{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## ARMA Modeling: Forecasting\n",
    "\n",
    "**Functions**\n",
    "\n",
    "`tsa.SARIMAX.forecast`\n",
    "\n",
    "### Exercise 70\n",
    "Produce 1-step forecasts from your preferred model in the previous exercise, as well\n",
    "as a random-walk model. \n",
    "\n",
    "1. Are the forecasts objectively accurate?\n",
    "2. Compare these forecasts to the random walk models using MSE and MAE.\n",
    "\n",
    "**Note**: Use 50% of the sample to estimate the model and 50% to evaluate it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_hdf(\"data/term-premium.h5\", \"term_premium\")\n",
    "term = data[\"TERM\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>TERM</td>       <th>  No. Observations:  </th>   <td>434</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 0, 1)</td> <th>  Log Likelihood     </th> <td>-67.547</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 27 Aug 2025</td> <th>  AIC                </th> <td>143.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:17:58</td>     <th>  BIC                </th> <td>159.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>04-01-1953</td>    <th>  HQIC               </th> <td>149.525</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 05-01-1989</td>   <th>                     </th>    <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>    <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td>    0.0550</td> <td>    0.021</td> <td>    2.663</td> <td> 0.008</td> <td>    0.015</td> <td>    0.095</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>    0.8995</td> <td>    0.016</td> <td>   57.608</td> <td> 0.000</td> <td>    0.869</td> <td>    0.930</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>     <td>    0.4596</td> <td>    0.019</td> <td>   24.241</td> <td> 0.000</td> <td>    0.422</td> <td>    0.497</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td>    0.0795</td> <td>    0.003</td> <td>   28.189</td> <td> 0.000</td> <td>    0.074</td> <td>    0.085</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.21</td> <th>  Jarque-Bera (JB):  </th> <td>1124.05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.64</td> <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>6.45</td> <th>  Skew:              </th>  <td>0.34</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>10.85</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &       TERM       & \\textbf{  No. Observations:  } &    434      \\\\\n",
       "\\textbf{Model:}                  & SARIMAX(1, 0, 1) & \\textbf{  Log Likelihood     } &  -67.547    \\\\\n",
       "\\textbf{Date:}                   & Wed, 27 Aug 2025 & \\textbf{  AIC                } &  143.094    \\\\\n",
       "\\textbf{Time:}                   &     15:17:58     & \\textbf{  BIC                } &  159.386    \\\\\n",
       "\\textbf{Sample:}                 &    04-01-1953    & \\textbf{  HQIC               } &  149.525    \\\\\n",
       "\\textbf{}                        &   - 05-01-1989   & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{intercept} &       0.0550  &        0.021     &     2.663  &         0.008        &        0.015    &        0.095     \\\\\n",
       "\\textbf{ar.L1}     &       0.8995  &        0.016     &    57.608  &         0.000        &        0.869    &        0.930     \\\\\n",
       "\\textbf{ma.L1}     &       0.4596  &        0.019     &    24.241  &         0.000        &        0.422    &        0.497     \\\\\n",
       "\\textbf{sigma2}    &       0.0795  &        0.003     &    28.189  &         0.000        &        0.074    &        0.085     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.21 & \\textbf{  Jarque-Bera (JB):  } & 1124.05  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.64 & \\textbf{  Prob(JB):          } &   0.00   \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 6.45 & \\textbf{  Skew:              } &   0.34   \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.00 & \\textbf{  Kurtosis:          } &  10.85   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                   TERM   No. Observations:                  434\n",
       "Model:               SARIMAX(1, 0, 1)   Log Likelihood                 -67.547\n",
       "Date:                Wed, 27 Aug 2025   AIC                            143.094\n",
       "Time:                        15:17:58   BIC                            159.386\n",
       "Sample:                    04-01-1953   HQIC                           149.525\n",
       "                         - 05-01-1989                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept      0.0550      0.021      2.663      0.008       0.015       0.095\n",
       "ar.L1          0.8995      0.016     57.608      0.000       0.869       0.930\n",
       "ma.L1          0.4596      0.019     24.241      0.000       0.422       0.497\n",
       "sigma2         0.0795      0.003     28.189      0.000       0.074       0.085\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.21   Jarque-Bera (JB):              1124.05\n",
       "Prob(Q):                              0.64   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               6.45   Skew:                             0.34\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        10.85\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.tsa.api as tsa\n",
    "\n",
    "nobs = term.shape[0]\n",
    "half = nobs // 2\n",
    "mod = tsa.SARIMAX(term.iloc[:half], order=(1, 0, 1), trend=\"c\")\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "mod = tsa.statespace.SARIMAX(term, order=(1, 0, 1), trend=\"c\")\n",
    "forecast_res = mod.filter(res.params)\n",
    "\n",
    "fcast = forecast_res.predict()\n",
    "\n",
    "errors = term - fcast\n",
    "oos_errors = errors.iloc[half:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "plt.rc(\"figure\", figsize=(16, 6))\n",
    "plt.rc(\"font\", size=14)\n",
    "\n",
    "both = pd.concat([term, fcast], axis=1)\n",
    "both.columns = [\"TERM\", \"One-step Ahead\"]\n",
    "both_oos = both.iloc[half:]\n",
    "fig, axes = plt.subplots(2, 1)\n",
    "both_oos.plot.line(ax=axes[0])\n",
    "axes[0].set_xticklabels([])\n",
    "axes[0].set_xlabel(\"\")\n",
    "axes[0].set_title(\"Term and ARMA(1,1) 1-step Forecast\")\n",
    "errors = both.TERM - both[\"One-step Ahead\"]\n",
    "errors.plot.line(ax=axes[1], linestyle=\"none\", marker=\"x\")\n",
    "axes[1].set_title(\"ARMA(1,1) 1-step Forecast Errors\")\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   52.28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Aug 2025</td> <th>  Prob (F-statistic):</th> <td>2.20e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:17:59</td>     <th>  Log-Likelihood:    </th> <td>  151.57</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   434</td>      <th>  AIC:               </th> <td>  -299.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   432</td>      <th>  BIC:               </th> <td>  -291.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Const</th>    <td>   -0.0181</td> <td>    0.012</td> <td>   -1.474</td> <td> 0.141</td> <td>   -0.042</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Forecast</th> <td>    0.0557</td> <td>    0.008</td> <td>    7.230</td> <td> 0.000</td> <td>    0.041</td> <td>    0.071</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>22.089</td> <th>  Durbin-Watson:     </th> <td>   2.250</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  48.969</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.239</td> <th>  Prob(JB):          </th> <td>2.33e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.574</td> <th>  Cond. No.          </th> <td>    3.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared:         } &     0.108   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.106   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     52.28   \\\\\n",
       "\\textbf{Date:}             & Wed, 27 Aug 2025 & \\textbf{  Prob (F-statistic):} &  2.20e-12   \\\\\n",
       "\\textbf{Time:}             &     15:17:59     & \\textbf{  Log-Likelihood:    } &    151.57   \\\\\n",
       "\\textbf{No. Observations:} &         434      & \\textbf{  AIC:               } &    -299.1   \\\\\n",
       "\\textbf{Df Residuals:}     &         432      & \\textbf{  BIC:               } &    -291.0   \\\\\n",
       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                  & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Const}    &      -0.0181  &        0.012     &    -1.474  &         0.141        &       -0.042    &        0.006     \\\\\n",
       "\\textbf{Forecast} &       0.0557  &        0.008     &     7.230  &         0.000        &        0.041    &        0.071     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 22.089 & \\textbf{  Durbin-Watson:     } &    2.250  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &   48.969  \\\\\n",
       "\\textbf{Skew:}          &  0.239 & \\textbf{  Prob(JB):          } & 2.33e-11  \\\\\n",
       "\\textbf{Kurtosis:}      &  4.574 & \\textbf{  Cond. No.          } &     3.00  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.108\n",
       "Model:                            OLS   Adj. R-squared:                  0.106\n",
       "Method:                 Least Squares   F-statistic:                     52.28\n",
       "Date:                Wed, 27 Aug 2025   Prob (F-statistic):           2.20e-12\n",
       "Time:                        15:17:59   Log-Likelihood:                 151.57\n",
       "No. Observations:                 434   AIC:                            -299.1\n",
       "Df Residuals:                     432   BIC:                            -291.0\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Const         -0.0181      0.012     -1.474      0.141      -0.042       0.006\n",
       "Forecast       0.0557      0.008      7.230      0.000       0.041       0.071\n",
       "==============================================================================\n",
       "Omnibus:                       22.089   Durbin-Watson:                   2.250\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               48.969\n",
       "Skew:                           0.239   Prob(JB):                     2.33e-11\n",
       "Kurtosis:                       4.574   Cond. No.                         3.00\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "oos_fcast = sm.add_constant(fcast.iloc[half:])\n",
    "oos_fcast.columns = [\"Const\", \"Forecast\"]\n",
    "res = sm.OLS(oos_errors, oos_fcast).fit()\n",
    "\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'statsmodels.stats.contrast.ContrastResults'>\n",
       "<F test: F=43.297878848680156, p=7.268319400268935e-18, df_denom=432, df_num=2>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "r_matrix = np.eye(2)\n",
    "res.wald_test(r_matrix, scalar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>TERM</td>       <th>  R-squared:         </th> <td>   0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   3.070</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Aug 2025</td> <th>  Prob (F-statistic):</th>  <td>0.0804</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:17:59</td>     <th>  Log-Likelihood:    </th> <td>  132.89</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   434</td>      <th>  AIC:               </th> <td>  -261.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   432</td>      <th>  BIC:               </th> <td>  -253.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Const</th>                <td>    0.0173</td> <td>    0.013</td> <td>    1.367</td> <td> 0.172</td> <td>   -0.008</td> <td>    0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>One-step RW Forecast</th> <td>   -0.0132</td> <td>    0.008</td> <td>   -1.752</td> <td> 0.080</td> <td>   -0.028</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>33.031</td> <th>  Durbin-Watson:     </th> <td>   1.417</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  89.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.335</td> <th>  Prob(JB):          </th> <td>4.51e-20</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.116</td> <th>  Cond. No.          </th> <td>    3.03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}       &       TERM       & \\textbf{  R-squared:         } &     0.007   \\\\\n",
       "\\textbf{Model:}               &       OLS        & \\textbf{  Adj. R-squared:    } &     0.005   \\\\\n",
       "\\textbf{Method:}              &  Least Squares   & \\textbf{  F-statistic:       } &     3.070   \\\\\n",
       "\\textbf{Date:}                & Wed, 27 Aug 2025 & \\textbf{  Prob (F-statistic):} &   0.0804    \\\\\n",
       "\\textbf{Time:}                &     15:17:59     & \\textbf{  Log-Likelihood:    } &    132.89   \\\\\n",
       "\\textbf{No. Observations:}    &         434      & \\textbf{  AIC:               } &    -261.8   \\\\\n",
       "\\textbf{Df Residuals:}        &         432      & \\textbf{  BIC:               } &    -253.6   \\\\\n",
       "\\textbf{Df Model:}            &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}     &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                              & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Const}                &       0.0173  &        0.013     &     1.367  &         0.172        &       -0.008    &        0.042     \\\\\n",
       "\\textbf{One-step RW Forecast} &      -0.0132  &        0.008     &    -1.752  &         0.080        &       -0.028    &        0.002     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 33.031 & \\textbf{  Durbin-Watson:     } &    1.417  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &   89.090  \\\\\n",
       "\\textbf{Skew:}          &  0.335 & \\textbf{  Prob(JB):          } & 4.51e-20  \\\\\n",
       "\\textbf{Kurtosis:}      &  5.116 & \\textbf{  Cond. No.          } &     3.03  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   TERM   R-squared:                       0.007\n",
       "Model:                            OLS   Adj. R-squared:                  0.005\n",
       "Method:                 Least Squares   F-statistic:                     3.070\n",
       "Date:                Wed, 27 Aug 2025   Prob (F-statistic):             0.0804\n",
       "Time:                        15:17:59   Log-Likelihood:                 132.89\n",
       "No. Observations:                 434   AIC:                            -261.8\n",
       "Df Residuals:                     432   BIC:                            -253.6\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "Const                    0.0173      0.013      1.367      0.172      -0.008       0.042\n",
       "One-step RW Forecast    -0.0132      0.008     -1.752      0.080      -0.028       0.002\n",
       "==============================================================================\n",
       "Omnibus:                       33.031   Durbin-Watson:                   1.417\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               89.090\n",
       "Skew:                           0.335   Prob(JB):                     4.51e-20\n",
       "Kurtosis:                       5.116   Cond. No.                         3.03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rw_forecast = term.shift(1)\n",
    "both = pd.concat([term, rw_forecast], axis=1)\n",
    "rw_error = term - rw_forecast\n",
    "rw_oos_error = rw_error.iloc[half:]\n",
    "rw_forecast = sm.add_constant(rw_forecast)\n",
    "rw_forecast = rw_forecast.iloc[half:]\n",
    "rw_forecast.columns = [\"Const\", \"One-step RW Forecast\"]\n",
    "res = sm.OLS(rw_oos_error, rw_forecast).fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'statsmodels.stats.contrast.ContrastResults'>\n",
       "<F test: F=1.5418527400748274, p=0.21515948042431157, df_denom=432, df_num=2>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.wald_test(r_matrix, scalar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/a9euxWq10qNHj2pnNTRr1ozmzZtz+PBhtm/f7pyRUGH8+PFVtunQoQPNmjXj+PHjbN261TkM/Vz8vXXV6Ro3blzp63Xr1gFw4403Vrv+zTffzHfffcfWrVspKSmp1KoqLCwMf3//SuubTCZiY2MBuOqqq6rd55VXXsnOnTtZv349N9xwA8XFxc6fweuvv77K+lFRUSxYsICIiAi0Wi3h4eFs3boVk8nkTJicrmL2zenvufDwcAAWLlxI27ZtGTJkiHNmTJ8+fSolLoUQQgghRP0kSQshhBBCCFFrjh07BkBmZuYZL54mJSUBVFtpcfqd09X5+/NarRaNRoPNZqs0i6FCxZ3/6mmDp/+JoihoNBpiY2M5cuQICQkJxMXFsX//fnJzc8+4L39//2qrDCouutpsNueyiiHRZ5pd0bJlywtKWnTt2pWZM2cCjsqT1NRUpk2bxg8//EBGRgadOnWqNmEB0KlTJwwGA3v37kVVVRRFYfPmzeh0Orp06VLpfDp27MiWLVvYu3cvnTt3vqAh3JfaDTfcwI8//khycjITJ07EaDQSExND3759GTx4MCEhIee0n4o7+g8ePHjGn9/09HTAMU/l9KSFTqcjKiqq2m1atGjB8ePHSUhIOK+kxY8//nhO6xUVFZGVlQVQbbIQHBf7PTw8KCoqIjk5mcjISOdzgYGBVdaPj493JvAeeuihaveZl5cHnHovp6amYrVa8fPzq5IEqVBdfEajkUOHDrF3714SEhKIj4/n0KFDzv8rTn/PDRw4kPbt27Nnzx6ee+45XnrpJTp16sQVV1zB4MGDadKkSbXHFUIIIYQQ9YckLYQQQgghRK2paJlTWFh4xrv+KxQWFlZZ9k+tkbRa7T8+X9GS52IsXryYDz/8kJSUFOcyo9HorADZunVrtdv9vY3V351+0bXivCsqAf7u9PZGF0qj0RAaGsrLL7+Mi4sL06dP58EHH2TGjBnV3r1fcY7btm0jPj4eu91Oeno6nTp1cg5MrtCtWze2bNnC9u3biY6OZvfu3Xh6elZpOXUpffXVV6xZs6bK8r59+3Lvvffi7e3NL7/8wpdffsnixYvJyspi/fr1rF+/nrfffpuhQ4fy2muvnfW1rfj5zcrKciYBzuTvP79ubm6Vhq3//bnT93+pVVTyKIpS5ft1Ond3d4qKiigpKam0vLrWWafHeq7v5fz8fODMP9vV2b9/P6+//nqlFmoajYYWLVowdOhQ/vzzz0rrGwwGvv/+e6ZOncqCBQtISUlh69atbN26lQ8//JBevXrx5ptvnnOiSgghhBBC1D5JWgghhBBCiFpTcbHymmuu4f3336/jaM7P2rVreeyxxwC47rrr6NOnDy1btqRRo0bodDo+/fTTMyYtzoeXlxfZ2dlVLhxXON+5B2fz2GOPsWnTJg4ePMgTTzzBwoUL8fDwqLJeTEwM27ZtY//+/c6L0NW1fOrevTuTJk1i586dHD9+nPz8fAYMGFBte59LJT4+vtoL56e3F/P19eW5557jmWeeYd++fWzcuJHVq1ezc+dOli5ditVqrXaew+kqKmPuu+8+HnnkkfOKsays7IzPVSQAqqsGuhQqkiKqqlJcXHzGxEVFHKe3hjqTivdygwYNqk0Y/dM21bVQq05mZiYTJkwgPz+frl27cs011xAVFUWzZs1wc3Nj06ZNVZIWFceZOHEiEydO5PDhw2zcuJE1a9YQGxvLhg0buP/++5k/f/45xSCEEEIIIWqfDOIWQgghhBC1pmK2wIkTJ864zoEDBzh06NAZL9rXle+++w5wtMJ5++23ufrqq2nWrJmz7VNFW6CLVXGhvWKg8N+dadD3hdLr9bz22mtoNBpSUlL4+OOPq12vYhj3gQMHnAmC6pIW7du3x9XVlaNHj9Zaa6h33nmHw4cPV3m88847AGRnZ7NlyxZKSkrQaDS0b9+ee++9lzlz5vDpp58CsHz58n9MLMCp780//fzu3LmTo0ePVkkumc1mZzujvzty5AjgGMxdEzw9PZ3tmA4ePFjtOomJiRQXFzurcM4mPDwcjUZDZmbmGStEkpOT2b17Nzk5Oc5tFEUhJyfHuezv7r//fh544AGSkpKYN28e+fn59OjRg++++44bbriBdu3aOZMq1b3n8vPz2bFjh3P/LVu2ZMKECXz33XfMnTsXRVE4cODAJX8fCSGEEEKIS0eSFkIIIYQQotbExMTg6urK/v37OXDgQJXni4qKmDBhAiNHjjznu7drS0VLqOp67ufl5TnnTFit1os6Tv/+/QH4+eefqz1OTbwu7du3Z+zYsYBjTsKhQ4eqrNOpUyf0ej0HDx5k165dGI3GaltJGQwGoqOjSU5Odg5qrssh3AD33HMPt9xyC6tXr67yXEVsqqo6Z4tUVIX8fT5J7969URSFtWvXVnvBPDk5mVtuuYXhw4dXm3RasGBBlWUVLbd8fX3p1KnT+Z7aOauYlXGmORgVyzt27PiPLaQqeHp60qFDB2w2G/Pmzat2nRdffJExY8YwadIk5zbt2rUDHIOy/y4xMZEVK1awfv16/Pz8nO+5li1bVtta67fffgMqv+deeuklbrzxxmpjateunfPc7Ha7c3lF67hznW0jhBBCCCFqliQthBBCCCFErfH09GT8+PGoqsqDDz7Ivn37nM9lZ2czceJE8vLyCA0NZfDgwXUYaVUVd9nPmjWr0p3lx44d46677nLe2W02my/qOOPGjSMgIIDNmzfz/vvvOy/I5uXl8eijj1Y76+NSeOSRR/Dx8cFms/H6669Xed7V1ZW2bduya9cuEhMTncO5q9O9e3dUVWXFihX4+PjQsmXLGon5XA0bNgyAd999t1JCxmQy8dFHHwHQuXNn5wXtijv5K2Z3VGjSpAlDhw6ltLSU++67r9Kw+KSkJB588EEsFgvR0dHOOSen+/rrryu1Mzp69ChPPfUU4Gg5daaZF5fCnXfeiV6vZ9GiRUyePNk5RNtutzNr1iy+++47FEVh4sSJ57zP++67D4CPPvqI33//3bncbDbz/vvvs3HjRvR6PTfddJPzubvvvhuATz/9tFISKT09nSeffBJwtF9zd3d3vueWLFlS6bXOycnh2WefZePGjUDllmkV3+uvvvrKmTQDx7D7L774gqKiIkJDQysN5K74fp88efKcz10IIYQQQtQcmWkhhBBCCCFq1SOPPMKxY8dYs2YN119/PY0bN8bFxYW4uDhMJhNeXl5MmTLF2XapvrjnnnvYuHEjGzdu5IorrqBx48YUFhaSmJgIONonbdmyhdTU1Is6jpeXFx9++CH33Xcf06ZN49dff6Vhw4YcP34cs9lMnz59WLdu3aU4pUp8fHx49NFHefnll9m2bRsLFy7kmmuuqbROTEyMs+XTP1VPVLSDKikpcVYn1KWbb76ZFStWsHXrVkaNGkVERAQeHh4kJiZSWFiIl5cXr7zyinN9Hx8fgoODSU9PZ9SoUYSEhPDdd9/h5eXFa6+9RkpKCnv37uXKK6+kefPmgKNllNVqpWHDhnzyySfVxhEREcHEiROJiIjAzc2NI0eOYLfbGT58ODfffHONvgYtW7bkzTff5LnnnmPSpEnMnDmT8PBwUlNTycrKQqvV8swzz9CjR49z3mffvn15+OGH+fTTT3niiSd49913CQ4OJikpifz8fBRF4c0333S+RgBDhgxhwoQJfPvtt9xzzz2EhYXh7u5OXFwcZrOZNm3a8PjjjwMwevRoZs6cSVpaGsOGDXMmGuLi4rBYLHTu3Jndu3dTWFjonNUxZMgQhg8fzqJFi7j11lsJDQ3Fx8eHkydPkpubi16v580336z0/0vLli3ZsWMH99xzDxEREbz33nuVYhZCCCGEELVLKi2EEEIIIUStMhgMfPnll7z11lt06dKFrKwsjh07hr+/P2PGjGH+/Pm0aNGirsOsonPnzvz888/0798fd3d3Dh8+TElJCf369WPatGlMmTIFvV7P3r17Lzpx0b17d3755ReGDx+OVqvl+PHjtG7dmm+//dY5W6ImjBkzhjZt2gDw3nvvVZlVEBMTUynGM2nbtq1zmHdNz7M4F3q9nq+//pqHHnqIyMhIMjIyOHr0KD4+Ptx0000sWrSIyMjIStt89NFHtGrViqKiIk6ePOlsVeTt7c0PP/zAs88+S5s2bUhOTiYuLo7Q0FAmTJjAvHnzCA4OrjaOyZMnc8cdd1BaWkpiYiJt2rThrbfe4oMPPqjRKosKI0eOZN68eQwfPhy9Xs+hQ4fQ6XSMGDGCOXPmcNttt533Pu+//35mzpzJoEGDsNvtHDp0CI1Gw8CBA5k1axYjR46sss0zzzzDF198Qc+ePSkoKODEiROEhoby0EMP8cMPPzh/dnx8fJg3bx5jx44lJCSEuLg40tLSaNu2La+88gozZ86kXbt22Gw2VqxY4dz/O++8w3PPPUe7du3Izc3lyJEjuLi4cO211/Lbb79VScy88cYbREdHYzabSU5OPuPsESGEEEIIUTsUVRp3CiGEEEIIIUSNqWiPtWbNGho0aFDH0QghhBBCCFG/SaWFEEIIIYQQQgghhBBCCCHqBUlaCCGEEEIIIYQQQgghhBCiXpCkhRBCCCGEEEIIIYQQQggh6gVJWgghhBBCCCGEEEIIIYQQol6QQdxCCCGEEEIIIYQQQgghhKgXpNJCCCGEEEIIIYQQQgghhBD1giQthBBCCCGEEEIIIYQQQghRL0jSQgghhBBCCCGEEEIIIYQQ9YKurgOoTdnZhcgED/Ffoyjg7+8pP/9CXAR5Hwlx8eR9JMTFkfeQEBdP3kdCXDx5Hwlxcf7L76GKcz8X/6mkharyn/thEKKC/PwLcfHkfSTExZP3kRAXR95DQlw8eR8JcfHkfSTExZH30D+T9lBCCCGEEEIIIYQQQgghhKgXJGkhhBBCCCGEEEIIIYQQQoh6QZIWQgghhBBCCCGEEEIIIYSoFyRpIYQQQgghhBBCCCGEEEKIekGSFkIIIYQQQgghhBBCCCGEqBckaSGEEEIIIYQQQgghhBBCiHpBV9cBCCGEEEIIIYT4d1BVFQBFUSoWQMW/T3M4o4gPVx5jQGQg4zoG47blQwwnY8m/aiqqq39thiyEEKIO2GxW7HZ7XYchRK1TFCgrK8NiMVP+Z9NlTavVotFoL/l+JWkhhBBCCCGEEOKS+Gj1CebvSeWqVkHc1dJK6xU3YncNoKTTfZhajAStnuWHM3n1j8OUWe3kZGdwT9ITGJLXAWA8toiydrfV8VkIIYSoKaWlxRQXF2C1mus6FCHqTE6O5l+UtFNwdXXHy8vv1E0rl4AkLYQQQgghhBBCXLQjGUXM3ZGCCizYm8aVhz6mrTYbTWk2Xisewbz5PdZ6XcOyBA8aqwEYFAuf2SdjSE537kN/crMkLYQQ4l+qtLSY/PwsDAZXfHwC0Wq1wKW7yCnE5UKrVbDZ/gVlFqiYTGUUFeWh1xtxc/O4ZHuWpIUQQgghhBBCiIv2+fo4VKBrhA+t7Ye4MmsrNlXha9twRmvXElh8kkHFXzHIUHm7AmMIStf78Vz3IoaUzWdsKSWEEOLyVlxcgMHgiq9v4CW9I1uIy41Op8Fq/XdUWuj1RqxWC0VFebi6ul+y97YM4hbib45kFLE7Jd/xhaUEw4mlKObCug1KCCGEEEKIemx7Uh4b43LRahSeGdic5/RzAdjmcyUrQu5lrMsUXrDexQo1hmyPKOwufgCssbXn5cBJlLW+EVVrRFOaiTbvRK3GrqoqG+JyyClxtCrRFCSjT9lYqzEIIcS/nc1mxWo14+bmIQkLIf5lXFzcsNttl7TllVRaCFEus8jE5HVxLDmQAajMik6gR+IktEWpWII6kDfqZ9C71XWYQgghhBBC1CuqqjJpbRwA17ZrQLOCTRhSY1G1RpqPfJUvPRsCYFd7YVfBrlHIBnYkZHLPLwfxT4VntUYswR0xnIxFf3IzNt9mtRb/7/vSef2vIzQPcGfmjW0I+m0M2oJE8od+hbn58FqLQwgh/s0qLmY6WkIJIf5NKgZx2+22S/Yel0oL8Z9gV1U2xedQYrYBoMlPQJexBwCrXWXGliSun76VJQcyaKkk8pPhNXofeA5tUSoA+ozdeC17COy2OjsHIYQQQggh6qNVx7LZn1aIq17Dnd3CcN/8NgCl7SdgL09YAGgUBZ3m1N21bUL9MWgVsovNJOaWYmnYHXDMtagtFpudqZsSADiWVcz+ZVPRFiQC4LH+FRRzUa3FIoQQ/w1SZSHEv01NVE9J0kL8J8zYksTEeft4ZP4+7CU5+P4yAp+fh6FPWsektSeYvC6OUoudgUElLPJ4k66aw5SoRiYzlqMDZqBqjRjj/sR945t1fSpCCCGEEEJcsDKLDau9fPCjql70RXmrXeWLdY4qi/Gdwwg9uRhd9iHsRm9Koh/4x22NOg1tQrwA2Jmcj6VhD6A8aaHWznDK3/elkVZoQqdRMGChddw0AFSNHm1xGm5bPsRktWNXT71mWEtrJTYhhBBCiP8qSVqIy16x2UpsfC5TNsTz5IJ97Nu/HZcDP+Kx4nE81j5PVn4B0zc77pbamZxP0uI30JTloKDi8udElm4/CMBzAxvzpdsU9JZCTIEduMfrCz4oG8mdm7xI6/M+AG67v8Zl74w6O1chhBBCCCEuVFaRiSu/2sz9P+/Baldx3/gG/t+0xxC/4oL3+cO2ZBJyS/F20XFrWw/cN74FQEn0/aguvmfdvlOYNwA7kvOxNIhG1ejQFqWiKa92qElmq51vyj8nTOzblMf8NtNQySZHG0DBlVMAMO6ezv8mzeaT1SfQpe3A94e++H8XUyvxCSGEEEL8V8lMC3FZe2f5UebvScWuQislge8M7xKckldpnaz4bMqs4/F3N+BTEkeH9HmggNU1EJfSTN7VT2VZ6w+4yTIPQ9pW7HoPiq78kue0DZgweyeJuaU8dSSSL7s9hUfse3isewlrcEesQR3q5qSFEEIIIYS4AFsS8yg229iZnM/MrUk8fmIpit2Mx6onyb1xxTklGU4Xn13ClI3xADzctynBW99AW5KB1bc5pe3vPKd9RId5Mx1H0kLVuWIN6oA+bTv6k5sxeTc6zzM8P7/tSyOjyEygh4HrWvsSuOtXAD4uG05ATms66nrR17qBN3TfsH7vNnwOLkBRHe1iXQ78SEn3p2s0PiGEEP8e33wzhW+/nXpO606YcDdpaaksXbrorOtOnPgYY8aMB+DBB//Hrl07qqyj0Whwd/cgPDyCoUOv5tprR6PRnLqP/fTtZs78iSZNmlZ7rNTUk9xwwzXOGO+8855zOh8hLoQkLcRlK6vYzK+7U1GBhl5GnjVuILgwjzJVz2FtJE1bdsLj4Gz6FC7iek0YI0Y9jP+S99CX2liniWGx2228WvIIQ7Tb6an5BretPwJQ1PdN7F4RBAAfjmrDbbN3su5EDr+2HMOY5gdxOfY7niseI3fMEtAa6/Q1EEIIIWpakclKWqGJ5gHuAGizD2H3DEM1eNRxZEKI83UgrdD571827eUpvaNaQFuSgcf6Vykc9Mk578tmV3ntz8OYbSo9m/hynfs+XA7/gopC4YAPQedyTvtp39ALrUYhvdDEyYIyPBp2R5+2HcPJWEytxp7X+Z0Pk9XOd7GO87+9awTeR+ZiLE0nXxfI3LL+mFefIJgbWemynWjNMaI5BipYAtujz9yDy+FfKOn6BGhkoKwQQoiz69t3AGFh4ZWWTZr0EXl5ebz44muVljdr1oK5c2cDcMstE2jcuMkZ9xsV1brKsokTH8Pb28f5tc1mIzX1JL//voCPP36PrKxM7rmn+haOq1Ytp0mT/1X73PLlf50xDiEuNUlaiMvW2mNZqEDrBp7MGN8Bv+/uA+BJ7VP8XtKGwSWB9HNVual0Nm8bp2M+6YtH6RYs6HixdBzxJX5468fxrHYWHgdnAVAWeS2mltc7jxEZ5MFdPSL4akMCH646QfdxL9M8ZSO6nMO4bfuMkm5P1sWpCyGEELXmpSWHWHcih2cHt2BMcBo+v1yDzbc5eaMXoho86zo8IcR5OJDmmF/h7aKjlfkEAHajN4qpAJfDv/DByTbYmw3m/s7euG39GH3GHgoHfoTNp+odl3N2pLA3tRB3g5bnr2iA56IJAJR2uBtrg87nHJOrXkvrYE/2phawIymfxg2747bj8xofxv3b3lQyiswEeRgY1coHtzmTAbB2ewS/LR6kFZpo0bQ5RWFP4b7lNQpUN6Z4PMDt192D/3ed0Ralok/ZiCW8T43GKYQQ4t+hefMWNG/eotKyqVO/BPIYOvTqM24XE9ON6Ogu53WsPn36ERLSsMry668fy80338CcObMYM+ZGfH39Kj0fFhbOqlXLueOO6pMWK1f+ha+vH7m5OecVjxAXQmZaiMvWqmPZAPRv7o8ubQfakgzsBi9GDR+NVqOw7HAmL+RexRq1EwbVjMemtwFIaXYziYQA4NHrAczhfQGweYZTdEXVQdu3xYTTMsiD/DIrb2/IobB8HdftnzNr8VLic0pQSrNx2/YZrrum1trQQCGEEKKm5Zda2Bjn+FDywcpjFO36GQUVXe5RPFc+Lr/zhLiMWG12jmQ6khZvj2hFF71jePYelxiWul8LwJ0Fk1C3TsHr+9647ZmOPm0bnssfAbut0r4Sc0v5ckM84GgL1XTPu2iL07B6N6b4Am7qiQ4/NdfCGhKDqmjQFiSiKTx5gWf7zzIKTXy9MQGACd0i8N35CdridGweodjb3cj08R35akx7PhrVBk2Xu0kYOpvB5g/4PLsT8QV2TC1GAuBy6KcaiU8IIYSoCT4+PlxxRT8sFgv79++r8nz//oOIiztBfHxclecSExM4evQI/fsPrI1QhZCkhbg8FZZZ2ZqYB0C/FgEYTywFwNxoAO3DA3m0r+NuMBUNOzu+jc0rAgC7qz8e/Z/i7RGtebRfU8ZGh1EwZDIl0Q+QP/x7VKNXlWPptBpeGhqJVqOw6mgW7yW3Zq2uJxrVytATb5C94FH8Z3TFPfY9PDa8ii5tW+28CEIIIUQN2xifg608L2Gx2dEc+8P5nPH4Elx3TamjyIQQ5+t4dgkmqx13g5bO4T5c458KwMKsEB7LHkG82oAQJYeX9TMxWPIx+UZhN3iiT9+B655vnPuJzy7h0fn7MFntdAv34qbSWbgenIOKQtGAD0Hvet6xnT6MWzV4YA1sB4D+5KZLcOaV2VWVV/44TH6ZlVbBHtzgdxzXHV8CUNTnVdAaCPQw0jncB0VRQFFwa96XFuX9vZccSKcs6gYAjCeWopgLz3gsIYQQor5xdXU743MDBgwGHC2i/m7Fir9wc3OnR4/eNRabEKeTpIWo99Ycy2LGliRsdhXFVID7xjfZv2cjNrtKE383Gvu6YjzhuIhianYVAGM6NeS2ruEMaRnI9d1akX/1N5hDe1HY/wNUoxcDWgQwvnMYiqKguvhS3ONZbH4tzhhDZJAHE7o6eg/O3p7Mo0W3kKt60lqTwJWli1FsJux6R69vl0M/1/ArIoQQQtSOtcccVRY3dGzIFd5ZhJKBGT0F3Z8HwH3TWxQfXQ2qilKShTb7ENitdRixEOJMKuZZtGrgiQYIKzsMwG57U6KbNKB08EeoGgP5ePCCZQJP+31Gca8XAXDf/C7avBOsP5HN7T/sJDG3lKYedr42foL7tk8BKOn6GJaG3S4otg4NvdAocDK/jLSCMiwNuwPUSIuo2duS2ZqYh4tOw5sDg/Fd+QgKKqWtb8Lc9MozbjesdTAAi/enYwrogNW3OYq1DOOxsw9JFUIIcXaqqlJqsdXbh1pHFcbFxUXk5eVV+ygoyD+vfVmtVjZtWo9Wq6VFi8gqz7doEUlERCNWrlxW5bkVK5bRp88VGI0y21XUDplpIeq1jEITzy06iNmm4qrXckfh57junUEP7a+48jb9m4ejzT6ItiABVWvEHNEfAEVReLDPqUFFNv9W5I+ae1Gx3NE9gu1JeSTklnJ9x/aU+n6A64qJbLFFkhJ1N1e1Dsbnt7EYj/3uuEtLd/53mQkhhBD1hdlqZ1O8I2lxdesgmunjYS+st7Vh4qZ2vGrvzfXa9YT8OQHtMgWdagbA6tuC/GtmY/eo2kdXCFF3KpIWrYM90RSnoi3JQFW0vHjr9YQGOHpa54RuYHeWyux5x1D3ZzGg5VCuDPsdQ/I6ihY8yOPZT2HEzK1BJ3le8z3G5COoWiOF/d7FFDX6gmPzMOpoFezJ/rRClh7M4J7QnrBrCsYTf1Dc62VUg8cleQ0Ophfy+fp4AB7v15Q2u15CW5yO1acZRb1f/sdt+zTzx8tFR0aRmW3J+fSLugGPTW9jPPQLZa1vvCTxCSHEf5Wqqtw1Zzd7ThbUdShn1KGhF1PHdXBU4dWiZ5994ozPeXh48Mcfq6ssLywsqFRRYbGYOXkyhdmzZ5CYmMCYMTcSHNyg2n0OHDiEb7+dSnx8nHMA+PHjx4iPP8EDD0y8uJMR4jxI0kLUazO2JGEu70vx6/qt3Kf7EYAAWzoP6RbQrsW7GE98DYA5oh/oz1zmdrH0Wg1Txp7+C6oRP1iiee2vo0QkuzJwUDQ2zzC0hckYT/yJKXJUjcUihBBC1LQdyXkUm20EuBto3cATvw2rAVhm70yRxc7z3EkrTTKtlXhQQUUBjR5d7lF8fr2OvGt+xO7T5B+PIYSoPRVJizYNPNBlONqZ2vwinQkLALtHCO08YEynEubuPMmLSw4zRbmRuepWIor3sMzwJI016WgL7I7t3YIpuHoa1uBOFx3fmE4NeXnpYWZtS+aGO3rh7t0EXX4cLnu/o7Tzgxe9/zKLjRcWH8JmV+nfIoCxulUYT/yBqtFTOOTzs36OMOo0DG4ZyLzdqSzan06PK67DffO7GFJj0eTHY/dufNExCiHEf1ntpgIuHw888EiVAd4VdLrqL+veccfN1S4PCAjknnseYPz4W894vAEDBvPtt1NZtWo5EybcDcDy5X/i5eVNTEx39uzZdX4nIMQFkqSFqLfSCsqYv9fRazfIw8CEsl/R2M2UuATjVpbO/3SLydc9fKo11D+Uc18qf8+oD2wZxPurjpOYW8qe1CJ6tByN+7ZPcDn8syQthBBCXNbWHMsGoE8zP3QlGejTdwIw+vrbGWYMpIGnEW9NN2avX8eXu0tJV/24rbWeZ7KfRZcfj++v15F3zWxsAa3r8jSEEDgu2B/PKgagdQNPdAf2AGAJ6lDt+g/0acL6Ezmk5JeRjw/v6cbzmm46zTSOv81tHiFYwnpT3P1p7O7V36l5voZGBTF9cyIJuaXM3Z3O/TEP47X8Edx2fkVZu9tQDZ4Xtf+1x7NJzC0l0MPA84Nb4DbvHgCKuz2FNbDtOe1jRJtg5u1OZdXRLAoGNsczvA+GxDW4HPqZkgsYQC6EEMJBURSmjutAmdVe16GckYtOU+tVFgAtW0YRHd3lvLZ56aXX8fX1Q1VVEhLi+fHHmdhsVp5//mViYrr/47ZNmjSlSZOmlZIWq1Ytp2/fAWdMkghRE2Smhai3vtuShMWm0inMm0mDfRijXQ3A88pDLLN1RocN77/uR5d9EFXRYm48qNZjdDNoGRAZCMDiA+mUlZfF65PWoSlKrfV4hBBCiEtBVVXWHnckLa5o5o8h3jGMzxLUkYjwJrQM8sDbVQ9GL4YMHMYtA3tiRce0Ayq9Mp/mgNoITWkmPnOH4vtFUwK+aob/V81hzk1o8uLOK47cEjP2ih7ClhIUc9ElP18h/u2OZBZjU8HPTU+wpxF9xm4ArEEdq13fVa9l8uh2PNG/GV+P7cDd971EwaDPKBj0Gdm3xpJz21YKB358yRIWAFqNwt09GgEwe1sK2RHDsfo0Q2PKw3XPtxe9/yOZjqTNFc388dZZ0ObHA1DWasw576N1A0+a+Lthstr5cXsKZVFjAXDd+x2K6fz6igshhKhMURRc9dp6+6iLhMWFateuAzEx3ejatTs33DCOKVO+RaPR8sQTD7Nu3eqzbj9gwGBOnDhOfHwchw4dJDk5icGDh9Z02EJUIkkLUS+lFZTx2940AO7p2Yj2Cd9iUGxssLVhfm5TXrHcilXrii7HMUDQEtoD1cW3TmIdXj6U769DmZS4hWMO6Yai2jEe+bVO4hFCCCEu1uGMIjKKzLjoNMRE+GKI+wsAc5PqP6xc16EhbwyLwqjTkG73ZpzpBTbaWqNBRaeaUWwmFGsZHFqE7w8Dcd/09hmTD1lFJn7ckcIzvx/gqq8289aUrzj4w6P4/DyMgGmt8f+2I5r8hBo7dyH+jZzzLBp4ogC6iqRFcPWVFgBhPq6MjQ6lU5g3LgYdppbXYWp5HXbP0BqLc1DLQJr4uVFosvLjrnRKYh4BwHXXFBTTxfU5r6g0aRbgji7nMAoqdtdAVFf/c96HopxKrMzalkxqyGCsfi3RmPJx2/HlRcUnhBBC1JTAwCBef/0dAF5//WWSkhL/cf2BAwcDjgqLFSv+wt8/gI4do2s8TiFOJ0kLUS9Nj03EalfpEu5NjHcBLoccQ7Sn68cBUOLakNLyDzFQO62hziQ63JsQLyPFZhtrjmU7hxC6HPoZKu4MFUIIIS4jFVUW3Rv7YlTLMCSvB8DUZPAZtxkSFcRf9/Xg97u7Mvvu/nje9hvfdl7EIPULepRNYrjpDQ57dEWxm3Hb8Tm+P/RFl7q1yn6e+O0AH606zoojWbQv28J3hvfol/cL+ozdKHYrirUMY9yfNXPiQvxLVRrCnR+PxpSPqjVi9Yuq48gq02oU7u7pSAr8sD2ZrLCrsfq2QGPKx3XPNxe172PllRbNA9zRZR8CwOp//uc/MDKAVsEelFhsTN+SQnH3pwFw3TMNTXHaRcUohBBC1JS2bdtz2213UlJSzBtvvIzNZjvjuhERjWnWrAWrV69g9eoVDBgwGI1GLiGL2iU/caLeOZlfxsJ96QD8r2dj3LZ9hmK3Yg7rw5BBI9AqMLJdA8o6/g9LUEfsRh9MTa+us3g1isKw8mqLRQfSMTUfjqpzQZd7DF3GrjqLSwghxPkps9hYdTSL2duS+XDVcSbNW8z+P7/ELfYDPFc8hudfD6IpTq/rMGtFxTyLvs39MSStQbGZsHk1wubX8h+3czNoaeDl4nh4uzK8e0e+vOMqenZoywGaMjTrYV5zfxGzZwTa4nR8FozB5cAPzu3TCsrYn1aIRoH7ezfm9WZHAYi1R3Ggy7uURN8PgL48iSKEODenV1o4W0MFtAatvi7DqtbAyACaBbhRZLLxw85USmIeBcB119QLbsFUZLKSVmgCoFmAG1pn0uKf/0+rjkZReLBPEwDm7U7lhE9vLCExKNYy3LZ8fEHxCSGEEGeydWssf/655IyPrVs3n/O+br31DqKiWrN//15mzfruH9cdOHAwx48fIzX1JAMHDrnIsxDi/MkEFVHvTI9NxGZX6RrhQ2fPPEfFAlDc9XEGhASw/IGeuBm0oCjkXfcr2G2gd63TmIe1CWba5kRi43NJM0Xi0fQqXI7Mx+XgTxQFd6rT2IQQQpybyevimLvzJABB5LLeOBGDUvkOJNXgQVG/d+oivFqTVlDGkcxiNAr0buKPcdMyoLzK4gJ6+fq46XlmUAv6twjg+cWHmJ7dimXubzEvdBZBKX/gueopdFn7Ker1irPCo0NDLybEhOL/7VoAPraOpjN9uKt5B9x2fIH+ZCzYLPXygqsQ9U2RyUpCbikArRt4oNtRMc/izK2h6pJGUfhfj0Y8/ftBftiewpg7h+Dm1xJdzmFcd35FSXllw/moaA0V5GHAy0XvrLSwXWClSddGvnRr5ENsQh5fbUzgne7P4jv/OlwOzqG04/+w+Ta7oP0KIYQQfzdz5j/PderYMfqsw7Ur6HQ6XnjhVe6442a+/XYqPXr0IjKy+t+FAwcO4euvvyAkpCFt27Y777iFuFiStBD1SnJeKYv2V1RZNMJt22soqg1zRF+sIV0A8DCe9mOrNYC2LiKtLMzHlegwb3Yk57Nofxr3thqHy5H5GI8uoKjXS3WeVBFCCHF2WxLzAOjWyIfrDIcxJNnIVL2I9+9PmzB/3PZMx+XwPIq7P4Pq4lOnsdakvamOO7Kjgj3xcdOjS98BgDm870Xtt3tjX357oBcTpm8hLqeEPgm3saJTFGF7P8F17wywlLE29zYA+jTzR5e2HU1ZDmU6L7aWtcSemMftXdtiN/qgMeWhy9jt/NtACHFmB9Md7+kQLyO+bgZnpYXlDEO464N+LRwtmA6mF/FNbDLPdXsK76V34rZ7KmXtbjvvAeDHTptnAaDLufD2UBUe7NOE2ISd/Hkok6tatWVw+EDck1bgvvENivq86ohRa7jg/QshhPj3++WX38/43PPPv8Lzz79yXvubPPnrs67TuHETVq7ccNbtQkPDWL9+W5Xl0dFdql0uxKUm7aFEvTJ9s6PKonsjXzq55+By+BcAimMer+PIzm5kO8eHp4V70zA17I7NqxEacyHG44vrODIhhBBnU2SyEp9dAsBrV0cxws9RcbHE1o27cm4iu9tLWP1boVhLcTk494z7ySg08fCve/lg5TGsNjsue2fg8+u16FIvnz/sK16HZv5uYLOgzY8HwHYBbVT+rnGAO9/e1JFOoV6YrCoflF1D/pWOD0kuh3/hZNIJAPo2D3DOrSgK7YcNLbtPFmCygSWsJ4BzzoYQ4p8dSHMMvW/dwBPsVnSZe4H6W2kBjmqLB05rwRTn1wdLgy6OFkxbPznv/R3Pcvy/1jzAHaUkC01pNioK1rO0vPsnUcGeDI0KBOCR+fu49tgQbKqCMX4Z/jN7EvBVM/xmxGA8+tsFH0MIIYQQ4r9Kkhai3kjKLWXJgVNVFu7bPkVRbZgaDcDaILqOozu7AS0C8DBqOVlgYmtSAWWtHEPDXQ78WMeRCSGEOJsDaYWoQEMvI35uBvTpOwFIMEaRX2blz8OZlLa/AwDXvd85WhP+TVJuKXfN2cXGuFzm7kwhduZTeK59Hn3qVnwWjkOfuLr2TugixOc4Lu418XdDmx+PYrdi17tjdw+5JPv3MOq4r7fjYuTSgxlkNByMObQHimpjnOYvGvu5EuHriiHe0ZZK3/IqAtwNmKx29qUWYA7r7ViesuGMxxBCnHL6EG5t7lEUayl2vUe9b2HUrbwFk9Wu8tXGRIp6PAc4/rbW5h4/r31VVFo0Dzw1hNvm3eiiq6Ef7NOETqFe+LrqOaKG8551HPH2YGwaAwoq2qJUx+8MUWdUVeVEdjEWmx0ApTQbCk7WcVRCCCGEOBtJWoh645vYRGwq9GziS0e3LIxHfgWgJOaxOo7s3LjotVwZFQTAb3vTKIsajapoMKTGnvcHKyGEELVrf/lFvTYhXmCzOO9EDmvtuEA+d+dJSluMxG70QVuYhCF+eaXtj2QUcdecXaQWmAj11POmYQajiucAYPZpgWItw3vxBIxHz1wCXl/ElSctGvm5oc07BoDNt/kFzbM4k46hXkQGumOy2lm4N82ZELpRu5IBTTzQ5h5Dl3cCVaPH0qgfXSJ8AEcLL0tF0iJ1O1hKL1lMQvxbVRrCnV4xz6IdKPX/o2DFwOs/D2ZwQNcaU+NBKKoN99j3znkfqqo6Z1o0C3B3toayXURrqAoNvFz4elxH/rq/B+sm9mJ32K30M3/M1B7ryBvpqMrT5Cdc9HHEhZu2OZGx323nqq828+nSLXjO7AcftcL7l1G47PkWpSSrrkMUQgghRDXq/1+q4j8hIaeEpRVVFj0a4bb1YxTVjqnxIKzBHes2uPMwqp3jLtTVx7LI0QRgbjQAgOS105i5NYkSsw1NQTLum966rFqFCCHEv92+8jkObUM80WUfRLGZsBu96RsTg4tOw9HMYnakmSlrMx4A1z3TsdrsHEgrZPa2ZO79aQ/akkwm+OzmjwZTuEnzF3ZV4XnLHVxre4fMsKtR7BY8/7of1x2f19uL7Ta7SmL5wN4mfm7ock5LWlxCiqIwNjoUgJ93naQwbCApaiB+ShGjDZsxxP0FOFpBqQZPYsJ9ANiWmIfNuwk29wYodjP6NPldKsQ/ySkxk1ZoQgGigj3QZdTvIdx/FxXsyeCWgajA5+vjHDOFUDAeX4yuvCLubDKLzBSUWdEq0NjPDW32QQCsFziE+0xc9Foa+7sBkJxvwhrQBgBtSQZYSi7pscS5yS0xM3NrEgD5ZVbCjnyLwZwLgD5tG57rXsR/RgyGE0vrMkwhhBBCVEOSFqJe+GZzInYVejf1o71LhrP3a0nX+j/L4nQtgz2ICvLAYlNZcjCd0vIWUSFJv/Hl2qM8Nf1XXOeOwG3HF/j+OgrPZQ+hKUqt46iFEOK/TVVV9qUWANCmgafzQpg1uCNergaubh0MOKotMlvchB0NhpQN3DN5Dh/98DOeG15lofoQW13u5+Wyd/FMWo6q0XGk+wcsNV7F/kwT3Y6NZ5nr1SioeGx6G//vu+G29ROUstw6O+/qpBWWYbLa0WsVGnq7oM09CoDVt8UlP9bQqCB8XPWkFZr4dF0iM6yDAGiR+CPG8qSFqfEQAGIa+QCOiphii81ZbSFzLYT4ZxVVFo38XPEw6pxJi/o8hPvv7uvVGK1GYWNcLrvNDTFFjQbAbevH57R9RWuocF9XjDqNsz3UxQzhPpNwHxcAkvNKUV18sBt9AJyzgUTtmrk1mVKLnVbBHkwd0ZAJekfbwSct/2N1xMNYAtuh2C14rHkezMV1HK0QQgghTidJC1HnkvNK+fNQBuCYZeG2fXJ5lcUQrIHt6ji681cxkHvB3jRePRJBhupDoFLAM66/8aX1RdzMmeQovqgouByZj9/svrhIr1shhKgzaYUmckosaDUKLYM8nPMsLEGdABjTqSEAq49mMWhWIn/YugAwV/sCvxlf4k7dUppo0h1DXf1bUdr2VvKunYd/l7FMH9+RoVGBKIqGu3Nv4hnLXWTpGqApy8F9ywf4zeyJNnP/BceenFfKjTO28/LSQ1hsdty2fIjvrN7ok6vOe/hyQzzXTI0lOa8UTV4cvj/0w33jG5XWic92VFlE+Lqi1Sho8xztDWui971Rp+Ha9o7fmT/vOslcW3/MihF99gFnBYW5yWAAQrxcCPNxwWZX2Zmcf2quhSQthPhHp7eGwmZCV1FlcJlUWoAj2TAoMgCAFUeyKIl+EABD0joUU8FZt69oDdU8wB1UO7qcI8ClaQ/1d6E+jhkZyXlljmN4NwJAWyAtoi6lzCITd/ywi3vm7sZqs+OyZzr+0zthOPGHc53sYjM/73LMrvhfz0b0yvwRo1pGpmcbfrb15YH4nsRd9RM2r0ZoSzJw2zG5rk5HCCGEENWQpIWoc99vTcJePsuijWsexiPzASjpMrGOI7swQ6OCMOo0xGWXMH9/FvNsVwBwpzoPP6WI3WozBpS+w5sNJmNp0AXFWoLn2hfQ5p2o48iFEOK/qaI1VIsAd1z02tMqLRxJi2YB7nSN8EEFLDaVP9xHAuCqmFF1rpS1GEn+VdPIvmsfueOWUdT3LawNOgMQ6u3KG8NaMe/OGMZ0DOVn+wC6Fb3Pwa4fYPWNRGMuxH3rRxcUd5nFxlMLD3Asq5glBzJY8vNk3Ld+jC4/Hu/FE9ClbXeuu+ZYFtM3J5JaYGLJvhS8lj2ILvcYrnu+BeupVlUV8yya+LmBqqLNrWgPdekrLQCu79AQbfmojHw8SA4d7nzOEtgOu0dD59ddyltEbU3MwxLWEwBd5l4UU36NxCbEv8GBtCLAMYRbl3UAxW7B7uKH3TOsjiM7P32bO5IWa49nY/NthtW3OYrdgiFx9Vm3PXbaPAtNfgKKtRRVa8Tm3fiSxxnuTFqUoqqq8xjavPhLfqz/quS8Uu6as5u9qQXsSM4j84/X8Vz3EprSTNw3vwOqY+D291uTKLPaadPAkz7BVlz3zQBA3/dp2oX6UGy28cWmVIp6vQSA266v0RQk1tl5CSGEEKIySVqIOpVRaGLRfscsiwldI3Db+RWKasMcfsVlNcvidJ4uOufdYHqtQkTfO53PmUN7kTP8R/LwZHaKP2kjfsEc1gcA47FFdRKvEEL81zlbQ4V4opjy0ZVXF1hO+z30ylUteaJ/M364NZoX7riV/Cu/pmDI52RN2EXhkM8xN70S1eh9xmOEervy5MDmDIkKwoaWTzI6UHDVVAAMcX+hzT1+XjGrqsrby49yNLMYbxcdbbSJ3JT1CQA2tyAUawnev9+CLnMfmUUmXv/ziHPbZocmoy9vEaPYTBhSNjmfiy9PWjT2c0NTlIrGUoyq0WHzanRe8Z2rYE8j/VsEAo7KC7fu9zifMzcZUmndmPJh3FsT87B7NMTq0xRFtaNP2VwjsQlxuVNVtVKlxanWUB1AUeoytPPWo7EvOo1CYm4p8dklmBs7qrAMcX+eddtjmacqLSqGcFt9W4BGd8njDPEyolGgzGonu9h8KmkhlRaXxLGsYu6es5uT+WUYtCov676nfbzjd6mq0aPLPYYhYSVZxWbm7Xa04L27ZyPcdn6JYi3DEhyNtVF/XhrRGoDf9qax170n5tBeKDYT7hvfqrNzE0IIIURlkrQQdWr29mQsNpVOoV508jXhcnAOACWdH6zjyC7O3T0bMTQqkEnXt6Nrx84U9XqJko73kD98Bm0ahdDA00ipxc6mhDxMLRx37BqOL67jqIUQ4r9p/+lDuMsv6tm8GqG6+jvXCfQwMjY6lBaBHiiKgrnZ1Y7/vw3u53Wsm7o47m5efjiTFG0opsaDUVBx3T31vPYzb3cqSw5koFXgw6vCmOP1Oa6KmTW29rwY+i2mBjFozAV4LxzPjIWLyS+z0sTPje6aA4wpmweA1TcSAEPCSud+47PLKy383ZzzLGzejUGrP6/4zsdtXcMw6jRc3ToIbXBrypqPwK53p6zFqErrdSlPWhzNLCa3xIwltBcgLaKEOJO0QhO5pY7Wd5FBHs5k5eXUGqqCh1HnrLZaezwbU5OhABgSVoHNcsbtrHbVmYxtHuiOLvswUDOtoQD0Wg0NPI2Ao0VURcJXmy9Ji4t1JKOIe+buJqvYTHN/N9ZHzmOCzpG02t/2eUo7OG4Uc901he+3JGGy2mkb4knvQBOu+2YCUNz1cVAUYhr7OQe8f7zmBIW9XkZVNLgcX4Q2ZROZRSb2niwgNj4Xu6kIbc5R9Ilr0Mj3UQghhKg1krQQdSav1MKv5XfA3N4tArfdU1FsJiwNOmNp2KOOo7s4Fe1AOpd/uCrt+D+Ke70IOhcURWGAsy9vJqamQ1EVLfqs/Wjy4uowaiGE+O+x2uwcynC0T2nbwOvUPIsaqvZrGeRBTIQPNhV+3JFCaSdHZYHLoZ9RSrLOun1aQRkL96bx4arjGDHzVqdCrtj/LJ6lSRS5hPCI5QF+2JvPFSfvJ8HYEk1ZDu/mTuR34wt8H7mJScav0CgqRxqMpLj700D5RT8cd2VXXNxr5OeGztkaqnlNvBROUcGeLL+/B88McrSgKhw8mew7dmP3aVJpPT83A5GBjiTRiiNZmCMc7ReNJ5aC3VqjMQpxOaqosmge4O4YQJ1enrS4TKuZr2juSCSvPZ6NNbgTdld/NOYC9Cdjz7hNcm4pZpuKq15DQ28XtDU4hLtCWHmLqKS80lOVFjKI+6LYVZU3/jpCQZmVdiGezOyZSVDcr9jQ8LD5ft7O6UNp+ztQNToMKZs4uNuRzL6nRxiea553fMYMicESfoVznxOvaIJRp2F7Uj4DfspjnjoQALf54wn6rhPtf+3OFYu6ETwtCr8f++Pz+034/nQVmqLUOnkNhBBCiP8aSVqIOjNnRwplVjstgzzo2UDBZd/3AJR0fuiyK1k/X4MiHa0w1h3PoUznjaV8oKhRqi2EEKJWHcsqxmS142HUEuHnWmWeRU2oqLZYsCeNHN/OWII6oNhMzn7bf2e12Xln+VGGfx3LiKlbWL58Pj/qXmafy92MPXAPhsQ1qFojlhHf8NjVMQR7Gkk3GxmZ/xh/2TpjVTW0U04QvutdAtUsTtgbMEl/J5awXqgaPdqCBLR5J8grtZBfZkUBGvm6OudZWGtonsXpXPRaNBW/+zVa0LlUu97wto7B3fN2p2KK6I/d1R9tcRqG+OU1HqMQl5tTraE8UMxFzuopy2VYaQHQp6kfAHtOFpBTZsPUeBAAhvi/zrhNxTyLpv7uaBTlVHuoWkhaJOeXYS8fxK0pSgGbucaO+W+39EAGB9OLcDdoeX94CwK3ONo4Zba+i4X23qw/kcMxkze5EcMAmKBZTL/m/gxM+Rxj/F+oWiNFvV6u9BkzxNuFCd3CAcgvs/JW2fVkql64KBYClAIClAK8FEci36r3xG70RmMuwH3Da7V89kIIIcR/kyQtRJ0oMln5aedJACZ0C8dt3ww0lmKs/q0wNxpYx9HVvLYhngR7Gimx2Ngcn4Op2dWAJC2EEKK2VQzhbtPAEw2cVmlRc0mLno19aeLvRonFxoJ9aZR2vBcA170zKg3FrrDuRA7zdqeSXmhCq8BbLrOI0RxBjwW7awCmpleSP/x7rEHtubJVEAvv7srXYzswsEMkLxif5aUmP1F4xRtYGnSh1DWEhywPsSapDKvOHUtIV8BRbVExhDvE2wUXvfZUeyjfZjX2WpyvYa2DMOo0HMsqZk+6ibJWYwGcrT+EEKc4kxbBno6h9ajYPBqiugXWcWQXpoGXC1FBHqjA+hM5mBs75t4Y4/4CVa12m4qkRfMAd7CWoS2vaq6p9lAAYT6OpGtybil2tyBUnSuKakdbmFxjx/w3KzHbmLzO8X27o1sEYSd+QJcfh901EF2vR+nXwlHBPnVjAk+m9gVguHYznwYsxG33NAAKB35cbYXRHd0i+PWOGObc1pkvbutP/HUrOTJiCZljl5MzbgXPh0yjXdk0rvX4kdxr5jhaSB37HX3imto5eSGEEOI/TJIWok4sP5xJoclKI19X+jXzx2W/42JDSfQD//oqC8DRIqr8D+zlR7IwNb3K0SIqc6/0ShVCiFq0r/yiXpsQLzSFSWhKs1E1eqwBbWrsmIqicHN5tcWcHSkUNR6KzTMMTVkOLofmVVl/9TFH26gRbYJZe0soTewJqIqWnHHLyJ6wk4KrpmEJ6+VcX6ModArz5plBLVh8T3ceHdadsna3k3f9AvJu30KioQUFZVb2pRZgbtQfAEPiylPzLPzcAE5rD1XzlRbnystFz5CWjguu83anUtr6JlQUDElrpMWiEKexqyoH0x2t704fwn05zrM43RXNyltEHcvGHH4FqtaItjAZbfbBatc/XlFpEeBoeaeoNuxGb+xuwTUW4+mVFigKNu+KuRbxNXbMf7MZW5PIKjYT6u3C+FYuuG39BIDi7k+hGjy5qXMo4PhMtSK/AbFKe7TY8d71OQBF3Z/B1OKaavetKArhvq40C3CnWYA7jRs2wDeiPQREYfNvyfjB/bHpPdmbWsCvaQGUtpsAgMea5zmamo29PFmmKUhCKcms4VdCCCGE+G+RpIWoE1sS8wAYEhWIIf8Y2uJ0VK0RU9Mr6zawWjSwfK7FuuPZmPQ+WEIdczyk2kIIIWrP/tQCANo28HRWWVgDWp+xPdGlcmVUEP7uBjKKzKyNy6e0w10AuO79ttJ6VpuddcdzABjRtgHeSY42KJbQntj8W513ol+nUejWyBeAjfG5mBsNAECfspmUrFwAGvm5opTloil1JEusPjU70+J8Xd+xIQDLj2SSo2+IJcJxZ63rgdl1GZYQ9UpiTinFZhtGnYamAe7OpMXl2hqqQsVci80JuZRhwFw+o8AYV7VFVJnFxo7kfMAxT0ifuBrAkZSuwZukwiuSFnmOyrmKuRYaSVqct9SCMmZvc1SoPNy3KT7bP0RjLsAS0JayqDEAdAj1pl2IFwDuBi2evR9ybl/aaiyl0Q9c8PGDPI3c08uRdJq8Lo6jUQ9QpA9AVxDPlp9e5auffsbt99vxn9kDv1l9pFWhEOIfffPNFHr37lLtY8iQvtx88w18/vmnFBc7bjowmcoYMKAnY8aMrHZ/Cxb8Qu/eXejTJ4bs7Kqz8fbt20vv3l14881Xzim+jIx0Jk/+hFtuGcPgwX248sp+3H77eL75ZgpZWZdfYjYpKbGuQyAlJZk+fWLo3bsLO3duP+N6vXt34cEH/1eLkZ27uoxNkhai1tlVlW3lSYuYCF/0yY5BaZaQrjV+kag+adfQiyAPA8VmG5sTcjE1Gw5I0kIIIWpLQZmF+BzHRaW2IZ7o0nYANTvPooJBp2FolKNiYEtCHmUtR6OioMs5jFKc4Vxve1I+hSYrvq562jf0whj3B8BFJfl7NnEkLTbF5WDzbYHNIxTFZsItfTPgqLTQ5h4HwOYRAgb3Cz5WTWjTwJNWwR5YbCq/70+jtO2tALgcnAvWsjqOToiLs+dkAZPWxlFYZgVrGW7bPsNQzQX5szmQ7qgiaxnkgU6joHdWWnS8lOHWushAdxp4GjFZ7WxJzMPcxNEiyhC/rMq6fxzMoKDMSkNvFzqGuDvnBpVF3VCjMYaWt4cqKLOSX2rB5lVRaSHV1Odr0to4TFY7ncO9GeiTgcuBHwAo7vOqY/5Rucf6N6VTmDfvj2xNYLsrKW1zC6WtxlHU9+2LTlCN6RRKi0B38susjPr+EE8XjwdgonY+L2c9hnuiI1GhsRThtXgCrtsnn7FdmRBCAFxzzbW8+OJrzscLL7zKrbdOwNXVlR9/nMmTTz6CzWbDaHShTZt2nDyZUm1SIjZ2E3q9HlVViY3dVOX53bsdn21iYrqfNaZVq5Yzfvz1zJ//M61bt+WBBx7m7rvvo3nzFsyePYObb76B9evXXvzJ15JPPnmfxx9/6Owr1rAlS35Ho9FgMBhZsOCXug7nsiNJC1HrTmSVkFtqwUWnoW2IJ4bkDQCYT2tt8V+gURT6l7eIWnkkE1PTK1EVDfqM3WgKkuo4OiGE+PdbecTxx39Tfzd8XbQYTywBwBzas1aOHx3mDcCO5DxUFx9H5QRgOBnrXGdVeWuovs390Zemo09z3KFTcaHuQvRo4hhmezC9iOwSi7NFVLP88qSFvxs65zyL+tMa6nTXtQ8B4Nc9qZRFDMDm0RBNWS7G40vqODIhLty83Sf539zdfL81iVU79+IzfzTuse/hueKx874IemoItydKaQ7aAsfdhtagdpc87tqkKIqzRdS83SfJbdgPFUdSRp+03rmeqqrMLZ+fN6ZjQ1zj/0RblIrd1R9T8xE1GqOrXkuAuwFwtIiyeTcBpD3U+coqNrPssOPO3sf6NcNt3wwU1Y6p2dVYGnartG7bEC++HtuBmAhfUBSK+r1N0YAPQGu46Dh0GoVnBjl+F6rAPq9+JPt2R6OoWNAxx9qP6/iA+