQ+jWrRuqVq0KY2NjVK1aFaNHj8ajR4/U6mZlZWHdunVo2LAhZDIZrK2tMWDAADx58kSj/aKu2YMHDzBp0iTUrFkTpqamsLS0RJs2bRASEqJxLF3rKp/RESNGYNGiRahbty5kMhkcHR2xcOFCCCFw4sQJBAYGwsbGBjKZDF27dsX9+/dVxxBCICQkBL1790a1atVgYmICa2treHl5YdGiRXrdAyIiospKIQQyc3V7KZNbGbmAzEjzWGb/dvPJyNXc9ryIBIHjCTYlSrQBgKmpackOQFRC7NlGRAaVm5urej158gR///03AMDNzU2n/bOysnT+y5WJiQmMjLT89C+G6OhovPvuuzh16hT69u2LJk2aID09HZGRkaokTteuXTFhwgQsXboU/v7+GDJkCADA3t6+yOPfuHEDkyZNQteuXdGzZ0+cPXsWe/fuxciRI3HkyBEAeQmUn3/+GdOnT4dEIsHo0aNhaWmJ48ePIzQ0tETnFxISgt27d2PAgAF49dVXcfz4caxfvx4ymQyrVq0q0bGXLFmCnJwcDBs2DCkpKdi1axf69++PKVOmYMWKFXjllVfQo0cPHDlyBMuXL4erqytmz56tdoynT59i/PjxaNKkCSZNmoSwsDAcPXoUAQEBCA8PV80Fde3aNQQFBeH8+fPo0qULRowYgYSEBBw4cABdu3bFjh07VAlRpU2bNuHJkycYNmwYqlevjvr16+t0XgqFAmvXrsXUqVORmZmJoUOHolq1ajhx4gQWL16Ms2fPIjg4GB4eHvjoo4+wZcsW7N+/H0OGDIG/vz9sbW3h6FjEuIp8Nm/ejKSkJAwfPhzm5uY4fPgwFi5ciNjYWKxbt04V17p16zB16lQIITBw4EC4uLggLCwMW7duRUhICC5dugRbW1soFAqsWbMGU6dOhUKhwMiRI2FtbY0jR45gzpw5Bcah7ZrdvXsXb7/9Ng4ePIgGDRpg8ODBiI2NxYEDB9CxY0fs3r0bvXr1AgC96iqFhIRgz549GDx4MKytrbF3717MmjULDx8+xJ9//gkPDw9MmDABFy9exOHDhzFy5EgcO3YMQN4Q3rFjx+Kff/7BkCFD4OnpiadPnyIiIgLBwcH48MMP9boPVDxeXl6GDoGIiApxMkYgcItuixpce90I3lUBc2MgS665PfPfJJt5ERmIBWcVMJIIyEXJFjjgVAVkcIKIyICuXLkiVqxYoXpt3LhRREdH67z/b7/9prZ/Ya+oqKgijxcXFyd69+4tAIjbt28LIYR49uyZGDNmjAAgjh8/LoQQ4uDBg8LS0lL06NGj0OPt3r1bABBjxozR2Hb8+HFRtWpV0aFDB40yAGLz5s2q8ps3b4oOHToIAKrziIiIEM2aNRMAREhIiKru8+Xa2tUlXolEIs6ePasqDwsLE76+vsLY2FikpKQIIYQIDQ0Vnp6ewt3dXeM43333nQAg5s6dqyqbMmWKACCqV68unjx5IoQQIjc3VyxYsEDV5vbt21X1T58+Ldzd3YWTk5OqTNkmADFp0iRVeUpKihg/frwAIN5++20hhBBpaWnivffeEwDEihUr1OI7ceKEqFmzpnB3dxcKhUItZmNjYxEZGanXdRNCiMjISNGiRQsBQOzdu1dVfvfuXdG1a1cBQPz8888a12PNmjV6t6WMVSqVigsXLqjKw8LCROPGjQUAcebMGSGEEOHh4aJRo0aiatWqIiYmRlU3KytLzJ8/X0ilUtV9ioyMFM2bNxcAxLFjx1R1w8PDVcd9/pkq6JrJ5XLx1VdfCYlEIgICAkRWVpYQIu9+r169Wpibmws3NzeRm5urV10hCn5G9+zZI2xsbIREIhETJkxQld++fVt07txZ7Wtn3bp1AoB499139b72+SUlJYnMzEzV+5SUFPHs2TPV+6ysLJGYmKi2z/379wt9/+DBA9VzWZnbuHbtWqU4D7bBNtgG26isbTxIVYhv/0oSa67IVa/vQh6L1ZdzVe+XnH0mVl7KEk8y89qtsyJLdN2UqdHGz5flAl/liMuPFIWex+SjuUL6VbbAVznFfkm+yhGd1yaU67ViGxW/jfLGnm1EZFC1atWCnZ0dcnNzkZiYiLt37yIzM1Pn/Tt16qQxZK4gpTk3lqmpKczMzHD58mVcuXIFDRs2LLVjA3lz1w0ePFj13sXFBd7e3jhx4gRu3LgBLy8v3L59GxEREWjevDnatGmjquvt7Y1evXrhwoULxW6/VatWaNmypUb7ERERuHPnDnx9fYt97DfeeEO1QpSRkRH8/f1hZmaGKlWq4LXXXlPVq169OurWrYtjx44hNTVVNcRY6aOPPlL938rKCoMHD8a2bduwc+dOLF++HLdu3cKZM2dgZWWFHj16IDY2VlXfzs4ODRs2xJ49e3Djxg14enqqtvXq1Qs+Pj56n9etW7cQHh6Oxo0bq/XCcnd3x/Dhw3HkyBHs3LkTb775pt7HLkiXLl3QtGlT1XtfX1/06tULYWFh2LlzJ1q1aoXw8HBcvnwZY8aMAQC16+Dn5wdHR0ccPHgQ8+bNw61bt3D58mU0a9YMgYGBqnoNGjRQHVeb/NcsMTERZ86cgRACM2fOVA3lMDIyQmBgIFq3bo2jR4/i0qVLcHd317muv7+/qo38z6ibmxs8PDwQHh6OyZMnq8qdnZ1Rr149HD16VPW1Y2VlBWNjY4SEhODOnTuoVatWMa5+nvzfV/I/p6amphrzkjk7Oxf6Pv8qnZW1jczMTLW5/V7U82AbbINtsI3K2kZ1Swneb5v/9+f8IzTU52htUt0If8UKKISAVCJRtfH3BTksjAFPe0BmXPB5fNhCih8vKaAo4TBSSb4FGirD/WAbJWujvDHZRkQGZWVlpfrmWatWLXh4eGDnzp3Izc1FkyZNitw//zfm8tKkSROMGTMGy5Ytg5+fH5ydndG6dWsMHToUr732GqTSkk2JWadOHbX3ynnbACApKQkAEBcXh6ysLHh7e6vVNTIyQt26dcu8/eKqXbu22vvn57vLX25hYaFq8/kfsra2thr33t7eHs7OzggLC0NaWhqePHmC+Ph4pKamap1XTunhw4dqybbiDm1T3g9tiUh3d3fY2tri5s2bxTp2QfIPcTUyMlLFr5y/LioqCkIIrF27FmvXrtV6HOV1jo2NRVZWlkay8fnjapN/W0pKChITEwHkJeqeZ29vDxcXF1WM9vb2Otd9PtmW/xl9/nnx8PDQWq58dtu0aYORI0diw4YN8PDwQM2aNdG+fXuMHDkS3bp1K/A8qXSVxcIzRERkWAM9JdgWLbAjWmCgV17CKzFdYOt1gT51JJAZFz481MVKgrcaAiuvAPISJNy4CBkZGpNtRFShVK1aFQ4ODoiMjNQp2ZaRkaHznG2mpqYwNi6db3u2trZYtGgRxowZg+3bt+P06dMIDg7G9u3b0bp1axw/frxEE7MWNrecrudbErq0n/8vhs8rrLdhQccu7XMWIm+y3ipVqmDp0qVa61hYWGgkx5SJmcrmtddew8CBA7VuUya0issQ16yw56WgbcrnyMnJCStXrsTbb7+NnTt34syZM9i1axfWr1+PgQMHYuvWrWUSM6njnG1ERJXPQE8JWjkDrx9QIDIJcDAHloUpIBfAp210+2P0qzbXsUriDZTgV968BbbMin8AohJiso2IKpzc3FxkZWXpVHfnzp1qK2QWpkOHDqX64c7IyAh+fn7w8/MDkDfJ+7hx43Do0CHs2rULgwYNKrW2tHFxcYFMJkNUVJRauUKhwD///FOmbQP/9Uh7fpVHIO/+xcTElGnbT58+RXx8vFrvtuTkZDx48ACOjo6wtLSEra0tqlWrhoiICPTp00ej+3lpc3V1hZmZGSIiIjS2xcTE4OnTp2rDfUtDZGSk2nu5XI7r168D+K8HYd26dSGRSGBsbIxhw4YVejxXV1fIZDJcu3atwOPqwtraGg4ODgCAq1evqvVCS05OVj0ztWvX1qtuaTIxMUGrVq3QqlUrAMDly5cxduxYbNu2DaGhoWjevHmptkeaLl++jMaNGxs6DCIiKkVGUgn2DTDC9BMK/HBJgYwcoHl1YG1PI3hV0W3RAyezXLzZUIKfL4ti927Lzs4u3o5EpaRk45yIiIopPT1da/n9+/eRnJyMatWq6XScTp06oVevXjq9dF3hVBfKYYrPc3R0hLu7OwDg8ePHAPI+0MtkMtX70lS7dm00aNAAoaGhOHXqlKr82rVr2LdvX6m3l5+trS2cnJyQmpqKc+fOqcrDw8NVK6aWpc8//1z1/9TUVGzZsgUPHz5E//79AeRdn9atW0OhUGDGjBka+2dnZ2skCkuidu3a8PPzQ1hYGA4cOKAqv3fvHjZu3AghhCq20nLkyBFcvHhR9T4iIkJ17/v16wcAaNq0KXx9fbFjxw6cPXtW4xhpaWlISEhQO4cLFy6oVtUF8pJg+jxTDg4OCAgIgEQiwZdffvnvX5fzEsHBwcE4c+YM3Nzc0KRJE73qlpbHjx9rfA9ycnJCjRo1VNuJiIioeOzNJPi5uxESxxsj7X1jBA81hn913VcXdXFxwUctmKqgFxt7thGRQYSEhCA9PR0uLi6wsrKCXC5HYmIibt68qepxogtDzdm2fft2vPXWW2jfvj38/PxgY2ODy5cv4+DBg7C1tUWfPn0AANWqVUPt2rVx6NAhTJ8+HS4uLqhatSpGjx5d4hi8vb0xevRoREZGomvXrhgxYgSsrKxUCwoAhQ/1LClXV1f06tULhw8fRu/evTFq1ChkZmbi6NGjOi9aUVw2NjbYuHEjoqKi0KRJE4SFheHo0aNwdHTEvHnzAOTNB/j+++/j8uXLWLZsGUJCQtC5c2dYWlri7t27uHDhAtLT03H79u1SicnLywtjx47FtWvX0LdvXwwbNgyOjo44efIkQkND0aZNG9UiBaWlRo0aaN++PUaMGAEzMzMcPnwY165dw4gRIxAQEAAAaNiwIaZOnYopU6agXbt26NOnDxo0aICMjAxER0fj3LlzeOeddzBv3jx4eXkhKCgIERER6NWrl+qZOnLkCNLS0nSOSyqVYtCgQThy5AgOHjyIZs2aqRapOHDgALKysrB8+XLVcE996paGH3/8EV988QU6deoEHx8fmJmZ4cKFCzhy5Ahq1qyJdu3alVpbVDBd/6hCREQvF4lEgpq2EgT5SrAuQiC3GL3b7OzsSj0uIn0w2UZEBlGnTh3cuHEDN27cUK0+amVlBR8fHzRq1KjMh/yVVKNGjdC3b19cvHgRZ86cQVZWFqpUqYLevXvjs88+U82B5efnh1mzZuGLL77A0qVLVedaGsk2IyMjjBs3DpaWlvj222/x66+/wtTUFL169ULHjh3x3nvvwdzcvMTtFEQqlWLUqFF4/PgxfvnlF/zwww+wsbHBmDFj4OLigunTp5dZ27a2tvj666/xxRdf4PvvvwcABAYGYtmyZWorD3l5eWH79u345ptvsGvXLvz000+Qy+Wwt7eHr69vqa4MKpVKERQUBEdHRyxevBg7d+5Eeno6qlWrhqlTp2L+/PmlNmeg0pAhQyCEwKZNmxAfHw97e3vMnDkT8+fPV4tr1KhRqF27NhYvXozTp0/jzz//hKmpKZycnPDKK6+oVr41MjLCG2+8AXNzcyxevBjr16+HqakpunXrhn79+un13NasWROrV6/G559/jt27d+Pbb7+FiYkJGjdujC+//FItoaVP3dLQrl07hIaGIjw8XJUcdnR0RFBQEObNm1dp5+2raMry+xMREb244uLi4OjoiI9bSrEmQl6sYzx9+hRA/pVUicqPRJTHTNtERFRuFAoF5s+fj08//RSff/45Zs6caeiQiIg0hIWFcc42IiLS8PzPhzH75Nh4Tb/ebRIAnas9xeHRVcskPiJdcCA0EdELLDc3FxkZGWplkZGR2LNnDwCga9euhgiLiIiIiKhYfHx8VP+f1UparEUSTExMSjEiIv1xGCkR0Qvs5MmTGDNmDAICAuDl5YVHjx7h2LFj+OeffzBo0CA0a9YM2dnZePDgAeTygrvhS6VSODg4VPjhu+VNoVAgOTn536EIBZPJZHB1dS1RW5mZmUUu2GBsbAxHR0cOv6NKoV69eoYOgYiIKqCYmBjUrVsXAOBZRYKh3hJsva5f7zaZTFZG0RHphsk2IqIXWI0aNeDv74+QkBDs3r0bCoUCzs7OmDNnDmbPng0AOH36NAYOHIikpKRCjzV37lzV4gKUJy0tDVOnTsW6deuKrFvSWRkOHz6Mvn37FllvzZo1CAoKKlFbRBVBQkICLC0tDR0GERFVMMqFvpQ+CZBiU5R+c7flHYMJNzIcJtuIiF5gnp6e2LlzZ6F1GjVqhFWrVqkWZ9DGxMQETZs2Le3wXnjm5uaYOHEiunfvXmg9W1vbErfVqlUrbNy4sdA6ZmZmOq/US1TRPXnyxNAhEBFRBWRmZqb23qeqBAM9Jdh5o3grkxIZAhdIICIiIqJyd/XqVTRo0MDQYRARUQWTm5ursYJ7RIJAw3W69W6TAOjrkYs/BpgVWZeorHCBBCIiIiIqd0y0ERGRNhERERplvo4S9K8LGEl0O0Zubm4pR0WkHybbiIiIiKjchYeHGzoEIiJ6gcxpbaTTyqQCQFZWVpnHQ1QYztlGREREROWOM5kQEZE21atX11reuJoEo+tLEBxT9M+Pbk5PAVQt5ciIdMc524iIiIio3MXGxqJGjRqGDoOIiCohIQQkEh3HnBKVAQ4jJSIiIqJyZ21tbegQiIiokrp27ZqhQ6CXHJNtRERERFTubt++begQiIioksrOzjZ0CPSSY7KNiIiIiIiIiCoNGxsbQ4dALznO2UZERERE5S4lJYVDSYmIqEykp6fDwsLC0GHQS4w924iIiIio3CUnJxs6BCIiqqSio6MNHQK95IwNHQARERERvXweP34Md3d3Q4dBREQvmdNxAlcSCx/gJwEwwkcCS1OuaErFw2QbEREREZU7IyMjQ4dARESVVEF/zMnKFRiwW474tKKP8c8TCRZ14M8qKh4OIyUiIiKictewYUNDh0BERJVUVlaW1vJ1V4VOiTYTKXDvQWIpR0UvEybbiIiIiKjcXblyxdAhEBFRJfXw4UONshy5wPwzCnBgKJUHDiMlIiIionInl8sNHQIREb1E1kcKxKXqXt/M3KzsgqFKjz3biIiIiKjcValSxdAhEBFRJZV/qoJchcCnp/Xr1WZiYlK6QdFLhck2IiIiIip39vb2hg6BiIgqqRs3bqi933hN4F4KUPgapOpSnqWUblD0UmGyjYiIiIjK3c2bNw0dAhERVVKZmZmq/8sVAvP07NVGVFKcs42IiIiIiIiIKg1ra2vV/zdfF7j9VP9j2NralmJE9LJhzzYiIiIiKnceHh6GDoGIiCopV1dXAIBC5PVqK07i4/necUT6YrKNiIiIiMpdSgrnwiEiIu2eZAqMOySH44+5sPwuF4Gb5bj4UPcZ16KiogAA26MFbiQDimLEkJWVVYy9iPIw2UZEL5379++jT58+kEgkuHPnDoC8D31BQUGQSCQIDg4utbaCg4Ph4OCAjh07ltoxK7vz58/Dy8sLNWvWNHQoJZaZmYlVq1ahQYMGsLS0hEQiwbx583Ta9/vvv9erfmnQ9rVBVFYSExMNHQIREVVACiHwyg45Nl4TmNBEikUdpHiULtBxsxw3knVPuCmEwNxTCkiLOVmbRMJZ3qj4OGcbEZWZJ0+e4J9//kFsbCyePXsGuVwOGxsbeHh4oGHDhpVmOe3r16/ju+++Q/fu3dGvXz9Dh0MVSEhICGbNmgWpVIqpU6dWiMRrSkoKNm7ciJs3b+Ljjz+GnZ2dQeOhlxc/xBARkTbbrgucvg9s7SPFQK+8/kGDvSTw/EWOuacU2NjbqMhj1KhRA7v+Ebj2uPhxVK1atfg700uPyTYiKjPXr19HZGQkatasibp160IqleL+/fs4f/48bt26hX79+sHYuGJ8G7KyssKKFSuwbNkymJmZ6bVvdHQ0li9fjoyMDI1kW/v27XH37t1Kk1gk/Vy6dAkJCQlYvHgxpkyZYuhwAOQl2/bs2YM9e/bgvffeY7KNDKZRo0aGDoGIiCqgbdECThbAa57//VHG0UKCwV4SbIgUyMoVkBkX/geb3Fw55vzbq02he2c4NYlJSQCqFW9neulVjE+5RFQp1a5dG02aNIGpqamqrH79+ggNDcWlS5cQFRUFX19fA0b4H4lEAplMVurHlUqlsLS0LPXjUunJzs5GVlaW2qpVpeXhw4cAgCpVqpT6sSuqsryeVLlcvXoVDRo0MHQYRERUwVx6JNDUSQJpvh7QLZwlWHlZIDoZaOhY+DE2X0lFRGIJe6aJYmbpiMA524ioDDk6Oqol2pTq1KkDAEhOTi7vkApU0JxtV65cwZAhQ+Ds7AyZTAZLS0vUrl0bH3zwAYC8ebX69u0LAFi3bh0kEonqBWifs01Z1qFDB2zYsAGNGzeGmZkZLC0t0adPH8THx6vFlpmZiV9++QW+vr6QyWSwsbHBkCFD8Ntvv0EikSAoKEivc/3zzz8hkUgwcuRILFmyBN7e3qrjjh49Gqmpqaq6hc2fpm1OsalTp0IikeDbb7/F22+/jerVq8PU1BT16tXDnj17kJWVhTVr1sDPzw/m5uawsbHBu+++i5ycHI3jCyGwZcsWNG3aFBYWFrCwsECPHj1w69YtjboxMTEYP348atWqBZlMBjMzM7Ro0QLHjx/XGvOHH36ImTNnolatWrCyssLEiRN1vn45OTn4448/0K5dO9jY2MDExAQuLi6YNm0a0tPT1a7b4sWLAQCvv/666rnQdy609PR0TJ8+HW5ubjAxMYGTkxNmzZqF3NxcVZ3s7Gxs3boVPXr0gKurK8zMzCCTyeDt7Y0VK1ao6l2/fh0DBw7Enj17AOStBqmMK//ccNeuXUP//v1hb28PExMTeHp6Yt++fWp1irqeDx48wKRJk1CzZk2YmprC0tISbdq0QUhIiMZ56lpX+fyOGDECixYtQt26dSGTyeDo6IiFCxdCCIETJ04gMDAQNjY2kMlk6Nq1K+7fv686hhACISEh6N27N6pVqwYTExNYW1vDy8sLixYt0uv+UPFp+7onIiJ6kAY4a/lbtbLsfmrhSTAhBH66Wa3Yc7UpyfQc7UL0PPZsI6Jyp0zmmJub61Q/KysLQse/LJmYmMDIqOh5HHQRHR2Nd999F6dOnULfvn3RpEkTpKenIzIyUpXE6dq1KyZMmIClS5fC398fQ4YMAQDY29sXefwbN25g0qRJ6Nq1K3r27ImzZ89i7969GDlyJI4cOQIAkMvl+PnnnzF9+nRIJBKMHj0alpaWOH78OEJDQ0t0fiEhIdi9ezcGDBiAV199FcePH8f69eshk8mwatWqEh17yZIlyMnJwbBhw5CSkoJdu3ahf//+mDJlClasWIFXXnkFPXr0wJEjR7B8+XK4urpi9uzZasd4+vQpxo8fjyZNmmDSpEkICwvD0aNHERAQgPDwcFSvXh1AXlIoKCgI58+fR5cuXTBixAgkJCTgwIED6Nq1K3bs2KFKiCpt2rQJT548wbBhw1C9enXUr19fp/NSKBRYu3Ytpk6diszMTAwdOhTVqlXDiRMnsHjxYpw9exbBwcHw8PDARx99hC1btmD//v0YMmQI/P39YWtrC0fHIv4Um8/mzZuRlJSE4cOHw9zcHIcPH8bChQsRGxuLdevWAchLyO3btw/R0dHo3r07atSogYcPH+Lw4cN455138PjxY3z00UdwdnbGm2++iYyMDISFheHjjz+Gvb09TE1NNeaSmzRpEqysrDBu3DjEx8dj165d6NevH6Kjo1GrVq0ir+fdu3fx9ttv4+DBg2jQoAEGDx6M2NhYHDhwAB07dsTu3bvRq1cvANCrrlJISAj27NmDwYMHw9raGnv37sWsWbPw8OFD/Pnnn/Dw8MCECRNw8eJFHD58GCNHjsSxY8cA5A3vHTt2LP755x8MGTIEnp6eePr0KSIiIhAcHIwPP/xQr3tExcMhzERElZ9CCGTLdasrM8obbZKRm/f//Mz+zV5k5Gpue96B2wLXU3X7nFFoPFo6DRDpisk2IipXCoUCly5dgkQiQd26dXXaZ/v27Wq9rQrToUMHeHl5lSRElTt37iAsLAw9evTArl27tNapX78+unXrhqVLl6JBgwaYNm2azsd/8OABNm/ejMGDBwMAbt26hTfeeANHjx7F9evX4eXlhaioKKxduxaZmZkICQlBmzZtAOQNvxozZgxu375d7PO7d+8ezpw5g5YtWwIAwsPDMXLkSKxduxbffvstrKysin3sjIwMREVFwdbWFnK5HB4eHpg9eza++uorbNu2Da+99hoA4MyZMxg6dCiWLl2qkWx79uwZJk2ahO+//x5AXpJ25syZ+PHHHzFv3jwsX74c6enpWLp0Kc6dO4cVK1Zg3Lhxqv1PnjyJ0aNHY+LEiaoVNpUePHiAy5cvw8fHR6/zun79On7++WekpKRg7969qgTQvXv3MHbsWBw+fBjr1q3Dm2++iaCgIFy5cgX79+9Hjx499O6BqBQbG4vQ0FA0bdoUQN59CgoKwq+//op3330XrVq1grW1NX744QeNoZt///03Ro8ejc8//xzTpk2DjY0NevbsiT/++ANhYWF466231BJnz/f+qlevHvbu3QuJRILc3Fx4eHjg008/xYoVK/D555+rtZP/eioUCnzzzTc4dOgQAgICEBwcDFNTU8jlcvz6668YP3483nnnHdy+fRsSiQRbt27Vqe7zifSYmBi157dz584YPnw4lixZgvHjx2PJkiUA8r6Ox44dq/Z1FRERgevXr+Pdd9/FsmXLinVfqOT0TTwTEdGL52SMQOAWhU51r71uBO+qgLkxkKUlQZf5b5LNvIgsxqrLCkghoEDJurY9e/YMQMmTdvRy4jBSIipXZ86cwcOHD+Hv769zr4ZOnTqhV69eOr3c3NxKLVZTU1OYmZnh8uXLuHLlSqkdV8nZ2VmVaAMAFxcXeHt7A8jr9QYAt2/fRkREBJo3b65KtAGAt7e3Rk8ffbVq1UqVqHi+/dzcXL2HOub3xhtvwNbWFgBgZGQEf39/mJmZwdnZWZVoA4Dq1aujbt26ePjwodaE6kcffaT6v5WVFQYPHgwnJyfs3LkTQF6C8syZM7CyskKPHj0QGxuretnZ2aFhw4a4d++e6noq9erVS+9Em7K98PBwNG7cWO36u7u7Y/jw4ZBIJKrYSkuXLl1UiTYA8PX1VbWtbMvIyEiVaFMoFHj69Cni4uJgaWkJHx8fpKSkICoqSq92P/jgA1WC0tjYGK1atYKZmZnGtQQ0r2diYiLOnDkDIQRmzpypGk5uZGSEwMBAtG7dGjExMbh06ZJedZ+X//l1c3ODh4cHhBCYPHmyqtzZ2Rn16tUD8N/XlZWVFYyNjRESElLiZ/3x48fIyspSvU9NTUVKSorqfXZ2NpKSktT2efDgQaHv4+Pj1XrzVtY2oqKiKsV5sA22wTbYBtsouI3a1rlY0jYda3pIVa9vWz3T+t7537/zVjOT40GaZhsP0vLe20kzCj2PId7SEifaAIHnx9VUlvvxMrdR3tizjYjKTWhoKK5evQpvb280adJE5/2UwwXLW5MmTTBmzBgsW7YMfn5+cHZ2RuvWrTF06FC89tprkEpL9vcK5dx1Ssp52wCofnjExcUhKytLlYRTMjIy0rlnYEnaL67atWurvX9+vrv85RYWFqo2n+9NZ2trq3Hv7e3t4ezsjLCwMKSlpeHJkyeIj49Hamqq1nnllB4+fAhPT0/V++L2flTeD20Le7i7u8PW1hY3b94s1rELkn+Iq5GRkSr+5+evCw0NxWeffYaQkBCt8yHqO0di/nulfD60PRv5r2dKSgoSExMBQGMCfHt7e7i4uKjit7e317muv7+/alv+5/f5Z8nDw0NruTL2Nm3aYOTIkdiwYQM8PDxQs2ZNtG/fHiNHjkS3bt0KvCba5F/8In+PUFNTU1Stqj5Bs7Ozc6Hv8z/3lbWN55PEL/J5sA22wTbYBtsouA13e1NMaJV/OGb+haPU3zdzNsZfsQIKISCVSFRt/P1ADgtjoLGrhdpqpPnPY6CnBO6WOYhJM1FLmOlHAlsbmwLbAF7M+/Eyt1HemGwjonJx/vx5XLp0CZ6enmjXrp1e+2ZkZOg8Z5upqSmMjUvnW5utrS0WLVqEMWPGYPv27Th9+jSCg4Oxfft2tG7dGsePH9e6AISuCptbTtfzLQld2n9+6GV+z0/Qr+uxS/uchRAQQqBKlSpYunSp1joWFhYayTFl8qWyuHz5MsaOHYsrV65g8ODBaN26NapUqYLc3Fxs2LABR48ehUKh2xAOpYLulbb7ZIjrWdizVFTsTk5OWLlyJd5++23s3LkTZ86cwa5du7B+/XoMHDgQW7duLZOYSV1hCXIiInp5DfSUYFu0wI5ogYFeeb+LJqYLbL0u0KeORC3Rpo2RVIL5bYwQdKhkcWRnZ4PDSKm4mGwjojJ3/vx5XLx4EZ6enujQoUOhCRxtdu7caZA524C8D+1+fn7w8/MDkDeR+7hx43Do0CHs2rULgwYNKrW2tHFxcYFMJtMYAqhQKPDPP/+UadvAfz3Snp/LC8hLtMXExJRp20+fPkV8fLzaX7qSk5Px4MEDODo6wtLSEra2tqhWrRoiIiLQp0+fEs0zpwvlSp8REREa22JiYvD06VO14b6lITIyUu29XC7H9evXAfzX+ywsLAxXrlzBqFGjVIsmKGPatGlTqcajC2trazg4OADIm1/w+V5oycnJquepdu3aetUtTSYmJmjVqhVatWoF4L+E5bZt2xAaGormzZuXanukKSMjQ6fFZIiI6OUy0FOCVs7A6wcUiEwCHMyBZWEKyAXwaRvdRpY0yI2Eh60v7jxFsXu3ZWZmArAt5t70suOcbURUpi5cuICLFy+iXr16xUq0AYabs005TPF5jo6OcHd3B5A3lwCQ96FdJpOp3pem2rVro0GDBggNDcWpU6dU5deuXcO+fftKvb38bG1t4eTkhNTUVJw7d05VHh4erloxtSw9PxF/amoqtmzZgocPH6J///4A8q5P69atoVAoMGPGDI39s7OzNRKFJVG7dm34+fkhLCwMBw4cUJXfu3cPGzduhBBCFVtpOXLkCC5evKh6HxERobr3/fr1A/BfT67ne68pFAocO3YMISEhasczMTGB2b9L2ZfFMwsADg4OCAgIgEQiwZdffomcnBxVTMHBwThz5gzc3NzQpEkTveqWlsePHyM9PV2tzMnJCTVq1FBtp7L36NEjQ4dAREQVkJFUgn0DjDDEW4IfLikw/YQCDubAscFG8Kqi22cJYykwN0BagmGkQImnfaOXGnu2EVGZuXr1Ki5cuAArKyu4urpq9MQyNzdXfbgtjKHmbNu+fTveeusttG/fHn5+frCxscHly5dx8OBB2Nraok+fPgCAatWqoXbt2jh06BCmT58OFxcXVK1aFaNHjy5xDN7e3hg9ejQiIyPRtWtXjBgxAlZWVjh27Jiqt19xEpi6cnV1Ra9evXD48GH07t0bo0aNQmZmJo4ePVroMNLSYGNjg40bNyIqKgpNmjRBWFgYjh49CkdHR8ybNw9A3vwN77//Pi5fvoxly5YhJCQEnTt3hqWlJe7evYsLFy4gPT29RKu2Ps/Lywtjx47FtWvX0LdvXwwbNgyOjo44efIkQkND0aZNG4wZM6ZU2lKqUaMG2rdvjxEjRsDMzAyHDx/GtWvXMGLECAQEBADIm+vMx8cHv/32m2qOv8jISBw+fBgODg5IS0tTHc/e3l61YMCECRPw6quvwtraGu3bt9eYH6O4pFIpBg0ahCNHjuDgwYNo1qyZagGLAwcOICsrC8uXL1clCfWpWxp+/PFHfPHFF+jUqRN8fHxgZmaGCxcu4MiRI6hZs6beQ92JiIiodNmbSfBzdyP83L14+7u4uKBBVQnmnAJiUorXu82hqkPxGicCk21EVIYSEhIA5PVICg4O1tju7OysU7LNUBo1aoS+ffvi4sWLOHPmDLKyslClShX07t0bn332mWridj8/P8yaNQtffPEFli5d+m+Xc5RKss3IyAjjxo2DpaUlvv32W/z6668wNTVFr1690LFjR7z33nswNy+7uSSkUilGjRqFx48f45dffsEPP/wAGxsbjBkzBi4uLpg+fXqZtW1ra4uvv/4aX3zxBb7//nsAQGBgIJYtW6Y24amXlxe2b9+Ob775Brt27cJPP/0EuVwOe3t7+Pr64s033yy1mKRSKYKCguDo6IjFixdj586dSE9PR7Vq1TB16lTMnz+/1OYMVBoyZAiEENi0aRPi4+Nhb2+PmTNnYv78+ao6fn5+WLJkCRYsWIBDhw7hjz/+gJubGz799FOEhYWpDS01NjbGO++8g5iYGBw4cAAff/wxFAoF5s6di3HjxpVa3DVr1sTq1avx+eefY/fu3fj2229hYmKCxo0b48svv1RLaOlTtzS0a9cOoaGhCA8PVyWOHR0dERQUhHnz5lW6Of0qKuXwfCIiotImkUhgYiTBnNZSjD2o37y1SkmPHwNwLN3A6KUhEeUxCzcREZUqhUKB+fPn49NPP8Xnn3+OmTNnGjokIiK9XLt2DT4+PoYOg4iIKqGwsDA0btwY2XKB2qvkuJ+qX+82EynwmksSNg11KrMYqXLjnG1ERBVcbm4uMjIy1MoiIyOxZ88eAEDXrl0NERYRUYlkZWUZOgQiIqrkTI0k+KSYc7eZymSlHg+9PDiMlIiogjt58iTGjBmDgIAAeHl54dGjRzh27Bj++ecfDBo0CM2aNUN2djYePHgAuVxe4HGkUikcHBzKfMXOF41CoUBycjKePn1aaD2ZTAZXV9cStZWZmVnkgg3GxsZwdHQs0+HBRBWBjY2NoUMgIqJK6vme00ENJJh3GohPK2QHLcz/XVCKqDiYbCMiquBq1KgBf39/hISEYPfu3VAoFHB2dsacOXMwe/ZsAMDp06cxcOBAJCUlFXqsuXPnqhYXoDxpaWmYOnWq2rxmBSnpzAuHDx9G3759i6y3Zs0aBAUFlagtooru+bkXiYiISlNMTAzq1q0LAJAZSzC7lRQTjyr06uGW94dYJtyoeDhnGxFRJZCcnIzg4GDV4gzamJiYoGnTpqhdu3Y5Rlbx5ebmIjw8HNHR0YXWs7W1Ra9evUrUVkJCAo4cOVJoHTMzM7Rq1YqJCKr0lPPpEBERlbb8P2MycwXcV8iRkFHwPs/jnG1UUky2EREREVG5Y7KNiIjKSlRUFLy9vdXKvrugwJTjuvVuM5ECY7yzsaoXVyin4uECCURERERU7tzc3AwdAhERVVLKIaTPG+cngb0eo0JzcnJLMSJ62TDZRkRERETlLicnx9AhEBFRJRUREaFRZmEiwUctpZDoeIzMDB3HnBJpwWQbEREREZW7+Ph4Q4dAREQvmXcaSWArA4wkeUNFC3rlKgAjCWfcouLjaqREREREREREVGlUr15da7mVqQTrekoRGl94Ik0CYGKTamUQGb0suEACEREREZW73NxcGBvz775ERFQxRUZGon79+oYOg15QHEZKREREROXun3/+MXQIREREBcrOzjZ0CPQCY7KNiIiIiMpdZmamoUMgIiIqkI2NjaFDoBcYk21EREREVO6srKwMHQIREVGBCpr3jUgXTLYRERERUblzc3MzdAhEREQFio6ONnQI9AJjso2IiIiIyt21a9cMHQIRERFRmeASUEREREREREREz3F3dy9w29TjcoTGC63b6thJsLyrFDJjSVmFRi8AJtuIiIiIqNy5uroaOgQiIqICZWVlaS2PSBD45oL2RBsA/BUnMNUf8HUsq8joRcBhpERERERU7oQo+IMKERGRoT18+FBr+WdnFWCnNSoKk21EREREVO7u379v6BCIiIj0EpUksPW6QC7/XkRFYLKNiIiIiIiIiOg5DRs21Cj77KwCRuzVRjpgso2IiIiIyl39+vUNHQIREVGBbty4of4+WeD3a+zVRrphso2IiIiIyt3du3cNHQIREVGBMjMz1d7/76wCUvZqIx1xNVIiIiIiKndpaWmGDoGIiKhA1tbWqv/feiKwPlJAwV5tpCP2bCMiIiKicmdhYWHoEIiIiArk6uqq+v/CvxVMnpBe+LwQERERUbnz8PAwdAhEREQFioqKAgDcfSqwNoJztZF+mGwjIiIionJ39epVQ4dARESV2JNMgXGH5HD8MReW3+UicLMcFx/qnzH7/JyiDKKjyo7JNiIqtm+//RYSiQS//vqroUMpsbS0NAwdOhQODg6QSqVwcHAwdEh6mzBhAiQSCf7++29VWVneo8aNG7+Q16m89e7dGxKJBCkpKYYO5YUgkUjQuXNnQ4dBRERELzCFEHhlhxwbrwlMaCLFog5SPEoX6LhZjhvJuiXcatSogZhnAr9cFpCzVxvpiQskEP3r0qVLSExMRGJiIlJSUmBlZYXhw4cbOiwqJx9++CE2b96MIUOGoGnTprCysjJ0SERElZqzs7OhQyAiokpq23WB0/eBrX2kGOiV18dosJcEnr/IMfeUAht7GxV5DLlcji/PKcA8GxUHk21E/woNDYVMJoODgwOys7MNHQ6Vs+DgYNSoUQO///47JJLKs6b3xIkT8dZbb8HMzMzQoby0tm/fjpycHFhaWho6lBdCamoqjIyK/gWYXny8z0REVFa2RQs4WQCvef73e72jhQSDvSTYECmQlSsgMy78d/7wOwlYebkqe7VRsXAYKdG/hg4dijFjxuCVV17hCmkVVHJyMhSKspkzISkpCdbW1pUq0QYAxsbGsLKygrHxy/O3laysLKSmpho6DBWZTAYrK6tK92wVRQiBx48f672fpaUlk8MvidjYWEOHQEREldSlRwJNnSSQ5vv9q4WzBOm5QHRy0cdYd8cBCibaqJiYbCP6l42NjaFD0KCc6+nu3bsYMGAAbG1tYWJiAm9vb+zatUutbmFzc2mbW8vJyQn169fH6dOn0aRJE5iZmcHGxgaDBw9GRkYGMjIyMHr0aFSpUgUmJiaoX78+Tp48qTVOuVyO9957D46OjjAxMUHNmjXx1Vdfaa0bHByMtm3bwtraGiYmJnBxccE777yDjIwMrTFHRkaiQ4cOsLKyQpUqVZCWlqbz9cvNzcXUqVPh5uYGExMTWFlZISAgAMePH9e4bg8fPsS1a9cgkUggkUgQFBSkcztHjx5Fz5494eLiAplMBjMzM3h7e2PJkiUadZX3NDo6Gj169ICVlRVkMhn8/Pxw4MABtbp37txRxbJ06VLUqlULJiYmqFq1Kt58802Na6ZNQc9FZmYm3n//fdW1sbCwQIsWLXDo0CGNYyQkJODVV1+FtbU1ZDIZfH19sW/fPp2vT36hoaHo2LGj6tmys7NDw4YNsXLlSrV6WVlZmDZtGjw8PCCTyWBhYYE6dergww8/VNVRzlN39OhRjBw5ElWrVoW5uTl27Nih93kKITB//nzUqVNHdR8bNmyI3377Ta3e8/dl/fr1qFevnuo8hg4dqnFftM3Zps/XNgA8fPgQvXv3hpWVlSqugwcPFnvePF3vga7Xb9++fZBIJJg7dy7mzZsHNzc3mJqaYsqUKejSpQuMjY1x48YNjTiuXLkCiUSC/v37q8oKmrPtjz/+QMuWLWFlZQUTExNUq1YNPXv2xO3bt9XqLVu2DA0aNIC5uTlkMhnq1auHb7/9VuN4GzZsQMOGDWFtbQ1TU1M4ODigTZs2OH36tF7XkoiIiCqeB2mAs5ZBBcqy+6mFZ9Hi0wS2xbFXGxXfy9PVgagcZGZm6lzX1NQUUqlu+e5u3brB1tYW48ePR3JyMtatW4eRI0fi+vXrcHFxKW64SEpKQs+ePdG5c2f07dsXx48fx9atWyGRSBATE4PU1FSMGzcOycnJWLt2LYYMGYLbt29r9DpZsGABMjIyMHToUBgZGWHbtm348MMPkZaWhnnz5qnqbdy4EWPGjIGTkxNGjx4NR0dH/P3331i5ciWuXLmCv/76S+2aZGZmon379mjQoAE++OADJCYmwsTEROfz69GjB44ePYpGjRphxIgRePToETZv3oyePXtix44d6NWrF7p164avvvoK8+fPh5WVFaZMmQIAaNasmc7tbNq0CTdv3kT37t1Rq1YtPH78GNu3b8ekSZOQkpKCjz/+WGOfnj17wsrKChMmTMCjR4+wadMm9OvXD3v27EGXLl3U6p48eRJbtmzBoEGD4OrqigMHDmD16tW4e/cuDh8+rHePqZycHLRq1QpXr15Fly5dEBQUhGfPnmHz5s3o3bs3tm3bhr59+6rqtm3bFtHR0ejcuTMCAgJw+fJlDBo0CNbW1nq1C+Ql7rp27QohBAYNGgQPDw8kJibi0qVLOHXqFMaNGwcAyM7ORsuWLREeHo7GjRujX79+sLCwwNWrV7Fv3z4sWrRI7bhjx46FiYkJXn/9dRgbG6NOnTp6nScA9OrVCwcPHkTbtm0xZMgQ5OTkYMeOHRg9ejSSk5MxYcIEtTZPnTqFHTt2YODAgRg2bBj279+PzZs3w9raGqtWrdLpeujytZ2VlYXWrVvj1q1b6Nq1K1q2bImrV69iwIABZXoP9L1+APDrr7/i2bNnGDBgAFxdXeHm5gYrKyscPXoUy5cvx+LFi9XqL126FADw5ptvFhrzwoULMXv2bNjb26tivnfvHo4ePYpr167Bw8MDAPDOO+9gxYoVaNq0KSZMmABjY2Ps27cPU6ZMQVxcHL7++msAwN69ezF69Gi4ubnhrbfeQpUqVXD//n389ddfuHz5Mlq3bq33dSX9eXt7GzoEIiJ6ASiEQLZct7oyo7w/3GXk5v0/P7N/MyAZuYUf5+tQBXK5CCmVhCAiDVu2bBG//fab3vutWLFC51dcXFyRx3vllVcEAPHKK68IhUKhKl+1apUAIGbMmKEq++abbwQAsW7dOo3jNGrUSFStWlWtrFq1agKAWLRokVp53bp1hUQiEc2bNxe5ubmq8jlz5ggAYsWKFRptVq1aVcTGxqrKk5KShIODg7CwsBCPHj0SQgiRkZEhbG1thbe3t0hLS1Nrc9asWRqxN2rUSAAQI0aMKPI6abNjxw4BQLRp00bk5OSoys+cOSOkUqnw8vIScrlc7Xr4+PgUq63k5GSNstTUVOHq6iqcnZ3V2lHe05YtW6rFdfLkSSGRSESjRo1UZbdv3xYAhEQiEX/++aeqXKFQiPbt2wsAYsuWLary8ePHCwDi7NmzqjJtz4Xyeq9cuVLjPBwcHISPj4/qefviiy8EADF69Gi1up988onq3utj/fr1AoD48ssvC6334YcfCgBi2LBhas++EELtuVSes4+Pj8jIyFCrp895rl27VgAQM2fOVKubk5Mj6tWrJxwcHERmZqYQ4r/7YmpqKkJDQ1V1FQqFqFmzprCxsRHZ2dmqcuU9f/bsmUaZLl/bn332mQAgxo4dqxabsrys7oE+12/v3r0CgLCwsBDXr19Xq5+bmyvs7OxE7dq11b4WFAqFcHR0FDVq1FC7pwBEp06dVO9v374tjI2NhYuLi9r3meePL4QQISEhqmcmv3bt2gkzMzPV/qNGjRIARERERKHXQBdJSUmqZ0MIIVJSUtTudVZWlkhMTFTb5/79+4W+f/DggdpzUVnbiIyMrBTnwTbYBttgG2yjbNvYFfFE4KscnV7XEvP2s/g2W7yxP1ejjb035QJf5YiNFxILPY8Ov2UK6VfZOreb/3XlkaLS3o8XtY3yxmQbkRbFTbbFxMTo/Hr+m0dBlB/IT548qVaemJgoAIiBAweqyoqTbLOzs1NL+AghxPDhwwUA8csvv6iVnz17VgAQU6ZM0Whz3LhxGm1OnTpVABDLly8XQgjx22+/CQBizpw5GtciLCxM40OyMtn2zz//FHWZtBo0aJAAIPbt26exTZmoioyMVLsexU22Pe/p06ciNjZWxMTEiIEDBwoA4vbt26rtynv6fJJMqXnz5kIqlYp79+4JIf5L6jyfgFM6duyYACAGDx6sKtM12Va3bl3h7Ows7t27p3Ev+vbtK6RSqeqHU6tWrYRUKlU7ByGEyMzMFObm5nonevbt2ycAiC5duoj4+PgC63l4eAgLCwuRlJRU6PGU5/zdd99pbNPnPDt27CjMzMxEdHS0Rt0JEyYIAOLEiRNCiP/uS/v27TXaHDp0qAAg7t69qyorLNmmy9d2y5Yt1Z4LpaysLGFhYVFm90Cf66dMtvXr10/rscaMGSMAiGPHjqnK/vzzTwFAjB8/Xq1u/mSbMun3xRdfFHpeo0ePFhKJRJw4cUIj3q+++koAEKtWrRJCCDFx4kQBQHz44YcaSVoqP5cuXTJ0CERE9AJ4kKoQa67IdXo9ycxL2NRdlSN6bsvVONbPl/OSbZcfKTS2Pe/oXXmxE23KZBu93DiMlKgU1ahRo0yO6+vrq/a+atWqAPIWDCiJatWqaUycb29vDwDw8vJSK3dycgIArROeN2jQQKOsUaNGAKCapyk8PBwAMH/+fMyfP19rPImJiWrvra2tUbNmzSLPQ5t79+5BIpGgRYsWGtt8fHxw8uRJXLt2DT4+PsU6/vPi4uIwfvx4HD9+HM+ePdPYnpCQgFq1aqmV+fv7a9Tz8vJCaGgorl+/Djc3N1V57dq1Neo2bdoUABATE6N3vPfu3UN2djbc3d0LrBMXFwdnZ2fExsbC1tZWo65MJoOLiwuePHmiV9s9e/ZEr169sG/fPri6uqJu3bpo06YNgoKC0K5dO7X23d3dUaVKFZ2Oq+0Z1Oc8b968iczMTHh6ehZYN/9k7tqOq/zajI+PL7RdJV2+tuPi4mBrawtXV1e1uqampnB2di6ze6DP9VOqW7eu1noTJkzAunXrsGrVKgQGBgIAVq5cCalUinfeeafQeKOjowEAAQEBRdYTQqBDhw4F1nnw4AEAYPbs2arhyD/88AMaNGiATp064d1331UNSaWyx4UwiIhIF9UtJQjy1W/alMbVJPgrVkAhhNoiCX8/ELAwBjztC98/0E2CFtUFLjyUcN42KhYm24hKUXp6us51ZTIZjIy0TCSgRUErSQrx33f+wubtksu1T3JQWPu6tKkP5X4TJkwocD6k5xNMQF4yoaKvoimEQPv27XHv3j0MGjQIrVq1QpUqVWBkZIS1a9fi0KFDZbaCanEJIVCjRg2Nec+eV69evTJrf+/evTh79iy2bt2K06dP47fffsPatWvxwQcfqObU0pe2ucv0PU9ra2ssX768wK+l9u3bq70v7OtH16+T0v4605Uu96A4z0lBKzn7+/ujTp06OHjwILKysiCXy3HkyBH4+flpJByLSwgBiUSCVatWFRiHci7GatWq4fr169i9ezf27NmDs2fP4uuvv8aPP/6IX3/9FQMGDCiVmKhwZfl9hoiIXm4DPSXYFi2wI1pgoFfe73aJ6QJbrwv0qSOBzLjw5J1EIkGQ812ci69VDtFSZVSxP8USvWA2bNigc93evXuXaHGD/KpVqwZAs3cYANy/f1/vSfT1cfXqVY0yZU825Yep+vXrAwDMzc0xbNiwMotFqWbNmvj7779x7tw59OzZU21bVFQUAJRKr7bz58/j1q1bGD16NNatW6e2bfXq1YXul78HzfXr1yGVSjV6Fd66dUtj/4sXLwLQTFDqwtXVFY8fP8Zrr70GmUxWaN0aNWrg3LlzuHfvnlrvvKysLNy/f7/ApEZRWrVqhVatWgHIe2YbN26MpUuXYv78+bCwsECNGjVw//59JCcnq3pb6kuf83R3d0dsbCzatWtXrGtallxcXHD+/HnExcWpxZaTk4MHDx7A3Ny8WMct6h7oc/10MXz4cHz22Wf4/fffkZaWplpUpSjKSfTPnj2rkfB8Xu3atfH333+jVq1aWlczzc/IyAj9+/dXrYR69uxZtG3bFv/73/+YbCsnV65cQePGjQ0dBhERVUIDPSVo5Qy8fkCByCTAwRxYFqaAXACfttFtkbpWVVLRzAm49AhQsHcb6Um3p4yIdNKrVy+dX8rhYqXFz88PAHDs2DG18p9++knr0M/StH37dsTFxaneP378GOvWrYOFhYXqg+zAgQNha2uL1atX486dOxrHSEtLQ0JCQqnFpPwQ//nnn6v17AsNDcVff/0FLy8vjaRWcSh7JuXviRQaGorg4OAC91u8eDFyc/9bBikkJATnz5+Hr6+vRrLn8uXL2LNnj+q9EEK1yuvAgQP1jnno0KF49uyZauXV/G7fvq36f9++faFQKDB37ly1Ov/73/+QkZGhd9vx8fFq5w0ADg4OcHV1RXZ2Np4+fQoAGDRoENLT0zFp0iSNa6trT0F9zvP111+HEAITJkzQevzn65a33r17Q6FQaAy//vLLL/XqTauk6z3Q5/rp4r333oORkRHWr1+PdevWwdzcHGPHji1yvzfeeAPGxsb44YcfEB8fr7Fdeb/efvttAMDHH3+MrKwsjXoxMTGq7wX5hwQDQJMmTWBqaqp1KDgRERG9WIykEuwbYIQh3hL8cEmB6ScUcDAHjg02glcV3TohuLq6YH4bKRNtVCzs2Ub0r+joaKSmpgIAMjMzIZfLVb2HrKysCp3LSams5mzTha+vL5o1a4Z9+/ahT58+aNasGS5fvoyjR4+ievXqyMnJKbO2bW1t0bx5cwwcOBBGRkbYunUrEhMTMXv2bFWPOysrK6xcuRIjR45Ew4YN0bdvX3h6euLp06eIjo7G8ePH8dNPP2H06NGlElP//v3RuXNnHD16FM2bN0ePHj3w6NEj/P777zAxMcE333wDqbTkf2/w8/NDzZo18fvvvyM7Oxv169fHjRs3sG3bNtSsWRM3b97Uul9CQgL8/f3Rq1cvPHz4EL///jtMTU21DqOsVasWBg8ejEGDBqFGjRrYv38/Ll26hMDAwGIl2+bPn48jR45g2bJlqt5CdnZ2uHfvHk6dOgUjIyNVb8WpU6di9erV+PXXXxEXF4eAgABcvnwZR44cgZOTk0bSpijLli3DsmXLEBgYiHr16kEmkyEkJATnzp1DmzZtVPN/zZ8/H/v27cOGDRtw9epVBAYGwsLCApGRkbhx4wYuX75cquf55ptvYteuXdi9ezfq16+PLl26oFq1aoiNjcWFCxdw7969Uk0G62P69OlYvXo1fv75Z9y9exctW7bE1atXcejQoWJ9betzD3S9frqoXr06WrVqhVOnTiEnJwfdunXT6Y8OtWrVwty5czFnzhz4+vqib9++8PDwQFxcHA4fPowlS5agV69e6NChAyZMmIClS5eibt266NWrF2rUqIEHDx4gPDwcf//9N5KTk2FtbY0RI0aoejLWqlULmZmZ2L17NzIyMvDaa6/pdT2p+JRzgRIREZUFezMJfu5uhJ+7F29/iUSCnh4SNHIEriSydxvph8k2on9dv35dNXm20vnz5wEAzs7OOiXbDG3nzp0YNWoUjhw5gsOHD6NBgwb4888/MWnSJK09OUrL7Nmzce7cOWzcuBFPnz6Fs7MzPv/8c8ycOVOt3uDBg1GzZk188skn2L9/PzZv3gxLS0s4Oztj5MiR6NixY6nGdeDAAcyYMQObN2/GV199BZlMBj8/P/zvf/9Dp06dSqUNIyMjHDx4EG+//Tb27duHnTt3ws3NDYsWLcL169fx448/at1v//79mDRpEn744Qfk5OTA09MTX375Jbp27apRt3379vD398fXX3+NuLg42NjYICgoCEuXLi3W8GATExOcOXMG8+bNw+bNm7Fs2TIAeYtj1K9fXy3haWpqilOnTuHNN9/E8ePH8ddff6FevXrYsmULZs2apfdz1a1bN5w7dw5//fUXdu/eDalUimrVqmHy5Mn47LPP1Nr9+++/MWPGDOzcuRM//PADTExM4OLionOCUZ/zBIDdu3fju+++w88//4xffvkFubm5sLOzg6enp8azXJ7MzMxw6tQpjB07FidPnsSJEyfg6emJHTt2YMKECao/EuhK13ug7/XTxRtvvIFTp04BAMaMGaPzfrNnz4anpye++OILbNq0CTk5ObC3t0fz5s1VQ9QBYMmSJWjZsiW+++47/Pbbb8jKyoKNjQ1q1aqFmTNnqobcjho1Cr/88gt2796NlJQUmJubw83NDd9//z0mTpyo93lR8ZTG8GQiIqKyEhcXB0dHR8xvI8Wrf1SsOZip4pOIsp6FmYiIVHr37o29e/fi2bNnWif1f96dO3fg4eGBMWPGYO3ateUTIL0w5HI5bG1tUbduXYSFhRk6HCK9hYWFcc42IiKqsJQ/p4QQaLhWjmuPde/ddmWMEXwdy27ObKr4OGcbERFRBZeSkqJRtnDhQqSlpaFt27YGiIiIiIioclMupiaRSDh3G+mNw0iJ6IVy+/ZtjQnz83NwcICNjU2J2klMTCxyonSZTAZXV9cStVNZ8HqVrcGDByMlJQUtW7aEubk5zp07p5o3b9asWQB4D+jF8yJMz0BERC+vmJgY1K1bFwDQr54E3lWA6GTO3Ua6YbKNiF4otWvXLrLOnDlz8A1KL7MAAFnoSURBVOmnn5aonaCgIOzdu7fQOj4+PoiMjCxRO5UFr1fZ6tatG3766ScsX74cmZmZsLW1Rc+ePfHdd9+pFjTgPaAXTXx8vE7f04mIiAzh+XlxpRIJPm0txZA9nLuNdMM524johbJlyxbI5fJC6zRr1qzEPSbOnTtX4EqiSlWqVEH37sVc3qiS4fUyPN4DetFwzjYiIqrIoqKi4O3trXovVwh4r5bj5hOgqCQK52wjJtuIiIiIqNxFRkaqrSZLRERUkeTm5sLYWH0w4G+RCozcV3TvNibbiAskEBEREVG5U048TUREVBFFRERolA3xlsDDFmAajYrCZBsRERERlbvw8HBDh0BERKQXY6kEcwOkRQ4jJWKyjYiIiIiIiIjoOdWrV9daPtxHAnfrgvczN5KjqnkZBUUvDM7ZRkRERETlLi4uDq6uroYOg4iISG/3UwWSM7Vvs5cJuFizX9PLzrjoKkREREREpcvS0tLQIRARERWLi5UELlbat0VGXoMLFwB66THdSkRERETl7s6dO4YOgYiIqNRlZ2cbOgSqAJhsIyIiIiIiIiIqBTY2NoYOgSoAztlGREREROUuNTUVVlYFjMEhIiJ6QaWnp8PCwsLQYZCBsWcbEREREZW7xMREQ4dARERU6qKjow0dAlUAXCCBiIiIiMrdkydPDB0CERFR4Z6lAxn55mCTAKhmZ4ho6AXCZBsRERERlTtjY/4aSkREFditeKDRFCA1U3Pb+a+AZnW07ubu7l7GgdGLgMNIiYiIiKjc+fr6GjoEIiKigt1L1J5oM5ICn24ucLesrKwyDIpeFEy2EREREVG5Cw8PN3QIRERE+pMrgD/PA2G3tW5++PBhOQdEFRGTbURERERU7oQQhg6BiIioeIylwPwtho6CKjAm24iIiIio3FWtWtXQIRARERVPrgLY+TcQcVdjU8OGDQ0QEFU0TLYRERERUbmzsbExdAhERETFZywF5m/VKL5x44YBgqGKhsk2IiIiIip3t29rn+uGiIjohZCrALadBq7FqhVnZmpZVIFeOky2ERERERERERHpy0gKfKbeu83a2tpAwVBFwmQbEREREZW72rVrGzoEIiKikslVAJtCgOj7qiJXV1cDBkQVBZNtRERERFTunjx5YugQiIiISk4qAf73X++2qKiosmvrSRow7ifAMQiwHAYEzgEu3iy79qjYmGwjIiIionL3+PFjQ4dARERUcnIFsOEkcCu+bNtRKIBXFgAb/wIm9AQWjQYePQU6zgFu3C96fypXTLYREb3kevToAYlEYugwiOglI5Xy11AiIqokJBJg4XYAQI0aNYp3jI6fAEFLCt6+7Qxw+jqwdgIwdwgwvicQPD9v3ri5m4vXJpUZ/pZDRFSOhBC4fPkyNm/ejF9++QW//fYbzpw5g5ycHEOHRqXk+PHjGDduHCIiIgwdCumA98tw/Pz8DB0CERFR6ZArgLXHgbuPIJfLy6aNbWcAJzvgtVb/lTnaAoNbA7vOAVn8PFGRMNlGRFSOzpw5g7Nnz8Le3h6tW7dG7dq1ERERgYMHD0IIYejwqBScPXsWq1atKtv5OqjU8H4ZzpUrVwwdAhERUen6fAcePHhQNse+dBtoWhvI3zO8RT0gPUttkQYyPCbbiIjKyePHjxEREYFatWqhW7du8PHxQUBAAAICAnD//n3cvMnJTan8JSUlGToEekmV2V/+iYiIDEGuAH45ApP4p2Vz/AfJgLO9Zrmy7D7nQq1ImGwjIionymRaw4YN1cq9vb1hbGyMGzduGCIslYSEBPTr1w82NjYwMTGBj48P9u/fr1ZHLpdj/PjxaNCgAezs7GBsbAwHBwf069cPcXFxGsdctGgR6tWrBwsLC8hkMlSrVg1dunTBvXv39I5v+/bt8PLygqmpKezs7DB48GCcPn0aEokE48aNU9X7/PPPIZFIsG3bNo1jNGzYEI6Ojhrlhw8fRps2bWBtbQ0TExO4uLhg/PjxyM7OVqt3+vRptG/fHlWqVIGJiQns7Ozg5+eHNWvWAADGjRuHjz/+GAAwaNAgSCQSSCQS9OjRQ+fz1PcaP3v2DEOHDoWdnR1MTU3h5eWFzZs3a52LT3n+4eHhaNeuHaysrODg4KDafvPmTfTr1w8ODg4wNjaGvb09+vTpg7t372q0m5CQgFGjRqF69eowMTGBjY0NOnXqhPDwcLV6yvuxefNmvPPOO3BwcICpqSk8PT2xb98+AHn3tn79+pDJZLCzs8O7776r9droep+U5/nPP/+gU6dOsLS0hEwmQ7NmzXD+/HlVvdK4X1R89vZaPjAQERG9yATgvVuH3vI5uUDiM/VXTm7eUND85QpF3j4Z2YDMWPNYZqb/bacKQ8udIiKispCQkACJRIJq1