From c3ac4cef6016dd1ecdcb8585d8943b4c7b641924 Mon Sep 17 00:00:00 2001
From: cmalzer <claudia.malzer@geo.uni-goettingen.de>
Date: Wed, 4 Sep 2019 23:05:13 +0200
Subject: [PATCH] update

---
 .../(3) R\303\244umliche Verschneidung.ipynb" | 302 +---------------
 ...und Darstellung des Versorgungsgrads.ipynb | 333 +-----------------
 2 files changed, 35 insertions(+), 600 deletions(-)

diff --git "a/Aufgabe/(3) R\303\244umliche Verschneidung.ipynb" "b/Aufgabe/(3) R\303\244umliche Verschneidung.ipynb"
index 6278f49..9bd9df1 100644
--- "a/Aufgabe/(3) R\303\244umliche Verschneidung.ipynb"	
+++ "b/Aufgabe/(3) R\303\244umliche Verschneidung.ipynb"	
@@ -16,43 +16,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   Unnamed: 0   ID                Fachgebiet1                  Adresse    PLZ  \\\n",
-      "0           0    2  Hals-Nasen-Ohrenheilkunde              Waitzstr. 7  22607   \n",
-      "1           1   52  Hals-Nasen-Ohrenheilkunde            Neuer Wall 43  20354   \n",
-      "2           2  102  Hals-Nasen-Ohrenheilkunde    Möllner Landstr. 26 a  22111   \n",
-      "3           3  125  Hals-Nasen-Ohrenheilkunde  Wandsbeker Marktstr. 73  22041   \n",
-      "4           4  148  Hals-Nasen-Ohrenheilkunde                  Sand 35  21073   \n",
-      "\n",
-      "   Privatarzt    Stadt     Land  \\\n",
-      "0         NaN  Hamburg  Germany   \n",
-      "1         NaN  Hamburg  Germany   \n",
-      "2         NaN  Hamburg  Germany   \n",
-      "3         NaN  Hamburg  Germany   \n",
-      "4         NaN  Hamburg  Germany   \n",
-      "\n",
-      "                              Adresse_kombiniert  \\\n",
-      "0              Waitzstr. 7,22607,Hamburg,Germany   \n",
-      "1            Neuer Wall 43,20354,Hamburg,Germany   \n",
-      "2    Möllner Landstr. 26 a,22111,Hamburg,Germany   \n",
-      "3  Wandsbeker Marktstr. 73,22041,Hamburg,Germany   \n",
-      "4                  Sand 35,21073,Hamburg,Germany   \n",
-      "\n",
-      "                                            Geocoded   latitude  longitude  \n",
-      "0  Änderungsschneiderei, 7, Waitzstraße, Groß Flo...  53.559497   9.885421  \n",
-      "1  van Laack, 43, Neuer Wall, Neustadt, Hamburg-M...  53.551104   9.989545  \n",
-      "2  Möllner Landstraße, Billstedt, Hamburg-Mitte, ...  53.539653  10.103738  \n",
-      "3  Adler-Apotheke, 73, Wandsbeker Marktstraße, Wa...  53.572075  10.066240  \n",
-      "4  Damian Apotheke, 35, Sand, Harburg, Hamburg, 2...  53.461323   9.979341  \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": []
   },
   {
@@ -67,45 +33,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   OBJECTID          NAME  code    Shape_Leng  xlsID        Bezirk  Einwohner  \\\n",
-      "0         1  Barmbek-Süd     3   7442.385726     49  Hamburg-Nord      31360   \n",
-      "1         3    Duvenstedt     0  13986.241699     68      Wandsbek       6220   \n",
-      "2         7        Rissen     0  26714.578295     32        Altona      14763   \n",
-      "3         8    Blankenese     0  13233.990172     29        Altona      12807   \n",
-      "4        10     Volksdorf     0  17677.783156     71      Wandsbek      19989   \n",
-      "\n",
-      "   Einw_u18  Anteil_u18  ü65  ...  Gewaltdeli  Gew_Dichte  Diebstahl  \\\n",
-      "0      2895         9.2  5099  ...          65           2       1654   \n",
-      "1      1573        25.3  1103  ...           6           1        137   \n",
-      "2      2542        17.2  4398  ...          19           1        439   \n",
-      "3      2243        17.5  3541  ...          28           2        472   \n",
-      "4      4100        20.5  5038  ...          35           2        572   \n",
-      "\n",
-      "   Die_Dichte    Shape_Le_1   Shape_Area  OBJECTID_1  FREQUENCY        NAME_1  \\\n",
-      "0          53   7442.385726   3121774.74           3          1  Barmbek-Süd   \n",
-      "1          22  13986.241699   6700717.44           8          1    Duvenstedt   \n",
-      "2          30  26714.578295  16812700.56          33          1        Rissen   \n",
-      "3          37  13233.990172   7839792.18           6          3    Blankenese   \n",
-      "4          29  17677.783156  11311905.78          39          2     Volksdorf   \n",
-      "\n",
-      "                                            geometry  \n",
-      "0  POLYGON ((568738.5000999998 5936170.3463, 5687...  \n",
-      "1  POLYGON ((574154.1001000004 5950369.9463, 5740...  \n",
-      "2  POLYGON ((552240.9001000002 5939762.5463, 5522...  \n",
-      "3  POLYGON ((551279.1001000004 5934212.5463, 5507...  \n",
-      "4  POLYGON ((578633.7001 5943958.3463, 578496.300...  \n",
-      "\n",
-      "[5 rows x 88 columns]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": []
   },
   {
@@ -117,30 +47,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            NAME  Einwohner                                           geometry\n",
-      "0   Barmbek-Süd      31360  POLYGON ((568738.5000999998 5936170.3463, 5687...\n",
-      "1     Duvenstedt       6220  POLYGON ((574154.1001000004 5950369.9463, 5740...\n",
-      "2         Rissen      14763  POLYGON ((552240.9001000002 5939762.5463, 5522...\n",
-      "3     Blankenese      12807  POLYGON ((551279.1001000004 5934212.5463, 5507...\n",
-      "4      Volksdorf      19989  POLYGON ((578633.7001 5943958.3463, 578496.300...\n",
-      "..           ...        ...                                                ...\n",
-      "95     St. Georg      10279  POLYGON ((568009.5000999998 5934687.146299999,...\n",
-      "96    Hohenfelde       8904  POLYGON ((566795.1001000004 5935675.9463, 5669...\n",
-      "97   Altona-Nord      33052  POLYGON ((563605.9812000003 5935419.763699999,...\n",
-      "98  Sternschanze       7723  POLYGON ((564561.5401999997 5935584.9878, 5644...\n",
-      "99    Hafen-City       1097  POLYGON ((566813.6781000001 5933346.6083, 5669...\n",
-      "\n",
-      "[100 rows x 3 columns]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": []
   },
   {
@@ -154,21 +63,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'aerzte' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-1e805efaf6b5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mshapely\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPoint\u001b[0m \u001b[0;31m#dieses Format kann von einem GeoDataFrame verstanden werden\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mgeometry\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mPoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxy\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mxy\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maerzte\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"longitude\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maerzte\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"latitude\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m#erstelle Punktgeometrie aus Koordinaten\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mcrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'init'\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m'epsg:4326'\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;31m#erstmal verwenden wir WGS1984 als Koordinatensystem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'aerzte' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from shapely.geometry import Point #dieses Format kann von einem GeoDataFrame verstanden werden\n",
     "geometry = [Point(xy) for xy in zip(aerzte[\"longitude\"], aerzte[\"latitude\"])] #erstelle Punktgeometrie aus Koordinaten\n",
@@ -184,82 +81,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     Unnamed: 0    ID                Fachgebiet1                  Adresse  \\\n",
-      "0             0     2  Hals-Nasen-Ohrenheilkunde              Waitzstr. 7   \n",
-      "1             1    52  Hals-Nasen-Ohrenheilkunde            Neuer Wall 43   \n",
-      "2             2   102  Hals-Nasen-Ohrenheilkunde    Möllner Landstr. 26 a   \n",
-      "3             3   125  Hals-Nasen-Ohrenheilkunde  Wandsbeker Marktstr. 73   \n",
-      "4             4   148  Hals-Nasen-Ohrenheilkunde                  Sand 35   \n",
-      "..          ...   ...                        ...                      ...   \n",
-      "115         131  3947  Hals-Nasen-Ohrenheilkunde  Tangstedter Landstr. 77   \n",
-      "116         132  3971  Hals-Nasen-Ohrenheilkunde          Spitalerstr. 32   \n",
-      "117         133  4030  Hals-Nasen-Ohrenheilkunde           Erdkampsweg 55   \n",
-      "118         134  4042  Hals-Nasen-Ohrenheilkunde         Bismarckstr. 115   \n",
-      "119         135  4057  Hals-Nasen-Ohrenheilkunde   Wandsbeker Marktstr. 8   \n",
-      "\n",
-      "       PLZ  Privatarzt    Stadt     Land  \\\n",
-      "0    22607         NaN  Hamburg  Germany   \n",
-      "1    20354         NaN  Hamburg  Germany   \n",
-      "2    22111         NaN  Hamburg  Germany   \n",
-      "3    22041         NaN  Hamburg  Germany   \n",
-      "4    21073         NaN  Hamburg  Germany   \n",
-      "..     ...         ...      ...      ...   \n",
-      "115  22415         NaN  Hamburg  Germany   \n",
-      "116  20095         NaN  Hamburg  Germany   \n",
-      "117  22335         NaN  Hamburg  Germany   \n",
-      "118  20253         NaN  Hamburg  Germany   \n",
-      "119  22041         NaN  Hamburg  Germany   \n",
-      "\n",
-      "                                Adresse_kombiniert  \\\n",
-      "0                Waitzstr. 7,22607,Hamburg,Germany   \n",
-      "1              Neuer Wall 43,20354,Hamburg,Germany   \n",
-      "2      Möllner Landstr. 26 a,22111,Hamburg,Germany   \n",
-      "3    Wandsbeker Marktstr. 73,22041,Hamburg,Germany   \n",
-      "4                    Sand 35,21073,Hamburg,Germany   \n",
-      "..                                             ...   \n",
-      "115  Tangstedter Landstr. 77,22415,Hamburg,Germany   \n",
-      "116          Spitalerstr. 32,20095,Hamburg,Germany   \n",
-      "117           Erdkampsweg 55,22335,Hamburg,Germany   \n",
-      "118         Bismarckstr. 115,20253,Hamburg,Germany   \n",
-      "119   Wandsbeker Marktstr. 8,22041,Hamburg,Germany   \n",
-      "\n",
-      "                                              Geocoded   latitude  longitude  \\\n",
-      "0    Änderungsschneiderei, 7, Waitzstraße, Groß Flo...  53.559497   9.885421   \n",
-      "1    van Laack, 43, Neuer Wall, Neustadt, Hamburg-M...  53.551104   9.989545   \n",
-      "2    Möllner Landstraße, Billstedt, Hamburg-Mitte, ...  53.539653  10.103738   \n",
-      "3    Adler-Apotheke, 73, Wandsbeker Marktstraße, Wa...  53.572075  10.066240   \n",
-      "4    Damian Apotheke, 35, Sand, Harburg, Hamburg, 2...  53.461323   9.979341   \n",
-      "..                                                 ...        ...        ...   \n",
-      "115  Ärztehaus Langenhorn, 77, Tangstedter Landstra...  53.651396  10.017983   \n",
-      "116  Praxis Forster & Team, 32, Spitalerstraße, Alt...  53.551292  10.000149   \n",
-      "117  55, Erdkampsweg, Fuhlsbüttel, Hamburg-Nord, Ha...  53.629615  10.022251   \n",
-      "118  Fachärzte für HNO, 115, Bismarckstraße, Hohelu...  53.577285   9.971537   \n",
-      "119  First Cut, 8, Wandsbeker Marktstraße, Marienth...  53.570492  10.061576   \n",
-      "\n",
-      "                                 geometry  \n",
-      "0            POINT (9.8854209 53.5594975)  \n",
-      "1             POINT (9.989545 53.5511039)  \n",
-      "2           POINT (10.1037383 53.5396527)  \n",
-      "3            POINT (10.0662405 53.572075)  \n",
-      "4            POINT (9.9793412 53.4613231)  \n",
-      "..                                    ...  \n",
-      "115  POINT (10.0179825746869 53.65139605)  \n",
-      "116         POINT (10.0001492 53.5512924)  \n",
-      "117  POINT (10.0222513142276 53.62961545)  \n",
-      "118   POINT (9.9715366 53.57728520000001)  \n",
-      "119          POINT (10.0615756 53.570492)  \n",
-      "\n",
-      "[120 rows x 13 columns]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": []
   },
   {
@@ -271,17 +95,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'init': 'epsg:32632'}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": []
   },
   {
@@ -293,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
@@ -307,95 +123,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     Unnamed: 0    ID                Fachgebiet1                  Adresse  \\\n",
-      "0             0     2  Hals-Nasen-Ohrenheilkunde              Waitzstr. 7   \n",
-      "19           19   592  Hals-Nasen-Ohrenheilkunde              Waitzstr. 7   \n",
-      "33           34   995  Hals-Nasen-Ohrenheilkunde             Waitzstr. 14   \n",
-      "87           99  2870  Hals-Nasen-Ohrenheilkunde          Beselerplatz 11   \n",
-      "107         122  3722  Hals-Nasen-Ohrenheilkunde             Waitzstr. 14   \n",
-      "..          ...   ...                        ...                      ...   \n",
-      "99          114  3387  Hals-Nasen-Ohrenheilkunde             Bornheide 11   \n",
-      "94          108  3148  Hals-Nasen-Ohrenheilkunde              Wolffstr. 9   \n",
-      "106         121  3693  Hals-Nasen-Ohrenheilkunde           Marktpassage 6   \n",
-      "115         131  3947  Hals-Nasen-Ohrenheilkunde  Tangstedter Landstr. 77   \n",
-      "119         135  4057  Hals-Nasen-Ohrenheilkunde   Wandsbeker Marktstr. 8   \n",
-      "\n",
-      "       PLZ  Privatarzt    Stadt     Land  \\\n",
-      "0    22607         NaN  Hamburg  Germany   \n",
-      "19   22607         NaN  Hamburg  Germany   \n",
-      "33   22607         NaN  Hamburg  Germany   \n",
-      "87   22607         NaN  Hamburg  Germany   \n",
-      "107  22607         NaN  Hamburg  Germany   \n",
-      "..     ...         ...      ...      ...   \n",
-      "99   22549         NaN  Hamburg  Germany   \n",
-      "94   22525         NaN  Hamburg  Germany   \n",
-      "106  21149         NaN  Hamburg  Germany   \n",
-      "115  22415         NaN  Hamburg  Germany   \n",
-      "119  22041         NaN  Hamburg  Germany   \n",
-      "\n",
-      "                                Adresse_kombiniert  \\\n",