EZjUVDx2PwOnn89UO2MKiGEAGjbtj1Dh17tfFx55TBuvvl2pkz5js6du7Jnzy42blwHQJcujvlze/furrQPq9XK9u3bGDhwCEajkdjYjVWOs3v3ThRFISam6z/Gs2PHNl555XnCwiKYPXsezz77EqNGjeb668fywguv8v33c/Hx8eP555/kwIF9l+hVqFnr16/FbrfXaQx2u52lSxfRqlUboqM7s3btanJzc+o0psuNJC1ErduS6Gg/0THMG72ioj/puEhi+Y8lLQAGRTrusl19LJtkizuWho4MuOvuaXKHjhBC1LBF+9MBGNY6GH3KBscFLaM35sYDa+X4HUO9UYD4nFKyi82YQx2/Ayp+L9pVlTXHsgHo1yLAebe1JTgau0fIBR83wN1AVJAHAJvic5wtorpatwMqjfzc0JbPs7D61q/WUBWGtgrC3aAlOa+MLUkFlLV23Pnqul8GcovLj8Vm553lR3ln+TFsdpVOylFG774dfcYuADSmPDQl6ee1zwNpFfMsPE7Ns/Bpimr0vqSx14WhrYJQgI1xuVw7J4GTvo4L2D4Lx+H5531oClPYkZzPsaxiXHQarmnbANc9jtZ7pW1urpXK7oph3Cl5padmWhRIpcX52BTnuLDTKtiDyEB3DImrAChrNa7WY2nf0IuvxrTnk+va8tMdMbhcP52CAR8SP3oNswMfZ0dZQ4Yev5YTnV9F1ehwObYQzzXPk1ti5qbvt/PY/H2oqorLnun4T++APqXqHdFCCKHRaBg+3DGDZ9cuR9vaLl0cv+P+nrTYs2cXJSXF9OjRiw4dotm6dUuli/R2u509e3bTokUkvr5+Zzymqqp8+OE76HQ63n33Ixo0aFBlnbCwcN566z0A3nnn9Ys7yf+Qbdu2kJGRTufOMfTp0w+LxcKiRQvrOqzLiiQtRK3bWt4aqmuED7rMvWhM+dgNXlgDL+87vy5E+1Avmvq7UWy2cfec3SREjAbAbc83uG94XRIXQghRQ5JyS9l9sgCNAle1DsLl4E8AmFqMAq2xVmLwdtXTPNDRemlXSr4zcV2RtNiXWkhWsRl3g5aYcB+Mx5c6YrwE858qWkQtP5yFKbQndo2eRpoMerkk4eOqdyYtbPU0aeGq1zKstWOQ7uxtKZS2Goeq0aFP3eps8yXE5cCuqjzy6z7m7U5FAe5ucII5htfxtmVj9WuJzd3xc67NOXbO+7Ta7BzJLE9aBHuiz9zjWH6Zz7Oo0L6hFx9f25ZwHxeyi81cnXoHSwxXoSoaXI79jt8Pfclc+wUAw9oE41N4GENqLKpGR1mbm2slxoph3El5pc6ZFtr8RLDbauX4/wYby5MWPZv4oc05grboJKrWiLl8DmBt6xzuQ68mfmgUBdXohanVWHyCG/HVmA50a+SDyWrnniMdybrqO8fP4qGf+H3e1xzJLGbdiRySd/2Bx/pX0JRm47Z9cp2cgxCi/nN1dav0dVRUKzw8PNizZ1el5bGxm9BoNHTp0pWuXbtRUJDPgQP7nc8fP36UoqLCs7aG2rNnFwkJ8QwYMJjg4KoJiwpNmzanV68rOHHiOIcOHQAcFRq9e3dh+fI/+f776YwdO4r+/Xtwww3XMG3aV1it1kr7sNvt/PLLHG6/fTwDBvTiyiv78fjjE6uc2z9JSUnmpZee5frrh9O/fw+uu24Yb731KmlpaQCkpp6kd+8upKWlkpaWSu/eXfjmmynO7RMT43n55ecYPnww/fv3YNy4a5k69UtMpsotZnv37sI777zOX38t5aabRjNgQE/GjbuOWbO+w2Y7t9/lixf/BkDXrt254or+aLVaFi6c/48VIGvWrOKuu25lwIBeDB8+mDfffKXa6ozY2E1MnHgvQ4b0ZeDAXtx55y0sWfJ7lfVKSoqZNu0rbrttHIMG9aZ//x6MGTOSSZM+pqSkpNK6eXl5vP/+W4wceSUDB/bioYfu4dChg+d0rjVFkhaiVlntKjvLB+J1ifBBn+JoDWVp2B00uroMrU5oFIXJo9vR1N+NzCIzozeFcaz9MwC47f4aj1VPgN16lr0IIYQ4X4sOOO5a7tbIlyC9yTkroqZ7nf+ds0VUUr6z3YUu5zBKaQ6rjzpaQ/Vu6ofRWoD+pOPOTPMlSFpUtCfcEJfDU0sTSPRxfKD5WnkdQ8JKdPU8aQEwLjoUnUZhc0IuK1J1mCKvA8Bj7fNyYVBcNg6lF7ElMQ+jTsMHo9pwn8tfGBUra+lE7nULsAY6Eg3a3CPnvM99qYWYrHY8jFrCfV3RJztaRvxbkhYAvZr6Mee2LjzUpwkWvTf3F9zCbdr3KAyKQbGWcUveFzym+4kxHUNw3TsdAFOzYRdVpXY+KiotkvPKsHs0RNXoUexmNMVptXL8y53VrhKbkAc4khaGhJUAWEJ7gM61DiOryqDT8MpVUfi66jmaWcxHceGUdHb0Ub87/zMakkVDsojc/DiK6rhQpU9aK+2AhaiOqoKlpP4+auGm0rVrHVVlUVGOtrFarZZOnTpz5MhhyspOXVjfsmUTLVu2wtvbh65dHcnc01tE7dzpuImna9d/Tlrs3u2o6GjfvuNZY+va1fFZZfv2rZWWT5nyBQsXzmf48JE8/PDj+Pj48t1305gx45tK67366gt88skHhIWF8+CDjzBu3M0kJSXw0EP3sGrV8rMev7CwkIkT72Xfvj2MHHkdjz/+NP36DWDZsj946KH/YTKV4ePjy4svvoaPjw8+Pj68+OJr9O3rqCo/cGAfd911G3v27OK6625g4sTHadOmHd9/P52JE+/DZDJVOt62bVt4881XiI6O4YEHHiYkJISvvprMG2+8fNZYCwoKWLduDYGBQbRr1wFfX186d44hNTWl2vkjAIcOHeCNN16iU6fOPPzw40RHd2Hp0kU8++wTldb79defeeKJiZSWljJhwt3cc88DuLq68tZbr/Lppx8617NarTz88H3Mnj2jfJ9PcO+9D+Lr68fcubOZNOlj57olJSXce+8dLFr0G3369OWBBx7By8uLiRPvPeu51qT/3lViUacOphVSbLbh5aIjMtADQ2x50uI/2BqqQqCHkSljO/DIr/vYn1bINTs78VOXN2iz6yVcD85FYy6iYMjn/8mkjhBC1AS7qrKkvDXU8DbBGI8vQrGWYfWNrPWLep3CvJm78yQ7kvNRXZtj9Y1El3sE3cnNrDrmSCz0bxGAIWE5it3quPPap+lFHzcq2JOXhkby9vKjrD6WzRhlPJ/r04nRHEFddBsKjg9l1no60wIg3NeVW2PCmB6bxIcrj9Fz7FOExv2JPnMvLvtmUNb+jroOUYizSs5z9L1vHezBFc388dzkuJD5hXkYr1iNuPu1gPi/0OUew/RPOzrNL7sdsxz6NQ9An3sMQ8oGVEWDqcnQmjiFOmPQabi1aziDWgby0Ly9rM1tSF/bkzznvZTR+d8yUbeA0j1GXI4sAKC0Fv9PCPN2XFhPzisFjRabZxi6/Di0+fHYPUNrLY7L1b6TBRSarHi76GjTwBPDFsdFPHNE/zqOrHoB7gZeujKSR+fv58cdKWijr+F6+2I6aY4x228a+UVFeNgLMPm3RTG4Y0iNxeXgXEq6PXH2nQvxX6Gq+Px6Lfq0bXUdyRlZQmLIu/ZXUJSL2k9paQl5eXnOr+12G9nZWfz11x8sWfI7kZFRDBgw2Pl8ly5dWbduDQcO7CM6ugvZ2VkcO3aU2267E4CmTZsRGBhEbOwm7rzzHsAxhNvFxYV27f75s01WlmN2UEBAwFnjDgx0VH9mZmZWWm42m5g162c8PT0BGDp0GNdeexW//77AGc/KlctZseIv7r9/IuPH3+rcdsyY8dx99618+OE79OjRGxeXM7dw3LYtlvT0NF599W0GDjz1+gQHN2DJkt+Ji4sjKqoVQ4dezdSpX5bHcjXgaIP19tuv4eHhwXff/YCXl+PGsWuvHU2nTtG8884b/PLLHG666TbnftPSUnnppdcZMuQqAK67bgzPP/8Uy5b9wTXXXEunTp3PGOuyZUsxm80MGDAIjcZRLzBo0FC2bNnMb7/No0ePqtdAy8rK+PzzqXTo0AmAkSOvIycnm127dpCWlkqDBiFkZKQzadJH9OjRm3ff/Qil/Gfxhhtu5I03Xubnn39k8OChtG7dls2bN3Dw4AFngqjC9dePZfToEaxY8RdPP/08AHPmzCI5OZGnnnqea665tvx8b+CLLz7lhx/qrv2uVFqIWlXRGqpzuA9a1Yw+dQsA5rDedRhV3fNx1fPFDe2JifChxGJj/M5IjvX6DFVjwHh8MR7rX5ZWUUIIcYlsT8ojrdCEh1FL3+YBuBz6GYCyqNEX/SHkfHUqr7Q4llVMXqkFS/lci9Lj60nOK8OgVejR2O+StoaqMKJtA6aM6YC/u4EM1Zvx5hfYGzTSmbCwG71RXc/+AaYuTegWQUNvFzKKzHy1q4Ti7k8D4B77Ppri85sBIERdSCpPWoT5uILdiq7QkbSItwcTl13iTBxqc86t0iKzyMTyI44qrbGdGuK69zsAzI0HY/cKv8TR1w8NvV34emwHIgPdySm18kT6YF6wTEBFwfXgXBSbCUtQB6zB0bUWU5hvRdLCcVfsqRZR8bUWw+VsY7yjFUb3xr7orMXoUx139ZrKZzDVR72b+jOmY0MAZu1I52HLA5g0rjQp2UNHzQlyVQ/mNX2Tsra3AOBy6CepChTi72r57/C68vHH7zN8+CDn45prhjJhwk0sWDCPESOu5eOPJ6PVap3rd+5ceRj3li2bUVW1UuunmJhuHDp0gKIiR3vI3bt30bFjNAaD4R9jqbjMpNOd/SbZUzFVvjbVs2cfZ8ICwNXVlUaNmlRqa7R8+Z8A9O07gLy8POfDbDY7l+3a9c8tXoOCHO2rvv9+OuvXr6W01PE31NixNzFjxhxndUp1jh8/RlzcCXr27I3drlaKoWfPPhgMRtasWVVpm0aNGjsTFgCKonDzzY6kxpo1K/8x1sWLHa2aBg06dcNI3779MRgMbNq0gfT0qpWXYWHhzoRFhdat2wKnkktr1qzCYrEwcOBg8vPzneeQn5/vPFbFefTu3ZclS1Zw3XVjKu0zOzsLLy8vSktPtYdas2YVHh6eDBt2TaV1b7rpNmdipC7IrduiVm1NygOgS7gP+rQdKNYy7K6B2Pwi6zawesDNoOWjUW24/+c97E0t5PatDfm578c0XPUArntnYPNuQmmHu+o6TCGEuOxVDOAe0jII16IE9KlbHXcht7yu1mPxczPQxM+NuJwSdiXnE9ywO677vkdJ3gRcSffGfrgpJgxJawAwNb36kh6/XUMvvr+pE0//foCDaYUUXPEOhRld8Vj/CubwK+r9h0cXvZanBjTnkfn7mLMjmWE3jSQmaC76jN24b3iNwiGf13WIQvyjpPKL2uG+rmgKU1DsVswYSMeXuJwSujd0JC0qWradzbzdqdjsKh1DvWjlY3cmZWuzyqAu+Lsb+GpMBx5bsI9dKQWs8hhBfp9ovFc+imK3Utp+Qq3+fxbm7bhTNKvYTKnFhocM4z4vG06cmmehT16PYrdg9W6M3adJHUf2zx66ognbk/M4nlWCX8MWlHZ4C+PKR1FReNjyANlHNQweeyV2ozfaopPok9ZiaVQ/q0eEqHWK4qhisJbWdSRnpnO9JL9Lxo+/pTzhoJKbm8svv8zl6NHD3HHH/7jxxqqzlxo3bkJgYJAzabF580bc3d1p06atc52uXbuzZMnv7Nq1g7CwcPLycs/aGgogKMhRPZGVlXXWdbOyMgAIDAyqtNzf37/Kunq9vtLsh8REx++/sWNHnXH/aWknsdls5OXlVnnOx8eXNm3actttdzJr1nc888xj6HQ62rRpR/fuPbnqquEEBASecd8Vx1+wYB4LFsw7w/FTK33dpEmzKus0atQYcMzWOJNjx45y5MghAgIC8fHxJTX1pPO5du06sH37Vn7/fQH33nt/pe38/Kq+jkajY9ai2WyudB6vv/7SGY+flnbqeDqdnt9/X8CePbs4eTKFkyeTyc/PR1EU1NNujD55Mpnw8IhKyTIAb2+fauOqLZK0ELWmzGJjT4pjnkXXCB/0x2YDYA7rWe8vitQWF72WD0a1YcIPu0jJL+Pe3Y34vttz+MS+hfv6V7F5RWBuMqSuwxRCiMtWsdnKyvK7kIe3CcblsGMwmzm8L3b3Mw+fq0nR4d7E5ZSwMyWfATGOXrFBpcfwopgBLVritvMrFGsZNq9G2AJaX/LjB3kamX5jRwpNVrxc9JQF34YpchSq3uOSH6sm9GrqR/8WAaw6msU7K+P4duDb+P0yHJejv1HWaiyW8CvqOkQhzig591SlhTbfMdgy1xiKWqYhPrsEa2vHXBlNaRZKaS5w6k5Gm11Fqzn1N7TZbOHX3Y4P3GM7heJy8CcUawlWv5ZYQnvW0hnVHU8XHZOub8fPu04SE+GDJbgr+Z4N0WXscc68qS3ernq8XHQUlFlJySvDTyotzllWkYkjmcWAo9LCEFu/W0OdzkWvZdL17Vh2OJNhrYOxuHSgQGckH3c2LjJgTSvkaK4V95bX47ZnOq4Hf5SkhRCnUxTQu519vctc48ZNiSn/mx9gwIDBPPnkw3z++Sfk5GTzwAMPV9mmc+cYNmxYh91uZ/v2LXTu3LVSdURMTDc0Gg07d2533pV/tiHcANHRXQDYuXM7V101/B/X3bFjO0CVtkjncie+3W7Dzc2dN99874zrREQ0IiMjnRtuuKbKcz//vJCQkIbcffd9XHfdDWzcuJ6tW2PZuXM7u3fv5Pvvp/Pxx1/Qtm27Mx4fHC2P+vTpV+06f682qa5KpSIR8/eL+6dbtMgxgDsrK7Pac6lY5667/sfpDZAq2kj9k4rzeOqp5wkJaVjtOr6+fgBkZmZw//13k5mZTseO0bRv35GRI6+jTZt2fPDB2855JhXUM3R3OdPy2iBJC1Fr9qYWYLapBHoYaOTniiHFMSTI8h9vDfV3fm4GPrm2LXf+uIs9Jwt42rMfn7ROwPXAbLz+eoDc63+rkYtWQgjxX7DiSBZlVjsRvq60beCOy7JfADDV8gDu00WHeTNvdyo7kvKx92tGhj6MIEsyI3ziuTqkJW4/OaoFirs/U2NJfkVR8HLRO79Wjd41cpya8nj/ZsTG57LnZAHri9syuN3tuO2ZjvuWD8mTpIWoxyraQ4X7uKDNiAegzCMC8uFETgkY3LF5hKItSkGbexTCIwDIKDRx54+78Hc38NbwVjQ//AU+27/kOvN1/O4+in5NfXCd8x1QXmXxH7lByEWv5ZaYU22wLA27YWnY7R+2qDmh3i4UlBWRnFdK6/KkhSZfKi3OZmO84w7bVsEe+LnqMSSWD+G+TC7uB3oYGd85zPm1qcU1uAB9mx9gxZEsftubRlT7cbjtmY4h7i+UkixUt/rdilEIUbN0Oh2vvvoWt98+nh9/nEnLllGV2gqBY67FH38sZv36teTl5VWpovD29iEyMoojRw6Rl5dLYGAQTZqcfQ5emzZtadasBcuX/8Vtt91JaGhYteslJSWyevUKmjZt5mxZdD5CQkJJTEygRYuW+Pj4VHru6NHDZGVl4erqitHowscfV62U9vPzJzs7ixMnjtOxYzQjRoxixIhRqKrKX38t5fXXX2LOnJm88Ub1SZGQkFPzpE5PGAHY7XZWr15Jw4aVZ04lJSVW2U9CQjwA4eGNqj2OxWJh2bKlaDQann/+FVxdqybhPvvsQ9LSUlm3bg19+pzf77aKRIWnp2eV88jKyuLAgX00bOhoUfnNN1NITU3hww8n0a1bj0rrZmdXrqwJCwsnJSUFi8WCXn/qM2FxcRG5uTlERFR/vjVNZlqIWlMxz6JLuA+KpQRduiOrZ/4PD+E+kyb+brx3TWu0GoU/D2exs9UzmMP7olhLcd/68XntK6vYzLztyRSbraCq6BPXoDu5pYYiF0KI+m3VUccfaMNaB6PP3Iu2KAW7wRNTHVaxRZfPtTiSWcSGuBxWlDrawdwddhKf9S+i2EyYw/tiav7Pdz/9lwV7Grm6taNU/a/DmZRG34+Kgj5tO5qCM5dvC1GXis1WckosQEWlRTwAGj9HC5z4bEevYZufo9pCm3vUue302ETSCk3sTyvk6Vl/4rrtM/T2Ul7Uz2a6z3TcEv5EW5CA3ehNWS1XGQiHcB/HRYOkvNLKMy1kTt0/2hjnaA3Vq4kf2pzDaItSUbVGzKE9zrJl/TaynaOac+nBDLLdW2AK7Ihit+Jy4EcwFzseNnMdRymEqCteXt688MKrKIrCBx+8Q0ZG5dlsXbo45lr89NMPAFUuQoOjRdSxY0fZv39flQva/+TZZ1/CZrPy9NOPVmplVCE19STPPvs4NpuNZ5558XxOy6l/f8dMoqlTv6i0vLi4iBdffJZnn30cs9mM0WgkJqZblYfRaGTx4oU8+ugDrF17avaEoijOYeOnVz9oNBrsdrvz66ioVoSENGTp0kVVWjstXPgrL730DIsXL6y0/ODB/ezcud35td1uZ+bMb1EUpdIg8NOtX7+G/Px8unXrwdChV3PFFf2qPEaPHgvAr7/+cs6vX4W+fQeg0Wj4/vvpmExllZ6bNOkjnnvuCQ4fPgRAfn4eAM2atai03urVK0hOdsxQs1qtAAwYMISSkmLnz1eFH3+cJZUW4t/PZldZX96bNCbCB0PiKhS7FZtXBHaviDqOrn7qEuFD32b+rDyaxdLDuUT1fB6/uWswxK9AMeWf9S5Yi83OnB0pfLM5kWKzjetDsnnLdTbGk5tRFQ35I37AEi5VLkKI/w6rzc6OJEebwl5N/NAnO/4wtYT2BJ1LncUV4GEkwteVxNxSnl90kMH2VtzIKiISfkZjKULVGim84o3/zJ3SF2pIVBC/7E5l9dEsyga1wBLaHUPKJozHfqc0+r66Dk+IKiqGNPu66vEw6pxJC9cgR5Iiq9hMYZkVd99IDIlr0OU4khZpBWX8ttcxwDHcx4Vbi+ah0VlJsgcSomTTNnsp6l+OgZdlrW8EvWstn5kACPNx/F5JyS/D5hWBioLGUoxSmi131p+B1a4Sm+CotOjZxA9DQnk74dCejl7yl7GuEb408DSSVmhi8BebGKftwjv6XXjEvotH7LsAqBoD+cO+xRLRt46jFULUhejoLowePY6ff/6Rt99+jY8+muxsvRQQEEjjxk3YtWsH4eER1bYG6tq1O99/P53CwgLuuuuecz5uVFQr3n33Y1555XluuWUMgwdfScuWjqHWhw8fZNmyP9Dp9LzxxrsXVGUBcNVVI1i5cgW//fYrycnJ9O59BVarlUWLFpCcnMj990/8x5kUACNGjGLBgnm8887r7N+/l6ZNm5GXl8fChfPR6/WMHj3Oua6fnz8HD+5nzpxZtG3bgbZt2/HUU8/z9NOPcuedNzNy5PWEhoZx8OABlixZSGhoGLfffmel4xkMRp588mGuv34sQUFBrFq1gl27djB27HiioqrvflKR+Lj22jNX8Y8YMYrp06eybdsWkpOTCAsLP+O6fxceHsEdd/yPadO+4vbbx3PVVcPx9PRi3brVbNmymV69+tC3r6N6o3fvvqxbt4bHH3+IESNGotPp2bVrBytXLsNoNGIymSgqKsLHx4exY8ezevUKvvxyEnFxJ2jTph379u1m7drVuLjU3edkqbQQteL7rUkczSzGTa+lT4iKxzrH0BhTs2F1HFn9dmUrx12jfx7KwOzXCqt/FIrdjPH44jNuk1lkYunBdMbN2M6Xa4/SwnKY13XTeS9nIsaTmwFQVDteyx6gOCuBpQfTWXMsi8PpRZTFbcJ48Gf0KRsd5etyt48Q4l/kQHoRJRYb3i46WgS5O9sU1oeKv06hjkR0sdnGAYPjw4DGUgRASfQD9X74aH3QIdSLIA8DxWYbm+JzMDUfCYDx2MKzbClE3Ug6bZ4FgDY/DgCdfzOCPBx9lONySrD5Vq60+GZzIla7Spdwb+aOCmSsbg0Aj1ru48uG72I3eqOodlRFQ2nb22r1nMQpjfwcLSGOZBSB1ojd09FyQ5e1vy7Dqtf2niygyOT4Pd26gSeGxPJ5FpdJa6h/otUo3N4tnIoxNL/benDCXnmWlmI3477t0zqITghRX9x774M0atSYrVtj+fXXnys9V1FtcaYB223btsfNzR1FUejS5fxaI3bv3pPZs3/mxhtv4dChA3z11SS+/PIzDh48wI033sLMmXPPOAviXGi1Wt5772Puv38ieXk5fPnlJGbO/BZvbx/efPM9xo+/9az78PX1Y/LkrxkwYDDr1q3ho4/e48cfZ9KsWXM+/3yqs+IC4K677qVBgxC++moyv/8+H3C0hZoy5Vu6dOnG4sUL+fjj99i+fQvXXjuaL76Yhr9/5RsKWrduwxNPPMvq1Sv4/PPPKCoq4plnXuShhx6rNr6srEy2bo0lJCSU7t3PPEvM3d2DYcOuQVVVfvvt13N5+Sq5/fa7ePPN9/D3D2DWrBl8/vknZGVlcv/9D/P66+86K06GDbuGxx57GpvNyhdfTGLatK9ITT3JU089x8SJjwMQG+v4PGwwGJg0aQrjxt3Mjh3bmDTpIxITE/ngg0l4enqdd4yXiqLWZZ3HOTh8+DCff/45W7ZsoaioiKCgIAYOHMjEiRPx9PQ8+w5Ok5VVKNW4dWB/agF3ztmNza7y8tAWjE94DmP8Mqy+keSOWXzZ3zVTk8xWO1d+tZlCk5UvbmjHFVk/4LHpbcwNu5N/7alSsrSCMr7amMDO5HxO5pcxTruSUdoNdNQcwwWLc71Ftu5orniGwYefR5+1n7204PqyF9Bj5WXd94wp/9BbQdUaKbhyCubGg2rtnIWojxQFAgI85ffIZe6bzQl8tSGBAS0CeHdYMwKmtUGxlpFz40psfpF1GtuSA+m8vPQwAM8PbsEdu65DW5iE1bsxueOW12klyKVSG++jj1cf54ftKQxpGchbA4Px/7YTimoj56a12HzO3tdXiNr0bWwiX6yP5+rWQbw6tAUBU1qg2M1k37KJ+//KJTYhjxeGtOD6gGR8f70Wm0dDTt6xnX4frMZmV/l6bAf6HHkd14NzSPTuytOur/Ls4BaEqml4bHwDS3BnqTKqQ2kFZYyYugWNAsvu70HohqdxOfQTJR3vobjXhbXX+Lf7cn0c02OTGBoVyBtDGjl+T9stZN+07pIl7+v6bzqz1Y69/MCT1hxn4e4Eejb25f3BQfjNvgLFbiV3zFKsgdUPkxWiPrjQ95HFYiY7OxV//xD0+qpDjoWoL3r37kLHjtFMnvx1jR1Dp9NgtdrPvuJl4lzf3xX/f5yLel1pceLECcaNG8fGjRsZO3YsL7zwAl27dmXWrFmMHz+ekpKSug5R/M3Pu07y9MIDbIjLQSlKxW3xXcT/9hI+9jwGtwxktPoXxvhlqBoDBUMmS8LiLAw6DYNaOrK9fxzMwNTiWsfyk5vRFKY415u8Lo7F+9M5mV9GpCaZd/TT6K45iAsW7C6+0HIY0xp/woOWiTy5zsTbHs+Sp7rTjqNMcf2C5W7PMUa3BruqEGuPosSjMarWiGIz4bLv+zo5dyGEuNS2VcxWivBBn7YDxVqGzS0Im2+Lf96wFvRs7EeAu4HeTf24pl0DylqNxa73oKj/e/+KhEVtGRLlqFBcezybEp03lvA+ABiPSrWFqH+SnUO4XdEUpaLYzagaPXaPhjTxdwcgLrv0VKVF0UmmLt+Dza7SNcKHzp55uBxy3IXpOeh5Pru+HSFeLti9G1Nw1TRJWNSxBl4uNPFzw646ZvuZy1v+GBJX121g9dj28haOMRE+6NO2odgt2DxC/1XVhgadBhe9Fhe9ljHRYZRhZFV8CQm2QGcXAtc939ZxlEIIIUTdq9dJizfffBOLxcIPP/zAo48+yrhx43jnnXd49tlnOXLkCD/88MPZdyJqTXxOCR+uPMbKo1k88us+ds9+HPf4P7jd9gsbXB7mQ+NUPDa8BkBxz+ewBVTfA05UVtEiasWRLEpcgp1D6IxHFwCOdlDLjzgGy75xdRS/dNgDgDmsNznjV5Nz5x648QeGX3UdnUK9KDbbmH5IwyOW+7Gj0F+NJcSejs0zjPeDP2Cs+SXebzaT3BuWAGBI3uAYDCeEEJexMouNPScLgPKLIcnrgfJ5FvVgVoSPm54l93Tjw1Ft0CgKJTGPkH33AUd84py1DvYg1NuFMquddcezKWtR3iLq6G8y/FbUO0nlMy3CTxvCbfOKAI2WJn6OG3vicopRXXyxuzr6PO/dvRWA//VshNu2z1BUG+aIvlgbdK79ExBn1b2xLwCb4nMxh1+BioIu5zCaotQ6jqz+KbPY2J9WCEB0mA/6FEdbW0to9W1Q/g0a+bnRs4kvKo6b/0rb3wE4fmcppdkXvX9VVckvPVV1r5gLwW696P0KIYQQtaHeJi3MZjPbtm2jc+fOREZWbtkwatQoALZu3VoHkYkz+XJ9PDYVInxdidYnMdC6FoB99sa4YMbryE8oNhPmiH7OP8jE2XUM9aaBp5Fis431J3IwRTqqLVwOO3rfzdudis2u0inUiyubGvE8Og+Aki4THXfmlV+M02k1vDWiNWE+LgR6GBg16hZKuj+Nqmgpi7qB3HHLCGnbD4A1x7Kx+UVi82qEYjNhSFpTNTAhhLiM7D5ZgNmmEuhhoJGvqyMhC1jqwTyLCoqioDk9gaLU2z/T6i1FURgS5bi4u+xwJuYmQ1E1BnS5R9HmHKrj6ISorKLSIsz3tKSFd2MAZ6VFfLajstzq56i2aKIm06OxL9FuWbgcdrQKLY55vBajFuejImkRG5+L3eiDNag9APqkdXUZVr20L7UQq93xezrMxwVD+Sw+S8N/b9ICYGynUAAW7kujwLcDlqAOKDYTrvsv/gbNN5cdZdAXm3jjzyOY0/bhN6Mbft93R5+0/qL3LYQQQtS0evtpWKfTsWjRIl5//fUqz2VlOe4q12jqbfj/OftSC1h5NAuNAu9e05pZjRx36a/Q9mFlzznkjfoJU6OBWBp0oWDAR3Ih5jxoFIWh5dUWfxzMwNRsmOMCTM5h7On7+XW3406tsdGhuByci2ItxeofhaVhjyr7CnA38NPtXVj0v270auJHaecHybrnCIUDP0Y1eNKzsR9aBU5kl5CcX4apyRAAjPHLau+EhRCiBlS0hoqJ8EFjKUaXsQtwVKWJf5chLR2/MzfE5VCIm3OAq7SIEvVJqcVGZpEZgHAfF+cQbmfSonyI88kCk2Ndo2N5C00K9/ZqjPuG11BUG6bGg7E2iK71+MW5iQ7zxqBVSCs0kZBTijmiHwC6hNX8sD3ZOYxdd3ILxkM//6crwnYk5wGO10yxlaFL3wWA+V+etOje2JcIX1eKzTYWH8xw3tznsm8G2Cxn2frMlh5M57e9aQD8sS8R0693ozEXoC1Ow2fhONzXvwLWsktxCkII8a+zfv22Gp1nIc5Nvb1yrNFoCA8PJyIiospz06dPB6Bbt261HZaohqqqTF7n+KB1detgWpXtwi15DapGR8cb3+bGzmFYQntSMHwGedcvQHUPquOILz9XtTp1ASbX7oa58UAAMmJnk1tqIcjDQN+mvrju/Q7A8cfuGdqd6LWaynfyao3Of3q76ukU5g04+oGby5MWhvjlUkoshLisbT0taaE/GYtit2LzaoTdK7xuAxOXXLMAN5r4u2Gxqaw+loWpvEWUy9GF/+kLgqJ+SSlvDeXtosPLRV+l0sLHTY+vqx5wtGBdlOYFQB+fHDqat2NMWIGq0clA53rORa91/m29KSEXc7hjroUav4ZPVx/jkfn7sBZm4L3oFrxWPIrx8Ly6DLdO7Uh2zLOIDvNGn74TxW7G5h6Mvfw98W+lURTGdGwIwE87UyhtNgy7ayDa4jSMJ/64oH2m5Jfy7vJjAFzdOog33ObSVE0iU/VmX+AIANx2T8P352EoxRmX5kSEEEKIS6zeJi3OZMGCBfz888+EhIRwww03nNe2iiKPmnhsTshle1I+Bq3CvT0jcN/8NgBlbW5C9Wlc5/H9Gx7NA92JDHTHalf5LjaR0vIWUaHJC2mipDKmU0Pck1eiLUjEbvTBFHltpe3P5+f/iub+gCNpYW0Yg93og6YsF3369jp/HeQhj7p8yO+Ry/dRbLZyMN3RJzsmwgd9ykYALGE96zy2/9qjNt5HGo3C0PIWUfN2p1LWeCCqzhVtQQKGpDV1/hrIQx6KAsn55a2hfFxRFJxJC/tpfzs38XdUW0zdmMDqvAAAojRJuJfPiCtrNwG7b9M6Pxd5/POjokXU5vgcbA06YdN74G7Lp60SR2JuKSlL30JjccyP89j4OhpT3jnvW0XFarefWmY31fn5XsjDYrOzL7V8nkW4D/ry1lDWht1RNMolP159+5tueNtg3A1a4nNK2ZpSQlnbmwFw3Tv9vPdlU1VeWnKYYrONDqFevBmVzA32pQA8YbmXUSnjOTn4G+xugehyDuO2e0qdn788Ls/Hhb6PhBD/bpfy/wFdzYV56c2fP5/nn38eNzc3PvvsM9zd3c9re39/zxqK7L/LZlf5cvYuAG7r2Zi2pk2Qvgv07rgOfQFXD3nNL5Xbezflufl7mb09hbjsBkxzDcGvNJXFhudQfd7A/cDvAGi63EZASNVqlnP9+R/VpREfrTrBrpQCdB7uaFpeCXvm4JO6CtoPuqTnJMTlRn6PXJ52HkjHrkKTAHfaNg2EvxxJC5dWg3AJkO9pbauN99GEvs2ZuTWZfamFrEoxM7bLBNj8Bd4bXob2G0HvUuMxiH+HI+mFfLshjugIX4a2CcZr22Qoy4P/s3fX4VGc2wPHvzOzvht3IwQLBHenUAoVoAUq1N3d7d5buZVbd3dvoZRSoS3ursE1xN02yfruzO+PDSn91WhLsgm8n+fpc8tldubMNll5z3vO6T4Fkvr87RWY6h3B3c2dEsOIjbZCXT4AEe27Q+PvSNeUCDYV2lmeU00cwZ3YusbjMEdjPuXfmM3iNay1O7VvKi8uPcimQju26Ah2m/vS3becU807qHHZ6FX+NUiANQ7ZUUHMludg4vN/el5V1Tj3rTXkVDYw/ZqhdKxdA5+dA4OvhVMeb/4bO4rW51bj8avE2gwM6BKPtDo4u9LY5QSMzfQ+3Zo+08UCk/um8OnafFYX1DFx7DWw/nn0JeuJNbnAduSdCp6fv5etxXWEGXW8NjmNqM8vCf7FkOup3DsSf3EdC+QBXDzpBZh+AZZ9s7FMfByUNrU0JLQSf/X3yO12U10toygSOl2b20MtCEfdsfR7oKoSsiwTFWXFZDo637XazDvTq6++yksvvURYWBhvvPEGvXr1+svnqKqqF10B/oaAqvHjzjJsRh29U8KJdeUgFa9nWUMaL+0ys7/aQ3djGTfav0Gb+TUS4OxzFU63Gdz1oQ7/mDGuQyTOkzvz7KIDrMptYCz384z+DYYrO2D+XQBokkxNx/NQK39+3iUp+GHiSH/+LQRbaxyodPLthnwmJ48mfOsXBHZ+T02/e8T2COG49Fd/j4TWZeH2YgD6poRTVZBHTOk2AKoi+qFVivepltKSv0cKcNWwdF5YksPjc3Yx4MJrab/1K5TqAzjmP4Vr0G3NG4BwzHho9jZWHazh83UFzJi9ny91Dwf/YuWLBCIycGdOxdXvWtCZ/9J5dxfVAhBv1lGdv59ovwtNUqjyR0Hj61KyVd90vGxLQJXDkb11ADQMuhO3QwGHeA1r7WIUiLMZqGjw8u7ifZRUdeZR/XLOjdpLD30pBk+ArYZ+tBt3L5Gzz0Hb8D72jCn4E/r+4XkX7KlgXW41AFe/v5Yfjfdh0AKw5lXqrZ3wdPtrXQlCadH24Iy+3snhVJVXEVOwDgmoiehL4Ci/T7fWz3T9ksL4FFi2p5zKke2JjO2OrnIH9dk/4cmcckTnKKp18fKifQDcc1InYlY+Bs4q/DHdqO1zO6fKVeworuOLNXmcdv5Qok3RyA1l2Dd/j6+x/bAgHIm/+3sUCPhRVRWv14cs6//8AYJwDNPpZPx+NdRhHDVerw9VVbHb3TQ0/P5MpkOvH0ei1SctfD4fDzzwALNmzSIhIYG33nqLrl27/q1zaZpoZfx3fLiugNdW5AIQQQNLTHcSSR2nAydqJg4aU+gh5SDtDT653tSROPtcK57ro07ijB5J9E+N5IEf9rCtBC703cfCftvJ2P48UsCDN2M8gbBU+I3n/q/8/J/QMYYDlU6W7q/itJNHo8kGFHsucvV+AtGdj+5tCUIbIt5H2qameRZpkegKg1UW/uhMVHPcb75eCs2rpX6PpvVNYc6OMvZVOHhxTQWPjniQ8HnXY9n4Cu7Ok1EjM5o/CKFNc3oDTa8faZEmRjdsAKBMiyJO50SxH8S67lmU2hzqx738l85d0DjTIjXSjFybA4Aaloom65tel9pHWZqOv/mEDgR2dEYu3Yg/OhNX1vni9avNkBicHsX3O8p4bkkOSVpw811k9RZGasHFivvqz+JiR2fOyDwL056ZWBffR+3Z34P821/XVU3jrVV5jWeHbrWLMRj2oiEhoWFbci++mCwCcd1b5A7/qU0FtUBwnoVSmo0U8KCaY/BHdmq2n/PW9pmuX2oEihR8bSiqdWNJG4mucgf6guW4uxxZ0mLu7gpUDQa0i+TkTmEYls0BoH7UY2iKifGZcTy/JIedZQ3sr/bRq8sULFvfxbh7Jt50kbQQ/rq/+nskyzp0OgNOZwNGoxlJbIgUhGOG2+1ElhUkST5q76+tOmkRCAS44447mDt3LpmZmbz99tskJCSEOqzjSpHdxbtrgmXoyREmbnS8QxR1VGs2DJKKTXLSkwMAeNqPx9nvevxJA0IZ8jEvNdLMW+f25rvtpRh1MmFZo6npdhLG/d/h6nnpUbnGqI4xvLe2gNUHa/DIXfGlDsOQvwRD7jxcImkhCEIbUu30cqDSCcCAtEgM61cD4E0dEcqwhBagkyXuGduJK7/I5tvtZZzefRSj0kZhKFhG2PJ/Y5/4iageFP7QmtxqfAGN1EgTX10+ENsnd0MdPOY7nwPWEXw8II+o5f/CtPdrvO3G4MmcesTnLqz5eaaFUpsLQCCy/S+O6ZkcTocYC+2jLYzLjMPjn4q+voCGMU/87mK20DoNbR9MWgRUjWIpHpetPeaGXAC2RZ7EjtL2PLXoAMYR13N67nz0lduxLbkH56A7UW1Jvzrfgj0V5FQ5sRkVHjm1Mz1+DFZdr0u9gj5KDsa8RUT8dBU1Z/+AZopswTv96/wBla3FwQqivqkRGPKCw8h9yYOPq9dom1FHj6RwsovrWJtXQ3raKCyb30BfuDy4KnwEz8X8PRUAnNo1HkPeImSfg0BYKv6kgQBEWQyM7BDNkv1VzNlRRmbW2cGkRc5cGty1rf5nRTg2WK3h2O2V1NRUYLFYURQdwfSrIBxfVFUiEGhF2fO/SdM0vF43breD8PCYo5qMbNWfdl988UXmzp1Lr169eO+99wgLaz19J48HmqbxzKIDePwqA9pF8tYIH9GzFgGwZeALdOk7Fm/dPnRVO/HH9SIQ3SXEER8/dLLElF4/f4EJxHTFGfP3KpB+S7fEMGKtBiodXn7aXc7Z7cdjyF+Ccd+3uLMuEB9oBUFoM3Y0DvbMiLYQadGjK9sEgK/xC7xwbOudEsEZPRP5Zlsp/1u4n14THyHuy/EY8pdiODAHb6eJoQ5RaMWWHagCgps5FHsulrr9aJKOLcaB5Fdr/K98CA8NvBXrumexLb0fX9IA1PB2f3pej1+lrN4DBCs4lLxcAAIR7X9xnMWgMP3SnzcDuXtegm3Mjfgr60WVRRszKD0KieB/tglZCWAZDds+QJP1xJzyIO2+qSS/xsWtc0vZE3Yh9/Aq5l3TMe2ZhbvLVNw9LiIQ2QHNGE5A1XhndXBT2fn9UhkbWEW4VIRds3B1zlBenHwZI6v3odTlE7bgFuomfNCqF/93lTXg8qlEmHR0jLWiX70WAG/ykBBH1vIGp0eRXVzHurwapmQNRFOMKA0lKLUHCER1+sPH5lQ52FfhQCdLjO4cg2nJtwB4Ok36xX//CVkJLNlfxQ+7yrl+xCD8Md3QVe3CuP873D0uatb7EwQAszk4m9bhqKO2tjLE0QhC6MiyjKoeK+2hJMxmW9Pv99HSapMWxcXFvPvuu0iSxLhx41i0aNGvjomNjWX48OEhiO74sGR/FStyqoM7FUe3J3zhWQC4u55Nz8GnABCIzSIQmxXKMIVmIEsSYzrH8uWWYh6Zu5el8cm8L+nQV+4g+qPBuLMuwNXnSlRbcqhDFQRB+EM7SoNJi6ykMPC70VXtAvjTXuHCsePGkRks2VfJgUonc0pTOaff9VjXP491zRN4O5widqwLv8mvaqzICc4LOKFTDMbcGQD4UoZwZ+9+3PzVdr7cUsyw089nYtIy9CXrCZ9/E7VTvvrTn6liuxsNsBoUIs16FHsuAIEI0bLsWBVp1jO+axxbiuq4elg6bs80TLtm4Ox3HfqYDN49N5XPNxXy5ZYSXq8fzk7ZxvW6bxjMbsy7p2PePR0AVW/Dbkhkcl1PvjJO4Ly+iVhmBYd2L446B3upmVc22Ol96ttEfnUGxryF6Eo3NO20b402F9oB6JMSgaz60ZcEh3D7jsOkxaD0SN5ancf6/FpUxYQvaSCGwhXoC5b9adJi/u5glcXQ9lFEyB4MuQsA8HQ+4xfHDe8QTaRZT5XDy9q8Wk7qeja2lf/FtHuGSFoILcZstmI2W5tmXAjC8UaSICrKSk2No1W1Kvy7FEVBlpWjft5W+y1t3bp1+P1+AJ599tnfPGbQoEEiadFMnN4AzyzaD8DFA1PpVjwDXdVOVGMEDcP+HeLohJZw48gMVE3j+x1lLCs3cIV8O/fpvyDTl48l+y1M2e8QsCZCeCoBWxKezmfgzRgf6rAFQRB+YeehpEVCGLrKHUiqH9UcK5Kux5FIs55LBqXx0rKDfLC2gNMuuAbztg/Q2XMxHpjzqwUdQQDYWmzH7vYTYdLRKzkCw/p5AHjbj2No+2jO65fC55uKeHh+Dj2nPkuH7yagL92IZcOLOAfd8YfnLqgNtoZKiwz285abkhbtm/OWhBB7dEI3NE1DkiT89KTy6j1NO+AjLXquG5HBxYPSmL21lG+2WbjQ3ocevr1crfuewfIuoqUGZF8DUb793KLbz/XSD2gLhqCrPYBqjKTD+Jvho91sKqilwjoEc8cJmPbOwnhgTosnLQprXXy3vRSDTibRZqBf3TzS/HnoIlNRbSkEItoRiO4KksSmxqRFv7QIdBXbkPxOVGMkgaNYRd5WdE8Mw2pQsLv97ClvoF/aSAyFKzAUrMDd6/LffZymacxrbA01rmschoPzkAIe/JEd8Mf+cq6JXpE5uWsc0zcX8/2OMkaMnYJ11WPoyzaj1OzHH9mRaqcPj18lKdyIFHAjN5SiWuLBcHR30AqCouhQjv46pyC0epIEJpMJvd53TCQtmkurTVpMnjyZyZMnhzqM49ZrKw5S3uAlOcLEld3A8tXTADiG3odmjglxdEJLsBgU7j2pM1cPS2f6piK+3KLjZE9vRsvZXKf7lsHybmRHMTiK0QPGfd9Se9a3YveyIAithqZpTUmL7klh6Mt+BMCX0KdVt8oQjr6pvZN4f20BeTUuFue5mNTrCqzrnsGy8WU8nU4XPw/CryzdH2wNNaJDNHpvLfqSdQB4Gjdo3DAygw0FteyrcPDEeg/PnfA/wuffGPyZ6jjhDxdcC2t/nmeBph1WadG++W5IaBV