aqplRsbG6Nq1apISEgo8hhCCGRlZencpkwm03nxg7Zt28LOzg4TJkxAUlIS1q9fj8GDB+POnTuoWrUqACAjIwNr1qxBYGAgevToASsrK5w/fx579uzBxYsXcf36dZibmwPIS7TNmDED9evXx+TJk2Fubo579+4hODgYsbGxcHd31/k8du3ahaFDh8LS0hKvv/467OzssHv3bowePVrnYxRkzZo1GDduHKpXr46goCBUrVoVZ8+exU8//YQrV67g5MmTAIC4uDj07NkTADB48GDUqlULCQkJuHTpEk6dOoXXX38dI0eORHx8PP78808EBQWhQYMGAAAfHx+d49HnGgNAly5dEBoaitatWyMwMBC3bt1CUFAQnJyctB4/MzMTnTp1gp+fH6ZMmYKHDx8CAK5du4Y2bdogNzcX/fr1Q926dXHjxg1s27YNrVq1wuXLl1WJyoSEBDRt2hQJCQno27cvGjRogPv372PTpk1o164dzpw5ozp3pVmzZkGhUCAoKAjZ2dlYu3YtBgwYgO+//x5TpkzBgAEDMHDgQPzxxx9Yvnw56tSpg2nTpul9n5SysrLQtm1b+Pr6YurUqbh16xY2b96MV199FXfu3IGJiUmp3C8qPuX3FSIiokpDroB01WFgzmCgeiF/VDoVBQTO0Sw/fR3YFKJedns5UKsaYG4KZOVq7pP5b5LN3LT4cVPpE0REVC62bNkifv31V63bDh8+LFasWCFyc3MLPcazZ8/EihUrdH49e/asyLi6d+8uAIh+/fqplS9ZskQAEHPnzlWVyeVyrcf87LPPBADx/fffq8ratGkjzMzMRFZWVpExFKVu3brC2NhYhIWFqcoyMzOFt7e3ACDeeustVfnChQsFALF161aN4/j6+goHBwfV+5SUFGFrayvq16+vEee0adPUjrNy5UqNc9SmsPZ1oc81XrNmjQAgXnnlFbW6yvL8P+Z9fX0FAPH6669rHL9Vq1bC2tpaREVFqZUfOnRISKVStWs8aNAgYWJiIoKDg9XqRkRECDMzM9G9e3dVmfJ6eHh4iPT0dFW58npKpVJx6NAhVXl6erqwtbUVnp6eqjJ97tPz5zl58mS1uu+//74AINavX68RX3HvV1JSksjMzFSL9fn7l5WVJRITE9X2uX//fqHvHzx4IBQKRaVv4/z585XiPNgG22AbbINtVM420vb+LQT66/3KsR0uRHpm4W08ThHpu8+ItF1nhDgcJsThMCFvOFlkBc5SvReHw0TipmNCZPz7u0/d90RGp1ma57HqUF7bl+9U6vtR0jbKG5NtRETlZOPGjWLDhg1atx07dkysWLFC7YeKNjk5OSImJkbnV05OTpFxKZNtFy5cUCuPjY0VAMSwYcO07pednS3i4+NFTEyMCA8PFwDE0KFDVdt79OghpFKpWLFihZDL5UXGUZBbt24JAKJDhw4a277//vsSJdt+/vlnAUD873//07h2586dEwDEmDFjhBBCbNu2TQAQPXr0EAkJCQXGW9LkzfOKusavvvqqACDOnDmjsa+rq2uBybb4+Hi18kePHgmJRCJ69eql9TlycXERXl5eQoi8ZKCVlZVo2rSp1rpNmjQRdnZ2Gtfj008/VWvzwYMHAoDw8fHRiL1Vq1bCyspK9V6f+6Q8T4lEIlJSUtSOe/jwYQFAfPTRRxrxlcb9Iv1cunTJ0CEQEREV7PgV/ZNtktfEo5k/F6+9DrOFGPNDwdsHLhLC6XUh8v9e/dYyISyGCpGZXbx2qUxwGCkRUTkxNjZGZmam1m3KicKNjQv/tmxsbIwaNWqUemyA5lxyrq6uAIDk5GS18h9++AFLly7FrVu3NCY4f/Lkier///vf/3Dx4kW8/fbbmDp1Kho1aoTu3bvjvffe02v42LVr1wAAdevW1djWqFEjnY+jjXI1xFmzZmHWrFla6yiH9w4YMADdunXDgQMHUL16ddSrVw9t27bFG2+8gYCAgBLFkZ+u1zgmJgYSiQRNmjTROEbNmjW1zvFmbW2tMcT0woULEEJg3759cHNz0xqTcvhzTEwMUlNTcfHixQLrahu67OXlpfa+evXqAP57zp5na2uL1NRU1Xt97pOSvb09rKystLbJRSEqhlq1ahk6BCIiotJlZwnbDweVzbEHBgDbzgA7zgIDW+eVJT4Dtp4G+vgDMpOyaZeKhck2IqJyYmlpiSdPnkAul8PIyEhtW1paGszMzDTK81MoFAUm7LQxMzODNP/y4AUwMdH+A1oIofr/Tz/9hMmTJ6NevXr4+OOPUatWLZibm0Mul2PUqFFqdZs2bYrbt29jy5YtOHjwIEJDQzFnzhx8++23OHbsGBo3bqzzeeijsPPNn7hSxvvBBx+gefPmWvd5PiFw8OBBnDp1Ctu2bcOZM2ewfv16rF69GjNmzMDChQtLHjz0u8ZKus7LB+TN45ef8phdunTBG2+8oXU/S0tLAHnPIAA0btwYH374oc7tFvRsF/XMPx+frvcJKPw50HYNqfylpqbCzs7O0GEQERGVDokEmNkfkXdvobF949I//sAAoJUn8PpSIDIWcLAGlh3IWwX106Gl3x6VCJNtRETlxNHREbGxsXj06BGcnZ1V5bm5uUhKSlIrK0haWhp+//13ndscNmwYrK2tixWvNhs2bICJiQnOnz8PGxsbVfm5c+e01rewsEBQUBCCgoIAAGvXrsXrr7+O+fPnY8eOHTq16e3tDQD4559/NLblX/kSgGp1TW0LTsTHx6sld5THtrKywrBhw3SKp02bNmjTpg0A4OHDh2jSpAm++eYbLFiwAFKpVOfkZkH0ucY1atTAxYsXcfHiRbRq1Uptm7YVRAvi5+cHiUSCnJycIq+Du7s7LCwskJaWpvM1K6ni3CddlfR+UfElJiaWWU9dIiKicmdjDrzXA/jnetkc38gI2DcbmL4O+GFv3uqjzesCaycCXpojBciw+BsmEVE5qVOnDoD/hsQpRUVFITc3V+swyfzMzc3Rq1cvnV/Pr1pZGqRSKSQSiVoPMYVCgY8//lijbmxsrEZZ+/btAagPhSxK7dq1UadOHZw6dUotuZaVlYWffvpJo76fnx8A4NChQ2rl3377rcaQ2GHDhsHGxgbLli3D/fv3NY6VkpKiGnJ4//59jZ5xTk5OcHZ2RnZ2NtLS0gBAldzUZXVZbfS5xv369QOQN2T3eWvXrtU6hLQgrq6uaN68Of766y/s27dPY7tCocC9e/cA5PVE69GjB27cuIEff/xR6/H0SfTpQp/7pK+S3i8iIiIiSCXAh/0AK3O4uLgU7xjBn+UlzgpjbwX8PB5IXAek/Z63j3/RnyGo/LFnGxFROalSpQoaNGiAq1ev4tChQ3B3d0dycjIiIiLg7OysU7KtLOds08WAAQMQEhKCFi1aYNCgQcjJycH+/fu1Dm1t3749rK2t0aJFC9W5btu2DRKJBKNHj9ar3S+//BJDhgxBhw4dMHToUNja2mL37t3IyckBoD6Msnnz5mjcuDF27dqFfv36oXHjxggLC0NwcDCqV6+O3Nz/lky3s7PDjz/+iDfeeAPe3t549dVXUa9ePSQnJyM6OhrBwcFYt24dBg4ciO+++w6rV69Gp06dULduXZiamuLkyZO4ePEiOnTooEraBAYGQiKR4Ouvv0ZSUhKsrKzg5eWFnj17lvo1HjNmDJYuXYo9e/agbdu2CAwMxK1bt7Bz507UrFlTr6TXunXr0LZtW/Tt2xfdu3dHkyZNIJfLcevWLRw/fhz9+vXDypUrAQDLly/HxYsXMXHiRGzbtg3NmzeHTCbDnTt3cOLECdSvXx8HDhzQue2i6HOf9FXS+0XFV1ZDyYmIiMqdpQyY0AuAftN7UCVmuLUZiIhePnK5XISHh4tNmzaJVatWifXr14vTp0+L7GzDrR6kXI1UGwCie/fuamWffvqpcHV1FcbGxsLW1lb07t1bxMXFadT99NNPRePGjYWtra0wMjIStra2wt/fX2zZsqVYcW7evFl4enqq2h00aJDYt2+fACAmT56sVvfmzZuiXbt2wszMTMhkMtGsWTNx5swZjdVIlUJCQkTnzp2Fvb29MDIyEjY2NsLLy0u88847Ii4uTgiRt2Jst27dhJOTkzA1NRVmZmaiZs2aYsqUKSItLU3teF988YVwdXUVRkZGWq9hUXS9xkIIkZycLAYNGiRsbGyEiYmJqFevnti8ebNo27atMDU1Vatb0PkrxcTEiOHDh4vq1asLY2NjYW5uLtzc3MSgQYPE6dOn1eo+ffpUvPPOO8LNzU2YmJgImUwmXFxcxCuvvCL27dunqlfYap8FXZuCnkld7lNh53nlyhWN1WuFKPn9ouKJiIgwdAhEREQF03U1UulrQny6WbUbV9smIYSQCMFZgomI6MW0bNkyjB8/Ht9++y3ef/99Q4dTobi7u0Mul+s1nJSoPIWFhbF3GxERVVzBEUDgnKLrWcqA2J8Bu7zFpPjzjQDO2UZERC8AhUKB1NRUtbKsrCwsWbIEUqkUffv2NVBkhpeSkqJRtmbNGsTExCAgIMAAERHpxtbW1tAhEBERlYxUAkzpq0q0AYCPj48BA6KKgnO2ERGRQSQnJ2ssWJCfsbEx3N3dkZ6eDldXV/Ts2RPe3t5ITEzEnj17cPfuXYwaNQq1a9cup6iLJycnBzExMUXWc3Z21ntRiwkTJiAiIgJt27aFnZ0dwsLCsHfvXlhZWWksnEBUkTg5ORk6BCIiopKRmQDv91YriomJ0WkuZqrcmGwjIiKDmDFjBlatWlVoHQcHByQkJEAmk6F169Y4fvw4du7cCSEEXF1dMXv2bHz22WflFHHxXb9+HQ0bNiyy3tatW/We5D8wMBDnz5/H6tWrkZ6eDisrK7Rv3x6LFy+Gl5dXcUMmKnPR0dEcZkNERC8uqTQv0VbFWq04/2gMejlxzjYiIjKI8PBwREZGFlrH0tKyUgwRTU1NxZ9//llkvcDAQFSvXr0cIiIyPM5pQ0REFVpRc7aZmQAxqwAHG7XiqKgoeHt7l3FwVNEx2UZERERE5e7x48eoUqWKocMgIiLSrrBkm5EU+KAP8NUYjU25ubkwNuYgwpcdF0ggIiIionKXmZlp6BCIiIiKx1gKTHtV66aIiIhyDoYqIibbiIiIiKjcPXr0yNAhEBER6c9ICrzbA3CyM3QkVIGxbyMRERERERER0fMa1UKKf01YP8tRLzcxAj7sV+BunH+XAM7ZRkREREQGIJfLYWRkZOgwiIiICiSEgEQiMXQY9ALiMFIiIiIiKnfR0dGGDoGIiKhQ165dM3QI9IJiso2IiIiIyl1WVpahQyAiIipUdna2oUOgFxSTbURERERU7qytrQ0dAhERUaFsbGwMHQK9oDhnGxERERGVu8zMTJiZmRk6DCIiogKlp6fDwsLC0GHQC4g924iIiIio3EVFRRk6BCIiokJxflEqLmNDB0BEREREREREVG4u3ARCrgFGUuDd7gBXx6ZSxmQbEREREZW7GjVqGDoEIiJ6WXWfDzxOBYQAqtkCg9torebu7l7OgVFlwWGkRERERFTucnNzDR0CERG9rLJy8hJtUgkwdxOgUGivxpWzqZiYbCMiIiKichcfH2/oEIiI6GWnEEBUHPDHOa2bHz58WM4BUWXBZBsRERERERERvZykEmDO73k93YhKCZNtRERERFTufH19DR0CERFRXu+2qzHAn6Eamxo2bGiAgKgyYLKNiIiIiMrdzZs3DR0CERFRHqkEmLNJo3fbjRs3DBQQveiYbCMiIiKicpeRkWHoEIiIiPIoBBB+B9h/Ua04MzPTMPHQC4/JNiIiIiIqd5aWloYOgYiI6D9GUo3ebdbW1gYMiF5kTLYRERERUblzd3c3dAhERET/kSuACzeBI+GqIldXVwMGRC8yJtuIiIiIqNxdu3bN0CEQERGpy9e7LSoqquzaepIGjPsJcAwCLIcBgXOAi5zPtLJgso2IiMpUjx49IJFIDB0GPWfcuHGQSCSIiIgwdCjFJpFI0KNHD0OHQURERJWJXAGcjQaCy/h3JIUCeGUBsPEvYEJPYNFo4NFToOMc4Mb9sm2byoWxoQMgIqLSI4TAlStXcO3aNaSmpsLMzAy1a9eGv78/TExMDB0eEZGKi4uLoUMgIiLSZCQF5m4CAhuiRo0axTtGx0+AWtWAtRO1b992Bjh9Hdg6DRjYOq9scGvAcwIwdzOw8YPitUsVBnu2ERFVImfOnMHZs2dhb2+P1q1bo3bt2oiIiMDBgwch8i1lTi+vpUuXIiUlBfXr1zd0KEREREQVi1wB/HUNOHkVcrm8bNrYdgZwsgNea/VfmaNtXsJt1zkgK6ds2qVyw2QbEVEl8fjxY0RERKBWrVro1q0bfHx8EBAQgICAANy/fx83b3IOCEPJycnBs2fPDB2GiqmpKaysrCCV8tcAMpz79zlMhoiIKigjKTBvMx48eFA2x790G2haG8j/u1iLekB6FhDNn5EvOv6WTURUSSiTaQ0bNlQr9/b2hrGxMW7cuGGIsFQSEhLQr18/2NjYwMTEBD4+Pti/f79aHblcjvHjx6NBgwaws7ODsbExHBwc0K9fP8TFxWkcc9GiRahXrx4sLCwgk8lQrVo1dOnSBffu3dMrtqioKPTs2RMODg4wMTGBjY0NvL298eWXX6rVUygUmD9/Pjw9PWFmZgYzMzO4u7tj7Nixqjqff/45JBIJfv/9d7z77rtwcnKCmZkZfvzxR9Ux5syZgzp16kAmk8HMzAx+fn7YunWr1th++OEH1K9fH2ZmZpDJZPD09FQd63nKOcz27t0LX19fyGQyWFlZ4ZVXXkFycrJaXW1ztinLQkNDERQUhCpVqsDExAS1atXC2rVrNdp79uwZhg4dCjs7O5iamsLLywubN28u9hx9yv3u3buHbt26wcrKCjKZDI0aNcLRo0eL3D8iIgISiQTjxo3T2KbtfHW950RERPQSkiuA4xGwCIspm+M/SAac7TXLlWX3H5dNu1RuOGcbEVElkZCQAIlEgmrVqqmVGxsbo2rVqkhISCjyGEIIZGVl6dymTCbTObHStm1b2NnZYcKECUhKSsL69esxePBg3LlzB1WrVgUAZGRkYM2aNQgMDESPHj1gZWWF8+fPY8+ePbh48SKuX78Oc3NzAHmJthkzZqB+/fqYPHkyzM3Nce/ePQQHByM2Nhbu7u46xZWdnY3OnTsjKSkJgwYNgpeXF548eYKIiAicPHkSM2bMUNXt1q0bjh49Ck9PT7z11luwt7dHVFQUDhw4oHHcjz/+GLm5uRgyZAhsbW3h5+enOsaxY8fQvn17DBkyBFlZWdi5cyeGDh2KJ0+e4K233lId44033sCaNWvQrFkzTJo0CUZGRti7dy8mTJiABw8eYMGCBWptXr9+HUOGDEHfvn0xcOBAnDx5Evv27cPo0aPx559/6nQ9Ro4cCWNjY4wdOxbZ2dlYt24dxo4dC39/f/j6+qrqdenSBaGhoWjdujUCAwNx69YtBAUFwcnJSad2ChIYGAgbGxtMnDgR8fHx2Lx5M1555RUcPXoUbdq0KdGxlfS551R2fHx8DB0CERFRwYykqPvbBSCoT+H1cnKBp+maZVk5QGK+kQ1VrPJ6s2VkAzIt6Rgz07x/M7KLHzdVCEy2ERFVEmlpaTAzM4ORkZHGNktLSzx8+BByuVzrdqXU1FT8/vvvOrc5bNgwWFtb61S3fv362Llzp+p9w4YNMXHiRCxZsgTz5s0DAFhYWODhw4cax1ywYAE++eQTrFq1CpMmTQIA7N69G2ZmZrh06RJMTU11jjm/s2fP4v79+5g8eTK+++67Auv98MMPOHr0KLp06YIDBw6oXUdt83lkZWUhKioKNjY2qrLly5fj6NGjmDdvHubO/X979x4WVbX/D/w9XAeY4SYooiCgiAqKIpKCZhopauXRvN8vR76ek6amnUrLUkuPesq0NFGPeDt9y0vaL7XUUisviVeQQC4KysUEFQVELsOs3x8e5us4gFxm9iDzfj3PPMXaa+/1WZ+ZZzZ+2HuvDzTty5YtQ4cOHbBgwQJMnToVZmZm+PnnnxEdHY2JEydqXVm2bNkyhIaG4pNPPsGcOXM0hUoAuH79Og4cOIABAwZo2kJCQnDw4EHk5eXByamSv6A+wdHREadPn9bcYjpgwABERERg5cqV2Lp1KwBgy5YtOHv2LAYNGoT9+/dr9t2yZQsmT5781DGq07x5c/z666+a8ceOHYt+/frhjTfewPnz5+t17Ao1fc/JsG7cuAFfX19jh0FERFS5cjXMfooDbt4FmjtX3e/kFaDPQt32U0nA1ye029LWP1o4wcYKKFHp7lP83yKbTd1/t6WGgbeREhE1EiqVqspncFUUhlSqSk7qj7GxscHAgQNr/Kq4yqwm3n//fa2fhwwZAgBITk7WtJmZmWkKbWVlZbh16xYyMzPx6quvAni0AEQFpVKJ0tJSbNmyBWq1usZxPKmiWHXixAlcv369yn4VRcgNGzboFCwrK2COGzdOq9AGADt27IBcLsekSZOQmZmpeeXk5ODFF19Ebm4uLly4AADYtGkTZDIZXn/9da2+mZmZGDRoEIqLi3WuqPPz89MqtAFAr169oFarkZCQUKN8zJo1S+tz1L9/f1hbW+PatWuatn379gEA3nvvPa19J02ahBYtWtRonKrMnz9fa/zw8HB06dIFly5d0rkdtq5q+p7XxN27d7WuBi0sLERBQYHm59LSUty5c0drnyef//Lkz3/++afWgiaNdYz8/PxGMQ+OwTE4BsfgGM/eGGr10xcOE+ZmKOjaCmjuXP0YgV7AkQ9w5+uZwJEPHr06tUJJ7/YQhxdq2vL3vIkSp//+7tzcCarMXJ15FKb897ZVd+cazaOxvB9SjCE5QUREjcLOnTvFtm3bKt125MgRERUVJVQqlcRRCdG/f38BQJSWlupsAyAiIiK02lavXi18fX2Fubm5AKD1erzv+fPnRdOmTQUAoVAoRFhYmFi8eLG4fft2rWOcPHmykMlkQiaTCR8fHzF27Fjx448/avVp0aKFcHR0fOqxli5dKgCI6OhonW0tWrTQmdOTr7179wohhAgODn5q3+XLl2uODUCEh4dXGc/u3bs1bdOmTRMAxOXLl3XaLl68qHMMFxcX0bFjR83PQUFBQiaTieLiYp2+oaGhoi6/XlR8TnJycnS2jRgxQgAQJ0+e1LQBEP3799f8fPnyZQFATJs2TWf/yuZbk/ecDOvKlSvGDoGIiEyVYrQQGPLUV8aOOv5u0Ps9ISauqXr7sBVCNJssRHm5dvu0dULYjhKiWPf3Znq28DZSIqJGws7ODvfu3av0VtHqbjF9nFqtRnFxcY3HlMvlNV7R0tLSstJ28dhfsb788kvMmjULvr6+mD9/Pry8vGBjY4Py8nKMHz9eq29QUBDS0tKwc+dOHDp0CGfPnsXChQuxatUqHD16FJ07d67xPDZv3oxZs2bh66+/xqlTp7Bv3z785z//wbhx47B9+/YaH+dxCoWi0rkqlUpERUVVuV/37t01fWUyGbZt21bl+xYSEqL1c3Xv7+O5q46FReW/GlS2f10WQjCU6mKp7IpOQ7znVDutW7c2dghERESVMzcDurdF0+F9DHP8YT2A3aeBb38HhoU+arudD+w6BbwSDFhX/nszPTtYbCMiaiRcXV01tyQ2b95c065SqXDnzh2ttqo8ePDAYM9sq4kdO3bA0tIS586d07oFMyYmptL+tra2mDRpEiZNmgTg/54ZtnjxYnz77be1GjswMBCBgYEAHl2q3r17d+zYsQMfffQRWrVqBU9PT5w+fRppaWnw9vau0/w8PDwQExODiIiIpz4/zdvbG+fPn0ebNm00BbiGomXLlrhw4QIuXLigE1t9b8s8e/YsBg4cqNWWmpoKMzOzah+o7+bmBgCV3mqanp5e6T5Pe8/JsOLj42tVFCciIpJMuRpYNAoJCQmGOVcN6wF0bwtM/gJIyARclMC6HzXj0rOPz2wjImokKq4SuXz5slb7lStXoFKp0KZNm6cew5DPbKsJMzMzyGQyrQUH1Go15s+fr9M3MzNTp+35558HANy7d6/GY+bm5uqswKpQKDT5zMnJAQCMGTMGADB9+nSdBRFq+sy4iqvzHl9x9HFpaWma///rX/8KAJg7dy7Kysqq7Su1v/zlLwCAjz/+WKt9y5YtyMrKqtexly5dqpXPn3/+GRcvXkTnzp2rLVA2adIEDg4OOHPmjNb+sbGxOHnypFbfmr7nREREZILMzYAQX6BvRwOOYQ4cfA8YGQasOQC8tQ1wsQeOLgL86vf8W2oYeGUbEVEj4ezsDH9/f/zxxx84fPgwPD09kZeXh/j4eDRv3rxGxTYLCwu0bNlSgmgr99prr+HEiRMICQnB8OHDUVZWhh9++KHSW1uff/55KJVKhISEaOa6e/duyGQyTJgwocZj7t27F/PmzUPfvn3Rtm1bKJVKnD9/Hvv370fbtm3RrVs3AMCMGTOwZ88eHD58GP7+/ujXrx+cnZ2RlJSEkydP4saNG08d6/XXX8f+/fuxZ88etG/fHuHh4XB1dUVGRgbOnz+vuTIReLQwQWRkJDZs2IDWrVtj4MCBaNGiBbKzsxEbG4uYmJinLnhhKBMnTsQXX3yB/fv3o2fPnujTpw+uXbuGvXv3olWrVvW6uu3mzZsIDg5GREQEbt68iW+++QZWVlZYtWrVU/cdO3Ys1q1bh27duuHll19GVlYWdu7cCU9PT6Smpmr61fQ9J8OquBqRiIioQSlXA4tHATIZ3N3d63aM40ue3sdJAWx6/dGLGh0W24iIGpEePXpAqVQiMTERN27cgFwuR0BAAIKDgxvU87WqMnv2bOTn52PDhg1YuXIl7Ozs0KtXL0RFRemscjlp0iTs3bsXu3btQmFhIRQKBXx9ffHJJ59g+PDhNR6ze/fu6NOnD86ePYtDhw5BrVbDxcUFkydPxrJly7T6/vTTT/jggw/w1VdfISoqCjKZDM2aNdNZAbQ6P/zwA1asWIGtW7di06ZNUKlUcHR0hJ+fn87qnlFRUejevTu++OIL7NixAyUlJbC3t4e3tzcWLFhQ4zH1zczMDD///DMiIyNx6NAhxMTEwMvLC1u2bMHnn39er9Wfjh07hqlTp2LNmjUoKytDu3bt8Mknn2iuWqzOqlWrcP/+fezfvx9Lly6Fh4cHVq1ahTNnzmgV22rznpPhVPV8QCIiIqMxN3u0umi/zgAa1vNp6dkiEzV9YjIRERHRU3h6eqK8vLzWt5NGRETg0KFDNV7IgZ59ly5d4jPbiIjIOJRjgMIqFgXbPx8YFAyA5yqqOz6zjYiIiGqtoKBApy06OhoZGRno0aOHESIiIiIiqgczM6BTK2BgV2NHQo0Ar98nIqJGKS8vr9KVKR9nYWEBT09PiSJqXGbMmIH4+Hj07NkTjo6OuHTpEg4cOACFQqFZOIHvAVWnXbt2xg6BiIjo/6jVwOLRwGO3jla3EjpRdVhsIyKiRuntt9/Gxo0bq+3j4uKC3NxciSJqXPr06YNz585h8+bNKCoqgkKhwPPPP49PPvkEfn5+APgeUPWysrI0K8ASEREZlZkMaNcCeFV7kaSMjIwaLTJG9CQ+s42IiBql2NhYJCQkVNvHzs4Or776qkQRmR6+B1QdPgeHiIiMprJntu2aBwwL1WriuYrqile2ERFRoxQYGIjAwEBjh2HS+B5QdaytrY0dAhER0aOr2to0B4Z219kkl8uNEBA1BlwggYiIiIgk17ZtW2OHQEREBKgFsGjUowUSnsBbSKmuWGwjIiIiIsldvnzZ2CEQEZGpk8mA1m7A8MpXUo+Pj5c4IGosWGwjIiIiIiIiItNhYQ5hJgOEAD4cCZibGzsiamT4zDYiIiIiklzTpk2NHQIREZmq7+cDx+MBC3NgVM8qu7m5uUkYFDUmXI2UiIiIiCR39+5dODs7GzsMIiIyUQkJCejQoYOxw6BGireREhEREZHkbty4YewQiIjIhJWWlho7BGrEWGwjIiIiIiIiIpNib29v7BCoEeNtpEREREQkuaKiItja2ho7DCIiMlE8D5Eh8co2IiIiIpLcrVu3jB0CERGZsOTkZGOHQI0Yi21EREREJLn79+8bOwQiIiIig2CxjYiIiIgkZ2lpaewQiIjI1AgBzNkMHL4ET09PY0dDjRif2UZEREREREREjV9xKWAzCgjvhJvb/gfNmzc3dkTUSPHKNiIiIiKS3KVLl4wdAhERmRqZ7NF/s+7y2aFkUCy2EREREREREZHpyCs0dgTUyLHYRkRERESSc3FxMXYIRERkqvIeoGPHjsaOghoxFtuIiIiISHIKhcLYIRARkakqKUNKSoqxo6BGjMU2IiIiIpJcenq6sUMgIiITVlxcbOwQqBFjsY2IiIiIiIiITIpSqTR2CNSIsdhGRERERJJr06aNsUMgIiIT1qJFC2OHQI0Yi21EREREJLk7d+4YOwQiIjJhV65cMXYI1Iix2EZEREREksvLyzN2CEREZMrK1YY57r0HQOSXgOskwG400GchcOGqYcaiBovFNiIiIgPavXs3ZDIZli1bZuxQNI4dO4bAwEDY2dlBJpMhMjLS2CGRCTI3Nzd2CEREZMI8lM76P6haDQz6CPjqN2DGAGDFBCDnPvDCQiAlW//jUYPFYhsRETVa2dnZGDNmDNzc3GBpaQl7e3t06tQJ+/bt0+s48fHxiIyMxLFjx/R6XEMoLS3FqFGjkJWVhVmzZmHlypUYN26cQceMjIzEhg0bDDoGPXs6duxo7BCIiMiEiTsFtd/phfeBSZ9XvX33aeBUErBlBvDBSOD1AcDxxYC5GfDBN3UPlp45FsYOgIiIyBD++OMP9OnTB8XFxfjLX/4CPz8/3Lt3D/Hx8UhPT9frWFeuXMHGjRvh7e2NPn366PXY+hYXF4ecnBzMnTsXS5culWTMjRs34saNG7yCjrTExcWhU6dOxg6DiIhM1N2rN+DSQ89/+Nl9GmjmCAzt/n9trg7AiFBgx69ASRlgbanfMalBYrGNiIgapREjRqC8vBwXL15E69atjR2OZO7cuYMmTZpUuT0jIwMAqu3zLCkoKIC1tTWsrKyMHQrVklptoGflEBER1YB5/kP9H/RiGhDkA5g9cRNhiC+w4QiQnA10bKX/canB4W2kRETU6OzduxcJCQmIjIxE69atUVJSgvz8/Fof5969e5g4cSKaNWsGS0tLODo6ol+/fvjjjz80fZYtW4bhw4cDAObPnw+ZTAaZTFbpLXJLly6Fh4cHLC0t4eLigtmzZ1c67pEjRxAWFgalUglLS0u4u7vj9ddfR2lpqVa/jh07wtXVFbGxsejVqxcUCgVcXFyqnE/Hjh0xdOhQnVjj4+MBPCp+LFy4EK1bt4a1tTXkcjk6deqEXbt26Rzr/fffR1BQEJydnWFhYQFHR0eEh4drjgU8ur1WJpMBAA4dOqQZr6KtYntlV7xFRkZqxQYAERERkMlkuHHjBgYMGAAHBwc4ODggOTkZAJCbm4vx48dr3Tbct29fxMbGah27sLAQkZGRcHd3h7W1NWxtbeHp6YmxY8dWmTvSP2dnAzwrh4iIqIZa2Vf9O1Od3cwDmjvptle0Zd/V/5jUIPHKNiIianT27t0LAPD29sZzzz2Hc+fOQa1Ww83NDXPnzsW8efOeeoySkhKEhoYiMTERvXr1Qu/evZGcnIxvv/0WYWFhOHPmDPz8/DBw4EAkJydjy5YteOWVV/D8888DANzd3bWOt2XLFuTl5WHYsGFwdHTEnj17sHr1anh6euLNN9/U9IuOjkZkZCTc3NwwadIkNGnSBL///ju+/PJLXL58Gb/++qvWcYuLi9G3b1906tQJb775Jm7dulXlnN59910cOXJEJ9aWLVsCAPr164ejR4/i+eefx8iRI1FSUoK9e/di1KhRuHfvHqZNm6Y5VlRUFAICAjB+/Hg0adIE8fHx+O6779CrVy/Ex8ejRYsWaNmyJVauXIm33noLHTp0wOTJk5+a95ro3bs3nJ2d8frrr+PBgwdwdHREbm4ugoKCkJubi1dffRX+/v7Izs7G119/jV69euH06dPw9/cHAIwcORIHDx5Ev3790KNHD6hUKiQnJ+P333/XS3xUM46OjsYOgYiITNid1BtoVl2HMhVwv0i3raQMuP3EH3GdFY+uZntYClhXUmaR//cK/IelutuocRJERESNTGhoqAAglEqlaNeunfjXv/4lPv74Y+Hh4SEAiI8//vipx1i0aJEAIMaOHavVvnnzZgFAhIeHa9p27dolAIilS5fqHKdim6Ojo8jJydG0379/XyiVStG2bVtNW0FBgXBwcBAdOnQQJSUlWseZN2+eACB27dqlaQsICBAAxOTJk5+elKfE+uWXXwoA4sMPP9RqLykpEa1btxaurq6ivLxc056Xl6dz7K+//loAELNmzdJqByD69++v0//y5csCgJg2bZrOtmnTpgkA4vLly5q2/v37CwDixRdf1Ok/fPhwYWlpKY4fP67VHh8fL+Ryudb4dnZ2Ijg4WOcYdXHnzh1RXFys+bmgoEDk5+drfi4pKRG3b9/W2ic7O7van2/evCnUanWjH+PcuXONYh4cg2NwDI7BMZ6hMYpLhcAQITBEZMz7stoxHhw4o+n71FfarUdj2I0WYsoXuvM4cE4IDBEl3595dnLVyMaQGottRETU6AQGBgoAomnTpqKoqEjTfvPmTWFrayscHByESqWq9hjBwcFCJpOJmzdv6mzz8vIScrlcc4yaFNsmTZqksy0kJEQoFArNz5s2bdIUAzMyMrReMTExAoCYOHGipn9Fse3PP/98ak6ejOfJWMPCwoRcLhfp6ek6Y0dGRgoA4uzZszrHU6lUIicnR9PXxsZGhIWFafXRd7Ht6NGjWn3Ly8uFQqEQQUFBOrFnZGSILl26CEdHR01/V1dX0aRJE3HixImaJY0M4uLFi8YOgYiITM1jxbaby/+3+r53C4Q4ckn71Wm2EP0W6bY//O8fSdv8XYgBS3SPtenIo3Hj0vU/J2qQeBspERE1OtbW1gCAQYMGwcbGRtPu5uaGnj174vDhwzh79iy6d+9e1SGQnZ0NJycnuLm56Wxr3bo10tPTkZmZiVatavaQ28oWaXByckJhYaHm58uXLwMAFixYgAULFlR6nNzcXK2flUolmjWr9iaIGklPT0dxcTG8vLyq7JOZmYng4GAAwM6dO7FkyRIkJSWhrKxMq19BQUG946lO165dtX7OyMhAYWEhLly4AA8Pj0r3qXhOHAB89NFHmDVrFnr27ImmTZsiODgYgwcPxtSpU2Fubm7Q2On/eHt7GzsEIiIyYU3aPOV3OCcFEB6o29bcSbe9Qmcv4LdEQK3WXiThTApgaw20da98P2p0WGwjIqJGp3nz5gBQaaGsojD1ZNHK0GpSxBFCAADmzJmDbt26VdrnyWJYRWGxvoQQUCqViIqKqrJPRXHy0KFDGD16NNzc3DBnzhy0adMGdnZ2msUOarrK5OMFsCepVKoqt9nb22v9XDFe586d8Y9//OOp40ZGRuLll1/GV199hePHjyMmJgYHDx7E6tWrce7cOa0CLRlOfn4+HBwcjB0GERGZqPT7ufDV90GH9QB2nwa+/R0YFvqo7XY+sOsU8EowYG2p7xGpgWKxjYiIGp2QkBB89913yMzM1NmWnZ0N4P8WBahKixYtcO7cOdy6dUvnyrFr167BxsZGc4zqika10a5dOwCAQqHA6NGj9XLMmvLw8EBMTAwiIiLg5FTJKlqP2bx5M9RqNX744Qd06tRJ037v3j08ePCgxmNWFEPz8vJ0tqWnp9f4OJ6enrC1tcWDBw9qnDd3d3fMmzcP8+bNg1qtxsSJE7Fjxw78+9//xowZM2o8NtXdnTt3qrwSkYiIyNDK7eX6P+iwHkD3tsDkL4CETMBFCaz7EShXA4tG6X88arDMnt6FiIjo2TJlyhTI5XLs379fq5Bz9epVnDhxAs2bN0eXLl2qPcbLL78MIQTeeustrfatW7ciLS0NYWFhmqvVKq7OuXPnTr3iHj16NOzt7bFu3TpNUfBxBQUF9R6jKuPHj4cQQmvF0celpaVp/r9i3hVX4lWYM2eOThvw6Oq7e/fu6bQ3adIEDg4OOHPmjNbVcLGxsTh58mSNYzc3N0dERARSUlKwdu3aSvtcv34dAFBWVqazYquZmZnm1tTbt2/XeFyqH30VqYmIiOrCxddL/wc1NwcOvgeMDAPWHADe2ga42ANHFwF+LfQ/HjVYvLKNiIgaHTc3N7zzzjv48MMPERgYiBEjRqC0tBRfffUVVCoVVq5c+dRjvP322/j666+xfft2ZGRkoGfPnkhJScGePXvg4OCAzz//XNP3ueeeg1wux44dO2BnZwcnJye4u7tjxIgRtYrb0dERa9euxZQpU9CuXTsMHjwYvr6+yMvLQ3JyMo4fP46tW7di2LBhtc7J07z++uvYv38/9uzZg/bt2yM8PByurq7IyMjA+fPnkZmZiZycHADAqFGj8PXXX2PgwIEYPXo0rKyscOzYMVy9ehVKpVLn2O3bt8fFixcxa9YseHl5QSaTYfbs2QCAsWPHYt26dejWrRtefvllZGVlYefOnfD09ERqamqN41+/fj0uXLiAmTNnYvfu3ejWrRusra2Rnp6OX375BR06dMCPP/6Iu3fvwsPDA6GhoejUqROaNWuGa9euYefOnbCzs8O4ceP0kk96usDAKp53Q0REJAHhaFf7nY4veXofJwWw6fVHLzJdxlydgYiIyJDWrVsn2rZtK6ysrIS1tbXo0qWL+O6772q8f15enpgwYYJwdXUV5ubmQqlUivDwcBEfH6/TNzo6Wnh7ewsLCwsBQAQEBAghql+ptGJ1zSedOHFCvPjii8LJyUmYm5sLe3t74efnJ6ZPny6ysrI0/QICAoSLi0uN5/O0eIQQYvny5aJDhw5CLpcLCwsL4eLiIsLCwsTq1au1+kVFRQkfHx9hZWUlFAqFeOGFF0RCQoJwcXHRzL3C+fPnRZcuXYRcLhcAtOZcUlIixo4dKxwcHISFhYXw9vYWmzZtqnY10qrcv39fTJ8+XXh4eAhLS0thbW0t3N3dxaBBg8TBgweFEEIUFRWJCRMmiDZt2gg7OzvNHCMiIsT58+drlUuqn8ffWyIiIkk8thopV8UmQ5IJUcn9HkREREREBnTp0iV07tzZ2GEQEZEpKSkD5CMBAJcuLuR5iAyGz2wjIiIiIsk5OjoaOwQiIjJh7du3N3YI1Iix2EZEREREknNxcTF2CEREZMIyMjKMHQI1Yiy2EREREZHkarMABhERkb4VFhYaOwRqxFhsIyIiIiIiIiKTIWQyyOVyY4dBjRiLbUREREQkOS8vL2OHQEREJko42qJNmzbGDoMaMRbbiIiIiEhyDx48MHYIRERkotT2NoiPjzd2GNSIsdhGRERERJLLzc01dghERGSiSu2sjB0CNXIsthERERERERFR42duBtjb4N5L7eHm5mbsaKgRkwkhhLGDICIiIiLTIoSATCYzdhhERGRq7j+AMJNBprQ1diTUiPHKNiIiIiKSXGJiorFDICIiU+Rgh8SMdGNHQY0ci21EREREJLnS0lJjh0BERCaK5yAyNBbbiIiIiEhy9vb2xg6BiIhMFM9BZGh8ZhsRERERSa6oqAi2tnxeDhERSY/nIDI0XtlGRERERJJLTk42dghERGSieA4iQ2OxjYiIiIiIiIiISE9YbCMiIiIiyXl6eho7BCIiMlE8B5GhsdhGRERERJIrKSkxdghERGSieA4iQ2OxjYiIiIgkd+vWLWOHQEREJornIDI0FtuIiIiIiIiIiIj0RCaEEMYOgoiIiIhMS3l5OczNzY0dBhERmSCeg8jQeGUbEREREUkuJSXF2CEQEZGJ4jmIDI3FNiIiIiKSXHFxsbFDICIiE8VzEBkai21EREREJDmlUmnsEIiIyETxHESGxme2EREREZHkiouLIZfLjR0GERGZIJ6DyNB4ZRsRERERSe7KlSvGDoGIiEwUz0FkaCy2ERERERERERER6QmLbUREREQkuZYtWxo7BCIiMlE8B5GhsdhGRERERJIrLy83dghERGSieA4iQ2OxjYiIiIgkd/PmTWOHQEREJornIDI0FtuIiIiIiIiIiIj0RCaEEMYOgoiIiIhMS1lZGSwtLY0dBhERmSCeg8jQeGUbEREREUkuLS3N2CEQEZGJ4jmIDI3FNiIiIiKSXFFRkbFDICIiE8VzEBkai21EREREJDk7Oztjh0BERCaK5yAyND6zjYiIiIgkV1paCisrK2OHQUREJojnIDI0XtlGRERERJJLSEgwdghERGSieA4iQ2OxjYiIiIiIiIiISE9YbCMiIiIiybm7uxs7BCIiMlE8B5GhsdhGRERERJKTyWTGDoGIiEwUz0FkaCy2EREREZHksrKyjB0CERGZKJ6DyNBYbCMiIiIiIiIiItITmRBCGDsIIiIiIjItJSUlsLa2NnYYRERkgngOIkPjlW1EREREJLmMjAxjh0BERCaK5yAyNBbbiIiIiEhyhYWFxg6BiIhMFM9BZGgsthERERGR5ORyubFDICIiE8VzEBkan9lGRERERJJTqVSwsLAwdhhERGSCeA4iQ+OVbUREREQkufj4eGOHQEREJornIDI0lnKJiIiI6qC8vBzJycnGDuOZde3aNa4ER0RERsFzkOlp27YtzM3NJRuPxTYiIiKiOkhOTkaHDh2MHQYRERERPUVCQgLat28v2Xh8ZhsRERFRHTzrV7YVFhYiJCQEMTExUCgUxg6nwWF+qsbcVI25qR7zUzXmpmr6zs2ff/6Jvn374ujRo3Bzc9NDhMbFz07VHs9NUFCQpFe2sdhGREREZILy8/Ph4OCA+/fvw97e3tjhNDjMT9WYm6oxN9VjfqrG3FRN37nJzMyEh4cHMjIy0LJlSz1EaFz87FTNmLnhAglERERERERERER6wmIbERERERERERGRnrDYRkRERGSCrK2t8cEHH3A1tiowP1VjbqrG3FSP+akac1M1fefG3t4evXv3bjS3XPKzUzVj5obPbCMiIiIiIiIiItITXtlGRERERERERESkJyy2ERERERERERER6QmLbURERERERERERHrCYhsREREREREREZGesNhGRERE9AxSq9VYtWoV2rVrB7lcDg8PD8ydOxcPHjww+P4jR46ETCZDQEBAfadhEFLnRqVSYc2aNQgKCoKdnR0cHBwQFBSEqKgofU5LL6TMjRACX331FUJDQ+Hi4gKlUgl/f38sXrwY+fn5+p6aXtQ3P8uWLcPw4cPh4+MDmUwGLy+vavufOXMG4eHhUCqVsLe3R0REBC5dulT/iRiAVLkpLi7Gxo0bMXjwYHh5ecHGxgY+Pj4YPXo0EhMT9Tgj/ZH6c/O4t99+GzKZDAqFoo7RG54x8rN9+3aEhYXB3t4eCoUCAQEBWLJkST1non9S5+aHH37Aiy++CDc3N9jZ2cHPzw/z5s3DrVu39DAb/apPbpKTk7Fw4UJ0794drq6uUCqV6Ny5Mz7++OMq909KSsJf/vIXODk5wc7ODr169cLRo0frPgFBRERERM+cN954QwAQQ4YMERs2bBBz5swRFhYWok+fPqK8vNxg+3///ffCzMxM2NjYCH9/f31OSW+kzE1JSYno37+/sLKyEpMnTxZRUVFi7dq1Yvbs2eLdd9811BTrTMrczJ8/XwAQffv2FWvWrBFffvmlGDlypAAgnnvuOaFWqw01zTqrb34ACGdnZxEeHi6cnJxEq1atqux7+vRpYW1tLXx8fMSnn34qPv30U+Hj4yMUCoWIi4vT46z0Q6rcJCYmCgCiZ8+eYvHixWLTpk1i/vz5wsnJSVhZWYmjR4/qeWb1J+Xn5nEXL14UFhYWQqFQCDs7u3rOwnCkzs/kyZOFmZmZGD58uFi3bp1Yv369ePvtt8XUqVP1NCP9kTI3GzZsEABE165dxaeffiqioqLEX//6V2Fubi68vb1FYWGhHmdWf/XJzdtvvy0UCoUYM2aM5vwzYsQIAUB06tRJFBUVafVPTU0Vzs7OomnTpmLp0qVi7dq1onPnzsLCwkIcOXKkTvGz2EZERET0jImPjxcymUwMHTpUq33NmjUCgPjPf/5jkP0LCgqEh4eHmDlzpmjVqlWDLLZJnZv33ntPmJubN8gCwJOkzE1ZWZmwtbUVQUFBOv8oGjt2rAAgLl68WL8J6Vl98yOEEFevXtX8v7+/f7X/8O3WrZtQKpUiMzNT05aZmSmUSqV46aWXaj8BA5IyN7dv3670s/HHH38IKysr0bVr11rFbmhSf24qqFQqERwcLF555RXRu3fvBltskzo/mzZtEgDEtm3b6hyzVKTOTdu2bUXz5s3Fw4cPtdoXLFggAIi9e/fWKn5Dqm9uzp49K+7du6fTXjHXzz//XKt9+PDhwszMTOu7p6CgQHh6eoq2bdvW6Y9DLLYRERERPWMqfln89ddftdofPnwobG1txYABAwyy/xtvvCHc3d3F/fv3G2yxTcrcFBYWCqVSqfnHgFqtFvn5+Xqaif5JmZuioiIhk8nEwIEDdY7z1ltvCQAiOTm5HrPRv/rm50nV/cM3JSVFABBTpkzR2TZlyhQhk8nEzZs3azWeIUmZm+oEBQUJa2vrWu9nSMbKzaeffipsbW1Fenp6gy62SZkftVotWrduLYKCgjRt+fn5DfIqWiGk/+y4ubmJDh066LSvXbtWABCHDx+u1XiGpO/cVIiLixMAxP/8z/9o2goLC4W1tbXo27evTv/FixcLAOLMmTO1HovPbCMiIiJ6xpw9exZmZmYICQnRapfL5ejcuTPOnj2r9/1jYmLwxRdfYNWqVbC3t6//JAxEytz89ttvKCgoQNeuXTFr1izY29vD3t4erq6umD9/PlQqlf4mpgdS5sbGxgbPP/88fvzxRyxfvhypqalIT0/Hli1bsG7dOowbNw6+vr76m5we1Dc/tR0LAHr06KGzrXv37hBC4Pz583obr76kzE1V1Go1bt68iWbNmhl8rNowRm6uX7+O999/Hx988AFatWql9+Prk5T5SUpKwtWrVxEaGoolS5agSZMmsLe3h6OjI6ZPn47CwkK9jaUPUn92+vfvj4SEBMydOxeJiYnIyMjAt99+iyVLlqB3797o27evXserD0PlJjMzEwC0vkfi4uJQUlJS5fdxRTy1xWIbERER0TMmOzsbLi4usLa21tnWokUL3L59G6WlpXrbX6VS4a9//Sv69euHESNG6GcSBiJlbpKSkgAAn332Gfbs2YMVK1bgm2++QWhoKJYtW4apU6fqaVb6IfXn5j//+Q/69u2Ld955B76+vvD29saUKVMwZ84cbNu2TT+T0qP65qe2Y1Uct7KxACArK0svY+mDlLmpyvr163Hz5k1MnDjRoOPUljFy87e//Q0+Pj5488039XpcQ5AyPxXfyd988w0++eQTvPvuu9i9ezeGDh2KqKgovPLKKxBC6GUsfZD6s7N69WoMHz4cq1evRocOHeDp6YnXXnsNAwYMwJEjR2Bubq63serLELkpLy/HkiVLYGFhgTFjxmiNVXHcysYC6vZ9bFHrPYiIiIjIqIqKiir9BRR49Fffij5WVlZ62X/lypVITU3Fvn376hm54UmZm4KCAgDA3bt38ccff8DPzw8AMGLECPTp0wfbtm3DO++8g/bt29drTvoi9efG2toa3t7emDBhAgYMGAAA2LNnDz766CPI5XIsWLCgXvPRt/rmp7ZjAah0vMfHaiikzE1lTp06hTfffBOBgYGYP3++QcaoK6lz87//+7/48ccfceLECVhYNPx/zkuZn4rv5NzcXBw5cgTh4eEAgNdeew1CCGzduhU//vij5vvI2KT+7FhaWsLT0xNDhgzBK6+8AltbWxw6dAibN2+Gubk5Nm7cqJdx9MEQuZk9ezZOnz6NpUuXas7XFccB9P99zCvbiIiIiJ4xtra2KCkpqXRbcXGxpo8+9k9NTcXixYuxYMEC+Pj41CdsSUiZGxsbGwCPbjN5/Bd3AJgwYQIA4Pjx4zUP3sCkzE1RURFCQ0ORn5+PrVu3YtSoURg1ahR27dqFkSNHYuHChZqrUBqK+uantmMBqHQ8fY+lD1Lm5knnz5/HoEGD4O7ujgMHDmj+8dtQSJmbu3fvYvbs2Zg6dSpCQ0P1ckxDkzI/Fd/JLVq00BTaKlRcEdmYvpNrQ61WIyIiAqdOncLOnTsxYcIEDBs2DBs3bsRbb72FTZs24aefftLLWPqg79y8//77+OKLLxAZGYl3331XZyxA/9/HLLYRERERPWPc3d1x+/btSn8xzMrKgouLS7V/7a3N/nPnzoWzszOGDBmC1NRUzUulUqG0tBSpqam4efOm/iZXT1LmpmXLlgAANzc3nb7NmzcHAOTl5dVpHoYgZW52796NlJQUDB8+XKfv8OHDoVarceLEiXrMRv/qm5/ajlVx3MrGAiq/pclYpMzN4y5cuICXXnoJDg4OOHbsWIPKSQUpc7No0SI8ePAA06ZN0/o+fvjwIYQQSE1NRUZGhl7G0hcp82Nq38m1ceLECfz222947bXXIJPJtLZVfE//8ssvehlLH/SZmw8//BAfffQRJk+ejPXr11c6VsVxKxsLqNv3MYttRERERM+Ybt26Qa1WIyYmRqu9uLgYly5dQnBwsN72v379OrKzs+Hv7w9fX1/NKysrCykpKfD19cW0adP0N7l6kjI3FQ9urnjg8uMq2po2bVqneRiClLmp+AdKeXm5znEqFo5oaAtI1Dc/tR0LAE6fPq2z7ffff4dMJkPXrl31Nl59SZmbChcuXEB4eDiUSiWOHTvWYBcCkDI3169fx4MHD/Dcc89pfR/HxMSgqKgIvr6+DeYWyQpS5qdjx46Qy+WVFk0a43dybTxr38n6ys2HH36IRYsWYeLEidi0aZNOoRF49Lmxtrau8vsYQN3eizqtl0pERERERhMXFydkMpkYOnSoVvuaNWsEALF9+3ZNW2pqqkhMTKzz/keOHBG7du3Sebm6ugoPDw+xa9cuceLECQPMsm6kzI0QQoSFhQmZTCbOnz+vaVOpVCIkJERYWFiI69ev62tq9SZlbvbt2ycAiIEDB+rEMWDAAAFAK2cNQX3z8yR/f3/RqlWrKrcHBwcLpVIpsrKyNG1ZWVlCqVSKF198sW6TMBCpc3PhwgXh7OwsPDw8xNWrV+sVu6FJmZtTp05V+n3coUMHIZfLxa5du8Thw4frPSd9kvqzM3bsWAFAfPvtt1rtQ4cOFQDEyZMnaz8JA5EyN5cuXRIARKdOnURpaanWtunTpwsAYs+ePXWbiAHoIzeLFi0SAMT48eNFeXl5teMNGzZMmJmZiUuXLmnaCgoKhKenp/D19RVqtbrWc5AJ0YCW4yAiIiKiGpk5cya++OILDBkyBAMHDkRiYiLWrFmDsLAwHD16FGZmj25g8PLywvXr13VWYKvp/lXx8vKCQqFAfHy8weZYV1Lm5uLFi+jVqxesrKzwxhtvoEmTJvjmm29w8uRJLFy4EIsWLZJ07k8jVW7Ky8sRGhqKmJgY9OrVC0OHDgUAfPvtt/jtt98wfPhw7Ny5U9rJ10B987N9+3Zcv34dAPD555+jtLQUc+fOBQC0atUK48eP1/Q9deoU+vTpg5YtW2LmzJmafW7duoWTJ08iMDBQiinXmFS5uX79Orp27Yq7d+/igw8+QOvWrXViGTJkCOzs7Aw53VqR8nNTmRdeeAHnzp1DYWGhAWZXf1Lm58aNGwgJCcH9+/cxc+ZMeHl54eDBgzhw4AAmTJiArVu3SjTrmpEyN8OGDcOePXvQqVMnjBs3TrNAwvfff4/u3bvjxIkTDWpF0vrkZu3atZgxYwY8PT2xZMkSnd9pmjVrhpdeeknzc2pqKkJCQmBpaYk5c+bA3t4eGzduxOXLl3HgwAH079+/9hOodXmOiIiIiIxOpVKJf/3rX6Jt27bCyspKuLu7izlz5oiCggKtfq1atRKV/cpX0/2r0qpVK+Hv76+Xueib1LmJjY0Vr7zyinBwcBDW1taic+fOIjo62hBTqzcpc5Ofny/effdd4efnJ6ysrIS1tbUICAgQy5cvF2VlZQabY33UNz+9e/cWACp99e7dW6f/qVOnRN++fYWdnZ1QKBSiX79+De6KvwpS5ebYsWNV9qt4paWlGXi2tSP156ay/e3s7PQ1Hb2TOj9paWlizJgxwsXFRVhaWop27dqJf/3rX0+9uskYpMxNSUmJWL58uejUqZOQy+XCyspK+Pr6infffVcUFhYacpp1Up/cTJw4sdrvkMo+NwkJCeLVV18VDg4OwsbGRoSFhYkjR47UOX5e2UZERERERERERKQnXCCBiIiIiIiIiIhIT1hsIyIiIiIiIiIi0hMW24iIiIiIiIiIiPSExTYiIiIiIiIiIiI9YbGNiIiIiIiIiIhIT1hsIyIiIiIiIiIi0hMW24iIiIiIiIiIiPSExTYiIiIiIiIiIiI9sTB2AERERERERERENTVp0iRs3boVAODv74/4+Hit7Wq1GkuXLkV0dDRu3LgBT09PXL16FStWrMDmzZuRkJAAM7PaX3u0fv16LF26FCkpKbC2ttba9tlnn2HOnDman3Nzc+Hi4lKH2dVNeXk5zp07h5SUFJSUlMDZ2RndunVDy5Ytn7pvWVkZYmNjkZOTg9zcXJSUlKB3797w8/OrtP/t27dx7tw53Lp1CyqVCvb29mjfvj0CAgI0fbKzs7F///5K9x88eDCaNWtWt4k+I3hlGxERERERERE1CIcOHYJMJqvytW3bNgCAi4sLtm/fjn/+8586x1i3bh0WLlyIoUOHYvPmzYiKikJ+fj6WL1+Ot99+W6fQtmjRIpiZmSExMVHnWFOmTIG5uTkOHDiASZMmobS0FFFRUTr9IiIisH37dgwZMkRPmaid48ePIy4uDm3atEFoaCjMzMzwww8/4M8//3zqvsXFxbhw4QLu3bsHZ2fnavtmZmZi3759KC4uRpcuXRAaGgpPT08UFhZW2j8gIAB9+vTRejk4ONRpjs8SXtlGRERERERERA1CbGwsAGDNmjVwcnLS2d6/f38cPXoUdnZ2GDduXKXHiI6OxksvvYSVK1dq2j777DOoVCqMHj1ap//f/vY3/POf/8Rnn32mVUj7/PPPER0djY8++giDBg0CAEycOBGffvopZs6cCZlMpunbrl07tGvXDqmpqdi7d2/dJl9HOTk5uHr1Kp577jkEBgYCAHx9fbF7926cOXMGgwcPrnZ/W1tbjBs3Dra2tsjNza0y/tLSUhw7dgyenp546aWXtOZfFTc3N/j4+NR+Us84FtuIiIiIiIiIqEGIi4uDg4MDZsyYUaNizpOKi4sRGxuLRYsWabVHR0fj1VdfhVwu19mnadOmGDt2LLZv346PP/4YLi4u+OWXX/Dmm2/itddew4IFCzR9R4wYgRUrVuDYsWPo27dv7SdoANeuXYNMJkP79u01bRYWFvDz88PZs2dRWFgIhUJR5f7m5uawtbV96jipqal4+PAhunXrBplMhrKyMlhYWDz1fSotLYWFhUWdbt19VpnOTImIiIiIiIioQYuNjUWXLl3qVGibOnUqbGxsUF5ejvfeew8ymQw9evRAWloa4uLiEB4eXuW+c+bMwcOHD7F+/XpkZGRgxIgRaNeuHbZs2aLVr2vXrnB2dsZ3331X6/gqo1arUVxcXKOXEKLSY9y5cwcODg6wsrLSam/atKlmuz5kZWXB0tISDx48wDfffIPo6GhER0fjt99+g0qlqnSfX375BVu2bMG///1vfP/998jNzdVLLA0dr2wjIiIiIiIiIqMrLS1FUlISevbsidu3b+tsd3BwgKWlZZX7jx07FpaWloiKisLq1avh7OyMVq1a4dSpUwCAoKCgKvf19/dHv379sHbtWuzbtw9lZWXYt29fpVeEBQUF4eTJk3WYoa4///yzyoUEnjR69GgolUqd9qKiokqvTKtoe/DgQf2C/K/79+9DCIHDhw/Dz88PISEhyM7Oxh9//IHS0lK8+OKLmr5mZmbw9vaGh4cH5HI57t27h9jYWPy///f/MHjwYEkXjzAGFtuIiIiIiIiIyOgSEhJQVlaG9evXY/369Trbk5KS0LZt2yr379u3L37++WfY2dlhxowZmtsW33//fQCAt7d3teO/+eabiIiIQE5ODg4ePIjWrVtX2s/Hxwfbt2+v6bSq1aRJEwwcOLBGfW1sbCptV6lUMDc312mvaCsvL697gI8pKyuDSqVC+/btERYWBuBRTtVqNRITExEcHKxZ/MDNzQ1ubm5a+3t7e2P37t2IiYmp8ZyfVSy2EREREREREZHRxcXFAQC2bNmCFi1a6Gz39fWt0TH8/f21ng92584dWFhYVPvcMgCa1Uh9fHzQv3//Kvs5OTnh4cOHVV5RVhvW1tZo2bJlvY5hYWFRaUGtoq2yQlxdxwGANm3aaLW3adMGiYmJuHXrVrUrjTo4OMDLywtpaWlQq9WN+hluLLYRERERERERkdHFxsbCwsICo0eP1nn+WG2OUV2hrCo//fQT5s2bB19fX6SkpODw4cPo169fpX0rnp1Wl+fKPam8vBwlJSU16iuXyystUNna2lZ6q2hRUREAwM7Orn5BPjZOXl6ezhV2FT/XZB52dnZQq9VQqVR1fo+fBSy2EREREREREZHRxcXFwdvbu85FmHv37iEjIwMdO3bUam/SpAlUKhUKCgoqfebZtWvXMHLkSHTp0gU//fQT2rZti1WrVlVZbMvLy4OtrW2Vt3XWxq1bt+r9zLYmTZogOzsbpaWlWrnLycnRbNcHV1dXZGVl4cGDB3B0dNS0VxT6apKPgoICmJubV/vsvcaAxTYiIiIiIiIiMrq4uDh07969XvsDQKdOnbTa27VrBwBIS0vT2VZYWIjBgwfD0tISe/fuhYODA/7+979j0aJFSExMRPv27XXGSUtLq7S9LvTxzDYfHx/ExcUhMTERgYGBAB5dMZeUlISmTZtqbp9VqVQoLCyEXC6HXC6vdaw+Pj64dOkSkpKStG7zvXLlCmQyGZo3b65pe/jwoU68d+7cwfXr1+Hh4aGXqwIbMhbbiIiIiIiIiMio/vzzT+Tk5GgKY3URGxsLQLfY1qNHDwDAuXPntLYJITB+/HgkJSXh2LFjmmen/f3vf8c///lPfPbZZ4iKitIZ58KFCxg7dmyd43ycPp7Z1rRpU/j4+CAmJgYPHz6Eg4MDkpOTUVBQgN69e2v65eTkYP/+/QgKCkJwcLDWMeLj41FaWqq59fT69euaK9YCAgJgZWUFFxcX+Pn5ISkpCWq1Gs2bN8fNmzdx7do1dO7cWet21Z9//hnm5uZo1qwZbGxskJeXhytXrsDCwgIhISH1mu+zgMU2IiIiIiIiIjKqikJZbm4uduzYobM9MDBQ5/bQJ8XFxaFFixZwdnbWavfx8UFAQAB++uknTJkyRdP+4YcfYt++fYiKitKsrgk8ul1y3Lhx2L59O5YuXap1G+b58+dx9+5dDB48uE7zNJQXXngBCoUCKSkpKC0thbOzMyIiIrSuNqtOXFwcCgsLNT+np6cjPT0dwKOFKSpuT+3VqxcUCgWSkpKQnp4OhUKBHj166Lw3Xl5eSElJweXLl1FaWgobGxt4eXmha9eu1S6i0Fiw2EZERERERERERlVxC2h0dDSio6N1tm/btq1GxbYnr2qrMGXKFCxcuFBze+PevXuxZMkSTJ8+HZGRkTr958yZg3//+99Yv349FixYoGnftWsXPD090bdv39pMz+AsLCzQvXv3am/DdXd3r3SuADBmzJgajWNmZoauXbuia9eu1fYLCAhAQEBAjY7ZGMlExTIaREREREREREQN3KRJk3D06FFcuHABFhYWWg/rr8r9+/fh4+ODFStWYOrUqXUat6SkBF5eXnjnnXcwa9YsrW3FxcUoLCzEihUrsHLlSuTm5sLFxaVO49CzT3fNWCIiIiIiIiKiBiwjIwOurq7o2bNnjfo7ODjgH//4B1auXAm1Wl2nMaOjo2FpaYnp06frbFu/fj1cXV2xcuXKOh2bGhde2UZEREREREREz4yEhARkZ2cDABQKRb1WMNWXjIwMJCUlaX7u3bs3LC0tjRgRGROLbURERERERERERHrC20iJiIiIiIiIiIj0hMU2IiIiIiIiIiIiPWGxjYiIiIiIiIiISE9YbCMiIiIiIiIiItITFtuIiIiIiIiIiIj0hMU2IiIiIiIiIiIiPWGxjYiIiIiIiIiISE9YbCMiIiIiIiIiItITFtuIiIiIiIiIiIj05P8D+bIwfU+DI9gAAAAASUVORK5CYII=",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Show the individual prediction for the lowest predicted instance\n",
+ "lowest_pred_index = np.argmin(shap_values.values[:, 0]) \n",
+ "\n",
+ "# Use waterfall plot for a single instance\n",
+ "shap.plots.waterfall(shap_values[lowest_pred_index], max_display=20)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "venv",
+ "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": "3.12.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/data_driven_risk_assessment/experiments/003_contactless_reduced_attributes.ipynb b/data_driven_risk_assessment/experiments/003_contactless_reduced_attributes.ipynb
new file mode 100644
index 0000000..7e3cf04
--- /dev/null
+++ b/data_driven_risk_assessment/experiments/003_contactless_reduced_attributes.ipynb
@@ -0,0 +1,2170 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "84dcd475",
+ "metadata": {},
+ "source": [
+ "# DDRA - Contactless (Reduced)\n",
+ "\n",
+ "## General Idea\n",
+ "The idea is to play only with numeric features (floats, integers or booleans) that are CONTACTLESS.\n",
+ "\n",
+ "This considers a subset of the features. This is mostly a copy from 002_contactless_full_attributes that just selects the most relevant attributes.\n",
+ "\n",
+ "## Initial setup\n",
+ "This first section just ensures that the connection to DWH works correctly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "12368ce1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "🔌 Testing connection using credentials at: /home/uri/.superhog-dwh/credentials.yml\n",
+ "✅ Connection successful.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# This script connects to a Data Warehouse (DWH) using PostgreSQL. \n",
+ "# This should be common for all Notebooks, but you might need to adjust the path to the `dwh_utils` module.\n",
+ "\n",
+ "import sys\n",
+ "import os\n",
+ "sys.path.append(os.path.abspath(\"../../utils\")) # Adjust path if needed\n",
+ "\n",
+ "from dwh_utils import read_credentials, create_postgres_engine, query_to_dataframe, test_connection\n",
+ "\n",
+ "# --- Connect to DWH ---\n",
+ "creds = read_credentials()\n",
+ "dwh_pg_engine = create_postgres_engine(creds)\n",
+ "\n",
+ "# --- Test Query ---\n",
+ "test_connection()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c86f94f1",
+ "metadata": {},
+ "source": [
+ "## Data Extraction\n",
+ "In this section we extract the data.\n",
+ "\n",
+ "This SQL query retrieves a clean and relevant subset of booking data for our model. It includes:\n",
+ "- A **unique booking ID**\n",
+ "- Key **numeric features** such as number of services, time between booking creation and check-in, number of nights, etc.\n",
+ "- Several **categorical (boolean) features** related to service usage\n",
+ "- A **target variable** (`has_resolution_incident`) indicating whether a resolution incident occurred\n",
+ "\n",
+ "Filters applied being:\n",
+ "1. Bookings from **\"New Dash\" users** with a valid deal ID\n",
+ "2. Only **protected bookings**, i.e., those with Protection or Deposit Management services\n",
+ "3. Bookings flagged for **risk categorisation** (excluding incomplete/rejected ones)\n",
+ "4. Bookings that are **already completed**\n",
+ "\n",
+ "The result is converted into a pandas DataFrame for further processing and modeling.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3e3ed391",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Initialise all imports needed for the Notebook\n",
+ "from sklearn.model_selection import (\n",
+ " train_test_split, \n",
+ " GridSearchCV\n",
+ ")\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from datetime import date\n",
+ "from sklearn.metrics import (\n",
+ " roc_auc_score, \n",
+ " average_precision_score,\n",
+ " classification_report,\n",
+ " roc_curve, \n",
+ " auc,\n",
+ " precision_recall_curve,\n",
+ " precision_score,\n",
+ " recall_score,\n",
+ " fbeta_score,\n",
+ " confusion_matrix\n",
+ ")\n",
+ "import matplotlib.pyplot as plt\n",
+ "import shap\n",
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "db5e3098",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Total Bookings: 21,384\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Query to extract data\n",
+ "data_extraction_query = \"\"\"\n",
+ "WITH \n",
+ "service_information AS (\n",
+ "\tSELECT\n",
+ "\t\tid_booking,\n",
+ "\t\tcount(DISTINCT CASE WHEN service_business_type = 'SCREENING' THEN id_booking_service_detail ELSE NULL END) AS number_of_applied_screening_services,\n",
+ "\t\tcount(DISTINCT CASE WHEN service_business_type = 'DEPOSIT_MANAGEMENT' THEN id_booking_service_detail ELSE NULL END) AS number_of_applied_deposit_management_services,\n",
+ "\t\tcount(DISTINCT CASE WHEN service_business_type = 'PROTECTION' THEN id_booking_service_detail ELSE NULL END) AS number_of_applied_protection_services,\n",
+ "\t\tcount(DISTINCT CASE WHEN service_name = 'WAIVER PRO' THEN id_booking ELSE NULL END)>0 AS has_waiver_pro,\n",
+ "\t\tcount(DISTINCT CASE WHEN service_name IN ('BASIC DAMAGE DEPOSIT','BASIC DAMAGE DEPOSIT OR BASIC WAIVER','BASIC DAMAGE DEPOSIT OR WAIVER PLUS','BASIC WAIVER','WAIVER PLUS') THEN id_booking ELSE NULL END)>0 AS has_guest_facing_waiver_or_deposit,\n",
+ "\t\tcount(DISTINCT CASE WHEN service_name = 'GUEST AGREEMENT' THEN id_booking ELSE NULL END)>0 AS has_guest_agreement,\n",
+ "\t\tcount(DISTINCT CASE WHEN service_name = 'BASIC PROTECTION' THEN id_booking ELSE NULL END)>0 AS has_basic_protection,\n",
+ "\t\tcount(DISTINCT CASE WHEN service_name = 'PROTECTION PLUS' THEN id_booking ELSE NULL END)>0 AS has_protection_plus,\n",
+ "\t\tcount(DISTINCT CASE WHEN service_name = 'PROTECTION PRO' THEN id_booking ELSE NULL END)>0 AS has_protection_pro,\n",
+ "\t\tcount(DISTINCT CASE WHEN service_name = 'ID VERIFICATION' THEN id_booking ELSE NULL END)>0 AS has_id_verification,\n",
+ "\t\tcount(DISTINCT CASE WHEN service_name = 'SCREENING PLUS' THEN id_booking ELSE NULL END)>0 AS has_screening_plus,\n",
+ "\t\tcount(DISTINCT CASE WHEN service_name = 'SEX OFFENDER CHECK' THEN id_booking ELSE NULL END)>0 AS has_sex_offender_check\n",
+ "\tFROM\n",
+ "\t\tintermediate.int_core__booking_service_detail\n",
+ "\tGROUP BY\n",
+ "\t\t1\n",
+ "),\n",
+ "listing_information AS (\n",
+ "SELECT \n",
+ "\tica.id_accommodation,\n",
+ "\t-- Defaults to 0 if null\n",
+ "\tCOALESCE(ica.number_of_bedrooms, 0) AS listing_number_of_bedrooms,\n",
+ "\t-- Defaults to 0 if null\n",
+ "\tCOALESCE(ica.number_of_bathrooms, 0) AS listing_number_of_bathrooms\n",
+ "\tFROM intermediate.int_core__accommodation ica \n",
+ "),\n",
+ "raw_bookings_checked_in_prior_to_TCR AS (\n",
+ "\tSELECT\n",
+ "\t\tb.id_booking,\n",
+ "\t\t-- Using group by on check-in date to remove booking duplicates\n",
+ "\t\tb2.booking_check_in_date_utc,\n",
+ "\t\t-- Using min as a conservative approach to reduce outliers\n",
+ "\t\tmin(b2.booking_number_of_nights) AS min_booking_number_of_nights\n",
+ "\tFROM\n",
+ "\t\tintermediate.int_booking_summary b\n",
+ "\t-- Note that by joining with BS we're only considering New Dash bookings\n",
+ "\tLEFT JOIN intermediate.int_booking_summary b2\n",
+ " ON\n",
+ "\t\tb2.id_accommodation = b.id_accommodation\n",
+ "\t\t-- Exclusion based on actual booking creation!\n",
+ "\t\tAND b2.booking_check_in_date_utc >= b.booking_created_date_utc - INTERVAL '30 days'\n",
+ "\t\tAND b2.booking_check_in_date_utc < b.booking_created_date_utc\n",
+ "\t\t-- Note that since is based on TCR we can remove Cancelled\n",
+ "\t\tAND b2.booking_status NOT IN ('CANCELLED')\n",
