{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1b5edbf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "from statsmodels.graphics.tsaplots import plot_pacf,plot_acf\n",
    "\n",
    "import pandas as pd  # 导入pandas库，用于数据处理和分析\n",
    "import numpy as np  # 导入numpy库，用于数值计算\n",
    "import xgboost as xgb  # 导入XGBoost库，用于梯度提升决策树模型\n",
    "from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier  # 导入决策树回归和分类模型\n",
    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor, RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier  # 导入随机森林、AdaBoost和梯度提升模型\n",
    "from sklearn import preprocessing #数据预处理：归一化\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error, r2_score  # 导入评估指标，包括平均绝对误差和R^2分数\n",
    "\n",
    "import matplotlib.pyplot as plt  # 导入matplotlib库，用于绘图\n",
    "import warnings  # 导入警告处理库\n",
    "warnings.filterwarnings('ignore')  # 忽略警告信息\n",
    "from math import sqrt  # 导入数学库中的平方根函数\n",
    "\n",
    "def mape(actual, pred): \n",
    "    actual, pred = np.array(actual), np.array(pred)  # 将输入的实际值和预测值转换为NumPy数组\n",
    "    tt1 = (actual - pred) / actual  # 计算相对误差\n",
    "    tt2 = np.isfinite(tt1)  # 检查相对误差中是否有无穷大值\n",
    "    tt3 = tt1[tt2]  # 去除无穷大值\n",
    "    return np.mean(np.abs(tt3)) * 100  # 计算平均绝对百分比误差（MAPE）并返回"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e9b7cf",
   "metadata": {},
   "source": [
    "# 数据导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dab898da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    人口出生率(‰)    出生人口（万人）  人口死亡率(‰)   死亡人口（万人）  人口自然增长率(‰)    增长人口（万人）  城镇化率(%)\n",
      "0      38.00  2756.44400      9.50  689.11100       28.50  2067.33300    17.98\n",
      "1      35.21  2624.62382      8.87  661.18754       26.34  1963.43628    17.86\n",
      "2      34.12  2605.67616      8.47  646.83696       25.65  1958.83920    17.76\n",
      "3      35.75  2807.59050      8.25  647.90550       27.50  2159.68500    17.63\n",
      "4      34.25  2762.98175      8.06  650.20826       26.19  2112.77349    17.86\n",
      "5      33.59  2787.70128      7.64  634.05888       25.95  2153.64240    17.99\n",
      "6      30.74  2619.93946      7.34  625.58086       23.40  1994.35860    17.54\n",
      "7      29.92  2608.33584      7.65  666.90405       22.27  1941.43179    17.55\n",
      "8      28.07  2504.15277      7.08  631.61388       20.99  1872.53889    17.63\n",
      "9      24.95  2266.93205      7.38  670.53942       17.57  1596.39263    17.54\n",
      "10     23.13  2137.67460      7.36  680.21120       15.77  1457.46340    17.88\n",
      "11     20.01  1875.27717      7.29  683.19693       12.72  1192.08024    17.69\n",
      "12     19.03  1807.35522      6.91  656.27034       12.12  1151.08488    17.92\n",
      "13     18.25  1756.72675      6.25  601.61875       12.00  1155.10800    19.99\n",
      "14     17.82  1738.19844      6.21  605.73582       11.61  1132.46262    19.39\n",
      "15     18.21  1797.41805      6.34  625.78970       11.87  1171.62835    20.16\n",
      "16     20.91  2092.50552      6.36  636.45792       14.55  1456.04760    21.03\n",
      "17     22.28  2264.85112      6.60  670.91640       15.68  1593.93472    21.62\n",
      "18     20.19  2079.73152      6.90  710.75520       13.29  1368.97632    23.01\n",
      "19     19.90  2076.70430      6.82  711.71474       13.08  1364.98956    23.71\n",
      "20     21.04  2227.10504      6.78  717.66978       14.26  1509.43526    24.52\n",
      "21     22.43  2411.38201      6.86  737.49802       15.57  1673.88399    25.56\n",
      "22     23.33  2549.96900      6.72  734.49600       16.61  1815.47300    25.76\n",
      "23     22.37  2483.65162      6.64  737.21264       15.73  1746.43898    25.81\n",
      "24     21.58  2432.15232      6.54  737.08416       15.04  1695.06816    26.21\n",
      "25     21.06  2407.85298      6.67  762.60111       14.39  1645.25187    26.41\n",
      "26     19.68  2279.39664      6.70  776.01410       12.98  1503.38254    23.54\n",
      "27     18.24  2137.19904      6.64  778.01544       11.60  1359.18360    27.63\n",
      "28     18.09  2143.97253      6.64  786.95288       11.45  1357.01965    28.14\n",
      "29     17.70  2121.34500      6.49  777.82650       11.21  1343.51850    28.26\n",
      "30     17.12  2073.59152      6.57  795.76497       10.55  1277.82655    29.04\n",
      "31     16.98  2078.16522      6.56  802.87184       10.42  1275.29338    29.37\n",
      "32     16.57  2048.48282      6.51  804.80526       10.06  1243.67756    29.92\n",
      "33     15.64  1951.26204      6.50  810.94650        9.14  1140.31554    30.04\n",
      "34     14.64  1841.50704      6.46  812.57756        8.18  1028.92948    30.89\n",
      "35     14.03  1778.20429      6.45  817.49235        7.58   960.71194    36.22\n",
      "36     13.38  1707.64926      6.43  820.64161        6.95   887.00765    37.66\n",
      "37     12.86  1651.90558      6.41  823.38373        6.45   828.52185    39.09\n",
      "38     12.41  1603.70707      6.40  827.05280        6.01   776.65427    40.53\n",
      "39     12.29  1597.55252      6.42  834.52296        5.87   763.02956    41.76\n",
      "40     12.40  1621.37440      6.51  851.22156        5.89   770.15284    42.99\n",
      "0      72538\n",
      "1      74542\n",
      "2      76368\n",
      "3      78534\n",
      "4      80671\n",
      "5      82992\n",
      "6      85229\n",
      "7      87177\n",
      "8      89211\n",
      "9      90859\n",
      "10     92420\n",
      "11     93717\n",
      "12     94974\n",
      "13     96259\n",
      "14     97542\n",
      "15     98705\n",
      "16    100072\n",
      "17    101654\n",
      "18    103008\n",
      "19    104357\n",
      "20    105851\n",
      "21    107507\n",
      "22    109300\n",
      "23    111026\n",
      "24    112704\n",
      "25    114333\n",
      "26    115823\n",
      "27    117171\n",
      "28    118517\n",
      "29    119850\n",
      "30    121121\n",
      "31    122389\n",
      "32    123626\n",
      "33    124761\n",
      "34    125786\n",
      "35    126743\n",
      "36    127627\n",
      "37    128453\n",
      "38    129227\n",
      "39    129988\n",
      "40    130756\n",
      "Name: 年末总人口(万人), dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_excel('data2.xlsx')\n",
    "X =df[df.columns[1:8]]\n",
    "Y =df[df.columns[9]]\n",
    "print(X)\n",
    "print(Y)\n",
    "\n",
    "#归一化\n",
    "X = (X - X.min()) / (X.max() - X.min())\n",
    "Y = (Y - Y.min()) / (Y.max() - Y.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "397a75f3",
   "metadata": {},
   "source": [
    "# 预训练GBDT模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "658ad7e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GBDT模型训练误差：\n",
      "   平均绝对百分比误差: 0.0033941166623282005\n",
      "   r2_score 0.9999999989954711\n"
     ]
    }
   ],
   "source": [
    "# 构建GBDT回归模型\n",
    "gbdt_model = GradientBoostingRegressor(n_estimators=100, max_depth=4, learning_rate=0.1, random_state=42)\n",
    "gbdt_model.fit(X, Y)\n",
    "gbdt_y_pred = gbdt_model.predict(X)\n",
    "# 输出GBDT模型评价结果\n",
    "print('GBDT模型训练误差：')\n",
    "print(\"   平均绝对百分比误差:\",mape(Y, gbdt_y_pred))\n",
    "print(\"   r2_score\",r2_score(Y,gbdt_y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e1a13a",
   "metadata": {},
   "source": [
    "# 建模前的准备工作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "71c7b487",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of ADF Test:\n",
      "Test Statistic                 -1.028023\n",
      "p-value                         0.742907\n",
      "#Lags Used                     10.000000\n",
      "Number of Observations Used    30.000000\n",
      "Critical Value (1%)            -3.669920\n",
      "Critical Value (5%)            -2.964071\n",
      "Critical Value (10%)           -2.621171\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:               人口出生率(‰)   No. Observations:                   41\n",
      "Model:                 ARIMA(1, 2, 1)   Log Likelihood                 -62.589\n",
      "Date:                Sun, 21 Jan 2024   AIC                            131.178\n",
      "Time:                        22:26:57   BIC                            136.169\n",
      "Sample:                             0   HQIC                           132.969\n",
      "                                 - 41                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.4101      0.199      2.063      0.039       0.020       0.800\n",
      "ma.L1         -0.9444      0.168     -5.631      0.000      -1.273      -0.616\n",
      "sigma2         1.3986      0.333      4.196      0.000       0.745       2.052\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 0.44\n",
      "Prob(Q):                              0.98   Prob(JB):                         0.80\n",
      "Heteroskedasticity (H):               0.06   Skew:                             0.18\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         3.38\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#建模前的准备工作：\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.tsa.stattools import adfuller, acf, pacf\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "#人口出生率(‰)\n",
    "data=df[df.columns[1]]\n",
    "ts = pd.Series(data)\n",
    "\n",
    "# ADF 检验\n",
    "def adf_test(timeseries):\n",
    "    print('Results of ADF Test:')\n",
    "    dftest = adfuller(timeseries, autolag='AIC')\n",
    "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "    for key,value in dftest[4].items():\n",
    "        dfoutput['Critical Value (%s)'%key] = value\n",
    "    print(dfoutput)\n",
    "\n",
    "# 差分\n",
    "def difference(timeseries):\n",
    "    differenced = timeseries.diff().dropna()\n",
    "    return differenced\n",
    "\n",
    "# ACF 和 PACF\n",
    "def plot_acf_pacf(timeseries):\n",
    "    lag_acf = acf(timeseries, nlags=10)\n",
    "    lag_pacf = pacf(timeseries, nlags=10, method='ols')\n",
    "\n",
    "    # 绘制 ACF:\n",
    "    plt.subplot(121)\n",
    "    plt.plot(lag_acf)\n",
    "    plt.axhline(y=0,linestyle='--',color='gray')\n",
    "    plt.axhline(y=-1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray')\n",
    "    plt.axhline(y=1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray')\n",
    "    plt.title('Autocorrelation Function')\n",
    "\n",
    "    # 绘制 PACF:\n",
    "    plt.subplot(122)\n",
    "    plt.plot(lag_pacf)\n",
    "    plt.axhline(y=0,linestyle='--',color='gray')\n",
    "    plt.axhline(y=-1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray')\n",
    "    plt.axhline(y=1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray')\n",
    "    plt.title('Partial Autocorrelation Function')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 执行ADF检验\n",
    "adf_test(ts)\n",
    "\n",
    "# 发现数据不平滑，则进行差分处理\n",
    "diff_ts = difference(ts)\n",
    "\n",
    "# 绘制ACF和PACF\n",
    "plot_acf_pacf(diff_ts)\n",
    "\n",
    "# 模型拟合定参\n",
    "model = ARIMA(ts, order=(1, 2, 1))\n",
    "results = model.fit()\n",
    "print(results.summary())\n",
    "\n",
    "# 残差检查\n",
    "residuals = pd.DataFrame(results.resid)\n",
    "fig, ax = plt.subplots(1,2)\n",
    "residuals.plot(title=\"Residuals\", ax=ax[1])\n",
    "residuals.plot( kind='kde',title='Density', ax=ax[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22e01d59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of ADF Test:\n",
      "Test Statistic                 -1.440383\n",
      "p-value                         0.562770\n",
      "#Lags Used                      1.000000\n",
      "Number of Observations Used    39.000000\n",
      "Critical Value (1%)            -3.610400\n",
      "Critical Value (5%)            -2.939109\n",
      "Critical Value (10%)           -2.608063\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:               出生人口（万人）   No. Observations:                   41\n",
      "Model:                 ARIMA(1, 2, 1)   Log Likelihood                -239.020\n",
      "Date:                Sun, 21 Jan 2024   AIC                            484.040\n",
      "Time:                        22:26:57   BIC                            489.031\n",
      "Sample:                             0   HQIC                           485.831\n",
      "                                 - 41                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.4167      0.195      2.132      0.033       0.034       0.800\n",
      "ma.L1         -0.9987      7.554     -0.132      0.895     -15.804      13.807\n",
      "sigma2      1.156e+04   8.68e+04      0.133      0.894   -1.59e+05    1.82e+05\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.06   Jarque-Bera (JB):                 0.35\n",
      "Prob(Q):                              0.81   Prob(JB):                         0.84\n",
      "Heteroskedasticity (H):               0.12   Skew:                             0.14\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         3.37\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#出生人口（万人）\n",
    "data=df[df.columns[2]]\n",
    "ts = pd.Series(data)\n",
    "\n",
    "# ADF 检验\n",
    "def adf_test(timeseries):\n",
    "    print('Results of ADF Test:')\n",
    "    dftest = adfuller(timeseries, autolag='AIC')\n",
    "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "    for key,value in dftest[4].items():\n",
    "        dfoutput['Critical Value (%s)'%key] = value\n",
    "    print(dfoutput)\n",
    "\n",
    "# 差分\n",
    "def difference(timeseries):\n",
    "    differenced = timeseries.diff().dropna()\n",
    "    return differenced\n",
    "\n",
    "# ACF 和 PACF\n",
    "def plot_acf_pacf(timeseries):\n",
    "    lag_acf = acf(timeseries, nlags=10)\n",
    "    lag_pacf = pacf(timeseries, nlags=10, method='ols')\n",
    "\n",
    "    # 绘制 ACF:\n",
    "    plt.subplot(121)\n",
    "    plt.plot(lag_acf)\n",
    "    plt.axhline(y=0,linestyle='--',color='gray')\n",
    "    plt.axhline(y=-1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray')\n",
    "    plt.axhline(y=1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray')\n",
    "    plt.title('Autocorrelation Function')\n",
    "\n",
    "    # 绘制 PACF:\n",
    "    plt.subplot(122)\n",
    "    plt.plot(lag_pacf)\n",
    "    plt.axhline(y=0,linestyle='--',color='gray')\n",
    "    plt.axhline(y=-1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray')\n",
    "    plt.axhline(y=1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray')\n",
    "    plt.title('Partial Autocorrelation Function')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 执行ADF检验\n",
    "adf_test(ts)\n",
    "\n",
    "# 发现数据不平滑，则进行差分处理\n",
    "diff_ts = difference(ts)\n",
    "\n",
    "# 绘制ACF和PACF\n",
    "plot_acf_pacf(diff_ts)\n",
    "\n",
    "# 模型拟合定参\n",
    "model = ARIMA(ts, order=(1, 2, 1))\n",
    "results = model.fit()\n",
    "print(results.summary())\n",
    "\n",
    "# 残差检查\n",
    "residuals = pd.DataFrame(results.resid)\n",
    "fig, ax = plt.subplots(1,2)\n",
    "residuals.plot(title=\"Residuals\", ax=ax[1])\n",
    "residuals.plot( kind='kde',title='Density', ax=ax[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "405ce6ca",
   "metadata": {},
   "source": [
    "其他数据的预处理操作类似，不再重复提供代码"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88b299e5",
   "metadata": {},
   "source": [
    "# 建立组合模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e48e5013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16    131527.751976\n",
      "17    132289.454506\n",
      "18    133034.553047\n",
      "19    133760.476130\n",
      "20    134466.401829\n",
      "21    135152.351948\n",
      "22    135818.697747\n",
      "23    136465.994585\n",
      "24    137094.840867\n",
      "25    137705.876650\n",
      "26    138299.719210\n",
      "27    138876.997346\n",
      "28    139438.303624\n",
      "29    139984.236417\n",