+0ef5N153rAYdFwxI5YIBqaiaRrVzMCX2s3l6TwU/bcslyl9BDymXqw0/0kM7APlLAXD2vZakuHi6JRSyq6yBxfurOLfTxKakhWP4AyDJzX5/mqbx3fYynl18AKcvQALVPKN/g17K9l8d6xh4O1V9b2FLUWPSIjUCQ/6XwKF5Fs0fb2ujU2T6p0Wy7EAVa3Nr6JkxEvgf+qJVEPCBoscfUNlUaCchzEh6tAW5roDSnK0U1Vgw6vSM6hiDcf6h1lC//f42qXsi0zcXs/RAJa9Hm5lsHkRP52r2fHE7e3zxJFJFslSJXq4iiuC8EU1nwtN+XHCjWvoYUIwt+dQIgiAIx6FWm7QQQufj9QVM31wMwH+GhZHww4XIPge+hH64s84PcXRCS4u2GLhuRAaXD0mn2O6mtL4nG2vP4rYlG0hQK7hrgIn+9p8w5i/GtuQ+as/+XrTaEAShVSiyu7G7/egVic6xVnTbtgDgj+8T0riElmc16JjWN5l31uTz/toCxp51CebNr6Or2o0hbyHe9ieFOkShFdE07RfzLAx5C5E0FX9MN9TwNACMOpkHT8nk4k82sWBvBUt7jmJ8h1Mw5vxE2NL7qZ0y8zcXXQOqxsaC4CJtWpQJXXk2ss+BhkSg8dyCAMF2rbFWA7FWAz2Tw7liSDu+3FLMon2d2TzgMlLD92Pe8haS5sfV8zIATuwcy66yBhbtrWDq5FGoehtKQwm6ss34E/s3a7w1Ti+Pz9/HksaE33Ux2dzoeg2rWo8LA1/7h9M7OkAXQxX6yh2Yt7zJ9/5xOLwBkiNMdImUMH//PgCeDqc2a6yt2eD0xqRFfi2XDuqBaopCdteQs30508vTWLi3AsVdzRT9Gm6I3UJ0zRZigDf0/ZnZ/mFsaj2GxmSWp/Ppv3mNLvFWOsVa2V/p4N01+RTJg3nDsJqh6iaG/saud4+mx+h3Y9r/Hab936Ga46idPF3MtBQEQRCa1fG3fUH4QzO3FPPSsoMA3DE4gnFbrkepyyMQ3o66U948Lne8CEFGnUxGjIWh7aM5u08ypwzsxWatM7fu6kjZqKdRjRHoK7dj3vZhqEMVBEEAfm4N1TnOhkEnoyvPBsAX3zuUYQkhMq1fCma9zJ7yBlaVarh7XAyAZcNLiLps4XAHq50U1rrRKxJD2kdjPBhsDeX5f20wM+NtnNM3BYCnFu6neuhDaDoL+pJ1GHd/+avz1rv93D57e1NCZJJuPZGzzwaCszLEzmXhj0SY9Vw5NJ3PLu7PKVkJ+FKHUzfxQ+yTPm1q2zO2SxwAG/JrqfUqTQlZ4/45zRqbP6By1RfZLNlfhU6WeL3bLu5xPIlVrccX35vlI77kfv9VnFl9PTkTv8UX1xPZ58C0+Q0ArhzSDuv2D5Dd1fgj2uPpMrlZ423NBrWLAiC7yI7br+FODrbDXrX4G2ZtLaG7dwvLjLfxH+UDomu2oCHhQ8c4ZSP/bXgI0+6ZSKoPf0y3300qSJLE3WM7MaZzLFN7JdFx6FR2pp5HScqplGddTe2IRygd9w7fDPicK+Jn0NXzARM8j1HU5TIClgRkVwWWdc+12HMiCIIgHJ/ECrTQ5PsdpTy5MDjH4toBkVxbdDe6mn0ErInUnvEFqi0pxBEKrcllg9JIiTBR3uDljc0OHEPuA8Cy9mlkR2mIoxMEQfh5CHf3xDAkjx1dbQ4A/oQ+IYxKCJVIs54pvYKfZT5Ym4+z91VoihF92aZg6w1BaLSscaf4wHaRWGQfhrwlAL85u+uaYenE2QwU1Lp5b6cfx6DbAVCW/pfL3l3I7V9vZ+n8L5E/nYD/vTGcXPAcU/Rr+K7rIvptvB3J78KbNoq6U95usfsTjl1pUWa6xFkJaLD0QCWeThMAMB6Y06zJ2cX7q8ircRFp1vPB+b0ZV/s5AK4el1A7dTZ9evUjKzEMt1/l4w1FTXNfztV+omeEm9M6WbE0JjCcA289rqu206PNxNsM+AIaq3Jr+KiiAwAjle38u/0+PjY9jU1yUajP4L++ixjsfoULPffRoJlJqNmAbeXDALj/ZF5T39QInjo9i/vGdebSIR2IO+NpdJPfRhrzAL7el6F0OYVhg0fyxNnDGNsljh1aBo8HLsR++qcAGHN+QLbnNe+TIQiCIBzXRNJCAIK7cR6ZuxeAc/skcnvNY+grd6CaY7GfMR01vF2IIxRaG5Ne4a6xnQCYvqmINRETqI7siexrIH/mneTv34JpxyfYFt2JefOboPpDHLEgCMeS7SV1PDZvLzlVDgj4sKx9GvOWt3+xKLPzsKSFrnwrAIHwdDRTVEhiFkLvwgGp6BWJzUV1bKox4u52LgCWja+EODKhNTlUCXFCxxgMRauQ/E4C1gT8cb1+dazNqOP20R0B+GBdAW+4x7OPNKwBO9c5XuXigvs5a+9txNRm00U7yCW6+TyvvETP3HcAcPa+CvvEj9BMkS12f8Kx7aTMYLXFgr2VeNuNRtNZUBqK0JVvabZrTt9UBMBZvZPo4dmMrmY/qt6KY+i9oOiRJImrh6YD8OWWYg6ED2M7HbFIHv4Xv5iw7R8ge2rxR3bA03lys8XZFkiSxOD04OeUf8/ZxSeVwdeX/vI+rih7GEX14elwKrrL5lPT/TLKiWKt1o2XUp5FNUU3ncfTadJRi+mCAakAzN1VTqkxA2+70Uiaijn7naN2DUEQBEH4/0TSQsDpDfDI3D2oGpzaLZ7741ZiKFqJprNQe/pnBKI6hjpEoZUanhHNiZ1jCWhw1YxtXFR2HgFNom/DEvrPnUjYknsx7/oC26pHiPj+EiR3bahDFgThGLCjpI4bvtzG7G2lXPvFZtTvr8e64UVsKx/GtuhOUP34VY1dZQ0AZCWGoS/bAjQO4RaOW3E2I5O6JwLw2oqDNPS9Fk3WYShcjr5geYijE1oDu8vH9pJgwnNkxxj0eYsB8LYf/7sD28d2iWVI+yh8AY1XVhVyr+dyACYo6xinbMKPwqfSJF6Oup/qrEvxxfUiEJ5O3YnP4Rjx4HG9q1w4+k7sHAvA+vxa7D4dnqYWUd83y/V2l9WTXVyHIkuc2TsJ87bgXAp313PQDGFNxw3LiKJHUhgev8q1X27lGe9UALqXfIl5y6Eqi9vE7wM0JS18AY16YxJOazskVCRNxdV1GnUnv46iN3HfSZ25ckg74m0Ghg0dQ+3UWfhjuuLOPAs1Iv2oxdMjKZw+KeH4VY3pm4tx9rkGAPOu6UjumqN2HUEQBEE4nEhaCLy6/CDFdR6Swo38a5CRsNX/A6Bh6H0EYrNCHJ3Q2t0+piMRpuCXC3tEVxZGngOASzOwKpDF2tiz0HRmDAVLiZw5EaV6XyjDFQShjdtb3sBNX23H6Qtg0sF9/tdJKPwRVdKhSTLm3dMJn3sdB8tq8PhVrAaF9Ghz0w5TMYRbuGxwGma9zJaiOmYckHF3vxCAsMV3g9cR4uiEUMuvcaEBCWFG4mxGdLUHAPAl9P3dx0iSxD1jOxFm1BFm1DF69Gk4ul8CgLfdCdSdv5Dx17/OuedfT2DMo9Se8wPVF63E0+2clrgl4TiTHm2hc5yVgKqx9EDVYS2ifmiWFlHTNxcDcFKXWOL9xRhyFwLg7nXZL46TJImrhwUX0ssbvCxR+1AR3gPJ70b22PFHdcbT6bcHRx9vBrePwmZUiLMZeHNab7SuwVZPzt5X03Di002JHUmSuGZ4e+ZcM4TuiWEEojpRc+4C6k964ajHdGFjtcWs7BJq44fgj8lC8jsx7fj0qF9LEARBEADENobj3KbCWmZsCX7Q/NdJnYlfeT2S34k3eTDunpeEODqhLUgIMzLrioF4AxqxVgNoA6movZFXsn18uLEUCuGKjLHc3/AYOnsukTMnYZ88A3/8r1ssCIIg/JHcKic3ztxGvcdPz8QwPkr8kqjdSwloErf4b+S8Ae0YtvVejDk/kl5diYXr6JaQgCxJTUkLMYRbSAw3cePIDjy9aD+vLD/IiPNupUfuApT6AmxrHqdh1GOhDlEIoSK7G4DkCBMASmPPdjU87Q8flxpp5usrBmLUyZj0Ck7tUdwDbkS1Jv5uhYYgNJexXWLZV+Fg4d4KTp90IprOjFJfgK5iK/6j+D5Y7fQyd3c5AOf2S8G8/TkkNDztxhCI7PCr44ekR9EzKZxtJXVkRFtRRt4Dcy4CDs2yUI5abG1ZpFnP11cMwnTo9STmLtw9Lg7pjMmRHWNoF2Umv8bFtzvKubTvNYQvuAXz1vdw9bkKFGPIYvstxXY3Gwtq6RJvo1OMBXPujyAb8CUNEG1CBUEQ2ghRaXEcc/sCTXMszuiZyAnOHzAUrkDTmagf8wxI4sdDODLhJn0wYQHBL+ZRGdw4ugsPn5qJIku8ezCC9aOn40sahOxrwLL+hZDGKwhC2+P1q9w8axs1Lh+Z8Tbey8omavdHALwbcxff+wdx2fokdo58C01nIaV2PY/p3yUrMRy5oQTFUYYmKfjjeoT4ToTW4Kw+SfRLjcDlU3lkcRF1o58CwLztQ/RFq0McnRBKxYcnLdQAckNwc0/gCOa7RZj1mPSNi66SFFxgFAkLIQTGdg7OtViXV0utT4cnfSwAxn3fHtXrzN5aii+g0T0xjJ4xMqZd0wFw9br8N4+XJIl7T+rEgHaR3D+uM4H00Th7X40r6wI8HSce1djaushfvJ7IIU1YAMiSxPn9UwD4fGMhjg6TCFgTUZzlGPfODmls/5+madz1zQ7+O3cvF368icdfe5GIn64h4ofLiH23J/63hlHyySVY5t1M2IJbCFt4m2gRKQiC0AqJVenj2OsrcymsdRNvM3BHfyPWlY8C4Bh8D2pkRoijE44Fp2UlcFKXYF/dz3d7qB/9JACGvAXI9cWhDE0QhDZmb0UDJXUewk06XjmzJxEF8wFwDLyNSdNuYnhGNN6Axk0bIqk49QMCyExRVnIay9CVZwMQiM4EvSWUtyG0ErIk8Z+Tu2DSyWwosDO9phOurPMBCFt0J/icIY5QCJVDSYuUCBOyoxRJ9aHJ+mDFhCC0Ee1jLHRLsOFXNWZsKcKTGZwfYdr5OZKn7ojPU1bv4add5dS7/aD60RcsR1+8BgB/QGVmdvDz/Dl9kzHumYnsrccf2QFfuxN+95xd4m28fnYv+qRGgCThGPEADWOeFFUWbcCErAQizXpK6jxcPn0725LPBSBs2X+wrnio1Xy/21laz94KBzpZwmpQOFMLfmas0WwAJPny6WVfiHXfLEx7vsK0+0vCf7q6KUktCK3RgUoH9363k6u+2MKD329lw6wn0D4+jbDvLsa25D7MG18RrbiFY45IWhynsovsfL6xCID7x3UmcdX9yL4GfIn9f3dnjCD8HVN7B3cFzd1Vjt2agTdlKJKmYtop+p8KgnDk9pT/PFQ70qxDV7kdAG/6iehkiQdO6UK0RU9OlZPHdsXysn8KAAN3P45x/3cA+BJEayjhZ6mRZq4fGdyk8dLSg+T3vpuALRmlLg/r+udDHJ0QKkV2FxBMWih1wdZQgbAUsaAqtDkXDwy2NJu+uZia5NH4o7oge+swbf/oDx9nd/mYlV3MNdOzmfTWGr7+8Vu2f3Q90e/3J/Lb84j8+iyM+75l1tZSKhq8RFv0jEvXY9nyNgCunpeJiv1jlEmvcMeYjhh1MrvKGrhgW0+2yV2R/E4s2e8Q/ckwbIvuhIA3pHHO3lYKwLjMOBZf3I5RyjYAvu7zAa/3m8tn7Z/gUd8FPOa7gL3d78IX1xPZW49t8T3NMvdFEP6JOrePZxbt54KPNrJwbyWRJcu44+AVnFryCvF1WzHlL8K842Nsa54g6stTUWr2hzpkQThqxKeJ49ChtlAaMKF7AmPd8zHkL0VTjNSf+Jz4UiYcVX1TIugQY8HtV/lhZxnu7hcDYNr5BQR8IY5OEIS24lDSIjPehuwsQ3ZVBds9xXQFINpi4KFTMwH4ZnspL/sns4WuKD4Hpn3fAGIIt/Br0/omk5UYhtMX4MtdDU3zLEzbP0by1oc4OiEUmtpDhZuQ6woAUI+gNZQgtDZjOsfSLspMndvPrK1lOPvfAIAl+23wuX51fGmdm2cXH2DiW2t5csFekorn8q3hX8wyPsTpvh9Q3FWoOjMA1gW3snjJDwBc1jeKuB8vRanLI2BJwNP17Ja7SaHFndItnu+uGsQVQ9qhmMKY5PwP1/MvPMlDkVQ/5l1fYNr5Wcjic3oDzNtdAQRbYFv3zEBCw5synEkjhnDW0O6Mm3AhpV0v5+3ABK7MGUblmBfQFCPG/MUY98wMWeyC8P9tL6lj6rvrmb65GFVT+Sz6XT4wPEVHuYR6JYqHfRdzr+9KtmdciS+2B5LfTdj8m0KeOBSEo0UkLY5Db6/OI6/GRazVwF0DLVhX/hcAx6A7CUR1DHF0wrFGkiTObKy2mJldgjtjPKo5DsVZhiF3XoijEwShrdhb7gCCSQtdxQ4AAlGdoHEBBWBo+2jO6xfstxxA4cP4e1ENYU1/7xNJC+H/kSWJCwekAjBrawkNqSfij+qM7GvAuPvLEEcntDS/qlFW7wEgJdKEUpcPHNk8C0FobRRZ4pJBwWqLTzcWUZcxiUBYGrKrCtOuL5qOa/D4eeinPUx+dz1fbCpilLqWZea7eNXwEj3lXAKKia/VkVzivYcHO39LSfxoFNXLm/pnuaaLi2tK/oO+bBOqMRL76Z+gGWyhumWhhURZDFw7vD3fXjUIm1HHD+7urBryLo5BdwBgyF0YstgW7KnA6QuQFmmiX7IN064ZALizzvvFcbeN7kCs1UB+jYvXdupxDLodANuKh5AdpS0etyD8lnfX5GN3+8mIsfDxSQrDnAvRJAVn76vxXLYSBlzFF4ETOS9nPPtPeBPVFIW+YhvWtU+FOnRBOCpE0uI4s6Okjk82FAJw79hOJK3+N7K3Dl98H1x9rg5xdMKx6rSsBEw6mYNVTraUunFlBfufmrd/HOLIBEFoC/yqxv7Kw5IWja2h/LHdf3XsDSMz6BxnBSAlPZOGEx4HQDWEEYju0kIRC23JmE4xxNsMVDt9LNhXiavXZQCYt74Pmhri6ISWVFbvJqCBQZGIsRpQGistAuFpIY5MEP6eU7vFkxBmpMrh5budlTj7XQ+AZfPrTTtxX1iSw5wdZQRUjQnJLt4wvkyqVoJqjMQx8DZqLlmHc9yLLFV78/HmCsYVXsI2tT0xUj33Fl6DoXg1qt6G/fRPCcR0C+XtCi3MatAxOD0KgJU51Xg6nAKAoWgV+H9dzdMSDrWGOqNnEoaiFSgNRajGiKbYDgk36blvXGcAPt1YyIaE8/DF90b22LEtuV+0iRJCzq9qbC60A/DwqZn0CRxqjTsWx4gH0IzhXD00ne6JYdR7/Ny/tBb76KcBsGx+44iHy+8pb2iqMsXrQF+4EtTA0b8hQfgbRNLiOOL1q/x37l5UDU7uGsd4dQnGvIVosoH6saItlNB8bEYdJ3eLB+Cr7GLcWRegIWEoXIFSmxPi6ARBaO3yqp14/CoWvUJqpAldxe8nLYw6mZem9uDOMR05p28yni5TsJ/yJnUTPgBF38KRC22BTpE5q08yAF9sKsLdZSqqIRyd/SCG/CWhDU5oUUW1wS/tSeEmZElqqrRQw0SlhdA26RWZiwcGq8k+Xl9AQ+czCVjiURqKMe79moNVTr7bEVzkfWFKD56N+RZZ8+NNGU7VxWtxDroDzRzN+K7xXD+iPQANqpF3Ux4nYE1CUv1oOhP2iR/hjxdzo45HwzKiAViVW0MguisBWxJSwIOhaHWLx3Kg0sG2kjoUKdgG27zzcwDcmWeCzvSr40d1jOGUbvGoGjyzNI/6E59Fk/UYc+ehL1jW0uELwi/sLW/A4Q1gMyp0ibOhL1oFgC9laNMxOkXmkdO6YtErbC6083ZlFq7uFwIQtuBWJFf1H15jf6WDSz7dzPkfbWRnaT1hi+8i8ptphC26XWzcEVoFkbQ4jqzIqSKnykm0Rc+dJ3bCsvE1AJwDbxO7T4Vmd1Zji6iFeyup1MXjTT8RANP2T0IZliAIbcCheRad46zIkoSuMtgeyh/X4zePj7UZmdYvBbM+mIz3dpyAL3lwywQrtEmTeyZiUCR2lTWwrVLF3W0aAOat74U4MqElNc2ziAgubsmi0kI4BpzeI5Foi57iOg8/7K1rqq63bHqVt5bvQdXghI4xnBBW1DQDyjH8P2Cw/uI8lw5K4+ZRGVw4IJW7Th+B/fTPcGeehX3Sp/iTB7X4fQmtw7D2wUqLXaX11Lh8eNuNAUCft7jFY/l2ezABN7JjDHFSHYaDwVbE7m7n/u5jbj2hAzpZYntJPTv8Kbh6XASA+U8G1gtCc9tYUAsEZ4Qqmh99yToAvCnDfnFcWpSZu8YG27y/szqPon7344/qhOIsC1bV/YEP1xUQUDUc3gDPf/Ujxv3fAWDa8xW2paLiSAg9kbQ4jmwvCQ6UHNM5lijFja5mLwCu7ueHMizhONE1IYzuiWH4VY35uytw92gcyL1npngzFAThDx0+hFvy2Jt2P/tjs0IZlnAMibIYOLlrsCJw+uYiXD0vCVYE5i9BqTkQ4uiEllJcF0xapESYwO9CcZYBEIhID2VYgvCPmPQK5/cPVls8tWg/qyMnoZqi0NXmcE7+QxgkP9ePbI919RMAuDtP/s1NAZIkcdHANG45oQMGnUwgujP1J70gNgUc52JtRrrEWdGA1bk1eNODSQtDfssmLbx+lTk7gq/ZZ/RMxLTnKyTVhy++D4E/+LwYYzUwtkss0Dh/sfE7qiF3PnJ9cfMHLgi/Y0Nj0qJ/WiS6im3IPgeqMYJA7K/b8E3ISiAz3oY3oPHd3jocQ+4FwLRrOgQ8v3n+wloX83aXA5ARY+GiwNdIaLjCMtCQMO/4BOvKR8RajRBSImlxHNlZFkxaZCWEoSvfCkAgLBXNHBPKsITjyIB2kQDk17iadgjI7mokjz2EUQmC0NrtPSxpoavcCTS+f5miQhmWcIyZ1jc4xH3B3krKlCS87U8CwLzt/VCGJbSgQ+2hkiNMKHXBGXCq3oZmjAxhVILwz53fP4URHaLx+FVu+S6X7AHP4kXPeGUjn8V8SKZzI4aCpWiyHsfgu0IdrtDGNLWIOliNL3UEmqxDZ89t0TbAq3NrsLv9xNkMDGkfjXHvLADcWb9fZXHI1MaOAHN3lWO3tMebMhRJUzHt/LRZYxaE3+NXNbYU1gEwIC3y59ZQyUNA+vUyriRJnNn4czwruxh3+lgCtiRkdzXGAz/85jU+Wl+AqsGwjCg+mBDDZGUlANc5r6Z46P8AsGS/hWXjS0f9/gThSImkxXFC1TR2lwUXfbol2pqSFv74XqEMSzjOJIQZASir94DejNq44Cg3iF0sgiD8Nk3T2FN++BDuxtZQvzHPQhD+icwEG31TwgmoGjOzS3D1uhwA4+4vkTx1IY5OaAmHV1o0zbMITwNJCmVYgvCP6RWZJyZlMaBdJE5fgHOXWLjGeys+TWFAw0Iifgi+3rm6X4gqKouEv2h4Y9JiTW4Nfp0NX1KwXZihBVtErToY7N0/ulMsBlcZ+sodaEh4Opz6p4/tmxJBhxgLbr/KDzvLcHdv7Aiw8wsI+Jo1bkH4LbvL6nH6AoSbdHSOt2IoDs6I8f2/1lCHO7lrPFaDQkGtm/UFDbizgh1VzNs//tWx5fUevm+sTLpsUDvid76DgsoGuTdLHOm8VDuEhhEPA2BZ/wKSs+Jo36IgHBGRtDhO5Ne4cHgDGHUyGTFWdBXBpIVPDEwTWlDi4UkLIGAL7mpVGkpCFpMgCK1bSZ2Heo8fnSzRIdby8xDu35lnIQj/xDmN1RbfbivFlTwcf1QnZJ8DY2Ofd+HYdmimRUqEGbn+0DwLMYRbODYYdTLPntGdXsnhePwqi9W+fNXuATRJRvK7UfVWnANuCXWYQhvUIzmcMKMOu9vPztL6prkWLdUiStO0pqTFsIwoDHlLAPDH9z6irhKH71KfmV2CO2M8qjkOxVmGIXdes8UtCL9nQ34tEEyoyaofffF6ALyHDeH+/ywGhdOyEgD4amsJ7qzz0CQFfck6lKpdvzj2042F+AIafVPC6RvlwbTri+D5B94EwPc7yqjOuhRfQl8k1RdsMyUIISCSFseJnaXB1lCZ8TZ0soS+PBsAf5xIWggt51ClRWlj0kK1JQMgNxSFLCZBEFq3Q62hOsRY0CsyusrGpEWsSFoIR98JnWKINOupdHhZlVvTtEvNtPPzEEcmNDenN0C1M7ijNjnChGIPVlqIpIVwLLEYFF6c2oO+KeG0izIzYNzF1I99DlVvwzn4bjRLbKhDFNognSwxOD0SCFY8HJproS9aDT5Xs1//YLWT0noPBkViQFpkU7LkUBxH4rSsBEw6mYNVTraUunE1tpX6rV3qgtDcNhYG22f3bxeJrjwbye9ENUURiOn6h487lHxbtr+SUjUKb8Z4AMw7Pmk6ptblY1Z2cNPopYPbYcl+GyngwZfYn479xpMaacLhDTBvdzmuxhkv5h2fgBo46vcpCH9GJC2OE7sOtYZKsCG5a34eYhrfM5RhCceZQ0mLWpcPty+AGhZ8U1XEkDNBEH7HoSHcXeJt4HejVO8DRHsooXnoFZkJjbvUvtlWijvzTDRZj75iK0rFjhBHJzSnQ62hwow6wkw6lPpDSYu0UIYlCEedzajjzWm9+erygUSY9Xgyz6Lqql24el8R6tCENuzQXIuVB6sJRGcSsCUjBTwYGnvxN6dVB2sA6JcaiUlW0RcsA2iq+DgSNqOOk7vFA/BVdjHurAvQkDAUrmjR2RyC4A+oZBcFkxYD0iIwFB1qDTX0N+dZHK5jrDXY6lSDb7aXNiUdjLu/Aq8DVdN4eVkObr9KZryNEWGlmLZ/BICz/03IsszUXsE1mq+yS/B0mohqjECpL8SQv6SZ7lgQfp9IWhwndjVWWmQl/jyE2x+RgWaMCGVYwnEm3KTDrA++7JQ3eJvaQ4mZFoIg/J49hw/hrt6LpAVQTVGotqQQRyYcq87omQjAypwqygM2PBknA2DeJaotjmWHWkMlR5gAkOuC7aFUUWkhHIOk/z+nRcxtEf6hoY1Ji11lDeypcDQlDIz7v8Xrqie7yM6ivRXUuX3I9UUYDsxBV7YFNO0fX3vlodZQHaLRl21E9tajmqLx/8VW2Gc17lJfuLeSAi0Gb/qJAJg3v/mPYxSEI7WjtB6XTyXCpKNjrLVpCLc3+fdbQx1uau9gN4vZW0twJQ/DH5GB7GtAv/dr/vvTHr7dHpxlcX9WLVGzz0b2OfAl9MObPhaASd0TMSgSu8oa2FHpx931HABMO0TVkdDyRNLiOOBXNXY3LvpkJYT93BpKDOEWWpgkST+3iKpzi/ZQgiD8qV8kLQ5vDSUWWIRmkhFjoXdycJfa9zvKcGedB4Bx79fgb/42F0Jo/DzPwgSa1lSVLNpDCYIg/LlYq4HMeBsAF368ift3Br/nmfZ8RcK73YmfNZHwn67G/M4AYj4aTMRP1xA1cyLRn4zAsuYplJr9f+u6Dq+fLY2tdIa1j2oa/u1tdwLIyl86V9eEMHonh+NXNa7+Ipvc9hcAYN75KdZVjx2VBIsg/JlNjT/P/dIikVUf+tLgPIs/GsJ9uBM7xxJp1lPe4OWub3exLWFq8C+WP0mnPa/TUS7hrcE1jNpwDbLHji+xP/aJHzV9t4q06BnbJQ6AWdnFuHtcBIAhdyFyXeHRvFVB+FMiaXEcyK1y4vGrWA0K7aLN6JqSFmKehdDyEg4bxh1oTFqIQdyCIPyWWqeP8gYvAJ3jrOga2/P440RrKKF5Haq2+HZ7Kd7UEQTCUpE9dorXf4XLF+zpK9cXoVTuDGWYwlFUdFilheSpRfYGq5QDYaI9lCAIwpG49YQO9E0Jx6BIfOfuySf+sRRqsegklV7yQU5V1pMsVeHXZHaq6bgxotTlYd34ElGfj8WQu/AvX3NDfi1+VSMlwkS7KDOGvEXAX2sNdbjHJnYjI9pCeYOXacvCyOl5FwCWza9jW3KP6OsvNLtDQ7gHpEWgK9uC5HejmmMIRHc5oscbdDKXDQ5+dlmRU82lWzMp0OKIVGu4Tf8VCw13MG7rTUh+F960E6g9/XM0U+QvznFoNsbc3RXUmNJwJQ9HQmPNNy9R1eBBX7yWsAW3Ytr+yf+/vCAcVbpQByD8c35Vw+0LYDMG/3NKnjo0g62p393OsuCXrq4JNmRJQlfR2B5KJC2EEDg8aaGmH2oPVQKa+qc9GgVBOL4cqrJIizRhM+rEEG6hxZyUGceziw9QWOtmU1E9vTOmkrT1JZzrP+KZkl683mEt1jVPgOqn9szZ+BP6hjpk4R86vD2Ucqg1lDkO9OZQhiUIgtBmDGgXyYB2ffD6VXaXN7C9pAvzjDoGRjlp59iG4ighz9CZbyqT+HRrDarbwVNZBZzs+QlD0Wpsi++m5ryFv1pA/SOH5lkMz4hGcZSiq9qFhoS33ei/dQ8JYUbemtabm2dtY1dZA5OyB/Blv4fplv0w5p2fIXnrqR/3yl+u4hCEI+H1q2QX1wHBSgvDwRnB/z956F+qMj+/fyr9UyOZu7uceXsqOKX+CSYZNnFHYjaxFauRtADujhOpH/ciKMZfPb5XcjidYq3sr3Rwz7c7SS0bzAvySgbbv8f55VYincHvZMZ93+DpcgaaIewo3L0g/JpIWrRhLl+A2dtK+XRDITVOL6+OUBlV+RmGAz/gSxmGfcIHoDezs3GeRbeEMCRHOUpDCRoSPrHoI4RAYliwV3RZvQfVmoImyUiqD9lZgWpNCHF0giC0Joe3hkINoGvc1e6PE+9fQvMy6xXGd43j662lPLv4AP7aLiyUJYYqO9EX3YqtZG/TsZaNr1B32rshjFY4Gg5vDyUfag0VIVpDCYIg/FUGnUyv5HB6JYc3/X9eOgAQD1wFJMWW8vBPe7l9Txc+PX8K/eZPQVd7ANvK/1I/9rkjuo6maT/Ps8iIxpA/FwB/Qh80c/Tfjj/Soue1s3tx5zc72Fhg56pdPfl+3GtELrgJ0/7v8KaPxdP1rL99fkH4PYv2VeLxq8TbDHSMNmH8aRYAvr+RhMtMsJGZYOPGURnsLK0nxnoChJuoclaiq9mLL2nw7ybfJEliau8knlq4nw0FdrbQh3+ZooiTasC5HU02oOmMyN56DHlL8HSe9E9uWxB+l9jW3Aapmsb7a/OZ9NZanlt8gKiGfbwvP8q4tRdgPDAHCQ1D0UoifrwSAh52lQUXfbol2NA3VlkEojqDwRrK2xCOU00zLeo9IOtQLfGAGMYtCMKv7a90ANA5zoZcl4/kd6EpRgIRGSGOTDgenNEzWBq/r8LBQV80mw39ARgg78UjGXEMuAUNCePBuShVu0MZqvAPaZpGkT04ryRYadGYtBCtoQRBEJrFhKwEhmVE4Qto/HdBHvYxz6AhYdo9o6nF05/JqXJSVu/BoEj0T4v4x62hDmcz6nh+Sg8iTDqK7G7maYNxDLoDAMuWN8V8C6FZzMoOrolM7pmEoWgVOvtBVL0Nd6e/nxSQJYkeSeEkhQc3j2qW2OB8jD+pFpqQlUD/tAgGpEXw1OTeaKP+zUEtkdf8p5N9+mLc3YMzXwy58/52bILwZ0TSog36KruE11bkYnf76RAhMTPsOYYrO/BpCrPVkWT3ewJNZ8FQsBTbT9dxsKIWgKzEsJ/nWSSI1lBCaBzeHgpADTvUIurYTlo4vH5W51azMif4z7bsNfj3zceQuxBD7kJ0FdvEh19B+H/ya4KLiO2jzehqcwAIRHYQJflCi8hKsDGoXSQ6WeKmkRlknHInqqRji9qBU92Ps6PTDXg7ngaAZdOrIY5W+CdqXT5cPhWApPCf20OJIdyCIAjNQ5Ik7h/XBatBYXtJPR8WJeLqfSUAtsV3I3nq/vQcqxqrLPqlRWKSVfSFKwDwpv/zpAUEqy7P7BOcwfjxhkJcWReg6SzoqnahL1xOXrUTXyD43oHfhWzPPSrXFY5P+ysdbC6qQ5GCs9XMOz4GwNP1zJBsOLYYFN44pzevn9ObkR1jkHpO497E93jKfy6LimU87ccDBJOFAV+LxyccH0TSoo1xegO8szoPgKuHpjO773Zs3nL8tmTuTHiPW73Xcd76DDYOehlNMWLOnceT8mvEm1RSIkxNSQtfXK9Q3oZwHEsIb0xa1HnQNO3nYdz1x27SQtU0bv96Bzd/tZ1bv97OzG8+Z/Tys0madxkRcy4hYs4lRM04FeuKh4KzPQRBQNM08mqcALSLtqA0Ji38kR1DGZZwHJEkieen9GDJTcO5eFAagXYjqL5yGy+kvUaOlsSH6wtw9r8RCPb0FYsVbdeh1lBxNgNGnYxSH6y0UMNFpYUgCEJzSQgzcusJwbZRb67KY1unG/BHtEdxlGJb/sAfbuhamVPNJxsKARiTZiBs4W3I3npUc8xRnd15Tp9k9IrE9pJ6sqtlXFnnAlC/7EXOen8DV32Rjd9ZQ9T0k4n+dBSGnJ+O2rWF48us7BIARnWKJVGuwZATbHfm6n5RKMP6hVEdYwBYfqAKf2J/VFM0sseOvmRdiCMTjlUiadHGfLaxkGqnj9RIE5f1CcO2+RUAnIPv4q4pYxjQLhKnL8DZi8y8Gf8AARROV1bzvXwHxpwf0ZeLIdxCaCU2Vlo4fQEaPAHUxqTFsVxpMXNLMZsK7Rh1Mv3iZZ41voMsaeSqCWynI3WRWQBYtr5L2KI7QPWHOGJBCL1qp48GTwAJSI0wodQeABorLQShhRh0Mkbdzx+XNUMYlw5JB+CnnWXkGzrjbTcaSVOxbHo9VGEK/1DRYfMsAGR7Y3soUWkhCILQrM7omciQ9Cg8fpVbvztA0bAngm2i9szEvPmNXx3v9gV4YsE+bv16O9VOH5Mjc7h850WY9s1Gk2Qcg+8C6egtc8VYDZyWFZy7+MmGQly9r0RDJq12LV2lfHaW2rHPvBZdbQ6SphK26E7khpKjdn3h+OD0BvhhZxkAZ/ZKwrTzcyQtgC9pEIGYriGO7meHkhbZxXXUulW87U8CwJA7P5RhCccwkbRoQ2qc3qbdBNcNb09E9hvIHjv+6Ew8XaZi1Mk8e0Z3JmTFowFPHMzgCu/tlGjRxKtlRPx0NbKrEk3W4Y/tFtqbEY5bJr1ChEkHNA7jPlRpcYwmLQprXbyy/CAAN4/qwKdpc0jQKvCFpXFn9KtMdD/C4KoH2NzncTRJwbT7S8J/ugb87hBHLgihdag1VFK4EZNeaaq0CESJpIUQWt0TwxicHklACy5gOPrfDIBp95dioaKNOpS0SI4wgaai1BcBImkhCILQ3CRJ4pEJXWkXZaakzsP1q63UDnsQANvqxzAcmNN07NrcGi74eBNfZZcQjoNPk7/iefd/0DuKCYSnUzv1a9zdLzzqMV7QPxWApfur2FwfwQJpCAC3W+dxnfItXetXEpAN+KM6IXtqCVt4m6ieF/6SubvLcXgDpEWaGJhmw7TjUwBcPVpPlQVAYriJLnFWVA2W51ThyQi2iDIenCdaXQvNQiQt2pD31hbg8AboGm9jfIofc/a7ADiG3NvU39tiUHjo1K68e14fuiXYWKL25UTPM+zscBWaEtzh7o/uCjpzyO5DEA6faxEIa6y0aFwgOJaomsZj8/bi8qn0S43gvNicpt6UjhOf4blzBjIkPQqXT+Wc9RnMaP8YmmLEeHAukbOmoivdhNMb4GCVE7+qgaYh23ORnJUhvjNBaH551T+3hgJ+rrSIEEkLIfQuGxxczP5mWwnlkX3wJg1GUr2Ys98JcWTC37GrrAEIVlooVbuRVC+arEe1JYU4MkEQhGNfpFnfNPR6R2k9txcMw9nzUgDC599M9f7V3Dl7Bzd+tY3CGgdXW5ayPvxuhld/hYSGK+s8qqfNw5/Yv1niy4ixMKJDNBpw/Zdbedl9KgDjAsu4Uz8TgMe5gsIT30DTmTEUrsC8+c1miUU49miaxleNraGm9ErClLcIxVGKaorG0zg7rTU5oVOw2mLZgSq8aaPQFCNKXT5K9Z4QRyYci0TSoo0osruYuSW4E/3GURnYNjyPFPDgSxrUVJJ1uF7J4XxwQV8ePjWTaYM6EzX+P1Sfvxhn76txjHy4pcMXhF/4OWnhPqw91LG3O/XrrSVsKAi2hXpgTAoRS+4Ggn0pfanDMesVnp3cnXGZcfgCGvfsasd12n14dTb0FVuJ+up0Nr15EY99+AUzXr0D95vDiflkBLHv9yHqk5HYFt6Bcc8ssatBOCYdqrRIjzKD14HiCJZMi/ZQQmvQLzWC7olheAMa32wrxdXvegBMu74AvyvE0Ql/RZHdxdL9wc0AYzrHYmlMPHkzxoGsC2VogiAIx412UWaePqM7ekVi8b5KJuybyBplAFLAQ/JPF3NPwdX8YLiPjWF3cL/6JkZvDf6oztSe/hkNY55u9kHFFw4IVlt4Axr7dJ2pixuIpPmRUfleOYl3nSN5aG2A+hHBtRbr2ifRlW1p1piEY8PO0nr2lDdgUCQm9UjEvOMjANzdpkHjxuPW5FCLqDW5Nbgx4k0bCTRWWwjCUSaSFm3Ee2vy8asag9pFMjTSjmnXdAAaht4PkvSbj5ElidOyErhhZAY6RUYNb4djxAP4kge3ZOiC8CuHkhal9R4CthQAZGc5BLyhDOuoqXX5eHt1Hi8syQE0/tezgu4rrkapyydgS8Ex7F9Nxxp0Mo9N6MqTk7qRFG7kJ2cXhjc8xZf+UQBMkZfxjfEBbpFnkBbIx6sFq6p09oOYd08nfMHNGPfN/lUMr684yOR31rE2rwalNoeoT08gbMGtou2U0GbkNSYt2kVZ0NmDraFUcwyaKTKEUQlCkCRJnNUnuAt/9tYSXKknEAhLQ/bYMe77LsTRCX/F5xuLUDUYkh5FpqUB496vAXD2uSbEkQmCIBxf+qZG8MDJmUjAwVovlzuuZ5vanjDJRZacR5acR5SvDNUQTsOIh6iZNg9f2qgWia1fagS9ksORgP+cnIk65BYAfPG9iTz9GXSyxJL9VVy5ozv2dicjqX4ivpmGbsfnzNleymcbCwmoGkrNfizrnkOp3NkicQut34frgy3gT8qMI7ZhD4b8pQC4mqHV2dGQGW8j3mbA7VfZUFCLt/04AAwiaSE0A7F9qI1Yk1sDwKWD0zAUzkHSVLzJg/EnDQhxZILw1x3eHkozt0dTjEgBD7KjFLUN94+udHj5aF0Bs7eV4PIFOFnewF227+m0ax8Ammyg/sRn0Qy2XzxOkiRO7BLHsIxoPtlQyCcbFJ4y3ExF2vlcYH+D8Nrt2OMGszl8LPfv7YDD7eWN4V761/6A8cAPmLPfxdNlStP5NhXW8t7aAgBu+3obK+KfR1d7AF3tAeSGEupOe/dXMQhCa5NfE2wPlR5l/nmeRWTHUIYkCL9wUpc4nl+SQ3GdhzX5dZzU/QJsa57AvP0jPN3OCXV4whGoc/v4dnspENxFa976JpLqw5c0sNnajAiCIAi/75Ru8fRICqOoNrjRKl+dherYSrsIPZIkARL+uJ4tvolFkiRenNqDKoeX9GgLPkZTfe5CAuHtyNSbeeCULjw2bx/rCuycZpzGV5EVJNo3EbXkLpICA1gQGMcZ+1aTULUUCQ3TnplUn78UFH2L3ofQumwsqGXxvkpkCS4akIp1xSUAuDufgRqRHuLofpskSYzqGMPM7BKW7q9i1PBg5xd9+Zbgeo41McQRCscSkbRoA6ocXsobvEhAj6RwdDnbAfAnioSF0DYlhpuAYNICSUK1JqLU5aE0FLfZpMWivRU8Pn8fdrefKOp42/YBI/xrwA+azoS727k4+1yDGp72u+cw6RWuHJrOFUPaoRGslvIykUpNBUmmBzDWmMMnGwp5qTCKVyb8D0PuQvTlW9CVbsKf2A9fQOWJBfsBiLboGeleTELNevyyEVnRYShaScQ307BP+gTNFIUvoFLt9BFnMyADkqcWJBnNGNEST5sg/Ca/qlHY+GU1PdqMsjs4z8IvWkMJrYhJrzCxewKfbSxiZnYxI04+F+u6Z4OvyRXb8Mf1DHWIwp+YlV2Cy6fSOc7K4GQ9pgXBuVOiykIQBCF0UiPNpEYePoPzJPwhi+ZnNqMOm/HnJbRATGbTv5/aLYFuCWE8+OMedpbCsLLbuUqZwx26GZysbOBkZQNUBY89NAPAtGcm7qzzfvd6voCKx682XVNy1wS/o0miYcqxIKBqPLc4+B1naq8ksuqWYihei6YYcQy9P8TR/bETOgWTFov2VXLDyAzCE/qiL9uMaefnOAfeFurwhGOIeLVrA/aUB4cDto+2YNYr6Cq2AeATX4aFNqqpPVSdB4BAWGOLqPrikMX0dzV4/Dz00x7u+W4XdrefC6J2szri34zwr0GT9Tj630TVxWtpGPXoHyYsDidJEvLhbd8O+2B6dp9kZAnW5NWQ4zTj6XwGAOat7wHBNhcHq5xEmvXMOLczj5o/B+B5zxncZXmUOikcfXk26ntjyHv1ZPJePQXHBxPwvD6E6De7EPtuT2Le7YVl7dMQ8B2lZ0kQ/ppiuxu/qmHUycSHGX8ewi2SFkIrM7VXsEXUypxqiv22poGJpu0fhzIs4Qj4AirTNwc/d1zQPxXzrunIHjv+iPZNrQ4EQRAE4Ui1j7bw7rm9uXJIOyRJ5qfwc/i+/4d4ozLxoWOG/wSm95uOY8i9AFg2vPi77ZG9fpVrpmdz2ptr2FVWj+HAHGLe60PY/JvFPMNjxHfbS9lb4cBmVLh2UBK2lY8C4Ox7LWrj+khrNaBdFB1iLNS5/by6/CCuXlcAYNn4CrI9L8TRCccSkbRoAw4lLbrEWyHgQ1e1GwB/XI9QhiUIf9uhpEV5gwdV0w4bxt22khYOr59LPt3MnB1lyBK80nEDj7n+i8lTGRwMd9Z3OIfcg2aOOWrXTI4wNQ2/mrG5CFevywAwHvieitI83l4d/JBw86gMUrY+T1ighgpDO94OTOCrsgSmuv9NiRZNvFbJAHYwVNnJYHk37bQilEBwhoCkBbBueJHIWZNRag4ctdgF4Ugdag3VLsqMLEkotQcB0R5KaH3Soy0MbBeJRnC2hbvHRQCY9n6N5KkLbXDCH5q7u5xKh5c4m4HxXaIwb30XAFefq0FWQhydIAiC0BbpFJlrhrdn4Y3DmHHZAEYOHY39vPm8M2g+d/uv4bltOuxdLyBgiUepL8S0e8Zvnuf1lblsK6nH5VN5+btlhC26C0kLYNo3u2m+qdB2NXj8vL4yF4CrhqaTuO8DlPoCAtZEnP1uCG1wR0AnS9x7UmcAZm8rZb11DN7UEUgBD9Zl/+GnnWXMyi5mf6UDqXo/5i1vY8hbhOSxhzhyoa0R7aHagN1lwaRF14QwlJp9SAEPqiEcNbx19rgThD8TbzMgAb6ARo3Th60xaaG0saTF8gPV5Ne4iLboeXJSFmOWBMs4XVkX0DDyIdCZ//gEf9O0viks2V/FnJ1lXD9iCLakQehL1rF33mu4/RPomxLO5NgSTEs/AkB3ylO8JvcMtuOiKyt8A0mp3UCUWSbCFOyj+tEOFz8W6SnVorkydie3et5AX55N1IyTcQy4FXePi9GM4c1yP4Lw/+VVBxNo6VFm0DRRaSG0amf2TmJ9fi2zt5Vy5ZBB2KK6oKvZi3HvLNw9Lw11eMJv0DSNTzYEB1+e2zcFa948lLp8VFMU7syzQxydIAiC0NZZDYcttUkyE/t04L1NVZTVe/h2dy0X9bsB24oHsWx4CXfXs0ExNh2+Nrem6T0q0iRzp+t5ZLkO1RiB7LFjW/4AvuRB4