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Experimental climatological rainfall zone using KMeans and Agglomerative Clustering
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"id": "7d893d13", | |
"metadata": {}, | |
"source": [ | |
"### Experimental climatological rainfall zone using KMeans and Agglomerative Clustering\n", | |
"\n", | |
"Notes:\n", | |
"* This analysis use monthly timeseries precipitation 1981-2010 and 1990-2020 from CHIRPS \n", | |
"* The input required monthly precipitation data in csv which consist of id, lon, lat, yyyymmdd, ..., yyyymmdd(n)\n", | |
"* the NoData (-9999.0) are manually cleaned before running this code\n", | |
"* Number of clusters assigned in the calculation, can be a specific integer or one of optimum result generated by Calinski-Harahasz or Silhouette method'\n", | |
"\n", | |
"Contact: Benny Istanto, GOST/DECAT/The World Bank\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"id": "a2b315e5", | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import pandas as pd\n", | |
"import geopandas as gpd\n", | |
"import numpy as np\n", | |
"from sklearn.decomposition import PCA\n", | |
"from sklearn.cluster import KMeans, MiniBatchKMeans, AgglomerativeClustering\n", | |
"from sklearn.preprocessing import StandardScaler\n", | |
"from sklearn.metrics import calinski_harabasz_score, silhouette_samples, silhouette_score\n", | |
"import matplotlib.pyplot as plt\n", | |
"from tqdm import tqdm\n", | |
"from joblib import Parallel, delayed" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"id": "a43a346b", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
"<style scoped>\n", | |
" .dataframe tbody tr th:only-of-type {\n", | |
" vertical-align: middle;\n", | |
" }\n", | |
"\n", | |
" .dataframe tbody tr th {\n", | |
" vertical-align: top;\n", | |
" }\n", | |
"\n", | |
" .dataframe thead th {\n", | |
" text-align: right;\n", | |
" }\n", | |
"</style>\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>id</th>\n", | |
" <th>lon</th>\n", | |
" <th>lat</th>\n", | |
" <th>JAN</th>\n", | |
" <th>FEB</th>\n", | |
" <th>MAR</th>\n", | |
" <th>APR</th>\n", | |
" <th>MAY</th>\n", | |
" <th>JUN</th>\n", | |
" <th>JUL</th>\n", | |
" <th>AUG</th>\n", | |
" <th>SEP</th>\n", | |
" <th>OCT</th>\n", | |
" <th>NOV</th>\n", | |
" <th>DEC</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>0</td>\n", | |
" <td>33.925003</td>\n", | |
" <td>4.174999</td>\n", | |
" <td>8.088668</td>\n", | |
" <td>13.915092</td>\n", | |
" <td>60.053000</td>\n", | |
" <td>90.902643</td>\n", | |
" <td>86.631900</td>\n", | |
" <td>91.712753</td>\n", | |
" <td>98.475047</td>\n", | |
" <td>109.974557</td>\n", | |
" <td>64.166463</td>\n", | |
" <td>79.296520</td>\n", | |
" <td>48.585107</td>\n", | |
" <td>22.960563</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>1</td>\n", | |
" <td>33.975003</td>\n", | |
" <td>4.174999</td>\n", | |
" <td>7.645988</td>\n", | |
" <td>15.179582</td>\n", | |
" <td>63.234697</td>\n", | |
" <td>96.147180</td>\n", | |
" <td>95.726670</td>\n", | |
" <td>95.923840</td>\n", | |
" <td>99.204340</td>\n", | |
" <td>113.000077</td>\n", | |
" <td>61.170353</td>\n", | |
" <td>85.418117</td>\n", | |
" <td>51.453167</td>\n", | |
" <td>23.465133</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>2</td>\n", | |
" <td>34.025003</td>\n", | |
" <td>4.174999</td>\n", | |
" <td>7.636106</td>\n", | |
" <td>17.176613</td>\n", | |
" <td>65.098473</td>\n", | |
" <td>97.812080</td>\n", | |
" <td>100.596047</td>\n", | |
" <td>95.845717</td>\n", | |
" <td>99.154027</td>\n", | |
" <td>107.560900</td>\n", | |
" <td>63.296063</td>\n", | |
" <td>85.716050</td>\n", | |
" <td>54.230960</td>\n", | |
" <td>24.428810</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td>3</td>\n", | |
" <td>34.075003</td>\n", | |
" <td>4.174999</td>\n", | |
" <td>5.951594</td>\n", | |
" <td>12.012614</td>\n", | |
" <td>59.818120</td>\n", | |
" <td>89.869180</td>\n", | |
" <td>73.881050</td>\n", | |
" <td>72.457413</td>\n", | |
" <td>81.721287</td>\n", | |
" <td>78.181587</td>\n", | |
" <td>51.945260</td>\n", | |
" <td>67.282288</td>\n", | |
" <td>52.560080</td>\n", | |
" <td>25.335905</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td>4</td>\n", | |
" <td>33.875003</td>\n", | |
" <td>4.124999</td>\n", | |
" <td>8.980482</td>\n", | |
" <td>14.813697</td>\n", | |
" <td>61.488367</td>\n", | |
" <td>104.600683</td>\n", | |
" <td>111.647470</td>\n", | |
" <td>117.044413</td>\n", | |
" <td>114.715580</td>\n", | |
" <td>135.794353</td>\n", | |
" <td>71.695987</td>\n", | |
" <td>104.778110</td>\n", | |
" <td>48.399707</td>\n", | |
" <td>23.021170</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>...</th>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>8092</th>\n", | |
" <td>8092</td>\n", | |
" <td>30.025003</td>\n", | |
" <td>-1.425001</td>\n", | |
" <td>63.860733</td>\n", | |
" <td>82.946740</td>\n", | |
" <td>145.910317</td>\n", | |
" <td>149.888110</td>\n", | |
" <td>113.535607</td>\n", | |
" <td>24.187128</td>\n", | |
" <td>7.740926</td>\n", | |
" <td>42.034117</td>\n", | |
" <td>96.621663</td>\n", | |
" <td>134.859930</td>\n", | |
" <td>123.205643</td>\n", | |
" <td>86.627350</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>8093</th>\n", | |
" <td>8093</td>\n", | |
" <td>30.075003</td>\n", | |
" <td>-1.425001</td>\n", | |
" <td>66.814717</td>\n", | |
" <td>79.246910</td>\n", | |
" <td>142.891830</td>\n", | |
" <td>151.630177</td>\n", | |
" <td>118.638950</td>\n", | |
" <td>21.835074</td>\n", | |
" <td>8.200012</td>\n", | |
" <td>42.983823</td>\n", | |
" <td>95.011460</td>\n", | |
" <td>140.173817</td>\n", | |
" <td>135.448703</td>\n", | |
" <td>86.049117</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>8094</th>\n", | |
" <td>8094</td>\n", | |
" <td>29.875003</td>\n", | |
" <td>-1.475001</td>\n", | |
" <td>75.061123</td>\n", | |
" <td>83.701627</td>\n", | |
" <td>135.825133</td>\n", | |
" <td>163.360500</td>\n", | |
" <td>115.644337</td>\n", | |
" <td>26.640957</td>\n", | |
" <td>9.397053</td>\n", | |
" <td>48.352220</td>\n", | |
" <td>107.835497</td>\n", | |
" <td>141.660773</td>\n", | |
" <td>135.213750</td>\n", | |
" <td>93.407247</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>8095</th>\n", | |
" <td>8095</td>\n", | |
" <td>29.925003</td>\n", | |
" <td>-1.475001</td>\n", | |
" <td>75.819240</td>\n", | |
" <td>88.351980</td>\n", | |
" <td>143.182193</td>\n", | |
" <td>166.503300</td>\n", | |
" <td>119.407767</td>\n", | |
" <td>27.252113</td>\n", | |
" <td>10.189302</td>\n", | |
" <td>49.488620</td>\n", | |
" <td>105.480023</td>\n", | |
" <td>138.718467</td>\n", | |
" <td>137.950787</td>\n", | |
" <td>95.524680</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>8096</th>\n", | |
" <td>8096</td>\n", | |
" <td>29.975003</td>\n", | |
" <td>-1.475001</td>\n", | |
" <td>72.389467</td>\n", | |
" <td>88.777333</td>\n", | |
" <td>144.566490</td>\n", | |
" <td>170.570767</td>\n", | |
" <td>121.454053</td>\n", | |
" <td>26.339266</td>\n", | |
" <td>10.426367</td>\n", | |
" <td>48.552157</td>\n", | |
" <td>101.422053</td>\n", | |
" <td>139.423143</td>\n", | |
" <td>134.830683</td>\n", | |
" <td>93.964817</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"<p>8097 rows × 15 columns</p>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" id lon lat JAN FEB MAR APR \\\n", | |
"0 0 33.925003 4.174999 8.088668 13.915092 60.053000 90.902643 \n", | |
"1 1 33.975003 4.174999 7.645988 15.179582 63.234697 96.147180 \n", | |
"2 2 34.025003 4.174999 7.636106 17.176613 65.098473 97.812080 \n", | |
"3 3 34.075003 4.174999 5.951594 12.012614 59.818120 89.869180 \n", | |
"4 4 33.875003 4.124999 8.980482 14.813697 61.488367 104.600683 \n", | |
"... ... ... ... ... ... ... ... \n", | |
"8092 8092 30.025003 -1.425001 63.860733 82.946740 145.910317 149.888110 \n", | |
"8093 8093 30.075003 -1.425001 66.814717 79.246910 142.891830 151.630177 \n", | |
"8094 8094 29.875003 -1.475001 75.061123 83.701627 135.825133 163.360500 \n", | |
"8095 8095 29.925003 -1.475001 75.819240 88.351980 143.182193 166.503300 \n", | |
"8096 8096 29.975003 -1.475001 72.389467 88.777333 144.566490 170.570767 \n", | |
"\n", | |
" MAY JUN JUL AUG SEP OCT \\\n", | |
"0 86.631900 91.712753 98.475047 109.974557 64.166463 79.296520 \n", | |
"1 95.726670 95.923840 99.204340 113.000077 61.170353 85.418117 \n", | |
"2 100.596047 95.845717 99.154027 107.560900 63.296063 85.716050 \n", | |
"3 73.881050 72.457413 81.721287 78.181587 51.945260 67.282288 \n", | |
"4 111.647470 117.044413 114.715580 135.794353 71.695987 104.778110 \n", | |
"... ... ... ... ... ... ... \n", | |
"8092 113.535607 24.187128 7.740926 42.034117 96.621663 134.859930 \n", | |
"8093 118.638950 21.835074 8.200012 42.983823 95.011460 140.173817 \n", | |
"8094 115.644337 26.640957 9.397053 48.352220 107.835497 141.660773 \n", | |
"8095 119.407767 27.252113 10.189302 49.488620 105.480023 138.718467 \n", | |
"8096 121.454053 26.339266 10.426367 48.552157 101.422053 139.423143 \n", | |
"\n", | |
" NOV DEC \n", | |
"0 48.585107 22.960563 \n", | |
"1 51.453167 23.465133 \n", | |
"2 54.230960 24.428810 \n", | |
"3 52.560080 25.335905 \n", | |
"4 48.399707 23.021170 \n", | |
"... ... ... \n", | |
"8092 123.205643 86.627350 \n", | |
"8093 135.448703 86.049117 \n", | |
"8094 135.213750 93.407247 \n", | |
"8095 137.950787 95.524680 \n", | |
"8096 134.830683 93.964817 \n", | |
"\n", | |
"[8097 rows x 15 columns]" | |
] | |
}, | |
"execution_count": 2, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"# Choose the clustering method\n", | |
"cluster_method = 'KMeans'\n", | |
"# cluster_method = 'AgglomerativeClustering'\n", | |
"\n", | |
"# Load the precipitation data\n", | |
"# precip_df = pd.read_csv(\"../csv/chirps_precip_1981_2010.csv\", sep=\";\")\n", | |
"precip_df = pd.read_csv(\"../csv/chirps_precip_1991_2020.csv\", sep=\";\")\n", | |
"\n", | |
"# Rename the date columns for each dataframe\n", | |
"precip_df.rename(columns={'id': 'id', **{col: pd.to_datetime(col, format='%Y%m%d') for col in precip_df.columns[3:]}}, inplace=True)\n", | |
"\n", | |
"# Melt the precipitation and temperature dataframes to a long format\n", | |
"precip_df = pd.melt(precip_df, id_vars=['id', 'lon', 'lat'], var_name='date', value_name='precipitation')\n", | |
"\n", | |
"# Convert the date column to datetime type\n", | |
"precip_df['date'] = pd.to_datetime(precip_df['date'])\n", | |
"\n", | |
"# Calculate the monthly mean precipitation for each location\n", | |
"precip_df[\"month\"] = precip_df[\"date\"].dt.month\n", | |
"monthly_precip_df = precip_df.groupby([\"id\", \"lon\", \"lat\", \"month\"], as_index=False).mean()\n", | |
"monthly_precip_df = monthly_precip_df.pivot_table(index=[\"id\", \"lon\", \"lat\"], columns=\"month\", values=\"precipitation\").reset_index()\n", | |
"monthly_precip_df.columns.name = None\n", | |
"monthly_precip_df.columns = [\"id\", \"lon\", \"lat\", \"JAN\", \"FEB\", \"MAR\", \"APR\", \"MAY\", \"JUN\", \"JUL\", \"AUG\", \"SEP\", \"OCT\", \"NOV\", \"DEC\"]\n", | |
"\n", | |
"# Check the data visually\n", | |
"monthly_precip_df" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"id": "cbd7b192", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Done!\n" | |
] | |
} | |
], | |
"source": [ | |
"# Compute the PCA 90\n", | |
"X = monthly_precip_df.drop(columns=[\"id\", \"lon\", \"lat\"])\n", | |
"scaler = StandardScaler()\n", | |
"X_scaled = scaler.fit_transform(X)\n", | |
"pca = PCA(n_components=0.90)\n", | |
"X_pca = pca.fit_transform(X_scaled)\n", | |
"\n", | |
"print('Done!')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"id": "40d8a133", | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"def get_optimal_plot_calinski(X, cluster_method):\n", | |
" \"\"\"\n", | |
" Compute the optimal number of clusters and plot the Calinski-Harabasz score as a function of the number of clusters.\n", | |
"\n", | |
" Parameters:\n", | |
" - X: input data array\n", | |
" - cluster_method: clustering algorithm to use, either \"KMeans\" or \"AgglomerativeClustering\"\n", | |
"\n", | |
" Returns:\n", | |
" - optimal_k: optimal number of clusters\n", | |
" \"\"\"\n", | |
" if cluster_method == \"KMeans\":\n", | |
" model = KMeans\n", | |
" elif cluster_method == \"AgglomerativeClustering\":\n", | |
" model = AgglomerativeClustering\n", | |
" else:\n", | |
" raise ValueError(\"Invalid cluster_method type. Must be either 'KMeans' or 'AgglomerativeClustering'.\")\n", | |
"\n", | |
" def compute_score(k):\n", | |
" score_k = model(n_clusters=k)\n", | |
" score_k.fit(X)\n", | |
" score = calinski_harabasz_score(X, score_k.labels_)\n", | |
" return score\n", | |
"\n", | |
" # Define the range of k values to explore\n", | |
" k_values = range(2, 21)\n", | |
"\n", | |
" # Compute the Calinski-Harabasz score for each value of k\n", | |
" scores = Parallel(n_jobs=-1)(delayed(compute_score)(k) for k in tqdm(k_values, desc=\"Calculating Calinski-Harabasz Scores\"))\n", | |
"\n", | |
" # Find the index of the maximum score\n", | |
" max_idx = np.argmax(scores)\n", | |
" optimal_k = k_values[max_idx]\n", | |
"\n", | |
" # Plot the scores\n", | |
" plt.plot(k_values, scores)\n", | |
" plt.axvline(optimal_k, color='r', linestyle='--')\n", | |
" plt.text(optimal_k+0.2, max(scores), f\"Optimal k: {optimal_k}\", color='r')\n", | |
" plt.title(f\"{cluster_method} - Calinski-Harabasz Method\")\n", | |
" plt.xlabel(\"Number of Clusters\")\n", | |
" plt.ylabel(\"Calinski-Harabasz Score\")\n", | |
" plt.show()\n", | |
"\n", | |
" return optimal_k\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"id": "2621d310", | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"def get_optimal_plot_silhouette(X, cluster_method):\n", | |
" \"\"\"\n", | |
" Compute the optimal number of clusters for KMeans or AgglomerativeClustering using the Silhouette score,\n", | |
" and plot the Silhouette score as a function of the number of clusters.\n", | |
"\n", | |
" Parameters:\n", | |
" - X: input data array\n", | |
" - cluster_method: clustering algorithm to use (\"KMeans\" or \"AgglomerativeClustering\")\n", | |
" \"\"\"\n", | |
" def compute_score(k):\n", | |
" if cluster_method == \"KMeans\":\n", | |
" clusterer = KMeans(n_clusters=k)\n", | |
" elif cluster_method == \"AgglomerativeClustering\":\n", | |
" clusterer = AgglomerativeClustering(n_clusters=k)\n", | |
" else:\n", | |
" raise ValueError(\"Invalid cluster_method parameter. Must be 'KMeans' or 'AgglomerativeClustering'.\")\n", | |
" cluster_labels = clusterer.fit_predict(X)\n", | |
" silhouette_avg = silhouette_score(X, cluster_labels)\n", | |
" sample_silhouette_values = silhouette_samples(X, cluster_labels)\n", | |
" return silhouette_avg, sample_silhouette_values\n", | |
"\n", | |
" # Define the range of k values to explore\n", | |
" k_values = range(2, 21)\n", | |
"\n", | |
" # Compute the Silhouette score for each value of k\n", | |
" scores = Parallel(n_jobs=-1)(delayed(compute_score)(k) for k in tqdm(k_values, desc=\"Calculating Silhouette Scores\"))\n", | |
"\n", | |
" # Extract the Silhouette score and sample Silhouette values for each k\n", | |
" silhouette_scores, sample_silhouette_values = zip(*scores)\n", | |
"\n", | |
" # Find the index of the maximum score\n", | |
" max_idx = np.argmax(silhouette_scores)\n", | |
" max_k = k_values[max_idx]\n", | |
"\n", | |
" # Plot the scores\n", | |
" plt.plot(k_values, silhouette_scores)\n", | |
" plt.axvline(max_k, color='r', linestyle='--')\n", | |
" plt.text(max_k+0.2, max(silhouette_scores), f\"Optimal k: {max_k}\", color='r')\n", | |
" plt.title(f\"{cluster_method} - Silhouette Method\")\n", | |
" plt.xlabel(\"Number of Clusters\")\n", | |
" plt.ylabel(\"Silhouette Score\")\n", | |
" plt.show()\n", | |
"\n", | |
" # Return the optimal number of clusters\n", | |
" return max_k\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"id": "c7520726", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"Calculating Calinski-Harabasz Scores: 100%|█████| 19/19 [00:03<00:00, 5.13it/s]\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"Calculating Silhouette Scores: 100%|████████████| 19/19 [00:09<00:00, 2.01it/s]\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"The optimal number of clusters using Calinski-Harabasz is: 8\n", | |
"The optimal number of clusters using Silhouette is: 4\n", | |
"Done!\n" | |
] | |
} | |
], | |
"source": [ | |
"# Calculate the optimal number of clusters using various method\n", | |
"# Calinski-Harabasz\n", | |
"optimal_c = get_optimal_plot_calinski(X_pca, cluster_method)\n", | |
"# Silhouette\n", | |
"optimal_s = get_optimal_plot_silhouette(X_pca, cluster_method)\n", | |
"\n", | |
"print(\"The optimal number of clusters using Calinski-Harabasz is: \", optimal_c)\n", | |
"print(\"The optimal number of clusters using Silhouette is: \", optimal_s)\n", | |
"\n", | |
"print('Done!')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"id": "b6e48b96", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Done!\n" | |
] | |
} | |
], | |
"source": [ | |
"def cluster_data(X_pca, cluster_method, n_clusters=None):\n", | |
" \"\"\"\n", | |
" Cluster the input data using the specified method and number of clusters.\n", | |
"\n", | |
" Parameters:\n", | |
" - X_pca: input data array\n", | |
" - cluster_method: clustering method, either 'KMeans' or 'AgglomerativeClustering'\n", | |
" - n_clusters: number of clusters, can be a specific integer or one of 'optimal_c' and 'optimal_s'\n", | |
"\n", | |
" Returns:\n", | |
" - labels: array of cluster labels\n", | |
" \"\"\"\n", | |
" if n_clusters == 'optimal_c':\n", | |
" if cluster_method == 'KMeans':\n", | |
" n_clusters = get_optimal_plot_calinski(X_pca, 'KMeans')\n", | |
" elif mcluster_method == 'AgglomerativeClustering':\n", | |
" n_clusters = get_optimal_plot_calinski(X_pca, 'AgglomerativeClustering')\n", | |
" else:\n", | |
" raise ValueError(\"Invalid clustering method. Choose 'KMeans' or 'AgglomerativeClustering'.\")\n", | |
" elif n_clusters == 'optimal_s':\n", | |
" if cluster_method == 'KMeans':\n", | |
" n_clusters = get_optimal_plot_silhouette(X_pca, 'KMeans')\n", | |
" elif cluster_method == 'AgglomerativeClustering':\n", | |
" n_clusters = get_optimal_plot_silhouette(X_pca, 'AgglomerativeClustering')\n", | |
" else:\n", | |
" raise ValueError(\"Invalid clustering method. Choose 'KMeans' or 'AgglomerativeClustering'.\")\n", | |
" elif isinstance(n_clusters, int):\n", | |
" pass\n", | |
" else:\n", | |
" raise ValueError(\"Invalid value for n_clusters parameter.\")\n", | |
" \n", | |
" if cluster_method == 'KMeans':\n", | |
" cluster_model = KMeans(n_clusters=n_clusters)\n", | |
" elif cluster_method == 'AgglomerativeClustering':\n", | |
" cluster_model = AgglomerativeClustering(n_clusters=n_clusters)\n", | |
"\n", | |
" pbar = tqdm(total=1, desc=f\"Performing {cluster_method} Clustering\")\n", | |
" cluster_model.fit(X_pca)\n", | |
" pbar.update(1)\n", | |
" \n", | |
" labels = cluster_model.labels_\n", | |
"\n", | |
" return labels, n_clusters\n", | |
"\n", | |
"print('Done!')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"id": "8186a8b5", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"Performing KMeans Clustering: 100%|███████████████| 1/1 [00:00<00:00, 1.81it/s]\n" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Done!\n" | |
] | |
} | |
], | |
"source": [ | |
"# Other ways to assign a single cluster to each row based on the 360 monthly timeseries \n", | |
"# of precipitation and temperature data. One approach is to calculate the distance \n", | |
"# between each row and the centroids of the clusters obtained from K-Means clustering. \n", | |
"# Then, assign the closest cluster to each row as its climate zone.\n", | |
"\n", | |
"# Cluster the data using the defined cluster_method\n", | |
"labels, n_clusters = cluster_data(X_pca, cluster_method, n_clusters=14)\n", | |
"\n", | |
"# Calculate the centroids for each cluster\n", | |
"centroids = np.zeros((n_clusters, X_pca.shape[1]))\n", | |
"for i in range(n_clusters):\n", | |
" mask = (labels == i)\n", | |
" centroids[i,:] = np.mean(X_pca[mask,:], axis=0)\n", | |
"\n", | |
"# Calculate the Euclidean distance between each row and the centroids\n", | |
"distances = np.sqrt(np.sum(np.square(X_pca[:, None] - centroids), axis=2))\n", | |
"\n", | |
"# In this code, we use the np.argmin() function to find the index of the closest centroid \n", | |
"# to each row. This approach preserves the details of climate characteristics captured by \n", | |
"# the monthly timeseries data.\n", | |
"# Assign the closest cluster to each row\n", | |
"monthly_precip_df[\"climate_zone\"] = np.argmin(distances, axis=1)\n", | |
"\n", | |
"# Group the merged dataframe by id, lon, and lat and take the mode of the climate zone for each group\n", | |
"monthly_precip_df_centroid = monthly_precip_df.groupby([\"id\", \"lon\", \"lat\"])[\"climate_zone\"].apply(lambda x: x.mode()[0]).reset_index()\n", | |
"\n", | |
"print('Done!')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"id": "35c4fdbc", | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"def plot_climate_zone_map(climate_zone_csv, shapefile_path):\n", | |
" \"\"\"\n", | |
" Plot the climate zones as a point map.\n", | |
"\n", | |
" Parameters:\n", | |
" - climate_zone_csv: path to the CSV file with lon, lat, and climate_zone columns\n", | |
" \"\"\"\n", | |
" # Load the data from the CSV file\n", | |
" df = pd.read_csv(climate_zone_csv)\n", | |
"\n", | |
" # Extract the lon, lat, and climate_zone columns\n", | |
" lon = df[\"lon\"]\n", | |
" lat = df[\"lat\"]\n", | |
" climate_zone = df[\"climate_zone\"]\n", | |
"\n", | |
" # Create a scatter plot of the climate zones\n", | |
" plt.figure(figsize=(12, 10))\n", | |
" plt.scatter(lon, lat, c=climate_zone, cmap=\"tab20\")\n", | |
" \n", | |
" # Load the polygon shapefile using geopandas\n", | |
" gdf = gpd.read_file(shapefile_path)\n", | |
"\n", | |
" # Plot the polygon shapefile\n", | |
" gdf.plot(ax=plt.gca(), facecolor=\"None\", edgecolor=\"black\", linewidth=1)\n", | |
"\n", | |
" # Add a colorbar\n", | |
" cbar = plt.colorbar()\n", | |
" cbar.set_label(\"New Rainfall Zone and the 1995 Climatological Rainfall Zone\")\n", | |
"\n", | |
" # Set the x and y axis labels\n", | |
" # You need to adjust the title with the year of data information if needed\n", | |
" plt.title(\"Rainfall Zone based on monthly CHIRPS, 1991-2020\\nAlgorithm: \"f\"{cluster_method}\", fontsize=16, fontweight='bold', ha='center')\n", | |
" plt.text(0.5, -0.15, \"The existing climatological rainfall zone based on study from C.P.K.Basalirwa in 1995\\nusing monthly record from 102 rain-gauge stations for the years 1940-1975\\nhttps://doi.org/10.1002/joc.3370151008\", fontsize=12, fontweight='normal', ha='center', transform=plt.gca().transAxes)\n", | |
" plt.xlabel(\"Longitude\")\n", | |
" plt.ylabel(\"Latitude\")\n", | |
" \n", | |
" # Show the plot\n", | |
" plt.show()\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"id": "dbb021ee", | |
"metadata": { | |
"scrolled": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Save the output to csv completed\n", | |
"----------------------------------------------------------\n" | |
] | |
}, | |
{ | |
"data": { | |
