{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe42094d-dbd7-434b-816b-bb7fdaf62616",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c954623d-c946-482b-ada9-8e74a229876e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "19a3a49d-ea49-4d19-9d51-53d57b7da7ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            saving_rate   R-squared:                       0.866\n",
      "Model:                            OLS   Adj. R-squared:                  0.866\n",
      "Method:                 Least Squares   F-statistic:                     2767.\n",
      "Date:                Sun, 27 Apr 2025   Prob (F-statistic):               0.00\n",
      "Time:                        11:10:06   Log-Likelihood:                 9596.7\n",
      "No. Observations:                3000   AIC:                        -1.918e+04\n",
      "Df Residuals:                    2992   BIC:                        -1.913e+04\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=========================================================================================\n",
      "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------\n",
      "Intercept                 0.0488      0.001     48.551      0.000       0.047       0.051\n",
      "first_nation              0.0201      0.001     36.305      0.000       0.019       0.021\n",
      "time                      0.0200      0.001     14.344      0.000       0.017       0.023\n",
      "treat                     0.0306      0.001     38.867      0.000       0.029       0.032\n",
      "culture_identity          0.0102      0.001     11.325      0.000       0.008       0.012\n",
      "income                    0.0004      0.000      2.125      0.034    2.85e-05       0.001\n",
      "culture_identity_time     0.0104      0.001      8.128      0.000       0.008       0.013\n",
      "income_time             9.87e-05      0.000      0.409      0.682      -0.000       0.001\n",
      "==============================================================================\n",
      "Omnibus:                        0.788   Durbin-Watson:                   1.996\n",
      "Prob(Omnibus):                  0.674   Jarque-Bera (JB):                0.718\n",
      "Skew:                          -0.012   Prob(JB):                        0.698\n",
      "Kurtosis:                       3.072   Cond. No.                         61.9\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# 设置随机种子，保证可复现\n",
    "np.random.seed(42)\n",
    "\n",
    "# 模拟样本数量\n",
    "n = 3000\n",
    "\n",
    "# 模拟数据\n",
    "first_nation = np.random.binomial(1, 0.3, n)  # 30%是First Nation\n",
    "income = np.random.normal(5, 1.5, n)  # 注意：单位已经变成万了（比如均值5万）\n",
    "culture_identity = np.random.uniform(0, 1, n)  # 文化认同程度 0到1之间\n",
    "\n",
    "# 时间：前期(t=0) vs 后期(t=1)\n",
    "time = np.random.binomial(1, 0.5, n)\n",
    "\n",
    "# 干预效果设定\n",
    "treatment_effect = (\n",
    "    0.02  # 基础干预效果\n",
    "    + 0.03 * first_nation    # First Nation额外提升\n",
    "    + 0.01 * culture_identity\n",
    "    + 0.0002 * income    # income系数放大了，便于观察\n",
    ")\n",
    "\n",
    "# 基础储蓄率\n",
    "base_saving_rate = (\n",
    "    0.05\n",
    "    + 0.02 * first_nation\n",
    "    + 0.01 * culture_identity\n",
    "    + 0.00015 * income\n",
    ")\n",
    "\n",
    "# 储蓄率 = 基础 + 干预（if after intervention）+ 随机扰动\n",
    "saving_rate = base_saving_rate + time * treatment_effect + np.random.normal(0, 0.01, n)\n",
    "\n",
    "# 整理数据\n",
    "data = pd.DataFrame({\n",
    "    'saving_rate': saving_rate,\n",
    "    'first_nation': first_nation,\n",
    "    'income': income,\n",
    "    'culture_identity': culture_identity,\n",
    "    'time': time,\n",
    "})\n",
    "\n",
    "# 创建交互项\n",
    "data['treat'] = data['first_nation'] * data['time']\n",
    "data['culture_identity_time'] = data['culture_identity'] * data['time']\n",
    "data['income_time'] = data['income'] * data['time']\n",
    "\n",
    "# 回归模型\n",
    "model = smf.ols('saving_rate ~ first_nation + time + treat + culture_identity + income + culture_identity_time + income_time', data=data).fit()\n",
    "\n",
    "# 打印结果\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "018719e3-2ae7-4aec-b9a2-262ccdf3e729",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       saving_rate  first_nation       income  culture_identity         time\n",
      "count  3000.000000   3000.000000  3000.000000       3000.000000  3000.000000\n",
      "mean      0.079073      0.302000     4.970137          0.488249     0.502667\n",
      "std       0.027000      0.459202     1.500699          0.283349     0.500076\n",
      "min       0.023884      0.000000     0.234944          0.000053     0.000000\n",
      "25%       0.060260      0.000000     3.938454          0.246911     0.000000\n",
      "50%       0.074270      0.000000     4.981703          0.482110     1.000000\n",
      "75%       0.089162      1.000000     5.977577          0.725032     1.000000\n",
      "max       0.172206      1.000000    10.889357          0.998905     1.000000\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 设置随机种子，保证可复现\n",
    "np.random.seed(42)\n",
    "\n",
    "# 模拟样本数量\n",
    "n = 3000\n",
    "\n",
    "# 生成数据\n",
    "first_nation = np.random.binomial(1, 0.3, n)\n",
    "income = np.random.normal(5, 1.5, n)  # 单位：万澳元\n",
    "culture_identity = np.random.uniform(0, 1, n)\n",
    "time = np.random.binomial(1, 0.5, n)\n",
    "\n",
    "treatment_effect = (\n",
    "    0.02 + 0.03 * first_nation + 0.01 * culture_identity + 0.0002 * income\n",
    ")\n",
    "\n",
    "base_saving_rate = (\n",
    "    0.05 + 0.02 * first_nation + 0.01 * culture_identity + 0.00015 * income\n",
    ")\n",
    "\n",
    "saving_rate = base_saving_rate + time * treatment_effect + np.random.normal(0, 0.01, n)\n",
    "\n",
    "# 创建 DataFrame\n",
    "data = pd.DataFrame({\n",
    "    'saving_rate': saving_rate,\n",
    "    'first_nation': first_nation,\n",
    "    'income': income,\n",
    "    'culture_identity': culture_identity,\n",
    "    'time': time,\n",
    "})\n",
    "\n",
    "# 计算描述性统计\n",
    "summary_stats = data.describe()\n",
    "\n",
    "# 输出结果\n",
    "print(summary_stats)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dfd12c67-4c9c-4fed-beb7-a99ed74ac668",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/rc/k08jtrdj1sd_sdlrm97rqnfr0000gn/T/ipykernel_1035/1425342792.py:29: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  axes[1, 1].set_xticklabels([\"Non First Nation\", \"First Nation\"])\n"
     ]
    },
    {
     "data": {
      "image/png": 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KP/7xD7z++us4cOBAu0GGQqHAkiVLcPDgQRQXFwMA9uzZg+effx6//vWvsWjRImcy5f/9v/+H/Px8Z9sBAwZg7Nix+Pjjj6FQKFBfX+/cwacn4uPjoVAorqu9ZLPZrluad9ddd+Guu+5CU1MTDh06hE2bNuGxxx7DxIkTnU8SXfGLX/wC+/btw4YNG3DTTTdh6NChAIBnn30Wn376KV5++WXccMMNCA8PB2BfiteVqKgo3HDDDVi5cuV1x65+2khERCQmjtH8Z4zWU1FRUe2KwzscPHgQw4cPR0xMDGpra687XlNTAwC96n90dDQA+/gyIiLiuuN9+/bt8XsTBTou3yPyI9u3b0d1dTV+8pOfdHj8hRdewKJFiyAIAtRqNW6++WZnzR1HYWvHU6Ge+Pbbb9sV7/z0009hsVgwZcoUAEBERATq6+vb7STiWCvv0NlAzWHy5MkA7Imcq3300UewWq1dLmXrzpgxY6BUKq977++++w4VFRUYP368y++Vnp6O+vr6DndFsVqtKC0tdSZkcnNzERUVhfvvv9+ZkNLr9cjNzb3uieG8efPw1Vdf4cMPP8TYsWMxcOBAN3v5A5lMhvHjx2Pfvn3tXv/iiy/a/Tv+6le/wkMPPQTAPiC744478OCDD8JqtTp3BXLnmmvXroXFYsG6deucr+fm5mLKlCm49dZbnQmpU6dOoa6urt09uPbrc/LkySguLsaIESOQlZWFrKwsZGZmYuvWrfj888/dio2IiMhbOEbznzFaT02cOBFff/21s4wCAJw9exb3338/8vPzMWnSJOTl5V23A/C///1vJCYmYsCAAT2+9qRJkwAA9fX1zvFOVlYWGhoa8PLLLztnUhGFIj6GJhJBc3Mzjh8/DuCHWS2HDh3Cjh07MG/ePPzoRz/qsN20adPw97//HU8++STmzZsHs9mMN998ExqNBlOnTgVgfxKTl5eHw4cPu10gsra2Fr/85S+xfPlyXLp0CS+++CKmT5/unO1y88034+2338ZTTz2FxYsXo6ioCFu2bGk3yImKigIAHD58GEOGDLnuyWB6ejruuecebNy4EUajEVOmTEFBQQE2btyIKVOmOIuA94RGo8H999+PjRs3QqFQ4JZbbkFZWRleeeUVpKenY8GCBS6/1/Tp03HXXXfhxRdfxNmzZ3HbbbchLi4OV65cwfbt23HlyhW8/PLLAOz1CN599108//zzuPnmm1FdXY3NmzejtrbW+WTQYc6cOVi/fj0++ugj/M///E+P++rw8MMPY/ny5Xj44YexaNEiVFRU4JVXXgHww1T6qVOnYu3atXjhhRcwc+ZMNDY2YuPGjRg4cCCGDx/u9jXHjh2LefPm4YMPPsBHH32EOXPmYPTo0fj444/x7rvvYsiQISgsLMTrr78OiUTSrr6E4+vz2LFjmDhxIh588EEsXboUP//5z/GTn/wEYWFh2LFjh3M2FhERkS9xjOb/Y7SeevDBB7FkyRLnkkqTyYRXXnkFo0aNwsyZMzFmzBj8+9//xsqVK/HQQw8hNjYW77//PnJycvDcc8/1Kqk4dOhQzJs3D7/5zW9QXl6OzMxMXLx4ES+99BLS0tJ69ZCSKNAxKUUkgjNnzmDJkiUA7E/N4uPjMWjQIDz//PPXFYC82syZM/GnP/0JW7ZscRbOnDBhAt566y1nfYNly5bh1KlTuO+++7B+/XokJSW5HNePf/xjGI1GrF69GkqlEnPnzsVjjz3mTG5Mnz4dTzzxBN5++2189tlnGDVqFDZu3IilS5c63yMyMhIrV67Ejh078OWXX+Kbb7657jrPPvssBgwYgH/961/YvHkzkpKSsHz5cqxevbpXv/AB4Je//CUSEhLwzjvvYOfOndBoNLj99tvxq1/9qsu1/h354x//iClTpuCDDz7A008/jZaWFsTFxWH69OlYv369c6r5Pffcg7KyMvzrX//CP//5TyQnJ2PWrFn46U9/it/85jcoLi527iaj0Wgwa9YsHDx4EHfeeWev+grYn/q9+uqreOWVV/Dggw8iNTUVv/nNb/DII484p4cvXboUZrMZ27dvxz//+U+oVCpMmzYNjz32GBQKRY+u+9hjj2Hfvn144YUXcPPNN+PJJ5+E2WzGyy+/DJPJhLS0NDzwwAMoLi7GF198AavVCplMhl/84hd47bXXcN9992Hv3r0YPnw4tm3bhpdeegmPP/44BEHA0KFD8Ze//AW33HJLr+8PERGROzhGC4wxWk+MHDkSb7/9Nv785z87x0mzZs3Co48+CqVSicTERLz77rv485//jGeffRZmsxnDhw/Ha6+95pExyfr16/HXv/7V+XAzPj4ed955J371q191O4uNKJhJhK4q0BIRkV/bv38/UlJSMGrUKOdrRUVFuOuuuzw2iCIiIiIiIvIGzpQiIgpghw4dwt69e/Hoo49i0KBBuHLlCl5//XUMHjwYM2bMEDs8IiIiIiKiTnGmFBFRADMajXjllVfw6aeforq6GhqNBjfeeCN+/etfd7jtMhERERERkb9gUoqIiIiIiIiIiHyud9XqiIiIiIiIiIiIeoBJKSIiIiIiIiIi8rmQK3Rus9lgsVgglUqdW6gSERERXU0QBNhsNsjl8l5vgx5MOI4iIiIiV7g6lgq5pJTFYkF+fr7YYRAREVEAyMrKglKpFDsMv8FxFBEREbmju7FUyCWlHBm6rKwsyGSyLs+1Wq3Iz8936dxgFOr9B3gP2P/Q7j/Ae8D+h27/HX3nLKn23BlHXS2Uv5Z8hffYN3ifvY/32Dd4n70v1O+xq2OpkEtKOaaay2Qyl78w3Dk3GIV6/wHeA/Y/tPsP8B6w/6Hbfy5Ra68n46irhfLXkq/wHvsG77P38R77Bu+z94X6Pe5uLMXHf0RERERERERE5HNMShERERERERERkc8xKUVERERERERERD7HpBQREREREREREfkck1JERERERERERORzTEoREREREREREZHPMSlFREREFGAOHz6MxYsXY/z48Zg+fTrWrVsHo9EIADhx4gQWL16McePGYfbs2di5c2e7trt370Z2djbGjh2LBQsWIC8vT4wuEBERETEpRURERBRI6urq8POf/xw/+clP8N1332H37t04evQo/va3v0Gn0+H+++/H3XffjWPHjuHZZ5/F+vXrcfLkSQDAkSNHsG7dOjz//PM4duwY5s2bhwceeAAGg0HkXhEREVEokosdABERERG5Li4uDt9++y0iIyMhCAIaGhrQ2tqKuLg4fPbZZ9BoNFi2bBkAYNq0aZg7dy62bduG0aNHY+fOnZgzZw4mTJgAAFixYgV27NiBvXv3YuHChS7HYLVa3YrZcb677ch1vMe+wfvsfbzHvsH77H2hfo9d7TeTUkREREQBJjIyEgAwa9YsVFVVYeLEiViwYAFefvllDB06tN256enp2LVrFwCguLj4uuRTeno6CgsL3bp+fn5+j+LuaTtyHe+xb/A+ex/vsW/wPnsf73HXmJQiIiIiClCfffYZdDodHn30UTz88MNITk6GWq1ud45KpUJLSwsAQK/Xd3ncVVlZWZDJZC6fb7VakZ+f73Y7ch3vsW/wPnsf77Fv8D57X6jfY0f/u8OkFBEREVGAUqlUUKlUeOyxx7B48WIsX74cTU1N7c4xGo2IiIgAAKjVamdB9KuPx8bGunVdmUzWowF2T9uR63iPfYP32ft4j32D99n7eI+7xkLnRERERAHk+++/x+233w6TyeR8zWQyQaFQID09HUVFRe3OLy4uRkZGBgAgIyOjy+NEREREvsSkFBEREVEAGTZsGIxGI/785z/DZDKhvLwcL7zwAhYtWoTbbrsNtbW12Lp1K8xmM3JycrBnzx5nHalFixZhz549yMnJgdlsxtatW6HVapGdnS1yr4iIiCgUcfkeERERUQCJiIjAm2++ieeeew7Tp09HVFQU5s6di9WrV0OpVGLLli149tlnsWHDBsTFxeHpp5/G1KlTAdh341u7di2eeeYZVFVVIT09HZs2bYJGoxG3U0RERBSSmJQiIiIiCjDp6enYsmVLh8eysrKwffv2TtvOnz8f8+fP91ZoRERERC7j8j0i6lJYWJjYIRAREREREVEQYlKKiAAANptw3WsymQyZmZku7RbRUXsiIiIiCgy9HctxLEhEPcHle0QEAJBKJdiVW4pGg+WHFwUbamprkZiQAEg6z2FHq+VYNKGfD6IkIiIiIm/ocCzoIo4FiainmJQiIqdGgwU6g9n5d0GwQdtkgCLCDEkXSSkiIiIiCnzXjgWJiLyNnzKJiIiIiIiIiMjnmJQiIiIiIiIiIiKfY1KKKEiwuCQREREREREFEtaUIgoSvSlO2Uejwh2ZfbwQFREREREREVHHmJQiCiI9LU4ZpeKPAiIiIiIiIvItLt8jIiIiIiIiIiKf4/QIoiBS32LCqXIdKhqMqG1uhcUmwCYIiFLJ0TdGjX5x4egXq4ZEIhE7VCIiIiIiIgpxTEoR+RGbTYBU6l7CyGYT8EVhNd7KuYyvztV0eE6T0YKKBiO+u1yPlGgVpqfHIy023BMhExEREREREfUIk1JEfsTdYuWXavXYV1CFCp3R+VqqRo20WDVSolVQyqWQSiTQ6ltR3mBAUVUzrjQa8a/vyzGyTzRmD0+CzM0kGBEREREREZEnMClF5GdcKVaub7XgwNlqnK/RAwDC5FLce8NAKGXSDpNMKTEqjOobg+lDLDh6qQ75ZTqcqWxEU6sZc7K46x4RERERERH5HgudEwUQQRBwprIRb+dcxvkaPaQSYHRaDJ68fTieunME4iKUXbaPCJPj5mFJmDumLxQyCUrrDNiVWwaj2eqjHhARERERERHZMSlFFCBMFhs+PV2Fz89UodViQ1JUGJZO6o+bhyUhUuXepMdBCRFYNCEN4UoZaptNePdoCWw2wUuRExEREREREV2PSSmiAFDb3Irtx0pwtqoJEgkwbUg8lkzsh8SosB6/Z1KUCnPH9IVMKsGZyia8vO+cByMmIiIiIiIi6hqTUkR+rqCyETuOlaK+xYyIMBkWjk/D5IFxbu/S15GUaBVuGZ4EANjwRTHOVTX1+j2JiIiIiIiIXMGkFJGfstoE7C+owmdnqmCxCegfF46fTu6PVI3ao9cZ0Sca09PjAQAfn7oCs9Xm0fcnIiIiIiIi6giTUkR+yGi24v3j5ThV0QgAmDo4DvPH9kW40jsbZt6Z2QepGjV0BjOOXKzzyjWIiIiIiIiIrsakFJGfqdeb8H/flaKs3gCFTIL5Y/piyqB4SCW9X67XGaVcimfmjQIA5JXUQ9vc6rVrEREREREREQFMShH5ldzL9dj8zUXUt5gRGSbH4gn9MDAhwifXzh6ZjKHJkbAJwJfnanxyTSIiIiIiIgpdTEoR+Ym9+ZX4yaYctJisSIoKw5JJvdtdryduH5UCmUSCsnoDyusNPr02ERERERERhRYmpYj8wK7cMjz0z+9hstgwNDkSiyakITLMO/WjuqIJV2JE3ygAwJFLWp9fn4iIiIiIiEKH7z/1ElE7b+dcxm/ePwUA+MnkfhiSGIkmo0W0eCYNiMOZikaU1hlQqTPCe5WsiIiIiIiIKJRxphSRiDZ9dcGZkFo5fSCeuyfLqwXNXRGtVmB4SjQA4OilelFjISIiIiIiouDFpBSRCARBwIb9RXh2bwEAYPXNQ/Dbu0ZCInJCymHSwFhIAFzWtqDeaBU7HCIiIiLyYyq5FDab0OP2vWlLRIGNy/eIuhEW5vli4y/tK8KG/UUAgEd/NBQPzc7w+DV6QxOuREZSJM5VN+N8nQUjBoodERERERF5k7a5FafKdThf04z6FjMaWkwQAMilEoQr5egfH45B8RFIiFRe9yBVIZdCKpVgV24pGg3ulaGIVsuxaEI/D/aEiAIJk1JEV7HZBEilP/ySlclkyMzM7HH7jvztq/POhNTTc0Zg1Y2Dexasl2WlxeBcdTNKdBa0WmxQKTixkoiIiCiYlNa14KP8SuzNr8TJMl2n59W3mFHeYMDh81r0i1Pj5mFJiA1XXndeo8ECncHszZCJKMgwKUV0leue8Ag21NTWIjEhAZB0nZRx5SnPu0dL8NzeQgDA47cP89uEFACkatSIDVegvsWMs1eaMKZfrNghEREREVEvldW3YG9+JT46WYkT1ySikqPD0CdGjfhIJWLVSshkElitAur0JlzU6lFS14LSOgO2HSnBlEFxmDgg1m/KTxBRYGJSiugaVz/hEQQbtE0GKCLMkHSTlOrO/oIq/M/ufADAL2YNwYM3pfc6Vm+SSCTISo3GV0Va5Jc3YnSahoMOIiIiogBU3mDAx/mV+PBkJY6XNjhfl0qAqYPjMWd0H2SPTMaHJyo7nOmUGqtGVloMGlpM+PJsDS7XteDb81oYTFbcmJHgw54QUbBhUorIB/LLdHjon3mwCcCSif3wxO3DxA7JJcNTovBNsRZavQmVOiP6atRih0RERERELtA2t+LDk5X44Hg5vi9pcL4ukQBTBsVhzui+uH1UChKjXK+fqglXYv7YvjhZpsOX52qQV9oAAcBPJ7MmFBH1DJNSRF5W0WDAf/7jGAxm+5OkP9yTGTAzjlQKGfrFyHGpwYJTFTompYiIiIj8mMFkxecFVXg/rxwHz9XA2rarnUQCTBoYh7tG98HtmSlIilL1+BoSiQRj+mkglUrwRWE1jpc2IC1WjR9P6u+pbhBRCGFSishDHFvhXl3o3Gi24hfv5KKmqRXDU6Lw2rLxUMgCq2D4II09KXW+Wg/LMBvkARY/ERERUbArrWvBlm8uYud3ZWhu/WH3uzFpMZg/NhVzRvdBcnTPE1EdyUqNgdUm4OC5GnycfwWnKzovlE5E1BkmpYg85NqtcAVBwJ6T9p1M1AoZfjQyGTu/K+u0fR+NCndk9vFhxK5JUEsRGSZHc6sFF7V6ZCRFiR0SERERUdBxZRfna5U3GPDnT8/i/ePlaJsUhbRYNe4Zl4q7x6ViSGKkFyL9wZi0GJTWteBCrR7/b/txLJ6Q5tXrEVHwYVKKyMMchdLzy3Q4XtoACYDbM1MgkUi63CI3SuWf344SiQRDkyPxfUkDzl1pZlKKiIiIyAuu2wW6C2arDV+dq0HOxTrnEr0bMxKw6sbBmJmR4LNSERKJBLeOSMa7x0pQXN2MfQVVuGEIC58Tkev881MwUYCraWrFwaIaAMANQ+LRPy5c5Ih6x5GUuqjVo9ViRZhcJnZIREREREHn6l2gO3Ol0YjPTl9BfYv9vDSNGrdnpuDpu0b6IsTrqJUyLJnYD28euojvLtVjWHIU4iNdL55ORKGNxWGIPMxsteGT01dgtQkYGB+OCQNixQ6p1xIjlYgNV8BqE3ChRi92OEREREQhRxAE5F6ux/99V4r6FjMilDLcNboPFoxPFX0zmmEpUfjRyGQIAL49rxU1FiIKLKIkpQoLC7Fy5UpMnjwZ06dPx+OPP466ujoAwNq1a5GZmYlx48Y5/+zYscPZdvfu3cjOzsbYsWOxYMEC5OXlidEFok59fqYKdXoTwpUyZI9MDpid9roikUgwLNm+bO/slSaRoyEiIiIKLRabDZ8XVOFQcS0EARiaFIn/mDoAQxIj/Was+fjtwyGRABdq9SivN4gdDhEFCJ8npYxGI1atWoVx48bh0KFD+PDDD9HQ0ICnnnoKAJCfn49169YhLy/P+WfJkiUAgCNHjmDdunV4/vnncezYMcybNw8PPPAADAb+0CP/cOBsNb67XA8A+NHIZIQrg2eF7NAUe1KqpL4FBrNV5GiIiIiIQkOrxYrdeeUoqGyCRALcNDQRd2T1gUrhX+UU0pMiMb6ffYWAPXkmiBwREQUCn39irqiowPDhw7F69WrIZDIolUosWbIEjz/+OEwmE86dO4fMzMwO2+7cuRNz5szBhAkTAAArVqzAjh07sHfvXixcuNCtOKzW7j9UO85x5dxgFIr9l8lkgGCDINgA2HdBcfxXKrV12dZgMuOp984BAMb2i0H/OLXzfVziOPeq6/ukbRftr+6/Ri1HfIQSWr0Jl2qbMTwl6rr2wfa1EorfA9cK9XvA/odu/0Oxz0Tkf1otVryfV4ErjUYo5VLcmZmCAfERYofVqVlDE3GyvAFXGo24pG3BoAT/jZWI/IPPk1KDBw/Gm2++2e61Tz/9FKNGjUJhYSEsFgs2bNiA3NxcREVFYeHChVi1ahWkUimKi4uvSz6lp6ejsLDQ7Tjy8/O9cm4wCpX+h4WFITMzEzW1tdA2tZ99V11d1W37rxoEVOqMiAmTYXCEGVeuXHHr+hHQABiAurp6VOuafdbWlfaO/iepBWj1wJkyLTT4obaUOUoNYAgKCgrQ2trq9vX9Xah8D3Ql1O8B+x/a/SciEsPVCakwuRQLxqUiKVoldlhdilTJkZkag7ySBuSV1jMpRUTdEnVtkSAIePnll3HgwAG88847qK2txeTJk7F8+XK8+OKLKCgowOrVqyGVSrFq1Sro9Xqo1e2L+KlUKrS0tLh97aysLPusmC5YrVbk5+e7dG4wCtX+JyYkQBFh383EZhNQXV2FpKRkSKWdr9cvrWvB8YpKAMD8canQhCvcvm5crP1rOy4uFlJ1pM/adtX+2v5Lwo0oqC1Hld6GhKRkyNvuiUZt7++IESPcvrY/C9XvgauF+j1g/0O3/46+ExGJwWoT8NHJSlxpNEIll+Ke8alIivLvhJTD2DQNjpc0oLTOgNrmViRwJz4i6oJoSanm5masWbMGp0+fxjvvvINhw4Zh2LBhmD59uvOc0aNH495778XevXuxatUqqNVqGI3Gdu9jNBoRG+v+7mYymczlAbY75wajkOu/RAqJxF5uzbFkTyqVOF+7lsVmw4GztQCA5VMHYGBCRLdb+XZ23Wuv75O2XbS/tv/J0WpEKGXQm6yoaDD+MH28rU2wfp2E3PdAB0L9HrD/od1/IiJfEgQB+wurUFpvgEImwT3jAichBQDRagWGJEWiuLoZeSUNyB6ZLHZIROTHRNl9r6SkBAsXLkRzczN27dqFYcOGAQD27duH7du3tzvXZDJBpbL/EM7IyEBRUVG748XFxcjIyPBN4EQd+P5yAxoMZkSp5Hjs9mFih+NVEonEOQ37Qo2+m7OJiIiIyF3HLtU7i5rfkdnH75fsdWRcPw0A+67N+laLuMEQkV/zeVJKp9Ph3nvvxfjx47F582bExcU5jwmCgPXr1+Pw4cMQBAF5eXl46623nLvvLVq0CHv27EFOTg7MZjO2bt0KrVaL7OxsX3eDCADQaDDj2KU6AMDc0X0QrXJ/2V6gGZxoX953oVbPXVWIiIiIPKi4uhmHL2gBADcPTQrYmkx9YlRIiVbBKgjIL9eJHQ4R+TGfL9977733UFFRgY8//hiffPJJu2N5eXlYs2YNnnnmGVRVVSEhIQG//OUvMX/+fADAtGnTsHbtWufx9PR0bNq0CRqNxtfdIAIAHDxXA4tNQJpGjbFtT4SCXb9YNeRSCZpbLahpag3Ip3dERERE/qaiwYDdeeUAgKzUGGSlxYgcUc9JJBKM6ReDK6eNOFPZiCmD4iCRdF6flYhCl8+TUitXrsTKlSs7Pb506VIsXbq00+Pz5893JqmIxFRS14ILtXpIJcBNwxJD5hetXCbFgPhwnK/R42KtnkkpIiIiol4yW21Y/c/vYTBbkRQVhpkZCW61V8mlsNmELjfm8bX0xEgckNegyWhBab0B/ePCxQ6JiPyQqLvvEQUqmyDgUJG9uHlWagziQ2xXkQHxEThfo0dJXQumDI4XOxwiIiKigPbq/iLklTRApZDizqw+kMvcq7KikEshlUqwK7cUjQb3azj10ahwR2Yft9t1RS6TYmhyJE6VN6KgspFJKSLqEJNSRD1QWNmEmuZWKOVSTBkUekmZAW2DispGI1otVgDBX0uLiIiIyBvySurxly/PAwDmZPVBjLrn46pGg6VHu0BHqbzzsXBUnxicKm9EcXUzbhpmRZicO7kSUXui7L5HFMjMVhu+vWCfJTV5YBzUytD75RqtVkATroAgAGX1BrHDISIiIgpIBpMVv/6/E7DaBMwf2xej+gZuHamOJEeHITZcAYtNQFFVs9jhEJEfYlKKyE15pQ3Qt1oRrZJjTAAXoOwtx2ypy9oWkSMhIiIiCkx//PQsLtTqkRwdht/PyxQ7HI+TSCQY2TcaAHCmslHkaIjIHzEpReSGVrMV31+uBwBMGxLv9nr/YOKoC1BSx6QUERERkbvyy3TY+u1FAMALC0cjJjw4yyEMT4mGBEClztijpYVEFNxC9xM1UQ/kltSj1WJDfIQSQ5OjxA5HVGmx4ZBKAJ3BjDq9SexwiIiIiAKGxWrDmt0nYROA+WP74qZhSWKH5DWRYXKkxqoBAMXVXMJHRO0xKUXkohaTBcdLGwDYZ0lJJf6z5a4YlHIp+sTYBxgXajjAICIiInLVPw5fxqnyRkSr5Hh6zkixw/G6jKRIAEBRdZPIkRCRv2FSishF312qh9kqIDk6DIMTIsQOxy/0j7cv4Ttfoxc5EiIiIqLAUN1oxIufnQUArLlzBBKjwkSOyPuGJEZCAqCqsZVL+IioHSaliFzQYrIgv1wHAJg2OB6SEJ8l5eCoK3VJq4fVJogcDREREZH/++OnZ6E3WTG2nwZLJvYTOxyfiOASPiLqBJNSRC44XqqDxWafJeVIxBCQFBkGpUyKVosNBdxRhYjIZwoLC7Fy5UpMnjwZ06dPx+OPP466ujoAwNq1a5GZmYlx48Y5/+zYscPZdvfu3cjOzsbYsWOxYMEC5OXlidUNoqBic+EBXX6ZDru+LwMA/HbuSEilofOg07GE71wVl/AR0Q/kYgdA5O9MVgEny+wJl0kD4zhL6ipSqQR9NSpc0rYg54IWmakxYodERBT0jEYjVq1ahR//+Mf461//Cr1ejyeeeAJPPfUU3njjDeTn52PdunW45557rmt75MgRrFu3Dps2bcLo0aOxbds2PPDAAzhw4ADUarUIvSEKHlKpBLtyS9FosHR4XBAEbP32EgQByEqNwfGSBhwvaQAA9NGocEdmHx9G63tDEiPx5dkaVDfZl/DFqINzt0Eicg9nShF1o7jODJPVvuMea0ldr1+sfebY4fNakSMhIgoNFRUVGD58OFavXg2lUonY2FgsWbIEx44dg8lkwrlz55CZmdlh2507d2LOnDmYMGECFAoFVqxYgdjYWOzdu9fHvSAKTo0GC3QGc4d/ci/Xo7TeALlUgkkDY9sdazZ2nMgKJlzCR0Qd4Uwpoi6YrTacq7MXY+QsqY45BhdHL9bBahMgC6Fp6EREYhg8eDDefPPNdq99+umnGDVqFAoLC2GxWLBhwwbk5uYiKioKCxcuxKpVqyCVSlFcXIyFCxe2a5ueno7CwkK3YrBarT0639125DreY9/o6j7LZDJAsEEQbNe3swn4tu0B3vj+GkSGydqf5/j/Ttp3K0DaD0mIQFm9ARdqmzG+f0y7ttfeW34texfvs/eF+j12td9MShF14VR5I0xWIEYtd66Dp/YSo8IQJpeiqdWCMxWNyErjEj4iIl8RBAEvv/wyDhw4gHfeeQe1tbWYPHkyli9fjhdffBEFBQVYvXo1pFIpVq1aBb1ef90yPZVKhZaWFreum5+f36N4e9qOXMd77BvX3uewsDBkZmaiprYW2ibDdeefrzejwWBGmAxIDWvFlStX2h2PgAbAANTV1aNa5/4sokBpH9mWgKpsMOJyWSXC5BKYo9QAhqCgoACtra3Oc/m17Bu8z97He9w1JqWIOmGx2fB92zr/CQNiQ6oQpTukEgn6x4WjqLoZORe0TEoREflIc3Mz1qxZg9OnT+Odd97BsGHDMGzYMEyfPt15zujRo3Hvvfdi7969WLVqFdRqNYxGY7v3MRqNiI2NdevaWVlZ9lkhLrJarcjPz3e7HbmO99g3urvPiQkJUESY271msdrw0fkSAMDkwfHol6q5rl1c28zzuLhYSNXuPwgNpPYJV0pR22xCiywSA1KioGmrLTVixAgA/Fr2Fd5n7wv1e+zof3eYlCLqREFlE/QmK9RyCYanRIkdjl8bmBDhTErdN3Ow2OEQEQW9kpIS3Hfffejbty927dqFuLg4AMC+fftQW1uLpUuXOs81mUxQqVQAgIyMDBQVFbV7r+LiYsycOdOt68tksh4NsHvajlzHe+wbnd5niRQSSfuyvSfLddC3WhGlkmN0qua64452nbV3SQC1H5wQidrmOlzUtmBk3xhn22vvJ7+WfYP32ft4j7vGQudEHbDZBORergcADItXQM5ZUl0aGG8vdn70Yh0s1h7UISAiIpfpdDrce++9GD9+PDZv3uxMSAH25Xzr16/H4cOHIQgC8vLy8NZbb2HJkiUAgEWLFmHPnj3IycmB2WzG1q1bodVqkZ2dLVZ3iIKayWLDd5frAABTBsVBLuPHr0GJ9o2DLmv1sNg4biQKdZwpRdSBc9VN0BnMUCmkGBzLb5PuJEerEKWSo8lowZnKRoxO04gdEhFR0HrvvfdQUVGBjz/+GJ988km7Y3l5eVizZg2eeeYZVFVVISEhAb/85S8xf/58AMC0adOwdu1a5/H09HRs2rQJGo1GhJ4QBb/8ch2MZhti1AqMSIkWOxy/kBwVhgilDHqTFeX1BsRHhIkdEhGJiJ+2ia4hCAK+v9wAABjXTwO5tLXrBgSpRIJJA+PwRWE1jl2qZ1KKiMiLVq5ciZUrV3Z6fOnSpe2W711r/vz5ziQVEXmP2WpzzryfPDCO9UnbSCQSDEqIwKmKRlyo0XPcSBTiOH+U6BqXtS2oaW6FXCpBViqfaLlqwgB7kdzctinqRERERKEsv1wHg9mKGLUCw1iftB3HEr6LWj0EQRA5GiISE5NSRNfIuagFAIzoEw2VggXpXDVpoL2mybFL9RxcEBERUUizXDVLauLAWMg4S6qdfrHhkEkkaDJaoNWbxA6HiETEpBTRVS7UNONcVTMAYFx/jbjBBJjRaTFQyqSoaWpFSV2L2OEQERERieZMZSNaTPYd91hL6noKmRR9Y+27gp6vbhY5GiISE5NSRFf5+zeXAACDEiIQG64UN5gAo1LIkNm23PG7S/UiR0NEREQkDptNwPclDQCACf05S6ozA+PsS/iKa5iUIgplTEoRtWloMWFXbhkAe4Fzcp9jCd93rCtFREREIaq4ptm5i/PIvpwl1Zn+8eEA7PVcjWaryNEQkViYlCJq88+jJTCYrUiODkNarFrscALSxKvqShERERGFGkEQnLWkxqRpoJDx41Zn4iOUiAyTw2ITcPQiH2gShSr+lCSCfcvet769DACYOigeEgmnWfeEYwe+4upm1LNoJREREYWYi1o9qpvsuziPSdOIHY5fk0gk6B9nny118FyNyNEQkViYlCICsDe/ElcajUiMCsMoTrPusbgIJYa0bfHreEpIREREFCoOn7fv4jyqbzTUSu7i3J2BbUv4vmJSiihkMSlFIU8QBLz59UUAwM+mDoCc06x7ZeKAtiV8rCtFREREIaS4ugnna/QAgHH9Y0WOJjD0iwuHBEBRdTPKGwxih0NEIuCnbwp5xy7VI79chzC5FMumDhA7nIA3YaB9EJZ3uUHcQIiIiIh8yLGL85DECMSoFeIGEyBUChlS22q5crYUUWhiUopC3j++vQQAuGdcKuIilOIGEwQcOxfml+tgsdrEDYaIiIjIB+r1Jvzre/suzmO5i7NbhiRGAmBSiihUMSlFIe2KzohPT18BANx7w0BxgwkSQxIjERUmh8FsxdmqJrHDISIiIvK6d4+VwGi2ISVahVQNd3F2R3pbUupQcS0faBKFICalKKT982gJLDYBkwfGYUQfFjj3BKlUgtH9YgAAx0sbxA2GiIiIyMssV+3iPGVQHHdxdlMfjQqacAWajBaOHYlCEJNSFLJMFhvePVoCAPjZDawl5UmOaevHSxpEjYOIiIjI2/YVVOFKoxHxEUru4twDUokEN2YkAgAOcgkfUchhUopC1ienr6CmqRVJUWG4bVSK2OEENJVcCptNcP59bD97sXN3nnZd3Z6IiIgoULydY58ltWRSP+7i3EMzMxIAMClFFIrkYgdAJJa32gqc/3RKfyg4gOgVhVwKqVSCXbmlaDRY0NxqAWDf3vf1A8UIU8i6bB+tlmPRhH6+CJWIiIjIY87XNOObYi0kEvuY8rPTVWKHFJBmDbXPlMov10GrN4kcDRH5EpNSFJJOV+jw3eV6yKUS/HRyf7HDCRqNBgt0BjMAIEolR5PRgnPVzegfFy5yZERERESety3HXgriluFJSIvleKenkqJVGNEnGgWVjfimuBYcnROFDk4PoZD09mH7NOvbM1OQFK0SOZrglNJ2X680GkWOhIiIiMjzWkwW7MwtBQD8x1TWJ+2tmUPtS/i+KqoVORIi8iUmpSjk6FrMeP94OQDg3hsGihtMEEuJsSelqnRMShEREVHw2XOiAk1GC/rHhWNmW6Fu6jnHPfymWAtBYK1RolDBpBSFnJ25pTCabRjRJxoTB8SKHU7QunqmFAcWREREFEwEQcBbbTPvl03pD6lUInJEgW/CgFiEyaWobmpFaaNF7HCIyEeYlKKQYrP9MIC4d9oASCQcQHhLUlQYpBKgxWRFk5EDCyIiIgoeJ8p0OF3RCKVcisUTuVmLJ6gUMkweFAcAOFnFYudEoYJJKQopXxXVoKSuBdEqOeaPTRU7nKAml0mREBkGgHWliIiIKLhsO2IvcH7X6D6Ii1CKHE3wuDHDXlfqBJNSRCGDSSkKKf9sG0AsGJ8GtVImcjTBj8XOiYiIKNg0tdrwYf4VAMByFjj3qBnp9rpSp2tMaLXYRI6GiHyBSSkKGVWNRuwvrAZgX/tP3ucodn6Fxc6JiIgoSHxxyQCTxYZRfaMxtp9G7HCCyvCUKMRHKNFqFZBXUi92OETkA0xKUcjYcawUVpuASQNjkZEcJXY4IcExU6q6qRVWG4udExERUWATBAGfXWgBAPzHVNYn9TSpVILp6fEAgEPFWpGjISJfYFKKQoLVJmD7UfvSvZ9ylpTPaMIVCJNLYbUJqG1uFTscIiIiol45crEOV5qtiAyTYd6YvmKHE5RmpNvrSn3DpBRRSGBSikLCwXPVqNAZoQlX4I7MPmKHEzIkEgmSWVeKiIiIgsSO78oA2AucR4TJRY4mOE0fYp8plV+hQ0MLC54TBTsmpSgkOAqcLxyfBpWCBc59ybGEr4p1pYiIiCiANbSY8MnpKgDAkon9RI4meKXEqJAWLYcgAN+e52wpomDHpBQFvYoGA75oK3D+k8lcuudrzmLnnClFREREAez9vHKYLDYMjJEjKzVa7HCC2phkJQDg66JakSMhIm9jUoqC3o5jpbAJwJRBcUhPihQ7nJDjmClV32KG0WwVORoiIiIi9wmCgO3HSgEAtwxWs8C5lzmSUoeKa0SOhIi8jUkpCmoWqw072gYQLHAuDrVShhi1AgBQxdlSREREFIBOlOlQeKUJYXIpZvZXix1O0BuZqIRCJkFpnQGXtXqxwyEiL2JSioLagbM1uNJoRFyEErdnpogdTshy1pVq5A58REREFHh2HLPXJ70jMwWRSn6E8ja1XIpx/TQAuISPKNjxJyoFtXeP2gcQiyakIUzOAudiSYoOAwBUN3GmFBEREQUWfasF/z5eAQBYMjFN5GhCx/T0BADAISaliIIak1IUtCp1Bnx5lgXO/UFyFGdKERERUWD68GQF9CYrBiVEYNLAWLHDCRkz0uMBAN+cr4XFahM5GiLyFialKGi99305bAIweVAcBiVEiB1OSEuMss+Uam61QN9qETkaIiIiItc5CpwvmdSPBc59KCs1BtEqOZqMFpws14kdDhF5CZNSFJQEQcDO7+wDiMUTOM1abEq5FHHh9l1Uqps4W4qIiIgCw9krTcgraYBcKsHC8RxT+pJMKsENQ7iEjyjYMSlFQem7y/W4pG1BuFKGO7P6iB0OgXWliIiIKPBsbytwfuuIZOfMb/KdGRlMShEFOyalKCg5ZknNyeqDiDC5yNEQACS1DeSqWVeKiIiIAoDRbMXuvHIAwNLJ/USOJjTNzEgEAHxfUo9mloAgCkpMSlHQ0bda8NHJSgDAjydxAOEvkqLbip1zphQREREFgE9PX0FDixmpGjVubEuOkG/1jw9H/7hwWGwCjlzQih0OEXkBk1IUdPbmVzp3SJk4gDuk+IvEyDBIAOhbrSx2TkRERH5vR1uB88UT0yCTssC5WBxL+L7mEj6ioMSkFAWdnbllAIBFE9K4Q4ofUcqliIuwFzvnbCkiIiLyZ5e1enx7XguJBFg8kTPvxXRjeltdqWImpYiCEZNSFFQua/U4erEOUgmwYHyq2OHQNVhXioiIiAKBY5bUzIxEpGrU7Y6FhbHguS/dMCQBUglQXN2MSp1B7HCIyMNESUoVFhZi5cqVmDx5MqZPn47HH38cdXV1AIATJ05g8eLFGDduHGbPno2dO3e2a7t7925kZ2dj7NixWLBgAfLy8sToAvmpXW2zpGZkJKJPjLqbs8nXHHWlqpuYlCIiIiL/YrMJAACL1eYcU/7kmgLnMpkMmZmZkMlkPo8vVMWEK5CVpgHAXfiIgpHPtyUzGo1YtWoVfvzjH+Ovf/0r9Ho9nnjiCTz11FN44YUXcP/99+Phhx/GkiVLcOzYMaxevRrDhg3D6NGjceTIEaxbtw6bNm3C6NGjsW3bNjzwwAM4cOAA1GomIEKd1SbgX20DiMUT0kSOhjqSHG1/sljVaIQgCFxeSURERH5DKpVgV24pvrtUj+qmVoQrZSitM2DLoYs/nCTYUFNbi8SEBEDyw/P9PhoV7sjsI0LUoeHG9AScKG3A10W1XE5JFGR8PlOqoqICw4cPx+rVq6FUKhEbG+tMQH322WfQaDRYtmwZ5HI5pk2bhrlz52Lbtm0AgJ07d2LOnDmYMGECFAoFVqxYgdjYWOzdu9fX3SA/9E1xLSp0RkSr5MgemSx2ONSBhLZi5y0mK/StVrHDISIiImqn0WDBsUv2FRzDUqLQ3GqBzmB2/mkwmKFtMqDhqtd0BjOajdzExZscxc6/Ka51zmgjouDg85lSgwcPxptvvtnutU8//RSjRo1CUVERhg4d2u5Yeno6du3aBQAoLi7GwoULrzteWFjodhxWa/cfiB3nuHJuMAq0/u/8zr72PzM1Gv/MueR2+xSNGndm9QUEGwTBBuCHadw2mwCp1Nb1G7S1ubq9W3rT3kvXdrn/Ll5fLgXiIpTQ6k2oajQgIiyiXXt/+1oLtO8Bbwj1e8D+h27/Q7HPRAToWy24WKsHAIzsEy1yNOQwvn8swpUyaPUmFFxpxKi+MWKHREQe4vOk1NUEQcDLL7+MAwcO4J133sFbb7113TI8lUqFlpYWAIBer+/yuDvy8/O9cm4wCoT+Gyw2fHa6BgDQR2VF4cVSt9/DnKIBsvqirq4e1brmdseqq6u6bR8BDYABHbZ3RW/ae/va3fXfnetHyW3QArhQqUW4tQkAYI5SAxiCgoICtLb6X72pQPge8LZQvwfsf2j3n4hCR365DjbBvjlLQiQLmvsLpVyKKYPicOBsDQ4V1TIpRRREREtKNTc3Y82aNTh9+jTeeecdDBs2DGq1Gk1NTe3OMxqNiIiwz6ZQq9UwGo3XHY+NjXX7+llZWd0WKLRarcjPz3fp3GAUSP3/4HgFWq3VGBgfjpEDUqDrwRTquFh7wjMuLhZSdSQA+wyh6uoqJCUlQyrtuv5RR+17e31ftO2qvav9d+f6Ayw6XNLVokVQICUlBQCgUSsAACNGjHA7dm8KpO8Bbwn1e8D+h27/HX0notAhCAKOlzYAAEb15SwpfzMjI9GelCquxc9nDRE7HCLyEFGSUiUlJbjvvvvQt29f7Nq1C3FxcQCAoUOH4ptvvml3bnFxMTIyMgAAGRkZKCoquu74zJkz3Y5BJpO5PMB259xgFAj9/zD/CgBg3pi+kEhlkEh6sITNUaxSIoWk7f8dS9akUonzNXfa9/b6PmnbRXuX++/G9VOi7QmsqsZWABJ7sfO2Nv76dRYI3wPeFur3gP0P7f4TUWg4Vd6I6qZWyKQSDE2OEjscusbMtrpSRy/WwWi2QqXg7yWiYODzQuc6nQ733nsvxo8fj82bNzsTUgCQnZ2N2tpabN26FWazGTk5OdizZ4+zjtSiRYuwZ88e5OTkwGw2Y+vWrdBqtcjOzvZ1N8iP1OtN+OqcfenevLF9RY6GupMQqYREAhjMVjS3sigoERER+YedufbyD0MSI5jw8EPpSZFIjg5Dq8WG7y7Vix0OEXmIz2dKvffee6ioqMDHH3+MTz75pN2xvLw8bNmyBc8++yw2bNiAuLg4PP3005g6dSoAYNq0aVi7di2eeeYZVFVVIT09HZs2bYJGo/F1N8iP7D1VCYtNwMg+0UhPisJX52rFDom6IJdJER+hRG2zCdVNrYhSKcQOiYiIiEKc0WzFB8crALDAub+SSCSYkZ6If31fhq+La5w78hFRYPN5UmrlypVYuXJlp8ezsrKwffv2To/Pnz8f8+fP90ZoFKD+3TaAmM9ZUgEjOVqF2mYTqhqNGJLofg0sIiIiIk/6/EwVdAYzolVy9IsLFzsc6sSNGQn41/dlOFRUC9whdjRE5Ak+X75H1BWbTXDr/EqdAUcv1QEA7hrDpFSgSIqy72ZT3eh/O+0RERFR6NmZWwYAGNNPA6mk681tyPvCwjre+XB6un121OmKRmibOY4kCgai7b5H1BGpVIJduaVoNLhWa+jweS0EAegXp8bJsgakatRejpA8ISlaBQCobmqFILiXiCQiIiLypIoGA74ustcnHZOmETeYEKSSS2GzCc6dnmUyGTIzMzs8NzEqDMNTolB4pQnfnNdiXttD6avbE1FgYVKK/E6jwQKdwezSuSfKGgAA6YmRaDayaHagSIhQQtpW7LzJaIEmXCl2SERERBSi3vu+DIIATBkUh7gIpcvjUPIMhVza/sG0YENNbS0SExJ+2OH5Kppwez3SLYcuorapFdFqORZN6OfrsInIQ5iUooBV32IvlC2R2HfjoMAhl0kRHxmGmqZWVDe1snYDERERiUIQBOfSvcUT+6GRCSnROB5MC4IN2iYDFBFmSDpISqW0zbgvrm5GQ4vJ12ESkYexphQFrLNXmgAA/ePCEa5kfjXQOOtKNRlFjoSIiIhC1dGLdbisbUGEUoY7s1LEDodc0FejhkwiQXOrBfUtTCISBTompSggCYKAs1X2pNSw5CiRo6GeYLFzIiIiEptjltRdo/vyIWeAUMik6KOxz5YqqWsRORoi6i0mpSgg1TS1oqHFDJlUgiGJXLoXiJKiWOyciKinCgsLsXLlSkyePBnTp0/H448/jro6+260J06cwOLFizFu3DjMnj0bO3fubNd29+7dyM7OxtixY7FgwQLk5eWJ0QUi0TW3WrA3vxIAsHhimsjRkDv6t5V+YFKKKPAxKUUB6VxVMwBgcEIElHJ+GQeihMgfip03skg9EZHLjEYjVq1ahXHjxuHQoUP48MMP0dDQgKeeego6nQ73338/7r77bhw7dgzPPvss1q9fj5MnTwIAjhw5gnXr1uH555/HsWPHMG/ePDzwwAMwGAwi94rI9/aerESLyYrBCRGYMCBW7HDIDQPaklLl9QZYbXy4SRTIOEeVAo4gCCiusSelMljgPGDJZVLERShR22xCZQM/DBERuaqiogLDhw/H6tWrIZPJoFQqsWTJEjz++OP47LPPoNFosGzZMgDAtGnTMHfuXGzbtg2jR4/Gzp07MWfOHEyYMAEAsGLFCuzYsQN79+7FwoULXY7BarW6FbPjfHfbket4j933f9+VAgAWjk+FzWaDTCYDBBsEwdZpG1tbAsRmEyCVXnWeo0037TsVyu2vadvpPb5KQqQCKoUURrMN5fV6APzadxd/ZnhfqN9jV/vNpBQFnNpmE3QG+9K9gQkRYodDvZAUpbInpRpZ7JyIyFWDBw/Gm2++2e61Tz/9FKNGjUJRURGGDh3a7lh6ejp27doFACguLr4u+ZSeno7CwkK3YsjPz+9B5D1vR67jPXZNZbMF312uhxTAUGU9CgoKkJmZiZraWmibun9YVl1d1e7vEdAAGIC6unpU65rdjieU23fW9tp7fK1EtQSlZiC/pAYAUFBQgNZW1ip1F39meB/vcdeYlKKAU1xt/2U1MD4cChmX7gWypOgwnKkEKhuYlCIi6glBEPDyyy/jwIEDeOedd/DWW29BrVa3O0elUqGlxV53Ra/Xd3ncVVlZWfZZJS6yWq3Iz893ux25jvfYPQf3FwGoxfSMBMyeNsH5emJCAhQRne/oZrMJqK6uQlJSMqRSifP1uFj791VcXCykavdn8ody+2vbdnaPrzXU1ojSxhpUNttnVo0YMcLtuEMZf2Z4X6jfY0f/u8OkFAUcx9K9dBY4D3iOHfgqdQYIggCJpPOBBxERtdfc3Iw1a9bg9OnTeOeddzBs2DCo1Wo0NTW1O89oNCIiwj6zWK1Ww2g0Xnc8Nta9ejoymaxHA+yetiPX8R53TxAEfHDCXuB84fi09vdLIoVE0vlDT8dyMqlU0v48x/93075Todz+mrad3uNrDEyIBFCDCp0R2uZWxEeGuR838WeGD/Aed43TTCig1OlNqNObIJUAg7h0L+AlRoZBIgH0JiuucAkfEZHLSkpKsHDhQjQ3N2PXrl0YNmwYAGDo0KEoKipqd25xcTEyMjIAABkZGV0eJwoFeaUNuKxtQbhShh+NShY7HOqhyDA5EiKVAICvimpEjoaIeopJKQoojllS/eLCEaZgtjnQOYqdA0B+mU7kaIiIAoNOp8O9996L8ePHY/PmzYiLi3Mey87ORm1tLbZu3Qqz2YycnBzs2bPHWUdq0aJF2LNnD3JycmA2m7F161ZotVpkZ2eL1R0ij7G5uAvb+3nlAIDbR6UgXMmFI4FsYLz9IfWXZ5mUIgpU/ClMAeV8NZfuBZukqDBom004Va7Dj0aliB0OEZHfe++991BRUYGPP/4Yn3zySbtjeXl52LJlC5599lls2LABcXFxePrppzF16lQA9t341q5di2eeeQZVVVVIT0/Hpk2boNFoROgJkWdJpRLsyi1Fo8HS6TlWm4Cd35UBANRKGbYcuggA6KNR4Y7MPj6JkzxnYHwEvrtcj6/O1cBqEyDrogYVEfknJqUoYDQZzahusu+oMTiRS/eCRXKUCgWVTcgv50wpIiJXrFy5EitXruz0eFZWFrZv397p8fnz52P+/PneCI1IdI0GC3SGzguVX6hthsFsRbhShrhwpfPcKBU/FgWiPjEqhMmlqG8x40RZA8b3d68+HhGJj8v3KGBcqNUDsP/y4VTr4JEUbS9KeaqiUeRIiIiIKNidrbRvBDAsOarLnd0oMEilEufDai7hIwpMTEpRwLhYY09KcZZUcEmIDIMEQE1TK6pY7JyIiIi8pNVixfm2h5zDU6JEjoY8xVHW4+DZapEjIaKeYFKKAkKrxYrS+hYAwOAE1pMKJgqZFAlR9tlSLHZORERE3nK+Wg+rTUBcuBKJbWMPCnzpSfbPBifLddA2t4ocDRG5i0kpCggl2hbYBECjViA2XCF2OORhfWJUAMC6UkREROQ1hVfspQKG9YmCRMKle8EiSqXAyD7REATgqyIu4SMKNExKUUBw1JManBjBQUQQciSlTjEpRURERF7QZDSjtN4AABiezKV7weamYYkAgAOFTEoRBRompcjv2WwCLjmSUly6F5T6xKgBcKYUERERece5qmYAQKpGjWg1Z90Hm5uGJQGwz5Sy2gSRoyEidzApRX6vUmeE0WKDSiF1zqih4JISrYJUAlQ3taKaxc6JiIjIwxxL91jgPDiN769BlEqOhhYzTpQ1iB0OEbmBSSnyexe19llSA+MjuHVvkFLKpRjStnPKqQrOliIiIiLPqdObUNtsglTyQ1FsCi5ymRQzM+xL+L48yyV8RIGESSnye5euSkpR8MpMjQEA5Jc1ihwJERERBZOiqiYAQP+4cKgUMpGjIW+Z1VZX6uDZapEjISJ3MClFfq3JaIa22QQJgP7x4WKHQ17kTEqxrhQRERF5UFG1vZ5UBgucB7WbhtqTUifKdKhtbhU5GiJyFZNS5Ncua1sAAMnRKqj5ZCuoZbUlpbgDHxEREXlKnd4Erd6+dG9IAmfdB7OkaBVG9okGABzkEj6igMGkFPk159K9BM6SCnaj+kZDIgGuNBpR08SnW0RERNR7Vy/dC+MDzqA3e7h9F74vCrmEjyhQMClFfstqE1BaZwDAelKhICJMjsFtTzA5W4qIiIg8gUv3QsstI+xJqYPnamCy2ESOhohcwaQU+a2KBgNMVhvUChmSosLEDod8IIt1pYiIiMhDuHQv9IxJ0yAhUonmVguOXaoTOxwicgGTUuS3HPWkBsaHQyKRiBwN+UIm60oRERGRh3DpXuiRSiW4eZh9ttS+giqRoyEiVzApRX7rUp29ntQALt0LGUxKERERkadw6V5oumVEMgBgf0E1BEEQORoi6g6TUuSX9K0WaJtNAIB+cWqRoyFfGdXXvmNKhc4ILbfyJSIioh7i0r3QdWNGApQyKUrqWlDclpgkIv/FpBT5pdI6+9K9pKgwhCvlIkdDvhKlUjiLnbOuFBEREfUUl+6FrogwOaYNiQcA7OcufER+j0kp8kslbUmpfnHhIkdCvsYlfERERNRbXLoX2hy78O1nXSkiv8ekFPkdQRCcSan+TEqFHO7AR0RERL2hbW7l0r0QN3u4PSmVe7ke9XqTyNEQUVeYlCK/U9PUCr3JCplUgr4xKrHDIR/7YaZUo8iREBERUSByzJLi0r3QlRYbjuEpUbAJwIGzXMJH5M+YlCK/c6HWvuteqkYNuYxfoqFmVKq92Hl5gwF1fLJFREREbiqu4dI9Am69ahc+IvJf/MRPfudCjT0pxaV7oSlapcDAePu/PetKERERkTvqW0zQNpsgkcC5eQoFN5VcCptNuO712W11pb46VwOTxdble3TUnoh8g9uakV8xWWy4XMekVKjLTI3BJW0L8st1mDk0UexwiIiIKECcu2Lfda9vjBoqLt0LCQq5FFKpBLtyS9FosDhfFwQBEUoZmlot+P2HpzE4IbLD9tFqORZN6OercInoGkxKkV/JK6mH2SpArZAhIVIpdjgkkqzUGHx4spIzpYiIiMgtZ6vsSanBiZwlFWoaDRboDOZ2rw2Ij8CZykacKm9EfESYSJERUVe4fI/8yuELWgBAv1g1JBKJyNGQWLgDHxEREblL12LG5bYdnLl0jwBgUNvXwcVaPQSBS/SI/JHbSakjR454Iw4iAMC35+1JqbRYLt0LZaPaklJl9QZu40tEQYdjKSLvOHC2GoIAxEcooQnnjHuylwORSSTQGcyobzF334CIfM7tpNTDDz+MW2+9FX/5y19QUVHhjZgoRBnNVhwvaQAApMWpxQ2GRBWjVmCAo9h5BWdLEVFw4ViKyDs+L6gCwKV79AOlXIq0WPvnigu1zSJHQ0QdcTspdejQITz22GM4deoUbrvtNvznf/4nPvzwQ5hMnM1AvZN7uR4mqw1RKjk0aoXY4ZDIMvvaZ0udKm8UORIiIs/iWIrI81otVhw8WwMAnRa0ptDkXMLXtsM3EfkXt5NSCoUCt912G15//XUcPHgQt956K7Zs2YIZM2bgd7/7HQoLC70RJ4WAw21L9wbGR7CeFCEz1ZGU4kwpIgouHEsRed6RC3VobrUgMkyO5GgWtKYfOJJSlTojDGaryNEQ0bV6XOhcq9Viz549eP/991FcXIwpU6YgLCwMK1aswBtvvOHJGClEfHu+FgAwMJ71pIjFzoko+HEsReQ5n5+xL90bmhzJh5vUTrRagYRIJQQAl2o5W4rI38jdbfDRRx/hgw8+wLfffovBgwdjwYIFeOONNxAXFwcAmDVrFlavXo1f/OIXHg+Wgpe+1YKTZfbkwyDulkIAMlOjAQAldS3QtZgRE84lnUQUHDiWIvIsQRCwr8CRlIoSORryR4MSIlDbbMLFWj1G9IkWOxwiuorbSanf/e53mDNnDrZv347MzMzrjg8aNAgrVqzwRGwUQo5dqoPFJiAtVg1NuBI6A3fHCHWacCX6xalRWmfAqQodpqcniB0SEZFHcCxF5FmnKxpRqTMiXCnD4IQI6E1cokXtDUqIwLFL9bisbYHVJkAm5Ww6In/hdlLq0KFDKC0tRXJyMgDg+PHjiIqKwpAhQwAAKSkpePjhhz0bJQW9nAt1AIBpg+NFjoT8SVZqDErrDMgvZ1KKiIIHx1JEnvVZ29K9mRmJkMukAJiUovZSolVQK2QwmK0obzCgfxzLhRD5C7drSu3fvx933303Ll26BADIy8vD4sWLcfDgQU/HRiHk2CV7UmoKk1J0lVF9WVeKiIIPx1JEnrWvLSl168hkkSMhfyWRSH7YhY91pYj8ittJqY0bN+K1115zTjdfuXIlXnnlFfz5z3/2eHAUGoxmK06WNQAAJg2MFTcY8iuOYuenmZQioiDCsRSR55TVt+BMZSOkEmD28CSxwyE/5khKXahphiAIIkdDRA5uJ6UqKytx4403tnttxowZqKio8FhQFFpOlDbAbBWQGBXGqbTUjiMpdUnbgkYj64wRUXDgWIrIc/YXVAMAJg6MQ1yEUuRoyJ/1jwuHTCpBo9GCOr1J7HCIqI3bSanU1FR8/fXX7V47fPgw+vbt67GgKLR8d7keADB5YBy38KV2YiOUSNWoAQCnOFuKiIIEx1JEnrO/0J6UunUEZ0lR15RyKfrF2seVF7iEj8hvuF3o/P7778fq1avxox/9CKmpqaioqMDnn3+OF154wRvxUQg4etFeT2oil+5RB7JSY1DeYMCpch1uGMJi50QU+DiWIvIMg8mKnAtaAMDNw5iUou4NSojAJW0LLtbqMWlgnNjhEBF6MFNq7ty52LRpExQKBU6fPg2VSoUtW7bgtttu80Z8FGBsNvfWZ1ttAr5vmynFXwzUkaw0R7HzRpEjISLyDI6liDzj8IVamCw2pGrUSE+KFDscCgCOulKVOiP0rRaRoyEioAczpQBgypQpmDJliqdjoSAglUqwK7cUjQbXfshfaTSiqdUCpVyKS1o9MttqCBE5OL4muHyPiIIJx1JEvffl2RoAwE3DElkCglwSpVIgKSoM1U2tuKTVO3d6JiLxuJ2Uqqqqwuuvv45Lly7BZrO1O/bWW295LDAKXI0GC3QG14pSn7vSBABIiVbBYLJ6MywKUJl9owHYt+9tNJoRrVKIHBERUe9wLEXUe4Ig4MBZez0pLt0jdwxOiEB1Uysu1DApReQP3E5KrVmzBrW1tbj55puhUPDDIfVORYMBANBXoxI5EvJX8ZFh6BujQoXOiDMVjZg6OF7skIiIeoVjKaLeu1CrR2mdAUqZFDekc2xArhucGImci3UoqWuBxWrrvgEReZXbSan8/Hx8+umniItj/R/qHUEQUK6zJ6UcO6wRdSQzNQYVOiNOleuYlCKigMexFFHvHWjbdW/K4DiEK3tUkYRCVEKkEpFhcjS3WlBab0B8ZJjYIRGFNLcLnUdFRUGpVHojFgoxjUYL9K1WSCVAcjRnSlHnslIdxc5ZV4qIAh/HUkS9d/CcvZ7UrKGJIkdCgUYikWBwW8HzCzXNIkdDRG4npR588EGsWbMGJ0+eREVFRbs/RO5wLN1LilJBIXP7S5FCSGYak1JEFDw4liLqHX2rBUcu1AEAbmI9KeqBwYn2pNTFWj0Ewb3dw4nIs9ye6/r0008DAD7//HMA9kyzIAiQSCQoKChw673q6uqwZMkS/OEPf3DuQLN27Vr861//aldj4cknn8SSJUsAALt378Zrr72GmpoaDB48GL/5zW8wbtw4d7tBfoD1pMhVjplSF2v1aG61IDKM0/SJKHB5cixFFIoOn9fCZLWhX5waQ9qSC0TuSI1VQyGTQG+yokJnFDscopDm9ie7/fv3e+TCubm5ePLJJ1FSUtLu9fz8fKxbtw733HPPdW2OHDmCdevWYdOmTRg9ejS2bduGBx54AAcOHIBazZpEgaaiwf4LoC/rSVE3EiLD0CdGhUrWlSKiIOCpsRRRqHLsunfT0CRIJBKRo6FAJJdKMSA+AsXVzThX1SR2OEQhze2kVGpqKgDgzJkzKCsrw0033YSmpibEx7v+IXH37t3YsGEDHnvsMTzyyCPO100mE86dO4fMzMwO2+3cuRNz5szBhAkTAAArVqzAjh07sHfvXixcuNCtflitVpfPceXcYNST/stkMkCwQRC63snCYLKirsUEAOgTHWY/39HGhfYd8kJ7m01w/lcq7eY9xYzfS9d2uf8eun5XX2tZqTGo1BlxvKQekwZo3L9GD4T6zwCA94D9D93+e7PPnhhLEYUqQRDw5Vl7Pambh7OeFPXc4AQmpYj8gdtJKa1Wi9WrV+PUqVNQKBTYtWsXFi1ahC1btri8jG7GjBmYO3cu5HJ5u6RUYWEhLBYLNmzYgNzcXERFRWHhwoVYtWoVpFIpiouLr0s+paeno7Cw0N1uID8/3yvnBiNX+x8WFobMzEzU1NZC22To8tzyJgsAIFopga6uBjoAEdAAGIC6unpU69wvOujN9tXVVaJeX+xrd9f/3l7fHKUGMAQFBQVobW3t8JxEqR4A8NWpy5gU5dvaUqH+MwDgPWD/Q7v/nuaJsRRRqCqubkZ5gwFKuRTTBieIHQ4FsIHxEZAAqGpsRVl9C9Jiw8UOiSgkuZ2Ueu655zB06FD8/e9/x8yZMzFkyBDcf//9+N///V+8++67Lr1HYmLHTzWampowefJkLF++HC+++CIKCgqwevVqSKVSrFq1Cnq9/rpleiqVCi0tLe52A1lZWfZZPV2wWq3Iz8936dxg1NP+JyYkQBFh7vKc4uZaAK3olxCJlBR7gcq4WPu/bVxcLKTqSLfj9UZ7m01AdXUVkpKSIZV2PT1czPi9dW1X+9/b62vU9hpyI0aM6PQcfZQW204dQ6legrFjx7p9jZ4I9Z8BAO8B+x+6/Xf03Rs8MZYiClWOWVJTB8dDrQytn0vkWWqlDH00KlQ0GLG/oBr33jBQ7JCIQpLbSamcnBzs27cParXauYZ71apV2LJlS6+DmT59OqZPn+78++jRo3Hvvfdi7969WLVqFdRqNYzG9oXojEYjYmNj3b6WTCZzeYDtzrnByO3+S6SQSLreTa+iwT4bJlUT/sO5V/23u/adXdfT7R1L1qRSSffvKWb8Xrq2y/330PW7+job08/+fV5ab0CDwYL4yDD3r9NDof4zAOA9YP9Du/+e5s2xFFGw+6GeFJfuUe8NTohERYMR+wqqmJQiEonbnx4VCoUzMeTYPlOv1yMiovc7X+zbtw/bt29v95rJZIJKZd+dLSMjA0VFRe2OFxcXIyMjo9fXJt8xW22obmKRc3JPjFqBwQn2nzMny327fI+IyJO8OZYiCmbNrRYcu1QHALh5eJLI0VAwcIwtcy5o0WTseqUHEXmH20mp2bNn47HHHsOlS5cgkUig1Wrxu9/9DrNmzep1MIIgYP369Th8+DAEQUBeXh7eeustLFmyBACwaNEi7NmzBzk5OTCbzdi6dSu0Wi2ys7N7fW3ynapGI2wCEBEmQ7TK7cl6FMLG9NMAAE6UNogaBxFRb3hzLEUUzL4proXZKmBAfDgGJTCJS70XG6FEfIQSZquAr87Vih0OUUhyOyn161//GuHh4bj99tvR2NiIGTNmwGAw4NFHH+11MNnZ2VizZg2eeeYZjBs3Do899hh++ctfYv78+QCAadOmYe3atXjmmWcwefJkfPTRR9i0aRM0Gk2vr02+U6mzPx3uE6PmNr7kltFpMQCAk2WcKUVEgcubYymiYPbVOXs9qVlcukceNDQ5CgCwr6D7jY2IyPPcnqYSERGBDRs2oK6uDmVlZUhJSUFSUs+nz549e7bd35cuXYqlS5d2ev78+fOdSSoKTFecSSmVyJFQoLl6ppQgCExqElFA8vRYiihUfF1kn8kyM4NJKfKcocmROHxBiy8Kq2Gx2iCX9aA+KhH1mNtJqWPHjrX7++XLl3H58mUAwKRJkzwTFQUtQRCcM6VSopmUIveM7BMNuVQCrd6E8gYDt+4looDEsRSR+y7V6lFS1wK5VIKpQ+LFDoeCSL/YcMSGK1DfYkbu5XpMGcyvLyJfcjsptXz58utek0ql6NOnD/bv3++RoCh4NRktMJitkEqApCjf7Z5GwUGlkGF4nyicKm/EiVIdk1JEFJA8OZaqq6vDkiVL8Ic//AFTpkwBAKxduxb/+te/oFAonOc9+eSTzhqdu3fvxmuvvYaamhoMHjwYv/nNbzBu3Lhe9IjI+74usi/dmzAgFpFhrElKniOVSnDzsCS8l1eOfQVVTEoR+ZjbP9ELCwvb/b2urg5/+ctfkJqa6rGgKHg5ZkklRIZxaiz1yOg0DU6VN+JkWQPmjO4jdjhERG7z1FgqNzcXTz75JEpKStq9np+fj3Xr1uGee+65rs2RI0ewbt06bNq0CaNHj8a2bdvwwAMP4MCBA1CruSMu+a+DbUWoZ7KeFHnBrSOT8V5eOT4/U4Wn7hzBEhFEPtTrrEBcXBwee+wx/OMf//BEPBTkrjSynhT1ztg0DQDgRFmDqHEQEXlKT8ZSu3fvxqOPPopHHnmk3esmkwnnzp1DZmZmh+127tyJOXPmYMKECVAoFFixYgViY2Oxd+/eXvWByJvMVhsOn2c9KfKemUMToZRJcUnbgvM1erHDIQopHpn7qtPp0Nra6om3oiB3hfWkqJdG97PvwJdfpoPVJkAm5ZMsIgp87o6lZsyYgblz50Iul7dLTBUWFsJisWDDhg3Izc1FVFQUFi5ciFWrVkEqlaK4uBgLFy5s917p6enXzd7qjtVq7dH57rYj1wXzPf7uYh30JiviwhUYnhzRaR9lMhkg2CAINvcv4mjTTXubTXD+Vyq96jwX2/f2+kHZ/pq2nd5jb1y7rV1kmBxTBsfh66JafH7mCgbFD3L/fQJMMP/M8Behfo9d7bfbSak1a9a0+7vZbEZubi5uuOEGd9+KQozFZkNNk33AncKZUtRD6YmRUCtk0JusuFDTjIy2bXyJiAKFJ8ZSiYkdzxZpamrC5MmTsXz5crz44osoKCjA6tWrIZVKsWrVKuj1+uuW6alUKrS0tLjVh/z8fLfO7207cl2g3mO5XI6Ro0ZBIb/+48k35+sAADMyEqFQdP3xRavVoqbRva9nAIiABsAA1NXVo1rX3O351dVVvWrf2+sHU/vO2l57j71xbQAwR6kBDMEojQ1fA/j3dxcwOUrn9vsEqkD9mRFIeI+71uuZUmFhYVi+fLmzeCZRZ2qbTLAKAlQKKWLUiu4bUMhQyaVtT8O6n/Ukl0mRlRqDo5fqcLy0wZmUcrU9EZG/8eRYavr06Zg+fbrz76NHj8a9996LvXv3YtWqVVCr1TAaje3aGI1GxMbGunWdrKws+6wUF1mtVuTn57vdjlwXDPdYJpNh53claDKY273+3vdl9v8RbNjy9fkO26Zo1Lgzqy/i4+MhC492+9pxsfZkbVxcLKTqyE7Ps9kEVFdXISkpud24w9X2vb1+MLa/tm1n99gb1wYATdvnkmU3ZeGNYwdxVmtGv4yRiI9Quv1egSQYfmb4u1C/x47+d8ftpNT69et7FBCRo55USrSKxQOpHYVcCqlUgl25pWg0WLo9X9pWDW/HsVI0GS2IVsuxaEI/L0dJROQZ3hxL7du3D7W1tVi6dKnzNZPJBJXKPkM5IyMDRUVF7doUFxdj5syZbl1HJpP1aIDd03bkukC/x01GK3TGH5Z8GExWVLSVf0iMVrc7drWo1rbXJVJIJD0om+to0017x3IyqVTS/jwX2/f2+kHZ/pq2nd5jb1z7qvb94iMxsk80zlQ24qsiLRZNSHP/vQJQoP/MCAS8x11zOym1ceNGl8576KGH3A6GglulzgCAS/eoc40GC3TXPB3tSGy4/clVSV2LS+cTEfkTb46lBEHA+vXrMWDAAEydOhXHjx/HW2+95VwyuGjRIqxevRp33HEHJkyYgG3btkGr1SI7O9vtaxH5QkmdfSlefIQSkWEeKYdL1KlbRybjTGUj9p2pCpmkFJHY3P7JXlRUhM8++wzDhw/HoEGDcOXKFXz//fcYOXIkIiIiAICzYKhDLHJOnpLc9jVU29wKi60HBS2JiETkzbFUdnY21qxZg2eeeQZVVVVISEjAL3/5S8yfPx8AMG3aNKxdu9Z5PD09HZs2bYJGo/FU94g8ypGUGhAfLnIkFAqyRyRjw/4ifFVUA6PZCpWCs1uIvM3tpJRUKsWaNWvws5/9zPnaBx98gAMHDuDll1/2ZGwURFpMFjQa7cuyOFOKeitaJYdKIYXRbENtkwnxEWFih0RE5DJPj6XOnj3b7u9Lly5tt3zvWvPnz3cmqYj8mSAIuFynBwD0j2NSirwvMzUaydFhqGpsRc4FLW4aliR2SERBz+1FtwcPHsSyZcvavXbXXXfh8OHDHguKgo9jllRcuBJhcj5xoN6RSCTO2VJVjcZuziYi8i8cSxG5pk5vgr7VCplUglSNuvsGRL0kkUhwy4hkAMC+Atd2/yOi3nE7KRUXF4djx461e+3rr79GSkqKx4Ki4OMscs5ZUuQhyVFtSakmJqWIKLBwLEXkmsttS/dSNWrIZT0oYE3UA9ltSan9BdUQBEHkaIiCn9vL937+85/j/vvvx2233Ya+ffuitLQUBw4cwKuvvuqN+ChIOOtJMSlFHpIcY1+yV6VrFTkSIiL3cCxF5JoSLetJke9NGxIPtUKGSp0RpysakZkaI3ZIREHN7UcOixcvxhtvvAGZTIYzZ85Ao9Fg+/btmDFjhjfioyBgEwRUNdoTByxyTp7imClV12KC0dzx9tBERP6IYymi7lmsNpQ12HduZj0p8iWVQoYbMxIAcAkfkS/0aF/VG264ATfccAPq6uoQFxfn6ZgoyNTpTTBZbVDIJIiPUIodDgWJiDA5olVyNBotqGgbtBIRBQqOpYi6Vt5ggNUmICJMxvEj+dytI5Px2Zkq7Cuowq9uHSp2OERBze2ZUmazGS+99BImTJiA2bNno7S0FAsXLkR1dbU34qMg4KgnlRylglTasy2uiTriWA5axqQUEQUQjqWIulfSVk+qf1w4JBKOH8m3Zg9PgkQCnCpvRKWO40wib3I7KbVx40bk5OTglVdegUKhQHx8PFJSUvDss896Iz4KAqwnRd7SJ8a+E09ZPQcLRBQ4OJYi6p6jyPmAuAiRI6FQlBAZhvH9YwEA+wr4wIDIm9xOSu3ZswcbNmzAjBkzIJFIEB4ejvXr1yMnJ8cb8VEQYFKKvMVRo6y83sDdUYgoYHAsRdS15lYLtM0mAKwnReK5ZUQSAOAL1pUi8iq3k1ItLS3O2geOD4EqlQpSKbdppeuZLDZo9fZBBYuck6clRoVBJpXAYLbiUtsOPURE/o5jKaKuOZbuJUWFQa2UiRwNhapbRyQDAL45r0WLySJyNETBy+3Rz9ixY7Fx40YAcK7vfvvtt5GVleXZyCgoVDfZZ0lFhskREdajuvpEnZJJJUiKCgMA5JXUixwNEZFrOJYi6tplrR4AMCCes6RIPBlJkUiLVcNkseGbYq3Y4RAFLbeTUk899RT27NmDmTNnQq/X484778Rbb72FJ5980hvxUYCramwFwFlS5D2OZaHfMylFRAGCYymizgmCgNI6e61ILt0jMUkkEtwyvG0JXyGX8BF5i9tTVxISEvDRRx/hyy+/RHl5OVJSUnDTTTchMjLSG/FRgKtq23kvKTpM5EgoWPWJViEPQF5Jg9ihEBG5hGMpos5V6owwmK1QyCTODU2IxHLLiGT84/Bl7C+ohiAI3AmSyAvcTkrddddd+Pe//4077rjDG/FQkHEkpZI5U4q8xDFTqvBKE1pMFoQruUyUiPwbx1JEnbtQa1+6lxYbDpmUCQAS15TBcQhXylDd1IpT5Y3ISosROySioNOjipoGA7dfp+61mCxoNNqLAiZHcaYUeUeUSoEolRxWm4D8Mp3Y4RARuYRjKaKOna9uBgAM4NI98gNhchluzEgAAOznEj4ir3B7SsGUKVOwePFizJw5E0lJSe2OPfTQQx4LjAJfdVs9KU24AmEK7pxC3pOmUaPgShO+L2nAlMHxYodDRNQljqWIOtbcakFpvX3nPRY5J39xy/BkfHq6Cl8UVuNXtw4VOxyioON2UqqsrAz9+vXDxYsXcfHiRefrXF9L1+LSPfKV1Fh7Uoo78BFRIOBYiqhjh89rYROAGLUCmnCl2OEQAQBubit2frJMh+pGI5L42YbIo1xOSv3Xf/0XNm/ejLfffhsAYDQaoVLxG5I6V9VknynFpXvkbWmx9qepeaUNLEJJRH6LYymirn11rgYAd90j31LJpbDZBEg7qWGWGBWGMf00OFHagC8Kq7F0cv/rzumqPRF1zeWkVF5eXru/z5w5E0ePHvV4QBQcBEHgTCnymT4xKsilEtQ0taK8weBMUhER+ROOpYi69lWRPSnFpXvkSwq5FFKpBLtyS9FosHR4TqxaAQD4+zeX0GKytjsWrZZj0YR+Xo+TKFj1eJsqQRA8GQcFmeZWC1pMVkgk9qcLRN6kkEkxsm80TpbpkFfSwKQUEQUEjqWIfnBZq8dlbQukEiAtVi12OBSCGg0W6AzmDo/10dgfsp+vaYa2uRVyWY/2CyOiDvT4u4nLY6grVW1FzuMjlFDwhzb5wLh+GgDA96wrRUQBgmMpoh98VVQLwL4kP0zODXLIvyRGhiEyTA6LTUBZA3dPJfIkZgvIK7h0j3xt/IBYAEBeSYO4gRAREZHbHPWkhiRGiBwJ0fUkEgkGJthn4l+s0YscDVFwcXn5nsViwfvvv+/8u9lsbvd3ALj77rs9FBYFuqqmtqRUFJNS5Bvj+tmTUmcqGtFqsfIpKxH5HY6liDpmttpw+LwWADAkMVLkaIg6NighAqfKG3FRq8dN3FiHyGNcTkolJCRgw4YNzr/Hxsa2+7tEIuFAigDYa2RUty3fS45mPSnyjX5xasRHKKHVm3C6ohHj+8eKHRIRUTscSxF17PvL9WhutSAuQok+MSo0GjsuNk0kpv6x4ZBLJWgyWqDVm5AQyc85RJ7gclLqiy++8GYcFETqW8xotdggk0oQzx/W5CMSiQTj+muwr6Aa31+uZ1KKiPwOx1JEHXPsujcjPYGzT8hvyWVS9IsLx8VaPS7W6pmUIvIQ1pQij6toK/6XGBkGmZQDC/KdcW2JqLzSBnEDISIiIpd9dc5e5Hzm0ESRIyHq2qB4e82zi7WsK0XkKUxKkceVtyWlkrh0j3xsXH8NACD3Uj23WiciIgoA2uZWnKrQAQBmZiSIHA1R1xzFzit1RrSYuMyUyBOYlCKPq9DZk1LceY98bWw/DeRSCa40Gp3JUSIiIvJfh4prIQjA8JQoJHHsSH4uSqVAYpT9wftlbYvI0RAFByalyKMsVhuu6Bw773GmFPlWuFKOUX2jAQC5l+tFjoaIiIi641i6N4tL9yhAcAkfkWcxKUUeVVzTDLNVgEImQWyEUuxwKARNHBgHADh2qU7kSIiIiKgrgiDg67Yi56wnRYFiUII9KXVZ2wKrjeUiiHqLSSnyqJNl9poASVEqSLl7Colg0kB7sfPvLnGmFBERkT8rvNKE6qZWqBUyTBzIXXMpMCRHhyFcKYPJamO5CCIPYFKKPOpkWQMA+w9rIjFMGGCfKXW2qgk6g1nkaIiIiKgzX52zz5KaOjgOYXKZyNEQuUYikWAgl/AReQyTUuRRjplSLHJOYkmMCsPA+HAIAvB9CWdLERER+auvuHSPApRjCd/FWj13fCbqJSalyGNaLVYUVDYCYFKKxOWoK/Ud60oRERH5pRaTBccu2h8eMSlFgaZ/XDhkEgl0BjO0epPY4RAFNCalyGMKK5tgtgpQK2SIVsnFDodCmKOu1DHWlSIiIvJLRy7UwWS1IVWjxuC2WSdEgUIplyI1Vg0AOFfVJHI0RIGNSSnyGEc9qb4aFSQsck4icsyUOlHaAJPFJnI0REREdK2D5xxL9xI4bqSA5EimFlU1ixwJUWBjUoo8xlFPqq9GLXIkFOoGJ0QgLkKJVosNpyp0YodDRERE13DWk8rg0j0KTAPbklIl9S3QtXBzHaKeYlKKPMaZlIphUorEJZFIMGGAfQkf60oRERH5l7L6Flyo0UMmleCG9ASxwyHqkRi1AvERSggC8OW5arHDIQpYTEqRR7SYLCiqtq+n7qthkXMSH+tKERER+aevi2oBAGP7aRCjVogcDVHPOXbh+6KQSSminmJSijziVHkjbAKQHB2GKBUHFyQ+R12p3Mv13KqXiIjIj3x1jkv3KDg4klJfnq2Bxco6pkQ9waQUeYSjyPnoNI2ocRA5ZPaNQZhcijq9CRdq9WKHQ0RERADMVhsOtc2UmjWMSSkKbCkxKqgVMugMZnxf0iB2OEQBiUkp8ogTbfWkxqTFiBwJkZ1SLsWYfhoArCtFRETkL767VI+mVgviI5QYncpxIwU2qUSC9KRIAMD+wiqRoyEKTExKkUfkc6YU+SHWlSIiIvIvX561196ZNSwRUqlE5GiIem9ocltSqoB1pYh6gkkp6jVdixmXtC0AgNGcKUV+ZOIAe10pzpQiIiLyDwfaklI3D0sSORIizxiSGAm5VILi6mZc1rJkBJG7mJSiXjtZ3gAAGBAfDk24UtxgiK4yvn8sJBLgkrYFNU2tYodDREQU0srqW3CuqhlSCYucU/BQKWSY1LbBDnfhI3Ifk1LUayfb6klx6R75m5hwBYYlRwEAci9zthQREZGYvjxr33VvwoBYxIRzt2YKHreMsM/8Y1KKyH1MSlGvnShtAAAWqyS/NLGtrtTRi6wrRUREJCZHPambuHSPgswtI5IBADkXtGgymkWOhiiwMClFvfbDTCkmpcj/TB4UDwA4clErciREREShy2i24pti++9i1pOiYDMoIQKDEyJgtgo4VFQrdjhEAYVJKeqV6kYjrjQaIZUAmZwpRX5o6iD7Gv8zlY3QtfDJFRERkRiOXKyDwWxFcnQYRvSJEjscIo+bPdyebP38TJXIkRAFFialqFccs6TSkyIRESYXORqi6yVFqzA4MQKCABzlLnxERESiOFD4w657EolE5GiIPO9Ho1IAAPsKqmC22kSOhihwMClFvXKyrAEAi5yTf5s62L6EL+cCl/ARERGJgfWkKNhNGBCLhEglGo0WHLnAB6FErhI1KVVXV4fs7GwcOXLE+dqJEyewePFijBs3DrNnz8bOnTvbtdm9ezeys7MxduxYLFiwAHl5eb4Om65yom2m1BjWkyI/5khKHT7PpBQREZGvXazV45K2BQqZBDMyEsQOh8grZFIJskfaC55/crpS5GiIAodoSanc3FwsWbIEJSUlztd0Oh3uv/9+3H333Th27BieffZZrF+/HidPngQAHDlyBOvWrcPzzz+PY8eOYd68eXjggQdgMBjE6kZIEwSBM6UoIDjqShVcaURDi0nkaIiIiEKLY+ne5EFxiGS5Bwpit7Ut4fvsdBVsNkHkaIgCgyhJqd27d+PRRx/FI4880u71zz77DBqNBsuWLYNcLse0adMwd+5cbNu2DQCwc+dOzJkzBxMmTIBCocCKFSsQGxuLvXv3itGNkFdWb0B9ixkKmQTDWbCS/FhStApDHHWlLnI6NRERkS8dOPtDPSmiYHbDkAREhclR3dSKvNIGscMhCgiiPKqYMWMG5s6dC7lc3i4xVVRUhKFDh7Y7Nz09Hbt27QIAFBcXY+HChdcdLywsdDsGq9Xq8jmunBuMuut/Xon9w/2w5CjIJfbzZDIZINggCD0o7udo40ftHU84bDYBUmk37ylm/F66tsv995N/u66+V6cMisP5Gj0On6/FLcMTXXrbUP8ZAPAesP+h2/9Q7DNRb9nHC+2LmLeYfqivw3pSFOyUciluHp6Ef5+owGenr2DCgFixQyLye6IkpRITO/5AqNfroVar272mUqnQ0tLi0nF35Ofne+XcYNRZ//efbAIA9FWZcfz4cYSFhSEzMxM1tbXQNrm/pDICGgADUFdXj2pds1+1r67ufmtXMeP39rW767/Y/3bmKDWAITh//jxMpo6X56Up7V+TBwsqsHDw9cetVissFkuHbUP9ZwDAe8D+h3b/icg1UqkEu3JL0Wj44ffp2aommKw2aMIVOHi2Gl+dq+mwbR+NCndk9vFVqERec3tmCv59ogKfnr6CJ+8Yzt0mibrhV4u61Wo1mpqa2r1mNBoRERHhPG40Gq87HhvrfgY6KyvLPqunC1arFfn5+S6dG4y66///fncUgB6zxwzB2LFpztcTExKgiDC7fb24WHvCMS4uFlJ1pF+0t9kEVFdXISkp+bonf764vi/adtXe1f6L/W+XEh0Gm024bqbl1ZIGGPG/h/bjYoMZaYOHQhOubHfcZhOum6UV6j8DAN4D9j90++/oOxG5p9Fggc7wwzjwdLl9U5z+seFoNHb88AcAolR+9bGEqMdmDU2EUi7FJW0LzlY1YXhKtNghEfk1v/rpP3ToUHzzzTftXisuLkZGRgYAICMjA0VFRdcdnzlzptvXkslkLg+w3Tk3GHXUf6tNwKm2Qca4AXHtj0ukkEh6UK7M0caP2juWrEmlku7fU8z4vXRtl/sv8r+dQi7v8OnstRIilahtNmH93kIMS/mhDlq0Wo5FE/oB6Pj7PNR/BgC8B+x/aPefiHpGEARcqNUDAAYnRogcDZFvRITJMTMjEfsKqvDpqSompYi6Idruex3Jzs5GbW0ttm7dCrPZjJycHOzZs8dZR2rRokXYs2cPcnJyYDabsXXrVmi1WmRnZ4sceegprm6G3mRFhFKG9CT3Z7YQeYPj6Wxnf/rE2Gdkna1qavd6V4ksIiIi6pmqxla0mKxQyqRIjVV334AoSNw2KhkA8MnpKyJHQuT//CopFRsbiy1btuCTTz7BlClT8PTTT+Ppp5/G1KlTAQDTpk3D2rVr8cwzz2Dy5Mn46KOPsGnTJmg0GnEDD0En2naTyEqLgaybZW1E/iKtbUBcVu9+HToiIn9UV1eH7OxsHDlyxPnaiRMnsHjxYowbNw6zZ8/Gzp0727XZvXs3srOzMXbsWCxYsAB5eXm+DptCxPkae53IAfHhkEv96mMHkVfdOiIZMqkEBZWNKNFy3EnUFdGX7509e7bd37OysrB9+/ZOz58/fz7mz5/v7bCoG8fLGgAAY/ppRI2DyB2pGntSqrbZBKPZCpWCy5GIKHDl5ubiySefRElJifM1nU6H+++/Hw8//DCWLFmCY8eOYfXq1Rg2bBhGjx6NI0eOYN26ddi0aRNGjx6Nbdu24YEHHsCBAweu20yGqLe4dI9CVWyEElMGxeHb81p8evoK7pvZwS47RATAz2ZKUeBwzJQam6YRNQ4id0SEyRHXVuC8vMH9HSKJiPzF7t278eijj+KRRx5p9/pnn30GjUaDZcuWQS6XY9q0aZg7dy62bdsGANi5cyfmzJmDCRMmQKFQYMWKFYiNjcXevXvF6AYFsYYWE+r0JkglwKB4JqUo9Nw2KgUA8CmX8BF1SfSZUhR4jGYrCq/Yd0nkTCkKNKmxatS1mFBWb8CQRNZDI6LANGPGDMydOxdyubxdYqqoqOi6nUjT09Oxa9cuAPYNYhy1Oq8+XlhY6Nb1rVZrj853tx25zh/usUwmAwQbBMHmXLqXqlFDKZdct8PtdRzH29q7zUftbTbB+V/HhjC+vH5Qtr+mbaf32BvX9mD7a7/3bhmeiLX/BnJL6nGloQWJUWHuv7cX+cPPjGAX6vfY1X4zKUVuO12hg9UmIDEqDH1iVGKHQ+SWtFg18st1rCtFRAEtMTGxw9f1ev11y/BUKhVaWlpcOu6q/Px8t87vbTtynVj3OCwsDJmZmaiprYW2yYDCCvuM5IQwK65c6X6mSAQ0AAagrq4e1bpmt6/v6/bV1VWiXj+Y2nfW9tp77I1re6K9OUoNYAgKCgrQ2tra7lhGnAJFdWZs/iwXtw8Jd/u9fYE/l72P97hrTEqR2/JKGgAAY9I0kEhY5JwCC+tKEVEwU6vVaGpqavea0WhERESE87jRaLzueGxsrFvXycrKss+KcZHVakV+fr7b7ch1/nKPExMSYFEYoT1zCQAwZlAKotWKbtvFtW1GEhcXC6na/ZnMvmpvswmorq5CUlIypFdt9hMo8ftj+2vbdnaPvXFtT7TXtH19jxgx4rpjC5su4vlPzuJkvQxPjh3r9nt7k7/8zAhmoX6PHf3vDpNS5LYTZToAwNh+MSJHQuS+iDA54iKUqNPbl/ClJ3EJHxEFj6FDh+Kbb75p91pxcTEyMjIAABkZGSgqKrru+MyZM926jkwm69EAu6ftyHWi32OJFJfqDBAAJEQqERPu4pIlidT5X4mkB2VvfdTesZxMKpW0Py9A4vfL9te07fQee+PaHmzf0ffd3LGpeP6Tszh6qR61ejOSo/1vlYnoPzNCAO9x11jonNzmLHLez72nqkT+Iq3tiVhpHZfwEVFwyc7ORm1tLbZu3Qqz2YycnBzs2bPHWUdq0aJF2LNnD3JycmA2m7F161ZotVpkZ2eLHDkFkwtt9aQGs3YjhbhUjRrj+2sgCMDe/EqxwyHyS0xKkVvq9CaUtH2Qz0rjTCkKTAPi7Gv6LzMpRURBJjY2Flu2bMEnn3yCKVOm4Omnn8bTTz+NqVOnAgCmTZuGtWvX4plnnsHkyZPx0UcfYdOmTdBoNOIGTkHDbLXhstb++3VIAnfdI7prdF8AwIcnmZQi6giX75FbTpQ1AAAGJ0YgxoX6AET+KDVWDakE0BnM0BnM/FomooB29uzZdn/PysrC9u3bOz1//vz5mD9/vrfDohB1sVYPi01AZJjc73YbIxLDnNF9sO6jM8i9XI+KBgP6atTdNyIKIZwpRW5xLt1L04gaB1FvhMllSGnbObJEy9lSREREnnL2ir3Q/uDECG6IQwQgOVqFSQPjAAAfcbYU0XWYlCK3OJJSY/ppRI2DqLcGxNmXFFyu04scCRERUXCw2QScq25LSnHpHpHTXaP7AAD2nKwQORIi/8OkFLlMEAQcZ1KKgkT/trpSZfUG2GyCyNEQEREFvrzSBuhbrVDKpEiLDRc7HCK/cWdWH8ikEpws0+F820YARGTHpBS5rLTOgPoWM5QyKUb0iRI7HKJeSYoOQ5hcilaLDRU6g9jhEBERBbxPTtmXJg1MCIdMyqV7RA4JkWGYmZEAAPggr1zkaIj8C5NS5LLjbUXOR/SNRphcJm4wRL0klUjQr2221PkaLuEjIiLqDUEQsDf/CgAgI4kPL4mudfe4VADA+8crIAicpU/kwKQUueyHIucx4gZC5CED2pJSFziNmoiIqFfyy3UobzBAIZNgYDyX7hFdK3tkMsKVMpTUtSCv7XMVETEpRW5gkXMKNs66Ug0GNBrNIkdDREQUuD7Kty/dy0iKglzGjxhE1wpXynHbqBQAwPtcwkfkxN8Y5BKz1YZTFToATEpR8IhWK6BRKyAIQM55rdjhEBERBSRBEPBx29K9kX2iRY6GyH/NH9sXAPDhyUqYrTaRoyHyD0xKkUvOXmmC0WxDlEqOQfHc4peCR/+2JQZfF9WKHAkREVFgOl3RiJK6FqgUUqQnRYodDpHfmpGegITIMNTpTfjybI3Y4RD5BSalyCUn2oqcj0nTQMrdVCiIOOpKfV3EgQEREVFP7G1bunfzsCQo5fx4QdQZuUyKe8bZZ0vtyi0VORoi/8DfGuSSvJIGAMBYLt2jIJMaq4ZUAlzStqBE2yJ2OERERAFFEAR8eNKelLojq4/I0RD5v0UT+gEA9hdUQ9vcKnI0ROJjUopc8v3legDAhAGxIkdC5FlhchnSYttmSxVzthQREZE7jpc2oKSuBWqFDLeOSBI7HCK/NywlCqPTYmCxCfjgeIXY4RCJjkkp6lad3oQLtXoAwLj+GnGDIfKCwQn2OmmHWFeKiIjILY4P1T8alYxwpVzkaIgCw6IJaQCAnbllIkdCJD4mpahbjqV76UmR0IQrxQ2GyAsGJ9qTUt8U18LCnVCIiIhcYrX9sHTPsasYUahRyaWw2QS32swb0xdKmRQFlY04XaFzuz1RMOHjDOrW9yVtS/f6c+keBae+GjVi1AroDGacKGvAhAFxYodERETk9w6f16K2uRWacAVmpCeKHQ6RKBRyKaRSCXbllqLRYHG5XXpSJM5UNuK5vQXYtmqqFyMk8m+cKUXd+r5tphTrSVGwkkokuDEjAQDwRWG1yNEQEREFhn+fKAcA3JnVh7vuUchrNFigM5hd/jM0ORIA8N2lerSYXE9mEQUb/vagLllsAk6U6QAA45mUoiA2e7i9OOsXhSx2TkRE1B2j2YqPT10BAMwfw6V7RO7qHxeOGLUCrRYb9pxgwXMKXUxKUZcuNVjQarFBE65wFoMmCkY3DUuCRAIUVDaiUmcQOxwiIiK/tr+gGk1GC/rEqDBpIJe9E7lLIpEgKzUGALDtSInI0RCJh0kp6lKh1gQAGN8/FlKpRORoiLwnLkKJcf00ALiEj4iIqDs7c0sBAAvHp3GMSNRDI/pEQSaV4GSZDifLGsQOh0gUTEpRl87WmgGwnhSFBscSvgNMShEREXWqqtGIr87Zl7svbNvanojcF66UY0RKFADgn5wtRSGKSSnq0tm2mVJMSlEouLktKfVNsRZGs1XkaIiIiPzT7rxy2ARg4oBYDGJ5B6JecXzO+uB4BRqNZpGjIfI9JqWoUxUNBmgNNsikEoxJ04gdDpHXjewTjZRoFQxmK3IuaMUOh4iIyO8IgoCd39mX7i3iLCmiXusfF46MpEgYzFa8n1cudjhEPsekFHUqt6QBADCyTxTUSpm4wRD5gEQicc6W2l/AJXyustkEUdsTEZHvHC9twPkaPVQKKeaM7iN2OEQBTyKRYNmU/gCAbTklEASOiyi0yMUOgPzX9yX1AOxFzolCxa0jkvDu0RLsK6jC2ruGix1OQJBKJdiVW/r/2bvvsKbOtw/g3wxG2FOWiMpyALJUnLi3tXVXS7VWa4u7am2rPzvVWq11W2etq1qt1l2rbd0KTtwKDmTI3psk5/2DklcEFRSSAN/PdXHVnJxxPyfpOU/u8wxk5MorvK2JTIqBfo5VEBUREVWF3/5rJdWjqS2M9XU0HA1RzdDfry7m/3kXd+MzcSkyFf6c0ZJqESal6LkuR6YBAHzrmWk0DiJ1auNiBQNdCZ6k5+FGbIamw6k2MnLlSM/lOAhERDVZRl4h/rgSCwB4u0U9DUdDVHOY6OvgjWb22HExClvORzIpRbUKu+9RmXIK5LgdlwmASSmqXfR1JGjvag0AOMYufERERCp7Lscgt1AB1zpGaNGAP5qJKtPwgKJE76HrcUjJLtBwNETqw6QUlSksKh0KpQBLmRj2ZjJNh0OkVt2a2gAAjt6K13AkRERE2kEQBGwNiQQADG9ZDyKRSMMREdUsXnXN4OlgigKFEr+GPtZ0OERqw6QUlal4PCl3S10NR0Kkfp0a1YFELMLd+CzEZVV8nCQiIqKa5sKjVNyLz4JMR4L+nHWPqEqMbF0fALDp3CMUyJWaDYZITZiUojJdivwvKWXFASyp9jEz0EWL//ryX4jN13A0REREmlfcSuqNZvYw4QDnRFWibzN7WBvrIT4jH4euP9F0OERqwaQUlaJUCqqWUo3YUopqqa5NirrwXYjN03AkREREmhWfkaf6gfxOgJOGoyGquXSlYrz73/9j608/hCAIGo6IqOoxKUWlPEjKRlpOIfR1xKhvxgkaqXYqTkrdTixEUhZbSxERUe218ewjFCoENK9vDs+6ppoOh6hGGx7gBD2pGNdj0nHhUaqmwyGqckxKUSkXH6UAALwcTCEVcxBLqp0cLQzgYW8CJYCjtzgLHxER1U7Z+XJsPV/UdW9Mu4Yajoao5rMw1EV/36Jx29affqDhaIiqHpNSVErIw6KkVPP6nOqXareeHrYAgMM34jQcCRERkWb8djEKGXlyNLAyRJfGNpoOh6hWeL9tfQDAX7fiEZmcrdlgiKoYk1JUSuh/SamWDcw1HAmRZhUnpc4/TEFKdoGGoyEiIlIvuUKJDWceAgDeb9sAYragJ1ILlzrGCHSzhiAAP595pOlwiKoUk1JUQlRKDmLSciEVi+BTz0zT4RBplJOlARqYSaFQCvjrJltLERFR7XLoRhyiUnJhbqCDAf91JyKiyqUvFUOpLD2g+fttGwAAdl6MQkZe4Qv3Udb2RNUFR7GmEoq77nnWNYWBrhR6enoajohIs1rV1cfDtCwcvP4EQ1vU03Q4REREaqFQClj6dzgAYGTrBpDpSjQcEVHNpCMVQywWYdelKGTkylXLBUGAtbEeEjPzMXVHGFo5W5a5vYlMioF+juoKl6jSsaUUlRDyIBkA0LKBJSQSCTw8PCCRsBJCtVeruvoAgLP3k5HKLnxERFRLHLgWi4iELJjoS/Hef+PbEFHVyciVIz23UPWXkSeHl0PRbJfnHxbVQ59+X7XeU4ksouqILaWohND/Zt5LzynAhlP3kZiUBGsrK0D08vylnZk+enrYVXWIRGplbyxFI1tj3InLxJGbcWwtRURENZZSKUAsFpVoJTWmXUOY6OtoODKi2qmRrTHO3k9GZp4c9xIy0cjWRNMhEVU6JqVIJS49D5HJORABMDXQQVpuIZIzc6FjWAhROZJSxvr8OlHN1MfLFnfiMrH3aiyTUpVEEASk5RbifmIW4g/nITolF2m5BVAqAbEYsDTUg62pPupbGsLTwRRutkbQk7LVJhFRVSruQnQmIhn3E7OhryOGVCzChtMPX7otH04SVT6pRAxvRzOce5CMi5GpcLcxhkjECQeoZmEWgVRCHhZ13bM11YeeVAJBUGo4IiLt0NfLDgv/Csf5h8mIS8+Dram+pkOqtpKz8nEjNgMRCVnIyi9/c3M9qRgtGligvas1enjYwtHCoAqjJCKqvVKyCvDPnQQAgI+jOfLkSuTJX14n5MNJoqrhVdcUFyNTkJxVgEfJOWhgZajpkIgqFe8epFI8yHk9/tgjKqGuuQH8ncxxMTIV+8NiMaZ9Q02HVO3EpObi3INkxKTlqpZJRCLUNZehY6M6cLQwgKWhLsRiEZRKAYmZ+XiSnod78Zm4HpOO9NxCnApPwqnwJMw5dBvN65vjTR8H9PG0h6kBu5UQEVWWC5GpSM8thIGuBN6OZpoOh6jW09eRwMvBDJcep+LCoxQmpajGYVKKVM7fL2opVd+SFzqiZ/XzccDFyFT8cTWGSakKSM0pwKnwJDxMygYAiERAQytDNLE3gaO5AayM9DDqvymPn0cQBEQkZOHEvUT8cycB5x4k48KjVFx4lIqv9t1Cp0Z1MKxlPbRztWKTdiKi15CSXYCT9xIBAK2cLaEr5ZxIRNrAu54Zrkal4Ul6HmLScuFgJtN0SESVhkkpAgA8Sc/Fg6RsiEWAk6UB8svRTJuoNtDT0wMA9Pa0w1f7buJmbAYiEjLhUsdYw5FpN6Ug4GpUGs7eT4ZCKUAkAjzsTdG8vjmMnxowV18qVg2s+zwikQiuNsZwtTHG6HYNEZeeh31hMdh9OQZ34jLx5804/HkzDg2tDTGiVX0M8KsLIz3e3oiIKmrp3+HIlythZaSLJnYcUJlIWxjpSdHYzhg3YjNw8VEKHLwdNB0SUaVhrZ0AAGcjilpJedY1g76OhEkpqlWelxiRSCTw8PAAAFgYShDoZo2/7yTgjyuxmNbdvcS6L0us1CbZ+XIcvhGn6qpXz8IAHdytYW6gW2pdHalYNbBuRaY0lorFGOzviKz8QqRkF2LXpWg8SMzGF/tuYsGRuxjoVxfvt23AsaeIiMrpTlwGtpyPBAC0c7WGmC1PibSKn5M5bsZm4FFyDhIz82FtrKfpkIgqBZNSBAA4cz8JANDa2VLDkRCp33MTI4ISiUlJsLayAkRimMqKWvhsPh8JcwMdVVcxE5kUA/0cNRG61nmSnouD154gu0ABHYkI7Vyt4WFv8tJudRm5cqTnFlb4eKYyHUzs7IZp3d2x+3I0Np59hAeJ2dh49hE2nXuE3l72GNu+ITwcTF+1SERENZ5CKeDT369DrhTgbmPM8UWJtJCZgS5c6xjhXkIWLkamcLZLqjGYlCIIgoBz/40n1cbZCvfiMzUcEZFmPJsYEQQlkjNzoWNYCJFIDDtTfehKxUjPLcSN2AxW2p9xJy4DR2/FQykAFoa66ONlV2brqKpgpCfFu63q452WTjgdkYS1px7gVHgS9ofFYn9YLNq6WGFsYEO0deG4U0REz9oaEomrUWkw0pOip4ctBE0HRERl8q9vgXsJWQiPz0KrhgUwU1M9i6gqcfRCwsOkbDxJz4OuRAw/J3NNh0OktaQSMdxtisaSuhWboeFotMu5+8k4crMoIeVibYQh/o5qS0g9TSwWob2bNTa/3xIHJ7bFm972kIhFOB2RhKD1oei99DT2Xo2BXFH+LsrF44oREdVEcel5+P7PuwCAT3q4w0TGGU2JtJW1sR6cLA0gALj0OFXT4RBVCialCGf+ayXl62QGma5Ew9EQabem9kUDv0YkZiGvUKHhaDRPEAR8d/gOjt6OBwD4OJqhl6etVszY1NTeFIuH+uDE9A54r019yHQkuPUkA5O2X0XgguP4+cxD5BTIoVQ+v01A8bhiEsnzr40v2p6ISJsJgoAZv19DVr4cPvXMMLylk6ZDIqKXaO5kAQC4HZuJzLyKD31ApG3YfY9w7r/xpNo4W2k4EiLtV8dYD5ZGukjOKsC9+Ex41TXTdEgaIwgC5h2+gzUnHwAA2rpYwbeemdZ1j6trboAv+jbFpM6u2HI+Ej+feYSYtFx8tf8Wlv8TgY86OMNAV4K8wjJaTz0zrtizOJ4YEVVnm89H4sS9ROhJxfh+gBcknLCDSOvZm+nDwUyGmLRcXIpMRV1zDidB1RuTUrWcUvn/40m1duEg50QvIxKJ0NTOBCfDk3AzNqPWJqWeTUj19LCF239dG9XpeTMnlsXMQBfjO7lidLuG+P1yNNacfIDI5Bx8e/A2jPSk8HMyh4e9CaSS/08+PTuuGBFRTRGRkIk5B28DAD7r2QiuGriGE1HFiUQitGxggd1XYnAjJgMd3etoOiSi18KkVC13MzYDqTmFMNSV1Nof10QV5W5rjNMRSUjIzEdiZr5qVr7aZPXJB6qE1Df9mqJQIbzS7Hmv67kzJ5ZDUEsnRKZk4587iYhJy8WJe4m4FJmKgIYWaGxnwunQiajGyitUYNL2q8iXK9HezRrvtqqv6ZCIqALqmstUraXO3E/C5K5umg6J6JXxsW8tdzI8EQDQ2sUKOhJ+HYjKw0BXCmdrIwDAteg0zQajAfvDYvHd4TsAgFm9GyNIC37MFM+cWJG/zHw5vOqa4d9pHdDL0xZGelJk5ctx7HYCtodGITo1R9PFIiKqEl/tv4WbsRmwMNTFgoFe5WptSkTao7i1FABcfpyGuPQ8DUdE9OqYhajlTtwtSkoFullrOBKi6qXZfy0L78Rl1qoBzy88SsHUnWEAgJGt62N0u4Yajuj16UrF8HeywIhWTmjnYgVdqRiJWfn4/XIM/roVjzw5BzInoprj90vR+DX0MUQiYMlQb9iY6Gs6JCJ6BXXNZbA304dCKWDV8QhNh0P0ypiUqsUy8gpVU4kyKUVUMfZm+rA01IVcKSAsKk3T4ajFw6RsjNl0EQVyJbo2scH/+jTRdEiVSioRw9fJHCNaOcHDoWiWxTtxWfjzfg7uxmVqODoiotd3+0kGZv5xHQAwubMb2rmy/kdUXYlEIgQ0KBoT+NfQKLaWompLK5NShw4dQpMmTeDj46P6mz59OgAgLCwMgwYNgo+PDzp16oSdO3dqONrq62xEEhRKAQ2tDeFowVkbiCpCJBLBq64pAOBiZCqUyprdmiY5uwAjfw5FWk4hmtU1xdKhPjV2liYDXSk6N7LBYP+6sDLSRYECOHIrAYevP6lVreKIqPqRSqXPnZQhOSsfo3+5iLzConGkJnRyUXN0RFTZ6prLUM/CAAUKJX46cV/T4RC9Eq0c6Pz69evo168f5s2bV2J5eno6PvjgA0ycOBFDhgzBhQsXMG7cOLi7u8PLy0tD0VZfJ+4Vdd1rz6dkRK+kka0JzkQkIzm7AGfuJ9XYJ875CgFjt1xGZHIO6prLsG5Ec8h0JZoOq8rZmcowxL8u/r0RhdvJhbiXkIUnGXno5WEHW1N2dyEi7SORSMqc/EGhFLDlfCRi0nJhYaCLlvUtsPHsoxLb2pnpo6eHnZojJqLXIRKJEOhmjc3nI7Et9DE+DHRmHYWqHa1NSvXs2bPU8r/++gtmZmYYPnw4AKBVq1bo27cvtm7dWuGklELx8qfdxeuUZ93qRhAEVVKqnYulqowSiQQQlBAEparlR9F068py7FSp+q8glGP9arB9hc6BJuOvomOXu/xa+NlVxvYvK7+OBGhkZ4Rr0RlYf/ohWje0qPixtVxhoRxLQ9JwJSYfJvpSrH/XDxYG0hLXxaevGxWmpZ99MREAjzq6aFrPGn/dTkR6biF2XopCe1creDqYAEJRcq4m3ieAmn0ffJnaWGaqOYonfyj2z50ERKbkQFciRm8vOxQolCjILXnNM9bXyp8FRPQS9S0N0KK+BUIfpWDl8Qh83c9D0yERVYjW3X2USiVu3rwJmUyGdevWQaFQIDAwENOmTUN4eDjc3EpOd+ni4oJdu3ZV+DjXr1+vknWri+gMOWLT8qAjBmSZ0bh6NQZ6enrw8PBAYlISkjNzVesmJMSXa5+GMAPghJSUVCSkZ1U4Jm3evjznQJPxV/WxX1Z+bf7sKmP7F5XfQV+JawCO303E4XNhsJPVrG58v4Rl4HxMPqQiYFqACbJiI3A19v/ff951o7y0/bMvJspLR8d6OrgQq0RMpgLH7yUhKjENfZtaQqkUihJzr6hQLsetmzchl8tfvrKG1MT7IFFtcS06Dddj0gEA3T1sYGGoq+GIiKgyiUQiTO7iimHrQvBr6GOMbtsQ9Sw5NAtVH1qXlEpJSUGTJk3QvXt3LF26FKmpqZgxYwamT58Oa2tryGSyEuvr6+sjJ6fi03Z7enq+9EeEQqHA9evXy7VudXPp9EMASQhoaImW/j4l3rO2soKOYSGUSgEJCfGoU8emXFMFW5gXfTYWFuYQy4wqHJM2bl+Rc6DJ+Kvq2OUtvzZ+dpWxfXnKbwvgUVYCbsdl4p8YERYMbFbh42urLecfY9+9OADAd/098JZv3eeuW3zdqCht/eyLPfsdcLQXcCUqHacjknE/VY5D97IxqoscR248QWZuxctvLNPBIP968PDQzqeaNfk++DLFZa+uDh06hGnTpkFPT0+1rEuXLliwYAHCwsLw7bffIiIiAubm5vjoo48waNAgDUZLVSU6NUfVMr61syUaWlX8OklE2q+1ixXauVrhVHgSFh29i8VDfV6+EZGW0LqklJWVFbZu3ap6LZPJMH36dAwePBj9+/dHXl7JWQXy8vJgaGhY4eNIJJJyV7Arsm518e/dJABAh0Y2pcsmEkMkEqu6K4nFoucOmvnsdk9vX2FauH2FzoEm46+iY5e7/Fr42VXG9uUtf2sXK9yOy8S+sCeY3qMR7Exlz123uvj7djy+OnALABDUzAxv+dZ98XVQyz67ytr+2e+ASAT4OVnAzEAXf96Iw72ELLy95jx6NLWF/FUGu//vmNp+j6mJ98GajuNzUkZuIQ5dj4NSANxsjODvZK7pkIioCs3o0Qinwk/jj6uxGNO+IZram2o6JKJy0bqk1J07d3DgwAFMnToVIlFRy4SCggKIxWJ4eXnhl19+KbF+REQEXF1dNRFqtZWeU4jQRykAgC6N62g4GqLqz8FMhoCGFjj/IAUbTj/EzN5NNB1ShRWNm1V0zb0enY7x265AKQBD/B3x9QBP1fWYijhbG2GgX13svxaLm7EZiM/IwxvN7GGsr6Pp0IgAVP34nBUdc6s2j0+mLk+f24JCOfZfi0VuoQJ1jPXQuZE1AAGC8ILkuZaP8act2z93vMlqEr9Wbv/MthzX9tW2b2xrhD5etjhwLQ7zD9/BzyP9X7gZr8tVr7af4/KWW+uSUmZmZti6dStMTU3x3nvvISEhAQsWLMBbb72F7t2744cffsDGjRsxfPhwXLp0Cfv378fKlSs1HXa1cvxeAhRKAW42RnCyrHgrMyIqbWx7Z5x/kIKtIUUzn1ga6b18Iy1SPFvT4+QcbDjzCLmFCjS0MoSLtQG+/yMU1lZW/9+q6Cm1ebYmGxN9BHdwxpbzj/EkPQ+7LkVjoF9dJqZI49QxPuerdm2szl0iqwM9PT0olQJ2hD5CUlYB9CUitLAVIzkx4aXbVpcx/rRl+2fHm6xu8WvT9s/bluPalk+hsQyAM27fvo2eDnIcvg6cDE/C5r9C4Fnn5fVRXperHs/xi2ldUsrW1harV6/GokWLsGrVKujp6aF3796YPn069PT0sGHDBsyZMwdLly6FhYUFZs2ahYCAAE2HXa0cvVV0ge/c2EbDkRDVHB3creHhYIIbMRlYffIBPu/VWNMhVVhCRj62hjxGVr4clka66NbUBpn5ciRn5kLHsLDM7m21fbamOsb62PlhK/RddhqpOYXYfTkGA/3qwlCvdp8X0ix1jM9Z0XHGavP4ZOqiUChw7949rDwegYepBRCLgL7e9rAr5/Tw2j7Gn7Zs/7zxJqtL/Nq4/bPbclzbijGTFT0Ma9y4MRoDGJZyC5tDHmPPfSXe6drsua3deV2uerX9HJd3fE6trDW3aNEC27dvL/M9T0/P575HL1cgV6oGvOzCpBRRpRGJRJja1R3vbbyATeceYXTbBqhjUr4fAtqgQK7EbxejkJxdAEM9Cfo1s4eeVPJqzchrmbrmBni3VX38fOYh0nKLElMD/BxgoKuVt1iqBdQxPuerjjPG8cmqVlhcLhb9HQkA6NioDuzNKjADl5aP8act2z93vMlqEr9Wbv/MthzX9tW2L762Tuziht+vxCAsOh1Hbyeip+eLW7Tzulz1eI5f7BW+9VSdXXiUgsw8OayMdOHtaKbpcIhqlA7u1vCpZ4a8QiVWHr+v6XDKTRAEfLb7Oh4l50BHIkK/Zg7sglZBpjId9PetCyM9KVJyCrAvLBYFcib0SDPu3LmDhQsXlhg/6OnxOcPDw0usz/E5a4b4jDwsOJ0EpQB4O5rBg4McE9VK1sZ6GN2uIQBgwZG7kCtYHyHtxqRULVPcda+jex1IytEclojKTyQSYVo3dwDAtpDHiEnL1XBE5bPk73D8fjkaIhHQy8MO1sbVazwsbWEq00F/Hwfo64gRn5GPQzeeQPEqM/IRvabi8TnXrVsHuVyO2NjYEuNzJiUlYePGjSgsLMT58+exf/9+DBgwQNNh02uQK5SYtCMMaXlKNLI1Rk8PW02HREQaNKZdA1gY6uJBUja2X4jSdDhEL8SkVC0iCAKO3S5KSnVpwq57RFWhtbMlAhpaoEChxPzDdzQdzkvtvBiFxceKWk308rBDfStOfvA6zA110a+ZA6RiESKTc/DPnYQXz3ZFVAWKx+f8+++/0aJFCwwYMACenp6YPXs2zM3NsWHDBvz5559o2bIlZs2axfE5a4AFf93FhUepMNARY9U7ftCRsIpPVJsZ6+tgYicXAMAPf91FWk6BhiMiej4OeFGLXI9JR3RqLmQ6ErRztdJ0OEQ1kkgkwqzeTdB3+WnsC4tFUCsnNK9voemwynTsVjw+3V00+OBHHZxhbaSH9NxCDUdV/dma6qOXpx32X4vFrScZsDTShW89c02HRbUMx+esvpRKoVyDOxc7eiseq088AAD8MNgbDawM8W9VBUdE1cY7AU74NTQKd+MzsejoPXzdz0PTIRGViUmpWuTg9ScAgE6N6nAAXqIq5OFgiqHN6+HX0Mf4Yu9N7J/QVuu6y4Y+TMG4bZehUAoY4FsXn3R3x89nHmk6rBqjgZUh2rta48S9RJwOT4KFgS5boRFRuYjFIuy6FIWMXPlL103LKcCaU0UJqZb1zZGelgrgxYMaE1HtIJWI8eUbTfH22vPYcj4SQ5vXQxN7E02HRVQK2/bWEoIg4NB/SaleL5mBgYhe37RubjDWl+LWkwxsv/BY0+GUcPtJBt7/5QLy5Up0blQH8wd4Pne6YHp1zeqaoqm9CQQAh2/EISWbTeeJqHwycuVIzy184V9aTgF2X4lBXqEStib68K9vjvRcXmeI6P+1crZEby87KAXgy303OaQAaSUmpWqJGzEZiErJhb6OGB0bWWs6HKIaz9JIDx93dQMAfHf4Dp6ka8eg51EpOXh3Qygy8+RoXt8cy4f5QsqxR6qESCRCR/c6sDfVR4FCif1hscgrVGg6LCKqIcKii4ZlkIpF6N7URuta5BKRdpjZqzFkOhKEPkrBvrBYTYdDVAp/idQS7LpHpH5BAU5o5miGzDw5Ptl1TeNPpxIy8xC0PgSJmfloZGuMdSOaQ6Yr0WhMNZ1ELEJvLzsY60uRlluIwzfioOSMfET0mlJzCnAmIgkA0NbFCmYGuhqOiIi0lb2ZDOM6OgMA5h66jez8l3cNJlInJqVqAXbdI9IMqUSMHwY1g55UjFPhSdgWqrlufAkZeRi65jweJeegrrkMm0a1gKlMR2Px1CYGulL09bKHVCzC45QcnL6fpOmQiKgaUwoCjt6Kh1wpwNFcBq+6ppoOiYi03Oh2DVHPwgDxGflY9k+Earmenp4GoyIqwqRULXAjJgOPU3KgryNGp0Z1NB0OUa3iUscIn/RoBACYc/A2wuMz1R5D/H8JqQeJ2bA31ce20QGoY6Kv9jhqM2tjPXRragMAuPI4TSPfAyKqGS4/TsWT9DzoSsTo0sSGYwIS1XL6UvFLW2Hr60gwu08TAMD60w9wPzELEokEHh4ekEgkbMVNGsV+XLXAnisxAIDOjWzYdY9IA95rXR/HbsXj3INkfLD5Ev4Y10ZtrZQeJmXj3Q0hiErJhYOZDNs/CICjhYFajk0ludYxhp9TPi5FpuLo7XhYGumxtRoRVUhSVj7O308BALR3s4KJPq8hRLWdjlRcrlk7BUGASx0jRCRk4b2fL2BEgCOSkpPR0NEOg/zrqTFiopLYUqqGkyuU2BdWlJTq7+ug4WiIaiexWITlw3zgYCbDw6RsTNp+BQo1PJG6Hp2OgavOIiolF06WBkxIaYHWDS1R10yGQoWAg9eeoECu1HRIRFRNKJRF3fYUgoAGVoZoYsep3Yno/71s1s6MPDnauVhBR1I0nMCZ+ylIzsxFZm6hpkOnWo5JqRruVHgSkrIKYGmoi/ZunHWPSFMsjfSwOsgP+jpiHL+biC/23ajSgc//vPEEQ9acQ3J2AZram2DXh62ZkNICYrEIPTxsYagrQUpOAfZfi9X4APhEVD2ERaUhITMfelIxOjeqw257RFRhJjIdtGpoCQA4HZGMnEI+HCPNY1Kqhvv9cjQAoG8ze+hw2ncijfJwMMWCgc0gEgFbzj/GF/tuVnpCQqkUsOivu/hwy2XkFCjQxsUS2z8IgLUxB7LUFoZ6UvTytINYBNyMzcDGs480HRIRabmMvEKce5AMAGjnagVDPQ7HQESvppmjGWxN9FGgUOLSkwI+HCONY5aiBkvPLcRft+IBAAN862o4GiICihLE8wd4QSQCNp2LxMw/bqBQUTlPqWLScjF8XQiW/jeryqg2DfDLey1gzDFHtI69mQxtXawAFA2AfykyRcMREZE2O3E3EXKlAHszfXbbI6LXIhaJ0LlxHYhFwJMsBcKi0zUdEtVyTErVYIevF41X4lrHCB4OrMAQaYvB/o6Y378oMbUt5DHeXnMecel5r7w/hVLAjguP0ePHkzj3IBkyHQl+GNQMs/s2gZQtJLWWt6MZmtiZQK4UELz1MpKy8jUdEhFpofuJWXiQlA2xCOjkzm57RPT6rIz0ENDQAgBw5FYCYtJyNRwR1Wb8tVKD7bxU1HXvLV8HVmCItMzg5o746R0/GOtJcTEyFX2WncJvF6MqNAC6IAg4HZ6EvstOY8bv15GZL4dvPTMcntQOA/zYOlLbiUQi9G1mB2drQ8Rn5GPCtiuQV1KrOSKqGQrkShy/mwgA8K1nDksjdsUmosrhW88MljIx8uVKTN8ZBqUaJuEhKguTUjXU7ScZuBSZCqlYhIHsukeklbo3tcW+CW3RyNYYSVkF+GTXNfRacgq7LkUj/QUzoaRkF2DL+Uj0XHIK76wPwa0nGTDWl2Jmr8b4bWwr1LcyVGMp6HXoSSVYHeQHA10Jzj1Ixg9H72k6JCLSIucfJiMrXw4TfSlaNLDQdDhEVIOIRSK0sNeDjkSEs/eTse70A02HRLUUR0msobaGRAIAujW1QR0TfQ1HQ0TP08DKEH+Ma4PN5yKx/N8I3I3PxLSdYdCRiOBTzxxOFgawMdFHToECabkFuBGTjnvxWart9aRivN2iHiZ2doWFoa4GS0KvyqWOMb4f6IXx265g1fH78HE0Q7emtpoOi4g0LDEzH1ej0gAAHRvV4YQ1RFTpjPXE6N7EBgeux+H7P++iZQNLNHM003RYVMswKVUDZeXLsedyDADgnZZOGo6GiF5GX0eCMe0bYrC/I3459wgHrsXiXnwWQh+mIPRh2QNgN7I1xiB/Rwz0rQtTAw5kXt318bLH5cg0bDjzEFN/C8P+CcZs8UZUiykFAX/fiYcgAK51jFDfktcDIqoaPo6mUAI4dD0OE7dfwYEJbTlJDqkVk1I10N6rMcguUKChlSFaOVtqOhwiKidTAx1M7OyKiZ1dEZGQhRsx6YhOzUF8Rj4M9CQwlemgoZUhWjSwLNUqSqkUIBZz7Ljq7LNejXAtOg0XI1Px4ZZL2BPcBjJdiabDIiINuByZiviMfOhKxGjvZq3pcIioBhOJRJj3lhfCotIRmZyDT3dfx/K3fTgmMakNk1I1jCAI2Hr+MQBgWMt6vJgQVVMudYzgUseo3OuLxSLsuhSFjFx5hY9lZ6aPnh52Fd6OKpeORIwVw33Re+kp3InLxMw91/HD4Ga8jhPVMgmZefj7TgIAoLWzJYz0WF0noqplaqCDpW97Y8jq8zh47Qn8nczxXpsGmg6Lagl2Tq9hQh+m4NaTDOhKxRjI2beIapWMXDnScwsr/JeVV/FEFlUNGxN9LHvbF2IRsPtKDLaFPtZ0SESkZt8cuI18uRJ1jPXgWddU0+EQUS3h52SBmb0bAwDmHLyNS5FlDyFBVNmYlKph1pwsmjVhgG9dmBlw0GMiouqmlbMlPunRCADw1b5bCPtvoGMiqvlO3EvE/rBYiAB0blQHYraUJCI1Gtm6Pvp42UGuFPDRlst4kp6r6ZCoFmB74BokIiETf99JgAiAhYEONpx+WKHt2YWHiEg7jG3fEJcjU/HXrXgEb72M/RPacnZFohoup0COmXuuAwBaNLDg7MlEpHYikQjzB3ghPD4Ld+MzMWrjRez6sBUM2Y2YqhBbStUga08WJaHcbIwhlYjZhYeIqJoSiURYOLgZGlgZIiYtF5O2X4FCKWg6LCKqQouPhSM6NRcOZjJ0dK+j6XCIqJYy1JNi/Uh/WBnp4vaTDEz8lXUQqlpMStUQCZl52HMlBkDRoJhEpD76UjGUr3mzft3tqeYx0dfBqnd8oa8jxqnwJCw5dk/TIRFRFbkRk451p4qGYPjmzabQlbKKTkSaU9fcAGvf9YeeVIy/7yRgzsHbmg6JajC2w6sh1p96iAKFEr71zOBoYYD03EJNh0RUa+hIxa81+52JTIqBfo5VEBlVd41sTTCvvyem7AjD0n8i4FzHCP28HTQdFhFVIrlCiU93X4NSAPp42aFTI5sKD8FARFTZfOqZ44fBzTB+2xVsOPMQDawNERTgpOmwqAZiUqoGSMjMwy/nHgEAxndywaOkHM0GRFRLFc9+R1SZ3vKpixsxGVh/+iGm77wGezMZmte30HRYRFRJfj7zCDdiMmCiL8Xsvk00HQ4RkUofL3s8SsrGwr/u4ct9N+FoLkMHdi+mSsa2wTXAquP3kVeohLejGccgICKqgT7v1Rjdm9qgQKHEmE0X8SAxS9MhEVEliErJwaKjRV1zZ/ZujDrGHNyciLTLuI4u6O/rAMV/M/JdeJSi6ZCohmFSqpp7kp6LrecfAwCmdnODiFMHE1U7lTEmFdVsErEIi4f4oJmjGdJyCvHexgtIzsrXdFhE9BoEQcDMP24gt1CBlg0sMNif3biJSPuIRCJ8198LgW7WyC1UYNTPF3AtOk3TYVENwu571dzyfyJQoFCiRQMLtHWx0nQ4RPQKXndMKjszffT0sKuCyEibyHQlWPeuP/qvOoPI5ByM2XQR28YEQF9HounQiOgV7LgQhZP3EqErFWNef08+WCQiraUrFeOnd/ww8udQhDxMwbsbQrHjg1ZwtzXWdGhUA7ClVDV2Ny4T2y9EAQCmdmUrKaLqrnhMqor+ZeVVPJFF2qGireSsjfXw88jmMNGX4vLjNEzafgUFhYoqjJCIqsLj5Bx8c+AWAGBaNzc0tDbScEREVFvp65SvLiLTlWD9yObw/q/V9vB1IXiYlA2As0jT62FLqWpKEAR8feAmFEoB3ZrYoGVDS02HREREFfSqreTe9HbA1tDHOHIzHlN3XcPiId6QiPlggqg6UCgFTN15FdkFCrRoYIH32zbUdEhEVIvpSCpWF+na2AZP0nMRn5GPfstPY3wnF3zQ3lkNkVJNxaRUNXXkZjzORCRDVyrGrN6cqYWIqDqr6MyN5oa66OVhi4PXn2B/WCx0JCIsHNgMYiamiLTeTyfu48KjVBjpSfHDoGZMKBORVqhIXeSNZvbYdSkaqTmFWP5PBDo3toEzW3zSK2L3vWoor1CBOYeKmnx/0K4h6lkaaDgiIiJSt4bWRhjgWxcSsQi7L8fg8z3X2XyeSMudf5CMH/66CwD48o2mcLRgHY6Iqh8DXSn6+9aFhYEuMvLkGLL6HG4/ydB0WFRNMSlVDS35OxxRKbmwNdFHcEc2lSQiqq0a25lg8RBviEXA9gtRmL3vBgSBiSkibZSYmY+Jv16BUgAG+NbFQL+6mg6JiOiVGelJMcDPAbYm+kjKKsCQ1edw5XGqpsOiaohJqWomLCoNq0/cBwB81a8pDHTZA5OIqDbr28wePwxuBpEI2HL+MWb+cQMKtpgi0iqFCiUmbb+ChMx8uNYxwjdvNtV0SEREr81AV4p3WznBz8kcGXlyvLMuBOfuJ2s6LKpmmJSqRvLlCkzbGQalUNSPt3tTW02HREREWuAtn7qY398LIhGwLeQxgrdeQh5n5SPSCoIgYPbemzh7PxkGuhKsHO7Lh4pEVGPo60iwaVQLtHGxRHaBAiN/DsWfN+I0HRZVI0xKVSM/Hg1HeEIWrIx08eUbfMJGRET/b3BzR6wc5gtdiRhHbsYjaH0I0nPKP3g6EVWNDWce4dfQxxCJgKVDfeBqY6zpkIiIKo2+VAyZjgTrRzRHl8Y2yJcr8dHWS1h/+mG598ExMWs3PqapJv69m4Cf/uu29+2bHrAw1NVwREREpG16etrB3FAXYzZdxIVHqRi0+iw2vtcC9mYyTYdGVCsduBaLbw8WTU4zs1djdGlio+GIiIgql45UDLFYhANXYhHQwAJpOQW4GJmKbw7cwpEbcejW1AZi0fNnGTWRSTHQz1GNEZO2YUupaiA2LRcf77gKAAgKcEIPDzvNBkRERForoKEldn7YCjYmergXn4X+K8/iRky6psMiqnX+uhmHyduvQhCAYS3r4f22DTQdEhFRlcnIlSMzX47WzpZo62IFAAh9lIJtIY+RlJWP9NzCMv8ycuUajpw0jUkpLZcvV2D8tstIzSmEh4MJZvVprOmQiIhIyzWyNcHu4DZwtjZEXEYeBqw6i12XojUdFlGt8e+dBIzfdgVypYA3ve3xTT8PiF7QUoCIqKYQiUTwczJHLw9bSMQiPEjKxq5L0cjM45ACVDYmpbSYIAj4ZNc1XH6cBmN9KVYM84WeVKLpsIiIqBpwMJNh90dt0KlRHeTLlZi2Mwwz91xHvpwDoBNVpZ0XozB600UUKJTo6WGLhYOaQSJmQoqIahdXG2P093GAvo4YCZn5+DU0CtGpOZoOi7QQk1JabNHRe9h7NRZSsQirhvvBydJQ0yEREVE1Ymqgg3Xv+mNKFzeIRMDWkMcYsvo8nqTnajo0ohpHqRSw5Fg4pu+6BoVSwFs+Dlgy1AdSCavbRFQ72ZvJMLR5PVgb6SG3UIHdV2Jw+XEqBIEDm9P/411SS20+H4ll/0QAAOa+5Ym2rlYajoiIiKojsViESV1csWFkc5jKdHA1Kg29l57GkZucrpmosqRmF2D0pov48dg9AMBHHZyxaHAz6EpZ1Sai2s1UpoNB/nXRyNYYggCcCk/CnzfjUKhQajo00hK8U2qhbSGP8b8/bgAAJnRyweDmnI2AiIhK05eKyz2Nckf3OjgwoS2a2psgJbsAYzdfwtTfwpCWU1DFURLVbCfvJaLX0lP4504CdKVizOvviRk9GnEMKSKi/+hIxOjWxAaBbtYQi4B78VnYfiEKCZl5mg6NtIBU0wFQSdtCHuPzPdcBAKPbNsDHXd00HBEREWmr4mmYd12KKvfsNf2a2cNMpoOz95Px++VonLufhIWDmqG1C1vkElVEQkYevjl4G/vDYgEADa0MsXyYL5rYm2g4MiIi7SMSieDtaAZrIz0cuvEEKdkF2HEhCh3c62BE6/oce68WY1JKSwiCgCV/h2PxsXAAwKg2DTCzd2M+ZSMiopfKyJUjPbf8s9r417eAvZkMx27HIzY9D8PWhWBEKydM6+4OY32dKoyUqPpLzsrH6pMPsOncI+QVKiEWASNa18fUbu4w0mPVmojoRRzMZRjesh7+uZOA+4nZ+OdOAoasPodFg71Rz9JA0+GRBvDOqQUK5ErM+uM6frtYNF13cAdnTO/uzoQUERFVGXszGca2d8bj1BxsC3mMX85F4vCNOPyvTxP08bLjPYjoGTdi0rHlfCT+uBqDvMKisVB865nh634e8HAw1XB0RETVh4GuFL097XA7LhMn7iXiYmQqeiw5iU97NsLwlk5sNVXLMCmlYU/ScxG89TKuPE6DWAR886YHhrd00nRYRERUC+hKxZj7lid6etjif3/cwKPkHEz49Qp2XIjC1/2awslCpukQiTRGEAREJGThyM04HLj2BHfiMlXveTqY4uOubujgbs0ELhHRKxCJRGhiZ4LGtsY4/zAFoQ9TMHvvzf/qIB7wczLXdIikJkxKadDxuwn4+LcwpGQXwERfiiVDfRDoZq3psIiIqJZp52qNPye3x5qTD7D83wicjkhCj8Wn8F4bJ7Qx5+w4VL0olQLEr/iUPSolB6cjknD+QTLO3k9GYma+6j0diQg9PewQ1MoJ/k7mz01Gvc7xiYhqGzMDXWwfE4CtIZFYcOQubsZmYMCqs+jR1BbTurvBpY6xpkOkKsaklAZk5csx99BtbAt5DABoam+CVcP9VH1oKzJg7dPszPTR08OuUmMlIqLaQV9HgomdXdHP2x5f7LuJ43cTsfrkQ2zTFWNCwUOMaNMAelKJpsMkeqnyDv6fUyBHTFounqTlITY9F7FpecjKL7mNVCyCk6UBmtiZoJGtCWS6ElyPTsf16PQy91lcF2Ndjoio/MRiEYJa1UdPTzt8/+cd7LoUjT9vxuGvW3Ho7WWPse0bspt0DcaklBoJgoCjt+Lx1f5biEnLBQCMbF0fn/ZsBH2d/6/oV3TA2mLG+vw4iYio/PSl4lKtOpwsDfHzyOb4+3YC5v95B+EJWZh7+C5+OfcYU7u5oZ+3Q4mxHtgqhLTRs3WpArkSiZn5iMvIQ/x/fxl5pZNGYhHgW88cMl0J6hjrwdZEH1KJuGgfCiUKcl/ccrC4Lsa6HBFRxVkZ6eH7gc0wul1DfP/nXRy7HY/9YbHYHxaLlg0sMLSFI3p62JX47UzVH+98ahIen4k5h27j+N1EAICDmQwLBnmhtTOn4CYiIs3QkYpf2KpkiJ8DTt+JwaXYPMSk5eLj38Iw5+BttHGxgqeDKcwNdTDQz1EDkRM9nyAISM0uwO24DMSmFSWgUrILIJSxrrmBDuqY6MPGWA82JvrwdjTD8AAnbDj98JWSSkRE9PrcbIyxboQ/bsamY83JBzhw7QlCHqYg5GEK/vfHTXRsVAfdm9qgnYs1TA04a3B1x6RUFYtOzcGSY+H4/XI0lELReARj2jXE+E4uMNDl6SciIs17XqsOQVCijkzAOwGOCIvOwKXIVCRnF2BfWCz+uZOANs6W6OVpx/sZaVxCRh5OhSfh7P1knH+QrGqR/jQjPSlsTIqST7Ym+qhjoleqS6quVKyukImI6CWa2ptiyVAfzOjRCLsuReO3i1GITs1VtZ4SiYqGwvF3skBjO2O425rAzcaI9ZJqhp9WJSvuxhAen4lVJ+5j39VYyJVFz+a6NbHBjJ6N4GxtpOEoiYiIyk9HIkbz+hbwqmuKGzEZuPw4FVn5chy5FY823/2Dwf6OeLtFPdS3MtR0qFRLKJQCwqLTcPxOAv65m4AbMRkl3heLABsTfTiYyWBrqg8bE30Y6bHaS0RUHdmbyTCxsyvGd3TB1eg0HLkZh6O34vEgMRs3YjJK3ANEIsDJwgBOloawN9OHnakMdqb6sDcr+q+dqQwyXXb/0ya8O1cipVLA0dvxWHjkLsITslTLG1gZoqO7NeqaG+DE3USc+K8L37M4uCUREWkzPakEfk7maFbXFLefZOJyVCpScwqx+uQDrD75AG1cLDGshRO6NrFhixOqdOk5hTgRnojjdxJw/F4iUrILSrzvVdcUrZ2t0MrZErdjM5BbqNBQpEREVF5ljW/5PGKxCL71zOFbzxyf9WyM+Iw8nH+QjGvR6bgbl4k7cZlIysrHo+QcPErOee5+LAx1YW9W9ODC3kwGe1MZHC2K/u1obgBzQ93KLCK9BJNSlWjViftYcOSu6rWztSH861vA1kQfAF46NgEHtyQioupAKhHDs64pWjtbop6lAbaFPsaJe4k4E5GMMxHJsDDURfemNujhYYfWzpbQkTBBRRUnCAJuxmbgxL1E/HsnAZcfp0L51MBQxnpStHezRsdGdRDoZg1rYz3Ve/cTspiUIiKqBl42vuXL2Jnp4399mqi2z86XIz4zD+k5hUjPLURGnhwZuYXIyCt6XagQkJJdgJTsglKtbItZGurCuY4RXOsYwaWOEdxtjdHU3hSmMo5fVRWYBalEDawM4W5jDEsjXbjbGMOEX1oiIqrBxGIRujW1RbemtohKycGOC1HYcTEKiZn5+DU0Cr+GRsFUpoOuTWzQpXEdtGxgyaeP9ELZ+XL8eycBJ8MTcfxuIhIy80u871rHCJ0a1UHHRnXg52TOhCcRUQ3xurOWPr29paEeLA31Sq0rCALy5Upk5smRmVeIzDw5FEoBJjIdxKTlIiYtF4mZ+UjOLkDywxSEPkwpsX09CwN4OpiiqYMJPOxN4eFgCjOZDmchfk1MSlWiXp526OVpxxlbiIio1nG0MMC07u6Y3MUVIQ9TcOj6Exy5GYekrALsuhSNXZeiIRIBjWxNENDQAq0aWsLb0QzWxnoQiViZoyIDVp3FnbhM1WsdiQgNrAzhWscYLnWMVE+pb8Vm4FZs6SfcHAqBiIieRyQSQV9HAn0diap1bV1zGQb5O6paWhXIlUjKykdSVj4Ss/KRlFmA+Mw8pOUU4nFKDh6n5ODg9SeqfTqYyeDxX5Kqib0JmtibwNZEn3WbCqiWSank5GT873//Q2hoKCQSCd544w3MmDEDUmm1LA4REVGNIZWI0cbFCm1crPB1Pw9ceJSCP2/E4UxEEsITsnD7SQZuP8nAz2ceAShqIt/YzgSN7YzRyNYE9a0MUNfcANZGenzyWEW0uR7lbmsMADAz0IGdqQz2ZvqQiv+/NRSHQiAioqrwdEsrQz0pDPWkcLL8/wlc8goVSMjMR0JmHhIz8pGQmY+03EJVC6sjN+NV65oZ6KCJnQka2RrBoCAXejYZcLExgb4OB1gvS7W8c0+ePBk2NjY4deoUkpKS8NFHH2Hjxo0YPXq0pkMjIiKi/0jEIgQ0tERAQ0sAQGJmPkIeJuP8g2SEPEjB/cQsJGcX4HREEk5HJJXYVlcqRt3/BiC1MtKFhaEeLI10YWWkCzMDXZjo68DGRA8NOaNthWlzPWrJUB8AYKtzIiLSKvo6EtSzMEA9CwPVMj2pGL5O5rgRk44bMem4/SQTEYlZSMspxNn7yTh7PxkAsPzCWYhEgL2pDE6WRTMDOlkawM5UH9bGeqhjXPRfE31prWxhVe2SUpGRkQgNDcXJkychk8ng6OiI4OBgLFiwQCsqU0RERLVFRWbMAQBrYz308bJHHy97AEBugQJ34jJwNy4Tt59k4G58JqJScvEkPRcFciUeJGXjQVL2C/e5YKAXBvk7vnZZagvWo4iIiCqHmUwHLepbqB6+AUUtqiISsnDrSVE38+IW4hl5clWrquJk1bN0pWKY6Be10jL670+mK4FEJIJYLIJEJIJEXPzvou6IcoUSSgAKhQC5UoBS+O+/SgFypRJKJSBXKqFQClAIAuSKkus0tDbCmiA/SDU4RmO1S0qFh4fDzMwMNjY2qmXOzs6IjY1FRkYGTExMXri9IBRN21JQUACJ5MXN5xQKRbnXLSaRSGCiL4JYqPiHaqQrgkKh0JrtlYIIShMZzPQlEJcjY6tt8VfG9hU5B5qMv6qOXd7ya+NnVxnb1/byAy8/B9oce2Vsz/K/uPzmBhIIghJ/XY9DdkHFZ8yxMtJDaxdrJKbnwLuuMbzrFnXdUigFpOfKkZpTgPScAmQXKJCVr0B2vhw5BXJk5SuQX6iASAQ0sJChoKCgwsd+meI6QHG9oaZQZz3qaRWpU9WkupQ6t1cKIhiJdLSuLlHTtn/edbG6xK+N2/M3iGa2Lz7PRjriahm/Nmz/onqQjgjwtDdEPQM5BvnaIq9QQHJ2PpKzCooGUs8uQE6+HHKlgMSsAmTlyQEIyM4rRHZeIRIqXJJXk5adj9TMHJgaVP5ENOWtS4mEalbb2rt3L3788UccP35ctezx48fo2rUrTpw4AVtb2xduX1BQgOvXr1dxlERERFQTeHp6Qle35swYyHoUERERqdPL6lLVrqWUgYEBcnNzSywrfm1oaFjWJiVIpVJ4enpCLBbXyv6aRERE9HKCIECpVGrF4N+VifUoIiIiUofy1qWqXU3L1dUVaWlpSEpKgpWVFQDg/v37sLW1hbGx8Uu3F4vFNeqJJxEREVF5sR5FRERE2kRzo1m9ovr168PPzw9z585FVlYWoqKisHLlSgwcOFDToRERERFpNdajiIiISJtUuzGlACApKQlff/01QkJCIBaL8eabb2LatGkVGnCTiIiIqDZiPYqIiIi0RbVMShERERERERERUfVW7brvERERERERERFR9cekFBERERERERERqR2TUkREREREREREpHZMShERERERERERkdoxKUVERERERERERGpX65JSycnJCA4Ohr+/P1q2bIk5c+ZALpeXue6JEyfQt29feHt7o2fPnvj3339V7+Xn52POnDlo3749/Pz8MGjQIJw/f15dxXhllVX+p+3cuRPu7u5VGXalqsxzsG3bNnTt2hU+Pj7o27fvc8+RNqms8ufl5WH27Nlo06YNmjdvjhEjRuDOnTvqKsYrq0j5ix05cgSdO3cutXzt2rVo3749vL29ERQUhAcPHlRV2JWqss5BbbgOFnved6BYdboOVmb5q+M1kNTrVb5vVHF37tzBe++9hxYtWqBNmzb45JNPkJKSoumwaiSFQoGgoCB8+umnmg6lRkpLS8Mnn3yCli1bonnz5ggODkZCQoKmw6pRbt68ieHDh8Pf3x9t27bFt99+i4KCAk2HVWOkpKSga9euCAkJUS0LCwvDoEGD4OPjg06dOmHnzp0ajFALCbXMO++8I0ydOlXIyckRHj9+LPTu3VtYu3ZtqfUePnwoeHp6CkePHhUKCwuFgwcPCl5eXkJcXJwgCILw7bffCv379xdiY2MFuVwu7NixQ2jWrJkQExOj7iJVSGWVv9i9e/cEb29vwc3NTV1FeG2VdQ52794ttG7dWggLCxOUSqWwf/9+oWnTpqXOkbaprPJ///33QlBQkJCamirk5+cLc+fOFTp37qzu4lRYecsvCIJQUFAgrFmzRmjSpInQsWPHEu/t3r1baNeunXDv3j0hLy9PmDdvntC7d29BqVSqoxivpbLOQU2/DgrCi8tfrLpdByvz/4HqeA0k9arI941eTW5urtCmTRthyZIlQn5+vpCSkiKMGTNGGDt2rKZDq5EWL14sNGrUSJgxY4amQ6mR3nnnHWHcuHFCenq6kJmZKYwfP1744IMPNB1WjaFQKIQ2bdoIv/zyi6BQKIQnT54I3bt3F5YvX67p0GqEixcvCl26dBHc3NyE8+fPC4IgCGlpaUKLFi2ELVu2CIWFhcLZs2cFHx8fISwsTMPRao9a1VIqMjISoaGhmD59OmQyGRwdHREcHIytW7eWWnfPnj3w9/dHly5dIJVK0atXLzRv3hw7duwAUNRCYOLEibCzs4NEIsHgwYOhq6uLmzdvqrtY5VaZ5QeA3NxcfPzxx3j33XfVWYzXUpnnYMOGDZg0aRK8vLwgEonQp08f7NixA0ZGRuouVrlVZvnv378PQRAgCAIAQCwWQyaTqbU8FVWR8gPAqFGjEBISgjFjxpR677fffsOwYcPg6uoKPT09TJ06FbGxsSWeimijyjwHNf06CLy4/ED1uw5WZvmr4zWQ1Kui3zd6NbGxsWjUqBHGjRsHXV1dmJubY8iQIbhw4YKmQ6txzp07h7/++gvdunXTdCg10o0bNxAWFobvvvsOJiYmMDIywjfffINp06ZpOrQaIz09HYmJiVAqldWqDl8d7NmzB9OmTcOUKVNKLP/rr79gZmaG4cOHQyqVolWrVujbty/vhU+pVUmp8PBwmJmZwcbGRrXM2dkZsbGxyMjIKLFuREQE3NzcSixzcXFRdU/6+uuvERgYqHrv3LlzyMzMRKNGjaqwBK+nMssPFJ2DDh06oHXr1lUbeCWqrHOQm5uL8PBwiMViDB8+HC1btsTQoUORm5sLQ0NDtZTlVVTmd2DUqFG4d+8eAgIC4O3tjX379mHx4sVVXobXUZHyA8CCBQuwbt061KtXr9R7z54fHR0d1K9fX+u7MFbmOajp10HgxeUHqt91sLLKX12vgaReFf2+0atp2LAh1q1bB4lEolp25MgRNG3aVINR1TzJycmYOXMmfvjhB/6AryLXrl2Di4sLfvvtN3Tt2hVt27bF/PnzYW1trenQagxzc3OMHDkS8+fPh6enJwIDA1G/fn2MHDlS06FVe23btsXRo0fRq1evEsvDw8Nf+ru6tqtVSans7OxSN5Hi1zk5OS9dV19fv9R6AHD16lVMnjwZ48ePh6OjYyVHXXkqs/x79+7F/fv3MWnSpCqMuPJV1jnIyMiAIAjYsGEDvvzyS5w6dQp9+vTBmDFjEB0dXbWFeA2V+R1QKBTo3r07Tp48idDQUHTu3BnBwcHIz8+vwhK8noqUHwBsbW0rtK/nXSO0SWWeg6fVxOsg8OLyV8frYGWVv7peA0m9Kvp9o9cnCAJ+/PFH/Pvvv5g5c6amw6kxlEolpk+fjvfee0+rH7xUd+np6bh79y4ePXqEPXv24I8//kB8fDxmzJih6dBqDKVSCX19ffzvf//D1atXceDAAdy/fx9Lly7VdGjVnrW1NaRSaanl1fU3gzrVqqSUgYEBcnNzSywrfv3sk12ZTIa8vLwSy/Ly8kqtt3PnTrz33nv48MMPMW7cuCqIuvJUVvkfPHiAH374AT/88EOZ/+Nps8o6Bzo6OgCA9957D66urtDV1cU777wDe3t7nDhxogpL8Hoqq/yFhYWYNGkS+vfvDxsbGxgZGeF///sf4uPjcebMmaotxGuoSPlfprzXCG1TmeegWE29Dr5Idb0OVlb5q+s1kNSrKq439HxZWVmYOHEi9u/fjy1btlSbyReqg9WrV0NXVxdBQUGaDqVG09XVBQDMnDkTRkZGsLKywuTJk3HixAlkZ2drOLqa4ejRozhy5AiGDRsGXV1duLq6Yty4cfj11181HVqNVV1/M6hT9alJVwJXV1ekpaUhKSkJVlZWAIrGxbG1tYWxsXGJdd3c3EqNixIREQEPDw8ARa1EvvrqK/z1119YsWJFtei6UVnlP3LkCDIyMvDWW28BKDoXAODv748vvvgCffv2VUNpXk1lnQMLCwtYWlqWmqmi+Fxoq8oqf05ODtLT00uUXyKRQCQSqX6saqOKlL88+woPD0fHjh0BAIWFhXj06FGp5rnapjLPQU2/Dr5Idb0OVlb5q+s1kNSrMq839GKPHz/GmDFjYG9vj127dsHCwkLTIdUoe/fuRUJCAvz9/QFA9QPz2LFjuHjxoiZDq1FcXFygVCpRWFgIPT09AEUtewCoxj+i1/PkyZNS926pVKrV9ffqzs3NrdRD+4iICLi6umooIu1Tq1pK1a9fH35+fpg7dy6ysrIQFRWFlStXYuDAgaXWfeONNxAaGopDhw5BLpfj0KFDCA0NRb9+/QAA8+bNw8mTJ/H7779Xix9iQOWV/6OPPsLVq1dx8eJFXLx4ET/99BMA4OLFi1r7Q6xYZX4Hhg4dihUrVuD27duQy+XYtGkT4uPj0aVLF3UXq9wqq/ympqbw8/PDwoULkZycjPz8fCxYsADm5ubw8/PTQMnKpyLlf5kBAwZgy5YtuHPnDvLz8/HDDz/AyspKVWHVVpV5Dmr6dfBFqut1sDI//+p4DST1qszvGz1feno6RowYAV9fX6xfv54JqSrw559/4vLly6prfp8+fdCnTx8mpCpZ69at4ejoiM8//xzZ2dlISUnBjz/+iC5dunASjUrStm1bJCYm4qeffoJCoUBUVBRWrVql1XWX6q5r165ISkrCxo0bUVhYiPPnz2P//v0YMGCApkPTHhqc+U8jEhMThQkTJggtWrQQAgIChO+++06Qy+WCIAiCt7e3sHfvXtW6J0+eFN544w3B29tb6N27t3D8+HFBEAQhOTlZaNSokdC0aVPB29u7xN/T22ujyij/s86fP19tpkIXhMo7BwqFQli/fr3QrVs3wdvbW+jfv79w4cIFtZenoiqr/ImJicL06dOF1q1bCy1atBDGjBkjPHjwQO3lqaiKlL/Y77//LnTs2LHEMqVSKaxfv17o1KmT4O3tLQQFBVWL8gtC5ZyD2nIdLFbWd+Bp1ek6WFnlr67XQFKvF33fqHJs2LBBcHNzE5o1a1bqekxVY8aMGcKMGTM0HUaNFBcXJ0yePFlo06aN4O/vL3zyySdCenq6psOqUc6cOSMMGjRI8PPzEzp06CAsWrRIyM/P13RYNYqbm5tw/vx51etr164JQ4YMEXx8fITOnTsLv//+uwaj0z4iQWBbSCIiIiIiIiIiUq9a1X2PiIiIiIiIiIi0A5NSRERERERERESkdkxKERERERERERGR2jEpRUREREREREREasekFBERERERERERqR2TUkREREREREREpHZMShERaYHIyEhNh0BERERERKRWTEoRkVqkp6fjyy+/RGBgILy9vdG2bVvMmDEDcXFxr73v2NhY+Pj4IDY2thIiLb9ly5ahcePG8PHxUf01a9YMXbt2xcaNG8u9n/nz52PVqlVVFygRERFVmaCgICxbtkzTYby2wsJCvP3224iKiiqxPDIyEi1btkR0dHSJ5SdOnEDfvn3h7e2Nnj174t9//33uvhUKBebPn4/WrVvDx8cHH330ERISElTvJycnIzg4GP7+/mjZsiXmzJkDuVyuej8sLAyDBg2Cj48POnXqhJ07d760PAMHDoSnpycSExNLvefu7o6QkJBSy5ctW4agoKAS63l5ecHHxwfe3t7w8fHBkCFDcOzYMdU6hYWFGDp0aKnzQ0Tlw6QUEanFlClTkJqail27duHq1av4448/UFBQgPfee69EpeNV2Nvb48qVK7C3t6+kaMvP398fV65cUf1dvHgRH3zwAebNm4dz586Vax+pqalVHCURERHRi61YsQLNmzeHo6Ojatnff/+Nt99+G2lpaSXWffToESZMmIBJkybh4sWLmDBhAiZPnoz4+Pgy971q1SqcOXMGv//+O06dOgV9fX3MmjVL9f7kyZNhYGCAU6dOYdeuXTh37pzqAV96ejo++OADvPnmm7hw4QLmzJmDefPm4dq1a88tS1hYGOLi4tC+fXts2bLl1U8KgLVr1+LKlSu4evUqzp49iwEDBmDy5Mk4ePAgAEBHRwcTJ07EjBkzXus4RLUVk1JEpBaXLl1C165dYW1tDQCwsrLC559/jmbNmiEjIwMAcP/+fYwdOxYdOnSAl5cXevXqpXrq9sknn2Dq1Kkl9jl58mR89dVXiI6Ohru7u+oJlbu7OzZv3ozu3bvDx8cHQ4cOxd27d1XbnT17Fm+++SZ8fX0xdOhQLFiwQPVULD4+HqNHj0aLFi3Qvn17jB8/vsSTvJfR0dHBoEGDYGZmhlu3bgEAsrKyMGvWLHTr1g3e3t5o164dfvrpJwBFFcD9+/dj//79eOONNwAASUlJmDZtGtq0aYO2bdti9uzZyMrKqvA5JyIiIvXavXs33n77bXz77bcICAhAq1atMHPmTBQWFgIA5HI5lixZgsDAQPj6+mL48OG4c+cOACAvLw/ff/89AgMD0bx5cwQFBZVIvLi7u2PHjh3o3r07mjVrhg8//BA3btzA0KFD4ePjgwEDBpQYDuDgwYPo27cv/Pz80L9/f5w+ffq5caekpGDTpk0YPny4atny5cuxaNEiTJkypdT6e/bsgb+/P7p06QKpVIpevXqhefPm2LFjR5n737lzJ8aMGQM7OzsYGRlh5syZOHnyJKKiohAZGYnQ0FBMnz4dMpkMjo6OCA4OxtatWwEAf/31F8zMzDB8+HBIpVK0atUKffv2Vb1fli1btqBHjx4YNmwYtm/fjtzc3OeuWxEymQyDBw/G+++/j/nz50MQBABA69atkZKSghMnTlTKcYhqEyaliEgtevfujS+++AJffvklDh06hJiYGFhbW+O7776DhYUFAGDChAlwc3PD0aNHcfHiRbRt2xZffvklAGDw4ME4duyYKjmTkZGBf/75BwMHDizzeAcPHsSWLVtw8uRJyGQyfP/99wCA6OhofPjhh3j77bcRGhqKadOmlahALVq0CLa2tjhz5gwOHTqEnJwcrFmzptzlzMvLw+bNm5GdnY02bdoAABYuXIjo6Gjs2rULV65cwaxZs/Djjz8iMjIS48aNQ9++fdG3b1/s27cPSqUSwcHBEIvFOHLkCPbv34+EhATMnj27wueciIiI1O/y5cuwtLTEqVOnsHr1ahw6dAh//fUXgKIWQwcOHMD69etx4cIFtGjRAmPHjoVCocCXX36J06dPY9OmTThz5gy6dOmCkSNHlhieYP/+/dixYweOHj2KS5cuITg4GHPmzMGZM2egq6ureuh14sQJfPHFF5g9ezZCQ0MxYcIETJgwAeHh4WXGvHv3bnh6esLGxka1bNCgQThw4ABatWpVav2IiAi4ubmVWObi4qJKsD0tMzMTcXFxJda3srKCqakp7t69i/DwcJiZmZU4trOzM2JjY5GRkYHw8PByHwso6gp45MgRvP3222jdujUsLS3x+++/l7nuq+rQoQPi4+Px4MED1bLevXtj27ZtlXocotqASSkiUotvv/0Ws2fPxpMnTzB79mx06tQJXbt2xb59+1TrrF69GhMmTIAgCIiJiYGJiYmqGbi/vz/s7Oxw+PBhAMCBAwfQsGFDNG3atMzjBQUFwdraGsbGxujZsycePXoEoKgy17hxYwwZMgRSqRT+/v4YPHiwajs9PT1cunQJBw8eRHZ2NtatW1eiefmzLl26BH9/f/j6+sLT0xMBAQEIDQ3FL7/8gkaNGgEoSrYtXrwYRkZGiIuLg56eHgCU2QLrxo0buHnzJr744gsYGRnB3NwcM2bMwMGDB9nNj4iIqBrQ19fHhx9+CB0dHXh5ecHd3R0PHz4EUNTCaPTo0XBxcYFEIsFHH32EJUuWIC8vDwcOHMDUqVPh5OQEXV1djBgxAg0bNsSBAwdU+37nnXdgZmaGOnXqwNXVFd26dYOzszMMDAwQEBCAmJgYAEUthd5++200b94cEokEHTt2RKdOnbB9+/YyYz5//jx8fHxKLLOxsYFIJCpz/ezsbMhkslLlzsnJKXNdADAwMCi1fnZ2dpn7Kn6dk5NToWMBwI4dO9CiRQs4OztDJBIhKCgIGzduhFKpLHP9V2Fubg4AJbo1+vr6IiQkRNV6iojKR6rpAIiodhCLxejXrx/69esHQRBw//597N27F5988gmsra3RqlUr3LlzB8HBwUhMTISzszMsLCxK3NgHDRqEvXv3YtCgQdizZw8GDRr03ONZWVmp/i2VSlX7efLkCRwcHEqs6+joiOvXrwMAZs2ahdWrV2P9+vX49NNP0ahRI8yaNQv+/v5lHsfPzw+bN28GAFy9ehWTJk2CtbU1/Pz8VOskJydjzpw5uHXrFurWrQsPDw8AKLNyFB0dDYVCgcDAwBLLdXV1ERUVpaoEERERkXaytLQskczR0dFR1UMSExNLjIGpq6sLb29vJCQkoLCwEHXr1i2xr7p165YYQNvMzEz1b4lEAlNTU9VrsVisOk5MTAxCQ0Px66+/qt5XKBQICAgoM+YnT56gc+fO5S6jTCZDXl5eiWV5eXkwNDQsc10ApbrQFa8vCEKp94pfGxoaQiaTITMzs1zHksvl2L59OzIyMtCyZUsARfWtjIwMHD16FN27dwdQdN4VCkWZ2+vq6r6w7EBR3Q4o+qyL2djYIDc3F6mpqapeAET0ckxKEVGVO3XqFCZOnIh///0XZmZmEIlEcHFxwdSpU3HmzBncunULDRs2xKRJk7B8+XJ06tQJAHDkyBFVc3cAeOutt7B48WKcPXsWd+/eRZ8+fSoci4ODQ6nZYZ5uFn/r1i0MGTIEEyZMQEpKClasWIHx48fj/PnzL923t7c3fvrpJwwZMgSmpqaYNGkSAGDSpEno1KkT1q9fD6lUitTUVPz2229l7sPW1hb6+voICQmBRCIBABQUFCAqKgpOTk4VLi8RERFpDzs7Ozx58kT1urCwEAsWLMD7778PPT09REVFwdnZWfX+48ePVfUiAM9tufQsW1tbvPnmm/jggw9Uy2JjY6Gvr1/m+mKxuEItidzc3HDz5s0SyyIiIlQP3p5mamoKGxubEl3+EhMTkZaWBjc3NyiVSqSlpSEpKUn1UPH+/fuwtbWFsbEx3NzccObMmVLHcnV1LXWso0ePoqCgAAcPHlTVowBgyZIl+Pnnn1VJKTs7O1Wrsqc9fvy41MPLsvz7779wcHAoUTcrTnI9fVwiejl23yOiKte8eXNYWlris88+w927d1FYWIisrCzs27cPjx49QocOHZCdnQ2FQqF6mhYREYEVK1YAKErKAICFhQU6duyoGjT86aeD5dWvXz/cvn0bf/zxBxQKBcLCwkokiH766Sd88803yMrKgomJCWQyWYVaJzVu3BiffvopfvrpJ1UiKzMzE/r6+pBIJEhJScG3334LAKpBT3V1dVVPAL28vODk5ITvvvsO2dnZyMvLw9y5czFy5Mgyn+gRERFR9dG/f3+sX78eDx8+hFwux+rVq3Hs2DFYWFhgwIABWLRoESIjI1FQUIBffvkFERER6N27d4WPM3jwYGzatEk1UPr169fRv3//El0Bn2Zvb1+hiV3eeOMNhIaG4tChQ5DL5Th06BBCQ0PRr1+/55Z71apViIqKQlZWFubOnYsWLVqgXr16qF+/Pvz8/DB37lxkZWUhKioKK1euVI0b2rVrVyQlJWHjxo0oLCzE+fPnsX//fgwYMKDUcbZs2YK+ffvCwcEBtra2qr+goCBcuXIFly9fVsW/evVq1fnJy8vD/v378c8//+DNN998brlzcnKwbds2bN68GZ999lmJJGFCQgIMDAxeqX5KVJuxpRQRVTl9fX1s27YNy5cvx0cffYTk5GTo6OjA29sbP//8s+qJ4CeffILp06cjNzcXtra2GDx4MBYsWIB79+6pnrwNHjwYR44cwdy5c18pFltbWyxduhTff/89vvrqKzRu3Bht27ZVjdf09ddf46uvvkLnzp1RUFAADw8PLFmypELHGDZsGE6ePIkZM2Zg3759mDdvHubOnYsNGzbA1NQUvXr1QpMmTXDv3j20bdsWvXr1wpQpU9ChQwccP34cq1evxvz589GtWzfk5+fDy8sLP//8s2osKiIiIqqeRo8eDblcjvfffx/p6enw9PTE2rVroaOjg08++QTLli3DyJEjkZaWBnd3d6xfvx4NGjSo8HF69OiBnJwcfP7554iNjYWZmRlGjhypmm34WW3atCnROv1lnJ2dsWLFCixcuBAzZ86Eg4MDli1bpop13759+OKLL3DlyhUAwLhx4yCXyzF8+HBkZ2ejZcuWWLx4sWp/S5cuxddff43OnTtDLBbjzTffRHBwMICi8Zs2bNiAOXPmYOnSpbCwsMCsWbNKdUW8c+cOLl68WOZYoE2aNIGHhwc2bNgAX19ffPTRR9DV1cWnn36KuLg