nNxG/b+2nyqnT7aRZmZ1lnB8sXLADiG3gd6S4ijOzJ9UyOY2D2B73eU8cTC/fSc8ChxM8Zjyl/Eiv0fM1cdxKnyWp7Vv4FFCnbY0JAIxGTi7H0Vnm7TQnwHQlsgKi3agN2NlRZd423oKhrnWcR1h8PbxwhCG6JTZGJtBiA410INa5uVFmtyqwGYkJVAv7BadPZcNFmHY/i/my1hAdA/LYKOsRZcPpXvdpSSm3E+AMPrviNM9vB82koiv7sACQ13lyn400bQOyWC8V3jGd81ntE9O9N55HnEDpiGvsdU9D2mcvk553PRuJHIehOvVvZhZP1jbNH1RvK7sa15guiPBmNd9Riyo6zZ7ut44A+oP/9BDYCm/v7Bx7H8mmDSol2UGdlZjuxzoEkygVY6kE44vp3QMYZYq4Fqp48lB6px9bgQAPP2j0ULh1ZqTV4NByqdWPQKU3omYtn8BgCuHpeAvvnevwVBEITjk0mvcOmgYKvg99fmU5t5HgFrAkpD8S8qJ2qdPh76aQ8Q3BQxI3Mlg+XdNGgmfuz/Id6UYUh+J2Hzb/rd1lJC6zZvdzmfbiwC4K5hscQsuB7Z58AX3wdPlykhju6vuXlUBuEmHfsqHDy/VeIjOdi6+iH9RzwbNZvXDS9ikTzsUNOpMaYioaGr2o1t+YOgtoZJNUJrJ5IWrVyd20exPTiMNDPe1jTPwh8r5lkIbVvTXIt6T1N7KKW+6HeP1zSNvGon324v5X/z9/FDdg76guVY1j2HbfHdSI7yFon78HjW5NUCMKR9FIaCpQD4EvujGcKa9dqSJHFO32Cfy7dX5zFxcTQlWjRxUh3rLLfRfsv/kL11+OJ60TD8wSM+5xk9k/jikgFM6ZVIlRLLlIa7uN17LUX6dGRvPZbNrxP9yUh0ZVua8e6OXT/sLGPESyu54vMtrNxTQOTMicS80x3r6sdFMuj/yWtsD5UebWmqslDD0kAxhDIsQfhNOkXmjJ6JAHy2sRB3lzPRdGZ01XvQFywLcXTCb/m0cQfrGT0TiarZjL58C5pixCUqYwRBEIRmckbPJOJtBsobvHy1oxpnvxsBsGx4CXxONE3jkXl7qXR4yYi2cHfXWjrvfQ2A//gu475VXnKGPIVqjEBfno113bOhvB3hb5i1tYR/z9lNQNU4t4ueU7KvRV+yHtUQTv3oJ9vckPUoi4EbR2YA8PmmIh5vmEAhCSRJ1ZzpCrY+WxM3jdO9j3JD9NtUXroJ1RCO7GtAV7kjlKELbUTb+o04Dh2aZ5ESYSLMpENfeajSQsyzENq2xMakRVm9h8BhMy1UTWPxvkqunZHN5HfWcdXbP7DgjetwvjaIuE9HcNLi07ht9zlcsHw0kd+eh3X9c5h3foZt5cMtGv++CgdVDi8mnUyflAgM+Y1Ji7TRLXL9U7vFE27S0eAJ4NF0rIg4HQCzvxbVFE396CepPes7NEvsXzpvcoSJ+8d14dsrB3HRwHS+5QRG1D/Gj92exRfXs3Fnz43gdTTHbR2zdpfV89i8vQRUja3FdnRzb0NfsS2YDNr0GtEfDcW2+G70BcuQvA2hDjfkDq+0UGpzAPBHiXkWQut1dp9kjDqZ7SX1rCwJ4Op+AUBwQUFUW7Qqe8sbWJtXiyLBuf1SsGx5EwB35pl/+T1TEARBEI6UUSdz2eB2ALy87CCLLacQsCWhOEoJ/+EKXlm8i2UHqtArEs+NkImdfy2SFsDZ6Qx2x55CndvP5d+Xs6138HuvZdOrWJc/CH5XKG9LOEIfrivgf/P3oQFXZkk8ar8bfeUOVHMctVNmEojrHuoQ/5YzeibSKznYRrpbSizaSf8DQJMN1I19Hv8JDxNAIbuoDp85Dl/yIAD0RWtCFrPQdoikRSv38zwLG6gBdBXBbKQ/TlRaCG1b/KGkRZ0HNSxYNSB77Fz6/nLu/nYnNYW7uMXxIjM813Fe4DvaU0K6XE66XE6aXIFOUqlU4vF0nICGhGnfN02VSL+l1unj7dV5LNpXCZqGIXch1tWPI7lr/1b8a3JrAOifFolBCqAvXAmAt90Jf+t8f5VZr3DP2E6M7BDNK2f2ZMw5d+PufAbOvtdRfeFy3N0v+EeDRGNtRm4alcGNIzPQkLl1WwpbR75HwJaMzp6LfvG/eGFJDnN3laOpKsZ932Bd+Uiba/HVEuwuH/d8uxNvQGNYRhSvZ6xmorIGr6bwiO9CNqhdkFQv5p2fEfnt+cS8k0Xk9FOwrHvuuCz7dnj9VDQE7zs9yoJSE0xaiL69QmsWYzVwVu9gAv6tVXk4+16HpjOhL9uEPn9JaIMTfuHTjcEqi7Fd4kjTijEcnAc0DuAWBEEQhGY0pVcS4zLj8Ksad805wPo+T6PqLBgLlzNy+33o8PPUADe9l16E4izDH52Jc/TjPD4pi6RwI8V2N1OWx7M8PtiK0rL1XaJmTECpELvWW7O3VuXyyvKDANzaV8d95Xegsx8kEJZK7dSvCMRmhTjCv0+WJF6c2oPnJnfntbN7YcocT83Ur6k5bwGermfTKc6Kzajg8AbYV9GAL2kwAPpikbQQ/pwYxN3KHaq0yIy3odgPIvmdaDqzWLwR2ryEpkoLNzn1CmYsWHESqC3gNuNmbpS+RCEAQE3MAEq6XERcfDt0OpmSOjdXf19OoRbD2z16M1IxYNr7NdbV/8N++me/uI5f1ZiVXcybq/Koc/vpLBdzavo3hJcsB0ByVdNw4jN/Of7VecGkxZD2UejLNiL7GlDNMS1aBXVoRgWABtSPf/WoX+O8/imsya1hTV4N984v5rMxLxD73TQi98+k1pvMM2p3MpZ/yHDfKgDM2z/C2f8mnH2uBp3pqMfT1qiaxn9+2E1xnYeUCBPP9K0j6cfXAVje/lY2OU7kk9KJ9PTt5DzdIgbJe0ijAn3l9uA/hSupO+XN42r376Eqi2iLnjCT7rAh3KLSQmjdLh6UylfZxewsrWdZqY5TelyCZcubWNc9Q2270WIWWStQVu9h7u4KAC4YkIp5y+NIaHjajyMQ1SnE0QmCIAjHOkWW+O+pmbh9AZbnVHPZUh3nxvyHe2oeYJyykaWxr5C8YzuS34kvoR/2iR+iGSNINcJnF/fnmUX7mbOznIvyT+NMW3v+o75GZM1eIr6cgDdxEFJEKgFbMr6UofhSh4f6dgXgo3UFvL06H4D7h1i5IudmlIZi/FGdsJ/+WVOr7LbMZtQxsmNM05/9SQOb/l2RJfqkRLAip5pNBXZ6pg0BQF+yLjjj8R9stBSOfaLSopU7vNLi53kWWeIXW2jzDrWH2lBg56JPNlGoRgMwM+w5bpG+QCGAp90Yas78Bv+5s4nrdyakDsSf2J+4LsMZ2DNYPvny8oM0DLoTTdZjKFiGvmBF0zX2Vzi48OONPL3oAPVuL/fop/OD/h7CS5ajyXoATLtnoFTv/Uuxu3wBsovsQGPSorE1lDd1ZJvrQ/lnZEniwVO6EGnWs6/CwTmLDLzqD7aietLwDvOM9zLctwqfplBk6IDkd2Fd+xTRn58odk8A767OZ3VuDUadzPPjYklcfCOSFsDdZSq9JtzKW9N6s/jGYVx/7rl8l/4fRnpeZLD7FWYm3Y1qCMNQspaIGadRdmADqqaB341Sm4Pkrgn1rTWb/OqfW0MBTe2hRLJeaO2iLQbO6Rv84vn26jwcfa5F05nRl2djyFsU4ugEgBmbiwioGv1SI+ge4cW0O9hvWVRZCIIgCC1Fp8j8b1IWg9pF4vKpvF+azo2B21AlHSk165D8Trxpo6g9/XM0U1TT42xGHQ+d2pUnJnUjwqTjq4Ysxjgf56fAQBTNj7lkFabdM7BueIHIb6ZhyPkxhHcpAHy5pZiXGyss7hwcweV5d6DU5REIT8d+xhfHRMLiSPRLjQBgU6Edf1xPVL0V2WNHqd4T4siE1u7YWl07xji8/qYdp8Eh3GKehXDsSAgP7sKvdfnw+FWcpuAQ03BvGareRt3Y56mb+BH+xP6/+firhqZj1MlsLa5jSYUNV/dgiax19eOgqQRUjX/N2cWBSicRJh1v9crlOuUb9FKA+YF+bBj/HZ6Mk5E0FeuaJ/9S7JsK7PgCGknhRtKjzE3zLLztRv/NZ6N1i7UZeeDkLgAcrHLyov9MCs3dsOEkXqqhRN+OKd6HGV73CNNT/k3AmohSl0/4D1cg1x+/7aIqGzy8tza4q+a+kzrTM+89ZFcF/piujYPWgruu9YpMz+Rwnjw9i6uHplNGNHce7MNZvkc4qCWhdxTT6cezsb7Vi7g3OxH96SiiPxmJ5KoO5e01m6Yh3FEWCHhR6oLPoUhaCG3BRQPSMOtldpU1sLREbhrsbBGzLULO4fUza2sJABcOSMW8/SOkgAdffG98yUNCHJ0gCIJwPDHqZJ6Z3J0h6VFEmfVMnXIR9Se/imoIw915MvYJ74PB+puPHdsljq8uH8gzZ3TnyhP7sar3M5zH49zqvZ6lyVc3fScNW3L/Mb3RqbX7fkcpTy3cD8D1AyK5puhudDX7CdiSqT1jOqo1McQRtpxDSYstRXZUScGfNAAAQ9HqUIYltAEiadGK7St3oAHxNgPRFsNhlRZinoXQ9qVHmTHpZIw6mbtO7EiXHsHeht6kwdScOx9P17P/sJVGnM3Ief2CszBeW3GQhn43o+qt6Cu2Ytw/hx93lZFT5STMqGPGxb04sfRtAH6IOJ+rfHfyzGYVx5B70CQZ48G56Eo3HnHsq3ODi8VD2kchu6ubfje9aaP+1nPRFozsGMONIzPokxLOq9P6Yz7zHbzJQ3D2vhrdZQu5cOIEQOKeA1nMHTEbX3wfZI+dsAU3B8s+j0OztpbgVzV6JYczsZMZ4+4vAWgY/hDozb86XpYkrhqWzpOTumHWy2xyxXOG578sCfTGJPmw+X/+0iF7ajHt/OxX5zgW/GIId10BkhZA01mOqw/2QtsVadFzTt/ge9Nbq3Jp6HMtms6CvmJr0+wEITS+3V5GgydA+2gzw9tZMG/7AABXn2tE6y5BEAShxZn1Ci+f1ZMfrh3CwHZReDtOoOqKbdSPfwUU4x8+NsKs54ROMZzTN5mbTujIKWNOZrY6gityx7Bp0Ev4ozojuyqwLX+whe5GONzGgloenRvs5nB+30Rurf1f09Bt+xlfoIanhjjClpWZEIZZL1Pn9rO/woE3eSgg5loIf04kLVqx3eWHWkOFgaY1VVr4xBBu4RhgM+r4/JL+fH3FQM7pm4Jz0B3UnD0H++QZqOFpR3SOiwemEW7SkVPl5OOd7uDCA2BZ9RhfrNgKwGWD00jO/QqlLg/VHEvqKXejkyXW5tWyqj4Od9ezAbCuevyId8EeGsI9pH00hoJlSGj4Y7LQrPF/9WloUy4ZlMbb5/ahb2oEakQ69ikzcYx4APRmxnaJ48zeSQD8Z0EhhSOfQ9VbMRSvwbz59RBH3vK8fpWvsoM7eqf1Tca0ewayz4E/qsuf9pc9sUscs68cxJvTevHRlaPpdO1srjY8yWmex3l/8Hzqxj4PgHn7hxDwNfu9tLS8xvZQ6dHmpnkW/sgOYlFRaDMuHJCK1aCwt8LBKxvqcPW6HICwJfci1xWGOLrjk6YF51sBnNcvBXPuPGRXFQFbEp6Op4U4OkEQBOF4ppMP+4wr/72xs6d2i2dEh2j8qsbD83OpHfMsmiRj2jsLQ+6CoxSpcCTK6z3c//0uAhqc0i2ef1m+xlC4HE1npvb0T4/L6nGdLNE7OVhtsbnQji/50DDutaISWfhDImnRijUlLeJtyHX5yN46NNlAILpziCMThKMjNdJMnK1xF4liwB/f+y/Nawkz6bhhRHsAXll+kLlhZxIIT0fXUMjj3v+RaoVzukdgXf8CAI6Bt5IUG9u0uP7q8oM0DLgdTTFiKFl7RD3HS+rc5NW4UCQYmBZ5WGuoY7fK4kjdekIHMqItVDm8PLDaS8PI/wJgXfcMurItoQ2uhS3YW0G100eczcCJnaIxHdrR2+vyI1p8j7YY6JcaSUqEGaNBT8/+o9mpteejbQ7cnSahmmNRGkowHJzbzHfSsjRNO6zSwvLzPIsoMYRbaDsizXruOSk41Pmj9QVMN56FPyYL2VVBxJxLkDx1IY7w+LO1uI7cahdmvczJ3eIx7fwcAHe3c//2ApEgCIIgtBaSJHHfSZ2xGRV2lTXwQUEcrt5XAWBbcg+Sxx7iCI8PvoDKvd/totrpo3OclUc652Ld+DIA9WOeJhCbFeIIQ6df2mFzLeJ7o+lMyO7qvzxfVDi+iKRFK7ancQh3l/jDhnDHdAXFEMqwBKFVmdIribN6J6EB980rYHnfl6jDSn95Hx9Gv0/UjneRXRUEwtNxZ50PwOVD2mHRBz/Q/VCow9XrMgCsqx6DgPcPr7e6scqie1I4YUYFfcEyALxpo5vtHtsKk17h0Qld0SsSy3Oq+cwzAnenSUiqn7D5N4LXEeoQW8yMzcEdvWf1TsZSuBSdPRfVGIE7c+rfOt/E7glY9AoHq52sK3Lh6n4BAJat7x21mFuD7SX1OH0BdLJEaqQJQ8FyAAKRImkhtC2ndkvg6qHpADy6pJglvV8kYE1AV72H8LnXHpNVUq3Z7G2lAIzLjCPMVYShcAUaEu6u00IcmSAIgiAcHfFhRm4fHfzM/NaqXBYlXoE/sgOKo4yw+TeB3xXiCI99LyzJYVtJHTajwoujLcQsuQ0AZ6/L8XSZHNrgQuzQXIvNhXY0WY8vITi7VLSIEv6ISFq0Um5fgINVwQW+rgk29GIItyD8JkmSuOPETgzLiMLjV7lsroNrvLfiR6FjxXws654DwDHk7qaEX7TFwKWDgy2onlt8gMKuV6OaY9DV7MWy4aU/vN7CPRVAcJ6FvmQdirMcTWfGlzywGe+y7egSb+PGkRkAvLw8l8JB/yVgS0Znz8Wy+bUQR9cytpfUsaO0Hr0iMblXIuZtwcSCu9u5oLf8rXPajDom9UgAYPqmItw9LkKTdehL1jW1DgRYm1fDipwqACSPHdO2D9rUwO63VucBwVJqc8VmDAVL0WQd7swzQxyZIPx1Vw5tx6nd4glocMuCanYMex1NZ8FQsAzbsn+JcvgW0uDxs6DxvfuMnkmYdk0HwNdu1HHXU1oQBEE4tk3snsCYzrF4Axq3fnuARZ0fRFOMGPMWEfHdRUje+lCH2CrUuX3M2FxMWb0HAH3+Egw5P/7is1lA1Vi0t4Krv9jCrbO246jIw7riIaI+G41x7+ym4/wBlZUHq/n3nF3M2FJMMpV8nrmazCWXIXvr8SUNxDHs3y19i61OVmIYRp1MjcvHwWonvpQhQGOLKEH4HSJp0UrtLKsnoEGUWU+8zYCudAMAfjHPQhB+RSdLPD6xG53jrGjAarU7m7o/AICEhi+2B55Ok37xmIsGpNIlzord7eeJFZU0jHwUAMumV1AqdvzmdTbk17IuvxZFlji1axzWNU8C4O4y+U+HpR1PpvVNITPehsMb4LUNtTSMfBgAy+Y3jot+7l9sKgJgfNd44jz5GPKXoiHh6nnJPzrv2X2SAViRU02+LwJPxwkAmLa+D8DS/ZXcNHMbt3+9g8LKWiK+v5iwZf/GtuqRf3TdlrK1uI41uTUoElwxpB3Wdc8C4M48CzUiPcTRCcJfJ0kS/x7fhb6pETi8Ae5eq6N2/Ktokox552eix3QLmbe7HLdfJSPGQs8EM6bdwaSFq9t5IY5MEARBEI4uSZJ4bEJXxmXG4Vc1rl2h5/vuL6MawjAUryFi9jQkV1Wowwy5TzYU8vSi/Zz34UYWbcshYs7lRPx4FeE/XIGnroyvt5ZwzgcbuOe7XbiLtzG18DFSZ4zCkv0Oupr9hM+/ETa+w/NLDnDqm2u5ddZ2PHvnM8PwMKtMN9Nz7wso9YUErInUnfy66JYC6BWZnsnhwKG5FoeSFmvERh7hd4mkRSukaRpvrAzuNh3WIRpD4UoMxWvQJAVv2sgQRycIrZPVoOO5yd3pGGthbJdY2p9wOQ1D7kU1x9Ew6hGQfvlyp1NkHjglE0WWWLSvkjmBwXg6nIqk+vF8eyNXfbqeXWX16AtXEjH7bKzLH+TVZcGhwFN7JdHBvgp9yTo0xYhz4O2huOVWS5Elbh8THDA2e1sJO2wj8KYMRQp4sK5+PMTRNa/CWhcL9lYCMK1PIubNbwDgzRiPGt7uH507PdrCsIwoNODLLcVNw31N+2azLy+Pf8/ZTfDjnopx7i3oSzcCYDjwA/hafzn4W6tyAZjYPZH2zm0YCpahyTqcA24JbWCC8A8YdDJPTOqG1aCwu7yBWY6euPpcA4B15X//tCWh8M8dag11Ro9EjAVLURxlqKZovBnjQxyZIAiCIBx9ekXmkdO6MrVXEqoGN62z8U768wRM0egrthI5aypK5c5fPKbW5eOhn/Zw08xtlNd7MOz/nsgvJ2Da9mGI7qJ57SgJVpzUe/zMnL8ASQ1+HjPmzsP00YnsWPg+E+s+Z77pHn403seZynJ0BNgg9aQyYwoAcWseImbLy0S58/nY9DQfGJ5ikLwHDQlvylDqRz9BzXkLUa2JIbvP1qZfSuNciwI7voQ+aLIBxVmOYj8Y4siE1kokLVqhRfsq2Vxox6iTuXZICrYVDwLg6nkJakT70AYnCK1YYriJLy4ZwBOTspAkCVf/G6m6fDP+pN9u3ZQZb+PSQcE2UU8tOsCn0TdTi40k9z5Or3wT78zLiPxmGoai1Vi2vsv4qvcx62WuGJSCdfX/AHD1vgLVltRi99hW9EuNZGyXWFQNnlt6kIbhD6EhYdr/LbridaEO76jLr3Hx2Ly9nPPBBmTVx60x6xm2aArmXV8A4Op52VG5zrS+KQB8lV3Caweiccf2Qgp4MH9/Bf3VbOKseu7QfUlW7UI0WY9qikb2OTDmzjsq128uWwrtrM0LVjFdfniVRddpqOFpIY5OEP6ZaIuBK4YEk5avrcilotf1qOY4dPaDmLd9ENrgjnF7yhvYVdaATpY4LeuwAdyZZ4ldj4IgCMIxS5El7j2pE5c0ftd9fJuF6/SP4rEkoas9QNSXE4ObqzSVVQerOe/DjczZUcaOvCLyPrmCiLnXoi/PJmzZv7CsffqY2gmvaRp7yoPzY0/tFk9PJReArWoGu9U0YiU7Lxpe4279DDpTgCYbqGl3Klfon+Qs130M2n0Wz/uCrWvv0M9kkfEuRrIZTdbj7HMN1Zeswz75S9zdL0QzRoTqNlulQ8O4txbXgc6ML6EvALqSDaEMS2jFRNIihPwBlfl7KjhY5QRAqd6HvHs2ry7ZC8DFA1NpX/g1uuo9qMZInANvC2W4gnBMumJIOzrGWqhx+fjvihoe8AZb+Fyu+4mTWEMAmYa0EwG4WTebJ9tvJbno+8bfywicfa8PZfit2k2jMjAoEhvya1lkT8CdFWzFYVvxEGhqaIM7ip5fcoCz3lvP7G2ltFcLWGm5g1sdz6Or2YtqCKNhyL34UocflWsNaR9F/7QIPH6Vt9cUcGPZBPwo9GU3nxr+xwrbvdyo+waA3X0fxtXjIgCMe2Ydles3lzcbZ1mc3iOB9IbNGIpWBj/4D7g5xJEJwtFxbr8U0iJNVDm8vL+5NjhnCbCsf0G0aWhG3zZWWYzuFEOMVtPUksuddW4owxIEQRCEZidJEjeOzODxid0IN+mYVxHJ6LqH2BM+Akn1Ylv1KLXvn86Wb55mkvsb7g6bxzzTfZymLiGARE3SCQBYN7yIdfkDx8z3t7J6D3a3H0WCf43vwlUd6gBYowzgk6x3Keh8KZpixJs6gvoxzwQ3QU56mzvPP4tOsVZUTeJ9/TSWtw92W5BQ8aSfSM15C3EM/4/Y0PgHOsZYASit9+D2BQjEZAKISotW7KN1Bdz33S7cvgByfTER35yLIeenFru+rsWuJPzCmtxqnl18gNxqF9Gyg3dS59G3YhaSFuBltQOPWm/l4l49sE5/GgDHoDvQTFEhjloQjj16ReaBkzO5eno2Olmi/eALcFXlYM75kfX04N+eC6kp6cRF/nBu0s1mUuFTaJWRADj73YBmigxp/K1ZSoSZ8/un8sG6Al5YmsPws+/AuO9b9BVbMe6eiafbOaEOkZI6NwlhRmRJAp8TyedEs8Qe8ePX5tbw2cbgDIsRGVG84nmKyOoKApZ4XL2vbNxhE37U4pUliVfP6sWifZV8uK6ABeU9Ge1/jptMP3G2vBijPdjC7EX/FIqdw7inb/CLhqFgKZKrCs0cc9RiOVrW5tWwIb8WnSxx+cBkLIvuA4KDy9WwlBBHJwhHh16RueWEDtz5zU4+21jI5Esm0T32A/SVO7CufYaG0f8LdYjHFE3TmLe7gjk7ywCY1lnCtuReJC2AL3EAgeguIY5QEARBEFrGuMw4eieH8/BPe1iXDyeXX8c0pRsP6D6is2sLD+q3BA/0Bf+nRErgRve17C3uzqy+I+m85VEs295H9jVQf+JzIEkhu5ejYU+5A4CMGCtGnUyUI7hpeNopJ+PtkAU8SqX2yK/uM9Zm5O1ze7Myp5rB7aOINA+jtng4oDXNZxD+WIRZh82o0OAJUFznJqqxk4xizw1pXMJva/D4eW1lLgFVo09KOJe7P8BQuAKleh/V6WNaZK6rSFq0sNI6N08vOsCyA1WAxgX6pdwhf0Z0Y3maW9PTR87hC+0u1Pl9kd3V+KM64+5+YWgDF4RjWFZiGLMuH4hZrxBm0tGgvoHLnovLE0fe9K24G7w8x1mcnNBAl6oFSK5KAtZEXL2OTsufY9mlg9P4bkcZhbVuPtrp4YYBt2Bb/Ri21Y/jzRgf0qTPyoPV3DprO6M7xfDk6VlEzz4bXfUeas+Yjj+x/58+3q9qPLckmCSY1jeZf6XtIHzeFjSdmdqz5zTbLhtFlhiXGcdJXWJZl1fLsgNVpPc+hWqrG/OOzzhY6+P57P5E7a7g1hMG44vrFUwU7f8Od89LmyWmv2vBngoe/mkPAOdmWchcdjWGkrVosgFn/5tCHJ0gHF2jOsYwsF0k6/NreWFZHs8Of4iYb87GtPNT3Fnn4Y/vFeoQjwl7yhp4ZvF+thTVYcTLQ5ELGbv0K2S/Ew0JZ78bQh2iIAiCILSo+DAjL5/Vk2+2lbKxoJZazuF/vuGcXDeTDmEBYqzBlomBiPa4u12Fb04u9cV13Fs0lPdPeomwhbdh2v0l7m7n4kseHOK7+Wf2Nq69ZcZbIeBBqQkmLfyxPX4+6HcSMzajjpO7xTf9ua0/Fy1NkiTSIs3sKmugoMZNV5G0aNXW5dUQUIOt4T7bVMQ1loUAKM4yjHu/aZFNqKI9VDNQNY1al6/pz5K7FlQ/flXj1q+3s+xAFYos8XjHvTymvEW01MA+LZXzvfcz2vMcW3S90akeDMVrAGgY8SAo+hDdjSAcH+LDjISZGvO4skIgqiPdEsN5dEI3ZAnSoqxYJ7+Gr3E+hmPIvaAzhzDitsFq0HHLCRkAvLc2n/3p5+OP6oTsqsS69qmQxjZ/dzkAS/ZX8fWyVejLs5H8bsJ/uhrJWfGnj5+9tYScKicRJh1XD4zHuuoxAJz9b2yRsmBJkhjcPoq7xnaiY6wVzRyDc8BNxIy5lSizgRqXj7X5tXgypwJgakUtolRN4/WVudz3/S7cfpWzUup4sPzm4PBtnZm6k19DDUsOdZiCcFRJksTtozsiS8HXnclzFQ7EnIikqUTOnETYgltQqnaHOsw27add5Vz0ySa2FNURofOzPOpRLnV/jOx34kscQO3Zc/BmjAt1mIIgCILQ4mRJYkqvJB6d0I1HJ3Tjlskn0fXiNzBMeZv68a9SP/5VnIPvwhYeyaMTugLBgclFKRPwZAZnOBj3fxvKWzgqDs2z6BJvQ1e9F0n1oxojRIV3C0mNDK6hFNa6CByetDiG5qYcK5bnVDf9u1yXj74xwQdg2fJmi/w3E0mLo6y0zs2ln27m1DfWsGRvBebsd4h5rzeRs8/h2y35HKgMLnB9fn4W0+xvA+DsdTkN58/HlTycBmM89tM/o2HEQ2g6C+7MM/G1Gx3amxKE49gJnWKYedlA3j2vDwaThdozvqD6nLl4up4V6tDajFO6xjOgXSQev8rTSwuoHxVc3Ddt/xhd2eaQxKRpGmvyapv+XLT5576MiqOM8HnXg+r/3cfXuX28sTIXgKuHtSdx97soDcUEbCk4+1zdXGEfEZ0iMy4zDggu4Lk7nY4myejLNiG3kl0sD/64h/fW5ANwd1YDT9Xdib4uj0BYGjVnfoO3wykhjlAQmkenOCv3ntSZCJOOglo35xadxRp6ImkBTHu+IvqLkwj/8UokV/Wfn0z4BV9A5eVlOWjAmM6x/DjsIPGu/aimKOrGvUzt1K9FNYsgCIIgHIGkcBM9k8LRgAV7K3F3Ph0A4/45f/gdqS3YW3Go0sKGrmIHAP7Y7m2+7VVbkRZpAqCg1kUgvB0aErK3HsktPvu2JqqmsbIxadEvNYIT5eC6jS+uJ6reiq56D/qCpc0eh0haHEXZRXYu+XQzu8oakFUvhvm3YVvxEJIWQF+yDt3q4HyKq4el0z3/46YFLsfQ+0iNDuOtab2Zf/0wspIicPW+ksqrdlE/9oXQ3pQgCKRFmYk0N1Y7KUYCcd1DG1AbI0kS95zYCZ0ssfJgNfNcmbi7TEVCw7b0flADLR7TvgoHVQ4vJp3MpO4JjJK3AlDdYSqq3oqhaDXWNU/87uPfXZOP3e0nI8bCWR3Bsuk1ABzD/tUqKnBOzQqWLS/ZV4lDH4MvdSQApr1fhzIsIDj87qdd5cgSPHRKJpcZFyL7GvAlDqDm7DkEYrNCHaIgNKspvZL49qrB3D6mI3JYAue67+N0zyNsDx+NhoQx5yeiZk5EqdoV6lDblB92llHe4CXWauDRkzuSsCu4Ocgx6E48XaaIxQhBEARB+AvGdw1ugpq3uwJfynBUUzSyqxJ90eoQR/b7fAEVt+/n75aSp+4Xf293+Sip8wDQJc6GrnI78P9aQwnN6vBKC3Smpg4FokVU67KztJ4alw+rQeGR07pykhJMWhyMG4876zwALJvfbDp+c6Gdye+sY+SLKxj94lKWv3wBtlc7EvZqR8Jf60j4a53QvzMI9dMz8P9w+xHHIZIWR8k320q4dsZWqp0+BsV4mW19nDNYQgAZV5dgKd1l2jdMiTzAWR0lLJteBX69wKWTD/tCJSviC5YgCMeE9jEWLh6UBsCzi/azvesdeHRh6Cu2sXz6o+Rtnot53fOE/3gV5k2v/uoD5tG2JrcGgAHtIrl3TDrDlZ0APFg+gtrRzwBg2fwGhpwff/XY/ZUOpm8uBuD2EzKIWP0okt+FL2kgnk6TmjXuI9U9MYy0SBNuv8qS/ZW4M6cAYNz7dchLb0vsbgASw4xM6J6AUlcIgKvHhWjm6FCGJggtxmJQOK9fCrOvGMilg9LYqnVkYvnV3BvzCr6wNJS6fKJmnvGbr0HCrwVUjQ/XFQBw4YBUwg5+h1JfiGqOw90C/XYFQRAE4VhzUpdYJGBbSR3FDQE8HScAYNz3TWgD+x3ZRXZOf3sdk95ex47CSmyL7yL2nSxsi+5sqg45VGWRHGEizKRDV9lYaSE2JbaYtMakRUFt8Dvhzy2iDoYqJOE3HGoNNbR9FPFGP8OU4Gaq9yq74up1BZqkYChcjlK5k7J6D/d8u5Miuxu3X+UGpjNVXooZDyY8GDUPRs1NpKeYhNqNJBYd+fcbkbQ4Cjbk1/LovH34VY2TOsfwUdhrZAX2YNesXOq9m39LNzI9MAZZ0nicl4lc+UCrW+ASBEFobpcNSiMlwkR5g5ezZuTxX9fZAEytfpsBq67Atv5ZjDk/Ylv9P6I+HARLHqamcBdVpblUleZSW5aLVF+M3BD8B5/zb8eyOi+YtBiSHoW1cjMWXFQTzveV8XzpHoCzzzUA2FY8DAFv0+OKal3c8tU2AqrGiA7RjC19G9P+b9GQaBjxUKtJNEuSxCmNQ+Lm76nAk3Eqms6Erjan6cN5qJTUNyYtwoOlwUp9MGmhhqWGLCZBCBWdInPDyAwem9AVo05melEUU3yPYk8YiuR3EvHjVVjWPQeaGupQW7WFeysoqHUTYdIxpWcClk2vAODsc1WrqH4TBEEQhLYm1makf1oEAAv2VODpHFy7Mub8+IvvR63BoU3ElQ4velcFEbPPxrzzcwDMu74gfO51EPCwp9wBBFtDoakolcGNa6LSouWkRgU/l5XWufEF1J+TFrW5oQtK+JVDraFGdIjBULgCneYjT41ner6Fg/6YpiSmadMb3PvdTmpcPrrEWVk4rpbrdcHZN/sHPMa8E37k0wGzeTHzC55Oep5nw+7ibfnINxTpjv6tHV80TWvqaz6xewKPZJVi+m4dmmJkXt8PWL7CB9kl/MBFjDLsJcldBDnB3umtaYFLEAShuZn0Cvee1IlbZ21HliV2xE8m17uR9o5sirUY1quZ7FVTOV1ZRaavkLgdbxO34+3fPZ+mGHH2vQ5nvxtAf+SLUi5fgOwiOwBD2kdh2PMxAOWxQ9EKZd5fk8+ki27HtGcWSn0hpt0z8PS4kBK7i+u/3EZ5g5eMGAsvJM5vWhhrOOEx/PG9/8Gzc/SdlBnH26vzWZtXQ4PWlfD0EzEe+AHj/u/xx4Xug3lpY0l2UrgRNBW5oQSAgE0kLYTj1/iu8aRHWbjjmx1sr4WRzpv4OqMDHfM+xbr+OXRVu6gb+wIYrKEOtdXRNI0PGqsspvVLIbJwPrqa/ajGCNw9LgpxdIIgCILQdo3rGs+GAjvz9lRw8YDBBCwJKM4y9PnLIGFKqMPDr2q8sORAUxX85enV3FDxIDFqFXbNSkGni+h+8D2MOT8S8f2l5Cp3A5AZb0Wx5yL7HGiKkUBUx1DexnElxqLHrJdx+VSK7W7CDx/GLbQK5fUe9pQ3IAHDMqIwrF0AwO6woWhVEnd+s4O7ss5mAt9i3Debaf46AsYzeHZ0LBlz7wHA2ftKIgZfQt/fOP9fWQYXlRb/0Lq8WrKL6zDqZK4fno5t3bMAuHpcxAmDhnBi51gAvLKZ6pNeRpODffHdXc9pdQtcgiAIzW1I+2h+vHYIi28cztvn98N60ddUXrYZ+2Xr2dz3ST4znM2kwJNc7b+LdWpXPJoer6bDq+nwHPaPHx1SwIN1wwtEf3YCxn3fHXHbo40FtfgCGsnhRtpFmZsGSMX1OJk4m4HSeg/f7LHj7H8DAJYNL1FTV88F76ylyO4mNdLE51kbiN0UfL1vGPZv3D0ubp4n7B/oEGMlI9qCL6Cx7EBV024Iw4HvQ9oiqqTu50oL2VmOpHrRJAXVlhiymAShNchMsPHRhX3pkxJOnRdO2jOBn9r/C002YMz5kahZZyDX5Yc6zFZn5cFq9lU4sOgVzumdhGVjMJns6nkpmiEsxNEJgiAIQtt1YqdYFFliT3kDubUePJ0mAq2nRdTrK3J/bts70Mq/a+4nRq2iSNeOM7z/5fSdo1ja9xU0nQVD4XKuKrgLHf7GIdyN8yxiuoEs9nO3FEmSDptr4T6sPVRu6IISfmHFwWCVRY+kcKLMOgy5iwBI6X86NqNCbrWLG1YozJFGoaBykW4BP0i30HXBecjeenxJg3AM/ddRiUUkLf4BTdN4c1UuAFN7JZFcvRp92SY0nQln3+uRJIl/je/M+Mw47j6xI/GdBlF/4rN4OpxKw9D7Qxu8IAhCiERZDBh1jW8/igHNEkes1cANIzOYd/1QVt56Ao/dcgsZNy2g7saD2G/MxX5jLjmX7eXOTnPJ9HxEJ/eH3KbdhseSjNJQTPi867AtO7I3xkPzLIa0j0Z2V6Ov2AaA1n40lzbO3Xh/bT72zPODu4kaipkz/QVyKhwkhBmZ3nMLiesfBcAx8DZcfa89ys/Q0TO2SzBxvnBvJZ70k9AUIzp7bkgH/JYcVmkh1xcBoFoTxZcFQQCiLQZeO7sXU3ologHX7u7Oy6nPETDHoavaTdSXE1Gqdoc6zFZD0zTeWxOssjizdxKxlavQV2xF05lx9boixNEJgiAIQtsWadEzOD0SaGw52/l0AAwH54H377fqPRr2Vzr4dGOwzezDp2Zytfs9ZG8dvrheKJf8RPesPqga3L45iuLTPkM1hNND3c3lyo+NQ7gb51nEinkWLe3QXIv8WpeYadEKrThQBfB/7N13mFTl/ffx98xsr5Sll6UXpUgviqhIrIjyGLuxoaKxxWhCEtOM/tSYaIwaEdFosCR2VOxiARcREGlKZ5ddettlYfvOef5YzjA7O7M7fc7sfl7XlSsu0+5zzt3O+d6Fk3q1IWHvGhxluzES0uhw3CnMmz6am07sQevURH5efiOXVN3D5swx2HBiL99PbVp7Dp3xFDgSw5IWBS1CsDj/IKt3lpKcYOdno7qSbs6yOP5nGOl1a4lnpSRy/7kDmTa0MwCV/adx6KxnMNJyYpZuEZF4lJOexB/O7M/zlw+jX7sM3qocxTT7Pzg04hcY2Ehd8x+SNr3X5PcsdgUtWpNU+BUANW2Pw0hvz9TBnWifkcSew1W8/WMxO4+7AYCLKl6jc4adtwZ+SZel9wJQNmwGZaPujNDRhsek/u0A+Cb/AIeNZKpyTwUgefP8mKVpl9tMC3M/i1rtZyHikuiw89vJ/fj1pD7YbfDIhjY83O1fVLcbjL3iANnvXIajeEusk2kJefkHWb3zEEkOG5cN70z6kr8BUH785RipbWKcOhERkfj3k/51z7Y+XreH8pwTqM3sir36CKxr+r4rUgzD4K+fbqTWaTCxd1umZq4nZeM8DJudw6f+FUdKFr89vS+5rVM5UFbNPza0YsPgumVrfpH4Jh2MvSTsOzrTIobL5rZUrpkWB48FLeyVJdgqDsYwVQJQUV3Lt9uKgbqgRVJ+3dJQVd1PBkcyWSmJXDu2O+9cP5rfTO7HuJPPJePK1zlw0UeUDb+FkvNexpneIWzpUdAiSIZhMDuvAKgb2dV53yIS93yPkZBK2fCbYpw6EZHm6/iOmTw2bRCtUhNZu6+GByvOp2zELQBkfvFr7KU7fH52R0kF2w6W47DBqO6tSNpWtzRUVfeJACQn2Ll6THcAnluyjYtXDGSn0YbOtgN83ub/6LzqnwAcGf3LuimPFt+XqHfbNHJbp1JVa7BoywHXElHJm2KzRJRhGG4zLVKwuzbh7hL1tIhY3YUndOZ3k/sBMGtVNY91eIiatgNxlO0he94l2A8VxTiF0VNcVk1JeXXdH85a7MVbcTqdPLmwblTeT0/oQuc9nx3ti6fV7XUkIiIiIZvYpy1JDhv5B8o5/9mlfJdRNwiKN28g8/3rSdi9Iuppem/tblZsP0RKgp27J3Yl48u6lUzKB1/tCkIkJdj59el9AHj9+x08VTKWJc4BpFJJ5qI/krBXMy1ipVurFAAKi8shMY3atLqH3FoiKvbyth6gssZJ+4wk+uakkrzlfQCqck+v976URAfThnTishFdsdts1LY7niPjZlLbdkBY02P5oMXBgwf5y1/+wqmnnsqQIUM477zzeP3114P+vsoaJ79/fx0vLau70Uva+jFZ713l2gjUH+XVtby1ehdrd5WSkmDjuv61pH17dGTX4Ksw0toFnT4REWlaTkYy9/yk7mHey8u383n7q6lufwL2yhIyP70NnLUUFZfz7DcFzHh1JQ9/vJaipW+Q9uHNvJD4IHe3WURmbTGJR2daVHU/xfXdUwd1pENmMvuPVLH9CPwv+UIAkvesAuDwSX+mbNQvLB+wgLo1Q83ZFp9t2EtVj6NLRBVvxnFgfdTTU1xeTWWNE4AOmck4ji4PpZkWIt6dN7gjd51atznkE8uKmd3tb9S06o3j8A6y37kE+5HdMU5h5JVX13LxC8v4f88tJX9/Ganfz6btSxM48M7dbNx7mIxkB1eP6kT6Nw8BUHbCDeqLi4iIhElGcgJ/OmsAOel1s9Gv3TqRz4yRgFG359brU6h5eixpL55G6/+eTqtXzyL1u6egtjIi6Skur+afX9UNWrhhfC49Nz5LQkk+tWkdKBtzd733juremjMGtMMA5q3ZzT3V11KLg+StH2Ev34dhs9ftaSFR1a21uadFOYD2tbCII1U1PPpF3WzuMwe2J231v0nYvw5nYjqVPSfHJE2WXkC6rKyM6667jg0bNnDZZZfRq1cvPvzwQ373u9+xb98+ZswIfB3xRVv28+GPe/hk3R4m9cth4OIHSDi4Eeeyf3L4lAd8fq6yxsm/l2wjb+sBNuw5zAms56nE95mQtImMN+umMBkJaZQN0ywLEZFomNinLf9vaCfeWLmTP368Fdu4e5m87xKSdnzDwudu56vSznS27eMy+w4m7/mOLFvduqt9HTDxyCqM52dhM5wYCalUdxrp+t6kBDs3ndiDP324nuM7ZnLu1DupmfcZCSX5lE76OxX9L4zVIQdlUt8cnvtmG4vzD3KEAWR2m0hy/sckb3qPsrYDcBoGt72xmopqJ//66RAytn9B5qd3UHrKg1T1PjusaTFnWbRNr9vXRDMtRJp28fAulFfX8uSifP76TQmDpz7D+MVXk1CST+aCX1Iy5cVGP3+kqoYl+Qf5fvshVu44RMr+H/hV350MYz0Ju5bjTGtP8QVvQFJ6lI4oMEu3FXOgrG6WxR1vreGz9LcAGLj9f1zjSCJ11E10zH+DhOItOFPaUD7sxlgmV0REpNmZ3L8dE3u35f0fdjN3WRHXHbyTvrYiZiS8y3n2PDrVFEHJsfcn7l1N5fIXeIirWGkfxD29tjK6bAGJu1dQ1f0Ujoz7Td2edkBNrZNvCg6yaschLhjSiS7sJXnTezhT21I58KIGaXly4VaKy6vpnZPGFb2rSXv1SQCOnPRHjKTMBu+/45TefL31AIcra9lodGVd7hUcX/ACALWt+kBiagTOmDTG3NNix6FKamqd1LbqATuXKGgRY49/tZVdpZV0zk7hxuMg/Y0HATgy/h6M1LYxSZOlgxYvvvgia9eu5ZFHHuGcc+qWtLj44ou5/vrreeKJJ5g6dSqdOnUK6DsXbqnbBb3WgM+XLmPwwY0ApKx/nSPjZmIkZzf4TFlVLXfNW8vSo+t6nWFfyuNJj5NEDdSCYU+ipsNQykbcGrMLKSLSEt0xsRffFZaw9UAZP/+0igsdV/K3xKeZVvk205Lqv3eX0YZ3asexz8ji1varyDxYNyW4qlvd+ozuzjm+A0O7ZNEpKwWH3UbJT9+lbXYilRWpEP1VlULSt1063Vunsu1gOYu27GdKn3Pqghab51M25i7W7CxlSUExAPN/2M3VW5/BXnGAjK//woEek8O2iRYc28+iU1bd+dZMCxH/XD2mOwUHy3lv7W7+75vDzD3nP7T932SStn1BUv5nVPWY5PVzR6pquHLudxQW15W9KxyfcF/iv8FtSwxH2R7SVj5D2ag7onAkgcvbesD139UlO0mp/MH19+8TX+Rg1ijSvnkUgLKRt3t9YCEiIiKhSUqwc/6QTkwZ1JEfd5eSlDaGkpKz+LxsN6tWL+e7orqoRW/Hbm6zv067qiL+yv1UGgkk/1Dj+p6U9W+QuOkD1vS8lleNn7B68xYyq3bTz1ZEyo/LaVt1rJ0/kHM8te2OLd+0o6SCd9bsAmDmpL5krfgzttpKqrqdTGWfKV7TnZOexE0n9uThBZsAqBz9C2r3f4rj8HZq2mlpqFjIyagbwFZZ42RXaSWZ2T0BzbSIpaXbDvLGyroViH4/uQ/tFt6AraaCqi7jqTj+8pily9JBi7fffpsOHTq4AhZQt9TF9OnTWbhwIe+++y433HCD399X6zTI23Lsxqd83YfHvremnJQf/0f5CfW/r6S8mjveWsOanaWkJTp4auBaTt74GDbDSWXPMygbNoOadoMhISWEIxURkWCkJDr4vykD+cP760iw29iXcQELy/czuOxbElt3wZHdldqMzlR3PYma9jb30b8AALsASURBVKNonV9Mjt1GRe+2VBdvIXH7Yp8P+8wNwgCM5CzIyISK0mgdWtjYbDZO65vD898W8tmGfZxxxmQMexIJBzfiOLCBzzce6wq89e0PXF+VB4CjtJDkjfOoHBC+mSW7SutmWnTMTAHDcJtpoaCFSFNumdCTzzfu48fdh3lvZxcuGnodaStmkf71vUeDrw0DjLPzCigsrqB1aiLTejqZWfBfqIVFtcdT1mUCJ+Zmk77kIVJXPEX5oCstN/jGMAxX0OK2k3tycEndkn4rnb1Y4+zJ5Qmf0XbBbQDUZnajfNAVMUuriIhIS+Cw2xjcOYucnEz27SvFMFozbMAABhUW8/fPN5O39wjv2sdzf5sPOfPIWyRTwzajPW/Xjud7Zx9+njCPEWxk6KYnGMoTdV9qDjarAgMbRnI29spiUja8yRG3wMJr3+/AacDY3NYMa1NN8oa3gbr9Bhtbuvf/De3ED7sOAdC1XVtKJz9G+td/oeJ49RtiwW6z0bVVCpv3lVFYXE4vLQ8VU2VVtdz3cd2A/v83tBMnHXqXpB3fYCSkUnrqw2CL3c4Slg1alJaWsmXLFiZPbrhu1tChQwFYtWpVQN+5bvdhDpZXk5HsICMpgXEVy8EBNW0HkrD/R1JXP0/5kOvA7gBgd2klt72xmi37y8hOcfDG4O/os7puvdzygRdz+JSHwG7ZUygi0iL0yUnn5Z+NcPuXf1ANVHu8Lxk4pW+O6+/aVr2obdUrCimMvdP7teP5bwv5eusBSulHZveTSc7/lOSN8/hiU90m5DZgYOlibEm1GNiwYZD23RNU9p8Wto7KsU24k7FVlmCvPgJAbWbnsHy/SHPWNj2Ja8d05/GFW3li4VZOu+LndF33GgnFm0ld8wLlQ6fXe/+GPYf533d1s5n+dFZ/zlw/k8Tacva3OoErd91FUlECb04eQb8t75O4dzVpy/7JkQl/jsWh+ZR/oJydhypJdNi48ITOJBVugZ3whfME3s64jP/XroaUwi8BODLm7gaz5kRERCQ6RnRrxX+uGM6mvYfpnJ1CVsoZFB/5NbaKYg4l9mDtwq0sWLeXz6uGcWXaEm43XqStcYAaRxpGVldWlGbyYflx9Bh/CVNzdpL9wXSSN87jyLjfgt1BeXUt81bXzbK4eHhnUn94CZuziur2Q6npMLzRtDnsNv501rENgqs7j6X4p/Mjej6kcd1apdYFLQ5W4OzaA1DQIlb+tWgrO0oq6JiZzC+GJZDx+v0AHB73G5zZuTFNm2U34t69ezeGYXhd/ik1NZXs7GyKiooC+s7F+XUjtcb1aMPlQ7IZY/8RgEOnPYIzORvHoW0kFXxGda2TuUsLufj5ZWzZX8a49B0sbP93V8CibOgNHD71bwpYiIhIXOjXPp2ebdOorHHy3trdVPabBkDimpfZXXyYJIeNn43uxhmOZQCUDbkWZ1IWCQc3kbTlw8a+OiDm8lAds1KwH10aypmaAwlaS1bEH5cM70KX7BT2Hani+ZUlHBnzKwDSlj6KrfzYbGKnYfDgp5uoNWBSvxxOsX9P8ub3MWwO+MlfGdqlFZU1TmZ/U8iRcb8BIHXNf7Af2haT4/LFnGUxvGs2qQ6D9vu/AeDH9NHcPbk/h8+cRVW3iVT2PofKfufHMKUiIiKSYLcxoEMmWSl1sz+d6R2pbTuADlkp3HfOQL64dTx5d0zg1pt+hXPGCvZN/4GDN66n+LIFfD3iSZ6rPYs3NhtU5Z5a94zuyC4Sd9S1/R/8sJvSyhq6tkphfPdMUtbU7UtRPuTaRmdZiDWZqxoUFZdTe/TBuL18P7bKQ7FMVouzoqiE/63YAcDvJveh/aLfYqspo6rTGCoGXx3bxGHxmRYAaWlpXl9PSUmhvLw8oO/8pqBuw+yTerXhDPtSkm01bHV2YFNVN048/jLSvnuKyiVPc8nnrdl2sJw2HOKhrHc4p/pDbHucGI5kysb+ivITbsCmSlHihJlVlWVFghfv5chms3HxsM48+OkmXl2xnYuvPpP09A4kHdnNWfYlFPc4j2uGtaXjypUAfJN5BuOGZJC27DESvv0nhe1OpWN2KlSXk7D/R2raD3XNSgyEOdOic3YyCYfrBh7UZnaJ2/MqgYn3cmQFyYl27jilF3fP+4GXlhVxwVXnMzDnBRL2/UD60r9zZGLdyKh3Vu9i9c5DpCU6uGtCFzLevR6A8qHTMdofx60nl3DdKyt5d80urhw5kiHdJpBUuJD0b//G4cn/jOUh1pN3dMDRib3akLh7OfaqQzhTWnPftZe66qBDU18C6maLNXcqQyKhUzkSCV2w5Sgj2e0RpMMBjixX+33mwHb888strNxxiB2HnWT0OZfUtS+RsuFNqruOdz1YvWhYZ1LzP8RxZDfOtHZU9T1X5TkOdWtdt8R+UXE5JGfiTM3BXr4Px6ECatsPjnHqIs8KbVFFdS1/+Wg9AFMHd+SU8g9JKlqIkZDC4UkPY7NHZp5DIMds2aCFYRj1/t/b6/YAT+CmvWXYbDBlZHfafFq3Yd/nzmEsWbMbe9//xyk8Tfv9S+hVlcfPU9fw/2yfk1BVNyqU487H9pO/kN6qO+nBH5ZIzLRtq40pRUIVz+XoZyf35slF+RQWV7C2uJZTR18Pn9/HNQkfsWnYTfQoXw62arY52/H4+nQyT7mCoctnkX5gDS+9+CR/PimFpOXPQNl+OP3PcNIdAadh9+G6oMXA3DZk5e8DIDGnBzk58XteJXDxXI6s4MK2Gby5ejeLt+zntx9u4qXT7yfrfxeQumYuqT1HsafvT3liYT4Ad/6kHwMLnoVDBZDVhbSz/kBacgaTcjKZvHIXn/ywm9lLCpl91l9g9imkrH+LlFN+AZ2GxPYggSOVNXxfVDfa7pzh3Wi18n8A2PueTk77VjFMWeypDImETuVIJHThLEc5OZmM79OWrzftZ+G2EoaOvgLWvkTKlg9YMfQPbNlfRlqSg6sn9iHrpdsBsI+6jpwOOU18s1jRoNy2wCZ2lFbW3Qvm9IbCfbR27oKc8bFOXtTEsi36y3s/UFhcQcesFO47vQ2Zz94HgG3SH2jTZ2jM0uXOskGL9PS60EBFRYXX1ysqKrwuHdWUwZ2ycJaV41z/EXbgM+cwvl67m4/WwlOJIzjLsZTnkx4GAzCgut0Qyk68h+qu46EG2Bd/m7BKy2az1VWE+/eX4iMGKCJNaC7laOqgjry4rIjZX2yi3Unn0td4kGH2TeTYNlDx/ZukAJ8wmu+LSrjoxRJ+n3Aa1yV8wAPGP2Dhse+pXv0WJQOuC+i3y6pqKS6r22kkpbaW8l2bSQXKkzpwRG1ri9BcypEV/HJiT6bvPMTq7SVMfTeN1/tfSdv1c+GdW3jRtpSS8jM4IQcuL7wPNrwBwKET/0hVqQFHZzNfP7orn/24m49/2M0f0rvy2z5TSNn0LrUvXcyhqa9Q27p3LA+RLzftp6rWSZfsFLJwUrPuIxKA0o4nUdlC6wyVIZHQqRyJhC5S5ej0o0GL15cVcvGg4bTJ7IKjdDvLPn4ZOJ5zjutA7ZZvoXAJhj2RA71+itFC+wTxLstWl3G2HShj955DZKd1I4UlHClaR3nH5n9NY90WrdxewnOLtgIwc1JvkuffBlWlVHccQUnvyyP67Ns8dn9YNmjRtWtXbDYbu3btavBaWVkZhw4domPHjgF/70m92uDYvQp72V6cienQfhxsO0x2SgL7+l4NG5cCUNXtZMqG3Ux11xPrzqg6NBLnDAN1zEVCFO/l6MITOvHSsiIW5x/kmcxkJjjHc6HjK9r98BxJhV8BUNXrLFgHyQl2dvS7hqotn5FEFTtS+pA5+moyv7qHhD2roLwYI6WV37+9o6RuEEJmcgLpSQnYS48uD5XRJa7PqQQu3suRFeS2SeOZi4fy89dXsfVAGedWT+W+VpVMKn6VO40X6Je+g8nOtSRv2IFhs1M28g4qe55Vrz/bs206d5zSm0c+38x/lhZhDPwZf2z1AwnFm8l+cxolU16kpl3spueb+1mM79kG+5HdJOz7AQMbld1OafH5R2VIJHQqRyKhC3c5OqVPDg8mbCL/QDnr9pQxou8FpH33BP33fggcz0VDOpC6YiYAlX3OxZnWQc/q4lS7jGQSHTaqaw12l1aSkd0DAHtxfouqm2PRFlVU13LvhxswgHOOa89plZ+StO1LDEcypaf9vW4PPItcA8sGLdLT0+nduzerV69u8NrKo2tuDx8+PKDvHGLbzEm9hpC05VUAqrtP5M8TB7NyxyFGdW9FaqKDg4M6YSRlUJtzXOgHISIiYiFdslOZ0LstX23ez7zVu9hs+wkXOr4iZfN7ADhT2zH1jHPJ6VfMkC5ZtElL4rNlc3n2q7UcSBvG/waPInX1CyQc3Eji9jyqep/t92/vOrqfRcesZADXRty1mV3DfJQiLUOPtmnMufQEbnl9NdsOlnNd6VRudtj4VeL/OLf2EyiD2qxcDp3+GDWdRnr9jkuHdyEt0c7/fbKRuT9WU9r7Af6acC9J+9aQ/fZFHDrneao7j4nykdUtA3ssaNGapIJPAKhpPxQjtW3U0yMiIiKRl5GcwIRebfl0w17mLi3iK+cwfgecYv+eP7ZbyLCP7yGheAsA5YOviW1iJSQOu40u2SnkHyin8GA5PY4GLRwl+TFNV0swb/UuCg6W0zY9iV9M6EbGq5cAcGT0L6lt3SfGqasvMrtqhMl5553H9u3bmT9/vuvfDMPg2WefJSkpibPP9v9hCcDLyf/HyEVXkbLhLQAqc08nOzWRk3u3JTWxbjO/ms6jFbAQEZFm6+JhnV3/vcboxZF2xwYAVPY6g8SEBE7pm0ObtCQA+hw/muXGALYcKGff4Uqqup4EQFLhQgKx81DdTItOWXWbrjnMmRYKWogErVNWCs9cMpShnbPonJ3KceffQ+nEB3EmZ1N+3KUcvPgjnwEL09TBnfi/cweSYLfx9uYarnb+kbIOo7FXlZL9zmUkr38jSkdzzNYDZew8VEmSw8bIbq1I2rYAgKrcU6OeFhEREYmeMwe2B+Dj9Xt5ZmMqa525JNlquab0KRKKt+BMzubwiX+gpmNgg5jFenJbpwGwdlepa1nShH1robYylslq1gzD4I2VOwG4dkx32u/8FHv5PmrTO1A+9PoYp64hSwctrrrqKvr06cOvf/1rHnroIV599VWuvfZaFi5cyB133EH79u0D+r4aEkjasQTHoQIMbLrxERGRFmdU91b0bFvXQTy+Yya1w6a7XqvsdWaD97dKTWRAhwwAvt1WTHW3kwFcy0n5a+fRmRadspKhuhx7+X4AnJldAj8IEXFpk5bEM5cM5e3rRjGmR2sqBl3B/uvWcPjUhzGSMvz6jkn92vGPCwaRnuQgb0c1Z+2/g/2dJ2GrrSTr09tJ//ov4KyN8JHUMQyDN4/eTA3v1oq0siIStx1dvq67+u4iIiLN2fieremcXTfI6ZQ+bbGPqNtHrza9I4fH/54DP1tC+Qk3xDKJEibje7UB4PON+6jJOZ7a9A7Yqw+TVLgoxilrvr4rKmHrgTJSE+2cfVx7UtbMBaDiuMvAkRjj1DVk6aBFSkoKc+fO5fzzz2fevHncf//9HDx4kIceeojrrgtsA1CAFT+ZR9kJN+JMzqay71SMtHYRSLWIiIh12Ww2bj6xB0kOG5cO70Jlr7Oobn8C1TmDqO4y3utnRue2BmBJwUGqu4zDsDlwHCrAfmib37+76+hMi45ZKTgO1y0N5UzMwEjODvGIRMRms2Gz2dz/IeDvGNOjNf++bBjdWqVQcBhO2nYda3rU9bfTvn+a7Pk/w1ZxMFxJ9sppGPzjyy38b8UOAK7ILaXVm9OwVx+mpnU/atoPjejvi4iISGwlOuz85/JhzL9hDA9PPZ6ccddw4LIvOXDl15QPu9HvARlifaf2aYvDBj/uPkxRSSWVvepW00k+unSxhJ85y+LMge3JPrKFpB3fYNgcdUELC7LsnhamNm3acN9994Xlu3rm9uJI999z5MTfh+X7RERE4tEpfXP4+o4Jrr+Lf9p4x3BMbite+LaQbwuKcSb2p6bjcBJ3LiWpcCEVx1/u12+6z7Swl24Cjs6yCOLhqohERs+2aTx/+TB+9946vik4yLnrJvGLju25pfQfJG37kjYvTuDImLuoOP4KsIf3NqLGaXD/xxt4b+1uAB4aWc7Z39+JvbKEmjb9KTnvZbA7wvqbIiIiYj3ZqW4jvm0219JB0ry0TktieLdWLN1WzIIN++jZ5xzSVv+bpK0fQ20VOJJincRmZf+RKj7fuA+A/zekMylrHgKgqsfpODM6xTJpPll6poWIiIjE3pDO2SQn2Nl3pIot+8uo6loX8EgMYF+LXaVuMy20n4WIZWWlJPLotEFcO7Y7CXYbj+4azNSKP7AzuRf2ymIyv7qH1v87g6T8z8K2ZJTTMLhn/o+8t3Y3Dhs8Oa6Mi9bdir2yhOoOwym+4HWc6R3C8lsiIiIiYg2T+uUA8NnGfVR3HEVtWnvslSUkFWmJqHB7Z80uapwGgzpl0r+NnZT1rwNQPuhnMU6ZbwpaiIiISKOSE+wM61K3jNOSgoNUmftaFC0CwwlAUXE5c5cWsv9IFRhOkte/SdKWDwGornWy73AVYM60OLo8lIIWIpaUYLdx04k9+N/VIzmpVxvW1OZyUsmfuaf6GkptmSQcWE/2/Kto88Io0hf+kYTdK0L6vZeXb+ezDftIdNj4+5ldOHPDPdhq6gKkxee9gpHSOkxHJiIiIiJWcUqfHOw2+GFXKTtKq6nqdRYASZvnxzhlzUut0+CtVXVLQ/2/oZ1I2TgPe1UptVm5VHeb0MSnY0dBCxEREWnS6NxWAHxbUExN+6E4EzOwVxbj2LOat1ft5LL/LOefX23l3tcWkPH2JWR9ehvZH0wned3r7C6txKAu+NE6NdFtpoU24Raxsu6tU3n0gkE8Nm0Qx3duzYu1kzmp/O88W3MWxWTgKNtD2qpnaf36FNKWPd7g89W1Tr4tOEhJeTUAjoObSdizCgzD9Z71uw/z5MKtANx1am/O2vYQjiO7qGnVi5Kzn4Wk9OgcrIiIiIhEVdv0JIZ1rRsct2DjPir7nANA8paPoLY6lklrVr7JP8jOQ5VkpSRwer92rg24y4+/AmzWDQ1Yfk8LERERib0xua2BrXxXVEw1Dqq7jCc5/2M+/fgN7t9zOmAw1bGYew8/R+qRMgybHZvhJPPzu6kckwWk0DEzGZvNhkMzLUTiyviebRjfsw07Sir4eN0enlnZlgdLL+XnXfOZkf0tKZvfI23p36nMPY3adscDcKSqhrvm/cCybcUk2G3c2mEttxQ/hMOopjYrl4q+UznUcwr3vH+YGqfBKX3acmnClyRvfh/Dnkjp5CcgMS3GRy4iIiIikTSpXzuWF5bw2Ya9XDF8DM7UHOzl+0jcnkd194mxTl6z8MbKHQCce3wHMg6uIXHvKgx7EhUDL45xyhpn3XCKiIiIWEafdum0SUukvNrJnz5Yz3O7ewIwovgDZiX9g7WZt/JY4hNk28r43tmbv/b4NxW9z8HmrGbY0jvoZdtBp6wUAOyaaSESlzpnp3D1mO78/fzjcdoT+UdRH97s+Rcqe5+DzVlD5oI7obaa4vJqbn5tNcu2FeOwwQW2z7nlwP04jGpqDDuOQwWkL/8nnV6fzFOHb+OutPncN2gfmYv+AMCRMXdT035IjI9WRERERCLt1L452IA1O0vZdbiayqNLRCVvfi+2CWsmisuqydt6AIALBncief0bAFT2PgsjtU0sk9YkBS1ERESkSXabjVHdWwHw8fq9vF7cB4De9p2caf+W9OoDGI5k1va6kZ9W/ZGnfkzkzqqb2Jw0gOSaQzyf+BC3lz9O9juXYz9ct56mZlqIxKd+7TO4ZnQ3AP762SaKRv0RZ0prEvetxfjmMW7430p+2FVKdkoCH41cycOJs3HYDN61T2Jo5TPcUnUrn9SOoMpwMNBeyC3Ol+j64eXYasqp6nIi5cNmxPgIRURERCQactKTOOHoElFvrNzJ9xl1eyzYNryPsX0ZSZveI3Xlszj2/xjLZMatBRv3UmvAgPYZ9GidTPKmumBQZb8LYpyypml5KBEREfHLVaO7caiihs7ZKQzt3I89e24g+8gWqjuOpLrTaGraD6F9Qgo3tyvisS+38P7GQ3zL7byV9Ae62/fS/dD7cKjuu2ozu+FMaxfbAxKRoF07tjtfbNrPpn1HeCCvhFuO+w0nfPcrWq94nE7VaZybtpMbW39HxuqVAJSdcCNjxv2OJ3cf5sN1fZi5fiLVRw5wX5/NnGksInF7HkZqW0pPf9TSa+uKiIiISHid3i+HFUUlPP9tIXNJ5dvkTNrWlJDy9vmu9xj2JA6d8S+qep0Zu4TGoY/X7wXgJwPakbjjGxxle3AmZ1PV7eQYp6xpNsNw2wmvmdu3r5SWc7QidWw2yMnJVP4XCYHKUWAMw+CNlTspLC6nY1YKfRy7Gbb7NbJat6c2owvOzC7UtBuMkZwV66RKFKkcNT8/7i7lmpdWUGsAGMxOfISfOJbXe49hc3BkzN2UD/95XSY4qtZpsPNQBV2yU7DZbNjK94M9ASM5O7oHEUdUhkRCp3IkEjqVIwm34rJqrn55BcXl1XTMSma6bR7TDr3IPrIoTmhP79aJpOxbhWGzU3ra36kc8NNYJzkk0SpDew9Xcs7TSzCAd64fTd/lfyT1h5coH3gJh0/7W+R+uBHmsftDMy1EREQkrGw2Gxee0NntX7oAwymLVYJEJCIGdsjkmjHdmfPNNjKTE1nUayYTt99IctUBqjsMp6Lf+VT2PhcjvX2DzzrsNrq2SnX9baS2jWbSRURERMQiWqUl8tZ1o7C5BriMZPHOu7n9zTWUHKmhd3Iyr/d+mdab3yDrs19wuPIQ5UOvi2ma48GnG/ZhAEM6Z9Ep3UHy5vkAVPadGtuE+UlBCxERERERCcoN43P5yYD2dMlOISnBTmn5FxyurcCZ0bnpD4uIiIiIgFvAos7xnbKYfclQbn19NZsPVHJd6tW8OLQNaSufIWPRH6nJGUh1l/ExSm18+GTd0aWh+rcjsWgR9spinKk5VHcZF+OU+UcLxoqIiIiISFBsNhs926aRlFB3W2GktlHAQkRERERC1qttOk9eOASA73eUsmfUb6noMwWApG1fxDBl1rejpILVOw9ht8Gk/u1I2fQOAJV9zgF7fMxhUNBCRERERERERERERCylR9s0OmYm4zTgx91HqO42EYCEXd/FOGXW9unRDbiHd2tFTrKTpC0fAlDRJz6WhgIFLURERERERERERETEggZ1ygJg9c5DVHccDkDinpXgrIllsizt4/XHloZK2vYF9qpSajM6UdNpZIxT5j8FLURERERERERERETEcgZ3zgRg9Y5D1LbugzMpC1tNOQn718U4Zda0ad8R1u85jMNu49S+OaSsfwOAyj7ngS1+QgHxk1IRERERERERERERaTEGH51psWZnKQY2ajoMAyBhd/NfIqqsqhbDMOr+MJxQXdbo+/ceruTueWsBOLFnGzr9OJvkLR8AUNnvgoimNdwUtBARERERERERERERy+nfPoNEh42D5dVsL6mg+mjQInHX8hinrL5Ne49wpKpuySpbZQmOAxtC+r5VOw4x6ck8bntjDUeqasiafw05zw0lactHXt9/sKyKn7++mqLiCjpnp/BQlzwyFj8AwOGxM6lpNyik9ESbghYiIiIiIiIiIiIiYjlJCXYGtM8A6va1qDm6r4WVNuNeu/MQl/5nObe+vganYZD58S20fmUSSQULgv7O2Xn51DgNvik4yL9e+S/JBZ9hqykn68MbSF73er33llbUcOsba9i6v4z2GUn8d/h6On37ZwCOjLyd8hG3hHR8saCghYiIiIiIiIiIiIhYkrkZ95odpa6ZFgklW7FVHIxlslyWbisG6oIqC9btIml7HjYM0vPuB2dtwN+3dlcpSwqKcdggOyWBs0peAaAmpS02o5asz+4gdeWzABiGwcx3f2D9nsO0SUvkPyeX0+2b3wFQNnQ6ZaPvCs9BRpmCFiIiIiIiIiIiIiJiSYM6Hd2Me+chjJTW1LTqDUCiRWZbrN9z2PXfHyxahK22EoCEA+tJ3vBWwN/3/JJtAJw5sD0vnpHIqY6V1Bo2LnXey4GBVwOQseiPpK54mgUb9/HttmKSE+w8fsFA+qy8H5vhpKLfNI6c+Eew2UI/wBhIiHUCRERERERERERERES8GdK5bqbFhr1HqKiupabjcBKKN5Ow+zuqekyKcepg3dGghd0GOYfXQxIYNjs2w0n6koep7HMuJKS43m8YBvn7y1hRVMLm/WVM7JrIuOStJO78loMVTpZvGoqNNK4a3Z2+S+8E4DPHSSw91JpbDl7E7FFtyVj6d9IX/x8LHKlAD64c2ZUh+94lYf86nMnZHJ7w57gNWICCFiIiIiIiIiIiIiJiUR0yk8lJT2LfkSrW7T5Mqw4jSFn3miVmWpRW1FBUXAHA9HG5dPw2H4Aj/S8mtXABjsPbSV0zl/ITrgfgleXbeW7JNg6WVdOOgzyd9ChD127GYTMASAfeSurMnM730ce+k+TN8wHoMPlukt47Ql5+Mf/ueRHX991Mysa3uafqUTam/52rhmaR/r+H63579C8xUlpH90SEmZaHEhERERERERERERFLstlsDD4622L1zkNUm5tx714R1J4R4bRhb90si05ZyVw9uhvDE+uWdvqiog9lo38JQNryf2KrPERpRQ1PLNzKwbJqkhPszGz9BcPtm3DYDAqc7VmYOomdRhv62Hdw3/7byfz8bmwYVPb4CZ37DOf2ib0AePyrLXze826KjHZ0t+9lTs5/afv9E9grDlDTui8Vx18Zm5MRRgpaiIiIiIiIiIiIiIhlDXbta1FKbZt+GAlp2KsP4zi4MabpMvez6N8+g0Q7HGcvAOCZLVns6n4BNa37YK84SOr3TzP/h91U1jjp3yGTL24ewQUsAODzgfdzeu1jXHnwOs6rvI/1iceRUF1K4s5vASgbcQsAPz2hMyf1akNVrcHN7xRwe9XN1GKn9+73SV35DACHT/wDOBKjfRrCTkELEREREREREREREbGsQZ2OzrTYcQjD5qC6wwkAJO5aHsNUwbrddUGLAR0ycJTkk1R7hEqSWFPVkX8v28GRsb8GIO372Xzx/VoArhjbnfT8j7CX76c2vQODTrmcWRcNpU1aIgfsrSk68yXKB14CQGXuJGqOziyx2Wz8/ox+tElLxACWG/3ZNmBG3WsYVOaeRnXuqVE+A5GhoIWIiIiIiIiIiIiIWNbADhk47Db2Halid2klNR3MJaJiu6+FuQn3gPaZJOxdA0BZq/7U4uC173dQ0PYUqjsMx1ZTzvmlL5OaaOf8YV1IXTMXgIrjLgN7AkM6Z/HmdaN4/ZqRDOnejsOnPsyBSz/n0Fmz6/1em7Qk/nxWf5IT7Ewb0omMU39FVdcJOJOzOXLiH6N78BGkoIWIiIiIiIiIiIiIWFZKooN+7dIBWFZY7NrXImn7N2AYMUlTeXUtBQfKAOjfIYOEvasBSO5yAiO7t6K61uDpvAKOjPsNAJc4PueK3lVkHtpE4o4lGDZHXdDiqPSkBLq2Sq37w2ajtk1fcCQ3+N2xPdrw2c/HM/P0PmBPoGTKi+y/5jtqW/eO8BFHj4IWIiIiIiIiIiIiImJpJ/duC8B7a3dT1eVEnInpOA4VkLhzSUzSs3HvEZwG5KQnkZOe5JppUdt+MLdM6AnA+z/sYVHNAD53nkCirZYba1+BZf8GoKrH6TgzOgX128kJdmw2W90fdofX4EY8U9BCRERERERERERERCzt3OM7YLfB8sISth2xU9n3PABSfnglJulx388Cw3DNtKhpN5jjO2Zyer8cDODueWt5qPoSnNjIKfwAvvsPAOWDfhaTdMcDBS1ERERERERERERExNI6ZqUwtkdrAN5Zs4uKgZcCkLzpPWyVJVFPz/o9pQD0b5+BvXQ79spiDHsiNW36ATDjxB44bFBe7WSd0Z0tHc6q+2BNObXZuVR3mxD1NMcLBS1ERERERERERERExPKmDq5bTum9tbupyBlKTZv+2GorSd7wdtTT4ppp0T6DhH1HZ1m06e9aqim3TZorvVkpCaSd9lsMexIAFcdfCTY9mvdFZ0ZERERERERERERELG9Crza0SUtk/5Eqvs4/SMVxdbMtor1EVFWNk8376zbhHtAhw7WfRU27QfXeN+PEXCb1y+Hu0/qQ2KYHh0+5Hwb9PyoGXR7V9MYbBS1ERERERERERERExPISHXbOOa4DAG+v3kVF//+HYU8icd8a154S0bB5/xFqnQbZKQl0yEyut5+Fu9ZpSTw45TjOHNgegMrjLoULn8NIyoxaWuORghYiIiIiIiIiIiIiEhfOG9wRgLytB9hdnUZl77q9IlJ++K/X9z/+1RZ+8q/FbNl/BHtJPm1eGEXGl78NKQ3um3DbbDafMy0kOApaiIiIiIiIiIiIiEhc6NEmjWFdsnAaMP+H3cc25N7wFlSX13vvpn1HmLu0iIPl1fzn20IyFj+A4/BOUtbMxV5SEHQa1u+pC1r0b5+J/chuHGV7MGx2atoeF/yBiYuCFiIiIiIiIiIiIiISN8wNrl9aVsSW9GHUZnXHXnWI9CUP1XvfvxZuxTj63zs3fEPy5vkA2DBIXf1CUL9dWeNk8dYDQN1Mi6StHwFQ27ovJKYG9Z1Sn4IWIiIiIiIiIiIiIhI3Jvdvx3EdMympqOGOt39gz8iZAKStnEPK6ucB+L6ohIVbDuCwQfdWKdxlexmAmtZ9AUj58b9QdSTg335+yTZ2HKqkfUYSEzo6Sf+mLlBibgouoVPQQkRERERERERERETiRlKCnb+ffzwdM5PZdrCcm1flUjLqVwBkLPwDifmf8fjCrUDdHhh/6L+D8Y4fqCKB/Wf9m5rsHtirDpGy4Y2AfrfgQBkvLC0E4M5Te9Nu2YPYK0uozjme8sFXh/UYWzIFLUREREREREREREQkruSkJ/HotEGkJzlYUVTCr/dOprjPhdgMJ+kf3kTGrjzSEgyuH9uNiUVPAfCfmsl8siuNisHXAJC66jkwjMZ+xsUwDP762Saqaw3G9mjNGembSFn3GgY2Dk/8P7AnROxYWxoFLUREREREREREREQk7vTJSeehKcfhsMGH6/Yyas15LLcPJrG2jJeT/o/vk6bT98MLSdy/lgp7Ok/WTOV/K7ZTMfAinInpJBzcRGLRQr9+65P1e/l2WzFJDhu/ntidzC9/B0DFcZdR03FEJA+zxVHQQkRERERERERERETi0pgerbn/3IH0zkmjhgSuKbuNd2rHcYg0kpzlJO5ZCUDp0BsptWezemcpa/YbVAy4CDg626IJhytreOSLLQBcPaY7fbe9RMLBDThT23Jk3MzIHVwLpTkrIiIiIiIiIiIiIhK3JvVrx6R+7ThUUc3qHaWs2X0cad2yGJ66m8SdS7FVlcLQ6fykZAvv/7CHl5ZvZ8hJ15C2+t8k5X+G4+Amalv38fn9n67fy/4jVXRtlcJVI7uQ+tKzABwe91uMlNbROswWQzMtRERERERERERERCTuZaUkcmKvNlw/LpehXVtT23YAFYOupHz4zeBI4tLhXbBRt9TTp3uzqOzxE2wYZH56O9RW+/zeLzbtB2DK8R1J278Kx5HdOBMzqOx3fnQOrIVR0EJEREREREREREREmr0BHTK5clRXAO7/eAP5I/6AMzmbxD0rSVv6iNfPHK6s4dttBwE4tW8OyVs+AKCqxyRwJEcn4S2MghYiIiIiIiIiIiIi0iLMOLEHAztkUFJRwz0LD3HolL8CkLb8CRK35zV4f97WA1TXGuS2TqVnm1SSzKBFzzOjmu6WREELEREREREREREREWkREh127j17ACkJdpZuK+a5g0MoH3iJa5koW8XBeu//fOM+oG6WhePAehJK8jEcyVTlnhqL5LcIClqIiIiIiIiIiIiISIvRo00ad53WG4AnF+WzcsDd1GT3xHF4Jxlf3eN6X2WNk6+3HgDglL45JG/5EICqbhMwkjKin/AWQkELEREREREREREREWlRzhvUkVP6tKXWafDwol0cmvw4hs1OysZ5JO5YAsCSgoOUVztpn5HEcR0ytDRUlChoISIiIiIiIiIiIiItis1m485Te5PksLG8sIRPD3Wl4rjLAEhf9CcwnHzhvjRUaSGJ+9Zi2OxU9vxJDFPe/CloISIiIiIiIiIiIiItTqesFK4Y2RWAx77cQvGIX+JMyiRx72oSf3yVrzbvB+qCFubSUNWdx2CktolZmlsCBS1EREREREREREREpEW6anR3ctKT2F5Swcs/VlA28g4AUvIepKailOyUBIZ2yT62n4WWhoo4BS1EREREREREREREpEVKS3Lw8wk9AHhuyTZWdvoph1K7kVK5j9sS3uTqLrvI/P5fJOxcCkBlr7NimNqWISHWCRARERERERERERERiZWzj+vAqyt28OPuw1z+0hpOt1/EnKS/c2PCfNg+H7bXva+64wicmZ1jm9gWQDMtRERERERERERERKTFstts3HVaHxIdNuw2KGp7Mj9kjAfAmdqWyp5ncHj8PRw68+kYp7Rl0EwLEREREREREREREWnRhnTOYv4NY0hKsJOelAC1L7G/fC/O9E5gs8U6eS2KghYiIiIiIiIiIiIi0uK1Tks69ocjEWeGloKKBS0PJSIiIiIiIiIiIiIilqCghYiIiIiIiIiIiIiIWIKCFiIiIiIiIiIiIiIiYgkKWoiIiIiIiIiIiIiIiCUoaCEiIiIiIiIiIiIiIpagoIWIiIiIiIiIiIiIiFhCQqwTEE02W6xTIBJ9Zr5X/hcJnsqRSOhUjkRCozIkEjqVI5HQqRyJhKYll6FAjtlmGIYRuaSIiIiIiIiIiIiIiIj4R8tDiYiIiIiIiIiIiIiIJShoISIiIiIiIiIiIiIilqCghYiIiIiIiIiIiIiIWIKCFiIiIiIiIiIiIiIiYgkKWoiIiIiIiIiIiIiIiCUoaCEiIiIiIiIiIiIiIpagoIWIiIiIiIiIiIiIiFiCghYiIiIiIiIiIiIiImIJClqIiIiIiIiIiIiIiIglKGghIiIiIiIiIiIiIiKWoKCFiIiIiIiIiIiIiIhYgoIWIiIiIiIiIiIiIiJiCQpaiIiIiIiIiIiIiIiIJShoISIiIiIiIiIiIiIilqCghYiIiIiIiIiIiIiIWIKCFiIiIiIiIiIiIiIiYgkKWoiIiIiIiIiIiIiIiCUoaCEiIiIiIiIiIiIiIpagoIWIiIiIiIiIiIiIiFhCQqwTICIiIiJSVFTEpEmTfL5ut9tJTU2lS5cunHTSScyYMYPs7GwA9uzZw4QJEwD44IMP6NWrl9fvmDBhAnv27CExMZFvv/2WtLS0Bu85cOAA48aNA2DBggV06dIloOM4cuQIZ599Nj179uT5558P6LPBKi8v5+DBg3Tu3Dkqv+ev0047je3bt/v13gceeIBp06ZFOEXxr6amhh07dtC9e3e/3j9z5kzeeustv7//N7/5DVdffXWQqRMRERERCQ8FLURERETEUgYNGkRSUlK9f6uurqaoqIgNGzawYcMG3n//fd544w1ycnJo3749PXr0ID8/n1WrVnkNWmzevJk9e/a4vmvZsmWcfPLJDd733XffAdCtW7eAAxaGYfD73/+eXbt20bNnz4A+G6wvv/ySP//5z9x+++1MnTo1Kr8ZqB49etCmTZtG39O2bdsopSZ+rVy5knvuuYezzjqLm2++OaDPtm3bltzc3Cbf16FDh2CTJyIiIiISNgpaiIiIiIilPPbYY3Tt2tXrawsXLuTXv/41u3bt4oEHHuDvf/87AKNHjyY/P5+VK1dy/vnnN/jc4sWLAejZsydbt25l8eLFXoMWK1asAGDMmDEBpbmmpoY//elPzJ8/P6DPhWrOnDl+z2aIlRtvvFGzKMLglVdeYcOGDZx11lkBf/bkk0/mwQcfjECqRERERETCT3taiIiIiEjcmDBhAjNnzgTgo48+4vDhwwCMGjUKqBuN7o0ZtLj++usB+Oabb7y+7/vvvwcCC1oUFRVxzTXX8Nprr/n9GREREREREfFOQQsRERERiSsnnXQSULfM07Zt24C6mRYA69evp6Kiot77nU4n3377LRkZGUydOpWcnBx+/PFHDh48WO991dXVrFmzBvA/aPHxxx9zzjnn8O2339KjRw9uvPHGkI5NRERERESkpVPQQkRERETiit1+rAvrdDoB6NixI926daOmpoa1a9fWe/+aNWs4dOgQo0aNIiEhgdGjR2MYBkuWLKn3vh9//JGKigp69Ojh99r+69evp7q6mssvv5y33nqLHj16hHZw1G0mfvXVV3PSSScxePBgTj31VGbOnMnGjRtd71myZAn9+/fn22+/BeBXv/oV/fv3580336z3Xd9//z233nor48ePZ9CgQZx22mnce++97N69u8Hvzpw5k/79+/P555+zaNEiLr74Yk444QQmTJjAL3/5SzZv3hzysfmrrKyMWbNmMXXqVIYOHcrw4cO56KKLePnll6mpqWnw/v79+3PyySezbds2Lr30UgYPHszJJ5/M22+/7XpPcXExf/vb3zjjjDMYPHgwo0ePZvr06Xz99dc+07F7924eeugh12fGjBnDTTfd5HVGT01NDa+//jrXXnst48aNY9CgQYwaNYrLL7+c1157DcMwGnxm8eLFzJgxg1NPPZVBgwYxYcIEbr/9dtfeKlA3k6d///6uDbUfe+wx+vfvz+OPPx7IKQ3Ym2++Sf/+/Xn00Ud58803mThxIkOGDGHKlCns27fPlV8WLlzIgw8+yIgRIxgxYgS33HJLvXPy0ksv8dOf/pRhw4ZxwgknMHXqVGbPnk15eXmD3zzttNPo378/e/bs4cYbb2TIkCGMHz+e2bNnA3D48GEeffRRzj//fIYPH87w4cOZNm0as2bN4siRIxE9HyIiIiISPdrTQkRERETiyieffAJAcnIyffr0cf37qFGjKCwsZNWqVYwYMcL17+ZSUGPHjgXqZlG8//77LF68mDPPPNP1vmD2sxg1ahRnn302vXv3Dv6A3MyePdu1T0dubi7t27enqKiIt956i/fff58XXniBYcOGkZmZyfDhw9mwYQOHDx92bXbtvqH1Sy+9xF/+8hcMw6B169b069ePgoICXnrpJd5//32ee+45jjvuuAZp+Oyzz3jjjTdITk6mV69ebN++nffee4/PP/+c2bNnM3LkyLAcqy979+7lqquuYvPmzTgcDvr27Ut1dTUrV65k5cqVfPzxxzz11FOkpqbW+1xlZSXTp09n79699OnTh82bN7vyx9atW7nmmmvYuXMnSUlJ9OzZk8OHD7Nw4UIWLlzIbbfdxs9//vN637dy5Upuuukm9u/fT1paGn379mXPnj0sWLCAr776ijlz5jBu3DgAqqqquOGGG1i8eDEJCQl0796djh07sm3bNpYtW8ayZcvYvHmza2kzgPfee4+77roLwzDo3Lkz/fv3Z+fOnXz44Yd8/PHH/OMf/+CMM84gOTmZ4cOHU1BQwP79++ncuTMdO3akU6dOEb0Opry8PJ5++mk6duxI586dAcjJyXG9/vjjj7Ny5Ur69u1LcXGxK+BXVlbG9OnTWb58OTabjd69e5OQkMCGDRtYt24d8+fP57nnnvO6Cfstt9zCDz/8QN++fSkoKCA3N5fKykquvPJKfvjhB1JTU+nevTtOp5P169ezdu1aPvnkE/773/+SmJgYlfMiIiIiIpGjmRYiIiIiEhcMw+CLL77goYceAuCyyy4jJSXF9bq5RJS5L4UpLy8POBa0MP/fc18LM2hhvu6PsWPHhi1gUVJSwj//+U9SUlL473//y8cff8ybb77JwoULmTZtGpWVlTz66KMAHHfccbzyyiuuoMPNN9/MK6+8wsSJEwFYvnw59913H6mpqfztb3/jm2++4c033yQvL4+rrrqKgwcPcuuttzZYSgvgtddeY9SoUSxYsMD1+xdeeCFHjhzhV7/6FVVVVWE5Xl/uuOMONm/ezNChQ/nkk0+YN28e77//Pm+//TZdunRh8eLF3H///Q0+V1xcTHV1NR9++CFvvfUWX375JYMGDaK6uprbbruNnTt3csEFF7B48WLeeecdFixYwJw5c8jMzOSf//wnX331leu7Kioq+OUvf8n+/fuZOnUqCxcu5M033+Srr77i5z//OTU1NfziF79wnYuXX36ZxYsX069fPz777DM++OAD3nrrLRYvXuzaR2Xu3LmUlpYCdTOEHnzwQQzD4Mknn+Tzzz/njTfeYOHChdx00004nU7++te/AtCuXTteeeUV18bxP/3pT3nllVe48MILI3odTKtWreKiiy5iwYIFfPjhh/znP/+p9/rKlSt5+OGHee+99/jyyy+5/fbbAfjTn/7E8uXLyc3N5Z133mH+/PnMmzePjz/+mOOPP55169Zx1113ef3NLVu28Pbbb/PWW2/x1Vdfcdppp/H666/zww8/MHr0aL766iveeecd3nvvPd5//306derEmjVrmD9/fsTPh4iIiIhEnoIWIiIiImIpt99+O5deemm9/11wwQWMHTuWG2+8kdLSUiZPnsydd95Z73PmZtyrVq1y/VtVVRXfffcdrVu3pn///gD06NGDjh07kp+fz86dO13vNYMWZvAj2rZu3Up1dTVdu3Zl2LBhrn9PTk7m7rvv5sQTT6Rfv35+fde//vUvnE4nd955J1OmTKn3Xb/97W8ZNmwYRUVFvPvuuw0+27p1ax5//HHatGkDQFJSEvfeey/9+vVj+/btfPDBBwEd129+8xv69+/v838//vij671Llixh2bJlZGZm8tRTT9GlSxfXawMHDuTJJ5/EZrPx5ptvUlRU1OC3rrjiCtdI/9atWwN1+45s2LCBwYMHc//995ORkeF6/4QJE7j77rsBmDVrluvfP/jgAwoLC+nXrx8PPPCA6zN2u53bbruNgQMHcvDgQRYtWgTUBcDsdjszZ86kY8eOru9JSkrizjvvJDU1lZqaGvLz8wHYv38/e/fuJT09nVNPPdX1fofDwS233MLJJ5/M2LFjw7bk0VtvvdXoNejfvz9Tp071+lmbzcbtt9/uWpbNPK+mvn37ct5557nSn5WVRWFhIe+++y4Oh4N//etf9fJtt27deOqpp0hLSyMvL49ly5Y1+M3zzjvPNUsmIyODxMRE1q9fD8DEiRPJyspyvTc3N5fbb7+dyZMnk5ycHMJZEhERERGr0PJQIiIiImIp5mbYnrKzs5k2bRrnnHOOazNud127dqVLly5s376dvXv30q5dO5YvX05lZSWnnnoqNpvN9d4xY8Ywb948lixZwvnnn8+uXbvYtWsXffv2rbf0TTR16dIFh8PBpk2bePTRR7n44otdy/G0adOG5557zq/vqaysdO3XcdZZZ3l9z5lnnsmKFStYtGgRP/3pT+u9du6555KdnV3v3xwOB+eddx5/+9vf+Oqrr3w+4PbGXLrKl7S0NNd/L1y4EICf/OQnXpcNGjhwICNGjGDZsmUsWrSISy65pN7rQ4cObfCZL774wvWdDoejwetnnnkmf/jDH1ixYgWHDx8mIyPDlY7zzjvP62cef/xxkpKSXAGSWbNmUVVV5XVposrKSrKzsykvL3fNbGndujWZmZmUlpbyxz/+kWuvvZZevXoBkJCQwDPPPNPwRIWgbdu25ObmNvoeX/uxdO3a1eu1MA0ZMqTBvy1atAin08nYsWPrLeFm6tChA5MnT2bevHl8+eWXDZYc8/ad3bt3B+CFF16gW7dunHzyya4lwi644AIuuOACn2kUERERkfiioIWIiIiIWMpnn31G165dgbp18T/++GMeeOABSkpKSElJce0j4M2oUaPYvn07K1eu5PTTT2+wn4Vp7NixzJs3j2XLlnH++ee7Nj4OZD+LcGvXrh1XXHEFL7zwArNmzWLWrFn069ePCRMmMGnSJIYPH14v8OJLfn4+1dXVANx6661e31NcXAxAQUFBg9eOP/54r58xR8t7+0xjbrzxRqZNm+bXe82ZCN722jANHDiQZcuWeU1Hu3btGvybuYH4G2+8weeff+71Ox0OB7W1tRQVFTFgwAC2bdsG4HNmS7du3Rr8W1JSEgcOHGDp0qVs2bKFbdu2sXHjRtatW+e6HubG8QkJCdxyyy088MADvPbaa7z22mt069aNiRMnctpppzFmzBgSEsJ3q3byySfz4IMPBvVZb+e0qdf9uY7HHXcc8+bN83od27dv3+DfzGWxioqKuO2220hOTmbUqFFMnDiRyZMnR22PDxERERGJPAUtRERERMSy0tLSOP/88+nduzeXXXYZL7/8Mg6Hg3vuucfr+0eNGsXbb7/NqlWrOP3001m8eDHgPWgBdXs/wLF9MCIZtPjhhx/4y1/+4vW1V155BYDf/va3DBgwgJdeeok1a9awYcMGNmzYwLPPPkuPHj247777XMtg+XL48GHXf5vBGF/MPRbcZWZmen2vOard22fCxVwOKT093ed7zNe8LZ3kbXkg83zk5+e7Hqb7Yh7boUOHABps9u1LeXk5f/3rX3n99dfr7fmRk5PDGWecwaJFi1yBItPVV19N9+7d+fe//82yZcsoLCzkxRdf5MUXX6R9+/b8/ve/5yc/+Ylfvx9JTS255O31UK9jUlJSg3/Lzs7m9ddf56mnnmL+/Pns27ePRYsWsWjRIh544AHOOOMM7r333npLR4mIiIhIfFLQQkREREQsb/Dgwdx+++08/PDDzJ07l3HjxjFp0qQG7zP3o1i7di1HjhxhzZo1dOzYkZ49e9Z7X+fOnenWrRtbtmyhuLiY7777DpvN1mRAIBSlpaVNBhEApk2bxrRp09i9ezdff/01Cxcu5IsvviA/P58bbriBDz/80LUskTfmg/aOHTvy5ZdfBpxOb5tzw7GH/557GoSTuVRUY3s5mOlo7IG4O3Oz9ueff77RWTrePlNeXu7X+3/3u98xf/582rZty5VXXsmQIUPo27eva8bAKaec0iBoAXDaaadx2mmnceDAARYvXsyiRYtYsGABe/bs4Y477uC1117zOfPFyvy5jmaAyN/rCHV577e//S0zZ85kzZo15OXl8cUXX7BixQo++OADampqeOKJJ0JLvIiIiIjEnDbiFhEREZG4cM011zBw4EAA/vSnP9WbUWDq3r07HTp04IcffmDVqlXU1tY2mGVhMmdVrFmzhnXr1tG/f/+IPpAfM2YM69ev9/o/qNv74Mcff2TLli1A3br/06ZN49FHH+Wzzz6jS5culJWVsWDBgkZ/p1u3btjtdvbu3ev1HAEUFRWxcuVKDhw40OA1czklTxs2bACgd+/efh9zoMx9F9w35/ZkvuZtiabGvtM8r55qa2tZvHgxhYWFruWbzM9s2rTJ62deffVVrrrqKt5++212797N+++/T0JCAq+88go33XQTJ554oitgUV1d3eA8V1dXs3HjRtextGnThnPOOYcHHniAL774gsGDB1NbWxvwpudWEYnruH//fr799lvKysqw2+0MGTKEGTNm8N///pfHHnsMgE8//dRn0E1ERERE4oeCFiIiIiISFxwOB3/605+w2Wzs2bOHJ5980uv7Ro0axYEDB/jwww+BhktDmcx/f+edd6iuro7pfhYAr7/+Oueffz4PPPBAg9fatGnj2l/BfLAOuPa4MAzD9W+ZmZkMHTqU2tpa3njjDa+/9fvf/56LLrqIxx9/vMFr77//vmsPBlNNTQ3z5s0D8DrDJVwmTJgAwEcffeQ1oLJu3TrXbJXx48cH9J1vvPEGtbW1DV6fP38+V199NRdeeKHruM0ZGe+99169822aN28e33zzDTU1NWzfvh3DMEhPT/e62fX7779PZWUlgOv38/LyOPfcc7nrrrvqXTuomylzwgkn1Hs/eL/WVjVhwgRsNhvffvut1yDYnj17+PTTTwE48cQT/frOG2+8kSuvvNK1sbo7sywbhuH1GouIiIhIfFHQQkRERETixgknnMAFF1wAwNy5c70+EDWXiHrnnXeApoMWn3zyCRDbTbgBJk+eTGJiIl999RUvvPBCvYflX375JV9//TV2u73ew3pzGZ6dO3fW+66bbroJgEceeYR3333X9e9VVVU8/PDD5OXlkZiYyOWXX94gHfn5+dxzzz2uEesVFRX87ne/Y8uWLRx//PGccsopYTtmT+PGjWPYsGGUlpZy88031zuudevWccstt2AYBhdccAE9evTw6zunTJlCly5dWLt2LTNnzqy3J8c333zDvffeC8All1zi2p/hvPPOo127dvzwww/ce++99YIOTzzxBMuWLSMnJ4czzzyT7t27Y7fbKSkp4eWXX3Z9d21tLW+//TZ/+tOfXP9mfs+4ceNo1aoVmzZt4uGHH663D8bq1atd1+zkk092/bu5jJLntbai7t27c84551BbW8vPf/5zNm7c6HqtqKiIm266ibKyMkaPHu33kl3nnHMOAA899BDr1q1z/XtlZSWPPPIIACNGjAhouSkRERERsSbtaSEiIiIiceWuu+7ik08+obS0lPvuu49///vf9V4396UoKysjNzeXTp06ef2edu3a0atXL7Zs2YLdbo/ofhb+aN++Pffccw9//OMf+b//+z+eeuopOnfuTHFxMdu3bwfg9ttvr7c/R//+/fn888958skn+fjjj/nZz37GBRdcwMSJE7n99tt57LHHuOuuu3jooYfo0KEDhYWFlJSUYLPZuP/+++nTp0+DdPTp04e3336bzz77jB49epCfn09paSmdOnXi4Ycfxm6P7LinRx55hGuuuYYVK1YwadIk+vXrR3V1tWupprFjx/rciN2b1NRUnnjiCaZPn84777zDRx99RJ8+fTh06BCFhYVAXXDg1ltvdX0mIyODxx57jBtuuIFXXnmF9957j9zcXLZv387BgwdJTk7mb3/7GxkZGWRkZHDJJZfw8ssv8+c//5nZs2fTpk0btm/fTnFxMdnZ2QwYMIB169a5Ag5JSUn89a9/ZcaMGTz77LO8+uqrdO/encOHD7Nt2zYMw+DCCy+s90C/f//+QN2MnNWrV3P22Wdz4403+nUOvvrqKy699NIm39ejRw+vM32C8cc//pGioiK+//57pkyZQp8+fXA4HGzcuJHa2loGDBjA3/72N9cMkqZcccUVfPbZZyxdupTzzz+f7t27k5GRwbZt2ygtLSUrK6tegEhERERE4peCFiIiIiISV9q2bcstt9zCAw88QF5eHh999BFnnHGG6/VevXrRrl079u7d63OWhWns2LFs2bKFgQMHkpWVFemkN+mSSy6hXbt2vPzyy6xZs4b169eTlZXFxIkTufzyy5k4cWK9919//fUUFRW5NurOz893vXbzzTczcuRIXnjhBVasWMG6devIzMxk0qRJXHvttYwcOdJrGs455xx69+7N008/zYYNG8jJyWHatGnccMMN5OTkRPLwgbpN0t944w2ef/55PvroI7Zu3UpSUhLDhw93bVLucDgC+s7jjjuOd955h2effZYFCxawceNG7HY7xx9/POeddx6XX345CQn1b41GjBjBvHnzePrpp1m4cCHr168nMzOTs88+m5///Of1Aj6///3vGThwIP/973/Jz8/nwIEDdOrUiSlTpnDdddexcOFCfv/73/PJJ59w2WWXATBx4kTmzp3Lv//9b77//nvWr19Peno6o0eP5qc//SlTpkypl57zzz+fH3/8kfnz57N161afe494s3//fvbv39/k+8rKyvz+zqZkZWUxd+5cXn75Zd555x22bt2KzWZjwIABTJkyhUsvvdS14bk/EhMTmT17Ns899xwff/wx27ZtY+fOnXTo0IHzzjuPG2+8sdEN6kVEREQkftiMeFgUVUREREREImrmzJm89dZb3H777dx8882xTo6IiIiIiLRQ2tNCREREREREREREREQsQUELERERERERERERERGxBAUtRERERERERERERETEEhS0EBERERERERERERERS9BG3CIiIiIiIiIiIiIiYgmaaSEiIiIiIiIiIiIiIpagoIWIiIiIiIiIiIiIiFiCghYiIiIiIiIiIiIiImIJCbFOQDTt31+KdvCQlsZmg7ZtM5X/RUKgciQSOpUjkdCoDImETuVIJHQqRyKhacllyDx2f7SooIVh0OIyg4hJ+V8kdCpHIqFTORIJjcqQSOhUjkRCp3IkEhqVocZpeSgREREREREREREREbEEBS1ERERERERERERERMQSFLQQERERERERERERERFLUNBCREREIm52Xj5zFhd4fW3O4gJm5+VHN0EiIiIiIiIiYkkKWoiIiEjE2W02ns4raBC4mLO4gKfzCrDbbDFKmYiIiIiIiIhYSUKsEyAiIiLN3/RxuQA8nVfg+tsMWNw4Ptf1uoiIiIiIiIi0bApaiIiISFS4By6eW7KN6lpDAQsRERERERERqUfLQ4mIiEjUTB+XS6LDRnWtQaLDpoCFiIiIiIiIiNSjoIWIiIhEzZzFBa6ARXWt4XNzbhERERERERFpmbQ8lIiIiESF5x4W5t+AZlyIiIiIiIiICKCghYiIiESBt023vW3OLSISC7Pz8rHbvC9ZN2dxAU7D4IbxPaKfMBERERGRFkhBCxEREYk4p+F9023zb6dhxCJZIiIA2G02rwFU94CriIiIiIhEh4IWIiIiEnGNjVDWDAsRiTVvM7+8zRATEREREZHIU9BCRERERERaPPfAxXNLtlFd632GmIiIiIiIRJY91gkQERERERGxgunjckl02KiuNUh0eN/jQkREREREIktBCxEREREREer2sDADFtW1BnMWF8Q6SSIiIiIiLY6WhxIRERERkRbPcw8L828I/947s/Pysdu8z+SYs7gAp2E0uheQiIiIiEhzpqCFiIiIiIi0aN423fa2OXe42G02r9/rng4RERERkZZKQQsREREREWnRnIb3TbfNv52GEdbf8xYQ8RY4ERERERFpiRS0EBERERGRFq2xpZgiFUBwD1w8t2Qb1bXeAyciIiIiIi2NNuIWERERERGJgenjcl2bfic6vO9xISIiIiLS0ihoISIiIiIiEgNzFhe4AhbVtQZzFhfEOkkiIiIiIjGn5aFERETEMmbn5WO3eR9tPGdxAU7DaHQZFxGReOG5h4X5N0RuSSoRERERkXigoIWIiIhYht1m8/rQzv3hnohIvPO26ba3zblFRERERFoiBS1ERETEMrw9tPP2cE9EJJ45De+bbpt/Ow0jFskSEREREbEEBS1ERETEUtwDF88t2UZ1rfeHeyIi8aqxZe5U14mIiIhIS6eNuEVERMRypo/LdW1Mm+jwvseFhNfsvHyfmwDPWVzA7Lz86CZIREREREREWiQFLURERMRy5iwucAUsqmsNnw/TJXzM/UQ8z7W5PJfdZotRykRERERERKQl0fJQIiIiYimee1iYf4OWTYkk7SciIiIiIiIiVhDxoMXBgwd54oknWLBgAfv376dHjx787Gc/48ILL2zys8uWLePyyy/3+tro0aOZO3duuJMrIiIiMeTtIbm3h+kSGdpPRERERERERGItokGLsrIyrrvuOjZs2MBll11Gr169+PDDD/nd737Hvn37mDFjRqOfX79+PQB33nknHTt2rPdaTk5OxNItIiIiseE0vD8kN/92GkYsktWiTB+X6wpYaD8RERERERERibaIBi1efPFF1q5dyyOPPMI555wDwMUXX8z111/PE088wdSpU+nUqZPPz69fvx6bzcaVV15JWlpaJJMqIiIiFnDD+B4+X9PD8+jwtp+Izr2IiIiIiIhES0Q34n777bfp0KGDK2ABYLPZmD59OtXV1bz77ruNfn79+vV07dpVAQsRERGRKHBfnivvjgncOD7X6+bcIiIiIiIiIpESsZkWpaWlbNmyhcmTJzd4bejQoQCsWrXK5+cNw2DDhg2MHTsWgNraWqqqqkhNTY1MgkVERERaMO0nIiLN0ey8fOw270vdzVlcgNMwGp3lJyIiIiLRF7Ggxe7duzEMw+vyT6mpqWRnZ1NUVOTz8wUFBZSVlVFWVsYVV1zB999/T3V1Nb179+aWW27h7LPPDjhNNlvAHxGJe2a+V/4XCZ7KkbQETsNgxokN9xO5fnwuNhvUOo2QyoDKkUhoVIaC47DbmPV1ATZb/cCrGaidcWKuzmkLonIkEjqVI5HQtOQyFMgxR3SmBeBzaaeUlBTKy8t9ft7chHvVqlVcffXVXHvttezcuZPnn3+eX/ziFxw8eJDLL788oDS1bZsZ0PtFmhPlf5HQqRxJc/a7qYN9vjZzyqCw/Y7KkUhoVIYCM3PKINLSknnkkw2kpSVz26S+/POzjcz6uoA7J/fjtkl9Y51EiQGVI5HQqRyJhEZlqHERC1oYhlHv/729brf73lIjNzeXW265hYkTJzJkyBDXv0+dOpVzzjmHv/3tb0yZMoWsrCy/07R/fyk+kiPSbNlsdRWh8r9I8FSOREKnciQSGpWh4F02tCNlZZU88skGHl+wkerauplllw3tyL59pbFOnkSRypFI6FSORELTksuQeez+iFjQIj09HYCKigqvr1dUVHhdOso0YMAABgwY0ODfMzIymDZtGv/6179Yvnw5p556qt9pMgxaXGYQMSn/i4RO5UgkdCpHIqFRGQrOdWNzefabbVTXGiQ6bFw3NlfnsQVTORIJncqRSGhUhhrne6pDiLp27YrNZmPXrl0NXisrK+PQoUN07NgxqO/OyckB4MiRIyGlUURERKxtdl4+cxYXeH1tzuICZuflRzdBIiISl+YsLnAFLKprDZ9ti4iINE+6rxCJLxELWqSnp9O7d29Wr17d4LWVK1cCMHz4cJ+f//Of/8xpp53G9u3bG7y2adMmALp37x6m1IqIiIgV2W02ns4raHCDYW6gam+Ju5eJiEhAzDbjxvG55N0xgRvH53ptW0REpPnSfYVIfIlY0ALgvPPOY/v27cyfP9/1b4Zh8Oyzz5KUlMTZZ5/t87OdOnVi+/btvPDCC/X+ffPmzbz55pv07duXwYN9bxjZHCgKLCIiLd30cbkNHi65P3yaPi43xikUEREr89ZmeGtbRESkedN9hUSCnt1GTsT2tAC46qqreOedd/j1r3/NmjVr6NmzJx988AF5eXn86le/on379gAUFhby3Xff0b17d4YNGwbAlVdeyfvvv88LL7zArl27GDduHDt27ODll18mISGBBx98EFszj4KaUWCgXuXpXqmKiIg0d2Yb+HReAc8tqVuPXDcWIiLiD6fhvc0w/3ZqMWkRkRZD9xUSbnp2GzkRDVqkpKQwd+5cHnnkEebNm8eRI0fo2bMnDz30EOeff77rfUuXLuU3v/kNF1xwgStokZqayosvvshTTz3FBx98wGeffUZWVhYTJ07k1ltvpWfPnpFMuiW4V6bm38FEgWfn5WO32by+f87iApyGwQ3je4Qt3SIiIuE2fVyu68Yi0eG9TRMREe9a8v1AY8eltkREpOXRfYWEU7ie3UpDEQ1aALRp04b77ruv0fdMmzaNadOmNfj3jIwM7r77bu6+++5IJc/ywhEFVtRPRETinbcNVNUBFBHxj+4HRERE6ui+QsJNM3giI+JBCwldqFFgRf1ERCSeebZZ5t/ge5RsSx5VLCLiWQe63w8sLyxmWNdsVyBD9wMiItJSBHNfIeIPzeAJPwUt4kA4osCK+omISDzytYEq0OgNhr+jihXcEJHmyFsdOH1cLssLi1lWWMJ3RSU4DXQ/ICIiLUaw9xUi/tAMnvBT0MLiwhkFVtRPRETiTbAbqPo7y1BLpohIc+SrDlxWWILdBk4D3Q+IiEiLEux9hUhTNIMnMhS0sLBwR4EV9RMRkXgTygaq/swy1BKKItJceasDR3bLZllhie4HRESkxQnlvkLEF83giRwFLSwsnFFgRf1ERKQl8meWoZZQFJHmyr0OtNtgWWGJ7gdEREREwkQzeCJHQQsLC1cUWFE/ERFpqfydZaglFEWkOTLrQHNJqJHdsnU/ICIiIhImmsETOQpatACK+omISEsUyCxDLaEoIs2Nex3oNAxWFJWwrLCkXv2m+wERERERsSIFLVoARf1ERKSlCWSWoZZQFJHmxtfePN7qN9VzIiIiImI1ClqIiIhIs+PvLEMtoSgizZFmWks8mZ2Xj93mfWnGOYsLcBpGowPxREREpPlR0EJERESaHX9nGerBnog0R5ppLfHEbrN5HSjgPrBARKQlUBBX5BgFLURERKTF0oM9ERGR2PI2w9HXEmciIs2ZgrgixyhoIVGlqLGIiESb2h4RERFrcw9cPLdkG9W13mdCiog0Zwriihxjj3UCpGUxo8ZzFhfU+3ezErbbbDFKmYiINFdqe0RERKxv+rhcEh02qmsNEh3eBxuIiDR308flcuP4XJ7OK2D8PxYqYCEtlmZaSFQpaizNgUZtxzddv5ZHbY+IiIj1zVlc4ApYVNcazFlcoDZaRFqk6eNyXbPOFMSVlkpBC4k6Tf2VeKd1JuObrl/LpLZHRETEujwHE5h/g/aYEpGWR0FcEQUtJEYUNZZ4plHb8U3Xr+VS2yMSPzQrTqTlcO+HOY36D+c8+2sq++JObYU0RwriitRpUUGLF74t5GejujX4dzVm0aeoscQ7jdqOb7p+LZPaHpH4oVlxIi2H0zAafThnBjJU9sWT2gppbrwNpvMWxBWJFCsFg1tU0OLfSwqpqnGqMYsxRY2ludCo7fim69eyqO0JTLQ7q1bqHIs1xHJWnPKjSHS5lyfNiJVAKL9Ic+MexHXnHsQViSQrBYNbVNDimjHdePyrrYAas1hR1FiaE43ajm+6fi2H2p7ARbuzaqXOsVhHrGbFKT+KxJZmxEoglF+kOWlsUITytESDlYLBLSpocdXoblTVONWYxVA4osYa/SZWoFHb8U3Xr2XVpfE6YimW1yjanVUrdY7FWmIxK075UST2NCNWAqH8IiKR1JLuncE6weAWFbQANWaxFo6osUa/Saxp1HZ80/Wr05Lq0ngdsRTraxTtzqpVOsdiLbGaFaf8KBJbmhErgVB+EZFIivV9WSxY4fl5iwtaqDGLfxr9JrEWr6O2pY6uXx3VpdZnhWsU7c6qFTrHYh2xnhWn/CgSG7Eu+/GmpY0A9qT8IiKRZoX7skCF2jZY4fl5iwpavPBtoRqzZkKj3ySW4nXUttTR9TtGdan1xfoaRbuzaoXOsViDFWbFKT+KRJ8Vyn68aYkjgE3KLyISLbG+LwtUKG2DVYLBLSpo8e8lhWrMmhGNfhMRCZ3qUuuL1TWKdmfVKp1jsYZYz4pTfhSJjViX/XgUjyOAw0X5RUSiKZ7unYNtG6wUDG5RQYtrxnTjZ6O61fs3NWbxS6PfRERC1xLr0nhbRiEW1yjanVUrdY7FGmI5K075USR2NCM2OPE2AjhclF9EJJri7d45mLbBSsHgFhW0uGp0N7ydWytnMPFOo99ERELXUuvSeFpGIVbXKNqdVSt1jkWUH0UkHsXTCGARkXgTr/fOgbYNVgoGt6ighTQPGv0mIhK6llyXxssyCrG8RtHurFqpcyzNR7CzqpQfRSQexdsI4HCLt5m0IhI/4vneOZ7bBgUtJO5o9JuISOhael0aD8sotPRrJBKqeJpVJSISingdARxOqvMlXingZn3huC+LxXV2r/+chsGKohKf9aQV85mCFhJ3NPrtGH8qvRtP7BH9hIm0YPHS6VRdav1lFHSNREITL7OqwiVe2p9I03mIHp1ra4jnEcDh1NLq/Jakudc1CrhZXzjuy9yv8/XjI3+dvQWzlxWWMLJbts960moUtBCJY2rcRKxH5TJ+xPNUWRHxTzzMqgoXtT91dB6iR+faGjQz85iWVOe3JM29rlHArWVwv842G8ycMiii19mzbXD//ZHdsnEahuXzmYIWInFMjZuI9ahcxgctoyDScrjPqrLb6v72HLXpPlIzXkdtqv2po/MQPTrX1qCZmfVZfSatBK4l1DUKuLUM5vWc9XUBz31TSFWtM2LX2Vvb4J7PVu44ZPl8pqCFxExzn+IXLWrcRKxH5dLatIyCSPPhT3/SbrO5AhZOA256dSUjurVylXfAVSfE+6hNtT91dB6iR+darEYzaZunllDXRCvgpmdxsWVe56pap8/rHMlrFE+BXXusExDPZuflM2dxgdfX5iwuYHZefnQTFGfMKX7mOTTPp3mzuKKoxPVe9/Opc9vQ9HG5rk6Z1SsdkZZC5dJ/0W5PG1tGwdykTETig2d/0uTenzSDEEvuPJmR3bJZVljC8sJibhyfy9N59YMUVh+1+egnG5qsL9X+1PHnPMTj/ZwV0xxonov0MTT1/Y9+siGk7xfrcg88590xwVXP+8oPEl+ae/vmLeAWCU31new2W0R+V+qY1znJYfd5nSN5jaKVz8JBQYsQqKCHxnw4ZJ5D83ya66stKyypF8Sw22w6tz7EU6Uj0lKoXPov2u3pDeN7+LzJmT4uVyOLROKIZ38SjtUdZn/SPQjx1EVDXf/+jFud89ySbZYPWAA47DZmfd14fan2p44/5yEe7+esmOZA81ykj6Gx75/1dQEOu/Wuq4TO10xaBS6aj+bcvkUz4NZY38nq/aB4Z57nGSfmsuH+s5hxovfrHKlrFG+BXS0PFYJor6tn1SlcoaTL/RwmOo51Hkd0a1Vv2n68jH6LBa3LXl8kyolVy55Yl8plYFrCOrUiEj6e7bJ7HfLM4gKcBq5ZUyO6tWpQhzx10VDGPPIVTgNX/zNeRm3eNqkvZWWVzPrae30JxKT9sVpfyd92OB7v58KV5nBds2D6PJE+7419/4wTc7ltUl/27SsN6TfEerQhefPWnO+vYrF0bUtYbgus1T/xdZ0Nw/t1Dvc1isclkhW0CJE/mSiQQtLYe1cUlbCssKTe75rfEcu1d82RLMGmy3M9tWvHdG8QxAi2gFqpgooEfyqd6+N0TeZghZofo/WdEt8aq1tuenVlg5G9Vu8MWEFL6TiLSOi8tcvTx+W6AhbmZtu+zDn6PnOkJmDZtc+9tTfuN7juQRpoOMAnWu2PlfpKgd6UR7P9Cdd5Ckeaw5GWUB6A+HsMwd7PqV/R8mhD8uYrHh+2NsazXnMPuLnXa5EOuHk+i4unc+gvK/VPggmsNnWNAmkj4zGwq6BFGDSViQIpJI29d1lhCSO7ZVtuJFCoI2U8p/gB9dYphOBHv1mpgoqEeKx0Ii0SI7fiZRR4cw/SWYk/dbXKZeBaQse5MSrDEi7NPS95a5dvenWlK2DhNPAZfPA2KwHg2jHdG3ynFbi3N94GopjBl+njcpmdlx+zfqGV+kqReCgQLuE8T6GmORxpCfVexJ9jCOV+rqX3K0SaC8+6xttDf1M89HM86zUzrd7qtUjWW96W22pu9WQk+ifB9rODCaw2dY0CaSPjMbCroEUYNJWJAikk/rzX/NtKI4GCHcnS2BS/cIx+s9INVCTEY6UTDZEYWRUPo7Wae5DOSoKtW6yUX6yoJXScG6MyLOHSEvKSez1szjYY2S2bpy4a6nPJCF/LKLn/t7m2r+dnY8X9OG02mDllkNf+8pzFBTHvF1qlrxSJhwLhFK7zFI40h5qWUPOcP8cQyv2ct+9vabPQRZoDz7om3vs5VnhO1ZyX2/IU7v5JtPKfP9fICnkpkhS0CFEw66U2VUiaem8oI0Y8I4Luf3uLCI4IYGZHoOlqqiAN7ZzlWg4rlNFvVrmBkuiKxMiqQL8z2iNdm3uDZTWqW8KrJXWcfVEZlnBpKXlp+rj6S0I9ddFQ179Dw36j+0hNb7MSPKfQW4WZxllfF/DcN4VU1ToBYl5f+lq6ymwTm1qmyypi0f6E2k8NZ5oj0Wf2RyDHEEyfy9f3m8E/EYlfzaGfE8t7yea23JY/wtnWRSP/BXKNgslLgTwbjuXsJQUtQhDMeqn+FpLG3hvKqBrPiKD59/LCYtca7J7HNvLohthNZX5v6XIahs+Htsu2FTPCbQkV99/0TE+oo9+8nc/mvnRCSxeJEXOBfmcsRoDoQXp0xepGv7lpiR1nX8JdhtXWtVzx0B6Emj/nuAUsPJeEcg9UmNy/y/N7vQ02shKzvfEMWJivQfTrS2/9HLOvBI0v0xUrnnnOs082Oy/flTcieT5D6aeGu82MxSzHYI4hkD5XY98/6+sC0tKSuWxox7AflxWo3Y9vun7+i4d+TlNidS/ZEpcZD3dbF+n8F+g1CjQvBfNsOBYUtAhBoJkokELi672hjqrx1hk0M6W5Bru3Tl5Tmd9XukZ2y/a5efjyopJ6Gd/9fDoNgxHdWoVt9Ju38xnvUwrFt0iMmAvmO2M1AkQP0qOnpS9nFC4tsePcmHCWYbV1LZvV24NQ8mcgU+abA7O9MQM0nmJRX3r2c9z/23PPEKtcC888Z7Y/UH+5sEiez1D7qeFsM2M1yzGYYwikz9XY99tsUOutEDUTavfjm65fYKzez2lKrO4lY72cZLRFqq2LZP4L9BoFmpeCfTYcbQpahCCQTBRIIfH1XveIVyijarxFBM3gwvh/LGwQIWwq8zc1UsbfzcMjNfrN1/n0NnPDCoVSQhOJEduhfGcsRoDoQXp0+Fuva8RU01pax7kp/pRhf/NVrIKnYg1Wbw+CzZ8tbXaWebwzTsxl5pRBPPjuGmZ97Xs52mjyFrjwvHZWuiae6b1hfA+feS4S6Q1H3g1XmxnLchTMw5hAHjg19f05OZns21cacLr9Fcu+n9r9yIr0tdX1C4zV+jmB5I9YBY1bmki2dVbJf8HmpUCfDXsT6TpRQYsoCKSQ+BMACMeoGs+I4FMXDXVlSvcIoT+Z35+RMiP8XGIq3Jo6n+6Bi3idUij1RWLEdqjfGc0RIOr8REcg9XqkR0wpKNK8+FuGA8lXsQieSuzFS3sQTP5sSbOzfLU3hmGdYMD0ccf2FvHs51jxmsSyTrRS3rVSWhrjb5/LSv2hWI+WV7sfOdG4trp+/rFiP8ff/NHSBl/EUqTaOqvkv1Dzkr/Phn2JdJ2ooEUUBFJIgi1QgRYKz4jgTa+udE05NyOEUH+atK81Xv0dKWPVtfpuGN8jrqcUSn2RGLEd6ndGKwKvzk9kPfrJBiorqrhubG6DusXzZti9ro70iKlY3xhHk5UeSERCIGU40HwVzeCpxF68tQeB5s+WNDsrHh4sz3ELWHjr51jxmsSqTrRS3g02LdFui/0tA1bqD0W67+dvGtTuh1+0rq2uX+Os2s9xT8OybcWM7N7K9bd7Wj33d/X8vBXa9uYiEu2ulfKfZxvp2Ua75yVvbbSvZ8OhLDMVzjpRQYsoCKSQRKMj65mBbnp1JcsKS+iclcyOQ5Wu5ZxGdM0O2xqvVl6rzypTuqR5imYEPh4ebMQzh93GrK8LMAwaNPTe6kh3kRwxZYUb42ix0gOJSAhmwzXwL1+prWtZ4q09UP70zUoPub2xykjDQCnPBS/abbG/ZcBq/aFI9v38oTweOdG4trp+jbNyP8c9fywvqtvf1XOwm+f+rt4+L9Zlpfzn2UYG0kb7ejY8sls2T100NKRlpsJVJypo0YTmNqrT242FmSm9/b/NRoN9NCCwitTKNzORTFtzyzsSuGhH4K3+YCPeeJbh2yb1payskllf1+0xNKxrtqtT4E+jHMkRU7G+MY4Wqz2QcBeOOj+YMuxPvrJyOyyREev2IJg1nUd0za43ItE9reo3WZOVRhoGQnViaGLZFvtTt1hp6d9Q+n6h9CuUxyMvnP16z2vt+XBxWWGxrp+HWPdzmuKeP0yz8/JZUVTi9flapPs5ejYVXlbOf/620U09G3YPlAazzFS4zoOCFk3wN0oVL5WAZ0TQ/W8zneb+E3YfAYtAWPlmJtJpa+4jgqVpVorAS+Dcy/D1brMolm0rZllhCd8VleA0Gm446kukR0xFMigSbqG0mVYN0MSqzm8qX1m5HZbmK5g1naHhfmOm5tBvaqzem/G/lWCDWRcNbfCale4jvC054HkfccP4Hk32c2J536Q6MTxi1Rb7U7dYqT8USt8v2H6F8nh0hLNf736tAa/t4sijz2hA18+TFZ/FeeYP8/ma06DBPrXReD4UjfsUK16HlsqfNtqfZ8Pu39XUs6tIPetQ0KIJ/kap4uUBtWcl4f63e7rD1dGz8kPbSKfNqiOCrdSYWCktwWrsGOw2W9j2obGK5nDN/OVehm02mDllkGsEgtnp9LeOjMaIt1hMIw82P4TaZlrpgYR7miC6db4/+crK7XC8iJd6z0rp9Lc8eMuf7oGLZYXFLA9xAI1VNFbvmctHNBZ0tALPY/CcLeOezsauVyzvmwKpE61UpqzIW1sc6XPmT91ilWV1Qu37BduvaO7tvhXKZbj79e7X2tsS3Z4PGsPJCuczVFZ7FucrfzgNXAODzXopWs+HonGfYrXr0NI1db/s77Nhb397iuSzDgUt/OBPlMqqD6iDEc6OnpWnTUUjbbEahdQYKzUmVkpLsJrDMQSipR2veYyzvi7guW8Kqap1uqZM+ltHRmPEW6yWAQg2P4TaZlrlgYSnaNb5/uYrK7fD8SJe6j2rpdOf8uCZP90/Y5bvWPebwqWpeq+x16xy/OG634nlfVMgdaLVypTVeGuLo3HOGqtbrLIsUrj6fsH0K5p7ux/rchmpfr37d6zaeajRZ07hFOvzGQ5WehbX1O+aMy1i8Xwo0vcpVroOEr375Ug/61DQwoOvSPP0cbk843bRvZ10fysBK0ezrdLRi7ZIXpOmIpyR0NjxAIw42lCa6YtVYxKNhs3faxtsHoh14xzt+iTWxxsLZhmuqnU2WDbPnzoy0iPeYrkMQCj5IdiOs9XbqWjV+c19JKWVxEu9Z8V0BlMeQi1Dsexnh2O9/Vg8yAjknIXroUc0g7zBsmKZsgpfbbHn8m6ROmfe6gkrLYsUzjY6FveSVuNeR3leUyCqs/Ii2f+KxbVuLvVcNNuUxtrMZduKGeG2/JNn8GdZYbFr1r6/1zmc/ZpI57F4aNtbgmjeL0f6nlRBC+pXAp6RZrMSWHF07XKzcvEVpfKnEghHNDsSN2SBdPSsHHgJRiDXJNBjj8WIYG9r8ZtpMY/HXBcz1o2Jex57ZnGBzz0Cgs1X/l7bUMplpI+hMbEYHdPSOiNmGXbYbNQaRr11SN3PxfLCYp7ysh55pJcJi0RHIVoPsALtOFvpgYQv0arzm/tISquJl3rPaukMpjyEWoZiOWo01PX2Y/VwMtBzFq50xsPDWKuVqUgJpN1vqi12D1xE6px5qyci/eAkkHMUzjbaqrNLo8mzjvIWuIhWufT32gbzvCRW19qq9Vyg5zCcbUpjv21upm3+pnualheVuNpMX3XlTa+urBe4aOo6h7NfE2geCyYfx0Pb3pxF+3450vekClrgvRIwH0AtKyyhc1YyOw5VMrJbNk9dNLTRKJU/lUA4otmRuCELpKPXHKYRugvkmgRy7LEaEex+PO5r8Xsej1UaE/eGzZtQ8pW/1zbUchnOYwj2gXEw6Q6W1Tsj4Qqsmudyxom5JKckkbdxD0u3ldSr26ePy3W1F551fjTqxEh0FKL1ACvQjnOsZxc0la88R/pZbRaIhMbq9Z4pFun0VjbM/D+yWzbDumb7rFfchaPfFKt20d/fbqzes8IDK1/pdheudMbLw9h4Kfuh8CyfZpkGGrT7niOJTe5t8Q3je0TsnDU2y8PX74Tj92Nx/+v+3eZASl9piLdBg4Fo6oGbFctloPkl1jOJo/XA3zOvBhsc8HUOQ2lT/BnMbLfZWFZY4lriadm2YkZ2bwU03IPEW11Zd79QElDfKFz9mmDyWDD1Xry07c1VrO+Xw01BC7xXAuYDKBvUC1j4ej8EVgm4f0cw0exI3JAF8uArljeEkeLvNfH32GM9Itj8bve1+D3TZ5XGxDMt4c5XwVzbQMtlOI8hmAfGwaY7WFbKP96E48bS87rl5GRy5bBOPJPXsG73FtCO5zoxGg+wguk4x3p2QVP5CohZnS+RZ/V6zxTrGZ7u5dncA2hEt1ZNlodw9pti0S7689uN1XvmZ2L5wMpXut2F68FarB/QBSJeyn4oPMuae5n2vH9wH0ns63sidc5ieX8V7ftfb2XE/WGptzQ0Z97qKMCy5TKQ/BLr5wZmGsJVZgO5D2vsvU3l98bOYTBtSlODmc0+jedvLC+qC6w0VVc2Vl8EErgIpl8TbB4L5p4wXtr25irW98vhFvGgxcGDB3niiSdYsGAB+/fvp0ePHvzsZz/jwgsv9Ovzb731Fi+88AL5+flkZGQwefJk7rjjDrKzs8OaTm+VgN2Ga0mopy4a6nUtRTNKZU7xCqQSmD4utGh2LG/IrPD7plis8efPsUc7wuntPJjHY67F7yugEmpjEso18JUWz3PrNHx3nvy5zoFc20DLpb/HEMzDFvPvpm6KQq1PAhFq/onG8nLhuLEMtAxbpU4MF1/H41kWPW9AlhUWN5kfrHBzFgz3NHqOrBrRNZuR3Vu58pqZj8NV5ze3ZRmbYrXjjZebMF/pNPNrpM6nZ/l1Hl1Kz1ff2Ft5CHe/KZrtoj+/7U+9F+s60Vu63cuit/zlvk+Hv+mMpzYgXsp+OLhfg0SHrcHr/vajInnOYj2CNJp9Pc9jdf/tkd2yXf0xKw+QCXdb7l5HAZYvl/7ml1jn63CX2UDuw/x5r/m3r3MYrjbF22eWu+1B4a1P422lBV/HGup1DqVfE8pv+5uP46lt90c07kUe/WQDlRVVXDfWGvc7VhTRoEVZWRnXXXcdGzZs4LLLLqNXr158+OGH/O53v2Pfvn3MmDGj0c8//fTTPPLII4wbN467776boqIi5s6dy3fffcf//vc/UlJSwppe90rADFi4R5p9raXoHgUOpBIIRzQ7ljdkVvh9iN0af00de7QjnN7Og3k8UJef5yyuez3cjUmw16Cphs3bzX6gv+H+Pn+ubaDlMpBjCESgN0WBpjvYRjgcnZFITK/3FbQz09XYXiO+BFOGw1knWuGhbWMP3kzu18z8b3PPHF/7fPiztESsBLKRrq+RVZ75OBx1vpWWZYxG3rTS8cbLTVhT6TTza6TOp+cDz6ZmNXoKd78plqPjvf12Yw8Mlm0rBlvD44x2negt3e5l0f0YvOU3f9MZ6wd0/oqXsh9Onu3+tWO6B/SAPtLnLNL3V/62b9G4//V2rO7ncuWOQyEHTdyP1/PY3Y832LY93G25+72tOyuXS3/uDWI5MjpSZdbXvay3gYhN3bM1dQ7DvfG9Z7rdnw165mPPlRYaqytDvc6h9GtC/W1/8nE4roMV7n9N0bgXcdhtzPq6AMOI/f2OVUU0aPHiiy+ydu1aHnnkEc455xwALr74Yq6//nqeeOIJpk6dSqdOnbx+dteuXTz++OOcfPLJPP3009jtdgCOP/54fvnLXzJ37lyuv/76sKbXrATMSslzD4sbxx/bVAz8H7nrq8CFI5odyxsyK/w+hGdENQR+Taxw7O48z4P7f985uR9lZZXM+rpuNHC4bxSDvQa+GjaTt82pgrnO/l7bYMplMMfgL38fgAeT7mAb4XB0RsJVZv05nmPpjs56t+GsF6zw0Lax4zFnF3gGLMzXG9vnw5+lJWIl0I103UVyxGMkyk2wopE3rXS88fKAtal0es6CisT59LfdirRYjo739duNnedZFw/1+X3RDLT4Srd5/9PYMQWSznhZuiBeyn44ebb7cGwQnz9lOt7PmT/tW6zvAcNZz7ofr/t/A/WON9i2PZxtufk5c2ar5/daNY/FOr80JZJl1ltebao99ic44HkOw92muKfb22Bmb/nY/DtS/Z9Y9mvM328qH4fjOljh/tcUjXuR2yb1dT2ni9RvxLuIBi3efvttOnTo4ApYANhsNqZPn87ChQt59913ueGGG7x+9t1336W6upqrr77aFbAAOPfcc3nkkUd48803wxq0MDOGOZXd/H/PBzTuHfdgp4SGK5pthYorlr/vzv38RWONPysduztvgYsZJ+Zy26S+7NtXimEcnVJ8tKPn6/Oh/ra/16Cxkfy+OqTm3/6OnPf32gZbLv2ZjWD+HejyHP50DvxNt+eoBff3LC8srrcRWGPnNFydQl/5xXP0jeeyFO7nyP3vxq4pRGe923DXC7F+aOvP8TydV8Cqnd5H+sXrPh/+nPdAR1ZFIm2xXIKssXM0omu2z8/NWVxAckoSVw7zPmClsd+J5fHGywPWptLpfp0idT6t8HAmlqPj43VkflPpDsf9TzyKl7IfLo21+/6W6Xg8Z96WgHa/l1pWWMzywmODLULt64U6kjic9ayvcg74HJQSym/4W394m/Xh/sDS/Rx5C1z4853uIjWC26rPDNzPhecxe7u/8ubpr/PJzEjhsqEdG7xmfofdZvOZV/29Z4vFOWxqMLM5MMtXPg53/yfWfYtoXoNY3/82lp5I9X+mj8t1PaeL9z5WJOrZiAUtSktL2bJlC5MnT27w2tChQwFYtWqVz8+vXLmy3nvdDR48mA8//JDS0lIyMzNDTqt7IXAahmuzQG+FMRxTQsMRzbZaxRXt3/dm+rjorPFnxWN3N31cruuBvud58HY8/lYs0Zgu7dkh9daBNvk7ct7faxuuUSZN5Q9/l+fwt3Pgb7q9jVqYPu7YOp3fFZUEvHxSqLyVWc/jNNPt3jmExpfgcW/wITrr3UaqXojVQ1t/j6ep8m6Vh86Baizd0R5Z5S1tVhjJ7uscmf/m/h44lqfunNwv4N+xwvE2F5E8n1Z5OBPLkd7xOsrcn3SH0sez0nIPkRCJ44v2OWvqodC1Y7oDgfVp4uW6+1oC2v3ew9cD/GD6eqGMJI5EPet+DO57mYSz3xZo2+N5jsw6ykyn+d+B1K3RHsEdjWcGwZaxcJwLh93GI59soKysst56/J6DgZsa/NTYPZu34ECkn7v4O5jZfWl4z/Pmz/5+gXBvoxvbZzdabUOkr4HV7h+jcS/SXO53POsWM7/CsXITqIgFLXbv3o1hGF6Xf0pNTSU7O5uioiKfn9+1axdZWVlkZGQ0eK1jx7po7vbt2xkwYEDIafX3BsO9UgpldEM4RqDEepNnzzVtw73haDBCuS6BXBOr35DOcQtYmOdh5pRBrtc90+1vpyUa06W9nVv3wIU50gnCP+IrXCPDwrE8h+e/meXPPXhjvtdus/nMc+5p8Na5mLO4wLWxWLSWT3LXWH5xT6fZYTU7h43dWLs3+BC9TU2DrRestHayO3+Ox9/yHq+dMG/pbuqhTjRGllthJLupsWvrrZ5zn/nnLysdb3MQqfNppQEdsRzp7fnb3h4umKz00NafcxZK3rHScg+REInji/Y582z3/R3ZbqVjCFZTdZX7pvThuAf01SdvaiRxJOtZz/YcCLrf5q1v6z5y3Z/6w/O4zH01vJ2jxr4nkFk04W4fovHMINgyFmwe9PyOtLRkHvlkg2s9fl8BC1+/6c89W6D7xoYi0MHMnp9xT384B264t9GN7bMbjbbBFK57XF9CvX8MZ9A8GvcizeV+x7Ocu+dX9wBgUoKdX559nF/fGdGZFgBpaWleX09JSaG8vLzRzzf2Wajb6DsQNpv3f7/xxB4+P3O9R6F3v/E2K6NZXxdgs0V3FFkgaQ4Hc4MY8zjN33c/H+b5jcTvNyWa1yXa5z4Qvs5DWloyl5/QcOom1KXZZqPe+fL8Hn/eB3WvhXINPM+t+2+6r60b6/LXmKbyx/UcS7fZELufZ6hr/N3/zSx/M07MZcaJudQ6DWw27+WvMe7n0/ztUd2zWbqtxHV+n/0mOg1kY2W2sXSO/8dCr+fM/Xvdp/PabPXrfvO7zXPoL/O94WhH3HnWre7HYZ6fZ7+p34mJxjVq6njM6+VPWfTshEUrj4XKW7o9y6ZnHbh0W11QMlJ1klX6IO7p8XZtvZXhGSfmusqCv2XPascb7yJ5Pj3LhinYOre58KeOD2dbFCmh5h1/+5rxKhLHF+1z5tnuh6NMW/W6eytH3totoF77Fs57QF/tZGPnJJL1rGd7DgTdb/Os98xrbvblR3XP9quvFMw5aiot7t9pilRejMYzg1DKWKjn12arvx6/+3fUOg2vSyK751Xz/ibYe7ZIPHfxt4y5/3a0+z+xbhs80+JLqP2fUO8fw9X/imTf2fz95na/4/kMzzSyeytGdm/FrK8LyEhOiH3QwjgacTN8RN4Mw6i3V0Vj3+GLw+EIKE1t24a2lNQ/P9vIrK/rlja4bVJfAGZOGeSKMKelJbv+vbnxdpzezkcstOTr4q6p8wD4PA/u73vum0Kqap1er6uv9wE88smGiFyDmVMGuX4LaBbX2f2Ykhz2ejNhAH43dXCD95vHaR5/sOXP/bcdNhtLt5XU+85onEt/yqznOXrt5pPo97sPfJ4zb99rHs/3O0sZ3zun3m+5f6bWafALP5esCbUd8dRU3Qo0uO6xzu+B1Lm+ronVy6uvdLsfs68yGKljDOS8P/rJBhx2m9ffDzTP+5sez+NurJ7zpxypbQ+vcJzPxvJVZkYKtU6DnJyG1zY5JQmH3eZ6zf17PPNjuPJnuAVbpiLVfw53W9SYcJVFf/ua8SoSxxfLc+bZF/VMl7+sct29lWGzHJll2Nc9R6Ta9abuBzyF65p48tbvBBiR2xq7zeYaAOfZ1/Gn3lu16zB5m/czvndb8jbvZ2yvNozvncOE/h289hk9vzPQc9RYWszfSktLdr0ezHdaTShlLNTzG8x3zJwyqEG/2lcZi/a1CaaMRapcNsbfax6N+wF/0hho/ycc94/h6H9F416kud7veNYLt5zWh0c+2UCSo/EYgDcRC1qkp6cDUFFR4fX1iooKr0tHuX/+4MGDXl8zZ2h4WzqqMfv3121EPGdxAbVOo9GooTelhyuYcWIulw3tWG9pg8uGdqSsrJLSwxUBLXkQb8zjfOSTDTy+YKMrku55Pp7+Oh+H3fdUrGDOfWNa+nUx+ToP5gyLw0caPw+XDe3I4ws2UlXrJNFh47KhHbl/3uoG19L9fXZb3d9Pf50fsWswZ3GB67ecBpSVVcb9dTaPKdFho6rWyYPvrmkyiu5v+fP3t+02qDXqZjCY3+H+GwvX7+api4Z6/XyoZdgzr7rXGea1fPDdNa50VtU6+em/FjV6ztxH/nsez6yvC/hmywHKyip9fqapc2iz1d3cmu1IsLzVj+7n/R+fbsBp1I36MtPu7ZgifY0a42+d29g1MdfAteLoEX/THUjbE452MZDfq6yoYtbXBSHl+VDPEdCgzF4/PtfvctTc2/Z47CsFm688P2f+vXD9bpZuK3F9Llz5MxJCKVPhar8hfG1RIILNO77aO88+pNWudSi89aUDOb6mzpkN7+cs0u1+qEI9L+HgXobd26Jn8o6VYbPvaTLvOSLVdwnmfiDc3Osw934nHJuNMKp7dr1jNz8zqns2Vw5r+DzHzI8zTsxl1tcF2G2Qt3m/63sf+WSDa/a4tz6je94IxznyVgcDMT3v4RZsGQv0/LrXUWZ75HnP1tR3xOv9gdX4c80jfT/gTxoD7f+EM3+E2v+K9L2IORMn3u53/LmHcdht9eqWsrJK138nOmz1ZmA0JWJBi65du2Kz2di1a1eD18rKyjh06JBrbwpfn1+7di1lZWUNlonatWsXdrudDh06BJQmw4Bn8o6t9RZoZ99c88zb58zNh6ywv14kNz27bmwuz35zbG2568Y2PI/miAxzbUP33w723DdG16VOY+fBXEO8sfPgOQXvmbwCr9fSfB/UBRGeySuI2DXwXCPSbMQ885aVrnNTfB3T0oJir9Nozc+Y+aOp8ufvbzsNgxVFJSzdVsIzecemW143Npdl24ob/Lvn5w0j+DztmV8885l5Tsx1DztnJbN0WwmdspI5b1Bdu+GZD5YWFDOiW3aDc2L+vaywuMFvmMcSyHk0jNDyma/60fxOc2+R68YeWzvZ2zH5e40iwd/yXus0fKbffN2KZdbfdAdS74WjXQzk98y0hiPPe9PUOVq67dga0WZdYz78mDllkKscBVJPNHa88She+kru9bxnvoL664H7ylfe8qNZh408Wm+7989DzZ+REGqZ8qf/HIhQ26JABJt3/O1DBvtwyoqbPHvrSwdyfE2dMwOY8b+V9QYsRKPdD1Wo5yUc3Msw1LVFZsDCPHdP5xUwoms2I7u3AuqX93D3Xfy9x4k09/bcvd8JuPrPZl1d6zRcdfXIbtlN9kO91Xvm95rn/YbxPXzW/+E8R+5pAWJ+3sMtmDIWzPl1r6OuH5/rGiXufs/m7Ttm/G8l2GDWRUMb9CHd62sr3x9YjT/XPNL3A/4ItP8T7vvHUPpf0bgX+cXkfj6f01n1fqepexjPPW3MugWOLTto1sX+iOhMi969e7N69eoGr61cuRKA4cOH+/z8kCFD+Oijj1i1ahVjx46t99rq1avp27dvwDMtXvi2MKDNheJVJDc982eDGPPvUDZ2ao6svBmdt06L+wbY7huWmf9tpjdSG2x6yzPe8lY8aeqYlheV1Ps3z8/4U/4C+W33f3f/3acuGtrg3719Plx5ut45cNt029v/+8qXy4tKGt1szv0YzDVXY1EfNVY/Qv0N5ht72OLvNYqlWG6GG4pIpDsW7aL7b4Y7zzf1IHC5R0fVLMPmEhOXDe1oibYvluKlr+Rrw0f3+tef9HrLj2a9bu5VZKXj9iaUMhVK+x2vvOWVcPYhrdav9tWX9kxfY5o6Z2Yf6aZXV9brB1i57ITjvISL+Xuzvi5wLVvhnhe9nUdv9V+orHSP496ee7btnv3nlTsO1av3muqH+qr3/KlLw32O3IN/nscY7HdaRTBlLNjz6/6e74qKXXuUmIEts7/nmS/Me1zP+xvP+joez38sBHLNI3k/4G9aA+n/hPs+rCX2vyKtsXsYz4CFp6Gds1hWWBLQ70UsaAFw3nnn8cgjjzB//nzOOeccoG6fimeffZakpCTOPvtsn58966yz+Mc//sGcOXMYM2YMtqO7lLz33nvs2LGD3/zmNwGn599LCi3XqYvEKKFI3QjHU+VoReG4LpHIL011Wrw9IPZMbyQ6ek7De54x/3ZaLeTsh6aOaVlhsc/8AQTcIfX3t5dtK2ZZYXG919zzwDOLC3AaDa97OOsa9++y23A1dk7DYES3Vm4PP4vr5Uv3Ub7+PDQz66NEh/dyFA3e6kcg4Gureja+xOJ6xSLPe9Y17sc9qnvdSM14eNAWDfFQhpt6iBFIvvLMj09dNNQVsIhlnRyIYMqUlR7aRps/Qa5gz4WVAn/hfMDa2DmbPi6Xm15dybLCEsY88pXXvpmVWOnhvMksw+YSFdPHHZthEK17jmjf44R6/+ir3musDWuq3muqLg3nOTJ/230WjbdgVDjOe6RngHl+v+e94uy8fNf3ux+j5+fcz697uvw5F+7X3dwf0fM6j+yWjdMwvN7Lmt9hhb6gFWfsNSWYejVW98Cx7v/E+vebM1/1v/uzG6h/DcyBFzeOzyUpwf+9LSIatLjqqqt45513+PWvf82aNWvo2bMnH3zwAXl5efzqV7+iffv2ABQWFvLdd9/RvXt3hg0bBkCXLl2YMWMGjz/+ONdeey1nnXUWW7duZe7cuQwePJhLLrkk4PRY8YYoUqOEwn0jHE+Vo5WFel0ikV/86RTeML6H68G157X01rkJRwcgXkdpN6apY3JvTN3zBzQcARboTV9jvz2yeyuezivwOfLA23V3T7eZjlDrmqbqDM9zZI6YiMeRru7HCrgaefc0eXbsvZUb1bPxJdrXq6k8H4mbNW/vd8/T3xUeotZLu2PVm8NImz4ul2fcrpPnwwXP8xLp8+QtT7hfP7MvAARcl3rmx5teXWmZOtlfgbYjkX5oGw8PXMw87m8fMtDvhtgH/sL9ELqxc/bURUNdAQurt/tWHIBkluEkh52qWmeTM1sbO7+hLpMa6O8FK9T7x8bqPW/9Gn/qPaDRujRc56ixB+ORmEUT6Rlgnt9vljGg3vd7ljHPz7n3KTzT5c+58CeQ5TkzxxTr+tqd1Wbs+SOYejUW98CxDlrH+veDEQ99Onf+3Ne651f3gIbN/y0tIhu0SElJYe7cuTzyyCPMmzePI0eO0LNnTx566CHOP/981/uWLl3Kb37zGy644AJX0ALglltuoW3btrz44ovce++95OTkcPHFF3PbbbeRkpIScHqseEPk6+FUOKLO4Xw4Ei+VYzzwdl38raAikV/86RTOcbtx8nYtPX83HjsA0ebrmvt6gBXMTV848hU0/WAqXHWNv3VGML9ntZEW5rGaG8yD93LjbZSSt+9pqfWsVTt3vtLlft0jfb38yfPhqqv9vQ7PLC6g1qg7frUNdcz2FY7lCfO6uI9IMt/rOaIyHPnf/Xs884T5PfajdxdmWgOtSz3zozlafGS3bK/L3VlRMO1IpB/axkN/K9A+ZKDc+wSedUv9NESuPYjEcha+zllT59NKrDYAySwXM07MZeaUQTz47hrXOtvBpCeW5S+Q+j+U+8em6j1v/VB/ZpZ7LiMZqfo/2Do42PY1ks92vH3/DeN7+Px+97bbW9DI12x1f47dbrM1CP41FshyT380Bu+E+9mKle43Aq1XY3UPHK7+T7Dn3opB86Y01aaM7Jbt9XOxuuf15zlEY0sQ+iuiQQuANm3acN999zX6nmnTpjFt2jSvr1166aVceumlYUnLNWO68fhXWwFr3RC5V5bhjDqH82FWvFSO8cDbdQmk0xup/NJYegO9lpHusDUHvq75Ta+uxGlQ76FmsDd9oeYr8O/BVDjqmkDymdVGugbK17F6LsXWVLlRPWvdB3be0uV+fa6PcP7zN8+Hq6725zqYD9ocNhu1hhFXa7JHimc+fTrv2N/ua0P7ui7hyv/evsc9aGKmxRt/6lJvdZX797rX4Vatw4JtRyL90Nbq/a1otFOegwDMusVbGuJBY+cMQlsqtCXzVYbNDbjNvwMRy/IXaP0fzP1jU/Wee2A9kGP3fFAeyfo/GvdQvr43Uvfq/n5/sHtS+fPgdFlhic/gX2P3adEabBXuZytWvd9oSizvgcPV/wn23FstaO6PxtoUb31miO99vPwV8aCFlVw1uhtVNU5Ldu5CjTo3tb6h51r5kWS1B4RWEq6HlaHml2DTa/42NH0tox1ciTfezmO4R54GejPlnq8Av657OBqsQPKZFUe6BqKpY3WvCxorN6pn61j1gZ23G0Pzv71Nk3f/TDBCWas4HHV1U9fBfM28wf3pvxaxdFt8rMkeKb7yqRm4AFw3KI2dpxFeNrwMNP97u37mwyhzjyEg6PXAPetgb/nTPR1WHP1mpXbEk1X7W9Fop3zN4InXoKg/5yyU82ml0cLRFqkyHKvyF0z/J9D7x8bOmWfAwleaAvlO83UrCLV/2dS5jtQ+I00dg7tAPuf54NRX8M9XIMsUrYecodwD+1qiOJDvswp/y5uV24Z4PffBaqxN8TbTLRbnIdrPIVpU0AKs1yCaQo06u0cgof6ahuZ/j+zWKioPs+KlM9KYWG947WvjY88pmZEepRDqtQy0cxysSFyvaDTe7tffvOZmwMLzdfe/g/2Npm6mPEcr+vou87qHq8HyN59ZdaRrIPzdQyaQ9SF9fU8sxKLTa9UHdu7psh9dt9MzXeG6XqGuVRyOutrXdTD/zf3Yn7poKKP//pVrVlmsr1WkNFYelm0rZsTRWRQm9+tgt9Vfu76xJbXM/l0o+d/b9XNvC7x9p7fAhTeeZd79b191mKdY31BbqR3xlQar7W8U6XbKW5/gqYuGxtVG1Z4aO2fLthWDl/oykPMZ7IjVWJe/cIhkGY5V+Qu0/xPo84bGztmwrtn1Nlv1TJOv/NjYd7ovZ+Qt7dHOZ6H0L5s615HcZ6SpYwD/lv71duyem+y6v9+fQFY0B1sFcw/c2Hmx6v1GY/yt96w+k8Sfc98c2imTrzbFKnkw2s8hWlzQAqxxc+EuHCOV3TPwiK7ZPh8UQOQfZln9xs4fVtjw2tcDCs8RDpEcpRDqtbTiFNBYfqc33h5Seb4OoZVbf26m/K2HPOuScDRY/uYzqz6oD4S/e8gEsj6kr++JhVh1eq34wA4CGwkX6u9A8KOQwlVXezteb3vyzHFr55wGEWsbYq2x8rC8qKRBefC8Du7LBbove+Pt2oYj/3u2R+6/7e29EL161+o31LEWrf5WICLdTvnqE8TTRtWeGjtnsy4e6vO1YO4Xzb/9aStU/hoXy/Lnb/8n3Et5RKJ8WzGfBdO/9Odch9JvC/Rauh8D+L8nVaDH3lggKxxB12CE8x7Y3++LR6HeR0RDU+feivVHsBprU6yQB6P9HKJFBi2sJJxTa9w/t2rnIa+RNytUOJEUrghrJCruQDa89vWAwteUTM+0enr0kw1UVlRx3djoRZ6juc5dJK5XtBrvQEZ2ROo34mUGQyR+z2qjMqJZbiIhVp1eKz6wi3a63M99IKNv/M1zgWzM6H68nuXnn59tZNbX8ZvHAxFIefC1zI05+66x0ePhymees+2aWqowmtcrHm6oYyXe241g+Wqbzb601dqDSAp2U2Z/2wqVP99iXf78qf+jvZRHsKyYzwJtXwM518GUxWCupXkMnvz9XDhm5oQj6BqMcN8DW/V+IxyCvY+IlqbOvRXrj2A01aY05zzoi4IWEeJv5zHcI4etEHmLJX82jvLG28PJaFfcvh5YuD+gaGxKJvjOLw67jVlfF2AY0Yk8x6JzHInrFek8EI0bHX9+o6nlCJYVFvt1IxztAEA4fs9KozLi5aayKbGqO32tt2++pyUEoALtAwSS5/zdmLGx452zuIBZX9ftbWEG0eMxjwfCn/LgLa94blTta6mocOUzz+toxU2yrXJDbaVgd3NpN8Il1g+QYyWYTZkDvV+0SvmzEvfz6zQMn/VlrAeIxdNMZSvlM3/Obyh7ipnf01RZdP8Nz2vpmbc8v7+pPrKvdDWHujTUe2Dz9UC+LxbC2Sex6rNEf899MPVHPPXp3JdgcxoGK4pKfLb98bQcVlMUtIgQfzuP4R453BIjb+4ai7B6uwGHxh9OTh+XyzNu59TzXIarQvBWQTW2lrU3jb1+26S+lJVVMuvr6ESeY9U5DrSh9beRikTjHY0HDf7+RmP5d2T3urXS/Sk30Q4AhOP3Gqszon2TFE83lU2JVqfX81qb/33j+FzXNXX/92iJ1YPEQPsAgeQ5f9rXpo631mlw5+R+XDa0I+7ZORZ5PJo3KU2VB382qjZHj7svqQUNlwENJp95PngzB0f4usE3xWoPm1jfUFsp2B1su9HYtZvxv5Vgg1key1WCtW+EW3IAJ9C+TLD3i1Yof1biWVd7qy+tMEDMqkuK+qqHmrr/jvTvA67Bg02d31D3FPOnLLr/hvu19PwNzz1BvL1uft5b4MLb5+K1Lg3HPbCv9t1q5yScfRIrPksM9NwH2k7FS5/O2yb35iAjX21/c6GgRYTE4kGYVaO/0dZYhNX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      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc(\"figure\", figsize=(16, 6))\n",
    "\n",
    "both.columns = [\"TERM\", \"RW One-step Ahead\"]\n",
    "both_oos = both.iloc[half:]\n",
    "fig, axes = plt.subplots(2, 1)\n",
    "both_oos.plot.line(ax=axes[0])\n",
    "axes[0].set_xticklabels([])\n",
    "axes[0].set_xlabel(\"\")\n",
    "axes[0].set_title(\"Term and RW 1-step Forecast\")\n",
    "\n",
    "errors = rw_oos_error\n",
    "errors.plot.line(ax=axes[1], linestyle=\"none\", marker=\"x\")\n",
    "axes[1].set_title(\"RW 1-step Forecast Errors\")\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 71\n",
    "\n",
    "Produce 3-step forecasts from the models selected in the previous exercises as well as a random walk model.\n",
    "\n",
    "1. Compare these forecasts to the random walk models using MSE and MAE.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "nobs = term.shape[0]\n",
    "half = nobs // 2\n",
    "mod = tsa.SARIMAX(term.iloc[:half], order=(1, 0, 1), trend=\"c\")\n",
    "res = mod.fit()\n",
    "res.summary()\n",
    "\n",
    "mod = tsa.statespace.SARIMAX(term, order=(1, 0, 1), trend=\"c\")\n",
    "forecast_res = mod.filter(res.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "three_step = {}\n",
    "for t in range(half, nobs):\n",
    "    fcasts = forecast_res.predict(t, t + 2, dynamic=True)\n",
    "    key = fcasts.index[-1]\n",
    "    value = fcasts.iloc[-1]\n",
    "    three_step[key] = value\n",
    "three_step = pd.Series(three_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1989-08-01   -0.125913\n",
       "1989-09-01   -0.025293\n",
       "1989-10-01   -0.322081\n",
       "1989-11-01    0.137237\n",
       "1989-12-01   -0.061069\n",
       "                ...   \n",
       "2025-03-01   -0.066969\n",
       "2025-04-01   -0.229963\n",
       "2025-05-01    0.104445\n",
       "2025-06-01   -0.015242\n",
       "2025-07-01   -0.095809\n",
       "Length: 432, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_step_errors = term - three_step\n",
    "three_step_errors = three_step_errors.dropna()\n",
    "three_step_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "three_step_rw = term.shift(3)\n",
    "three_step_rw_errors = term - three_step_rw\n",
    "three_step_rw_errors = three_step_rw_errors.iloc[half:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ARMA</th>\n",
       "      <th>RW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>0.203035</td>\n",
       "      <td>0.135943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>0.343436</td>\n",
       "      <td>0.280856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ARMA        RW\n",
       "MSE  0.203035  0.135943\n",
       "MAE  0.343436  0.280856"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors = pd.DataFrame({\"ARMA\": three_step_errors, \"RW\": three_step_rw_errors}).dropna()\n",
    "pd.DataFrame([(errors**2).mean(), errors.abs().mean()], index=[\"MSE\", \"MAE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>     nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Aug 2025</td> <th>  Prob (F-statistic):</th>  <td>   nan</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:18:00</td>     <th>  Log-Likelihood:    </th> <td>  5.0461</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   432</td>      <th>  AIC:               </th> <td>  -8.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   431</td>      <th>  BIC:               </th> <td>  -4.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     0</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>         <td>HAC</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.0671</td> <td>    0.023</td> <td>    2.949</td> <td> 0.003</td> <td>    0.023</td> <td>    0.112</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>87.071</td> <th>  Durbin-Watson:     </th> <td>   0.478</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 222.948</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.987</td> <th>  Prob(JB):          </th> <td>3.87e-49</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.913</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 7 lags and without small sample correction"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared:         } &     0.000   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.000   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &       nan   \\\\\n",
       "\\textbf{Date:}             & Wed, 27 Aug 2025 & \\textbf{  Prob (F-statistic):} &      nan    \\\\\n",
       "\\textbf{Time:}             &     15:18:00     & \\textbf{  Log-Likelihood:    } &    5.0461   \\\\\n",
       "\\textbf{No. Observations:} &         432      & \\textbf{  AIC:               } &    -8.092   \\\\\n",
       "\\textbf{Df Residuals:}     &         431      & \\textbf{  BIC:               } &    -4.024   \\\\\n",
       "\\textbf{Df Model:}         &           0      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &       HAC        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &       0.0671  &        0.023     &     2.949  &         0.003        &        0.023    &        0.112     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 87.071 & \\textbf{  Durbin-Watson:     } &    0.478  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &  222.948  \\\\\n",
       "\\textbf{Skew:}          &  0.987 & \\textbf{  Prob(JB):          } & 3.87e-49  \\\\\n",
       "\\textbf{Kurtosis:}      &  5.913 & \\textbf{  Cond. No.          } &     1.00  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 7 lags and without small sample correction"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.000\n",
       "Model:                            OLS   Adj. R-squared:                  0.000\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 27 Aug 2025   Prob (F-statistic):                nan\n",
       "Time:                        15:18:00   Log-Likelihood:                 5.0461\n",
       "No. Observations:                 432   AIC:                            -8.092\n",
       "Df Residuals:                     431   BIC:                            -4.024\n",
       "Df Model:                           0                                         \n",
       "Covariance Type:                  HAC                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0671      0.023      2.949      0.003       0.023       0.112\n",
       "==============================================================================\n",
       "Omnibus:                       87.071   Durbin-Watson:                   0.478\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              222.948\n",
       "Skew:                           0.987   Prob(JB):                     3.87e-49\n",
       "Kurtosis:                       5.913   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 7 lags and without small sample correction\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = errors[\"ARMA\"] ** 2 - errors[\"RW\"] ** 2\n",
    "x = np.ones_like(d)\n",
    "maxlags = int(d.shape[0] ** (1 / 3))\n",
    "res = sm.OLS(d, x).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": maxlags})\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>     nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Aug 2025</td> <th>  Prob (F-statistic):</th>  <td>   nan</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:18:00</td>     <th>  Log-Likelihood:    </th> <td> -34.333</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   432</td>      <th>  AIC:               </th> <td>   70.67</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   431</td>      <th>  BIC:               </th> <td>   74.73</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     0</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>         <td>HAC</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.0626</td> <td>    0.024</td> <td>    2.585</td> <td> 0.010</td> <td>    0.015</td> <td>    0.110</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.200</td> <th>  Durbin-Watson:     </th> <td>   0.552</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.905</td> <th>  Jarque-Bera (JB):  </th> <td>   0.227</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.052</td> <th>  Prob(JB):          </th> <td>   0.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.957</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 7 lags and without small sample correction"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared:         } &     0.000   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.000   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &       nan   \\\\\n",
       "\\textbf{Date:}             & Wed, 27 Aug 2025 & \\textbf{  Prob (F-statistic):} &      nan    \\\\\n",
       "\\textbf{Time:}             &     15:18:00     & \\textbf{  Log-Likelihood:    } &   -34.333   \\\\\n",
       "\\textbf{No. Observations:} &         432      & \\textbf{  AIC:               } &     70.67   \\\\\n",
       "\\textbf{Df Residuals:}     &         431      & \\textbf{  BIC:               } &     74.73   \\\\\n",
       "\\textbf{Df Model:}         &           0      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &       HAC        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &       0.0626  &        0.024     &     2.585  &         0.010        &        0.015    &        0.110     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  0.200 & \\textbf{  Durbin-Watson:     } &    0.552  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.905 & \\textbf{  Jarque-Bera (JB):  } &    0.227  \\\\\n",
       "\\textbf{Skew:}          &  0.052 & \\textbf{  Prob(JB):          } &    0.893  \\\\\n",
       "\\textbf{Kurtosis:}      &  2.957 & \\textbf{  Cond. No.          } &     1.00  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 7 lags and without small sample correction"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.000\n",
       "Model:                            OLS   Adj. R-squared:                  0.000\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 27 Aug 2025   Prob (F-statistic):                nan\n",
       "Time:                        15:18:00   Log-Likelihood:                -34.333\n",
       "No. Observations:                 432   AIC:                             70.67\n",
       "Df Residuals:                     431   BIC:                             74.73\n",
       "Df Model:                           0                                         \n",
       "Covariance Type:                  HAC                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0626      0.024      2.585      0.010       0.015       0.110\n",
       "==============================================================================\n",
       "Omnibus:                        0.200   Durbin-Watson:                   0.552\n",
       "Prob(Omnibus):                  0.905   Jarque-Bera (JB):                0.227\n",
       "Skew:                           0.052   Prob(JB):                        0.893\n",
       "Kurtosis:                       2.957   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 7 lags and without small sample correction\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = errors[\"ARMA\"].abs() - errors[\"RW\"].abs()\n",
    "res = sm.OLS(d, x).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": maxlags})\n",
    "res.summary()"
   ]
  }
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