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4hRYK11LH87AhfCKEmCeMINypQgiLEGIAFf7HEOQ7KioCzYek0AshEoUQKVSORWkVQqQEFktQ3puEEPsHzmuaEGIGhrsOwFsB95KyvFcJIfoLIRxCiE4BhaxM0VskpVwalDeqrD0qv3eTA+kxAFLKOdV8UE4K1JGA8Qwqc+14TEp5tZSyqpsAGL1mZT7dNwTu8TSM3hUwntdzA/WWdZ9PCuz7DsMyuVe4NynlCir8lo8RQpwnhEim4j0AFb7Yin2N+ozg2VcWah7VmILRFSIx/IgGBdIXBuUvH40ZtL4oqI45QelpgbS0oLQ5QXnTaqhjTFD6mKD0RAwrTW2yBB/P1kDa1rrqruE8/VtNW9UtxwTyX0rDRnmn1ZBeVm8UxpdzbTKk1XEswfXWNcr7/mr2ZxE0ojio3u9rkemToHwv1yH/mEC+mkZ5B4+cXlTbsQbqacgo761V6ihLlyG0t6jqb0HN1/ekan7jho7yDuV+6ozRHV3tNVOf46eO+wfDz7psfwFVQkLVcv40jDiItV0jtwbll1VlC+H5Vt/0mp6Hx1R3nms5tt/qOK5PquTf67zXUf/WOuoPfu5UF8VAYlx7CVXqXVtD3s1At1rOeXXLnBCOY0wddZRfs4H819aS77Ea7oualuD3zMFUH+1AAj8TFL5KLfvWoiyU9UAaAwnKLAWCiq+yyzAsT3kYD9h5GKOAW1q+PIzRrr9hfJ3uwniQNcZ6UBsJ9ckspZyP8bL5AsMq5MNwE3gfOFRKuaghQkgpSzGsGRMxRl+WYnQRbsYI4zGWCqtgKJyNMVq+AGOAz4cYX+xlX/xPYIyu3Blo52eMwTJVRz2C4WP1FYaPowuj23QjRgy/8tHBUsobMK6jnwP1eANlfsLorv86kE9idLO/ESTfx1SMuA0JKeW1GN2ufwTq8GKcr+cwYqfuqaV4iyMrfNYexlDu3Rjn81+McFWjZPWuFqHUvRMjVuVqKvwcm4tng9bnB11TtSKl1KWU12C4TXyAce15MJ45/2I8ixY0qaR1y1TT87C+PIBx3S3BcAfxYtzD/wL3EfQsFUKYqHjuLKXpmY9xf5Y9Q1ZgfEAeKfeeOOJjjGumLI7reoy4pEOllPX2n2xqpDHg8QyMbvpijONZghHJ4L7aytZR71IM//EPMZ7jHoxz9ghwqlSz5OyzCOP9pFAoFIrmRlSeDm+QlPK/2vIrKiOEOBzjg7kEI8j4+jCLpFAoAqi5vBUKhaKZEUI8jjGYKy2Q9LFSJhvEqYH/tyhlUqGILJSFUqFQKJoZIcQcjNlf8jHcF66XlUMQKRQKRatGKZQKhUKhUCgUikahBuUoFAqFQqFQKBqFUigVCoVCoVAoFI1CKZQKRQshhPijypRqe02jJ4Q4pqYp18JBTfIE0icFlrRqypWVWdSC4oaMEGJRkIzHBKWnCCGWB+17KZBedUq8z6up884qeea02AEpFApFmFEKpULRAggh9gOGV0keXV3eVsIxGLMIPUTFyOVWTWDGjx+AgYGkV6iYL7kqpwkhugeVFRhzVCsUCsU+iVIoFYqW4bJq0i4JKCIRR2A6NquUcpGsMgVcWyRImTwwkPQaxkjsmkYtalRWIE8E9m8+CRUKhSKyUQqlQtEylCmUTuDdwHp34MhQKwh0qW4TQjiFEL8H5nCudl7jQNftdCHERiGEWwhRFOhyv7JKvuAu7YeFEPcLIbZizGI0orou78D+h4Kq+am67uOgNo4TQiwOyL1JCHFXsCId6DYvK3+dEOJ5IURuYHlGGHM7nx7oii4RQvwthDiiShvBdYwJ9ZwGSMKYIvOgwPZsYFwtymR64P9VomIe6LJ5tLfW1IgQoq8Q4i0hxE4hhEcIkSmE+FAIcWCVfBcKIRYKIbYLIUoDv99mIcRMIUT7KnnLfv+tQohhQoifAmW2CSGeFEJYg/I6hBCPCyHWCSGKA+dysxDiIyFEVeu5QqFQ1I9wz/2oFrW09QWjq7tsrtuPgVOCtl+tkveYoH2TgtIfYO95cwsDS6V5jYEO1D5/8Ss1tJddJd8x1clTR93HBPIE1+mtJt9lQTJMCkrPqibvFxgKbnBaPpBYQx1jQvhNFgXlzw1af5PAnOBV8o8JyvMcsCewfgHGXOBlx3hvUL45QeWPwJjOr7pz5sSY2q8s78xazu9awBqUt+y3KKX6eagnBuV9qZZ6bwr3faIWtaildS/KQqlQND/B3d0fYnSt5ge2zxNC2GorLISIx1BUwFBcRmLMZ/wGEFtNkUcwrJ8Ac4BkDOtbmWVtnBBiRDXlkoHxQDzQFWMe472QUqYBk4OSjpUV3eKLqqnzSSCRyv6INfmPeoD+wJCgtJHAvEAdzwfS4oHTaqijviQG/v+IMc9xXXOCezHOPcD1wDUYs44tBpbVUOZVwIHxGxwM2IDBGAq0HUPZK+NtjLmSUwAL0B7DagrQh+qP24Fh+U4BRgWlB5/nowL/FwOpQDRwAHADxvzoCoVC0WCUQqlQNCOBLtELA5tu4AsppRcoGyWcAJxeRzWHYSgMAF9JKb+Sxiwr92EoN1UZGbR+u5QyV0q5HJgelF6dUrJQSvmClLJQSrlDSplTh1yhkAE8KKXMx7D+ldG9+uzMllKullIuAzKD0h8J1PFlUFq3shUp5aQgpXZOA2U9AsN6HAqzAB3Dgjs+kDajuoxCiF4YihsYx70U41pYhqHYAQwUQnQIrO8Gbg7sL8U4h8GuCn2qacaPMR1hjpTyc6Dstws+z1sC//tiWLwvxejuf11K+UMtx6pQKBR1ohRKhaJ5OQXDagTwD9BNCDEACJ7Hua7R3ilB69vKVqSUpVQoDsGUKSnFUsrcoPT0oPV21ZSrybrWGDZJKf2B9ZKgdHsN+bcGrTuD1stk9wSl1WrZrQeLAv+twMdCiJPqKiCl3Ap8E9hMxOg2f7+G7NWd6+pIDlijf8OwanfFsFBWxVFNWoasPJVj2bkOPke3AX9jWHfHYyjF/wN2hnLMCoVCURtKoVQompfg7u7DMLqRVwDTgtJPFUIk1VJHdtB657IVIUQURpdyVbIC/2OEEIlB6d2C1oOtf2U4q0mriVDnbC23oEopQynjq7YxKatNbyImA88E1m3AJ0KI40IoF2yRnC2ldNWQL/hcLwyypJYvGH6bq4BjqVBAfwA6BvaPp3aqWqr3OtdSyo1SykOBThij0m/FsIamAC/UUb9CoVDUilIoFYpmQggRR2V/tpqwYgzuqIk/qFD2RgohTghYsh6jegvWF0Hr04QQiQGr6ISg9C9pHMGW0QOFEGF9ljRylDdSytup8M90AJ8LIY6qpQjAVxj+jp9SQ3d3oO4NwPrA5olCiFuFEAmBZagQ4kEqRv4HK84uoEQI0R+jC7xRBKIEXIhhHf4VeA/YFdjdrcaCCoVCEQJKoVQomo/zqOjafa8aq9TJQXlr7PYOdGU+Hti0AQsxBvVcBRRXU+RBKrqIx2J0x66gIgD5K1LKP+p9NJVZHLT+HOCvGrqotSGlvAV4ObAZBXwphDi8lvy6lPJSKeVZUspNdVQ/DkNBBMOXNS+w/I1hIS0LB/Q7FRbmkRij+FfW91hq4GQMxXVzQJbdGAOEAL5tojYUCsU+ilIoFYrmI7i7e241+78HdgbWRwghetZUkZTyEeBuYAfGgI7/AScEZckJyrsHGAo8C2zC8DssxlACx0opr6ORSCmXYHTDbqL6gUGtlZswfAsBYoCvmyJGo5TyZwzlbS7Gb+jFUPSXY3Q33xfIlweciuFHWYphQZwETG2sDBiDor4KtO8KyLARo7u/Nc/apFAoIgARmluTQqEIJ0KIbkCKlPKfwLYZuAcjRBAYFtCLwiWfQqFQKPZtzOEWQKFQhMSBGH59ZSO7U6gY7ZtBwMKlUCgUCkU4UF3eCkXrYAPG4I88DH87gRGM+mngICnl5jDKplAoFIp9HNXlrVAoFAqFQqFoFMpCqVAoFAqFQqFoFK3KhzIlJUWmpaWFWwyFQqFQKBRhZOnSpdlSytS6czYvA486Vhbl5dadsQlIX7n8WyllqNPDtjitSqFMS0tjyZIl4RZDoVAoFApFGBFCpNedq/kpysvloY+/bpG2xvbunFJ3rvChurwVCoVCoVAoFI1CKZQKhUKhUCgUikahFEqFQqFQKBQKRaNQCqVCoVAoFAqFolEohVKhUCgUCoVC0SiUQqlQKBQKhUKhaBRKoVQoFAqFQqFQNAqlUCoUCoVCoVAoGoVSKBUKhUKhUCgUjUIplAqFQqFQKBSKRqEUSoVCoVAoFApFo1AKpUKhUCgUCoWiUSiFUqFQKBQKhULRKJRCqVAoFAqFQqFoFEqhVCgUCoVCoVA0CqVQKhQKhUKhUCgahVIoFQqFQqFQKBSNQimUCoVCoVAoFIpGoRRKhUKhUCgUCkWjUAqlQqFQKBQKhaJRKIVSoVAoFAqFQtEolEKpUCgUin0OKSUlJSXhFkOhaDMohVKhUCgU+xS6rjNu3Dh69OjBsmXLwi2OQtEmUAqlQqFQKNosfq+fwpwSnEVuY9vv55qrxrFq5WqenvY0J598Mgu//Z6C7BK8Hj8AvrIyxe5wiq5QtCrM4RZAoVAoFIrmYOfGbLavzUIIgZQSi93M488+xLpNa3ju4deIjopmyj3PcMGFF3Lf+Ec44pBjiUmwU5zvQtOMMlFxdvoO64rFpl6XCkVtqDtEoVAoFG2OvMxitq/NQvdLQCKlZNr0SazZuIpnJ72K3erA79Ppv98Qpj0wg7un3EjB6HxOO+4sAPy6BKA438m6v3cw4Ii0sB2LInLZ6fFy747McIsREagub4VCoVC0OXZvzAkok8YAnOden8rqDcuZ/tAsoqNiKuXtu/8AXnhkNq+98yKffPt+5YokFOU5cTu9LSW6QtEqUQqlQqFQKNocHrcPMJTJR5+/j+9+/oJnHppFTHRstfm7d+nJy4/N5cXZT5G+c0ulfUIT+AL+lQqFonqUQqlQKBSKNsGObBcL/83ms78yyYyJwWMy8dzrU9mybRPvvvwlcTHxtZbvkNqJy88bx+vvvFR5h5Q4Ym3NKLlC0fpRCqVCoVAoWj1b9pTyz+YCil1+/LrE6fLx8qwpLF+zjGcnv0pcbEJI9Zx/+qX8u+pv1m9eU54WlxyFpolmklyhaBsohVKhUCgUrRopJat2FOPXK7bfmv4Ay1f/YyiTdVgmq9ZltznIL8wrTyvILsXvVV3eCkVtKIVSoVAoFK0aj0/iDxqAM+eFSaxY/me9lUmAF+dM48C+Qzh00IjyNE0TuErVoByFojZU2CCFQqFQtEqK851kbS/A79OxenRcQvD2c5NYs+x/PPPIG8RGVT8ApzY2bFnLJWddWSlN1yVWh3pdKhS1oe4QhUKhULQ6dm7IZvu6rPLQQHFI3przNMuXL+aFya81SJkEGH3O1bzx3sscc9iJCGH4TcYlRWGxqtelQlEbqstboVAoFK0Kt9PLtrVZleJMznjzGZb89wfPT36NuNgEBNCQYTRHDjsOq9XGHY9cx+ZtGwEoLXIhpWy6A1Ao2iBKoVQoFApFqyI/s5iA8RApJbPmP8+fy37jucmvhzyauyaEELz46ByGDDiUex67CZ/fh9+nl88FrlAoqkcplAqFQqFoFfi8fgqySvC6/ZQZDN9492V+++snnp38OvFxCU3SjtVi5ZKzx5KS1I4ffv0aJGgm9bpURDZCiDeEEJlCiJVBaU8JIdYKIZYLIRYIIRKaq311hygUCoUi4tm5MZsl36xn7V/b2bE+C6lLZr83gx//9y3PP/w6ifFJTdqeEIKzTr6An/73HVaHBXu0tUnrVyiagTnAKVXSFgIDpJQHAuuBe5urcaVQKhQKhSKiyc8qZvvaLHRd4vfp6H7J3A9f5btfvjSUyYTkZmnX5XYS5YjGZFavSkXkI6X8BcitkvadlNIX2FwMdGmu9sM+bE0IYQKWADullKeHWx6FQqFQRBa7N+WWD8ABmP/x63z14wJeeHQOyYmpTd7e9799zY+/fcOS5Yt54JbHKS1y43Z6sTksTd6WQlEPUoQQS4K2Z0kpZ9Wj/FjgvSaWqZywK5TALcAaIC7cgigUCoUi8vC6feXr7346h0+/+4CXprxJalK7Jm8rOzeLh6bdwcRbHuPO6x4kMSEZIQQ+j18plIpwky2lHNqQgkKI+wEfML9pRaogrAqlEKILMBKYAtwWTlkUCoVCETlkbc9n+7os3E4fZosJgA++mMdHX73Li4/OJjW5fbO063BEYbXa8HjclA0l1/06K3/bCkBSx1jS+rfHYosEe4xCUTdCiCuA04HjZTPGvwq3Y8izwF2AXlMGIcQ4IcQSIcSSrKysFhNMoVAoFOEhY2sem/7bjavEi9QlXrePj756m/c+m8sLj7xB+9SOzdZ2tCOaKXc9y9IVf3HZzaNYt2k1AH6fjt+nk72jgOW/bEHXVVxKReQjhDgFuBsYJaUsbc62wqZQCiFOBzKllEtryyelnCWlHCqlHJqa2vS+MgqFQqGIHKSUpK/JrOQz+cm37zN/wRs8/8gbdGjXqdllGDH0KB6+Yxp3Xv8Qdz56A9t3pQfJZ3TB5+4ubHY5FIr6IIR4B/gD6COE2CGEuAp4EYgFFgoh/hVCzGyu9sNpsz8cGCWEOA2wA3FCiHlSysvCKJNCoVAowoju0/F7/eXbny38kLkfvMLzj8ymU/tmG6BaLcccdiKFxQXc/MCVXD96AicceRomkwndLykpdJHSOb5F5VEoakNKeXE1ya+3VPths1BKKe+VUnaRUqYBFwE/KmVSoVAo9k2K8pxs+m83m5bvLp9D+8sfFvDGuy/z3MNv0KVjt7DINerE85h025N8/PW7XDHhHHbsTkdoAke0LSzyKBSRSrh9KBUKhUKxj7NzQzarft9KxtY8sncUouuSb376jFnzn+e5h1+na6fuYZVvUP+hzJw6D6vFxur1KwBJcmcVmEShCCYiFEop5SIVg1KhUCj2PTwuH9vWZlXymfzu5y94ee4zPDv5Vbp37hFG6SqTlbOHAQcMQiAoLVRzeysUwUSEQqlQKBSKfZOCrOKy6DyAEVT8xTlP8eykV+nRdf/wCVaFzekbsNui6NS+C7ouycsoCrdICkVEoRRKhUKhiEDcbjd5eXk4nU4A/H4/+fn5FBYWIqVESklusZc9eW48vhojr0UsPq+fvIxi3E4vZbbJn/73Hc+99jjPPDSLnt17hVW+qixZvpihBw4DQGhGGKG8jCI8LiPousflIy+jiJICVzjFVCjChorMqlAoFBGElJJNmzaRk5ODpmlIKbFarXg8HoQQSCnRhYV8U2c8PiP2tq5L+naNoXen6HCLHxI7N2azbU0WmmYcj/RLfln8A8/MepSnH3yF/dP6hFvEvVi6fDEnHW14ZkkdMrbkkZmej9+vExVnw1nkKT8ee7SVfiO6Y1XBzxX7EMpCqVAoFBHErl27yM3NRUqJ3+9H13VcLhd+v58vv/ySl2fMYEdpDE6Pjl+X+PwSXcLaHcVkFXjCLX6dFGSXsH1tFlKX+H06ul/y218/8eSMyUx7YCa9e/YNt4iV2LR1PQ88dTsbt67jkINGlKfrAfmRUFrgrnQ8pUVu1v+9I4xSKxQtj/p8UigUiggiIyMDXa/chZ2ens60adMoKCggJjaeBWcdxtRXv6RDl7TyPH4dNu0pJTXe2sIS14/dm3MrDcD535KfefylB5k28WX67NcvjJLtzb+rlnDf1Fu49JyruPemh4lyVFiAi0uK+OTb98jKySS/MBez2cKlZ19Fz277gzTCIHlcPqx29ZptyxxgLWJB1x9bpK3IcgLZG3WlKxQKRQTh91cE9Xa5XMyePZtPP/2UK664gvPPP59f/ljC/XdN4J5xI7nx3ukccuRJ5flbgy+lz1NxfH8u+40pL0zkiftepG+vgWGUqno2bFnH4Yccg91m5/EXHmB31k7ap3QkNbk9K9YuIyWpHUMGHEq/3geyYs0/vPXRqzw04QnAcEXwe/2gFErFPoK60hWKfZDS0lK2bt1KUVERmqaRmpqKlJLs7Gx0XSc+Pp60tDTsdnu4RW1zuN1utm7dSn5+PkIIkpOTMZvNZGVl4ff7MZlMAPzxxx88/fTTHHDAAcx56x2sqX3IwsGaLV8RHRvPfU+9xRP3XMn2resYdfF1WMwWipw+PvkzA7tFo1/XGLqlOsJ8tAbbspys2VGM06NjszmIsnr49+9fefjZe5l67/MM6HNQWOSSgNNhoyTaga5pmH1+4gqKsQSU+ryCbL768RNKSos5atjxdO7YlczsDLJyM+jdsy/HH3EqNqsR4PzggcO47OZReLwerBYrQhPYYyLbWqxQNCVKoVQo9jHcbjerVq0qt4T5/X727NlTKU9+fj4rV65k0KBBmM3qMdFU+P1+Vq5cidfrBYwBOFlZWZXy7Nq1i2effZb169dz++23M+ywEWTRFTcaINizcxvRsfH07j+YKTM+Zda0e/jrl2+4/ZFXSO1gTE3o9Oj8u6UQvy7p0T6qpQ+zEumZpfy3tQh/wHjq8fhZv/Q3Jj19J1Pufo6BBwwOm2wlUXZKoqNAM+IWSQHmIAvx5eeN47Tjzg5plp6UpFQ6d+zKmg0rOKjfwcSnRJfP+KNQ7AuoQTkKxT7Gnj179vLRqw5d18nMzGwBifYdcnJyKnVpB+P3+/n444+54oor6N69O/PmzeOwww6jlBgkAjCUk6zdO4hPSAYEHbqkMfGZtxl+9GncNfYU/vmjwpfLr8OaHSVIKattryWQUrJqe0m5Mgmw8Y+fmfTUHTxy5zMM6n9w+GQDSqMd5cokQHSxs1Ieu81RqzK5fVc6v/75I7//vYjF//yKlFBSWgxAfmaxMWhHodhHUKYHhWIfo6RkbyXjs88+4/vvvyc5OZkLLriAvn37ous6JSUlYZKybVJcXFytMr9u3TqeeOIJLBYLL730Ej179izf58OGDPr2z8vJoFO3/cq3NU3jrMtupM/AoTz9wHUcf/pFXHDVHZhMJjxeHV2CKUyGMinB7a043tX/LuapR27k4dunMWTgoeERKoCuaQFFvQKLz0eopyp9x2ZuvH8M/XoNQJcSXfcTHxtPh9ROAAghcDu9RMWqOb8V+wZKoVQoqsPrhJ1/Q/5WiG4HXYZB0S7Y8x+YLNBpKFgcsGMxuIsh5QC8Kf3JzM6luLgYh8NB+3ap2Ao2QsYKMFmh81Dj/46/wFMMqX2hw0GgNf9t6PF4yMjIoLS0tFqFZvbs2QwcOJB+/fpx++2389BDDzFs2DC8Xi/r1q0jOjqa9u3bY7FYml3WlsLv95OZmUlhYSE2m4327dvjcrnIzs4GIDU1FavVSmZmJm63m/j4eJKSksjJyaGwsBC73U67du3KywghSE1NxWKxkJGRgcfjISEhgYSEBHJycigqKiq3Tvp8PlavXs2SJUv4559/2Lx5MzfccAMjR45ECIGORikxeLCjIwCdsg6lwoJcBnc6dq/j6XvQMJ6a/S3PPnQDD99yIRMenkFySipLNhRgNgvS2jkwaYItGU48Xp2OSTZS46ykZznJK/ESH2UhrZ2dnCIvu3LdWEyCtPZRCGBLZilen6RTko3kGAvpWS7yS70kRlvonuogq9DD7jw3VrNGWjsHUkq2Zjrx+iQmzbCWrl3+N0/eO5Y7Jj7PoD4H47RZcdssaLrE4XQhhcBptyE1gd3lwez14XTY8JlNWLw+okpdTdqlpuk6NrcHr8WEw+nG7PMjMSyXdSmV6Tu38OC027nmkps48+QLqs3j9+tsX5tpXBfdEkhIVV3giraNUigViqq4CuCvF8HnAt0H2esg/RfyS738+t8WpAS//h66lLg8XmIcFjzCwTZbP6RmRUqJJgTn9HKRFutB6F5AwO5lxptKBl5buRtg+x8w9FpDSW0mSktLWbVqFbqu19j9eeihh1JQUMD5559P7969uffee7ngggsYPXo0JpOJ/Px89uzZw4ABA9rEQB2v18uKFSvw+XzlCvaePXvKA4cD5bEgy8jPz2fr1q1omlZeZvfu3ZXK5OTk7FWmbHvz5s0sXbqUP//8k3///ZdOnToxdOhQLrroIoYMGUJUlOHr6MdENp0DiqSGoUwKylQdZ0kRnbtXPyVhYnI7HnzuPd57bRp3jDmJCZNepP+QwwHYnuUCEbj8gD35bqQMBEaXkJHnYd3OEjQNyr45tlUtk7d3mbU7Ssq3AdIznZXKCGD9qn+Yes8Yxj/4IgOGH0sOGBk0DaTE6Qiy4gmB22qhfD5GIfBYLNjcHoRfD9mCWBcCiC8srrQdagf1JTcaAc6zc7NYt2l19eGOJOTsMqZnzN1TREqXePYf1KlxQisUEYxSKBWKqmz8BjwlUD4hnI6uS8564D3yi92kJkRhs5gQAnILXSTF2SnREnGzBTC6unRXEc9s3cyrE07ltGH7BeqSFVUC+D1Qkgm7lkLX4c12OFu2bKnRb6+oqIjHH3+cjRs38sQTRriTgw46iNdee40nn3ySH3/8kccff5zOnTvj8/lIT0+nT5/Im8WkvuzcuROv17uXgh28XdO+qhbe6spIKdmxYwdLliwpt0JGRUVx8MEHc/LJJ/PAAw8QHx9frWyFJKEHBuAYVLbLeTxuuu1Xc/Bvk8nEJdfeTd+DDuXpB6/njIvGceYlN6BpWqXrr0wBLBO//GoPOjwZvKOWMsGnqmqZ9auX8dido7np/mcZcthxFTuCFMa90Cofs8PlxtyEymS5CFWbDbHc4/c8z5qNK3G6nYy9/Xw+n/MzsdFx7M7cRcd2ncgvzCM1uX15ft0vyd5RQMceSUTHt/4PMoWiOpRCqVBUJXsdwW/ENenZXDP9G3QpWfLyGMymvV87f0Wfhi4qrIw9Xf+weulvXDntK074PY1nrjuO2KhqfKl0L2SuaDaFUkpJUVFRtfs2btzIPffcw7Bhw5g7d24ly2OnTp2YPn067733HhMmTGDevHlYrVYKCgqaRc6WJi8vr8kHq+Tl5fHXX3/x119/sWTJEqSUHHzwwRx22GGMHz+e9u3b110J4MZBTZ2uHpcLqet0r0WhLGPw8GN54rWvePqBa/nfD5/Rqet+xCYkEZeQTNxe/5OJjUvE1MQj+jetW85jd4zmhnufYejhJza4Hrvb0+TKZGM4avjxJCYk8e6nbwJwx8PXs27z6kp5npv8OkMPqrivdV2Sl1GsFEpFm0UplIp9A2ee4QNpT4DYToYil7cZhAaJPUGYoGCb4dsY8Gn0+XWuevor5i5cyT0XDeeh0UdUq0wC2HQnzqBu61IthqMP6s7SmeMY/8ovHHjdXObedRpHDOhMkZaET1iJ9ediwYNTi8OZm4vdbi/v9qzzcJxOnE4nDocDh8OBz+ejsLAQk8lEXFwcAIWFhTVaJr///numTZvGzTffzMiRI6vNI4TgoosuYunSpbz22mvccMMNCCHIzc3FbDYTGxsbUT5hHo+H4uJiLBYLMTExSCkpLCxESkl8fDxCCEpKSsrnxK4Lt9tNYWEhhYWFFBUVVbtetuTm5rJ7924OPvhgDj30UK644gq6du1aYzs6Ag92BBIrLgC82NAxUdmMXZn0zWsRQsNmDy2+ZGqHLjzy8gJWLfuDgrxsCvNyKCrIZcv6VRTmG+sFeTkUFeRQXFRAVHTc3spmfBJxicl7KaBxCUnYHTXPHb5l/UoenXAJ1971JIceeXJI8taEz6Rh9YIuBB6rGU2XWLyhD6BpDl6d/wJLV/yJ3eZgwAGDuH/8FLbt2oLL7SIpIZmHnr6D91/5lujAORKAz+MjZ3chMQkObA4L7lIvxflObA4L0Ql2dL+kMKcEhCA+OQqthueNQhGJKIVS0baROqz+CDKWG0ojOliijS5tUfawlmCygd8NCPB7+PCXtYx9+iu8Pp2/X7yCoX061trMAOfPrHQcjdNkKHN7LL3INXXCG+1g/ANncNAvv3LWI09z9qiRXHLp0cRGR+NHw66X4vbEom3ahJSSmJgY+vTpUx7cuiq6rrNu3TqKiorKffesVitutxtN0wz/zUB3YXUWuG+//ZbFixezdOlSnn32WQ444IA6T+E999zDDTfcQEpKChdccAGbArKazWb69u2LwxHe4NlSStLT08nIyChX4EwmE36/v/wcuVwuPB4P+fn57Nq1ix07dtSoIJZt67pOXFwccXFxxMbGlq+XLT169ChPT0hIYP/99w9p0FIp0RSSQpniKAENGRjJXfG/Oivltk2rsdrqN2rYYrEy6NCj68zn9/spLswrVzIL83MozM+lqCCXrD072bR2eZACmkthXg4IsZeSGZeQTExsPN98PIdrbn+M4cecVi95q6MoLoaSKD+62YQIXNcWt5eEwuKwKZVPPTCDrJw9dO7Qrfy62y+td/n+Pvv1Y8HX73DZOVcDhlvAnq15ZKTn4/frOKKtuEq9aJpxjZotJnxef6WPkD6HdiEhNaZlD0yhaCBKoVS0bbb/YYyy1n2Az0jz5++VLTM7jzMf+ogtewrILXLi9ekcc1A3Pn7oHBJj6+6i2m3phUsLsi4KgccUDQgkJo48+jj69B3Aiy++yHkXXMI555zDhRdeCLExICum2ysqKmLr1q3st99+1baTnp5ebnUrw+UyLFxlddQUY/KPP/5g5syZXHrppdxwww2kpqbWeVwAycnJPPvss1x99dUccMABHHjggYBhEVy7di2DBg0Kq6UyJyeHzMxMpJTl58XtdvPWW2/x0UcfUVhYiKZpREVFER0dTadOnejUqVP5qO20tLRKimN8fDxxcXHYbLYmPy4fZgpIoaq3nr6XAlm9lXJn+iYcUc2jYJhMJuITU4hPTCFoivAakVLidpWWK56F+TkU5OVQkJdNQW4WN94/nYNHnNA0wgmBbjaBEEhhjPiJLwqfMglgs9ro0rF7jftvvOIObnpgDAcfOJy++w8ACMxhbvy2zmIPAP6AY6rHH3g+Bf32a//cztCTemO2Vv+BqVBEEkqhVLRttv9hdG/XwZ2v/sTiNbtY+vIYzp70Mbtzi5l3zxkhKZMAGZY0pKh6O1V+3bVr146HH36YrVu3Mm/ePC688ELOP/98zj//fGJiDCWhbPrDnj17VqvMZGVlNcj3z+l08uyzz3L77bdzxBFH1Lt8x44dufvuu5k8eTLz588v97f0er2UlpYSHV1z12dzs3v37kpK9MaNG3n44YdJSUlhxowZdOjQAas1MqbAcxJD9f6RVdOqV5UydqUTG5/Y1GI1CCEEdkc0dkc07UKYSaYJGixftbk9zd9eI9kvrTe3j5vIXY/ewCN3TmdQ/4MpdZbw/BtPsHT5X+QV5ODzeRnQZxCjz7uGYYMPr7aenN2FtO8eGb+5QlEbSqFUtF2kDHRj187unGIWLt3KNacdxJBeHUiff0O9m/KLIAtCWVyVGkhLS2PixImkp6czZ84czjnnHM4//3xGjx6N3W5HSomu63t1e5elh0pmZib//fcfa9eu5eeff2bQoEENUibLOOqoo/j000/55JNPuOiiiwBDqfD5fEgpm91KWaZIB7cjpawU23Hu3Ll88MEHXH/99ZxxxhkR5eMJBEZvl1H2YRC6jDlZu0lMDm1wT1tG02VtrqYRwwlHnkaUI4YHp93OuaddzLZdW/F6vTz94EySE1PQNI3f/17EEy8/xOknnMPYCys/e6SU+L16i9xfCkVjEeGclqu+DB06VC5ZsiTcYigiGanD5h9h++/gc4PZDj5jOrX8YheT5v7GG98u56Ce7Zh3zxlk5JVw5kMfsX+nRD5/5DwSYho2AtMposk0daOdfxt2WYJX2Nhq6U+upUutyiUY8Q9feuklVq5cyZ133snhhx9e7guZnJyMEILs7OxyJbOmgTYABQUF/Pjjj3z33Xds3ryZIUOG0KtXLw4//HB69+7d6JfStm3buO6663j00UcZMmQIQLmfYnx8PD169GjyOJVer5etW7eSm5sLQHx8PFFRUWRmZuLz+TCbzaxfv55HHnmE+Ph47rvvPtq1a9ekMjQVEigmDgtebBjXZSkxFJJMKEFrrj93GP2HjOCm+6c3r6CRjpREFZcSU+qKqNHfNZGdm8U1d16Ey+Pkw1e+I7qK20Jefg7X3HUxt427nxFDK/u7mswafp9OdLydHgM7EJcc3rnZIwUhxFIp5dBwyzFwYFe5YMGEFmmrV6/bI+KYa0IplIq2xZpPYPc/e3Vz+/06R0yYR7/uKdxwxhDmfLecFz/9B4fNzKxbT+GyEwY0uulgLzgfFpZFnYBfWOpUKMtYvHgxzz77LHFxcYwePZojjjgiJAXQ6XSyePFivvnmG5YuXcrw4cM5+eSTGTZsWLN09S5evJipU6fy7rvv7qU8ms1mBg0ahLmJws9IKfnvv/9wu93VdvX7/X7mz5/PO++8w3XXXceoUaMi3pJT1S6ZRwouoglFobz8pD6cedmNnHv5+OYSr3UgJUk5BZj9/lahUEL1FvZgvv35C776YQGP3/s8UTWMntdMgoFH9lChh1AKZSSiurwVbQevE3YvDQzAqcxXf21Cl5LXbjsVIQRDerVn8P7tOeag7vTsmNAkzQe/JjItXZFCC1mZBBg+fDjz5s1j0aJFzJw5kzfeeIOLL76Yo48+GluVkb1SSn744QcWLlzIkiVL6NevHyeddBIPPPBAuT9mczF8+HD69OnDhx9+yGWXXVZpn67rZGVl0bFj7aPiQyU/Px+Px1OtMrl161YeffRR7HY7b7zxRpO12dwEXxF+TCErkwAuZyld0no1i1ytCYvXh0lvPcok1KxIlnHoQYfx+XcfMurKozmo38GcfPQZHDX8eOy2iigKul+yY30WfQ7p2tziKhT1RimUiraDMy8QGqiyQrls4x6uf/47bjl7aPlDXQjB2FMOajZRSrUE9L0G6dSN2WzmhBNO4LjjjuO3337jgw8+YMqUKbz22mv06mUoEj6fj2nTprFixQpGjx7NfffdV+OsK83FNddcw+jRoxk8eDD9+/cvT9d1ndLS0iZrx+Vy7aVM+nw+5s+fz7vvvss111zDWWedVR4qqbXhx4xAhuwO6PN56dGr8db01o7Z528VPpT1ITEhmRenzMHpKuXXP3/k258/55lZU7j7xskcO+Kk8nylhXX7hSsU4UAplIq2gyMRZGX/Qiklox78iHOP6M31ZwxuMVE6eDZRLGKJlsUk+3agYybT0h2/MNPeuwWLdJNn6kimpWtACa6MpmkcddRRHHnkkZx77rmV5o5+4oknMJlMzJo1Kyyjqz/99FOeeeYZAB577LFKcgghQg7OXhN+v5/MzEzy8/OByvE0t2zZwiOPPEJsbGyrskrWhBk3Flx4iKKuwTm5WXsASGnfuQUki0CkxOF0YXN725ouWQmHPYqTjj6dk44+nUV/LOTtT2ZXUiijVHe3IkJRCqWi7WBxQEIPyN1QnvT9P1vZkVXE9OtPQNNaroMsWhZwkOtndDRMgSiDSf5dgb1G6Gqr7iLb0hld1tw1np6eDhixICdPnszvv//OKaecwvjx45vMTzFUdu7cyR133MG2bds455xzmDBhAvfeey/ff/89Z555JmAofykpKQ1uw+fzsWLFCrxeb6UR7T6fj7fffpu3336ba6+9lrPOOivifSVDQQPslAQUytrZsn4lJrO51VpjG4PQJUm5BWh+f3nY932B4pIiulaJddmhhwohpIhMlEKpaDvofijcUSkpJd7wP2pJZRIqbE0m9PJtUeU1uN3W15hqL4RR4GeccQbnnnsuCxYsaHGrpK7rTJs2jU8++YQePXrw4Ycf0rFjR9xuN6tXr+bss88uz6tpGsXFxSQmNuylt3v37r18JtetW8fDDz9McnIys2fPbvVWyWAkgiJSCCV00PYt67HZ980Rvg6nC1PQAJzW/ykRGlu3b6JHt4pJDoSAgswS4pPDF/dVoagJpVAq2g4lmZW6vLfuyWfI9XO44Oi6pxcMBwWm1KDpH6snLS2NBQsWsG7dOkaMGNHiVsk///yTBx54AI/Hwz333MOoUaPK961YsYLExESGDRtWnqbrOnl5eQ1WKPPy8sqVSa/Xy/z583nllVcYO3YsV111VZuzznkJfRS+x+3E63Gj63qbOw91YXN79hklMpgt2zdxdv8Ly7elhJzdRXTrG5lhsRT7NvvWU0nRNvG5IXMVFGw3rJRAYYmbHqNnAjBrwinhlK4SEkGBKZUcUyc0ao4nGUz79u056qijWlSZLCoq4oYbbmDChAkMGjSIb775ppIyCcZUfQ6HY6+uZ13Xyc7Oxuk04ix6PB5ycnIoKCiodrS2lJKioiKys7PL61q1ahVjxoxhxYoVfPzxx1xzzTVtSonSEbiIwos15O7b0y+8BiEE0x+8rllli0R8gSD/fk3gslnxWMz7RLf3lm0bSOvas1Ka2dJ27gNF20JZKBWtm4wVsOqDCkuf9OPy+Dh70sekJkSR+UHkxOsr1WJZYx+BX5gQGCFj6ppVJxzMmzePV155hYSEBGbNmsWAAdWPKo6LiysPNh5Mbm4uubm56LqOzWbD4/GUK4pms5m+ffvicBiuCF6vlzVr1pTPR+71ennttdf49NNPufXWWznxxBPbhK9kMKVEU0BKkMVNwF5zee9NVEwcd0+dzaO3XcJhP37OiOPOaF5BI4iiuGhKoh3oJg0R+CixerzEF4R3Pu/mxO/3k5WbScd2XSqld+iRFCaJFIraUZ86itaLqwBWvW8EMfe7y6dZvOaZr1m5NYtPJp0TZgErkMBa+3C8woYuLIGA51pEOYNt3LiRc845hxkzZnDZZZfx+eef16hMAnTr1o0dO3bspVTquo7f7+eDDz5gyJAhPPPMM+i6jq7reDwe1q1bV26p3LRpE6WlpeX7V6xYwZw5cxgyZAgnnXRSm1MmfZgpIAXQkIGlzMM2FAYPP5bjz7iUZyfdSH5uVnOKGlkIgW4yBq9JzThncYVtV5kEowcgLjaBgqK88jQhMD5CFYoIRFkoFa2XjP/2erjquuSdn1azfNZV9Ove8NHGTU2xloiv2llzwv9K9Hq9PProoyxcuJADDjiAV199leTk5DrLWSwWevXqxRdffIHD4WDPnj3lCuK6devKg7G///77WCwWbrrpJsDoAnc6ndhsNgoKCirVOWjQIGbOnMn48ePZsWMHXbp02avd1oyTGBr7m99w7zSWL/mFidefxYvv/d40grUGgu4dq8cTRkFajpTEFLJzs0hKMJ5lUsLuLXmkdk0Ir2AKRTUohVLRevG594o7+cR7i+nZMSGilEkAHS0CVMe9+eGHH5gyZQpCCB599FGOO+64epVPTk5mxowZHHjggRxwwAF07dqVI488kgEDBhAVFYWUktGjRzN//nwAbrrpJoQQ5XNwV8dBBx3EoEGDWLJkSZtSKCXgb6JOocdmfsa15wzl9ekTuWrCo01SZ2tC6HKfiB0U5YihpLS4Upru02vIrQgHHk8m6dteCrcYEYFSKBWtl5Q+sPUXkMbMOL+v3MHj7/7BH8+NDrNgexOn59DdtZzN9kHVBjJvaXJzc7njjjtYu3YtJ510Evfffz8Wi6Xe9UyaNAmz2VxjKCMhBPPmzePtt9/mhRde4MQTT6RPnz6sWbOm2gE6YAzSKSkpYcuWLfWWJxJxEkURSeWz4oTiL1kXSakduOn+Z3n+4ZsZfsxI+g8+rElkbS24HDaErhNb4ozID7WmIicvi6TEoI9jAcmd48InkEJRC8qHUtF6ie5QqRts7vcrmXDOIfRPSw2jUNUjgFxLp7C//KSUzJw5kzPOOIP8/HzmzZvHpEmTGqRMAsTHx4cUF7Ms8PnKlSvL5aiJjRs3kpGRwfXXX98gmSIJNw7yScWPBSMSaZnPZOM5+pTzOHjECUy5/TJcrqab7rK14HC17W5vKSVZuZmkJgWFCJIQl7JvxiJVRD5KoVS0XrJWliuUbo+PWV/+y+gTInOeY5eIosDUDhlG66Su61xwwQXMnz+f66+/no8//piePXvWXbAJKFM6p02bxvfff092dnaNedesWYPNZsNub/1TzBWRwN6P2ab7rLhr6mwsVhuTbr6gyepsDVi8Pky6P+wfaM1JUUkhFrOFKEflD7bdm/aOrKBQRAKqy1vReinJAr9hpfh88UaG9GrP/p0jc1oylxaNhm6ECgoT06ZNIzMzky+//JK4uJbvNrv66qvZvXs33377LU8++SSJiYkMHjyYQYMGMXjwYNq3bw/ATz/9xHXXtY1Yiz4aZvkNFbPZzCMvf8yE0cfxybyXOeuyG5q1vUjB7PO3eR/K4pIiYqP3vk+dRe4wSKNQ1I1SKBWtl5gOoFlA9zLnuxVcO3IwAJl5Jfz4bzq6lMTYrYwa0SvMgkKsP4dYXzb5lvBMG7hx40Y++eQTHnzwQeLi4pAISonBRTQafqIpxErzvqiuuuqq8nW/38+mTZtYtmwZP//8M8899xxRUVEMHjyYoqIi8vLyaqkpsvFipZg4dMwIdGQzf0R063kAl113H2/NeJQhI46jW8/InBmqKXHZrFidboQmcDpsSCGwu9yYvT6cUQ58ZhNWjxeby4XHZsNts2Ly+3GUutBNGi67HSnA7nJjd0XmLDzf//o1nTt03Ss9Or71W+4VbROlUCpaL6n9gI/YnVPMd0u3MPWqYzjhrndYumEPRwzogq7DV39tIuvD8aTEh9fvyIROnD+LAnN7ZB3TLTY1UkomTJhA//79OeWUU5AIsumEDzNGd6zEjBsL7hZ7sZpMJnr37k3v3r258MILkVKydetWli1bxpdffklCQgLnn39+C0nTdJQFLa+ILanTFINw6uLs0Tfx589f8+CN5/La5/+1+BSdLY3UBAVJAetdwO3FY7FUnGYh8FrMlEQ7KralGZfdVqkMEuwuTwv8QqEjpWTuh7P4fOFHzHj8rUr7NJOgS+/I8xFXKEAplIrWTOEOAF7/5j/6dEnmoOve4LZzD+Gd+84kNSEKl8dH6nnPo+vh7xvzYWaHrV+LK5MAL7zwAvn5+bz99tsAlBITpEyChp8YisL6QhVC0KNHD3r06IHZbOaJJ55odXEoJVAYCFpeQcv93pNe/ICxpw1g2n1Xcc+Tb7ZYu2GhuoD3mqg9T9VtKYkrKokIRXLO+zN597M3OWC/AezYnU58XAIzp84nNTkVk1nD79OJSXSQNqADUXG2cIurUFSLGpSjaL3kbADdS3Kcg5xCJ5cd35+nxh1HaoJhjbRbzRQ7PSz4fX2YBYUSU2IgZEzLsm3bNt59911uu+02YmNjAXARRfCtb8UVUe5oo0aNonPnztx1113hFqVe+LCGtX27PYr7n57P3799x8/ffBhWWVoDmi7R9MiI6di9S0+Kigs5/ohTePrBmbzyxNukJBmWyM69UzlsVD8GHtmD2ERHmCVVKGpGKZSK1oslCoSZ688Ywq73buLNu06vtFvXJYf06YjDFn5DvFUvAakjpE6CL4Nk73asuhOAKH8+Kd7txPhzm3xatVtvvZVevXpx9tlnl6dZcAMSDR92ijHjJdJGOEydOpX09HS++uqrcIsSMho+jC7u8NF/8GGcdv5VvDjlVnKz9oRVlkhB8/uxO91Y3R6QEqFLbK7AdoRw7IiTGHfpeD7++h06d+iK2WQ8s4QmMFvUa1rROgj/m1ahaCgdDoJN39WoC23LLGDl1izOGtG7ZeWqBrssZUjpd4E4hGAodDoeYcciK15s26x9ybD0rL5Lr57MmjWLzMxMXnvttUrpseQTRREm/MhAh5+gJTz9Qqdnz54cd9xxPPXUUxx77LE4HJFvmTGhk8wesulEOL/Vr5rwKMsW/8Sk8Rfw/Du/hE2OsCMlsUUlOJxuZIXLJJrE2JYVaZFw3V9+3jj+W72U+Qve4IrzrwWM78vkTiqQuaJ1oD59FK0XWywceCmYbBVL0KthR3YRA3ukEhcdfp8jgaFwmPBhxocZPxoSm3Riwo8JP04tlixL9yZRJnfv3s2bb77JjTfeSFJSUjWyGDH8NCRaQK2MhJdqMPfffz9CCB555JFwixISEsijHZFwJu+f9hY7tq4nY2d6uEUJGza3B7vTGGimyYqlfBsi6roXQnDTmDv54vuPK6XLCPABVyhCQSmUitZNSh84eiIcNBr6ngVaRYiW+Ggb2zOLwidbNVR9eQVvZ5q7ozfRLXnrrbfSrVs3Lr744pDkiETsdjsPPPAAixYtYvXq1eEWp078WNAxEwlnt2PXnqS078xbL+9783yXEVXqasaQ8s1DUmIKGdl70AO+nUJAzu7IeoYpFDWhFEpF60czQ0J3iEqFoFHUnZNj2Z1bHEbBQkMCfkz4hLmS/A1l7ty57Nixg+eee67xwoWZY489lgEDBnD33XfXOl1jJBBp0p167pUs+X0hlM8fHm4kFWGUmrJaWdn3OLAtImTATajkF+bx9MxHOOPEc9E04zkgJej+1nUcin0XpVAqWjfOXFj6Kvz4IPz9Mvi95bscNjMWc2Rf4pnmbiyNOpm/o08j39wBpL9R9eXk5DBr1iyuuuoq2rVrV3eBVsCTTz5Jfn4+s2bNCrcotWLGSzwZhHtgThlnXzwWv8/Lf9++Qge2hlE2SRQFtGcbHUinHduwUtLoWoWuE59fSLvMXNpl5pKYk09MYQmpWXm0y8zFpOsRoUaHysdfv8PmbRu4fvSEikQpSWwXEz6hFIp6ENlvW4WiNvwe+HsG5G0BJMgK60epy8s107/hsL6dwypibWSZO7PVNhCfZgehoYuK2JAN5fbbb6d9+/aMHTu2aYSMABISErj22muZO3cuWVlZ4RanRgTgIjbcYgBGbNH25kwGDRrE3DffRADOMMkWRSGx5KGhBwZ/aXhp5CArKUnKLcDm9pb7QVp8fqKcLjQpy/0kWxPFJUWcceK5xERX/E6aScMWHd5wVApFqCiFUtF6yVgRmMt77zfHBY9+QqnLy9ePXdDycoXIDusBASUyiEYMyPnmm29Yv349Tz/9dCMlizwuu+wyOnTowB133BFuUWrEj7ZXjM9w4aAQkFx33XVs3bqV7PwSPNhpedkkseSjBd2jJcSVRxdoKFaPD03XK9VS3QCbSPeZDMblcmKzVVa0JZCXoXwoFa2D8D/5FIqGUpodUCgr4/L4WL45i1vOHkqU3RIGwULDI5ouFI7L5WLq1KmcfvrppKWlNVm9kcTTTz/Nhg0b+PLLL8MtSrX4MUeMAmPBiwYMGDCA+Ph4XnllRlgC6wOIKt3sXqw0VtUz+f2R4RbahGiahtdb+Xmm+3VcxZETL1OhqA2lUCpaL7EdQausMLo9Pi589BO6t49jaO8OYRKsZgpMKayzH8IqxxGYpLfuAiHywAMPYLFYWt3sMvUhLS2Nc845hyeffJLS0tJwi7MXFjzYKMGCkwQySGYX0eRhwkMMOSSzi3iyMOPGQRFJ7CaRPdgpoSHakTXQThK7iCYfEx5iA+1oeMvVuJEjR7Lo+2+x4mxQO42lmHi8WMgnmWw6BqyTjfPldNmsuK1mXDYreQmx5CbGUeqwtWodc8TQo3nzw1ksXfFneZoQgqh4exilUihCRymUitZLSt+9kp54bzGFpR7eu/9Moh2R5Xu0y9KTdfZh5Jk6UmRKxicsTTIzzj///MPvv//O5MmTMZvb9lwFt99+O9HR0dx5553hFmUvBJBANsnswU4pVtzEkE8qO4mhECtuHBSTwi7iycaGCztOLDjr3VYUBSSSgZ1SbLiJJY9UdhIdaMeKp9wGOHbsWEpKSlj+109NeryhISgmkWw64yQWL3Z8VI4X2xCkJihMiKMgPgaPzYrXasFtNT4uW6tSOWLo0Tx8xzQefOp2/lnxFwCaJkhIjQ6zZApFaCiFUtF6KdheafPzPzbw6Nv/45VbT6FTSmQMjijDh5nt1n6Gz2SZn6QwNTqIudfr5b777mPw4MGMGDGiCSSNbIQQPPXUUyxbtoyff/453OLshaCyL1/V4NlV90sExSRRHwVLoBNHXnlA+urqDV6PioqizwEHMHvWjHq103TUJl1DqxQVCwRmxSmNqEDlDeHggcN4cMIT3Df1Fnx+H1KXlBS4wi2WQhESSqFUtF5yN4Je0W1c6vZhs5jp3SWplkLhodiUiNYMIVumT59OaWkpU6ZMafK6I5X+/ftzwgknMHnyZNxud7jFaRSGP2H9bGoW3PW2wl1x1Q1sWP0vHlfbVE6ElJhaWdzJmhg2+HA6tOvEqnX/IaWkIDvy3DsUiupQCqWi9VG8B3b+Dd4Sw8oXYFdOEcVOD74IDARs14sR0o+QfhJ9u0n1pmPTGxeLLz09nc8++4xrrrmGhISEphG0lfDAAw+gaRoTJ04MtyiNwoSPssDjNkpwUIQJ4yOpzNeyzPdRw4eDIiy4qa8Sevjhw7E7ovhgzvSmPoSIQAqBrrVm22RlBvYdzOr1K0CAx+klIz0PZ1Hr/nhStH3atsOVom2h+2HFO5Cz3tgWojwQ+PMLlnDnrJ+YfcdpmCLwxWKTTgaXLkQGvuEEEh2NFVFH4xFRDer6fvDBB0lOTubSSy9tanEjHovFwqOPPsptt93G33//zSGHHBJukRqECT/t2V4pjI5A4sdUyaItEWjo5fmMeI6hd++a8XHMccfx/edvc+l19zbdAUQKQpCbGE9ybn6riz9ZHQP6DOKHX7/i4rPGkJGejxFnF5I7x7H/4E6IRrrKKBTNgbJQKloPO/4wlEndayyBkEFSSj7533omXX4EY04+MCIftgIwoWPGhxkfJvzssPbBK+wNUiYXLVrExo0befTRR8unadvXGD58OMOGDeP+++/H5/OFW5wGY/j9SbTAYlwr/vJtYzFiLgbnqe9Vc8F1kynIzWbz2uVNfgyRQFxRCaINKJMARxxyDP+t/ofc/Gx0v47ul+i6JGdXIVnbC8ItnkJRLfvmm0jROtnxZyWfSTCUyW/+3sxP/27jihMHhkmwhpFt6YYM6rIPFZ/Px+OPP86hhx7KwIGt65ibmsceewyPx9PqfUjrCsjd2E8kD3YSktvTuft+vDXjsUbWFnkIXWL1eFv1gJxgoqNiOGLYsfzw27eV0nW/ZM+W3DBJpVDUjlIoFa0HvbIVavHqnQy67g2uf/5bbjhjCJ0jbGR3TegIfJgbPFvIrFmzKC0tZdKkSU0rWCvE4XBw33338e2337Jy5cpwixPxnH3pjaxc+ht+X9sKli1bbbCgmunTsx/bd23dK13X296xKtoGyodS0XpoPxDSf6NsQMKzC5Zw+rD9efiKIzGZIv/byI+JLbaDyDF3AgQCvzH/uAhd9oKCAt5++23GjBlDfHx88wnbijjppJP4+OOPufPOO/niiy8wmepv9W3rWHGRQAZnjTyGV54S/Prh05x70WjyaI9O5M4mFTKaRk5iHIn5RZiaILZrJNAupUN5PMpyBKR0Vvd9JKFpPYmOmt1CrR3WQu00jMh/CysUZcSnETy69ZPf19M+MbpVKJMA6+yHkmPuhBQmpNDQhaXe/pMPPvggcXFxjB07tpmkbJ089dRTlJaW8uSTT4ZblIhEADZcRGkuRowYwQfvv48XGzptR/m2uz2INqJMAsTFxFNYXMVfUkJsUtNN2apQNCWt402sUABs+7V81eXx0Sk5hn83ZYRRoNBximiKTcnV+EyGrlCuWrWKv//+m8mTJ++zA3FqIjY2lrvvvpvPP/+ctWvXhluciKRsIM+NN97I7t272bSzgDbzCpCSKKerrRwNADHRsZSUFu2VvnuT8qFURCZt6f5TtHWcFQ/SuQtX0qtzEjNvOSWMAoWOW4syurgbwcSJExkwYECrDZHT3Jx22mn079+f22+/Hb2NBLluDrp27Ur79u158+Unwi1KkyGkbDMjvMuIjY6jqLhwr3RnsYpHqYhMlA+lovUQ2xncxgN2yfrdnDy0B1ZL6+iyi/Nnk+DLwIeVDr7NWKSHXFMH9lh7Gl3fdfD++++TmZnJa6+9hl0vpqNnA9F6AaVaPHssacT7s0n27cInLOwx90AIaO/dgll6yTZ3ptCUQgfvZqL0Qkq0BPZY0kjwZZLs341XWNlj7oEmdNp7t2CSfnLMnSk0JdPRuwmHXkSxlkimpTuJvgwS/XvwChu7LT0pNLdr8nNllm46eDaT4M/EI+zstvTEJl2086YDkmxzFzIt3av1PX366ac5/fTTefrppyNyvu9I4ZxzzuHVV19l69qlHHhANyx48GGlmFjsuLBTgo5GKXEARFGEhh8X0ZQQR6TZIqQQlETZiS51tZmR3gnxieQV5CKlrBQKLSZRdXkrIhOlUCpaD12GQ/YawOjy3p1bHGaBQkdDsp/7H0CgBfxAJZI97AdS1upL6fF4eOmllzj33HPplqDRr3QRGn4EEK0XkOrbhkQrD4Qd58+q1I7DU4CGDATHlkTrBbTzpVcqE+/PrFQmypNfqUyUXkB739ZKZXJNHeo9qKguzNLNgaU/YZbeQCBvSPDvgaB23X47WZZuyGrOW1xcHHfddRePPfYYZ511Fr169Woy2doSl112GX/++Se3jz2da8aN48oxY7DgwYFxT5WdVSuuveYeNxTK+oRVbwGEwGsxXmcRJlmDcdijMJlMlDpLiI6KKU9vn5YQPqEUilqIrM9MhaI2MpYDgvSMAt76fhX3XTwi3BLVCw3KFTaA7db+xqCIOgbmPProo1gsFm699VbS3CswBZRJqPCLC55VpWo7pkCAorK0hpTRyvMZZVwimmxL1yZVJgE6ejaWK5NlsmpB7Upgm22g4Ytaw3k7/fTT6du3L7fddpuhdCr2QtM0XnrpJcaPH89rr77K1VdfjdtVWXkE49wHbxeRXE1qBCAlcUWlDQr4HskkxqeQm59Tvi0E5O5qPR/Sin0LpVAqWg+5GwDJg2/+yugT+pMYaw+3RI2iyJRUpzK5ZcsWvv/+e+699140TSNGz2sh6fYmWNJCUzKiGWL/JfoyKim6VfEIB35Rd8fK9OnTyc/P59lnn21C6doeF110EW+99RY7duxg5MiRrFixosa8EvBibTnh6oGQEq0N+s22T+3Au5+9SUbWbsDozMjL2HugjkIRCSiFUhHZ6D7IXA07FoNmxe/X+XzxRu65KLLjcZXhFnYyzN3JMnfBhxk/JrLNncgwp6HJuqcLnDBhAn169+aCo/rQzrsVf4R4qUT5CxDSh0l6SfFup513K1bdCVIS78ugvXcLMf5ckBKHXkh77xaSfLsQ0o9JekjxbiPVm45Fd4LUSQiU8ddh8TRLN2bdHSizh/beLUT791ay4+LiuP322/nggw/YsmVLc52GNkHPnj354osvOOigg7j22muZOXNmtfkEYCYyA6JLIfBrhj3dbbVQ6rDhsZhbfbjz26+diNvt4srbzuO3v34CQDMJdm/JJXdPEVKXeJxeMtLzyNyWj9fTuIF/CkVjEK2pS2jo0KFyyZIl4RZD0VKUZsOSV8