Assignment04_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader.data as web\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.stats.diagnostic as dg\n",
    "import statsmodels.stats.stattools as st"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this worksheet, we're going to examine the Fisher hypothesis for the relationship between interest rate and inflation. The hypothesis states that interest rate $r_t$ fully reflects long-run movements in expected inflation $E(\\pi_t)$. For the interest rate, we'll use the 3-month Treasury bill rate. We don't know what consumers *expect* for inflation, but we do have data from consumer opinion surveys conducted by the OECD that ask for opinions on future inflation. We will use that information as a proxy for expected inflation. \n",
    "\n",
    "We can compare the survey results with actual inflation and inspect how accurate consumers were in predicting inflation. The data is obtained from FRED below and are all in monthly frequency.\n",
    "\n",
    "- **TB3MS** is the 3-month T-bill rate, denoted using $r_t$ below.\n",
    "- **CSINFT02USM460S** is the survey of future inflation, denoted using $\\pi_t$ below.\n",
    "- **CPIAUCSL** is the CPI data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data from FRED\n",
    "data = web.DataReader(['TB3MS', 'CSINFT02USM460S', 'CPIAUCSL'], 'fred', start='1981-01-01', end='2020-12-31')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Create a new column in the dataframe for realized (that is, future) inflation. You can name it however you want. I'm going to use $\\pi^r_t$ as the symbol. Using CPI, the realized inflation is defined as: $\\pi^r_t = 100 * \\frac{\\text{CPI}_{t+12} - \\text{CPI}_{t}}{\\text{CPI}_{t}}$. Since the data we have are in monthly intervals, we've defined realized inflation as the *annual change in CPI*. Pay attention to the time subscripts in the formula.\n",
    "\n",
    "Note: You can use the **diff** and **shift** functions in Pandas but it's not required. If you've done this correctly, the realized inflation values from Feb 2020 to Dec 2020 should be *NaN*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            TB3MS  CSINFT02USM460S  CPIAUCSL  Realized_Inflation\n",
      "DATE                                                            \n",
      "2019-10-01   1.65              3.0   257.155            1.087282\n",
      "2019-11-01   1.54              3.1   257.879            0.919036\n",
      "2019-12-01   1.54              2.8   258.630            0.875768\n",
      "2020-01-01   1.52              2.9   258.906            1.196960\n",
      "2020-02-01   1.52              2.8   259.246                 NaN\n",
      "2020-03-01   0.29              2.5   258.150                 NaN\n",
      "2020-04-01   0.14              2.4   256.126                 NaN\n",
      "2020-05-01   0.13              3.3   255.848                 NaN\n",
      "2020-06-01   0.16              3.3   257.004                 NaN\n",
      "2020-07-01   0.13              3.6   258.408                 NaN\n",
      "2020-08-01   0.10              3.4   259.366                 NaN\n",
      "2020-09-01   0.11              3.3   259.951                 NaN\n",
      "2020-10-01   0.10              3.0   260.249                 NaN\n",
      "2020-11-01   0.09              3.2   260.895                 NaN\n",
      "2020-12-01   0.09              3.0   262.005                 NaN\n"
     ]
    }
   ],
   "source": [
    "# Calculate realized inflation\n",
    "data['Realized_Inflation'] = ((data['CPIAUCSL'].shift(-11) - data['CPIAUCSL']) / (data['CPIAUCSL'])) * 100\n",
    "\n",
    "# Display the dataframe to check the new column\n",
    "print(data.tail(15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Estimate the following linear regression model using OLS: \n",
    "$$ r_t = \\alpha + \\beta \\pi_t + u_t $$\n",
    "\n",
    "Save the results and print out the summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>TB3MS</td>      <th>  R-squared:         </th> <td>   0.457</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   403.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 25 Apr 2024</td> <th>  Prob (F-statistic):</th> <td>1.79e-65</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:58:09</td>     <th>  Log-Likelihood:    </th> <td> -1120.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   480</td>      <th>  AIC:               </th> <td>   2245.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   478</td>      <th>  BIC:               </th> <td>   2253.</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>   -4.3924</td> <td>    0.431</td> <td>  -10.185</td> <td> 0.000</td> <td>   -5.240</td> <td>   -3.545</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CSINFT02USM460S</th> <td>    2.1028</td> <td>    0.105</td> <td>   20.077</td> <td> 0.000</td> <td>    1.897</td> <td>    2.309</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 9.396</td> <th>  Durbin-Watson:     </th> <td>   0.168</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.009</td> <th>  Jarque-Bera (JB):  </th> <td>   9.725</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.343</td> <th>  Prob(JB):          </th> <td> 0.00773</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.871</td> <th>  Cond. No.          </th> <td>    16.4</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:}    &      TB3MS       & \\textbf{  R-squared:         } &     0.457   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.456   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     403.1   \\\\\n",
       "\\textbf{Date:}             & Thu, 25 Apr 2024 & \\textbf{  Prob (F-statistic):} &  1.79e-65   \\\\\n",
       "\\textbf{Time:}             &     19:58:09     & \\textbf{  Log-Likelihood:    } &   -1120.4   \\\\\n",
       "\\textbf{No. Observations:} &         480      & \\textbf{  AIC:               } &     2245.   \\\\\n",
       "\\textbf{Df Residuals:}     &         478      & \\textbf{  BIC:               } &     2253.   \\\\\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}           &      -4.3924  &        0.431     &   -10.185  &         0.000        &       -5.240    &       -3.545     \\\\\n",
       "\\textbf{CSINFT02USM460S} &       2.1028  &        0.105     &    20.077  &         0.000        &        1.897    &        2.309     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  9.396 & \\textbf{  Durbin-Watson:     } &    0.168  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.009 & \\textbf{  Jarque-Bera (JB):  } &    9.725  \\\\\n",
       "\\textbf{Skew:}          & -0.343 & \\textbf{  Prob(JB):          } &  0.00773  \\\\\n",
       "\\textbf{Kurtosis:}      &  2.871 & \\textbf{  Cond. No.          } &     16.4  \\\\\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:                  TB3MS   R-squared:                       0.457\n",
       "Model:                            OLS   Adj. R-squared:                  0.456\n",
       "Method:                 Least Squares   F-statistic:                     403.1\n",
       "Date:                Thu, 25 Apr 2024   Prob (F-statistic):           1.79e-65\n",
       "Time:                        19:58:09   Log-Likelihood:                -1120.4\n",
       "No. Observations:                 480   AIC:                             2245.\n",
       "Df Residuals:                     478   BIC:                             2253.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "const              -4.3924      0.431    -10.185      0.000      -5.240      -3.545\n",
       "CSINFT02USM460S     2.1028      0.105     20.077      0.000       1.897       2.309\n",
       "==============================================================================\n",
       "Omnibus:                        9.396   Durbin-Watson:                   0.168\n",
       "Prob(Omnibus):                  0.009   Jarque-Bera (JB):                9.725\n",
       "Skew:                          -0.343   Prob(JB):                      0.00773\n",
       "Kurtosis:                       2.871   Cond. No.                         16.4\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data['TB3MS']\n",
    "X = data[['CSINFT02USM460S']]\n",
    "X = sm.add_constant(X)\n",
    "model = sm.OLS(y, X, missing='drop').fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Below, you'll perform a range of diagnostic tests on the OLS residuals. All the modules you'll need for this have been imported above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Plot the OLS residuals. Does it look like the residuals follow a white noise process?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fGEEQBEEQBEEQxAyjqorzQ4cO4d///d/R0tKCUCiEo48+Gq+++mq5D6tqYYFwTTVeAEBiAsV5OqNY4lOZ4q3xBEEQBEEQBEEQMxVPuQ/AKcPDwzj11FNx9tln45///Cfa29uxe/duNDY2lvvQqpYoV879ACamnKdV5ZyKc4IgCIIgCIIgiMKpmuL829/+Njo7O/GLX/yCX7Zw4cLyHVCVk8nKSKSVQpop5/FU8YU1s7OzIp0gCIIgCIIgCIJwTtXY2h944AGsW7cOl156Kdrb23HMMcfgpz/9qe19EokEwuGw7h+hIKrkzSVRzsnWThAEQRAEQRAEUSxVU5zv2bMHd999N5YtW4aHH34YV155Ja699lr8+te/trzPzTffjIaGBv6vs7NzCo+4smFhcJIENAaZcj6RnnNma5chy1OvnmeyMu5/7SC6hqJT/rsJgiAIgiAIgiAmStUU59lsFsceeyy++c1v4phjjsEnPvEJfPzjH8fdd99teZ8bb7wRo6Oj/F9XV9cUHnFlw4rzoNeNoM8NYILFeVZTzDNlsLY/ua0P1/9xI7724JYp/90EQRAEQRAEQRATpWqK846ODqxevVp32apVq3DgwAHL+/j9ftTX1+v+EQrRlJLUHvK5EfSy4rx4S3pKGKGWKsM4tZ19YwCA4Whyyn83QRAEQRAEQRDERKma4vzUU0/F9u3bdZft2LEDCxYsKNMRVTcsqT3ocyPgVd4GE+k5F9XyVHbq+84PjSh29nJsDBAEQRAEQRAEQUyUqinOP/3pT2P9+vX45je/iV27duF3v/sdfvKTn+Dqq68u96FVJczWHvJ6EPBO3NYuBsGly1AgHxqO5RwHQRAEQRAEQRBEtVA1xfnxxx+Pv/zlL/j973+PtWvX4mtf+xpuv/12vP/97y/3oVUlonLObO2lmHMOlKdAPjSiFOfl2BggCIIgCIIgCIKYKFUz5xwA3vnOd+Kd73xnuQ9jWhBNaj3nTDlPTKDnPFPG4lyWZVLOCYIgCIIgCIKoaqpGOSdKC7e1+7S09oko5+W0tY/GUhhXn085+t0JgiAIgiAIgiAmChXnMxTN1u5BwKMW58mJzzkHpl69Pqiq5sbjIAiCIAiCIAiCqBaoOJ+hMJU85HUj4FPeBvF0BrJcXHGr7zmf2gKZ9Zsrv5uUc4IgCIIgCIIgqg8qzmcorOc8KPScyzKQSBdX3KZFW/sUW8sPDYvFOSnnBEEQBEEQBEFUH1Scz1CiYs+5WpwDxYfClTOtnZRzgiAIgiAIgiCqHSrOZyhiIJzX7YLbJSmXFxkKJ6rlU25rp55zgiAIgiAIgiCqHCrOZyhiIBwArp7Hiy3OhaJ4qgtknXKezRbdN08QBEEQBEEQBFEuqDifoYi2dgAIeJW3QrHKuaiWT/U4M7E4l2X9zHWCIAiCIAiCIIhqgIrzGUospQTCacX5xJTzjGhrLzJUrhiiyTSGxpO6y9JUnBMEQRAEQRAEUWVQcT5D4bZ2r744L1o5FwriqSyOu4YU1dzv0d7KSQqFIwiCIAiCIAiiyqDifIaiBcLpe86LTmvPiIFwU1ccb+0OAwBWz6kXjoWUc4IgCIIgCIIgqgsqzmcoWiBcaXrOM7pRalNXHG/tUYrzNXPqoQbO6zYKCIIgCIIgCIIgqgEqzmcouYFwE+s5T+nS2qdSOY8AAFZ11MPjVt7OZGsnCIIgCIIgCKLaoOJ8hhJL6gPhghPsOdfNOZ/CnnNma1/VUQ+fWpyTrZ0gCIIgCIIgiGqDivMZiCzLiKaMtnamnBfbcy7Y2qcorX1gLIH+SAKSBKycXQePW/G1p6d4lBtBEARBEARBEMREoeJ8BpJIZyGrtbQxEK5YW3tal9Y+NcUxU80XttQg5PPAy2ztaVLOCYIgCIIgCIKoLqg4n4GwfnNAHKWmvBWKLs51ae1TUxxrlvY6AIDXRco5QRAEQRAEQRDVCRXnM5Co2m/u97jgVgvagGpvjyUnrpxP1Sg1HgY3WxmjxgLhpnKUG0EQBEEQBEEQRCmg4nwGEjMktQNAwDPBQDhdWvtUK+dKce5Ve86ncpQbQRAEQRAEQRBEKaDifAaijVHz8MtYMFyxgXApXVr71CjX+wejAICl7bUAwHvOKa2dIAiCIAiCIIhqg4rzGQgrzoM65XxiPecZ0dY+BYFs8VSGq/zNtT4AWnFOtnaCIAiCIAiCIKoNKs5nILGUfsY5ICrnJbC1T4FyHo6lAABul4Q6v+IA8HBbOxXnBEEQBEEQBEFUF1Scz0C4cu4VlHPvxHrOU1Oc1j4cVYrzhqAXkqQU5V4XU87J1k4QBEEQBEEQRHVBxfkMJGoWCDfBOeeZKU5rH4kmAQCNQS+/zOuhUWoEQRAEQRAEQVQnVJzPQIbG1cI25OOXacp5kYFwQkGenoriXLW1N4S04txDyjlBEARBEARBEFUKFeczkIPDSsr5vKYgv4xZ3BOlUM6zk18cj6q2dp1yToFwBEEQBEEQBEFUKVScz0AODscA6IvzgFd5KxTdc65La58K5TxX/WdzzqdCuScIgiAIgiAIgiglVJzPQLTiPMQvC06w51wsiNNToJyPCIFwDI+qnCfJ1k4QBEEQBEEQRJVBxfkMQ5ZlU1v7RNLas1kZYj0+JYFwas95Y0i0tZNyThAEQRAEQRBEdULF+QxjcDyJeCoLSQI6GnKL83gqC1kuTHk2KuVTUZyb9pyrgXBTodwbGRpP4pE3e6jfnSAIgiAIgiCIoqDifIbRNaSo5rPrA/B5tD+/OFYtXmBiu3F0WXoKbOWmPefqKLXkFPS8G/n2P7fhP37zKh5+s2fKfzdBEARBEARBENUPFeczDLMwOEDrOQeA8WS6oMfMUc6nsufcZJRaOeacHxxRNj26R+JT/rsJgiAIgiAIgqh+qDifYZiFwQGAyyVx9TyaKKzv3KiUT0lau+koNUU5L8ec83BM2dAodGODIAiCIAiCIAgCoOJ8xmEWBscI+TwAilDODX3WU6Fcj/JAOHGUWvnmnEfiyvFEk8Wl3RMEQRAEQRAEMbOh4nyGYWVrB4Aav6qcT9DWPtk956lMFmMJ5RgbTUapTUXPu5FwXDkedlwEQRAEQRAEQRCFQMX5DIMp550GWzsgKOcTtbVPsnLOVHMAqBeKcx+3tU+tci7LsqacU3FOEARBEARBEEQRUHE+g1BmnJv3nANAja845dxYjKfSk6tcs37z+oAHbpfEL/dwW/vUKufxVJb/znGytRMEQRAEQRAEUQRUnM8gxhJpJNSwtrY6f871QV6cF1ZgZoy29klXznPHqAGAx1Ue5Zyp5kDhGxsEQRAEQRAEQRAAFeczCtYP7XFJCHhz//Q1PBCusOLcWAxPtnI9PM7C4Ly6y9nc9qkepRYWivNCWwIIgiAIgiAIgiAAKs5nFONqcV4b8ECSpJzrQywQrsC+6Zye80lWrkfUnvOGoL44Z3POp9rWzsLgAO01JgiCIAiCIAiCKAQqzmcQEbWIZAq5kWKVc5bW7puitPSRqLmt3VumQLhwTLS1k3JOEARBEARBEEThUHE+g2C29rqAeXFevHKuFMPMKp/KZiHLk1eg8xnnBuXcW6ZRajrlnHrOCYIgCIIgCIIoAirOZxDc1u6fHOWcBcrJcm5IXClhae3GnnOPqpwnyxkIRz3nBEEQBEEQBEEUARXnMwhua7cozkNFjlLjxbnXnXPZZGDVc64p51Nta9der2Qmi2R6an8/QRBENfHgpsP4wD3/wsBYotyHQhAEQRAVBRXnM4gxIRDODFa0F5o4rtnateJ8Mvu+WY93fU5xrijnk7kxYIaonAM0To0gCMKOX7+wH8/uHMDzuwbKfSgEQRAEUVFUbXF+8803Q5IkXHfddeU+lKqB2drrSqycs3R0fXE+eQUyK4brDZsMTDmfauU6bCjOC20LIAiCmEmMxJRQz3iKzpUEQRAEIVKVxfnLL7+Mn/zkJzjyyCPLfShVRSRhb2svtuec9Zf7PC641Altk2ktZwFs9QHzUWpTr5zrNzMKDdQjCIKYSbBQzwS1ABEEQRCEjqorzsfGxvD+978fP/3pT9HU1FTuw6kqxuL2gXBFp7VnlQWW1y3B42aJ7VqBLMsy7n5qNx56o6fgYzaDKed1AXNbezlHqQFa+wBBENMTWZbxhb9sxt1P7S73oVQlrDinfA6CIAiC0FN1xfnVV1+NCy64AG9729vy3jaRSCAcDuv+zWTyjVJjynmhs7qZhd3jcvFZ5ylh0bWzbwzffmgb/vsvmws+ZjOYUm18HqUcpdYbjmPfwHhBx8OgWecEMb3Z0TuG3/3rAG57dMekjo2cjiTSGcRTWfVnKs4JgiAIQqSqivM//OEPeO2113DzzTc7uv3NN9+MhoYG/q+zs3OSj7CyGc9na1eV80JndWdU5dzjkvg4M6amA+BF7uB4csKqdjqT5cWvsTj3lFA5v+RHL+C87z/rSAXP6Tkn5ZwgpjX7B5VzWjKT5W02hDNGBadRgnrOCYIgCEJH1RTnXV1d+M///E/ce++9CAQCju5z4403YnR0lP/r6uqa5KOsbCL5bO1MOS8wrZ0r526J932LgXCHRmL85+FosqDHNiIWy0ZbO1ftS7AB0DUUQyyVwaCDUT8R3gNfnPOAIIjq4sBQlP/s5BxBaIijJxNT3IJEEARBEJVO1RTnr776Kvr6+nDcccfB4/HA4/Hg6aefxg9+8AN4PB5kMrkFkd/vR319ve7fTCbvKDW1OE9msgUVuCz8zeN2wWeiXh8c1orzofGJFeesEPZ7XPB59G9fj0Nb+4ObDuMLf9lsGVoXFdScmANlh/WcdzQEARTuPCAIorrYPygU5xM8p8009Mo5FecEQRAEIWJepVUgb33rW7F5s75n+cMf/jBWrlyJz33uc3C73Rb3JBj5RqkFfdprGE1m0BB0tnfD0tEVW3uucn5wWFvIDo1NbCEbtgiDY78fUDYX7Lj10R3Y0z+O9xwzF+sWNudcL9rSY3lU8HQmy9PtZzcEsL03QrZ2gpjm7Ncp51ScF4IYoEk95wRBEAShp2qK87q6Oqxdu1Z3WU1NDVpaWnIuJ8wZy9Nz7vO44HVLSGVkRJNpNARzC2AztOLcpfWcWynnE7S1Gy3kIkxJzzdKjT2Glf18XLD1x/MoO6LNfnZ9IOf+BEFMPw4MamGRg+Nkay8EUTmntHaCIAiC0FM1tnZi4uTrOQe0vvNCCkxWiHvdktD3LSrnpbe1myXOM+U8k5WRNRTo8VSGpyozNdxKtRGV73geWzs7noDXhcaQspkRJVs7QUxb0pms/pxGynlB6GztadrIJAiCIAiRqlHOzXjqqafKfQgVQSKdQddQDEvbay1vk8pkeTFqNUoNAGp8bozGUgUVmEypdgtp7Sk1rT0ST+kWYxO1gFrNOAcAr9CDnspm4XcpNv3DIzG87dan8c4jO/DtfzuS94RbLQzFnvF8xTl7bvUBr7axQYFwBDFt6R6N69w51HNeGKNkaycIgiAIS0g5nwZ87cEteNutT+O5nQOWtxHVYCtbOwCE/MUo58pC1et28bR2dpmY1A5MrnLudWlvZzEUbsvhMKLJDF7ZN4xEOgs2ltgqjEh87vkC4cTj4aPoqOecIKYtYlI7QMV5oZCtnSAIgiCsoeJ8GrD54CgAYP/QuOVtxJRzr9v6z16jhsIVopynhDnnxnFmB4cMxfmEe86Zcm5ia1dVe/H3A1pfeCSR1lvWLZTzqE45t188soC6+qCXb3pQzzlBTF/EpHZAGaX2mxf34bRvP4Hd/WNlOqrqgWztBEEQBGENFefTgEMjcQBAykaFYFZtO0s7gKKs2RlVpXa7BVs7K87VpHa32g+erz/z0EgMn/vfTdjeEzG9XlOqrdPald+vKecRtSAfi6d1IXClVc69CAkbGz2jcV64EwQxfWCboIvbagAobqA/vNyFg8MxvLAr173UNRTFHU/spPOBCqW1EwRBEIQ1VJxXOYl0BgNjSlqwXUr5WNw+qZ3BrNnRAqzZ7Pd6Xa6cWePM1r5M7YfPZ2v/w0sHcN8rXfjVi/tMrw/b2NolSYKXpcVnBeVcvU8sleHFNFCaQLgw7zn38DnxXcNRvOV7T+HSu1+0vS9BENXHAVU5P3Z+EwCgJxzHzl5FMR8azy3A73hiF777yA785sX9U3eQFQzZ2gmCIAjCGirOq5zeUW2Mj918b6Ye2yW1A8Up50wl97gl+HKUc6U4P3JeA4D8/Zmsn3MgYj6eyC4QDgC37KfS2kbFWEJbDPZF4vznUgTC6ZRzdWOjayiGaDKD7b0R3Ug5giCqH2ZrP2Z+IwBgJJri595hk7ad7rByznll39DUHGCFQ4FwBEEQBGENFedVjhi4JoagGRlzMEYNKE45z/A55xIPhEupl7Hi/Ih5jQCUxSsbaWbGIfX2I1FzC6hdIBw7BuX35yrnANAnFP0lUc55z7nH9LUdtngeBEFUJ4dHlXPUUeo5TcTMGTSkzkHf0DVie+6bKYRJOScIgiAIS6g4r3K6R7XiPGWj0rKCczJ6zll/t8ft4j3n6UwWsixj/6DSn3mUqpxnsjLCMevC/7C62WCmQAGacl5v8Ty8hkA6QHMNAEBfWFDOrXrOk4X0nOeOUhOZaDo9QRCVgyzLfIOwrc6fcz41O2+xnI2RaAp7B6xDO2cKFAhHEARBENZQcV7ldI9qxaadrZ0lluftOS8irT1tkdbeP5ZAOJ6GSwKWz6pDnfq7B8fNLeupTBY9avFspTjbBcIBWnEuugislXMLW3uigLR2daOhXhilJmL1XAmCqD5iqQx3CtUFPGip8emuN27GybKMAeGy1w6MTPoxVjKpTFa3+Um2doIgCILQQ8V5lXPYoa094tDWXtScc9HWznvOZexSQ5IWtNQg4HWjSV3IWqnJPaNxsEy7EdX+/sKuATyxrTfneVja2g0974C2MQEAvaJybmlrd66ch4UeeFLOCWJ6wzb6XBIQ9LrRUuvXXW/8vI8nMzrr9oYDw5N/kBWMaGkHyNZOEARBEEaoOK9yxOLcia29No+tvSjlnAfC6dPad/Qq49CWqkntzXmKc13/fFbGSDSFj/7qFXziN6/y488XCKcp92IgXPE95wmHgXD1QQ9/7USoOCeI6UNY2OSUJImf0xhD4/pMDePoyJmunI8ainNSzgmCIAhCDxXnVY5oa7crzlmBWmui7ooU03POFHuvW7O1p7NZ7OxTlHM2Rq0lX3E+HNP9/86+McRSGaQyMkZjKWSyMj+ufMq5mJKus7WHheLcovAWNyYKUc49bhf8Hv1HajDPXHeCIKqHsYS+rYad0xa2hAAoxaZ4zmBtLSF14257T1i3+TfTYJsbbCMzk5VpogVBEARBCFBxXuXolXMbW7tD5TyoLprihRTnqhfd7XLxtPRkRijOZynFObO1W41TE5VzAFx5B4BoMqMrsq3T2l389zPEQLh+J8q5GAiX53Xgyrm6WGc9/R0NAQCknBPEdGLM0FbTXq98zk9Y1Mw3JsXPPPt5cVsN5jQEkJWBNw6NTuUhVxRMOWevG2CflUIQBEEQMw0qzquYsUSaKxGAQ1t7np5zs1Fk+WCBcF63pLO17+LKeR2AIpRzoTiPJTNcpfZ5XPB7ci3kAOD12AfCiQvBiQbCybLMeyjrg8rruqStBl63hPOP6ABAxTlBTCe0thrl8/6+Ezrx4VMX4qqzlnKL+/C4Zt1mG5EtNX4snaWcB/cNztzEdlactwm9+lZTMwiCIAhiJmJfqREVTbdBaba1tTsMhGO2cJZI7AQ+Ss3lgk+9f284jqHxJCQJWNKm7zkfdqycj/GfY6kM3HHlsest+s0BwKtuLrANg3Qma2lNn+ic83gqy10DzOb6iw+fgOHxJF5Tg58orZ0gpg8RwyZnR0MQX75wDQDFGdQTjmMomquct9T4UBvw4BkA+wajU3vQFQQrzptqvPC4JKSzMvWdEwRBEIQAKedVzGGh3xywt7U7HaXGbOF2j2Ukw23tmnK+pTsMAOhsCnGrPEs27o3ETR5FK86Zwr6zT7S1p/POOAe0UWpJ9fjtUufNFBtZlnW2drvinCn5Lknroaz1e9DZHMobfkcQRPXBp16YbBA21yiXDZvY2ptrfFjYUgMA2D+DlXPmNGoIeuFTXU6U2E4QBEEQGlScVzGFKOdMPTabxS1iFqiWD3Zbxdau3H9Pv7IAZWFwgJbavq07oks0BpSimBXna+Y2AAAGhDC1WDKTd4ya2fFHEubz0gFzW3sindW5BuwC4cTkeEmSdNdRcU4Q0w9jz7lIUyj3M88CIZtrfVjYqoTG7RuYuco529Cs9Xt5eKZVexFBEARBzESoOK9iWDFbp6rhtsW5qgYHvHmKc1U5L8rW7nbxUCTG0llacb5ydh1cktKHKQazAUohnkxnIUnAqtl1Ob8jmszwQttqjBqgKefstRizSUY2s1Mak5Ttes5HY9oYNSMtNYpLYDiaQraA15IgiMpljJ2DTBxIvG1HZ2tXznMtNT4sUJXzfYPjGI2l8PFfv4L/ffXgZB+yYxLpDK77w4ZJPaZUWjkX+r1abgjZ2gmCIAhCg4rzKmbvgKJOL1EVaTsrOlOAg/mKc3fhgXCskPe4JLx11SwsFwryY+c38Z8DXjfvP3/zcFj3GGyjYVZdAG11fhiJppwp5152/OprIYbBGTFbFEYN6eyxVCZH5Wdw5dyfu1nQpFpcM1k5Z7YvQRDVScQmu4Mp54OmtnY/5jUF4ZKUc8w9z+7Bo1t68e2HtlmeX6aal/YO4a+vH8Ztj+6YtN/Bw0NdEre1U3FOEARBEBpUnFcxLPV3GS/OrRc5rHea9X9b4XXlpp3ngxXyHpeERa01ePi6M/DIp8/Arz9yAs5dPUt329Vz6gFoPemMnlGlOJ/TGOCLXJFYMm27MGZoafHM1m5TnJtY1pnSHvBqHw2rxSNLyjdTzv0eN1fXrEbHEQRRXUQS1huEZoGXg0LPud/jxpzGIADgDy93AVBGO24XplKUEzYto3s0ZvtdMhFSvAXKRbZ2giAIgjCBivMqRZZl3ru4XB3RY1VQpzJZriTnU87dPO3ceXHOAn1YYSxJEpbPqsMZy9tyerFXd6jFuUE5Zzb39roAV51FokLPud2sdp9bH2jHe0RNCnpz5Vy5PbOlA9ahcGLPuRnNtdR3ThDTCbtAuCaTnAkxrR0AD4XrE9p6nts5MDkHWyCHVfdSVga6R/ShnbIs45aHtuH+1yZmeRdboPxeUs4JgiAIwggV51XKwFgSY4m0MqqsXVnwWakdYnGZr+fcW2AgnCzLPPSILUDtWDNHCXt78/Co7nJWnLfV+dFoppynMrwf3KzQZrA57UlDz/nshkDObRPpbI6llKW71we9/LWw6jsPs55zq+KcL9ZpnBpBTAfGDHPORVoMPefxVIa3ybCNugUtoZz7PVshxfkhoSA/OKwPrXvzcBh3PbUb3/zH1gn9jpQQHso2UimtnSAIgiA0qDivUpilfU5DEDU+ZaGYtCioWb+5JIFbCa3QbOHOlPPxZIY/vlmvuJFVHYrKv28wqgtr6x/TinNzW7tWnNuNg2PXscdmyrlZcQ7kvmb8d/jcCKiBRVaJ7RGbhTqgLdbJ1k4Q04Mxmw1CLa1dOS+wz73XLfHbL2qt4bdn0yv+tXeQW7t/9PRufPGvm6esD/3QSAx/39QNWZbRPapN/zg4rJ8Ewr5vwjYZHk5g3yuKrZ0C4QiCIAjCCBXnVQoLg1vUWpO3oI4nlcVP0OvOsZkb8RRoa2eKd43PnXeGOqDMOp9drxTK24S+c1E5bwpZ2NodFOetqkI1oD4eK6A7LIpz48KQzTiv8XsQUPvzrWztbCxQfTCPcj5GxTlDlmXs7I04dmZksjKu+8MG3PXUrkk+MoLIj11rjZjWLssy/9w31/j4eZcltgPAB09ZiLY6P+KpLF7dP4x0JovvPrwd964/wM/vk0k8lcH7frIeV//uNTy3a4Db2oFc5Xz/oPL/yXS2oDGbRpJizzmztduMqyQIgiCImQYV51XKPnXxtrA1JPRZ2yvn+frNAWFOuMO09r6wYoVsrzcvfs1goXBiYjsvzmv9qA944TLsIYjKuV1ae2utot4PqEo8K+iba/zcpi6SSFko5343D4WzVs6Zrd38eJrVvnVSzjUe3NSNc257Brc6TITe2RfBX18/jNsf20kj6Yiyo805z92QawxpExrC8TQG1XaWZiG/YqFgaz99aStOW9oKAFi/Zwg94TjfFDWOmpwM7nluLw4MKUX3hgMjODwq2toNyrmwWRCdQDGdNrO1T1L4HEEQBEFUI1ScVynMZriwpQZeDxsfZl+c5+s3B7Q556mM7Mhaye3otfkt7Qxm52QLQ0ALSGqr88PlktBgUKOjybRgObcuzpm1fkBVrcaE8Wss5d2jG+OjX2iOJ7XfwTYzLJVzdUSaVc95i0lA1HRjZ28EP39ur+O+0Q0HRgAAmw+N2t9QZVi1CCfTWfSE43luTRCTRzYrYyxpPTEi4HXzz/yO3khOGBwALG6rxVtXtuPdx8zFgpYQb/PZOzCuK4jZeXWy6BmN484nNTfKi7sHdZ9hY3HOlHNA2SgtlpRoa1fPr8YNUoIgCIKYyVBxXqXsVZPaF7XW6ApqM9hiKt8YNUCztQPa/HI7+sJqUV3vvDg3jhzKZmWudLPimvVvstuKtna7tHajcj4mqO3sfkGfGwGLGbtiX3sgT3Geb+56i2qxN1pEpxNf//tWfPXBLXhmR7+j27MNmUMjsTy3VBiNaRsbYoFATA9kWcYNf9qIWx/ZXu5Dyct4Mg22X2n1mT95SQsA4Nkd/djROwYAmCW4itwuCfd86HjcdvnRkCQJ85sVm/uBwXE+ygzQ2nImi9sf24FoMsOfx8v7hnTXdxnOWXsHBeV8AsV50nSUGhXnBEEQBMGg4rwKkWUZ+5ly3lqT19YeL8LWDjjrOy9GOW9mwUlqqvFoLMU3FlhByyyi85sVG6iY1m4357y1TivOZVnmxXmt34Nav/KYIZ/bUrVhae2KrV0NhEtazTm37zk/YVEzAGBD14iun3M60aNaYZ26A7rU4vzwSIw7M5LpLD5wz7/w3YdzC7RR1Z0AgL/nienDgaEo/vfVg7j76d3lPpS8sHOJ1y1ZBmuesbwNAPD0jn489EY3AODslW2Wj8nS2/cPRadMOe8ejeHP6ki0r1+8FoB2rmfn255wnCvp44m0zmY/nig+FI7Z2j1uzb1Eae0EQRAEoUHFeRXSF0kgmszAJQGdTSHHtnYnxbnXrb0lnBTnTDlvn4ByzhaijSEvT/DtaAwCAJbPUizwSs+5FtZmBbOQpjIyRmMpLcDJ7+GJyTU+D+8nz7G1qwvPkG/iyvm8phBOXNQMWQb++vohy2OuZkZUZZvNh7dDlmWunMdTWYxElcL7jcOjeHbnAH77r/25jx8VivMhUs6nG2xTJ5WRJxQ0NhWI5xKrYM0z1eJ848FR7BuMwu9x4ewV7ZaPyYrhkWgKW4WAzIHI5LXC/PSZvUhlZJy4qBnvOmqObrPziLkNCHhdkGXw9HajY8Uqg8MJbBPWp1POKRCOIAiCIBhUnFchT29XLMTzm0PweVx5+8SZrT3gwNbuFmztThbLxSjnTYYRY2IYHOPz71iJr120Bv927DwAipVyzIFyHvC6ebE8MJbQ7mOwtbNNAOMMc2bZrPG5EcwTCJev5xwAP/77Xzs0ZeORphJWPMcc9I0OjCV1ryWztveq6rvZPPkRQTk/QLb2aYe4+RKvcAU1YhMGx5hVH8DK2XX8/89c3pZ39CNrxXlxzyC/fLKU86HxJH73krIJdvXZSyFJEt8ABYC5TUHMa1I2DJiSb3SsTMTWnhKUcxqlRhAEQRC5UHFeZYwl0viO2p95xYnzAYDb2gFztVtTzvP/ucWec2fKeeFp7TnKeUTfbw4Anc0hfODkhWhULfDiYtWuOAe0Ir8/ktQC4fxefr+Qz22p2vBAOL99IFw6k+Vj1+zS4887Yjb8Hhd29Y05DkGrFmLJDF9Yxxwo5wcMyjez+nez4jydydnA0CvnZGufbgxHNYXYyqFSKbCxjPnOP8zaDgDnH9GR93GZtV1s4RiYpOL8+V0DiKeyWDGrDqcvU5LiVwibCXMaApjXpLiWWFbGPqNy7uCzbkVK6DknWztBEARB5ELFeZVx91O70B9JYGFLCB86ZREAcFs7YD7rvJCec0mStFnnFgFzIgPF9Jyrxfl4MoN4KmNanDNCqtrPFnAuCdySboUYCmemnId8HsswIqeBcGNC36WdklYX8OItKxVb67M7B2yPu9oYEcLanKhpBwzFNSvOWQq7LOeGGuoC4Qai09J9MJMZFpXzCivOB8YSOPGbj+F//u8NAPpwSTuYtd3rlvCWVdaWdsaC5lDOZVaBcAeHozj2a4/ixvs3531cM1hGxIrZddyav3yWUJw3Bnlx3jVkrpyz9qJiYBu+Yt8+2doJgiAIQoOK8yoincni58/tAwDceP4qrjwwWztgPjO2kLR2QAuFs+phF4+HWdPNCmsr6gMebp8fjiZtrfHGY7br92SwY+mLJHRW+Dqdcm5uqeQuA59bKM5zXwemmvsEBciK2Q2Kq2AiQUqVCBtzBjjrQz0wqA/FY3OVu4X5ynHDQl1UEyOJtK6YI6qf4XFROS+/gnrXU7vwjtufweBYAq/sG0ZvOIH7Xu5CPJXRjWW04+TFLbjyzCX4xruPsG15YcxvMSnOx5KmG1F/fvUQhsaTuP+1g443M17dP4QfPr4TmazMP2vsnAQAK3KKc+V4WNsJG9vJTrsTmXOeSlNaO0EQBEHYQcV5FZFIZ3kRdKZgnfQKCetmBXUhc84BrdjPN0ptcDwJWVb61JuFWb75kCSJj0obGk86Us4Z+SylANCqJr7vHRjT3a9OVM5ZIJxhock3MrxCWrvJYrSQDQ/2HCbSq1mJiMo5ez1iyVxrOoPZ2lloH1v894xqRbux4BgxFOOU2D69qCRbuyzL+Plz+7CtJ4L1e4b4pmEincWGAyO6QDg7XC4Jnz9vJS5b1+no9y4QivN29RyYzGQRjuVu5v1TTYBnx+SE6/+4Ed97dAee3tGHXtWlIo53Wy7Y2uc2BvkmKXNFsawHpvDHkmn0ReL46TN7dJsrTkip3ykel1ack62dIAiCIDSoOK8ixB5wl6Ae57OiF5LWDmjKeTprv2hiRXVLjU8XJOeEFt53nrItzgMe/THbhSsxmK39OdVG3tEQQNDnxvlHdOD0Za24bN08S9WGqXdBr9u257yQVgG7x6lmxMI5msxgV98YjvrqI/jqg1tMb8/GqJ24WBkxZ+w5B3JH27HfwTY4jH3rRHUjvofKbW8+NBLjBWlPOI7+sPa+fHHPICKJ/IFwxTBfsLUvbqtBvbqJ2D8W191uT/8YtvVEdMckkkxncfGdz+PT973OL9s3MM7T1vcNRHkLSYegnLfW+vHZt6/A9ecsR1ONj4+zHBxLIpuV+SbFkjYlOC6azOCe5/biG//Yit+sz52wYAfbPPZ5KBCOIAiCIMyg4ryKEJVsj6EY9trMOi+kkFQeW0t/t6MvwsLgnFvaGU01ygJ3cDzBi/P2utxQOZdL0h13bR5LKaDNOmdBRkfNawQALG6rxW8+eiJOXNzi0NZundYu3i4fQZ9yzEw5l2XzVP1qQyysYqkMNh0cQdJG0WOF9UmLWwAoxXk2K3M1D8gt0Jitfe2cBgBKgUFMH/TKeXmLtNe7RvjPveG4LoTyxd0DWiCcg3NQIcxvruE/z2sK8fNXv2Gc2j/f6AGgBYC+uFufYbG7fwyvd43gr68f4p+jp3f08+sPjcR4z/ksQ4Dn1WcvxbVvXQZA29wcHE9gNJbi3wOd6iZCNJnhIzTZBptT2Oaxx+XS3EvUc04QBEEQHCrOqwimZEuSUrSKMGt7SXrO1cfOZ2s3G4HmFDGxnRX5Vn3rorXdma1d/zhHz2/MuY1VGJFoa2evl1HNFW/npFUgKNjjZVnGh3/5Mi750Yt5X99Kx2hrZ7ZfMyUsnspw1Y4V532RBHojcd0mkFigpTJZnhlw5DylOKfE9ulFJQXCvS5sKvWMxnkBCiiFe596vsvXc14orbU+fo4TbeXGcWoPqcX5R09fxI8pKiSn9wrBit0jys/PCMV511CU30bsOTfCzs1D40n+nBtDXjQElQ3VaDLNN80GxgqztbPvJ6/HxTcZyNZOEARBEBpUnFcRzGVuVM0BTTm3s7U77jl3GAjHFq9minc+2AKwOxznC3TWK25E3FSo8TnvOWcc3dmYcxut51x7jrIs614rZqm3Vc4djKdjC282euyp7f14df8wukftVadtPWFc+qMX8Mq+oby/oxzobO2pNFcWzZQwNjO51u/B0rZa+DwuyLK+IAL0BZoYBreqox6AljZNTA9GKkg533hwhP/cY1DOUxmZt8nUOdggLARJkri1fW5TkCvnYmL74FiCj2L86GmLMLcxiFRGxiv7hvltxM2Eg8MxJNIZnfV986FRpLMyJEnrbTeDnZtTGRl7+pXcjrZavy47g302B8cLG/mWZsW5SxKUcyrOK5XBsQQ2H5xeI0AJgiAqHSrOqwimnJv1d9vZ2mNCH7UTeKGfTzkfs+4Vz0ezGgi3fo9SeDaFvJahcjrl3ImtXVDOXRJwxNyGnNuY2drFn4M+NwI+fa/4AxsP44xbnsQbh0a1VgEHbgQxWE5MbO+3GJfE+L/XD+PlfcP4+t+35v0d5UAsrHTKuUmRxTYi5jQG4HJJmKMqd6/uH9bdTizQWAFQH/DwHtm+PK8ZUV1USiBcKpPlxS+gqNCs2F3WrvRas/djMee7fHzolIU4dn4jzl7RzpXzx7b24kO/eAlvHBrFa+om1rL2WrTW+nHyEsV9cvdTu/mGldgecnA4ilf3DSOazPDvC5bt0Frr5+d4MwJeN9+A2Kr2uLfV+XWbjGFWnBegnGeyMthXipLWbu1MIiqDT/72NVx4x3PY1RfJf2OCIAiiJFBxXkVkhKRbI2zWuZmtPV6grd3tYM55Nivjxd2KKjNXnYtbCE1qIb5JVavWzm2wHJGm6zl3oFqJi+fls+pMQ+TMbO1icRDwuBDw6HvOH3qjGweGonhmZ7/O/p4PUXESE9vzFedMiX69awRbDofz/p6pRrQkx5IZhLmtPbfIGlJTndkGDHvPPLOzX3c78b5MmW8IedGu9siyAuSe5/biyt+8mtfdQZSedCaL9XsGJ1RMpzNZxJIZ3WaMcYzeVLK9J4J4KsvHhfWMxnk43DVvWYpavwdHzmvAFy9YhbeumlXy3//eE+bj/qtORVudn5+/Xtg9iKe29+Pup3bjlf3KJuZxC5oAAJet64TbJeHFPYN4261PY0//GHojWnF+aCSGZ3cpSv9bV+pnrc+uz+90YqFw27qV805rrR8h1bU0LirnY843y8TPqsct8RGUZt9ZRGWwp19pI9rSTcU5QRDEVEHFeRXBlGxT5dyV39buPBAuf1r7Q2/2YGffGOoCHlxwZIejxxVhRRrLRVs9p97ytsECe85F5YeFwRkxU87Z6+Rzu+Bxu/jvZQUEKxZjyUxBrQJBQYEfF3pEjT2lRthcZQD4/UsH8v6eqWZUZ2vP8M0EM3sye+3YCL2zlisFw47eMd3t9Mq5UtA3Bn2YpYYORuJpxJIZfO3BLXjozR48sa2vVE+HcMifXj2I9/5kPe54YldR9//l83ux+ssP45EtPbrLy2lrZ5b24xcokwQS6Sw/3563tgNv3PR2PHDNafjY6YttVedSYMzweGZHP3cYseL8hEXNePBTp2FZey3GEmn8fVM3eg229o1qwN1bV7Xr+uSNYXBmsPPz9l4z5TyNsPpZH09m+EZlPsTiXDfnfJpNsZguyLLM3VGHhgsL/iMIgiCKh4rzKkJTzgu1tRfWc27Xvw4oqvkPHt8JAPjIqYtQX8RoIaOFnaVxmxES+sydjFIDtMR2szA4wLznnC0y2XUBwwg0VmBGheK8kFFq0WQa4wnnyvmYYIH/64ZDuvCnSsA6EC53sc3sy41qcf5vx83jgVAiohrLXu/GkBe1fg9/HfcJs87NHoOYXHb1KRsq23qKc3M8v3sQyXQWf3ylS3d5OW3trJA9flETmkLa+awp5OUK71RxytIWzG8O4YMnL0BrrQ+RRJof37qFzfx2qzrqceFRcwAA+4ei6BNs7V1DUbyh2vTXzm3AvCZtXFuHTRgco0XdIGATFtrq/HyTcSSa0m2kOO07F79PvG4Xf12p57wyiSTSfIPq0AhNySAIgpgqaGVbRbDFjalybmNrLzStndvaLXrOn9zeh209EdT5PfjIqYscPaYRpqAy1pr0hTP0yrmz53DR0XOwqLUGb13Vbnq9ma3dWHDzlHX19WNWzmgyU1CrQFDo1RQL7HzFeVhQziOJNJ7e3m9z66lnWDejOstfn1RGzkmi15RzpfBprvHh7Wtn5zxm3MzWHvRCkiSunouj2pxuOBGlg1mZD48UF87HHBYv79PnDZRTQd2i2rePmNugU5aLCbucKPOaQnjmv87GTRetxZnLtfNXS40PC1tCutsuUP//wGBUp5xvOjSKcDwNr1vCsvY6zG3UWo/sktoZLFSTOZvaBFt7T1j/d3ea2M42jl2S8h3D3EuU1l6ZjIxr53dSzgmCIKYOKs6riIyNrd1jY2svdM45G8uWtugF3Kimt55/RAcaQoWr5oDW0wgoVvUFzSHL24YKnHMOANe9bTmevOEsy8U1WxgeHonhXXc8h1+/uC8n5I1Z6FkxwSx+0WS6IOWc20FTGVPlnFlEjTBbO1OYRmLmtysHsizrbO2AfrPBqJ4z5VzclLnihPn8Z5YeLToZ2PNlI5zY33LDAa2os2u9KJRX9g3hi3/drEuJJ3JhxVi+aQNWMIeFsSiLT3KRJsuy6d82lcliR4/iBljdoS/OJyP8rRDEzcVjFzTl5HIsaFFmpO8ZGNe1ybDXdsXsOvg8LswTckGc2NpbavTPW7S1R+J6B4/TvvMUc36pbhc/KecVjRjWeKjAefYEQRBE8VBxXkXYpbX7HNjanfecq49loZyzoCQnCowVYpG2uqM+Z267SKjAUWpOYAvD1w6MYNPBUfzplYOIJfWp9s3qBsJ4UumnHlfV8mihPefqbVIZmaccA0rP+SNv9uDIrzyCe57bm3M/ZmtvVVsAKqk3M5rM5Lg0+oRAKmMC87BgUWectLgZx85vREPQy8fdico5e63YfdqZcq5afAH70MJCufup3bh3/QE8vrW3ZI85HWGf/+FoynG/sYixuGNMtq39+4/vxFE3PYIX1KA0xu7+MSQzWdT5PZjXFNQFptmNHJsKTlvWytuY1qn95iJsU3NgLIFMVoZLgs6Gz9qFxOLcia3d2HbUKoxSM+I0sT2lFuHsu0oMhMvmmQxCTD264nw4BlmmvxFBEMRUQMV5FWHXc241m1w3u9vn7M/NHitjoUoypcRqLrkTAl43X+ytmWsdBgcAQaEgdxII5wS/YT75WCKdU3DX+T38td43oPXciSnThdjaAWBA6M/sjyTw9A7Fqm42y5wV52yToJIUJqZq+9wu3eYDw3isIybKuSRJ+N3HT8ILn38LV/PEXlZ2n8agch+mnLOeZ+V3lu41Ya93ocr5G4dG8bFfvYIdvTMj0XhwXFu0F6OeRyycIpNdnLNxZM8ainM2CWGVukk4q6FylPP6gBfnH9EBn8dlmhLfGPKiXnATtdb6dYX4GjVoU7S1O1LODed2RTk3P/cOOO05V79P2PeLX9hEoMT2ymNEcEaNJzO6/ycIgiAmj6opzm+++WYcf/zxqKurQ3t7Oy6++GJs37693Ic1pdimtXPlXL+7nUhned9goWntxsdiMKWkpXZiC1emzqyxCYMDCp9z7gRma2dE4rlWdUmS+Mi3vUII2XiBtnaf2wX2JxuIaIVNfyTBCzqz/nNma2cWU6e9maOxFC7/8Yv4zYv7HN2+GIbVAq0h5DVV1IyFFre11+jbIAJeN2r8HgR4QJ/Qc85s7apyznrORaxyEYqBFQjiLHon/PGVLjy2tRd/2XCoZMdSqWSzMh+LB4DP2HaKLMu6LAVAK9ImO62dvWfFzR1AK87ZxIjZFWRrB4DvXnoUXv7vt2GpOm9dRJIkbm0HlMJbDH9bM5cp59plznrOteftkpRztdVGpFPlPJlWPqtebmvXHq+SNh4JBVE5B8jaThAEMVVUTXH+9NNP4+qrr8b69evx6KOPIp1O49xzz8X4+Hj+O08TsnZzzi1s7brZ3U6L8zxp7QNcOZ/YwvXc1bMxq96PM5a32t5OZ2svkXIeyFHOU6Yhby1qcb6nX1vQx4RAOOPjmCFJEledxGTjRDqLzWqisnGsWiKt2cZbClTOn9reh3/tHcLX/r5VZzUvJUxdbgp5Td9XyqaQrCXdjzOLurnbwmy0HU9rD7LiPLeoKKVyzqz4rH3h0S29eNnE0WCEFatjFnbt6cRoLKUL+ztcYHEeS2VywgKZzXqylXNWbOw2FudqGNzqDrU4b9DOa5VQnPs8Lp67YMZ8ISRuVr2fq+QuCVg1W3lOi9tq0BjyYvmsWkfuI9HW3lLrh9sl2djaC1POvepOpdct8Y3gsQI3xIjJZ9iglB+kUDiCIIgpoTSVzhTw0EMP6f7/F7/4Bdrb2/Hqq6/ijDPOKNNRTS32yrl5iBtTeL1uyfF83nxzzjXlvHhbOwD8z4Wr8aV3rsoJOTIiFn8ls7UblPN4KouIukAU1XBmw947YK6cO93wCHjdGEuk+caG+HsBoC+cgCzL/LUQC71m9RicWj971TTlZDqLnz+3D58/b6Wj+xWCVjj7IMu5ylkincGXH3gTf3ylCw9+6nT+2hpT+hlsk0Ms0EZzAuFMlPMS9pyLyvngWAKf+M0raAz58NqXzrG9H3stxits1N1kYHz/dheoppn1m89uCGDfYHTSA+GYcr5/KIpkOgufxwVZlrXiXFXOKykQzglignt7fYAX50vba/lGY43fg6dvONvxWDjx3M7mrnvdLvjcLv458XlcSKazujYHO9hGmlc9BkmS0FrrR084joFIQme9J8rPCCnnBEEQZaFqlHMjo6OK4tjc3Gx5m0QigXA4rPtXzfCec7e1rT1pKFZiycKKSOXx7ZPfIzyobOIL13yFOWCwtZesOM996zNrufhaNXPlXCvOYwXOOQe052BlAY2lMlyxBTQlqcbn5gtsu0C4RDrDVfKeUa2Aunf9/pxU9VKgzS23srVn8fK+YcRTWTy6RQlYkyRYKoDGmfKAVpwztb3dRDkvZVo7S5gfT2QwMJZEVlZU8XxBSGzeezRROYF9k4VxbFahyjnrNxeLxI4GpSibTOU8kdY+X5msjP1qm0r3aBwj0RQ8LonbxispEM4JC5oFW3tdACcuaoZLAt6yUt+j3hDyOh6n2SxsorUKr4F4/0Wqnd75KLXczBQW8phvrCQx9TDlnH1X0jg1giCIqaEqi3NZlnH99dfjtNNOw9q1ay1vd/PNN6OhoYH/6+zsnMKjLD3Oes7NlXOnRSSg2Q7NCh+mknjdEuqDU2O8YMWfJMHSWlkoxkA4QFsgBoXgPFaci8p5NJkpeHY8e/2NyqPZ7wc0hbE24OHpxna29k/e+xpOufkJdA1FuXIOKEX+/7520NExOmFoPInLfvwi7nxyFwClODe3tWsz3TcdHAGgFOZm710gd6xSOpPN6VNvN+k5t8pFKAbW0z+eSGMsoW1o5Otrn0nK+aAh/KvQQDjWb95e5+czutl/J3MagTHMivWdv6n2my9pq+Xv4+YaH+Y2BlEf8GBuo/WIx0rBaGtft7AZr3/5XHzuHSuKfkyP24UmNeuhTWhfEs+/i9tYce5wlBpTzgUHF3vsPirOKw6mnK9S2z0ODkftbk4QBEGUiKoszq+55hps2rQJv//9721vd+ONN2J0dJT/6+rqmqIjnBxYerpZWruVrZ2pUYUUtW6bQDjWX9hS43ekepcCltZe6/OU7Hcabe2A1vets7WrxbnYExlNZhBNFrbpwYr4IRsLqFlxXhfw8o0Eu0C4zYdGkc7KeL1rBD1qcc4Wz/sHS5fL8MyOfry0dwjdqmI6tzFk+t5KpLL8Ndp0UHG5WFnaAcBvUM4HxpKQZeW9yBwadX5PzuttfL9PBLYxMJ5M60LL8gXx8eJ8BvTNMudHnepgKTQQTnxf33b50fjaxWtx/ELF/TSZgXDGcCtWnG9Ux/IdOU8LpZQkCX/71Gl47PozHW++lZMFuuJcUf3rA94JnyvZxmSbhXLOzi9D40lHo9CYE0t0TZByXrmwz8xadZoK2doJgiCmhqorzj/1qU/hgQcewJNPPol58+bZ3tbv96O+vl73r5phCp7LZNFlbWtXFrzF2NqNwU1A6frNCyEk9E2WCse29pC5DZstXJy+rqyoZC+p2Rg6cYHKNgNq/R7TsDQRWZZ5P23XcJQXTCzgym5DoFCYLXnt3Hrc9K41+NApC01HLMXTGUTV58AWdY0WryUg2tqV58jU/7ZaP1zqZpEkSTmJ7SVNaxeVc4fFeSqT5X+raBEzv6sNtjnHxh8eNizY46kMHnqjm7cIGGHvn7qAB8fOb8IHTlqg5Q1Y3KcUGD8Du9SAx42qq+Oozkbd9c01PtM2ikpkVl2Av4ZOxqQ5hU3jEIvzGuGzvlC1tWeysqPxg6xXXdxcblPHI05WcCVRPMNqiOdadZrKgcEod0MNl/A7hSAIgtBTNcW5LMu45pprcP/99+OJJ57AokWLyn1IU46TnnNLW3sBCpCVCg9o6vJEk9oLYc2ceqyZU4+Lj5lbsscUi3PmFOhXF4i64tzieTIF0OnralSXxfFHTIHqj4h2dK2I8XHLt3nxEo6leZHaNRTlC91Vk1Gcq4Xoqtn1+OApC9FgYWuPJTOIGmzKtsq5R1+gMZur0crOZp0zSmlr58p5IqNzStgF8elnAU9/5XxAfS8doY7oCsfTOsfAvev348p7X8N3HlLGXHaPxnTODfa5EWdzs82n2CRubpjZ2rOq0wQAjjYU59WEyyXhc+9Yifed0ImVs+tK9rinLmmFz+3CugVN/DLxfNda6+cOCqMzwQymnOts7XWknFcqzNZ+7IImtNf5EUmk8dk/bcK1v9+AY772KG57dEeZj5AgCGJ6UjXF+dVXX417770Xv/vd71BXV4eenh709PQgFps5Viu2uHGbjlKzT2svpOec29orRjn34O/Xnl7S1HFR7V0xS1nQsn76oE45t3+eTl/XgKE4Z6qT2yXhxEWKrVccp8aUW0U5t+85F/uAN3aN8oJ1VYfyvEpZnLNCTJw3b2ZrH42lYMxRc6KcJwzKubEYNxbrpbK1pzNZvvk1lkhzhRewV85HY9prOyMC4dQian5LDS/MxL7zPWo2w982HUY0mca77ngeF/zgOf4eDMfYppP2XjALAyw17PcvU0PfdvePYc/AGCLxNPweF1aUsKgtBx8+dRFufs+R3GVSCv7zbcuw6Svn6lwF4me9PujlbiYnrhGznnMWuEc955VFMp3lAYqz6gO444pj4XFJ+Pvmbjyw8TAA4I4nd+ENdRQoQRAEUTqqpji/++67MTo6irPOOgsdHR3833333VfuQ5syuHJuGwinr4jiBfZGi49lbmufeuV8Mgj63Pjh+47BXe8/FvOalLRoVkyK6pA479f0cZymtRtut2yWUiQsbavlI4R0PecF2NrF4ntbjxJw1Vrr44VtKYtzcdOAYVacmylpdsp5wEI5N9rY33fCfJywqBknLVY2NMw2kIpBVMejSYOt3WYDQJwFPBNmNbMNrNYaHzoalffX4RHN8cEmA/SGE7j5H9vQH0lgLJHGszv7AYg959r7R7O1T17POVMBj5zXCJ/bhXgqiwc2dgNQXABOx0zONIyuGPGz3hD0IORX/t9J3oJWnIu2dlLOKxH2eXG7JNQHPDhhUTO+fOFqAMqGysmLW5DJyviv/91kuk4gCIIgiqdq5pznG2c0E7BLa2dWdytbu1G5tcPjMn8sQEvmNeuZrjYuPGoOAOCxrb26y4Mmo9SscJzWbrjdW1a2I5OVcdLiZh5Opes5F9Pa8yrnWiHM1kmz6gP82IejSd0M9YkgbhowzGztQ+O5PahNBSjnfRbK+alLW3Hq0lZ85YE3sX7PEA9JnCiiOj6e0MYFGq8zItqlE+ks0pksz2yYjvDNuTo/OhqC2NE7pguFGxGcBL9Zv5///PT2flx09FxdzzmD/e2T6SyyWbmk6i+DvR/b6vw4qrMBL+8bxo+e3g2gui3tU43oOKoPenkPurGFxQw+Ss1EOe+PJEp2jiImzhAblRnUggU/cPJCrFvYjLlNQSTTWZx5y5PY0h3G1u4w1s5tsHs4giAIogCm7ypyGmKf1l66UWrs8c3mnLNCsKUEM84rhTpD0FxAl9auLyiNr71ZsJwZxuK8LuDB1WcvxXELmjX1aMwkrV2wtVsViWbKuFicpzIyLzZlWcYLuweKDmASNw0Ypsq5yTE12qa1q+ppyl45Z9i9R4tB3PhIZrK64zd73aPJNGLJTI5DwEmRUs3wtpYaH+rVmfXiRsawyaYMADyzcwDZrCz0nOfa2gH7cYETgSmBTSEvrj9HGTHG/q7GMDjCGp2tPaDNTXfS0sG+m3xCcc4cWMlMFuHY9HeeVAvsc2xsRVrVUY/6gBettX7MV1uzBikcjiAIoqRQcV5FZGyUc5+FrT1WhK2dKRtmSdgDZeg5n2zEQhPQF9J+j1unEotpyEGv27HSY3z9RQWqrVZ5TLO09rqAV+g5N18AWxXnAa+bL6ZZsbnx4Ciu+Om/cMOfNjk6biNjJsq5fsHusTwme1u73rrPe86tinOL93uxGAvw3rD2tzDa2jNZGW+//Rm87danc2Y8T+e+83hKcxS01PpR42NBblpRZUztPnVpC0I+NwbGEtjaE+Yj6nQ958IG16GRGO58chf/+5cKpgQ21fhw8pIWnLt6Fr+OlHPnsHOj3+NCwOvm7wEnYYisOBcDTQNeNxrUTR5KbK8ctM0s63N2o/p3G3EQBkgQBEE4h4rzKiJt03NuZWuPF5HW7rFJax+YJj3nIrV+vTpgLKRF9XxOo1CcF/CaGtVl8f+Zcj4wlsTvXzqAWx/dwe2/oq3dSjlnaqbIbHUTgannTN1gydn7BoqbfT6eMOsZzn0uQ6Y9505GqSnvV1YcG23tDB6AWCJbu3Hjo0coDo2v++BYAl1DMRwaieH1AyO666Zz3zl7D3ndSh9qkBdm2mvHnAQs4fvjpy/GKUtaAADP7BgwtbV73C5+TvvZs3vwnYe34+fP7S3psbPNKVZsfOH8Vaj1e7B8Vi3PnCDyE/IqfzdWUIdYIJyjnvPctHaA+s4riT39Yzjxm4/h5n9uA2DvdmKqupMxegRBEIRzqqbnnBCVc7O0dntbe0Fzzpll2KCcZ7MyV0SnU3FeZ1TODa9Vc8iHrqEYgl43GoI+y9vZId7W53HpFqjMhZDJyrjx/s0ANGXaWSBc7qJ2doPy92mu8eHgcIwXJ2wh5WT0kRlMORXnHetcAHV+7O4fN1VTbG3tHs2tEU9leAK91dxmj6u0yrnxtRX7qI3KuWjjfHnfkO666DQep8b6zVtq/JAkib8HmDsnkc7w1O673n8sYqkMFrTU4MBQFI9t7cPTO/pMA+EA5fw0lkib5i+UAhbc16xutC1srcETN5yJQAHuF0LbVGQtDTUmGzSM7z68Hc/uGsBvP3Yiav0evtnrNYwCba/zY1ffGCW2VwCPb+3TuYbsNlRZcW4cU0gQBEFMDFLOqwgtEC73uny2dpaI7ARW+BiL85FYim8Q5AtKqyZyinOf/rViz7Ux5NUp3oW8pkGhgK0xqOhetyvn9RxLmATCWfQzs2JxSZs2O32WhXLO0rQj8bRp4F8+8vWcs00bVgyJPeN27xlx8+jQSAyyrLRvtFjcx87dUQzG4jwmvNZG5Vy07A8bFqbj09jWzp43+zvyfmN1Q4K9t1yS8j5YoPaknrq0FQCw4cAI37QRbe2A9lnqGo4qj1ViNY5tTokbRO11AV3vO5Efls7OlXPDBo3IXzYcwsauEazfPQjAfJQaQMp5JeExbJywv7MZbKOainOCIIjSQsV5FWGnnFvZ2lmB7TW5jxVWM9OZctYQ9PKCcTpQaxMIByh9qoDyvPXFeXHKuag0M9osnAi6QDiLQpQVTWKw1ewGtThXixFWnIwIRU8xiyreCy+0AgRNinP2Xl0xux5BtffdGK4nIgbrHRhSCrS2Wr9lcrdma5+cnnO76+wCkKazcs42HtjGTMigmrL3VkPQq/u7LWqpQa3fg0Q6i8OqI6HesCHG3CFMtStlcZ7KZLnjo9nGvUHkZ35zCACwqFXZeAnZ9JyzVhE23jHF27L03x3tJoGYRHmIp/TnOrfb2lXClfMY9ZwTBEGUkulTYc0A7HrOrWztrEgqZDyR28IyHDHpN54OGItzM1s7wIpzj+Xt7BCL+hp/7v3YYvedR3bojy3g4UnmiXTWdKQgK87FYCtjz/mQwdYOFG5tz2RlblsWlfOgSc85o7XGh99+/ETc+7ETeQFmhsslcfdHl1qcW4XBAaKtfXKUc5Ec5dymiJjOPeds44G9l422djN1GlD+tqs76nWXWSnnjHC8dMU5e59LkmbHJorjtKWtuP+qU/CVd60BANTwnvNc5ZyNRdzWEwEApNTPkdej/y5i54y+EocAEoXDNlRaa/04Zn4j3nv8fMvbskC4UVLOCYIgSsr0qrKmOWyUmllau9fC1p5RizmbDfAcmApvnCEdL6J/vRowprUbn19zrblyXkggXCCPcv4/F67GhUfNwdvXzMIzO/p5qnWt3wO/W7mvLCt/X5+wuJVlmSu56xY0I+h1oz7o4XbEJkNxLqrlZuPO7BDVMXGDQRduZ3AAhPxuHDu/ydHj+70uJDNZ7B9Ui3OLMDhAdHcUp5xnszIyssw/N7bKecba1s5or/OjL5LgmxfTEfbcWFFutLUz5dw4fgkA1sytx0tCf75Zz7lIKZVz9p5vDHpNz52EcyRJ0n2ebZXzjL44t3Jxsc85KeflhynnFx09B19652rb22rKORXnBEEQpYSU8yrCXjk3t6Jn1OLFbdaoboGVZZgpIYX0WlcDdX6jiqcvFN66chaWz6rFxcfM1RXkhWxS5FPO5zQGccGRHfC4XThhUbPu2PzC620sFMeTGV5YLmwN4W+fOhV//uQpPOSqxVCchyegnLN+c5/bpVPBGwVngXGjw2wjwgr2ejJbu9WMc0Ac91e4ci7LMi750Qs459an+WtnNaYOcGZrn6smfo9PY+V83KCch3hxrtra1fdTo4k6vWZOA//Z7ZJyphdMZnE+ZEhqJ0oH26gxbkrJssw/N3sHxhFPZfi5y6rnvC9MxXm5YedBv4O2Na3nnGztBEEQpWR6VVnTHK3QtlbOk5bKeTG2dgvl3MaeXI2IKp7P48pR11bMrsMjnz4T5x/RoQtzKyit3WevnIucuKiF/1wb8HC7N5AbCjekjlELeF0I+TxY2l6HeU0hfj1XzqNmtvbCCiAxpE6krc6P2y8/GndccUzOxo2xCLOD3ZeNe7NTztkGVTFp7X2RBF47MIJ9g1EcHokBsFfOjZ8DM+V8TqNSnE835TyblfnfndnXteJcX5gxhdqsCF47V7O11wU8OQnpxvdNPJW13TApBDZqcDqFWFYKLCDOmLUgbiJmsjJ29Y3xjWNj6Nhc9bNzYChasoBHwjmpTJZ/tplybteCxKBRagRBEJMDFecVRjqTxV1P7cJ3Ht7Gi2F+XRE951mbhHcrvC5ma9cXPvH09LS11/id95EX23Mu3taY1m7kpMVKcV4X8MDtkuBySdzNYFTOB8a18VZmMOVcC4TTCkuzItOOiGC1N3LxMXNx+rK2nEVdTQHKObvvvgFFObebP+2dgHK+XbXZApp7wK7n3HgdU85ZqJlL0nr8p5tyfsOfNuK4rz2Kg8NRHgjHZltz5Tyht7U3mNjal7bVcjXOLLPCbMMvHCvNa8lm1s9qsN7sIYrD6J5gGD8z23sifCPNZ/gymt8cQsjnRiKdxT51Y46YOv7t7hdw1nefRDyV4RtiTtxx4ig1sywUgiAIojioOK8wXJKEWx7ajjuf3M2LIUZWtk5rt7S129zHCo9F/3p8mtrafR4XLxzyFueCJb2QnnOdcm5S3IqsnVuPz5yzHF++cA2/jM86N6TpMuWczUo30mQcpaZLay/Q1p6wLs614zQo5yYWfivY+4ptQMy1Kc616QSFLwp39OYW54WktbNNjRPVTZTGkI+/JuPJNJ7d2Y8ntvUWfFyVyKZDo0iks9jWHeHqKNtcYu0Z0ZTe1m6mnHvcLqycXQcgt40EMN/wK5Ui16sW52wDhSgdbLPSuCll/Mxs6wnzjWOjcu5ySVihvje2dEdATB2ZrIxNB0fRG06gP5LgmypObO2Nqq09nZVN59wTBEEQxTG9qqxpgMsl8cWvccHjTDk32NqLUM75DGmLQDj/NFPOAU3Ny1dwFztKTddznud3SJKET711GS45bh6/jM86VxdP2ayMDQeGuSpoZdllynkknsZYIq0blTM0XljxM+6gODe+JgXZ2g3qqZ1yztLai7HBbhOVc/U1sO05t7C1n6bO726v8/NCdWg8iY/96hVc+ZvXTGc/VxvsdYkkUlwdDfJAOHNbu1kgHACsmav0nZsp536TDb9SJbb3jFJxPllY9ZwblfNtPRHLOecAsEpN89/WHZ6Mw5yxhOMpPLdzIMcFx4gJ7rxYKsPbppx8twW8Lv69RH3nBEEQpYOK8wqE2ayNY5l4z7lJcW4155yPUiug55wV/8YkbK6cT7Oec0ArOPMtSoLe4mzt4mtWSEgag886Vxe9D27uxrvvegE3/e1NANbFeX1AS6jeN6C3jBasnMfNe87NjpNRyHMVCzS3S7ItpiYy53wiynkmK/P7nLd2Nr54wSp8491r+fPc2TuGRDqLZCaLSAnHgZUL9tzH4ukc5Tykvv+T6SzSmSx/XYyj1Bgs5bvDxF5eKuW8P5LAfS8f0G22kK198mDOmPzKeYR/n3hNMlNYcb6VivOScvM/tuHf7/kX/rG52/T6qPB3iyUzmnLuwB0nSRIPfxyhcWoEQRAlg0apVSC1fg/6IglL5dysOPflmXNeyAghrkoae85TzvvRqg1WcAbzPLcana3d+evgckkIeF2Ip7Kmae358HPlXPkbvH5gBIDmlGixKM5dLglNIS8GxpLYayjOhwosziOObO3F95yLGxiz6wO8vcIMntZeoK09m5VNi3O2KK3xuXMsmuJnaiSaBGuvbKrx4WOnLwYAdA0pwXLiaxxLVb9yzoqscDyd23MuvI+jqYxuZJkZFx09B1lZxunLWnOuM+85L3zBf9tjO/C7fx3Atp4Ibwth87Nn1Vmn/xPFwWfdp4zKufL/Po8LyXQW/ZEEb9MyVc5VW/tWsrWXFHaue+3AMC48ak7O9aLjIZ7KFBz62hjyoi+SoFA4giCIEjL9qqxpAFcjkmnIsoyuoShkWeZzx4uxtZvdxwqxf/3nz+3Fqd96AgcGo9M2EA7Q+mALsbUXopyLty9GOTfa2vcbgpNaa60LD9YDbCzOC1U7nCjnxo2bYmfB2/WbA1poYaGBcF3DUZ21nyXWsyK0yWSTQ1QBmaW9IejVFRnsfSFuaMVThVvuKw2unCfSvLecKeY+tzbZIJbM2Ka1A8o56rJ1nehoyP3biu8bdq4qpjhntujfrj+AwyMxyLLMlfPZpJyXHPb5TmVk3eeE/dxa4+PvkQF1jrnZpttKVTnvCcd5eCUxcVhLx87eMdPrxeI8lipMOQe0vnNSzgmCIEoHFecVSA0P2cngly/sw+m3PIk/v3bIVjm3tLXLhdva3cKYqofe6MGhkRhe2jc0beecA6JynsfWLhTWhW5SsKK8OOVcsxAD4KnGV521BBcdPQcXHT3X8r7t6rzwLYeVwoXZkgtNax9LKAuwukKU8wKeq2iJt+s3B4pXzsV+c0Cz9rNFqVl7gFh0sGA9YwBfjclrMi2Uc/V8EomnuAWWbR5KkjavfDyR5pMArHrO7RA/dwtbawAUZ2s/MBTlx/3DJ3YhHNNyFmZRz3nJETcrxXFqWpHnRoPqpOhXi3Ofia291u/B/GZlBCRZ20tDNiujL6IU59t7zR0JsZT2NxOVcyej1ABtMoM4BYQgCIKYGNOvypoG8OTnRBrbVJvf7v4xWxXcytaeLcLWzhTBTFbGeJLNOE5P2znngFZw5iu4dXPOC1CFlcdWXteJ9Jwn0hlksjK3Ub/vhPn4/nuPsVUFl89SLKMv7RsCoC9+CglUYxkIZoUoP07Dxk1Bo9SE135eY77iXN1AKlA536EW56wYZBsUrJgQVV/2t05kcpVzYxuBWfBdtQfCZbMyd+IoPefK8xH/pux5D0eTvAgupjgXP3dL22oBFF6cR+IpDIxpRcKfXunCxoMj/Jimo+On3HjdWiiY2A6SFFK/WXHObO0ei8khqzpUa3uPeSFJFMZQNMk/v/2RhKkjwUo5d7oBTz3nBEEQpYeK8wpEDIQbU4vjVDorKOdmo9SUy7Kyfj65NkqtgJ5zIa2dfXlHk0I/2jRc5DLlPG8g3ARs7UvblaJjSVtNgUent7V3j8aQzGThdUuYk6eIBcBHWLHCckFLiF9XSAE0pvYcFzRKrSBbu6ich2xuCXhdxSnnTEE6boESTsYWlaxHViy6m1V13Ew5NyrsZq9JvMqVczGlPiIEwol/U7bRdGhEUeg8Lsn2/WGF+Ldnn5NCi/P9g4pq3lLjwxFzG5DOyvjbxsMAKKl9MqkxzLsH9D3nDYYMAq/FmK6VsykUrpQwSztjh4l6rivOk1nujnOqnLONOOo5JwiCKB1UnFcgNX7N1s76fJOZrK1yLs6OFdXztE3CuxUewdbOQumU4nz62tqZApCvsPC5Xfz1KbQ4//57j8Gz/3U2lrbXFXx8fqE4Z0VIZ3PI0d+VLXoZLTV+1KubEcMOQuH++EoXbrx/E4bGFVuqXc+5JEl8IwEozCUgbozks7WLrReFsKtP6b08YVEzgNxAOLHnvLnGr/4OoThXrbnsOobZ7Ppqt7WL47AiiTRXRsXnygr17hHFydEY8kIqoIWGwVwTdX4PZqltGOFY2u4uOTBL+4KWEI6d3wgAeHSrMm+eLO2TR8hknJqonBudFF6Lc9ZcdaORfcaIicEs7Ywdfbl952IrQjyVEXJlHCrnIdZzTrZ2giCIUkFp7RVIrRAIx4rjVCZr23MuhlOlMlle6GSZcl7QKDXB1q7+/ngqw9WQ6Tjn/NJ1negJx/HvJ823vZ0kSQj63IjE0wgUbGt3o7PZXhG2QlTOWb/5whZnCvzyWXWQJPCU8YagF801PoTjaR6IZsetj+xATzgO9hay6zkHlAV5Mp2FJBW2kSOq7nkD4dyFB8JlszIPxVunKufD4ynIshZmJSriTEU3C4Qz2trNZtdXu61dfN4j0ST//xqdcq78fEgtzo0qqVPY+aqt3o/6YHFqnPi5OKqzEXhxP3dGkHI+efDcAZOe80KUc+ZKMs5MJ4qjZ1S/ybHDpF0gx9ZeoHLeQLZ2giCIkjP9JNBpAFMixhJp3uebTMu8f9xjEqijL84FWzubc16ErT2ZyfKEZr1yPv2K887mEG655ChHqvbcxiAkaWrVODEQjinnoj3djqDPrSvkG0Nerng4CYVj6jIr7u2Uc0B7f9T4PAWpqOx+kgTTRG+RYgLhusNxJNJKO8DaeQ0A1Pe4MN9X7DlvNinOrWztZg6BalfORVt7X0Rb6AdNbO17+pXCuNjPRKe6GbNiVh1f8Bdsax9QPhfzW0I4urNRdx1T44nSw5wU0YT2fueBcB53zmg9s1FqgOZEqvbPTaXAphTUqedrM1t7TGdr15Rzx2ntPBCOinOCIIhSQcp5BVLLFztpHqKjKOfKgscsed3tkuCSlJ5zMeSrmFFqTDkXi5Ko8MUdsFA+Zgo//sBx6BmNcxvmVCAGwu0bKEw5B5Sih6nG9apyDuS3I8aFkCCGXSCceKwFB+ap95tVF9BZ483QWi+cK+d71QJyfnMIdX4Pn8E8NJ5EUn1v1wU88LgkpLOyppyLgXBj5mntPo8LPrdLd9tCes5lWS7KDj6Z6JVzZfHtcUk8fBLQVFPWLuAkA8GMozsb8bdrTsOC1hB/rHC8wOJ8SPtcLGqtQX3Ag7B6/pxFY9QmjRo75dztQoNhtJ7VdxF7L1W746RS6FV7zk9Z0oKH3+zFjt5IznlGVM4j8RTfgHXcc66OUhsl5ZwgCKJkzOwqq0LRAuEyfMGTTNv3nAOaIiEWCHyUWhHKuUgslZ7WynkhLGipwYmLW6b0d3Jbe6pw5RwAVnZojoCGoJcrxEPj9osqswLJia0dMLd6295PfV/l6zcHtPe6OFc8H3sHlKJvUWstJElCU0izZCaEHlnmDGgxCYRjLgKzkWshw9g4p0XGXzccwrFfexT/2jPo+LlMBcl07sZHyOfWLe7ZBgxT6YotziVJwhHzGlAf8Foq54l0Bk9t7zM9LkALhJvfEoIkSYq1XYVs7ZOHbc+5N9fWbrXxRrb20sI+kyep31XD0ZQuUR/Qv9ai+m0M9rSisQJHqclyYTkkBEEQlQYV5xUImw09nkjzQLh8PeeAVrCItnbWkltIz7nXJA0+lswgMY3T2isdpmTE0xmdQugUltgOKOF3WmFqv6gKm9gVndraCx0Zd8qSFixtr8Ulx83Le1u2gZTJyo4XY3tU58BiNS2fbVAMC/3UPo8LHz11Ec5dPQtr52rWdwZzstQFcnurjWPjnNpzn9nZj+FoCk/v6Hd0+6nCvDjXP0fjc57bOPEiWBy9JU6e+OMrB/GhX7yMj/zyZQBKSwZLpI6nMuhWf2afi2OE4pwC4SYP9n0lFno8rd3tyrG1W20us+K82qccVAq9anG+uK2WrxnGE/qQxZjgdhD7xp0W5+yzOhxNVURR/PPn9uL4bzyOXX00jo8giOqFbO0VCLO1D40neUGuS2s3UbYBISRLTGtXq/NC0trdJo+vH6VGezpTDesBPDgUQzyVhdsl5Q1NExET2xtCXl6ssBAtK8z6fvMl2nPl3F/YJs6Clho8dv2Zjm4rbiClMjJ8nvzvb2brX9SaW5yLPbKfeusyAMCGA8MA9EUqK7jNRsSxy1h7idPinG2miX3dlUAyk3v8RneA8XXIlxXghHph42MsnkaDupH01w2HAADP7RrAweEoLv3RixiLp/Hc597Ck6nrAh6+8XS0mtgOALPJ1j5phExGqYnKeU5au0XPecibq8ATxcOU89n1AdT6PRiNpRCJpzFLGN4hKulso9bvcTlusRFzOWKpTMEbsqXmye19GBhL4JV9w0VNRSEIgqgEqMqqQJitnX25AsqXnzYWzWJxo34xigUVq9OLGaUmEktlEE+Trb1csD5fpv7Org9YLnLNmN8cQmutD163hNn1AW753XBgxFbxYOOs2uv8cLsk1PjcOWqpEfb+CE7iQk3coHKa2J5TnNeoqs+4XjlnsJ/12QvK62E2Ro99buerifxOFcCU+vi94XieW04tLLlZxPi3N+YKFGtrF/F5XPz1Fc9lqzu0quJ9P12P7tE4Iok03jw8in1CqwcrLI7pbILf40JzjQ/Nodw2BKI0sO+d8WRuIJzP7c5Na7c4bwV8yuWxVKYiVNhqJp7K6CYV1PJWOaNynmtrL+T7PeRz8/Pk4Fj5re3sfUehggRBVDOknFcgNSZFdirPnHMAWD6rFodGYtjaHca6hcocZz5KrYDi3GzxpFPOHYbFEKVDU86VIqRQJdDlkvCH/zgJ4XgajSEfjpjbAI9LQl8kgcM24XbsPbi0vRa3nrUUXreUN7+g2J7zQhCLcyezzpPpLLrU125xjnKe0sYEisW5IcMhk5V57oJZKN77TuiESwJOXNyCu5/a7bjnnIXa9VeYcp4wCdszFuPGYn1OCWztgGKXjaUyunOgqKh2DcX4zzt6I3xRvkBo9Wiq8eHPnzwFAa+7oMwNojBqeK+4U+XcKhBOey/FU9mCAyUJDbbRF/C6UB/08MR21ibHiJrY2p1a2gElK6Klxofu0TiGxpNFjwotFex9R+4LgiCqGVLOKxAzO3AqI+e1qLMe2TcOhfllfJRaAT3nZg8fE4pzp2NWiNLBes4jCU3JLpSl7XU4dr4y3zvoc2OVqkS+tn/Y8j6sOGoIenHaslZHQXjsWCfT4ija2tMOEtsPDEWRlZVCok197cx6zv02yrmoxpjZ2i8/fj7uv+pUvtHhVL1hxX+lKedmPefGDRexgGoKeUv2N+fzk4WgKaMTgZ0Hd/SN4c3DyjlPVNcB5Zy4tL22JMdEmMNGqY0nzHvOG4KGtPY8o9QAfdFIFA7LYphdH4AkSYJyrm9TEotYFv5ZqDOOh4vmyS+ZCthGJ+UWEMTksH7PIG7400aa0DDJUJVVgZj19DpRztfMUYvzw6P8smJGqUmSlKNuhGMpsGwmUs6nHmPCcSkCro5Ve3I3HBixvE1YKM6dwjIJCu05LwSXOjoQgC40zApmaV/YWsNtz001onKu9Zwz2GvOFnysYJAke3UpwOc1O7Pbs8cXFfxKwDQQznBuEv/GpbC0M5jaOhwVlXPl9V8zpx7HzG/E9ecsBwDs6Inwc96aOfUgphY+Ai1lrpznpLVbFOdul8Q/c2RLnhisJY59T7AQz4hBORdfZ22MWmHLQjbVYqgCbO2knBNEaclkZXz816/gm//YCgD4weM78b+vHsTfNh0u85FNb6g4r0DMLLPJdP60drYwVWyeypdTJs99rPAY+tojQq8aKedTj3HB1F5fuHJu5BhVRX/tgDPl3CmswJ1sWypT4FIOivPHtvQCAFYIqfViYr1pz7kw/SCblRFVlcEan8c2MIkpgHHHtnbt+CvJ2m5anBtUtaBXO1eVIgyOwdQ4cZoAKySuPHMJ/nLVqTh7RTsAYGt3mG++sA1KYurgPecJs55zF3wel85pYhVoqjwWzTovBX1h5TzCi3OLnnNjejtQ+Pc7C4UbGq+A4jxDPecEUUr2Dozh0S29+OmzexBPZfjIUvadS0wOVGVVIEGvG8a1vy6t3SIQbl5TEA1BL1IZGTt7lZnOxcw5V36H+e3zqYbE5JBTnNeVQjlXivMth8OWii0rzusLKM4Xqj3dS1on107sdeVOJzCjLxzHX9Sk7/efOJ9fzgrA/kiCb3yZ2doB5fPH1Jh8mw5BNdgq7lAFTwnH3xuuoOLc5HU1bhyKRVcpxqgxtLA+TTlnBRv7nYvbauCSlCAyWVYsvG1FtHsQE8O+51y5ThynZufiCnLXCRVXE2FY3dRihbNVz7nZJkihzriKsrWr7zunG6MEQdgzpH4HyzKwvSeCw6NK3gsV55MLVVkViMsl5ShUoq3dojaHJElYO1dRz984pNg8i7G1A9bqRiFjVojSYSzOZ5VAOe9sDqK11odkJqvLKRBhfYiFFOf/ccZiPPLpM3DpuvzzyieCR1C27fjlC/uQzGRx3IImHLegmV/OCjkWFAeYp7UDSqHKbLtm/eYi3NbucIEoKtR9FdR3bj7n3BAIN0m2djEPgME3R9TXN+B185nmAFnay4V5z7lanKuf0Qb17+l1S/auE17o2392NhwYxt82kq3SCqPjyUo5j5psghSqnLfUVJCtPUO2doIoJaIj5ukd/bz9ZR8V55MKFecVilGhSqbzK+cAsFa1dbKApGIC4QDr0B4ao1Ye/AY1oxQ955IkYfksxeZ9cDhqeptibO1ul/K4k72Jw3IR7EapRZNp3Lt+PwBl00Cks0lJFhZHQImbIGLoXDKd5cVHvtCzQtU/UTmvpFnnZm4KY3Eu2tonuzhnr6foXFg2S3NnrJlLlvZykC+tHQAagsr7JN/4x6CDja2xRBr/756X8Knfb8D+QVogmpFbnCv/jRiL8xIo581qz/lgHlv74FgC3314+6T+zWiUGkGUFrG17PFtffznA0NRR2G8RHFQcV6hGEPhlLT2/P3jbIH6xuFRZIVe3MJ7zs1vT2Fw5cEYCFdMWrsZrNC0UhpG1TnnhRTnUwXbpErbKOcbu0YRjqcxuz6Ac1bN0l1XH/ToPmcuSb8p5XJpwYgpwdaeTzlnxaPjOefC8VdSYru5cl6+QDjN1q4dA9tcAkg5LxdmareY1g4AjWpiez4HlxYuZ/3Z+fOrB3mRWao2kKyD3IpqIhzXn7drTWztmaxs+hkvuOfcZCPNjD++chB3PLkLP3t2b0GPXwhmkzUIgigesV1l08ER/nM6K+PQSMzkHkQpoOK8QslRzh2ktQPKrHNAsZywfnOgiOLcwtYeoDC4smDshS5VsRzKYyNlae31gckbi1YsHqFwtoJ9eSxtr83JXZAkCfOatILS6E4AhFnnaee2difqn0jF9pyrC13RAGFM4A/qes5LV5yzXtkRG1s7ACwTivO1pJyXhfqAci4Kx1OQ1e8co3LONluMm4xGgnk2C7NZGb96YR//f3Z+mgiPvNmDo256BA+90TPhx6oUjFkhdSa2dqtxdWbnQTucBsIdGlHcWaxVajJg51IKFCSI0jAibJDLhj3MPWRtnzSo0qpQjIvgVCbLv3jsCm2mbKczsm7EVKHFudfCOk+29vIgLpja6/wls4xr6cjmC7ViRqlNFcwim7ZRvQ4NK8W5VeEoXm5WOIizzjVbu8PiPJXhxYodup7zSOUo5wn1fCOGeQUNynlTyIfWWj/mNJQ2jK0xpF/wy7Jsams/Ym4DXBLQ0RDAnIbSBdIRzmGjtOKpLG8R0dLalb8VO3/YtWQBQNBrP0rt2V0DugVhKQq9h97sQSSRxvO7Bib8WJWC8bxtppyzDRDj0qDQDXg+Si1Pcc4S5CdrBnk6k+XjXkk5J4jSYPe5pr7zyaPy5DACQK6tXZa1sBO7UTSsCE9nDcV5gcWcVTHvp+K8LIiFYyn6zRl2AUyZrMzto5VYnDMHib1yrqg1c5vMi3O9cm5dnCfSWVNbtRkB9TXNqp/ZfEqUmIreV4HKeUutn9vLawwbE163C49dfwYkSSp4A9AObcyd8nvjwsx4sThf1FqDX33kBLTWlm7DiiiMkE9pDxlLpNEfSaDW78ntOVf/nl5PPlu78tmy2iz886sHdf8vKudD40n8fXM33nXUnILOV7v7lQXmZCq6U41VIFwkkVuch3wepLNZ/hkrVDln+RCjsRRSmSy8bhfufmo3Ht3Sg5/+v3VoqVU27ViehvhZLiXieZSUc4IoDcMmxfmqjnps7Q5TcT6JkHJeoZgVAEyEs1sEa2piVmdrzyNY5GAZCEdj1MqCX1ecl06htLO1R4TFaiFp7VMFe4/a9ZwzW7ulct5kr5yzz1NBo9SEDax4Mv9CVB8IN3XK+d6Bcfzo6d2WShYrsJhtFTA/LzWGfCXfvGEL/rFEGsl0VmfBDRo2CE9f1oZVHdRvXk5aa7WxhIB+zjmg9ZxbObIY2qQD888NCxNrVQu+sKAE/+jp3fjSX9/ArwXbez5kWcaefmXs6GgJLPKVgCzL1sp5QnuO7DMV9Ll1n6lCe84bQz7e+jIcTSKZzuKOJ3bitQMjuP+1Q/x22ntjcgrnVFr7HiDlnCBKg1mWxBnLWwEAewfNg4SJiUOVVoVi7DkXsbMG6pRzoWjJZyc04hXUefFnsrWXB7FwLMWMc4amVGmLmb0D47jozufx+5e61Nu486YslwOPK39a++ERpdi1Vs5D/Gc75TwlFIhG9diI1+3ix+ZkkSgGwg1HU5O2eDVy9W9fw7f+uQ1f/r83Ta9nxTkrvID8lv5SUR/0crvtSDTJX0e/x1VShZ4oDaylIac497C0dlU5z3Me4ZuFKXPlnGUyLGtXslXEDcRdfUqRvUP9rxP6xxKIqAV+KfrXK4FoMsNbferVlHzecy5sZrBzfo2xOC9QOXe7JN76Mjyewkt7h3h7w0NvKn38sizz98ZkKeeJjHbeJOWcIEoDc80xUUiSgNOXtgEA9g44P9cShVF5K24CAFDrt/6CtFucsqJAloGUULQUup4VfwdTKQAKhCsXYuHYXkLlnC3KxHm3T2zrw8auEXz/8R0AKtPSDoiBcObKeVZIE3XSc24bCKdTzvN3Azkdp5YxtJ8AU2dt39KtjFu875Uu0+sTJsq5MQtjsnC7JP6+G46m+GI7n2uBKA+sOB8Y06uj7Ly1pF2ZRz+n0X5j0S5MMZOV0a8+/lK1OA/HtGLzwFBU918n7O7TbJnTRTlnz8PrlvjrqSnnaZ6DIZ7PxE33Yr7j2TlicDyBx7f18stf3T+MvnAco7EUt51PVs+5mN0RS2WmXQI/QZQDppwfv7AZADCnIYjls5Xz76HhmOnEB2LiUKVVodgp57bFuaByJ9QdapeEgvsxRfthi6CckXJeHsRe/1klVc7V4lzoRRxVT8ZM4ajU4tybZ5TawHgCyXQWLgmYbREWNi+Prd0vBMI5HaUGaH3n+RQc0dLOEq1LaW3vHo3h1y/uMz2O2UJ2wYiJdY33nNdom0H5+u1LiTjrnG1yhOj8U5GwDVymjiYNyvnK2fX4+7Wn4fb3HmP7OEGbz83geAKZrAyXpGQNAFqfuCzL6GLFeQFztPcIyo9oka9mxH5z9r3Pes5TGZlvuonns8AElHNAO0cMjSfxhDoLmZ07H36zh/ebA+YjGkuBcZM2QUUDQUyITFbm55Mzlitq+YrZdWir9aPG50ZWBrqGydo+GVAgXIViDIQTsRulJtrXeYBcoQ3n0Bf5baJyTnPOy4LPPUnKuUnPuVFBYqOSKg32HrWytbOk9tn1AUs7bXONDwGvC/FU1tbWnizA1g44V87F4ry11o+RaIrbbEvB9x7Zgf999SBkGfjgKQt117XW+dCjzlV/cnsf3n3MPN317PzRWueH2yUhK8tcgZsKGnkoXBKAUqiTcl6ZtAnFuSxrBaBY6K2Zk3/UnWZrz/3cMEdJa62fK7WsOO+PJPjvHI6mEI6nLM9b6UwWV/zsX/C5XVyBB6aPrT1sGKMGADXCptpYIo2A183PZ8bNRrPzYD6aapTf9cq+YewfjMLnduETZy7BDx7fiYfe7MGiVu11ngrlHFDOvXS+IIjiGY2leNbVxUfPRX3Ag6M7myBJEmY3BLC7fxy94TiWtNXaPxBRMKScVyglVc6L+CuTrb2y8LolHrpTyrR2tmgTF8M5xXmFKucsEM7K1s4s7XNs5m8rs86VvnPngXDObe35FqLisbPPfCmVpb1qmurmQ6M514m9n49t6cu5nh1Hnd+Db1y8Fje9a82UbtRo85PJ1l7p8J7zsYTuPZ1vrrkR/rkxUc571Y2kWfUB3kvNNrKM6s0Bm6Ci3f3jeGnvEJ7bNYA/v6alvyfS2UkrHKcSPuNc+Ky6XBLf8Gd95/x85tX3nBfjjmtWlfP/e10JgDtxcTMuOVbZ7Fu/Z0jnUIhPkqJtPG9azXEnCMIZbIxaXcADn8eFd6zt4C5EY84IUVqo0qpQxN3sOkOhbjcWTbyO9f0VOkYN0IoSSdL3nJKtvTxIkoSTF7dgYUsIC1pC+e/gELM558bivHJt7apybjFKjc84twiDY7C+c9Oec0E5j02ird3tkvgkhKTNaLh8xJIZvOP2Z/Clv74BADisblBs74nk3FYsRJ7e0Z8TRCdak997wnz8v5MXFn1cxdBoamsns1clwjZwB8YSuvdRoSqs3WhHFgY3q97PC0+mEhv7zLts+s6392qfBaNLZTqMUzOOUWPw4lxtYeKBcH6PwdZe+LKwpYZ9VpXfffHRczG/JYS5jUFksjIe36pt/iUmSzk3nDenw0YLQZQT1u4m1gCMNrW9korzyaHqivO77roLixYtQiAQwHHHHYdnn3223Ic0KbAvUknSZsQCSv+4y0Y5d7kkHv7GbH52t7eCWedrfB5dnynNOS8f9370RDz+mbOK6gm0womtvVKLc83Wbq+cW4XBMVjfuamtXVDOxy1soGYEVYdJPlt7Uhg5JW4EFMuW7jC29UTwp1e7kExnudq4ozeSEzwnLl7HEmm8sm9Yd30iox+HNdU0CbZ2p2PsiPIgqiji+7fQ945dOwh7L7fXB1DHinOmnA/FdLfdb1Oc7zDZqGJOMTFgrlqxLM4DereB+JkSP1fFbMA3CYv3t65sx3uOnQsAOKpTaWV4cfcgv37qlHMqzgliIjDlnG2Ui7QLbimi9FRVcX7ffffhuuuuw3//939jw4YNOP3003HeeefhwIED5T60ksMsrrU+j84a6KR/nNl9WXFu16Nu/RjKfUI+N4I+7XeSrb18uFxSycdImY1SG1EXd6xYZRbSSkObcz4x5ZyFS5k9T3PlPP/rESiw59zrlnThc8XClMR4Kos3Do+C1eOJdBb7DEFZzNZ+7PxGAMCzOwd01zOFq1BrcqlgCwLF1q7OZKbNwYpETGuPCxtOhW4Mm52PGCwocVadZmsPx1KQZZkr5+z7ab+NrZ0p5+x9XR/w8BT5Sk9sf3JbH7724BZc/bvXdAWviHHGOcOonLNxdSGvm7t2gOKU87nq69da68e3LzmSB9EdOa8RgF7VzmRlXdZGqTAq5zROjSAmxojqhGkO5Qo0xdra9w2M4y8bDvKpEYQ5VVVp3XrrrfjoRz+Kj33sY1i1ahVuv/12dHZ24u677y73oZUctivVXu/XqQ9OijNWjLNFfjEFHdsEqPV7dD22FAg3vQiZKOdscfeJMxZjcWsN3rKyvSzHlg9ua5+gcn7pcZ249q3L8IkzluRc5ysyrb3QnnOfR1DOJ7BwHYlpqevr9+gX79u69YohO7ZzVs8GADy3q193PTuOchXnzEonzjmfqjnrRGGwiR6pjDbPupj3DdsINptzbmZrT2dlxFIZbmNn437sbO071OL82rcshccl4cTFLbyQLbet3WqjEQD6wnF85Fcv457n9uLvm7px99O7TW/H3ATGzcY6Pk5NeY7RhPaZmqhy/tZVs/CF81fidx8/UZdRc+Q88xDAyUhSNwuEIwiieIZUW3uTiXLeVltccf4/D7yJT9+3EX/f3I1oMo0rfroe3/j7lokf7DSjaorzZDKJV199Feeee67u8nPPPRcvvPCC6X0SiQTC4bDuX7WwuK0WP3jfMbj98mN0SdNOVHB2G9b75yqi55wr5363bnwR9ZxPL/joInUurCxrozPee8J8PHHDWVz9qDTsAuEyWZkr5/PyKOcNIS+uP2c5FqoKuojfJK29tMU5U85dmoV+AgtXttMNKEFMItt7tPNfKpPlmxpvW6Vsvrx5OMxtbOJxFKOklQJmax8mW3vF4/e4eYF7UA1nK+Z9E/Qy5Tz3MyAGwoV8br7pHImneTF+6tJWAMD+IfNxatFkmqvs7zthPp684SzcfvnROT3s5eBz/7sJJ3zzcT4r3khvOAFRbDILzQMc9Jzn2No9OkdKMX83r9uF/zhjCZbPqtNdfsTcBpgtPyajH9yoxpNyThATg804bzLtOS+uON+rhkM+sbUPj2/twwu7B/HrF/eTkm6gaorzgYEBZDIZzJo1S3f5rFmz0NPTY3qfm2++GQ0NDfxfZ2fnVBxqyXjXUXNwxLwGnQLhdjsozg2L/KJs7S5ma/foihGytU8vxL9tPJ1BLJXhxW6l9pozvG7rQLjf/ms/Iok06gMensZe3O/ITWt3ZGvngXD2hXZSKM7F31UsYnH+yj6lOGcf/61Cr624OO5sDmHFrDrIMvDCbs3abpxVPdVogXBCWjttDlYsbLHGNsWKed+YBVQymHLeXu+HJElcCR4YS6BbLdxPU4vzwyNxU+v0zt4xyDLQWutDS60fnc0h1Pg9mnJexuL8ie19GBpPmoY3AkAkoT82qxGS+YrzCAuES2mbjbpAuBJ+x9cFvKZjliajOCflnCBKy/A4U86tbe1Wm4lmyLLMR2I+s3MAj2zpBaA4acIlHCE7Hai6SksybMPKspxzGePGG2/E6Ogo/9fV1TUVh1hyvEJB7iR53c2V8wkEwrk1W3tggpY3onIR2xTGExm+sPO4pIq3ELPWi5TB1t4bjuM7D20HAHz2HSsn9J5lavZ4IsOVZifqbb45592jMYTjKb6g9LqlkgTCiT2zbDPhuAVNAPSJ7eIYNb/HhdOWKUXNc0LfOdskKJdy3lxjktZe4e/JmQyzObJ2kqKUc8HJIyopqUwWg+PM1q70NzO1e2t3BLKsfOZWd9TD73HxhHDjxh3rNzcqvPWGgLmpJp3J8kXueML8GIzJ8saAR0a+QLhc5dxgay9x65pobfcY1ialhIpzgigtbPqCnXI+OJ60bccRCcfT/LM/MJbAPzd38+v61A1WQqFqivPW1la43e4clbyvry9HTWf4/X7U19fr/lUj3gJ7zlkvLgt0Kqbn3OvSAuH0tvaqecsQDnC5JK2QTGZ0CzurTa9KwWOhnN/+2A5EEmkcNa8BV5wwf0K/gxXMYi/3RG3tI9Ekzv7uU3jvj9frbe0lKM7Z6BORs1YotvUDQ1EeBsWOK+B1QZIkXpy/IIRMaUny5SmIG9Xd+tFYih93gIrziqW1BMo5KxKzsr6AGxhTLN0el4Rm1VHBeqrfPDwKQGlfcbkkrtReee+reNcdzyMrFLEsqT2nOFcfq1yBcANjSW5Zt0oZNxbnZu08gKb+i3POAW0kK/sssRaW5pBPb2sv8Xf8UWpbVMDr4ov8SVHOTWztA2MJRKbBeDyCKAeacp5bnDeFfHC7JMgydO1wdvRH9AW4mBfEnFGEQtVUWj6fD8cddxweffRR3eWPPvooTjnllDId1dTgK7DnnFnf2eKmmDnnTDk3jlKjQLjpBw+FS6UxGjVXXSoRtmllDITbcljprf7EmUsmnG7PCgz2uvgE+7kdXAE0WWh3DcUQT2WxZ2CMF+e6QLiJFOcmxcWaOfV8l3t3n9LvxfIomKtgTYeycXlwOMoVuXLb2ptCPkgSIMvAQbXgC5Fzp2Jhyjn7WxUz8lEsEsXPDre01/m5E4wVn5sPKsX5ghalfeW7lx6Ffzt2HgBltOCwsGHFlPMVs/XFeblt7b2CamRVnI+pRSazmOZTzuvzKOesV7Stzq/bdC/1dzwL6VvUWst/j+jcKRXG82b/WAJnf+cpXHTH89TPShBFMGwTCOd2SWhRN9v6HPad2xXgfRFSzkWqpjgHgOuvvx4/+9nP8POf/xxbt27Fpz/9aRw4cABXXnlluQ9tUim059zr0o9SK6ZAYV+idQGPzvJGc86nH+Ksc6uFXSXCNqqMvaXsObCCdCJoyrnymE4DyexGqbFE6Hgqi0RKU879Jeg5N1P+5jYG+Zcou571wrOFeHONUghnZeULOZOV+aZHuYpzr9vFC769A0rAl5N+f6I88J7zkeKVcyV7Qflci58dccY5gxXnmw4pxfnSdqXgXj2nHt+77CjeYy1a1dnjdBpyKNj5rlzKub44t7e1s4Vy4T3nyv9HEmnIsozBMWXh3Vrnn7Sec0D5e/ziw8fjziuO4ecbtjlYSoznzS2Hw4gk0tgzMI7Do7TwJwinpDJZ3PXULj6Skk3jMNJW4KxzVoCLE3RY+xop53qqaqVz+eWXY3BwEF/96lfR3d2NtWvX4h//+AcWLFhQ7kObVPRp7fm/ON0lGKV2yXGd6B6J470ndBrGrFTVfg7hgJDP3NZe6WhzzvWqyEgJnwNzrbAdZKc9z3Y956I6x8KZStZzHs0tLjoag7xQYQv/eFqffu5xu9Ac8mFwPIn+SAI1QhFcruIcADoaAuiLJLjKR7b2ymWuOhWB2aaLzSoIet1IZdI6BZlZ5duFDTcWCMc+L8tn6YPH6gMejCXSus8bn0Bg+B4r9yi1XkF5Gk9YKOfq68raPcxGSMZTGb4p3xCyVs7DsTQvZltrDbb2SXDHna221rBNgMQkKOcpw3lzT7+W2P/GodG8IzUJglC4+6nduPXRHQCA84+YjWXtuaGOQG5i+66+CP706kFcecYS0z51FgZ3/MImrE7Wo2c0juMWNOGXL+wj5dyA4+L8Bz/4geMHvfbaa4s6GCdcddVVuOqqqybt8SuRQnvO3SUYpbaotQa3Xn40AK3vBKBAuOkIm2MfrbLiXJtzri3KslmZL8YbS1GcG2ztjotzH7NvWivngFaol6znPKYdZzSZQUPQi1q/ByG1OGcLf3ZcYgHVVufnxfmcBm0hW65AOADoaAhio2pbBsjWXsms7tBnuhS7qRP0uRGOp3WfnUe2KFkzR89v5JcZ3T25feReHB6N63q1tRwF/bFpo9TKEwgnhiGZzXgHNAcAV85Nes7ZuUWSgFqDy6SOp7Wn0D+m/L76gAd+jxYI53VLE24FsoOdS6ai5/zwaIz//ObhMN6+ZnbJfydBTEde3T8MALj67CW44dwVlvlDxlnndz65G3/ZcAitNX58/IzFABRB4PN/3oy3r5nN7e/t9QF84fxVAICfP7cXgFa4EwqOi/PbbrvN0e0kSZrU4nwmIi5ynPSce42j1BxY4e3Q2drLuFAnJocabmtPV1VxbjbnPJJIgwlKpbDms0X8KC96nZ0yxZA9I2KxwIpznzDnPFGkrT2blXkg3No5DXhp3xDmqGqR+DcGtJ5PcbOtrc6PbT0R9EcSSGSU45ak4kYxlorZDQHd/1Nae+WyqLUGQa+bu0WK/a5QPmMJrpx3j8bwr73KWMB3HTWH304MPJMk5IzsYsq6uBmWzJjnKJQ7EE5na7dQzlmwGRsxaNZzLobBGae0MCVrcCyJ/ohmaQe089VkqOYiXDmfxLR2t0tCJivrZsJvOTxqcS+CIIywTf5jOptsg4GNynnXkGKD7xqO8ts88mYvHth4GBsPjuBINRxSdECx6Ru9lNauw3Fxvnfv3sk8DsIGnzhKrSDlXB2lNsHUbb/HhfY6P8YSad4fQkwfqtXWbjbnnC1O/R5XSVweC1pqlN9RwBg1IE/PuVAAMDVMCYRT7lOscj6W1DYmjpinFOdzG5UvPrapMK4WPDEhrZ3RWqvNLRUVxnKm9ncYinOytVcubpeElR112HBgBAD4+7lQ2GdnTJ3r/cDrhyHLwAkLmzFP6BVnBTUAzG8O5Xw2WfEupnUnLEIOy25rF1SjcYue81xbu/488eLuQfzh5QMAzM/fzNbdE46jW1WVmfLFXjun57di0QLhSq+cs79tfcDDR0Ax3jgULvnvI4jpyqi6yd9oMt9cxNhz3q1mOxwe0QrtXWoI7f7BKN8EFLND2uuVx3AaKjdTIBm0CvAWmNbuNaa1T1D5kiQJf/7kKXjgmlMpkGkaUq22dva+Fuecs+PP96XilHULm7gdFNAU6HywL6GdfWO45aFtusVo2EQ5F4OwjAF3TmHWe7/HhfOP6EBLjQ/nqlbOGr+qnOeMUtMr54CyC17upHZGh6FPlJTzyka0thernC9qVQrwWx/dgXgqg79sOAQAuOiYObrb1QnK+bJ2vaUd0JwzolU9v609VZZkb1E1MnPbAGIgXG7PeSYr46O/ehn/9/phAFpyvUhrrQ9+jwuyDGxWQ/SYcr5ydj3ec8xcXH3WkhI8G2uYMj8ZxTk7bzaaJEv3hOMYdBhaRRAznRGH6yhxzZDJyvw81hPWWkpYcQ4A29RRljrlvE5TzmmqgkbRldbBgwfxwAMP4MCBA0gm9TPubr311gkfGKHhFRY5RquaGcae82JGqRnpbM79siemByFB5a2m4lwLhNOK2ZESj4Lzul04fXkr/rFZ6Xl1ujm1ek49VnfUY0t3GHc9tRsZWcaN5yk9VnrlvHQ95+LGxHELmvDKF9/GVW+jcp5QF8diEBTvHxtLcPtvudtYjMp5yEubg5XM6jlacV7sxs5/X7Aa6/cM4Y1DYZxxy5PoiyTgdUu44IgO3e3qA9p7wRgGB+Ta2mVZ1t7XXqOtXTlfZGVFoa4LTO35TyzOxy1HqTHlXLW1C+08qUyWtwF8/eK1eOeRHTn3lyQJcxuD2DMwjte7RgBon3m3S+IZM5MJe90n09Zu1c705uEwzljeVvLfSxDVjizL+NUL+7Cyox4nLGwW1oH2Tll2/hiIJDA4luAbht2Ccr67fyznfmJxzpTzRDqLcDxdFWvPqaCob8/HH38cK1aswF133YXvfe97ePLJJ/GLX/wCP//5z/H666+X+BCJQuecs0R3log6mQEvRPXDrIzjiXRVjVJjgXCjsRQ+8suX8adXurQCNc+XSiGwpGHAue2zLuDFg586DZ8/byUA4NkdA/w6fSAcs7VLvBAutjhnGxPsuYt2dKc954CyC87OHUaFcaqZXW+0tZPZq5JZM6eB/1zsxs7cxiDuvOJYuF0S+iIJSBLwqbcsy1FExXOUMQwOEG3tyns+LfQh+936z3HA6+abCaKzZSpIpDM6GzZztxiJ8DnnyuuQEmztotvmkuPmmarHgJao/6Zq8y7FuMlCYOebSZlzntFs7SJHzlPek28eJms7QZixtTuCr/xtCz73502IxNP8PJmvUO5Qg2MPjsRwcERTywfHk4inMkhnstg3OJ5zP9HWHvC6+We2j/rOOUV9e9544434zGc+gzfeeAOBQAB//vOf0dXVhTPPPBOXXnppqY9xxqObc+6kOFftsezLiopzwo6QyZzzUtnCJxOmnK/fM4QntvXhJ8/swUhMcfGUcnPhzBWa2uLU1g4oLpeLjlasuNt6whhXF92izdZUOS/S1s6eu3GEEgDUqNb8MUNau2XPuUVw1lQzqz4A0fhDbTWVzYpZdWBfNxN575y8pAX3fvRE3HLJkXjpC2/DtW9dlnMbMRBumZ1yrp7TxE0vs2Njj2c2jrCUpDJZfOgXL+FrD26BLMs5KcVRK1t7Qm9rFwPhxOR2r82GGus7F8eoTSVsznl8EuacW9naT13aCgA4MJRbJBBEtfP9x3biwh8+x9duxbTFsTFm3aNx3djYfOfwuU1B+D0uJNNZvKyGdjJ6RuM4MBTVBfayx63167/HtVA4aj1hFPXtuXXrVnzwgx8EAHg8HsRiMdTW1uKrX/0qvv3tb5f0AAmthxxwNuecqetM/XJihSdmLmIgXLiKbO1ewxSC3nB8UjYX2usCXH0JFlgcdjQE0dEQQFYGNqkjwSxHqbknFginKedmxbm+51xL1LbvOZ/s9OZ8+DwuvmkA6G34ROUR9LmxWE1Nn+h75+QlLbhsXaeluttUo7zP3S4pJ6kdEHrOVSU8kac4bwjmprtPBlsOh/HU9n7c89xe/Pm1QznzfaMmgXDZrCwEwqmj1LJ6WzugpNbbbcYbZ31PtXLObe2ToZyz+e5CUGBTyIsWNcTWatODIKoVWZbxyxf2YvOhUby8dwgHBqM4+qZHcMVP1xc0N3xU2MA8pCrgTkbRiufeZ3cO6K47PBrj/eadzdp5p93knMOK858/vxfv+8l6HBZU+JlKUcV5TU0NEgllh2POnDnYvXs3v25gYMDqbkSRTHTOeTlHIRGVDw+Eq7aec8NGVTieRq+aFlrq479sXSckCTi6syH/jQ0cO78JAPDaAWV2qNhzPlrCOed2GxNaz7ne1i7a9NlCfTia4ip/uZVzQOs793lc5AKqAtaqfed1/sl1OXQ0BHH12UvwP+9cbTqZgYe8xfXKudtlPstbC5Cb3OJ8RHj8mx54E6/tHwGguXLMes7Hk5rVlG1KyLKmnrNQTG+ezXtma2eIG19TwWQq5wlenGvnv9kNQX6Oo+KcmG70jyV4S4wycnIQ48kMXtg9iAt/+Bx29UUcPY44QnKP2iPeYNEaY4S5ll7ap1fOu0fi2KU+1nHzm3iB3m5oVQO0gv2JbX14cc8gntjW5+h3T2eK+vY86aST8Pzzz2P16tW44IIL8JnPfAabN2/G/fffj5NOOqnUxzjjKXTOObP7lmqUGjG9Ycr50HiCW5Cqojh3576vd/QqXwZOdn0L4d9PWoD3HDu3KFv1MfMb8ffN3XzElNjTyhbiPrfEP+fFhiWN8PEnuV+qXDlXfx9bHAcEdbMx6IXHJSGdlflIlEopzjcdHKWk9irh2rcuQ3t9ABcfM3fSf9dn377S8jorW7tVjkKdoUddpGsoioff7MEHTl4wYUeAuBCOJNK45eFtAICFrTV483DYNK2dqeYel8RbVABlnJrb5eahmEY3kRGjcj7lxfkkjlJj7gHxu6ujIaBzhhHEdGJHjxa2dng0rju39YYTuPupPfjeZUflfRyxlWd3v9L+4XQNtaxdKc6NokL3aAx7B5R550vaapHMZNE1FDNVzo0F+2ScH6qNolZet956K0488UQAwFe+8hWcc845uO+++7BgwQLcc889JT1AonDlnBXwmlIwOcdFTA/Y4oUlbHpcUlUUQmYL0R29yk6xWd/1RCm23/kYVTnfcGAYGcGeKqLY2ifYc26TVM+V85xRavpJEC1qDyqztpU7EA7QQmdCZGmvCha31eIL56+acsu0EaaEs2I7mVE3wiw2nGr9TLnO/Xx+75Ht+Prft+IXz+8r+DhGo/rxbGyG8Jo59Wit9fENUdYOoKjk+j5N9hzqAh6dOs6Vc/Wc4cnzeTUq5y1T3HPun+AGpB1JU+U8gKA64SFGC35imrGtRws5PDwSQ9ewUgwft0BZc2w+NOLocUQ3D0tXd9oauNQwxnJxaw0ApX+dKedL22tx+jIlu0ec6ME4b+1s3cSNYtdA04miVl6LFy/GkUceCQAIhUK46667sGnTJtx///1YsGBBSQ+QMKS159kZB4S0dvXLykmfOjFzYX283YIlXKoCt4XZ+3pwXA1FqyDlf82cenjdEgbHk9hikRjs9ZTO1m723GuEWfaA+ZxzQLO2HxpWi/MKUM5nq7b2QBVsGBGVQ71hlBr7PrR6T7PPiNnm2f4hZdH71PbC7Jb/9/ohHPXVR/CBe17CvgFFkWKf07VzGvDLD5/Aw5GWtCmLWlnOTTNnxXltwKPboE/z4ly1tedZH8yuD/D7NwS9U54pwc43iUkolJNmynl9QBd4ShDTCSZGAIq4clD93j5v7WwAyoxxswwLI6KbZ7faJ+50DWUM4zxW3Rg4PBLDnj6tOL98XSceu/4MXHnGkpzHOKqzEY98+kx84CSlfpyMaQ7VRvlXXkRe9GntBQTCqdZVCoQj7GCqKlMWOhpze4IqEbuNqkoqzgNeNx8x9fQO88W9Tjkv1tZu13POVMGEoefcWJyrNteD6g58JRTnrOe8GtwcROXAbOpjiTSyWTmvrZ3ZxcdNinOWqP7KvmHT4t2KjV1KCORzuwbwju8/g/2D49omWsiLtXMb8JuPnoB/P2k+3n+iJmwYF9RsjFqd36trbWMp7WlenNt/Xj1uFx9PWA5nw6SOUlP/vnUBL5/wMKshwHvOYw6KFIKoJrb3aMX54dEY31Q/dkET2uv8yMrA1m5FEHjj0ChOuflx/Pjp3TmPMyLY2g8zkcahcr6gOaQ7p7KMned3DyKSSKPG58aClhq4XBKWttfZ1iOas4Y20opaeblcLrjdbst/RGnR2dod1NluNyvOs47vQ8xcjLO7j1NPrpWO3UK0kopzAFjVoVi5XlP7zo2IPefFjEIBtL4xsxnvTJ0bT2YgyzJXzv1e/WvIlfORyuk5P31ZG47qbMRl6zrLfShEFcF6zmUZGEumtQkEXitbOyvO9QvDbFZGrzp/N52V8eLuQcfHICpS8VQWz+wcyGk/OWZ+E75+8RFoq/PzzTKjyiva2l0uiY+rS6uzztnMcyfOOmZtn+oxasDkLr6Ze8DncfHXsaMhYPmaEkQ1k83KPGMHUEaXdY8qxfm8piCOmKsIApsPjiKdyeKdP3wOh0fjuPmf23IeyywE02wdYYbH7cJi1fXjkoCj1NBcdr695Lh5jtcRkznNodooqonyL3/5i+7/U6kUNmzYgF/96le46aabSnJghIZoVXOinHtd+uKclHPCDqMiedzC5jIdSWHYhSOahaKVE9aHtbFrxPR6n2BrT2dlZLNywZ9bNufcPK1d+RtnsjIS6aylrV2cdQ5oi+ly0lzjw/9dfWq5D4OoMgJeZU5vMp1FOJbitmcr5Zy5S4zK+FA0qRtb9syOfqQyWYR8bpy1ot32GIxj2QbHEvbtJ343YqkM73v/w0sH8IPHd+Ltqk2VbTh4XC4kM1mt51z9rs+X1g4A8xqDeAlTHwYHTI1y7nO7MLshgH0D41jcVsst9BQIR0wnDg7HEEtl4PO4kMnK/Bzl97jQVuvH2rkNeHxbHzYfCuNXL+7n9zNzoLG1g0gh42iXttdiW08EbXV+dDaHdNf9v1MWOn4cFlBLynmRxflFF12Uc9kll1yCNWvW4L777sNHP/rRCR8YoaHrOXc0Sk1vj6VRaoQdNYagMxYmUulUk3K+SC3OWU+8EXGUGqD0TwZchbmQnATCAYqCFFMXx1Y954xKKM4JoljqA14MjCUQiQvKuWUgnLmtvWdUPy/4dy8dwG/W70fA68Lmr7zd9jzECvFFrTXYOzCOwbGkffuJzwMgyVXe+zccwuHROP7wUhcAzarvdklARrC1qwtzJ8r58tl1/JimGv8kprUzMcLrduFn/28d+iIJzG0M8r8fBcIR0wkWBre0rRYj0SS3o89tCkKSJK6cP72jD//Y3M3vZza9adRUOXe+hlrWXgegG7MbgqgPeFHr92Askcbpy1r5HHQnkHKuUdKV14knnojHHnuslA9JwNBz7uDL1xgKQ7OBCTtEW3tHQyBn3E6lIi5E2TgPBguDqhSY7csKseccKDzNmCniAHSjlhhul8ST2ccTaa4mGXvOV87WJ6lWQlo7QRRLfVAbp5YsMhCuL6Isehe11sDrlrhaHU9lMWYydk2E2UWZc2ZoPMkvM99EUy3YqrX+oBpExwpLtoHANtzThrT2fD3nAPDBkxfirvcfi0+cmRvMNNn4uTJW+sU3ew18HhcWt9XipMUtALTvt7SQO0AQ1cRT2/vwf68f0l3GwuBWzK5Dh7Bmm9ekKNdHzFOK84GxJGKpDI6Z3whAOb8ZlWmx55xRyMSb05Ypn7XjVWGHJa9/5LRFjh8DEAIj6XNauuI8Fovhhz/8IebNm1eqhyRUvAUr5/rb0Jxzwg7R5lQtqjmgT2s/VuiTr/N78o4Ummo6m0O6z6Xxc+x1u3SbaoUuIkUlKmDRUysmtpuNUgOAkxY342PCF2oqqx/pRBDVBFOaw/G0Zmu3Ks4tlPNeNQxucWsNPnDSQixtr+WfVbOZ6CJhQTkHlHaREZtsCHYuHld75LvDetWe29rV359Re82Zgu7kvBf0uXH+ER280J9KJnPOuZUzQtyAJGs7UW3IsoxrfrcB//mH17F/cJxfzuaRL5tVizm64lz5eVZ9AHPUMNV3rJmN33/8JL4GGR4XszAypsVwIe7D4xY0Y8OXzsF/X7AKAHD75cfg1x85AWfnafsxwj67NOe8SFt7U1OTbtSSLMuIRCIIhUK49957S3ZwhEKxc86t/p8gRMTFy7oqKs7FYvaY+Y247xXF+jkZM84nitftwvzmEPaq45Ta6/zchgYAPo8ESZJ4j2yhcz51xbnFeKSQ343BcWXhH0+b29olScIXzl+FnnAcD27qxlHq7jtBVCPMQROJp7jKbOUGsQqEY7bo9voA/ufC1QCAE77xGPoiiZyeciNhtXhf1Ka1tdj3nKuTM5IZHB6JwTDuHLXq82GtaywEjSvnFf5dr/WcT94oNaN7wOdxweOSkM7KiKbSaEDlfT8QBKB87j9x76s4ZUkLrlSdLeF4mrt5/rVnCAtalHMJc/R0NAR4GCygFecA8IP3HYOt3WG874T58LhdaAr5MDCWwOB4go8oNbO0A4Xn9jTVaLef3xLC/JaQza3NmUxnTbVRVHF+22236Ypzl8uFtrY2nHjiiWhqqp7FfbXg81grbmYYd88pEI6ww+WSUOf3IJJIY12VhMEB+vf50vZa1AU8iMTTFddvzmB9p4Cy0BeLc7ag9LvV4rxQ5VwIQ7L6vHPlPJHhCpJZIe9ySfjh+47B589bWTUtDgRhRr16LgjHUnCrnzFr5dw8EI4tgtkIMkBRsPsiCduxaulMll/PlPOe0Ti3qJttIrKN0vFkGl3qOEMR5gRg6wAeCJd1Nkqt3Ghp7aVdfGeyMn8tzP6+QZ8bkXiaEtuJimb93kE8s6Mfz+3sx9tWzcLS9loMquGsAPDSviFcdrwytWQgouTXtNUGMNogFudaUbxuYbNuTddco2RwiMo5K87rAkqfONsQLKTnvFRMprOm2iiqOP/Qhz5U4sMg7PAJ4+mcpLUb1XU32dqJPHzxnavQPRrHmjn1+W9cIYgq0ZzGIGbVBxCJjxWUMjqViAFMs+r1wWtesXBIFG9rtxoTBWiq3Fgijbjacxbwmd9ekiTdlzxBVCOacp5GSH3/+yycJdq4QfNAOPEzW6sWyXa2dvG6xa1KDyYr1iVJab8xwj6j0UQGXUOxnOvZfZitnbkB0hnno9TKidhTKsuyTuSZCOL4SbPiPKQW52RrJ8pNOpPF7Y/txMlLWnDq0lbddf0RpRDPysDtj+3AHVccqwuRfXnfkHZbtWhvrfMhmsy1tZvRrKrbg+Nawc+K85YaH7xuF4bGk/C6JdNU98mGlHMNx8X5pk2bHD/okUceWdTBEOZ4C1TOKRCOKJTLj59f7kMomNqAB36PCwGvG+11fsyq92NX31jFKudiKNwsQYUDtOKc/bfY4txoUxdhX7ajsSTfHbe7PUFUO/W85zzFnTZWtvZ8PeezGrTPrGiXt4Itemt8brTV+SFJ4J+7+oDX1OHCwsuiyQyGosqiXHTcaKPU1OI8o+85r3TlXDzfJNLZkp1/xMW82d+XORIosZ0oNy/sHsQdT+7CY1t78dB1Z+iuGxBU8gc3dePqs8M65Xz/YBR94TiaanwYGmfKuZ9//gGg02ZTnRXnw0LBz6e8hLTivCHoK9nGWSFozhr6nDouzo8++mhIkgRZ/Xax+8NlMvTClhLxC9eJRd2orlNxTkxHQj4Pfv8fJyHgccPjdmFWnbJ4bjAJWqoE9Mq5vjj3GSy3yQLPoWxusDF9XYTZ2gfGtC9mq/50gpgOsGI2HEuj1q8U6vkC4VIZGYl0hqs4vWooGzu/iI9rp5yz4rw+6IXbJaEppC2ordw9Nbw4T+PQiKKcX3hkB37wxC719wqj1KAp56zfutLzZcSwtkSqdMW5uJlpFCcAICiEYRJEOdmnhrqxz7cIU84Z973chaWGSTQv7xvGuoVK+zA7r/g8LoR8btQHvGittV7/sOJ8KJpra28IehHyurGzr3zuQz/PpCDl3HFxvnfvXv7zhg0bcMMNN+Czn/0sTj75ZADAiy++iO9973u45ZZbSn+UMxxxMeGo59xoa6/wL2yCKBYxpX3ZLGV+74IigkimAmZtBZRAOBHmjuHFebqwlHSr9HWRkNpTywoEl2S+kCWI6QLrOY8kUkhmlIWp1ZzzGsHGGU0oxXkyneW20tmCcl6nFvp2PecsLI45eVoEtcvK3RPyadb6rmFl8b56TgOOX9iEjQdHsbBVObexDfuMwdbutXhulYLX7YLbpYyji6czJQtn42PU3C5T4Yi5hmJJ+3R9gphsutTxiEoGQpp/5gGtOF/SVoPd/ePoGoqiyRDM9vK+Ib7GaanxKZlBAS/+fu3pCHjN3/+MZvWxhgRb+4jq0GkMepFVxddyuQ9JOddwXJwvWLCA/3zppZfiBz/4Ac4//3x+2ZFHHonOzk586UtfwsUXX1zSg5zp+ApNazcsuGmUGjET+PCpC7F6Tj1OXFSZoXaz6v0Iet2IpTJot7C1s8+6mNaeycp5P/dObO1MOWc2uaDXXRbrGkFMFdzWHktbjtpieNwuBLwuZX55Io2mGh/v6/S5XWgS1CSWmm6X1s6Vc/UYWmp92NmnXGddnGu2djbjvLM5iN989ERE4mm0qZt67HzAilKmoFd6WjsABDwujAvjHEtBvhn24utKEOVEzJLoCyewsDW3OD+6swm7+8dxaCSGuWoPOWtveWnvEM5c3gYA/HzArs+HZmvXzlthQTln55VyhMEBmrhAPedFzjnfvHkzFi3KHS6/aNEibNmyZcIHRegpdM45jVIjZiIBrxtnLm+r2D5qSZLw8TMW45QlLTkj63Js7eqX059fPYg1X34IT+/ot31sZgOzs6kz5ZwpgZX6OhFEqagTesPzFXCAtoHFQuG0MWp+3UaWE1t7OKZcV8+Vc20hbVmcq9b6/kiCf047m0MIeN26hbgxrZ3b2iu85xzQh8KVCm2Mmvlah3rOiUpBnMLAWmYYrOf8qE5lhOnhkRgG1Ta0t61SZobv6I2gWz0vtRkcePloMgmEGxGKc/Z4zTXlaQ1krUSU1l5kcb5q1Sp8/etfRzyuvbESiQS+/vWvY9WqVSU7OELB7ZLA6mtnc85plBpBVCLXn7Mcv/v4Sajxe3SbZl6L4nz9nkHEU1k8k7c4d5DWrhYeQ1ScEzOEOh4Il+YFnFUgHKAPhfvn5m785JndAHIzItjjjjnoOee2dqEXNF/P+Y7eCL8dU95FWBGupbWzQLjK/65nzoWpVM6D3NZOi36ivDBbOwD0GnrMmXJ+1LxGAMp5i/WoHzGvESGfG+msjFf3DwMAWmsLK87ZBqHZKLXGkBfvOXYuLj1uHj506sKCHrdU+AXlnOWbzVSKGqX2ox/9CBdeeCE6Oztx1FFHAQA2btwISZLw4IMPlvQACQWfR7HbOZtzTj3nBFHpBL1uRNSeVbao9BsC4dj88u7R3PAYET4azc7W7tcX53aFPEFMB0JCwFrCiXKufkY2do3iqw9qLsBlhlCmOgdp7czyXh9UbutIOfexEDplkW6VvOw2KOe857yKlPNShj7xjReytc94ZFlGOitX5GdhNJZCWNjQ6xOU83gqw69b2FKDxpAXI9EUtvcoG3WttT4saavF5kOjWL9nEEAxyrly3hk0SWuvD3rR0RDEdy49qohnVhrYuUGWlc+0fwYH1hZVnJ9wwgnYu3cv7r33Xmzbtg2yLOPyyy/HFVdcgZqa/H0PROF43Upx7nZwwqE55wRR+QR9WnHOFC+fYZQaU5cOj8RNHkGD29pte85VW7tqk7NLdieI6YA4N9yJrb1Wbf144/AoAGBOQwCfPmc5zjuiQ3c7Nm/cSVq7qXJuMVFCDIcCcjcFGB5Dz3lKLdKNrrlKxM9t7RnIsozvPbID85tDuOz4zqIfk/1trQqyoJfS2mcKn/njRjy5vQ+PXX8mWgpUlicbUTUH9Lb2ASHfoj7owZyGIEaiKe6Oaa31Y2m7UpyzpPe2YpXzaBKyLEOSJE05r4ARtLppDmkqzosiFArhP/7jP0p5LIQNbNHurOecRqkRRKUTFNKhrWztWnGeRzlngXA2hQfrZ2UqE9naiekOV0xTzopzVszv6VespKvnNODSdblFI7O12/ecGwLhhD5OK+VcvPzIeQ349DnLTW/nMaS1p9L2PdeVhGZrz2J3/zjueHIX/B4X/u24eUWvVVJ5WhYorX3m8NSOfgxHU3jzcBhnqMFplcLBYWNxrtna2YjT1lplxvjcpiC2dIf59S01vpyxaq1FKueZrIxwLI2GkDdnE7GcKNMWFOU8kcoCgfz3ma44Ls4feOABnHfeefB6vXjggQdsb/uud71rwgdG6GGLd2c952RrJ4hKR1Su2eeb/ZdZcBOqIt4/lkAynbUsLBKO0tr119mNXSOI6QDbAMtkZV5IO+k539M/BkCZsGAGs7XbjVLLVc61x6q3WAivnduAa85eirlNQVy2rtPyu5t9x/Oec5bWXoFWXiPsvBNPZfimYyKdxcHhKBa0FOe8zJfEz3vOpzBoak//GL741zdw9dlLcerS1in7vTOZRDrD27aGo8k8t556WFK7z+NCMp3VKees35xZ1ec2Bvl1LgloDCm2dpFClXO/x41avwdjiTSGokldcd4YKk8InIgkSfCrLbwzPRTOcXF+8cUXo6enB+3t7baj0iRJQiYzs1/UyYAtyp1Y1HNGqVFxThAVByuk3S6JL8K5cq4qQWwxKcuKBa6z2bwHlfWm2845N1hm7ZLdCWI6EBI2q1gqsa2t3cdGpClFtzEIjlHnYJRauIhAOLdLwg1vX2H5mOLtAC0ILsXT2iv/u15Ma2dp+IDiVphocW4ZCOd11nP+8Js9eHZnP/7nnWts3ydO+OcbPXhh9yBq/R4qzqeI/og4v9v6s1ksmayMeCrDN/EKhSW1Hzm3Aa/sH0afcLzG4nxOo3buaa7xwe2ScpTzQnvOAUU9H0ukMTSeQFaW+ZzzSlDOAWUDIZ7Kzvhxao7PPtlsFu3t7fxnq39UmE8OzK7m5MvXaGunUWoEUXmwBaNoRbWytQP21nYnc85Xz6nXzUIN+qg4J6Y3HreLf6bYItRKXQWQs+i2Us5rBeU8mzVPFWYFPlPJWx0EwjmFnTMyWXXOeaZ6lHNmMY/EU+gRlMPdqluhGJJ5AvFCDtPav/73Lbh3/QG8tHeo6GNhsM2ZvQPjE34swhmiTXxovPTK+Yd/+TJO+ubjRT826zk/bqEySrU3HOep5KznXFPOtY141iu+oCWkW88XqpwDQLP6WFsOh/GBn/0LWRk4bkGT5bluqmHn50R6ZteSJTuTj4yMlOqhCBMKsbVTIBxBVD6sOBYXlMxyy5SwuPAF1T1qHQrnpDhvCHrxyKfPwPcuPQpnr2jDFSfOL/7gCaJKYO0cTEmzCxligXCMdgvlnPWRy7I2E92I0dZeH/Sg1u+BJBWneIm4XfpRalw5r4KN+DkNil330HBMd07bM4Ei1ukoNTvlfCyR5rbjoRJYotnmzP7BKM8GICYXMf18pMS29kxWxou7BxBJpLGrr7iNpK5h5f21bkEzAOX9yFpjmHLOxqOJyjlz3XjdLixUN9hZcFyhNKuunS/935s4PBrH4tYa/OQDx0GqkDphMqY5VCNFFeff/va3cd999/H/v/TSS9Hc3Iy5c+di48aNJTs4QoPtJjn58jWGwpCtnSAqD6aciz2w/hzlXPuCOmSrnNv3XDK8biV46RcfPgGnLCGrJTH9Ye0crEXESSAcY1adeXHu97j496xZ37ksy1ognLqAliQJd77/WNx++dEFzyc24jHa2quo55y15nQNR9EjjIjcUwLl3DoQTk1rt+ljZbPlAWC0JMV5ih9bvkBPojSIPdzDJba1Hx6JIaV+3tjGWyGMJ9JcOV8+q5a3xjC1P6fnvEnrORfzKpaqfedtdf6iCmqxvW1uYxC//ugJFZVqT8q5QlFn8h//+Mfo7FQSTB999FE89thjeOihh3Deeefhs5/9bEkPkFA4Zn4TAl4XVsyuz3vbHOW88r+vCWLGYaqc29ja7WadO1HOCWImYmzfKKg4t7B6SpJkm9geTWa4qi1a2M9c3oaLjp7r7MBtcBsD4TLVk9be2awUHV1DBuW8f+LKuTdPz3ncRjnf0aMV56XoVw4LBdxELPuEc3qFHu5SB8IdEMagFaPK//y5vUiks1jQEsK8phDPs2Bqfz+ztauFcmuNn282iZMeWN95a21xAW5L2rTWtvs+cRLmNZnn2JQLv1cfijtTKSrVoLu7mxfnDz74IC677DKce+65WLhwIU488cSSHiCh8OULV+Pz5610tPg27p67q2D2KUHMNHjPuUfoOWdzzjP6tHYA6LaZda4FwlFxThAixikF9mnt4gQFCU02Cca1fg+GxpOImITCMdXU65Z0UxlKhbHnnCl6nirYie9Ui4GuoSjcwmZCXySBSDzFNz0Kgdn6/VZzzvlIPet0/e2icl6EMmpE3LTZOzCOs/Ln/BETRK+cl7Y43z+oFeeFvj+GxpP48TN7AACfOXcF3C4Js+r92NU3ht6IcszGnnOXS0JHYwD7B6O6QvzYBY0AgGWz6op6Hh8+dRGaa3w4/8gOtFs4g8oJC6pNzPC09qLO5E1NTejq6gIAPPTQQ3jb294GQLFyUSDc5CBJkuOFN/WcE0TlY6ecJ9JZZLIyL9KBfLZ25bw7GYUAQVQzBSnnguWzvS5g2xKmJbbnFnyjwozzyejlZN/xKUNaezXY2plSF0mkuULNNlCKDU/L13PuJBBuu6icl6A4F5P8KRRuaugTAuGGx0tra9cr584fO5bM4CsPvImxRBpr5tTjnUd0ANBaZoy2drHlhW1kiRkVZ69ox/9eeTK+8q41RT2PphofPnTqoooszAFSzhlFKefvec97cMUVV2DZsmUYHBzEeeedBwB4/fXXsXTp0pIeIFE4uXPOy3QgBEFYEjDpORdt7caeK7tAOG3OOX3YCULEOELQdpSaYGtvz5NezGedmxTn4Zhy2WSNJ2ITWTJ8znn12NqDPjdaa/1cKQz53FgztwEv7R3Cnv5xHDmvseDH5Lb2PGntdoFwYs95aWzteuWcmHx6JzEQ7sCQ9jcciekfO5HO6IImD43E8L6frAegbJyx7+7Pn7eSb/ixsMme0Tgi8RR/b4qF+NVnL0V7vR9vXzObXyZJEtYtbC7lU6somHI+0+ecF7WSu+2223DNNddg9erVePTRR1Fbq/RAdHd346qrrirpARKFY7S2ka2dICoPHggnFAtscZlMZ3PSSkdjKYybhE8BWiAc2doJQk+oIFu7VpxbhcEx7HrOmXJeN2nFub7nnNvaq+S7nvWdA8Ds+gDvgy02FC6pPv98c85jqQwfXSUyMJbAwJhWcI3GJl7Yie0OE+mnJ5wjFufjyUxJQ8VEW7u4eXP3U7ux5n8exr/2DPLLHt/aiwNDURwYiqJ7NI7Z9QH87P+tw+nL2vht5qpp7AeHo1yVb67x6c5BJy9pwa2XHY1Gm/aa6QYp5wpFKederxc33HBDzuXXXXfdRI+HKAGknBNE5RNUv4RMA+EyWb5z7HUrLS2ReBrdozEsbc/tNWMj10g5Jwg9xuLc6ZzzfHN/69TbmvWcjyWYrb2oJVZeWK82C4Jj//VUgXIOKHbdDQdGAACzGwJY3KoIPLuLLGKdjlKTZWXRb9zEFMPggIkr54l0RldcHB6NIZ7K0ObpJBJLZnJaTEaiKcyqL/41f2XfED7529fwlQvX4IBFz/lzu/qRzsr4w8tdOHFxC4D/396dxzlV3vsD/5zss2aWzMKwDYtssqigSEFFq4h1vbbWhVJpvdYNtbZWpbUXbnuttmLVqr2t9pYuv95qrevVlrrgLqggCCjKKiAwDMy+Zj2/P5LnyUkmySSZbCf5vF+vedVmTjInczhn8j3f5QnejDl3+jCcN20Y5h7jkMsvCuNqgv/mxRR3sZJBIbPKnvPCDs6T/iT35z//GfPmzUNDQwP27t0LAHjggQfw3HPPpWznKDnhPecG9pwT5Zxgz3nw/NQupaadwC4nu2qm0WqJXspYazgTFaLky9oHy5wHytojVLOIbHpZmoLzaJnzWFUBuSQkc263YVggiygmVifKFZh1NNhSakDk0nYxDG54hX+/htpzrq2mKLOaoKrA5y3MnqdTc2CwWpHZiKrAdPOhDoVbvbUJR7qc+Nk/tqFLc55rg3PRK77m02Y5+0FM5z/1GAfOmTZsQGAOAOMCU9f3tfbKddNHMziXCQaWtSfhv//7v/G9730P55xzDtrb2+UQuIqKCjzwwAOp3D9KQvjdc72UuhEVktHV/lJO7VImlghl7TazUfaudkb50Mil1IgiG5g5j36OaKe11w0anEcvaxePlVrTFZyH9py7dZg5F4bZbfK65/Ymly1ze2KXtRsNivxer2vg8RLB0Ulj/L28HX3uiOXv8RLX6TKrCWMDQdgelranlRisVlduRWWx/9xs7RlacH4wsHxp+DBWbWWFaIfo6HNj/edtAIKZ87GB7HgktWVWlFpN8PpUvLn9KABgFIPzYOa8wMvak4raHnroITz22GP40Y9+BKMx+Mds1qxZ2LJlS8p2jpITHowzNifKPbPHVOGFG+fhpxdOlY+JD5Burw99miFvFYHgXHwo2HO0J+TOcnApNZ7sRFoDes7jnNY+aFm7nNYeqaxdBOeZ6TkX/6uXG/Ha8t36cptcn9yV5AdysapFrMqBWBPbxXVVrCEdaeZHIjo1lRON1f73qp32Takn+s1ry21yCcShticcCFu+VKw3LobNub2+kBsAr2w7jH63Vwb1Yx0liEZRFDlrYcM+f1A/qprBuVWuWBM7c77tUKe8qdbS7cThzv4h3VDLNUldyffs2YPjjz9+wONWqxU9Pbw7mG3hd8+5lBpR7lEUBVOH20OWetIupSYnsJuCmfOOPjc2f9GO01e+jtv+vhkA4POp8kMtM+dEobQlzUaDMqDtS8tgUGQvef0gmfNSm+g5j5Q5D2ROM9RzHlxKTR9/67WZ83p7kVyfPNnM+WA950BwKFyksnZxM6Wu3CZvfIRP5E6EOP7lRWbUBqZvH4nSkkSpIYLzunKbHKA21LL2g2EZ8+kj7AD8N1+8PhUt3aGv/8q2w9hztAeq6l+pQZTXRyP6zkUFDDPngNUsprVHvxb0OD24+Nfv4pLfvAuP14f/fW8fZv/sVSx7On+Sw0kF52PGjMGmTZsGPP7Pf/4TkydPHuo+0RANHAinjz/YRIVOlrV7fZohb0bYA2V67X1ufBoYXvTxwQ4AoeVfDM6JQmkz5/H0ZN+2cCK+PXeMzKJGI0rWI62g0J3mnnNz+FJqgZ5zPaxzDgDDKmwQH0uG2W0hS0gmwznIUmpAcMZHX4Re1h5Z6WBERXFoldK7u47i9JWv4/09rXHvj1hGrdxmlutJJ9tPT4Nr7urH1gP+v4d1ZVZUlYQew2Q4PV55Q0VUpE3TLPPX2eeWywHai8ywGA3Y29KLf25tAgCMrSmBMkhibFzYNWY0M+dxZc4Ptvehz+1FW68bX7T1yaUK82mgXlJX8h/84Ae44YYb8MQTT0BVVbz//vu46667sGzZMtx2222p3kd8/vnnuOqqqzBmzBgUFRVh3LhxWL58OVyu1K5jmC/Cg3EG50T6YDFF6jk3hGTO2wJldGI4nLa83RYjc0RUiCJVpsSyeE4j/uP8KYN+sI4ZnDvT23NuHDAQTl8952ajAZefNAonNlZiQl2ZDKrFYLtEifcf6/jGKmsXx6vEapLXWhHY/eaN3dhztAcvbj4Y9/50aionxLrVzZ0MztNhw95WfOnuNXh2k//41NuDZe1D6TlvCqxNbjMb8J1TxsKgAGdOrpXndEefWwbvIyqLcOoEBwDgD+/sAQC5AkEs4zQ96RaTYdDlGwuBCM5jZc4Pa86lPUd7sCcwbHFMjDYCvUnqL8e3vvUteDwe3Hbbbejt7cUVV1yB4cOH46GHHsIpp5yS6n3Ep59+Cp/Ph9/+9rcYP348tm7diquvvho9PT1YuXJlyn+e3pnD+s4YnBPpgyXKtPYKbXAe+NDY1e9Bv9srM+wmgwKTTjJnRJmi7SOPJziP+3Wt0ae1i57j0nRNaxdl7b7AUmo+fWXOAeCuf5sm/1vbzpOMeMrai83+YxGprL3HNTA47+hzocfpwbpd/vWr2xLIwmrL2kVwzsx5eny0vwMen4rKYjPmT6zFRccPx5PrvwAwtLJ2MQSuoaIIt5w1AUvPOAYWk/9GebfTg3ZNcF5TZsX5MxrwyrZmee6PrRk8UBxfG9xmZGURDPysLqv/YmXOtevZ7zrSLTPn+RScJ30lv/rqq7F37140NzejqakJ77//PjZu3Ijx48encv8AAAsXLsSqVauwYMECjB07FhdccAFuvfVWPP300yn/WfnAYFCgvenPpdSI9CGkrD1w59hqCpa1d/QGM+eAPxujnepORKESLWuPVzBzHiETK8va0zUQzv8+RDm7O46y7lxmlqtUJLd8UnAgXPTPOrbAvwOxBr1Wt2a6foVmmNg7O4/K104k0AuWtZvYc55mYubDOdOG4f5Lj4OjNDitfShl7QcDw+CGVxRBUYLT/oOVFS55w8VRasWZk+tCBrKOiyM4H1VVIpNnYvWWQmeN40addknZjfva5XFuzKPfYUJX8vb2dixatAg1NTVoaGjAr371K1RVVeGRRx7B+PHjsW7dOvz+979P176G6OjoQFVVVcxtnE4nOjs7Q74KhTZ7rpdSN6JCFzlzbkBFkf8DY0efG62aD4mHu/pDtiOiUMWa0nJrGjLn2ShrF3Nl5FJqgQx6+LwZvbCa0l/WPiHQ3/tuIBOuJW6wlFhNIVVKaz5tltskUiIdHAgYzJx39LkLfu3mdBAtBNq1xCtTsM65GAbXYC8KeVzMJOgIy5yXWE348qQ6uV2sZdQEi8kg1zbnMDg/mxwIF1/m/M3tRwD4Z1cUWfInQZHQX6of/vCHePPNN3HllVeiqqoKt9xyC8477zy89dZb+Mc//oEPPvgAl19+ebr2Vdq1axceeughXHvttTG3u/vuu2G32+XXyJEj075vuUJbys5p7UT6EBKcawbClYu79X0uuYwLIDLn/u1ird9MVKiKE+w5j5dYE73H5RmwhE/ap7UH/r67dToQLpxZUzGUDFnWbox+DTxnWj0A4NVtzSElsy6PT/7cUqtJVim19YYG520JBOeitLm8yCSHhQGQA8QodeSa8ppzTfScJ3LMwh3UlLVraQcGisx5Tan/Bsz5M4YBAAxK/MPdjqnzB/H5VJI9FPFkzrVVKF2BG6H59vtL6Er+4osvYtWqVVi5ciWef/55qKqKCRMmYM2aNTjttNMS/uErVqyAoigxv9avXx/ynIMHD2LhwoW45JJL8O///u8xX3/ZsmXo6OiQX/v37094H/VKewedfSxE+iA+xHl8qhxcZDMbgnfre90hGZzmrv6QwXFEFKrInJ7gXGTFfWroBHBVVWXmPF3BuaiG8/p8UFU1uM65TqvkxHHx+lRZDZCIeHrOjx9ZibpyK7qdHry946h8XFv5UGIxyiqld3YeRXOXU7YIJtJzLgLGcpsZiqIEh8KxtD3luuSNEE3mXHODJV4ujw9ftAXXog/2nIcOabMXBdsejgaOpyNwfE+fVItzptbjO6eOi/tm+ffOmojr5o/DRccPj3tf85lYSs0ZcyBc/4DH8i04T+gvx8GDBzFlyhQAwNixY2Gz2QYNkGNZunQpLrvsspjbNDY2hvz8008/HXPmzMGjjz466OtbrVZYrdak90/PtH+kmTkn0gfth0vxoUO7znlnvyfkZltzlxONDn9gkE8lXUSpUpKmsvYisxEGxR+cdzs9cj11p8cny7PTV9Ye7DnXloLrNXOuve65vT4YDYldy1xxrPNuMChYeGw9/rh2L/65tQlfnuwvQRY3UmxmA0zG4I3QLYGlueaNd+CtHUfR5/aiz+WN6zrbFTZzwFFmxYH2Pvadp0GwrF2TOS8JtoF5vL64BqXe/tRmPLPxAF64cR6mDrdHzZxrV04Jz5xbTUb89zdmJrT/E+vLcPvCSQk9J5+JFWf6Yw2E62JwHsLn88FsDt6dMhqNKClJ/hficDjgcDji2vbAgQM4/fTTMXPmTKxatQoGgz7/CGWKUfP74bR2In3QfkjtCGRfbOZgcA6EDrk53NkPp+g5Z1k70QDpKmtXFAUlFhO6nB5/z3KZ//HukExs+pdSExPbgdjBaS7T7rfT40t4uGU8mXPAPzTsj2v34qWPm+C+eBrMRoOc1C5upGivtQDw9VkjsW53C9xeFW29LhRZiga8bjgZMBb5X5ND4dInUs+59hh29XtksB7Lxwf9N2M+berCsQ3lciBc1LL2PldIzzmlxmCZc1VV5bKEjlKrbBUp6OBcVVUsWbJEZqP7+/tx7bXXDgjQUz1F/eDBg5g/fz5GjRqFlStX4siRI/J79fX1Kf1Z+UL7x47BOZE+WIwGWEwGuDw++Ye/yGKE2WhAicWInrBlgI50cVo7USxWkwGKAqhqaqe1A/6svD84DwbkXZrJ3+lqKTNpgnNt5tyk06SF9ri4k+g7F5nzwSojTmysgs1sQGe/Bwfa+tDoKJHHTlRYiJ5zwJ9N//LkWlQWW9Dc5URrj2tAsBaJLLUOBIwsa0+fLk1/v2A2GlBqNcklz+IJzkW7WHuvC+29btmqMsweWtYuBgY2dzrlzxaZcxq6wXrOO/s88nuzx1bhxc2HABR4cH7llVeG/P9vfOMbKd2ZaF566SXs3LkTO3fuxIgRI0K+Fz6IhfxCBsIxOCfSBUVRUFFkRnOXE02BviqrZgmX8OBcOxCOPedEA4kMd7fTk9LMORAcCqfNlgeXUUtP1hwI7Tn3ePWfOVcUBWajArdXlVnwRLjiXErOaFBQajWj3+2U6513i0ntgSqHCk3W9cuT6lBsMcngPN6lucKHlIngjZnz1Av+rkMrHuR65L0uALEDN59Plf3pbb0u2W/uKLUMuOktMuc7m7sB+G8saW8M0NDIdc6jTGtvDpS0l9tMmDKsHC9uPgSjQcHIPJt2n9C/qFWrVqVrP2JasmQJlixZkpWfrVcmBudEulRR7A/OD3f4/wiJP1b2YgsOdoT2WjVrllKzMnNOFFGRxRgIzlN7johSaBGQA0BXYB3tdPWbA5F7zk0G/xBdvbIYDXB7vUllzuNZSk0QbQ59bv8x065xDkCucw4A5033T9+uLPEHZK1xLM3l9alygrQYUlZbLoLzgb2ylDxVVQdUKQgVxWYcaO9De9/gN1Q6+91yEGFbb3CJtNoy24BtxUA4cfO8psyq6/Mu1wyWOT8cKGmvK7dhbCBbPqqqWLfzNqLJr3dDknYABgfCEemHmBbcJQcVBYJzzd35kVX+0sq2Xrdctoc950SRlQQCsnSUtQOQfcuApqw9jZlzY0hZe2CNc51mzQXtMpKJ8GlK++M5viI4F5nzYFm7//G6civKbSY4Si2YP7EWAFBVEv/SXNoqCmbO06vP7ZUrFYRXqmhXOOlzebFud0vUlQBaNMe1vdcl+5gdEXrJpwwrD/lZjtLBS+YpfiI4d3l9EY+XmNReV27DaRNrcO70YfjumcdkdB8zgcF5nuJSakT6pO15BILl6iJoB4AxjlL5QXR/a2/IdkQUqihQspz6svZA5jxiWbs54nNSwSzL2oPLqJl12m8uJLvWuXZ7cwKZ82BZe2jPebHFhP+7cR6evWGunMwu1s1ujSM4F2XWVpNBLqdVW+7PwDI4T63OPv+xMxqUkMGPQOhU9ZUvfYbLHl2HZzceiPg62uPa1uPG0W7//3dE6FW3F5vxl3+fLZdrq4mQXafkadsIIt2oa5ZVDVYUW0x45IoTcOFx+bcMnb6v5hSViQPhiHSpImxasMiIayfQVhWb5ZChfTI4Z+acKBLxwT2VS6kBwVJo7UA4ucZ5Gsvaxd90t9cnM+fxBKa5LNnMubYMPr7Muf+49IVlzrVtCKOrSzCiMtjDKoLz9jjK2oOT2oPXa3GtPtLt5JykFOrSLKMWXlquXY/8s6YuAMDmL9ojvk5IcN7rQkuMzDkATB9RgSevnYPzZzTgO6eOHdJ7oFDaa/RbO47g86M9Id8XmXNxwytf6ftqTlFpl1IzMTgn0o2KAZlz44DHK0ssso9xHzPnRDGJ4Dx9A+GCw4tEwJCJnnOvtqxd53/nRWCdaHCu3T6e4Fxkw0UrQrdrYHAeTkz7bh1kINy+ll781wvbAITeTBWlz26vGvdQORqcuBESqUpFu+SZCOh2hwV6gjY47+hzy7L26hhT3sfXluGhy4/HSWOqktt5ishkNMhr2Xf+vAGLf/9eyPePaDLn+Yyf5vJUSFk7e86JdEM7kAgIBt3aTExlsUX+cTokBsex55woonRlzksiZM7FrIh09pyLyjiPT4Un0G+t94FI4saJdmm4eIiydrNRiauFTw6EG9BzHv14VQUGwsXqOVdVFZc+uhZrd7fAZjbgutPGye9ZTUYZrB/pZml7qoiy9kjT0kUFWkevWwbne+IIztt6XbIH3cEl0rJCe2Nrf2tfyA04bc95PtP31Zyi4rR2In2yh5e1mweWtVeWWAZMkmVZO1FkYpmsVA+EK7VEKGvPxFJqBm3PeZ4NhPNGXkIpGrcnsZsTAwfC+f83Vua8Io6ec7dXlTdKn7thHr46M3TZX5GFjadvneIjM+fW6JnzQx39cmjqgfY+ubqJlvaY9Lt9ONDmX0qtmsPesuK+r8/AivOnyOtcS4//hla/24uPD3YCAMbW5Ne65uEYnOcp7R9qxuZE+jGwrN0w4PHKYjPmjKuOuB0RhTpjci3qyq340vjqwTdOQKSBcF39g5dJD5W259yVYHCaq+RAOE+imXN/sBVvy4LoOY82EC6Sqjh6zvs0Qd8Yx8DAoTKBie8UHxF0R8qci57zHc1d8jFVDQ5Q1Qq/YbI3sA0z59kxf2ItlswdI3//opT9vT2t6HN7UVduxaT6smzuYtrp+2pOUYmeNKPO1z4lKjSVYWXt1ogD4Sz4yrRhuEYzjMbKsnaiiM6b3oD3fngmZo5ObX9ozIFwacyci0A2JHOu87vwliSntYv1kOOtigiWtYeucy7mB0RSJXvOowfWIiNrNChymr6WuK63sec8ZYID4QZmzsXfSzF5XYjUd94SFpyLJbwYnGeXHKQYCM5f+7QZAHD6xNq8j2vS99eDskr8oeYa50T6Eq2sXbuUmiizvOOcSeh3e/HilkOY2ViZuZ0kIk3PeTBrmoml1LTrnOdLz7mYNu9OciBcvO+/KLysPYGBcP1uH/pcXhRZjGju6kd1iVUeC9HDXmQ2RgwcxNJbbXFMfKf4iJ7zWAPhwkXqO2/tiTwHoCrGQDhKP21wrqoqXvssEJxPqs3mbmWEvq/mFJX4g6HzpU+JCk60svaQzHngQ4OiKPjPC6figx+diXE1pZnbSSLSTGuPMBAurdPaA8G5dik1vfecJ5k5FwPk4h32V2yOvc55JCUWo/z9tva6sPVAB06661X88OktchtR1h5t9kcVe85TLrhsXYSBcNGC8yMDg/O2nuDa9IK9yJzy1R0oMTWasvY9R3uwt6UXZqOCueMdWd6z9OO/vDwl7iKbGJ0T6crAae3+D3tVpRaYDAosJgMqS0I/eOR7iRdRLpJl7S5tz3lgKbW0Tmv3/133qcFg1qTzzLnF5L+GJbuUWuI95/5jFmmd83CKogTL0ntc+HBfGwBgy4EOuY0IzosskfeDPeep1xWjSkVbaQYAdYGlR/e0RCpr92fOx2pucDs4DC7rZOa824k1gZL22WOq03rjM1fk/zssUDJzzs/sRLpSYjHCZFDg8akwGRR5o63UasKvLj8eJoPC/nKiHBBpKTXZc56BgXCAv9QayJ/MuTvBzLkYCJd0WXugJSFW5hzwZ76bu5xo63Xhi8A0b22Jer+mrD3i84sH71unxHT2iZ7zgcfOZjbAYjLImzcnj63Gc5sODihr73V55Dk0rqYE2w75p4FXs98867Rl7WLt+XnH5H/WHGDmPG/JnnNG50S6oiiKLMkLL5H8yrRhWHBsfTZ2i4jClIZNa1dVNSM956aQ4NwbeEzfH+dE5tuZcOZcDXn+YEQrQp/bC1VVZdVDrIFwQHA4WFNHP75o80/z1paoy8x5lOBcZs45EC5l5EC4ooHnmqIoIa1gJ4/1r9RwpMspnwcALYGBcRaTAcMriuTjNQzOs04bnO843A0AmDysPJu7lDH6vppTVGIpNaPO/2ATFSLxoYLLoxHlLpFtFYPCfvXqTngCk57TWdZujBCc634gXNKZ88SmtReZg0up9bq8UAMrt0VaK1trfK2/5Hn74S7sb/Vnzp0enxwEN1jPuRwIx7L2lOnsj70yQoUmOD+mtlQGe58E1soGgtUPVcWWkJYyrnGefeJ4HerolxUPx9QWxmwdfV/NKSqjXEotyztCRAkTHxJYvk6Uu7TZ1mv+3wbc/8p2AMA5U+vT2hepDcRFcKj7snaTWOc8yWntcfecB8ranR7ZjmBQBr8ROqHOv67yZ4e7ZeYcCJapy2ntlkEy5wzOUyZY1h75xop2KFxduQ3zAoPERP8yEFxGrarEIm+gAEB1CTPn2SaqFw6098HjU1FiMWKY3ZblvcoMhm55Svyh5lJqRPpTwcw5Uc6zmoJTvN/cfgQAcO/XpuPXi05I68/Vdqv1ewJl7Tq/Ey+ntScYnLsTzJzL4NztlZP1S6ymQYdqTqz3Z+w+2t8eUpougu3+QcraRc95l9OT8HukyMRAuGjBuV0zFK6mzIozJ9cBAF7edlg+3hooa68uDc2cO8qYOc82kTkXxteVFczwW31fzSkqUfZm1PnddKJCZI/Sc05EuUU7SKyy2IyvzRyR9g+QiqLIvvM+V54MhDMlWdYeCHTjXUpNOxAunkntgsicd/SF9oyLvvPBes7Li8zypkp7H7PnQ+Xy+OTvPNJSakAwc15ZbIbNbMSpExwwGxXsPtKDXUf8PcytzJznrBKrSd5MAwqnpB1gcJ635EC4ArnLRJRPxDIwDM6JcluJJRgYzBhZkbHMjpgrIwIUs87nyyS7znmyS6m5PD509g2+xrlQZjOHDAwT2mRZu38/bFHK2o2G4IAysa42JU871C3azRVRgVZX7i+FLrOZ5WC4VwPZc9GWUFVika0HAFDDzHlO0GbPGZyT7okSNwOntRPpTnBaOy/RRLlMGxgcN7IiYz9XTGd3imntOs+cm2XPuZrQ80QwH2/lgDYTJ5Zniic4B4CJ9WUDHmuLM3MOBPvOW9l3PmSipL3EYoza0mEPC84B4Kwp/tL2Vz7x950fbO+T21Qwc55ztFPzj6ljcE46x8w5kX6J8joxWZiIcpN2KFwmg3PRutaXJ9PaM5U5t5oMsry8uasfAFA6yDJqgiht12oN9J8P1nMOBPvO27jW+ZC1B9oL7BGWUROOH1UJRQFOGlMlHzt9Yi0AYP3eVjg9Xnze4h/u11hdjMpiC8qsJhSZjSEBPWVPaOZ84PmXr/jJL0+Z5LR2BudEerPg2Hqs+bQZi04ele1dIaIYSrKUOReZ4uBSavr+Wy8y5+5Ep7XLgXDxBdiKoqDYYkK304PmzkDm3BJv5jyYubMXmdHR5w5mzgeZ1g5o1zpncD5UzZ3+GyvhQ8O05h3jwOblC1CmGRg3orIIpVb/8d/X0ou9Lf4lukZXl8BsNOAvV8+Gx6fGPI6UOeL42syGiG0l+Urft1opquA65/r+g01UiOrKbVj1rZPkXX4iyk2irL2xujhk2nO6hWfO9T6t3TrEzLnZFP9nHRF47Q8siRbvmtYT68rlf08fYQeg6TkfZJ1zgGudp1Jzl//GSk1Z7Ax3Wdgkd0VRMLamBACwcX872gOVD6OriwEA00dU4IRRlaneXUqSKGsfX1taUG26+r6aU1RyWnsB/WMmIiLKJJE5z2TWHAhWx/W7A8Gpzv/Wi+A62aXUrAncnBB953sDJc2DBXjCuNoS+Zlq2vDIwXl8PefZGQjX2e/G7X/fjI372rLy81NJBOd15Yn3ho91+IPz1wLrndeUWeWgQMotUwM3wWaPqc7ynmQWg/M8ZWJwTkRElFYnNlbCaFCwcOqwjP5cU1hZu94z56IsPd0950AwgP48UNJcG6M0WstqMuLWBRNx2YkjZR+zCLRlz7kl+n5ku+f8qQ1f4In1+7H8+Y8z+nPdXh8eeW0ndhzuStlrirL22jhvrGiNcfjbE97ecRSAv+qFctPpE2vx5g9Ox7JzJmV7VzKKt4ryFAfCERERpdelJ47ChccNz/iyh+LGe3++DIST09qTLGtP4P2LagdRdZDI8K/r5o8DAGz5ogMABvac5/C09t1H/DcjNn/RgcOd/RkbevbyJ4dx778+wz+2HMKLN52SktcUmfPaZDLngbL2rsA696OrS1KyT5Qeowrw5om+r+YUFZdSIyIiSr9MB+ZA8AZ8X74MhAvsvzvRzLk38cx5cdiwr3gz51qVJf5e5tZeF1RVjavnXGTO27OUOReVAgDw6rbmjP3cI4FA+uODndhztGeQreMjJu0nc+xEcC6Mriq84I9yG4PzPMXMORERUX4SPeciY2vS+Y34oWbOkylrF5LJvlYGAm2Xx4c+tzeupdS0AX02aAPjV7YdztjP7eoP9ti/uPlgSl5TTNpPrqw9LDh3MHNOuYXBeZ7iQDgiIqL8JHvO5bRyfX+cS3qdc7mUWnKZc0UBHKWJB+fFFqO8IdDa45Il8jGXUgsE9K3d/mx7Jjk9Xhxs75P//+2dR9Hr8mTkZ3f1B3/OC5sPDfn1vD4VR7uTHwhXbDFhmD0Y1DNzTrlG31dzimr2mGqMcZRg4dT6bO8KERERpZC48S57rg36/jhnSXKdc3cSZe1FmsncVcWWpPr1FUUJDnjrccc1rX2Y3b9Oc4/Li7bezE5s39/aC58KlFiMGFlVBJfHhze3H83Izxa93QDwaVMXdjZ3D+n1Wrqd8KmAQQGqk7ixAoSWtjey55xyjL6v5hTVqOpivHbrfHzj5NHZ3hUiIiJKofBg3KTznnNZ1p7stPYkM+c1SfQsC3LAW69LthfE6jkvshjREMjY7jk6tAA1UXuO+peNa3SU4LQJNQCQsSXVtJlzAPjHlviy56qq4pVPDmN/a2/I42IYXHWpNenqUFHaXlFshr3YPMjWRJnF4JyIiIhIR8KDEr0vpSay15noOS/RBOdDmVheGQjq2npcwcx5jLJ2ABgTyNiKyempoqoqfvfWbry7K3I2fG9gGFyjowTH1JYBAHaleB+iET3nM0dXAgBe/TS+YXRbDnTg3/+0Ht//20chjw9lGJwwNrCcGkvaKRfp+2pOREREVGDCM+UWvWfOk+w5dyaxlJq2rH0oAZ7InB/q6A++9iCT+0XGVjs5PRW2HujEf724Dcue3hLx+2IY3JjqElnSvTtD2fvuQOb8ghkNAIDNX7TLCe6xNAV+r7vDJryLYXBDubFy1pQ6TKgrxWUnjUr6NYjShcE5ERERkY4MyJznS8+5N7FBacn0nGvL2pOZ1C6InnPtoLXBltUT/c2pWlJM+KLNX/p9oK0PPt/A3+Hnmsz52Bp/1nhfSy88Cd4MSYYoax9fW4qpw8uhqsDrnw2ePRfVCC09zpD9lGucD+HGysiqYrx0y2m4nME55SB9X82JiIiICkx4MK77nvNA5tvrU+GNEFxGk8w659rS82SW4hKqApnz/YHA2GIyDNoDPTZNZe0iYPX4VBztGZiV/lz0nFcXY1i5DTazAR6fiv1tfQO2TTVR1l5qNeGMibUAgNfiCM57A338qgq09ASXnzvcOfSydqJcxuCciIiISEfC1zUfUanv3lltcJ1I3/lQB8INJcATWfd9Lf7Ad7CSdgAYE+h13tvSGzHDnSzRhw0AhztCg/N+txcHO/xBeKOjBAaDIjP4u4+kv7RdZM7LbCacMbkOAPDW9qODHucezZR3UcoOBG9E1AyhrJ0olzE4JyIiItIRoyZTXmYzYaxD38tBaXvGE+k7F2XwyZe1Jx/giay7yJzHE5yPqCyCyaCgz+3FYU1APVSHNcFrU2fo6zZ19ENV/ftXHcj2jwuUtqcqg7+/tReL/+c9vL0jdCCdz6ei2yWCczOmD7fDUWpBl9ODTfvbY76mmIAPhN58SEVZO1EuY3BOREREpCNmTeZ8xogKGJJcUipXmDU3G9KfOU/NQDjxXHGDYLBJ7YD/JsSowITwPSksbW/uih6ci97tEqsRiuL/PYvBdOHD1pL1jy2H8NaOo/jzus9DHu9xeaAGCgTKbCYYDAqObbADAHYNkrXvdQeDc+0AuSOB9zeUgXBEuYzBOREREZGOGDU958eNrMjejqSIoigywHYnkDlPZim1VK1zHj5MbrBhcEJjigNjAGju1Ja1hwbn/YEg12oK7l+w9z01Ze2tvf6e8OawKeyipN1sVGANHKPGav/NicEm1odmzv2v6/R45c0HsWY8Ub5hcE5ERESkI9qe83wIzoFggB1v5lxVVVkCn8hSatWl/qC6rtwad0AdiaPUCkVTsFBkjm8f5HJqKQzOtZnlw2GZc7HcnFWzf2Jie6puELT3+Ie+aXvDAaDbGSxpF1n7xjjff69L03MeKGvf29ILn+rPwg/lxgpRLjMNvgkRERER5Yp+TzCreNyoiuztSAqJ0vZ4M+faZdcSyZwPryjCw1ccj2H2osR2MIzZaEB1iQVHu/1Z43jK2gFNcJqitc7dXl/INPPwsnaRObdpMufiBsGRLie6+t0os5mHtA8ic36kywlVVWUgLia1l9mC4YYYRrc3MEgvml5t5jwQ9O9q9mf6x9WUyp9BlG+YOSciIiLSkY8Pdsr/dpTmRwZRBNjOODPn2sFx1gSCcwA4b3oDZo6uTOg5kdRolmKLZyAcAAyv8D8nPIhO1pGwUvJomXObJnNuLzLDUeofDieWWRuK9kBw7vL60NHnlo93BsraS62a4Fxzc0JVo0+sj1TWLvrUxUA7onzE4JyIiIhIR7QBUL6QZe1xZs615e+JlLWnknagXLwl8mLKe1PHwPXIkxHe590UR8+5dj8irYueqLbe4L9H7c2Cbs0yasKIyiIYDQr63b6QKfPhtJnzIzI491cbjKvV9+oERLEwOCciIiLSkZ9/dRocpRasWnJitnclZUSA7Y4zcy7K340GBcYsTavXBufxZs7rA4PMWnqcCQ2/i0YMgxPL6XX2e0Kyzk73wMw5AFQFllVr05TEJ0v7GtqbBcE1zoNl82ajASMq/S0FsUr7w6e1q6oqM+djHcycU/5icE5ERESkI2dMqsP6O8/C6ZNqs70rKSOmtSeaOU9kGbVU005sj7fnvKrYArNRgaoOLElPhgiGx9aUyhsE2pJ5Z2A+QXhmv6LYHzBrs97JUFUV7ZpKDu2a5LLn3Bo64mp09eBD4fo0A+FcXh/ae92y53w8M+eUxxicExEREVFWibL2eLPJopdau0Z6pmnX2o43c24wKLKkPLw/PBkiOK8rt8qsvLa0vV9mzkP3L1WZ885+D7y+YO+4dmJ7cFp7aHA+Ri6nFr3fXVvWDgBbDnSgx+WF0aBgVBWDc8pfugvOnU4njjvuOCiKgk2bNmV7d4iIiIhoiGTmPMGydosp+eXQhiqZnnMgmHFPSXAeeI3aMhvqIrxusOc89CN/ZXEgOO91we314T+e24ofPPkRfvvGLrzyyWHsb40cOHt9akhAHx7cD1bWDsSbOffvt2hZWLu7xf/cquKEpvMT6Y3ullK77bbb0NDQgI8++ijbu0JEREREKZDwtHaxfncWA7WQae1xlrUDQJ3MnKeurL223Ir68oEZ+eC09tD9q5Rl7S68u6sFf1q7d8BrT6grxaLZo3HllxrlYz96Zgse/2A/zpxch5u+PD4kaw6Elup3BsraS8My540OkTmP0XMeCM5HVBZhb0sv1gWC87Gc1E55Tle3nv75z3/ipZdewsqVK7O9K0RERESUInIgnDf68lpaLpk5z2LPeRID4YDgULihLKfm9anY19KLQx0ic25FXYTXlZnzsIFwlYGy9tYeF5o6+gAAjdXFOH9GAyYPK4fJoGD74W4sf/5jtHQHA+5N+9sBAK9sO4yv/ve7AwJsbc95pGnt/p8TXOtcu5za7iPdeG7TAXh9KvoC+y2y7B8Ffi4ntVO+003m/PDhw7j66qvx7LPPori4OK7nOJ1OOJ2aO3idnTG2JiIiIqJskEupxZk5F8PByovMg2yZPjVJBuepKGv/8XNb8b/v7ZP/v67chga7fwr6tkPBz7v9nshLqYmy9vZet8zgnzy2Gvd8dToAoKPXjXm/WIOufg/ael2oLvXvs3YZP7dXxft72gD4j5/L44urrH1EZTEUBehze3G02yV/j7c++RE+3NeOisC+AcD4mlK8uf0IRIJ+Qm1Z/L8kIh3SReZcVVUsWbIE1157LWbNmhX38+6++27Y7Xb5NXLkyDTuJRERERElI9hz7h1kS7+nNx4AAJx9bF3a9mkwNrMR9sDNAVsCZe2Rys8T9eHeNvnfVSUWjHGU4IxJtTAowLrdrdhxuAvA4Euptfa45H7Uagbc2YvNcqJ7R19wcroIzkdW+W8EiEz6MbX+cvMjmlL9Lmfkae0WkwHVJaE3KHw+FZ8EbirsDNx4AYDvnDoW3547BpfOGonr54/DudOHDfq7IdKzrAbnK1asgKIoMb/Wr1+Phx56CJ2dnVi2bFlCr79s2TJ0dHTIr/3796fpnRARERFRsmTmPI5p7ftbe/H+nlYoCnDRccPTvWsxiSFsiWTO68qH3nMuMtT/76rZeOf2M1BiNWFkVTHOnOy/WfHHtZ8DAPpFz7kp8lJq2sy5tkwfAMoDGW+xJJrT45W94DNGVAAAPmvyB9QT6/wZ7S5ncJ31aGXtAAYMrzvQ3icnyx9s95fZF5mNqLfb8B/nT8HPvzYdty2clNDgPSI9ympwvnTpUmzbti3m19SpU7FmzRqsW7cOVqsVJpMJ48ePBwDMmjULV155ZdTXt1qtKC8vD/kiIiIiotySyLT25z86CAA4eUw1GiqK0rpfg/n23DGYO74aJ42pivs5MjjvSC5z7vL40BqYkj55WFnIMLolgeFtT394AJ397qg95yJz7vL6sOdod8h+CSKo7gwE2SJrrijA1OF2AJDl5iOqimV2XgyFi1bWDmirB/zbarPlIjgvTqAagShfZLXn3OFwwOFwDLrdr371K/zXf/2X/P8HDx7E2WefjSeeeAKzZ89O5y4SERERUZol0nP+TKCk/d9OyG7WHAAuO2kULjtpVELPEVnjLqcHPU4PSqyJfRw/GhjQZjIosndcmDOuGhPqSrH9cDde3HwoOK09LHNeZDbKPnGx3rjYL0FkzjsDQXlHr/9/7UVmjHGEDmarKjajtsyGfa29aO7qx6jqYhmch09rB4Il9GJ4XaTgPJEJ+ET5QhcD4UaNCr3olZb6+1rGjRuHESNGZGOXiIiIiChF5FJqg5S1Oz1eGcidMak27fuVDmU2M0osRvS4vDjc2Z/w8mCipL2mzApDYB1wQVEUnDSmCtsPd6Opo19mzsPLwRVFQVWxBU2d/XI5tNqy0My5GLYnguz2QJBeESE4ryyxoLbMGgjOneh3e2WLQqSydpE5b44QnB9o9z/GzDkVIl0MhCMiIiKi/BVv5lxkbw0KUBWWNdaTofSdH9EE55GUWPzBcK/LA6coa4+w5JzoOwf8peqO0tDfZ7Cs3f87bxeZ82ILRlWFrpxUUWzBqGr/Y5u/6EBLoOxeUYBSy+A95zuPBINzURlQFOF5RPlOl8F5Y2MjVFXFcccdl+1dISIiIqIhssYZnLdpSqvDs8Z6MpTl1MRa4uED3ITiQFDb4/IGy9ojDFITfecA4Ci1wmQMDQvCB8K19/oD7ooiM2xmI4bZg5n2qmILTp/or2R4ZdthrNl2GAAwfbg94nEKrsnuhKqqIZlz+T44/I0KkC6DcyIiIiLKH/FmzmWAqOOsORAs625KJjjvFJlzW8Tvl1j9QW2v06Mpax/4kb9SE5xHCvRFWXtnX+hAOJFxH10dzJ5XFJtx2sQamAwKdjZ343dv7wEAnDe9IeI+1pUFy9qPdrtC1k8Pfx9EhYTBORERERFllZzWPkjPucica0uy9UhMmd/f2pvwc0XPebTMuRgw1+PyyuXJrKaBgW6l5ncYPqkdGFjW3qHpOQeAxupg33lliQXlNjNOHlsNANgbGDL3lSjrktcHMuctPS5sC6xvbjaGZthZ1k6FiME5EREREWVVvGXtInMePqVcb8RAtc9behJ+rug5ry2PVtbuD8R7nB44PdEz59qe/YiZc1vYQDhNzzkAjA4E52ajgpLAzzxzcnBI3wmjKjA8ylJ3lcVmeUPm3V0tAILLs8n3wbJ2KkAMzomIiIgoq+Iuaw/L3uqVDM6PJp45PxLoOa8pjT0QTps5j9Rzrm0NqI2QOS8XmfPA71z87u0yc+4va68stkBR/FnvL0+uk88/N0pJO+CfFi9uLry14wgA4IRRlVA0yXMupUaFiME5EREREWWVDM4HLWvPj57zxkBwfrCjT/aFx0uWtUcIqAGg2BrMnPd7ok9r1w6EC1/jHIiwlJpmIBwAzBxdiTKrCSeNqZLPGVlVjNMn1sBRasX5MyKXtAd/pn//Pz7YKV9PZOsBLqVGhYnNHERERESUVRajPxBzxrmUmt57zqtLLCizmtDl9GBvSy8m1pfF9TyfTw2WtQ+ylFpHnxuqfwlzWCNmzoO/w/A1zoFgWfuAnvPA82rLbfjgzjMHBP7/c+WJ8KoqzMbYOcB6zc0FgwLMHedAZbFZ/hwG51SImDknIiIioqyKt6y9Tfac6zs4VxQFY2r82fM9R+PvO2/vc8Pj80fcjmhl7YHMeVtgrXEgSs/5IJlzMRCu1+WF2+uTPefaoN5mNsqSdsFgUAYNzIHQnvnjRlbAXmwOqYjgQDgqRAzOiYiIiCir4g/ORYCo77J2IDjtPJGhcGKN86oSi/ydhRPrnIsgXlGC0/C1KkMGwkWf1g4A3f0emdG2F6Xmd6/NnJ9yTA2A0MCfmXMqRLwlRURERERZFe9SavlS1g4E+84/TyBzLtc4j5I1B4Jl7YLVZBiQ3QaAmjIryqwmmE0GOEoHBtwmowHFFiN6XV609bpkeXuqfvfa5dtOneAPzrU3DBicUyFicE5EREREWZV4Wbv+M+djHP5p57sTCc4HWUYNGDjlPNKkdvH48zfOg8mgwBSlDL3cZkavy4sD7X2yf92eokn5Yq33MpsJM0b4l1HTBv5FXEqNChCDcyIiIiLKqnjWOVdVNbiUWh5kzsc4SgEkljk/1N4HwJ/1jsZiMsBiNMgqBJspepArlnSLprzIhKZOYH+r/+eWWIxx9ZPHY9boSlxz6lgcN7JC3hwIzZwzTKHCw3/1RERERJRVInPu9ERfVqzP7ZXBez70nI8J9Jw3dznR4/SgxDr4x/I3tvvXBJ8xoiLmdsVWI1y9/t+VNcIwuHiVBSa272v1r8eeyt+7waBg2VcmhzymHfTHdc6pEHEgHBERERFllew5j5E5F8PgzEYFJXkQuNmLzTIYjWcoXHNnPzbsawMALDi2Lua22r7zWJnzwZQHhsLtb/MH56kqaY+mgj3nVOAYnBMRERFRVsme8xgD4doD/eb2IkvEAWd6NLLK33d+sL1/0G1f+uQwVNW/7Ngwe1HMbbWBbaRl1OJVHgjG98vMeXqDcw6Eo0LHsnYiIiIiyioRnLu9Knw+FQbDwOBbrLOt9zXOtcTQs373wHJ+j9eHXUd64FNVDLPb8K+PmwAAC6fWD/q62hJ56xAy52I5tU+bugAA9faBS66lUgXL2qnAMTgnIiIioqyyatbsdnl9sBkGBmbB4Fz//eaCLUZwftPjG/GPLf6A3KAAgWHpOPvYeILz4O9vKD3n5YGec9FuMGdsddKvFY/Qdc4ZplDh4b96IiIiIsoqS3hwHmEZLbGMmj2PMuei5NwZodf+o/0dAPx93h2BKfXHNpQPOmEdCA1soy2lFg8xEE6Yd4wj6deKR22ZDfXlNtjMBhRzKTUqQAzOiYiIiCirLJrluaINhRMBaj6VtYuS80iZc3Ez4vmlc2FQFKzb3YKTxlTF9bragXnaqoRElRcFQ4VxNSWD9roPlcVkwKvfPw0GRYnY2kCU7xicExEREVFWKYoi1+aOFpy39fiD1XxYRk2Iljnvd3vR6/IH7JUlFpTbzHJ4XDyKranJnJdrMufzxqc3ay7Es6QcUb7itHYiIiIiyjo5sT1acB7oOU/3xPBMEoGzMyxzLvrrTQYFZUkEqyUpmtYuBsIBwNwMBedEhYzBORERERFl3WDLqXX0+TPn+TQQTpSc94fdkGjVVAkks2yctud8KNPaxVJqRoOCk8eldxgcEbGsnYiIiIhygOg7j5Y5F9nkiqL8y5yH95yLfvNk++u109qHkjmfXF+OacPtmDm6MqTEnYjSg8E5EREREWWdyJxHmlwOQPZgF+dRT3KwrD30PcvgvCS5KoGQae1DyJwXWYz4vxvnJf18IkoMy9qJiIiIKOsG6zl3evzBuXayu94Fy9rDMuc9qcucD2WdcyLKLJ6tRERERJR1sqw9Ss+5eDyfgk1r1LJ2fwl/VSoy51wvnEg38ufqRkRERES6NWjmPFD6nU+Zc1uUUv7WIS4bV5KisnYiyqz8uboRERERkW4NFpyLzPlQBpzlmmiZ8/ZAz3lVksF5McvaiXSJZysRERERZZ1VZpG9Eb8fzJznTyZYZM77wwbCtQbK2pMdCFeSoqXUiCizGJwTERERUdYNtpRaPvacy2ntYe+5PUeWUiOizOLZSkRERERZJ8vaIwyE83h98PpUAMEMez6Q1QJhZe2i55yZc6LCkj9XNyIiIiLSrVg959qA3ZJHwbktas95oKw9BT3nzJwT6QfPViIiIiLKOmuUyeVAsN8cyLNp7RHK2p0eL7qdHgDJD4SzGA0wGZSQn0FEuS9/rm5EREREpFvxZM6NBgWmPArOrXIgXDBzLrLmBgUos5kiPm8wiqKgrtwGRQGqkyyNJ6LMS+6MJyIiIiJKITGFPVLPucic51O/ORA5c94mh8FZYAhkv5Px6DdnornLidpy29B2kogyhsE5EREREWVd7My5N2SbfCH6wT0+FR6vDyajQQ6Dq0hyUrtwbIMdxw55D4kok/LrCkdEREREuhQrOO/P08y5dpJ6f+B9i7L2KpajExWc/LrCEREREZEuWePoOc+3zLn2ZoNYTk0uo5bkMDgi0q/8usIRERERkS6JKeyxe87za/K4waDIGw7BzDmDc6JCxeCciIiIiLIunmnt+bSMmhA+sb21J7DGOcvaiQpO/l3hiIiIiEh3LDHXOfcHrlZz/n10lRPbA9UB7X2pGQhHRPqTf1c4IiIiItKdWGXt+Zw5FxPb+z3+GxCdfR4AgL2IwTlRocm/KxwRERER6U6wrN074Huy59ycXz3nQLCPXpS1d/b7y9rLbFzxmKjQMDgnIiIioqwr1J5zkTkX5fxd/f7MeZmNmXOiQpN/VzgiIiIi0h0ZnEec1p7HPecm0XPuf49dgcx5OTPnRAUn/65wRERERKQ71kBWXJSwa4mA3ZqHmXNxw6E/8L47+0RZOzPnRIVGV1e4F198EbNnz0ZRUREcDgcuvvjibO8SEREREaVA7My56DnX1UfXuMjMuccLn09Ft9Nf1l5exMw5UaHRzVn/1FNP4eqrr8bPfvYznHHGGVBVFVu2bMn2bhERERFRCojBaIXWc67NnPe4PPCp/sfLmTknKji6CM49Hg9uvvlm3Hvvvbjqqqvk4xMnTsziXhERERFRqsQaCCeGpeXjtHabZlq7GAZnNiqwmvLvRgQRxaaLs/7DDz/EgQMHYDAYcPzxx2PYsGE455xz8PHHH8d8ntPpRGdnZ8gXEREREeWemNPaPfmcORdl7T4ZnJfbzFAUJZu7RURZoIsr3O7duwEAK1aswJ133okXXngBlZWVOO2009Da2hr1eXfffTfsdrv8GjlyZKZ2mYiIiIgSIIJzZ6Se88Da5/mYTRbvqd/t5RrnRAUuq1e4FStWQFGUmF/r16+Hz+e/SP/oRz/CV7/6VcycOROrVq2Coih48skno77+smXL0NHRIb/279+fqbdGRERERAkQWXGXxwdVVUO+J8raLXkYnNtCMueBZdSK2G9OVIiyeltu6dKluOyyy2Ju09jYiK6uLgDAlClT5ONWqxVjx47Fvn37oj7XarXCarWmZmeJiIiIKG20gbfbq8JiCpZ1y57zvAzONZnzPn9ZOzPnRIUpq2e+w+GAw+EYdLuZM2fCarXis88+w7x58wAAbrcbn3/+OUaPHp3u3SQiIiKiNNMG3i6vLyRYlz3npvwbCGeVA+GCmfMyKzPnRIVIF7flysvLce2112L58uUYOXIkRo8ejXvvvRcAcMkll2R574iIiIhoqLTD3lweH6ApfiyEzLnT40VnP9c4Jypkujnz7733XphMJixevBh9fX2YPXs21qxZg8rKymzvGhERERENkcGgwGRQ4PGpAya2uwID4fK557zf7dMMhGPmnKgQ6SY4N5vNWLlyJVauXJntXSEiIiKiNLCYDPC4vAOC83zOnIv35PR4Q5ZSI6LCk39XOCIiIiLSJbnWudcb8rirEKa1u33o7ONSakSFLP+ucERERESkS6Lv3Bk1c55/A+HktHZN5pzBOVFhYnBORERERDlBZs4H9Jznb+Y8OK3dy3XOiQocb8sRERERUU6IFpw7AwPh8rHnPDit3Qefysw5USHjmU9EREREOSFaWbsrrwfCBTPn/W7/TQgOhCMqTAzOiYiIiCgnWKNmzvO359weKGFv63EDiv8xBudEhSn/bj8SERERkS6J4NvlLZye8xGVRagrt8Ll9cn3ybJ2osKUf1c4IiIiItKlSD3nXp8Kj08FkJ9l7Yqi4EvjHCGPMTgnKkz5d4UjIiIiIl2KFJxr/zsfM+cAMGdstfzvYosRJmN+vk8iio1nPhERERHlBDkQTlPWLia1A/mZOQeAOeOCwTn7zYkKV35e4YiIiIhId2Jlzg0K8jajPLKqGCOrigCwpJ2okOXnFY6IiIiIdCdScJ7Pk9q1RGk7g3OiwsXgnIiIiIhyQqzgPF/7zYUzJtUCAEZUFmd5T4goW3hrjoiIiIhygug5d3mDfeai5zxf+82Fs4+txx++dSJmjKjI9q4QUZYwOCciIiKinGCN0XOe75lzRVEwf2JttneDiLIov69yRERERKQbsXvO+bGViPIbr3JERERElBOCZe2RMuf5PRCOiIjBORERERHlBJE5dzJzTkQFiFc5IiIiIsoJsdY5z/eecyIiXuWIiIiIKCdE7jkvjGntRES8yhERERFRTojVc87gnIjyHa9yRERERJQTZM+5O1LPOQfCEVF+Y3BORERERDlBBOCRp7XzYysR5Tde5YiIiIgoJ1jZc05EBYxXOSIiIiLKCZzWTkSFjFc5IiIiIsoJMjj3cp1zIio8vMoRERERUU6Q09o9A4NzZs6JKN/xKkdEREREOUFOa9cE57uP9gAAKostWdknIqJMYXBORERERDkh2HPuHwLX0evGuzuPAgDOmFSbtf0iIsoEBudERERElBNkWXug5/yVbYfh8amYWFeGsTWl2dw1IqK0Y3BORERERDkhfCm1f249BABYOLU+a/tERJQpDM6JiIiIKCeIsnaf6i9pf3OHv6T9nGkMzoko/zE4JyIiIqKcoJ3I/uaOI3B5fGisLsbEurIs7hURUWYwOCciIiKinCB6zgGgqaMfANDoKIGiKNnaJSKijGFwTkREREQ5wWQ0wBCIw1t7XQCAUqspi3tERJQ5DM6JiIiIKGeI0vbWbn9wXmZjcE5EhYHBORERERHlDFHa3tLDzDkRFRYG50RERESUMywmIwCgpccJACi1mrO5O0REGcPgnIiIiIhyhljrvFVkzlnWTkQFgsE5EREREeUMa1jPeanVmM3dISLKGAbnRERERJQzxEC4LqcHAMvaiahwMDgnIiIiopwhgnOBZe1EVCgYnBMRERFRzhDT2gVOayeiQsHgnIiIiIhyRnjmnOucE1GhYHBORERERDljQFk7M+dEVCB0E5xv374dF154IRwOB8rLyzF37ly89tpr2d4tIiIiIkqhAWXtzJwTUYHQTXB+7rnnwuPxYM2aNdiwYQOOO+44nHfeeWhqasr2rhERERFRioRnzkssDM6JqDDoIjg/evQodu7ciTvuuAPTp0/HMcccg3vuuQe9vb34+OOPs717RERERJQi2uC8xGKE0aBkcW+IiDJHF8F5dXU1Jk+ejD/96U/o6emBx+PBb3/7W9TV1WHmzJlRn+d0OtHZ2RnyRURERES5y6oNztlvTkQFRBdXPEVR8PLLL+PCCy9EWVkZDAYD6urqsHr1alRUVER93t13343//M//zNyOEhEREdGQaHvO2W9ORIUkq5nzFStWQFGUmF/r16+Hqqq4/vrrUVtbi7feegvvv/8+LrzwQpx33nk4dOhQ1NdftmwZOjo65Nf+/fsz+O6IiIiIKFHasvYyZs6JqIBk9Yq3dOlSXHbZZTG3aWxsxJo1a/DCCy+gra0N5eXlAIBf//rXePnll/HHP/4Rd9xxR8TnWq1WWK3WlO83EREREaWHNjhn5pyICklWr3gOhwMOh2PQ7Xp7ewEABkNoot9gMMDn86Vl34iIiIgo8yxGo/xvrnFORIVEFwPh5syZg8rKSlx55ZX46KOPsH37dvzgBz/Anj17cO6552Z794iIiIgoRaxmTebcas7inhARZZYugnOHw4HVq1eju7sbZ5xxBmbNmoW3334bzz33HGbMmJHt3SMiIiKiFNEOhCtjWTsRFRDdXPFmzZqFf/3rX9neDSIiIiJKo5Cec5a1E1EB0UXmnIiIiIgKAwfCEVGhYnBORERERDnDqgnOS5g5J6ICwuCciIiIiHJGSM85g3MiKiAMzomIiIgoZ7DnnIgKFYNzIiIiIsoZ7DknokLF4JyIiIiIcoa2rJ2ZcyIqJAzOiYiIiChnaDPnXOeciAoJg3MiIiIiyhnsOSeiQsXgnIiIiIhyhpU950RUoBicExEREVHOKLL4A3KryQCryZjlvSEiyhzejiQiIiKinNFgt+FbcxsxorI427tCRJRRDM6JiIiIKGcoioLl5x+b7d0gIso4lrUTERERERERZRmDcyIiIiIiIqIsY3BORERERERElGUMzomIiIiIiIiyjME5ERERERERUZYxOCciIiIiIiLKMgbnRERERERERFnG4JyIiIiIiIgoyxicExEREREREWUZg3MiIiIiIiKiLGNwTkRERERERJRlDM6JiIiIiIiIsozBOREREREREVGWMTgnIiIiIiIiyjIG50RERERERERZxuCciIiIiIiIKMsYnBMRERERERFlGYNzIiIiIiIioiwzZXsHMklVVQBAZ2dnlveEiIiIiIiICoGIP0U8Gk1BBeddXV0AgJEjR2Z5T4iIiIiIiKiQdHV1wW63R/2+og4WvucRn8+HgwcPoqysDIqipOQ1Ozs7MXLkSOzfvx/l5eUpeU1KPR4nfeBx0gcep9zHY6QPPE76wOOkDzxO+lCox0lVVXR1daGhoQEGQ/TO8oLKnBsMBowYMSItr11eXl5Q/8D0isdJH3ic9IHHKffxGOkDj5M+8DjpA4+TPhTicYqVMRc4EI6IiIiIiIgoyxicExEREREREWUZg/MhslqtWL58OaxWa7Z3hWLgcdIHHid94HHKfTxG+sDjpA88TvrA46QPPE6xFdRAOCIiIiIiIqJcxMw5ERERERERUZYxOCciIiIiIiLKMgbnRERERERERFnG4JyIiIiIiIgoyxicA3jzzTdx/vnno6GhAYqi4Nlnnw35/uHDh7FkyRI0NDSguLgYCxcuxI4dO0K2aWpqwuLFi1FfX4+SkhKccMIJ+Pvf/x6yTVtbGxYvXgy73Q673Y7Fixejvb09ze8uf2TqODU2NkJRlJCvO+64I91vLy+k4hjt2rUL//Zv/4aamhqUl5fj61//Og4fPhyyDc+locnUceK5NDR33303TjzxRJSVlaG2thYXXXQRPvvss5BtVFXFihUr0NDQgKKiIsyfPx8ff/xxyDZOpxM33ngjHA4HSkpKcMEFF+CLL74I2YbnVPIyeZx4TiUvVcfp0Ucfxfz581FeXg5FUSKeJzyfkpPJY8RzKXmpOE6tra248cYbMXHiRBQXF2PUqFG46aab0NHREfI6hXguMTgH0NPTgxkzZuDhhx8e8D1VVXHRRRdh9+7deO6557Bx40aMHj0aZ555Jnp6euR2ixcvxmeffYbnn38eW7ZswcUXX4xLL70UGzdulNtcccUV2LRpE1avXo3Vq1dj06ZNWLx4cUbeYz7I1HECgJ/85Cc4dOiQ/LrzzjvT/v7ywVCPUU9PDxYsWABFUbBmzRq88847cLlcOP/88+Hz+eRr8VwamkwdJ4Dn0lC88cYbuOGGG7Bu3Tq8/PLL8Hg8WLBgQcg17Re/+AV++ctf4uGHH8YHH3yA+vp6nHXWWejq6pLbfPe738UzzzyDxx9/HG+//Ta6u7tx3nnnwev1ym14TiUvk8cJ4DmVrFQdp97eXixcuBA//OEPo/4snk/JyeQxAnguJSsVx+ngwYM4ePAgVq5ciS1btuAPf/gDVq9ejauuuirkZxXkuaRSCADqM888I///Z599pgJQt27dKh/zeDxqVVWV+thjj8nHSkpK1D/96U8hr1VVVaX+7ne/U1VVVT/55BMVgLpu3Tr5/bVr16oA1E8//TRN7yZ/pes4qaqqjh49Wr3//vvTtu+FIplj9K9//Us1GAxqR0eH3Ka1tVUFoL788suqqvJcSrV0HSdV5bmUas3NzSoA9Y033lBVVVV9Pp9aX1+v3nPPPXKb/v5+1W63q7/5zW9UVVXV9vZ21Ww2q48//rjc5sCBA6rBYFBXr16tqirPqVRL13FSVZ5TqZTMcdJ67bXXVABqW1tbyOM8n1InXcdIVXkupdJQj5Pwt7/9TbVYLKrb7VZVtXDPJWbOB+F0OgEANptNPmY0GmGxWPD222/Lx+bNm4cnnngCra2t8Pl8ePzxx+F0OjF//nwAwNq1a2G32zF79mz5nJNPPhl2ux3vvvtuZt5MHkvVcRJ+/vOfo7q6GscddxzuuusuuFyujLyPfBbPMXI6nVAUBVarVW5js9lgMBjkNjyX0itVx0nguZQ6otyvqqoKALBnzx40NTVhwYIFchur1YrTTjtNngsbNmyA2+0O2aahoQFTp06V2/CcKw9jeAAADphJREFUSq10HSeB51RqJHOc4sHzKXXSdYwEnkupkarj1NHRgfLycphMJgCFey4xOB/EpEmTMHr0aCxbtgxtbW1wuVy455570NTUhEOHDsntnnjiCXg8HlRXV8NqteKaa67BM888g3HjxgHw9zrX1tYOeP3a2lo0NTVl7P3kq1QdJwC4+eab8fjjj+O1117D0qVL8cADD+D666/PxtvKK/Eco5NPPhklJSW4/fbb0dvbi56eHvzgBz+Az+eT2/BcSq9UHSeA51IqqaqK733ve5g3bx6mTp0KAPLfe11dXci2dXV18ntNTU2wWCyorKyMuQ3PqdRI53ECeE6lSrLHKR48n1IjnccI4LmUKqk6Ti0tLfjpT3+Ka665Rj5WqOeSKds7kOvMZjOeeuopXHXVVaiqqoLRaMSZZ56Jc845J2S7O++8E21tbXjllVfgcDjw7LPP4pJLLsFbb72FadOmAQAURRnw+qqqRnycEpPK43TLLbfI7adPn47Kykp87Wtfk3dYKTnxHKOamho8+eSTuO666/CrX/0KBoMBl19+OU444QQYjUa5Hc+l9EnlceK5lDpLly7F5s2bB1QmAAPPh3jOhfBteE6lRrqPE8+p1Ej1cRrsNZJ9nUKW7mPEcyk1UnGcOjs7ce6552LKlClYvnx5zNeI9Tr5gsF5HGbOnIlNmzaho6MDLpcLNTU1mD17NmbNmgXAP7X44YcfxtatW3HssccCAGbMmIG33noLjzzyCH7zm9+gvr5+wCRjADhy5MiAO0uUnFQcp0hOPvlkAMDOnTt5wR6iwY4RACxYsAC7du3C0aNHYTKZUFFRgfr6eowZMwYAeC5lQCqOUyQ8l5Jz44034vnnn8ebb76JESNGyMfr6+sB+LMLw4YNk483NzfLc6G+vh4ulwttbW0hWdnm5mZ86UtfktvwnBq6dB+nSHhOJW4oxykePJ+GLt3HKBKeS4lLxXHq6urCwoULUVpaimeeeQZmsznkdQrxXGJZewLsdjtqamqwY8cOrF+/HhdeeCEA/1RIADAYQn+dRqNRTi6eM2cOOjo68P7778vvv/fee+jo6Ij5h5cSN5TjFImY5K69wNDQRDtGWg6HAxUVFVizZg2am5txwQUXAOC5lElDOU6R8FxKjKqqWLp0KZ5++mmsWbNmwI2PMWPGoL6+Hi+//LJ8zOVy4Y033pDnwsyZM2E2m0O2OXToELZu3Sq34Tk1NJk6TpHwnIpfKo5TPHg+JS9TxygSnkvxS9Vx6uzsxIIFC2CxWPD888+HzLoBCvhcyuT0uVzV1dWlbty4Ud24caMKQP3lL3+pbty4Ud27d6+qqv7pga+99pq6a9cu9dlnn1VHjx6tXnzxxfL5LpdLHT9+vHrKKaeo7733nrpz50515cqVqqIo6osvvii3W7hwoTp9+nR17dq16tq1a9Vp06ap5513Xsbfr15l4ji9++678nV3796tPvHEE2pDQ4N6wQUXZOU9681Qj5Gqqurvf/97de3aterOnTvVP//5z2pVVZX6ve99L2QbnktDk4njxHNp6K677jrVbrerr7/+unro0CH51dvbK7e55557VLvdrj799NPqli1b1Msvv1wdNmyY2tnZKbe59tpr1REjRqivvPKK+uGHH6pnnHGGOmPGDNXj8chteE4lL1PHiefU0KTqOB06dEjduHGj+thjj6kA1DfffFPduHGj2tLSIrfh+ZScTB0jnktDk4rj1NnZqc6ePVudNm2aunPnzpDXKfS/TQzO1eBSC+FfV155paqqqvrggw+qI0aMUM1mszpq1Cj1zjvvVJ1OZ8hrbN++Xb344ovV2tpatbi4WJ0+ffqAJbtaWlrURYsWqWVlZWpZWZm6aNGiiMs7UGSZOE4bNmxQZ8+erdrtdtVms6kTJ05Uly9frvb09GTyrepWKo7R7bffrtbV1alms1k95phj1Pvuu0/1+Xwh2/BcGppMHCeeS0MX6RgBUFetWiW38fl86vLly9X6+nrVarWqp556qrply5aQ1+nr61OXLl2qVlVVqUVFRep5552n7tu3L2QbnlPJy9Rx4jk1NKk6TsuXLx/0dXg+JSdTx4jn0tCk4jhF+xwCQN2zZ4/crhDPJUVVVTX5vDsRERERERERDRV7zomIiIiIiIiyjME5ERERERERUZYxOCciIiIiIiLKMgbnRERERERERFnG4JyIiIiIiIgoyxicExEREREREWUZg3MiIiIiIiKiLGNwTkRERERERJRlDM6JiIh0pLGxEQ888EC2dyNlXn/9dSiKgvb29mzvChERUVYxOCciIsoR+/fvx1VXXYWGhgZYLBaMHj0aN998M1paWrK9aykxf/58fPe73w157Etf+hIOHToEu92enZ0iIiLKEQzOiYiIcsDu3bsxa9YsbN++HX/961+xc+dO/OY3v8Grr76KOXPmoLW1NSv75fV64fP50vb6FosF9fX1UBQlbT+DiIhIDxicExER5YAbbrgBFosFL730Ek477TSMGjUK55xzDl555RUcOHAAP/rRj+S2XV1duOKKK1BaWoqGhgY89NBDIa+1YsUKjBo1ClarFQ0NDbjpppvk91wuF2677TYMHz4cJSUlmD17Nl5//XX5/T/84Q+oqKjACy+8gClTpsBqteKxxx6DzWYbUHp+00034bTTTgMAtLS04PLLL8eIESNQXFyMadOm4a9//avcdsmSJXjjjTfw4IMPQlEUKIqCzz//PGJZ+1NPPYVjjz0WVqsVjY2NuO+++0J+bmNjI372s5/h29/+NsrKyjBq1Cg8+uijyf7qiYiIcgKDcyIioixrbW3Fv/71L1x//fUoKioK+V59fT0WLVqEJ554AqqqAgDuvfdeTJ8+HR9++CGWLVuGW265BS+//DIA4O9//zvuv/9+/Pa3v8WOHTvw7LPPYtq0afL1vvWtb+Gdd97B448/js2bN+OSSy7BwoULsWPHDrlNb28v7r77bvzud7/Dxx9/jG984xuoqKjAU089Jbfxer3429/+hkWLFgEA+vv7MXPmTLzwwgvYunUrvvOd72Dx4sV47733AAAPPvgg5syZg6uvvhqHDh3CoUOHMHLkyAG/iw0bNuDrX/86LrvsMmzZsgUrVqzAj3/8Y/zhD38I2e6+++7DrFmzsHHjRlx//fW47rrr8Omnnw7hKBAREWWXKds7QEREVOh27NgBVVUxefLkiN+fPHky2tracOTIEQDA3LlzcccddwAAJkyYgHfeeQf3338/zjrrLOzbtw/19fU488wzYTabMWrUKJx00kkAgF27duGvf/0rvvjiCzQ0NAAAbr31VqxevRqrVq3Cz372MwCA2+3Gr3/9a8yYMUPuw6WXXor//d//xVVXXQUAePXVV9HW1oZLLrkEADB8+HDceuutcvsbb7wRq1evxpNPPonZs2fDbrfDYrGguLgY9fX1UX8Xv/zlL/HlL38ZP/7xj+X7++STT3DvvfdiyZIlcruvfOUruP766wEAt99+O+6//368/vrrmDRpUpy/dSIiotzCzDkREVGOExlz0Zc9Z86ckO/PmTMH27ZtAwBccskl6Ovrw9ixY3H11VfjmWeegcfjAQB8+OGHUFUVEyZMQGlpqfx64403sGvXLvl6FosF06dPD/kZixYtwuuvv46DBw8CAP7yl7/gK1/5CiorKwH4M+l33XUXpk+fjurqapSWluKll17Cvn37Enqv27Ztw9y5c0Memzt3Lnbs2AGv1ysf0+6foiior69Hc3NzQj+LiIgolzA4JyIiyrLx48dDURR88sknEb//6aeforKyEg6HI+priMB95MiR+Oyzz/DII4+gqKgI119/PU499VS43W74fD4YjUZs2LABmzZtkl/btm3Dgw8+KF+rqKhowIC2k046CePGjcPjjz+Ovr4+PPPMM/jGN74hv3/ffffh/vvvx2233YY1a9Zg06ZNOPvss+FyuRL6XaiqOuBni5sTWmazecD7T+fgOiIionRjWTsREVGWVVdX46yzzsKvf/1r3HLLLSF9501NTfjLX/6Cb37zmzJoXbduXcjz161bF1LOXVRUhAsuuAAXXHABbrjhBkyaNAlbtmzB8ccfD6/Xi+bmZpxyyikJ7+cVV1yBv/zlLxgxYgQMBgPOPfdc+b233noLF154oQzYfT4fduzYEVKqb7FYQrLfkUyZMgVvv/12yGPvvvsuJkyYAKPRmPA+ExER6QUz50RERDng4YcfhtPpxNlnn40333wT+/fvx+rVq3HWWWdh+PDhuOuuu+S277zzDn7xi19g+/bteOSRR/Dkk0/i5ptvBuCftv4///M/2Lp1K3bv3o0///nPKCoqwujRozFhwgQsWrQI3/zmN/H0009jz549+OCDD/Dzn/8c//jHPwbdx0WLFuHDDz/EXXfdha997Wuw2Wzye+PHj8fLL7+Md999F9u2bcM111yDpqamkOc3Njbivffew+eff46jR49GzHR///vfx6uvvoqf/vSn2L59O/74xz/i4YcfDulnJyIiykcMzomIiHLAMcccg/Xr12PcuHG49NJLMW7cOHznO9/B6aefjrVr16Kqqkpu+/3vfx8bNmzA8ccfj5/+9Ke47777cPbZZwMAKioq8Nhjj2Hu3LmYPn06Xn31Vfzf//0fqqurAQCrVq3CN7/5TXz/+9/HxIkTccEFF+C9996LODk90j6eeOKJ2Lx5s5zSLvz4xz/GCSecgLPPPhvz589HfX09LrroopBtbr31VhiNRkyZMgU1NTUR+9FPOOEE/O1vf8Pjjz+OqVOn4j/+4z/wk5/8JGQYHBERUT5S1EiNXERERERERESUMcycExEREREREWUZg3MiIiIiIiKiLGNwTkRERERERJRlDM6JiIiIiIiIsozBOREREREREVGWMTgnIiIiIiIiyjIG50RERERERERZxuCciIiIiIiIKMsYnBMRERERERFlGYNzIiIiIiIioixjcE5ERERERESUZf8fP8roV2NSL4MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals = model.resid\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(residuals)\n",
    "plt.title('OLS Residuals')\n",
    "plt.xlabel('Observation')\n",
    "plt.ylabel('Residual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the plot of the OLS residuals, it does indeed appear that the residuals are following a white noise process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Test for autocorrelation in the residuals up to order 12 (i.e. 1 year). Interpret the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7087835910746958"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals.autocorr(lag=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The autocorrelation coefficient of 0.709 is relatively high and positive. This suggests that there is a significant positive autocorrelation in the residuals up to the 12th lag (or 1 year). Positive autocorrelation implies that periods with higher-than-expected residuals tend to follow periods with higher-than-expected residuals, and the same goes for lower-than-expected residuals. The positive autocorrelation in the residuals up to the 12th lag indicates that the model might not be capturing some important dynamics in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Test for ARCH up to order 6. Interpret the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bhale\\AppData\\Local\\Temp\\ipykernel_12472\\4220693008.py:2: FutureWarning: the 'maxlag' keyword is deprecated, use 'nlags' instead.\n",
      "  arch_test = dg.het_arch(squared_residuals, maxlag=6)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2.3796022339956124e-49"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squared_residuals = residuals**2\n",
    "arch_test = dg.het_arch(squared_residuals, maxlag=6)\n",
    "arch_test\n",
    "p_values_arch = arch_test[1]\n",
    "p_values_arch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value obtained is approximately 2.3796022339956124e-49, which is extremely close to zero. A very low p-value suggests strong evidence against the null hypothesis. In this context, the null hypothesis for the ARCH test is that there is no ARCH effect. (i.e., the residuals are homoskedastic)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Test for normality in the residuals. Interpret the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SignificanceResult(statistic=9.724724890258493, pvalue=0.0077321954094918596)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jb_test = stats.jarque_bera(residuals)\n",
    "jb_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test statistic obtained is approximately 9.7247248. The p-value obtained is approximately 0.00773219540. The p-value of 0.0077 is less than the commonly used significance level of 0.05. This suggests that you should reject the null hypothesis that the residuals are normally distributed. In this context, the null hypothesis for the Jarque-Bera test is that the residuals are normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. If the Fisher hypothesis holds, $\\beta = 1$. Test this restriction and interpret the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'statsmodels.stats.contrast.ContrastResults'>\n",
       "                             Test for Constraints                             \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "c0             2.1028      0.105     10.529      0.000       1.897       2.309\n",
       "=============================================================================="
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.t_test('CSINFT02USM460S = 1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The t-test strongly rejects the Fisher hypothesis β=1 with a p-value of 0.000. This indicates that the interest rate does not fully reflect long-run movements in expected inflation as the Fisher hypothesis suggests."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. You've learned how the residuals behave from your tests in steps 4, 5, 6. What effect does this have on your conclusion for step 7? Enter your answer below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rejection of the Fisher hypothesis (β=1) based on the t-test suggests that the interest rate does not fully reflect long-run movements in expected inflation.\n",
    "\n",
    "However, considering the issues with the residuals (autocorrelation, ARCH effects, non-normality), the estimated coefficient for expected inflation (β) might be biased and inefficient.\n",
    "\n",
    "The rejection of β=1 might be influenced by these residual issues, making it harder to draw definitive conclusions about the true relationship between interest rates and inflation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some claims that the Fisher hypothesis might hold in some periods but not others. We'll inspect that by performing subsample tests. Estimate the same model, that is $r_t = \\alpha + \\beta \\pi_t + u_t$, but over 4 subsample periods (just print out the summary):\n",
    "- Jan 1981 to Dec 1990\n",
    "- Jan 1991 to Dec 2000\n",
    "- Jan 2001 to Dec 2010\n",
    "- Jan 2011 to Dec 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary for Jan 1981 to Dec 1990:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  TB3MS   R-squared:                       0.658\n",
      "Model:                            OLS   Adj. R-squared:                  0.656\n",
      "Method:                 Least Squares   F-statistic:                     227.5\n",
      "Date:                Thu, 25 Apr 2024   Prob (F-statistic):           2.68e-29\n",
      "Time:                        19:58:09   Log-Likelihood:                -218.31\n",
      "No. Observations:                 120   AIC:                             440.6\n",
      "Df Residuals:                     118   BIC:                             446.2\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const               0.2203      0.561      0.393      0.695      -0.891       1.332\n",
      "CSINFT02USM460S     1.6162      0.107     15.082      0.000       1.404       1.828\n",
      "==============================================================================\n",
      "Omnibus:                        5.376   Durbin-Watson:                   0.555\n",
      "Prob(Omnibus):                  0.068   Jarque-Bera (JB):                4.866\n",
      "Skew:                           0.473   Prob(JB):                       0.0878\n",
      "Kurtosis:                       3.278   Cond. No.                         22.1\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Jan 1981 to Dec 1990\n",
    "# Filter data for the subsample period\n",
    "subsample_1 = data['1981-01-01':'1990-12-31']\n",
    "\n",
    "# Define dependent and independent variables\n",
    "y_sub_1 = subsample_1['TB3MS']\n",
    "X_sub_1 = subsample_1[['CSINFT02USM460S']]\n",
    "\n",
    "# Add constant to the independent variables\n",
    "X_sub_1 = sm.add_constant(X_sub_1)\n",
    "\n",
    "# Fit the OLS model\n",
    "model_sub_1 = sm.OLS(y_sub_1, X_sub_1).fit()\n",
    "\n",
    "# Print the summary\n",
    "print(\"Summary for Jan 1981 to Dec 1990:\")\n",
    "print(model_sub_1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary for Jan 1991 to Dec 2000:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  TB3MS   R-squared:                       0.001\n",
      "Model:                            OLS   Adj. R-squared:                 -0.007\n",
      "Method:                 Least Squares   F-statistic:                    0.1724\n",
      "Date:                Thu, 25 Apr 2024   Prob (F-statistic):              0.679\n",
      "Time:                        19:58:09   Log-Likelihood:                -161.48\n",
      "No. Observations:                 120   AIC:                             327.0\n",
      "Df Residuals:                     118   BIC:                             332.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const               4.9175      0.569      8.641      0.000       3.791       6.044\n",
      "CSINFT02USM460S    -0.0625      0.151     -0.415      0.679      -0.361       0.236\n",
      "==============================================================================\n",
      "Omnibus:                       11.749   Durbin-Watson:                   0.040\n",
      "Prob(Omnibus):                  0.003   Jarque-Bera (JB):                9.117\n",
      "Skew:                          -0.567   Prob(JB):                       0.0105\n",
      "Kurtosis:                       2.268   Cond. No.                         26.9\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Jan 1991 to Dec 2000\n",
    "# Filter data for the subsample period\n",
    "subsample_2 = data['1991-01-01':'2000-12-31']\n",
    "\n",
    "# Define dependent and independent variables\n",
    "y_sub_2 = subsample_2['TB3MS']\n",
    "X_sub_2 = subsample_2[['CSINFT02USM460S']]\n",
    "\n",
    "# Add constant to the independent variables\n",
    "X_sub_2 = sm.add_constant(X_sub_2)\n",
    "\n",
    "# Fit the OLS model\n",
    "model_sub_2 = sm.OLS(y_sub_2, X_sub_2).fit()\n",
    "\n",
    "# Print the summary\n",
    "print(\"Summary for Jan 1991 to Dec 2000:\")\n",
    "print(model_sub_2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary for Jan 2001 to Dec 2010:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  TB3MS   R-squared:                       0.102\n",
      "Model:                            OLS   Adj. R-squared:                  0.095\n",
      "Method:                 Least Squares   F-statistic:                     13.43\n",
      "Date:                Thu, 25 Apr 2024   Prob (F-statistic):           0.000372\n",
      "Time:                        19:58:09   Log-Likelihood:                -224.74\n",
      "No. Observations:                 120   AIC:                             453.5\n",
      "Df Residuals:                     118   BIC:                             459.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const               0.0474      0.585      0.081      0.936      -1.111       1.206\n",
      "CSINFT02USM460S     0.5882      0.160      3.665      0.000       0.270       0.906\n",
      "==============================================================================\n",
      "Omnibus:                       29.531   Durbin-Watson:                   0.052\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):                8.249\n",
      "Skew:                           0.335   Prob(JB):                       0.0162\n",
      "Kurtosis:                       1.904   Cond. No.                         15.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Jan 2001 to Dec 2010\n",
    "# Filter data for the subsample period\n",
    "subsample_3 = data['2001-01-01':'2010-12-31']\n",
    "\n",
    "# Define dependent and independent variables\n",
    "y_sub_3 = subsample_3['TB3MS']\n",
    "X_sub_3 = subsample_3[['CSINFT02USM460S']]\n",
    "\n",
    "# Add constant to the independent variables\n",
    "X_sub_3 = sm.add_constant(X_sub_3)\n",
    "\n",
    "# Fit the OLS model\n",
    "model_sub_3 = sm.OLS(y_sub_3, X_sub_3).fit()\n",
    "\n",
    "# Print the summary\n",
    "print(\"Summary for Jan 2001 to Dec 2010:\")\n",
    "print(model_sub_3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary for Jan 2001 to Dec 2020:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  TB3MS   R-squared:                       0.153\n",
      "Model:                            OLS   Adj. R-squared:                  0.146\n",
      "Method:                 Least Squares   F-statistic:                     21.30\n",
      "Date:                Thu, 25 Apr 2024   Prob (F-statistic):           1.01e-05\n",
      "Time:                        19:58:09   Log-Likelihood:                -131.43\n",
      "No. Observations:                 120   AIC:                             266.9\n",
      "Df Residuals:                     118   BIC:                             272.4\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const               2.5401      0.428      5.937      0.000       1.693       3.387\n",
      "CSINFT02USM460S    -0.5521      0.120     -4.615      0.000      -0.789      -0.315\n",
      "==============================================================================\n",
      "Omnibus:                       17.766   Durbin-Watson:                   0.085\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               21.941\n",
      "Skew:                           1.047   Prob(JB):                     1.72e-05\n",
      "Kurtosis:                       2.995   Cond. No.                         24.7\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Jan 2011 to Dec 2020\n",
    "# Filter data for the subsample period\n",
    "subsample_4 = data['2011-01-01':'2020-12-31']\n",
    "\n",
    "# Define dependent and independent variables\n",
    "y_sub_4 = subsample_4['TB3MS']\n",
    "X_sub_4 = subsample_4[['CSINFT02USM460S']]\n",
    "\n",
    "# Add constant to the independent variables\n",
    "X_sub_4 = sm.add_constant(X_sub_4)\n",
    "\n",
    "# Fit the OLS model\n",
    "model_sub_4 = sm.OLS(y_sub_4, X_sub_4, missing='drop').fit()\n",
    "\n",
    "# Print the summary\n",
    "print(\"Summary for Jan 2001 to Dec 2020:\")\n",
    "print(model_sub_4.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on what you observe in the 4 results above, make a comment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results clearly show that the relationship between interest rates and expected inflation varies over time. While the Fisher hypothesis holds during some periods, it's strongly rejected in others.\n",
    "\n",
    "Different economic conditions, such as inflation rates, monetary policy regimes, and global economic events, can influence the relationship between interest rates and inflation expectations. For instance, periods of high inflation or significant monetary policy shifts might affect this relationship.\n",
    "\n",
    "In summary, the Fisher hypothesis doesn't hold uniformly across all periods. While it holds strongly in some periods, it's rejected in others, indicating the importance of considering the specific economic context and conditions when examining the relationship between interest rates and inflation expectations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's see how well consumers did in forecasting inflation. Create a plot with time on the x-axis and two series: $\\pi_t$ and $\\pi^r_t$. What can you conclude from the plot? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Actual Inflation (pi_t) and Realized Inflation (pi_rt)\n",
    "plt.figure(figsize=(14, 7))\n",
    "\n",
    "# Actual Inflation (pi_t) from the survey data\n",
    "plt.plot(data.index, data['CSINFT02USM460S'], label='Actual Inflation', color='blue', linewidth=2)\n",
    "\n",
    "# Realized Inflation (pi_rt)\n",
    "plt.plot(data.index, data['Realized_Inflation'], label='Realized Inflation', color='red', linestyle='--', linewidth=2)\n",
    "\n",
    "# Set plot title and labels\n",
    "plt.title('Actual vs Realized Inflation', fontsize=16)\n",
    "plt.xlabel('Time', fontsize=14)\n",
    "plt.ylabel('Inflation (%)', fontsize=14)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "# Show grid\n",
    "plt.grid(True)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the blue line closely tracks the red dashed line across most of the time series, it suggests that consumers' expectations of inflation were generally accurate. This analysis can provide insights into consumer behavior, perceptions, and the factors influencing their expectations of inflation over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}