diff --git a/module2/exo5/exo5_fr.ipynb b/module2/exo5/exo5_fr.ipynb
index 26ad6d94fa840f788a57621b06dc6af83a848391..020527121d0e5b2038bbf320a6f0142dcb3d6dcf 100644
--- a/module2/exo5/exo5_fr.ipynb
+++ b/module2/exo5/exo5_fr.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -261,33 +261,33 @@
        "</div>"
       ],
       "text/plain": [
-       "         Date  Count  Temperature  Pressure  Malfunction\n",
-       "0     4/12/81      6           66        50            0\n",
-       "1    11/12/81      6           70        50            1\n",
-       "2     3/22/82      6           69        50            0\n",
-       "3    11/11/82      6           68        50            0\n",
-       "4     4/04/83      6           67        50            0\n",
-       "5     6/18/82      6           72        50            0\n",
-       "6     8/30/83      6           73       100            0\n",
-       "7    11/28/83      6           70       100            0\n",
-       "8     2/03/84      6           57       200            1\n",
-       "9     4/06/84      6           63       200            1\n",
-       "10    8/30/84      6           70       200            1\n",
-       "11   10/05/84      6           78       200            0\n",
-       "12   11/08/84      6           67       200            0\n",
-       "13    1/24/85      6           53       200            2\n",
-       "14    4/12/85      6           67       200            0\n",
-       "15    4/29/85      6           75       200            0\n",
-       "16    6/17/85      6           70       200            0\n",
-       "17  7/29/85      6           81       200            0\n",
-       "18    8/27/85      6           76       200            0\n",
-       "19   10/03/85      6           79       200            0\n",
-       "20   10/30/85      6           75       200            2\n",
-       "21   11/26/85      6           76       200            0\n",
-       "22    1/12/86      6           58       200            1"
+       "        Date  Count  Temperature  Pressure  Malfunction\n",
+       "0    4/12/81      6           66        50            0\n",
+       "1   11/12/81      6           70        50            1\n",
+       "2    3/22/82      6           69        50            0\n",
+       "3   11/11/82      6           68        50            0\n",
+       "4    4/04/83      6           67        50            0\n",
+       "5    6/18/82      6           72        50            0\n",
+       "6    8/30/83      6           73       100            0\n",
+       "7   11/28/83      6           70       100            0\n",
+       "8    2/03/84      6           57       200            1\n",
+       "9    4/06/84      6           63       200            1\n",
+       "10   8/30/84      6           70       200            1\n",
+       "11  10/05/84      6           78       200            0\n",
+       "12  11/08/84      6           67       200            0\n",
+       "13   1/24/85      6           53       200            2\n",
+       "14   4/12/85      6           67       200            0\n",
+       "15   4/29/85      6           75       200            0\n",
+       "16   6/17/85      6           70       200            0\n",
+       "17   7/29/85      6           81       200            0\n",
+       "18   8/27/85      6           76       200            0\n",
+       "19  10/03/85      6           79       200            0\n",
+       "20  10/30/85      6           75       200            2\n",
+       "21  11/26/85      6           76       200            0\n",
+       "22   1/12/86      6           58       200            1"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -322,7 +322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -355,6 +355,14 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4/12/81</td>\n",
+       "      <td>6</td>\n",
+       "      <td>66</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>11/12/81</td>\n",
        "      <td>6</td>\n",
@@ -363,6 +371,54 @@
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3/22/82</td>\n",
+       "      <td>6</td>\n",
+       "      <td>69</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11/11/82</td>\n",
+       "      <td>6</td>\n",
+       "      <td>68</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4/04/83</td>\n",
+       "      <td>6</td>\n",
+       "      <td>67</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6/18/82</td>\n",
+       "      <td>6</td>\n",
+       "      <td>72</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>8/30/83</td>\n",
+       "      <td>6</td>\n",
+       "      <td>73</td>\n",
+       "      <td>100</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>11/28/83</td>\n",
+       "      <td>6</td>\n",
+       "      <td>70</td>\n",
+       "      <td>100</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>2/03/84</td>\n",
        "      <td>6</td>\n",
@@ -387,6 +443,22 @@
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10/05/84</td>\n",
+       "      <td>6</td>\n",
+       "      <td>78</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>11/08/84</td>\n",
+       "      <td>6</td>\n",
+       "      <td>67</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>1/24/85</td>\n",
        "      <td>6</td>\n",
@@ -395,6 +467,54 @@
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>4/12/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>67</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>4/29/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>75</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>6/17/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>70</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>7/29/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>81</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>8/27/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>76</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10/03/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>79</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>10/30/85</td>\n",
        "      <td>6</td>\n",
@@ -403,6 +523,14 @@
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>11/26/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>76</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>1/12/86</td>\n",
        "      <td>6</td>\n",
@@ -416,22 +544,38 @@
       ],
       "text/plain": [
        "        Date  Count  Temperature  Pressure  Malfunction\n",
+       "0    4/12/81      6           66        50            0\n",
        "1   11/12/81      6           70        50            1\n",
+       "2    3/22/82      6           69        50            0\n",
+       "3   11/11/82      6           68        50            0\n",
+       "4    4/04/83      6           67        50            0\n",
+       "5    6/18/82      6           72        50            0\n",
+       "6    8/30/83      6           73       100            0\n",
+       "7   11/28/83      6           70       100            0\n",
        "8    2/03/84      6           57       200            1\n",
        "9    4/06/84      6           63       200            1\n",
        "10   8/30/84      6           70       200            1\n",
+       "11  10/05/84      6           78       200            0\n",
+       "12  11/08/84      6           67       200            0\n",
        "13   1/24/85      6           53       200            2\n",
+       "14   4/12/85      6           67       200            0\n",
+       "15   4/29/85      6           75       200            0\n",
+       "16   6/17/85      6           70       200            0\n",
+       "17   7/29/85      6           81       200            0\n",
+       "18   8/27/85      6           76       200            0\n",
+       "19  10/03/85      6           79       200            0\n",
        "20  10/30/85      6           75       200            2\n",
+       "21  11/26/85      6           76       200            0\n",
        "22   1/12/86      6           58       200            1"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data = data[data.Malfunction>0]\n",
+    "# data = data[data.Malfunction>0]\n",
     "data"
    ]
   },
@@ -448,12 +592,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -500,7 +644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -509,10 +653,10 @@
        "<table class=\"simpletable\">\n",
        "<caption>Generalized Linear Model Regression Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>      <td>Frequency</td>    <th>  No. Observations:  </th>  <td>     7</td>  \n",
+       "  <th>Dep. Variable:</th>      <td>Frequency</td>    <th>  No. Observations:  </th>  <td>    23</td>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>     5</td>  \n",
+       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>    21</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Model Family:</th>       <td>Binomial</td>     <th>  Df Model:          </th>  <td>     1</td>  \n",
@@ -521,16 +665,16 @@
        "  <th>Link Function:</th>        <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>               <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -2.5250</td> \n",
+       "  <th>Method:</th>               <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -3.9210</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>           <td>Sat, 13 Apr 2019</td> <th>  Deviance:          </th> <td> 0.22231</td> \n",
+       "  <th>Date:</th>           <td>Tue, 17 Oct 2023</td> <th>  Deviance:          </th> <td>  3.0144</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>               <td>19:11:24</td>     <th>  Pearson chi2:      </th>  <td> 0.236</td>  \n",
+       "  <th>Time:</th>               <td>17:53:24</td>     <th>  Pearson chi2:      </th>  <td>  5.00</td>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>No. Iterations:</th>         <td>4</td>        <th>  Covariance Type:   </th> <td>nonrobust</td>\n",
+       "  <th>No. Iterations:</th>         <td>6</td>        <th>  Covariance Type:   </th> <td>nonrobust</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
@@ -538,10 +682,10 @@
        "       <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>   -1.3895</td> <td>    7.828</td> <td>   -0.178</td> <td> 0.859</td> <td>  -16.732</td> <td>   13.953</td>\n",
+       "  <th>Intercept</th>   <td>    5.0850</td> <td>    7.477</td> <td>    0.680</td> <td> 0.496</td> <td>   -9.570</td> <td>   19.740</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Temperature</th> <td>    0.0014</td> <td>    0.122</td> <td>    0.012</td> <td> 0.991</td> <td>   -0.238</td> <td>    0.240</td>\n",
+       "  <th>Temperature</th> <td>   -0.1156</td> <td>    0.115</td> <td>   -1.004</td> <td> 0.316</td> <td>   -0.341</td> <td>    0.110</td>\n",
        "</tr>\n",
        "</table>"
       ],
@@ -550,24 +694,24 @@
        "\"\"\"\n",
        "                 Generalized Linear Model Regression Results                  \n",
        "==============================================================================\n",
-       "Dep. Variable:              Frequency   No. Observations:                    7\n",
-       "Model:                            GLM   Df Residuals:                        5\n",
+       "Dep. Variable:              Frequency   No. Observations:                   23\n",
+       "Model:                            GLM   Df Residuals:                       21\n",
        "Model Family:                Binomial   Df Model:                            1\n",
        "Link Function:                  logit   Scale:                          1.0000\n",
-       "Method:                          IRLS   Log-Likelihood:                -2.5250\n",
-       "Date:                Sat, 13 Apr 2019   Deviance:                      0.22231\n",
-       "Time:                        19:11:24   Pearson chi2:                    0.236\n",
-       "No. Iterations:                     4   Covariance Type:             nonrobust\n",
+       "Method:                          IRLS   Log-Likelihood:                -3.9210\n",
+       "Date:                Tue, 17 Oct 2023   Deviance:                       3.0144\n",
+       "Time:                        17:53:24   Pearson chi2:                     5.00\n",
+       "No. Iterations:                     6   Covariance Type:             nonrobust\n",
        "===============================================================================\n",
        "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
        "-------------------------------------------------------------------------------\n",
-       "Intercept      -1.3895      7.828     -0.178      0.859     -16.732      13.953\n",
-       "Temperature     0.0014      0.122      0.012      0.991      -0.238       0.240\n",
+       "Intercept       5.0850      7.477      0.680      0.496      -9.570      19.740\n",
+       "Temperature    -0.1156      0.115     -1.004      0.316      -0.341       0.110\n",
        "===============================================================================\n",
        "\"\"\""
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -587,9 +731,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "L'estimateur le plus probable du paramètre de température est 0.0014\n",
-    "et l'erreur standard de cet estimateur est de 0.122, autrement dit on\n",
-    "ne peut pas distinguer d'impact particulier et il faut prendre nos\n",
+    "L'estimateur le plus probable du paramètre de température est 5.085\n",
+    "et l'erreur standard de cet estimateur est de 7.477, autrement dit il faut prendre nos\n",
     "estimations avec des pincettes.\n"
    ]
   },
@@ -605,12 +748,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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NHDlygXNuUE3tAjlbprqkOfQTIQbIAk4DEoHPzewL59yqgxZy7gngCYBBgwa5ESNGBLD5n8rOzqa2y4ab7OxsbvrFCJq0Wc9dby8jp6Q1t47uEeqyaiXa3pdo6Eu09APUl6MVSLjnAu2qTGcCm6pps905txfYa2YfA/2AVUhAfnlCB1ZvLWRq9lq6NE/hgiyNIikitRfIMfccoKuZdTSzOOBi4K1D2rwJnGRmMWaWBAwFlge31OhmZtx1Ti9O6NyU219fwoLvdoa6JBGJYDWGu3OuHLgBmIMvsF92zi01s0lmNsnfZjnwLrAY+Ap4yjn3bf2VHZ1ivR4evWwgrdMTuO75BeTt1hk0IlI7AZ3n7pyb7Zzr5pzr7Jy7xz/vMefcY1XaTHHO9XTO9XbOPVhfBUe79KQ4pv1yECVllUx4VmfQiEjt6ArVMNSlRSoPXzqAlVvy+a+XdaNtETl6CvcwNbJ7C/77rON4d+kWHv7P6lCXIyIRRgOHhbFrTuzIii0FPPj+arq3TOXMPq1DXZKIRAjtuYcxM+Oe83ozsH06v3v5G5Zt0m36RCQwCvcwt/8+rGmJsVz73Hx2FJaEuiQRiQAK9wjQIjWBJ67IYnthCb/+59e6i5OI1EjhHiH6ZqZz/wV9+XL9Tv749tJQlyMiYU5fqEaQMQPasnxzPo9/vI6erdO4dGj7UJckImFKe+4R5veje3BKt+bc+da3zN+gIQpEpHoK9wjj9RgPXzyAzGOSmPTCAjZpiAIRqYbCPQKlJcXy5BVZFJdVct3zCyguqwh1SSISZhTuEapLi1T+dlF/luTt4fbXl1DTTVdEpHFRuEew03u25Hend+ONhXlM+3R9qMsRkTCicI9wN4zswuherfjz7OV8snpbqMsRkTChcI9wHo/xwLh+dGmRwg0vLmTjjn2hLklEwoDCPQqkxMfw5BW+++Ve+5zGgBcRhXvUOLZpMv+4dACrtxZwyyvf6AtWkUZO4R5FTuranNvO7MG/v93CIx+uCXU5IhJCCvcoc+1JnRjTvw1/eW8V7y/7IdTliEiIKNyjjJlx3wV96dWmCTfPXMSarYWhLklEQkDhHoUSYr08Pn4Q8TEeJj43nz1FZaEuSUQamMI9SrVNT2Tq5Vls3LmP38xYSIVusi3SqCjco9iQjhnceU4vsldu44G5K0Ndjog0II3nHuUuH9qeZZvymZq9lp6tm3B2vzahLklEGoD23KOcmfHHc3ox6NhjmPzqN3ybtyfUJYlIA1C4NwJxMR6mXp7FMUlxTHxuPtt1k22RqKdwbySap8bz5BWD2LG3lF+9sIDSct1kWySaKdwbkd5t0/i/sX3J2bCLO99aqiEKRKKYvlBtZM7t35blmwt47KO19GydyvhhHUJdkojUA+25N0KTR3Xn1B4t+OPby/hs7fZQlyMi9UDh3gh5PcZDF/enQ7Nkrv/n1xoDXiQKKdwbqdSEWJ66YhCVDiY8l0NBsYYoEIkmCvdGrEOzZB69bCBrt+3l5hmLNESBSBRRuDdyw7s0486ze/LBiq1MmaMhCkSihc6WEcYffywrt/jOoOnWMoXzB2aGuiQRqaOA9tzNbLSZrTSzNWZ22xHaDTazCjMbG7wSpb6ZGXed04thnZpy22tLWPDdzlCXJCJ1VGO4m5kXeAQ4E+gJXGJmPQ/T7n5gTrCLlPoX6/Uw9fKBtElPYOJzC8jdpTNoRCJZIHvuQ4A1zrl1zrlSYAZwbjXtbgReA7YGsT5pQOlJcUy7cjClFZVMeHa+zqARiWBW0yXo/kMso51zE/zT44GhzrkbqrRpC7wInApMA95xzr1azbomAhMBWrZsmTVjxoxaFV1YWEhKSkqtlg034diXpdsr+MuCYno383LzwHg8ZgEtF459qa1o6Uu09APUl/1Gjhy5wDk3qKZ2gXyhWt3/7EM/ER4EbnXOVdgRgsA59wTwBMCgQYPciBEjAtj8T2VnZ1PbZcNNOPZlBJCW+R1/mPUtnxS24M6zewW0XDj2pbaipS/R0g9QX45WIOGeC7SrMp0JbDqkzSBghj/YmwFnmVm5c25WUKqUBnf58ceybttenp63nk7NkjUGjUiECSTcc4CuZtYRyAMuBi6t2sA513H/czObju+wjII9wv2/nx/Hdzv2cudbS8nMSGJk9xahLklEAlTjF6rOuXLgBnxnwSwHXnbOLTWzSWY2qb4LlNDxeoyHLxnAca2bcMM/v2bZpvxQlyQiAQroPHfn3GznXDfnXGfn3D3+eY855x6rpu2V1X2ZKpEpOT6Gab8cTGpCLNc8m8OWPcWhLklEAqDhB6RGrdISmHblIPKLyrh6eg6FJeWhLklEaqBwl4D0apPGI5cNZOUPBdzw4teUV+g2fSLhTOEuARvRvQV/Orc32Su3cceb3+o2fSJhTAOHyVG5dGh7cnft49HstbRNT+SGU7uGuiQRqYbCXY7a5FHd2bKnmAfmrqJVWiJjszSKpEi4UbjLUTMz7rugL1sLSrjttcU0T43nlG7Na7WuWQvzmDJnJZt2F9EmPZHJo7ozZkDbIFcs9UXvX/jSMXeplbgY3yiS3Vqm8qsXFvDN97uPeh2zFuZx++tLyNtdhAPydhdx++tLmLUwL/gFS9Dp/QtvCneptdSEWKZfPZimKXFcPT2HLXuP7gyaKXNWUlRWcdC8orIK3REqQuj9C28Kd6mTFqkJPHvVEBzwwPxifsgP/CKnTbuLjmq+hBe9f+FN4S511ql5CtOvGkxhqeOKaV+xZ19g48C3SU88qvkSXvT+hTeFuwRF38x0bhqYwPrte7n62Rz2ldZ8FevkUd1JjPUeNC8x1svkUd3rq0wJIr1/4U3hLkHTs6mXBy/uz8KNu5j0wteUlh/5GPyYAW259/w+tE1PxIC26Ynce34fnW0RIfT+hTedCilBdVaf1tx3fl9+/9pifjtzEQ9fMgCv5/A3cBkzoK3CIILp/QtfCncJunGD25FfXMbd/1pOcryX+87vi+cIAS8iwadwl3ox4aROFBSX89AHq0mM9XLXOb040i0YRSS4FO5Sb27+WVf2lZbz5CfrSYjzctvoHgp4kQaicJd6Y2b891nHsa+0gsc/Wkd8jJffnd4t1GWJNAoKd6lXZsafzu1NaXklD3+wmliPceNpGklSpL4p3KXeeTy+gcYqKh1/eW8VMV4PvxrROdRliUQ1hbs0CK/HmHJhP8orHfe/uwKH49cjuoS6LJGopXCXBuP1GH8d1w8z+L93V+IcXD9SAS9SHxTu0qBivB7+Oq4/hm9UwfIKx02nddFZNCJBpnCXBuf1GH8Z1x+vx8Pf3l9FSXkFk0d1V8CLBJHCXULC6zGmjO1LXIyHR7PXUlxWyR2/OE4BLxIkCncJGY/H+PN5vYmP8fD0vPXsKy3nnvP6HHEsGhEJjMJdQsrMuPPsnqQmxPD3/6yhsKScv47rT1yMBiwVqQuFu4ScmfFfZ3QnJT6Ge/+9goLicqZePpCkOP3zFKkt7R5J2LjulM7cd34fPlm9jUuf/JJde0tDXZJIxFK4S1i5eEh7Hr0si2Wb87nw8c/J0/04RWpF4S5hZ3TvVjx71RB+yC/mvEfmsWxTfqhLEok4CncJS8M6N+WVScPwmDHu8c/5ZPW2UJckElEU7hK2erRqwhvXn0DmMYlc9UwOM3M2hrokkYihcJew1jotkVcmDWNY56bc+toS7n93BZWVLtRliYQ9hbuEvdSEWJ6+cjCXDGnP1Oy1/OqfC9hXWh7qskTCWkDhbmajzWylma0xs9uqef0yM1vs//nMzPoFv1RpzGK9Hv58Xm/u+EVP3lv2A2Onfs4mnUkjclg1hruZeYFHgDOBnsAlZtbzkGbrgVOcc32BPwFPBLtQETPjmhM7Mu3KwXy/cx9n//1Tvlq/M9RliYSlQPbchwBrnHPrnHOlwAzg3KoNnHOfOed2+Se/ADKDW6bIj0Z2b8Eb1w8nLTGWS5/8guc/34BzOg4vUpXV9J/CzMYCo51zE/zT44GhzrkbDtP+FqDH/vaHvDYRmAjQsmXLrBkzZtSq6MLCQlJSUmq1bLhRX2pvX5nj8cUlfLOtguFtYriiVxzx3uAMOhYt70u09APUl/1Gjhy5wDk3qMaGzrkj/gAXAk9VmR4P/P0wbUcCy4GmNa03KyvL1daHH35Y62XDjfpSNxUVle5v7610HW57x43620du/bbCoKw3Wt6XaOmHc+rLfsB8V0O+OucCOiyTC7SrMp0JbDq0kZn1BZ4CznXO7QhgvSJ15vEYN/+sG89cOZjNe4o5+++f8q/Fm0NdlkjIBRLuOUBXM+toZnHAxcBbVRuYWXvgdWC8c25V8MsUObIR3Vvwr5tOpHOLFK5/8WvumPUtxWUVoS5LJGRqDHfnXDlwAzAH3yGXl51zS81skplN8jf7H6Ap8KiZLTKz+fVWschhZB6TxMvXDePakzry/BffMeaReaz+oSDUZYmEREADZjvnZgOzD5n3WJXnE4CffIEq0tBmL9nM7CVbAFj1QwFnPfwJY/q3Zd6a7WzeU0yb9EQmj+rOmAFtg77tWQvzmDJnJZt2F9XrdgLxh1lLeOnL77m5dxnX3D6bS4a24+4xfUJSi4SG7oYgUWPWwjxuf30JRf7DMZUOXIXjlQW5B9rk7S7i9teXAAQ1eA/ddn1tJxB/mLWEF774cRyeCucOTCvgGw8NPyBRY8qclQfCdb/qTvQtKqtgypyV9b7t+thOIF768vujmi/RSeEuUeNohiMI9k1ADrftUAyRUHGYa1cON1+ik8Jdokab9MSA23oM3lyUF7QrWw+37aOpKVi8Vv2FXIebL9FJ4S5RY/Ko7iTGeg+aF+sxYg+5ajU+xkPmMUn8ZsYiJjw7n9xd++pl24mxXiaP6l7ndR+tS4a2O6r5Ep0U7hI1xgxoy73n96FteiIGtE1PZMqF/Zgytt9B8+6/oC8f3jKCP/z8OD5bu4PT//oxT3y8lrKKyqBu+97z+4TkbJm7x/Th8uPbH9hT95px+fHt9WVqI6OzZSSqjBnQttpArW7ehJM6Mbp3K+56ayl/nr2C1xbk8cdze3F8p6ZB3XYo3D2mD3eP6UN2djZrLxsR6nIkBLTnLo1a5jFJPPXLwTwxPovCknIufuILbnppITuLa78XLxIOtOcuApzRqxUndW3O1I/W8thHa3m3spLvvKu47pROJMXpv4lEHu25i/glxnn53end+OB3p9C/hZeHPljNyAeymZmzkQrdt1UijMJd5BDtMpL4df8EXpk0jNZpidz62hLOfOhj3lv2g24KIhFD4S5yGIM7ZPDGr0/g0csGUlbhuPa5+Yx59DM+Xb1dIS9hT+EucgRmxll9WvPeb0/m/gv6sC2/mMunfcm4xz9n3hqFvIQvhbtIAGK8Hi4a3J7/3DKC/z23F9/vLOKyp77kgqmf8Z8VOlwj4UfhLnIUEmK9XDGsA9mTR/Cnc3vxQ34JV0+fz1kPf8obC3PrdCGUSDAp3EVqISHWy3h/yD9wYT/KKir57cxvOPn/PuTxj9ayZ19ZqEuURk4n8IrUQazXw9isTM4f0JbsVVt54uN13PvvFTz4/mouyGrLFcM60K1laqjLlEZI4S4SBB6PcWqPlpzaoyVLN+1h+rwNvDw/lxe+2MjQjhlcfvyxnNGrJfEx3ppXJhIECneRIOvVJo0pF/bj9rOO4+X53/PCF99x40sLyUiO44KBbRk3qB1dtTcv9UzhLlJPMpLjmHRKZyae1IlP12znpa828sy8DTz5yXr6ZaYxNiuTn/dtQ0ZyXKhLlSikcBepZx6PcXK35pzcrTnbC0uYtTCPVxfkcsebS/nj28sY0b05Z/drw8+Oa0lyvP5LSnDoX5JIA2qWEs+Ekzox4aROLN+cz6yFeby5aBPvL99KQqyHU3u04MzerRnZowUpCnqpA/3rEQmR41o34bjWTbh1dA/mf7eLt7/ZxL+/3cLsJVuIi/FwUpdmnN6zJacd15LmqfGhLlcijMJdJMQ8HmNIxwyGdMzgrnN6seC7Xcxespn3lv3AByu2YraEvpnpnNajBSO6N6d3mzQ8Ht0PVY5M4S4SRrxVgv7Os3uyfHMBHyz3hfzf3l/FX99bRUZyHCd2acaJXZtxYpc9/m12AAANA0lEQVRmIbkJt4Q/hbtImDIzerZpQs82TbjxtK5sLyzh09XbyV65lU/XbOetbzYB0LFZMsd3yuD4Tk0Z0jGD1mkKe1G4i0SMZinxB+7T6pxj5Q8FfLp6O5+v3cE732zmpa++B6BdRiKDj81g4LHH4Aoqqah0eHUYp9FRuItEIDOjR6sm9GjVhAkndaK8opLlmwv4asNOvlq/g49Xb+P1hXkA3Jczh76Z6fRrl06/zDT6tkunTVoCZgr8aKZwF4kCMV4PfTLT6JOZxjUndsQ5x8ad+3jh3c8oSWnNwo27mfbpOsoqfEMTH5MUS++2afRs7Tvsc1zrJnRslkysV2MJRguFu0gUMjOObZrM8LaxjBjRG4CS8gpWbC5gce5ulm7K59tNe3hm3gZK/cMUx3k9dGqeTPdWqXRtkULXlql0aZFC+4wkhX4EUriLNBLxMV7foZl26QfmlVVUsm7bXpZvzmfFlgJWbMln/oZdvLlo04E2sV6jfUYSHZul0Kl5Msc2TaJj02TaN02idVqijueHKYW7SCMW6/XQvVUq3VsdPJBZYUk5a7YWsnZrIWu2FbJ+217Wb9/Lx6u3UVpeWWV5o216Iu0yksg8JonMYxJpm55I22MSaZOeSMvUeGK01x8SCncR+YmU+Bj6t0unf5W9fIDKSseW/GI2bN/Lhh37+H7XPjbu2Efurn3M3bSFHXtLD2rvMWiRmkCrtARaNfE9tmgST4vUBFqkxtOiSTzNUuLJSIrThVlBpnAXkYB5PEabdN9e+Qldfvr6vtJyNu0uIndXEZv3FLN5dxGb9hTzQ34xa7YVMm/tdgqKy3+ynNdjZCTH0TQ5jmYp8TRNiSMjOY6MpDgyUuI4JimO73ZU0HJzPsckxZGWGEtCrEdn/ByBwl1EgiYpLoYuLVLp0uLw49XvKy1na34JWwtK2FpQzPaCErYXlrKtoIQde0vZXljCxp372LW3lIKSgz8I7s/55MDzOK+HtKRYmiTEkJYYS5PEWJokxJKaEEPqgccYUuJ//En2//iee0mKi4na7wwCCnczGw08BHiBp5xz9x3yuvlfPwvYB1zpnPs6yLWKRK1ZC/OYMmclm3YX0SY9kcmjuvPK/I3MW7vzQJvhnTO4cFD7n7QDfjJv/nc7eenL77m5dxnX3D6bS4a24+4xfQLa7pgBbQ87P5Dl92+7wjm8Zj/ZdlJcDB2axbDo+9019uXOs3tycrfm7NxXyofzcujQrSe79pXx2drtZK/cxraCEgqKy/B6jLIKx4bte8kvLqeguOzAaZ81iY/xkBTnC/qkOC+JcV4SY70kxXlJiPU9T4jzkhDjJSHWQ0Lsj4/xMR7iY/yPsT8+j9v/4/3xebzXS2yMEev14FxgtdVFjeFuZl7gEeB0IBfIMbO3nHPLqjQ7E+jq/xkKTPU/ikgNZi3M4/bXl1BUVgFA3u4ibp656Cft5q3deVDY5+0uYvKr34CDskp3YN7vZi6isspyFc7xwhcbAQ4K2eq2e/vrS5j/3U5eW5D3k/nAQQFf3fJ12fbkV74B40Ao5+0u4o43l3Lv+X0YM6AtW5p6GdGnNbMW5vHB8q0Hli0uq+T7nUUH2u1XXFZBYUk5hcXlFBSXU1hSzt6ScvaWlrO3pIK9JeXsK61gb2k5+0p9z4tKKw487thbemC6pLyC4rJKisoqqKisezCf2TGWkSPrvJojCmTPfQiwxjm3DsDMZgDnAlXD/VzgOef7OPrCzNLNrLVzbnPQKxaJMlPmrDwQVEerur3TymraAbz05fcHBWx12y0qqziw133o/ClzVh4UntUtX5dtl1UTmoFut7p2vj1sL81SgjtccllFJcVlFZSUV1JS7nte6n/ue6ygpKyS0grf9IHH8krKKnw/nl0bg1pTdaymPw/MbCww2jk3wT89HhjqnLuhSpt3gPucc5/6pz8AbnXOzT9kXROBif7J7sDKWtbdDNhey2XDjfoSnhqsL3GtumTV17or9u3Bm5R2YLp0y5oFddluXZYPwrLNgO1HWrbqNsJcXf59Heuca15To0D23Kv7tuHQT4RA2uCcewJ4IoBtHrkgs/nOuUF1XU84UF/CU7T0xczml+/ZGvH9gOh5T6Bh+hLI1QW5QLsq05nAplq0ERGRBhJIuOcAXc2so5nFARcDbx3S5i3gCvM5Htij4+0iIqFT42EZ51y5md0AzMF3KuTTzrmlZjbJ//pjwGx8p0GuwXcq5FX1VzIQhEM7YUR9CU/R0pdo6QeoL0elxi9URUQk8mhEHxGRKKRwFxGJQmEf7maWYGZfmdk3ZrbUzP7on59hZu+Z2Wr/4zGhrjUQZuY1s4X+awMiuR8bzGyJmS0ys/n+eZHal3Qze9XMVpjZcjMbFol9MbPu/vdj/0++md0coX35rf//+7dm9pI/ByKuHwBm9ht/P5aa2c3+efXel7APd6AEONU51w/oD4z2n5FzG/CBc64r8IF/OhL8BlheZTpS+wEw0jnXv8r5upHal4eAd51zPYB++N6fiOuLc26l//3oD2ThO7nhDSKsL2bWFrgJGOSc643vRI6LibB+AJhZb+BafFf69wN+YWZdaYi+OOci5gdIAr7GN27NSqC1f35rYGWo6wug/kz/G3kq8I5/XsT1w1/rBqDZIfMiri9AE2A9/pMLIrkvh9R/BjAvEvsCtAW+BzLwndH3jr8/EdUPf50X4htscf/0HcDvG6IvkbDnvv9QxiJgK/Cec+5LoKXzn0vvf2wRyhoD9CC+N7bqEByR2A/wXYE818wW+IeVgMjsSydgG/CM/3DZU2aWTGT2paqLgZf8zyOqL865POABYCOwGd91M3OJsH74fQucbGZNzSwJ3ynj7WiAvkREuDvnKpzvT81MYIj/T52IYma/ALY65yJl7IuaDHfODcQ3Iuj1ZnZyqAuqpRhgIDDVOTcA2EsE/Ll/JP6LDc8BXgl1LbXhP/58LtARaAMkm9nloa2qdpxzy4H7gfeAd4FvgJ/eraQeRES47+ec2w1kA6OBH8ysNYD/cWsISwvEcOAcM9sAzABONbMXiLx+AOCc2+R/3IrvuO4QIrMvuUCu/69BgFfxhX0k9mW/M4GvnXM/+KcjrS8/A9Y757Y558qA14ETiLx+AOCcm+acG+icOxnYCaymAfoS9uFuZs3NLN3/PBHfG78C35AHv/Q3+yXwZmgqDIxz7nbnXKZzrgO+P5n/45y7nAjrB4CZJZtZ6v7n+I6HfksE9sU5twX43sy6+2edhm8464jrSxWX8OMhGYi8vmwEjjezJDMzfO/JciKvHwCYWQv/Y3vgfHzvTb33JeyvUDWzvsCz+L4x9wAvO+f+18yaAi8D7fH9Y7jQObfz8GsKH2Y2ArjFOfeLSOyHmXXCt7cOvsMaLzrn7onEvgCYWX/gKSAOWIdv+AwPkdmXJHxfRnZyzu3xz4u498V/yvNF+A5hLAQmAClEWD8AzOwToClQBvzOOfdBQ7wnYR/uIiJy9ML+sIyIiBw9hbuISBRSuIuIRCGFu4hIFFK4i4hEoUBukC3SoPyniX3gn2wFVOAbIgBgiHOuNCSFHYGZXQ3M9p83LxJyOhVSwpqZ3QUUOuceCINavM65isO89ilwg3Nu0VGsL8Y51yCXokvjo8MyElHM7JfmG99/kZk9amYeM4sxs91mNsXMvjazOWY21Mw+MrN1ZnaWf9kJZvaG//WVZvaHANd7t5l9hW9coz+aWY5/fO7HzOcifMNRz/QvH2dmuVWurD7ezN73P7/bzB43s/fwDVYWY2Z/9W97sZlNaPjfqkQjhbtEDP+AcecBJ/gHkovBN5QDQBow1z+YWSlwF77L1i8E/rfKaob4lxkIXGpm/QNY79fOuSHOuc+Bh5xzg4E+/tdGO+dmAouAi5xvPPWaDhsNAM52zo0HJuIbUG4IMBjfIGzta/P7EalKx9wlkvwMXwDO9w05QiK+S+0Bipxz7/mfL8E3TGy5mS0BOlRZxxzn3C4AM5sFnIjv/8Hh1lvKj0MtAJxmZpOBBKAZsAD491H2403nXLH/+RnAcWZW9cOkK75L0kVqTeEukcSAp51zdxw00ywGXwjvV4nvDl77n1f9d37ol0yuhvUWOf8XU/5xW/4BDHTO5ZnZ3fhCvjrl/PiX8aFt9h7Sp1875z5AJIh0WEYiyfvAODNrBr6zampxCOMM890zNQnfmOHzjmK9ifg+LLb7R8W8oMprBUBqlekN+G51xyHtDjUH+LX/g2T/fVATj7JPIj+hPXeJGM65Jf7RAt83Mw++UfYmAZuOYjWfAi8CnYHn95/dEsh6nXM7zOxZfMMbfwd8WeXlZ4CnzKwI33H9u4AnzWwL8NUR6nkc38iAi/yHhLbi+9ARqROdCimNhv9MlN7OuZtDXYtIfdNhGRGRKKQ9dxGRKKQ9dxGRKKRwFxGJQgp3EZEopHAXEYlCCncRkSj0/wHRUJwHFwSFegAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -638,54 +781,31 @@
     "scrolled": true
    },
    "source": [
-    "Comme on pouvait s'attendre au vu des données initiales, la\n",
-    "température n'a pas d'impact notable sur la probabilité d'échec des\n",
-    "joints toriques. Elle sera d'environ 0.2, comme dans les essais\n",
-    "précédents où nous il y a eu défaillance d'au moins un joint. Revenons\n",
-    "à l'ensemble des données initiales pour estimer la probabilité de\n",
-    "défaillance d'un joint:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.06521739130434782\n"
-     ]
-    }
-   ],
-   "source": [
-    "data = pd.read_csv(\"shuttle.csv\")\n",
-    "print(np.sum(data.Malfunction)/np.sum(data.Count))"
+    "On peut prendre la valeur 0.85 pour faire une grossière estimation mais il faudrait quand même avoir des datas plus froides que celles qu'on a pour faire un travail sérieux.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Cette probabilité est donc d'environ $p=0.065$, sachant qu'il existe\n",
+    "Cette probabilité est donc d'environ $p=0.85$, sachant qu'il existe\n",
     "un joint primaire un joint secondaire sur chacune des trois parties du\n",
     "lançeur, la probabilité de défaillance des deux joints d'un lançeur\n",
-    "est de $p^2 \\approx 0.00425$. La probabilité de défaillance d'un des\n",
-    "lançeur est donc de $1-(1-p^2)^3 \\approx 1.2%$.  Ça serait vraiment\n",
-    "pas de chance... Tout est sous contrôle, le décollage peut donc avoir\n",
-    "lieu demain comme prévu.\n",
+    "est de $p^2 \\approx 0.7225$. La probabilité de défaillance d'un des\n",
+    "lançeur est donc de $1-(1-p^2)^3 \\approx 98 \\%$.  \n",
     "\n",
-    "Seulement, le lendemain, la navette Challenger explosera et emportera\n",
+    "Le lendemain, la navette Challenger explosera et emportera\n",
     "avec elle ses sept membres d'équipages. L'opinion publique est\n",
     "fortement touchée et lors de l'enquête qui suivra, la fiabilité des\n",
-    "joints toriques sera directement mise en cause. Au delà des problèmes\n",
-    "de communication interne à la NASA qui sont pour beaucoup dans ce\n",
-    "fiasco, l'analyse précédente comporte (au moins) un petit\n",
-    "problème... Saurez-vous le trouver ? Vous êtes libre de modifier cette\n",
-    "analyse et de regarder ce jeu de données sous tous les angles afin\n",
-    "d'expliquer ce qui ne va pas."
+    "joints toriques sera directement mise en cause."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -705,7 +825,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/module3/exo3/module3_exo3_weekly_in_situ_co2_mlo.csv b/module3/exo3/module3_exo3_weekly_in_situ_co2_mlo.csv
