diff --git a/.gitignore b/.gitignore
index ad8665a..4b3bb25 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,3 +11,4 @@ mapclassify/tests/.ropeproject/
 .DS_Store
 .vscode/settings.json
 __pycache__
+/notebooks/.ipynb_checkpoints/
diff --git a/mapclassify/_classify_API.py b/mapclassify/_classify_API.py
index 034677f..27fa19c 100644
--- a/mapclassify/_classify_API.py
+++ b/mapclassify/_classify_API.py
@@ -51,6 +51,7 @@ def classify(
     initial=100,
     bins=None,
     lowest=None,
+    anchor=False,
 ):
     """
 
@@ -94,6 +95,9 @@ def classify(
         Scalar minimum value of lowest class. Default is to set the minimum
         to ``-inf`` if  ``y.min()`` > first upper bound (which will override
         the default), otherwise minimum is set to ``y.min()``.
+    anchor : bool (default False)
+            Anchor upper bound of one class to the sample mean.
+
 
 
     Returns
@@ -182,7 +186,7 @@ def classify(
         classifier = _classifiers[scheme](y, pct)
 
     elif scheme == "stdmean":
-        classifier = _classifiers[scheme](y, multiples)
+        classifier = _classifiers[scheme](y, multiples, anchor)
 
     elif scheme == "jenkscaspallsampled":
         classifier = _classifiers[scheme](y, k, pct_sampled)
diff --git a/mapclassify/classifiers.py b/mapclassify/classifiers.py
index 0c792ca..627d396 100644
--- a/mapclassify/classifiers.py
+++ b/mapclassify/classifiers.py
@@ -1520,12 +1520,14 @@ class StdMean(MapClassifier):
 
     Parameters
     ----------
-
     y : numpy.array
-        :math:`(n,1)`, values to classify.
+        :math:`(n,1)`, values to classify
     multiples : numpy.array (default [-2, -1, 1, 2])
         The multiples of the standard deviation to add/subtract from
-        the sample mean to define the bins
+        the sample mean to define the bins.
+    anchor : bool (default False)
+        Anchor upper bound of one class to the sample mean.
+
 
     Attributes
     ----------
@@ -1539,6 +1541,17 @@ class StdMean(MapClassifier):
     counts : numpy.array
         :math:`(k,1)`, the number of observations falling in each class.
 
+    Notes
+    -----
+
+    If anchor is True, one of the intervals will have its closed upper bound
+    equal to the mean of y. Intermediate intervals will have widths equal to
+    the standard deviation of y. The first interval will be closed on the
+    minimum value of y, and the last interval will be closed on the maximum of
+    y. The first and last intervals may have widths different from the
+    intermediate intervals.
+
+
     Examples
     --------
 
@@ -1562,11 +1575,20 @@ class StdMean(MapClassifier):
 
     >>> list(st3.counts)
     [0, 0, 57, 0, 1]
-
+    >>> stda = mapclassify.StdMean(cal, anchor=True)
+    >>> stda.k
+    9
+    >>> stda.bins
+    array([ 125.92810345,  672.57333208, 1219.21856072, 1765.86378936,
+           2312.50901799, 2859.15424663, 3405.79947527, 3952.4447039 ,
+           4111.45      ])
+    >>> cal.mean(), cal.std(), cal.min(), cal.max()
+    (125.92810344827588, 546.6452286365233, 0.13, 4111.45)
     """
 
-    def __init__(self, y, multiples=[-2, -1, 1, 2]):
+    def __init__(self, y, multiples=[-2, -1, 1, 2], anchor=False):
         self.multiples = multiples
+        self.anchor = anchor
         MapClassifier.__init__(self, y)
         self.name = "StdMean"
 
@@ -1574,6 +1596,10 @@ def _set_bins(self):
         y = self.y
         s = y.std(ddof=1)
         m = y.mean()
+        if self.anchor:
+            min_z = int((y.min() - m) / s)
+            max_z = int((y.max() - m) / s) + 1
+            self.multiples = list(range(min_z, max_z))
         cuts = [m + s * w for w in self.multiples]
         y_max = y.max()
         if cuts[-1] < y_max:
diff --git a/mapclassify/tests/test_mapclassify.py b/mapclassify/tests/test_mapclassify.py
index 3a5b607..8e41b73 100644
--- a/mapclassify/tests/test_mapclassify.py
+++ b/mapclassify/tests/test_mapclassify.py
@@ -631,6 +631,30 @@ def test_UserDefined_lowest(self):
         assert ud.get_legend_classes() == classes
 
 
+class TestStdMeanAnchor:
+    def setup_method(self):
+        self.V = load_example()
+
+    def test_StdMeanAnchor(self):
+        sm = StdMean(self.V, anchor=True)
+        bins = numpy.array(
+            [
+                125.92810345,
+                672.57333208,
+                1219.21856072,
+                1765.86378936,
+                2312.50901799,
+                2859.15424663,
+                3405.79947527,
+                3952.4447039,
+                4111.45,
+            ]
+        )
+        counts = numpy.array([50, 6, 1, 0, 0, 0, 0, 0, 1])
+        numpy.testing.assert_array_almost_equal(sm.bins, bins)
+        numpy.testing.assert_array_almost_equal(sm.counts, counts)
+
+
 class TestMaxP:
     def setup_method(self):
         self.V = load_example()
diff --git a/notebooks/06_api.ipynb b/notebooks/06_api.ipynb
index ca6a3a0..1eae870 100644
--- a/notebooks/06_api.ipynb
+++ b/notebooks/06_api.ipynb
@@ -23,7 +23,8 @@
     "ExecuteTime": {
      "end_time": "2022-11-05T15:10:19.167785Z",
      "start_time": "2022-11-05T15:10:14.404320Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -46,13 +47,14 @@
     "ExecuteTime": {
      "end_time": "2022-11-05T15:10:19.182165Z",
      "start_time": "2022-11-05T15:10:19.171353Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'2.5.0+8.g34341a22.dirty'"
+       "'2.4.2+107.gb97c316a.dirty'"
       ]
      },
      "execution_count": 2,
@@ -71,7 +73,8 @@
     "ExecuteTime": {
      "end_time": "2022-11-05T15:10:19.586837Z",
      "start_time": "2022-11-05T15:10:19.187232Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -295,7 +298,8 @@
     "ExecuteTime": {
      "end_time": "2022-11-05T15:10:19.595711Z",
      "start_time": "2022-11-05T15:10:19.589037Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -339,7 +343,8 @@
     "ExecuteTime": {
      "end_time": "2022-11-05T15:10:19.603460Z",
      "start_time": "2022-11-05T15:10:19.598526Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -374,7 +379,8 @@
     "ExecuteTime": {
      "end_time": "2022-11-05T15:10:19.611996Z",
      "start_time": "2022-11-05T15:10:19.608075Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -399,7 +405,8 @@
     "ExecuteTime": {
      "end_time": "2022-11-05T15:10:19.619168Z",
      "start_time": "2022-11-05T15:10:19.614412Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -440,7 +447,8 @@
     "ExecuteTime": {
      "end_time": "2022-11-05T15:10:19.627988Z",
      "start_time": "2022-11-05T15:10:19.621853Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -474,7 +482,8 @@
     "ExecuteTime": {
      "end_time": "2022-11-05T15:10:19.634396Z",
      "start_time": "2022-11-05T15:10:19.629847Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -508,7 +517,8 @@
     "ExecuteTime": {
      "end_time": "2022-11-05T15:10:19.641115Z",
      "start_time": "2022-11-05T15:10:19.636017Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -537,258 +547,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2022-11-05T15:10:19.691302Z",
      "start_time": "2022-11-05T15:10:19.645124Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "\u001b[0;31mSignature:\u001b[0m\n",
-       "\u001b[0mmapclassify\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mscheme\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mpct\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m90\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mpct_sampled\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mtruncate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mhinge\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mmultiples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mmindiff\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0minitial\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mDocstring:\u001b[0m\n",
-       "Classify your data with ``mapclassify.classify``.\n",
-       "Input parameters are dependent on classifier used.\n",
-       "\n",
-       "Parameters\n",
-       "----------\n",
-       "\n",
-       "y : numpy.array\n",
-       "    :math:`(n,1)`, values to classify.\n",
-       "scheme : str\n",
-       "    ``pysal.mapclassify`` classification scheme.\n",
-       "k : int (default 5)\n",
-       "    The number of classes.\n",
-       "pct  : numpy.array (default [1, 10, 50, 90, 99, 100])\n",
-       "    Percentiles used for classification with ``percentiles``.\n",
-       "pct_sampled : float default (0.10)\n",
-       "    The percentage of n that should form the sample\n",
-       "    (``JenksCaspallSampled``, ``FisherJenksSampled``)\n",
-       "    If ``pct`` is specified such that ``n*pct > 1000``, then ``pct=1000``.\n",
-       "truncate : bool (default True)\n",
-       "    Truncate ``pct_sampled`` in cases where ``pct * n > 1000``.\n",
-       "hinge : float (default 1.5)\n",
-       "    Multiplier for *IQR* when ``BoxPlot`` classifier used.\n",
-       "multiples : numpy.array (default [-2,-1,1,2])\n",
-       "    The multiples of the standard deviation to add/subtract from\n",
-       "    the sample mean to define the bins using ``std_mean``.\n",
-       "mindiff : float (default is 0)\n",
-       "    The minimum difference between class breaks\n",
-       "    if using ``maximum_breaks`` classifier.\n",
-       "initial : int (default 100)\n",
-       "    Number of initial solutions to generate or number of runs when using\n",
-       "    ``natural_breaks`` or ``max_p_classifier``. Setting initial to ``0``\n",
-       "    will result in the quickest calculation of bins.\n",
-       "bins : numpy.array (default None)\n",
-       "    :math:`(k,1)`, upper bounds of classes (have to be monotically\n",
-       "    increasing) if using ``user_defined`` classifier.\n",
-       "    Default is ``None``. For example: ``[20, max(y)]``.\n",
+       "StdMean\n",
        "\n",
-       "Returns\n",
-       "-------\n",
-       "classifier : mapclassify.classifiers.MapClassifier\n",
-       "    Object containing bin ids for each observation (``.yb``),\n",
-       "    upper bounds of each class (``.bins``), number of classes (``.k``)\n",
-       "    and number of observations falling in each class (``.counts``).\n",
-       "\n",
-       "Notes\n",
-       "-----\n",
-       "\n",
-       "Supported classifiers include:\n",
-       "\n",
-       "* ``quantiles``\n",
-       "* ``box_plot``\n",
-       "* ``equal_interval``\n",
-       "* ``fisher_jenks``\n",
-       "* ``fisher_jenks_sampled``\n",
-       "* ``headtail_breaks``\n",
-       "* ``jenks_caspall``\n",
-       "* ``jenks_caspall_sampled``\n",
-       "* ``jenks_caspall_forced``\n",
-       "* ``max_p``\n",
-       "* ``maximum_breaks``\n",