      "30    140515.355764\n",
      "31    141032.227062\n",
      "32    141535.377704\n",
      "33    142025.341086\n",
      "Name: predicted_mean, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "data = {\n",
    "    \"年份\": np.arange(1990, 2006),\n",
    "    \"死亡人口（万人）\": [762.60, 776.01, 778.02, 786.95, 777.83, 795.76, 802.87, 804.81, 810.95, 812.58, 817.49, 820.64, 823.38, 827.05, 834.52, 851.22],\n",
    "    \"年末总人口(万人)\": [114333, 115823, 117171, 118517, 119850, 121121, 122389, 123626, 124761, 125786, 126743, 127627, 128453, 129227, 129988, 130756],\n",
    "    \"人口自然增长率(‰)\":[26.41, 26.53, 27.63, 28.14, 28.62, 29.04, 29.37, 29.97, 30.40, 30.89, 36.22, 37.66, 39.09, 40.53, 41.67, 42.99],\n",
    "    \"出生人口（万人）\": [2407.85, 2279.40, 2137.20, 2143.97, 2121.35, 2073.59, 2078.17, 2048.48, 1951.26, 1841.51, 1778.20, 1707.65, 1651.91, 1603.71, 1597.55, 1621.37],\n",
    "    \"城镇化率(%)\": [26.41, 26.53, 27.63, 28.14, 28.62, 29.04, 29.37, 29.97, 30.40, 30.89, 36.22, 37.66, 39.09, 40.53, 41.67, 42.99],\n",
    "    \"老龄化率(%)\":[6.1, 6.2,6.3, 6.5, 6.6,6.6,7.1, 7.3, 7.5, 7.7, 7.8, 8.0, 8.2, 8.4,8.9, 9.1]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "df.set_index(\"年份\", inplace=True)\n",
    "\n",
    "# 为每个外生变量建立时间序列模型\n",
    "# 出生人口模型\n",
    "model_birth = ARIMA(df[\"出生人口（万人）\"], order=(1, 3, 1))\n",
    "model_birth_fit = model_birth.fit()\n",
    "future_births = model_birth_fit.forecast(steps=18)\n",
    "\n",
    "# 死亡人口模型\n",
    "model_death = ARIMA(df[\"死亡人口（万人）\"], order=(1, 3, 2))\n",
    "model_death_fit = model_death.fit()\n",
    "future_deaths = model_death_fit.forecast(steps=18)\n",
    "\n",
    "# 城镇化率模型\n",
    "model_urbanization = ARIMA(df[\"城镇化率(%)\"], order=(1, 2, 1))\n",
    "model_urbanization_fit = model_urbanization.fit()\n",
    "future_urbanization = model_urbanization_fit.forecast(steps=18)\n",
    "\n",
    "#人口增长率\n",
    "model_gr = ARIMA(df[\"人口自然增长率(‰)\"], order=(1, 3, 2))\n",
    "model_gr_fit = model_gr.fit()\n",
    "growth_rate = model_gr_fit.forecast(steps=18)\n",
    "\n",
    "#老龄化率\n",
    "model_old = ARIMA(df[\"老龄化率(%)\"], order=(1, 3, 2))\n",
    "model_old_fit = model_old.fit()\n",
    "old_rate = model_old_fit.forecast(steps=18)\n",
    "\n",
    "future_years_corrected = np.arange(2006, 2024)\n",
    "# 预测年末总人口\n",
    "future_data = pd.DataFrame({\n",
    "    \"出生人口（万人）\": future_births.values,\n",
    "    \"死亡人口（万人）\": future_deaths.values,\n",
    "    \"城镇化率(%)\": future_urbanization.values\n",
    "}, index=future_years_corrected)\n",
    "\n",
    "# 进行总人口预测\n",
    "gbdt_model = GradientBoostingRegressor(n_estimators=100, max_depth=4, learning_rate=0.1, random_state=42)#重新调用GBDT\n",
    "gbdt_model.fit(X, Y)\n",
    "gbdt_y_pred = gbdt_model.predict(X)\n",
    "model = sm.tsa.ARIMA(df[\"年末总人口(万人)\"], order=(2, 2, 2), exog=df[[\"出生人口（万人）\", \"死亡人口（万人）\", \"城镇化率(%)\"]])\n",
    "model_fit = model.fit()\n",
    "forecast_total_population = model_fit.forecast(steps=18, exog=future_data)\n",
    "\n",
    "# 输出预测结果\n",
    "print(forecast_total_population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "932270a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中短期预测平均百分比误差: -0.15727673720921162 %\n"
     ]
    }
   ],
   "source": [
    "#模型评估\n",
    "#2006~2015实际人口\n",
    "Y_actual1=[131448,132129,132802,133450,134910,134735,135404,136072,136782,137462,]\n",
    "Y_pre1=[131527.751976,132289.454506,133034.553047,133760.476130,134466.401829,135152.351948,135818.697747,136465.994585,137094.840867,137705.876650]\n",
    "Y_actual1=np.array(Y_actual1)\n",
    "Y_pre1=np.array(Y_pre1)\n",
    "\n",
    "# 计算误差\n",
    "errors1 = Y_actual1 - Y_pre1\n",
    "# 计算平均百分比误差（Mean Percentage Error, MPE）\n",
    "mpe1 = np.mean((errors1 / Y_actual1) * 100)\n",
    "\n",
    "print(\"中短期预测平均百分比误差:\",mpe1,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc2abaee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "长期预测平均百分比误差: -0.09766960732055358 %\n"
     ]
    }
   ],
   "source": [
    "#模型评估\n",
    "#2006~2023实际人口\n",
    "Y_actual=[131448,132129,132802,133450,134910,134735,135404,136072,136782,137462,138271,139008,139538,140005,141212,141260,141175,140976,]\n",
    "Y_pre=np.array(forecast_total_population)\n",
    "\n",
    "# 计算误差\n",
    "errors = Y_actual - Y_pre\n",
    "# 计算平均百分比误差（Mean Percentage Error, MPE）\n",
    "mpe = np.mean((errors / Y_actual) * 100)\n",
    "\n",
    "print(\"长期预测平均百分比误差:\",mpe,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9315c8e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2006    131524.725758\n",
      "2007    132293.672665\n",
      "2008    133062.686960\n",
      "2009    133831.721788\n",
      "2010    134600.762873\n",
      "2011    135369.805865\n",
      "2012    136138.849438\n",
      "2013    136907.893188\n",
      "2014    137676.936992\n",
      "2015    138445.980813\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#对比试验:单一模型中短期预测\n",
    "population_data = [\n",
    "    114333, 115823, 117171, 118517, 119850, 121121, 122389, 123626,\n",
    "    124761, 125786, 126743, 127627, 128453, 129227, 129988, 130756\n",
    "]\n",
    "\n",
    "# 将数据转换为Pandas Series（假设每个数据点代表一个年份）\n",
    "population_series = pd.Series(population_data, index=pd.date_range(start='1990', periods=len(population_data), freq='A'))\n",
    "\n",
    "# ARIMA模型拟合\n",
    "model = ARIMA(population_series, order=(1, 2, 1))\n",
    "model_fit = model.fit()\n",
    "\n",
    "# 预测未来17年的人口数量\n",
    "forecast = model_fit.forecast(steps=10)\n",
    "\n",
    "forecast_values = forecast.values\n",
    "forecast_years = list(range(population_series.index[-1].year+1 , population_series.index[-1].year+11 ))\n",
    "forecast_series2 = pd.Series(forecast_values, index=forecast_years)\n",
    "\n",
    "print(forecast_series2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ec573ccc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中短期预测平均百分比误差： -0.40437626810177363 %\n"
     ]
    }
   ],
   "source": [
    "#对照组误差分析\n",
    "# 真实人口数据\n",
    "real_population = np.array([\n",
    "    131448, 132129, 132802, 133450, 134091, 134735, 135404,136072, 136782, 137462, \n",
    "])\n",
    "\n",
    "# 预测人口数据\n",
    "predicted_population = forecast.values\n",
    "\n",
    "# 计算平均百分比误差 (Mean Percentage Error, MPE)\n",
    "errors = real_population - predicted_population\n",
    "percentage_errors = errors / real_population\n",
    "mean_percentage_error = np.mean(percentage_errors) * 100\n",
    "\n",
    "print(\"中短期预测平均百分比误差：\",mean_percentage_error,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "da9261f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2006    131524.725758\n",
      "2007    132293.672665\n",
      "2008    133062.686960\n",
      "2009    133831.721788\n",
      "2010    134600.762873\n",
      "2011    135369.805865\n",
      "2012    136138.849438\n",
      "2013    136907.893188\n",
      "2014    137676.936992\n",
      "2015    138445.980813\n",
      "2016    139215.024638\n",
      "2017    139984.068465\n",
      "2018    140753.112292\n",
      "2019    141522.156120\n",
      "2020    142291.199947\n",
      "2021    143060.243775\n",
      "2022    143829.287602\n",
      "2023    144598.331430\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#对比试验:单一模型长期预测\n",
    "population_data = [\n",
    "    114333, 115823, 117171, 118517, 119850, 121121, 122389, 123626,\n",
    "    124761, 125786, 126743, 127627, 128453, 129227, 129988, 130756\n",
    "]\n",
    "\n",
    "# 将数据转换为Pandas Series（假设每个数据点代表一个年份）\n",
    "population_series = pd.Series(population_data, index=pd.date_range(start='1990', periods=len(population_data), freq='A'))\n",
    "\n",
    "# ARIMA模型拟合\n",
    "model = ARIMA(population_series, order=(1, 2, 1))\n",
    "model_fit = model.fit()\n",
    "\n",
    "# 预测未来18年的人口数量\n",
    "forecast = model_fit.forecast(steps=18)\n",
    "\n",
    "forecast_values = forecast.values\n",
    "forecast_years = list(range(population_series.index[-1].year+1 , population_series.index[-1].year+19 ))\n",
    "forecast_series3 = pd.Series(forecast_values, index=forecast_years)\n",
    "\n",
    "print(forecast_series3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ee30a811",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "data2 = {\n",
    "    \"年份\": np.arange(1965, 2006),\n",
    "    \"出生人口（万人）\":[2756.444,2624.62382,2605.67616,2807.5905,2762.98175,2787.70128,2619.93946,2608.33584,2504.15277,2266.93205,2137.6746,1875.27717,1807.35522,1756.72675,1738.19844,1797.41805,2092.50552,2264.85112,2079.73152,2076.7043,2227.10504,2411.38201,2549.969,2483.65162,2432.15232,2407.85298,2279.39664,2137.19904,2143.97253,2121.345,2073.59152,2078.16522,2048.48282,1951.26204,1841.50704,1778.20429,1707.64926,1651.90558,1603.70707,1597.55252,1621.3744,],\n",
    "    \"死亡人口（万人）\": [689.111,661.18754,646.83696,647.9055,650.20826,634.05888,625.58086,666.90405,631.61388,670.53942,680.2112,683.19693,656.27034,601.61875,605.73582,625.7897,636.45792,670.9164,710.7552,711.71474,717.66978,737.49802,734.496,737.21264,737.08416,762.60111,776.0141,778.01544,786.95288,777.8265,795.76497,802.87184,804.80526,810.9465,812.57756,817.49235,820.64161,823.38373,827.0528,834.52296,851.22156,],\n",
    "    \"人口死亡率(‰)\":[9.5, 8.87, 8.47, 8.25, 8.06, 7.64, 7.34, 7.65, 7.08, 7.38,7.36,7.29, 6.91, 6.25, 6.21, 6.34, 6.36, 6.6, 6.9, 6.82, 6.78, 6.86, 6.72, 6.64, 6.54, 6.67, 6.7, 6.64, 6.64, 6.49, 6.57 ,6.56, 6.51, 6.5, 6.46, 6.45,6.43,6.41,6.4,6.42,6.51,],\n",
    "    \"人口出生率(‰)\":[38,35.21,34.12,35.75,34.25,33.59,30.74,29.92,28.07,24.95,23.13,20.01,19.03,18.25,17.82,18.21,20.91,22.28,20.19,19.9,21.04,22.43,23.33,22.37,21.58,21.06,19.68,18.24,18.09,17.7,17.12,16.98,16.57,15.64,14.64,14.03,13.38,12.86,12.41,12.29,12.4,],\n",
    "    \"年末总人口(万人)\": [72538,74542,76368,78534,80671,82992,85229,87177,89211,90859,92420,93717,94974,96259,97542,98705,100072,101654,103008,104357,105851,107507,109300,111026,112704,114333,115823,117171,118517,119850,121121,122389,123626,124761,125786,126743,127627,128453,129227,129988,130756,],\n",
    "    \"城镇化率(%)\": [17.98,17.86,17.76,17.63,17.86,17.99,17.54,17.55,17.63,17.54,17.88,17.69,17.92,19.99,19.39,20.16,21.03,21.62,23.01,23.71,24.52,25.56,25.76,25.81,26.21,26.41,23.54,27.63,28.14,28.26,29.04,29.37,29.92,30.04,30.89,36.22,37.66,39.09,40.53,41.76,42.99,],\n",
    "    \"老龄化率(%)\":[3.58,3.63,3.62,3.71,3.75,3.79,3.84,3.95,4.06,4.15,4.25,4.36,4.49,4.57,4.69,4.77,4.86,4.91,5.02,5.13,5.19,5.22,5.26,5.39,5.4,5.57,5.64,5.78,5.89,6.01,6.24,6.35,6.45,6.68,6.79,6.96,7.14,7.32,7.56,7.64,7.86,],\n",
    "    \"人口自然增长率(‰)\":[28.5,26.34,25.65,27.5,26.19,25.95,23.4,22.27,20.99,17.57,15.77,12.72,12.12,12,11.61,11.87,14.55,15.68,13.29,13.06,14.26,15.57,16.61,15.73,15.04,14.39,12.98,11.6,11.45,11.21,10.55,10.42,10.06,9.14,8.18,7.58,6.95,6.45,6.01,5.87,5.89,],\n",
    "}\n",
    "\n",
    "df2 = pd.DataFrame(data2)\n",
    "df2.set_index(\"年份\", inplace=True)\n",
    "\n",
    "# 为每个外生变量建立时间序列模型\n",
    "# 出生人口\n",
    "model_birth = ARIMA(df2[\"出生人口（万人）\"], order=(1, 1, 1))\n",
    "model_birth_fit = model_birth.fit()\n",
    "future_births = model_birth_fit.forecast(steps=17)\n",
    "\n",
    "#出生率\n",
    "model_birth1 = ARIMA(df2[\"人口出生率(‰)\"], order=(1, 3, 1))\n",
    "model_birth_fit1 = model_birth1.fit()\n",
    "births_rate = model_birth_fit1.forecast(steps=17)\n",
    "\n",
    "# 死亡人口\n",
    "model_death = ARIMA(df2[\"死亡人口（万人）\"], order=(1, 3, 2))\n",
    "model_death_fit = model_death.fit()\n",
    "future_deaths = model_death_fit.forecast(steps=17)\n",
    "\n",
    "# 死亡率\n",
    "model_death1 = ARIMA(df2[\"人口死亡率(‰)\"], order=(1, 3, 2))\n",
    "model_death_fit1 = model_death1.fit()\n",
    "deaths_rate = model_death_fit1.forecast(steps=17)\n",
    "\n",
    "# 城镇化率\n",
    "model_urbanization = ARIMA(df2[\"城镇化率(%)\"], order=(1, 2, 1))\n",
    "model_urbanization_fit = model_urbanization.fit()\n",
    "future_urbanization = model_urbanization_fit.forecast(steps=17)\n",
    "\n",
    "#人口增长率\n",
    "model_gr = ARIMA(df2[\"人口自然增长率(‰)\"], order=(1, 3, 2))\n",
    "model_gr_fit = model_gr.fit()\n",
    "growth_rate = model_gr_fit.forecast(steps=17)\n",
    "\n",
    "#老龄化率\n",
    "model_old = ARIMA(df2[\"老龄化率(%)\"], order=(1, 3, 2))\n",
    "model_old_fit = model_old.fit()\n",
    "old_rate = model_old_fit.forecast(steps=17)\n",
    "\n",
    "\n",
    "# 设置时间索引为1965年到2005年\n",
    "df2.index = pd.date_range(start='1965', end='2006', freq='A')\n",
    "future_years_corrected = np.arange(2006, 2023)\n",
    "# 预测年末总人口\n",
    "future_data = pd.DataFrame({\n",
    "    \"出生人口（万人）\": future_births.values,\n",
    "    \"人口出生率（‰）\":births_rate.values,\n",
    "    \"死亡人口（万人）\": future_deaths.values,\n",
    "    \"人口死亡率(‰)\":deaths_rate.values,\n",
    "    \"城镇化率(%)\": future_urbanization.values,\n",
    "    \"人口自然增长率(‰)\":growth_rate.values,\n",
    "    \"老龄化率(%)\":old_rate.values,\n",
    "    \n",
    "}, index=future_years_corrected)\n",
    "\n",
    "# 使用 ARIMAX 模型进行总人口预测\n",
    "model = sm.tsa.ARIMA(df2[\"年末总人口(万人)\"], order=(1, 2, 2), exog=df2[[\"出生人口（万人）\", \"死亡人口（万人）\", \"城镇化率(%)\", \"人口出生率(‰)\", \n",
    "                                                        \"人口死亡率(‰)\",  \"人口自然增长率(‰)\",   \"老龄化率(%)\"  ]])\n",
    "model_fit = model.fit()\n",
    "forecast_total_population = model_fit.forecast(steps=17, exog=future_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "445ff202",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "长期预测平均百分比误差： -0.7706325817316322 %\n"
     ]
    }
   ],
   "source": [
    "#对照组误差分析\n",
    "# 真实人口数据\n",
    "real_population = np.array([\n",
    "    131448, 132129, 132802, 133450, 134091, 134735, 135404,\n",
    "    136072, 136782, 137462, 138271, 139008, 139538, 140005,\n",
    "    141212, 141260, 141175,140976,\n",
    "])\n",
    "\n",
    "# 预测人口数据\n",
    "predicted_population = forecast.values\n",
    "\n",
    "# 计算平均百分比误差 (Mean Percentage Error, MPE)\n",
    "errors = real_population - predicted_population\n",
    "percentage_errors = errors / real_population\n",
    "mean_percentage_error = np.mean(percentage_errors) * 100\n",
    "\n",