4iEQiuLu7Y9myZfD19S2x7ZgxY1Stn2QyGRo3boxVq1ahdevWJda7dOkS2rVrV+5zSERFRAJHYiOiWuTJkydITU1FkyZNVMu+++47JCYm4ocfftBgZERERESakZSUhO7du+PQoUMlZsF7HSNHjsTGjRsrZV/VQc+ePfHZZ5+hffv2mg6FqFph9z0iqlVSU1MxbNgw3LhxA0DRU7V9+/ahY8eOGo6MiIiISDOsrKzwzjvvYNOmTZWyvytXrqBDhw6Vsq/q4MSJE7C0tGRCiugVsKUUEdU6O3fuxNq1a5GYmAgrKysMHz4cI0eO1HRYRERERBpTUFCAoKAgLFiwAPXq1dN0ONVGYWEhhg0bhkWLFsHR0VHT4RBVO0xKERERERERERGR2rH7HhERERERERERqR2TUkREREREREREpHZMShERERERERERkdoxKUVERERERERERGrHpBQREREREREREakdk1JERERERERERKR2TEoREREREREREZHaMSlFRERERERERERqx6QUERERERERERGpHZNSRERERERERESkdkxKERERERERERGR2jEpRUREREREREREasekFBERERERERERqR2TUkREREREREREpHZMShFRpRMEQdMhVLqaWKbK8LLzwvNGRERUcbx/lqapc1KdPotXjbU6lZFqHialiLRAUFAQ3N3dS/z5+/vj3XffRWhoqMbi6tSpEz799NMKbfP3339jxowZlXL8X375BW3btoWXlxdWrlz5wnWPHDmC999/H61bt4a3tzf69OmDFStWICsrq8LH/fTTT9GpUyfV68osU3m5u7tj2bJlz30/JCQE7u7uCAkJeeF+ni1LZSkoKMC8efOwf//+5x5LE+eNiIjU7969e5gyZQratGkDDw8PtG3bFpMnT8atW7cq/VjR0dFwd3fH7t27K33fr6JTp06l6nCNGzdG8+bNERQUhDNnzlR4n5cuXcLYsWNfO7by1hVeRVl116f/Ll68CODV6pLPKqvOUZZOnTrB19cXsbGxZb7/srpVWZ79LDTx/du9ezeGDh0KX19fNGvWDL1798aPP/5Yqo67atUqrF+/vkL7Lu+5JapKUk0HQERFmjRpgi+++AIAoFAokJqail9//RXvv/8+du/eDVdXVw1HWD4bN26slP1kZWXhu+++Q2BgIN5//33UrVu3zPWUSiWmT5+OP//8EwMGDMDbb78NQ0NDhIWFYf369Th69Ch++eUXmJqavnIslVWmmiQhIQEbN27EvHnzVMuCg4Px7rvvql7zvBER1Xzh4eEYMmQIvLy8MHPmTFhZWSEuLg5btmzBkCFDsHnzZnh7e1fa8erUqYMdO3agXr16lbbP1xUYGIjg4GDVa7lcjsePH2PNmjUYO3Ysfv/9d7i7u5d7fzt37kRERERVhFqpnq67PsvFxQUAsHz5chgZGb3WccqqczxPdnY2Zs2ahQ0bNrzWMYs9+1mo+/u3fPly/PTTTxg5ciQ++ugj6Ojo4MaNG1i3bh1Onz6N7du3Q0dHBwCwePFijB8/vkL7r8i5JaoqTEoRaQkjI6NSlbbWrVujVatW2L17d61rcZKeng6lUomuXbuiefPmz11v3bp1OHDgAJYvX46uXbuqlrdq1QoBAQF4++23sWzZMsyaNUsdYddq2vQDgYiI1OPnn3+GmZkZ1q1bp/pxDABdunRBz549sXLlSqxZs6bSjqerq1upSa7KYGFhUSomf39/eHt7o2fPnti7dy8++eQTzQRXhcqquz6rSZMm6gnmPyYmJjhz5gx+++03DB48uNL3r87vX0FBAdauXYtRo0bh448/Vi1v3bo1GjZsiHHjxuHYsWPo2bOnWuIhqirsvkekxWQyGfT09CASiUosP3ToEPr37w8fHx+0adMGs2fPRnp6OoCiFkadOnVCjx49UFBQAKCon/ioUaPQqlUrJCUlqZoeHzx4EB9++CGaNWuGwMBALFu2DEql8rnxZGZmYt68eejSpQs8PT3Rp08f7Nq1S/V+UFAQQkNDERoa+tLm4mfOnMGwYcPg5+eHli1bYurUqXjy5AmAombKxd3APv/88+c+XSwsLMSGDRvQvn37EgmpYt7e3pg8eXKJVmZlNd1etmzZc49RVpme1xw+KCgIQUFBqtedOnXC3LlzMWLECPj6+mL27NkAgDt37mD8+PEICAhA06ZN0a5dO3z77bfIy8t77vkqj/T0dHz22Wdo2bIlmjdvjgULFpT5eR47dgz9+/eHp6cn2rRpg2+//RY5OTklzkfXrl1x/Phx9O3bFx4eHujevTv27NkDoKjpeufOnQEAn332meqzerr73rPn7ezZs2jbti2mTp1aKp6ePXvis88+e62yExGRZiQlJQEoPSaNgYEBPvvssxI/mBUKBdasWYM+ffrAy8sL3t7eGDp0KM6dOwcAuHz5Mtzd3XHs2LES+7p//z7c3d1x+PDhUt2ndu/ejSZNmiAsLAxDhgyBp6cnOnTogLVr15bYR0JCAqZMmYIWLVqgefPmmD17Nn788ccS3c5v3ryJESNGwM/PDz4+Phg5ciTCwsJe+dwYGxuXWpaSkoKvvvoKHTt2hIeHB1q0aIFx48YhOjoaQNG9dM+ePYiJiSlRzvz8fHz//fcIDAyEh4cH+vbti0OHDpUrjoiICAwbNgyenp7o2rUrNm/erHpv4sSJCAwMLFVfmD17Njp37vzaYw093X2v+LP7+eef0bNnT7Ro0QK7d+9Gfn4+vvrqK7Rv3x4eHh7o0aOHqqXT8+ocLzpeixYtMH/+fFW98nle5bMoq/veo0ePMHHiRLRp0wbe3t4ICgrCpUuXVO8Xb3P48GFMnDgRPj4+aN68OWbOnIns7OznxpeVlYW8vLwyP4PAwEBMmTIFjo6OAKCqxy5fvrxEnfbYsWMYNmwYfHx8VOd2y5YtLzy3ZQ39UFa5N2/ejB49esDT0xPt2rXDl19++UrDZhAxKUWkJQRBgFwuh1wuR2FhIRITE7Fo0SIUFBRgwIABqvVWrlyJKVOmoFmzZli6dCnGjRuHI0eOICgoCHl5eTAyMsKcOXPw6NEj/PTTTwCAbdu24cyZM5gzZw6srKxU+/ryyy9hZGSEZcuW4c0338TKlSvx/ffflxlfXl4ehg0bhn379mHUqFFYuXIl/Pz8MHPmTNVxvvjiCzRp0gRNmjTBjh070LRp0zL3tXfvXowaNQo2NjZYtGgRPvvsM1y5cgVDhgxBcnIyOnTogOXLlwMAPvroI+zYsaPM/dy8eROpqano2LHjc8/r2LFjMWTIkBec+Rcrb5meZ+vWrapEWL9+/ZCQkIDhw4cjNzcX3333HdauXYuePXti8+bNr9XdTalUYvTo0Th+/DimTZuG+fPn48qVK6UqrPv378e4cePQsGFDrFixAuPHj8e+ffsQHBxcotKTmJiIr7/+Gu+++y7WrFmDunXr4tNPP8X9+/dRp06dEp9P8b+f9ux58/Lywptvvoljx46VqLCEhYXhwYMH6N+//yuXnYiINKdDhw6IjY3F0KFDsXXrVty/f191P+nRowfeeust1boLFy7EihUrMGTIEKxbtw5ff/01UlNTMWnSJOTk5MDX1xdOTk5l3ruMjY2fm5BQKpWYPHkyevXqhTVr1sDPzw8LFy7EqVOnABS1OBkxYgQuX76Mzz//HPPmzcOdO3dKdPHKysrC6NGjYW5ujqVLl+LHH39Ebm4u3n//fWRmZr7wHDxdh5PL5cjLy8Pdu3fx6aefQiqVok+fPqr1xo4dizNnzmDq1KlYv349goODcfbsWdWDq+DgYAQGBsLa2ho7duxAhw4dIAgCxo0bh+3bt+O9997DqlWr4OPjgylTpuCPP/546Wc0b948NGvWDCtXrlQ9CPvtt98AAAMHDkRcXFyJB20FBQU4fPgw3nrrrVIPRl9U7uK/lyWyfvzxR7z//vv49ttvERAQgDlz5uDEiROYMWMG1q9fj86dO2P+/PnYvXt3ueocTxOJRJg7dy6USuULW8m/6mfxrIiICPTv3x9RUVGYNWsWFi5cCJFIhBEjRpQaF/aLL76Ag4MDVq5cidGjR+P3339X1aHLYmFhgWbNmmH9+vWYMWMGjh07hpSUFACAjo4OPvzwQ3h4eACAqq48cOBA1b+PHz+OcePGoWnTpli5ciWWLVsGBwcHfPPNN7h8+XKFz+3TDh48iPnz52P48OFYv349xo0bh7179+Lbb78t9z6IirH7HpGWuHDhQpkJj48//hjOzs4AilrCrFq1CoMGDSrRh9/NzQ3Dhw/H7t27MWzYMLRq1QrDhg3DmjVr4O3tjYULF2LIkCGlKnNNmjTBwoULAQDt27dHTk4OtmzZguDgYJiYmJRYd/fu3bh37x62bdsGPz8/AEC7du0gl8uxcuVKDB06FC4uLqpxA57XtFmpVGLBggVo3bo1fvzxR9VyX19f9OrVCxs2bMD06dPRuHFjAEVdwp63r7i4OAB47nhTlaE8ZXqROnXq4NNPP4VYXPQM4PTp02jcuDGWLFmi2m/r1q1x7tw5XLhwAR9++OErxXny5Elcu3YNq1evVlWaAgICSnzmgiBg4cKFaNeunepzB4D69etj5MiROHHihGrb3NxczJkzB61atVKt07FjR5w4cQKjRo0q8fmU1TS/rPM2YMAArF27FkeOHFElWvfs2YN69erB39//lcpNRESaNWzYMCQmJmL9+vX4+uuvAQDm5uZo27YtgoKC0KxZM9W6xa2Vnm5VrK+vjwkTJuDu3bvw8fHBG2+8gfXr1yM3NxcymQxA0Q/gHj16QE9Pr8wYBEFAcHAwBg0aBADw8/PD0aNHcfz4cbRr1w779u3DgwcP8Pvvv6t+xAcEBKBLly6qfURERCAlJQVBQUGqek7Dhg2xfft2ZGVlldnqqdgff/xRKjkklUrh4eGBDRs2qO6TCQkJkMlkmDFjhuq+17JlS0RHR2P79u0Aiu6rFhYWJbqJnTlzBqdOncKPP/6IXr16ASiqg+Xm5mLhwoXo06cPpNLn/6zr37+/ahiIdu3aIT4+HitWrMDAgQPRtm1b2Nra4o8//lDd848dO4bMzMwSCcWyPK/u+v3336Nfv37P3a5bt24YOHCg6nVoaChat26N3r17q86JgYEBzM3Noaur+9I6x7McHR3x8ccf49tvv8XOnTtV34unvepn8XTLcqCoZZKOjg42bdqk+o506NABffr0wYIFC7Bz507VuoGBgarPoVWrVjhz5gyOHz9eZivyYkuXLsX06dNV3zGRSARXV1d06dIFI0eOVI2ZWhyfra2t6t8RERF48803MXPmTNX+fHx80LJlS1y4cAG+vr4VPrfFQkJC4ODggOHDh0MsFqNFixYwMDBAampqufdBVIxJKSIt0bRpU3z11VcAiipXGRkZOHnyJH788Ufk5ORgypQpuHr1KgoKCtC3b98S2/r7+8PBwQEhISEYNmwYAGDatGk4deoUxo4di3r16pXZPeqNN94o8bp79+7YtGkTrl69ivbt25d4LzQ0FA4ODqqK2tP72LVrF8LCwhAYGPjScj58+BCJiYkl+sYDRTdDHx+fCs0QU5zoeVGXQ01zdnZWxQkAbdu2Rdu2bVFYWIiHDx/i0aNHuHv3LlJSUmBmZvbKx7l48SJ0dHRKfG4GBgYIDAzEhQsXAAAPHjxAXFwcxo4dC7lcrlqvefPmMDIywpkzZ0o8BXw6CWdrawugdGWsIho0aAA/Pz/s3bsXAwYMQEFBAQ4dOoQRI0a88EksERFpt0mTJmHkyJE4deoUzp07h5CQEOzfvx8HDhzAZ599hhEjRgAAfvjhBwBF3aYiIyPx8OFD/PPPPwCKuuQDQL9+/bBs2TL8+++/6NWrF65du4bHjx9j7ty5L4zBx8dH9W9dXV1YWFio7lnnz5+Ho6OjKiEFFI2H1LFjR1W9w9XVFRYWFvjoo4/Qs2dPBAYGolWrVuUaC6pjx44YN24cBEHAo0ePsGjRItjY2GDZsmWoU6eOaj0bGxts2rQJABAbG4vIyEjcv38fly9fVpW/LOfOnYNIJEJgYGCJ+3enTp2wb98+hIeHq5ILZSlOZBXr2rUrjh07hgcPHsDFxQVvvfUWfvnlF3z55ZeQyWTYs2cPWrZsCQcHhxeW++m669OKu5Q9j5ubW4nXLVu2xPbt2xEfH4+OHTsiMDAQ48aNe+E+Xuadd97BkSNH8N1336Fdu3aqekyxV/0snhUaGoqOHTuWSFpKpVL07t0bK1asKNE979mHm7a2toiJiXnh/m1tbbF582ZERETg5MmTCAkJwYULF7By5Ur89ttv2Lp1K+rXr1/mtqNHjwZQVHd7/PgxHj58iOvXrwNAhcpYloCAAOzYsQP9+/dHt27d0KFDB/Tt25f1OXolTEoRaQlDQ0N4enqWWNa2bVvk5ORg3bp1ePfdd1XjRj3dBa+YlZVVieblBgYG6N69O9auXYuAgADV08anPV1RAoqaCQNARkZGqXXT09Ofe9znbVOWtLS0F5ahItNHF1eWXnRDT0lJgaGh4XOfrla1Z8upVCqxaNEibN26FTk5ObCzs4OXl9drx5eeng4zM7MSCTAAsLa2Vv27+Nx/9dVXZVYiExISSrx++jtTvN/XHVti4MCB+PzzzxEbG4uwsDBkZGS89EksERFpP1NTU/Tp00fVVe3WrVv45JNPsHDhQrzxxhswNzfH9evX8dVXX+H69evQ19eHi4uL6l5efH9xdHSEr68vDh48iF69emH//v1wcHB4aYtafX39Eq/FYrFqn6mpqbC0tCy1zdP3aENDQ2zduhWrVq3CoUOHsH37dshkMrzxxhuYOXPmC+/TZmZmqjqcl5cXmjZtigEDBmDMmDH47bffSmy7b98+LFq0CE+ePIGZmRkaNWpUKvZnpaWlQRAE+Pr6lvl+QkLCC5NST9cFAKjORXG9csCAAfjpp5/w119/oXXr1jhz5ky5ZmMrq+5aHs/WjWbOnAlbW1vs27dPVT/x8fHB7NmzX3mg9OJufG+88QZmzZqFdevWlVrnVT6LZ72ofiwIQokhC56tiz/9HX0ZFxcXuLi4YNSoUSgsLMTu3bvx9ddfY9GiRVi6dGmZ26SkpOCLL77AsWPHIBKJ4OTkpHq4/Lr1uV69ekGpVGLbtm1Yvnw5lixZAgcHB0ydOlXV4o2ovDimFJGWa9y4MeRyOaKjo1VNdIsHFX1aYmIizM3NVa8jIiLwyy+/oHHjxvjtt99w8eLFUtsUJymKJScnA0CZFTdTU9PnHhdAiWO/SHFroPKU4WUaN24MKysrnDx58rnrfPnll2jXrl2JQcQVCkWJdSra+qf4KdCzLbReNFhlsTVr1mDjxo2YOXMmLl68iOPHj2Pp0qWqhOCrMjc3R2pqaqmyPf0ZF3fJ/OSTT7Br165Sfy9qPl5ZevToAQMDAxw5cgSHDx9Gq1atYG9vX+XHJSKiyhcfH4+2bduW6KJUrEmTJpg8eTIKCgoQFRWlGrPJwMAABw4cwJUrV/D777+XGDezWL9+/XDy5ElkZmbi8OHDr90Cw8bGRlXHedqzyxo2bIgFCxbg/Pnz2L59O958803s2LEDv/zyS4WO5+zsjEmTJuHOnTtYsWKFavnFixcxY8YMdO3aFSdOnEBISAh++eWXlw4PYGxsDAMDgzLv3bt27SrRSqwsxcmnYsV1sOL6nqOjI1q0aIHDhw/jyJEjkMlk6NatW4XK/Dp0dXXx0Ucf4fDhw/j3338xe/ZsREVFvXa9pF69epgyZQpOnTpVYmIe4NU/i2dVVv24LL/88gsCAgKQm5tbYrmOjg6GDBmCwMBAREREPHf7adOm4dq1a/j5559x9epVHD58uERXvucRiUTlqiv36dMH27ZtQ0hICBYvXgwzMzNMnz4d8fHx5SwhUREmpYi03JUrVyCRSODo6IhmzZpBV1cX+/fvL7HOxYsXERsbq3qCJpfLMWPGDDg4OODXX3+Fh4cHPvvss1I3lOIm88WKKyJPj/9QrHnz5oiJiSkxmwhQ9JRJR0cHXl5eAFCqpc6zGjRoAGtr61JliIqKwtWrV5/7FLAsYrEYI0eOxPHjx/H333+Xev/ChQv4559/0L17d9WTLyMjI9VYVMUuX7780uM8rXispKdndUlPT8f9+/dfGvOlS5fg4uKCgQMHqpp6x8fH4969e6/VDbFVq1aQy+UlZiwqKCjAmTNnVK8bNmwIS0tLREdHw9PTU/Vna2uLH374oUKt1CQSyUvXKeu7YGBggF69euHAgQM4deoUW0kREVVjVlZWkEql2LZtG/Lz80u9/+DBA+jp6cHJyQkPHjxAWloa3n33Xbi6uqruEcUPlp6+BxbP2LdkyRIkJiaWGm6golq0aIGoqCjcvn1btSw/P7/EQ60///wTAQEBSExMhEQigY+PD7788kuYmJiUqjeUR1BQENzc3LBhwwY8fPgQQFGdTqlUYuLEiaruZAqFAmfPngXw/+fg2ftnixYtkJOTA0EQSty/w8PDsWLFihJd+spSPOB7sYMHD8LOzg5OTk6qZQMHDsTZs2exb98+9OzZs8wW9lUhLy8P3bt3Vw06b29vj+HDh6N3796q816eOsfzvPvuu/D398d3331XYvmrfhbPat68Of79998SvRUUCgUOHjwIT09P6OrqvnLsLi4uSE1NLTFb4tPHiIqKKtEV8tlYL126hO7duyMgIEAVx7P/v5V1bg0NDZGamlri/+ln68qTJ0/G+PHjARQlTXv27Ing4GAoFIpSLe+JXobd94i0RFZWFq5evap6XVhYiL///hv79+/HkCFDVC1pPvjgA9Wgip07d0Z0dDSWLFkCFxcX1Qxmq1evxs2bN7FlyxbIZDJ88803GDBgABYuXKiaUQQoqoBZWVkhMDAQoaGh2Lp1K6ZMmQIDA4NS8fXv3x/btm3D+PHjMXHiRDg6OuKff/7B77//jvHjx6ta4ZiYmODKlSs4d+4cmjRpomrdVUwsFuPjjz/GZ599hilTpuDNN99Eamoqli9fDlNTU7z33nsVOm8jR47EhQsXMHHiRAwaNAgdOnSAWCzGxYsXsXnzZri6uqoGlQSKBp88ePAgvLy80KBBA+zZsweRkZEvPMazZXJ3d4ednR2WL18OY2NjiMVirFmzplwVOC8vL6xcuVI1CH1kZCRWr16NgoKCUk/CKqJVq1Zo27YtZs2aheTkZDg4OGDTpk1ISUlRPQmVSCSYMmUKZs+eDYlEgo4dOyIjIwMrV65EfHx8hWYWLE6onTt3Ds7OzmUmMp/3XRg4cCCGDBkCIyMjtT6JJSKiyiWRSPDll19i3LhxGDBgAIYPHw5nZ2fk5ub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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Set style\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Create subplots\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
    "\n",
    "# Histogram of Savings Rate\n",
    "sns.histplot(data['saving_rate'], bins=30, kde=True, ax=axes[0, 0])\n",
    "axes[0, 0].set_title(\"Distribution of Savings Rate\")\n",
    "axes[0, 0].set_xlabel(\"Savings Rate\")\n",
    "axes[0, 0].set_ylabel(\"Frequency\")\n",
    "\n",
    "# Histogram of Income\n",
    "sns.histplot(data['income'], bins=30, kde=True, ax=axes[0, 1])\n",
    "axes[0, 1].set_title(\"Distribution of Income\")\n",
    "axes[0, 1].set_xlabel(\"Income (10,000 AUD)\")\n",
    "axes[0, 1].set_ylabel(\"Frequency\")\n",
    "\n",
    "# Boxplot of Cultural Identity\n",
    "sns.boxplot(x=data['culture_identity'], ax=axes[1, 0])\n",
    "axes[1, 0].set_title(\"Boxplot of Cultural Identity\")\n",
    "axes[1, 0].set_xlabel(\"Cultural Identity Score\")\n",
    "\n",
    "# Boxplot of Savings Rate by First Nation Status\n",
    "sns.boxplot(x=data['first_nation'], y=data['saving_rate'], ax=axes[1, 1])\n",
    "axes[1, 1].set_xticklabels([\"Non First Nation\", \"First Nation\"])\n",
    "axes[1, 1].set_title(\"Savings Rate by First Nation Status\")\n",
    "axes[1, 1].set_xlabel(\"First Nation Status\")\n",
    "axes[1, 1].set_ylabel(\"Savings Rate\")\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "326654f2-82ff-45f5-84c9-4e6c686ed173",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "026a2de8-06cb-455a-a17a-f2cb5f37b852",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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