-      "0                Waitzstr. 7,22607,Hamburg,Germany   \n",
-      "19               Waitzstr. 7,22607,Hamburg,Germany   \n",
-      "33              Waitzstr. 14,22607,Hamburg,Germany   \n",
-      "87           Beselerplatz 11,22607,Hamburg,Germany   \n",
-      "107             Waitzstr. 14,22607,Hamburg,Germany   \n",
-      "..                                             ...   \n",
-      "99              Bornheide 11,22549,Hamburg,Germany   \n",
-      "94               Wolffstr. 9,22525,Hamburg,Germany   \n",
-      "106           Marktpassage 6,21149,Hamburg,Germany   \n",
-      "115  Tangstedter Landstr. 77,22415,Hamburg,Germany   \n",
-      "119   Wandsbeker Marktstr. 8,22041,Hamburg,Germany   \n",
-      "\n",
-      "                                              Geocoded   latitude  longitude  \\\n",
-      "0    Änderungsschneiderei, 7, Waitzstraße, Groß Flo...  53.559497   9.885421   \n",
-      "19   Änderungsschneiderei, 7, Waitzstraße, Groß Flo...  53.559497   9.885421   \n",
-      "33   Volksbank, 14, Waitzstraße, Groß Flottbek, Alt...  53.559661   9.884339   \n",
-      "87   Köpi-Bar, 11, Beselerplatz, Groß Flottbek, Alt...  53.559699   9.887951   \n",
-      "107  Volksbank, 14, Waitzstraße, Groß Flottbek, Alt...  53.559661   9.884339   \n",
-      "..                                                 ...        ...        ...   \n",
-      "99   Deesmoor-Apotheke, 11, Bornheide, Osdorf, Alto...  53.582789   9.855380   \n",
-      "94   Ärztehaus, 9, Wolffstraße, Stellingen, Eimsbüt...  53.579169   9.933645   \n",
-      "106  6, Marktpassage, Neugraben, Neugraben-Fischbek...  53.470557   9.853249   \n",
-      "115  Ärztehaus Langenhorn, 77, Tangstedter Landstra...  53.651396  10.017983   \n",
-      "119  First Cut, 8, Wandsbeker Marktstraße, Marienth...  53.570492  10.061576   \n",
-      "\n",
-      "                                        geometry  index_right  \\\n",
-      "0    POINT (558649.2475721425 5934877.526663299)           22   \n",
-      "19   POINT (558649.2475721425 5934877.526663299)           22   \n",
-      "33   POINT (558577.3466108972 5934894.802930685)           22   \n",
-      "87   POINT (558816.5614957435 5934902.040986323)           22   \n",
-      "107  POINT (558577.3466108972 5934894.802930685)           22   \n",
-      "..                                           ...          ...   \n",
-      "99    POINT (556628.3174588972 5937444.31490787)           11   \n",
-      "94   POINT (561814.8077995365 5937106.683680706)           31   \n",
-      "106  POINT (556636.7223994726 5924957.048849167)           21   \n",
-      "115  POINT (567283.4957914886 5945218.391117038)           10   \n",
-      "119  POINT (570298.9358385277 5936260.128897806)           27   \n",
-      "\n",
-      "                   NAME  Einwohner  \n",
-      "0           Othmarschen      12335  \n",
-      "19          Othmarschen      12335  \n",
-      "33          Othmarschen      12335  \n",
-      "87          Othmarschen      12335  \n",
-      "107         Othmarschen      12335  \n",
-      "..                  ...        ...  \n",
-      "99               Osdorf      25203  \n",
-      "94           Stellingen      23037  \n",
-      "106  Neugraben-Fischbek      26782  \n",
-      "115          Langenhorn      41459  \n",
-      "119          Marienthal      12239  \n",
-      "\n",
-      "[120 rows x 16 columns]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": []
   },
   {
@@ -407,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/Aufgabe/(4) Berechnung und Darstellung des Versorgungsgrads.ipynb b/Aufgabe/(4) Berechnung und Darstellung des Versorgungsgrads.ipynb
index 27b5465..0bc0e58 100644
--- a/Aufgabe/(4) Berechnung und Darstellung des Versorgungsgrads.ipynb	
+++ b/Aufgabe/(4) Berechnung und Darstellung des Versorgungsgrads.ipynb	
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
@@ -30,59 +30,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Harburg               8\n",
-      "Neustadt              7\n",
-      "Bergedorf             6\n",
-      "Poppenbüttel         6\n",
-      "Othmarschen           6\n",
-      "Altona-Altstadt       5\n",
-      "Hamburg-Altstadt      5\n",
-      "Eimsbüttel           4\n",
-      "Winterhude            4\n",
-      "Ottensen              4\n",
-      "Barmbek-Nord          4\n",
-      "Eppendorf             4\n",
-      "Eidelstedt            4\n",
-      "Billstedt             4\n",
-      "Wandsbek              3\n",
-      "Uhlenhorst            3\n",
-      "Bramfeld              3\n",
-      "Niendorf              3\n",
-      "Hoheluft-West         3\n",
-      "Blankenese            3\n",
-      "Volksdorf             2\n",
-      "Fuhlsbüttel          2\n",
-      "Osdorf                2\n",
-      "Rahlstedt             2\n",
-      "Wilhelmsburg          2\n",
-      "Horn                  2\n",
-      "Tonndorf              2\n",
-      "Hausbruch             2\n",
-      "Schnelsen             2\n",
-      "Barmbek-Süd          1\n",
-      "Farmsen-Berne         1\n",
-      "Hamm-Nord             1\n",
-      "Langenhorn            1\n",
-      "Duvenstedt            1\n",
-      "Marienthal            1\n",
-      "Rissen                1\n",
-      "Neugraben-Fischbek    1\n",
-      "Stellingen            1\n",
-      "Hamm-Mitte            1\n",
-      "Rotherbaum            1\n",
-      "Harvestehude          1\n",
-      "Lohbrügge            1\n",
-      "Name: NAME, dtype: int64\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": []
   },
   {
@@ -103,30 +53,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                   NAME  ANZAHL_AERZTE\n",
-      "0           Othmarschen              6\n",
-      "1           Othmarschen              6\n",
-      "2           Othmarschen              6\n",
-      "3           Othmarschen              6\n",
-      "4           Othmarschen              6\n",
-      "..                  ...            ...\n",
-      "115              Osdorf              2\n",
-      "116          Stellingen              1\n",
-      "117  Neugraben-Fischbek              1\n",
-      "118          Langenhorn              1\n",
-      "119          Marienthal              1\n",
-      "\n",
-      "[120 rows x 2 columns]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": []
   },
   {
@@ -140,43 +69,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             NAME  Einwohner  \\\n",
-      "0    Barmbek-Süd      31360   \n",
-      "1      Duvenstedt       6220   \n",
-      "2          Rissen      14763   \n",
-      "3      Blankenese      12807   \n",
-      "4      Blankenese      12807   \n",
-      "..            ...        ...   \n",
-      "173     St. Georg      10279   \n",
-      "174    Hohenfelde       8904   \n",
-      "175   Altona-Nord      33052   \n",
-      "176  Sternschanze       7723   \n",
-      "177    Hafen-City       1097   \n",
-      "\n",
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-      "4    POLYGON ((551279.1001000004 5934212.5463, 5507...            3.0  \n",
-      "..                                                 ...            ...  \n",
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-      "177  POLYGON ((566813.6781000001 5933346.6083, 5669...            NaN  \n",
-      "\n",
-      "[178 rows x 4 columns]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": []
   },
   {
@@ -188,48 +83,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "NAME              object\n",
-      "Einwohner          int64\n",
-      "geometry          object\n",
-      "ANZAHL_AERZTE    float64\n",
-      "dtype: object\n",
-      "             NAME  Einwohner  \\\n",
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-      "1      Duvenstedt       6220   \n",
-      "2          Rissen      14763   \n",
-      "3      Blankenese      12807   \n",
-      "4      Blankenese      12807   \n",
-      "..            ...        ...   \n",
-      "173     St. Georg      10279   \n",
-      "174    Hohenfelde       8904   \n",
-      "175   Altona-Nord      33052   \n",
-      "176  Sternschanze       7723   \n",
-      "177    Hafen-City       1097   \n",
-      "\n",
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-      "4    POLYGON ((551279.1001000004 5934212.5463, 5507...            3.0  \n",
-      "..                                                 ...            ...  \n",
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-      "177  POLYGON ((566813.6781000001 5933346.6083, 5669...            0.0  \n",
-      "\n",
-      "[178 rows x 4 columns]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": []
   },
   {
@@ -241,50 +97,16 @@
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   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             NAME  Einwohner  \\\n",
-      "0    Barmbek-Süd      31360   \n",
-      "1      Duvenstedt       6220   \n",
-      "2          Rissen      14763   \n",
-      "3      Blankenese      12807   \n",
-      "4      Blankenese      12807   \n",
-      "..            ...        ...   \n",
-      "173     St. Georg      10279   \n",
-      "174    Hohenfelde       8904   \n",
-      "175   Altona-Nord      33052   \n",
-      "176  Sternschanze       7723   \n",
-      "177    Hafen-City       1097   \n",
-      "\n",
-      "                                              geometry  ANZAHL_AERZTE  \n",
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-      "1    POLYGON ((574154.1001000004 5950369.9463, 5740...              1  \n",
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-      "4    POLYGON ((551279.1001000004 5934212.5463, 5507...              3  \n",
-      "..                                                 ...            ...  \n",
-      "173  POLYGON ((568009.5000999998 5934687.146299999,...              0  \n",
-      "174  POLYGON ((566795.1001000004 5935675.9463, 5669...              0  \n",
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-      "\n",
-      "[178 rows x 4 columns]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": []
   },
   {
@@ -296,56 +118,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             NAME  Einwohner  \\\n",
-      "0    Barmbek-Süd      31360   \n",
-      "1      Duvenstedt       6220   \n",
-      "2          Rissen      14763   \n",
-      "3      Blankenese      12807   \n",
-      "4      Blankenese      12807   \n",
-      "..            ...        ...   \n",
-      "173     St. Georg      10279   \n",
-      "174    Hohenfelde       8904   \n",
-      "175   Altona-Nord      33052   \n",
-      "176  Sternschanze       7723   \n",
-      "177    Hafen-City       1097   \n",
-      "\n",
-      "                                              geometry  ANZAHL_AERZTE  \\\n",
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-      "4    POLYGON ((551279.1001000004 5934212.5463, 5507...              3   \n",
-      "..                                                 ...            ...   \n",
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-      "177  POLYGON ((566813.6781000001 5933346.6083, 5669...              0   \n",
-      "\n",
-      "     Verhaeltnis  \n",
-      "0        31360.0  \n",
-      "1         6220.0  \n",
-      "2        14763.0  \n",
-      "3         4269.0  \n",
-      "4         4269.0  \n",
-      "..           ...  \n",
-      "173          inf  \n",
-      "174          inf  \n",
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-      "176          inf  \n",
-      "177          inf  \n",
-      "\n",
-      "[178 rows x 5 columns]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": []
   },
   {
@@ -357,56 +132,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             NAME  Einwohner  \\\n",
-      "0    Barmbek-Süd      31360   \n",
-      "1      Duvenstedt       6220   \n",
-      "2          Rissen      14763   \n",
-      "3      Blankenese      12807   \n",
-      "4      Blankenese      12807   \n",
-      "..            ...        ...   \n",
-      "173     St. Georg      10279   \n",
-      "174    Hohenfelde       8904   \n",
-      "175   Altona-Nord      33052   \n",
-      "176  Sternschanze       7723   \n",
-      "177    Hafen-City       1097   \n",
-      "\n",
-      "                                              geometry  ANZAHL_AERZTE  \\\n",
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-      "..                                                 ...            ...   \n",
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-      "1         6220.0  284.163987  \n",
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-      "3         4269.0  414.031389  \n",
-      "4         4269.0  414.031389  \n",
-      "..           ...         ...  \n",
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+   "outputs": [],
    "source": []
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   {
@@ -418,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
@@ -432,32 +160,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f752fcd8358>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