    "print(\"长期预测平均百分比误差：\",mean_percentage_error,\"%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "deef96e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vwr59+6xmvgOZs9inTJkCR0dHTJgwQWp/8OABNm/ejPr162P//v34+OOPcerUKWm9+L179xZIn4mIiIiocLGQTkRERERlSl5rXecn+9IfzZs3R/PmzR/r3tkL8tmdOnUKL7zwAqKiovDee+9h1KhRuZ5Xvnx57Nq1C23btsW2bdvg7++P7777Ds2aNcv3vrnNlC8oZrMZqampGDRoEDZs2ABBENC3b1+kpKTAw8MDBw4cQKdOnbB37140bNgQn332Gd58880cS738/vvvmD9/Pjp37oz3339fal+wYAGaN2+Onj174vDhw5g6dSoAoH///gU2hpdeeglA5lr0KSkpCAoKwrp16zB+/Hhs3LgR3bp1y/GYRYsW4cKFC5g4cSLKly8vtX/88cfQ6/WYNGkS+vXrh127dmHNmjV49tln8eabbxZYn4mIiIiocLGQTkRERERlStZa1xkZGTYvjZJVAL9y5cpjLcsRExNjdZ3sfZk/fz4+++wzGAwGTJo0CbNmzcr3WvXq1cOhQ4fQo0cPnD9/HgEBAXjjjTfw6aefwtvb2+pcs9kMAPjwww9tnrl+48YNBAcH57om+MPu3r2LmJgYxMTEICoqCvXq1UNISAhat24tnVOjRg389ddfGDhwIPbt24cxY8Zg5syZWLduHTp16gQAOHbsGF599VV4eHhg8+bNUCqV0uPr1KmDZcuWYfLkyVi9ejX27duHJk2aWC3/kjXW9PR0ODo65trXjIwMJCcn5zqL3WKx4MMPP8TatWvx8ssvo1+/fjhz5gxq1KiBHj16YNasWZg4caJ0fmRkJD755BOoVCq8/fbbUvuvv/6K4OBg+Pv74/XXX4cgCNiyZQvq16+P8ePHo2PHjqhTp450/u3btwHI2ySViIiIiIoGC+lEREREVKZkFcKNRqPsQnqXLl2e6N7ZC+nJyclo0aIFzp07B61Wi1WrVuW6AWpu6tWrh+PHjyMoKAg//vgj1q9fj/79++copGfNpJ8wYQIqVKhg07UPHz6M4OBgmzbE3Lp1q/T3t956CwsWLJA2YM2uSpUq2LNnDxYuXIipU6eiT58+6Nixo3T8119/la6XfXZ3loEDB6JixYp48cUXoVAoMH/+fKvi8/Lly/HJJ58gJiYmx+z8AwcO4MMPP0RiYiKMRiPq169vdfz06dMYM2YMDhw4gJdeeglr164FADRs2BCHDx/GiBEj8MEHHyAyMhIrVqyARqOBm5sbOnXqBEdHR3h4eAAATpw4gYEDB0KtVmP58uVS/ypXrozZs2dj5MiROHjwIOrUqYOQkBB8/PHHuHfvHpRKJapWrfrI55qIiIiIilbBLCpIRERERFRCxMbGAoCsmeVZBelr165BFEXZX1OmTAFgXUh3dHTEV199hdatW+P48eM2F9GzuLm5YevWrfj111/xzTffoGvXrjnOSUtLk3XN7GwppL/55pvo2rUr1qxZg2XLluVaRM+iUCgwYcIEXLx4EQsWLLAqhE+ZMgU3b95EQEBAno9/9tlnERgYiI8//hidO3e2Ota7d2+kpqaiZ8+e+Oqrr6yOBQQE4Ny5c0hLS8OIESOsZpAvXboUTZo0wd9//43p06djy5YtVjP3NRoNVq1ahXHjxmH9+vXYsGEDAKBatWrYsWMHFi1aJJ3r4OCADh064KuvvsoxjuHDh+PgwYMIDAwEADz//PMwGo147rnn8PPPP+c5i56IiIiIig9BfJxFIomIiIiIypCUlBSkp6fDzc2twDa4LC1EUSzUpUnyup/ZbLZaEsbWa61YsQJt27ZF3bp18z137969OQr4RERERFR2sJBORERERERERERERJQPTqchIiIiIiIiIiIiIsoHC+lERERERERERERERPlgIZ2IiIiIiIiIiIiIKB8spBMRERERERERERER5UNV1B0obSwWC27fvg1HR0cIglDU3SEiIiIiIiIiIiKiXIiiiOTkZFStWhUKRf5zzllIL2C3b9+Gp6dnUXeDiIiIiIiIiIiIiGxw8+ZNeHh45HsOC+kFzNHREUDmk+/k5FTEvSlcJpMJERER8PX1hUrFaNGjMTMkFzNDcjEzJBczQ3IxMyQXM0NyMTMkFzNDcpXlzCQlJcHT01Oq6eanbD0zhSBrORcnJ6cyWUh3cHCAk5NTmfuho8fDzJBczAzJxcyQXMwMycXMkFzMDMnFzJBczAzJxczApiW6udkoEREREREREREREVE+WEinAiMIApydnbnJKtmMmSG5mBmSi5khuZgZkouZIbmYGZKLmSG5mBmSi5mxjSCKoljUnShNkpKS4OzsjMTExDK3tAsRERERERERERFRSSGnlssZ6VRgLBYLoqOjYbFYirorVEIwMyQXM0NyMTMkFzNDcjEzJBczQ3IxMyQXM0NyMTO2KZurxxcjGRkZMJvNRd2NAmEymRAdHQ0XF5cyuzEByVNcM6NWq6FUKou6G5SLrH/c3d3doVDws2B6NGaG5GJmSC5mhuRiZkguZobkYmZILmbGNsWnclXGJCUlISYmBgaDoai7UmBEUYRWq8WNGze4phLZpLhmJmttMHd392LVLyIiIiIiIiIiKhospBeBpKQk3Lp1C+XKlUOFChWgVqtLRbFOFEWkpaXB3t6+VIyHnr7imBlRFJGamooHDx5Ap9PBxcWlqLtERERERERERERFjIX0IhATE4Ny5crBw8Oj2BQPC4IoihAEAXZ2dqVqXPT0FNfM6HQ6GAwG3L9/n7tWFzMKhQIVK1bkr5qRzZgZkouZIbmYGZKLmSG5mBmSi5khuZgZ2wiiKIpF3YnS5FE7vWZkZODy5cuoVq3aI3eCJaKik5ycjOjoaNSuXbtYrd9OREREREREREQF41G13Oz4MUMhy9pYVK1WF3FPCp4oikhPTwc/myFbFefMZBXPTSZTEfeEsrNYLLhy5Qp3EiebMTMkFzNDcjEzJBczQ3IxMyQXM0NyMTO2YSG9iJTWpSJYdCS5imtmSuvPaElnsVjw4MED/uNONmNmSC5mhuRiZkguZobkYmZILmaG5GJmbMNCOhWau3fvYu/evUhNTX2sx1+7dg3JycmPff9Lly7h66+/xq1btx77Go/r559/xt27d63aEhIScrQRERERERERERFR8cNCOhW4jIwMGAyGHO27d+/Giy++CI1GY9VuNpuRlpaW7zWvXLmCmjVrYtWqVY/drwsXLmDUqFG4du3aY19Djtdffx3r1q0DALz66qs4ceIE/v77bwQEBMBsNuO3335D9erVERcXVyj9ISIiIiIiIiIiosfDHfSoQGk0GmzYsAHjxo2DVqu1Wh4jKSkJZrMZnp6eVo8xmUwoX748Lly4IH3/8HIf1apVQ4sWLRAcHIwRI0bkuK9SqXzkuvNZxytVqmTTWJKSkpCUlGTTuYIgoFq1alZtY8aMQZcuXdC2bVtoNBpoNBosWLAAnTp1glKpRHh4ONq0aQM3Nzeb7lFaPfzBClF+FAoFPDw8uJM42YyZIbmYGZKLmSG5mBmSi5khuZgZkouZsQ0L6aWI2WzBoUM3cOdOMqpUcUTbttWhVBbeD4AgCNBoNBg6dCiGDh2a4/jgwYNhNpuxfv36fK8zYcIEfPXVV3ke1+l0Odpee+01bNq0Kd/rZhXSbX1TWLRoET799FObzlWpVMjIyJC+v3fvHi5duoTJkycjLCwMoiji0KFDcHFxQZUqVXD58mXs3bsXAwYMkB5jsVhgMBhyHV9plZUZIltl/eNOZCtmhuRiZkguZobkYmZILmaG5GJmSC5mxjb8mKGU+PHHc/D2XoSOHddgwIAf0bHjGnh7L8KPP54rtD6Iogi9Xg9RFAEAmzZtgiAIUKlUUKlU2LBhAzZt2iR9r1KpIAgCoqOjra6j1WrRsGFDiKKY40uv1yMjI8OqrWnTpjYVn1NSUqz+fBSNRgMvL69c+5H9a9WqVbCzs7N67L1797B8+XLs2bMHr7/+OlJSUrBz505cu3YNv/zyCw4ePIgTJ07g/fffhyAIEAQBSqUSjRs3tqlvpcXDmSF6FLPZjHPnzsFsNhd1V6iEYGZILmaG5GJmSC5mhuRiZkguZobkYmZsw0J6KfDjj+fQt+/3iI62Xobk1q0k9O37faEW07P/wGUVt7OWann46/DhwwCQowhtsVhgb28PAJg7dy6Cg4Ol6x47dgwuLi7Yv3+/dH6tWrVQvnz5R/bt5s2bAIA7d+7YNBY5M6UfHkOTJk1w+PBh9OjRAxUqVIC9vT1eeeUV9OzZE3/88QeSkpJQpUoVJCUlITk5GVOnTkXHjh1x7Ngxm+9ZWvBNmuQQRRGJiYn88IVsxsyQXMwMycXMkFzMDMnFzJBczAzJxczYhku7FBOiKCItLePRJz7EbLbgnXd+Q245F0VAEICxY39Dly4+spZ5sbdXW61v/jiUSiWAzBnmucn64Xz4PnPnzpX+Xr58eXz++edYuHAh5s2bBycnJ6SmplqtR75lyxab+hMWFgYA2LdvH3r06PHI8+WM/+Fz4+Pj8d5772HXrl3Ys2cPnn32WTg7O+OTTz6BTqfDmjVrkJaWBkdHRwBAbGwsqlatCldXV5vvSURERERERERERIWDhfRiIi0tA+XKzSrw64oiEB2dDGfnL2U9LiXlQzg4FMza1TExMbm2Hzt2DJ06dcr12OTJk6FWq/Huu++if//+mDFjBmJjY6FUKqFUKuHl5SW7HwcOHICvry/WrFmDzz//PM8Cf0HYt28foqKicPToUej1erRv3x7+/v7Ytm0bQkNDceHCBVgsFly/fh1eXl64ffs26tWr99T6Q0RERERERERERI+PS7tQgXp4iRMAcHR0zPUrryI6ALRr1w47d+5E3bp18dtvv2HatGkYOHAgoqOj4enpKXuDyv379+PatWtYu3YttFotli9fLntscvTs2RM//fQTKlSogOHDh8PZ2RlNmzbFM888g2rVqmHcuHHw9/fHgQMHAAAXL15EgwYNnmqfiqvcMkOUF4VCgRo1anAncbIZM0NyMTMkFzNDcjEzJBczQ3IxMyQXM2MbzkgvJuzt1UhJ+VD24w4evI6ePTc+8rxffx2Adu1sn8Vtb6+W3RdBEKBW53zc119/nev5165dw+zZs63aMjIykJaWho4dO+LIkSP46quvUKlSJZhMJukxPj4+0vcAYDQakZ6eDicnJ6hUuUd64cKFeOaZZ9CoUSO89dZbmDZtGl5//XWb1lZ/HMuXL8f48eOt2tatWwcA6NChA/bt24epU6di+/btePHFF3HmzBm0bt36qfSlOMsrM0R5USgUqFSpUlF3g0oQZobkYmZILmaG5GJmSC5mhuRiZkiu8LvhmBg6EbO7zkZA1YCi7k6xxY8ZiglBEODgoJH91a1bTXh4OCGv5bwFAfD0dEK3bjVlXfdx1kcXRRGpqalISUmx2kDSxcUl16+s9cGzGI1GHDlyBC4uLtBqtdBqtXj//ffRpk0bqNVqqNVqTJs2Dfv27ZO+V6vVcHBwQPny5XHixIlc+/XTTz9h+/btmDNnDgBgzJgxcHJyQlBQkOwx2urNN99EQkIC3n33XTz//PPQ6/VIS0tDnTp10K9fPwDAgAEDsHPnTsyZMwc1atRA9erVn1p/iqvMvQHSuJkF2cxsNuPkyZPcpJZsxsyQXMwMycXMkFzMDMnFzJBczAzJtebEGuyL2oe1J9YWdVeKNc5IL+GUSgUWLXoOfft+D0GA1aajWbXwhQufk7XR6JO4e/cuatWqZdXWv3//fB9TsWJFAEDDhg0RFhaGmzdvQqfTSZuVZvnuu+/w3nvvISwsDG5ubtKSIAaDAenp6XB3d89x7Zs3b2LkyJEYMmQI2rVrBwAoV64cQkJC0KVLF3z44YeYNavg16bX6XRITEzEN998gx49euCPP/5AdHQ0UlNTMXjwYABA7dq10b17d0yfPh0LFiwo8D6UFBaLpai7QCWIKIrQ6/X88IVsxsyQXMwMycXMkFzMDMnFzJBczAzZ4nrCdcSkxUAQBGw+sxkAsOnMJgzxHQJRFFHBvgK8XOTvUViasZBeCvTpUx9btryKsWN3ITo6SWr38HDCwoXPoU+f+oXWlwoVKuDSpUtWhfC9e/fCaDSiR48e0nmbNm2Cg4MDunXrBrVaDbPZDFEUodVq4eHhkeO6ISEhGD9+PJYsWYLdu3dj3rx5mDp1KgYPHpzn+k1Xr15Fjx49ULNmTSxZssTqWOfOnfH555/j008/RUxMDBYvXgydTpfjGmazGdHR0fmOOT4+Ptf2SpUq4Y8//sDevXsxadIkREZGokaNGvj+++8xcOBACIIg3ZP/uBERERERERERUWHxXuSdoy0mLQb+3/pL34tTWK/KjoX0UqJPn/p44YW6OHToBu7cSUaVKo5o27Z6oc1Ez6JUKlGzZk0AwO7duzF37lzs378fAwcOxIABA2BnZweLxYLY2FjMnTsX7733HoYMGYJx48bBx8fH6lrx8fHYtm0bli1bhnPnzmHZsmUYNmwYbt++jZs3b2LUqFGYP38+Zs2ahV69elk9dsOGDXj77bfh7e2NnTt3wsHBIUdfP/nkEwiCgE8++QQHDhzA9OnT8corr0jL2mQV0T09PR857nLlyuVoUygU8PPzw9mzZ3Hr1i1Mnz4dbm5u+P7779G2bVuMGDECt27dwrx58/Dee+/BYDDggw8+eKxldYiIiIiIiIiIiB4l1ZiKH87+gNputXEp7pLVMRGZhXOVQoXVL6wugt4Vb1wjvRRRKhXo0MEb/fs3RocO3oVeRAcArVYrbQg6YsQItGzZElevXsXq1aulpVgUCgWmTZuGqKgozJ07Fz/++CP69+8vbSA6Z84ctG7dGpUrV8aYMWPg5+eHyMhIDBs2DABQtWpVzJ07FxcvXkSNGjXQu3dvvPDCC9Ks7hkzZmDQoEHo06cPDh8+DBcXlzz7+/HHH2PHjh0wGo1Yv3691TIj6enp8PLygiiK+X6tWrUK6enpVteNiorChAkT4OXlha+//ho///wzPvroI7z55pvw9/dH48aNodPpcOTIEUyYMAGLFy/Gxx9/jN69e5e5pU60Wm1Rd4FKEKVSiXr16uVY+okoL8wMycXMkFzMDMnFzJBczAzJxcxQbo7fPo6RO0aiyrwqGLp9KC7FXYIij9LwP4H/YGCTgYXcw+JPELmmRIFKSkqCs7MzEhMT4eTklON4enq6VGguzQXEK1euwMfHJ89lV7JLT09HWloa3NzcAGQuyfLZZ5+hV69eeP7553OdTZ7dtm3bpGVigMyZ5Pv27UOXLl1s7m9aWhpMJlOur9njsFgsmDJlCrp27SqtzZ7lxx9/hJ2dHZ5//nmr9j///BM6nQ6+vr4F0gd6MmXlZ5WIiIiIiIiISqeE9ARsjNyIkPAQRNyNkNprutZEoF8gfN198dyG56CAAhZYpD+PjzgOvyp+RdjzwvOoWm52LKQXsLJcSBdFEWlpabC3t+fyJGST4pyZ0vyzWpKZTCZERETA19cXKhVXJ6NHY2ZILmaG5GJmSC5mhuRiZkguZqZsE0URh28cRkhECH448wP0Jj0AwE5ph5cbvIxA30C0924PhaBAdFI0mgc3h4ejB7qU74I9sXsQnRyNY0HH4OGUcw/D0khOIZ0/TVSg+LkMycXMkFxms7mou0AlDDNDcjEzJBczQ3IxMyQXM0NyMTNlz4PUB1hzcg1CwkNwIfaC1N6oUiME+QVhYOOBKG9f3uoxHk4eiBobBYWowPHjx/H5/z6HRbDATmVX2N0vEVhIJyIiIiIiIiIiIiphLKIFe67uQXB4MLaf344MSwYAwEHtgH6N+iHILwjPVHsm31UA7FR20r6FgiCwiJ4PFtKJiIiIiIiIiIiISojopGisiliFFRErcD3xutT+TLVnEOgbiH6N+sHRzrEIe1g6sZBOBUqn0xV1F6iEYWZIDqVSiSZNmnD3ebIZM0NyMTMkFzNDcjEzJBczQ3IxM6VThjkDOy/tRHB4MHZd3gWLaAEAuGhd8HqT1xHoF4gmlZs81rWZGduwkE4FSqFQFHUXqIRhZkgujUZT1F2gEoaZIbmYGZKLmSG5mBmSi5khuZiZ0uNy3GWsCF+B1SdX427KXam9g3cHBPoGok/9PtCpn3ySIjPzaKxgUYFKTU0t6i5QCcPMkBxmsxlhYWHcOIdsxsyQXMwMycXMkFzMDMnFzJBczEzJl25Kx8bIjei0phNqf1UbXxz5AndT7qKyQ2V80PoDXBxzEfsG78PAJgMLpIjOzNiGM9KJiIiIiIiIiIiIitjp+6cRfDwY606tQ3x6PABAgIDnaj2HIL8g9KrTC2qluoh7WXZxRjoVOJPJVOCzjC9evCjr/B9++AHff/+9VZsoipg6dSru3LkDAFi6dCmuXLnyxH0LDw/H9OnTn/g6TyouLg7p6ek52nft2gWLxZLrY7755ht8/fXXT7trRERERERERESUixRjClaEr0DLkJZo/HVjLD66GPHp8ajuXB2ftf8MUeOi8OvAX/FS/ZdYRC9inJFOBe63337DkCFDsG/fPjRt2hQAkJGRAYPBkOv5Op0u380MoqOj4evri8mTJ+Ojjz6yqQ8//fQTLBYLXn31VRgMBigUCqjVanzxxRfo168fqlSpgk8++QQ1atSAt7c3DAYD7O3tAQCRkZHYunUr7OzsIAiC1XUNBgN69+6Nhg0bIiMjA+XKlcOFCxewfPlyfPzxx4/sV0xMDLRaLVQq6x89s9mM9PR0uLm5QRAEhIaGYubMmVi9ejW8vLxw/vx5mEwmq8e4ubmhatWq0vdNmjTBJ598gjfffFNqO3nyJHr06IH58+dj/PjxOfpz5MgRmM1mvPXWW3n2efjw4Vi5cqXVa2Q2m6FQKKyeH7PZjF27dqF79+6PfB6IiIiIiIiIiMoqURRx7PYxhISH4LvT3yHFmAIAUClUeKHuCwj0C0TXGl2hVHDzz+KEhXQqUA4ODti4cSP8/f2lIjoALF++HO+8806uj8mv+CqKIkaPHo3KlStjyZIlGDRoELy8vB7ZD61WK63rFBISgilTpkClUsFgMKBNmzZQKpVITEzEwIEDoVQqERAQgN9++w0AkJiYiIiICFy/fh13795Fhw4dEBMTg7179+Lll19G69at8dVXX2HLli34+++/YWdnBzs7O+neoaGhePnll2FnZ2dVfP7xxx/Rrl072NnZQa1WIzExEQ4ODlCpVDCbzdDr9bh79y4qVKiAOnXqICUlBf7+/tizZw+ef/55mM1maLVaAJkF+WHDhmHx4sXS9R/uR9bz3rBhQ4wZMwZAZrHbbDZLG0ioVCqrYrjRaITZbIZO99/6WlqtFu3bt8f+/fultmbNmiEwMFC6LgAIgpDj/rZwcHCQ/Rgqu7J+XrmTONmKmSG5mBmSi5khuZgZkouZIbmYmeIrTh+HDac2IDg8GJH3I6X2OuXrINA3EG80fQOVy1Uu9H4xM7ZhIZ0KVFJSEnbs2IEjR44gNTUVFosFjo6OcHR0hJeXF6KioqzOV6lU+RZfv/jiC/z11184fvw4Vq5ciW7duuHAgQNwd3fP9Xyz2QylUmlVHB45ciRGjx4NAPD398e2bdtQvXp1NGnSBEuXLkXbtm2trtGmTRu0adMGc+bMwf79+7Fp0yYcPnwYe/fuxZYtWwAA586dk/r98MzsNm3aIDIyEhqNBgqFAn369IGrqyuaNWsmzSpPSUmBo6MjDh8+jGbNmuUYh5eXFw4fPozFixejYcOG0Gq1WLFiBTp06AAAGDJkCDQaDX777Te88sorSElJyTF7/tq1a1i5ciV+/fVXqNVqbN26Fa6urujcuXOO+61du1b6e+vWrXH48GHpezlvoo/zhmuxWKBQcJUpsp3RaLT6sIfoUZgZkouZIbmYGZKLmSG5mBmSi5kpPkRRxIHrBxASHoItZ7fAYM5csUGr0uKVBq8g0C8Qbau3zVHXKWzMzKOxelUKxM1eibh5q3M/Nm814mavLLS+LFu2DN27d0fTpk3x888/o27durhx40aOpUxs8e2332LWrFn4+eef4enpiSlTpqBdu3Zo165dnmum9+3bFyqVCitXrsTatWshCAIWLFiAVatWoV+/fqhduzYmTpyIfv36oUGDBli6dCn69euHlJSUHNcyGo15FvkFQcjzDS5rqZp3330XM2bMgMViwdatW6WlY4DMNd/VajUaNGiQ6zVEUYSdnR3ef/99qNVq6V4fffQRli5dKp2n1Wpz7aPZbEZgYCCee+45dO7cGTt27EDfvn1x/vx53L17FwkJCUhOTsbgwYMxcOBAJCcnIzk5GTExMdi+fbvVtRQKBQ4ePAitVit9nTp1CuPHj7dqy3pe5NLr9bIfQ2WX2WzGqVOnuJM42YyZIbmYGZKLmSG5mBmSi5khuZiZohF2Owyd1nRC2O0wAMC9lHv48vCXqLukLjqu6YgNkRtgMBvQtHJTLOmxBLcn3Mbal9ainVe7Ii+iMzO24Yz00kCpQPwXKwAAbu8OkZrj5q1G/Bcr4DppeKF0IyYmBgsWLEBoaCiAzEJ48+bNUb16dQCZxeHcCta5mT59OubPn4+dO3eiZcuWUvs333yD0aNHw8/PD9OnT8fo0aOhVv+30cLmzZuhUqnQvHlzNG7cGN9++y0UCgUmT54MURTx/vvvW93HbDajZcuWWLEi8/m7efMmJk6cCI1Gg8jISMTHxyMwMBD37t0DkDkTvGLFio9cXqZy5cqwt7fHV199hT179kjF7lmzZuGjjz6SZqs7OTkByCzaT58+HZMnT4bFYkGXLl3Qu3fvHOuaX7p0CbGxsdL3eRX0v/zyS/zxxx/o2rUrXn/9dRw6dAg9e/bEW2+9BYPBAKVSCbVaLX3AUa5cOWnd8+wF/6znqF27djYt7ZLXpqZERERERERERKXZ2pNrsS9qH6YdmAalQolfLv4CkyVzZYJymnIY0GgAgvyD4F/Fv8gL5/R4WEgvJkRRhJiW/liPdRn5GkRjBuK/WAHRmAHXdwYhfvF6JMxfC5cJb8Bl5GuwpMqb9SvYa2X/UB86dAjx8fHo27cv0tLSEBcXh7Nnz0rHb9y4AUdHx3yvcebMGYwZMwY3btzAypUr4eXlhejoaKtzPvzwQ7i7u+Ojjz7C4sWLMXz4cAQGBqJy5crQaDS4d+8eTpw4gcqVK+OXX37Byy+/DJVKhR07duDAgQO53jdrSRK1Wo1q1apBo9HgyJEjqFatGjw8PKQCsYeHB1xcXJCRkZFj888sWZubhoSEwNPTEy4uLtIxrVYrFaWvXbsGk8mE2rVro0OHDtKvzygUCowYMQLDhw/H2bNnERwcLD1erVbD0dER6en5Z6Vly5YYNWoUGjRogO3btyMxMREhISEQBAF9+/bFzp07rc5fs2YNAKB8+fKIiYmxOibn00h+cklEREREREREZcX1hOuISYvB3ZS7WBmRuSLEzxd/lo77ufth9DOj8WrDV1FOU66oukkFhIX0YkJMS8c1725PfJ2E+WuRMH9tnt/byidqNwQHeesi9ezZE0uXLkXPnj0xdOhQPPvss/Dx8ZGO57VGepbffvsNL7zwAvr164dt27bBw8MDqampud5r4MCBuHTpEqZPn45ly5YhMDBQOrZp0yZYLBbExcUhKCgI3bp1gyAIGDp0KJYsWWJ1HZPJZDWj3d3dHXPnzsWNGzewaNEizJ07Fy+++CIOHTqEVatWYdq0aRAEAV988YVUzLZYLFYfOowdOxbffPMNlEolzGYzpk2bBlEUUb16dbz77rvSefPnz8f58+elGfzZ+9GvXz9UqlQJZ86cyTH2rGVU8tOpUyd06tQJUVFReP/997F06VJUqVIFALB69epcr2OxWHIt0JtMJhw+fNjqtTKbzRg7dizGjRsntSmVShiNxkf27WH8FJbk4uYnJBczQ3IxMyQXM0NyMTMkFzNDcjEzT5/RbIT3Iu98zwm/G45hvsMKp0NPiJl5NBbSqcBotVqMGDECu3fvxqVLl3Kstf0oPXr0wPHjx9G4cWMAQGxsLNRqdY6NKE0mEywWCzQaDZYtW4Z58+ZJs7lNJhMWL16MatWqoV69eqhQoQKWL1/+yJnS2Y8fPnwYw4YNQ/369dG7d28AkArE6enp0Ol0mDRpEiZNmoTg4GBs374dzs7O0uO/+uorLFu2DElJSXB1dUVUVBRmzJiRo0it1+tRu3btPPvUqVMntGnTBgCkGfEPHjyAn58fbt68CVEU8x2TKIoYPXo0WrVqhaFDh0rt5cuXz/P5KFfuv09Hk5KSkJaWhnnz5mHx4sVW57Vo0QJDhw7FyJEjrdrNZjPi4uLg5uaWb9+yCIIABwcHm84lAiAt3URkK2aG5GJmSC5mhuRiZkguZobkYmaergsxF7AiYgXWnFyT73kqhQqrX1hdOJ16QsyMbVhILyYEey18onY/0TWylnOBRg0YM+Ay4Q24vjPosfsjlyiKuHz5MoYNG4avvvoKCQkJOHnypFQsNpvNuHv3br7XaNy4MW7dugUPD49H3u+5557Db7/9ZrWj8FdffQWtVosOHTrAbDbjzTffxK5du+Dq6ootW7Zgy5YtSElJgdlslorflStXhtFohIODAzIyMjB16lRoNBps375d+jSuXr16GD9+PObOnYtPPvlEul94eDguXbqEOXPmSG3ZZ5ZncXNzQ+vWrXH16lWpLTExEU2bNs1zfGazGX5+fpg/fz4MhswdnW/duoVq1aohMjJSastNQkIC3nnnHfz666/4+OOP8eGHH+LChQt47733cO3aNQwalHsu+vXrh++++w5A5hr3D68pn9348eNzrOEOADVr1sTly5fzfFx2oijCbDZDqVRyZjrZRBRFJCYmwtnZmZkhmzAzJBczQ3IxMyQXM0NyMTMkFzNT8PQZemw5uwUhESE4eP2g1O5ezh09avXAqhOrcjzmn8B/4FfFrzC7+diYGdsoHn0KFQZBEKBw0D32V8LyzUiYvxauk4aj5q0/4DppeOayLss3P9b1HveHpk+fPrh16xb69+8PLy8vvPbaa1JRNTo6GlWqVLH6ym1mdNbGnEePHkVycnKuXwMHDrQqoANASkoK5s6dixkzZkj97927NxYsWIApU6bg7t27uHv3LgYOHIigoCDp+zt37kizotVqNX744QccOHAAarUaiYmJSElJgbOzM5o2bYp58+YhJSVF+po5cyYOHjyIXr165fu8zJgxAz179rTajPPs2bOoVauW9H1GRobVY3bs2IFz587B09MTQUFB0Gq1OH/+PKpXr46XX34ZPXv2hNlsznU5leTkZKxfvx4ajQa//vorbt++jSZNmqBOnTrQ6XQoX7489Hq91degQYOsntORI0ciMTERJpMJoigiLi4OjRo1wrhx49C0aVN89dVXaN++PcaMGZO5xr8oIjk5GX/++We+z8XDHrXeO1F2ZrMZ58+f53r8ZDNmhuRiZkguZobkYmZILmaG5GJmCs7Juycx5tcxqDKvCt746Q0cvH4QCkGBXnV6YXu/7bg5/ibGPDMGAKD4t8yqKIHlVmbGNpyRXgrEzVuN+C9WwHXScLi9OwQApD/jv1hh9f3TNnXqVJjNZjRt2hQ1a9aUZnRfvnz5kWukZ8l6jE6ns1pq5OHHPbzkS7ly5fD333/D09MTO3bskNrnzp2LuXPnQq1WQxAE3L9/H6IoYuXKlShXrhyMRiMUCoW0yaaLiws+/fRTTJs2Ldd757Zh6oMHD1ChQoU8npX/ZM0iv3TpEs6dO4eWLVtKxx4uKC9duhR9+vRB/fr18dlnn2HVqlVwdXVFeno6vL290bhxY4SGhubaH09PT0RGRqJ27drQaDRWx7Ket4fXSFcqlVbPafbnPiIiAoMGDYKnpye+/PJLPPPMMwCAb775Bm3btkViYiIWLVoEV1fXPF8zIiIiIiIiIqLiLsmQhE2nNyE4PBhht8Okdm8Xbwz3HY6hzYaimlM1qb2SQyW4l3OHp5MnhvsOx4qIFbiZdBOVHCoVRffpKWIhvTQwW6yK6Fmk782WHA95Wrp16wYHBwekpaXhxIkTOHv2rLTOuK2e5FdIPD09c7RNnjwZkydPBgCsXbsWb775JgYMGIAdO3YgNDQUjRs3zvGJ27vvvosxY8ZAo9FIxf6ffvoJ48aNs/owICMjAwaDweY1wV966SW0atUKEyZMQKdOnQAAv//+O9auXWu1Vvjhw4exZ88eHD9+HEDmJ4Nffvkl+vTpg6+//hrHjh3D8ePH0bVrV9y6dSvXezVo0AB3797FlStXcOHCBZw/fx7du3eXjqekpFidbzKZrD7YEEUR//zzD5YsWYLvv/8eo0aNwty5c63OqVu3Lg4dOoSBAwfC09MT/fv3R69evdCjR48cBXwiIiIiIiIiouJIFEX8Hf03QsJDsOnMJqRlpAEA1Ao1Xqr/EgJ9A9G5RmcohJyzzT2cPBA1NgoapQaCIGCE/wgYzUbYqewKexj0lLGQXgq4Tcx799/CmokOACdPnsTo0aNx8+ZNxMfHo0GDBmjUqBFq1KiBW7duISMjA+fPn8/xuKioKERGRkqbjGZtopn1fV5efPHFPI9lLTWS5cqVK5g3bx42b96MH374Ab169cLkyZPRqlUrzJw5EyNGjIBSqYTRaERkZCS0Wm2O3Yrj4uJgNpsRHR2d437379+HWq1G/fr1pXOvXbsGAFbLpZQvXx6TJ0/GiRMn8Ndff+HGjRt4++234e/vL23oaTQaMWbMGHTq1Am+vr4AgAULFuD69ev46KOP4OrqCj8/P4waNQqbN2+Wrp19iZeFCxfivffeg9lshr29PWrXro3atWvDxcUFSUlJiI2NzXUm+7Bh/2Vp69ateOWVV9C1a1f8/fff8PP7b12v7EvK1K1bF0ePHsXGjRuxZMkS/P33349c6ia7h3+zgCg/giBAp3v85aeo7GFmSC5mhuRiZkguZobkYmZILmbGdrFpsVh3ah1CwkNw5sEZqb1ehXoI8gvC601eR0WHio+8TvaiuSAIJa6IzszYhoV0KjANGjRAmzZt0K1bN7Rr107adLNly5Y4c+YMlEql1VImQObyIW+//TYqV64sraWetY748ePH0aBBg1zvFRQUJC3FkhuDwYCMjAwYjUb06tULp06dwuDBg3H27FlUrlwZADBz5kx07NgRkyZNwpw5c3D06FGYTCa0aNECdnZ2uW4amjWe3O7n6+srrQ8+bNgwnDp1Cu+99x7Kly8PAJgzZw6+/PJLtGrVCuHh4ahYsSI8PDwQGRmJsWPHomHDhggLC4NarYZarcY777wDIHOt9EmTJmHatGnw8vICkLnsy8CBA3H79m1cv34d48aNw82bN6Wx9e/fH9WqVUNAQAC8vb2t3ggvXryI8uXL53j+hgwZYrW8zMsvv4zIyEg0atQox3jT09OtCvcKhQKDBg3CoEGDkJqamuNDiLwIggB7e3ubziUCMpcgym+TXqKHMTMkFzNDcjEzJBczQ3IxMyQXM5M/i2jBvmv7EBIRgh/P/QijObO+oVPp8GrDVxHkF4RWnq3KVFGZmbGNIGaftktPLCkpCc7OzkhMTISTk1OO4+np6bh27Rp8fHxyrFFd0omiKC0P8iRvNrdv30a1atXyLOLKlZiYCEdHx3xnPickJMDFxeWJ75Wfu3fv4syZM+jcuXOux0+fPi2NNyMjQ1qz/M6dO1i3bh3ef/99q+f1wYMHqFixIvR6Pd555x107doVr7766iP7kZycjJiYGPj4+BTMwJ5AQWXmaSjNP6slmcViQUxMDCpUqMDfZiCbMDMkFzNDcjEzJBczQ3IxMyQXM5O728m3sfrEaqyIWIGr8Veldr8qfgj0DcSAxgPgrHUuwh4WnbKcmUfVcrPjjHQqUAaDIdcNROWoWrUqCvLzHWfnR78JPu0iOgC4u7vD3d09z+PZPzTIPhu+SpUqmDhxYo7zK1bM/NUinU6H4OBgm/vh6OiY67IuRaUgMkNlh8ViwdWrV+Hm5lbm/nGnx8PMkFzMDMnFzJBczAzJxcyQXMzMf0wWE3679BtCIkKw8+JOmMXMPfKc7JwwsPFABPoFwq+K3yOuUvoxM7Zh9YqIiIiIiIiIiIhKjWvx17AiYgVWnViF28m3pfY21dsg0DcQfRv0hYPGoQh7SCURC+lERERERERERERUohlMBmy/sB3B4cHYc3WP1F7BvgIGNx2M4b7DUb9i/SLsIZV0LKRTgbJ1k0miLMwMySEIApydnYvdmvpUfDEzJBczQ3IxMyQXM0NyMTMkV1nLzLkH5xASHoK1p9YiJi1Gau9aoyuC/ILwv7r/g53Krgh7WPyVtcw8Lm42WsDK8majRKUJf1aJiIiIiIiIiqdUYyp+OPsDQsJDcOTmEam9qmNVDGs2DMN8h8HH1acIe0glBTcbpSIhiiIyMjKgVqv5CRbZhJkhuSwWC27fvo2qVatyAxSyCTNDcjEzJBczQ3IxMyQXM0NylebMHL99HCHhIdh4eiOSDEkAAKWgRK86vRDoF4jnaj0HlYLlTrlKc2YKEp8ZKlBGo7Gou0AlDDNDclgsFkRHR8NisRR1V6iEYGZILmaG5GJmSC5mhuRiZkiukpyZsNth6LSmE8Juh0ltiemJWHZsGfy+8UNAcACWH1+OJEMSarjWwMxOM3Fj/A381O8n9KrTi0X0x1SSM1OYmC4iIiIiIiIiIiIqcmtPrsW+qH1Ye3It0k3pCA4Pxg9nfoDepAcAaJQa9KnfB0F+Qejg3QEKgXOEqfCwkE6FThRFLuNBRERERERERES4nnAdMWkxEAQB353+DgCw7NgyfHX0K+mchhUbIsgvCIOaDEJ5+/JF1VUq4/ixDRUoleq/z2bCwsLQqVMn6PV6q3NGjx6NwYMHw2w2F3b3ip3g4GDs3r3bqi0+Ph6fffYZ9Ho99Ho9Fi5ciAcPHjzxvfbu3YulS5c+8XWeVExMjNVyLiqVCkajEXv37s3zMTNnzsQPP/xQGN2jYk6hUKBixYpcs41sxsyQXMwMycXMkFzMDMnFzJBcJS0z3ou8ERAcAP9v/RGTFgMAMIvWNaPItyIxtuVYFtGfkpKWmaIiiKIoFnUnSpNH7fSanp6Oa9euwcfHB1qttgh6WHhSU1PRsGFDDBw4EDNmzAAAJCcno2rVqhg+fDgWLlxYtB0sBlq3bo127dph1qxZSE9Ph0qlQmxsLNzd3ZGeng6DwQBnZ2ecPXsWtWvXRkZGBnQ6HQDg0KFD2LdvX64bdaanp2Pw4MFwd3eHKIqwt7fHrFmzsHPnThw+fDjfPlksFsTHx8POzs7qgxEAMJlMMBgMKF8+8x+uVatW4ZdffkFwcDDc3Nxw5syZHNerXLkyKlasKD1erVZj165d6N69u3TO9u3b8eKLL+LHH3/ESy+9lOMa7dq1Q9u2baUc5aZz587Yt2+f1Zu+2WyGQqGwen7MZjPOnz+PunXr5vs8lKWfVSIiIiIiIqLCFp0UjVURq7Don0WI1cfmeo5KocLqF1ZjYJOBhdw7KiseVcvNjh8zUIERRRHp6enI+mzGwcEBX375pVT4BYANGzbAzs4On3322VPty6JFiyAIAq5evWrVPmTIEAiCAEEQoFQqUbNmTXz66adIT0/PcY0hQ4agWbNmOdqzHh8bm/kmHxsbK7VlZ7FYUKFCBbzxxht59lOr1cLOzg4AMGXKFFSpUgWNGzcGAHh5eaFOnToAMgvJ7u7uGDdunPTYBw8eIDw8HKtWrUJwcDBOnDiB7du3Y9q0aYiMjERKSgref/99DBs2DABgZ2cn3QsAQkJC4OzsjEqVKsHd3V36OnLkCCpXrozKlSujUqVK0Ol0VufUrFlTuoafnx9OnToFf39/XLt2DY0bN8Zzzz2HXr16oVevXggICMC6deuk81UqFRQKhdSPrMwsX74cXbt2lYroGRkZyMjIsHpcVlFfFEUYDIYcr5lWq8Ubb7wBk8kkfTk7O2P79u3S95cvX5aeCyqZLBYLrly5wg1QyGbMDMnFzJBczAzJxcyQXMwMyVWcM5NhzsBP539Cr4294LXQC5/u/xSx+liU05TL9fx/Av9hEb0QFOfMFCcspFOBMplM0Ov1UmG5X79++OSTT6Tv33rrLcTGxsLV1RWCIKBDhw5PpR+hoaEAkGPZFCCzQH3s2DHs378fw4YNw+zZszF69GjZ9zh16hQA4OTJk7keDw8PR2xsrNSX7LKWtckqvouiiJkzZ+LBgwe4ePEi6tati1u3buH69euoXLkyjh07hpiYGHzzzTfSNfr06YOffvoJXbt2xXPPPYdNmzZh9OjRqFixIrZu3YrGjRtbFc8fnpn96quvIjIyEqdOncI///wDJycn9O7dG61atYLJZEJqair+/PNPAMCNGzeQkJCAlJQUJCQkSNdo2rQpjh8/jrFjx6JGjRpQKBQ4fPgwoqKiEBUVhZYtW0Kj0eDrr79Go0aNrMac5fDhw9i7dy8WL14Mo9GI7du3Y8OGDdBoNFJu9u3bh88//xyCIEChUECr1SIwMNDqOkql8tEv2mOcS8WLxWLBgwcP+I872YyZIbmYGZKLmSG5mBmSi5khuYpjZq7EXcGHez5E9YXV8dLml7Dz0k5YRAvaebXDupfW4fdBvwMAFP+WKhUsWRaq4piZ4oibjZYyYbfDMDF0ImZ3nY2AqgFF0oesZTC2bt2KVq1aSX//4osvcOzYMem8zz//HGfPni3w+2dkZODAgQOws7NDaGgoRo4cmaN/AQGZz03btm0RFxeHpUuX4uuvv4ZGo7H5PqdOnULHjh3zLKTv3r0bdnZ2uHv3LiIjI6WZ5gAQEBCAyMhIWCwW/PHHH5g6dSp++OEHXL16FeHh4WjWrBkGDsz8xLVDhw6YNGkSdDodVq1aleM+RqMRjo6OufYht5nyWZycnBAVFYWFCxciLS0NjRo1wrfffmt1/sWLF+Hl5QUXF5dcryGKIpydnTF+/HjpfgAwYsQIdO3aVTov+8z77NLS0jBmzBiMHDkS9erVw5IlS/D2229j+/btuHfvHrRaLRQKBXr37o1nn30WkydPlmakP7zsjEKhwLp167Bp0yapzWAwoE+fPtJyL1m/LcHNbomIiIiIiIiernRTOrad24bg8GDsi9ontVe0r4ghzYYg0C8Qdcpn/iZ+dFI03Mu5w9PJE8N9h2NFxArcTLqJSg6Viqr7RDmwkF7KrD25Fvui9mHdyXVFVkjPKlK6ubnB3d0dAODs7AylUil9DwD29vZP5f5//vknUlJSMHHiRHz77bcwm835zkAOCAiAwWBATEwMqlatatM9nn32WamAfurUKbRo0QL//POP1TmhoaEYMGAAdu7cid27d1sV0v/++2+oVCq4u7tj1KhRmDx5MlQqFfr374+qVati0KBBVteKjo5GUFCQ9H14eDjmzJkDjUaDv/76S5qhfeXKFcTGxmLw4MFo2LDhI8fh4+OD+/fv47fffsO5c+ek1+7NN99ESEgI1Go1gP8+HDEYDNiwYQMGDBiA1NRUtGvXDu+//z769etndd0TJ06gRo0a0vd5FfTHjRuHCxcuoEGDBhg0aBB+//13vPXWW/jf//4HvV4PtVoNlUoFpVIJjUaDcuXKwWQyQaVSWS0ZBGTO8n/99dexevVqqc3FxQXr169Hr169AABRUVHw8fHhJ6xERERERERET8np+6cREh6CdafWIU4fBwAQIKB7re4I8gtCrzq9oFFaT2T0cPJA1NgoaJSZv50+wn8EjGYj7FRcmpWKDxbSiwlRFJGWkfZYj72ReAOxaZnrdG86nTkb97vT3+HVhq9CFEWUty+P6s7VZV3TXm3/WLN2s2Z0K5VKdOzYMcfxh6/Zvn176e9Go1HajFKlUtl0f4vFAqPRCKVSKRV9Q0ND4ePjgzfeeAOzZ8/G0aNH8eyzz+Z5jbt370IQBGkDTVs0btwYx48fB5C5tIu/v79VIT0tLQ1//vknhg4diuTkZOzevRvvvvuudNzOzg5hYWGIiYnB1atXceDAAXTr1g0qlQorV660mlUNIMeHAVqtFh4eHtBoNIiLi0PLli3h4eGBuLg4KJVKVKtWDeXLl8etW7dgMplyHUN6ejp0Oh1++uknTJo0SXr+sq6fVZQ+f/48dDodvLy84O3tLRWwHRwc8Prrr+P111/H5cuX8fHHH0uPV6vVec6Sz65z587Q6XRo0KABvvnmGzg5OWHOnDkAgObNm1ttXrp3715MnToVAODv74+wsLAcz5Gt5JxLxYtCoYCHhwd3EiebMTMkFzNDcjEzJBczQ3IxMyRXUWQmxZiCzac3IyQiBH9H/y21ezp5YpjvMAzzHfbI2lT2orkgCCyiFyK+z9iGhfRiIi0jDeVm5b6xwuN4kPYAbVa1eezHp3yYAgeNg6zHCIIgFdJFUcSvv/6Ktm3bAgA2b96Mzz//3Kow+tFHH1ktizJ//nx8+OGHj9XfVatWYciQIQAyl1Rp3749GjRogIoVKyI0NDTXQrooiggLC8OCBQvQo0cPWRtQNm7cGGvXroXBYMC5c+eke2fZv38/jEYj2rdvj6SkJEycOBEGg8HqHhs2bAAAXL58GWPGjMH58+chCAI+/fRTvPfee1bXu3z5Mtq0+e/1bNCgAebMmYNjx45h1qxZmD59Opo1a4Z169bh+PHjmDlzJgBg5MiR0qacFovF6sOJF198Eb///juUSiXMZjMWLlwIi8WC9u3bw9/fXzrv/fffh7u7O4KDgwHAquA+btw4VKxYMdfnLmsWe3769euHfv364Z9//sGpU6cQGhoKB4fM3O3duxcqlSrHtc1ms9VGpFlMJhPWr1+P9evXW537wgsvWI1bqVTCaDQ+sm9UPGX9405kK2aG5GJmSC5mhuRiZkguZobkKqzMiKKIsNthCA4Pxnenv0OKMQUAoFKo8L+6/0OgbyC61ewGpYL7lBV3fJ+xDQvpVGBEUUR6ejq0Wi0sFgs0Go1UTFWr1RAEwaq4+vAa102aNEFgYCDUarV0/qOYzWYYjUbUrl0bABAfH4/jx49j9OjREAQB7dq1w+7du/Hpp59Kj7lw4YLVtVu3bo2QkBBZY61bty4sFgt++eUXmM1m1KtXz+p4aGgovL294eXlhQ4dOkCv1+PQoUPo0qULACAhIQGrVq1C1apV0b17d2zduhVbt27Nd6Z09mOiKGLHjh0ICgrCCy+8gGbNmgHInNWfVTgHgOXLl0tF8i1btlgtXfPzzz9DrVbj5MmTCAgIgMlkQo8ePdCyZUskJydL5+n1eun5zc3AgQOlwnTWGuQPHjyQ1lXPasuNKIpISEjAyJEjMXToUHTq1Ek6VrFixTyXYMn+CWlcXBwyMjKwbdu2HEv4uLu7Y9WqVejRo4dVu8lkQkJCQp5rv1PxZTabcfHiRdSpU4ebxpJNmBmSi5khuZgZkouZIbmYGZLraWcmXh+PDZEbEBwejFP3Tknttd1qI9AvEIObDkblcpUL/L709PB9xjYspBcT9mp7pHyY8tiPP3H3RK4z0A8PPYxm7s0eqz+PI6uwDUAqGmeXfTYzYL20S8+ePdGzZ8/Hum+WvXv3wmKxYMiQIdIscZVKhaSkJDg5OQEAvL29sW3bNkRERGDYsGGYMmUKqlSpIus+arUadevWxbp161C3bt0cm5SGhoYiKirKqmC/e/du6TmZMmUKWrVqJT1XI0eOxNWrV2FnZ4e5c+di7ty5SExMhFKpRLlymb+p4OzsLF0rKSkJH3/8MerWrYs1a9ZI7W3atEGfPn2wfPlyaZNVpVKJ3bt3Q6/XWy0vk9vGqh4eHnj++eetlpZJTEzMd+34xMRE+Pr6YvPmzbBYLBBFEbdv30a1atUAZK6rntcyL7dv38aQIUNw+vRp9O/fH++99x4uXLiAxYsXY+PGjVbLxWQ3adIkzJo1CwDw6aefYunSpXn2r0+fPrm2d+7cGXv27MnzcVQ8iaKIxMTEfD+gIcqOmSG5mBmSi5khuZgZkouZIbmeRmZEUcTB6wcRHB6MLWe3wGA2AADslHZ4peErCPQNRDuvdo+1TDAVPb7P2IaF9GJCEATZS6lkp1NnrlutgAIWWKQ/dWrdE133cWg0Guj1equ27777Dp999hkuXLhg1V7Qay/t3r0btWrVwg8//AAAuHPnDnr27Il9+/bhhRdeAJC5PnmzZs3QrFkzfP3115g+fTq6du0q+16NGzfGxo0b0b9/f6v227dv48yZM5g7dy46d+4MILNwHhoaCgC4efMmVq1ahf3792PixIkAgNGjR8NgMECpVEoz9bt3747u3btjwoQJADLf1IxGIzQaDZydnbFnzx6oVCoYDAYkJSVBoVCgWrVqqFq1Kr7++murDUvXr1+PjIwMVK6c/yfCWcu3bNy4EcB/n0jWqlVLOufhZVVWr16N+/fvw8PDA++++y4uX76M1NRUVK9eHcOHD0eNGjVw/vz5XJdTuXr1Kvbs2QOdTodt27ahfv36aNGiBapVqwadTgc/Pz8cOXLE6jHdunWz2mh0xowZmDVrFhwcHKBQKHD9+nW0atUKEydOxJQpU7By5UpMmjQJb775Jt59912Ioojk5ORcl4chIiIiIiIiov/cS7mHNSfXICQ8BJfiLkntTSo3QZBfEAY2HghXnWsR9pCo8LCQXkpUcqgE93Lu8HTyxHDf4VgRsQI3k26ikkOlQu3HmTNnUK5cuRy/BhITEwOTyYSoqKgcjzGZTHBwcICPj88T3z80NBRdunSRljpp1qwZqlSpgt27d0uF9Ow+/fRT9O7dG4cOHZLWc7dV48aNAWQuSfNwH4DM9b+zZmX37NkTb731Fu7fvw9PT0+cOXMGnp6eVo975513sGXLFqmQfv/+fezevRtTp06FTqeDwWCAj48PwsPDAWQuffLGG29g3bp1ufbv4VngDg4OSEmx7bceDIbMT5b/+OMPmEwmqzFmXzpGFEUsW7YMb731FqpUqYK5c+diypQpaNKkCY4ePYrOnTujatWquHDhAuztc/6WQ6tWrXD06FH4+vrmWOpHoVDkWA4oqz37BzDZZ+rv3bsXQ4YMQffu3TF27FhMmTIFGo0GK1euRI8ePXD37l18/vnn0m8nEBEREREREZE1s8WM3Vd2IyQiBD9f+BkmiwkAUE5TDv0b9UeQXxACqgZw9jmVOSyklxIeTh6IGhsFjVIDQRAwwn8EjGZjoe9wPGjQINy9ezdHIT0jIwN6vR4tW7a0ardYLDAYDHjttdewdu3aJ7r35cuXERUVlaMg3rZtW+zevTvXx/Tq1Qt+fn6YPn06fv/9d1n3yyqkZ/2ZJTQ0FD4+PlIRHQDatWsHURQRGhqKgQMH5iiiA8A333yDb775BgDw+eef46uvvkKnTp0QERGBP/74Ax4eHjnWUF+4cCHmzZsHjUYjPeeLFy/Gli1bcPDgQek8o9Eoawb2m2++ibS0NIwePRr9+/fH+fPnYTabsXfvXlSsWFE6b8OGDbhx44a0ZExiYiKWLFmCCRMmYNasWTCbzThy5AiGDRuGYcOG5biPQqFA48aNcfv2bVy5cgUXLlzA+fPnpWV5LBZLjuL/w8+ByWTC/v37sXDhQuzZswdTpkzJsWltmzZtsHv3bgwePBgrV67E66+/jp49e6Jr1678h7+EUSgUqFGjBncSJ5sxMyQXM0NyMTMkFzNDcjEzJNfjZuZG4g2sjFiJlRErcTPpptTe0qMlAn0D8Vqj11BOU66gu0vFAN9nbMNCeimSvWguCEKhF9EFQcClS5dyPbZu3Tp88sknuc5ILyhZM8HbtLFeK75du3b4/vvvce3atVwf9+mnn+LFF1/E0aNH8cwzz9h8v+yF9KtXrwLInKG9Z88edOvWzerc+vXro2LFiti9ezcGDhwotYuiaLX+1KlTpzBjxgz8+eef2LFjB/z9/TF8+HD4+flhwYIF6NevHwAgJSUFFy9ehJ2dXY4PLRITE2E0GhEdHZ2jz3fv3kW5cuVQs2ZNiKKI+Ph4XL16NcdscTc3N4wdOxYmkwnz58/H/v37MWrUKPTp0wczZ84EkLnJ5wcffICBAwfC3d0dAPDRRx9BpVLh7bffxhtvvIFmzZrh008/xZdffgkgs+idfbzvvvsuFi1aBIvFAicnJ9SpUwe1atWCs7MzLBYLIiIicl1fPfvzu3DhQnzwwQd4+eWXcfr0aatlaLKv2f/ss8/i9OnTCA4OxrJly3DhwoUcrxMVfwqFApUqFe5v2lDJxsyQXMwMycXMkFzMDMnFzJBccjKTYc7ALxd/QXB4MH6//DtEZP4/u5vODa83eR3DfYejceXGj7gKlXR8n7GNIHIV+QKVlJQEZ2dnJCYm5rp8RHp6Oq5duwYfH58cS1aUdKIoQq/XQ6fT5Zjlu3LlSnz22We4ceNGEfWueGrTpg1at26NDz/8EB06dEBiYiKCgoIwevRoqyVLstaYt7e3x+HDh3HixAl06NABWq1W1m7KBoMBL730krQGur+/P5KTkzFy5EhMmDABFosFH3zwAb755hv07dsXX331FRwcMtfYT0xMxODBg3H27FmEhYXh1q1beOGFF7BlyxY0adIEX3/9NUaNGoUNGzZgwIABADJn2c+ePRunT5/Grl27MGXKFERGRuLMmTNo0KABLl68iJMnT6Jly5Y5ZunPnDkTP/74I8LCwqzaO3TogNatW2PGjBkAMn+moqOjrQroWdRqNdatWyd9AJFdamqqNLbclOaf1ZLMbDbj9OnTaNSoEXcSJ5swMyQXM0NyMTMkFzNDcjEzJJctmbkYexErwldg9cnVuJ96X2rv5NMJgb6BeKn+S9Cq+P/CZUVZfp95VC03O85IpwJlsVhybU9KSpLW3ab/HD58WPr7vn374Oqa+wYd/fv3R//+/ZGYmAgHBwe0bt26QDbLPH78uNX3CoUCI0aMwCuvvJJjdr6zszO2bduGs2fPwsnJCU5OTjh16pRUZO7atSuWLFkiFdEBICgoCH379oVOp4O/vz/atGmDOXPmoEGDBgCA2rVro2rVqrkWtN966y288cYbOdr3799v9b1Wq821iA7k3Bg1u/yK6FR8ZX1gx8+AyVbMDMnFzJBczAzJxcyQXMwMyXXs1jGMOTgGS1yXoGX1/5bY1WfosfXcVoSEh+DA9QNSu3s5dwxtNhTDfIehllvu/39NpU/c7JWAUgG3d4fkeJ+Jm7caMFvgNjHnMr1lGQvpVCjGjRuHcePGFXU3irW8iujZZZ+l/rTUrl07z2OCIKBhw4bS99lnateqVStHQVuhUKB8+fIAgOrVq2PZsmU298PV1dWm54SIiIiIiIiI/rM+cj2Oxx3HhtMb0LJ6S5y8exIh4SFYH7keCekJAACFoECPWj0Q5BeEnrV7Qq1UF22nqfApFYj/YgUAwGnsIKk5bt5qxH+xAq6ThhdVz4otFtKJiIiIiIiIiIhKsOsJ1xGTFgNBEPD92e8BAGtOrsEf1/7A2Ziz0nlezl4Y7jscQ32HwsPJo6i6S8WA27tDAADxX6yAxWQGWtRGwvy1SJyzCq6ThkvH6T8spFOB4lrSJBczQ3IolUrUq1evzK3ZRo+PmSG5mBmSi5khuZgZkouZIVt4L/LO0ZZsTLYqou8etBuda3SGQlAUYs+oOBJFERkXoqB0Kgd1HS8kzl2NigoFEi0WFtHzwUJ6ESmNa5sJggCVipEi2xXnzJTGn9HSQBAEuLi4FHU3qARhZkguZobkYmZILmaG5GJm6FFi02IxqPEgrI9cn+txlUKF1S+sRteaXQu5Z1RciKII07Vb0B8Oh/7QceiPRMD8IN76JIsF0KhZRM8HP4IqZGq1GoIgIDU1tai7UuBEUURqaioLkGSz4pyZtLQ0AJk/s1R8mEwmHDt2DCaTqai7QiUEM0NyMTMkFzNDcjEzJBczQ7mxiBb8ce0P9N/aH1XnV82ziA4A/wT+g4FNBhZi76g4MN26h6RNv+He6Bm44dsXN1r0x4N35yDlpz9gfhAPQWcHXfsA6NoFAABElRIwZmRuNEq5Kp5TQUsxpVIJZ2dnPHjwAAaDAU5OTlCpVBAEoai79sREUUR6ejoUCkWpGA89fcUxM6IoIi0tDffv34eLiwt/fbIYMpvNRd0FKmGYGZKLmSG5mBmSi5khuZgZynIn+Q5Wn1iNFRErcCX+itTu6+6L7jW744sjX0ABBSywSH/S02c2W3Do0A3cuZOMKlUc0bZtdSiVhTt/2XQ/Dvoj4dAfCkf64QhkXIu2PkGtgjagIXRt/KBr4wetfwPEL9mI+C9WwPn9objcrgFqHTwrbUDKmek5sZBeBNzd3aHT6XD//n0kJSUVdXcKjCiKMBqN0Gg0xaYoSsVbcc6Mi4sL3N3di7obREREREREVMaZLCbsurwLIeEh2HFxB8xi5gcrTnZOGNBoAIL8g+BXxQ/RSdFYfXI1PBw90Mm1E/6I/wPRydGo5FCpiEdgm+JQjH4cP/54DmPH7kJ09H81Pg8PJyxa9Bz69Kn/1O5rjk+C/s8T0lItGeevWZ+gUMDOtx50rX2ha+sP7TONobD/b5+6uHmrEf/FCrhOGg6nsYOAsDC4THgDCqWCxfQ8sJBeBLLWN3N2dobZbC41v55lMplw+vRp1KpVq9iue03FS3HNjFqt5kx0IiIiIiIiKlJRCVFYEb4Cq06swq3kW1J7a8/WCPQLxCsNXoGDxkFq93DyQNTYKChEBY4fP47pL06HRbDATmVXFN2XpaiK0U/qxx/PoW/f7/HwirW3biWhb9/vsWXLqwXWf0tKGvR/nZTWOTeevoyHb6xpVBu6Nr7QtfWDtmVTKJ3K5X1Bc+bGos7j3sD+/ddw5MgdpKZeR4dxb0jHyZogFsfFiUuwpKQkODs7IzExEU5OTkXdnUIliiL0ej10Ol2xm11MxRMzQ3IxMyQXM0NyMTMkFzNDcjEzJBczU7YYTAZsv7AdIeEh2HN1D0Rklu3K68pjcNPBGO43HA0qNsj3GiUtM3kVo7O6XpDFaFtZLCKMRrPVl8FgyvZ3M/T6DLz88vd48CAt12sIAuDuXg4nTrwJJyct7OyUsl4Pi96A9GOR0B8Kh/5wOAwR54GHlnlS1/GCro1/ZvG8VTMoy7vIGmdJ/QCjIMmp5bKQXsDKeiHdbDZDqZT3xkBlFzNDcjEzJBczQ3IxMyQXM0NyMTMkFzNTNpx7cA4h4SFYe2otYtJipPauNboi0C8QL9R9weaZ5SUpM2azBd7ei6wKuQ+rUMEey5b1hMlkeaiwnXehO/fjtp9vMhX8bGyFQoCDgxoODhqUK6eR/p71p5NOgZqGGNRKjIbX/RuoeP8mlA8Vzk2VK8HUrBGUzZtA28YX5bzc4eCghlYrf//F4vgBRlFgIb0IleVCuslkQlhYGAICAorVMh1UfDEzJBczQ3IxMyQXM0NyMTMkFzNDcjEzpVdaRhp+OPMDgsODceTmEam9qmNVDGs2DMN8h8HH1Uf2dUtSZvbvj0LHjmuKuhuPpFIpoNEoYWenhEaT+WU0mnHvXupjX1MJCxqokvGsOh7PquPgr06AvWBdwL9rtsNfGa74K8MNf2e44rZFl+u1shfp8/8z8+/29mrMmXMECQmGXK8nCJkz069dG1si1ql/EnJqucX7p4mIiIiIiIiIiKgUCb8TjpDwEGyI3IAkQ+ZMbKWgxPN1nkeQXxCeq/UcVIqyUbK7dSvvmejZ1anjhmrVnGBnp5IK2dmL2rl/n9+5th9Tq5VQKHLO9t6/Pwrbnv8UZlHAUn2NHMdH665CKYj438+foXnzakhOSkda5GUY/zoBhJ2C+vQ5KPV6q8ek6xxwy90L19yq46JjVdyEA1LTTEhNNaJqagacU41ISTEiNTUDqalGGAyZM9YtFhHJyUYkJxtlvwa5EUXg5s0kHDp0Ax06eBfINUuDsvFTSUREREREREREVEQS0xOxMXIjQiJCEH4nXGqv4VoDw32HY0izIajqWLUIe1j4wsPvYNq0gzad+803vYtdQbdt2+rY7aTFcNM5ALAqpo/WXcV4h6vYqqwJv6gTSNuwCsYjEVDEJUKb7RoKp3LQtm4mrXOuqeeDhgrbZ4CbTBakpWUW1bOK66mpGf8W263bsv8ZGXkfBw5cf+T179xJtrkvZQEL6URERERERERERAVMFEUcuXkEIeEh+P7M99CbMmcfa5Qa9KnfB4G+gejo0xEKoXQvnfGwpCQDPvnkDyxZcgwWiwhBQI51urNkLTHStm31wu2kDZRKBQKWjsfC17/AeIerAICfDFXwkcMFdLOLQYpFiZdxBXGTFkiPEex10LZsAl1bP+ja+MGucW0ISuVj90GlUsDJyQ5OTratn5/F1iV1qlRxfNyulUpcI72AleU10kvSZhZUPDAzJBczQ3IxMyQXM0NyMTMkFzNDcjEzJc+D1AdYe3ItQiJCcD7mvNTeoGIDBPkFYVCTQahgX+Gp3b+4ZkYURfzww1mMG7cLd+6kAAD69WuELl18EBT0y7/n/Hd+Sdj00vQgHgfn/4z0FVtQV0zIcVyw08CueSPYt/GDrq0f7HzrQ1AX/bzmrE1eb91KyvVDDK6Rnruif+WoVDEajdDpct/4gCg3zAzJxcyQXMwMycXMkFzMDMnFzJBczEzxZxEt2Ht1L4LDg/HT+Z+QYckAANir7dGvYT8E+gWipUfLQitsF7fMXLkSh9Gjf8Xvv18BANSq5YZly3qia9eaAABXVx3Gjt2F6Oj/1kz38HDCwoXPFasiuiUlDfq/TkJ/6Dj0B8NgPHMFXg+dIwoCXMa9Dod2/rALaAiFVt5s8cKgVCqwaNFz6Nv3+xy/EZAV0YULnyv1RXS5WEinAmM2m3Hq1KkSsSs0FQ/MDMnFzJBczAzJxcyQXMwMycXMkFzMTPEWnRSNVRGrsPLESkQlREntAVUDEOQXhH6N+sHJrnBXLChOmTEYTJgz50/MmHEI6ekmaDRKTJ7cBh980AZa7X9969OnPl54oS4OHbqBO3eSUaWKI9q2rV7khVwxw4T042czC+cHwpB+/AxgMludo2lYC4K9FoZjpwGNGoIxAwo7NXRt/Iqo17bp06c+tmx5tUR8gFFc8B2YiIiIiIiIiIjIRhnmDPx66VcEhwfjt8u/wSJaAADOds4Y1GQQAv0C0cy9WdF2shj4449rGDVqJy5ciAUAdOlSA0uX9kSdOuVzPV+pVBT5hqKiKMJ49opUONf/dRJiqt7qHFX1KtC184d9uwBo2/ghae12xH+xAq6ThsPt3SGIm7ca8V+sAAC4vTukCEZhu6wPMPbvv4YjR06hdesm6NDBp8g/wCiuWEgnIiIiIiIiIiLKJux2GCaGTsTsrrMRUDUAAHAl7gpWRKzAqhOrcDflrnRuO692CPQNRN8GfaFTF5/lVIrKvXspeO+9UKxffwoAULmyAxYufA6vvdawWK3ZniXjxh3oD2Yu1aI/HA7zg3ir44ryztC18Yd9e3/o2gZA7V1VOpZVNM8qogP/Fc9LSjFdqVSgfXsvODg8QECAF4vo+WAhnQqU8gl2GqayiZkhuZgZkouZIbmYGZKLmSG5mBmSi5kpfGtPrsW+qH1YdWIVLsVeQkhECP649od0vKJ9RQxpNgTDfYejboW6RdjT3BVFZiwWEd9+exwffrgXCQnpEARg1KjmmD69E1xctIXen7yYYxOgPxwB/aHjSDsQBlPULavjgr0W2pZNYd8+ALq2/tA0rAlBkUdx2WyxKqJnkb43Wwp+AE8J32ceTRDF3PZmpcclZ6dXIiIiIiIiIiIqHq4nXEdMWgwEQUDXdV0Rp4+DAAEi/iudda/ZHUF+Qehdtzc0Sk0R9rZ4iYi4g5Ejd+Lo0cyitJ9fFSxf/jyaN69WxD0DLGnpSP/7pFQ4N56+bL27plIJrV996P4tnGsDGkLQqIuuw1So5NRyOSOdCowoikhMTISzs3Ox/FUdKn6YGZKLmSG5mBmSi5khuZgZkouZIbmYmcLjvcg7R1v2IjoA7Bq0q5B68/gKMzPJyQZ8+uk+LF58FBaLCEdHDWbM6IRRo5oX2RIhoskEQ8R56A8eR9rBMKSHnQGMGVbnaOrXgK6df+bXs82gcHQokr4WF3yfsQ0L6VRgzGYzzp8/Xyx2haaSgZkhuZgZkouZIbmYGZKLmSG5mBmSi5l5ukRRRNjtMASHB8NOaQeD2ZDreSqFCqtfWF24nXtMhZEZURSxdes5jBu3C7duJQMAXnutIebP746qVR2fyj3z60vGhSik/bvOefqfJ2BJTrU6R1WtEnTtAjJnnbfxg6py7huellV8n7ENnxkiIiIiIiIiIipT4vXx2BC5AcHhwTh175TU7uHkgeik6Bzn/xP4D/yq+BVmF4utq1fjMWbMr/jtt8sAgJo1XbFs2fPo1q1mofXBdOueVDjXHzwO8/04q+MKVyfoWvtKy7Woa3hwpjU9MRbSiYiIiIiIiIio1BNFEQevH0RIRAi2nN2CdFM6AMBOaYe+Dfoi0C8QjhpHBAQHQAEFLLBIfxJgMJgwd+6fmD79ENLTTdBolJg0qTUmTWoDne7x1xSPm70SUCpybNgJAHHzVgNmC5xHvAL94XDo/y2eZ1y5aXWeoNVA27IpdO38Yd8uAJpGtSBw80wqYCykU4ERBAE6nY6f8JHNmBmSi5khuZgZkouZIbmYGZKLmSG5mJkndy/lHtacXIOQ8BBcirsktTeu1BhBfkEY2GQg3HRuAIDopGi4l3OHp5MnhvsOx4qIFbiZdBOVHCoVVfdlexqZ2b8/Cm+9tRPnz8cAADp18sGyZT1Rt26FJ7+4UoH4L1YAgFRMt+gNePD+PKRs/g1K9wqIn7vaeoNQhQJ2vvWgaxcA+3b+sAtoCIXW7sn7UkbxfcY2giiK4qNPI1vJ2emViIiIiIiIiIgKntliRujVUASHB+PnCz/DZDEBAMppyqF/o/4I9AtE86rNcy0cGkwGaJQaCIIAURRhNBthpyqbRdr791Px3nu7sW5d5vI3lSo5YMGC7ujfv1GBFl3j5qxC/OyV0LULAEQL9H+eBMxmq3PUdbxg3y4Aunb+0LZqBqVz4a7FTqWTnFouZ6RTgbFYLIiJiUGFChWgUBTNzsxUsjAzJBczQ3IxMyQXM0NyMTMkFzNDcjEz8txIvIGVESuxMmIlbib9t/xHi2otEOQXhFcbvgpHu/wLsNmL5oIglLgiekFkxmIRERISjg8+2IOEhHQIAjByZABmzOgEV1ddgfQz4+Zd6PcfQ9qBMOgPhgGA9CcACOXs4dCzHezb+0PX1h+qKhUL5L6UE99nbMNCOhUYi8WCq1evws3NjT90ZBNmhuRiZkguZobkYmZILmaG5GJmSC5m5tEyzBn45eIvCA4Pxu+Xf4eIzMUXXLWueL3J6wj0C0Tjyo2LuJeF50kzc/LkXYwcuRN//5256aqvrzuWL++FZ56p9mT9Sk6F/nA40vaHQX/gWI51zhWODrCkpGUu4aJWwefqLi41Ukj4PmMbFtKJiIiIiIiIiKjEuRR7CSHhIVh9cjXup96X2jt6d0SgXyD61O8DrUpbhD0sWZKTDfjss/1YtOgfmM0iHB01mDatI0aPfgYqlfziqmgywRBxHmkHjkG/7xjSj5+1Xq5FqYTWrz50HZrDvkNzpO0/hvg5qwCNGjBmIH7+mlw3ICUqKiykExERERERERFRiaDP0GPrua0ICQ/BgesHpPbKDpUxtNlQDPMdhtrlaxdhD0seURSxbdt5jB27C9HRSQCAV15pgAULuqNaNXn7/2Vcu4W0/UehPxAG/aFwWJJSrI6ra3hA17457DsEQNvGD0qncgCAuHmrET9nFVwnDYfbu0Myv39oA1KiosZCOhUYQRDg7OzMX7shmzEzJBczQ3IxMyQXM0NyMTMkFzNDcjEzmU7dO4Xg48FYH7keCekJAACFoECPWj0Q6BeI52s/D7VSXbSdLCbkZObatXi8/fZv2LnzEgDAx8cFS5f2RI8etn0YYU5Ihv7Q8X/XOj8G0/U7VscVLo7QtfWHfcfm0LVvDnX1KjmukVU0zyqiA/8Vz1lMLxx8n7GNIIqiWNSdKE3k7PRKRERERERERES5SzYkY9PpTQgOD8ax28ekdi9nLwz3HY4hzYbA09mzCHtYchmNZsyb9yemTTsIvd4EtVqBDz5ojcmT20Kny/sDCdGYgfSwM9AfCEPagWMwRJwHLJb/TlCroG3eCPbtm0PXsTnsmtSBoFTm25e42SsBpSLXYnncvNWA2QK3icMec6RE+ZNTy2UhvYCV5UK6xWLB7du3UbVqVW5MQDZhZkguZobkYmZILmaG5GJmSC5mhuQqa5kRRRH/3PoHIeEh2HR6E1IzUgEAaoUaL9R7AUF+Qejs0xlKRf7F2bLsUZk5cCAKb721E+fOxQAAOnTwxtdfP4969SrkOFcURWRcvoG0fcegP3AM+iMREFP1Vueo63jBvsMz0LUPgK5VMyjK2T+dgdFTU9beZ7KTU8vl0i5UYCwWC6Kjo+Hu7l7mfujo8TAzJBczQ3IxMyQXM0NyMTMkFzNDcpWVzMTp47Du5DqERITg9P3TUnvd8nUR6BeIN5q+gUoOlYqwhyVHXpl58CAV778fijVrTgIAKla0x/z53TFwYGOrJT3MMfFIO3Qc+n3HkHYgDObb962ur6jgAvt2AZmbhLYPgKoqX5eSrqy8zzwpFtKJiIiIiIiIiKjQWUQL9kftR0h4CH489yMMZgMAQKvS4tWGryLQNxBtqrfhus0ymM0WHDhwHUeO3EFq6nV06OADQRCwcmUEJk4MRXx8OgQBGDHCH7NmdYarqw6WdAP0RyOh35+5XIvx1EWrawp2GmhbNoGufQDs2zeHplEtCCy2UhlU7ArpsbGxCAgIwL59++Dt7Z3j+HPPPYd+/fphyJAhAIDTp09j6NChuHz5MgIDAzF79mzpDfZpHCMiIiIiIiIiokcLux2GiaETMbvrbARUDZDa7yTfweoTq7EiYgWuxF+R2pu5N0OQXxAGNB4AF61LEfS45IqbvRJnL8Si/24VoqOT/m09hcqVHTBScwXxsamIT6uJpk0rY/nXz8PX2Qj9pu24ve8Y0v8+CVFvsLqepmHNf2ecN4e2ZVModHaFPyiiYqZYfXwUExODXr16ISoqKtfjGzZswO+//y59bzAY0Lt3b/j7+yMsLAxnz57F6tWrn9oxyp9CoUDFihX5KyBkM2aG5GJmSC5mhuRiZkguZobkYmZIrpKcmbUn12Jf1D6sO7kOZosZOy/uxEubX4LnAk9M/mMyrsRfgaPGESP9RyIsKAzhI8IxqvkoFtEfw9kLsajy8894IfaEVXvfpEi8rj8NB5WIH4ZVwK5nH8A9cAyi2w9B7KdLod93FKLeAGXl8ij36nOo9PUn8DqzHZ77V6PCZ6Nh3/EZFtHLgJL8PlOYitVmo126dEHv3r0xbtw4XLt2zWpGelxcHBo0aAAXFxdMmjQJQ4YMwU8//YRhw4YhOjoa9vb2OHnyJEaPHo3Dhw8/lWO2KMubjRIRERERERFR2XY94Tpi0mIgCAJ6bOiB+6n3Ya+2h4PaAQ/SHkjntfJshSC/ILzS4BU4aByKsMcln9lsgbf3IrwQewLjHa5iQWoNrNB74fNy59FHewcPzBpUVBqtHiPo7KBr5QtdhwDo2jeHpp4PV2OgMqnEbjb67bffokaNGhg3blyOY++++y5eeukl6PX/7Qx88uRJtGzZEvb2mbsBN2nSBGfPnn1qx3JjMBhgMPz36y9JSZm/PmMymWAymQBkfqqjUChgsVhgsVikc7PazWYzsn+ekVe7UqmEIAjSdbO3A4DZbLapXaVSQRRFq3ZBEKBUKnP0Ma/23MZksVhw48YN1KhRA6Ioloox5dd3junJx2SxWHD9+nXUrFlT+r6kjym/vnNMTz6mrMzUqFFDOr+kj+lRfeeYnmxMWZnx8fGBSqUqFWN6VN85picbU1ZmvL29oVarS8WYHtV3junJxpSVGS8vL6jV6lIxJlv6zjE9/phMJpOUGZVKVSrGVBpfp+I0poyMDCkzSqWy2I/Je5E3HpaWkYa0jDTp+5MjTqJBxQZS3wEU6zHl117U2UtMTMc33xxHdHQi9igrwl+dgPEOVzHO/iqy6uIVlUZYRCCjpjcq9mwFXYcA2AU0hMpeJ40pe/+Lekyl8XUq7mMymUy4du0avLy8pLaSPiZbX6fs5zxKsSqk16hRI9f2ffv2Ye/evTh9+jTeeecdqT0pKQk+Pj7S91lPYHx8/FM55urqmqNvs2bNwtSpU3O0R0REwMEh8xPVihUrombNmrh27RoePPjv01cPDw94eHjg4sWLSExMtHoeKlWqhNOnT1t9cFCvXj24uLggIiLCKlBNmjSBRqNBWFiYVR8CAgJgNBpx6tQpqU2pVKJ58+ZITEzE+fPnpXadToemTZsiJiYGV69eldqdnZ1Rv3593L59G9HR0VJ7bmMSRRHp6enw9vbGpUuXSsWYgNL3OhWnMYmiiOTkZPj4+CAuLq5UjAkofa9TcRqTKIpISEiAu7s7dDpdqRhTaXyditOYsjKj0Wjg5eVVKsZUGl+n4jSmrMykpKTA19e3VIypNL5OxWlMJpMJCQkJiImJQdOmTUvFmErj61ScxnT//n0pM56enqViTKXxdSpOYzp37pyUGXt7+2I9pqiUKDxb4Vn8FfMXcqNSqLC061KkXU9D2PWwUvU6FVb24uIMuHNHg1OnEvDXrlNwvxGFNqpYHHGNReVss84FARBF4AdDVRw2uuGvDDeMeycA3bpVAWAGIk8VmzGVxtepJI7p8uXLiInJ/G2S0jImW16nihUrwlbFammXLIIgSEu7pKeno0mTJliwYAGef/55DBkyBB06dMCQIUPwwQcfICMjA/Pnz5ce6+npib///huLFy8u8GPVqlXL0dfcZqR7enoiNjZW+nWA0vyJVfZ2s9mM8PBwNG/eHIIglIox5dd3junJx5Q9M1n9LOljyq/vHNOTjykrMwEBAdLs4pI+pkf1nWN6sjFlZcbf3x8ajaZUjOlRfeeYnmxMWZnx8/ODnZ1dqRjTo/rOMT3ZmLJnRqPRlIox2dJ3junxx5SRkSFlRq1Wl4oxlcbXqTiNyWg0SplRqVTFbkxpGWn4/vT3WHFiBY7cPIL8HB9xHL7uvqXydXoaYxIEAVFRiThwIAqHDt3AX4evwyEqCm01sWinjkVjVRIU2VZjSRMVuGvWooYqDUZRgEYQsSC1BpbqMyeu7tkzCO3bexXpmErj61QaxmQ0GnH8+HH4+flBqVSWijHZ+jqlpKTA1dW15C3tkptp06ahefPmeP7553Mcc3Nzw+nTp63akpOTodFonsqx3NjZ2cHOLuemCyqVCiqV9dOb9YI9LCs8trY/fN3HaRcEIdf2vPpoa3vWr2SVpjFl4ZiezpiyMlOaxvSoPnJMTzYmQRCkr9Iypsdt55hsaxcEQfp7aRlTdhxTwY8p6z/KH6fvxXVMtvSRY3r8MWVlJuu/a0rDmJ6knWPKvz0rK1mFivz6XlLGVBpfp+I0puyZKU7/PkXciUBweDA2RG5AkiFzmVuFoMDztZ9HJ+9OGL97PBRQwAKL9GfWmErj61QQYxJF4MyZGBw6dOPfr+vAvRi0U8eirSYW76jj4ORiXagUanvDsWtLBAZHwyfxFt62vyYVz0frrmK8Q+YSL9vLN0OHDj5QKnPetyz9PHFMebdnvc9kv15JH5Mtr1Nu5+Sl2BfSN27ciAcPHsDFxQUAkJaWhu+//x5Hjx5F3759ERISIp0bFRUFg8EANzc3NG/evMCPUf4UCgU8PDxkBZDKNmaG5GJmSC5mhuRiZkguZobkYmZIruKUmcT0RHx3+jsEhwcj/E641O7j4oNAv0AMbjoY1ZyqITopGl/++SU8nTwx3Hc4VkSswM2km6jkUKkIe1/8GAwmhIXdlgrnR47cQHpiGlqo49FWE4th6ljUckuzeozg4giHDs2h69QC9h2fgcq9AgDg/eg5qPLzYSxM+28G+lJ9DQgCMM7+Kl7r2ijXIjoRULzeZ4qzYr+0S3R0tNWvBbz33nto2bIlhgwZAhcXF1StWhVz587FG2+8gZEjR+LWrVv45ZdfYDKZCvyYLeTs9EpEREREREREVJyJoog/b/6JkIgQfH/me2nTUI1Sg5fqvYQgvyB09OkIhWBdgDOYDNAoNdLSr0azEXaqnL/RX5YkJxvw5583pcL50aO3kJ6egdrKVLT9d9b5M+oE2AnZNj9UKKD1bwBd58zCuV3TuhBymQ0cN3slzl6IRf/dKkRHJ0ntnp5O2NjVhAZ1y8Nt4rDCGCZRiSKnllvsC+kPy75GOgD89NNPGDBgABwdHWE2m3HgwAE0bNjwqR17lLJcSDebzbh48SLq1KmT5694EGXHzJBczAzJxcyQXMwMycXMkFzMDMlVVJmJSYvB2pNrERIegnMx56T2+hXqI8gvCK83fR0V7CsUWn9Kovv3U3Ho0HWpcH7ixF1YLCKchAy0VsehrSYW7bVxqIx0q8epqlWCruMzsO/UArp2/lA6O9p8T7PZgv37ryEi4jJ8fWvluZwLUXZl+d8mObXcYrm0S361/dWrV1t9/+KLL+LSpUsICwtDq1atrHZafRrHKG+iKCIxMTHf148oO2aG5GJmSC5mhuRiZkguZobkYmZIrsLMjEW0YO/VvQiJCMG2c9uQYckAANir7fFaw9cQ6BeIZz2elfaEKK3MZgsOHbqBO3eSUaWKI9q2rf7IYrQoirh2LcGqcH7xYiwAQAERjVVJGG0Xiy6OCahviYcC/72eglYD7bPNYN8ps3iuru312M+xUqlA+/ZecHB4gIAALxbRySb8t8k2xbKQLle1atVQrVq1QjtGRERERERERFRa3Eq6hVUnVmFFxApEJURJ7f5V/BHkF4T+jfvDya5s/Nb9jz+ew9ixu6yWR/HwcMKiRc+hT5/6UpvFIuL06ftWhfPbt5Ol45UV6XhFG4vnK6TC33IfOuO/s87/XbVFXdcb9h2fgX3HZ6B9thkUurK97A1RSVAqCulERERERERERGQ7k8WEXy/9iuDwYPx66VdYxMwKr7OdMwY2HohAv0D4VvEt4l4Wrh9/PIe+fb/Hw5Nyb91KQt++32P69E5QKoV/Nwa9iYSE/5Zk0cCMdtok9PVIR0vhAdzi72ce+PcUhVM56NoHZBbPOz0DVbXKhTQqIiooLKRTgVEoFKhRowZ3+CWbMTMkFzNDcjEzJBczQ3IxMyQXM0NyFXRmrsRdwcqIlVh1YhXupNyR2ttWb4tAv0D0bdAX9mr7ArlXSWI2WxAxaj5GaY1Yqq9hdUwUgdG6q7g34woWp9XMakWDckYMqmlGG1UM3O9EQWE0Agn/HhYE2PnWg32nfzcJ9asPQVU4ZTi+z5BczIxtiuVmoyVZWd5slIiIiIiIiIiKn3RTOn46/xNCwkOw99peqb2ifUUMbjoYw/2Go16FekXYw6K3d+9VbOs9FeMdrmJBag2rYvpo3VWMd7iKpWneMNeugf9VSoXnratQ3n9gdQ1l5fL/zjhvAV37ACjdnAt7GEQkU4nfbJRKJrPZjNOnT6NRo0ZlbodfejzMDMnFzJBczAzJxcyQXMwMycXMkFxPkpkz988gJDwEa0+tRZw+DgAgQEC3mt0Q6BeI/9X9HzRKzdPodrEniiIuX45DaOhVhIZexe+/X4b+3+L5eIerAIBleh9MdTiPAbpbiDZr8abuOlS3ooBb/15Eo4auZRPo/i2ea+rXKBYbsfJ9huRiZmzDQjoVGFEUodfrucMv2YyZIbmYGZKLmSG5mBmSi5khuZgZkktuZlKNqdh8ZjNCwkPwV/RfUruHkweGNRuGob5D4e3i/ZR6W7zFxemxd+9VqXgeFZWQ45zv0j1QT5WM8Q5XMc7+KrLq4h7KzMXOM6q4o8LzraHr1AK6Vs2gcNAV4ghsw/cZkouZsQ0L6UREREREREREJZgoijh+5ziCjwfju9PfIdmYDABQCkr0rtsbQX5B6F6zO5SKsjXT1Gg0488/byI09ApCQ68iLOy21UaiarUCbVp54NXGdmgl3MP1TftQzxIPxb/Fc0HIXB891FgRhzPK41J5L/wVMRlKJdeRJiqLWEgnIiIiIiIiIiqB4vXx2BC5ASHhITh576TUXtO1JgL9AjGk2RC4l3Mvwh4WLlEUcfbsA2nG+f79UUhLy7A6p0GDivhfO3f0LJ+CGnevwnh4KyxnEjOPAYAA3DdrUElpRIYoQC2IOGt2xHcGD2xZ/DKL6ERlGAvpVGCUSiXq1avHtZTIZswMycXMkFzMDMnFzJBczAzJxcyQXBH3IjDxzETM85yH5tWaQxRFHLpxCMHhwdhydgvSTZlLjtgp7fByg5cR6BuI9t7toRDKRsH33r0U7Nnz33Itt28nWx2vVMkB3br44MV6SgQY70J5LAKGrZsAUUT6v+coHB2g69Ac9p1b4szOcFQK3S1tOJq10ehr/RqhTZ/6hT/Ax8D3GZKLmbENC+lUYARBgIuLS1F3g0oQZobkYmZILmaG5GJmSC5mhuRiZkiudafW4VD0IXx7/Fvsj9qPkIgQXIy9KB1vVKkRgvyCMKjJILjp3Iqwp4VDr8/AoUM3EBp6Bbt3X8WpU/esjmu1KrRr54Xn21RGZ4cElL90Hvr9q2HZnQATANO/52ka1oR955aw79wS2uaNIKhViJu3GpVCd8Nl4jD0bd4Rre8ko0oVR7gc2wfMXom4eRXh9u6Qwh6ybHyfIbmYGduwkE4FxmQyISIiAr6+vlCpGC16NGaG5GJmSC5mhuRiZkguZobkYmbIFtcTriMmLQYW0YK1J9cCAEIiQqTjOpUOAxsPRKBfIJ6p9gyErB0xSyGLRcTJk3cRGnoVu3dfweHDN2AwmK3O8fV1R9cuPni+phL14qNgPHAYhiXnAIsFqf+eI5Szh3375rDv0hL2nVtAVaVizpuZLXCdNBxu7w5Bh+ztHYZCUAiA2fKURlmw+D5DcjEztuEzQwXKbDY/+iSibJgZkouZIbmYGZKLmSG5mBmSi5mhR/Fe5J3vcb1Jj+D/BRdOZ4pAdHSSNON8796rePAgzeq4h4cTunatgedaVUZrVSxUx05Av2MnzA/ikX1hF02DGv/NOn+mMQR1/mUwt4nD8j5WAmaiZ8f3GZKLmXk0FtKJiIiIiIiIiIpYhjkDv1z8BSHhIfmep1KosPqF1YXTqSdgNltw6NAN3Pl3eZS2bavnuVFncrIB+/dHSeucnz8fY3W8XDkNOnTwRtcu3ujmrUCly+eQtncXDB+dRbrlv1nimbPOA2DfuQXsO7eEqmqlpzpGIipbWEgnIiIiIiIiIioil2IvISQ8BKtPrsb91PtSu38Vfxy/czzH+f8E/gO/Kn6F2UXZfvzxHMaO3YXo6CSpzcPDCYsWPYc+ferDZLIgLOw2QkOvIDT0Kv76Kxom038FcYVCQPPmVdG1aw10e9YdjVNvwXjgKNK+2QTzgzjEZ7uXpn4NqXCufaYxBI26EEdKRGWJIIqiWNSdKE2SkpLg7OyMxMREODk5FXV3CpUoitDr9dDpdKV6fTYqOMwMycXMkFzMDMnFzJBczAzJxcwQAOgz9Pjx3I8IDg/GgesHpPbKDpUxpNkQDPcdjmRjMvy/9YcCClhgkf48PuJ4sS6k//jjOfTt+z0erjYJAiCKQIsW1XDhQiwSEtKtjteo4YquXWuga2dvtKtigeroCaTt/RvpYWeA7LPOHXTQtfOHQ5dnoevUAmqPyoUxrBKF7zMkV1nOjJxaLmekU4HSaDRF3QUqYZgZkouZIbmYGZKLmSG5mBmSi5kpu07dO4WQ8BCsP7Ue8emZ86oVggLP1XoOgb6B6FWnF9TKzBnV0UnRcC/nDg8nDwxtOhSrTq5CdFI0KjkU3+VKzGYLxo7dlaOIDkBq++efWwAAFxctOnXyySyet6iEytcvI23v30ibEoykB3FWj1XX9c5c67xLS+haNOGscxvwfYbkYmYejTPSC1hZnpFuMpkQFhaGgIAA7vBLNmFmSC5mhuRiZkguZobkYmZILmam7Ek2JGPT6U0IiQjB0VtHpfbqztUx3Hc4hjYbCk9nz1wfazAZoBAVOH78OPz9/WERLLBT2RVW12Xbt+8afuo1BWZRwFJ9jRzHR+uuQimIqDt7DIa0doZh3z9I2/tP5qzzbBsdCvaZs86zlmxRe7oX5jBKPL7PkFxlOTOckU5EREREREREVEREUcTRW0cRHB6MTac3ITUjFUDmRqEv1nsRgb6B6FKjC5QKZb7XsVPZwWQyAQAEQSi2RfS7d1OwYcMpLFz4D14QBYx3uAoAVsX0CfaXMco+CqczyqHO/M9x57NEq2uo63hlzjrv3BK6lk0g2HF2LBEVLyykExEREREREREVgDh9HNafWo/g8GCcvn9aaq9Tvg6C/ILwRtM3ivXSLHIYjWbs2HERq1efwK+/XoLZnLngwVJkFs/HO1xFRYURdyxa9NdGw0OZuSZ6I3UKkAQI9lro2mabdV69SpGNhYjIFiykExERERERERE9JotowYGoAwiJCMHWs1thMBsAAFqVFq80eAVBfkFoU71NqdjATxRFnDhxF6tWncDGjZGIjdVLx1q0qIah/etjzxc/oUqKAckWJQbpoq0ef8Vsj3BNVYzZOBL2rZpBoS2eM+yJiHLDNdILWFleI10URZjNZiiVylLxHwj09DEzJBczQ3IxMyQXM0NyMTMkFzNTetxJvoM1J9cgJDwEV+KvSO1NKzdFkF8QBjYZCBetyxPfpzhk5v79VGzYcAqrV5/EqVP3pPYqVcph1EteeKVqGpwiI6E/HA7RYJSOiyIgCIBJFNA1oRWiLTps2fIq+vSpXxTDKDOKQ2aoZCnLmeEa6VRkjEYjdDpdUXeDShBmhuRiZkguZobkYmZILmaG5GJmSi6zxYxdl3chJCIEv1z4BWYxc4NMR40jBjQegCC/IPhV8SvwQlRRZMZoNOPXXy9h9eoT2LnzEkwmCwBA93/27js+qir///hrZtIrCTUhtKCUEEACKNJFqlJDaIoKAuuqa9lddf19d/e73123uKi72HdJKApKFQFFUJEqigIJJYTeQ0IJgfQ6c39/RAaQNjckpL2fj8c+WM65c+85mTfX5JM753hYeKZ3IDEhOdQ/uIOihUsByP3pdW6NGnAqvBXxGw/R33GCQsOCh8XgkbrpdHz31yqi3ya6z4hZyszNqZAuZcZut7Nz584aucOvlI4yI2YpM2KWMiNmKTNiljIjZikzVdOxC8eYkTCDWdtnkZx5abmSro26MrnDZEa3GY2vh2+5XPt2Z2bHjlPMnr2duXN3kZZWUh6vZSlkQmsHIxvkEHr8IEZ8FgBFAFYrXp0j8enfFZ9+9+LRqhn+//qAOuvXkTFmJPu63U/LTd/w+IJPCDryA6BCennTfUbMUmZco6+MiIiIiIiIiMjPFNoLWb5vObHxsXx96GsMSlbGre1dm0fbP8qkDpNoU69NBY+ybKSl5TqXbtm+/RRg0MKWw4R6mQyrk0WDtJNYzjrgLBiANSgAnz73lBTP77sbW9Cl5RDS35jN+VdnEPTyJJr/dgJRAOPakt6sFudfnQFA8G8nVMAsRURujQrpIiIiIiIiIiI/2Zu2lxnxM/hgxweczT3rbO8b3pfJHSYzvNVwPN2q/iaZRUV2Vq48yOzZ2/n88/1Yi4q41/08rwScY5D/eWoVZIMDOFNyvEdEOD5978WnX1e8OkVgud5Tq3YHQS9PuqpY7vy73VFeUxIRKVcqpEuZstlsFT0EqWKUGTFLmRGzlBkxS5kRs5QZMUuZqXxyi3JZnLSYuPg4Nh7f6GwP8Qvh8Q6P83iHxwkPCq+w8ZVlZnbtOu1cusWWdo7eHmm8651Gt1rn8fxpzXcKwOLlgXf3qJKnzvvei3ujBi6dP/ilx6/fpyfRbxvdZ8QsZebmLIZhGBU9iOrEzE6vIiIiIiIiIlL+tqZs5aWvX2Jqv6l0Cu3kbN9+ajux22L5aNdHZBRkAGC1WHnwzgeZEjWFQXcOws1a9Z9BPHcul48/3sUHsxIwdu3jPo807vNIo5Vb9hXHuTWsh0+/e/Hpdy/e3Tti9fGqoBGLiNweZmq5Vf+/BlJpGIZBRkYGgYGBZb5DuVRPyoyYpcyIWcqMmKXMiFnKjJilzFSMD3d8yNqja5mzYw4tardg3q55xMbHsi11m/OYZrWaManDJCbcNYGGAQ0rcLRXKm1miosdrFp1kAWxm8lavZme1rO873GO4FpFlw6yWvHqGHFpo9CI5splNaD7jJilzLhGhXQpM3a7nb1792qHX3GZMiNmKTNiljIjZikzYpYyI2YpM7fPsQvHSMtNw2KxsCBxAQDT46czPX46+cX5AHjYPBjRagSToybTp1kfrBZrRQ75msxmZnfiaZZN+5q05RvpXJjCH9wycPO5bDGCAD/87v9po9A+92ALDizH0UtF0H1GzFJmXKOvjIiIiIiIiIhUO03fbHpV28UC+kUnf3OSOj51btOIzLPbHaxff4xNm1LJyTlG797NsNmuLvafS83gm6nLSFu2gTYXjjHG9tM83X86T9NG1B7So2Sj0M5trr9RqIiIXJfunCIiIiIiIiJSbTgMB2uOrOGehvfww8kfrnmMm9WN2cNmV9oievrUmSTtO8e4r9xITs78qXUnYWEBzOtfTETL2viMfYD4dz7j3PKNNE07TkfLTxuF2qDIYiO/TWsajbufgIHdcG8cUmFzERGpLlRIlzJjsVjw9vbWWkriMmVGzFJmxCxlRsxSZsQsZUbMUmbKT0pWCrMSZjEjYQZHLhy54bE/TP6BqJCo2zQy85L2nSNk+XKG5YTzLuEAWDH45YXNhCxP4Zi7NwGvzaI+UB/AAues3mS3b0erSYNoNrgrVl/vipyCVCDdZ8QsZcY1FsMwjJsfJq4ys9OriIiIiIiIiJResaOYlQdWEhsfy4oDK3AYDgACPQN5uO3DdGvcjYeXPIwVKw4czj+3/WJbpS2k2+0OmjZ9k2HntvNr38OsyK9HIVYGeJzBx+pwHucwIJEgstu3o92TD9JuZBcVwURETDJTy9UT6VJmHA4HaWlp1KlTB6u18m3QIpWPMiNmKTNiljIjZikzYpYyI2YpM2Xj8PnDzEyYyazts0jJSnG292jcg8lRk4mJiMHH3YfkzGQa+DWgUUAjJnWYxIyEGZzIPEE933oVOPob+27xVvqkJdLZ4wJ2Ax70OuPsy3dY+aaoLmsL69Dl10P59Z8H4OFhq8DRSmWk+4yYpcy4RoV0KTMOh4PDhw8THBysf3TiEmVGzFJmxCxlRsxSZsQsZUbMUmZKr6C4gKV7lxIbH8s3R75xttf1qctj7R9jUtQkWtVpdcVrwgLCOPrcUTxsHlgsFn7R8RcU2gvxdPO83cO/LsPhoCB+DzlfbiL3y02E7jnM//pd1m+AxQJFhoW70ntTTEluRrdrpCK6XJPuM2KWMuMaFdJFREREREREpNJKOptEXHwcH+74kHN55wCwYKF/8/5MjprM0JZD8bB5XPf1lxfNLRZLpSiiO3LyyNuwtaR4/tX32M+mO/uKDQvbigNZU1iXBpZ8JvqcoNCw4GExeML7KO/mlayZHhLiX1HDFxGpkVRIFxEREREREZFKJacwh4W7FxKXEMd3J75ztocFhPH4XY8zscNEmtZqWnEDLIXiU2nkfvUdOV9uIm/DVoz8QmdfrtWdtXnBfFNYh/WFdcixefKE+0Em+pzg3znhvJsXztPeh/m172EsFlhW+y569GhcgbMREal5VEiXMmOxWAgMDNTmJuIyZUbMUmbELGVGzFJmxCxlRsxSZq7PMAziU+OJjY/l410fk1WYBYDNYmNIyyFMiZrCgOYDsFmrxnImhmFQmHiQnK82kbtqEwXb917Rn+VXiy+yg1iRUYstRUEYbm6MGNGKpU91xu3DRYR8dphpueHOJ9DfzQvHYoHnfQ4zpl8kNpuWX5Br031GzFJmXGMxDMOo6EFUJ2Z2ehURERERERGp6S7kX+CjnR8RlxDH9lPbne3Ng5ozOWoyE+6aQAO/BhU3QBOMgkLyNiWQs2oTuV9tovjkpY1CsVjIa9aMbwrr8H6ilX3FvoCFhg39eeKJjkyeHOVcriV96kyS9p1j3FduJCdnOk/RqFEAH/crJqJlbYJfevw2z05EpPoxU8vVE+lSZhwOBykpKYSGhmpjAnGJMiNmKTNiljIjZikzYpYyI2YpMyUMw+Db498SGx/LoqRF5BfnA+Bp82RkxEgmd5hMr6a9sFoq/9fIfu4Cuas3k7PqW3LX/oiRk+fss3h7YuvSgc0eofxzYyEJP+Y7+/r1C+fJJzsxZEhL3NyunGfwS4/THThqd7B+/VGSkk4QEdGIXr2a6kl0uSndZ8QsZcY1KqRLmXE4HCQnJ9OgQQP9oxOXKDNiljIjZikzYpYyI2YpM2JWTc/MmZwzfLjjQ+Li49h3bp+zPbJeJFOipjC+3XiCvYMrcIQ3ZxgGRQePl2wUumoT+VsSweFw9tvq18anf1eON2nJO5sLmLdkP4WFJU+V16rlxcSJd/HLX3aiRYvaN72WzWalZ8/G+PicoVOnxiqii0tq+n1GzFNmXKNCuoiIiIiIiIiUG7vDzurDq4mNj2XZvmUUO4oB8HX3ZWzkWKZETeHuhndX6rV5jeJi8n/YVVI8/3ITRYeTr+j3aHMHvgO7Yel5Dwt35PH+f+PZsWO7s79Tp1CeeqoTY8ZE4uPjfptHLyIiZUGFdBEREREREREpcycyTjBr+yxmJszkWMYxZ/vdDe9mcofJjI0ci7+nfwWO8MbsmdnkrfmRnC+/JXf1ZhwXsi51urvh3T0K3wHd8BnQjQOZVqa+v4UPBn5OVlYhAF5ebowbF8mTT3aic+eGFTQLEREpKyqkS5mxWq3UrVtXHwERlykzYpYyI2YpM2KWMiNmKTNiVnXPTJG9iM/3f05cQhyrDq7CYZQseVLLqxaPtHuEyVGTaVe/XQWP8vqKjqWQ8+V35H61ibxNCVBsd/ZZgwPx6XsvvgO74XPf3RR7eLJ06V7eG7+S9esv/aLgjjuCefLJTkyYcBfBwd63PKbqnhkpe8qMmKXMuMZiGIZR0YOoTszs9CoiIiIiIiJSHRxMP0hcfByzt8/mdM5pZ3vvpr2Z3GEy0a2j8Xa/9aKyGelTZ4LNSvBvJ1zd98ZssDsIemECBQl7yFlVsmRL4Z7DVxznfmcTfAd0xWdAd7w6t8Fis3HiRAbTp28jLi6BU6eyAbBaLQwd2pKnnurE/feHY7VW3mVqRETkEjO1XD2RLmXG4XBw5MgRmjVrpt9giUuUGTFLmRGzlBkxS5kRs5QZMasqZ2ZrylZe+volpvabSqfQTuQX57NkzxJi42NZd3Sd87j6vvWZcNcEJnWYxJ2176y4AdusnH91BsAVxfRz/4jlwr8+xKNdCzJnL8N+Nv2y19jwuqftT0u2dMWjeWMAHA6Dr1cf5v33t7J8+T4cjpJnEhs08GPKlCimTImiUaPAcplGVc6MVAxlRsxSZlyjQrqUGYfDwdmzZ2nSpIn+0YlLlBkxS5kRs5QZMUuZEbOUGTGrKmfmwx0fsvboWv79/b+p7VObuTvncj7/PAAWLAy8YyBToqYwuMVg3G0Vv6HmxeL5+Vdn4MjKxSM8jAvvzafo0AkACnfuB8Dq74t3n3tKlmy5vwu2oEtPJKan5zF79nbef38rBw9eKrj37t2UJ5/sxPDhrfDwsJXrPKpyZqRiKDNiljLjGhXSRUREREREROSajl04RlpuGrlFuczePhuAjxM/dvaH+IXwRMcnmNhhIo0DG1fQKK9mGAZFB45hsdmwhdYl4915V/S7NQ5xbhTqfW97LB5XFv63bDnJe+9tZf78RPLziwEICPDk0Ufb8eSTnYmIqHvb5iIiIpWDCukiIiIiIiIichXDMGj6ZtMbHpOancqfev/p9gzoJgyHg4JtSeSs3EjOFxudT55fwWYlbM1MPFqHY7FcuY55bm4R8+cn8v77W9m6NcXZ3r59fZ56qjMPPdQWPz+P8p6GiIhUUiqkS5mxWq2EhYXpIyDiMmVGzFJmxCxlRsxSZsQsZUbMqgqZSc9LZ+7OucTFx93wODerG7OHzb49g7oOR34BeRvjyVm5kdxVm65c79zDHZ8eHTFsVvK++g6HzQ2rvZjsFRuoHdHcedj+/ef4z3+2MmvWdi5cyC95qYeN0aPb8NRTnejSJeyqovvtVBUyI5WLMiNmKTOusRiGYVT0IKoTMzu9ioiIiIiIiFQGhmGw7ug64hLi+CTpEwrsBQB4uXlxX9P7WHlw5VWv2faLbUSFRN3uoWLPyCJ39WZyvthI7jebMXLynH1Wf198+t2L76Ae+Nx/D989/x4hy5fz75xw3s0L52nvw/za9zApg4eQNnQI7723ldWrDztf36xZLX75y05MnHgXdev63va5iYjI7WWmlqsn0qXM2O129u/fT4sWLbDZynezFakelBkxS5kRs5QZMUuZEbOUGTGrsmXmVPYpZm+fzYyEGRxMP+hsb1+/PVOipvBQ24c4cuEIKw+uxIoVBw7nn7dTccoZclZ+S87KjeRtSoBiu7PP1qAOvoN64PtAD7y73uVc7/zbSa9dUUQHnH/++vPPWLBgN6vzwrFY4MEHW/Dkk50YMKA5NlvleiKzsmVGKj9lRsxSZlyjQrqUGcMwyMjIQB9yEFcpM2KWMiNmKTNiljIjZikzYlZlyIzdYefLQ18SGx/LZ/s+w26UFKX9PPx4KPIhpnScQseQjs7lTHKKcmjg14BGAY2Y1GESMxJmcCLzBPV865XbGA3DoHDvEXJ/Kp4XbN97Rb97y6bO4rln+5ZYfrYcgd3u4MsV+zl/WRH9oot/d7PCSy915cknO9O0aa1ym8utqgyZkapFmRGzlBnXqJAuIiIiIiIiUgMcu3CMmQkzmbl9JsmZyc72e8PuZXLUZEa3GY2fh99VrwsLCOPoc0fxsHlgsVj4RcdfUGgvxNPNs0zHZ9jt5G/Z7dwstPjoyUudFgtenSPxfaAHPgO749G80Q3PtWHDMf56Ouy6/ReL6WsH3Vmpi+giIlJ5qJAuIiIiIiIiUk0V2gtZvm85cfFxfHXoKwxKnjYM9g7m0XaPMilqEpH1Im96nsuL5haLpcyK6I68AvI2biXni43kfLkJR9qFS9fx9MC7Vyd8B3bHZ0A33OoF3/R8u3efYd68RGJj4126fmpqVmmHLiIiNYwK6VJmrFYr4eHh2uFXXKbMiFnKjJilzIhZyoyYpcyIWbcrM/vS9hEXH8cHOz7gbO5ZZ3ufZn2YEjWF4a2G4+XmVa5juB77+Uxyv/6OnC++JXftDxi5+c4+a6AfPv27lmwWet/dWP18bnq+w4fPM39+IvPnJ7Jr1xlTYwkJ8Tc9/ttN9xkxS5kRs5QZ11gMLX5Tpszs9CoiIiIiIiJSVnKLcvkk6RNi42PZeHyjsz3EL4SJd03k8Q6P0zy4eYWMrSj5NLkrN5ZsFvrdDrBf2izUrWG9ksL5Az3w7tIei/vNn/lLTc1i4cLdzJuXyA8/XFoCxt3dysCBdzBmTBt+97vVpKRkca2qh8UCYWEBHDnyXKXbXFRERG4fM7VcPZEuZcZut5OYmEhkZKR2+BWXKDNiljIjZikzYpYyI2YpM2JWeWRm+6ntxMXHMXfnXDIKMgCwWqw8cOcDTImawgN3PoCb9fb++G8YBoVJh5zrnRfuOnBFv0eb5vgO7I7voB54tGvh3Nj0RtLT8/jkkyTmzUtk3bqjzgK51WrhvvuaMm5cJNHRrQkK8gbA29udmJiFWCxcUUy/eKlp0wZWiSK67jNiljIjZikzrlEhXcqMYRjk5eVph19xmTIjZikzYpYyI2YpM2KWMiNmlVVmMgsymbdrHrHxsWxL3eZsb1qrKZM6TGLiXRNpGNDwVodrilFcTP4Pu8hZ9S05KzdSfCz1UqfVitc9bfF9oAe+A3vg3jTUpXNmZxeyfPk+5s1L5MsvD1JU5HD2dekSxrhxkYwe3YYGDa7eJDU6ujWLF4/muedWkZyc6WwPCwtg2rSBREe3Lv1kbyPdZ8QsZUbMUmZco0K6iIiIiIiISBVgGAbfJ39PXHwcC3YvILcoFwB3qzsjWo9gStQU+jTrg9VSdk9Zp0+dCTYrwb+dcHXfG7Mx8gvximpdslnoV9/hSM9w9lu8PPC+7+6SJ8/7d8VWJ8ilaxYUFLNy5UHmz09k+fJ95OUVO/vatavPuHGRjBnThmbNbn6+6OjWDBvWko0bj5OamkVIiD89ejSuEk+ii4hI5aJCuoiIiIiIiEgllpabxpwdc4hLiCPpbJKzvXWd1kyOmswj7R6hrm/d8rm4zcr5V2cAOIvp9nMXOPObqeR+sRHcbFB8ab1za1AAvv274vtAD7x7dcbq6+3SZYqLHaxde4R58xJZsmQPGRkFzr7mzYMYNy6ScePaEhFhfp42m5XevZuafp2IiMjltNloGavJm40ahkFGRgaBgYEurW8nosyIWcqMmKXMiFnKjJilzIhZrmbGYThYe2QtsfGxfLr3UwrthQB4u3kzJnIMkztMpmujrrcld+lvzOb8qzPw7nMPRn4B+d/vuGLRcbfGIfgOKlnv3OuetljcXHtmz+Ew+P77E8ybl8iiRUmcOZPj7GvY0J8xY9owblxbOnYMqdH/vnSfEbOUGTGrJmfGTC1XhfQyVpML6SIiIiIiInJrUrJSmL19NjMSZnD4/GFne1RIFFOipjAuchyBXoG3ZSyFB46R8/l6clZsoGDHviv6bPWCCZgwvGSz0DbNXS68GIbBjh2nmTdvF/Pn7+b48UtLwdSu7c2oURGMHRtJjx5NsFprVjFHRERuPzO1XC3tImWmuLiYhIQEOnTogJuLTyBIzabMiFnKjJilzIhZyoyYpcyIWZuPb+ZXy37FO8PeoUvjLgAUO4pZeWAlcQlxrNi/ArtRslRKgGcA49uOZ1LUJKJCosp9bIZhULjrADkrNpD9+TqK9h+71Gm1ljyFbhjg7kbT3ctMnXv//nPMn5/IvHmJ7N2b5mz38/NgxIhWjBsXSd++4bi728pqOtWG7jNiljIjZikzrtFXRsqU3W6/+UEil1FmxCxlRsxSZsQsZUbMUmbEjLm75rItfRsfJX5EPf96zEyYyazts0jJSnEe071xd6ZETSEmIgYfd59yHY/hcJC/JZGcFRvIWbGB4uOplzrd3fDp2QnfB3tSeCSZjLc/Bg93KCwi/Y3Z19yA9HInTmSwYMFu5s1LJD7+0nk9PW08+GALxo2L5MEH78Tb272cZld96D4jZikzYpYyc3MqpIuIiIiIiIiUo2MXjpGWm4bFYmFh0kIA/rvtv7yz5R3nMXV86vBY+8eYHDWZVnValet4jKJi8r7bTs6KkmVb7GfSnX0WHy98+tyD7+Be+PS7F1uAH+lvzCbj7Y/JGDOSfd3up+Wmb+BnG5BedPZsDosWJTF/fiIbNx53tttsFvr1a864cZEMH96KgADPcp2jiIhIWVMhXURERERERKQcNX2z6VVtRY6iK/5+8jcn8bB5lNsYHHkF5K3fUrLm+ZebcFzIcvZZA/zwGdAV3wd74XPf3Vh9vJx9FzcaneHWmn+8mwHvLgHg/zVozaSfium2yWNZunQv8+Ylsnr1Yez2S1ux9ezZhLFj2xATE0Hdur7lNj8REZHyps1Gy1hN3mzUMAzy8vLw9vaucTv8SukoM2KWMiNmKTNiljIjZikzciM5hTksSlrE3zf+nQPpB655jJvVjdnDZvNwu4fL/PqO7Fxyv/6e7M/Xk7t6M0ZunrPPVjcI30E98H2wJ97do7B4XHt5lU0T/8n8hUm8kxt+RbvFAk95HaZxQ3/+90QDCgouLQnQsWMI48ZFMmZMJGFhNevn4vKg+4yYpcyIWTU5M9psVCqMh0f5PUEh1ZMyI2YpM2KWMiNmKTNiljIjP7ctZRux8bF8vOtjsgpLnvy2YsWB46pjf5j8Q5luJGpPzyBn1bfkrNhA7rotUHjpyXe3hvXwfbAXvg/2xOuetlhsN97Y0253MHa1B8k/K6JDyZ6j7+aFw0EAO61b13EWz1u0qF1m85ESus+IWcqMmKXM3JwK6VJm7HY7W7dupVOnTtrhV1yizIhZyoyYpcyIWcqMmKXMyEUX8i/w8a6PiYuPI+FUgrO9eVBzJkdNpkODDgz8aKCzoH69wnppFJ9KI+eLjeR8vo6873bAZRvGuTdvhO/gXvgO7oVn+5amnjTcuPE4ycmZNz0uNnYIkyZ1qHFPMd4uus+IWcqMmKXMuEZfGREREREREZFSMAyDb49/S1xCHIt2LyKvuGTpFA+bByNbj2RK1BR6Ne2F1WIlOTOZBn4NCPMPo09QH9acX0NyVjL1fOuV6tpFR1PIWbGe7BUbKNiSeEWfR+Sd+A7uid+DvXBv2bRUBe5Dh9J5990fXTrW19ddRXQREan2VEgXERERERERMeFszlk+2PEBcfFx7Du3z9nepm4bpkRNYXy78dT2uXJ5k7CAMI4+dxSrYWXbtm38dfhfcVgceLp5unRNwzAo3HuEnBXryfl8A4W7D17R79U5suTJ8wd64t40tFTzSk/PY+HC3cyZs5Pvvjvh8utCQvxLdT0REZGqRIV0ERERERERkZtwGA5WH15NbHwsy/Yuo8hRsva4r7svYyPHMjlqMvc0vOeGT2Z7unlSXFwMgMViuWkR3TAMCrbvJefz9eSs2EDRocuK2zYb3t3uKlnz/IEeuDWoU6p5FRQUs2LFAebM2cmKFfspKipZbsZqtdCnT1Pi41M5fz4fw7j6tRYLhIUF0KNH41JdW0REpCqxGMa1/nMopWVmp9fqxjAM7HY7NptNH+sTlygzYpYyI2YpM2KWMiNmKTPVX3JmMrMSZjEjYQbHMo452zuHdmZK1BTGRo7F39P1J7JvlhnDbid/805yVmwg54sNFJ884+yzeHrg3bszvg/2xHdAN2zBgaWak2EYbNp0grlzd7Jw4W7On8939rVvX59HHmnHuHFtCQ31Z8mSPcTELPzpdZfOcXHoixePJjq6danGIa7RfUbMUmbErJqcGTO1XD2RLmWqsLAQb2/vih6GVCHKjJilzIhZyoyYpcyIWcpM9VNkL2LFgRXExsey6uAqHEbJU9q1vGoxvu14JkdNpn2D9i6fL33qTLBZCf7tBODKzKS/MRujsAjvu9uRvWI9OSs34ki74Hytxccbn3734vdgT3z63YvVz6fU89q//xxz5+5k7tydHDly6RoNG/rz8MNtGT++HW3b1r/iNdHRrVm8eDTPPbfqio1Hw8ICmDZtoIrot4nuM2KWMiNmKTM3p0K6lBm73c7OnTu1w6+4TJkRs5QZMUuZEbOUGTFLmaleDqYfZEb8DGbvmM2p7FPO9l5NejE5ajIjW4/E270URQablfOvzgAg4Lnx7Ny5k6iINqS/8AY5n36DxdODC//60Hm4tZY/vgO74zu4F949O2H1dm0d9WtJS8tl/vxE5szZyY8/nnS2+/l5MHJkax55pB29ezfFZrNe9xzR0a0ZNqwlGzceJzU1i5AQf3r0aHzD10jZ0X1GzFJmxCxlxjX6yoiIiIiIiEiNlV+cz5I9S4iLj2Pt0bXO9nq+9ZjQfgKToibRonaLW7rGxSfRz786g/xdBwhIT+fElj1QbAfAKCjEVi+4ZMmWB3vh3fUuLO6l/3E9L6+Izz7bz9y5O1m58iDFxSVP1NtsFvr3b84jj7Rj2LBW+Pi4u3xOm81K795NSz0mERGRqk6FdBEREREREalxEs8kErstljk753A+/zwAFiwMvGMgk6MmM6TFENxtrhear8eekUXuqk0UJOwFm5W8FRu4+Hy5NdAP/4cH4/dgTzw7tcFiLf0T3g6HwcaNx5gzZyeLFiWRmVng7OvYMYTx49sxblwk9ev73eKMREREaiYV0qVM2Wy2ih6CVDHKjJilzIhZyoyYpcyIWcpM1ZFdmM2CxAXExsfyw8kfnO2NAhoxqcMkJnaYSOPAxrd8HfuFLHJWbiTns3XkrtsCRcVXH+TuRtMDX9zypm579pxlzpydfPTRLo4fz3C2N24c6Fz3PCKi7i1dQyqe7jNiljIjZikzN2cxjMv33ZZbZWanVxERERERESlfhmGwJWULsdtimb97PtmF2QC4Wd0Y2nIoU6Km0C+8HzbrrRUQ7OkZ5Kz8luzla8nbsNW5bAuAe8um+A29D3t6BpkzloCHOxQWEfTyJOeyL2acPp3NvHmJzJ27k23bUp3tAQGejBoVwSOPtKNHjyZYrbdWpBcREanuzNRy9US6lBnDMMjIyCAwMPCWn6qQmkGZEbOUGTFLmRGzlBkxS5mpvNLz0vlo50fExsey68wuZ/udwXcyOWoyj7V/jPp+9W/pGvZzF8j5YgPZy9eR9238FcVzj4hwfIf0xm9IbzxaNiP9jdlkzlhC0O8mYZ08HEfcUucGpK4U03Nzi1i2bC9z5uzkq68OYbeXPBPn5mZl0KA7eOSRdgwe3AJv71tfjkYqF91nxCxlRsxSZlyjQrqUGbvdzt69e7XDr7hMmRGzlBkxS5kRs5QZMUuZqRhbU7by0tcvMbXfVDqFdnK2G4bB+mPriYuPY3HSYgrsJeuEe7l5ERMRw+QOk+nZpOctFQmKz54n54sN5CxfS96m7WC/rHje5g78ht6H79DeeNxxaYmY9Ddmc/7VGQS9PImA58azdetWOj0/HizcsJhutztYt+4oc+bs5JNP9pCdXejsu+eehowf344xY9pQt65vqecjlZ/uM2KWMiNmKTOu0VdGREREREREqpQPd3zI2qNrmbNjDp1CO3E6+zSzt88mLiGOg+kHnce1q9+OKVFTeLjtwwR5B5X6esWnzzmfPM//bjs4HM4+j7Z3lhTPh/TGo3mja5/A7iDo5UkEPv8o69YdYdOmVHJyjtH7+Ued/Zfbtes0c+bs5OOPd3HyZJazvVmzWowf347x49vRokXtUs9HREREzFMhXURERERERCq9YxeOkZabhsViYcHuBQB8sOMDdp3ZxYZjG7AbJU+G+3n4MS5yHFOiptAptFOpnz4vPpVGzufryf5sHfnf74DLthfzvKuVc9kW92YNb3qu4JceZ8mSPTzX9E2SkzN/at1JWFgAb745kOjo1qSkZDFv3i7mzNnJjh2nna+tVcuLMWPa8Mgj7ejatZE+ci8iIlJBVEiXMmOxWPD29tY3duIyZUbMUmbELGVGzFJmxCxl5vZp+mbTq9oyCjJYe3St8+8zhs5gdJvR+Hn4leoaxalnyf5sPTnL15L/464ri+dRrUuePB/cC/cmoabOu2TJHmJiFl5+OgBOnsxk5MiFtGtXn8TEMzgcJQe4u1sZPLgF48e348EH78TTUz+612S6z4hZyoyYpcy4xmIYP/9PudwKMzu9ioiIiIiIyM0V2gt58asXefvHtzG4+kdYm8XGB8M/4OF2D5s+d/HJ05eK51sSr+jz7NQGv6G98R3cG/dGDUo1drvdQdMrnkS/vm7dGjF+fDtGj25DcLB3qa4nIiIirjNTy9WvtaXMOBwO0tLSqFOnDlartaKHI1WAMiNmKTNiljIjZikzYpYyU772pe1jRsIMPtjxAWdyzlz3uB+n/EhUSJTL5y06cYqcz9aR/dk6CrbuvqLP6+62Py3b0gu3hvVLOfJLNm487lIR/aOPonnooba3fD2pfnSfEbOUGTFLmXGNCulSZhwOB4cPHyY4OFj/6MQlyoyYpcyIWcqMmKXMiFnKTNnLK8pjcdJi4hLi2HBsg7O9gV8DBt0xiFnbZ2HFigOH809XFB1LIfuzdeQsX0dBwp5LHRYLXve0++nJ8164hdQtk3kYhsGWLSm89toml47Xp+nlenSfEbOUGTFLmXGNCukiIiIiIiJS4Xac2kFsfCxzd84loyADAKvFyqA7BjElagoP3PkAp3NOs/LgShoFNGJSh0nMSJjBicwT1POtd81zFh05+VPxfC0FO/Zd6rBY8Lq3fcma5w/2xK1BnTKZg2EY7NhxmvnzE1m4cDdHjlxw+bUhIf5lMgYREREpHyqki4iIiIiISIXILMhkfuJ8YuNj2Zqy1dneJLAJkzpMYmKHiYQFhAGQPnUmPjYrR587iofNA4vFwi86/oJCeyE5b84j3e4g+KXHKTx0gpzla8n+bB2Fuw5cupjVine3u/Ad0hvfB3riVr92mc0jKeks8+cnsmDBbvbvP+ds9/FxZ/DgFnzzzWHS0/Ou2mwUSp5EDwsLoEePxmU2HhERESl7KqRLmbFYLAQGBmqHX3GZMiNmKTNiljIjZikzYpYyY55hGGxO3kxcfBzzd88ntygXAHerO8NbDWdK1BTuD78fq+VnHy23WTn/6gwAPH87ASj5+ue8OY/zr87Au0dHTvSeQOHuQ5e9xoZ39w6Xiud1g8psHgcOnGPBgt0sWLCbxMRL67d7ebnxwAN3MnZsGx58sAU+Pu4sWbKHmJiFWCxcUUy/GJtp0wZis+mj9HJtus+IWcqMmKXMuMZiGNf6nbiUlpmdXkVERERERGqKc7nnmLNzDnHxcew+e2mDz1Z1WjG5w2Qebf8odX1vvD55+huzOf/qDIJenoTf4F6cfXka+d/GX3mQzYZ3j6iSZVse6IGtdq0ym8PRoxdYuLCkeB4fn+psd3e3MnDgHYwZ04ahQ1vi7+951WuXLNnDc8+tumLj0UaNApg2bSDR0a3LbIwiIiLiOjO1XBXSy1hNLqQ7HA5SUlIIDQ3VxgTiEmVGzFJmxCxlRsxSZsQsZebGHIaDtUfWEpcQx5I9Syi0FwLg7ebN6DajmRw1mW6Nurn8BFzhweOcffGNq4vnbjZ8enUuefJ8UHdswYFlNoeTJzNZtCiJBQt2s3lzsrPdZrPQt284Y8a0YfjwVgQFed/0XHa7g/Xrj5KUdIKIiEb06tVUT6LLTek+I2YpM2JWTc6MmVqulnaRMuNwOEhOTqZBgwY17h+dlI4yI2YpM2KWMiNmKTNiljJzbalZqczaPosZCTM4fP6ws71Dgw5MiZrCQ20fItDLtWJ30dEUspd+Q/bSNRTuPnhlp8VC3TdfxndQD2y1ym6zzjNncli8uKR4vnHjMedyLBYL9O7dlLFjI4mObk2dOj6mzmuzWenZszE+Pmfo1KmxiujiEt1nxCxlRsxSZlyjQrqIiIiIiIjcsmJHMasOriI2PpYV+1dgN+wABHgG8HDbh5kcNZmokCiXzlWUfJqcZWvIXrqGgu17L3W42XBrFELxkWRwd4OiYopTzpRJET09PY8lS/Ywf34ia9cexeG49OHtbt0aMXZsJDExETRo4HfL1xIREZGqR4V0ERERERERKbUj548wI2EGs7bPIiUrxdnerVE3pkRNISYiBl8P35uep/hUGtnL1pK9bA0FWxIvdViteHfvgN/wPhQdTeHCWx8R9PIkgn87wblmOkDwTxuQmpGRkc/SpXtZsGA3X399mOJih7Pv7rsbMmZMG0aNiqBRo7JbKkZERESqJhXSpcxYrVbq1q2rj4CIy5QZMUuZEbOUGTFLmRGzampmCooLWLZvGXHxcXx9+Gtne23v2jzW/jEmR02mdd2bb6BZfPY8OZ+tI3vpN+Rv3snla6h4dWmH3/D78R3cC7d6waS/MfuKIjpcKp6bKaZnZxfy2Wf7WLBgNytXHqSw0O7su+uuBowZ04bRo9sQHh7k0tfCrJqaGSk9ZUbMUmbELGXGNdpstIzV5M1GRURERESkettzdg9x8XF8uPND0nLTnO39wvsxOWoyw1oOw9PN84bnsKdnkLNiPdlL15D3bQI4Lj0F7tk5Er9hffAbdh9uDepc8br0qTPBZr1msTz9jdlgdxD80uPXvGZeXhFffHGABQt28/nn+8nLK3b2RUTUZcyYNowZ04aWLetc8/UiIiJSPZmp5aqQXsZqciHd4XBw5MgRmjVrpt9giUuUGTFLmRGzlBkxS5kRs2pCZnIKc1iUtIi4+Dg2ndjkbA/1D+Xxux7n8Q6P0yyo2Q3PYc/MJueLjWR/+g15G7ZC8aWnwD3vaoXf8D74DuuDe1j9m47HbnewceNxUlOzCAnxp0ePa2/aWVBQzFdfHWLBgt0sW7aP7OxCZ98ddwQzdmwbxoyJJDKynitfhjJTEzIjZUuZEbOUGTGrJmfGTC1XS7tImXE4HJw9e5YmTZrUuH90UjrKjJilzIhZyoyYpcyIWdU5M/Gp8cRui+XjxI/JLMgEwGaxMbjFYCZHTWbgHQNxs17/R0pHdi45X24ie+kactf8AIVFzj6PNnfgN7wPfsP64N6soctjWrJkD889t4rk5ExnW1hYAG++OZDo6NYUFdlZs+YI8+fv5tNP95CRUeA8rkmTwJ+ePI+kQ4cGWCwWM1+OMlOdMyPlQ5kRs5QZMUuZcY0K6SIiIiIiIjXQ1pStvPT1S0ztN5VOoZ0AyMjP4ONdHxMbH0vCqQTnseFB4UzuMJnH7nqMUP/Q657TkZtP7tffk730G3JXf4+Rf+kpcPeWTUuK58Pvx+OOxqbHu2TJHmJiFvLzz1SfPJnJyJEL6devGfHxpzh3Ls/ZFxrqz+jREYwZE8k99zSssOK5iIiIVH0qpIuIiIiIiNRAH+74kLVH1/Lhjg/JL84nLj6OhbsXkldcUoj2sHkwsvVIJkdNpnfT3lgt135CzZFfQN6aH8heuoacL7/DyL1UyHYPDyvZMHR4Hzxbh5d6rHa7g+eeW3VVER0u7U/69ddHAKhXz5eYmNaMGRNJ9+6NsVpVPBcREZFbp0K6lBmr1UpYWJg+AiIuU2bELGVGzFJmxCxlRsyqapk5duEYablpWCwW5iXOA+C9Le/x9o9vO49pU7cNU6KmML7deGr71L7meYzCInLXbSF72RpyV36LIyvH2efWOKRkw9DhffBoe2eZPAW+cePxK5ZzuZ7XX+/Hc891wc2t8r4fVS0zUvGUGTFLmRGzlBnXaLPRMlaTNxsVEREREZHKzfLnmxe1Hf/ruGbx2yguJm9jfMmT519swHEhy9lnC62H37D78BveB88OrctsCRXDMNix4zR/+tNali/ff9PjP/44mnHj2pbJtUVERKT602ajUiHsdjv79++nRYsW2Gy2ih6OVAHKjJilzIhZyoyYpcyIWVUlM8mZycxKmEUdnzqk5aZd8xg3qxuzh82+oghu2O3kf7+D7KVryP58HY5zGc4+W71gfIf0xm/E/Xh1jsRShk+xJSWdZcGCRBYs2M2+fedcfl1IiH+ZjaG8VJXMSOWhzIhZyoyYpcy45pYK6YcOHWLHjh2kpKSQl5dHSEgITZs2pWvXrvooQA1kGAYZGRnoQw7iKmVGzFJmxCxlRsxSZsSsypyZInsRXxz4gtj4WFYeXInDcADghxfZ5F91/Feev6f9qgKMSAf5WxLJWbqG7OVrsZ9Jdx5jrR2I3+De+A3vg9e97bGU4Q/bBw+mO4vnu3adcbZ7ebkxaNAdbNhwjPT0vGuuk26xQFhYAD16mN/E9HarzJmRykmZEbOUGTFLmXFNqQrpM2bM4L333mPPnj20adOG0NBQvL29SUlJ4cCBA9jtdsaOHcsf//hH6tatW9ZjFhERERERkes4lH6IGQkzmLV9FqeyTznbezbpybhTLak9dzOjR+3CihUHDuefmbOXkdu0C5kfrcCecqmQbQ30w/fBXvgN74N3jygsbmX3weZjxy6wcOFuFizYzbZtqc52d3crAwbcwdixbRg6tCX+/p4sWbKHmJiFWCxcUUy/+AD9tGkDsdn0QJeIiIiUD1PfASUlJTFhwgTOnz/P73//e6Kjo69aO8YwDDZs2MDbb79Ny5Ytef3113n88cdveaDnzp1j3759tGjRgjp16tzy+URERERERKqL/OJ8Pt3zKbHxsaw9utbZXs+3HhPaT2BS1CRa1G4BwC7bv6hz9mUa+YfxeLsJxH37DictF6id607BlkQALH4++D7QA79hffDp3RmLh3uZjTU1NYtFi5KYPz+R779PdrbbbBbuvz+cMWPaMGJEK4KCvK94XXR0axYvHs1zz626YuPRsLAApk0bSHR06zIbo4iIiMjPubzZ6Jdffsno0aN54okn+Mtf/oKXl9dNX/P5558zceJEJk6cyNSpU10a0Llz5+jUqRNr166ladOmAMyfP58nn3ySpk2bsm/fPmbOnMnYsWMBeOaZZ3jnnXecr2/evDkHDx4EIDExkYkTJ3Lw4EEmT57M1KlTnev9lbbvZmryZqMOh4O0tDTq1KmjpX3EJcqMmKXMiFnKjJilzIhZFZ2ZxDOJxMXHMWfnHNLzSpZgsWBhwB0DmBI1hcEtBuNh87jiNYUHj5P84j9xfLsDCxYMDAqtBl5evvgO6Fry5Hmfe7B6eZbZOM+ezeGTT/awYMFu1q8/6nyi3GKBXr2aMmZMG0aObE3dur43PZfd7mDjxuOkpmYREuJPjx6Nq9ST6BWdGal6lBkxS5kRs2pyZszUcl0qpNvtdu6++26eeOIJfvGLX5gaTFJSEsOHD+fTTz+lTZs2Nzw2LS2NIUOGsHnzZo4cOULTpk25cOECd955J2vXriUyMpI5c+bwhz/8gWPHjgHQtWtX/vCHP9C1a1cAbDYb/v7+FBQU0KpVKwYMGMCLL77Is88+S0xMDBMnTix1nytqciFdRERERETKX3ZhNgsSFxCXEMfm5M3O9kYBjXi8w+M83uFxGgdeuVZ4ccoZspeuIeuTryncuf/KE1ot1J/+Z3z63YvV5+YPTLnq/Pk8Pv10LwsW7Oabbw5jt1/60bNr10aMGdOGmJgIQkMr/wahIiIiUj2VeSEdSorppd211dXX9u3blyFDhvD88887C+knTpxgw4YNPPzwwwDs3LmT7t27k5mZSXFxMcHBwaSkpODn53fFuZYuXcrjjz9OcnIyPj4+7Nixg6effppvv/221H2uqMmFdLvdTmJiIpGRkdrhV1yizIhZyoyYpcyIWcqMmHW7MmMYBltTthIbH8u8xHlkF2YD4GZ1Y2jLoUzuMJn+zftjs14agz09g5zP15P1ydfkf7/j0sLiNhvuTUIoOpwMHu5QWETQy5MI/u2EWx5nVlYBy5btY8GC3Xz55UGKihzOvo4dQxg7NpLRo9vQuHHgLV+rqtJ9RsxSZsQsZUbMqsmZMVPLdXmN9NJ+EfPy8sjKyqJevXo3PXb69OmEh4fz/PPPO9saNWrkLKIXFRXx+uuvEx0dDZQU1Q3D4K677uLkyZP06tWL6dOn07hxY3bs2EGXLl3w8fEBoF27diQlJQGUuk9uzDAM8vLytMOvuEyZEbOUGTFLmRGzlBkxq7wzcz7vPB/t+ojY+Fh2nt7pbL8z+E4mR03m0faP0sCvgbPdkZNHzpebyP7ka3LX/ADFdmefV5f2+EXfT/GJU1x4+2Nn8Tz9jdmcf3UGQKmK6bm5RaxYsZ/583fzxRcHyM8vdva1bVvPWTy/447gUnwFqh/dZ8QsZUbMUmbELGXGNWW33fp1vPnmm8TGxrJs2TIiIyNveGx4ePh1+3bs2MF9992Hh4cHe/fuBWDPnj20adOGt99+mzp16vDss8/yxBNPsHLlSjIzM2nWrJnz9RaLBZvNxvnz50vdFxQUdNW4CgoKKCgocP49M7Nk05vi4mKKi0u+gbRarVitVhwOBw7HpScyLrbb7fYrgnq9dpvNhsVicZ738nYo+e2RK+1ubm4YhnFF+8V5/nyM12u/1pwuH291mdONxq453fqcLr9OdZnTjcauOd36nC72X/xfdZjTzcauOd3anC6O9Vr/raqqc7rZ2DWnW5vTxWvY7Xbc3NyqxZxuNnbN6dbmdHlmympOVquVdUfXERcfx5K9S8gvzgfA0+ZJTEQMj7d/nB6NeziPNQqLyPlmM1mfriZv1XcYefnO83lE3oHP8D74DuuDW1h9MqbN4cLbH1PrpccJeG48xcXFBDw3Hgw4/+oMHHYHtX7z6E3HXlxssGrVIebP38Xnnx8gJ6fI2deyZW1GjWrNqFERRETUrRTvkyvvx+3K3uWZqS5zqo7vU2Wb08U/q9OcbjZ2zan0c7o8M9VlTtXxfapsc7r8XNVlTq68T5cfczPlWkg/ffo0r732GtnZ2S5tTnoj7dq145tvvuGFF15g4sSJfPrppzz88MPOp9UB3nnnHcLDw8nMzMTNzQ1Pzys3x/Hy8iI3N7fUfdcqpP/jH//gz3/+81XtCQkJ+PqWbJRTt25dmjdvzpEjRzh79qzzmLCwMMLCwti/fz8ZGRnO9vDwcOrVq0diYiJ5eXnO9latWlGrVi0SEhKuCFS7du3w8PBg69atV4yhU6dOFBYWsnPnpSdXbDYbnTt3JiMjw/kLCQBvb2/at29PWloahw8fdrYHBgbSunVrUlJSSE5OdrZfa06GYZCfX/JNe3WZE1S/96kyzckwDLKysgCqzZyg+r1PlWlOhmFw4cIF8vPz8fb2rhZzqo7vU2Wa08XMpKam0qRJk2oxp+r4PlWmOV3MTFJSEh06dKgWc6qO71NlmlNxcTEXLlwgPj6e9u3b39Kc0gvS+ersV3yR+gUH0g84j23u15wxd47hhX4vkHMuh+TkZLad3oL77iPU+nEv1nXbcFzIch5vD6mNx+BehE4YwSF7HiczMuDUCTh1goZZ2QS9PIkTfTtw4LJxtpo8nCALpBw/wcHL2i9/n4qLHWzZco6vvz7Fpk3nyMy89EBRaKg3/fqF8Pzz/WjSxJN9+/aRm3uMrVuPVYr3qTJl78yZM87MNGrUqFrMqTq+T5VpTnv27HFmxsfHp1rMqTq+T5VpTjt27HBmxmKxVIs5Vcf3qTLN6dixY1dkpjrMydX3qW7durjK5TXSS6N///588803/PGPf+T//u//XB+UxeJcI/3nTpw4QZMmTTh37txVhe2MjAxq1arF3r17Wbp0KYmJicyZM8fZX6tWLQ4cOMDMmTNL1XetL+y1nkhv1KgR586dc66rU91/Y3Wx/WJRNCgoyPmbrKo+pxuNXXO69TldnpnLnxitynO60dg1p1ufk2EYZGZmEhQU5HzSoqrP6WZj15xubU4XM1OrVi1sNlu1mNPNxq453foTfxfXSdQT6ZqTK3O6mJmAgADc3NxMz6nYXsyqA6uYuX0mnx34jGJHydfJz8OPMRFjmNRhEp1COjnHkr99L1mffE3u8rXYU9MuXaNeMD7D+uA7og8ed7XCZrPdcE6FhUVs3Hic1NRsQkL86NWrKW5utqveJ7Cwbt1R5s9P5NNP95GefumH5oYN/Rk1KoJRo1rTuXMoFoul0r5Prrwftyt7drvdmZmbvU9VZU7V8X2qTHMqLi52ZsZqtVaLOVXH96kyzamoqMiZmYvXrOpzqo7vU2Wak91u58KFC87MVIc5ufo+ZWdnExQUVLabjZr1y1/+kunTp9OvXz9WrlyJ1Wp1+bWXF9LXrFnDypUree211wBITU2lYcOGnD9/nj//+c906dKF0aNHA7B69WoGDBhAVlYWmzdv5oknnuDAgZInOY4ePUrr1q3Jzs5m/fr1peq7+CbfSE3ebFRERERERFxzPOM4MxNmMjNhJicyTzjbu4R1YXKHyYyJHIOfhx8AhQePk71kNdlLVlN06NKx1gA/fIf0wi+6L97dOmBx4ecVgCVL9vDcc6tITs50toWFBfDmmwOJjm6Nw2GwadNxFizYzaJFSZw5k+M8rl49X0aNimDs2Ei6dm2E1Wq51S+FiIiISIUpl81GXZWTk8MTTzzBxx9/TPfu3fnkk09MFdF/rlWrVgwfPpw777yTQYMG8Yc//IH+/fsTGBjIXXfdxe9//3saNGhAcXExzzzzDBMmTMDHx4eePXuSkZHBhx9+yKOPPsqrr75K3759sdlspe6TGysuLiYhIYEOHTo4n8YRuRFlRsxSZsQsZUbMUmbELDOZKbIX8dn+z4iNj+XLg19iUPJMU5BXEI+2f5RJHSbRtn7bkvOmnOHC0s/J+uRrCnfud57D4u2JT/9u+I/si0+fe7B4epga75Ile4iJWcjPH6c6eTKTkSMXMnhwCxISUjl58tJSMcHB3owc2ZqxYyPp1asJNlvpf74T3WfEPGVGzFJmxCxlxjVl+pVZvXo1Tz75JIcOHWL8+PFMnz79ltdGDw0NZdGiRfz617/mhRdeYMCAAc5lVx599FH27NnDsGHD8Pf3Z8SIEfz9738HSj4KMH36dB566CFefPFF7HY769evv6U+ubmffyRD5GaUGTFLmRGzlBkxS5kRs26Wmf3n9hMXH8cHOz7gTM4ZZ3ufZn2Y3GEyI1qPwMvNC3t6BpkfLifrk6/J/34Hzmq3zYZP7874jeyL76AeWP18SjlOB889t+qqIjpcutTnn5cU7QMCPBkxohVjxrShb99w3N31YFFZ0n1GzFJmxCxlRsxSZm6uTArpmzdv5i9/+QtffvkloaGhzJ8/37ncSmn8fLWZAQMGkJSUdM1j//GPf/CPf/zjmn3Dhw/nwIEDbN26la5du16xxnlp+0RERERERC7amrKVX235Fe+EvkOXxl2c7XlFeXyy5xNi42PZcGyDs72BXwMm3jWRxzs8zh3Bd+DIziVn2UbOL1lN7pofoPjSD7FeXdrjF30/fkN6Y6tz5f5QpbFx4/ErlnO5nldeuY8XXuiKl5eeSBMRERG5yKXvjDZu3MjJkydp0KABnp6e5Obmcu7cOfbv38/SpUtJSEigcePGvPHGGzzxxBN4e3uX97hd1rBhQxo2bFimfSIiIiIiIgBzd81lW/o2Pkr8iC6Nu7Dj1A7i4uOYu2suF/IvAGC1WBl0xyCmRE3hgTsfwM0OuWt/5PSSj8hZ9S1Gbr7zfB6Rd5YUz0f0xT2sfpmM0TAMtm1L5Y03vnPp+ObNg1REFxEREfkZlzYbffTRR5k7dy4Wy7U3kunevTvTpk2jQ4cOZT7AqqYmbzZqGAZ5eXl4e3tfNysil1NmxCxlRsxSZsQsZUZccezCMdJy07BYLAyaO4gzuWfw9/CnUUAjktIufZK2SWATJnWYxMQOE2noF0r+9ztKNg1dvhbHhUtrkLs1bYh/dF/8ou/Ho2WzMhmjw2Hw448nWbw4icWLkzh2LMPl165d+xi9ezctk3HI1XSfEbOUGTFLmRGzanJmzNRyXSqkL1u2jOPHj+Pu7o67uzsOh4OTJ09y5MgRvv/+ew4ePIjFYqFTp078/ve/Z+jQoWU2maqmphfS7XY7Nputxv2jk9JRZsQsZUbMUmbELGVGXGH5882z8eX4L7m/2f0U7zpI1idfk710DfbUs85+W71g/Ebcj190Xzw7tC6TvDkcBt99d4LFi5P45JM9Vyzj4uPjzgMP3MnatUdIT8+75jrpFguEhQVw5Mhz2lC0HOk+I2YpM2KWMiNm1eTMmKnluvR5vWHDht2w/9SpU8yfP5+3336bESNG0LdvX2bMmEFYWJjro5Yqz263s3XrVjp16qQdfsUlyoyYpcyIWcqMmKXMyM2cyz3HaFsXFto3X7PfhpW3C8fR6ZNkTi55hKJDJ5x91gA/fIf0wi+6L97dOmCx3foGnna7g2+/Pe4snqemZjv7/P09GDKkJTExrRkw4A58fNxZsmQPMTELsVi4oph+8WfmadMGqoheznSfEbOUGTFLmRGzlBnXlMlXpkGDBjz//PM888wzvPnmm/zxj3+kY8eOfPrpp3Tt2rUsLiEiIiIiIlIhHIaDdUfXERsfy5I9Syi0F1732E8WtCPy7DHOMwsAi5cHPgO64z+yLz597sHi6XHL4ykudrB+/VEWL05iyZK9nDmT4+wLDPRk6NCWjBoVQb9+za9a6zw6ujWLF4/muedWXfHEelhYANOmDSQ6uvUtj09ERESkOirTXzHYbDZ+85vfMHDgQIYMGUK/fv1YsWIFvXv3LsvLiIiIiIiIlLvUrFRmb5/NjIQZHDp/yNl+V4O76HkulLeKvsBigGHB+ScANhs+vTvjN7IvvoN6YPXzueWxFBXZWbPmCIsXJ/Hpp3s5dy7P2RcU5MXw4a2IiYng/vub4el54x/zoqNbM2xYSzZuPE5qahYhIf706NFYT6KLiIiI3EC5PKsfERHBd999R/fu3Rk6dCibN28mIiKiPC4lIiIiIiJSZoodxaw6uIq4+Dg+3/85dsMOgL+HPw+3fZjJbSfQMrGA/T8u5uPa7oRkezI6qT4LI05zKrCIO3/9FE1HxGCrE3TLYykstLN69WEWL05i6dK9nD+f7+yrU8eHESNKiuf33dcUd3dzy8TYbFZtKCoiIiJigkubjZbW/v376dixI6GhoWzfvh1vb+/yulSloc1Ga+bGBFI6yoyYpcyIWcqMmKXM1FxHLxxlRvwMZm2fxcmsk872bo26ManD4wzJjsBYupHsZWtwZJSsQ15gdeBhWLAYFgx3G42Of4mnm+ctjSM/v5ivvjrE4sVJLF++j4yMAmdf/fq+REe3JiYmgp49m+DmpifIqyLdZ8QsZUbMUmbErJqcmTLfbLS0WrRowT//+U9+9atf8be//Y2//vWv5Xk5qQQKCwtrxC9MpOwoM2KWMiNmKTNiljJTcxQUF7B833Ji42NZfXg1BiXPGNX2rs1j7R/j0dqDCPvqCNmTV5FxfJbzdbYGdfCP6YcjN5/MmZ+ChzuWwiJy3pyH528nmB5HXl4Rq1YdZNGiJD77bD/Z2ZfWYA8J8WPkyJLieffuWn6lutB9RsxSZsQsZUbMUmZurty3Yf3lL3/J119/zbPPPlvel5IKZrfb2blzp3b4FZcpM2KWMiNmKTNiljJTM+w5u4e4+Dg+3Pkhablpzva+4X15vPk4+uz0oui1NRTs+DMXfuqz+PngN7gXfqMH4N31Ls5Pm0PmO/MIfHEiB3tGcMeGJM6/OgOAYBeK6Tk5hXzxxQEWL97DihX7yckpcvaFhQUQE1NSPL/33kZYrTXrybDqTvcZMUuZEbOUGTFLmXFNuX9lrFYrn376aXlfRkRERERE5Lpyi3JZtHsRsfGxbDqxydke6h/KxDaPMOZcBLWX7SRv/Ydk20vWRcfNhk+fe/CP6Y/PgG5YfbwASH9jNudfnUHQy5MIeG48bN1Krd88itVmvWExPSurgM8/38/ixXtYufIAeXnFzr4mTQKJiYlg1KgIOnduqOK5iIiISCWjXzGIiIiIiEi1FZ8aT1x8HB/t+ojMgkwAbBYbD97xAI969KbL2iwK3tyEkbuJvJ9e49kxAv+Y/vgN73PtTUPtDoJenkTg84+ybt0RNm1KJSfnGL2ff9TZf1FGRj6ffbafxYuTWLXqIAUFdmdfeHgQo0ZFEBMTQceOITVuTVIRERGRqsTlQvrJkydZsWIFY8aMITAwsDzHJFWYzWar6CFIFaPMiFnKjJilzIhZykzVl5Gfwce7PiYuIY741Hhne7NazZgQMozoXbXx+9sW7Gc+Jf+nPremDfEf1R+/kf3waN7ohucPfulxlizZw3NN3yQ5OfOn1p2EhQXw5psD6d27KbNnb2fx4iS++uoQRUWXCustWtR2Fs/bt6+v4nkNpfuMmKXMiFnKjJilzNycxTAMw5UDN27cSO/evTl48CDNmjUr73FVWWZ2ehURERERkbJhGAbfnfiO2PhYFu5eSF5xyfPlHjYPhjd+gHFnWnLX0mTs+445