+ "\tGROUP BY\n",
+ "\t\tb.id_booking,\n",
+ "\t\tb2.booking_check_in_date_utc\n",
+ "),\n",
+ "bookings_checked_in_prior_to_TCR AS (\n",
+ "\tSELECT\n",
+ "\t\tid_booking,\n",
+ "\t\tLEAST(\n",
+ "\t\t\tcount(booking_check_in_date_utc),\n",
+ "\t\t\t30\n",
+ "\t\t) AS listing_check_ins_prior_to_TCR_in_30_days,\n",
+ "\t\t-- Capping\n",
+ "\t\tLEAST(\n",
+ "\t\t\tGREATEST(\n",
+ "\t\t\t\tsum(min_booking_number_of_nights),\n",
+ "\t\t\t\t0\n",
+ "\t\t\t),\n",
+ "\t\t\t30\n",
+ "\t\t) AS listing_occupancy_prior_to_TCR_in_30_days\n",
+ "\tFROM\n",
+ "\t\traw_bookings_checked_in_prior_to_TCR\n",
+ "\tGROUP BY\n",
+ "\t\t1\n",
+ "),\n",
+ "raw_known_bookings_checking_in_prior_to_TCI AS (\n",
+ "\tSELECT\n",
+ "\t\tb.id_booking,\n",
+ "\t\tb.booking_check_in_date_utc,\n",
+ "\t\t-- Using group by on check-in date to remove booking duplicates\n",
+ "\t\tb2.booking_check_in_date_utc AS other_bookings_check_in_date_utc,\n",
+ "\t\t-- Using min as a conservative approach to reduce outliers\n",
+ "\t\tmin(b2.booking_number_of_nights) AS min_booking_number_of_nights\n",
+ "\tFROM\n",
+ "\t\tintermediate.int_booking_summary b\n",
+ "\t-- Note that by joining with BS we're only considering New Dash bookings\n",
+ "\tLEFT JOIN intermediate.int_booking_summary b2\n",
+ " ON\n",
+ "\t\tb2.id_accommodation = b.id_accommodation\n",
+ "\t\t-- Exclusion based on check-in\n",
+ "\t\tAND b2.booking_check_in_date_utc >= b.booking_check_in_date_utc - INTERVAL '30 days'\n",
+ "\t\tAND b2.booking_check_in_date_utc < b.booking_check_in_date_utc\n",
+ "\t\t-- that are known!\n",
+ "\t\tAND b2.booking_created_date_utc < b.booking_created_date_utc\n",
+ "\t\t-- Note that since is based on TCI we cannot remove Cancelled\n",
+ "\tGROUP BY\n",
+ "\t\tb.id_booking,\n",
+ "\t\tb.booking_check_in_date_utc,\n",
+ "\t\tb2.booking_check_in_date_utc\n",
+ "),\n",
+ "known_bookings_checking_in_prior_to_TCI AS (\n",
+ "\tSELECT\n",
+ "\t\tid_booking,\n",
+ "\t\tLEAST(\n",
+ "\t\t\tcount(other_bookings_check_in_date_utc),\n",
+ "\t\t\t30\n",
+ "\t\t) AS listing_known_check_ins_prior_to_TCI_in_30_days,\n",
+ "\t\t-- Capping\n",
+ "\t\tLEAST(\n",
+ "\t\t\tGREATEST(\n",
+ "\t\t\t\tsum(min_booking_number_of_nights),\n",
+ "\t\t\t\t0\n",
+ "\t\t\t),\n",
+ "\t\t\t30\n",
+ "\t\t) AS listing_known_occupancy_prior_to_TCI_in_30_days,\n",
+ "\t\tCOALESCE(\n",
+ "\t\t\tbooking_check_in_date_utc - max(other_bookings_check_in_date_utc),\n",
+ "\t\t\t30\n",
+ "\t\t) AS lead_time_between_prior_known_check_in_to_TCI_30_days\n",
+ "\tFROM\n",
+ "\t\traw_known_bookings_checking_in_prior_to_TCI\n",
+ "\tGROUP BY\n",
+ "\t\tid_booking, \n",
+ "\t\tbooking_check_in_date_utc\n",
+ "),\n",
+ "incidents_prior_to_TCP AS (\n",
+ "\tSELECT\n",
+ "\t\tb.id_booking,\n",
+ "\t\t-- Using distinct count on check-in date to remove booking duplicates\n",
+ "\t\tCOUNT(DISTINCT b2.booking_check_in_date_utc) AS listing_incidents_prior_to_TCP_in_30_days\n",
+ "\tFROM\n",
+ "\t\tintermediate.int_booking_summary b\n",
+ "\tLEFT JOIN intermediate.int_booking_summary b2\n",
+ " ON\n",
+ "\t\tb2.id_accommodation = b.id_accommodation\n",
+ "\t\t-- Filter on Check Out date\n",
+ "\t\tAND b2.booking_completed_date_utc >= b.booking_created_date_utc - INTERVAL '30 days'\n",
+ "\t\tAND b2.booking_completed_date_utc < b.booking_created_date_utc\n",
+ "\t\tAND b2.has_resolution_incident = TRUE\n",
+ "\tGROUP BY\n",
+ "\t\tb.id_booking\n",
+ ")\n",
+ "SELECT\n",
+ "\t-- UNIQUE BOOKING ID --\n",
+ "\tbooking_summary.id_booking,\n",
+ "\t\n",
+ "\t-- CONTEXTUAL SERVICE INFORMATION --\n",
+ "\t-- We're not including number_of_applied_services as it 1-correlates with upgraded services\n",
+ "\tbooking_summary.number_of_applied_upgraded_services,\n",
+ " --Removed! booking_summary.number_of_applied_billable_services,\n",
+ "\tservice_information.number_of_applied_screening_services,\n",
+ "\tservice_information.number_of_applied_deposit_management_services,\n",
+ "\tservice_information.number_of_applied_protection_services,\n",
+ "\t--Removed! service_information.has_waiver_pro,\n",
+ "\t--Removed! service_information.has_guest_facing_waiver_or_deposit,\n",
+ "\t--Removed! service_information.has_guest_agreement,\n",
+ "\t--Removed! service_information.has_basic_protection,\n",
+ "\t--Removed! service_information.has_protection_plus,\n",
+ "\t--Removed! service_information.has_protection_pro,\n",
+ "\t--Removed! service_information.has_id_verification,\n",
+ "\t--Removed! service_information.has_screening_plus,\n",
+ "\t--Removed! service_information.has_sex_offender_check,\n",
+ "\tNOT booking_summary.has_verification_request AS is_contactless_booking,\n",
+ "\t\n",
+ "\t-- CONTEXTUAL LISTING INFORMATION --\n",
+ "\tlisting_information.listing_number_of_bedrooms,\n",
+ "\tlisting_information.listing_number_of_bathrooms,\n",
+ "\t\n",
+ "\t-- CONTEXTUAL TIMELINE OF OUR BOOKING\n",
+ "\t-- Defaults to 0 if booking_created_date_utc > booking_check_in_date_utc\n",
+ "\tGREATEST(booking_summary.booking_check_in_date_utc - booking_summary.booking_created_date_utc, 0) AS booking_lead_time,\n",
+ "\tbooking_summary.booking_check_out_date_utc - booking_summary.booking_check_in_date_utc AS booking_duration,\n",
+ "\t\n",
+ "\t-- SAME-LISTING, OTHER BOOKING INTERACTIONS: PRIOR TO TCR\n",
+ "\t-- Removed! bookings_checked_in_prior_to_TCR.listing_check_ins_prior_to_TCR_in_30_days,\n",
+ "\tbookings_checked_in_prior_to_TCR.listing_occupancy_prior_to_TCR_in_30_days,\n",
+ "\t\n",
+ "\t-- SAME-LISTING, OTHER BOOKING INTERACTIONS: PRIOR TO TCI (KNOWN)\n",
+ "\t-- Removed! known_bookings_checking_in_prior_to_TCI.listing_known_check_ins_prior_to_TCI_in_30_days,\n",
+ "\tknown_bookings_checking_in_prior_to_TCI.listing_known_occupancy_prior_to_TCI_in_30_days,\n",
+ "\tknown_bookings_checking_in_prior_to_TCI.lead_time_between_prior_known_check_in_to_TCI_30_days,\n",
+ "\t\n",
+ "\t-- SAME-LISTING, OTHER BOOKING INTERACTIONS: INCIDENTAL BOOKINGS\n",
+ "\t-- Removed! incidents_prior_to_TCP.listing_incidents_prior_to_TCP_in_30_days,\n",
+ "\t\n",
+ "\t-- TARGET (BOOLEAN) --\n",
+ "\tbooking_summary.has_resolution_incident\n",
+ "\n",
+ "FROM\n",
+ "\tintermediate.int_booking_summary booking_summary\n",
+ "LEFT JOIN service_information \n",
+ "\tON\n",
+ "\tbooking_summary.id_booking = service_information.id_booking\n",
+ "LEFT JOIN listing_information \n",
+ "\tON booking_summary.id_accommodation = listing_information.id_accommodation\n",
+ "LEFT JOIN bookings_checked_in_prior_to_TCR\n",
+ "\tON booking_summary.id_booking = bookings_checked_in_prior_to_TCR.id_booking\n",
+ "LEFT JOIN known_bookings_checking_in_prior_to_TCI\n",
+ "\tON booking_summary.id_booking = known_bookings_checking_in_prior_to_TCI.id_booking\n",
+ "LEFT JOIN incidents_prior_to_TCP\n",
+ "\tON booking_summary.id_booking = incidents_prior_to_TCP.id_booking\n",
+ "WHERE\n",
+ "\t-- 1. Bookings from New Dash users with Id Deal\n",
+ "\tbooking_summary.is_user_in_new_dash = TRUE\n",
+ "\tAND \n",
+ " booking_summary.is_missing_id_deal = FALSE\n",
+ "\tAND\n",
+ "\t-- 2. Protected Bookings with a Protection or a Deposit Management service\n",
+ " (\n",
+ "\t\tbooking_summary.has_protection_service_business_type\n",
+ "\t\t\tOR \n",
+ " booking_summary.has_deposit_management_service_business_type\n",
+ "\t)\n",
+ "\tAND\n",
+ "\t-- 3. Bookings with flagging categorisation (this excludes Cancelled/Incomplete/Rejected bookings)\n",
+ "\tbooking_summary.is_booking_flagged_as_risk IS NOT NULL\n",
+ "\tAND\n",
+ "\t-- 4. Booking is completed\n",
+ "\tbooking_summary.is_booking_past_completion_date = TRUE\n",
+ "\n",
+ "\n",
+ "\"\"\"\n",
+ "\n",
+ "# Retrieve Data from Query\n",
+ "df_extraction = query_to_dataframe(engine=dwh_pg_engine, query=data_extraction_query)\n",
+ "print(f\"Total Bookings: {len(df_extraction):,}\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Preprocessing\n",
+ "Preprocessing in this notebook is quite straight-forward: we just drop id booking and split the features and target."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Drop ID column\n",
+ "df = df_extraction.copy().drop(columns=['id_booking'])\n",
+ "\n",
+ "# Separate features and target\n",
+ "target_col = 'has_resolution_incident'\n",
+ "X = df.drop(columns=[target_col])\n",
+ "y = df[target_col]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exploratory Data Analysis\n",
+ "In this section we focus on explore the different features."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### EDA - Dataset Overview"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape: (21384, 12)\n",
+ "has_resolution_incident\n",
+ "False 98.8\n",
+ "True 1.2\n",
+ "Name: proportion, dtype: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Shape and types\n",
+ "print(f\"Shape: {X.shape}\")\n",
+ "\n",
+ "# Target distribution\n",
+ "print(round(100*df[target_col].value_counts(normalize=True),2))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "