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   "source": [
    "### Using and understanding probit models - Exercises & Answers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e4cf7dc-4a44-4796-a54f-420b60515fd8",
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   "source": [
    "The exercises for this week will ease you into the slightly odd world of probit models. In many ways the logic is the same - we fit the models and interpret the predictions - but since the predictions are probabilities, and a threshold has to be imposed to actually recover a binary outcome, things can be a little trickier. On the whole, these models are a little trickier to understand than a standard GLM, so take care."
   ]
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   "id": "cc169d99-ad30-49b3-8b8f-cc1fda935e3b",
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   "source": [
    "## 1. **Iceberg**, right ahead!\n",
    "Our probit regression practice begins by examining the `titanic` dataset. This is a genuine dataset that records the full passenger log of the Titanic, with many interesting variables, including whether a passenger survived the disaster or not. \n",
    "\n",
    "### a. Imports\n",
    "To get started, import the following packages with their usual aliases: `pandas`, `pingouin`, `statsmodels.formula.api`, `seaborn`, `marginaleffects`, and `numpy`."
   ]
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   "source": [
    "# Your answer here\n",
    "import pandas as pd\n",
    "import pingouin as pg\n",
    "import statsmodels.formula.api as smf\n",
    "import seaborn as sns\n",
    "import marginaleffects as me\n",
    "import numpy as np\n",
    "\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7954a603-33c3-4dc6-8e7f-9eb43f35d57e",
   "metadata": {},
   "source": [
    "### b. Loading up data\n",
    "Load the Titanic dataset from the following link using `pandas` into a variable called `titanic`, then show the top 10 rows. \n",
    "\n",
    "Data: https://vincentarelbundock.github.io/Rdatasets/csv/Stat2Data/Titanic.csv\n",
    "\n",
    "The dataset is fairly self explanatory, but you can find a more detailed description here:\n",
    "https://vincentarelbundock.github.io/Rdatasets/doc/Stat2Data/Titanic.html"
   ]
  },
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    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rownames</th>\n",
       "      <th>Name</th>\n",
       "      <th>PClass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "      <th>SexCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss Elisabeth Walton</td>\n",
       "      <td>1st</td>\n",
       "      <td>29.00</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Allison, Miss Helen Loraine</td>\n",
       "      <td>1st</td>\n",
       "      <td>2.00</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Allison, Mr Hudson Joshua Creighton</td>\n",
       "      <td>1st</td>\n",
       "      <td>30.00</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Allison, Mrs Hudson JC (Bessie Waldo Daniels)</td>\n",
       "      <td>1st</td>\n",
       "      <td>25.00</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Allison, Master Hudson Trevor</td>\n",
       "      <td>1st</td>\n",
       "      <td>0.92</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Anderson, Mr Harry</td>\n",
       "      <td>1st</td>\n",
       "      <td>47.00</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Andrews, Miss Kornelia Theodosia</td>\n",
       "      <td>1st</td>\n",
       "      <td>63.00</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Andrews, Mr Thomas, jr</td>\n",
       "      <td>1st</td>\n",
       "      <td>39.00</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Appleton, Mrs Edward Dale (Charlotte Lamson)</td>\n",
       "      <td>1st</td>\n",
       "      <td>58.00</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Artagaveytia, Mr Ramon</td>\n",
       "      <td>1st</td>\n",
       "      <td>71.00</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   rownames                                           Name PClass    Age  \\\n",
       "0         1                   Allen, Miss Elisabeth Walton    1st  29.00   \n",
       "1         2                    Allison, Miss Helen Loraine    1st   2.00   \n",
       "2         3            Allison, Mr Hudson Joshua Creighton    1st  30.00   \n",
       "3         4  Allison, Mrs Hudson JC (Bessie Waldo Daniels)    1st  25.00   \n",
       "4         5                  Allison, Master Hudson Trevor    1st   0.92   \n",
       "5         6                             Anderson, Mr Harry    1st  47.00   \n",
       "6         7               Andrews, Miss Kornelia Theodosia    1st  63.00   \n",
       "7         8                         Andrews, Mr Thomas, jr    1st  39.00   \n",
       "8         9   Appleton, Mrs Edward Dale (Charlotte Lamson)    1st  58.00   \n",
       "9        10                         Artagaveytia, Mr Ramon    1st  71.00   \n",
       "\n",
       "      Sex  Survived  SexCode  \n",
       "0  female         1        1  \n",
       "1  female         0        1  \n",
       "2    male         0        0  \n",
       "3  female         0        1  \n",
       "4    male         1        0  \n",
       "5    male         1        0  \n",
       "6  female         1        1  \n",
       "7    male         0        0  \n",
       "8  female         1        1  \n",
       "9    male         0        0  "
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   "source": [
    "# Your answer here\n",
    "titanic = pd.read_csv('https://vincentarelbundock.github.io/Rdatasets/csv/Stat2Data/Titanic.csv')\n",
    "titanic.head(10)\n"
   ]
  },
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   "cell_type": "markdown",
   "id": "9e2b352c-bc4f-4240-9258-2c83ea4af408",
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    "### c. A tiny bit of cleaning\n",
    "The dataset has a missing data value for one of the `PClass` entries, where it is recorded as a * symbol. \n",
    "\n",
    "Remove this by querying the data so that `PClass` is not equal to '*', and reassign this back to the variable `titanic`. \n",
    "\n",
    "Then, drop the missing values in the `Age` column, and reassign this back to the variable `titanic`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "efcfc500-4b83-4582-864c-6e3def00ce81",
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   "outputs": [],
   "source": [
    "# Your answer here\n",
    "titanic = titanic.query('PClass != \"*\"').dropna(how='any', subset='Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5672c467-b2e9-48d2-a8d7-63496d715aa7",
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   "source": [
    "### d. Estimating survival probabilities\n",
    "The sinking of the Titanic is one of the worst maritime disasters in recorded history, with over 1500 deaths. The dataset contains only the passenger manifesto (i.e., crew are not included). Surviving the disaster was not equally likely for all passengers, however - the phrase 'women and children first' may come to mind.\n",
    "\n",
    "Probit regression can help us estimate survival probabilities for the different passenger classes, ages, and sex. \n",
    "\n",
    "Fit a probit model predicted whether a passenger survived from their sex, age, and class, and print the summary:"
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.461503\n",
      "         Iterations 6\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Probit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Survived</td>     <th>  No. Observations:  </th>  <td>   756</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                <td>Probit</td>      <th>  Df Residuals:      </th>  <td>   751</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 28 Aug 2024</td> <th>  Pseudo R-squ.:     </th>  <td>0.3196</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>14:57:41</td>     <th>  Log-Likelihood:    </th> <td> -348.90</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -512.79</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>1.098e-69</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>     <td>    2.1643</td> <td>    0.217</td> <td>    9.967</td> <td> 0.000</td> <td>    1.739</td> <td>    2.590</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PClass[T.2nd]</th> <td>   -0.7540</td> <td>    0.152</td> <td>   -4.966</td> <td> 0.000</td> <td>   -1.052</td> <td>   -0.456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PClass[T.3rd]</th> <td>   -1.4076</td> <td>    0.150</td> <td>   -9.356</td> <td> 0.000</td> <td>   -1.702</td> <td>   -1.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sex[T.male]</th>   <td>   -1.5460</td> <td>    0.112</td> <td>  -13.821</td> <td> 0.000</td> <td>   -1.765</td> <td>   -1.327</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Age</th>           <td>   -0.0221</td> <td>    0.004</td> <td>   -5.130</td> <td> 0.000</td> <td>   -0.031</td> <td>   -0.014</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}   &     Survived     & \\textbf{  No. Observations:  } &      756    \\\\\n",
       "\\textbf{Model:}           &      Probit      & \\textbf{  Df Residuals:      } &      751    \\\\\n",
       "\\textbf{Method:}          &       MLE        & \\textbf{  Df Model:          } &        4    \\\\\n",
       "\\textbf{Date:}            & Wed, 28 Aug 2024 & \\textbf{  Pseudo R-squ.:     } &   0.3196    \\\\\n",
       "\\textbf{Time:}            &     14:57:41     & \\textbf{  Log-Likelihood:    } &   -348.90   \\\\\n",
       "\\textbf{converged:}       &       True       & \\textbf{  LL-Null:           } &   -512.79   \\\\\n",
       "\\textbf{Covariance Type:} &    nonrobust     & \\textbf{  LLR p-value:       } & 1.098e-69   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                       & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}     &       2.1643  &        0.217     &     9.967  &         0.000        &        1.739    &        2.590     \\\\\n",
       "\\textbf{PClass[T.2nd]} &      -0.7540  &        0.152     &    -4.966  &         0.000        &       -1.052    &       -0.456     \\\\\n",
       "\\textbf{PClass[T.3rd]} &      -1.4076  &        0.150     &    -9.356  &         0.000        &       -1.702    &       -1.113     \\\\\n",
       "\\textbf{Sex[T.male]}   &      -1.5460  &        0.112     &   -13.821  &         0.000        &       -1.765    &       -1.327     \\\\\n",
       "\\textbf{Age}           &      -0.0221  &        0.004     &    -5.130  &         0.000        &       -0.031    &       -0.014     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Probit Regression Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          Probit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:               Survived   No. Observations:                  756\n",
       "Model:                         Probit   Df Residuals:                      751\n",
       "Method:                           MLE   Df Model:                            4\n",
       "Date:                Wed, 28 Aug 2024   Pseudo R-squ.:                  0.3196\n",
       "Time:                        14:57:41   Log-Likelihood:                -348.90\n",
       "converged:                       True   LL-Null:                       -512.79\n",
       "Covariance Type:            nonrobust   LLR p-value:                 1.098e-69\n",
       "=================================================================================\n",
       "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept         2.1643      0.217      9.967      0.000       1.739       2.590\n",
       "PClass[T.2nd]    -0.7540      0.152     -4.966      0.000      -1.052      -0.456\n",
       "PClass[T.3rd]    -1.4076      0.150     -9.356      0.000      -1.702      -1.113\n",
       "Sex[T.male]      -1.5460      0.112    -13.821      0.000      -1.765      -1.327\n",
       "Age              -0.0221      0.004     -5.130      0.000      -0.031      -0.014\n",
       "=================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your answer here\n",
    "titanic_model = smf.probit('Survived ~ PClass + Age + Sex', data=titanic).fit()\n",
    "titanic_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5681ddd-4154-4b00-b59d-5869160e2e8e",
   "metadata": {},
   "source": [
    "All of the coefficients are significant, but again, we do not want to dwell on interpreting these. Instead, we should focus on the predictions. Generate a set of predictions for each predictor, to see how survival probability is affected. Bonus points if you can plot them as well."
   ]
  },
  {
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       "<seaborn.axisgrid.FacetGrid at 0x159ed9750>"
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",
      "text/plain": [
       "<Figure size 1073.25x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your answer here\n",
    "dg = me.datagrid(titanic_model,\n",
    "                 PClass=['1st', '2nd', '3rd'],\n",
    "                 Sex=['male', 'female'],\n",
    "                 Age=np.arange(0, 90))\n",
    "\n",
    "# Predict\n",
    "preds = me.predictions(titanic_model, newdata=dg)\n",
    "\n",
    "# Plot\n",
    "sns.relplot(data=preds,\n",
    "            x='Age', y='estimate',\n",
    "            hue='PClass', col='Sex',\n",
    "            kind='line')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004b2f0b-c9a3-48df-ba2b-edf746089fa0",
   "metadata": {},
   "source": [
    "This story is not apparent at all from the coefficients. Younger individuals had a higher chance of survival, as did women, which is particularly pronounced for higher-class passengers. \n",
    "\n",
    "Let's check the importance of these differences. \n",
    "\n",
    "Use `marginaleffects` to contrast the differences in survival probability for females and males, regardless of class and age (i.e., the marginal effect of sex)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dc6ac360-319f-421f-8d77-2be441df3f91",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (2, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Sex</th><th>estimate</th><th>std_error</th><th>statistic</th><th>p_value</th><th>s_value</th><th>conf_low</th><th>conf_high</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;female&quot;</td><td>0.637297</td><td>0.034537</td><td>18.452395</td><td>0.0</td><td>inf</td><td>0.569605</td><td>0.704989</td></tr><tr><td>&quot;male&quot;</td><td>0.202482</td><td>0.015936</td><td>12.706124</td><td>0.0</td><td>inf</td><td>0.171248</td><td>0.233715</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (2, 8)\n",
       "┌────────┬──────────┬───────────┬──────┬─────────┬─────┬───────┬───────┐\n",
       "│ Sex    ┆ Estimate ┆ Std.Error ┆ z    ┆ P(>|z|) ┆ S   ┆ 2.5%  ┆ 97.5% │\n",
       "│ ---    ┆ ---      ┆ ---       ┆ ---  ┆ ---     ┆ --- ┆ ---   ┆ ---   │\n",
       "│ str    ┆ str      ┆ str       ┆ str  ┆ str     ┆ str ┆ str   ┆ str   │\n",
       "╞════════╪══════════╪═══════════╪══════╪═════════╪═════╪═══════╪═══════╡\n",
       "│ female ┆ 0.637    ┆ 0.0345    ┆ 18.5 ┆ 0       ┆ inf ┆ 0.57  ┆ 0.705 │\n",
       "│ male   ┆ 0.202    ┆ 0.0159    ┆ 12.7 ┆ 0       ┆ inf ┆ 0.171 ┆ 0.234 │\n",
       "└────────┴──────────┴───────────┴──────┴─────────┴─────┴───────┴───────┘\n",
       "\n",
       "Columns: Sex, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>term</th><th>estimate</th><th>std_error</th><th>statistic</th><th>p_value</th><th>s_value</th><th>conf_low</th><th>conf_high</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Row 1 - Row 2&quot;</td><td>0.434815</td><td>0.037036</td><td>11.740354</td><td>0.0</td><td>inf</td><td>0.362226</td><td>0.507404</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 8)\n",
       "┌───────────────┬──────────┬───────────┬──────┬─────────┬─────┬───────┬───────┐\n",
       "│ Term          ┆ Estimate ┆ Std.Error ┆ z    ┆ P(>|z|) ┆ S   ┆ 2.5%  ┆ 97.5% │\n",
       "│ ---           ┆ ---      ┆ ---       ┆ ---  ┆ ---     ┆ --- ┆ ---   ┆ ---   │\n",
       "│ str           ┆ str      ┆ str       ┆ str  ┆ str     ┆ str ┆ str   ┆ str   │\n",
       "╞═══════════════╪══════════╪═══════════╪══════╪═════════╪═════╪═══════╪═══════╡\n",
       "│ Row 1 - Row 2 ┆ 0.435    ┆ 0.037     ┆ 11.7 ┆ 0       ┆ inf ┆ 0.362 ┆ 0.507 │\n",
       "└───────────────┴──────────┴───────────┴──────┴─────────┴─────┴───────┴───────┘\n",
       "\n",
       "Columns: term, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here\n",
    "# marginal effects here\n",
    "display(\n",
    "    me.predictions(titanic_model,\n",
    "               newdata=dg,\n",
    "               by=['Sex'])\n",
    ")\n",
    "\n",
    "# Contrast here\n",
    "me.predictions(titanic_model,\n",
    "               newdata=dg,\n",
    "               by=['Sex'],\n",
    "               hypothesis='pairwise')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e849631-0f0e-4bef-8411-2dd4b03ce891",
   "metadata": {},
   "source": [
    "What about the marginal effects of passenger class? What are the probabilities of survival for those in each of the three classes, and do they differ significantly from one another?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "df356244-37ed-4b11-a82c-8eee2d324740",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
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    "tags": [
     "hide-input"
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   },
   "outputs": [
    {
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       "}\n",
       "</style>\n",
       "<small>shape: (3, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PClass</th><th>estimate</th><th>std_error</th><th>statistic</th><th>p_value</th><th>s_value</th><th>conf_low</th><th>conf_high</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;1st&quot;</td><td>0.61146</td><td>0.02734</td><td>22.364725</td><td>0.0</td><td>inf</td><td>0.557874</td><td>0.665046</td></tr><tr><td>&quot;2nd&quot;</td><td>0.405421</td><td>0.032443</td><td>12.496293</td><td>0.0</td><td>inf</td><td>0.341833</td><td>0.469009</td></tr><tr><td>&quot;3rd&quot;</td><td>0.242787</td><td>0.024009</td><td>10.11215</td><td>0.0</td><td>inf</td><td>0.195729</td><td>0.289845</td></tr></tbody></table></div>"
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       "shape: (3, 8)\n",
       "┌────────┬──────────┬───────────┬──────┬─────────┬─────┬───────┬───────┐\n",
       "│ PClass ┆ Estimate ┆ Std.Error ┆ z    ┆ P(>|z|) ┆ S   ┆ 2.5%  ┆ 97.5% │\n",
       "│ ---    ┆ ---      ┆ ---       ┆ ---  ┆ ---     ┆ --- ┆ ---   ┆ ---   │\n",
       "│ str    ┆ str      ┆ str       ┆ str  ┆ str     ┆ str ┆ str   ┆ str   │\n",
       "╞════════╪══════════╪═══════════╪══════╪═════════╪═════╪═══════╪═══════╡\n",
       "│ 1st    ┆ 0.611    ┆ 0.0273    ┆ 22.4 ┆ 0       ┆ inf ┆ 0.558 ┆ 0.665 │\n",
       "│ 2nd    ┆ 0.405    ┆ 0.0324    ┆ 12.5 ┆ 0       ┆ inf ┆ 0.342 ┆ 0.469 │\n",
       "│ 3rd    ┆ 0.243    ┆ 0.024     ┆ 10.1 ┆ 0       ┆ inf ┆ 0.196 ┆ 0.29  │\n",
       "└────────┴──────────┴───────────┴──────┴─────────┴─────┴───────┴───────┘\n",
       "\n",
       "Columns: PClass, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "</style>\n",
       "<small>shape: (3, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>term</th><th>estimate</th><th>std_error</th><th>statistic</th><th>p_value</th><th>s_value</th><th>conf_low</th><th>conf_high</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Row 1 - Row 2&quot;</td><td>0.206039</td><td>0.0389</td><td>5.296636</td><td>1.1796e-7</td><td>23.015252</td><td>0.129797</td><td>0.282282</td></tr><tr><td>&quot;Row 1 - Row 3&quot;</td><td>0.368673</td><td>0.032987</td><td>11.176456</td><td>0.0</td><td>inf</td><td>0.304021</td><td>0.433326</td></tr><tr><td>&quot;Row 2 - Row 3&quot;</td><td>0.162634</td><td>0.033226</td><td>4.894787</td><td>9.8412e-7</td><td>19.954661</td><td>0.097512</td><td>0.227756</td></tr></tbody></table></div>"
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      "text/plain": [
       "shape: (3, 8)\n",
       "┌───────────────┬──────────┬───────────┬──────┬──────────┬─────┬────────┬───────┐\n",
       "│ Term          ┆ Estimate ┆ Std.Error ┆ z    ┆ P(>|z|)  ┆ S   ┆ 2.5%   ┆ 97.5% │\n",
       "│ ---           ┆ ---      ┆ ---       ┆ ---  ┆ ---      ┆ --- ┆ ---    ┆ ---   │\n",
       "│ str           ┆ str      ┆ str       ┆ str  ┆ str      ┆ str ┆ str    ┆ str   │\n",
       "╞═══════════════╪══════════╪═══════════╪══════╪══════════╪═════╪════════╪═══════╡\n",
       "│ Row 1 - Row 2 ┆ 0.206    ┆ 0.0389    ┆ 5.3  ┆ 1.18e-07 ┆ 23  ┆ 0.13   ┆ 0.282 │\n",
       "│ Row 1 - Row 3 ┆ 0.369    ┆ 0.033     ┆ 11.2 ┆ 0        ┆ inf ┆ 0.304  ┆ 0.433 │\n",
       "│ Row 2 - Row 3 ┆ 0.163    ┆ 0.0332    ┆ 4.89 ┆ 9.84e-07 ┆ 20  ┆ 0.0975 ┆ 0.228 │\n",
       "└───────────────┴──────────┴───────────┴──────┴──────────┴─────┴────────┴───────┘\n",
       "\n",
       "Columns: term, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here\n",
    "display(\n",
    "    me.predictions(titanic_model,\n",
    "                   newdata=dg,\n",
    "                   by=['PClass'])\n",
    ")\n",
    "\n",
    "# Contrast here\n",
    "me.predictions(titanic_model,\n",
    "               newdata=dg,\n",
    "               by=['PClass'],\n",
    "               hypothesis='pairwise')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e9c7c61-1843-4cce-8cbd-854f900fba07",
   "metadata": {},
   "source": [
    "What about the marginal slope of age? How did probability of survival change with a 1-year increase in age, regardless of passenger sex and class? Hint - since you have just one variable of interest here, you should use `avg_slopes` rather than `slopes`, which will helpfully average across the results for you. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74ad1d33-1cf9-4361-96f4-ac7278333a20",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
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    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "}\n",
       "</style>\n",
       "<small>shape: (1, 9)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>term</th><th>contrast</th><th>estimate</th><th>std_error</th><th>statistic</th><th>p_value</th><th>s_value</th><th>conf_low</th><th>conf_high</th></tr><tr><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Age&quot;</td><td>&quot;mean(dY/dX)&quot;</td><td>-0.005577</td><td>0.00087</td><td>-6.40739</td><td>1.4803e-10</td><td>32.653374</td><td>-0.007283</td><td>-0.003871</td></tr></tbody></table></div>"
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      "text/plain": [
       "shape: (1, 9)\n",
       "┌──────┬─────────────┬──────────┬───────────┬───┬──────────┬──────┬──────────┬──────────┐\n",
       "│ Term ┆ Contrast    ┆ Estimate ┆ Std.Error ┆ … ┆ P(>|z|)  ┆ S    ┆ 2.5%     ┆ 97.5%    │\n",
       "│ ---  ┆ ---         ┆ ---      ┆ ---       ┆   ┆ ---      ┆ ---  ┆ ---      ┆ ---      │\n",
       "│ str  ┆ str         ┆ str      ┆ str       ┆   ┆ str      ┆ str  ┆ str      ┆ str      │\n",
       "╞══════╪═════════════╪══════════╪═══════════╪═══╪══════════╪══════╪══════════╪══════════╡\n",
       "│ Age  ┆ mean(dY/dX) ┆ -0.00558 ┆ 0.00087   ┆ … ┆ 1.48e-10 ┆ 32.7 ┆ -0.00728 ┆ -0.00387 │\n",
       "└──────┴─────────────┴──────────┴───────────┴───┴──────────┴──────┴──────────┴──────────┘\n",
       "\n",
       "Columns: term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your answer here\n",
    "me.avg_slopes(titanic_model, newdata=dg, variables='Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00cbcc28-c31f-477f-aebe-8d1da54c4ae5",
   "metadata": {},
   "source": [
    "### e. Individualised survival estimates\n",
    "\n",
    "Based on your model, who was more likely to survive the Titanic disaster out of the following three people:\n",
    "1. A 10 year old female from the second class\n",
    "2. A 30 year old male from first class\n",
    "3. A 60 year old female from first class\n",
    "4. A 1 year old male from third class\n",
    "\n",
    "Hint - you may find using `datagrid` gives you more than what you need here, so you could specify your `newdata` through a simple DataFrame. Try it and see. Once you obtain the four predictions, you can use a different argument to `hypothesis` - try using 'reference' to see if you can compare all predictions to that of a 10 year old second class female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7025fc94-a82c-4cbe-8afd-ceec243a99a1",
   "metadata": {
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    "slideshow": {
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   "outputs": [
    {
     "data": {
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       "}\n",
       "</style>\n",
       "<small>shape: (4, 11)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>rowid</th><th>estimate</th><th>std_error</th><th>statistic</th><th>p_value</th><th>s_value</th><th>conf_low</th><th>conf_high</th><th>Age</th><th>Sex</th><th>PClass</th></tr><tr><td>i32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i64</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>0</td><td>0.882865</td><td>0.028276</td><td>31.222681</td><td>0.0</td><td>inf</td><td>0.827444</td><td>0.938286</td><td>10</td><td>&quot;female&quot;</td><td>&quot;2nd&quot;</td></tr><tr><td>1</td><td>0.482342</td><td>0.044893</td><td>10.74414</td><td>0.0</td><td>inf</td><td>0.394352</td><td>0.570331</td><td>30</td><td>&quot;male&quot;</td><td>&quot;1st&quot;</td></tr><tr><td>2</td><td>0.799332</td><td>0.041966</td><td>19.046915</td><td>0.0</td><td>inf</td><td>0.717079</td><td>0.881585</td><td>60</td><td>&quot;female&quot;</td><td>&quot;1st&quot;</td></tr><tr><td>3</td><td>0.208569</td><td>0.039494</td><td>5.280984</td><td>1.2849e-7</td><td>22.891818</td><td>0.131161</td><td>0.285976</td><td>1</td><td>&quot;male&quot;</td><td>&quot;3rd&quot;</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (4, 7)\n",
       "┌──────────┬───────────┬──────┬──────────┬──────┬───────┬───────┐\n",
       "│ Estimate ┆ Std.Error ┆ z    ┆ P(>|z|)  ┆ S    ┆ 2.5%  ┆ 97.5% │\n",
       "│ ---      ┆ ---       ┆ ---  ┆ ---      ┆ ---  ┆ ---   ┆ ---   │\n",
       "│ str      ┆ str       ┆ str  ┆ str      ┆ str  ┆ str   ┆ str   │\n",
       "╞══════════╪═══════════╪══════╪══════════╪══════╪═══════╪═══════╡\n",
       "│ 0.883    ┆ 0.0283    ┆ 31.2 ┆ 0        ┆ inf  ┆ 0.827 ┆ 0.938 │\n",
       "│ 0.482    ┆ 0.0449    ┆ 10.7 ┆ 0        ┆ inf  ┆ 0.394 ┆ 0.57  │\n",
       "│ 0.799    ┆ 0.042     ┆ 19   ┆ 0        ┆ inf  ┆ 0.717 ┆ 0.882 │\n",
       "│ 0.209    ┆ 0.0395    ┆ 5.28 ┆ 1.28e-07 ┆ 22.9 ┆ 0.131 ┆ 0.286 │\n",
       "└──────────┴───────────┴──────┴──────────┴──────┴───────┴───────┘\n",
       "\n",
       "Columns: rowid, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high, Age, Sex, PClass"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<small>shape: (3, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>term</th><th>estimate</th><th>std_error</th><th>statistic</th><th>p_value</th><th>s_value</th><th>conf_low</th><th>conf_high</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Row 2 - Row 1&quot;</td><td>-0.400524</td><td>0.051269</td><td>-7.812249</td><td>5.5511e-15</td><td>47.356144</td><td>-0.501008</td><td>-0.300039</td></tr><tr><td>&quot;Row 3 - Row 1&quot;</td><td>-0.083533</td><td>0.051703</td><td>-1.615623</td><td>0.106176</td><td>3.235471</td><td>-0.18487</td><td>0.017804</td></tr><tr><td>&quot;Row 4 - Row 1&quot;</td><td>-0.674296</td><td>0.043832</td><td>-15.383747</td><td>0.0</td><td>inf</td><td>-0.760205</td><td>-0.588388</td></tr></tbody></table></div>"
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      "text/plain": [
       "shape: (3, 8)\n",
       "┌───────────────┬──────────┬───────────┬───────┬──────────┬──────┬────────┬────────┐\n",
       "│ Term          ┆ Estimate ┆ Std.Error ┆ z     ┆ P(>|z|)  ┆ S    ┆ 2.5%   ┆ 97.5%  │\n",
       "│ ---           ┆ ---      ┆ ---       ┆ ---   ┆ ---      ┆ ---  ┆ ---    ┆ ---    │\n",
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       "│ Row 2 - Row 1 ┆ -0.401   ┆ 0.0513    ┆ -7.81 ┆ 5.55e-15 ┆ 47.4 ┆ -0.501 ┆ -0.3   │\n",
       "│ Row 3 - Row 1 ┆ -0.0835  ┆ 0.0517    ┆ -1.62 ┆ 0.106    ┆ 3.24 ┆ -0.185 ┆ 0.0178 │\n",
       "│ Row 4 - Row 1 ┆ -0.674   ┆ 0.0438    ┆ -15.4 ┆ 0        ┆ inf  ┆ -0.76  ┆ -0.588 │\n",
       "└───────────────┴──────────┴───────────┴───────┴──────────┴──────┴────────┴────────┘\n",
       "\n",
       "Columns: term, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your answer here\n",
    "# Option 1 - create a dataframe\n",
    "dg = pd.DataFrame({'Age': [10, 30, 60, 1],\n",
    "                   'Sex': ['female', 'male', 'female', 'male'],\n",
    "                   'PClass': ['2nd', '1st', '1st', '3rd']})\n",
    "\n",
    "# Predict\n",
    "opt1 = me.predictions(titanic_model, newdata=dg)\n",
    "display(opt1)\n",
    "\n",
    "# Opt1 contrast\n",
    "display(me.predictions(titanic_model, newdata=dg, hypothesis='reference'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "730edd18-88fd-45e6-a76d-590ed57da1fb",
   "metadata": {},
   "source": [
    "### f. Evaluating the model\n",
    "Compute the phi coefficient for the model examine its magnitude. How good is this (higher is better)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4be97584-a646-4dc6-ab45-2883e4499935",
   "metadata": {
    "editable": true,
    "slideshow": {
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      "text/plain": [
       "           n         r        CI95%         p-val       BF10  power\n",
       "pearson  756  0.557665  [0.51, 0.6]  5.325949e-63  3.067e+59    1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your answer here\n",
    "# Get predictions for all datapoints\n",
    "preds = me.predictions(titanic_model)\n",
    "\n",
    "# Binarise\n",
    "binary = np.digitize(preds['estimate'], [.5])\n",
    "\n",
    "# Correlate\n",
    "pg.corr(binary, titanic['Survived'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f67bcd46-f238-40e6-a284-ddff8f946da7",
   "metadata": {},
   "source": [
    "## 2. The Challenger Disaster\n",
    "Probit regression is really morbid, isn't it? Lets continue with another example.\n",
    "\n",
    "The Challenger was a space shuttle that exploded shortly after launch in 1986, killing all seven astronauts on board. The cause of the explosion was traced back to a simple mechanical fault in the form of a rubber O-ring in the fuselage. In normal temperatures, these O-rings kept extremely powerful and flammable rocket fuel from the booster, allowing the fuel to enter when needed. In low temperatures, they became brittle, allowing fuel to leak into the booster in greater quantities. \n",
    "\n",
    "What is very tragic about this example is that the data was there to suggest it all along - engineers had stated that the O-rings failed in low temperatures, but those in charge of the operation pressed forward with the launch date.\n",
    "\n",
    "The recorded engineer data can be downloaded here: https://vincentarelbundock.github.io/Rdatasets/csv/openintro/orings.csv\n",
    "\n",
    "And a short description is here:\n",
    "https://vincentarelbundock.github.io/Rdatasets/doc/openintro/orings.html\n",
    "\n",
    "Read in the dataset into a dataframe called `oring` and display it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "904c2943-c1ca-4857-a333-25b753ca67d8",
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    "slideshow": {
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    {
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      "text/plain": [
       "    rownames  mission  temperature  damaged  undamaged\n",
       "0          1        1           53        5          1\n",
       "1          2        2           57        1          5\n",
       "2          3        3           58        1          5\n",
       "3          4        4           63        1          5\n",
       "4          5        5           66        0          6\n",
       "5          6        6           67        0          6\n",
       "6          7        7           67        0          6\n",
       "7          8        8           67        0          6\n",
       "8          9        9           68        0          6\n",
       "9         10       10           69        0          6\n",
       "10        11       11           70        1          5\n",
       "11        12       12           70        0          6\n",
       "12        13       13           70        1          5\n",
       "13        14       14           70        0          6\n",
       "14        15       15           72        0          6\n",
       "15        16       16           73        0          6\n",
       "16        17       17           75        0          6\n",
       "17        18       18           75        1          5\n",
       "18        19       19           76        0          6\n",
       "19        20       20           76        0          6\n",
       "20        21       21           78        0          6\n",
       "21        22       22           79        0          6\n",
       "22        23       23           81        0          6"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your answer here\n",
    "oring = pd.read_csv('https://vincentarelbundock.github.io/Rdatasets/csv/openintro/orings.csv')\n",
    "oring"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d19f7ab8-860a-4548-a693-23c2775502a1",
   "metadata": {},
   "source": [
    "There are two columns of interest here. The first is `temperature`, displaying the temperature on the day of the test flight, and `damaged`, which records the number (out of six) of the damaged O-rings. Before we fit a model, create a new column in the dataframe called `any_damage` which should have a 1 where there is 1 or more in the `damaged` column, and a zero elsewhere. You already know the function that will do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc3decc5-497b-4164-b966-ebd3c6a4c9d7",
   "metadata": {
    "editable": true,
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   "outputs": [],
   "source": [
    "# Your answer here\n",
    "oring['any_damage'] = np.digitize(oring['damaged'], [1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acf04804-bd40-48f2-b95a-c2645e1a9b88",
   "metadata": {},
   "source": [
    "Once you have done that, fit a simple probit model that predicts `any_damage` from `temperature`. Show the summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c7436cc8-54c8-4709-b331-869911594c15",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.442994\n",
      "         Iterations 6\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Probit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>any_damage</td>    <th>  No. Observations:  </th>  <td>    23</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                <td>Probit</td>      <th>  Df Residuals:      </th>  <td>    21</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 28 Aug 2024</td> <th>  Pseudo R-squ.:     </th>  <td>0.2791</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>14:57:41</td>     <th>  Log-Likelihood:    </th> <td> -10.189</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -14.134</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>0.004972</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>    8.7750</td> <td>    4.029</td> <td>    2.178</td> <td> 0.029</td> <td>    0.879</td> <td>   16.671</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>temperature</th> <td>   -0.1351</td> <td>    0.058</td> <td>   -2.314</td> <td> 0.021</td> <td>   -0.250</td> <td>   -0.021</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}   &   any\\_damage    & \\textbf{  No. Observations:  } &       23    \\\\\n",
       "\\textbf{Model:}           &      Probit      & \\textbf{  Df Residuals:      } &       21    \\\\\n",
       "\\textbf{Method:}          &       MLE        & \\textbf{  Df Model:          } &        1    \\\\\n",
       "\\textbf{Date:}            & Wed, 28 Aug 2024 & \\textbf{  Pseudo R-squ.:     } &   0.2791    \\\\\n",
       "\\textbf{Time:}            &     14:57:41     & \\textbf{  Log-Likelihood:    } &   -10.189   \\\\\n",
       "\\textbf{converged:}       &       True       & \\textbf{  LL-Null:           } &   -14.134   \\\\\n",
       "\\textbf{Covariance Type:} &    nonrobust     & \\textbf{  LLR p-value:       } &  0.004972   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                     & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}   &       8.7750  &        4.029     &     2.178  &         0.029        &        0.879    &       16.671     \\\\\n",
       "\\textbf{temperature} &      -0.1351  &        0.058     &    -2.314  &         0.021        &       -0.250    &       -0.021     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Probit Regression Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          Probit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:             any_damage   No. Observations:                   23\n",
       "Model:                         Probit   Df Residuals:                       21\n",
       "Method:                           MLE   Df Model:                            1\n",
       "Date:                Wed, 28 Aug 2024   Pseudo R-squ.:                  0.2791\n",
       "Time:                        14:57:41   Log-Likelihood:                -10.189\n",
       "converged:                       True   LL-Null:                       -14.134\n",
       "Covariance Type:            nonrobust   LLR p-value:                  0.004972\n",
       "===============================================================================\n",
       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "Intercept       8.7750      4.029      2.178      0.029       0.879      16.671\n",
       "temperature    -0.1351      0.058     -2.314      0.021      -0.250      -0.021\n",
       "===============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your answer here\n",
    "oring_model = smf.probit('any_damage ~ temperature', data=oring).fit()\n",
    "oring_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c83dee0-b76d-4d45-b4b9-2e77f6a7541d",
   "metadata": {},
   "source": [
    "Notice that this is a very small dataset, and the coefficient for temperature is not particularly fairly weak. Compute the phi coefficient for this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f859ca0-5224-412c-9e24-9c9129aba481",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>r</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>p-val</th>\n",
       "      <th>BF10</th>\n",
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       "    <tr>\n",
       "      <th>pearson</th>\n",
       "      <td>23</td>\n",
       "      <td>0.693688</td>\n",
       "      <td>[0.39, 0.86]</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>144.722</td>\n",
       "      <td>0.973117</td>\n",
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       "          n         r         CI95%     p-val     BF10     power\n",
       "pearson  23  0.693688  [0.39, 0.86]  0.000242  144.722  0.973117"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your answer here\n",
    "# Get predictions for all datapoints\n",
    "preds = me.predictions(oring_model)\n",
    "\n",
    "# Binarise\n",
    "binary = np.digitize(preds['estimate'], [.5])\n",
    "\n",
    "# Correlate\n",
    "pg.corr(binary, oring['any_damage'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc4b3b5-8bcc-4683-b998-713bb9c32d3d",
   "metadata": {},
   "source": [
    "Plot the predictions of the model, showing how probability of damage changes with temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5d270866-6956-4bb3-ac24-65fd79b8cdf7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='temperature', ylabel='estimate'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your answer here\n",
    "# Get predictions\n",
    "preds = me.predictions(oring_model)\n",
    "\n",
    "sns.lineplot(data=preds,\n",
    "             x='temperature', y='estimate')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "496214d7-52ae-4b85-b3fd-3dde8a6a8533",
   "metadata": {},
   "source": [
    "On the morning of the disaster, when the shuttle launched, the temperature was 36 degrees Fahrenheit.\n",
    "\n",
    "What probability does the model give for any kind of O-ring damage for this event?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2c443afc-a9ca-4f7d-8c2e-ae9b198a936f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1, 14)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>temperature</th><th>rowid</th><th>estimate</th><th>std_error</th><th>statistic</th><th>p_value</th><th>s_value</th><th>conf_low</th><th>conf_high</th><th>rownames</th><th>mission</th><th>damaged</th><th>undamaged</th><th>any_damage</th></tr><tr><td>i64</td><td>i32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>36</td><td>0</td><td>0.999954</td><td>0.000368</td><td>2713.616681</td><td>0.0</td><td>inf</td><td>0.999232</td><td>1.000676</td><td>20</td><td>16</td><td>0</td><td>6</td><td>0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 8)\n",
       "┌─────────────┬──────────┬───────────┬──────────┬─────────┬─────┬───────┬───────┐\n",
       "│ temperature ┆ Estimate ┆ Std.Error ┆ z        ┆ P(>|z|) ┆ S   ┆ 2.5%  ┆ 97.5% │\n",
       "│ ---         ┆ ---      ┆ ---       ┆ ---      ┆ ---     ┆ --- ┆ ---   ┆ ---   │\n",
       "│ str         ┆ str      ┆ str       ┆ str      ┆ str     ┆ str ┆ str   ┆ str   │\n",
       "╞═════════════╪══════════╪═══════════╪══════════╪═════════╪═════╪═══════╪═══════╡\n",
       "│ 36          ┆ 1        ┆ 0.000368  ┆ 2.71e+03 ┆ 0       ┆ inf ┆ 0.999 ┆ 1     │\n",
       "└─────────────┴──────────┴───────────┴──────────┴─────────┴─────┴───────┴───────┘\n",
       "\n",
       "Columns: temperature, rowid, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high, rownames, mission, damaged, undamaged, any_damage"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your answer here\n",
    "me.predictions(oring_model, newdata=me.datagrid(oring_model, temperature=36))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09b30431-63bf-491c-91d7-19881086bd1d",
   "metadata": {},
   "source": [
    "## 3. Marginally significant meta-scientific statistics\n",
    "Effects are either statistically significant, or they are not. Historically, researchers in psychology would refer to hypothesis tests that are just above the .05 threshold as 'marginally significant', a practice that is heavily frowned upon these days in light of the replication crisis. \n",
    "\n",
    "In 2016, [Pritschet, Powell, & Horne, 2016](https://journals.sagepub.com/doi/10.1177/0956797616645672) carried out an analysis of just over 1,500 articles in three different areas of psychology (social, cognitive, and developmental), over the past four decades, and examined whether articles used the term 'marginally significant'. This interesting dataset allows us to essentially examine a cultural trend in psychological science in terms of the language used. \n",
    "\n",
    "For this exercise, we will fit a probit regression to this data and examine the probability of this phrase appearing over time and in different fields. First, you will need to do some data detective work - follow the link above to the paper, and then head to the OSF page linked in the paper. There, you'll find an Excel file titled `marginals psych science revision_corrections.xlsx`. Download this and then place it in the same folder as this notebook. Read it in using `pandas`, which has a `.read_excel` function, and show the top 10 rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7fb8cc61-e717-40a5-8d3e-97dc577ab0a3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "# Your answer here\n",
    "marginals = pd.read_excel('marginals psych science revision_corrections.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ba554e6-b445-4680-96f2-ab5a72fabe3e",
   "metadata": {},
   "source": [
    "The first thing we need to do is rename some columns, as we can't work *easily* with these column names. \n",
    "\n",
    "Create a dictionary that has the keys 'Number of Experiments' and 'Marginals Yes/No' (the existing column names) and the values 'n_exps' and 'marginal_yes' (the new names). Call it `newcols`, then use the `.rename` method on your dataframe to alter the column names. Make sure to overwrite the dataframe so the names actually stick! You may need to refer to Week 1's content to figure this out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2485361f-9d4a-47f6-aa96-644ebd6e96b3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "# Your answer here\n",
    "newcols = {'Number of Experiments': 'n_exps', 'Marginals Yes/No': 'marginal_yes'}\n",
    "marginals = marginals.rename(columns=newcols)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "841c2583-cf16-473c-9f48-1f1cf6cfc777",
   "metadata": {},
   "source": [
    "Next, we need to replace the values in the `Field` column so they are no longer numbers but represent the actual fields (this makes it easier for us to interpret the outcome, but it is not fully required). You can again make a dictionary that has the numbers 1, 2, and 3 to the values 'Cognitive', 'Developmental', and 'Social'. Then you can use the `.replace` method to recode the values in the `Field` column. Again, reassign the dataframe so the changes stick!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9ee2b0ba-ce69-4468-9da7-c52418b1146e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "# Your answer here\n",
    "newfields = {1: 'Cognitive', 2: 'Developmental', 3: 'Social'}\n",
    "marginals = marginals.replace({'Field': newfields})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aefd5be-35a5-46ae-8b6b-bb90cd4f01e2",
   "metadata": {},
   "source": [
    "Now filter the data so that the number of experiments is 1 or more (there are some papers that don't have any statistics, e.g. commentaries). Again, reassign the dataframe. As a final step, just take the columns `marginal_yes`, `Year`, `Field`, and `n_exps` out to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e6e4b257-727e-4b47-a58d-34603cc442b7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "# Your answer here\n",
    "marginals = marginals.query('n_exps > 0')\n",
    "marginals = marginals[['marginal_yes', 'Year', 'Field', 'n_exps']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76e8aa9d-aeea-448e-9f46-1544df1b849c",
   "metadata": {},
   "source": [
    "Now fit a probit regression to this data that predicts whether a paper contains the phrase 'marginally significant' from Year, Field, and number of experiments. This examines whether different fields use the phrase at different rates, whether it has changed over time, and whether papers with more studies are more likely to use it. Call it `marginal_mod`, and print the summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c3929033-0db7-4157-8a05-82e7cecf9f9e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.597290\n",
      "         Iterations 5\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Probit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>marginal_yes</td>   <th>  No. Observations:  </th>  <td>  1469</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                <td>Probit</td>      <th>  Df Residuals:      </th>  <td>  1464</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 28 Aug 2024</td> <th>  Pseudo R-squ.:     </th>  <td>0.07556</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>14:57:42</td>     <th>  Log-Likelihood:    </th> <td> -877.42</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -949.13</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>5.202e-30</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>               <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>              <td>  -40.1238</td> <td>    5.205</td> <td>   -7.709</td> <td> 0.000</td> <td>  -50.325</td> <td>  -29.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Field[T.Developmental]</th> <td>   -0.0067</td> <td>    0.162</td> <td>   -0.041</td> <td> 0.967</td> <td>   -0.323</td> <td>    0.310</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Field[T.Social]</th>        <td>    0.3672</td> <td>    0.151</td> <td>    2.426</td> <td> 0.015</td> <td>    0.071</td> <td>    0.664</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Year</th>                   <td>    0.0197</td> <td>    0.003</td> <td>    7.525</td> <td> 0.000</td> <td>    0.015</td> <td>    0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_exps</th>                 <td>    0.1164</td> <td>    0.027</td> <td>    4.303</td> <td> 0.000</td> <td>    0.063</td> <td>    0.169</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}         &  marginal\\_yes   & \\textbf{  No. Observations:  } &     1469    \\\\\n",
       "\\textbf{Model:}                 &      Probit      & \\textbf{  Df Residuals:      } &     1464    \\\\\n",
       "\\textbf{Method:}                &       MLE        & \\textbf{  Df Model:          } &        4    \\\\\n",
       "\\textbf{Date:}                  & Wed, 28 Aug 2024 & \\textbf{  Pseudo R-squ.:     } &  0.07556    \\\\\n",
       "\\textbf{Time:}                  &     14:57:42     & \\textbf{  Log-Likelihood:    } &   -877.42   \\\\\n",
       "\\textbf{converged:}             &       True       & \\textbf{  LL-Null:           } &   -949.13   \\\\\n",
       "\\textbf{Covariance Type:}       &    nonrobust     & \\textbf{  LLR p-value:       } & 5.202e-30   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}              &     -40.1238  &        5.205     &    -7.709  &         0.000        &      -50.325    &      -29.923     \\\\\n",
       "\\textbf{Field[T.Developmental]} &      -0.0067  &        0.162     &    -0.041  &         0.967        &       -0.323    &        0.310     \\\\\n",
       "\\textbf{Field[T.Social]}        &       0.3672  &        0.151     &     2.426  &         0.015        &        0.071    &        0.664     \\\\\n",
       "\\textbf{Year}                   &       0.0197  &        0.003     &     7.525  &         0.000        &        0.015    &        0.025     \\\\\n",
       "\\textbf{n\\_exps}                &       0.1164  &        0.027     &     4.303  &         0.000        &        0.063    &        0.169     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Probit Regression Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          Probit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:           marginal_yes   No. Observations:                 1469\n",
       "Model:                         Probit   Df Residuals:                     1464\n",
       "Method:                           MLE   Df Model:                            4\n",
       "Date:                Wed, 28 Aug 2024   Pseudo R-squ.:                 0.07556\n",
       "Time:                        14:57:42   Log-Likelihood:                -877.42\n",
       "converged:                       True   LL-Null:                       -949.13\n",
       "Covariance Type:            nonrobust   LLR p-value:                 5.202e-30\n",
       "==========================================================================================\n",
       "                             coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------\n",
       "Intercept                -40.1238      5.205     -7.709      0.000     -50.325     -29.923\n",
       "Field[T.Developmental]    -0.0067      0.162     -0.041      0.967      -0.323       0.310\n",
       "Field[T.Social]            0.3672      0.151      2.426      0.015       0.071       0.664\n",
       "Year                       0.0197      0.003      7.525      0.000       0.015       0.025\n",
       "n_exps                     0.1164      0.027      4.303      0.000       0.063       0.169\n",
       "==========================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your answer here\n",
    "marginal_mod = smf.probit('marginal_yes ~ Year + Field + n_exps', data=marginals).fit()\n",
    "marginal_mod.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd7e4e8b-b703-4571-a01f-85b2d7941e60",
   "metadata": {},
   "source": [
    "Generate a set of predictions for this data. This is a little tricky but `numpy` can help. You will need:\n",
    "- Each of the fields\n",
    "- Years from 1970 to 2010 (DO NOT TYPE EACH NUMBER OUT INDIVIDUALLY!)\n",
    "- The number of experiments, from 1 to 10 (these are what are observed in the data).\n",
    "\n",
    "Create a datagrid and generate these predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7d6f7cef-158e-48ca-aa72-5a5ab8fbfa3c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "# Your answer here\n",
    "dg = me.datagrid(marginal_mod,\n",
    "                 Year=np.arange(1970, 2011),\n",
    "                 n_exps=np.arange(1, 11),\n",
    "                 Field=['Cognitive', 'Developmental', 'Social'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "590deff2-33ac-4c31-b36b-bed0094fa27f",
   "metadata": {},
   "source": [
    "Plot these predictions. It should have year on the X axis, the probability of the phrase on the Y, separate lines for the number of experiments, and separate plots for each field (use `relplot`). Can you add a reference line to the plot at .5?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8df485cb-815c-457f-8645-fb35aeb72f53",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x15a69e190>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NzUVOTg6USiV+/vOf4/DwENPT0/jud7+Lg4MDve/Fax8RnWXc2UAkQJ/61Keg0WjwhS98AUtLS3Bzc8OLL76I/Pz8Rx77wgsv4Hvf+x6eeeYZGBsb4w//8A8f2UXwOJKSkvCrX/0KP//5z3H58mXs7e3B2dkZJSUl+NznPgcTExMAwC9+8Qt8//vfR3FxMQ4PD5GWloZXXnkFxsbGcHd3x49//GP83d/9HV5++WWEh4cjIyMDa2tr7zmehx8CpVKpXm3oh4KDg+Hn5wdjY2O9chHv5WdKRERn11m6RgLHpRgelj0yMzNDQEAAXn75ZVy6dAnAcQmhX/ziF/jbv/1bFBUV4eDgADExMXjllVdgZmamfZ6PfOQjeOmll/QS5V5eXviXf/kX/PCHP8Tf/M3fQCwWIycnBy+88MIjccTGxuJHP/oRfvrTn+LrX/86HBwc8PGPfxz/43/8D+1jJBIJ/vmf/xlf/OIX4efnh//zf/7P25ageCslJSXY3d3F17/+dczNzcHe3h6f+tSn8MlPfvJdff21a9fwzW9+E729vfjlL3+J9vZ2XL58GWKxGAEBAfijP/oj/Md//AcODw/fc2xERHS+nJXrurm5OX7961/jJz/5Cf70T/8Uq6urMDExQVxcHF555RUEBgYCAP7u7/4OP/zhD/Hcc89hd3cXcXFx+Ld/+zftTox//dd/xfe+9z288sorsLCwwCc+8Ql89KMf1fZmAoDnnnuO1z4iOrNEmofFU4mIzqj5+Xmsra0hIiJCe+z73/8+lpeX8fd///cGjIyIiOiD4a/+6q8A4E13RRAREREREQEso0RE58Da2hp+//d/H729vQCAwcFBXL9+Hbm5uQaOjIiIiIiIiIiIiACWUSKicyAiIgIvvPAC/vzP/xzLy8twdnbGZz/72fdVJ5qIiIiIiIiIiIhOH8soERERERERERERERHRY2EZJSIiIiIiIiIiIiIieixMNhARERERERERERER0WNhsoGIiIiIiIiIiIiIiB6L4JMNGo0GKpUKbE1BRER0+nidJSIienJ4nSUiIqLzRPDJBrVaDYVCAbVafarP2d3dfarPaUg8n7ON53O2Ce18AOGdE8/nyeJ19p3xfM42ns/ZJ7Rz4vmcbWftfHidfWc8n7NPaOfE8znbeD5nm9DOhx4l+GTDk6DRaHB0dCSY1SU8n7ON53O2Ce18AOGdE8/n/BHaOfJ8zjaez9kntHPi+ZxtQjufNyO0c+T5nH1COyeez9nG8znbhHY+9CgmG4iIiIiIiIiIiIiI6LEw2UBERERERERERERERI+FyQYiIiIiIiIiIiIiInosTDYQEREREREREREREdFjYbKBiIiIiIiIiIiIiIgeC5MNRERERERERERERET0WJhsICIiIiIiIiIiIiKix8JkAxERERERERERERERPRYmG4iIiIiIiIiIiIiI6LEw2UBERERERERERERERI+FyQYiIiIiIiIiIiIiInosTDYQEREREREREREREdFjYbKBiIiIiIiIiIiIiIgeC5MNRERERERERERERET0WJhsICIiIiIiIiIiIiKix2Js6ACIiIiIiIiIiIiISJg0Gg1mZ+ahaO+FoqMbgQFBsHewM3RY9AQw2UBEREREREREREREp2Jrcxt93YPo7hxAd2cfehQDWFtd144HBgbg45963nAB0hPDZAMRERERERERERERvWcqlQpjw5PHSYXOAXR39mN8dAoajUbvccYmxggND4S3rzuKn8k1ULT0pDHZQERERERERERERETv6P7qOnoU/ehq70N3Zz96uwawu7P3yOM8vd0RkxCB6LgIxMRHICwiCMYmxlAoFLCztzVA5PQ0MNlARERERERERERERHqUSiWGB8bR3dmH7o7j5ML05Owjj7O0skBUbDhi4iMQHR+BmLhwOLk4PvI4lUr1NMImA2KygYiIiIiIiIiIiOgDbn1tA10dfVC096K7vQ89XYPY39t/5HH+gT6ITYhETEIEYhIiERjsB7FYbICI6axhsoGIiIiIiIiIiIjoA0StVmN8dAqK9l50tfeiq6Mfk2PTjzzOxtYa0XHhiEmIREz8cUkkWzsbA0RM5wGTDUREREREREREREQCtruzi+7OASjae6Bo70NPZz+2NrcfedzDXQuxiVGITYxEQJAvjIyMDBAxnUdMNhAREREREREREREJyML8EhRtvVC09aCzrRfDA2OP9EwwtzBHdFw44hKjtGWR7B3sDBQxCQGTDURERERERERERETnlEqlwujQBDpbe9DZ1gNFey/mZxcfeZyHlxviEqOO/5NEITgsAMbGvD1Mp4e/TURERERERERERETnxP7+AXoVA+ho7UFnaze6OvqwvbWj9xgjIyOERgQhXhKFOEk04iRRcPdwNVDE9EHBZAMRERERERERERHRGbV2fwM9igF0tnajQ96N/t5hKI+Ueo+xsrZEbEIk4iRRiEuMRkx8OCytLA0UMX1QMdlAREREREREREREdEbM3VtAh7wb7fIuNNe3Yu7eoyWRXN2ckZAUg3hJNOKl0QgOC4BYLDZAtEQ6TDYQERERERERERERGYBGo8H4yBQ6HuxaaJd3YWFu6ZHHBQb7IV56nFhIkMbA09sdIpHIABETvTUmG4iIiIiIiIiIiIieAqVSiaH+UbTLutDR2o3O1h6s3d/Qe4yxsRjh0aGIl0TB3skaz/3eM3BydjRQxKdjY20TPW396GzrRkhQCGzsbAwdEj0BTDYQERERERERERERPQFHh0fo6x5Cm0yBdlkXFO292Nne1XuMubkZYhIikZAUgwRpDGISImBpaQGVSgWFQgF7BzsDRf/+3V9eQ0/7IHrb+tHbPoipsXvaMV8/H1z9eIkBo6MnhckGIiIiIiIiIiIiolOwv3+A7o4+tMu70daiQE9nP/b3D/QeY2NrfVwOKSkGiUmxiIgKgYmpiYEiPh2Lc8vobRs4TjB0DGBuauGRx1wI8IJXgCuyilMNECE9DUw2EBEREREREREREb0Pu7t76GrvRVtL13FyoWsAyiOl3mMcnOyRmBSLxOQYSJLjEBTqf66bOWs0GizMLqGntR89bQPobhvA8vyK3mNEIhECQn0RlRiGaEkEIuNDYW1nBYVCARs7awNFTk8akw1ERERERERERERE78Luzi4U7X1obelEW7MCfd2DUCpVeo9xdXNGYnIsEpNjIUmOhX+Q77lu5qzRaLBwbwndrf3obutHb9sAlhdW9R5jJDZCcEQAoiXhiEoMQ0RcKKxtrfQeo1Lp/5xIeJhsICIiIiIiIiIiInoTuzu76GzrRVuLAq3NnejvGXokueDu6QpJShwkyXGQpsbB28fz3CcX5mcW0d3aj562fnS3DmB16b7eY4yNxQiJDkJ0YjiipeEIjw2BhaW5gSKms4LJBiIiIiIiIiIiIiIAe3v7ULT1QN7cidamTvR1Dz2yIt/Dyw3S1HhIkmMhSYmH1wX3c59cWJxdRndrH7pa+9HT2o+VxUeTC6HRQYiWRiBaEo7wmGCYM7lAv4PJBiIiIiIiIiIiIvpAOtg/QFdHH1qbOyFv7kSP4tGeC57e7pCmHu9ckKTEweuCh4GiPT1L8yvolvehu7UfXa39j/RcMDYWIzQmGLHSCERJwhEWEwxzCzMDRUvnBZMNRERERERERERE9IFwdHiEnq4ByJs60NrUia7OfhweHOo9xs3DBdLUeCSlxkOaGi+I5ML95TV0yfvRJe9Dt7wPC7NLeuNiYzFCogIRI41ArDQSYbFMLtB7x2QDERERERERERERCZJKpcJg3whkTR2QN3WiQ96N/b19vce4uDodl0VKjUNSajwu+Hqd67JIALC5vnW8a+FBcmFmYk5v/GFD55ik4+RCRFwIyyLRY2OygYiIiIiIiIiIiARBo9FgdGgCbTIF5E2daGtRYGtzW+8xDo52xzsX0hIgTY2HX8CFc59c2N3eRW/HILrkfeiS92FiaBoajUY7LhKJEBjmh9ikSMQkRSAyPhSW1pYGjJiEiMkGIiIiIiIiIiIiOrfuTc+hpbEdLQ3taK5rfSS5YG1jBUlKHJLSEpCUFo+gEH8YGRkZKNrTcXR4hIGuEShkvVC09GK4bwxqlVrvMb6B3ohNjkRMUiSiE8NhY2dtoGjpg4LJBiIiIiIiIiIiIjo3VlfWIG/qQEtDO2SN7Zi7t6A3bm5uhoSkGG1yISwyGMbG5/s2qEqlxtjgBLpkfVDIetHfOYSDff1eEx4X3BCbFInYpEhESyPg6GxvmGDpA+t8v8qIiIiIiIiIiIhI0Ha2d9Eu60JL43FyYWRwXG/c2FiM6LgISFPj4ehqg+c+/AwsLC0MFO3p0Gg0mJ2ah6yqE7f/f7XoaevH9uaO3mMcnO0RmxSJuJQoxCZFws3TxUDREh1jsoGIiIiIiIiIiIjOjKMjJXoVA8elkerb0KPoh1Kp0ntMSHggktMSkJIhQUJSDKysLaFSqaBQKGBqZmqgyB/P2uo6umR96GzpgaKlF8sLq3rjltYWiJFEIDY5CnHJkfAJ9D73vSZIWJhsICIiIiIiIiIiIoPRaDSYGJ1Cc0MbWhra0daiwM72rt5jvH08kZyegOT0REhT4+Hk7GCgaE/P/u4+ejsGtcmFieFpvXFjE2NcCPRARkEK4lNiEBzhD7Gx2EDREr0zgyQbVldX8Y1vfANyuRxisRhXr17F1772tTetnfZv//Zv+Ld/+zesr6/Dy8sLX/ziF1FcXGyAqImIiIiIiIiIiOg0rCytoqWhXdvYeWlhWW/czt4WyekJSMmUICU9Ed4+ngaK9PSoVGqM9o+js7kHnS09GFAMP7JjIzDMD3EpUYhPiUZoTBAGhwYQFxcHsfj8Jhm2N3Yw2DaEPvkAxocm4PJ1V/iEXDB0WPQEGCTZ8JWvfAVubm6or6/HysoK/vRP/xS//OUv8ZnPfEbvcbW1tfjFL36BX/3qVwgICEBpaSm+8pWvoLy8HN7e3oYInYiIiIiIiIiIiN6j/f0DdMi70VTXipaGNgwPjOmNm5qZIkEa/SC5IEFYZBCMjIwMFO3pWZxdRkdzNzqbe6CQ9T7Sd8HV0xnxKdGIS4lGXFIk7BxttWMqlep3n+5cODo8wljvBAZaB9HfOoTpoRloNBrt+L3RWSYbBOqpJxumpqYgl8tRV1cHCwsLXLhwAV/4whfwgx/84JFkw/j4ODQajfY/sVgMExOT99U9/jRfnA+f67y+4H8Xz+ds4/mcbUI7H0B458TzeXced5UMr7NvjedztvF8zj6hnRPP52zjdfb84fmcfUI7J57Pu6dWqzEyOI6WhjY017ejs7Ubh4dH2nGRSISwyCAkpyciOT0RcZIomJubacc1Gs17jusszM/O1i662/qhaOlFZ3MP5mcW9catrC0RkxSBuOQoxKVEweOCm17fhZOxn4XzeTc0Gg3mJxcw0DqEgbYhjHSN4XD/UO8xnv4eCE0MhqWLORLz40/1nM7zrg+hEWlOppWegoqKCrzwwguQyWTaY0NDQ7h69SpaW1tha6vL3i0tLeFP/uRPMDw8DLFYDJFIhB/84Ae4dOnSu/5+DxvDEBER0VtLTEx8X1/H6ywREdE743WWiD4o1u9voq9rCL1dQ+jvGsbmxrbeuIOTPaLiQhAZG4qImGDY2FobKNLTo1KpMTe5gJGeSYz1TeLe+DzUat3tViOxES4EeCAw0hdBUX7w9HOHWHz+d2zsbe9jdmgOM4NzuDc0h90N/R4bFjbm8A71hHeoJ7xCPWFlZ/nEYnm/11k6fU99Z8POzg4sLCz0jj389+7url6y4ejoCGFhYfjOd76DsLAw3LhxAy+88AICAwMRGhr6nr5vdHT0qWW5VCoVenp6TvU5DYnnc7bxfM42oZ0PILxz4vk8HbzOvjWez9nG8zn7hHZOPJ+z7ayeD6+zb43nc/YJ7Zx4PvoODg7R2dqD5vpWNNe1YWRoXG/cwtIc0pQ4JGdIkJopgV/ABb1V/Kftac3P0vzKcd+FB6WRdrb0b7R7+bo/KI0UhWhJOCyt39+N9rP0+6Y8UmK8bxL98kEMtA1hZvieXmkkEzMTBMcGIlwSijBJKLwCPB6Z67N0PvRkPPVkg6WlJfb29vSOPfy3lZWV3vFvf/vbSEhIQExMDADgQx/6EG7evInXXnsNf/VXf/Wevq9YLD71X+In8ZyGxPM523g+Z5vQzgcQ3jnxfJ4sXmffGc/nbOP5nH1COyeez9l21s6H19l3xvM5+4R2Th/U89FoNJgYnUJjXSuaauVol3Vhf/9AOy4SiRARHYLUTCnSsqSITYiEianJkwz9TZ32/Ozv7qOnfQDtjd3obO7GzMSc3ri1jRXiUqKQkBqN+LQYuHm6nNr3Bgzz+6bRaLA0s4y+1gH0ywYx1DmMgz390kjeQV6ISApDhDQMwTGBMDF7d3MttNcP6Tz1ZENwcDDW19exsrICZ2dnAMDY2Bjc3d1hY2Oj99i5uTlERUXpHTM2NoaJydN/kyIiIiIiIiIiIvqg2dzYQktDOxpr5Wiub8XC3JLeuKubM1KzJEjLSkJKRiIcHO0NE+gp0mg0mByZQXtjF9obu9DXOQTlkVI7bmQkQmhMMBLTYpCQGoPgqEBBlEba393HYPsw+mQD6JMNYGV+VW/cxt76OLmQFI5wSSjsne0MFCmdVU892eDn54fExER897vfxbe+9S2sra3hZz/7GT784Q8/8ti8vDz86le/Qm5uLsLDw1FWVgaZTIY///M/f9phExERERERERERCZ5arUZ/zxAaa+RorJWju7MfarVaO25qZorEpBikZSUhLVuKoBD/J1oa6WnZ2thGZ0sP2hu70NHUjdWlNb1xV09nJKbFIiEtBrFJkbC2tXqLZzo/NBoN7o3OorelH32yAYz2jEOt0s212FiMoJgARCaFIyIpDN5BXjAyOv9JFXpynnqyAQB+/OMf41vf+hby8/NhZGSEa9eu4Qtf+AIAID4+Hi+//DKuXr2KL37xixCLxfjSl76EjY0N+Pr64qc//SnCw8MNETYREREREREREZHgrCzfh6yxHU21rWiub8Xa/Q298YAgX6RlJyEtS4rE5FhYWJgbKNLTo1KpMdI3jvZGBdobuzHcO6rX2NnM3BQx0ggkpMUiMS0GXn6P9iA4j7bWtzHQOni8e0E+gM37W3rjLl7OiEwOR1RyBELig2FuaWagSOk8MkiywdnZGT/+8Y/fdKyzs1P7/8bGxvjSl76EL33pS08rNCIiIiIiIiIiIkFTKpXo6uhDfVULKu7WYnpiVm/c2sYKyemJSM9JQnpWEjy83AwU6elaW11HR2M32hoU6GjuwdbGtt64b6A3EtNjkZAeg6iEMJiamRoo0tOjVqkxOTiN3pZ+9Lb0Y2pwWq+xs5mFKULjQxCZHI7I5HC4ep9uvwn6YDFIsoGIiIiIiIiIiIienqXFFTTWyNBQI0NLQzu2NvVvtEdEhyItS4r0nCTExEfCxOT83zZUKVUY7BlFW4MC7Q1dGB2Y0Bu3srFEfEr0cYIhLQYu7k4GivR0ba5tHe9caOlHX+sgdjZ29Ma9Aj0f7F4IR2B0gEGaeJMwnf93DSIiIiIiIiIiItJzdKREV3svGh4kGIYHxvTG7R3skJopgbefGz72Bx+Ci5uzgSI9XVvr26h4oxbtTd1QNPdie0v/RntQuD8SM2IhSY9FWEwwxMZiA0V6etQqNSYGprS7F6aHZvR2L5hbmSNCGoaolAhEJofDwcXecMGSoDHZQEREREREREREJAAPdy/UV8vQ0tCmd6NdJBIhMjYMGTnJyMhJRmRMKABAoVDA0dnBUCE/NpVKjaGeUbTWd6K1vhPjg1N649a2VkhMi4EkIw7xaTFwdLY3TKCnbGttC33yQfQ296FPPoCdzV298QvB3ohKCUdUSgT8I/1hLICkCp19TDYQERERERERERGdQyqVCj2dA6ivbkF9dQsG+0b0xh0c7ZCWlYSMnGSkZknh6GT/yNefRxv3N9He1IXWegU6mrof6b0QFOEPaWY8JBmxCIkKglhsZKBIT49arcb08D30Nvehp7kfkwNTersXLK0tEJF0vHshIikc9s52BoyWPqiYbCAiIiIiIiIiIjon1u6vo6m2FXVVzWiqa8XG+qZ27OTuhczcFEREh0AsPv8r2tVqNUYHJtBWr0BrvQLDvWN6N9of7l5ISI+Fqa0IGVnpgjjvna1djHZMQHG7D/3yQWytbemN63YvRCIg0k8QJaHofGOygYiIiIiIiIiI6IzSaDQY6B1BfVUz6qtb0KMY0LvRbmNrjfTsJGTmpSAtKwlO57gk0kk7W7voaO5Ga10n2hoUWL+/qTceEOYLaUY8JJlxCIsOgthYDJVKBYVCYZiAT4FGo8HcxDx6mvrQ09yHsd4JqFVq7bi5pTnCpaGITo1k7wU6k5hsICIiIiIiIiIiOkN2d3bR0tCO2spmNFS3YHlpVW88NCIIGbnHuxdi4iNgbHz+b/FpNBrcm5xDa10n5HWd6OscgkqpK/NkYWWB+JRoSDPjkJgeC2c3RwNGe3oO9g8x1DGsTTDcX1zTG3dwt4ckJwHRaZEIig6Ascn5n2sSLv52EhERERERERERGdj05D3UVbWgvqoZbbIuHB0eaccsLC2QmpmIjNwUZOamwM3dxYCRnp7Dg0P0tA2gtf44wbBwb0lv3NvPE9KsOEgz4xGZEAYTgdxoX124j56mXnQ392GofURvrk1MTRCaEPxg90IYZhZnEBcXd+7LQqmUKkz1TmBKPoGI0AhYWFsYOiR6AoTxCiUiIiIiIiIiIjpHjo6U6GztRl1lM2qrmjE1PqM37u3jiaz8VGTlpUKSHAtTM1MDRXq67i+vQf5g90Jncw8O9g+0Y8YmxoiRRkCaGY+krHh4XHAzYKSnR61SY6J/Et1Nvehu6sPs2JzeuIOrA2LSIhGdGonQxBCYmR/PtUqlwszizJs95bmwu7GD0fYRjLYOY6xjBAe7x3Pt7e2NpGdSDBwdPQlMNhARERERERERET0F62sbaKiWoa6qGY21cmxtbmvHjI3FiJfGICsvFVl5KfAL9IFIJDJgtKdDrVZjbHAS8toOyOs6MdI3rjfu5OoASUYcpFnxiE+JhoWluYEiPV2723volw2gu6kXPS392NnY0Y6JjEQIjPJHdGokotOi4BXgIYi51mg0WJpcxEjrEEbbhnFv8B5wor+IpZ0lHAOcEZ4RacAo6UlisoGIiOgcODo8Qru8Gw01LYCRGnFxcYYOiYiIiIiI3oFGo8HYyOTx7oWKJnR19EGt1jX8dXC0Q0ZuCrLzU5GaKYWNrbUBoz09+7v7UMh6H+xg6MD95XW98ZCoQCRlJyApMx6B4X6CuNEOAIszS8fJhaY+DCtG9Zo7W1pbIColAtFpUYhMDoe1nZUBIz09ysMjTHZPYEQ+hJHWYWyubOiNuwW4I1gaimBpCNwCPNDV3QVLW0sDRUtPGpMNREREZ9TDVU+1lU1orJVje+t4JYyZuSm+8pd/auDoiIiIhOXe9DyqyxswMT6B6Ojoc18bm4gM5+jwCG0yBWormlBb2YzZmXm98ZDwQGTlpSI7PxVRceGCeb9ZWViFrK4DspoOdMn79PoQmFuYISEtBklZ8ZBkxsPR2d5wgZ4ilVKF0Z5xdDf2oruxF4sz+j0nPPzcEZ0WiZi0KARG+UNsLIy53l7bwkjrMEbkQ5hQjOHoQDfXxqbG8I8NQJA0FEGSENi52GnHVCrVmz0dCQiTDURERGeERqPBxOgUaiqbUFfZDEVbr96qJ0dnB2TmJCM6MUQwK3+IiIgMRaVSoVcxgJrKJtSUN2FseEI79qGPXkVkTJgBoyOi82Z9bQP11S2oKW9EU10rdrZ3tWOmZqZISo1HVn4qsvPT4OEljD4EGo0GowMTkFW3Q1bbgbHBSb1xN08XJGXHIzk7EdGScJiYmhgm0FO2u72HPlk/uht70dPcj90t3VyLjcUIiQtCTHoUotMi4eoljEbeD8sjDcsHMSIfwtzwrN64jZMtgqUhCE4KhV+0P0zMhdFfhN47JhuIiIgM6OhICUVbD2rKG1FT0YSZKf0PbcFhAcjOT0N2QRqi48Kh0WigUCgMEywREdE5t7uzi+b6tuPVxlXNWFtd146JxWLES6MRFR+C0IggwwVJROeCdqFQRRNqKhrR1a5fHsnZxRGZeanIKUhDckYiLC0tDBjt6TnYP0SXvBeymg7I6zqwurSmHROJRAiLCUJyTiKSshLgG+QtmEVSS7PL2t0Lv1seycrOCtEpEYjNiEZEUhgsrIQx18ojJaZ6jssjDcuHsLmsXx7JI8gTwUmhCEkKg1uAu2Dmmh4Pkw1ERERP2dbmNprq5Kgpb0J9dQs2N7a0YyamJpCmxCE7Pw1Z+anwuuCh97XcdkpERPTeLC4so7aiCTXljZA3d+Lw4FA7ZmNrjfScJGTnpyEzNwVW1pZQKBS8YUJEb0qpVKJd3o3f/voNvNTzD5ievKc3HhIeiJyCdGQXpCEyJhRGRkYGivR0ra2uQ17bCVlNOzpbenCwr3sfPS6PFIvknARIM+Jg72T3Ns90fqjVakz0T6GroQddDT2Yn1zQG3f3dUNsejRi0o/LIxmJhTHXe1u7GG0bwbBsEGMdIzjc0821sakJ/OMCEJwUimBJCGycbA0YKZ1VTDYQERE9BXP3Fo5vdFQ0orVFAeWRUjtm72CHzLwU5BSkIy1LCitrNssiIiJ6vzQaDYYHxlBd3oCa8ib09wzpjXv7eCKnMB3Z+WlISIqBiYnuz2Im9Ynod+1s76KxVo6a8gbUVekvFDI2MUZSajxyCtKRlZ8KT293A0Z6ejQaDWYm5tBS3QZZTTsGu0eh0Wi04y7uTkjKTkBydgJipBEwNRNGyZyD/UMMtA2hu6EHXY292FrTzbWR2AjBsYGISY9CbHo0XL2FUR4JAO7PrWJYPohh2RBm+qehObFDx9rRBsHSEIQkhcEvhuWR6J0x2UBERPQEaDQa9PcMacsjDfWP6o37BfogpyANOYXpiE2IFExTOCIiIkM4OlKiXabQXnfn7ulWoIpEIsQmRCK7IA05BekICPblzgUieluLC8vH7ycPdkSdbHRsZ2+LyNgQXPu9y8jISYa1jZUBIz09KqUK/Z3DaKlpg6ymA3PT+iv5gyMDkJydgOScRASECud9dPP+Jrqb+tDV0IP+1kG9RsfmVuba8kiRyRGwshXGojCNWo3Z4XsYlg1hWDaIlZllvXFXPzeEJIchJCkMHkEeEAlkhw49HUw2EBERnZKjwyPImztRXdaAmoomLC3oPrQZGRkhThKl3VbtH+hjwEiJiIjOv63NbTTUyFBd3oDGGjm2Nre1Y+bmZkjNkiKnIA1Z+WlwcnYwYKREdNZpNBqMDI4/+BzfiL5u/R1RPn5eyCnMQE5hOqLjwtHb24O4uLhzv2Bob3cfrfWduPtaBf6u/+fYWte9jxqbGCM2KRIpuYlIzkqAs7uTASM9PRqNBgtTi1DUd6OroQcT/VN6uzYc3RwQlxmDmPQohMQFwdhEGLdOjw6OMNk1jqGWAYy0DmFnfUc7ZiQ2gk+UH0KSwhCSHAp7N14z6f0TxiuGiIjIQDY3ttBQI0NNeSPqq1uws72rHbOwtEBalhS5RenIzE2Bg6O94QIlIiISgIW5JdSUN6K6vAGtzZ1QKnVlj5xcHJGdn4qcwnQkpyfCwsLcgJES0VmnVCrR2dqDqrIGVJc1PLIjKiY+ArlFGcgpSIN/kG4l/3kvt3Z/ZR3ymnY017RD0dKrt2vDxs4a0sx4pOQmIiEtBpYCaXSsVqkx3jcJRUM3uup7sDizpDfuG+aD2IxoxKZHwTvISzC7NnY3dzHSerx7YbxjVG/XhpmlGQIlIQhNDkNgQhDMrYUx12R4TDYQERG9RwtzS6gua0B1eQPaWhSP3OjIKUhDXlEmktLiYWZuZsBIiYiIzjeNRoORoXFUlzagurzxkf4LAUG+xzcDH6w2FkozViJ6MnZ399BU24rq8gbUVTZjY31TO2ZmZvpgR1Q6svNT4eTiaMBIT9fMxCxaqtvRUt32SP8Fjwuu8A+/gCu/V4LoxHCIjc/3bo2HDg8OMdwxetzg+Xf6L4iNxQhLDNHuYHBwsTdcoKdsbeE+hlsGMSQbxEz/FDRq3VzbOtshJCUMIclh8I30hVgguzbobOFvFRER0TvQaDQYHZ5AdWkDqsoaeKODiIjoCVIqlVC09aK67Pi6Ozszrx0TiUSIS4xCblEGcosy4OvvbcBIieg8WF1ZQ21FE6rK6iFraMfBwaF2zN7BDtkFqcgrykRKpkQwO6LUajWGekbRXNWGlup23Juc0xsPiQpESk4iUvMk8PLzQFdXF6Ljws99WajtjR10NXSj9lYDXhn+NQ73dXNtYW2B6NRIxGVEIzIlHBYC2bWh0WiwMDaPoZYBDMsGsTS5qDfu5u9+3H8hOQzugR6C2bVBZxeTDURERG9CpVKhq6MPVaXH26pnpma1Yw9vdOQUpiO3KAN+ARcMGCkREdH5t79/gOb6NlSV1qG2ohnraxvaMTMzU6RkSpBXlMH+C0T0rsxMzaLybj2qyxqgaO/VW8nv7eOJvKIM5BRlIC4xEsbGwrg1dnR4hC5534MEQxvWVnXvo8bG4gf9FyRIzkmEs5tu18Z5Lwu1unAfivpuKOq7MdI1BrVKrR1zcLVHXGYMYjOiBdV/Qa1SYbpvCkPNAxhqGcTmim6uRUZG8I3yRUhyOPsvkEEI41VGRER0Cg72DyBr7EBVWT1qKppwf2VNO2ZqZoqUjETkFWUiu4A3OoiIiB7X5sYWaiubUFXagMZaOfb39rVjdva2yC5IQ25hOlKzpLC0FMYKVCJ6MjQaDQZ6R1BdVo/K0nqMDk3ojUdEhyKvKAO5xRkICvEXzOru3e1dtDZ0oaWqFfJ6BfZ29rRjltYWD/ovSCBJj4WVjaUBIz09Go0GcxPzUNR1o7OuG9PDM3rjXgEecAt2RfGHC+AX5iuYuT46OMJI1xCGWgYx2jqEvS3dXJuYmSAwMRihKeEIkgTDQiBzTecTkw1ERPSBtrW5jfrqFlSV1qO+Woa9Xd2HNhtba2TlpSK3KAMZOUmwtOKHNiIiosexML+EmrJGVJbWo12m3/fIw8sNuUUZyC/ORLw0WjCrjYnoyXi7Bs9isRiSlDjkF2cipzAd7p6uBoz0dK2trqOluh3NVW1QyHqhPFJqxxxd7JGSI0FqngQxSZEwEcpKfrUa472TUNR3o7OuC8uzK9oxkUiEoJiA4x0MmdFwcneEQqGAT8iFc59o2N3cxVDLANrLZCifuAXloW6uLW0tEZwchtCUMPjHBsLEzMSAkRLpCONdh4iI6D1YXb6P6vIGVN6th6ypQ+8Duqu7C/KKMpBXnIHE5DjBfEAnIiIylImxaVTerUNVaT16uwb1xoLDAh5cdzMRFhl87m8MEdGTdbB/gOaGNlTefbTkmrmFOdKzk5BXnIGsvFTY2dsaMNLTNX9vEc2VrWiqasOAYlivLJSnrzvS8qRIy5MiJDpQMP3jlEdKDHWMoLOuC4r6bmze1zV4NjY1RoQkDHFZxw2ebR1stGPnvSzU5soGhloGMdTcj6neKWjUurJQ9m4OCE0JQ2hqOLzDfGAkFsZck7DwDgoREX0gzM7Mo6b8eCWlok2/bqt/oA/ySjKRX5yJyJgw3uggIiJ6DMflTIZRebcelXfrMD46pR172PcorzgTuUXp8PFjg2cienvbWzuoq2p+053I2gbPxVlIyUgUTINnjUaDyZEZNFXK0VzVhvGhKb3x4MgApOVJkZonwYUAL8H8/XKwd4Be2QAUdV3oburD3rZurh82eI7PikFkcjjMLYUx1wCwem8Fg839GGoewNzIrN6Yq58bbP3skX0tF+4BbPBMZx+TDUREJEgajQajwxMov12LW6+XYWZyTm88MiYU+SVZyC/OhH+Qr4GiJCIiEgaVSoXOtl7tDob52UXtmLGJMZLTEpBfkomcgnQ4uzoZMFIiOg9WV9ZQU96IqtJ6tDS24+jwSDvm7umKvOJMwZVcU6vVGO4ZQ+ODBMPctK4slJHYCFEJYUgvSEJKrgQu7sJ5H93Z3EF3Yy866rrQLxvUm2tbRxvEZcYgPisWoQnBgmnwrNFosDA2j8Gmfgy1DGBlZlk3KBLBO+wCwlLDEZoaDlsXOygUCrj6uZ37RMPh3gGmOkYw3zGGsMAQWNnbvPMX0bkjjFcpERERjj+09XUPouJOHSrv1mFq4p52TCw2QkJSLPKLM5FXnCmouq1ERESGcHhwiOaGNvzfX7+Ons5vYW11XTtmbmGOjJxk5JdkIisvFTa21oYLlIjOhfnZRVSWHu+I6mztgfpE+Ri/QB/kl2SioCQLEdGh5/6m60MqpQo9bQMPEgytuL+8rh0zMTVBQmo0UvOlSM5OgJ2DcMpCbaxuorOuC521XRjqHIFapZtrZw8nxGfHIj4rFgGRfoIpFaRWqXFvcAaDTf0YbO7H5rKuBJiRsRh+Mf4IS41ASHIorAVUFmp/axfTHaOYbBvGXN8U1A96NU2EDCGqWGLg6OhJYLKBiIjONZVKhc7WHlTcPU4wLM7rVoWYmpkiJSMRQWG++IM//iicXRwNGCkREdH5t7e3j8Ya+XG99MombG/taMds7WyQU5iO/OJMpGZJYW5uZsBIieg8mByfQeXdOlTcqUVf95DeWER0KPKKM1FQkomAYD/DBPgEHB0eoaO5B02VcrRUt2NrY1s7ZmFlgaTMOKQVJCExPRaWVhYGjPR0rcyvorO2Cx21XRjvndAra+sV6In4rFjEZ8XAO0g4ZaFUShUmuycw1NyPoZZB7Kzr5trEzASBicEIS41AkDQE5lbCKQu1c38LU+0jmGobxsLgjN5c27o7wNzHDsGZUQaMkJ4kJhuIiOjcOTo8gqyp47hUQ1mD3kpKSysLZOWlIr8kExk5KTC3MINCoYCDo53hAj4lywuraK5uxfbuJuLi4gwdDhERfUBsbW6jrrIZFXfr0Fgjw/7+gXbM2dUJ0fGh+MgnnkNSWgJMBFLigoieDI1Gg5HBcZTfqUXl3TqMDk1ox0QiEeKl0dpSp57e7gaM9HTt7e6jvaELjRUyyOsV2NvR9SKwdbBBSo4E6QVSxCVHwcTUxICRnq75qQV01BzvYJgentEb84/wQ0LO8Q4GV28XA0V4+o4OjjDeOYrB5gGMyAaxv7OvHTO3MkdwUijC0iIQEB8EEzPhzPXm4hqm2oYx2TaC5TH9EsZOvq7wlYTAVxICW3cHKBQKGAvo3EkfPwkSEdG5sL9/gKZaOSru1KK2shlbm7pVIbZ2NsgtSkd+SRZSMyQwO7GS8rxvO52fWURDuQyNFXIM944BAExMjfHRT33IwJEREZGQ3V9dR015Iyru1KKlsR3KI6V2zNPbHQUlWSi4mI3I2FB0d3cjLi4OYrHYgBE/Po1Gg6GeUYz1TzGpT3SKNBoNxkemUHtXjqqyekxP6hrgGhuLkZSWgIKLWcgtzICTgHYib2/uQFbbgcYKOTqaunB4oOtF4OTqiLR8CdILkhEZHwqx8fl+/3xIo9Hg3ugsOmoU6Kjtwvykru+EyEiE4Nig4wRDZgwcXB0MGOnpOtw7wEjbMAYb+zHaPoKj/UPtmJW9FUJTwhGaGgG/aD+IBZSUX5tdwVTrMCbbhnF/eklvzDXYC76SYPhJQmDjYq89ft7/Pqd3JpzfcCIiEpzdnV3UV7eg/HYt6qtl2NvVrQBydnE8XvVUkoXE5FhBraScHruHhgo5mirkGB+a0h4XiUSIiAtBdFqoAaMjIiKhWl5cRWVpHSru1KGtRaFXL90/0AcFF7ORX5KF8KhgbYmL837TQKVSY0AxjMaK48T+yuJ9AIAkKQEBoX6GDY7oHFOpVFC096LiznGJpJOlTs3MTJGWnaRtGm9rJ5wmsRtrm2ipbkdDuQxdsl4olbr3SHdvV6QXJCG9IBkhUQEwMhJGLwKNRoPJgSm01yjQUdOFlbkV7ZjYWIxwSSgScuIQmx4FGwfhzPX+9h6G5UMYbOrHeOcolIe6pLytix3CUiMQlhYO7zAfwfSd0Gg0uD+9hMnWYUy2DmFj/r52TGQkgnuYD/ykIfBNCIKlgOaa3hvh3JkhIiJB2NzYQm1lEyru1KGpVo6DA92qEA8vN+1KytjESEF9QB8fnNQmGGYmdNtOjcRGiJFGIL0gCal5Utg52EChUBguWCIiEpSFuSVU3K1D+e0aKNp69eoqh0UGP7juZgmqXrpKqUJP+wAay2VoqmrD2sq6dszC0hwRkmC4e7sZLkCic0qpVKKtpQsVd2pRWVqP1WXdjUhzczNkFaSh6FI2MnKSYWllacBIT9f9lXU0V7aisUKO7rZ+vWbHvoHeSC9IQlpBEvxDfATTi0CtVmOsZ0K7g2FtaU07ZmJmgqjkcCTkxCE6LQqW1sLpO7G7sYMh2SAGm/ox0TWubXYMAA4ejghPj0RYWgQ8gjwFM9cajQbL4/PaHQxbS+vaMSOxETyj/OAnCYFPQhDMbYTzuqb3j8kGIiIyuPW1DVSXNaD89qOlGnz8vFBwMRsFF7MQGRMmqA9tI33jaCiXoaFchoV7um2nxibGiE+NRnpBElJyEmFrr1sVct5XkBIRkeHdm55D+e1aVNytQ09nv95YdHwECkqyUHgpG94+ngaK8PQdHSnRJetFQ7kcLTVt2Fzb0o5Z2VgiOTsR6YVJiE2KxMBgP8zMTQ0YLdH58bCXWsWdWlSVNmB9bUM7ZmNrjZzCdOQVZ8DK1hTSJOm5L7f20MbqJq7/+i6aKtvQ3zmkl6gNDPM73sFQmIQL/l4GjPJ0qZQqjHSNor1agc76bmyubmrHzCzMEJ0WicScOESlRMDMwuxtnul82V7bwlDzAAaa+jHVMwnNiV1/Lj6uCEuLQFhaBFz93ITzt6pag8WRWUy2DmGqbRg793XXTLGJMbxj/OEnDcGFuCCYWgpnrul0MNlAREQGsbqyhuqyepTfroW8qVPvJnpgsB8KLmah8FIOgsMCBPOhTa1WY6hnVNuDYenEFmNTMxNIMuKQXpCEpKwEWHFVCBERnaKpiXsou1WN8tu1GOwb0R5/2JC14GI2Ckqy4O7pasAoT9fR4RE6mnvQWC5DS3U7trd2tGO29tZIyZUivSAJcSlR2nKMTOoTvbPDg0M01bei/HYtasob9Xqp2TvYIa84AwUXs5GclgATU5PjkkoC2Jm7OLuMxgoZ6stkGOoZ1RsLiQpERmEy0guT4CGgnVFKpQpDHcPoeJBg2F7XzbWFtQVi06OQkBuPSGmYoJodb61uYrB5AAONfZjumwJOJJPcAzy0CQbnC8JpbK1Wq7E4fA+T8mFMtg1hb113zTQ2N8GF2ED4SUPgHRMAEybk6W0w2UBERE/NytIqKkvrUXarBu2yLr1a0KERQSi8mI3CS9nwD/I1YJSn62Et6IcJhtWlE9vJLcwgzYpHRmEypBlxMLc0N2CkREQkNBOjUyi/XYuy2zUYHhjTHjcyMoIkJQ6Fl7KRV5QJFzcnA0Z5ug4PDtHe1I2GchlkNe3Y3db1e3JwskNavhTpBcmIloQLpiEr0dOwv3+Aplr5cYKhohE727vaMScXR+QXZ6LgYjYkKbEwNhbOrab5e4toKDveiTzSN649LhIB4XEhyChIRlpBElw9nA0Y5elSHikx0DaEjhoFFPXd2NnUzbWVnRXiM2OQkBOHsMQQGAuob97G8gYGm/ow0NiPewPTemOeIV7aEkkO7sJpYq5WqbEwOIPJ1iFMtg1j/8Rcm1iYwifhuMGzV7Q/jE2FM9f0ZPE3hYiInqjFhWVU3qlD+Z1adMi79bYYR0SHovBSNgouZsPX39uAUZ4ulUqNvo5B1Je1oKmyVb8WtJUFkrMTkFGYjIS0GJgLaIsxEREZ3ujwxHGC4VYNxoYntMeNjcVISk9EwcUs5BVlwtHJ3nBBnrKD/UO0N3ahvqwF8toO7O3ua8ecXB20DVkj4kMhFkiTTqKnYW9vH401cpTdrkZdZTN2d3TJO1c35+OdyJdzEJcYJZjySAAwOzV/XOq0TIaxwUntcSMjEaISw5FWIIWtqwUyczIEc94qpQo9TX3orOtCV32PXqLWxt4a8dmxSMyNR0hckKASteuLaxhs6sdAYx9mh+7pjXmHX0B4WiRC0yJg72pvmACfALVShbn+6eMSSe0jODgx16aWZvBNDIZfUig8I3whFlAyiZ4e/tYQEdGpW1xYRvntWpTfqoGiXb/ZZFRcOIou5aDgYja8fTwMGOXpUqnU6G0fQMPDBMOqrl6tlY0lUnISkVGYjPjUaJiacdspERGdDo1Gg5GhcZTfqkH57VqMj05px4xNjJGSnoiiyznIKUyHvYOdASM9Xft7B2irV6C+vAWtdZ3Y3zvQjjm7OT4oZ5KM8NhgGBkxwUD0bu3u7qGhugXlt2tRV9WCvV3djUg3DxcUXspB0aUcxCRECOq1dW9iDvXlLWgsl2N8SPc+amQkQow0EhlFyUjNk8DByV4wZaGODo7Q1zqItsoOKOq7cLh/pB2zdbJFQnYsEnPiERwbCCMBJWrXFtYw2NSH/vpezI/O6QZEIlyI8EF4WiTC0sJh6yyca+ZxgmEKE/IhTLeP4GBHl5Q3s7Y4TjBIQ+ER4SOoZBIZBpMNRER0Khbml/QSDCfFJkSi6HIO8kuy4OntbqAIT9/DBEN9aQuaKuVYv69rkmZta4XUPAkyCpMRlxKtrQVNRET0uE4mGEpv1WByTFfuwcTUBKmZkuMEQ0E6bO1sDBjp6XqYYKgra0ZrnQIH+7oEg6unMzIKkpFRmIyQ6EBB3QQletIeJhhKb1ajvqoF+ydeW57e7ih4UOo0Oi5cUK+thwmG+tIWTI7MaI8biY0QlxyFjMJkpOZKYOdoa8AoT9fDBEN7VQe6Gnqxf2InmJ2zHRJz4pCYG4fAqADBJRgGGnsx0NCnl2AQGYngE+mL8PRIhKZGwMZRONdMbYJBNoSpjhEcnkgwmNtYwlcSDP+kMLiHXRDUXJPh8c4HERG9b2+XYIhLjELh5RwUXswWVLNJlVKFnvYBNJTJ3jTBkJYvRUZhMmKTo5hgICKiU/N2CQZTM1OkZUlRdCkH2QVpsLG1NmCkp2t/dx+tDQrUl7U8kmBw83RBRlEyMotSEBwZAJFIZMBIic6Xd0owFF3OQdHlHETGhAnqtfVWCQaxsRhxyVHILEpGSq4EtvbCuel8dHCEPvkA2qs7H0kwOLjaIz47Fjaelii6VggTE+E0eV5bWMPwgybPjyQYovwQ8SDBYO0gnGumSqnCXN8UJuVvkmCwtYSfJAR+SaFwD2WCgZ4c3gUhIqL35GGJpLKb1Y8kGOIl0Si8nIOCi1lw9xBQguFtdjDY2FkjNU+KzKJkxCZFCqpJGhERGdY7JRjSs5O0CQZrGysDRnq63inBkFmcgoyiZARHMMFA9F48TDCU3apBXVUL9vd0NyK9LnhoEwwR0aGCem29XYIhPiX6uERSrgQ2dsK56fxOCYaEnDhIcuPhH+kHjUYDhUIhiF0rawtr6K/vQXt5K+7M68raioxE8I3yQ3hGFEJTwgWVYHi7HQwWdlbwTQyGf1Io3MIuCGKO6ewzyB2R1dVVfOMb34BcLodYLMbVq1fxta99DcbG+uF85jOfQXt7u96x3d1dfPSjH8W3vvWtpxkyEdEH2tLiCipu16L0VjU6W3u0x0UiEeIkUSi6fNyDwc3dxYBRni6VSo3+zkHUlbagqUKu14PB1v5hgiEFMdIIJhiIiOhUjQ5PoPRG1ZuWSMrISRZmgmHvAG0PEgzy2k4mGIhOyd7ePuqrWlB2q/oDlWCYnZpHfWkL6staMDGsex8VdILh8AgDrUNorepAV33P2yYYTt50VqlUhgj31KwvrWOg4bhE0tzIrPa4yEgE32h/hKdHIiw1HFb2wplrtVKF+YFpTMgHMdWm34PBws4KvpKQ4wRDqDcTDPTUGeTuyFe+8hW4ubmhvr4eKysr+NM//VP88pe/xGc+8xm9x/3Lv/yL3r9/+9vf4ic/+Qm++MUvPs1wiYg+kFaW76O6tAGlt6rRIe/Wa/IcL41G8eVcFFzKhqubswGjPF3HCYYhNJS3oKFcjrWVde3YcYmkJGQVM8FARESnb2J0CndvVqPsZjXGRia1x01MTZCenYTiy7mCSzAcHhyirbEL9XebIavt0GvyzAQD0fu3v3+AxhoZSm9Vo7aiWa/Js6e3O4qv5AoywTA/s4j6shbUlTZjfFDX5FlbIqk4RXAJBuWREgNtQ2ir6oCivgd727q5tnexR2LumycYzruN5Q1tD4bZoXva4w93MFj52CL/w4WwdRJOvw21So2FgRldguHEXJvbWsJPGgr/5FC4hTDBQIb11O+UTE1NQS6Xo66uDhYWFrhw4QK+8IUv4Ac/+MEjyYaTxsfH8e1vfxv/+q//CldX4ZTmICI6S1aX76Psdg1e+++bGOob10swxCVGoehKruBKJKnVagx0jaC+tBkN5TLcX17XjlnbWCE1X4LMohTEJUcxwUBERKdqcnwGZTerUXqrGiOD49rjJqYmSMuSovhyLnIK0wWVYDg6PEJ7Yxfqy1rQUtOBvR3dzRJXT2dkFqUgsziFCQai9+jw4BCNda0ou1mN6vIG7O7oJxiKLueg+Equ4BIMi7PL2gTDaP+E9riR2AjxDxMMeVJhJRiUKgy1D6O1sgOKui7snkwwONshMTcekjzhJRg2Vze1OxjuDerKYUEkgm+ULyIyohCWFgFzGwsoFApY2Z//a6darcZ8/zTuVQ5i8F+b9BMMNpbwk4awRBKdOU/9rsnIyAjs7e3h5uamPRYYGIi5uTlsbm7C1vbNs44vv/wyrl27BolE8r6+72luC3v4XOd9q9lDPJ+zjedztgnhfNbub6C6rB6lN2vQ1qKAWq3WjkXHR6DoUjYKfqfJ83k63zebI41Gg+HeMTSUydBQLsPK4n3tmJW1JVLyEpFRmILY5Ei9Js9n4byf1O+cWCx+rK/ndfat8XzONp7P2Se0c5oan8HN/1eB773wUwwPjGmPG5sYIzVDgsLLOcj5nSbPZ/nc3838HB0poWjpQUOZDC017Xo3xpzdnZBRkHScYDjR5Pnk55GnidfZ8+eDfD5Hh0doaWhH2e0a1JQ1Ynt7Rzvm7uGKwsvZjzR5NsRr67TnaHlhVfs5frhX9z5qZCRCjDQSGQ+aPNs56Jo8n/fXkEqpwrBiFO1VnVDUd2Nnc1c7Zutoi4TsWCTkxiEw2l9701mj0byrGM/ya2h7bRuDTf3HCYYBXTksiIALEb4IT494pMnzWT6fd0Oj1mBpdPa4B0PbMPZPzLWZtQV8E4PhlxQCtxNNnt/tXJ8FZ/U6S6dHpDm5bPUpeOONN/CP//iPqKmp0R6bnp5GYWEhamtr4e7u/sjXtLW14TOf+QzKy8vh4vLe6oGrVCooFIrHjJqISFh2d/bQ3tIDeWMn+rtH9P7o8A+6gKT0eEhSY+Ds6mjAKE+XRqPB/NQSeuSD6G0dwvqKrsmzmbkpwuKDEJ0UisBI3w/kDobExMT39XW8zhIRvbOVpftobVJA3qjA5Jiu3INYbISImBBI0+KQkBwFK2tLA0Z5ulQqNSYGptEjH8RAxyj2TtSTtnWwRqQ0FFHSUHgHeMDISDirrN8Kr7N0WlQqFQZ6RiFv7ESHrAc7J5J3Do52kKbFQpoeh4BgH0GtdN5a30Zv6zB65YOYHp3THheJRPAL9UZ0UhjCE4NgbXv+V7M/pFarsTC+hLGOCUx0TWFv+0Rdfmtz+Mf5ITDeD+4BroKa68OdAywMzGO+dxb3p1aAE3ctHXwc4R7pBfdwD5jbWhguyFOm0Wiwu7CJ9eFFbIwu4WhbV1ZQbG4Mu0BX2Ae7wvqCA0QCmuvT9H6vs3T6nvrdFEtLS+zt7ekde/hvK6s3vyj85je/wcWLF99zouGk6OjoU8tyqVQq9PT0nOpzGhLP52zj+Zxt5+l8trd2UFvZhLKb1Wiqb4PySKkdC40I0jaH8/ByOzfn9E40Gg3Ghybx6n/cxEjXJOZnFrVj5hZmSMqKR0ZxChLTYmBqZmrASN+9s/o7x+vsW+P5nG08n7PvvJ7T0uIKKm7XovRWNbo7+rXHxWIjhEYG4fmPXkZ+SRbsHewMGOXjOzk/gAh9HYOoL21BU2UrNte3tI9zcLZDekEyMotTEBYTdGZvjJ3V3zdeZ9/aB+F8VCoVOlt7UHqzGpWl9VhbXdc+3tnFEQWXslF0KQexiZFn8rX1fudo4/4mmipbUVfajL6OIW2JV5FIhIj4UGQWJyMtTwoHZ/snFPmbe5K/c2q1GhP9U2iv6kRHjQIbq7oFUlZ2VojPikFiXjxCYoO0q9of11l4De1t72G4ZRADDX2Y7B6HRq3LMHiGeCE8IxJhaZGwdX7nHgxn4XzeDY1Gg9XJRUzKhzDZOoSdVd0108TCDD4JQfBLCoVbqBf6BvrP/Pm8Wwc7e+huaUd8VsoHcpHfB8FTn9Xg4GCsr69jZWUFzs7HTUXHxsbg7u4OGxubRx6vVCpRWVmJn/70p4/1fcVi8am/KJ/EcxoSz+ds4/mcbWf1fPb29lFX2YS716tQXyPD4cGhdiwo1B/Fl3NR/Ewe/AIuaI8/3M54Vs/p3bg3MYe60mbU3m3GzPis9riZuSmkmfHIKk6FJDMO5hZmBozy8Zy1+eF19p3xfM42ns/Zdx7OaXVlDeW3a1B6sxod8m69G2OS5FgUP5OH3KIMTM9MIi4u7syfz7uhVqsxNTILWWk3GivkWFtZ147ZOtggoyAZWSWpiEwIg/iUbow9DWft943X2XcmtPMRiUTo6RzA3ZtVKL9Vg+WlVe2Yg6MdCi5lo+RKHhKSYs7Neb+bOdra2NYmGLrkfVCrdDuww2ODkVmciozCZDi7GX4H9mn9zmk0GkwNzaCtsgOtlR1YW1rTjllaWyAuKxbS/ASEJobA2PjJzfXTfg0d7O5jWDaEvroejCvGoFbqyuq4B3ogIjMKERlRsHdzeF/PfxbfEzQaDdburWC8ZQATskFsLa1rx4zNTeATH4SA5DB4RftD/OBGvBD+PlcdHmGpfxJzilEsD0xBrVRh0c4FPskRhg6NnoCnnmzw8/NDYmIivvvd7+Jb3/oW1tbW8LOf/Qwf/vCH3/TxQ0NDODg4QEJCwlOOlIjofDo8OERjrRx3blSitqIZe7u63WR+gT4ovpKL4iu5CArxN2CUp29xdvlBgqEJ44NT2uPGJsYIivLDld8rQmqeFBaW5gaMkoiIhGZjfROVd+tx90Yl5E2deqUJ4xKjUPxMLgov5cDV7XihlUqlwvTMWz3b+aDRaDA6MIG6O82oLW3GyoLuJqi1jRXSCqTIKk5FbFIkxE/wxhiR0Gg0GvR1D+E3/3Ydfy3/Phbml7RjNrbWKCjJQsnVPEhT42FsLJwVwbvbu2ipaUft3WZ0NnVDeeKmc1CEP7KKU5FZnAI3z/df7eKs0Wg0mB2fR2tFO1orO7Ayt6IdM7c0R2xmNKR5CYhIChPU6u+j/UOMtA2jv64XI23DUJ3Ybe/q54aIjChEZEbB0dPJgFGevo35+xiXHScY1md110yxqTF84gLhnxwO71h/GJuaGDDK06VSqrAyOIW5zlEs9U9Adaiba7GtBewuuL7NV9N5ZpB3rB//+Mf41re+hfz8fBgZGeHatWv4whe+AACIj4/Hyy+/jKtXrwIAZmZmYGdnBzOz87v6lIjoSTs6UkLW2I7Sm1WoKm3A1ua2dszrggdKnslD8ZVchEYEaZvDCcHK4n00lLWg9m4zhnpGtcfFxmLEp0QjuyQV0qx4jIwNC2YFKRERGd7O9i5qyhtx50Ylmupa9UoTRsaEovjK8XXXw8vNgFGevqnRGdTebUbd3WbMTS9oj5uZmyItPwnZF1MRnxoDEwHdGCN6GkaGxnH3ehXu3qjCzJRuV66VtSVyizJQciUPqZkSmAjoRuT+3gHa6hWovduE1vpOHB4cacf8Q3y0CQZPn0f7ep5ni9NLaK3qQGtFO+Ynde+jpuamiEmPgjQvAZEp4eemxOu7oTxSYrxzFH11vRiWDeJoX7fb3snbGRGZUYjMjIbzBeEkkwBga3kDE7JBTMgGsDqlSxwaGYvhHRuAgOQwXIgLhIm5cOZarVJhdeQe5jpHsdg7DuWJubZwtIVHXBDcYgIwtjgD6/e5Y4XOPoN8CnR2dsaPf/zjNx3r7OzU+3dJSQlKSkqeRlhEROeKSqVCh7wbd29Uofx2LdbXNrRjrm7OKL6Si5Kr+YiKDRNUgmHj/iYaKmSou9uM3vZBbYkKIyMRoiURyC5JRVpBEmztj0vzPdx2ep5pNBqM902itaIdHbUKmNuYIe6VOEOHRUT0gbK/f4D6qhbcvVGJuspmHJwoTRgSHnh83X0mDxd8vQwY5embm15A3d3j0oRTo7otGWbmpkjKTkBmUTKMbUSQSiVM6hO9B9OT93D3ehXu3KjC2PCE9ri5hTmi48PwsU8+h6y8VJiZC2fh5dGREm0NXai724SW6nbs7eoaHnv7eSKrJBVZJanwCRDW++jqwn20VnagrbID08O691FjE2NEpURAWpCAmLQomJ3jEq+/S61SYaJrAv31PRhsHsDBjm6u7VztEZkVjcisaLj6uQnqb9XdtS1MyIcw3jKI5bETjczFRvCK9IN/chh8E4Nhaimcudao1bg/Po85xQgWusZwdOJ1bWZrBY+4IHjGB8PugitEIhFUKhVES/cMGDE9aVxyQkR0jmg0GvQoBnD3RhVKb1Tp1251skfRpRwUP5OHBGn0mWwO937tbO2iuar1eGt1S49e7daI+FBkl6QivTAZjk+5OdyTpNFocG90Vru1enXhvnbM2VxY24qJiM6qo8MjtDS0486NSlSXNWBne1c75uvvjZKr+Si5kofAED/DBfkELC+sov5B76ORvnHtcWNjMSQZccgqSUVyTiIsLM2hUqmgUCgMF+wpUavUGOwYhryiHQuzCwj7XhisbKwMHRYJzMLcEkpvVuPO9Ur09wxpj5uYmiAjJxklz+QhMzcZQ8NDgtmVq1Kq0Cnrweu/vovvK36GnS3d+6ibpwuyLqYiuyQN/iE+grrpvLG6ibaq4x4M4726ZJKR2AjhklBICxIRlxkDS2sLA0Z5ujRqNWYGptFX24OBpn7sbuxox2wcbY57MGRFwzPYS1Bzvb+1h8m2IYy3DGBhcAZ42NtaBHiE+8A/OQx+khCY21gaNM7TpNFosDG9iDnFKOYVozjY1M21qbUF3GMC4RkfDAc/D4iMhDPX9O4w2UBEdA4MD47hzvVK3L1ehdmZee1xG1tr5Bdn4uKz+YKr3bq/d4DW+k7U3mlCa70CR4e6rdVBEf7ILklDZnEKXD2cDRjl6VuYXtQmGBamFrXHzSzMEJcVg8TcOBya7L/NMxAR0eN4uHPwzvVKlN+uxcb6pnbMw8sNxVdycfFqPsIigwV1s2Tj/iYaymWoudOEvo5B7XEjsRHikqOQVZKK1DwJbGytDRjl6dJoNBjvnYC8oh3t1Z3YvL8F4Lgx78bqJpMNdCpWV9ZQfqsGd25UorO1R3tcLBYjOSMRFx80jre1E86uXLVajYGuEdTeaUJDWQvW7+veRx1d7JFVnIqskjSERgcK6n10Z3MXHbUKtFa0Y6hzBBr18V1nkUiE4LggSPMTkJATBxt7Yb2Pzo/Oob+uB30Nvdha0c21pa0lwtMjEZEVDZ8IH4gEtBjuaO8AUx2jGG8ZwGzvJDQnFsO5BnshIDkMfkmhsBTYXG8t3Md85zDmOkexd+J1bWxhBvfoAHjEBcMpyAtGYuHMNb13wrkrRUQkMG+3tTq3MB0lV/ORniUVVD3PoyMlOpq633Rr9YUAL2RfTENWcSq8/TwMGOXpe7i1urWyHTPDui2lxqbGiEmLgjQ/AVGpkTAzNxXMClIiorNEo9Ggt2sQd65XouxmNZYWdY06nVwcUXw5ByXP5CMmIUJQOwd3t3fRVNmK2jtN6JT16u0cjEwIQ87FNKQXJMHeyc6AUZ6ut9s5aGVrifjsWDgHOcCNjSvpMWxtbqOqtB53rldC1tihTSCIRCIkJsei5Jk8FFzMhqOTvWEDPUUajQbjQ1OovdOE2rvNWJ7XvY/a2FsjLD4Qz/3+FURLIiAW0I3I/d0DdDf2QF7Rjj7ZAFQnmlv7R/hBWpAASV4C7J2F8z4KAMvTS+ir60FfXQ/W5nXvo2aWZghNjUBkVhT8YwNgJIDdOQ8pD49wr2sC4y0DmFGM6TW3dvJ1hX9KOAKSw2AtsLneWVnHfOco5jqHsb24pj0uNjWGa6Q/POOD4RzqA7GxcOaaHg+TDUREZ8jiwjJKb1ThzvVK9HU/urX64tV8ZOWnwtJSONttVSo1etsHjlc+lcuwfWIL5sOt1TkX0+AXLKyt1VtrW2iv7oS8oh2j3boSFUZiI0RIwx5srY6GhZVw5pqI6KwZHZ7AnTcqH2nKamNrjYKL2bh4NR/SVGGUMnnoYP8QrXWdqLlz3JT1d3cO5jxI7Du7C6tk3+LMElor2o/LJL3JzsGkgkSES8MgEoFJfXpf9vcPUFfZhDtvVKK+RobDE31domLDUPJMHoqfyYObu7Ca4M5OzR8nGO40YWZCV6PewtIcqXlSZF9MQ4w0HL19vYiOCxdEouHoUInJnmm0vdGFnqY+HJ5ogusV6ImkgkRI8hLg4iWsHdhrC2vorz9OMCxN6t5HjU1NEJIUisisaAQmBsFYQI3M1So15vomMd48gKn2Eb3m1nYejghICYd/chjsPYV1zdzf2MG8YgRznSPYmDnR3FpsBJcwX3jEB8M1wg/GZsKZazo9TDYQERnYxvomyu/U4s7rFWiTdZ1oeGyE5PQEXLyaj7ziTO3WaiHQaDQY6RtHzZ0m1Jc2Y3VJt0LCwdkemUUpyLmYhtCYIEElGPZ29tBZ143W8nYMtA9pV5CKRCIExwZCWpAouK3VRERnzb3pedy9UYk71ysxMqhL9gp556DySInOll7U3mlCc1Xrm+4czC5JhZevsHYOri2vo62yA/LyNkwNnWjKamqM6NRIJBUkancOPiSEEjb09BwdKdFS34Y71ytQVdaA3Z097VhgsB8uXs1HydU8+Ph5GzDK07eysIq60mbU3GnCaL9uB7aJqQmkmfHIuZgGaVa89rUlhNeVWqXGUOcIWiva0V6jwN62bq6dPZ2RVJCIpIJEeAYI6310e20bA4196Kvtxr1B3fuokbEYgfGBiMyKQUhyKEwF1Nxao9ZgcWQW4y0DmJQPYf9EnxErJ1sEpIQhIDkcjr6ugvpb9XB3HwvdY5jvHMHq2Ky294TISASnIG94xAfDPToAJgKaa3oymGwgIjKA3d091JQ34s71SjTWyqE8sQUzXhKNi1fzUXg5B07ODgaM8vRNj89qVz7NTS9oj1vbWCGtIAk5l9IEt7X66OAI3c19aK1oR3dTL5SHurn2Db2ApEIJJPkJcHCxN1yQREQCt7p8H2W3anD7jQp0dfRpjxubGGt3DmYXpAlq56BarcaAYhg1txtRXy7D5tqWdkzITVl3NnfQXq2AvKIdI4pR3SIOsRHCE0MhLRReU1Z6utRqNTrbenDnjUqU3arB+tqGdszT2x0Xr+bj4tV8BIcFCOq1tbm+hcYKOWpuN6K3fVDvtRWfEo2ci2lIyZXASmBNcCcHpiAvb0drVQc2V0/0I7CzRGqRFMlFUviGCet9dH9nH0PNA+ir68ZE17i29wREIvhG+yEqKwZhaeGwENhc359ewnjLAMZbBrFzYq7NbSzhnxyKgJRwuAZ5CarhsfLgCEt9E5jrHMHy0LRe7wkHPw94xAfDIzYQZgKaa3rymGwgInpKjg6P0FTfituvV6C6vBH7e7pVhaERQccrn57Jg6e3uwGjPH1L8yvaBMP40JT2uJm5KZJzEpFzMQ2J6bEwEdB2W5VShaHOEcjL29BR24X9Hd1cu/m4IrlQAml+Itx8WA+aiOhJ2d7aQWVpPe68UYGWhnao1cd/QBsZGUGaGo+LV/NRcDFLcDsHtTXT7zRheWFVO2bvaIusklRkX0xDWIywmlsf7B2gq6EH8vJ29Mn1a6YHxQQgqSARibnxsHEQzlzT06XRaDA8MIbbb1TgzvVKLMzpyoo4Ojug5EHj+JiESEG9tvZ399FS046a201ob+rSe21Fxoci+2IaMotSYOdoa8AoT9/cxLy27NryrK73hKWNJRJz4yHJi8O2ZgsJCQmCKbOnOlJhsKkfAw19GGkd1utH4BnshcjsaERkRMHGSVhzvbm4hvHmAYy3DGB9TnfNNDE3ha8kBIGp4fCI8BVUw2O1UoXl4RnMdQxjqW8CqhOL4Ww8neAZHwLPuCBYCOx1TU8Pkw1ERE+QWq1GR2sP7rxRgbJbNdhY162Q8PbxxKVnC3Dxaj4CQ/wMF+QTsLG2iYZyGWpuN6GvY1B7XGwsRkJajHblk4WluQGjPF0ajQYT/VOQl7ehraoDm/d1K0gdXO0hfbC1+kKwt6D+CCUiOksO9g9QXy3D7TcqUFfVrF8zPS4cl54tQPHlXLi4Cau28vzMImruNKLmdhNmxnW9JyytLZCWf7xzMFYaKajmjcojJfrlg5CXt0HR0KNXM907yAtJhRJI8xPg5O5owCjpvJuZmsWdN47Lro2NTGqPW9tYoaAkCxefzYc0NR7GxsK5tXJ0pERHUzdqbjeipbodB/sH2rGAMF9kl6Qh+2IaXD2E1Y9gdeE+Wh+UXbs3qnsfNTU3RVxGNJIKJYhICoOxiTFUKpUgeruoVSpMdk+gp6YbA429UB6caHjs7Yyo7BhEZkXDUWD9CHbXtzEhG8RY8wBWxue1x8UmYlyIC0RASji8YwME1XtCo9ZgbWIes53DWOgaxdGu7nVt6WQLz/gQeMQHw4bXTDoFwrkiEhGdERqNBkP9o9qVT4vzy9oxZxdHlDzYWh0VGyaom877u/torm5HzZ1GdDR16618ikoMQ+7lDKQXJMHWXlirCucnFyAvb4OsvB0rc7qVT1a2lpDkJUBakIigmAAYGQlnNQwR0VmiUqnQ2tyJW6+Xo/JuPba3drRj/oE+uHytUJA10++vrKO+tAXVtxow3DumPW5iaoKkrHjkXEqHNDNOUL0n1Go1xnrGIStrQ3t1J3Y2dXW0XbyOa6ZLCxLh6S+smun0dK0sraL0VjVuv1GJns5+7XFTM1Nk5aXi0rMFyMxNhpm5cOqWq9Vq9HUMoeZOIxrKZNja2NaOeVxwQ/bFNORcTINPoLDeR7c3dtBe3Ql5eRtGunTvo2JjMSKTw5FUkIjYjGiYCahGvUajwdzwPfTW9qC/vhc767q5tnW2ReSDBIObv7ug/lY93D3AZNswxpv7Md8/rS0DJhKJ4Bnpi4DUCPgmBsPUUlhzvTW3grmO40bP+yde12Y2lvCIC4JnQgjsLgir9wQZHpMNRESn5N70HG6/UYk7b1TorXyysbV+sPKpANLUOMFstQWOVxV2NHej5nYTmqva9FY+BYb5IedSOrJKUuHiLqzVMPcX19Ba2Q55eTtmRu5pj5tZmCI2IwZJhYmIkB6vfCIiotOn0WjQ1z2IW69XoPRGFVaW72vH3D1dtTXTQyOCBPUH9O72LpoqW1FzpwmKlh6oH9TRNjISITY5CrmX0pGaJxVczfTZsTnIytogr2jH2tKadszW0QbS/EQkFSbCL9xXUHNNT9fDsmu3Xy+HrLFDr+xaSkYiLj5bgLyiDNjYWhs40tOj0WgwMTyNmtuNj5Rdc3C2R/aDsmshUYGCem0d7B2gq7EX8rI29Mr6oX5Qo14kEiE4NhDJRRIk5MTBytbKwJGerpWZZfTWdqOvrgdr87prpoWNJcLSI2DqYY7cZ/IE9feL6kiJmY4xjLf0Y0YxBtWRbjGcS6AnAtMi4J8UCgs7Yc31zsoG5jtHMNc5jO1F3TXT2NwU7jGB8IwPhlOQF0RcDEdPiHDeRYiIDOD+yhoqbtfjH//mX9Dd8cFZ+TSgGEb1rUY0lLdgc11/5VPOpXRkX0yDT4CXAaM8fQ8bTsrK2zCiGNUeNxIbISo5AklFEsSmRwlq5RMR0VkzOT5zvHPwjQpMTeiSvXb2tii6koOLVwuQII0W1G6yo8MjtDYoUHO7EfLaDhweHGnHQqODkHs5HZnFKXBwsjdckE/AytwK5BXHif25CV2ZC3MrcyRkxyGpMBFhCSGCqqNNT9fhwSEaao7LrtVWNOHgRNm16PgIXHo2HyVX8uDkIqyyIouzy6i53Yia242YGtO9j1paWyC9IAm5lzIQLY2AWECvLaVShYHWQcgr2qGo68LBnm6uL4R4I7lAAmlBAhxcHQwY5enbXNlAX10P+mp7sHCiXJCJmQlCUsIRlR2NgPggQAQoFApBND5Wq9WY75/CTHk/+v9PA472dIvh7D2dEJAWgYCUcNi62hsuyCfgYGsX84pRzHUMY316UXvcyFgM1wg/eCaEwCXMB2IBJZPo7OJvGRHRe7S7s4uq0gbcer0cLQ1tUKl0K5+S0uJx6dkC5JdkCWrlEwBMjswc/2FypxFLJ8oFOTjZIaskFTmX0gW38unw4BDdDb2QVbSht7lfrzRUcGwgkgolSMyNh7XAVsMQEZ0lS4srKL1Rix+89Av09wxpj5ubmyG3KAOXrhUgLVMKEwHVVlar1ehtH0T1rQY0lsv1SkN5+3ki53I6ci+lw+OCmwGjPH1ba1toq+qEvKINYz0T2uPGJsaITotEUqEE0akRgioNRU+XWq1Gu6wLt14vR/ntWmxt6hbN+AX64PKzBbh0rQAXfIW1aGbj/ibqy1pQfbsRA4ph7XFjE2MkZSUg51IakrLiBfXa0mg0GO+bhKysFe1Vndg6sUDK2dMZyYWJSCqUwMPP3YBRnr797T0MNPajt7YLU71TwINyQUZiIwTEByEqOwYhKWEwNdfNtUqlequnOxc0Gg1WpxYx1tSPCdkgdtd0c23laIOAlHAEpIbD0UdY5YKU+4dY7J3AbMcwVkdmoHmw2xEiEZyDveEZHwy36ACYcDEcPWVMNhARvQtHR0o01clx+/UKVJc3Yn9vXzvmF3gBH/74M7h4tUBwDSeXF1a1K58mhqe1xy2sLJCeL0XO5XTBNZxUKVUY6hyBrKwVnbXd2N/VzbV3oCeSiqRIKkiEo5uwVj4REZ0l21s7qLxbh5uvlUPe1KGtrSwWi5GaKcHla4XILUqHpZWwygVNDE+j+lYDam43YXVJV+bCydUB2SVpyLmUjsBwP0HdLDnYO0BXQw9k5W3okw3olTQJTQhGUqEECdmxsBRQaSh6ujQaDYYHxnDr9fJH+qm5ujmj5Go+Ll8rQFhksKBeW9p+arcb0NHco100IxKJECONQO7ldKTlJ8FaYOWC5qcWIC97tJ+ajYMNpPkJSCqUwD9CWGXXlIdHGGkdRm9tN0Zbh/UWSF2I8EFUTizC0yJgKbAFUptL6xhv6sdYcz82TpSGMrUyh7W/I6SXM+AZ7iuIHRsPqVUqLA/NYK59GIt9E1Af6Zp6211whWdCCDzjgmAmsNc1nS9MNhARvQWNRgNFWy9uvV6Osls1WF/b0I75+Hnh0rVClFzJxdrmKuLihNOLYXtzB2213fjNT26ir2NIe4PH2FgMSWY8ci6lITk7EWbmwlr5NDU0A1lpK1or27F5f0s75ujmgKRCCZKLJPAK8DRglEREwnZ0eISGGhluvV7+SEmTwFA//N7Hr6Lkaj4cBVYuaHHuQUmTW/olTaxsLJFekIzcy+mISgwXVEkTlVKFgbYhyMrbHilp4hNyAclFEkgLEmHvbGfAKOm8m7u3gNtvVODW6xUYG9btlLGxtUbhpWxcerYAicmxgvkMDxy/tjpbelB9uxHNla3YP1FCJijcHzmX05FdkgonV2GVhlpf2UBrRTtkZW2YHp7RHjezMEN8VgySi6QISwwR1AIptUqNqd5J9NZ2Y7CpHwc7ugVSLj6uiMqJQWR2DOwFVi5ob3MXE7JBjDX1Y3lsTntcbGKMC/GBCEyNgEekD3r6euEedkEQiQaNRoPD5S30v1aHha5xHJ1YDGfpbAfPhBB4JYTAysXecEESncBkAxHR7xgfmcSt1ytw6/VyzN1b0B53dHZAyTN5uHytEFGxYRCJRFCpVFhTrL7Ns50PhweHkNd1ovpWA1rrFVCeWCERlRiG3MsZyChMho2dsEpDLc+uQFbeBllpKxZnlrTHrWwtIck7XvkUGO0vqNrfRERniVqtRmdbD26/XoGyWzXYWN/UjvkH+uDyc0UoeSYXy6uLgkrs727v4c5vK1F7uwl9nbrSUA9LmuReToc0M05wJU0m+qcgL29Da2UHttZ0iX1tSZMiCTx8hVXShJ6ujfVNlN2qwc3XytDZ2qM9/rCf2uVrBcjMTRHca2u4dwzVtxpRd7cJ6/d176Pu3q7IuZSO3MvpuOAvrNJQu9t76KztgqysFUMdI9oFUnr91DKiBbdAanFiAb3VXeir78XWqm6ubZxtEZUVjaicWLj6uQlq58bRwSGm20cx1tSP2d4JbbkgkUgEjwgfBKZFwFcSAtMH5YLOe1moh7YX72O2Yxhz7cPYW9vCw1bPpjYW8IwLhmdCCOwuCKs0FAkDkw1ERACWF1dx90Ylbr5WjoFeXR1TSysL5BVn4vK1QiSnJ8DYWDhvmw/rQVfdbEBjhQw7W7vaMTdvZ5Q8n4/cyxlw9XA2YJSnb2t9G+1VHWgpa8N4r26Vm4mpCWIzo5FcKEFkcjiM2TyLiOiJGRuexM3Xy3DnjUq9xL6LqxMuXs3HpWuFCI8K1ib2l1cX3+bZzoeD/UPI6zpQdaMebQ0Kbc8nkUiEaEk4ci6lI70wSXA9n5buLUNW1gpZWRuW7unK11jbW0OSF4/kIikCIoVVGoqerv39A8gbFfjlz36Lxlq5dtGMSCSCJCUOl68VouBiFmztbAwc6emanZpH9a3jcqdz07r3UVsHG2QVpyL3cjrCYoRVGkp5pESvbACy0lZ0N/bi6PBIOxYY7Y/kQgkS8xJgYy+s99GNpXX01najt6Yby9O6BVLmVuYIS49EVE4MfCN9IRLQAim1So25vimMNfVhqn0EygPdXDv7uyMgNRwBKeGwFNhc72/uYL5zBLMdw9g8cc0UGRvBIzYIXomhcAryhpGAdjuS8PBOChF9YO1s76KytA63XiuHrLEDavXxH/3GxmKkZSfh8rVC5BSmw8LC3MCRni5dPehGrCzqals6uzki51I6skpSsbF7X1ArSI8OlWit7EBbRQd6Zf26etBGIoQlhiKlSIL47FiYWwprromIzpKlxRXcvV6Jm6+WYbB/VHvcytoSBRezcPlaIaSp8YK59gDHif2etgFU32pAQ7kMu9t72jH/EB/kXs5AzsU0OLsLq+fTw0bPsrJWjPdNao+bmpsiLjMGyUUShEvDYCygkib0dKlUKrS1dOHWa2Uov1OLnW3dopnQiCBcerYAF5/Nh7uHqwGjPH0b9zdRV9qMqpsNGOrRvY+amZshNU+C3MvpiE+JFtSiGY1Gg/HeCbSUtqKtqgM7m7q59vBzPy53WpgIZ09hLZDa297DQGMfequ7MN03pT0uNjFGsDQEUTkxCJKECG6uVycWMNbcj/GWQext7GjHbFztEZgajsC0CNh5COuaqdw/xELvOObah7Eyck/b1FtkZASXMB+4xwVhQbmBaEmioD4jkXAJ512JiOhdODpSormuFbdeL0d1WQP293V1TGMTInH5uUIUX8mFg6O94YJ8AlYWVlFzpwnVtxr0Gj0/rAeddyUDUYlhMDIygkqlgkJx/22e7XxQq9QY7BhGS6kc7dWdODrQlYbyCbmA5GIJpPmsB01E9CTtbO+i4m4dbr1WBnlTpy6xb2KMjJxkXHmuEFn5aTA3NzNwpKfrrRL7Lh7OyC5JhXuAI4qvFArqpsHh/iF6mvshK2vVb/RsJEKEJAzJxVLEZcbA3FJYc01P1/DAGG6+Vobbb1RiaUG36tfJ2QFXP1yCK88XITg0wIARnr79vQPIatpRdasB7Y1d2teWkZEI8akxyL2cjtQ8KSwEtmhmYXoRstLjXVEr87qytbZOtkguSERSkRQ+Id6C27kx2jaM3upujLQO6Ro9i0TwjfLVNno2t7YwbKCnbGt5HWNN/Rhr0m/0bGZtgYCUMASmRsAlyFNQc61WqbAyNIPZN2n0bO/rDq/EEHjEBsHU2gIqlQqLCoXhgj1FB5s7WO4dhWpoCvu+gbBytjd0SPQEMNlARIKn0WjQ2zWIm6+W4u7NaqytrmvHfAMu4PK1Aly+VogLvkKrY7qLhgo5qm82oLu1X6/RszQrHrmXM5CUFS+4mrX3RmfRUtoKeXkbNk7UMXVyd0RykRTJRRJ4+LEeNBHRk/IwsX/z9TLUlDXqJfbjJdG4/Fwhii7nwN5BWMnelYVVVN9uRPWtBkyO6BqUWttYIaMoGbmXMxCZEAqNRgOFQG4aqFVqDLQNofo39fhlz3/i4EQzWt8wH6QUSSHJT4Cdk60Bo6TzbmF+6bjR82vlGBkc1x63sbVG8ZVcXLyaDxgrkZCQIJgEnlqthqKlF7V3mtBYIcfeiYawwZEByL2cgaySVDgK7Ebd5v1NtFZ0oKWsFVODugVSZhZmSMiJPW70nBAiqBIyGrUaMwPT6KnuxkBDL/ZPNHp29XM7bvScFQM7F2FdMw+29zAhH8RoYz+WRma1x8UmxvBJDEJgagS8o/1hJKAdcBqNBhszS5htG8K8YgSHJ+baysUenokh8IwPgZXAFsMpDw6xNjSFlb4xbE7Na49vzSww2SBQTDYQkWDdm57DrdfKcfP1ckyN6/7od3Cyx8Wr+bjyXCEiY8IEtUJCeaREe1M3qm81oKW6DYcnaltGxoci75lMQTZ6vr+4Bnl5G1rKWjE3rvsAY2ljicTcODj42aHk+SJB9dwgIjpLHib2b71Whjs3qvQS+36BPrhyrRCXrhXA28fTcEE+AW+Z2H/Q6DnvSgakmXEwMTXRfo0QGlfOjNyDrKwN8vI2rK9saI87ezghufg4se/u42bACOm8297aQcWdWtx4tQxtLQrta8vE1ARZeam48lyhttHz8a5chWEDPgUajQbjQ1OovFGPyhu12FrXlZBx93JF7pUM5F5Kh7e/sN5HD/YOoKjvQUupHANtQ7qdG2IjRCaHI7lIKrhGzwCwMrOMnuou9NZ2Y2NpXXvcxskWUTkxiMqOgZu/sBZIKQ+VuNc1htGmftxTjOl2wIlE8Ij0RWBaBPwSg2FiIawdcLurG9pGzzvL69rjptYW8IwPhmdiKOy8XQR1X0KtUmNjYhYrvaNYG52BRqn77GPt7YpdGxM4RwcZMEJ6knjXhYgEZWN9E6U3q3HrtXJ0tvVoj5ubmx03en6uEKmZEkHddNZoNBjqGUP1rXrU3m3G5tqWduyCvyfyrmQi51I63LxcDBjl6dvd3kNHjQKyslYMd47q3eCJSY9CSrEUUSkREBmJoFAoBPHhTaPRYGVmGXvru+/8YCKip+CtEvtOLo4oeSYPV54rRER0qCDegx9SHinR0dyNqpuPJvajEsOQdzkD6UXJgmv0vLa0Bll5O2RlrZgdm9Met7K1hG/0BVz6/RIExwQKaq7p6To6UqKpTo6br5ahprwRBweH2rHE5NjjXVGXcgTX6Pnhrqiqmw2YGtW9j9rYWSOrOAW5VzIRHiusRs8Py53KSlvRUdultyvKP8LvuNxpXgJsHIQ119tr2+iv70FPdRfmR3Xvo2aWZghLi0B0bix8o/wE1ehZo9ZgcfgeRpv6MCkfwuGubq4dfVwRlB6JgJQwWApsrg939jHfNYq59iGsTeoauItNjeEWFQCvxBA4BV8Q1i4djQY78ytY6R3F6sAElCde1+ZOdnCODIRzRACMbSwF8/c5vTnh3G0jog+sw4ND1FY249brZairaoHyQb1DIyMjJKcn4PJzhcgvzoKVtaWBIz1d8/cWUX2zAVW3GjA3pfsA4+Bkh+yLaci9koGgcH9BXcSVShX6WvrRUtqK7sZeHB3qbvAExwUhpViKxJw4WNro5loIK0g3VzbQW9ONnuouLE8vwdTSFKk5aYYOi4g+oDbWN1F2qwY3Xy3TT+xbmCP/QWI/JSNRcIn94d4xVN9qQO2dZmys6cr0aRP7l9Ph5imsxP7ezh46arogK2vFUMfImyb2w6Wh6O3rRWCUsD5z0NOhV+70RhXW7ut2ygQE+eLyc4W4fK0Qnt7CWuH9cFdU1Y169LQN6O3cSMqKh2+4Jz78yWuC62dzb3QWrRUdj+6K8nRGSrEEyUVSuF0QVlPvo/1DDLUMoqemC+OdY9CodTs3AhODEZ0Ti+CkUJiYmbzDM50v67OrGG3qw3hzP7ZXdNdMK0cbBKRGICg9Ag7ewrpmqpQqLPVPYq59CEsDU9A82LkBkQjOwd7wSgyFW5Q/jAW2S2d/bRMrfeNY7RvD/onPRyZWFnAK94dzVCAs3Zy0nxGE8Pc5vT3h/AVARB8oGo0GnW09uPlqGUpvVmNrc1s7FhYRhMvPFeHis/lwdXM2YJSnb2tzG/WlLai62YD+ziHtcTNzM6TlS5B7OQPxKdEQC6y25eTAFFpKW9Fa2YHtdd1ce/i5I6VYiqRCCZzcHQ0Y5ek72N3HQFM/equ7MNkzCTz4I1RsIoZn7AXDBkdEHziHB4eor27BzVfLUFfdok32Cj2xv3BvCdW3G1B1owGzJ+oM2zvaIudSumAT+/3yAbSUtqKrvkc/sR8beJzYz43XJvaFctNgc3UTfbXHif3N+5sI+v8CYedib+iwBOve9DxuvVb2pruijsudFiE8Slir+d9xV9SV43KnFlbmUCgUMDERxu2ateV1tNyVo/Z6A+7Pr2mPW9laQpKXgJRiKQIElqxUq9SY7JlAT3UXhpr7cbin26XjFeqN6NxYhGdEwcrOyoBRnr69zR2MNw9gtLEPq5OL2uMmFqbwk4YiKC0C7mE+EBkJZ641Gg3WJuYx2z6E+a4xvdX8tl7O8EwMhWdcMMwFNtfKvQOsDk5gpXcM27NL2uNGJsZwCPGBc2QQ7Pw8BLVLh949YVy9iOgDY2riHm6+Woqbr5Vjdkb3R7+bhwsuXyvE5ecKERwaYMAIT9/RkRKt9Z2outEAeV3HiZ0bIsQmRyHvSibS8qWwsDQ3cKSna2V+FbKyNrSUyrE4rfsAY+Ngg6TCRKQUSeETekFgf5ioMN45hu7qLgy3DEJ54gaPT6QvonPjEJIShsHRQQNGSUQfFBqNBl3tfbjxailKb1Zjc0NXpi80IgiXH/RhEGJiv6FMhqob9ejTS+ybIjVPirwrwkzsTw3NoOWuHK0V7dg6kdh383FFSnESkoskcPZwMmCUp+9w7wCDzQPoqVZgomtCm9g3sTDRrjSn07O5sYWyW9W48WoZOlsfLXd65fkiQe6KGukfR9WNBtTeaXrzXVG/U+5UCAm8/d0DdNYq0HxX/ju7osSISY/Wljs1FkhC5aGlyUX0VCvQW9uDrVXdXNu7OyA6JxbRubFw9BTW+6jy8AjTHaMYbezDbM8ENOrjuRaJjeAd7Y/A9Ej4xAfC2FRYOze2l9cx1zaE2Y4h7N3XfT4yt7OGZ0IIvCQhsHEX1lyrlSqsj93DSt8o1sfu6e3csPX1gHNUIBxDfCEW2FzTeyesd3YiEqS1++sovVGNG6+VoaezX3vc0soChZey8czzxZCkxMFIQFlzjUaDwe4RVN2oR11pC7Y2dH/0+4f4PPjDJA1OrsJazb+7tYv2GgVaSlsxohjVHjcxM0F8ZgySi6WIkIYJ7gbPwtg8eqoV6Kvrwc6JZoBOXs6Izo1FVE4M7N0cAAjjD1AiOttmpmZx49Uy3HqtHDNTs9rjrm7OuHStAFeeL0JIWKABIzx9R0dKtDcojlcc17RrE/sikQixyZHIv5KJ1HwpLK0sDBzp6VpduA95eRtaSlsxf6KmtI29NaQFiUgpSYKvABP7E13jD1YcD+DoxOryCxE+iMyOgdJWBRsnWwNGKRxHh0doqJXjxv8rRW1lk3anjEgkQlJaAq48X4SCEuHtilqaXzkud3qzHjMTutr89o622nKnwREBAnttqTHQNoSWUjk667pxuK9bzR8UEwDPcHdc/eQV2NoLqzb/1v0t9NUd74paHNe9j5pbWyAiMwrRubHwDhPW+6hGrcHC0AxGG/sw2TqEoxM7N5wD3BGUFgn/lHBY2ArrdX24vYc5xQhm24ewcWIxnLGZCdxjAuElCYVjgJfgdm5szy5hpW8MqwMTUJ14XVu6OMA5KhBOEYEwtRHWXNPjYbKBiM6kg/0D1FY24+ZrZWioboFSeXyDVSwWIzVTgivPFyG3KAMWFsJaza/tw3CzAXPTug+rTq4OyLmYjrxnMuAf4mvACE+fUqlCv2wAzXfl6GrsgfJQd4MnNCEYKcVSxGfHwkJgN3g2ljfQW9OFnuourMwsa49b2loiMisa0blx8Aj2FNQfJkR0dm2sb6L0ZjVuvloGRXuv9riFpQUKLmbhmeeLIE2Nh1gsrGTvcO8YKm/Uo+5uEzZPrOb3DbqA/GeOVxw7uwkrsf+wD0PzXTlGFKN6teJjM49XHEckhcNYYIn9xYkF9FR3oa+2G9trurl29HR6kNiPhYO7A1QqFRQKheGCFYCTfRjuXK/C+pquNn9QqD+uPF+ES88WwN1DWLX5T/Zh6G7VLZAyNTNBaq4Eec9kIj4lWnCr+WdG7qGltBWy8jZsnljN7+rtgtSS411RDm4OUCgUsBLIDcnD/UPMds9g8I1eTHaNa1fzGxmLESwJQXReLIIkIYKb6/W5VYw29mGsqR87J+ba2tkWgWmRCEyLgL3Adm6ojpRY6p/EbNsQlgentT03REYiOIf6HPdhiPQT3Gr+4z4MY1jpHcPBum7nhom1JZwjA+AcGQhLgS18pNMjrHc+IjrXNBoNFG292nINJ/swhEeFHP9hcjUfTi7Cuqhtb+6gvqzlkXIN5hZmSMtPQt4zGYhNioJYLKydG9NDM2h+k3INnv4eSCmWHv9h4upgwChP38HuAQab+9FTpfidPgzGCE0OQ3RuLAISggS1c4OIzq6jwyPUV7fgxqtlqKtq1uvDkJIpwTPPFSG3OAOWlsJK9i7OLqPqVv0jfRgcnO2RcykNeVcyERDqK6hkr0qpQn/rIGSlreis69brwxASF4SUkiQk5MTB0lpYc721uoneB30Ylk7UD7ewsURk1vGKY88Qb0HNtSHN3VvAzdfKcePVUr0+DM4ujrj07PGuqNCIIEH9vFVKFTqae1B1ow7Nv9OHIUYagbwrGUgvSBbMTfaH1lc2IC9rQ3OpHLNjup0bVnZWkOYnIKU4Cf4RvoJqCKtRqzHZM4meKgUGm/r1dm54h13Q9mGwFNhq/r3NXUy0HPdhWJnQLYYztTQ77sOQHgm3EG/BreZfm1zAbNvgo30YvF3glRgKz/hgmAnsda3cP8D9wUks945i+55+HwbHUF84RwXB1sedfRjoHTHZQEQGd296DjdeLcPNV8v0yjW4ebjgynNFuPJcEQJD/AwX4BOgPFKivakblTfqIKvpOHGDR4S4lGjkXclAWp4U5gLrw3B/cQ2ystZHyzU42CC5SIKU/z97/xkdV5aeZ8NXAYWcI5FzIRdyBgiABMAcO8pBYSzZI49sWVpjWZL9vZJmlqxZtoL1jb8lLemVJVmyxtORmSAJgETOoVDIKOScc0ZVne8HwCpwerqnewYkyN11/eMGWdwHT52wz76f+z6fir9CrEW/Xqc/tGtQfcau4XkOQ1R2NNaCveAxYcLE68lzxfG9Tx5TfLeM1RWjMjEiOowrNw8Uxx6nxFImbm1sUV3SQNm9KjqaewzjVtaWZBWkcvbKaRLSY4Xb7B3XTFD/qPFAcXzEU9or8NThxn4qbl5iiTj2dvboq++h46mK4SOKY3O5OeGHG/uhSWGYC6Y4Pik21jcpeVjOvU+f0FyvMoyLnsMw1DdK2b0qKh7WsLxo7Nww5DBczuaUj8cXfMqbx+72LqoqNXWPGulp7jOcW3ILOXHZscLmMCyMz6N+qqKzXM3agrHWNi62JJ9LJe5sgoA5DFrGVYMM1HQyoR42ePM/z2EIy4nBPyEMuaVYtd5cWGWypY+plj62jnRuWDvZ45Mcjm9yBA6C3TP1Oj2rQxMsdA6yPDD2Qg6DU5A37rFhuCgChOvcMPFyEevKYMKEiTeG9bUNSosrPhMQZ2tnQ+HFgxyG1Ezxchg+LyBOZLuGna0dWivaqf+RgDgLSwsSTivJuJAmXA4DwNzoLB1PVXSUq9k48oLH1ccN5dl4YvMO7BpMmDBh4lXweYpjD083Lt0o4upb5wiPEiuH4fMUxzKZjLjUaAquniarME24HIbPUxzbO9uTVphMxvlUAiMDhNrYl/R6RjtHUD9tp7e2i70j/uF+UQHEnU0gKicGG9PG/rGg1Wqpr27h3iePefq4it3dg9+3TCYjNTORq2+do/BinnA5DItzS5Q/rKXsXiUjGuN11NHFgfyLWZy9elq8HAa9nn7VAPWPGml5pmL3iMI7VBlCxvlUUs4mYSeYmn9zdZPuyg7UT1VMDxivo9Z21kSfjiUmT8n8ziKJieLYC0qSxJxmkqG6HoYbetnbMtbaPdiLsGwxcxj2t3aYbh9ksrmP5RFjt6O5lQVeyoMcBrdQ8XIYNmcWWegcYLF76IXODRsPZzxiw0w5DCZ+JkybDSZMmHhl7O9rqX5Wz//5uw9pb+5m76hdQ04yV986L6Rdw8LMIs8e1lB2r5KxQWPnhoubE3mXsim4Kp5dg16np7e1n7pHjbRVtL/QZiyyXcPG8sZBQNzTdmaGjA+rNg42RJ9WEnfWZNdgwoSJV8fG+ubBxv4nj2n6EcVxwYVcrr59nvTsJGFelDznQHFcSfmDzyqOC67lcuZyDh5eYqlQnyuO6x810d3c+1nF8YU0YtOjxFQcP1PR+exFxbGzlwvKM/EozyTg6i2WiOMk6e8Z5O4nj3l4u4SF+SXDeEhYIFffPs/lG0V4+YiVw7CztUPds2bK7lWhqu9Af+TcyjiTTMGV0yRnxwt3bk2PzFD/uJH6x80szy0bxt193Mk4n0rGhVQ8fcXq3NDu7aNp6kf9VMVgiwb9ocLbzNyM0GQFcWcTUKSGI7e0QKfTsaBa+gmf+GawNreCpqqD3nIV6tVtw7idqwOhWdGEZsfg4ut+gjM8fvQ6HfN940w29TLXNWyoNTIZ7go/fFMiOBUbgtxKLDX/7toGi11DzHcOsHPk+cjCzga36BDcYw9yGExrVRM/K2LdEU2YMPHaIUkSvV2ag4XJnVKWF1cMPwuLCOba2xe4dKMQz1NiPcDsbO1QU9ZE2b1K2hu6DGp+SysLMs8e2DUkZSqFU/NPDU1T96iRhidNrBxZ9J/y9yTjMCDO3VusFzzavX36G/oOFiatA4bQMDO5OYrUcJRn4lGkhJvsGkyYMPFK0Gp1dLT18uHfP+DZk2p2dg7UajKZjJSMBK69fV5IxfHS/DLlD2sou1fFcP+YYVx0xbFGNUjdo0ZanrX9iOI4mIzzaUIqjrfWtuiu6kBdpmJKYxRxWNlZE50TS9zZePyixOrcOEkW5hZ5eKeUe58+oa97wDDu4urExWsFXH37PNHKCKF+33q9no7mHsruVVFT0sD21o7hZ9GJERRcPU3OuXQcHO1PcJbHz/rKBk1lLdQ/amSkx3gdtbG3IeVsEpkX0ghVBgtVa0mSmOwbR12moruqk51NY629w3yIO5tAdK4SOye7E5zl8bO3tctwQy8DNV3M9k8YxuXWFgSlRBCWE4NXpL9wLgNrkwtMNvcy1aZhb8O4sWLv5YpfSiQ+SeFYC1Zr3d4+S32jLHQOsHYkp0omN8dVEYB7bBhOwT6mHAYTx4rpzYcJEyZeCnOzCzw4tGsY6Bs2jLu6uZCSqeRf/eq/JCo2XKiHVb1ej7qpm6f3qqguaWDnyKJfmRJFwdXTQgbErS+v01h6sDAZ7TO2lNs62B7YNVx4MSBOBCRJYqJnDPVTFd3VXeweWZj4RvihPJNA9GnxAuJMmDDx+qLpG+Lux495cLuEhblFw3hQaADXDhXH3r6nTnCGx8/uzh71h4rj1tr2FxTH6XlJFFw9TXJOAhaCbfbOjs9R/6iR+sdNLM4YlbXu3m5kXEgj43wqnn5iKY51+1oGmjWon6rQNPej1x6EzcrMzAhNCiOuIIHwtAjkJk/pY2FnZ5dnT6q598ljaiub0B8KKSwsLcgvzOLq2+fJzksX7tyaGJmm7F4lT+9XMz+9YBj38vXk7NXTnL2Sg0+A1wnO8PjR7mvpqO2i7lEjHXVd6A7PLTNzM2LTo8m4kEZ8diwWgim8V2aX6Shvp+NpO0tTxnumg5vjYVdUPB4BYnXp6HV6JjtHGKjuZKx1AN2+9uAHMvCJDkTuZ0fOzULhMgN3VjeZau1jormPjSP3TEt7G3ySDnIYHH3dxVqr6vWsjc2w0DnAUt8o+ue1Bhz8T+EeG4ZrRBBya8sTnKUJkRHr6cCECRMnyvb2Dk8fV3Hv0yfUVzUbFiaWVpacKcrm2tsXSMtOorOzg4joMGFu6PPTS/xD9YeUP6hhfsb4sOoT4HWwMLmcg5efWA+r+3v7qGu7qCtuoLO++4U247isWDIvpBGbGY2FYIv+5ZllOp6p6HjWzvK08WHV0cMJ5Zl44s4k4OYnVpeOCRMmXl8WF5YpvlvG3Y8f0dulMYzbO9hx+WYR19+5QExcpDD3WzjY7O1u66P0biVVT+rZOqJMjIpXcPbKaXIvZOLgJJbieHNti+anrdQ9amSo0yjisLazJuVsIpkX0gmLE6tzQ5IkpjVTqJ+20VXZyfb6luFnXiHexBUkEJOrxM5ZrFqfFJIk0dbcwd2PH/HkQTkb65uGnyUkx3L17fOcv3IGRyeHE5zl8bO+ukHlozrK7lXRqzZeR23tbTh9LoPCa7lEJ4rVuSFJEqO9Y9QWN9BU2sLmmvHcCgj3J/NCGqlFyTi6iFXr3a0demq7UZepGOscMYxbWFkQmRVNXEECgbHBmJmLpfBeGptDU93JUF0P26vG89rZ142wnFhCM6OxdrJFpVIJs6mk29tne2SB5ub7LGom4dBlwExuzqnYYHxTInAPDxCu1tuLK8x3DLDYNcjekXumlYvDQQ5DTCjWzmKd1yZeT0ybDSZMmPiZ0Ov1tDaqufvxI0qKK9jcMN7UElOUXH37HOcuGxcmOp3upKZ6rKyvblBRXEvp3Ur6OwcN4/YOdpy+cLAwiYxTCLcwGe4epa64gaayVraOPMAERQWQcSGN1IJkHARb9O9u7dBd3YX6qYrxrlHDuKWNJZFZMcSdjScwNsjUemrChIlXwt7uHuWltdz79DE15Q1oD1Wocgs5uWczuHLzHA4u1qSkpgiVxbA0t8L//atPeXq/mpmJOcO4h7c7BVdPc/bKafyCvE9whsePVquju6GH2uIG1DWdaA+ViTIzGTFpUWRcSCPhtBJLK7GUiWsLq3Q8a0f9VMXihFFdbu/qgDI/DuWZBDyDxOrSOUkmxqa49+kT7n3ymIkxYxCuj58XV986x5W3zhMY7HeCMzx+tPtaWmraKbtXRX15i+HcMjM3IzkrjrNXc8nIT8ZKMNXv0uwyDU+aqHvUyMzorGHcyc2R9POpZF5IwzfE5wRnePzodXqG24dQP1XRV9eD9jAzEJmMIGUQcQWJRGZGYWljdbITPWa2VzcZrOtmoLqLpTHjPdPawYaQzGjCsmNwCzplWKuKsEaX9BJLw1NMNvUy3T6I7nmtAZdgb3xTIvCOD8NCsFrvb++w2D3MQucAm0c6ssytLHGLCsZdGYa9j4dQ7yVMvP6YNhtMmDDxUzE+OsndTx5z75PHTE3MGMZ9/Ly4+vZ5rr51joAg8RYmzdUqyu5V0VDRalyYmMlIzkmg6FoeaXmJwi36ny9MaosbmD3ysOrs4UzG4cLEO0islvKDhckg6jIVffU9aPeetxnLCI4PIe5sAhGZUVgKtgg1YcLE64kkSajburn7ySMe33vG2uq64Wex8ZFce/sC56+ewcXVGZ1Oh0qlOrnJHiOb61tUPamn7G4lXW19hnEbW2tyzqVTcDWX2ORIoTylAcY1E9QWN9BY0sL6srHWvqE+ZF5II60oBWd3pxOc4fGzt7NHX10P6qdtDLcPG1SocksLIjKjiDsbT3B8qHAq1JNiY32TytJ6vv+9v6OtqcMwbmtnw7lL+Vx75wJJaXFCnVuSJDHUO0Lp3UrKH9ayurxm+FlweAAF13LJv5SNq7vzyU3yJbC7vUtrRTt1xQ30tWoMOXIWVhYk5saTeSGNqJQI4c6t+bE51E9VdD5rZ33JeB1183UnriCB2Lw4nDydT26CLwHtnpZx1QADVZ1MdAwj6Y1q/oDEUMKyY/CLC8FMsMzAzYVVJpt7mWzpY/tIrc3trAjKjMUvNQo7we6Zep2e1aEJ5jsGWBkYN2QGIpPhHOKHuzIUlzB/zOSmV74mTgbTN8+ECRNfmvW1DZ48KOfuJ49eWJjY2dty7vIZrr59nqRU5ddmYRISGcjZK6dx83cgJzdbKAXp/u4+DY+bqH/c9JmFSVJ+ApkX0ohMChdvYTI6h/ppGx3lajaOLkz83Ik7m4DyTDyOgj2smjBh4vVlZmqOe58+5u4njxkdMmbieHp5cPWtc1x96xwhiqCTm+BLQKfTo6rvoPRuJXVPm9jbPVAmymSQkKGk8FoumWdTsRZMmbi2tEbDk2bqihuYGDSqyx1cHEg/l0LmhTT8FWKJOCS9nrGuUdRPVfTUdLG3vWf4WUBMIHEFiURlR2MlmH/468DP3/wWI4fXFJlMRkZOMtfevsDZC6exsRHr9720sEL5g2pK71YyojFeR51dHcm/nEPhtVxCIgJPcIbHjzE8voGWZ6oXwuPDE8LIuJBG8pkEbOxsTnCWx8/W2hZdlR2oy9qYHjBeR63tbYjJVRJXkICPwlcohbckScwPTjNQ3clQfQ97W8Zae4R6E5YTS0h6JFb2YtV6f3uX6fYBJpv7WB42hh7LrSzwig/DJzmckZUZwhIThVmjS5LE5uwiS11DLHYPoT1yXtt6uuIeG4Z7TAgWgp3XJt5MTJsNJkyY+EJ0Oh311S3c/fgRTx9Xsbt7sBCUyWRknk7h6tvnOXv+67MwcXFzIv9yNoXXcgkODxRKQfp8YVL7sJ7mZ63s7xqDpMITwsi8mE7ymQThQsO2VjcPFiZPVS8sTGwcDhcmZxPxVvgItTAxYcLE68vW1jZPH1Vx95NHNNS0GjZ7rW2sKbxwmmvvXCA1U5zF83PGBicovVvJswfVLM4tG8b9Q3w5eyUHzyBncs+cFuq493f3aatXU/eoga6GHkP+kdxCTlx2LFkX04lOj0IumAp1aXqJjqcq1E9VrM6tGMadvVwMG/suXq4nN8GvAQHBfuj0Om6+d5mrb5/nlJdYgeJ7u3vUl7dQdreSllr1C+dWxplkCq/lkpwVj7lg59bcxDx1jxqpf9zI4pFsMQ9fdzIPw+PdfcTKFtPtaxloOQyPbzKGx5uZmxGWEo7yTDyKtAjkgoWZbyysMVDbxUB1J2szxnumnasDodkxhGXH4OzjdoIzPH4kvZ6F/nEmmvqY7Rwy1BqZDPdwf3xTIvCKDcbc0gKdTseoavaLP/ANYW9ji/mOAXQtGno2jIJPCzsb3KJD8FCGYetpumeaeL0Q64prwoSJY2Owf4S7nzziwa0S5maN3n8hYYFce+cCl28WCbkwaahopfROBS017egPW0+FX5hMzlP/qJG6R1+jhYlWx0Bz/+cuTOIKEghLCRduYWLChInXk6P5R08elrO1aQw9TslI4Nrb5ym6lI+dve0JzvL4WVtZN+QfabqGDOMOTvbkXcyi8FouipgQ9Hq9MBv7kiQx1DVC1Yd1/EP7B2wfCbgOjg4i82IaqQVJ2DnaneAsj5+dzR16qrtQP21jvHvMMG5pY0V0TgxxBQn4RweaNvZfEf/f//e/olKpSEhIEGYDT5Ikets1PH1QTdWj+hcCriPjFBRcOwyPdxQrW2xrY5uWp63UFjcy2GG8jooeHj8zOI36qYquCjVba1+P8Pj93T1GmvoZqO5kumcMDpaqyC0tCEwNR5ETi1eUv1AuAwDrM0sGm6TdI7W2P+WCb0okvsnhWDuJVWu9VsuyZpyFzgFWhowB1zJzM1wUAXgow3AK9jVlBr4hTExMUFBQwB/+4R/yl3/5l6yurhIXF8f3vvc9vLy+2I5akiT+8R//kX/6p39icXGR8PBw/vN//s/ExsYyOjrKjRs3+I//8T/yL/7Fv2BjY4ObN29y4cIFvv3tb3P27Fneeust7t27x9zcHNHR0XznO98hLCwMrVbLH/7hH1JSUoJWqyU0NJRvf/vbJCcnH8sxm96imDBhwsDK8iqP7j7l7ieP6GzvNYw7OTty6XoBV98+T0xcpHAPq30dg5TeqaDyUd3XZmGyvblNyzMVtQ/rGVC/uDBJzk/APcyF8zfPIRfI59G4MGmjs6KDrVVjrb1CvYk7m0hMnhI7J7Fe8JgwYeL15fPyj/wCfLj2znmu3DyPX4BgoceH+Ueld6torGgxBFyby81JzUmg4FouabmJWFhanPBMj5fluWXqHzdR+7CB2XFj/pGL52H+0cV0vALECj1+MZi125B/JDN7nn+USERGJBam/CMTPwPzM4uU3q3g4celLB5ReHt4uXH2ymkKrp7GL1i80OOelj7qHjbQVqlm/zAIV2YmIzolksyLaSTkxgmXI7exvE5nuRr1UxVzI0bVup2zPcr8OOIKEoULj5f0EjP94wxUdTLc1Id2xxh67BXljyInlqCUcOFCj/c2d5hq0zDZ3MvqkXumha0VPonh+KZG4uQnVuixJElsTM2z0DnAYvcwul2jtaCdjwfbTpbEFeZgZSeW8OTrRHl5Obdv32Zvb49vfOMb/MVf/AXf/e53v/Df/OAHP+Dv/u7v+Mu//EtCQ0O5c+cO3/jGNyguLiYwMJDf//3f5zvf+Q65ubn8+Z//OZ6envzGb/yG4d9/8MEH/PVf/zXBwcH80R/9Eb/6q79KcXExd+/epa2tjeLiYuzs7Pj+97/Pd77zHe7evXssxyrOWyQTJkz8VOzva6mpaODux48pL60xhB7L5ebknMng2tsXyCvIFG7RvzCzyNMH1ZTeqWRixGid437KlYKruWIvTIobaatsZ3/3xy9MzOXmqFQqYR7eNlc2GK4doOXv6782CxMTJky8vmxubPHkwTPufPyI1ka1Yfx5/tG1d86TlBonzDX4OUN9owc2SferX8g/Co0MOghmvZiFs5tYmTi7O3uoKtupLW6kt7nPYIllaW1JYKwfF//ZeaJTIoXLP1oYn0f9VEXHs3bWF421dvf3IO5sArFn4nF0czzBGZp409nZ3qXuaRMldypob+gynFtW1lZkF6ZReC2XuLRo4RTe06Mz1BU3Uv+4iZX5FcO4d5AXmRfTST+XgouH84nN72Wg3deiaeyjvbSNwdYBQxCuuYWciPRI4goSCEkMxUyQDp3nrM0uM1DTxUB1FxsLq4ZxB09nFDmxhGbH4OAh1j1Tr9Mx3zfOZFMvs13DSIf2ZzIzMzyiAvBLjcIzKlC4gOvdtU0WugZZ6BhgZ8lYa0tHO9xjQnGPDcPS2R6VSoXcWoxNJUkvIdvX/uS/KBj/+l//axwdD55/zp49S1tb20/8N//0T//EN7/5TSIjIwF45513+Pjjj7l79y7/6l/9K27cuEFNTQ2/+Iu/yPb2Nrdv336hY/GXf/mXiYqKAuB3f/d3SUlJobW1FWtrayYmJvj444/Jzc3lP/yH/8Bv/uZvHtuxmjYbTJj4mtLXPcDdjx/x4E4pSwtGFVBkdBhX37nApeuFuLm7nOAMj5+DhUkzZfcqaavrOLIwsSTr+cIkNQZzwRb9M2Oz1BU3UPfoxyxMLqSRfj71hYWJTqd79ZM8Zp4vTNRlbQy0aJAOLbHM5eaEHy5MQpPChFuYmDBh4vVEr9fTWNvG3U8eUVpcyc72DnAkmPWdC0LmH60srlL+sIbSe5UM9Y4axn80/0gkJEliQD1EXXEDzU/b2NnaMfxMkRBG1sV0EnKV9Pb3EpUQIcxGw/72Hq2Pmul81s5k34Rh/Hkwa3yBKf/IxM+GJEl0tfZRereSqif1bB+xm4tNjkQRH8jP/at3hOtG3lzboqmshbriRoa7Rwzjtg62pBUlk3UxncDIAKHOLUmSmNJMoi5T0VXZwc4RuznfCD/iChKJPh2LjWChx3vbu4w09qGp7mT2yHXUwtqS4PRIwnJiORUuVsA1wNrUAhNNvUy19rN3pNaOPu74pkbik6jAykEsNb9uX8ty/yjzHQOsHRE+mlnIcQ0PxF0ZhmOgt6HWIqzPAXZX11gbGGV1aAS7rR02PL1wChHrOfCLcHc3WlPL5XLD+6gvYnJykv/23/4bf/Inf2IY02q1xMbGGv788z//89y9e5cbN25w6tSLIsrAQOPv18bGBmdnZ+bn57ly5Qr7+/t89NFH/Nmf/Rlubm786q/+Kv/sn/2zn+UQDZg2G0yY+BqxtLjCwzul3P2omN7uAcO4q7sLl68Xcu2dC0REh53gDI8fSZLoadcc2CQ9rmNr48WFSeG1XHKK0rEVzAd7a32LprJW6oobGOoaMYyLvjCZGZymvayNrgo12+vGWjv5upBxJZPYvDhsBHtYNWHCxOvL6PAEdz9+xP1bT5ieNHZWBYUGcP0w/8jL2/MEZ3j87O9raapso/RuJU1VbegObZLkFnIy8pMpuJZLclaccJk4izNL1D1qpK64gflJY9aVu7cbGRfSyLyQhofvwSJTlJcGep2OobZB2kvb6Ks3BlzLzMwISw4jriBRyGBWE6+W2al5yu5VUXa3kulx43XUy9eTgmunKbiai4e3GyqVCls7MV4+67Q6xromaLqlQl3Taeg8NzM3IzY9msxL6cRlxQjXeb6+uEZHeTvqMhUL4/OGcQc3R5Rn44k/m4ibn1g5cpJeYrpnDE1VByPN/egO7eaQgU9MEIqcWAKTFcitxKr13sY2U239TDT1snbknmlpb4Nvcji+KZE4CpYZKEkSG5NzzHcMsNgzjH7PaInl4O+FhzIM14ggzAWrtW53j/WRcVYHR9iZN+ZDSuZmyO1N9sU/CS8vL37913+dy5cvG8bGxsZwdnYGYG9vj9/7vd/jypUrPH78mEuXLpGXl2f4u7Ozxvvm5uYmy8vLeHt7Mzw8TExMDDdu3GBnZ4dHjx7x27/926SkpKBQKH7meZ/Ik9/i4iL/z//z/9DY2Ii5uTnXrl3jt3/7t3+sN3hjYyN//Md/zMDAAI6Ojvzzf/7P+eY3v3kCszZh4s1kf19L9bN67nxcTGVZncEb2cLSgryCLK69c57svHQsBFsIzk0v8PReFaX3KpkaNfpgn/LxoOBaLgXXTuPtJ5Z1jl6np7upl9riBlRVaoM3smFhcjGNuOxY8RYmS+t0Hi5M5seMnp4Org4ozyYQmx/H+MKEUCGIJkyYeH1ZX9vg8YNn3Pv4MW3NHYZxB0d7Llw7y/V3LqJMiBJus3eod4SSO5WUF9ewtrxu+Fl4bCiF13LJu5iFg2ABjrvbu7RWtFNX3EBvS79h3MrGkuQzSWReTEMRHyqclcv82JzBJmljyVhrj0BP4gsTic2Lx95FrFqbeLXsbO1QU9pIyZ0K1E3dhnEbW2tyzqVTeC2PmKQIw7klygbe1NA0tY8aqH/UxNqS0YLMN9SHrEObJEdXsSzItHv79Df00V7WxlDbgKEbWW4pJyIzmviCBILiQoTpAnvO2uwymqpOBmq62DxiN+fk7UpYTixh2THYuTqc4AyPH71Ox3zPGBPNvcx1jxhskszMzfCMCcY3NRKPCH/hOs93VzdY6BpkvkPD7pHnIysne9yVYbjHhmHtLFatJb3E1vQsqwMjbIxNGuzPkMmw8/XCISSAgYU5rN3EctJ4Gbz33nv85V/+JZGRkYSGhlJVVcW3vvUt/vzP/5yCggL+5E/+BJ1Ox/e+9z1++MMf8ju/8zvcvXsXDw8PAP7u7/6OzMxMPD09+d73vkdISAiJiYn8/d//PR988AH/63/9L/z8/HB2dkYul+PgcDzfxRN5u/gbv/EbnDp1iqqqKhYWFvi3//bf8vd///f8yq/8ygt/b3BwkH/zb/4Nv//7v8+NGzfo6+vjF3/xFwkMDOTChQsnMXUTJt4Y+roHuPPxIx7cLmF5ccUwHhMXwfV3LnLh2lmcXcTyefw8/1ZrGytyzmVQdD2XmKRI4Rb90yMz1BU3HPi3HvH09AnxJvtSBmlFKTgJ5o2s3dun/9AmabB10PAAI7eUE5ERRVxBAsHxoZiZm6HT6RhfmPgJn/hmsDa3wmBdN0vrS5CQcNLTMWHCxCE6nY6mujZuf1hM2aNKdg9D/czMzMjKTeX6uxfIL8zGShCv3eesLK7y7GENpXcqGO4fM4y7ejhz9sppCq/lEhDqd4IzPH4ObJIGqX14YJO0u71r+FlEkoKsSxkk5cVjJVhY5/bGNt2VHbSXtTHVP2kYt3GwJSYvFksfG3Iv5v1Y8ZgJE18GSZLobOml9G4F1U8a2D5iQRafFkPh9TyyC1KxthXLbm5zbZOm0hZqixsY6TFeR63trMi8mE72pQz8FX7CbVBPaSZRl7Yd2CRtGmvtFxVAXEEC0TmxWNuJVWuDTVJVJ7P9xrWJpa0VIRlRhOXE4hHqLVSt4fNtkpz8PfFNObBJshSs1rp9Lct9o8x3aFgbnTaMm1nIcYsMxl0ZhoP/KeFqvbuyxtrgCGuDo2i3jee1pbMjTmHBOIYEILexPtggXpr/gk8y8Zxf+qVfQpIkvvWtbzE3N8epU6f4vd/7PQoKCqisrOQHP/gBH374IZaWlvz8z/88paWl/M7v/A5/8zd/A0BycjK/9mu/xtTUFKmpqfz1X/81ZmZm/MIv/AKzs7P83M/9HBsbG/j6+vI//sf/wMvL61jm/cqfBkdHR2lsbKSyshIbGxv8/f351re+xR//8R9/ZrPhBz/4AQUFBdy8eROAyMhIfvjDH2Jv/9WVMsepdnj+WaIoKEzH83rzVY5naXGFR3fLuPvJY/qO2CS5ubtw+WYRV986T1hE8Gc++1Vy3PV5bpNUZvBvNd7UlClRFFw9TVZhGjaHCxNJkoS4Hmyub9HytI26R42MdBt9sO0cbUktTCbzYtoLC5OvMr/X9Rx6bpOkLmuju6qTnQ1jrX0j/FCeTSAqOwZr+8Nac1Dr1/V4viz7O3uMNvczUN1l8G81k5uhe+/csf4/P2vXhwjn1cvCdDyvNz/L8YwOT3D/08fc/7SEmWljZ1WIIpCrb53n0o1CPE8ZbQBexe/sZddnf19Lc7WKsjuVNNe0G2ySLCwtDm2STpOQHov5YYDjcczjdfjOLc4s0fC4ibpHjSxMLRrG3X3cDPlHbl6uhvEvmuvrcDxfBr1Oz1DbIB1PVWga+wy1NjM3IzRZQdzZBEKTFWAGHR0d6PX61/6Yvgzri2vsLG0e+7GY7rM/nrmpBcruVfH0fhUzE8brqLe/JwVXczlzOQdPny++jr5Ox/Nl0Gl19DT3UVfciLqmA+2+8dxSZkaTdi4FyU5HQuJBV67+uSr4DUan07Gztk3tJ1V0PlOzOGG0znluk6Q8E4+rj9sL/+Z15ct+5yS9xEzvGAPVXYy2aAw2STKZDJ/YQEKzY/BPDEV+2Hl+UrU+7nNob2ObaZWGyeY+1o/cMy3tbfBJCscnJQKHL3nP/Gk4iWvCc5ukxc5BlvpGX7RJCvDCLSYUl/AAzH+KWr/O1zjd7h4boxOsDY2yeyQL1MzKEocgfxxCArBydX7hvcTLOp7X1cXAz8+Pvr6+F8b+/b//91/q35qbm/Mrv/Irn3lfDpCbm0tnZ6fhzzKZjH/4h3944e+kpKTwR3/0R5/5t3K5nN/93d/ld3/3d7/UPL4qMunLJFIcI6WlpfyX//JfaGhoMIz19fVx7do1mpqaDMncAO+++y5ZWVmMj49TU1ODq6srv/RLv8T777//pf8/nU6HSqU6zkMwYeK1QqvV0dHaQ/XTRtpbutEdtiPK5eYkpMaScyaV2MSI1/bC+9OyuriGqq6btuouFmeNNzUXDycSs2NIyIp+IfRYBPR6PZN90/Q1DjCiHkWnfe6NLCMg2o/wtDACY/wML3hEYXd9h0n1BJOqMTbmja2n1o7W+Mb74xPvj727YK2nksTm5ApL3dOsDsyh3zc+iNkHuOKZEoiDv+sXfMJXJzk5+af6d6b7rImvG9tbOzTWqKh51oSmd9gwbmtnQ8bpJLLPpBIc5i+cWm16bI626k7a63peyD/yC/EiMTsWZXokNoIpE/d39xlWj9HXMMCUxqhMtLCSE5IQTER6GF4hnsLVemN+nQnVGFPt4+xuGDs3HE454psQgI/SDyt7sTo39Fo9a8PzLHVPsz66CBJE/EIG1i7H5ydtus8a2dvdo7tFQ1t1F0NH1PyW1hYoUyNJzIkhQCFeEO7yzAp9jQNomgbZWjNeR119XIhID0ORHIKNgxi5E8/R7euY65thUjXG/OAcHL59MpOb4xXljW+CP27BHsjMxKr17soWyz3TLPXMsL9uFEhZudjiGu2NS6QXFvZi3TMlvZ7dqRV2hubZnVyB568azWRY+blgE+yBpbezcLWWdvaQppbRTy3D9p7xBzaWmHm7IPNxQWZjeXITfBlIEuYb28hX1pGvbSE7rLUE6Bxs2XdxQGdvC6+41j/tfVZUzp49y7/7d/+Ot95665X/36+8s2FzcxMbmxdvoM//vLW19cJmw+rqKv/wD//A//gf/4P//t//O21tbXzzm9/EycnpK9soKZXKY3vZqtPp6OjoONbPPElMx/N683nHo+kd4u4nj3l4u5SlRePL9mhlBNfePs/5q2deS5ukn6U+uzt71D1rpuxu5WdskrKL0ii8lkd0YvgrtUl6Fd+3mbFZ6h810fCkiZX5IzZJwd5kXkwjrSgFx2P09HwdziHdvg5NUx8dT9sZbNW84N8anhFF3Nl4ApXBX8q/9XU4ni/L+vwqgzVdDNV0sbFg9G919HIhLDuGkKxorJ1sX8vjMd1nPx/T8bzefJnj0ev1NNa2ce+Txzx9XMXOzsEL2Oc2SdfeOU9uQRZWVie/mDzO+qwurVHxqJbSO5Uv2iS5O3PmSg5nr5wmINT3Z53yT+RVfuckSWKwY5i6R420PmtjZ+tFm6TMC2kk5Mb9TDZJr+M5tLOxQ3d1J+oyFdOaIzZJjrbE5CqJO5vAqZAf31r/Oh7Pl0GSJBZHZhmo7mK4oZe9I92x9gGuJKQlYf0aBQ6/6fdZSZLoVvVTdqeS6pIXbZLi0qIpuJpLVkEK1jZf/QXs6/wd3FzformslfpHjS/YJNk72ZFalEzmhYNu5KO8zsfzZZAkiemBKTqeqj7bjRzpR1xBIpFZ0W+0TdKPq9H+9h4jzf0MVHcyd8RuzsLGiuCMSMKyY3AP8XotN9F+lu/c+vQik819TLdpXrBJcvTzwDclAq+EMCxfsf3Zyz6HdPtaVjRjLHYOfsYmySUiEPfYUOz9js8m6XW5JuytrrM2NMr60Bi6H7FJcgwNxD7IH/mXuIa/Lsdz0vzd3/0d3//+9z/351evXuW73/3uK5zR8fHKNxtsbW3Z3t5+Yez5n+3sXlSOWFpaUlBQQH5+PgCpqalcv36d4uLir7zZYG5ufuxf4pfxmSeJ6Xheb8zNzVlf26D4Thm3Pyqmp9MYROjm4cqVm+e49s55FBEhJzjLL8+XrY8kSfSpByi5U0HFo9oX1JTKlCiKrueRXZRusEk6KY77+7a1sU3L01ZqHjYw1GlUzto52pJWlELWxXQCIl6ucvZVn0NHbZI6KzrYXt8y/Mwv0p+4gkSic2Kwtv/pFv6v6zVhf3ePkcZ+NNUdzPSMG8YtbCwJTo9EcVqJZ5jPZyyxXrfjMd1nfzKm43m9+XHHMz46yd2PH3H3k8dMT84axkPCArn+7kUu3yx6wSbpdeKnrY/20Cap5E4FjZVtBuscuYWczLMpFF7LIylTeSJddC/zO7c0u0zdo0bqihuYmzD6CLv7uJN1MY2MC2m4e7t9wSd8dU76HNLr9Ay3D9JepqKvrgfd/qG9h5kZitRw4goSUKSEY27x5ZaMJ308X5atlQ0G67oZqOpk+YiVi52rA6HZMYRmRTM0M4q1nc1rdTxv6n12fmaRsruVlN6tZGpsxjDu5edJ4fU8Cq6c5pSvx7H8X6/Ld1Cv09PT3EfNw3pUVWq0h9Y5BzZJMWRdSkeZGYP8J5xbr8vxfFk2ltfpeNaOukzF/JjREsvB3RFlfhxybyuyC3LeqGP6SZjJzJjrn0RT2cFIUz/aQ+scmUyGjzIIRU4sAUkK5JZvRp7Nl/3O7W3uMNXWz0RTL2tH7pmWDjb4JkfglxKJwzHfM38ajvMckiSJjal55tUalnqH0e2+aJPkoQzDNSLIYJP0MjiJa4Jub5/1kXFWB0bYmTdaYplZWeIYHIBTWNALNklfhTftGnfcfOMb3+Ab3/jGS/v8p0+fvrTP/km88iueQqFgZWWFhYUF3N0PFmeDg4N4eXl9JvU6NDSUvb29F8Z0Oh2v2PnJhIkTRavV0d7czT/9v7epKKtj//ABRm4hJ78wm+vvXiA7L024QL7FuSXK7lVReqeSiZEpw7injzuF1/IouHYab79TJzjD40ev19Pb0k9dcQOtFe3sHz7AyMxkxGZEk3UxnbjsWCxe4gPMSbC5uknns3bay9qYGzG+zHNwdUB5NoG4swm4+x/PIvR1QZIkZvsn0FR1MtzYi3bn8GFVBj4xBwuTwGQFciuxam3CxJvA1uYWTx6Uc+fjR7Q0tBvGHRztuXitgGvvXECZEPVaKhN/Fob7xyi9W8Gz+9WsLBk7qxQxIRRdzyPvYhYOTl89N+11Zm93j7YKNbXFDfQ29xnWGFY2liSfSSTrUgZhcSGvtGPyVbA4uUB7WRsdT9tZXzTW2iPQk/jCRGLz4rF3EavWOq2O8bZBNFUdTKiHDB2T5hZyApMVKHKVeEcHYGZmdrCpPzP6Ez7RxBexu7NHbVkTpXcrUNV3vtCNfPp8BoXX8ohNjhTuOjo7NkdtcT11j5pYmV8xjPuG+pB1KZ30c6k4uohl/anb19Lf2Ie6rI2BlgGkQx96uaWciMxo4gsSCIoLQUISyg5sfX6VmfohBn/QzMaRznNHLxcUp5WEZcdgd4yd568Dep2ehb4xJpp6me0aRjq0b5aZm3EqJhjf1Eg8IvwxE+zl8d76FgudA8x3DLCzZKy1paM9Hsow3JVhWDuLVWtJktianmN1YISNsQlDrZHJsPP1wiksCDs/b+FqbeL4eOVvJ4OCgkhOTuaP/uiP+O53v8vy8jJ/8Rd/wTvvvPOZv/tzP/dz/Mqv/Ap37tzh2rVrNDc3c+/ePf7kT/7kVU/bhIlXzpBmhDsfP+Lep09YmDPuIEfGKLjx7kUuXi/AxdX55Cb4Etjf26fuWTOldypprW1Hf7gQtLK2JLswnaIbeShTooRb9M9PLlBb3EBdcQNLR/InvIO8DAsTZ/fXzxLrZ0Gn1THYoqG9tA1NUx/6wwcYcws5EemRxBcmEpwQ+qVskt4kNhbXGKjpYqCqg7XZFcO44ylnwnJiCcuOxd7d8fM/wIQJEy8FvV5Pc0M79z55TMnDCra3DrroZDIZmbmp3Hj3ImeKsrGyFsunfn11g/KHNZTcqWCg29hF5+zqyNkrpym8nkeQwv8EZ3j8SJLEcNcINQ8baCprYeeIdU54QhhZlzNIykvA2lasWu9u7dBd1UV7WSsTR7robBxsiMmLI74gEa9Qb+Fe/i6OzqKp6mSwtpvdI92xHqE+KE7HEpweidUbbOXyOvGmdCMfN9ub2zSXtVJb3MBgx4/pRr6UQUC4n3Dn1szgNO2lrV+6G/l1DLb9quzv7jHS1M9AVSfTRyyxnncjh59W4nGkG1kUNmaXmGjsZbKlj90jtXb0dccvNQqfRAWWP2Xn+euKXqtlWTPOfIeG1eEpQ/6EmYUc14hA3JUKHANeT0usn4W9tQ3WBkdYHRxBu2m8hls6OeIUFoRjSAByW7FqbeLlcCJS6O9///t897vfpaCgADMzM27cuMG3vvUtABITE/nOd77DtWvXyMzM5C/+4i/4/ve/z3e+8x1cXV357d/+bQoKCk5i2iZMvHTW1zZ4dO8ptz8qpqOt2zDu4GjPtXcucOPdi0REh53gDI8fSZIY6Bmm5HYF5Q9r2FjbNPwsJjGCwut5nD6Xjq297QnO8vjZ2dqltbyNmocNaFQDhnFbextSC5PJupROUFSgcA8wc6OztJe20VnezuaKsdY+Cl/iCxOJzlViI9jDqnZvn9EWDZqqTqa6RgzBeHJrC4LTIlGcjuWUgItQEybeBKYmZrj9UTGf/N97zM8aN/YDg/24/u5Frrx1Di9vzxOc4fGj0+porVNTcruC+vIWtIfWOXK5OWl5yRRdzyU5O/4n2nu8aawsrFL/uInah/XMjBq76Ny8XMm8mE7mhTQ8fF9PS6yfFkmvZ7RzhPbSNnpquo32HmYyQpPCiC9MQpEWIVytd9a3GKztRlPVydIRKxcbZzvCsmNQ5Chx9j15ew9RWJpfPuxGrmB8+Ee6ka/mUnA9V8hu5P42DTUPG2gtV322G/lSBnFZMcJ1I2+tbtJZoaa9tI3ZYaMllujdyHOaSTRVHQw39LG/c+i8IQN7P1cSL2YQnBohXDfy/vYu0yoNE429rIwZ75mWdtb4JEfglxqJo49g90xJYnNmkYUODQvdQ+h2jC4rDn6ncFeG4RoZhPw1yOc6TvT7WtZHJ1gdGGZ71mgtaGZpgWNwAI5hQVi7uZjWqia+EifyZOnu7v65IRhtbW0v/DkvL4+8vLxXMS0TJk6Eg9DJVm5/WEzZo0p2dw9uanK5OafPZnL1rXM4uFiTkpoilJ/dytIqFcV1lNyuYHTAqLBzP+VKwdVcCq/n4hvofYIzPH4kSULTPkjtwwZanrWyu31Qa5lMRlRqBFmXMkg8HYeFYA+r2+tbdFV20l7ayvSAcRFq52yHMj+euMJEPAPFWoRKksTC0Ayaqg6G6nvYOxIw6hXljyJHSVBqOBbWYj2smjDxJrCzs0tZcSW3PyqmsbbVYO9hZ2/L+StnuP7ORRJSYoVbVE0MT1Fyp4Kn96tYnDN20YVEBlJ0PZ/8S1k4uYjVWbW/t4+6ppPa4ga6GnoMXXQWVhYk5yeQdTmD8IQw4TomV2aXaS9rQ12mYnVuxTDu5udOfGEiyvx4HNzEqrVep2dCPYSmqpPxtgFDrc3k5gQkhaE4rcQ3Nki4jsmTYn9vn4byVkruVtBS0274fT/vRi68nktcarRw59bC1PNu5EYWZ5YM4yJ3I+t1OgZbBmgvbaO/qQ/9YY6PudyciIwo4goSCEkMFc5OZXNpnYHqTjRVnawd6Tx38HRGkRNLcFYUA+NDhCRECbNGl/QSC/3jTLX0M9sxZKi1zEyGR1QgfqlReEYFYnYCmU0vk/3NbRa6Bpnv0LB9xP7M0sEW99gwPJRhWLuKdV5LksT27AKrAyOsj44jaY2dR7Y+p3AKC8Y+wEe489rEq0MsGYsJE28Qnxc6GRYRzI13L3L5RhFuHq7odDph/C21+1oaKlr55B/voekYNoROWlhakHU2haIb+cSnx2Iu2EJwaXaZuuIGaosbmJ80qgU8/TzIupROxvk0XE+5nOAMjx9D6GRp20Ho5GGtzczNUKRFEF+YSGiS4kQCRl8mWysbh2rKDlYmjSppe3fHA5uknFgcPZ1PboImTHxNkSQJdVs3dz4q5tG9p2ysGzurUjMTSEiN5hv/5l9g7yCWT/3WxhaVj+spuVNBj6rfMO7obE/+pRyKbuQRGhl0chN8SYz1j1P7sIGGkmY2V421DlUGk3Upg5SzidjYidVFt7+zR09tN+1lbYyqjVYuVrZWxOQqiS9MxEfALrrlyQU0lR0M1nazfaTWbkGnUJxWEpIRhbWDWLU+SQZ7Rw67katZW9kwjEclhFN0PY/c8xnCdSPvbu/SWq6i5mED/W0aw7iNvQ2pBUlkXcogOFq8buSF8XnaS1vpeNbOxrKx1t5hPsQXJhKTq8TGQaxaa/e0jLUediN3jhjECHIrC4LTIlCcVnIq4uA6qtPpYPwnfOAbwubCKuON3SzUdTK3ZVTz23u54pcaiW9yBFaC1Vqv07MyOM5CxwArg+OGHB+ZuTmu4QG4xylwCvRGJtiG6f7mFmsDBzZJ+0eehS0c7Q9tkgKxsBOr1iZOBtNmgwkTr5CtrW1KHlZw56NimutVhnEHR3suXS/kxnsXiVZGCPewOqIZp+RO+WdCJ8NjQw8WJhczcXAU6wXP3u4eqko1NQ9/NHTSitSCJDIvphMWFyJcrZemFmkvbUP9TMX6grHWnkGnDkIn8+Oxc7I7wRkePzqtjnHVYehk+4uhk0Gp4Qehk5EByMzEqrUJE28CC3OL3Pv0CXc+KmZowBj46uPnxbV3LnDt7Qt4+3qiUqmwEcSDVq/XM9QzxtOP66kta2T30AbAzNyMlOwECq/nkp6fjIVg1jnrKxs0Pmmm5mE9EwOThnFndycyLqSRdSkdrwDxuugmesdpL22ju6qTve3DLjqZjOD4YOILkojIjBKuY3Jva5eh+h76KztYGJo2jFs72BKaFY3idCyuAWLZn50kq8trlD+speROOUO9xuuom6cLZ6+epuhaHn7BPic4w+NHkiQGO4apfVhP89M2drYOsl1kMhmRyeEH3ch5cVgKZqeys7lDV2UH6rI2JvsmDOO2TnYo8+OIL0zCM0i86+jiyCz9lYfdyEdyfE5F+KE4rSQ4LUK4bmTt7h4z7YNMNPWyNGTsPJfbWOKTGI5fWhROfh7CrVW35peZV2tY6BpEu2WstZ23Ox5xCtyigpELls+l1+nYGJtkdWCErSmjyFUml+MY7I9jWBA2Hm7C1drEySLWKsOEidcQSZJQNXdy+8OHPH7wjK3Nr0fo5MbaJhXFtZTcqaC/c9Aw7uzmREyqgn/2K+8QEh54gjM8fiRJYrR3jJoH9TSVtrwQjPc8dDI5PwErG7Fqvbe9S3dNF+0lrYx3G8PSrO1tiM0XN3RyaWwOTdWBmnJn/UdCJ3NjCUmPwlKwgFETJt4E9vf2qSir4/ZHD6kpbzSEUlpbW1F4MZfr714iNTPBYO8hQmglwOzUPGV3Kym5U8Hs5Lxh3D/Yh8LreRRcPY2rh1hddDqtjq7GHmofNtBe3WHoopNbyInPUZJ1KZ3o1EjhuujWF9foeNZOe2kbi0c6Jl28XYkrSCDuTAJOgnXRSXqJqe5RNFUdjDZr0B1mjcjMZPjHh6LIVeIXHyJcrU8KnVZHS207JbcraChvQXvk3Mo4k0zR9XySsuKE60ZeWVg96EZ+2MDsuDHvw93HnaxLB9kubl6uJzjD40fS6xnpGEZV0kZfXTfavYNzy8zcjLCUcOILEwlLCRfu3Npe22KwtgtNVSfL48Z7pp2rA2E5sShOx+IoWOe5JEksD08z0dTLdPsAusOsEWTgpvBH62FF6qV8LAV7L6Hd2WWxe5h5tYbNGeM9U25rjXtsKB5KBbaCPR9JksTO4jJrAyOsDY+hP8xsArDx8sApLAiHAD/MBBOemHh9MH2zTJh4SczOzHPvk8fc+fgRo0PGHsuAIF+uv3uRq2+fFy90UqenvbGTktsV1JY1sX94UzOXm5OWm8i5G/kkZMTS2dVJYKjfCc/2+Nha26b0g2fUFTcyNWxU2LmeciHzYjpZF9PFC52UJMa6Rg9DJ7sMYWkyMxkhiWHEFyYSnh4pXOjk7sY2g/U9aCo7WBwxKkNsnOwIyzGFTpowcZL09w5y+8NiHtx6wvLSqmE8PimG6+9d5PzlM8J10e3u7FFb1kjJ7QraG7uOdNFZkn8pm/M3zhARFybcZu/M2Cy1D+qpe9TI6qKxiy4g3J+sy+mkFaZgL1gXnXZfi6axj/bSVgZbBwxddBZWFkRlxxBfmEhATKBwlg/r8ytoqjoZqO5k40jHpLOvO4rcWMKyYrARrNYnyfNsl7J7lSwd8S4Piw6m6Hoe+ZeycXAS6zr6PNul5mE9XQ09hnPL0tqS5DMJZF3KQBEfKlz+xPLMMuqyNtRPX8x28QjwJL4okdi8eOxdxKq1IdulsoMx1SDSYdaIuYU5AckKwnPj8I4OEK7W2ysbTDb3MtHUy9aC8fnI1t3pwCYpJRJLBxtUKhXmgqzdJL0e/eI6Q/cqWdaMI+mM+RPOof54xClwCvETLsdHu73D2tAYqwPD7K0Y75lyO9sDm6TQQCwFsww18dOxtLTE+++/zx/+4R+Snp5+7J8vxpXEhInXhL3dPcpLa7j9YTG1lU3o9QcPMDa2Npy/nM/19y6SlBon3KJ/enz2YGFyt5L5GaNPfWCYP+du5nPmUjbObgehSqIoSLVaHZ313dTcr6Wjtgv980W/pQWJefFkXUonMjlcuIfVtYVV1GUq2svaWJ42BuO5+roRX5CI8mwCjqKFTur1THeN0l/ZwVirBt2+MX/CPzGM8Fwlvspg4R5WTZh4E1hbXefh7VJuf1RMd0efYdzdw5Vr71zg+jsXCA4Tr4uuv3OQJ7fLqXxUx+b6luFnCemxFFw7ja2bJWnpqcKEVgLs7exRfb+O+keNDHYYMwnsnOzIOJdK1qV0/BXiCBmeMzM0TXtpG53laraP1NovKoCEoiSismOwEqyLTru7z0hzP/2VamZ6jIIdS1srQjKiUOQqcQ/2Eu55+qT43GwXFwfOXj7IdgkWrBsZYFwzQc2DehqeNLG5Zjy3QpUhZF8+yHaxtrU+wRkeP5+b7WJnTWyukvjCJLwVPsKdWyuTi2iqOhio6Xoh28U92AtF7kG2i5WdWLXWaXXMdQ4x0dTLfN84HIoRzC3leMeH4ZcWhUuwsfNclDX6zvIa8x0DLHRo0K9v8Xy1auPhgodSgXtMCBaCZTZJej2bEzOsDgyzMTFtqLXM3Az7AD+cwoKw9fYU7rx+HZEkCST9q/sPZWY/VV1bWlr4nd/5HcbGxn7yX/4pMW02mDBxDPR2abj94UMe3C5l9cgOclJaHDffu0TRpTxsBQva2dnaoaa0kZI7Faibug3j9g525F3KouhGHopo8TIJpoanqX3YQN2jRtaX1w3jQVEBZF/OJLUgCVvBArS0e/v0N/ShKm1lWDVoVHzZWBKdE0t8URJ+kf7C1XptdtmgptxcMtbaxd+D8FwloVnRWAtWaxMm3gR0Oh0NNa3c/vAhT59Us7d70Fklt5CTX5jNjXcvkpWXilwu1mPu8uIKT+9VU3KnnLFBYybBKR8PCq/nUngtj1O+Huh0OlQq1clN9BjR6/VoVINUP6ij5Vmbwd5DZiYjNiOa7MsZxGXFCtdFt7W2RVeFmvbSNmaOZBI4uDqgLEggviARNwE7JucHp+mvVDNc32vomEQGPtGBKE4rCUxRILcUK3/ipNDr9Qz3jvHsk3pqSpvY3TnI+3ie7VJ0I4+0vCThsl02VjdpLGmm5kE94xpjJoGzuxOZF9PIupjBKcHyPiRJYrLvINulq/JHs11CiC9MJCJDwGyX7V2G63vpr+xgftCYSWDtYEtodjThuUpc/DxOcIYvh9WJeSYae5hq62d/a9cw7hrig19qJF7xocgFyxrR7e2z1DfKvFrD+viM8Qdyczxiw/CMD8fOS7xMgt2VNVYHhlkbHEW3Y6y1tbvrgU1SsD/mlmLV+nVGkiTWB3vRbm3+5L98TMht7XEI/WqZr7du3eL73/8+v/Vbv8Vv/uZvvry5vbRPNmFCcFaWVw/UlB8+pLd7wDB+ytuDa29f4Pq7FwgIEkthJ0kSvWoNJbcrqHhUx/aR/InETCVF1/PIPJsiXFja1sY2zWWt1DyoZ7h7xDDu4OJA+rkUXIIdOXvxjFAKUkmSmBmcpuNZO53lanaO5E8ExAaRUJREZFY0loKFpe3v7jHS2I+mqoOZ3iNqSjtrQjOjUeTG4hZ4SriHVRMm3gTGRye589Ej7n7yiJkpo5d2eFQoN967xOUbhbi4Op/cBF8C2n0tTdUqSm5X0FjZiv7Q8sHK2pKswjSKrucRlxotXBfd0uwydcUN1DxsYGHK6K/sFeBJ1uVMMs6n4uzudIIzPH70Oj3DqkHaS9voq+8x5E+Yyc2JSI8kvjCRkMRQzAR61gDYWtlgsKaL/soOVo90TNp7OKE4HYsiR4m9u1gdk68D/59vfo+O5h7Dn0XOdtHr9HQ39VLzoJ726g60h3kfome7bCyv0/GsHVVJK4sTxuuos5cL8QWJxJ0VM9tlpm+c/soORpr60B3ZoPaLDyE8Nw7/+BDMBKv13uYOU619jDf2sD5ldBmwdrLHNzUCv9RI7NydT26CLwFJktiYnGO+Y4DFnuEXMgmcgn1wiw1ldHOZwOQkodbour191sdGWB0YYWfBeM80t7bCMSQQp7AgrFzEej56s3j93xHk5ORw9epV5HK5abPBhInXBZ1OR21lE3c+KuZZSY0hk8DC0oKC86e5/u5FMnKShbqhASzNL1N2r4rSOxWMDxuVIV5+nhTdyKPgai6e3mIp7PR6Pf2qAWoe1NNarmL/MEDLzNwMZVYM2ZcyiM2MQSZDGAUpwNbqJupnKhru17E+a+zScXB3PFiYFCTi6i1YMJ4kMTcwhaZSzVBDL9odY1iab2wwilwlAYlhyC1Nt0wTJl41W1vblBZXcPvDYprrVYZxRycHLt8o5Pq7l4iKVQi3ATg2OEHJ7QPf9JUl47U4QhnGuZv55J7PxE6wzqr93X1U1WpqHtTT09RnyJ+wtrUm5WwibmEunL9RJFzHytLUIu2lB77p60fyJ06FeJFQmERMXhy2jmLVWq/VMd4+RH+lmon2IUPHpLmlnKDUCMJzlXhF+CMzE+u8fp1YWVrDytqS/MviZrvMTcxTc5jtsnIkf8Jf4Uf25QzSisTLdtFpdQw09aMqbWWgWYN0aOkrt7QgKjua+MJEAmODhMt22VhYY6C6k/6qDjbmjZkEzj5uKA67kW2dxfKpl/R65vvGmWjsYbZr2JA/YWZuxillCH6pUbiH+wlX672NLRY6B5hXD7BzJJ/LytkBD2UY7sowrBzt0el0jKlWv+CT3hwkSWJrZg6r8TlGekYMtUYmw97PG8ewIOz9vIWr9ZuGTCbDITTitbdR8vB4NR1dYj2tmzDxkhgdnuDOR8Xc/eQxczPzhvGo2HBuvHuRSzcKcXIWS3Wl3dfSWNVGya1ymqpVR9SUVuQUpXHuZj4xSZHCqSkXZ5aoK26g9mEDC9NGZYh3kBfZlzPIOJ+Ko6ux1iL4W+p1eobaBg7UlA296A/VlOYW5kRkRBFfmERwfIhwmQRbKxsM1HSh+RE1pYOnM4pcJWHZMdgLlj9hwsSbgCRJqFu7uPXhQx7ff8bmxoGXtkwmIzM3lRvvXuRMUTZW1mL51G+ub1H5uI4nt8rp6zB2TLq4OXH2ymmKbuQRECpex+RY3zg1D+tpLGlh60gmQXiiguzLGSTlxSO3lKNSqYR5Gbq3vcdE2xidH6kY7xo1jNs42BCbF0d8YRJeod4nOMOXw/LEAv2VagZru9k54pHvGeaD4rSS4IxILG3EOq9fV/5/H32PtjYVyYKpfne2dmmvUlPzsAGNyngdtXO0Jf0w2yUg3P8EZ/hymB+do720lY7ydjZXjBYefpH+xBUmEnM6FivB8ie0e1rGWjX0V3Yw1TUCB3uWWFhbEpJ5kO3iEeItzH3jOZvzK0w09TDR1MfumrHWjn4e+KVG4pMUjqVgtdbrdKwMTDCv1rAyNGHIJDCzkOMaEYhHXDgO/uJ1nu9vbLI6OMrawDD7G1tYcPA1t3RyxEkRhGNIIHIbsWr9piOTyUAmzj31Z8G02WDCxOewtblFycMKbn34kNZGtWHc2cWJSzcKufHuRSJjFCc4w5fD6MA4T26X8/ReNavLRoVdVEI4RdfzyD2fga29WAq7/d192qoO1JS9zUfUlHbWpBUmk3Upg+DoQOEeYJamFlGVttLxtP0FNaVXiDeuEe6c++cXsRdMBaTT6hhXDaKp7GBCbVRTyi0tCE6PQHFayakIP+FqbcLEm8D87CL3bj3mzofFDA8aA8v8A325/u5Frr19Hi8fsby09Xo9Hc09PLldTm1pI7uHPvXmcnNSTydy7kYeKTkJwmUSrK9s0PikmZoHdUwc8dJ28XQh61I6WRfT8TiSSSDCxr4kSUz0jtNe0kp3VSd7h7WWmckISQglviiJ8PRI4Wq9t7XLUH0P/ZUdLBzJn7BxsiMsJwZFjhJnX7cTnOHXEzMzM8wFEZFIksRg5zAV/7eGv2//v+weZhLIzGREp0aSfTmT+JxYLATL+9jZ3KGrsoP20lam+o05PnbO9sSdjSe+MAl3f7EyCSRJYnF0Fk1lB4O13ewdySTwjgpAkaskKCUcuWD5E9rdPWbaBxlv6mH5yHXUwtYa3+Rw/FIjcfQVq9YAW/PLzKs1LHQNot3aMYzb+3rgEReOa2SQcPkTep2OjdFJVgdG2JqeNYybWcjZtbchOD0ZW09301rVxGuPWE+zJkz8jEiSRHvLczXlU7YOMwnMzMzIPJ3Czfcvk1+YJVwmweb6FhWPanlyq5z+zkHDuIu7MwVXD9SU/sG+JzjD48egpnxQT2NJM1tHMgkik8PJvpxBQm48VoJlEuxt79JT042qtPVH1JS2KM8cqCndAzxQqVTYONic4EyPl53FDZp+WM5QbQ87R5SzngpfwnOVBKdFYGFSU5ow8crZ39dS9bSOWx8+pPpZg+GlsrWNNecu53PjvUskp8UJt6iam16g9G4lpbcrmJk05k8EhPpy7kY+Z67k4OLmfHITfAnodXq6GnsMvunPMwnklnISTseRfTmDqOQI4bro1pfW6Ximor2kjcVJo2+6rasdqRfTiS9MxFGw/AlJLzHTO3bom96P7tAjX2Zuhn9CKOG5SvzixOuYNPFqWVlYpf5xEzUP6pgdM15HPXzdyb6cQeaFNFw8xcqfkPR6RjpGaC9to7e2C+1hJoGZuRmKtAjiCxMJTVIIlz+xs77FYG03mqpOlo7U2s7VAUWuEsXpWBw8nE9ugi8BSZJYGZlhvLGH6fYBdLvPbV5leET445cWhWdMsHC11u7ssdgzxLxaw+a08Z5pYWeDe2woHnEKbAR7PpIkid2lFVY1w6wNj72QP2Hr5YmTIggbXy/UnZ1Yu7sK90xsQkxMmw0mTGBUU97+sJiRI2rKgCBfbrx3iatvn+eUl1hqgaNqypqSBvYOH2DM5eak5yVRdCOflOx44R5gvkhNmX35QE3p7iNW/sQLasrqTva2jWrK0KQw4guTUKRFGNSUIihI4aiaUs3C0Ixh/EBNGYvidCzOPiY1pQkTJ8Fg/wi3PnzI/VtPWFpYNozHJ8Vw471LnL9yBnsHsby093b3qHvWQsntctrqOgxddLb2NuRdzKLoej4RylDhFpGf55seEO5P1uV00otSsHMUq9af55tuYWVBVE4scWfjWdhbIjExUSgLm43FNYYOXwi+4Jvu6054rpLQ7GhsBKu1iVeLVqujo7aTmgf1dNZ3G2xeLa0tCYrz58q/vEREong5PqtzK7SXtdFe2sbq3Iph3CPAk/iiRJT58dgJ1o2s1+uZ7BhBU9nBWKvGUGtzC3MCkhWE58bhHR0gnKXvztomk819TDT2sHnknmnr5ohfWhS+KZHYCFZrSZJYG51mvkPDUt8o0qEYQWYmwznMH4+4cJxDfIXLJNDu7LI+NMbqwDC7y8Z7ptzOBqewYBxDg7A8fBYWZX0uSRJ7KytsTkzgtrzM3uoqNq5i5UGaOMC02WDia8sXqSnPXzlQUyalfs3UlDfPcPZyDs5uYinsvkhNmXg6juwrmUQmhQunsNtYXkf99LNqShdvVxKKklCeTcBRsEyCz1NTYiYjICGU8Lw4k5rShIkTYn1tg8f3n3Lrw2I62roN424erlx7+zzX37lAiCLo5Cb4khjsGebJ7XKePahh44i/clxqNOdu5pNVkIa1YJ1Vu9u7tJSrqHlQ/2N907MvZ+CvECt/AmB+bI72kh/jmx7lT3xhEtE5sVjZWqHT6VhULX/BJ705aPe0jDT3MfiwjfbxMqNvuo0loZnRKHKVuAd7Cfc8beLVMjU0Tc3DeuofN7G+vG4YD1UGk305k8S8OHr7e1HEi7Nhq9vX0V3VibpMxXD7kMGn3srWipi8OBIKk/BW+AhzvM9Zm12mv7KDgepOtpY3DONuQacIz40jJDMKKzuxfOr1Oh1z3aNMNPYw3ztqsHk1t5TjFR+Gf2oULgLmT+yubjDfMcBCh4bdVWOtbdyd8YhT4B4TioWdOJ32cLBW3ZyaYXVghI3xSTistczMDPsAX5wUwdh6eSIzE6vWut1dtqam2JqcRLt58HxkAeh3dr74H5p4qfT19b20zzZtNpj42vFcTXnv08csL64YxhOSYw1qSjvBMgl+kpry3I18wmPFeTh/zhepKbMvZ5BWlCymmrK5H1XJj1FTZseQcC4JfwHzJzYW1hio7qS/quNH1JRuhJ2OZctBT2pWmlAKUhMm3gT0ej0tDe3c+vAhpQ8r2Nk58FeWy83JLcjkxnuXyM5Lx0Iwn/r11Q2ePajmye1yhnqNlnXup1wpupFP4fVcvP1OneAMjx9JkhjqGqHmQR3NZW3sHPory8xkxKRFkX05g7hsMX3Tu6s6aS9tZbJvwjBu52xPXEEC8QWJwvmmAyyOzNJf2cFgXTd7m8aXBd7RAYTnKglMFs833cSrZWtjm+ayVmoe1DPcPWIYd3R1IPNCOlmX0/EO9ALEUf0CTA9M0VbSgvqpCu2O0U4lKC6Y+KIkIjOisBDM5nV/d4+Rxn40lWpmjlxHrextCM062LR0CxArswlgfWaJicYeJlv62Dti6esS5IVfWhTe8WHIBau1XqtlWTPGvFrD6rDRZcDcygK36BA84hTYeYmXSbC3tsHqwAhrgyNot4y1tnJzwSksCMfgAMwFs+qW9Hp25ufZmpxkZ2HBsGEqMzfH2tOTia0tvE6J9SxswohYKzsTJj6HjfVNHt1/yq0PHv5YNeWNdy8SHBZ4gjN8OQz2jvDk1rOvnZqy9VBN2f81UlMujM+jKmml45nqC9WUIqHb1zLaOoCmQs1k18gLasqQjCjCc+NwD/FCr9ejUqlOcqrHyvbKBvoji08TJl5HZqbnuPvRI25/VMzEmHExGRIWyM33L3PlZhFuHmK1Tet0etobOg/Cnsua0B52Vskt5GQVpHLuRj7x6bHChLI+Z21pjfpHTdQ8rGd6xGhZJ7RvuiQx1jmCqrSNnuoutIf+ymbmZihSI4gvEtM3fXdjm8G6Hvor1J/xTbcPcyP7nUKcvcQ6r028WiRJol81QM39OlrKVezvGs+tuKxYsi9nEJMRjVywc2trbYuuCjWq0lZmj1h/Oro7El+YRFxBIi5e4l1H5wen6a9UM1zfy/7Ooc2rTIaPMojw3DgCEkMxF0yMsL+zx7RKw0RjDyujxgBgSwcb/JIj8UuLwv6UWLUG2JxZPAh77h5Ed1hrAMcALzziw3EJDxSu1vp9LetjE6xqRtienTeMm1lZ4hQSiGNYENauzic3wZfE/sYGW5OTbE1Nod8z1trS2RlbX19svLyQZDKGBVqfm/gsYp3NJkwcQZIkmhvaufXBA0oelH+t1JTlD2t4cqucwd4Rw7joasrhrhFqHtTTVNZqVFPKZESnRZJ9OZP4HPHUlLtbO3RXdaEqafmsmvJs/EHYs4hqytFZNJUdDNZ2s3tUTRkVgCJXSVCKeGpK3d4+0+pBJhp7WBqcwszGAjJST3paJky8wN7uHuWltdz64AF1Vc3oDzur7OxtuXitgBvvXUKZECWcWm1mYo6SOxWU3qlgfmbRMB4SEci5m/mcuZyDg5NY/so6rY7O+m6qH9TRUdtl8NK2sLIg5UwiWZczCE8IE67WawurqMtUtJe1sTy9ZBh39/cgvjAR5ZkE7F3EqrWkl5jqHqW/Qs1oiwb9oRWlmdycwGQF4XlxeEb4olarcfAQy4bTxKtjeW6Z2uJGah/WM3/E+tM7yIvsSxlkXEjF0VUs60+9Ts9w+xDtJa301fcYbF7N5eaEZ0ZhF+hAwc1C4dYv26ubDNf3oqnsYGXKeM908HQ+CHvOicXO1eEEZ3j8SJLE8tA0443dTLcPoj8UI8jMzPCMDsQvLQqPyADMBOvA1m7vstA9yHy7hq054z3T0sEWD6UC9zgF1s7i1XpnYYlVzTDrI+OGWgPY+XrhFBaEnb+PcLXWa7VsT0+zOTnJ/qrRZcDM0hJbHx9sfX2xsDc+H4nUjWbixyPWW1YTJoC5mXnuf1zK7/3mnzI+OmkYDw4N4Ob7l7n61jnh1JR6vR5VQydPbn1WTZl5NoVzN/JJyFCKp6ZcXqf+USM1D15UU7r7GNWUroIpQyRJYrx7FNWTVnpqugyKL5mZGYrUcBKKkghNFlBNubnDYF03mgo1i6Mvqimfhz07Cljr1bFZxht7mG7ToD2sNTKw8hHrWE282fT3DHLrw4c8uFXCypGAu5SMBG68d5HCi3nY2orlubu7s0dtWSNPbpXT3thlGLd3tOPM5WzO3cgnNCr4BGf4cpgZm6X2QT21jxpZW1wzjAdHB5FzJYOUgiRsBPNX1u5r0TT2oSppZahtwOClbWljRUxuLPGFSfhG+Am3sbI+v4qmqgNNVSebR2rtGuBJeF4coZlRWNkf1FqUlwZ6nZ75vjHGG7pZnltgLzwSG8GC6l8n9vf2UdcchD13NfYYzi1rW2tSC5LIvpxBcEyQcOfW8swy7aWtqMtUrC0Y75mnQrxIKEomNk+Jpa0VKpVKmIwxvU7PuGqQ4ftq1CPPkJ6HPVvKCU6NQJGnxCvcXzif+p3VDSaaeplo6mXrSK3tT7nglxqFb0oEVg5i2TdLksTqyBTzag3L/aOGWsvMzXBRBOARF45TkLd4Yc/bO6wNjbKqGWFv1XjPtLC3w0kRjGNoIBZ24tV6b2WFrYkJtmdnkZ4/C8hkWHt4YOvri7W7u3C1NvHlMG02mBCC/b19yktruf3hQ2oqGg1qSls7Gy5cPcvN9y4RlxQj3MPq7OQ8JbfLKblbyfy0UQX0XE2ZfykbR8HUAjqt7iDs+X4d7TWdL6gpk/MTyL6ciSIhFDPBbmrri2uon6pQlbS+oKZ083UnviiRuLMJ2LuIVevnakpNZQejLf3o9g/VlOZmBCQrCM9V4hMbJFytdze2mWzpY6Kxh40ZY61tXB3xT4vCK0lB38jAF3yCCRMvn63NbT78P3e4+/EjutTGcDFPLw+uv3OB6+9eICBILMs6SZLQdA9Rcquc8uJaNte3gIMuuoSMWM7dyCfzbAqWgnnu7mzt0vKsjer7dQx2DBnGHZztybiQRvalDHxCvE9whi+HuZHZQ3vCdrYPaw0QEBNIfFESUdkxWArmpa3d22e0WUN/ZQfT3casEUs7a0IzownPVeIWJFZ3LMDG/AoTDT1MNvey+7zWZjK023tg2mw4diYGp6h5UEf94yY2V43Wn4qEMHIuZ5CUn4CVYDav+7v79NZ1017Syoh62DBubW9DbP5B2LNXqPE6KsoG3ur0Iv2VnQxUd7J9pNYeod4ocpWEpEdhKZjNq16rY7Z75DDseczgUy+3ssA7UYFfWhTOAaeEey+xu7LOfMcA8x0a9o7YN9t6uuIRp8AtJgQLG7GCvSW9ns2pWVY1w2yMT72QSeAQ6IeTIhibU+LlT+h2dtiammJzchLdlvH5SG5nh62vL7Y+PphbiXVem/jqmDYbTLzRDPQPc+uHD7h/6wnLS0a1gCIqmH/5jXc5f/WscGrKvd09asuaeHK7nPaGLkPYs72DHXmXsjh/8wyhUeKpgGbH56h9WE9dcSMrR5QhQVGBZF/OILUwGVt7sWqt29eiae5H9aSVwVbNETWlJdE5scQXJeEX6S9crTcWVtFUdaKp6mBjwagMcfH3MKgprQVTAel1ehb6xxhv6GGua8QQ7G0mN8crPhT/tChcQ3yRmcmEWYCaePPQ6/U016v49IcPKCkuZ3/P2EWXX5jNzfcvkZWbKlwQ++ryGs8e1PDk1jNGNOOGcU8fd4qu51F4PY9TPmJZ1kmSxFDnMNX362h+2sru9qGXtpkMZUYM2ZczUGbFIBfMinJnY5uuqk5UT1qYHjBmjTi4OhBXkEh8YSKuPm4nOMPjR5IkQ9jzUF03e1sHtqPIwCc6kPC8OAKSFMgtxaq1dnePmfZBxht7WB6eNoxb2lnjnRTOhqMMWzexbHtOkq2NbZpKW6h5UMdIz5hh3NndicyL6WRdSueUv1gBwJIkMTM4jepJC52VHUbrT5mM4PgQEoqSiMiIRC6YTdL+zh7DjX1oKjuY7TfavFo72OCg8CDzrTO4B4i3abk+vch4Yw9TLX3sHbF5dQ3xwS8tCq+4UOFsXvX7WpY0Y8y397M2aryOmltZ4h5jDHsWjb219YOw54ERtNvGWlu7u+KkCMYhyB9zwc7r52HPm5OT7M4b8ydk5ubYeHlh6+eHpZOTcO8lTPz0iPXUaOJrwfraBo/vP+XTDx7SqeoxjHt4unHtnQtcfescy2uLJCQkCPXCY6BnmLK7VZQ/qGFj3agWSEiP5dzNfDLPpmIlmMJud3uXlnIVNffr0LQPGsbtne3JOH8Q9uwb4nOCM3w5zI/NGdSUW0dUQP7RAcQXJRGdHYOlYIov7Z6WsVYN/RVqprpHDWHPlrZWB2HPeXG4BYmnAtpcWGGisYeJpj52j6iAnPw98UuLwidRgYVgtTbx5jEzNcfdjz8b9hwWHszNn7vM5RtFuLo5n9wEXwI6nR5VfQePbz2j/lmLwZ7QwtKCrIJUzt88Q1xatHCdVauLa9Q/PrAnnDkSXOnp53FgT3gxHWd3sXz5Jb2e0c4RVCVt9NZ2oT3cRDMzNyM8LZKEc0mEJIYK56+8s759YE9Y2fFC2LO9uyOK00oUp2OxF63WksTK6CzjDd1Mtw+gM9gTyvCIDMA/LQrP6CAkGahMwZU/M3q9/iDs+UE9rc9U7B8JUo/PUZJzJZPo1EjhrD+31rboLFejKmlhbsR4HXXydCa+8KAb2VlA68/5gSn6K9UMNfSi3Tm0eZXJ8IsLRpEXh48yiI7ODlx8xXn5rN/TMl7fzWRzL6tHrqNWjrb4pUbilxqFnYfzyU3wJSBJEluzi8ypNSx2DaHbPRL2HOSDR5wC1/AAzORivWrU72tZH51gdWCY7Vmjo4S5lSWOoYE4hQVj5SLWPRMOw54nJtianv5s2LOfHzanTglXaxPHg+lbYeKNQJIkWhraufXhwx8b9nzz/ctk56Uhl8vR6XQsqxZ/wie+GayvbvD0fhV3/u8jZo48wHh4uRnUlF5+4qmAhrtHqblf92LYs5mMmPSog7Dn7Fjh1JS7Wzt0VXXSXtL6QtizvYs9cWcTiC9Mws1PnIfz5yyOztJf0cFgXfcLKiDv6ADCc+MITFEIp/j60bDn51jYWuObEoFfWhSO3mIpZ028eXxe2LO9gx3nr5wlOiGUm+9cRS7YAuPzwp7DooIpuplP/qUsHBzFCgDWaXV01HdT8yNhz5bWlqScTST7cgZhcaHCbfauLazSXtZGe2kbKzPLhnF3fw8SziWhPJOAnZNYFjp6vZ6pzlE0lWpGWwcMYc/mFuYEJoejyFXiEx0onG/67voWk819TDT1sDFrrLWtuxN+aVH4pURg7WQKrjwulueWqX3YQM3DBhamXgx7zrmSScb5VBwEs/48CHsepL2k7cWwZws5kZlRxBclERwXLJx3+fbqJgM1XZ8Je3Y85YwiNw5FTgy2h7UW5bySJImloSnG67uZbx9g/nkmgZkZnjFB+KdF4R4RIEzexnO027ssdA0yr/6RsGdHOzyUCjyUYVgJZt/8uWHPMrDz8cJJEYy9nw8ywWr9uWHPVlbGsGc7sZ6PTBw/Yq0QTQjH7Mw8dz9+xJ2Pihkb+XqFPZfcKqf2abNBBSS3kJNVkMq5G/nEp8cKF/a8vrxO/eMmah7UM3Wknf152HPWxTRcPMVTAX1h2PO5JMKSFcKpKb8o7FlxOpaw00ocPZ1PboIvAUmSWB2fM4Y97xwqQ2TgERGAX1oUnjHBwqn7TLx59PcOcvvDYu5/+uSFsOfUjARuvH+Jwot5WFpaoFKphHn5vLuzR3tdDx/95UPUjd2GcXtHO85eyaHoRj6hkUEnN8GXxMzYLDX366l7/GLYc0hMENlXMkk5myhc2LNuX0v/54Y9K0koSsQnXMSw5xU0lYdhz0vrhnG3QE8UuS+GPYvC87DniYZu5rpHDfaE5pZyvOLC8E+LwiXEW7hanxTafS3t1R0/Puy5MPkg7Dk6ULjf9+eFPXuFeBNflERsnhIbAa0/J9RD9Fd2MK4aNAQAyy0tCEqLIDxXyakI8a6jhrDnxh62jtwz7U654J8ejW9SuJBhz2sjU8z9uLDn8EA84xQ4BgoY9ryzy9rgKKsDw+ytHAl7drDDKSwYx9AgLAR7PjKFPZs4bkybDSZeO/b39ql8WsetDx5SXd7w9Ql7npqn9E4FJXcqmDuiAgpSBBCdGsa/+Nfv4iKYTYVepz8Ie35QT3t1h0EFJHzY89I66kM15dIRFZCbr/uhmjJeyLDn6Z4x+ivUnxP2HIdPbKBwtd47DHse/5ywZ9+UCGwEq7WJN4/1tQ0e3XvKrQ8e0Nneaxj3POXOtXcucOO9iy+EPYugTpQkiYGeYZ7cKqf8Yc1nw55vniHzTPLXL+z5cgY+weKFPc+PzqEqaTmwJ1x7Mew54VwykVnRAoY9axlt6ae/4seFPUcRnhsnZNjz5vwK442HYc9Hau0ccAq/9Ci8ExRYCFbrk2RyaIrq+3U0PGlmY2XDMC5y2LN2b5/e2h5Upa2MtBuvo58X9iwKqzNLB5uW1Z1sr7wY9hyeG0dwRqRwNq96rY657hHGfyTs2dzKAq/4ULaczUktyBGuy3N3dYN5teZzw57dY0KRC1ZrU9izKezZxPHyU10Va2pq+Md//Efm5ub4q7/6K/72b/+Wb3/728JdZE28WoY0I3z6wUPuffqY5cUVw3hiqpKb713i3OV8bO3EUgvs7e5R96yFJ7eeoarvNIQ92znYkn8pm3M38wkOD6C9vR1HgdoSVxfWuPM3D6h/1MTK/IphPCgqgOzLmWKGPWt1DDT3oyppZaBZY1DYWVhbEnNa5LDnNTTVHWgqO9k4ovhy8fcgPFdJaFa0cGHPkl7PQv844w09zHYNG1RAZnJzvOJC8U83hj2bMHFSPLcn/PSDB5Q+rDDaEz4Pe37vEll54oU9r69u8OxBNU9ulTPUZ3wB6+zmyKV3Cym6kS9k2PNw1wjVD+poKm1ld/ug1jIzGbEZ0eRczkSZHYtcsM6q3a0duio7UZW0MNVv7I61d3UgriCBhMIk4cKegcOwZzWDtUfCngGfmEAUuUoCk8MFDHveZ0Y9yHhjN8tDL4Y9+yRH4J8ehYOXeLU+KbY3t2kqbaX6fh0jPcbrqJObI1mX0sm6lCFc2DNwEPZc0kJnuZqdI9afwQkhxBcmEZkZJZz15/7uHiON/Wgq1cz0vRj2HJodQ3huHC4C2ryuzywx0djNZEs/exvbhnGXYG/806Lwig9DJjcTqstTr9Whn1mh78MS1n8k7NktJgRPYcOeN1gdGP5s2LOby0HYc3CAsGHPW5OT7CwsvLCxYuPlha2vL5bOzsJ8t028SF1dHX/2Z3/G4OAgNjY2XLhwgd/6rd/C2tr6WP+fr/ykee/ePb73ve/x7rvv0tTUBMDTp0+RyWT8p//0n451cibEZ3Njy6CmVLcZrQvcPVy59s4Frr97keDQgBOc4cthqG+UJ7ee8exBDeurRhVQXGo05986Q1ZBmiHsWQQFKRxsrLSWt1N9r5Z+1YBh3M7J7jDsORO/UPHCnhfG5w/aq5+2s3lE8eUXFUBCURLROeKFPev2tYy1DdBf0cFk57Ah7NnCxpLQzGjCc5W4BXsJ9wCztbh2GPbcy86R89rRzwP/9GhT2LOJ14LPsycMVQRx8/1LXL55Djd3sSzrntsTPrlVTm1Z04thz2dTKLyeh95yn6SkRKE2V9aW12kqaaHmQT3TIzOGcaHDniWJsa5R2ktetCc0MzdDkRZBQlESoUlhXy97wlwlitNKHDzEq/Xq2KzRnvBo2HOEP37p0ZyKDsJMsE20k0KSJDTtg9Tcr6P5WdsL51Z8dizZVzKJSYsSzg5ye2ObznI17SWtzBzZyHL0cCK+MJH4gkQhw54XhmYOwp7retg/tP6UyWT4xgUTnqvEPzFMuFrv7+wxrdIw0djDyqgx2NvKwRbflEj80qOwPxL2LMoafXN2kXm1hoWuQfQ7ezw32nMM9D4Mew7ETLCsRL1Wy/roJKuaYbZn5w3j5laWOIYE4qQQOOx5cpKtqanPhj37+mLj5WUKe/4ZkSQJSfvqrg0yuflXeqeytLTEN7/5Tf7gD/6AGzdusLCwwC//8i/z13/91/z6r//6sc7tK3+T/vqv/5q/+Iu/ICEhgR/84Ad4eHjwV3/1V/zCL/yCabPBxJdCkiTamju4/cFDHj8oZ3vrQC1gbn4Q9nzjvUvk5KdjIdhNbWNtk4riWh7fesZA97Bh3P2UK4XX8yi6kYe3n1jt7JIkMdo3Ts39OhpLW9h+rgyRQXRqJDlXsojPicVCMLXA3vYu3dVdqEpamegZM4zbOdsZwp7d/cVSzgIsjc/TX3Ggptw9ogLyivInPDeOoJRw5FZi1Vq3r2WmY4iJhm4WB4wvbi1srfBNPgx79hFPBWTizWJ/X0tlWe1n7Ant7G0P7Anfv4wyIUq4DcDZqXlKbh/YE85PG+0JQyIDOX/zDPmXsnFwsken06FSqU5uoseIXqens76bJ//0jNGufzCEPRvsCa9kEp4QJlytP9ee0M+dhKKDsGd7F7GCvQ32hJVqRpuP2BPKzQlMVhCeq8Q75utjT2jr5ohfWhS+KZHYOItV65NkZWGVuuIGah7UMzdhfCnnFXiKnMuZZFxIxdHV8QRnePxIej0jHcOonrTSW9eD7nCD2lxuTkRmFPGFSQTHhwgXALyzvmUIe16eMN4zHTydDzYtc2KxcxWn0x4O1qrLw9OMN/Yw0z6Abu+g1jIzGZ5RQfilR+ERGShcrbU7uyx2DzHXrmFr1njPxMoC76QoPOPDsRbIVQEOar27uMyKZpj14TFj2DNg63MKZ0Uwdv4+wokR9Fot2zMzbE1OsreyYhg3s7Q0hj3bm+6Zx4EkSYwVP2NnfvEn/+VjwsbTDf8LZ770c72rqyu1tbXY29sjSRIrKyvs7u7i6nr8Obhf+W3uzMwM8fHxAIYDCgwMZOuIv5cJEz+OhblF7n76mNsfFjMyaHwBGxjiz833L3HtrfO4e4rV4qzX6+lo7uHJ7XJqShrYO1QByeXmZJxJoehGPklZccKFPW+sbtLwpIma+3VMDE4Zxt28Xcm6mI5jgD05Z7KFUpBKksRE7xiqJ610V3eyt32oAjIzIyxFQUJREmEp4cKpgPa2dhmq76G/Us3CkFE5a+tij+K0EsXpWBwFU3wBrE7MM9HYw2RrP9pDSxJk4K7wxy8tilOxwZgLtmFq4s1jSDPCrQ+LuffpY5YWlg3jSWlx3Hz/MkWX8rC1Fcuybn9vn9qnzZ9rT3j+5hnCooNPeJbHz9zk/EHY86PGr709oaWNJdE5sSScS8I3QkB7wsU1Bqo66a/qYGP+R+wJ8+IIzYzG2kGsWpvsCV8tWq2OjtpOau7X09nQbdi0tLKxIrUgiewrmYTEBAl3bq3Or9Je2kp7aRurcyuGcc+gUyQUJRObH4eto1jWn3q9nqnOEforOhhr1RhqbW4hJyg1nPC8OLwi/IU7t3bXNplo7mOisYfNI/dMOw9n/NKi8EuJwMrR7uQm+BKQJIm1sRnm1f0s9Y0a1NcyMzNcFAG4KUMZWpnDNzFBqDW6bneXtcMuht1l4z3Twt4Wx7BgnMKCsBDMqtsQ9jw5yfbMzIthz+7u2Pr5mcKeXxJvwm3R/nBzKS8vj9nZWVJSUnjrrbeO/f/5ym9CgoKCKCsro7Cw0DBWW1tLYGDgsU7MhBhotVqqyxu49cFDKsvqDO2G1jbWnL+Sz833L5OYohTuYXVhdonSOxU8uV3OzISxnT0w1I9zb53h7OUcnARTAen1enpb+qm+X4eqUm2wqZBbyknKjSf7SiYRSQokSRJGQQqwubLJUM0AjX9Tw+IRFZCrjxvxhYnEnU3AwU2sWkuSxGzfBP0Vaoab+owqIHMzAhLDCM9T4qsMFk5Nub+1w2RrPxMNPawdCXG3cXHALzUSv9RIbAQ7r028eWxubPH4/jNuffCA9tYuw/hze8Ib710iKMT/BGf4chjuH+Xxp+U8e1D9gj1hfFoM526eIasg1WBPKAoGe8L7dfS3aQzjdo62BCcGcuMXrxIQLl6tFycWUJW0/Hh7wnNJRGcLaE+o1THWOkB/pZrJDpM9ocme8OUwMzZL9f066h81sra0bhgPVQaTfTmTlLNJWNuK9fvW7mvpb+hFVdLKUNugwbvcys6a2FwlCeeS8Qr1Fu7cWp9fOQh7rupk80it3YO9UOQqCcmIwsrueP27Txq9Ts98zyjjjd3M94wi6Q/Dni3leMeH4ZcejUuQeNfRvfVN5jsGmFdr2F0x1trGwxmPuHDcY0KxsLVGp9MhU81/wSe9OUiSxNbULFbjswx3j8ChGEFmZoZ9oC9OimBsvTyFq7Vud5etqSm2JifRbhqDvc1tbbF7HvZ8zL78JozIZDL8L5x5rW2UjvLkyRNWV1f5j//xP/Lrv/7r/M3f/M2xzu0rbzb85m/+Jt/61rcoKChgd3eXP/iDP+D+/fv86Z/+6bFOzMSbzejwBLc+eMDdjx+xMG9scY5LjObm+5e5cPUsdvZi7SDv72tprGjlya1ntNS0oz98gLGxsyH/YhbnbuYTHhsq3E1tcWaJ2of11D5sYPFIO7t/uB85lzNJK0rB7ogKSAR/S71Ox2DrAKqSVjSNfQYVkNzSgqicGBKKkgiICRSu1lsrG2iqOtFUdrA2a1RJO/u4ochTEpYdg41oKiC9xOLABOONPcx2DKE/fHgwMzfjlDIEv7Ro3BV+wim+TLxZSJJEe0sXtz54wKP7z36sPeHpM+nIBfNh3Vzfory4hie3ytF0DRnGRbcnHOsbp/pH7AllMhnRqZFkX84gNiuGru5OfAXKQdrb3mOibZSOD1qZ6Bk3jItuT7g8sXBoT9jFzvrX3J7Qxgqf5HD806NN9oTHyM7WLqpKNdX36xjsMF5HHVwcyLyYRvblDLwDvU5whi+HudFZVE9a6XjWzva60aEhUBlEQlEykZlRWAi2Qa3d0zLa0k9/RQfT3cZgbys7a0KzY1DkKnELEC/Ye2N+hYmGHiabe9k9UmvnwFP4p0XjnRCGXLBa63U6VjTjzHdoWBmaNGyimVla4B4djEdcOHbe7sKtVfc3NlkdGGF1YATt5hbP745Wrs44KYJxDA7A3EqsWkt6PbuLi2xOTLAzP/9i2POpU9j6+ZnCnl8hMpkM2RvibmBtbY21tTW/9Vu/xbvvvsvq6ipOTseXVfKVfwtZWVn88Ic/5IMPPiA9PR29Xs/f/u3fEhcXd2yTMvFmsrW1TcnDCm598IDWRrVh3MXViatvnefG+5cICxfPumBsaJInnz6j7F4Vq8trhvGYpEjO38wnpygda1uxdpD39/Zpr+qg+kEdPU19BpsKW3sb0s6lkHMlU0g15dL0Eu0lrajL2lg/ogJy8nUm81oOyvw4rASrtV6rY7x9iP5KNRPtQwYVkNzagpD0KMLz4vAQUPG1vbzORHMvE429bC8Zz2sHbzf806PwSYrAUjDFl4k3j8X5Je5+8pjbHz5k+GtiTyhJEp0tPTz+tJya0gZ2D4Mr5XJz0vOTOXfzjJD2hJtrmzQ8aab6fh0TR17Aunm5GsKe3bwO/FZF2NSHg1pP9k2gKmmlu7KDvZ2viT3h9qE9YUUHC0cCaW1d7AnLiSU8V2myJzTxMzM1PE3FD2v4e9X/Zffw9y0zk6HMiCH7SgbKrFjkgp1bu1s7dFV2oippYarfeB11cHUgriCR+MJEXH3EumcCLI7M0l95kKm2t2U8t3xiggjPVRKQpEBuKda5pd3dZ0Y9yHhjN8tHrqOW9jaGTDUHr+P3KD9pthZWmG/vZ6FrEO3WjmHcwe8UHvEKXCOCMBcsK1Gv07ExNsWqZpitaWOwt5mlBTv2NoRmpGDrId55rd3cZPOwi0G/u2sYt3Byws7XFxtvb1PYs4nP0Nrayn/+z/+Zu3fvYml5sPG2t7eHhYUFNjbHa8H5lb99/+t//S9++Zd/md///d9/YfzP//zP+Y3f+I3jmpeJNwRJkuhs7+XWBw8ovlvG5saBWsDMzIys3FRuvn+Z/MIs4QKAtza3qXxUx5Nb5fSqjdYFLu7OFF7LpehGPn5B3ic4w5fDxMAk1ffraHjSxOaaURkSmRxO9uVMEvPisBRMLbC/u09vbTeqkhZGO0YM4zYOtijPxBNXkMDk8hQJCWL5W+4sb9L8YQVDtT1srxrbMD0VvoTnxRGcFiGc4kuv1THbNcx4Qw8L/WMGmwq5tSU+SeH4p0Xh6Och3MaKiTcLrVZLTUUjtz54QGVZHVrt18OecHFuidK7lTy5Vc70uHEx+XWwJ6y5X0fbj9gTJp6OI/tKJpHJ4cJZ1m2ubtLxTIXqSSsL40ZLB1tXO9IuZxBfkCimPWH/BP0VHYw09qHdO8j4kpmbEZAQSnhe3IE9oWCbaPvbu0y19jPe2MPakfBhaxd7/FKj8EuNxFaw8/p14U///f9k61Dl7ennYdi0dHY/PlXj64AkSYx3j6J60kpPTRf7h/l5ZuZmKNIiSChKIjQpTLhQ2N3NHQbrutFUqFkcNVr62rk5ojh9sGlpL2CtV8fnGG/oZrpNg/aw1shkeEQG4J8WhWd0EGaCbaLpdvdZ7B1mvr2fjSnjddTCzgZ3ZRgecQpsXMWqNcDO0gqrA8OsDY2h390zjNt6e+IUFoyNnxfqjg6sXJ1PbpLHjSSxPT3N9tQUe8tGlwEzCwtsfHyw8/XFwkGsYG8Tx0tERAQ7Ozv86Z/+Kd/+9reZn5/nv/23/8Y777xj2Hw4Lr7UZsPS0hKDg4MA/M//+T+Jj483KJkB1tfX+d//+3+bNhu+Rqwsr3L/Vgm3PniAptfYcusX4MON9y5x7Z3zeHmL1YYpSRLdqn6e3HpG1eN6dg5VQGbmZqSdTuT8W2dIyUkQTmG3tbFNU2kL1ffrGO01KmddPJ3JuphO1qUMPHzFameXJImZwWlUT1rorOxgd/NQGSKTEZIYSkJREuHpkcgt5Oh0OiaXp774A98Q9nf3GG7oo7+inTmN8ZisHW1R5MSiyFXiLKDia316kfHGHiab+9g/ogJyDfXFPz0KL2WIcCogE28eYyMT3PrwIfc+fszcrDEzRJkYzc33L3HhylnsHcSyMdPua2msauPJp+U0V7cZ7Qltrcm7mMW5m2eIUIppT1hX3EDNw3oWp432hH5hvuRcyST9XAp2glnW6XV6htoO7An7G/sMlnXP7QnjChJY3FsiMTFRqI397dVNhmp76K9UszZjfHHg5O1KeF7cgT2hk1i1lvQSS0OTjDf0MKMefNGeMDYEv/SoQ3tCsTZWXjfOvH2awb4hLv7ceSKTwoW7jm4sr6MuU6EqbWVpctEw7ubnTkJREsozCdi72J/gDI8fSS8x3TtGf4Wa0WYNusMNajO5OYFJYYTnxeEdEyjcBvXexvZBplpjD+vTxlrbujnilxaFb0okNs6C1VqS2JicY75dw2LvMPrDWiOT4Rzqh2d8OM6h4l1HdXv7rA+PsaoZZmfReM+U29rgFBaEY1gwlofPwiJ1ee6vrbExMYHn0hKri8bvuJWbG3Z+flh7egpXaxMvBzs7O/7mb/6GP/qjPyI7OxsHBweuXr3Kr/3arx37//WlNhssLS359V//dZYPd8/+5b/8l5/5+fvvv3/skzPxeqHX66mvbuHWBw94+qSa/UPVlZWVJYUX87j5/iVSMhKEe4BZXlzh6b1qntx6xviw8QWsb6A3527mU3D1NK4eYrWzS5KEpn2Q6nu1tJSrDCogc7k58TlKsi9nEJMWJZzCbnt9i85yNaqSVmaHZwzjTp7OxBcmEl+QiJOn88lN8CUgSRLzg9P0V6gZauhBu/NcBQR+cSFE5MfjHx8inApof2ePaZWGiYYeVsaMKmkrR9sDNWVaJHbuzic3QRMmgO3tHUqLK7j1wUOa61WGcRdXJ67cPMeN9y+hiAg5uQm+JCaGp3h86xlP71WxvLhqGI9JjODcW2c4Lao9YXUnNffr6G7qNYh6bOxtSCtMPrAnjPAX7oXg8swy7aWttJe1sb5gtKzzUfgSX5RETK4Sa7uD4Mol1fIXfNKbg16nZ6xtgOH77ahHnhrtCa0sCE6PJDwvDs8wH+FqvbO6wURTLxONPWwtvmhP6JcWhW9SOJb2x9vCb+LzufKNi6hUKsITwoT5rul1ejSNfajLVGia+pEOQ2EtrC2JPh1LQlESfpHiXUc3l9bRVB2EPa/PrRjGXfw9CM9VEpoVg7WDWOeWpJdY0Iwz0dDDbOeQIT/PTG6OV1wo/ulRuIb4Cpeptr+5zXznQdjzzpHnI2tXRzziFLjHhmEpWC6mJElszy6wqhlmfXQC6fkmgpkMe39fnBXB2HqfEq7W+r09tqan2ZqcZH/9wL7ZDDC3tsbW1xdbX1/kx2x7Y+LrQVhYGH/7t3/70v+fL7XZYG9vT11dHQAXLlzg0aNHL3VSJl4vpiZmuP1RMXc+KmZ60vhSLio2nJvvX+LS9UIcncRq19JpdbTUtvPkVjkNFa3oDlVXVtZWnD6XzrmbZ4hJihDuYXVlYfVATfmgnrkj7ezeQV7kXMkk43wqDi5i1VrS6xlWD6MqaaWvrsegAjKXmxORFU1CURLBccHCqQV21rcYqOmiv6KDlUmjStrxlDNhObHsOENqTrpQClJJklgammK8sYeZ9gF0ewe1lpmZ4RkThH9aFO4RAcJtopl4s5AkiZ7Ofj794YE94fraBnAQOJaZm8pb71/mTFG2cPaEO1s7VD6p58mtcrrb+gzjzq6OFF7P49yNfPyCxQk8fs7k0BTV9+uof9zE5hHLuvBEBTlXMknMi8dKMMs67d4+vXU9qEpaGWk3dsfaONgQmx9PQlESp4LFC6RdnVmiv6KDgerOF+0Jw3yM9oQ2Vic4w+NHr9Ux1z3CeGMP871jhuBKubUl3okK/NOicPL3FO552sSrZWlqkdbHzbQ9aWZ3w+hd7hfpT3xREtE5sVjZinVu6bQ6xtsG6K/oYLJj2LBBbWFjSUjGQaaae7CXcOfW9tIa4029TDT1sLO8YRh39PPAPy0Kn6Rw4a6jkl7PytAk8+p+VgbGDRvUZhZyXCOC8IhX4OB3Srhaa7e2WR0cYVUzwv66sdaWzo44hQXjGBqI3FqwWksSu0tLbE1MsD03B4cbpshkWHt6MrW9TVRqKnJTFoOJN4Cv/C39vI2GpaUlXF3FC9n5urK3u8fTJ9Xc+uAB9dUthgcYB0d7rtws4ub7l4mMUZzwLI+f6fFZntwqp/RuBYtzRgVdeGwo5986Q96FTGwFUwtotTo667qovl9HZ323QRliZWNFamESOZczCY4JEu4BZnV+FXVZG6qSVlaPqIBOBXuRUJREbH4cNg5i1Vqv1zPVOUJ/RQdjrRpDrc0t5QSlRhCep8Qrwh+9Xo9KpTrZyR4ju+tbbHZPUVPSy+b8imHczsMZ//SD9morwWpt4s1jdWWNB7dKuPXhQ/q6BwzjPn5e3HjvItffuYi376kTnOHxI0kSfeoBHt96RuWjOrYPbczMzGSk5CRy7q180k4nIhcsFHZ7c5vmslaq7tUx0jNqGHd2dyLzYjrZlzPw9PM4wRm+HGaGplGVtNJZrmZnY/tgUCYjOD6EhHNJRKRHIhdsE027u89wUx+aCjUzfROGcSt7GxwVHmS9fRa3ALFsRwE2ZpcYb+hhsqWPvee1BlxDfPBLj8I7LtRkT2jiZ2J/Z4+e2m5UT1oY6zJeR22dbIk7k0B8URIeAp5bK5OL9FeqGajuYmfdmJ93KsLvYNMyNQK5lVjnlk6rY7ZjiInGHhY044ZMNQsbq4NMtfQoHH3Fu2fuLK8xr9Yw3zHA/oax1nbe7njGh+MaFYxcsKxESa9nY2KaVc0wm5Mzhg1qmVyOY7A/TopgrN1dhXsvod3eZusw7Fm3bbxnWjg4HHQxeHsjmZszolIJc+ySJGGO9JP/ook3lq+8elOr1fz3//7fmZ2dRX+407a/v8/S0hKdnZ3HPkETr5b+3kFuffCQBA3rzQABAABJREFUB7dKWFk2tualZSXx1s9dpuD8aawE20He3dmjtqyRx58+Q93UbRh3dLbn7JXTnLt5hiCF/wnO8OUwMzZLzf166h41sLa0bhgPVYaQcyWT5DOJWIumAtrX0t/Yh+pJC0OqQYMyxMrOmthcJQnnkvEK9RbmJv6c9fkVNJUH7dWbR2rtFnSK8Lw4QjKisLITy5JEr9Mz3zfGREM3c92jhlZ6c0s53vFh+KVH4xIknuLLxJuFXq+nsbaVTz94wNPH1ewdBtxZWFpQcOE0b71/mbSsJOHsCVeX1ii7X0XJrXJGB40vYH0CvCi6kUfhtVzcPMUSsEiSxIB6iJoHdTQ/bWNv56DWZuZmxGfHkn0lk5i0KOFyn3Y2tums6EBV0sLM4LRh3NHDyWBP6HxKPCvKxeEZ+irUDNX3sL99UGuZTIavMpjwPCU+ccF0dHbg7CtODpJ+X8dEUy9TTb0sjxitKK0cbPFNjcQ/LQo7D+eTm6CJNx5JkpjWTKEqaaGrsoPdrYMuBpmZjJDEMBxDnSl69zyWgnWD7e/sMdzQS3+FmrkBo6WvjZMditMHmWpOXmLdMwHWphaYaDzYtNzfMnasuCn88E+L4pQyBHPBxAj6fS1LfaPMqftZHzNeR+U2VrjHhOIRH46tYPbNAHur66wODLM6MIJux1hrG083nMKCcQjyx0ywWkt6PTtzc2xOTrK7YHQZkMnl2Hp7Y+vri4Wjo2GtKkoGhW5vl92lBXaXFgiwgP3VZcxdxcr/NHHAVz5jv/vd7+Lv749CoWB8fJzs7Gz+4R/+gW9/+9svY34mXgEb65uUP6njT/7gr+hSG60LPL08uPHuRW68dxG/APGsCwZ7hnn06TMqHtaysX7Qzi6TyUjKVHLurTNk5CcLZ1Oxu71Ly7M2qu/XMaA2Whc4uDiQeSGN7CsZeAeKZ10wPzaHqqSVjmftbB2xLgiIDSLxXDKRmVFYCLYw0e5pGWvV0FeuZrrbqPiytLMmLCsaRV6ckGrKzYVVJhp7mGjqZXfNWGsLN3vC85PxTQpHLlitTbx5zEzNceejYm59+JCpCeNiMiI6jBvvXeTKzXM4OTue4AyPH51OT1udmie3yql/1ozWYE9oSXZhOuffyic2OUq4DcC1pTVqixupeVDH7NicYdwr8BQ5lzPJuJCKo6tYtZb0ekY7R1CVtNJb2412zxhSGpEeScK5ZILjQ4SzrNtZ32awrpv+CjXL40YrSnsPJ8JzlShOK7FzPbCiFOWlgSRJrIzOMlbfxUJbP/Pag419mZkMj6hA/NOj8YgMFK7WJl4tW2tbdJa3oyppZW7EaOnr7OVCQmEScQUJ2LnYo1KpMLcQY8NWkiQ2p1epbX3McGMf2sP8PJmZDP/4UMLz4/CLE+86ur+9y1SbhonGHlbHjfdMayc7Q6aarZvTCc7w5bA5s8CcWsNi1xC6Q+EJgFOwLx7xClzCAoTLz9Pva1kfnWBVM8z2nPFlu7m1FY6hQTgpgrByEuv5CGB/Y4PNiQm2p6bQ7+8bxi1dXA7Cnk+dwkwgG2M4eC7cW1thd2ke7YZR+KiTwMxSLHGrCSNfebNBo9Hwf/7P/2FiYoL/+l//K9/4xjdITEzku9/9Lt/4xjdexhxNvAQkSaK1Sc2tDx7y5EE5O9sH1gVyuTn5RTncfP8SWbmpQvm1A6yvbVD+sJbHnz5lqNf4AtbTx51zN/IpvJ6Hp7dYO6uSJDHSM0r1vTqaylrZObSpkJnJiM2IJudyJsrsWOSCPcDsbu3SXdWJqqSFySPWBfauDsQVJJBQmISrjziKwucsjs2hqVAzUNvN3uaOYdw7OpDwPCWByeHILcVShuj2tcyoBxlv6GFpcNIwbmlnjW9KBD7JEQzMjOGXECXcNc3Em4N2X0vJwwrufFRMbWXTC/aEF68V8NbPXSYqNly4l+0zE3OU3Kmg9E4F8zOLhvGw6GDOv3WG/IvZ2AlmY6bT6uhq6KH6fh3q2s4j9oSWJJ9J4vTVTEJig4Wr9driGuqyNtpL21ieXjKMewR6klCUhDI/HlsnuxOc4fEj6SWmukfpr1Az2qJBf7iJZm5hTmBKOOF5cXhHBggXXLm7vsVkSx8TjT1szBptR23dnfBPj8Y3JQJrR7FqbeLVIun1DLcPGTPVDOeWnMisaBLPJREYG2TIVBNlA297bfMwU03N6pTxOuro5UJ4bhxhOTHYOtuf4AyPH0mSWB6aZryxm+n2QfSH+XkyczNOxQTjlxaFR4S/cPl52p1dFrqGmG/vZ2vOWGtLR3s84hR4KMOwchKv1jsLy6xqhlgfGTfUGhnY+XrjFBaMvb+3cLXWa7Vsz8ywOTHB/qrRPcTMygpbHx/sfH2R24l3z9Rub7G7tMDeyqIx2BuQ2ztg4exGz/AIrjZirQFMGPnKb5wcHR2xtrbG398fjUYDQEJCApOTkz/hX5p4HViYW+TuJ4+59eFDRofGDeM+fqd4/xdu/v/Z+++4uLP0zBv+Vi4KKGIRi0yRRBIIEEKxlaYlTXerxzPtsOv87Npez3rGaXbX64/j67D7rud1Gtu7r73z2N7Hbo9n1N0jqbsVWjkAEoIiU2SKXKSiKCr/nj8KVUG3pG71oEY6zfWXOBz9OHDXOed3zn3d18UrX/oCCYlileYFAgHMzV1cOHOVW5ea8HqCGWSlSsmewzUcO32QyrpS4WQqlhcdNF5o5ubZO0wMhqULEtMS2XtqN/Uv1xEnWDm7JElYe8ZovdhC140OvGsyFTK5nILaQiqPVpFXnS8cW8C94mLwbjd918zMrWN8RcZHh8qrowWLNcCSdZaxxi4mWvrwrcUaGRgKMzHWFpO8Iwe5UhE8gE6Nbu1gNwl+j5e53mEC846P77yN5wIDfcN895/P8tZ3zuNYV22za3clr79xksMv7yciQiwZM4/bw50P7vH+967Q2hiW2IzSR/LSqb0cO32I3MKsLRzhs8GMdZZb5+5w590mFm3hw2TujmwaTtVTc7gKrU6sWPt9fizNvbReaGGgxRKSJ1RHaNixv5TKo1WkFRiFS6w4bHYsN9uxXG/HYbOH2uMzkyg4WE5efYlw8oRSIMBs7xjWpm6mO4ZC8oRylZKU8lxW45TUHN23bVy5jR8ISzOLtK0lLdd7qqXkplJ5rIodB8qJiIrYugE+AwQCAcbbh7FcNzPS0o+0lqCWKeXk1hVTeLCcZAHXUbd9Beu9YNJyvadaVHIcxtpi0qsLhfNUkyQJ+8gks2YL870joQtYmUJOXEEWSeUm9NlpwsXa53JjHxxhyTKEZzG8Z6qiI9fMnrNRRYo1ryVJwrO4iHN8nNWpqfBlu0yG1mBAl56ONjFRvMSK34dncR73vA3/athrRK5So45LQBOfiEKtwe/3IzHyhCdt40XHU78N5ubm8k//9E/8yI/8CDqdju7ubtRqtXALokjw+XzcvNrImTfPc/3ynRDzI0IXwRe++BKvfvkLBGQedu7cKRTr1zY9xwdnb3LxrWtMWcNlmDkFmRw7fYhDJxvQx0Zv4Qg3HwF/gO57vdw8e4e2m+341tgCKrWKqoMVNJyqp6AyX7jEysqiA/MHrbRebGHOGi7DjE9PoPJoFeUvVRIVJ1asJUliqneMvmvtDDf34n8oU6GQk1llCmpCl2YLF2uv08V4Sx/Wxm7sE+FYR8RHY6wtxririAgBY+2YmGXWbGGue5CAxwdyGby0d6uHto3HYMXh5L3vf8CZN89hfhD2AkpMSuDVL3+B0185QWa2cQtH+Gww2DvChTNX+ODszQ2JlcrdpRw/fYj6l3ahFszM0OP20HK1jZtn79D3wBJqj4qNov54DQ2n6knLSd3CET4b2MZm1+QJW1lZDMc6oySTyqPVFO/dIZxuut/rY/RBP33X2hnvGAqZlKp1GnLrSyg4UEZitnhSlM45e0ie0LUUTnTHZCSRUVdMaqUJuVpJq0jGlYFA6MJ3G88ePq+PvsYeWi+2MPhgIGQKq4nUUnqgnMqjVaTmiyfpuzy7SN+1dvpvbvRUS8xNxbSvlOUID9V1u4Q6n0sBiZnOYcbv9TDbPRJKUCvUSlIrTWTUlRCblSzMWvIQbvsKtvag2bN7MRxrnSEOQ0UBCTtyUQlGPJECEs7JaZYsQzjGJkIJaplCTlSWkdj8HCJSDMLF2u92h8yefSvh9yOlTofOaESXloZCI5Z0kCRJ+FYcwSqGpfnQGo5Mhlofizo+EVWUXrhYb+PJeOpkwy/90i/x8z//8zQ0NPAzP/MzfOUrX0GhUPAjP/Ijz2J82/gBMDJk5a1/Oc87//oeszNh6YKKqh2cfuMkx08dIjJKh9/vp7W1desGuonwen3cvXKP7/79O/R3DBNYe4HRRUVw8OUGjr1+EFNJrnAL3dzUPLfO3eX2+bvMrytnzyzIYO+p3dQe3YVOMGZIwO9noKWf1ostWJp6QzIVKo2K4r1BNmVGSaZwsXYuOrDc6MBy3Yx9ejHUHpueQMH+cvIadhChFyvWUkBirt/KWFM30+2DIZkKuUJOclkeGXXFJOQbhZOp8Dpd2DoGmDX3sWpbDLVrYqPxpsdu2bi28WhIkkTrvQ7OvHmO989dZdW5CgTlCfe9tJvyXUX8m5/8YTSCXbavLDu5+u4tLpy5iqUz7AWUmBzP0dcOcvTVA6QYxfKHkSSJ0d4xbp69Q9Ol+6w6grGWyWSU1Bax94t7qGgoRSmYmaFn1U3XrU5aL9zH2h2ujo2MjaT88E4qj1SRYBRLihJgwWqj75qZ/luduNdiDZBanBmUJ9xVgFIwjy+/18d0+yBjTd3MWcJSlCqdhvTqQox1JehTw1KUokjYuOaXmDFbsLX343e5cecXoIsXTx/+ecHMyDStF4KeaqvLYQZsVnkOlUerKKovQaURa275PD5G7vXRd93MZFe46lYTqSWvYQcFB8qJzzAIdT4HWJldZLSxC9vdDmZWwzr1sVkpwaRlRb5wnmoBv5/F/jFmzRYWB8dDF7AKjYqEklwM5QVEpiQId1b1OlZY6h9mqX8Y30p4XmviY4kx5aDPzUShFivWUiCAe26OFasV1+xsKNYyhYKI5GR0RiPq2FjhYh3wenAvzOGetxHwhI29FRotmvhE1HEJyJVireHb+OR46lNQVVUV169fR6VS8cYbb1BcXMzy8jINDQ2f+Blzc3P85m/+Jk1NTSgUCl555RW+8Y1vPLLs9md/9mdpbGzc8L0//dM/Zf/+/U879M8FVlddXHr3Gt/753Pcb2wLtcclxPLF149z+isnyCvI3roBPiOMDo5z4XtXuPz9GywthEvzSquLOH76EA1H69BGiJVB9nq8tN1o5+a5O3Q394b0v3VREdQdr2HvqXoyTOIxZxem5mm92IL5civLc+FYpxWkU3msmh37StEIJlMR8Aewtg3Qe82MtW0wxAJSalXk1hVTcKAcQ16qcC8wq4sOxpt7GGvqZnU+HOvotAQyaktIqy5ALVispUCApeEJZtssLFhGwzIVSgXxhdkYKgrQpSXS1tb2MU/axmeFOdsC3//u+5x58xxDA+GLg6zcDE6/cYJXXj9OXEIsra2twnjjSJLEUM8YH/zrXW5fbsK9JmOmVCrYfWgXx04fZGd9OQrBjCtX7E4aLzZz8/t3sPaH5UMTUuNpOFnPnpfriE8WS4pSkiTGe61r8oTteFYfyhPKyK82UXm0mvyaAhSCfLYfwrPqZuhuD33XzcwOhKUodXFRmPaVYdpXil6wWAPYx2cZa+xmoqUP7+raxYEMEk0ZQXnCslzhYu33+pjvHWa2rY/lsbAUJVqVcIaszwPcThedNzpou9iywVMtOj6a8iM7qThSRXxq/BaO8NlgbmSavmvtDNxZ56kmg/Qd2ZgOlJNVlY9CsAS13+NlyjzIWGMX84MTofagp1oRxtpiolPEi/WqbZEZcx+2jgF8zrB/XnRGMobyAuKLsoWLdcDvxzE2wZJlCOdEeB2Vq1XoczKJMeWgTRBvz/Q5nTjHx1kZHyfgDl+2q2JiiExPJyI1Fblg0oKSFMBrX8I9b8O7HJYMRS5HExsflEmKiBTuXmIbT49P9cmfn59nfHw8dLmpVqtpbm6mpqbmE/3/r33tayQnJ3Pjxg1sNhs///M/z7e//W1+9md/9iN9Ozo6+Nu//Vtqa2s/zVBD8Hg8jyxBlMvlGxIZHo/nsc+QyWSoVKoNfR9X1vjhvl6vN/T32uy+gUCAnk4Lb3/3PS6evYrDESzXUqmV1O2p5vUfPsWBw/UgW9OO+9Dv6Pf78fl8G9p8Ph+BwOPLh9XrstEf11elUoUWm83s6/P6uXnhLu+fuUpPW1+I1RyfFEvJLhM//FOvY8xJCz33Ifx+/xMZWEqlMiQ98zz0DQQC+Hy+0OdtfGiSu+820XT5Ps6llWDiXIKi6gLqT9RRtqckJFPx4VgrFIrQZ/bhcx+HT9tXkiS8Xu9j+67Hx/V9OD+9bi/dtzt5cOk+ox1hbb8IfQQ79pdRfriS5OyU0Fz+pM99iCfN+4/r+3D+eDwelErlR9aIx+GTzHv79AKWm50M3u7CvRRmhiTmp5K/dwdZuwpQrbGAHv6+T7OewMa5/LDv+t9p/Rr3NPP+064RHpebqa5hxpu7sVnCLCClVkVqRT7Zu0uJyUhCJpOFxvhJnhsIBJ64Zq/vuxXz3r3kYKFriLnOATz2FQKSRACJyOR4EktNxBdlodQGE6YP1/2H2Ky5rP4BWUYi77Mf7uvz+Wi8eZ93vvs+1y7dxrdWbaOL0nH05X188UtfoKJqR/hz7fG8kPvsh+eFbXqOK+ducvn7N5hcdymXZUrn6KuHOHxqHzHx+rXPuo/HTY3nfZ9dj0AgwIB5iDvnG2m53obP60MmA6VWSeXeMuq/UIepMm/Dz30R99kP93XaV+i81oH5g1Zs1tm1h0F8anzQ7PlQBVp9UF/ZH/Dj9/gf+1z4wfZZCO+1Xq93Q4x+0H12fV+lUsmMZZy+a2YGmnrwrXl8yZRyMspzyd9bStqOLBRKxQu9z364r3fVzaR5gPHmHuzjNuSADBna2ChSqwtIrS5AtyZP+OFYv0j77Pq+kiRhn5hhuq2P+Z4h/O61OSKTEZOdSsKOXMaciyEN8e19NohPu3c+9FQzf9BG7+1OvB4fskCwQtVUW0jpS+XkVOSGPNU+PP5HzaEP43k8z7pXXFhud9B3o535kbCkb2SCnvy9O8jfU0LsOmb7+s/vo9aI52kOPa6vJEnYx22M3+thum0Avys4tyQZxOWn4TNEUH1sL6oPnVU/izPqZu6z6+eMJEmsOlaY7x3B1mHBsU7mVRUZgaE0j9TKIrTxMaHn+h8zRx+3J39cX9iafdaztIxzaAz74Ah+twff2ryISDYQk5dFZEZaKGnr9XqfyXs7fLb7rOT3szozg3NiAs9CWFFCrdGgS08nMj0dWURE8PMWCMAj/n6f9l18K/dZyevBPW/DOTez4fOr1EWhjktArY9FplAgVyo/0Rj8fv9zeZ79PMHv9/OTP/mTpKen80d/9Eeb/nyZ9KRZ+wj81V/9FX/6p3/60QfJZHR3d3/s/x8ZGeHYsWNcv36d5ORkAM6fP89//+//nStXrmzoOzY2xrFjx2hubiYqKupphhnCwxLE995775EfyPz8fH74h3849PUf//EfP3ZRz8zM5Md//Mfx+/20t7dz+fJlVldXH9k3NTWVn/mZnwl9/ed//ucsrXOeX4/ExER+7ud+LvT1X//1X2Oz2R7ZNyYmhq9+9asALC3a+Z9/8z9xe12P7KvVavnVX/3V0Nd///d/z+joo01SFQoFv/7rvx6awP/8z/9Mf3//I/sC/Nf/+l9D//7ud7/7xNj/+q//emjSv/POO5jN5sf2/frXv05kZCQA7777Lvfv339s3+6rVuxzQe3YzPIkUk2PZ0b8+3//7zEYDABcu3aNGzduPLbvT//0T5OWFkxS3Llzh8uXLz+277/5N/+G7OxsAO7du8d777332L5vvPEGJpMJgLa2Nr7//e8/tu/rr79OSUkJEEy4vfXWW4/ta4zJ4uSXXsaQlojFYuHNN998bN8vfOEL7Nq1C4Dh4WH+8R//8bF9Dx8+TH19PQATExP83d/93WP77tu3jwMHDgAwOzvL3/zN3zy2b11dHQaDgbKyMpaXl/mLv/iLx/YtNhUTtRRB17V2Vt0uApWPZ5iVl5fzyiuvAMGXov/23/7b459bXMyXvvSl0Ne///u//9i+n2aNeIg/+ZM/wel0PrLv06wRSo+MnKV48vaUkL+vlH9+5zufaI0A+Nu//VsmJycf2Ven0/HLv/zLoa+ftEaoVCq+8Y1vhL7e7DXCs+DA2tTD9Qd3mZE/+m8GT7dG/OIv/iKxsbH4/X7efPNNBgcHH9v3eVgjqpQGkhQRKLRqFhI13B7ofHzfqiqOHz+OQqGgq6uL733ve4/t+8UvfpGKigqAJ64Rv/Vbv/XYZzwJIu+z8OQ55F71cPafblJaUcRrXzmB3WVjfNz6yL4v4j77i7/4i0RHRXPvZisXLlxEpnv8IeB5mEPPap9d6FjGORlkrKWXpUDK4/8OL9o+W11dzcsvvwzA8vLyI9/xHyI3I5cf+fEfQSaTCbfPRmsjyZyLwT4VvDiYzHDhUz/6iPSi7rOfdI04mrGTvN3lJJjSee/994XaZ3/o9OvEuWTY2vuxTI3R4Zt/bN/tffbZ7bNyn4yXKg5SdqiCyNioTZtDz8s++2NffANro4WRexZsMU4cMY+/xHs4hwAuXbrE3bt3H9v3eZhDT7PP5jmjSdcnkl5ThCNGxvffO//Yvp90DsHzsc/u3r2bgwcP0n6zEeXiKm93PD5u6/fZlZUVvvnNbz6274t2no1XazmWlguAQqflnaEellcf3fdZvYt/Vvvsd/75n+l9Qt9f+9VfRaMNVtxv9rv4Vu+zXzpykLTo4HjNg8Nca3v8GfVp1ojnaZ/dLEiSRMD7+HPCZkOuCid3nhZ/+qd/yl//9V/z6quvPpNkw1NXNnz729/mL//yL3nppZc+1S9lsViIjY0NJRoA8vLymJiYwG63o9frQ+3t7e1ERkby9a9/nfb2dhITE/nJn/xJfuiHfuipf+7jYLfbN+ghPimr6HA4NvR9UvbP6XRu6PukjLDL5drQ1+V6dPLg4XP+z//9L9y43Mj9u2b2vbyTeIP+kX0DgcCG5zocjkf2e4j29vbQv+12+xN6suG5i4uLT+xrNptDmfT5+ce/1EPwwK9ZM8x53ObzEK5VNwnJcVTtK0WfpsU6PvbYvj09PYyPByUPpqamnvjcvr4+ZmaC7JOJiYkn9u3v7w/9/lbroy+YHmJwcJCVNZOgsbHHjxWCL05ut5upwRk67nVC5OP7JmTHMj5jZXzGyvT09OM7ro3xYSw+7u87MTERivPHxXhqairUd3l5+Yl9Z2dnMRgMtLe3P/bF5SF6bnUiHw0e9rXxWpw8nt0xPz8fGsOTsuIQ/H0+qQ7qD7JGPGkc69cISZJwrz5+3qujtBS8WodMIWd4Zuxj14j1Y3jS39jn833iNeLD68lmrhHXvvnPBOaC4/Rr/fAEQsLTrBFdXV3odJ/Mv+J5WCNk0VrkuRmQFMPKxPgT+0J4zf64MYyOjobYQB+3RjwLvIj77Pq+K+vM3T4MlUbF737zV8nICr6s37796MPNQ7xo++zf/H+/TevNbhxLK2SUGUgrSHhs3+dhDj3tPvvwM/NxY1CoFJQ0FFJUb8Kv8nHv3r3H9n3R9lmbzcbtK7cZbx1lzDwCeY/v65E8IQm3F22fhSevEat2J/apAHKlnJiCZOZkU/hcj/7bvYj77MM1wu/0MDP45HlEUSLW1Tms5jnh9tnBszdIkgerFfgE59jtffbp99mAP4Ctf4bF6QV4DEdIE61FmxOJZbg/9HMeh6eZQ/B87LNX/uxtFIHg50uhVQGP/xuvn0MPP/OPw3Mxh9bts4+70H2IyNJ0oovzWJbJNnUOPQ/77HT/CK1934UVN8vSk/dDm80Weq57ndTOo/CinWclwKfX4Y3T44+KwDf0+ITdD/IuvlX7bFtLCxFuNxEuF56PmfftHR3P5F38edhnAy4XUlQkqxLYP8ai6WnexeHF2Wc/CSRJousfz+MYf/JavpmIMiZR8mMnnvpu/s6dO1y4cIFjx449o5F9isqGhoYGrl+//tjSnY/D22+/zTe/+U2uXr0aahsdHeXo0aNcu3aNlJSUUPtbb73F2bNn+eVf/mVMJhONjY189atf5Q/+4A9C2eGPw0MmSGFh4aaVnT5kgjzumev7PsQPWiY2PTnLubcucu7MRayj4axu0Q4TX/zSMY6dPIg+Jvojz/6kZaednZ3s3Lkz9PtsddkpgNvtobWxnQ++f5N7Nx6EZCrUWjX1h3Zx9NWDlFYXIZPJPlJ22tnZyY4dO16ostOHsM8vc//yA26/28jM2CzIguehpAwD9S/XsetwFdGx4UqfF63sFIKbZllZGXK5PPi5DEiMdY/ScaWNvsYefN6wAXBhTREVR6vIKsvGH3j833eryk7Xf96eVkbJ7/YxeLub/hvtzI3Phr4Xlagnb88OcvcUo4uNemZyMfD4stMPz6EP9/20a4QkSdjHZhi/38d0+wB+jx85wb+zoTiLlCoTcfnpyB+j9f5p1hO/309bWxvFxcVbJu/gc3uwdQ4y096Hczr8AqqK1pFQkktCSS6RCbGf6Ll+v5/u7m4qKipQKBSbNpcjIiIe+4wnQZR99sN9ezotvPUv53nv+x+E5AllMhm19Ts59fpx9h2qQ61Rv9D77If7ulwu7n5wnw++f5Putj4C/uDvEhOn59CpBg6dasCYnfbC77Mf7jvWb+X7/3ieobZRVhbDh9S88lzqj9ew80AlEZssp/KD9P00++yH4fP66L/XR8cHZkbMQyAFLw200RqK95VSdqiCpKzkDf9nq+UdOjs7KS0tRavVPrHvQzxq3ttnFhm43cXg7W5Wl8KxTsxJoXB/Odm1hagjNC/0PvthBE1KJ5m414utdxR/IIAEKNRKUsrzSKsuQG80hObvi7rPfrivZ9nJXNcAc11DeNZiLUdGZHI8hjITMQWZyNSP5t1t77Mb+z7Ekz7rC9MLdF/toP1KG475ZaS117jU/FTKXqqkcE8xmjU5yM2YQx/GVuyzAZ+f8Y5hBm51MdE5HPJUU2vU5NYVk7+/lNiMxCf+bo+bF8/zPuu2Oxlv7sHa3INzMXwpH50aT2p1IakVeagiNB+RK3S73Y9c9+D5P89KgQDLo1PMdQ6wODCOLCAhl8lALiOuMIu4klwi08Pr6Hp82r3zedhnfasu5vsGsQ+O4FsOk2+U+ij0uVlE5xiJiA7fQX2WkqbrsZn7rCRJeBYXcU1O4p+fh7Xv+yQJTWIi2tRU1HFxH4n1s5Y0fdb7rNftxmtfwLs4T8AdTgTJVWq0CQa08YnIVepNW0+et312MyBJEl3/5zwO6/OdbJibm+PLX/4y3/rWt/j2t78N8HxUNvzYj/0Y3/zmN/m5n/u5TyVtpNPpPlKq+fDrh6VED/Haa6/x2muvhb7eu3cvr732Gu++++4nTjY8RERExCdKkDzNh/OTPhP4xP3W9/V6vFy5eIszb57j9vXm0AIbrY/ixKtHOP3GSUrKCp76uR+G3+9HqVRumMCfZryb1Xd6fJYLb13l0tvXmJ2aC7WbduRy7PRBDr7cQGT0xuzu+uc+/H0eF5+t/N0e19fv89PZ2M2Ns7dpv91JwB/ccDQRaqoP7cRQkMDx144+0kT9w89dvzFvRV/gieN8uOEoFApWFlcwX35A68UWFqfCmodJWUnsPFZN6YFydDFPKOv4lGP4MJ523q/Hkz5vj3quFJCY6Bqh75qZkfsWAmtJNJVKSdauAgoPlJNSlBnyIHkUnvXn8uPm0Kd5rnvZyfj9XqxN3Timw7GOTowlo64E465CNPqni/XTjEEul3/iNXuz/r6SJLFsnWa2rY/5nuFwrBVKYk0ZJJUXEJOThkz+0cTKk57r9/uRy+WhNftZzuWnwYu4z34Y9qVlzr99iTNvnqe7oy/UnpqezGtfOcGrP/QF0owpj/y/T3ru87bPru8rSRKWzkHeP3OFa+/exukIvpPJ5TJq9lVy7PQhag9UoVpnZvgi7rMfhsvppvGDe9w8e4fBjqFQuz5Bz56X62g4uZvkjKRHPvdF2mcVCsWGvtNDU7RebKHjahury+H38eyKXCqPVlFUX4xS/cnG8VntsxD+zGm12g2x/STP9Xm8jNzro/eamanuMMsuIkpHfkMJBQfKiTMaNvyfF3Gf/TAcMwtYm7qx3uvBsy7WCdlpGOuKSa3IQ6l5srbx877PfhgyYHlwgllzH0uD4QpBjVZLwo5ckspNRKYkfuxztvfZR+PD/bxuLz23u2i9eJ+R9uHwM6N1lL9UQeXRagxZH11HP+65n6bvZ7nPOmYW6bveTv/NTlaX1sgIQEpBBgUHysiuLQx5EjwNnufzrN/nZ6ZziLHGbmx9o8HsNKDRakirKiCjroSYD62jH37uwwvvj/vMPU/7rGtxGZvZwmy7Bc9ykF2vACJTDSSU5THudZBXU/1Uf+On2Tu3Yp/VqNWsTkyzZBnCYZ0M+eep1Cr02RnEmHLRGuIfebn5PL3/Pe0+63e5QmbP/nX3lcqoKCKNRiJSU1F8Qj+AZ/W7bfY+K0kSPocdz7wNj30RJAkFoFCpUMfEoYlPRBkZvSHWm/W7Pa/77A8CmUxGyY+deK5llAKBAL/2a7/GT/3UT1FUVPQMR/Ypkg25ubn8yq/8Cn/7t3/7ke99Es8Gk8nE4uIiNpuNxMTgy97AwAApKSlEr8uKAvzrv/4rkZGRGxILHo8nVHIkKvr7hjjzz+c4e+YCC/NhXcya3ZWcfuMkR04cQKsV62/gcXu488E93j9zhbbGzlBiJUofyUun9nH89YPkFGRt8Sg3HzPWWW6du8Pt840szYXL+HJ3ZNNwqp6aw1WoNCpaW1s/tRbb8wa/z8909yT/craLwZb+MAsoQsOO/WVUHqsizZQuzO/7EA6bHcvNdizX23HYwrFOyErCdKCcvPoSNJHaJzzhxYMUCDDbO8pYYzczncNIa6wNuUpJakU+GXXFxOWkChdrj8OJraOfWbMF13w41tqEGJLKC0gszQuZTm5j6xEIBLh3t5Uzb57n0rvXcLuDbC+VWsVLx/Zy+o2T1DVUPdXL9YsA++IyH5y9yYUzVxi2hC9gU9KTOHb6IEde2U9iSsLWDfAZQJIkBjuHuXX2Ds2XW3CvBuUM5Ao5GcXpnPjR45TvKUWhFCvWrhUXndfbabvYwoQlfAEbnain4vBOKo5UEZcSt4UjfDaYG56m95qZwTtdeJxr0hUySC/NoeBAGZk781Gonvoo9FzD5/YyZR5grLGLhaFwJbQ6KgLjriKMtcVEJYsXa+fsArNmC7aOfnyrYZmS6MwUksoLiC/MQi5YrLcSk/0TtF64T8f1dtwrawxYmYzcnXlUHq2ioK4IpWB/b6/bw3BTL33X2pnuC0sNafU6THtLMe0vIzZNrD0TYHlqjrHGbibu9+JZCbOd4/PSyagrJqU8T7h1NODzsdA3yozZgn04LO2i1GqCScuKAnRJ8fj9fiY+oYzRiwDPsgP11DzDA+/hXyfvqzUkEGPKQZ9tRP4cXvD+IJACAVyzs6xYrbjXyRnJFAoiUlOJNBpR6fXCnVX9HjeehTnc8zYC3nCVi0KrQxOfiDo2HvlTJLq2EYZMJkPxCUk7W4G/+Zu/Qa1W82//7b995j/rqT9Bf/RHf8RP//RPs2fPnk918M7Ozqa6upo/+IM/4Hd/93dZWFjgW9/61iN9GBwOB3/yJ39CVlYWRUVFXL9+nbNnzz4y0fGiY8Xh5L3vf8D33jxH+4OuULshKYFXv/wyr33lZTKzjVs4wmeDob4R3v/eVa6cu8nyunL2yt2lHD99iPqXdqH+FMyQ5xlul4eWq63cOnuHvtawyVBUbBT1x2toOFlPWm5qqP1JZWovEuasNlov3qftcivOpXAZZkZJJpVHqyneuwO1VqxY+70+Rh/003etnfGOoRALSK3TkLenhIL95SRkJz/5IS8gnHNLWJt6sDZ341oX65iMJDLqikmtNKGKECthKgUCLA5YmWnrY3HAGmIByVVKEopzMFQUEJX26PLqbWwNpqdmeedf3+PMm+exjoYPk/mFOZx+4ySnTh8lLj526wb4DBAIBGht7ODCmavcvtyMb415o1KraDhSy7HTBymvKQmVP4uC5YVl7r7fzM2zd5gcDmvYJhkN7D1VT+2xaobGhiivLBUmqSRJEqOdI5gvt9J9sxOfJyjFIFcqKKgtpPJoFbk78x8rWfeiwr3iYuBOF5ZrZuZGwmXsUYl6TPvLMO0tIyrx0V5nLyokSWJpbIaxxi4mH1jwuddkN2QyDEWZZNSVkFSShVyQz/ZD+N1e5nqGmGnrY2UiLEWpiorAUGbCUG5CGydWrLcSq45VOq6aab14n+nB8DoakxRLxZGdVBzeSUxS7NYN8BlAkiRsg1P0Xmtj6G4PXlfwUk4mk2GsyKXgQDkZFbnIBUtQ+1weJlv7GWvsYnE0rI2u0esw1hRjrC0iMjF26wb4jLAyPc+suQ9b5wB+V/gCVp+dRlKFiThTpnAXsAGfj+WRcZYsQ6xOz6Im6DCi0KjR52UTY8pBEyveOup1OHCOj+OcmCCwTlJKHReHLj2diORk4WItBQJ47Yu45m34HGEynEyhQB0bjybegDLik3lDbOPFxdtvv83MzAy7du0Cwt4ply5deqIn3afBU8+g5eVlfuVXfuUH+qF/9md/xu/+7u9y+PBh5HI5r732Gr/wC78AwM6dO/md3/kdXnnlFX7iJ34Cp9PJL/7iLzI3N0dGRgZ//Md/HPrDvOiQJInWex2cefMc75+7yqozWK6lVCrYf7ie02+cpOFA7VOVz70IWFl2cvXdW1w4cxVL52Co3ZCSwNFXD3D0tYMkpz++DPNFhCRJjPaOcfPsHZou3Wd1TaZCJpNRUlvE3lP1VOwtE44F5HF56LrZQeuFFqzdYRMxdaSGqmPV7Dy2iwTjx5ezv2hYsM7Sd81M/60u3I5wGWZqcSYFB8rI2lXwiWUqXhT4vT6m2gexNnYx1x9mzqp0GtKrCzHWFqNPEy/WrvklZtbYlN51sY5KT8JQbiKhOOe5Zjd83uD1+rh++TZn3jzPzauNIY3UyCgdL79ymNNvnKS0oki4pNDMpI2Lb13l4tvXmJkIM7dyi7I4/vpLHDyxh2j900tjPs8I+AN0NnVz69xd2m62438oY6ZRsevQThpO1WOqyAvp4DL2MQ98QeBYWKb1UgvN5+6yMhdO9iZmGKg8WkXZoQoiY8WKtRSQmOwZDcoT3rPgX0uiyZUKsqpNFBwoJ60k64nyhC8iPI5Vxlv6GGvswjEV9gLSJegx1hZjrClCGyNYrCUJx/gMs20W5nqGwlIFMhlx+RkYKgqIzU1/pDzhNp4eUiDAcPswrRdb6LndFZpbCqWCwvpiKo9Vk1OeI9zf27XsZOBWF33XzSxYw3tmdFIsBfvLyN9bSmT8R70SX2RIksTC8BTWxi4m2/rxe4KxlsnlJJVkYawrwVCYKVyC2ufyMNc9yGybhZWpcKzV+shg0rIsH02seLF2zy+y2DfI8tDoBskXX1QExqpy9JlGZILFOuDzsTo1hXN8HM86s3C5Wo0uLQ2d0Ygq8tPJNz/P8LlWcc/P4lmYR/KHY62MjA5WMcTECbeGb+PxeO+99zZ8/Z/+038CnhPPhqNHj3Lx4kWOHj36qX9oYmIif/Znf/bI7z148CD0b5lMxi/8wi+EEhGiYG52nne++z5v/ct5hgbCF7BZuRmcfuMEr7x+nMQkscowJUmi434373/vKrcuNeJeYwsolQp2H9rFsdMH2VlfjkKwTW3FvkLjhaAmtHXdBWxCajwNJ3ZT/3IdCSnxWzjCzYckSUz0WWm92ELn9Q48a+XsMrmM/F0FlB+uxKF0UlUtliyJZ9XNaLOFvutmZgfC0gW6uChM+8ow7StFL6B0wZJ1FmtTN+MtfWHpAhkkmjIw1haTXJojXHm13+tjvmeYWXMfy2NhxpdSpyWxNI+k8gIiBGR8vcgY6h/hzL+c553vvs+8LewZUlVbzuk3TnL0xAF0OrGkrbweL3ev3ufC967Qcqc9JE8YGa3j4IkGjp8+RH5JzhaPcvNhm7Bx69xdbr/byMLMYqg9uziThpP11BypRhclVqwDfj/99yy0XmzB0twXkqxTaVXs2BeUJ0wvzBAuibYyv4zlRjuWGx0sr4t1XIaBgjV5Qm20WLGWAhI2yxjWxm6mOwZDHl9ypYKUijwyakuIz00TLrHiXVnF1tHPjNmCay4sMauN12NYkydUR20zMjcLdtsSbZcf0HbxAYvrfLaSspOpPFpN6cFydHqx/t5SQGKic5jea2ZGW/pDPlsKlZLsmgIKDpSTUpgh3NxyLzsZv9fDWFMPKzPhWEcaYoNJy0/hqfa8Q5IklsfWPNV6w55qMrmcOFMmhgoTMdmP9lR7keF3e7APjrBkGcK9EF5HVVE69Pk5ROVk0GnpIyozXZxEgyThWVrCNTHB6tQU0jrFCK3BgM5oRJuYKFysJb8f9+I87gUbfmeYeCJTqdDEJaKJS0ChEUu+eRvPH576BsjlcvFLv/RL5OXlERsbu+Hg8vd///ebOjiR4PP5uH2tmTNvnuPa5dv41jY1bYSWYycPcvqNE1TVlAt3EJyfXeDSO9e58NZVJkbCJbdZeUaOvX6Il07uJSZerNK8QCBAz/0+bp29w4MbZnxrzBClSsnO/eU0nKqnqLpAOJkK59IK7VfaaL3YwuxoWLogLjWeyqNVlL9USXSCHr/fT6sg+paSJDHdZ2X0Qhedf3UtFGuZQk7mznwKDpSRXpYjXKy9q24mWvoYa+rGbg1LF2hjozDWBDWhdYLNa0mSWJmyBdmU3YP418lUxOamYyg3EZufIZxMxYsM54qTC+eucubN8zy41x5qTzDE88qXjvPaV06Qk5e5hSN8Nhi2jHHhzBU+OHsD+2JYnrC8poTjrx9iz+FaNIJJ1nndXlqutXHr3B167oeNvSP1OuqO1bD3i3sw5qVt4QifDeYn5mi92IL58gMcC+FYpxcaiStM5NiPfAGdYBewfp+fsZYB+q6bGTcPhZJoqgg1ubuLKThQTmJOinDv06vzdsaa1+QJ18VabzSQUVtMWlWBmPKEg+PMmvtY7B8LeXzJVUrii7JJKi8gypgkXKy3Cn6vj76mXlovtjD4IOypptE99FSrJjU/Tbi/t8O2RN/1YNJyZZ1/XkJ2MgX7y8mtLxbOUy3gD2DrHWWsaaOnmkKtJKUin4xagT3V2tc81RbCsY5IjA0lLVU6sWItSRLOqRmW+oZwjI6HYi2Ty4nKSifGlIMuJSlc5SkI/B4PK1YrCYuLzDc3h9oVOh2R6eno0tJQaMWLtc/pwD1vw7O4AFJg7TsyVPoYNPGJqKJjhJvX2/jB8CwqGh7iqZMN+fn55OfnP4uxCImxkXHOvHmed/71PWamw6V5pZXFvP7GCb7wxcNERYvFFvB5fTTfbOXC967QfLM1xLqK0Gk58PIejp0+RGFZnnAL3fz0ArffbeTWuTvMTYbL2Y356ew9VU/dsV1ECsYMCfgDDLUN0Hqhhd7GnhAzRKlWUdxQQuXRKjJLs4WLtXPRQf+tTizX21laF+uY1HgKDpST37CDiBixYi0FJOYHxxlr7GbKPBBmASnkJJfmkFFbQmKBUThmiHfVxVzHADNmC6uzYcaXJiYKQ7mJxDKTcIyvFxmSJGF+0MWZN8/x3vc/wLkSlLZSKBTsPVTH62+cZO+h3agEq7ZxOpxce+8O73/vCn0dA6H2hKQ4jrxygGOnD5KaIZ4/zGjfmjzhhXs418kTFu0qZO+p3VTuK0clmIyZ1+Wh+3YXrRfuM9o5EmrXxURSfqiCiqNVxKcn0Nraikagy+fFiTkmbljo/bvbuJbDknUphUZMB8rJqSlEqREr1n6fn5mOQcYau7FZxkK+T8oIDelVBWTUFaMXTHYUwLVgZ9ZsYbajH++yM9QemZpIUkUB8cU5KAXzc9tK2MZmab/civlK2wZPtcwdWVQeq6Z4TwkqwRLUfq+PkfsW+q63M9E5/CFPtR0UHCgjIUu8PXPFtoS1qRtrcw9uezjWsZnJGB96qgkW64A/wOLAGLNmy0ZPNbWShOKg2XNkaqJwZ1XvipOl/mHs/UN4HeF1VBMXQ4wpF31uJgrB1lFJknDbbKyMj+OamQFJQgUglxORkkJkejrquDjhYh3wenEv2HAvzBFwh4295RotmvhENLEJwhl7b+PFwFOftn/xF3/xWYxDKLhcbi6/e53vvXmO5jthWajYuBhOvX6M02+cwFSYu4UjfDawDk9y4cwVLn//Bgu2xVB7yc5Cjp0+yL5ju4kQjC3g8/pou9nOzbN36GrqCTHsIqIiqD1Szd5T9WQKKF2wOL1A26UHtF16gN0WLsNMzU+j8mgVO/aXoRVOpiKA1TxI3zUzY60DIcaXUqNCn5dI3WsHguXVgsXateTA2tyDtakb5zrGV1RKPBl1JaRXFaAWLNaSJGEfnmDGbGGhbwRpLWEqUyiIL8zCUG5CnyUe4+tFxvzcIme/9z5n3jzPgGU41J6Znc7pN07yxS8dJylZLM8QSZLoetDL+2eucuP9u7hdQRkzhVJB3YEqjp0+SPWeChSCGVc6l500XbzHzbN3Ge0LGy3EJ8ex58RuGk7uFlKecLJ/IihPeM2M2xmWJ8yryqfyaDWmmoKQZJ0o7ESvy8NQYw9918zM9IdN3CNiIjHtK8W0v4wYwWINYJ+cw9rYxfj9PrzO8MVBQn46GXUlJJflCidPGPD6mO8bYbatD/touBJaGaEhcUcehooCdAbxpCi3Ev33+rjz7essWsNEiqj4aMpfqqTyaBXxaWJJ+gLMjwU91QZuf8hTrSSTggPlZFUXoFSLNbf8Xh9T5gHGGruZH1jvqaYlfVchGbXFRKeKF+vVuaWg2XPHAN6VdZ5qxiSSyguIL8oWzlNN8gdwjE2w1D/Eynh4HZWrVOhzM4Nmz/Gxwp1ffE5nyOzZ7wrvmUq9njm/H9OuXagErGLwLi/hnrfhtS+GvyGXo46JRxOfiFIXKVyst/Fi4RPvpr/927/Nb//2b/Of//N/fmyfP/zDP9yUQb2o6Grv48yb5zj/9iWW7cESZ5lMRv2+XZx+4wSHju5FLVgG2eV0ceNiIxe+d4XOB72h9th4PYdf2c+x0wfJyEnfugE+I0wMTnLz3B3uvt+MY51MRcFOEw0nd1N1sFI4mQqfx0vv3R5aL95nqG0oxAzRRkVQdrCcymPVJOekbPEoNx/26YW18up2VhfDLCBDXhqFB8vJrDbR2dtFkildmA094PfjGpunpeU8s71joVgrNSpSd5rIqCshJkM86QL3koPZdguz5n489vC81iXHB8urd+Si1IrDFH7R4ff7uXPjHmfePMeVi7fwrRncabUajpw4wOtvnKS6rkK4z+m8bZHL71zn4lvXsA6HL2AzctI4dvoQL31xL3EJsVs3wGeAQCBAX2s/t87eoeVqG15PUMZMoVRQua+cvV+sp7i6UDjjSqfdScdVM60X7zMzHPaHiU2OC8oTHq5EnxizhSPcfEiSxEz/BH3XzAw19uBbk6yTyWVEZydQfaqBrJ0m4WLtXXUz2WphrLGbpbGwFKU2JhJjTTHG2iJ0CWLFGmBlysaM2cJc5yB+tyfUHpMTlCeMM2UiFyxh+rzgzH/7Dj6PD5lchqmmkMpjVeRXm4STg/Q43Qze7abvuhnbYPgCNuSptr8MfVLs1g3wGWHJOstYYxcTLX341rwSkUFiQSYZdcUk7cgRjozg93jXPNUsLFs3eqoZyvIxlJuIEOz9CMC9sMRS/xD2gZEN62hEioGY/Byis9KRK8VKokl+P6szMzitVtzzYZUBmVKJLi2NyPR05JGRWFtbhWL1K5FwTU/gWZxH8nlD7QpdJNr4RNQx8cgEW8O38eLiE686Dxnb29gI+9Iy585c5Myb5+jp6g+1pxlTePXLL/PqD32BNKNYF7CSJNHbPsCFM1e49t4dVtfYAnK5jOq9lRx//RC1+3aiFIx15XK6aL50n5tn7zLUNRxqj02Mof7lOhpO7ibJKF45+/TQFK0X7tN+1YxrHQsopzKXyqPVFO4uQikYM8Tn9jJ8r4++a2amesLMWW10BHkNOyg4UE5cepAlLQqDFMAxvcBYUxfj93rxrIt1XE4qGbXFpFTkCydTEfD5WbCMMmvuY2kofHGr0KhJ2JFLUrmJyBSxGPEvOqyjk7z9nfO89Z13mZ4Me4bsKC/k9BsnefmVw0Tro7ZwhJsPv8/PvZutvH/mKk3XW0LyhNoIDfu/UM+x04corjAJl1hZmF3k9vlGbp27i20iLEWZlpPK3i/WU3eshuhYsWItBQIMtQ3SerGF3jvd+NeZlBbtCcoTZpdlCydZt2pfof9mUJ5wcWIu1K5PjqPgQDk59UX0DfeTWZkvTKJBkiTmByeYuNfLZNsAAW/Y9yl5Rw7G2mIMhRnCxdrncmPrHGC2zYJzJnxJpNZHYSjPx1BmQhMj1rx+HnHyP75Kb3svR3/4ODECJi2nesfou9bOcHMv/jVPNblCTmZVPqb95aSXZYvnqeZ0Md7Sh7WxG/u6PTMiPjqYtKwpIiIuegtHuPmQJAnHxCyz5qCnWmAt1kFPNSOGChOxeRnC7BsPEfB6sQ+NsWQZwmULr6PKCC36/Gxi8nNQC/YuDOC121lZq2KQfL5QuyYhAV16OhFJSaHLdlHO6FLAj2dpAdecjQwVuG3BRJpMoUQTl4AmPhGFViyVgW2IgU98G/w7v/M7APzwD/8wFRUVH/n+9evXN29UzzkCgQBd5j7e/N9n+eDCTTxrGWSVWsXh4/s4/cYJ6hqqhXuBWVqw88HZm1z43hVGBqyh9tSMZI69dpDDr+wnMVmscnZJkug3D3Lr3F3ufdCCZ40ZIlfIKd9Tyt4v1rOjtlg4ZojLsUrn9XZaL7YwuU66QJ8YQ8WRnVQc2Ulssljl7JIkMTc0Rd/1dgbudOFdDbOA0styKNhfTmZVvnCx9rk9TLb2Y23qZmF4XcmtVkXm7h1k1pUQlSRWrAGcM/PMmi3YOgfwrbpD7dGZKUFN6IIs5IIlTF9kuF1uLr9/gzNvnqfx1v1Quz4mmlOnj3L6jZMUlojnJzU+MsmFt65y+Z3rzM8uhtqLK0wcO32Ifcd3o4sU64Dh8/ow3+7g5tk7dDZ2hyTrtDotNUeq2XtqN9nFWcIlVpZmFmm7HJQnXJpZDLWn5KZSeayKHQfKiRBMsi4QCDDePkTftXZGH/SHJOsUaiU5tUUUHCgjucAonHGly77CWFM3czfbmFkOSz5EJcdhrCshvboQjWCxliQJ+8gks2YL870jSP6w71NcQRZJ5Sb02eKZDz/PKNlbiifKR1ScOBeSXoeb9rON9N/swD69GGqPTU+gYH85eQ07iNDrtm6AzwBSQGKu38pYUzfT7YMhTzW5Qk5yWR4ZdcUk5BuRycWaW16nC1vHALPmPlbXyTdrYqMxVBRgKM1HHS1YrCWJ1Zk5lixDLI+MIa3FGpmMqIw0Ykw5RKYlC5egDni9OCcncY6P47WHJX0VWi269HR06ekoI8TbM/2rzjWz53mkgH+tHVTRerQJhqDZs2Cx3oZYeOqblJ/6qZ+ipaVlQ5vD4eCXfumXePDgwWP+lzjo7xvil372vzA2Er6ALSjO4/QbJzj52lFi48Rihvj9AR7cMXPhzFXuXrmHb21TU2tUNByt4/jpQ5RWF4mXWJmzc+e9Jm6du8P0aLicPTkzib2n6qn/Qi36eP0WjnDzIUkSIx3DtF5ooed2J76HLCClgsK6IiqPVZFTkSccM8S1vMrAnS76rplZGAuzpKMSYyjYX0b+vlKiEsSL9eLINGONXUy29eNfJ1NhKMoivaaQcfcChVVVKAQqxfS5Pdh6R5g1W1iZDDO+VNG6YHl1mQltnFixftHR29XP2995l3NvXcK+tBxq3723mtNvnOSlY3vRCCZt5Vp1c+tiI++fuULH/Z5Quz4umsNf3Mfx04fIzDNu4QifDSaHp7h59g5332tieZ08oakij4ZT9VQfrBTK8BiCiZW+NXnCwdbBsDxhpJbSg+VUHq0mJS91i0e5+bDPLGJZkyd0LoRjnZibSsGBMnJ3F6MWLNYBv5/Z7hHGGruZ7RkJJdEUahVpO00Y64qJzUwW7rLdbXdga+9n1mzBvbROntAQh6GigIQduagixNLS3sZni4DPz1jbIL1X27CawzKvSq2K3LpiCg6UY8gTz2drdWEZ670erE09rM6HL2Cj0xLIqC0hrboAtWBeiVIgwNLQBDPmPhYtY0iBNU81pYL4wmySKgqIzhBvHfWtulgaGMZuGcZjD78Lq/XRxJhy0OdloRRsHZUkCc/CAitWK6vT07AWa2QyIpKS0BmNaBIShIt1wOfDsziHe96G3xVWGZCr1KhiE7BMTFKWlSfU+Xwb4uITJRtGRkY4efIkfr8fSZIoLi7+SJ+qqqpNH9zziE5zL2MjE0TotJx87Shf+pFTlJQVCrfQTVlnuPj2NS69fY3ZqXA5e35JDsdPH+LAy3uI0kdu4Qg3H36fn87Gbm6cvU377c6QTIUmQk31oSr2nqonryxHuFjb5+yYLz2g7fIDFibDZZiGzCQqj1ZReqiCyBixYi0FJCa6Rui7ZmbkviXEAlKoFGRVF1BwoJzU4kzhWEDuZSfj93uxNnXjmA4bAuoSY8ioLSa9pgitPhK/389E69ITnvTiQJIklsem8XeMYb7SGYq1TC4jNj+TpAoTMTnp28yQ5wzvfPd9/u6v/g8jg+EqupS0JF798su89uWXSc8Q6wJWkiQsnYO8f+YK1969jdMRlies2lPBsdMHqTtYjUqwahuX08W9Dx5w8+wdBjuGQu36BD17vlBLw8l6kjOTtnCEzwYzw9O0XrxP+xUzq8vOUHt2eU5QnrC+GJVgknU+j5eRe330XWtnsns01K6J1IbkCeMzxJOidMwsYG3qxnqvB89y+OIgNjuFQLKOmlOH0Ah2IRjw+1mwjAXlCQfDhrQKjYqEklwM5QVEpoh3SbSNzxZLk3P0XWun/1Ynq0vrPNXy0yg8UE5OXREqwfzzAj4/051DWJu6me0dhTWVa6VWTdqap5reaBBubrkWl4PVyO0WPOv2zMiUBAzlBSSU5AjnqSYFAqyMT7FkGcJhnQwl0WRKBdHZGcSYcogwiLeO+l0unBMTrFit+FfDe6YyKorI9HQi0tJQqMWa15Ik4XPYg1UM9sVQrJHJUMfEoYlLRBkVTSAQwD8x9cRnbWMbzxM+0ak1KyuL73znO9jtdv7dv/t3/K//9b82fF+j0VBQUPBMBvi84ZUvHSe/IJslxwJ1dbVCZRU9bg+NV1u4cOYqrY0dofYofSQvndrLsdOHyC3M2sIRPhvMWGe5de4Ot883sjQXZobk7sim4VQ9NYer0Ap2EPR7fVia+2i9eJ+Blv4Qw04doWHH/lIqj1aRtiZdIBIctiUsNzqw3GjHYQvHOj4ziYID5eTVF4snXRAIMNs7ylhjNzOdwyEWkFylJLUij4zaEuJyxWN8eRzOEJvStRCMtQRoE2JIKi8gsTQPlWDyM6Kgp9PCb/3aHwNBecKXju3lta+cYPfeaqH2XAjKE145e5MLb11l2BL2h0lJT+Lo6QMcfeUAiSkJWzjCzYckSQx2DHHz7B3ufdCCezUsT1hWv4OGU/WU7S4RTrLOteKi83o7bRdbmLCEL2CjE/QhecK4FLGkKAFsw1P0XWtn8E4XHueaZJ0M0ktzMO0vI6sqH4VgSTSf28Nk2wDWxm4WhidD7eqoCIy7ijDWFhORqKe1tVUoHyTJ4WLsg2bmugY3yhNmpGCoMBFfmC1crLfx2cLr8jDU1EvfNTMz69ZRrV5H3p4SfAYlu19qEO5dYXlyjrGmbibu9+JZCUuvxeelk1FXTEpZLgrB/PMCPh/za9XI9pHwOqrQqknckRdMWgom3wzgsS+zZBlmaWAY/2o41lpDAjH52ehzMoQyPIbgWdU1O8vK+Dju2bDKgEyhICI1lcj0dFQxMcKdVf0edzDBsDBHwBs29lZoI9DEG1DHxgtn7L2Nzxc+8af3YTXD2bNnycjICLU7HA7UajVqwTKMj4NMJqNoh4nW1tatHsqmYbB3hLP/eJk/bv4rHPYgM0Qmk1G5u5Rjrx2k/qVdqDVixdft8tBytZVbZ+/Q1xo29o6KjaL+eA0Np+pJyxGLOQtgG5ul9cJ9zFfacK5jAWXsyKLyaBXFDTtQC8YC8nt9jNy30He9nYnO4RALSK3TkFtfQsGBMhKzxTJxB1ixLWFt7ma8uQfXuljHZCaRUVtC6k6TeIwvf4DFgTFm2ywsDlpDzBC5SolkiKbw4G70ApZXi4Y8Uzb/11f/LY6VZf6vX/gJEg1iHSb9/gCtd9t5/8wV7l65j2/NFFalVtFwpJZjpw9SXlMinDyhfd7O3feauXnuDlMj06H25IwkGk7uZvcXaokV0KR0tGOY1ostdN/qwucJStbJlQoKagupPFpF7k5xDI8fwu1YZeB2F33X25lfJ0UZlajHtL8M094yohLFkqyTJInF0Wmsjd1MtFpC8oTIZCQVZ2GsLSapJAu5YMaVPreH+e4hZtr68E/aeDizVVERGMpMGMq35Qm38YNBkiRmBybpu2ZmsLEbn2tN+lMmw1iRS8GBcjIqcpFkCHU+97k8OPunuXvjeyyNhddRjT4SY00RxtoiIhNjt26AzwgrU3PMmvuwdQ7id4cvYPXZaSSVm4gryBTuAjbg9bE8amXJMsTqdFjmVaHVoM/NIsaUgyZWvHXU63DgXDN7DnjCsVbHxQXNnpOThYu1FAjgsS/inrfhc4SJjzKFAnVsPJp4A8oIsbxGtvH5xVPPXo/Hw3/4D/+Bv/zLv+TixYt8/etfJzIykm9961tUV1c/izFu4xlg2e7g6vnbXPjeFQZ6hkPthtREjr56gKOvHiA5XaxydkmSGOkZ5ebZOzRduo9rjRkik8vYUVtMw6l6KhpKUQrGunI73XTd7KDtYgvWnjBzNiouivKXKqk4WkVCeuIWjvDZYG50Bsv1dgZudeJexwJKLc6k4EAZWbsKUArGAvJ7fUyZBxhr7GZ+IMz4Uum0pO8qxFhbjD5VLJY0wOrcYrC8umMA70q45DbKmIShzERsQSbtXZ1EpYtXWi4iVGoVv/D1n6K1tZW4eHEunxdsS/yfv/oul9+5vlGesDiHY6cPcuDEHqL14hh1Qlie8ObZO5hvd4TkCdVaNdWHKtl7qp788jzh5uXynB3zB620Xmz5/MkTXjczet+C3xuWJ8ysNlGwv5y0kiwx5Qlb+rA2dm2UJ0zQY6wrwbirCK1osZYklq3TzLZZmO8dJrCWMEUGsXkZJFUWEpu7LU+4jR8Mq3YnA7c66bvezuJ4+AJWnxyLaX85pr070MVFh9pFSOBJksTC4CRjTd1MtfXjX5tbMrmcpB3ZZNQWk1iYKVyC2udyY+scZNbch3M6vGeq9ZHBpGVZPprY6Cc84cWDJEm4bAss9Q+xPDS6YR2NTEshxpRDlDENmWCxDvh8rE5N4Rwfx7O4GGqXq9Uhs2dVpFh7JoAvZPY8h7RurVJGRaOJS0QdE7e9Z25DODz1reof/MEfkJSUhCRJ/Mmf/An/8T/+RyIjI/mjP/ojvvOd7zyLMW5jkxAIBDA3d3HhzFVuX27Cs8a6UqqUFFXm8eWfeo2q+nIUgm1qy4sOGi80c+vcXcYHwsbeiakJNJzczZ4TdcQlxW3hCDcfkiRh7R6l9WILXTc78bqCbAGZXI6ppoDKo1XkVZuEk6lwr7gYvNtN3zUzc8Nh5mxkfDT5e0sx7S9DnxS7dQN8BpAkiSXrLNambiZa+vCtxRoZJBZkklFXTNKOHOFi7fd4me8ZZsbch8MaZnwpddqg2XO5iYiE2GBfAQ6gD+FeWGJxYBjVvBieGqLD4/Zw+3IzF85cpa2pIyTDGqWP5NDJBo6dPkReUfaWjvFZYHpshtvn73Ln3SYWbeHPanZxFntP1VNzpIoIwWTM/D4/luZeWi+0MNBiWSdPqGbH/jKB5QntWG60P1qecH8ZeXtKPlfyhCnluWTUlRCfmyZcrB8lTwigjY8hsSyPSWmV/Loa4SRstvHZIRAIMN4+hOV6O6Mt/aEEtUKtJLumkIIDZaQUZgg3t1z2Fcabexhr6sa5bs9U6LXk7asko6YYTbRYbGdJkrCPTDJrtjDfOxK6gJUp5MSZMjGUFxCTnSrcBazP5cY+MMJS/xCexfA6qoqKDJk9qyLFi7VncRHn+DirU1Phy3aZDK3BgC49HW1ionCxDvh9eBbng2bPq2GvEblKhTouEU1cIgqNWF4j23gxsLi4yB/8wR9w7do1AoEANTU1/PZv/zZJSZvrk/fUyYbe3l7++q//mvHxcUZHR/nRH/1RIiMj+R//439s6sC2sXmYnZrj0jvXuHjmGlPj4Uu5nIJMjp0+xP4v7GZweIDKyjJhEg2BQICupm5un2+i7Wb7BpmKqoMVNJyqp6AyXziZCseCg/YrQTblnDXMAopPT6DyaBVlhyqJjheMGRKQmOoZpe96O8PNfSEWkFwhJ7PKhGl/Gell2cLF2rPiYqKll7HGbpYnwyzpiPhojDXFGGuKiIgTLNaShGNiltm2PuZ6hgh4HrKAZMTmGjFUmIjNyxCO8eX3eFkeGmWpfxiXLcj4UgvGDhYNA91DXHjrKlfO3QrJEwJU1O7g+OuH2HO4Rkx5wisPuHnuLpZ18oSRMZHsPl7D3lP1pOembeEInw0eyhO2X21jZXGdPGFJJpVHqyneK6g8YUs/lmtmxj9v8oRN3Yzf+5A8YUYSxtpi0naaUEWIdXEQkic0W1gc2ChPmFCcg6HcRFR6EoFAgCmRJGxWnMhdno/vuI1NgX16IeSp5lxwhNoTc1Mo2F9O7u5i1DrR5pafma6RoNlzz0goQa1QK0mtNJG2q5DhhUlydlYKlcCTXB4mbpuZ6+jHvRSOdYQhjqRyEwmleagixPJKlAISzslpFi1DOMbGYS3WMoWcqCwjsfk5RKSIV33td7txTkzgHB/HtxLeM5U6HTqjEV1amnCX7ZIk4VtZDlYxLC1sMHtW6WPRxCeiitILF+tthCFJUkg29bOAUq166s/TV7/6VWJiYrh48SJyuZz//J//M7/5m7/J3/zN32zu2J72P/h8PiRJ4tatW+zYsYOoqCjm5+fRCLZQvOjwen00XrnP+2eu8OCOmcDapqaLiuDgyw0ce/0gppJcZDKZUKxf24SNm2fvcP2dWzjWHfqzCjNoOFVP7ZFqdIIxQwJ+P/33LbRdfICluTfEAlJpVBTvDZo9Z5RkCreprcwvY7nZgeV6O8szi6H22PRECg6UkbdnBxF6sWItBQLYLFasjd1MdwyGYi1XKkguyyWjtpiEfKNwMhXelVVsHf3MmC245sKML01cNIbyAgyl+agFm9eSJLE6bQuWVw9bN7CAIo2pzIqlACYEHitPmJLA4Vf2k5afwKGjB8W6NJAkhrqGuXn2Ls2X7uNyrskTymSU1Bax91Q9FXvLxJQnvNFB26XPlzzh/OgMfdvyhACodBrSq9fkCdPEi/Vj5QnTkzCUm0goykEhkLk1BN+nV8YmWOofZmV8igjAU1xMRHzsVg9NSPjcXobv9dF33cxUd3gd1URFkLenhIID5cRniCXpC+CYXsDa1I31fg+e5fDcistOwVhbTGplPkqNGr/fz8ji1BaOdPMQ8PlZ6B9lprUP//AED3UGFBoVCSW5QbPnlAThzqqeZQf2/mGW+ofxOcOx1iTEEZOfgz43A4VgvqdSIIDLZsM5Po5rdjZ02S5TKIhITkZnNKKOjRUu1gGPB/eCDffCHAGPO9Su0EYEZZLi4pErxdozt/FRSJLEud//f5ixjH98501Ckimdk//1Rz/xnOro6KCtrY3bt28TFRWU7/293/s9ZteZs28Wnvr0t2fPHr761a/S09PDz/zMzzA2Nsav//qvc/DgwU0f3DaeHsOWMS6cucIH525iX1gOtZfXlHDs9EH2HK5FKxjryuP28OCamVvn7tBzvy/UrovWsft4DQ0nd5NhMm7hCJ8N5sZttF1swfxBK451LKD0QiOVR6sp2VeKRjAWkN/nZ+xBP33X2hlvH0Jae4FRadXk7C6i4EA5htxU4V5gnPP24MHkXg+udbHWpyVirCsmraoAtU40FlCAxcFxZs19LPaPhRhfcqWC+KJsDOUFRAto9uxzrrI0MMySZRjvcjjW6pjoYHl1bhYytYppgRikLzICgQDmpi7eP3OF25eb8XrC8oT1L+3i2GsHqdxdBkhCGVc6Fh2Yr3Ty/f/f+0wMTYbaH8oT1r9cR3yyePKEY12jtF36fMoTWq63YxsKX3x9LuQJG7uYeGD5qDxhbRFJpbnCxdrv8TLXPcSs2YJj/EPyhKVr8oQCGtK6F5ZYsgxhHxzZYEjrj4pAKdi71VZDkiTmhqbou97O4N1uPM61SzkZpJfmUHCgjMyd+SgES1D73B4mW/uxNnWzMBxeR9VRERh3Bc2eo5Ljt3CEzwbOmflg0rJzAN9q+AI2OiMZQ0UB8YXZwsU64PPjGLWyZBnGORVeR+VqVcjsWStgAtO7soLTav2I2bMqJobI9HQiUlOFNHv22hdxL9jwLoclsZDL0cTGo4lPRBERKdxZdRsvNsxmM/n5+fzLv/wL//RP/8Tq6ir79u3jG9/4xqb/rKee8b/3e7/H3/3d37Fr1y5OnTrFzMwMO3bs4Fd+5Vc2fXDb+GRYWXZy7b3bXDhzlb6OgVB7QlIcR145wNHXDpCWKVY5uyRJjPaOcevcXZou3sPpCLIFZDIZRdUFpO1I5tUf+yJawQ4JHpeH7pudtF5qYaxzJNSui4mk/FAFFUeqMGRtrtba84AFq42+a2YGbnfiWscCSik0YtpfTnZtASrBJEn8Xh/T7YOMNXUz128NyVQoIzSkVxVgrC0mxige48s1v8Rsez+z7f14HWF9y8jURJIqCogvykEpmCSJ5A/gsE6y1D/EyvhkKNYypRJ9TgYx+TloDfGhl1WRqtFeVMxM2rj0znUuvbVRnjDblMGx04d46dRe9LFiGVcG/AE6m7q5de4ubTfb8fuCv5P48oTLmD9ope3iA+bGPypPWP5SJVGiSdZ9juUJx+/3Ym36nMkTjs8wa7Yw1z20zqR0TZ6w3ERsvsDyhJYhXHNhY29lhBZ9fjZRuZl0DfQjV20zUTcDrmUnA7e76LvezsJYmD0ZlRiDaX8ppr1lRCXqt3CEmw9JklgYnsLa1M1kqwX/OunPpOIsjLXFJJVkIReo2hHA5/Iw1z3IrNnCymR4z1RF6UgozWNW4aFwT51wVZ6uuYW1pOUoAW9YQkWXmkyMKZuozHThYh3w+VidnsZptX7U7DktLWj2vMacFgk+12pQJmlhDsnvC7UrI6PQxD80exYr1tv4ZJDJZJz8rz/6XMsoLS0t0dvbS2lpKWfOnMHlcvHrv/7rfOMb39h6GSWNRoPX6+Xb3/42f/mXf8k777zDgwcPcDgcRESIZQD3PEOSJNrvdXPhzFVuXWrEvca6UioV1B2s5tjpg1TtqRDGg+EhHEsrIbNna3+4PCkhJZ49J+rYc2I3sYYYWltbUQlS3i1JEgtj85y/9Q7dNzvxrD5kU8rIqzJReawK064C4ZghnlU3Q3d76LtuZnYgzJyNiI3EtMamjEkRjwW03uzZu44FlGAyklFbTHJZrnCx9nu8zPcOM2u2sDwWNvZWRmhIXGNT6gxisaQB3Iv2MJvSFY51RFIiMfnZRGdnIBcs1i8yvB4vd67c48KZqzy40x6qrHqUPKFImBmf5fa5Rm6/28ji7GKoPdGYwJEfOsTu4zXCyRP6fX767/XRdukBlua+kAGwSqumZO8OKo6IKU/omLPTf7MDy42Oz5c8YZ+VsaYuZjqGPlfyhLMda2bP6+QJtXF6DOUmEoWVJ5xlyTLM8shGecKojDRiTDlEpiUjk8uFSBBvNQKBABMdw/Q9NHteS1ArVAqydhVQsL+c1OJM4eaW277C+P1expp6WJkJJ7J0iTFk1BaTvqsIbUzkFo5w8yFJEsujU8yYLcz3DiOtxVomlxGbn0lShYmYnHQCkoRNoCpPv9uNam6JsfMf4FkIr6PKSB0x+dnE5GejihIv1p6lJZxW60azZ0BjMBCZno7WYBDU7Hlhzew5LNUtU6rQxCUEqxg0YpFct/HpIJPJnmsSrHpNuu03fuM30Gg0REVF8bWvfY2vfOUrrKysEBm5eWvWU99i/Pmf/zmNjY382Z/9GV/72tdITEwkJSWF3//93+dP//RPN21g23g0bNPzXHr7Ghffvsbkuku5zLx0jp0+xOFT+4iJF4sZEvAH6L7fy62zd2i9ETZ7VqqV7NxXTsOpeoqqC0IMO1EOCCuLDtqvtNF6sQXbOhZQXGp8iE0ZnSBWrCVJYrrXSt81M0PNvSEWkEwhJ6Myj4L9ZRjLc4Vj2HmcLiZa+rA2dWNfx5zVxkZhrCnCWFOMTsBYr0zamGnrC7IpHzIAZDJictIwlBcQZ8oQjgXk93hZHh5jqX8I1+x8qF2h1awdTHJQx4jFnH3RMdg7woUzV7ly7ibL68wMy2tKOPraQRqOiCdP6HZ5eHCtjZtn79D3wBJqj9TrqDtWQ/3Ltdgcs1RWimVcOTduo/2DNsyXW1lZDMfaWJRBxdEqSvYKKE/o9TH6YIC+62Ym2ofD8oQRanJ3F1Owv5zE3BThEivOuSWszT1Ym3twrYu13mjAWFNMepUJlWDVsVIgwOKAdc3seZ08oUpJfGE2hgoT0Ubx5Am9K07sAyMs9Q/hXQ5fEqlj9EF5wrwslFqx5vVWYnl2Ecv1diw3OliZD0v6JmQnB82e64vRRIo1twL+ALM9I1gbu5npHt5g9pxSnk9GXTFxOeLJvLrtK9jWkpbuxXCsIxJjQ0nLDeuoAGf0h2bPS/1DLI9OoAkE8BCUVIzKTCfGlIMuNUm4WD/O7Fmh0xGZnh40e9aKNa+DZs8O3As2PIsLIAXWviNDpY8Jmj1HxwgX622Ijfz8fAKBAF6vN+S7HFgjVT08A2wWnjrZ8P3vf59/+qd/Ijk5+DKq0+n4wz/8Q44ePbqpA9tGGA/Nni+8dZWW220hs+eIyAgOvryHo68dpLAsT7iFzjZh4/a7jdw618jCOmZIhslIw6nd1B3dRaReLLZAwO9n4H4/rZdasDT1bmDYlewrZeexajJ3ZAkX65X5ZfpvdWK5bsY+vRhqj0mNp+BAOfkNO4gQjQUUkIJmz01rZs9rLCC5Qk5yaS7G2mISC4zCMUO8K6vYOgeYNVtYtS2G2jWx0aGDiUaweS1JEqszNpYsQ0E2pW8dm9KYGmRTpqcIF+sXGQ77ClfP3+LCW1fp7xoKtSckxXP0tQMcffUAqRnJWzjCzYckSYz0jHLr3F0aL97DtRI2ey6uKWTvyXoq9pWhUqvw+/3YWjffSGwr4Fl103mjnbtv3eTdsXACMDI2Mmj2fKSKRAFNSufHZrFcN9N/qwu3Y508YVEGBQfKyd5VgFKQCtGHeGj2bG3qZm5ddawqQkNadQEZtcXo08WL9ercErPtFmzt/RvMniPTDCSVm4gvzkH5HLPwPg0kfwDH2ERQnnBiKiRPKFcpic7OCOqmJ8YL9z69VfB5vIzc66PvejuTXaOhdnWklvw9JZj2l5GQJdaeCeCYWcDa1MP4vR7cy2Hpz9is5DWzZxMqwaQ/g2bPY8ya+1gamggZAMvVKhJLcoJmz6mJws0tz/IK9oFhlvqH8K1bR/1aNcllxcTmZaMQbR19nNmzXI42JYXI9HTUcXHCxTrg9eBemMM9b9tg9izXaNHEJ6KJTdiW2NvGC4s9e/aQkZHBf/kv/4U//MM/xO12881vfpMjR46EDKM3C0+dbHA6ncTHB6VLHmY+tFqtcLqtzwOG+ka5+NbVj5g976gq4vjpg+w9WieeJ8HjzJ6jIqg9tou9p+rJLMjYwhE+GzzJ7LnspUp8MQFqdtcIxSD1+/yMtQTZlOPmsNmzUqsit66Ygv1lGPLThHuBcc7b19iU3RvMnqNTE8ioKyatqhC1YIyvJ5o9F2ZjKDcRnSkeczZo9rzGprSvM3vWR4fZlBFixfpFRiAQwNzcxYUzV7l9uQmPe83sWalg96FdHDt9kJ315cLJEy4vOkLyhOMDE6H2hNR4Gk7WU/+FWhIEk6yTJAlr9yitlx7QdaNjndmzjPxdQbPn/F0FwhkAu1dcDDV203dto9mzLi6K/L2lFOwvQy+gsfd6ecL1Zs8J+UYy6kpILs0RU56wZ5gZcx8O60az58TSPAxlgsoTPsbsOSI5kRhTDtGZxm15wk3CE82ed2Rj2l9GZpUJpVqsv3fY7LmHheGwzKs6KoL06qCnWnRKwhaO8NngiWbP5aag2bNarAvYoNnzOEuWoUeaPUfnZtI9OkxsYZ5QZ3TfygqOqSmc4+OfL7Pn5SXc8za8y2FJrJDZc1wiCt222fM2XnyoVCr+4R/+gT/6oz/i+PHjuN1uXnrpJX7jN35j03/WU68SlZWV/MVf/AVf//rXQ5PtH/7hHygrK9v0wX0e4bCvhMyeLZ2DofaEpDgOf3E/R187QHpW6haOcPPxOLNngOJdhTScqmfnvnJhPBgewrPqpvtWF60X7zO2jgWki4mk7FAFlWtmz36/n1aB9C0Xxm1MXLfQ+3e3N5g9JxcaKdhfRnZt4XOtc/dp8FizZ62atKoCMupK0KeLxwJanV8KHkw6+vGum9eRqYkYyk0kFOd+jsyeFehzMonJz0ZrSBAu1i8yZiZtQXnCt64xPRFm62flZ3D89EEOndpLTJxYMmaPM3tWqpVUHahk76ndFOw0CUckWZ5fpv3KI8ye0xJILE7i+I+9TIwhdusG+AzwZLPn/KDZc2mOePKEjlXG1+QJN5g9x0WvyRMWESGY7OgGs+eeIQKe9WbP6RjKC4jNN4opT/gEs+eY/GzU+m15ws2Ca9nJwK0u+m5smz1vMHsuzkIuWILa53Iz1z3EbJuFlamNZs+GsnwMZflo42O2cISbD0mScM8vBpOWQ6NhmVdAl5pEjCknZPbs9/th9AkPe4EQ8PlwTk4Sv7iI7c6dULtcrUaXmorOaBTW7Nkzb8P9KLPnuETUsdtmz9sQD8nJyXzzm9985j/nqZMNv/Ebv8FP/MRPcObMGVZWVjhx4gQrKyv87//9v5/F+D4XCAQCa2bPV7h1KcymVCgV1B2o5vjrB6mqLxeOYRcyez57B+s6NmV8chx7Tuxmz4k6ElPFYoZIkoS1Z4y2iy103ez4qNnz0SpMNQKaPTvdDN7tpu96O7bBR5g97ysjJlU85qzdOstYUzcTDywbWEAJJiPG2mJSRDV77lkze7Y+wuy5LB9dklixhjU2Zf/wttnzCwKP28OdK/e5cOYKrXc7Pmr2fPogph1imj3fOnuXO+81bTB7zirMoOFUPTWHq4kUzADY7/PT39xH66UW+u9ZHmn2nFaYTltbG1Hx4lxIeuwu2t65Q//NThyzYZae+GbPY4w1djPdOYS0TooypSwoTyii2bPH4QzJE36uzJ6nZoO66SPWUKwfZfa8jR8cgUCA8fZhLNfbGW2xhGReQ2bPB8pJLRLT7Nl6rxdrUzcr6/ZM0c2e7SOTzLZbmO8dWWf2LCfWlEFSedDsWbS55Xe5sa8lLd2fJ7PnxUWc4+Mhs+eHFLBts+ftyvNtbOMHxVPffGRkZHDu3DmuXr3K+Pg4KSkpHDx4cNP1nT4PmJm0cemd61x66xpT4+HSvKw8I8deP8RLJ/cKafbc1dwTYlN+nNmzKHAsLIfMnuesYWaI0GbP69mU9/o2mD1HZyew61QDmZX54rEpV1xMtPQy1riRTSm62bNjYpbZtr5HsynLTMSKbPZsGcJlW2f2HKElJi9r2+z5OcRA9xAX3rrKlXO3cNjDB4zymhKOv36I+pdqhDR7brnayq2zd+hr7Q+1R+p17D5ey54TdWSYjFs4wmeD2dEZ2i49oP1KKyuL4VgbizOoPFpNccOOkNmzXwDTSghW0Y209NN3zcxEx3CoPWT2fKCcxBzxJOtWbEtYm7sZb+7BtRSOtd5oIKO2mLSd4pk9B/wBFgfG1syerWHddNHNnh1OlgaGsfcP43Vsmz0/a9inF+i73k7/zQ6c66Q/E3NSMO0vI3e3iGbPfma7Rxhr7Ga2Z2Sj2XNFPhm1gpo9LznCZs9L4VgHzZ4LSCzNE24dDZk9W4ZwjE2EyAgyuZyorHRi8j9nZs8RESzKZORWV6PWiZeg9q0s45634VlaCO2Z22bP29jGs8OnollGRETw8ssvb/ZYPhd4yKa8+NZVHtxp38CmPPDyHo69dpCCUvHMnmfGZ7l9rpHb7zZuYFNmFmSw52SdkGbPfp+f/nt9tF16gKW5L8ym1Kgo3ltK5dGdZJSIZ/bsmLPTf7MDy40OlmcWQ+2x6QmY9peRs7uY3sE+MirzhEk0SAGJ2d5RJu71MtMxtMHYO2T2bBKPBfQ4NqUmLhpDeQGG0jzU0WLNa0mSWJ2eZckyvMamXGf2nJFGTH72ttnzc4blJQdXz9/i/TNXGOwZCbUbUhI48uoBjry6n1SjWMaVkiQx1DXCrXN3ab50H5czbPZcUltEw8l6KvaWohJMX9ntdNF5o4O2iy2M91pD7ZGxUZQfrqTi8E4hzZ7nRqaxXG9n4HYX7jVjbxDb7Nnn9jLVHjR7nl9XHavSaYLyhIKaPTtti9jMFmY7+vE5w7GOSjdgKC8gvihbOLPngN8fNHu2DOGcCFdMylVKonMyicnPQZsonknpVsHr9jDc1IflupmpdeuoJiqCvIdmz5lJWzjCZ4Plqflg0vJeL5510p+xWclk1JaQWpkvnPRnwOdjwTLKrNkSNHteg0KjIqE4F0O5SUyzZ7sjWI08MIzPGY61Jj42mLTMyRTX7NlqxWWzhc2eFQoikpPRGY0ooqMZb2tDoREnYatAwjUzhXdpboP/hGLN7Fkdl4BcKdb70Ta28bxgW9PhM8KT2JTHTh9kz+Fa8diUq+4gm/Lc3Y+wKeuO1dBwcreQbErb2CytF1s+yqYsyqDiSBUl+0pDbEpR8JBNabneznjHUEinXqVVk1tfjGl/GYbcIAtIFAYpBNmUY41d2O52MOMMv8Do0xMx1paQVmVCLRgL6GPZlOUmojMEZFOuOLH3D7P0ETblmtlz7rbZ8/MEvz9A6912Lrx1lTsf3AtX0amU1L+0i+OnD1FRVyqc2bN93s7d95q5df4uk8NhA+DEtEQaTtRR/3Id8QIaAI92DNN66QHdNzvxrekry+RyTDUFVBzZKabZs2OVgTvdWK6bmRsJV8dGxkeT17ADT7yMuoN7hDKtlCSJxdFpJu71MvnAgm9NdhQZGAozg7rpO3KEi7XP7WG+e4hZswXHOl8ZpU67pptuIiIxdusG+IzgWq+bvs7sWZdiQJ+fQ3RWunAmpVsFSZKY7Z+g73o7Q409eNeM1GUyGWll2RTsLyNzZ75w0p9el4fJVgvWpm4WR8KJLHV0BMbqIoy1RUQliyf9uTI1F/RU6xrA71p3fslMwVBeQFxhlnCxDvh8LI+Ms9Q/xOpUeB2Va9Toc9eSlvGxWzfAZwSvw4FzfBznxMSGy3Z1bCy69HQiUlJC66goZ3QpEMBjX8Q9P0uGEtyzQQlnmVyBOjYeTXwCiohts+dtbONZQ6xd5DnD8pKDK+ducuGtqxvYlInJ8Rx59QBHXz1AaoaAbMrO4SCb8nLLNpsyNorylyqoOFL1+WJTFmdQsP/zxaZURmhIry7AWFNMjFG8WD+WTZlmwFDxeWVTZqNNjN9+WX2OMGmd5uJb17j09jVs02Fpq9zCLI6dPsjBEw3oY8WStvL5/HTc7eLWuTu03+4MVVapNCp2HdrJnpO7MVXkCSdPaLctYb7cStvlByxMhmOdmGGg4shOyg5VEBUnVqwDgQCTnSP0XW9n5L6FwJqWtlypIKsqH9P+ctJKs5AkidbW1q0d7CbCvezE2tzD3E0zM0uNoXZdgh5jbTHp1YVECBZrSZJYHp0K6qb3DIdijUxGbJ4RQ7mJ2LwMYSpEH8Lv9mAfHGWpfwj3/GKoXamLICY/G31eNmr9tnTvZsG74qbj3WYGbnayOBGW/oxOiqVgfxn5e0uJFMjLBoJza35wImj23DZAYI2MIJPLSCrOxlhbjKE4UzjpT++qi7nOQWbNFpwz4T1THa3DUGYisdyEVrD3I0mScNnmWbIMsTw8Foo1QGR6SrAaOSNNuFgHvF5Wp6ZYGR/HuxSuPJer1ejS0tClp4tp9rzqDMokLc6FKs9lMlBERqGNN6COid02e97GNj5DbCcbNhkfx6Y89tpBKneXCcemXJqzc/f9Jm6fb/wom/Lkbuq/UCsemzIQYKRzhLbHsCkrj1aRV20SjmHnWl5l8E4XfdfbmR/dyKbM31uKaV8petFivcamtDZ1f4RNmWDKwGfQUHPiIGrBdIL9bg9zfaMfYVOqIiNILM373LEpI1IMxGyzKZ87uFbd3LrUxMW3rmJu7gq1R+kjOXSygWOvHSSvOGcLR/hsMDk8xa3zd7n7XhP2+eVQe+6ObBpO7qb6pSp0URFbOMLNh8/ro6+xh7aLLQw8GAhVVqkjNOzYX0rFkSrSC43CJQDtM4v032jHcqODlXWxjs9MomB/Gbn1JWijw7EWgZ0Y1E0fxdrUzUz3SEiKUq5Sklqeh7G2mPjcNAENaR3Y2vuZbe/HvRiOtTYhJmj2vCMPdZRgWtoPddP7h3CMfkg3fc3sWZeaLFystxqN/3iZng/aQuuoQq0kp6aQggPlJAu4jq4uLDN+rxdrczfOOXuoPSo5DmNNMenVBWgEk/SVAgGWhieZNfexYBkNGanLFHLiTJkYKgqIyUoVTvrTt+rCPjDCUv8QnqXwOqqKigwmLfOzUUUKto5KEp6FBVbGx3FNTYXWUWQytImJ6IxGtImJwsU64PPhWZwLmj27wpJYcpUKVUw8/ZPTlGabhKry3MY2XhRs35ZsEiZGp7j49jUuv3P988WmvNPJrXN3ab+zkU1ZfbCShlP1QrIpl2aXMH/wgLZLD1icWgi1i86mnOgYwXLdzEhL/2PZlKLF2r3sDB1MHNPhWOsS9MGDya5C1Hodra2twpQbS5KEfXQKf8cobVc6N7Ap4/IzMJSbiMk1isemdLmxD42y1D+8zaZ8ASBJEr3mfi6+fY1r793GuaavLJPJ2FlfxtHXDlJ/qBq1YNU2qyur3Lvcwq1zdxnsHA61R8dFU/+FoNlzWk7q1g3wGWFqcJK2iy10XDOzuhw+TGaWZlN5tIqiPSWoBdPS9rm9DN/ro++6manusVC7OlJLXn0JBfvLSMgWqzoWwDE9z1hTN+P3e/Gsi3VMZhKB5EhqTh1EK9hle8DnX6ebPh5ql6uVQd30snyi0sUzKfXYHdgHgvKEH9FNz89Bn5OBQjASx/OE4aZekCQMeakU7C8nZ3cRasEkff1eH9MdQ1ibe7D1jYZkXpUaFak7TRhri4nNFE/607VgZ7bdgq29H8+yM9SuS4rHUG4iYUcuKsGkP6VAAId1Env/MA7r5AZPguhsIzH5OUQki+c/4VtdDZk9+1fD66gyMhKd0YguNVUoDwYIngG8DjueeRse+2LY7FkmQ62PRROfiDJKTyAQwDc588RnbWMb23h2EON2bIvgWnVx5/I9Lr59jfZ73aF20dmUE4OTQTbl+80sL3yUTbnrcBURkQKyKe/20HqphcHPE5tyegHLjQ76b25kUyZkJWHaX07u7uINbEoREPD7mekawdrczWz3CFIg7EmQWrHGpswJsylFYJACuJcc2Do2siklgmzKpHITCSKzKS1DOMY+xKbMTCcmP3ubTfmcYWFukQ++H5QnHBsMX8qlpCdx5LUDHHllP0mpiVs4ws1HIBDA0jrArXN3uH+1Fe9aZZVcIad0dwl7T9VTWr8DpWBVdE67k85rZlovtTA9GK6YjE7QU/5SJRVHdhKflrCFI9x8SJLE7MAkfdfNDN0N66Yjg/TSHEz7SsmsMqFUi/X6/ljd9KgI0ncVYqwpRmeIobW1FZVAl6GSfZXRS43Mdw/jc7lD7dEZKRjKTcQXZqEQTHY04PWxPGplyTLM6vQ63XS1Cn1uVlCeMEGs6tjnFad+59/S0dYunLcLwJJ1FmtTNxMtfXhXw3MrPi8dY20RKWV5wsm8+j1e5nuHmTX3szwW3jMVWjWJO4LVyJEpYu2ZAO6FpaDZ8+AI/nXrqNYQH6xGzs4Qbh2V/H5WZ2Zwjo/jngvLn8kUCiJSU4lMT0cVEyPcvYTf7cK9MIdnwUbA6w21K7S6oNlzbPx25fk2tvEcYXs2fgr0mPt563+/T9f9v2B1JexJUFVfxtHTB9l9UDw2pdOxSvOl+9w+38hQ13CoXR8fze4v1NJwYjep2SlbN8BnAEmSmBqYpO3yAzo/xKbMKsum4oiYbEqv28NwUx+W62am1vlPqCO15O8pwbRPTDbl8tQc1qZuxu/34XGEYx2blYyxtpjUShMqwWId8PlY6Btltt3C0lDYf0KuViEZoik8WIfeKB7jy2NfDh5MBkYewabMRp+bhUKwNfxFhs/ro+nGAy69dY2mGw9CVXQarZo9R2o5+uoBymtKhKusmpua5857QXlC24Qt1J6anULDid3UHa8hJkG/hSPcfAT8AYZaB2i92EJfYw/+tcoqhVJB4e5iKo7uJKciT7jKKueig4FbnVhudHxEN920r5T8vaVECRZrKbBON938KN30IgzFWSEtbVES+w9102fMffhnFnjIuVRH60gsM2Eoy0cbJ1isJQnX7DxL/Y/WTdfnZxMloG76847IuGg0seKQSDwrLpy9k9y+amF53TqqjY3CuKuI9JoiIhNjtnCEmw9JknCMzzDb3s9c9xABT/gCNiYnHUO5iThThnAXsH6Pl+W1amSXLawoodBq0OdlE5OfjSZWvHXUa7cHzZ4nJ5F84XVUHRdHpNGINjlZuHVU8vvxLC3gXrDhW3GE2mUKBerYhGAVQ4Q469g2tiESxNp5PgPcutTE/+eXvxn6OsWYxLHXDnL4lf0YBGMLBAIBelss3D5/l5arbXg9YTZl2Z4dNJysp3R3iXhsyqUVOq6Zab3YwsxwmGEXnain4vBOyg/vJD41fgtHuPmQJIkZyziWG+0MNvbgc4U9CdJLczDtLyNzZ754bMpVNxMPLFibu1la5z+hjo7AWB08mESniBdr5/QcM2YLc52D+Nd5EkRnBtmUMXlG2rs6iUoziJNo8AewDwyzPDDK6kz44lauUaPPzSQmPwdtfOzWjW8bH8H0uI2//eD/4eq5myzOh/WVi8pNHHvtAPuO1xMZLdYBw+v28uCGmdvn79Ld3Iu0VkWn1WmpOVxFw8nd5OzIFmdermF+Yo62Sw8wf9DK8jot7ZTcVCqOVrFjfxk6vVixDvj8jLUN0nfdjLVtMFRF91A33XSgjJSCDOEqq1bn7Vjv9WJt7mF1/nOkmz40wazZwkJ/WDcdmYy4wiySyguIyRZQN925ytLACPb+YTz2D+mmm3LQ52UJp5u+jc8WAX8AW98o1qYepjuHQnNLrlSQXJqDsbaYRJNRuLnlcTiD1cjmflzzYQNgTWx00NulNA+NYNKfkiThnJphqX8Yx4h1wzoaZUwlJj+HSGOKcLH2ezysTkywMj6OzxG+bFdotSGzZ6VOrHVUkiR8Tgfu+Tk8S/Pw0H8CUEXrUcclotbHChfrbWxDNIh1c/gZIMEQR3p2Koa0OL7yU6eFZFPaJue4fb6RO+82MjcVZgs8ZFPu/kIN+nix2AIBv5+Bln7aLj2gr6k3pFOvUCkp3F1ExRFB2ZQLy1hudmK50Y59nf+EPjkW074y8veWEhkvlv+EFJCY67dibepmqn0wFGuZXE5SSRbG2mIMRZnCMUO8The2zgFmzRZWZ8OxVusjMZTmk1huQrvmKyMKg1SSJFZn5li0DBI5NMpMYDj4DRlEpqUQk59N5Dab8rnDyrKT3/va/9hg9hyXEMNLX9zH0dcOkpmbvoWj23xIksRI7xi3z92l6eK9kP8EQMFOEw0nd1N1oAKNQPIxAJ5VN123Omm79ICxzpFQe0R0BKUHyqk4UkVKnnj+EwvWWfqutTNwuwvXOi3tpPw0TPvLyKkTVDe9fZCxpm7m+q0f0U3PqC0mRkDd9NX5pZDZs9exTjc9OYHE0jzGfQ7yancJJWEj+QMsWydZ6h9mZXwqrJuuVBCdJa5u+jY+WzhmF4PVyPd6cdtXQu3KuEhMB3aSXl2IWieWJ0HA72fRMsZsu4XFwfHQ3JKrlMQXZmEoMxGdmSLc3PI6VljqX/N2WQmvo+oYPTGmYDWyUkD/CffcHCtWK67Z2bAngVxORFISuvR0NAkJwsU64PXgXgiaPQc8YUksuVqDJj4RTWwCcvV25fk2tvGD4J133uG3fuu3NrR512TJOjo6NvVnbScbnhJFFSb++sx/p7W1lbLKYmESDT6Pj6aL97jzbhM99/tC7dpILbVHqmk4uZvs4izhNrU5q422Sy2Yr7ThWOdJkJqfRsWRnezYX0aEYMxZv9fH6IMBLDfaGTcPhZizSo2KnNpCTPvLSC4Qz3/COWfH2hw8mKyu8xqJSonHWFtMelUBGsFiLQUCLA6OM2vuY7HfGvYkUCiCB5NyE/qsVOFi7V1xYh8YYWlgGK89yAKSAaroNTZlbjYqwXxlRMKCbZGO+93IFXJq9+/k+OlDVDdUoBTEhP0hlheWuXuhmVvn7jIxOBlqj0uKY8+JOva8XIchXSz/CUmSGOsaoe3SA7pudoY8CWRyGbmVeVQcqaJgd5FwsXavuBi8243leju2obCWdkRMJPl7d2DaW0ZsuljVsZIksTQ2E9RNf2DB5wpX0SXkp2OsLSalLFc4LW2/28tczxCz7RYc1nDFpDJCQ+KOPBLLTEQmx+P3+5lobd26gW4y3AtLqCdtDFnOE1hXMak1JAT33WwjcpVYsd7GZwufy8NkWz/Wph4WhsN7pkqnJb26gNTqAgZnrGRWlgqVwHPOzDPfOYitcwDfOv+JqPSkoLdLUTZKwaQ/Az4/jjVvF+dUeB2Vq5RE56xVIyfGCXh+WQnKJE1MEHCHY63S69Glp6NLTRVuHZUCAbz2RdwLc3iXw1U6yOSoY+OCMkm6KOFivQ1xIUlSyF/vs4BKo3qq+fHKK6/wyiuvhL6enp7mS1/6Er/2a7+26WMT6zS3jaeCJEkMdY1w69wdGi8241kNT4qi6gIaTu6mcn8FGsF06t1OF503OjBfeoC1ZyzUrtPrKD1YQcWRnSTniOU/ATA3Mo3lRgcDt7twr2POJhcYMe0rJae2UCjjRQCf28tU+wDWph7mB8JmskqtmrSqAow1RcRkJAn3ArNqW2S23YKtYwDvSjjWkSmJGMpNJJTkoNSKFeuA349jbAJ7/zArE1Mh5qxMqSAqy8h0wEPZnt0oBdOtFRHGnDS+9b3/xtDIIHv3Nwh1aeD3+Wm/08Xtc3cx3+4I+U8o1Up27iun4VQ9RVUFwlXR2W1LmD9ope3SAxYmwxWTcanxVB6touylSvSCeRIEAgEmu0bpu25m9L4Fv3etik4hJ3NnPqZ9pRjLc4WLtXvZyfj9oEySY111rDYuCmNNMcZdRegEi7UkSSyPTTPbbmG+ZzjsSSCTEZubjqHMRKwpQ7gqOr/bg31wlKX+Idzzi6iBAKCI0BKTl0VMfg7qGLGqY7fx2UKSHnq79DBl7sfvCc8tQ1EmGbXFJJVkI1cqglW5M9YnP/AFgW/VzWxHP74mC13L5lC7KiqCxNJ8DGUmIhLE859w2eZZ6h9meWh0g7eLLjWJmPwcojLTkQsm3xzw+VidnMQ5MYFncTHULlepiEhLC5o9R4u3jvpWnbjnbXgW55DWVdQrdVFBs+eYOGSC7ZnbEB+SJPF/f+P/j7V77OM7bxKMxZn8xB//zKe6z5IkiV/7tV/j4MGDvPrqq5s+tu1bl88hFm1L3H2/mTvvNjI5HGbYxafE0XBiN/Uv15GYKhjDLhBguH2YtksP6LndhW/Nf0Iml5NfnU/FkSpMNQUoBGNTupadDNwJsinn13kS6OKiyN9bimlvKTEC+k8sDk9hbe5hstWCzx32n0jIN4bZlILF2ufyMNc9yGx7PysTs6F2pU5L4o48DOUmdIa4LRzh5kOSJNzzi0Gz58GRDcZ4EUmJxORnE52dgSSXMdnaKkxSSQoEwiXVgiI9K5XZhemP7/iCYHJ4ijtvN/NPv/Nd7Ouq6LKLM9lzYjc1h6uJFMyTwOfx0tvYQ9ulBwy1DoQ8CVRaNSX7Sqk4vJOMkkxh5uVD2KcXsNzooP9mByvrYh2XYaBgXxm5e0qIECzWAb+fma4RrM3dzHaPhqro5EoFKWW5GGuLScg3Cuc/4V5yBHXT2/txL4ZjrY3XB3XTd+SjFq5iUmJlYgp7/zCOsYlQrJHL8EVFkFFVQXRG2raW9jZ+IKwuLGO918N4cw/OdT4+kYbYYDVydSHaGEG9XdotLFjC3i4yuZw4UyaG8nxictKFm1s+5ypLg2veLkvrvV10IbNnVZRgsZYkPPPzrIyP45qeDq+jMhnaxER06eloDQbhYh3wefEszuOet+F3hclwMqUKTVzQ7FmhEUsSaxufR7w477pvv/02/f39fOtb33omzxfrtm0bj4XP68N8u4Nb5+7S2dgdYlOqNCp27q8gqSCBl3/oOCrBSvMWpxdou/wA8+VWlmYWQ+0JxkQqjuyk7FAl0YJ5EgT8Acbbh7DcaGe0pT8Ua7lSQVZVPqb9ZaSVZgsjAfYQrqUVxu/1YG3uYWV2MdSuS9CTXlOEcVcREXFixVqSJOwjk0E2Ze8I0pr/BDIZsXlGDOUmYvMyhGPO+lxu7GsHE/dCuORWqYsIHUzU64zxRPCgkCQJz+IizvFxVqemECsdLCacy06aL7dw69xdhrvDngTRsVHsPl7DnpO7Sc9N28IRbj4kSWKyf4K2Sw/ovN6Oa10VXeaOLCqOVFHcUCKcJ4HX5WGoqRfLjXame8PMWrVOQ96eEkz7ykjIFs+TwD45F5RJaunDsy7WMZlJGGuKSdtpEq5iMuD1Md83wmx7P/bhiVC7XK0ioTgHQ1k+UeniVUx6lpaDif2BYXyrrlC7Ji4maMyalU57TzeRRvGMrrfx2cDv9THVPoj1Q94uCo2K1Ip8MmqLic0Wz5NgdX6JWfNaNfI6b5eIpDjccRGUHdmLNlqwy/bHebsoHnq7ZBORYhAu1r7V1ZBMkn81vGcqIyODMklpaSg0Yu2ZkiThXV4KyiTZF8NkKZkMtT4WdXwiqii9cLHexucTMpmMn/jjn3muZZQeIhAI8Fd/9Vf83M/9HFFRUR//Hz4FtpMNgmPMYuX2+UYaL97DsegIteeW5tBwoo7ql6rQRKhpbW0V5vLZ6/Yy3jZG13fbGGkfDrVrdBpK9pVRcWQn6YXieRIsTsxhudFO/61OVhfDZmkJ2cmY9pWRV1+MJkosnXq/z89M5xDW5h5me0ZDLzAKtZKU8nyMtUXE56QJx6Z0LS5jM1uY7ejHs84YLyIxFkOZicTSPOE8CaRAgJXxKZb6h3FYJ2CNJS2Ty4nKTCcmPxtdarJwsfatrrI6McHKxAR+Z/gQGhAsMSwKAv4A3fd7uX3uLg9umPGtST7IFXIyitN5+UeOUd5QhlIwGYCVRQftV9tou/SA2ZFwFV10op7ylyqpOLyT+DSxUmSSJDHda8Vyo52hpt5QFZ1MJiOtLBvTvjIyd+ajVIv1qu1xuphosWBt7sZuDVfRqaMjSK8uxFhTTHSKeBWTK5M2Zs0W5rqH8K/zJNBnppBYbiK+IEs4/4mA14t92Iq9f4jVmblQu1yjRp+TSUx+NtqEYMWkCEn9h/Da7TisVmKWlwn4fEJJ+T1vkCSJxdHpYDXyI7xd0muKSCnLQ6kRa2753B7me4aZNVtwjG/0dkkoycVQbkKbGEtraysqgYyu3QuLj/d2WatGFm4d9ftxTU/jHB/HPR+WFpQplUSkpARlkmJihLuX8LtWg2bPC3NIvvDFqyJChyYuEXVsPPJtadttCAiZTIb6BZChb2xsZGZmhh/6oR96Zj9je4YLiOVFB00X73H7fCNjljDDLiZBT/0Xaqk/UUdqVtiTQIQDgiRJWHvGgqaTN9rxrK69wMhkZJfnUHFkJ0W7i1G9ABP/aeBxuoOmkzc6mB0IM+y00REhNmV8ZtIWjnDzIUkS9nHbmulkH15n2EArLjsVY20RqRX5KAWLtd/jZb53mFlzP8tjYfkzhUZNQkkOhjITkamJwr2suheWQjJJflc41tqEOPT52ehzMlEIZown+f2szswEDyZz4QsemUJBREoK2pQUuoaHydrCMW5jI2ass9w+38id9xpZWFdFl5abSsOJ3ew6XMXg6AAVlWXCXFwF/AH67vbQfqWN/nt9oSo6hUpJUX0xFUd2ki2gJ4Fjzk7/zQ4sNzpYXhdrfXIcpv1l5DfsIFKwikkpEMDWN4a1qYfpjsFQrGUKOUkl2RhrizEUiudJ4HE4sXUOYGvvZ9W2GGpX66MwlOWTWJaPNlawWEsSq1OzQd30Ueu6ikmITEsJVjFkpAoXa7/HE9RNHx/HuxyUcomAoEmrYEzj5wFu+0rY22V6IdQeERcdqkYW0dvFPjqFzWxhvm/k0d4u+RkhTwIRzucQrEZeHhplqX/4kd4u+vxsNDHixXp9NfJ6TwJNfHxQJik5Wbh1NOD34VlcwL1gw+8Mk+FkCiXquHg0cYkoI8SSFtzGNl5UvP/++xw9ehSd7tnNye1kgyDw+/x0NnZz6/xdzLc68K8dDpQqJeUNpTSc3E1JTREKwdiU9jk77R+00nb5AfPj4Uu5iFgdu16upeJIFbFJsVs3wGcAKSAx0TWC5UY7I/cs+NdeVmVyGcbyXEz7y8iozBMu1u5lJxMtfVibe1ieDMdaGxNJ+q5C0muKiTLEbt0AnwEkSWLZOo3N3M9c7xABT9gsTZ+dhqHcRLwpE7lg/hN+twf70Cj2/mFcc+FDqEKrQZ+bRUx+Npo48YzxvEtLOCcmcE5OIvnCsVbHxaFLTyciORm5Uhk8gI6MPOFp2/gs4HK6uX/lAbfP38XSNhBq10VFUHt0F3tO7iarMAOZTBaM2egWDnYTMTM8zYOL92m71ILHGWYmphWkU3Gkih37StEKVkXn83gZuWfBcqOdia6RkLyHUqsit64I074ykkzpwiV7HTMLWJt7GL/Xi3tdFV10WkJQJqmqQLiKyYDfz6JljNl2C4uD42F5D6WC+MJsDGX56LNShYu1Z3kF+0BQJmm9lItaH40+P5uYvCyUOrFiLQUCuOfmgrrpMzMb5D00BgOTLhcpkWLJ12wlAj4/013DWJu6sfWOhnx85ColKeW5GGuKSchLF65C1bW4jK29H1tHP+6lsMqANiEmVI2sjhLrAvZx1chBbxcdGdUVRAsoueZ3uYLv8ePj+NZVIysiItClpaFLT0cZIdg6Kkn4HHbcC3N4lhY2eMqpomPQxCeiio4RLtbb2MaLjvv37/PjP/7jz/RniHVD9TnExNAkt883cvf9pg2mk5kFGew5UUft0V1ECWag9dB00nzpAYPrTSc1KoobdlB6qIJ57wI7q3YKwyAFsM8sMnir6yOmk7HpiZj2l5JXX4Iu9tnorW0VAn4/s90jWJt6mOke2WA6mVyag7GmmMQCo3AvMG67A1vHALPtFtwL4Vhr4qJDBxONXqxYSwEJ5+R08GAyOr7BLC3KmBpkUxpThIu13+0OH0xWwpd5Cq02fDB5hoyDbTwdAoEAlrYBbp+/S8vVVtxrVXQymYySmiL2nKyjcm85KsEkH5x2J53X22m71MLUwGSoPTI2krJDQZkkQ5Z4VXSzA5NYbrQzeLcb72o4sZJSnIFpXxnZNQWoBKus8q66mWzrx9rUw+JIuIpOpdOQVlWAsaaYGKNhC0f4bLAyNcdsu4W5rkF8q+Equqh0A4YyE/FFOcJVTAa8PpZHrdj7h3FOhSWx5Col0Q9lkhLjhUuseB2OkG56wBOe1yq9Hl1aGhGpqaBQMNLaunWDFAShauTmbiZaLHidYb+P2KyUUDWyaN4uwWrkEWbbLSyPrq9GDnq7JJaZiEoTz5PAvWhnqX8I+8DGamRNfOyat0sa7d3dRKYL9D4vSaxOTeGanPxoNXJyMrr0dNRxccLF2u92BRMMC3MEvOF1VKHRoo5PRBObgHxb8nUb23huYbVaSUp6tme37WTDC4gVu5Pmy/e5fb5xg+lkVGwUdcd20XBiN8b89C0c4ebjSaaTGTuyqDi8k+KGHWh0Gvx+Pwuti1s32E2Ed9XNQGMP/e/dp23icqhdrdOQW1+CaV8piTnimaXZJ+cYb+5m/P6HTCczkjDWFpNWmS+Uhik8yXRSSUJRUCYpyvj5Mp3U52ejz81CqRXrECoFArhmZnBOTOCy2cIsILk8dDDRxIt3wfMiwzY5x933mrh9vhHbusqqJKOBhpO72X28hrikuC0c4eYj4Pcz0NKP+XIrfY09oYpJuVKBaVcBkTl6jnzpKGrBLtudC8v03+rCcqOdpcmwvnJUop78vaWY9pUSLVoVXUBirt+KtbmHqfbBDfIehqJMjLXFJJVkC1cx6XW6mOscYLa9H+dMONaqKB2JpXkYykxEJIhXRbc6M4d9YJjl4bFwrAFdahIx+TlEZaYJp6Ud8HpZnZpiZXwc79JSqF2uUhGRlhbUTY8OS2KJImGzVXA7VoPVyE3dG6qRNfpgNbKxpogowfZMSZJwWGeCScueR1Qjl+UTX5AlXjWyx4N9cAz7wDAuW3gdDVYjZ6LPy0YbHxvsK8i8kiQJr93OitVK0vw8S+uSDB+uRhYJkt+PZykok+RbCVfpyBQK1LFBmSRFhG77/LKNbbwAePDgwTP/GVuyAs7NzfGbv/mbNDU1oVAoeOWVV/jGN76B8gkLcl9fH1/+8pf5n//zf1JXV/cZjvb5QMAfoPteL7fO36X1Q6aTZfU72HOijrL6HSgFe4FxLCzTcdVM2+XPkelkQGKqdwzLjQ6Gmx9hOrm3lMwqk3imkyuu4MHkXs9G08moh6aTRUSnChZrScIxMYut/aHpZNhAKzojBUN5PvGF2cKZpfk9XpaHx1jqH8Y1+2jTSU18rFAvq5Ik4V1eDuq3Tk4S8IZjrY6NDbIpU1K2WUDPEdwuDy1XW7nzbiM99/tC7Vqdll2Hd9JwYje5pTlCfU4BZkdnMF9+QPuVNhwL4cNkSm4q5Yd3UnqgDE2UltbWVmEun/1eH4uWaS598D0mOoaR1hKACrWS7JpCCvaVklKUKZy8x4ptifF7PVjv9eBaF+uo5DiMNUWkVRei1YtVHRvwB1gatAZlkvqtoSo6mUJOnCkTQ7mJmOw0cVi3a/A6nNgHR1jqH8a7HI61KjqSmLxs9HnZqESTcpEk3HNzwX13ZgbWVUxqExODuukGg3Cx3ioEq5FHsTZ3M9O1rhpZISe5NBdjbRGJBRnC/b3dSw5snY+oRo6NDnq7lOajifl8VSPr87OJSk9FJphnU6gaeWICnyO4jsoBuUZDZHo6urQ0lIJJr0mShG9lOVjFsLgAUiD0PVWUHnV8Imp9rHDzehvb2MYPji25rfza175GcnIyN27cwGaz8fM///N8+9vf5md/9mcf2X91dZVf+ZVfweVyPfL7ImNqdDook/ReE4u2MBPnoelk3bFd6OPFMlXyeX1YmnoxX35A//3+0AuMUq2kcLe4ppPLs4tYbnTQf7MTx7pY61Pi0OXGs/eHDqNPjN26AT4DBPwBZntHGW/uYbpzCOnDppM1RRiKMoUz0PIsr6zJJPXjmg/HWhMTRWKpuKaTzsmZtYOJNRRrZDIi01OIyc8m0iio6eTEBCvj46GDCQQPJg9lklSCHUxeZEiSxED7ELfP3+XeBw9wrUk+yGQyCqtM7Dmxm50HKtAIJqey6lil60YHbZdamOgbD7Xr9DpKD5ZTcaSK5JyUULsI7ERJkrANTWG50cHg3W48K+F3zOQCI6Z9pWTXFqIWTN7D5/IwaR5gvLmH+cFwFZ1SqyZ1p4mMmiJiMpOFS6I5Z+aZbe/H1jmAb52US2RKIobyfBKKc1EKFuuAz4djdIKl/iGck2HCjkypIDorg5j8bCKSE4WLtXdlJSyT5A5LuSijotClp6NLTUWxbfq8abBP2LA29zDR8ohq5JoiUneaUAtWjez3+lh4VDWyKliNnFiWT3SGeOvo46qR1bF6YvJz0OdmoowQK9YPq5FXJiZwf6gaWZuUxITTSXFNzRNJsy8i/B437nnbR2SS5GoNmocySWqx3oW3sY1tbC4+81VxZGSEpqYmrl+/TkREBBkZGf8ve38eHFl6nveCTy4n9z0TuSKRQCb2pfbq6upuimw2KYtyiGKMaSviaiY8YUu2SCkcVowX2ZJ9LYZE3RmFx3OpMUcRDisUcsh23FHYsuURZYXIbpK9d1cXUNiXTCAB5L7v21nmj5M4J7O2ru5GFYCvvl8E/+hzuoGTfJHnnO973vd58M1vfhO/+7u/+0ix4Td/8zfxpS99CTs7Ow89TxrNWhMf/uAu3v7ee4it7UvHjRaDGDr5lVsY64dOkoIgCEhHU7j3/btY++E9tGryy+robBCXXruK+VcWiAud7HW6OHh/B7tvriK9eSQdZ/QahF+cw9Qri3BMeLCysgKjnZzNZ7bcxPb/eAfJuzvoDtTaEnCJoZNXp6AhrNY8y6K0e4jcvT1UDpLSy6qSUcMxE8LI0hTMY+RZYnWrNVT24qjGDsA25FprrBZYJ8fFhQmBoZPtXE60Scrlhm2S3G4Y/H5oXeRt8FxkCuki3v2fH+Cd772H7MBklcvvwks/fQu3f+oFOL2OM7zC04fneOwvR7Hy/bvYfncLXN9ORaFUYurmNC69dgVTN6ahImxislmuI/rWOnbfXEM5IU9WMSYtZr9wBdM/sQQrYbUWeAHFWEK0SboXBXdi76EAXFNBjN6chWcpTFyte802Chsx5FZ30cwM2CQZ9XAuhDGyNAXDCHlWLu1cAZW9B22S9N4RWCPjMIdGibNyObFJaiaT6JbL0nEFw8Dg88Hg94OxWOhz95To1ltI3t3B8QdbqCby0nGNWY/ANYKnkRNZ5Fb3UNy6bxp5zIuRxUk4Zuk0MgkMTiM3UykIj5hGFpRKHCwvE/PZBY6DSSGgvr8Lrjlgk6RUQWOzQ2N3QW0wEvN5KRTK0+WZv2nu7u7CZrPB4/FIxyKRCJLJJKrVKiyW4S79P/3TP0U8Hsdv//Zv47vf/e6n/r2n2YV38rNO82fyHI+tj3bwzvfex8qPV9Hrig81pUqJhRfm8OJXXsDS7QUwfescnucf9+M+EU/j8zwpjXId6z9cxb0fLA/ZJJkcZiy9eglLr16Bc9T1wLU+jrP8PE+CIAjI7CQQfXMNBx/ugG33X2AUgG8+hMmXFzB2bRLqfsDoef88T0q30UZ6ZQ+J/sLk5HVVY9TBd20a/uvTsPg/Wa3PA4+rjyAIaKTyKKxFH1iYmEbdcC1Owj4TkhYmp/m9/ix81r85vttDLX6MWuxweGGiYWAeD8IcHoPWKYelPe1aP6vvUK9WQyuZRCudHlqYMBYL9H4/dB6PZJP0WWr9tD6P6jNOlZz35+z9dNtd3P3RPbzzF+9j56NdyTpHq9fg2uev4PZP38LkpfCp/J2ep/t4/iiH1ddXsPbGPdSLsuXDyJgbl167goXPL8Foky0fHnbN5+nzPAlcj8XRcgzRt9aRWN2HwPdtkhg1xq5PInx7Djm2gkuXL0GlUl2Yz/UoTq6/lishc3cPyTs7aA3YexhcVgRuzMB/bRq6j6n1eeFJ/+Z4jkd1P4H8WhSV6IBNklIJ2+QonIuTsIz7penYs/rMp/0dYhtNVPePUIvGh2yS1EYDLJEQzOExMCZxik44xd97wlncEwRBQLdYROtE2B+wctE6ndD7fNAO2CR9kucufc4+CM9xyG8fIfHhNnKb8Qemkf3XZ+CaGZUmVJ/1d+tp1axba6CwHkN+bW/IJkljNcG5EIZrIQLtwDTyaf3+s3zOCryAViaLajSOxlFyaBrZ4PfAEgmJIc/9Wj/pd+u8vztwnQ7a6TRaqdQD08h6nw96n0+ySRq8j57Xz/MkCIIArllHt1xEr1LGiBqS0KA2msHYHGAGbJLOy1r1SSChPoPQz/NkfNbnLOX0UAgnq+tnxH/7b/8N/+bf/Bu88cYb0rHDw0N8+ctfxg9/+EN4vfKYfjQaxd/5O38H/+k//Sf4/X7MzMzgj/7ojz5RZgPHcVheXj7FT3C6lDMVbL+/h90Po2iUm9Jxu9eGmVuTmLoRhsFClocqx3LI7WSQWD5CbjcjbfAoVUp45nwIXBmDKzxCnDdyp9JCaTOF0lYa3cpgh7cejnkf7LM+aCykjZ4K6KbKaMVy6CRKQH+DBwoFtAEbdOERaP3k+TwK7R6EVAl8sgQ05RF+6BgofHYo/XYoDISN8AsCVI0W1KU61NUGFP3vtQCAM+nB2s1gzQaAsForeR66Tgf6dhvMwMsSp1SipdWipdWCuyCj1devX/9U/915f84OIggC0rEsdt7fQ/TuAXoDAqB/youZFyYxcTkERktWZ2Kv1UVqLYHj5SNUEiXpOKNn4F8aReDKGCw+K1HdamIQbg3FjRTK22lwncEgXCsc8z7YpjxQaS/G9/NJ4XscOkcFtGI59LLyxpiCUUEXckI3MQLGZSKq1gAg1FrgkyUI6TIwEMwKs1585nptUBCWdQWeh7rahLpUg6rRwklFBYUCrNUI1mYGZ9QBhNVaxbLQdzrQdzpQDWx89VQqtLRatHU68Of0XeMiPmd7pQba+3m0D/Lg2/IzU+0wQj8xAt24E0rCnpkCx0PIViCkShAK8qYzlAooPDYo/HYo7OR1eCs6XTClOtTlGpTswDutlhHf460mCIRNRUEQoO12oW+3oe315PsogLZGg5ZOhy7DEHcfVUOASQmYlAAz8NF6AlDjgToPcCDrM1OeDz7tc5Zy+jzzp4XBYECr1Ro6dvLPxgHf6k6ng1/91V/FP//n/xx+v/8z/96lpaVTU7k4jsPq6uqn/pnNWhN3Xr+Ld//iA8TWD6TjBrMBN790Dbd/6oVnapP0WT/Pk3Bik7T6g2Ws/3gN7QHrHN9UAJdeu4K5VxagPwXrnGfxeZ6UXruL+Ic7iL61gfTWgE2SToPxm9OIvLIA91TgsbU+T5/nSamlCkje2XnAv9Xsd8J3bRolTReXb167MJ/ncZzUZ2F2DtX9JApre6gepKTzSrUK9ukQnIuRC2OT9En+5rrVOmqxOGr7h2Cbcq0ZqxmWcAjmieCZ2ySd9ndI4Hl08nm0kkl0SiXZJkmhgM7tht7ng8bheGoi2nm9J5yn5+z9FNJFvPeXH+Ldv3gfuQHLB6fPids/dRMv/rWbcD5Fy4ezqNmJTdLqD1aw8/4WuJ64caBQKhC5PoVLX7yCyI0pqD/FxsF5/RsERJuk2DubiL61PmSTZLCbEHl5AZGX5x+wSTrPn+dJEG2Skkje2Ub+XkyyxIICcE6NInB9Bu7FiQttk/SwGvWabRQ391FYi6KZlW2S1AYdnPNhOBciMLjPp03Sp/2bE22SiuJzN34MYcAmSed2wRIegykUkKbonhVP+zvE93poZzJopVLoVeSsK4VaDb3XK3Ycn6JN0nm9Jzyr52y33kJqeQ+JD7dRSw7YJJn08Penkc+bTdJnrZlok5RDYT2K0tYB+O7ANHLQA9diBPbp0DOzSXpWf4Nct4v6wTGqsUN08vJ9VJpGjoROzSbpvHyvBEEAezKNnMl87DTyozgvn+dJETgOvWoZ3XJxyCYJSiUYix1qiw370SiWli5diM/zcVy0+nwc9PNQLhrPfNUxNTWFcrmMfD4Pl0u0TIlGo/B6vTCb5RHE1dVVHBwc4Nd//dfx67/+69LxX/qlX8LP/uzP4l/9q3/1iX6vSqU69T/iT/IzeY7HxgdbeOd77+Huj++B7XddKVVKLNyaw+2fuoXLryyCOUOfx6fx/1GtWMPaGyu49/1l5A5lmySzw4ylL17BpS9egSs4cqq/84Sn8XmeBIEXkN4+wu6P13DwwTbYjmyT5J8PYepziwhdn5Zskp6Us/o8T0q30Zb9Wwc8z08WJqM3Z2Hxu8BxHCrLy+f+8zwJJ/6t3MYx1n64CW5gYWIOeuDq+7eqtRczQOtRNXqkf6uGgWViDJbJcegGbJLOC5/lb04QBPSqVTSTSbRSKfCDCxOrVQx79nqfaVjaefsOnfVz9n46rQ4+emMZ7/zF+9i6I2c+afUaXH/1Gl7q2yQpn2EH7LOoWS6exb0f3MXq6yuol+TF5EjIjctfuorFz1+GyW56zE94cs7L3yDbZXF4dw97P14TbZKEYZuk6c8twbcQ+than5fP86Q08mUkPtjG8Z0ttAdqbXBZoQxYcP2vfx5Gp/UMr/D0UUCBSvQY+dU9lKNHkiWWaJMUxMjSJKzhUckm6bzzpH9zvXoT1egBKvfZJDEmAyyRcVgiIWjMp/O9/iyc5ndIEAR0CgU0Ewm0stkhmySdywWD3w+d2/1Up2PP2z3haT5neZZDdjOOxIdbyG7EZQuyvk3S6AtzGJkJSjZJ55VP+v9Rp1JHfm0PuftskrRWE1yLk3AtTUI3YJP0rHkaNRd4Ac1UBpW9A9QPE1KtoVDAGPDCOjkO46jvqdX6rL5XXKeDZjKJZjL5gE2Swe8Xs11Mn/w+et7uE4MIggC2UUOnVEC3XAIEeRpMbbJAa3dCY7VBoTyxkFSc68/zaaCf53xD2uehyDxzsWF8fBzXr1/Ht7/9bXzrW99CqVTCd7/7XXz9618f+vdu3LiBe/fuDR2bmZnB7//+738iG6WzJnWQxjvfew/v/s8PUM7LnTj+sA8vfeUWbv3kTVidlsf8hIsH2+1h5/1t3Pv+XUQ/2pMWgmqNGjMvzuHSa1cwcTlyYRaCT0o1U8LeW+vYe3Md9YFaWzx2TH1uEZGXF2AirNY8xyG3Gcfxh9vIbhw84N86emMWI3Nj535h8knpVOrIr0eRX91Du1QFAHAANBYTRpYm4VqMQGcnq9ZDC5OjxIB/K2D09xcmQT9xtX7swsTngyEQ+FQLE8rTged57K5E8c733sOd15fRack2ZjPXpvDSV27h6uevQEeYjVmz2sTGj1ex8ld3kdpLSsf1ZgMWv3AJl754Bd6I79wJgJ8FQRCQi6aw9+YaYu9uojtgWeeeCmDqlUVM3JqFhrBa91odpFeiOP5gC6WBKTq1TgPf1SkxmDXgwsrKylAew0WnmSmC207i3ls7YJtt6bjR64RraRLO+TAYPVlWlHyPRe0wgWr0AM2U3LCjUKtgDgVhnQxB7xkh6nsNAL16XQpm5Tvy91ptMsEQCMDg80GlJet7fVYIgoBqIo/UR/1p5Ib83bIG3QjcmIX/6hQ0RrK+W1y3h+JOHPnVPVTjA9PIjBqO2XGMLE3BHPQQ993qlKuSaMm15FprbBZYJydgCY9BTdh9VOA4tHI5NBMJdAoFeRpZqYTe7YYhEIDW6SSu1lynLQoMpQL4Xlc6rtRooXW4oLE5oXqGDVIUCuX540zmqb/zne/gW9/6Fl577TUolUp87Wtfwze/+U0AwNWrV/Gbv/mb+OpXv3oWl3YqNKoNfPBXd/D2997HwWZcOm60GHDrJ2/i9lduYWx6lKiHmiAISGwf494PlrHxo1W0B15WR+eCuPTFq5h/ZQG6U7BJOk90mx3sv7+FvTfXkdk5lo4zeg3CL85h8pVFuCf9xNW6msgj8eEWknd3h2ySLAEXRm/OiQsTwmr9uIWJ4DJj6iduwjZOVq0BeWFSjcbB3r8wiYyLC5Mztkk6bQSOQzuXQ+NRCxO/X1yYnFNP6OeRXCKPd/7ifbzzF++hkJJtAEYCLtz+yi28+NduwnXOLB8+KxzLIfrRLu59fxk772+D7/srK1VKTN6YxuXXrmLyxtSFts55GI1irS/sr6EyUGujw4zJVxYx+fICrD7HY37CxUPgeeR3j5H4YAvptX3wkk2SAiMzQQRuzMIzYJNESnhgr9GShP1mTswaYQEwRj1cCxG4liZhGDmfNkmfFkEQ0MrkUYkeoHZwDIEdyBrxjsASGYc5NAolYd9rrttFK51GM5FAr1qVjisYRhb2zWbi3rHOktTyHorfW0W2/J50TGs2IHB9BoGbMzB7yXpmCoKA2lEG+dU9FLb3wQ9ku1jGvHAtTcEx8+xskp4VXLuD6sERqnsHaBfkzCaVVgPzxBisk+OnZpN0XhAEAb1KRWwWSqWG7qMamw0Gvx96r/eZ2809bXiORbdcQrdUADtgk6RQqqCx2aGxu6A2kJc1QqFQzidn8qbqcrnwne9856Hn7t69+8j/bnt7+2ld0meGZTmsv7uBd/7ifdx7aw1sT7ZJWrq9gNtfeQFLtxfO1CbpaVDJVbD6+jJWf7CCwoAPttllwaVXr+DSa1fgDLjO8ApPH57nkVqPY/fHa4jf2ZW8kRUKBfyL45h8ZQGh61NQE1brTq2JxEc7SHywhVpKts7RmPUIXJtB4OYsLIRt5j1uYWIe82JkcRLWqSBWN9ZhuSB5DE8C1+mCKVRw9L3X0RlYmCi1GljowuQMr5IySLvZxp3Xl/H2997D7vKedFxn0OHGa1dx+6dEmySS/k4BIB1L4d73l7H+o3tolBvScW/Yh0uvXcHC5y/BaDU+5idcPNhODwcf7mDvrXUk1w/E5EYAKo0a4zemMfW5JfjmxqBQklXreqaI4w+2kfxoG+2KXGuTx47AjVkErs9AR1iteZZDae9ItEmKHUtir0KlBJxmRD53HY5IkDixt1uroxqNoxo9QK/elI4zJiMsk+OwRkJgTGTVWuB5tPN5NJNJtLPZ4fwjlwuGQAC6kRHian1eWPs/XgfPclColPAuhhG4OQPX9Bhxk+ftcg351T3k1/bQqcgbsFqbWZxGXohAe4Y2SU8DgefROE6jEj1A/TgJ8PJ3yzTqgyUyDtOoT7yvEgTXbsvTyA35manS6aA/sUkyEnYfFQSw9ao4xVAZyJEDwJgs0Nid0Fjt9D5KoVAkotEovv3tb2NlZQUmkwk/93M/h7//9//+qVsLk9UWcwYc7R7j7e+9h/f/8kPUyvILzOhkALe/8gJuffkGLA6y7FS67S6239nEvR/cxf7KvvRQU2sYzL40j0uvXcH40gRxL6vlRAG7b64h+vY6mgPeyDa/E5OvLCLy0jyMDrJeVrkei+zGAY4/2EJ++1CyxFKqlHAvTmD0xixcMwQuTEpV5NeiT7QwIaWDVOB5NBL9hclRClqeRwcgfmHCtlpopVLP1cLkIsNzPLY+2sE733sPH/1wBb1+Jo5CocDczRnc/qlbuPITl6DVkTUaXi/VsfZDMf8oe5CRjhttRix+/hIuvXYVngnvGV7h6SPwAjI7x9h9cw0H72+j15ZtADwzo5h6ZRHjL8xAoyfLTqXbbCN1dxfHH2yhciRb5zB6rWSTZA26iRLRBEFAI5VHbnUPhc0YuIFaG/0jGFmchG16DGvbm7CFR8nZNOF4VPcOUIsdopWVG3aUjBrm8SAskRD0bhdRtQaAXrWKxkn+UVeuNWM2wxAIQO/1UpukZ8CV/8tPYm9jGze+8nnozIazvpxThe10Udw6QH51D7Vj+Zmp1DBwzo7DtTQJ8yh5NkntYhnVvQNU9w/BtWULMq3DBmtkHOaJIHE2STzHoZ3JoJlMitPIfRRKJXQej2iT5HAQV2uu3UKnVECnXBgKuFZqddDandDanVAyZL0LUygXAUEQhmx8nzZavfYT3d8ajQZ+4Rd+AS+//DJ+7/d+D6VSCb/0S78EjuPwK7/yK6d6bVRs+BRUizXce30d/+M7f4lEVPZGNtvNuPWTN3D7p15AcGr0DK/w9BF4Hofrcdz7wTI231pHtyUvDsYWQrj0pauYe2kBWsK8kdu1FmLvbWLvzTXkY2npuMaoQ+S2aJPkmiCnox0Qb5DlwwwSH2whubwHduBmaRvzIHBzFv4rk2AMZL2ssu0uituPWJjMTWBkcRKmUfI2eDrFsthNed/ChNNp4FmYhTUSIm9hwrJoZ7Oif2tRtmEhfWFykUkdpPHOX7wv5h/lytJxb8iD2z/1Al78azdhd5NlpyLnHy3384/EnBSVWoXpW7O49MUrCF+bhEpNVk5KNVvG3ptr2HtrHfWcnH9kGrFi8uUFTL6yCIvbdnYX+BTgOQ65rUMkPtxGdn0f/En+kVKBkdkQAjdn4Z4fJ67WnWoDhfUocmt7aBfkWjNmA0YWxfwjvdMGgCRhX0AznUV5dx/G+DGywoF0zuD3wBoZh2nMD6WarCUa1+mgmUqhlUyiV5NDeJUazZBNEuXZMTIXQqJTAkPI2k3gefCFGmL/48co7x5K1oIAYJ3ww7U4Cft0iDhrQbbVRjV2iGr0AJ2SfB9V6bSwhEOwRELQOWxnd4FPAUEQ0C2V0Ewm0UqnIQw8HzR2uyhaejzE3Ud5lkW3XESnVADXkhukFCoVNDYHtHYXVHoDXb9QKGeEIAj4R3/7X2FzeeeZ/c75qzP43T/8X5/4e3/nzh0UCgX8y3/5L6HRaGAwGPCNb3wDv/3bv41f/uVfPtX7B1l34GfA1p1t/O//t/8PuP4LjJpR49LLi3jpK7cwf2sOasIWgsVkAauvr+DeD5ZRyZal4zaPHZdeu4KlVy/D7iXLG5ljORyvxLD35hqOlqNDi/7Ry2FMvbKI4JUIcS+rrVINiTvbSHy4jcbAZp7OakLgxjQC12dh8pC1mSfwPCoHSeRX91DcPYTwvCxMmi1U9w9RicbRfcjCxDQRxGZ8H7a5SahICXwWBHSKRXTSabQymedmYXKRaTfaeOO//hjv/c8PcLB5KB03mA144UvXcfsrL2B8LkTUoupx+Uf+6QAuv3YV859bhJ6wLtRuq4OD97ex++YaMtsD+Uc6DcZfmMHUK4vwTI8SZZMkCAJqyTyOP9wWg1kH8o/MfidGb8zCf20aWsJqzXV7KO0cIre2h+qB3LCjVKtgnwlhZHESlpCPnOmFPlL+UewQbFOstQIAYzHDOinmHzFGsmotBbMmk+jk88M2Sf38I53LRVytKc+WVr6M3Noe8mtR8PUmTlpIdE4rRhYn4VyIQGsha0KV5zg0jpKoRONoJNKy3ZxSCWPQD+vkOIx+D3HfLbbRkOxOuZb8zFTp9TD0p5HVBsLuozyPXq2CTqmAXq0ybJNktkLrcIExW4mrNYVyUTnv61Ke58EwDJgBa2iFQoF8Po9qtQqr1Xpqv4vuqnxCel0WPM/DHXLhi/+nL+DWl2/ASNgLTLvewsZb67j3/WUcD2zwaPRazL2ygEtfvIKx+TGiHmqCIKBwkMHeW+uIvbOBdk1+gXGMuTH5ygIit+ehJ8wbme10kV6NIfHBFgrRxJAPtmcpjNEbs3BOBoiqNQA0cyXk1/aQX4+iN7DBo3NaJZskjZmsWvMch/phEtXoARrJtFRrhVIJU9APy8DChOM4IP74n3dRYBsN1BMJjJRKKA2MV5O8MLnodDtd/If/+3/CB9//SBJ7lSolFl+cx+2v3MKllwjMP8qWsfrGyoP5R04Lll69jEtfvAJXcOQMr/D04XkeybU49t4czj+CAvAvjGPqlQWErk9DrSWr1u1qA8mPdpD4cHs4/8ikh//aNEZvzsLiJyvrShAEVA/TyK/tobh9MJx/FPTAtTgJx+w41FqyLB/Ydge1/SNUo8PBrEoNA/N4ECm2jaXbt6AmSOQWBAGdchnNRELsOB7IP2KsVvG56/VCqSGr1pRnS6/ZRmEzhvxqFI20/MyEWoWRxUm4L03B6CPLgkwQBLRzBVSicdQOjsB3ZescncsBS2QclokgVITdR/leTwyPTybRLZel4wqVCnqvFwa/Hxq7nbhac62mmMNQLkLg5PuoSqeH1u6Cxu6AUk3W+xGFctFRKBT43T/8X8+1jdK1a9eg0+nwr//1v8Yv//Ivo1gs4t//+38PAGi321RsOEuWbi/g3/7g/4mVlRVcuXKFmK5fnuMRvbOLtTfuYfvdLTn0WKnAxJUILr16BTMvzoIhzAe7Waph7+0N7L25jvLABo/eakTkpXlMvrwAx5j7DK/w9BF4AYVoAokPt5C+FwU3sOh3RAIYvTED76UI1ITVutdso7ARQ251D82MvMGj1mnhnA/DtRSB0UvewqSVK4ie0AfH4Ac8PXUjTlgjIZjHn4+FiQoDC5NAABobWQHXJJFL5PHeX34IQMw/eumnb+GFL9+AxU6WxUa31cHm2xu494NlxFcP7ss/msOlL17B+KUwcZk4peMc9t5cx97b62gNBFxbfQ5MvrKIyZcXiMw/yqztI/HhFnLbR1KtT/KPAjdmMTIThJKQd8oTWsWKKOyvRdGtyrXW2sxw9W2SdKQFs3I86okUqnsHqCdSDwlmDcE46oMAILG8TMxziGu1YGw2kX/nHXBNOeCa5h9RTgue41DeO0Z+bQ/l6JGUI6dQKmANj8I5H8ZBo4DQtWvErM8BoFdvoHRwjGo0jl5NzpFTG/WwhEOwRsahsRJ2H+V5dAoF0SYpmwX6NpIAoHU6xfB4t5u4Zybf66JTKqJbyoPryJOtCrUaWpsTGrsTaj1tkKJQzjMKhQK6c2w3brFY8O/+3b/D7/zO7+ALX/gCxsbG8LWvfQ2rq6uwWE43a5iKDZ8CUhYGAJDZT2Pl+3ex8v2P0KnLCpwrOIJLr13B4hcuw+IkK+Ca7fQQv7OLvbfWkVw7gNBf9KsYFcauTmLyc4sILJIXcF3PlESbpDvbaA+EmRtcVozemEXg+jT0hIWZ8yyH8t4Rcmt7qMSOhxYmtkgQrsVJ2CZHiXtZ7dbqYg5DLI5eTd7gURsN/YVJiMiFSTufRyuZRCuXG1qYaJxOZLtdzNy4ATXtpjz3BMJ+/Or//is4Shzitb/+RaI2DXiOx+HaAe59fxlbb29IAdcAEFoax9IXrxCZf