X2MNDsRvWB/8R/XHs1Mbl4vaS5bsISZmIdf7ScpqteBwXOqMiKjrXPM8MrKeiuciIiIilYSZWq7LT6R7enoC4OXldWujk2rLMAwyMjIIDAzUDwfiEmVGzFJmxCxlRsxSZqqeszlnmbNzDnHxcexJ2+NsjwhuxSOWbjy4Bnze3AekYQcsnh74DOiG/+gB+Nx3NxYPd1PXs9sdPPfcqusW0QEcDoO2besxalQEI0dGEBFRt3STk2pJ9xkxS5kRs5QZMUuZcY3LhfSLj/df/pj/4sWLeeqppwgKCiIoKIjg4GBq165NcHAwderUoW7duoSHh3P//ffr4wE1gN1uZ+/evdrhV1ymzIhZyoyYpcyIWcpM1eAwHHxz+BviEuL4dM+nFDmKAPBx92FkYC9G76pHq+nHsBTuK3mBxYJXtw74x/THd0gvbAF+pb72p5/uvWw5l+t7661B9O7dtNTXkepL9xkxS5kRs5QZMUuZcc0tfWXsdjtpaWlERUWRlZXF8ePH2blzJxcuXCA3NxcAi8VC586d+e6777BarWUyaBERERERqXlOZp5k1vZZzEiYwdELR53tUQFteOhMK/otyMD3bBaQBYB7q2b4jxqA/8i+uDWsX+rrHj+ewSefJPHJJ3vYtOmES69JTc0q9fVEREREpPK55V8xWCwWli9fjoeHxxXtBQUFpKSk8J///IfXX3+dtWvXcv/999/q5UREREREpAYpdhTzxYEviI2P5YsDX+AwSjbuDHQPIMbekei1brTclQecBsDWoA5+I/viHzMAjzbNS/3x5MOHz/PJJ0ksXryHH388afr1ISH+pbquiIiIiFRO5fasvqenJ82aNeP555/ntddeY+PGjSqkV3MWiwVvb2+tpSQuU2bELGVGzFJmxCxlpmJsTdnKS1+/xNR+U+kU2gmAQ+mHmJEwg9nbZ5Oaneo8tqtHa0Yn1qfvNwV42YuAIiy+3vgN7oXfqAF4d++ApZTLSu7bl8Ynn+xh8eIkEhJOOdstFujZswkxMREMHdqCbt1mcfJk5jXXSbdYICwsgB49GpdqDFL96T4jZikzYpYyI2YpM64p90VvfHx88Pf3Jzg4uLwvJRXMZrPRvn37ih6GVCHKjJilzIhZyoyYpczcPulTZ4LNSvBvJ/Dhjg9Ze3Qts7bP4mD6Qd5b+hc22i9tHFrHVotRZ+5kxFcQfs4TKAabBz59O+M3egC+A7pj9fEyPQbDMEhKOsvixSVPnicmnnH22WwW7ruvGTExrRk+vBX1619aV/3NNwcSE7MQi4UriukXf/acNm0gNpuWtZRr031GzFJmxCxlRsxSZlxT7oV0X19fjh49SlBQUHlfSiqYw+EgLS2NOnXqaD18cYkyI2YpM2KWMiNmKTO3zwlLOkdnfESA/Sgf8REA7295n/e2vFdygAH3Fd5JzCZv7tvrh4ej5P3wjGpdsmno8Ptxq2v+ZwzDMNix4zSLF5eseb53b5qzz93dSt++4Ywc2Zphw1pRp47PNc8RHd2axYtH89xzq67YeDQsLIBp0wYSHd3a9Lik5tB9RsxSZsQsZUbMUmZcY7qQbvYRfzc3NxXRawiHw8Hhw4cJDg7WPzpxiTIjZikzYpYyI2YpM7fPXbkvwhggb7uzzeDyx7shNrZkg1C3JiH4x/THb1R/PJqbXzLFMAy2bk1xFs8PHTrv7PPwsDFgQHNiYiIYMqQFQUHeLp0zOro1w4a1ZN26I2zatJNu3drRu3czPYkuN6X7jJilzIhZyoyYpcy4xlQh3TAM7r//fmrXrk1gYCDp6ekALFy4kN69exMWFlYugxQRERERkarPMAy2pmwlLj4OL5sX+fb8ax7n5rAw9ft2BEwcjn9Mfzw7R5p+oMfhMNi8Odm5Yejx4xnOPm9vNwYNupOYmNY8+GALAgI8SzUfm81Kr15N8PU9S6dOTVREFxEREanGTD+R7u3tTWZmJqdPn+bUqVMYhsGjjz6KxWKhcePGjB49mqeffprGjbW5joiIiIiIwPm883y06yPi4uPYcXqHsz2k0I9Uj+yrjl/Xdjpdf/8YFg93U9ex2x1s2nTC+eR5SkqWs8/X153Bg1sQExPBoEF34OvrUfoJiYiIiEiNY6qQbrFY+Oyzz6hXr56z7fz58+zdu5e1a9fy5Zdf8tprr/Gvf/2LmJgYpk2bRv369ct80FI5WSwWAgMDtcOvuEyZEbOUGTFLmRGzlJmyYxgGG49vJDY+lsVJi8kvLnn63NNwY+DhuozaWRu/QhvDx+zAYoBhwfmnd7col4voxcUO1q8/yuLFSSxZspczZ3KcfQEBngwd2pKYmNb0798cb29zhXlXKDNiljIjZikzYpYyI2YpM66xGMbl+8xf37Zt27j77rtJTU29opD+c7t27eKvf/0rixYtok6dOrz77ruMGjWqzAZc2WVmZhIYGEhGRgYBAQEVPRwRERERkdvqdPZpPtzxIXEJcew/t9/Z3iozkNHbazNsXz0CC9xwaxLCmWZ+PNBsLo38w3hi0O/478p/ciIrmTV1X6Xti7+57jUKC+2sWXOExYuTWLp0L+fO5Tn7goK8GDasFTExrenbNxxPT9MfwhURERGRGsJMLdfl7yov1ttvVndv27YtCxYsYOLEiTz22GOMHTuW5ORkfv3rX7t6KamiHA4HKSkphIaGamMCcYkyI2YpM2KWMiNmKTOlY3fY+frw18TFx7Fs3zKKHcUA+NrdGbw3mNG7G9DujB9WP1/8Yu7Df8wg8jbFU/zPmezo/A77Ovfj1IFs3uj+DS23fE3e1DmkW4MJ/u0E5zXy84v5+utDLF68h+XL93HhwqX11evU8WHEiFbExERw331NcXe33ba5KzNiljIjZikzYpYyI2YpM65xuZBeXFx8xZ83M3DgQBISEujfvz8vvPAC3t7e/PKXvyzdKKVKcDgcJCcn06BBA/2jE5coM2KWMiNmKTNiljJjzvGM48xKmMXM7TM5nnHc2X7XmQBG76rHgwfr4mt3x7tnR/z/MgjfQT2w+ngBkLdxG6lDhzLu7RySkz90vjYsLIB5Q4cSZHeQm1vEqlUH+eSTPXz22T6ysgqdxzVo4Ed0dEnxvEePJri5Vcz7pcyIWcqMmKXMiFnKjJilzLjG5UJ6fn4+VquVvLy8mx/8k9DQUNauXcvdd99NaGhoqQYoIiIiIiKVR5G9iM/2f0ZcfByrDq7CoOQTq4GF7oxIqsuoPfVpec4X95ZN8f9/A/GP6Y9bSN2rzrPujnuJeXkhP//A68mTmfSYCffeG8yOP79Gbm6Rs69hQ39iYiKIiYng3nvDsNn0g56IiIiI3B4uF9J79+5NUVHRzQ/8mbp167J+/XoaN25s+rUiIiIiIlI5HDh3gLj4OGbvmM2ZnDPO9i7JgYzZ3YD+h2vjHRiE34j78R87CM/2La+7YZXd7uC551ZdVUQHnG3ff58MQNOmtRg5sjUxMRHcfXdDrFZtgiUiIiIit99t2XlHRfSawWq1UrduXX0ERFymzIhZyoyYpcyIWcrMlfKK8liyZwmx8bGsP7be2V4n152Re+ozKqk+TXP88Ol3L/6/H4Rvv3uxeLjf9LwbNx4nOTnzpsf95z8P8otfdLxuQb4yUGbELGVGzFJmxCxlRsxSZlxjMW62e+hPXn/9dUaPHm26KO5wOPjb3/7G888/j7+/f6kGWZWY2elVRERERKQy2nl6J7HbYpm7ay4X8i8AYHVAr+NBjN7dgN7HgvBr2xr/MQPxi+6LrXYtl857/nwey5bt4803f2D79lM3Pf7jj6MZN67tLcxEREREROT6zNRyXXoi3W63s337dqZNm8YXX3xBu3btXBpIXl4e48aNY+/evUyYMKFGFNJrMofDwZEjR2jWrJl+gyUuUWbELGVGzFJmxKyanJmsgizmJ84nNj6WLSlbnO0NMz0ZlVSfkXvqE+YXiv+o/viPGYRHq2YunffcuVyWLdvHokVJrF59mOJih8tjCgmp/D8/1OTMSOkoM2KWMiNmKTNiljLjGpe+Mjabjblz5zJ58mS6dOnCH//4x5tuOvrFF18QERHBuXPn2LRpE40aNSqTAUvl5XA4OHv2LA6H6z8cSc2mzIhZyoyYpcyIWTUtM4ZhsDl5M5OXTybkjRB+8fkv2JKyBXe7hUEH6jBrWRvWLezKy40fJ2rm2zTZvpjaf3rqpkX0tLRc4uLiGTBgLg0avMGkSctZteogxcUO2ratx//9Xy/q1/fleiu2WCzQqFEAPXpU/iUia1pm5NYpM2KWMiNmKTNiljLjGlNrpP/f//0fQ4cO5emnn2batGkMHTqUbt260bBhQ7y9vUlJSWHfvn0sWrSI8+fP88orr/Dkk09W6jUNRURERERqmnO555i7cy5x8XEknk10toef92b07vqM2FePhu064f/rgfgOuw9bgN9Nz3n2bA6ffrqXRYuSWLv2CHb7pRUk27evz6hREcTERNCyZR0A2ratT0zMQiwWrth09OKPDtOmDcRm0xNRIiIiIlI5mN5sNCoqiu+//574+HgWL17MF198QUpKCvn5+TRo0ICmTZsydepUHnjgAby8vMpjzCIiIiIiYpLDcLDu6Dri4uNYkrSEAkcBAF5FVh44WIfRSfW5x6MlAaMH4T99AO7NGt70nKdPZ7NkyR4WL97DunVHcTguVcQ7dGjgLJ7feWftq14bHd2axYtH89xzq67YeDQsLIBp0wYSHd26DGYtIiIiIlI2TBfSL4qKiiIqKqosxyJVnNVqJSwsTGspicuUGTFLmRGzlBkxqypnZmvKVl76+iWm9ptKp9BOzvbUrFRmb5/NjG1xHMo47GyPOOPLmKQGDE1pTOjA/vg/OxCvLu2w3GTup05l88knSSxevIcNG45dUTzv2DHEWTxv3jz4pmOOjm7NsGEt2bjxOKmpWYSE+NOjR+Mq9SR6Vc6MVAxlRsxSZsQsZUbMUmZcYzGMyz9IKbfKzE6vIiIiIiK3In3qTLBZCf7tBJ5d+Sxv//g2z979LG8MeIPFr73Ih4Xr+MqxCzt2AHwLbQzbV5fRSSF0btMb/zED8X2gJ1afG3+SNCUly1k837jx2BVLsdx9d0NiYloTExNBs2ZB5TldEREREZEyZaaWW+on0kV+zm63s3//flq0aIHNZqvo4UgVoMyIWcqMmKXMiFlVLTMnLOkcnfERAfajLLAuACAuIY6Pf5hJmiXbeVzHlABGJ9VnqCWK+jFD8H+7P24hdW947uTkTGfxfNOm41cUz7t0CXMWz5s0qVUeU6syqlpmpOIpM2KWMiNmKTNiljLjGhXSpcwYhkFGRgb6kIO4SpkRs5QZMUuZEbOqWmbuyn0RxgB5251tuUW55FouHfPlZz3ocP8I/N8aiOddrbBYLFed56ITJzJYvLikeP7ddyeu6OvatRExMa0ZOTKCxo0Dy3gmVVdVy4xUPGVGzFJmxCxlRsxSZlyjQrqIiIiISBW05+weBjUfyMpDq67Z72ZY+U/4/9Bv0x+xeHpc9zzHjl1g8eIkFi1K4ocfTjrbLRbo1q2xs3geFqZlC0VERESk5lIhXURERESkisgtymXR7kVM/+4dvju79YbH/vDEFqJCoq7Zd+TIeWfxfMuWFGe7xQI9ejRh1KgIoqNbExrqX6bjFxERERGpqlRIlzJjtVoJDw/XDr/iMmVGzFJmxCxlRsyqrJlJSE1g+g/v8fHOj8k0cgGwOaD30WDuPluPf9yzF4sBhgXnn2vWHCHq4UuF9MOHz7No0W4WLUpi27ZUZ7vVaqFnz5Li+YgRrQgJUfHcjMqaGam8lBkxS5kRs5QZMUuZcY3F0OI3ZcrMTq8iIiIiIteTkZ/BRzvnEvvtO2zP2utsb5Thyag9IYyvO4j0xt34KHYJnz32GdbMAM5svZd6nb7HEZDJ4A+G0ul/XyIzs4BFi5JISDjlPIfVaqF376bO4nn9+n4VMUURERERkQplpparQnoZq8mFdLvdTmJiIpGRkdrhV1yizIhZyoyYpcyIWRWdGcMw+O7Ed/x341ssPriUPAoBcLdbGHCoNuMy2jOg/2MEjh6EpW4wfwz7JZOK9/B6fhP+k30HYAEMful3kBe8jvHvnHDezQsHwGazcN99zRg1KoLhw1tRr57vbZ9fdVTRmZGqR5kRs5QZMUuZEbNqcmbM1HK1tIuUGcMwyMvL0w6/4jJlRsxSZsQsZUbMqqjMnM05y4fbZhH73bvsKzjubL/jnA9jDzXioVZjaPbr0Xh2jMBisQCwbt1RcjLz+bcRzn9+KpaXsPCf7Dux2W3YLAadOoXyxBMdGT68FXXq+NzWedUEus+IWcqMmKXMiFnKjJilzLhGhXQRERERkQrgMBysPrSa6Wv/zfKTX1NksQPgXWTlwQN1eMStF72HTsTv1fuw+nhd9fpdu07zVm7z657/4pPoH/+mC+PGtS2fSYiIiIiI1BAqpIuIiIiI3EYnM08yY9O7zNgax3HH2ZJGC7Q97cfY1Dt5qMskGk4dgXuT0Ktee+pUNosW7Wb+/N18990Jl66nzUNFRERERG6d1kgvYzV5jXTDMMjIyCAwMND5kWORG1FmxCxlRsxSZsSs8spMsaOYz/csZ/rqN/jy/Pc4LCXfgvsX2Bh+sAGP1RvCvaMn4d09CovVesVrz53LZcmSPcyfv5t1647icFz69t3T00ZBgf2a17RYICwsgCNHnsNms17zGLl1us+IWcqMmKXMiFnKjJhVkzOjzUYrUE0upIuIiIjIlQ6lH2L66jf4IOkjTlsyne2dTwbwcG4UY+5/krrRg7AFXvnUeGZmAcuW7WX+/N189dUhiosdzr4uXcIYO7YNo0a1YfPmZGJiFgJw+Xf1F3/+Wbx4NNHRrctvgiIiIiIiVZg2G5UKUVxcTEJCAh06dMDNTdGSm1NmxCxlRsxSZsSssshMfnE+S+Ln8d+1/2ZD/q6SRgsE57oTc6wxE1s9RNRvJ+LRstkVr8vNLWLFiv3Mn7+bFSv2X/Gk+V13NWDs2DaMHt2GZs2CnO3R0a1ZvHg0zz23iuTkS4X6sLAApk0bqCL6baD7jJilzIhZyoyYpcyIWcqMa0r1lUlJSWH27Nm89NJLLn1x33vvPZ566qnSXEqqGLv92h8tFrkeZUbMUmbELGVGzCptZhJP7eI/K/7Bx8eXct6aB4DFgO4ngnjUszcjBz9DrX49sLhf+v65oKCYL788xPz5iSxfvo+cnCJnX6tWdRg7tg1jxkTSqlWd6143Oro1w4a1ZOPG46SmZhES4k+PHo21nMttpPuMmKXMiFnKjJilzIhZyszNlaqQnp6ezh//+EdGjBhBaGgogYGB1z12zpw5PPPMMwQEBDB+/PhSD1REREREpLLJLsxm3obpTP/uXbYah0sardAgy4MxZ1vxeOfJRDw9Hre6l54iLy52sGbNEebPT2TJkj1kZBQ4+5o1q8XYsZGMHRtJ27b1XF6j0maz0rt307KcmoiIiIiIXKZUhXR3d3cMwyAyMhKAoKAgIiMj6dmzJ/fffz+9evUCYPPmzTz99NO0b9+eMWPGlN2oRUREREQqiGEY/Hh4E//9/O8sOreabFvJU+Rudgt9TtZjQr3BDH341/jcFeEshDscBt9+e5z58xNZvDiJs2dznecLDfVnzJg2jB0bSefOoTVugycRERERkaqgVJuN7tu3j4iICDZu3EhycjLJycns3buX9evXc+DAAdq0acOECRP461//Sq1atVi3bh1NmjQpj/FXOjV5s1HDMMjLy8Pb21s/AIpLlBkxS5kRs5QZMetGmUnPTeeDFa8zY9dsdttSne1NLnjxcH4nJt73DOFDh2H18nSea8uWFObPT2Thwt2cPJnlfE2dOj6MGhXB2LGRdO/eGKtV+ayqdJ8Rs5QZMUuZEbOUGTGrJmemXDYbPX/+PEFBQVe0de3a9arjvvrqK0aOHMlLL71EUFAQP/zwA/Xq1XP1MlLFeXh4VPQQpIpRZsQsZUbMUmbkZtKnzgSbleDfTmBrylZe+volpvabSueGnTn3+iw2Ze7gI++tLMvbTIHNDjbwKLYw6HQjJjUfx4DfPI9HowZAyQ8hO3acYv78RBYs2M2RIxec1wkM9CQ6ujVjx0bSp08z3Ny0hnl1ofuMmKXMiFnKjJilzIhZyszNuVxIHzNmDMeOHWPEiBHccccdV/XHx8ezcOFCZs6cidVqZeLEicydO5f333+fP/3pT2U6aKmc7HY7W7dupVOnTtrhV1yizIhZyoyYpcyIS2xWzr86A4APWm1l3bF1/OeH91i+8QQfGd9xJCgPCgEbtEj35VGP3kx48AVCe/bEYi0phu/bl8b8+YnMn7+bvXvTnKf29XVn2LBWjB3bhv79m+PpqRxWN7rPiFnKjJilzIhZyoyYpcy4xuWvzN133w3Au+++S05ODgCjRo0iOzubrVu3cu7cOVq3bs3zzz/Pk08+SVBQEH379uXhhx8mMjKSkSNHls8MRERERERuQdak+zhmP8rOGdOYM3ofWGDmztlQq6Tfq9DCiAutmNxhEr1+80ts/r4AHD16gQULSorn27efcp7P09PGgw+2YOzYNjz4YAt8fNxv/6RERERERKRMuVxI/+tf/wpAUVERGzZsYNGiRcyfP5/MzEzq1q3LV199Rd++fa94zdixY/nyyy/51a9+Rf/+/fH39y/b0YuIiIiI3KKmbzYt+T9jLmu8bGnIfA+Dj99NAiAlJYtFMzczf/5uNm9Odh7j5malf//mjB3bhmHDWhEQ4Fn+AxcRERERkdvG5YUZExMT2bJlC+7u7nz99dcUFRWRmprKv//9bzw9PXnllVdITEy84jVz587l2WefJS8vj7feeqvMBy8iIiIiUhqFxYXMW/ov+v5PSzCuf5wNG+/1ncF//rOV3r1nExb2L55//ks2b07GarXQp08zpk8fzKlTv2XFiod45JH2KqKLiIiIiFRDFsMwbvCjwyV/+MMf+Pvf/86gQYOoU6cOBQUFzJkzh4EDBzJ8+HAGDhxIy5YtadWqFS+//DIPPvggTZo04Z133sFutxMTE0NgYGB5z6fCmdnptboxDAO73Y7NZqtxO/xK6SgzYpYyI2YpM/Jze/Zv4T+f/oWPM74mzbPA2X7HKX8ONsi6+gX//QXW06E4HJeaunVrxNixkcTERNCggd9tGLVUZrrPiFnKjJilzIhZyoyYVZMzY6aW63IhHWDbtm28//77BAQEcObMGfz8/Pjuu+9YvXo19erVw2q1MmLECFasWIGvry82m42UlJQatUh9TS+k5+Xl4e3tXeP+0UnpKDNiljIjZikzApCbl8W8Ja8zY9dsvvc+7myvk+vBWMu9JCyLoGH+cRZOWAEOSj6z+dOfo2c/yMKjnYmKasC4cW0ZPboNjRtX/4dDxHW6z4hZyoyYpcyIWcqMmFWTM2OmluvS0i6GYfD5559z/vx5xo8fT0pKCmfPniUyMpL/9//+HydOnHAe+/7775OUlIS/vz9FRUXs3bv31mYjVYbdbmfnzp3Y7faKHopUEcqMmKXMiFnKTM22LX41U17pQ8hfazP54F/43vs4Vgf0Od+YufX/h+T/OcPQ+2dw17EcnjHO4JPtBamh8NlgSA3FJ9uLZ4wzPO19mDfeGMALL3RVEV2uovuMmKXMiFnKjJilzIhZyoxrXHpUvLCwkKFDh2KxWLj4ALvFYmH16tUADBs2jDZt2mCxWLBYLISHh5OdnU3jxo2Jjo5m165deHpqrUgRERERKV8ZGWl8uOjvzDzwMdt9Tpc0ekBojjePePVmyvDf07T9vaxbd5Snf7ueefMSmWwxmH+mNbn/HgB2G2CBbR3JtdmZ73Ecm8UgNfUay76IiIiIiEiN4VIh3dPTk507d+Lu7o6Hhwf//Oc/mT59OnfffTcffPAB+/fvJzo6GoB//etfDBs2jJycHNavX8/dd9/NG2+8wf/8z/+U60REREREpGYyDIONGxbz39VvsNTYSq67HXzAzW5hQPYdTO4wmQdH/optO9N564NdLFz4b06dyna+/i2aX+OsFrC78W5eOAAjQvxv02xERERERKQycmlpF4DIyEg2b97Miy++iL+/P7169SIrK4vXXnuNIUOGcPz4cQzDYMaMGfTs2ZOuXbtSq1Yt/vd//5d///vf5OXllec8pJKw2WwVPQSpYpQZMUuZEbOUmerrzOljvPrWJFr/rja91o3mY7cfyHW3E57lx5/dR3N0/E7+MXEjP+7pSMvIWO69dwZvvfUjp05lExTkxZQpUXz99XjCwvy53lKQFgs0ahRAjx6Nb+/kpErRfUbMUmbELGVGzFJmxCxl5uZc3mz0vffe4/nnn+eVV17h5MmTpKSk8NprrxEVFcW7777LyJEj8fHx4cCBA3z++ed4eHjwy1/+koKCAkJCQpg2bRqPPvpoec+nwtXkzUZFREREypvdYeerL2YSu+kdVrglUujmAMCz2MqQgjb8ouvTNIuIZsGiPcybl8ju3Wedr/X1dWfYsFaMGxdJ//7N8fAo+WFhyZI9xMQsBODy74wvFtcXLx5NdHTr2zNBERERERG5bczUcl1a2gVK1kFv1KgRQ4YM4amnnqK4uJhmzZrxt7/9jWnTpjFkyBAMw8BisfDss886X+fp6cngwYPx8vIq/YykSjAMg4yMDAIDA2vcDr9SOsqMmKXMiFnKTPVx4vBuYhf/hQ/TvuCYbzb89K1lRFYQE0NH8EDv3/DVhiz+8I9EfvzxPefrPDxsDBp0B+PGRTJ4cAt8fT2uOnd0dGsWLx7Nc8+tIjk509keFhbAtGkDVUSXG9J9RsxSZsQsZUbMUmbELGXGNS4/kX49RUVFJCUlERkZydatW+nQoQMeHlf+gJKTk4Ovr+8tDbSqqMlPpBcXF7N161Y6deqEm5vLv6ORGkyZEbOUGTFLmanaigrzWfbpW8Rti2O19wHsPy1K6FdoI9roxPh7nuHIqRbMX5DEunVHnU+TW60W+vRpxrhxkURHt6ZWLdce6LDbHaxbd4RNm3bSrVs7evduhs3m8kqIUkPpPiNmKTNiljIjZikzYlZNzky5PJF+Pe7u7rRv3x6Ae+6555rH1JQiuoiIiIjcugO7NvPfT1/ho5w1nPLJh5++leyUVZ9HGo/G32cknyw9yQOvHaK4+KDzdffeG8a4cZGMGtWGBg38TF/XZrPSq1cTfH3P0qlTExXRRURERETEqdx/xVBQUICnp2d5X0ZEREREqrD8nCwWLpjKjN0fsNH/BIYF8IGgAndGu3XjrjqPsDYpgJff20de3jrn69q3r8+4cZGMGRNJ06a1Kmr4IiIiIiJSzZVrIf3//b//x2effcbGjRsJCgoqz0tJJWCxWPD29tZaSuIyZUbMUmbELGWm8tuxaSX/XTmVBfZNpHsVwU+fpuyR3Zi+/sM5ktKV+UuP8N+ME87X3HFHMOPGRTJ2bCQREXXLdDzKjJilzIhZyoyYpcyIWcqMmKXMuOaW10i/FofDwYQJE5g7dy6PPfYY//3vf69aN726qslrpIuIiIi4IivtNHPn/Y1ZRxayJfC0s71+nhdDjB64nX+AJcuKOXMmx9nXsKE/Y8a0Ydy4tnTsGKJv8kVERERE5JaV+xrpb731Fp6ennh4eGCxWCgoKKCgoIBnn32WwsJCRo8ezfLly3nllVf4/e9/X6pJSNXjcDhIS0ujTp06WK1aU1RuTpkRs5QZMUuZuX3Sp84Em5Xg305ga8pWXvr6Jab2m0qn0E6kvzEbo9jOvvY+TF87jU/ctpHtYYdAsDqgV3Zz7sgewFdfNiPuWA6QAUBwsDejRkUwblwkPXo0wWot/+K5MiNmKTNiljIjZikzYpYyI2YpM64pVSH9+eefv6rNYrHw7LPPAiVf/NjYWCZNmsSrr77KoUOH+Mtf/kJISMgtDVYqN4fDweHDhwkODtY/OnGJMiNmKTNiljJzG9msnH91BobD4O9B61l7ci3/+OJdpia34aP1/2FR27Mk5meCT8nhYbm+dM/oxr4furB2u5W1AOTg5+fB8OGtGDcukn79wnF3t93WaSgzYpYyI2YpM2KWMiNmKTNiljLjGpcK6Xl5efzrX//ipZdewt3dHYCEhASaNm0KwP79++nSpQsAHh4eLF++3PnaTZs28cUXX5CUlMSmTZvKePgiIiIiUhlkTbqPdQcSqDPrLVYN3gO+8NmxD/ncMCjsXbKSoLvdQrf0O2FPd9atDWO+UfJNuqenjQceuJNx4yJ58MEW+Pi4V+RUREREREREruJSIf2jjz7ij3/8I9OnT+ell17CYrHg7+9PYGAgwA3Xj/nss89YtmwZo0ePLpsRi4iIiEil0/TNphACjAF+2oGnyM1xxTHub/yadbkl3zfabBbuvz+cceMiGTGiFYGBXrd1vCIiIiIiIma4VEiPiYkhICCABQsW8Nvf/talE58+fZqgoCA8PDy46667KC4uvqWBSuVnsVgIDAzU5l/iMmVGzFJmxCxlpvzZ8/P5bMGb3HEqiIP1z4OFkv9dcZAVlg4nNzeAbt0aMW5cJKNGtaFePd+KGPINKTNiljIjZikzYpYyI2YpM2KWMuMai2EYhpkXpKWlUa9ePQ4ePEh4eDgA+/btIyIiArvdDsCnn37KE088wWOPPcZrr73GsWPHCA8Pd/ZXZ2Z2ehURERGpqg7v2Ezskr8xN/cbkv3ybnzwf38BqaHMmzeSsWMjb88ARUREREREbsJMLdf06vF16tS5bp9hGDz99NPExMTQqlUrfvnLX5o9vVRhDoeD5ORkHA7HzQ8WQZkR85QZMUuZKVsFWVnMj/sT/X7dlDuX3Mur1s9J9svDr8CN9vEtGbq8a8mBF7/cP/052vMkUPK9YmWnzIhZyoyYpcyIWcqMmKXMiFnKjGvKdBtWi8VCq1at+N3vfse6deto3rx5WZ5eKjn9oxOzlBkxS5kRs5SZspG0+Rue/0NfGv21LuNO/oXVtY7hsELLk3Vovbwvua+/SPev7+HFc0X4ZHtBaih8NhhSQ/HJ9uIZ4wxPex8mJMS/oqdyU8qMmKXMiFnKjJilzIhZyoyYpcy4xqU10i/6+OOPMQzjhuvlPPPMM1e1paWlmR+ZiIiIiFSY3AvpzP/478w8MI9NtVLAHXCHWrkeNN11Jye2dmXf2YYEBHjy6MOt8P/8MxacbU3uvweA3QZYYFtHcm12FngeJyjQgx49Glf0tERERERERErFpUK6YRj85je/4a233qJfv343PX706NHs2rULLy8vDMMgNTWVWrVq3epYRURERKQcGYZB/Lpl/Per11jEFi54FUEtsBjQ8mh9jG1R7NvTkT1ungwe3IKHHmrLAw/ciZeXG0uWtCQmZiEW4NICLhYsDjfezQtn8ZzR2Gxl+mFIERERERGR28alQvoPP/zAe++9x6uvvsoLL7yAzWa74VPpHTt2pE6dOnh6euLh4YGXlxddu3Yts0FL5WS1Wqlbty5Wq35IFtcoM2KWMiNmKTOuyTidwofzXmH28cXEB6aBV0l73Swvwna04vDWbuzLqEufPs343W/aMmJEa2rV8rriHNHRrVm8eDTPPbeK5ORMZ3tYWADTpg0kOrr17ZxSqSkzYpYyI2YpM2KWMiNmKTNiljLjGovh4q5PR44coVmzZkDJF/dahXS73Q6UrKtTU7/wZnZ6FREREakoDoeDTas+Zvr6aXzqtp0cj5Lv49zsFlodCqVgWycO7G9Hp46NePjhtowZ08alNc7tdgcbNx4nNTWLkBB/evRorCfRRURERESkUjJTy3V5jfSLRXSAt956C09PTzw9PbFYLBQXF5OXl+fsf/zxx9m+fTvDhw8nJiaGyMjIUkxDqhqHw+H8hUtN/UWKmKPMiFnKjJilzFzt7IkjzJr/Z2afWsaegAvgU9Ieet6H+tvbsH9bVwpCmvPQQ2156KG2tGhR29T5bTYrvXs3LfNx3y7KjJilzIhZyoyYpcyIWcqMmKXMuMbUZqMX/epXv7phf9++fSkoKOCtt97ilVdeISIigvnz59OmTZtSDVKqBofDwdmzZ2nSpIn+0YlLlBkxS5kRs5SZEg67na+XTWf69+/xudduCt0MCACPYgut9zcmc+vd5OZ0pte4tkx/vR0dO4bccBm/6kyZEbOUGTFLmRGzlBkxS5kRs5QZ15SqkH4z48ePZ/z48RQXF/P555/z4Ycf0rx58/K4lIiIiIhcx8mDicQu+gsfnFvJUf9s8Ctpb3LWn+CEtiQf6E3U4Lt5eHpbevduqiVYRERERERErqNcCunOk7u5MXz4cIYPH16elxERERGRnxQXFvDZJ2/x3y3T+cbvEMU2A/zBp9BGqz1NuZDQlfZRD/Lwb9ry4IMt8PIq128HRUREREREqgX95CRlxmq1EhYWpo+AiMuUGTFLmRGzalJmDib+yPuL/8zHuWs45ZsPgSXtd6TUwi+hPXUCRjPuoXuJntmaWrW8KnawlVhNyoyUDWVGzFJmxCxlRsxSZsQsZcY1FsMwjIoeRHViZqdXERERkWux2x1s3Hic1NQsQkL86dGj8TWXXSnIzWbBvKlM3zGL74KTMX5a1jwgz407dzfH78JghkWPZMyYSEJD/W/zLERERERERCo3M7VcPZEuZcZut7N//35atGiBzWar6OFIFaDMiFnKjJhV1TKTPnUmSfvOMe4rN5Ide6Dv17C6H2HW1szrX0xEy9oEv/Q4O3/8hn/N/z8+c/+RdJ9CqF3y+tbH61D30L307PFLxr9+Ny1b1qnYCVVBVS0zUvGUGTFLmRGzlBkxS5kRs5QZ15gupAcEBODu7o6Hhwfu7u43Pd5ut1NQUEBRUREZGRmlGqRUDYZhkJGRgT7kIK5SZsQsZUbMqmqZSdp3jpDlyxmWE867vfdC+FFot5PhGwoIWnGAf2V4seJXL7O97lnn0i21czxoubc1HUIn8tgvY+jUKRSLxVKh86jKqlpmpOIpM2KWMiNmKTNiljIjZikzrjFdSM/Ozmb48OEEBgbe9NiMjAyWLl3KY489VqrBiYiIiNQUdruDUd9l0sMvkIfr7mRm5F7yAO/2CRyqfYi7m6ST6+EAwOqAyKMNaFs0iEcefZG+/2h5zaVfREREREREpGyUammXV199lRYtWtz0uL1797Js2TJmzZpVmsuIiIiI1BgbNx7n1Ni/sghYBPDTwyB53oWsvDPNeVy/nV0Y0/9lHnr5Aby9b/7pQBEREREREbl15frokj5WXLNYrVbCw8O1w6+4TJkRs5QZMasqZeZkcgb1N9zjLKDz82+jHBZYMoKJMdOZ9MQwFdHLSVXKjFQOyoyYpcyIWcqMmKXMiFnKjGu02aiUGavVSr169Sp6GFKFKDNiljIjZlWFzOzZlcjr77/MN+7rOd0z+/oHxk6B1FBCQvxv3+BqoKqQGalclBkxS5kRs5QZMUuZEbOUGdfo1wxSZux2Ozt27MBut1f0UKSKUGbELGVGzKqsmSkqKmLaa6/QfVI47Re1Y2b9FRwLzsaryErng3VLDnJwxZ+jvU7SqFEAPXo0rpAx1xSVNTNSeSkzYpYyI2YpM2KWMiNmKTOu0RPpUmYMwyAvL087/IrLlBkxS5kRsypbZrZs+o5/f/BH1vl9T2pgHvxUE7/zbCD9bAMYdPYOan33NQMaZJGbEQzxURAVj09gOs84zvBMv2JtKlrOKltmpPJTZsQsZUbMUmbELGVGzFJmXKNCuoiIiEg5ysvN5Y3X/pdlyfOJDz2Jo2FJu1+Bje6n2jCh968Z86cJAKRPnUmSdy2CFo4i93gOYIFtHQlu4gv3Q0TL2hU2DxERERERkZpMhXQRERGRcrBm1UreWfwKG4O3keZbCGEl7ZGpdRjoP4SXfvsP6jaof8Vrgl96nO7AMbuDjRuPk5qaRUiIPz16NNaT6CIiIiIiIhVIhXQpMzabjVatWmGz2Sp6KFJFKDNiljIjZt3uzJxPS2fq6/+PL84vZWfoGWhU0h6U607PtLt4YtjvGfSnYTc9j81mpXfvpuU7