-       "* ``natural_breaks``\n",
-       "* ``percentiles``\n",
-       "* ``std_mean``\n",
-       "* ``user_defined``\n",
-       "\n",
-       "Examples\n",
-       "--------\n",
-       "\n",
-       ">>> import libpysal\n",
-       ">>> import geopandas\n",
-       ">>> from mapclassify import classify\n",
-       "\n",
-       "Load example data.\n",
-       "\n",
-       ">>> link_to_data = libpysal.examples.get_path(\"columbus.shp\")\n",
-       ">>> gdf = geopandas.read_file(link_to_data)\n",
-       ">>> x = gdf['HOVAL'].values\n",
-       "\n",
-       "Classify values by quantiles.\n",
-       "\n",
-       ">>> quantiles = classify(x, \"quantiles\")\n",
-       "\n",
-       "Classify values by box_plot and set hinge to ``2``.\n",
-       "\n",
-       ">>> box_plot = classify(x, 'box_plot', hinge=2)\n",
-       ">>> box_plot\n",
-       "BoxPlot\n",
-       "<BLANKLINE>\n",
        "   Interval      Count\n",
        "----------------------\n",
-       "( -inf, -9.50] |     0\n",
-       "(-9.50, 25.70] |    13\n",
-       "(25.70, 33.50] |    12\n",
-       "(33.50, 43.30] |    12\n",
-       "(43.30, 78.50] |     9\n",
-       "(78.50, 96.40] |     3\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/Documents/p/pysal/src/subpackages/mapclassify/mapclassify/_classify_API.py\n",
-       "\u001b[0;31mType:\u001b[0m      function\n"
+       "( -inf,  1.50] |     0\n",
+       "( 1.50, 19.97] |     5\n",
+       "(19.97, 56.90] |    37\n",
+       "(56.90, 75.37] |     3\n",
+       "(75.37, 96.40] |     4"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mapclassify.classify?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-05T15:10:19.641115Z",
-     "start_time": "2022-11-05T15:10:19.636017Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "UserDefined\n",
-       "\n",
-       "    Interval       Count\n",
-       "------------------------\n",
-       "(  -inf,   0.00] |     0\n",
-       "(  0.00,  50.00] |    40\n",
-       "( 50.00, 100.00] |     9"
-      ]
-     },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mapclassify.classify(y, \"User_Defined\", bins=[0,50, 100])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Finding bins for new data"
+    "mapclassify.classify(y, 'Std_Mean')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-11-05T15:10:19.641115Z",
-     "start_time": "2022-11-05T15:10:19.636017Z"
-    }
+     "end_time": "2022-10-26T03:01:45.977181Z",
+     "start_time": "2022-10-26T03:01:45.931234Z"
+    },
+    "tags": []
    },
-   "outputs": [],
-   "source": [
-    "r = mapclassify.classify(y, \"User_Defined\", bins=[0,50, 100])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\u001b[0;31mSignature:\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_bin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mSource:\u001b[0m   \n",
-       "    \u001b[0;32mdef\u001b[0m \u001b[0mfind_bin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"\u001b[0m\n",
-       "\u001b[0;34m        Sort input or inputs according to the current bin estimate.\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m        Parameters\u001b[0m\n",
-       "\u001b[0;34m        ----------\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m        x : numpy.array, int, float\u001b[0m\n",
-       "\u001b[0;34m            A value or array of values to fit within the estimated bins.\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m        Returns\u001b[0m\n",
-       "\u001b[0;34m        -------\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m        right : numpy.array, int\u001b[0m\n",
-       "\u001b[0;34m            A bin index or array of bin indices that classify the\u001b[0m\n",
-       "\u001b[0;34m            input into one of the classifiers' bins.\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m        Notes\u001b[0m\n",
-       "\u001b[0;34m        -----\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m        This differs from similar functionality in\u001b[0m\n",
-       "\u001b[0;34m        ``numpy.digitize(x, classi.bins, right=True)``.\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m        This will always provide the closest bin, so data \"outside\" the classifier,\u001b[0m\n",
-       "\u001b[0;34m        above and below the max/min breaks, will be classified into the nearest bin.\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m        ``numpy.digitize`` returns :math:`k+1` for data greater than the greatest bin,\u001b[0m\n",
-       "\u001b[0;34m        but retains 0 for data below the lowest bin.\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m        \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m        \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdigitize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m            \u001b[0mright\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mright\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/Documents/p/pysal/src/subpackages/mapclassify/mapclassify/classifiers.py\n",
-       "\u001b[0;31mType:\u001b[0m      method\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "r.find_bin??"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([  0,  50, 100]), array([ 0, 40,  9]))"
+       "StdMean\n",
+       "\n",
+       "   Interval      Count\n",
+       "----------------------\n",
+       "[17.90, 19.97] |     5\n",
+       "(19.97, 38.44] |    24\n",
+       "(38.44, 56.90] |    13\n",
+       "(56.90, 75.37] |     3\n",
+       "(75.37, 93.83] |     3\n",
+       "(93.83, 96.40] |     1"
       ]
      },
      "execution_count": 15,
@@ -797,18 +611,20 @@
     }
    ],
    "source": [
-    "r.bins, r.counts"
+    "mapclassify.classify(y, 'Std_Mean', anchor=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1, 0, 2, 1])"
+       "(38.43622446938775, 18.466069465206047, 17.9, 96.400002)"
       ]
      },
      "execution_count": 16,
@@ -817,35 +633,15 @@
     }
    ],
    "source": [
-    "r.find_bin([7,0, 51, 33])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that `find_bin` does not recalibrate the classifier:"
+    "y.mean(), y.std(), y.min(), y.max()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([  0,  50, 100]), array([ 0, 40,  9]))"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r.bins, r.counts"
-   ]
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -864,7 +660,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/07_std_anchor.ipynb b/notebooks/07_std_anchor.ipynb
new file mode 100644
index 0000000..58ef19d
--- /dev/null
+++ b/notebooks/07_std_anchor.ipynb
@@ -0,0 +1,522 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to mapclassify\n",
+    "\n",
+    "`mapclassify` implementsbins = [ ybar + trim * ystd for trim in range(-2, 2+1) ] a family of classification schemes for choropleth maps. \n",
+    "Its focus is on the determination of the number of classes, and the assignment of observations to those classes.\n",
+    "It is intended for use with upstream mapping and geovisualization packages (see [geopandas](https://geopandas.org/mapping.html) and [geoplot](https://residentmario.github.io/geoplot/user_guide/Customizing_Plots.html) for examples) that handle the rendering of the maps.\n",
+    "\n",
+    "In this notebook, the basic functionality of mapclassify is presented."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-10-26T02:53:25.104870Z",
+     "start_time": "2022-10-26T02:53:23.858480Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.4.2+55.g0155c6e6.dirty'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import mapclassify as mc\n",
+    "\n",
+    "mc.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas as gpd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf = gpd.read_file(\"data/nyc/nyc.shp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot: >"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdwAAAG0CAYAAACYBJbCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAADoUUlEQVR4nOydeXRb5bW3H82WZUuWPM925jjOPIdAGBNoAqUTtOGmTUuhLQQuDdAWOnwt9xZoSWkLFMqFFlqm0JZ5CgkEEkJmJ07iJM7oebYlS7YsazzfH4oVK5ZtyZYdm7zPWl4LS/scHSlG+7z73fv3k0mSJCEQCAQCgWBIkZ/vCxAIBAKB4EJAJFyBQCAQCIYBkXAFAoFAIBgGRMIVCAQCgWAYEAlXIBAIBIJhQCRcgUAgEAiGAZFwBQKBQCAYBkTCFQgEAoFgGBAJVyAQCASCYUAkXIFAIBAIhoGIEu5DDz3E3LlziY+PJyUlheuvv55jx44FxbS3t7NmzRqysrLQarVMnjyZp556KijG6XRyxx13kJSUhE6n47rrrqO6ujooxmKxsGrVKgwGAwaDgVWrVtHa2hoUU1lZybXXXotOpyMpKYk777wTl8sVFHPo0CGWLFmCVqslMzOTBx54AKFmKRAIBILhJqKEu2XLFm6//XZ27tzJpk2b8Hg8LF26FLvdHoj58Y9/zIYNG3jxxRc5evQoP/7xj7njjjt46623AjF33XUXb7zxBuvXr2fbtm20t7ezYsUKvF5vIGblypUUFxezYcMGNmzYQHFxMatWrQo87/V6Wb58OXa7nW3btrF+/Xpee+017r777kCMzWbjqquuIiMjgz179vD444+zbt06Hn300QF9WAKBQCAQDBhpEDQ2NkqAtGXLlsBjU6ZMkR544IGguFmzZkm/+MUvJEmSpNbWVkmlUknr168PPF9TUyPJ5XJpw4YNkiRJ0pEjRyRA2rlzZyBmx44dEiCVlpZKkiRJ77//viSXy6WamppAzCuvvCJpNBrJarVKkiRJTz75pGQwGKTOzs5AzEMPPSRlZGRIPp9vMG9dIBAIBIKIUA4mWVutVgBMJlPgscWLF/P222/zve99j4yMDD799FOOHz/On//8ZwCKiopwu90sXbo0cExGRgaFhYVs376dZcuWsWPHDgwGA/Pnzw/ELFiwAIPBwPbt25k4cSI7duygsLCQjIyMQMyyZctwOp0UFRVx2WWXsWPHDpYsWYJGowmKue+++ygvLyc/P7/He3I6nTidzsDvPp8Ps9lMYmIiMplsMB+XQCAQCEYAkiTR1tZGRkYGcvnwtTINOOFKksTatWtZvHgxhYWFgccfe+wxbrnlFrKyslAqlcjlcp599lkWL14MQH19PWq1GqPRGHS+1NRU6uvrAzEpKSk9XjMlJSUoJjU1Neh5o9GIWq0OisnLy+vxOl3PhUq4Dz30EL/5zW8i+SgEAoFAMAqpqqoiKytr2F5vwAl3zZo1HDx4kG3btgU9/thjj7Fz507efvttcnNz2bp1K7fddhvp6elceeWVvZ5PkqSgFWSo1WQ0YqQzDVO9rVbvu+8+1q5dG/jdarWSk5NDVVUVer2+1+sXCAQCwejAZrORnZ1NfHz8sL7ugBLuHXfcwdtvv83WrVuD7g4cDgf3338/b7zxBsuXLwdg2rRpFBcXs27dOq688krS0tJwuVxYLJagVW5jYyOLFi0CIC0tjYaGhh6v29TUFFihpqWlsWvXrqDnLRYLbrc7KKZrtdv9dYAeq+MuNBpNUAm6C71eLxKuQCAQfIEY7m3CiIrXkiSxZs0aXn/9dTZv3tyjJOt2u3G73T1q4gqFAp/PB8Ds2bNRqVRs2rQp8HxdXR0lJSWBhLtw4UKsViu7d+8OxOzatQur1RoUU1JSQl1dXSBm48aNaDQaZs+eHYjZunVr0KjQxo0bycjI6FFqFggEAoFgSImkw+pHP/qRZDAYpE8//VSqq6sL/HR0dARilixZIk2ZMkX65JNPpNOnT0vPPfecFBMTIz355JOBmB/+8IdSVlaW9NFHH0n79u2TLr/8cmn69OmSx+MJxFx99dXStGnTpB07dkg7duyQpk6dKq1YsSLwvMfjkQoLC6UrrrhC2rdvn/TRRx9JWVlZ0po1awIxra2tUmpqqvStb31LOnTokPT6669Ler1eWrduXdjv2Wq1SkCg81kgEAgEo5vz9b0eUcIFQv4899xzgZi6ujpp9erVUkZGhhQTEyNNnDhR+sMf/hA0huNwOKQ1a9ZIJpNJ0mq10ooVK6TKysqg12ppaZFuuukmKT4+XoqPj5duuukmyWKxBMVUVFRIy5cvl7RarWQymaQ1a9YEjQBJkiQdPHhQuvjiiyWNRiOlpaVJv/71ryMaCRIJVyAQCL5YnK/vdZkkCdmlvrDZbBgMBqxWq9jDFQgEgi8A5+t7XWgpCwQCgUAwDIiEKxAIBALBMCASrkAgEAgEw4BIuAKBQCAQDAMi4QoEAoFAMAyIhCsQCAQCwTAgEq5AIBAIBMOASLgCgUAgEAwDIuEKBAKBQDAMiIQrEAhGDZIk8dctpzjR0Ha+L0UgiJgB++EKBALBcPP2gVoe/qCUhz8opTBTz1dnZnHdjAyS4npaagoEIw2RcAUCwahh+8mWwH+X1NgoqTnCb98/ylWTU7nlkjHMykkYdo9TgSBcRMIVCASjhgPVrT0e8/okNhyuZ8PhetINMXx7US5fnp5JRoJ2+C9QIOgDsYcrEAhGBXanh+P97N3WWTt5c18ti3+3me89v4cPD9fj9vqG5Hp8Pol3DtRyrF7sJwvCQ6xwBQLBqKCkxoovDDNRj8+HT4LNpY1sLm0kJV7DN+dm8815OVFd9f7yrRIOVlt58eb5UTun4IuNSLgCgWBUUFRpCSvOdc6KtrHNyWObT/LEJyeZlWNk1cJcrilMR60cXIFvUlo8VxWkolDIqLd2kmaIGdT5BF98RMIVCASjgt1l5rDipF4qyD4J9lZY2Fth4dexh7l+ZiZfm5XFlAx9xI1WrxVV89ctp2mwdZKfpKPS3MENc7L54aVjyRR7x4JekEmSFEaR5sLFZrNhMBiwWq3o9frzfTkCwQWJx+tjxgObaHd6+o1N1KlpsbvCPve4lDi+PjuLr87KJCW+/1Vqab2NW/65lyqzo8dzchl88N+XMDEtPuzXFww/5+t7XTRNCQSCEc9HRxvDSrYAka4gTja28/AHpSx8aDM/eGEvn5Q24umj0crc7gqZbMG/il7z8j6+8/fdbD3eFOGVCL7oiJKyQCAY8by0qyLs2FiVgvCKz8F4fRIfHm7gw8MNJMVp+OqsTL41L4f8JF0gZufpFv7vs9N9nudEYzsnGttJjFNzyYTkAVyJ4IuKSLgCgWBE4/L42FMefgpN0Wuobg29Ag2X5nYn/7f1NM98dpo5uUa+MSebpQWpvLGvhk+PhbdyPVJrQ5IkIcQhCCASrkAgGNEUV7XS6Q5/llYpj95OmSTBnnILe8ot/ExGRJ3NpfVtbCip55qp6VG7HsHoRuzhCgSCEc32U80RxXvDGdYdAD6JiBI/wIeH64fkWgSjE5FwBQLBiGb7qZb+g7qhVIycEu707ITzfQmCEYRIuAKBYMTicHnZH6bgRRdDtcIdCKea2s/3JQhGECLhCgSCEcuW4024vZElUCniwaCh48PDDfhG0A2A4PwiEq5AIBixHKppjfiYwTZNZRmjpxTV1ObkrleL2X6yGaExJBBdygKBYMTy8dHGiI8ZbF5rbncO7gTn8PaBWt4+UEteYiw3zM3mKzMzSTcI+ccLEbHCFQgEI5IqcwelA7C+szrcQb/HqOR8eUZG2MdH2okcLuUtHfx+wzEuengzq5/bzSeljYH95pONbdjDVNISjF7EClcgEIxIdkTYnQyQa9JyrJtn7phkHf99xXhe3VMVzUsbFD4JPj3WxKfHmkiJ13D9zEzKm+1sPdHEFZNTWTE1nYvGJ6GPUZ3vSxVEGWFe0A/CvEAgOD/84s1DvLizMqJj5uYZ2VMeWVfzSESrUnB1YRqXTEjiysmpxIvkG1XO1/e6WOEKBIIRyYEqa0TxGqWMo3WRl6CVchme89hJPD3bQJJOw+4yM21nysoOt5c39tfwxv4aNEo5VxaksrQglWVT0ohRKc7btQoGh0i4AoFgxFFab+NQTWQJ16BVk22KpcbioN7WGdYxY5N1aJRyjgwgUQ+WcSlxxKjkHKiyIpPB/1tRwHuH6uhweYnTKHF7fdidXmQyqLd28tLOStbvqSI5TsOCMYlcXZiGSace9usWDJyImqYeeugh5s6dS3x8PCkpKVx//fUcO3asR9zRo0e57rrrMBgMxMfHs2DBAiorz5aGnE4nd9xxB0lJSeh0Oq677jqqq6uDzmGxWFi1ahUGgwGDwcCqVatobW0NiqmsrOTaa69Fp9ORlJTEnXfeicsV7IN56NAhlixZglarJTMzkwceeEC05wsEI5zXiqr7DzqHxjYnRRUW6m2dJOrUTMsyMC/fxNhkHQp5aPWpGJUCrWr41h1yGUxIjWNOrpGTje2U1NgAf2f1X7ec5ptzczhca2NXmZl9la0ca2ijtL6NogoLu8vN7DjVwtsHanl6yyluf3kfP/nPATaU1ImGq1FCRH9pW7Zs4fbbb2fu3Ll4PB5+/vOfs3TpUo4cOYJO57ewOnXqFIsXL+bmm2/mN7/5DQaDgaNHjxITc9bY+a677uKdd95h/fr1JCYmcvfdd7NixQqKiopQKPzlkpUrV1JdXc2GDRsAuPXWW1m1ahXvvPMOAF6vl+XLl5OcnMy2bdtoaWnhO9/5DpIk8fjjjwP+Ov1VV13FZZddxp49ezh+/DirV69Gp9Nx9913D/7TEwgEUcft9fHG/ppBnaPF7uphQj8rJwGVQk5pfRtWh5upmXoO1diYk2sc1GuFS0G6nuZ2J8cbQqtPuby+Xm8MzqXC3EGFuYMdQEmNjec+LycpTsNXZmZyZUFqFK9aEE0G1TTV1NRESkoKW7Zs4ZJLLgHgm9/8JiqVihdeeCHkMVarleTkZF544QVuvPFGAGpra8nOzub9999n2bJlHD16lIKCAnbu3Mn8+fMB2LlzJwsXLqS0tJSJEyfywQcfsGLFCqqqqsjI8Lf8r1+/ntWrV9PY2Iher+epp57ivvvuo6GhAY1GA8DDDz/M448/TnV1dVi2WaJpSiAYXj460sD3/7l3UOfQqhSsmJbOoRorXp9EY5uTMck69le2AqDXKolRKmhsczIt08DBCMvXkVKQHs/JxnZcfahmpeljWLUwl0c+7Fk1DAelHH581URuv2zcQC/zguF8fa8Pag7XavX/kZpMJgB8Ph/vvfceEyZMYNmyZaSkpDB//nzefPPNwDFFRUW43W6WLl0aeCwjI4PCwkK2b98OwI4dOzAYDIFkC7BgwQIMBkNQTGFhYSDZAixbtgyn00lRUVEgZsmSJYFk2xVTW1tLeXl5yPfkdDqx2WxBPwKBYPgY7OoWYP2tC3jkG9PZcNcl/H31XGQyv12eVuX/yrM5PDS2+QUuDtdamZQWF1WFqS4yE7TMyTVyoqHvZAswL99ERYt9wK+VGKdh5bwcAD470YT5nBW+4Pwz4IQrSRJr165l8eLFFBYWAtDY2Eh7ezsPP/wwV199NRs3buQrX/kKX/3qV9myZQsA9fX1qNVqjMbgMk5qair19fWBmJSUlB6vmZKSEhSTmhpcOjEajajV6j5jun7vijmXhx56KLBvbDAYyM7OjuhzEQgEA8fa4WbT0YZBnUMplzEtywBARYudG5/eQWuHG4fLS0G6oUe8V4LS+nYyEmKYl29icnr8oF4f/GIbc/OM1LQ62FthwR1GF/RF4xIHlSR/sbyAWI2CV/dUctuL++hwiX3dkcaAE+6aNWs4ePAgr7zySuAxn8+v0PLlL3+ZH//4x8yYMYOf/exnrFixgr/+9a99nk+SpKASb6hybzRiuirovZWT77vvPqxWa+CnqmrkDMwLBF90rA43316Qi0E78LnTWy4Zg0wmQ5IkfvxqMbXWsx3Lfe0i7S6zsLvMjDyMrab+GJccx6nGyJyCFo1Noqlt4LKScRolT285zU9fO0Sb08Pi333CjAc28j/vHhnwOQXRZUAJ94477uDtt9/mk08+ISsrK/B4UlISSqWSgoKCoPjJkycHupTT0tJwuVxYLMHD6Y2NjYHVZ1paGg0NPe9ym5qagmLOXaVaLBbcbnefMY2Nfm3Wc1e+XWg0GvR6fdCPQCAYWiRJ4kcvFvHzNw9h1Kl57UcLidNE3j2cEq9h7VUTAPjX3ir2ndmzBX/T1N6K/kUxVAoZs3ISIn7t7pTU2nB4fMzLN6EKw59Xp1bQ4fJwuHZgW1iFmXouHp/E7jJz0OOtHe6olOgF0SGihCtJEmvWrOH1119n8+bN5OfnBz2vVquZO3duj1Gh48ePk5ubC8Ds2bNRqVRs2rQp8HxdXR0lJSUsWrQIgIULF2K1Wtm9e3cgZteuXVit1qCYkpIS6urqAjEbN25Eo9Ewe/bsQMzWrVuDRoU2btxIRkYGeXl5kbx1gUAwhHx0tJEPSur57EQzj3x4DEuHm+e+O5evzcoiKU7T/wnOcP+XJqNSyHnvYB0/e/1Q4PEYlTwsb1qVQkZZsz3sOd6+cLi81LQ68IRhLzh/TCIGrZrMAe4j3zQ/F6VCHrIy8LVZmQBn5npFmfl8ElHCvf3223nxxRd5+eWXiY+Pp76+nvr6ehwORyDm3nvv5dVXX+WZZ57h5MmTPPHEE7zzzjvcdtttABgMBm6++WbuvvtuPv74Y/bv389//dd/MXXqVK688krAvyK++uqrueWWW9i5cyc7d+7klltuYcWKFUycOBGApUuXUlBQwKpVq9i/fz8ff/wx99xzD7fccktgVbpy5Uo0Gg2rV6+mpKSEN954gwcffJC1a9eG1aEsEAiGnk63lwfePRz0WJo+hp2nWli9KI/d91/Bf364kOVT0+lrakYfo+S66RlIksRTW04GuQZNSTdgdfSfbKZnJ+Dy+KhtHXzCBaixOMhM6D+JLhqbyP5Ky4BLyk63F4AjdcErZJ1awXcvygNALpOhG0DVQBA9Ikq4Tz31FFarlUsvvZT09PTAz6uvvhqI+cpXvsJf//pXfv/73zN16lSeffZZXnvtNRYvXhyI+eMf/8j111/PDTfcwEUXXURsbCzvvPNOYAYX4KWXXmLq1KksXbqUpUuXMm3atKBRI4VCwXvvvUdMTAwXXXQRN9xwA9dffz3r1q0LxBgMBjZt2kR1dTVz5szhtttuY+3ataxdu3ZAH5ZAIIg+/7f1NFVmR9Bjj246jrnDxbVPbGPJuk94eXcli8cn8fL3FzAlw39DbdIFr+ZsnR5u/L8dPL+9PCAo0YVP6t8BKFat4HRjO4owbsbn5hnDbq4KZ9VakKHH0uHmtkvHhnXOLmblJHD0gav5rwW52DrdlJ/T5fzVWVko5HL2VVrCnvEVDB3CvKAfxByuQDB0VJk7uPLRLTg94VniaVUKXrh5Hve9foiWdhd6rZLylo5+j5uVkxC0nxuK+fkmdpWZiY9R0tbZ92q4MENPSa2NbJOWVH0Mx+rbej1mXr6px97qufxyRQH7Ki28d7Cuz7hz2XLvpWQbY7n3Pwf5oMQvC9mdeI2S5dPSkclkPPTVqRGd+4uMMC8QCAQXHL9972jYyRb8ov5TMgxIkoS5w4W5w4VKIcPdzz6pUtF3MS8vMZY95f6kqFLImJ9vCjwnSYDM38PikyQkicD4TpXZQZXZgU6tYE6ukX2VFs6dAApn37Sowky9te8ydvcbgTWXjaPD5SU3Ucd7B+t4bV9oKcw2p4f1e6q4pjCt32sQDD0i4QoEgvNCcVUrGw6HnofvDX2MkhiVnMpuJegpGQaKq1pDxstlMDvX2OcK0xirQiaTBRJlW6eHXf2sSM/F7vKyt8LC+JQ47E5P0CjSkTobyXEamtp735/dU27hxjlZ7KtsRadWMCMngeLKVuzdVqzjU+LIS9Lxxv4aCjL0XDk5Fa9P4k8fHe/z2uJjlKxelBfR+xEMDYNSmhIIBIKBohzAnuKY5DgO19pwec+uiktqWpmXZ0Kt7Pl1NifP1Kc/bmGGHqVcTlmzHbVSTrohBrdX6nNety9ONLZj6XAxL98UaPCSJBiTrOvzuKY2JzOyE/jzN2dw99KJPL1qDuNSg/eI91W20trh5pGvT0elkKNWynnvUB0nepn3zTZpuf2ysbx/58XMH5M4sDckiCoi4QoEgvPCfyJ0BJLL4GfXTOrRievxwe5yMwoZTM8yMDfPiFIuIz5GydHanhrJaYYYtGoFs3ISKKm1BVae+Yk6uvLsYNqLHG4fu8vMTEiNJynOb593qqmdGdmGgAJWKL7/zyLiNEoyErQ02DopSO+5t7i5tJFPjjVyxaSUPle3P7hkDFvvvYx7l00i2xQb9rUXVZhxnLMPLIgeoqQsEAiGHa9PYseploiOuePy8czPN/Hg+0dDPu9w+zhQ7U+w6QYNSXEaDp3TrTwzO4HSehtxGlWPJqpjDX5PXLmMHvuwA6G0vg29Vhlo2Gpu9+/7ZiVoqW51hDzG0uEmQaukpd3Z677sR0caWPPKPq4qSOV0U2jt5Vm5xohGH30+CZfXx+xcU//BggEjVrgCgWDYeWN/TSDBhUN+ko47Lh/HhpJ6Dlb37+xTZ3UGJds0fQxz84zsr2rF4fb1uZ9qjI2eqbvN4WFfZSuzchICylkZCTG9xrd2uNBrVZTWtZESH1rww+nx8f6heu7+14FezzMxNTI9aJ8YVhkWxApXIBAMK51uL49ujMyCbmlBKj6JAVnXJerU2F2ePvdyuzMUmjj7KltJjdeQadRyqNZGnFpBe4jSbVObk3n5iczINtJsd3GoupVPjjWFPGdvq/AYlZycbmVki92FUdf3TYRSIUep6DNEEAXEClcgEAwrL+yoCOri7Y/MBC1fnZXF2n8Vc7rZTn6SrkeDVLxGybx8E7NyEijoJkghl0GaQdPvXG13NEOUeRranByvb2NapoEpmaH3cl/eVUljWydqpZy7rhjP31fP5dEbpkd0EzAhNR55t4a03pKtrdONNxq1c0HYiIQrEAiGDavDzROfnAw7XiaDR2+YzoeH63n3YB2JOjUp8Rpc3WZ3E3VqJqfHs7vMzL7KVtqdZ1eOmQlamtsis7zTaYZuqScBu8rMWB1uskOUltVKOU1tTnacaqHa4kAmk/HVWVncfNFZ3fpVC3L7fI1ZOcHWp71pGzVYOwflvyuIHJFwBQLBsPHUp6ewOtz9xsVplMRplPxoyViUChl/+ug4SjmMS4mjuKo10IUM/q7j3WfKxQXp8VSa/cpTMSo5yfEaGiLUJ9aqhr62Wm1xBM3YdvGHG6Zz978O8K1ndga5/NyzbCITUuO4YU4WV0zu6RXexZWTU/h/1551a9t2opkv/+XzkLHjU+PJS9TxVnENf/nkZK+JWRA9xB6uQCAYFkrrbTz72emwYp0eL2/dvpj4GCWr/rYLn+Tfs2zr9JBuiCFVH8O+CgtTMvRBlnYnGtuZl2fC6fHSYncFzeuGiyrEPG+0KUjXs7u8p7jGzf/YGyjzdi+bx6gUPPy1aczISuD1Puz2Lh6fjEwmo7bVgdcnkZ4Qw6M3zOg1Xi6X8a+9VXx+soV2p4efXj1p4G9K0C9ihSsQCIYcr0/iZ68dwhPmnmFCrBqTTsVXnvw8SCu5awa3qMKM2yf18I91eyV2l5s5UG0lTqPsYWIQDg6Xh9k5CWH52A6Escm6kMkW/J+TRinnx1dOCLj8dDErx4hcLqPeGnqkCGBOnpE6q4NVf9vFp8ebGJscx7iUuD6vpyvBP/XpKd49WBvZmxFEhEi4AoFgyPnH9vJe5RdD8Z2Fubyyuyowu9qd8pYOwpFfDje5n8vh2jaUCnm/+swDQR+j7Fc7+s4rxvPfV44nppfSdqiGs3RDDD+7ZhKT0vT8e281p5rsAcs+gNN9eAF3/5h+/kZJv5rOgoEjEq5AIBhSqswdEY3zGGNVfGdRHqebB97QMz3bwMleJA/PRSbzJ8LudLqHRm1pXEpcvypWf9tWhq2z933u1o7gm5DcxFjevP0ifrhkLAq5jPwkv4zkkW6r/063j5ON7ZSF+Ey77916vD7eOxSZY5EgfETCFQgEQ4YkSdz/xiEcESSwOy4fz4nGdt4bRHkzEnlCSYL7vzQ5IEwBQzMaNDfPyL7KVqosvZeEwe9E9K89Vb0+/4vlBUG/T8nQk6o/2/G8fGo6E1Pj2XayOXDjUJChZ1xKXCAZd0cp96cBg1bF7p9fyc2L83vECKKDSLgCgWDIeH1fDZ+daA47Psuo5fJJydz0zK4ByyuOSdJxvCG81W0XP3v9UNCebYs9ss7mcGgJUR7vjbzE3s0OdGol6YazCXbTkQasHWdXxHK5jDuvGE9jm5P7Xz+Er58Pcnyqf493Vk4COo3oox1KRMIVCARDwjsHarnvjUMRHXPP0on8c0dlRCvic0mMG5g045w8U2AkqKLFzgDMjPqk2tLRf9AZTjW19zqmY4hV8cy35wSu1SeB8pwGrysLUlAr5by+v4a7Xi3u87W6pCxDuS0Joov4hAUCwZBw978OBAlU9EdBup45uUbW76kc8GvGa5QcCkNrORRp+hgmn1GpKsgwRMXAoItEnRpXBE1YD31QSnUfpefCTAO/+/o0ANZeNaHHyrTG4gh89u8crOVgdWuv5/reRfmoFDLyQpSbBdFFJFyBQDAkKCJcIv706on8/M0SOgZhDzc5Q09nBEm+OzOyE2iw+UvJTW2dpOpDmwcMhPiYyEu1zX0YLABcOy2dyyelcOslY3o8t/X4Wf1lSYJfvnW4z9Ky2ysxP184BQ01IuEKBIIhIRKJxIvGJdLU5mTL8dBC/eGgVso5GYED0bms23iMmjO2eS3trqj6wsYNIOEWZPT0w+2OTCbjsW/NRKUI/hp3e32s2xjsk3ugqpWj9aFnkqvOlLozE8L3zRUMDJFwBQLBkDC1F4H+UHx7QS4PvHtkUK83LdOAuaN/2cjeqOs2fzoxLR5bBIYH/REzgK7nUDPI5xIXoslJKZehVfd8vf8UhfbXnZKh59sLc/mfd4/022AlGBwi4QoEgiFhdq6x/yBgfEocW443Yev0MCE1jrHJOnJMsUzPNjAv38ScPGOvDT3dq9Z1URRssDujO4cbaXkd4NU+RoP6QiaTcd81wRKNchncODe71/hfriggKU4dkUexIHJED7hAIBgSwlmhAXx5RgaPbT6JPkZJh9NL9ZmybmU39cOUM16y+ytbg46dnpVApbkDk07NiTCFLvrDoFX2KNMOlv7UpbqjlMtYOiV1UKvNr8zM5K9bTgXGo4yxaial9V6iVink/OmbMwf8eoLwECtcgUAQdSRJ4pNjjf3GJcdpyE2MxeXxkW7QBpLtuTS2Odl/xsS9MFPP7Fwj8/NNJMSqaLG7opZs5TIwaPtf6WlVCvKTdExIjWNGtgFlHytYlULG4drwO6c9PokGm5PbLhsb8vnaVgf/t/VUvx3g107LCPx3i93FxsP1YV+DYGgQCVcgEESdE43tVLT0P3e6cn4O/9pbzYzshLDKmQ1tTkpqbBRVWNhVZh5UR3MoZucaA/Z+ACadilk5CczPNzEzO4FxKToSYlU43F7Kmu0cb2inuMpKYaah17ndzARtxLrMRRUWtvUiGJKRoOXjo419etnKZDJuu2xckHHBnz8+EdE1CKKPKCkLBIKo82FJ/6upzAQtK+dl89KuSrKNPc3Yw2GgBgWhUMqhqc1JrFpBZoKWWLWCA9VWzPbWfo8trmplaqaeU012OlxeCtL1OD1ekuI02F0Da74qqbWxdEpayOde/cHCfo+Xy+BLU9N5fPMJJAnKmu14fdKA9pMF0UEkXIFAEHU2HmnoN+Zb87Ipb+lAp1FQXDUwsYr2PkT+I8Xjg1i1kg5Xx4BK1IdqbOQn6UjQqjhWb6PD7SM+RjUgi0C1Qk62URvxcd2RyWSsvWoCTW2dvLK7CoVMhk+SUPRrnyAYKkRJWSAQRJXaVgeHavpOoAlaFd9emMszn50mNT6GgaxTJ6TGcSxCzeS+mJmTwPFBdumWNdvZX9VKh9u/v6oeYPNVekIM35gTuqs4UpZMSEGjlPOnb86IejOYIDLEClcgEESVN/bX9BuzYno6jW0uPjraGNLBJhx684uNlDS9BpNO06MD+nwy2MTo8fqwdLhJjtdwdWEan/30MlLiB1a2F0QPcbsjEAiihs8n8VZx3wk3M0HLhJR43j9UR7xGGdKjtT8yEmI4OEDN5C5kQFaCFq8ER+oiL/uGQ6dnYE1dNsfgSuVKhZzk+LPSlCLZjgxEwhUIBFFj28lm4jTKgAnAuaTqNbi9PgyxKqotHeQkDkxOMCthcPubAHPzjdRYHTS1Rd+KrwvNAB14GtucdAyw2UowchEJVyAQRAWfT+J3G0pRyGUcrWtjdm4CKd1WWVq1AoVcRmObk+R4DTaHZ0D+qzq1gpJ+9oj7I92g4WC1lV4c8KKG2R6+B+65DGTlLxjZiIQrEAiiwruH6jhce7Y0W1TRisXuYl6eifgYJVMzDdS2+uUXO5xeYkPo/YZDYaYh0JQ0UEw6DZ2DPEd/zM83capp4Enz9CCOFYxMIkq4Dz30EHPnziU+Pp6UlBSuv/56jh071mv8D37wA2QyGX/605+CHnc6ndxxxx0kJSWh0+m47rrrqK4OFta2WCysWrUKg8GAwWBg1apVtLa2BsVUVlZy7bXXotPpSEpK4s4778TlCr6jPHToEEuWLEGr1ZKZmckDDzzQq7GzQCAYGC6Pjz9s9H8XWLvtP7p9ErvL/RqN3T3SK8wd6LWqAckXNrU50Q0wWQNMyzIE3RgMBd9dlMfeCsugzvHPHeXiu+oLRkQJd8uWLdx+++3s3LmTTZs24fF4WLp0KXZ7zzuxN998k127dpGRkdHjubvuuos33niD9evXs23bNtrb21mxYgVe79kGg5UrV1JcXMyGDRvYsGEDxcXFrFq1KvC81+tl+fLl2O12tm3bxvr163nttde4++67AzE2m42rrrqKjIwM9uzZw+OPP866det49NFHI3nbAoGgH17dU0lFSwf6mNBNUG2dniDZxtNN7Rhj1RRVWjBoIysrJ8drsA9CYUobpe7mvvBKEt5BinLsKbfw2r7+O74Fo4eI/tI3bNgQ9Ptzzz1HSkoKRUVFXHLJJYHHa2pqWLNmDR9++CHLly8POsZqtfK3v/2NF154gSuvvBKAF198kezsbD766COWLVvG0aNH2bBhAzt37mT+/PkAPPPMMyxcuJBjx44xceJENm7cyJEjR6iqqgok9T/84Q+sXr2a3/72t+j1el566SU6Ozt5/vnn0Wg0FBYWcvz4cR599FHWrl2LTCYGwAWCaPDOgTrAb2u3pzz0yi7doKXK3GVM0MH1MzP588cQq1ZgdYTXIDQt08CuMnOPx2UymJNrxOcDu8uNPkYdWFl3kRCr6lPzOJr8a08V+hjloC3+3jlQy9dnZ0XpqgTnm0Ht4Vqt/sYFk8kUeMzn87Fq1SruvfdepkyZ0uOYoqIi3G43S5cuDTyWkZFBYWEh27dvB2DHjh0YDIZAsgVYsGABBoMhKKawsDBoBb1s2TKcTidFRUWBmCVLlqDRaIJiamtrKS8vD/menE4nNpst6EcgEPROp9vL4VorM7INvSbbpDg1B6tbA7+b7S5MOjWT0/XUWcPvEu5t9jYjQcvRWhtFlRZK69s51dTO3DwjY5J0gR+Px4dSLsfhjq7+cig6PT4unZgy6PP84JIxUbgawUhhwAlXkiTWrl3L4sWLKSwsDDz+u9/9DqVSyZ133hnyuPr6etRqNUZjsFdmamoq9fX1gZiUlJ5/rCkpKUExqampQc8bjUbUanWfMV2/d8Wcy0MPPRTYNzYYDGRnR0ftRSD4ovK3bWWkGWI43UdXbV6iLqhJqaXdhdcnhSVekRKvYWZOAjmmWCwdrqC94C5qLA7SDGdnTVvsLvaUWzjdbA/8xMUoqbd1DukYUHfyBjjy1B25XIbPJ7GhpI6vPPl50E2LYPQx4IS7Zs0aDh48yCuvvBJ4rKioiD//+c88//zzEZdrJUkKOibU8dGI6WpC6O367rvvPqxWa+CnqmpgJtACwYVAc7uTZz87jc3hwdZLWTgvMZaiyuCVr9nuIsMQQ39zOSaditzEWIqrWqk0dyA7Y593Lmn6GE7209WbmeBPgJ4InXsGSnWrg4vGJQ7qHM9/Xs6yP23lhy/uY39lK//eW93/QYIRy4AS7h133MHbb7/NJ598QlbW2f2Fzz77jMbGRnJyclAqlSiVSioqKrj77rvJy8sDIC0tDZfLhcUS/D9gY2NjYPWZlpZGQ0NP8fOmpqagmHNXqRaLBbfb3WdMY6Pfo/PclW8XGo0GvV4f9CMQCELz2McnyEvU0dTe+6oxRqXokVddXh8KmYzOPjxd0w0xxKqU7Cm3kJeoY06ukbrWTtITYoJUlABS9Br605iwOlwUpOvJSBge1aXiqlb+a37uoM6x4XB9kJHC9lOhLfsEo4OIEq4kSaxZs4bXX3+dzZs3k5+fH/T8qlWrOHjwIMXFxYGfjIwM7r33Xj788EMAZs+ejUqlYtOmTYHj6urqKCkpYdGiRQAsXLgQq9XK7t27AzG7du3CarUGxZSUlFBXVxeI2bhxIxqNhtmzZwditm7dGjQqtHHjRjIyMgI3AAKBIHI63V6KKixsP9nSqy+rXqtkXp6J0vrQhgAyuYxGW+hEnZsYi1wmC3Q2lzXb2Vthoc3p4XCtjaY2J+NSdMzLN5IS7xexSNX3nUhPNtk5UmfDFKfGoFVF8G4HxukmO3PzTP0HRsCpJjvWjug5JAmGl4i6lG+//XZefvll3nrrLeLj4wOrR4PBgFarJTExkcTE4BKKSqUiLS2NiRMnBmJvvvlm7r77bhITEzGZTNxzzz1MnTo10LU8efJkrr76am655RaefvppAG699VZWrFgROM/SpUspKChg1apVPPLII5jNZu655x5uueWWwKp05cqV/OY3v2H16tXcf//9nDhxggcffJBf/epXokNZIBgg1g43+6os/Pa9o9TbHLQ7Qzchpeu1PTqFu9Pb/4J5ibHUWBy4+xmrOdloB/zJPt2goeaMqEZ/7Dpt5oEvT+Gefx8MK34w9OeaNBCON7ZFPZELhoeIVrhPPfUUVquVSy+9lPT09MDPq6++GtGL/vGPf+T666/nhhtu4KKLLiI2NpZ33nkHheJsA8VLL73E1KlTWbp0KUuXLmXatGm88MILgecVCgXvvfceMTExXHTRRdxwww1cf/31rFu3LhBjMBjYtGkT1dXVzJkzh9tuu421a9eydu3aiK5XIBD4kSSJelsnP3/9EPEaZe/J1qDhWB9WdzkmLW6PD2W3DqiukR2NUtFvsj2XSDqdO1xeqswdXN2LuXu0WD4tHZc3+mpWP3/jEO1OobM8GpFJQsqkT2w2GwaDAavVKvZzBQL89nuv76vmsxO97yfOyzexO8S8bBdpeg3v3HExK5/ZGdijTDdo0Meo+0zU0UImg//58hQ+P9nCByWhJxYGQ3yMkr2/uJK7/3WAdw/W9X9AhOQmxpIcp+H2y8Zx6cRkUbGLkPP1vS60lAUCQdiY7S5++95RWjv6FuW39CPan22KRSYjYF4wPiWOOqtzWJIt+Jujf/HmYaZmGvjZNZNI1Wv6PygCbrl4DBqlgvJe9rcHSlezWEVLB3srLHz3+T3c+PRO2jrFvu5oQCRcgUAQNr/fUEpzuxOtuvf2j6wEbVBnbY/njVr2VbaiVspJjPOP+Bh1PUd9hoN1G49RVGHhka9P56//NYuLxyeFddy0LAPqXkzip2cZuOPycQDkmAY/i9sdSYJVC852PqsVcpZPSx+0jKRgeBAJVyAQhEVbp5u3imvJSIhhf2XvwvzdBShCYYpV4/VJKGUyrB1ukuM1HB6C5qJw8Emw6UgD3/77btZtPM71MzL57VcK+z3uWH0by6el843ZWaycn4Oim2RkQYYhUOK1DsBIXi6DMUm6kF66ze1OFHIZ18/wK+zdddV4vrMoj4TY83PDIoiMyM0oBQLBBclft5zC4fYyIS4uYLN3LpPT4/t0yZmSoedgjZX4GCUalQKVQkabw93nPO5wcbKxnfvfOETRL67kk9JGPjra2Gus0+Pjjf1+Y4HMBC2/XD6Zf+6s4HSTnaUFZ2f8y5s7wnrt3MRYVkxLZ1aOkVk5Row6/01JeYudQ9VWPjnWyFvFtQCcamrnrivHs2hcEjfMEUp4owmRcAUCQb+UN9t5ZmsZk9PjOVAdejWaEKvqVzax44zLjzFWjdnuwu7yjohk24XT4+PBD0p5+GvTuPpPW2lu799AvqbVwT92VHDfNZP427YylkxIBvwd3X11KctlMClNz73LJoZsfFLIZYxNjmNschzXz8xkTp6JX799mM9ONOP1Sbz0/fm9nFkwUhEJVyAQ9Mv/vHsEl9eHsxfT9nSDhsyE2H49YJPi1FgdLmpbHcQo5ZxoaO+3o3m4+bS0kf/9ciFvrVnM0VobVZYOHG4vK6Zm8HFpA79550iPYwoy9Fw8Phm9VoX8THlZJpPxrbnZPLb5ZI/451bPZf4YE7F97IWfy6oFuWQbtdz20j62n2rhic0nueWSMWHpUQtGBmIPVyAQ9MknpY18XNrI3DxjSIOC2blGmtpcPfSSQ7Gn3EJ+Yhwen8SBaivjU+No7/SENCQ4X9RaO9lTbiYzQcuVBal896J8brt0HDmJsVwxKbQkbEG6Hq1awYIxwcI/Ny3I7WEJqFMruHRickTJtotLJ6bw6q0LmZGdwKEaK1f8YQu/31BKbTevYcHIRSRcgUDQK06PlwfePYIxVsXBEKVktUJGRYsdj0/qz4cAAJVcRo3Vnxx2nG7mS1PTOVJnY5j8BMLmzTP7peeS0sv4UE0vCS9VH8OXpqYHPTYuJW5Qc7NTswy8eftF/N+35/Dcd+ey6UgDF//+E+5/4xDmfsaxBOcXUVIWCAS98vdt5ZQ125mZk8D+ytYez8/MMYY0hO8iM0GLx+sjMU6DT/Kh16oD5eN/bK/gzdsX8cruSipawmsu6g19jJJxKXFkGmPJTNCSFKemvMWO1yfR1unhw8P1uCPI6p+daAr5uFIuI1atCOxFd/33y7sqmZtn5Csze5rFf29xPm8fOJvAx6XER/juemdCajxvrbmIp7ec5tU9Vby8q5LMBC2/uW4KVxaEXo0Lzh8i4QoEgpA02jp5fPMJAOS9rMjK+vDAVcoBGZg7XDScaabqPj7T7vTw1KenefArU7np2V2DutbvXzyGO68Y3+vzHx9t4OZ/7A37fFdMCm0er1TIeeybM3n/UB0ymYyfXj2RxzafwKBVUdkSepU7IzuBN25bxImGdk40tjEzxxgybqDEqpX8+KoJLJ+WzspndlLT6uDDw/Ui4Y5AhLRjPwhpR8GFyk//c5BX91YxPjWO0032kOIK6YYY6qyhR4QmpcVzorG9T1EGlULG5z+9nN9tOMZr+wbm9fq378zhonFJ/TYPdb2f/tAo5Wy597J+54m78PmkQKPU+abd6eFYvY3pWQkoexHmEAhpR4FAMII4WmfjX0X+5KSQyUImTY1SFiithqK0vo1JaX2XT91eiX8XVbPm8nG9ugf1xXXTM7hicmog2bb04ct7//LJpOo1xKjkXDQuke9elMfyaelMydCj6ta1tfaqCWEnW2DEJFuAOI2S2bkmkWxHKKKkLBAIgpAkiQffPxpogpLJYEyyjtNNweXj6VnGPu33JqXFc7jW1u/rbTzSwO2XjeOVW+azq8xMjimWNoeHX719uM/jjLEq1pyRUASoMndw6bpP+eXyyay+KL9HvEGr4j8/XER8jLKHMtOzn53mf987yriUOG5e3PNYgSAaiIQrEAiC+PR4U5AT0NE6v6HArJwEqi0OGs/sxx6utTI/30RZsz3wWHc8Yer7HqxupbXDxZqX9weEJr40NY0fLRnLU1tO9XrcPcsmMiH17Aq6sa0Tr0/qszkquxdt4+9elM+cPBOFGfrzujqsszpIitOgEivULyTiX1UgEATweH389r2jIZ/bV+lPjPPyTehjlBRmGthVZsbS4WJ+volY9dk91Fk5CZzsw8CgO5IEO061BKk6vX+onjf2V3Pl5NDNSzfMyeJbc3OCHssx6Xj11gV8/+LQK9S+2lUUchkzss/vvuemIw0sfXQrNz69Q8zVfkERCVcgEARYv6eqz0Tp8krsLjPjkySKq/xCF26vxK4yMzFKOXNyjejUCqoskSWMUPug9TYnHx1tZNmUNPQxZ4txX5+dxUNfndbjmOR4DfPHJAbNuJrtLn7ynwPc/vI+Vj+3B/sIM24vb7bj80kcb2hjzcv7aHN62FfZypce+4zNpQ3n+/IEUUaUlAUCAeB3A/rjpuNhxbY7g5ulrp2eQYYhhue2lzMnN4HtpyKTapyWZej1ua3Hm9j208s4XGvD4/Nx2cSUXoUj/r23ijRDDBeP9+sZ/+adwwHRf4BvPbOT51bPJTEuuv63feHzSUgEj0QBdLq9bDhcz3Ofl9He6cHZTVO6tcPN957fyw8uGcOROhtrLhvH/DGJeH1S4DwjqTtaEB4i4QoEAgCe+vQULQNUKnrnQC2Pf2smSyYksfV4c/8HdCNWreDdA3W9Pu9we3n/UB2rFub1e65vdHPPsXa4ef9Q8HkPVlv58l8+56szM7loXBIzc4yoQ9jgRYvHPz7Bs9vKuO3Ssfxgydig517aVcnDH5T2efzTW08DsP1UC+NT4ihrtnPx+GRS9Bq2Hm8i2xjLjJwEvjYrk7HJfStYuTw+fvyvYvaWm9HHqLhxbjY3L84flOqVIDJEwhUIBNS0OvjbtrKwYuUyuKYwnU+PNWLvNhb0zGenUSnkQSu1/jBoVfz8S5N5ZOOxPuO2HG8OK+F251hDW8gGqmqLg8c2n+SxzSeJj1HyqxUFfH121oASj63TTYxSEZS0t59sZtPRBialxfPEJydxenwUhTB1WLUgl79uOdWvwxKA1ydRWu9vXvvo6NlSc7XFwY7TLTz16SmyTVqeumk2hZmhqwUKuYzTTXYabE4abE7+972j+CSJWy8ZGzJeEH1EwhUIBDyyoTTsROmT4L1DPVek1g43FebwJBpjVHLuuHw8qxfl8fq+6j6TTnK8hp9cPTGs83YntRfd4+60dXq49z8H2XSkgQe/OpWkCEvNq57dRavDzd1LJ5Kmj+HDw/Uhb1z2VbYiSVJQUlfKZSFN5gdKldnB/kpLnwn3pvk5/OLNksBjj28+yXcvyhdd0cOESLgCwQVOcVVrr2L9kaDXhvd1YtKpef1Hi8hL0mHrdPPXLad7jc0wxPDSLQvIT9JFfD25iTrGJus41dS7/GQXG480YLa7WH/rgog6ldudHipaOrjzlf19xjW3O6m2OILGkv7+eRnVETaX9Ue6Qdvn81aHO+j3tk4PG0rquXZ6RlSvQxAacVsjEFzg/Pmj8Bql+mJyejyHavoXuQC48/Jx5CXpsNhd3PTMrl6ddnITY/nXDxf2SLYdLg81rQ58Ycz5/nz5ZL4yMzOs69pbYeGPEX4W3w0hsNEbO063BP77reIa/reX8avBcKyhLaAKJklS4LP6994qfvhCES/vquxxzK/fPozHG/42gGDgiBWuQHABc7qpnU+OhXbGiYTejOnPRatS8I052UiSxPVPft6nS9DfvjOHLGOwUEVzu5PvPb+Hg9VWxibr2HDXJagUcj44VMcVk1N7NEBdPimVqZkJvFlcE5Z94JOfnuKisUksGpcU1vuJj1EikxHWuf/80QnGpcTh8Uq8sKMirPNHysu7KrE7PfgkeHlXBbbO/segWuwuDlRbmZ0bXVMFQU9EwhUILmD+sb08ovhYtYLMBC15STpa2p3sq2xlbq6Rspb+y7YAK6alo9MoeXlX35Z8C8aYgmzsPF4fr+6t4qlPTwXKsKea7PzoxSJuXjyGH720j+nZCTzxrZk91KSS4zUsyE8MWmH2hiTByab2sBNuZoI2rGQL/sa0rz65PbzgAVLT6uDJT3tX5+qNJzaf4LnvzhuCKxJ0RyRcgeACxepw8++i8B16VAoZe35+JTqN/2vj5V2VlNbZKGuxY3O4mZtnpLSujbZexCVMOjW/vLaA2lYHD77fdzn1W/OCVaT+590j/CPEqvCjo41IEqTpYzhQ1cryxz7jnmUTuXFuNhrlWeWrl2+ZT5XZwb5KC0fqbByptVFc1Up7t2uVyeDaaRlcNjG0ulV3Ot1ejtTZ+PU7fes9j3Q0SjlfmprOJRPCu8EQDA6RcAWCC5QnNp/o0+3nXMYkxQWSLfjLqVOzEgIG9HvKLRhjVczNM7K/sjVISzkhVsX/u7YAfYyKZz8rC0p052KMVbFsSlrgd7PdxYsh9h67+Li0MfDftk4Pv3rrMDaHmzWXn/XHlclk5CTGkpMYy/Vn9nTdXh/H6tvQqhUkaFUYtCqUCjkdrt6vzdrhZn+Vhb98cpI95T1HfUYbbq+Pb83LYV6+6XxfygWBaJoSCC5Aiqtaw5677UIm88+dgv+L+m/byjhQ3RoUY+lws6fcQlKcmpnZCQBkGbV88N8X8+UZ/kSn6kcd6RtzsgN2e26vj+e3l/fpqXsu+hglVxem9RunUsgpzDQwNjmOxDgNSoWcmlYHix7eTHFVa8hjYjUKnB5fwNBhtOOT4L/X78cyQMETQWSIhCsQXGA4PV5+8p8DRJDDAL+/7bI/buVkYzsv76pEKZfR2UuzVL3Nyf6qViamxvH4t2YGjavccskYHvjyFC6ZkIz6nBGcMcm6gOVetaWDZX/cymMfnwjr+pLjNWSbtDy9ak7Q/m+4SJLEr94sobXDzcdHQ+sYqxRylk1J4/sX52PQqiJ+jZFInbWTe/9zsE9zh1A4PeFXRwR+RElZILjAePazMo43hOfkcy511k5W/W0XSXFqSsLwuj3W0B7oNG7rdNPW6SEjQcu3F+bx7YV5WDvc3P/mId47WMfE1Hhev21RoGz9t21lnG4Orxnra7Oy+NGlY6iyOHh66ynanR6uKkjt97jPTzYjk8GisUl8UFLPx6WNXFOYFliN98ZdV05g0dgkdpxqoam9k1ON9rCaskYqHx1t4B/by0P6CJ+Lx+vjvtcPseN0C//43jzGJscNwxV+MRAJVyC4QGhud2K2u3jms96FJkKRZdQGCTTUWTtJitOE3Z27u8zM8mnpbC5tJFGnISPh7GrXEKviiW/NZGlBKjOzjYFk+/SWUzz3eXlY54/TKLnzinFc8+fPAnvSDTYnM7ITSI7vWznqH9vL2Xikga/OymTr8SZ0agW///o04mP6X73OyzcF9j7fKq4Z1QkX4MH3S/2ewL0oVXXxzsHaQLPd157azrPfnsOcPLEHHA6ipCwQXCB0ur18dLSB1g53/8HdiFEpgmQS/SIX1rCP/+SYv6npysmpLB7fsxtWJpPx5RmZ5CTG4vb6ePiDUh7qR9S/O+1ODztPtwQ1gB2ts/Ffz+6itSN4b1KSJP733SN8+++7uejhzWw84i8dv76vhuZ2F3aXl1v+uZcjYazeuzMQJayRhsvr47fvHcHVR6lYkiSe6jZ21NrhZuUzu/hPBN3uFzIi4QoEFwBur48399cEfVn2x0+vnoRKIeNkYzsNNr/WsQxwRNDZDLDvjHB/9w7nF3aUs+Lxz3hx59lRn5pWBwse/Ji/bolsjlSlkPHrt4/0eLzF7uRHL+4LUqSSJHhldyVbjzf1qnC187SZ657YxiMfltLpDu+9puljGJs8epJuVoI2sDpXyWXMzjEyLiWOHafNfW43HKi29nje5fVxz78P8PAHpWGpf13IiIQrEFwAtLS72HnaTFsYykMA6YYYrp+Z0aOxak6ekfI+BCtCMSE1uIHJ2uHmdxuOUVJj45dvlfDPHeVsLm3gv1/ZPyB7QLdXwnFOYhyfGofd6WViWjzdTYDkchmT0/X9ntPjk/jLJ6f40mOfsbe8f2/fFH0Md105IeJrH04SdWrm5ZvIMcVS3epgd5mZOblG4rVKiiotnGz0J9LeOrQBNh2p7/W5v245xa0v7O1RVRCcJaKE+9BDDzF37lzi4+NJSUnh+uuv59ixs7Zabrebn/70p0ydOhWdTkdGRgbf/va3qa0NFkZ3Op3ccccdJCUlodPpuO6666iuDi5JWCwWVq1ahcFgwGAwsGrVKlpbW4NiKisrufbaa9HpdCQlJXHnnXficgX/Yx86dIglS5ag1WrJzMzkgQceiLgbTyAY7ZxobOsxwtMXcpmMogoLj31zJrmJ/qan+BjlgJqtrjyneWnzsYbAHK4kwa/eOsz3nt/L3hAWdgMlQavCcaaEfm4n9fjU8Jt8TjfZ+cbTO3jo/aP9rt5a2vu32RtuYpRyZuf6V68tdhe7y8xUdnN02lthwWwP3mJ4eVdlr+9Vq1KEfLyLj4428qU/f8ax+i/G2FS0iSjhbtmyhdtvv52dO3eyadMmPB4PS5cuxW73dxJ2dHSwb98+fvnLX7Jv3z5ef/11jh8/znXXXRd0nrvuuos33niD9evXs23bNtrb21mxYgVe79m71JUrV1JcXMyGDRvYsGEDxcXFrFq1KvC81+tl+fLl2O12tm3bxvr163nttde4++67AzE2m42rrrqKjIwM9uzZw+OPP866det49NFHB/RhCQSjDafHS3FVKzaHO+zVLfjLuz/9z0FONLbRYOtkfEocE1Pje7jN9IdcBpdPClZuemVXVUTnGAh7yi3MyzdSbXHw76Lg1xsf4ciQJPmN4K99YhslvexdS5LEa/tqBny90SbdEMO8fBM6jZKiirOr13A4Umfjw8OhV7LXTE3v9/haaydff2p7r6NVFzIyaRDLvaamJlJSUtiyZQuXXHJJyJg9e/Ywb948KioqyMnJwWq1kpyczAsvvMCNN94IQG1tLdnZ2bz//vssW7aMo0ePUlBQwM6dO5k/fz4AO3fuZOHChZSWljJx4kQ++OADVqxYQVVVFRkZfmup9evXs3r1ahobG9Hr9Tz11FPcd999NDQ0oNH4mz4efvhhHn/8caqrq8MynLbZbBgMBqxWK3p9/6UogWAksf1UM0fr2thXYea9Q72XA/sjx+TvVI50i+4bs7O4bkYGP3pxH898ew7z801M+MUHQSpUQ8mUjHgKMwzMy0+kwtxBa4eL0vo2dpf1XyYOhUIu4/bLxrHmsnE9jBIuW/cpZWGOMQ0FsSo5Y5LjUMhlHKgOv6ktFGOSdLx358Vo1T1XtF/+y+cc6KPs3J21V01gzWXjkPcjdjLcnK/v9UHt4Vqt/n9Uk6n3lnCr1YpMJiMhIQGAoqIi3G43S5cuDcRkZGRQWFjI9u1+Ye8dO3ZgMBgCyRZgwYIFGAyGoJjCwsJAsgVYtmwZTqeToqKiQMySJUsCybYrpra2lvLy8pDX63Q6sdlsQT8CwWjEfsar9dNjjVjsbgrSIxeD6MIYq4442V43PYOfXD2Re/59gHanhymZeuRyGTcvzifb1Ldva7Q4XNvGq3ureXVvFY99fIJ/7qgYcLIF8PokHvv4RA8N5beKa85rsgX/vrPT4xt0sgU43Wznf97r2Yi26UgD1ebw9/Af3XSctf8qxuUR9n8wiIQrSRJr165l8eLFFBYWhozp7OzkZz/7GStXrgzcRdTX16NWqzEag62gUlNTqa+vD8SkpPQUEE9JSQmKSU0N3hsyGo2o1eo+Y7p+74o5l4ceeiiwb2wwGMjOzu7zcxAIRiI+n4Td6eFonY095WZKG9o4UtfG1Ex9YE82XCalxUX8JV6Qrufhr07l7n8fpMHmJDcxFp3a36V835cm8/qPLorofIPFGWa3cbicK6qhlJ///lOXV6K1w93vPmu4vLyrkg0ldUGPbS5tiLix7c3iWr75fztoaht5e9zDzYD/StasWcPBgwd55ZVXQj7vdrv55je/ic/n48knn+z3fJIkBZV4Q5V7oxHTVUHvrZx83333YbVaAz9VVUO/3yQQRBuH28vGIw3YHG4SdRosZzpHD9XYqGjpYHausV9RiC7c3siWtkq5jMe+NZOPShvZeryJVL2Gp26ajUIuw+310e708K+9w/v/lSXC2WOAuXlG5ocQ9b94fBJLxicHPVZhPr+r2y6a2p3otdHTM/rJfw4GjU+FIwgSin2Vrax4/LNBVRe+CAwo4d5xxx28/fbbfPLJJ2RlZfV43u12c8MNN1BWVsamTZuCauRpaWm4XC4sluCOxMbGxsDqMy0tjYaGnhvuTU1NQTHnrlItFgtut7vPmMZG/xD+uSvfLjQaDXq9PuhHIBhtHK61YdCqGJcSR3K8uocqVFGFBWuHi/n5JsYk60jTx4Q8z6ycBE41RZZMvrc4n9