9SqNhF7ZwN7b62jMBBwrTXpEX5xDlOvLMBJYP5RaT+FxIfbSK3sgR0IPbaFvBi9OQPfZRLzjzoobO4jv7aHeiInHVdpGThmyc0/ahdKqEbjqO0fguvItdY6bLBOjsM8MQa1Tv5ek5BBwbMsWpkMmokEuqUSzAA4iMK+zuOBwe+n+UeUz8RJeHx+bQ+FjX2wA9liBo8TI0uTcM5NgDHqwXEc4sulx/y0iwPX7aF6cAjdfhLxtZh0XKFWwRwahSUyDoN3hLjvVq9WE22Sksmh8Hi1ySRORfl8UOnIemYKPIdupYxuqYBevSqfUCigsdigsTtFmyTCak2hUM6GbrcLlmXxR3/0R9J95T/+x/+IyclJ6PX6U/1dVGx4DqmXalj74T2s/mAFmX059FhvMWDx85dw6YtX4I34iHqoCbyA9PYR9t5cx/4HW2Db8gaPeyqAyVcWMPHCLLRGsl5guo02knd3kbizhcphVjqu1mvhuxzB6I1Z2MbJ2+CpJ3PiwmRzH9zABo/R64JrMQLnfJi4DR6u20Pt4AjVaBytrDylo1CrYQ6NwjoZgt5D1sJEEARpYdJKpR5cmAQCMPh8gFqNw+VlKAjZtBYFUuFj/72LzPSVSTRR//h/8YJQOM5j+6828ObvvY5aQV5MOvxOLH3xMpa+cBk2wjJxuB6Lo+Uodt9cw/G9fQgDllijVyKYemURo5fDxIUeN/IVJO5sI3lnG82BWuvsJoxen0XgxgyMI7azu8CnAM/xqMSOkV+LorR3KNUaCgWsE36MLE7CPjUGJWH5R71GE9VYHNVoHN2KHHqs0uskYV9rP71x9POAIAhSx3E7k5GC6wGgwzBwT03B4PPR/CPKZ6JTqSO/EUP+/vB4kx6uhQhci5MwjJD1zBR4Ac1UBpVoHPXDBASOkzZqDF43LJMhmMdGibuPPjI8nmGgHwiPJ239wjZqok1SpTTUIKU2GKGxu6Cx2aFUkVVrCoVyPvi7f/fv4p/8k3+Cr3/961hfX8fv//7v41d+5VdO/ffQO9hzQq/Tw857W7j3g2XE7kalxYFSrcLUjWkYx8340t/4SWgIs86ppIrYe2sde2+tozGw6De5rJh8ZQGTLy/AQtoGD8shtxlH4sMtZDfj0qJfoVRgZDaEwI0ZuOfHiQs97lTqkl1DuyTXmjEb5IWJy3Z2F/gUEHgejWQG1Wgc9aOEvMEDwODzwDoZgikYIG9h0m6jmUqhmUyCrcsb0kqNRlyY+P1DCxMSOkjvX5gEyCopkTQrDaz/eA2rP1hGcjchHdcZdZj/iSVc+uIVBGZGiVtAZ/eS2HtzDfvvbaHbHJiYDHsx+coiwrdmoSMs9LjX6iC1vIfEh9soHaSk4yotA+8lUdh3hP3EBVw3MwXkVvdQ2IiBHfCn1Y/YpGBWjYmsWvO9HmrxBKrROJppuYlDoVLBNBaANRKCwecmLutK6jhOpcB35FqrjUZxgsHjwerWFkJ+P3HTopRnA9fpobhzgPxaFNW4fB9VqFVwTIfgWpyEdZzA8PhSBZV+eDzXkq1zGIsJdR2D6ZdvQWchbBqZ49DMZsXw+ELhuQmP5zpt8T2+VADfkxuklBoNNDYntHYnVFqymuEoFMr5QqPR4Lvf/S5+53d+B9/+9rfhdDrxi7/4i/hbf+tvnfrvotsVBCPwPI42D3Hv+8vYfGsdnYFFf2BmFJe+eAXzn1uExqDF8vIyVAwZi4NOvYX8vWP8+f/YQC4qv6wyeg0mXpjF5CsL8EyNErforxxmcPzhNlLLu+gN1NoyOoLA9Rn4r05BS9gGD9vporh1gPx6FLVDeUpHyajFhcnSJCxjXqJeVgVBQKdUkWySuIGRco3VAstkCJaJEBjj6Y7BnTU8y6I9uDA5QamEvr8w0TqdRNUaALh2SwqIG1yYcGQPNlxY2B6L3fe3sfr6CvY+3BkKuHZF3Hj5a5/DzItzUBMmAFazZUT7wn4tW5aOGx3mfv7RImwB59ld4FOA5zjkto9EYX/9ADzbFzUVCrimRhG4MQPPYpi4gOtOtYHCRhT5tSha+bJ0nDHqxfyjxQgMbrKscwReQDOdRTV6gNphAgIrC9h67wis4RBMoVGoCAuu5zodMf8okRjqOFYwDAz9YFbGKtp7kCDqAyfCfh3tUh5ulQCB4wAqnjw1BJ5HNZ5Cbi2K0k4cfG8gPH7MK05FzYTIC49vtVGNHaIai6NTLEvHlVoNLONBWCLjYOwWrKysgDGSsXYTBAHdUgmWWg3ZH/1I/G710dhsMPj90Hs8xIXH8yyLbqWITqkArilb2yqUKmhsdjGHwWAi6plJoVDONzdv3sR/+S//5an/HrJWuxQAQDFZwL0fLGPtjXsoZ2TvSqvbhqVXL2Pp1ctwBlzScRIWCBzL4Xglhr0313C0HJU2eBRKBQJLE5h8eQFj1yahJmwh2CxWkbyzg8SdbTRyZem41mJA4NoMAjdmYPaRtcEj8Dwq+0nk1/ZQ3D0cWvRbQj64FifhmAkRt+hnmy1UY4eoxOLoluSRcpVWA/PEGKyT49A6yAo9FgQB3WJRtEnKZB6+MPF6oWTIqjXP9tAt9xcmLTloU6FSQWN1QG21YX9nF94zvEaKjCAISGwf4d4PVrDx4zW06y3pnG/Sj6VXr2D25XnsHuxi9so8MRkUXKeHnR/eQ+ztTWR2jqXjai2D8ZszmHxlAb7ZMeKE/Woij8SHW0je3UV3oNYmrwOjN2bgvzYNndV0hld5+nDdHorbceTXo6geJKXjCrUKjqkxseN4wk+c2Ct2HMdRi8XB3tdxbA2HYImEwJjICj0WOA6tR3Ucj4yIHccjI8TV+mEdxwaF+DwGYZuf54FmriROI6/H0KvL7zk6uwWupUm45sPQEhYez7Mc6kfiVFQjmZG/W0oFTKN+WCIhmAI+KPqZgSSszwGAbTSkqSiu1YIBohGoSq+HoT+NrCYsPF7gefRqFXRKBfRqFbnWABizFRq7ExqLjbj7KIVCoQxCxQZCaFab2PjxGlZfX0ZiW170a/RazL2ygEuvXsbYQoioh5ogCMhFU9h7ax37726i05AXgjqXCYtfuoHJl+ZhsJG16O+1OkjfiyLx4TaKMXnRr2TU8C5OIHBzFq6pUeJq3cwUkV+PorARQ68hb/DonNa+XUMYWgtZteZ7LOpHCVSicTRTGcmmX6FUwhQUFybGAFmTGwDQq9elHAauLX+vVXq9GBDn90NtIKPT6wSB59GrlvsLkyrkTAYFGIsV2pOAOKWyvwAlZwP3olJKF7H6+gpWX19BKVWUjpudFknYHxlzAyBn04BnORyv7mP3zTUcfrQ34M0P+BfGMfnyAkI3psAQ1oXaKtWQ/EgU9usDTRwakx7+a9MIXJ+BJeAiS+zleVTiKTGH4f6O46CnL+yPQ02Y/eYjO441DCwTY7BEQtC5CJvc6HccS8I+K9easVolYV9F2KY7z7KisF8e7jiGUgnGYkM8X4SNWpqcGt16E4WNfeTX99DMyM9MlU4D53wYI4uTMPoIu48KAlqZvDgVFT8euo/qXA5YIiFYxoNQDYTHkwDX7YpTUckkehW5QUqhUqGhVmN0cRE6p5O4WnPNBjplcRp5sEFKpdNDY3dCa3MS1yBFoVAoj4KKDRcYtsdi74MdrL6+gt0Pd6QRfoVSgfCVCJZevYKZF2fBELYQrOXKiL61gb231lEdWPTrbUZEbs9j4vYcDgsJLFy5QkwHqcDzyG3Gkbq7i8za/oBdA+CMBBC4MQvvUpi4RX+31kB+PYb8+h5aA5Mbar1WXJgsTcLgIexllRfQzGRRjcZRiyeGFv16twuWSAjm0ChUhG3mSXYNySR6VTlzQ6FWQ9+3a9DYyJvcYJt1dEsFdMslCPzAwkRvgPYkIE5NFybnhVa9hc0317H6+jKONg6l44xOg9mX5nHpi5cRWpyAUkWOACgIAvL7aUTfWkfs3S20a3IXqi3gxNQriwjfnofRQVYXKtvuisL+nW0UoglJ/1OqVXAvjGP0xixcM0Hi/Omb2SLya1HkN4Y7jrV2M1wLk3AtRqAjruOYRf0wiWosjkYyLWu9SgVMAZ8o7I/6iKt1r9FAK5lEM5kcFvZ1OlFg8PvBPFcdxxYxmNViBS8AnVzpMT+J8iRwPRal3UPk1/ZQ2U9K/38rlErYIqNwLU7CFhmFUk3Wd6tbqaHStztlG/J9VG00SOHxGitZ91GB59HO5cTw+FxuaCpK63SK7/FOJ1ZWV6Gx24l5n1dDQDubRq9SBN+VrW0VagZauwMamxNqPVkNUhQKhfIkULHhgiEIAo63jrD6+oN2DZ6wF5devYKFzy/BZCfrBabTaOPg/W3svb2OzMDkhlrDIHRjCpMvL8C3EIKy3/V7WEg85qddDARBQPU4h6MPt5D/cAvZdk86Z/LYxRyGa9PQE1brR9o1qFSwTwX7dg0BojbzANGuoRqLoxo7BNuUv9eM2QhLOARLOAQNYZMbkl1DKoVOPj9s1+ByyXYNhG3wPDIgjmHkgDgdWZkbFxmux2Lvzq4o7L+/DW7Am3/i8oRok3R7Dho9WZ2J9XwFe29vIPrWOioDkxs6iwETL86Cdapw+8ufg1pNzqskz/Eo7h4j8eE20mv7Q12ojrAfgesz8F6OgCGs1t16E4X1GPLrUTSzcq3VOi0c8xNwLURg8o8QszkE9DuO0zlUYnHUn7eO41QKzVRquONYrYbe4xE3BAnaCAROhP2GKOxX7u84NkgbgkMdx4RMo50FgiCgephGYS2KwvYB+O7A+sU/AtdiBI65CTB6sqZG2HYHtf0jVKMHaBdkoUrJqGEeD8ISDkHvIW9yo1suS/eUoakos1kULX0+qLTifZSYKU+ORbdcQqeUR5ABOrl+RqRCCY3VBq3dCbXJQlStKRQK5ZNCzgqRcIrJAlbfWMHaG/eG7RocZiz27RrcIc8ZXuHpw7EcEvdi2HtrA0fLe+B6cje/by6EyVcWMH5jmrjJjVaphsRHO0jeb9dg1Ml2DaOELfp5HpWDFPLreyjtHA7bNYx6xIXJ7DjUhC36H2fXYB4PwhoJQTdC2OTG4+waLBZ5YUKcXUOvvzApgGsN2zVorHZxYWI0E1Xri4wgCEjuHEs5DK2Bbn73uAdLr17GwucvweK0nOFVnj7dZgcHH2xj7611pLeOpOMqRo3Q9SlEXl5AYHEcAgQsLy8T8fcqCAKqyTxqdw7ww/++MpTDYByxicL+9WkYHGTVmuv2UNo5RH49isrBfR3HkwMdx4SJvZ1yFdVoHNX9ONgBS0bGZJCFfdI6jjlO7ji+T9g/6TjWu93kCvvlh3UcO8VgVirsnxrNfBmF9Sjy61F0q/J7jtZqgmshAudiBHqH9Qyv8PThOQ6NoyQq0TgaifTQd8sY8MISDsEU9BM3ucE2GmimUuJUVEu+jyq1WimHgTETdh89mYoqF9GrlqVaCwLAmMzQOpzQWOzE3UcpFArl00LFhnPMo3IYGJ0Gs7fnsPTFKxhfIs+uIRdNIfq2aNfQGVj02wJOTL68iMhL5Nk19NpdpFf2kLizg2JUnspQqlVwz4+jbVfj5k/9BBiCNmAFQUAjXXh4DoPdAtdiBM4FAu0aHpHDQLxdw6NyGE7sGnw+MCbCJjdOchjKRfSqFcjF7ts12JzQWG1QKMmq9UWmlC5h7Q0xh6GYLEjHTXYTFj9/CUtfvALPBFnR3Cc5DNG3N3D40R64AbHXOxfE5MuLGL85PTS5QUJ3YqtUQ/LurpjDkJabOBiDDv6rUwjcmIE16CZCUDlB4HlU+zkMxftyGEyBEbgWJ+GcnYCasMmNRwr7Jx3HkRD0bgI7jh8n7Pt8Qx3HpPC4HAaN1Q6tzQm1iQr7p0W33kRhcx/5tSiaGfmZqdJq4Jgdx8jiJEyjhN1HT3IYYnHUDo7B9+TJDa3TDms4BPNEEGrCJjcel8Og609FaR3k5dnIOQwlCJx8H1Xp9GCsduwcJ7E0PkmMdTOFQqGcFlRsOGew3R52+zkMe3d2h3IYJi6HpRwG0uwaqtkyom+vI/rWxnAOg9WI8ItzmHx5AY4QWS+rPMchv32ExJ3t4RwGAI5IAIHr0/BeikCpUWN5eZmYzedurQH+IIv1u3+Gdr4sHVfrtXDOTcBFYkAcL6CZzooLk/tyGHQjTrGbcnyUPLuGTke2a7g/h4Fku4ZGHd3yQ3IYdAaxm9LmoAFx54hWrYmNN9ex+voKjjcHchi0DGZuz2Hp1cuYuBwm5h4M9HMYYmnsvbWO/fc20a4NCPt+JyIvLyByex4mF1nd/L1+DkPyITkMjM+K+S++AM/8OHFdqI1MEfn1PVHYH2ji0NrMcC1E4FoIQ0dax/FQDkPmgY5jayQEY9BP1Pca6Av7qRRaD8lh0J90HJMo7D8qh8FkgcZOhf3ThOv2xByG9eh9OQwKWMOjcC1GYJ8MQkmQxR4AdCr9qajY4X05DHppKkprI+uZKfA82n2706EcBkCaitK53cTVmut00C0X0CkVHpyKsjmg6dud8jwP7jh1hldKoVAo5xeyngwXFIHncbR5iNXXV7D55jraDXlx4Al7RbuGn7gEM2Hd/J16C/vvi3YN2V25m/8khyHy0gL8CyHiJjcqR1kk7mwjtbw3ZNdg8tjhvz6DwH05DCR0kLKdLkonOQxx8aWsjX4Ow2QQrsUIrOEAUYt+QRDEHIZoHNX9Q3At+XvNmIyiHzSBOQw8y0oLk06h8EAOg97vh57EHIZ2S7ZreEgOA7VrOF+wPRZ7fWF/98OdIWF//FIYS69eJjKHoZotI/b2BvbeXkc1LQv7OotBEvad4x6iBMDHCvthP/zXp+FenMD69ibcC+PEPIeEdhep99ZQ3NxHayDoVq3TwjE3AddCGKYAWU0cAi+gmcmiGn2IsN/PYTCPB4mzZOQ6Hbnj+HkS9pv1fg5D6b4cBj00die0NgeUDDkTwWeJNBW13p+K6g5MRflH4FyIwDk3AcZAVjc/22qjtn+ESiyOzv05DKFRcSrKQ5i1bT+HoZlMopVOf2wOAynwLItupYhuqQi2WZdP9HMYNHYnGJrDQKFQKE8MFRvOkPxRTsphqGTL0nGzy4Klz1/CIoE5DGyXxfFKFHtvb+B4OQqe48UTCsA/H0Lk5QWMX58iLnyxWaj0cxh20MiVpeMak160ayAwh4HneFT2E8ivR1HaPYQwsMEDmxGhF5bgmg8Tt+jvNZqSXUO3LC/6lVoNLONBWMJjROYwdAoFsfMpkxla9DNWq2zXQJANGADwvZ5s19CSu9wUShUYqx1au4PmMJwjZGH/HjbfXBsW9idOhP0lmAnLYXiUsK/S9HMYXhJzGJ4XYd/otiNwfRr+a3IOAwmiPiAL+7m1PXCHaZxUW6FSwhYRhX3SchgkYT8WRy12CPZhwv7EGHE5DB8r7Pt8ZOYwtFvolIvolgpDwr6CYaClwv6pIggCmtmSmMOwEUOvLr/nED0V1WNRP3r0VJQlEoJplLwchl69Lk0jD+YwED8VdWJ3et9UlNpkFqeRaQ4DhUKhfCqo2PCMqZdqWP/RKtbeuIfUXlI6rtFrMPvSApZevYwQaYt+XkB65wjRtzZw8ME2uk15HNEx5kbk9hzCt8nLYeg22kit7CF5Zxulg7R0XMmo4V2cgP/6DFzTQbJqLQhopPLIr0VR2IyBbcm11jmtcC1EYJ8dx+b+HkYuT5Pjb8nxqO4doH5whGY6Jx1WKJUwBv2whMdgCvigIKzWvVoNrWQSzXQafEeutUqvh8Hvh8Hng9poPMOrPH0EnkO3Uka3XECvVh04oxBzGOxOaCw2KJTk1Pqi80hh32nB4hcuYekLl+Eep8I+CXycsO+/PgPrcyTsmwJujCxNwjE7/vwI+xpGzmF4HoV9r5e8juNeD91KEZ3SsLAv5TDYnVTYP0U61ToKGzHk16NoDdxHVTqNaHe6ECFzKirdn4o6PB66j+qcdnEqamKMuPvoI+1OVSrovV4YfD5oCMxheLTdKZ2KolAolNOCig3PgG6rg+13t7D6xgr2l6MQeFE1V6qUiFybxOKrlzF9cwaMjqyHWimRR/StdUTf3kCjWJOOGx1mhG/PI/LSPBzBkTO8wtOH67HIbhwgeWcH2a04BGmDRwHX1CgC12fgWZyAmrBat0tVMeh5PYZ2SX5ZZYz6fg5DBAaPuOgnpYNU4Hg0EmlUogcwHiWRFQ6kc3rPCCzhMZjHR4nr5mdbLXFhkkyCbcjhiwqGgcHrhd7vh8ZqJW5h0qtX+3YNZUDgpXMqg1HsprTZoVTTHIbzQq1Yw8aPVrH6xgrSUdlPV6PXYu7leSx+4fkS9u3BEUy+vIDwi3NkCvvLu0jc2UE5PizsexYnELg+3Rf2CRG3Id6T6skcCutRFDb3HxD2HXMTyKCN2dsvkCPqAx8j7PtgCYdgDHiJq7Uk7KdS4LtyN/+JsK/3+cA8l8K+leYwnBJsu4Ni3+60dijfRxUqJWyTQbjm+1NRBHXzC4KATrEsipaPsjsleSoqmRSnok6gdqd0KopCoTxXFItF/NzP/Rx+67d+C7du3QIArKys4Ld+67ewt7cHu92Ob3zjG/ibf/NvfqqfT8WGpwTPcdhfjmH1jXvYfncTvbb8UAvMjGLp1cuYe2URRitZi4NmqYbou5uIvb2BQjwrHWf0Gky8MIPISwvwzgShUBK0EckLKMYSSNzZQfpeFOxArS0BF/zXZ+C/MgUdYbXuNdsobMaQX4+hkZQX/UpGDfv0GFwLk7CO+4jq8BYEAa1coe8HfQy+I9ZaAYCxmmHtL0wYE1m15ns90Q86lUK3JHvWQqmEbmREDIhzuYirNddqolMWFyaDnrVKjRYamwNauxMqLVn+xBeZTrOD7Xc3sfbGCvZXYg8K+1+4jOkXCBT2j3PYe2sDsXeeP2E/cWcbuc1DCPxzIuwXK8ivix3HnbJc6/uFfZ7nkV1ePrsLPUUEjkc9kUI1GqfCPp4jYb9aBngq7D9NeI4Dn60g+t9+iHL0eGhSxhz0wrUYgWMmRFw3f6/eQHX/ENXoIbqVh9mdhqAbIaybn+flqahs9sGpKL9fnIoi7D7K97qS7RrXlq2hFEoVNFY7NNTulEKhnDKCIKA1IF4/bfR63ae6h925cwe/9mu/hsPDQ+lYpVLB3/t7fw//4B/8A/zcz/0cPvjgA/zyL/8yZmZmcOnSpU/8O6jYcIoIgoDkbgJrb9zDxo9X0SjLiwO7z4GlVy9j8fOX4PA7z/AqT59uq4P4hzvYe2sDqc040Lc7VKqUGL0cRuSlBQSvhKHWkLM4EAQBtVRB9IO+u4t2Ra61zmaC/9o0AtenYfaSVWuu20Np9xCFjRjKscSQj6l13A/nQhiO6RBUBNUaADrlKqqxQ9T240OetSq9DubxUSS7LSy9+ALUaoJuqYKAdiYj/i+XG/Ix1Tgcol2DxwMlQ1atuU4H3XIBnXIRfEd+UVCo1NDY7NDanFAZjHRhck7gWA77y1Gsvr6C7Xe3wHZ70rnR2SAWv3CJSGG/Uawh9u4mom9voHgoC/sagxbjN2cQeXke3mnShH0ehb0Ekh/tIL0aGxb2R0cQuDYN39Up6Cxk1brXaKGwuY/8ehSNVF46Lgr7IbgWImQK+9m8+Nw9OALf/14rAGisZlgi431h33C2F3rK8N0uWpkMmskkuuWyfEKphH5kBHpihf0GOqUiuhUq7D9Ltv74e+AzRZy0kOhdNrgWInDOh6G1kuXNz3W6qB0coRo7RCsr30cVSiVMQb80FUWc3WmlgmYqhVY6PTwVZTDA4PMRaXfKcyx6lTI65QLYuizKQ6EAY7ZCa3OAoXanFArlKSAIAv723/gVLN9Ze2a/8+qNJfzhn/zeJ9qb+K//9b/iO9/5Dv7xP/7H+NVf/VXp+F/+5V/CZrPh53/+5wEAt2/fxs/8zM/gj//4j6nYcFYUkwWs/fAe1t64h2JSHkc0WAyY/9will69DP/0KFGbUxzLIXEvhr23N3B0NwquJy8O3FMBRF6ax8QLs9CZyRpHbJVqSH60g8RHO6ini9JxtV4L3+UIAtdmYJ/wEbfBUzlIiX7QO3HwA7U2el1wLYThmJuAhrBFP9tsobp/hGosjk6xLB1XqNUwhwKwhEMweN3gBR7Hy8tEfL8FQUC3VEIjkYC7WER5YLxabTJJnU9qPVnfa57toVsuoVsugG3KwiEUCmgsYucTY7ZAoaALk/OAIAhIbB9j7Y0VbLy5juaA2OvwO7H4hUtECvudRhvxD3cQfXsDqa3DYWH/SgSTL81j9HIEag05r3aCIKCayCP50TaSd/fQqcq11tvN8F8Tg57NXscZXuXpw3V7KO0cIr8RRWU/OSzsT/jF/KOpMfKE/VJF7DiOHYJtPFzYjxAm7Asch1YuJ3Yc3yfsax0OMeiZSGG/jW5ZzGHgu7INmCjsO6C1O6DSU2H/aaJUqwAtA8/SFEaWJmFwk9XNz7McGsdJVGOHqCdSAC9/twzeEVjCIZhCo8TdR9lGA82ToOemfB9VajRSDgND2lQUz6NXq4o5DNXycNCzwQSN3QGN1QElQc8OCoVyPrkI99ZXXnkFP/MzPwO1Wj0kNuzu7mJ6enro352cnMSf/MmffKrfQ++4n5JOvYMP/8d7WP/RKpI7Cem4WsNg5sVZLL16GRNXIlCR5G3JC8jsJhB7ZwP7722h05C7fq0+ByIvLyByew7mEdvZXeRToNtsI70SRfKjHRRjcqi3UqWEe2EC/mvTGJkLkVXrk6Dn9SiKWwfoNeTRU63NDNd8GM6FCPRO6xle5enDdXuoHyZQjcXRTGelzTwoFDAGvOLCJOgbflm94BEUgiCArdWkzieuLX6vlQCUWq3Y+eT3gzGT5Vkr+0EX+37QAwsTk1m0a7DaifOsvcgUjvNYfWMF6z9aRSkli70GqxELn1vE4quX4Z8KXIiXvCeF67E4Wokh+vYGjlei4HryDcczPYrIS3OYeGEWWhNZAmCzUEXy7g6SH+2gnpGt2xi9Ft4rkwhcm4Z9nCxhn+d4VA8SyK/HUNo9HBb2fS6x43huAoyRrFr3Gi3U9sWg506pIh1XMmqYxkZhiYzB4CFP2O8Ui7DUasj+6EfDliZmM/T9jmOVjqxuflHYL6JTLoIbEvaV0Fht0NiosP8smf1fvoLl5WUEr1whJ9tFENBMZVGPH6MePx66j2rtVljCIZgngmCMZDVIcZ2OZHfaq8j3UYVSCZ3HA4PPB63TSVQ3vyAIYJt1dE+mogbuo0qtDlqbAxq7EyoNWTZgFArl/KJQKPCHf/J7595GaWTk4fa6jUYD+vuaSnU6HZoDwvUngYoNn5BqoYr/3//7vyF2Zw9CXzVXKBUIX4lg8QuXMfPiLDR6sh5qpeM8ou+IftD1vOxtqbcaEX5xDpGX5+EMeYhYAJ7A9Vhk1/eR+GgHua1DOegZgCMSQOD6NLyXImAIq/WJH3RhYzjoWa3Xwjk3AedCBCb/CFG1PvGDrsUOUT9ODtVaN+IQFybjQeI8ayU/6FQKbL0uHVeo1dC53UjU65i7eZOsDlJBQLdWebgftN4gdlPaHFAyZHnWXmTqxRq23trA2g/vIbUni72MToOZF2ex+PlLRAr76e0jRN9+MOjZFnAh8tI8wi/OwTxCltjbrbeQuhdF8s42SgcDQc9qFdwL4whcm8bIbIi4gFIx6DmGwtY+2Ka8ONHazXDNR+BaCEPnIKvWXLeLWjyBWiw+FPQMpQKmgA+W8BiMo/7hWhMg7EtBz+k0+E4HBogyt0qnkwQG4oR9jkO3+rCgZ4AxnQQ926iwT/nUnAQ9V6JxGHYPkVzfl86pjQZYwmOwTIxBayfrPioFPadSYtDz4FSU0ynmqbndxHXzs+2W+B5/X9CzQs2IAoPNAZXeQNRalUKhXBwUCgUMhovZGKTX61Gr1YaOtdttGD+l3R5ZT59nwO57W4h+uAsA8E0FsPSFy5j/3CJMdrK8LeuFquQHXTqSF4JqHYPxGzOIvDQP3/wYlAR1SPAcj8LeMZIf7SKzGgXbkb2/zT6naNdwdQp6O1kLwW69icLmPgrrMTTSA37QahXsU2NwLkRgnQhASZiPaSuTR3V/2A8aADQWM8z9hYnGQtb3mut20X6YH7RCMRT0zAPYJ6iDlG024FAKqG2vQeAe4gdtc0Clu5gvBaSy9fYG3v+Tt/EXB/9dCnpWKJWIXItg8fOXMf3iLDQEhf8KgoDiUQ6xtzcQe3dzKOjZYDchcnse4X7QMwnfyxPYTk8S9vPbRwNBz4BzchSBa9PwXIoQF+rdypeR34iisB5DpyKLvWqDTgx6XojA6HMRVWue49A4TqEaO0TjOCXXGoDe7RKDnkOjUJEm7Deb4uRgKjUc9KxWo6FWY3RhATqnk6haCzzfD3ou9i1NBoV9I7QnliaEWUNRni3dal18j98/RLciPjOVAJQaBubxICzhMejdZN1HBZ5HO59H6yToeeA+ylgsctCzlqz7KNftoFsuolsuDgU9Q6mExmqH1uaA2mQhqtYUCoXyrJmensZbb701dGxvbw9TU1Of6udRseETcvnL16AxaFHqlvHya6+QM3YKoFNv4fCjPcTe3kB6+1g6rlQpMXopjPBL8xi7EoFaS87iQBAEVI6ySH60g+TyLro1+QVG9IOegv/qNMw+sry/2U4XpZ048usxVOOpB/2g5yOwT5PlBy0IAjqlimjXsH8IdsAaSqXXwTIxBkt4DFqHjaiXVZ7j0M5mxYVJPv9kQc/cBW8hBcC1W+j0FyZ8twOrChA4Vgp61ticUNOg53NJZj+N//r/+P9K/0xy0HMtV0bsnU1E39lAOSFnpIhBz9OI3J6HZzZImLDPIb9zJAr7azFwXVkAtIyOiML+lSnoCKt1t9ZAYSOG/HoMzaxsAyYGPY/BNR+BZdxPlrDPC2hmsqJn+n2WJhqbReo4Zkxk1fpRliZQKqF3u6H3+cDY7Vi5dw8au52I55AgCNAqBLSSR+hVyw8I+1q7U+w4pkHPlM8A22pLQc/tvHwfVaiUMI76kBNYLL58G2ri1i8lcRo5nYbQkxukToKe9T4fGNKCnlkW3Uo/T60hi/InQc8am0OciiLo/YhCoVDOki9/+cv43d/9XfzhH/4hfv7nfx537tzBn/3Zn+G73/3up/p5VGz4hKgZNeY/t4jl5eWzvpRTge30EP9oF/v/cwWr8dfBD1jIeGdGEX5pHhM3Z4jzg67nyqLA8NEOmnl5IcgYtPBdnoSfRD9olkM5dozCRgylvSMIrLyhbPSPiDkMBPpBd2sNSWDoluURfiWjhik0CktY9IMmqdYCz6NTKIiBk9ns8+MH3etKAgPXGvAWVChR53i4x8PQWm3UD/qc4xx14dbXbqNYKeG1n/tJuAKus76kU6VVbWL//S3E3tlEdlfOfFIxKgSvRBB+cY7IoOfyQRqJj3aQXtlDdyDzyeC0SJODJg9ZQc9su4Pidhz59Shqh7I1lEKpgHViFM6FMOyTQfKE/WIZ1Vgc1f0jcAO+tWqjHpaJMZj7liYkbLKf8HGWJlLQc9/ShCNA1AcAttWUchj8aqBbEidkFWoGGpsdWpuTWppQPhN8r4faYVLMU0tlBxqkAIPPA8vEGExjAUClRHp5GQpCBNtevQ5To4HcW2+Bb8v3USno2e8HYyGrm1/gOXSrlX6eWmU46Nlogqafp0aaNRSFQqGcB+x2O/7gD/4Av/3bv43vfOc7cDgc+I3f+A28+OKLn+rn0Tv1cwjPckhuxBF9ZxPxOztg23KHhGPMjfDtOYRfnIPJaTnDqzx92pU6kst7SH60g+qxbA2lZNTwLEzAf30aI9NBsvygeR7VowwK61EUt+PgOrK3pc5hhWshDOd8GDo7WbVmW23U4sdi51NO7hZWKJVS0LNx1EdWrQUB3XIZrX7QMz/Y+aTXy51PJrKsoeTOpyLYxqDHoAKM2SJ2UprM2L+3ioDZSoWGC4CaUeOL/9efxPLyMuxe+1lfzqnQa3dR2krhr34QQ3L9QLKGggLwzYUQeWkeoetT0BrJEgBr6QKSH+0ieXcHrQFrKI1JD18/6Nk6RlbmE99jUYoeobAeQzl2PJQDZB71wLkQhmNmHIyBrFp3KzVU+8J+ryp3oUqWJhNj0HvItTRpZbNDOUCM1So+d58jSxNeALR2B3R2J7U0oXwmeI5DI5FGbf8Q9aPUUNOMzuUQbdfGg1Dr5fsoCQLe/XlqJgA8AIVKBb3HA73PB63DQVQ3vyDw6NVq6JYfkqem04sTDDYnVBqy7BQpFArlPLC9vT30z0tLS/jP//k/n8rPpmLDc4IgCMjuJRF7ZwP7722jXZO7fo1OC4wTdrz4tS/ANeY5w6s8fXrNNtL3Ykje3UEhmhCT+CB2Fbqmg/Bfm4ZncQJqLTkvMIIgoJkpiEHPm/vo1eVaMyYDnPMTcM2HYfCQ5RF80vlU2z9EI5kZ6oYx+Nxi51MoQNzLaq9Wk/yguYd1Pvl8YKxkdZAKPI9etYzOwzqfDCZo7I5+55PYLUzCApRy8eBYDol7MUTf2cTh3b0huyDXhFcU9m/NwkBYDlCzWEXq7i6Sd3dRS8lir0rLwLsUhv/qNJxTo0TZBfEcj2o8icJ6DMXdOPiBWutH7OLk4HwYWitZYi/bbKG6f4Ta/iHahZJ0XKFSwhT0wzIRgjHgJabTGOgL+8Wi+NzNZCCwcq3VBoM0OagmztKkh265L+w3H7Q0YSw2bOwf4HIgRJTFLOXZIfACWpmcmMMQPx7KU2MsJljCIVgmgtBYyHpmcp0OWpkMWqnUA3lqbYaBd2YGBo+HqBB1QRDQa9T6omVp2HaN0Yjv8TYn1DRPjUKhUC4sVGwgnOJRDrF3xMDJel62kNGZDZi4NYvI7Tk4JjxYWVmBnRCrCq7bQztewN2Vv0Bu63Coq9A+4YP/2jS8lyLEWUO1ihUUNmIobMTQLsq1Vmk1cMyOwzUfhjnoIaobZrjzKTlUa53TDvPEGCwTQagNZNWabTYlP2i2Li/6FSoVdB4PDER3PhXRrZYe0fnkgEpDVgcp5WLB8zwyW0eIvrOJgw93huyCNFY95r5wBZMvzcNKWA5Qp9ZEaiWK1N1dlA5S0nGFSomR2TEErk3DPT9OnF1QPZEVn7tbB2CbA7W2mGRh302WNRTX6aIWP0Zt/wjNdFY+oVDA6PfAPDEG85ifqPBfQRDQq1Ylz3S+05HOKbVaeXLQbCZL2Oc4dKtldMsF9GrVoXNqkxlamwOMRbQ04TgOAuJndKWUi4ogCOgUSv2pqPts1/Q68T2exDw1lkU7kxFt14rFYds1hwN6nw8alwv31tcxTpDQwLVbsCsF1HY3IPTkaXsxT018j6d5ahQKhUIGVGwgkJPAydi7mygd56Xjah2D8evTCN+eh38hJHUVktD1KwVO3t1FZnUf3EA3jNnn7AdOTkLvIMsuqFNtoLi5j/xGDM2M3EGqVKtgmxqDaz4M60SALLsgXkAznUVt/xC1eGLILoixmMSg54kxaKwEdj6l02il0w90PulGRmDw+aAbGSFmQQKIi1C2WX9059PJwkRvOMOrpDzvCIKAwn4a0Xc2sf/+FpolWQA02E2YeGEW47dmcFRO4crVq8R0/fI9Fok728isRJHfORqyhnJGAvBfnYb3Upg4u6Bmtoj8RgyFjX10B+yC1AYdnLPjcM6HYQq4idos4VkW9aOUKOwnUqJfTh+92ykKDONBqHVkib29RkO0SEqlwDblKVGFWi1NDpIS8HyCwPPo1Sp9Yb8CCAPCvt4Abf+5q2TImhKlPFsea7vWz1PTu0fIylPjONl2LZcbtl2zWGTbtX6eGgnrc0Bcv3QrRXRLBXCdNmwqiEKDUgmN1Q6NzQGG2q5RKBQKcVCxgRCa5Tr2399G7J1N5KJJ6bhSrcLo5TAit+cQvByBWktQpxkvoLifRPLuLtIrUfQGugqVRi3Gb80jcG0GZsI6SHvNNorbByhsxFA7ysgnFApYJ/xwzUdgnxqDiqRaCwLa+aI4Wn1w/PDOp4kgtE6yFv18r4dWNovWSeDkACedT3qPh7gOUq7dkvyg+cHOJ7UaGivtfKKcD8rJAmLvbiL2ziaqGdlCRmPUYfzmNCIvzsEzG4RSqQTHcTheTj/mp10MuB6L3GYciY92kNvYR46TN52tQTf8V6fguzIJHWF2Qe1SFYXNfRQ2Ymjly9JxpUYNx3QIzvkwLCE/UdZQAsejkUyjun+E+lECAitvfGns1r6wHwRjIssuiG21RGE/lUKvJueMKJRK6Nxu6H0+6FwuwiYHBbD1qmhNWClD4OVaKzVaaO1OcXJQS5ZwSHm29OpN1A6OUN0/RKdYlo4rVKq+7doYDAEPlIQI8oAo3nWKRSnX5bmxXet10a2U0CkXwTUb8gmFAg1OwMjYOLQ2siawKRQKhTIMFRsuMJ1GG/EPdxB9ZxPpzUMIwn2Bk7fnELoxTVTgpCAIqB7nkLy7i9TyHtoVuRtGY9bDd3kS3ssR7BdTmCKog1RgORTWYyhtH6Cyn5A7SNEPnJwPwzFLXuBkp1QRBYb9I/Tq8suqUqsRO58mgmR2PuVyaKZSaOfzz0/gZKeNbrmITrkIviOLSQqlCozVBq3NQQMnKWdOPV9F7D1RYCgeyhYyag2D4LUIIrfnEViagIqgaTKe41HYPRYnB9diYNuyAGhwWRG4Ng3ftWmYRmxnd5FPgW6ticKWKDA0UvKUqEKlhC08CudCGLZIECqGnFfpIc/0wwT4jlxrxmToC/tj0NqtZ3iVpw/X7UoCw/2Tg1qnU5wcdLuhVBNUa0EA22yIwn6lOLQJqmAYaPvCvkpvoM9dyqeGbXdQOzhCbf8Irax8HyXddq1bLkvTyHxXvo9KtmteLxgLWe+0PMuiVxUFBrZeGzon2q45oTKZsb+6Br/VToUGCoVCIRxy3pqfE3qdLo7uRhF7ZxPH92LgBzzqRyI+hF+cw8StWRhsZHUV1jMlJO/uIHl3F818RTqu1mngXQrDd3UazskAlCqxg1RRuvgdpDzLohxNIL8RBbd7iP0BgcHgccA5H4ZzdoK4wMlurY7a/hGqB0foluRaK9QqmIIBWCaCMPoJC5zkeXQKBVFgyGYhDIxOq41GyQ9abSDLLojrdsXR6nIRXEu2qIBCAcYiCgyM2UoXJJQzpVVpiJOD724iu5uQjitUSowujiN8ex5j1ybB6MixFRF4AaWDlDg5eC+Kbr0lndNZTfBejqBq4HDj1ZehJmgDlm11UNg7QmEjhurhwHuEQgFLyAfn3AQcMyGi7ILEycESavuHqB4Me6ardFqYJ4KwTIxB53IQtzHWzmTQTKfFycEBz3SN3S4KDB4PVBqCvtePmxxUqSVLE7XRRFStKc8WrttD/TCB6v4hmqns0HdL73HBMjEGU2iUuPsoW6uh2RctufbAtD3DQO/1Qu/1Emi7xqFbFW3XerXKUK1VBqNou2Z1SGISKdZQFAqFQvl4yFkhEgzXY5FYO0DsnU0c3t0D25E96u2jLlFgeHEOFrft7C7yKdAsVpFa3kPy7g5qyYE8AkYNz/w4fFenMDIXIq6DtBpPorCxj9JuHNxArbV2M1zzETjnJ6B32s7uIp8CvUZL7Hw6OEI7X5RPKBUw+r2whMdgGvVDSVIHqSCgWyyKC5NMBsJA9oRKp5NHq01kLfp5toduuYRuuQi2WR86x5gtYg6DxU5U9gTl4tFptBG/s4vYu5tIrceHJwdnxxB+cQ7jN6ehNZETPi8IAqqJPFJ3d5Fc3kW7PDA5aNTBe3kS/qtTsI/7wAs8lpeXibg3cd0eCtsH4O7uY+X7axAGpslMgRE458Jwzk2AMZJTa+BkcvAItYND9GoDk4N9z3TzRBAGj/v5mRy0WMTn7oBnOik8anIQSiU0FpvomW62QKGgwj7l08GzLBrHaVT3D9E4Tg3dR7VOOywTQZjHg2CMZDXNSLku6TTYhnwfVahU0Hk8MHi90DqdRDXNiLku1X6uS3k410WnF9/jrQ7iJrApFAqF8skgZ+eOMHiOR2rzELF3NxH/cAfdZkc6ZxqxIvziHCK352AfHTnDqzx92tUG0it7SN7dRTku5xEolEq4ZoLwX52CZ2ECapI6SAUBtaMMCpsxFLcOwLbkWmvMBthnx5FXdrH4udtEdZBy7Q5qxylU94/QyuTkEwrA4HX3R6sDUGnJqnWvUkEzlUIrkwHfkWut1GjEziefDxqrlYhNvBN4jkWvUu6PVleHzqmNpv7CxA6lmpwxesrFQ5ocfG8Lxysx8AMe9a6wD5EX5zBxawYGO1nh8/VMUbImbOTK0nG1loFnKQL/1Uk4p0aHfbQveHMi32NRjh2jsLmP8t7RUK0Nbgec8xPi5KCNrFp3q3XJM71blu/FCrUKplFyPdPb+Txa6fRDJwdPnrsMYZ7pXLcjCvuVh0wOmq19Yd8KhZKcWlOeLVKuy8ER6ofJISsujdUs5alpLGTdR6Vcl3QaverAO61SCZ3LJU5FjYwQ1TTzcbkuGpsdGpsTah1ZojyFQqFQPj3k7FwSgMALyOwmsP/eJvbf30a7Ki8O9DYjwrdmEX5xHq6wl6iNyG6jjfRqFKm7uyhEE8DJBKYCcEYC8F2dgncpAg1h2RONdB6FjX0UtvbRq8m1Vht0cMyOwzUXhmnUDZ7nUSCog7QaP4LuIIX99f3h0Wq3E+bxMZjHR6HWk1VrabQ6nQbXku1IFGo19B4P9D4ftA6yLCoeO1qtN0Bjc0BrdUBJkEUF5eLBdlkkVvcRe3cTR3ejYLvyhJEt4EL49hzCt2Zh8djP8CpPn2ahguTyHlJ3d1FLDUwOqlVwz4/DfzI5SNA0Gc/xqB4kUdiIobh7CH6g1lqbGV27HvNfeBEmj/MMr/L06TWakmd6uyAHmSuUShgDXpgngkRODkqhrJnM0CaoSq+H3uuFweuF2mwm6rnL93roVh4zOWh1gLHaoFSRU2vKs0XgeTTTOfGeEj8euo8yJoP4Hj8RhNZOVtMM1+lIAsPDcl30Xi/0bjdx2RNss97PdSkN57qoGfE9nua6UCgUCuUR0LfNM0YQBBT204i9t4X997bQKMqBSlqTHhMvzGDixVl4pkehJGgEs9fuIru2j+TyLvLbR0PjtraQB/6rU/BenoTOQk6nmSAIaOVKKGzuo7C5j05ZrrVKq4FjOgTn/AQsIR9R47Z8j0X9OIXa/iEaiTQEnpduPFqHDZYJUWBgTOTUGgB69bq0MHlgtNrtht7rhc7lIqrW4mh1pT9aXRkerdbqxE5KmwMqLTliEuXiwbMcEutx7L+3hfidHfRasm+52W3DxK1ZhF+cgyNI2ORgpS5aEy7vojIQbq1QKTEyMwbflUnyJgd5HtWjDIqbMRS34mDbA5ODFiOccxNwzk1A67JhZWUFepft7C72FGHbbagLFRz/5Q/RzspiEhQKGHxuWMaDMIUCxOURPDKUtT85aPD5wJA2OciyosBQeUgoK50cpJwC4vqlgNq+aHfKDdxHVXodLONBmCeC5OW6dLtoZbNoneS6DEB0rkurKQkMNNeFQqFQKJ8WKjacAYIgoHSUkwSGWrYsnWP0GoSuTyP84iz88yEoCcojYDs9ZDcOkFrZQ24zPmRbYPY74b8yBd+VKRicljO8ytOnVShLAkO7IAceKxk17JNBOOYmYAuPElVrnuPQSKRR2z9C/TgJYaDWjMWMhlaFyRdvQu+wnd1FPgXYZlMera4NLPr7o9V6nw86lwtKguywBIFHr1ZDt/Ko0WpRYKCj1ZSzhOd5ZLaOEHtvCwcf7KAzEHhsdJhFgeHWLJwTZE0OdmpNpFdjSN3dRXE/OTA5qIBzMiBaEy6FoTGQIwAKgoB6IovC5j6KWwfoNeRaqw06OGcn4JyfgCnglmpNQmgl1+midphAbf8QzXQWOgE4cefXu10wTwRhDpE3OdirVqXn7mAoq4JhoO97pmtImxzkOHSr5f7kYBXyF1ucHJRCWQnaBKU8WwRBQLtQkgQGtinfR5VaDcyhUVgmgtC7R4jKdeFZFu1sFs1U6oHgeMZqhaEf9ExSrstQcHylBL4ri0kKpQqMtZ/rYjLTXBcKhUKhPDHk7HhdAMrJAvb7AkN5IPBYrWEQvBJG+PY8AksTUGvIKQvXY5HbOkRqeRfZjQNwXXkE0zhig+/qFPxXJmHyOM7wKk+fdrmGYl9gaGblwGOFSgVbJADn3ARskSBUGnI6zQSeRyOZQe3gCPXDBPieXGvGZBQ3OsaDUFtMWFlZgcZKhocr127D0Gqh8P77w96t/dFqg88HndtNmMAggG3U+guTMgROrrWSYaCx9icY6Gg15QyRrQm3cPDBNloVecJIZzGIk4O35uCZChC1WdJttpG5F0NyeReFvcTQZol9wgfflSn4LkegNZMT1ClZE/YFhm5VrrVKp4FjZhzOuQlYxrxETZNx3R7qR0lxcjCZGao1p9fCPTcNa3iMqFBWQRDQO7EmTKWGrQmJDmV9jDWhFMpqp5ODlE+NIAjolCqS7VqvPhAcz6hhGgvAPB6E0e8h6rvFsyzauZyY63JfcLzaZILB54Pe64XaQM59FIAoMFRKDwbHK5TQWKz94HgrUbWmUCgUyrODnN2vc0o1W5YEhuKAbYGKUSFwKYzwrVkEr0bAEBSCy7Mc8rtHSC3vIbO2D7Ytj2DqHRb4r07Cd2UKZp+TqI3ITrWO4tYBCpv7aKTy0nGFUgHrRACOuQnYp8agJqjWg96t9cMEuI5ca7VBD/PJaLXTTlgH6bB3qwXAiWut1uEQJxjcbuJGq9lGHd3Kw7xb1X2BwQ61gY5WU84OQRCQj6URe3cTBx9sD1kTagxajN+cxsStOfjmxqBUkbOA7rU6yKzvI3V3F/md4yFrQmvQDd/lSfiuTEJPULi1IAhoZksobj1oTajUMHBMjcExNwHrhJ+owGPJmvDgCI3j1FCttXaruBk45sd6dA/2+SmoCPnsbKMBY7OJ/LvvgrvfmnBkRLYmJOTzAoPWhCV0q+Uha0KlVgeN1S56ptPJQcpnoFuponGYRO3gCN2KfB89CY43jwdhHPUSdR8VOE4Ojs/lHh4c7/WCMZnO8CpPH67TEd/jy0VwbVmopcHxFAqFQjltzkRsKBQK+Bf/4l/g/fffh0qlwle/+lX803/6T6G+r/OX53n823/7b/Enf/InqFarGB0dxTe+8Q389E//9Flc9hNTz1ex/4EoMORjaem4QqVEYHEcE7dmEbo2BY1Be4ZXebrwHI/C3rEoMKzG0GvJI5g6qwm+KxH4rkzBGnQTtRHZrTdFgWFrH/VjWUyCQgHLmBfO+TDs02NgSLIt4AW0snkpHG7Iu1WnhTk0CvPEGPRussQkrttFO5NBM51Gt1gcOtdVq+GMRGD0+aDSkvO9FgQBvROBoVyCwMpBgAqVSvRutTqgNpEVskm5WAiCgGI8K1oTvr+Fek62q2N0Goxdn0L41iz8i+NQEWRXx3a6yK4fILW8h9xWHDwnb0Sa/c7+BMMkjC7rGV7l6dPKD1gTFoetCW2TQThnJ2CLBIiaJuNZDo1ECrWD4wesCTVWsyjsjwehtYk2lCSI+oAoMLQyGcma0AyAA2RrQq8XupERomot8Dx69Wo/+6g81GWt1GjkyUGdnj53KZ+JWuwQ+r1jHK7FpGMKpRLGUR/M40GYRn1kBcfzvCwwZLNDAgPJwfFctyMFx3Ot5sAZhRgcb3NAY7ERJdRSKBQK5ew5kzeIf/gP/yE8Hg9+/OMfI5/P4xvf+Ab+8A//EL/wC78w9O/98R//Mf70T/8U/+E//AeMjY3h9ddfxze/+U0sLi5ibGzsLC79kTSKNRy8v43997eQ3UtKxxUKBbxzYwi/OIvQ9WnozOR0Hwk8j0I0idTyLjKrMXQb8gimxqyH75LYSWkf9xFlUdFrtFDcjqOwuY/aUXronHnUA8fcOJyzE2CMBNV6MBwufgyuJddapdWIo9UTQRg8I0SN2w6FwxWLD/Vu1YyMYHVrC2PBIBEdpIIggG01YVcKqO2uQ+gNCAzUu5VyThAEAeVEHrF3RWG/milJ59QaBsFrEYRvzZFnTdjtIbsZR2p5D9nN+JBdnclj708wTMHksZ/hVZ4+rWJFtCbc2kcrV5aOy9aEYdgio0RZE/Ich2Yig+rBEepHyaFpskFrQq2drMBjttmUBYb7rAnbajU809MweL1kCQwCj15dtCbsVctDm6DUmpDytMi+dxcqjgMUChgDXlFgCPqJuo8KPI9OoSBOI2ezQ/dRlU4nTzBYLER9t/huV7RIqhTBNRtD59Qms3hPsdqJuo9SKBQK5XzxzJ8w8Xgc77//Pn70ox9Br9cjGAzim9/8Jn73d3/3AbHh53/+5/E3/sbfgMFgQLfbRbFYhF6vh+5ThDKdZpfXyc+qF6s4+iiKg/e3kd1NyP+CAvBMj2L8hRmErk9BbzU+les4LU6u6UmuTeAFlA5SSN+LInMvhu5AyCZj1MGzGIb3cgSOsE/adOYFvt+K9mz4JJ/nSWFbbZR2DlHcOkDtaNgX2egfgWMmBPvMODQDHtin9fufxud5EgRBQKdQQj1+jHo8MRwOp2FgDPphCo3C4B0ZqLUAfMx1ntXneVL4Xg+dXA7tTOYBgUFtNkPv8UDr8UCtF8Wk8/55ngRBEMC3W+hVy+hVSuB7XdhUEIUGpRKM2QrGYhcnGE5qzQt4pl/szwAJNRrkaX2ezyqWPY3n7P0/s5wo4OCDbRx8sI1KUp4wUjFqBC5NYOKFGQQuh8FomQd+1lnyWWrG9Vjktw6RvhdFbiMObkBg0Dst8F2ehPdyBCav45nZ1T2L71S7VENp+wDFrQO0crKYpFAqYRn3wTE7AdvkKFQD1oSf9nrOyz1C4Hg0UxnUDxNoHCWHxCS1UQ/T2ChMoQC0A9aE/EDn+wnn5fM8KVy7jXYmg3Ym84DAoLHbofN4wDidWN/aQtDthqBQXJjP9ihYloVOIaBxHAdXrwwJDAq1GozFDsZqg0pvfGytzwsX7W/u43genrOen3gBBzt7mL51AxqD3CB1UWt4ct1sr4dOoSDeU3K5oaYZpUYDnccj3lOs1nP93fqkf4N8rye+x1dLDwgMKoMJjNUGxmKDUi2+Hwmf4GefFvQ+cb6hn+d8Qz/Pk0FC8ycpKARhYCftGfBXf/VX+PVf/3W899570rHt7W189atfxQcffACLxfLAf/Pmm2/iF3/xFyEIAv7ZP/tn+Nt/+28/8e/jOA7Ly8uncekAxM324kYSpe00GsfloXMGnxW2aQ9sk24wJsKsVPJ1tOMFdI4K4FsDnc4aFbRBB3RjTmg8VqImGIQeCyFbhZApQyjWxbeyEyx6KD02KDxWKPTk+PJDEKBsdaCuNqCuNKAc2OgQlAqwFiNYqwmcUQ8QVGsFz0Pb7ULX6UDb62Hwk/VUKrS1WrS1WnBEPbwEaAAYleL/mIEPzQtAUwAaPNASAAHk1JryaK5fv/6p/rvTfs7eT7vUQGUni/JuBu3CoFe7AuaQE7YpDyxhF1QETTAIHI9OqoxOvIBOogSBHbBSMWqhCzmhG3NCbSer01lodSFkyuDTFaA26CcNKBxmKLxWKEYsUBBk7QFBgKregrpSh7rahGJg04tXq8Baxecur9cCBNVayXHQ9Z+7moFuYwFAl2HE565GA4GgaUlAgE4BGBXic1c1UE6u/8xtCEBbAECfu0RyXp+zFx5BgKbXk+4pyoHtDU6hkN7je2o1WfdRCOJ7vALQKYY/Wrt/P2nwAEfvJxQK5Tnh0z5nKafPM1+tNRoN6PXD9jIn/9xsNh8qNrzwwgtYXV3FBx98gG9+85sYGRn5xLkNS0tLp6Jybb++gnvf35L+eSTiQ+jmDMZvTsPouJjBixzHYXV1dej/I4EXUD7MiBMMqzF0KvIGj1qngXthHN7Lk3BOBc5dYNjDPs+TwrY7KO8eobh9gFo8BYGXX1b1bjscs+NwzIxDa3t2tf4sn+dJEAQBnWIZ9XgC9fgx2Ibs56lQq2AM+GAaH4XB7zmVWj/tz/Ok8CwrTzCUSsMTDEaj1PmkNhof81POz+d5EgRBAN9pyxMMXTlvAwoF1Gar2PVkMCG+vn4hPtOTcJFq9CSc189zmtdTSRfx7n//EdpHFZSP89JxpUoJ/+I4xm9OI3h18sJkHz1JzXiWQ37nGJl7UWQ3DsC2u9I5nc0E76UIvJcjsIyOnLnAcJp/g51KHaXtOEo7cTRScq1Pso/ss+OwTwWhforZR8/6OyXwPJrpHBrxY9SPUuC7cq1VOi1MIXGCQTfy6bKPzus9gmu30c5mxQmGSmXoHGOziZODbvdDs4/O62f6OARBANeso1cp9y2SZGGFEwCt3QGt1QGV0QTHBd4Evaj1eRTn9fOc5vWc18/4pAiCgG6phHY2i042O3QfVTAMdG43dB4PNHb7mT8zPw2Pqg/P9tCrVsQJhkZ96L9R6Q1grHYwFhuszPlrhrvof3P3Qz/P+YZ+nvMNaZ+H8iDPXGwwGAxotVpDx07+2fiITT2NRnxY3r59Gz/7sz+LP/uzP/vEYoNKpTqVP+Lg5TCyL86hpe7hpZ99FVaCfJGVCiWqRzmkVvaQXomiXZFfYE4EBt/lSbhmxi5EyOaT1pxtd1DaOURhax/VgxSEga5Cg9sBx+w4nLPj0DnONmTztP6GAVlgqB0co3ZwhF59oFtYrYJp1A/z+CiMAR+UT6nWp/l5nhSeZdHO5cRwuHx+KHhRbTTK3q0m0yf+2WfxeZ4Utt0SwyYrJfAdOW8DCgUYs1UMhzNbpXC4k3HG8/yZPg308zxdTut6qtky/uxf/gfJLkihUsK/EEL41izGrk1Ba3x6m85Pm/v/P+JYDvmdI6RX9pBZ2x8WGKxGeC+L2Ue2Mc+53Cz5tDXvVOoobh2gsLX/UIHBOTcB+3QIjOHZ1vppfqcEnkczlUUtfozaYQJ8Z1hgMIdGYR4PQu92ndqU6Hm4R3DttpTB0C2Xh85pbDbxuevxQPWEFqnn4TN9HIIggG3U0a2Iz91Bn3iFSgWNxQ6VxYr1vSiuBELn/vN8Ei5CfT4J5+3zPI3rOW+f8XGcCAytdBqtTGZYYFCr0VCpEJifh97lIiZDTqVSQSHw6FbK6FaKYOu14fN6IzQ2OzRWO1Sai9GAcZH+5p4E+nnON/TznG9I+zwUmWcuNkxNTaFcLiOfz8PlcgEAotEovF4vzObhbvH/7X/73wAAv/ZrvyYd63a7sNlsz+x678c8YsPn/v5PY3l5GSbXg1MYFw1BEFCOZ1D7KI4f/fka2uUBgUHLwL04Ad+lSbhmglARZFvAtjso7YoZDJX95LDAMNKfYJidgN55tgLDaTIkMMSP0KvdLzD4YB4PwhggK3iRZ1m0s1m0MpmHCwweD/ReL9Qm07nczPs0CIIArtN+vMBgtUNjsUkCA4VyXtDoNXCMjaDV6+DSazcwfmMGOrP+4//DC8LjBAatxQDvpQh8lydhH/cRZU0oCQzbB2gkc/IJhQKWoAeO2Qk4ZkJgjOTU+mSCoXZwhPphAtx9AoNpLADLeBB6zwhRteY6HWkzsFsqDZ37NALDRUAWGEp9gWHAclSlAmOxQ2OzgzGZoVAo+6I+OTWnUJ4WHycwSO/xVitW7t1D2OkkQmjg2R7MCgH1g92HTjCIwfEXR2CgUCgUyvPFM99RHB8fx/Xr1/Htb38b3/rWt1AqlfDd734XX//61x/4d2/cuIF/9I/+EV577TVcv34db7zxBv78z/8cf/AHf/CsL5soBF5AOZ5G6l4U6XvRIYFBpWXgWZiA73JEnGAgTWB4xASDfsQG5+wEHLPj0DttZ3eRp4wsMByhdnA8PMGgUsE46oPlZIKBoFo/TmBQGQww9CcYiBMY2i1po+MBgcFkgcbmAGOxQqkip9YU8tCZDfjp3/hfsLy8jKkrZIzW8iyHTqKE1e0fILt+8HwJDNsHKGzdJzAAMI95xecuiQJDf4LhUQKDeTwIg4eczltgYILhUQJDf0OQPIGh1n/ulh8iMNigsTpEgYGgWlMoT5snEhg8HmgHhAUSQlN5tjc0weBSQxIaRIHBDo3NQQUGCoVCoZx7zmTH6Tvf+Q6+9a1v4bXXXoNSqcTXvvY1fPOb3wQAXL16Fb/5m7+Jr371q/jSl76E3/iN38Bv/MZvIJ/PY3x8HL/3e7+Ha9euncVlX2gEXkApnkZ6ZQ/pe7EhiySVhgHjs2D28zfgmR8nS2BodVDciaO4fYDqQXI4g8FlkyYYDC7b2V3kKSMIAjqFkmjV8FCBwQtzKAjT6PMjMKgNBqmTUm02U4GBQqE8dbge259giCKzLk4wlPvnSBYY2uUaSicCw6BFEgBz0Avn3Djs0yFoTIYzusLT57EWSVoNTKFRmMdHYfCMELXp/DiLJMZqhcHrFbOP9ASJSUMCw4MWSaLAYAdjshBVawrlaSPwvCgw9EXLBwQGtxt6r3dIYCABvtdDt1pCt1wC2xi2SOoIgNXjh9bufGiWDYVCoVAo55Uz2X1yuVz4zne+89Bzd+/eHfrnr3/96w+deqB8PAIvoHSQEjMY7sXQqQ6EPGsZuBcm4L0UgWMqgNX1NXgWJ4joIGVbbfCJInZ2/wq1w/tCnkfscPZDnvWECQztQgm1g2PU48cPZjD0Q55NpE0w9HpyBkOh8HCBgdgJhiK65QdDnk8skqjAQKE8e7gei9zWIdIre2LIc0fudFbqGYxem4H/yhR5AkOpiuL2AYpbcTTS9wsMskUSUQIDx6ORykgTDHxXrrU0wRAahcFLlsDAtlpon0wwPCyDweMhU2Co9wWG6qMEBjrBQKF8UgSeR6c/wdC+P+T5ERMMJMD3epJgeb/AcDLBoDZbsb+xCfeIh4j1OYVCoVCeL+hOFGEIPI/ifgrplSjSq1F0qk3pnBTyfF8GAwljp71GC6XdQxS2DlCNpwBBQLV/juQMhnauKHZSxo/BNuRaSxkMoX4GA0kCQ7eLVl9g6BQKgCCLSURnMLSa8gQDFRgolHMD1+0hu3WI9EoU2c0DcAMCg85qhPdSBO7FCRyU05i7epWYTQOh0UHq3VWUduJoZoryiYEMBvv0GFECA89x4gTDwRHqR8kHBAZzaBSm0ChxFklsqyVNMPQqlaFz5GYw8LLAUClD4O4XGPoZDEYqMFAonwSB59EpFsVJ5EwGfG/AfoxhxAkGIgWG7oDA8PEZDCSszykUCoXy/EJ3pQiA53gUowmk7kWRWY2hW29J59Q6DTwLE/CeZDCoydjkAIBuvYnSThzFrQNUjzJDm84w6xC4Mgfn3AT0DoIEBl5AK5cXOynjCbBNudYkhzxz3a5okZROo1MsPigwnFgkEScwNNAt9wWGntztRUOeKZSzhe10kduMI3UvitxmHFxX3ojU2UySRZJtzAOFUgGO4xBfzpzhFZ8OrXxZymDgciUkTk4oFLCEfHDMhOCYJiuDgWc5NJJp1OLHaBwlwffkWqv0Opj7GQx6t4uoaRW20ZDsTHrV6tA5jd0udRwTJTDwPHr1KrqVEnrVMoSBzT6FSg2NVbRIUvdDnikUypMh8Dw6hYJ4T8lmIQwIDEqGge5kgsHhIEpg4Lod9E4EhmZj6JzKYBTf46005JlCoVAo5EHObuRzBs9yKOwdI7USRWZtH72m7NXO6LVwL4oWSa7pIFECQ6daR2k7juJ2HLXj4Y0bo9cFx2wI1skgtuIx+AgJFhV4Hqp6C7n3l1E/SoJrybVWMmoYg36YQ6Mw+r1QElRrrtORBYZSaVhgMJkkgYExmc7wKk8XQRDANuviRkelfJ/AoITGYgVjtUNjtlKBgUJ5xvRaHWQ3DpC+F0Vu6xA8K29E6u1meC9H4LsUgXXMQ5To2cqVUNw6QGH7AO3CQFe7ArCEfHDOTsA+HQJjIGfTme+xaCRSorB/nIIwUGu1XidlMOhHyBIYevW61G3cqw1bexAtMNQqfYGhAoEfEBjUamgs9gGBgZxaUyhPG4Hj0O4LDO1sdsh+TKnRQHeSwWC3kyUwdDrSBAPXGhYY1H2BgaECA4VCoVAIh4oNFwgpbPJeTAybbMlWKhqjDp6lMLyXInBOBqAkaCPyJGyyuB1HPZkbOmfyj8AxMw7HTAhamxlAf+w0fhZXenoIHI9mWg6b1He6ONniUWoYmPoCg8HvIarWUthkJoNuqTR0jjGboe+HTTJG4xld4ekzHDZZhsDK3V5QKqEx20SrBrMFCiU5taZQLgLdZhuZtX2k70VR2DkCz8m5MAanBd5LEUiqsBwAAE4WSURBVHgvT8I6OkLMRqQgCGikC2IGw/YBOiV501mhVMI64YdtagxHrTKmX7hBhKgPAOB41PaP0DhKopFID3W1q40GmEOjMIdGoRtxEFVrti8wtDIZsPUBaw+FAlqHQ8xgcLuJCicVeA69agXdShndWnko70mhZqRuY7WRnGlJCuVZwHMcOrmcKDDkckP3UaVWKwmWGrudqO8W125LIc9cuzl0Tm00iRZJVhuUjOaMrpBCoVAolGcLFRvOOWynh9xWHOl7sQe8oDVmPbxLETHkOeyHUkVOV0irUEZxO47i9sGwFzQA86gHjpkQ7DMhaC3kdLWfWDXU4wnUj4e9oAWVEpbxIKwTYzB43VAQVGu22ZStGu7zgmYsFjmDwUCO77fA8+g1av3R6vu8oJUqMJZ+BoPZSlS3F4VyEejUmrLAsJeAMLARaXTb4bsUgfdyBGafk5jNEkEQUE9k+8/dOLpVedNZoVLBFg7AMTMO2+Qo1DotOI7D8fLy2V3wKcG1O6gfp1A9OIIxmUZGOJDOMWajlMGgc5KzMSYIAnrVqvTc5ZoDG2MKBbROpywwaMjZGOM5ti8wlNCrVQFB/l4rGUacGrTaoTZQgYFC+STwvR7afYGhk88PPTNVOp1kkaSx2Yj5bgmCAK7d6k8il8B12kPn1SZz3+rUDiXDnNFVUigUCoVydlCx4RzyOKsGndUIz1IYvsuTsI97idmIFAQBzWxJnGDYiaOVL8sn+2GT9v4EA1Fhkz0W9UQK9YdYNah0WpjGAjAG/dhJJzBFULCoimVR399HJ5t90KrBZhM3OjweqPXk+H6LVg1VdKsP94JmLKIXNGOiYZMUyrOmVa4jsxpFejWGYiw1ZNtm9jnhvRyBdykCs9dxhld5ugg8j+phGsWdOEo7cfQG8p6UjBq2yCgcMyHYwkGotORslrCtNuqHCdTix2imc1KtFQAYiwmW8SBMoVFo7VaiNsa65bJkkcS1BzbGlEronE5xcnBkhKiNMZ5l0auWxQ3BenXoe61kNLJfusFITK0plGeBlKWWyaBTKAx9t1R6vTTBwFjJuo9yrYY4EVUpge/KDgNQKMAYzX3R0galmpz7KIVCoVAonwYqNpwTOvUWsutiJ2V+9xjC/VYNSxF4L4VhDXqI8QcWBAGNVL5v1RBHpzxs1WAZ98ExMw771BhRXtBcp4v6sSgwNJLpoVqrjXqYx0ZhCgUkL2iO44BM8gyv+LMjCAJ6tRra/U7KkUYD9XJZPKlQQGu3S51PRFk1cBy6tYo4wVCr3GfVQL2gKZSzpJEvI30vhvRqFJXD7NA5y+iIOMFwKQLjiO1sLvApwLMcqvEkittxlHYPh+wYVVoGtkgQjpkQrOFRqBhyXhF79QZqhwnU4wm0svmhc1q7FcagH8fNGpZu3YRaTcjnFgR0ikV0+x3HfFfOAFKoVNC5XNB5PKLAQMpnhthl3a32A1nrw80MSq1ODHm22KHSG+hzl0L5BHCdjiRYPpClZjRKjUKMmZx32sdnqSnAmPuTyBYrlCpy7qMUCoVCoXxW6FPxDGmV68isxZBZjaEQTQ69tJk8dklgMPtd5Ly08TxqRxmpk7Jbk8f3FWoVbBMB2GdCsE8GodaRs+ksdVIeJtBMZYdqLVk1jI1C5yLLqqFbLkudT1xL7poVAGidThi8XvKsGh7bScmAsdihsVGrBgrlWSMIAurpItL3xAmGWqogn1QA9nEfvEtheJfC0DssZ3ehpwzXY1GJJVDcOUB572jIjlGt18I+NQbHTAiWkB9KNRnTcwDQrdT6AsMx2oXhDCCdywFTKADz2Cg0FhM4jsPh8vKFvydLgazpNNzFIkoF+W9coVZDNzIibgi6XFAQMikJAFy3I20Gss360DmVTi9PMOjImZakUJ4FbLOJVjaLdiaD7kmTUB/GbJYahRgTOba2As/3s9TK6FZLQ8HWYpaaVZxgMFuJuo9SKBQKhXKaULHhGdPIlZFeFQWG8mFm6Jwl4BLDJpfCMHnIsWp4XCelbNUwDltkFCoNOWOnj+uk1NgsMI8FyLNq4Hl0SiVxgiGbBd+Ra61QKqF1uaAdGcFOKoXLJNlCQUCnmANbqzzYSanRyhsdtJOSQnmmCLyAynEW6XsxZNZiaOTK0jmFUgFHJADvpQg8ixPQWcgJnmfbHZT3jlHcOUAllhiyY2RMejimQ7BPh2AZI8uOsVMsS8J+t1wdOq/3uPrCfgCMkSA7RpZFO5dDO5sdCmRVAlAwDPRuN/QeD7ROJ1G15jttdPu5R/cHsqr0RnGCwWqHSkvOZCyF8rQRBAFsoyFNMNxvdcpYrZJFEllZapxodVopo1e7z+pUqepbndpolhqFQqFQKE8IFRueMoIgoJYqSALDA52UIR88l8LwLoZhcBLUSdntoRxLoLQTR2nvaCjsWK0TOyntMyFYx31Eje93KlXU4+JGR+f+TkqnHabQKMxjAWis5jO6wtPnpJOyncmglctB6Mm1Huyk1LpcUKpU4DgOQibzmJ94MeA6bXSrZXTLJQTVQDt1LJ076aRk+hsdVGCgUJ4dPMejGEsisyoKDO1KQzqnVCnhmhmDdykM9+IENARZ9HXrTZR2D1HaiaMaT0Hg5akqrdUE+0wIjukQTAE3MfckgRfQyhVQP0ygfniMXn047Njgc4vC/lgAaj05tea6XVFgyGTQLhSGLPqUWi10bjeOKxXM3bgBNSEZDIIgQKMQ0M4k0atVwN8fyGo8CWS1QUnQtCSF8rQRBAHdSkUSGNjmsHincTgk0VKlI+c++rjQ+BOrU8ZqA2OkWWoUCoVCoXxSyNnlPUcIvIByPI103yKpWZC76xRKJZyTAXiWwsR1UvaabZT3jlDciaNykBwKOya5k7JdKPU3OhLoVgY6gBSA3j0CcygAUzAAhqhg6x7auRxa2Sw6+fxQB5BSo4HO7Ybe7Sauk5Jrt8RFSbUMri3bQikUgEpvgMbqgMZqo52UFMozhuuxyO8cIb0aQ3Z9H73mcBaBezYEz1IYI3MhMDpyNiLb5RpK23EUd+OoHw/nTuhdNvG5OxOCwe0gR2DgeDTTWXFy8DABrj0wQadSwRjwwjQWgGnUB5WWnFpz7ba4GZjNPuCXrjIYxG5jtxuM1Qqe59FdXr7wz1+xy1q2MwmogU6+36ygUIAxWWS/dBrISqF8Inr1Osz1OnJvvjk0iQyFAlqXC3q3mzyr014P3WoZvWoJvXrtgdD4k4BnanVKoVAoFMpng4oNpwTPcijsHYsTDOv76NbkjUilWoWRmTF4LoXhnh8nqpOyU6mjtHuI4k4ctaPM0Eub1maGY0YUGEz+EWJe2gSeRzOTlwQGtinXGkoFjD6PuNER9JPVSdnpSPkLnWJxeKNDpxMFBo8HGjtZuRNss45epYxutQy+2xk6rzaZoTZZsXN0jKWFGWJsoSiUi0Cv1UFuM470Wgy5zTi4ruyrzBh08CxOwLMUhmuKnLBjQRDQypX6uUeHaGaLQ+eNPpf03NU7rGd0lacP32PRSKRRO0ygcZwCPzBBp2QYmII+mMZGYQx4iJqW7NXr0nO3Vx22hRr0S1cbjeQ8d3letDOpltCrViBw8veaFwCt1QaNzUH90imUz4AgCCh++CGMLAse/dD4kRHo3G7iQuNPJpF7lRLYZmPonEqrEwUGi41anVIoFAqFcoqQ8yZxBrDtLrK7x0iv7SO3GQfb7krn1DoN3HP9TsrZENRaMjquBEFAq1AR7ZF24mikC0PnDW4H7NNjsE+FYHCTs+nMsxwaybQoMBynwHfkWivUKpgCPpjGAjCO+ojKnWCbTamT8v5gOLXRKAZNut1gLBZiai3wPHqNGnqVErrV8nAwnEIBxmzt+7baoFSrwXEcuKPE2V0whfIc0a42kF3fR3p1H4W9YwicbHugs5rgWZqAdykC+4QPStXF7uo+QeB51BM5UWDYPUSnPDhBp4Al6IF9JgT7VAhagqYl2XYHjaMkaocJNJMZCANWQSqdFqaxAMxjARi8bihIqbUgoFepiIGs2SzYxvDGmMZmEwUGt5sov/TH2pmo1GAsVqjNVmxEY7gcnKDCPoVyChhDIWSPj+GfnYVhZIQY8U4QBLCt5kMnkYGTSeS+wEBD4ykUCoVCeSpQseETIvA8Eh9uofTmFl7/Pz4YCl3Umg1iJ+ViGM7JAJRqcl7a6smc5AXdLg5315lHPaLAMB2CzkZOFgHX6aJ+nEL9MIFGMj1kC6XSamAK+mEaC8Dg90BJ0At6r1aTAp7Zen3oPGO1imPVHg8YIzmbWjzHolerigJDrTLkfy0Gw1lFqwazBQolGbWmUC4KjVxZnBpc20f5MA3IQ1Uwuu3wLE7AuxSGNUhOFgHPcqjGkyjuHKK0ewi2KfvTK9QqWMf9cEyHYJsMgiFoWrJXb6B+KAoMrWxuqNaMyShODYYC0LucUCjJqLXA8+iUStJz9wE7E6dTtjPRas/uQk8ZvtcVc48qZbD1GgaLrWQ0UiCr2miGQqEQ855ARs0plLNGoVDANDGBvUoFYQKEBtFyrQ6HUkBtdwNCrzt0Xm0yQ2OhmS4UCoVCoTwrqNjwCTl6bxNrf/KG9M8GlxXexTA8S2HYxjzELH55lkP1MIVSf6Oj1xjwp1cpYQn5RC/oqTEwRnK6Qnr1JupHCdQPk2hmckNWQWqjQQqa1LsJyiLgeXRLJamTkmsPhC4qFNDa7VInJVHBcL0uutXKQ31bFWoGGqsNGkt/o4OQWlMoF4V6uoj6yhHe/P42GpnS0DnbmEcS9k0e+xld4enDtrsox45R2omjHDsGP2ALpdJqYJsMwjE1Bms4QMwEnSAI6JQqqB8lUT9MoFMsD53XOmzSBIPGRs4EHc+y6OTz4nM3lxuaoCPVzkQQBPB9O5NupQyuRe1MKBTKp+dhlmtWFUShQaEEY7YMTSJTKBQKhUJ5dtAn7yfEOTUK76UIakIHl7/8Eqx+FzELIrbTRfkgheLuIcrRY/DdAU9kDQN7ZBT2qTFYI6NQExK6KAgCuuWqFDR5/0aHxm4VBYagH1qHjZha8yyLTqEgCQz3b3RonU7RIsnlIqoDiGu3+4uS8gO+rUqtDpp+J6VKT47/NYVy0Wjkynj7//Unkm2OQqmEczIAz1IYnoVx6KymM77C06NTbaC8J4r61Xh6yCqIMRlgnx6DYzoEc9BLlC1UK1sQbQmPEujVm/JJBaB3uySBgTGRM0HHdTpo53JoZbPoFApDE3RKjQa6kRHoPR5oHY4L32V8gph71ECvWka3Unow98hg7E8w2KHSktPMQKFQng48y4r3k2r5IZZrKlR7HDyhCWitNjqJTKFQKBTKGULFhk+I0WXF5f/zl7G8vAyz13HhNyS7tQYK23Fwd2NY+f7afRsdetinxPwFy5iXHFuok42Oo6S40VEb2HRWAPoRlxTwrLGQs6l1stHRzmbRvn+jg2HELkq3Gzqnk8yNjmoZfKc9dF5lMIoCA/VtpVDODRqTHs6pAGrNBqZfvgLvYhiMngz7GEEQ0MyV+raEh2ik80PndU4r7FOiwGD0kdPMwLMsGsmMaEt4nAI3mHukUsLo98IU9MMY9EOtI6PWAMA2GpKof3/ukUqvl3KPNDZymhkEnkevXpWeuw/kHpnMYCx2aCxWKBlymhkoFMrTget2+oJlGWyjNnRu0HJNoTMgtrKCUQsVGigUCoVCOWuo2PCcIQgCWicbHbtHz89GR6+/0XH0kI0OpRIGvwfmfsCzWk9Od12v0RDFhUdsdOjcbug9nudqo0NtNEsWSXSjg0I5fzB6La7/3b+O5eVl+K9MX/gwWIHnUTvOgNtJYu2D/eGAZwCmgLsv7I9B77Se0VWePmyrjcZxCvWjpJh7NBDmrdRqYBr1wTQWgNHngZIh43VUEAR0KxXpuXt/wDNjsUi5R2ojORN0PMuiV6v0u40fnnvEWGzQmK3ENDNQKJSngyAI4FpN8X7ysIBnnV6eiNLppfsox3EP+3EUCoVCoVDOADJWd5THIvA8qkcZlHdFq4ZOZTj01+hzoWVUY+4nbsHkdpzRVZ4+bKuNWjKD+lESzWRmaGpD2ugI+mH0e8na6CiXxY2OXO6hGx06txt6txtqk4mwjQ6x66lXrz640WG2grHawJgtUKrIqDWFQjm/cN0eKvsJlHaPUI4egW2J9jEdiFYP1nEf7NMh2CeDROUedSrVfv5CEu1cYegcYzLAFCQw94jj0C4UpOcu3x0IJlUooHU4pOcuSblHXKeDXrWEbrUMtjH8XqlQM5ItIc09olAoH4fA82AbNUlg4Hu9ofNqowkaix2M1QaVhpzpNwqFQqFQSIXuuhEK1+mhvJ9AafcQ5egRuPagZYEK1nE/7NNjsEVGodJrsby8fOE7KsWOwhpq8WPoowkcrMWGzjMmI0xjfpiCZG108ByHznO50dGWFiX3b3QoGUbseqIBzxQK5RnRrTdR3jtCafcQlYMUhIEuS5VOA95uQPjmZdgng+QEPPMCmvm8bEtYHb4Xax02mIJ+mMYC0NqtxAjcXLcr2RJ2CoWhWivUauhcLtGW0OWCkiGk1k/abUwDnikUyhPAcyx61Yo4iXzfRBSUSjAmCzQWGxiLFUo1GfdRCoVCoVCeF6jYQBBi0OQRSnuHqMZTQ5YFar0WtkhQDHie8A9tdFzksVMxf+FkoyOFXk3c6DgZ0tc57XL+gs1CzOL3cfkLJG90PDZ/gW50UCiUZ4ggCGjly31bwkM0UsO2hFqrSbZH8rlwb/Ue7DOhC28LxfdY1BMpaI+zONj9c3CdgdBfpQIGr1sUGIJ+MEbD2V3oKSPZEuZy6JZKQ+dUOh10IyPQud1iwDMhAvewLWEFAnt/t7G5vxlog0pLu40pFMrjkfIXqmWw9ToAQTqnUKul+wljshBzH6VQKBQK5XmEig0XGEEQ0MwUUNoVBYZmpjh0Xms3Sxsd5oCbmJc2rttDI5kWfaCPU+C78uJXoVRC7x1BUeAwe/smtGZCAp4FAb16Hc1CAa1sFr1KZei0SqcTxYWREcI2Ojj0atV+J2UFAjeQvwAF1CaTvNFBx6opFMpThuc41I4y4tTg3tFDbQklgcFlI8ZLutdooXGcFG0JU1kIPA8GAAdAqWFgDPhgGhNtCcmZ2uBFW8K+sM82m0PnGbNZfO663WDMZmIEbp7toVc9yV+oAsJ93cZmq/jcNVuhVNNlBIVCeTSCIIBrNtCtie/xD0xEaXVyo5CBnBwbCoVCoVCed+gq4YLBsyyq8TRKe4co7R2hVxte/JpG3bBPjsE+FYTOQY5lQa/eQP1IDJpsZrIAL3fCqLQaGKX8BQ8EpRLZ5WWoDRfbB1vgeXRKJbQyGbhKJRTefXfoPLH5C70uuv2x6l69CggDXU8DQZM0f4FCoTwL2FYH5dgxSntHqMSOwXUGBO6T/IWpMdgmg9CYyOjkFwQBnWIZ9X7Ac6cw3MmvNhnQ1KoxfvUSTD4PMQI3z7Lo5nJoZbNo5/MQBn3DT2wJ+xMMav3Ffsc4QRAE8IO2hM3hrKcTW0LGYgNDbQkpFMrHIDYK1fr2SGUILDt0XpyIsvYnosixd6VQKBQKhSJDd+ouAL1GC6U9MWSysp8E35Nf2pSMGtYJP+yTYv4CKUGTAi+gnS+ifpxE/TiFbmm4k19jMcPYt2nQjzihUMob7Re5g5TrdtHJ58VOynxeekFXA4BSCa3TCf3ICHQjI8TkLwz5QNcq4FrDApqS0QzkL5joRgeFQnnqtIsVlPaOUNo9Qu04MyR6qg062CeDsE0GYR33E9PJz3McWulc35YwCbY53IGqG3GI9kijfqjMRqysrMDgvfhTk2yrhWY6DXulguwPfzgscDMMdCMj0I+MQOtyEdPJL4ax1qVuY77bGTqv0hvkqUGdnphmBgqF8nT42EYhs6XfKEQnoigUCoVCeR6gT/tziCAIaGZLKEePUNo7QiOZGzrPmAywTwVhnwzCEvIR89LG99i+PVIKjUQKXHtg8asA9CMuyQdaYzWf3YWeIoIggG00RHHhIT7QSo0GWqcTyXodMzdugNFozuhKTxcFBPRqFbTrNXSr5Qd8oFUGo2TTQDc6KBTK00bgedSOs2LuUfQI7cKwwK0fsYmi/mQQJv8IMfckttVG/TiFxlESjVQGAjsYdKyC0ecRpwZHfVDrZYH7Iov6giDI9ki5XN83HDgx4lMbDJItocZmu/Biygk8y6JX628G1qoQ+IEaKhRgTBYwFis0ZhuUhLxrUCiUpwNtFKJQKBQKhfI4yNilJgCeZVE9TIsTDHtH6FaHx9iNXmc/4DkIg8dJzEZHr96QbBpa6RyEgaBjJaOGMeCFcdQPU8ALlY4MT37h/9/evUfZVdf3/3+d+3XOXJOZhAQpkIAiNUNosKKsAqZ8l5ZLEdGKWHWZUqBFdLWiy9UWseqyLhpFly6LF7ygXUq1/tKqqAu5fWm4qKHoVyQXhCSTSeY+537O3vvz+2Of2TNnZnKZzOWcs3k+1spSzt4zsz/5ZOY95/P+fN5vx1FlbEzF2kKHPasOdDiddssjrVqlSHu7HMfR87t2KdjyTUXdXU+VyXGdGpYKL+6bvhgMKpLOTNeB9klTawDNyyqVNb7voMb37Nf43gOyyxXvWiAYVNupfd4JhniHfxLcU+WR8vsHVJpdHikR904NJvtWKxhu7bgzxbEslYeHVRwaUnloSM6s8kiR9naNVCo6/VWvUiyTadyDLikju1RUJZ9VdXJCVqG+v0ggHFakrcMtZ5LOKNDiv2MAWF5TfdSq2Yn5G8YnU97pBTYKAQDw0kayoYEquYLG9x7Q+J79mvh9fXmkQDik9petVeeG9eo4Y52ibakGPunSMY5RcXhE+f2HlDswoMr4ZN31SFtK6XVrlVq/VsneHt/shLErFZVryYXSyEh9/VIf14E+2q6nYEAKhCOKtnco0tahSJo60ACWlzFGpdEJ9/TCnv3KHjhSXx4pEVPHGetq5ZFOUTjuj93djmWrMHhEuf0Dyh84NLc8Unenm2BYt0axrg7fLBBZhYJ3eqE8Olpf1iMcVrynx427PT0yoZD279qlcKq1f9dyyyNlVZ4Y17qwlNv7bN31UDxR223crlCCZqwAjs2ulGsnoibmlEeq2yiUaVcwzEYhAADgItmwgowxyg+O1HZR7ld+cKTueiSdVOeZ69Rx5qnKvGyNQhF/TI9drrjlkQ4cUv7goJwZu0enyiOl1q9Rep1bHskPb36NMbKyWa/3QmV8vO56MBr1Fjp8VQfatlXNTbp1W+dpChdKphROZ7Tv4CG9/BXnKOyTcQNoTo5lK7t/UON7D2hs736Vx7J11xOrOtxTg1PlkXyS9KzmC8ofOKTcgUMqHDoiM6PsUSAUUmptr1Lr1ii9bo3CSZ8kuB2nvjxSvv6EaDiZ9JL6s8sjtXJZKKda8XYaV3OTUu2EaCSgWnmktundxlF/nBAFsDyMMbIKOTe5kJ2QXapPTk+XR2pXmIbxAADgKFjpW2Z2uaqJ3w9ofK9bpqGar/+lLbWmxy3TcMY635RHMsaoMpH1FjqKR4brdsIEoxGlTulzTzCs7fVNeSTHtlUeGfEaPNulUt31SFubu9BRK4/kh7mWJLtc8hY6rHx27q6ntnZF29q9XU+2batycNA34wfQXLxTg3trpwYrM04NhoLKnLpGHWeuU+cZ6xXzS3mkqVODtbhbGavvORFOJrzeC34qj2RXKl7MLQ0Pzzk1GO3snI67LX5qYcrxaqUHwhGF0xkdGB7RhleeqzD9FwAcg7EtlWf2c7HrNwqFk2lFMu1uw/hYnN/fAQDAcZFsWAal0YnaLsoDyu4flLFn9CGIhtX+B6eo8ww3wRBJ+WNH4VSZhvzBQeUPHFI1V7+jMNqeUXr9GqVOWaPE6m7f7ITxyjQMD7tlGmb0nAgEg4p1d3sLHaF4/BifqXW4ZRpyqtTemDiVct31YDTmNZmkKRyA5WaMUf7QcK258wEVDs86NZhKuOWRzliv9tPWKhTzR6mHmacGCwcH63pOKCDFe7q9uBvr9EeC2zs1WEswzDk1GIkoVou58e5u3/T/mT416CYY5pwaTKTc3gtt7QolknIcR4WhUfowADiqysSY+kJGk88+U/d6IBRSJO1uEoq0tfvm9DUAAFg5/PawBBzL1uT+QU0cpUxDrLPNTS6cuV5t63tbvtHvlGquoPzBo5RpCAaV6Ful9Lo1Sq1bo2hbuoFPunSOV6YhFI97yYVYV5dv3ugfrUyDK6BwKl1LMLQryK4nAMvMKpU18fyAe4Jh3wFZhfqTZKk1PbXySP46NVgem1D+4CHlDwyqODQszTxIFo0otbbPTTCs7fPPqUHLUnlkxEvsO+X6BLcfTw0aY+SUS7Wk/oSsfE71kz3j1GBbu2+SKgBWhjFGxYMvKFHbDxSKxWvJhdpGIR/8HAUAAI1DsuEkmVJFQ08/p8nnB+Y2dw4G1ba+Vx210wuJ7vYGPunSMY6j4pERL8Ewu7lzOJlQat0apU7pU2pNr4K+6TlRdk8uHKW5c7Sjw+u/EE774xd0t2Zr3m0KN2+ZhrC70JHpUCSd8U1SBUBzMsaoODSm8X0HNL73wJzmzqFYRO2nnVI7weCjU4NVS/lDh5Wv9Tya3dzZOzW4bo0Sq/xxatCNPwU35g4NqTw2Vt/cORRSrKvLl6cGq7msqln39IJTqdRdd08Ndija1s6pQQCLEggElFz/B3rx+X36g7NfoUgi2ehHAgAAPuKP1eAVVBwZ157/7yHZh0f1wozXI+mEOk73X5kGq1jySiPlBw7LqVanL041d17X57syDZWJCXehY3hY1cn6pIpXpqGnR/GeHt/sKHSsqqrZyVqCYW7N1lAi6SYXamUa/DDXAJqXsWyN79mvyecPanzvAVWy9UnPeHe7m1w4fZ1vTg0aY1SdzCl38JDyBw6peHhYZmZ5vlBIyTWrlTpljdLr+hRJ+6QPgW2rPDrqlUeyi/VJlVAiMX1qsLPTNwluu1L2GrFWc1nJzDg1GAgokmrzSpmEYv5IqgBoDpG2dmWdgII0jgcAAEuMZMMC5Q4OqXB4VJKUWrvKbe58+jole7t8sfhqHKPS8IhyBwaVP3hI5dHxuuuhWNQ9uXCKe4IhFPNH48G6JpMjIzIzkyqSIpmMd3rBT2Ua7GLBLY+UnZBdqC8JFQiGFGnLKEKZBgAryK5Utfe/Hpa9e7/2mN94rwfDIWVetkYdZ6xT++nrFPdJc2fHslQYHPJOL8zueRRJp5Rat0bpdWuU6F3lm+bOM3seVUZH65IqCgTc0wtTpwb90tzZcWTls6rUEvtOub78VzASUaStw00wpNsUCPpjrgEAAAC8dJBsWKCeV56haEebdh98QS/fcr5CPthdZ5VKCo9lNfjIEyocOiynUr/QHu/u9Mojxbu7FAj6Y6G9OjHh7qIcHlZ1YqLueiAcVrynR7Ha6YVQzB+7fhzLUjU36ZVHmtNkMp5wkwuZdoWT/igJBaC1VCbzGnvuRUlStD3t9jw6Y50yp/b5ojzf1OmF/MFB5Q4eUnFwqH6hPRhQsndV7fTCGkUy/vhZbGxb5bExL8FgF+pPqoTicTfm1noe+aUpqV0puzF3ckLVfHZWzyO5PY9qSf1QPOGLuQYAAADw0uWPd3IrKBAMKn3KKgWGDjb6UU6acRwVh0dVODio3MFBlUfGFJeUq12fajKZWten1No+hRP+OLpvl8tuk8nhYZWHh+tLQsltMjm10BFtb/dFPeTjnV5QMKhIOuM1mgxG/XFSBUDrSvR06OXveKOe27Nb577mAoV9sOjsWJZC2YKGntilwsDhOacXwqmEd2IwtWa1b06SWYWCl9SvjIzMOb0Q7ehwyyP19Pin55HjyMrnvLg7+/RCIBypxdyMwm0ZBUOt/+8bAAAAAKbwDuclwioU3d4LBweVn+f0gh2PqufMP1Db+rWK93T5Y6HdcVSenHTLI83TeyEQDk83mezp8U2TyeP2Xogn3ATD1OkFH8w1AH9J9XYrcGh/yy4+u71/sm7MHRhUcXBICceRd4YuGFBy9apaWcI+RTsyLTvWmRzbVmWq98I8pxeCsZhXGinW3e2f0wvl0nTcnd17QVI4mZ7uvcDpBQAAAAA+5o93eZjDOI6KR4a9BEN5rL5MkHd64ZQ+xXt79OvnfqfuTee0fFkou1RSYWhI7ZOTOvLww3PKBPn19IJVyHkLHXaxfnEnEAwpnG6bLtPA6QUAWHJ2parCoSPKD7hx18rX/yx2IiF1vGy90uvX+ub0gjFG1VzOOzFYHhurLxM0dXphqveCb04v2Krmsl7cdSrluuvu6YVaz6N0xjdJFQAAAAA4Ht79+Eglm1P+4GEVBgaVP3RkzkK713thbV/t9IL7ht+27UY87pIwjqPy2Jh3esHKucWgEpKM/Nt7wa5UajsoJ2VlJ2Wc+jn0ei+0tSucSikQaP2kCgA0E2OMymMTtaT+IRWPjEjGeNcDwaASve7phUTfKv2/5/dqY39/yyf1nWpVpeFhZbJZDT36qJxy/UK713uhp8c3pxeMMbXTC+6JQSufrZtrBQLu6YVagoHTCwAAAABeqlr/HeBLmFO1VBg8ovzAYeUPDqqazdVdD8WiXomG5No+heOtv9BujJE9VQN6ZESV0VGZWcmSSCajsWpVp55zjuJdXb54wx+QUTU3qVI+Jys7IXt2DehQyOu9EGnLKBjh9AIALDWrWFJ+oJbUHzgsu1S/0B7JpL1Tg8neVV5Da9u2pRaNRcYYVScmVBoZUXl4WJWJCckYJSU5khQMKtbZ6SX23QR3a451JseyVM1Neol9M6vPUzAS9WJuJJ1RoMWTSAAAAACwFEg2tBBvF+XAoAoHD6t4ZHhOs8XEqm4vwRDr6vDNG/7yyIjX3NkuFuuuB6NRb5Ej1t0thULav2uXoh2tO/6pXZRWrcHkqWGp8MLeunvCyZS30BFK+GNxBwCaibEdFYeGlT94WPmBQZVHx+uuB8IhJftWu3F3bZ+imXRjHnSJ2aWSl1wojYzMWWgPJZOadBytO/tsxXt6FPTFQrtbkrCcz6mam5RdqG/irUBQkXRakXQtqR+LE3cBAAAAYJaGJBtGRkb0D//wD3riiScUCoV0xRVX6LbbblN4nqP23/72t3XPPffoyJEjWr16td7xjnfouuuua8BTN8b0LsrDyh86LLtYv6M9kk4qObWLsm+1QlGf1ICeZxelJxBQrLPTK9MwuwZ0q5aFcqxqrQb03F2UwcBUDeh2RdsyClMDGgCWnDFG1Wze67tQGByaU5Iw1tWh1NpetyTh6m5fLLQb23ZLEtaS+lMlCacEwmHFurq8xH4gGtXBXbsUa/FEg10pq5qd9JL6+ed3110PxRO1U4MZhVNtvujzBAAAAADLqSGrlbfeeqt6e3v1yCOPaHh4WDfeeKPuuecevec976m772c/+5n+9V//VXfffbde9apXadeuXfqrv/or9fT06LLLLmvEoy87x7ZVPDLsJhfmaewcCIeUrNWATq3tUyTjj2aLVrHonl6Y2kU5a3EnnEwq1t3tnl7o6vLFQrtxHFmFvKo5twb07MbOCgQUSbUplGrTngMH9YpXnDNvQg4AcPLscsUrSVgYGFQ1V/+zOBSPKbW2103sr+1VOBFv0JMuHWOMrKnGziMjcxs7yy1JOJVciLa31y20t2pS39i1xs61uDuzsXMoMKskYTqjYJSShAAAAACwECu+cvnCCy/oiSee0MMPP6xEIqH169frpptu0qc+9ak5yYbDhw9r27Zt2rRpkySpv79fF1xwgZ588knfJBuMMaqMT7qLHIcOu7soZ72Jd3dRuoscftlF6ViWyqOjXnkkK19friAQDivW3a14d7di3d0KJ5MNetKlY4yRUy7VakBPqprPzlncmW8XpW3bqh4Y8EVSCQAazTiOikMjblJ/4LBKI6PSjMNzCgaUWN3jxV2/lCS0y2Xv5EJ5ZEROpVJ3PRiLuTG3VpIw5IOFdmOM7GLejbm5ydrvGqbunnAyrVCqTc8fOqSzX3EuSX0AAAAAWIQVf0e1e/dudXR0qLe313vtjDPO0MDAgCYnJ5XJZLzXZ5dLGhkZ0ZNPPqkPfehDC/66S7kLb+pznezntApFFQaHVDx0RIXBI3NKI4UScSXXrFZyTa+Sa1YrNKOxs1nE1z2axY7nRBjHUTWbVWVkROXRUVVnl0aSFGlvV6y7W9GuLkUymZPeRbkS4zlRjlWVlc/Jyk3KymVlrPq614FQWOF0m/snlVEwMl0GyzFGsu2mGs9SYDzNz29jYjwnJrTIRHYzxdmZjDGqTuZUGDzixt3DQzLV+tNz0fY2JWpxN7G6x2vsLEnOrKTwyWjEv0Fj26qMj6s8MqLK6Ojc0kjBoKKdnYp2dyvW1aXQrMbOx3rWZv6eciplVXNZWfms7Hx2zgaOYDSmcGoq7rYpEArJtm2VBwblOE5Tjmmhmnl+TpbfxsR4mhtxtvUwnubntzExnubGeJpbs8ZZLJ2AMbNWfJfZD37wA23fvl0PPvig99qLL76orVu36qGHHlJfX9+8Hzc0NKQbbrhBnZ2d+uIXv3jCO89s29auXbuW4MkXwXEUypcUyhUVyhUUKtcvOJtAQHYqLjudkJ1OyolFpFbfRWmMQo6jaKWiWLWqaLWq4Kx/alYwqEo0qnIkokokIuODWsgBGcUCUiIgJYJSbNY0GiOVjFQ0UtGRKrWPAoBG27x580l9XFPE2VkClj0dc/NFBav1v8iaUFBWLeba6YRMxAe72Y1R2LK8mButVudEl2oopHI0qkot7rb87xqSgjKKz4i7kVlDsmfFXYuYC6BB/BRnAQBoNicbZ7H0VvzddTKZVLFYrHtt6r9TqdS8H7Nr1y69973v1fnnn69PfOITJ3XE/dxzz12yLJdt23rmmWeO+jmN46g8Oq7CoSMqHDqi0vCI5NQvtMe6Omq7KFcrvqqxpZGON54T5VQqKo+NqTI6qsroqOxZ8xwIhxXt6lKsq0vRrq5lK420VOM5EcYYOaWirHxWVi4rq5Cbc2IjGIsrnM64uyiTaXUsMKmykuNZCYyn+fltTIxnZaxknJ3NsSwVj4x4JwYrs/odKRhUYlW3kmtWK7Fm9YqXRlquObOKRe/EYGVsTKZav5khGIt5JwZjXV1L1oOgkf8GjePILubdmJvPzu13JCmUTCmccuNuKJE87lw36/fUyfLbeCT/jYnxNLdmHU8j42yzYzzNz29jYjzNjfE0N7+NB3OteLJhw4YNGh8f1/DwsHp6eiRJe/fuVV9fn9ra2ubcf9999+mf//mfdcstt+jd7373SX/dUCi05P+Ipz7nVImG/KHDKgy4fRecWW/4w6lkrcFkr1J99aWRmsVC/44c21ZlbMzru1DNZutvCAQU7ejw+i5E2ttXdHFnOeZckuxKWdXspKzcpKq5rIxdX44jEI4o0pZxey+k60sjLcZyjadRGE/z89uYGM/yWs44O5txjEojYyoMHlZh4IiKR4ZlZpU7inW2uzF3Ta8SvT0KNkEt/sX+HdmViiqjoyrV4u58Sf1YV5diM/odLWfcXYl/g8YY2aXijL4LOcnM6ncUiys8FXdrpZFORrN9Ty2W38Yj+W9MjKe5Ndt4VjLOtirG0/z8NibG09wYT3Pz23gwbcXfeZ922mnavHmzPv7xj+uOO+7Q2NiYPv/5z+uaa66Zc+/999+v22+/XV/4whf0ute9bqUf9ZgCVUvZfS+qeHhYhUOHZRXq3/AHoxEl+9yTC6m1vYq0pVu+waQxRtWJCZVrCx2VsbE5u/jD6bTX2Dna2dkUizuL5VhVWbms29g5l5VTKdffEAwqkmrzEgzBWLzl5xoAmokxRpXJrAoDR1Q4dLSkfkKpNb1ev6NwIt6gp106J5TUr/U7ivX0KDqr31ErMsZM9104alI/7CX0I20ZBSOt38waAAAAAPygISvBd911l+644w5deumlCgaDuuqqq3TTTTdJkvr7+/WRj3xEV1xxhT73uc/Jtm3dcsstdR9/+eWX64477mjEo6s8PqmBh3cqNTahw3rRez0QDCqxukfJtW6DyXhXpwLB1l5wNsbIyue9RY7y2JiMVf+GPxSPezsoY11dCsWa78TGQhnbVrVWFqmam5RdKs66I6BwKqVIOuOWR0omFQi09uIOADSbQNXS5L4XVBqsJfWLpbrrwUhEyb5V3umFSMYHSX3HUWVy0ou7lfHxl0xS30suZCflVCv1N8xI6ofTGYVI6gMAAABAU2rIO9Senh7ddddd81771a9+5f3/HTt2rNQjnbDS8KgqYxMykuJdHW5ppDW9SqzuUTDc+sd/rGJxOrkwOiqnUv+Gf2aJhnh3t0LLXKJhJRjHkVXIu+UZcpOyCgVJ9Ys7oXhC4XTboks0AACOzS5XtP9nDys1PKYjs5P6vT1KrvFZUj+bVXl01Ptj7Ppm1l5SvxZ7/ZDUd2xLVi6nan5SVi47N6kfCCicJKkPAAAAAK2m9bfDrbDMGS9TOJPWs8/v04bzN7d8fTG7XFZxeFiZbFZD//f/zq3/HAwq2tnpLXJEMpnWTy4YI7tY8MoizVf/ORiNeosckXSbguGl6bsAADg2u1xReWTcTep3d7qlkdauVmJV6yf1jTGq5vOqjI5OJ/VnlYMKhMNeQj/W1eWfpH4+58Vdu5ifc08onpiOu6k0SX0AAAAAaEEkGxYoEAgo3t0p7W/NN8FOpaLy2Ji7g3JkRFbefcOflGRL0/Wfa8mFaEeHL+o/26WiVxbJyudknPqdo4FwWJFUxj290JZRKNr6O0cBoBVFM2md9uf/R7/+7f/Ths2tn9S3ikWVhofVns1q6NFH5ZTr+/4EQqH6pH5bmz+SC8V8Le5mZRVyc8pBBaOxWt+FNoVJ6gMAAACAL5Bs8DnHstzmkrXkwpzmknLrP09Ylk45+2wlurtbvv6zMUYRGZVHh2QX3MWOOc0lgyE3sZBqU7iN+s8A0EzCyYTUokkGu1z2Ti2UR0e9E4MJSY7kJvU7OrzSSNH2dn8k9Yt5VaeSC/OcGAxEItPJhVRGoShNnQEAAADAb1p7VRlzOJalyvi4t8hRnZyc21wylXJ3UHZ1KdrVJYVCOrBrl+I9PQq24OKOMUZOpVxb4HAXOtZFpNKhA9M3BYKKpNMKp9y+C6FE65elAAA0nl0uqzw25pZGGh31Tgx6AgFF2to0Vq1q/dlnK97d3ZKxdiZjjKIyKg8fll3Iq5rPSs6s5EIo7J1aiKQzCkZjxF0AAAAA8DmSDS3OsW0vuVAZHVVlYmJOciGUSEw3l+zqmtNc0p7VjLLZucmFiqy8W/u5ms/KzKp57Rgpkk4rmnZLI4UTqZbfOQoAaDy7Upk+MTg6KiuXm3NPJJOZTup3dsoEAtq/a5diLZpomC5HOKlqPqdqLqtTIlLp8IB3TyAUqiX02xROc2IQAAAAAF6KSDa0GGPbKo+Pezso500uxOPeqYVYV5fCiUSDnnZpGGPkVCtu7ed8VlYuK6daqb8pEFA4mVI41aZQMqXf7N6jV522oeVrfQMAGutEkgvhdNrruRDr7FQwUt9/oBWT+l6vo7x7atDMGoNjpGhbRpG2jHtiMJ4guQAAAAAAL3EkG5rcVFmkqYWOoyUXphILvkkuVMq1kkg5Wfl5kguqJRem+i6kUgoE3cSCbdsyYsEDALBwC0oudHYq2tXV8v0HTiS5oGCwFm/bFEwm9evfPadNLzuDpD4AAAAAwEOyocl4DZ1rCx3z9VwIxmJeYiHW1aVQorV3E87puTBPWSQ3uZCcLtGQSnvJBQAATpZdLrvJ/FrsfckkF4oF77Sglc/JOPMlF9Je3A0lUt7vGu5Jjdb9vQMAAAAAsDxINjSYU626jSWnkgvZ7NFPLnR2+ia5YJdLsmqLHNV8TsaalVwIBBROTJ1cSJNcAAAsCatY9BILldFRWYXCnHvCqVRdz4XZvY5ajTGOrELBjbt5N+7Obuh8rOQCAAAAAAAngmTDCrPL5elFjrExN7kwSyiR8BY4/FIWyS4WvAUOK5+Tsa36m2b0XIik2xROpkguAAAWxRgju1CY7nU0Nia7WJxzX6StzYu50c7O1j+54DiyCm68dcsi5SVTn1wIhEIKJ9NeOcJQIklyAQAAAACwKCQbllndDsqxMfcN/yzhZLJukaPlkwuOI6s4vYPSyufnlmcIBBVOpbz6z25yIdiYBwYA+IIxRlYuN31icGxMTrk8575IJjOd1J+noXOrcWxLVj4/HXeLhTmnJAOhsMKpdK0UYRsNnQEAAAAAS45kwxIyxsjK5+uSC3apNOe+utrPfijPYNuyCrnaqYWsrEJ+7iJHMKRwrRySt4OS5AIAYBGM46g6OVmXXDDW3JNz0fZ2L7EQ7exUMNzav/441Wot7mZl5XKyS3NLQQXCETehn3ZLI4VicZILAAAAAIBl1drvthvMOI4q2axbnqG20OHMbmwcCLg7KGsLHLGODgVbvDyDY1VnlGbIyS7Os8jBDkoAwBJzLEuViQkv5lYmJmTs+pNzgVBI0Y6O6eRCe7sCodYty2eMkVMp1+Kum9R3KnNPawSjMS+hH06lFYzGiLsAAAAAgBVFsmGBHMtS7ve/V+fEhI48+KDMrAaLgWBQkfZ2L7kQ7eho6R2UMxc5pk4vOOW5pzWCkahbDimVViSVVpAdlACARbIrFVVmnFyoZrNzT86Fw9MJ/c5ORTKZlj45Z4yRXSrWSiK5cddY1Tn3heKJ2onBNjfuRlp7IwMAAAAAoPW17ip4gxQOHlRu717FJBn5dJGjWHAXOAruDso5JSk0Y5Ej6e6ibPXTGgCA5mAcR5PPPaeesTENPfzwnOuheLyuJFI4lWrp5LZxbFmF/PTJhUJOmrWRQYGAwomkl9QPJ9MtvZEBAAAAAOBPvFNdoERfn6xSSQNDQzr93HMVy2Rae5HDtlXNTaojaJT7/W63JNK8ixwpr+cCixwAgOVi5fMqvPii9wtKOJ2uOy0YTiQa+nyL5VQrMxL6RylFGAwpnEq5yYVkWuFkqqU3MgAAAAAAXhpYMV6gUCymtjPPVDGXUySdbqlEgzHGW+SwphY5SkVJUmdIsvM5SW6963AyPZ1cSLDIAQBYGZG2NnX84R/q+d//Xmf19ysSjzf6kU6aMUZ2uaRKdlI9IaPsc7+RU63MuS8YidbFXfocAQAAAABaEckGHzOOI7s0VRLJLdEwX93nQCSqyXJFq05Zr2hbRiH6LQAAGii+erXKAwMKRiKNfpQFMXatJFItoW8V8jKO28C6LSgv0TCzFGE4lVYoGmvkYwMAAAAAsCRINviIY1W9xQ2rkJNVKEhmbkmkUCJZ67XglmYwwZD27dqldV09CoVCjXl4AABayLFOC9YJBhVKJDU8mdMpp5+hWDqjALEWAAAAAOBDJBtalDFGdqlYW+BwkwtOpTznvhMpiWTb9ko9NgAALck4jqxivi6pbyxrzn3BSLT+1EI8IcdxtGfXLp1GogEAAAAA4GMkG1qEY1VrixtTpxbycxs5SwrF4tOLHMmUgpREAgBgQepPLbhx1y4WJZn6G2eeFkymFE6lFYxEG/LMAAAAAAA0GsmGJmSMI7tYrEsuzHdqQcGgl1QI10oiBUNMKQAAC2EcW1ah4CXzrUJ+/h5H4UhdzJ3vtCAAAAAAAC9VrEw3AadamXVqYZ5eC5KCsbi7uFFLMITiCU4tAACwAMYYOeXyjMTCUXotKKBQIjFdijCZUjASJe4CAAAAAHAUJBtW2PTuybysYl52ISenOs/uyWDITShM7aBMpBQMM10AACyEY1kzSiHVTi3M06soGIkoNHVakFMLAAAAAAAsGKvXy8jdPVmq67Mw/+5JKRRP1J1aoNcCAAALYxxHdrHgNnKunRictwxhIKBwIjVdEimRUjBKrwUAAAAAABaDZMMSMcbIVKvTCxy1/52vibNX83nm7slQqAFPDQBAa/LKIRVnJfSNmXNvMBqb7nGUTCmUSCgQ4NQCAAAAAABLiWTDSQrKqJqbVMU7uTB/M0kFggonk9OnFtg9CQDAgsyX0LcLBRlnbjmkQChcl9APUYYQAAA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      "text/plain": [
       "<Figure size 1565.5x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your answer here\n",
    "preds = me.predictions(marginal_mod, newdata=dg)\n",
    "\n",
    "# Plot\n",
    "sns.relplot(data=preds,\n",
    "            x='Year', y='estimate',\n",
    "            hue='n_exps', col='Field',\n",
    "            kind='line').refline(y=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deeaa088-57b2-4364-b8ce-46f2ee1df787",
   "metadata": {},
   "source": [
    "Using your datagrid, or by developing one you might need, test the following hypotheses:\n",
    "\n",
    "1. Regardless of time or number of experiments, which field is most guilty of using the phrase?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8d6d6ee5-5155-4da4-bbca-e59bd54f458d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (3, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Field</th><th>estimate</th><th>std_error</th><th>statistic</th><th>p_value</th><th>s_value</th><th>conf_low</th><th>conf_high</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Cognitive&quot;</td><td>0.42801</td><td>0.05757</td><td>7.434588</td><td>1.0481e-13</td><td>43.117357</td><td>0.315174</td><td>0.540845</td></tr><tr><td>&quot;Developmental&quot;</td><td>0.425579</td><td>0.047817</td><td>8.900135</td><td>0.0</td><td>inf</td><td>0.331859</td><td>0.519299</td></tr><tr><td>&quot;Social&quot;</td><td>0.562763</td><td>0.035215</td><td>15.980838</td><td>0.0</td><td>inf</td><td>0.493743</td><td>0.631782</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (3, 8)\n",
       "┌───────────────┬──────────┬───────────┬──────┬──────────┬──────┬───────┬───────┐\n",
       "│ Field         ┆ Estimate ┆ Std.Error ┆ z    ┆ P(>|z|)  ┆ S    ┆ 2.5%  ┆ 97.5% │\n",
       "│ ---           ┆ ---      ┆ ---       ┆ ---  ┆ ---      ┆ ---  ┆ ---   ┆ ---   │\n",
       "│ str           ┆ str      ┆ str       ┆ str  ┆ str      ┆ str  ┆ str   ┆ str   │\n",
       "╞═══════════════╪══════════╪═══════════╪══════╪══════════╪══════╪═══════╪═══════╡\n",
       "│ Cognitive     ┆ 0.428    ┆ 0.0576    ┆ 7.43 ┆ 1.05e-13 ┆ 43.1 ┆ 0.315 ┆ 0.541 │\n",
       "│ Developmental ┆ 0.426    ┆ 0.0478    ┆ 8.9  ┆ 0        ┆ inf  ┆ 0.332 ┆ 0.519 │\n",
       "│ Social        ┆ 0.563    ┆ 0.0352    ┆ 16   ┆ 0        ┆ inf  ┆ 0.494 ┆ 0.632 │\n",
       "└───────────────┴──────────┴───────────┴──────┴──────────┴──────┴───────┴───────┘\n",
       "\n",
       "Columns: Field, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your answer here\n",
    "me.predictions(marginal_mod, \n",
    "               newdata=dg,\n",
    "               by=['Field'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d45eca42-e883-4242-a925-ec380d6ea4db",
   "metadata": {},
   "source": [
    "2. In 2010, what number of experiments in social psychology led to the highest probability of using the phrase? At which point was the highest value statistically significant from others (you can use the 'sequential' argument to `hypothesis` for this!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "532a2029-275d-4782-b3ae-34a5a4c02275",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
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       ".dataframe > tbody > tr {\n",
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       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (9, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>term</th><th>estimate</th><th>std_error</th><th>statistic</th><th>p_value</th><th>s_value</th><th>conf_low</th><th>conf_high</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Row 2 - Row 1&quot;</td><td>0.046179</td><td>0.010886</td><td>4.242234</td><td>0.000022</td><td>15.463599</td><td>0.024844</td><td>0.067515</td></tr><tr><td>&quot;Row 3 - Row 2&quot;</td><td>0.045337</td><td>0.010624</td><td>4.267511</td><td>0.00002</td><td>15.626575</td><td>0.024515</td><td>0.066159</td></tr><tr><td>&quot;Row 4 - Row 3&quot;</td><td>0.043911</td><td>0.009966</td><td>4.406169</td><td>0.000011</td><td>16.536303</td><td>0.024379</td><td>0.063444</td></tr><tr><td>&quot;Row 5 - Row 4&quot;</td><td>0.041959</td><td>0.008966</td><td>4.679607</td><td>0.000003</td><td>18.40838</td><td>0.024385</td><td>0.059533</td></tr><tr><td>&quot;Row 6 - Row 5&quot;</td><td>0.039554</td><td>0.007702</td><td>5.135353</td><td>2.8161e-7</td><td>21.759773</td><td>0.024458</td><td>0.054651</td></tr><tr><td>&quot;Row 7 - Row 6&quot;</td><td>0.036786</td><td>0.00627</td><td>5.867274</td><td>4.4302e-9</td><td>27.749991</td><td>0.024498</td><td>0.049075</td></tr><tr><td>&quot;Row 8 - Row 7&quot;</td><td>0.033752</td><td>0.004781</td><td>7.059261</td><td>1.6740e-12</td><td>39.119843</td><td>0.024381</td><td>0.043123</td></tr><tr><td>&quot;Row 9 - Row 8&quot;</td><td>0.030552</td><td>0.003378</td><td>9.044419</td><td>0.0</td><td>inf</td><td>0.023931</td><td>0.037172</td></tr><tr><td>&quot;Row 10 - Row 9&quot;</td><td>0.027283</td><td>0.002284</td><td>11.944306</td><td>0.0</td><td>inf</td><td>0.022806</td><td>0.03176</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (9, 8)\n",
       "┌────────────────┬──────────┬───────────┬──────┬──────────┬──────┬────────┬────────┐\n",
       "│ Term           ┆ Estimate ┆ Std.Error ┆ z    ┆ P(>|z|)  ┆ S    ┆ 2.5%   ┆ 97.5%  │\n",
       "│ ---            ┆ ---      ┆ ---       ┆ ---  ┆ ---      ┆ ---  ┆ ---    ┆ ---    │\n",
       "│ str            ┆ str      ┆ str       ┆ str  ┆ str      ┆ str  ┆ str    ┆ str    │\n",
       "╞════════════════╪══════════╪═══════════╪══════╪══════════╪══════╪════════╪════════╡\n",
       "│ Row 2 - Row 1  ┆ 0.0462   ┆ 0.0109    ┆ 4.24 ┆ 2.21e-05 ┆ 15.5 ┆ 0.0248 ┆ 0.0675 │\n",
       "│ Row 3 - Row 2  ┆ 0.0453   ┆ 0.0106    ┆ 4.27 ┆ 1.98e-05 ┆ 15.6 ┆ 0.0245 ┆ 0.0662 │\n",
       "│ Row 4 - Row 3  ┆ 0.0439   ┆ 0.00997   ┆ 4.41 ┆ 1.05e-05 ┆ 16.5 ┆ 0.0244 ┆ 0.0634 │\n",
       "│ Row 5 - Row 4  ┆ 0.042    ┆ 0.00897   ┆ 4.68 ┆ 2.87e-06 ┆ 18.4 ┆ 0.0244 ┆ 0.0595 │\n",
       "│ Row 6 - Row 5  ┆ 0.0396   ┆ 0.0077    ┆ 5.14 ┆ 2.82e-07 ┆ 21.8 ┆ 0.0245 ┆ 0.0547 │\n",
       "│ Row 7 - Row 6  ┆ 0.0368   ┆ 0.00627   ┆ 5.87 ┆ 4.43e-09 ┆ 27.7 ┆ 0.0245 ┆ 0.0491 │\n",
       "│ Row 8 - Row 7  ┆ 0.0338   ┆ 0.00478   ┆ 7.06 ┆ 1.67e-12 ┆ 39.1 ┆ 0.0244 ┆ 0.0431 │\n",
       "│ Row 9 - Row 8  ┆ 0.0306   ┆ 0.00338   ┆ 9.04 ┆ 0        ┆ inf  ┆ 0.0239 ┆ 0.0372 │\n",
       "│ Row 10 - Row 9 ┆ 0.0273   ┆ 0.00228   ┆ 11.9 ┆ 0        ┆ inf  ┆ 0.0228 ┆ 0.0318 │\n",
       "└────────────────┴──────────┴───────────┴──────┴──────────┴──────┴────────┴────────┘\n",
       "\n",
       "Columns: term, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your answer here\n",
    "me.predictions(marginal_mod,\n",
    "               newdata=me.datagrid(marginal_mod, \n",
    "                                   Year=2010, \n",
    "                                   Field='Social', \n",
    "                                   n_exps=np.arange(1, 11)),\n",
    "               hypothesis='sequential')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6e4654a-d34f-4e32-9443-e369a2b95fec",
   "metadata": {},
   "source": [
    "3. For each field, what is the difference between the probability of use in 2010 and 2000, averaging over the number of experiments? You may need to run it a few times to compare each field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1c635286-c85e-42e5-bfe5-81a144832e2f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>term</th><th>estimate</th><th>std_error</th><th>statistic</th><th>p_value</th><th>s_value</th><th>conf_low</th><th>conf_high</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;b1=b4&quot;</td><td>-0.074131</td><td>0.01057</td><td>-7.013193</td><td>2.3295e-12</td><td>38.643135</td><td>-0.094849</td><td>-0.053414</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 8)\n",
       "┌───────┬──────────┬───────────┬───────┬──────────┬──────┬─────────┬─────────┐\n",
       "│ Term  ┆ Estimate ┆ Std.Error ┆ z     ┆ P(>|z|)  ┆ S    ┆ 2.5%    ┆ 97.5%   │\n",
       "│ ---   ┆ ---      ┆ ---       ┆ ---   ┆ ---      ┆ ---  ┆ ---     ┆ ---     │\n",
       "│ str   ┆ str      ┆ str       ┆ str   ┆ str      ┆ str  ┆ str     ┆ str     │\n",
       "╞═══════╪══════════╪═══════════╪═══════╪══════════╪══════╪═════════╪═════════╡\n",
       "│ b1=b4 ┆ -0.0741  ┆ 0.0106    ┆ -7.01 ┆ 2.33e-12 ┆ 38.6 ┆ -0.0948 ┆ -0.0534 │\n",
       "└───────┴──────────┴───────────┴───────┴──────────┴──────┴─────────┴─────────┘\n",
       "\n",
       "Columns: term, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your answer here\n",
    "me.predictions(marginal_mod,\n",
    "               newdata=me.datagrid(marginal_mod,\n",
    "                                   Year=[2000, 2010],\n",
    "                                   Field=['Cognitive', 'Developmental', 'Social'],\n",
    "                                   n_exps=np.arange(1, 11)),\n",
    "               by=['Year', 'Field'], # averages over experiments\n",
    "               hypothesis='b1=b4' # e.g., for Cognitive, this is the comparison\n",
    "              )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "370de054-a1b1-4f5a-b063-902b26faa1b7",
   "metadata": {},
   "source": [
    "4. In developmental psychology, what combination of year and number of experiments has a probability *significantly* grater than .6 of using the phrase?\n",
    "\n",
    "This is a very tricky one. You will need to specify the right data grid, and rather than specifying a string to `hypothesis`, you can pass\n",
    "a float value. Then you will need to convert your output to pandas and filter it appropriately - this is difficult, but this is an extremely specific hypothesis.\n",
    "\n",
    "Nevertheless, this is the kind of science you can do by leveraging linear models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "66d7a8ff-0f88-4b86-83dd-ba5c7fbf0d80",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Field</th>\n",
       "      <th>Year</th>\n",
       "      <th>n_exps</th>\n",
       "      <th>rowid</th>\n",
       "      <th>estimate</th>\n",
       "      <th>std_error</th>\n",
       "      <th>statistic</th>\n",
       "      <th>p_value</th>\n",
       "      <th>s_value</th>\n",
       "      <th>conf_low</th>\n",
       "      <th>conf_high</th>\n",
       "      <th>marginal_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>Developmental</td>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>389</td>\n",
       "      <td>0.750526</td>\n",
       "      <td>0.073478</td>\n",
       "      <td>2.048579</td>\n",
       "      <td>0.040503</td>\n",
       "      <td>4.625816</td>\n",
       "      <td>0.606511</td>\n",
       "      <td>0.894540</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>Developmental</td>\n",
       "      <td>2009</td>\n",
       "      <td>10</td>\n",
       "      <td>399</td>\n",
       "      <td>0.756750</td>\n",
       "      <td>0.072383</td>\n",
       "      <td>2.165564</td>\n",
       "      <td>0.030345</td>\n",
       "      <td>5.042420</td>\n",
       "      <td>0.614882</td>\n",
       "      <td>0.898619</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>Developmental</td>\n",
       "      <td>2010</td>\n",
       "      <td>10</td>\n",
       "      <td>409</td>\n",
       "      <td>0.762890</td>\n",
       "      <td>0.071286</td>\n",
       "      <td>2.285035</td>\n",
       "      <td>0.022311</td>\n",
       "      <td>5.486114</td>\n",
       "      <td>0.623173</td>\n",
       "      <td>0.902607</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Field  Year  n_exps  rowid  estimate  std_error  statistic  \\\n",
       "389  Developmental  2008      10    389  0.750526   0.073478   2.048579   \n",
       "399  Developmental  2009      10    399  0.756750   0.072383   2.165564   \n",
       "409  Developmental  2010      10    409  0.762890   0.071286   2.285035   \n",
       "\n",
       "      p_value   s_value  conf_low  conf_high  marginal_yes  \n",
       "389  0.040503  4.625816  0.606511   0.894540             0  \n",
       "399  0.030345  5.042420  0.614882   0.898619             0  \n",
       "409  0.022311  5.486114  0.623173   0.902607             0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your answer here\n",
    "# Get predictions\n",
    "ps = me.predictions(marginal_mod,\n",
    "                    newdata=me.datagrid(marginal_mod,\n",
    "                                        Field='Developmental',\n",
    "                                        Year=np.arange(1970, 2011),\n",
    "                                        n_exps=np.arange(1, 11)),\n",
    "                    hypothesis=.6)\n",
    "\n",
    "# Now filter\n",
    "ps.to_pandas().query('statistic > 0 and p_value < .05')"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "5. Averaging across all years, for which combination of number of experiments and fields show a significantly-greater-than-.50 chance of using the phrase?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4219f4f4-75f4-48bc-90d7-ede7db4984b2",
   "metadata": {
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     "hide-input"
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   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Field</th>\n",
       "      <th>n_exps</th>\n",
       "      <th>estimate</th>\n",
       "      <th>std_error</th>\n",
       "      <th>statistic</th>\n",
       "      <th>p_value</th>\n",
       "      <th>s_value</th>\n",
       "      <th>conf_low</th>\n",
       "      <th>conf_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Social</td>\n",
       "      <td>6</td>\n",
       "      <td>0.588244</td>\n",
       "      <td>0.042696</td>\n",
       "      <td>2.066821</td>\n",
       "      <td>0.038751</td>\n",
       "      <td>4.689621</td>\n",
       "      <td>0.504562</td>\n",
       "      <td>0.671927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Social</td>\n",
       "      <td>7</td>\n",
       "      <td>0.631692</td>\n",
       "      <td>0.050721</td>\n",
       "      <td>2.596420</td>\n",
       "      <td>0.009420</td>\n",
       "      <td>6.730044</td>\n",
       "      <td>0.532281</td>\n",
       "      <td>0.731102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Social</td>\n",
       "      <td>8</td>\n",
       "      <td>0.673518</td>\n",
       "      <td>0.057668</td>\n",
       "      <td>3.008925</td>\n",
       "      <td>0.002622</td>\n",
       "      <td>8.575258</td>\n",
       "      <td>0.560491</td>\n",
       "      <td>0.786545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Social</td>\n",
       "      <td>9</td>\n",
       "      <td>0.713270</td>\n",
       "      <td>0.063214</td>\n",
       "      <td>3.373783</td>\n",
       "      <td>0.000741</td>\n",
       "      <td>10.397408</td>\n",
       "      <td>0.589373</td>\n",
       "      <td>0.837167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Social</td>\n",
       "      <td>10</td>\n",
       "      <td>0.750570</td>\n",
       "      <td>0.067164</td>\n",
       "      <td>3.730709</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>12.354578</td>\n",
       "      <td>0.618931</td>\n",
       "      <td>0.882210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Field  n_exps  estimate  std_error  statistic   p_value    s_value  \\\n",
       "5  Social       6  0.588244   0.042696   2.066821  0.038751   4.689621   \n",
       "6  Social       7  0.631692   0.050721   2.596420  0.009420   6.730044   \n",
       "7  Social       8  0.673518   0.057668   3.008925  0.002622   8.575258   \n",
       "8  Social       9  0.713270   0.063214   3.373783  0.000741  10.397408   \n",
       "9  Social      10  0.750570   0.067164   3.730709  0.000191  12.354578   \n",
       "\n",
       "   conf_low  conf_high  \n",
       "5  0.504562   0.671927  \n",
       "6  0.532281   0.731102  \n",
       "7  0.560491   0.786545  \n",
       "8  0.589373   0.837167  \n",
       "9  0.618931   0.882210  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your answer here\n",
    "# Get predictions\n",
    "ps = me.predictions(marginal_mod,\n",
    "                    newdata=me.datagrid(marginal_mod,\n",
    "                                        Field='Social',\n",
    "                                        Year=np.arange(1970, 2011),\n",
    "                                        n_exps=np.arange(1, 11)),\n",
    "                    by=['Field', 'n_exps'],\n",
    "                    hypothesis=.5)\n",
    "\n",
    "# Now filter\n",
    "ps.to_pandas().query('statistic > 0 and p_value < .05')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45773f52-e1fb-4566-ae4f-c71ca56b67c4",
   "metadata": {},
   "source": [
    "Social psychology papers with 6 or more studies are, across time, most likely to use the phrase!"
   ]
  }
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