new file mode 100644
index 0000000000000000000000000000000000000000..aa6d86651093447ce7f8d196a424d643e9727d83
--- /dev/null
+++ b/module3/exo3/module3_exo3_weekly_in_situ_co2_mlo.csv
@@ -0,0 +1,3376 @@
+"-------------------------------------------------------------------------------------------"
+" Atmospheric CO2 concentrations (ppm) derived from in situ air measurements                "
+" at Mauna Loa, Observatory, Hawaii: Latitude 19.5°N Longitude 155.6°W Elevation 3397m      "
+"                                                                                           "
+" Source: R. F. Keeling, S. J. Walker, S. C. Piper and A. F. Bollenbacher                   "
+" Scripps CO2 Program ( http://scrippsco2.ucsd.edu )                                        "
+" Scripps Institution of Oceanography (SIO)                                                 "
+" University of California                                                                  "
+" La Jolla, California USA 92093-0244                                                       "
+"                                                                                           "
+" Status of data and correspondence:                                                        "
+"                                                                                           "
+" These data are subject to revision based on recalibration of standard gases. Questions    "
+" about the data should be directed to Dr. Ralph Keeling (rkeeling@ucsd.edu), Stephen Walker"
+" (sjwalker@ucsd.edu) and Stephen Piper (scpiper@ucsd.edu), Scripps CO2 Program.            "
+"                                                                                           "
+" Baseline data in this file through 09-Jul-2023 from archive dated 10-Jul-2023 15:15:05    "
+"                                                                                           "
+"-------------------------------------------------------------------------------------------"
+"                                                                                           "
+" Please cite as:                                                                           "
+"                                                                                           "
+" C. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and       "
+" H. A. Meijer, Exchanges of atmospheric CO2 and 13CO2 with the terrestrial biosphere and   "
+" oceans from 1978 to 2000.  I. Global aspects, SIO Reference Series, No. 01-06, Scripps    "
+" Institution of Oceanography, San Diego, 88 pages, 2001.                                   "
+"                                                                                           "
+" If it is necessary to cite a peer-reviewed article, please cite as:                       "
+"                                                                                           "
+" C. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and       "
+" H. A. Meijer, Atmospheric CO2 and 13CO2 exchange with the terrestrial biosphere and       "
+" oceans from 1978 to 2000: observations and carbon cycle implications, pages 83-113,       "
+" in "A History of Atmospheric CO2 and its effects on Plants, Animals, and Ecosystems",     "
+" editors, Ehleringer, J.R., T. E. Cerling, M. D. Dearing, Springer Verlag,                 "
+" New York, 2005.                                                                           "
+"                                                                                           "
+"-------------------------------------------------------------------------------------------"
+"                                                                                           "
+"                                                                        "
+" The data file below contains 2 columns indicaing the date and CO2      "
+" concentrations in micro-mol CO2 per mole (ppm), reported on the 2012  "
+" SIO manometric mole fraction scale.  These weekly values have been     "
+" adjusted to 12:00 hours at middle day of each weekly period as         "
+" indicated by the date in the first column.                             "
+1958-03-29, 316.19
+1958-04-05, 317.31
+1958-04-12, 317.69
+1958-04-19, 317.58
+1958-04-26, 316.48
+1958-05-03, 316.95
+1958-05-17, 317.56
+1958-05-24, 317.99
+1958-07-05, 315.85
+1958-07-12, 315.85
+1958-07-19, 315.46
+1958-07-26, 315.59
+1958-08-02, 315.64
+1958-08-09, 315.10
+1958-08-16, 315.09
+1958-08-30, 314.14
+1958-09-06, 313.54
+1958-11-08, 313.05
+1958-11-15, 313.26
+1958-11-22, 313.57
+1958-11-29, 314.01
+1958-12-06, 314.56
+1958-12-13, 314.41
+1958-12-20, 314.77
+1958-12-27, 315.21
+1959-01-03, 315.24
+1959-01-10, 315.50
+1959-01-17, 315.69
+1959-01-24, 315.86
+1959-01-31, 315.42
+1959-02-14, 316.94
+1959-02-21, 316.61
+1959-02-28, 316.62
+1959-03-07, 316.81
+1959-03-21, 316.73
+1959-03-28, 316.71
+1959-04-04, 317.72
+1959-04-11, 317.12
+1959-04-18, 317.64
+1959-04-25, 318.32
+1959-05-02, 318.27
+1959-05-09, 318.78
+1959-05-16, 318.05
+1959-05-23, 318.43
+1959-06-06, 318.53
+1959-06-13, 318.14
+1959-06-20, 317.88
+1959-06-27, 317.76
+1959-07-04, 316.86
+1959-07-11, 316.83
+1959-07-18, 316.45
+1959-07-25, 316.16
+1959-08-01, 315.62
+1959-08-08, 314.91
+1959-08-22, 315.00
+1959-08-29, 314.15
+1959-09-05, 314.45
+1959-09-12, 313.93
+1959-09-19, 313.57
+1959-09-26, 313.54
+1959-10-03, 313.04
+1959-10-10, 313.15
+1959-10-17, 313.43
+1959-10-24, 313.46
+1959-10-31, 314.12
+1959-11-07, 314.42
+1959-11-14, 314.88
+1959-11-21, 315.20
+1959-11-28, 315.11
+1959-12-05, 315.06
+1959-12-12, 315.64
+1959-12-19, 315.86
+1959-12-26, 315.77
+1960-01-02, 315.72
+1960-01-09, 316.40
+1960-01-16, 316.73
+1960-01-23, 316.57
+1960-01-30, 316.68
+1960-02-06, 316.61
+1960-02-13, 316.98
+1960-02-20, 317.42
+1960-02-27, 317.00
+1960-03-05, 316.96
+1960-03-12, 317.76
+1960-03-19, 318.07
+1960-03-26, 317.77
+1960-04-02, 318.61
+1960-04-09, 319.34
+1960-04-16, 319.01
+1960-04-23, 319.03
+1960-04-30, 319.77
+1960-05-07, 319.96
+1960-05-14, 319.82
+1960-05-21, 320.04
+1960-05-28, 320.06
+1960-06-04, 319.46
+1960-06-11, 320.08
+1960-06-18, 319.42
+1960-06-25, 319.09
+1960-07-02, 318.18
+1960-07-09, 318.64
+1960-07-16, 318.41
+1960-07-23, 317.91
+1960-07-30, 317.33
+1960-08-06, 316.62
+1960-08-13, 316.70
+1960-08-20, 315.10
+1960-08-27, 314.78
+1960-09-03, 314.72
+1960-09-10, 314.58
+1960-09-17, 314.28
+1960-09-24, 313.33
+1960-10-01, 313.66
+1960-10-08, 313.32
+1960-10-15, 313.92
+1960-10-22, 314.21
+1960-10-29, 314.29
+1960-11-05, 314.50
+1960-11-12, 315.10
+1960-11-19, 315.10
+1960-11-26, 315.41
+1960-12-03, 315.84
+1960-12-10, 316.07
+1960-12-17, 316.21
+1960-12-24, 316.40
+1960-12-31, 316.67
+1961-01-07, 316.73
+1961-01-14, 317.02
+1961-01-21, 317.03
+1961-01-28, 317.01
+1961-02-04, 317.06
+1961-02-11, 317.75
+1961-02-18, 317.94
+1961-02-25, 318.00
+1961-03-04, 318.04
+1961-03-11, 318.51
+1961-03-18, 318.92
+1961-03-25, 318.71
+1961-04-01, 319.41
+1961-04-08, 319.55
+1961-04-15, 318.90
+1961-04-22, 319.74
+1961-04-29, 319.67
+1961-05-06, 320.67
+1961-05-13, 320.46
+1961-05-20, 320.61
+1961-05-27, 320.36
+1961-06-03, 320.09
+1961-06-10, 320.02
+1961-06-17, 319.62
+1961-06-24, 319.49
+1961-07-01, 319.26
+1961-07-08, 319.07
+1961-07-15, 318.49
+1961-07-22, 317.16
+1961-07-29, 317.97
+1961-08-05, 317.91
+1961-08-12, 317.33
+1961-08-19, 316.76
+1961-08-26, 315.26
+1961-09-02, 315.28
+1961-09-09, 314.97
+1961-09-16, 314.53
+1961-09-23, 315.06
+1961-09-30, 315.61
+1961-10-07, 314.97
+1961-10-14, 315.43
+1961-10-21, 315.48
+1961-10-28, 315.72
+1961-11-04, 315.82
+1961-11-11, 315.70
+1961-11-18, 316.21
+1961-11-25, 316.55
+1961-12-02, 316.52
+1961-12-09, 317.02
+1961-12-16, 316.94
+1961-12-23, 317.18
+1961-12-30, 317.46
+1962-01-06, 317.99
+1962-01-13, 318.12
+1962-01-20, 318.07
+1962-01-27, 317.78
+1962-02-03, 318.08
+1962-02-10, 318.35
+1962-02-17, 318.92
+1962-02-24, 319.38
+1962-03-03, 319.28
+1962-03-10, 319.46
+1962-03-17, 319.95
+1962-03-24, 319.78
+1962-03-31, 320.27
+1962-04-07, 320.26
+1962-04-14, 321.03
+1962-04-21, 320.78
+1962-04-28, 320.36
+1962-05-05, 320.89
+1962-05-12, 320.78
+1962-05-19, 321.05
+1962-05-26, 321.16
+1962-06-02, 320.91
+1962-06-09, 320.87
+1962-06-16, 320.52
+1962-06-23, 320.15
+1962-06-30, 320.23
+1962-07-07, 320.10
+1962-07-14, 319.95
+1962-07-21, 319.01
+1962-07-28, 318.75
+1962-08-04, 318.68
+1962-08-11, 317.24
+1962-08-18, 317.48
+1962-09-15, 316.65
+1962-09-22, 315.72
+1962-09-29, 315.92
+1962-10-06, 315.10
+1962-10-13, 315.40
+1962-10-20, 315.72
+1962-10-27, 315.78
+1962-11-03, 315.94
+1962-11-10, 316.49
+1962-11-17, 316.92
+1962-11-24, 317.08
+1962-12-01, 317.19
+1962-12-08, 317.31
+1962-12-15, 317.66
+1962-12-22, 318.18
+1963-01-05, 318.54
+1963-01-12, 318.73
+1963-01-19, 318.90
+1963-01-26, 318.82
+1963-02-02, 318.77
+1963-02-09, 318.95
+1963-02-23, 319.37
+1963-03-02, 319.35
+1963-03-09, 319.70
+1963-03-16, 319.80
+1963-03-23, 320.34
+1963-03-30, 320.29
+1963-04-06, 320.38
+1963-04-13, 320.96
+1963-04-20, 322.05
+1963-04-27, 321.92
+1963-05-11, 321.97
+1963-05-18, 322.28
+1963-05-25, 322.27
+1963-06-01, 322.32
+1963-06-08, 322.09
+1963-06-15, 321.83
+1963-06-22, 320.88
+1963-06-29, 320.49
+1963-07-06, 320.12
+1963-07-13, 319.82
+1963-07-20, 319.56
+1963-07-27, 319.06
+1963-08-03, 318.60
+1963-08-10, 318.27
+1963-08-17, 317.73
+1963-08-24, 317.12
+1963-08-31, 316.44
+1963-09-07, 316.81
+1963-09-14, 315.99
+1963-09-21, 316.03
+1963-09-28, 315.93
+1963-10-05, 316.28
+1963-10-12, 315.65
+1963-10-19, 316.14
+1963-10-26, 316.36
+1963-11-02, 316.63
+1963-11-09, 316.83
+1963-11-16, 317.13
+1963-11-30, 317.56
+1963-12-07, 318.07
+1963-12-14, 318.27
+1963-12-21, 318.59
+1963-12-28, 318.74
+1964-01-04, 319.04
+1964-01-11, 319.49
+1964-01-18, 319.88
+1964-05-30, 322.07
+1964-06-06, 322.06
+1964-06-27, 321.57
+1964-07-04, 321.14
+1964-07-11, 319.95
+1964-07-18, 320.28
+1964-07-25, 320.02
+1964-08-01, 319.14
+1964-08-15, 318.61
+1964-08-22, 318.23
+1964-08-29, 318.11
+1964-09-05, 317.48
+1964-09-12, 316.57
+1964-09-19, 315.59
+1964-09-26, 317.01
+1964-10-03, 316.93
+1964-10-10, 316.51
+1964-10-17, 316.86
+1964-10-24, 317.05
+1964-10-31, 317.65
+1964-11-07, 317.77
+1964-11-14, 317.65
+1964-11-21, 317.52
+1964-11-28, 318.11
+1964-12-05, 318.42
+1964-12-12, 318.58
+1964-12-19, 318.93
+1964-12-26, 318.91
+1965-01-02, 319.08
+1965-01-09, 319.17
+1965-01-16, 319.16
+1965-01-23, 319.79
+1965-01-30, 320.12
+1965-02-06, 320.20
+1965-02-13, 320.16
+1965-02-20, 320.70
+1965-02-27, 320.89
+1965-03-06, 321.07
+1965-03-13, 320.98
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+1965-03-27, 321.40
+1965-04-03, 321.95
+1965-04-10, 322.27
+1965-04-17, 322.17
+1965-04-24, 321.86
+1965-05-01, 322.46
+1965-05-08, 322.22
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+1965-05-22, 321.69
+1965-05-29, 322.26
+1965-06-05, 321.87
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+1965-06-26, 321.99
+1965-07-03, 321.97
+1965-07-10, 321.85
+1965-07-17, 321.76
+1965-07-24, 320.47
+1965-07-31, 319.49
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+1965-08-14, 318.02
+1965-08-21, 318.82
+1965-08-28, 318.73
+1965-09-04, 318.39
+1965-09-11, 318.05
+1965-09-18, 318.26
+1965-09-25, 316.85
+1965-10-02, 316.60
+1965-10-09, 317.26
+1965-10-16, 317.86
+1965-10-23, 317.56
+1965-10-30, 317.63
+1965-11-06, 318.30
+1965-11-13, 319.26
+1965-11-20, 318.99
+1965-11-27, 319.17
+1965-12-04, 319.09
+1965-12-11, 319.37
+1965-12-18, 319.39
+1965-12-25, 319.70
+1966-01-01, 319.69
+1966-01-08, 320.49
+1966-01-15, 320.63
+1966-01-22, 321.09
+1966-01-29, 321.16
+1966-02-05, 321.26
+1966-02-12, 321.75
+1966-02-19, 321.67
+1966-02-26, 321.80
+1966-03-05, 322.10
+1966-03-12, 322.14
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+1966-04-02, 323.55
+1966-04-09, 323.62
+1966-04-16, 323.88
+1966-04-23, 323.91
+1966-04-30, 323.53
+1966-05-07, 324.00
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+1968-02-10, 323.17
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+1969-01-11, 324.06
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+2021-08-07, 414.56
+2021-08-14, 414.66
+2021-08-21, 414.42
+2021-08-28, 412.68
+2021-09-04, 412.58
+2021-09-11, 413.15
+2021-09-18, 413.09
+2021-09-25, 413.05
+2021-10-02, 413.05
+2021-10-09, 413.56
+2021-10-16, 413.97
+2021-10-23, 413.97
+2021-10-30, 413.64
+2021-11-06, 414.32
+2021-11-13, 414.77
+2021-11-20, 414.91
+2021-11-27, 415.59
+2021-12-04, 415.77
+2021-12-11, 415.91
+2021-12-18, 416.58
+2021-12-25, 417.36
+2022-01-01, 417.17
+2022-01-08, 417.38
+2022-01-15, 418.03
+2022-01-22, 418.33
+2022-01-29, 419.08
+2022-02-05, 418.74
+2022-02-12, 418.90
+2022-02-19, 418.79
+2022-02-26, 419.55
+2022-03-05, 418.35
+2022-03-12, 418.56
+2022-03-19, 417.95
+2022-03-26, 419.00
+2022-04-02, 419.91
+2022-04-09, 419.38
+2022-04-16, 420.57
+2022-04-23, 420.11
+2022-04-30, 419.81
+2022-05-07, 419.64
+2022-05-14, 421.36
+2022-05-21, 420.55
+2022-05-28, 421.34
+2022-06-04, 421.18
+2022-06-11, 420.90
+2022-06-18, 420.45
+2022-06-25, 420.16
+2022-07-02, 419.89
+2022-07-09, 418.92
+2022-07-16, 418.47
+2022-07-23, 418.02
+2022-07-30, 417.64
+2022-08-06, 417.42
+2022-08-13, 416.84
+2022-08-20, 416.24
+2022-08-27, 416.11
+2022-09-03, 415.74
+2022-09-10, 415.85
+2022-09-17, 415.75
+2022-09-24, 414.82
+2022-10-01, 415.12
+2022-10-08, 414.85
+2022-10-15, 415.31
+2022-10-22, 415.60
+2022-10-29, 416.07
+2022-11-05, 416.30
+2022-11-12, 416.81
+2022-11-19, 417.41
+2022-11-26, 417.73
+2022-12-17, 418.41
+2022-12-24, 419.29
+2022-12-31, 419.19
+2023-01-07, 419.09
+2023-01-14, 419.32
+2023-01-21, 419.18
+2023-01-28, 419.66
+2023-02-04, 419.68
+2023-02-11, 420.63
+2023-02-18, 420.19
+2023-02-25, 420.76
+2023-03-04, 421.12
+2023-03-11, 420.19
+2023-03-18, 419.96
+2023-03-25, 420.87
+2023-04-01, 421.33
+2023-04-08, 422.20
+2023-04-15, 423.02
+2023-04-22, 422.99
+2023-04-29, 423.95
+2023-05-06, 423.76
+2023-05-13, 423.78
+2023-05-20, 422.77
+2023-05-27, 424.44
+2023-06-03, 424.40
+2023-06-10, 424.01
+2023-06-17, 422.93
+2023-06-24, 422.21
+2023-07-01, 422.80
diff --git a/module3/exo3/review_1_module3_exo3_exercice.ipynb b/module3/exo3/review_1_module3_exo3_exercice.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..1bf3c76df9ea23b05bca6bc8e849815e703a9728
--- /dev/null
+++ b/module3/exo3/review_1_module3_exo3_exercice.ipynb
@@ -0,0 +1,845 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sujet 1 : Concentration de CO2 dans l'atmosphère depuis 1958"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En 1958, Charles David Keeling  a initié une mesure de la concentration de CO2 dans l'atmosphère à l'observatoire de Mauna Loa, Hawaii, États-Unis qui continue jusqu'à aujourd'hui. L'objectif initial était d'étudier la variation saisonnière, mais l'intérêt s'est déplacé plus tard vers l'étude de la tendance croissante dans le contexte du changement climatique. En honneur à Keeling, ce jeu de données est souvent appelé \"Keeling Curve\" (voir https://en.wikipedia.org/wiki/Keeling_Curve pour l'histoire et l'importance de ces données).\n",
+    "\n",
+    "Les données sont disponibles sur le site Web de l'institut Scripps. Utilisez le fichier avec les observations hebdomadaires. Attention, ce fichier est mis à jour régulièrement avec de nouvelles observations. Notez donc bien la date du téléchargement, et gardez une copie locale de la version précise que vous analysez. Faites aussi attention aux données manquantes.\n",
+    "\n",
+    "Votre mission si vous l'acceptez :\n",
+    "1. Réalisez un graphique qui vous montrera une oscillation périodique superposée à une évolution systématique plus lente.\n",
+    "2. Séparez ces deux phénomènes. Caractérisez l'oscillation périodique. Proposez un modèle simple de la contribution lente, estimez ses paramètres et tentez une extrapolation jusqu'à 2025 (dans le but de pouvoir valider le modèle par des observations futures).\n",
+    "3. Déposer dans FUN votre résultat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Librairies**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import r2_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import des données"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from url\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = 'weekly_in_situ_co2_mlo.csv'\n",
+    "\n",
+    "if os.path.exists(filename):\n",
+    "    print('Using local file')\n",
+    "    data_path = filename\n",
+    "else:\n",
+    "    print('Downloading data from url')\n",
+    "    data_path = 'https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/weekly/weekly_in_situ_co2_mlo.csv'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On importe les données en supprimant toutes les lignes de commentaires qui existent au début du fichier. La série de donnée commence la semaine du 29 Mars 1958. La 2ème colonne est renommée pour plus de lisibilité."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "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>ppm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1958-03-29</th>\n",
+       "      <td>316.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-05</th>\n",
+       "      <td>317.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-12</th>\n",
+       "      <td>317.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-19</th>\n",
+       "      <td>317.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-26</th>\n",
+       "      <td>316.48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               ppm\n",
+       "1958-03-29  316.19\n",
+       "1958-04-05  317.31\n",
+       "1958-04-12  317.69\n",
+       "1958-04-19  317.58\n",
+       "1958-04-26  316.48"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(data_path, skiprows=43, delimiter=',')\n",
+    "raw_data.columns = ['ppm']\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 3332 entries, 1958-03-29 to 2023-07-01\n",
+      "Data columns (total 1 columns):\n",
+      "ppm    3332 non-null float64\n",
+      "dtypes: float64(1)\n",
+      "memory usage: 52.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "raw_data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On vérifie si des données sont mal renseignées : Non"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    },
+    {
+     "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>ppm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [ppm]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(raw_data['ppm'].isna().any())\n",
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "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>index</th>\n",
+       "      <th>ppm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1958-03-29</td>\n",
+       "      <td>316.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1958-04-05</td>\n",
+       "      <td>317.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1958-04-12</td>\n",
+       "      <td>317.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1958-04-19</td>\n",
+       "      <td>317.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1958-04-26</td>\n",
+       "      <td>316.48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        index     ppm\n",
+       "0  1958-03-29  316.19\n",
+       "1  1958-04-05  317.31\n",
+       "2  1958-04-12  317.69\n",
+       "3  1958-04-19  317.58\n",
+       "4  1958-04-26  316.48"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.copy().reset_index()\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On vérifie si toutes les semaines sont présentes dans les données : ce n'est pas le cas pour les années ci-dessous. Deux cas de figure se présentent :\n",
+    "- pour l'année 1958 (première date de la série) et l'année 2023 (dernière date de la série) $\\rightarrow$ pas de problème\n",
+    "- pour les autres années $\\rightarrow$ quelques semaines sont manquantes, on décide de continuer l'analyse tel quel mais on garde l'information en tête"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1958 25\n",
+      "1959 48\n",
+      "1962 48\n",
+      "1963 49\n",
+      "1964 31\n",
+      "1966 49\n",
+      "1984 48\n",
+      "2003 49\n",
+      "2005 49\n",
+      "2023 26\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calcul du nombre de semaines présentes par année\n",
+    "data['year'] = data['index'].apply(lambda x: x[:4])\n",
+    "years = data.groupby(['year'])['year'].count().index\n",
+    "nb_weeks = data.groupby(['year'])['year'].count().values\n",
+    "for x, y in zip(years, nb_weeks):\n",
+    "    if y < 50:\n",
+    "        print(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Affichage des données à décomposer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data.set_index('index')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.plot(y='ppm', kind='line',figsize=(12, 5), lw=2, title=\"CO2 dans atmosphère\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On observe bien une oscillation périodique ainsi qu'une tendance à la hausse plus lente."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Decomposition des séries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data.reset_index()['ppm'].values\n",
+    "x = data.reset_index()['index'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = seasonal_decompose(y, model='additive', freq=52)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fbHKVc8OL6dQ1NvHS9dM5bVS8v0uSDqagJSIi4geLNu/nlws3ENs7nH/cMINRA6L8XZL4gIKWiIhIJyqqrOfBxdt4a10+U4dG88y1U4nrE+7vssRHFLREREQ6gcdjeWX1Xh75eDt1jU389MwR/Gz2CMJDgv1dmviQgpaIiIiP5ZXWcOebm/h6VwmnjIjjt/PGMTxet23oCRS0REREfCSvtIZnl+/m9fQ8QoIMD82fwBXTknTbhh5EQUtERKSDZRdW8pfPd/HuBu/X58w/MZHbzx7J4P6R/i5NOpmCloiISAew1pKxt4znVuTwUWYB4SFB/MfMofz41GEKWD2YgpaIiMhxKK9p4K11+fxzTS47C6uIigjh1jNGcN2sFGL1acIeT0FLRETkGDR5LH//eg//88l2ahqamJTUn4fmT+CiSYPpHa63V/HSniAiItJOu4qquPONTaTvLeOs0Qn857mjGDe4n7/LkgCkoCUiItJGjU0enl2+myeW7iQiJIjHLp/EpScO0acIpVUKWiIiIm2QU1zNz19bz0ZXBXPGD+S388aREBXh77IkwCloiYiIHIG1ln+szuXBRdsIDQ7iqauncMHEQf4uS7oIBS0REZFWuMpq+O93trBsexGnjozjkcsmMqifbtUgbaegJSIicpjiqnpe/HoPzy7fDcBvLx7Hf8wcqrFY0m4KWiIiIo7ckhqe/XIXC9e6aGjycNGkwdw1ZzRDdMNROUYKWiIi0uMVVdbz4KJtvLMhn5CgIL43dQgLTkllREKUv0uTLk5BS0REeqzqejdvrXPxx092UNvQxA2nDmPBKakM6KtPE0rHUNASEZEeZ3dRFX/5fBeLNu+npqGJ6akx/OHSCYxI6OPv0qSbUdASEZEeo6iynieX7uTVNbmEhwQxb/Jg5k9JJG1otAa6i08oaImISLd34GAdr63J49nlu6hze7h6ejK3zR5JfJS+9Fl8S0FLRES6rVW7S3hw0TY2uioAOHfsAO6cM5rh8bpEKJ1DQUtERLqdsuoGHvk4i3+uySMpJpK75oxm9ugERg7QpwilcyloiYhIt7HjQCUL1+bx+to8qhvc3HTaMH5+9igiw4L9XZr0UApaIiLSpVlr+XpXCc8s383yHUWEBhvOHTuQ22aP5ISB6sES/1LQEhGRLmtNTil//GQ7a3JKiY8K545zR3HNjKFE9w7zd2kiQBuCljEmAlgOhDvLv2Gtvc8YMxl4GogA3MAt1to1zjp3AwuAJuA2a+3HPqpfRER6oMq6Rn7/wVYWpruIjwrnd/PGccW0JMJDdIlQAktberTqgbOstVXGmFBghTFmMfA74LfW2sXGmLnAI8AZxpixwJXAOGAw8KkxZpS1tslHr0FERHoAd5OHL7OL+WJ7EYs276e4qp5bzhjOz84aqTFYErCOGrSstRaocp6GOj/W+enrTO8H7HMezwNes9bWAznGmGxgOrCyA+sWEZEeZOeBSn6xcANb8g8SHhLEKSPiuOXMEUwdGu3v0kSOqE1jtIwxwUAGMAJ4ylq72hjzc+BjY8wfgSDgZGfxIcCqZqu7nGkiIiLtlrG3jOteWENocBBPXDmZ88YNJCJUPVjSNQS1ZSFrbZO1djKQCEw3xowHfgL8wlqbBPwCeM5ZvKXvMLCHTzDG3GiMSTfGpBcVFR1b9SIi0q2t2FnMtc+tJqZ3GO/+dBbzJg9RyJIupU1B6xBrbTnwOXA+8EPgLWfWv/BeHgRvD1ZSs9US+fdlxebbetZam2atTYuPj29n2SIi0t0tyyrk+r+vJSm6FwtvnklidC9/lyTSbkcNWsaYeGNMf+dxJHA2kIU3PJ3uLHYWsNN5/B5wpTEm3BiTCowE1nR04SIi0j1lF1bywIdbuenlDEYN7MPrN51EQlSEv8sSOSZtGaM1CHjRGacVBCy01n5gjCkHnjDGhAB1wI0A1tpMY8xCYCve2z7cqk8ciojI0XyWdYA/f5bNutxyQoIM540fyAOXjKd/L90TS7ou4/1QoX+lpaXZ9PR0f5chIiJ+kFdaw58+3cFb6/JJjevNldOSmD8lkfiocH+XJtIiY0yGtTatLcvqzvAiItKp3E0e8spq2ZhXzgeb9vFZViFBxvDTM0dw2+yRhIW0a/iwSEBT0BIRkU5R0+DmgQ+38a8MFw1uDwAD+0Zw8+nDuXbmUAb1i/RzhSIdT0FLRER8bmNeOT9/fQN7Sqq5Ii2JKUOjGT0wivGD+xEU1NJdgUS6BwUtERHxGWstzyzfzR8/3k5CVDiv3nASM4fH+rsskU6joCUiIj5RXe/m129s4sPN+7lgwiD+cOkE+vUK9XdZIp1KQUtERDpcxt5S7nxzM7uLqrh7zmhuPG0YxugSofQ8CloiItJh6hqbeGzJDv7vy90M7hfJS9fP4JSRcf4uS8RvFLRERKRDrM8t445/bWRXUTXXzEjm7rlj6BOutxnp2XQEiIjIcalpcPOnJTt4bkUOA/tG8PKC6Zw6Ut9hKwIKWiIichyWZRVy7ztbyC+v5eoZydw9ZzRRERrwLnKIgpaIiLRbXmkNv/9gK59sPcDw+N4svGkm01Nj/F2WSMBR0BIRkTara2zi2eW7eWpZNkHG8OvzT+CGU4bpa3NEWqGgJSIiR1Ve08A/Vu3l71/vobiqgQsmDOK/LhjDkP762hyRI1HQEhGRFllr2ZxfwWtr83hrnYu6Rg9nnBDPzacP56Rhuru7SFsoaImIyLcUV9XzZoaLNzJc7CysIiwkiEsnD+G6U1IYPbCvv8sT6VIUtEREBICsgoO8+PVe3lznosHtYUpyfx64dDwXThisr84ROUYKWiIiPVhtQxOLt+xnYXoeq3aXEhYSxPemJLLglFRGJPTxd3kiXZ6ClohID5RdWMnLK/fy5rp8qurdJMVEctec0VyRlkR07zB/lyfSbShoiYj0AIUH61idU8rqnBLW5JSy40AVYcFBXDBxEFdOS2J6aoy+9FnEBxS0RES6ocLKOr7KLmb17lJW55SSU1wNQO+wYKamxHDZ1ETmT0kkrk+4nysV6d4UtEREugl3k4ePMgt4fW0eX2UX47HQNyKE6akxXD09mempMYwb3JeQYN1cVKSzKGiJiHQD6XtKueutzWQXVpEYHcmtZ47gvHEDGTuoL0FBuiQo4i8KWiIiXZi7ycOjn2znmS92M6R/JH+9ZgrnjRuocCUSIBS0RES6qIraRn766jq+3FnM1TOSuWfuGHqH67QuEkh0RIqIdEG7i6q44aV08kpreGj+BK6cnuzvkkSkBQpaIiJdzKrdJdz0cgbBQYZ/LJjBDH3voEjAUtASEekiGps8PPPFLp5YupOhsb154UfTSIrp5e+yROQIFLRERLqALfkV/PqNTWzdf5ALJg7iD5dM0PcPinQBCloiIgGspsHNU8uyefqL3cT0DuOZa6dy3riB/i5LRNpIQUtEJMAcrGtkbU4py7YX8sGm/ZTXNPL9qYnce8FY9WKJdDEKWiIiflbvbmKTq4L1uWW8tS6frIJKACJCgzh7zACum5XC1KExfq5SRI6FgpaIiJ9syCvnuRU5fJJZQL3bA8CkxH786rwTODGpP1OGRhMRGuznKkXkeChoiYh0oiaP5ZPMAp5bkUP63jKiwkO4PC2JU0fGMSGxH4P6Rfq7RBHpQEcNWsaYCGA5EO4s/4a19j5n3s+AnwJu4ENr7a+d6XcDC4Am4DZr7ce+KV9EpGtocHt4I8PFM8t3sbekhqSYSH5z4Vi+n5ZIVITGXYl0V23p0aoHzrLWVhljQoEVxpjFQCQwD5hora03xiQAGGPGAlcC44DBwKfGmFHW2ibfvAQRkcDV4Pbwzvp8nvxsJ66yWiYl9uNO5/sIg/V9hCLd3lGDlrXWAlXO01DnxwI/AR6y1tY7yxU6y8wDXnOm5xhjsoHpwMoOrl1EJGDVu5t4ZVUu//flbvZX1DExsR+/v2Q8Z4yKxxgFLJGeok1jtIwxwUAGMAJ4ylq72hgzCjjVGPMAUAfcYa1dCwwBVjVb3eVMExHp9jweyzsb8nlsyQ5cZbXMSI3hwfkTOF0BS6RHalPQci77TTbG9AfeNsaMd9aNBk4CpgELjTHDgJbOJPbwCcaYG4EbAZKT9WWoItL1Zewt43cfbGVjXjkThvTjD5dO4LRR8f4uS0T8qF2fOrTWlhtjPgfOx9tT9ZZzaXGNMcYDxDnTk5qtlgjsa2FbzwLPAqSlpX0niImIdBU7D1Ty2JIdLN5SQEJUOP/z/UlceuIQgjQGS6THa8unDuOBRidkRQJnAw/jHbd1FvC5cxkxDCgG3gNeNcY8hncw/EhgjY/qFxHxm7zSGv706Q7eWZ9Pr7AQbp89khtPG0bvcN05R0S82nI2GAS86IzTCgIWWms/MMaEAc8bY7YADcAPnd6tTGPMQmAr3ts+3KpPHIpId5JTXM1flmXz9vp8goMMN5w6jJtPH05M7zB/lyYiAcZ4s5F/paWl2fT0dH+XISJyRNsLKnlqWTYfbNpHaHAQV01P5ubThzOwX4S/SxORTmSMybDWprVlWfVvi4gcgcdj+WJnEf9YuZelWYX0Dgvmx6cN44ZThhEfFe7v8kQkwCloiYg0c7Cukb3FNWzOr2CTq5wV2cW4ymqJ6xPGbbNHct3JKUTrEqGItJGCloj0ONZa8kpr2eAqZ7OrnN1F1eSW1lBwsI7KOvc3y/XvFcrkpP7cef5ozhs3kLCQID9WLSJdkYKWiHRr1lryy2vZ7Kpgy74Kdh6oYu2eUspqGgEIDwkiNa43qXG9mTUijgF9Ixga24vxg/uRFBOpm4yKyHFR0BKRbqe0uoGPthSwdNsB1ueVU1rdAEBIkGFIdCTnjB3A5KRoJiX1Y9SAKEKD1VMlIr6hoCUi3UZxVT3PLt/Nyyv3UtvYRHJML2aPTmBiYj8mJPZn9MAoIkKD/V2miPQgCloi0qVZa8ncd5B3N+Tzj1W51LubuGTyEG44dRhjBkXp0p+I+JWCloh0SbuKqnh7XT7vbMjHVVZLkIF5k4fw07NGMDy+j7/LExEBFLREpIuoa2xiTU4pX+4sYtn2IrILqwgyMGtEHLfNHsmZJyTovlYiEnAUtEQkYNU0uPliexEfZRawdFshVfVuwoKDmJYazTUzkpk7YRAD+uqu7CISuBS0RCSg1LubWLL1AO9v3McXO4qoa/QQ3SuUuRMGMmf8IGYMi6FXmE5dItI16GwlIgFhT3E1r6zey5vr8imtbmBA33CuSEvivPEDmZ4SQ4huwSAiXZCCloj4VcbeMp5als2y7YUEG8M5Ywdw5fRkThkRR3CQPjEoIl2bgpaI+EVpdQMPfLiNN9e5vN8jeNZIrpmRTILGXIlIN6KgJSKdylrLGxku/rBoG5V1bn5yxnB+euYIeofrdCQi3Y/ObCLSabILK/nvdzJZubuEqUOjeXD+BEYNiPJ3WSIiPqOgJSI+19jk4S/LdvHnZTuJDA3mgUvHc9W0ZII0BktEujkFLRHxqXW5Zfz3O1vI3HeQiycN5jcXjSWuj24sKiI9g4KWiPjEmpxSnli6g6+yS4jrE8bTP5jC+eMH+bssEZFOpaAlIh2myWNZvrOIv3252wlY4dwzdwxXz0jWYHcR6ZF05hOR41ZUWc/C9Dz+uSYXV1ktcX3CufeCMfzgpKFEhAb7uzwREb9R0BKRY2KtZXVOKf9YtZePMwtobLLMHBbLXXNGc+7YgYSF6E7uIiIKWiLSZtZasgurWLa9kNfX5rGrqJq+ESFce1IKV89IZkRCH3+XKCISUBS0ROSIrLVszq/gw837Wby5gNzSGgAmJfXn0csmcuHEwUSG6fKgiEhLFLRE5DustWx0VbBo834Wbd6Pq6yW4CDDycNjufG0YZxxQjyJ0b38XaaISMBT0BKRb+wuquLV1bks3lJAfnktocGGWSPiuG32SM4ZM4Do3mH+LlFEpEtR0BIRKmoaeWLpTl5auYcgYzh1ZBy/OGcU54wZQL9eof4uT0Sky1LQEunBmjyWZ5bv4q+f76K63s0V05L55TmjiI/SndtFRDqCgpZID+LxWL7eVUJJdT1BxvDSyj2s3VPG2WMS+M9zT2DMoL7+LlFEpFtR0BLpIZbvKOLBxVls23/wm2n9e4VnvSQlAAAgAElEQVTy2OWTmD8l0Y+ViYh0XwpaIt3clvwKHlqcxYrsYpJiInn8ismMH9KPxiYPSTG96KOvxhER8RmdYUW6qbzSGv74yXbe3bCP6F6h/ObCsVxzUjLhIbrnlYhIZ1HQEulmSqsbeGpZNi+v3IsxcMsZw7n5jOH0jdCnB0VEOttRg5YxJgJYDoQ7y79hrb2v2fw7gEeBeGttsTPtbmAB0ATcZq392Ae1iwjem4vml9eSsbeM1TmlvLM+n7rGJi6bmsgvzhnFoH6R/i5RRKTHakuPVj1wlrW2yhgTCqwwxiy21q4yxiQB5wC5hxY2xowFrgTGAYOBT40xo6y1TT6oX6RHcjd5WJNTyuItBSzddoB9FXUA9A4L5pyxA/jZWSMYkRDl5ypFROSoQctaa4Eq52mo82Od538Cfg2822yVecBr1tp6IMcYkw1MB1Z2VNEiPVG9u4mvsov5aEsBS7YeoKymkYjQIE4fFc9Npw9n6tBoRg+MIiQ4yN+lioiIo01jtIwxwUAGMAJ4ylq72hhzMZBvrd1ojGm++BBgVbPnLmeaiLRTWXUDX+8q4ePMAj7LKqSq3k2f8BBmj0lgzviBnDYqnl5hGmopIhKo2nSGdi77TTbG9AfeNsZMBO4Bzm1hcdPCNPudhYy5EbgRIDk5uc0Fi3Rn7iYPK3eXsCyriJW7S8gqOIi1EN0rlLkTBjJn/CBOHhGrTw6KiHQR7fpT2Fpbboz5HO/lwVTgUG9WIrDOGDMdbw9WUrPVEoF9LWzrWeBZgLS0tO8EMZGeJK+0hhe/3sPb6/MpqW4gLCSIqcnR/OLsUcwcHsuJSf11SVBEpAtqy6cO44FGJ2RFAmcDD1trE5otswdIs9YWG2PeA141xjyGdzD8SGCNT6oX6eI2uyp4ZvkuFm3eT5AxnD1mAJdOGcLpo+KJCFWvlYhIV9eWHq1BwIvOOK0gYKG19oPWFrbWZhpjFgJbATdwqz5xKPJv1lqW7yzmmS928fWuEqLCQ/jxacO47uRUBvaL8Hd5IiLSgYz3Q4X+lZaWZtPT0/1dhohPeTyWDzfv56ll2WQVVDKgbzgLTknlqunJROlmoiIiXYYxJsNam9aWZfVxJREfq6hp5L1N+3h55R52HKhiREIf/vj9SVw8aTBhIRp3JSLSnSloifhAk8fyVXYx/8pw8XFmAQ1uD6MHRvG/V53IBRMGERTU0odzRUSku1HQEulAe4qreSPDxZvrXOyvqKNfZChXTUvi+2lJjBvcl8PuOSciIt2cgpbIcbDWklVQyceZBXyceYBt+w8SZOC0UfHce8FYzh6boHteiYj0YApaIsegtLqBt9a5eG1tHtmFVRgDU5Oj+a+5o7l40hB9elBERAAFLZE2s9ayJqeUV1bn8tGWAhqaPJyY3J8HLh3POWMHkBClcCUiIt+moCVyFNZalmw9wJ+XZbPJVUFURAhXTU/iqhnJjB7Y19/liYhIAFPQEjmCNTmlPLR4G+tyy0mJ7cWD8ydwyeQhRIZp3JWIiBydgpZIC3YeqOThj7L4dFshA/qG89D8CVw2NVHfNygiIu2ioCXSTFbBQf76+S7e27iPPmEh/Oq8E7h+Vqp6sERE5JgoaEmPV1HTyKfbDvDOhny+3FlMr7BgbjptODeeNoyY3mH+Lk9ERLowBS3pUay17CmpYXN+Bdv2H2R9bhkZe8tobLIM6hfBHeeO4gcnDaV/LwUsERE5fgpa0u3VNTbx5c5ilmwt4PPtRRRW1gMQEmQYO7gv189K5fzxA5mU2F9fjSMiIh1KQUu6nSaPJavgIKt2l/JVdjGrdpdQ09BEVEQIp42KZ9bwOCYn9Wd4Qm/dtV1ERHxKQUu6LGstB2vdHKisY3dRNdv2H2RDXjnr9pZRWe8GIDWuN9+bksjZYwdw8vBYQvWpQRER6UQKWhLwrLXsKqri8+1FpO8po+BgHaXVDRw4WEe92/PNcsbAyIQ+XDx5MNNSYpieGsPg/pF+rFxERHo6BS0JWPvKa/nL59m8v3E/FbWNgLeHKjE6kqGxvRjQN4KEqHAG9I0gKaYXowdGERGqS4EiIhI4FLQk4DR5LH/9PJv//SwbC1wwYRAzUmM4dVQ8Q9RDJSIiXYiClgSU4qp6fv7aBlZkF3PBhEH81wVjFK5ERKTLUtCSgODxWN5c5+Lhj7ZTWdfIQ/MncMW0JIzR7RZERKTrUtASv6prbOK9jft4fkUOWQWVTE7qz4PzpzNmUF9/lyYiInLcFLSk03k8lu0HKnl/4z5eX5tHSXUDowdG8cSVk7l40mD1YomISLehoCU+5709QzVr95SyLKuQVbtLOFjnJsjAWaMHcP2sFGYOj1XAEhGRbkdBSzqUx2PZXVzNJlc5WQWV7C6qZl1uGaXVDQAM6hfB3AmDmJYSw6wRcQzsF+HnikVERHxHQUuOWXW9m/S93i9lzthbSnZhFWU1jTQ4NxENCwkiMTqSM09IYHpqNNNSYkiN662eKxER6TEUtKTNKmobSd9TSsbeMtbnlpORW0aD20OQgTGD+nLayHhieocxPKEPkxL7Mzy+NyH6yhsREenBFLTkiMqqG/g4s4BFWwr4OrsYt8cSEmQYM6gvP5gxlDNHx3NicjR9wrUriYiIHE7vjtKiza4Knlm+i48zC2hssgyN7cWCU1M5Y1QCk5P6Exmmr7oRERE5GgUt+UZjk4dPtx7gha/2sGZPKVHhIVx7Ugrzpwxh3OC+GlslIiLSTgpaQl5pDf9ck8vCdBfFVfUM6R/JvReM4fJpSfSNCPV3eSIiIl2WglYPVFHbyHsb97FyVzHb9leSU1zt3NMqgatnJHP6qASCg9R7JSIicrwUtHqA3UVVZBVUsr2gkqyCg3y+vYh6t4fE6EjGDurLldOSuGjSYAbry5tFREQ6lIJWN+Fu8vDmOhcvfLWHvpGhnDt2AOGhwfwrPY9NrgoAggwkxfTi8rQkrpiWpHFXIiIiPnbUoGWMiQCWA+HO8m9Ya+8zxjwKXAQ0ALuA66y15c46dwMLgCbgNmvtxz6qv8ez1vLptkIe+SiLnYVVjB/Sl4O1jfy/D7cBcMKAKP77wrHMSI1hWHxveoUpW4uIiHSWtrzr1gNnWWurjDGhwApjzGJgCXC3tdZtjHkYuBu40xgzFrgSGAcMBj41xoyy1jb56DX0OBU1jazKKaGyzs0/1+SSsbeMYXG9efoHUzhv3ECMMeSW1FDT6OaEAVHqtRIREfGTowYta60Fqpynoc6PtdZ+0myxVcBlzuN5wGvW2nogxxiTDUwHVnZY1T2QtZZt+yt5edVe3lmfT22jN7cO6BvOA5eO5/K0JEKb3YU9ObaXv0oVERERR5uuIxljgoEMYATwlLV29WGLXA+87jwegjd4HeJypkk71DY0sa+ilg255Wx0lbN8RxF7SmoIDwli3uTBfD8tiZjeYSRGRxIeopuHioiIBKI2BS3nst9kY0x/4G1jzHhr7RYAY8w9gBt4xVm8petU9vAJxpgbgRsBkpOTj6H0rs3d5CE4yHznst6yrEL+6+3N7K+o+2Za77BgpqbEcMOpw7hgwiCie4d1drkiIiJyDNo1MtpaW26M+Rw4H9hijPkhcCEw27nECN4erKRmqyUC+1rY1rPAswBpaWnfCWLdQea+Cl5bk0e9u4n5UxI5aVgsrrIanvh0J2+tz2f8kH7837VTSegbAXi/9uaWV9aRHNOLH5w0lISocCYl9Wd4fB/d10pERKQLMv/OR60sYEw80OiErEjgE+BhvL1YjwGnW2uLmi0/DngV77iswcBSYOSRBsOnpaXZ9PT0430tfufxWHYWVvHlziLeXp9P5r6DhIcEERYSRGWdm5EJfZybgxounDiIjzILGD0witdunElJdT3z/vwVocFBvHPrLOKjwv39ckRERKQFxpgMa21aW5ZtS4/WIOBFZ5xWELDQWvuBM8g9HFjiXP5aZa292VqbaYxZCGzFG8Zu7a6fOKyud7N2TymZ+w6yPreM9L1llNc0AjAxsR/3XTSW+ScmEh4axEsr97Asq4izxiTww5kpDO4fyexN+7n11XXc8so6coqrqGlo4o2fTFfIEhER6SaO2qPVGbpKj1aTx7I5v4IVO4v4cmcx63LLaGzytl9qXG+mpUQzLSWGGamxbf7U358/28mfPt1JVEQIf71mKjOHx/ryJYiIiMhxak+PloLWYay1HKx1c6CyDldZDbklNeSW1rKnpJqMvWVU1Hp7rMYN7supI+M5ZUQcE5P6HdeXL9c0uAkNDvrW7RlEREQkMHX0pcNuocljWxxQXu9uYllWIYs2F7Alv4L88lrq3Z5vLRMZGszQ2F6cO3YAp4yMY9aIOOL6dNzlPd2tXUREpHvqEe/w1fVuLnnqK66YlsR1s1IJDjJsya9gYXoe727YR0VtI3F9wkgbGsPZYweQEBVOfFQ4idG9SI7pRVyfMN1dXURERNqtRwStmoYmkmJ68f8+3MYbGS76RoSyZk8pYSFBnD9uIN+bmsis4bGE6NKdiIiIdKAeEbTio8J57odpfLBpP799fyvlNY3cM3cMl6cl0a/XsY+tEhERETmSHhG0AIwxXDRpMBdOHIS1EKQbgIqIiIiP9ZigdYgxBg23EhERkc6gQUkiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjxlrr7xowxhQBe32w6Tig2Afb7c7UZu2nNjs2arf2U5u1n9rs2KjdjmyotTa+LQsGRNDyFWNMurU2zd91dCVqs/ZTmx0btVv7qc3aT212bNRuHUeXDkVERER8REFLRERExEe6e9B61t8FdEFqs/ZTmx0btVv7qc3aT212bNRuHaRbj9ESERER8afu3qMlIiIi4jcKWiIiIiI+0qlByxjzvDGm0Bizpdm0ScaYlcaYzcaY940xfZ3pKcaYWmPMBufn6WbrXOUsv8kY85ExJq6V3zfVWS7bGPOkMcY40292pm8wxqwwxoxtZf1fGmO2Or9nqTFmaLN5Dxtjtjg/V3RUG7VQQ5vbzJk30ZmX6cyPcKa3tc0eMMbkGWOqDpt+mjFmnTHGbYy57Aj1hhtjXnfafLUxJsWZPrlZXZsCpc2MMdc028c2GGM8xpjJzrzj2s+ceZc7+1CmMebVVtZvtW2NMT80xux0fn7YEe3Tmna2W6gx5kVn+jZjzN3N1jne47PV4+6w9VtsN2PMUGNMhvP/mWmMubmj2qiFGtrTZmHGmBec6RuNMWc406MO2weLjTGPt/L7WmuzoU5bbTLGfG6MSWxl/UBosyRjzDJnv8k0xtzuTI8xxixx9vUlxpjoZuvc7bzm7caY85xpx91uzry2HKMt7pOd1W7tbTNjTKyzfJUx5s/NttMR+1pbj88W3wuaze9rjMlvXl+3Za3ttB/gNGAKsKXZtLXA6c7j64HfO49Tmi/XbPkQoBCIc54/Atzfyu9bA8wEDLAYmONM79tsmYuBj1pZ/0ygl/P4J8DrzuMLgCVOLb2B9Obb9GObhQCbgEnO81gguJ1tdhIwCKg6bHoKMBF4CbjsCPXeAjztPL6yWZuNAkY6jwcD+4H+/m6zw9abAOzuwP1sJLAeiHaeJ7SyfottC8QAu51/o53H0b5os2PY164GXnMe9wL2OK+jI9qtxeOuHe0WBoQ7j/s4tQ0OgDa7FXjh0L4AZABBLWwzAzitnW32L+CHzuOzgJcDuM0GAVOcx1HADmCss6/c5Uy/C3jYeTwW2AiEA6nALiC4g9qtrcdoa+8FndJux9BmvYFTgJuBPx9hu8fSZm09Plt8L2g2/wng1SPV111+OrVHy1q7HCg9bPIJwHLn8RLge0fZjHF+ejsJuy+w7zsLGTMIb/hZab3/qy8Blzh1HGy2aG+gxU8EWGuXWWtrnKergEN/JY4FvrDWuq211XhPAucfpe5j0s42OxfYZK3d6KxbYq1too1t5qyzylq7v4Xpe6y1mwDPUUqeB7zoPH4DmG2MMdbaHdbanc629uF9M27TXXXb6zj2s6uAfzqPj3s/A34MPGWtLXPqKmyl3tba9jxgibW21NnGEny0nzl1tKfdLN62CQEigQbgIB1zfLZ23B1eb4vtZq1tsNbWO0/D8WHPfTvbbCyw1FmvECgHvnVDSGPMSLwh7MvDf9dR9rVvtg0sw3sctlRvILTZfmvtOudxJbANGMK3zx0v8u/XNg9vqK+31uYA2cD05ts8jnZr6zHa4j7ZWe3W3jaz1lZba1cAda1t81jbrK3HJ628FzjbnwoMAD45+qvv+gJhjNYWvL1KAN8HkprNSzXGrDfGfGGMORXAWtuIN0VvxnsCHws818J2hwCuZs9dzjQAjDG3GmN24f2L4LY21LkAb6oHb7CaY4zpZbyXRc48rG5fa63NRgHWGPOxc3ng19CuNusIQ4A85/e6gQq8PWvfMMZMx/uX4C4f1dCSI+1nh1yBE7Q6aD8bBYwyxnxljFlljGlvSPqmLVvYdmdprd3eAKrx9kzmAn90AmGHHJ/NND/u2sy51LIJb/s97IT7ztJam20E5hljQowxqcBUvrsfXoX3L/+W/vg7Uptt5N+B7lIgyhjzrePuaPzRZs7lpBOB1cCAQ3/kOf8mOIu15Tg41nY7lmP0W/tkZ7dbG9usLY61zZo70vHZ4nuBMSYI+B/gV+2otUsLhKB1PXCrMSYDb5dogzN9P5BsrT0R+CXwqnNNNxTvifxEvJegNgF3f3ezmBamfbNDWWufstYOB+4E7j1SgcaYH+D9y/NRZ91PgEXA13jfmFcC7ja92o7RWpuF4O0uvsb591JjzOx2tFlHOGK7O38pvQxcZ609Wu9YR2qtzQ7VNQOosdZucZ53xH4WgvfSxBl4T2p/M8b0b0fNR2zLTtJau00HmvC2TSrwn8aYYR11fMJ3j7v2sNbmWWsnAiOAHxpjBrR3G8ehtTZ7Hu8bVjrwON7zx+HnjSv5d6/q4Y7UZncApxtj1gOnA/ktbPuIOrvNjDF9gDeBnx92leE7i7Yw7fDj4FjbrV3HaEv7ZGe2WzvarC2Otc0O1XK047O1bdwCLLLW5rUwv1sK8XcB1tosvJe8MMaMwjv+Cac7tt55nOH0Po3C+c+z1u5y1lkI3GWMCcZ7vRngPeCvfLtLM5GWL5e95iyLMeaBZr//0IDos4F78I65ONRFjLX2AeABZ5lXgZ3H0Qzt0lqb4T2Jf2GtLXbmLcI7fuSgs94R28xa+5v21tJCm7nw/pXuci4r9cO5tGK8g4I/BO611q5q7+86Hkdos0MOP+lMdtY7nv3MBaxyenlyjDHbgZHGmEs4bD9rhQvvG0DzbX9+lJfaoY7QblfjHdvYCBQaY77Ce9KNddY7ruOzpeOupeOzDfXvM8ZkAqfi7YXzuSOc09zALw4tZ4z5mmbnDWPMJCDEWpvhPG9zmzm9KPOd9foA37PWVgRqmzmB/E3gFWvtW87kA8aYQdba/c4fZIcu4x06pxxy+L5yzO1GO47R1t4LDvF1u7WzzY62reNps7Yen629F8wETjXG3IJ3XFuYMabKWntXuxqkK7GdPCiMwwa54ww+xNu79hJwvfM8HmfAIzAM719oMfx7IHW8M+/3wP+08rvW4h3cfWgw31xn+shmy1wEpLey/ol4L2+NPGx6MBDrPJ6I91JBSAC0WTSwDu/g5BDgU7w7fpvbrNnvqGpl+t858mD4W/n2AMiFzuMwvGNIfh5I+1mzaS5gWLNpHbGfnQ+86DyOw9uNHnuEmr/Vts7+nuP8v0Y7j2MCod3w9gS/4Lzm3sBW51joiHZr8bhrR7slApHNjokdwIQAaLNeQG/n8TnA8sO28xDw26P8rtbaLA5nYD3ePwB/F6ht5tT+EvD4YdMf5dsDux9xHo/j24Phd9NsMPxxtlubjtHW9snOarf2tlmz+T+ihcHmx9lmbTo+aeW9oC31dbefzv1l3h6D/UAj3je2BcDtzs65w/nPP3S3+u8Bmc4Btg64qNl2bsY7GHAT8H5LB4azXBreELQL+HOzbT/hbHsD3oGj41pZ/1PggLPcBry9PgAReN9YtuIdDDg5ENrMWf4Hzmvb0vyga0ebPeL8Ho/z7/3O9GnO82qgBMhsZf0IvJ+Aysb7qZVhzepqbNaWG3zVbsfQZmfg/av28O0c735mgMec/WQzcGUr67fatngvQ2U7P9cF0PHZx/l/znRe3686sN1aPO7a2m54Q8wmvOeOTcCNAdJmKcB2p20+BYYetq3dwOij/L7W2uwyvL1jO4C/4XwSLkDb7BS8l5A2Nfs/nou3N3Sp8zqW0uyPCry9J7uc9pvTge3W1mO0tfeCTmm3Y2yzPXh7kKqc//OxHdRmbT0+W3wvOGyZH9EDgpa+gkdERETERwJhMLyIiIhIt6SgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPqKgJSIiIuIjCloiIiIiPhLi7wIA4uLibEpKir/LEBERETmqjIyMYmttfFuWDYiglZKSQnp6ur/LEBERETkqY8zeti6rS4ciIiIiPqKgJSIiIuIjCloiIiIiPhIQY7Ra0tjYiMvloq6uzt+ldEsREREkJiYSGhrq71JERES6rYANWi6Xi6ioKFJSUjDG+LucbsVaS0lJCS6Xi9TUVH+XIyIi0m0F7KXDuro6YmNjFbJ8wBhDbGysegtFRER8LGCDFqCQ5UNqWxEREd8L6KAVCFwuF/PmzWPkyJEMHz6c22+/nYaGhnZt4+mnn+all14C4Ec/+hFvvPEGAGecccZR7x/2+OOPU1NT883zuXPnUl5e3s5XISIiIv7QY4JWRW0jB2sbW51fVtNAVd2351trmT9/Ppdccgmr1m9hw5atVFVVcc8993xn/aLKeuoam1rc9k033cR5l1xOg9vT4nxrLfsranE3fXf+448/TlVVNfsramnyWBYtWkT//v2/tYy7ycP+ilqstS1uv8Ht4cDBulbnZxdW8cwXu1qcB7DJVc7Lq1q/N9vKXSW8tc7V6vyl2w7w0ZaCVue/v3Efy3cUtTr/9bW5ZOwtbXX+8yty2Lb/YIvzrLU8tSyb3JKaFud7PJY/frydosr6FufXu5t4cPE2qurdLc4/WNfIg4u3tfp/W3iwjv/5ZDseT8ttv6e4mqeWZbf6f5O5r4K/f5XT4jyAtXtKWZie1+r8z7cX8uGm/a3OX7x5P8uyClud/0aGi9W7S1qd/9LKPWzJr2h1/tNf7GJ3UVWL86y1PLZkBwUVLV/Cbmzy8NDiLCpaOW6r6908uGhbq8ddSVU9j3yURVMrbZ9XWsOTS3e22vbbCyr525e7W5wHsC63jH+uyW11/lfZxby7Ib/V+Z9kFrBk64FW57+zPp+vs4tbnf/K6r1syGv9j66/fbmbHQcqW5xnreWJT3fiKmv5uHA3eXjkoyxKq1v+o7KusYkHF22jpqHl46KippGHP8qisYVzGsD+ilr+tGTHMZ+TNrsqjnhOWrW7hDczWj8nfZZ1gI+2tH5cHO2ctHBtHul7Wj8nvfBVDlv3HfmctLekusX5bT0nVda1fFxUdsI56YUjnJPS95SycG3r56QvdhTxwaZ9rc4/2jnpzQwXq45wTnp55R42u1o/J/lDjwlae0uq2dPKjg3ek+7u4m/P/+yzz4iIiOC6664jv7yWnJJa/vSnP/H888+TmZnJ9OnTmTx5MhMnTmTVhi1kF1bx0ksvMXHiRCZNmsS1114LwH//5j4eeeTRVg+syjo3t//0VqZMTWPcuHHcd999ADz55JPs27ePM848k4vnnEtxVT0pKSkUF/9/9t48zJKruBONzHtvVfWmvRFoQ8IYowXQgIz9jA14zHhj8TLyGGYxGvvB2A8DY7CxsD9v82zG8837wDAMGLwIb4ONLYQBgT1gVskYkIwk1BIgELKRkIRoSd3q7uqqezPz/ZF5MuPEiV9E5r1d9H2vlP90V52Kk5m/OCf2OFkL39e+9rV00UUX0YUXXUSve93v0IH1Kd1xxx10/vnn04te9CK68MIL6Xu/93vptrv2070Hj9LRqb7xfvwtn6D/+v7PQYX1vDdeS7/yrpshdi/4vX+gV7zjRjj+U390Hf30n14Px1/69s/QT/zhp+D4L175WfrXb/4EHP8v772FfuD1H1fH7ju0Qf/9bz9Pl12hz//JL99Pb/zwF+nyK29Sx//yujvpLR+9nd7wd7ep47/zgdvoLR+9HRqav3jlTfQ/PvRFuv6fH1DHX3jFp+i//+3naT9QaM9+wzX06++5RR0jIvqx3/0Eveqv9GcnIrrsik/TS/7XP8Lxn/mzf6T/+LZPw/Gf/8sb6cff+g9w/Ff/eh89539co44d2ZzRb7//c5D+5rsO0hv+7jZ6+Z9/Rh1/32fvpt/96Jfot9//OXX8rR+7nd7ysdvpT4HC/bV376M3feRL9NEv6EL7P/3J9fTaD3yB/vl+3dh43huvod+8+laokH70TX9Pr37nZ9UxIqJ/9/ufpJf/+Q1w/MV/cj296I9xRPs//8UN9G9//5Nw/Jevupl++H9eq44VZUW/efWt9ENv1Me//PXD9LoPfoH+05/o+/Ijn7+P3vSRL9Gvv3ufOv4nn/gnesvHbqe3fkw3RH/7b26lN3/kS/R+4GC9/O030Ov/7jbaB4yR57+1lknrm7pMeu4brzFl0vPf+g/0yr/EMukn3zA7WG0AACAASURBVHYd/fSf4n3hyaRXXXkTXfq7WCb9xntuoR98gy2TXgjm//Qdtky68vq76C0fvZ1e/0FdJr3+g7VM+itgaAaZ9GlgKF7WQyb9hiGTLv3dT9CrwLMTEb3wDz9FP/u/9D1P5MukV/7ljfR8Qyb9yl/vo+e+UZdJx+vaNobWPNe+ffvoKU95SvS7E044gc455xz62Z/9WXr5y19ON9xwA33yU5+m0x95Bn3hc7fQb/3Wb9GHPvQhuvHGG+n1r399RAvkNVVE9NJX/Qq9+4Mfp5tuuok++tGP0k033UQve9nL6IwzzqAr3/s39AfveA+VzMO4/vrr6YorrqBPfvKT9K6//Qhd+fY/phtvqIX6bbfdRi95yUto3759dNJJJ9H73/uu5j76AxxuvNINYIi1zwk8nON5aVHA6Goe+eBR3fMO74QiViEagjz3zaJWBBvAewzG7RSMH96o6WeFjS2KDCzzNZ3V74QiyWE9IuwDJusA+4AJwj78Hnn2641jMQXYB/qjM13ZL/MV3nkdOE/hguu+stf9tLTnD7JkA4wHuhkQimFfbDjYL6NMQhHU9mqGHwIyKZBjmVRjewRgu9nuC328lUlg3R/Z/P+vTDpe19Ie78Cv33jPPhiG7XsdbhbtrtX6lS844wT6tedeaNJUVaUWjVdVRc985jPpNa95Dd155530vB/6YVrbfTp96u8/TpdeeimddtppRER0yimnxHTA0MmI6G/fexW96+1/TDmVdPfdd9Mtt9xCT3ziE7s/ENc111xDP/IjP0K7du2i3RsZfc/3P4f+/tpr6NE/9qN03nnn0cUXX0xERE95ylPorq/8c3gA9Ro177g+LehEwudqbRYlrY5HcPx4XJvOZg/jJRDIeV6/O5LXYRzJzoAdEq6jhr4ANxg1ro6nUI5OC5qMlssv8ozcjcYIhdhnDrYtds44wt6ZvyGHzxeu9c2Cdq5gUVmWVbtOluVCxqW80Lu76zpgC8b77itv36xPCzpJ/Yv6WkqZ5GAfZBKWCWHdetguuq/s+3syaX0JZZJr5B6na7lQWrLrwgsvpOuuuy5a0AcPHqSvfOUr9Au/8Av07ne/m3bs2EHP/sEfoE9e+zFsmCX/ia8vf/l2+uO3vJH+9J3vpZtuuome/exnR0cvBGHG9wV/JukVrq6utv8fjUa0OZ1Zt6fDjQeDvNdwHd1cvoiXF4XrvDM76jErUcSpxqQA2v7BJlqDNnios0Ce+70H63Ev8uCNu5G9Lbg8IzesF/TugTcFwN4bD5EyNP/+wzb2oTYMpczlc6DreES8PIXiPdMhZ10HeoRdiMag8QeatBMa/9rBGnv0HuH5POxRajFcx0UmOdi36xpGUr1oX4M9oD/QyiR93wSZhMbvbvaFu+6d8eMik3o6GN/o6/8TES0v8uRdRVm2tQBPOPPExBhCgvZ7vud76PLLL6e3/P4V9B0/8KNUFAW98pW/QJdddhndc8899JjHPIZe9rKX0Wdv/QLddus++vbveiZd/jMvpJ/7uZ+jU089le6//3465ZRT6KGjM5qsTqBi+uJd99GOnTtpZeduuvfee+n9738/PfOZzyQioj179tA/3XM/nXXOnkgoPf3pT6fLLruMLr/8crr3/gfpQ3/zXnrLH1yhzh88Iy/Ue8/Bo/SYvbvN8RN34ojX1w9t0t49q9HvOLabs5JWxrFt/xVQHxOua41iYCKit38aFyMTEb3uA18gIpw6/KWmxubWu/Wi4VAfdN8hvTD1r2+oizofOKLXM3y+KUZ+ANQ7hOueA0fp8Y88AY7fe2CDHrFnDY8/tEFnnrQj+h2PNhzZnCVRma891BnzmpNwo1FoTUT0rs/gglYiojd/9IvN3Pp4qP/5wr16sfwvXVXz5m5QLP9Hn6hrsx4E2H/6jrouDmEfHIx7DhylJ56lPyNRjdPZp+yE4/ce3KDzTsOi9MCRabJvDrJC5qKs2ihCuFARe7j+9z7cXEJEdMW1d5jjYV1/FWB7+ZU19qiu9Y0frnmLsP27ppgZ7YvAU74Gteveg0fpmxyZdNLOFTh+36F033BDaGNWJBExTyb9/ZdsmfTnRiE4EdHvfLCWSQ+B1OCrW5mkZ3Fe8z5bJl31mboB4/7Deso+yCQ0Hq57Dx6l8x+FZdI9B4/SI07AMumeg0fprJPjfcNl0uGNWZthChdvANBk0k132jLJaj45nteWRLSyLDs7y7IPZ1l2a5Zl+7Ise/lW3KfvNcq719S8BO718P9nWUZXXXUVveuqK+m53/UUet7TL6G1tTV6zWteQ3/xF39BF110EV188cX0xds+T8/918+nx37L+fTLv/zL9IxnPIOe9KQn0Ste8Ypez/e48y+ix1/4RHrOM76NfvInf5Ke9rSntWMvfvGL6SU/8WP0U//mudGzP/nJT6bLLruMnvrUp9K/f+6z6Edf8BN0wROeZN4H5eTDde9BW+jd44xr9Ny4/LoiGPg7aQav57F6ES1UvyPv6UWM9gOhFq4HjnhCy6ZfFHutc2/KPNavKffnhrtmiHLeaZEBz3P3eNM3EuQZqagzru+4i/0Bm3ca9hwvjXe8Zk97Ph6t0CIDHnZ9UtHWFfbDg866RgXT4UJGcLhQx2m4fN449ArveNTj64cU7B2Z5GHnyiRvXzTjqHkpXN669rDvY+Ra1zy8i2SS0lXpyaSpK5PqcctAPB7XVqUOZ0T0yqqqzieibyeil2RZdsEW3avXFSIteqi6s5rl+Nlnn01/9c530Xs+fj1dfe1n6A1veAOtrq7Sq1/9atq3bx/dcMMNdNW7r6YTTz6ZiIhe+MIX0s0330w33ngjve1tbyMiol/9tV+jF/70S2mc5/S2t72NLr30UiIi+shHPkKXXHIJ7d2zRv/3695EH7j2err66qvpne98J1122WVERPTSl76UPv6pG+gP3vEeyjOiO+64o60Be8UrXkE333wzfeDa6+jf/58/Q0VZ0bnnnks339x14/z8z/88/dyrftl49+56wPFw5lF4PDyujfM6BM375ZEATcB5G+pZ559OREQroJbgsu8415znJ/6PRxMR0Sm7dK/5ey+o50ch6yeddSIRYc9+z1rt0XnepYe9Ns75fb9yf67MNXqOmeZ9n3vqrvb/mtB72mNPM56Y6PnfejYREZ19yg51/EXfVX8e6syT9fHnPekMIsKH737beXWNJDoeIkQAPew17Pil8TbC3uGNRs9f6UHl+U9nURptXz/5nJPxA1OH3UkgQv0zz/gmIiJ6/CP3qOM/fknNu50ren3UMx63l4hwQffjTq+jVGhfhMvjjWcIaryLsHdkksa7E9ZsmfT4R+mYhetZjcyQUcxwXfYdtcxBMumFjkz6vgsbmQQyGE86u656QzLlxB31+7kyaQ7eefuCl3Boz8drwjRD7JxT6wjarz7nuJobybUlhlZVVXdXVfWPzf8fIqJbiejMrbhX32tXkzbRCgx5kbo+Tmw8ndsrAwjjuF6ggvcmotYORIZS2K6w6NcprjynSYt4NVqHlfFCpKfkNYvGU6HElX3oNOIX92BU+tL2cEIdAhI6oSDUiwAcBpG1SZMKRdiF0PhhoHCCskf0wdjRsI+fT/P+GPYKtpw3Gj3HVqPnvNGipajuTd4fefiBndq6ICIaj7Lm2XRsdjRGAML+1N21okLYB0WG5g+XNv/M2RdTkUJJ6B3ecXotGtsXe+QgBFmBsA9JAlTHs9rsC0QfjBU0/uhGYbrYK9hGKXOFflrY2HsyLdpXjkzTZFKIUCJ5HraVW+sFsAvGCBrfFfYF4N2jTqyNeLQvPN62z+diZ697V6YZ9LtXl6sqasuL4bMsO5eI/gUR4QNhvgFX8BBVW4P9zjOktI1TkTdeJX+n0SM7qzXUAL117z7j4fncbhllnAt0LU3Hxz16bZwLI29cV/Y2JoEeFZZ6CilE7NC4R++Nh3WrjXN+utgVqVAsPN4UQ+bXeOtgXwQHQx93sQvYIyO6tMcDPUov5wb2/PLeXcXOwTbaFwvyTndAbOduUeyKvusezB8ey+0qVrGz16W3brkDob2fJ5Ni3s2xLxp6TyZtzLvuPZnmjIeuRm1+vtbmw26AzFHH69+haOHxurbU0MqybDcRXUlE/7mqqoNi7MVZll2XZdl1992HT+A9VlfX8uoZSimtP16pf2v/Mp2zdP4QGmLeeGUrtCAX0MYN1zwLP1LWqsLor6z9+e3n065jZkjNKfTCOFJYQWDMZ+h4Ru5ihtoQetWzX1DZe9jNHN5584drHmVfOOvSU8YcO9WBOUZGMAp8LaqMe++LOecPl/fu8zh/gwypBZ1D7Tpm2M9Jv4hM4q+2Fdj1lWmT0TYxtLIsm1BtZP1ZVVXvlONVVb21qqpLqqq6ZO/eveocx7I1N8CuGko8YqUYO/w5VGMoinjh4fkjWlXyHNo4Ulja/flcwYObR6hNXYVib5yp59k7hpq38aY9w/hIIUyd1GO4P1b2NrZT5/7hWtQIdZW15tkvqLA49lZqESuMntjNS+/eHyskL5o4dSO9C+4Lj77n2kAyw123rrJfjHfeYbRWpHe6YKR2YZk0YH7LAYEyqXUg9NSdxzuft7ZMCs+sv9sA7J1ooirzesq0bRHRyurq1D8golurqnrtPHOsra3R/v37j5mx1aYOlbEoItUz+hTT83HNUOvG1PdpDSlwT+fZXEOO3T/8u3//flpbq3PxloejGWT8ilJ3RiiXiGhTUbZ9Q8H1OBZK+PlK9W/l79DJ7eH50NEY3visL/0ckQH/3T1Dpz92Kn3Rn956fi89tSj2XlQFzm+kJodhbyuMRXmnRsSc+rk2PVXqMslft6U53jetOy9vrdRi4WA7JLXoYe8Z0Rb2cP6w7qFMCtgDI7ln6tBb955MmmfdDysFmWNftRGt5ToidKsqxp5GRP+BiD6bZVn42NcvVVX1vr4TnHXWWXTnnXfSsUorTouS7j24QdP9K0mnzNFp0bb5lvev0OokHj+yOes6KB5cTZj40NFZ29mUH1hLrOkD69P2gL/xwbWkS+qBI5t0eKOgPCPKDqQdVvsPbdD6tKRxnlHxQHpuyX0PbdBGc0bV5tdXk/F7DhylWVnRwUlOR75Wj6+trdFZZ53VYkPUIwyvKox+yhTNH9NrBddOxGygdyp519aaeApj3qiKU8sydRSS5X1G2M3lXfaPSHlRFe39EnqxNDm22pk5i9a3edi7vDWiMv66c7B3Il5+VMabv3/Ea1pUtDIW2Lvrvp8yn5d3fWXSXBGtIeNzRNGHRHo3i/RswTZKDqPs/db9vLWN056G2jwpbTdi5WLXb18sW0RrSwytqqquIfXDMf2vyWRC55133jF6IqJ/3n+EnvenH6b/58eeRJc+KT6d8IO33Esvenf9cdcrLvtW+u7HPyIaf8env0Kvenf9kcx3veRpdP7Z8Uch3vyRL9F/+5v6ELmPv+q7k8MNf+vqW+j3Pl4fYrfvN74vOaTtle+4ka78x7tpdZzT53/zB5Jn/49XfIo+/Pn76FEnrtEnXv09yfjlb7yGbrzzAF14xgl09csuTsZf9N8+RHc+sE7f9c2n0Z/8VDpu1aq4htKQGqpF64TmUPaSfpdU9qWn7G1Dyq+X8OjxeFlWbc3D4tjZNVbHp3DVUfZ9sZ1b2c/P28hQmsNzH1Qs79XHzVOML+hTZW87IIvWCXm8s2WSbaQOqxsdLrOOaV2q5oA4MsmKtPL55y138DIcfXnjNiLMUe7Qt/ZwvF1qtJbtCoJkq73LXp49uP9mUdo5e8+7nNtzx6HgY2tIOd6hEgpfOCrjpa+cFMsx8+zniJgNqsFS6x0WjQY6nr1bT8GxncO7DXU6c0YbA/086a2qqkzeHts6nzmiLu78Nu/6RtyCskf0bh2Qgz2M9FpGrpcaXLBu1Cum71t7COkH6JN5ahsXqT2s74npF3W8t772sP7dOF8u02a5nmYLr9CF4Hn2c1nhA71HRF9V8Vzt+ILdU4t0AA1pQ58rtTioe2oO3h2j7qy5PfsFuhYX7SpctA19SETqWEQbET1S9seqO0t7Nr4Nt2bPe4aOh92i9P15p9cZHRvs54smLiqPHefMMUILN7U4IFrpPb9V2zgv9gvok2/Unsf377fuly11uG0MrRDRsoQGkV4AOKxwdY6N33N+LxTsF0/qnrvVzssVgl8Mj5VhTW9j7wtNh3dzFL7OHN7zotz5iob7jXuG0HzrjiuEOdZ9hJ3H20WVuU1vNTIs6mB42C2O/XD6xRsZFqTvWVA+d8G1Me6lpzwj1m3COJaNCK48d+hdmWrrA72TejGZZBlyi68bb1+x8TkiweHdts3xDst2tanDeVIsQ0LB80R1PA+rb2pxK9JTjlCIsbPrEXTs+P0XS08tXviKvVscbazHYcF13zD/VqSnhoTx57j/oGL6OXjXN7Lgpq/mSZE4tSKD2tjnGB+SepyrXMEpd/BTl45MWgT7nukhNL7ovnDLHTzsh0SS5zAm+n6RYe7aRiPtO6hUw4t4zaWL+/Hu4YjWcbrCp0wWDcMvnJ5aIA3hpRaxsseG1qIHhrodQIPC8IsKxTmwX5B3i6RIyrLq2tS3Igy/8LsNEJrHI721YOfbIumphQ/aHYT9POUOA2TaXCkcRyb1xX6ulPmxk7dqucOCMslLLR7TkoA5yh08mVT2lElz8e5Y6kpj/snDNVrH58qyrD4eQTkKmVvRliFDRCo9p9HouYejnQo8c+4/LRz65pnQicNhXJ+7ex/92bpx/d3s8aK0sXOxd+hd7AfM7/HOen+IfXN/DzuLr2h86vAmprcFur/ubKGpPl+E3XB6b1/w2sbSGNdoq6pqf++te4832qdSXHpvX7kyyeZtTH/ssXdlkrHuo3GnAWa+Z+uPfaHybgj2Dr36fv33lYu98fzzyKRY3s7zbkNk0hz0PfZllnXf912Wa9sYWkR1OFFbHFEtiFfH43SZaPTxAXWOwlOfz6YPz+99G8t7d6+ewMfOpvdqtOZ6vsLm3RDeerxTx5nQ0tO62Mh163wcoTYI+zmE5qC1oSiMwpnfq/eIDUGbt1NVYZXRv0NoCw87xwAfhp3DW+f+C+8rzwhfoC4Vfdy62xfe3I683WLezbWvjuXaWGDtFp5MWrD20MN+Hl26sD4oq6WLZhFtM0NrMspdD2hhD2YB76+mnyNy0Cp7feGHvbZo1EKbf4j3OZ9nPyCysAURuyFRG9Ozd+oR9Hdz1oV36r3n+XrYeRE3jp3m3S4YsfOMfH9tVNG/aG4Nm5ivc0QdPHrHuRqy7rdmX3jv1+/+Gi1PT1nyrB73nKMtkGmekRvtK2zgI/qtlmm+PsP7wt/zx07XLRzNA87VstVnEW0zQ6uOaHlWuGdlD9/4i3q3fb2AskpTKMM8EGfTu+823AMa5L152C/q3c7hvVqRB56emsezdT8f5CiMKBI6D/YFf/cFsff23RxCt+++cveUNveAaJwbxZ6LdwPWvRcNXXj+OdZu87uqShXuolGNxdeFI9MiB8KJcrsyZQ7sB0Wah8n0SCYZfKvnHi4vB8m0RfcVoB8/bGgd32syynrkpT2F4ilbm16z8ocYekM9oIh2jqjGkBTGPDl9/90c7AYoRI23Hu9c/Azvkf84T73CEKE2F3ZDeOspxC1fG8MjglYKpXBo/X2xWDRxUeyKAcbG4mtjeJTdMsIXkWfpsw3fVwuv20H64NhjP4S3kp7/+dbIjAEybZ591eP5lu1UeKJtZmihGq3wu1GeAe+zbK1kFOa3xjm9FhmYsnAnSs+19MADGgP6oIzGeQbqWLpxK701zjNT4I4BdmEz1PQadozewQ6Fuq1xHkqea36Hd9yDkvgFXqFI6mwAtlaKo8YO06N17/K2ZNgBodp/3Tv0Dm+Rd4ywD2cxofkDLyF2Zb99AXlb9uedtzaQ5z4ysJ+yd0dRnYVkGl8bIJKN6F3sPZnE6H3shs8/K33s0bojqvHuvS8A70YL7AtLXwU8oD4omcwySkWOCfaG82TJxBCwQtiPHq7ROr7XONdrtIqmU2FllEMrfK350DTyYMK4R4+827XmnK+h84dQMHq+8Pdrk5HpYaxNRqZ3tjYZgahDyebHQgvP79O374awM3gzY9i6vHXmH7o2WuzGuZrWLRxshmFn0I9zM7Vo0Y9z3K07K4bsC4e3C+4rSc+x0+jdfVH03BdjLDNM+qK0x4NMGucAu9KRGaWN/QDewfG++0piXzjYD5BJHv3i8+vYrS6CvbOuC85bkN5bWCZNRmq3brvux0hX2tgO0jdGpNeUSaMcZqeKsly6w0qJtpuhNcLeZVAoyApfm4SFr1vRYRx1P7X0cxgTU4O+W5j6xpy1Aj2naZGmUGZlN44iQtb4tOjub9VDrE1yMzKwOs5hLctklFGWAewc3nCF4vIWeFgtb0HhK8Y+VvZJVIVja6Ql0biLPVfmc9HX+wJGgssB+wLM7/POwr7qgb1O72Jfeth32NrYo/krczzIpBGM9HKZMQf2Du/68Tas61QmVRVB+qmQOUNl0jSSaTgqsyhvoUwqS5qMc8ozHE3sve5dI1jXV+a+KPH8yb6Q444hxLFD2ZlufA59wuc3onUjkCGZFdXDxfDH+0LpqzrFkNN4lEErenVseXdlO46sfIt+xjwglJNH9OHnblzfOGFc3j5sptXxyAyjr45tDwaNR/MD+lGe0WSUgxqyxoMB0chZD+xt76+cm7ehewpj3yljjZ5jZ9X2zY19T8/dmr/2HhH23r6oTN549H1462GP6Idgv0hUZRHejvMae4ydHQXfauxxNLE/9tq4J5P6rNuW3kjJLyST8pzGYF8UhY19tG6hETzfvgopczS/zxseabWxs2oPa97Msa8Km7chyj7JsQMzGS2fWbN8T7SF1zjXoyoHjkzp6KygUa5b0Q8c2WzrqLTFt//wJmWNEa3Rf+2hjbZAT1sc9xw42n4iSBs/vFnQKvLcw8J1PPtV4AGF03VXgYdxdFq045r3dGSzG9ewPbQxM8cPHp22gk3zkB48skmbsxLWwtx/eLP9v4b9fYc2KKTsNWzvPbjRhpq18QeOTBl2A7EX4/L9IuyVe4fT0vtgr40f5tgr2D50dFqPj3X6B49s0sasMLDfaP+vPf/XI+xT+q8dPGrui3sPbrTGhMbbzaLssEfKGvBuysbLKk2hbEz5vkiffT3si7Hu2R+J1n367IeOduMa9p1M0mXOg0c226iXKpMObTCZlI7fd/CoWRfKZZIWmTi0MesMPU/mOLyRvPNlEt8X6Xgkk8Czd+OKTFrvI5MKWKcUySSFPpJJyvi9B+x9cf/hTYYdcKzRvpAyScwfvifbSyYp2K5PuT5QdJmjDx7acPbF+pQ2ZiWNQHbqgUZfLNs1Pt4P8I28UMTqL677ChHVXYmaFf6hz32NiGqhqm3cm+480P5f0ldVRXcfONr+LOkPrE9pWlT05a8fbujj8dvvO9T8e1ilv/aLX4/GJf1f/eOd5vibPvxFIqo3r/Zuv/rX+4iIKFNoiYh+6arPEhHRGvBQXvuBL5jjf/yJfyKigH06/v6b7yEioj1rY5X+03c80P5f413AlSjFbn2zoEMbs1bwSvo7HzhCRBi76//pgWhczn/1TXfH9ELw/ME1XyYioq8+uN6mULKsC3u/5upbiahWlBo2P/+XNxIR9vz/6/s/R0QN9orQ+72P1/dH++JdN3yViIh2r+rYX/vF/URElGU69p+756H2/6khVNL+w5u0v1FKUmje91BtxN0O9sUtXz1Yjwfsxfv9XbNnEe/+7JP1ugvrY1ZWtMJSDq9r1u2hozPVc//Fv7qJiOqz+Y4qn8j59ffcQkR43b+x2XdrwHNvZRJI237wVlsm3RjJpDTq8VUukwpPJsW8Cb+//ev6uv/7L9XrAq37K4VMkvRv/siXiIho/yFdJv3aX98M342I6NXvZDJJwY7LJI3+jxyZ9L7P1jLpBCCTPnXH/d3zKfe3ZNLRaUEPbcxag0Nif9eD60SE172USZL+fZ8VMknc/w+vrWXCXQ8AmfS+WiZtTHWZ9Ip3dDJJM5QimaTQv/VjtxNR/Qkdbf6rPnMXERHtWZuo9B+/7evJ75bh2mYRLb3eIVyoFoXTW+NEOOrR/iwWX4gYdfTxOPeONPp/3n/EpP/8PQcFffw8QSA/eGSqKut7DtYCeVZWC2GH6iH60s+DvbykMt+YSexj+oeaqENLX+iGGJr/tq8diucX4/vuqnkTPHQpWD5/70PtuCa0wsXPxtEuVA8RLhTJ7cZ93mjeLb8kvfxOmRw/shljL7G75+B6TJ8YAwJ7cb9gqHWH+cbjN3zlQSKqPXAtKnG4iZrMytLEZgU0IoRrAuqAwjUCRb8t/cjeV/UzxuNJqk5R9vySa8OTSf+0/3D0s8TvC/c+FP0s6W+6s8b+wLouk4KRWJSViW2e6xGjcKEar3C5635k85ZIjwbyS84fIqktvRg/JGWSmP+uB+J9IemlTJL43nxXrQ9C5EquleA8bcwKXyYZ4yiiFS5UmxguFE1c1mtbGVqjXPdQQggc1XAREe3dswrpiYiecOaJRITrDZ567in1z0DIfdc3n6bSh1z6915wukr/zafvJiKiH7r4DJX+X13wSCIi+vFLzm7o48X5k087j4iI/sO3P1pNoVz2Hee2z6dFLcJznXPKTlXoXHTmCUQUuqdS7E7aOSGi0BGqb5w9q2MagRotIqLHP3IP5ZleQ0VE9ORzTiKiVKiFjfydj9WxzxtP7lnnP6KmF+NnnryDiIie88RHqfM/7bGnEhHRpU85S53/+U+tefLvvu0cdf6ffsY3ERHRs5/wKBW7wPPzH3WCit23nnsyEWHv74wT14iIYFchUb1nULcuUc13a19cfPZJ6rsF7L/9MfW+SHjX/Pjd37K3HhfYnrhjhYiIfvAJj1Tnv/js+t1/9F+cqc7/QxfXmgCW8QAAIABJREFUv3/BU3Xs/6/vrrG/9Clnqe/2goZ3lzz6FBX7sJ/37llVlfVj9u4iIox9J5Mw9qft7ieTUErbk0lPf1yDfSKT6mfDMmkPEWGZ9Kzza7pOJsXjfWXS0795r4rN911Yz//oU3ep40EmoTqgU3bVa8uSSbtWRib2jzt9dzMe04d3ecqj6/WZ7Ivm77/jm2rZIecPwSUkk844qZZJz25kkqQP8waZJOmf/601T/4tkEk/88x6XzzniWeo7/7DDc8veNQJKvZBJp2wNlHpz2yefwyyS0REeUawGJ6I6OxTdqi/P57XtjK0xiPdg3nM3t30rPNPhx7K6Ses0r/8lkfQBNBPRhl9ZyNYUYfNJc0Ck+Ph5yAUpQcU6B/7iN3N3+uGXFig0gsIG/vU3bXwkIszbNw9a2N1fGWc09okh57znrUJnXnSjqajMx1/xJ41esKZJ9bYKdg++tRd9PTH7a3plfnPPmUHPeuC0+t2XgX7XSsjetpjT6t5W+rYf9tjTm3eTS+YfuJZDfagM+1xjeJAHTznnLKzGdcjB4/Ysxr9fbhCNOXEHROVfpxnlGe4HmLnyphO270Ko32n7lqlx52+G3p/Z528k779MadA3n3T3l30/Rc+EnbrnrxzQs943F6a5Om+CJ1kQbCjdf/kc+p9kXQ/NePnP6pWiin29c/nnrqr+VmPHD+yMSbTgxur9h00+mBk71odq926q+MR7Vkbw3V78s4VOvfUnbAN/YwTd9CTzzkJRtlbmQR498gT1uhfPn4v7JReGeX0tMeepnbrhr/3ZNJFZ5wQ/b2kxzKpHu9kkl5H1MokELlAMml1nNPq2JZJZ5y4BtOuj9izRhedeQKNRzr255yy05RJ55yyk/7VBafTBPBu9+qYvvOxe9V92cqk8xojF0R2n9Q4KK5MSrCtf350I5MkvSuTmn9PMmRSluGuxJ2rYzpt9wrELsikCcD+zJN30LeddwoMejz2Ebvp+y96ZKMPdCP5GY2DsEzX9jK0gAcyK+qzN5DCmhX1abOaB1NVdUpt0hwPgYpyd8AW//pndO5KSq9vrB2gsy3cD90/bFREPy1KmuQ5XPizsmyxQa3SCLtwf4RdeP/QzqvRT8uaN+M8S6IeHvZy3MUOdPDsALyTvEXdWS29IpTHo9xft/CLB6XZTTstS5o086MOnpa36vzNuDI/73jU393GfurxLlnX0sgetu6RIQe7dTl2cF/kNAK1JvW+MDqdG95i7G36aclk2sB1L7H3eauve0jvyjT/+SbNu+udcT2wz419VXoyqZkfYV/0xz7VB448X1CmpftK1zfo3Mda14V1D7DPc5iBCB2bpj4Y5U3jGsDeoA+8XbZr+Z5oC6866oEURg4V1rRhfh3R0qMWiJ6fZRTuFd873lhJjZekB+mv9qwo54iB9PRyh74xMlG0LxhCExBmn7Ub0ziryZh/WnRHDKBzWZAhKM+MSbGLsUm7o2zeJdgB3u1YAPtJk7qrzyZKFV5tZOIzyibNuPVFAZQiqcebozUAfRCK6bvXP09Gudo5l2APjFi87u1xb98kxkJi5FatEcvvx99vnDfrFmBf7wt88vzE4F2QSRNj/rA2NJlUVdQYEyn9tDw22HsOyLHirSbTxoZjHJyvOmqC1m0GMxyuTGp4p0Vy6+dj9CBi5a9L0LE5FDtvX4AMyg4okxrsR5n+bd2iwx59baLFHhz/EObH+iCD2aXA22W7tpehBeohpk1UxbbCMzXnPmsNrUy1wsNGWVuxPZSwsRIPRNAjQw3N3ypzg36UdwoliQqxqIplhCLvjgtFFfvW80dRlc6z1+odyoqgUJQKBUVFWuyQIYSwK2zeToXQHEofPGf4GZnGyJw76sGiKpi3RjSyLOHJ8QF7FJVJ3/3YYi/HoZFsYZ932MOoihH1CEYm8tzHRqS4k0l2VEWrZWk/k+JEVRD2HnaJAzN0X/SY35JJ08YBCO+WOCBsX+B1iyNaYV/gT7bhqEr36Sedt2FfrLbyXo+kdlFub1/o+siVSSvA0HP0UdCF+NNWVbfuoWNtYd9FvJBMc3n78Dlax/fyUoMTUA/RLo5R2l0VhNok10PZrQfRngdke/aoXqL73AbYeGD+hF7ZWMEzJ0o9GO45VxXYeKMM1itMGXYI+0kePCDAm0ZoJWchtcpcT3+Fn1uhh7AHBz+2RmyLHYgWetgbnn+WEa2OwNooO89bpefeI8I+N04Xb6MqdupQq6cIKXPE2w77TK2VSbEDnvec2E893rn7sotK8Pfh9+88b4zdWImCh/nbtK8lkxBvyy6tm0Y96p/R/K0yBdin+8KJkqNoZU/sNQfJkkldyhwYA07Ug/MW8c6VSYC34WcP+/owWk2f1D+vOqlBJM8l9qj2EfJWpMw1fVWnbTH9OM9gTW74hqaVGpw0vLP2hZad4mU8y3ZtL0PLqmUZYSua1zugOiDkPYaNszLOm/OGQFTFSU/tcDx3OF7G45qhNhlhz517zjp9l5NfqN4BRBPbegetDogpcx37zrPXPCCJHax3cKIiHm+sWpbgnen0cVRFU3gm9iHlbYxb9RQdb1Psws+4NjFgr9O72CXrdrF9oXneRPjUfl6DFX6W84+bKLiW1uW1jehbhW1qD407EbFJk9ZNZVKDPeBtUIArzWdkXOyBMYGxc/aVM//Mk0nMyNTmr41QO+rRRSOxYw33BaNPMxzdutc6Rl2ZJFJ3MLXXVx8AfWXtC54yR0YsjrJ72Fc29ox3VgZFw56X8SzbtXxPtIWXWcuSBys6ZV5U7wBSJO3GAp53UHjQe/TqHVoPwx5HHo71SYZo4yipT2s85MSteghUrxDmn1jRxPb+qXfaGbm5Xg/BPHutvi7FDnnmTh0Q4I2bumT1Dhp9hx3gDcfeqHdApyh39Q44kotqWWalwB4Uo3fY67xbGelRjxR7FNHS6+v68G7cRC0CFvL5xix9paZtR3nrPWtGehvxMj1zpFA6mSR5y1Pmev0bxz7lbRTp1bp1JXZOwTSORnoRL2dfIJkUjEzAm7bBRokYhfla50yNNpa2TGJrAzkA3bghkxSZljTQoBqvdt3b+wKWU5j7onP+NJkWOX+KTA3Yqd/WZTIPYp8bMomlLtGZlWHPLtO1zQwtHFUZAc89qndQi927jWXVooxQjVe7sZx6h3k724qK8oygUJo6CoV/xFMdb2q8vC4QG3vc0VmUFY1APUVr5DpRlY63Onbe5yog9l5URPJWEQzhnCqNnn/sXH++DruqSgtTeUE2jOQavKvnB9gXAnugbOG+kmlfNxroRVVArYrR2TZqIlI1PdgXEPuGHnj+05a3OEpufrDbkkls3WvprfCunkwLz4eiHl4DzxqoM0qNhXT+LKsPa1XpS08mCd4p87fvhmSSk/a1ZBJX9mjdY33AeKNG6YNjjPRB7LzB+jcv9WhE2UPqT5s/OAgjmDqMeSPh5fsKpWUDvR7JrWgEMhxcJi3btb0MLaPeoYvKACsZbqxuXKunaGu4msWBoiq4My0WariLBEQGmhTEBEQOuvQUGu8+bqs/X2i1rsPksDPOqcHSoh6tZ57rHlCUtlWx7+i1eggeVckzHA3sjT2qhzBqWUKbOr8fpw8RI52+inirdgiF9BGMquRq1KStdwD1EAn2QODDaKJDL5W5V5Dt7ws9ItVir6VlR3hfhILpSY7r8yYg2heep/tYOo70Wh2dqGCbY6t160Y1XJoxILEH+wp2Sju8m5Yh7YmiiY5MamWazrsutWd064KOzPp5SiiTokiuwjs+rnXrRjJN421DH9K6WoaBaP513xpy0PnrjuMJWCTjjfOm0bcpcyTTQkQMZDha3uZ6lD6sHTV7xCK1y3Yt3xNt4QW7Cgt81hOvNbFSh6jDqDPUUGTA8x6dqEniuadCN3QvIXoz594IfEzfdZ6p44Uj1Erc2ZbWO+hG6ATUonB6rQsmSfu60UTHO9TSrlZURERNUFRlBIQaP2tJfb7SbrXmnW0SmyJZtzhlbmIPoom8jkjt1vWwF2nZeTx7vu5hLYoRVTH3leO5ByMWduuy9BmKgrfHS6B9MUJRdLbuVXoPu2ODvYmdUwdkrnuHnt8fOX9QJrkOhsTekmm4Y7Q9+sPF3ovk2jJJx64zYlG3bV99YvHG3BdAJlWVUQ9ddtgt27WtDC1N4bT1DrBegXl/TlRFj8rw1KPiATF6rd5Cbiy3a1GLakTeH/LssQdk0YeNadeyOAeSho5OFJGCUQ+OPa5VaWu8APYdb0G9AzhTJq3B0rDvUoOpIeZ08JTh/DacvuL1Elr9X3uOlRbRKjuhiqIe6LyiFrtcr2WRCin94oHkrYP90FqWsqQ8qyMDfD4+zqMmandVFFXRPX9U48WjKnq3bhM1ydN1H8skfLo47LYVRjLcV16UvuU9SpmjaJ7Dm0J0FSrzmzKpjapg3gV5rtHzGi99X5RYJgXsc4A9i6poGZKp2BcWvSqTBLZofhP7UZca1PZVOOooYDGEPonCQ5lnNEe1uhgZsU6n88PF8Mf3sqIabVeh45nDLhJQD9EpcyQUvfnrn60ukyxjOX0lqlJ7COjcFhl10cdRPQQ3pGp63cPRPo4b6h2CwIfRRK8eAkUTWcRL71ociD3Y2FZnXNxVqGBrFP12njeuheHYe1GVtDAVf/FAprxhbSLoEIo9c60OKPbsEfarqFu3obc6RsOzadjw4xW08bSzTU+ZoxqvNCKm816rRZEyCWOvR2Uk77yoih+NHBbRCvSozqg1pOC6LU2ZNHV4OysrIZPSqA/CXn7pw4po2VF0O0MCD1kW9MO7Du194XY6tylzVOzenXOl0XPs+Pt0z+d06zLe4exQiPbhSO+yXdvK0NJqUbjC0FIYMmJldZGotSy8YNurZTEMMXTWU5uzhimOuEtEq6cwI1IiqpLWsnSpwXC/+P2C5467AlEKw6uHiGqwtK5EJyojO+PcM9CAUENGrhf1kGlXNRrYp5YF1d+VsXepKfTu1H5c72CdLt6deQOwR926hRCaRkRM69ZtUyTwPKGqXRf8ft38fmcbj+SiqIpZX2d0Z3Vfm0i7dSOZpGDLU+b6uo2xx/Pr3cB96+usRoVAq727123Lz2fT5u/O0TL2Bae3sBfYFZ5MYg6C1hnXYddEZSyZZKRtw5dIEG9XwRll02RfaDKByaTBadtwzpWlT6xu3VCbaOirEAVX9hQR/goLj/Qu27V8T7SFV55lJEtVeMRplOvf+iMKBdMZyTKj1kMZ5TTK9M6vbtzwcEY55UYNlxXRmowyGmU4qjEZd5659nwrHv0oaz+yq31ywczZF1Vd2JnXn2zgHszMwYZjb9U71NhRwlseaja9S8TbYIg1hakJdqUfUQp8Jeo+ZNw9n1/vMBlnLb0+zuiV+Vd4LQsbD/UO4f7pu5Xtu4/yLJmb8y7PM5IZGBf7Zv6VMH+CveCtck4VEYuqJAXPZc23RsLpvHPW7Yhhr7x/WLdEyvOVFa2Mdezr52v2VZ4ltMm+UGiJqF4b6rqNeYewa/eV4F28L7R3a6ImsHaxbPmmvrtY98n7l6Utk0qbPqnhUu9fY1dJmVQK7EHKO6wNa1+MMow91AelLZOSfaUdn5CzTuaEt1U7N5GmD8p2XRAZMg2se7kvtPeL9hUbL8o6Zd5iB3RhWBtW9mjZruV7oi28Rnm66TZmBRHVHoK2cDdmJRtPF2Y8njI/jK9NcnVjSnq5cFv6sS7UNmYFrU5GUJlvzEpaHY+g0NmYFbQ6ZvTa/B79JFcVUlVVDT1XSDEtf3eEXZhfDEfjHu9yRWHF9D14K+mnZYydhv0EC516fASVeYs9EGob0xhbuDZaoRqPde+uv1t4d81BiXinGKGSd1LgR/MrhhQf14TqxsxWCDVvsBHa8i7riT3gHb5/Ec3P6WdFSUVZtfNrfOuwSZVtNz7S120P7Nr5Ld6hfTm1Da1a5uQ2dta6lfsK8dbaVwD7TibpvIuwU9+dYa8YA71lGuLtdIC+UQyxgC1yMFqZhNZtS4/2hdhXCHtrX7J9w/f9ZvtuIyDPO+xzxTnk2C/btXxPtIWXL3Q05nnKPCiska7M+27MCRa6ISKUKd6lL3SK9t30cbHxgLEA6adSqHZjs8ZDCUJD0kfKOFMMqUhh6PeuxxvsDSNXi9pI3iDehWimboR62HvK2FBI0lhwhCLHvlYoZYw9ez/fkIoFPt43I1MZu4aYa2yM1EivxAY6CM6+wPQO9g3vbAdHp5fKVkZ6E+fOMkI9B0OJ9Lq84zIJzL8yMmSSt+6lcwect77OocdbTh/LpHScOyCqTPKcL2lIAfq1CeCtNKQ83qrY9XCs53YwBPaId4pzqDne8b6IHQy4bicONpPlM2uW74m28NI8JFeoMGWsKlsh9Ob1DjuhqysUImwoRh6IozDQ/K4hpSjzbv6RrsyZQNeEKjeUdGXuKFvhPWoRn25+XygiZZtlemFrokwRtnMoYz7uRyspuX99KjNB3sbK1or0on0hjWSH3ovaKO+WZV2bPFTGUGEMxN5zMBB9i203xo1cTSFJZVo/n4INdL5iBwN79k5kYAIivYkRrhsyRIZMmjjYe5Fezzkc68rcm186d0Qy0uvJJLHuUbRxMtIdDOncGUYuitqsBJmk0Q+W53NEvLghNcA55Eau5hy6hpTQlTgaOKJlu7aVoaUJ5aNCqFgRLz+1aI2PgEKq29DHOdg4jTIlIqCQYu9RN6RGqkLoRd+O1z/z+5dlRZtFvLEjIzZEhHp6MCa2ADsitjGdaOU8vI0UiirQ9RQFp8fYixSH412qvAPRRqls5fPFqT8nbbtoisRL24JI8qqlUFhURb5bS98nbdszGuk6GIx+MzRJTHhkIJ67HVfoj0rnCypjR+ZMwLrv4Ry2Mgk5GE3UQN23XpS9V6TWkknYGKiqijZF+irCPnK+6t9pEa0VJJMEvYetlzocHK1kjrfufPYwpPo4h7BcYVjqsFQdjNoIlfePMhSmczYC0cCHU4dLcWlRH1kYqhUHEuHixU1RUA3px7iNfjLiURNKxleaVYkKBCfjDCrjzcIpCk6K6eX94+LHCDtR0JyMR40G6cbeFNhi7HShxXmjNxLEhaNaYWqWNUcUAN6uMKGWPN9MHhhKCb1VFDwtyrhgWsN+nHX0QqHIYnr+frxzTMOeY6sZK9OZLJh2sEcF10CZh/lRsfzmrGyLWjWFNG3WNRHB5+ONCMnamJVRo0Ay/ywUVNc/y/nrfZWpCinGPqWfzrp9oSmkSCYpRqorkwTvzIJsA7ssy1Qjecp4g/Yt2vNEfF8A7JvONiiTZpg+KpgGkd7w7ppM25wxeqMgG2GX6IOBvOOfkUHj4fugiLdRMTrCHjiHCe8UfRRhJ1KDm4VsZGC07Z7XD5vtsMtIi5h5jWUc22W7tuSJsiz7wyzLvpZl2c1bMf+8lyZUA7NGwXsTC3PGxjVlG+jbc1MqfTzQJ10gZdV2rWk596Ks2u+p6d1Z9bcAW0NKoeeni2vPx+m1LpExUEgSOzk/PxNG29gtdn2xl+PsiAAt4tU+H+pSibBX6IuqfS/0fKO8LiiW7x7uPwZCI4zX36PT6dtP6CjKPMJOURjt9+6AUJ1p9IC3WlRE0qN94e2rcTM/WrdEenpqloxTPF7E617dVzlW5h1vMe+ifcXXdRHeHWEfK9OEXmIn352te0smhfdDvLN4O2brXl+3tkwKfJXvHujHFvaFJ5MwfffuuiEVYa84GN2+Al19YV+FdYv0AeAdl0macxjeLct0mRdhD3gX5KV8t0Bvncrf7gskk8S659HCMFUk0xSZMwJGcox9ev8Ie2Vd8qMzlu3aKtPvbUT0/Vs099yXrpCEwBebPjKkFO8uFVoxPT/TRsvZ99k4oVU3zzSFUUKBHehH5ng5UCFh7Ih0oTXK9I3dbTy91boQhhQUWkgh8edTioKHYZ/yNmBfe/7K0RetIWVjbymkyIjVsMtxq3R4d9UILmz6maAfbEgV8drQ6POMYP1bwC7cI8GeGcHQQTENqdqIRZHeQuwLGU3sHBTDwUCGmOugCEPKcN4smRTo0b5peWspc885VGWSXLcpfWRIOc6hPr9uiKmG1BzOYZDniUyShpTjHFq8Qc6hL5MM57AxpMIzIOewc64oGfdlkm0IRY71PM6hJ9OALua8XbZrSwytqqo+RkT3b8Xci1xazr6MNp6+6Ym40IrnLNtxvR03zJdnQWFoGwPXO5SJ99hfGQf6MVu46cbsIj4afSHp2XjpCC2+MTSFVAqhJe8f/hYZUmF8nOvHP3jGQMENKY23Fcde4y3FvFF4Z2FflmTyrqhi+gi7imFnCDVkJEf0mkKS2MN9oRcFlx59JbAXvAvY1ePKuq2YIaYZ4c38KNJbVnjdhvcLylaOh1tF2BvrVtIXDHtNoZWCd3jdA5lUxfSuoWbJJM05rHyZNDYivYG3MNJbVbZMqiq47sM6ROs+kkka9hrvgJGsRQNLNn+vSC+Qt0RAJpUswwF4F++LlD44nvLdOb0V8YLYcewdea4ZUoG+l0xzHIhlu5YvmbmFl1aANxMbA0dN9HbcmWQ+iDiheocQcaqfTwuHllEYH3l/Waa3Ws/K0oxohefTjIHwzbVenreXAlGKgiV2cv5EmSveVze/LhQ4PTJS6zl03kbpKcBbIlKjNn2jiaizTUZltNSfpxBiI5l0eo13hcBeiUoQNUa6UhQ8S7CP6WPPHUUDbYUSK6T5sPccDC29xD1vF3uNd4XNOy6TtOMfYoWjrFseRTecszo9lUZ6I+w1+qIzkpFMaudXeVs266ancyfqgOxIL4uqOIaSFelFkeAoqpLj9FUvB0VxMGLskUwyIr2OgzKT2AF94sokC3vgHMoMBVH8fF25Qz+ZZuniZbuOm6GVZdmLsyy7Lsuy6+67775vyD1tD0UvCpahXs1QCeOWsg33RymKMG4ZA+78hkKyTheHylbxvBF2nUKK5w70WqhZGkKIvo02JgI/3thI2eeZXjgaGbGAt116SsFe1nAB3npRE02Zh+f36oCQQpLYyftLgV/fnyC9Fem1FEprDCjY2eu+bFMgKAUT7SuUUkfKvBC1LNr8xqn64d01haQaUgMivdG+ahUSe7aoHMGvA9IcFCsSKx0IC3tdJpVm+soypOr3w86h5kCg2kJdmafK3opYEVkybXikN5JJWrSw7KKJOGJlGVKdzEIyDcnz8Hx83SPHX1v3umO9qIMSPxuf33Ksl+06bk9UVdVbq6q6pKqqS/bu3fsNuaelkHBRcL8CPOjBSIXiCS0rqgIMscizRzl5INSk5+4aUgg7Q5njomDPGBCGFPJgQpjeiCb6QksvtrcVEquHMBQS/HyRxF5EE6tKYKcZUtHnLuJnI4qN2Mj75MaAEumNPPceBdWaIVXfH0d6Qwpknkhv4RjJgXc40hs3KkAHxahNjI3kmJYoTk/p9PigXyKxr7R9ObJl0jhHkV5hSM3jHI5smdTHOewbTVSxA00m0hCS9LpjTSk96FqUMg1GepFzyGUScA5NmeQ6hz0jvcCQspxDLcMxD/aRcwedQ8vBAZ90Y/TLdi2f6beFl+7B1Dslz+yi4NaKVgypaFwu3Kpq50VFwXZUJKbXio5thVTRKCdVGYf3y4F32b27Th/ePXcUEt54ceFpQi+wle/W1lMYQiu3hE7hRwPN8apqnxt1f8XeG8XjRRVhpxuhpNLzd1eLgouU3i0KVrxPvi+iOqTmYVrsFUMq4AKVbWYp83jc4t083VlFWcE9T1TzljdxRNhFEavu7/m7EdXYWYZYPX9KXzB6c19mQCYJ3qnKlmOv1FBZDsasZOseGckG7wJvUaTXkkkxdliZu9jl1Ns5VPVBhiK9cdQmHadYH2gOSOavW6L5Ir2Bt+j4h8BbtbmpUrDX1m0OIr2ePmHY2rq4o+fwtDJpuxhaWZa9nYg+QUTfkmXZnVmW/dRW3GfoZXZX9TzCAHr2GW617h3RypWcvYiIzdNq3ev4B8OQmr/Dp1O22sbU6iHUrkUgtGSHj3Y8A/f+LGVstVqHcfMIAu35QnpKiRjx+f1oYkqvRWL1dasfHKl1+LgdQmpErd8RAShtisa9SG8fQyreVzb2sjYxRBO1fdG327ZXm7pFDyJuiTIHMsnsbDMKqiX2VtTElUnAEEMF1Xx+C/sIO21fgLOcOuz6GFIab1gdkRHpxd22dqlIog+0fWWcq+jtizbKrrxboHdlkhOpjeU5pfTO8Q/RkThqdsk5PmIJDa3xVkxaVdULtmLeRa95urOivLHmIVR1m3qe694lb1PXioJ5d5Was6/sVuuoeyvTOtuk0OrG4zb1bj4+d3h3VSFVHnbU0vfu8AH3563WWbiXd/yDxE4Z54bYTOQBoq5DmJ4yeFuxM3Egb/VWa68zrdDqgBTvc5xnVFbpuNa1qBt6sXfZfEc46Vo0162X4tCilVyhAN5ZDobkrTa/a0iN9O6sbt3q3VkRdgq2bXcVKPpVa1mAEazKpLKiLOscnKHppcSQco1kSulZfZ2OvRHprbBM0juV43vXz6U7h1wm9Y70Vjo9N1ak4WI5h5YDEK17RSb1yYAs4hwGI1yVSZohpfAGOYecvqp60iN9Yuijh7sOj/Nl54317ixpiOmeOz4iIK7RQt1VvItE2xhGq7Wz8ZIuk0hhUPvubvGi48HojQS2wugbGcDpLSHUXKETDbvdVbPC4W05kLdA6GnK3DOk1O4q1fsDRcHOOVpRVEURakNqE2Fatcd5P4FeVebt/HrnnXUOVxtVUSK9nrJVu21RNFDFboAhZaWV5z2IV0Q99M40uyC7NaSgTMLrPsjMoA+12kQsk8K7+8rc7GxzHAx8yLJCD9K6bjQQpdT7GlJzRHpT3lJCj2SSVls4JNKr1mAhfeJFcq1o5MOG1vG93AI8ZeN3zNcL8GJDB7fThnsgZUvUoyhYNaTmP/5hUBd04B05AAAgAElEQVSIhl3f7iq4cRyFJtrUtflHeXd0hm5IBaFCqdCShpSizG3elnEKxuEtFzpqm7rCm6ig2onEakcIuIWpIEwvaxMT+h7HP7jpI/MwWDvSGxlSbsRMT5ljge2c3K6se9XBgKm/xRRSGtEyZJLjHPoOCgHeYvrI0AO87fbtAu/uOYc9ZdqQaKLmXMr5cxZNnNf5IgKlJK4+mP/4h6qq2ufzI6n176AR2hd75BwauhoV6/MMx7Jd28rQMgvwnMWRgwI8r6Cap59QUXDONo6W0+f0WuTAKra32n219JBmSIW0KMIGpUi87ixZ7I7mz3NS231DwXI9h25IBa8X1Tu02GvK3GmVLqtuTejHP5RQKHrYte+OakVC+irz1m2PgmqDnhemat1ZedZg79a/UTIe5KEeDWRFwa4xAAq2HQcFvXt4T1xQ3S8iFWOXYs95q6XHomijwrs8y3SZVPrF6DnbF9JBKSR2Gm+zDiNtPGf70txXYn5XJgXsB6xbFbtcdzC0iBjaV8gYsBzrOm1a/x85KC12MFqIDTHOO+uLCQEjLcMRonHy3dsGGccIjWSawrsI+zmMbL+B6GFD67heXgGdZWxEha3CiucLH7Xwh3tYZ9aoYXZxxIA7v7sxgYfgeN5WqBd5IIWGnSdU1VA0pkfv1mHTRT3Q8Q/hGTzszFZrpJC4UDNSb/W79/feotSfVRQc8TZ+tpbe9B5zNdLLoxJuigJEA/scekmEIr1xNNEtOhaee1lR+/wo0ouL0fs5GNDzDuPu8Q9cIVFKn7OuxTLlTcBOGlLxvlAclFJGpCzeOocsA0MMOZ9DvgNpdraB4x8KJ9Kr6gN09AZ4vvjdolcXESeAvVGbKKP0LvaePtCwB+doeUaoluGA9JaRjGRajyh9nlFbw7tM17YytDTmdcWVendW2MR5pqceZdGtel5Q34WvFe1WtqEmI2ZWKFoqJFm4yd+X/x8p4674EJzlFLBjESkNW2RI8WJ5dPyDWfBc2WF2/gkdtd5B8lbSe8dDVKJWptSw02s9VKGjCa2ooDq+d0tvnMWEeNeuDXRKcxULbPl8ZelgV5K57qXCSXnbRRNdI1nMzwW+9nyaEaqvW68o2I5Wet1ZgxwUMd4eeqkoc358gx7pjSNOqMEmjGtHGIyMYvhC0kfrhtrfe0aqpsz1YvkBMi2SiTENnwsZYoU0pCznCzoYXUrdxT7hLcW8dZxD9/NFA9adbJAx6Y35UaNDqfBO6qtlPKyUaJsZWn09GBmxCjVOngejeiDyvB9P6Bg1Wn3a6KXQqT13PWfPvbPgBMA2da34MSqojmkSemVjqqFmxUOKFVLMG6+g2i4KdlqtC08hie6qAUJV95w9Q0qjB8q4SHmnFnyP9KiI2ugglTkzVOT9+brNUMTJOT3cUkhcYaCiYMRbHvUgciK92rpVU96U0rtHxthHd1hGNo8m8jkDfW9l62Gnyhxx2KzGW8A7fuhlO78WTXQcDGSIecc/uDKtt5FsR3oxdna3rbVuA70X6bWcT+v5tE/oDIpoDcFOMVJjekroZwrvpJEb5l22a1sZWlYBHu5SiQW+pOdRFdhO2+P0cKIeRcHa/LK7ig23njfvEEICv68h5XmXKD2mbTz2CR1dIZVRm7q8vxRaVSWLgu1oYmJIObybp8MHFQXLwk1siIHjH7ghNEe9g9ohpOwLxDuJnRz3DKmkWH6oQnI+fzQrK3jyvBbR0pW5c+o+OP7B3VcK9hK7MI54Z8kkeWr+vJ3KRPMd/2DxlkcDw/wQu7wfdvgMM8eQWsA5zDPkuJfRu6XPh+vTAr0tk0REag7eIn3FjVjt+fygRD/svGhh9Ek28Hyqg8J08bJd28rQmqsAr4gNIUlflLygWk8f8YJp8+O64Jtw1vy1UKPm+XSh0xWmAs8+RwI7NZS0egpelIsUhlkUbHhILXbK/eszzITCAcaAJnSkoaQJLYRdwArxNrSp54C3snBTtlq32Ge2Mo8KqlWFZXf4IPqoTR0YUlLZJw6Kl9pj2LnYa9HKKCJFCT2/PypG754vpq1/T+rxD14x+0zhnbYvUEF14abPSnNfzHpgF0XZlfRUtC80+oyvW4e3ikxBxfZcJgWdqckkd93Ck9+dY08857BZ91mGI71cXmvPb2UoCoGt1zzlOe7aQbxIn/SWSWDdds1TpDoYWupQ1wdYnlv0RVm277Zs1/YytIAhFcZQUXDiPYrFIc9qGnT8g/MtxCKaPw6VdvPrrdbc+9Pm5ye/t0XBiiHlHhGAsBty/AOYv280Ec0fC50YO69DSPI2UeZRNBEXoxOl0Uzuudfzz9fZho5/0M4LQsX2uiHGDr0ECilJHQLeaam9Xsc/ROmpaFgYyVpRsNxXMW1493p+pGxB3Sb33A0HAyokdn+PHimkZN2L5+PYpZHe/tFEz0GBxz+Ak+flvkgivQx76/iHqFEh4m168vuwY09S5xBGE0GkV0a0LJmmrtseH+QO2A2J9MpoInYwGG8Vee412Iz5l0iAIafr4pLRW86hr4uX7dpWhpZagBcK7DJ98fBidFSAJzceX/v80Mt5jn+o5ze6syqRnjJCwUlRsNx40gNSig+HnKrfFkeC7ipemIrqiFxlzhQKkcjZV1xhkIp9LLSi4diIRcocKBR+ejiRkiJRsI/olVP1ZYqinh8UBbP5zYgX6s5ylHm0bnMF+8TBoOgqSxGRUr6YEBmxyr5AZ0F1Bc36Qb/du+v03BDrGy3UIlKoa1E7vkFVSCBiVvJ17zgY0BDrbUjpxz9YxgAv2Ja8lQ6G5K00ghPnkNEHZV5p2APeuQXVSkG4jAqZhlSVYg8dFIBdLK+j4YS3pj4ADgZy7hLsDZmmHvTL5TlYt2F+XRdTR6/JpKo7E1Kdn2G/bNe2MrQ6hdT9rk5RhDB+97twxe20Cj03pLxWa+TB9D3ATix8fuhloEcF1e38wHMOz6cdIeAe/+AWbHvHP/i1KKjVOsEeRbQUbGciIqUpnHmPf+gfTeQKSTGk+rRaK0LHO/6hjzGRYJ9EEzsjVN4/UsYw5W0d/+BEekVKXy1mh+ve5g03YrXjH6J17xjB9tEZOFoZZBI6/kE6IGnnmzC0QJR+3uMfUPqLH3pZ37+HMgepu/b5BhnBaTQQNjqA9JM1vxbRGuwcGoZUvO5tmaRFeq3jH9Qou+pAsEOcAb1XKuIVy+syrT/2KFr5cERrCS7U4cNPqCZKjQG+8DV6SyEVlV0M74bxC9xqPURhhPdDQifcf54OH6szLczrdvgAet6mLu8fHTEAw/jsHC3PkNLSWyxiNuT4B2lIwQ6fkc/brTj+Qe0QAvsCpQmsqAkX+KiDyepsSxWSZqjZDgZe9+m+iLt163/R82mGFIyIGQ5KhH2CTed88WeS2GCZJGSadQSBwNY//iF+vjgKTu27E9nYhfndiBbC3jOkDAcFpa96ySR33WPnMMHe0gdIJlmRXsM5TPZFoi9S7GGDjPbuThlOwWSepU8Qb4s+pSLB61uya0s+Kr2sFy5mr/8/jxUtO9s0equgWnaZDGvHrVq68HzqxmL31+oV4hO0+bP1U+Y5MqT4Z1pAtC/PSLSpx/cP91UVUhEXPGvPx3lrdviohhg+nTxEE9vnMxRCGFeLgjObt7wYfS6FkTnnAWVcIVE0HpxDHOk1HIyyaiNd3FDMKWvpOXbawYsR9gMUEj89PIyr2AHe8hOsw78udorCyLOMmtcdFumN1i212HF6qxi+xsamR+/Wzs+dLwV7vG77rXuPd1HBNipXMPYF7FTuEen1ZJK57qOz9Si9P+ctjPRaMonzNsamlUnAwUiwTxxrBXtgSFmR3hFwMCKZBuR5S6/IpJl4N23+cN9lu7ZZRMuOeqACPN9zt6zsuN1XP55BFyph/rjewfb+UDF6+HdIUbCuzONnq+mZIaXRG8c/dNjHNPX/y8SDGXT0Bjt93C0KdhQOUraovk0TaigMH+afp9U6jniRSu8f/0A6diMj0iuUMb9nSy/3FeItivSaqUdxjpbhuUPs5jz+Qe9Mo5R+NKS2kb9bGmVP0k8jse4TmWTRL3r8Qynq6waseyfSm8gk6Rx62LFyhN7ncCHsNZlUaOs+ph+JaCSKViIHyvwgdxHLNC8aqDU3Id4OlkmGTLMivcjB6OMcJvrg4XO0lu/ST6NNI1Juhw9gPsobo4Up53ePfxDKXG/HVTYeP0cLCK3wr75xctBqzeqIDGVu5fQT7JEhhRRGUgyPDSlJPytlvUOqcFxlC+qANKFldfhAoRUZGxq93eETnyeUYh8+yD0cu/S8oMQY8Ixk51MjSNnyT+i09J4h5Rhi/r6ghD46Rwvsy3kjvR52MrWInDst6pMce6JEek3nsIdM6uscotpG9Hy8rhM1AgQ6K9Lb57NeuNvWqslVztHyZFriHNoyyVv3EDuJveSdW+6g8NaLNirzwy+JlGltotyXI4k9cKyX7VrOp9qiSw01V3bOXXYFJvSOQipKeXp4HFWJPxOTFgXL54ueLVn4adddTQe6s6pu4bfjYONordbzdPjI7iwTe4GdHHejiRpvq3h+pGzDvdDJ85rCQBGl+l9cUN3Or30CBx7/wIxY8G5EtSGoOxjkr1tpSCF6EPHiRqKcX3ZvuYfBOgpDx47x1opYAd51kQOjkQC8W6BXI73N//MMGVJxxEmbf7BMEjKHY2dGej0HBMgkZIjxzjRt/jLBXhi5kl7s24LJNC3SG8kkgN1gfRC9X7ovPEMsfn/8buH9vK5AFOlVMxwGfeJgKLyDh8UqhlSLPTrot1IcCCQzVGw7umW7tqWhBTvTNA+jcJS5ZgwIwZJ0LbK9IzuI5MKqKq5ss4SWiOI2dU8hGR4MDEVDY8PprgrzZ0ZEy8DO6/AphdCR80enl7dClbpxbgzkqWcvo5WmEZsYoQI7VIPVw5DS6KMjAox6CFwUXKZCy8Me0CNDrcU+17Hvq8xlNFBihw6D7d1dBXiHFBKnt6ImPKKlYYc/60UKdsAQQzLJMMRUB8UwNuS6K6v4+TSZ5BVk8/QZqgut/0XdtuzIHBDVUSO9PT5M7Mkke917kd7UwYFGNpBJEfZg3Wn0/LidQF8ovOvmT+vPiIRjrkXZnUiv1W1r6mJl3cpi+/Buy3ZtK0MLd6YJ5skuEZGeSqMqxvEP0cakhJ4feok2TqesU6FAJD1724MZcvyD153VJzWYZaIwVdCnber9vUvtMzCId+7xD8KQ0trUJd/Cu+vYSUPKiaokxoBy9MZAQyrQa0JNFVpDsNcMKaSQGtkHjYE8jfT2OSIAFQUnhlSeGnkBu5be2FcoRROMpbQoeECbOlIYjiGVKCTPkHJqXayuRW5IdREjWyZFRwRwZZ7IJCfSaxhS9fxi3Sqf9ZLOYZBJ6KDftkkCGMmSd9AxVyO9pRnptWRSeH9U2yhlkowGJg4Kcg7BuY8J9plhSIF3C/TIMbZS3h52fN0v27WtDC3dCuafNGh+J4VOJplPEX3bXaXRF3F3lbx/vTgaepAicTvb2POh4xXCuNrhk7GN6dErGwd2+JRp+int8LGVuYVdnCKh5P58fq9LRbZahz/D2HXvHuY3sU+ElsAezI/TX+wzLsD7C/edJ72kdvg4tSxpvUU/Q02L9BZyX1hGLKqfg4aY6Cr0lHkS6XW6sxi935kG1v1AQwoe/wAUmpw/padmfl1ZRl2LFvZAmaNoZtSxSanzKR0QqxPb3/M99wVw7pBzaNJrMg1FeqVMKuNP6MwbDYTdtuwTOu38oJREo3c/68UyHMjByCV2QBfDRoLsYUPruF+4w0cKnf7KXOvwsc4LCr+L59drqPgncAL9sC6Qrlg9PJ/nuZuhaKCQrA4fSxmr2Lt1PjG91V2lhflhhw/Ajkcz5z3vh8goCuYfrZ5DIUVnmAn6vtFE1GptRXqlQlDnFykY10hu6LuDePVIrxdx4p9hCe+nCXy07hNlDowJWBTM29T7RpyQzJjDwdA+A+NG2ZFMAw4AlEnss17h+S2ZlkZ6hcyT95dRG2AM4Eiv42AUvkzyDCk7WulEeo3mqTQSiyK9eimJViqiY9fJRLdrcWCkt5VJjryG3baeIZY/bGgd90stwCur1IoWi8sK09ehZDLolfmrTqHMGD08a8kR+J4yRxEvPadPKT0Xipp3m+FW6c4DoejdA32KvUOfeDAU0eNopbYxY+yJurUho4koGsg9LPfMGi0i5niXfH53baBIbBuRoo6+SCNO8vm68YbGSV/BaKUaVSlT77SKsY+igVok1lFIXqQX87afQuLvpzooWY9aFIS95aCw8+OQQrJ4N1N4lxq5pL5bMKTgum2VMbV/Z65bYAQjY0Nb9+7xD867+/qATPohkV4eTYSGGojauNhp2FvOW4JdTI8yGDjS6z1f+m7JujUcDGvdhv8/bGgtwaUJvQfXN+nEHRMi0jfOg0em7bjGfG2cG1IH16fJ/GFxPrQxo7Kijl4szAePTImoG5fKVo5LZa6N8/kPyPmzWOgk9EIhPXhkk1bHOa2O9eMfImwUofbgkU02d0p/4MgmnSDpAe80Zc7nl9gfnRZ0dFqm9M38CfaAN/z5XOxM7AXv1m3ePXhkSqM8o90rYzZO0Xi3bih6d4xdR39A2ReYdw72YnxWlPTQxgzyrnv3cT/scgd7id16P97y99PmPyF6Po7dlPasjmGb+oPr6b6I6BXeyec3ZZIm05ghdXB92q1bocwPbcyoKCu859c3Y+ykTJLrNvdlmskbb35hBB9Yn9JKkElKREuV1xDb+ndF5WDP9926ItPK+Pnkvgjvd3Ra0Pq0wDJJw36IvBb0cvyAsi9MfaLwNs+Idq+O2+dzsXP0hdwXybqtdJmzbNf2MrQUZbz/0Cadumu1Hlc25v2H2bhCf//hTTp1dz2uGVKbRUmn7V6J5g/09x+qF36YXxYF7z/cjAd6oRDul+O5PS6LgpP5E/oNWpvktCtsHKHM9x/epNN2rzZh4vT4h/sPb7TYaELNwo7PD+kPMXoxfnRa0OHNIqVvsO2w0ek1bOW6IKKIt1yopNjH0cL9Gr3ALs+ITtrJhVpMf/LOFVavEQudGnux7iqJfbcuiAZiz/aFLAouy4oeODLteC+M5AfXp1RVlK4Ngzf83VrsdrF94WFfKvSctxr9Ls5bisb3rI1pdTyq588oeb4E+5LTb7Rza5He/YcVmYR468gkSd/KpF0O9ru6fVkpMuk0xlu5JyN6tC8Y71TsDZm3Os5jZc73xaFNOm3XCmUgWqliJ/alJZM47zRD7H6mT2Skd2NW0KGNWYQdUce7B47YMmm/wBave8AbuS+Ec5jMr+iTPCM6iTsoYt2esmsljuIn8lquW+rGD28m+yKWSRvdnkX0zfiyXdvK0EJh+tUxPvxvVla0MtZP8g2daSsjnT6EwSV9O95YPWFcFgXPmlW0yo9vEOkXOT8bpmmYHxz/0NKzE8D5+LTo3q2mT1ulw73V+cuKJkadDp9fDaOzca3DZ8p4I5V5SCG08wuh1vJG3D/QTwW2KHXYYp/FHUJTgW3ygVeFd7I7a2WctwojOf6hKNt1G94vwY49G3/3+vkqWmkMBVSrktCL8YnANrxfwD7ZVxJ7MX+gT7AH6aUJx07DntFL7Oz5S8ozYifjx7ybSuyVfdm9GyXYTQtl3ZY6b1ExuyWTtPn7Yx+Pe7xDqcMIe45dM76K6BveTEaYtyti3cv0lxyXqb9OplD07uH+K9ap+8q+iN+v8rGTMs3AliiVaRA7Ie9lxGuqyDxTpgmZNC1Lmoy6SGGSeixKoS8k9mxfBOyreG0k2EldLHiX6osRLeO1rQytPu20clx+1JmoYz4vvNTok+LBdmM2cwt6GWpWc+JcGUv6LH03SW/VeMmNU5TdR53D86N6hPb5xMJPP0ws5pfYyZz8SGKn00uhJj/qLIuCk486i+fTeBsVgytt6hr2+OgMQS8NsbLDLjyfVY+gdX/JT4EMb9LIIL08g4zTa7V9RFxhpEW1Gj06/iGlB0cE5Dr26fOlhf4R9go9x16tj2vo9YN+7YMZ+9RwoZPhwzToLCeIPZBpifOoHfliyaQ8lRkRvXQwwniGeTt2sef7Qjoo3qn6fc5ysnmHDvrVmouIUkMK7UvtyBf3ZHdRjE4kv5hAHb0jk3iDTPg7WdPLP+osI73aUUgIO+RgJDJN7ouHz9E6/pdWgKcd/oc2dmpINQtTKJwgtFOh1fxeRLywQpLKGhlS3PtLNx4XLFoHDQqzp0Ir9R6Tjeco66QYPcEuFgzIkOqKdm1ljs7RgsaAMIIjQ0pV5sg71Ixkh94zpBJlG2OPu5uQwrCU+RxnOUnsE6Ep332oQtLpreMVwvy6MaDzTnMgUuzzaBwZsdr8w84gcwyxvs4dMqQcenn8gybTPOdOc1A6B0wpRs+cgmqOfa4ZA/0cFNyp3M9BketeyiTpHGrOF/+9ZkjV9BI7HRs5/+DjH3qcDRjvC8c5VGSafy6iiNR6Ms2Qmct0bStDKzCB8U490yZpYxcbI9AjQ6pMDCk91JxsPCAU0aGXmgckP6VBRFFUCHVxhOfjCqlQDCnU6tzN39FHH5/NFOyVjScVYiLwDWWrjaf0yHukaDwxhBTs5Pwa9sjI7boOqaVHhk64P/pMiv586cGKiLcoWoiwl8+H9sUI7QtgSKX7Ku9FjzoyI2VuRVUU3sp1b2Kv8DbdV8CBEOtSzq/vCx97tK+Qc5fQj/rxDkVVOO9U7JlMRdE6bf4kquLJtGT+MjF0rA/N1+9OHb2S4agSmQGwR4ZUO95PJvkORMe7eWSS5RyORWrQ1AfK2gjj4fgH+XyWTOK62HMOl+3aVoZW4EHinY4C89Px2IPpfhdoiTpDKBMeDkotpkJLr6VJzutRBHZN341bZ95kIlQsDSnNQ0k9e4rGOX2mKLQRwE7O72IPBb6ohTGwI0qNXOlBoahKJoqCNd6aUZXE++w+wxLGLaGVzl9G4ylvq9687Txzisblug/0yWdYHOwzsa6lwE/oixR7m94+/807R0uLSMUpkJS344Q3xOjlvkpT6mlUpaPnxkAmsG+fz5VJYl8B586TaTJqIz/DAg/qBWf36TLJXveeERunr+LPsKi89bDvK5MEvcxwQOzlvnIyHFImSd5BmSb1gbKvtEgukkkq9kImxfoAR9nV5+OfZFNkkvbZsDC/lEnLdm0zQysW2OH/iQfTjMvPsCRCJ/FAYnqtBorToRRKF6bvdwpydHifEPjx/W1Dygr11u+PBb5Grx0YCkPJyIMBOX2MnS50ZPrLN8TSgxP5vOoZZMKQks8nvU8TO+G5p7yN6xF68dYzYvnzK/UQLfaVjn2algWNCkihAGOgiwwY2EdRDwV7wZssY+mpPBX4CfZC4MdRk7QOKOUtp+eHuVL0bmE8kRkg4oYNIQ87fV9A5xCt+x4yyXIO00aFNGKVOCijmDdD1r3mQFgHhvJ3b+d3yhFQtFJmOKBMG+nYo31lyTQ1tcixH6QPFAfCibJbzqFsVPBkEn++pBxByKRlu7aVoaXl3IcUlkr6eYp663kBvdj4utBKoyqRIaWFgkENlu9h9DEGRE4ebDztpOB5CkstZWtilwg1uyhY+04kH9cKS6UhFdFn4t00QypS5nFUReMtr1XRGhX8qIrd4ZN849MzpEode29fpEXBep2R5B1sVBCRhfTAUy8iJRVG2m1rKRRpBOeZZkgZXYVcJonx8BkW1OjQvXtPB0XyDq17ZAwkMqmfcxjJJKHMo2ii0qjgYt+Tt1qjgtXkIefHRihwUIA+QNFEV6Yl86cyzfzSRyKvbQfD1QcCe9XBsBwUx8GwDDEpk5btWs6n2qJL1vEQ1QwKG0bWoiBDKownOXNAL095RvTS+wzjkj6sPa3LJD2du6P3QsGjXNSiFGmoOD15naJx+WFgWZiaKhyK3jGMh8LSFLugEEDRboutwD73sYvpdd629InCEMpUidqg07/reZSPKmd8XMGOOW9arYxVLxFhD+qE+Onf9bvbhlSLXRGv27QWpee+ygB9zw8Tc3q05wM9x35aCOxV7GKFlPDWoJ8x3sk9T0TRqf05wF5+jcLHLjZiJW/huge8a+ndfZFiz6OJeZ5FdZt9mjw4bzTnzeKtJvNguYMik/i+RPI68BZiD+S55F0qk3TeJdjnCHulYxR8iSO8h1WbqO4rYUiZ2Gtp3UQX6zITR2ppKa8lfaytubSce1ywXf8utZLjcdg1GOhhGJ6i32t1QPz+aS1JfH/5fLLewe2OUgwpST+S9EmKJYf0Wr1F/DmKjl5uLOR5y861BHuBLcI+xW4xelijxesdXM8bY5fwtnB462HPeKudB8TTZwn2Sa1HjE3yPTuwLzC9zVstxWJ3fGpRbAf7kYG9Qu/WoiS8ies6UVQljcSGdxP1bRXCPsxPET2WSVIZ1+OuTJO8ZXVKNvZp1CJZtwMNJVmjhbCvnw93lCZ1QFW4J6qpbbAb6fRpRKp5JhDJhTJJ1tcZ+0rdF1lHnxiZTm3iSPDOSvtK3vXhLUqbBnovZf5wRGsJLhkqDof7TUCoeDqr/53IjRPG5aGUkl45fC+eH9BX/eg3xbgMFU9npajjkQeSli1tmD86oI4d3tfS8/kZdt14R785k/UU8caMsBfKPByu1717/ftSvDvEXvJOYKthF9Gj8UDf8I4fmlkKbMd5FnmvLvYM200xLhXKtChpMpa8pXicz59J7LWDGeuxWVFSWaXYp9jp3qXknUxfIeytw2L5/cM4V4hV1XnPGu8S7Mdy3RvYZwr28ny50uZtKeZfSXjHZVKZ7ouqo63fTVdILfbjwBtSsZvIQzcF79BBvwk9kEmcPsJ+ZmO3WaTOmzwsNjkk2cJe3H9zVsYyS5kfHUvSvXswlBB2gB7ok0LwbmUs9yXQB3JfzcS6B+8W1SY667YQ+mRlwLrXeJvsC0AvZVJR1hkOpIu7ddfxdvqJIT8AACAASURBVJmubWVoEcVCbWNWUlFWtHOl+5wDUbdxDm/OiIho14o4Qbth7pHNgoiIdjbjUpkf3qjHd6029EKoSXop1A4347tWum+qxfQzGuVZfFIwEyqHN4v23YjSUPHhzVk7d33/2IM4sjmL6YUyP7w5o52rfDxW5keS+buNV5YVrU8Lhj1F79Ziv6qfXt5itxo+gyKwl7wTGzPQo/kPg/kl/c5JN38RYVe0fG2xi7Av2k8bBfoEu1Udu0Af8zbmXcLbXD5fxzvpGR+ZgnVbduuOiNr7p+u2GV+N90VLD/ZFIcbbfSXufxjxjvFmbZKLFAnDZqNI1mW07jds7A5vFmLdS97avDuyEe8rXhS8WZQ0i2QSxdi1+yKWCS32GzFvEuwC9lCmBXoh0xz6ViZtFJRn+PTyZN3mEvui5WtLX8Xj0b7KhExSeCdlWspbajE6wnibyKSNIFPidR+mPyKxD0au2BcYO7GvkD6R9EAfJfpAYiccgHpfMOwTmaTJNIe3gncJ9glvgUzaFNjDddvNv0zXtjO0eJh/HRg60hDaIQV+YijFG6+S9BNpKJFKL8+0WW8Wz47k+Tr6nZORaMft3nV9cxZtDFnLcmSzaOcOz2dtLJmzX2/u3437G7PFfgqwL2Pe7JjYvNkphE4leLsjEWoU009ihdTR60KP06+M8+4zLXmMvTRSZa3I+uasfbcwf0wvsSOHtyn2iLebs5KmRdXyLkR6fexsByPZVxOA/dSmTww5wLu1MeKtxF7si+lMwaYbl9hJ3q5vzuJ1n/BWw77+f1VVdGSa8lZiL9c1xF7si2RfAfodEHsk0wJ2Ym1oMmllzD4dFddgJetWyKwj04J2GM7hkalw/hSZ5PE2NtS6dz86s2VSil09B3K8XZmEnDfB2yH7cmWUR1EfHuldF4ZQgv1mjH2KXeoguLwtY/poX7H7T4uSNouypXdlkrLuOHbLdm1PQyssbCnwRah3fVM3hKRCkIZYSz+NvUNZi5LSy/GCxnnWhsolvRQqMqqRCpXYA1kX47IWJZ3fidqw8aKsaGNWphvLESpJxKrBXtaytNgJQylgn9JL7GzedUayzhspVMI4f7/U88bYyVqW9c0iMsRkVGYe3kpljHgreSPPsRrqgEjvtMNeRl2ad0v2ZUq/YzJq92Nby8LuH2NH5rrNxPi6NISy1EFxeTvRebsxK6mq+mMv94V0QLChJNd14D2IeEFDjKL7hyh6SCEl+2oqlSmZ6z7P43FpxGrYS0Mq4u1U4S2rYUpkEnNAXOyBvvAcc0+myUiyJ5MQ/bpiyNTPp2Mj98X6dGbKpNQBsXkra8TUfVXq2IbnhzJJRBsl9st2bTtDi4eKQ+daW+shmDcVLfwy595+3FaciSNz7t55QEmNGBuXNVZEcT2GrONBxYX184lakzL9lmE8bufsZ0UZ0fP5Q2Ener7uEzlxrYesf0Ot1hL75PNGgHdpHZGgZ/PnWXffpB6jTDsqo/sXdlGuHPfq51TegkaF0B2VfKeyfbeUN7xOSe6LZN3KGqhkX6RHXxApdUQ54J1yoKikH4s6m/r+Hf1ErHt5cKN1uvjU+YxLP97qvGvrbMDRHPJkdVQX6mIPCrqnAltEL2vAOLb8oF1VJglsk0YC40DRhLeJzInnV7GXnc7h2TWZFO0LgT2SSeDoDTk/KpZP9IHEXnwRgdNnTCZ1EbEGu1LWzAr6wj6DbKp+yzDm3cRa94q+kvXQyb5IsNf3rdQX6Z5PebtM13I+1RZe3EOSXRz+oZj1HPCbbEIhdfT64tA614jirkN5ThW/f3JwoggVq2ctsY2hfWInGlfOC5LttuhwQflu4fmkkZp2EFFEnygEMW5hF9HnPbHnRq5mSAV65RwsPp5gL4Sa7NjUzgOS2MVnOZUJdmjdduM6duH+8BwsoYwT7EQdUd9ztCT2haCX+4rTy3cjiiMX8t3iNvMy2VfWvrA+2B3o027cAfsiT42B1MEglb6vTEq+MwmcQ0+maccv8PsnnWvCWNGwM89/U8Zd7IGhBrGXyhwYG6k+CNiEezvYJTKpeScxvzwdHWMv6GUXucI79+y9kb5uw7iJfaHsq2Y8TIO6EmXHZri/K1Mkdmz+Zbq2zNDKsuz7syz7fJZlX8yy7PKtus/Qiy+O0tkYknmyBiv8nTSkEL1c+IkhJjZuWaXKNqKvUqEQ0StCke0bKspU6PCce1HZ9MnzGYZSTa9hJzZ2+266Qkk/gaNHXVJ6fRzRa+8m6aWhUz9fh42JfbUo9qn312Iv3r0eZ9hp2Cu8SwwpQK9hF9GDfSGxb9dGmX7sXN5fHjtCRFQxY8IypEpl3cf7QhwWK2pREt6yWpSqSj8FwnkrDZ3wfHzPx9jFv0cOCMeOzy+/VejKNOAccnpTJgEHhctc6yDdRJkL7BNDTNQZJbzNHJnEeBf+TZ3LmF5++5Z/morPLyNOiUyC+wLsK4R9MCSrYdhLQ0lin8gkxbHm9Mm617AT92/lZYNRb5nUrltq313SL9O1JYZWlmUjIvqfRPQDRHQBEb0gy7ILtuJeQy8e7pTnisg6oEKMY8+bIno/YkURfVgb4V8eKtY3Vvi7Mj4gTqPPunGZcy/EeJ5R4uHw+WUti3quinj3PIufH3nO8vn6Rj2CXMDnDdlCDdHLAz+184jkuxPFkYUEu0pgL+hlNFHyNsY+pUfKNDy/TE8h3rrRwjKml7Uo6flyA7Ev5aGUgr7oDlsN7x7dP8E+Pr3cwi48R7QvRC2KjCbKOiD+7mF+iW0O9p0mkzKFtwn2nkyTzxdkmogMSJmkyTRvX3Bdl84vZY5i5AreSpkTH4opo41lwrsEO4h9fI5V/f6WPiDxbvH8mRjvva8MfSOxI4rfTx4gLemTfSFPzee8kzKpjA+Dld9XVfcVkCly/hZ7oa8SmSSxBzJp2a6timg9lYi+WFXV7VVVbRLRnxPRD23RvQZdPFSMrOzkkwfii+Ed88PGBLUs0ntMQsVl5LlrGyd+tphe+x6eSZ+lOXfpuSc5d+ABdZ8C6YddeD9LIfHIg/cJHBlNdD/RI9JfsB4DpGXTGrDUs+bvlaRlk9Rf/2+yhflT71PnnQyzy3GUWnSxY4YOp08862TdC+xLgb2cX/kmmqRXU+oG9kQUeccIu/AcftRFYGc4EHGKJMWez6+m1A3eou9Epute0ut1ROHd0hosaunj9FGHWT2enoPF6TWZJg2lsYiqWGndETNCtWiiih2oU3JlksQeOHdpDZa3L/R96crzPOWtxK6+P7V/JzMM6WGwVl2oLDWRjjv+RI+WluVpYYR9IpNAza6USct2bZWhdSYRfYX9fGfzu+N+qQonMTbq/3epRZCeEuFOqRBkKDlVSOnC4s+lhck5vUxvaTVgVmFpEsbPHYXCsVNCtTxiJdOy4f/J6d7SGGlunxhSwPtDhpRMgchalPD84fZJHZKibDm9DONrQtUypGT6SSrzhLeJwtE+Oo2xjxwMJbUYnXGGjNhSp0/WbZKWpej3Mj0mlXlRpilnPr+f1pXrjsS4s+4dI1hL66YOgL3uEW9belDMj7D30rLJ2gANOmlKvHvnQG/JpFSmxfRSJskaLM1QMh2M3HYg+LpHMklii8oZUKlJWs4g9gXgDYr0IplWY6cYUiC1KL+d6zkYflqXxPdXnfq6CLuOht8ffe1BPp/UpUmkV5Fpy3RtlaGlvW0V/UGWvTjLsuuyLLvuvvvu26LHSK96Y9b/l6FaojjUnHy3K1n4ZUOTtbREhjGgeN5xmDvemNo30zi99jkKTi9Dvdo32fj9teJI+XxJCsTzQDK5MSmmB8+HvukmsR8J7FEKJlVIIpooDbGySt6N0ydfolewl2H+qoobFeT8qXepK6TguedSaIGIVBhHUY8wvyza5WmISCEhhVB1z87pUaOCFemN06YSe8/BKJN34+OzQn5n0sZeKwqWz2dFtCzsAj1KP4X7twoJfKNTFru32Cfr1pNpenrKlTkIe8X5Q+9Wz4+bPNrnY+P8SBotfaRFtBKZJ9alTE32de6kTIPOoZBJnD7PCMqkRB8kkd4ykbecXpPn8fMp2MtyB5n2LYVMEveXTR6ekezqA8A7bV8t07VVhtadRHQ2+/ksIvoq/4Oqqt5aVdUlVVVdsnfv3i16jPTKMlzMThQvHsm8wGMU0ZIKwS8KTiNCRHFhqky9xfSp0CFiRcFJ1KRLn4T5Y4UUjxel/PZV+gHUZH6jsy3yQBQPJs8Uz1t8083DXhb1og6epBFA0MNoIqDXamF03lH7fEPSU8FQC2P83Yli3mmee8Z47/FO3xeYPpxpgwqq0yaOqp2zfrZ03MIeFgWDfaHtSxT1COOSt2xYWRse9n0aFWJ6iL2gT2RSEgkO4/HfYZmmY89lpol95ewrLZLLsVf2VSSTZDSR8UaLJvL5kUyy6X3sccQqzBveTcqk+L6ymF3VB+LZInogkyLsDX1SKrzzZBIq5E/GlXWv7Qsok4CDYsm0Zbq2ytD6NBF9c5Zl52VZtkJEzyeid2/RvQZd3EqH9RDAGEjTQ7GVLqMa7fzSw+FRFfU8oPpnLaoR00uhQ8n9Lc/cO9Mmzemnnrfs/kLYhfun3qFeEyDrIdKiYIG94nlzereGSvIOKGvkuScdRonnTxG9Nr80ciVvLc9dazTw01fgLCck1AA9wt6K5EZnMQnP369FSY9fqOkRdunzpe/W/qi20XMHQCok7pnr9XFpnQ462kNGA8P7pekxUc7gRl1imZR0trH5tUgufz67LhTsK3b/NJIaNyokdUBe6tBat3nq/KFyCjcaKeo65bdz232Bzv6TMkmR51aUHOmDaF8p5y66GRKAvSuTNOyTtHBHSyQzHCzS6uhivb5OS7su54lVW/JUVVXNiOhniehviehWInpHVVX7tuJeQ695zrTJhUJA6am0XqE+YI5/xLP+PbX0skOGP5cM9WqGmJVaTDwQTahm+sZRn09TtsyBiEPFVYRJO39CH89fCUNKhso7+vidUaNCyzsh1FAHD8KuU0gMG82QQvNrQlUIHWkEo+4qzXvjaVstDK8rpHZYFAXbYX6YAgFGsJa+MpW5cx5Qum4puj92UKill7yzum0jZVvp6xZ1BSbYFTr2KCoS6GFKXcikorCxl+UOfZRtNL9I62pNJnLdxvR62pfzBqX+wvsnkdqwbsHZfFZa1mvQ0dLCMqWO0ltJDVUV318zgudy7pAh5TiXSaOCTKkrMgk5KKohxXjjNXl4uliVaYqDwu+/TNeWfYGxqqr3EdH7tmr+eS+3KJhtTBlK1gwVIuzBpEWzFN0XFbO7XYfMu16d4OLIpLCVCc2cMjWMj1J3Epvu3dEBdanC8ML4sSFW/w4WBYNo4pBGBVnYGdM7RcEovYUMNcWQM4uCpfeohuH1qIieltWEGuJt/Tu3KBic/N4HezMti9ZtVBSMFRIqCrZ4wxWKlj5De55IdyBgUTDgnVksb+zLtFGBIvpk3YF1y/ed5WCkUQ2K6MuqSk5ej+glbxl9wBE1Knidzm6DjiKT9Eivzlsk02CjQs9oIp9fxZ6lZa1ooZYSr3Gj5u/ku1H03FqTiGzgSRyUUn93iB34UomnizUHRG0A4p7/El3LGWfbwivP+MJLPfc867wrzQrn49KKD3/W0pfpuSNELEzew3OPvTfbg3FrVeT8yVlNXb2DGvXQDCXxfil2jD5XsEuEYrh/GtHKM0qfD2AvPfesHe+wG4m5iUTqT+EdD3UPwl4Zl/RhXYRP6MCoSZuSbodj7FXPPa2vS+cP4yn22TzYJ+MA+1zS62eUeeu+gvN3DkY7v7UvS6XY3Vj3akQL8Lb3vhL7XvJWrl2JfRvREhGj9N2oefeO3sJerltNJnn0Q2VaooxFVMOKJkbYazKpF2+pfTaVPhgbMpqYROt0mdTtK4A9ODexk2nUvp/GW9kAxJ89Htf0QWdo6eUOQiYl6zZe95C3QB8kMk1gn+yrJY1obUNDK63Rcr1XYGXLnL5myFi1IkgZo3oILeJl1qI4h25atSha1IOHitXPVWRKtA/WoihGbo5bpSW9jCy42EnPH2EPFIZWR6TWQ6D5cwf7yPMOc0rvraOt3z0+z8isA8od7BnvtHqIOGoDahMlPdoXZXp+m6T3Ut5WfZ1n5MraxCQyoMyfpChk1EUoBB87m7fovKKWHrTZp9hT+05EikxSeKdhXyDsejp3SCZpkWRUB6RFE+uIl4WdlhoU2Bu8i+uAUt6NFN6PJPYgYtR33XJ9pco0A7uIHu0LQ9/wPSGjiVpXIIzCazKJ6wMU1PBkGshwLNu17QwtHiqWHTjh/zIcigwxebaHFqa3alHQxuM5fU0oxcocH1AnQ8laqDgVOvjd46Lg+JnC/03s2MbUOny0RgS5sVGKJjGkEHaOUOK8NQ2pKj06g79X2h1F0f1LhXdlVRuaWuqPF4626zYT2DveoTzTZsg34fQwfj0uGxXKsoprE7W0qWZIMfq5FJJjBPMOJrQ22mhikvLuaPm71+/nOChMmatpWaWoN6lFacdTek0mJUZwpe8LqczTtKvtoGgyyXIOE5mkOZ9Jgw5FfyM701BaNdDzs/EkPXcO1XKJyLkj+/5ifs+B8PZFIpOkTFP0jWVIwXVvOIetTELyOtGlunPX7Yt2OGpUQGldKzUZ8VZZG8t0bUtDy+uukgoFFrZ6Qs1RKElRcLtx6p/7pRb5u1F0f79zTtBHYXDw7u3cWlQkS7w/eKaN5sH0UPYoFJ0WBdtCR4bJVe9PSU9BQ03hnde9JbEnqqNZUNmKdTc0EouaPML/E0NNhvnF/a3zitTUHcc+07Bj+0LD3lH28WdglPkd3pYliyZGRmwarXPXbXR/MrF36+tyzluFXpNJbdq2L3bAGNCUseVgSOzVda/Qg5Q9b1SADTZGWlaLBrryXLyfjJq4ZwdKfcBkqiXP/a5AP9JrOZdetDLBXpNJri5sh6NIr5rhcHgXyyStQSeNwvPnW6Zr2xlacS2KrpBkLYt1enmesQPmmn85vfSOarqGPjHEunmJlKiMzPlX+rkr6Lyh8P+q8dyT52NCA0Xz5BlkyBBSvb88jWjJ97Puz8dlZCHpLAMRrRg7Rdky3urYNfQJdh1d+FczsquItzq9Gw10jFA9GulEE/MsSdui+TX6SKj2wE6P5FL7flpEqoLzU/RcWlFvmDf8C7FHhk6ybuN93Rs70OThFQWnKRggk8pK7XRG58Ml2Dv7IlHmyrh8t2j+hL67b3j/vns+PF+KPZBpKBpo8S6aP6XnxobclyHSG+95BTtGb529B41ksK9kqYjWYEMkZJqIotfzV+q64zW9ejQwjdQi7FHzlYl9ZsukZbq2oaGl5X2zaNyKqvAUSZ3Tjg8W5HQyYiTHtRQFkaxF8ebXNhZ6vs7DCptzHL1bFypGESsvqtKNozogMuntA0+VOqX/l713j7bsKOtFf7X23kknnU7nQRKSNHkgBBJCCCEBo4eXgIAEUMEryktAo0e86vWKkANIPOc4hoKeK165OhhcUFQuHjnX4fWEw4GgvMJD0xCSQIAkEExDTEIn5N3de69V9485a86qmt+j5pxr7V7J/n5j9NirV62v5pxfVX31PWt2+qc3FJr33fa4HLnP2OYv3+XGlvOIxfRUYinlUeI9sV3eOxflojD9i16VEt5H3kSS97HlvBLfW/V3xvIOyX2xvPPt2FH0XI5WPDZc2FXKRaG8gfn9yeui6wnOPW55HlHOv1QmEc/GnO/msrHpFHnkSmyHd22/bf8K74l0h6n3UR5Q2x4Oiw0h3erZ5fBUh/fFXnhu3svrapaPjeAppo/OaOklmcTKtCTCQdCzEQ4kzy3JpHbNp2PL5QY2z57JLO2NChw9L9N4mbhM2HKKFuXuzHNtpMHLF3Z+pkr4PtCTL/lkQ3/ZhjGjz6SJwxCSqzh348dCjXwlAuEq5p6dfV1FZB2R9EJYNuE9eSZOmq8wca0wyPvnz1JqlQEtRJIfGhl4F/rPEzuT/vOxjYRW8CZSocUZNzYTWZFKChXIM2u6BkYn/CVtGJOUd93+03WVC9T4unnojpq3cUUllRQseQbYdTVj1kVEz1W2xeGfwK+WN91qWdZTS/B2SFJwJz+PfbbqLxc2VYs8tLBuRyalY0flIXG8p0N/kQHCKTKZEpuHZUuPbyBD6hPCwBD6j72JoZ1TcjuFCty64HhPGNZq2JeQidq6jD1a+bmJuSLU2Q8EJZiSad29GM29xfcUPktnqC0TtpyilSYFdy2YooWZWCip9RVbl7NcESOEDun1mLX0UlJw/roKNSk4ciXTyexo6HjLvvpMJ7PL3kDSayIoUp37yyyYmPfN/UW8ozbj5vpe8Th5RpGaxWMrCzVSyY7ygLikYC7xMz4Pp8ObHmfaqEnBHO/z/jsbUksvVnwyIRSOdzm9WiTCjK1a/TVjvIkTJSmYKlQQjLNu/92kYG3sZJnUtuWnl3PnxyW8oxQpJiROKsmUx4qTSRE9l1Ad2tnipCCTuNCeEBqMN3M6pN4Nf3EVnznvq/5z40vjHSHPo/1Eq5Ylee/psY2NQ64QIPTfPntewBPuvfpOK27q5IVmMi3dr9J5Gz9z+NwUmRAyaZmw5RStOO5LWigT2frMcwJyBTq2XnmPFhr63EIA0vOKSAumaadfIhrH3NPkStT0kdeEEaq05Zy6oWOa8Fm03Anec9VVfBl9xPts9sZjx1tnLW8oy1l66XNM3wnREP2rvGfoaQU/5j3t8dLPpMmEHkPPFSrE9B3LPRtbzlMKdL2Jnfy13EAheE9tSNrYTmcgvYkx7yVPr/e05Z6seYZ38Zrs9D/RQ5OSNzE/TyhPCJbWhXZ2X+6pZY+M4Xgf8S60U4UKFe/Ds7X0cd4rXaAjrwsyHSG5fnquYU6veSMn2bEq8bM19IzHqGMAMIpYMrYCPedN9NHcoZRYlveUTMq9edFeFN9ToI/nBdCtpPbZ2OXFAJ1Cg8xbK8mkZcKWU7Qor0on7ivmoqSu5tWVlIV5ySkVE49Df3IeUP7KhJR+mlmnOX3+Oo0kD4h5NiAoA92FoXmk+uURdftPXjLKKQuM16O9v5aezANKlAHKmxjoc96l98WOrY/nBm2d0s9W0888n0OV826S807mfa4McHlEmqKWK5md+8s244Z3Ku+VdcF5TXLeT2l6Pqk3aie9ie31KUVI5x06BoSW4yXxHugaf6k3MB0bl/XPrXmAyk3M14Um03KvSk0XrwtibsxmsTeRlonU2X1apXK8GWvynN8PCteVsi5ybyKdU0vIDOZUflpR48cuz02MZVr77MS64XjvuhGMvD2vtu0T4SDXFfNuXor3y4Stp2jF7sZmcNMKpLxqr5MHxLzSoOo/m/iEV0UN/cX0yqGYlFclnAc087RlzlVXxa5iylVLeqyy/qWwbK4IdegnVEw/Lwdu6Tu8zywcKvTHhWXzDYF9FQk3tp3+GSV2Rm/2VAiFqvCJz9nijlcgz3qKNyQmNNitWqTPE8rDsuH+4vOK8pB6LHQ74adm3qP5y/EO6M77TgiFoefCslSIhFxXjIGSzFsq/BTzjlTk+r0GZuK63sSY9/mLdfN0CHpdtPRpbmL1N34+KqzLhY1j3pMyadIdG21daJ5arSqw8+oraewimUefoabIpEmuDFC8Q8O7PKQc31euSOX03f2g/T70k3uMwnVV3hPpCnF+nhZWbcYu83hJBgblTZTGNuf9MmHLKVpUUnCeRxVbMHnMNz8pOG/v0DNCp2qnk9m55MhOUvCMccMLG0Lol4yZN9fnEz87Fka+cKTE0UmX92xScHN/TXMnKThfWIkyMUvDuvmG0Am7djZzuj0ZW6qMnes/Elok76Prc4mf4fqa0OGKPGaZgcFt5tS6yMe2M+/zEAoR1uWLPNA8e0XPFHkkY0sbGFX/zLrivInUutDoc+NLC6mHsVFefMzdH1eZVrVHMmmahu6a/qOxI3nPGSgZb6c5fS6Tpv14H/e/QYRl1XVByCQunYFNZpdC6ppHKzMOSZkUGTC5PAvfA0OKPDL6bGyoAh02L5Qq8kh4z4fUp8y6SKI/XFi20MAgZZrjQ+LLhoW9VHpZEVsolOWen5nTWTjJwqQ8Wik9fR5QuD7nNUFzf5SF0ZxlxVj2M097LSZEO3V6ubQh5Zs1F3Pnknq7FkoaZkjc7JOuN5ErVab6pwoVWt4VeKzI84Boyz3mPWW5t0KL411LzyV+hnYuvFTfGlPkQST1amObeXrjHCp5XTCFCuq8pdcVFSKhzwOix5ZSpLjzhOgiD4L3eQgj533HMq8+64UOBO9duuZF3nuK98K8JTfzHpt1LpNymRYbf8yzB3oumT30q85bZuyasWFlEppny+nj97OSlc4u9Yjl3sR4bkyntKeXPWcrXxeeDut6VSbF5ybSMk3yaM08zTsqp7d7Nl/LO6Ar86Sxi8eW89LHY2cerSVCr1yUKRNzjyzveOI09JEWTnkt1DygxDrl6XOvChVz53JRuKTdcH1KGWjoZ1xMv3s8Qy5YOpZ7tmFxCdVN/zHvs/Y8T4mkj7weeX5YfF8dN3y24WxMZ9nYVn+njNCJeU/Pu5aezgMi6LPrl55pQyq50WZMexOz6qrMbSLlAQV67liSbsh81hG44dnD/VG5LvERC9yhm/Rmjeb6lNeiZF2JuYkTdOa9yvssITy27OncRIH3goFCpiskzy4rYhrv4zPOaN619NzZe+G64rqa0WM3cZEXmpRJhJe9owTzvHfR2OXnx4Xrc8cvhP5TmZPKy/i+eN63Mokau+msPTeRM/5E3jMyiTIeWd4TMss5dGRSTi/n12XyOnflLhGW984WBK1CKHVFp29LBwih1QmhOH7DyBSpzmtaMqHWPRclpc9Di7H1ybnZAx0nVML1uarAcP2SpNyKX0joOxtSHoKJeN8J22beRKrCJ6EnLP84n4HLVwj3R4cOkoqzOQAAIABJREFUW3pa6ES8I6zD6YznXaCnQovx2FGFCmToLw8dSrwneOeYsc0NiHB9Lmza3l/LO+7Zm/7JeRvo0/7jXJTgTaQr27jNONqQqHUz6Y4tFzos2azje255IyhiWXtuuEuKVLhXbl2Q52Ax87Zqz0Pi1V8utNjyjjsLCQ0daUBMumNHzg0vHM0hyaSJ6xgoUlhXrXTOeR+NHRUhyc9t5ELWVbseUuc8YnSlcvWXL9CJ5j1T8RnoubBqaQ6Wvhd3vYn5vM9l0jJh6ylasdDyhOUenWmTh+6AdPDJhZNv5pmFEVs4nKuX67+TFCzQc8nsoZ07jyf024ZQ6DNxuJh6Nyk4T2Zvn61zfy5OLO2+TiFXxHJvYrKZexBCLXfD85t5HqKhkoI5oUcms0e85XgX6KXQYnyeUDcHi9+sqUKFPDTa8rbLu3hDyUN/oT0eu+66yMJPZJEHyP7ppGB6Q+HCP4FODsvSlnuyLpgNyfvsheDMZj0jxobakGL2dcKymeWuFipkc4NUBuLQJGlcoabveqHj5+qGFms6gXdANnZKgQ5bqEDJtNjTSsmkkghH8mwZ7zVFyqWKFKUEx2dFyTKJluclMo1MZlcMb1WmOZ33fYo8OveX7dUd3mfz3qoOlwjpmTaVx4nLA+Ld8NXnPHmxouffK5b3r51enpesxhvCbNZ9XQVl/XG5KNxmXNHzSbsdeibmzpZCZ6eXd2P6keWdh2Wj/jlFKqanlOQ4KZlUpGLekzlU9NygjsagvIme21CIsaNKtf0MjeWej13DG0aRyscmnxv5uoixEo8dtS4SRYpWgnneoXl2qv/OeUDMhpTwjjqoV1kXaVi3y/uEnsmv4+il0GLKey43ESRvgHxs5bEjZZJL6dN5U/3l1lUuk2a+u+ZDu6TEaoUK7LqiZFJm3Inhpwn1btucvm2nFKl4bCh5H/rnDBRubLsyacbMW/r+YnrqeIaWd3IltM57T8ukAt53ZVIq0/J1EUPz5C4Ttp6i5QDOVVu1p3lG5IYSaem0R4sf/D5n2kynTIXPjH5dBWUdUhVGUx/naMX3Vv3lqhbDx6RCKI+5N/fOvOswEtid+3NpTL8bOkzpqdAiF5bN21neR2NL5tcxSnDCe+YcrKpfuoLGUZu943hPj42UD5HOW+pwwfTZct4mY0uEZaXcxNDOjW3+RgUutMitK/J8OCr8FOWSULxPLfv03iX6ZmxmfFi43QzpdSHLjDg8RM17ft42/QsbksvoOa9FoKfzTunXF5EhcU2mkfTosS7iZ4uUTEYm5esmZk/aTs37VCZ1ZU5Kn7dLY0vKJEoeM3OH4j3n0aJzExUlmKCf5Lz3uSJH5yZSYd1EJrF7ceBBd2yWCVtO0dLcjYmrmHAFx252KrQYb+ZshRCjqFFn2nBJwa2rlT4ThwyhxG54in5CCE2FvlPh0/AOHfo8bCsVGnAVn41QobyJGX33dRj82Ma5HtT9xUIreBPpHKw4REEouTM6LEuNnebGJy1/ZuySeUtUd8XzPj9YMdDHYyuFdVneS+GrLISTn3UUvg/9s6FDad5q3sQkjyituOzQM55kKiybvB6J4n3GOyq/Tap0TsfWdwt0JtnYCmM3za4fh00DPSeTKE9qIlMomUTRT1LehetTY0fn16Vj167p9J6aZ8/kdZ6bqPKeUVLD8yVjQ9Bzlc4dmSTwvo1w0GMnKkqeCakrMilOh5Bk2szTihQVts2rKuM0G3kv7sqkZcKWU7TypGA5mV2vEiFfdxH1T29IaPvPvApAGkLhkoJJjxaRWMolfFP06cKkX/MCpMmR/GZOv2aGe7bw2zg5knIVi/kQE9mVLI1tnhScexMpJZTLRaEs9yR8RXgT1fdMkmNHC2Xack/z2zr3F/GmpMiD8hbGY9cZ22zdkGMbjY1aqMBtCELImy3yiPOIFK8MmcyueWWIdZEn60uVzlqRR0mhgji2saeXe8UO4zUhN2OKd56RSdpmTngbybnB8D7nHdW/uh/EvBc8tZQ3UTKsu/eX7wdd3nMFMpI3kZMppIFBrYsZ7Slui7fo/Yii7xylFD1bdc8xfVolLu/FXZm0TNhyilYiVAjLPU5m185qIoXiJD0pmHLjs3lAmZu+a8HU/Sbhqa5His0lSRberENPVZlwScGcUPS+chVzuTDNs1Gl0BN5M042JEqJ1TbzeGzJ/Lp0Q+JOL5dCILHA5woVWt5xeUDC4YAzWgmOvT5cPoVoABTwLs5vkwsVCgwMamyZ+0uTeruWe1yoIOVgsQI/VpQERY2jVw0U191Q2FP7mUrnqTR2mVek99jmxif57CDptaMxYnpKJsXGoUSf5ngpvGcOEmZ5L4XEcyVV2g9mXW9ivi4045DeT2SPViJTtLxPxsCQlFw+NzGmJ/YT7fquy3v2jLIBe/EyYcspWvEBdKSr1/EHgob2uCoxH3wXKVKsG58JLcYTuz1gjj6gjgxPkTH17rksVTsEeroKJKEXrw/SFV0pmTzvgqIW2ruKGJ/4Wd1fj7ElQ5f5/UW8i5KCpbBsEj7ieK+MnVTho42dj8auc9aTwHvn0sRTiTd0dZXWf3owo9h/Nu+D7O3DO5Ze2FC4QoUJMXbUuvBCyD5e886lSnryjk+GN17jvaDEuoielEnC2E4i3lH3F/OOrAokeU/LNLLSmZRpjEwSZRpf9dh6nLrhp1gmzUjeycny8dhSilQ8dmTFaCbTqHfbes/Nu9Av7S2kcrC4eS/KtFkP3ncKdFre95dJ8uHiy4Stp2hlrlzSzR5Zd2RSrxBa7LqK+ZLU3FUdW19hcZGl1jP5dRWsq7fpvz2gjgyhcNZl5Comw2MUffZ8shsdifVIJrNrFk6PsZW8NlX/8b3FvOe9iWk7Ou2zyPpjz9kSDpOteC+fiaOeacNUy2ohcZF3mVdF9BZShQqd/lPa6tnlV+RwCdEp7xWPVX2P+dEZLT3lsULn+pOM9zPfenq74Sn04h3pqdXotXXBeFXiQgUyN5FcFxzveJkUF+jkh7WG/smxS2QSJdOIuZGF11pFputNXHEAJ6/D83Ghv+r66dhSoUXJY9aRScqzcQU8lCLV8o4LqRNjS4UOOZlGzI1uXijq31Bn76W8pwts0LSborVEkFyxQLohURZMvpl3FKl44XmPjJzwaBH0nIURTXz5dRV6YmlDz1nuCj1pQUXXpyz3PGwrhQ75UmheKHXGlgoLM97Ehn5GexNjodZYZxE5ndTLJbtD5h3hNUl5j047nRSc8n5skUevZHahnfImJuGvzJu4Qm0YlOU84xKiI94LHrE4BMKFLqmxizckrkikem6G9z3CtlxSsDS2iSLGja2kZNf3Rxd5dJ+dnvd65RqZUB0bGOq6kOYG7U1MDAzSi64V6ChKcr4upEIFbl0y90eteXI/YAwIet7SoT/VQJFk2qzlUW7YxwU6UtiU3osByXBfJmw5RavjFckHz+X5CrQiFOilDamyPmWPVmfy1Fq6NLH1PB4+hAEEC4ZPdo+FIkcvWZfBgtHyeCRvIMl7lytiaXvX49UduzQPiOjft95EnnfBDd6j4jPx9sljx1l/gV4tVGA8B+q8TXgvj13f/LpuoQI/tnn/VJ6N5k3UFCmad60iRW7mDO/1s54ieiY3UeU943HK20klN+6fkkklY8sZEMSza7mJ/c5qqv5ySjC9Lml6MnSX8Y6USYI3scM7IVUkNyAaemlsg0yadc9NJNf8Cm0ctrxn8kKZk9mr54pkGvP+V9XTS+X8JmueODexRCYJ836ZsOUUrXyzVasOs7HLB18KT+Xhp7YdTXvHHTrJFCkydBgpYkmIo/rbR5GiQpecK5oOjxHt9cLuPluf1xd1eZeHl8SqRWLsqv7R9E/en8o7mffTaOy4yjnSsqcqRrUNiQkjTAnLPS9UEIUWV/HphXnvFG9jRwlOmpu5EbyJZOgyCT+lfVfPHoWPOCWUTHZHxDu6Mg0QlIFYCZbCvjU96THq4RUhKz4TRarnusrCV9T9pUpu2lb1i+bZ87Bp++yaccjzfsqOnSLTMo+aVBVYokiR3kJpP8jnfS6T4nYmpK7yngnLUsVNZJU5e3wDmvsmZRphHJIyKTJQyLkx40Lq+V4sK2I575cJW1PRCpst42ZPT1EmvCI1PZks3+k/87pM5KTgsPDJVyZExz9QFka8GZNnLWmhwbo9SWxl+pfCACEpWDqBmjqrKUkKZrwePmrXTsDu8N7JhQphbOnXVaB9diE85D0T1u3Be9Lyp+iFsC/l1ajauaTbPPzE854Nqcdj25n36dhS3sQqxIDOszftPtoQoh0jPr2cTspFyxtlXUheGTahOl4Xnthwov7VsCk1NhNl3se852SaIJNWJmWnl1MerUQmKWFTUiYlMg1d+km0rsjQIcF7hp7nXTzvZXkuJsszoUcpwpGMLTs3Yt5RoT06JJ6seSqsS4wdux+Q/Uf0lEyKec/sdYGenbeCkhuPHSWTlglbTtFyDrLlnm3G+UtEJ64VOPpJwfyBppzlHtrpt6m3FkDj9aCsN8YVrClarfVHWyh9Ty+XT0lmEkcF6y8+KZiyTpOxZSx3KVl+4lKPVP7srh5b+eR3euxKTy/nvIkUPZeLwj1be3+MdZgouejQS5a764xtl74X7yfd/mcc7wjek+tiJs97Ltk+7V/yHID1Jsb03WeTX5Gj8b6zriiZNJPWRfV96+1MfxDGlvImpryjCnTa5xJlUkzPjm2XPpFJ6rrsyqT89HJ9P+hu9nKhQmu4VcoEOu2Sl75dF12PVNu/p72J2ZoHUpmRynPhyBhmP1FlWrwfMQZE3L9oIDDGoeRlXyZsOUWrKAcrUqSoPKDS5MeNaVcRaxJLiYkPtK5iyesRL7xeOVRhYnN5QET4inPzy2fm0NZj7CpmeecV3kcbhpiLUjS2xMJmnr25P0aRIs+0YSxvsjqLUqSU08vJilHP56KE609nRG7iJN+su5b3VON9si6kXJTuuxCled/QR7zXznfrlwekKLEZ7wD0zgMCakWMq/iUwrKTNIRCHzGg8D6SSZ11NUlzE6kQThL6o3ITOZlE8Y4ZO/KcLSo8pskkxoDhvODV/ekhb+qsJrWSepIqA10ve1nuJHVuYtw/tS6oHCq2KpDIoSqu+CwMqVOKEtAaKOS6iGQSzRs011jmHK3Vg30Dm4144muuXurFxUnMnqEP7TOCPvRPuWrj+5MqfHRFinHjRxuCJ65PhadWCVfzdOab8JMjhFp4Pir8FK5PhY/SqkOeN6EP6jUx4ti6Ng+IGpswtpSi09xfSQiD2uyjDWFIWDatGNWVbGrDqK4v8D52wxP04mbuZEUszmWRxpYzQNqk4Pb33LMDuSJE8J6iL9jMJW9ivC7Se6/+hnVNFmHMIt4LScGaIiV50atnpA2ImTDvw/1RIXFVJsXzjpJpUXv8PPnnGcP7RCZ5j4mjlYlQtdjlXUTPKMlita5DIpOkAh02LDujK51DO8e70P9sxsikZM2jQx/vF6VhWZZeUrJnDO/i3EhuL43kuaSIUbxbJizvnS0IeVIwV8oMANpJwVTyYpoU3PWa5B4t0h06Q3SmjLwh0R6vyOvBbEiliaXUmTbBgulYvh0LJmlOvD5S0i1Ah2W7iaX8hkR6Eyc672e+Xbxc4qx8+nhEnwid6i9bFUjQ8+ExfeyoZwdaJTc3/jq8p7wqiVBL6fsVKhD9Z+tiroUKhNeFLvIAOTeo08u5QgcuqTe0V+GnpLmbFKxUOlNHxkgh9dTTO2BdTDKPFuNxI3k/6c5brsiDotdebdWRSdJmLsikplCBmpdCBCP39KoFOow8D3omJdOSAhumf604SfLC6x4pTqYp9NnYUUpi1X/BfjCgyGOZsMS3thjkWrh8BAFv/QG0Ryumn826C6NamLR1VtEjsVBoRa21oqgNIT5PiC21Jumjzbi5Pl1GTyYvZv1ThQQAnxScbBicNzDaMMRzVxj6qad5F/7PWW9N/5oipVl/jEeMCtuS5wn51nJ3lLLAeBNzrw81Nq1Hi99swzXIZHbBmxgUKc6b2Hh6Oa9KsyERvKs/ch6x8DFZF0LoL+8/SdoVlWwmz2ei8N6lc4OqdJYs90QmcTJNGluXedkZJZjyJrbtkZLKFegUGiiat1LyqvDhKd6bGNPrr9Dpmc7QGTtCkZrJ8z4ujmL3A4V31NipvCdlEk1PyaQ89MgZf2FdiseWUPtNZy9eXnVmee9sQWiErqetO+fi11VQeTxtZRQ1eVzUTucBpZVlnNCjBH57f/RmrJWhUyESip67v/zcFc5C8Z4TKqjphVOQC3lPbxht4mkJ77mkY8rbB7SvV5LCR2kpNs+7il7hPVVVOAt5QHnCckRPjg3q69O8D6+78LX1K72mhVYmonlPeBPDupB4z70qJL4+l9SbrwsuBCIlBbOvgVEs97xIZHWFVnKDIsgqYs266tL7iPfSutiYydW85Lqox45KJo/7b/KAOv1Lr6ZC/Wx0Xqdr2mllo49Mo9ZFLvNYRczT3kTtNTAricxiCh2UsfO+ZN4LRSJFvKeKq0K7LNP4/aCll3jPFxqU8b6tGO2XL71M2HqK1iRbeIKrmKwCmaQhEs4rEix3LrGVU6SCq5hbeMHCol5XkYeHcnpqw0joIwtFfA2Mr5Iru25sNNfnFKmq/4qecvPHrmKxwod5m7tYIeRCQjXD+4nO+5R30WYeb6ZqdZU8dlJlWxP6y1ZunhTMhg453jdCjwk/uXjeM7wXLPdgmVO8C/Tcs8f9syF1YWypQoUk9BeHMKYC72dt2JXyJoZCBYp3Tf9E2LRTMZqPbZIUzKQzNLynwrJo8oBImdTwjt7M80IFUqbNlHk7U5Rghn6FGDsqNNmEZbO9NqaXKt+ahOwOPdL9gAhpJzKpwxsk64KUaTHvmf6pZ0/pCZnUg3diSJ1TcqOxo+dtSk89W2inQn95oQLHO4BeF8uELadodZKCBVdxWVIwLbR4yz244YXNXKBvkoIpy1tzs8eu3hlBH7uKfVh4IOnJ5MXMDS+5ilneCeGndDPnldBwD1yhAsv7un82tJiNHZsUTNBTYVtq7EJYlqMPG0I39IeEXvKa0EUeLT2XmDpr1gXD+yS0SLezIfFs3nNjy9E3GxLFO8WTm4SfAj1XqECFn7JCBbHqkORdS08mVE/idcHMW0kmTQoqnRneNfQ+nreMTFIUKTH0x4aFu2Mnhb863sRo7NRCBWpdOWU/SBQpiAU6nMzkijzC9cVChUnKe8rTq/OODqmXKslscVO+HxBFFkCk5PYtVIi8hZRMWiZsPUUrmxxk0m1kwahJwZQFEltn5MGOvOVeLZwoD4i6v8RyJ55tRtNPMo9Tfv1kQ6ktBEfRNxYMo0h52nLPeU/xrg1fyR4tqVChynGjLXcpLBvGhnXj52PHbUiSUItCKJRHKw4tpkINNX2pG54WapxHKw9fkR4jyXJ36bqgPFZcWDX8X/Im5h4vLnGW8iaS6yIu8iAse6lQoXu2XstbKSE7Xle96CMDQ0pmD31wnl6Jd5Ii1ni8GG9iVyZR8572iJUX6ARPK1iZJHsT+SKPQE+ObaQosgU6sUxiPLGe2W9aTy/tTcyV5O4Zaq6Rl/HzJvfHKGpUoQK/H5R5evN7B1pPr2SgUN5EtVAh9/Rm7cuELadodXNd5A2D22wBOh8ibOasqzeb+F2hmLrROQumPWBuktACqSKVWO6JxyuciUNbONJmzeVoqUnBOe+JMvaYXjoiQNqQ5GT28opPtWKUHFv5jLJKGQhn3hDnEUWvIokvn/KO3mwrelmRChumrIgVvKdSeDkuua7CvGXzgOSwrurpnaS8j+nDMM2UdVFUdUjkUFEGSv5sAB9ST70+jCIVNnPm9UixTNLmPefpFRUpJo8nvj+ad+mar+hpbyLtNan+Bt5xyeyB95THKe5fNDCIs/tyRTGnd5pMynjTh3dAV0nuLdMmfEieVKQoJVk5N7Hh3YqsSOWKWC7TpEIFMmc3Mby7MmmZMHdFyzn3U865rzjnZs658+fd/1i0k0NSpKrPZGgxcVcKr6tg3PBN4qmgSKUTn0oArCY/0HUVT1z6Ch02BCKdicOEQPIQihieongX8V4MLTJjszLp+aoQ8gWvEIVeqOqr/t/dUKWxC69XmhFjl/Iu/S7Qtu3Vs5F5QPXYqbxnFCnv6bBsydj6eF1QSq4PuUDMhhFVy1LXD/dWtcu8Jw0UT4fUEwODCk8lHic+vBWS5VkDZFaNLcvbGf8Knvb6NO+Cp5cMy8YyiRk7MTToct5TBowg01y+ruixI2VSlIcjhdRZ3hfwrm2n5XnTTs3bbF1w8xaQU00a3pGHzcrrQp73WRU78Xzx2FKKFDd28bqgzk1MQ38FvJeq0Jl0hJiecmo0r7MjeL9MWIRH61oAPwngUwvoezTCWPBel+pvk+DXcdXG7spu+Mu54LUQhFJk3VGuYMlrMgn9E6+riOkpCyZ9ZQL/uosm/NR5ttT65D1W/KtCACE5MluY1LPHvM8tb+f4xM9AH2+m3NhSYdnQv5QULPE+TWaXX69EFmHkoT+Fd92QM5rrc1WH1fW58JOWFNzmGOXPHugT3lP9R14VivdSUnA3rNu2NV4T33oTSd4z4bXUI0VVVKZjy44dE5bN6fmx5cKymUziFCFOJk2ydAJqXYlel9RjRb62jPGqOELJpc7u4zyxuUyTNntaJhXS16f6ywnZTNUhc75b4I1U5BHGlvdYFco08hU6iJ6d531zvhux1wEF87ZApknrqi1UoOUtsPyv4Jn7yfDe++uANI6+TMgtFDaEwlgwIURCeS2q/yuJoxM+mR1oXcXcwmpdxdX/udPLpYRszqOVJ8tT+WUtvXw4IJfMDgjJ8Jmbn3u28BvVeiTauXyF0L/kTeyGAYjEWcabmDw7XE3fHRs2odqlY8eH/mhPrFqokAhN+VUkbGJqvCEwRSZs+Mql64I7K0qy3KfMumySgme0N1ENUSTtguU9o+dlXuiwlodY8g2FeD1Se31ZJlHexJWwWTMyqW+BDieTRJk2Y2RSZrzl9HFoUfVoCd7EQD++UIGet+EeKHotJJ7yjpFJpTKNeduGuB/MPKYTgfeaN1FZFyrvGW9i940KFG/aKMJW82gtNbRclKQUnHjbethM2Ryq3CPFCB3urKbOhkRsmMGyJumDUGvygGhFajqbdV9+G+URcflrLb1y0i9zkm9oF5UBYbOPD6hjX1fB5Fh1ChWYQzfZ0GLusWI2PPLk+EnK+5y+Yz2yvKvoqbBl1a7kAXlmbPOxYTckT+cmdpRQxgAQlFiOd03/0rqaKLksLs1tpELmqiLllTwg1gCp/u89l8eT0ksbEpebKBl3E4V3KxrvFAOjczQHN3aUTIo289bLzz07/aYNoF0XUuiPlklorq8r2TR9SW4iVRUY+hcLbAoUqT4FPmRuImPY54UKrMwIvBPkuVygA2Zd5XsxTT/zdL70MmGQR8s5dzmAhxNNb/be/31hHxcDuBgATjnllCG3MQh5UrBcOUec7ZEJrT5VgQ295IafuLoyjQ4NdqxLoj1OXuROL+cqKoE4eVHezKl7C/1PZx6HrNIxebZ/xVUdCy3vu8+uCp3cW0jQBzd2fD95/6JlH23maVgWzbP7UModteeKjrQhkLxRwq65gUE9W6AXhSIb1s3O8yGuX7Yu6FeNdDYkov/gkeLoS5J6pTPGAr06b7mxY8ZWnff5Zk+ueT4s2622RaddlGkdAyOjb2QSb/xxFaOpl7tb6dyt+BRkkljkAVIm5RWf7H7AjG17PEMlkwZV286U/WDmyXMTw/OJMilfl1S17cxjA/y6COtKDv3x+8mMUYJXsrGjijAAQSbl62pJo2jAQEXLe//ssRf23r8bwLsB4Pzzz/dj+ytFvCHNvCLUPD34qSu3239swdBCCwJ9mzRbtVObuZQcmSYFs2564tm0qsF4Q6LDT2n/0oZChm2jhT2jhKpzjVeg+j1S+hAC8cyG0xFqDL34qhF+7PPE1Jg+FCrMZh515DC5v1aR4fMRgHZucBtCo0gxQosd2yy0yCli3NiGzVYKP3El/uH/IQQR309CnygTINrB0odcFmpdppsxTQugeT7NU8vmAXFKsDLvcwOF9KQKBkQTnhKMQ2mzD14Rjr6VSSCv34Q2hfCVxnuNd0HmScahJHMamdbX+MxlkmAcBl7kvJFyD1ecw8Zs1vKOWxecTMrGjru/kNEf314ikzTekTIJNT1vnMX00l5M9a+lAS0TtlzosPXq1K7ebGaGsZ7VG95ax02OJLG068pt+wbQyceYuPBKgW6Jf9XuGjd31Z5OHuey6igmz4g8YiD2ekw91jp9V/9vXnnAuIKb8FOHd1H/MynPCPSrRiZR+6ybyxLo10MIguBtwvvOKx3S111QYxeHZalXjVTzghs7JGfaUPwLvFvL+667KuI94cbPeZ/331ZvhRwumrft/fGWv/f0s8e8p9ZVnBRMrYtZ5MnlXgHE0bt8XVHVwjN6XbRrXg6BtPOeb5d4HzzRWv+duRHGjs2/03jvCmQSTz9xbYELRd/KJHnsaJmE5tkl3vsC3lO8m+QyjZVJdP/augqbOy+T0nnX3U9cKpM6B67qYxunI5DrcqaMrW9Dd7E3sSOTCKMeqPsvkElj1tXGrLtfhfYNRiYtExZxvMNPOOf2ALgQwGXOuf8572uMQR5zl0qxOcs/Di+Rm+lMcKNnrmAtn0LbMFivC9F/J89GsMypM2vKeRfetp6HDiHTZwuTcxUf2OjmeoTnkyo+g/XW5LdR+W8x7xmvTXF+nWC9Sh4l+nC/jPdCoUI1tgzvg1DjrEOm6rDDe64Ig+Ndo2SWHczIJQWL9IyBkd/fxGXexM66kEPe2mbde11NlHlf/3fKja06b1NFSPM2cgnfUmhQza/ztEyKTy+X83hk3mte+FDxyb1sXZVJM4hzg5VJTj6eIRQqsPPdGvUQAAAgAElEQVS2GVu6/zC2rEzKxo4rYtF4L73bdsooUi3v9HVBjm0sk5h8aYCXScuERVQd/h2Av5t3v/NCsiERL79tFg6RuAm0g7+ubua6wCfpa1dxaVLwkOTIdmHQE7cJXwmuYvrQTNT0hRuSUF1Fv/w2HRs+F4T3amiKlC/kHdW/mhTsQgiFPmsIkMNDgKBIJeGvIRsSmuvTL+TWed/nhOvxScFyOxfaJJXYfMPg5q2miIU8oLUVmp5bV9HYSUr2uiqThHWhbOba2B3YKJdJVMpAkXEobLbTmbxZt57eEcahYqBIc4NdF1nVIWmgKIa5pkipEQ6J9xMnysP42dkqdHVdKIoUowR3jEPi2YBW0bLQ4RKh1EJqBo/R0iULJvZYqdafUgZPH1haJrRWJq5zzMaKa5OCu5t59bc9qZffEPQNacYuzCBYOFfztFZGuA2J470q8DWh43LLnqiuUoRWeno5VRVJb7b5Zt6b972qDodUVxXwXvKKlBgAMy+E5AsqPhMDh66ME5XYgZa39jJ1jfelp5dzG4oqkyYFnljJG6kYh910B6YamJFJ8enl3GYd0h3408MLjUPOuCtQsul1W/3VPb2CgSKkipQcSaPJJM6b2PTPzNuuTBrh6RU8UoONw5z3pmgtDzoWEqMM7BeEFgAcmE7r39Nl6mzoryO0iA2FyWdo6CMLiG6nFamqf6lMPbNgREVsuCtYK/fleJ+PzfASf2kzpyvTwv+5UunQnrzLMGP/xEmbbSx0qDL16m/wOEkbCjm2hYqUdvzDfmazD7kmrKe345Gi5n15xSj38l9JGQivV6IEcurpZXjn+QNBgbZyTlSkqFfoJJ5efuw0mcSNTWfeM55eVol1qaImKVJA1zidCJt5uH4I+UsGRpFxyI1d0ZE0/Loql0nEvJ0JMinnHTG2kjeydF2IBkqQ151X6MhKbMewFmVagXEopkPwMrPdi03RWhqEhdckBTMLu3XDczF5IY/Hx5s1lRwpKFIuTeqlLXdeGQivgaEEfng+qVQZ0DdbPdldVqS45Mo8RMLlU3AhlJVOPgSliAmKVJZfR/YvKGJBkaISS8PvuTyc8F/2vB+Ndwrv89AjR78xDYde9uT9JG8nDBAP0dObJgVr64pZF5zlHq0L6rydMHekzZxbV3n4iltXXAgkX1fc2LGhwZz32ovsiXXhPV/gE3jPKlLZ2NAhefAyaaLznkuW13nX0kvGYbFMYhK2pXWRhv5kmcS9Qkc0zBVFKtCTEQ6J97FMEuc9s5dOUnouOsSuq0gmzYgIR+vppefdMmHLKVrdV5HQVYd8aLBuZ4Sec5BDKNFmTNHn4SmWnkumV6zHOITS300ebRhCYqmmiLXWKZ0UrPKe9apoiaUQFamm6pBRBtqx7Z9YGtpDWJkKmzoX8U442Z3qP389Ej+2dGJpoD/AbNadkDpR3ZS2d3kneXrbsRPW1cyTh14G+jC2zrVzKSC27CmB7BzvkUq9HsK6mMljp64r5v5y3uvrgqg6jCo+KXrZo1WwrkSZxs/b6v6idAKBd5JxyBZ5ZLwXx5bwqjiF963hLfBeUEJb3nGKFBJPLz3vIcgkmfeTSKZpMolTxDSZxO4Hk3TsOHo2N7GTH7e86szy3tmCUGqZc4qUJvRKk4LFZPlZvPD4DUNqpyZ26L/k9HEpsZQTWgk9qUihbpe9MnxiqaIMFFj+kiIVck1Er4rE+zB2THgqPu+I86q0QotWQvk8oVaoDUks7W4YeXv1VysS4drzQgUuRCLlCSVeGSYkzylSUsVnQz/jeNduCKQiFfF+SoxdbPlLVYehTJ0bu2ZdMEdvsLxvNuvCqkPmWBLxQNJ4XTA5WJxMiseW8+QONQ7jsSPXldJ/VyYxxqEw74HxhQpqsjyrJDtWkYr7l8K6rAGQzXspXUFUkhWZxvFey01cJmw5RUvfUPpaMIrQIiandpKvlFgab0iUKzjOU6I0/Ob+SEWq+qu9SiR4HvomlmrHN+SWed8qk9JChVGKlOJN5DaMcH+t0OqOjbQhqYpUYS6K9p5JiXdxu8b7vu86XHHyhqFVHcZjy3kTuaTepv+CzVyr+JS8Kmx+XD5vFW9ib5lUwLt5GYcTR3sTOd7l98du5qpxSI/tvKoOh3rZi4+kGXpsiZNlUizTJANDMsyLjqSRDJQS43AI75V5v0zYeopWwWYct/NelSoBjxKKXslFCROzun6/xNKVws2c9ao4Plk+Pr1cTCxlYvLJhiQdnTFw4ZRUV4n0mSLVO1neFXq0Zt3E0radTiwN12cTSzVFKi9UECo+5eMb6MTSztgpHjHOwCjhXUXfLxcltuxJRWri2GNLmvtTN3MlqVcxUNiKz2xD0Xg/dF1pnl7JONSOpFF5L8mkUuOQ6L/XkTSkTKr+quGpRckkJytSfY4t4dolb2JpoQIV4eh4ejklmVOkCo1D3ctO78XLhC2naKkVOll731yVDn3fCp2Jnlja5DNwFohQdRhegkrlQzT9a4mlBdaltPD2c14Pl/NOodd4P6DiU0p2z3lDl6nrFZ/UZtv0rylSXGJpIvR4RWpjxhydoWzWOW+5MIBY+aYoUppHq7TqkPZo8UdnhOvxHq3qr1Ytyx6LohzfoFbbauuigPdAPLY9jy1xuYEiH9+Qo9TTy4YWBeNwXkfSFO8HqkxSxk49zofYDyTD3CkySUhHCNdrKz77GYfqcT6FxiG7LnrvxaZoLQ20HK3wX63y7QAbPsrp03bnstddEPRSPkSVnChs5i6q4iAUKU3oucQ6ZRZWadXhgAqduL1v5Zs2djHvOPqgxHLtM0bgh3ap4jPOAaN5L1QNKvlxWsWnzrt0XnPJ8qW8117R072/unqJzQFL6XP2tWMrVbbVVYecIlbTU55eJ4xtvi4o+tAu5brwMkNu744tl0fE5/EkFZ/M2HHKQMN71mMV6DmZJMu0Yt4ThQpB7xgqk/J2TpHiKj679N11FUdAVN4TyfricT71fsNHOHTeN8YhUcAC8FWHk5j3hDdxLO+1vXiZsOUUrTzuyyaWspZ9Rs8ITTbEEiUnUvRaYmm7YciW+ZCqw3C9wYmlE1kR6+Tx9Kw6VHmvjZ3T8yG0xFLunKrQP5f4mbfLyoCQWDpQEeuGn3rO+0JPrrYu1oX+k3kvvNpqlbDcG8tcmvczumow7b+b7J7fH/ds3NiWHp1RmoPV9SghaVfXBZFbKXm0mrCtlAcUlNievAv9cxWfoX9u7HKZxB7/ULwuespzbV0VyLTEyy4UeXD04nE+E533jReciXDwr0RL5z3HO1am5bwbnZu4vOrM8t7ZgqBX6KTtQzZziT6e+BR9fIDcxLWTNe5fcgXHScHsZi4pA5PWQtKqDvM0pO7hgnTVIVu55tJ23oLR8ueGJ5ZKR2cUCS2B90nVoaSIUUdnFG4Y3NzQqga1DWceVYdxOxciaTYcQpESc0lCu5CbyB2d0dDXvJs4ZmwLChXId7LlvOE2a/bsPmTt8rqgFClAViaqhGrBQKk3Y6fIJG3ei8YhowQnhQo9j87oGIdEaA3oM2+Z44BY+rQ9n1vzqDoUZZJTFCmn7AfC2HaOpOGMw9A/p0gV78X9DJRlwpZTtHpbMOyGVJg0TG0oSmJpW6bOVKbN+MTS2CvDJ5YGVy+lqPEhkHYzBzamMyJEUf1Vj84YmFiqKwMK751SqJApweyGJHkDS6oOmXyI4g2JUqQaj9fIxNINvshDpFfaSz29atWhpEiFzVZRpFgDpcDTq1V8Ul6VeVTLSu2qTGvopzR9tNkCjFdFUaSkHC3NOExytKjQ4iT2VnKVbbJxuM54PbpeFcY4nJdMGnokDWOAqPO2yLCeg3FIGtatojXEy65VHWp78TJhyyla/UMkspbNLWyt1JpLLA3J7NLEHpNYOnF8Mnu43+r+qHyI6q9W7rvICh2RvnTslKRgLrFULVNPlGBOSZbHNvCeD8HoVYeSkqwdISC9LD2h76tIFWxIabI8YaBo81YzUBglNNBzilR7f3qhQsm64OiLqw57VnyWenpF3iubrVR1WOrp1aoO6bzR6q9W8Rnmtebt670fbIJMC7ybOMKbOCmQSUrV4aYcSSMYh8PlefV33XK0lg8lQiduZy0kbUMSElNnQmJpiLlL72Sb+Wry0tafnNwYPF7y6zC4xFLXJnx7/YA5Lbyk8o7zDAwcu87YC6954XknJJaq9PXYivkQvDIwaeaGcHRG8SnKcx67niEYal14aV24iDfE0RmVR0tSYlvei2MrGShMQrVWdZiHzPvOe423pWO7ziZcu6RAh+O9dHxD4B23WXulPYwdrwzQ9LFMkqoO55WOMO91USLTvMT74NESQ+Z0Kkfov533w8eWlEkun3cj10XPsVsmbDlFq+vRYmLuhWXsXCWG9JqY9PqEBSMkljone6TypGGqXUoKTqoOmapFLkyQv66CD2HIXpXyhO1uhU5C36nQSdvJqkJBCXVOf50F9062cP/S6eQlY8t5Bjq8U8JXuvWo8L7TP0R6fV2lXonu0Rl8IUCg14/O4C1759rT06l113p1+h+doXnRNcu997piKka1ozmkdSWFBuN5yxkIklel5b12NIcs00iZpHistLHReF9ydIZEP5737ZE0Eu8kA0KSaU7lPR8aLN1Lh64LTSYtE7acoqUllnYskAGJpQm90L9zXVdwfO6JlKcjVpkIiaVqUrAgtMLzcW58zQLREksbRUl4lUhKL1s4QxKyJaETJwX3rUwL91c2tsJ5QkqZ+nDeL5i+YG5ovCupTJM2JHVshQ2ppOpw83jXL1lez31M6fPHb8O2sszhj86IQuIM/ZixFWVST49V/wgFRPru3OhZ6BDJc5o3snEXeCdWfBbsJ1SEo7m/GePp1daFwnu9yENeV8uE5b2zBaFvHk9eJdKhJ/J4Enpu4W3MOrTh+m3VoSDwp0x1VLRhcEJtKvU/ic5lYa7PlanrCdFI23Pe9eS9lgfUqfCZ6GMn5qLEQoXifbwhcGPrC8ZWEopM/yrvOgZC994S+rFjp6wrbkNanyoGBMO7eLOVleSCsRU3JH5sS9Z89dsubdzenbcQ6csLHQpk0oTOTQwGQE7btAte8njes7wPY8spasrYBpmkzbuxOVT5Xt6X95pXhgzb1kooyXtNJkXGIce7Zj/paWCE63PrQt0PFrwXLxO2nKLVd+HxWjhd6VBetchbd9rLccXzfpQNJz69nHPzcy/HDf0PDR+pm21PRUpbmJIrmvImxkJraAVOcWKpGJ4qSfjOnw3is+vnYPWll9t7J7ZG7WrYVChTVxWpkuMftA2HuX55ZZqSFDzwbRQq76d81WGgH8S7icL7Qk+vVqjAeVUSmaTOu54J2Z11MVKmqcpE99nE/aDUgFA8UvOoOuytxKrtSNr78n6ZsOUUrc0oUy/tny9Tr5MPufCUUKFTCS2husqBLZVu+g9hAuYU56Gb7ejjG/pu5oKiJiaOEomd4fqadScpA5MJooRtgffE0Rnh/tY3+JPbk7FZGbgZlyqxfY8wyNs7Hjd5bMJmO/gcrUlbdah7tPpVHVb9z28z761Ijd7QlHVRstkqxqGqSEnnaGnGoRvDOyTtfY3D3jJNkUnUK3TCkTRDPbElR9IMUaRC/9yp9+H+yveDnhX8uSJmOVrLg/zcEm7D0V+pwJ21BLH/sI7Wmc184uJXdVDltPwrDwK9VxQxKWlYs5DihaXxLqfvvEqEETo87+T+O2PXaUfTzlnWAPhzrlzEe6ZQwQsVPhrvXdQ/Nze4eRn6XxTv5sn7iSPCVxE9zbvwih4+obq06lAdW3ZseANF5n377MAQ3o3lfSqzpLHlZZLmBZfnbassMLwXlFiV96JMQvNswHjec0ruPNaVJJO4kHpckcnJhFYm8WPHjW0qk+ix447OaNtpRcxle6XKO+L1QxL9MmH1YN/AZkNPiEbS3rtMXem/DT3OyDL1YJnPBDd78xJPwcKZOPpckda67Co6oV0LoewvTfzsmRwZ/jv8+AaZPr4/upS5peeUTNEbGI2dlGc0m9FCoQmREEdnhHaplHkitGu8D+3c2Or0SNrZEIrC+/3chhIsc897A2d1+yGTlS59bZnPiKrA0L8WguE2y3D9sbxfZJGH1B6fXi6FTSve8zJJOiJg6j0wY2SSKxhbaV0IvA+nl+/nZJLGu57tbNi3oPiKS3YP7aS8dopMcqjnPR96nCoyTXs1lSiTonZ9L+23LgL9fmFdLgu2oEer+lueB8RUmRS44Vcm9DvZ4vbO/dUWiJYULMXUpTL1pHKOEVobU3lD4lzB4f4Gh2UVV3L/dx3yIRzu2UK7VPU3pDIt3F9Z9RZPL/JeyhPqGxLvW+igjp0+76V2tSpwUhBCkXiv5Qk5fl6H/gfzfmRIXc0jyngryyTaYwRUp6tLipQ0bxvjkDs/rmBsJS+7FD6Sxq5PTi3VvyaTuqFLXqbpMok2UNT9oMnRYhQp5WR5cWzH7Af5vO55JI22LpYJW07R0kqtiw9e1ISq4grmLRidflSZesGGxD176J9TQpt21s2O5tmqdmYzHmrZZ/T57aceLfrZQrucD6F5vIYrUtrxD5L1KLWrvFcVMYVesz413scbysAiD82A4A7iDfRTQRlQ14XI+2zeqmXqzGaujR0bkq/ptc184LqIj2+Qxk7bzLncSMkrEu5P9fSqxU26x4mi78ok2cvekUnxfkBFODTeKzIpzsEaUuSRjG1PRSr0X+rt6+2pVYy7ZcLWCx0qWvLYxNKSpN7Qzm2WGr2qSPlw/AKzMMVXkfDevNC/ZMGIScEa7/pa9oIFRCWWllRXSe1xeGmNEYrywYvt0Rnc2HFHZ4R2kfduhEeqt6e3nwGSeKyYzbhpZ3gXTqAWN4yCpGHO67Exmw33aDnFqzLhDZTeScE9X8ET04uKVIlXRfFIcZvtdEbntIb702Qa9+x5e1/jUK34nGMlNSmTxu4HTpFJBQbEVDMOhZC75EUP/Q/fS5G0W9XhgwjqSzx7Jj9yWjiX3Bi3czFzkX4SKuOEhOuZXHUoJcuvODkXZTKRkw8lei0xdEUZG5W+gHehXeM9e4SAmFgqv8YlvIJnyrnxY971TCwNzye9nDY8G9V/Pu/HrAvn2v9T11d5r4ydtC74ik/lNTCTeGz7JVxX9MrYSOtCSQrujJ0ikyR6SZFi530h76XKNC+ui4j3zNiVyyR5Xei8pzfzeci0sTJJn7cDZJKTk+X1/UCvOiwtVOCMQ35e5/SmaC0NNK+JVlKqJkdGWjhXxRHaaesupu8Oj3NKTN7xL/EM9y/lorgSy11UBoR8iL65Kup5QrL12H02NO20mzxqZypsxDNnHMQXEyehQaZqUU0sFT1afHvxa1wYodWhF8aG401o554tXJ/jfdO/8GoqqTpKeum0c5W3zHvdW5gfnRHuXxob5zZn3gPtBtVpVzbzMplEP5scEo9f8zJsXUle9iLeF4efRvK+t0way3vlHKxkP6CVVO31SDLveZnRtDP0oVBhaITDZTKLkunLgi2naDWVCoq7snmXYc/E0knUP6fhh3YusbSlpy0MLbFUO4BOSixdmTj2PY7h+lpS8H5mYcTPRvWf856zzLmxiekl67GE9xzvAF4RC2FbrVBB4r2WWMrxJvTftDNzgxub0pA5P7Zo2iVFiuc9mutLvGfXVRS21ZLdtXmvrgvm+YbS57ztyiSF9xFvJ67rTdTnLRp6SRHTZZJcdcgrA040DleczPuVAt5rObXaeyCLZZpAL0U4ON6X7AeAMLaqIhafT0ePnTbvNcO8dOw4mcTuB4oitkzYcopW76ReIWYvxtRHJLs39AMSS0MekXTo5qISSwMNbz2ieTaKXjs6Q0ssTUqlByaWSu1aKXZSqCCcXj40sVTzJo4Z2zzfQU245qzPqazEqvNe21CkalylMk3kvcK7ibIu4nnfl/fjj29A007yXvU2xmMjj92oqkMh/CXyXpApeXvf/Dr1PZX5fsAYGMOP82npNZmkHUnD7geKIqUVKqjzXstNLBw7iXdU/7nMovpfFmw5RStM1v3rtCLVWBDr9MJr6DULZF3xqqzTilT4TqKvjn+ghdrqxGFDSixdceyzh/65Zw/ftRYGYwGt0wvDOZe05/SdseksvLadSiwt4V1MTz2b1L4S90+N3UROLF2dOKwLR2do1uNqPHYcfWF7LrSbZ+esx5V8XfBjN4j3K+31h64rqToqXhdDeLdasi4k3q8U8J6d93PiPaPErmbrino2rX/veUVOlUmL5v1E9tJL9BrvVzLec8nuPO9i3nflqc776PrMs2m835iWrQuWd5ont3Ds8v6L1wVjHC4TlvfOFoQwVpwFEwZL07I1ocV5pML1pCMEGnpJ6AkbkuRVWZ3I1t/qSmxh9Hclx/2zlXOc9bgiW5c67yLeC5t5CT0n8KX+V1b0V/SIvC9QYkvHjhPaJfOa6j9+doDwJqrzVubtSmTADOH96kQPy86E0GLKu4GW/UDeTyYuySPiFLGhR2ckSqxU8Tl0XRXQz0bIpF68V5UBbV0pvGeUgaEyo5dMU/YTmncT8egMdT8o4H35ftBPJnY8WopxSHS/NNhyipZzDqsTh33r9Euhw/+b9txr0mjZU9Hy3rc+FRfOPoZ+tZB+//qUFBqrkYXC3V/77LTQCe28BULzrpSe4/1qznthbMbwroSeE1pS/5r1uLqi835/Me/kseO8KtzYxPM6/DbvG2h5w3kTed7L7cm6opSBFZn3K5MJvOcNkLDZ8kdnTFreESGe1YR3A3gfrwtmw2Tn/Uq6Lrix27c+FY/O2K/wfi7rivHSr0tHZ8S8V+a9zjtibFZ6rCth3lP0mkzS94OIXlCkxqyrjWkh75m5I8vzSbQfKPtJz7Er3Q/CXpzLpGXCllO0gDoEw2jha5H1N3HdxNLYOqQmVkyvWZeiG59JGtb7n2BjNmNj8muTiWKBTERX8OqKTL+2otALFlBuoXTaNd4pvFmblNMPGtvJRDyFeVXjvco7hfcjxrYJ63LrotDjxPJe8aqsThTeK/1rY5fMW+L6a8KzV9/JY7Om9K+uK2HsYt5TR2fo815Od+i1rijeqTKtMv5YmSTI4+r6Me+UsVXWRV+ZFs6m4ug13sX0o3mvrSuGfhZSTZixG8d7mX5txNhpMml1RZZJy4StqWhNJkWHqNEhANmNHtNzHiex/5WYXuhfcAVzL48FKqtFSh5cnZQnu+cCv2lXFu7QA+wa3mvhJY137NjG7SPGVpgbKu+lEIgydkX9D0xcXVmReZOMjXogqTDvpzMQ5P3WFReeKuUdYRkXrQtxM1fWRUwvJP2qvFdlhiDTtNAiw/uSdbUxE2RS8uxM2HUE77XQozQ3wn+5uZHwTpEZlLzU6HWZFo/9WJk0kPelyfI9ed+9v5z38rMtE7amohUJk1wwrEX/lywMrj0+nVcSimz/E4V+ZP9rk/jZhzyfQj+R6VcnPO9DWDfQcuGpqp3fMKi+K/py3pPWX4+xUXnHeD3k+5N5r/avja0wNuq4Cmuqurc+83YAvTa22rztQc95BsT+e/GP3lDKaHlPLHtvyroaz3ttXcxxXc15bjjnmv4njvcmVn2P5b22H/AGCt//yHWl7ScLXpfSfqXxbpmwNRUtQXCFxNT8dxStZP1Rfec0kgXE0sftymbKxeRL+6efr8/C4S0sgM8JKLq3kbyj84Dm2P9Y3g+gX1HoJSU3p8/JU94p47oQ3inzfp68V5TgYXND3jCl+5tMXDMe6ppaNO9H0i/jutKUifCdxnv92TR6Or9Murd+hu3yzY1S3lPtyZoiZNIyYbnvbkHQhGbwXNCKjOYx0iwEeWLqXo/yzXRQ/5oFpAgGzSukeeSChTTauhrg0Rnjdej0P8Tr0cc6HjB2pZ6F1Qlfps7R6uOqPbviTRzpGRjrye3nyZaVWN0rw4/dMN4v2GPU49kGrSutf00m9bo+JfME3o/1+KgRiHIFfzzvhsz78v2qb4Sj6r/lPSWTwlfm0VpChEGhEkvjdt0rQND2cQWP9IhJ+RLs/fWysBZhwcj0xR6tIbxTleAeQnGQV2WevO9PX9o/xbs4rDtkXoz1xo32Bqph53Lej1ek5P6pPUPi/eI9WuPCPysjjU9NEer3fP3pw9xZxgjHmmKgrCiK3nwjHFpoU+F9zwhHfH/Usy0TtqSitSosnPh7XWgpAltdeP3zjHoJLSXEM0iRUiwYfcON2pmkY+7e4rDuQtzco5VYRegsWpmY04bLrYtiJXgBCng/3o3lff/w3LxCn5Q3MaYfrUgNMlDmyDvydPRx3sCxMql0bnIvvN4smaQ922gldogi1WduDEh1GSOTlglzV7Scc+9wzn3NOXe1c+7vnHNHzfsaYyEpUkCsiI3zGiwiOXJNXThjwwSbkxw5cbQ3MTw/xTugdaWPdnMPGNs1RREbm3A9Nqm3T4hIaufWhRjW7RV6G8K7ketKnbflvB/S/0J5ryhSekL0SJmmXl9ZV6oipKQEzCl8x0U4VgWZU92/tC56hAYHhN5UmbTo/Uq7P/X6ZeuGXRfK2CwLFuHR+hiAs7335wD4BoBLFnCNUWgXDv34YcJxIRTZsi+PqY+1gMaGERZ9f7LXhOa9JtRa3g/xBpYL9CG8m6vXY8H04tgwiaXl876/N1CrOhw9b+fo9ZDCS1z/xbzX5j3RdyqTFuCJ3UzeLXpdCYrgUK+JLJMU3qmK0sgIR5I3uuB1NVYm9YxwAC3/tpxHy3v/Ue/9Rv3fzwPYNe9rjMWqMjhrysKTBn/08Q89vBZDLOd+ian9E0v1pF6Z96Xti+C9JnTG8l534yvW7Zw8disTOjxVvi5ogS2FUJJjU4Z4PXoczbFIrwfXv+aVSedW/3UR+ucNFH5dqGu+lxI8kneD5kYPZWLA9bUIhzTv4+uP9gYOerZxzz5epo31slffTdywCIfmNFkWLPruXgvgf4o8iBUAACAASURBVFANzrmLnXNXOueuvP322xd8GynGxn2lhbmZHqNhHi3ZQhpLX+7RknlPbWYx3SI8PsmGtIB8iBVNkVKEcq8jAgRlhrfMS72J8tgMOTpjrCd24d7EXgnfVAim/Y5iX8N7zpuoGSAjZJJWLTtPr8cixnasTNMjHMu7H/QLDS6C9zH98CiCFl3SnB4PSY+Wc+5y59y1xL8XR795M4ANAH9N9eG9f7f3/nzv/fnHHXfcsLsfiNJ8CN6rEoTiEAuhjwWjeJSYd7K1/csx9yGeAd0jVmYBUQIdkEup4/6pvrWwrpYPoXu0eghF1esxIE8pplf6l3jP5pqomzk/7+N21aM1YN6M9Yjph93KY9cnl0XakFYntDdxTZi3Vbuy4QjrQuX9aK9H2bzLP7f991Fy50+ve3KH8z4/hJmj5eh7HaQ7ZF3Nk/fquhzCe0UmBW8iI5OWBatDiLz3z5banXOvBnARgGd57/2QaywSsdAT21WvSnfixF0OyUXRBf648NSK2v98LCA2sbQwH2KoBbMycdULVNV8CG1DGZkPMShPSeH9nPIhtFwTfkPRFDGevtezD/Imjsvx0r2J5byXNrShvB/jVQmVcd4vyutRzvvFeNnLx5Zi3yJ5H77f4F40X7hmufsbvx/MM8Ix/PkWEeFYJsw9dOicex6ANwJ4kff+/nn3Pw80Qm+AIgXIykL8yoYhiaV6FYY2cXt4BgZZQIpnYaSrV+JddX/ywlsTxi4VWiM3Y/LojJGegTmOHZlYqii5qjdxhCI2VpEZW/E5OhdlZO6kFgIp9qIrvNfHdgjvxvJeWVejvTJlOV6sN7GQ90O9KlL/Yz2xK5pM2sIRjmXCInK0/gTADgAfc85d5Zz7swVcYxRaoddfkYrphlin87TOSMt60XlGhfQ8b8ryIXTe96dXratebvQF8G5OuSwTJ5ep67xj1oU67/mxTV8joxkYC/AGLphe9WgVeD042vh7Law7bF1sHu+HHPS7eJlUxns9ZL5g3iupKkO83GqEY04yiT0cfMERjmXBoNChBO/9o+bd57xRqgwMX5gTALNhik6PiT3I1dxr4QxwJWsVOsUbxkDeC9ZjnxDIwd/Mh4dQBiuxxeGt4WN7YDoT5w13f4s+emMsffHYMMnu5crAUAOl7n90WHbAZq2sq9HKQPG66M+bmH6IIpX0r6WaDIhw6PvBcsi0wQaEEuHQ1sWyYLnvbkHQQiBr2uQoTdAj2ueZmEpvxn36H+lKpugLF87QEIi+mfP0WmLqaKHVKzxF8a4sBMLSF85LlXdsCGRxY9vH2zg6NDioiEVZdxFvhhydIR2SXN1f4dhyXpd58X5kARDVrr+jdKxMKw0/ybwfqyxwMkka2z5edNWwHqAE90s1GcB7Lbqk7sVy/8uCraloFVruQzbz+HuKfqItjEToKTF5dWH2p++T8E09/thyXJX3I4WeZGH1eR+d7k0c7pHirq/Tl/GeU6TGe3ILx3bR4aMBVYO9vImkV6jmHaFkxX3qIXGF90peKR+C4fvvJ5PGzduxuZHa2X60TAq8G+pNLMzR0sZW8ZiNVYKHRDi0AqFFe9lH816Z98uCraloKYNXqoVTMeekf0boBkhVedX1iXtTPVYKfY88JDrPp/2OTix1LG3V/3x4r1mn2oYlvUyco+9juVNypU8O2ETwivD3V8h7Zl42ljWnLKwo8750bIV3+XH0fV5fRPIu7l+YtwDnTVToFUHfzDuWt/KGE3jH0isyRxvbAFomRbwnn71tp+dtPHb8vbH0ythpMqnUk8vyTmlX9wMtD8nxc6NPhEJtV8ZukEzTZGYz77u0cf/8vC8bO473y4ItqmhpWrRioRQm8HEWTH4f6XdKcmSvXJax1qO8IVKYm9djoAWj9Z//LsbaaN7JbvR55lNIljsr1A4y76W5EW+Qw/J4ykJ7ZfTKZi94Bjzo02ya8JOaBzROJg3xJib9jHj2+D54empd9Bg7JceMgh5+KuT9YG+i3H+Y+4sIqY9PZldkWuFBvJQCHF9zURGOZcHWVLS0UuhSC2hgTD9gUKn0yIXXJxdF8+pQWNUs70Le60KRcUUr9OFbMjFVETp9ytzVXBRNKCq5KlKZOodSTy67oZSWuTPt4Wtu7AKoMvU+LwRXldgB1VV9clkoaCEQ9UDS0hAKm18n99/ep7zZL+Ig3oR+SB5R4bzXvIFDjmeI6YaObSOTiOuPTTVRx26sTIu+k7yJHLTjGdTDYpV1sSzYkopWeS4KF1eWB7/VwjUBMMB67LXwxtEP8WgFGi5CUXpEgP5uq2H0kvWY9rNY3g3Kpyi8Z+6EYO28H23elucBMbyHfP38OtS95Z8bGsWrMpb3Wki94T3DfJV3IwsVSnO8+HWRXoei5eg3UyZp9BRK1/pgb+LY/UDgfdrPOC/7wYhwBBru3PJFe9mXBVtS0dJfEjrOgpmM2My1EEq/yjbNgukv1NZUC6Vs4bEVPppQU6qrNI9Y+FYVECMr02jrT/OqlOchUdDm21hPrEqvKAPSS6djqLxTNxR57IbQa+XjpeuC86povCv35NLt4ZF0r0qXfrRMmmO1rpZfR4FLgs/ph4YWtbEpl0n9DfOxr0farAgH3y7zZmyEY1mw3He3IIzVkjULJ/8dh0W48XXrrzwfgnIFlwoDzrLXLHctl6TZ7JkNSxvbph9FOI/2aI32qsi5LBQ03re8o9vH5kMU816b98TYxl6kIVWLfTxiQ7wmYWxYb2Ih/XBvYhnvFy6ThvC+x7qivYn9FRSKXt8Pxo2NlrC9aC/7wYxwcOsi8J7jje5lLwuJH2xsSUVrbNy3idkrljtrwdRfD1EGtFcyaMc7JAtPyYegoHtVFHolH0J91YgyNq75HXOfpbwfILQ0odQnz4i6Pa2yTbceC3NVNN4PzAMK345VgrXQnuYNHJLjNdqTO9qrUphfx/Je7j8YVYPWRa/cxP55QBpvS8eGC1+NjnAoMkmLcIzivWpALHeEI8g07leqTFIiHMuCLaloFXu0lFyW0R4t1atCbQjywkmsR0VR0xYufU9jn6m27oZWoZQWGowMs1G8T0IoipI69gw02ptYaD0yG4qaw1W6LsYaECPnmB6eGsD7HvQUSnNRWE/vSN4fXI9WD96rye79eT/eq1Jv9kw3pfl1ByPCockkbd5vVoSDY35brSvTDzmDbJmwJRUtVZEaWXWY/47DEPr4q0H5ED1cyaX3tAh6NU9oAYrUWPo+R2/QSnJZPgN/TyN5MjI0WF5oMI73izg6I6ahLq95asfyfmxIXFMGSu9Df44uH3SZJK+LPmNH39Oi13rhfjBaJin0gwzzmPf9IxwHY49L6IvzRk3RWjqECced7dEmZHOJpYplj7DwuOqrcB8jLRjFK6IJLfLwv8JSaQ5amXtpCGVoTL51w8+f9+l99Fdi+5ziTF9T491I3mt5QsWhQYb3c1LEBuX5KEdnrCmWux6+Uu65NKzL/EwLDU4myrx38n2066I/71VPb6+wbn/eq4rY6LCuku6gyqTqL2+glN3noCp29cgYzTgcp/zNK9WEzxu1HK2lRTM5WDe+PLjt7xZtPfan72M9aie/k9cs9KqoIRSWXssTkjeU/Hfafc6TXt3seygDY+5Jc8NroUVu31kW3pOeXtUrUr4uhtxTMe+ZX7TrRu5/rNdzs1ID0mv28CYuQCZpYdu5ebQOtvdnJO+H5MeV7pHakTPqvFdTTZZblVnuu1sQtAmrHjgaLJDBFkypZd/fGuhT5k72qSYvjrUey3jPJpbWl9e9Kovm/RChJY+NbpmPLFSYU1Lx6BytOQnvPv0vukx97NEbxbznCnDqv0N5pxXwaPQBQ7zspQeOchjrRVfHRotwqDKplmkLiHDEGCKTkrdhKK8AojB27OZVfGUerSVEq2Vrlv3me0VSei0coVnuQ5J6x+aiVPRjvSpcsnz5fWy+5Z6GdWXek+GpkffcWHWK5a55E9XjIRasSI0NV+gereHJ7vw1Fd6plvuceD/2ORbgERvN+znNJ1ber8i8L/WWLGOEQ6OPx3uQN7GU9yMjHJpHzHK0lhi33r2f/H59OgMA3Ld/g2y/+4H1+nf08N92T9UvpytMZxXdIhQ5LYSyaK9HuOYtd+0Tf8e1B97cef8Bsv3ufdWY7N+Yke1776vouIV5Vz12xUoLgyFK8MI9TvUz3cPM26Dc7bnzAbJ9VgvD791Lr4v7DkwBAA+sT8n2O++veBvGMMetd1djvmglesjRGWM9sdo9hWf+9t77yfawEd16D70ugqy5dx89tmFeH5jS64Ib07x/7aXTQwy12KjQvCp0n/ORSZy8D9TfvYteF9NZxVNOJt2jyKTv3Vddl1PkwrpZBO81+rERCvXVUzV9kB05wiPvuYNeF5pMuv9AxXtOJi0LtqSidcKR2wAAu799J9l+1OGHAAA+c8P3yPbth6wCAK7e832y/fBDVgAA37z9PvE+7riPXrgBM25l1hiyIR1e3zuHh20/VGwPvOOw6+jDxPaTjqrab7jtXvr6R1TX53i/49Dq/r/IjF3