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eKiBQFEVpRaZKA0m//hupz/zT3VNpkNAILqDn6a/28t5DOv44RUfvUA06raB3qIY/TtHx3kM6nv5qb7PKHwM8/PDDbNiwod7xV155hQkTJpCWlsaECRN45ZVXADhy5AhffPEFhw8fZsOGDfz617/GZDLV6//HP/6RAwcOkJqayu23316TpfC9994D4ODBg2zevJmnn34as9nslDnPnTuXV155hYMHDzJjxgz++c9/OjTnhvrff//9NdkWP/nkE6Kiohg+fLhDc3aECggURVFaScnpc2weO5u0dz5391QapQ8J5JPEs8wepWVEb9sbC0f01nLPKC3Ldp1p1jXGjx9PSEhIveNr1qxhzpw5AMyZM4fVq1fXHJ89ezaenp706dOH6OhoEhMT6/UPCAioeV27/PCRI0eYMGECABEREQQFBeFoorqG5nz8+HHGjx8PwMSJE1m5cqVDc26of22ff/65y1Mdq4BAUZROw2Q2sjVzDftydpBRdIIiQ77NKnaucHHrHtaPmEFeyuFWuV5LeIWHsmZ/FrNGNf4Wcc8oDWtSnVN2uNrFixfp1q0bAN26davJ/+9I+eG//OUv9OzZk08//bRmhSAuLo41a9ZgNBo5c+YMKSkpTis/HBsby9q1awFYvnx5zbj2zrmh/rV9+eWXLg8I1KZCRVE6jUN5iezM/q7OMZ3GgxDPCIK9IgjxDKeLT08GhyQghPM+LxkKitg2/ddUFTVeTrgt0Pl6o/P1Jr/USI8gj0bbdg8S5JcZW2VejpQffvnll3n55ZdZuHAhb775JgsWLODRRx/l6NGjJCQk0Lt3b6677jqnlR/+8MMPeeKJJ3jhhReYNm0aer3eoTk31L/anj178PHxqbd3wdlUQKAoSqcgpZld2fU3dRnNVVwqz+JS+ZVPbvtytnN3zK/x1Ho75dqnPljRLoIBAK9wy5J4sK+OrAJJ79CGyxufL5AE+zj3baRLly5kZ2fTrVs3srOziYiw1EpoTvnh++67j6lTp7JgwQJ0Oh3//ve/a85dd9119coP79mzh1/96lcANW/O9hg4cCCbNm0C4MSJE3z77bcOzbmh/tW++OKLVqmMqG4ZKIrSKRzPTyW34oJdbTOKT/D1qfcwy/obwBxVfOosJ978b4vHaQ1Cp0UfEgjA9LgefJXU+Ka7L5PMTB/ew6lzmDZtGkuXLgVg6dKlTJ8+veb4F198QWVlJWfOnCEtLY3Ro0fX65+Wllbzeu3atQwcOBCAsrIySkstGyA3b96MTqdj8ODBdfqOGTOmZhOfvcEAXClrbDabeemll3jsscccmnND/auPLV++nNmzZ9s9n+ZSAYGiKB2elJKfsuvvDm/M6cLDfHP6Y5Iu/kBRZfPKDksp2T7jN5SmO/c+u6t4hQUjNJa3hTnX9eGLJBN7M2wHRXszTHyZZOKha/s061r33nsv1157LcePHycyMpIPPvgAgHnz5rF582ZiYmLYvHkz8+bNA2DIkCHMmjWLwYMHM2XKFN566y201kyKc+fOrdkgOG/ePGJjYxk2bBibNm1i8eLFgOVNd8SIEQwaNIhFixbxySefOG3On3/+Of3792fgwIF0796dRx55xKE5N9QfYNu2bURGRtK3b1+H5+soVf5YUZQO70zhUT478Xqz+4+KuJlJve9ptM2O89+RW57N7X3moNVYltEvbNnFlgkPN/u6rtJQ+eOg2Bi0Xp413/9w/BJPf7WXe0ZpuWeUhu5BgvMFki+TzHyZZOJfs0ao8sdtmCp/rCiKcpWfste3qP++y9sxmCvp6tOTrr69iPCORK+1vHFKKSk05LL9/DrM0kRX396M6XoLAKfeW97iubcWj0C/OsEAwE0DIlj16+tZtusMM9/JIr/MSLCPjunDe7Dq1ypTYUejAgJFUTq0rJIzpBcfb9EYRnMV+y/vZL/1e4Eg1KsrJmmiwliKSRpr9htklZzGLM0IBBe31n/mvK3yCg+1ebx3qC/zb49l/u2u3eGuuJ8KCBRF6dAO5e5x+pgSyeUK2wWIjuan4PVWFuKD/VRcyHH6tZvDMzSInndPIePzb6kqLK53Xqv3wCPQzw0zU9oSFRAoitKhlFYV4a3zQyM07M7eRPKlH1p9DkWaEsypR1v9urVpfbwJio0hMDaG3rOn0m3iWDQ6nc0nHjzDQxp8pl/pPFRAoChKh2Eym/j46CIqTeV08+nN6aIjbplHSVczPm65soVHUAAzc/fUPDFQLe7vT2G4aoVACIFnWHBrTk9po1RAoChKh5FTcZ7CyjwkZrcFAwB5ERVuDQiERsOpD1cSPffuOsc9/P24btk/OJiyF31gAIaCIvTBAWg8Gn4ryMgt5cOdp1idmklRuSTAW3Dn8EgeHdtPbSrsYFQeAkVROoyuPj15YOBTjO9+BxphuyhPazBpZL1P563JkFdA0uN/o+j4aZvndT7e+Ef3InBINF7dwhsc54fjl7j9/7ZyMO9Hply/mUfv/IYp12/mYN6P3P5/W11S/ritlxI2mUzEx8dz++231xxbvnw5Q4YMQaPR2CyYdPbsWfz8/Hj11VdtjtlQyefExMSa+cbFxbFq1SqH5+sIFRAoitKh9PLvT7h3d6dkGWyuiCxvpIOldZ1JaLUMePIhvHt0abSdztsLnbeXzXMZuaU88XkSN12znZFDDhHgV4ZGIwnwK2PkkEPcdM12nvg8yenlj9t6KeHFixfXe7Y/NjaWr7/+uqZi4dWeeuopbr311gbHbKjkc2xsLMnJyaSmprJhwwZ+9atfYTS6rnaECggURelQKoxlfJfh3lTB3oXu+9Uaes1wpqR8zYhXn8XDr/lL+h/uPEVM1Gm6hObbPN8lNJ/oqNN89NOpZo3fHksJZ2Zm8u233zJ37tw6xwcNGsSAAQNs9lm9ejV9+/ZlyJAhDY7bUMlnHx+fmgJMFRUVLt/4qQICRVE6lMSL/6Pc2LxPrQ0xm8wc257BDx/s5dj2DMymxj/9lwe1fgZYj6AARv/nBSbt/JzguIEtHm91aiYxvW3fcqjWv/dpVu/LbPG1amvLpYR/97vf8Y9//AONnbeDSktLWbRoEc8//3yj7Roq+QyWgktDhgxh6NChLFmyxGkVGm1RAYGiKB1GhbGcpItbnDqm2WTmq3nfkbrsf4wMSiZ12f/4at53jQYFugqnTqFJUQ9O5/Zj64n+5T1O27tQVC7x8ylvtI2fTzlF5c4Nfj788EPeeustRo4cSXFxcbNKCdvqX625pYTXrVtHREQEI0eOtLvP888/z1NPPYWfX/NzPIwZM4bDhw+TlJTEwoULqahw3T8u9ZSBoigdhqfWi1kxv+FA7i6O5iVTaWr5L88TP52DgkvsTTLi4QEvvWgkPuESJ346x8Dre9vsE7qngiKbZ5zLL7o3o/+zgK43X+v0sQO8BSVl3gT4lTXYpqTMmwBv5y5ju7OUcGPlj3fu3MnatWv57rvvqKiooKioiAceeID//rfh21N79uxhxYoVPPPMMxQUFKDRaPDy8uK3v/1tnXYNlXyubdCgQfj6+nLo0CESEmyWImgxtUKgKEqHIYSgp380U6Me5Mnh/+TOvj+nb8BgoPlvWtkncrltiiUYAPDwgKm3Gsk+kdtgH016/WyArjDsxSddEgwA3Dk8krSMxivsncjoy53xkU69rjtLCTdW/njhwoVkZmaSnp7OF198wc0339xoMACwfft20tPTSU9P53e/+x1//vOf6wUD1T+brZLPZ86cqdlEmJGRwfHjx4mKimr0mi2hAgJFUTokD42eIaGjuXfAk/y/uIUEezb8eF1juvUP5bsNOqqqLN9XVcG363V06287939rytnuuuqtj47tR1p6Xy7m2k5adDE3mJPpfXnkun7NGr8jlRJetWoVkZGR7Nq1i6lTpzJ58uQm+1xdstlWyecdO3YQFxfH8OHDmTFjBm+//TZhYWFOnXttqvyxoigd3uHcRFaf/qBZfav3EMj8S0y91ci363WI4AhmvXIbGq3tz1Txrxoo+OqnlkzZbuNWvEGvu5p+A6rNVllcW344foknPk8iOuo0/Xufxs+nnJIyb05k9OVkel/euHeUKn/chqnyx4qiKLWUVhWxIePzZvfXaDXMeuU2Tvx0jpQTucTPCaX/dT0bDAYADIMCmn09R+2Y+QRxLz/FkD8/1nRjB900IIJ1/+9GPvqpJ6u397uSqTA+ktfvUJkKOxoVECiK0qGdKDhAhanhjXH20Gg1DLy+d4ObCK92/JZKYpNHUfhtUouua6+LPya5JCAAS/njv90xjL/dMcwl4ytth9pDoChKhxbi2fpL2iatmaqj51vtel7hqjiR0nJqhUBRlA4t2CucLj49uVhWP0GNq0gNFP1tOKGb+4KXjvI1+zFcynPd9TrRXjDFdVRAoChKh3Qsfx+7sjdwvjS9Wf27nvWh6+ZyUh+WoIXYb7zQJeVgHhLCyVuhLKCq4c4awbmBpZwbqAUkQ7b6ujQgQMUDihOogEBRlA6nqDKPb05/hMFcafP8kO+80aw4jm5wVzLvDSSnR92MfOHnvQl8fBeFOfkMzx7N0d/7U/X2LkpzC2EDxJTdzP5HbA5Nn/1+eJRIfI6WUd7fh5PXlFGZmePsH7EuF64QZOSW8t62NFbty6TMAD56mBEfyS/Gx6hNhR2MXXsIhBDpQoiDQohUIUSy9VicEGKX9fg3QogA6/EoIUS5tW2qEGJJrXFGWtufFEK8Iax5J4UQnkKIL63H9wghomr1mSOESLP+mVPreB9r2zRr37r5KRVF6bT+l7mywWAAQH+ggJJDpyn46if8Zqxn+IJSup32ASDkohehv07GkGMp6lO4NpG+DxzCkFtY0784zrvemB6VGnoe9UX7xPdUPfUtRR9sw/T8FuIXlGIqd20uY//+US4Z94fjl5j8+hbe3bmSE7lvkV74Midy3+LdnSuZ/PqWFpU/hvZXSjgqKoqhQ4cyfPjwOtkC58+fz7Bhwxg+fDiTJk3i/HnL/pHNmzczcuRIhg4dysiRI9my5Upa7ZSUFIYOHUp0dDRPPPGEzds+6enpeHt718y7dpKlL7/8kmHDhjFkyBCeeeYZh38WW+zKQyCESAcSpJSXax1LAv4gpfxRCPEo0EdKOd/6Zr5OSlkvUbQQIhF4EtgNfAe8IaVcL4T4NTBMSvmYEGI2MENKeY8QIgRIBhKwLIqlACOllPlCiK+Ar6WUX1iDjv1Synca+zlUHgJF6djMJhP7//QvsiKLEOtOIiuMlk/PZgkmM9IsQUrKT2RhLCqp11/r44WprOk3b5++PRCeHsjKKkyl5RhLyjGV1l1lCB0zjPzUY/j1iaToWONFglrqpk0f0m3iWLvb25OHICO3lMmvb+FM/scYTPWLCOm1PegT/DAbf3dzs1cKXnvtNZKTkykqKmLdunU1c9NoNPzqV7/i1VdfrZem96677kKj0TBmzBj+8Ic/1BvzmWeeISQkhHnz5vHKK6+Qn5/PokWLKCsrQ6/Xo9PpyM7OJi4ujvPnzztULCgqKork5OR6yYGKiooICLA8avrGG29w5MgRlixZwr59++jSpQvdu3fn0KFDTJ48uaYg0+jRo1m8eDHXXHMNt912G0888US9Esnp6encfvvtHDp0qM7x3Nxc4uPjSUlJITw8nDlz5vDQQw8xYcKEOu0czUPQkqcMBgDbrK83A3c11lgI0Q0IkFLukpYoZBlwp/X0dGCp9fUKYIJ19WAysFlKmSelzLdeZ4r13M3Wtlj7Vo+lKEonlb1+G0f/+QFFTy6ncPM+irYfpGjHIYp+OkzRnqMUJx2jOPm4zWAAsCsYACg7nUXp0XTKTmdReTGvXjAQMKAPuXsOYK40uDwYAAiObzrJkKPe25ZGXnmizWAAwGDKIq88ife3n2zW+B2plHB1MACWCofVY8fHx9fUWxgyZAgVFRVUVlaSnZ1NUVER1157LUIIHnrooZp52uP06dP079+f8HBL9s1bbrnFZplnR9kbEEhgkxAiRQjxS+uxQ0B1sue7gZ612vcRQuwTQvwohLjeeqwHULtOZqb1WPW5cwBSSiNQCITWPn5Vn1CgwNr26rEURemEDPmF/DjtcXdPAwDPiNZNa6yxpvB1plX7Mskvb3xFNVPjjXIAACAASURBVL88ia/3Ne/pjfZYSlgIwaRJkxg5ciTvvvtunXN/+ctf6NmzJ59++ikvvPBCvb4rV64kPj4eT09PsrKyiIy8UgOioVLOYKlnEB8fzw033MD27dsBiI6O5tixY6Snp2M0Glm9erXNMs+OsjcgGCulHAHcCvxGCDEeeNT6OgXwBwzWttlALyllPPB74DPr/gJb4Vj1/YqGzjl6vB4hxC+FEMlCiOScHBdv7FEUxW2K0jJcurnOXt7dI8j5aV+rXtNsMjl9zDIDGM0FjbYxmgspa3irRoPaaynhnTt3snfvXtavX89bb73Ftm3bas69/PLLnDt3jvvvv58333yzTr/Dhw/z7LPP8p///Aewv5Rzt27dOHv2LPv27eO1117jvvvuo6ioiODgYN555x3uuecerr/+eqKiohwObmyxKyCQUp63fr0ErAJGSymPSSknSSlHAp8Dp6xtKqWUudbXKdbj/bF8iq9dFisSqM7ckYl1hUEIoQMCgbzax6/qcxkIsra9eqyr5/6ulDJBSplQvbyiKErHYzYYmm7UCvTBgXh1CSVs3EhCEuptpXIJaTI7fUwfPeg0QY220WkC8fF0fOzqUsJRUVHMnj2bLVu28MADDzTaZ8+ePTzzzDNERUXx+uuv8/e//73eGy9cKSUM2FVK+OprVG/gW7t2bb1+1cv/ERERzJgxg8TExHpt7rvvvjrL95mZmcyYMYNly5bRr5+lEFRkZCSZmZl12tgq5ezp6UloqGW1aeTIkfTr148TJ04AcMcdd7Bnzx527drFgAEDiImJqdffUU0GBEIIXyGEf/VrYBJwSAgRYT2mAf4KLLF+Hy6E0Fpf9wVigNNSymygWAhxjXUPwEPAGutl1gLVTxDMBLZY9xlsBCYJIYKFEMHWa2+0nvvB2hZr3+qxFEXphDJXfe/uKQBgLC2j4vwlLu9IabVrShesEMyIjyTY2+besxrB3qP4WXzPRtvY0lZLCTdW/ri0tJTi4uKa15s2bSI21hLwpaWl1bRbu3YtAwcOBKCgoICpU6eycOFCxo69sumzW7du+Pv7s3v3bqSULFu2rGaeteXk5GCy/t2ePn2atLS0mkqN1bdC8vPzefvtt+vtxWgOe1YIugA7hBD7gUTgWynlBuBeIcQJ4BiWT+cfWduPBw5Y268AHpNSVmfkeBx4HziJZeVgvfX4B0CoEOIkltsM8wCs/V4Ekqx/Xqg11rPA7619Qq1jKIrSCZWcPkf6Z+vcPQ0Ays5mo/G0PAVtLG1ZDQV7eAT6ow9yfjGlX4yPIcR7NHqt7e1Zem0PQrxHMff6aKdet62WEr548SLjxo0jLi6O0aNHM3XqVKZMmVJzzdjYWIYNG8amTZtYvHgxAG+++SYnT57kxRdfrFl5qH4jf+edd5g7dy7R0dH069ev5gmDtWvX8txzzwGWcs3Dhg0jLi6OmTNnsmTJEkJCQgB48sknGTx4MGPHjmXevHn079/f7p+lIar8saIo7d6ZT9ey64E/unsaAAitFs+IECqycxBaLQGD+lresAXkJh/G7MScBDd8+y7+Mb0JiIlyqJ8j5Y8f/+8e8sqTyC9PwmguRKcJJNh7FCHeo3jngTGq/HEbpsofK4rSqRjLyilNt71D2x3CxsaTs83ywUOaTBQeurKcHDRsAEVpGU4JCvr/9gF63HZDi8dpzE0DItj4u5t5f3svvt43mrJK8PGEn8X3ZO710SpTYQejAgJFUdqltCWfk/7pN5bn/asaqSvQysyVDc+l4MBx/Pr1QprNVF7Ox1hWAc24/+8b1YO4hb9vyTTt1jvUlxfvjOPFO+Na5XqK+6iAQFGUdqnw6ClyWnHjnr00Ho3/Wi05dbbmtW/fnpSedvz58dHvvoiHn/p0rjhXSzIVKoqiuE3XCde6ewq22ZlopyX8Y3q3eIzOtH+sM2rO368KCBRFaZe633YDd2Zt4/bjG5iydxVRD9Z/bMsdHMmIqw8OQB/a+LP+thQeOeVwn9q8vLzIzc1VQUEHJaUkNzcXLy8vh/qpWwaKorRLGp0On+5dAKjMzSd7/bYmerQOR95j81MOo/HUEzp6KKVnL1Bxwb5sqh5+Ps2cnUV1YhyVvbXj8vLyqpMe2R4qIFAUpd3zDA3Gv38UlZfz3T0VDPmFTTeqxVxpIDfxICGjYtHoPfDuHoFGrwMEOduS6rUPihtI+PWNJwxqioeHB3369GnRGErHowICRVE6hH5z7+ZyK9cQuJpHgB+FB080q29ekiWNbtlZaxZ2G/cefKN6MOb9l5xaqU9Rqqk9BIqitAlStiwff6+7p6Dzd+/Oe+NVZZBb5Kp7D71m3cqt+1YTmjDUeddQlFrUCoGiKHVIacZSoqSRNiYjVFWAoRyqKpBVFWCogKpypKHCcq6qAmk9j+Gq7619Za1zZr0fpQlPEjpqWLPm7eHnS+/ZUzn13lfN6t9SWi9PQkYPrUlK1FxeXcLQenvi3S2ciou5lGfnkPB/f6XvozPVyoDiUiogUBSlhizNx7hyAQR2RUT0gcpS5IWTYCizvnlb39BNRqdfO/2YJ/uffpDrPvsXPe+8pVlj9Jt7t1sCAp2/L379erU4GADoftt4rvlwIYAlgVFeAV5hIS0eV1GaomoZKIoCgDSUY/ziz8jcs003drKLeeH89NouTGUVIAQjXpvHwN89bHf/kpISfrviNb47uIOxO3MQUlKphUqd4MZMDX3PlLhs7kFxAynPvEhlrnM2NOr8fPjZhZ3ofFv2JIGi2KJqGSiK0iTTDx+4JRjIvBDBnte2Ia2laZGSvU8tpORMJiNe+xMarbZO+wPnTvDEV/8m5dwxSirr37NfHe9Z5/ufYiT/rPQn+HyxS+av9fFyWjAAYCwp49zXm+jz4J1OG1NR7KECAkVRMB3bjvnID61+3byiEBIX77gSDNRy4o1PKE3PYsATD9Fv5TOUyebVKyjXC/48UcOdqd5M3u/ETX9WWg8Pp495eulqAmP7ow8OwC/KsWfJFaW51FMGitLJyYILmP73n1a/bkWVNzvfOoi50tBgm6y1W9hyy8ME5bWsOmCRt4ZPxniCr2OZ25oSfn0Cl2zkCmipi1t28/3195P4i/nk7NqH+aqASWJC4li+A0VpigoIFKUTk6YqjN+9Ztks2MqOJHpQmWPfUvuEYy2vZig1gk+HCYS+5Z/ohU5H+PUJriuuJCXG0jIufP8Tm8feS+m51Uh2IDmI5BJQBJx0zbWVTksFBIrSmRVcRF5sWV785qio8uLM6j12t5+yv5wB2S0PCr6L8+LJmd4cim5+vgKvLmGWJwq2JzuWp7iZ9CGBeHcLAKqAy8BhIBXwbLSfojhKBQSK0olJU8PL9a5kNHsgTSaH+szZ75xr5wRoWXiLnhen+VHQzd/h/n79elJ8/LRzJmMHQ24BJ/9jK3gyIGnZrRRFqU0FBIrSicnMI+6egt16p5fQPd+xIKIxx7p7MH+CBvy86xzXhwYROmoooWOGofHU1+tXVeiapxUa493dVuBSCGQiaVmGR0WppgICRenEzGcPuHsKDvGtdO4SfZ6fhndG1c3+Fzgkhtykg+TuOYBv7+54BNZ9My48fBLfqB5OnUdjQkcPpOddjaUrTkGyE0nrrVooHZMKCBSlk5JmEzKr/awQAPy/JPAyODco2NFfT2Yvv1pHroxffCKdwNiY+p0keFtLL7tanzmjEZqGflWHIhgFDAYuI2leYSVFARUQKEqnZUlJ3PpPF7REaFYxM/dWOndQIVgx8EoQcHWBooqLl+t1Kc3Iwrd3N8LGjXTuXGzwCGrsUckzSC4Cp4BSIAvJIZfPSemYVECgKJ2UPHfQjRdvfteoYuf/2krqq2fFaMtegvyUwwQM7ldzzlRm2bjn06s7gUOi8YnsitbLk8u7Urm8I4WAQf3QBTi+OdFehxasJz81q4GzhcARoPa+hpx6OQokZiQX1X4DpVEqIFCUTsqd+weMpubnAhiUXkFUjvOLK60a4cXH47ypDPPjD9EX+Wq0N8Lbk8rcAsKuHY40Gik8fJKyzAuYKq6sUhQdPUVQbLTT51Ot+MQ5Dr3wvYO9DiC5jMSI5DKwE0vgkOb8CSodhkpdrCidVRMljl2pqqoF1zaZ+NkhE6/d5PxfX5tjvdg8RIIQHO/uwc4YE2+srODyrtRG+13elYo+NAhDboHT5wRwadtRpNncyF6CqxkBWytAF5DEINRnQcUG9a9CUTopTV+bBc9ahbFKNN2oESPOSzRmFyUFEnXnJkXTc9UHB6APCnDNfLDkIri8O8MJI7Xsv7vSsamAQFE6KXcGBFe/6TrcvbicvjnOy0lg8xpS8vfNEsqb3sSoDw6k5JRrK0VmrnLGEyF91OqA0iD1L0NROqvALqBzT/pbZ9yt8K1wbdrg0BIzvpeaTkIUMLify4MBgMxVe5EtTpV8DolrAyml/VIBgaJ0ViV5YHTyI3x2Ei15zMBqVKZr3tjCik2MO17JL/bYN75XeIhL5nG1klNZFBw838JRKoHdSK6unmhGUtrCsZX2Tm0qVJROSpbaV2nQFZwRENx0sJxNA3ScDXPur7GEM1U8+JMD+RkkeEd2pTzzglPnYUvW6qMED2tplkQDsAtJIJbHFk1UPwcqGYugfrpmpXNQKwSK0kkJ/zD3XVvjnM1tU484f5XA28Giipe2JaHzaSx5kPNc3OqsxwaNQK71a+3g7KiTxlfaIxUQKEpn5RMIHq3zRlafc+7/X3/KiF+5+5PtFJ9IJ3y86zdp5u4+jtnoyj0AeUjaV/ZKxXlUQKAonZQQAhHYOvn461/bOePISgM+Tqxt0KXAxJC85v1azNmWTPj4UQQM7Is++MojiOHjRxF+vXOCBVN5BcVpOU4Zq2Htq76F4jxqD4GidGZBXeGyM55vd4wQznsTl058tH70mSoGnSpH6HVIg+PZEHO2JdW89gjww6trWM2xwKH98QwJrNPeVGkgd/d+m2MNe3kW0Y+NwVhSScH+bLI3pqHRa512u6VhRUhKEPg13VTpUFRAoCidmPANdtLivaMXdt6b2riTVawaoXXKWMlRHkzM1PHyeEG/i0YmpQtiMirA5PgyfVVRCVVFJTXfFx6sX4nQPyYKz7AQKi/n1TsXPKI7niG+eIb44tsrhB53DHF4Ds2XByog6HRUQKAonVlF08/Zt3XXXdCxykljZQdreeIOy+uLgVp+6g8eRg/Cis2MPlPFrETn3l8vTksHIDh+EEVpGZhKygAY/Oc76XqLjbLLrUIDRLrp2oo7qT0EitKJyVLX5N5vSoBPIeP+fBMhcS1/0+txvgwPo+vWOap0guxgLUVerluqz993lICY3qC1rHSYK41odM5Z9XCcGShpspXS8aiAQFE6KfPJPchM92wg89BW0TUsB9+ugU03boI0muid6/rse/6Vrr25kr/vKKGjYhE6HVofd+cCSEGyHclRVTK5E1EBgaJ0QuYLaRjXv46zHv9zt1+mSLoWOCEoMJhgcxp8mmr5argy5mU/1/+6zN29n8Ah0fhFBbn8Wk0zAheAH63BwV4kBndPSnEhFRAoSicjCy9iXLMQjB3nl3vkRQP5vi38dWYw4b8ymYEeeTzzeCUDPfLwX5lcExSc6NI6S/gF+49RcsZ9WSRtM2LJapjt7okoLqQCAkVpJkv+90x3T8NhpsNboKzQ3dNwquM9PKj0aOE9/h9P070rHDgAixZZvnbvajkOUOTder8uyzLb6t+Pr7snoLiQCggUxQGSw0gKkeQAO6EdBgTCydkJTWbJt4ez+fumo3x7OBuTuenbEFJKLheGUXDSOfn/oy9LFn9jZPE3RkJKmnnP+2Ix06aBh4flWw8PmDbNchzAoAOhd/2DWf3m3sLwRVOa1ddkMrFu3S5efPET1q3bhakZj0s2zAuB+9JdK66nHjtUFIdcsv6pZkSSCAxA0PgGOYm5bdSi13s7bSiTWXL3JzvINucx8VYTL63X8kFyCMsfHIe2kQQ6Kdv8SV+z1Wnz0BSVEVZked210I+85tzvl/DNN/Dyy5ZgoKoK1q6lZpvF0EzHExU5KmBgbxKW/AyN1vH5m0wmfjbjGbKyjjFxUgV/e96L994dyNer/oFW64zbHRVI8hEEO2EspS2y61+dECJdCHFQCJEqhEi2HosTQuyyHv9GCBFwVZ9eQogSIcQfah3bKoQ4bh0nVQgRYT3uKYT4UghxUgixRwgRVavPHCFEmvXPnFrH+1jbpln7untbrtLBWFYC0pGUIMlAsqeBlqXAXuvGqx+R/IBkK5JUJGlIDEjOAmlIjEjykWRZzx1CtvZ9WSeuEGw4eoFscx67U0y8sgh2p5g4b8pjw9GGP/mXGXzJWJfU4PmWGp3RzE/FwpIvacwY+NOfLF811t+QT2ytZN63Jc3KXuiIPg9f26xgAGD9+kSyso6xe3c5ryyU7N5dTmbmUdavT3TiDE85cSylrXHkX95NUsrhUsrqpNzvA/OklEOBVcAfr2r/b2C9jXHut44zXEpZ/VHr50C+lDLa2m8RgBAiBHgeGAOMBp4XQlSHp4uAf0spY4B86xiK4hSSYmAvcAZIAk4DZU30MkLNI1oSyz/LTCy3Fk4B54HtQCpwwnouBziGpG6de4nr8gMIJ64Q7M8qYOKtpjrL7JNuM3HgfMP3wNNPeCOdupRtsa+/D/93iy+7+jTz03AXf8xmeO458PW1fDWZIM7sy5hjTf3dO4df3+Z/+t637yQTbiln40Z48UXYuBFumVhBaupJJ86wwoljKW1NS9YvBwDbrK83A3dVnxBC3InlN+hhO8eaDiy1vl4BTBBCCGAysFlKmSelzLdeZ4r13M3Wtlj73tmCn0VRrtLaVQCPI9ljXYlIBfYh+cn6HHhuoz0lDr65OjEgiOsRxOb1WqqsJYOrqmDTd1qGdbd9+8RsFpzZYO+vBfsJb09evdmT3dF6jnfzaN4gN/Tl/AWYNw+Kiy1fL2XD78pDnTvZRhQeudR0owYMG9aXZUs1PP88lJXB88/DsqWCoUP7OnGGVUja6oZHpaXsDQgksEkIkSKE+KX12CFgmvX13UBPACGEL/AssKCBsT6y3i6Yb31jB+gBnAOQUlY/3xJa+7hVpvVYKFBgbVv7uKI4SaobrlmOJY6ufuSsEstz4AeQ7ERyDkkykhTAEghIdgPb6tx2kFxq/OkHJ94ymDKoK900IYwZoWXeszBmhJbu2hCmDOpar62UkgOJAZSfd361vuIAJ9wx1GspviuB48YQFr+hJ/hkEP8u7oee1ssYmPFZIpV5pc3qK4CQENi9GxYutHwNCRE4P79i/ZoMSsdg76bCsVLK89Z7/puFEMeAR4E3hBDPAWuhJmPFAixL+SWifgGT+6WUWUIIf2Al8CCwDGz+m5XNOF6PNYD5JUCvXr0a+REVpTZ31IRvbHe+Abiy9GsJCkq4covimHUloT/VC3OVxrOsPZNKRnEaPf2iuaXXTEK9ulB14aTT3iS0GsHyB8ex4egFDhwsZP6YQKYM6mpzQ+HRw+Gc/Gqrk65c18ZeRpyxR7pLGXQZ0Jt5GRUuua3RlOIT5zj7xX5ifn2dw333HzjNtGnyqqckJAcOnmba9LFOnGUJkjIEPk4cU2kL7Po/SEp53vr1khBiFTBaSvkqMAlACNEfmGptPgaYKYT4BxAEmIUQFVLKN6WUWdZxioUQn2HZF7AMyyf8nkCmEEIHBGIpt5UJ3FhrKpHAVuAyECSE0FlXCSLhqpuwV+b+LvAuQEJCQsdIy6a4lCVVa+u/GTimyMaxHOsfCzPFnCiwlNY9WXiAkwcPoBMe9Knyder9Na1GMHVIN6YO6dZgm/M5ERz5cIsTr1rXzn4O3iaQkvBiM1VaQUGthEaxWUZ+kWJG+HhjLHJPPn/PiOZVGYyPj+Zvz3vx4ovlNU9JbNrkyYIXop08QwC1h7sjavKWgRDC1/qJvvp2wCTgUK0nBDTAX4ElAFLK66WUUVLKKOB14O9SyjeFEDohRJi1jwdwO5bbDmBZYah+gmAmsEVKKYGNwCQhRLB1M+EkYKP13A/Wtlj7rmnBfwdFqaV+Kdr2yEunx9+jbiIZo6zitK64VbPTlxt8SF6S7LLxi7v6czHI/mX9hDMGPl5u5PXPivj792Y8q658Tggrk5jKKggcHO3UEs2OyN9n87NNk269dTQ9egxkzBgv5v1JMGaMF5GRg7j11tFOnmEYQj2x3iHZs4egC7BDCLEfSAS+lVJuAO4VQpwAjmH5dP5RE+N4AhuFEAew3KDNAt6znvsACBVCnAR+D8wDkFLmAS9i2eadBLxgPQaWfQq/t/YJtY6hKE6Q5e4JOM3MmPpLzyZMbI3v1yrXl2ZIXleOId/WioZzbOzZ9KOA3QpMzE6sYHZiBU/uknjkWT79B2YX8+Q2A55Vkv7ZRm5PswQBubtTCRo6wGVzbkxpRvPSFmu1Wr5e9Q8WvDAfX9+HWfDCfCfmIKjNXWWZFVcTlg/bnUNCQoJMTnbdJxWlY7DkDGiN560FrVFcaNnRLWSW1N29LtDw2z15eFW59rn6I4fCOPLhVpdeY+lYbzYNrb9RUpglf9tcRfQlE6LK2HgOASHgqt+FYWNHcHnnXmdPt0ldJ47gxo2/wMYerDYgAMFId09CaQEhREqt9AF1tIG0aYrStgh6YXvfqnOv0lqVBh8YcCO+HnU3gEnMbBrRx6XXvZQXxpGPfnTpNQBmndAibKRL/tneSqLPlEJpRdMJhWx8MLq8cy+ho4fh1aX1HjsEuLB5L1tvfZ+Df9tM0fGLrXrtpqnVgY5MBQSKYpOr36xbb2VOo9EwMrz+s+gnPAoo9XLN5jApJQdWnbX5Ruts3jklXHOqqs6x649X8rPklj8pkpt4AGNpOeHjbX6gcpkLG1M4tGAlx17b0arXbVxXBAFNN1PaLRUQKEoncE23gVz9RLpE8v3w3i653qX8CAoOn3bJ2Lbce9qTsGITfuVmfrW9kse2Ou+xUWNJGTnbkgkcEo1PVHenjWsPz9C2Ul3QD8Egd09CcTG1VVRRbGq9Jf3WoNPo8NZ5Umasm3r2tK7Y6dcymrQcWnPW6ePaogvwI3j4IHT7jrD401KE3gNpqGq6YzMUHj6J1tuLsOviufzTPpdc42r60LbwrL8HqH0DnYJaIVCUq0gu4tpgwD2bxW6KHFbvmBEjR/pGNtrPiLD7MUWzWbBnHeQfbJ0iOAED+pCzLQljsSW7n6uCgWqm8gou70oldHT9/5aO0no3nTHSM9R5aaabRwAJbaNKp+JyaoVAUahORnQOOIulSJFrr+YOceF92X3hOLkVdXPRb+0hGNzA6n653oP/jAkjQHrx6I4zNtuUeuk53yWEnABv0suM+J10XSXD2oTewy3ZBJGS3MQDhI9PIGd7isP7JPyiujPyof4E+xewZ50k+8f9V04KgX/fHpirjBjLKvAMc/0tA7PZDBnnkEUlYDYjL+Uicy6DRoPw64IIyUJ06YvoNwqNRr1ldGTqb1fp9CzBwA7syU5oMBh4+aVP2bHzEOPGxvKXv96PXt8+srYtO7qlXjAAUCrL+de4CCQSgcBXeKFDQxkGKqUBZBUSzzp9jELDt2P6cVJXiBkzliCqGAJB8/4AQvb3Ify9TCpOuaa0s9DpCIqNIX/vEZeMb4+cbckEjxhM0Yl0TCX2V0Mc+VB/woMuA3DdHXC8/02UXCqn6wAfwkML8fSorGkrdM5JkmVOO435aBry8mWorLSUcTSb0M65D9PGHyDrnM1+8uJ55Cnr7RGNFu34OWjjp9psq7R/Kg+BogCS7TS1MmAwGOgfMxMfnxKmTYO1a6GszI8TaSvsDArcuy9hUfJyTLJ5n6j9hQ+PbbfsC9g3oBfbIqowyMaX572PleOZd+Xn9Sg2E/ZJNhUnWpb4yTM0GP8BUa12H78pfn17YjJUUZ55wa72wUP7cd39oXjr7QsiNNeNRzN6qF15CcwnTmLefwR58SIYDaDVWVYwTA382w6NgLwch1Y5RPQ1aGMnYEpPRR7ZAmYTePlDQDjCLxSZcwa0HmivuQdtzBi7x1VaR2N5CFRAoCiA5CCWEhkNe/65j1i+/BMOHKAmV/zQoTBr1oMseOGR1ploCyxM+hLZzIBEh45xeV4cDtGQY7OOgp2MZnp+VYrHW/uRDiZFCh+fQNm5C5SeaaSSo5voQ4Pw69uVvKRjdrWPnDiSa26tbLqhlWb4aDQ3jrIZFJgv5WDelYzMOAsm1+6hcJjeGzFwPNpxD6DxbAsbJJXGAgJ1y0BRAOyo3LZj5yGmTaNONbnp02HnT4ca71iHe1YJjGZjs4MBsGw+3BrihGI/Og3n7vPHL3Yk4XP3NNxOoyF83EiQElNFJaVnMi2P/rkpnXBjes68htjnJ+IfE0HiL74i/ZPtTfbJ+t8+yieMxFtv3+OR5tRE8PdDmzAY87E0TEmpUFYKlRUNf/pvCwzlyAMbMR7YBF36oRt3P5peLd+QqbiGCggUBYCKJluMGxvL8uX7eOmlKysEa9bArFmxrTC/ljmW17Y+VZcM86bnDQOo+PF4neNaH2/8+vVEHxxIzrb6mxP1wQGWzW46rcufKGiMZ1gQve4ZRd9HRxEy4kpZ9WuX3U/U/fHs/d1qio41/OilNJs5n+lPv77250sw7/wRWVyITE1p0dzdQ8LFkxhXLgDvQHQPvY7GRyU5amvULQOlU7NsKDwANF1QpnoPgbd3CdOnW4KB8nJH9hC4z7dnkth/uXUeBbRXr2VFaN+sVStAqyVgQBRFRxqZp7XmQMiooeQlHXTJvEJG9Wfo3ybh3SOIvJRznHp3N/mpZ0BKRi25j8DYbgTFRaLVN/x5qiwzn+9iX6aq8MqqSvDQfsTf1QOdzkxWuo7e0WX42LmPoIaXP1Q4P3dEa9KMn4Nu5DR3T6PTUrcMFKVBuwH77uXq9XpOpK3g5Zc+ZedPh5g1D99yKwAAIABJREFUq/08ZZBTXv/pAnerCtBQuw5f+LXDydnRxKdf6weYsrPNKxFcm8ZTT/jYAaCxPGMfNLQr3SYPoMstA9FoLceC4yLp9+i1mE1mDLkleEXY96nWJzKYaz95iG3T3wEpCYrty81zvBAay1MDAYObMWGtDiodDCDaEqFBe/sf0EarjYZtlQoIlE7O4FBrvV7fLjYQXq2gstTdU6ghKk2Eby0nZHMh+mED0Pl6c3lXqkNjVF4uwDMshMrL9j+W5xfdm5CESDzDfNAHe9Nv7jX49gqxq69Gq7E7GKjW445hJLx9P8mP/5eqojLs2afSKA/v9rs6ENwd3a1PoelSv6aG0naogEDp5DrHLbNKk2OBT3MZDUa2frSPs/sv0iuuCzc+Eo/uqqV1TbEJ/5cPUlZRSfXnXd++kRQds7/2gTSZ8IwIwTMsqNF+QqcjdMwwKi/lUpyWwXWfziZ0tGurPNYW89g4fHoEcPyNnVRJLXoHA9AagV2gsK1VPnSAoVwFA+2AykepKK3CvbXtzdLe5MPNZzQYeWvWf7mwcx8zbz7PhZ37eGvWfzFeVXrYFKZHzq5bKKf0dCaVl5vex1Fb0ZGTeAT62zwn9B6EjxuJZ3gwl3fupTgtAwCz0fX/Ha7W445h3LTpcfTaThoMgCUngtLmqYBA6ZQkZiTOyQJn7xXdJb+ipEWPHNpr60f7CA0wcOAALFoEBw5AiL+BrR/tQ5QZwWAGk0R3sRKzp3MCpKtvGfhEdSd8fAI6by9ydqRQkZ1T57x0Q0AAIPOa+chmQAcIBpR2Q90yUDodSTmQhD2pip3HfVkKt2Tub7qRE5zdf5GZNvI0rPj+Av03HMOQ1Xjip+YoOZ1J2HXxCJ0OQ14BhYfSKEtveMPh7jmfog/xQ+utR+ulI+bxa+l5V7zT51WHBNPGTY73C4iAog4SDFSWUrX1QzxufNTdM1EaoQICpVORlALJYHf9Pudd2R1BwenCCxzPt52n3tl6xXVh7drz9fI09LquK5q9fuCCgAApHUphXJp+gdL0K9+bDSaXBwRSAhfteyrCZJZsOHqB/VkFxPXOZUpMCFqNe283Ocv/Z++846Oq8v7/PnNnJr0XSAKB0HuHiBQ7gihYWXeRVddddVfdvpZHsVBWcd31UR9/a0esu6IgqIBgpZfQQzGkQSrpfSYzc+/5/TEJScgkmZnMJEHn/XrxIrlzT5nkZs7nfM+3yJxj3T0FHx3gOzLw8ZNBUo3dMtA9ZuPusBBkVnqnuJAjLr1zPKVVRkaPhocesqd1Lqs2culd4ym9KbrL5uEKlgovh/FJsH3+pVO3qprklne3s2zvXsxjT7Bs53ZueXc7qvYjcHzVG9Ff/uvunoWPDvAJAh8/CSTlwH5+KlEFAN/nHmXf2bQuG09v1HPfR7cRN308H38dT9/ho3n65vn02lFPdX/FnlSohxHcP8prfUsJ1u37IOOUU/dvOlFIgVbG7v0qzzwDu/er5KtlbDrhXNGkHkvvwejveQtdwvCO7/XRrfiODHowEhV7BT6JwL+7p3PBIrEBh+leMdB1xwU1FjPrMndzurrrFxK9Uc+V90xmyNxDWPdXAIcJAHpaktqQoX1JWjSJoX+6zHOdSpBWDVlrRtaZ0DZ8ATXOF4I6nFfBVXPUFj4Ys65ROXK0krkj4zw3T28SHmd3gmyIatFNuA79JXd075x8OI1PEPRAJBlALk2m7V6AO6nNfNhJo/stA10z/qbsFA4Up3fJWG3R67MarMUV3TqH9gjsG8vcE//jVDlhV9Bq6lHfeN3t9mMTwlm2Uc/SZbZzPhibNygsTg7z4Cy9hxhzNYYr7karLsW2dikiur9PDFxg+ARBj8RCy3PuoO6ayAWPzWbmHwdfYlBYPAB9gqO5KH5YN8zE+xaC3OrirhMDUqLPr8e/WMNQ0/S+grNV9C8eaKdh9yN0Oo+LAQB1v/POjY6YPX4gb6akkzyhjFnXqGzeoBCvRDJ7eG8PzdC7CL3dtKELicL4y//t5tn4cAefIOiRnB8O5xME7qLX+6PoDKRV2qv9pVXmougEk3u7V0Y3rSyXb3IP0yswghsGXezJqZ7DYrGwfNn7bN+RyvRpztVLKDVV8d9T270yn0Z0lVbCD1uIPGBB+S4Xo38gNZldE8HgSeJme8nalp3lfludgqKprF40nU0nCjlytJLFyWHMHt77gokykBdynQUfgK/aYY/B7i+QA5QA5+crn+rzIegENpuN5w79AVU2ZczToSMqIJRAxY9AvRGrVLGqKhbNhk2qqJoGSEw2CxbNitpOpj+BYHLsEK7s1174mnMWApPJxMABNxEWZmb+fFi/Hurq2q+oeLz0NOsyd3st+ZAx20zCpnp0HxxHM9sLQSn+fkRMGOFSyF9PIHnlHSTdnuwVC4H1rXeh0s0iUoFhUNfzClA5hRCIAZNQrv0rOp1vj9nT8VU7vCCoARztMBTAr4vn8uOi1FxAsCGMSkvpuWsaGsUmz5xzSyR7i35gX1EaNw2cxpDIPg7v6ghVVZk44VeEh5s5csTuVLZsGYweXcPyZe87LKr0acZujpdld/5NOMA/w0y/D2qwfJYKtDzEipwyBtVk8sq43iJy8hCviQGp4b4YCOsNlRdoJEHsAPQ3P4XOr5OFm3z0CHyCoMfQVmrTAEQ358G/UCmqzeetE8tbWAa8iUTyccZ2Rpb3Y/7AqS6337hxL2Vlxdx5Z+tsfzt2pra6f23GLk6UnXbYlzHXzMB/lFC4IILyac4fOekqLCSuMROwtxzzgaw2S/FIq42yfamEDh9I1YkMp/v3JDqDnqip46gvqUCzWtEHBaL4+9lLF+t02GrqKD943H6vn5FRi6/0jhiQYHvnQ/c7qPFCwqauIDAM/c+f9lkFfkT4fpM9hrbKmtYjkT5R4CKHirbzxel3u2XsY2WnubLvBIKMrll2Dh5MZ8YMjS1bYOnSpmx/n34KP/vZqHP3aZrGhuyUNsVAwIk6+v4lDXNJBeHDx1MxUUX6K003aBJ0An2xhfiNZnIWBCH9FQKPm+jz5xNYy6owdzBXS4U9nK6t4kKOCB02AHNRKZYyz5jGo5LHUry1/SPAsFGDCR0ez/jnrna61LErSAm2D9dAeWnHNzsiKAJqXSvq1CPwC0L/i3/4xMCPDN9vs8fQliCwAvXg8yFwisPFO/ki+11kt2UjtLPv7A9c2neMS23Gjx/Ep2v9iI+vJzkZZs2yi4GqKn8efWwhAGabhZXHv6K83kF8uyYJSDPT/5kCzCX24xDdB8fpv9JC0VuTqR0ZiKhXGfL3MoquCCT6qWPYqusY9F4wqBq26jqsTswzZuakcwtx+f5UjBGhWModx9uHjhiEEuBHXU4BVScziUoeS+ke12orxEyfCEJQeTwdS6n9fYUOG0Dx9v0dtq1MPUWfG4Z7RQwAaFn5Tqcl/tHQayD6BcvQ6dt3dPVx4eETBD0A++JV284d1fgEQcdsOb2avUVfdfc0AEivzHNZEMyZM4XXXxtBTs5xBg6sZ+VKHZGRMWRkvoXRaKTaUserRzdh0Rwb8uPX1OD37P4Wu/tGJ8A+j2YgRscijxRhKSglfKM95RWArcL5SnyxMydTtHVfU/9WG2GjhzjcqUdfPJ6ylFQ0i11mCL2emuw8p8cC8IuJpHTfUbR6CygKMTMmUXH45Lk+O0QIkhZNdGlMp5GgrVvjfvvw3lBxYfkOiLGzMVz+m+6ehg8v4RMEPYJa2nc6qwZiumguFyabsv/D/uJvu3sa54gPcj0lrqIorFn7LBs37uXQoXRuv2MQc+ZMQVEU6mz1vJ76pWMxICVx62rx/9fhNp8iS34p5Ltp1m4gZsakFmKgkfqiMpTAANQ6U+MbIWbquFY7+IDe0dTlurYAhg0f2DSmqlK8LQVDeKhT4Y4B8dEM+f0lhAzu5dKYziItnaiWGRR5YYkBoUO55k8oQ7wTauujZ+ATBD2Cto4LnH39p82GrPc5WLK1u6fRgln93NuVKorCtddO5dprm5wSaywmXk3dSL3q2DIw+F0Ttv9L8Wrao5jpEyne5vi8vupkJiGD+1OdmYMxLJjAPr0dmvPryytBUUB1biENHtCX4l2twxqtFR2nA45KHsoV39+P4mdwaix30PLcLE2sU8DsfErjbkfo0P98BbpeA7p7Jj68jK+4UY+gIyeraq/FmF/o9EQxAKAIz/1pZVedbSUGYgIiuDhuBH8efyO9crz7bATEx1Ka0jrKoTnVp7KJHD8czWKl4sgPDu9Ra03EXDzOqTF1fvbzaWl1L0Lkond+4V0xUGt1/7ggMAzUrol86TwC/Y2P+8TATwSfhaCbsfsPdORlbMWeztiXj6A5ewu/6ZFiwNP0D20yeeuEjunxI5keP/LcNUOod/1LAuJiMeUXdXhfWQeiAcBW11H8gp3QYQOoOHzSqXsdEZzkhXLLEqQqUTPOIDd85l4fF5jfgHLFb9Alju7uafjoInyCoBuxi4FD2KMIOuIYkiQg3BeCCGRVnmBLzn+7exptYtNUDIpn/ryCjQHcNfJqNKkR5R+G8bx+heK95yFm5mSKHfgNuEt9iXMhdobQYLfH8IuNQGdQWlxzJx10CyTYTp1BfrHe7XkBYLtQLAN2tPS9KGOu7u5p+OgifEcG3UoRHR8XNFKJXTwcRFL2kz5CUOstrP9oeXdPo13eTN2EJ9OC9wqMIC4oqpUYALxSM8m/VzSRk0d5VAwAmPI6tjR0luCk2BbfWywWhgy+mdWr32XypIOsXv0uQwbfjMXSVtqllkgJtgPHOi8GAHQX1keuPH0YreYCzJPgwy0urKfzR4R9Qc91o2UlcBg4gKTkJykMctZsptcDhzEe77mOWRbVCl1gyTEVVpL32SGP9ukXE4lqrqdsX8dHAK4ibTb8YzvOCeCsJcERSXdMbvH98mXvExhYw5EjsGIFHDkCAQH2dNBOYdGQWz0UweKFTIneRaJuebm7J+Gji/AJgm6jjs5FD1QBR4EUJMU/KWFQnZaNVm+h36PZGDKcj6HvShYOv6JLPvtTl2zGUuZZYRTULx5rpfciW/QdZDc0Roa5nQ458WdTGXzv9BbXtu9IZd4859JBO0JN70QVw1ZcaIIAZPYhrOuexvrtW909FR9exicIuglBEDCGzpc2rgFSgX1Iin4SwqA63Z6y15ZXRp+FBwje6H0ztCuMiUoi0j/U6+NITSN3jWerDcbMnOyUc6BTCEH/X05j+pp7uGr3QwTER5+73h4hQ5LcHjJhXuvSxtOnjWL9ensaaLD/v24dTLt4VKt7z0faJHLzRrfn04oLTw8AEpmZgjz6JZrWvRlAfXgXn1NhNyKIQhIBFGCvdOhk9jWH1ALHgMCGPoMRxHtglj2P6lPNcvhrGjFPnsCS6I9lZOcXYYvJwpplW8k7VkzCyBhufGwmxgDXUrTGBoR3iXWgdG825rNlHusvZGhSm7kGXCUwMZbJr/6M+NlN0RBXH3iQ3LWHSH91b7ttFaN74YKzDz1GxNjWlSYffWwhq1atZfToGubPt4sBkyn4XDro9lBPZbs1F4cER11QEQatUG1oKZ+im3Jjd8/Eh5fwWQi6GYEOQQKey0RYB+QBp5DnlaixOyN2IrtaD6GFIACQEmNW50vxmmvMPHfN2xQezOSyi6qxnsnkhetXYTE553zWSHZ113zoVxwp8Fhfir+fvRJgJxwhhU5H4q0XccXWvzAv68kWYgAgoFcog++dyeB7kwEI7BvLgF/NYOgfr2pxn2p1XRiHjxngUAwAGI1G0k59zIIFi0jZP54FCxaRdupj56IM3NwRq5rki2MF/H3zCb44VoCqSfBQ1InT43kBLaN9MefjwsZnIegxVHi4Pw04gWQcYALSgVKgDzDYw2N1DbVn8jn0yD+xlLX+WfV6Jh2dClXz3UtTq6kar/7yIxL72s+Xt2yBQYNASsmaZVu5dfmVTveVXlmAqmkI0RggKtq0GJSmnKbuTBmWCjNZq1KQ1paCzS86mJGLryRqcv9WbQPiPXcsET52KKV7jrjVVh8SyMC7pjHkgZkED+hY2A749TQSF0zAEB6IEAJrlYmsd/aQ+LOJ9Jk/iqz3DlC6y7U59J41tN3XjUYjTy2507VOAWE0unwIp2qSW97dToFWxlVzVJZtVHgzJZLV912P0nFzl2lzvEXTUXQeNlVpF/6GwkfbOCUIhBDZ2D3gVMAmpZwkhBgLvAIEA9nAQillVbM2icBx4Ekp5XMN1yYCbwMBwAbgD1JKKYTwA94BJmJftX4mpcxuaHM78FhDt8uklKsaricB/wEigQPAIimla1u5HoKkHvvO3tNUYA9VrKQpNi0XSSQC13PtdxfW6hqOP/M6J/+1EtXsOGeDZrEStfwExrMWSu7u6/IYaTtziAgyc+SI3els6VJIToZRo+DbPcUu97di/0fnvh4Q2psFg2ag1VvRrBJDiB8lu7NIe3E7Z/7b8cqX91kKCfMmMerJWUSOTzx3Pai/Zyr4Na9e6Ap+sREM/eOlDP7dTIxhAU630yk6jBFNvjOG0ADmnV6CIdieYKm+rI7T7+1wuj+hKAy+b5rzE3cB3aC+aNGxUOK8n8qmE4UUaGXs3q/an6VlKskTyti0J5W5EwZDvWcdYdsc70Qhc0fGeXQsnyD4cePKkcFlUspxUspJDd+/ATwspRwNrAX+dt79zwPne+P8G7gb+xZ1MDC74fpdQLmUclBDuxUAQohI4AkgGZgCPCGEiGhoswJ4Xko5GHuqv7tceC89DG/G+VbQOlC9ZznhtUfRthQ+GzKbY39/pU0x0JyQNzII/9D1crQFaaXMn9/SE33WLNi6FRJGdO44pzY/l7o336PiXx/zSfif+I/+Pr6a9g+nxEAjeetT+HLi0+z+1YfUl9gXlNBhvVH8O5e9MnRoEsU7XHNMNEaEMHbFjczLeoqRj1ztkhhoi0YxANDvZxPpdUXHDn+NDPn9lQT390JmQuz+j8rNN7jU5nBeBVfNUVs+S9eoHMmvBOn5BbXd8TyN7YLcc/lwks74EAwFGvPGbgFuanxBCHE9kIndy63xWhwQKqXcJe0ZW94Brm94eT6wquHrj4ErhBACuBrYIqUsk1KWN4wzu+G1yxvupaFtY18XIJ0XBEVb8zn5fAoH/rSZ7Td/yM6FH3Pin22d97m+g+4uMt9eg7nQtR16xPNpRK7Kc+k8PG5IFBu/1LfwRP/0U6gzwY2PzXRpfKQktsbGzDMWfnvEyi8PVeBXW01oYDmT/nQFgxdMQyhuGI+lJGvl92ya8CxHn9hA8bZ0wkb3c72fZiiBAU4XG4qeOpRJL/+c6zKfYsSDs9AHuuZs6SxCp+OiVQubohLaodcVYxj37DyvzOPcfPxdc3IcmxDOlo1Ki2dp8waFMfFhYDFBmGerL7Y7nqepdt1a5uPCwVkfAglsFkJI4FUp5WvYY93mAeuAW2hYZYQQQcBDwFXAX5v1kUDLTDy5DdcaX8sBkFLahBCVQFTz6+e1iQIqpJQ2B321QAhxN3arBImJiY5u6VbsYYLuC4KarGr23bOawi2tF//60mSG/2XKeVdjEbifFrarKfrevUx5YS+fwlCmcvYPfcGJc9QhF/fl0PpYxk8q4prZNtZ9CuV1/vz5swVORRn4WzWGllpJqtHRr0rDWNc6jl8IQf++Z6EvnD2QQNWpM269t7qcIlKXrIcl0HvWRW71AfZyxh1FFYQMSSBm+gD63jyW+DnO79o7S2BCBNekPsJ3s1+hdO8ph/dETBjEjDV3otN742TefWYP782bKZEkTyhj1jUqmzcoxCuRzB7e235D5VkIifHY4trheJ5EtaGV5aGLdPhx6+MCx1lBME1KmS+EiAW2CCFOAr8CXhRCPA6sx159B+Ap7Kb8GtHSk8rRp7Ls4DVXr7e+aBcvrwFMmjSpBwbpm3CuloFj/KL8qMlwbCIPTGg8Y44CooFIBN4thONJ8jdtpSbDvUUTIPDDTBKqVPL/1hcZ2P6jrlN0LHjmGtJ25nAgrZQp90Qx5OK+6JT2jWh6VXLFGRujC+sQqvPe8YFx4W4LguYUbt5NzIxJVGecwexEAaJGoi4a16EYCBuZyOXfPoB/TPuJhLyFMSKIS764h4y3dlK66zTFOzOpL7KL5+BBCVy66R4MoZ0/rvA0ik6wetF0Np0o5MjRShYnhzF7eO+WDn4e9CNwajwPoqV+jW7mL73St4/uxSlBIKXMb/i/SAixFpjS4Cg4C0AIMQSY23B7MnCzEOJZIBzQhBBm4BPsLu6N9AEaV7Jc7BaGXCGEHggDyhquX3pem++AEiBcCKFvsBI07+sCo3PHBYZQI0P/NJf9D7za6jW7yTUOGIjAe6VgvYGlspq9v1nc6X6MX5xm0O4yzt6dYI9AaOdDUqfoGDajH8NmOGeG97dq3JamEVHu+lmtf3gAOoMezc3yvs0p3pYCQhAxYSTmIJX68gpEwymAqKzHlt/yGQsfN5zSfUfb7C/2spEM/eNM4ueO7lAQeRu/6GBGPDgLgOqMYlJ+t5qKw7nMXP/rbhMqzqDoBHNHxrXt1Gcxgd4PbO5vBlwaz4Nopw97fQwf3UOHgqDhCEAnpaxu+HoWsEQIEdsgEHTYowBeAZBSzmjW9kmgRkr5fw3fVwshLgL2AL8EXmq4dT1wO7ALuBn4piH64Evg780cCWcBjzS89m3Dvf9paLuuMz+IrkCSjj0oIwJxrpRx58INazKr0Ac7NmmXH8xBMKxT/XcXB/+6grpcz8Tz20qriXr6JDGfV1IyJ5TKeb3A2PmFbl6WRkS5e7+/SZfW4hd6MT+856HyzVJSfuAYppuTKPxny9+5MKnoi+rR55iI2FqNpVg69BsIH5vE+Ofm0fvK4Z6Zk4cJGRjDZV/+DqlpiAusSJBDgsLtxwcXGpWey3/ho2fhjIWgF7C2wfyvBz6QUm4SQvxBCHFfwz1rgJVO9PVbmsION9IUhfAm8K4QIh27ZeBWACllmRBiKdB4kLxEStmYmu0h4D9CiGXAwYY+eiwSE83dISRBQASdtRCo9Sp77nzJ4WsFm7ZhLi7DP8Yz4WldhaWymow3Vnu8X+1oAZFHC4hdXcqZR/tSP8Z9p6vYGhv9ijon5iL7eD4NiGJpfSomAxSs/QKx9gvEND2KfEDURGPMrsOQZya+OoiZc2cSM2MQ4gIovtPlYsBbB42VZyG894WXvVC5sKyNPpynw08kKWUmMNbB9ReAFzpo++R536cArTyTpJRm7I6Jjvp4C2hVVaNhXud7zPVgSs/7vrbhX+cIGx7BsL/dwsl/tF5Apapy+j9fMPSBRZ0epyvRB3rXz8GWVULCveVUL+pP2U2xqLGuhe75WzVuzOz8KhES4hlzcXN0Zucy68lgPfWjQqkfFUpkr2HEJl6Yyaq6BG96HlUUkh4XwaCCC6fEsP7a8yPMffxY+BHY3Xo+9nTBnXcga4txKy5n9uHlDLr3ulavpS79f9SezvPa2N5AZzCgBHhXFEibSvDKDPpdvxe/I675APSvsBJS1fkYb3+D5wWBvkJ1Of1wldUbSbG8i6qqfP75LpYufZfPP9+F6mTopDsIBfR/vB8muR/R0R7fJXW2wFnXIYbPRNe366JNfHQtPkHQJeTSmUiCjhBCEDEmir43jWn1Wn1xGd/P+y3Wms5bI7qKmswcRBc5s0mbSuKyHERNa+e+MLPKxbkWZmVZuOWUjTuP27j3qJU5pz2z+BgUEzqDh48N9ubR58lsUJ0XBaV1ni2f7G1UVeXGGx7kySeWUVf3Nk8+sYwbb3jQu6JAgC6pv8f7NRkNlOvMnIzpedESrQiOQpn1QHfPwocX8QkCLyOxAKc7vM+bVBz5gV2/fAjZw0uXWsor2XXHw3w25GpsNV23a7VllzBo4TH8Drb0CRhcZmNadhVj86rof7aC6LIKQior0Zs9MzchBH4xER3f6CKGjafxP+C8f0Op2bOpdL3Nxo17ycs7ye7dJp55WrJ7t4nc3BNs3Ojlwjte8K/Ij7WnEN8wNBRzD/40Fn1GYvzNa+h+DM6cPtrEV9zI62RDF1QYlFJybNmGNl/PXbuFo0++xJglf/D6XFwh462PMYQGE5gYz77fPkn5gWMdN/ICtoJyQvZGUj8+/Ny12C74hPa4haABLdD5ZD3h/q13pxaLheXL3mf7jlSmTxvFo48tdK46YBdw8GA6V80yt0zVe3U9hw6lc+21U7t3cm2wZ2QSRyMldViwSBuNjgkSu2VKReWjSXH8cm/P9OCXtReOj4MP9/EJAq9T0iWjCCEo2ZXa7j2pS/8fYaMG02/BNV0yp7aoOHaKnE++JGfNFioOn+zWuTRHMbU0s9fremAeKyfRgp3/0w41tjzDtlgsDBl8M4GBNcybB6tXH2TVqrXOlwz2MuPHD+LJJ/xZusSEwdCQqvdLP55aMsir42pu6sMvJg/iuF9Fh86JZ41W9vcNYWJO6yyX3U55Ppq5Bp3/hZPl1Ifr+Ow/XqfrFhUlsONzyIN/WeFUkSBv8v3cezj6xEs9SgwA6Ewtj1S+6Wfg64EhaIbOFRDqcoRAC3VeEJis9S38EJcve5/AwBqOHIEVK+DIEQgIqGH5sve9MNmWWCwWnnh8JVdc8ReeeHwlFkvrYjpz5kwhIWEYycn+PPyIIDnZnz59hjNnjneDjjL8zLwyI5HDQ5xPgf7lpAYx4CTf9vOnygM5MryBtveT7p6CDy/TM5+8HxVd50GsD+rYM78ut5BPYqdy4l8r0azOp9rtDJJqJJlIspGcJmZma+fHnoBibinepBAciPPjdKT3Ih7CB3s2s5y+dzgFL49Bi3B+J19gKmd7fuo5UbB9Ryrz5rWs/Dh/PuzY2b4FqrM0WiZWr36XyZMOsnr1uwwZfHMrUaAoCmvWPstTSxYTFHQHTy1ZzJq1z6K4UzDKlflpNqplHZtj6/hw2kBsDjKo24TS67XkAAAgAElEQVSOM3HR7B2ZxCcXDeKIv2u5KiSS/0z0Qg0CD6Cd8FASLR89Fp8g8DpeqDjWBvog5zyVbdW1HPzLM3xz5Z2o9V1RzvQMdsfKLCATv6ie+djp6h1bc2q9eLA28rIAjyXa0S7pS8Y7IzBPct1RcVt+Kpuy9yGlZPq0UaxfT4vqeevWwbSLvRtu5oplQlEUrr12Ko89tohrr53qdTEAYG1WqyJXVPJF8kAAjg/sw8rpSfzvjDienxbJfwfC9xHVZOrdS1xVqVjYNNw75Zw7RV0FWnF2d8/Chxfx+RB4nb7Yyyx4d+G1Vlupy3EtDWrR1n3su/cJBt69gPDRQzAEe8uaEQk0Fd4J6NN1IgkgdNgA/GIikQ1haUKvBynRbDak1UZZin3nq7N0fRRGaEAl/ecnk7V2V6f6USKCyXo6CfQdiwubxcZ3Kw9y5vBZEsf24tI7x6M36jlYkoFE8j+PLmTVqrWMHl3D/Pl2MWAyBfPoYws7NceO8KRlQkrp8ayLfkpLq8tZo5VPpg4iU6kAzB49HTwaBWemxhNklSxM6TmOhrbPn8N45/919zR8eImeuVX7ESHQA97PApf7aTqqyexyu8y317Dl4lvZcvGtmEvKOm7gIpJKmosBgGF/nsGEF27DENo1DkqGsBCKt6VQsvMgJTsPUrx1H8XbUijddYiylFRChw0getoEzt7sYGctJQO8HKYfndT5GPTaS6OdFgMvL3iPwh0HufnyfAp3HOTlBe9hs9i93Q+VZCIVQdqpj1mwYBEp+8ezYMGiLnEo9IRlojq9iFP/3srXM19EupigqSPiglqmAK+UtQ1iwDtUKhbKepr7SkUB6uEvu3sWPryEz0LQJURjr9jsPQfD2qxShF6PtLlXPa/iaBqnXv6A0U/c77E5SaqBA62uCyEY+vvp9Pv5WNJe2kX2e7upzfJesUqdsf3c61UnMwGIP+jPmTf9sQ5sspQkVNsIrG7KSqhqOgQSnQcjEHr1qkIJ8HdL0DVSO9w5P4fvVh4kKtTCkSP2HfiyZTB6tIXvVh7kynsmA5BZUcCI6ESeWnKn2/Nxh0cf65xlwmaysDn5OSxldgX3xbDl53IHTH3nF0RNSerU/EKMnRdubVln2kIVGhYdGHtQChH1+5WIkZeh03d/xIkPz+KzEHQJtXg72mDU49O5oeAfTHzxbrf7OPrkS6R7tKhQbruv+seEMGbJLK7LWMxlX/2FhPkXeSX5i7Opm7U6M31ebGmelQdqOHogkhPHotm9yY+1fzvKV69VYrV5rsCLv8HMgPmTO9WHNapjbW+z2Di04RTXXdfaLH/mSNNxU63N1Km5uIvRaDxnmdi7byyxsaMZMGAgy5e97zDaoDlSSo7/ffM5MQBQnZZL9Q85VP+Qw+kPD3V6fgZd5/wUOrLOOMJqs7LR4PnkVZ1CtaJubLeMjY8LFJ8g6BK6Jq7YLzqAIQ9MIG6O+znX9969mIyVngovci7fvxCC3lcMZuand3Bd+lIG/242Oj/P7D78oiIwnz2/sFTb6GpaJpEyZ9Xyw3tbOfbmd+Ru3g9SUpV2hrx8z35IJ43q3BZQC2sSKMKsEvviGYxpTc9d42IUrNSwYUNrs3zimF7n7q2zdV9YqtFo5NHHFpKZkUFx8VGmTDncZrRBI+aiKrZe+yrHln3eZr/1pZ7JLnll3wlut21unWl0mowMsVtnzqfXhhqG35XFoFkpWO/fjqnKPcuft5Dpu30Ohj9CfIKgS+ja1LCWsk6MJyV77nqUzFVrPTAT5+O1GwkeEM2kl69nXvZShj80H0N4SKdmEDyoL5oLkRQipxrlbJPp/vxkRY1YPewjGhpYSVCi++FmUm+3rAiTSv+/ZhL0Xib9/pqBUmRf3BsXo1OnYMAASE6GBx+EUaOgrNrIpXeOP9dXpH/nfuadxZVog/LDuWwc+wz5G9q3AAgn/Cs6QgiY1Mt9f6Azh886dJo8Z52Rkvi11Qy7+ThBSw5gPnoataYOpCQvo+d9VNs+e7a7p+DDw/S8p+xHhCQNyRGg69J+Vh4vp/zAD53rREp23/kIWe+t6+Rs3HdRCegdxrhn5nBD4d+Zse4BEm+dieJiWeSo5LGU7jniUhu1ooakP2US/WoOwqyimBzv3M2Vns/hMObnIzp1ZKIrtZD05wzYaz8isRVWkPCC/evGxcjfH9auhSVLYNcuKKoO5r6Pbmtxjh0bGO6w/67C2WgDzaqy+/b3MRd27AxrCPVu9UxnSBzby6HTZOKYXog6G0OXluD39H7qzxS1qjtSeMq150254m6IG+KpqTum8izqwS+8O4aPLsUnCLyEpAJ7uGEp0HWFenb87A3PJBySkt23P0z2B5+53LTyeDrpb6zm0CP/7vQ0FD8DfeaNZNqHv+DGohX0unycU+2iksdStt+9ughqehEhb2YQ9HUJSrVjQVByosjh9c6Q0KuI8OH9XW4nYsOp/LiEAb84gtzf0jlT2XKGkM/PtliMFAWuvhqKi2HcNYNbObWF+3VB9Ids+bWUIG0aUnM+2uDY8k1UHM5yajj/mEAPTdx9Lr1zPKVVRkaPhocegtGj7daZK68ZwbDf52D5vO3ntfyUC89bSDTKmKsRigeO3YQO/IIgsg9iwCR0U26C3k1WEnXbu2hazzrO8OE+vigDLyCpB47RlWmLG6lJb9+RzxWkprFr0YOo5noG3HFjhwl0pJTkrt3CrjsexlZdS8J1ycBMj81HH+RH/NwRnP2mYwcxoejcjrhoJOapE22+VnYkA7N1PP4GzzrghSRGUXHcuUUOnY6scbE8P9xEmKmCEeWOj4pi/p7GrfP78HiFgdGjrec8+Ctr/Hl/yf0MiEqgor6ar3NSOFtXjlHnnY8FKUGarajHf4Ct34HBaN/+17Uszf3w737ebrSBtcpExlu7SF3Sts9Ac6KShzLk95d45D0IBFf1nciWnP0ut9Ub9dz30W18t/IgH397lgEjo3g0cAjG245gNrXvtyE15z9LdCMutbcpdvI5EjowBkBQBCK8NyIqEV3cEEgY7rB2gTb5Bmz/vgM0GyJuKDovPS8+uh7fb9LDSDQgFW8nInKEarZ5vE6B1DT23PUox55+jWF/up3+C+dhDGt5xqya6ynde4SjT77E2W/3nLtujPT8rkwJdG7XI7ycuU7abGSlBTF8pIc98p08MvhhYm9eHVLH2bB6QEdpCFj7x2LIbr2TlDaVkE9O86+QEbw9ycrGbWZuvmU6ixf/4lxugXC/KH41Yjbbco+Ag5S8nUFK0ApK0P77n5YvWC0OnTH0H37IiW2v8/QbG9i5I5UFC5qqLdbllbNl6vPU5Ti/Y57075swhHjmyEAI6BfSq+Mb20Bv1HPlPZPptaGGwCdTgKMeroUq0E26wT7WTY9j++BhQDZb9MMRYb0R0f0QvQch+oxA56LPiM4YgG76bWipX6Hc9IRHZ++je/EJAo9zCvByJhsHaDaVrHfb3tG6i87PSOSkUaBppNy/lEMPPsfopx5g6B9vR9pUdt/xMGc+/tLhbty/l2sfNJ4suVt28DjGyDAsZc5FOrhD2uoUkoYM97iVoCPWXRLJR8PrgZaiZ9kEM0tLA9GqHR9RiWoTfzWP5/Ktv2HTpj2sWPFfxo8fxJw5U1AUBSEkM/uOBzq2rDj7u5KaxPbya2Bz7RhL/8lqFkcAC8chps9ElNei2iopX73fJTEw8DeXETnededWbyLqbIS/muXalsHZJEtRfdEZ7eJH12sQ+ttfgMBQlxf9jtBPvA5t/Fx0Hkq77aNn4BMEHkRSgN1voOvJXZPBvrtf9mifugB/QocmUbKjKbmQrbaOg39dwbHlr+AfG0nVD22bJfvcMMLpsTxdcldabegjvCsIrFU1lJSG0qe35wRB0MAoIsYPwFRQ0aaz3JgqIx/R2hKUHqvwwgzBAxsc9x0xYRgz/ruIm278G3l5J7lqlpknn/Dn9deGNSsO5JwYaOt3ZZIWpIQwYxBaYWlrq4CrVFYiv/js3OFbjJREjB5I+dGMDpsGD4hn/L/md258B4T5dc7ylbSyBkuB86GwAJaKanattRASrWfUjLYXYWXyDS2+10UmuDVHZ/CJgR8fPkHgIexZ+dJcaqOqKhs37uXgwfQWOzV36HvzIPrfdhXZ721xq70jwkYMpLwNxzxLeSWWcseL7dA/zSVyUgLRF/V3eqzmoWZNGfTsoWbOZswTRgPRF43DUlpO1Q/Z1OV4Pwe8ixvfcyj3/RahE6in85BHj6GbPBERHMT40EAQdotP7tpDHFm8keofWvqFDDhjgmTHH8a7E3XcPKY3cUcKW1wPGz2ISzfew1c7D5GXd5Ldu00YDLB0iYnk5BNs3LiXa6+d2jg7aMeQ3dbvas7v/ocr7pnE5QUKEzNcq6uhapJNJwo5nFfB2IRwZg/vjaJrfXQhhGDo7P7sbkcQ+MVE0PvK4Qz5wwwMwZ6PLjAqekKNAVRZXBeCkVtrke8cdmvcvG32z5f+I4YSHNU6MZZy6V0owz3ns+Pjp4dPEHiMNMD55DKqqnLjDQ+2s1NzDaHTkfzWDVgq6sj/fIfL7R2hM7j3eIz8n8vwi3bNU93ZUDOdn56oKWMQBv15To6S+uJyirfuc2vO7lJXbrPXr2qDthY6YVAQAvQD+8KAvq2O7XV6hcRbJpIwfyxZb+8i7d39fKaVsj2mnsIOfrRvJ9XxSLNoy7jZFzHto4UYQgwcPJjOVbPMLX7Os66u59Ch9GaCQKU9UdDW7+rjb+0iZJwbYuCWd7dToJVx1RyVZRsV3kyJZPWi6Q5FQULvQqInD6dkn+MjstFL5jL43ukuzcEVhIC5/afyYdo3LrXzzzAT8ehhtE7WWMjLVBga1eyCTkE//3/Q9XcuAseHj7bw2Xw8RiKu/Dg3btx7bqf2zNOS3btN5Obad2ruUHWynMItZxhwx1T8YiM7btAB0RePp3S3ezsZnb/rQsLZUDOt3kbp3iOU7DhA8baUZv/2n6tJ0JUcf/t7ThyPdlhIp3GhW7Z3L+YxJ1i2dy+3fH4M7YabWt7Yjg+fYtQz6O4ZzNn6R379wb0ci1MoDWn/OUuNUzg8xe74Fj1tDNPX3IYhxL56jx8/iC2b/Vv8nDd/6ce4cYPOnz3n+yg00tbvKnFMLxR0bbRqm00nCinQyti9X+WZFbB7v0q+WsamE4UO7xdCED8xzuFrY5++if63TXJxBq7TJ8S18sS6MgtJzxW5lCSrLYqzm5mlDP7ob/unTwz48Ag+QeAhBDFAv4bvhgDt57pvb6fmDiW78/l+7jNsv/k56os6X7VQp3ffS1+tc/1D79HHFlJXF9wiRrsrSu56gmNvfMf+74PRtJYru8OFrvIMW1KzXc4/JARMSExg599+6/gGiwpbTsH7h2DLKZ4bVkfd7FHMXP8r9AFNAm3OnCkkJAwjOdmfhx8RJCf706fPcObMmeKgU8cWAke/q8Zsh8KNCIXDeRVcNUdt+bdwjcqR/Lb9P8JjWguwAb+6hBEPX+WVY4LzUZr9Av3TTQy79QeE+byfl1UjdlMNwxeeot/snZj3e0awlh7LtwtQnR79r19FF9WOicqHDxfwCQKP0hsIAqKA+HbvdH6n5hyxl3rWk7ozpWPTX91LXa5r2RmbF7bpypK7niJ7/R62faRSY26y5ztc6Ga1L/pqamq4ZOYfiIyYxyUz/0BNTVNuASEgOSmRfQ+fV5HSohLySQrDDGU8+Nt6hhnKCFq3n5s++jV+kS0XR0VRWLP2WZ5aspigoDt4aslil4+pGn9XQy67mI+/jSdu+vhz2Q51bgiCsQnhbNmotPxb2KAwJj6szTYB/k1OlYawYCa/uogpb9zq8tju0ih8jNlmEj+qpT6zgGH3naH/m5UkvVLBsAcLGHR1CkGPp2A+5VxxLWexVtVyOlWjSklymCfAhw93EZ6uGd6TmTRpkkxJSemSsSRmYFebrzf6EOTmnmDW1fVs/tKPPn2Gu+1DALC+/1PUnvaMI13o8IHUns5DrXOvJO/Mz35PwrXORxk4S9Y7+9l9++se79dTKAH+XLF4IqH+lXxxrIBle/eye79dFFitkDzRwFPLn+Ta66a2altTU0OfhHnEx8O8ebB+PeTnQ27eeoKDW37wN55Dl9XW0Xfyr0ni7DknP6vVvmsfN+4yPvzPYk+8K5pbC1RVZW3mLtIqWifBUtDx5+2uZXFsPFrJV8uYdY3K5g0K8UrbPgQAdfVBbHhkD/rgQK74/o9ETuja0EIp4a0n3iBwadd8njhi8v97gsG//UW3je/jwkQIsV9K6fBczWch8BICf6Dtc0ZP7NTOJ/bSMW63PZ+qExkE9e+DPiTIrfaWMu/E5uuDe7bFQDWZObC2DKlJZg/vTZwukuQJCg8/BMkTFOJ14cy+2nGp47nXPEp8PC2K+sTF2a+fj04IdEIQHRxEfL3OoZPf3n0nPfWuzn31WeZuVhxY7VAM2O/UePci1xZnRSdYvWg6i5OnEHB0BIuTp7QrBgCM+nomPP8Lrkl9tMvFAICtVutWMQAQ2NexH4UPH+7iEwQeRGJD8gOSQ0j2AO2f5SuKwrXXTuWxxxZx7bVTOyUGAEKGuF8tzxFVx9MJiI91y0mxdG+OR+fSSE8XBAAl+06QkdWrzYWOIscx6EePZjlc2I+mtp+CdsqUYQ6d/KZMHubJt8X/HlzL0dLsDu8r1NexYZxr59qKTjB3ZByPXDWMuSPj2hUDmibYtU4FnUZQv6g27/McArtPUOM/MATrMIR1b1XImizPpSn34QN8gsDDZGNPTFSOvaBR52rcu4o7znwdUf1DFjqDweXdSOFXxz0+FwBbTdenhHaHI29sp7w60uFCJzdvdljmYvToJIcL++hRSe2O9dbKv3L2rNLCya+oSOGtlX/16HuqszmfFvtYsIlPJ3p+515eE8nOdYKz249SsKmpqqeqqny6dhuXXfonxo79NYsfexOLxd1nRaH54m//ZVmb/bMTMqiPm/17hupTp7t1fB8/PnyCwKMUd+vo1hrP1jFoxJR3FludiZAh/Z1uU52W06qEqyf44fnvPd6nN9DqLXz39B5OHIumsCSG4opoSquiKauOpCJfoTw1j7q8lo6XX2xYTn4+LRb2ggL79fYICAggN28948ZdxuqP4xg37jJy874gICDA6fmqqsrnn+9i6dJ3+fzzXaiqAPwa/oUBzvfVyKmAOkwGx1av1MRop3L4W2xGSqujyDrTi71fBfL141sp3GZPslCw8SCle7NQVZXr5/+NRx55gilTDqOpmbz66vsMHnSTi6JAoclfouXi74jIKYPbfd3biHasKD58uIMvMZFHGYi9ymH3YKtxzwGwOUpwAHJib6pH+RO+tRrrUbtZ0lJaQcgAF8zAUrJ1/ttctHKBy0mK2sNUWOGxvryNaq7n2JvfOX5x8VYQgiH3X87EF28BIDg4mNy89cy95lFeez2L0aOS2JeyvJVDoSMCAgLccCA0AgGoqo0bb7ifvLzUZkmyhrNm7YqGYyy70AwxhFBtrXZphJVTevG7HU3pvFXgu1F9OBBu5svEWO46UEG4A8tWRW0Eu9/MoCa7/VTgZ9Yf48XPv2PfviO8+ipcdx0sXw7JyVBUVMvyZR/w1JK7sJv9JY5DKQV2IeBadczw0e1HEnmbvjfO6tbxffz48AkCj9K1RW6aY1GtHBpZSmRECLZy1z60G1Gm9yfrkTjUGD8Ayi4LIeRgGJZoPdZoPWcrVHq5kAhQ6HRoNs/WctMHeT/GvMuQkow3dzDhf2+iLqecwMRIgoOD+X7rCx4aoHGh8wcclUa2ABY2btxFXl7qeemMj5+XzhiuSpzImozvXJpBrbCwbkJf5h/I4YeEaNYnAdiFq4bG+xOiuW+7fdGXGpRWR3PmaD1Z63Yi1fafHQ3J799+j8qIMu64Q2PpUnjrLVi7Fq6+GrZvhx07j9JeGma7KLLgqhgAMIS7bjXxFOFjhhIzw/sJmHz8tPAJAo/iuB59V7Dq+PeUTg+g/MPRJL5UhPiiday7flIiOb+Nxjy8accZuLeShGXZlN2eQOktMdDMDGlLDKQ8samQiwUQVw5EftVxYRmAPjeOIqB327Hk7mDKK/Fof12OEBgjQtHpFXR+fvjFRJL++g5OvbSNMX+fS595Yz04mMR+Ktj+c+lcOmMYFtkbkSGQjhwg2iEt0MS3faN46LWvOXOsiISRMdz42EyMAUbqsPD9iATG7DWx/7+5VBz7zul+D1FLdVAJBw5oTWGdyfDZZ/Dll1BUBL/61ajzWumxL/467ILJfZ8Uv2jPl/d2lphpExCuZrfy4aMDfILAo3SPIDhemk+x2b5QapFGsp/og/HWMOI/qkRfaqN8aiCVU4OxJbb+AKu7OIJT68LA4Jw7Sc6vI+nzdaZT5VhTn/yCAbd7dhejBPjhHx+L4mdE8TMiDAb0gf7U5RZiynMth35XEjNtAqaCYkyFxS0qMNblFJByr/2Yaf/9nxA+JoHg/q6lxW2fjhe88eMH8eQT/ixdYjq3sG7+0o+nlrROkqUIBZt0bTdtMVn42d2riOsl+c1tsH59NS9cn8UfPr0dY4CRlCAzNa+cwJTvmg/OaeqZM19rIWSuugruuQc0DQICghxkumyce+f9W6Iv6o3OYECzulnhqhP4RUd0+Zg+fvz4BIGHkKjYIws6T0FNJRmVRRTUllFeX0WdzdRu5kCT2vqowjI0hOzFToZFOSkGAKwDgjAMj8N6vP2z3X4LL2HU4sud7tdZDGFBVKa2TgGrDwkiZvpEyg4cczuZkldRFGoy2w/FrMspYstF/6L/bZMY9pcrCIjrrHWlcTfcPnPmTOH114aRnNwySZajdMZ6nR6b6pogWLNsK3G95HnVESVrlm3l1uVXEvFpsctiAKAffmxcJ1i6XJ4TMuvX6wgMimHhL65k8eOLvJrp0hBiIOaScZz9qmsLasVdPZ1Ri3/XpWP6+GngEwQew3PWgZUnNnqsL29QPzgIXQdRhZrZSujQXh4fu9+tkzjyWGtBYKuupXj7fpTgQIIH9qUmwzt5ENzGyYyg5rNlnPznZqyVZqa83pksdM6JAWhKkrVx414OHUrnqSVtl+K+YcA0Pjz1tUszyTtWzG9ua51f4Y1VRSQ9lQsbnDuCOp9xBLE115+JI81cc4Nk8xZ/Bg/uXLZPV+n/i4tcFgQ6o4GAuEh0/gZ0Rj2Knx6dUUEoOkx5FdSeLmrTf8IQGsz01S+ga/xh+vDhQXyCwGN4ThD4K/6Y1R64y21AX612aHDN+WQnlvKbMEa4l+mwLfx7t+9xr9bUYRw+kPCQIEx5RdQXd77QkyfQrK7tqjNX7mDony8jbLg72eicFwONNCbJau4z4Iik8BiCDcHUWJ1/3hNGxrB+fTXLljWlVl63DoaW6sGBr4uz6BD8vq4Ph07VkvW8xpOf3M811yR3mRgA6HvTYFJ+54dqbgr5NUaEYAgPImRQDDEzBxAQH4Yh2Ig+NICQQTEEJUWjU9q2ymk2lZr0YjJX7SHjtR1YyqrOvRZ98XgMIb76BT68g08QeAzPCYLEkHjSKrq+lK+zKAXmDgVB2KgBHhcDADq/jh/Zsn1Hz32tDw0msE9vjBGhCL2CZqrHVFBEXY7j0rreQrooCKSqsv/+T5j52d3oA50ze5/99gdipg9GZ3DdY94VEkN6c7zM+YX8xsdm8sL1WYweLZk/3y4GzubDw7bOZxnUIZhAMFMjIpl7zUXtLrTewBBqJPHWmRRvTcUYGcSYZXOIu3pkp/rU6RVCh/Vm3NPzGbV4DlmrdvPD899TfSoPxclnwYcPd/AJAo/huQ/hGfHDeq4gkBItt+NcAOOeneeV4RU/13Z/tqoaqo63XrwiJ43CVlNH1cmu+Tm743h29ptUNo1bQfLKnxMzre0qmPVltRx59HPSX/mWmBnjmLb6lwT08p4HfKDeD1SJvqiekF3V+J9VUSwSpUbDL7MOLOfJxSoz/1s+gFfLz/L2s2YG4M8zk2dRty+1U/MwhAaTcNkoeg8NJKa/4kadRdfQNA1yitFOnEIWnYW6aqg3MfGKSAwrn/DKmPpAI4N/O5OBd0+n7kwZQf1cTyPuw4ez+ASBx/BcTHKvoFAErod3dQW6KhtqdfvOk6OevIn4OZ7No39ufCcsBM5QlpKKMBoIHzecikMnPNJne9hM7mWRrD6Vx1cz/smQ+y9nzN+vwxDcMg9D1ru7OfCHT7A05J4o3naILyfmM/2T+4hO9rwPB0Cv9yqZsLaE8v2tk3C1JXuMKDzQrCS4rrwa/7gYzAWuOxMKnY6+sycx5hINf0MNUANmkGlnEW4dsbSNZrPB6bOo+w5C4Rl7soTzqa5B0zR0Ou9ZJ3SKjuAke/SJZBcwBoHnLXA+ftr4BIHH8OyHQZAhyKVzWk9js9j4buVBzhw+S+LYXlx653j0Rj1aiJ4zn0/FkG/GL9+Kf6FKQnogumoban09QhEk3T7ea/Ny1ULQHtJipeLQCWJmTqZ4q3c9xc1nO5E/QUrSXvqa/C+OM2vPX/CLDqYmu4QTK74m/ZVvW91uyivClFcNeF4QlOwq5ORT/0GzqcRMn0jx9v1u9VOTfhqd0YAwGpAWxzJCKAr+sRH4R4cR3CeS2KGhREbXE+xfhaJrLUrVlD2IYfPdjs/XzBYoLLNbAM5kganaOWdQswlKyyGmKwotgT2x014kOmAQgoQuGtfHjx2fIPAYjivYuUt8UAxpFd0jCCwmCy/f8h4xkTZungfr1+fz8sZj3PfRbeiNetRYP9RYP8zjoBIIfvAM2p4m0/vGUcuJmTGS3rOGMvh3F6H4ec4j2lMWghY42vV5GFtVDcaocCyl7qdersksYO9vV2MINpD93m6kzQZCoA8ORB8UgFAUey4GIUj7v23svvNVbNWtnyFDaDDD/noDg+4ej3+s85aturxatt34f+ccJEt2H9yOL2cAACAASURBVCb64vGU7Dzo1vvRLFZiZkykeFuTqND5GZl0/3RiYmrx19cizulsEx1mAi3JRaYVIdqJbtE0DXksC3k6B1leBrXVYDGD2slcAgF+nWvvFhqQhiQNiAGGIXwf6T46gVNPjxAiG6jGngPUJqWcJIQYC7wCBGMv87dQSlklhJgCvNbYFHhSSrm2oZ/vgDia/rJnSSmLhBB+wDvAROwr68+klNkNbW4HHmu4f5mUclXD9STgP0AkcABYJKXsllJ4EjNQ1eF9rqCI7qk7paka/170MTGRtvPixi18t/IgV94zuVUbU6KB5h+HtloTBZtSKNiUQtbbu5n63kKP5H2XmkbxtuxO99OqX7VrqlKGDk1ye/EE0PWP5FhMKWH/bvKJiJk+keJtKdiqazFGhBIzczJafT1F3+5psx9rZTVHF79Dyc5TXLrhdqfGlprG3l+vxlzYZOmQNhslOw8SPm449UWlmPKLXH5PFUfSMEaFo5nrSbx6PAPGK4QHud5PI+qu7YghN7ayEti27EJmpdvP/b0hAP27O6V2MVCMRA/0RdC/m+fj40LElVXnMinlOCllY+q5N4CHpZSjgbXA3xqupwKTpJTjgNnAq0KI5sJjYUM/46SUjX/5dwHlUspBwPPACgAhRCTwBJAMTAGeEEI0puhaATwvpRyMvd7wXS68Fw/TvVUOPUnazhzqymqZN6913PiZI60zASpF9QRvbNtjv+JIBl9O/DvHln/TqboGtWfKWJ+0hKOPf+x2H21RX1qJ8OL5L4A+NKTd0EMlJpTcDyaRt3IC9VcnIAz2PxklJAAxLIaaBYmUTw0m45UjrKWUA9SgIak6mUnYqMH4x8VgKa+ieOs+SvcccWpOBRt3kfWec/4Tx5btpGDTboevVRw6gaWymuip4/CLdc3pzVpZTezUMVzx6DgmTK8iPKi840btUV6AzGgSLVp5Nda3P0Km7ofaSq9Zg+QPp7zSr+vYgN7dPQkfFyid+RQcCmxt+HoLcBOAlLJOynO5Tf1xWPm9FfOBVQ1ffwxcIewS/2pgi5SyTEpZ3jDO7IbXLm+4l4a213fivXSSH48gKEgrZcwYyfr19nhxaIobTxzT2hSbtKIQa3Flq+vN0aw2jjz2EVsueoHqdPd+VkqAgbozroUK2tD4L0XcSzq/4hQPksleqtDOeySrf8gkevpEt+blLKFD+7cIhzwftbiK2DcLsA4KIn/pYDK3TyN76wzSv5pC7sN9Cd5Xw8p1O9gyOI+Bfytly5ACXgzMxVRcSu3pfGy1rhXWqo0KZPswf+5643Vmv7iq3XuLt+eT+tQH7d6j1poo2XUIoVMIG+V8WeDoaRMo/H4/NTWec8pV9+9H3XoA27qvUFe9C+XuWxycRdvyjT0KoUfQLYZSHz8CnBUEEtgshNgvhLi74Voq0BhbdgtwrjauECJZCHEMOArc20wgAKwUQhwSQiwWTXa9BCAHoOHeSiCq+fUGchuuRQEVzfptvN7lSOqxT9fT/XYPcUOiqKzWUVsLo0fDQw/Z/y8p13PpnS2dBUWNDW3nGaf7LtufxteXvEhVmusf0H7RwSj+zp/T2tB4MCiDndEVJI7U+P3fJCHDbLwfXcj/BuS0EgXFW/cRO7P1cYjTCIESFoQ+JhTdeefJSnBgu2KgEePXeUS+euZcf9JfASGwjAjly7uCMQ+wkJIqeeZZSEmVmBPMHKIWY3ioUxYOGeTHNyP8eeL6EO6+2ci/Lw3gu+F+bD5xklU7HTsH1uXXsnX+C0gnFztzYTGVx9KJuaT9n2VQ/wSiLhpHyY4D2KprSfuqbbGnapIvjhXw980n+OJYAarWwV9HaT7a/p3IzJPe9w/x90eMG4NuxsVejTJwDe9Hzfj4ceKsB8o0KWW+ECIW2CKEOAn8CnhRCPE4sJ5mslRKuQcYKYQYDqwSQmyUUpqxHxfkCSFCgE+ARdh9Bxy5BUs3rreiQcDcDZCYmOjcu3UJ71Tfq++sk5ObDLm4L4fW9yZYFhIeqvHyy4LAqCDuW30zemPT4xLxaTExbxdi7aBE7fmY8ov55rKXuPzbBwgdEut0OyEEwYMSHNYxcMRayjCESxJ6we7d9mOP5cth4kQoLDXzYbSNvqFhaEJi08GZMEFm9AmqF4Y2jSlBr4JRlcybGEdir2CkXiAVAXqB1AtQBFqQHi1YD0qzx9KmoatTMabX0m9JDmqNc3UuwnfVUPr71tfzciqYM1dtcYxzzfWSjH/UM3tAH4q+bydKQqfj03FG1o33x2IQ5+bHwVworIHewdxp+wi9orAwedy5ZlJKUn63tkUxJqeQkuLv9xE9bQIlOw60erkxOqE2O+/ctYqTZ9C0JHS6ln/Gqia55d3tFGhlXDVHZdlGhTdTIlm9aDqKro1ognrP1BRxCr0e/WUzu248p3AvxNWHD6cEgZQyv+H/IiHEWmCKlPI5YBaAEGIIMNdBuxNCiFpgFJAipcxruF4thPgAu1/AO9h3+H2B3AZ/gzCgrOH6pc267AN8h30VDhdC6BusBH0Ah9V2pJSv0eDkOGnSJC9svL1zXFBndc0E7Cl0io4Fz1xD2s4cCtJKueGmKIZc3LdVBrjYT8ux5LsXWWHKL/7/7J13fBxnnf/fz2wv6tUqVrEtyd2SZTtxbCeB2ImTkAI5juNCCeX4HdzvCndHOeA4UjjgOA4OOPqFI/A7CJBGEqeQZie24265yEVyUbd6WW2fmd8fK6uutki72pU879dLlnZ25pnHq9HM9/mWz5dXbvwOJe+/nrSVeeRuKyVlaU7QfRWfTH9dG+7OYU4vNeN12DEqYJQFBgUGTCqdZoUus8qgCVwGcBsEx495WVsGW7ZMzIXYuRP27oXnPT5YH7nHwXp9Dh5rFNUSegklVcJdk87lL6gs/Y6d4XOXwx83zRW6qCKL53+h5+GH/KPyv88/JdiBKWxSZP1iE7/dOC7pza9gf+oIhdkyd/85PP3MIK1PXeEDwP/9zTO8+rcfZd3iQi7+z2lan34z8v/zJLrfOjKhpNOUk4mtpCBoqaJv0EHvUCbZaROvqRfqO2hXetl/OGAMPfSwzKaaXl6o7+COlSE0B4Q0J9UjTFMymVi0PgcaMyOsQSCEsAHSyEPcRsAIeFAIkTtiIEgEqgB+OLJ/GdCsqqpfCFFCINfg0siDPl1V1W4hhAG4E/jjyGmeAT4E7APuA15VVVUVQrwIfHVcIuEO4PMj7702su+vR459OgafR1SoeIGZl5GFwi0nLg4o6SSqtpZQtbUk+A6yiu/87OKy7o4ezn7r2dHXaavLSVmWj600k+Wf2YYlLxVPt4PnVn4VT+dIP4LFdr54e4Q3u8FULl5y4HTCgw+Oaejv2gXdvcCa6JT8wrqpQ+DekEHTvR6yvh5mx5W5XH64LOhbAc9NLutXd3D7XQrPPyVIdeSyDhu+EN6HN5abeWbtpM/saCuF2TInJlSRyJw92kr/hmJq/vV76GWVn+3SzbqIrWv3QTI3rMLT049/YJjeQ9OrE/a0S2RPavB4vLWf7TsnekZ23C5Td2IgtEFgMIE3tFEtKyov1HdwvLWftYXp3LY8f3qvw3T44ysTPTPmSg9BY6ERyd97HvDkSLhfD/w/VVVfEEL8jRDiUyP7PAE8OvLzFuBzQggfgULZT44YATbgxRFjQEfAGPjJyDE/Ax4TQjQQ8Ay8D0BV1V4hxEPAVX/og6qqXu1W81ng10KIh4GjI2PMMd3EK9rvSaBBEA7hkqPW5g/HwIkLDJwIhAMaf/wqOrMRT/dEY8vui+JmXV1I/xMdeJoUqqvh9tvhmWeguwecaXYoTo9qft9+vJ4vP7CWGWreMHxD+rS3aV1xBkM1drr+ajFKWnCDR9JJ/OkXd9C//WUa/83NDkxszCrGvjYPV8vU6g+AU2UWfrzNxJRJdzi4+8+nVpF841djmgV+neBjt8ncdNbKn543YOmYeZ5M78GTpFcvZ/hCS8j9+ludUJMGzpFz6QysLS/g4V3neejhMc/IS8/r+dKmiZbDlIf7+ip0IXQLpoQintfx9Tds7FhSQHVRRsTGgbR+Xdh95p54hEY1rgWEGmFb1oVAbW2teujQoZiNp9JJQDbBPfLlIVYGwjcO/x6/EsYd6Vco+nYHrmI9g5ts+EqsU2/+cUDX5WHxHfvifp7J9BSm8NfvimLN6lfgUDOc6Q78bDfAhmIoy4QoV4Jry9P5k5um8ZhEOJfyrW8FbWvb+9dLGbi/KOwQlv295P91ZCWFvXk2PnOHAZcxyP/zYDOVw22jHgKfD1atgnP2RbBx6sNEqCorW/x85JSOvEvRGwbGrHS8fYMQJjEx/9ZN3PzCA6MhEKGTkGWZd9/7GVpa6tlxq4eXXjRRWLicJ37+INKAC7WrD/+lS9z3tZ/RLvewfafMy7t0LDLk8tv3XzftQ/25U+08fODAaCjC5wskz65aCRfP61gkhclTGEH38Q8j2ZOp+2AJgvJET0IjiRFCHB4nHzABTdZqFghygbHEuEDvAQ9jxoFr3OurX5HFNcMaA8Cin3ZiePwcBiAVMBRl4dqUSd8GM851KSiZ8emMJpwz1xOYDf5obR29BNeVBL5myV03hH9gT2ay/PPHc+wwaaUtLc3Gsyyy8IX1UOTiV/+7iuDGAEB1Ia1PXmHVKpm774bnn4feXrAoQ7gUdYqxpArBmTwdv2nopphBSjCxDhtShO2E0lYupWt35Ia4GJevotPpeOLJb7Br1wGOHWvgKw8uZefOjYEWx1k2KM9mV1c37dIg+w+MyzNY3x0yzyBYKOKee8Buh//9dWR5CqK0JMmMgRzNGNCYFZpBEEMEgoD0QnDVsoDB4GOigTDZePBzuidofuQErAf6MT96dsI2X0sP+pYecn4feG0oy8