DvBekHXWfFlj2c9/AnrJs9LtzS1clOSy92WPsELHmGP6Jh5TPWZVnXYQ1WypkzZzJ/3jy2v3cLKfFRgRLGCyPc3XoS0NEQUD7jj0DHjxmBRASsjH7MmPAFymiBUjWXEYEzU9vx6YAeUKwFOhLBWvtwik3Je53LK6+8ktzcXD755JOInOM90liwYAHTpk1j//33Z8aMGURFRVXa70cjm87l5z+ikLI8DqVEgJQkFBRh9frCfr80lpXr/uOex27m7Zc+Jysng+5demCz2ZBIw4056AD3H9KJlE5tP/i5EGKplHJouOXo27evnD27ZQKbH3bYYRFxzDWhLJSKyGX5fPCUgN8dmHZRZ822HBJibOGWrE6KtQR2WnsjhQldmNGFBSnMldYRWo3K5K5du3jrrbeYOu54OsRbMeOr5DsZbgRgQkdDx4QfE340JGa8AVl1TOhY8JSvl6XXVkYLwaakASZ85WWyzV0pNSVUey6nT59Obm4uL730UpOfg7bI2WefzXvvvUdOTg4jR46k6gd8IUmRGwxdCKQQSE0DTWD3eLG0AWUSYECfgxh64DAuvP5U7nj4ek6//Cgeeuoulq/6F6lLdH/FsnHpLryeuns/FIqmRimUisjElW9YKKsoGI+/8wdjTz4wLCLVhyxz14AtrmFMmDCBHt06c8s5h+61L5JfkFVlq2s71Dy1lcm0pKHX4FeZkJDArbfeyttvv822bdtCqFnRpUsXPvvsMw499FDGjx/P9OnTAeNOdBFDRF+BQR8VjjYW6PzGMXfw+D0v8PFr3/PWC5/SZ79+3Pno9aTvrOLSISB3txq4o2h52tL9pmhL6D6qe3Gt2ZbDSQf3aHl5QkQCHmELKJMNe/HOnTuXHTt28PrTDzapbG2Vcq8dKTHrboSs8CMzSS8XnDOKXr16ceutt4ZFvtaIpmk88cQTTJw4kY8++ohLLrmEwoICqn7gRTStyJ0rFFKT2zOo/8EIIUhNaseFoy7nwjMu5833X6mcUYJsgwOUFJGPUigVkYkjGSxV/Lf8Oj6/TqfkmBoKhZc95jSWRJ/KsqgTjZA6tYxWrgln5kZmzXqFh0YfzpFxm9FQXVd1McD1K92d/zKk9FuGlH7HISVfsb/zb/qX/sLBJV9zcMnXfPn4hWRlZTJ16tRwi9uqOO200/joo48oKSlh1KhRbPzjfTS84RYrJPKS4nFZItDXswk5//TL+Ovf//G/Jb+Up+lSktghNoxSKVozwuAyIcSDge1uQoi9u8qqQSmUishECIhpXylpwe/r6dM1GZs18l4SWebObLP1xy+sSGEKzNldv9vLqju5ZcLtdG8Xx4OXHY4WGJzStuwsTY8JPx386VilGy3go5ns30WMnoeGNM5jSn8emzKFzz77jHfeeSfcIrcq2rdvz4IFCzjmmGO4+/YJvPTEveEWKSTMPj82b9v+IIuOiuHhO6YxbeZknK5SAEwmDast8p6RiuZHCPGGECJTCLEyKC1JCLFQCLEh8D+xjmpeBg4DLg5sFwEhOaErhVIRmXidkLe5UtKyjRlccPQBYRKodnZaD9jbj6+eo4q/+fANVm3J4rvHL6yogoj2WIsYqvPDLEvzYSbb0oUjjzqam2++mRdeeIFffvkFRehomsbkyZO589GZ/PTFe9xw/nDyc7PCLVatRJeUhluEFmHIwEPp0rE7S5YvBowP0LxM5UO5jzIHOKVK2j3AD1LKXsAPge3aGCalvBFwAUgp8yC0ALRKoVREJu7CvWZhcXv9eH2R6RvkEY0Lsl5YWMikF95hwrmHsF/nuj4gFfXBK2zlYYwuvvhizjzzTO677z7Wr18fZslaFxIYftwoXvlkKVJKxp11MH/89GW4xaoRk1/fZz7GunZOIzN7DwBSl7hLW4dbgqJpkVL+AuRWST4TeDOw/iZwVh3VeIUQJgKdY0KIVEL031IKpSIycSSVO9Vn5pVw/sMLePrDv+iSEpm+QWmu5Zhkw4M+33bbbSQkJPDgtWc1nVAKAOyyhHaedOJ8WfRxLmb+TYcw/MD9uXbcOBy7f+XAkh/p41xMjD+n7sr2YQQQSw7tkuN4+8PPOfmU05h2/9U899C12H1ZpLCDJHZjoxg7xSSxixR2Ek0+FlwkkEkKO4gnCzNuYsgrL2Ol6a2JRTFR+PaR2KM2qw23xx3YkkTHt+5ZxBQ1kiKEWBK0hBKQub2UcjdA4H+7OvI/DywA2gkhpgC/AY+FIpxytFBEJiYLJPeBrJXc+8bPbNyVx7IZV3LQfnXdC+EhWuYbgbulrH9X9zffsHr1at56czZmscOoopnk3BcRQDfvKoS3YnvR1DPpe+WrnDR2EunzrifeVsRuuR9GlGj1nV0T0RQRjTH138T77uaYo4/ggYkTOfuEbxg7diyXX345VqOnrPwaDp5dx5htx4uD4qA8XpzEYBhEmu7K95lN5T7Ibf1+ysrJpM9+/QEwmU3EJjnCLJGimchu7sDmUsr5QoilwPEYt85ZUso1oZRVT05FZKL7IW8jAD06xLM1o4BenRMjdraTbdYBRsDnespXWlrK448/zqhRoxieZscmnW3+5RcOyoI4lSs5msaymVfi9escctNcCrVkik2JSpmsA0Hl83jE4Yfzww8/cM455/Dqq69y0kkn8fVXX1W6hquWqbrtw4yTaJpa7YsqdaHJ2mddaiukJrcrn+9b9+uUFrrrKKHYh8gQQnQECPzPDKHMBgwr5WdAiRCiWygNhe3pKYSwCyH+EkL8J4RYJYSYHC5ZFBGIM8ewFgETLz0ch9XC8i2ROwigyJRUb2UyNzeXq666Crvdzl133UWCLwMTai7eliLGYeXfmWPZsDOX8x+YH7kzwEQ4mqYxfvx4Fi5cyIgRI5gyZQpnnnkmf/31V0jlPdibRemzebz7hDIJ0KVDN/Zk7QKMTpLCnH1jQJIiJD4DrgisXwF8WltmIcTNQAawEPgC+DLwv07C+TnuBo6TUh4EDAJOEUIMD6M8ikjCElWuUALcc9FwTrn3fWZ8/g8erx+fP7IG58T6c0BCjFcj1WUmzmMCCVa/IMVlJsltxqSDy+PljXnvc855FzBy5Eh27dpFUVERDz/8MFkyKYImV9w36NYujkXTLuHHxf/x4vPPhlucVo3dbmfSpEl8+eWXpKWlceutt3LJJZfUOfjJjLt80FRT4rFa9pmQW106deefFX9SUlqM0AQWFTZon0QI8Q7wB9BHCLFDCHEVMBU4UQixATgxsF0btwB9pJT9pZQHSikHSilDmp4ubAqlNCiLbWAJLPvK/a+oC2sMJO4HwrAajT97KH++cDmPv7MY22lPMfSGOeQWOsMsZAW9nUsZWKDTo9hOR6eV7iU2BuQ7OKDQQSenlaXf/8q4seM4/thjePO1GXTt2Y/pb33Hjz//xuSHH+bXX39l8MhreOmHneomaGGG9+vMW3efwdvvfcCCBQvCLU6rJyEhgeeee4733nsPm83GmDFjGDduHBkZGdXmt+Aljhya+vFfHO3AYzHvE/fToYNGcMD+A/joq3eQuiSpQ2RO/qBoXqSUF0spO0opLVLKLlLK16WUOVLK46WUvQL/q44Cr8p2oKAh7YfVYUgIYRJC/IvRp79QSvlnNXnGlY1oysqK3C5PRTMw8CJI7AGaGUw2DuiWwoY517L97RsocnpYlZ4dbgnL0f0DMckoTAg0BCYEqzdv5ILJt5B81qFc9tgdlJot3Pn467y7aCv3PPkmA/fviIafE084ge+++46RJx7FhCfeYsBVr7JpV164D2mf4pzjh3LV2Ct56qmn+Pvvv8MtTpuga9euzJ49m1deeYW8vDzOPvts7rzzTgoLCyvlkwgKSKWpfSjtbg8Wr2+fsfkfPHAYO3anA+CP0PBqilbBZmCREOJeIcRtZUsoBcOqUEop/VLKQUAX4FAhxIBq8sySUg6VUg5NTU1tcRkVYcRshyFXwYjbjf+ORGxWE11S47js+P6MeepL3J7ImAnDL3sAJrIL8rjlpSl0uegoht90Hmu2beLBy25g06fLeOrVLxh21KnlZRyUlL/sTCYTL992JhvnXovZpNF7zCx+Xb49LMeyL5Jr7sTVV43lxBNP5LbbbmPbtm3hFqnNMHDgQD744AOmTp3K6tWrOfXUU3nsscfweIzR326iaI7OKYfTvU+NOo2LjaegKB8hIHdPUbjFUbRetmH4T1qB2KClTiLC0UJKmS+EWIQR4X1lHdkV+xr2BGORFS+dyVccyZptOZx6//u8cssp9OqSFBbRfJjx+AXPfTSP1776mC27t5MUl8C5R57M/ZdeR7uEZAByTWZElfglVV+hUkKPDgn8N+sqRt7/Pmc8+CFZH4zHYlGDRZodKZEIJk+ezM4d27jyyitZsGABcXFx4ZaszXDUUUdx1FFH8dFHH/Hyyy/zzTffcOmll3LZNbeAZtwYAj8CGRgg1Tjbogw8LySgaxpC6mhtuP+7a6c00ndsBmlYKN1OL1a7OWIjYygiEynlZAAhRKyxKUOedimco7xThRAJgXUHcAKwNlzyKFoBHQ4yur8DvHPfKI4+sBu9r5zV4oN0fJh56W+dQbd+TOxpU5g8bya9u/XkjxfeZ8e7v/DcjfeXK5MAiX4fBxUXoAUNNMqiC96gb7ot9kHsNqchgQWTzkVKyXmPKJ++lqCjbxO9XH8zsOQn/nn6TJKiTVxzxUVoUs040tSce+65LFy4kEsvvZT58+cz6sTD+fmjF0liF+3ZRju2k8IOBI3rfchPiicvNorslESyUxLISk2iMCaqzfpUduvUnczsDEqdpaSvzmTZ9xtZ8u16cnYV1l1YoQgghBgghFiGYdxbJYRYKoToH0rZcPYIdAR+EkIsB/7G8KEMaWi6Yh8l7RiISgGTMa2oyWzl0D4d6Z+WgtnUMpfyqq1ZnP3QR8Sc/iTjJ05DSpgyZQr/ffsbHz70PAftV/1c4wLY5IgmOCqendJKYYKs0kU733YEYLWY+GTyuXy+eCOf/W9DMx+VQgBJ/j1EyUKsZsGSl65g2548iksiZ+BXW0LTNK699lq+//57jj/+eJ5/ZirnjDyBX3/5BQE4iTEmCmgMQuBx2NFNWnlIr+jStvt7ms0WOnfoSvqOLUhdousSr9vPhn92UpTXdo9b0eTMAm6TUnaXUnYHbgdeDaVg2Lq8pZTLgcHhal/RCjHb4NCbIHsN5G8DdwG9u+WxdlsOG3fmsX8zzYFdXOphytv/Y853K9iTV0Jah0SuvmosF1x4MVarodx2KLBgqqWLrlgzUWi2IIO6n2LIRwuyl7Tzbq0UPuXYQd259Lh+XPTYp2R9MJ5oh7UZjk5RRvCvl5SYhNlsZsk//3LUUUeFTaa2jtVq5b777uOmm27ikUce4Z577qFz587c/NAMeg04pPENBN1vNrcX0cYDnffsvj+b0tfTZ79+5Wm6X7JzQzYHHNo1jJK1XQoKCvjmm2/CLUZTEi2l/KlsQ0q5SAgRHUrBfclnWdEW0EzQbgD0Pg0cSezXIZbkOAeT3/qtyZt676fVDL7uDeLOeoaXP/+HUw/tyc53buS/+fdxxWWXlCuTAGa99teUUzOhycqdbVWDmDv04koKJsCcO08nxm7htPs/aOTRKOqDR7MTHx/HP//8E25R9gni4uJ46qmnWLBgAYmJidw9bhR3Xnkyu7dvbrI2NN1v+DG3Yfrs15/5C95g0R8L8fsrni+uEk8tpRSKSmwWQjwghEgLLBOBLaEUjIhBOQpFg4jrCpqFGeNP5sYXv8Pr82MxN24Ay5r0bO6f/TPf/L0Fj8/PiH6dWTj1Io4fklaeR9dzSPZuxyvsdPRuwiI9SHMn8A0EbNXWm+zz0MHtwqtppLlLsUpJkcVOkcVNqtuKw68hxRB84g/MoiJMmMmk8e3Uizj4htnM+XY5Y04OKb6sopFE6YWkdUxm09oV9HX+jlV3UWBKYae1D17NHm7x2izt27fnlVmz+Hf9Lh6fdC83XXg4Bx5yNBMmv0RckE9yQ3DZbZg9XtA0nA47EnC43ESVutqM1fKC0y8jNakd73wyh+ffeILLzxvHmSedj65L/vl+I44YK532S6Iwz0n2jkI0TdChRyLtuiWowTuKMsYCk4GPA9u/AFeGUlAplIrWS3IvAM4c0YvzHlnAjM+XMf7sofWuptTlYcrbfzD72+Xszi2hR4d4Jl9xBBPOORSzeW8jvgakeVYgEIiARdFHNj5MGGNK934wa0B/V3H5OkCU20QHtwMgUFMUmtmLlJVncRy8f3vGn3Uw1z77DWcM35/k+Kh6H6OifghgWM9YPv19PfF+I96p9MM20Y+9fiBFkyKAvr3347m3F7Fs8SJefvx2rhw5kCNOOIvr752G3d6w619qGkXxgegngd/P6zMG/lR/17Y+zGYLJx41khOPGsmaDSu4+7Gb6bNfP/rub0Tkc5V4yMsoRoiKoBlbVuyhIKuE3kO7hFFyRbgRQkRJKUullHnA+Cr7egB1BkdWXd6K1kv+FkBgMmlMvvxI5i5cSWZeScjFP/xlLQdfP5uYUc/w4qdLOengHmx/+wY2v3U9d14wvFplsgwNypVJAL9/ENQR6kSj8g1n1GH8AZhFOgJntbrK9OtPoENSDCfc/W7Ix6doHMce2I2sgoo5kXda+xjhbJQy2axIoJBkQGPw8ON49dNl3HjfM/zzxw+MPrEPs5+fhK43MKqDEBW/n5TEFpUiaBvKZFX69hrIGSeey8JfvtxrX7D3je6X5O4porTQ1YLSKSKQAiHEZCFEdS++j0KpQCmUitZL3hbQDd+guy8czkH7teOhubX7Uv6yfBsn3/Me9tOe4qIpn+KwWfj2sQsp+PQ25tx1Ol1SGxZ3UMpkGvta0rTdCFF9qBQhBD88eRHLt2Tx9Ad/NaodRWiceHAaHp9ePsVnoSkFqn3WKpoSicBfpfPsuJEX8eY3a7jo6jv49uM5XHr8/nz2zsxGtaPp+l5+zW2Nk44ayfe/fl3Jn/K9z+YyY+4z5XE6yyjKVSPB93E2A/sBvwcsksGE9HJTXd6K1ostDjQL6F6sFhOnHtKTN75Zvle2lVuymPTWr3y7ZCslLg99u6Uw7drjuG7k4FqtkFXRZSy63gnQMWnbAR9+2Q2kDfBiTEffcPx6JwQ70bTqrS/7d07iodFHcPdrP3H+0QfQrZ0Kut2cxMfYaZcQxTXTv+ajh84h3pdJtrkLcXoO0Xo+HuEg19wRq3SR6NuDRJBr7ohfWEjy7cIi3RSZkinWEpVVsx4IJCY8+LEGQmt58WLFozk494pbOPPSG5nz/EO89dKjfPTm81x92xSOPOnserejaxp+k4bJr+O2WfGbNCxeX5uarrF7l56kJrfjpolX0Ge//hQU5fPj798Q5YgGITj75Avp0K4TACVFLnZuyCY+NZqYBEeYJVeEgRIp5WVCiMuAX4QQ90sp5wb2hfTlJap+pUQyQ4cOlUuWLAm3GIpIwVsKvzxmOLcBX/25iftn/8yymWPZllnAw2/9zif/20BOoZO0DvGMOXEgd15wKFH2+off8foH4Nf7YXyoBd8zkgpDv0bjrJQ6Zu0vTNrmWvWPfle9itens+HNaxvRliIU3l20mksf+5xd791IamIMOmYEEoGOjhZYr7giBLI8fqJAx4eFVY4jcWvRSqmsBxLKY7YaM+cI8mmHBwdl95iztJiXpkzgj5++JKV9J25+4HkGDBlRz4YkBEIJSSEQuk5CfhEWn7/NKJUlzhKW/PcHuzN3YjVb6dm9N4kJSbzzyWx+WfwDN465g5HHn41mEui6RNMEiR1i6X1w54geqCOEWCqlrL/TfBPTqVMnOW7cuBZpa/Lkyc12zEKIf6SUQwLracBbwE7gWuCnsn211qEUSkWrpSQLFj9XrlCOf2khH/y8FpMm2JlTTIekaC44ui8PXDKClISGD2TR9SQ8/hOo26DfONd+TWRgMS1CCH+t+fbkFtP1kpe564JhTBl7dIPbU4RG90tfpneXJBY+cdFev3Bdv/g2a192W/ZDCjV9Zn0JPrdOoikgpdpg53k5mUx/8HpWLfsf3XoewISHZ9CtZ/UTDFTfUMUgK0eJk5ji0n3GF2z1+uVMfHIC77/yLWZTxfNNMwn2G9SJ1C7xYZSudpRC2bQIIZZJKQcHbWvAA8AVgENK2bGuOvaV+0bRFslcSbC18LeVO8gqKOX4wWlsnnsdu9+7meduOKFRyiSAX+9OaLdKI30oxWagdmUSoENSDM9dfwJT313Mmm3ZjWpTUTevTjiVH5ZtZdOu3L1+4bp+8SxzV6VMNpDgc1tKbI0z5yQmt+Phlz7i2bd/Rmgmbr3sWCbecDa5WXtCbKiiJYfLvU+9FPv1PpD2qZ2Y8vz9lDorBjTqfklGep2DehVti0qjt6SUemBe70uA/0KpYF+6dxRtBd0Ppbng91TqfR6QlsLMW07hzbtPp0fHhEY3I6WGLmOQsmVuEylFyL2iN5w5hIN7d+CEu97dy7le0bScNLQHvbskcenjn9e/cOC3EdKPTS+pmBtcSqy6E4uuRtaGgiy/0SUmvIhqPry6pvXmmbnf88hLH5OdsZNrzhzMk/deRWlx6HNZ74t30tUX38i/K5cw5fn7K+/YF0/GPoyUcmIN6YullKeEUocalKNoXez4EzZ8A+iGYhmYqnD9jly+XbKFW89pgunaAK//APz6wMBWyyiUfjkM/GbM2vqQFMuFUy+k/QUvcP1z3zLz1pDud0UDeeuu0xl281yWbdjD4F4dQi43yPkjuVp7EvXMgL+lJM/Ujii9EKs0AmoXaYmscwxHF+pxXBPJZODEgQ13+XksJYZCUqhqJ+4/+DBmfvQXv//wGa9Ou5crTunLCWdexlUTpmA2136O85Liic8rwO6tu6egrXDwgcN5d8ZXjBpzNHn5OSQmJIOAdt0Swi2aopWhLJSK1kPWGlj/JfhdAeuk8dB3ur2c/8gCJl46giH1eNnXhM/fHb9+IMaobQt1xZdsKgQlmLVNIVsp42PszLlzJLO++pfFq3c2r3D7OIcc0IkhvTtw+ZNf1KucCT8p+i7M+DDhR0Mnyb8HhyzFhI6Gzk5rnxq7cxUGAnDgDJwzw3RWTGKtZQ4/fhRzvl7FFTdP4uevP+DS4/fn/TeerjWGpcXrw7YPKZNl2Kw2eu/Xl9+X/GwkSHDE1H/womLfRj3FFK2HLT+B7t0refpHf5MQbeemMw9ukmb8+gD2Nt43v0Jp0jZQZnENlYuO7cfxg9MYOfED/P4GBntWhMT8e85gVXo2i/5Lr1e52vwuXSKKIlMSUsW3rJPg8+bGEVDC674vT7/wGuZ9v5GRF1zFh7Of5YqTD+D7z9+uNm90ibPNjO6uL1dddCPvfvZm+fbuzbm15FYo9kY9xRStB3dBpc0NO3K59/VFPP3hXzxx9TFNFuJCEp65moUoRoj6Oy59/vC5uL1+Ln7s02aQSlFGn67JHD2wG2OnfdVkdXqFHa2eHxEK8GOul4ufpmlcfuMDzFu4gaFHnsyMqXdw7dmH7OVfafLve9bJMvr1PpCdu7fh8RqTRbhKPWGWSNGSCCE+F0J8VtMSSh3KaUfRekjoDhkryjdPvvc9sgudzBh/MsP6dmpU1X69Cz69L1LaAR9gpaUnZPP6B+PWdFYldCM9an8s0ssBhf/Ru2RNrZLYbRY+fPBsTrv/fb5dspmTh/ZsMZn3Nd665wy6XfoSC35bx9lH9Kk2T66pA7usvfAKG/G+TJJ9O8m0pFFsSsSml9LRu4FiLZEcSxeQEj+taxS4ByvFJOLDghkP0RTiIgo3UQh0oinAQUmz3j0OinFjw00M9blPrXY7tzz4AmNveZgJo4/j5ouO5KUP/yifH7wgLoa4giIs+r43IsVqsdK310D+XPYbRw47jviU6HCLpGhZpjW2AqVQKloPHYeWK5RnPPABbq+fbfNvICGmcRZFr79vYABO2e0QHouRT0TzdcdRlJoFumYoGbowvMZMdch0yiE9OfeIPpw3eQE5H9+K1dK6lJTWQpfUWE4fvj/XPvctow7rhclUuZNnl2V/dlj7lA+wybR0J9OSBkgQGm4tmkJTCgK9IpyQ1GlsDNOWwo2dXNpDYAZsP2bclIXlMuRvCVVMqzShQP2JjU/khXd/5fpzh3HrJUfz4vt/YDab0TWBSbaWX6PpOezgI1m28m+OPPQ4UiI4BqWi6ZFS/tzYOlSXt6L1sPsfQODy+Phi8Sb+nTm20cqklOYqyiQ0fsabhrEl1o7TbCpXJh2+EnoVr6lTmSzjnftGIYG7Xv2pGaVUzL3zdFxuH32unIXLXeHT68dUSZkEjLm/hag8B7gQlWNTivBcbw2hkGQq3x8iaAETXqIobfaj8WLBHTRrTkNwRMXwwru/UVJUwG2jj0PXdaJLXQgpW8mv0fTousSkmRACsncU1F1A0WYQQqwQQiyvaQmlDqVQKloP+VsAidVsvIyXbcxodJVSxhMui2RVMqIs+LWKV1mKJxO9HkGxzWYTd5w/jJlfLMPn23d9wZqbhFg7m+ZeS0Gpm7TRM8grdALg1GIRbTh4nwR8dcxXb8HdImfA00R+zrHxiTz39i9kZ+zgrrGnYHa591llEmBT+nr2694bKaEgu6TuAoq2xOnAGbUsdaIUSkVk43PDziWw+UcwWfH7dY6YMI+OSTGceHBag6vVZRw+f1/8shORcht0LnZjCgppkmNNwaXV78X5wKUj0DTBxDm/NrV4iiBSE6LZ+tb12C1melw+k79zk8g1tcffwGvJJD20826ls2cdsf6c8oDokYQALFQfiN2Cm2jyA/ubX3YrLjT8aPiIpoBo8jHTsEEkSakdmP7WT2zfso7xE69sw58EdbN+82p679cPACEE29dlkbWjAF1FkGjzSCnTa1tCqSMy3qQKRXUU7YbfpsL6z2HzQnDm8cWfG/lj9U6Wzxrb4FHdXv8APL5T8OkH4tf7YcSZDP9rpHuxl67FnnJlotQUw59JR6LXw2ZiMmmMP2soz3+yRIURamaiHVZ+mv8Uye07ceTlD/DbDhOg1VsZjPHnMKTkO7q7V9LFs5b9nX9jlc6IVCoTyArMUlMxc008mSSxm1jyiKIYQfPfTWa8pLKDdmwnhlxiySOBTEQDexvad+7Ok298w5o1y7hzyo0R8DRoeZyuUjKydtOtcxoAJQUutq/NYtO/u1i6cCPu0r1DtinaHkKI4UKIv4UQxUIIjxDCL4QIaboppVAqIhMpYfl88AWCmANIP3aLmbgoGynxDZufW5cJASXSjKFIlgUtD39HV6bDwvYYW/ncwib8HJ29sDyQc6g8OuZIdF3yyPzfm0NMRYBSLZas6H689dY8+vbtyxVjxrJ8xQpCjkwPICW9XX9jwo8JPwLYZe2NV9jqV08LUUhypfiPNkqxU4qG4XdY9r+5JTfaqvy/gBQa4/3Yfb++THv2A/7651cmP3NX0wjaivD5fFgsVn76/VvAmM+77L/X42PjMjV5wj7Ci8DFwAbAAVwNvBBKQaVQKiITVx649/4o+nbpFg7v37nB1fr93YjUy35LrBV/0PuwvWtXg+oxm02MGzmIpz/4S83z3YzkmDqho6FpGjNmzOCII47g+uuv55dffgm5jmg9H5P0Va7X0rnyoJ0IQUJgRHfFRRpFcb0/eJoDHYEXG41VZQelHcAzD83ih9++5qkZDzeNcK2E2Jg4Zj4+j2dfn0r6js2Vd0ooyClVXd/7CFLKjYBJSumXUs4Gjg2lXGS+WRWKali/I5fpH/3Nw1cc2YhafHVnCROmsth3UhLr8WMN0i5LTVEUmBNC7v6eNu443D4/T733Z3OIqgA0WXng0+OPP86ZZ57JPffcw+effx5SHdX+nlIP1O/DoRdikoGuRimx68VYdWej5G4cxjUq0AM+i+FXJg0kFbL5A7LVX/mRwNADh/PYPc/x6Xcf8NKbzzSplJFOz+69OOGIU/npf9+FWxRF+CgVQliBf4UQTwohJgAhBSVVcSgVkYk9EWxx4MwpT/pzzS76dU9haJ+ODa7WbFoNugjM1R1ZXYqHZJfSqdRLstuHRZcIoigxncD3HaLIsqWiSR2z7mXkng+J89fu0mK1mLjypAN57N0/uPPCYU02i5Cigk6+jUgEO2x9y7un77rrLhISEnj88cfJz89n9OjRtdbh1OJZbT+Mfq7/lYeHGlz6PXlaOxL1LCQCDZ18UwrReiFm6UUgKdSSWOcYhhQt9wgXQHvScePAjisQq1FGRMxGDWjHNrzYsOEq7/rOIxVPFatqbeQlxWN1eegz8lxuscBzD4/HkpDANWeODfsxthRHDT+e516bymXnXo3ZVHF9xSU50EzKBrUPMBrjlroJmAB0Bc4NpaC6OhSRiRBgTyjfLHF6uPu1RYw/q7HzddsCPpSR93oQQOdSLw6/xCxBk/BTx+5k2jqgCzM+zUqvkjU49NKQ6nv2+uMpcXl58dN/mlfwfRQfFnbZeu3l6zhu3DgmTJjAjBkzeO6552qvRAi6eVZXCjdkQidZ34MJP2Z8aOgk+jOxSRcm/GjobLf1DfgytiwaYMeJQJb7S0YKJiQ2XOW+nD6seOobq1IIPHYrUtM46pTzGXfnVObOeYYPv5jXXGJHHIP6DSU1uR1PvPQQelDUCUeMLYxSKZobIcQPgdUbpJQuKWWhlHKylPK2QBd4nSiFUhGZeEogf2v5ptevszu3mKMGdm1UtT49jUhUJssIlizPZqLEYkIGKSwDCpdhkaF12zvsFi45rh+T5qoQQs1BjqULNV1L559/PpMnT+b9999n8uTJNdZh00uI0fP38kOsWmvwtlPE4NTiKgdLb0Fqky3cBMtSTDwNki7ofjvzlIu4dvQEnnt9Kl/9+EljxWsVmEwmHr1rOtt3beWF2U+W+2Fn7ShQPtltm45CiKOBUUKIwUKIIcFLKBWoLm9FZOItAc0EfsNPbeHSrezfKZH9Oyc2rl4ZRWu57J0mDSErdyhadXe96njp5pOY/8MqXvvqX64+bVDTCriP4xaOyrPiVOHEE08kPj6e2267jby8PKZPn76X64FFugKWxtD9/TyavU0HUG8q/JhprLqr6TqXnXMVTmcJj70wkWhHDEcfdkLTCBjBOOxRPDnxZW66fwxvfzKbS88ei65LdL/EZI6kT4jw0zE6kXuGhdQj3GgmU/PHaRPwIHAP0AWo6jwsgePqqkBZKBWRiSOJ4JfB9I//5vozBmMxN270q9m0CsGORgrXMnQs9ZJW4GK/fCenbstn1NY8SvTDkdIach0xDivnHdWH+94IfeSxIjQ6eTaQ4N1da55DDz2U119/nX/++YexY8fi91ceyFOsJZFh7l6v4SNx/mxS3VtI8aQzsPQnBpd8Rw/Xv5j16oOO76skkIWJ+n2AVaXUYcNltTDu0vGce9olTHxyAn/9+78mkjCyiYuJZ+p9L/DWR6/idJWimQTLftzEsh82snNTNnvSc/n3p00s+W49m/7bhcel4lS2ZqSUH0opTwWelFIeW2WpU5kEpVAqIhXNDMm9WZ2ezVG3zeOP1Ts5+/DeTVCxF4ghUqZbrA0NGJLrZGh2KQkeP9E+HYtuAfz1ink9c/wp5BQ5mff9yuYSdZ/EjA+PFlU+Krsm+vTpw9tvv016ejoXXXQRbneQkiMEUmjU51EsgO6+Nezn+ZdovRCbdGLXiwxrqeqSLEego2OmUSPRhcAkjYFHE665j5OPOYPbH76OFWv/bSIpI5tO7bsw9MDhvL1gNrpP4nF6KS1ys/x/69n87x5KC914nD4y0vP5b9FmvJ7IjaKhCA0p5SNCiM5CiBFCiKPKllDKKoVSEZnoPtJX/cmg697g2IO6s2nudfTomND4amUXJNG0lku/qqphNS1DCH+9Yl4nxNoZddj+3PXqoiaWbt8mz9QBlxYdki9jly5d+OCDDygqKuKcc85h1y4jxqhJeunk3YRWzw+cqsHDt9v6GwqlGs1fTgnxlYKwNwSL14fZ6yuvYeItj3HEIcdw0/1XsH7z2iaRM9K55ap7+Pjrd1i/eQ2lzhLOuuo4Trv8cJYuX1yRSYLPq7NnS174BFU0CUKIqcDvwETgzsByRyhlW8dbVbHv4czFbNLQdcm9Fx9GzyZQJgF0PQWwNEld4UBQ0KBys249lYy8Ej76Zd94CbYERabEWn0oq5KcnMxHH31EQkIC5513Ho888ghmdw56EzyGS7X4RtfR1vBgp7E+lJYgZbKMx+99noP6D2Xc3Rezc/e2RtXfGkhNbs9t4yZy2+Rreeuj14hyRHP1xTfx57LKM3FJXVKQVRImKRVNyNlAHynlaVLKMwLLqFAKKoVSEZlYY+iYGEWPDgn8trLpfB6FtgsIZ2DoxqHLjngxsSmqN0vjh7EpujdeTGTYOvBP/KGsjB1EqWnvGLSpCVGcckgPJsz8oZpaFQ0hwZ+BWXfi8BfS2b2OTp712PVizNJDe89murjXEO/LrNQNHR0dzfz583l04h0s+ukHjjjtIuYv/Lfxsvh2I3Q/Sd6ddHGvIcW7DU36iPbn0dm9lo6eDVhDDDcVqRgz9dgpJIES4vDX8fqyU0xjXVs8FjN+TeAzaZREOSiOduAzmXh20qskxifzwpynGlV/SyABl81KUbQDp92GLsBrNlEc7aAkyo5f09CFoNRhpyjagcdi3stJ4PgjTuHRu6azYctaLj/vGvrs169aC609uvV+rCvK2UwDrS6tY7irYt/DEoWW3JM+XZPYvDu/yao1iQx0sRVdHkBkBTwJjUKO4btOsXhM4NOsmHQPvycZ/tJ+YUKTOlnWdhyds/cc4K9OOJUul7zEl39uZOSw/cMhfpsizp/D4NKFCAQCHYmgs2ctIjAGW0PH47WxKupII7h2oDu6q3sldx9l5a7Db2Hc9G8Y+9SXTH3nDz6dfA69uyY3SJb93UvLu3dN+PGj0cP9HwTkkGjE+HPZYD8kbOGGGoME8miPB3sg+qXEh5k4cmu8i6MpxE10o6Zk9FnM5CQnVirutlpIyitkQJ8D2bR1fYPqbSl0IchNikPXNKSmgS4hrvIHZ3FMlLESCCjhtNtIzitA0yvHGR3U/2AG9TfiAGflZrJhyxqklJUiF3TomdTMR6RoAUoxZsn5ASpGtUkpx9dVsPU9WRT7BlJCaQ7RdgtWc9NdppJEdNmL1qhMAixNicFptuDTjJHefs2KX5jxaxYQGhqSI3J/rHZ+5U4psRx7UHdufnFhS4vdJikLoK0FJlDUkPyfvbMOc9vK+vB7JZk9TJmZMDdJMUmZu4Vtyu3XLTMzbJnb3TLulrbMzJBykzK3SdMw40yGySzpfn/Y4xkPehii93n8zEj2la9lWTo68DtqbFmNrSuyjyUinHFj0mNUMiSyKipario89c/9Wf7smThsKhNPfZxjbn2PcMRo621bJJpra6ISHatiJswDYIVzmwFpTAIE8BLCGTeaFSSpVLb5Kw6Qgo6dLv3WhQBFRP/GHmk1dQBMGrc5pRUlnd92L1DndWGoatSYhNhnIeHzxB+xz+n1BZoZk03JzshBVVWKStbH1wlFEKwN9+jnsegV3gNuAb4Hfmv0aJeBeXaxGPwEKiEcPXHbbV2TCmqMYQxjIB/26z32BKFzIKEQIz+4rs0OKk9e+ndWbazm899X9tQUNynaM1XKtaFI0XD8ZunrUUg0GEfmpzPnf6fw2rUH8+FPy0g/9D6emDm3y3NpvFyrDmzPkR8vjX+3Dvzt1m4H8HZ7NyHFMFCNqJE+dYsdCAT7dxpB0OlooVCr7aPWGQq3e1wLIdhq8nT++OuX+DppSkrWVnVqnhb9Bynlsy09khk7cK+sFoMTKcFXCoEKkCbjh2ayaE15++PaISI0ym3ZBNWBneUhYvl4NsMkPaRjMxJzxCKNikRq1RQqbFmNfFQwckg6O00eyrn/+bR3JryJI2TUeFSkjtuoRrQhMXTErptR8dZFnLzP5pz5wMeMO+l//LWqtNvmUW+A2U0/bqO6Xbmj/oRokgvZ2H+moxHBFv98JgoR7D0i/S4bScqPGj0BKSUr1y6PPRfNTTSU/hP9EKaM/9UiOqK+laKUqLqOqjfc3CiGgRbRk5aemrr5trzx4Ys88vx9rIrtAyT4qoPo4Y572S36FiHEa7G/84QQfzZ9JLWNgdRKadq0afLXX3/t62lY9BQ16+HPF+OeSUydN75eyFMf/8nMW4/s1CYlMDd1Gn+mTUPBxBAam1UE2bwyOCCD3hGg1KWRG9Qxid4R/p7lZnlaLKwqJWnhcoSAWi0dgYlmRjiw+DW8RnS/LltfwfiTHuPb+49jx8lD+/LjDHpMBFVKDmlmORIR80627zdbX1bDwde/xe/Lijls5wm8cMUBOB2dL3iQQAAPUlFxmXXxQP1frl0IqSmd3m5vEc2hzCaEF2I5lBphJAIDLfZbNrERIYwj9goRG9m9fhNhmGi6QcSuccxeYzn2zCs59OATiNjtgEQKgcsfJKXO3+fnGAmE7BqOsI4UIGS00EgzDEQsrG0oAqkoaLoRfw20732vrath5pfv8Nu8n/C4vdxw8R3RyHlMnSNnWBpjtshH9JCBLYT4TUo5rUc23gGmjpssv/vPK73yXq79t+ixzyyEyJdSFgkhRrT0vJRydXvbsDyUFv0DPQi/PQ7BSjAjYEb4/PeVnHLPTKaOH9Lpza7wjGde2jQMxUZEcWAzBJtVDUxjEqKld/kBHU2CXYLPprIqtSFHDyGotmdRZcvCUDR0xc6k2j9xNuqiMrYwk20n5nPGfR/3zYfYhFCQZJgl0ZxJdBTazk2rpzA7lV8fPol3bjyML/5YRcah9/PgO52/mRaACx9usyaWW6lTYhtBRHF1epu9SQQHITw0mDkCHTsGNuozWaMmpjO+3Fyts3uQqkLEHtX8zMjOY8mCPwg77MiYYSakxOsL9ItzjAAc4aj0kSKjy/aIjmo27CHVlGi6kfCaZOae4k3lHwedwFnHX8zc+b9iGNGGC4ZuIk1J2dpq1izq3zmmFg1IKYtif1e39EhmG5ZBadE/2DivWQjulVkLuOboHbnx+J07vdk/U6eiKw2enZG1A9eYrKfx/JekOTGbpUiJBgNTSjarnYsmEztYPHnp/ixYXcZvS9puHWjRdTqWwZbIQTuOp/zNCznrwK256JEvGHXcI/yxbGOn59H4vYtsYzuko9mX+Eil5T3Z3t7toV977PdVMGw0a1cuSchTdAbD/apjUTJ7qCt7afTwsWRl5vL9b18lrDdNSdGKSgZSFNQChBDbCyF+EULUCSHCQghDCFGTzFjLoLToH4TrwEw0ehasKWfrsXmoaucP06DqTlh26RJtEJ3f/JrSvEinCTbZvMfu5JE5bDU2z/JSDgAUReG+s//G+lfOJT/Tw9Rznuag697A38XeyYYYOJqBBhr9UZlhzMQtKW9U6QygmGY/nGnPcsSMY7ny1vNZuHRewnrTMLvU+dKiT3gQOBpYCriA04D/JjPQMigt+gdpI6L9u2OYpqSyNojL0TUPyp4lH1JYV86uG2o4dGUFQ31hBlO32ekldWT725DqEILZ2fsSEvZmTx2/12SWd6PGp0Vy6Gissk/mN/c+/OH+G2tt49ioDWeuew9+c+/LMsc2hHA0G5eX4eX7/5zAR/8+kh8WrCfj0Pu55/WfOj2PccGfUc1Q+y/sB6RTikaw/Rf2MlOm7oSvNtF543O7CHYh33UgMnXz7QAoq0wsInOl2Hssh9Ki55BSLgNUKaUhpXwa2COZcZZBadE/SB+REDYqrfazbEMlI/O61lIuOxRk12KdAn8EpyFJ0U1UBs9Nsyahzq61GWLThQ1VNq+6zEhxEtGtaszexEThL/eubLSNIqK4CCke1tsnstKxJQEllYjiJCjcaERaPUb3nT6akjcu4OLDp3HFE7MZdsxD/LxwfSuvbh0FY8CEvEHG8iX71y93/JRpmKZBZXmjXEEBmmH2s5n2HLoe4eBTdictJZ0tJm4TX6+ogtGb5/fhzCw6iV8IYScqbn6nEOJioHn7tRawDEqL/kHFsoTFvAwPp+63BXe8+iNzl29kv6te5YrHZ1Ht65iXwjC2BJSEEFTPpOr3DctSHUQUWtCaa2Dbym/QaG44pnuc6MamctnrH1Ro+YSFK0GbEqEkCI4PDy9IECRvCSEEt5+2BxtfO5/RQ9LZ/sLn2e/KV6nxJe9xXGOfkjiPfoyP1Fg5U//65drtdmx2J3N+mt2wLhxBjRW5bArc9cjNALzx2KcMKczF6bGTmZ/ClJ1HkpaTlB1i0b84nqhteB7gA4YBhyUz0DIoLfoHNevBSAzdzthuDA+99ztbnfU0TrvGd/PXs83Zz3D4TW9x5C3vsMMFz7Wb8G3KLAbzYV7ismEobXw+KUnVq1t8KjPViWEOHC3CwUCtktmuV9BtJpX/DkBWmpuv7j2WL+48mjnLN5J9xAP8+8XvkxrrV/q/XFA9kVj1dn8kLSOLRfMaBL5tEX2TMSZveeAqPvjiLS4941rcLg82u8Y2fxvLxG2H4U0fGAoCFonEqrqDUsoaKeVNRLvmHJXM2IES77AY7DjTQbUnGJXTxuez77RRPHPZDIZkejFNybcLNvBjicLikiA/fv0q3y4pY9KUzVjlHotAMsq/FJfhZ5lnAn7VQ5ZPkO+PXooMw8AfCpLiHjx3zSNrQpQ4NYzWCpeEYJ1rBHnB9axyj6PKnklmuJwR/qXo2SMxJfyZug1jfIvxGL7enfwmSIa+gXItH70NuZ5yrYBcfU2HjJI9thpB0Wvnc9Pz33Lj89/y0Hu/8dp1h7DzlGGtjsnUN1CpDiHL2IDbrCGgpFCuFuCR1aTrGzGwUW4bSkhxt7qN3sJJHRFs2AnhIIiJQgAvAomLOkASwoOODRd1qESI4CCYIDXUMwwpHMnqZQvjy0G7Dac/iFQVgo5o7rIzFEY1TAJOB6aqYItEcIQiA97w/HjWezx863NsOSna49vpbZ6rbTEwEEIMA64DCoB3gJeIGpPHAy8nsw3LoLToH+RNgaUfghGhPk8qP8vLx7f9I/4SQ7VTsfel5Gtp5Ch2FhQHOPWpuVzz0vWYIhrWnpe6DQiBkCaGYsPpCWN/73OeePN5PvvtOwzTwGGzc9FhJ3LFUafjcjj75vN2E8N9YVYH7Wxw21sNe3+eMwNFRqWsdcWOaob5LnN3apylSHkrf6RtR42axo6Vs1vsAW7RfaSZ5WTqxZTYRrb6fa21b0a6UYJddkziSgjBjSfswkWHTeOIm99h10teZPcth/PmdYeSkdrcgB0b+g1iypgqBgYKI/kTYgLsEoHTrOkXPcA91OKmlvo+3ibgbdLL20N1XNBcQWIg0NHQcdCTRuWoCZvz5QcvY5omiqJg2DTKszMS3tLvie1/CSgCRbej6TXxNo4DkeqaKlRFZeLYKfF1+SMHdovPTZzngK+AN4H9gB+B+cAWUsriZDbQP2MIFpseqh2mnQUpBSDU6MObDzmTo9Xfio0/M7anxpaJrkTvgg8871KW/v4rId0AoSCFgqlomELFiGlP5q3cyBFXnk44EubLu5+l8p1feOiC6/nvO89z4LVn8dOijvdM7k+s99gpcbVuTAIgVEyhxvebodgxhYYjLXbyD/nZvupry5jsBeqUDMpsw9r8voaFF2GToU4bGuleF5/feTTf3Hsci9aUU3DUg3z084pmr4sKW5uosfxaFTMmem7EDDOFVc6t+tyYhKhtpkD8GFUgQbpcNFquf00ALzp2etpDeehx5xIJh/j3P4+LTVaAIhr0YBs/YhXPrkAQZQAbkwA2mw3DNOJtF4Ui8NcODNUAixbJlFLeKKX8REp5MZAHnJSsMQmWQWnRn/DkwHbnwS5XRh/bXwBbHge7XQc7XsqKjG0wGhUR5I8Zy+SdduXeU45l+ZzfWtzk7x99wOQRY/nw1sfYduIWOO0Ojt3rIL68+zm+m/87u19yPHUBf299wm5nVYodPRlZjqYGjBBo9qg0TVr5YkzrVNArVGj5RHUGWidT39Atxv1OU4ay/pXzOHyXCcy49jUufOizZq9pS/S6Rs1u1Ll64BEkhd64xKVn5nDLw28z56fZPP/wv5Ma4wyGB/wvzu3ysPnErfAHoqky0pSUrq3q20lZdAkhRIYQIlMIkQkUA+5Gy+0y0I9pi8GI3Rt91KPawZmG0oJX57yHnyI9N5c7jjmcJR9/1Ew+56u5PzN+2Khm47YcMxHfB3M4ed/D2P2S41iwelmz1wwE7IbsdFcOEfOalFYHgGg0rkrLoMyeE68xDiguSu15hJTmuoiDgYiwUWrPxaf2Tl6tKsNA9DtzG9W4jJr49+cwfXiMym59PyEEL1x1EC9ceSAPv/8Hm5/+RNKV4NG5DlxEC8oGPcW4SVtz/jX3884LD/L1p2+1+3oZO5WZAiKa1nZhXT9maP4I1hY1dOWzNCcHNGnAb40eqcDvsf+T6vtq5VBaDBjGpbqZU1FLY6WbQjRmnncL19tSWfz6a1wwaQc+L0xFjxWpqFtO5svnn25xe4qi8NAFN/DcZ++w7xWnct3x53D6/kdGDa0BwralPhQpWZbmbDvs3QqKEKwKOvlg3JGYQsGveqLeMWmSFSmjxDEE1TQwFJWdyj5njH/pgA7TNWZu6lTmpk1HwcREZULdPLat/LZHP19hZBmpRhku048SM3gi2DGEDaesQ6KgoCPp3kDtMXtOZqfJQ9nxwufJ/8d/mfnvI9lty+FtjkkxKxkX+JnFru0HjLxQYzLZSCU5TXqA9xy7738kq5Yt4D83nU/B8DGMnbhlq6+tyErHFo4QsdsQUiKFwOUPkFLXP3qAJ8vIYaPjIW8E5I5I79P5WHQeKeXIrm5jYN4WWWySTM7wkuu0owmBAnhMye4banCbcPp+R/DOt5/xvwU/EIyE+OmDd9m4eiVrFi1k39POZoPXHs1fUhVQY7lMikBoCifucyhf3P0Md7zyOLe88HBff8wOUW1XWZnaOWMSQKgqdVVVVNsyqNXSMBQbEcVORHVS7CjEFBoR1UFWuJSRgZUD6mLXFqvcY/gzbXrs8zpQpc42VT/1+OcTQIpZhY0wKgYqBg4CuGUNKiYaeo+dlEfkpbH2pXPYZ9oo9rjsJa5+ana7Y1Y6t4zpPw48wjgJ4aY3tStPuuBGpkzbievOOoTqyvLWXygEEbsNhEAqCkJKvAPMmAQYNWxsg0EJ2LvY2cxiYGN9+xYDBlUI9i3MojQYoTQYJqekFpsQICUTho3ijIOP5Z7rL0OPRAj66lBtNjLyhnDyv+9icU46w1ERLhtKbiqEIhgltUjdwFhWwviho9hqzETSval9/TE7xNI0J11RklRVFX9tdcsGaaN1k2vmoMjB07RyXuo26EpDe7xR/qX0VheWtvIW21rXHSiKwts3Hs4TM+dw1gOf8Nlvq/jqnmNxO5u3CqxTMogIR78oyukMPtLoC5/J9fe9wrn/twOXnrgXj771K5rWymW20e/LGRyYxSwjhjbyUEooWllJSmbfy0xZ9A0D80xhsckihCDXZWdyhpcMIcBsMAJuOO8q/vPVr5x6+31c/eq73P3VL9z03md4MzIJCNBGZaMOSUMoAuGyo43IQs3yxisvZ2y/O9c/8wCBUP/rGdwafk1BdiFvSdE0fDXtC2m7Dd+gOlkE1MSLnsvwow0ig7k9Ttt/KxY+eTqrN9Yw5Mj/tNi6MSIGtqSW0U7xU0+hKAp3P/sZAV8d1519SHJjBmjFd35uIdW11fHCnHAg0sczsuhLLA+lxYBFyfRgrCoHI+qjSw0baA47U/fdP/F1QKGn5YujSHEipWR5qoPwXrsR+s/NnPLawzxx0qV4BkBbwm031vF9npcST+cEhTXNRqC2tt3Xzc7al6nV37HWNZoi5zDsZogp1b8zwTd/QF4I/7bxfeamTWOsbzE54WIiwo6BgtYlf+/AYtzQTDa8ci4HXPcGO1z4AjeduDPXHrtT/PkMo4i8UCpSKAzRV6FKgyotl9X2KegDoEgrgxJqSceJPy6GXktarPq7Z49ajzeVO56cycXH7cnDt/+Tc668u83X+zwuFMPAHRpYBpmqqgwrGMHq9SuZNG4KGXne9gcNMiJ1BmVfVfX1NLpMe5XcUsqK9rYxmJwOFpsYSpYXkeaKexhVYOtyP2qjqmcFsKsKm2e0fKITqsK80dn8nu3hi4/fBcCekorDbNuYDEXCfD//j275HF1BAaodWqcrvW0OB7XlZe2+zm9L4ZusfVnlHktIdVFrSyfFqBmwckMZegW7l3/K0OBqnGaIFKMWFXMAi+R0Dk1T+fi2f3Df2Xtyw3PfsvNFLxAKRz21AhiuL2Z4ZBFOGcBGGEXqGO20juwvaOikU4YTf0xfU8eg9zq5DB05nstufYIv3nuRj954qu0XC4FdNwbk8TesYCRr1q9CSsjMHzjtPC2aUV/N/VsLD6vK22JwI4TAPn0kxtoKjPVVIGDi0EwyM938VeXDrxsUuB1snuHFpbUc/goZJoswMRTBIRdeRjgQ4LXH/kNuSQV3nn4ZQgj+XLGYl758n+0324pgOMTJd10VHx+Y+WcvfdqWWZ7qiOpQdrIoZ9SWWzPv61nJvVjUS0hDZriU3FAR6gD16EWFuxP9VAPR09pdXHDodPbcaiS7XvIiQ/7xX766+xi2GJMXEw1vMHPWOiYPqIrvxt9pBEevCJ03Zttd9+PoM67giXuvZdjoCUzZZqcWX+cIhVHMgRn2HlYwgrUbViEUQenaaoZvltvXU7LoBFLK5vp6HcQyKC0GNEJR0EZko43Ijq8rAApaCXE3pTqsowiBISWKonDU1Tdy+uit+L/rz+XDH2dz3XHncMrdVwPwAM8BsN/0Xbj0/07h+Nsu6/bP01FKXDaMLuRQzjjrfG45fAaBulpc3uS9C1mhkk6/Z39hIF68e5Ipo3IoevU89r7iVbY++xnuPnMPLj582/jzEgiK3tHr7AkiOPrEA3jESRexcul8br7oaB589Tty85v3V9ciOspAdE8SNSh/+v1bpCmpKR+4TSIsGhBCZADjgPiFVEr5dXvjLIPSYpPGa1Mxm4SLd95xdyrf+YV/v/ho3Jgseu1bXA4nqqKgqRpFFaV9Md1mjK4OUurU4rqbHWXUlC1xuj188uT/OOTCfyY9rsg5lFo1hUy9e4W4W6PCls1yz3ikEIz0LSM3vDHheROFNe5RFDmG4jFqGVu3CL/mYYV7PBCt4s4OVaObo0F6UJQSpNjAGs8oNjoK8Bi1jKtbiMsM9Mrn6a847Bpf33cst730A//83yxm/ryCmf/6P2w2FQFk6uupUvPI1tfhNmvwK6mU2oYhB0AY3I4flRQE4KIOkATxoGPDTS0qOmGcBHHjJICdAAYaAbyYXbxU/vNfj3Hx8Xtw2Un78Ni7v+NwJvZWDznsOAMhpKoSdNoBGe2mY5oEnA5MVcEe1nGEwv3uRmh44Shef/8FAFze3kspsOgZhBCnARcCQ4E5wPbAD8Ce7Y3t/2cBC4sexK2pFLqdrPcH44Lp8zJd7BAxuOXkC7ngsOPxBwPN5IQEAlP2fbi30B8hO6hT7LZ1Ouw9eedd+eG9NztkUNZpqSzxTmbbqu96vAd4vQB5fdvNUvsQ9t34Dlq9MLjQmJl3ODW2dHTFjmrq/J62HUIQz/EssW3GHkV1qNIENILmaD4bmo7PJmJjIijSYHLt3H53we4LrjpmB/42dQR7XfYKBUc/yLf3HceEYVmMDf1GNAiuoGKgoxJUPNSoOZ0+/noLDZ1s1id8v25qYukPAgWJi1rSGy1Hf+ESH+l0xacthOD2Jz7izEOmcsWpf+e+F2YlNFDQbRrlORkJY/xuV+MNELLr2CJ6vwuNDy8YyeIVC5BSMmRUUh36LPo3FwLTgR+llHsIISYCNyUzcGBm1FtYdCO7DUlnlNcV1ToHKtNdBMbmgk0lJyOLEQVDUQrSESnOhmtKPzmjr/fYKXNpXbqYzzjrAkrXrcWfhHxQPU4zwLSqH3rcmKzVUpmbti2GYovpIQp2Lv8ibkwCLEjZimpbJroS9Y4YioZUNEyhxcdsXxJElQr199ALMlKotdnjY1L1aibWDcyK9Z5i+oQCil87n1F5aUw+7Qn+98EfRPemRI3t/yptCHVqZr83JiH6k1Vif0WT5frjuOmyiRbTs+z653M6Xdzz7GdsWLuCu64+rcnkRNsPwO0P9TtjEiAldrNdUV2Or3rgSK5ZtEpQShkEEEI4pJSLgAnJDLQ8lBabPJqisMuQDHYw09FNE4eqIIRAjsmFsAE2BRHrtSsjBma1H75oV0GhV1idYkfvYh/gkZM3x+Xx8vFTj3LYRZcnNaYwsBpTKCB7tl/yWtfIhGWX4cOjJ8ocLfdMwFBaP5