g/df/7R7xPjRFcL+ycDUjl+L9oasrIk2YdxyO3yHzPvCWQxib2++hhdZxOyref/bGvWT7jm0V77/wrTvI9u0177/y3bvJ9jA2/8oI1YB7GGUigCtiCdCqaSn6I7etiX0ed4S8Lk7cKY/NI445HACvCD28pv/Sv9Iy5eh6blxxI7Mu6rH58s00/WFzkkmKSBokk8K9cTh2pEw6WVkXJx1V0XO8eVi9Lq64QV4Xu7/NrYuq/ev/Rq+LMB1vuVuWSfvW6bkToMuk7g8OWZWJVJl0pDw2Gu93HV2ti73MvDs+yCSW99W6/cI36fZlwZZUtH7s8SeK7S85bxcAXuC//AdPAQDcvW+dbH/tD59etT9At//sUwI93f8Fpx0tXv/ow6vJpQk9yhWsLaydh8sbTtgwOJx98k6x/Ucee7zY/hPnnQyA9ya+7Mky71/9Q6eJ7b/4tEeK7c98zHF1O339RxxTCQ7Omxkw5OiMIw6VleCHK5v5Yx6+Q2z/oR94mNj+oiecBIC3zH/qSY8AANzDzOtXXXgaAH7e/8JTHym2h+tzY3PmiUcCAO7fTyvBhzQHsnbbNOVs25qy2SuK1iOPO0JsP++Uo8X255+tyaRqXbAy6SmnAgDufoBub2QSw9uXP0VeV08+/Rix/Zjt1YbMhYgCllEmPfMxskz6yXo/YGXSBTXvGN7/XCOT6PaLnyavi2fVMpPj/SlBiWfWbQCliI2VSZqSe8YJsky68AeOFdtfdG4lEzgD5aeeVI0Nx9tlwcqll156sO8B7373uy+9+OKLN+16E+fwle/ejd94zhnkRDjskBX8y7fuwFsvOgunHNtdxMftOBSfuWEvfvuix5ET7ZRjDscVN3wPb7noLNIiOOOEHfjCN+/Abz3vMaSH6XEnHYlrv3MXXv/MR5FC6JyTd2LPnQ/g5U85hRRcjzlhB/ZvTPGic08mn/+EI7dh52FreO7jHk62b1tbwRkn7MDTa6Ujx771GZ766OPwg4+kF8mtd+/HS887GecSm4tzDtffei9e/8xH4bH1xhnjkJUJdn/7TrzlBWfh9Idt77QfdfgaPvfNvXjrRWeRHpxdRx2Oz9xwO9560eMa4R/jUccfgc/euBf/4cfOJIXIWScdiS/dfCd+7dmPJj1Q55y8E9+8/T685odPJ4XU2SftxJ33H8BLzttFbu67jj4ch66tsMr+jm1rOPWYw1mFdDbzuOC0Y/BDj6KVpjvvO4CLzjkJTzqV3thv+t59eO2/Ox2PO6m7+ayuTHDVzd/HJT92Jh51fFdx2LFtFV/45h14y0VnNZZojIfv3IYrbvge3nrRWY13LMbpx23HFTfsxX94wZk48rDu5vnYE4/ElTfdgd/80ceQis/jT96Jr996Dy5+2iPJBOdzHnEUbrt7H152wSkk709/2HbAAxfVCl2OY7YfguN3bMNzzjqBbF9xDufs2omnPppeF/fu28CzzzoBF5x2DNm+584H8PIfPAXn7Dqq06bJpG1rlUx6y0Vn4tRju+viuCMOxRU3fA+//cKzSJl06rHb8Zl6bGSZ9FhGJu3ENYJMevyunbj5zgfwih88lZVJ+9aneDEjkx5+5DYcuW0Nzz2bl0mPPuEIPINRivZvzPDUR42TSb/8zB9olPkYayuukkkXnYnTH9ZdF0dvX8PnbtyLt150JimTTj76sHpdnIljCO9ckEmXPP9MHLGNkEknVjLp1599BimTHl8ok176JFomPeLow3HI6kSUSacccxh+5LH0upiLTPrh0/E4QiFenUzw5Zu/jzc9n5ZJR2xbxRfqvZqSSYvE7/zO79xy6aWXvrvkt06zQDYD559/vr/yyisP9m0YDAaDwWAwqHDO7fben1/y2y0ZOjQYDAaDwWDYDJiiZTAYDAaDwbAgmKJlMBgMBoPBsCCYomUwGAwGg8GwIJiiZTAYDAaDwbAgmKJlMBgMBoPBsCCYomUwGAwGg8GwIJiiZTAYDAaDwbAgmKJlMBgMBoPBsCAsxcnwzrnbAXx7Ey71MAD0W1kNFIxf/WD86gfjVz8Yv/rB+NUfxrNynArgzd579TU8S6FobRacc1eWHplvMH71hfGrH4xf/WD86gfjV38Yz/qhlF8WOjQYDAaDwWBYEEzRMhgMBoPBYFgQtpqipcZSDQmMX/1g/OoH41c/GL/6wfjVH8azfiji15bK0TIYDAaDwWDYTGw1j5bBYDAYDAbDpuGgKlrOufc6525zzl0bffcE59znnHPXOOf+wTl3ZP39ac65B5xzV9X//iyi+Zn691c75z7inHsYc70n1b+7wTn3x845V3//S/X3VznnPuOcO4uh/w3n3Ffr63zcOXdq1Pb7zrlr638/PS8eZdcv5lfddk7d9pW6fVv9fSm/ftc5d7Nz7t7s+6c5577onNtwzr1UuN9DnXN/U/P7C8650+rvz43u6+pl4Jdz7uXR3LrKOTdzzp1bt42aX3Xb/1LPna845z7A0LN8dc692jl3ff3v1fPgD3H9Pvxac879Rf39dc65SyKaseuRXWcZPckv59ypzrnd9Th+xTn3S/PiUXb9Pvw6xDn3vvr7LzvnnlF/vyObd99zzv0Rcz2OX6fWfLraOfcJ59wuhv5g8+sRzrl/qufLV5xzv1Z/f4xz7mP13P6Yc+7oiOaS+nm/7px7bv3daJ7VbSVrkpyLm8Gzvvxyzh1b//5e59yfRP3MY46VrklS5kftRzrnvhPf35aA9/6g/QPwNADnAbg2+u5fADy9/vxaAP+p/nxa/Lvo96sAbgPwsPr/bwdwKXO9fwZwIQAH4H8AeH79/ZHRb14E4CMM/TMBHF5//vcA/qb+/AIAH6vvZTuAK+M+DxK/VgFcDeAJ9f+PBbDSk18/COBEAPdm358G4BwA7wfwUuF+fxnAn9WfXxbx6wwAj64/nwTgFgBHHUx+ZXSPB/DNOc6vRwP4EoCj6/8fz9CTfAVwDIBv1n+Prj8ffZDn188C+GD9+XAAN9X3Pw9+keusB78OAXBo/fmI+t5OOsj8ej2A94XxB7AbwIToczeAp/Xk198CeHX9+UcA/OWS8utEAOfVn3cA+AaAs+o58qb6+zcB+P3681kAvgzgUACnA7gRwMqceFa6JjmZv3CeDeDXdgD/DsAvAfgTod8h/Cpdk6TMj9rfCeAD0v09FP8dVI+W9/5TAO7Ivn4MgE/Vnz8G4CVKN67+t73Wvo8E8N3Oj5w7EZXy8zlfjfj7Afx4fR93Rz/dDoBMXPPe/5P3/v76v58HECzHswB80nu/4b2/D5VweJ5y373Rk18/CuBq7/2Xa9q93vspCvlV03zee38L8f1N3vurAcyUW34xgL+oP38IwLOcc857/w3v/fV1X99FtTEfp/TVGyPm188A+H/qz6PnF4BfAPAu7/2d9X3dxtwvx9fnAviY9/6Ouo+P4eDPL4+KJ6sADgNwAMDdmM965NZZfr8kv7z3B7z3++v/HooFee578ussAB+v6W4D8H0Ayfk7zrlHo1LCPp1fS5lfTd8A/gnVuqPu92Dz6xbv/Rfrz/cAuA7AyUjlxF+gfa4Xo1Lm93vvvwXgBgBPjvscwbPSNUnOxc3gWV9+ee/v895/BsA+rs+h/Cpdk2Bkft3/kwCcAOCj+tM/tLCMOVrXovIqAcBPAXhE1Ha6c+5LzrlPOueeCgDe+3VUGvY1qAT6WQD+b6LfkwHsif6/p/4OAOCce71z7kZU1sKvFtzn61Bp/EClWD3fOXe4q8Ikz8zue5Hg+HUGAO+c+591uOC3gF78mgdOBnBzfd0NAHeh8qw1cM49GZV1eOOC7iGHNL8Cfhq1ojWn+XUGgDOcc1c45z7vnOurJDV8JPpeNDh+fQjAfai8kf8K4A9qRXAu6zFCvM6KUYddrkbFt9+vFfrNAMevLwN4sXNu1Tl3OoAnoTv3fgaVB4Ay9CR+fRmtQvcTAHY455J1pmGz+VWHlJ4I4AsATggGXf33+PpnJfN+KM+GrMlkLm4mzwr5VYKh/IohrUlS5jvnJgD+EMAbetzrQwbLqGi9FsDrnXO7UblLD9Tf3wLgFO/9EwH8BoAP1PHeNVSC/YmowlBXA7ik2y0c8V0z2bz37/Le/wCANwJ4i3SDzrlXoLJG31HTfhTAhwF8FtUG/TkAG0VPOx4cv1ZRuZFfXv/9Cefcs3rwax4QeV5bUH8J4DXee807Ni9w/Ar39BQA93vvr63/P4/5tYoqVPEMVILuPc65o3rcs8jHBYPj15MBTFHx5HQA/7tz7pHzWo9Ad531gff+Zu/9OQAeBeDVzrkT+vYxEBy/3otq47oSwB+hkhW5jHgZWk9qDolfvwng6c65LwF4OoDvEH2L2Ex+OeeOAPDfAPx6Fk3o/JT4Lp/3Q3nWa01Sc3GzeNaDXyUYyq9wL9qa5Pr4ZQAf9t7fTLQ/5LF6sG8gh/f+a6jCXnDOnYEq/wm1m3Z//Xl37X06A/XAeu9vrGn+K4A3OedWUMWiAeD/A/CnSN2du0CHzD5Y/xbOud+Nrh8So58N4M2o8jCC6xje+98F8Lv1bz4A4PoRbCgGxy9UQv2T3vvv1W0fRpVPcndNJ/LLe//bfe+F4NceVFb7njrEtBN1qMVVScKXAXiL9/7zfa81FAK/AnJBdG5NN2Z+7QHw+drb8y3n3NcBPNo59+PI5heDPag2hLjvTyiPOhcI/PpZVLmM6wBuc85dgUoAH1vTjVqP1Dqj1mPB/X/XOfcVAE9F5YVbKAT5tQHgfwu/c859FpGMcM49AcCq9353/f9iftWelJ+s6Y4A8BLv/V3LyK9aEf9vAP7ae///1l/f6pw70Xt/S218hTBekB8B+RwZzDP0WJOczA9YJM968kvrawy/StckJ/MvBPBU59wvo8ppO8Q5d6/3/k29GPJghT/ISWLIktxRJyWi8ra9H8Br6/8fhzoREsAjUVltx6BNpj6ubvtPAP6Quda/oErwDol+P1Z//+joNy8EcCVD/0RUIa5HZ9+vADi2/nwOqvDB6kHm19EAvogqUXkVwOWoFkQxv6Jr3Mt8/+eQk+FfjzQx8r/Wnw9BlVPy68syv6Lv9gB4ZPTdPObX8wD8Rf35Yahc68cK95zwtZ7n36rH9Oj68zEHeX69EcD76mfdDuCr9dyfB7/IddaDX7sAHBatg28AePxB5tfhALbXn58D4FNZP78H4HeUa3H8ehjqxHpUxt5/XEZ+1ff9fgB/lH3/DqTJ3W+vPz8OaTL8NxElw4/kWdGa5ObiZvCsL7+i9p8DkWw+kl9FaxKMzC+5v4fyv4N78cpzcAuAdVQb3OsA/Fo9ab9RT4xwqOpLAHylXnhfBPDCqJ9fQpUoeDWAf6AWTP2781EpQTcC+JOo73fWfV+FKpn0cQz95QBurX93FSrPDwBsQ7XRfBVVouC5B5tf9e9fUT/XtfFi7MGvt9fXmdV/L62/v6D+/30A9gL4CkO/DVVF1A2oqlkeGd3XesTHqxbBswH8egYqKzfvZ+z8cgD+Sz0/rgHwMoae5SuqkNQN9b/XHOz5hcoq/dt6fn0VwBvmyC9ynZXyC5UiczUqWXE1gIuXgF+nAfh6zZfLAZya9fVNAI9Vrsfx66WovGPfAPAe1NVwS8ivf4cqjHR1NLY/hsoL+vH6GT6OyIhA5UG5sebd8+fIs9I1ycn8hfNsIL9uQuVBurce67PmxK/SNUnK/Ow3P4ctpmjZyfAGg8FgMBgMC8IyJsMbDAaDwWAwPCRgipbBYDAYDAbDgmCKlsFgMBgMBsOCYIqWwWAwGAwGw4JgipbBYDAYDAbDgmCKlsFgMBgMBsOCYIqWwWAwGAwGw4JgipbBYDAYDAbDgmCKlsFgMBgMBsOCYIqWwWAwGAwGw4JgipbBYDAYDAbDgmCKlsFgMBgMBsOCYIqWwWAwGAwGw4JgipbBYDAYDAbDgmCKlsFgMBgMBsOCYIqWwWAwGAwGw4JgipbBYDAYDAbDgmCKlsFgMBgMBsOCYIqWwWAwGAwGw4JgipbBYDAYDAbDgmCKlsFgMBgMBsOCYIqWwWAwGAwGw4JgipbBYDAYDAbDgmCKlsFgMBgMBsOCYIqWwWAwGAwGw4JgipbBYDAYDAbDgmCKlsFgMBgMBsOCYIqWwWAwGAwGw4KwMEXLOXeUc+5DzrmvOeeuc85duKhrGQwGg8FgMCwjVhfY9zsBfMR7/1Ln3CEADl/gtQwGg8FgMBiWDs57P/9OnTsSwJcBPNIv4gIGg8FgMBgMDwIsKnT4SAC3A3ifc+5Lzrn3OOe2L+haBoPBYDAYDEuJRXm0zgfweQA/7L3/gnPunQDu9t6/NfrNxQAuBoDt27c/6bGPfezc78NgMBgMBoNh3ti9e/f3vPfHlfx2UYrWwwF83nt/Wv3/pwJ4k/f+BdTvzz//fH/lJVNZ2wAAIABJREFUlVfO/T4MBoPBYDAY5g3n3G7v/fklv11I6NB7/28AbnbOPab+6lkAvrqIaxkMBoPBYDAsKxZZdfi/AvjruuLwmwBes8BrGQwGg8FgMCwdFqZoee+vAlDkVjMYDAaDwWB4KGKRHq1RWF9fx549e7Bv376DfStLjW3btmHXrl1YW1s72LdiMBgMBoMhw9IqWnv27MGOHTtw2mmnwTl3sG9nKeG9x969e7Fnzx6cfvrpB/t2DAaDwWAwZFjadx3u27cPxx57rClZApxzOPbYY83rZzAYDAbDkmJpFS0ADwol67LLLsM111xz0K7/YOCRwWAwGAxbFUutaB1srKys4Nxzz8XZZ5+NF77whfj+97+ftH/kIx/BJz/5SZx99tliPz//8z+Pr361e7rFn//5n+NXfuVX5nrPBoPBYDAYlgemaAk47LDDcNVVV+Haa6/FMcccg3e9611J+/Oe9zy8/e1vV71K73nPe3DWWWex7d/eex/uuG//XO7ZYDAYDAbD8sAUrUJceOGF+M53vtP8/x3veAcuuOACnHPOOXjb294GALjvvvvwghe8AE94whNw9tln42/+5m8AAM94xjMQTr5/3/vehzPOOANPf/rTccUVVwAA7npgHa/5udfgQx/6UNP/EUccAQC45ZZb8LSnPa3xrH3605/elOc1GAwGg8EwHktbdRjjd/7hK/jqd++ea59nnXQk3vbCxxX9djqd4uMf/zhe97rXAQA++tGP4vrrr8c///M/w3uPF73oRfjUpz6F22+/HSeddBIuu+wyAMBdd92V9HPLLbfgbW97G3bv3o2dO3fimc98Js4991zx2h/4wAfw3Oc+F29+85sxnU5x//33D3hag8FgMBgMBwPm0RLwwAMP4Nxzz8Wxxx6LO+64A895znMAVIrWRz/6UTzxiU/Eeeedh6997Wu4/vrr8fjHPx6XX3453vjGN+LTn/40du7cmfT3hS98Ac94xjNw3HHH4ZBDDsFP//RPq/dwwQUX4H3vex8uvfRSXHPNNdixY8dCntVgMBgMBsP88aDwaJV6nuaNkKN111134aKLLsK73vUu/Oqv/iq897jkkkvwi7/4ix2a3bt348Mf/jAuueQS/OiP/ih++7d/O2nP87nCK71XVlcxm82q77zHgQMHAABPe9rT8KlPfQqXXXYZXvnKV+INb3gDXvWqV83/YQ0Gg8FgMMwd5tEqwM6dO/HHf/zH+IM/+AOsr6/juc99Lt773vfi3nvvBQB85zvfwW233Ybvfve7OPzww/GKV7wCv/mbv4kvfvGLST9PecpT8IlPfAJ79+7F+vo6/vZv/7ZpO2nXKdi9ezcA4O///u+xvr4OAPj2t7+N448/Hr/wC7+A173udZ0+DQaDwWAwLC8eFB6tZcATn/hEPOEJT8AHP/hBvPKVr8R1112HCy+8EECVuP5Xf/VXuOGGG/CGN7wBk8kEa2tr+NM//dOkjxNPPBGXXnopLrzwQpx44ok477zzsL6xAQB4yc++Cpf8+1fhyU9+Mp71rGdh+/btAIBPfOITeMc73oG1tTUcccQReP/737+5D24wGAwGg2EwnPde/9WCcf755/tQlRdw3XXX4cwzzzxId7R5WJ/OcN0tVaL/ObuOGtTHVuGVwWAwGAzLAOfcbu/9+SW/tdChwWAwGAwGw4JgipbBYDAYDAbDgmCK1ibixtvutRPgDQaDwWDYQlhqRWsZ8sfmifsObGDPnQ/Mtc+HGo8MBoPBYHgoYWkVrW3btmHv3r2mSAjw3mPv3r3Ytm3bwb4Vg8FgMBgMBJb2eIddu3Zhz549uP322w/2rcwNt9berOvuOaz5bjrzuPWufZ3vS7Ft2zbs2rVrPjdoMBgMBoNhrlhaRWttbQ2nn376wb6NueL5b6regXjT772g+e6Wux7ARX/5j53vDQaDwWAwPPixtKHDhxq4EKhFRg0Gg8FgeOjCFK1NwoxRqEzPMhgMBoPhoQtTtDYJU07TMhgMBoPB8JCFKVqbhBkbOjQFzGAwGAyGhypM0dok8IpW+/nyr966SXdjMBgMBoNhM2CK1iahJHJ40977Fn8jBoPBYDAYNg2maG0SuByt2KN1/4HpJt2NwWAwGAyGzYApWpuEOBdrxihdljBvMBgMBsNDC6ZobRJiJerAdNZ89tEBD85t6i0ZDAaDwWBYMEzR2iTEzqr9G5GiZU4sg8FgMBgesliYouWcW3HOfck5998XdY0HE+Kqw/XEo2UwGAwGg+GhikV6tH4NwHUL7P9BhSR0mHi0TNUyGAwGg+GhioUoWs65XQBeAOA9i+j/wYiNKa1oGQwGg8FgeOhiUR6tPwLwWwBMo6jx3iu+1XzmQoc7tq1t4h0ZDAaDwWBYNOauaDnnLgJwm/d+t/K7i51zVzrnrrz99tvnfRtLhz//7E3NZy4ZfsWqDg0Gg8FgeEhhER6tHwbwIufcTQA+COBHnHN/lf/Ie/9u7/353vvzjzvuuAXcxvIiPt4h9mlNLV3LYDAYDJuAe/dv4PZ79h/s29gSmLui5b2/xHu/y3t/GoCXAfhH7/0r5n2dBzPWGY8Wd5CpwWAwGAzzxPPf+Slc8LuXH+zb2BKwc7QOAlKPVgvuxdPv/9xNOO1Nl+HufesLvCuDwWAwbBXcfMcDB/sWtgwWqmh57z/hvb9okdd4MCI53iH6fsooWn/2iRsBAHfdb4qWwWAwGAwPJphHa5Pw7DOPbz4nVYeRbsUdqXX3vg0AwKGrNlwGg8FgMDyYYDv3JuHA1GPHtlUAWdVhnAzP5Gjdu79StCyFy2AwGAyGBxdM0dok7F+f4sj6nKwDTDI8p2gFUDlc3nuc9qbL8Icf/fp8btRgMBgMBsPcYIrWJuGB9Sl2HlYpWuvMOQ7a63goRSt4x/7Pf7xh5B0aDAaDwWCYN0zR2iTct38DRx0ePFrT5vvEo6UoWlTzPXX+1pqddmowGAyGnrD37S4epmhtEh440Hq0DkzpHC0tB4sKLd5TH/mwtmJDaTAYDIZ+0FJWDONhu/Mm4cDU4/BDqmT4OHTY58BSKnS4974DAEzRMhgMBkN/bJiitXDY7jwn3HDbvXjn5dezbtiN2QyHHVKxm7Mg9GT47ndv/rtrAAB3PWBnbBkMBoOhH0zRWjxM0YrwXz76dfzUn312EO3L3/N5/B+Xf4NVeNY3Zjh0dQUAP7G1+U4pcaccs73fjRoMBoPBUGNqL9ldOEzRivDH/3gD/uWmOwfR7luv8q7O/Y8fw511OC/G+sxjbWWClYnDdMa861CtOux+98RTjhp0vwaDwWAwrM/oV8IZ5gdTtHrgu99/IDnVPUbsbbr+tns77evTGdZWXK1oRXRJMnz/HC17EbXBYDAYhsKS4RcPU7QKce/+DfzQ7/0jfv2DV5Ht3IuigWoie18lrK8KHq0hB5baGjEYDAbDUHDOA8P8YIoWgf3ROVcBt929DwBw2TW3YO+9+wmadrK67EirMJFXa49WnKMV60maR4tq1s7eMhgMBoOBwyI8Wl+++ft2PlcEU7QI/Of/fl3nux/5w082n3/8/7qi0y7NqaBoHdLkaDHJ8Iphwb2Ch/psMBgMBoMG7k0lQ/GZ67+HF7/rCrzvipvm2u+DGaZo1fj7q77TfP7ynu+Lv735jgc635167OHN5/yM9jCR29AhrRxR3qn4t5R+prUbDAaDwcBh3h6tm++8HwDwjVvvmWu/D2aYolXj16Lcq/sPdEOHGh5/8k62bSMLHU57hA7j+DnVPjWPlsFgMBh6IqS4LCpHy7ajFltG0XrgwBTXfueu4t/2hTRZQ6L82soEK871Ohk+zv2iFKn0eIg+d2ww/P/svXmcHkW1Pn6q+31nS2aSkJ0ESFjCDoKAKAgCsom44FUuqFfv9fd1365eFXG9Ii6goIIbi15FBUFQEJCdsBMIYQskZE/ISvZlMsv7dvfvj+pTderUqe6ZSSYZzZzPJ5/MTG/V3dVVp57znOcM2qAN2q5qce5pbW/B0sGqu77tMo7Wl/7yPLz9ikdhnaBxxa29u97r83dTh4htq5vQoYIoUsxhoqFD/7yUmF8WOsy8Kw/aoA3aoA3aoPkWR9olSgZ1tPrddhlHC3lXWzrLnagNW3tfzoaiVDzmXaOIVqSC2YUSokUdOGl7msno2KAN2qAN2qANWsgquaO1vcnwaIMLf2u7jKNVifStXnDLC3BhXh+wNzZqaEPhdqqj5TtamWlDpJSDXFHnSAo/0tChhGil6aCjNWiDNmiDNmi9M4tobefQ4WDs0LNdxtHCTvX4/LXwp2lLen18VNJ7KPLEY95G3qGiIFIuCkV3lTp8V62Yo+WQ4QdXEIM2aIM2aIPWA4sNojVIhu9v22UcLYRJ+2plYqK1JIVRQxv1vsxhqucxcES0UkeSwf5ck0KHSTGiNZ3UZhwkww+aZLc+twxOu/wh6Kz1Pslj0AZt0P457ZpHFsA3/jYzuD3OozzbHdEapMN7tus4WvG2vfyyzthdT6G5QT9Ojmh1162OVqRcjhZ1uupS6LBGyfB+G2avtFolg/IOg8YtSTP43A3PwZxVW2DuKr8G57+6XXLXbPjO31/e2c0YtEHb4fbdO2bBdU8uDm6P89m/vzha/Wkn/Wgq/OjuV3Z2M3psu4yjhd57X63M0aolKTRX43zf1NsGYLMOnXBhZjMS60KHv+KBeebn8qLThZsHbRe0rSSDtrkh3okt2Tn2i6nz4TePLdzZzRi0QRtwhrzl+j9h1uHCNe1w5YPzynccILbLOFpFoUPJieLhvzKwqLtOHS13G3ZkjWi558ZrN1ZiqAkd/tF5a2ybhEY4QqmDjtagMWvvsojoIEl10HYlm7Nqc4+1E/tiP7lvDpxwyYP9dv7+sjTN4JQfT4VlG3SFk/6odThorg06WgBw78urvL9xp6aoeHOWZbB8Yyc05o7WorXtznYMHaIyvEuGR0crEhEtp03CwqMe4Hv9K9mWrroYVh0o9rdnl8HLyzcFt89asQlWbuzcgS2yRjXhJHmQQRu0f1U77fKH4e1XPNpv5//JfXNhybqt/Xb+/rLOegLzV9s5ima2D1r/2K7jaBVwtKYtXOv9jc9J3OunfKhbZug6iU8tXAcAAJey2DEiWg1xBEqx0GHexxsrkZj98d7XTyRtkmohhoVS/1XskG/dDV/6ywt9OjZNM7hx+qtOVuj2ts//+Tl4288eCW4/86ePwLHfv1/ctnJjJ0y64A6n1mZv7QPXTINfPTRf3EarHGxvBehBG7Rd1ej3+s/Ojd3uWYf5VFv0VPb72p1w/tVPFp4mSTOYdMEd8O3bXtp+bdtJtss4WkUcreHNvkYWdaxeWr7R8/rp9rLimVSwlMs7mNBhNRYnwt2Ifld5Uel/7g9eMnSQ/vps3xyRh+auhi//5QX44V2zt2eztpth37lp+lJxey1J4aO/n16ImD06bw384B/y/dF+Oxgi8K2rnsCMJevLdxy0AWe/mDoPHifUih1lz726wamN21nbOYjQmi1dsKWr91VMuNW28yIUIY2i6aiWZPD4fB/goHZJPmb/3+OLnL9PW1B83EC0XcbRKuRoCT3i908sMj9Lkxx1ijqEtHlaOscIlsYKYp516IQO/Q7vCpJKiNa/tmDpeSWrnjLDel6vrAw7w6+u29pvYbWy1SLqsz0amDBeWbkZ7nl5FXz55uf7dH2atTroaPn264cWwDm/eByeWbxuZzdl0Hppl9z1Cpx/zbQdft125txs7UPJtm21jR01OOq798Fplz1Uui+dNzZ11uB/bnLHklDW4eK17XDcDx7YabSHR+bKY+K5VxXPCZ/+0wy47fnl/dGkPtugowUyd2V5ThQEABjSWPG2lzk4FEkwgqW5vEMikOEbKpHY4WnTpHmyXuKI7Wybs2ozfOT/nnYcz97YM4u3DW1AUb4Q2veZ65+FN1/yIPx5+qvbdJ2Qfa2kCkGZvBvqqFX6mDXbWR8MHRbZsvX6O59d4IgP2r+WXfXwfDjom3dtt/N17wT+6EW3a8mS5T1wgujQd+0jC+HOF1c620Pt/+O0JbBsQwfc9nzfaQ3bYn0drW5/YQV89vpnt2tbttX6xdFSSu2hlHpQKTVLKfWSUupz/XGd3ljRah4RrREtVWiI/UeC4Ss6KdJJS0LEKLG9bhCtCKKIO0+5oxVHYpptWWjQQby8rTvfLrzlRbh/9mvwwtL+y/4pMiwiHnK0/p6vfELh33qSwoV/fRGWrO0b6fUfM1cWbr9/9muF22lBcsnKnGtaWaCvzu6/ssUxOuI7uSGD5tnqzV2wcE17+Y69tO/dORu2difbDeHl/M/fP7EIfnb/3O1y7pD1BkWjY58EOJTxV3fG+v1zNzwLs1aE6RL/bNZfiFYdAL6YZdmBAHAsAHxKKXVQP12rR7bnyJbgtjTNoLESwbPfPM1oDSmSC29QBeKE0TBfkk+GqAwP4GqTODpaSjmTowkdViNZZkLYl1p9gIcOo36qp0Wtq57A0vWyI/SZfGVTJhVTFRxsAIBnX90Af5q2BL70Fzl099rm4hXlkAYfDaV27aPFGk/1EkSr7LlSROuHd/3zCPztKMPQ8mBGZt9t+YYOmHTBHXD/LD97e1vs6Ivvg5N+NHW7npOaRPnoiXFXhTsq37z1Jbjs3jl9bFVP22BbsWC1K0T8s/vnwi0zLOeTdu1YWLBJ9IZlGzrgLmGRmGUZLF7bM+e3JyXhHp6zGl7b5I+htz43sEJ/22r94mhlWbYiy7IZ+c+bAWAWAEzoj2v11BorYbHGJM1MiEky/JAo2kUnuJZGfe437L0b/OCcQ73tNYNKSKFD2z45dFjsSCVpZtCOgUiGx2f3k/v6Z+Dprqew/9fvguN/+GAhYlOG5oSQoTLJjU/9cUbh9jLtqk++ZZ/C7dbJl09EkwRWb+7ytlNE6/lXN3jbF61pH5Ah5x1l/VVYd6DZ6Zc/DDc+3T/h8ReW6n715346//Y2HMc516qvFpJHWL25a5uznZ9/dUNpO9/KeFqX3TsHvnCjXRj+5RnrdFWFBRt3tDZ31uC4HzwgSldc9+RiOPHSqfCcMJagUZDihqeWwDWPLAju+x+/eQrO+eXjwe1Ftq1l9Xak9TtHSyk1CQCOAIAdz1okxr3rNVvspJRkmVnZSpMOfixj2yhiZfdDJOv75xxqBm46QTtZh0wZHlfSITI8/VMI0UK0o7+mim2ZiDd31gAA4MkF2042lhwJmrRQ5BRt7e7b6hXvfen6DnG71CZqZbyooU3FiBfWJ1wlrPoAAH7/hC2x8XNBKZnWN/zUSa5T9/SidfCWH039p5kg+8MwGWEgLlK2p72yajN8+ea+SaT01AaSIO7GjlpwG47RXdspWzDEcTr64vvgCzc+J27ria3a1Anv/Plj8I1b3XqFx/3gAbjjxRXm97I1wpUP2DCmtGDjC/yzC7THsLbuu37+WPFFQY8vF9zyInz3jlmF+4XG1jKLBEdroC4a+9XRUkoNBYCbAeDzWZZtYts+qpSarpSavnr16v5sBgDo7CJqVF8oTTPz0qTXhI7Sbz58NBy553AAAJHQPqShYjpyPXUdrUjpD9yTdyBZh1JRaTfM6LctTTNzzf4If6RpBpO/eiecV5LpEbLtScCe+5rPo6IOVNGVyhyt0PeJ72TZBnkwaC4JDZYJrZaNCxjemBOoU0gdKYm3QVfbnCeHtQ+LVqf/6oZjdcjRau+q96u6+KD1j90niFCj4aK7s57AJXfN3maJhCLUijpE3Jau3wovLff71t+fXw4nXvogzMgTge54gThVaRYci0LWTsa+ikCR4IjcIsZH/X5AOqbMqXl1Xd8cqCInmVp3PYVXGeo2UIHpfnO0lFJV0E7WH7Msu4Vvz7LsqizLjsqy7KjRo0f3VzOCRifeJLOhw7amKgC45GP8kHYf3gz/8cZJAOA6EPhzpCyXhgqJdtYSaMpV47m8AzpplTgqlW8IIVoIhffFmZ+2YC3c81KYsD09/9if6KN2ybZC56/fa4T5ORKWzDTkWxT+CZHJ0UKHbir56IeU1A8sczTLnOOO7uLn1ynIiDjbyaqdp0v3hEPxr242dChv/8jvnoa3X/Fovwrebqutb+8ubN+2LsCeXLAWvnjj87Bha/c2nWdHWk/u+OZnlsIvps6HH9/TS+4iG0qKnn3RmHz8Dx+Es37mo0efuf5ZWLx2K3wipyW0NVfNttVbihF0yei4GAtjaG8ES+nthBzUMmCziJf63Ksb4PD/vafH7XnwFTeZCO/1I8dP7vE5doT1V9ahAoBrAWBWlmWX9cc1ttVoaZIktZP4Ze87HAAADiE1BGuGkKxIaJAKQaZQiRQopUzcmE6wnbUUGiv6USulHGI2Ok/VWMmIVWYng5Aj1pCfu6hMUMjOvepJ+Oh1z8C812TEJMARN9bRncDl984JDjYrtlGDpZWE1qSYfCY4rdSOnqQdtQPHtxVepzPA4dpQ4mjtPry5cHsZ96dsDkSUasrYoeJ2umqUQhih+wKwk8BACvnsaItK5D8w5I0h8IFoR1x0L3zsuunB7dsaFv33q56Em2cshU/8QeYjlp3++/+YBZMuuEP8FmpJWppQUmbS2NOTENKvH9ZRji2dssOwfEOHw2+yJ3d//cXU3hU3vvbRhTDla/8wv5eh3k1VOwiXURUkcxwtYTzvlaNFnuv81e1wxk8edhKRVm3qhOeXFiPkvw5UsQAAuPKB3j1LvvjGvk4T0waC9ReidRwAfBAATlZKPZf/e1s/XatP5iBFaWY64Ji2JgBwB4+uJIWGShR0pOqETC9xtCiiFVKGr0SRLN+QZcbZWL/VH+zrqS1mvS2lFFZslGFeRP5CHffKB+fCT++fW6pDtedu4azPIpv6ig0rK8EjoI6GNJAjwliGSPxp2hLx72XK023NxaHDspBl2SSIocGepLlLyEVXzfaPQfMNV/hlDvGmwGS8sw3f+YOvhOkX2yucwmu4ouHpVQDLQNqGVCbqm7fOhGMuvn+bRD+P/+EDfT4WIMyx+tBvnoL/uel52FTiZPeWf3rR7S871/xySXkxmoTVFyoGXYBLY2hfM/z+77GFMHvlZocnetKPpjq/SxbK8O6L8SQ2/I634yW2i/VX1uGjWZapLMsOy7Lsdfm/O/vjWj0xaQKijhAlw+N7c7IG6zY8hzFuh6OVZMYBkzhaHTR0GIVChwqyzF+JpVkGI1oaoKUh9lYzWZZBmoE597aEN0LzPepADWmUJ+vN+QSUlDh5RcVXz7vqSTjjJw+XtlGaDKmDJ23HQaavooJUB0taJfdm3ON8An5O6fzoqNWSrPT9Sts76wm0BMKbT5pw8K4LaZVxtPC77tiGZIqf3jc3uJAB0P22r45GTyQKypz5epL2yJEPZWZfnksZ3FVAQQDwS6kAANz9kuZSFS1Iyvr9awLKU3TH/HGEFqjr81BpJ3/G2/lzuaWkvFgDyZgvWxCIYxQ5RqJfUOvNHCItPkLvkbbLe57Q9/A2D4XieF92nzvaBpjf1z8mTbIc0cIQgpSF1J0kJjxXhmhJHK0tXXWDSnF5Bxs6lHlWHd0JNFaiXH/L3YYk5pdzYTc+YKC+zRMlNaUAwp0fkZ59R8uhK3SgJJJlTyzLMnhiwdoeKXNLjgo1KXSKz/qRuWvEe0Q7ZtJupdcXdc56MUB86k9+6KVM+Z86qNL1Tz5gjPl53LAmb3tXLTWOODdEuka3DiyYfXtZT94Nfvc/uU8WmMQz9LWw7TdunQmX3zcH3vfrJ4L7nHf1k/CGi+Wi42u3dMGX//J8sO/2JJu2zNH60T1z4KQfTS39vkKO1twA7YBbVTi+zNEFALjm0bA8QNB6MW+HnAuUBNpe2YkhQ1pJyBrI9jJH614hCaDuOFrFbfncDT1XVMf5piffGd1F6st9TZr6/ZOL4N+vst9WahCtQUdrhxv9UPYf2woArqAoJcNLnA2KaFnyrFusFx2NihA63NRRMyT7KHIdJuxfoVIx97y8Cmav3AwKfPLyYyysxcnQWCyXSiBQo6uMC/8609v+ClFLD30GGNrrq6bJ5K/2HOi89G6XtMo/WOmDpwNTEV+sK7CqHTnEFvWWBgNHUFbYPrzFElk3CyvAMkHa22nGUWAyOnTCMBjT2igOwp31BBqr8mc+Mg8H/+z+ucFV/cNzVg+4umE9NZrxFeLslK188Zk+tUgOD53500fgpwEnDcBqSxVlYD21cB1sDhCLL737Fbhx+lK4LRDe6QldoGwOw+y2smw2iUi97abP+Y8Xw2jY9nZ0+OMI1fpDB6c3FRW4eGhPrLXJjhHSN9wY0G+U7NUSqYRQf8exq6ySBb06tuWaEtFlui+A/Lx7o2NHaSgzl21yQrf1QUdr5xklVF/63sMAwJdnwEHEKEWT996dpFCtIGLlO1IiR4ucYGt3YhTnQxytauRfF4XqxrQ2glI+2sUnfj7oNuUrstBqmCJ9a4RsliuJLlPZh9AfUC0nif77MXs4v/P7LXOEpMkWv0dptQ3gomSSo0ObIF1/bKtFmaSsLRfRKuMJ+VyRJM0gUhoRlQawrloSFOulIOT6QEbZf/zmqQFXN6ynRp93qP9ua6+dtWITXH7fnCChe1vlTbBLhPpGT85e1q9wbCoLX0q6Rb0x+VHoP36rADEsCruGLJRR+/snFnkIVuj5YDg1JEiKdsgEm2jzzit9fak7icMvFbcfRnie1z/lc0WpxmjZOFySXB1MfMF+WjqMk8v3xjmi+0oRplpZ6Y7AubihwzgYOtwJRgcIfAF0AEwzGjrUf+MdgyNaTq3DPOsQwIbQeGgR5QUipdzJm8g7YFvQMOPtC6dOAcVK9/A2YjuprcxFLhsC0HQnkQ44YFyrt53qtzw0p1jrLKRcTo07RmWZQfx+OImSOxbSB0gdYun7HD9MZw2GsvMS5lBzo/cg1aqk71MK89DjJUeplRQ0l8JX2Hd+sxbJAAAgAElEQVTjSDkoK1pXPXWyltxr258lsdeXl/9z1xqjd/S7AEF3exHFQ4hMbxL+xIVA/upCp+lJ2OaFV4t1wFqMo1WM3GyrEvdKQXS3J5P1jdOFzD9i+41xaQ2dtQS+crMt5k4rIlzziI++lLWB1y3kpH+6kJGQyU+S6hF0QYOh+70JLUPim9HmlWWWlyE5EhkewI5dIaJ6WXm4MpPGRuf8JRU4enoufD6DiNZONnQIQoiWES6lHK16ajpgyJGyHC0/tKizGvVxEZN3wI6B7XLCigQGjZQ/2CJSNwnrOLIdcPU0ts3n7gC4zsXx+44S9+mp0RBZyDhxt2yAw5XnWYeOBwB/UuGOm0RYLkO0cPv6djmziOqUiaFJcs6y0KC0MqbbfzXVz8qiz0xqY5plECkFlViJgredtcQgm9zo3lII6p+9CDXNtrzo9pfFfbaXInxf0R5a5+3mGRIpulh+oifN/8C1xUU5mgscLYqG98fktT1KH50wxeowdtUTeHaJKy9wMVEmHyGMU2VtQMI+gB4DbnrGzbDuzT1QpOWg3TUSRt+h9B3ScafMsS7rh8FSY/l5GwOOFraLIoVP5yrxPTE65+0ucEnLEDUqQFv0vE3W4SCitXNNIrMnKXhkeDembHWwJEcqSW3WIQ5GNR5azN87Dx0aREvgaKXEO1dM6BQAYGijHjQuOPNA71gAXZcKAGDkEJnsvIhkGhXpLRUZomUbBOmJF5kSOQ9hhrgRaOiYHLvPSADwV3M8BPCqUFia7iN9n/jMQvo0HbXEoJESokX7iRR+LRuCKVK4nHFk6knqVhgQVnK4SKhGcgmnrnoa5GjRxzlrRXkyQn9YlmWwUeg71L7xt5nwlZIUeMl64hhsr5IdfR3Yv/13i1KuEDhSeAuhZtKJT0q26IlhskSXQDHARBuA8PPsq3QLQN9lMzJhnAQA2P/rd8F5V7tVLCi/bgThXErHl9mdM1fALcwh7o2jRbsJfq8pW9QXta8sFC2FzCaPGiKeixoi2iGu4LZIBwG4KJSkPVj2DKk2V5JmsM/oIeJ+eJltDXNvb9tlHK2L3nUInHHwOIMsOauEzOpo2SwYeyxFtCSdLAfRCiFmkT2el+DRiJXvaOE5IqXBap+jpXsVOoGh8SL0cdFBrqx4cpnRIqZof5zmhms4qbUsLo+DTlMFw6rudv7xS4OUK0wrIVr5sUnqOYJPLVyXn0P/XVpN0kcrhwa9PzlGS+vw98QHVf6ONnbU4MkF6+CpRevy0GFvES27///c5L+/nqR6v7R8I8zvAwEY7coH5sHh37knyHHqrqdw3ZOLS3XaJOvJ/PfQ3GKdtJ76T33V7aGhHIm7ooyjJd8MvUca6u+N3Z4nO0j9p40QtUOOVig03Z9Gm9obX3lEi+Roub8vXtsOtz4nSy5IDkfvEC37M37fUjk35/w0dFhyLckRoxzl0JBbJpq6rfNDKUerF6XK6mkGQxtl/UIbOuxDI/vRBlhz+s8+eOxe8KsPvj6AaPmhQ2cVkdh6gqIjldjCzvg/Pz9eVyNTQLbp1bASSPj4c5QjWryrY+c30hCBew8N0mUfeJl11pLCyZhzw7gjU/bxIqLVUIk0EuiFDotDiQAAa7dYToRE9qXPhof+eB2yMkSrLHRZZnxPfiy/P9q+aqxkMjxDtJxwoNPX/GN7oj121s8ehVN+/JC4rbuewrdunVlYu+zHuQZTKHRblm5+1cPzYb+v3SlOFPSeQoK7z5fUeexpSD3EfaG2WBD8pKn90rekwB8XqJUhcv/1f0+XtgsXXGUcRMlJoW0L6bVtD+P3GUK0ykxqIx/7Trx0KnzuBrkYdGtj70OP1OiuOP6V0ht6MU5LeoZ0nA0dXYaUmdBhYLey+oQ0GiH18/IKGu44HWovBScGku0yjhaaJM/gkuEFRytNiSOVhwZ7wdGqpy7ZnqNpUURDBP4AEinIsw7dzmUyFmO/zdRCfdhxtHowWPHrl6X9c8eDc7TKVlH4QTZWIo3YlDge/ONbtqHD+dtvH1vkXSPN7IqPZ/V1MgSujBAqapEVPFd+//z8/HL8/iokHakSRyJJlCNa1KGhbZO0tLY1rf4vzyyF3z2xuEe1y0LJFEXp5vUkhe/dORtqSeYUzjVGHteoob6TQJ3OUC3Mns7hElGc61JJxbupoyVx+PCb+dZtL4kFiIvmpzTN4IHZr4V3YPZLgSNIvzmKjLht0Pv0NrzUGw06/i3SI3vjaIkcqF4cP0RAUnqTMUevj9+rq6voH0PbV1bepmwxWBY6RPkjbjjflWVgBttFnD3J0SqtCZtvxoXRS4FEHUq3GUi2yzlaZYiWJO+QZEDkG3xB0iRNzUQhcbTSzOVw8fBgrGjoEJzjsE2RIO+AH3i1j6FDOjH3ZKzh+5TxUsa0uZM3Hywl8jY1nAgbcsFWPjBzxIU7LmsY7+rA8f4gkmYZDMuLtnJEiz83EdZPM0O6llS6i57rOiapwPfl1+P3RweTOFIiQthZc7MO6SCXZXaFP1xAK/qqpo8mZUGGrC+hCSrmKvGL8P0dPWmE6ARffq/NJgutqMt00tCkgZ1zheSMLnuclFBCa+1JdfeKEK2eOgA4ua5tF+RHSuRLdBvy6yWZ10dfKHAMykrbUONotIsC9fg0Ynh/Wwn5W7t6zm91KpIIocOy0GQZUFMmnRDqL7jPgeNbxYSBWj5utAc4XGVOM70HyVkry0rEdoeusjbnxw6S4QeIWUTL7fCRCe2BsJ3IN/SBo1VPUiuIKijDR3lWIf5O26XbpECBT4bHlFhbC6t3iJZ0rSLjiBIPDfKPePIoP+2aGh+U+cfaZThaschBqrEPlofOuOMkhdbSNDM8FF5cto2t4KWJpjtJjaMmTeZZBvDWA8cCAMDr9hjubadGBTaxbUXtpyhKNVZi+7rqCTSS7DuK4mSgp/lTDxoranxRp0xCH8uVxHs+vPSFbEsV77kTD2D7fWMlFt/9KpLxl2bFWakAUMgTm7/aDwvyBJEtwiTVm/lA5AgW7N9T57XIIXMpFPJ+C52kGnefIjFXybFD4++COki/fWwhfPy6Z0gbg6fxTArvO/yhEsRG6iMbOsL3wY3285oQOpSvaX8uQ5klR4jeH7/UkXsOd/ZJMnnRhe1uD2itlUVE6HbR0Srpq7aCiv5gDmdjKUqAmNDhIKK1c81wqBjiZBAtQd6hnpST3a0jJnO0qKPloFapTs9XQsgSf4wjBSs3dXp6MogIVWIfDaMWTOlFR60S9Sh0yAcE7mhd/5Q7EfGJoSzrkA/4uH9jNYJYSaFD/fs5R0wAAH9VxOUJpMzKJM0M4sPPj/o4XznjAH0/wjN6ZO4aszIPcbRGtFTh8D2GQ1uzu1LsDS8BwL8/iqJUhKzDLMs0ohUIT2WZduLbmqriQEcnnQ0CB+O+WTbleu4qP2uROhFlK15pJf73ktA0vX/p3SLZv6ES9YjELH0C1DkKZaYC+FpLAH44dFMJj0XqP1QjSvpGi0PTPfNAivYr43Hex0q+8HsociQl5x6NX4qixf/795edItq9yRy9XwilUuchVDg71C4A7eRjG8pCVjUB0aJzBR8jANz3Hqq5id9Xu4CuFWkJYiiUltOR7gEXMhOHyxmmvUG0ugPjcJHhZryXU0jpMQA71tMo0ECyXc7RioNZgVzeAZztPDRYqqNFTkBL/FRiJZb/wevSMcMS++R7QaQNCbP0WNrx7315lUgEx/M3xlGP+BJ8POOO1nOvuroqfGLwOFppMSKFH3djJYbNXXWPYzV9sc4KPPfoPcTj+S1J43EtzUxIx0MM89/REeMTEg5OuNruqPmO4oqNnZqjJ3DsyibCeayGHN+fjiWVyEe00HmhiJbjyEMGKj9WGuhoWSJpUvzfv1ttKklag57zVw/7/B9qEjp2bUlpj/K0eP1/YyUq5a4A+P31tU2dMHNZWN6gbIKvMERPkjKgZ5TCWqkzyUoaS+Hr84WLlBCQZVmhA0mfyX2zfCeFyj8ASKhdeMIrAjH5u8FnI/WT3nCspPl31SZ7/2V1B0PXwvaWFvB2uLvWuUET+zFNuAlU+cD3VIbYzV7pvq8hDdrRwrGlnqaik4ILmX3GyLIKeP0JgnQDPT+AjD6Xc7Rs+wCkRCuX7zbI0drJJnK0MvCU4XlYDR0hi4hxHa1cvkHQXKKOXBwxwdJUO3fSdS0ZXu40iLQZIj3Qe7I/L1jTDh8jUDvfp1qJejRY8cHvN2wi5N+KT+52f1/Jag9yRAZXKaGiyFgIGFEa7ojw63kcr3rKOEts4k0tIiKdj8P0fBC8+uEFAADw12eXBeQ5ip/5Jay2o6+sr/8/fI/h2oEPOqqRd4z5Wek+K4ZFybNZF8gKRGuu+iRhSp5+RhA3pJOClOXVHHjvaBKfkRq+z4ZK5IWZAXxHn/fX5ax/8sG7bG1SJYuzhjiSswrJKSVHi76XjUKIipeaod8Q7w/S3NNVT4OTN0A5UsGfO9eSKwIWisac0LcmJeD0JnTYUo3hrMPGO38rq8NX1C5zDuQQlbSFvpM1eUZ0WViNJ2ZR++zJ++pzCOiYdMzvWYUE5GhaqQk57Ibfasg5xusfOL5N3I73kKQZdBNdStPGHso74PvhfEeMfpg5c9DR2rlmESsSK6+n0BBb+QWlmKOV+YKkIUSrGgodGkfNRbQ0VAsBMnze5kCnqechS6Xc/fGa1LhaMt2nGqvC1aVtj3vOR5gGER+Ucf/PnbJfvt0938f/4Dp/Vz+ywPkd+QghnR4kD48f1gRK+YMQTrRv3Htk3h73eCy2PS3Xy+Ltw/Y3CNUAANzVe1M1EkKj9oRanoM7ciXaNZwMH3i+nzlpX6jEfngMHVWKaPFhGBGtNVu6PEeThl7L0relrD3qKElloH7wj1ne36iF6tWh0f72jisfg2kL1rrH55sb4igg9srOx+6ff3Y8s7DMkad80IZKJCrt01N01HzEi07M+7BSMwBWFPiovUYAgBuC5f1hmBSW6kXIRzK+mTuLvFcU8YXc88qIloRU9CrrMM28Sd7h05aSsuW/9zTvQwqR0z9111NPOZ06Yvx98EolEspa5Dw2oaNFxFOlDFruyHDD+w+hvOgo4zfAdbCkxABqeF5Dd2HjTUIcRYDB0OFONwnR2tpdh5YG++JjpsKukSP9qKpi6DH1HLGEdPyUIGY669AOyknODzPOEjkvlXeQTIc0IwATOnSdP2oSEZdytIoGKyMfUTKY7MEUovEZnHKgjqfzK3AI+SbGQeskiNbYtkZPgfq8Y/YEAID9xrZCNfKLKuMj+PY7DoZhzVXvHnkoJ5RliE4C56MgH+Lidx8CnbUUrnrYdRTpqkvMGs3bG3Ik0RFDFeS37D/a2Y73F0cKmiqxtxo2jqqDaGXOz0opkzL9hyfd1S5FYDh6l2UZjCGSEPIEYq+1n5A2LtV1o1aGVPDnyaUMjKNckQtuc0eXX4/XtPMRLfeA2axgMH3/jRUZ0XKysYSQiuM41f17uOAWXdPPqrvT0FQxwgtQHr4u427yiZU7m3y+o85fUejVDx3q/ic5Ab3ws6CepMEi63p78URfhmiVJbxI56fPrJak3qLEjYBwRwupKuH2FaFFQxiiRaWIqJnQaJqJKGVS8ny+d6deVOGYOTSQaBRKisHbNpn2DNHC66Oj3Is8nB1iA6w5/W9RjgCljqOVwJBG+/HpzEB7jER2rzGInnO0DLkQJ0PlbjcdM8WsQ59n9VrOHQh557VEO3iSI9aTLEKbyREV7n/S/mOc/UO231h3xY3nxGcWOh4RI57ejquopmoMB4xr89KOU7I61aEzPnFaR5WXPgIQJomQo5W378oH5znb0XkN8RIqZDAQs0bz83/nnYeIx+OgvPfooTBxRLOn8I7nU0pG1MoQrQz0c1m0VvNenvdKJtnnyUtz/OHJxY6jxMstAbiT9Ng2nx9UFqYp68OliBciWhXdv7kTwB0Rfj3+2fFJnndn/n7p/g0BR4su8CSnho4zEuKFhs56tzMuFTuSAOUIDm4+dMKw/PeefTMho8/867fOLL0uGiJaEo2CtqEwVJnqRW8RDys00VvESD6OIjJ8QeSeR3C2GdfQc7QKFtAWbc85SpmPLhfJ6DQjR4s4UpKkHLYxyTJRpsQAC4HrLF2vy0shMr4bK4WEx+M3MoXNJYajRcABZ3vqbg8Vx95ZNrBas4OsErkFeLd2J86AF0U+OoTcq2qsHTU6aFKyPDpyHArF9251uAiiFSnjgdOPCmuXrdgolyfB60oZiz1xtPDjipQSyfJouFouW93yS+LutmC2fPzVHzoKACxChdZJEBlEAnn7qeyGv4LX/6tcp6xsUuDnp46oZIjyhMpB0AFPCYgWtnd0QLXcOqo5xycghxEpBc3V2HO0OkVEixyfI1o4OVGHfuPWGvzqIUtgv/FpN6OUh42//4/Zwfbra/n3VzbJl/E2PASKtD/LMlNIGvsIbwJPYy9DY/hKn/cfHhqkjnZDxX9/eh/97g7fY7j8jBJ3nApZhY0rAFJyiH+B/3t8UfCcAPabR0fZ64OZvD8ad4zoO10gSGKE2ooOkCRsS9tQpAhuCicXlAwK8SbxPYQEdFMy3heFrURRY3LNbgHR8qSIWBIMbXeaZUTuB9se/o5aWOiQ8o2lNqSpf368rv5fvs5/HT8ZAIijlX97B+WcLny/+D8fU/G82E4+JuP9dxNJoIFku6ijZVPhsyyD9u66gVABwJuUE5KJoZSCxkrkhGk02d0+ymoUOR0fwA7S/MPwBUv9nhoiq9by0j/43dHU3p44WptzWYJ5r20prMSOH2MIkbrkPYcBgEAmz9DJ9NE6AL1KPmn/0XBIXsWeh2Y6awnEkYJKrEvweFliBOauChwl64jo588fCeet+Y6Y/r8aWAF3EMTtkAk+CZQiGqKjJUwedNDFPhJHCqpxJJD9wWxvqsZePxERLSd0qIPO2Pfo+MprD764zEWs+LuaNNJP+6btnU4K+0rbJeOOQhkCSec3is7FAUT1sBylCZ2Pvy8+geL+px2kddJ4ViB9/42VSAwN1hO9WIiV7+gB6In39IPHwqihDYWOFiUbm3OXkP0BAK54QKO0yAsKhQJxseU7WsXvyA8dlo9LAL7DhseVIlrCubAqgMmyLpiEL7hZLl6OIaubZ/iisbS9aSbLI6BJGZ6UX/XA7NfgZaZ6Tl+blqOx7eccrTTNvPEqzcLzAY7t+Hz1mCrcn5nPZD4mpcpIhq/NOEL5PRhpHXSUEjmrEM+LAEkI0cJ5uciZ3hk2sFqzg4zWhdMaKAAtxIOWQoexM2i6fBiKrACAI67JlWoNh4vE1LGWof7dXhfJq3vT6uvO6iZ1pCG+/reZZJvb4UcKVet//qCbck+Poen8h07EsIF3CgAAGGsGaffvhmxvJjp3O/IRzL0LoR38qEOIlEG0hKw7mrUpySs8kZOn33PkRLF9eLzEC6H3V4kVHD5xuPeMeejQyxAjjtT/nDbF+RuARcwqkYJqRfmOJAkdIuJH75FytL759oMAwA8dKmUnJzqJlU2HHN15x+G7e/tQlPTW55Y7beusJbByk4zUovE+fPMMt9BvEceHXhvfX0i+I/g7u77H0cpfB2oRcbSCOtAhRCtJU4hjBQvXtMOj8/wC17UkhSljW2HNlm649+VVDmq2uZOWU/LbTPvS8JZqYQYhTnyhcCo6Jzx7k5+zLKmmCMWkPEBvLMhPLDkxCfnuuR/W2lSBNVu6Icsy4ywVFcGWhGf19Yu/CAfRKnC0JK4szxgMjUO4L3W0EF2i84mEOIVCopiwwiMsoTaWbU+zDA6Z0GZEmtGofASAfU+83yEPkSNW+AiwH/B7xG/XVBMZDB3ufKPoB4rVtTiIlt+5vdVp3UWPXPFIOylyVKdi5B9yDldqOUQA7uRx+sG6s76JFLal2VN1Ezr075GHMcqkBABsGQMAgIfmWEHAuABtAwComtCMvBrGsCs/vrueQjVHq/TxrtVJse5IKd+Ryyg3zs8so4gPV+Snds6RWvA0tJoPIQkP5uTrWClxIqV9Roej/fsD0P1RKmaOCNWw5ipUouLQocTx6ySI1pScjE6365+V4azw8jxFRtEdqQ4lAMDqLa4cAW3/+Vc/6emEcePvk6MB/HXS8Z82B59taCFgzsfmIq+2JFvJdyX6+WIFAY4oVgk0EJJ3WL6xE7Z01mF9riLPw0RZ5k46tNzLXSSMZXWGiKNFnIPTDxpXGPpHfiQXVcVjEEHg5ygLHXKTHJaDc0Sbvl9+HrMoKdB4AvDfMVaHqCWZUEmD7qfvmy5q3XYXe5CUw1QkLdAtJDTguyvjf+HPzQ6i5c4nSeoTzfX2EKKFHC0bOpQqOpjQYJqJqCL2/SzTzts1OR3EXD9/ftgOXoQd7xHnTV5Xs5Sjld/eIKI1gIyGYWqCh6zDTO7KMCaDbDV2Q4dISkeLYzup08kQwM9KTLLMmSjp91BPM5g4wiVaO6V/EjkVF8B3DopCiRe+TSufU3Iz/Zhw4KDnpJMGIjd8ojI6XQGODPIRMLtLWskZIdlIQBxIEkJVQLTWtev7UcpX5KfWHAiN4vVCZTluymvPRQGdJHo9iQyPg1scKVKM3O7z5v20g/2VMw+AhpLQoaTD1kV0tPB1us6kziDC+6fvt4xoTrtdrGR5kIVr2h1JgUvvsrpgMwS5EW7+83TbtJnVyqNZgnTXEKJVqrvGfucTEKLiqOb9meufZfuXk+HvfXmVMwm6JVp8PgptE51sxNAhOdej89bAqk1dns4V2rgcleZlcXD8QmdcCt9L+6Nx50FCVt60j5ZfoY64hH4DyA7DVKISH9LcyyAz55D2ufCvOhowSiiuHrouNTrex0p5iA5aUYWCoHQCm4uoE4F9g4buKGhgjgs4ilLoUCLDUyqMhGghjxh5n9QiBdCNiFb+P45t73rdhPz6un2YuY6SPGj4+ENZhxj6NnSMAZZ2OLBas4OsEhPECR0h6ih5HC3XoanGyltleOVQGBzMsxKdLI/ARMmvC8AcrVQjPtIKijtaRZD9ITlXhcLa1NHCleaX/vK8+duvCVEaw3t8mEjZs+WIUS1JoRpFoPJe6Cmnp26NSMkRoiFZfo/fu3O2OXblpk64/QW5pAsSLznHBtt/Wo4s8lUWWqwUVGIfMUP5gNs+fZzmaLHjPvzbpwEA8sxRt18A6MHvwPFt0NJQKQwdRspC8bQJXYQPgW/TcbMyHTbEjDI6QP9lusxFQaN9LooC3MLuxMkkvfaxhYXn9Gs7hjlG81dvgff88onguaijGBtHy92Hyzvw91cm+PnFG7XIaqhfUARM0w3Kiw/Te+42jpaCk/JMNto/6OLQhFaoPiDZd9kGnfU1c5mfHQoAMLZNT1S8ELtZLCFyEqiOgFaujO5vN/wv4oj6oUN0BIqRJe5E4YSdZYTfIyBaiOaHyiSVALxmkYkoe7B4s+DwcETrgjMPgL9/+nhy7szZlxK9Eb3CElm4cPez9vT/w5qrcDYJ81tHjSJaAg/OhEZlnhyGlNPM/06aqrFFtPL/D50wDBZ8723w5imjzHUBrDzGB47dyzkH19FC3p25vglN6v9DAMTOsl3S0dIChu4KkELSiq3Q6wxOjSMlODxu6NDEzJmjZQVTLaIVR3LmIJWNQHNDh6kOHQr3iHwRHoOXDPehSt2uYrV2wGgKP619V1TChnLIpMGTtt8P7bjPzeeD0GxQWSsJj03SzKlJRg3v/8K/vuj8fXNXHVobK1CNI3jfUROD2YV4j/z+56zSobE9d2vRgqWBVxBHyjoD5B67iZCuFDrESVCRWpnUwTB8hUpkiFg8dKiURswAAA4jGkA3sCxDr800dBgIyz42b40jdIrXDpV84ZPw/uPagttP+fFD3vH0U6Gnso4Wd+SLESx+T/z9PblAE/xpCRdq+F2Mbm10eKFokuPFEWs8z5mHaDVzOlFLSJd7fAlhitjoHMlZzwphl1VH4Chd2TOTHA2jAVb3k3kue9/hWgctxaw0/ffbPn0cPPuNU42WG01uonZi7qBmmZx8gobdeXNnXeRwldbyM6E1Pd6EwvyynpuLaEXK8mL13+2+9SRz2rf7MB3xwALpaabHgwvfdqB7fULWp1xSdKBpgWvJkaJtFMnyxlnMPP25auwDD5VIQUTGPZocpheOjA+JZPj8uY4c4iKPNPQJAE4EaiDYLuloVWJlPHBKaEaLibxDKjhiuuOEsw7jSJmBIczRoh1L5tjU09SDQOlAioiXxNHC8//h/zsGPnvyvoXQN8bpKceEnlLKiKTPy/wsOUqKlgjy26g5WjLHi2Z7SvIONJVaQpRs++3P0i4h8urGrTUYliMysSAfQY9XyiWjt3fV4bpcADSOVF6CRz4+y3x9NQBXvFCqR2hVkiOx/5hyFRGph0mesa51qMwKn04mFIn62Al7e9pDdMKMhLZlWQYdtQQ2bPURggeZsOjHTtxbX589n6ZKBAeMazXPoNRxIB8CPRf2kYyHtj1Hy93OEdIQWoPO5IlTXP2kCnGedWUAazOXbYT9v36Xd65Q6LAiIEr0u0fNPSyxsnBNO9w4PewsL1i9BZ5ZbDNBseZdCKFqiGWyPEXd6P4hkzha2LecTG4yblbJwhbfyd6jh8KIIQ1GNgDbwc+OjyjNMvM9VAREi96PhHiVcRZp8lMc+aV0EuIo8PHGy1Dn/CVy8TTLnKxJSSBb8jHMe8nc86OQMH5bqOP1hsm7BdsYK2X4w+b85vq+WGg1to4nvj98B7ykneYdSxwx/X+Ig8UjVIPK8APAYkl+gXKSyArdeOCOI8YRrdTZTkOLftahG1PHmHcwdMjF59hAXIkicQVikbQI4iiCLAuvynCFtDWAaEkZkZToGxIkxdVPiINVzwcdvNZdTKOGljba0lWHJaygLIW5K5Ev78Dbj8f428XDctkPPQHFgjOB5tSbzHehbdHvKBx+qCWpCcXRa76EA4MAACAASURBVGCyAIDszBj5BsLBou8AHTuqlUUbgTA/H6wBXOierkjRNhNV/a5aCovXutlaRQgq59qNyleneI2H5qyGX06dbxzNGz56LAAAjGjxM2eDJiBaoazPUJvxWZ58QF7ZIHBLk0a2wP5jW73ajA/P1dwhHc5xHe3nl8octRp7/wB67LFp/HIfR2f073ktwDN/+jDc/dIqbz/8Fk7+8UNO6BXHAF8iRf9frfh9BMA+2/3Htebb3ev5z9xvP8qPrCXJE5TbWq1EwMnU6MSiU4TPii9m7CKO8nHDiE0tScWJvlwcNx/v8/E8hPTVktTRtaMLKHy0IaI4gL5/imhxbucjc9fAjCUbvHPgY9EcKv3zMZN28xb+mzpr0NZcNWXTvPvLgYFff9Alu1N5Cz4fUTkl4+xGdgFNr0+rrEjXX9feDS0NsRciNhwzBmwMFNslHS39DvDF6r/RF0OJ01Zw1A0NhopG4751EzrMz8k4Ws4KqIAMzzsdHQjxulKXolk6/GPiJoWtlON4+sc4qev5wMTPfsuMpdBZSws4WNqRxHvnBOmUOJr35uVvaGYkfe4SGV5qv7TiDvkEVKWZDhb++ZWXmRmx/qCE0CIafc+Oo5VYR4vzBgEo2T0mjrp/X7SP0DPo0KESMx5x/397/USDFtL+0U7kE7qTFB4khGS8p5DxbVzy5EO/eQp+eNdsc/9H7DEcqrHyiNrcaDd1EK0gR0t2rEw78/ZY+Q/5nj74xkkeR3D+6i1GMBYnJ3o8XajsPdpmujlFoTFsF0di6TDanHcdoUnF6PDw8lb2GPkeECXhz+SxXHLCKJCzb2xczu366b8fAQAAj85z+wF3WqTQGToeNJkAmxEplWcU6z/c8cIKfd78eSCy0UU4QtToAqRuJvmwIGetHpro9f97jx4Cb2dFqenxmJUX4he2dydOVqA7V6Bz6Z87I44OdTKkRRJAuMZklp9//vfeBjd89FivX23qqENrU8Uby/F2UI6IG7YdQ5fUNL/UBR5wXDcZz8SRlpwk7LabO2siJ9IIriaDjtaAMcmRcrPsCOcBoU7qaLGSNdwhosRX62FD/j927HxgSPX1JERCIibyYtaVWIkqfdRBDH2MaLid1oqTQofUKD+kEggbYHV6Kaxl2l/wQdRJaDC4PSKOUGC1Ty8rPQOuDYRGVZolMjtaHFFnxd9eMaFD+fgDx7d53D0AzZlDgrruk+5xOLk0VGjo0O0/AEiW998BrtJj4drYzy5+9yHmXVN+4KkHjQMAgJs/8SbxnoocrVDxZf7+avUsr8SAgq3FoUPaVeh9SmgxAMDc19zahHx7J0GU+DmpNVYij4PFNaF41iv9rqlCejB0iM+IUgdIg971Ok1w/rfXT5QbWWIG0WLv5pZntXZZA8s6vOaRBXDqZQ8ZhAWzS+98cSWsJw7x0BwRvuY/NALyi6mudh+AK6iLZscvXETpZ/FSLuaJz68M0aL9vpa675Oa0XFK5RIzlANXVIImiGgRSkFbkw3LV0nB89TMFWFHL0ndotjSIkkyytFSSpkxi4fuamkKjXEk8GXtfCWNyVR0lW+tkmgDzwrk7y+USU/lKyRHuUaeP8Bg6HBAWKT8dGgHkSKrT3F7ZFcsaaq1buj25gar0s0dOQ/RYoRxp/QPIcOj+rSbvp05GWvUaLsrgYls1NAGeP8b9jTH3/WSDd2VIVp0VWFKnAS+dTych7U0x0wOfeL+fNChl9iwtds8r4pANjbnoeij0MhQ1pgmo+doXabDZZKj5hQFN6RQux0FaUPPZ2hjRXSGdQ1O3TYpGYCGDs29OoiWRdeMvAN9gpkVOwVwn41ZeUaRR5gFsAkEUp3HH941G/40bbF8s1CAaLG/a0dXXydWslYXNYrtuvXv5MnosXlasPYbuZjrI3NcwdDP5giL4bAFrq85VGFHP8nDNSGOIzX6jN3Qof+Mrn10oflZGj8kC90DOjsJcfS+dJPNMjbCmPnx371jFsx9bYvJHKYTG0U762kGE4Y3w255ltjDc1zEC8AtEcXbGeWIPEfS8J3y50L3uuOzx5NEm8wsqCRHqZboRJlakorK5zbrrTi0heglJ8PfmSNx7V11IwcCAHmGoj6Ohw5R9gIgrHMlIZ0AAAfkyOZJJBkA/6fDra3Nq1EzRLlDVTKwLwMA3P6Z48l2FzGjRt+f4Y2SknXVWJlFY4ijVUbGpyWElPIFlXe27ZKOliKrSxw4HHFJJXCsyHbKwdqSDypDSK3ElobY8J34KoXHpDGmLYUOabz7vUft4bQHz1GJIzF0SJG00ERWK3DUaD+VOi3ly+DAcMPTS4SWuDwJ2nYACJL5AVxHE1fs1NmYvng9zF6pUQmeoEBtCMkW5M7KOUdMgJGk1qCTXk9Ch0hsv/4p4R6VIOrK5jM+0XKTnJ0tXbY0lCQK2k2IodJESycrO+HY4zOwGmMA7rOpJ6lxwmgyxPVPLYF7XlppHDbp3f1y6nwjrSEZT9GXEgEAdOZsAxmQyzK/HESL/D2EqKJNGK7DXxffOcv8beNWP6s2JFwbRwpWbuyEx+attbw48lX+5NzXeY52qH4mdSioxp/kLD610JLZpfGD2rW5gGRoMYKSAXTCRp04AFs5YytTNq8nOvOXjhFuwo7mr1al2RGvXYho6WMlVX293eWu0Wc8ZWyrw50sSv3vrqd5iA5gVD4eYJIGgH2utRJHABOUOKKFWbzd9dSRUaGL4Mx8r3rbn/7fsfDVPCM4STOoJyks29ABKzZ2mOOlb/fkA8bAmLYmWPSDs4yUA10A0r4Z5fxS6ujFkXIQWQCaVWnH5ENICauH89qnEkeLCoRL8ynVIKQcLQxLO+0IRDnqifv8B5r1m6OllDpDKfWKUmqeUuqC/rpOX4wSUyUdrQVr2uH2fAUiI1qWM4AETlqNvKkaG6kETobnxV8xrGDCG+SDoZ1WWrng6orHxOl54kjmd2AbsI6g/4zsHyUWGH64933hRHP8zGWbvP0AwmFRAJ2GG/osaOj0DbkeTgjVqET+qnfiiGZ4+2HjHUeLOpuVSMH44e7HTAeYLuJo4XFYmoiXW+ETHSfPln36kjNMHT1JPsKEDqm6vuOoY9uAIFrWMBXbXttuo2FdOph/9ZYX4aPXPQOPz1/r3VeaZmLRXG58zjSLD/b+Zq/c7OjQ0cG+zKTQIXWisZ0n7T/aC0Vs7KjBrc/bcj+ImnzrtpcAQGcMXv3wAucY1Klaul7/T9//GYeM98jwHKn99tkH5eexCR947zQzN3jr7Bvj598zF4IMJYw0GjK8fAF0RvH+aBsrUeRcj14Dw/8cwaMlknhGK72PEKKFVmVjm/OMlSubY0KxwvVqic1KPOXAsXDFeUfAF0/d32zH09YDiBe2F79ZdBz2HaP1rLBWaC1Jnfulfd9+r8rfnmYmgQK/Pbqd0gSwUDM9l0WcMm+8r+ThSyovQRcVOhHIhjel0CYilWkqcLRi6yjjYoYuNBqrlmpDOVqPfOUkeOprpzj7S47c7sOaHLmmosLiO8v6xdFSSsUA8HMAOBMADgKA85RSB/XHtfpidNIyA1Pg5UiroCrpeNhBmskqpUUKHXJEi2RJuDpa9tpp5iNhFA1AR4QjVdc/tQRmrdxk7mtTniHGxQpR0V76cPBv3zq7+LVR/k/IzCRPmmkGvYLQYZLZyV7iEVELFZXmq2UnPCZ8tNThphwtey96O82AHNZcNffIM4jMeRmiwe9DIsNT4mmkFGzpdFeZXfUEKpHOSMMswAVr2p3j8VjLVbHnt4hW3iYatiYDnuQEIikZFMAXT51ijj/7ikehzEKik+f+2hcgRdVvWn805GzTsBS9TyyY/vMH55m/HXPxfQAAcOSeIwyBHO3zNzwL37z1JfM7d3DffsWjcPGds6CtqQLnHbOHsy30KXCOFp+sUTD043+YYf5WI6FD60jL9+5NnuQPE4Y3GxQm5GiNz5W1Q+8GUR6ekICOB70+Oiz3vLQS7nhxhUNfANB6fAd9827zu4Ro0bA35V8eNnGYCYcB+OMpfcaI1gBg1mHuuAroWnc9dQoan3347qLyPi0LRi1J9XhTTzNoqERw+iGaw4ioDM1ip/IMlHtHHR2+PQlc14yLme4bdM7AZ0CfS5r5fRTlM/D6Sik4ixD+Y+fbKx7vaVYjWoMwNtP+QBEtytGqxhGMaW2CA8a1stAhc7SGNzuhw10J0ToGAOZlWbYgy7JuALgBAN7ZT9fqtWm+i/7ZOkLyvhbRkgVLTVYhebfNBNHijhznFKDSrjSQ0k4rkWFRh4pP2l+95UX49UN6xR3HymQOXfnAPGc/ncosI2I4mR2+x3AxtfmZxevz+1beIM+tiKhN5R28NqTE0cABI8CHrsS+zhV11NAsV0GHCbijRbWWuuuaGOoej6Eh14yEgIHoGaKl3AmbhzklMjxdvd08Yyms31ozwoQAOusQJ4P7ZumszCsemGuPJ/xAbK8TOsz0faj8HVKkqKM7MTwsfPa/fWwRcFOgjDhgkmYwt6R+ob5399lgEsbyjeEi03Fk2/fychk5HUVCwJ/4o3VYUOfq+qesrhQuPtqaq97AvWitKyPSFcjg29RZ9/pPKNupLHQshcasRlU5osW17ihi8NgFJ1uenVBrb8/dWoxDEEK09ho5BJqqkSlrhcYzh/U5Uph0wR3w0eueMfxVGm47+0rXGaeaSDbSoH+PlXIWrrXEDd35HC3WfvJcigRLRw5tEOUfcBFhBDNT7Vje9fk3O8cnaeaUh/nOOw6GZ77+VmcBDqDHb+rAYZ+f+9pmx9FBi/N7be+ui+Fm++2lhFdJHC02r2TC4hIX63YuU15mowEmUlmnixr/BGjWIT0nWmOVhg790CwFRqTQIUU8y2pN7izrL0drAgBQtbyl+d8GhNFBr0jgLMsyGdGKi3W4mhuE0KHH0bJk+jjyVx64Dc8r6SyhDtWQRrm+F94Xju/0Fl9cuhHSDOCJ+WsD6bTFSB8KI+pJXN5neEsVDpnQJk4SlBQZWiHR1Ql+e+HQoV8LUFLW54iTt52cn4buuPEm+6FDf3/6N95WCTVKM3/QWr7Bhm666jYMISEW9B4l5fiMtBtDc39+egk8Pn8NtHfXjZAttuHKB11HHe8rFJoOGQ/9bWF8EMkoGf6dP39M3Acn41dWutmEbYFkBwA9SPM+znsjrzdKbXqOltF2ipbpsBu+n+delUvhUKOCtDT83llL4Ir75zr78v7HnQmT3SU4dGcdNt5kokk8x2P31uKVu7U0wLp2V4DWkOFJR+XfaCVWhSgDFQjl3Ngo0skqmzp0H+GhO8vRQkfCPbe5bGYz06TQ36kHjnWU+NEQ7bShQ+1YUqoIAIYm7fuqxBGMHNro9Yd66oYOj9xTV2PY3FU316BzCZYEuvCvM8V2W6TfPgOnPBbrF2nm92+MBtiwM7sGBRZY6BDr5NLnwB05CdGiY34DqR1cF3S06CI6SX3OMA1N7mqIlnSnzieglPqoUmq6Umr66tV+Jkp/Gg3jSBwstO4kFbfrjuem5DqOVtWuwLiuUjjrEJz99c/2uiJHK9Xp761NVXjTPiO9+lbYVkNcBgUnXvogfPHG52HaQh3nn7Fkg4hI0dVNwUIclAKjk8VteHMV9h41VMw6lJBCbnXqaJaGDn3tmhrRoUIoHI9/Iuc58HunbSwKHXIz4bfAgM8zeXDg+vCbJgGATIZHFWbp+rUkheueXGwGUAmxcOQd8r+5HC476mJW41dufhHOv3oa3DJjGazN0YuiVGkF9h0e8q27g/uZa6aZIyHwkeMnm/Iykn3lDD2Q94QM31FLIE0zOP0nDzt/L1rh0soFIZs8aojz+4GEA8NFdLGF/P2jTMI1jywEAICfMUdJlB0hCAxFrK55ZAH8+N45zr74ipCszsNM+B1IEhnrcp4p1wdEG92q0a5hLQ1OSSV9Pj1+0T7C5VLiKAqS//V2eyznWsVKQVtz1RQQR04YGh1PkzTzFNmpWHKdIITcMqDPm8gnkOeOC28pUzpJM4PKNAgcLADd99PM3b7XSN23kIyvr2nPi0XAn1m0Thx78FGkmQ39SYgW1eLi50FEKKRMHzFggh7/0RP2cfZNhShBNZYLqqNRTpskaUST07Sj5x5PE6Gk4weC9ZejtRQAKHlhIgA4FX2zLLsqy7Kjsiw7avRot3RFfxvVySpSkq0nmeho0QFJ2t7SEEM9//AoBE73o0Wldcd0oX88N55Wmogpj6a1qQIK/GKmmD4MoNWoF6/dCjfPcAsGi8ry5qP0NjmmlOtVUzTB8s/07xJHK5TmDoAwsvvc8P7WM66IFDqsEZj+lFzdG+/rA9dOAwB/EnYcrbpcjmPmso3w1svkyTwYOgRwlhoYZkCxSo5o0VRr7zygxWABbFhM0rqSlOHd0KHVvJG4fih6WUQuVaoYreD2lh9NhV8SLaU4UqZot2SozyRlXXqWAfzuiUXen4v0t6gGGhq/XR7KGEoQZF4KJM0yeHzeGieES21Tp1+S6Lh9Rxpn89yj9oAZS9bD4/PXiKHDLNOil9ywzT/L6QEc/cB2SseiYULJxo6ak12L99sgLGa6cyV1+sy4sxOr4u+cIuL1VIf48N6jSGlEKw/1It3BnJtECKisBJrlaGWiI4WWZplXTkhfPz8+o8kJCkYNbYRvn30QXHm+FmpNKNmeOmqkbxkdL+Ioon5ZZy0RHR2sQdnaVDXf82dP3tdstzpYNvrCxbfx/qTIBp6jTkKHOObc/fkT4OfnH+noRkqLv2Mm72ZQT7p4Q6tWLOIkycE0ViyiVUt8RKoSlYQOCeK2qzlaTwPAfkqpyUqpBgD4dwC4rZ+u1WuLlK07xos+U0uyzHjKjqMVS2UT7HEY3+6oJR4HjE+oHNGiU4mbdZhnv7Gisfh3BRq54gvSWNkCo3QApFwf2dGy247aa4S3nR5Lj//qLS/Yc6SWiB0pl5ck8Qm4Uc0Yg2jlH9xXbtbXwZpcUgkeqokTkrjAj/aTb9nHtBlNCh1mWQa/ZhlntH2h0CHtcwAW0cL3x9tHn79k3Sz0WBVChz8jnDxb69Da+vaaCYEU6VQVIUIKIBheRfvth48GAID9xgz1ECCl3ImJ10E0Qr8qXAIJrZ6mIn+LO+CzV9p9OL8oy7Igaok2nEibcEc8zQDOv2Ya3DfrNX4YAPhQ/8/PPxKu/dDR0NwQQyVSMKq1Ac75xeNw/tXTYHHOFaNZpUnqC2be9PE3ev2EOzYtDRVoa6rAygIeXCUXhT3vqifhq7fYAuv4TUhaYe1duggzfWY88zTNSr5zurhMMnj/NdPgw799GgA0mby1qQqbOmoaUUoyVv7Lfjc8WQSAZjwXyzukmSunYY+3jor5ZvPtHz5uMuwxQmdzpgTRckKb5FJUhBXNzBXdif3mSfs+eOxeAABw3jF7mu0HOFmF+bmzTFRFpw56xv6GphepbtYhgA6bnnXYeI0oZXZc4kEIpw6rgGjR0OGEEc1eHUWOaHkcLRa6lEKHVD5ioImVAgCEyQvbYFmW1ZVSnwaAuwEgBoDfZFn2UslhO8xoGMfEtSWOVio7BHEUOdXO9d8oopUXae62qxQr7+AiWtgxxKww0mkl9WwnBT/S+3PCbRwrk9pN7eo8hHHaQWM9J3N9e7e5ThRZaQVsnytm6pLZ6aSmHUX9c3M1dsqC8OKikj336gZH3gDAvg9EcvB2eQkezAAytQIZWZi2HwBgr5EtznaaQUQtzWR5ATrg0XahzXttCyxc4yuAG8SO3R+mg/P24m/f+NtM5+8hiQQ0i2jZ7eu3dpu+URSaK3hFoBQ4SteSnXTAGNh71BDYf1yrR5aPlXImttmMX0U5imXq10maeYjRlecfAbNWaMcKhWk//Jun7fWZ2GZIK4sadWY5olUmGBopBfNX22cwurXRTLY0sxIA4LI8PEgTVtIsM5IGaIdNHCaqcXNrqsZQI3QINOzjGztq8Luce0mNag1yUvOaLV1eaLWTIVrPverX3nP2J45ZLU1hGtEHGzm0AdqaqlBPM+ispV5dWRohQMmIE6eMNokcdFyli+qPn7gP/OohjaxGKg9p1xGxkkKHFJGSr28cNUG5HcDyUun3QhflXEeLtqVaUY4jdPm5h8OC1e1OIouoCSmEFvmbQEQoJA1SiZRx4iT5hDhS0FEjjpCHmNnjU2FMrUQRdCQo7+AX3Y6V5Q5S8OH2zxwPQxor8LP75zocrbgAPd1Z1m86WlmW3Zll2ZQsy/bJsuzi/rpOX0zHnPXPEqKFmjZJJsOxWrA0dY6nnQ87Eo27WzJ85ByXppmbhkzGMZ1BoX9GCHkFWZHWSadSoD9ECbEp6nifPGlf58NYvbkLjrjoXvjJfXPze9UN+O+3TjHXvOQuK0YZKRcK39RRg0/9aQZc+NcXHRiXquXjeQDCK118PrjSwfOgn4kDVBcJB1AntJvB+JJWFIAdCBVzdLqFQRMAxGesnwO2L3e0GKb1cj7ZU70Y3T4ZcUNVcl6suawMTZk5qBpxRItCc4WhQ1DQ1ly+XlNKFgyNI+UgAA++whEt64iiaCM3LDtTTzOvkPIbJo+Ez+d9F5FZh2+plMMxXNfeLXK2PvymSYZUT/km6CSiXlIoKxZt0dp2uOj2l83vtM/qxZL/kKqxJZtnGXiZbJwztGTtVkfmAw3fwfE/fMD5e1O1eBpITF/1yyAtXrsVxg9zw0GSllpLQzhhhy4OniZOFoAWUkUHeVNnDWqsBA5F+tFJ/sCxe8GvPvh6AHC5iXTMueBMS+LGrDqTNUi+eXt8JpLlcXyoJzb0SBcOXzrdanHhs2trrsIP33MoAAC8MV/EdtQIosUWsth+mpX47iMmwhdP2988g3pKaS4+IpcUhA6R42SjM74jRYtGi44YQeJlxIw6Yv75KfBQTIa3iNUhE4bB5FFDoJEiYkJocyBYvzlaA9k0wduG7gB8sjtAvgowH6cs75CYVQY9HszxXEeLI1q6Y9rQDl3R0045vEWvgig8nqQERld6EuXjNCorh4xn/WFW27wceUCHBu/p0blr3HplynVGuuop3PHCCvjTtCUOzNtUjaGToAV1Aeamxid9nnXIYe5qvtrG98rDACEyPR7P1cORLL9wtTth0RCEex57/JxVm+FCEnoBANgn52Lh9W37rPK7fH/675/JeRmh9Psykzha1BGOVFFR7oKBS8k6SGiNBJGUZEK0IKXtP0+xida0L9Joz5EX3eud45L3HAZvPXCM6ABrRy6CI/YcDvWcEExRKI/InaRiFi3VwXIcrVwTCUPPIQVztNtfWOGE4scS9WtK+qVGQ4dbuupOTVJ9nDt5vv/aJ8VrI71gBQsfFr0/AILqCzzIeprB2LZG529dgqNVjSNH/4raIRPaTImxmcvdbEzkaAHogsKo/YfmIlr6utSpo9+lJE4NYCuFmBI9UugNSJ0+sjCgIXsJEaNOaJ08x3OP3hNe+PZp8Ob9RgEAQGd34o1p9Gcs9UbbZJ9RHtoUQpOInm3cWhPlI/T+nAzvbNbnN+OWr+cVk4zvNPNrHTqOVOY/f+2oWY6WRIa3wIQfuqQcr1BR6p1tu6ij5cs7OEroBJ2wKbP2eCrvYDs/Pb+dNHnokQuPcsL4pXe/Ys7jyDtwDlB+bjyfAgDIJAdFFiR9b44CHLz7MG8bNaxDhs/Em0jYvHDCfnYwRScSwM3EzLIMHp6rM025KjR+UL5DJDtKeGc4WSdkQADwESMefsJQFnWOAcBUBng01yCziGPmnAOJqRFxBD/xh2fgwVfcTNrzjtkTAOxgi9pMeP+mXwScnROnjM7vy33+OEGVDS1EScv8jaZSx5GMRAAUO1qRCpeTAQC47dPHm3NIaA/e9/H7jiq8dpxPJkiKfs+RE83xUaSgsRKLDjBOirjq3vvCO50izrwElSS4iNfnaCcAeKFtRCyLjHbBfUbbTOFYuaKm9B6Mw339s44yOAAmO9hGr9nsJorY/WRU8T+Pm1zcXmxHTpngPDo+cXMyPJqUYXzClNGglILTD9YCn9I7xDBgZy3N5RUIokV0tFBShyJ+lqPlj8VokcprIeKYQUN/xNEx8hDkPnB84YKn1A6fOAwayJyB129rqoJSyoyNkiNEhaxDjhCivbQaCNqYPBKyektXODQY60onIfFuRMwAZGV8imhlmZDVGFkkNBNCiw5wISBaVSL/gPMltYZKZMbTdNDRGjimyKAvpcTaSZlytBiiVVBxnQp0csQrlHUYIqRbxEH/DVEBHnpSOdna5yDJujENlQhGMi0Yel60xqqLuHAeDx8XMYQC4MK8NHT4x2lL4JK7tEPZzFbT3AFGWQbuKPFBCQfcunG03DCAQcRYg+et0o4WJ9vjYNnAHM2UOVrHTB7pHJ9mvho9gKvwPHPZRnjHz7VoI6IrHNHCUif2/vIQBfNWPnvKft61JBMRLbJ6jJVyQrsAFg3kf3fPq8TMTDTse6+s2uwULUfDfh0aHHnoEG1IYwxvmLwb/OoDrzf7hdAg3N5dT0XEl87/nJd3TE7cjUj4xA0d4venf+claiQL6Y0p5X+/AHqiKaF+ORZ6Xwr8JA0AWz7ssInFiy7Ng0y9zE7+7kIOu9RNED0yDpOw0GgyjlZiBEPRaK3AQkQLgCA+PmKSpHLWIHV00FmgxxuaSJIGM6n3H9cKI4c2WESMOZw4NkqIlXH0MrlEDwCivbLmI44vDo3Fc4Q0ohQ8v7LjEqUboMWxnQ8lR4o6YkHB0dSen/N225orpvKFxBFrrNgSPjSBaiDZwGvRDjCKaEkhLPwxyNGKfHkHThDX23wOGOfycESLGl1d8xI9tt24onYJn2h8tYsmiXmObWv0BnpEtGJ2fdpGAIDf/9cxzu94jzR0iCtOSgrHQRSRHfygsYYk1u3iZHGOJOIqEwc7o2nDQ4d8xlLgbMfQ4+vz8M4P33OYcx0aggDw32uWZZ7zCABG4bmeZvDdlqDwHAAAIABJREFUO1427cdyHKGsSLwuDp48dIiD+ri8hEprQJzTOFqgCcy/mDovL5Cbtz9STrICgJ1EVm921cCd84LsyKMVoV0A9v5D58D7n7+6HR4iJXZqSQZ//tgb4dQc0atEstgmXr8SRdApoE0xQ7Te9tNHHHTwoncekrfD0g2oo4XH4jf2KaJIHzL8Rr7xdre8FXKF/HuQ7623xrXcuB2xx3CRn4b33VjRz3Azy+7jEyfvR9/M71MqIYPfv+FaCe3DsWFrt3ZGXB0ti2QjGb6lar8BCdHC9l71wdfDzZ94o0ESRXkH891bxIs6Ug0kdEgFZqmhsrl0PIBNFJIQKys0TOca9/mgI5OYBCbiaOXUhO56arURRUTPzkn8/HGkSOgw9dpfxtGKHY6WjHhZR8wXLG3Ls0718QJHLFYmSUk7WjDgbAA2qf9N603pn7mgKIDrDCVipklkJlw8j8vxsufmHDBP3qEI0WLeOzpTACRrj4QONbzcs2ewtr3buafj9h0JE0e0OBP9J9+yj1ld2InavQDeD678Q4KbejBJnGMA7ECLhGUUdPz+P3Tx0X/M1CE8q5zvOloYEjOIVuIiWg0sNMcdUWwKDc1e++hCk9W3R56VR4UPNxHRRuOoEOeahi7OOVIXRKBp6K1O1pHryHqh0bx9lHRLDY9DCY9jWOq0OQ/KO2QAR333Prjkrldg9eYuJ1mDIyE4wRUjWsV9rkz6Ad/ryCGN8vb8/rh6/NL1rkxEHCkRDaHfnSSaWIlcjlaa6dppZjvJCsVnRUNjyvQf/X9P1PHraQYNcQQfOX6y8/f1W2sm25SaCnC3QoYZtP55QIa0cmuoRIXvcnhLA2zYWjPlt5zzEuOIFg/fU+OIrpT5imMEFnx3sg4Jko3XdUOHtt/zxKTTDh4Hr99rN4MkSvIO2D+7k9SMuXTxQDlakhQQtoFGR/jio6kauVJAAqKkdfXk7UhWx4i2U8WkYttv5iL2GtABDwmWxk7oMPM4v5SDJYXeqe5kKjhCdJEkcawaK67yO3fsbb3HbJAMP5DMESxlgqIAbhjIbCcv34apZDVfmsHGPx5aBBTAxpRlRMtvF0fiKk7oUM6Ik2z5hg7HuXxs3lp4ZvF6uPW5ZeZvWGiW3j8/PZJ58f4o4Z3WnaIcLcqRsRwX/fuld78CqzZ1mjAA3j+eH/V9+MrclKDJP9hVmzQKY7LqyDuhiJrlwIHZftHtL5u2NgiO5vNLN3rHU+eahg7RWcHkgr8886ozUU8a5QqWhuQnLKLFxCAJYliNwyr+IUf5xulLzXX4BIlt/39vdh0C57xQ7ASUIVro/GCyB7dQSPGRuWuc3yuxchTnuVUiJXKHIuHbW0fEcIfngqlaXkJPeJSjhQ5sYcIAsxueWhK8r2eXbBD/3tPyRgAAU8a2isKQSoEjncCtLOQyvKXqoVn6OPde+LsB1oepmXqa+SaaDHTFeVoMFMcIdLal0CFVhafJDrgn5bRyRCWKVDCrENv34d8+TbTviCNjHC2isxVxRAsRMdkRQ7TfhO6E0CYNHYbI4pKjZxA3IQOePiO6SOfdgNYZ5fIa+DwSsz0TtkeAyvShrENcJNGi0nS7pYz4z4fWWq0nfmhzINjAa9EOsGocmYnUcKjIk3A5Wn7cnX7c0ioAJ/Us8zlcRteEhQ6lcVdn7dnfKXrA4/Eqv16Zjg/aio0d4sBHC+/SKvM2vVw+P56KimR21lLzXJCH8MLSDaYAMj2OfvyrN3d5Ks747aCII7YCny9yPXCwO+9qnXlldLTwnWUZnPSjqeZaBpEIODqc7Mxv3zx/4pzT0CFOTCik+dCc1fAwCYFxMjxHrLB9dOVMy6DQQV2pIokGENtPt3NHC++9tUAnSymA/Vjpp5+c+zrbvkBI8PCcD3TUJB2ileQIANw+SI3XH4wj31GktqWrbvoOP45PvFRLC8VJaehcQsZ64wit31rrNWG3N4hWZy2BMW0+QqhAwbINYQ5ZSGoF/x6SaOBte2XVZnE/SfgWFyV750kB9ExnH747AFjHCvW5OF8WAHWscCygZHa9fd5rW4IZaZhR2i1wrCg6JnG4sG1d9TTIAcNxm/Nq0RorWnBT0tGi7QtV60DESOJgVfKM1W5Sy1By5IDMddzhpmLGXF4D75dyY7mj6ZRJynzBUS7fwDlaDmKWCmR6sojurqeDjtZAsYkjmmHZhg69OsVVUMXPVKEEQ9p58UOqBepD0Qy0UAkek8af4rH+AKBhWPt3pfwajaZTqhwe76GjtX5rrXRyoGEf6khIFiYz6/9XbeqEV9d1wA/+MdvZzjkuAADPLllvanxVWWgOQE8kptRN/jdDFvcU093jOdXFy+pk29ERuvS9mqslTfD8eElQURqk6XG4Hw+R4eOmZH+qfu4sEJSLaI0c0gDvf8Oe+W/l72/OKldMdORQOZzHrRpHJk2dthXA56ug7TakAWZfdAYcPUmHOnkmGxqW4OH2h4+8wfm9EkWFjlYIycFu+0YiyksdWRt61L+v3twFGztqpl3YLamkQWtjua5YTwppU+uNI9fRnYhOURnoFv6G9c03N8j3VSZpgZ2yCNHCELq0kMOJG9FyEdFKUuIouGMFAMAn/jjDoTJQi5RbQof2WfocqTwDmlLK1FlNAo4U8vtCZHjMqjOIEmsjhjaDtQgj5WTIc91ErDUYIsNjhEeK7gC4iSZd9cTLFKfbNaIkI071nGojIloUMRMQLeSQSVmHdE7tTlIna3Sg2MBr0Q6whjjO0Sabjk07Dw3jSHArLdCKpVAaKsTRIk6JETSN3G1uCR5/FaOP56FDOxBxGJrLP/TEyjKknOKogiOC0gIAPsHRtln/HVPSeWo6rm7ot/mNW18ykz4XHAUAOOzb93gcO/w4l2/sgD9NW2LvgSFi3BFVxtFxz8ePP2ov7RBwR04KHTrnz/8fn4dyqG6Svm/X0frWbW4BBVxRU0QL0ToAd9COlEVK56/eAmvbu4kjC6Jd8m9I9rc7vO8oLZ3waVJT7ZwjJojHm5AkuW3aJj4oox237ygnxEqv/7ET9zY/hxytSUyNPI581fLe2JmHjjM/8zJBAAAj8sy879yu3w86Y9hsqobe0ug7OVKCRMgkXhuWLcHkkBGBUCuA5tRJ1yvD0EKIFo5tLYF7oIXMJcOt0vtp4hwt4VS4DRX1Kdne8Le6E0/SBcDt91JYCsA6QkZHizhaTRUf0eKOUkMcQa0e5mghIhUSaW7IEa0QYqUURivs+aghRystOH83cQRDoclQaBEdoSTV6vySYC46kTq0KCNaiLpxkJvKR0i1CikHi/OW6fNIU+0sNwRQ9J1pu6SjRVP9uZ4RgOsoSXF37Ac0jIDCk87xaZgMbwTYMrcED7WEwaSUo8U1YVR+rpAOU1/MdT7zNpEZ9XISIgoZPouwThKeX/44eAkdAL2Cnp4TclGiAkMi/+/30+HCv1qx0CoL/S1jziWeVdIJQ40m2k5ODEfOEx4/leln4XU/l8swUOQHgDqa8v1jiRycfDtYiRguK4IT1Sk/fihvl/4d+wkXBH3fUXt453nn6ybAoh+cBSftP8b87UBSX42aEpCyKlvxc/vVB46E/2LaTXTuuuAMq9otOVq3fuo472+9KWwtWaj8ENo4xkU8Jkfi8P4pojWipcHjnF1+7uGlbTj94LGw/9hWMTTZVI1hwvBmg6gW8aleWr5JXEQtEkKn1EIVJJrzLD7qAGKlCgCA7qRYOwy7Bi9mD0AEkdkClBqOATc8rWkN975sqQdN1RiaqhFs2NptyrfQPnfQ7rbfJmkq3iNqvEk6WvTnuuDI4T4065A7YohI8VqJaA054hRErHJHMKijFalg9AUAjHJ6SCeLk/VD8hc49g1hyGZM+Iu1xC8RRdX7gxwtE3oUOFqEg1WIaOWhw7IEnJ1hA69FO8AoH6eznkBDxS2KSlEn4yjRjwv5OkBCj0xtWp8fPLiWeuc6kwRMvSpqj89fAxs7arCWEHPpRIo8ElvTSw9o1FEIEYx7alLo0NUyKg+RvJSHuT56wt7idnweIUTs3KN9R4Daj96rJzD8ODd31h1nhiNiz7/qko1RSBTf+catNmxEnQdsH3d0+Crz8vvmONvxOHRaQ/N56P7+442TnON/fI97fjoo4YBODdvFHTYAXeRZur7EccBv5rCJw0g40jpyR+w53LaphCMxpq1J5omYc9qfhwpyFVLtztVbwhIUktH2ApQjwVTLCQDgP4+bBAAE0SKO1rDmKjz3zdOc46kwacjiSAX5TbhdkiBAowrtvGZkT0xy9t//hj3hv0/dz1wf7VSCZkuOIbUi3mijGb8Q9fDPxb8NHqoc2liFLV2JiFgdMK4NRg1tgLcdOi6YkRbl3430bOn5QvVZq3EE3QlBhMTQIamVKIT2NEfL7s+Pp3OJp+yuLOIE4D8vL3TofXuu5qOXFRgrRz6jyUO0lJMYhs/n/i+eCI9+5SQneYxK/vDjAWSOlsPxkrIOyfZaMsjRGjBGV09dtRQaBfIdgJsS7BTqxB8zgO66m50G4HK8OAERt/EVBC/7cV1e3HX6IptKjagVAMDKTXrFOjbPDMTyGjwrbVuM3hM+sydY6K/MMCWbw81oNnQnOxqoGB0aq5GsTL89mnGFg+aYVv2cuHL3CVO0U4b3d9MzNhmAXhObx4sOh1aZ/Dhs32Pz1oj7lQl24rvgk4wjSxKp4KSmlIIJw5sd/hF1yqhWlrQixP5fjSO4+N2H2vPm/3/4TRah4s4ot3aBn8Qnl0e+fBL88v1HFkoCUHtVCPeF7NwcxdOmz39ioDwMGjpaqzd3gVJ+f6bhLGmBU8aP0vsU7xRHCtbnCy98LnQBM6LFFyAO2c/yjD5qi4VnePG7DzXJEPRdZBnAW/bHagU9Cx1KXXzC8Cbn3Ij60PZZUWb9+2kHjwVq+u+ZSNQGABg1tBHqSSaGpfTxOvRWF8j09HxGB4s7MpHK5R0y8zs1dOQS4fwAeWivLuto4f3R0J7HgYpcR4s7cjw06ZUgAnc+4t9iW1MV1myxUjD8/jiihtffZ/RQmDiixUlYSLMwIqf38TlakSLHC44a5WhJJYIGgg28Fu0Aoy8mEdJR8T3SzuOsuImmklR2wWYd+pkgUrV1Sd4B0SIaqkLUCgCgo1tfF8mauI06Wsgrovf8v+84uOjROOZWodf/3/b88h4fD2Adm9Aqw3LMis9TRrilm1FKAcA6KCOH6knob8+57efX50WJ0fD9L1jjEsYRCQkhIvhe8fi/PrtM3I9OALT99jxK5Du5CwC/hAvtV8Oaq44GGPXJaP07icAeyjpF54AO/ps7a8DtonfafocE+FA7AbR+2ZmHjvf2C7VPyoyk4S1qb9p3pPe3ySOHCHtaQ+R42YYOGCogueOHW+6ddF3uRH3h1CnePg+xsDO3OFJGWmQdc7gALDoEAPDnjx5beC4M5VN0sixjmSIJS9dvhbceqB2eUMkde179P39HV5x3hAlP47kRiTt2b3/swvO883UuXzBSWDTalx7Q25UJ3YkcrciOnZQuQK8NAKL8gz5eOZqLso6WLL+A56sRRMyTn0BELFAfFuUPQo5SQ4wcrXx/6fxEGog7KrsPb4a1W7rN/cuIFFHWD2Qd1gOhP6qzJTnDeHxXLYHlGzthBtNxo8CJVMJnIFh57Odf0DiHivMdaGgxMQRI4mjlP2ZAUooFkTta65DCyZa86LaHGg4qjkoxQSy4eF57dwKvbe4yaMIFZx4AH8rDTgCayLq5q25q5gGAx5PhRjMxe6MTpLNwdDtx6A51/qJvYhIRXixD6ig6M518iFYZXL4QDgplaALeP+dg7Z4jimV1AsueHx1c3nrZQ+I+DaR4qnRcpHyy/0wigDmsueogWqEM1SYBMcLnx4/Aq9PB9+zDd4d5q7fAp06yZPp9x7SS8/vo5jGTdoP7Z7/Wo0FSCk1KWXb3fP4E8XgZ1Si+JhLC1+QVC/A5YL857+g94Wt/1SK3k0f5YUJ6+uP2HSmWTqKZiF8/60B4x+t2d5IvePFrALdfU0dp75JQZVtTBWZ95wzxXYeMjmHVOLLlZ0ocLfw225orTv9D+QYAl6MH4H4vIbkANAVWeV3qPwbxEaQFACBXhsewU7gj1AOhPxzPawUcLeoo8Wvgd42vTwrtubUaZfmDemC7Hzp07wtL0lmyPPjHE0TMzwqMnPuTdLQANEcrY5n09Hgd+pN1tAAsx5BLwZj5OtXOnPSOd7btkogWTZVPhBfrkOEFRwl3zzIQRexoBqCUaYICc6GYOIDVNHEcOIJYcM0YHGTXb9UTwWEThznhDU4KB+jJ5GKv3ZMabmh0/g7xDmwb7LPidu8XTjQ/NwX0lNBOZ+EEcxzjgHAzZPSSj1MJ76ipGsGYNt/RosRqg2iVfGk9cWRDkwg9R5ZlMGOJdTQpItraVHEEJ0NSDxJiExq8ONkeQD/zr555ILQRBEMK91H70hn7AwDAngFV8y+dvn/h8fT5feT4ybDoB2eZTEEAW88PQIfYJudZi1iyqMzR5tt3H6bD00fmXC/6fNoEXpnEAS2yDxy7F4xpbXJU6nnoDsCdVOnrHDW0OIxYiSNoboiD9/2Tc18HHzh2T+dvjj5TpAwpuowSc1yOnv32w8cE9wllkgH4masyxwjLt8j8wiSTCxYD6An81ueWa2mAgpuRdLoA0FEDcVGN94KIG4BEhlduaI810SByAUcPyeohDhY6cmGyvSsfwREvbF9IJwzbg9EL/oy5wLdU6xCALB48Ko/+HTli3Gi9yzQbmCV4dnlESxKxo+KEkhdPM62s4KnviKWpvArhMXVp4JVWB/hBANgCp7j92L1Hwu0vrIC/v6BL1vDBqTfp5Wj0HHQlWmZ7jx5i5BkMohVYKfKwADU66B1aUvB23zGtMKKlCuu3uu0M1f7jVrYIkki0NPumi3B0aHiyzNFE6wmSI53DqxyQAnw9R1YAAFobrbODad68bdxaBEcr1L4yjh1aSOaBnz90lgPHtwa2YDvsz58mSBra7/7zGDj7Sl3Ie48RLXDxuw6Ftx82HqaMlc/74P+8RcxERtt/XCvc+98niCR3iStFj+6JWKmE+tHjzLgU+3+7+RNvLHUcJTvzkPFGsPhdR0yAdzFJD9oHKrGC0w8eC594yz7wsUCiy73/fQLssVuLuRdacJ4bfyaUFB9FCg4Y12rCih6ilTs6mUADwf2TNA0KlqJpaYBwP+Vjrr0+ODpW/sIdDOImbbccKtzfP3+Syot6AD90KDlaVOdLyirMKCggkPXTzJcUotcHsGOgJ3jaA44WAIgl2gAs5xEX++9m/ZJGoOqCYOpAsIHXoh1gLkfLj+tLHC3n5RNEK5UcNefF+x8HKu3ygtPUkJxMJ0McUADAlBvBtuMAcUfuaI0b5gprIrpFEY6iArMALqJVRnCmdtPH3mR+lngL+5PJzTilrC1zvnumd16KSgD4UgncyQIIq1n/91unmGK3AGHExrazGE2SChYD2PTy3oQOQ3+TJk/u4KdZBi+vsIKmtOQODrhoIUmDIcIzK3KIAfxwBbeQwntPrez50e0jhviOTnODbV9rUwWaG2I4+QAZBQUAmDxqiIMmSd1jv7GtYr+ZPFqjZW89cIzRJKPND0l5lC0KHD5W/jxp/5ZQrt7YCVOKEwLovSpQUIkj+MoZB5iElJs+/kZn//3GtnoO48z/PV0+N3smmLyC5lTmYM6QUjmNI5XJ8JiVl6bFROnuuszx+sCxe8LIIQ3emGvOny+c60I4F++N1lLkiw4M7YV0tMa0NsHS9Vt9kWpy/XoS5oChfIQECujfmeYjawBGQzpYWTQ0w6Gqy/ePz7yeyGT2inG05ONRwue1fE78t9dPdO/fcLTkotYDwXZNR4uQ56RVjsvR8hEpureUMuzIQ6QpKOVnh0kaW9Rw4nYcLbAhwufymmgIY1cr7jl4rbPzcxmDUUMbTKHjvYnoo8TXooMkZv8V2WXvOxw+c/K+MIxkXeGKnz6j6z5iQwi2tI290We/caqY+cY/oCvPP7K0TaGV/ckHjIH/IkV9yz5OaTMdEM45cqK/AwB88Ni98vP3vp1Urbyn5+C+E/29IY4c5E3SFBreUhXbgv0/5JrHkYKnvnYKzPrOGeL2MkSrzPAbCRZMzv+npHtq9P32RJak6Pgyw3u95kNHwyX/5utnhZz6knWPM0588i37wGdP2Q/e/4a9zN8klGt7WhkSR5McDgsg0FJYGsDt21x6A8Adf31EC2kcfsYaQO5I9IC/0xUo31KJUCdLJnv//+2deZxdVZXvf+vWkEoqA5lJIGSABAgkEAhDBBKGMDeDigINguBjElGkBUXsbrptuxVeK9ra+GjEoT8O8MTnaz7NE4kfG2kFERACqCCTjDKFKSFVdYf9/jh7n7PvuWudfceqe+uu7+dTnzr3nLv32XefaZ015lzUovDi7K5LLiGq+1woJS/l6XNt+mAfhvLFJD0EY4HxTX9SwtL4xT6tMUO2RsyN1wlCUtSfMx1KzuzOtMfl0cps70yPdv4r20f/Y2f4NkxY2p2mQ895jrPb++VUkqjDyu1Oo5W+B6d9tDhVbzH1BpG+ySZFNJMN/vdc8j93UoVyh5x1wCJ8YM1C9PXk8MX374nz1+1YZva46NCdcMMvnypr45sbD1zKJxz18YWNWz96EK694wn803tWxL8ZiCKy5njZ0eOLhhLNHKeRACodzqsxwUi8PVyu/Qp1xT1ofWdy6QHihJZ6TDlp4cQfw9UnrawQGJ5/Y2tFUkhfgO3rKS9T862zKn1mJNNJvDpDGkhrIXxC56cT6hcJ0X/pDPoVUHk/Fe29dlkOzwDw6WN2qVhXi4NtlvkJkM+1kIbZ/w2TJvTiQ96LAgBssT4sUzNqU4b4/jn7i35ytVxvlx1ZOYdZ+H1z81dmEWBNX9aRWoiYdaazLBP9cJ43HbqiyQXB2bvHvTiX+KLV7lOcpyt1XTtN5htbI//ayvQNORRKxczM7cOFZHycRitfkAWpSCMI8cXfZVofknyw0hqttDN87IMlRx2WtRc0+bLp0jrb22PQjhqtrhS0/CznkUYrrYqN/jspP33xuEV/u0+OaV+2nShWc7rPaZyLQrmgFX32I8mcMBR6kLmaXI60bwqn3Wgkw+7y+VPxL14uHCmNg/vsHqRO28aRFrQaCeOdkwrB546Bn6OI21XInHrzBYkppZqh7rd4RllNvnSSV9dHb47wvrJcUDL+szsStKIT69Ijd2b9i6Rj7q6ReiN6Qm+Z20+fhH87YzX2W1KZ+gHw0kgIph/nNymJKlJCVI5z1+7ItE+Wf5gykaUJXYtSYeegRqvMH69y+1dOWYWb7n22QptdC2t2rEx9we0/nY8uTa3WS/+YsHnchCSiQJIHqlDiowZ9QSVLWBwuFHnTYw8hXzKxRokrqpw8Syr7v8MWkf+uLQ2WHqPz6Xv5reF4vGX9UxKRB/A+Ws7Z33326XW1GEVneCrLGSlqtOz9rlKjFW1PfLT49gXrh5a+/Fz7kVhQqww2APz0Fun20f+i9Zlux/QO3Wk6TEnAkgTtDhz3BgAkbwEVmWpTUYtpv4AcVWbi9X1phgvFWMDifLR+83TyMHYnZegtOkSZo2sLT1RJbeznWpHYa+H0zL7ScL5GDj/dAFCpTXjwb4/Ap4/ZNXNfq3aYXrHO8dkTd8feXh6zdPtZTMHmG89LHuD/cOLu2C9lOqzW8dzH17r5zvBiXTvhPHLfD0V/SlSTrfnw5XNFbUwsqAvjjqdXOH0aDfn2j99qJg+YDydU+qdXuni3Y297fn/jzNXsdv9YHruyMsfYqh2m45/es7Jl4e3+MdwyXL3PJse8abL2k/Pnc/dQVmNEBIPIB4j1dSSUaZwkhvKlCheMaN8pjRb74hz5aKWTeQLA069F6Qj+ZNMTpF8WnJ+dS+/BpU+IEq5KUXmRs787P2QLSvK5fPypur5pHy27P+ffK2u0sre7hKOyjxffPklYypsOy1112jO9Q3dqtAI+WumEo9KBN4Z37qPUgedUvc450/XnnzyX3/xQHGHjyx1RYj3DJmf0b+5nrllYsT2EP8ZfXHYIm87hy6fsibeHCrj+ziex2/zsKMA0saNnar2bq8ScKwta3zhzNS656cG41llI4LjlogOrHl86F1C6xl6ZM3OOcMtHDhRNLFMHemPfLL+Nz92XH8oKldtPn4jnXt+KqUyNP6cJCSWI9Cn30UrGIM2dpNFyxy+UpkEiZK4L4Z4toXxskvlNckCvllqah4TKJbN48+i1p++FXz7+Kg7blXfS9+tUZplpW8X2MxJN2efevXvd/dz7mfWiiRfg/fnc+SrlQHPpDziNZ+ysbmshSgwXiqyzfG8uF5d34cbw1Ktb8ObWPBbPGmT7L88JRowzevR5RDC9bfh9dL9bb8se8YKerJHy84hFnyvHV5beIa2R6i0XtKQSOO6+lNbI+emUsny0hgSNmPu6K14utedyy7UL3SlolTnPySn/XUitdHGWDDKLXLrcKeyFkXI+XODVb/uRlz38X09LHL4JhB/99vmy7el9AsBMRlsSwr/BzN9mYlnElcNlZD59/9oFOacpkYSTuJ5Vhv1kykAfdp8/Dbf/7iUQVV6Qnz1xd/z1j5PUBqGkjT4h4aU8D1J5sdo0d11+WHB/vT05cMohtxsuF1NdpHy0HJI20M8U7uPmp16n9sZDrrO1efHLj9C60UKztSXsrdyXPy5JmzNloA9H7c5nw6+Ha07ekx3LcqFAeAhf21jLtZWG0+b6cEK5M79xyVGHRop45e1hlEqGPc4u/UGplG3CHi6UKgomA35UXBF9PZUaNZf65rGX3mZ9xPxTNqu8leQM7njRvmilj2mv9RGTfLQ2bRnBc69vjeeuMn1ElBZG9vHKFoSCebTiqMPIdFgRdZhuz/jAARCDDdznkQI/vnagK02HZQneZqeIAAAgAElEQVRJGUEqlxLE5DB7g2KJTwCXtOcFMS7qcCmTZ2bfxb75Sf5N/sVXT1HNVp+bc6YO4NrT9sLXT9+b3X7ILnOwYrtpuOjQyozZPu4i5OSxtBYpzSn7yH5NxQxNWpqgyZJxjKfAzTb+nhUoOK1lPZRptLz9Sg+cD3jVBHzcTdCZdaToP4lGNVpxxJfQj+83yTE4obH0EtVoxDZcsg5XHrc8eKOvJzCiHk5ctR1rYrz1YweNyv7rJVQ7Mc0Lbw7hnqc2IV+SneELGRotF1QwXOBNh06j884Ir/FylEqSxi1Zl1Ww3b3MSH6II8US+4IZ/b6SqJFy1SzueOxldnsUtWlEH64k6pAXBEOCTiJI2e2SRixO78AHAUmmQ9efJEi2A10paMV5tIRwUN80yEUNuo9JHi2w7aX6Ws55MZ23RMr5lO7X8aWTk9Bx/+SuR2gajZv/0SvmxTl30kyb2IdbLjowM6khEH5g+6kj0nz2RNnc4bJXV0ParFgNvt9JVsmSajRatWhnfN+/3io0WhLuJuhKttx28Vo8dOURVbdv9PxyxbylqMSDlkY5oHYXTNqtzuMFRAk5Pxgoa6WE+c+HXqyr3dBIkfWRiitxCD5aO1hrguQM73w93xoqZGrE8iXeR8tfI+X5AjxBRdjH1pGikL6CUlVI+PvD61sizRunGDBInkdcwlK3f0BO3xDKo7XZVqXwc9pV0959DCVMbWfTYVcKWn4tQj7qMNle4pzhvaLSbB4t52/kog6ZhKicRss3H3Kk7/W+n5T/FpR2Gh9PpI9VGvfA5ci6AHtyhIN3jtp+/fTs/Fw3nZcddSZxzIpwLjIXgZQlTIVqVPp4CbZx2yN/jpdrvRm5ZLFOEB7o66lZ63bZUTvjPz5yQE1tHGuXzcalR+6MvxWKoh+zYh42XnkE9lhQmYOpGbgSTPXKi36ajcuOyi4npNTHa1uGWUHIjwpkBRW7aijP59FyGuo33hnJtBYUipX3+qj/ZF0/sz0taEgarc3DBVaIclGJUq1ChxNg076hSYkf3hneKQBcsXgpvYIUdei6cyV0JqbMs3F7wXSaiwUpwcfMadRcnq02FLS60kfLndjxW07quJTlwSpWRg26E2fLSIF1hvdL+HBRh7FzYkqjFXrrlpwA08shzVgnU40J6uYL1rAaiJBWxT0LQ8dhkeDM/MmjdsnMhURigZmEfz1tL9zy4AvxWzbHZYG6fz4rtkuE8dc2D8fLm2xNzGo5fPlcfOusfbA2Q5AN8eGDK0vjVEtPjsqKVHM0kj8qhDufGr2FL541iF22rc9Hqh04efWCOF9Xq6hXEH39nbxoOpTcOIDkvjCcL7JRt07QenNrvi5ner9JOocW4JsOs3203hkpCoJkrux5EvKHfCN17bto9rgWYmr/ToO/6R1e0KqIGhQqrWyxGrF0ObiKzPCChUlKWBrSiLUDXSloVUYdVqZfAOSQ4Kds9fDLfrgRu82fyobLuv7FPFpMuO2S2fwD3JGWE/wTyhdA6qlr2Cm4uTp1X9nfyk+rwPHBdy1i18ciknCdHrtiHo7aXdZKXXBwZf4lH3cj4/xmHPO3mYjz1mX3U8sb20fXJz5vV520Eu+99i4AwGM2qrVaiAgH7zynpjadyNypvKN2o/fu2ryOeBbMmIhnN23FNSfv2YTe6uMLJ62s6nuLhZeRagj5WkpICUldCR5J0PKj5jhBpt8rQZP1EN+aL7IvaSEfLbd/Z5pLv0yefcBi3PDLp6ISQez4y8vFSUOcP20AL7w5hP2ZihPG+IJaeQdOc+0ENMl0J0Udut/vfLykIuGJMz3/PM4Lzvrp/atGq02ITYdC1KE7cCXDp38YtifME69sxi7bThFNh8aGHHP9m7Jw2mj9+et2xNW3PRp/78rjlpe1y9Jo+W9y7VhUs1mUqnxrk3j688eK24zgDOr42mnhkj9ZOGXXX6xoXmRZFteetldZ8solsxL/t3OEQsDjmdsuXpvpY3PHpQdjm4m8D2GzHGwb6cWZlBoRYkaLedPqT5qaZZIOBRqwpjVPo8UdR9elVIJngnMGLxQzz4OtI3zUoj8kLlAmFrRsZvq05v3QXeZEglaxJGvsTBK4JWnuF80axGtbRipSa+SsRisxPaZ9tMoFpVprHbpPUsLVuL1Q69DNeV7QaDnh1iVUVY1Wm+D7YPE+VL4gxpQMsCe7serWirwogfYuwZ3z0ZISUR6UKvLqimpGbVDmWO6fXG0YdFHB8XvMxwPPvlFzuxGhDEYzcIJQq6YvOd4t2oEHJ1BO8iLvJqUeCHsvnI77/vR6y8c1luy87ZTM7QsFR3vAMx3WefAWzRzEiXvOZ7POV0tWLql24Yfnr6krWKRaQj+drZGas0WdSyV2u39MX3prqGK7M/cN5UtxMIiPSyszlOdNe75gwAW59HgaH77Ga/R/pMA727sodikzvYOLWHTrMxOWer/f7a9sfLGgJPhYpUx/YY0Y37/kDD/Biwrlxt8OdKWglRaEKiToWCMVHdwKCdy7mIpMpttKH63K/v2oQ+nESK/ftCWxrU+e0FtWX88fUzuqTtN8xSvPUwtJmH/ztXYuQq9V4cHujTArWWMr8f1P0jf07/6P/bBluLW+N51Mo6dET45wzSn1nfN+H80YSysJZc2vl2NWbItbH/pz8NqcPolJ5pyL0jvkC4b1wfLvs3f+8dWK7f1eegOu2sSuVoDfmi+ywTr+iDk/Qt90KAmKgBW0mPQTPURx1nVOm+N+vzH8syYuwSPWOsxO7xCuVRj9l/JguWdXnHle8PFK+zQ7YkErX2D7bwea/rQioquJ6A9EtJGI/g8RtSYMqAFCpsH4wBqDoUIJExhVq6NYYmo/xZlwS3weLUr8v/zxSONkSTl9NJreAQBuvuBduPOyQ+prPErkA/mUGsH5zLXqQeZuVJz5YDQo9xUp/5EDfT11JbrtFnpzhLXLZuP6M/jyOKMyhp7kBbHbOHK3yDcy9BDdhhG0nAVhpMibBkNeCK7NcL7ECnrOwiH5iPnPBy6fW2I6LLL3NbfPkSKf+T7na7SY8X3R+vQVGOsN4Gm0BB+oPs90ym3vDQhiFaa/Co1Weeb5/p7yOUrXOkxPgRNO3xnODiYYS1rhzHM7gN2NMSsBPAbg8hbsoyHiLOQl/uLwaxUO5YsYSL1l+JFlUfqH8v7dG8BIocRGLaZLIkhvaVmaqa2pAsv+BViv6nTvhdODKSbGmsTO3zqNVqsu0y+8dyVOXr0Aq1qUgqAW2jGpXztDRPjO2fvikF3GLiDAPZCy6oGOV+JSXYHzVvLRKpUM8oIPlm/qXM2kxunvTQQd7p7sC0d8+ohkXdpkDwAzJ0cuIG9uzQdNh5xpsjdHohsMkKSU4KwvbnxlGi3GFzhHkQ+a+1zef7npTro3S7Ui3WcXDJCeA0oJaunxOR8t90xsR4tO01+tjTE/9T7eDeCkZu+jUXzTIRd16A5sqWQwnC9WJNn0I/i5k9ddeCMFXqMlRR2myRKY0jdb/wIbrczTY4HLDtxoSRWOeEpbNH1LZk+uOmqrEf4tQ+vyk4sPwvd//QxmDvJO30r74vxzCjVmTnesWzY7flh1Gu4WGXqGSlGFRWOQLxnW9OY77nOCtC8ocfdk3xzJ+2gly5xGa9ZgoknOyhwfRR3y2wuCG0yEs7DwBZdzVqNVKhkQ8YJKX09O9MFygmhcCzFt+nM+VqXszPAuz1b63u52J5ke/ahQoDud4c8GcCO3gYjOBXAuAOywA19frVX4pkNWoxVvj+zO6XBUv/Ax5wxPROjvzWG4WEKhVMKEvnSCNoqEMKHIp6MWpY2v0WrD86xpuLfPVggKR+62Le55alNmDqux5P6/PryqY3v4cr4oMQDssu1U/N0J9RcEVsaOpOZbfcLSt8+Wqya0C9ILlMtBF9JWcNudBaFQ4n20fI3UsrmVARO+oMW9w/qRgJyPlv9OzGm0crnoeTFSKLHj802HbB4tL/M9nz8w+l8slUSNVuxGI7yk9/fkEtNh6jvO1CcJOrGgJJgme2NBi9dopTO/V5