WLkm3WfELGVGzFJmxCxlRsxSZsQsZcY1KqRLmbFYLNSqVauihyFViDIjZikzYtbtyszShfOJWzWVb+vtIsO7GLzBYkCHlBAG1x/FC//zCv6BAeU+Drl1us+IWcqMmKXMiFnKjJilzIhZyoxrSvUZ4ZSUFI4fP37T/508eRKAEydOcPz48TIduFQ+xcXFbNmyheLi4ooeilQRyoyYpcyIWeWZmZTkkzz364dp/VQwI/aMY0WTBDK8i6mf5cWY5J5823sd26an8OdX3lQRvQrRfUbMUmbELGVGzFJmxCxlRsxSZlxTqifS77//fpePNQyDpk2bAmC320tzOalC9B6LWcqMmKXMiFllmRm73c5HM2OZ8/3bfBeyj9xaJed2s1volNKY6OaP8Oxff4+nl1eZXVNuP91nxCxlRsxSZsQsZUbMUmbELGXm5kwX0v/5z3/i4eGBh4cH7u7uWCyWGx7vcDiw2+3k5uaWepAiIiIiFenAnr289s7LrLat4UjtLGhS0t7ovC/35ffgt1P+RruOURU7SBERERERESk3pgvpL774YnmMQ0RERKRSsdvtvP/mVBbunsEPoYcprGcA4FlspcvJcMa0f4Jf/OHX2pBHRERERESkBrAYhmFU9CCqk8zMTAIDA8nIyCAgoGath2oYBnl5eXh7e9/0kwoioMyIecqMmFWazGzfupU3Yv+HtT6bOFnr0ifqwtMC6Mf9/O7Z12h2Z/PyGrJUMN1nxCxlRsxSZsQsZUbMUmbErJqcGTO13FKtkV4Rzp07x759+2jRogV16tSp6OHIdXh4eFT0EKSKUWbELGVGzHIlM/l5+fz79T+z7NhHbG14AntoSbtvoY1uqa15rOfzjPnDBD19XkPoPiNmKTNiljIjZikzYpYyI2YpMzdnLe8LnD9/nn379rl8/Llz52jWrBlHjx51ts2fP5877riDp59+TixOmgAAZDJJREFUmsaNGzN//nxnX2JiIp07dyYoKIgXX3yRyx+wL48+uT673c7WrVu1OYG4TJkRs5QZMcNud7BmzWH+/vfPWLPmMHa746pjNn7zDaOm9KDx/9bifxyv8kOjE9itEHGqNs9nPsrBp4/z5cxdPDRhkoroNYTuM2KWMiNmKTNiljIjZikzYpYy45pyL6S/+uqrdOvWjc2bN9/02LS0NAYPHnxFEf3ChQs888wzbNy4kYSEBP773//yu9/9DoCCggKGDBlCx44d2bp1K0lJScyePbvc+kRERKTyS586k28nvUbTpm/S99F/8qdDv6Hvo/+kadM3+XbSa5z4yzv88fe/osMTDei1sS+Lw77lrF8BgXnuDDnekeWRi9j9fhr/fuMDGoSGVvR0REREREREpBIo10L6gQMHeOeddygoKKBp06Y3PX7s2LGMHTv2irasrCymTZtGZGQkAO3bt+f8+fMArFy5koyMDP71r3/RvHlz/v73vzNjxoxy6xMREZHKL2nfOUKWL2fYue3QfgeEH4V2OxliWc2sc9Non/sb/urxLttDT2NY4K6U+vy+4CmOvXSa5TO2MmRkTEVPQURERERERCqZclsjvbCwkHHjxpGfn8/bb79NgwYNbvqa6dOnEx4ezvPPP+9sa9SoEQ8//DAARUVFvP7660RHRwOwY8cOunTpgo+PDwDt2rUjKSmp3PpERESkcrPbHYz6LpMefoE8XHcnM9vuIQ+wdf6R97tcWqqtTrYnvc934ldj/kSvfv0qbsAiIiIiIiJSJZRLIb2oqIiRI0cSHx/P+PHjeeqpp1x6XXh4+HX7duzYwX333YeHhwd79+4FSnZVbdasmfMYi8WCzWbj/Pnz5dIXFBR01bgKCgooKChw/j0zMxOA4uJiiouLAbBarVitVhwOBw7HpfVZL7bb7fYr1mG/XrvNZsNisTjPe3k7cNU6Rtdrd3NzwzCMK9ovzvPnY7xe+7XmZBgGUVFR2Gy2ajOnG41dc7r1ORmGQYcOHarVnG40ds3p1ud0MTNWq/WqMVbVOd1s7JqTuTlt2HCcU2P/yiJgEcBPw7Tbrtzv5MP7ttKvfyusVqtzLJV1TtXxfarMc7p4n7moOszpZmPXnG5tThczYxiG85iqPidXxq45lX5Ol2fG4XBUizlVx/epMs3p8szY7fZqMafq+D5Vpjldnpni4uJqMafq+D5VpjlZLJYrMlMd5uTq+3T5MTdT5oX0lJQUxo8fz7p16xg1ahSzZs0qk/O2a9eOb775hhdeeIGJEyfy6aef4ubmhqen5xXHeXl5kZubWy591yqk/+Mf/+DPf/7zVe0JCQn4+voCULduXZo3b86RI0c4e/as85iwsDDCwsLYv38/GRkZzvbw8HDq1atHYmIieXl5zvZWrVpRq1YtEhISrghUu3bt8PDwYOvWrVeMoVOnThQWFrJz505nm81mo3PnzmRkZDh/IQHg7e1N+/btSUtL4/Dhw872wMBAWrduTUpKCsnJyc72682pbt26hIeHV6s5Vcf3qTLNyd3dnaioqGo1p+r4PlWmOdntdu666y48PT2rzZyg+r1PFTGnk8dOMOeT/1CnkRdpAfklB1q4kt0KS4ezxXKAoODsSj8nqH7vU1WYk91ux8/Pr1rNCarf+1SZ5nSxsFWd5lQd36fKNKeLmalOc6qO71NlmtPFzFSnOVXH96myzGn79u0UFhY6i5XVYU7V8X2qTHM6evQop06dcmamOszJ1fepbt26uMpiXF6CvwWGYTBjxgxefvllLly4wP/8z//w5z//GYvl5z/BujAoi4UjR45cc131EydO0KRJE86dO8f06dNJTExkzpw5zv5atWpx4MABZs6cWeZ91/rCXuuJ9EaNGnHu3DkCAgKA6v0bq8vb7XY78fHxdO7c2fkb0Ko+pxuNXXO69TldnpmL46zqc7rR2DWnW5/Txcx06tQJNze3ajGnm41dc7rxnAoKCnh32qss2f8BPzY8StFPT567F1socrvGtzj//QWkhrJ69Xh69WpSKedUHd+nqjSni/eZqKgoPD09q8WcbjZ2zenW5nR5Zjw8PKrFnFwZu+ZU+jkVFRU5M+Pu7l4t5lQd36fKNKfCwkJnZtzc3KrFnKrj+1SZ5lRQUODMjM1mqxZzqo7vU2WaU2FhIdu2bXNmpjrMydX3KTs7m6CgIDIyMpy13Ou55SfSHQ4HCxcu5G9/+xu7d+8mKiqKd955hy5dutzqqQFYs2YNK1eu5LXXXgNKvqBQMvnOnTsTFxfnPPbo0aMUFBQQHBxcLn3X4unpedUT7BfHeXGsF118w37uYnhcbf/5eUvTbrFYrtl+vTG62n7xFyfVaU4XaU7lM6eLmalOc7rZGDWnW5uTxWJx/q+6zKm07TV5Tps3bOTNuf/Lev/NpAbkQ+OS9jvOBlAvsR1hqV4sfGg1OAArzj9He53k+0at6N27GTbbpfNVhjmVtr0yv0+lba/oOV38prw0Y6+sc3JljJpT6ed0MTMXv6+pDnO6lXbN6cbtF7NysVBxo7FXlTlVx/epMs3p8szov083b9ecuCIzl4+rKs+pOr5PlW1O18pMVZ+TK+/TtY65HpcK6UuXLmXv3r00aNAAT09PcnNzOXfuHPv372fFihWcOXOG7t27s2TJEoYPH+7yxV3RqlUrhg8fzp133smgQYP4wx/+QP/+/QkMDKRnz55kZGTw4Ycf8uijj/Lqq6/St29fbDZbufSJiIhIxcnKyOT11/7IijOLSAhJxdGwpN2vwEa3UxE81ut5xv3pcb6d9BocXcTn2V7kZgRDfBRExeMTmM4zjjM806/4iiK6iIiIiIiIyM24VEhftGgR8+bNu25/9+7d+c1vfsPgwYPLbGAXhYaGsmjRIn7961/zwgsvMGDAAOeyK25ubkyfPp2HHnqIF198Ebvdzvr168utT25Ov3AQs5QZMUuZqXk+X/IJ01e8yrd1dnDepwh+KqC3Ta3LwICh/O7Ff1D7suXXIlrWJolRBC0cRe7xHMAC2zoS3MQX7i/pF7kR3WfELGVGzFJmxCxlRsxSZsQsZebmXFoj/fvvvyctLQ13d3fc3d1xOBycPHmSI0eO8P3337N582ZycnIICQnhpZde4qmnnrruI/vl4eTJk2zduvX/t3fncVHV+x/H3zMDCCqiKCrIouhNU7PSvNdroZbabbGyzTRv3hZbbmV7tllWmpWZ2W6lZnWzsl3rdtN2Wiw1c0m0XBFERUQ2Wc+c3x8EP0nF+eIwwwyv5+Phw+s5A3y/zgu8fRjOUf/+/Q+4jnl9nKtNfn6+oqKiPLquDgAAOLjtGZmaOv0OLSr5RGnt9lQfb1MUppQ9fXTdefdq8Bmn1/o+LMut1NR0ZWUVKDY2UikpibwSHQAAAABQzWSW65WbjZaXl+vjjz/W9OnT9e2336pHjx567bXXdNxxxx3puw44jXmQbtu28vLyFBUVVaebzKLxoRmYopngZlmWXp01U6//+Kx+iP1N+8IqbzDjckt9MuN1duIo3XLb/Ypo2tTj90kzMEUzMEUzMEUzMEUzMEUzMNWYmzGZ5XrlZVmhoaEaPny4vvnmG73zzjvKzs7WiSeeqHfeeccb7x4BwrIsrVu37oA77gKHQjMwRTPBKW31Gl1x7TB1uaWlLt9xvT5PStO+MEvxe5vqku2naumZP+nHWdt0z31TjYboEs3AHM3AFM3AFM3AFM3AFM3AFM14xuvXXznvvPN04okn6uyzz9aoUaNUXl6uUaNGefvDAACAAFZeXq6nHn9I7/3+in7qsFUV7Sp/QC683Kl+WZ01otdVumrCzVynDwAAAADQINTLhczbtWunL7/8UoMGDdKll16qhIQEnXTSSfXxoQAAQAD5/quv9dTr9+mrqJ+0M7JESqw8ftSulhriGqrxNz2qpORO/l0kAAAAAAB/Um93BG3atKkWLFigXr16acSIEVq9erVat25dXx8ODYDD4VBERESju5YS6o5mYIpmAlPenlxNe/xefZz9rn6J2yE7vvJ4i5IQnbizpy4ffIsumHhJvXxsmoEpmoEpmoEpmoEpmoEpmoEpmvGMV242Wpv58+dr5MiRGj9+vB555JH6/FANQmO+2SgAAPtb8PZbeul/jyk1ZpXyIsqrj/fa3lantxquO257WK3aRPtxhQAAAACAxsxklltvr0ivMmLECH3//fe699576/tDwc/cbrd2796tNm3ayOn0yn1sEeRoBqZopuHL2JquqTPu0uLST7SuXW71pVtiCptoYO4JunbEfTr51FN9th6agSmagSmagSmagSmagSmagSma8Uy9D9IlacaMGb74MPAzt9utTZs2KTo6mk86eIRmYIpmfM+y3EpNTVdWVoFiYyOVkpIol8v5p8dYmvvCs5q37Hn9EPubilu6JUkhlkMnbE/Q8E7/1E2T7lWT8HCfr59mYIpmYIpmYIpmYIpmYIpmYIpmPOOTQToAAAgse6bO0dr1ORq1KEQZ7jRpyGLps6GKdx6tN06tUPeurbV9aB89/uLd+jLsG22NLpSSKt82IbeZTikZoFuunKxefXr7dyMAAAAAAHgBg3QAAHCAtetzFLtggc4pStazg9ZJyVukXqt0TmqxfvvlZ91TsUffF+5URfvKW61ElDvVb3sXjerzb10+YZxcLpd/NwAAAAAAgBcxSIfXOBwORUVFcYdfeIxmYIpmfMOy3Lrw+3ylNI/S6JhVmtNznYolhfVZpjePWa5nm1VUP7brrlYaGvoP3X7jw0rs1NFvaz4UmoEpmoEpmoEpmoEpmoEpmoEpmvGMw7Zt29+LCCYmd3oFAKAh+uqrLTr5607/f8CWdJD/PzXV9bRun3C9z9YFAAAAAIA3mcxyuXo8vMbtdisjI0Nut9vfS0GAoBmYohnf+Hrxf5W8MbZygC4dOES3nNK75ym+80BfL80YzcAUzcAUzcAUzcAUzcAUzcAUzXiGQTq8hk86mKIZmKKZ+pOTna3x48fqmGtidH/YddrUOeugr0KXJM0aK63updjYSJ+usS5oBqZoBqZoBqZoBqZoBqZoBqZoxjNcIx0AgEbsvTfnadaix/RtuzUqaFYhNZOcbumYze2VtLWtFpyySnKr8lvvf/w+IjxTPyR0U0pKop9XDwAAAACAbzBIBwCgkdmekalHHx+vRaWfaF27XCmp8nj7/HCdXPB33fyvySqd85209W19VhiufXnR0s+9pd4/q2nUHo1z79K4oRVyufjBNgAAAABA48AgHV7jdDoVExMjp5PBCjxDMzBFM0dm3tzZmvvNdH0fu15FLS1JUojlUN/tiTq/yxjd8Mi9Cg0NlSTt+W6d1upCtZp/ofalF0lySMv7KDqpmTRY6t61tR934jmagSmagSmagSmagSmagSmagSma8YzDtm378A+Dp0zu9AoAQH3bummzHpkxXp+5F2lDTH718Q57m2pwcYpuvXKKevXpfci3tyy3UlPTlZVVoNjYSKWkJPJKdAAAAABAUDCZ5fJfwvAat9utjRs3cmMCeIxmYIpmPGNZlmY/95ROubyrjp7TRTNbv6MNMfkKtRxKSe+sZ1o8oq3T8vXKzP/VOkSXJJfLqUGDOmrUqGM0aFDHgBui0wxM0QxM0QxM0QxM0QxM0QxM0YxnAuu/htGgud1uZWdn80kHj9EMTNFM7db/ulZXXHe2utzSUmOzb9SXSb+pONStpD3Ndfmus7T6ojX6ZvYGXXfzHXK5XP5erk/QDEzRDEzRDEzRDEzRDEzRDEzRjGe4RjoAAAHMsiw99+RUvf3rLP0Yt1llbSuv2BZe7tTft3fR6BOu06UTrms0g3MAAAAAAOoDg3QAAALQquU/6/GX7tYXEanKaLlPSqw83iW7hYY4T9WdN01VUnIn/y4SAAAAAIAgwSAdXuN0OhUfH88dfuExmoGpxt5MeXm5nnp8kt7d8KqWxqWrIrby1edNy1w6MaubLh1wsy6eeIWfV9mwNPZmYI5mYIpmYIpmYIpmYIpmYIpmPOOwbdv29yKCicmdXgEA8MTS777X9Ffu0VeRS7SjRUn18W47W+nUJqfrjlunKi6+gx9XCAAAAABA4DGZ5fJtBniNZVlKS0uTZVn+XgoCBM3AVGNqpnjfPk1+4Hb1vTJe/RadqDc7fKUdLUoUWRKi07cep3e7vq605/boySdeZ4hei8bUDLyDZmCKZmCKZmCKZmCKZmCKZjzDpV3gNbZtKy8vT/yQAzxFMzDVGJpJ/fxzPfXGRH3Vapl2Ny+V4iuP99jRRmdEnqM7bn9YrWNi/LvIANIYmoF30QxM0QxM0QxM0QxM0QxM0YxnGKQDAFDPLMut1NR0ZWUVKDY2UikpiXK5/v+Hwgry8vXYY/foo13v6Je4HbITKo+3LA5VSvaxuuasu3XGxHP9tHoAAAAAAMAgHQCAerJn6hytXZ+jUYtClOFOk4Yslj4bqnjn0Xrj1ArtC9+h50pSldrmF+1pWi79cYWW47a305mtz9ft4ycrKrqVfzcBAAAAAAAYpMN7nE6nkpOTucMvPEYzMBVozaxdn6PYBQt0TlGynh20TkreIudxK3RK9grd6vpNP7XNr35s66IwDdxzgq4fMVEnn3qqH1cdXAKtGfgfzcAUzcAUzcAUzcAUzcAUzXjGYXPxG68yudMrACB4WZZb8T0nKqVkhUZHZGrU2WtVHFEmhy3ZjsrHONxS7+1xOjvuIt1624NqFtncv4sGAAAAAKARMZnl8m0GeI1lWVq5ciV3+IXHaAamAqmZ1NR07Rg5WW9f+rGGX/SLiiPKJP3/EF2SbKc0bfR3uu+B6QzR60kgNYOGgWZgimZgimZgimZgimZgimY8wyAdXmPbtoqLi7nDLzxGMzAVKM38unKVnnhxjFoU13IFNcspvXuesrIKfLewRihQmkHDQTMwRTMwRTMwRTMwRTMwRTOe4RrpAAB4yWuzX9Cc7x/Xdx02qLxr5f8BaV4SosLwigMfPGuslBWn2NhIH68SAAAAAACYYpAOAMARyN29R1MevU0L932g9W1zpcTK40ftaqnWv5yghJ0hmn/J/yS3Kn8O7I/fR4Rn6oeEbkpJSfTj6gEAAAAAgCe4tAu8xuVyqVu3bnK5XP5eCgIEzcBUQ2pmyTepOn/sier8WHtNa/6y1rfNVajl0MCtXfRK3PNa/2yupnY7VeMqctS0MFzKipMWDpOy4tS0MFzj3Ls0b2iFXC7+Ka5PDakZBAaagSmagSmagSmagSmagSma8QyvSIfXOBwOtWzZ0t/LQAChGZjydzOWZem5GY/q9bQXtLRDutwJlcfbFjTRkIKTdNc1j6vn8cdWP75719ZaqwvVav6F2pdeJMkhLe+j6KRm0uDK86hf/m4GgYdmYIpmYIpmYIpmYIpmYIpmPOOwuYq8V+Xn5ysqKkp5eXlq0aKFv5fjUxUVFVqxYoWOP/54hYTwPRocHs3AlL+aydiaroem3aJPnJ9qa3Rh9fFeWTE6N+Zi3XHXFEU0bXrIt7cst1JT05WVVaDY2EilpCTySnQf4esMTNEMTNEMTNEMTNEMTNEMTDXmZkxmuY3rbwb1zrIsfy8BAYZmYMqXzfz3g/f1zIIH9U3sahW1qfy4EWVODcjqoWtPv0dnT7zIo/fjcjk1aFDHelwpasPXGZiiGZiiGZiiGZiiGZiiGZiimcNjkA4AwH5KS0o09ZEJei/rP/olbqeUVHk8IbeZTqsYqntuma6k5E7+XSQAAAAAAPApBukAAEhKW71GDz9zqxY3/0Y7WpRIcZLDlvpmxGvkUWN1/d13KzQ01N/LBAAAAAAAfsA10r2sMV8j3bZtFRcXKyIiQg6Hw9/LQQCgGZiqj2bmzZ2tWd9M1XcdfldZSOU/iVHFIRqU3Vu3XjxFKYMHe+XjwD/4OgNTNANTNANTNANTNANTNANTjbkZrpEOvwkLC/P3EhBgaAamvNFM3p5cPfTI7VpY+J7WtcutvnxLl+woDWtyjibcOU2tY2KO+OOgYeDrDEzRDEzRDEzRDEzRDEzRDEzRzOE5/b0ABA/LsrRs2TJuTgCP0QxMHWkzP337nS4Ye6KSH22nx5rN1rp2uQqxHBqQ3llz2j+jdU/m6InHX2GIHkT4OgNTNANTNANTNANTNANTNANTNOMZXpEOAAhqlmVp5lPT9Pqvz+unDltlJVQejylsoiF5/XXn1dPUq09v/y4SAAAAAAA0aAzSAQBBaXtGph6aerP+6/hEW6ILpT8G6D2z2ujcNiN1x30Pq1lkc/8uEgAAAAAABAQG6QCAgGBZbn399VZ9912Wioq2atCgTnK5DrxC2acLF+jp9+/X1+1XqbB15Y+lRZQ7lbK9u/592j0aPnGkr5cOAAAAAAACnMO2bdvfiwgmJnd6DTa2bcuyLLlcrkZ3h1/UDc3AE3umztHa9TkatShEGe40achi6bOhincerTdOrVD3rq3V7IaLNf2x+/X2tle0osOO6reN39tUp5UN0d03TVenv3T24y7gL3ydgSmagSmagSmagSmagSmaganG3IzJLJdXpMOrysrKFBER4e9lIIDQDA5n7focxS5YoHOKkvXsoHVS8hap1yoN/6ZUEZ+u1x05Zfp4x/XKiiqWOkgOW+qT2UEjO1+uG6beq9DQUH9vAX7G1xmYohmYohmYohmYohmYohmYopnDO/Bn4oE6sixLq1at4g6/8BjN4HAsy60Lv8/XXc2jNLTjKkX0XCVJijh2hdIu+Eh/v+wnzTp+hbKiitWiJERnp/fV5/0/1dKXMnTrnQ8yRAdfZ2CMZmCKZmCKZmCKZmCKZmCKZjzDK9IBAA1Wamq6doycrLclvS1Jf1yMrDiiTJ8n76l+3KVbL9DUKc8opn07fywTAAAAAAAEOV6RDgBosLKyChS24EzJ/ceBP1+qze2Q3j1Xpw69jyE6AAAAAACoN7wiHV7lcrn8vQQEGJrBoSz97nu99t8bFPaPX1R2qG/7vnSllBWn2NhIn64NgYWvMzBFMzBFMzBFMzBFMzBFMzBFM4fnsG3b9vcigonJnV4BAAd65/XX9MznD+q7+I2qcFX+E9U+v4l2tCitfGW6U9W/j3jlTP1QMVibN98ol4sfsgIAAAAAAJ4zmeUydYDX2LatvXv3iu/NwFM0gyqWZenxR+7T8Ve314UbxujrpA2qcNnqmdVG9y8/TfPfPUZNC8OlrDhp4TApK05NC8M1zr1L84ZWMETHIfF1BqZoBqZoBqZoBqZoBqZoBqZoxjNMHuA1lmVp3bp13OEXHqMZ5O3J1c23/kt/ubmVbiudpF/idsrllk5M76RX42dq9cxsjTvpQjkGj1Cr+Q9WXspl+QnSS1cq+u0HpcEXqnvX1v7eBhowvs7AFM3AFM3AFM3AFM3AFM3AFM14hmukAwB87ve0dXpgxjj9L+ob5bQokyQ1LXPp5B3H6o6Lpypl8ODqx0aPv1wnSdpqufXVV5v13XerdOKJvTRoUCdeiQ4AAAAAAHyCQToAwGe+XLRIj715h76KW6XiOLckKaawiU4vGKT7b31Wnf7S+ZBv63I5NXBgkpo1y9YJJyQxRAcAAAAAAD7DIB1e43A4FBERIYfD4e+lIEDQTOPx8gvP6qWlj+nHDlvlTqo8lry7hc4LP1/3TZihyCjPbs5MMzBFMzBFMzBFMzBFMzBFMzBFMzBFM55x2FxF3qtM7vQKAMGstKREj0y5S/OzX9Pa9jnVx3tnxuqSv/xb4265Wy6Xy48rBAAAAAAAjZnJLJefi4fXuN1u7dq1S263299LQYCgmeCUvWOn/j3uAnW+q7Xud83Q2vY5CrEcOmVrV71/9Bta/uJ23XT7vXUaotMMTNEMTNEMTNEMTNEMTNEMTNEMTNGMZxikw2vcbrc2bdrEJx08RjPBZdXyn3Xh2JP0lxkJmtnmXWW23KcWJSE6b1t//TTsR30+Z52Gjxh5RB+DZmCKZmCKZmCKZmCKZmCKZmCKZmCKZjzDNdIBAEdk4bvv6MmP71Vqh/UqS6i8WlhsXoTOKjtND9z9jNrHxfl5hQAAAAAAAEeGQToAwJhlWXruyal6Je0ZLY/fLv1xA9FuO1tpRPQlunvKo2oSHu7fRQIAAAAAAHgJg3R4jcPhUFRUFHf4hcdoJvAUFRRq0uRb9G7RW9oQky/FSw5b+mtGoq7sfauumHhDvX58moEpmoEpmoEpmoEpmoEpmoEpmoEpmvGMw7Zt29+LCCYmd3oFgECxddNm3f/Y9fpvs8+1K7JUkhRe7tTA7T10+wWPavAZp/t5hQAAAAAAAGZMZrncbBRe43a7lZGRwY0J4DGaafi+/+prnX3FCeox+y+a2/6/2hVZquiiMF28/RT9Mmq1/jdnlU+H6DQDUzQDUzQDUzQDUzQDUzQDUzQDUzTjGQbp8Bo+6WCKZhquN16ZowFXdNaALwdpYeJyFYVZStrTXDfsvVgbxmfp9Rc+V9ce3X2+LpqBKZqBKZqBKZqBKZqBKZqBKZqBKZrxDNdIB4BGxLLcSk1NV1ZWgWJjI5WSkiiXq/J7quXl5Zr2yH16I3O2VsdmS4mVb9Nre1uNThyrm6fdr9DQUD+uHgAAAAAAwD8YpANAI7Bn6hytXZ+jUYtClOFOk4Yslj4bqnjn0ZpzcoG+LP1cr8cuV3qrIilWCrEc6p+ZrGsHTtBFEy/19/IBAAAAAAD8ikE6vMbpdComJkZOJ1cMgmdoxnfWrs9R7IIFOqcoWc8OWiclb1FY76X6a+k3GhW7STlNyyVJzUtdOmVnb931r8fVb0CKn1d9IJqBKZqBKZqBKZqBKZqBKZqBKZqBKZrxjMO2bdvfiwgmJnd6BQBfsCy34ntOVErJCo2OyNSos9eqOKJMsiU5Kh/TujBMZxUO0QPjn1Vip47+XC4AAAAAAIBPmMxy+TYDvMbtdmvjxo3cmAAeoxnfSE1N146Rk/X2pR9r+EW/qDi8rPKE4/8fk9O8TP+6qOEP0WkGpmgGpmgGpmgGpmgGpmgGpmgGpmjGMwzS4TVut1vZ2dl80sFjNOMbn/33XSVtj658BbpUY4AuSbKc0rvnKSurwNdLM0YzMEUzMEUzMEUzMEUzMEUzMEUzMEUznuEa6QAQpN54ZY6eTH1APyakS81qeeCssVJWnGJjI322NgAAAAAAgEDCK9IBIMi88uJz6ntlB1285Qr9mJAuhy0d/1u8zl/w98oHVH2D+Y/fR4RnKiGhhVJSEv2yXgAAAAAAgIaOV6TDa5xOp+Lj47nDLzxGM971wtOPa+aax/RL3E4pXnK6pZMyOuu2Mx5Rq22bpZy39UlhuPblRUs/95Z6/6ymUXs0zr1L44ZWyOVq+M8DzcAUzcAUzcAUzcAUzcAUzcAUzcAUzXjGYdu2ffiHwVMmd3oFgCNlWZaeeeJhzdrwpNbE7pYkhVgODcg4SneeP01DzxwmSdozdY7Wrs/RyM+kzPQiVV4o3VZ8UjO9MVjq3rW1osdf7r+NAAAAAAAA+JjJLJdXpMNrLMvSb7/9pqOOOkoul8vfy0EAoJm6syxLjz9yn+ZmzlRauz1SbOUA/eSM7rp39JNKGTy4xuOjx1+ukyRttdxKTU1XVlaBYmMjlZKSGBCvRK9CMzBFMzBFMzBFMzBFMzBFMzBFMzBFM55hkA6vsW1beXl54occ4CmaMWdZlqZMukP/yZ6t39ruldpJTSqcOiWzl+6//Bn99aQTa317l8upQYM6+mSt9YFmYIpmYIpmYIpmYIpmYIpmYIpmYIpmPMMgHQACQHl5uR68/2bNy39Vm9oUSG2liHKnhmT11oP/fl7HnXCCv5cIAAAAAAAQtBikA0ADVrxvnybef4Pml76lrdGFUhupWZlLp+78qyaNm6kex/by9xIBAAAAAACCHoN0eI3T6VRycjJ3+IXHaObQigoKNWHitXrbfleZLfdJzaTmpS6dkX2iJt/ygv5ydDd/L9EvaAamaAamaAamaAamaAamaAamaAamaMYzDpuL33iVyZ1eAeDP8vbk6u4HrtH7IQuU1aJEkhRVHKozcwdoyh0vKSm5k59XCAAAAAAAEBxMZrl8mwFeY1mWVq5cKcuy/L0UBAia+X852dm66vpzddSUWD0XPV9ZLUoUXRSmf2WdrrXXb9brL3zGEF00A3M0A1M0A1M0A1M0A1M0A1M0A1M04xku7QKvsW1bxcXF3OEXHqMZacf27bpr0pVaGPmZcmLKJEltCpto+L7T9Mj9L6l1TIyfV9iw0AxM0QxM0QxM0QxM0QxM0QxM0QxM0YxnGKQDgB+kb96iux+5Uh+3+lp725dLktoVhOvcsmGact8LatUm2s8rBAAAAAAAQBUG6QDgQxvX/6Z7pl2pT9p8r/y4CklSh7wIXaDzNGnic4qM4t4KAAAAAAAADQ03G/WyxnyzUdu2lZeXp6ioKDkcDn8vBwGgMTWTtnqNJjx5tRa1/VGFTSqvOZa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      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "plt.rcParams[\"font.sans-serif\"]=[\"SimHei\"] #设置字体\n",
    "plt.rcParams[\"axes.unicode_minus\"]=False #该语句解决图像中的“-”负号的乱码问题\n",
    "# 将提供的序列转换为数组并进行处理\n",
    "# 序列1：实际值\n",
    "Y_actual = np.array([\n",
    "    121121, 122389, 123626, 124761, 125786, 126743, 127627, 128453, 129227, 129988,\n",
    "    130756, 131448, 132129, 132802, 133450, 134091, 134735, 135404, 136072, 136782,\n",
    "    137462, 138271, 139008, 139538, 140005, 141212, 141260, 141175\n",
    "])\n",
    "\n",
    "# 序列2：组合模型预测值\n",
    "Y_predicted = np.array([\n",
    "    121121, 122389, 123626, 124761, 125786, 126743, 127627, 128453, 129227, 129988,\n",
    "    130756, 131527.751976, 132289.454506, 133034.553047, 133760.476130, 134466.401829,\n",
    "    135152.351948, 135818.697747, 136465.994585, 137094.840867, 137705.876650,\n",
    "    138299.719210, 138876.997346, 139438.303624, 139984.236417, 140515.355764,\n",
    "    141032.227062, 141535.377704, 142025.341086\n",
    "])\n",
    "\n",
    "# 序列3：单一ARIMA模型预测值\n",
    "Y_predicted2 = np.array([\n",
    "    121121, 122389, 123626, 124761, 125786, 126743, 127627, 128453, 129227, 129988,\n",
    "    130756, 131524.725758 ,132293.672665, 133062.686960 ,133831.721788, 134600.762873, 135369.805865, 136138.849438, 136907.893188,\n",
    "    137676.936992, 138445.980813, 139215.024638, 139984.068465, 140753.112292, 141522.156120, 142291.199947, 143060.243775, 143829.287602,\n",
    "    144598.331430,\n",
    "])\n",
    "\n",
    "# 对预测值进行处理，删除逗号，并转换为整数型\n",
    "Y_predicted = np.array([float(str(val).replace(',', '')) for val in Y_predicted])\n",
    "\n",
    "# 绘制折线图比较实际值和预测值\n",
    "plt.figure(figsize=(15, 7))\n",
    "actual_line, = plt.plot(range(1995, 2023), Y_actual, marker='o', label='真实人口数', color='navy')\n",
    "predicted_line, = plt.plot(range(1995, 2023), Y_predicted[:28], marker='x', label='组合模型预测结果', color='crimson')\n",
    "predicted_line2, = plt.plot(range(1995, 2023), Y_predicted2[:28], marker='*', label='单一ARIMA模型预测结果', color='g')\n",
    "\n",
    "plt.title('中国人口长期预测', fontsize=16, fontweight='bold')\n",
    "plt.xlabel('年份', fontsize=14)\n",
    "plt.ylabel('人口总数 (万人)', fontsize=14)\n",
    "plt.legend(handles=[actual_line, predicted_line, predicted_line2], fontsize=12)\n",
    "plt.grid(True, linestyle='--', alpha=0.7)\n",
    "plt.gca().xaxis.set_major_locator(mticker.MaxNLocator(integer=True))  # Ensure integer years on x-axis\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}