V5dBOnkehNXuN1YDbKPR0WWInSw59lIOJ22vj5oZO45qmvOPs/n/DNvLW8cNVB8efLtGg3pcFKkO4V6c7OK+T6+1/hhvOO4LWn7uHIUy5NeqwzFOp3xmQ9W06ayqo1yxi9bgS5w9P7ejoWXWOdECIdeAf4TAhRCWxIZqDlobSwiKEpAqemxkNRQgiEQ4sbkwDCpiJsKi67g7KaKq59+v4+zad06gZIiWJKsgIRUkN6pySEJu+0Kz++93bSrw8q0VxtSTS/sdSeFw8v16leNjry46+JCI2NjnyqtfT4mPL4GJEwJhTTN4wIGxsd+YSFncYdbHShxZdq1RQ2OvIR7VSa66J5Dxy7mTgmKFwDtsVgb/DvU3bjszuO4qVZC5g1Z1V8vc2MdngR0sBrVOAya6LHn5S4jBq8RgWiH6SGdBaF7r/JmLLNjpz+z1t59Ym7+f6L95IeZ/ZjL/DIYWNYuXY5EC3MsQTOBy5SykOllFVSyhuB64AngUOSGTt4by0tLHoIkeoiLTWV+U98wANvP8/Usw7lsF324ZIjTmZ0CxWcPcm0sgAja8KkR6IadgpQ4tT4uiAV2YEL0IyzL+CmQ/bDX1ODO7X99pPrXSN5P/9IdGEjrDii0jJSkh6posKejSINTKGSHSqmzJGHgomJQopeQ1hxEFbsUfNNmqTqVVTZWhujYjRq/RdRnbw69FRS9CqqbVkoGOhCA9OEVjy1IU3lo2Fp7Le2GnvMsvzbuhpm56dQEss9XZIyGQWd7Su/sczKVthr65HstfVIjrvtA9a/eh4Ao8NzyYmsxiNrkQgEkjBOhDCxyTASgYnCX+7dCCsDryWfCx8SqCGH7sxz2e+wk1i/ahn3Xn82OfnDGDdp63bHVGSmkVFZg13vf570UTGDsqbMz8LKNZimJHNICuO2KUDpZMGgRd8ghNgemC+lrJVSfiWESAG2Bn5qb6z1TVtYdBAhBGgqI4cM5b6zr2Lu4++Rk5bJbhcfx1Mfv4HspMh4p+YCZIcNbBLsElQJv+R6O5zZOGLSFFwpXj5+8pEk31hQo2XgV73oip2I4iCiOil15GEoGhHVgaFobHQWYig2IooDQ7FRZcvEr9WPsRNRnZTb2xqj0fRCHlEdVNhzo2MUR1QXsR3jeWqZH7XRTlnntVPuaihkcphBplX9aBmT7fDqNQexscrHfW/8DES/mRRZhYqBho6KgRMfDhmIr1tvH09EDMzqX4mglmx6Imn61Ev+xVbb7ca1Zx9CSdHadl/vDIbR+qExCTBu1GYsXj4fKcHQTaQpqSyuZfWCgS8vtgnyCFDXaNkXW9culkFpYdFBzNogBBtCOjlpmdxwwnl8dufTPPzuS5x811XU+n29Np/Gl7pSp0a4k0LnU3benR/fTz7s3bhgIGFdR5Z7akwj7IbJEH8kobPzonRXgn7nKP/SdkPnFpCZ6ub8Q6Zy9dNfE45EjZume140WieBUtuIASEr1BJBPD1adnb13S9QMHwMl56wF/66toviPP5Av71gTxw7iRWrlxGOhOPrTFOycXVlr95gW3QLQjb60qSMSWMkQX89Pi0s+i8RI97usTETh4/mm/tfwu1wssMF/+DPFYt7fWrBLoSXDjj7AsrWrcVfXd2NM+p77IbEbPJ1BdXEFU4jgGoV5STF3WfsiV1TOO3eme2+NhryHjiddZoSnXvP+a0VReGOJz/G4fJw4bG7o+t6669tpx1sX+J0uBhWMIKlKxclrDcN2TyB2aK/s0IIcYEQwhZ7XAisSGagZVBaWHQQkeqEVk7uLoeThy+8kWuPPZsZV5/By7M+7NW5DfWFGV8V7FRhzrCJk3ClpPJRsmHvAUKdTeHXbA+LUx28Pzyd10dlRH2RjfbRvNSpLPFMtK59SaCqCg+dtw8vfrGAtaVte9UUJBOCP5ITXs3m/llMq/uQyf6vyAqvYXzgZ6bVfcjWvk/IDy1maHAB29R9zLS6mYwJ/oZqhtvcdm/goRoX1aRQRi6ryWM1KZRBN3qz7XY7978wi4CvlitO3Q/TbHnbFRmpnW5g0BtMnrAl8xfPTVjnTnUgutDJy6JPOAvYEVgPrAO2A85IZmD/PTotLPopQlPRJuSB2vqJ8qg9ZvDgBdfz6Psv9+LMoj/oaofa6TZuU3bpYNh7ICAEq1Id/J7joc6uoqsKQVui18wUCnmhYqvSO0mO23sKo/PTOeKm9o+VdKOE0eE5eMwaNHRSzCrGhv8gwyhCQ8chgwyPLKJQX4qdEBoRTARmP+gZLoA0KvFQi4qJgonsgcumNzWde579nLUrl3Db5Se2PBcpUY3+m5YxadzmLFz2V8K6zPzk27la9A+klCVSyqOklLlSyjwp5TFSyqSSYS2D0sKiE2gjc7BtMxIlJwWR5gJH8xSTvIzsFkb2LDU2hWK3PUFjsSMccNb5lK9fR11V77RU7DXayfccFliJx/D1uFD7YOL16w7hlyVFfPHHqjZf1zinsrV1TfMu19knRYut+gFN5xbtmtP9l868whH86+G3+eOHL3nsrquaPe+t699tQSdP2IK/FiV6KEvXVPXNZCw6jBDi8tjf/woh/tP0kcw2LIPSwqKTqNle7NNG4thxLOjNPQd9kYxeZdcQXXjfYRMn4U5N46PHH+7GWfV/MkOlaNLSzusIW43NY8rIHO6NVXx3FyYaej+tCjd6WGlv/JSp/PPfj/PJ28/wzguJv0Gbrvdr//mIwtHU1FZRWd3Q9CEU0DH7ce7nYEQIcbEQYr4Q4i8hxMtCCGeSQxfG/v4K/NbCo10GZumdhUU/wPSHMdZWIMM6aAo0CUfNWb4Qj9PVyuieIScQxhtxUdWFXKvc4SNYMfePbpxV/2e1ezSjfYtJM5JvP9kadWoKS7yb4Ve9FATXMsK/DHWQej4nDM1k4Zrybt2mgk66Xkytmkm2vg63WUOdkkGpbVifV4ur6GgEMbDhog4VnQhOArhxEcBGABMNPymo6Lhi6isBvBhouKlFQSeCiwAeWir42X73/Tn5wpt5+oHryckdwj477odimJiCpFJZDEUh4HJgKAqOcAR7KEzQ5SCiaWiGgcsf7BFPkqIoTBw3hfmL57LztnsAoNnUgdCVc9AghCgELgAmSSkDQojXgKOAZ9obK6V8P/b32c6+v2VQWlh0AmNjDZE5a2JdQWh2Xfjyjx+57aVH+fj2J3t1Xk4TxtYE+c3h6ZCweWOKli/j4AuSbwk3GKi057DeNYKUur+6FPZe7xzOFzn7I4XAFBobnEMZElyPy/T3a+9SZ5k0PJvZc9d06zYFMC70C9Gu4QIVkwyK8akZ+JS0Pu0bLoAsNiYsm9SRRrSiXUEiAS9VCeNc1Ma/fwHoBAjhwKS5zirAAf84nYr1q7nvpvMYfcszbDV5KpIWTzUJhOw2qtJjeYtCEHQ6ogMkUWUKw0QxDJyhSI8cj5tP3Ip5i+fEDcrcEenxzmMWvYYGuIQQEcBNkm0T6xFCvE/z2vxqop7L/9X3+W4JK+RtYdFBpGkS+XNttNK7/mfX6Oc3d/kiTrrzCl68+h4mjRjbq3OLKII/sjtvTJasWU0o4GfX/zumm2fWv8mIlDHet7BLxqSJ4KvsfTAUW7y/9RY1v+Mwg4PSmATYZnwe1f5Qt29XIWpOqrFq6nKtkIDi7VNjsh5BYl6lEvu//tgRLTyUJmN8pLYtSSQllxx3CbtsuycX3nAqa9evbjEXNWEIUJ3mTcwXVkRsAtFlm2Hg6CFjEmDSuC2Y89ev8eW6yv6d9zkAyRZC/NrokVB9LaVcD9wNrAGKgGop5acdfI8VRIXNH489aoCNwPjYcqtYBqWFRQeR1YFWtdVWb9zAYTeex71nX80um0/r3YkBJS4NpQs5lLNefg5vRkZS7RcHE8P9q1C6qENZacvCaFJIMsK/PG4UDUZ2nFRIRDeJ9EAHl8ZGT5ltaNxIHwwE8dDW5VfTDQSSW698gPGjJnLypUdQVdN2oZyutVbE1LAnncFQj97cbD5xK5atWoxuRPU0a8r9lrB591ImpZzW6PFY4yeFEBnAwcAooADwCCGO6+B7bB2r7H4/9jgO2FZKeS6wTVsDLYPSwqKjtNIzuqK2moOvP5uLDj+RI3bdt5cnFcVhyC6dwP+c/QVjtprajTMaGPg0d7zv9EZHPhsdQzBbufQGFScbnEOp1tIA4mOqbBnNZIcCiiv+d4NzKDXa4DLUc9I9KALmLu+ZFntB4aZaze5SoVl/RKFtA9wUIn7T+t87XyYtNYPjLziEYLh5tFECYZuGrqrtyl4ZrZy7uosUbypDcgtYvmoJ0C8cypsafwNWSilLpZQR4C2impIdIUcIMbx+IfZ/vWRJm+Kwg+eWz8KilxCpzmZFOIFQkCNuuoD9pu/C+Ycc32dzyw7q7FJcx+zC1A6HvaWUbFy1kkMvuryHZtd/WeaZRIl9CEG1Pl1A4NLrOLD4Neyx6m8J/JK+M4tSNkfBwEQhRa/Cp6bGnEAi6qGUMn4lfbfgaLJCJVQ5slFMA1MoTK6ZwzbVg6dvuNtp46dFG5g2Ib/btmmgssQ5jRo1J7avNZAmiMHhA8miiAqGEMFJS4FsU1MpzclANU0MReXeF2dxxuHbcsqlR/LCA++gxAzDkN0WDXPXI0g4/pri97gQSDy+nkvDmDJhK/5aPIcJYyaRmZ9i5VD2LmuA7YUQbiAA7EU097EjXAp8K4RYTvSIGgWcI4TwAG0W7AyOX6eFRW/TqIraMAxOvusqhubkcespl/ThpKJGz49DvJ3KBFz88w9I02Drv/WNd7VPEYIaWyZh1UlEcRBR7OxUMQtVNrTCW+rZjMUpUzAUjYjiwFBsVNmyiaiO+BiE0kRgUaHckYchNCKqA6cZZMuaXweNMQmQlepi7oru9VCusm9OjZqDFCqGsCOFMqjcXUG8RHDQZlakomCoKigCpzeVRx//mOKN67joxtOBqLexKi0FqSjxR4t6q41QDbNHjUmA4YUjKdq4Pvp+rYbhLXoCKeVPwBvA78A8ojbeY20Oar6NmcA44KLYY4KU8kMppU9KeX9bY/vMoBRCDBNCzBJCLIxpJl3YV3OxsOgIsi4EoZjXSkr++didVNbV8Pgl/4p7DvqKUpdGRGn7otIaX736IlmFQ1HVTfQi0GifufU6skMbE+R+/krdGl2xtTqm0cpWXzO2biFCDq6cyqHZKSxd131C+CaCMtvQFoTNB49B6SOVpC6/jcX33Wk8dudLzJn/C//+zzUEnPYO7xJnsPsLqJridnnw+aNySWXrq60cyl5GSnmDlHKilHKKlPJ4KWWHvnQhhA04E7gOuBY4LbauXfry6qcDl0opNwO2B84VQkzqw/lYWCRHxIif6O9942m+nfcrr157Hw5b34sxhxWl1YKh9lj88w9M2nHX7p3QAMVuhjCbhFfDSrL6wK3jNAKDrkhnbGFGuz29O4JEGfQtMKMV3h1DmJKxIydyxzUP8dGsd3n+hf92+MZRMVvLDO4+PC4PvoAPANOQnT4fWfQZjwBTgYdjj6mxde3SZzmUUsoiomXtSClrhRALgUJgQV/NycKiNWQwQmRREWZJbfQkrpu8POtDHv3gFWbd8xzp3r4rtqjTFH7P9lDstqFKid4J72Q4FKKqtITdj+67/M/+RJUtkx8zdiU9UsHC1C0JKU5sZrjLeXy/ZuyIzQgyLrCkG2fbt2wxKpe3v+2+z6NiMCH4E8scUzGUvr9J6wkyKaKSXAwcSY+pzEzF5Qswfq8ZnBW+i0fvuIz0ocPZ88DkJb5qvR4MwLDZCDuiTienP0iKL9BthmZGehaV1VGxe0+aE9HJNrAWfcZ0KeWWjZa/FELMbfXVjegXRTlCiJHA1sBPLTx3BnAGwPDhw5s+bWHR40jdJPT9Mgg15NN9+cePXPn4XXx02xMMzR7SZ3MLqoJPhqURjoW5DUQ0Kb+D/PTeW2g2GyMnb94DsxyACMFy72bx/wEMxdapfduUgvD6dgWqBwrBUISp44bgC3Zv28qwcDbzEA8uFAySiiLGMTSNutSozuTeBx9HafF6Hrrtn2QNGcqW05OMLCgCf4on+n/suNaM7pV8ys7MobQ8mlObMcTbzqsHPmGHn9Xj/uzraXQnhhBijJRyOYAQYjS0I0sQo88NSiGEF3gTuEhK2SxuEtNZegxg2rRplvPcotcxNlRCI529euHyl/pAuLwpS1OdUY9kY69kJzyUP37wDgVjxnfjzAYBLe3HLhaGjPIvxW6GB7wxWeMLceo9M3nr2yVoqoJhSorKa8nPSunytiWw1j6pz9ss9iS1ZNCpW4pGx98xZ15BafFa/nXxMdz97GeMGLNZh7ehGCauYPcejymeNGpqq5BSUrK6iuETc7tx6xa9wGXALCHECqIH6Qjg5GQG9uktYCzR803gRSnlW305FwuL1pA1QTCi9zL1wuX3nXMNO/eBcHlTyp0aZjeElFb9NXfTrO7uZbJDJdhk93rzepNwxOCMez8i6/D7+erPtbx41YFcfNh0AEYd/yjn/OcTSip9XXoPEw0juRqAAUsEO93ho77whgeZsMV0rjxtfyrLO15prxoGspvvbjLTsygYMpQ3Z75EOKhjmpYfaCAhpfyCaJX3BbHHBCnlrGTG9mWVtwCeBBZKKe/tq3lYWLSE1A30VWWE56zBDES1XOuFyy8+/CQO32WfPp5hlHFVARyRroWsqko2EqittfIne4FlnolUaamscw7nm6y9+D5zd0rseX09rXYxTZMrn5hF6sH38upXC7n/7L9R8sYFHLXHJG4/fXc2vHIup/19C575ZB6FRz/E369+jUVryjv1Xgo6GfqGbkkv6K+4qYZuKs66+cE3ycot4KJjdycY9HdobERT0dXu9QQrisINF9/JU688TEllEYqVQzkgEEIcVv8AZgBjgTHAjNi6dunLmMJOwPHAPCHEnNi6q2MaSBYWfYYMRqI5kxEj2q9bQDAc4v9ujgqXn3dIRztZ9RxDAjqpukmp1nmdvtmvvIArJYW07Jxunp1FU8ocebxTcByKNGM5mSYhYSe7/HOUflj9LaXkntd/5sbnv8UwJVcdtT3XHbdTM3ms/KwUHjx/X+45cy8enzmHf7/8A1NOf4LJI7K5+8w92Hvq6KTfUwAFkWVUaUMwpTqo9CfrcVOHn7RYN6aufT5FUbj32S8487BpXHLcnjz42vfJy5cpCn63A1uN3q1h72EFIxg3ejOqw6XduFWLHubANp6TRLvutElfVnl/y+DIS7cYZESWFENYj8tdGHpUuLwwu++Fy5uy1mun0qF16aL7xxefMGrzrbtxVhatIgQSNd7zW0GyU8XsfmlMPvvpn1z86JfU+cOcdeDW3HPGnthsbcvdOOwa5x0yjXMPnsoXf6zi8sdns99Vr5OX4ebaY3fkzBlbo6rtGzsrHVtGpXUGoTEJUEc6JgrddQm0O53c98Iszj58O64562Bue+z95AZKSWqtr0cuxAJBoLbndS8tugcpZVJ5km0xmMvoLCw6hVlSGzcmpZRc9vhdVNZW9wvh8qas8drRuxhSKlq2lO0POrSbZmTREbL6iQcnGNZZsLqMD39cxr1v/EThUQ9y6j0fMWPbMVS9czH/OXfvdo3Jxggh+Ns2o/j9kZNZ9twZbDexgAsf/py0Q+7jwoc+wxdovSWwgYpPSRu0xiRAEDfdfflNz8zhzqc/ZtnCOdxz7RlJjbFFdHrKryMEBGpDSCuHckAhhEgTQtwrhPg19rhHCJGWzNjBW0ZnYdEBpJTImiAyrCecX+978xm++fMXPrvz6T4TLpdAuSPaASc7qGOTkjpNocauRp9so3dve6z8ay66HmHbGQd365wtkiOgOJHdrPwcCuv8vGgDqzbWsLa0hg3ldWys9FFa7aeqLki1L0xdIIw/rBOOGOixnvRCgKYqOG0au24xjOcuP4DMVFeX5zNqSAZv33Q4vkCYm174jkff/4OH3vudv08fzX/P24eRQxKvVQIDVeoYYnBqUEI0T9TsoGxQMgwbOZ4bHniVG847nN2++5RpO7Wd620qAhk7AnVNxVQUbBEdRUoMRUHXVFTDQDM66UEXwopDDjyeAv4CjowtHw88DbSbR2kZlBabPDIQIfzLSmQwEj356dGT5yuzPuTR91/hy3ue7TPh8hqbwqyCVMKqgpASQwhSwga1dhU1ttwVZr/8Ahm5edgdyQssW3QfdVoaM/OO4MDi19CSk3prFdM0ufbpb7jnjZ8wTIndpuKya3hddlLddtK9Tkblp5OX7qEgy8vQnFRG5KUyKi+N4blpaFrPet89Ljt3nr4Hd5y2O89+Oo8bnvuW0cc/wpZjcrn/7L+x25ZRnWEF2Mr/OXPde6ArXTdo+yNZFFPBECI46W6La8o2OzJ9l335z80X8sxH89uMqhiqSll2BoqUyNi5RAqBqhsYmoqIrffU+vAEQh2eaUqmCzGIPc2DlDFSysMbLd/UqM6lTSyD0mKTJ/z7KqQ/lNAibNacH7mij4XLJTCrIBV/vOAmemKudkRzy+JN1LpQDbvw+2+YsN2OXZ+sRecQgt3KP+1yDuXTH//JRY98TihicM2xO3LdsTv12wu5EIKT9t2Ck/bdgp8WbuDChz9jj8teIj/TywPn/I0jdp3IaseUQe2hDOBFx0FPue8uuulhTthnIi8+ehvHn3NN6y+MadiaTaIchhY9x0SNS71TxiSAVeA9IAkIIXaO1bkghNgJCCQzsH8lhFlY9DKmL4SsSzQm/1yxmBPvuIIXrrq7T4XLy5waYVU0D2e3t5wkhq5TvmEdux6ZfOs2i+4lLVxBil6N0smw9+y5qxl+zEOcdt9HHLrTeKrfuZjrj9u53xqTTdluswJ+/O+JbHjlPLadkM8//vUuPyzcQLlWiBQd73c9UPCThuzBy6/T6eaYM67kvZcepra6sv0BbZxTXIHOF9bUlAeQg1j+aZByFvCQEGKVEGIV8CBwZjIDLYPSYtMmYiScPOuFy+89+2p26WPh8rAiED14Lv7+nTcQqsqE6dv33JtYtInDDHXKsFi2vpKpZz/Nnpe9zLjCDIpePY9nLj8Ah31gBp2GZHp5+6bD2XfaKPa+/GV8/qQcIgMWsxcuvYccdw5pmTncdc3pXdqOYpqd9qNKKenmFGGLnqcm1st7C2ALKeXWQG0yAwfm2cfCogvIYITIwg3Ram4hIJZwXlFbzSHXn8OFh53AEbt2vGtMtQnrdYUQYANyFEmdhBopEECWYlKoJh8GKvBHmFzuZ06Op9srXkvWrua5669kx4MP71Zv1qjqIFMqA7h1kxq7yi85HspcPd/1JCsYYZtSPxkhnbAqWJLqJCViMNwXrSZe67ZR49AYVx3EYUiqHCorUhyMrg2RHjIIqYKlaU5SwwbDYmPWeGzU2VTG1YSwG5JKh8bKFDtjakKkhw2CqsKfmU5Wpjo7/f2UOIbwc/qObF/5NVoSYe+q2gDH3fEBM39ezmbDs5j76ClsPnrgt7aLCI1f03fkqGdP4/vdduSUs8/lodfepNKZSkRRsRsR8n0VuPXWq8MHEllsoII8DNrKXZZ4qMZDNQomERwE8OKmFo0wJip1pGInhJMAEgjgxkTDTS0KJjfe8m8uOPs0iufNZKvNN4tW0JOCnXCjMR5M1PiYME6qyI4XDdWkeJGyFneo4x2enB47wop7DzTeBLZp0gr7DWBqewMtg9Jik0IaZlS0PKTXrwEahMv3mbYz5x/S8Y4xNSas0BVk7F4+DKyP2wcCCXg6eF4VQKnbhiLB7MZzcjgY5JbD9qdg3HhOua37mlSNrQqwdbkfLeaRUKWkytHzp5j0kM6e62vi7+syJFtURi+W9X6gEb4IwheJe1qyQgaZIX982W1ItqhIHDOyLoKgYUx2SCcr1CAA7TBNit1dzPMTgtzwRtrLpTMMkwse/pzHPvyD7FQ37998BDO279s+8t2FBD7NPZhyey6GonHt6x9w5d925N677ua4G2+NvkYoOPQIksFRNGyiYrRT5Z1COW7q4ukQdkLYaMhlVDFIJRrOrs+wduOLLwNM33ISW221FTdeeyXvvvsuGgapVDUZU5cwRiKiGqAxBBJnWO/UvvemD86iqsGIEGIiMBlIa9IZJxVwJrMNK+RtsUlhFlXHq7jrMYwG4fLbTu2ccPn6RsZkAw2FNDYk6UrHktRrbQrFbnu39OpuzL//cRAIwTWvvtd9G5VRg0xrFN5amO7C6IWr/+QKP0qTsJog8eSWjIR0R8es8diJtJTj2gGchp/RviVtVni/+c1iUg++j2c/nce9Z+5F0WvnDxpjEqDMnkuFPRtDid58ZBUUcsY9D/Hly8/x++cfA5AZqKGlX9hApZYM2jq6BAaeRsZkw/qmr0tc11Lfndtvu42ysjJefvnlpMZE59bwS3AGQiA7t++rSuo6Mcqij5gAHACkE+2aU//YBkgqb8LyUFpsUpg1gXiIG6I5Ppc/fhcVtdW8d8sjnRYuby9t3SminXs7svVqmxrVg+vGy+jjl1/IhqVLuPWTr7E7k7rpTAqbKdGaCBhXOLS4FElPkhE2OnVn3JmZNR5T5dDQuyh0nxqpwhAqmmxuUBqGyT/+9S5vfbeYk/bZnMcv/ntSXWYGGlW2zGbrpv/9AOZ/dzSPXHgWd3zxAyOdYtAYkwA6dto6AlU65xFsibS0NI466igeeeQRDj30UJzt/O6bek5tEb3TnqdIWMc0pdXPewAgpXwXeFcIsYOU8ofObMMyKC0GPTJioK+rRFb4kKYZPUvHbJ/73nyGr+b+zOd3PdMl4fIcRbLRhNYuAT4JARNS2ilcNYF1XjtrPNG5dFVnsjFfvPAUP777Jhc9/hw5w4a3+3qHT6dwUQ3eyhA1OU42jE0ha0OAnNU+wm6VdRNTcboURteE0EyJKUBtZFNOrPTzR5abgqBOvi9MQFNYluqkpqNhcCnJDeiMqg2iSFid4qDapjC2JkRKxECn48Z6Z4kIwYpUByUuDQOBYpqYXTAqy+05lNlzcZt+Fnsn41e9DAuswv/XN+x92Uv4QxE+u/0o9thmFGtdo1jpGYdmRhjnW4jNDLPYO4WA6mZ4YCX5gdUs925GqWMIGeEyxtctoMyRx0r3WGxmhAl188kJb+zwHOuUNEq0kejCRqZRRIpRTqk2HJ+ahseoIi+yChsdz6+rpzCwmvRwOWXO/IT1J/3rLpb+9gv//r8DePTT2RT4I2iDpMLDQxU+0nHiw04QAw0/KWjoOKlDIGOJMomoMkJOZA2pRhkBJYWN2gjcspbsyDpMoVBqG05EOMiLrMRuBqnS8qhU87jl9H2Z+f473HnDpdxy291E2tD3dFONj3Tqz2V+twMtHMbWiV2v2VXLmBx4/CGEOJdo+Dt+9yGlPKW9gZZBaTGokaEIoe+WRau5m3jQulO4PFuRlJkCoxW/gomgXIJHth72NoHZBSmUO2Oer3q5jS50wqlnya8/8dItN3DoRZez+a57tvt6b3mI6R+uRxgS1YTstX7G/FaBVASqETUexwZCuEe44qFiAxI+/QhfhOG+akwBmox+Pr+qUGtXO+S53Krcz9jqIJqMbntoXRgl9l4q0HPN4xIJqoKPh6YRVhUMRTT6fjo/AUOx8WnuQbGvVyCFwuNPvsZrDz3JdpsV8Nnt/8DtsvN5zgyKnYXoih2kyTLPhLg2qRQK61zDMYWGIg0MxcZa5wj+TJseX0aaKNIgK1zSIYmiYm0kaxyTo1XJQqFSy0OiIJBIoVKtZJNmlKKZVZ3+DlxmgKGBVZQ78pAi0Ti/5tV3uWSXqdx+8QXce9u/UU19UHgqPdTioTaesysBDzVIBEqj3kmNDy2bGWTzwFeoMoKKgWkUkx9ZikRBxUQCWfq6WAg7GqJONzYyColEcMepu3LBQ59xr1lNlWi9kMxNHX5SY+oDAl3TMDUNGen4vk/P9nR011j0Pc8Di4B9gZuBY4GFyQwcfPETC4tG6EtLIKw3MyZnz/mJKx6/i3dufqhbhMvXGyKWBdfyKVdBMqydCu91HnuDMQlx0eGuGpNVpSXcc/LRbLXX3hxw9gVJjZn0bSlqJGpMAqgmCAmqEd2Pmk3gHepEpeEkotJyblZ9XqVfU1iS7uqQMZkSNhhXHcQmG7atxd5TbbTcG8zLcBPUYsYkNPp+urhhRUUKFV9dHTccvA+vPfwId5+1F98/cDwel511zhENxiSAUJCKhhRq3AAzFDtSiKjxCLHnG5Y1qbNt1XcdMiZ1bKx2TMEUGsTeRwotJngd3ftZRjFus7ZLuyCgevgzfXozYxLAnZrGJU++yJxZn/Heqy8PCmMSGn4bSrNlmbDc+PMODS9CkyHU2JlGof53YMbHqMiEXFMVE4FExeS4vSYRihgsLom0eU6pJSNuTAI4wjq2ThiTAL6azmtYWvQZY6WU1wE+KeWzwAxg82QGWgalxaDG2FjTTAftzxWLOeGOy7tVuLxGtpQO34BHtC/HttZr73JOXlMMXeemQ/YlM7+Q8x56MqkxwpCkljfvjNF42ZZhS0qwuPGYIretxTBeW+T7w0np2PWGobHea+uxnNDfv/iEi3fYitqKcv565lwuPqxBA3WNe3SDMdkmrX9jeaGiDmsf1qhZrXTwadhupr4+buB0lg3OoSiydcmk8dO248Qzz+KB++9n2bJlXXqvgUyGXtwpAfy4koHTTqrXzYcffdrm60O4afwd20OhThsKQV8YaXYiVm7Rl9Tnr1QJIaYAacDIZAZaBqXFoENKiVnljxqTauJFtqeEy21xaQ9JmpA4Y4rkCpJUIXGI9qskU8JGl9ootsQdxx9BKODnhrdmJq03KQVE7O3I2ASNDnfA8EYMhClRpCTPHybfF0aNXWzSQzqFdWHckahx4tRNCn1hHIbZb7LmPJGutUdsSsmaVbxw87Vcvsf2PHjOqWw742Du/uoX8oYNA6BWS2W1axQGCqINgysZfKoHk6htXuwoYI1rJCElqoFYraWz2jWKKi0DgJCws9Y1kgp7RtSclJIUo4x0vRhVRq81IUWj1uaiWstAAiHFwVrXSIodBR3+vryR2nY/3z9OP4stt9ySs84+m3JD4NfsSb+PgUIQN2EcvXosSQQhXIRwJV2fLhEEcRHC2WyuoW7oaz5xVAE//fRTC+8LYRwEcVF/B6fqBo5g9Iau0/utOzz4Fr3NY0KIDOA64D1gAXBnMgOtHEqLQYUMRAj/shIZjERPZI0kgtaVFnPgNWd2Wri8LSbZJEFp4GjkiQxLEpYV2k6H3LwygC5gcYarW4TMX7z5WlbM+Z2b3vsMpzcl+YGK4LsjRrDDW2txBlr2PunVBnWLA6RO9iRtqA4J6MxYWx0Ngcd2hGJKApqCyzCjRT0m1NgUUnUTQ0TD7P3lJLXn+hq+KEyh3Gnr1PdjmiZzvviUr159gWW//0qgrpbU7Gwm7bAL5z30BMMnTQHg3YKjSYtUUqeloUgDU6hdNoSqbFm8UXgiKia60BBIDFRS9GrqtFQUTEwUPHotPi0VBQMQpIUr2afkPVQZ1W01hcLM3COocmQgJKxP+Rspkan4tLT4mJxgEXuXvp+0Ny0vvIHdyj7li9wDWgx7AxR5s7jw6Vc4b/ftOf/sM7nh9Q/wRAIU1pW3aq9IoJZ0fKTFPOMCB37SKe1xGyeIiyoaBOcVdLLZ0OY+CeKmipzYkkTBSBgz37kzk4Pf4u1CvuoBU/O5+43fEk5EOhoVDIlrT0oJadU1OEIRZOw32Bl0Qyc9O/nzg0X/QEr5ROzfr4DRHRnbX87VFhbdQvj31Uh/qNktdTAcYp8rTmFl8Tr22nqHbn9fIaLSQI1PnU2Xoe07/Uq7yrL07jEmv3v7db588RnOvO8RCsdN6PD48T+VYwu1EcpUIGWiu0MXC0FUdDw+Iubh9Oqx1m6xnZMeiS7XV4z3Fw/l8lQHVY6OGZO+6io+f/4pfv3oQ4pWLAUgf/RY9jnlTPY67iS86RnNBwmFalsmCIFRf4ruqudaCCKqk0iTO5qG94lSY8uIv6+QJnuVfoBdBuPf2Q/pu1JjT0MKBSnqx2Q2GmOwW/mnHQrNGqh8nb1Pq8Zk/fxNl4erXnmX62bswav33Mo1p5zYpmEVwo2fNOozEwUmaZT1uDFpoFBJLo0DgOmUtZnuYaBSSU6TMcUJY/L0VV3OV/2/v03n+me+IRgK4XRGvaDRjj0a9Wcrj8+PPRQV9Bdd+A2WlBVTMGRoF2Zr0RcIIdKBE4iGueM2opSy3QR8y6C0GDSY/jCyLtjs7FcvXD5twhTys3LZ5aJjWfj0THLTs7r1/dvKOWxrXT3L05zd0hFn9YK/eOqqS9jv1LPYdv8DO74BU5K/ohaljSikPbNz7RST2SfJ7Me+YEm6s6Egpw1WzP2Dz557koXff0NNeRkubwpjt5nOYZdczpZ77J2c1mlTo7W7vDztbbfRcl5oA5qMJOz/Zd5JcQHylsYUBNehtKCp2RbrXcOTy00VgvzRYzjhljt49prLOHyHbZm82cRWX+6LVypHcca6yPQ0QRIrmxV0bITbPI4DTcaoRLCRuO+HRFZ2OV/VPWxLbDYbX3zxBTNmzEDHjtnImARwBYLNcuE6evSZpsnG0iIcpCKltLyUA4uZwI/APEiiH2wjLIPSYvCgG7GLW4NFKaXkssfvorK2mndveQSbqrH56Qcy/ZzDWfzMxzjt3Sfu3VXCiuhy0Yevuorbjz6UidvtyP9dfm2ntiFk+2EuoW56Fwi9FWNSD4f59q1X+e7t11mz4C8i4TDZhcOYvv9B7H3iqeQOH9m7E+0m7GaYxqaEBHTRtpCqzex4r+2IYu9QD5xdjziaX995g4svvICZM2eiaS1fxpoWIUW9fT3v725cIR19X7NdZSkziTH1aQddQcfG0KFDmT17NjNmzGhxv4tuyOOuqavG6XRh1+xdktWy6BOcUspOtYyzDEqLAY0MRojM34BZWhs9aTWpKLzvzWf45s9f+OzOp+PC5T8/+DrjT9qXHS44it8efqvT3XG6gzUeO3Oy3PhsCnZTRuffSSFg0zS56dD98KSnc8lTL7X5Wm9FiInfl5FeEsTQBGsmprJ8WhYoAqkKfplRyJafbcARatifqlfFO9aFlqoijbiSzCbD3murWZzmZHRtmIq1q3ng3Rd4/+evKS5ej8NmZ8sxEzn+3KvY54DDmRQGuympECqrK/2MqQmRGjEJqoI5mW5WpTq6z+vYQ6xxjeKn9J1QkSz3TsAQGjYzQkSxtzr3Ve6x2I0gACu8EzGEypDgOnYt/RS3DLQ4ZoxvMT7h4ffMHZI+qM57/AUu2mFLLr7+Rs599BkkAk/YT2FdBfUZp5kUU0caLnzYCCPpnW47HqoxUGLhdoGBjSqySac0wcQN4aSGLHRsiHjJVH1eo50qskinLD7mL/eu5IWXU2obSVDxYpMhciMr8anpVKt5gCRTX0/QdLPavjkR7GSYGymMLGGVfXPqRAZ2GWSH3fbhnddfpIiRtGTtVWSmkVZZg63RuVRXFWpTPITt0ciEMxBCYBJ0OpFCYIvo2ENhAi4npqqwwVdGWloW3gwXwhI2H2g8L4Q4HfiARk3gpJQV7Q20DEqLAYs0TEI/LIdgTOWgyY11a8LlHpebnx96gymnHcBB15/NB//6Xy/OuoF1Hhs/5nnjYdSwKrqUK3f/6cdTU1bKXbN/QVVb9yQ56nSmf7AeNRL1T2gRSdkID0LKuMfCWxFEa+QQUZwKaVt6UbTo80KLen83JeeDx5CUfPIpJzx6O8uL1pKbnsnuW23HuZfdzrYTtwCi8SERbPD7ZIUMMkOBBg1NU1Lk6XxHpl5FCJalTI7/D1FvYntjlqRMAWTcOPRGqrG300lnjXdMVCw9yanZXS7Of/Rp7jrxH/z88Uym//0AHEaiVqKKSSqV8XUd2X5XCSWEsAV2QtCoRVcYBxXkUZ8zKVFJPIEJHAQTPk9YcbPWMaXhuxBO1ttjIf/YuuXaVgTxxnVCK9V8KpUh8efDwsX+x1zCS888xopF8xg9cYvY+zb8klXDTGijagpBRWZaNHoS207Q5Uh434hNI2LT4ss1dTW40zNwDG/eVtOi3xMG7gKuoeGglCRRoGMZlBYDFnNjTbQDTgvMmvMjVzx+Fx/d9kSLwuWF2Xl8cdcz7HrxcVzw4C3857zrenq6zZiT5W6ek9dJr9Xb99/F/O++5prX3icls+2T+PD5VYhGxTE1WXbqMh3I+j7RUjL298q4iDmAa6i9mfNoU8qLeu2rj7j6yXvZUF7CbltM54NbH2NkXmGz17XkX2u8l1alOKKh84Gy7zqTyxnr4AOAlEyt/gmtjXBtmT2PKltm3AhKls2234ldDv8Hj192AZN33JnxRm2zYqC+yMcN4o5VTMduvjDxUJtQYFPbqLVhS7MTGLjxNZ9vG9+HgUJApNDsKGwyxp2SRl7BMN547gEuv/XJZvPw1voT1gRcMbmlxttp57ioq6nEm5rO8vIwE0Y2/RCDC19NDT9++lFfT6M7uYSouHlZRwduYkEri8GEWRsEo3nO8J8rFnPiHVe0K1y+zbjJvHLtvTzx0Rvc9+YzPTjTlvHZOnYBbY3fPvuIDx55gBNvvpPRW2zV7utTy0PxDjgAvnR7gmdUMSS2UOJ+1bzaJhm6euzDVxl29G6cfOdVbDVmIitf+JKPbnuiRWMyGarsWlKFPYMFBROn2XKou54qWwuV7kly4r/uIiUjgztPOLJbcv+6A53EnFAVvZlnVMdOW+ZtS2PawyD5xgG77ns4f/78dcvvbSTepEc0FTqYFlRZUUpaZg6hiIlpCZsPNOYD/s4MtDyUFgMWxeuIXpwbnbA6Klx+wPZ7cMdp/+SKJ+5mVF4hh+y8d7fNzwTWeO2s8drRpGRMTQjNlCxLdRLUFGyGSUjruFFp9+sMW1BNanmI+aFiHrniLPbb70jOT98d/auNrJuYBsDQRdVoIZOSUV6KRnvjIu8rtkjHWV2C2x81GrPW+UkpjxZTFC6uQYuYGJpAizTsV/+aAN5xblRn14xgXddRFKVP81bbwzRN7n79Se55/Wn8oQBH7Lov959zDWmeDmh5tsLYmgAbXRq1jrZPvQ4jwITaeeSGiqmyZbIwdQt8Wtf6zfcFJgqLvJszNLCaxSmTqbBlkxMuZlztQja4h7PaNRoapVp0FEVRuPz517l6v9144qWXOfG446hyphBSbbgjQdKDdWi9LDzlopYgLlRMXNQCNDP0vFRSQyYNDUQTidaFu1CIECAVAw07fpz4CeIljBOVCG5q0LETwAuQ9H48+Jizef3p+1mx6M9Y2LsBn9uFOxAk4HYSscX2nml2yKjcuG4VWTkFOGwKyiZ0AzVIMIA5QohZJOZQWrJBFoMXkZcKf62PL1fUVnPI9ed0WLj8/EOPZ0XRWo67/TJm3/sC08ZP6fLcTGB2QQplTlvU6JWSNR4HCOLhI2HKtpXOW8BTGWb6++tQDEkw6Oech49mi9xxPLL1eahr/ZjAkOV1sQKlaAjCHjTYONKDqUTftzrPie7UkP6olIktZDL1ow0gQJjRoYZITNePVBlIgy5JgPzn7ee46ol7MaWJpqo47Q5S3V4yU9LITc+iMDuPkUMKGVc4gonDxzBx6Chsts7JE3WGSCTCjc8/yKPvv4xuGJywzyHcefo/cTm63qGknrSwiVc3qbPLViv6PXoNBxW9imZG0DDID65lvWs4PtU78CqhhODHzF0Q7IrAxBQaG5xDmZO2Lao0or3G67vkdPC3UE/eyNEcfsmVPHPv7Yw/4P/IG1kAQhDUbKSGA0izc32oO4uKQTZFQMPvJzFLERz4Uchoo/67XsuyfrRCCCe1ZMWXQeInNWG54fVtf2K3N7VJ2LsBv9eF3+uKbbbjed0BXx1ffvgKN/znNUbm9B8VDYukeSf26DCWQWkxcKkNxv8NhkP8380XsO/0nTn/kOM7vKn7zrmalcXr+NtlJ/HXEx8wNKd53mVH2OCxUe5sFN4UIi4EXY/sxJ37xB9Ko55DKTnwubOwqzbeOPa/8XxHBRquXjGWbJeFqTXk7eUvq8NT06BxFxcwbjRGbXINcQ6xozqVThmThmFwyI3n8uXvP3DTiRdw/N4HM3/VUhatXcnKonWsKdlAUUUp3/31Gx/+NBtfMEBYjyClRBEKDrsdr9NNujeVnLQM8rNyGZ6bz/DcAvKzcslNzyTdm0K6J5WMlJQOS0EFQgEue+wunv/sXTRV5awDj+bG48/rEWN2o8tGqUtrUx5qeuV32M0GLcANruGUOoYMPGOynlinn3pNSKlogMQQsf3bDZ9r/zPO5ZeZ73HH8Udw99e/oghBetCHZhq9XjSWjNaqj/SEPMu2t1T/t95oVFp5vmOfdNd9D+fD155o/kT9sSmaLCfJuy8/wpbb7saIMZtRUhNmUodGW/Q1UspnOzvWMigtBixGaS2YMi5cXpidx62ndEo+C4C3bnyQbc89gm3PPYIlz3yK1+3u9LbWue3oPRDWTS+OVn6e895NrK5az7dnvIxDbb3yVgLVuc5EwepVPlS9Y14He469U9qTK4vXsfslx+MPBZh1zwtsO3Hz6BwystmznY5FdQEfC1evYOGaFSwvWs2akg1sKCth/qqlfPvXb5RWVyBNiaIITCmb9RYXQqAIBVWJPVQVTdWwqRo2TcNus2HXbKwoWovb4eKaY8/in/93ao+G4zd4bOjtXKALAmsSktvXukaht1ddPeDofjPvsude4+Idt+Z/l5zD2fc/SkrY36GOPb1JVPy8M/ug+/ZbW2HvzuKrq2Hm609x51MfA1BZp2NKiTJQitA2YYQQr0kpjxRCzKMFwVYpZbsHiWVQWgw4TH8YWR0A3UBKyeWP30VFbTXv3fJIl4wBRVH4/oFXGH/yvmx73hH89cQHnd5eWtiIyvB084k06FF5/osX+WDRLF456j7yU3PbHWP36YS9NrzlITzV0f68HZX70X0G9vSOnS5e+uIDzrz/OiaPGMfndz2D19UxA93r8jB94uZMjxmhTZlw0r7sPXVHHjz/hvg60zTxBQNU1dVQVVdLta+Gal8dNf46avw+6gL1jwC+oB9fMMC5Bx/HGTOO7NDcOktaqP3jolZLxREpo1pLp8KejYFKVPize4q4Bivu1DQu+N8z3HvqsTx3/ZVccdk/cRkRIopKQLOjmiZuPYQEfDYnIHBHgrTUKb1+jGaauPQQEoHP5gAEnkiwy4aqRpgwWqwjTggDjQgOBBI7AUAQIjpHGyFUDMI4Yl1tuodo2Ht4i2FvAJUwNsLo2NBxJLXN91/5H1N33IshhSOAaMTcMiUHDBfG/h7Q2Q1YBqXFgEFKSWTeOsyi6nhHnPvefIav5v7M53c9Excu7wp2u51fH36LiSfvx96Xn8wXd3fO+z+xOkhIFSzM6J7e3BA1lm4JfMELsx/muj3OZccR27Q7RgC7vLqGYIqGI2AgBQij4xdD/7IAigqOPHu7YW8pJafcfRWvzv6I8w4+ljvPuLzD75cMJVUVbLfZVgnrFEUhxe0hxe1hWG5+j7xvVxhTGyKoCuZluVs9Lt7P/wcpkWr8mhcFs1kXFYvWmbzTrpz38JM8eM6pgOTU6/+FrmrxCnAhJaYi4p2gFGkwuqo4blRKYKM7nSqnFyFjAWZpYipKq2M6QyYb0dHQMOJbMRFNDFWJRI2JnkeXy8mPGXfdczzsuu9hLYS9JRmU4CAQn00t6XGh9taoLC9h5utPcdfTH8fXFWY6Nil5sYGMlLIo9nd1Z7dhGZQWAwZjTUXUmDSjGVmtCZd3ley0DL65/yW2Pff/OOXuq3jqn7d1eBvVdpUl6V0zJv011fzw3tvMnfU5axbMo6aiPJrviGBM5vCkt6MArtrEwoSOXgptGVo07N3O5ymtqmDXi4+lqKKUd25+iH2m7tzBd0oOXdcJhkP8rZ2weX+jzqawILN1YxIAoVBrSwchGjo39xNJnIHA1nvu08iohBNuvqPBIxwr/KnPZy6sKUsw4mrsbqqcXqRQ4q8xpEgYM7S6tMseSgFo6DRS7ES0UKMtacgBrSO1XbmhjtJS2NtLJXYC8flEsMWKf9p+36cfuJ59Dj6OvIIRjeZvGZMDBSFELS1fGgQgpZTtXmQHaJa3xaaIsbo8LhFUL1z+zs0PtShc3lU2Gz6Gd29+iFdnzeRfLz7S4fErUhwYHTyXbli+lDfvuZ2bD9+fs7cez3nTJvH6nf+iuqyE7Q88lOvf+ogVV3/NkZv/nZPfvJJHf3o56W13VeDZWeBAaSeH8tNfv2XcifsghGDps5/2mDEJ8NPiP1EUhfys9kP+/YmVXgdmMju/M4LiFnHqjcqvXnuJ566/suGJRvtRMU3ckcRuNBXOFGRzBf/4v6pp4NJD3WImJfObbLwuatR17yW7cdg7vo5EgXg/Ka3MroH5f/zAwrk/83+nXJywfkNFsFlus0X/REqZIqVMbeGRkowxCUl6KEXULXEsMFpKebMQYjgwREr5cxfmb2GRFPUnJBkTMZ+3cgkn3nEFL159T5vC5V1lz6134L/nX895/72ZUXmFHPu3g5Keb7hxN5T6E2qjC5Npmvz19Sx+/uh9lv3+K+Xr12HoEbwZGQydMImDz72EHQ4+jLSc3IRtKD+t4O79r2RCzihu+fJhFpet5L4ZV3fbZ24NobV9QbnssTt56N0XOWr3/Xnyn7d2KszVVJKo/nuvX9d4+Zs/fyHN3XVdyN5EAhE1Welpi67S3FN5e8LzTb8JSbTNYGNWzZvLJ08/xvzvvsFfU4Vms+FyOPB6vaSmppKenk52djZDhgyhoKCAgsJCRo4YQWpq92uG9pS3r2nYu/l+aT3lQkrJ5++9yIuP3sp51z6Aw5mYJ23KjudrW/QNQojpQLaU8qMm6w8ENkgpf2tvG8mGvB8mKq23J3AzUAu8CUzv0IwtLDqAlBJ9WQnGqjLQTdAU1pQUcegN5yYtXN5VTtnvcFYWreWM+64nEA6xy+ZTGZo9BE8LBSbBkjD+FQHMkGSMvQZ1YhppJUEyioPUhOt4puQbPlnzI6sX/kVNeRmKopCdlce03M04cMZJ7Lb5rmycnEVWUZD0jUHMD+vYODKa85W/og7FkOg2ARHJ6dP/wdisEZzy5lX8tv4v7ptxNVMLu66f2Ro1f9XhnejGmZ2Yp1rjq2PvK05mweplPHnpvzl6z47lc0spCawLEVgTQuoSxangGGIjXBrB8JmIWN6mETKJVOggo+H3XxfPpzB7YHgn17lt/J7twWdToj2SrStsr9GWUWkIhdWpuXgiISqdXkyhgKHz0wfv8NWrL7Ji7h+EQ0GyC4ay/UGHsvVee1OxoYiqNSuo3rCekooKiirKWbZ2PaG6bwgGgkQiYaQZvfFVVRW73Y7b7cbr9ZKWlkZGRgY5OTnk5eVRWFjIpEmTyMvLS+qzZLOBCvKSLpBJlqZh71IKyaQYG9F2mWmUIZEEY55KX10Nv3//BQ6ni/df+R9+Xx3/euQdho4c32zbmV7NqvAeONwFnNTC+oXAY0TtvzZJ1qDcTkq5jRDiDwApZaUQYrDpWFj0M/SFRRhrK+Jh7srKKg65/hwuOLRjwuVd5ZaTL6KspoqLHr4VU5pxT5kiRPSiodmwqzYcigO35sRjd5Hi8JDi8LKxroyl5avxhf3YHU7yRo5i2/0PYoeDDmM77ximfF3a0DM7At45VUDU3lANSf7yuvgykNC9Zo/R2zP7tBe44INbOPSFcxmXNYK7/n4l2xR2v/KbYlOwZzToMi5bv5oLH76V2XN+YkhmFn8+/j6jhgzt8HYDq4P414aorzswgyaBVfHmDEgDghvCCWNsaRrLNqxmyqhxnfswvUiRy8b3Q1LieqS62nGhaIuu0apRKQRBzUFFMMSX/3uInz58l6JlSxGKYOj4iRx2yRXseuSxOJvKh7UQcWgsym4aBmlL5rB+1UrWrV1LcXExJSUllJeXs27dOhYuXIjf7ycUChGJRABwOBykpKSQlZVFQUEBI0eOZOLEiUyZMoXMzEyAWLV192ujNhU5txFGbcjeRaIQwgNAKOjntstOIBIOoWo29tj/SPaccRSqFjUloqWS0epuRRFsNXrgdXfahMmSUq5qulJKuUwIkZXMBpI1KCNCCJV4kw+RQ/wSYGHR/UjdSDAm64XL9566Excc2nHh8q7yyIU38siFNwLRcHVZTSXryzZSVF5KcWUZa+ZvoLSqgspADdXBWmpCdayuWk+GM5WLdjyR/bfdn6UnbYWpNeRAjX19dYMxGaO9vKqmyyMyCnn3+EdZVr6aSz68jUNeOItxWSO73bB0DXUgRDRP8son7mHRmuWMLhjO81feyWG77NOpbUpT4l8X6tiZRETnUlRRykn7Htqp9+1N5mW6mvfutjw2vU5To3LfU87k46ce5c/ZX1BZXITT42HsNtM57JIr2XL3vdqWC2vp+2u0Lk0Pkp+by9DcHLbbdts252WaJmvWrGH+/PksWbKEtWvXsmrVKn7//Xd8Ph+6riOEwOFw4E3NICMnnyEFIxg2egKjJ2zB+Mnb4E1N78wuSWCXfQ5n5uvRsHcKlU1yKL1IBMGAn1v/eTy5+cM479oHmu0jAaR7NJx2hVS3jdF5Lpx2S+pqANFWSzBPMhtI1qD8D/A2kCuE+DdwBHBtkmMtLDqMDETi0kCmaXLK3VeTn5XLbad2Xri8u1AUhdz0LHLTs9g6lsJZ5qlq0zAqK3ShGBKz0S/OVad325zGZo3gvRN6xrA0TZNHv3qJ+z94ltLqSnactDU/Pvg6W4ye0KXtSl12uNxcsUcv3LUBH7tt2fbFuj9Qa11Q+w1b77kP5z70BA+efQqzX3metJxcNt91D/Y5+UyGju/asdwYh6EnXQWuKAojR45k5MiRzJgxo9nzuq6zYsUKFixYwJ9Li9mwdhXLF//JHz/NIuCrwzB0hKLgcLpJScsgKyefIUNHMnzURMZutiVjJ2+N09m+/ushx57NG89Ew955ExO9ihEcgMIbT99PakZ2i8YkxDyTimD7CRlJfXaLfsfnMfvuWtmokkoIcRPwZTIbSMqglFK+KIT4DdiL6I3IIVLKhZ2YsIVFq0gpMTfWYKyrjIaQzGh4+bLH7qS8pqrLwuVdnVuoJEJoYxihCJz5dtAgtCGMGZYIDWS49fHpxUHSiwNEnBrDFlRjDxqE7QrOYPc6+hsMy1Vc8uHtXTIs60J+/jXrId6c/wmGNDl8x33412EX4w16UEKCSI2OLbXtU4g0JIGiEJFyHcUucBTYQYdgURhptmxQ6jbBugmplA914/DpDJ9fRWpFNDRohiQL5i1DSslofTjVc+vQ0lRchQ4UW/8TrZhS7mdRhpPRNWFyghFqbSqL0l3UWYZmn7Dg26/RbHZu//xbMvMLe+Q9qh1u3OEAhqpS7Yg6dtJCPmyGTqUzhYii4YkEyAjW0t5RoGka48ePZ/z48eyNlxqyaFzpHQ6HWbVkHssWzmHVsoUUr1vJoj9/5pevPybg92GaBoqi4nS5SUnPJCu3gPyhoxgxZjPGTdqaURO2iOZ5Ngp7T7n1HtzUEiCFCI7YT9Tkl+8+5dyr72v1HCwEZHi6PyRv0WtcCjwBLBNCzImt2xL4FTgtmQ20eTUQQmQ2WiwBXm78nJSyoiOztbBoi8jctZglNdAoDHz/W892q3B5Z5BSUvOnj0iNHvdChssjDQlDSaAZki0/3wgi2jdbAEYnOtYky9iskU0My7MZnz2SO/e7gm0KJ+ELByiqLaG4towSXzllvgrK/dVUBqupDtRSVFvK3OJFpDq8nLv9cZy/w/GoigolEDGjnlXVq6J5VUQrPclNXVL1Wy1m2Izvt1BJ2/stYlf48ZChhF1qND3AlKQXB/BWRlBiYz6Z9R1um5PIxug8dJ+BM9/RrEq8PzC+JsS4mmh3FhXICeis9dipsylW6LuXWTV/HrNeepZT73ygx4xJgIiisSYtD4GMSxDVxTrzACAEYVUlPeRDSjPp378jpg0pG5017HY746dMZfyUqS2OCQb9LF84l+WL5rJ6+UKK161i3m/f8v2X7xMM+JCmiapqON0epGny67efsabET3buUBr6hktKi9dRWVbCmIlbtjo/VRGMye98u1qLvkVK6QOOFkKMBibHVs+XUq5IdhvteSh/o+GaNxyojP2fDqwBRnVwzhYWLWJW+ZsZk6/M+pCH33uJWfc8163C5R0lUqEnGJNxOhqubTJG7YXajHrDcmnZSi6ZeTsHv3BW/DlBtNe1Ldbf2qHZcWtO3HYXqQ4vDx90IzMm7p64wdg+UBwKrgJHq8YkQHB9KMGYjNPG5149OY2QS0XGck09VWHyV/jixiTAnKIF5Hga7nVdQx0oNtHvjElouFmo9+