zEWP6w8RhKuYzXb7uIggw9DpeXKx/dwqWPfMojHx7r/0RRpMrcEfHK3uHycrjWxuychKDHPzvRzE9eO8jzn5cF9IS/uyifRJ06Wpc7KCIVNukLW6eHu9bvx+P1l4RTe/k7CYcGm5P/enYXL++qvGAnRSJKuJIksWbNGl5//XU2b95Mfn5P3c2uZHvixAk++ugjEhMTg56fPXs2KpWKTZs2BR6rq6ujpKSERYsWAbBw4UKsViu7d+8OxOzatQur1RoUU1JSQl3d2ZLHxo0b0Wg0zJ49OxCzdevWoFGhjRs3kpGRQV5eXiRvXSAYNbR2uGhpd5KfpOOjow0UV4UuB7u8ErvKzJxustPS7mRevol4jZLp2QnkJ+lQyWXUWTsjeu0so5Y1l49j/W6/wfw35mSRmaDloyMNzP7fTVS0dNB8HsZnJMAUG1lCLKm1YdSpqGntZH6+CcWZotiVk1N55OvTWDw+iYfeL6XK3IFWreCb887/9pNeq8QZ5f3SPeUWbv7HXooqzEGKYwPB5fVx/xuHuPc/B8MWFfkiEVGX8m233cbLL7/MW2+9xcSJEwOPGwwGtFotHo+Hr33ta+zbt4933303aBVpMplQq/1/8D/60Y949913ef755zGZTNxzzz20tLRQVFSEQuHff7jmmmuora3l6aefBuDWW28lNzeXd955B/CPBc2YMYPU1FQeeeQRzGYzq1ev5vrrr+fxxx8H/A1bEydO5PLLL+f+++/nxIkTrF69ml/96ldB40N9IbqUBaONdw/WUmNxsLfcTKXZwbGG8Gci42OUTM9K4FhDG/mJOnaHIfrQnRdunodOo+Rb/7cTrVrBx2uXYOlwcf1ftvfpgTsYYlVyOj2+Hk1dSXFqxiTp6HT7kMn94h/dNaHDZVyKjoqWDsalxPG7r02jIF2P8ozLUafby1OfnuLHV02guKqV6//yOQBqpZwVU9OJj1FyuM7G3mHyzh2brIu4IhEJmQnaXhW6ImVsso7ffW3aedFhPl/f6xEl3N72PZ977jlWr15NeXl5yFUvwCeffMKll14K+Jup7r33Xl5++WUcDgdXXHEFTz75ZFCDktls5s477+Ttt98G4LrrruOJJ54IdDuDX/jitttuY/PmzWi1WlauXMm6deuCupIPHTrE7bffzu7duzEajfzwhz/kV7/6VVgjQSASrmB0caKhjZ1lZsztLjYfa+BAL6vb/jDFqpmYFseeckvYIzw/uGQM31mUx4rHt2G2u3j0hul8ZWYmVzy6hdNDmATm5hlpd3qCPGqVcsg2xVLWHJkqVm/MyzOyu9zCxeOTeP6781B0G3NxeXyBEaGPjzbQ2OZkfr6J3753lI9LG/narEzeOVg3LJ262SYtbZ2eqJaVhxK1Us7Tq2Zz2cSeTbJDyahIuBciIuEKRgten0RpvY3PTzbz4s4KbFH44h2fEkesWtFvl/KktHjeuv0ibn95Hx8dbWT51HSeWDmTpnYn83778aCuoS8yEmJoavM7A+3ptoqcn29iV5QbdMalxHGysZ17l03k9svG9Rp3qqmd7z2/h4ozEpg6tYKZOQlsOzk8bkI5ptggNamRjlIu43+vL+Sb83KG7TVH5RyuQCAYOfxzRzmNbU7+vacap8cXlVWOUafmQLWVcck6JqWFnuNVymX84YbpfHKsiY+ONpKq1/DbrxQik8l48pPIjAgiJT5GhdsrUdWtVDw+NY49EZbCw0F1ZhP30U3Hez1/UYWZrz65PZBsAewuLzOyjSHjh4LRNn7j8Un87PVDPPDOEbxfcPMDkXAFgi8IV05O5af/OYgxTh1w9xkM6QYN+yv9q8aTTXZK69sozNCTd06372++PIUcUyy/ftsvBrHuG9NJiFWz8XA9z28vH/R19MbcPGNAs7fe2olG6U+ISrksYpGOcDha18bcPCNen8St/9xLeTehC0mS2HSkgf96dndI+cs0w+CajSJhcno807L69rSNFiqFDLUiOmnk75+X8d3n9wRG2L6IiIQrEHwBqG11sPVEEzEqecAOb7Ck6rU9ZnBLam2Un5njTYpTs3pRHjfNz+UPG49Tb+tk9aI8Lh6fTG2rg3v/czAq19EbxVWtGLRn50KnZBi4aFxi0F5u1F+zspV5eSYMWhXXPrGN3753hIc/KOWaP3/GLf/c28O1CCA3MZbm9uFLIvsqW6Nuk3fFpNB7rD4JHvnGNOJjBjf7mxynYV6+iYoWO199cjsnG7+Y5gci4QoEoxyXx8eHh+t57KMTxKqVUdEpHp8a16dNW1GFhTR9DD+7eiIHqlr5545yUvUafnL1RDxeH/+9fn/ERgcRX2NKsJnCvspW3N6hbUxy+yR2l5upaOlAr1XxzGdl/HXLKUr7cMdZNDZp2LqUu4hW01SiTs2rty7ooRvdhdcn4fL4+NGlYwf8GmOSdLS7POwu83+uZc12lv5xK49/fOIL568rEq5AMMppsHWy/VQLmUZtn1/8kdBfD79aIef/XTcFmVzGT/5zEJ8EP7tmErFqJY99fCKogWmoCJkEpOERyZfwm7iHw6UTkthXOXxjQfExygH5CvfGz98s4YOS3o0v7v3PQZ77vHxA51bJZbQ7PThcwZUBnwR/2HSce/59ICC68UVAJFyBYBTj9Ulkm2JRKWRBjUMDRSn3m8z35Xsrk8HT357N3DwTT2w+ybGGNr53UT5fmZnF9lPNPP7JyUFfRzhUW0J04g6jKc2ppnb6M8HJTYxFkmR0uIZH5EEmk9HW6QlZ2h4ILXZXWNZ+A23Umpyhp7GPY1/fX8Mdr+z/wpgfiIQrEIxirA43v99QSku7KyrdqbNyTP2WP79UmM5lE1MoqjDz5KenuHxSCj9fPpmWdid3rS9GJZeREDswzd1wmZQWH3Jf1OEaGnGNUOSYYtFp+t67/NqsLD4qHT5fWLViZNng9UWcRsGR2v7d2D4oqefWF/aOuu7rUIiEKxCMYsx2Jw63d1B7dnqtkhxTLPPyjByt7/sLMD5GyU+unojPJ3HbS/sozNDz52/OQCGX8b/vHSU/Scf07AQ8Xol5+aZ+V4C9kRCrYl6+ifEpcWhVCpLjNUzLMjAvz8iktPheV4wlNTZSwjRlGCxur4+2zr4TvEIu4/V91czNG56xoPPpWhRJt3J+kg6VQh52v8Gnx5pY8sgnfH6yeaCXNyKInq2EQCAYdiwdbqotHRwfRFfnxNR49pRbwhJL+M11U8hN1NHU5uTi8cn8z5cL0aoVbD/VzBv7a4Jid5eZyTZqMcSqKKnpmchT4jVkm2JRdKm+ycDp8WJzuKlo6QhylnG4vWGtcCQgL0nXZ5kyWmjV/X99Ntg68Un+JrOUeA0OtxeVQobZPjQNZWEK6A0Jbl94Zd/4GCVenxSxg1OHy8st/9zL374zl4VjE/s/YAQiEq5AMIrRKOW0drh7uAGFS7ohhv2VrWHFLp+WzldmZgKQHK9h3TemB57rrWmmyuKgyuJgepaBKrMD85kZy4L0eE40tg9JYhyuJpvaMDSFj5/RsfZJ/nJ3m9PLnFwjZvvQNFENlV51OIT7NzgpLX7ATXUdLi/7Ki2jNuGKkrJAMIrZcWpwcoEpeg3KMPb90vQx/Pb6wpAa5FXmjn6v40C1FYfHy7w8I+NT4qho6YjYZzdcaqLQPBar6v+rMSOMLuWjdW0Bh502p78MXmuNjvh/d0yxfivGoTQuiAbJcZqwb/B6wzNEfzfDgUi4AsEopabVQY4pFrlMFpAdjJQDVVZ0aiVJcb1b1ynkMh69wa8edS62Tjf/t/U0C8b07/jicHnZXW7hVFM79iHs2m1oczIty0C6oW/v1pR4DVMy4pmXZ2JOnpEpGXoMWhUqhYy4PozW5TKYlmnAHsZq0upwc8vFYwLCELEqObWtkVkensvK+TlcOz0j6DFzhxvTCPHj7YsxybpBz4mb7aO3eUqUlAWCUUp7pwedRolGKR/UajHdEENJH92iN87NZtG4pJDPvbGvhhd2VkT0esOhZXCw2opKLmNevokOpwelQo5GKcfp8WF3emiwddLY5uy1pN3Y5mRCalzQeFRhpp4YlYKTje0crAnfhcnu9GLQqnB7fKToYyhvGZyxgMfrC6kmtrvM3OOaRxrRkG2sGeQNy/lEJFyBYJTS6fbi8vgGPaPY14xouiGG+66Z1OvzIWdhRwhun8TuMjMzcxIGVMY0aFVkJWjJNGqptjhCNn6FQ3GVhXanh6lZhqgIgvxrb3Wvz3WXuowElUJGjFJBfrKOGosjqsIZXRRm6jkcxhhQf4zmFa4oKQsEo5RDNVaON7YNasU4J9fI6ebe9/1+evUk4vsor9oc569JJ1xUAxTXP1bfRnWrg11l5kGZrru8/lV1OCXowSKPsE1ZJoN5+Sa8PolOt5eD1VYyEvouxQ+EaZkGyps7Btzc153hEhEZCkTCFQhGKcunpqNWyJEY2LeYViXnVFPv5ccMQwzXnbNX2J3fbyjl1b1VYb9e4nnaY7Q53MzONZJjiu0/uPtx/czYhsvnJ1twe6UhN4WPj1FyIgxVKJ1awdRMA3PzjJhi1ewuM+OT/BUBgCO1tgGvlEMxL9/EwRpr1DqoR7ObkEi4AsEo5XCtDZlMxtE6G/PyTb0KzPfG1KyEPmchF4xJRN6HcsXHRxsjer0rJqeclznR0vo2iios/TZRDSUzshLwSTAhNY55+f03mEVKrEpOSrwGcy+l4DiNkjm5/sawTreXQzVW9pRbQpaOvWeuMxrMzTMGzVNHg5Z216g1NRB7uALBKCU+RsmjG49hd3nZXWbGFKtifHYCR+ts/a7OTDo1B/pwAwL69FR99rPTnOy2OpbL+m6GyjDEcLDaGpWS4kCpszqYn2/iSK2NCWlxeLz+2sDB6vAboAaKWiWj3tZJvY0h6SaemKZn/zn/nrFqBeNT4kAGR2ps7I3AtrG3xB0Jc/OMQ2Ji4fFJmDtcJMUNn8dwtBAJVyAYhThcXqosHeQm6jhS529EMXe42VVmRquSMz/fRGl9W68WeWOSdP1+AYeaue2irdOD1ycxJlnHdxbmkm2MpcXu4sVdlSET+UXjknjnYG34b3AIqDQ7qLY4kAFFFa0AKGQwPcvAgSFOuopun6X3nDuTialxxKoVnGhop/2c/UmFzL/i7I/Wbv/OXTdKR+tsA35fp5rsZJu0VJkHtnc9P9/EriivbLsz1DaMQ4VIuALBKESjlHPFpFTePVAXSLhdONw+dpWZiVMrmJ9v4kB1K53us19QeYmxFPVjF6dWyHF6em9OKWu2s+aycVwxOYUH3j3C/spWdGoF35iTze2XjeWDQ/XUWztJiFXR6fby76LeO2uHk+65TqWQMT4lLuJSfG9MSI0jRqkIjAytnJ/D1EwDJxvb2XSkHoVchtcn0dbpZlyyjpNNdubmGTlQbcXl8SGX+f9tkuI0yOUyJEmiuKoVtUKOTq1AKZejUsrQqZWolXJUcjnIziRzGSTFqalo6Yjaij3DEHnClctgdq6RoghW05GSEKsalatbEAlXIBiV2F0eiiosNLX3PiLR7vKyq8yMKVbN1Ewd+ypb8fokdBplv6Xdf948j+w+moxuvWQMHx1t4GtPbQ8kMbvLy/Pby/nnjnKumZrOw1+bSkVLB+s2HhvIWxxyJqfpOVpvG9AMs0IGBRl6tCoFyGS0d7o5UtfGlAw9AJdPSgkocz30wVEqzQ6S4tQ0t7vwSf6GrPn5JvZVWgKv75OgvKWjx5yu2+vt4Rc7HJwYwDzv7NyhKSN35weXjB1w5/n5RiRcgWAU4vVJaFUKOsPwPTV3uDCXu8hM0JJm0ATKqX1x978OcNmkZL6/eAx5Sbqg51weH58ea+RPH50IeaxPgvcO1jElQ09bp4f7vzSZ4w1t/Oqtw2G9t+GipNYacX935pm53GP1bRwKMZd7vKHNP9OqkgdK8un6GOQygsr7fYlujBTMHa7AqjwcxqXoOBSBIMhAyDHFcsvF+UP6GkOJSLgCwSjEbHfR1O4MK+F2UdPqwOvzi+efamrvs0N5Vq6Ra6dl9Ei24Jdz/MeOvtWldGoFHU4vT316iv8UVfPeHYv54FA9O04PTvu5LzITtKToNXh9Enan54ycogwkkMv940Enu2kN+6TwG3umZxlweyWO1Nn6nMl1eyWmZMSz9XgzjbZOUvRnVbyGSjt6qMhIiMFid+Pw9f83lm6IoaXdFbR1MRT87JpJKEfp6hZEwhUIRh0VLXaON7QxM8eIWqmI6NiEWDV7KyzEqRXMzTMGyszn0un2MiMnIeQ59pab+7XKs7u8PPd5GQBNbU7WvLKf2y4dO2QJVyEDpULWp6JUtkmLQasi3RCDPkbJqSY7e8otzMs39RhdmZKhx+Hy4pMkrA53RM1HGqWCdqeHxzaf4H+vn0qNxYFPgvEpcWHNyY4EjLEqbA4PjjBu6PKTdHS4PBHb7UVKQbqeawrThvQ1hprRe6sgEFyg5CbqWDgmiSO1Nk40ROaDG6fxCxq0u7zsKbeQpo9hRnZCj7jNpY297vOOSQ5vRrO7QcHuMjNFFRYKM/URXW+4zM4zUdGPRrEMf1m3tL6N3eUW7C4P8/KNSN3e6NhkHZPS4jlca+N0s53ylo6IE8mhGiuZCVreLq7F6fGSbfK7CsVqIrs5Op9YOtxMTI3vNy7bpMXS4aLBNvTl8TWXj+uzc340IBKuQDAKufc/B/h/b5VE7Lxy7vdVTauD4qpWxibrAg0/4N8jvvWFopCr3/EpcQHLuUh44pOTrF6Y3+d870DIT9Kxv5+ua6CHRGWn28fuMgu2Tjdzco3Myk1Awi+UMRjcXolYtX9/ff3uKu5dNomrClI5UGVlenZ03/tQUlRpYVYvVQ6AXJMWu9M75ApaAJPT9Vw9ZXSvbkEkXIFg1OFwebG7PAMSmO9tLvdUk53DtTYKM/UBCcStx5uoNPdcNQ50lSFJ8Ot3DnPLxWO4e+n4qIx2yGUgSVJY+6Pn3pzEaRRMydBT19rJ3goLB6taMWhVUVHDOtHYzvjUeH7zzmEa2zr5v1Wz+dLUNMx2F32Id404jtbZQkpizsw20NzuiopARjj85OqJfaqejRZEwhUIRhkxKjkvfX8Bd14xPqLjNEpZyATanZIaG1XmDmbnGhmTpMMYG1pTN1E3sGTZ7vRwxyv7+fNHJ8lPiuVrszL5xfLJYfnphmJunilsu7tai4MZ2QbGp8Rh0qmwu7wcrj2ryuXxwf7KVmbnGgd0LedyuNbGnDwTv3n7CNtONqNTK6kyO5iVE53zDwcOt49Ot5f8pLNJd3qWAblMNqSext2ZnmXg0gnJw/JaQ41omhIIRhnbTjbz+r4adkXYgDQxTR+WKIIEFFVYmJQWH9J0HsDjG1w3qscnsafccqZDuIa//tcsiiosEXXyzotQzajN6aG4qv/3XzlIv9ruFFe1Eq9RsupvuwOPHWtow6RTD9vqcLA0tjmhza/XnGHUEqOUUzrEnruZCVqS4tTMyTOxcn7OqN+77UIkXIFgFCFJEsWVrVxdmEaqPoa/bjkV9rExESoqjUvpvTnqyZtm8ffPy3lrf01UVjqHaqysnJfT77hRF6E6i6NFY5uTdEMMddbBG527PD7GZsXRYvdf65emplFjcSBJfnnCtig5Eg0HMSoldqeXk43RF7bITNCydEoqC8ckMivXOGqVpPpDlJQFglGEJMGVBam4vT7eORCZNnFfqlSh6Ksb+ZEPj2GMVbFoXFJE5+yNN/fX8p1FeX7lpn4YymTbRWaCNmrnqm71r5hn5xp58qbZPPPtOdSeSebj+7ipGWmYO1xkGaP3uehjlFw7PYOXb5nPZz+5jP937RSWTkn7wiZbECtcgWBUIZfLmJyuZ3K6ng8O1YdtjJ4cp6GsObJS6djknqIXAFuON/Hh4QY+PNwQ0fn6oqbVQWl9G3+8cQa/31DKimnpvLCzImgkJz5GyaS0+CFPtgB7KyxRm5utbe1kaqaeQzVWOt1eUvQxGLRKTjU5GZ8yur6CZQy+tHvx+CRuvWQMC8ckjmoRi4Ewuv61BYILHK9PYtORBpraOjlQ3Rr2cXlJsRGvcMeGWOF2ur3c//qhiM4TLn/bVsZrP1qESadm1d924fQE7xNnJmiHXKe3Ox0uLxqlvMd1DARJ8peXG21OchJjA1rAp5pGhxBGF5VmO8ZY1YBELkw6Nb/72jSuKkgdgisbHUR0e/HQQw8xd+5c4uPjSUlJ4frrr+fYsWBhckmS+PWvf01GRgZarZZLL72Uw4eDNVSdTid33HEHSUlJ6HQ6rrvuOqqrg91ELBYLq1atwmAwYDAYWLVqFa2trUExlZWVXHvtteh0OpKSkrjzzjtxuYIbEQ4dOsSSJUvQarVkZmbywAMPBA26CwSjCUmSMMaqUCvlNEYgNtAR4T6rSiFjfAgT8qe3nA57VR0pRRUWiqtaOVZvC5nkSuvbKEgfGuGMUNS0OqI2M1xSayPXFMt/9lUjSVLgM7Q6PFEt0w419TYnGQMstyfq1Bd0soUIE+6WLVu4/fbb2blzJ5s2bcLj8bB06VLs9rP6pL///e959NFHeeKJJ9izZw9paWlcddVVtLWdHSa/6667eOONN1i/fj3btm2jvb2dFStW4PWe/VJYuXIlxcXFbNiwgQ0bNlBcXMyqVasCz3u9XpYvX47dbmfbtm2sX7+e1157jbvvvjsQY7PZuOqqq8jIyGDPnj08/vjjrFu3jkcffXRAH5ZAcL5xuL08u62MN/bX4ArTEzRGKY94JVWQrkdzjmxkTauDp7acjOg8kfLd53ZTbek9oSuHeRZzT7klSBBkMHQ1uf1h4/GgZqmU+JG/Z6lSyLr9t5w4TeTF0RON7ZxsHJyoyGhHJg1iudfU1ERKSgpbtmzhkksuQZIkMjIyuOuuu/jpT38K+Fezqamp/O53v+MHP/gBVquV5ORkXnjhBW688UYAamtryc7O5v3332fZsmUcPXqUgoICdu7cyfz58wHYuXMnCxcupLS0lIkTJ/LBBx+wYsUKqqqqyMjIAGD9+vWsXr2axsZG9Ho9Tz31FPfddx8NDQ1oNP4/6ocffpjHH3+c6urqsFrNbTYbBoMBq9WKXj98d9cCQSi2n2pm5TO7+o2blZNAh8tLaX0bs3MSKOpDYzgUd14+jrVLJwZ+9/kkvv333Ww72RzpJYdNql6D1wd/uWkmNz69M/B4QqyK8Slx1LZ2Dtnqui/iNApUCvmgtYJVChljknQcO2ekZjiawAaKWiFjRo6RanMHsRolSrlsUEpc/+/aAr570fl3+zlf3+uD2rG2Wv0zbSaTf2i9rKyM+vp6li5dGojRaDQsWbKE7du3A1BUVITb7Q6KycjIoLCwMBCzY8cODAZDINkCLFiwAIPBEBRTWFgYSLYAy5Ytw+l0UlRUFIhZsmRJINl2xdTW1lJeXh7yPTmdTmw2W9CPQDBSOBDGHKlOreB0s51jDW1MzTTQMAAbuCUTU4J+33C4fkiT7Y1zsnG4vDS3O3lm62nm5ZuYk2tkfEocNoebPeWW85JsAdqdXjITtINWoHJ7JWpbO3s0o7k9PqZlGZiRbWBGdgKJutCzz8NBcpwmIOc4OycBvVbF7jIztdZOTDrVoGUv90V44/dFY8AJV5Ik1q5dy+LFiyksLASgvr4egNTU4Dp9ampq4Ln6+nrUajVGo7HPmJSU4P/hAVJSUoJizn0do9GIWq3uM6br966Yc3nooYcC+8YGg4Hs7Ox+PgmBYPgoruq/aaggQ09rhxtJ8s+39lWiDUVCrKqHocGHh0P//xINsk1aKsz2gOJTu9PD7jIzeyssnGhsJ0K56CGhpNbGnCgoULU5PTS2ORmXcjbp7q9qpdLcgUIuDxgsDDep8Rrm5Zkwd7gormoly6ilqLKV5vazPTGHa2wYtKGVx8IlHM3rLzIDTrhr1qzh4MGDvPLKKz2eO7dUK0lSv+Xbc2NCxUcjpquC3tv13HfffVit1sBPVVVVn9ctEAwnR85o2/a2CpqaqR90J+/lk1JQnLNXuuPU0NjqTc00YNCq2Hn6bElVHoXRk6FgT3nfYv7h0tbpIUGrYnyKjvn5JsYm60jXx1BUYWF/VWvEhhQDJT5GybRMA1Mz9TS2O9ldbsbrk/BJhLxJs7u8TAjRSBcJ1RYHzRF2y3+RGFDCveOOO3j77bf55JNPyMrKCjyeluZ3czh39djY2BhYWaalpeFyubBYLH3GNDT0nPFramoKijn3dSwWC263u8+YxsZGoOcqvAuNRoNerw/6EQhGAo1tnfz8SwX87Ttz0IdYaSTFqaNSdr2mMD3o96Y2p1/eL0qMTdYxL8+/DZUQq8JxTgd1aYONMSGM70cC596IREpeYiwTU+M5XNfGiUY7u8rMnGqy93AyGiqS4/wr2Ymp8bg8Pg7WWDlUY+vVivFciiosZA6yq/pAVeugjh/NRJRwJUlizZo1vP7662zevJn8/ODN7/z8fNLS0ti0aVPgMZfLxZYtW1i0aBEAs2fPRqVSBcXU1dVRUlISiFm4cCFWq5Xdu8/qj+7atQur1RoUU1JSQl1dXSBm48aNaDQaZs+eHYjZunVr0KjQxo0bycjIIC8vL5K3LhCcd/780Ql++GIRV/1xK2XN9h7Pm+0uchMHl6gWjDFxxaTg7Zz/FFX3Eh0Zs3MSyE+K5VSTnd3lZnJNsdRbOznVFPxezHY3eu0XTyJgXp6JaouDYw1tPW4yDtdamZ9vIikuevu3Jp2ahFgV+Un+G5wxSTqazqxkjzW0DWi+2CdBuj5mUNdVfAEn3Ij+qm+//XZefvll3nrrLeLj4wOrR4PBgFarRSaTcdddd/Hggw8yfvx4xo8fz4MPPkhsbCwrV64MxN58883cfffdJCYmYjKZuOeee5g6dSpXXnklAJMnT+bqq6/mlltu4emnnwbg1ltvZcWKFUyc6O+cXLp0KQUFBaxatYpHHnkEs9nMPffcwy233BJYla5cuZLf/OY3rF69mvvvv58TJ07w4IMP8qtf/eoLI4YtuHDYXNrY5/M+CWosDrJNWqrMZ1e6MSo5WcZYTLFqDFolB6qtIVesM7IT+N3XpvWwQTtWP7jGwdk5fp/Z8paOIEvBVEMMCpmMmlZHjznhsuYO9FolNsfI0hoeiNLSuBQdGqWC3eW9dyLbXV52lZmZl2+iuX1wHctpeg2ZxliKq1rx+iRaO9whb9AGSn+OU/2x/wJunIoo4T711FMAXHrppUGPP/fcc6xevRqAn/zkJzgcDm677TYsFgvz589n48aNxMfHB+L/+Mc/olQqueGGG3A4HFxxxRU8//zzKBRn5/5eeukl7rzzzkA383XXXccTTzwReF6hUPDee+9x2223cdFFF6HValm5ciXr1q0LxBgMBjZt2sTtt9/OnDlzMBqNrF27lrVr10bytgWC847T4w1LTL+xzYkxVkVeYiyp+hgyEmLITdQxN8/EzJwE6qydvHOglsO1NrQqBXmJsYxNiWNKhp5xKfFB5+p0e4lRKQYtb6iQy9gdYl+5xuLA6nCxYlo6/9p7dhUdp1aQbdRSMcgv9qHAG8EU5bgUHVqVkkM1/XeWd3GwupVJafFhdwPPyzNS1tJBok4FyIhRyTlYbaU+AlGUSPE3fcVxcoB/FztPt9Du9Axolne0M6g53AsBMYcrGAlIksTkX22g0x1eGbAwQ88vVxQwf0zigF5v1+kW7ly/n4e+OpUnNp8c1DhHX3Omk9LiyUiIwdbpQZKgwdYZaNiZH6H93nAwLcsQ0uIwUacmRa/B7fURq1Yil8kGXDqdk2tkb0X/jW/dP1eVQhaRteFgmZdn6nPF3h/7f3kVxvM4/nS+vtcvvFsMgWAUYnW4w0q2cpm/y/gvN83qoRQVLl6fhFeScLi8/Pcrxdx+2TjKm+2YByj84OlDEau0vq3X1dyuMjOzchI4UmujMwp6xtGge+lbq5IzJcNAh8vDicb2oHL5YOitS1ku8yuAldSeKfF3CxvOZAtwcpAa0OoIrSK/KFyY71ogGGXoNEqMsao+rcvUCjk3zMnm2ukZvZqbn5v8JEmirNnO9lPN/PmjEzhcXnySRHlzBwUZetqcHmI1CvJ7cQ4Kh8EoNO2rbMWoU5M/QrqWy5ramZGdwOwcI1OzDOytsHCkri2qCa+4qjVoTtcUq2Z+vgljrJqSWlvgs6g9T0Ig4G/QG0wnuVJxYfbQiBWuQDAKUCnk7LjvCr7+1+0h5xiT4tR8/+IxTE7XMzvXGLQ/5nB5+cWbJWQZtewuM6NSymnrdNNg7cTqcAcZyO8ub+HHV05g5fwcOlwedp42Y7a70A9ibKXJ1sn8fH8JciAbWHXWTjRKObNyEs67UpFXOttlOyktvu/gQaBTn/33yzJqg0rrJp0aY6yqR3f3cDI710hRGGXv3rB2uEnRD6wCM5oRCVcgGCXEqBR8Y3Y2JTXB7lsmnZrrpmdyVUEqeYm6oFnRzaUNfFjSwGv7whvt2X6qhVULXEiSxLiUODITtPzpoxNkJmj73FNVyv2rcOuZruKMhBiyjbHsq7TQfqYDd6C2bgBOj499la3MyTOydxgt+mJVcgyxakw6FZ1uH/XWzsANSmm9XzozkqaocDlQbSUzQUtNqwNbZ/BnNphEFy16q6CES4PNScogx4tGIyLhCgSjiFULcvnoaAOfnfDrGmcmaPnrf80my6ilw+0NSrblzXZ+/fZhmtrC/3KUJHhpVwULxpg4UGVlTp4RXZ2C4w3t1LQ6QjZAJcSqSInX4PZKuDw+sox+791dZWZyTLH4JIlqiwOjTj1oAwD5MI3zTUiNo6Klgw63jw5rZ1CHeFfj1OwcI0Ppnz4uJY5MYwwgo7xlZHVslzXbyU2MpWKA19Xpicwu8ouCSLgCwXlg4+F6lk5Ji/g4uVzGz5dPxtrhRqtWUJhhoM3pwaBV0V3p1+uTOFJnI92gpdIc2V5fvbWT+984RJzG321b3y3Z7C4zB610ZYBW5U/I4FeRKm+xB/Y0K80dTM3Uk2PSUtY8+KSxp9zMjKwEiqtbB32uvkjQqjjeS6OWVqVgbLKOoiHWBXa4PewuO/+r2d5I08cMOOFG4uX8RUIkXIHgPLCn3IxWreDi8ckRHzspLXiMIZSgfG2rg+0nmwdUfrR0uHn/kF/URi6jh3lAcVUrSjl4fP5G2e77sqH2FUvr2nBHSR9YkqDdOfTi/n31QA3XqFJZU0fIz3+k0O4cuCiJU6xwBQLBcPHz5QVDdm6L3ckfNh5DKZcPSAi/e1NWqMOT4zVkG7V0enw0WDup7UeQY+YZlx2n24vbK9HW6aYqQgej7jTYnEzN1HOoZuisM0eCPEFTu5OC9HiO1I1M03atauBNTxkJg9NjHq2IhCsQfMHYU24hx6Tj1b2VQ3L+aotfijHcxplz93xjVPKwxR1C0eb0UFJrGzJhDFOs+rx3Q3cRpxkeU4OB4BvETUnmBZpwxRyuQPAForiqle2nWkg3aGgYwn0ys91FzADFCzrdPvZW+K3uNMqBNUFJkr+0OyktnjhNdMdLzB0upmeNDFW5kppWtOqROT5TWmdDp1ZQmKFnXp6J3MTYsI+9UBOuWOEKBCMcp8fLztNmDFoV07MMfRpvGGKUtNhdvL6/dsiva2xKHJLkFzEIJXfYH/sqW8lM0KJWygcsru/1SbQ7o7MfGK9RMCFVj1fyoZCPjLVIh9vH3DzjoD2Oh4IOt4+EWFVA+Wp8SvheuReqd4xIuALBCOd/3z3KCzsrABiTrOPuqyayfFp6yNhtJ5tJilOz5VjfzkLR4PCZL1rTIDRxa1odqJXyAScVj08KzKsOhklp8dTbOgOdxzmm8FdrQ021ZeQ2T7WeGfNKjddEJPfo9UkXpNrUyLiNEwgEISmuauXl3Wf3Yk832bn95X088mFpj1i314elw01rhxtbp4fh+j4z213EDaLs6fL42FNuYUa2IeLycFmzHUuHi5nZCQN+/bl5Rk40tgeSB/jHmaJdqh4odVYn0wfx/oaD3MTYiFTEBtLM90VAJFyBYIjx+iQu+f0nPLP1NL4IvmgkSeK37x3BG+KYbSdbejy241QLaqWMjUfqiVHKiYtRMSfXyOT0oZEgNMaqmJQWz5xcI6ooiNEXV1nRx6gi1k3ucHnpcIdXVp6ScXZvNjlOw+wc/8o61Gecm3h+9Zt1agVz84xMSovH3O5EPkIXhOmGGPZH6Iw0mIar0YxIuALBEKOQy/jS1HR+/2FpRKXP9Xuqei2zus8RZSitt/FBST11rZ3YnV4KMvRYHW72Vlg4WteGSadmepZhUO/jXJLjNZTWt7G3wjJoBakuaq2dVFs6mJ9visjq/VRje58r0nRDDNkmLU/eNIupmQZm5xppdbj6FK/ormc8VExMDd73VMqhMFPPrJwEnGdW/qX1bVSYHcwYoavcdENMxOYN3V2XLiTEHq5AMAz87JpJ3Dg3m+ww9wZ3nm7hl2+WhHxuTJKOv9w0K/B7ndXBa0XVKOUy/rHDv9frPCchm+2uQe21hiI+Zmi+PtxeiV1lZgrS9VSa7WE1RXl8EhNS43sd5/nuRXnYnV62HG8i26Tlg5L6fkugkZjND4SufetJafFIkoQhVs2x+jZKepkvrrM6kMkYkAHEUJGXGDugEaq2Tk+fzldfVETCFQiGiXBLpV6fxL3/OdBjn0urUrDm8nF8/+L8IK9bn09iT5mZ4jOdwinxGo7UBX9pZ5u0nGwcnIfpuchkMianx6NTK/H6fOyviq6I/5E6G2n6GNINWk6Ece3uPnx3D1RZyUuM5Z0DtRRVWMJKWvZBKCmFQ3O7f465vMUeltdxndVJQbq+x7/t+USnGVgKqbM6Rozl4nAiEq5AMMIoa7ZTdY7+8dw8I3/65syQ84u/euswlZYO5uWbaO90o1LIaWwLnsFN12t7nHOwdHftGYw3al/U2zqJ0+iYl2/sV1fY6vD02s373qG6iDuhK8xDYxhQkB6PUiEPdHmHk2y7UI2gzt7B2CXGDTBRj3bEHq5AMMLISIjhonGJgd+/NiuLF78/v0ey7ZIfnJaVgNnuZneZmSN1bRwIMRNr6wxPFercPcVwqbP1Le84GHQxSnaXWZiZndCn2EaluYM5uabA712dy8ZYFVMy9BGPHTlcXrKM0RFoyDJqmZdvwqRTc6SujYPV1pCNWv1xoNpKtun8i0bI8CuODZQNJfXRu5hRxIV5myEQjGBi1Upe+v4CqswdyOWykIn275+XU9li59fXTenhlxqKE4125uYZOdVk71WScUqGnuMNbczKSUClkFFc1YrTE15ScLi8pMZraGgLX91KBqTqNSTFx1Db6sDa4SLNEENCrJqKZjvtZxprusrn+6tayUuMxeOVqO6l+exQrZWkODU5Jv/e4rx8Eycb2zg6wDJscpxmwIklTqOg3ellUlocpfXtg0pQ3UnTx0S9WhEpE9PiKa0fuMbzcNksjjTEClcgGKFkm2JDlpB/+VYJm47U85OrJ7Fu4zH+tq2s33N5fRJ7yi10uDzMyzOSGt+zYcXrk3B7JfZVttLY5kJC5pftyzeRF4ZsX2oEhuJjk3Uo5DLqbU5Kaqz+1bpMRk1rJ4drbUxIOzvK1H2EpLylA0uHi6mZoaUXHS4v2UZtwLVod5kZs909YNEItVJORkLf76sgXU/imYa0OLWC2TkJFGbqcXslNEoZpfXtJMVFr2GttL4tyPf4fKAfZMPcSO24HmpEwhUIRgkt7U6e/ew0/9pbTZ21kz9/fIK/fHIqonN0un3sLrewYEwis3ONmGL9iWBialzQiiU5To3L46Ok1sbuMjPlLR0YY1XMzElgbp6RrBA3AvFaJZkJ2rD2GRvbnEFNYZYOd1CJVdnN2b3znBlbu8tLSa2NeXlny8exqrPxtk4PVkd0xpR2lZlp7+y7eSrujJxmfpKODreXospWSmpsOD0+pmclMCVDH2iQigZtnR4KIpitXr0oL6oJH6CmdXBbCK4+Gty+yIiSskAwCmhpd3LZuk+xnfnyr2jp4P+2nh7QuYyxKsrNdg5UWdEo5czLMwWVpeWy0F+olg43lm5NMinxGnJMsbi9PlQKOQerrbSduT6NUk58jJL4GCWxaiUxKsXZVZnkH7nZX2npdeXp6jbWFCrhSRLsLjcz58w8bbvTiwF/12y01362Tg8ZhhiyjFqa2p04PT6cbh/xMUoMsapAcg+lB717iDSQlRGscJdMSKa03kZze3SclWbnGgfks9ydkTTaNJyIhCsQjAJKam2BZDtYripI5V97qwH/vO7u8uAv4sJMQ1hmBI1tzh7d0F04PT6c7a4Br+ya2zqZnmVApZCDzF9KDsXeCgvjUnT4fFKv1xIN9FoV+yoteHwEZmFb7C7oKfg1LByqsZIQqwqSo+yNogpL1PaPFTL/SM9giVFdmMXVC/NdCwSjjBMN0TMhd/XTCHWw2srE1HgKe9knHQ4UcjkHqq3srbAEjR+F4mSjHYfLM6T7gqX1beSY/KNPI2F15vGF787j9vpYe9WEsPbh+2NWrpHaQZaT5TKYGmXVs9GCWOEKBKOAaVkJAz42PkbJddMzcHp8GGNVAeehvjh2JsGnGzSkG7TDbsgeqQdsm9PLycbo3ZSEwqAdWWbwLk94+6Bur4Td5aVyEHPF41J0GGJUvVYaImHx+GRS4sNvsPsiIRKuQDAKmJdv4pIJyWw93hTRcXIZXDYxhZd2VfYfHII6qxO3Vxp2ScHYAbgPtTu9pMRrsHS4yDJqUSsUgRuHaKCOgkFDNDnZ2E58jDKwb94bKqWM14qqB9ypPT/fxK6y6Oz/Alw/IyNq5xptjKy/IIFA0CtP3TSLH1wyJqJjvjEnm41HBicyYLa7hn11N9CxF7nML3VZ1txBe5SlGaN9vsFid3nDKis/veU0xRG6+XTH0hG9DmuACalD4141GhAJVyAYJeg0Su770mSe/facsIwDDFoVSFJE0oGh8El+o/FcUyzz8k1RHzGJJvU2J13GNTWtjqg250Sr8SiaDIeJ+/GGdqZm6pmWGZ1918QR/Pcz1IiEKxCMcNqdHj4pbQz8fmVBKm+vWczEc1YK1xSmBf1+7bR0Nh5piNp1VJg72F1mJlalYFJaPPPzTRSk64dE3zdaSkTTshKi5gdsdbiZk2tkdq4xKueLBu4wlcAGy6EaW8T76r1hjBUJVyAQjFDiNEqe3XY6SMwhP0nHn745IzCP+d2L8lg25WzC1aoUqJVyxg9B+a6hrZNjDW3sKjNzpM6GTCZjXEocUzL0zM41Mi/fxNy8wSWlaBmU7y4zExtFX9u9FRZKaqLrijQYhlNxKhr/InEa/0z2hYpomhIIRgEXj0/m85PNfGlqeuCx3MRYNEo5U1Ljue+ayfxh0zEWj0vi67OzmJ1rpMPlZeM/9jA2WReQOowGTo9Emj6G+jOGBS6Pr4f1n0YpZ2Z2AkqFDLlMhk+S8Hh9OD0+zHYX9ba+Z2YjNTTvi5b26M7nOj0+shK0veo5f1GRonATlBA7sjq9hxuRcAWCUcBlE1P4+7ayoIT7/qF6FHIZT3xrJmqlnBlZCfzs6km8sb+GW18o4pIJSdRbOxkb5rxmJCTGqQMJNxROj4/9vTTqzMk19plw49QKmqKYJKvMHcSo5IPey+5Osl4zIhLusYY2Es6oXWmUcuI0SjQqBeZ2F5IkYdSpUCvkJMSqUSnkyOX+mx+vz/9jc7hp6/SgUclJitMQo1Lg80m0Oz2Y7a4gMZF66+AdoUbaaNVwE3FJeevWrVx77bVkZGQgk8l48803g55vb29nzZo1ZGVlodVqmTx5Mk899VRQjNPp5I477iApKQmdTsd1111HdXV1UIzFYmHVqlUYDAYMBgOrVq2itbU1KKayspJrr70WnU5HUlISd955Jy5XcEfdoUOHWLJkCVqtlszMTB544IGo3KkJBMPJhNQ4TjS2IUkSkiSx63QLz352mj/cMINsk1/QYF6+X1u43enhZGMbT285jccn0TEE3bUDGdvpQgI0ytCl0Nk5CahVcmqi2KA0LSshKNmm6WMG/cU/Unxp2zo9tDncaJX+G4rmdhc1FgcOt5fp2QnUWZ1UmB0BEZHdZWb2llvYX9nKwWor5S0dtNhd1LZ2crDa6n++wkJpfRuNbU6UchnJcRryEmOpisK/SVoEBhdfRCJOuHa7nenTp/PEE0+EfP7HP/4xGzZs4MUXX+To0aP8+Mc/5o477uCtt94KxNx111288cYbrF+/nm3bttHe3s6KFSvwes+KlK9cuZLi4mI2bNjAhg0bKC4uZtWqVYHnvV4vy5cvx263s23bNtavX89rr73G3XffHYix2WxcddVVZGRksGfPHh5//HHWrVvHo48+GunbFgjOKzKZjCUTUnjg3SMs/eNW1u+p4rdfKeSqgtRAzMYjDfz2vaN8a14OL9w8H/AnhoHOX/bFYJqaiiosOD0SMSo5iTq/ld6SCcnkJ8WikMsx26NjPNBF7ZmVqFalYH6+ieb2TiYM0Pe3C5tjcDcx4apEhSI/Scf8fBO5Z3xxvRJ0uH0o5aDt1pXticI/vMcn0dTujIrgBcC+SkvUjCVGIzJpEMs9mUzGG2+8wfXXXx94rLCwkBtvvJFf/vKXgcdmz57Nl770Jf7nf/4Hq9VKcnIyL7zwAjfeeCMAtbW1ZGdn8/7777Ns2TKOHj1KQUEBO3fuZP58/xfHzp07WbhwIaWlpUycOJEPPviAFStWUFVVRUaGf5B6/fr1rF69msbGRvR6PU899RT33XcfDQ0NaDR+O7KHH36Yxx9/nOrqamRhfGnYbDYMBgNWqxW9/vxJ3QkEkiSxr9JCtjGWlG4rBUmSkMlkdLq9rPvwGMVVrUxIi+flM2IXM3MSOFJrDdvbtj9kwNx8I7vLoiPMPysnIaBkpZDLmJQWz+HagfnX9sbMnATKm+1YOtwYtEompsUP+PpVChmT0vQcGmDz1MTUeI41tJGoU5Np1KJVKXB7fTjcXprbXP2W06dlGjhYY2VevondZwQp5uUZKam10eHyEqtWkJGgpanNOSKT27afXkaWcfAyk4PhfH2vR71LefHixbz99tvU1NQgSRKffPIJx48fZ9myZQAUFRXhdrtZunRp4JiMjAwKCwvZvn07ADt27MBgMASSLcCCBQswGAxBMYWFhYFkC7Bs2TKcTidFRUWBmCVLlgSSbVdMbW0t5eXlIa/f6XRis9mCfgSCkYBMJmN2riko2Ta1OfnaU9u59JFPeHN/DcsK0yiqtASSLcD+ylYyErSkG/zHFWbqA7Z8AJkJMaSE8MftjTl50Uu2AAeqrUzJ8H/peX0SFS0dZBu1KGQQLXGn/ZWttHW6mZdn8jsNDeL6p2UZwk62chk9NIxrWjtQK+W02F0crLayq8zMvspWjta10dTuJEYlJy8xlhnZBmZkJzCtm+6wSi7jRGM7CrmMRlsn45J1zM0zsrvcQofLXyHscHk52dg+IpPt7FzjeU+255OoJ9zHHnuMgoICsrKyUKvVXH311Tz55JMsXrwYgPr6etRqNUZj8NhAamoq9fX1gZiUlJQe505JSQmKSU1NDXreaDSiVqv7jOn6vSvmXB566KHAvrHBYCA7OzvSj0AgGBaO1bdx/V8+Z19lK+UtHfz2/aPc/a8DISUYy5o7sDrczMszUmV2MC5VF3guTqOi3elhZk5Cnwlu0dhEIHojO114fRKnm+3kJ/mvqd3pweuTyE/WMSM7OjOvM7MNpMTHsLvcPGjXJYUs/K/NBWMSuX5mZtBj7U4vU9JDr6omp+t54MuF3HbZOGbn+vfkY5QKpmUZ0GuVTEiLw+H2MiVDj8Pt5WSTnT1DZAE4FBRVWKiMUnl6NBL1LuXHHnuMnTt38vbbb5Obm8vWrVu57bbbSE9P58orr+z1uK6yWBehyr3RiOmqoPdWTr7vvvtYu3Zt4HebzSaSrmBEcLqpnXanh2lZCUiSxJbjjaQbYmiwdeLxSf4Gmj6SSYfLG/Bn3V1mYX6+CQkCZcn9la1kG7XExSg5WhesQTw9OyGQyO3OYEP4aBCnUZIcrwl4ytZaO0mIVXGy0c68PFMPC8FwmZQWj8cnsb9q8LOzUzMNqJVyKs3hj1gdqGrlkgnJPR73+Hp2TP999RxsDg9/2HSMKrMDucxvC6hRymk409VdKfn3o8OxTxypvFlcw51XjD/fl3FeiGrCdTgc3H///bzxxhssX74cgGnTplFcXMy6deu48sorSUtLw+VyYbFYgla5jY2NLFq0CIC0tDQaGnoq5DQ1NQVWqGlpaezatSvoeYvFgtvtDoo5dyXb2OhX7Dl35duFRqMJKkELBCOBEw1tfOuZXdgcbn55bQFjknQ8+ekpjGfGPTy+yJNgKEH6rk7U7oL1k9LiyUrQkpcUS6vDTfMQ+M66PL5A4u+iy+t1d7mZ6VkGDkSQZDITYkiK00R0TF/MzTMOaCVpd3lJjgv+PjHGqjhSayMpTh3wC56crmdfRStPfHIyENclqdmd/owKRgPPfV7GrZeMuSAFMKJaUna73bjdbuTy4NMqFAp8Z+7oZs+ejUqlYtOmTYHn6+rqKCkpCSTchQsXYrVa2b17dyBm165dWK3WoJiSkhLq6uoCMRs3bkSj0TB79uxAzNatW4NGhTZu3EhGRgZ5eXnRfOsCwZBR0+rgm/+3k+Z2Jy6vj1++WcJ//W0XrR1uyprtONzRX3Ge6GZ1V9ZsZ2+FmdxEHQvHJNLU7oyqc47fiajvMvWxhrZAybk3CtLjmZdvYnauEVunJ2rJ1n+NMi6bmMxgFSfHJuvQaZR4JRibfLZT+eLxSRyobh3cyUcJlg43x6Po4jSaiHiF297ezsmTZ+/CysrKKC4uxmQykZOTw5IlS7j33nvRarXk5uayZcsW/vnPfwZGcQwGAzfffDN33303iYmJmEwm7rnnHqZOnRooOU+ePJmrr76aW265haeffhqAW2+9lRUrVjBx4kQAli5dSkFBAatWreKRRx7BbDZzzz33cMsttwS6zlauXMlvfvMbVq9ezf3338+JEyd48MEH+dWvfhVWh7JAcL5xerzc9tI+WuzB8+VDPUputrvJMvrHTt5ZsxidRkmHy8ORM93DqXoNDdZOXL0oQhljVRi0KmJUCjQqOWqFHJlMhiRJeLwSDreX9k4PrQ4X7U5vv/uqnW4fNoebzIQYfJJEnTV4lT0719/pPFSfy+4yM2+vuQi70xtxedvW6Q7Y6JW3dASa17rfKC0el8Tr+6p7O8UXjoqWjkF5PI9WIk64e/fu5bLLLgv83rXf+Z3vfIfnn3+e9evXc99993HTTTdhNpvJzc3lt7/9LT/84Q8Dx/zxj39EqVRyww034HA4uOKKK3j++edRKM6WGF566SXuvPPOQDfzddddFzT7q1AoeO+997jtttu46KKL0Gq1rFy5knXr1gViDAYDmzZt4vbbb2fOnDkYjUbWrl0btEcrEIxkfvveUQ4MwlptMCTGqSlINxAfo+TB90t5YWc5bq9EXmIsBq2K1PgY9lb0LLN2jatYOqLbJdt105FzRuhjdm4CcpmMtk4PRRWtQbEqhSyq8pDg/7dYVpgaccI9Vt/GzBwjW4834fVJZCZoqbY4aOym1JWREBMoL18IVJovzMapQc3hXgiIOVzB+cJsd7HsT1tpGoI903CYl29iSoaeDIOW375/FGOsirxEXUCycXxKHCfO0VAG/+hHUYhEHE3iY5Qkxakpa+75xT09y0CVpYMYlYLa1sHLEXYhl8Hvvz6V1/fVsv1US9jHFWbquWpyGn/86DgA37son79/Xgb4pQ6tDjfPfnsOv3yrhLooyCeOBq6dnsHj35p53l7/CzOHKxAIooNJp+bdOxYz5zzZwdkcbp77vJyHN5QC/nGdmm76wbW9aAm7vdHTLO6NmdkJvTYQ+Tt8ZSTqotv86JPgwfePsWhsInddGX6X7dG6tiBlK58kkXtmNjf7jFrUwepWpmREx292NPBpaSMOV/R7D0Y6IuEKBCOUkhorq5/bQ5rh/OjPqpVyZPjnZAF8Pimwrwv+Dtxso5b4MyM9OaZYJqTGERPFhqpQzMk14vR4ey3B5iXF0tbp4VCNlRnZCVF9bbPdxWcnmpmUFk9SXHgJ3euTcHW7CTlW38aSM6NCujPWgQeqrRRmfvEraNkmLf99xXg+vffSqPnrjiaEW5BAcA6S5NePTYk/P4mu0+0lRqXgV2+VcLTOxtG686N2drDayviUOE41tTMrx0hNqyMgwdiFUaemyuKgbQgMEkLRfVypuyRkF93lDsE/TpWZoA1amQ+WJROSubownXEp8fxrbxWSJKGQy/nrllO9HmPr9JAQq6K1w82Jxnae++5cPj7aSKfHv8o7WN3KqgW5UbvGkYI+RslXZ2UxMS2eKyalBKmkXYiIhCu4oPn8ZDMbD9ez6UgDCoWMsclxHKq20u708I05WVw3PTPgwjMc/GHjMRJi1RRm6Fk8PrlHQhluMhJiaHd6QjZHgV8FaTiQy/xl5O6zw0fqbOQlxlLe0oFaIWNKhqHHLK/d5SU5XoZaIeu1o7ov7rpyPM99Xh4kk7hoXBIA41LiuP9Lk7E63Fz/l8/7PE9JtZUlE5J5q7iW5nYnh2ttvHDzPH7/YSnfXpBHRYudbFMsK6al8+7Buj7PNRrQKOV8c242d1wxPuxKwIWASLiCC5Jj9W0889lp/lMUPIpRZT67EnpxZyUmnWbYEu5nJ5p4fLN/5G58ShwT0+KH5XWzjX6dZQn/jGR3M/m2Tk+fjTzDMV1n0KpIN8RQdM7NR6fbR4fLy4TUONze3v13y1s6mJmTwP4B3LzsONXC/HwTG4+cFeKxn7Oa31zaEFDI6o0D1a188N8X8/PlkwPvSaNU8NRNs5EkiRa7G4NWxRMrZ7F0Si2/eOPQoCUozxerF+Wx5vJxItGGQCRcQZ98cqyR+fkmYtVKmtqcxKoV6DQj78/G5fHh8vpwe3wcrbdxtK6N8mY71xSmsWhcElXmDvZWmDne0E5xZSs7TofXZfrctjLyk2KZlWMkN7Fv4YXBMjfPxJdnZPBWcS0nGttDdgBHk9R4DdmmWPZVWgIKU3PzjMSq5Pgkv6zgYHxvo0FeYiwOl5fS+tBCCY1tziCT9N7YX9kallrUJROS2Xq8KfD70Tobf/7mzKCEe67t3VUFaSwaW91n5/LJxnZcXl+PbQqZTIZMJiO5m3nEddMzmJf3/9u777Amz/UP4N9sQkgCYYWwXQwZIjhw1FVR66hd2tpStXto7VF7Ttvf6bB7aHd7urVaqx1WW2tr1bbugQIqCIqy9yYQRubz+yMQCQRIQhLW87kur5bkSfK+hOR+n3XfEvx710WjYxko5kf50GDbBbotqAdDcVvQsauV+O1CKc7m1yC/uglf3BuLmaHeWLk5CdmVjfj5sUn95gNV16TCjqRC7L1QgivlDYYFPu1F+oqRU6lAYy9WRU4d6WGoMWtrtY1KvLn/CsTOHBzKKIe7gGd17mBzuHBZCJWJcL6grlPwYDMZ0OgIRnq5IK+6EWotMWxdMWW8Dcv0dTTCywXl8mY02DB3c095mT+8KwZeQh42HchCuEyEKSM8cMMoT6zZmYopIz0wK9Tb5CI2jVaHPedLUNHQgvpmDaoVSlQplLhWqcAtMX7wETvhtrF+FmXoIoTg2zMFeG1fpl2yiVkrJsAVGi3psmJS6nOz4Sbgmryvv+ir73UacHswlAKuRqvD1ydy8drvl8FiMgzBa9WMEfjnSgWuViig0ugwL0KKD++KAZvVd4vcC2ua8NXxXPxwrtBQlszeNt4Rjdtj/Wz6nIQQrPouFUeyKhEqFXY5V2orXkIeuGwmimrNX0QU5SfuMln+GH9XnLdTYg5r8xd3h8NiYGmcP6obVfgjvXPFsDAfEf5YM9Vmrydv1g8V90ZhTRPe+OMy9qX1n7ndUKkQo7yF2HuxxCi7l68rHyeentl3B2amvvpe739jg1SfyCpvQHl9C3aeLQQAo55i+4TqAPBHehl+OFcEFhNwdeYirUiOJ28cCRaTYXXKzIYWNU7n1OBqRQMmBLvDXcCFv8QZLGbn57t/y1n8dbnCqtfpjU+PZOPWGF8wTRyTtX69UGL4Ij2XX4u41qQRtrgK9nPlo6jd6txR3i5mD8G2x+8myXxBTSOcOUw0qW2z9zZUKoSYz8HFYjla7NCrU2sJTmZX45dVkzEhWIKXfstA+05+26rwsC7K51mqt8EWAPwlzvj47rFYmVeDl3/LsGmOaGtdLmtAQ4sGt431M1oH0Vdb2AYKGnApqLU6vPjrJX15Ngm/5wcAeHZ3mtHPW07mISbAFR/fPRZCHtuswHutQgEOi4G86iZ8eSwHx65WGe5z4jDBAAMP3TAMS8b5QypywpWyBnx/tgBHr/bNvNa1CgXOF9VhbIDtElGkdxiWO5dfiwiZCOX1SlQq9IExNsANyQWW9fRiA9yQWlhrGEKN9hcjs7QBKo3lgVGh7DpFY02j2uT2HGuJ+BxUNCghFTkhrdg+26Fyqhrx758u4r4pwVg9cyTe/+uq0f17UottFnBtKS5Igt2PTcbeiyV484/LKOnjrFTFdc1gMRiG7U5A5/ltyhgNuEPclbIGrNmZaliUklVu3UIdhVKDY1ercCyrCq//kYkgdwES4wMRP9wdGi0Bm8WAWqPDtQoF6prV4HNY+O+edDQqNZ0S8wP6FagA8NvFErz/11XD3GJfyyprsGnANbUALb2kHuODJahUKDE+WNJjJZ2OgtydkVYih47oS9sN9xTgQi/qwRbUdD/8nGLmgqTuRPmJ4cRholzegnwH5Nn9I70Mf6SXYc5oaaf7rB3W1+kIGpQaq3q1uVWNPVZDAgAmk4Gbx/hizmgptpzMw8f/XOvTkn2/XSzBLWN98e3pAgCA1kSdX+o6GnCHMKVGi8e/SzHaBtJba3amQqMjKKptxvFrVfjuwQnYe6EE5fVKsJkMw2pPLotplH2nK9mV+u0W/SHYArDpymGtjmBXFxViUgv0BeJTC+sMyfrN4S3koUWtM+rJtv0OrdXQooGP2Knb7UEXiuSIC3QDAQGzdeUtIQRNKg0ulfRcio3PYZmsz2tvXkKeUQ/NmcvCszeFWfw8GSX1+O+eNFyrUOCpOSFYNiHQ5HTI+cI65FQqcEuMr9Eo0OodKfh42VizV8I7cVh4ZNpwLI3zx4d/XzMUlnA0NotpNIebXlyPUnkzfMTmjZQNNTTgDmHfny20abAFOgfGB785Z1gdzG73BWROsO2PbLnE8J/LFUb7fttTa4khAF2rUJjVg2QzASGfY/P3FNCX4+su4Ko0ui4rB5mjr5Zu7kgqwLIJAdh2Kh8cFhNf3BuHWAtyVyuUGrx7MAtbTuYZ1j0898sleLjwMC/Sp1P7JpUGa3+4gJSCWrx8c4Qh6DLAQGpBncVbz9wEXDy/MBzLJwXi7T+vODxpRoSvCAU1TXhwajCi/FzBZTNxvqAOPpE04JpCA+4QpVBqsCvZ/vU322/F6S+91N7Q2HDIbMvJPLPbns2rRbSfGDlVjV0OIcYE2H5VLwBEyERd7oPtibnD4do+3CxxOqcaD90wDBOHu2NyaxapnhBCsD+9DBv2ZqCs3vhChM1kGLJRddRWoODb0wVggIF74wOxI6kQVysa8PflCsyNkMKpm0VqXQl0F+CjZWPxwNQ6vPZ7ZqeMW/YS4++G2EA3xA93hxOHhbf2X0ZedaPJiw2KBtwhSaXR4ZFtycirbkK4jxAZpdZ9mQ5FShutxi2u0w+5W+JCkRy+bnyTAXdsgKtdgi2g7223WHnepq6xQqVCCJ30C+t0rYn9Kxv6ZgHQfVOCsSTODwADI7xcemzf5ve0Mjz+XYrJ++KHu3c5jyvmc+Av4aOwphnbTudj2+l8w32/XijBcE8XrLGgElFHY/xd8f1DE/HGH5fx2dEcq5/HHP4SPm6K9EG47PoCs0VjZPDuoxzkAwENuENMeX0L1v5wHieu6bPiNLSojRLCU91TamyzVeWX88VWPa64thmhUmGnHmdFvRJeQp7FW366w2YCYwMluNCLfbamSvWJ+ByH9cC6w+ew8PANw+BuRRKXnWcLTN7uzGXh0WnDDT9XKZTYnVKMi0V1+PfcUPhLnDHaR9zlVMKFojqLj6UjBoOBp+eFImG0Nx7fntqpB95bUpETVs8agSVx/uB02IsfKu1/q7v7Expwh5g39182BFtA3wM5k1uDcB8RyupbUGNixTB1ndKKbTUdEULw0znrh/OvVSg6ZXjyk/Bt2sMNlbqgoUXb68Co0ujgJeTBw4UHPpcFJqNzLuKeTBnhgbL6FpvOTYuc2Pg0MdaqYFtU29Tl6MS2+8cjNlACeZMa/9l1EYcyy6HREUT6ig2LqLoLgOsTQgDo/0byqpvMWrlsCoPBQGygBFvuG4cHt57rMsBbYpS3C5ZPCsJtY/2sGvamaMAdUrQ6/byTKRml9XBz5mC0TIRLJX1TDm4g6HhFb43LZQ3I6SHZfXc0OoKkXP2c7rUKBSJ8xTidU4PxQW44l19rchjXXAIuC6FSkcX7fk0R8zmob1FblWyjvcLaJjwzLxQ5VY344K+rVg9vt/F15eOb+8ZhhJd1xSHSi+WYECxBs1qHFpUWLRotmlVaTBjmjthA/TaudT+ex6HM68lZZod7Q+bKR02jqttebJNKfzFyOKsSKzefxVu3RWHJOH+rjhPQ9ziPrJ+BH5ML8Z9daT0/wARPIQ/rZo/CHXH+JldeU+ajAXcIyalUdJsGsbZJjdomNYZ5CqDT6a+wKWMNLV0ngTDXn5dMX/RY6kKRHCIntmE6ICmvFqNlIuRVNVqdN5rLZtok2AL65CV+bs4oqevdkGZ+dRMe+TYF86N8sPOheHz09zUcyizv+YEmDPcU4PEZI6wOtgAwN8IHcyOMFwURQvDNyTw0qTRw5rJR33y9Fy9yYmPF5CAAQFJutckV2QESZ5TKm/HCr5cwLkiC2ABXAMBzv6RjRqiXUXEDSzGZDCyJ84dUzMdnR7K7LbLQntCJjUemDcfKyUFw5tJQYQt9lwyXcjhz8+fmVDaiuK7ZoXVgBwpbDLlftTK5iCkdS7hdKqlHhK/Y6uerbVIbbd/qjSalxqZztfsulmLF5iQkxgfi88RY+LpatvWEy2Ji50PxuHWsefmwk/NrcDK789CxUqPF50ezodURfHokG/9cqcA7B7OwK6UYvNb6wI/OuD6P++DUYRA5cUAIwaEM0xcK/hI+In3FuFRSjy0n81BY2wwumwmlRofLZV2PONU0qlDX1PPfJIPBwLRRnvjuwYnYsnIcRvawQCzQ3Rl/r5uOx2eMoMHWhmjAHUIsWTyh1hIk5dYgyk8MIY/O17TpqmqOJWy9iKWjpNwajAuyPhtWgLv5iTa64+ps+4oxdU1q3L/lLJpUWhxcewMenT7crAuEQHdnfLk8zqyeolqrw6YDV3DHp6fw1I8XjeacCSFY/+NFvPb7ZUx8/S989Pc1rNx8Fh/+fQ0vLgo37MWdPsoT4T4iiPkcLG/t3Wp1pMtqQREysdGWpIzSejy3IBwiJzZe/i3D5GPSi+XYsPcSxrx0EAs/PI4MM6eCpod44Y81U/Hy4ghITFT1cXPmYMvK8b3qVVOm0YA7hHSXuKArF4vkcOaxESo1f8vEYCZvVkPTy6QdZXbOgUug37cbF+iGsQGumGDhSIUbn4O4IDeM8nbpVeAWO/c+cb8pGh3Bk9+fx3dnCvDvOSH4Y81UTBzW9TmO9HLBr6um4IZRnj0+d06lArf/7yQ+/PsadES/fevY1SoU1zWjuK4ZhzIrsPdCCQCgskEJRWswduIw8dj2FEx9628cyigHg8HAo9OH44EpwRA56X8Pv6eXdZmprEWtxcRh7oafg9wFSMmvhZfICVnlCpTUGY9OpRbUIvGrM/jlvP5YLpXIkZRr3lAxoM8QlTgxEIefmo47O8wRb71vgtWLtaju0YA7hJTJrVupWF6vxOUyBWID3CCxQ69lIKltUveqVi0hxO493DbZlQqwWQwk5dUg0lcMNzMDYHJBHc7l1SKrXIFqhcrqijfOXDZ4bPstsnllXyZe/PUSgj0E2PHgRKxPGGWy3e2xfj2eAyEE350pwPwPjneqxhPmI8SKr5Mwa9NhvLrPdG+zRa1Deb0S5fVKPLEzFdUKJW6K9MF9U4INbbIrFGBA//sY4+9q9PgLRXLI2g2RbzmZh8dnDDckDmlbyKjVEfzr+/NY9sUZ1Lamo+SxmZg60hPHr1VDpyNoVGpwNq/GrC1sIicOXlkcYVTf2ktEe7b2QgPuENLbbIrJBbVQarQYH+SGobxY8f1DV6Gzcikwg8FwyO9ufJAEGh1BQXUzCAHSiuVgMRkY6W3ZSEVOVSO8rBxaTMqtQZC7fUdGvjmVj4e2JaO+WYNVM0fi03tiO/1+/c3IRb0/vQzP7k7rVOhd5MRGgMQZk0d4YEaIl1kLCZtUWvyVWQEWk2EoTnGlrAHv/3UVDIa+5m5WufE+6romFYI9BIgNdIOvKx9OHCZK5S24eYwvAH1BCgA4ca0Ku1OLjY5TqdEhzEeET+4eiz/Sy3Db/07ijk9P4eN/slFhxsWdUqNDTeP1VeS2Su5CdUYD7hBibg+nO40qLZLyauEvcR6yw8xncmuw2YK0jB3Zew9jqNQFyQW1aGjRGPWmqxQq5FY2YryFw8TlDS2I9BVjfJAEoVLLVve6mKiGZGt/X67A3PePYkdSAWaHe+Ph1sQTrs4cLIqWYZoZQ8kHu1jMFOYjAoPBwIuLRoPXxfwroA/Md08IwOqZI/Dt/RM6beU52zoqQqCfI+44P5pX3YSaRhWqFErMDvfG6WdmYepIT3i48OAjdkJQ6xCvqdGRUKkQT80JAZfNxE/JhVBpdZCKnPDBX1cx6Y2/sfVUXrfnfqW8wbCV7L/zw2w2h091RpefDSEuTrZ7u/Nbr/QjfcVQKDXI7cW+0oHovUNZuG2sr1ULg5y5LLuVVGvLNqXtogeu0REk5dUiNtANF4vqzKowU9+sQVpr3V5L53TZLMcMhZTKW/DMz2n44Vwh3r49GmtnjzJ7z7RGq8M/VypM3ufa7iLV1PamaH9X3DMhAAujZd1eSGWUti5oIvrEJaYWT72yLwP51U04cKkMBzPKEejujE/uHov/HbmGJpUWYj7T5EKmm8f4gsVkILWgFv9cMa4VrdERw2e1K9kVCoyWiTA/ygdLxvmDEGJWPWvKcrSHO4Q0W7k3sztpxXLkVTdifLAE3XQABp0gdwHYVibBcBfYZ46MxdAnm6hp7HkldXJ+LaL9XC1+je72cXc03FNgVcH73kgtqMOdn5/uNDTcnZSCOsN8aEcuvOsBt0mtv0hiMxlYGueP31ZPwS+PT8Ydcf49jlpwW/9WCPQXOKtnjOjUprCmCdNGeaJE3oLiumYk5dagUaXF9FFeSG+94LlhpKdRucabIqXwc+NDpyP47550k6+9+UQuPj2S3eWxnbhWBW+RE+aESzFz42HsPFvY7blQ1htCX5FUpJ/1+zO7Q4h+vi7QXQAf8dBYcJFWLEf8a38hv9rynn2wp31WgMYEullUr/dqhQLhPpYNEdeaseezjbuAh9Re5GG2VpVCiQ//ump2+79MJNEIcnfGhkWjseHm0UgrkuP+LWeRXdGIG0Z54sC/bsCbt0eZvd9ZodRgWoh+WFunI7hhlCeGeXaejskqV7QuYOLChcfGpiXR8HXlY3GMDFdb53xZTH2FoTa+rnzMi5AitbC2ywxxOgIcvlJhMqXm0axKpJfU4783hWHFliRUKVQ99ogp69Eh5SFk0nAP3BHrh7RiOa5WKBAgcbbpUHB2ZSNceGxE+4uRViTvVYrBgcBTxDNa3WmusQFu2GfjuqVxQW44Z2EuZXmzGg0taowPlpidoKJM3gIum2lWz9VWhR5M4XNY3fZiN5/Iw9Jx/mZllDqYWQ4GA4jyFWPqSE9MHemBcUESMFtXX42SuuDR6cNxT3wgpo/y7Ha4VaPV4fHvUsBmMfHxsrEok7fghrf+gbB1OkdHCJ5fEIZ7vkwyehyLycA/66dDIuDC1ZmLO8f5GxZMxQZKECC5fpEWE3B9WF+h1ILNYuKzI91XBjqdU4OfU4qQGB9kuK2uSYWXf8vApjui8dh3KYZ8yx23IFG2Q3u4Q4hEwMXbd0Rj/5M34NKGOdh633ibv4ZCqcGFQnmvsh0NFCV1zVblnZ4z2tumxzHS2wXnC+qseqyudXQi2k8MF27Pi7l0BGYnRKhrUhlW19qal0i/mKgrGh3Bhr0ZPdbjbVFrsXb2KKT8dzZ+WTUF6+eEYMIwd0OwBQAem4W4IAlmhHgZgu32M/n4+3LnnnGpvAV/XirHqtYhYzGfg3mRUlS3ZijT6oC3/7zSafGTVkew/XQ+tDoCrY4YFlm1af87b7+3eoSXC05lV+OgGakuP/rnmmFoGgBUWh1euzUSSbk1RmsKaMC1HxpwhwhCCHYkFeDApTKczqmGE4cFH7ETBGZ8yVqjWaUd9BmqWtQ63LflLMot3Ffr5+aMqSPNK3TeE4kzB7WNKmh6OZxwoUgOsTMX/pKe0yWas9qdxQDcXawbATCFwQCE7VY851c3YWygW7dblo5drepy9XEbJw4LC6JkcGuXcamrBWftuQt42HwiD4QQbDmRi7f2XwYhBGnFcjAY+hJ2AMDnsvDe0jFYGqdftTxaJsLdEwLx9u1R4HeY9910MAsxLx1AblUjUlovoEwNAzMYDPi5OUPAZWFhtA/+9f15k/mZOyqvV+LW/500VF3yEjphXJAED94wDCeenonk/96IrfeNR2K7IWvKtmjAHSLO5dfimZ/T8NC2ZLx/SD+/dSSrsodHWe9qhQLuLjxI7JRtqL9QKDX4OcXy2rbPLwjvdeUVNhPwEjmhSmGbkorFdc2oUqg6JWXoyJnT/UxUbKAbOGwmUgrqDJmYesOFx8a7S8Z0uqjYd7EUd40PgBOn66+xl/dloMWCBVQA8PXxXBR308vT6ghK5c1w5rKQU9WIN/ZfxuYTeWAwGDibVwNCgDnvHcUfaaXIKm/AnvPFCPIQwInDxMPThuOGUZ64I84fe1dPxqJomVGWrLbc2JUNSmw5mYvlXyeZPIZIXxFeXhwBtdayRCptOZ3bqLU6/PunC3ho6zk8/l0KXtx7CdvPFOCiDeryUp3RgDtEaNpt/wjycEaLWosX916yuqqMOfKqm8DnseFpo15ORy48FnzEPLgLuOB386Vrb79dLLH4MSO9hUic2LuexJgAt06F6HurWaXF+cK6LhNkxPi7wonbw++awFBCzxbFHppUGgR5CEzO2X51PBe3xHRdjKCwphlfHut+frOjBqWm2/3DP5wrxIa9GTidU4PHvk0Bh8mEM5eFFrUWP7bWOa5oUOLR7SlIePcoqhpUmB/pg1cXRxr20wLACC8hPrgrBjNCvEy+TqhUhDvHB5i8b8XkYNwS44s3/rhs9nmNlonw66rJGOl9fV5bqdHhp+QiHMgox+mcGuRUNiIptwbfnNQPb3c3JN+s0uJgRjkqe1F6caix+Fvq6NGjWLhwIWQyGRgMBvbs2dOpTWZmJhYtWgSxWAyhUIiJEyeioKDAcL9SqcTq1avh4eEBgUCARYsWoajIuCB3bW0tEhMTIRaLIRaLkZiYiLq6OqM2BQUFWLhwIQQCATw8PPDEE09ApTL+gKelpWHatGng8/nw9fXFSy+91OO8zmC3fFIQvj2db5Oi1D0prm0Gh82Au4kk6dbgMBkYF+SGUKkQCqUWpXIlqhtV0BJ9ztxwHyFCpS4YHyzBuCA3TAiWYHywBOOD9D/HBrohwIxhU0tcrVDg29P5Fj/uXzeOsjoZSWyg5YukLCF2Mn1cXDYT1e161Gwm4OfKR5SfGBOCJYgNdENR3fVVrhUNSjj38mIoLlACZRe9VIVSgytl9Rgf1HUu5Y//ybZoXjLG3xW/ntePWuh0pNNj/76s37Mrb1bjSnkDGpQa8LkscFlMkxcFMQGuCHB3xm2xpi8M7psSjBmtq5jvahdgr1YoEBvY9b5ntZbg7gkB+GhZDG4d69vl35K+Zz0MPz4Sb5Q+EtCPHnyeGNepiMGulCJMefNv3Pn5aeRUKpBZen2tgkKpwdfHc3HjO0fw4NZzmP/BMUN+aap7Fq9SbmxsRHR0NFauXInbbrut0/3Z2dmYMmUK7r//fmzYsAFisRiZmZlwcrq+wOHJJ5/E3r17sXPnTri7u2PdunVYsGABkpOTwWLp5zWWLVuGoqIi7N+/HwDw0EMPITExEXv37gUAaLVazJ8/