WzJo3BVUYcq634Sq1Rr4yDYWpJTBazX0qcN8uoj66kzO/18/33/pKsVO+o/POnhyXeTx7NeCkz2cn+3kZ8azPCDzZC+ltDEZmTcoqFg2Uhci30Eo71ZThON2Czwde+Btu2QekSB67fnYCyDKguDBhUAIqKcc8ZXNIwS/5RYdfTgt0tZv7aWRSZUTBL1U2dTsedd17PnXdeH/T9o0cb2L7DMynPwE9d3fR5BmsL03l4l46HHh7zELz00li+Sdg8BSEhvWvnrP5fsWdJoiegMc9JlsLZawKBQGBEkIogF8FiBBUIViPYiGArsJUM0wYKbGUYpOBZ8FKvl6KHLoVtvOK72In09HnSHzlF0fsOsmznEZZ8pomcX1zBfGQA4Z7ZSr/wXyPIlo8DcvyjITHj9UePkpXqpa4Ovv51OHoUZJPCCxXtlP9jD0+XXOabX/kDboc7ovFMdQMojZFVdTy/VMWnD/Nh9bn5s/cFyjAPHoSqKsjLhs98zEXlcBvWJw7B203wh3p4pYFCg4NDJ5QpGgihSFlWEtAa2HeUrOvWYSsL7WVxtXYzkxBmdfVSXn7JPEFI66UXTawpSJv2mNuW57NIymRTjY7PjWhtFBQEqlACeQq6kMeL0mIkfTKtp65HxLDjqsa1STJd0RqAQE+BvYIHVnxuwnZZlhnw9tDjuUL7N35Py5W9UY/t7xuC14ewvx5oQCEMevSrFuFYbcVVbMCbrcOXo8e/yIySEmKFmWWFzvhUV4TCP4/M18vHOviTcfLPf/wj5OUFDAODAR5+BFavdvOtd/2Cf3j+wxgtocM7GU9HWN6q1/HiygjKKTPM/M/P4bVXAzL8aWmM9q74whegqEil0tnO3X8OTz0NOhWu6u6M10CoIbjLPHfbBjp3H4TzAeOxZ/8xhF4/rTaBMSsdv9PDya/sx9HYhrc/UDlhKcgg96ZlpCzNQG83YC20YUid+Fnt3LmRn/y4ik2bxuUZ5Fdy2/LpJXx1kuC3H9jCC/UdHKvrJ8XbSuslB1/4J4WXntdRoMuc/nghkG7bHuYDnkvWIabxSmpoRINmEMwTdDodmZZcMi259LSHlpGNFNXnx3e0GdNRJjQnQggMawoZ3GSnf6MV9wr7mPsYULIS0dkNeo3JIg0bHllWeOaZQEmfwQCHDgUqHSbHrH/2M3ji4d2875FbQo6nG45s5fzicgN99ggsp+5hsrICok233cZo7wpZhk2boLCQSc2t4A9/CMzZ54Pnn9Gxg6nXga20EENaSsAYmITq99Nz8MSUro9XjQdvTz9n/+P36O02XG1jlRONPx4bo/T+7Wz44V3obWMGa7A8g9tu3ID60x+G/Ah0kuCOlYu4Y+UiPndLFS/Ud1B3YoAvbUoLWWUgysuREt7M6CpLEEQedtLQCIVmEMxDhpvC5xjMClXFd7wFy3Gw/Bj06Xb8m/Lp22BmaJMdX4YuIbGmdtv8qYiRJEF3d+BBevfd8Ktfgc0WUEscH7Petg1eezuC1X+EH/iTNREaax0OVl4XyB/IyQn0qnj4YXjxxUCC4QMPTDRe7roLPvZx2LcXdj0lMLcauXnD9bgam/D2DiAZ9GRfX03X/mOo4xQIJ6N6fdjLiunrHyKjejnABOPBN+ggbXXFBINgPJd++TKXfvVHjJlp6G0W9DYz5vxM7GW5lCzOYrFvMcoeF+6V7iDmyvSMNw7CIb1jaxQjx5M8hFZiqBFDNINgHjLc1D6n5/P3O+DFBjJehAzAtrQY67YNuNo6cTTMXT6BJ6FXa3QJDKXVi2hzdPCe98Dbb8MHPgDf/37AQLjnnoAxUFAAJ09C4YrgKo3j8dslIqkZUSKZpqJilT2cPg3LlsH589DZGSg9zM0NqDu+/DI89NCY8fL009CTlsnpH1nYMaiyDhsDB08g9HpspYU42zqDegWCMXj2ApLFNK1IUe+BOnQ2C/J0DZtUFW9P/6iXYbD+Ip2vTdzl3HefYfunS7FnxfiisViTpLJAIFiR6EloLDDmUVRWAwL68s7muTUIJjPc0EzX7oM4Gi5jzEonbdUysm+oIfuGGiyFUzsixopEBgx+8MxZzjUPoqoqqho2n5ObHqimz2Hk978PJO49+SToLRLNLYEwQVlZ4EF8pUvw7i+G18J3roisNHFRfwSfUnM/RVl+6uoCHoL9+wOGQOMlePO0hROnYNGiQOjgc58LGDFtnRLcvITc8lxqsI9WF6h+P8OXWlGjkPD1Dw2H7O2g+Pykr1ga8XjBkF1ums7G/vYmliVLWV92oiegsQDRPATzDE9XL4oneVQMx6/UrmIpysdWUoAQAseFZlxtsWk/m8gqg7YeN794+eKEbf/yodXodcEfOnqjnk89fj+vP3qU3712hcVb8vizB6pRZIUnHt7Na293Ubgih7/5ybawCYUAju055PxbY1iFyM++qfDx96ihBaq6ndx9pzIxJHA3/NsrhbC+kNbXzqBeGGLNcpWf/gwcGPG8ew3oJX6xwcAt9UbUOF+DkztGzoQr55ys2BzL1bxAun5jDMebDVWJnoDGAkQzCOYZcc8fiAGulg5cLWPtbG1lRViL8/E7nAycapixQaMkmT9LVlT0uunf1xv13PKJqW2AwyUQBkO16ZHXZiEdCh5bv8q5NBlEGAMj28rTz0k88rAyGhJ46lkJSmwgCZw3V9HQ3E9DsxPjOguVBjuNqsBJQNLYlWHG3JE8RimAtTgfV2vnhDbNffUtyN4KdMbYXDi6v/xoEiUTHkBlI0K7hWvEEO1qmmc4m6fvG5+sDF9sYfhiQMdeGPSkrVyGMSsd2elioL5x+ljxJCKKjy9gLn+/CvPRRSz6P8em3ed8ZgQDFafT0mBn5ToH99yp8NSzEq2+cf0dBGzy2bhtIJXK4y5U7zBquo3HVqn0WsDS5YlTB48xwnVwTF2+BHNOJn6nm+HmdpzNHWRUr8DR2IRvMNCTQfH5cTkU7JmxMQiSxxiAgIjZHlS2akaBRszQrqR5xnzwEIRC9fkZOHV+wjZ9agq2kgKMaXaGzl/CfSW4AI+cJB6Cd1bnctO6/BFlyjlECDxVoV3g762TGbB4eH25EXW6sMGIF+B8cz//9poTSqwBY0AS5PXL/N0BQfGFgOjQ1Qe/6B/mg2+CvbwYqcrM4KTfYSwRkoTebiV7czWoKqqiBlb+V79LAkdjM4P1jROO6zt6GvuSxQiDfjSM5YuVqOYc9AiZGReAikRPQmOBoBkE8wznHFcYzAX+wSEGTozJMJvzc7CVLEJnNuHtH2LwzAUUjxclSW7KXr+KSPBcdFYzxsy0KZ0OVY+PPz9p4FShQmfa1HiG2auiCkh1KaSYUzDn2xEqiFY/S3oU/vSoP2R+gONCMwhB1qa19Lx9POb/LyFJZG5aQ8eLb87oeEdjE2mrlo0aBC7HLKr0CwuhdaSE0pQY7Y3wzL1AmMbCRTMI5hmJrjCYC9wdXbg7xmrzJbOJjPUrKR6+THGPn5ZM3fSr3zmgozeyEEcskAZ86Nvc6Hq86Lu8GNo9ZNSsZODUeVwtV5DMJvRWCzqLCVNmOsJk4B+WXA5qDEiKyldf8JHXNr3kcEShAFWl//gZzAW5uGeYMKqzWTBlZSDpdQiDHqHTIel16GwWevZNHxKJBG/fWBOo/i5BwbIZDKI3oLvvbugfaUZlDKHcmVC00kON2KEZBPOMudYgSAYUt4e+w6fYDmw/AlhNdGcaabXIdFpUuq3Qa5PotwoGLIHvwyYRNzfvB3fEt/RMuGX0rS7MxwfJ/W4TyvDEfgd9435W3B687oBf3NUa8Bb82xkzBwp8HFis4/jisRbI1ZdDGwPRILs92DPT8HT1hq18mIylMA//sAvnHIS/XI4ZZjv4fTA4iJQ51b/g9Xp55OFfsefNE+TlZlBZWUTthqqxLoxzSg9MIx+toREtmkEwj3B39aLKiWk9nFQ4PWQ7PaErsXU6VKsRr1mHyyBw6FUcBpVhvYovTC7CoQLBgfLgK0KzHoSsEmwtLQ360be7MbS5MbX58JvBn2NANUjoBv3oBuXR78ZuP6YOH1K7E9U77oGqguIaC3zPRHtBONxsOgebzgFmI0+ulPjDOjN3nomtksPAyfOkrliKpNfTX3cm5L45W9ajyDKSToe3f2jUeIk3fs8MDQJJF+iFPAmv10vFsvuwWh28610BOee6OvjDH0z85McreOLJb8yxUdCOSp7Wy0AjJmgGwTzA3dVL23Ovc/yfvoXrSg8pFaUMnbuU6GklN7KMGHJhGgr0aUiP4tDr6oFXQuzwg91RTUVI0oRyuKuoQNzNO7eXew/DnY2CvJXLcflaYqYLATB4ugEA86IcUpaV4O7onnJt5mzbQFeEKoaxwNM7QGplGYNnLyJ7wxtBQ10+3C5BWrbAaA08zKV1q4N2M3zk4V9htTpG+zw88khAwOmf/9nDgw/Ws2vXgWnbNMcHF3Ac2DSH59RYqGgGQZLT8swr7L7nUxOk8WSXh6zr1qH6/dPKv2okDzqbBf9QbFz1M8XQ76L3rSNIJiM5W9bT9ebhmI7vbu/C3R7I+7CVFWFbvAhn6xUsBXlzagwAKC43pvxsOHsR2RfcIFBklZ4WmdYGicYXGlBlGWNmKnlrFpFTncWyv9kc9Lg33zo52ggKAt9vvTUgQb3jVg/HjjXMsUEAAaNAQ2P2JEkhl8Z09B2tn6KT62xup2f/MXoPnQyUZmkkNRlrq8isXZXoaQCgeLx0vXmYlMoysjfXIBlivyYYvthC5xsHcTQ04enswVZeHPNzhGPgxDmyb6jBnD3VN+Qd9vPGY8O88Z0zNDx3ejQM5+0dpPn1sxz/0RH8QxPrFWWvn8u/PkhJd6CLpW9EqdnnCzSEWrUKXnrRxLp1s5NcnhnqyL9+VM6gsheVN1DZPfLzYVTqUalPwNw05hNCDSfKvoCora1VDx06lOhpRMXud/8VLU++PO37wmggY20lvQc1T0EyIwx67OXFDJ29GH7nOURnt5JWVY7OasbVciVQVhhj0tdU0l93NvyOcaJ85wrWbFPxuhSazuk490Iz3v6hkMcYM1IwpNvQ282ofpmh8+2ofj9+FD5rv0BaocJdd8Ezz4Akgclkorg4ETkEV8kHwouWCW6O/1Q0khohxGFVVWuDvqcZBMnNrpp76Tt6OvROkkTO5mqETkLx+hg43YhvIPQNT2PuiYerPtakVJVjzs3E2zuAY0RdUm8xI5mMs0oETK0qZ/DMhVhNM2rybt7AlddiE7rwo/AkvZzDRV5+FuvvqGHbPZsSVGUQHZpBoBHKINByCJIc//D0XeFGUZQJDxphNJCztZbeQyeRXe4QB2rMJb3H6pM+IXTozAWGJj24r0pL20oLsRbm4bjYgt/hHJUIjgRjVjRpnbFH8cdu4aNH4k+u1rh0gPifBiprqpPeGIi2hbfGtYeWQ5DkFL97R9THqF4fXXsOYc7NIqN6eRxmpTETZIcTb+8gxsy0RE9lRgxfaqXrrSO42jrxDTowZWeSu20DmetXYcoN3USh71g9ppxIGi3EASFiWlkxGdXv59Cn/pcjn/590GqSxKNHcDOCmxI9EY0kRzMIkpyyD90z42OHL7fSdzSwKs3fcUPibsgao3i6e0mtLEv0NGKCp7uXzt0H6T18Ek9nL+ZFOWRvriF9beWUfeVhF/alixMwS8i+fh2Oxqa4n+fsf7zMhUf3xf080aNpFGhEhmYQJDlpVUvIum7drMbwDQzR8dJb+F3usCs5jfjTve8Y5ryQskrzEnd7F917j9B//CypVeWkLl8y4f2efcdIrYqvymMw4ukdmEzz7+vm7FyRU5DoCWjMEzSDYB5Q/uF7p31Pn2Ijd9sGcm6owZCeGnSflIrAilR2OKfcpDUSQ8qykkRPIa4MnrmAzmYm98YNEySk59pLlbFuOcOXWufsfL7+ZMvZSUFQmOhJaMwTNINgHlDyp7ez6LatCGnir0syGkitKqdz90G63jqCzmwibdXETi7pqysYPD3WJrbnwAlyt21Y8A+kZEf1L3wJar3FQucbB0lZspjszdXYyovmvH23PnVudf6HVo7bLwAAIABJREFUGq7Q9WZD3MaXZZlnn93HQw89xrPP7kMOK2VeFbe5aCw8tLLDecQr7/wQV17dP/o6fd1y+o9NFRvJ3lyNZNAjuzz0HDwxRdjoKqlV5ZiyM+g9eno0k1xjbshYv4q+wwtXOyJYiaUwGsjdVkvvoVP4+genOTLG89haS9eeuf+bv+PMv5BamR/TMWVZ5t33fobW1jNs3+Hm5ZfMFBZWhdE+sCPYENN5aMxvQpUdah6CeUTJn9054bWnqzfoft17j9L5xkF6DtRNawxAwK3b9eZh9GYzWRvXxHSuGmFIymz02CG7PVO2qV4fV/64D0mnI2vT2rjPwVqUz1DD5bifJxiGVEvMx9y16wCtrWfYv9/F1/5VZf9+Fy0tgf4J03PtLPg0Zo+mQzCPmJydrl7VT50lnp4+PD19pFSWY8nLwtnagaMx9op1GmOo8sI1CDJqVjAwLkw1mavXW9rqCnyDDpyXYxNGsBblI4yBJgOSTofs9oz2V5hL9DYLppzYhyqOHm1g+w73hD4K4fonCDbGfB4aCxfNQzBPkD1eDn3qKxO2+RyxdfMPnb1A5+6DOBqbMaSnkrF+JTnbNmApzIvpeTQAdWEaBEKvZ+BUA7Iz/LU5cOIc7s5eMjeswr60BPuSmZclZtauwtnSwfCFZoYvNDN0/hLO5vYZjzcbcm+qQtLHXqSounopL79kntBHIXT/BG29pxEdmkEwT1A8XvpPnh99rbOYI7rpzhRf/yB9h0/Rtfsgxsx0sjfXkL25Gr3dOmXfeDTIWegoYZPB5ieZtStRPN6I91dcbnoPnsTRcBmfw0n6mqkaBpHg6enHUhTbmP1MyN+xhusfu392g0zj5d+5cyOFhVVs2mTmc58XbNpkpqhoOTt3TucFyJndPDSuObSkwnlE99vHOfDxL9J/4hxZG9cEcgTmmPR1y3G3d2FfUkxf3VlUnx/JaEh4e9/5RkplWdI1Opot1sWLcHf1ocxSLjt7czX9dWfxOyKQ7Z50fktBLqpfTlhb8J0nvkT6qpmX+al+Bf93/wvpz96PLi9zitqwLMvs2nWAY8caWLduaZj+CUUIlk3znsa1itbcaIT5bhAAKD4fh//2q1z+zfN4e/oTPZ1RdGZT0EQyjeDYK0pxxLmngTEzlZV3FuEagoaXLuKPcyVJxvqV9B0+FZOxbKWFqIqCsyk6t7+5IBd3W2cg/KCqceneGAyh15NzQzVDDZeo/LsbWf73t0Q/iAr+S62oTz0ZeF1Ugv49dyKkmfYgSEEQ9L6vcQ2jVRksICSDgRWf/XhSGQNAQnrea0zP4ndWcdNfFLBkncSqrRK3/G0JlvysuJ0vdcWSmBkDEOib4B8ajlpIS9LpMOfn4GhswlKQG7P5hCP7+nV0vnEQV2sXx/7hdzT9NvKulqoKctcAvjcPjBkDAC2X8X/n+8id/TMsFkg2kSSNZEczCOYhtsUFGOZYcCUcpuzEdrPTmIjPpZCaO5bbYc/SU7k9fsmhpuyMmI/p7RvE2dJB6orIjQJnc/uo6Nbg2YsY0lJiPq/JmPOyR1tFX2Xfnz/Ki7X/xv4Hfkn/iemVElUV/L/8DcovH4NDwcsHlV/9EmVgOFBBHIlhoILv6Gnkk4lJqtSYv2gGwTzl3vY3uadlNyJJWq4u5DK6+UhO6dREz7LV8I6/r8JeGvvku3g11vUPDTN8uS0q3QLV7wcCOh3WxYviKpdszs8OdFNs6ZiwXfH56T3cyMWfv8kbt/+QoYYg/RRU8D/7MnSHL42UH30U/7e/h6+uPpS0SGBYWYHXX0V54xmU1qnCZRoa06EZBPMUvdWCtTCPwjtvSvRUAKJOANMYQzIZYz5m0bKpBppOL5FZqKfqHbF9QKavraJr77GYjjkeedhFz9vHydlai9CHr2iRvWP6HAMnzgGQtnK60rzZYc7Lxt0R+oHubOni5c3fou/YuHwGFXwnzkDD2ehO+Oor+L/9PRSHe1pvgXq1HNnrxP/EQyiXj0d3Do1rFs0gmOeUf/S+RE8BAMfFFnQWrc1qKOzlxeRsqyVny3pSS/PIXldGyfbl1H4gtg2nUpcVYk2b/sFZukpizftXTOmNMRuursrjSdeeQ6QsXYytrCjkfop3omCXp6uXoQstZNSsiOl8crdtoP/4mYj29XT18/rOHzB8uScwR5cHXvnjjM8t/+Sn+C+2TDUKVJAf/+3Ya78H/9Nfxf/8t1DO7WUuk8hVRUZpeHvOzqcxe7Qqg3mO4vfz9OKbcCVAkW0ywfTrNQJkbVpL/8lzoz0jUpcUsP2TGcg+Fb1RovGoQt1vG2NSqVF+23JqdoQPJXU3+9j3WAee7oGQ++ntViy5aRhsJvwuH75hNwarCVOaGVOKkbb9FxCSQHbNTZWJMBqwlxYyNE2VhrU4H2dzx5TtQq8nfU0FfUdOz3oOWZvW0PN29GW/qcuLeecbf4Pulz+f9RwAKFuK/s4dIAlAoHT1ofy/X027u8hfhm7bh5EK49v0SFVk5Be/h9KwH8PHf4IwJ1fO07VMqCoDTVFmHuHq6MKYnorObBrdJun1lH3oXk5/7ccJnNkIIl6R5PlN9uZquvcenbBNkRWEEOiNgc9sSbVEfmkZ7RclOi/66ThwAcU3s1V3el5kv4fsYgM3fDif3T/wjpYkWgtzyFqWSUaBjvRshfQcMNqmv004B/x01pnZ/tcFGG0STfVwfs8A3sGxEJJvyBlTY0H1+gLeDSGC9upwNncELYFU/X6Gzl/GXl4883JEIcjZsn7GGiCD9c28dtP3ueGD2ViMMSgDvdiA/7uRd1dUO87jf/wLiGXXo99yPyI99vkkAWPguyhn9gCgnH4dXc2dYY7SSAY0D8E8Ys99f03b82+Qs7WWgtu2svQTf4reamHw/CWerbg10dMja3M1/cfOxFVB0bq4AEtBDpLBEMhkU9VAAteR06je2PR2iCWGtBQQYkp3P2tRDrd/evqsf49D5lK94NLbfQxdiC5bfPtnK0nLM0S8v+xTAkrKAvTG6MIIqqKCABHCGHT2+9j3Oxd9p5uiGjscuds20Ln7YND3bCWFONs7g14T5rws9HYbjsbo5iMZDWRUL5+RZ2Ay9vJC3vmXuRh0CbxmJT3Sup3oNt0XsxW8qsjIL3wH5exbYxszCzF88DshrxGNuUMTJhphPhsEXfuO8sdt90+I1drLi9n4owfJv2Uzf7zx/mlvjnNF2uqKQAdGIeLSVCZt1TIcDU1B3eqpVeXobBY8XX0osoy9pCAQL5UVPH2DOM5fivl8wiGMBrI3rQ3afteSn8Udn1kUdgxVVelpkbl8WnDlRBfOtu6Q+2esLOGdH41/qV20yH6Futeh8fnZu+vHk7VpLT1vB0+ay9lWS9fu4H/v+lQ7tsWLGBgnBx4KIUlkVK+gN4Ytq4tvq2XjdlfiH5SWVPQ7/xapZHYdKFXZj7zr2yjn9015T3/fvyAVr57V+BqxYdbCREKIS0KIE0KIY0KIQyPb1goh9o1s/4MQInVk+8aR/Y4JIY4LIe4dN876kf0bhBD/KUb+EoQQJiHEb0a2vy2EKB13zIeEEOdHvj40bnvZyL7nR46Nfap2EtH448enJG45LjTz6vYH2Pfhz1F0zwyU0WLM0PnLKCNSxua82IjgCIOenK3rSa0sY+Dk+Wlj7INnLtB3+BTOpjZSyoro3nuUnn3H6DlQh+r3k7a6AkthXkRZ6rEipbw4qDEARBwOEEKQXaxn/a06dv59Hrf+UxVr3r+CjBXBGwFll9tmPN94otNLVN8iseEjyynaVok5Jza6BaEWNN1vHiFlUofQq/gHHQw1NEWWaCgEWdeti6kxAND8wiFaO5KgcZhrEP8TDyHvexxVmVmPDVX245/GGABQTr02mxlqzBHR+AdvVlV13TjL4qfA51RVXQ08CfzjyPaTQK2qquuA24AfCSGu3oV/APwFsGzk67aR7R8F+lRVXQr8B/B1ACFEJvBlYBOwEfiyEOLqneTrwH+oqroM6BsZY0GiqipXXps+W/fi/zxJ3T9/Zw5nFBzF7SFtxdKRdraClIrSWY2XunwJ1oI8uvYcZjAK3f+B0w2kVpWPvh6+2MLAiXO4Wq+g+v2krlhC9vXVcc150KfYGGqY3iXtd7qR/dFpNwghSMnWU1Er8c6PpXLLZyop3b4caaTlr95mYVFZcutBlKzScd09BhZvDu8diYT+42em1RlQFQUUZVojUHF76K87R86W9SHPkXNDDd17j8x6rsE4+vOjeP2m8DvGHRV5/2/wP/UIqmsw/O7jj5T9+J//Fuo0xgCA6o2vbLZGbIgoZCCEuETgId89btsgkKaqqiqEKAZeVFV1xaTjyoD9QCGB1luvqapaNfLenwE3qar6CSHEi8C/qKq6b8R46BjZ/31X9xk55kfA68CvgS4gX1VVvxDi+pHjQwbS52vIoOutw7y85f2JnkZEpK+ppL8uUFstJAnzohzs5cWB6oMg15o5P2e0g6LQSRgz0/APDeMbcAQe4MrMH3A52zbQFSKMkrFuOY6LLfgGhmZ8jsnYyorQ260YM9JCnhtgy19VkV8+e4+F7FfwOhUkSWCyJ4dQVTgu1skc/nlsRHPC/Z4zN6ym98hpCNFhMndbLZ1BwgsZ1SvoOxrbMMdkyu69nvVbY3cNzhpbJvqbPoJYdt2EcIaqyDDUjdrXNvbV24ra2wLDfWGH1d3wfqTaexDS/LhGFyqxqDJQgZeEECrwI1VVf0zAE3AX8DTwJ8ComL0QYhPw30AJ8IGRh3YhMF7fs4WAocDI92aAkX0HgKzx2ycdkwX0q6rqDzLWgqPxv3+f6ClExORselVRcLVewdV6BeviRRgz0hA6CSFJ+B1Ohpvawoq6zIaut45gKytieJKs7FX6jtVjKcjFVlZE/7HoH0620kJcV3pAUcjauAZPTz+DpyPP+PZ5YuOh0OklLKnzS1Ikr0RFMhqmaAbMjNCLmt6DJ0LmEwB4+gYxZqVP6RGis5pBpwtpTMyWlldPsH5radzGj5rhXvzPfRNRWo3+5o8h0vNRGg/if+7fQZ7570sd7gO/F4yW2M1VI6ZEehe5QVXVGmAn8CkhxDbgIyM/HwZSgNEm6Kqqvq2q6kpgA/B5IYSZ4OqmV/+Sp3sv2u1TEEL8hRDikBDiUFdX4mv1o8XnGKbpN7sSPY2w6KwWhhouT/u+s6md/uNn6Dtymt5DJxk8cwHZGefmK7KM3hb65uNq66T/WD3W4kXkbtswbcx5PMJoIGdrLcOXWklfuZSMtVV07TkUlTEA4Pddu1nX1jQ9q98bo9a8EeRF99edC/n+wIlz+J1ucraOLZysJQX0150lZWnwfI1Y4RtwBKo8kgz10lF8v/g7lKYTqD3NszIGAJRju/D99ydRzu+P0Qw1Yk1EHgJVVdtGvncKIZ4ENqqq+k1gB4AQogK4I8hx9UKIYWAVgVX8eImxIqBt5OcWAh6GlpGQQRrQO7L9pknHvA50A+lCCP2Il2D8WJPn8GPgxxAIGUTy/00m2l/Yg384uWWBdTYLqZVlMRF8iTUDJ8+js1lGBYGmw9ncjrM5UN5nzsvGvnQxQjfVXlY8XnoPnRpNFuw9NPNEM583/D4LmaXVgoY3chluDqLzHw0RhD0VT/gPW3G56dpzaLSc0VZSiPNyG0NnL5J9Qw3db8UnjwBARSBm1tIwvshe/K/9FNwxCmm4BvE/9+/o7/wHpKWbYjOmRswIaxAIIWyApKrq0MjPO4AHhRC5IwaCBHwR+OHI/mVA84jrvwSoBC6pqtothBgSQlwHvA18EPjuyGmeAT4E7APuA14dyU14EfjquETCHcDnR957bWTfX48c+3QMPo+kYz7IAeuMBjzd4WOIiUIfgUEwHveVbtxXQpf3xYJrPc9KCEH+6mwaZ2kQRJJnYkizI7si80h17j6IuSB3Ql5Cz/7jM1YnDIfebkWSktAYuEpv8JDbjFEV5FOvagZBEhJJyCAPeFMIcRw4ADynquoLwJ8JIc4BZwiszh8d2X8LcFwIcYxA9cEnxyUj/iWB6oQGoBG46gv/GZAlhGgAPg18DkBV1V7gIeDgyNeDI9sAPgt8euSYrJExFhyeSTHNZMTbN4iqqHHtKjcbdMbkrEi9ctaR6CkknOpbJG77QiWV96yYcZOnUInR9vJizAW5uDuiM/DcbRONFFWW6Tlwgpwt69HHofX4taQHA8RFIVFj9oT1EKiqegGYolihqup3gCm1bqqqPgY8Ns1YhwiEDyZvdxNITAx2zH8TSFAMNq+NYaY/7+l46c1ETyEiXC0d2JeVoMoy3t7Q2vhzjTBGrto3l/iG50b7P9mxZxlYvQ0qasrxOEGWVRQZFBlkWaAqoCoCRSXwswqKIlAVFUURON0SPdNUvBkz03DMIqwzAVWl683DGLPSybpuHT37Y9Ph0e9w0taVR2HuLEMn8wiRHpuyU43YovUySGJURaHj5b2JnkbEOM5fRjIZw2Z0zyWGtJSYlhXGEtkbv8z1+YjJrsc0g8V347HpQwE6S+xr/L09/fT0HAupkhgt+77+Grc8uIV0W/KG3mKJUr8bTDZ0VVsTPRWNcWgGQRLTX3cWd2dPoqcRFYrHS9fuQ0nT+TC1qiwucd9YMNPmRRoTcYSKqon4lWP6Bh2kVJRO23UxKlSV9ks60lfObpi+oUw6miT8bgW/Vx6twMhcbKK4oCtpchXU9rPInRcQeiOicAXCknxy29cimkGQxLTPk3BBMPrqzkWU3R9P4p0ZPlsUr2YQzBZVUelpmr4cLtJEwpkwWN+IzmzCVl7M8Ey7J47jzK/2kf9Pm8iw94bfOQhOr5U93zkeNGTXCNSXFbD83uUUF3Qmh2Eg+/D/4Rug02P45C8Q+mRQbLy2mV9qJtcY7S+9FX6nJMU/OET6qhjVmc+QSBvXJArfoAMlSvlijYlcuSTTX38Za3HwmHTvwRMYs9Ljdn7Z7UGIka6WMRjr9a8doLktN+okQ1VVOfSMM2T+juNiGwe/9Qovfa+HK705s51uzBAl1SBpa9NkQDMIkpiBE6HFVJIaIXA2dyR0CpIpOZMJx+Ma0gyC2XDpZCBMFaqZlhRETyKWOBqbMedmYsxMm/VYstPN2998lboD0TV/6uzLpXNvZMmTjktt9LQmgYdgBP22D2pyxkmCZhAkMTc9/2NWfPbjGOJQ5hRvMmpW4GpLbNZ0spYbjsc9nOgZzF9kr0L7/kDTK701uCKlMSMVd+fMXPDR4OnuI6M6gs6JEXL+N29y8VIuqqLil3XIcugH5sWD0SXOpmQlxwNY5C4Be3KWK1+LaH6aJCZz/Soy168i98YNvH7HJyJSZEsWdObExwOlJC03HE9vp0RWUfj9NKbS3aqMtsMePNMYtDdC2splcU9utZYUgKJy5ZV9ZNaumpV65XgO/+erHNHpUGUZa2EuGz+2nKzU7gkNh64y0BC5N27x7RspLrgSkznOGEsqurU7kWrvQhiSX3ztWkEzCOYBBTtv5Lqff40z33qU/uNnEj2diBBS4p1PV7soJjMX9/WzrEZbIc2Ey+OUst2dvWRfv47ufWPaAKbsDPrC9DCYLbbSQvwOF57ugBdi6PxlMqqX03c0Np0c1ZGmSs7WTl7/SieGVDvpVYuRjDr8Ti8+hwt3zwDensi1P4aae/DKaRh1idPO1m18N7qadyXs/BrBSfxdWyMiyj94D7cfe5p7Wnaz4vOfQDeNizRZiGd2d6TMBw/BYGMbzn6t2iBa+tr9NL0y0Tj2Dg6TUbMCyRBY59jLi/EPxk+DIm3lUvxDzlFjAMA3METfsTPkbFkfl3P6Bh10HTjNlTdP0HPkLIPnmqIyBgD6TjSy9zdOVCWBHketoiAp0QyCeYa1MI91X/00dzW+TMVf3Y9kSL6HnmQxM3Aqus5/8aD30Emyb6hJ9DTCcmL3/AkFJQsnXp9aajh46jx9R05jLsxl0W1b6TkQP/2J7BtqGDx3CU9PECGhEUXDnG1BW84nBd2HznC5JS9h55eFjov9sfGiaMQOzSCYp1jyc6j97pe489wLlH3o3qRw0V8lq3ZVUngIAHoOnkh6o6D59bM0n55fqoVev8JXdp3m1v/azVd2ncY7h+WT/R1+Og82Bn1PMhqwLsql/eW95GyrJaNmBWmrlmFIT43Z+XO2baD7rSOoYYSlunYfImfreoQ+OSOzx366H6d37sJqLquVxmwrJ/Os/Kfj1/y/89/m6cafz9n5NcKTPE8RjRlhLy3i+p9/jZr/+HyipzJGEjV3V70+ut86QvqaykRPJSRHHm+mp2V+hA68foUV33yOJ5rPsPHd3TzRfIYV33xuzoyCiyenuW0JQfqaykAegSzTtfsQfUdOB1pgG/WB5L9Zkrttw4QuiOHo2nOY1OVL4qqFMFP8Difnj06txFHjVAJo8vp5sdzMrmVWPPrA7/Bk7z6+f/wLcTmfRvQkp+mqETX2JYsTPYUx4igXO1P6686Ss7WWrj3J0WNhMr5BB7t/eIktf1FKzuLk/rP815fPYMv0UVcHBgM8/DCsXu3jX18+w5d3xq70Lhh97T4uvBg8HJWztXbah7W7sxdLUT452zZMO7bq99Oz//i07ZRzt22gMwpj4CoDJ85izssibXVF0mmLDLYNjv58LsfKvkIjnTYdJlmlut3N1svOmJ1L8nvJH/LRmDUxf6DfO7UT5fne4/z+wk+QVR96YWB78XupydsWs7loBCe57zwaEZN93VpqvvV50tdW8tr2j0TUIz5eOBqbEnbuUMiu5O4uKDvd7P3vFjZ/pCipjYK9F7u5690BYwAC3+++G/Y9Gf++G01ndKOZ95PxDYZuJ+1q6cDVEro8L6WiFNUv45gkRZyztXZGxsBV3Fd6cHf1JZ1R6ulz4PFlourh2WUWZClQ0ujRC/rNsfUUDKTY6LAHv64fOfgJDMKEAKoy11PXM9bUza/62NX0K1Zl1GA0zj9NlvlE8i3lNGaEKSuDqr/7MKmV5Qk1Bsz5OQkXJApGztZahi+1JHoaYfENOnjjO2e5ciF5wweby7J55hnwjeT1+Xzw9NNwfen0aoGxIpQUh946+3r2oXOXUHy+UTEwnc1C1nXrYvMQVxS69hwi54aa0UqIRNN/8gJ/+Owhnv7V5VFj4Cr+GT4dZEXluVPtfPWlep471Y6sqPSl2vjFChPDpumNDJ/qwat6JhgD49GMgfijGQQLjP4TZxN6flMSxkoBhmwuPN3zpLWsqnLxZPJWHnx+exXDvQZWr4bPfhZWrwZnn4HPb69K6Ly69x4NGRKIFGdzB+ZFuaSvrcSUk0HP/mPhD4qCrreOYC0tJGP9LFsbxpCe90ztbaCfwbpCVlT+5LE3efjAAdxr6nn4wAH+5LE38SsKbv1UQSWN5EIzCBYY/XWJNQiMWekIvR5jRirW4rw5FwcyFmRhKsufsr3+S5k4HqpFn2qb0/nMlJbXz9LbmpxeAqNe4vQ/3MF7Fldx8Mkc3rO4itP/cAdG/RzcTsLYSV27D5K9uXrWpxk6ewG91YrzUtusxwqG4/xl+g6fIm11BZkbVsflHBGzJJPhd0w1CAxy9EbpC/UdtCu97D8s87Wvw/7DMm1yL3uOz+5zNEqamuFckBx+K42Y4He6qPvid+b0nCV3biSt0EZKliDF7sKkd6G/u3JUXtXnL+DihQwanz/NcFMgfisZ9ChhSrYiYdGNaynZlIUkQYdO4kj2AAOrjKgGQdFThehcgSXOQIUeDDq6brbit60g/dMzjwXPJfX7FDa/Rw0qVZtojHop7gmEwYgkGta99yg5W9bHXbI4FlxNMrQuLsBWWojscjN8qRVPV/z7L1yl97Z00E29xtpT9CgGI5IvckXD4639bN8pT8gv2XG7zDNtfaycxXVcmpLcVUILBc0gWED4Bh0ovul7w8cMIchct4ySLUUsKesEJmcij/3hG/Q+Kio6WbY0i15HJSajD5txAIcnjcEhM6oqcA6odBy/Qvfhc+gsJoRON6WNq2Q0kF1TgdBJZC7NpKTSi908BATyFfSFRbxSNrb6b37/1Ha0trPueWMMALTvPUdD6QqW1SafQZAoZF9kq9Zk0uWIBGdTG86msVW0OT8H6+JF6Mwm/MNOfH2DePsHQ7Y3nimujcE7K3bb9PymysJ7zwh0vsgSclcXpPGlZ+ChhwPGgM8HTz4FNQ/MLr9kZ+n7ZnW8RmRoBsECwpSdQcHtN9L2/BsTtuusFsruv4uUyjK8fQMMX25j4FQDzuZ2LItysBZkYzQrGMx6+hq7UBWV0hsKcTtVmnc3MnhurGqgaMd6VtxoJtUywNWHcSQISZCVejULXZBiGSTFMlLylAcVFQL1vgqEELi8Fp7/whFUWcaYkUrpzrUsXePBanQS8BlPzWY3RuDeHF5uxpCViq9nMOy+ycKJ354jr2QJqTnJp0iZCGRveBdBzpb1dO07OgeziR/uji7cHV1T3xCClIoyzLmZ9J84h69/dteyXJODt2r6ZL2WNAP/s1rw3rN67MMTW3P6TBZOZAnqsw1saPdR0eVEVaG3B9asgbvugueeg95eeOHfX6dqawl648weOXaj1u9jLtAMggWEpNfj7Ru7QWRUr6D0/rtY8sC7MWZM36td7e/A9+inAi82Xr0kAg/7ZZWZ7P5VoG64YvtiihZdAeJTvnfVNW4xuii8pZrcylRKinvQ6cInA6YMOkDVQyi3pF5i+KPLMH4j+V3JV1F8fprO6Fg1NcR7TRKJ/r5v0BFWRXDeoqoMnb3A0NkLCIOelKUlow45yWTCkJ6CEAK/04Wnqw9X65VpyzQBOj9SEPpvBuix6vnBOh3FAzqKBv04jBK9Fh1tqXrUkWOfTjVQnqPn9Mtn2bwF6urAaoWvfQ1uuQXWrpV5/dGj3PKJ2Sd9asQPzSBYYKz43MdRZYXMmhXYSgojO8g2fWWAXufn5g8YRx7Wc9cyddOyLukMAAAgAElEQVQOF0JELn8s+TwYZB2+UJnMioq10ct8e1Scf6GR4opS0vI0L4HRFr42frC+kYz1K+k7fGpW55rLOP5MUH1+hhouh95JkrCVFGLKyUBnteAfdo5+LsrqbNwbIqwKEoLmdCPN6VOVDa9yIdNIfbqO3S/Bxz4GX/7y2Hv33AO/e23m949HDv4fMozZfHLtwzMeQyM8mkGwwCi6653RHxRmhZCIpLZoz/lyRTo+fYhHvayy7N/78P/+xCxnNvfILg/nDgs23J7omSQesy38daH4/IGHuSRFloUYhIyaFfQdOR1+x2RHURi+3Mrw5dbAayHI2LCaDlsXXX9ZHPZvP1r67AYkKaBL8dBDY3kETz8NfovMf3/yWRavzeOmB6qjDB+o9HmDhFA0Ysr8yrzRiBPz9zLoyy/khVV5HM+Y3hgQTj/LP9OO/3fx634XbxydbtRQqjzXCKYIq1idTe3kbJl5UyvJNP1KeF6jqvSfPMuVz5ThK4t9CW5BVTbZeYL+fli1akynoq0NzM4r3PeONjreOsr33/tL/N755qtb+MzfJ4FGDJmfD5rW4mJ+usTNifTQnQLTj3hw70kuDflo6am7xKk35+fvKZboDZF/Bj37jpG6fMmMzjN8sXVGx80HVJcX+7PxCf9VbC5Gl7WIjGyJrGz4/vcFrZ0G8vPh5En4+tcD+QWZKV5efzT6xM9eT3KHceY7mkGggXLs+URPYUa8kDsckcsz/eTCWImceao+acWK5gp9FGkUis+Pb2AIferUEtRw6GIgg5zUxCkKKOkk3vu121n/wHasq2u598Ed5JRnc++9U3tfNNVFb5QcbH8pxjPWGI9mEFzjqO5h5L2/TvQ0okdI9Jkju6td+rAd15fWY95aMb9dwarK+SOJnkRiSc2KLifA1dZJWlVZ1Oe52stgoaJY4nfrl3QSVVtLuPmjNVRtLaFkXX7Q3heL1+RFPfalocQqsS50NIPgWsfjAHkOxIxijCrpRkuewu5r1tHxrhTq/72AC3/cRPePNuL97HqM71qFZDaFHyCJaH2rkaGu+ff7ihWpOQYWba6I6pieA3XkbK2N6piFbBBI6TacN81dHetND1TTM2ic0Puid8jITQ9ELzE96NVCBvFEMwiudfyRy5ImC6rOQF9u7syONekYqrbS+p4Uzn4pm4t/qMX9T7WYb6xEJEkHulAoXh+njtgjqsdfqKy9WWDMSI3qmO63jpBRE3kzoa7dB8m/dQupVeUIXWzbACeajk8vRs6dO0NYb9TzqcfvZ9GWan73WgGLtlTzqcfvn5FIkVdJ7hbm8x1xLWUu19bWqocOJU8v8mRAuXwc/xMPJnoaEaOY7fxyhYkr1tgHQaUBH7l7PGS82I/77YaYjz9bhCSRfUMNXXsOsfb9K1hWe+3a81cu+tnz3TNRHWNelIOnqw/VHz4Pw5STid/pQh52IZmMpFSUYkxPBVR8/UM4Lrbgd0yW7E5+/Btzaf7u8piXG841JsnC+yv/lgJ7aaKnMu8QQhxWVTWoy0wzCK5x5MPPIO/+n0RPI2J+vzaHCynxv2YNbR5SzvkDuVcy5D7Vl3AjIXPDKnoPngy8EILV71tORa1IyuZHc8GRlxUu7IpOKyB7cw3de8MnYmTfUEP3W9PvJwx6MtYtR2c2MXyxBWdLR1TzMGalI+l1+BxO5GFXVMfOFGE00PzLdfhK50fHz0hZlrqG91Z+KtHTmDeEMgiS30eqEVfUrkuJnkLEqEYLRW4DKf6xxLIKh4HFnX1IHhcoMr7ULL69ZvYPSF+Bid6CMbdqzzuspB7IZNELw/heOBNSDjZeDJxuHHuhqpz439MMdFZRc4tAb1pYbu1IWL1Vpf1YFq72qb0tpkNVwv/ehCTRG0aUSB0RP3J3dCO7PVgK87AvKcY/5GTgdAOKZ2ooLmP9ShSPj+HLrXh7+ke320oKsS7Ox3GhBVdr/NRAez5StOCMAYDzg3U8cvAT2PRp/G31NxI9nXmNZhBc46iu+dPoR3hdbDobejXlMRmBOCTdCcHgJiuDm6wYP5TCkp8M4XmlHnWGSngzIVjeQNMrZ+i7uIjae9LIKrq2/pwNZh3V9+aw978iNwiGzk8v9WsrK8JakAs6HV27w3fF1NssyO5ATNvVemX0Ya5PTSFr4xr8zsC1KhkMCAHd+44FHWdUSVCnI2frenoPnUJ2RS7bHQmuWxcx8MHisPtZ9nST/mw3kldF8qr035zG0H0RSqAnmGH/AD+s+zL/Z81XEj2Vecu1dQfRmMoCczc7jTriYhCMw1tqpuN2mbxj6aQsK0Ho9Xi6exkcv4KPMWmrljFw8nzQ94YutPPaf3Sw9PblrLxBxWC+drwFBUv1LH5HFU2vRpZP4O3pJ3P9KnoPnxzdllG9HFVWQJLoChEmGI99WQkDp4KHkPyDQ3TtmUFoUpbp2nMYW1kRqizjbGqPfowgeLfk0fFQZUT72vf2Y35tLPxhqJpfegw9ng5eufwE7yx5Nw6vg1+f+zZ3ln6QfPviRE9tXqAZBNc6YmElpjmMc2TgqIHmN+Mb4FgK89BbJt5ALYW5DJxunHWjHL3NEmY+Kg3PnabtaDZbPphNau6186e99iZB2wFrxEl+im/MYExbXUHf0fqoz2ldlIsjhLdhNgxfbCF9TWXMDIKh9RGWUCoqtpMzz2ew/rET7/IU/IVhrtVY4FdAH/zetb/zRQ53vY5PDXhvflb/CAZh4pNr/kVroxyGa+euoRGcBWAQtBYXU5cWiNl2mJzETYZtHHrnVPd9sPjv1W50qSuXYc5KZ6jhMq62zqjOlb62ip63I+vD4Gzr5o2f+tn6kXzS86+NP2+TXUfR5hIuvRTZg72/7ixZG9fg7u7F0x2+tXYw4p3W6myOjTEg2cw47siPaN/U37Yizk4Mv9jq3fQqKkih/6akQR+LvnkZzHou/mIVSmocO3P6FZbcfRRHbQpDN2fgui4TdZJX7KoxMP71ie5DXF+wI37zWgDM/6eBxuxYAAaBVwcn02VOpst0W+bGQ6CPsuJs8NR5OncfxNXRTdZ168jZWos5PzvscZaCXIYvt0V1Lk93P6999yIX65RrpiFSyYro/p86mwVvzwDu9pl10IukdHE2eAccMRlHGXZTdn8dtheuQIhrQbhlcn7YMmW7/lAn2d9qRLhCJ2OmPt6K0juM0jZA+mNTx4klhssulK4hrLvayPvMKcpvfRvLnu6wx2nGQHiujSWExvQsgByCXMfcK/d5UwQziq4qCj37A8llOVtrcXeEvpFZixfR8/bxqE8juzwc/vlpWjctZfVNOtLy4rhiSwKyi3TYinMZbg7vfdGZTTgam/ENDM34fL1HTmPMSp9QLZCsKJ1D5P5zPepvuui+L5vhW3JQJ1WlmI4PoAwHT2RMebwFVRL0fHr6RlEpx8dCDRmPt+OusuGtsOMvssT8HmM8P9FYUlxeCr54jr4/HWLwXXn4i4O3xHzs9DdxyU663K3ohIHP1X4vpvNaCMz/5aHG7OiPrn46GbH1drF8QCIviBs/XvTVGGbfAEdV0dun7+ebtXHNjIyB8XS83cDLXz/LKz8b4vwhBWf/wmyOJCRB5tLw8WGh05FevRxnU3Rel8kobg8pFaVIxjgZWnHw7IhT3eR85Qzl9xwl/WeX0XV5YKRyxbo/tGFjvOKb3kugqBhPjVUrKS4veZ8/TfF7DlD+vjpS/7cZfZNz9FyzxXR2eOoUXF7Sfn6Z4vccIP1nl4N+fk3D5+lyB7pYyuq1K/8dCk2Y6BpG9Xnwff9+UOeudC6eOHIK+EHl3Ekxl/+gH/XR4KVk0aBPsWHJy0Znt9J/LBAHT19TibOlA2/vwKzHn0zGisV03w45aUZutJlZKDUJp/Yq1P9uev0AncVM2oqlEyoMZoutvBghwNHYHLMxr2JelDPjkEakCJ0OkWFFHXKjekI/JCWbmeEtGQxty8B5QyaqNeBg1jc5Kb7vQNhzSXYLvgo7wxVmPJU2vEts+IosqPboHNXF/7ce/duh9Rr6PlZK/1+Uhp6P0PH52v+K6twLAU2YSCMo6pXGBWMMeP0KDz55gD80tbB4bR43PVA9I630aGh6t4XFj+lnHU/2Dw0zNDSy6tHpMNit9NfFr6tb3+km/nFtKlccOozA32faudFiotJkIFOnwyRmn5bpdcp0t6gM9OoY7lPwexTMKTpMNrCmqGTkqKRk6xBhktWiISV9+sWNpSAXfYotpsYAwPCFZsz52ZjzsnFfCR/HjgZDig1VlvF0xq+hjyrLqN2RhU6UYTeWF9uxvNiOZDMzeGsWkkclt91OJBkPisOF7oiL1EmVnVKmHX+RGXehEU+hEX+RGW+pFV+Jlf/f3nmHx1VcD/udrepttZIsyZIlW5Yt23KT3CvFgAGbYnoLkACBJJAEAiQhkEA+QkIq5BcghRSSUEIPxYYYN2zj3nHvkm31Xnd3vj/2ypas3dWutE3yvM+zj1Zz586c0dXePffMKTL6rM+wlJj2NtDTXSvxL0dpmmWhbcRZpa/bHKAXoBc4ZPCTi4U7SiE4h5GN1RCdBI39u4JYm81BwbMfEJ3UzqIF8N57pfz+o529LqDiLbZUM7qrC7C/5l0EgFfY7X3a2/aWRrPzi7gNeLqqgac73dJHmAz8e1ASI81GrxUDKSV1ZTZKD+s5sbORqp1HezR7G+NiMMVHMW5BEoOG9v06JSS7ni+hMJ+mYyd8ju7wlpaTFURmppFQmO9XRa5+72FMlgSsM4uo3bU/rPwVHI0txLzlNL9HzizySiFwO1ZVA7qqBqK2wdkbaLpB8Rx/MpfWwngA9GWtOKq7bxl0H9TBoJ8f4cgfRzkVAI2kPx8h8T9ltIyJw/jdmVDcB8EHIEohOIfR509HVhzGse6tUIvSJ57+ZDfRSe1s2wZGIzz1FIwZ08aylzdzwd2B/cSfuDiSlNcCOoXfscdGnFYIXLG7zcb4I2Uk6XR8pUHHVSeSSc6AnMKumwvSITl12M6JAzpO7qjwyqGvM+11DbTXNXBkeyKD3PureU1ssh5jfAztnTz0LZMKqdq0K+BRAc3HT9J8/CTWmUVUrt2Co90/87VV1lC+cgMmS4LH5FSuEHo9SRNHgQBbYwvSZsPR1k5EmtWreg7e4gjg39ZxopaYpZWnFQLzHu9VD92OCuJeP07dDc4MjaLRRuJb5TjqmzGtbubi524MiMz9Ga8UAiHEYaAesAM2KWWREGIs8AIQAxwGbpJS1gkhLgR+BphwPoA8JKVcqo2zDBgEdLikzpNSlgkhzMDfgYlAJXCdlPKwds5twA+1/k9JKf+mtecArwJJwCbgFill/6vlG2qCmHo3UKw+VMGCq5zKADh/LlwI//kscHnhO2gcGUHEpKG0rAtclkJ/s9UqkV54flc5HJza0siRbeUcAWouKcCkhXXa2iUlG8toKu27mfz4ij2sastj+CQ91mx9r4s1CZ0gb14W+5ac2c9vPlEWcGWgM+UrNxA/Ko+WUxW9znHgirbKGtpq6rHOLqZq3XaPqY3jCoYRkZxA7a6DVK7rbr1qOHiMxImjqN64s89y6SPMAd3eAkhYVkvtNc3YMiIx7fXNFmH5zUFn2vHrM4l95wSOWme8sNDriS8YFghx+zW+WAjmSik7f/r/BDwopVwuhLgDeAh4DKgALpdSlgohRgOLgc7JsG+SUp7t2XcnUC2lHCaEuB54BrhOCJEEPA4U4cwFslEI8Z6Uslrr82sp5atCiBe0Mf7gw3oUAF4UewkWdofk4y9PsrWkhrEZCVw8Mg29F3vM03KSeeu9Cp56yqkMtLfDu+9C1ozUnieVEtHmQDQ7FSNHgo9e4zrBl7/JYOSjBlqWB/bG6C+qvE0kJyWzSs4EIh3wsbKgL5xcu4+TayFh5GCyxscyeLiDyDjfDZjxFtllyyUY2y9nU7tzH+bUZKKzM5w1CvyF3U758vUYE+JIGDcCJEi7DWl34Ghrx9bUQltlDXW79tNThZLWskp0ZpPLIky+kFQ8pndpmn3AUVpL9s1bKbt3MFF7fazxICWWX+3HYRJYX68443sgBFseeZbc268mcewIf4vcb/EqykCzEBR1VgiEEHVAvJRSCiEGA4ullAVnnSdwKgjpUspWzULw4NkKgRBiMfCElHKNEMIAnASswPXAHCnl3Vq/F4FlOC0D5UCalNImhJiqnX+Rp3WoKIPu2Jb9BcfmD0ItBnaH5Jp/rOKEo4oLL7HzyUd6BumSeOOWGT0qBR0+BFGJ7Sxc6FQGqupNXvkQ5PyxFv64+fTvRmsC+uEptGdE0DrIQEuy7nSWNrsJ2mMF7XECW7QOXTvomx0YGiUZr9fR6mU+/VDzzoQI3pjUs1YwpNzGT98M/hcqgM5kZMw1eQyb4Ft556O77Kz7k++piAOB2ZqEISrSv0qBH7HOLOrTl7kxLgZ0OtprwrtAmjEhzqOMafNmMOXPPyUq07uMjv0df0QZSGCJEEICL0opXwJ2AAuAd4FrAFeltK4GNkvZJY/ky0IIO/Amzi0AidOCcAxA+4KvBSyd2zWOa20WoEZKaTurvRtCiLuAuwCyslSBi26EiYXg4y9PcsJRxdqNdoxGePIpO5MnVPHxlye5dNQgj+eaDDp2PXgpT3+ym9Xv1ZI7OY0b7vIuyiBicy2dnznay2toL3c6bxlw7od5Q2vPXcKG7V5WRZxwJHSx2o62drb+cxeVx/OZfLnB62iE9GECY1wM7XX+yfTXF1rLq9BnpTsT84RheHdf/RwiM1Kp+zL8t8pihg72uD1ycskqPixcwJSX/x+ZCy8IomThh7cKwXRtCyAF+EQIsRu4A/idEOJHwHs4/QVOI4QYhdOs3zlf5E1SyhIhRCxOheAWnL4Drj7tshft3RudystL4LQQuF/iOUqYhB1uLanhwkvsXfwA5s23s217bY8KAYDDmkb2D6xEReJ1ZrSIAy20bDjYB6n7IWYje7yscVDct9w9fuH48j1EJxYwZpZ319Rg0pEyLoOSFeGxfdN0tJSkotFUbfBvuKM/aDrec1Iy66wiGg87LRxmaxJ6swl7axs6vR5dhJl4k5HareFtGfOU/KuDtupa2mpCYw0LJ7zKVCilLNV+lgFvA5OklLullPOklBOBfwOnVUUhRKbW71Yp5YFO45RoP+uBfwGTtEPH0SwM2pZBPFDVuV0jEyjFuQ2RoPXt3K7wEV3ORHTDp4M5OqRyjM1I4JOP9HQUomtvhyUf6ilMj/fq/HeyHZRHCZ/SpGa+3/vKbuFGgyWKo+kRNCdGgt79x7o8wYD04mk7ok2SdSo8fHT3vLOLIzu8t2QlZ4VXqiXppwx9/qb5+Emistwr29E5mVRt3EnT0RM0HT1B9cadVKzeTPXGnVSu20b5ivXUbt1NdE4mydMnBFFy37DV9xymmFQ8hpxbFgZBmvCmR4VACBGtPdEjhIjG+cS/Q7MWIITQ4YwCeEH7PQH4AHhUSvl5p3EMQohk7b0RuAzntgM4LQy3ae8XAUu1rYTFwDwhRKIQIlGbe7F27DOtL9q57/buT3Buo8stwnDpd9CNnBVSOS4emcYgXRKTJ+h55GGYPEFPuj6Ji0d62NcTOqoGZfBmoZVj3tr2O2MPzxt1b/jvMMmjCyL56nUR/OCKaDC6tgKsGOzdmosOtYE9PKxHABv/eYCqEu9M3MmDwkdugOpNOzFbw6/srnXGRPRRkS5TcMePyqOtqhZ7Y89Kc+Oh41R8vomYvOxAiNlnvMkiOfan30boVCb/Hp0KhRC5OJ/2wbnF8C8p5U+FEPcD92ntb+FUAKQQ4ofAo0DngNl5QCOwAjACeuBT4DtSSrsQIgL4BzAep2XgeinlQW3+O4Dva+P8VEr5cie5OsIONwM3n+Wr0A3lVOgex941OI5sRtaVI4/6MdGOD3REGWwrraUwPd5jlMGxrMH8N7WRBnPvP8Smoy0MvnEjjrYBkNdcr+M718ZwKt75dJxaa+fCHa2MK9cxqKINbM4n7Htvjac2qoe/mZQ8/1YrieXhZUGJTLNw3j3WHqMPHHbJu48fwN7ko0d6ALFMHos+wgQ43QnaKqup3bk/ZPLEj85zzi8l8aPzQErq9hxG2mzEDB1MS3k1Nh/9MFJmFVO2Yn2AJO4d3kZ6RKRYGPPENxj61WvQGQd2ITBPToWqloGiC46j27C9+eNQi+ERe3Q8vxrvnw9tzgu18JfNPXfsB7w4J4oVI8zd2uObHNz4hfPL8Q9ze95PBZi6r41vrGyDMFOWCm8sYHhRz0rg8leaKN8U3v4hyVPGUbtrf9AdIPXRkZgS42l24UMgTEaQEtkLh8NwVAiSp03wKQlTfMEwZr//AjG5rnzkBwaqloHCa8Sg/FCL0CO6thachqa+o2+0Ex5xFn0nucG1qbw2Sue1ItDBmjwTx5N0fG2NnqEn209bGEKNLiKaM3nN3GPJjqDcf8n4AkLF2i1EpqeSMG4kIJ1bNHo9bVU1PmUk9JXEMflUrHVdlEuGmQLYV4TetyRXtbv2s2Tqdcz58CWSJo4OkFThi9o0UXQlTKIOgoaPN4xw5b3xESxzYR0AwOaA9cfg/S+dP23eXeNjFgM/uiySR64MrcNpB4njC8h77kVEVmGPfS1h5kfgjubSU5SvWE/5ig2Uf76J8hXraT5ZQfLUcQGZL2VWsVtloK+Eo7W5pdz3bJEtZZV8OucWTixZFQCJwhulECi60g9SGQtbm9/iultSB4aRbFe6gaoYFx9nm4OYdzaR31jK926qI7+xlJh3NnmtFACY2yWYQ7+vOv7Z72GMisZ49ePopt3gsa8lXSD04RVt4C0Jo4ZRsWYLydPG+3UNSUWjA2rSd7TbMMSGh/IIzrTK9XsP9+pcW0MTy+bfxbYnnsPRPrCsJp5QCoGiK0YzutEXILIKEZbBoA/9F0E3pCTS5qenkX5qIIhItZA8fQLWmUUkTxtPo8lNx80lZCTb2b4NnnkGtm+DDIsdNnuXPW/SgTZ+/GELtIb2pph63hTSzpt6+nfD5EUYFv3Y7f+nKUrPyCvDf/vrbCyTCilf43yCr1i9mcTxBejcRIz4gjEhjoZDx/s8jicq125BZ9BjnVVEVFY6hFghixmW1acHHGm3s+PHz7Nk+g00+7m0dbiiFAJFF4TegOHCr2O8+nGMt/4G4z0vo5/3DYiMC7VoXYix+eeb3OBm3z2cicoahK25lYrPN1G+cgMVqzcz9YAbJ7CTDSxc4Ez0ZLfD4sWQnAzsLIM2z34BxQfbuP+TxrBwLBz70293a9MNHo3hqy9AnNXlOSMmC5LH5QRaNL9hnTGRynXbuuzjV23YjmXqeI/nCZ0O68yJWGdOxJTo+nMaXzA0KOWT26rrKF+xgaajpQgpiR81jLgQFREyJSX4ZZyq9dtZf8/jHH/3U5Yv/HpYZMEMFEohUHhEmCLRj5qL8cafI1L8UKPWT0Ta/aMQtKT0L7NyTO5gpEN2Cwmbv9dOcp2LL/i0GN59D1pa4Mor4fHHYdo0yM+2EfvmBo9KwUV7wsORMOfWK0ie4npPXReVgOH2/0MMndT9mF5QfLnZmXM/zIlMT6Fqi+saDHW7DnhMuBU7IpfylRspX7kRoTcQnZPZ5bh1xkQqVgc/kkY6HNTu3E/drv0kT5+AyeKfL2hv6Wvhps4cf+dTVl79LUreW8onM26g8ejAzIOnFAKFV4g4K4brnkRXMDfUoviVmpHhrRBEpqcQO3wIscOHYJk6juZTFS7DxWhpZ9p+FzfA8RmUVOgZOhQOHoS1a+HnP4ft2yE9DVjWPRe93i6ZvreVkcdDm6nQlJTA9Fd/zZS//sxjP51Oh3HBw+jn3N7tizM60UDRDZluzgwPDLHR6CMjsDc0uTzeWlFFgoeKfObkxC59bU0tp5WguIKhlK/a6F+Be0HF55twtLVjnVnkUzbRvuDvL21pdyrINdv3snjytVRu2O7X8cMBpRAovEYYzOjn3Yf+vK+BbmA447XkRqCP9rYesH9Inj6BlNnFWGcXn7lBCoF1xkSSp09AZzISVzAU66xiWsqqqN97mPq9h6lcs8Vj5riEZhd+FQYdDVdMoLRMx6WX0qVWxKWXAkdrukQfGG2SX7zfyr1Lm0KeqbDouR+Sfd18r6sd6sdfhuG25yC6a1bAjHwDeZcXuDkr9MSNyKXhwFGPfVpOVhCRmuzy2NkKYuupChIK850Jh8IobbKtvpHylRtInjI24HOZLAm0nCgP2PgtJ8v5dNbNHHv7k4DNEQpUYiJFr3CU7sb232eh0fewHn/wr/HJlPjJoXnk/cdpWbMfhMCcnEhreZV/BnZDTO5gGg6eSadqSopHHxV55sau0/XKGcoeG8FjFxk5kuxCWXttGyNSm9m2zakMNDZCRgakpeEsGf0elFToabhiAtE2yKy2k1Vp58YdYKoJfsZC6/QJXLDyXz6VPu7A4XBg//QF5M7/nWmzOVj290aqdhzxp5h9xpcSxBGpFmKH5+BobUMYDQi9npZTldTvcZGAKUwrLALE5edQt+dQQOdInFBA9aZdAZ0DACEoeu4xht93U+Dn8hOeEhMpC4GiV+jSR2C88ReIdPemzEDSpvPfzU5GOL9A0+fPZuGRz8i6br7fxnaFLqJrvoC2qtquT3m99IzW17fw+Kc2YppdnF+cTkkJjBkDDz8MWVlOZWD79u7RB40ROvYMMvLJ6Ai+fbmJg5nBtaAADLpkVq+UAdC2EObdi+Gan4DJKbvOoGPK1ZHEDgmfmvdxBcOo+GKr1/1bTlVSvnIDleu2OR1KV6x3rQwASIk+KvjXzRvsQQjjM8QEKfxRSjZ+66mwy9DYW5RCoOg1IiYRw6In0I27JOhzt/pRITAL5xd01OA0DJERTPjlI0z4zfcZ+rVr/TZHZ0wJsQhDYLZczDXN3LFay+EvJUPKbc4nxSEW2qzRHD8BL74INbVOy0DnLYSFC+qTl0UAACAASURBVIGTXZ0Va6J1PHGJmY/HuEl6FCB2PvUHtj3xHLam3lsndJmjMNz9MiLbaaKOijcw+/YE4oeH3qfAMmUsTcdOBDQzoM4UhiHDQKSbrQ9/4mgLnv+LdDj4/MbvUrFum0/JmRzt7bRW1dBSVklLgK2S3qK2DBR+wb7zM+z/ewnswfkgvjzRQkVk352T8hIKubD5fBYXLyJ52njOX/r300+mrVU1fDTuCpqOnejzPF0QAp3J6Fcv6LM5mh6B0SEYVGPnwYURnEjUO/eTj9VARRNUNZFvrGK7toXQ3u60HsSUW9l4ba7LMaftbeW+lW3Qizz3vWXe2tdJntz3PWdHQxVy31oc5YdoKytn0zsVnPj4c2yNrh35AoU+wkzCuJFUBihbYAcJhfnUbNsT0Dl6i3VWMeUBfqL2tqiRv0mcMIr8b95M7leu6rHvwb++xdrbHz39u3VmEcO/cRODr7wwoAWWVC0DRcDRj5qLSM7G9v4zUB/4JB7pLQYqIvsWFpdkTmHR0K+j0+kY/8tH2Pf8K5xYsor0i2YCYE5KYPqrv2Lp+V/B3uKxkGaPRGWmEZmegi7SjGy3+1RwpTdklTqtBCtGmJ3KAIBOQHai82VzUPJOLWPG2J0+BO9CaYWe56viuatd0mrsrmytHm6mJlrH9//Xjmjq29/DGzIWnOcXZQBAF5ME4+ejx1kFY+bNYG9tY+/zr7D5wWf8MkePMphNxAzNCrgyAGBMiA34HL1FBnjLQB8dGRJlAJylrtfe/ijSbmfondd47Hu2H0X5yg2Ur9xAZHoKw+6+jmF3XUdkmuscG4FCbRko/IYuNRfjTb9ADB4T8LnibH0PF7xt5MPotBro+d+4mct2f3xaGejAOm0Cc5f8xadYdn10JNZZRSRPHUf8mOEY4mJoOn6SynXbKF++PuDKQAeOmAhWD3XzpKFFH+yJSefn/4zjsHkQ1yWNwICOr3ze7NYhbVeGkR9eYgJ9gG8dQlD45P0BnUJvNjHyu3cw+rF7AzpPB5bJY6ndGbiiRZ2xNYRX6erO2AJcljomJ7RbQvEFwxh01n3EFXW7Xft/NJeWsf3x53g3ay77/vAvf4vnEWUhUPgVERmH4arHsH/2JxzblgRsngRb376QsmPziTJ69yWfMrOI8z/7O0svvIO2qp6zvSVNHB1wk2iPCMEvZ+g5YtGT1ODAaJeYbGC0SeJbHFy+086aLEFLXArEpXDDDkl8ufNLZNbuVmoj4I3iSOxnF3+Skrm72wIekpi16CISC4PjsDrmx9+i5VQl+196LaDzNJ8oC+j4nTHE+FbdMpgEOtOfKTE+oON7InnaeGa//wJmL7Ik9hRpoY80k7HwfH+J5hVKIVD4HaHToz/vLkRcCvZVrwRkjryyRkjT9SrJiVkfyY3DH/DpnKQJoxj54B1s/f6veuzbXlvvs0z+xjK5kIc+9uzBnu8h9P3yLa1MOqnjB5eYaTaf+Rtfsq2VC3YGfrvg2JtL+HTOLWRecT6ZV1xAzJDAPfUJISj6v8dprajm2FuBU2IjkhNp2BecsMfGw4GtW9AXojJSaQxgXQUZogJtKXMmMeeDlzB4Ed3hsNlo2O8598S4Zx4kKj3VX+J5hdoyUAQEIQT64ivRX/IA6P2vdxrqq0ntpVV0xqD5p7cKfGHo165BZ3ZXRchJ/Og8mlxlEgwyffV5AEg92cydn5/5IxcebefmLwKvDIDzpl62fB2bvv007+Wcz0fjr+DQK+8GbD6dXs+0fz5LypzuKZD9RcWaLUTnZmKdWRTwkMDWsiqXynLixFFYZxWRMquYpKLRXo0VkZJE/Jh8IjP9E7JZvmojlsljXUba6MymHj9jPdFSFhqP/cZDJTi8dLhtOHTcYxVF6/QJDLvrOn+J5jVKIVAEFP2ImRiu+hGY/R8XnNTumx+BUWdiWtrFTBk0r1fzRSQnMeKB29wet86YSO2OfUEpIuMJ68wiarbs9stYU/e28o2lTYwoaeeh5baQlceu2b6X+AAXydFHmJn1zv95TBPcVxoPHqd85Qavv4x7i72l1WVRoebjpyhfsYGyFeup3rK7x/oCxoQ4HO02arfvwd7YRPzo4X6Rr/KLrcSNyCF5+gTiRjqzcposCTha23C0thGXn9MrBURnNHRJ+hVMGo+U8NlFd9LqxbaiO/8BAJ3RyKSXnkT04qGlryiFQBFwdJmjMFz8Lb+Pm9rqfWhORnQu35v4HHMHX9mnOcc88U0SJ4zq1m6ZXEj558FxFuwJf3uxT93byo8/c5AydqRfx/WFgkfvIsnF393fmOJjmfvxn4jOzgjoPA37j2CMD2wkgDGuuxLeuRqitNlIGJ3ncYz4kbm0VdcBzkqGtTv2kjihwC/1CGp37KPi803UfXmA8hXruyjSdXsOYatrINJHk3l07mBnWc8QUfnFVtbf83iP/TwpBAXfvzvgyq87lEKgCAq63CJ0oy/w65g5Dd7dlGKM8dw64rt+mVMfYeaCz/5O2gXTTrdZJhVSuWFnWKSKNacme2229AVbQxMVqzdjmTIOg4/VA+Py+1aCOGHM8KBFAgBEplmZ/vpvAhoL3lxaRmIALREAlWu2YDkrbPNsc3zl+u1EZrj+0o3KSqd25/5u7dWbdpE0MbAWDnA6H0ZnD/LpnIhOhZ5CxdE3PqZqs+e0yfVuHArjRg5l1KN3B0Isr1AKgSJo6Gd/BeL95ySTfKKE2FbPJmyDMHJnwQ/R+bEYkzEuhtkfvMiQmxcQPzqPyo07Q/pU0hlzgG+I1Zt2klQ0GrM1qefOwNinv8slW95lyM0LPPYbdtd1XLrrQxLHa0WIhMAQG01kegpTXn4avalv+8q+kjypkHHPPBjQOfqShdFb2mvqQH9ma63lZNeCP/amFkyJcRgT4rq068wmdCaj24gAEeiw0w45fM22GAIzuyu2/eh3Ho+7sxBMeukn6PvoQ9EXVJSBImgIUySGi76F7Y3HQPphL1pKMloM7Da7HksguGXkg8SY4lwe7wt6k4mpf3uGFVfeR+2O4MSWe4M+gOlqhV5Pwph8ypauJWZYNvbWNmweQsjyvn4DBQ9/DSEEIx64jcOvvOey34zXf0P6ZXMxREZwyaa3sTU1o4+M6HUtA3+R/8BtnFr2BSXvLfX72EKn67PznDfU7TlEbN4QTJYEhF6H0OloOVXZpU/tjn2YkxNJKhqDzmRA2uy0VtbQsN9DRESQrGE2D9U9XdFWXRsgSXyj9L+fsf3J3zP6B1/v5gvQeKSEGheWl2H3XE/KDJcJBIOGSl2sCDq2Vf/Esf4tv4y1tCCNjUmuTeQLcm5nTPIUv8zjjqP/+ZhV1wQ2gY4vJE4YRfWmnQEZO3nqOCrWnPFPiB+dR/3+ozhcRDRkLDiPmW89j057OpUOBxVfbOXIqx9w7I2PadZK02bfeBnT//nLgMjrD1qravho/JU0HS3167iWyYVUfrHNr2MGk9hh2dR7Uhj8RESqhZaKGu8scEKgizDjaA5s4iNfiMpMQxj0ONptyHYbjnYb7fWNSFvXe1bkICuXfvkRpgD7lYCqdqgIM/RTr0VY+7av3EGD3rV1YErqhQFXBoCAhql1oDObuu0Fu8MYwIQ0FWu2YJ115j5Su2Mf8SNyuz3pWiaPZfq/f3VaGQDnE7F16niKfvtDFh5bzvnL/kHevTcy/uffC5i8/sCclMCM137t92JU0tE/H8QiUpOxTp9AfQDzCHSm5VQlkWnJXkVlROdkhpUyANB0/CSNh0toLjlFS1klbdW13ZQBgKLnfxQUZaAnlEKgCDpCb8Qw/wEwRvRtoIgYTkV1/xcuSpnD+VmL+ja2l7RVBdZEGTU4jajMNCq/2Eq8G49wkyUB68yJJBWPAb2OqOz0gIUsVa3bjui0LVG95UviC4aeVgpihmUz+/0XPCZn0en1pM6eRPHvHyfKjUNbOJE8ZRwTfvWIX8eUtvDwOfGFpOLRtJyqcEbTBNFnprnklDO3Rw//05GpliBJ5F+sMyYy+KrehUL7G6UQKEKCSMpEf+HXez+AORrjdT9lWEbXnOFFKXO4KPuGPkrnPSc/WR2wsZOKR9Ne30TDAWdGs+bSMqyzikmZVYx1djHWWcVYJhXSVlNP+cqNVK3fTtlnX9B0pJSE8YEJEbS3tBKTld6lrXrzl1gmFWKMi2HuR38kwkuHw/7E8G/c7JdEMdYZE7HOLKK6By/0cKS1sqZbGGDcqDysM4sCVs67g5aTFVgmFXrsIwIYFRJIWiuqObXsi1CLASgfAkWIsX32JxxbPvL9xKQMTLf9DofDxpby1WytXE169BAuyr7e/0J6oL2hkdbyKhYXL6LVTwmJYvOy0UdHUbPly16PET8mH3OS5kwpnZX9Kr/wnMrYW1yVr9WZjJiSEpj2j593CckcSDja21k67w7Klq3r1fmRGak0l5zys1TBxRATRXROJnqTicYjJbRWVAMQnZuJ2ZJI1frtAZs7rmAodbsOuD0eziWfvSHv6zcw8bc/CGi4K3j2IVAKgSKkSFs7tjceQ5700VM/PhXTHf8XGKF6wfvDL6J+32G/jCWMBqSfcwn4uwa9dWYR5Ss3dGurWr+dkd/7KoOvnkfCmPyQRwr4m9bKaj4uXtSrXPyu/mYDjaSi0VRt2BGw8eNGDqXuS9dKgT4mCntDU8DmDgbDv3kLRb/7YUDnUE6FirBFGIwYLv0ORPiW7AZHeO3B6iPNfR5D6HRYZxb5XRmwTB3nN2UgfsxwEscX0F7f2O1YW1UN9pZWdvzk93w0diEfjr6MY+98ykB66DBbEhnfi/wEEWnJlK/eHACJwgtXiYz8idlNquWo7PR+rwwAZFw+N6TzK4VAEXJEXAqGi+8HPD9Nitxi9HPuRAydhC5nQnCE85Lc26/y+Zy4EblYZ04kZXYxKbOLickdHJAnSJ0f93drt++levMul9sZZ8eM1+7az8or7+OT6TdQNoCejAdffRHxozyn/D2buOE5YZO8KpAEOmFR1aad6KO7O6xGDkoJ6LxBQQhSgxC15AmVmEgRFuhyJqCbfDWOL/7j8rgYPBrDgu8hhA79+PlBlq5n8r91K8ff/pQyL5/E4wqGUbdrP/inBpFHHC7CnHpDZHoKzaVlbo+7K/tcsWYzn866ibE//Tajvn+PX2QJJUKnY8zj97HqWu9KaOujI6nux3vbvmCIjcYWwCd1e1OLWx+W/oIxIQ7LpEKSJxeSVDT6dE0LnUEfcP+BnlAKgSJs0E+5Flm6B3msu2OSLmssQoSvQUvodEx5+Wk+LFyArdHzDVFnNjmVgSBRt+sAydMnUNGL4kuGmCji8nPRR0X06BDXVl3ncZ9822O/dZbeDXE2Nn/QYSWo3dmz70tiYX6XhE4DmajMVFpOlPfcsQ+Ur1hP8rTxVG/bc3qbQHooJRxuXPblh0SmWUMthkuUQqAIG4ROj2H+t2l/5UFo7FrTXKTnh0gq74nJHUz29fM58Gc3Vg6DAVNiHFFZg6jeGJhsgq5or62n4vNNxAzLJjLdeSNytLajMxtxtNsQHVs1wmnyFUIgEdTu2EdMbqZPTmLlKzdgmTqOynXbu5nIpcPB6hsf5JIt72BO8lx2N9zxxUrQn55e+0JUZhpNJe4tSP6kYvVmYvOGnHbkbaupC8q8fcGUlEDciBwcbeGrvCiFQBFWiKh4DJd+t1u9A2EdEjqhvKTkw+UcfNl1Sub40Xk0l5yitbyK1vIql30CTcP+IzTsP4LQ64lMT6Hp2AmP/ZNnTKRi1Uaf56lcs4XEiaOo2banm4Nk07ETfHHnD5j51vP9PgLBWytBf81K6AuRGak47PaAWwc6U7/vMKakeNqqamkqCd68vmJOTmTUD+4h754b0Ef03fk4kISvDVZxzqLLGIF+5i1d2uxL/4j0R0GkACGlZOP9P0U6ustonVVE3e5Dp+vKh5rECQU9KgMAuj44iFVv3Eli4YhuCWuETheUdM/BoMNK4A5TUjxR2elujw8kgrFV4IqY3CyMCXHY6lz7r4Sa9PmzuWzPx4x44CthrwyAUggUYYpuwuXop1wHOmc+fMfuldhXvxpiqdxTtWG7y+pw1pkTKV+xwWX+8lARLBN21cYdXXLQGxPimPPRHxlx/2393jrQgauIg4TCfCLTU51PrkdKB3zuAYDGYydDMm97bT1RWYNCMrdHhKDwyfuZ/f4L/Wp7TCkEirBECIF+6rUYrn/6dI4Cx5fLke3hVbwEnFkAN3zrpy6Ptdd1j9cPNVXrtxPpRQ0Bhx/yIVSudRZEihuRy0Xr3mDQvBl9HjOcEDodBQ9/9fTvhthoanbup7m0f2ck9BV/hrb6Qv2+wxiiIpx1PsJEyTRbEpi7+M+M/uG9AaspEij6l7SKcw5d6lB0BXOcv9RXYP/0hbBLdLPx/qeoXNs/vMiFXk/8qDzMPdQbsEwZR4WfEukYY6K5cM1rxOUN8ct44cbgRRefDh2z1TeeE/kGzkZvNhGREpoaFpVrt1K7Yx9xI4diTIgLiQwdWCYVcvGmtxl04fSQytFblEKgCHt0o847/d6xeyWy8lgIpenKgT+/wf4XX3N7PNyeEMwpSW4TC3VgiInya/Gd8b/4HuYQ36gDiSEygiE3Xe78JUyeUoNN/b7DtJRVYUyI61HZDBR1u/ZjSoojIkRVD/Puu4kLVvyT6Kz+6zcSXncrhcIFuuRsROrQMw2t4ZGitGLdNtbf+2OPffyR0thX9FERJE4cRVLRaJKKRncpm2yIjnJ/ohBEZqQSO3wI5uREIjNSMSX27ovcEBvN1Fd+wYWrXyVmWFavxuhPDP3qNYBTmTqXaa+pQx8VgXVmEWZLYtDnbzx4HF2EOajWCn1UJFNf+QXFz/8IvVYGvL+iwg4V/QLdqPOwnzqAbtR5iJQhoRYHR3s7qxZ9q8eY4rbq2oDMH5GWjNmSgLTZqdtz6HR73IhcWsqruuU5sM4qpm7PIRr2H3GbcdAYG01zyakuCYisMyZS7iH00DpjIoaYKNqq64gdlkXM0CyM8bHU7dpP1jUXozf17xuktySNLyBxwiiajp1wbhucwzQdKaXpSCmg5d6Ij0EfHYXZmkj15i/BRSSOv+ePyk4nIiWJlrLAhvjGDh/CzDefI2H08IDOEyyUQqDoF+jyZyJSh6FLGxZqUQDQGY0kjh/ZY/ievanV6zHnrX0dU2IcTcdO0nTsBNXb9nDwz/+hva6hSz+TJYG2mnpaTlYAztS4xrgYDNFRNB4/6bLIS+dUr8a4GGKGZtF4pMTpOCglINBHmbvNhZstD2N8LON/8T2G3rko7LZFQsXwe29g6w9+E2oxwgppsznLglfW0HS01O9VN93RdKSU2PycgCoEWdfNZ/JLT2KM87EwWxjjlUIghDgM1AN2wCalLBJCjAVeAGKAw8BNUso6IcSFwM8AE9AGPCSlXKqNMxH4KxAJfAjcL6WUQggz8HdgIlAJXCelPKydcxvQUQ/yKSnl37T2HOBVIAnYBNwipWzr9V9CEdaIiGhEmCgDHYz87h2UvLfUYx97i3cKQVRWOpZJhQghnIVwNAqf+Cb7X3qNvb//J42HSwBIGJ1H2fIzN1V7YzP2swoLeaJu90HYfdC7zi62xM3WJOatfpXYYdlez3kukHvHIhztNjY/9POA5vPvz9TvPdRzJ3/NtecQ0dkZNB4p8d+gQpC16CIKHr2bpPEF/hs3TPBFtZ8rpRzXqY7yn4BHpJRjgLeBh7T2CuByrf024B+dxvgDcBeQp70u1trvBKqllMOAXwPPAAghkoDHgcnAJOBxIUTHxtQzwK+llHlAtTaGQhE0kqeNx5QY7/a4Zeo4r2P+m46WsnjyNZxa9kWXdmNcDCMfvJMFB//HpTs/YPwvvocpKR6h1/dJdq+xdzXvGqKjmPPhS0oZcIEQgrx7buCy3R+Tde0loRYn7LBMKgy6X4G/EkPpjEZy77iay778kBmv/3ZAKgMAwpsQLs1CUCSlrOjUVgfEa0/4g4HFUsqCs84TOBWEdJxP8p9JKUdox24A5kgp7xZCLAaekFKuEUIYgJOAFbi+o492zovAMpyWgXIgTUppE0JM1c6/yNM6ioqK5IYNAz9JiCI4ONrbeSt1uks/AeusIspX9O5/7bLdHxGXn+uxT+PRUt7NDmzt9KxrL6Gp5NTpokjCYGD2+38g/eJZAZ13oHBiySqOvbWEyPQUItNTMcREYW9qxtbQhK2hifb6RppKTlHy7v+6b9UMICLSkokZlt2rNNj+QGcyEpmegikpnoYDx9xW5XSFPjKCYXddy4jv3kH04DBMgNQLhBAbOz3Yd8FbHwIJLBFCSOBFKeVLwA5gAfAucA0w2MV5VwObpZStQogM4HinY8eBDO19BnAMQPuCrwUsndvPOscC1EgpbS7GUiiCgr21ze2WQHNp79K4pl0wrUdlACA6K73HcsR9wWxJYNq/fgkOByc+Wc2Rf71P2rwZShnwgUHzZniViMnW3ELJe0s59Pd3OLF4FXIA5TEwW5NwtLWHTBkAcLS103i4hJbyKq+31uJG5JJz6xUM/eo1RIQojDIUeKsQTJdSlgohUoBPhBC7gTuA3wkhfgS8h9Nf4DRCiFE4zfrzOppcjCt7OOZrezeEEHfh3KYgK2vghz8pgocxJppB86Zz/N3/dTvWsP8IhrhYn3OsFzz8Na/7Dr/vJrY99luX9RP6SuoF09Dp9aDXkzF/NhnzZ/t9DoUTQ2QE2dfNJ/u6+TSfquDoGx9z+JX3qPxia6hF6xOWKWOp33eEtqrARNr4QuzwIQiDwWPZcbMlgewbLiPn1oUkFY0ZMOm1fcErHwIpZan2swynv8AkKeVuKeU8KeVE4N/AgY7+QohMrd+tUsqO9uNAZqdhM4HSTscGa+cagHigqnP7WedUAAla37PHOlv2l6SURVLKIqs1PGtQK/onx9/7HyUfLHd7XKf37YaSOL6A1POnet1/1PfvYd4Xr2OdPsGnebxh0EUDK8VwfyEyNZn8b9zMRWtfZ8brv8Fs6T958M/GVt9EW2VNqMUAnKZ/V8qAzmgk88oLmfn277midCVFzz2GpbjwnFQGwAuFQAgRLYSI7XiP84l/h2YtQAihwxkF8IL2ewLwAfColPLzjnGklCeAeiHEFM234Fac2w3gtDDcpr1fBCyVTueGxcA8IUSi5kw4D6evggQ+0/qindsxlkIRcE4sWcWqa+73a9Gikd/7qs83IkvRGC5c9W+uOLacBQc+ZcGh/zHhN9/vsywDreZAfyTrmku4YNW/Qpb5ry9YZxb1WBY6WESkJlOzdXe39sFXX8Rluz9i1lvPM/iKC86ZnBme8MZCkAqsEkJsBdYBH0gpPwZuEELsBXbjfDp/Wev/DWAY8JgQYov2StGOfR1ndMJ+nBaFj7T2PwMWIcR+4DvAIwBSyirgSWC99vqJ1gbwMPAd7RyLNoZCEXBOLV/Hiivu6zEpkcmHKmfROZlkLfLoE+uRqMw0YnIHEzMkk7yv30DahdOJykzDlBhP6tzJPo0VP3o4UV4UP1IEnvgRQ5nzwYvojMGpUNlXTJYEkmdMDKsKj7H5OV1+Txw3kvOX/YOZ//kdMbmuXN/OXXr0IZBSHgTGumj/LfBbF+1PAU+5GWsDMNpFewtOx0RX5/wF+IsbuQZGYXVFv6HxaCkrrrgPe3PPVReNcdGn3w+6ZBbjnv4u7fUN2OobT3uZt2vvLcVj/FYxTm8yMXfxn5F2O3VfHqBq405OffaFx3NMSQk42tux1Teq7YIww1JcyNinv8PmB58JtSg9Ej8qLyiJh3yiUyRd4VMPUPDIXU7/GEU3VKZChcJLpJSsufVh2mvqvOpft/sQY//fd6jbfZCc264gceyIAEt4BiEEwmAgYUw+EanJPfYffOUFTHrpSer2HsIYE91jf0VwGfHtr3Dyk885sXhVqEXxiD+30PxF+coNWGcVk3n5XEY+qNLVeEIpBAqFlxz6x7uULV/nVV9jQhxz/vsC1ukTAyxVzwi9553B+FF5Ti9snY74EUM99lWEBqHTMe2fz7Ji4b2Ua3khwpGqDTuIGGSl5USnsFudjqTxBdgam5xZMkOAUga8QykECoUXOOx2tj7yrFd9o4dkMOfDPxI/Mjy+XHtKozv6sXvJvm5+kKRR9BazJZHzPv0rSy/4StgqBY62dszJicQNHwLSaVVrOVlO1cYdgDO+P9hKQcGjdytlwEuUQqBQeEHlum00n+g52VDSxFHM/u+LRKaFT4hruweFwJycSNyInhMhKcIDfYSZ4hd/woejLwu1KG6p3b7X7bGIFEtQFYK0C6Yx9qkHgjZff0eVKVMovKD0v8t67JN24XQuWP5KWCkD4N5CEJ2dwVVla4Lq26DoOwmj8og7y3O+v1C5fjvR2UFKKisEE371iKrG6QPKQqBQeEHpxys9HrdMGcest5/HEB0VJIm8R2fs/jFPmTOJSS89ec4mYOnvFDxyF2tvfzTUYviMvbkFQ2zUmSyGWuKiiFQLsXlDnOlmpUTa7Ei7nbbqOhoOHO3VXJkLziNhTL7/hD8HUAqBQuEF5y35M8fe/pSjr3/EqaVru+Sbjy8YxpwPXgxLZQCc+7YdORH0kWYSxgwn47K5xOUNCa1gil6Tc+sVHPjTG2HrS+CJ2h3OhEWWyWOp1BSC2Pwcl8XA9JERROdk0njoeLdjPTH0qy4j2RUe8Kra4UBBVTtU+IOW8iqOvbWEo69/RMOh41y44p9EZaaFWizFOUbpR8tZNv+uUIvRa5KKR6Mzm6lYtZGkotFUbdjhsl9EWjI6o4GmYye9HjtykJWFR5f5LbfHQMIf1Q4VCoVGhDWJvLuvJ+/u63HY7SrJiSIkpF04HbM1idbyqp47hyFV63dgSowjZXYxdbsPue3XcrKCyPQUDHEx2LwsE53zlauUMtALlLeFQtEHlDKgCBU6g6Hfh4u2VddRtnw9LacqPPZrX9rM+AAACyhJREFULi0jsdB7f4Chd1zdV9HOSZRCoFAoFP2U7BvDN/zQ35Sv2kiCFxExKbMnETssOwgSDTyUQqBQKBT9lOQp40iZc+6UdGmvre+xz9A7lXWgtyiFQKFQKPopQgimvfIsZov3lTX7M42HSzwm0jLGxTD46t5XDT3XUQqBQqFQ9GOiMlKZ8tefhVqMoGG2Jrk9ln3DpRiiIoMozcBCKQQKhULRz8m4bC75D9wWajGCgq2pGbObCp5D71wUZGkGFkohUCgUigHA+GcfPieS8VRv3ElrWSWWSYWYOm2VJIwZTlLRmBBK1v9RCoFCoVAMAHR6PZNeepKRD50Dlf2kpHLdNnQGPfGj8gDIvXORSsXdR1TmBoVCoRggCCEY98xDmBLj2fr9X4VanIDTcqqS9oYmEseNJOfmBaEWp9+jLAQKhUIxgBBCMOrRuyn6/Y9CLUpQsDc2E5ufg9mSGGpR+j1KIVAoFIoByPB7b2LqK79AnAPZNFVmQv+gFAKFQqEYoOTctICZbz+PzmwKtSgBIyornbQLpoVajAGBUggUCoViAJN5+XnM/fhPGGKjQy1KQMi9/SqETn2V+QP1V1QoFIoBTuqcyVy88S0SJ4zy67j6qEgSxo5g0MUzSSoajT7C7NfxeyIqM438b94c1DkHMirKQKFQKM4B4vKGMG/1q2z9/q/Y85u/IR2OXo0TmZ5CxoLzyFx4PqlzJndRAhw2G/X7jlC1aSel/11GyX8/w9bQ5K8ldMGUGM/Mt59XzoR+REgpQy1D0CgqKpIbNmwItRgKhUIRUmq272HzQz/nxOJVXvVPGDuCTE0JSJwwyut4f3tLKyc/Xc2xN5dw/N3/0VZd2xexTxORZuW8T/5CwujhfhnvXEIIsVFKWeTymFIIFAqF4tykatNOjr25hBNLVtFyqhJbYzMRKUlEpqcQOciKZfJYMi6fS8yQzD7P5Whvp2z5eo69tYSS9z+j6fjJXo0z6OKZTHn5aSLTrH2W6VxEKQQaSiFQKBSK8KCp9BRVG3dStWEHleu303z8JLbGZu3VhK2xGSEE0TmZxOXnEDt8CJZJhWRff6nKSNgHPCkEyodAoVAoFEEnKj2VqPRUMi8/z+VxKSXS4UB3DuRRCBeUQqBQKBSKsEMIcU4kVQonVNihQqFQKBQKpRAoFAqFQqFQCoFCoVAoFAqUQqBQKBQKhQKlECgUCoVCoUApBAqFQqFQKFAKgUKhUCgUCpRCoFAoFAqFAqUQKBQKhUKhQCkECoVCoVAoUAqBQqFQKBQKlEKgUCgUCoUCpRAoFAqFQqFAKQQKhUKhUCjwUiEQQhwWQmwXQmwRQmzQ2sYKIdZo7e8LIeK0dosQ4jMhRIMQ4vmzxlkmhNijjbNFCJGitZuFEK8JIfYLIb4QQgzpdM5tQoh92uu2Tu05Wt992rmmvv85FAqFQqE4N/HFQjBXSjlOSlmk/f4n4BEp5RjgbeAhrb0FeAx40M04N2njjJNSlmltdwLVUsphwK+BZwCEEEnA48BkYBLwuBAiUTvnGeDXUso8oFobQ6FQKBQKRS/oy5ZBPrBCe/8JcDWAlLJRSrkKp2LgLQuBv2nv/wOcL4QQwEXAJ1LKKilltTbPxdqx87S+aOde0Ye1KBQKhUJxTuOtQiCBJUKIjUKIu7S2HcAC7f01wGAvx3pZ2y54TPtiB8gAjgFIKW1ALWDp3K5xXGuzADVa387tCoVCoVAoeoHBy37TpZSl2p7/J0KI3cAdwO+EED8C3gPavBjnJilliRAiFngTuAX4OyBc9JW9aO+GpsB0KDENQog9XsgZTJKBilAL4UfUesKXgbQWUOsJZwbSWmBgrSfb3QGvFAIpZan2s0wI8TYwSUr5LDAPQAgxHLjUi3FKtJ/1Qoh/4fQL+DvOJ/zBwHEhhAGIB6q09jmdhsgEluG8MAlCCINmJcgESt3M+RLwkjfrDAVCiA2d/DL6PWo94ctAWguo9YQzA2ktMPDW444etwyEENHaEz1CiGicSsCOThECOuCHwAs9jGMQQiRr743AZTi3HcBpYeiIIFgELJVSSmAxME8Ikag5E84DFmvHPtP6op37rndLVigUCoVCcTbeWAhSgbe17X4D8C8p5cdCiPuFEPdpfd4CXu44QQhxGIgDTEKIK3B+kR8BFmvKgB74FPijdsqfgX8IIfbjtAxcDyClrBJCPAms1/r9REpZpb1/GHhVCPEUsFkbQ6FQKBQKRS/oUSGQUh4Exrpo/y3wWzfnDHEz3EQ3/VtwOia6OvYX4C9u5JrkZp7+RNhuZ/QStZ7wZSCtBdR6wpmBtBYYeOtxiXBa3xUKhUKhUJzLqNTFCoVCoVAolELQF9ykdH5CCFHSKT3z/E79H9XSM+8RQlzUqX2iNs5+IcTvOvIz9Calc7DWI4QYIoRo7tT+QqdxQr4eV2vR2r+p/f13CiF+3qm9310bd+sJ92vjbj3a/B0yHxZCbOnUP2yvjy9r6cfXZpwQYm1HmxBiUqf+/e3auFxLf7g2AUdKqV69fAGHgeSz2p4AHnTRtwDYCpiBHOAAoNeOrQOm4syv8BFwidZ+L/CC9v564DXtfRJwUPuZqL1PDPJ6hgA73IwT8vW4WctcnM6sZu33lH5+bdytJ6yvjbv1nHX8l8CP+sP18XEt/fLaAEs6yTMfWNZfr42HtYT9tQn0S1kIgsdC4FUpZauU8hCwH5gkhBgExEkp10jnf9LfOZOG2aeUzsFcjDvCfD1fB34mpWwFZ16NTnL1x2vjbj0u6Qfr6ZBTANcC/+4kW3+8Pq7W4q5fuK9F4owcA2eemI68L/3x2rhbi0vCfC1+RSkEfcNVSmeAbwghtgkh/iLOFGNyl4Y5Q3t/dnuXc6R3KZ2DuR6AHCHEZiHEciHEzE4yh8N6XK1lODBTM+0tF0IUny3XWfOHy1p8XQ+E97Vxt54OZgKnpJT7zpbtLBnCZT2+rAX657V5APiFEOIY8Czw6NmynSVDuKzHl7VA+F+bgOJt6mKFa1yldP4D8CTOf8QncZoL76B3aZj7nLrZR3xZzwkgS0pZKYSYCLwjhBjVg2zBXI+rtRhwmu+mAMXA60KI3F7KFQ7Xxt16wv3agIv1SCk7iqXdQNcn6nC/Pr6spV9eG5xJ4L4tpXxTCHEtzrwvF/RStlDfB9ytpT9cm4CiLAR9QHZK6YyzBPQkKeUpKaVdSunAmXipw/mmIz1zBx3plo9r789u73KO6J7S2dVYQVuPZiKs1N5vxLl3ODxc1uNqLdo8b0kn6wAHzhzl/fLauFtPuF8bD+vpmPsq4LVO3cP6+viyln58bW7DmYAO4A36yX3Nl7X0h2sTcNw5F6hXj84q0UBsp/erce4RDerU59s499cARtHV+eYgZ5xv1uN8yutwWJmvtd9HV4eV17X3ScAhnE+Hidr7pCCvx9pJ/lygpEOGUK/Hw1ruwZntEpwf9GOajP312rhbT9heG0/r0X6/GFh+Vv+wvT69WEu/vDbAl8Acrf18YGN/vTYe1hLW1yYYr5AL0F9f2j/MVu21E/iB1v4PYDuwDWeNhs5fqD/AqXXuQfNS1dqLcNZ1OAA8z5mEURE4Ndj9OL1cczudc4fWvh+4PdjrAa7W+m0FNgGXh8t6PKzFBLyiybYJOK+fXxuX6wnna+NpPdqxvwL3uDgnLK+Pr2vpr9cGmAFs1Nq/ACb212vjbi3hfm2C8VKZChUKhUKhUCgfAoVCoVAoFEohUCgUCoVCgVIIFAqFQqFQoBQChUKhUCgUKIVAoVAoFAoFSiFQKBQKhUKBUggUCoVCoVCgFAKFQqFQKBTA/weh1Myu2wHPDgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "fig, ax = plt.subplots(figsize=(12,8))\n",
-- 
GitLab