lsdNsod2mWMdnLmKbJfacey7hp27HjwYf37JvVy1w1NhWbaFtmBWpRO2BMAtSR0aaET0s4nW4mb70Dk1tpAOCvq2HJgj9YvuhP1ixfwNyfv+bMQ6ay3W77c9619+P2RCu7f/9hFlN33AtVbfCpCkBVoqfs7BQbW4xMxe2wPO8DnZgBmbQR2Zg2DUop5SgAIcSjwHtSypmx5b8Df+vMG1pYtIRZXpdgTM6e8xNXPH4XM299vEeEyztCqDwy4EvQxmWP4v0T/keFvxopTTJcaV1KH7Cla1GPYBsXt1BZx/db6UhP3JgEyNoQaFYVvbh0JSMzGirK7dm2Ng3bvqbxzIrcNvQ+StvYlHnqyksI+n1c9PjzfT0VAFIigQ5HJkK46e54htubylbb7sZW2+4WX/f9l+/z2F1XcuK+E9n74BM45eJbmPvzV2y7634JYxVFsOWoFIbntFXLYbEpkWxRznQpZdy1IaX8SAhxSw/NyWITwvSFkFV+zJAeD4XOW7mEE+64nBevvofJI3tHGkZKSaRSx4xIbGkaqlNB9xnodUY012+QkOlO65btGH4DKSXSkIQrdTAltgwNoQn0GgMjaDZ1yiRFSnmIukwHRmUZ+urlVFarmCIdNIWyQjcIWF9bwi6jGjRI9ToDzd2/PSPVNpUKh0pECIQpsUlJnj+CKQTFbhumgLxABIchKXVq+G0983mEKclc78cWNqkc4iLkGfzdd5f8+hPfv/sG5/738eYSQH1EULVhMw1CikZQs6OZBm49hCkEPpsTAG84iEDi1xzoioqpm2CqaISxEUZHI4IDgRnruy0I4QIEdoIoGERwYGBrNqYtw3THPQ9kxz0P5P1X/sdL/7uDLz54mUg4yGmX3prwOgHYNOvmyKKBZM8mZUKIa4EXiAasjgPKe2xWFoMeKSWReeuivbnjHWXodeFyiBpH1XPrkEZ9Rx5QHAIzIjuUJ7kpodcaVP5UCzH5PQlggLCLaPW2iC53lElfl1D56StsKJ0LioIfQdmYzXEdeQpCRr+MylAt00ZuFR9Tt9CPYgNbuq3fhb1N4NshKRS7bQgpkUIwqjbI1DI/0Y700ZxaUxEo9R2hgM+GplFjV7s1NO4tDzH1ow0osWIoUxX8dPBQgimDt5BC13X+c+ZJbL7LHkzd5+99PZ0461Jy0AwdQ1URca17EylUROw4ML2gShkPnZvAsJpiPEYwfkoyEaixrMooEokS73YjkBhoKI1+jHWk4iOD9rydBx51JtN23peLj9sDb2o6mdmJAuyKAnlplhz1YKajtTLJ3l4cDeQQlQ56B8iNrbOw6BTG2grM4uqozqRhgmFSWVvDwded3avC5VJKav7yYYYl0ogakxCtJsYkahQN8HB3jxHbN9IgbjzKcKP91gnW1i1kY/Fc0CMQDoGqYP+/EzBtKoZdoTZYh2nobF/QUBzgyLOjpfY/YxJgYbqTYrcNQxHoqoJbN5la5keTYJNgl2AD7KbEFlu3KMNFna17jUlMyTafFGEPmWgRiaZLlm+dSdjVvz27XeXRi87GMA3Of/jJvp5KIkKgqxpSKJiKglQUTKEihcBUousQCkbseVNRyAzW4jaiXksl9lAxE5YVQMGMLwtARY8vG2j4SCeZ0Lmh69x22fEc8I/TuevpTwDQVIGmCBw2hZ02y0Dpx6kmFh0j5jSs/3+SEGIJ8JsQYpUQYrtktpGsbFAFcGHnpmlh0RxjTUVCzmS9cPk+03buVeFyw29ihCyLsb+wrOZ3DBmJL9smbR3L1YyyePZX2FQb3lhYEMBZaEdR++eFbVmaM0HYfGRtMKHAqJ7Gs1+e6sTs5gt1ekkQVU88zovGpSQI7Q82/vpmNr9/9hGXPvUimr0fetKa3jC0I5ieEaprpm/Z0lHSVjOEAN5WRiVSUVrMm88+QGp6FseedRVCCISAaWNTsakKWSn98wbOokscBvwr9v9dwIWx9MZtgfuBHdvbQFIGpRBiFi0E/qSU7fZ2tLBojJQSTInUG1xYhmH0iXC5NCWmYTaEbC36nIjZYEwqQkVoDkSsiEWYkqW//UKGq0nFf+MOeGY03N7bF7v6PNumxUFGPJ1DohgSzWj/SDPqNyElqowtx7YjjOjnkx00OBW9ISwqRfRh9lMjvDvQw2EeOv8Mpu07g8k77db+gN5CSlQMDKHFFmXUG69q8eNcGlE5LKFqCWPqUyKQMhoep0F6Skgz1sZOSXyNSPRAyxaXRKNlyerli7nh/CPYac+DuOTmRxt+SxJy0xyolldyU6BASvkRgJTyZyFEUpVXyeZQ/rPR/07gcKD72nxYDHqklOhLN2KsKo+GuGOeESkllz9+V68KlxsBg9olfvQqw8qR7GfsVXAMi6t+Y7h3Aim2DIyNBkU/lOMMQtY6Py//uoQsd3rCmJo5dTgK7ITLdMyACQq4hjlwj3D2uGFpBAzqlgSIVEWLymxZGqkTPYiYsfb3tVUsCApy51bj9BuYmsA/womnwN7q3PZfU8WKFDujayM4DRNdESxWVZzzakkvCSEFFI/0sGCX3ISK+LaoKHQxf5dcVN2kZJQXUxGouokhlGjj5UHGf84+BVXTOOPeh/p6KkDU4Num6gc2q52HKnVqtVQ+K8tn7edfYlZVgM2OfesdMUuL0FcuAQGOcZPZbZ/NmBhaiioN/IqXCiWfPGMtdhnEQKVUG47brCbVrEACVUouEcVBtr4OBZOASGGxaxohJXoTlkolKjo2wtgJARDEhYmKGx8geevT59hmu904/Z+3JXyGVLdqGZODm9FCiPeIXhWHCiHcUkp/7LmkEq2TDXn/1mTVd0KIr5Kfp8Wmjr6oKBrmrq+YjoXfelu43NQlVX/UISP1d/s9/pYWHcCmOJicsUPc2NJQGLrUBzGBIl/Yj8fmShCElwYE1zZqUyTBOcTR43OVRvNjyZGVeAyrpWFGzPfF83A1U+LObfvc7DYkk6tC8c+nhQwKfq7C1KOhfyGheExKxxRkhKB0pCf+P4BhV5tJMg0GfvvkQ/76djZXvfQ2mtY/qti3q/iKcb5FaDLqh6lZu571H32HWe+xDocI/zSrYYCE3YdUMqFufryS2mvW4jFrG44LdIboUblAEXtkmBvBbDg0QoqbiGiobBeAh9r4/wBOAgnLBx1xDKed8A82rF1BwbDR8bHZKf0wbcCiOzm4ybICIITIAx5JZgNJ3d4KITIbPbKFEPsCfSsOaDFgkLqZaEzGqBcuf+eWh3tNuDxcEo5Xc1v0T5p67kQjtUt/OIDH7m7TlrJn2RBaz4e9Q02OJWETOHJtce8kgG9VMKGoy5FnTyok3/jZYHG0TWX9uoBXo7LAhVQ76M0XIrm8vQFM0FfHY5ddwE6H/h/jpk7v6+kAYDNDjKtbGDcmAb7/rQS9jfOQ06EycXRaM1melvIjRRvLa+0TMYXWoTGOnAlst9v+/PDlBwnj1pQFkYPwBsQiipTyqyaPutj6jVLKpFz9yd6+Ne6YowMrgVM7M2mLTQ8Z1pudCftKuFz3G1bV9gDGHwlSkJrX5mtUl5Jg1PUUuj+xC5DqVJBmYlMUM5B4sKluBaWDhTCGL/GY9afYUAyJ2T+cb/2K+08/Aafbw8m33tPXU4nj1n2YQmmQkAAqq0Ntjkn12jBMmfQFujVCiqfDY77+/EN++vojrrrzuYT1uiGjcxrEubedwZM5lB2OubN33uy1D3ts00IIBTiRaErjMKK23lLgUSnl7GS2kezxupmUMtjkzXs+pmQxYJFSYhZXo6+rjHomG10Q+0K4vB5ngYNwdQSzzrrTHogE9RCpjuYXSaEJnAV2bBk2ICoBJXrY4HLm24lURdA8atTzSNThF6nRCawLYYbMaAPvRtnmgfVhFK+KDJiENkb7mjsL7AhNENwQxgyZ2LNs2LNshIrD0ZafTfIy0jcGyNwQIOxUGL6gBnvAoGSEm3UT0jDtg7dquz2+e/t1lv72M9e99VGv5GK3iZSM8i9lXN0ChJSoMlFHa5fpecz6oYhQuOW729KKIMtW15CRauf3+eXU1UUYMSyFCaPSmL+kknXFPjLS7GwzOZuSiiALllaiKoItJmbidmn8Mb+cOl8E95ivGLLlNArFelKMCoKKl/X2sYSUlBbft6qqinuuO5srbn+SCU36g9s1YeVQDm6eBFYDtwNHADXAN8C1QojNpZT/bW8DyZ5yvwe2abLuhxbWWVgAEPlzHebG6gRpIOgb4fLGKE6BqqmYVk3ZgCRqUHoT1gmbIH1qCoomEKqIV3o3zrPsCVSXQvo2KdGctZjXJlAUxLcs2KoX3AyY1M7xJRSDRSoTj0W91sC/MthqwZhqwuZfboyGzmMa8mrEZP3EtGhO5CALYyeDv6aaZ6+7nD2OPYmRkzfv28lIye6lHzE0uBpbLMxtkHg8Divwtpn2ICV8PHtdVIEidgwUlfr5/teNKAqYJmzY6Gf+kipUVWDEzrNr1tdFa7VjYzJDf7DPuBrsdg0FicespFQbRkh4mvUXB0hPT+fscy/g3ZceZfou+6EoDVXiw3N7vsjNok+ZKqU8Ofb/t0KIH6WU1wshvgbmAO0alG3exgkhhgghpgIuIcTWQohtYo/dgf7Rw8qi32FWB6Ki5U2Myb4QLm+KXmXEvD4WA5GQHiGtiWyQa5gDxSbiRp1QRFQ3r4fnEtXma3hfaUr8y1s3JhNoy0Eu23+NAiiywUBZNi0TQ20hR3ITQA+Hue2Yw0jJyOSYa2/u6+mQEy5OMCYh6qhu/M38+HsJoXD76v+NUxZNM/FvPUaj86wpE8fsNDUXu6bE9Sur1Tx8anqLxmQ9+x9zAdKUfPTm0wnriyvDrYywGCREhBBjAIQQ2wBhAClliCTLV9vzUO4LnAQMBe5ttL4WuLqDk7XYRDAr6ppVj/aVcHlTwpURK4dyABMxImQ4Ew1Ke5atmf5jX2D4OtkeqBuozHcNSvmf9vjjy0957JLzEIrgujdm9n2oG8gPrkOVbd+0rlxb2ysF9sMLvQndbKrU3GZFOk3RFTfnXXs/15x1EOMmbcP4ydFAZF3AQDesHMpBzGXALCFEkKhM0FEAQogc4IO2BtbT5pElpXwWeFYIcbiU8s0uTtZikGPWhTCrfJjBSEK4zjTNPhEub4xeZ6DX6tE+0xYDloipk+FKS1in1+ho7s61ENR90eNCsSvYMjSkLglX6AgBtkwNoQoiFTpmxERL1dDcavxYUpwKtvSGMTJiIvvoZsVbEaImt7n2sN2vk7U+gKEJyoa5B01nnKCvjv+cdTKLfv6B7WYczKl3PtDnEkFp4QpywsW4dD8makL/bADTlKxZX0edP4Jm653voaQ8yPCChhQRr1FJqTkMqSTuKwmEYnqUYFI4YixnXHYH991wNrc//iFpGdkIEe3fbTE4kVJ+KYQYAWRJKcsarS8FLk9mG23+AoUQx0kpXwBGCiGaWQJSyntbGGaxiSGlJDJvHWZRdSyuI+JeQCkllz12Z68KlyfMzZTUzPdFhactBjyGaZLpTjQo6xYHUBxR4y7ZHC8pJbUL/IQrop154t2STBoSgWK5kVLK+iYiKDaBWX9TUq+3YjQa00f3K9t8VMRPhwwlkGqLh71HzK1kzB+VyNg8a7Ls/P73wg532elvfPPGKzx/49W4U1K49rX3GL1l36byC2mya9mnDA+siHXPFqhNjMnqmjCvfrCCUNiINnnoJemytz9exbGHjCErI5r/mGOsJRRxs94+IX6cRLBRTj71QXkZ8wbsuOeB/P79F3z81jP849R/UpDpQNkEUyo2MTzA7kKIxlXen0qZ3K1ye7d09eWU3haes1w9FgCY66uiOZNm8+Sv3hYub0pgXShqTFph7kGBKU2yPZkJ6xxD7NhSkzcmAYIbQlFjsv7Gp/GTjWyBph5tM9zKaa/vot0ArNgmg5BHixsJqaVBxsypRI0ZLlLAvD2HDGhjsqa8jPtOPZY1C+ez53Enc/Q1N/WLEPf42vkMC6xEa1TJ3fQoee/z1dT5Ir1+0Zy+ZQ5pqY74byOgpFBkHxs/TiRQwZBoG8eELM/oTB0uN3aHEwDdtC75gxkhxJFEw95zgT2IFmNvD9wZcy7+2d422gt5/y/27+dSyu+avPlOnZq1xaBDX1PerAAH4NXZM3n4vZeYdc9zvSZc3pRgUdgyJgcREkmWMz1hnbPA3mHdyeCGwXVcbBiXmhDOLlhSg2hkDFflOjE6KoTej5j52EO8dd8dZOYX8K+PviJ/9Ji+nlKciXXzEgpwINE0q6kLU14V6hMPzBYTMxPE0Uu1YZiNanF17C0Yk8SXFUWNtxXYWBXGNGVCTqbFoOJaYHsppV8IkQ28KKXcVwixBfAosGN7G0g26eS/NJcIammdxSBBShltj6gq8YIHaUSvwCJ2YZKmBMNE6tE780AoyJvffEIoEqHaV8v9bz7DR7c90SvC5fUdSxpX3EoTTGMQWQ2bOC/+8R4Ap759FW8e+yBKfaVq7PomZUx/Um3oRNPsuIi9xhwk3ZIiSCKAbPT5QhETEWkwG/xIQirUe52EIVEMiVGvWSklWsREtylxz1U4YqAoAq2PjdDStau555RjKVu3hgPPvZiDz7u4T+fTEops3T0d0U1CIaPPiu+bOnBN2WA8mgiMBG0qiUDGDMxo7ntp8VrCoUD901ZYcnAjINaHE3xALoCU8k8hRFIeofZyKHcgapXmNMmhTCWqhGAxCDHKaonM3wCBMAiBkpeKDBvIirroC1JdKKkuzA1VUQ0LRUFKyRn3XU9ReQkTho0C4NXrHuhx4XIjYFC7yI9eGz2pa6kqilMhXBqJnv0GrlPGohERI8JNXz7I/uN348sVP7Lv06cw88THsak2qufU4RhiJ1IWiYakBThybBhBE70melyoKQqaWyVUMjiOiwCSewnwOTomkP36AkaPTmfu8gqCYRO7Ivg7KvMxWI6J2FDLlFlBrtA8FK7xgQR/mo2yQjcFy2rRIiaGpvDlWDePFVVQWhVECMHkUekcuMvwXjcsTdPk9Ttu4dNnn6Bw3ATumv0zGXn9s9vvB/lHslfx++RHiuLrqmvDfPLVOtYX+/pwZvDMG0s54u+jyM2OFmyNjMwnpNn4I2M6QS2agmQLm+TXVeDGB0gMNCrI44sP32DFknmccuEtAKR5VEvYfHAzE/hYCPEV8HfgdYi23iZJSd/2PJR2ovmTGtBYWr+GqJK6xSDDrA4Q+W11Qz6klNFim8ZUBzCrAw3LhsnNzz/EquJ1fHbn0zjtvdNESRqSqj/qkJGG+2a92oDqRh6DPs5ts+geLv/4LhRF4eGDb6SotpS9nzqJ3R4/ji9OfRYXTkLrG2nkSaJdaBph1JgYNY281QP8uLgWP39gUP8pNwYibJxfGn8+aErebhLTP2WdTh51KLFrg7cqgqeqOn6lKA3r3L1gQ9xFgZRM3yy7x/U8m7Jq/jweOOME6qoqOf6GW9n96L6TGUuGjHA5OXrDvtd1k5ffXY4/qPeKNFBbFOZ5yEhrOB+HFAe/ZU0nrNjj3ui8UCkugvHvOYKDsCF45Ym7+fsRJ7P97vsDkJnS+znwFr2HlPIKIcT+wCTgZinlZ7GnqkgyGt1eDuVXwFdCiGeklKu7MlmLgYG+orRRcU1yvPPd57w86wNm3fN8rxmTAKHScDykaTF4WVO1gbfmf8L9M65FVVSGpg3h2zNeZo8nT2Cn/x3FrFOfJ83Vciu5wch6TP7AoCMy08NRmISKvYl52HjpbcIJ/aPyMl3kZrpQe9E7+e6D9/Hef+9h7DbT+fdHs3GnprU/qI/ZqvqXBN3JpatqCOtmnxuTADtMzcXWSKJomWcChmhIbdAMHU8kmOCwryWdrz5+E81m49DjzouvX1MaZIsRKVa3nEGMlHImUU9l43Um0Hbz+RjJ5lD6hRB3AZMBZ6M32jPJ8RYDBOlL6riJ8+eKxZz/31t45+aHyc/M6aFZtYzhNwdVYYVFy5z+9rWMzhzOoZP3jq/L8mTw3ZmvsucTx7PTY0fx+SnPMiQluw9n2XsUYWKDDhmUhSjtNhtdiUljv25Gih2zl6wiXde556SjWPLrTxxz3b/Y67iTeuV9u4PUSFWCYV5VEyIS6R8nplRvolex2pZJRCqYkQglq1dRtmgesxfMpba6mtTUVP766y901cvShXO47N9PJBiPhiExTNCsZLdNDiHEPClluz1NkzUoXwReBQ4AzgJOBErbHGHRL5FSYhZXo6+pAFOiFqQh0twYq8qQvnC0GAcor6ki3ZOCqjY/e4QjESSSc/9zEy9+8T7PXnEHU8dP7pX5myGzQQqoXgewH3gCLHqGj5d8w8KS5Xx52vPNnktxuPnmzJfY56mT2e3xY/jkpKcZmVnYB7PsfooxeZkQf2EwEoUjsDMfg8/RUUjSXdCI39D5iQgeBG8SoQqT3bCxHRpvE2ZxrDxDg7jhuWxdDSvW16KpCj8vKCUYNpg0Mp1pm2Vjt3WfVVG6djX/+r8DiYRD3PDWRwyfNKXbtt0b/JSxM1ut/5yFc9ewZ0snQQAA/qhJREFUen0dikK833ZfM/vHDWwzJZtvfy/itW8Wsqr6dRYVV1FdUU5G3hCyC4cyKieLnOxs6urq2HbbbbFnjeW0S28lr2BEwrbsNoUBLBRg0Q5CiMNaewpIKoE5WYMyS0r5pBDiwkZh8K+SHGvRj9D/Wo9RVA2x6me92t/MIPttyXx2vuhoRuQV8MA517DFmInc8crj1PiiRTmvf/0xUkpmbL87fzz6DhOHj+6VuRtBk6rfaqNhbsuIHPSYpsk/Z97O38fvytis4S2+xqk5+PLU5zjo+bPY66kTeO/4R5mc17OFYD3NagzOwEeIqHG3BJNP0dEg7kHsaNAxDNwUy5OrTx9dSoj/EUKhQZu9sQ2kG5I3vlyFEA1ZMKoimD4pByllt4Q+f/7ofR679DwKx03gqpffwel2d3mbvc1qPZN5by6GgA+zn2k1zl9SxfwlVbw/ZwEbqmqYPmoou40eyaizLsMxPupwyq8rJy3kR8ROqiGcVJKXcIpVFZgy3GuFuwc3rxJ1HrZ0EDtbWNeMZA3K+vNYkRBiBrCBaH9viwGE6QthbKhKzJFscuiUVldw8PVn89Rlt1FUXsLFj9zGyuJ17Dd9F9aWFLH7Vtvx+yNvYbfZGJ5b0KsnGP/qgNU6cRPi1tmPENCDPHDgtW2+TlM1PjzxcY565WIOeO4MXv7HfWw/fKvemWQP8BAh/DQWc4nSOBydzK/ADPrxL/+JwIrfCJesAD3S/qB2qPXYuO7+7nFThYIBqks2suexJ3LcDbd2yzb7gsBXMzEDgQ7nnvcWy0vL+XX1es7cbTsK0qPqL5EPXsF76eaoikLW0GHkh/2UbCzGMAzyUpyMyfKyrDhMbcDA5VCYNMxLQWZSNoXFwOVP4G4p5V9NnxBC/C2ZDSRrUP5LCJEGXEpUfzIVuCjJsRb9BLPCl+DaqPbVcscrj3Pq349gaM4QHDY7Nz/3EAdsvwdH7zEDgHMPPpZAKNhnwuSNiVRa7RM3FSr8VTzx6+tcu8c5OLX2C72EELxy1H2c9c71/OOVizhuq4M4dquDmJQ7thdm273MRe+wA94M+/EvjRmPxcswakqReghUG1pKNrackShOT/sbaodJ47PouH+0Zfy11cz5/FM2LFuKaZr9outNZ4gsnQ9m/5MNCEZ0Zi9eznfLVnPE1M3jxiSAXl3JEUNScXrrm+ClM7SwIGH80N5Nibfoey4iquDTEocms4GkDEop5Qexf6uJtuRBCHFRMmMt+h6zLoRZUYf0h+OujZPuvJJXZ88kKzWd+958hiN3+zu3nHwRvyyexzXHnk1AQp0psKl2Uj12dAnVZjQokqZIbH0Q+dBSVMKhTdCoVMCeZUNogkiljhk00dJUNLeK4TeJVA++fXLmO9eT583mtOlHJj1GCMH/Dr2F2776H6/P+4jn/3gHm2pjbNZw9hy9A8dseSDD0vN7cNbdwwRUfm9H1yhcvpbq718jvHEZRnVJovGYO5qUaQfjnrAjmrt7q6T3P2QiQ7Jc7b4uFDZYsraacMRk7NBU0rwtS86sXbSAf//jIK7eZ2dufPdTnJ6Wuvz2b9ThYzAry5qtF24vtgnRsHJk8Tykv65X5qMbJu/Omc/PK9cycUguF/1tZ7K8iakEQgg0R+8pclj0HkKIdOAJYArRK/4pUsof2hsnpfymjed+Teq9ZSer+IQQa6SULSc29RDTpk2Tv/6a1OeyIFqAo/+1PhrmBhACM6Lz6lcfccpdV/Hl3c+x3cQt+PiXbzj8pvOBaPjwo0fex5M7LDqEhvBavQ2pIdnMJulgt7suY+om1XPrMOr6QbZ7L6GlqqRuHrvIiuh3YBoSoQiEACnBt8zfTHdxIPPz2j854qXzeef4h9mmoPPFXsFIiPcWfcl7C79gbtEiqoI1uGxOJmaPZr/xu/CPLWaQ5U7vvol3E3WYnI6Pta34Kc2wn3UPHo+wu3EUboZz5Na4x++A5knv8bm5HSpnHjqRVE/rmoQrN9TyymcrQETPQU67ypmHTMTjsrX4+pryMm448G9EwmFufPcTsguH9dT0ewQzEqb2qXsx1q6Ir7NvtT2eg49DxipzzKpyah65tVvSDlqcg5T8sHw1SzeWsayknJHZGRy73da47M33uWr7f/bOO06K8v7j72dmtt7u9U47ei8CgiJib9h7jbHXJJpiovnFqGkmMZoYTTTGEks0orHXCCICCgoo0nu7g+ttb/vMPL8/drkC1+seztsXL29353nm2d3Zmc98q43RRx7Nabf+qJmZ+g9CiJVSyul9vY4xIybIpx+c1yv7Ouqc8W2+ZyHEs8BiKeWTQgg74JZSVvfG+trr8m4OKzo3wTGLa2MJOPHYns2F27no17ezac8OHr/9Po4cNwWAuTOPYd1T71IXDFBY58eTPQgz/vU2vqTt/3uIZvbJlx/aF4mVCvq2ICB5QhKK1vTTVpSG1oLRqijhskNHTALc8ta9zBp8WJfEJIDT5uCiiadx0cTTAKgO+nhl7ft8sPlT/vrZc9y/6B8kO5KYkDOaM8Ycx/njT8Ztb9v61tM8R4SSVpzeJf/5BcLmZMAtz/a6m/jkmQNxO1u+bER1k//M305Eb/idnj1nIA57y1nhyRmZPPDJF/z6gtP5+SlzuOPZeYycdni3rrsnCa9YglFcWP9YSUkj6ewrELaGqp++eU+B3nOehJIaH++v2cSZk8dy/rRJpHo9qDYNI6qTO2IkxVs3o9ls6FGdIROncOJ1t/TYWiz6jniLxDnAVQBSyggdqzDWJboiKBMzAtmiHn1PRX02N8DND9/LhXNO5eeX3XRQMs2wvJhVwBFV8MuW5aKKJElAX3TgCu2LfKvqTtpStGZv2xp/d+HiQ+sz+fuyF6kIVDH/mme7fe5Ul5frD7+I6+Nu9H21pbz0zbvM37qU+z5+hJ//70HSXalMHTCe00cdw9zRx/SJwHyfaItXgJrPXyFSvJW8a//WJzGH44elttqGcfteX5NDVggYMzilzeLomt3OfW99xCO3XsvvLz+Pa+5/iKPOvbCbVt2zRFZ8CtGGb8w2fhqNf7hGdQVGRQk9ecmsDYUZmJbCzGGDsTmczDz3IgaMGUf20OHYnS4CtTVUFu0hOSub5MzsHluHRY+TKYRo7KZ9Qkr5RKPHw4iVdHxGCDEZWAncJqXslR6gbfXy9tH8r0AAfX8rb9E6B2Qd2lSNQVl5rWZmSyT7Hd0qMa0i4ydHBYnaB/cR0pRIQ37ruuLIRp+1NCTSlAhNIIRASomMSsxD6DMJREI8uOQprj/8YlJ7ofNNXnI2P5p9NT+afTUAW8t38e/Vb7Noxxf87MMH+OF7vyPF6WVc1nBOGD6Lc8efRLYnAxNJDRIPAls32urDSIJIoi38xiJlu6he/Bypx1yFPaN33cKaKtBUBaWFc4eUklDEIBxpmlAkZcMNkGFKQmEdl0NDUWLHcDBsYNMUbFpMcN74lyd49cHf8dSdP6Rk1w7Ou/2nPf3WOo3Uo8hwCDN6gIdAUervuGUoiPTX1Xem6SlqQ2GSXbEsbCklTo+HgWMb6nm6k1P6RdchizYpb8PlrRFrk/h9KeVyIcTDwJ3A3e3dgRAiB/gdkC+lPE0IMQ44Ukr5VFtj22q9+O3pZ3YIouanolc31Jk8/Yhj+Wz9V1x58jktjhmlScpMgzSl4eCoMUETkBQ/J0r2Xyh6cvUxIVm3NUi4JJ5M9C0LstCrDWrX+hGqqM9wFw6BPc1GpDSCNDmkPpMfvPMr3DYXd865oU/2PyJzCPec0NBqbkdlIa+u/YBPd37Jn5c+w28++Tt2mxM1fSCOgimkjD+eG7NGcjn2+v7YnSGI5E8E+TheUry5k7Jp6pS8dBf2vNGkzDy/0/vqKE67yllHD2bkoFiGsG6YaIgm1tFdxXW8tXg31XWRWJ3LA25kH563luEDklm3oxrDlGiqwqRhaWwtqqXWHxNjIwZ6ieomO4v9kHMmBecl8e7jf6Z4x3ZuefjxXnu/7UEaOoH3XyW8YnHsRHiApTi85H+YVZWYtZUYRfs7Fnfvjd/izTuo9AdIdjlJdjlYW1hMZjzxxjRNhh7Wf0IGLLqVQqBQSrk8/vhVYoKyI/wLeAb4v/jjzcRqVHZNUFr0b0SKq8l57MMvlzB2yPBWxygCspWmYjE1fr7c/5zsJXFXtzkQiw/c79I9dIxx7UavaZrtK0OS8L5GDtFD5DPZVLaD/21ZyjPn358w5WOGpg/kjjnXccec6wD4MFDOXes/oHbrcmq/+R/Vy1/lHkXjydQ8jhg4hXPGncjMwZNRRMfW/0uCrESvrzPZXERs+Ru/R+oRci7+TdfeVAe54tTh5KS7GtzcKjRO5KysDfPvD7cR1VuOu6j163y1ubL+sWEYfLmxaVb0pt1Nq5XMueASzj1jBn+94Qp+dd5p/Hze22haYlyu/O+8ROTrZQ0JNs0k5EfX9Vzy6I6ySv63bjOzRw7FFwqzt7qWQCTK0Kx0AGx2O5609B7bv0XiIqUsFkLsEUKMllJuAk4A1ndwmkwp5TwhxF3xOXUhRLvqYiXGL9SiRzB2VtT//eArT7Pgq8956Oa2b1YOtDy29bgnMHWTcGn0kBFMFq1z/ev/x4SckZwwYlZfL6VFXnO7cE8/C/f0swCYGzUZuWUFH236hIU7lvGfb94BAbmeLKbkj+OM0cdy8sjZOLSWM6JLMFmJ3mrUvH/9IoJblpN96e9Q7L1XXDo3w0VWqvOgmMnGITPL15ViGN0bxCsEzJqYg8Oez2/e+4RfnXcqPz1uJr96ez6e1LRu3VdHkeEQka+W9Vi2dpv7l5Jl23cze+RQTpkwqoVtTHZ8vZLh02b08uosEoTvA/+OZ3hvB67u4Hi/ECKD+NVXCHEEsZKRbWIJykMY6W/o+PvW5x/jcbkZNXBoH66o/ZhhSX1POItDmnlr3mNXdRGf3/RKXy+lVfYdkP1UYHNx4bgTuXBcrImEKU0+272KN9cvYPmeb/jfliXopk66K4Xx2SM5aeRRzB19DDmezPo5ijGx0XIapl5XSfm7f8Y79XRcgyf20DtrnlSPHbONsnLl1eFubxBj0xS0eExlzpACHlj0BfeceRJ3HDuTX7zyNgNGju7eHXYAs672IBd3b/Lxxm3srqzmrCnjWtzG0HVqSkt6cVUWiYSU8mugK+WUfgS8BQwXQiwFsoAL2jPQEpSHMEqaG6MmCEBxZTnfPbldxe57jGiNTnBPCCNkYkvRcOTaCZdGiVZFURwKroEOjKAZy1yW0hKT3wJ0U+fujx7mwglzyU/u3ezTMkxeJMwqDHJRuBgbuzB5jygKgtPRyEfhZaKUYXJgGeh5RBiGQiEmHxJFEYKzhkziwiGTkUQZhsnQ0j3o6z5mxa5V3P/JP/jl/IdRFRW3KwU1LY+UvJGECg7DPngiSjOWzJIX70RLziL9pJt650NpxJbCWrbsqWXCsLQmVsnSqiBLV5dQXBlExCs+dKeojERNPv1qH7Mn52LTFNyeZP6w4HP+cPn53HP2ydz2+L+YOOe47tthB1DSMnFMm0142UJiQcy9h24YLNy4jVuPn0WSo2Wrt6Ko5AxtPbTJwqIlpJSrhBDHAKOJBbdtklK2yyRvCcpDGHVgOsbOCqSU7C7di6q0XAuupwmVRqjbFKiPhzT8EUJ7I/WV0w2/SbRSb1pJ3eKQ564PHwQkvz/1x7263xJMrqaOAKAD2zD5DB2NhhjGTRiYNITwHmiXKkdyB8FWxxjZg3g8+xbcgEBQFw3ynV2fs3XXVwSLt1C7fhHmirdBGgjNgepJx5Y5GMeAsUTKdqLXlDLg5qd77oNoBcOQ1Pqj8USamKDcU+Ln+Q+2ohsmneyJ0S4KywJAzMUrRCwJ6K6XXufJn93OX67/Dpf94tec8J2OevK6jlAU1AFDYlbKbnb1t8X2sko8Djt5KU1zZYWi1BdQV212socOI3/02F5d27eZQE2Ur94v7utldDczgALiWePxyiLPtTXIEpSHMEZJDQhBNF7WYt3OLX2yDikl/i3B5uslHnhRssTkt4bSugrmrXmfP556B5rSu6eiZwjjp6kRXNI0IebAMtQtHb6tjbkWB05iYhJglU2jbsR0UkZMp3ERF91XQXD7CsK71xAp20lozxpkOICj4DA0T98kWCS5NGaOy2oSQ/n+53taTcDpLuYeObC+lFBjrvvDX8gdOpwXf3M3xTu3c/ndv+7xtTRGmibB9+aB0fvtTtcWlTC9YBDejEwioSCe9Eymn3EOxdu2snnZEhRVZfwxJ3LE+Re3WhrOwqI1hBDPA8OBr2k4RUogsQWlEOJp4AygVEo5oa3tLTqGWeoDKbHFsyMPG9Fy3E2PriNkIrs70Mqi33PLm/cyIDmbiyed3uv7/hK9VyIqDkNFbVQSYQU6wWa207wZeCefgnfyKfXP+TcuofzNP1C97BVSj+j9It+DspPQTVl/kZBSsq+iudV3LzZNIdXbcp/pM276PrkFw3j89psp2bGd2598vtcqA5jVFchorzUeaUJxrY9x+dnYnC5u+Pu/6p+fcOxJnHjtzX2yJotDkunAONmJvtx9XZ/jX8CpfbyGQwppSozSWvSd5fXFdSt9sQStcW2UDOophF2g2GKNqO2ZNpwD7GjevnO/W/Q9X+/dwBeF3/DImb/sk/1Po3eOv6/RkUhWozOPMH4kzXe0PpikMbNJO/EGahY9h2/N/B5dZ3OUVAYJRwzCEYPVWyr4ckM5Nq3nLV9R3aSyJtTqNtNPPZ1fvPI2m75cxi/mHkc4GBO6pinZtKuGZWtL2VVch5SS4ooAy9eVsnZbFVHdxB+MsmpTOV9uKKOmrmPiUHiSEUleUFVs4w7DccTxqL2U6FhSW0eW10NSqlUSyKJHWQvkdmZgn1oopZSfCiEK+nINhxIyHCXy+TZkxIgltcRvMDKSUwH448tPcunxZ/T6uhRVIW1Gcr2VUgiBNCW1a/zoPivz5tvIrW/dx7QB45k2oG8cEz/GyXYCbOzhvpV3E8JDiCgxd3jjeMv2kDztTAx/FZXvP4yalIZ72LSeWWgzVPki/PWVdSiioTuT0UudmV74YCs3nTcWl6PlS1TBxMn8fv5S7jnrZO44dgZ3vPwe/11WQzBiYBgSRYCmKUT1WLynqgreXCzjtchj72f5ujJuOHs0dlv7bjAUu4PU23+NjJcNEqqKjEbxPfswRtHO7njrzRLWdQLhCOneJKafcU6P7cfi24sQ4m1irm0vsF4I8QVQXypGSnlWW3MkfAylEOIG4AaAwYMH9/FqEpvomiJk8ODL1WtL/gfArPGH9faS6hFKzEK5P7YnuC+M7rfE5LeRV9d8QGHtPl67/NE+W8NrRNnRC03QTaBxye7OVC9Mm3MlRl0lZa/eR+6VD+HIHdFNq2sb0wQz1hur1/YJcMbswe0SeWk5ufxp0Rfcd+6p3Dd3DtmX/R5HXqw+owFEjYZzTOMOPvv/PvvowfUJR+1FaBpCVesL8oa+/BSjZG+H5ugoUsYasSpCwVdZ0eb2Fhad4E9dnaCvXd5tIqV8Qko5XUo5PSsrq6+Xk7BIw8Qs9zV5LqpHefSNF7j8dz8B4GeX9E1Lu/00DhQP74s0n+Vg0W/YUbmHH7/3ey79zw+5b8GjvL3xYyoD1a2OMU2TX85/mHPHnUyON7PVbXuSd4g23Hr3AzLn3o6z4DCKX7iDaNW+vl5Oj6IqgmEDklGV9gk9u9PJL17/CMfgSRQ//xP8Gxe3a5zTrpKX6e5c/GXjc9mKJaD3bFylPR4HHwyHWLPggx7dl8W3EynlIinlImDu/r8bP9eeORLeQmnRcaQpiYSi5F0+m2A4RGZyGu/86gkGpncqLKIeM2qCBMUeOwFLQ2LqEsXe4BIzIxJFE4j4XX9LY0wrSadfsq5kM39b9iKf7vySmpCPXE8mud4s3t64gOe+ep2IEUUVCl5HEjneLIalDWRCzmiOGDSJw/LH8cdP/0nEiPKLU39CFSZp8XvaMJIaJOkINAQmkkokbgTuFvp8hpHUxseo8TEVSDwIXPExNXH7Wmp8PyEkPiTRfng3k33hvex79nb2/esH5N/4JJo7pe1B/RJZ/43rukkgrJPksqHG3dR1AR27TcFhj1kwg2GdQNgk9+L7KP/fPyh/849EireRMufKerHocWuEI2aTDPXGidBR3SQY1vG4bPXucF8gitOu1ltKA6FYZrfbqTUZY8atoCaCgOrGYYaxye7NAt9TWU2Ky4nTZqvfn4VFD3ES8LMDnjutmecOwhKUhwhCVSDFRd3KSkJ7w8z75n2C4Vhg+xe3voqtxEZlcQ2OHDueka6YC7qdGCET34aGeEfFJdCSNCIV8TgiRWDL1IhW6EhDggR7uoYRlhhxt7biFmgujUhl37Qss+g8y3Z/zRNfvMxne77CHwkwKCWXyyafyU0zLiHdndpk24geYcXedXyxezVrSzezrXIPn+3+igeXPIUZLwQ9Zs5VXKCFEYQZgGAEKovjBXdswPFoLMGgjpib71xsfA8nSlxmRJE8TIj3iCIAB3AsGosx8MfHHIlKCZLtceE4BMEgVJai14uV/lbyVAhB7pUPsfeJG9j31C0MuOkpFFvvtWLsLQwT/vLyWgryvGzcVR2Lf1QE44elsWVPLcGwjpQwLN9LMKKzrzyWkCOQpJ94A1r6QKoWPIHvq3cZceqV3HLvz3A7bQgBKzaU87/lRUggGDb4+383kJ/pZtPuWOKiTVUYW5DCpt21hKMGUsLIQV5q/TollbH9ZKY4yEhxsnlPfEzq2QwTm9jmHooubEjgsJrVzKz+ottcgDvKKhmTl4Wiqoybc3w3zWph0YAQ4mbgFmCYEOKbRi95gaXtmqMTmeHdhhDiJeBYIBMoAe6RUj7V0vbTp0+XK1as6KXV9T+q39lG3Wd78QX9jPvLaQAsvfE/DE7Nr98mbYYXxaG0W1BKU1K1vBYz0p8uvRZdZf7WpTy14lVWFK0lrEcYlj6Qc8adzHWHX4jH7u7UnJe9/GM+K97I4Nteard9UANexUNa3AoJ8ABBPuhnLuvuxIyGKHrsGhRHEnnXP47Shw0LEhVTjxBd9hxlX7yLzW7n9Jt/wMnX3Mwjr2ygLhDt/hsJKetNnpoZ5ao9z+Mww90mKJ9dupIxeVkcMXwIF/ziNwyeMLmbZu6/CCFWSim70mKwWxicNVr+7PzHemVf3/vHCT32noUQKUAacD9wZ6OXfFLKyvbM0ddZ3pf25f4PJaQp8X9ZAiZsq9wNwJe3vEZuozg1W5qGsIkOWScjlTpmL2V2WvQdpmny1saPeXbVa3xTvAndNBidOZSfzbmeKw47G6fWcl3A9rCjcg9Ldq5gwHl3d8jZfBQarkZiMojkfaIt9r7+NqDYnORd8zf2PnE9Jc//hNwrH7IKWR+Aotn5zj2/ZXjuQ8z746954+E/8fbf/orniAtJmn5e99etbPT5j/BvR5Vmt4lJ3TDZU1XNcWOGI6VkxbtvWILSotuRUtYANUCndZnl8j5EkFEDGY1dqn/x0Z8BSHYkNdlGdSpNA4fagRkyreSZQxTd1PnP6nd5afU7rCvdihAwPnskvz3pR1ww8ZRu7V5z05v3kJM5BNfIGR0Sg7koTeo21iATP5OwF9A8aeRd/Vf2PnULpf/9FTkX3NPXS0o40rwO7E4HV/zyN1xy5y/5yx13sv5/L1C19GVSjrgI78zze6Qgulf3YWtf6+N28fm2XWR7PQzOSAWgpvjQTsqy6L9YgjKBiRTVUbtwN3pJAFu+B8+ReYQ2VBLcUIHituE5Kh8ZMahbVhyrPakJisqLWb1vIwBuu6vJfKGSCFqKCiYE90ZAShzZsSLjwaIwZsBES1Zx5tkJl0VjvbVV+legmUWrhPQw/1r5Gq+u/YAtFTvRFI3D8sbx6Fm/ZO6oY7r9AluJya+3L2V96TaOvPYxSjs4/l0ijEehFslbRDGgxzrcaMA52JlLLA7uHSLsxeQSHGQhWIHOs4Sp6qH9dxRbWj65l/2B4hfuoOKDR8g49ft9vaSE4p2lu7n0qEHMNiV5gQhzbv8Fv5lzFW++8jeql/ybms9eQnQkfEOAlppP0tij8Uw8CcXeEL/q0euYVr2KgaEidKGho2Drhjvxwsoa/rduMzcec0R8DQoDxozv8rwWFj2BJSgTlNC2air+tQ6px7Kk9bIgwdVlse43pgSC7NhUxGPLXyIcjTBtwHhGZA7hpKeuAmDepQ8fPKkJdRuDTbIRAjtDTQSjETQJl0T7X8aCRYvUhur455fzeGPDfHZVFeHUHMwYNJFfHHcLxw6b2WP7rcLkKvx8896fcA6bxt7MgR2eow64hxAqDX2ye8K5K4AHcTMOtT5DfChOVGjSOvGJBIvcdOSPJuvc/6Ps9d+geNJJm315Xy8pYQhVhzi3pI5Uu4oKpAA21Ub2yTeRetw1+Fa+jRnyt39CUydUuI7qT56hav4/UJLScA6eSPakY7lUrMVmRlDjJ83uOHWW1tbx+KJlnDl5LAPT4xn90mTC8Sd1w+wWFt2PJSgTlOo3t9a7sBsTjoT5+7J/s6FsGx9vW0bYiDkQn2yUq/TchQ9w5OBWipjLFv5uaRuLfkdpXQWPL3+J9zYvoqi2BK89iVlDpvLn03/O9F7qTjOPCLu/eA0jUEP+mT/p9DySBjG5/3F3Mw2VsY3EJID9AOn6DGFabwjYN7hHziT9lO9R+cGjqElpJB/WrpJxhzxXD0zGoyr1TTbX+SJ8XhVGl7EYy5SZ53d67kjZLnyr3iW0YyU7N3zKL4UgJ9nD+AE5zBo+hGRX17PvA5EoKS4nM4c1NPRQVJWNSxeRP3JMl+e3sGiMEMJHK6dXKWVyW3NYgjIBkYaJXhZs8lxpXQV//PSfvLzmPQCyPRmEjQgXTzyd24/6LpvKd/D6uo84dtgMZgyc2BfLtuhjdlfv5W/L/s38rZ9R6q8gzZnMscNmcssRlzMma1ivr2eZHqBi8fN4p56B4vT0+v47wiQ02pIAX2EkbDixd/IpGP4qqv73GFpSGu5RR/b1kvqc2elOHI264KysCWN0U1UTe9YQMk65BYArdj3Hzp2bWbGziCVbdjJ//VaS7HZOmTCSWSMKOr2PNYX7GJOX3eQ50zDY9c3XXVi5hUXzSCm9AEKIXwHFwPPEnDeXEysd1CaWoEwgDH+U4NpyzHAsHpKoZNmer7nwxR802W7xDS9RkDaA297+DQu3L+NPc3/GwJRcThhuXUS+bWwq28Hflr3AJzuWUxWsJSspnZNGHMUtR1zOkEblovqCio8eRwiV1OOv69N1tIfV6NRiI7UVh/oMVD6gewtWdyepsy7B8FVQ9sb95Fz2B5wDx/b1kvqUhRUBhrtUNvh1vqwJUxTU0QR0dwW03UkFTB7sJ2fkNIodOQhfGeuXvc8bX63ng7WbOW3iaI4cPoSo0NjuHopfdZMbLiEvXNxq+EZlIMjwrIyDnvekH/ychUU3coqUsnEs1GNCiOXAH9saaAnKBCG4qZLKFzYAsK+mjFe/eZ8/LvpnvEwz/HTO9XzviCtYX7qVgrQBANx5zA3MeOwCNpZt7xMLlEXfsKpoLY8tf4mlu1fhC/sZkJzD+eNP5eaZl5LtSYyLTbm/itVr/seUk79HZQ9k0nY3KzF4iTA3NiqgfiA/xMkm/OxI4HiQjFNuxairpOSlu8i/5hFsGYP6ekl9xl931vL0Hh9RCbqU2IUg0AMm5k8zZrMsbQamUDGFgppmMCF1MOeVLuGNVWt5fdU63luzGe8xV5M8eA6GULGZEc7b9xYZ0cqDjrYF67eyfMduqvxBzpjU9KZAczg4/KzOu+otLNqBIYS4HPgPMRf4pbQzF9ISlAmAGTGo/PeG+pjJm1+7mxVFaxmRPphzx5/M94/8Tn2dufE5I+vH5SVnU5A6gPs/eZxnL2zz5sGiH/Ppji954suX+aLwG0LRMENS87lm2gVcf/hFpDjb5Y3oVW558x5SvZkEppzc10tpF/kIrm5FTEIsJrQogcXkfrLO+wXFL/yEfc/+kPzrn0Dzpvf1kvoECfga1dDVe6qJh1CIqA72p4t5dD9HVH+JZtO4dOYUzp06gQc31lE6/5+UL3mJ1Dnf5bghmaTqNQcdbVtKynl/7SZOnTCKY0cPR9NUFFVFs9mQEuZcfhUFk1qJj7ew6DqXAQ/H/0liXXIua89AS1AmAOGt1fX1IauCNawoWgvAwutfaHPsdYdfxH0LHsE0zR6pqWbRN5imyXubF/GvVa/x1d716KbOiIwCfnjUVVw19Xxctq4VGu9J/rv2Q5btWc2sy/5IYV8vpp0ch422+s28108KqgshyLn8j+x78hb2PXUz+Tf+E9XVZjy9RZdokIbDA9tjnXPiRFxppJ1xM55TI1R8+Dcq//c33rPbUCaOapJwE9Z1/rFoOePzczhxXMxwoNlszDz3YoZNPZz0/IFodnvvvSWLbyVSyp3A2Z0ZawnKRMBsOPmc8sw1AOz+6aJ2Df3OlLO5b8EjvPD1W1w59Rwg1k2kEkkW4qBMVYvExTRN5q15nxdXv8Waks1ICeOyh3PvCT/g0smnd2uh8Z7il/Mf5l8rX+PymZdQMWg8/aUqfhRZ/0spj685M15CvQ5JLRIjAayT2QgiQHUba1EUlbxrHmHvP29k75M3k3/DP1EdnWuZaREjy64gEJRGmvf+CWni1evQzGjTs66UCCSK3U3+6bdhO/Y7qO/+ildXrmHBhq1cd/QMspM99dbxgsy0xkOxu1xkF1ghTRa9gxAiC7geKKCRRpRSXtPW2MS/Qn0LcIxIRRqxi9g+XxnXTrug3a3UFEVhdsE0/rliHpdPPZtHCfMWkfpuIj/HxbFoCEtYJiQRPcLzX7/JvG/eY2P5DlShMDlvDA/NvYuzx57Yb6zOhmlw4Us/YEXROrLP+ikrxh6NjtlvypnOI8puTIqR7I0LyoEIslFYiYFKTBr31fsZj8rdOMlEQQFWofNzgq1aTBXNTv61f6foH9ez76lbyL/hHyhdbKH5bWSoS+PBsZkMT7IhpWRbIMpV35Ti0xuOhLG+Dcyu/CzWclGaiEY3Ul6jjouLXqHGlsKgYBFSCMQRU6ibNJJnFn/JAx8sYmBaCmr8t14bbFrrdMT0I3rnjVpYxHgTWAzMp4N9JCxBmQAIh4rithGo9AFwVMG0Do2/c86NnPKva3jIt5cPvd760sujUDjSEpMJRTAaZmPZVj7duYLX133E9so9ODQ70waM58nzfstJI47q6yV2mDJ/Jaf961qqIgEGX/0IZA0h2PawhGPZAefO7Ui2x5/rvkZ6HScDwZ9x4270O36WcLtsv4rdRf51j7P3ievY99T3yLvuMRTVOu23F4cieGFKDqk2BVUIQPBcoY9Io9jMQYE9HFOxBJtsqADQ+KZDAGl6DWn7YybjL6a4nPzw5KP5/XsL2VNVg9tu4+rZ0xmbn4OixmInZ1/6XZKzmpYOsrDoYdxSyp91ZqB1ZkkAIrtqkSGD+z95HKDDomJczgiyktL516J/knbGj+qfvxR7kz7IFj2HaZoU+UpYX7KVLRU72VFVxN7aEkr9lVQFa6gL+wkbEUwpUYQgye5m5sDJ/O6UHzOrtSL0Cc6KwjVc8vIPyfNm4b3u70QP6B9v0XVOPyC+czcGmzHbXcBIdXnIv+4xip64geJ/3Ubu1X9FUdqKGLUAODHDhUMRcTEJdbrJ+2WBJqWHptesaiIm4eBuTs3d0ktT8sG6TZTXBZgzaihnTRmHomkcfsZ5aA4Ho46YTXr+gO59QxYWbfOOEGKulPK9jg60BGUCYNSEQUh2V+/r9BwXTDqdx1e8QhoNgnIAapO2cRadIxAJsbF8GxtLt7Otcje7a/ZR7CujPFBFbaiOYDRE1IxdUGyKhtvuIsXhJTMplZEZQxicms/w9EGMyRrO6KyhOA8Rt+Nzq17n7vl/4YThs3jo3N9wjtKBNnYW7WYACo5Gv+NSJBp0qAmkmpRG3jWPsu/JWyh54afkfudP7Q6r+TaT61RxKA2fU2XUiInLRkk3Xt3XrrkM06S4xsfmknJW7CykpLYOgPOmTmDWiCEAqKrGxBNOJiU7txvfhYVFh7gN+LkQIkzMOSMAaXXK6SfYB3iRuuSSyaczf9tn7K7ey+B2FKU2kbxDhNeIEph1HnLZC/i3LCdpZKwm6e8J8kucjLC+5mYxTZNCXwkbGlkVi2qLKa2LWxUjASL1VkUFh2bHa08izZVMtieDsVnDKEgbxKjMIYzPHkle8rfHNfXj9+7nlTUf8KPZV3P7UVchkdyEgycI94tM6P7EPwmTDGzGZD46GnQqpMCWkkPuVQ+z75nvU/KfX5BzyW8OOVGZH9rL9OqVpEZrKHbksDz1cGrsqZ2e7+W9dYxNslEeNXl1nx9TSnQpGRQsZHr1Srx6HTIeMdlatPOm4jL++ekX9Y+HZqZz07FHUJCRhqY2jFRUFU96ZqfXa2HRVfZ3zOkMltJIALRMF2qqg5NHzAbgzg//xIsXP9TmuN8SZBF6rL+wZsORN5qapS/WC8pKJOkomMhW6+t9W1hZtJb7P/kH68u2EoyG0M1YfFy9VdHpJdOdyqjMgkPWqthVwnqEc1+4hQ1l23j6/Ps5ccQsAASCPOsY6xHKkdxFCI2Gnuad/aTtGQPJ/c6DFD/3Q8pe/y3Z5/2im1bZ9wyv28ZJ5R/Xu58VaRJWHTFrYieFc50huWNjJaqA/Tk4430bOLpRzOT+ZK3WcNpil9pB6SncduLsZrfR7A6OvvwqVM26LFv0LUKINGAkNHSklVJ+2tY468hNAPTKEEZNGCEE54w7sV2u790YLERvYg1KOfoKSl++GzMSQLG7uQAbSYhvtZgM6WH+uvQ5Xlz9NpXBGkZnDeXWmZczNns447NHkuO1rAHtpbCmmNOfvR5Dmiy87nkK0gbWvyaRPGxZJ3uUxlF6Xck0d+QMI+fS+yl58U7K33mQzDN+3NWl9T1SMqeyaWLM1ymTiCj2TovJ+qlpEJOKNJhV+VmT/bSnDsOQjDQOLxjY1CIsBMmZ2Zh6FG9GFjPPu5jh02Z0aa0WvU+2O8Sth23qlX19rxf2IYS4jpjbeyDwNXAE8DlwfFtjLUGZAET2+BCqQOqSN9bPZ3LemDbHrIuXMmmMq2AKwuGieslLpB9/LYejNYm9+jax3xr5ZdEaXJqDM8Yez8+PuZF0d2pfL61fsnjnCr77yk8Zlj6Yt658HLfN2eT1OmIWcYv+gXPgOLIvvJfSV+5B2N1knHxzXy+pSzjNEE4j1OS5QudATNG9yUce3Y8qO1db1ZCSdFej342UCCG48fHnuml1Fhbdwm3A4cAyKeVxQogxwH3tGWgJygRA9dpBNgi/M8e0fCOwHoOV6FS0UDTEM3Y24fUfcvq1A6muG0q0bAp6yk4C6RtRIx68JYej6odmJm69NfKbt6kMxKyRj519L3NHH9vXS+vXPLb8Je7/5HHOHnsCfz3z7iZWlnJMFhIl1KgwuEVTBDATjVEo7MNk0QGehZ5kWLx0WAjJQvQmot81dCqZZ99F+Zv3ozjcpB3z3V5aVfeRpNcxwr8dTR5c2GmYfzsVWiq50TIGhPYSUF1sTRpORGkjfEVK8kP7yA/vw6+62Zo0HKcRYnhgR6xouexQab5G00octqaXXG9GRqfmsrDoQUJSypAQAiGEQ0q5UQgxuj0DLUGZANgLksEu2H+V2Vax+6BtTCS/IsiS+MXIxsFZnunOSn75031ce1mAMfprJE/0sgMFhIkUUYRpI5iyldz11yDa5ajpH1jWyJ5BSsktb93Luxs/4e7jbuH6GRc3ef0jItwfi+DFoH8UMO9tXMCjJDEIBQcQAtIIMa8XKlvehoMzsdfHXs5A5y6CTW5Fk0bPQs69nYp3/4KwO0k98uIWZks8Rvk2c0LFJyBBxFNjJA3xjIfXrGRK7TcoSFSpYwiNzHA5n2Yc3aIbXJEGZxa/R264GE3q6ELjmPLFiPjRHatEKZvsp714HA7WFBZz3JjhsSeEYNrp53b8jVtY9CyFQohU4A3gIyFEFbC3PQMtQZkAyKgJYRMZL0UxJuvgNlufoLNkfwIOzZcMuWr8SwxMCzBkiI1nnqrggT85MBvFowvdRs7GKw8JMdnUGlnNqKxh/P2sezl9zLF9vbRDgkA0yBnP3siu6iL+c8mfmTVkapPXa5H8npAVM9kGV+GgoFHZn0IM3uwFMXkYKmdgxxnfr0Ty6wPE5H48E07ADAepmv8PnIMm4hw4rsfX11WcRpATKj5Ba8VaqAB22dAGMajYWJo+q9WYygm168kLF9fHSNple6t9tkxY1/nbx5+zt7qWaUMa6koqikJVcbuu0xYWvYaUcv9dzr1CiIVACvBBe8ZagjIBCG+tBkXw4uq3Abh8ylkHbfMeEUIHPduAJqKMSduCqkguviSVB/9Uhq6baFqDePSUTwbROXdNovDV3nX8duHj9dbI08ccx13H3EhmUlrbgy3axfbKQs587gZsqo0lN7zUbDmkZegHxfBaHMzJ2JrEMS8g2isiPLbfBr5poxN58rQzCGxeStkb9zPw1ucSvpxQQWBXvLFn6zTeYoe7oN7S2BLj6jYcVKS8K1TUBbj/vYUAnHvYeI4aWVD/mmkYrPtkPoefeV637c/CojuRUi7qyPb931R1SBA7yRX7yrlo4lwcmr3+lQCSXRjobZwIJQ21dk88MQlVFbz+Wm2TbUyxv0Zp/yKkh3ng0yeZ8shZnP38zVSHffz9rHvZ+KMPeXDunZaY7EYWlX7BCU99h6Hpg/jilldarK1pp8HFnYUgvx8eV73BfhkXjv+Ow70UGBA5YD/tsRxknfN/mMFaqhe/0DOL6kZ0odLRc5lqNtxMe/Q6kqO19SdNhxEmLVLVpGB5d1AVCADw+/NPayIm9yO7eX8WFn2JZaFMABwj0pCGyZJdK7hyaszabCD5KyHeJopGLLxS0HKcmiFt3LH4Pu4+4gHSnbWceKKHf/yjkrfequW0uV4uuCCFutwv2Sc18tdeh+gH9iXLGtl7lGqV3O19hPcf+hjPGA/pt2dSu8tPpm5vdvtj0EjHhQuFwSiYQAUmt+CnsneXntBchp8jUVmOgQK9FiLwl3gJp0uwIxBMQuXXuPgZwRYd7qrLQ+rR36H60+dInnYGagL/zrYmjUA1DU6s+ASlnSJ9jH8zXr0WlxkmRa9FIggoTqrsaQwM7sUUCoo02ixS3hFqgjG/kqIcLH41u50Jx57YTXuysOh7LEGZAAi7gmmHDWXbmFNwOADPEOaduHusvRehk4csJMkW66Hxox9ncdbZyTz/XBUv/ruaZ56uYthQO9ePS+OSsRI1AW3TpmmyomgN721axBsb5tfHRj561i9bzXy36BomJj/Jf5DFd36OLcPGoJ8M4vTyOXgMd4tjBIKJ8dOHiFuKXiOK1XyxKRFgEb0fZpKD4Jy4mITYd/QY4RZqQzSQcsQF+L56j9L//oa8Kx/s+YV2EoFkVvVyOpIKJoAB4eL6vwFSjDqSg3Wxx/F4zO6yGdYGQ7y0fDUASjyEQLM70KMRbA4H2QXDOOy0g8ObLCz6GiHEEGCklHK+EMIFaFLKNnuMWoIyAYjsrEVGDPyRIFJKJJJ5RDrUq1dgcvygJTjUBvvDiBEO7vtVrCfsihUB5j2r84v3HuMX7z3G1Pzx3HrE5Rw//MhufjftQzd1Ptv1FQu2fcaqvevZWVVITagOIQTprhROGD7Lskb2Et+4NrPs9yuQpmT4vcNRhcpZlcfglK2XVxGNXI46kjc7eMxa9BxnYW/ig9iCwR7MdknbrPN/QfEzt+Hf/DlJo/rm/NAWQ4K7sZnRDlsSm3OSH/hcR4M3pJTsrKgiEI7y9ur1lNcFcNk0gtFYLObI7FhpIM3hZPSRs0nJziF/1FgGT5ic8LGqFt8+hBDXAzcA6cBwYgXOHwdOaGusJSgTAKM2gqZqXDjhNB7+7FnuOem2DvfqtatRNKXlYPLp092ckHsMmesv4Y01i3hq5atc/d87sat2Zg+Zxu1HXdWuguqdIaJH+GTHlyzcvoyv921gV3URvrAfVShkJqUxImMI10y7gFNGHs24nBE9sgaLlvnVq48S2hdi5O9HojpVNFPFLpt3dbdEEPrADmfREvkI7I2kUTlmu4NcHNnDcI2eTcW7f8Y1/HAUNfEuE0m6Px5o0ffc8cp7Bz2X7HJy8YzRTBiQW/+cEQmTmpPHEef1n9JMFt9KbgVmAMsBpJRbhBDNB9MfQOKdKb6F2Ad5MXTJ4MPm8peXf8qaOZeS5HBR18Y4gclxgxZz7KAl2NUoYcOOW2k5F7wm7zMc1UM5bjBMvTGLiJ7MOy85eOOTzZzx3A14klTmTB3Ij2ddS/K4bfgz1qFGkknfMRdv6eFNLFItEYiE+Hj75yzcvpxvijeyp6YYfySApqhkJWUwOnMoZ4w+ltNGz2FY+uAOflIW3UWlWsNLme/zn4/fYee63Qz76TDsmTERGVV0Hsl9kRtKzsctXe2azwP8AAd/J9xqNQKL3uERwqjAOkw+IYodCHRgfOYZP6Lwr5dQ+eHfyJx7Ww+tsvNs9IwmO1xKRLGz3jsOQ6iM9G/h8KqV2Hrp1sYXCnPfW/MBGJ2bxXdnTUNTlGbjJQE0h4P8UT1z025h0Y2EpZSR/dZzIYRGOyNBLEGZAGjpTv6YajLfGIotbyQ71i0gZeoZbY67YeKzTM5ah0OLRVkapkDKVsqsqVFKxz0LCFBiRTfOvEHhzJu81PldvPjvGhYsKOSExb8kI0PluOOSuOKKVLL0pFi5Idn0cKkN1fG/LUv4dOeXrC3ZTGFtCcFoCLtqI9uTwdjM4Vw8cS6njprDwJTcZpdk0fv4FD/fG3o/u1YVsvvd3eR/Nx/3mKbxktWaD7UDiVsCQSoiQWxGFhVI7iaESucsx4pmI/3kW6l498+kHHEBtvQBbQ/qRQxFY2HmMSjI+vaKPsUTO/f1QuL0+2s2sWDDVgCmDhnAZTOnNN1ACBRFwTRin75mt5NdMJxB4yf1/OIsLLrGIiHEzwGXEOIk4Bbg7fYMtARlArCrws9HNX7CgDQMUNQ2L8x5ScVMyV6LvVHMpKq040yqSJqccdXYnjwejRtuzOCGGzMoLo7y3HNVfPBhHa++WktB2q+5ZOIZpLqSWbJrJetKtrDPV0ZID+NQ7eR5sxiXPYKrpp7PqaOOJttjtRNLZN5J+5SyPRXs/sdu0k9KJ/3Y9KYbSLip5EIcHXB7SySPxDOLLRKHrtjqPBOOp/aL1yj9768YcP0/um1N3YZo5PSWktnVn7da6Lw7qA4E+c07HwMwMC2F20+aHV+LIDkzCxB40zOYdua5VBUVsnbRAoSA8cecyNS5Z1sxkxb9gTuBa4E1wI3Ae8CT7RloCcoE4JvCGjRVENYhvG8zKUdc0OaYguTdmLJnTk65uTZ++tNYyETxp5N59sVi/vr5c5hSMiA5h0m5Y7j1iMs5ecTRpLmTe2QNFj3HMuMbNv1+M0ljk8i/LP+g121SIyua3szIlqkDqqzmi4ccWRf8kr2PX0vtqndJnnp6Xy+nRZxmCIfR87czCzduA+DY0cM4Y/LYhhekxDRMbnzsXw3PHQ4zzrmwx9dkYdGdSClN4J/xfx3CEpQJQG6KEyQk6XU4nS5KX72Pad/7B1WuHEYEtpMaqabCns52dwE5kTIGBfbgjdYSLVBxent2bYOnVPK4vAN7OLNnd2TRo0RElCXer9ip7OW9++djS7Ux5MdDmt02KnTWO7cxMTSyzXmLMfmYKEGk1SXhEMSWnI136plUffwkngnHo9jbF1Pb24QUB2X2DPIipT22D8M0Wb1nHz877ViyvEkHve7NsM6RFv0fIcRRwL3AEGIaUQBSSnlwT+gDsARlAjB9SBqT9V1MLnyP4qOm8pf/fcJx2+eR4U1ComCXUSJonCBi36wmdfRalcovU/EcG0T04JU84t1D1ZAPyN582SHRA/zbSJlWxQ8LHiCgBFnzq3VEdZ1R945CUVr4PgU8NOB5Ht1xF27T2WIy1vtEeIAQJlixk4cwaSdcj3/dQsrf/CPZF97T18tpHqGwIOs4Ltr7327pv90cRVW1eJ2OZsWk5nAw89yLemS/Fha9zFPAD4GVdDBqxlIICUA0FGTGng/RpEFdMFYaOsttR5MGdhmLkbSjY5OxfwJw2iIMmlPco2ISQA2nkrX1IktM9mMezX2JKq2GTU9sIVwYZth9w1DdrSfc/GDfpThNe4tisgaTBwgRAXSwROUhjBCCzLN+SnD7CsKFG/t6OS1yQvknaD0kJgH8kQheZ6w2qxACRdOwOZzY3W6OueJahk+b0WP7trDoRWqklO9LKUullBX7/7VnoGWhTAB2rfkaVVUxiNUvg1ix3NYK7noH1SHNnk9p9JRP7vF9WPQcJiYrPOspea+U2i9qKbijAEdW6wXLVakwKTCq1SzvZej9oHmnRXfhGnoYjsETKH3jtwy89bmESy6xm2Gyw2U9dtsrpeTTTdsZnRcvxycUDj/zfCYceyLezCxUzbqUWhwyLBRCPAC8Bg29KqSUq9oaaP0K+hDTMKjcW0jI19DRSIu7Ic02NJwZ7R2LoamEsQRl4mNgUGgvwWU6ydZjCTWlWiUBJQhIyt4pI+PkDDzjPG3OJZEYmKio+JQA5bZKcqKZuE1n/TbORrc3ma4KbEqUYn+2FUl5COOdeiblb/yO3Q+dj+L0oLq8qEnpqMlZ2NLy0TIG4cguQPFmtRxO0UOYiPqzVFBx4FeTSNFrsXWTxTIQibKltIJjRsfCyISIlQJKzc3rlvktLBKImfH/T2/0nATa7H9sCco+YuuXy/jw8YcxdB3T0DGiUcJRnec+X8XciaOxa63bf2p3e9i9MJeCk/b2qNvbl7cMTI289VcjLJtUQrLM8w0P5T1PVOiYwmRgJBsJFNlLUaRCaFcIM2iSdXZWu+YzheTSUT9jTHAoa9xbsEkNXRjctvdyjvPFCtzPQeNBl4+KKY+S6S7HlIK6SBK//eJH1EZSevYNW/QqpmlS+d5f8K9biHP4DFzDp6NX7UWvKcXwVRDdtZrg5s8wIyEw4wJOURE2B4ojCdWVgupJx5YxEPfo2TjyR3X7GnXFztODryQrXM4+Vx6KNJEITi6dz7Dgzg63UzwQl82GEJDljd2QCSEYdcRRXV+4hUWCIaU8rrNjLUHZB5Tv3sm7f30APdLQ+diUkkc//gy33cZxY4a3PYmAAUf1XEZj/W6kRvaWi+l4h1uL3mCXfR+/H/AUYaWhHul2R1Hsj/hXVvxuCbZMG5q7/T/3gBpiVdIGEBBFZ1RwCLPqpjTEVAoT9+EPYLfXIOL1T/+75SyCemJmAVt0jkhFISUv/RwZriPrvJ/jHtl2b2/dV0G0bBfR8l1Eq/ah15ai+8oJ79tE7RevgxConnTsOcNxDZ9B0pijUZzuNudti7DqotA1EITAEJAf3MvgUGG3nLkkEikhxRUPFxECd3JqN8xsYZFYCCFSgHuAOfGnFgG/klLWtDXWEpR9wKoP3sHQo02em79+Kw5N44Y5M9sVn+Qd4Ee1mz2elJNUOgVhalZSToLyTtoiouKARLwDDp+6tXWkn9CxupIHznNOxfHYG3VK8mesRWqhejGpmwqLi44ganasB7hF4lLz2Tyql7yAPWcY2df9HdXZdrgEgObNQPNm4Bo29aDXTNMkvPsbAhuXECpaT3DBP6j88BGEzYWWno9z0ESSxs7pvBWz0blzas3X3ZakUx2INRTVTYmmghAKGz/7lCknz+2W+S0sEoingbXA/rIF3wGeAc5ra6AlKPsAX3kp0mzIid2wr5Tl23dz24lHoantE26aS6c3Yhu1SCpCWq7uRKXUVokpWs6vDu4OYgZNMud2rUZetp6G0uimQndUIxsdfxHDgSmtm45DASNYR8l/7iJatovUY75Lyszzu21uRVFwFUzBVTCl/jm9rpLA+kUEd6zCv/4TfCve7BYrpkev6xbr5O7Kap77bCVzJ47GaYtdMvVImLqqdiW+Wlj0N4ZLKRv/6O8TQnzdnoGWoOwlCjesZem8f1NZtAe704WiqpiGQbnPz8tfrOa7s6bVZ3i3hC0pSu70MpIH+TF1gVB7RlDaAllkbD2PpMqxmGoIzEPL3e1XgryU8QGLUlagSYWTq2ZxYeVJaP3w5/C94kv5o/I0a5O2Nft62dtlHXZ3N8ef8p/lZ4XXMCY8FIDkvbMIeXahGA5Si45FmDZ+pKg8akCwS3uy6Ev8G5dQ/s6DqO4U8q9/HFvawZ2UuhvNk07yjHNJnnEu0D4rZvLM89A8rVvdP8w+kVNKPyIrWtnpte0oq+TJxV9wwfSJHDa4oZ+55nAwYPS4Ts9rYZHABIUQs6WUS6C+0Hm7Tuv97wraD9m5ehVv/um39TGTgZpqAIKRKE8vXcHJ40cxNKv1k6Pq1Bl9/g5Uh1Hv5pb723J3o97TQukMWXYviu6sT8KR8f9aqknYn4ii86OCP7HPVkZUibnDhBAYwkTrh8nsLtNBkb2sxeOg0+7uAwiJCPl6Vv1xoEgbOZuuACHrLdgughhE25jJIhExDZ3yN35HcOuXJE06mYxTv9dnpYHatGKu+xjfyjdxjZpF5tzbUOzNWy6jwkayXtelU+TmknLG5uU0EZP7GTxhUidntbBIaG4CnovHUgJUAVe1Z6AlKHuBhc/+s0kCDsTqmj08fykD05KZNaL5FniNyRxfiWJrGjPZE+f7tB2noRj2Jhndh4KQ3M/n3tWUapX1YtJtODm38ngcsn/G/r2fugS/Gmz2itld7m6As6uOw2k6mhwLAqU+6kIi+Rs6Pd9N2aK7Ce/bQsm8u8E0yL7kt7iGJJ5QOtCK6V//KZXz/8Gev1yCZ9LJpJ14A4rW9De8P4ayK2cvp03DlAffaQoh2PH1KkZMn9nMKAuL/ouUcjUwWQiRHH9c296xlqDsYaSUVO4tPOj5TzZtR1MVLp4xuV3zePODKL1gQnNXjUZIW4/vp6/Y4NpOSG0Q94MjuehC77eCco17CxGleatgd7m7ASYFRmJv5bjwATVWvdJ+R+XHT+H78g0cgyeSfcE9KLbWi94nCknj5pA0bg61K9+i+tPnqVu7gOTDzyXl6Cvqa2AOCO1F7WL/JgHYmolrj4ZCFG/bbAlKC0IlpWz481/7ehldRgjxOfB/UsqPoamQFEIskFKe0NYclqDsYYQQuDxegr4Gkb+5pJxPN+/gthOPwqa2nPCiaCapw2txpoaRUmKa0OF6wVLgrhiHu3Ichs1Hbf7nKLoTb/FMhGnDn72KUMqO+s1r85di35yLQv8UWG1xRN0kPk1eSZqewlG+KdhNGzaz//4Mjqs5nLXurQTV8EGvddXdbReSw9wGOZpJVe5SIrvyiaZuJ5CxDiXiIaX4SEwlSm3u5xhqlBFlo9hZl8uM3FVku0vZ7RvEqpKJjE7fxtj0zdRGvCzbN53aSHKT/SQBJ2JjAAobMFiE3mdtHE2PhpETK32kFgdR/D3Xyq8v0f3VlPz7p+g1JaSf+n28k0/u6yV1iuRpZ+E57Axqlr5I7fL/4lv5NqlzriB5+tls9IxiRtUX7HUNpNCZT5IRYHTdFtxm+6N8Jw/OZ/HWnawtKmbCgNz6520OJ8mZ7avramHRTxgMPCqEeA+4S0rZ2FLRrgtJ/72S9iMmnXgay19/GYDyOj8vLvuK7xw5lVR3yzX77J4II8/diaKZqHaJEe2Ei9tUGPDVD3FXj0QYdqTQydxyPoi4G1sqeMomsXvGb5FazFlZNWgB3uIZOGuHHVKu7v1MCozima2/RgqJJtV4TKDSb2NEj/ZNZVHKSpZ71jRxewf3dM3dnaGa3J4Twi7AoUDE8zE7hi1GSIFUIwjTRvnI/xLbqQRh8qPBH2IKiSEVnFqEoG7n6vEvYkqBU4sQMWykOaqYt/nc+o46Q1H4G25sCFwIqjEpIsDmPpCU0eFejKGe+h+amW7HvqICcYgZXqPVxex75vsoDg8Dbv4Xmietr5fUJRRFIe3oK0g58iKqFjxJ1cdPU/PZy/hPuJ5vxl6DAHTFhmrq+FU3s6qWtbsIWorLydEjh7J+b2kTQWmaBmNmzWllpIVFv6MEmA38FVguhLhUSrkp/lq7zoJWnY9eoGx3zAIYNQyeXbqSE8eNZHh2RqtjBs4pRnUaqPbY96jaOh5YnrzvKNxVI1EMJwIFRdpRsKFIG0LGakuWjPsXspHLNLVoDo66gf1SXLUHgcCGhkPaUVHR0FBR+u37Xe5dy2r3poMOjq66uy9Oj5CkxMQkgF0BqURjNx4CpBoFxQBFj/1fSFQtiqboOOM3Jy4tgk2J1j8O6C5e23pWk/aMv8CFJy4mAf5HlF19ICbNJC0mJlUFFAEC7GuqDzkxGSnZzr4nb0ZLzSP/pif7vZhsjKLZyTjlFgbd/hLOIZMof/chdj1+Pb5dawBwmiGOqP6ywxe9SQNzWVNUjHlAP9zasp5vLGFh0ZtIKQNSyuuAXwMfCSFuir/UrgukJSh7GCklO79eCcDW0gocNo2j2krCERLvAP/B7u0Oap6UvUehmC2XIjLUIKHk3aA0nCiT9x6FYvaPOKrO0l/FY3MsTP6CkHpwKkxwR5Ck0UmdmlNFMsJhohzwMbXHQn7gNo0ff1M2HtFILHqBYSgojb6PD4hysPO+5zGznU0WKwI6RPvK8d4zhHavYd9zP8QxcBx5Vz2Mohya9WUVu5uss+9kwK3PY8sYROm8eyj6502k7VyK7MRvP9XtwqGpVAYC9c+ZhsGWL5d157ItLBIGKeXrwJHA+UKIt4B2dTWwBGUvsL/8xs7ySkZkZbRdjkOCjF/LfBEPO2oG44+2v6Wdz4DdYYU6JXYCjIgoW5y7KbE1LcQrpAIythY1nIyzZijyUDPJHOJ4jKRmnRGKTcHshCDK0kzybWaPpNe4tACi0fEVBYz4niow2YCB6KvEnqjZ5HOUimB/cq90qpjJNuSBCjuOQDLGFmGkLcL+SfJVnYn2MI540XmPMJlkD5OlGs3O0dP4N31GyX/+D/eoWeRc8tteKQmU4y5lsHcPyoGdnHoJLSmVnIt/Tf4NT6A43Cz97yP87aNFlPn8VNrSKLNntOto84cj6EbT35IQAqWV+HcLi35IE5O7lLJISnkSsBgY1J4JrBjKHkYIwYgZR7Lps8XsLK/i2Pb06Uaw+rkxLBs7k6/9k7AJHV1qfGfsf5iV/2WLliJdwkuVdlYHVDQBRvZTTHB/ykbXLgQCXRgc6ZvEHXuvQkVFMR2M+ORhwt5CnLVDkUJHMe1IjCZlgywSlx8UX4pNKryTtriJBVtxKJiB9gvKTM3k2swwGarEJCaLTMlBVsquMD1nNbqp8dTa7yBRCAHnUMcwFDZiYgPC1Edl9ipqYQCpgDEmNWapdKpEjs5BhAyk1xb7MJDYVlSg1jaEiBzuCPFodjlJSsz2VWUIKkyV0bYoUSlQhOTLoJMjXCEiCOxIHqtJ5uHqFLq1gGwr+FZ/SOUHj+KZejoZJ93U9oAukukq5/tT/kmWuxxTKkR0G79e/hOqwl2vh9oZbGl55F35EOG9Wyh/94/84f1FuIZMImfubXiddi7a9xous2W7+D1vfoTX6SAjqaHepaKqjDriqN5YvoVFryClPLWF5x8AHmjPHJag7AWS0jLQTUlhVQ0FGe2LWVrqmcWamvHoig0dGwXJu5ie+3Wrbsc3qm18E1DREegSQPJV0ub665YmVW4quaiJy1c1knBVj4o/F8vsllb5l37Dbkcx/0tddpA2UZwKRqh9liGB5NasMCmqbCIgu/so0KXGfzad38TtWAd8E3eD92kNS01gjExpcHvHRaV0KLG/VYGyN4BS15D1naEY/Cu3jKRGISNuIRmAGRse/wSPcYfqH5foKk/UJNNbYrL683nUfPo8KbMvJfWoy3p8fwKTn05/hFRHNWr8c3lr26nURdvXB7wnceSPJOe6Jwju+IqKDx9h52PXoGk2HnE5yEv1MjwrnXH5uaS6G8KEPly7GYDZI4diczgBiZSSoy+/mrTcnu8iZGHRn7AEZS+wbuFH7K2qIT3Jjcvedo1HCaxJjonJ/Zw4eBE20XIJE0PCcr9G9MALVaOH0+vGY5Nak57MsU1Eq48tEpe3Uxehc7BwVFwKel37St6MdJi4FHlwzGR3LLARq8vGo5tqD8zcdYy8FvpEN7qD03b4EI0SM872+FEOkN3N3fA1fu6/dW6MXrpfq1zwJL4Vb5J20o0kTz2jV/Y5Km0rblugXkxKCZ8UziZqJkgZMiFwDZvKwJufQVYWMnnFo+wsK2dvVS0b9pby2qp1KELgttsIRqMYpmTOqKGcPHEMR55/CXaXm+HTZ+LN6HqzAAuLQw1LUPYwUkrCwQA7yyspyGyfdVIi0EXTrybNWY2itHwl0iVtXqjSdC+qtMJmDyXKbVWYysGubdWtIsPtUy7eHuoJfyC1YS+GTMxQCmlXQG1d6IpI0885WzVwdfDnVKxrRHohdL387T/h37CIjLN+imfs0T2+v/2kOHxNHhtSIWokZqMET3IqR40axjEjG5IkdcPk3W82sHjLTgBG5mRy1pRxqJpGwZRpZBcM66PVWlgkPpag7AEMU/L0ku089/ku/BGDwQVnsnXpr5iU3767WgXJaaUf8nHmsYTVmPvlyTXf4ZYRbzOrYhpJFeMxtSBlQz7gtZQlfO5XiUqBTUBrGuKjlGUUhPI5s/rYHrdChkSElzLfY0HKckwkx9RO47tlZ+Pspx1pEpXb9l3OH8QzfOPZ3OR51a1iRlqOoUxVTU5PiTLWaRCR0BuX/KMHfE6hL5dFRUfTF1ZKI9eFPsyLdCgo1RGUogDmADdmqj12R2bKVkVlZGoGkzYUc5ezgsmOCD5TEDTpkKj8SVo1IRPGOaKcnhQgiuBlXxIPe/IJF3iQNgWlMoyyL4gxOAnptSGCBupOHzLNESu6bkqUQj8iKjGGeMAmUMrDaJtqEGGD0lfuIbTza7Iv/BWuoYd1wyfXflYUT2GIdycnFyxCEaApJjdNeoYVJVM4echCst3lFNXl8fLmc9hV23bL2Z7EryaxKH02+aG9fJ0yBZ/mISdcyuL/vg/AqB+/gtsMsqHmKybpe0gf0K68BAuLfokQ4ketvS6lfKitOSxB2QP8eN5qPly3j2A8yzY5EGV3eTnnTB7VgVlkUytlOJ1Z627BCzGXdTSZJ+rq+BonkfosytYtTbpiMCiSi46BrQe/eonkzsF/YYezqL4toLAc6T2CTWrscu6NffWNPmA1SUXqzR8PSYrkJzkhXI2McqY8aIpuR1MM9vnzUIWBIXv31BMdkoQxIhm0mPozs5yYWfFYOSFi4cPN9GxuzFhHhNcy9uEyYyWV0tTYkI58bsmq5PdZVZiAFh9UMSINmZqMjGcNm9kuzBxXva9c2lX0SemxHcXjEszhybGd7t/GoSAjUUqev4NI6Q5yv/MgjryR7VxV92GikuWuwpBqfXb3tJzVTMtZXe/6z00qpTyQSc8fcW0gBOu9Y1jvHVv/Oe4gBYDMM+8grLkI4+KTjDlkj7Cj2RLT0mph0U14uzqBJSi7mT2VAd5fu4+w3mAdGr/rf3wlIK2VzjgHsiT9KAyl4eu5ADux8uTxE5+jiNVJmxuJSWjr5DwyOJixwWE9KiYBvnZvYpdjX72Y1KTKFeWn99t+2YnM+6lLCCrhg7561dOyoJztieIQTY1x3ZnN3RLbawrY5Rvc62JSKjQRk0DbwY7NcGfFHlyyaX3OzlTfUURDvbZKReP5tFzCjYvONvdlCNH0Oz5gG3VdOcVP3IJRV0H+NX/Dlt43CSO57hImZm7ApjSclw78jBbsnkPYtJMQsbSiqXm54sNHEXYX7tENGdy6YuO1PYL/C+skOaxLpsWhiZTyvq7OYf06upl1e2uxqUqDoJSSiuJdFGSkt7v2m4GCT2t6szAJFUejE/BW5x6E7NgJeURocIe27yzbnHuIKg0JIZnR1FjNS4tuZ617a71wb4yWrLUoKIc7TGx98HXsqh2E2cFjtjuQzu45zU0J+bs9+nGL3YVDmoS7MLPpr6P4T1dh6hHyb3gCzdM35XkABicXYpgKrVUd21w1HN1MPGtfeO8mgps/J+24axBq02NGUxR2VQQYl5/cwmgLi/6NEOKvrb0upfxBW3NYgrKbGZjmwjAlma5yjsz7kiQtwOIPKhiUlcEa71gqbBlkRisYWbeFEmcuO12DcZhhxtRtJstdQdqoGlSbwcTgOrbVjOck7AxFASQRDFYnbWRl0joCSrhdZV0UKZheN57D/GNwG64eKwlkYLDM+w2r3ZupUwLYTBUjXsS5zFbFVuceBkVyWJCynBJbBeOCwzmqdkqPW0shVth9qfdr1ru2kx/J5ITamRTZyliSvApNahxbOx2vkcSClOVUajVM8Y9min8Mn3m/ZqNrJwMj2RxXM4M9jmKWer/Cbto4rnYGbtPB/JTl1Kg+DguMZaJ/JEuTv2azcxeDIjkcXzODHY4iPvOuxmnaOb52BnbTxoKU5dSqdUzzj2d8YBiLk79iq3MPQ8J5HFdzOEmyqSW72FbO/JTl1CkBDvePZ0xgKJ+mrGS7o4io0FFMgXlAwpaarCLN5r/r5XUq+ZrJnqjChpCKW5EcnmSQofVscs7Y9I1kOI+mOJDb9sbdiAjqCF8Umda1DlD/Sc7ilqq93XrEjg37GRwJUaHZuLC2jGwjyiJ3CvOT0pDtuAE1KsqpuOZ8hGpj2PVPcG66YIK9ki1RjTfrkhjviHKiO0DAVHi9LokogvM8daQqJp8EXawI2TkzKcg4e4RNURtv+JIIdEHcbqkaRkUoDVvE4LN90wnrTqZkr2Wwdw/L902jyJ+3f+W0qjp7mWhFIcXP/xjH4El4Dpt70OsRwyQn+dDuIGbxrWdlVycQso24oURi+vTpcsWKFX29jDb58XMPcGLeUyjCRBEGV1xehOe0uxFZ49AVO5oZwURBicdJKhgcmfslV45/GUWYCAUUXx6Dv7wbabhwIahD5/5Bj7HetZ2QGkYzVfT97u4WrjuqVPjt7u8zKjgEl3QSIYqGFgu96kZ3U1hEuGPIQxTaSwiqYWymRlToTWP6zFi/bAWFiBIlNerlj7t/yMBITo9GV/qUAD8s+CMVWg0hNYzdtGHGP/2oiMZ7nMf2LxBEFR2nYccQJqpUCKmRA8bosc7fB4xxGHZMYaKiEFIi2A0bpmjYj4LSxKKsK0bDmPh+nIaDO/Z+lyPqJtWXdlrsXcmf8p/DxEQXBg6z6Ri7qRE54LMGCO0LsfWurUz414RmPhWJSszlHZECFcmMpCgXpuk96vqWEj7YeTz/3dK0n3dHMU2Tivf+QnDzZwjNgeL0oLpTUL0ZaCnZaKn52DIHYc8sQHHGygEZqTai07PazORujVQ9yoe71zBYD3frERurvxlrSulEskuzc8bgiZSptlZ96nrhbipvuAQlK5vRf3uOj4o3k6LrJCmSgBmTayYCh5AYxOJkhYhFLtqAkIxtoyNIUiQ+Q3BbWQYLg65OtSiMIREY9ceRIRXsShRTKijCJGI60EQEXdpICJc3IE2Dstd+g9DsZJ1z10GvOzSFk8bl8OhlU/tgdRYtIYRYKaWc3tfrmOB0yVcKCnplX+M2bUyI99wSloWym9F1P2cMfhbTjLkh6+oMSksjuDPHIpVYDKGu2AGJGT+hOrQIV0x4FU1tiLscsPkKHIazXmwtS1nBevc2QkokPkfbRatPqj6S0cECnDJ2Z23voVzet9I+aRIz2djdvR+jvrRNbN1zq2eTHU3v8VSdFzPfpcRWiR5fU4N7OLYOiYl5oBiL98bev+WBYwzkQdfC8IFj1APHmG2OmRQYyWH+MfViMiTCPJj/fBOX9kH7aeazBrClxr5raUrEQSpRYNBQZsqhwLmpPSsmAarDKby57fQuicngrtWUv/47pKmTfMRFSD2CXlOC4asgUraT0J41yEgIqUca+peqNoTLhfB4UVJTUdIzUbNzUfMHoQ0qQB02AiUjC0VpfV3fryoiV490+xG7v53Afv6SPpAqVWtVTEY2raPqB1ejDR9F2iPP8qvSbWSZ0fpQBreyP+UlNq8CBx1/7ri4dMS3+SzkZFnI2QUxSXyPWpMSZhHTEXt/8RsqXdppK4GwN6n44BHMSJCsM34MxMJTHZrCfgP/WVPy+fXZzd2YWVgcegghsoCfAeOA+ir/Usrj2xprCcpuprr6C4RocOWsWxfGOWg8Uj3QXdJw0h6bvqVp3JEUuCvHIhpdeBcmf1EvJtvL8TUz6sVkT7Iw5ctm4/ha49jaw3slSefT5FX1YjLROaZ2Oi7Z0KVjrXtrp+uGKs7YOCNgoHla/5mPchh0vOt3x/mmfFzcDtdxTD1C+Zt/ILj1C5zDp5N1zl0oWuvHj2nq6FXFhGv2EE6uwSjei1lajFlSjL5lIzLgR4ZDoMePD1VFSUvHNuVw3Kefh33ytCbzneOrqBdfPcn73nR00fL3Hvryc2p+/n3s044g9f5HEEJwSl3VQbeL7ZGFjbd5q85NoMdinQ++qUkEQnvWEtr5NfnXPYZij4WamBKSXTbe+f7ReJ0aTlviuOYtLHqBfwMvA6cDNwHfBcraM9ASlN2EaYbx+dYTCpfQOIxg3doQ7oGxckHJ9lqy3eWUBjKpjSSjKVEGewtJttcAAimhqC6PiOFgKAZ2FCq1GvbZyjtlNahWfW1v1A149Ra6jLSCT/ED4FeC7HLsJV1PITeaiYnJdmchAMNCA1FQKLaVU6nVMCScT5Lpqh+TEU0lR8/AwGSbcw+KVBgWHoCCwl5bGdVaLUo/SgaqVmsxMevfT7lW3emY1/0JYHqN3qag9Jvday+qCqVQHkwnL6kEjz1AWLezp24A/ogbITq+J/+mz6h490FQNLIv/jWugintGicUDa1gKKp9GLbDMpt1d3sNndGRIHtqa9m9exfhxQvgy8+pWvA+KCrqoCFkTpvB6JNOR+8lXeExDKpVG2lGlBGRIHs0B8U2B1SUErn3DmrWr8F54lxS7voNwyNBUg2d7rhlylANQGIHxjsi1JmCLdHEcU13N2bYT/m7fyb9pJvqxeR+bKpClteKmbT4VpIhpXxKCHGblHIRsEgIsag9A60Yym6gpORdNmz8OQBSGphmiP2X6Dt+spdzzk8ne8wYCpILiZoaNkVne81gBicXgRQoikFlMI1Hvr6eqlBqLPbSVBif9zrbvGuxSxshJYKJQUeSZIUU/LjoSo73zehR17KBwa8G/oMvPGvbfe1RpMI4/1A2u3ejyVg86MBwDpW2GsIiZom1SzuZ0RT2OErQpEpU6IwJDGWTe2d8jM7gUB6l9kqiQkcCLtNOqpFMkb0UTaqElDBmMy7qRERIwajAYPY4SwAwhBn7LDq59rXXrKXgjgI8Y9vqoywZ5zC4NivSlRBDIoaNJ765krUV49CUKFHTxvCUHeyoGYIqTHSpEjU1aKfL2wz7KX31V4QL15M0/ljS596OorRP1ZkpNiJTMhqKPSqiSd1GpORHFYV8v6qIiFCwS5PNNhcDjAgO00SaJvO+Xs0Ly5bx+bp1RHQdp83GqOxsThwzhqtnHcGEAQM6/iG1gwiwwulhathPWCiISJjLXprH+58tZWB6Os9cew0jR40mKBQG6hF0IXCZBgpdS3MxJawK2RjtiMlTFVgRsnNNSTZ6f/gBdQApJeVv/wnF4SbjlFubvOa0Kdxy7Ah+cELv1/G0aD9WDGXPIIRYJqU8QgjxIfBXYC/wqpRyeFtjLQtlF6mr28T6DT+Ni8imGIZk48YwE8fb8Hp3IgTY1NjJelTa9vprmykFD668lepwSn18mT37bTZ41yIUneh++0MHtf+44DCOqpvS43GKKz3rWZ20uUPCxxQma5O2gYBIPCJwu7OwyRxBwtSovibbrEna0uTxVteeJmNCapgqremY/oIUkk3uXU0/xy7c7wlFoNe2bbtSgIszuh4b+OLG81lbMZaoaSMaLwuzqWoknXlDvq8/pHL+YyhOD7lXPtShIt1SE0SmZzatO3kAZ9VVcGvVXlxS4pKxONdJkUDDSgVcd9hkrj1sMtc8+xyfbNrM/809jde++poXvviCPy9YgKaqDMvM5NhRI/nOzJkcNaLN8227sANHhuoQwCMfvs8v334Hm6ry5Hcu5+pZs2LvUd+fykP9R9pV04AiYJoz2ui8BD+vyGimU3z/x792AdGyHeReGWv+4bIrSDNm2Z86JI0bj7FaLFr0T0Qs5m4FUCSlPKMTU/xGCJEC/Bh4BEgGftiegZag7CKFRS9ims3HNm7fHiErSyM5+WC7QeN4+02VIwjqrkbJChJ72heIA2P/OnjFP7fyeOyy5+u9/Td9PuEOxncC7Quramub9ozpT3RjqJnQBEZt23JgpMPEIbpW3Fw3VZbtO7xeSDZaRRuPD5inrpLSV+4lWrYDz9QzST/h+nbXb92PkdN2A4FbKveSJJvGc7Z0KI3IyuKtb77hutlHcd3sWMHriK7zxurVzFuxknfWrOWJJUtRhGBQWhpHDR/OZYdP59Tx49pM9GmJ99es5ernnqfS7+cHxx3HA+ef22SunjrsG3/UK8MOqgyli0k6iUe0soiqhU+Tc+nvUGxO7Krg9Il5TBqYyqSBqUwemNLhY87CIoG4DdhATAi2GyHEH6SUPwNcUsoaoAY4riNzWIKyi0TCJYCJlJI3Xq/lhReqGDjQxoABNmpqTCZNdrY5hy96oEvSBNH1qKj0aGp9xnBPUqX1TqymRccQmsDwty0oPWrXw14ihr3LRctrPp9H9eIXUJOzyL/2cWwZnXQp25U21XGG0X7r9ZjcXPzhpjdMdk3jomnTuGhaLHHHNE0+2rCRF7/4kiXbtvKfFSswpSQ/JYWZBQVcOG0q5x02BbvW+il3U3ExFz7xJGv37uX0iRP49zVXk+xqf4et7qTCUA4xKQlSj1L+9gOkHHUp9qwCACKGJNVl58ojC/p0bRYWXUUIMZBYMs1vgVZ7czfDXCHEL4C7gFc6s39LUHaCz/d+zsOrHmZn7U5ynV5OdTlIqqjjb3+r4MhZbjIyVMrLDGw2+O6VaW3ONy3na/bWDiBz5+mchQMHgi/33Mg/8p/DpwU6vc7fDnyCuwqvY3yodVfcYu8qXsh8l3JbFUPDA5hbdTSLklew1r0Vr5HEmVXHUKXWsiB1OQYGs31TGRkazOvpC6jQanCZzmaLa1v0LcIm0P3N35ikqyZnpkYY7TSJmmDronJwaUFumPgsT5Vfg68gB+lUEXVRtN1+jDwXZpoDoibqrjqkQ8Uc4AYBSkkIY8sOKp68C6N0H0kXX0/qzIsw812EUmNjknfU8BP/Ps7zxjrVvOt382tHHr6hKUiHiqiNMnFbKf9nL+cwZ5gKQ+OJynyGmmHO85UjJLzhSWezw80N1cVkGVH8QiEK7SqkNWlAPhG99Rs8RVE4Zfw4Thk/rv65pVu38cIXy/lk81a++69nufSpp8nyeJg2ZDDnTpnCZYdPx+OM3XDWBoJc9vQzvLd2LRMG5LPh3l8yOrd3C8AfyEnuIDck1xBFcFmyH7cwWRR08UuRS8nwNKRLQwR01F11sd7omU7QTZQ9foQCxkAPqAKlLIRSEcIY4omN8euou32xnuUZ8TG76xCagjEgCRSBUhZEqQxjFHgbjqU9fowcF2Z67LjQdvhQCwNNRK+R40QfkRw7LnxRtEI/Rm7D8ed/6I+o3ky8Uxs8gUl2laNGZvb+B2xh0XEyhRCNE0mekFI+0ejxX4Cf0rm+3B8A5UCSEKK20fMCkFLKNi2elqDsIEuKlvDDhT8kZMRiJvcZdWS7Q/z9XzHL5K9/3fGLgCokN/uH4RYaWjwrWVWihDtYiudABAoFkXwkssU4yvdTlvCP3FfrXdbr3NtY59q2fwICaoinsl9HIDBFzEX4QcpS3k9dUu9jC6ihRCorZxFHsSvNWig9iuTHOSGcSjzxWYnFy8XqFnYOIcCfk0YwNwsp4jUw0xxEU+0NG9gUjNEp8cUJpJRUv/V3gq/9B23YCDJffh81Iwt9f6KgEKAJXvYUMdYWwBk/yGoHe4hkpiHjCTrDvSavpe3Fbcb6bCcT5TeVu5A0nOC+W1uKQkM6kAcjdpZsx3senpWFBMrr6sj0tJXg1MBRI4Y3iatcW1TEs58v56ONG7h93ivc+O8XSXW7GJ2dw8rdu8lISuKdW29m7sSJ7d5HT6IK+F6aD0lDgrya46A6L7P+s5cpdvSJ8Zvm+Hdsjohfd+JWYjPXhZnnqveny1Q7ekp60zEjU5qOyXNj5rsbxhx4LGkCURcv6B8/XPQBbvSxKaDGvmWZ7iCa1jAmvOozAms+JffupxFFsUEum8LEASkcMzKrGz85C4seo7ylpBwhxBlAqZRypRDi2I5OLKW8A7hDCPGmlPLszizOEpQd5E9f/qleTAJMCAe57659VFca/PGBvFZGtoy9Lg9PxXiURvGOT2e/1uHajgdybuXx2E1bi2LSxOSZ7DcPjn88YHMpZJPyNbI5S+Sh5hs7BFAcCmbw4LqPR3ui2EXTKjpdLWpuIviPuJKoOKDUyoGxaPEdRTatp+b/foDpq8V7+124z7yg2TFHBWsZGQnWi8kIgj9nDCLUKNv7hxV7cEqzyXs4MGr5wBNdrJlp+w5bRVHQFIXVe/Zwwtix7RjRPBMGDOCBC86rf7yrooJ/fbaM+Zs28rtzzuaOk0/q9Nw9xYHHxa+zhjT57IEWv+MWH3fDGFETRamNIholJOmjkuvF5IFjjPJSav94Lym//ANiQj6HOYKgm1wwdSAXTh+E0tNV/S0sep6jgLOEEHOJFSRPFkK8IKW8oiOTdFZMgiUoO8zO2p31f0spefr67QC8PG8wGRmd+zgdvsFI0XDh1zEot1V1aZ0A4wPDWu2V7VMDhJRwl/djkZgozuYF5VCHWd9RpbsI4iZA2/VITcPA94dfElrwPraJh5Hx24dRklq2+k0I+7E1Km1WotkwDxAjh4X8nTqRdURCuOx21hcXd0lQHsiQjAzuOfN07jnz9G6bsyeRwE5b2zHhvYHii9LELWJTDhaTcaRhUPO7X+A660Lsk6fhUhXuvngSU1OSemexFha9gJTyLmLxj8QtlD/pqJiMjz0P+AOQTbzYGpbLu2fIcmdR7C8GwAzHLta/fzifEredBZUqLgVmJOnk2Jpa8YImfOHX2BsRDLBLprl1dkYU1gZVkpVtnG8rw1RtzE9Zhl8N4jQdBNWOiz2P4ebk6iMYHM7HkBIDA7WF6nRew80U/2g2OXdyUs2RDIrkst69nU+Sv2ixpZ9F/8EMmWgpB//EP/Np5NsieDpYtNBEYRXT+ZqpJFPNsSwkhJPFHEMIZ5tdcMIrl1Nz7x1IQyfl3gdwHn1Cm/tc4E7lclsJo6Ixr0CuHmFa0McGu4tLassZGg0SFAom7a1u2TlSXU42l5T24B4SHwGc5yvnNW9mq20hewMj3Y7q0pCKwMhzIdWWAxj8Lz4NSJKuuA6AiJQMcPZ8ly6Lbwe+URl8+tY1vbOzoT/tjb38EThTSrmhowMtQdlBLh1zKX9e+WcAQntiF7kXU9JQKyAqBQqSOgMuSo/WuxTLdcGfS5xEZGwbW0DyRrUNVewf42f+kD8gUJBCYggT1VQ6HNQ2OJzLgzt/giY1nNJOmEirWd4KCncX3oggVkTbIe3M9E1gi3MXOxxFlhu7nxOtjOKdfHBs9jchhdlRheGK2W5doKNxP79kB8MICxeqjPI256AQE5omAoVYp5X9B45RV0vks0WEv/gMffMGjKLdOGYdQ/Iv/4Bia98FfavdxTuedG6r2otKLInmxaINSASmAKeUBOn5QzXT42VnRWUP7yXx+VFFIR8lpeFT1L4VlW6NyKzs2N/7lyEBKQ9al/+Zv5P++L8RauwOarLXRY6j58upWVj0FVLKT4BPOjm8pDNiEixB2WG+Lv0agUAiCRfFLIgmAjNukLQJOD8t2iQ+7dUqOwGT+npu0fj/948xEaBIaFRC2FA63vP49r1X4Dad9SLSgb3N1n02NCQSTcYOhYUpX7LXXmqJyUMAI2CQNOZgt940t8kge/vFJMCnHMt2hhMRMZenEU+82X+UGnW1BJZ8QmTF5zHxWFoMkTDY7KjZOWgjxuC9414cE6d06D0MiYa5pXpfExt7POWn3uPpoudzwvJSktlXU9PDe0l8fpFdgF9R+txCiRDtLnFqnzmb6OYN2EbFMvC3+MPopkSz4iYtLJpjhRDiZeANoN5NKqV8ra2BlqDsIEuLliKR1HxRw95/7SX9+PQmrw93mE06S0gJm0M9XxzYbtoYFRpykEWyPV1yGm+zMOXLLmeXW/Q90ZoomOAedXBc41S3jqOD/uHPOLpBTNbVEm5DPLov/i6Oo45FTW27bFZrnOivapdY7GlpUJCRwdqivT28l8RGAovdqQfFsCY6zuNPJfzpfNxnnA9AVErW+YNM9rYd82th8S0kGQgAJzd6TgKWoOxuNEUjYkao+SJmrcj7TtPM7jojJiKlhKKoIGT2dOPDGIYwCIsIbtm1Isjp0RRwgdtwMjw0iBrNx25HcTet0qK3qFtXh7AJlGayb6rjdzxhE/ZEFFyKJN8W63e+k2GEcTCMrdiJUiozef9jnfWfP0rFluIG8Wi3o2bloo0cg/uS7+I46jjUlNRuW7/X0JkY9pNs6DELfidskNI0CZWUICW4cnMQnexaAzAyK4vqYLDT4w8VPIZOjda/3MWOGbPw/fX3yEgEYbdjSnBY1kkLi2aRUl7d2bGWoOwgpw09jde2vEZwe+zicmCLrt1Rhd8XO1EF+E0RjzGDrlX5axtDmHx3xN08tv3/yDQ6bxW6u+hGNlfsZFh4ILowUKXCUu/XPJj/HFL0tGPRorsIbAmgepvPuplX5eAzn0mxoaASOz6djnwqs35CEC8CSdTnI/L4Hyj7+DMwdNTc/Jh4vPQqHLOO7VbxeCDfryjiR5V7iAoFm5Q4ZMfDPwJFeyl8+x2kboAA1eVm6GWXoDodbQ9uhrF5eQQinWgvegghgBU7VnH8kMnssSdGtnd7UFLS0IYMJbJmFY5pR5BuVxnt7j/rt7DoDYQQP5VS/lEI8QjN3MFLKX/Q1hyWoOwgyfZY5rzqVbFnN5dYIKg1++bu95rSc/CaXSuFoaIwJjQUgcAhISTC/D33ZUtM9jNCe0LYs5pPfJEI9ugxsRkrvqKwN+P/kHiJbNpA3d8fILruG0RyCklXXIv7kqtRNI2evikCOKmuitsrC3FJiUsa8fV2DCMUZvfrbyKjDaEbA04/BmHr/Olu8sAB6KaJaZqd7s99KPBQxkDK+pmFEmJxlJFlS3BMOwKvqlq9ui0sDmZ/Is6KVrdqhT4VlEKIU4GHidUhflJK+fu+XE97eG3ra/WJLqGiUBtb9x6KVDi+diYO2fWTfWMn/TLvN20m9lgkHtHyKMmHt1k2DICIYxyB996j7t/PYZaWoA0bQer9j+CYcdQBW/b8RfiGqr0kHWCR7Ohea7dsobEMVV0u3AMHoqgdrJPUiNyUWCeXXRWVDM369rbpez4l9+DC5v0Ax8yjqfnNnXhv/Qk7gmF2BsMUuDpnrbawOBSRUr4d//+znZ2jzwSlEEIF/gacBBQCXwoh3pJSru+rNbUHf9QPQOqsVIpfKiZSEcGe0fc1zWyyoW1jd1Kr+jHEwe37LBIbo84gaVTr1mojYFA8r5jqzzcidYnjyDl4H34aNadzHZ+6gwyj6wlhRigUc3XHUez2hpIKXcCuqnxTVPStFZQSYhne/RBt5BhkMIi+ZxdawVAqo7olKC0smkEIkQX8DBhHrOMOAFLK49sa25cWyhnAVinldgAhxH+As4GEFpRTsqawomQFaXPSKH6pmOCOYEIIyrAS4d6Bj/GTvVeRYra/3zBATe5nVAx/E91ZhaNuACl7jsOXt4xgylZG606O90HBzgs4rmYmKgpLPV/xWO48Quq3O6YsUTHCBlKXJI1rEJRh11T8KRdiaFkYm5dR9+gfCW3Yh/B4cV9wGe4rb0KxdS2hqz24TYM7y3dzYW05KpIPk9JY5Uji+ppicvUotYpKBOjoLypYUkrpp4sJFpfEBGSj7jrRmhr2zf+YvJOOR7F13oKf5HCwobiYs5nc6Tn6MwJ4dc86XkvO4rrqfQyJhtlpc/Jkai4n+6uYHaghoKg8n5yNHcmltaU4TZOPk1K5M3sYlX3oKhdC4Jg5m/CyxYQGDeHS1dsxpeS0zBTuHpFPlr3/ufEtLHqIfwMvA6cDNwHfBcraM7AvBeUAYE+jx4XAzAM3EkLcANwAMHjw4N5ZWSucOOREVpSsQHXF3D4VH1WQMj2lj1cVI01PxiE7dimuGriAstEvI+PiMJSyg1DyjtiLAlAj3PDVPdjqBqDF567V6jCtmMqExb/RDypoSbGfd8g1A1/GDUS3bqfmd9/H2L0TdcgwUu77E87Z8ZtOKenxGEkpeaVwPePDfpxxwXeer5zzfeX1xa7chklHVxKuqGDXvFeReqy7k6Ef3OXJlto+939rpLldbC1t13n1kOXIkI9ZIV/9dzMuEuDB0u2YxOKWPIbJ7VVFSBouLmEhCCSAZdN+5NEE/vsixoVXUBO3YL9WWsXnNX4WzxiDs4XWjRYW3zIypJRPCSFuk1IuAhYJIRa1Z2Bf/oKau140l1n0hJRyupRyelZWVi8sq3XmbZpX/7djgIPApgBSTwBxJeHqsnNwdkBQSkzKR7xWLybr2d+9E3BVjcEeyKsXkzoGL2V+QMSqVZmwBDYFUJMa4txqwsdR8YMbqbz5coQrifRn3yDzmf82iEmIF6ru2RjJmUEfo8OBejEJMRFy4EmooyspW/YF0mg5LEOoKpmHT++SdRIgy+tld9W3u1uOQvP1xBtHVao0iEkJ/C5zSELEXTqmH4m+dRN6WUMLTV1CZUTnnbLqvluYhUVisf/ivk8IcboQ4jBgYHsG9qWgLAQGNXo8EEj4ysG7anfV/z3optjy1123rq+WU4/LdOA1Opbhbdj8yDb6hTt8AxGNYjOrNZ8VU5ngBHcFsWXY0P06O/+yi7LvXomsqSL9kWfJeOwFbIOG9Mm6xkUCLXSV7xqhktImLu4DsXm9yFZeby8DU1Mprqnt8jzfJqIIirW+DwkCEA4njqOPJ/TJh02e95sm3/isGqMWFnF+I4RIAX4M/AR4Eri9PQP70uX9JTBSCDEUKAIuAS7rw/W0ixx3Dnv9Md1buzJ2cfFM6FjMYk8QVMIs9X7F0b6prfbvbowaTcJdPgl/9qoWTUJ1matJLjqanbrJgpRlhJUIppX1ndBESiJIKdn4/Y2oHpXMu65BPeEWEL1//2iTJmf4KjjeX41dmhjdIOwOJP2wyZR9vhzv8GG4Bw9Cr/VRvXYtmtdLypjRCFWp7+PcFYZmZPLZtu3dsOJDn7UONy8lZ+NTVNymgV9NjAp1zmNPxv/s4yRd+J3651wCRiZZCToWFgBSynfif9YAxwEIIQ4s+dEsffYrl1LqQojvAR8S85I8LaXse1NfG1wy5hIeWvkQEItVc+Q7KPhJQd8uCkDAvIz/MbNuIg5pb2fLRYX0HacTyFyDVKLNikrdXcYTY3/HQp+DCAYSGROsPV+S0KKTRCuiCJsg77I8Mk7MIKoVUi3DgLNXezA7TYM396xjeCSIR5pEiJ1wuvvQSZs8idRJE8E0UWw2TF0nY/pUpJQN3XGkjD3uwvsfmZNNbShxSoUlKk+n5PDrrCGEhYIpBDbTjFmQE6D2o/2ww6n53f9h7CtCzRsAgAGck5Xap+uysOhr4pV3LiKW3/KBlHKtEOIM4OeACzisrTn69LZRSvke8F5frqGjrCxZiUAgkUSro6TN6Vqv4u7kB8WXYZe2donJ/ZSMfwapthwPWWEI5tcp6KIh0cGk451LLHqH6mXVAIz52xhUW8wq50+/EoSj1y/oV9SUMDISxB2vK9lTjs/9onG/FTJWhL3773cmDsgnFLVih1ujUtG4L6uAcKMknGgCJOTsR2g2nMecROjjD0i6/FoAFCHY4A8xI7XvPU0WFn3IU8TCEL8A/iqE2AUcCdwppXyjPRMkzi+9n/D53s/rC33rtTreyd4+XlEMu2ljRGhQu93dAIbmJ5K0r9VtNgRVyxDZjyh7p4yk0Un1YlIiiDrG9Im7+7za8nox2dP0xjE6MT8fQ0oizWSRW8RY4k7G1kvfeWdxnjiX4Pz36uNqw6bkw/KaPl6VhUWfMx04SUp5FzAXuBA4tr1iEixB2WFsakOmqCPXQaQ4MWox6sIgKFpPsNmPxCSQuplgatOOIs2RopkIq0RQv0Cv0wkXhsm+ILvRsxJMf4/tU5GSw4O1zA7U4DRjyVrDIkGO81cRTQAXZ3ficTpRhGDJtm19vZSEJUePIKRESMm0oI+j/dW4zcRK4rONn4wMh9C3bQZAFeDuhhhbC4t+TkTK2N2glDIEbJZSFndkgsSIlO5HnD70dF7Z/AqmaRIpieAa3vPFoNuDKUyuHvFLHt/+CzKM1Ba3CyXvoPCwP8dKBUkByFaD2iY4TS5Oi/JCpR1p2SoTmpL/lqB6VJJGNGT7CyCj6EdU5f0O09a9Zbcmhup4oWgjbmkgESimyW67k6HRMBEhcJkmBvRIZndfMS4vl9+89z7Hjx7d10tJSGaE6li862sUBC7TQAqBDhw/ZDLFtsRIfBFC4Dz+VEIL3sc2YjQCOD83cUKXLCz6iDFCiG/ifwtgePyxAKSUclJbE1iCsoO4tJiADBeHUdwKttTE6bBwfel5eM2WSweZSoQ90x7AtAXaPWdEwrwqS0z2B2qW1ZB2zMEXRn/qhZhq9xbfd5gm8wrXk3aA9WlsJIgAXHGj9qFm2/7Zyadw9XPPoes6mmadPg9EADmGXv83Eu7IHkpVgmR578d1wlyq7rwVz/U/AFXFfohZ0y0sOsHYrk5gubw7yOtbX0ciCWwJ4B7h7uvl1KNIhWNrD8cuWxa4/qzVSNGx+KY1wUPJvnTo4lvjwwyZZJ+T3eR5CYQ8x4DSvSkxxwWq0ZopAdRc0etDictmTEdTVR5e+ElfLyVhaVyY3gTmJWcTToDC5o3Rhg5HSU4humZVrKVkSVVfL8nCok+RUu5q7V975kis28Z+QECPWfcCWwO4RyaOoLRJDVW2fn9gaH7oYIZ20BQYh5qZKYEwdZPw3jDhojDh4jCRsgh6lU60OooZMDFCBvZMO1lnZ5E6I7XFeUpfL8U1xIXqPPDCLUB0f351qqF/K+9GFUVh7vjxPLLwE3580ol9vZyEx0AQSVDrn/P4Uwl9Mh/75OlURa1EKwuLrmIJyg4yLXsay4uXE9waJOOkjF7d9+BwLjeWXMiEwAiCSpgPUpbglA5OqJmJTWqERAQ95ysqRrxG1FmN3Z9D2u7jqc39klDqVoThiNWb7ACzPDqVOiyss3Ho2Zt6llBRiNCeEOF9TYWi4TcwQyYy2hC/KjSB4lBQk1S0ZA3nICf2TDu2DBu1K2opfLyQoqeKSJ6eTO6FuU1CLcyQSXBHkCG3HdwBRyBJKfk1YfdMXHUfoeoVGFo2dWlXEnWN79D7GRMO8KvSHcwI+QgKBXsvZfMaoTCli5dQu2kzUko8w4bizMqiavU3GIEAjsxMUieMp2bDBkKlZShOB2mTJhKpqcG3JZZA4xk2FEdmBtVff4MRCuHIyiTvpBNwZmZ2eD2/P/dsRt1zHzvKyhma1fHxhxpbbU7uzi7gM1cKbmlwSXUJulCYl5JNWAg8poEvwVzeAEpqOvrunbgUwXHpXe/1bmHxbSfxfuUJznGDjmPp1qVEq6M4Bzp7bb+Z0TQe2nkHLtOBgoLdsHFB5ckAqHFbkX/Ax5SP+U99b+6It4iScc/HJhAgVb3DQW0KsCkc20NiFwNJDIK7g5S+Xkrd2jpkVCJszQtFe64dR74D50BnM1bFpmSckIGpm5S/U07lwko2Ld2EY4CDrDOzSD0ildK3SlGcCt4pzZewske2YItsqb8dEDKI7hjaoWLTA6Nh3t69liRpoABOaWDS8/XtpZTsmvcq4aoqMGNHoG/zFnybt9RvEyotpfjjhv7Mhj9A+efL46Hksed8mzbj29RoYtPEntK5uNKROTkMSkvl52++yUvXXdupOQ4VSlQbcwdPxKeoSCEIo/B4+gAEYMaPrZCUCVPYvDGmvw7hcmEC05MTx9tkYdGXCCGOB5ZJKdufbBHHEpQd5D+b/kNwaxDXMBdC6b0T5NmVx2E3bU3qTKqN/pZIKke+Xi8m6+liUNuWsEKFrmBa1skW0X06pa+XUvNFDUadgS3bRvZ52WSclIGidY9jWNEUss/JJvucbAI7ApTMK6HwiUKKni4CCSlHti6OGn97Qc/JSGHr0AX+xqq9OOJisn5NHXsLncK/ezeR2tp6MdkhWrl5yjxyZpfaMd549NH87oMP297wEOfp1FzCQiAbHUtSiKYffYIJyf2YpftQs/PQELxTXsNFuel9vSQLi0TgKuBxIUQFsDj+b4mUss1AY0tQdpA9vj2x+MnhvXtHOzo0GFsrX5dUQxhah28o2mRfVLFiKJvB1E0qPqqgamEVkdIIqkclZUYK2edkoyX37M/KPdTN0J8NxdRN9r2wj6pPqsg5N6fd43XHUBAdq04wOVTXY51uWiNcXoHsgTqGzszMhraMneCOk0/inrffYd7KlVw0bVo3rqx/8bXTk3AJN+1FL9yNa9I0/KbJWl+Qi3L7ekUWFn2PlPJKACFEPnAB8Dcgn3boRUtQtoNNlZt4ZfMrVIWqSLYns3XrVjLn9m7s1Kvp88mPZKBlbaIu6yvUSDKphcci1TA1+YsxtSDCVJFK9158Jzh1vrSp2BWY4dbRBKwKaOyNwlEegyzNZHtYZVmdRuRbYMWsWVVD+TvlBHcGEarAM97DoFsH4RrS+/VIFU2JWUSzbNjSmgrEqH0ooaRjMBU3juAKbKHNhDzHoNuHINFARjskKh9Ly6egeBvZsuvHV6CwiOp165GmQfKoUTiyMqn+Zi2RqipcA/JJHj0K/46d1O3chTSMeJef7g24KF+xkswZh+Pbup1A4R5sySmkTpxAtKaGmo0x33jquLFoHg/Va9YS9flIGjyIlLFjUGw27JrGrOHDuP/9Dw8pQakD73oyeM+bTrKhc3lNKQrwfEo2tarGab5KjgjW8FJyDmucSQSFQDVNjARqr9hejF070AYPxSUEo5N6L3zJwiKREUJcARwNTATKgUeJWSnbxBKUbfD6ltf53fLfETEjmNJEMzSCO4O9bqFc6VnDV7NWMUhTQIuAKagZuJCY49EARYKpdHtQW7oGP8wJYxIrUK0ImOQyUEVsV5qAoXaTdUGVCoPu3XmCENoTouS1EurWxeIiXQUuBt08iJTDu7e2Y2fwrfGRNbdpwfKA50T8qZeA0ECoRFxTY39LI1Y+yIzQ0Z/+QncqpTYHmZFAl1zdpZ99TuXKr5Dx9oW1W7bF3NlCgGni27GT0k+XIFQ1to0Qsfi7bqZ69Rqqv1nbsB9FoXLVVwhFiYlYoHbT5obYP9MkuK8Yz7BhCE1DCMGvzjyT4//8F2oDQZLdidHgoCvowMUDx/GV00NAUVGkyUvJ2QgBBmAKhQ+T0ogKgSYlYUXFbpoYCerSbg0ZDmGUl6LmD8AEzs7q+9+yRf+k1FfBo4ue6+tldCd/AbYBjwMLpZQ72zvQEpStEIgG+N3y3xEyQvXP+fb4sKXbUJN6180zw6OTbdcbAtcUSUzSNbLcKN2fNiNETEg2frf2AxTF536NGqNx9bn+j16nU/pGKTXLazB8MStg9tnZZJzSfXGRXaVuQx0yIsk8pcFabipJ+NMubVoqSHEAEkT8W1TsdDQ765LaUoZFQ10Sk5GaGipXrKoXbADs/3u/aIw/3i84e0JM1iNlw37iMZpN1rY/bjO+hozDp6G6nIi4gDp29ChS3W7uffddHrrwgp5bZy/xjjejXkxCTECaB/ykQ4oKSIx4b/hIP7RMAuiFu1DzByI0G0LAWn+II1I9fb0sC4s+R0qZKYQYD8wBfiuEGAlsklJ+p62xlqBsha9Lv0ZTtNjteZzQ7hDOIb3vHjnMbeBI0HP31wEN/RAQk6ZuUrGggqqPq4iUxOIikw9PJufcnB6Pi+wMZW+X4RjgQHE2HBhRxxiE1JEH1Z7sWnbW2b4K3F0sE+TftTthEzTag3f4cJQDEnkumT6N55YtPyQE5duejHox2Tr99zvcT8zdXQBAyJT8r7zGEpQWFoAQIhkYDAwBCoAU2hlzlHhXyQTCbXMjD7DkBHcHcQ3uGfeWQDLMYWIXsD2sEJaCbM0kSzMxetJS00UyNJNiPUHVbhvoAZ3KjyqpXlZNpDiC0ASecR4G3TwIV0HiujFN0ySwKUDeFXlNnleMSuiB+pBb7U6ODPm6NIfN6+2XgtKZk42WlISpH1z8+jdnncnjny5m+fbtzBw2rA9W130MjwQRUjbJ2D5U0XfvRBsc+75sArxa/0wssrDoAZY0+veolLKwvQMtQdkKk7Im4VAd+KP++udCe0IkH9b9RXAH2ExuzAphj8cmqkClIUhXJQaxk54pYzGMicY1GRGerIANIZX+YL2IlEcof7+c2q9q0St1FKdC0ugk8i7Pwzuh+VqOiUbtF7VIKUk9JrXJ81pkB97yv1Kb/bMGF3c38LOc4TilyQW+ik5/w0kFQ8g76UT2vvd+t62rJ9GSkhh03jnYkr0gJYpmQ5pmk+zwdI+HsXm5/N9bbzP/9tv6cLVd586KPQSEwpNpef1S+HcEffcOHEfOAWIRDeflpPXxiiwsEgMp5SQAIUSSlNLf1vaNsQRlGyiiUa1HKQntCeEc1L0ubwXJTVkhvAdc/3OEbHJeT1Qj5ZqQytZwYovJwI4A5e+X41/nx/AbqMkqnokesk7N6vbvszeo+LAC1zAXykExbAq+zFvp7u/iqEANp9dVdWlWGY1S/NH8bltTTzPgjNNwpKc1EZCymR/hz045mWufewFd19G0/ntK/caRxL9Tcw55MQmg79xG0iXfBUAIgfoteM8WFu1BCHEk8BTgAQYLISYDN0opb2lrbP89+/UCa8rXEIg21HaMVkRR7Eq3x9ONcprYmjmfHXiOS9Rz3ic+jahMrMWZponvax+VCyoJbAkgIxJbpo3Uo1PJPDWzSevC/oapmwR3BRn0vUEHvRZ1jImVAxLdG4JwbXVxl2Mofdt3dNNqeh7N48GZnX1QrUrRzI/wihkzuPGFF/nrJ5/woxP7b3/v51NyCCfqSaYbkZEwxt5CtILhQOzW67/FldxWYBWitLAgluV9CvAW/9/eecfZVVQP/HvuK7tv+ya7KZtOGiQh9N5BelNBpYMFRSn6EwUUVIoFRRQbIopI70UEERAIivQOgfSeTTbZbC+v3vn9MXezd1+279vd7O755rOfvFtm5ty5c+eee2bmHMAY876IHNydhKpQdkJDvKGNhTK6Nkr25Mxbs7Ikfabm0CKavhR0kHCTLjX/qaH6v9U0r2kGA1kTshjzmTGMPmx0m8UrQ5mqF6uQgFC4+7auTozTP+6sClPbzh/sKW4s3q6Fb3vEyQpjuhmdx3Ecjpk7l9+/+NKQViirA0HcDH+IbI8kVy0nOHEyEs4CIGEMNcnMO89XlKGKMWZt2sdztx4QVSg7YZfSXUikElu3o2syN9w9NuhySnGc6VkuCUO7FsqhwoVjovxlc5jViYFvTm7SZfOTm6l9rZZ4hV1UE5kWYeJXJlKwT0E7Q8JDC0OIhqLPEcs7FCNhQrHFNNbciOTF2DzpDpzUFrLrnyORvQuJ7B177LC8M75QW8FlW9YxLhmn2gkShx5Hy2neWMHGBS8R3ViBEwq1uunZzolvqWL9v54la9Qoaj/8iFQsRnZpKQU7zab2o4+JVVURiGRTuv9+FM2by/Wf/TQ7/uga1lRVMXnU9hHCb2Uom++Pmcp/cwoJG8Nn6zaT5bo8WDiGJifAPs11HNlQxd+KxrM2lEVhKknQdUkO8WemK5LLlxLcYebW7SwRjhid+XnxijJEWSsi+wNGRMLAJcAn3UmoCmUn5IXzmFI4hSXVSwCrUBbs1feOpzDg8q2xUbLELrIJeL6bM+yTfMCoTwnlyYFdJZmKpthwzwZqXqnZujK77Nwy8nYaXq4/6kouJp491/MdaYe0g0dfjPvPL+EaIFhKU9HpWD+Tjl2IY1p8lPa+NZ1VU8G1m1dtHeYucZM9tqLHqqtZ/fCjmIT9KHPj8S5SbF80Ll9B4/IVW7ejmzYR3bRp67Ybi5M3bSoAs8eOZUJREVc89jj3fvlLAy3qNlQ5QY6dPI86J4ArDkmBewvHYGiNu/1KpIBXIgVb59JUB0Pb70TtDJJcvYLg1NYV+UZgt/yBDVShKNsxFwC/ASYA64BngQu7k1AVyk5YW7+W1XWrt25H10YZc8qYPud7YF6SIG1XbA/lqUvP1oVIDtB7KNmQpPzOcureqsOJOIz9/FhGHzl6yFsi2yMZHEs8e85WZRIAcQjOmQ8iJN57i6zd9/YaTwYbkzFcsWXNNnMme5rrljffHjIWyd5QMGsmTji8dV7lVw86kF888+wgS2W5t7CUqDhthrC3Gc5ur50M5Y6omyRXryBywme3bgcQnthcw+njRw+iVIqyfWCMqQTO7E1aVSg7YWXtSkJOiFgqRqo5RaImQdbYrD7nOzHkEhpG+k95wsH0s201UZOg/PZy6j+oJ5AfoOzcMkYdsn0MLfYXqVAZQmoby6DjODijS4kueMYqlBkm27gUZ2DOZHTT5mFt8coqGU0g3KrsX3H0UVzz5FM88s67nLL7boMoGXyQnedFtVHSSa5aTnBKq4WyyXX5pCHaSQpFGf6IyA87OWyMMdd1lYcqlJ0wrWAaCdcO10XXRcmekI0E+q44PV8fZEwozughXPuVSeHl+iCbkkKAvg+xdkR8c5z1f11P46JGgsVBJn59IkV7F2W8nO2RUPQTArGVJCNztz02ew6JD9/tl3Kj4nBPwRjOqNvUpw6iZJ+92PDvF3Cjw/NlXbPwY3ImTyYyxsZSDweD7LfDNH769L8GXaH8atUGPg5FmBdv5qT6ShqcAHcXjeWNyMieK+g2N+HWVBMYP3Hrvogj7Jg39FyHKUqGac/nZC7wZWA0oAplX5hUMInJ+ZNZWrM0owtyVsYcGlNCccBsl47Ku2J51OFPlVkkDbi0KJSZJbo+yvq/rqd5eTPhMWGmfGsK+bsMDafjmcIxUbKa3yaZPRuk7aOatf+hxG76Sf8ULMK9hWM4tb6SgHF778h88iSCkWziw1ShjFdVk6ipIbtk9Fb3Qj8+6SQO//VNPPfxJxw5Z6dBk22PWAML1nxAQhwixsUF6pwA72TnkRwBK7k7IrV6pV3h7QuhmTJwUum2HhMUZSRhjLmx5beI5APfBL4I3A/c2FE6PyO3Z+kGjYlG1tSvATLrMmiPXJexoaGpTBoD91aFiRuhRdVIkTaHrw80rWhi2Q+XsezKZbhRl2nfm8asX8waccokgCsRGotO20aZBMg69EiIx0lWbOiXsq/ftJKcPiiTANUffkSivm/hGrdnIhPKyJs6pY2vykNnz+Lze+zOyX+8haqGhkGTTbDWgog3D7bRCfDj0ikjWpkESCxf3GaFN9hFkR/UD8+PHkXpCSIySkR+DHyA7UJ2N8ZcbozZ1EVSQBXKTnlv03uEHOuCJZMWyl0iSbKGaM3Xu1Cbyrwm3PBJA0u+t4QV164AgelXT2fmT2aSOzs342UNFRJZsxDan8voZEeQ3Dyizz2V8XLDrsvOsR5F3GqX+iVLMcPYv1/e1KlIaFsXTfd++UuU5OVxwC9/NQhStc+b2fmEhvF81u6SXLaE4IzZbfZFXcO/t9QOkkSKsn0gIjcAbwL1wM7GmKuNMdU9yUOHvDshN5SLa1yMa4itj/VZoRwXchkbdDFm+43L3RWScKE6RnO9IdmYxG1wSTWmSDWnSDWlcJtcUtEUJmpwY679i9s/k/B8I6Xhxl1S9SlyZuYw+WeTyR6v85kAnFQlxnSskAWm7ED8rVfhrK9ktNyECFsCQcb0cWFOePRoohWbCObmEBk/nmRzM83ryzMk5eARKigge+wY61czlULSwi06jsMrl32HHa76Id+49z5uPuP0QZIUapwAr0YKWBPK8kYSRjbJFUvIOuiwNvuCAgVBXcCkjHguBWLAVcCVPsfmgl2U0+UEbFUoO2F+6XwioQjV66oJ5AcI5PSu0wmJ4fySGFPDLilsBzaYuK7L668188KLDVRuThKNGmIxQzxhSMQNiYQhmTSkUpBKGVwXtgka4o1yiyMQAAkIEhScoIOEBAkJTtjByXII5YRwsh2cbKfdRU0SFEYfPZrw6J66zR7eBBPrKdz8a2rHfr/9Ye8996XpsfszXq4R4ZApu/Di6g8Y53Ps31PKjjyCkr32JFSQj0mlAKFh1SrK//mvzAk7kIgw/shPUTB7plUkHQfpYCX1xOJi7v7SeZz259s4bt5cTpg/f4CFhTsKx/Cj0qmEjI3E1eQ4ds7KCHAN1B7GGJIrlhKaPqvN/oAIp4wb3h4jFKUrjDF9HjdVhbIrTN+Hu08ujDMt3NZV0ECOPrUokP9+voGFC6NsqUzhODB2bJAxY4KMGu0QiTjk5DjkRITc3AB5+UJeXoC8PIeCggAFBQ6FhQ55eQ63bomwNOqoxaPfEepKLqajmSnZRxxH45234sbjOOHMKuPXbl7dZ9dB4jiEi4usn8ZgkFQ8zobnns+QhANP8fydKZg1AycYBM8q2Vkoyc/vsQdPffARp976F9b89MeMKRi4FdYfZeVwdelUok4AnR1ocTeWI5EcAoXF5AXsM5U0ht/uOJlJ2foxqyh9RRXKTvhg8wdEk9E+L8jZKze1jd/J/jYSvPFGI88801aBHDcuyN5753DUUXnMmxfpVb7NLqpMDhCJrFkYJ9tGwGmH4KQpEAwRe2UBkUOPyli5jjGcXL+FcAZW7/vjwTYsX2HHTvqc6+BQvOsuOGlzJqWLB/n2c8/mv8uWccANN7L0umv6U7w23FswhtgIX4CTTmL5EoLTZ5HtCFdPL6M0K8QBxXnkBnS4W1EygSqUndCYaEREiK6JUnRQUS9zMQMap9sYww9+sJHXX2umrKzvCmTbvCHuDs3wkEMNg+BKNoLpVAELjBtP7MVnM6pQBozpF1dQbjzRqUVve8cJtyqTxpgulUlonU855ftX8aU77+Kv55zdnyJupTYQxPXkE2/Ie6QOdbeQWreG4MQpBESYk5/DbgUablFRMokqlJ2w65hdSaQSfbRQCjdszOb80iij+rm2EwmXiy4sZ82aOL/5bRlz5mRmcUtjCh6uDvNBs1Uz1O7RfySDZTSMOo9E1mwgCXRuPYl85nQa/nADyc0VBEvHZkSGhONw7MR53Fm+iHFu5kInFs2fR7K5icpXX89YngPJynsfoHiX+dR98gnx6hqc7GxK99uH4l3md6pcjiss5MHzv8JnbvkTx82dy6l77N7vst5YsZxZsSb2jDawb3MdCRHuzy/lqjHTSA7DMKXdIVWxwfqgBObl9f0DW1GUtozMnqWb5IZyGSfjSDWlCJf0fo7NxHCKvH6u6fr6FGeduZaNGxPc/reJGVMmXQO/2ZTN+80BUljfk0m1UfYLrlNAzbgfWmVSHJAw9hHt2KqX+5nTCEyYRM3lF2ZUlj1i9RSY9JVYfadu8dIhaylLNTZS+cqrxKtrADDJJAUzZ3Qr7cm77sJXDjyAM/96O+U1Nf0npEe2MVxSXc5+zXUEvO1n8kaN6Cc3VVGOM248O+dHCA1FFxuKsp2jCmUnrK1by5KPluBGXRoW9t5J8XGFCcL9WNPr1yc484w1BAJw732TGTcucxPMl8QcalOtTsyV/qM571AMobZzJqVrp/FF1/+B1OqVND3xUGYEMYbvbFlPToYVyqa160jW1w+b+N4Fs2YioVC3hr4Bbj3rTKaMHsX+v/gl7jZuEzKP0NrBrwxl82pOAYkRap0EcDdXECgdx9u1TWyO9957gaIo7TNye5dusLJuJaPmjaJw70JW37iaLf/e0otcDEX9OOf7ww+b+fKX1jJxYog775pEbm5mC6tIOKSGx/t/uycZngpOzz8GguMnEPns6dT/4QbcDERnyTYuRRkc6m4hVlWFGQBFaqDIGj2KQA9X179y2XepqKvj7Nvv6Cep2mdZOHvEOzZ3G+px8gsIO8Ka5vhgi6Moww6dQ9kJOxTuQJIkk74xCSfbYfOTmxn9qdE9zEV4vj7IofnJjPuffP75en5+/Wb2PyCHH/1obLcsJcbAoqjDq41BEgb2yEkxKezyckOQjQmH6Vkp9spJ8mFzkI+jAWTIrskdGqQCRTTnHUkyawdrnXRj4GT1OJ+8r3+b6AtPU/PD/2PUr/7cK1kOaKrlnJqN5LkpGiVAQSdO1buLG49TvfBjGlasBJEhvSgnneoPFxKZUEayrp7aTxaB41A0by5506Z2+CyW5OXx6AVf4/jf/4ETdp7H6XvvNSCy7t9Ux57N9dQHgpxXs5GSVIIn80bxUEEpsQ58aQ43TEM9kpdP3BimRnr+jCmK0jmqUHbCxPyJTCmYwpLqJdS9W0eqvncv2HebAhyUlyRA5qaP3XtPNbffXs0ppxZywQXdV3KfqA3xv4YgcWMFWRoNYF1Og4uwIubwTF2IAJBEulxlrPSeZHAcNeOutoqkEwaTwD6Shp6upXcch6LrbqL64nOJvvZfsvc9qEfpL61cyzeqy4kYFweI90qKtqRiMVbd+wCJhgZMMmkb/zBSKBM1Nax+4CHECXiO28EJh8mdMhnpxBXNsfPmctGhh3LO3+7ggBnTmTyq/51q5xqXO8sXAUIAgwMUpZI8WlA6IpydG9fFNDchObnsWZDD6LC++hQl0+iQdyfUx+tZXbcagLw5eYw6vHcd/2eLE4Qkc332jb/cxO23V3PRxaN7pExWJoX/1rcqk2CVRkPrHMmUt92y8MbQ9Rw+pXc0FJ+FkUjrMLeEujVnsiPCc3Ym68DDqfvx93CT3R+yHpuMc3H1enI9ZRIg3GspWql+/wMS9fVWmYRhpUxuxbBVmcRxGHfYITjd8Gv429M+z+yxYwdsPiVACAh5yiTAT0om0yzOsFcmAUxTI5KVjQQCfFDfTGwYTb1QlO0F/UzrhA82f0DICRFLxZCwENsQ60UuhmlhNyNxu13XcPllG3j//SjXXjeW/fbL7VH6pVHHvjuG4Xt9KJLI3qlDp+W9peDKn1L56UOp+8WPKPr+T7qVZv+mWhII2RluGPXLlrcqWyOA7JKeTYd5+buXUnb59/jcrX/hkQu+2k9SdcxrOQWYEaBMArjVVThFxYDt/j5uiKofSiUjzA0V8daEEwakLOGjASmnt6iFsh2qo9X8a+W/WFq9lJRrX4huzCUyrTe+y4SqDKxviEZdvvyltSxcGOMPN0/osTIJUBo0BFWbHFQMDvHsnYnm7Atu5hcGOOEw+VdcR+z5p0ksW9TpuWOScU6uq2RqPJqxjwxjDM0bNlC7aDEywlYUJxoaMakUxnVpXLOW2kWLSdTVdXh+UU4OT170DR57/31u/9+rAyipZXJ85ARlTK5ZQWDyNPvbGAqDI2PeqKIMJGqhTOOej+/h1+/8mqDYqmlONQPQ+Ekj2Uf1zrfjLyuyuXxclOJe1nZlZYKvfXU9AHfeNZGSklAXKdpnepbLF0bFuWNLljeUrQwkydBEasZcAeIFNZQwGDfjVsrIwUfQPGc+Nd+/hNIHn233nEsr13Jx9XoSXjvIpe9DgMnmZtY88hjxmlo7J7cHw+7DgVRTE0v/8lcCobBVLL39Uz9/KlmlJe0u1Dl89my+86kj+Oo993DwrBlMLy0dMHn/teZDDp8yn7Xh4e/k262uxhldAsCk7DA75OiiHEXJNCPLhNAFn2z5hJveuYlYKkZjspHGZOPWY4HcAJse2UR0bc+/6k8qSvTasfnSpVHOOXsdBQUB7rl3cq+VSYC4gfuqVJkcDAxCbel3ME4+xomAEwEJ9Nv8taKf/Ra3ppr6v/xum2MHNtXyjepyso0h37jkm8x4Gd3w3PPEtlRhEgncRGJ4zpnsipRLKhrFTSQwiQTFu84nXFzUqQeGX5zyWeaVlXHAAM6nBLi+ZDKbgpnzWbu9I14rTxozrLwNKMr2giqUPh5d+ijxDoYhp/9wOgCbn9rc43z3zEkR6kVN/+/lRi78Rjnz5mVz218nkp3dt9u1MKrDPINFMrwDxslpxxrZPwqlk1dA3le/RdP9fyO5uaLNsXNqNhJJc1reVyncZJKGlatAFzu0oXj+zjihrj8C//udb9MQi3HSzbcMgFSWB0aQyyA/G2MJljb1Zj68oiidoQqlj8ZEI24H0UE23LsBgLJzynqYq+mV/8mHHqzh6qsrOO74fH5xw3icDKzqibvqAmiwMIQZ6NVQeaecQaBsIjWXX+QJYQi7LvmpZEYffOO61iKpbIMT7N48l7zsbJ655GL+uXAhf3zpP/0slSWe4akWQ4WACE0p/fBRlEwzMnuUDvjUlE+RFWg7tyZWEePjr31Mzcs1jPvCOAI5Pf2iF362MZstPXjf3vTrzdx6axVfu2AU3/pW5uZU7ZPrcnR+XJ2VDwKh+Cfkbvmb52uyf8lLJfnNhmWsWvo6n3ztK7irl3Pwfbfy0Yq3WLnsdfaINZKJ5UCpaJR1Tz3Not/dzNI//WVEuJ/pKSvuupfmTZu6de4BM6Zz1bHHcPH9D7C4oqLrBH3kpVXvMSne3O/lbG84wNy84T9vVFEGGlUofexcsjPJtJBzDR804MZcSo4voeTYkl7lu0NWivxu6KGu63LZd8v55z/rufqaMZx6alGvyusIA7zRFNIZlIOAALHc/QfESHnP+kV8ur6SLAwzS0u4+NBDeeL2P5Pd2EAAKHBT9H4mrsUYw+qHHqF+2XI7zG0MjCAXQd0lu7SErOLibp9/7UknsseUKRx4wy9J9vOiprcj+VSOoDmULczNixDKhB83RVHaoAqlj8eXPU7QaTtENeow68w8a1zvVwUeW5Ag3EVNx2IuX/7SOhYujHHzzWUccEBer8vriOUxh5pUqxNzZeBIBUpJZM8Bp6+qXOfMiTWyc6yRLJ/m+uvPnUJhJMLJf2ydn9fXFtC8vpx4bZ3OmeyC0v327dYcSj8vfvtbxJIpzrvz7n6SyvLz0ZNoHoFzKN+pa2JzXKdoKEqmUYXSx5LqJcRSbSdrizcBsurFql7maijsos+urExyxulrqKtzufOuScyY2Tv3RF2xKelkwDmM0htSoXEI/f8Smx6Pkm7XchyHx7/+NV5cvISnP1qYkXJi1dUjcxV3DwkVFfY4TU44zAUHHci/FmbmXnVEeWhkus4JO8Ka5sz7gFWUkY4qlD7mlcwjy0mbQ7nJKpilJ/Z2LqPwTF2QeAea3NIlMc45ey2FhQHuvW8yo0f3n2vQXSJJpoaTzM1Ocn5JlG+URtk7N8m4YIovFMe4uDTK8YVx8kUVhUxgCNCcewg1Y66gKf9Yb2FO//JCTiGvRwq22b/fDjvwmV135fTbbuv1UKobj7Pl7XdY/eAj1C1eglHrZJdsevkVYlXVbHr5FVY98LDnWmlLl+kuOuxQtjQ2UtnQ0G+yfadyDdnuyJumEDeGaeqHUlEyjiqUPk6cfiJJ0/Zlu+kxO6G+YLdtX9Ld5aNma6JMN+i8/HIjF164np13zuYvt00gK6t/b0euAxeUJvhiSZy5EZdZ2S6nFsW5bFyMvXNTTM922Ts3ScAxaHzGvmFwqBnzPRqKzyKRPZdEZGfrd7KfrXqNgSAfZOVudVju576vfImk6/Klu3o+lOrG46y89342v/IaTevX07R2nQ53d4Pajxay4o672PL2OzSXl1Oz8GOSjU1dKuOTR42iMJLNzf244vtTjTX2BTDCLM17FeQwKqQxPRQl06hC6eONDW8QSpvjVv9uPaHRfZv3dkqxnUPpXwT70IM1XHN1Bccfn8/PfzEeZwDC1IlAQGjjxijsgOPtB3i1IUhDSugv/4gjhXhkD5LhKeD4pi+I0+8roUuScS6sKSfUzgfB2upqABZt7PkK4uoPPiJRX48ZYdFvMoanQOZOmUz2uLHdCku5z7RpPPLOu/0m0tWlU2kagDa5vfFufTNRdRukKBlHFUofC9YuIJpqjYSz4f4NuFGX6ddM70Ouhinhtp3Xr39l3QJdcMEovplBt0CZ4MPmAElVJvtMLLJrW2VygNinub5d/4Kvr1jBnKuvZadx43nlu5f2ON/65SswyZE3PJpp8qZOIRDu3tSH8/bdl082buw3WV7PyR9xyiTYT+WPG0eeuyRF6W9UofQxKnsUAWldQbPlX3auU1ZegLnZSfbKSVIU6OmXrbA5aTtt13X5zqXlPP10PddcO5ZTMuwWKBOkK78tZIlh10iSPXKS5DoGMcIujbM4smZfpkZ76ux9+BOMLwd34FeSLg9nb+Nj8u/vvc8BN9zIMXPn8Ob3LifYTWfbfiLjx2ZGwBFO88YKUonuWXm/sOcepFyXFxYv7hdZZoxAH5RgQy8W9+IZUBSlc/Sp8nHqrFO5d9G9ACQbbKc//oBCri1rxhH7ZZs0cGNFNtWp7uviN1VkcUlRE1d8Yx2VlSn++McJTJ+xfU4KP6UoQXVS+CQWoGXYe052inNHx7YOombFCil94/tEEoWIsbMtr514C+/nLtGRco9Iw4u4gRKaC04YUCvQonAOXx8/i/vWf0IQuHnBS1z0wINccNCB3HzG6b3Ot/SA/UnU1VO/dFnmhB2B1C1aTKggn9L99+s0vjfY1fnTRo/mlv/8l8Nnz864LI+v+YhPTd2FdaGBt6QPJlMiYV2Uoyj9gCqUPpqTzTjikDIpKh6288x+e9Uocn1ufx6qClGf6pmCcGiyiW+ctYZAAO66exKjRm2/1f5+NMDyeKsyGRHDeaNjbfxoTvzoa+TEShBsxTxX+BqLclapMukjGZpMc/5RAz6kGMLw5w1LCALff+zvXP/MM/zk5JP43rHH9CnfxlWrbaxupU+Ei4sp2XuvLpXJFo7feR73v/V2v8hyfekUNgVGnmPz5pTBGNPte6AoSvfYfjWbQeCxZY+R8txoNCy07joiaYG432oK9miOYdPyJm76+WomlAW5+Y8TCHfl4XyQ+V99kLhpvb6dI6k2yzucRA6RmplblUmAJ4tfIuaoXzc/0dyDQPrXiXk6rusy+fkn+fRj9/HWihU0JxL87bxzOGffffucd/UHH+qCnAxQsNPsbi3IaeGSww/jty8uoKapiaKcnIzK8lBBKfEBWAy4vVEZT7KkKcbs3JFlmVWU/kYVSh/NyWZcz/V30bxCNr9Yuc1a2ZS3QzCEBeIGTAcKZv0bNay+ZR177Rnhpz8dl7Ev4phrV2q3rMxOGnANW62IrrFyZUmrgay7aWJpUyiDvqtLGpBUqyKZwiXqxIiJKpPpGMmyq7r7GTfaTPM/Hib67FMkVy2j0sC8svFcc+IJfP2Qg8np5gKQrjDdnPendI4EAj2yWk8vLSUvK4tb//sylx19VEZlac+11HDGeL25IxBVl1eKknFUofRx1JSjWLroIy5Y9znKx1RwNt9h8ofns3ne7RCwL9Tvj2/m3aYA++SmyHasEvZwVYh3moO0jPlmiaHk3xt59u4tnHxyAedfWIKh7yPCK2IOD1SF2ZwUHGDnSJK4ERZFAxigLOiyQ7bLG41B4gZyHNgnJ8GH0SCVXppdcpI0pYTFMasYTgy5TM5yebMxSMJASKyy3KJGvtIUJGqgyQhLogEgyaypNzC9aRrPF75BwkkQdsM4ruA6I8ufXWfkV98OJkks/4h+GfZObiyn5spvklq1HLKyCO00n4KrfkbWgYdzWH0ll2xa0ed43X4mffZk1v/zaRqWr8xgriOPzS+/giCM2mO3bn9g7jV1Cg++/U7GFcr/rXqXUybsxJqszFo+t0t8VR0E5uZGBk0URRmuqELpY//CfZi48puEUgHeqfsIgIaJ/23TGY0KwuH5qa06QkNK+CjaqkwaYwjfvYZnn6/nGxeO5jOfKfT29022zQnhls1ZW4ejXeDd5pbbZ/etSzqsa3C2bje48HxDaOu2C7zd1DbNmoTDmkRrmtg2coqnLLemWZS9jkVZ67bWS3Mgqn7Q0zBOLvG8/fsl7/pbf0PTg3cSmDyN4pvvJjx7Tpvj59Ru8j4IMndTEnV1NK5em7H8RipOOETRvDldn+jj7L334ev33ZdxWf6XU8jmERh+cce8CEFnZFlnFWUgGHkTaDqh+c1NZEkWDs5WDTBatAKctsN9fsPCyw3BrcPgxjWsv34Fb7xYz49/MnarMpmepjcsqA+SbEfZa2v3bM8heXvbXaXpqpxuFDPCac49BEPPhje7IrF8CZs/fxRND99N3oXfpeSvD2+jTO4cbWDHeBPhDGv4VW+/i0mpH8q+Urjjjkgg0KPpL2fvsxfxZJJXV6zIqCw3jJ5IsxPo+sRhxrv1TWyKDbxLL0UZ7qhC6SNR0QhJO7emLtYIgLidd7gbE0IKIdmUZMllS6hfFeWmP05gn31yMyrbxoSDq1rbkCEVmgBOZqw/rutS+/MfUvXV0wiMLaP00efJ/cxp7Z47NRGjP9S+2JaqEReirz8IjyrGCfVsMkIwGGTyqFH8YUFmwzBuDI486yRAWIR1UZ33rQw/RGSSiLwoIp+IyEIR+eZAlq9D3j7CkwtoWryR6jEv8q+n7wfA7WL18rGFCcrXxnjth6vJiQhX/XUKM8ZkRk+vSAgv1AXZkHBI0XZuo7J9E6l7gmSwlFB8DdlNr2KcLJrzjiAe2aNHVsvYO29Qe/V3MYk4BT+4nsih286jy3FTnF1TwQkNW0iIkJUhxS+2pYotb71NbEsVxlXrZCaoeuddskaPJl5dTe0ni5GAQ/HO88ifNbNTq+Wx8+bw9/c/zKgsV25ezQ0lk0aElVJEtn4QxY1hB/VDqQxPksClxph3RCQfeFtEnjPGfDwQhatC6SOyezEfVP2EWGQ9Bx8pvPkBbKmKMbok2OGQ7oYPGnnlyo1M2yHMb39bRijk4HrOvvui+q2MOfxxc5ZdjY14yqQyVAgmN1C86SdAEMEOryVC04hHdqU7j52bTFJ79XeJv/oS4X0PovBHP8cJb+vmJNtN8dSaD5mSiJFjrHU9Rd/bX9P6ctY8+rgd5lbLZMZI1Nax5uFHkUBg6xSCrJLR5M2YbleAd8BFhx7GLf95mYZolLzszLi7OaC5lhuYZO/vCPLJuHdBLkUhffUpww9jzAZgg/e7XkQ+ASYAA6JQ6pC3j8q6fxMvqMAE4hx6WB4Ab7/b3OGb+Ym/1/K9KzZy0MG53HzzBEIhW50tUXX6wkPVYeJGtg5zm27NdVS2F+zdMluVSVdyaCr8NEjXL7Lk2lVUfu4oEu++QdENt1D8k9+0q0wCnFq3mck+ZRKg1S1979nw/AvW76Qqk/1CizIpwSBjDtgfpxNlEmBu2XhywmFu+98rGZPhR6VTaRZnRCmTAO/UN9GcUrdBypCkRETe8v19taMTRWQqsBvw+kAJpwqlj8otL+K6TQBEIg7HHJPPfffWtHvu739Xye9+t4Vzzy3myivHZjTqQsrAhsTI6uSHO8msHbCjEZ3T9PgDbPniKQTGT6DkkX+TtfvenZ5/ZGM1uSazL0c3mSReVZ3RPJX2yS4twXTTJ+JukybywNuZi5rzViR/xCmTYD+2PmkcmXHMlSFPpTFmT9/fre2dJCJ5wCPAt4wxdQMl3Ii3+xtjqK17h4aGxbhuHGvfsdaDzZVJ4vG2FhrXdbni8o28916Uq34whkMOycuYLDEXFkYDxFwrhbqSHj44iY2IG8c47fv8c5NJaq+8hPhbr5F7zlfJO/eCbuX7anYBhzfWZMTnZCoep2HFStx4wioaap3sd+K1dbiJBIGsruf0zSsr45mPMzdytWOsiY+yM9d/bfd4zTlpDKN0yFvJFA2b4OWbB1uKrYhICKtM3mOMeXQgyx7RT1Uq1cS7755LQ8Mi3wxFq0zW1qb48IMov/1d2dbzm5tdLvjaOjZXpvj9H8qYNStzobuWRh3+UmlfKoYWZTIT7tCV7YFAqpKCyt9SO/b72wx7J9euouqSL0I8TvHv7iA8Z+du53vrqDJmxps5o35zn1pK45q1rH3iSc97pQGNJDIgpJqaWPf3J5ly2ue6HPYuzc+jMZa51ckPrfuYI6bsQnkwPPwtlb7rmxbJYmpEF+Uoww+xQ6W3AZ8YY3410OWP6CHv5ctvpL7hI1JuE67bjOu2DoO89moTe+wRYcYM2/Fs3Jjg9NPX0NTkcvfdkzKqTCYM3FaZRcwIMSOe83KdMzm8EOpKL9lGmWwd4p5IySP/7pEyCbBfcx2fbtjSp5biJhKse+JJTCKBm0homMUBZsKJx3Urvvf4gkKiicz5T/xpyWQqA6Hhr0ymUZ9MYdT6rgxPDgDOBg4Xkfe8v+MGqvARbaHcsPExb5h7W1zXbNXnPvygme9etoEpk8P8/g9lWxffZIol0RGt148IkuEZIK1Wkd4OcadzRm0FkT7OoWxcvWbEKRXbC1mlJQSys7s1B3tCURGxZOaU/UcKSol3Q5EdblQlUixuirKjhl9UhhnGmJcZREvUiFYojem4cx5dEmTZshj/erqOG2+s5IADc/jRjzK7+KaFlNHIhcMdg0PLXe7LEHc6YddsHWaocQKEjWmz4rtbsrmuzpccLHowV3VCURHJDE5F2Dby1shABGLuCL14RelHRt7nqY+SkiPwK/MVFUkWLYqyeHGMYBA2b0rxy19WcsYZRVx99bh+USYBdo64fLYohqNq5bAlFF9M3pY/0fTYvX0a4k7n62UzuXTMNA6csgs7T9+T2dP34uKx0+nJTLv8mTMYc8hBaqUcBGKbNrP+qadJdWMoe2rJaNwMKv6vr3qXKfGRstq5VXEPC8zLU+ukomSaEW2hLCjYjYqKJwD45JMoF19UzqxZYYyxfU9+vsPRx+TzxS+N6ndZXmrIxDpdZXtFgI0PbqHh0bv6NMSdTlIc7ikc61MGDefXbOzxmEfVu+9lRB6l5xTvMr9bH6ujcqyHgJqmJopy2vcW0BOezy1mUzDc53yGGjNzIgT040lRMs6IVijXrfvb1t9VW1LMmp3Fr389nqysgTXcro47VCY1VvdwJhkcR/PrH5J16FEZUya34ns57hJrZHq8uUduhJrLN5Coq9dh70EgmJdH7pTJOMGuu2LHcRAR1lRVZUSh/NXoiSMi7GI67zc0sTGWYFyWfsQrSiYZ0UPesdjGrb93np9NwIEHHqgdcDmqUqKq5DDHDZbi1lYTnDq9X8uZnIjR01l2iboB83urpBEqyMckux8nPeQ4rKmqykjZFSPQOgkQFqE8mjn3S4qiWEa0hTI3dyb19R8BUFAQICfXIZkYeCvN3OwUs7JSfBgd0bdjyBHPnkdT/rG4gQKymt4l3Pw20bzDSWRNJ5BYT07dsySyZxLL2Q9IYZoeIjRzxx6Xc2RDFedXb6DQTfHPvFG8mFPIl2or2DnayEdZudxWNJZDm2o5vqGKuAjhHi7KyZu+AzlLl9G4fEWPZVP6RvOGjdQtW0bR3DndGvYOB4OU12bmo/fqTau4vmQSjYGR0u/Yvj1uDNNz1A+lomSakdKTtMuECWeyaNH3tm5v3pRk//36PpTUU7IcKAoaAhhSaqscEjTlHUNj0SngWH+kTcEJNBWeDBiQAKnQROI5+wIpkBBuMgmpJKGderYI57uVa7igesPW8IqzY41cvmUtLvbhnR1v4nP1m0mIkOUNWbv0zCV+IBwmlJsLjqMOzQcaY4hVbsGkUkg3hr2zQ0E21GTGorxntB63ZZX5cJ9T6Lu+vQtyKNRIOYqScUb0kHdFxT/abH/ms4W89150wOWoTgqvNgRVmRwiuJJNY9HntiqTADieg2jx5qRJAMQBsfO0ksuXgAhOYVG3yxmdTHBhdXmbWN3Z2Ie25XUY9LazfPMfHXrmiCxeW0ftwo9VmRwEJBRizAH7dWsOJUBuOIuK+swolD8cM9XOoRzuymQab9c105TStq4omWZQFEoR+ZyILBQRV0T2HAwZAGpr32mzHW12ee+9Zmpruz+nKROsjDsERlafPqRJhqcg7UZa7/gmJhcvRLJ6Fl1p92g98QF4RJvLy7sVqUXJPNmlJdYPaDfJz86isrExI2W/O5LiePsICCxqGCnukhRl4Bist8hHwGeB/wxS+QCEgoVttv/0pyrq610WLhxYK2VZ0CUshmwx7JWT5IC8BKMD+gVtUoa1t6xl8XcXs+Z3a2hcnJkXaV9xUvUYerY6NrlyGVJQ2PWJPhaHIzQOgKIXyMnRKJ+DRKqpuUfKfEF2hKoMKZQFqYH9cN5eSBjD6LAOeStKphmUp8oY8wnQb47Cu8uECWewYuVNtEzWHjMmyDHH5rPvvgM7j3JsyPCD8VEESGG1/CPyEty4KUKjOzLf9CZl2HDvBqJrokz8ykSaVzSz7i/rCBYGCeanNVvZti0ZY+w+X0h06cAMLCHBJA0ty6ONa9MaTOtkxJaJiQCsIR75P4zjtRNxwLitBTlOqwsexwoQf/2/mKYman961VaZ7RB5OzHbAw6C8JHAgW6KkxKN5ASDOI5D0HEIBQIEHAfXuCRSLq7rkjKGlOv6/gwp42Ja9nv/J1MuSTeFa4zPS5Chft06gqkUTheaZXce2Uy02Ey1+sF7erou2WBoKl9Pzi23EsjO6lblvrJiBaNzczntL7fZPPrg6ikcvof6cA7dUit7UY5pbECyI94z0J0EYFJJO/XCdcFNYVL2/7bbrfuMmwJvn2k5L5XaZtsk4kSO/yxBgfn5OUyJ6KIcRck02/1nmoh8FfgqwOTJkzOadzxeie34DdVVSTZtSnL22UUDruiK2OgNwFb/gY9XhYmOMCNl8+pmomujRNdGqXmlhuwJ2ezwvR2IFEYYO28s448eT+n6UpZXLicgAZImyfic8VRFq0ikbKSRrEAW+eF8NjVvIkCAlJtiSv4UVtWtImACJNwEKbftKzRZm8QkDJGxEYISxBWXyXmTWV2/moATIOkmmVQwiU1Nm0iRwmCIBF3cUUezKQkB18UVYWp2iJWxJAHXJYkwJTtMeTSG6xoSc+aTxCD5BbZQ4ymrW1/UrS/sllCIAdflG5tW4UYDxJJJXGNIplJEEwlSrovjKZeOCOFAACcYJOA4rX8iiIi3LQTEIRgIEHQcHBHEESQQABdiO89j4+tvkaqvt8q0I4TGjiOxbh0SCmKSSYITJ5EoX2+Pp1wCxcWYeBy3qclL4xAcU0pifTkStGlCkyfZPBzHphlVjIlGcZuj9jkLBAiOHkViwwYvTYrgJH+aFMGSEtymRtxozOpcwSDB4mISGyuQQACTiKfVZXqNDiwmvWRxQAQJhSCVJDhuPKktlZiUS7YjRIIhSvbbDycWaw3FWFgItbWtC6XyC6CxgcPnzCESChLKjkAgAM2ehdMYKCiEuprWNAWF0FDfWjfhsD0WjYIILnDPxBm8ljAERUgaw7RImLVR+yy5GIq8uZ01ySSO2PChk7OzWNkcs2kwTMvOYo3nhsfFMCoYJAXUNkcRx8EJhZiUHW5NYww7RLJYFbUTOlLGUBIKEjeGeiM4AYeAE2BibjYrY0mCgQBJx2FOXg6LYwkCToCU4zA+O5tmoM4FJ2jb9rS8HBY1xwkHgiQcYffCPN5vjBEMBElmZbFzfg5/nTdtYBqCooww+k2hFJF/A+PaOXSlMebv3c3HGHMrcCvAnnvumdF3xMaKv9NilqqsTDF5cmjQraYAroGPmgMjxtG5MYaqf1dR8VgF+fPzCeYHmf6D6YTHhAk7Yb4474scNPEgZhTNIDeUS0O8geW1yxkTGcP4vPG4xmVR1SICEmBW8SxEhHX169gS3cLMopnkhHKoi9exomYF/1r5L+5ffD8p01apjAQjfHX+V9lr3F7MKp5FJBihNlbLqrpVjM8dz5icMSTdJIurFhMOhJlRNAMRYWVTjOpEkp3yIkQCDtWJJMubYkzKDjM2K0TSNXzU0MzSpma+t2Q9DT1YDHBgUy3nr19MgemnoclgEE44CbKyoKgIYwzx5ctxm5vJ3nFHJBQiuXkz8XXrCE+dSrC4GDcWI7ZoEYGiIsJTpmCMIbZ0KSYWJ3unHZFgkETFJpIbyglPnUqgqAi3uZnYkiUEiosJT55s0yxZikkkbJpAgERFBckNGwjvsAOBggLcpiabpqSE8MSJXpolmGTSyhYIkNiwgWRFBRt++ENiS5b2Tx1liOx58xh31ZUEx48nNHYsJpUi+skiJBQia9ZM2+/U1lplb9QoCIXs79oayMuH3FyrJG6phEAQiout8llbA9EYjB5t72c0CjU1UFAAOTnWWrdlC4SCUOSlqamBeAxGl3B6IEBlPMmq5hhTImFKwyHirstHDc3kBwLMzLXzfpc2RqlPpZiXFyHsOGyOJ1jdHGdqJIuScJCY67KwoZmCYIAZOdkYY1jaFKMx5TIvL0LIETbFEqyJxpkWyWJ0OEg05fJxQzNFoSA75GRhjGFxU5RoyjAvL0LQESpiCdZF4+yQk0VxKEhzyuWThmaKQ0GmeWkWNUaJG8PcXJtmQyxOeTTB9JwsikJBmrw0JeGgWiYVpR/pN4XSGPOp/so7Uxjfi3rsuCCVlUlc1+B0d4imHzE9cvwydDHGsO7WdcQ3xpn+g+lkjW/b4bvGJRKMsEvpLlv35YXz2mw74jBn9Jw26SbmT2Ri/sSt2wXhAnYdsysvrn0Rtx0/jcYYCsIFbfItzCpssx10gswtmdsm3bScLKbRKnNxKMiehUFfGmHXghwaUz1fwx/qoT/JHmMMjB27dVNEyJoxo80pwdJSgqWlW7edrCwiu+zSJk32rFlt0oTGjiE0dkxrmkhk2zSz09OMJeSTxcnJIbLrrmlpZrdNM348ofHje7SoZbAwrtv2egIBIvPatiUKC+1fC9nZkO37JnccKB2TlqYI/FNzs7NhnC9NIABj0tIUFbXZLAkHKfHNKQw7DrsX5LY5p0WxbKE0HKI03BppJistjYgwKy3NmKwQY3zRabIDDrsXtk2zY27bGNtjs0KM9aWJtJNmp7S43OOzwozPanXanhNw2KOw7fUoipJ5RvTSzpLRR9CitBUUBMjLC1C+PjG4QmGnHP1gXJRCZ/t7UaYaU6z65So2/WMTqca+W86aljTRtKSJqZdP3UaZBAg4AQ6bfFify2nhyClHkhXYthwXl4MnHpyxctLZszC3x0Owr0UKCPTXwK0ITJnSP3kPMIUnnIhkbb+WJ8nOpvDkkwdbDEVRlH5lsNwGfUZE1gH7AU+JyDODIUdB4Xz8M6123CmLjz+ODYYo2/B2c5AmMzgWyviWOA2fNBCvjJOsTRJdG6X2zVqaljbxyYWf0PBRA5se2cQnF37CJxd9Qvmd5TSv3tYNhzEGN+bSuLSR5lXNGNe0WUSQbEiy4d4NFO5bSCDbrpp2fP+yA9mcN/c8dijcIWPXNq9kHqfMOoXsQDaCEJAAWYEsLtntEsbltjdDIzNkOQ43z5lCxBHC3rSKiAhZIuR4q3wj3lzaiGNDcSYDAb49fgbJQMBap8BanBzHDm+2t+3Y+Xo4TsdpgiGIRGD/A/rtegeSUeeeQ9aMGYgX31qysyAYRFrmDDqOnT8YCiHZntUsEkGysnxpsu3x9tK0KKtdpQkE7FC1L42Tk0P2jjtSfNoXBrZSFEVRBpjBWuX9GPDYYJTtZ+3aO9ps19elKB7VM3cw/YEx8EJdiMQgKJTGGJZcuqTTc2bdMItQSYjo6ij1H9aDC6t+sYoxp4whd2YuTrZD1YIqql6swiQNoVEhUo0pUvUpQqUh3GYXkzK4zS65c3IZc3LrkFzACXDa7NPICmRx9LSj2XFUz0MVdsUVe1/B8dOO57nVzxEMBDl+2vFML+rfGNsAR5UU8vI+O/HwxiqqEikOG53PXgW5PLm5lg8bmpidm82nS4v4qDHKs5W15AUCnDJuJ4LJvWDJEojFYPIUO4S5YrmdGzd6NOwwHTZvhtWr7HzIWbPsd9JSL83UqVBSCsuXQVUVjC6B6dOt8jMMcCIRpj5wPw0LFtD4xpuExo+j8KSTSFZWUvfUP8EYCo49huD48dQ+8Q8S69eRs8ee5B10IA3/fZmmt98iNGEihSedSHLDBuqe/heIUHD8cQRLSqj9+99JbKwgd++9yD3gABoWLKDp3XcJTZpE0YknEl+7jvpnnoFggILjjiNYXEzNE/8gWVFB7r77knfIwXbxk6IoyjBG+uJ2YqDZc889zVtvvZWx/F5cMAfXbbVIfuqIFdx19yTGjx/cF23KwKXrIgzGHErjGhZ+aSGTL55MsjZJaEyIQFaAnJmdu1JqXNzIpic2EdsQw8QMefPyGHXYKMJjw4SKQ9YtTwCiq6M0Lm4kkBsgf9d8gnltv2lygjnce/y9A6LgKYqiKEMTEXnbGDNogVFa2LMsYN766sAECZBr6raLa+6I7d5tUH+Sl7cjdXXvAxD1fPSMGTP4VRIQOLkwzj9rwyQGWKnc8swWAPJ3y0d6sDgpd3Yu077bsTsOCXrDvFMjRKZGOjzPYJiQN6Hb5SqKoiiKMviM6EU5ZWWnbf29bl2CyZNDBLaTGIhloRZvdgNrQa54pIIJX57QRpl0cAhIoM22tKPo+ve1zE/sSZrsQDYX7HIB2cGehShUFEVRFGVwGXxz3CBSsbHVHeZLCxqZOXP7WSn6aE2Y5CAMeUtYCOQEKI2UknSTFGcXc9ZOZ7GpaROPL3+clJvimKnHMKdkDncuvJONjRuZWzKXU2eeypMrnuTtircZFRnF2TudTXlDOU8sfwLXuBy3w3HMLJrJnR/fyaamTcwvnc9nZn6GJ5Y9wbub3qUkUsL588/n2GnHDvg1K4qiKIrSN0a0Qllb997W3+vXJ5g0eftYpJAysCk5OPMnSUHT0iZi+8X43+n/a3P8wt0ubLN9wg4ntNk+YsoR2+R58e4Xt9k+acZJbdNM3jaNoiiKoihDixGtUIZCRcRiGwHYsCHBfvsPbAzvDXFhcSxAthjm56QwBj5oDhBzhSAwUB4x3YRL9UvVbPr7JtyYi5PjUJRVNEClK4qiKIoy1BnRCuWEsrNYsfJGwLDjTtk8+2wDRx6Z3+/lGgMPV4d4oymIMdaR+cPV9pgjNvSidRne/9FyTNKw7AfLMHFD2TllFOxRgOM4nLXTWf1arqIoiqIMdWoK5/HYMc8OTGHX9J+v5EwwohflRGPltChshx2Wy7vvNDMQbpQ+jjq82RQkYYQkQtz73//bIAyE26AtL2wh1ZRi1i9nUbhXIeIIjjiUN5b3e9mKoiiKogwPRrRCuWnTPwDrLuj5fzdw9NF5iPS/EvdGY5D4IEXBSSe6OsroT41us6o7ZVI8ufzJQZRKURRFUZShxIhWKFuskamUYfXqBEVF/RvNImFgS1JIbEchuuverSN//rbD/C7bkZCKoiiKomzXjOg5lCUlR1FR8RiplOGjj6IcfHBuv5TjGni6NsiChhDibQvGG9YeHNyES8VDFZikIWtCW3dJISfE0VOOHiTJFEVRFEUZaoxoC2V+/k4AhMPCLX+awG23VfHqK40ZL+fF+iAvNdjY3G3nSA4OJmWofqma2tdqmfnjmTghh+yAdSaeE8xhQt4ELtr9okGTT1EURVGUocWItlCuW/c3WiLRzJiRxY9/Mo4fXLWR/fbP4corx2asnBfqQ9vNnMlkXZJVN6wiFU0x7rRxhMeECQfCHDjhQKYUTGHO6DkcNvkwQs724ZNTURRFUZTtnxFtoYzHt7TZ3m23CGeeVcyLL2TOSmkMNG5H0xGXXb0MCQmzfj6Lov2LAIin4hRlFfGtPb7FUVOPUmVSURRFUZQeMaIVyry8nbbZN2VKiJKSzC3OEYEvFMXJloGNyd0ede/WkaxKMvW7U9us6o4EI+w+dvdBlExRFEVRlKHMiFYoy8o+32a7udnlhz+o4KSTCjJazuiQIWWgZXh9MKh9s5Z1f1rH1O9OJRhpnekQckKMyRnD0VN1EY6iKIqiKL1jRM+h3Ljh0Tbbq1bFmTgxxBlnFme0nMdrwiQGeBFOoiqBSRlCo0M0LW2i/G/lTPm/KeTOzsURh/G540maJMdMPYbz559POBAeUPkURVEURRk+jGiFsq7+wzbbVVUpysoyWyXGQHliYJXJqgVVlP+tbaSbyd+cTO5s6xYpEoxw7QHXste4vQZULkVRFEVRhicjWqEMhYqJxTZs3Z46NczKlfGMliEC08Muy+P96zS9baFQdEAR4bFhkrVJRh06iuxJ2VsPJ90kY3LGDJw8iqIoiqIMa0b0HMqJE8/GHy+7rCxILGZYvz6R0XLOHh0j13EZqDmU4gjRdVHGnDSGsrPL2iiTQSfIjqN2ZErBlAGRRVEURVGU4c+IViibm1bjVyhFhEMOzeOxx2ozkv/999Vw5fc3ctdSodmVNmX1F8YYtjy7hfxdbThFQQg5IXKCOYSdMHuM2YPfHf67fpdDURRFUZSRw4ge8q7Y9E9Ii1l99NF5XP+zzX3OOx53eeCBGpqbXZKvL2fm9TPJGpfVdcI+ktiSIFYeY4cf7QCAwRAJRrjz2DspCBdQmlPa7zIoiqIoijKyGNEWyhYkDoEKkCisWZNgh+ndW/FcX59i48YEsdi2nsuvuHwjubkOf7tjEgCNizIf0rE9ApEAJmXa+Jk0GKYXTVdlUlEURVGUfmFEWyjHlh5D3W2PkfecgIC48Ekwxs4HZHearqYmxVVXbmTNmji5eQ6bN6XYcccsfvDDMYwdG+LZZ+rZtCnJ3+6YRDAo7H9JGa/8tpycmTlkT+g8774Q3xInuiYKQO0btRTtW4SDw9FT1MekoiiKoij9x4hWKEvemYl5LoDE7WKZ5CjD++uaOW5efodp3nmnmRtu2MT+++Xym9+WEQgIzc0ujz5ayxWXb+RPt07g/vtr+MJpRYRCWRiTZO9DilmyNM6yK5cRGhVi3BfGUbhPYUavpXllMytvWEn2xGzGnjqWvHl5gLVO7lC4Q0bLUhRFURRF8TOiFcqa2+7ZqkwClO8Wp/L9ZIdD3hUVSS777gYu/U4Jxx7bGk0nEnE488xili6J8eUvrWPDhiTTp+czccKZhEJFvPHq3ZQcmwID8c1xmlY09UmhdJMujYsaCZeEiW+KEyoOsfza5Yz7/DhKji1pc67BcM+iezh77tm9Lk9RFEVRFKUzRrRCmaqubrP9bn0zk6eEcV1obk4RDAoffhilucmlqjrFiy80cPbZRW2UST8/+OFYli+P8+KLDYwdG6awaHfGlB5L/X9uJVgQZNxp49j85GaSdcley2yShhXXrSDVkCJRnSCYFyRZl6T44OJtlMkWqqJVvS5PURRFURSlK0a0Qpk9Zw7N77yzdXvFh3EWfRLj+ONWEg4LqRRkZws775xNUXGAgw/J49Of7jjOdyAgzJqVxaxZWTiOQ0H+LogI04umk2hazNEFCVbMhOdfjDI+lKI80TNn58Y1VD5bSbImyeybZiMipJpSlN9VTtGBRR2mmzt6bo/KURRFURRF6QkjWqEce8XlrD7nXEwsBsbwzWQpp99XRKokgDgpUimDMQGCQQFSXqqA91todVQuiAQwxloeHSfCuHEnEYlMAOA7c0+kbtUHBAUaCgS3MUVDqiV9931Tbrh7A03Lm5h88WREbLpAToBJX5vkSRbAxcV4cglCVjCLS/e8tC/VpCiKoiiK0ikjWqGMzJ/P1PvvY/Pvfk/0448JT5vGpBnnUVH0Cps2PU0gkMOEsjMJBCOsXXs7yWQdJSVHUFpyOGvX3kF9wyJyc2cwedIXqar6H5s2P0MwmMvECedQVva5reUEtjxE2HPQNGVKmIUfRtklati6swOMMWy4awNuzKX0pFJM0lB0QBE5M3Ja85YAk/InkTIpjppyFHuM3YM7P76T1XWrmTN6Dl/f5evMHjW7X+pPURRFURQFRrhCCZC9445M+sPv2+wr4CBmzri8zb6y8Z9tsz1q1IFttkePPpiZM7+3Tf7GGBobl27dzs11cEJCU3WSrLGdD3nXvVlH3Vt15MzOYenlSwmVhggWtr1lkWCE7+39PfafsP/WfQdNPKjTfBVFURRFUTLJiFco+xsRIRQsJJGsASAvz2HG/AjNH9cza1KQ2dkuURfeaQriYNgtJ0WWA28uT/DJneVMunASeTvl0fBRA1ue30LuTrlt8k+6Scbmjh2EK1MURVEURbGoQjkAlJV9gdVr/rR1+4BdsqjY3MSFpTkEBZIGTixMIAKugbdfb2LNX7Zw6MlFVHgKZN68vK2+JVsIOkFmFM9getH0Ab0eRVEURVEUP6pQDgCNTSvwL+I56KBc/u9b5Rx0QA577ZVDCMNHH0Z56T+NfPhBlOXL4/zo6rHsc0A+d1YHWBHPxjUuh0w8hDcr3qQ52UzKTbHH2D34+cE/H9RrUxRFUZSRytqaJv7v8bcGW4ztAlUo+xljDFu2vEjrinCYPDnM5ZeP4be/qeSaa8Zy0UXlJBKG004r4msXjGbWrDB5eQHA5YqdDqNw0rcYmzuWSDCCa1zW1a8jL5zHqOxRg3ZdiqIoiqIoLahCOSBs6xpo731ymP1sFl/96noOPiSXq64ag+Nse17QCTG1cOrWbUccJhdM7k9hFUVRFEVReoQqlP2MiFBaeiSbNz+71U9lC1deNYYrr2KrT8l0HCeH8eM+PQBSKoqiKIqi9B5VKAeA2bN+RH3dR8QTW0ilojhOFiKCMSmMSSISwhjrOF3EwZgUIgHKxp9KcfH+XeSuKIqiKIoyuKhCOQCEwyXsu++zVG55gabGFeTmzmD06MNobFxMVdXLBIL5jB1zLMa4bNr0L1KpRkaPPoS8PHVIriiKoijK9o8qlAOE44QYU3o0lLbuy8+fS35+2zjbEyeeOcCSKYqiKIqi9I3OY/8piqIoiqIoSheoQqkoiqIoiqL0CVUoFUVRFEVRlD6hCqWiKIqiKIrSJ1ShVBRFURRFUfqEKpSKoiiKoihKn1CFUlEURVEURekTqlAqiqIoiqIofUIVSkVRFEVRFKVPqEKpKIqiKIqi9AlVKBVFURRFUZQ+oQqloiiKoiiK0idUoVQURVEURVH6hCqUiqIoiqIoSp9QhVJRFEVRFEXpE6pQKoqiKIqiKH1CFUpFURRFURSlT6hCqSiKoiiKMgwQkWNEZLGILBORKwaybFUoFUVRFEVRhjgiEgD+ABwLzAFOF5E5A1W+KpSKoiiKoihDn72BZcaYFcaYOHA/cPJAFS7GmIEqq8+IyGZg9WDLMYCUAJWDLcQwReu2f9B67T+0bvsHrdf+oz/rdooxprSf8u42IvIv7HUOBNlA1Ld9qzHmVp8spwLHGGO+4m2fDexjjLloIIQLDkQhmWJ7aDwDiYi8ZYzZc7DlGI5o3fYPWq/9h9Zt/6D12n+MhLo1xhwz2DL4kHb2DZjVUIe8FUVRFEVRhj7rgEm+7YlA+UAVrgqloiiKoijK0OdNYKaITBORMHAa8MRAFT6khrxHILd2fYrSS7Ru+wet1/5D67Z/0HrtP7RuBxBjTFJELgKeAQLAX40xCweq/CG1KEdRFEVRFEXZ/tAhb0VRFEVRFKVPqEKpKIqiKIqi9AlVKLcTRCRbRN4QkfdFZKGIXOPtHyUiz4nIUu//4sGWdSjRSb1+ztt2RWRYu7XoLzqp2xtEZJGIfCAij4lI0SCLOqTopF6v8+r0PRF5VkTKBlvWoUZHdes7/h0RMSIyUH4FhwWdtNmrRWS912bfE5HjBltWpf/QOZTbCSIiQK4xpkFEQsDLwDeBzwJVxpjrvbicxcaYywdT1qFEJ/VaC7jAn4DvGGPeGkQxhySd1G0B8II3QfznANpmu08n9fqxMabOO+cSYI4x5oJBFHXI0VHdGmNeE5FJwF+AHYE9jDHq7LybdNJmjwEajDG/HFQBlQFBLZTbCcbS4G2GvD+DDZt0h7f/DuDTAy/d0KWjejXGfGKMWTyIog15OqnbZ40xSW//a1hfaEo36aRe63yn5TKADouHC530swC/Bi5D67XHdFGvyghBFcrtCBEJiMh7wCbgOWPM68BYY8wGAO//MYMo4pCkg3pVMkA36vZLwNMDLtgQp6N6FZGfiMha4Ezgh4Mo4pClvboVkZOA9caY9wdXuqFLJ33BRd5Ujb/qlK3hjSqU2xHGmJQxZlesRWdvEZk3yCINC7Re+4/O6lZErgSSwD2DJN6QpaN6NcZcaYyZhK3TAYnPO9xop27nA1eiCnqf6KDN/hGYDuwKbABuHDQBlX5HFcrtEGNMDbAAO/+kQkTGA3j/bxo8yYY2afWqZJD0uhWRc4ETgDONTtTuNZ202XuBUwZanuGEr25PBqYB74vIKqxC9I6IjBs04YYw/jZrjKnwFE0X+DOw92DKpvQvqlBuJ4hIactqWBGJAJ8CFmHDJp3rnXYu8PdBEXCI0km9Kn2ko7oVkWOAy4GTjDFNgyjikKSTep3pO+0ktB33mA7q9l1jzBhjzFRjzFRsPOTdjTEbB0/SoUUnbXa877TPAB8NgnjKAKGhF7cfxgN3iEgAq+g/aIx5UkReBR4UkS8Da4DPDaaQQ5CO6vUzwO+AUuApEXnPGHP0YAo6BOmobpcBWcBzdvEnr+lq5B7RUb0+IiKzsd4JVgNapz2n3bodZJmGAx212btEZFfsAp1VwNcGT0Slv1G3QYqiKIqiKEqf0CFvRVEURVEUpU+oQqkoiqIoiqL0CVUoFUVRFEVRlD6hCqWiKIqiKIrSJ1ShVBRFURRFUfqEKpSKomQUEWno+qw+5f9PESny/r7Ri/SHioi6ilEURckgqlAqijKkMMYc50XjKAJ6rFAqiqIomUcVSkVR+h0R2VVEXhORD0TkMREp9vYvEJGfi8gbIrJERA7y9ueIyIPe+Q+IyOsisqd3bJWIlADXA9NF5D0RuSHd8igivxeR87zfx4jIIhF5Gfis75xcEfmriLwpIu+KyMkDVyuKoijDB1UoFUUZCO4ELjfGzAc+BH7kOxY0xuwNfMu3/xtAtXf+dcAe7eR5BbDcGLOrMea7HRUsItnYOMInAgcB/hjNVwIvGGP2Ag4DbhCR3F5cn6IoyohGFUpFUfoVESkEiowxL3m77gAO9p3yqPf/28BU7/eBwP0AxpiPgA/6IMKOwEpjzFJjQ4Pd7Tt2FHCFiLwHLACygcl9KEtRFGVEorG8FUUZbGLe/yla+yTpRT5J2n4kZ/t+dxRjVoBTjDGLe1GeoiiK4qEWSkVR+hVjTC1Q3TI/EjgbeKmTJAAvA58HEJE5wM7tnFMP5Pu2VwNzRCTLs4oe4e1fBEwTkene9um+NM8AF4uIeGXt1r2rUhRFUfyohVJRlEyTIyLrfNu/As4FbhGRHGAF8MUu8rgZuENEPgDexQ551/pPMMZsEZH/ichHwNPGmO+KyIPeuUu9dBhjoiLyVeApEanEKqvzvGyuA24CPvCUylXACb27bEVRlJGL2ClFiqIo2w8iEgBCnjI4HXgemGWMiQ+yaIqiKEo7qIVSUZTtkRzgRREJYec5fl2VSUVRlO0XtVAqiqIoiqIofUIX5SiKoiiKoih9QhVKRVEURVEUpU+oQqkoiqIoiqL0CVUoFUVRFEVRlD6hCqWiKIqiKIrSJ1ShVBRFURRFUfqEKpSKoiiKoihKn1CFUlEURVEURekTqlAqiqIoiqIofUIVSkVRFEVRFKVPDEuFUkSuFpG7B1mGM0Xk2V6mXSgih2ZWoi7LXCUin/J+f19E/jKQ5XeEX64+5nOLiPygj3kcKiLr+iqLL79u17NYbheRahF5oxvnLxCRr3i/zxORl/sqb3/hl3UQZfibiPx4MGXoK325BhH5uohUiEiDiIzOtGzKtojIVBExIhL0tp8WkXMHWIbJ3j0PDGS5yvBkSCqU3gPQ8ueKSLNv+8zBlg/AGHOPMeaors5r7yVgjJlrjFnQb8J1gTHmp8aYfnnBZ0pB7CnGmAuMMdcNdLmd0cN6PhA4EphojNm7H8VSBoFMf6z0sOwQ8CvgKGNMnjFmyyDIMF5EbhORDSJSLyKLROQaEclt59xDvX6/wTt3sYh8sZ1z1vm2wyLyqIj8T0QK2slzgYhEvTxrReQ/IrJz/1xt+xhjjjXG3DHAZa7x7nmqp2m9On3Y69NNuhFERIpE5A4R2eT9XZ12fH8RecO7hx+IyIG+Y/573PI3oMq20nOGpELpPQB5xpg8YA1wom/fPYMtn6K0WB0yyBRglTGmMcP5KspYIBtY2N7BfmjL6fmPAl4FIsB+xph87MdTETC9g2TlXv9fAFwO/FlE5nSQfxbwqJffUcaYug7yvMjLczSwALirN9fTH/T3PegDLwNnARvbOfZrIAeYCuwNnN2i+Hv3/AngBux9+QXwDxEp9qUv97/rB1rZVnrOkFQou0lYRO70vn4WisieLQdEpExEHhGRzSKyUkQu6SgTEckSkV+KyBpvSOgWEYl4x/4pIjf6zn1ARP7q/d46zOgNV/7a+0qr9b7G5onIV4Ezgcu8L7B/eOf7h5+vFpEHO7mW3UXkXe/YQ54MHQ57icj5IvKJd/7HIrJ7O+dsnTIgrcMyXxSRtWKHXC8Qkb2866gRkd/70k4XkRdEZIuIVIrIPSJS5B27C5iM7TgaROQyb/9J3nXVeJaCnTq5FzeJSLn3d5P3smg5fplYC0e5iHzFk3uGd6yNJVhEThaR90SkTkSWi8gx3v4v+upnhYh8raO6bEc+IyIXishSYKm37zdevdWJyNsiclAX9Xyu19YqReRK79iXgb8A+3n1do2IFIvIk14brvZ+T+yurD4Zfi9trQBJ8SwJIrKTdz9qvPtzki/d30TkDyLylFdXr4vIdN/xHUXkORGpEmtB+nwXokwXa62oFZG/i33htOT1kIhslFbL0VzfseO8dlwvIutF5Du+Yyd497hGRF4Rkfm+Y7uJyDteugewClVHdeSIyFUislrsM3yniBR6xzq8bx3ktY28Yq1wTwNlvvtQ1k6bTbe6dXgNIvKRiJzo2w55su2aJs8sYLG3WSMiL3j722vL54vIMu+ePiEiZb58jIh8Q0SWevJcJ7YveNVr+w+KSLiDavk2UA+cZYxZBWCMWWuM+aYx5oOO6tI7zxhjHgeqgW0UShHJAf4BhIDju/NBZoxJAvf78xORvb1rqRHbx/y+5XrEsk3/7h07Xmz/XCe2H7i6o3Jl22kq//PyrQKu9trfHt7xs7w6n+Ntf0VEHu9K1nbKTB92X+Ddu/959/FZESnpoJ7ixpibjDEvA+1ZOE8EfmGMafLu623Al7xj+wMVxpiHjDEpY8zdwGbgsx3VjzIEMMYM6T9gFfCptH1XA1HgOCAA/Ax4zTvmAG8DPwTCwA7ACuDoDvK/CfslNQrIx3ZOP/OOjQM2AYdjFcMVQL537DzgZe/30V6ZRYAAOwHjvWN/A37c0TV1cS1hYDXwTWyH+Vkgnp6fL9/PAeuBvTw5ZgBTOijzbu/3VMAAt2BfWEd58jwOjAEmeHVwiHf+DKx1IQsoBf4D3NTR/QJmAY1emhBwGbAMCLcj17XAa165pcArwHXesWOwX8lzsV/Fd3lyz0ivZ+zXcq1XpuNdw47eseOxVhEBDgGagN29Y4cC6zppiwZ4DttWIt6+s7AWjyBwqSdjdif1/GespWYXIAbslN6evO3RwCneteYDDwGP+44vAL7SXtpO5N8V26nv5t2LZcD3se3scOxLf7avPqu8ugwC9wD3e8dygbXAF71juwOVwNwOyl2AbZfzvLSPtNSLd/xL3jVmYZ/H93zHNgAHeb+Lffdqd2y73Af73JyLbUtZtD43/+dd56lAgo6fmy95dbEDkIe1dt3VnfvWTl4dyXsoaW2LtL7Bf05X14B9jh7wpT0Z+LADmVquIdhRW/buf6VXr1nA74D/pJ3/BNZiONerg+e9OisEPgbO7aD814BretDn++vBAT7jXfvstHM2Ay95cmV1kecCWp+XMPCTtOvbA9gX256nAp8A3+pG/34osLMn53ygAvh0e/XOts9sErjYKzMC3Alc6h2/FVgOfN3bvhP4v65k7ereezIsx/bLEW/7+m7ck3XAoWn7KoG9fdtXAtXe7xOBj9POXwr82ldvca++VmKtnbndbSP6Nzh/gy5Any+gY4Xy377tOUCz93sfYE3a+d8Dbm8nb8EqO9N9+/YDVvq2P4t9eVYCB/r2n0erQnk4sMR7yJ20Mv5G1wplR9dyMPZFLL7jL6fn5zv2DPDNruqR9hWdCb5ztwBf8G0/0kmH9Wng3Y7uF/AD4EHftuNd06HtyLUcOM537tHYYWCAv+Ip+t72DDpWKP+E13F1o3093lJndE+hPLyL/KqBXTqp54m+c98ATktvTx3kuyteZ+1tL6AHCiVWQV/lK+8grPLr+M65D7jaV59/8R07Dljk/f4C8N+0/P8E/KiDshfge2lh23gcCLRzbpFXT4Xe9hrga0BB2nl/xPvY8O1bjP1IOBgop+1z8wodPzfPA9/wbc/GKjAtL+wO71s7eXUk7zZti84Vyk6vASjDfgAUeNsPA5d1IFPLNaQrlIf7tm/DWptatvO8OpjqO/8A3/G3gct92zfi+7BMK38pcEF3nkdfPbhADfaj5r30+vbOiXrt6JRu5LkA+/FY46WpBY7o5PxvAY95vzvs39tJdxOtSlObemfbZzb9PfVl4Anv9yfAV2j9iFuN93HSmaxd3XtPhqt8x78B/Ksb9deeQnk39uMrH9sfLwdi3rHRXl2fjv0gOte7p3/yjo/D9gMOMA1rmPhTd9uI/g3O33Ae8vbP6WgCsj2z/hTs0FJNyx/WCjO2nTxKsRagt33n/svb38KTWAvIYmNN/9tgjHkB+D3wB6BCRG6VdiaG9+JayoD1xnsCPdZ2ks8k7EPdGyp8v5vb2c4DEJExInK/N5xXh+1U2h0y8SjDdoYAGGNc7DVM6Opc73eZ75j/2ntVDyJyrIi85g3r1WAVpc7kT6dNuSJyqdgh9Fovv8Iu8ku/13kdyJkjIn/yhsHqsB1ukfRitabYRRkPA/caY+73dpcBa7370cJq2t6XjmSdAuyT9oydiX1JdIS/3lZjXzIlIhIQkevFTkuowyq90FqHp2Dv0WoReUlE9vPJcGmaDJO862rvufG3q3Taa3dB2vYZ3bpvncjbUzq9BmNMOfA/4BSxU06OxVqRe4L/nqQ/pw3YD0t/e+hWH9EOW4DxPZSt3BhTZIwZZYzZ1ddu/VQCpwF3iMjR3cjzEmNMEXYk5gTgYfGmSYjILLHTSjZ67fCneG2ws/5dRPYRkRfFTk2pBS6g+/1Jeh/2EnCQiIzDvnMeAA4QkanYfuW9rmTtJt1ty11xCfa+LwX+jv0gXQdg7MKvk7HTHSqwI0z/9h3faIz52BjjGmNWYi3up/ZSDmWAGM4KZUesxVoYi3x/+caY49o5txL7QMz1nVto7MTtFn6C/VocLyKnd1SoMea3xpg9sMNBs4Dvthzqw7VsACaIiPj2Terk/LV0PMk9U/wMe03zjTEF2CFfv3zp11uOffkDdj4S9hrWt5N3m3Ox8zHLvd8bAP8cwh7Xg9j5mI8AvwTGei+Xf6bJ3xVbr0/sfMnLgc8DxV5+tT3MryMuxVrK9vHq+eCWYnuR1++w1qyrfPvKgUki4u8jJtP+fUlnLfBS2jOWZ4z5eidp/PdrMtb6VQmcgX3xfAr70pzqnSMAxpg3jTEnY6dBPA486JPhJ2ky5Bhj7qP952ZyJ7K11+6StFWYukUn8rbXDzRiP2hb8Cvk3bmGO7DP3+eAV40x3bl3bcT1/U5/TnOxVqae5tke/wY+k9bWMoIx5lHgfKxyeFg307jGmP9ipzm0eOr4I7AImOk9b9/H96x10r/fix1yn2SMKcROHeruM9qmTRhjlmEVvEuww/H1WOXvq9gRiJaPv05lHSiMMVXGmDONMeOMMXOx+sYbvuMvGWP2MsaMAs7G9mcduUQzDMI1KD1jJCqUbwB1InK5iEQ8C8g8Edkr/UTvAf0z8GsRGQMgIhNavnZF5GDsPLFzvL/ficg2ljWxC1j28SxBjdihmJZJzBXYeUa94VUvn4tEJCgiJ2PntHXEX4DviMgeYpkhIlM6Ob835AMN2An+E2jtWFtIv94HgeNF5Aivfi7Fzr96pZ287wOuEpFSb6L4D7EW0JZ8vih2IUmOd6wjbvPOPULsgosJIrIjdu5UFnbuVVJEjqX1hdIb8rGKx2YgKCI/xM4xywT52I+dGrELWH7Um0zELjo6BDgjzRr5OratXiZ2Qceh2HlP7VmC0nkSmCUiZ3tpQ94z0O5iK4+zRGSOd++uBR421pVJPrY9bMEqVz/1yR4W6++10BiTAOpofa7+DFzgPXciIrliF0jkY5+bJHCJ99x8ls6fm/uA/xORaSKS58nwgLGLN7pNF/JWAKPFW+zj8R5wnIiM8qxS3/Id6841PI6d8/hN7By7vnAv9pnZ1fvw+inwuvEW0fSRX2Gfizta+iPvmfyV+BZS9RbvI+Ii4O8ickB30niW4zm0rnzPx96vBq+v+Lrv3M7693ygyhgTFZG9sR9IfeEl71pe8rYXpG13KmumEbtQsmUxWFhEsls+csQuyhrtvWOPxSq+/kVmu3l9QwH2I36dMeYZ79ihYn1kiohMAq7HWjmV7ZgRp1B6L6kTsXPOVmKtIH/BWj/a43Lsl+pr3vDBv4HZ3kNwJ9bVxHpvuPs24PY0qwHYzvLP2Plzq7Evx196x24D5ogdlnu8h9cSx87h/DJ2PspZ2Jd5rIPzH8JaVO/FWqQex066zyTXYF9itcBT2Dk0fn6GVQprROQ7xpjFnty/w96LE7FuoOLt5P1j4C3gA+BD4B1vH8aYp4HfAi9i79erXppt6sIY8wb2Q+DXnpwvYRcn1WO//h/E3qszsNaF3vIMdvXuEux9j9L5UHxPuAk7ab4Su6jhX73M53Ssgl8urSuMv+/V/0nYodJK4GbgHGPMoq4y9OrxKOxwYznWivJzrLLeEXdh5wxuxA45tnheuBNbd+uxCzteS0t3NrDKezYvwLYljDFvYS1Tv8fey2XYeWn+5+Y879gX2Lad+vmrJ99/sH1GFLtYojd0JO8irOK6wns2yrwy38cO8z+LHeKku9dgjGnGWtyndXF9XWKMeR473/kRrHV0Ovb+9hljTBV21W8CeF1E6rHzVmux9w2xXgY69THc2TnGupy5FHhK7CroM0Uk3U3SVo8H2Lq/yutXAL6D7Q/qsX35A750nfXv3wCu9a7ph7RapHvLS1iF8T8dbHcla6ZZjP2wnYDt75pptWTvge2n67H9/pnGGH+dX4btW9Zipzx8xndsd2wf3og1LnxEa5+gbKdI2yk4ylBHRF4HbjHG3D7YsgwmnjXsI+zqzh5ZkhRluOBZxWcZY84abFkURRnejDgL5XBDRA4RkXHesNe5WNcUvbVWDWlE5DPesGIx1iL2D1UmlZGKNxXiy1gXM4qiKP2KKpRDn9nYYbFa7JDOqcaYDYMr0qDxNex8xeXYOUz9NndIUbZnROR87FDi08aY/3R1vqIoSl/RIW9FURRFURSlT6iFUlEURVEURekTqlD2I96qw0MHW45MI74Y2e0cO0+8GOZDFUmLb9vO8dnSGj9dVx76EJGnvbm8Si/wVh8/O8Bl9nt7Fl+cakVRhieqUPYjxpi5xpgFgy1HX9AXQbtcBizwHOL/dqALF5HrRORDEUmKyNXtHD9DbASdRhF53Fuc0XLslyKy1FMeFonIOZmUzRhzrOeiZcTR04+p9j5cjDH3GGP64vu0N2S0PYvI1SJyd9dnDh9E5DCxEXFqRWRVO8f3F5E3vOfuAxE5sIN8bk//YPd8Pf5VROrERr/5dheyfF5EXhGRJhFZ0M7xE0XkI89F0isiMsd37Baf+7AGEYl5Lo9aji8Qkajv+OLu1ZAyElCFUtku6cg6uJ2UM4VWh8ft5dnj8Ic9ZBlWCXiqnbLnYuNmn40NDdiE9SHZQiPW12chNn7ub0Rk/+4UOlD3RBlwOm3PnTFc20QvrqsR6680PZBDy2r7J4AbsLHofwH8w/NG4T/vQNqPZHY1MBN7nw7DBhs4phNZqrB+aq9vR5aZ2BCcF3iy/AN4ouV6jTEXeFGt8ryIcPcBD6Vlc5HvnNmdyKGMNMx2EFB8e//Dhn2a4dv+G/Bj73cJ1pl4DfZB/i/geMdWAZ/yfl+NdWp7J9bR60JgT1+euwPvescewjqj/XEH8pyHjdP7a6/cFVjHwOdhV3ZuAs71nV/olbsZ63j3Kp+M5wEvYx3xVmMdNx/rHfsJdrV0FBv95ve++rgAG6O1GhvDVvz5eb//ANyYJvs/gG91Us8Xevmu9PadgI0YUoN1cDvfd/4krMPmzVhnwi3yOd41rvbq4k6g0Ds21Svny8AarEPggHf9lV5dXuidE2xHxhfS6mSW1x7+iA3T2IgNE7gTNopFjXevT0prPzdjnZ43ePdyHPYlUI0Nm7ZbN9rl3cDVaft+io3J3bI9HYgD+R3k8QRwaQfHDsXG1r0c63D8LqAY2943e7I+CUz0pVkAfKWrttVBeQHgRu8+rMRGANl6H7DO6D/BPiMrgK+lPRMvd/TcYsME/gMbQeRNrEP8l33n7gg8h32GFwOf70TO87zy6z05z/Tud0uElAagxjv3eOxzXYd9Nq/25bPGk7HB+9sv/Tqwz/WbWC8ObwL7p9X1dV77qcc6Py/xjmV77WMLtg2+iQ0n2p323FV/0dL3VJHWR2FjMsexTsobgPe7ktU7vi/2+a7Beq04tIO6/y7wSNq+3wE3+fq627DO19d79zngexZe8OqkEqtYFfnyWYVt6x9gAyIEve31nsyLgSO6eCY/BaxK23cCsDBt3xLgy77toNdO5rPt+2Y9cJRv+zrg/m70D1/BWp79+y4CnvJtO1hn5NtcF5DrXfch7T3f+qd/6X+DLsBQ+GvnAf8brQrlz7DxWUPe30G0KleraKtQRoHjsC/OnwGvecfC2I77m14en/U65c4UyiT2BRvwOs01WAUuCxulpB7I886/Exu2Kh+rUG3tzLy8EtioIgGsq51y3zVs04F49fEk9gt3MvbFc4wvvxaFcm8vr5aXUQnWYrbNi82X73PY6D0RrJK9CdjHk+1cr06zvO33sS+2XOwL9EAvny9hrXg7AHlYpfMu79hUr5w7vXQRrHK8CKugjsJG22lXoWyvTrDtoRY4ANtB53vlf9+7t4d792O27/xKbCSJbOxLbiU2fGfL/XyxG+2yPYXy78DlafsagD3aSR/BvniP6SD/Q7HtrCXKTQSrmJ2CDYOYj/34eby9uqGLttVOeRdgo+FMxCqu/6atQnk8VikQbLjIJmD39HbX3nOLDRl5vyf3HKxy19JOc73tL2Jf7Lt792duOzLmYpXDlns5vuW8DmQ4FNjZaxfzsSEWP53WFoO+87fmgW2L1VhrcxAb1agaGO2r6+VYJTDibV/vHfsaVoHO8ep+D6Cgm+25q/4iiY0UFAQi7eR3NXB3O2V0JOsErJJ3nFdPR3rbpe3kPR770VbkbQexfcQe3vbjWAt9LjZe+ht4Hx7ADC/vLKAU+zF5ky/vVdiP10mejLOx7aLMd7+md/FMtqdQngh8nLZvKfBr3/Z3gd+0026Lve2xvnNPBT7sRv/QnkJ5MfBP33YA+176Zjvpz8F+OIlv3wJsf1+J/Tg4tCs59G/k/OmQd99JYDu5KcaYhDHmv8YY08G5Lxtj/mls+Me7gF28/ftiO8bfenk8iu0IO2OlMeZ2L68HsJ3gtcaYmDHmWaxCOsMbfv0C8D1jTL2xsXdvxL6kWlhtjPmzl9cd3vWM7aL8640xNcaYNVgFbNf0E4wNcVgLHOHtOg3bwVV0ku/PjDFVxoaNOx/4kzHmdWNMyti5eTFsfe0NlAHfNcY0GmOixoa/BGsx+pUxZoUxpgH4HnBa2jDW1V66ZuDz2BfLWmPDwP2si2tvj78bY/5nbDzsXbGK7PXGmLgx5gWsAn667/zHjDFvG2OiwGNA1Bhzp+9+7tYLGfDKrU3bV4tVDtK5BauUP9NJfi7wI69dNRtjthhjHjHGNBkbYvEnWOWuI3rStj6PfamuM8ZUkzZkZ4x5yhiz3Fhewlq5DuqkbGDrFIRTvOtoMsZ87MnSwglYJeB2Y0zSGPMONsTgqR1k6QLzRCRijNlg2oaTa4MxZoEx5kNjjGuM+QA7hNhZffk5HlhqjLnLk+s+7IfPib5zbjfGLPHa8YO0PocJrPI/w3t23jbG1HVVYDf7i3JjzO88mZq7eS2dyXoWVsn5p1dPz2FDrB6XnoGxPnb/A3zO23UMUGmMeVtExmJDhX7Le7Y3YT84T/PSLjPGPOe15c3YGOLp9+K3Xj/QjLXcZmFD44aMMauMMct7cL0tvAKUicjpYmNXn4v9MMoBEBur+mvY0Izp5Hn/+5/pjp7n7vAccIjYWNlhWj96c9o591zgzrT32eXYD/UJWIf5/xCR9obplRGIKpR95wasNepZEVkhIld0cu5G3+8mINtTcsqA9WkPblcxn/1KWTNAmqLWjO2MSmi1gLawGtshbCOXMabJ+5lH56RfS0fn34EXr9j7/64u8vVf9xTgUrGxjWtEpAarOJd5/6827UfCKWPb6w3SVpFZm3b+2rTze8o2+XnKpT9Pf52n36v27l1vaMDGFvZTgLWQbkVEbgDmYYd2O/oAAtjsKb0t6XJE5E/eop867Mu9qJN5o+22LRE5yDexv0UhS78PbZ4BETlWRF4TkSqvLRyHbd9dUYq9/x3lPQXYJ62dnYmdhtAGY0wjVuG6ANggIk+JyI4dFSwi+3iLNTaLSK2Xrjsyw7btGDp5dmn7HN6F/VC4X0TKReQXIhLqRpnd6S96G4++I1mnAJ9Lq/8DsR8f7dFRnzIFO8KzwZfPn7CWSkRkjIjcLyLrvbZ7N9vei63XZoxZBnwLa3Hd5KUt69EV23y2ACcD38Y+58dgre/rvFNuwhoD0j8EwT7P0PaZ3vo8py2i+X43ZFmEVRR/jx2dKMGOCqzzn+cpuYdgrdX+9K97Hxox7wP/f7Sj+CsjE1Uou0cTbb/gtr5ovIfrUmPMDljLwbdF5Ij0DLpgAzBBRMS3b1KvpW1LJdZaMcW3bzJ2Xk536EzZ6A53AyeLyC7YeWaP96C8tcBPjDFFvr8cz1KzFpjcweT5cra93iRtlTZ/ORtoW9+Tu5CxK7nLgUki4n++elLnfWEhrZZvRGQHrJVliW/fNVhLzlHdsFql3/9LsUOB+xhjCoCDW7LtiZDGWvJbJvbP9XZvwA53t7D1nohIFtZq+Evs8F8Rds5qS7mN+J5REfErg5ux97/dvLFt6aW0dpZnjGk30pIx5hljzJFYhWcR8OeWQ+2cfi92nuokY0wh1iosnZzvJ70dQzfbkbEjHdcYY+Zg52GegB3C7Iru9Bddyd3TPmMtdkqKv/5zjTHbLCrxeByYLyLzsNd1jy+fGHZuZks+Bb729TNPtvle2z2LbdttG9mNMfcaYw7E1ofBTv/oMcaYl4wxexljRmGtvbNpHYU6ArjBW8HdonS/KiJneJb6Dfieae/3Qi9f/yKan3ZTloeNMfOMMaOBH3nX9mbaaecArxhjVnSVHT189pXhiyqU3eM94AwRCXir67YOk4jICSIyw1MG67DDJKke5v+ql+YisTG5T8YO6fYZb6jxQeAnIpIvIlOwX8rddetRgR3i6G3567Cd1V3YyfQ9GSL7M3CBZ+UREckVkeNFJB/bGW8Arvf2Z4vIAV66+4D/E5FpIpKHXajyQAfWTLD1c4mITPRWXnZmZe4Or2MVnMu8Ia5DsR8b9/cxXwC8PLOxz2/Qu/YWC+E9wImeBTAXuBZ41NjhaUTke8AZwJGe5aSn5GMtqDXe6tUf9fV6fDwIfFNEJohIEXZ4rYUwVjHeDCRF5FjsXOEW3gfmisiuXt1c3XLAewYeBa72LKw70la5ehKYJSJne3UbEpG9RGSndAFFZKyInOTVbQxrQWp53iuAid5QYgv5QJUxJioie2PrvoXN2OHzjp6vf3pyneH1C1/Azv98soPz/XIeJiI7e+2iDqskdtkvZaC/AFsPU9M+qDrjbmybPdrrY7PFDslObO9kz2L+MFZZf8PYaTctw+HPAjeKSIGIOCIyXURa+ut8vAVTIjKBdlZk+xHrn/Nw72Mmim337dahV1Y21kIq3jWEfcd389pVAfajaJ0xpmWqySyskrgrrdMATsROhQFrJbxKRIq9tns+dh52R3IHPFmCgOPJEvId38M7pxRrwf2HZ7n0c056GSJS5N2jbK89non9oOxsyowyglCFsnt8E/uA12CHwh73HZuJHb5owCqGN5se+p40xsSxC3G+7JVxFvalEeuT1K1cjFVwVmBX3d6LdXHRHX4DnCoi1SLSWx91d2AXJnQ13N0GY8xb2M7z99jFCMuwiwJaXnwnYifar8EO2XzBS/pXr6z/YBe7RLF10BF/xnaK7wPvYJWPXuPdz5OwVsBK7Iruc9rptHvLn7Evt9OBK73fZ3tlL8QOq96DXayQD3zDl/anWIvT0p4Mlfm4CbtgoRJ4DfhXn66kLX/GKgQfYFe8/hNrWUx5CvElWGWnGquYPdGS0BizBKs8/xu74CHdH+RF2BXALavV78N7vry8j8LOtSv3zmlZiJSOg7XSlmNXOR9Ca/2+gLUcbRSRSm/fN4Brxfry+6Enf4vMTdg5qP8TO0S7r78gT+E/wStvC9ZV1AnGmEq6ZhxW6arDrox/ie4rhX3pL6DVzcwWEXmnq5ONMWuxQ8LfxyrZa7HKXmfvp476lHOwHx8fY9vJw7QOnV+DXXBVi3W51dVznoWdx1uJbRNjPBnb42Dsc/hP7PPVjG3LLVzm5bPWk+czLQeMMZuMMRtb/rzdlb6P7x9hFzStxt7HG4wxnT13Z3vl/xE7x7iZVis62D69BrtqvQbbx25FRPbDWvPT3QWFsAsGWxblXIxdYKa+KBVAY3lvt4jI68AtxpjbB1uWviIiB2NfZlPT5hUqSod4VshbjDHpw76ZyPvnwDhjzLmZzlvpf0RkMna6wbhuTNtQFGUAUAvldoKIHCIi47yhhHOxLkYyaf0ZFLyhlm8Cf1FlUukMEYmIyHHeMzABa5l5rKt03cx7RxGZ702d2Bs7GpCRvJWBxRtK/zbWF6Mqk4qynTAsoxwMUWZjh8PysMMbp3pzgoYs3hy0t7BDyV8cZHGU7R/BDks+gB2me4r2Xan0hnzsMHcZdirAjVhfi8oQwpu7WoEd/u0sWoyiKAOMDnkriqIoiqIofUKHvBVFURRFUZQ+oQqlMmwRkVUi8qnBlqOniMjVItKtFbkistBzS6RkALGOon8w2HIoiqIMNVShVEYcno+79MgQ3VbiellmWEQqPb+Y22z3FmPM3J66qeoLnk/Ee73ft4rIYhFxReS8ds79P7HOmmtF5K+eP7+O8u11XiIySkQeE5FGsRF8zvAd21dEnhMbXWeziDwkIttEYBGRJSIyy3MUfV3Pa6ZjxEapWSsidZ58V/qOlYjI/0Rki+c66FVp9aeaHgmlQURingui7lx7WEQe9j6sTPqHh9fmE2n57+A7fp2IfCgiSRG5up3rOsMrs1FEHhfrl7TlWJZ3n+q8+/bttLSHi8g73vEVIvLVXlewoijbBapQKsrAcDDwnrGxxdvbHlSk/YhD7XEc1tce2MVW38D67kzP72isg/gjgKlY593XdJJvX/L6AzZ2/Visn9g/ikhLdJRibMzhqdiIIPVAG1dcYmMRO54vy/7gNmBHLzrL/tggCZ/1jjUAX8KGhyzG+r/8R8v9SIuEkoddWOT3D9jZtYP1I3kWbcMe+nnAn39aZJRlWP+JT6Un8sr4E9bn4VhsNLGbfadcjfXROwU4DOvk/xgvbQi7wv5PWN+gXwB+JTaalqIoQxRVKJXhzq4i8oFn2XrAWyX6NFDms8qcgXVY/AVv+30AEVkgIj8TkTe89H9vscKIjRZxt8+y9KaIjO1YjDaKWJttsRF9XhKRehF5jrT4wmIjsyz0ylkgvggu0smwvoiUicgTnnVumYic7zt2tWe9ultsXOPzPDn+48nxbxH5g/istmLdtRyJ587KGPMHY8zzWMfx6ZwL3GaMWeiFj7sOzyl9e/Q2L+9+ngL8wBjTYIx5GevwvMXR+9PGmIeMMXWeI/HfAwek5X88rffibyLyY981n+/VXZVXl2W+Y3Ol1fpZIR04iDfGLDY2BngLLtYhP8aYqHfcxa5yT2EVy1Hp+fiu9Y5uXnvcGHOTt7+n0bswxtxhjHmatDjwHmdiI6z8x/so+gHwWbFRrMA6GL/OGFNtjPkE61j7PO/YKGw86ruM5U2s8/U5PZVRUZTtB1UoleHO57HuRaZhfXuejY1gU+6zytxLa3jGPGOM31JyDtaCVIaN2tISLehcrHVlEjAaG52mGUBErhCR9PB4x9HW0uPfvhd4G6tIXufljZfXLKxV6ltYK9Y/sRYsf3i/jrgPG0GoDDgV+Km0jTN/MjaSSBE2ss692JCWo7EWprPT8tsbWNHNSC1zsVbHFt4HxorI6G6k7Ules7CRdJakHfdb6fwcjBcH2Uf6vQHssCw2/vPnsdFNVuOFz/QUp39jlesyrIL4fEcX4LWJBuz9yMXWtf/4B1hl+gmsz9ZN7WRzCjZKyX+87Z5ee3uc6CnEC0Wk3djlHdDmnhhjlmMtpbPEhi8tY9t7Ntc7twLbNr8oNgTgflhLZnp0I0VRhhDqh1IZ7vzWGFMOICL/wMbK7UkIxLuMMR956X8AvCfW8XwCq3jNMMZ8gFUIATDGXO/PwJuXFmoJUebfFhvxYy/gU8aYGPAfT84WvgA8ZYx5zkv7S6yj+P2BBR0JLSKTgAOxofqintx/wSqJLYrPq8aYx73zSz05jvBCR74sIk+kZbvVktcN8rAh7lpo+Z2PDSPYEzrLK/1Yy/H8tH2IyHysX8uTfftysNf9Ujvlngn81Rjzjnfu94BqEZkK7AdsNMbc6J0bxcZwbxdjzPVio/PsCnw6XWZjzHyx8Zc/gw0d2B7nAneaVl9v3b72DngQOx2gAtgHeEREaowx93UjbWdl5/m2O5LrPuAv2DCAAF/3QjAqijJEUQulMtzxzx1rovVl1138L7nV2Hi2JdgYws8A94tIudiFF6EO8khXxPzbZUB12pDoat/vMv+2NzS6FpjQhdxlQJUXp9qfrz/d2nbOb+rgOGw7bN8ZDdhhzRZafrc3fNqXvNKPtRxvU46IzMBOdfimMea/vkNHAK94Snc66XXfgFWGJ2At08t7chHe8O67WEv2NvNJveHv+4Ar0ucTeh8IhwB3+nZ369o7kedjY0y5MSZljHkFq9yd2s3L6azsBt/2NnKJyI5Y5/UtcbfnYudYHt/NshVF2Q5RhVIZibTnzb8jD/+TfL8nYy2TlcaYhDHmGmPMHKy18ATsC7I9Ohvu3gAUe/Ph/OW0UI4dDgRARMSTaX0HZfnTjfLNaWvJ15/Of80bvPNzfPu2XruIjMMO+26zaKYDFgJ+pWgXoMIY01PrZFd5LQGCIjIz7fjWYW0RmYIdnr7OGHNXWt7tDnd7pNd9LtYqvR6rbE/vxbWAHRnqLG0Iu/DIzzlYxde/aKbLa+8hBjuPszu0uSee1T0LWOLNc93AtvesRa55wGJjzDPGGNez3D+FnYqiKMoQRRVKZSRSAYwWkcK0fVO9hSd+zhKROZ6idS3wsDEmJSKHicjOIhIA6rCK5jYLH0Qkgp17uKC9bWPMamx4ymvEunk5EDjRl8WDwPEicoRnAb0UiAGvdHaB3vDhK8DPvAVE87Hxq+/p4PwWOa725NgvTY7jgH/5hltb3NJkY5WQkFdOS/3dCXzZq7ti4Crgbx3J29u8PMvuo8C1IpIr1uXOyVgLMmJjgr8A/MEYc0s7RR9Lx1bXe7Hz/HYV66bop8DrxphVwJPAOBH5llgXOfkisk871+WIyNdEpFgsewMX4k07EOvW6EDv+iMicjl21XT68Pk56fXX1bV7+Wd59QoQ9upVvGMnp8l1Cb5wlCIS8tI6WMU122vvYNvRiSJykKdoXws86rOI3wlc5eW/I3C+T/53gZliXQeJ2FX2J9B2zqWiKEMNY4z+6d+w/ANWYecmtmxfDdzt/f4rdviyBju0ORq7KKAaeMc7ZwF2UcYbWKXxH0CJd+x0YDHQiFVGfwsEvWPfB572fp8APOmToc22t28H4L/YocLnsCuR7/Yd/wzwMXYe2kvA3PauETvnb6Hv2ESs4lOFHZ69oL268O2b7slRj1V4bsWurga7eOfUtPMXYK1a/r9Dfce/7dVNHdZVT5bv2NPA9zOU1yjgce9erAHO8B37kZdXg//POzYP+Cjtmv4G/Ni3fYFXd1VeXU70HZvn1VM1dmrFFen3AauM/ctL34C1Kn6f1rC3h2AVqXrvnJeAg9Nk2s+7tvx22niH1+5rH+n1OtU7dh/2GWjAziu+pJ26SE97nu/4GV6ZjVhFdJTvWBb2Gavz7tu30/L+PPCRd93rsO6SnMHuM/RP//Sv938ay1tROkBEFmCVrr/0IY+bsUrLze1tb8+IyANYReM6rMI03RiTvhBjyCIil2E/EC7z7bsTWGaMuXbwJFMURRl66JC3ovQv72GdOHe0vd0gInuJyHRvmPYY7PDp41gr2A+GkzLpsQqfk3OxzsRnAysHSyBFUZShiroNUpR+xBhza2fb2xnjsHPyRmOHIb9u7KpkgD8OmlT9hDHmwbRdG7Hunx4ZBHEURVGGNDrkrSiKoiiKovQJHfJWFEVRFEVR+oQqlIqiKIqiKEqfUIVSURRFURRF6ROqUCqKoiiKoih9QhVKRVEURVEUpU/8P2js/7kmsn4rAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<Figure size 864x720 with 2 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"----------------------------------------------------------\n", | |
"Done!\n" | |
] | |
} | |
], | |
"source": [ | |
"# Save the grouped dataframe to a new CSV file\n", | |
"output_df_centroid = monthly_precip_df_centroid[['id', 'lon', 'lat', 'climate_zone']]\n", | |
"# Adjust the output filename with the year of data information if needed\n", | |
"output_df_centroid.to_csv(\"../csv/uga_climatezone_eucl_{0}_{1}_p_ai.csv\".format(n_clusters, cluster_method), index=False)\n", | |
"print(\"Save the output to csv completed\")\n", | |
"print(\"----------------------------------------------------------\")\n", | |
"\n", | |
"# Plot map\n", | |
"plot_climate_zone_map(\"../csv/uga_climatezone_eucl_{0}_{1}_p_ai.csv\".format(n_clusters, cluster_method), \"../shapefiles/uga_cli_climatezone_unma.shp\")\n", | |
"print(\"----------------------------------------------------------\")\n", | |
"\n", | |
"print(\"Done!\")" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "2b398723", | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3 (ipykernel)", | |
"language": "python", | |
"name": "python3" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.9.7" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 5 | |
} |
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The existing 1995 climatiological rainfall zone are available here: https://www.dropbox.com/t/43zseE37tQaYHmzw
Precipitation data in csv format for 1981-2010: https://www.dropbox.com/s/atbudxdcpx4bv1y/chirps_precip_1981_2010.csv?dl=0 and 1991-2020: https://www.dropbox.com/s/205vgwvq3ydcpv2/chirps_precip_1991_2020.csv?dl=0
Agglomerative Clustering, 1981-2010
Agglomerative Clustering, 1991-2020
KMeans Clustering, 1981-2010
KMeans Clustering, 1991-2020