oeCb05igW1dtTW1yVoEdEGAFxCoSuMMf/XfucKAAUA3+X6MMZcB+A6AFi9evWo6sLduVwoGRSKlfWvfOe7QslUvGX4g5XUsRN6cqJGy71hpUvwpKkmwaXfZzL+9jvRmsVZByzGlIE+vG+1nEerXs4+YBHet3r7lia+bIQZAeHy+jNWx9mRlfHHP75nBa76yR+w9zit/HDPpw8TkwW721vILYLb3kMUuxxwEZG+cHTIzpUJef0uOY1ZyHR43Mr5+NKGxwCAdaYHgAEnaGWYDqX+Q5nvHVxdXwAAJVVOJEG2t4eQH+bdNvrTUX9pxUOsUcuOOoxNhz21mQ6ByCH+nfGm0TLGrM/aTkRnAvgLAIcZv/ZEm5DzTINZUYclm+sqfVx9Z9SioI51byh8rURq3Bk+RV+HRR3WS19PDqfu2xoNKBG1rZBVDeszNFlK57Pj7Mn4Xx8YO2f8VjNn6oC4LfbRCtzbOOuA32b+NpX5vfz7M3fP9V9cuZfY+dMmWvMb/5C/6NCdYkGL02gBiAKuhgqZGi0AYh4tY6KIbNbZ3f6XErbmiAAbBS8JKb6yQXJGj6MGBcVF4sye1miVC2rpOQhpxNwYtgqmzXagFVGHRwH4JIDjjTHvNLv/ZuAXleYiMfyi0MZUXlwm7QwvlHtxSeA4Cb7kOR9yF+/NF7wrqMFI7y8Zf9XNFEVR2h53e+PubXt6wSVsVKDXiE3v4G3n/Fv9VayPU45iTRnro+W1mcT4aAFJsXbWR8sXtDiNlmdalJ5FQJagFbXNF2XToT+utEarN5XQtGKOYkGJT//gggGefT0SF4KmQ2aMA309eHuoi9I7APgqgCkAbieiB4jo6y3YR0OkE5bKUYfWByt13CpMh8LJCxibh6tSQi+WTCyw+e3X7zoH86YNNGQeGM+mQ0VRug93T+MEgR9fmBQql4pKO7hagyFTk38/fX0LXyPUCSKhovdc5ni/PZ9HyxtrprO8UMvR/s+XKt1kAOCPL20GAHzrV0+LGkN/XOmf2BvvPzuze0FwlRmc0Iv+3lwsKFU6wztBS9ZoLZgxKS6N146CViuiDuuvHDtKxM7uYtRh9F8q27DH9tEb1IzBfhQN/5ZDFOVlkqIOI0f85LPj+jP3aei3pftTFEXpdOKoQ+Ehuu3UAfz5rSGhxI4naHHpEQLJj/0un9nEG2mcoBQS2iYJPlr9WYKWP36m/1jQYV7qAe95VjTo6ats7wQUv6805X5ovCAkZna3210yWLaepPVp7slRpY+X9zyO+qscn+/71o6C1vgtipdBkgCNl8Ddgbr6tkdhTKU6ef3yuZjU34MDdpqFQpEvi5AjggHvoJjOo9XsJOfjuNShoihdSGI65B+iU2xpGz69Q7IcMh1yVPPi6nIrzpsm+5kBcrLiWKMVMB1m+ZANjRQzf4vk7O77HEuCbJmPVuorlRqtlOIi1RenlXTCV1bm/nypBCLevNtXFvmpglZbENdmCkjgAJ+QFIguqFLJROkfmPpXBMQ+WhUlBaqMOpRYtcM2uOeKw8TtqtFSFGU8IdWDTbZH/9moQ+/Bz/lAcQ93ru8snKAVqq4gabQefeltu7PKbf5P5sb/4weeBwDc8/QmzJ5Suf+4BE6Rf5blvXQhIR+tHCPo9HgatfR4o8+Vz780TjjLyowvlRACyk3C7fj860pBqyeo6kyWIx8t3u5fLBmMFEpsSDLZbLtciR8/7wm3/xAT+3owZ4r85tSOJ5qiKEq9uDuadKt093I2j1ZAUOEKRftUcz918VH9gdJgkkbLaYOe9sx43P45s9vzb2yNl7kAKvISlnI+ZH4CXOlZ5AQg3jRJ8XHp78lVapwEDRjXf7rcXdR/sixq3AKRo2NNVwpauQpVZ9omHH1eu2w2SozpEEiyuw8XihUJTaM+opIupYw8WqESPBJDqfI7lWOrqTtFUZS2xt0jpXvln16LfKeef31rxTb//tvPZIYPVdKo5XYaqlgxOVDnlHuW+NoaTtDxTX9sDVjPx4nb7FcZkQUtq9ESfp5r50y4PiENFwC8+OYQgEiJkMYX5CYI2kdfgG7H6gddKWilw0W5k2vnuVMwqa8Hhok6dG2c6ZC7uIiQaLTSqlMilEpJhvlqJfANl6wDAOy7eGbm98ZzCR5FUbqPOOowcK88mEk46mtBOI1WtfvOwokqkqB10t7bAwgXlOesIzO8EnCcNuhTR+8SL3O/z7UoCFGHxRoELamOoYsI5AQtydTIIWkX3THg3HSAcmHUJT5tJ7pS0AqFowJJRfMSk0cL8OpnCYVKo0KdTqOVzguClDN8dYLRTnMm487LDsEnjljGbr/0yJ2r6kdRFKWTcLfg0EskJ8j0ViFobbhkHX768bXstlpMh1L/V5+0Ek/+4zHBfjhBY2J/T6zp4kyH63dNEhWzglYgj9YZaxbGy0GNVmAqJnIlhqowHToGGI1W1IcVtITKAf1lzvrtp2joSkHLrx0FyLlPDPg8WlGbpH4Wd/I7Z/jILl7ZtlSnM/yCGZPEcOQLD9kJT3/+2Kr7UhRF6QTcHXJiwJ+Ke4j791dJ47TTnMlsnUMAoKqektG9XHKsJ6KqKnZIgoQTIDlBasZgfywgcaZHB+cvDAAXr09e3MVah9bkGkqFEUpPAfB+VotmRvVlB6QSTLZb6ff5x/2AnbItPmNBVwpaPVVptCJn9qiiOedg6QlaYnoH9xZRWbvJFbT2x6MoiqJU4gSNyYESWVKJGkc9psNq7s6xRivgoxVCMo25aEW+1mIufpa46Ecf14KrcuJw/UrCoBOAQkqBLEHPwT0vD7flw6TfP5SPntWSoOz/6nZ0nWl6wtJOwB3nESHqEHAnp2w6zLlcWCXD262t6bFo+MzzxZKJq7qrnKUoiiKzYrtp+NhhS/H+fbKLyXPO4OWCVu0321osDpKzdrVIPlAuozxnPfEJlRCSXupzBBQh53Rctm2k7ds6UsjcP6fRSw+JE+acyVAyHYbw/czaka7UaLkoBhcSzJr+rDO7aDokik2PrEYMAAxfdNqZHd3bx3guAq0oitIouRzh44cvw3ZMUWifUFHoUM4sdt81OMM3qtGS/JdcjcS+QDZqXmmQrJP6T/KU8f3PnRrl59oiOJpfdpTsH5w2R/K5zqJ1t//uJbEfQNZIPvHK5sx2Y01XClpAdGClPFpAYvqLEpbyJ8Zw3glqvF26UJJrP7lkpoBsF1cURVGqhxNEfHNayMeIo5aEpfUIcj7SS7dLexByM+G2luWhkkyHcS1Jvl/Jd8oh+ZYB0Zzf8pEDk30xv+HNrfnM/h2S6dDVSWxXulrQip3hBXWrizpkfbRyhOFCJN1LUYsFoQhmvVGHiqIoSiVH7hb5+HCCCue3VAu1vAfX4wMGACfuOR+ArHFyAkYtBbAd/pos02HW9tAzykVLGvBzPW1in/fdSqHMCVqhyHlJkA3llhxrulfQIsKIE5SE3CPGIDOPVpbpMUeEvODsni7Bo4KWoihK/Xzl1FX4zRXr2W2lBvNXVlXr0N7LQwlLJVZsvw0A+VnwyAtvAQBuvPfZzH5CQ5UFLSr7nybkG+Y0WpJMO2fqBMyeMgHXn7Ga3e4EqOXzpmbuRzLNXnn8bpntxpqudIYHImEnH2ucmC/EpkP+5M0RZUYtAkBB0Ji5EjwFNR0qiqI0zITeHsyewpuvig1qtKorKh39D5XzkSgKbiYOF813+v4L2e0ONuKuCmd410wSFCXfLUes0RKmeqCvRxSEAeDyo3fF4lmDWLesMuGsj6TR2n9J+6V08OlejVaOvKhDSaMlRxX25Hxnej5JnGw6tCV4SpG2TJ3hFUVRWsNwg2alau7Ozt+23qi5+KVb0By5Z8ju86ex2z904GIAfAS77wwvvdQnebj48btnpUSs0RJMhyGmTerDeet2DD4Ls9JHXHDwjnjXju0pcHWvoBXQSOUoKdHDbS8X1IT2JalodZSjK1/kHe0VRVGU5rDVClpOGKkVIuDsA7LbNipoFe1LuRgVaP9LzwvnLM/6aFWh0XKC3q+eeJXdHvJzcwJQg8pDEXfsstJzfPKoXfC9c/ZvzQAapGsFrZznzM6GxFJiWpSiCut1hncXQ6HI155SFEVRmsPcqQMAgKVzJtfVnojwN8ctz/zOOltjcaBOH63l8yPfpJXWV0tC8pVymiReo5UgPW9c1N47QvoGE5CgXL+t8oJZMD1K61GvD9xY07U+Wn05it90pISlIY2Ws8tLgpikMXNfHymWxAR1iqIoSuMcv8d8zJo8oSlmpWNXzGPX/8upq/DnN4fqSh8BAIftOhd3XnYIFsyYlPk9SVByzyI+YSl5y3UND8VAQIGTw6gqQ2vtuN9Qb1TnWNO1gtbcaQP47TNvAJA0WsBIrNHKToInJSx16ti03dl9HimoRktRFKWVEBEO2GlWw/1k1ZEd6OvBolmDDfWfJWTFCUUFSSkWdJjNzvIChDOouzQTaUIBBU6j1uq4rk59XnameNgEJntV3nlBiRKNlpD+IbM9URx1yJXgASJBK5QXRVEURVEAWdBwpj1Oo+RcWIDk5V/i4J3nsOtdtN9p++3Abp85GGWOD6VnqJdS/Ps6k67VaPkaKfbkJYiCElCujpVK+OQzog4BYLhY0ohDRVEUJRP3lJB9tCK4x4nvyB5yVpf6P2L5XHzq6F3wASG9xPL5U3HTeWuwaodsH7N6MRmm0U6giwWtZFmMGsyIBPGPN+dnFZkO5ahDAMirRktRFEUJ4GodDuV5ZymXMJWTQ3xzYSgnmOQDlcsRzl+3Y2bbfRfPyNzeCO1dMjpM15oOfeFHMh3GRaNZH61kmWv/wLNv4KW3hqN9pROWOh8tjTpUFEVRAqzfNSox5Lu8+CQarcrnia/R8kvhcGSlTxhLnGm0muSx7UjXarSozHTIJRz1ow45jZVvOuQKmSbLXAkeQH20FEVRlDAfPXQpjt9jPpbM5lNUZOW58p9FUwayH/mh4tFjTYfKWV2s0QpFDVJk2svaHvcVEJYqTYfR/7xqtBRFUZQAuRyJQhaQ+DBxGp9aTIfTB/vrG2CLSdJHdCZdK2j5SirOIZ2QFIWWSuw4OHXr/GkD8XK6fXnUYdceAkVRFKUJJKa1ym2+tiukEZrRpoJWHHXYoZJW1z7lg3mwfNOhEFXo4LRSR+y2bbzcJ5kOi0ajDhVFUZSGcEor7nniWxVDT5ttJmX7cI0V7id0atRh1wpafvSGVILHnaBSLUQHF6mRlT4i0WgV1UdLURRFaYisPFOhJKUAcMUxu2LmYL9YVHqsyUrI2gl0rTP8ht+/FC9Lmd2T7dnO8FJ6iLh92nToRR1OUUFLURRFaYAsjU+xCtPhOWuX4Jy1S1owsuYQZ57vUC+trtVo+bA+WmWCUvb2PkYQ84WvtA+XRh0qiqIozcJk+DCFCkJ3Ap2u0WqZoEVEnyAiQ0SNF5lqMWGNVnatwx5WEKvGdKhRh4qiKEpjZEUd7r1w+iiPpvmYDi/B0xJBi4gWADgcwDOt6L/ZSD5ajlBkYNrZPerT215hOoz+54tGBS1FURSlIUoZUYc7zZmCDx8cZXXvVGdy1WjxfAnAZeiQzPlcRXT/hOXkrFCtxKyoxpxqtBRFUZQmUYrzTGXXQuxUYh+0DtVpNV3QIqLjATxvjHkw8L1zieheIrr3lVdeafYwaoIXdrIFqXIfruw8W2mNWCxoFdVHS1EURWmMkMan0920ClaS7FTFRF1Rh0S0AcC2zKYrAHwawBGhPowx1wG4DgBWr1496qfBjMF+bNoyAoBXp5YVnQ5s54SlsszzKR8u/2Thyv8oiqIoSrVcvH4pnt30Do7cnXssJ3Sq6W3EVmnp7+3M52VdozbGrDfG7J7+A/AkgMUAHiSipwFsD+B+Iso++mPAl07eM3N7n3dA+ahEWZAC0ukd+KhDgBfSFEVRFKVaFsyYhJvOX4OpA+2ZcLRRnKA1oUMFrabm0TLGPARgjvtsha3VxphXm7mfZsA5sPv4B5TTaIXybJULU3wJHoCPWFQURVGUZmE63EtrpFgE0Lkara5NWMr5Vfn4GXJDUYlBZ/gKjZY3DtVoKYqiKC3kQwcsxgPPvIGTVy8Y66HURX9P9DyePKEzRZaWjtoYs6iV/TcCZ+7zmRA0HWb3X1aiJ0ujpYKWoiiK0kLmTB3AjeetGeth1M1fHbEMMwb7cPwe88d6KHXRmeJhE+gPaLQm9nsarYAzPEe5w7vsDK8aLUVRFEWRGZzQi48cunSsh1E3nWnwbAIhjdb8bSbGy1xgYCifh29aTJfg8a3lqtFSFEVRlPFL9wpagbQKsyf3x8tZGq2DlvIVhnzZKp0+YutIMfmeClqKoiiKMm7pWkGrFMjgNm2iJ2hxwpAVnqZN5MNpOb8uR75YipdDAp+iKIqiKJ1L1z7lJ/b1ZG4fnJBsZ53h7X9JI5VVU8oXtFSjpSiKoijjl64VtBbMmJS5faAv2xn+7aECALm0AdfGsWT25HhZneEVRVEUZfzStYJWiIFAHq0Hn3sDAPD4y5vZ9lny07K5UzL7VhRFURRlfKCClsBAn5dHi9FOnX3A4sz2WT5aQJLhVjVaiqIoijJ+UUFLIJQZfvJAlIJMcqrnhDMOLSqtKIqiKOMXfcoLkDcznNLJ5cYqlnhBK5APFcYKaKF8XoqiKIqidC5dmxkeADZcsrZMc+XjO7NzEYQus3wxoNGSLINOPlMfLUVRFEUZv3S1oLXTnCnitpDpz/lYlQSNFsWCFt+PMzmqj5aiKIqijF/UdCgQcp3qsxqtgiBoOdOgvN3up0pfLkVRFEVROg8VtARCApATtCSN1l1PvFbVfp57fWttA1MURVEUpWNQQUsgK+EoEPbRkjRZabbmC7UNTFEURVGUjkEFLYGQRc/5aHnVdMqQohHTqDO8ok3UcC4AAA0zSURBVCiKooxfVNASyKpVCCRpGaQ8WtVqtLSotKIoiqKMX/QpXycuWlDSXB27Ytua+lEURVEUZfyhgladOJOfZPrbd/HMqvrpDWU2VRRFURSlY+nqPFqNMHlCLz566E44duV8dnvImd6hGi1FURRFGb+ooFUnRIRLjthZ3l6lokrTaCmKoijK+EXtVi2iWo2WoiiKoijjFxW0WkS1aRuEoEVFURRFUcYBKmi1iJBC6z17bTc6A1EURVEUZcxQH60M9l8yA+t3nVtX25Dp8Mrjd0OxZHDOQUvq6l9RFEVRlPZHBa0MfnDumrrbhkyHUwf68OVTVtXdv6IoiqIo7Y+aDltEKLO8oiiKoijjHxW0WoymyVIURVGU7kVNhy3k70/YDfsvqS5DvKIoiqIo4w8VtFrIGWsWjfUQFEVRFEUZQ1piOiSii4joUSJ6hIiuasU+FEVRFEVR2p2ma7SI6BAAJwBYaYwZJqI5zd6HoiiKoihKJ9AKjdYFAD5vjBkGAGPMyy3Yh6IoiqIoStvTCkFrGYCDiOjXRHQHEe3Tgn0oiqIoiqK0PXWZDoloA4BtmU1X2D6nA9gfwD4AbiKiJcaUV/UjonMBnAsAO+ywQz3DUBRFURRFaWvqErSMMeulbUR0AYAfWcHqHiIqAZgF4JVUH9cBuA4AVq9eraWVFUVRFEUZd7TCdPhjAIcCABEtA9AP4NUW7EdRFEVRFKWtaUUerRsA3EBEDwMYAXBm2myoKIqiKIrSDTRd0DLGjAA4vdn9KoqiKIqidBrUDsomInoFwJ9a0PUsqNmyVnTOakfnrD503mpH56x2dM7qQ+ctm4XGmNnVfLEtBK1WQUT3GmNWj/U4Ogmds9rROasPnbfa0TmrHZ2z+tB5ax4tKcGjKIqiKIqiqKClKIqiKIrSMsa7oHXdWA+gA9E5qx2ds/rQeasdnbPa0TmrD523JjGufbQURVEURVHGkvGu0VIURVEURRkzRlXQIqIbiOhlm8zUrduDiO4iooeI6BYimmrXLyKirUT0gP37utfmVPv9jUT0EyKaJexvb/u9x4noK0REdv35dv0DRPTfRLRcaH8JEf3O7udnRLTQ2/YFInrY/p3crDlixlD1nNltK+22R+z2Abu+2jn7HBE9S0SbU+vXEtH9RFQgopMyxjuBiG60c/5rIlpk1+/pjWtju8wZEZ3mnWMPEFGJiPa02xo6z+y299tz6BEi+p7QXpxbIjqTiP5o/85sxvxI1DhvfUT0bbv+90R0udem0etTvO5S7dl5I6KFRHSfPZ6PENH5zZojZgy1zFk/EX3Trn+QiA6266ekzsFXiegaYX/SnC20c7WRiP6LiLYX2rfDnC0gop/b8+YRIvqYXT+DiG635/rtRDTda3O5/c2PEtGRdl3D82a3VXONsufkaM1brXNGRDPt9zcT0Ve9fppxrlV7fbLPAm/7VCJ63h/fuMUYM2p/ANYC2AvAw9663wBYZ5fPBvBZu7zI/573/V4ALwOYZT9fBeBKYX/3AFgDgAD8PwBH2/VTve8cD+AnQvtDAEyyyxcAuNEuHwvgdjuWQQD3+n2O4Zz1AtgIYA/7eSaAnhrnbH8A8wBsTq1fBGAlgO8AOCljvB8G8HW7fIo3Z8sALLXL8wG8CGCbsZ6zVLsVAJ5s4nm2FMBvAUy3n+cI7dm5BTADwJP2/3S7PL0Vc1bHufaXAH5glycBeNr+jmbMG3vd1TBv/QAm2OXJdmzz22DOLgTwTXcuALgPQI7p8z4Aa2ucs/+NqAoHEJVA+/c2nrN5APayy1MAPAZguT1XPmXXfwrAF+zycgAPApgAYDGAJwD0NGneqr1GpWfBqMxbHXM2COBAAOcD+GpGv/XMWbXXJ/ss8LZ/GcD3ssY3Xv5GVaNljPkFgE2p1TsD+IVdvh3AewPdkP0btBL2VAAvVHyJaB4i4ecuEx3V7wA40Y7jLe+rgwBYRzVjzM+NMe/Yj3cDcG+JywHcYYwpGGO2ILoJHBUYd13UOGdHANhojHnQtn3NGFNElXNm29xtjHmRWf+0MWYjgFJgyCcA+LZd/iGAw4iIjDGPGWP+aPt6AdHDuKpkb7XSwHl2KoDv2+WGzzMA5wD4mjHmdTuul4XxSnN7JIDbjTGbbB+3o0XnmR1HLfNmEM1NL4CJiMptvYXmXJ/SdZceLztvxpgRY8yw/TgBLdTc1zhnywH8zLZ7GcAbAMryFBHRUkRC2J3pfQXOtbhvAD9HdB1y422HOXvRGHO/XX4bwO8BbIfye8e3kfy2ExAJ9cPGmKcAPA5gX7/PBuat2muUPSdHa95qnTNjzBZjzH8DGJL6rHfOqr0+ITwLbP97A5gL4KfhX9/5tIOP1sOItEoA8D4AC7xti4not0R0BxEdBADGmDwiKfohRDfw5QC+wfS7HYDnvM/P2XUAACK6kIieQPRG8NEqxvkhRFI9EAlWRxPRJIrMIoekxt1qpDlbBsAQ0W3WPHAZUNOcNYPtADxr91sA8CYizVoMEe2L6E3wiRaNgSPrPHOcDCtoNek8WwZgGRH9kojuJqJahaR4Lpm+Rwtp3n4IYAsizeQzAP6nFQibcn16+Ndd1VhTy0ZE8/cFK9yPFtKcPQjgBCLqJaLFAPZG5Xl4KqI3f+7lL2vOHkQi0L0bwBQiKrvuQozFnFlz0ioAvwYw173k2f9z7NequQ7qnbd6rtGyc3K0563KOauGeufMJ+v6ZJ8FRJQD8M8ALq1hrB1NOwhaZwO4kIjuQ6QSHbHrXwSwgzFmFYBLAHzP2nT7EN3IVyEyQW0EcHlltyBmXXxCGWO+ZozZEcAnAXwma4BEdDqiN8+rbdufArgVwK8QPZjvAlCo6tc2B2nOehGpi0+z/99NRIfVMGfNIHPe7ZvSvwM4yxgT0o41E2nO3Lj2A/COMeZh+7kZ51kvItPEwYhuatcT0TY1jDlzLkcJad72BVBENDeLAfwVES1p1vUJVF53tWCMedYYsxLATgDOJKK5tfbRANKc3YDogXUvgGsQ3T/S941TkGhV02TN2ScArCOi3wJYB+B5pu9MRnvOiGgygJsBXJyyMlR8lVmXvg7qnbearlHunBzNeathzqqh3jlzYwldn1IfHwZwqzHmWWb7uKTpRaVrxRjzB0QmLxDRMkT+T7Dq2GG7fJ/VPi2DPXjGmCdsm5sAfIqIehDZmwHgPwBci3KV5vbgzWU/sN8FEX3O279ziF4P4ApEPhdORQxjzOcAfM5+53sA/tjANNSENGeIbuJ3GGNetdtuReQ/8pZtlzlnxpi/qXUszJw9h+gt/TlrVpoGa1qhyCn4PwF8xhhzd637aoSMOXOkbzp72naNnGfPAbjbanmeIqJHASwlohOROs8EnkP0APD7/q/AT20qGfP2l4h8G/MAXiaiXyK66c607Rq6Prnrjrs+qxj/C0T0CICDEGnhWk7GPa0A4OPue0T0K3j3DSLaA0CvMeY++7nqObNalPfYdpMBvNcY82a7zpkVyG8G8F1jzI/s6peIaJ4x5kX7QubMeO6e4kifK3XPG2q4RqVngaPV81bjnIX6amTOqr0+pWfBGgAHEdGHEfm19RPRZmPMp2qakE7CjLJTGFJO7rDOh4i0a98BcLb9PBvW4RHAEkRvaDOQOFLPtts+C+CfhX39BpFzt3PmO8auX+p95zgA9wrtVyEyby1Nre8BMNMur0RkKuhtgzmbDuB+RM7JvQA2IDrxq54zbx+bhfXfQrYz/IUod4C8yS73I/IhubidzjNv3XMAlnjrmnGeHQXg23Z5FiI1+syMMZfNrT3fn7LHdbpdntEO84ZIE/xN+5sHAfzOXgvNmDf2uqth3rYHMNG7Jh4DsKIN5mwSgEG7fDiAX6T6+TyAvwvsS5qzWbCO9YheAP++XefMjv07AK5Jrb8a5Y7dV9nl3VDuDP8kPGf4BuetqmtUOidHa95qnTNv+wfBOJs3OGdVXZ8QngXVjG+8/Y3uziKNwYsA8ogebB8C8DF7cj5mD75LovpeAI/YC+x+AMd5/ZyPyBlwI4BbuAvDfm81IiHoCQBf9fr+su37AUSOo7sJ7TcAeMl+7wFEWh8AGED0YPkdImfAPdthzuz3T7e/7WH/oqthzq6y+ynZ/1fa9fvYz1sAvAbgEaH9AKIIqMcRRa0s8caV9+bygVbNWx1zdjCit9p0P42eZwTgi/Y8eQjAKUJ7cW4RmaEet39ntdH1Odke50fs77u0ifPGXnfVzhsiIWYjonvHRgDntsmcLQLwqJ2bDQAWpvp6EsAugf1Jc3YSIu3YYwCuh42Ea9M5OxCRCWmjd4yPQaQN/Zn9HT+D91KBSHvyhJ2/o5s4b9Veo9KzYFTmrc45exqRBmmzPebLmzRn1V6f7LMg9Z0PogsELc0MryiKoiiK0iLawRleURRFURRlXKKClqIoiqIoSotQQUtRFEVRFKVFqKClKIqiKIrSIlTQUhRFURRFaREqaCmKoiiKorQIFbQURVEURVFahApaiqIoiqIoLeL/A1Gk5vnUtu0TAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 720x1080 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(3, 1, figsize=(10, 15))\n",
+    "axes[0].plot(res.trend, label='Tendance')\n",
+    "axes[1].plot(res.seasonal, label='Oscillation')\n",
+    "axes[2].plot(res.resid, label='Résidus')\n",
+    "for i in range(3):\n",
+    "    axes[i].set_xticklabels(x[[j for j in range(-500, 3500, 500)]])\n",
+    "    axes[i].legend(loc='upper left');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Apprentissage d'un modèle simple sur la tendance lente et prédictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On cherche à apprendre un modèle linéaire sur la tendance lente."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = np.arange(0, len(x)).reshape(-1, 1)\n",
+    "\n",
+    "# Model\n",
+    "model = LinearRegression()\n",
+    "model.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.976691466648821"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Score du modèle sur les données d'entrâinement\n",
+    "r2_score(y, model.predict(X))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "78"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Nombre de semaines restant jusque 2025\n",
+    "def count_weeks_until_year(start_date, target_year):\n",
+    "    start_date = datetime.strptime(start_date, '%Y-%m-%d')\n",
+    "    target_date = datetime(target_year, 1, 1)\n",
+    "    delta = target_date - start_date\n",
+    "    weeks = delta.days // 7\n",
+    "    return weeks\n",
+    "\n",
+    "start_date_str = x[-1]\n",
+    "target_year = 2025\n",
+    "\n",
+    "weeks_until_target_year = count_weeks_until_year(start_date_str, target_year)\n",
+    "weeks_until_target_year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prédictions\n",
+    "weeks_untils_2025 = np.arange(int(X[[-1]]) + 1, int(X[[-1]]) + 1 + weeks_until_target_year)\n",
+    "preds = model.predict(weeks_untils_2025.reshape(-1, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1)\n",
+    "ax.plot(res.trend, label='Tendance')\n",
+    "ax.plot(weeks_untils_2025, preds, color='r', label='prédictions')\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ce modèle n'est pas particulièrement adapté, il sous-estime très nettement la quantité de $CO_2$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Utlisation des lags comme attributs du modèle. \n",
+    "\n",
+    "On complexifie légèrement le modèle afin d'utiliser des attributs correspondants aux \"lags\" des valeurs de $CO2$ dans celui-ci."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['ppm_lag1'] = data['ppm'].shift(1)\n",
+    "data['ppm_lag2'] = data['ppm'].shift(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "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>ppm</th>\n",
+       "      <th>year</th>\n",
+       "      <th>ppm_lag1</th>\n",
+       "      <th>ppm_lag2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1958-03-29</th>\n",
+       "      <td>316.19</td>\n",
+       "      <td>1958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-05</th>\n",
+       "      <td>317.31</td>\n",
+       "      <td>1958</td>\n",
+       "      <td>316.19</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-12</th>\n",
+       "      <td>317.69</td>\n",
+       "      <td>1958</td>\n",
+       "      <td>317.31</td>\n",
+       "      <td>316.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-19</th>\n",
+       "      <td>317.58</td>\n",
+       "      <td>1958</td>\n",
+       "      <td>317.69</td>\n",
+       "      <td>317.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-26</th>\n",
+       "      <td>316.48</td>\n",
+       "      <td>1958</td>\n",
+       "      <td>317.58</td>\n",
+       "      <td>317.69</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               ppm  year  ppm_lag1  ppm_lag2\n",
+       "index                                       \n",
+       "1958-03-29  316.19  1958       NaN       NaN\n",
+       "1958-04-05  317.31  1958    316.19       NaN\n",
+       "1958-04-12  317.69  1958    317.31    316.19\n",
+       "1958-04-19  317.58  1958    317.69    317.31\n",
+       "1958-04-26  316.48  1958    317.58    317.69"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Suppression des lignes contenant des NaN dues au décalage\n",
+    "data = data.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = data[['ppm_lag1', 'ppm_lag2']]\n",
+    "y = data['ppm']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LinearRegression()\n",
+    "model.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On utilise les 2 dernières semaines disponibles dans les données pour prédire la semaine à venir, et ainsi de suite (en utilisation les prédictions au fur et à mesure) jusqu'à 2025."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ppm = list(data['ppm'].values)\n",
+    "ppm_preds = []\n",
+    "\n",
+    "for i in range(weeks_until_target_year):\n",
+    "    new_data = {\n",
+    "        'ppm_lag1': [ppm[-1]],\n",
+    "        'ppm_lag2': [ppm[-2]]\n",
+    "    }\n",
+    "    pred = model.predict(pd.DataFrame(new_data))\n",
+    "    ppm.append(pred)\n",
+    "    ppm_preds.append(float(pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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tAosHA5M9jycD1xRYPsM5l+Wc2wFsBboWT1wRkV9bteswt729jBoVy/PqzZ0I0YnbsyrsnnkReATIK7CsrnNuL4Dnzzqe5Q2B3QXWS/Qs+wUzG21mcWYWp3fHEpELkZfneOP7bdwwYTFhIUG8P6ob4VVCvR3Lp5239M1sIJDsnFtRyNe0Myz71RvdOucmOudinXOx4eGBe0m0iFyYQ8dOcPu7y/nHnI1c0bYu/7n/ElrUqeztWD6vMLN3egJXm9kAIAyoamZTgP1mVt85t9fM6gPJnvUTgUYFto8AkooztIgEtjW7U7ln6koOpGfx/65pxy3dGmN2puNNOd15j/Sdc4855yKcc5Hkn6D91jk3DJgN3OZZ7TbgM8/j2cBQMws1s6ZAS2BZsScXkYDjnGPq0p3cMGExAB/d1YNh3Zuo8Ivgt8zTfwaYaWajgF3ADQDOuXVmNhNYD+QAY5xzub85qYgEtJzcPJ74NJ4Zy3fTu1U4L94YQw3dS6fIzLlfDbeXutjYWBcXF+ftGCLiow4czeKuKStYsfMwY/o0549XtCaonI7uzWyFcy62KNvoilwR8WnbD6Qz4p3lHDiaxUtDY3TR1W+k0hcRn7Vg8wHunbaS4KByTB/dPaDey7akqPRFxCct2HyA299dTss6lXlzeCyNalb0dqQyQaUvIj5ny/6jjJm6kpZ1KjPrrot1p8xipGuVRcSnHEzPYuTk5YSGBDFpRBcVfjFT6YuIz0jLyGb4pGUkH8nizeGdaVi9grcjlTkqfRHxCclHMrn5rSVsTU5n4vBYOjaucf6NpMg0pi8iXpd4OIPhk5ax70gmE4d3pncr3Y+rpKj0RcSrZq9J4vFPfgIHk0d2pUtkTW9HKtNU+iLiFUczsxn32To+XrWHzk1q8OKNMZqWWQpU+iJSqpxzfLMhmb9/uYGElGM80Lcl9/ZpQbDe+KRUqPRFpNSkZpzg3mmrWLj1IA2qhTHtzu50b1bL27ECikpfRErFoWMnuPnNJWw/cIynr2nHTV0a6ejeC1T6IlLiUtKzuOWtpew4eIxJI2K5pKVm53iLSl9EStTB9CxufnMJuw5l8PaILvRsUdvbkQKaSl9ESkzykUxueWspuw/nF36P5ip8b1Ppi0iJWJ5wiDFTV3I0M4d3RnTl4uY6YesLVPoiUqycc7zzYwJ//3IDETUq8N6orlxUr6q3Y4mHSl9Eik16Vg7/O2sNc+L3cUXbujw/pANVdZdMn6LSF5FisXp3Kn/8YDUJKcf4vwEXcUevZpTT+9j6HJW+iPwm2bl5vPLNFl6dv426VUKZckc3nbD1YSp9Eblge1KP89DM1SzZfojrOjXkyaujNJzj41T6IlJkzjkmLtjO+LmbMYPxQzpwXacIb8eSQlDpi0iRnMjJY9zseKYv282VUXUZNyiKBnqHK7+h0heRQlubmMqfP1vHmt2pjOnTnIeuaK2TtX5GpS8i55Wb53hh7mZem7+VmpXK8+rNnfhddH1vx5ILcN7SN7MwYAEQ6ln/Q+fcODOLASYAYUAOcI9zbplnm8eAUUAucL9z7qsSyi8iJSw14wR3vhfH8oTD3NA5gj8PaquTtX6sMEf6WcBlzrl0MwsBFprZHOAp4K/OuTlmNgB4DvgfM2sLDAWigAbAPDNr5ZzLLaGvQURKSPyeNB6auYYdKcd44cYOXNtRJ2v93XlL3znngHTP0xDPh/N8nLy2uhqQ5Hk8GJjhnMsCdpjZVqArsLgYc4tICTqRk8fYGauYE7+PSuWDeHdEF3ro7phlQqHG9M0sCFgBtABedc4tNbMHgK/M7F9AOaCHZ/WGwJICmyd6lomInxg3ex1z4vfxYN9W3HpxE2pWKu/tSFJMCvW2Nc65XOdcDBABdDWzdsDdwIPOuUbAg8Akz+pnOpXvTl9gZqPNLM7M4g4cOHBh6UWk2M1dv5/py3bxh97NGNu3pQq/jCnSe5U551KB+UB/4DbgY8+nZpE/hAP5R/aNCmwWwc9DPwVfa6JzLtY5FxsernfREfEFB45m8djHa2lTvyoPXdHa23GkBJy39M0s3Myqex5XAPoCG8kv8t6e1S4DtngezwaGmlmomTUFWgLLiju4iBSf3DzHB8t3cc2rP5KelcOLN8ZQPljvX1sWFWZMvz4w2TOuXw6Y6Zz7wsxSgZfMLBjIBEYDOOfWmdlMYD35UznHaOaOiO86kZPHw7PWMHtNEtER1Xh+SAda16vi7VhSQix/co53xcbGuri4OG/HEAkoacezmb8pmVe+3crW5HQe6d+au3s3x0xX2PoLM1vhnIstyja6IlckAI3/ehOvf7+N7FxHo5oVeGdEF/pcVMfbsaQUqPRFAsyE77fx8rdbGRhdn9t7NqVjo+q6f04AUemLBJD3FifwzJyNDIyuz0tDOxKksg84Oj0vEiBmxu3mL5+to2+burxwY4wKP0Cp9EUCwOdrknj0o7Vc0rI2/765IyFB+tYPVPqXFynj/hu/jwc/WE1sk5pMvDWWsJAgb0cSL1Lpi5RhM5fv5p6pK2jXsBqTRsRSobwKP9DpRK5IGTXh+208M2cjl7SszYRhnakUqm93UemLlDl5eY5n/ruRiQu2MzC6PuOH6JYK8jOVvkgZkpKexROfxjMnfh/DL27Ck4OiNAdffkGlL1IGOOeYvSaJv36+nqOZ2fzfgIu485JmuqWC/IpKX8TPxSUc4un/bGDN7lQ6RFTjnzd0p1Vd3TBNzkylL+Kn0jKyeX7uJt5bvJP61cJ47vpoft8pQhddyTmp9EX8TE5uHtOX7WL83M2kHs9mRI9I/vfK1pqdI4Wi/yUifiI3z7FgywH+9dUm1iUdoXuzmvxlYBRtG1T1djTxIyp9ER+Wl+f4cdtBPli+mwWbD3AkM4cG1cJ49eZODGhfTydqpchU+iI+6suf9vLsfzeyMyWD6hVDuKpdfS5pVZt+betp3r1cMJW+iI9xzvHvb7fy/NzNtKlflZeGxnBlVD3dM0eKhUpfxIc453juq028Pn8b13VsyHPXRxOsO2JKMVLpi/iQt39M4PX527ilW2OeHtxOV9NKsdMhhIiPWLwthb9/uYF+beuq8KXEqPRFfMCe1OPcO20lkbUq8vyQDip8KTEqfREv25t2nFsnLSUrJ483bo2lSliItyNJGaYxfREvWrHzMGOmriQ9K4d3bu9CizqVvR1JyjiVvkgpy8nNY96GZD5emcjcDftpVKMiH4zoTlSDat6OJgFApS9SSlLSs3h3UQIfr9zDntTj1K5cnrt7N+cPvZtTrYKGdKR0qPRFSphzjk9X7+Gpz9eTdjybXi3D+fPAtvRtU0dz8KXUnbf0zSwMWACEetb/0Dk3zvO5+4B7gRzgP865RzzLHwNGAbnA/c65r0omvohvO5iexaMfrWXehmRiGlXn2d9H07qe7nUv3lOYI/0s4DLnXLqZhQALzWwOUAEYDEQ757LMrA6AmbUFhgJRQANgnpm1cs7llsyXIOKbftx6kAc+WE3a8Wye+F0bbu/ZVPe6F687b+k75xyQ7nka4vlwwN3AM865LM96yZ51BgMzPMt3mNlWoCuwuJizi/iknNw8Xpi3mdfmb6N5eGXeG9mVNvV1+2PxDYUaUDSzIDNbDSQDc51zS4FWwCVmttTMvjezLp7VGwK7C2ye6FkmUualZpxg2KSlvPrdNoZ0bsTse3uq8MWnFOpErmdoJsbMqgOfmFk7z7Y1gO5AF2CmmTUDzvT7qzt9gZmNBkYDNG7c+MLSi/iQ+D1p3Dd9FXsOH+f5Gzrw+84R3o4k8itFmjrgnEsF5gP9yT+C/9jlWwbkAbU9yxsV2CwCSDrDa010zsU652LDw8MvML6I9znneH/JTq57bREZJ3KYemc3Fb74rPOWvpmFe47wMbMKQF9gI/ApcJlneSugPHAQmA0MNbNQM2sKtASWlUx8Ee/KzM7lkQ/X8udP4+nZohZzxl5Kl8ia3o4lclaFGd6pD0w2syDyf0jMdM59YWblgbfNLB44AdzmOem7zsxmAuvJn8o5RjN3pCxasfMQj338E5v3p3P/ZS14oG8r3ShNfJ7l97R3xcbGuri4OG/HECmU1IwT/OvrTUxZsosG1cL423Xt6dO6jrdjSQAysxXOudiibKMrckXOISc3j4SUY2zZn87GfUdZtuMQK3YdJic3j5E9m/JQv1ZUCtW3kfgP/W8VOc2RzGyWbEvhv+v28c2GZNKOZwNgBm3qVWV49yZcHxvBRfU0FVP8j0pfBMjLcyzYcoApS3by3aYD5OY5qoYF07dNXXq1rE3LOlVoXqcSFcvrW0b8m/4HS8DbmXKM+6avYm1iGuFVQrnjkqb0bhVOl8iahOiGaFLGqPQlYOXmOWav2cNfP18PwD+vj2ZwTEPKB6vopexS6UvAyczO5bPVe3jj++1sP3iMdg2r8u+bOhFZu5K3o4mUOJW+BIy0jGze/GE7U5buJDUjmzb1qzJhWCf6ta2n+fUSMFT6UuYlHs5g4oLtzIpL5Hh2Lv2j6nFbj0i6N6uJmcpeAotKX8qszOxcXvl2C2/+sAMcXB3TgJE9m9K2gaZaSuBS6UuZtPtQBndPXUH8niNc27Eh/3tlaxpUr+DtWCJep9KXMue7jck88MFqnHNMui2Wy9vU9XYkEZ+h0pcyZdLCHTz9xXra1K/KG8M607hWRW9HEvEpKn0pE5xzPDNnI28s2E7/qHq8cGMMFcoHeTuWiM9R6Yvfy8nN47GPf2LWikSGX9yEcYOi9AbkImeh0he/lnDwGI98uJZlCYd4oG9Lxl7eUtMwRc5BpS9+KTfP8e6iBP751UZCgsrpPWlFCkmlL35n24F0HvlwLSt2Hubyi+rwt2vbU69amLdjifgFlb74jaOZ2bz8zRbeXZRAxfLBvHBjB66JaajhHJEiUOmLz9uVksGUpTuZvmwXRzNzGBIbwcNXtqZOFR3dixSVSl981rYD6bz8zRZmr0minBlXtavH6EubER1R3dvRRPyWSl98zu5DGbwwbzOfrtpDaHAQf7i0OcMvbqLbKIgUA5W++JS4hEPc8V4cmdm53HFJM0Zf2ozalUO9HUukzFDpi8/4z9q9PPDBKhpWr8DkMT1pUktvaiJS3FT64lX70jJ54tOf2LD3KHtSj9MlsgZvDe9CtYoh3o4mUiap9MVrVu06zOj3V3AsK4fLLqrDVe3q8VC/1rpnjkgJUumLV3y2eg//++Fa6lYNZeodPWlVt4q3I4kEBJW+lKqsnFzGf72ZNxZsp2vTmkwY1pmalcp7O5ZIwDhv6ZtZGLAACPWs/6FzblyBzz8M/BMId84d9Cx7DBgF5AL3O+e+KoHs4kd2H8pg3ob9zFi2m037j3Jzt8Y8OSiK8sHlvB1NJKAU5kg/C7jMOZduZiHAQjOb45xbYmaNgCuAXSdXNrO2wFAgCmgAzDOzVs653BLILz4ufk8ar83fypz4fTgHzcMr6d2sRLzovKXvnHNAuudpiOfDeZ6/ADwCfFZgk8HADOdcFrDDzLYCXYHFxRVafN/S7Sm8On8bCzYfoEpoMHf3bs6NXRppGqaIlxVqTN/MgoAVQAvgVefcUjO7GtjjnFtz2g2vGgJLCjxP9Cw7/TVHA6MBGjdufGHpxaekZWTz4cpEvlibxKpdqdSuXJ5H+rdmWPcmVA3TFEwRX1Co0vcMzcSYWXXgEzOLBh4H+p1h9TPd8tD9aoFzE4GJALGxsb/6vPiPzOxc3lywnQnfb+PYiVza1K/KXwa25eZujQkL0fRLEV9SpNk7zrlUM5tP/hBOU+DkUX4EsNLMupJ/ZN+owGYRQFKxpBWfs2jbQR7/JJ4dB4/RP6oe91/ekrYNqno7loicRWFm74QD2Z7CrwD0BZ51ztUpsE4CEOucO2hms4FpZjae/BO5LYFlJZJevCY7N4+Xv9nCv7/bSuOaFXl/VFcuaRnu7Vgich6FOdKvD0z2jOuXA2Y6574428rOuXVmNhNYD+QAYzRzp2zZmpzOQ7PWsGZ3Ktd3juDpwe10Fa2InyjM7J21QMfzrBN52vO/AX/7TcnE5ySlHuflb7Ywa0UilUODee2WTgxoX9/bsUSkCHRFrpxXSnoWr83fxvtLdoKDW7s3YUyfFoRX0S2PRfx2RG/kAAALaUlEQVSNSl/O6mhmNm/+sINJP2zneHYuv+8Uwdi+LYmoUdHb0UTkAqn05Vdy8xwfLN/N819vIuXYCc/dL1vRoo5uiibi71T68gs/Jabx6MdrWZd0hK6RNXl7RBs6NNJ70oqUFSp9AcA5x5Slu3j68/XUqBTCyzd1ZFB0fU672lpE/JxKX0jPyuHxT37is9VJ/E/rcF4YEkMN3e5YpExS6Qe4+D1p3Dd9FTtTjvHQFa0Y06cF5crp6F6krFLpB6ik1OO8MHczH61MJLxKKNPu7E73ZrW8HUtESphKP4CkpGfx6eokvt24n2U7DmEYI3s25d7LWlC9ooZzRAKBSj8A/JSYxts/7uCLtUlk5zpa163C7T2bMvziJppzLxJgVPplkHOOdUlH+HrdPr5ev5+N+45SqXwQt3Rrws3dGutNyEUCmEq/DEg+ksnsNUms33uEw8dOsGnfUZLSMilnENukJn+9OoprOzXUG5mIiErfn+1KyeDJz9fx3aZknIMG1cKoWbk8MY2rM7ZVOH3b1KVWZd0fR0R+ptL3UwkHjzHkjcUcz87lvj4tGNyxIc3DK3s7loj4OJW+H9p9KINb3lpKdm4eH97Vg9b1NEYvIoWj0vczK3Ye5t5pKzmWlcO0O7ur8EWkSFT6fuJIZjbjv97M5MUJNKxegemjuxPVoJq3Y4mIn1Hp+zDnHKt2p/L+4p18tW4fx7NzGdatCX+66iIqh+qfTkSKTs3hQ5xzpGflsHT7Ib7ffIBvNuwnKS2TyqHBXNOxITd3bUy7hjq6F5ELp9L3spMXUs2J38uXP+1jx8FjAFQsH0SP5rV5qF9rroiqqzn2IlIsVPpekppxgilLdjJrRSI7UzIIKmf0aF6La2Ia0iWyBrGRNSkfXM7bMUWkjFHpl7Kc3DymLt3FC/M2k5qRzcXNanF37+b0i6pHTd3DXkRKmEq/FP2w5QBPfb6eLcnp9Gheiz8PbEub+lW9HUtEAohKvxTsSsng719u4L/r9tGkVkXeuLUz/drW1VsRikipU+mXkMzsXD5fk8T7S3by0540woKDeLhfK+68tBmhwUHejiciAUqlXwwys3PZmpxOm/pV2bz/KJMW7uCr+H0czcrhonpVeODyVtzYpRH1qoV5O6qIBLjzlr6ZhQELgFDP+h8658aZ2T+BQcAJYBtwu3Mu1bPNY8AoIBe43zn3VQnl97rtB9K5e8pKNu0/Sq1K5Uk5doLKocFc1a4eV8c0oFeL2hrGERGfUZgj/SzgMudcupmFAAvNbA4wF3jMOZdjZs8CjwF/MrO2wFAgCmgAzDOzVs653BL6GrziSGY28zcd4P8+/omQIGPs5S1Zm5hKu4bVuKNXM6pV1Lx6EfE95y1955wD0j1PQzwfzjn3dYHVlgDXex4PBmY457KAHWa2FegKLC621F6UlpHNi99s5r3FO8nNc3SIqMZrwzrTsHoFb0cTETmvQo3pm1kQsAJoAbzqnFt62iojgQ88jxuS/0PgpETPMr+VlpHN+r1HmL8pmQ/idpN2PJuhXRrRu1U4fS6qoxOzIuI3ClX6nqGZGDOrDnxiZu2cc/EAZvY4kANM9ax+pgFsd/oCMxsNjAZo3LjxBUQvGdm5efy0J432DasRElSOL3/ay/3TV5GT5wgqZ1x+UR3G9m2pO1yKiF8q0uwd51yqmc0H+gPxZnYbMBC43DMMBPlH9o0KbBYBJJ3htSYCEwFiY2N/9UOhNDjnWJ5wmFe+3cL6pCMMvziSH7ceZFnCIQZ1aMDfrm3HXz5bR6u6VfjjFa3o1qwmVXQPHBHxY4WZvRMOZHsKvwLQF3jWzPoDfwJ6O+cyCmwyG5hmZuPJP5HbElhW/NEvXFpGNp+vTWLKkp1s3HeUahVCqFc1jBfmbaZqWDAdGlXn8zVJrN59mJRjWbw9IpboiOreji0i8psV5ki/PjDZM65fDpjpnPvCc4I2FJjrmZK4xDl3l3NunZnNBNaTP+wzxhdm7mTl5PLVuv18uCKRhVsOkOcgqkFVnv19e67u0JDyweWISzhEq7pVqBQazO9e/oEtyenc0q2xCl9Eygz7eVTGe2JjY11cXFyxvmZWTi7rk46walcqaxJTWbQthQNHs2hYvQLXdGxAv7b1iI6odtY59PuPZLJo20EGtK+vE7Ui4pPMbIVzLrYo2/j9Fbn70jJPXemanpXDlCU7+W5jMqt2pXIiNw+AulVD6RJZgyGxjbi0ZTjlyp3/Yqm6VcO4tmNEiWYXESltfl36yxMOcctbSxk3qC0h5crx3FebOJieRVSDqtzWowkdG9cgtkkN6lTV7Q9ERMDPS79dg2r0alGbxz+JByC2SQ3eHN6Zjo1reDmZiIhv8uvSr1A+iDdu7cxL87YQUaMCN3ZppPvciIicg1+XPkBIUDkevrK1t2OIiPgFvQmriEgAUemLiAQQlb6ISABR6YuIBBCVvohIAFHpi4gEEJW+iEgAUemLiAQQn7jLppkdAHYWcbPawMESiFOSlLl0KHPpUObSca7MTZxz4UV5MZ8o/QthZnFFvaWotylz6VDm0qHMpaO4M2t4R0QkgKj0RUQCiD+X/kRvB7gAylw6lLl0KHPpKNbMfjumLyIiRefPR/oiIlJEfln6ZtbfzDaZ2VYze9TbeU4yswQz+8nMVptZnGdZTTOba2ZbPH/WKLD+Y56vYZOZXVmKOd82s2Qziy+wrMg5zayz5+vdamYvWwm+g81ZMj9pZns8+3u1mQ3wlcxm1sjMvjOzDWa2zszGepb77H4+R2Zf3s9hZrbMzNZ4Mv/Vs9yX9/PZMpfOfnbO+dUHEARsA5oB5YE1QFtv5/JkSwBqn7bsOeBRz+NHgWc9j9t6socCTT1fU1Ap5bwU6ATE/5acwDLgYsCAOcBVpZz5SeDhM6zr9cxAfaCT53EVYLMnl8/u53Nk9uX9bEBlz+MQYCnQ3cf389kyl8p+9scj/a7AVufcdufcCWAGMNjLmc5lMDDZ83gycE2B5TOcc1nOuR3AVvK/thLnnFsAHPotOc2sPlDVObfY5f/ve6/ANqWV+Wy8ntk5t9c5t9Lz+CiwAWiID+/nc2Q+G1/I7Jxz6Z6nIZ4Ph2/v57NlPptizeyPpd8Q2F3geSLn/o9ZmhzwtZmtMLPRnmV1nXN7If+bCqjjWe5rX0dRczb0PD59eWm718zWeoZ/Tv4K71OZzSwS6Ej+EZ1f7OfTMoMP72czCzKz1UAyMNc55/P7+SyZoRT2sz+W/pnGrHxlClJP51wn4CpgjJldeo51ffnrKOhsOX0h/+tAcyAG2As871nuM5nNrDLwEfCAc+7IuVY9wzJfyezT+9k5l+uciwEiyD8CbneO1X05c6nsZ38s/USgUYHnEUCSl7L8gnMuyfNnMvAJ+cM1+z2/huH5M9mzuq99HUXNmeh5fPryUuOc2+/55skD3uTn4TGfyGxmIeSX51Tn3MeexT69n8+U2df380nOuVRgPtAfH9/PJxXMXFr72R9LfznQ0syamll5YCgw28uZMLNKZlbl5GOgHxBPfrbbPKvdBnzmeTwbGGpmoWbWFGhJ/kkZbylSTs+vzEfNrLtnxsDwAtuUipPf1B7Xkr+/fSKz5/UnARucc+MLfMpn9/PZMvv4fg43s+qexxWAvsBGfHs/nzFzqe3nkjg7XdIfwADyZxZsAx73dh5Ppmbkn2FfA6w7mQuoBXwDbPH8WbPANo97voZNlODMlzNknU7+r4/Z5B8tjLqQnECs5z/mNuDfeC72K8XM7wM/AWs93xj1fSUz0Iv8X7XXAqs9HwN8eT+fI7Mv7+doYJUnWzzwF89yX97PZ8tcKvtZV+SKiAQQfxzeERGRC6TSFxEJICp9EZEAotIXEQkgKn0RkQCi0hcRCSAqfRGRAKLSFxEJIP8fLjcfJ6BI7CwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1)\n",
+    "ax.plot(res.trend, label='Tendance')\n",
+    "ax.plot(weeks_untils_2025, ppm_preds, color='r', label='prédictions')\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ce modèle semble plus cohérent que le précédent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}