H56enjh+/Diqq6uxfPlyEELw4YcfAgDq6+sxe/ZszJgxA2fPnkVWVhZWrFgBgUCAdevWWf7bGsDyWrevhEqFkIqccDavBkwGHLKSuKSuBb6ufIwPkuiH3Mx8nETAhUTAAZ/DAp/DhkqrRW5VE86aCDQqjc6iLTGRviKotcQmPUSVRof/7kmHmM/BwmiZ2Y8TO3OwLiGky/2TXRnhKbD7kF/7RUNsJuAt4sPdhQuVVofcqkaE+QhRrVChUqFEUV0ziroJaFIxH/nVjRgb6IZqhQo5Zq6MvzXGF6OkQqyYFNRtTy6loA7f3DceaduSTQa8ZrUWr/2eiY+WjTXrdaeHeGLWO0ewbEIgzubVwMPFOBiF+4g6zQ2P8haCydRfWFa06/FxWAyjBYSfHbkGdxcn3N4afKsVSri78LB2dgjUWoI5o72xI0nfOSmsbsJVYUOnQgInr1VhbKAbSuqa8fXxXFQ3qjBxmAS/PzEVj25Pwfl2W7FGerlg6/3j4SPWB1pCCN49mIWT2dXwFjlhwjAJ7o0Pwu++U/F/u9Pw1+XrCUBK5S3wEjlh8ccnUN+igY/YCfdMDDS8ZpuKBiU2HbiC+ZE+Rn83VGcWB9x58+Zh3rx5Xd7/f//3f7jpppvw1ltvGW4bNmyY4f/lcjm++uorbNu2DTfeeCMA4Ntvv4W/vz8OHTqEOXPmIDMzE/v378fp06cxYcIEAMAXX3yB+Ph4XLlyBSEhIThw4AAyMjJQWFgImUz/Jbdp0yasWLECr776KkQiEbZv346WlhZs2bIFPB4PERERyMrKwjvvvIO1a9cOqWwqhTX6XoenkIe/Mivw56Vy/Ryig3r7bdVWgtyd4S1ygq71dRkMBkAAjU4HtZagWa1Fk0qDygYlahpVNhmSNCWtWH/FHiIVgstiGH7ujWd/vogZIV4WZfRaNj4Al0rqDV+yPXFz5qCuWW1WhqjeKKxpwpQR7rhSrkCVQml4/9pklpp/oeLEYWK0TIyzrRmuYGbAFfE5eGTacDSrtNiVUtRt2zM51Xh6Xihe+PWSyft/u1iK1TMbEGJGakoGg4Hn5uvn2OMC3TolOFkzayRqGlXY1jqqESoV4p0l0dh7oaTTIqdwmdiQFOOvzHJsOpAFXzdnVDYo8ej04bj36yRsXjkOYT5CsJgMZFde/91sO52PUR2Ot0qhxGhfMTJK65H45RnDlFByfi2OX63Cl/eOQ+LXZ3C5rAGhUiG2PzAB7u2mdDJLG/DB39cMP+9LK8UYf1dE+bkiJsDVKOACQE6FAg2t51Qqb8Hbf14x+TvLq27CocxyJIyW9vj7HcpsOvGl0+mwb98+jBo1CnPmzIGXlxcmTJhgNOycnJwMtVqNhIQEw20ymQwRERE4efIkAODUqVMQi8WGYAsAEydOhFgsNmoTERFhCLYAMGfOHCiVSiQnJxvaTJs2DTwez6hNSUkJ8vLybHnq/V7bl0aLWouC1uBrzmpMW8urbsKZ3BqczavF2bxaJOXWICmvBikFdUgrluNahQIldS12DyhtrpQ1IK24HqFSIWICXMHnMMFlMRDsIUCErwjeZm6BGeYhgI8rHxv2XrKoZi6TycBj04f33LCVp5DnkOTypfIWqDQElQ3KXl+TZZQ24GKxHMM89CXnzLXlZB52JRfhdG51p1SYHa+VNTqCxImBmDhMAh6biTmjvfH+nWOQ9mICDq+fjt2PTTIr2LaJH64vlWcqmxiTycCc1sDi58bH1vvG4+ldaVi9I7XTmoixAa6G/3/jj8tQaQlyqxoNCVO+vX8C3AU8sFlM/O+esdhyMtfQvlmtNRrO/vFcIf7z00WI+Rx8dTy302tdKJLjoW/PYXaYN8J9RPjuwYlGwRYAfjZx4XKhtUec1m7LUJsGCxa9/UKHlXtk04BbUVEBhUKBN954A3PnzsWBAwdwyy234NZbb8WRI0cAAGVlZeByuXBzM56b8Pb2RllZmaGNl1fnhQReXl5Gbby9jfczurm5gcvldtum7ee2Nh0plUrU19cb/RsMov3EYDEZOJtXi4nDJIZtC5Te5bIGpBbUoVmtgwuPg7zqRqQX16O8QYlhngKMDXA1WeXH19UJkb5i5FY3IqtcgR+Ti7D9jGXzuRIB1+w5biaDgUaVffIwd0TMHvw3T05VI+IszMX87O40eAh4eGFhuOE2HpuJ4/+ZaXSh4ufGB5PJwP/ujkXKc7PxWWIcbh7jC6ETB0EeAsQEuIEQgor6FrOSdvSUqjHIwxmJEwOx7f4J8BTyIHEx/f6NbZekwt9NP6wb7iPCva0JKNwEXLCYDKi1Ojz6bYrRuooRXi64JUaf/EKnIziVU42Nd0QDQJcZziJkYqyaNQI7H57YaV52y4lcfHk81+g2AZeFORH6i4fuVmabY9/FUvyU3P1IxFBn00xTOp3+D/nmm2/Gv/71LwDAmDFjcPLkSXz66aeYNm1al4/tmDDb1HCvLdqQ9kOZJrz++uvYsGFDl8c5UM0K88b/7h6LVTtS8dXxXPxnXgj+9f2Fvj6sfkfAZeHUszPx1fFcvLVfP3yW0zrM5yXkIchDAAb0OYWduSykFNSiuENS+80n8nD/lGDw2ObtQxbw2PjvgjA8t+dSt9toJK3zg44amLAkb7K5UgvqECoVmj13rtTo8Mi3yfh11WRkVyrw7ekCKDU6HMuqxL/nhmJJnD+K65oN85xuHYKMQqnBqexqHL5SgSNZlSiqbcaqGSOwfk5Ij699obAOPA4ToVJRp/v83Jzx8uIIw8+vLo6AmM/B/w7rcxYP8xDgoRuGIaFdkpMXF43GG1wWPF14Jr9/rrT7nUwa7o5Xb4mEl4iH788WQCpywtu3R4MQgqvlDYiQiZHeYRqEy2Zi8ggP8NisTn971yoUeHFv51q+c0ZL4SV0Qnl9i1VJXDpa/+MF/J5Wik/viTVZoGGos2nA9fDwAJvNRnh4uNHtYWFhOH78OABAKpVCpVKhtrbWqJdbUVGBSZMmGdqUl3fesF5ZWWnooUqlUpw5c8bo/traWqjVaqM2HXuyFRX6OYqOPd82zzzzDNauXWv4ub6+Hv7+/ibbDjQJo6V4adFoPP1zGp6eFwqG46ZwBwwXJzaKapuNVuK2qWhQGi2I6UplgxKldS1GW0B6ckuMHxLCpfjlfAkul9Ub3pe2Xua1CgUI0acGVGt1dqs21J49/jY0OoJSeQt8xE4olesvVBaPkcFL5ITPj5revlNc14xV36Xis3tjkRAuBY/NhI+Yj5K6ZjAZDHiLnFDfokZNsQr1zWoU1jbhQpEcFwrrcLmsodPUyXdJBVg1c0S3vdgmlQbDPAVmlxdkMBj4z9xQyFz58HThISHcu9MCogD3rv8eOCwmWtqlwpwZ6oVqhRJLPzuFigYlHps+HNNCvFBS14IGpQYPTxuO0TIRTmZXgxBAxGfjjjh/jOuwWvtSiRwiJw6C3J0RIHE2TCcZXidMP5Io4LHBZDCgtcGb/vflCrz8W4bRBQmlZ9OAy+VyMW7cOFy5YjyxnpWVhcBA/Z7D2NhYcDgcHDx4EEuWLAEAlJaWIj093bDQKj4+HnK5HElJSRg/Xp9+8MyZM5DL5YagHB8fj1dffRWlpaXw8fEBABw4cAA8Hg+xsbGGNs8++yxUKhW4XK6hjUwmQ1BQkMlz4PF4RnO+g83Scf64XNaAIHcB/Nz4DtkaNJBodcCLv14y6m1YakKwxKJg20bAY2PZBNP7LpuUGpzKqUZ1owr5VY04XySHUq3FOQvmRC3Ft1MWMnmzGgIeC5G+YmSV12OYpwtWzxwBhVKD786YXjx2KqcaUS8esMnr1zSqsPdCCe6I6/pC+lR2NWaFeRs9Zs3OVDAZDCybEIBZoV4m53et3Vvd0KI2WnA1cZg7Kttd4LWV95O58g1be4I9BEa5kTv650oFVn+XilcWR6BE3twp2HoJebihtVawC4+N2AA3JOWZl1O7JwczymnANcHigKtQKHDt2vVVbrm5uTh//jwkEgkCAgLw1FNPYenSpbjhhhswY8YM7N+/H3v37sXhw4cBAGKxGPfffz/WrVsHd3d3SCQSrF+/HpGRkYZVy2FhYZg7dy4efPBBfPbZZwD024IWLFiAkBD9UFBCQgLCw8ORmJiIt99+GzU1NVi/fj0efPBBiET6IaBly5Zhw4YNWLFiBZ599llcvXoVr732Gp5//vkhtUK5PQaDgRcWhuP4tSoabE2oUihx7GrvNvLf08uEFqY489hGAUCt1WF/ehncXbg4lVON+mbb93jZdtziUVLXgpK6FkT7i/HOwSyU17fgxYWjca1CYXYhhd7YcjIPt8f6mfwekDerUdeuXF9WeQNWbj5rmOM8klUJbxEPS8cF4M5x/p32tlrj3YNXDQsF50f5IMJXjJzK69vcknJr0KLW9ji33Oa7MwV47pd0zI/0QZSfGM/uTjO6XypywjBPAaoVKoha91yH+QhtFnA1OseWZRwoLB5kP3fuHGJiYhATEwMAWLt2LWJiYvD8888DAG655RZ8+umneOuttxAZGYkvv/wSu3btwpQpUwzP8e6772Lx4sVYsmQJJk+eDGdnZ+zdu9ewBxcAtm/fjsjISCQkJCAhIQFRUVHYtm2b4X4Wi4V9+/bByckJkydPxpIlS7B48WJs3LjR0EYsFuPgwYMoKipCXFwcHnvsMaxdu9ZoyHgoYjAYNEG5nfi68jE3wv5bIzgsJhZGy/D+nTGYEy5FqAUrcM3liNmGC4VyTAiWYPuZAry1/zL+d/dYBLk7g8tmYna4N56ZF2qXucBLJfVdjg7odARh7coW+oidMNzLOPNWeb0SH/x1FVPe/NuQKtUahBBkVyrQrNYY8k6L+foAOMzTxVCKz4XHNgq2FwrrsPdCCZLza7A/vQzyZjXK61twsagOz/x8Ec/uTsOSOH88vzAcD29LNszHzwz1wq5H4/HOkmiczqnGkSvXtwG5OtsmOQ0Ai1aEDyUMMtTTLvWgvr4eYrEYcrnc0HMeDJ75Oc3svZ+U+V6+eXS3w3z2oNRosfq7VNQ1q5GUWwMxn40gdwEuFHXe5mEpFx4LCqX90n+2GRvgipSCOjwxayQenzEcKo0OaUXy1v27NXj422Sbb2ObH+WDj7tIhpFSUGu0wlit1eH5X9KxI6lzcfb3lo7B4tbVxObS6gj2p5fh0yPZSGvdMtWWEOTZm0Lx0A36FdjyJjUS3juCuCCJ4VizKxWY994xqLRd9yJvDPPGl8vj8MSOVPzabrvOZ4mxmDbKE3d/eQbJ+bWI9hPj58cmgxCCzNIGrNicZJTUwlr3TQ7G8wvDe27YR/rqe53Wwx2CdDpiKGhN2Y6HC6/beUF74bFZ+PjusXho6zmM8nYBg8HAhSI5YgNckWxF2T6JMxdSMQ8CHhs1jSooKm1XM9kUFx7bkID/g7+uwpnLwiPThuN/R7JxuawBPz2i75Gt2Xnepq97MKMc8ma1oUfZ3rAOc/AcFhOv3RKJAIkAb+6/nvVqRognZod3XW6REIJGldaw+Eqt1VeYSiuWGw1btwVbN2cO5o72MdwuduZg76opRivTLxTWdRtsAWBJnB8OXCozCraAfhWxhwvPUAf7QpEcCe8eQUldC0J9hPp5exu83aaqHFE04A5Jf1+usOtim6HqganBZs+x2RqHxcQHd8Vg0UcnkFuln/uzZJ2Ct5AHVwEXZfIW1DSpUNOk7+X4udk277QpCqUGIVIhSlq3V73xx2U4c1nYtCQat3x8Ev87nI03bovC1XIFPvrnWg/PZj6VRoc/0kpNFggwNbzKYDDw6PTh8HPjIzm/FssnBXVKu9jmYlEdfk8rw6HMcuRWNeL+KcF49qYwbDmRh2NXqzq1ZzMZSBjtjTWzRiGgQw1hr3Z75tVaHSYN94BU5NRtlSB3F57JamANLZpOK9zbslulWllT2RQRn4YWU+hvZYipb1Hj1d8zzW7PYTGQMFqKcnkLDdLdEDmxcXcXK4wdRejEwauLI3AyuxoKpQbZlQqMC3IzmXsaAHhsBpQafdepvkWNchNbnqrsWAmGxWTg3vhA/HiuCBeL6jDCS4BrFfov/+d/uQQnDgtfrxiH5V8noUmlwaqZI/DDuUKztmaZ6+fU4i4r8nRlYbSs25zZyfm1uP3Tk0bbqr47U4B1CaMQ5SfGzFAvuPI5iPAVY1aYF7xFTtDoiFlbkD4/moP96WV4el4onvz+fJftcioVhl6svXkKebgxzBvFdc1QabQorGnGH+llWDk52CYLygYTGnCHEJ2OYN0PFyz6IG5aMgbvHLiCwlq6yKo7j80YAWEXFXYcadIID0wa4QFCCF789RK+OZWP8cGSTit/R3gKUNGgRLS/CGqNDrVNKuRVN3V6Pg6LAXtt+dXqCBLCpeCwmPj8aA4aWjQQ89mQt664fnrXRbx/ZwzeviMKGSX1iAuS4KEbhuGVfeZfMPYkKbcGRbVN8HNz7rmxGVQaHf6z66Ih2Aa5O6OwthkKpQY3vX8MW1aOx9crxln13PsulmJHUgGKaptRpVDCU8jrsjTe639ctlse8jYsJgPzIqR4cdFow4KvNoQQh6VnHUhoKpAh5NOj2Z2qnHQnyN0ZNQol8qqb+iTv8kAxPcQTD00d1nNDBwuRiuDnxkdSbo1RUXlXZw7qWzSob9EgKbcGqYV1JoMtYDycaQ9/Xy43lHYrr1fC19UZrNaRcB0B/tXai4trTehwR6x/twXnrfHLeeN5TnmTGks/O4Uvj+VYXLyew2JAqdHP2TpzWdj+4ERD6b3sykbM2HgYa384D6XG/Oc9mV2FTQeuYPWOFBACBLo74839l9HQ0nXObnsH2zmjvXHy6Zn4aNnYTsEW0A+/00xTndEe7hBxNq8GG7uo9NEVBoMBf4n+yn+UtwtuivRBerEchzIrenjk0OHryse7S8b0u7JkDAYDYwNdDQGjpK4ZbCYwNlCCmkYVrplZylDM54DN1J+nRMADh82ERquDjsCoDJy11FpiWGDDYADhMhFcnNiGHrlGR/DItmR8/3A8InzFEDtzsDTOH9+csrz+cFd+TinC4zNGAND3zJ7YmYozuTU4k1uD9w9dxV0TAvDsTWFmPReDwcCR9TNAoJ9vdeKwkNxuKkajI/jtQileujkC5iSx+vxoNt7afwWa1gve4rpmiPkc6Aig7kURCzGfg2mjPBEb6Aap2Al8Dgstai0uFsmRVixHbZMKGi0Bl80El80Ej80Ej82CiM9GiLcQD04d1u/+5gcCGnCHAKVGiyd3nrc4B29uVSNGeQvx3tIxGBcswYqvkyyqOTvYcVgMfLQsplP+3v7i+T2XUNWaorKiQYlQqdDipBKZZQ3QESC/phn57RKluHZR7NxSl8vqsXrmSGw/ox/63nuhFM1qrdHcc6NKi29O5uHt1sT9tv6iz65shEqjM/TILrSrNdyo0uDzozlYNj7A7OxhbcfHYuoX0Pm68VHbbkXylJEePc7XarQ6vLIvE1tO5nW6z5JqVO35S/hICJfixjBvxAW5gWMiUxYtr2dftM8/BKQX11tdCeRapQKXSuS46f1jNNh2sD4hBDEBllW/caT1c0KMtqyYWzCgvWaV1uSFmrVf+h2dya3BCC8XfPfgRPxwrshQQP5sXi0mBF/PC1yp0M9VanUEf3UYYWExGZ3K9Vmq7fEMBgMjW5NcCHlsbLhZn57wx+TO+2/N9ci04fB15UPIY4PPYeHpeaFdtiWEYPuZfKzYfNZksLVUgMQZq2aMwL4npuDoUzPw3IJwxA93NxlsKfujPdwhwNzSbx09PG0YxgVJkFfViCg/MU5lVxuGtoa6icMkeKAfztu2N8LLBedslKqvI0L0vdz2e0mt9c2pPKxPCNGXuzt9faj4TG4NxgW5wdWZi42363u3nx7JRkFNE+ZFSFHfokawhwBPzQnF0axKrN6RavUxsNpF7BtGerZmDPPB50f11X8q6q1fGb0gSoYFUV2vagb0Pdpj16pQ16TC/+1Ot/q1ZGInRPu7ItrfFVF+YowPkpjM+Uz1DRpwhwCRiY39PQlyd8Z/5oRi+eYknLhW5bCScAOB0ImNTUvGgNXP57Ce+yXdaCjT1pg2yEfuLXRCXZMa93x1Bu8tHWMUcPkcFpgMBh65IRiXy+pRVt8CJzYTux6dhNhA45GFBVE+2HTgSpeLv3rSfph69ayR0OoI1v94ASmte1MnDHO36nl7klOpwOErldh2Ot/ibTxcNhMx/q4YHyxBtJ8rovzF8BLSOtf9GQ24Q0BXxaq7Q6Bf4BHo7oxj1qeKHZRevjkCvgNgf+EdsX5oaNHgVHYVQqUihEiFOJ1TjSIbbfGy1WphQgjqm9V4a/8VLI8PQqm8GQ0talQ0KHGhqA63fXra0PaTu8d2CraAfijY2mFSDsv4wkGnI3j25zTsTi0GoM8g1r6urS0QQnA2rxZLPjtl9mN8XflYNEaGicPcESoVwtOFRxcuDTA04A4B1mSQya9uwtZTeVgeH4RvT9Ocy4B+nu+FBeEW583tK9NDvDA9xAtqrU5fb1WtxdiXDxq14bKZ0OqIVdu+RE4clECf7UjoxIZU5IThni44m1eD5ZOCIG3bUsTQp/rLr24Cn6tfDVvTqEKZvAUuPP2KZDaTiYzSekMZuq68tf8y5kVIDVm0MkvrMcLLBRwWEzor08I/dIPx1EBqYS2+P3d9zvaZeaGGijq9VVTbhF/Ol+CX88XIKu95TYSHC9eQaCPG33XIVjkbLGjAHQJSCqzLEPX+oau4eYwvJg13x8nsahsf1cDCZTHx7tIxmB/l03Pjfqat51dU22SoGgPoMwTdNc4fhzIrjALd7HBv5FY19rh16GqFAm7OHHi48JBT1YiNd0Qjt6oRrs4chEqFGOPvavY+XkII6ls0qFIoUa1QoUqh1P9rUKJSocLUkR7IKm/Ax/9cQ3WjyrD383xhHR75NhkPTB2GxImBeHFvhkW/mxBvIZ6YNdLotkhfVwid2Gho0WBckBtuHdv1BRYhBIezKpFeJIdU7IS4IAmC3J2NAuP+9FKcy6vFhaK6LrN+tcdjM5EwWopbY3wxZaQHXeA0iNBqQT0Y6NWCKupbMPH1v8BkMKxa8PTErJEYLRPh4W3Jdji6gUHIY+Pze+MQP9w+83i9RQjBj8lFSC2oxeQRHjiUUQ53Fx6evHGkUfYrQgiWfHbK8KUfG+iG5PxaSARczA7zxl+XK/D4jOHIq2q0ap9r+4o3bXzETvBuDboEgFKthVKjQ0vrfz9aFoNJwz3Mfo2aRhXcnDmGgEYIwd6LpXjBivlqFpOBXx6fjAhfcaf7ntypr7Lz2+qpCJeZ/twX1zXjmZ/TcLRDzmI+h2U0v68wM5H/2ABX3BHnjwVRPv0ia9lgRqsFUXah1RHcPyUYqYV1GO0jRqiPCxqV+i+73KpG/JRc1O3jJc4cTA/xBIvJGLLZpjYtie6XwTa1oBav/3EZyfm1hvemffm4r0/kYqSXC8b4u2KMvxvG+Lvi42Vjseq7VEjFTjiUqc86VtOowo/JhZgfJcOHf1+zOkuRm4m9uaXyFpTKu06y37ZP2FySDivuGQwGFkXLMGm4O57/JR2/p5UZ3T/cU4C7JwTiZHYV/rlSCXcBFyFSIWL8XTF5hIfJYAsAt8f6Y3a41GSwJYRgV0oxXvz1kslg2mxBdio/Nz5ujfHFLWP9uiyEQA0eNOAOct+fK8QXx3IBAOfyahHmI8LeVZMNWwWGe7rgzf2X4S7g4r4pwXB15qCgugkZpfWYHuIFf4kzbnr/2JAMtr6ufCyOkfXLZADl9S2449NT3Y5aEAJklSuQVa7AD+f0F1YuPDZuDPNCYW2z0fCyjsCQYtFaLCuGPmttlILQw4WHT+6Oxb6LpXjpt0uIDXTDPRMDET/MHQwGAysnB4EQ85NmTBlputdd06jC/+1Owx/pZSbvN+9YubgxzBuLY3wxPkhCFz4NITTgDnIdF0wFeziDzWLivUNZ8BY5IS7IDb8/MRVXKxrwzck8zAz1wt0TAnG+qA5MBrBm53mzh8QGm6kjPfDUnK6TFPSl2iaVVVMECqUGe873LrB2pVll+d+JrXP+zo/ywbwIaacgxmD0PjlGUm4NVu9IQbkVe3I5LAYWj/HFknH+GBvg1u+3lFH2QQPuICZvVuNktnHtzRGeLpA3q7H1VL7hyy7YQ2DYA5hSUIcQqQj/tzutU93MviDgshDoLoDQiY36FjUySy3PlmSNf88NwaPThjvktawx0ksIMZ9js4xPtlBtYfAM9hB0W+bOWrbsMTartLhS3oC/MsvxyeFsi0d6RE5s3DMxEMsnBRnmsqmhiwbcQexaRUOnEln+Emeczqk26ll03HC/8c8rWD1jBF7747JDjtMUFy4Lo33FKKptNlpBG+0nxoUiuV1f++XFEUicGGjX1+gtFpOBmyKlRnO2juQv4WOEpwt0RJ+rW6nRAUQ/tBsgcUa0nyskAi4C3Z2RU9mIqkYl5E1qyJvVaFRpEe0nxl3jAyAwJ4N/H3n5twxsPpFrVdKXAIkzVkwKwpJx/mbVuaWGBvqXMIh5uPCwPmEUXHhsqLQ6NKt0GBvghvf+6j6TxZXyBhTXNePHh+Pxwd9XcexqVbftbYHHZsDX1RluzhywmExkltbjjIlE+04clt2OgckA3rwtCnfE+dvtNWzppZsjsCBKhvu2nNUHPDvxl/AR5eeKSF8xIn3FiJDpq/aYq6203kBSWNOEb07mWRxsF0XLcHusHyaP8KDDxlQnNOAOYs5cNqobVRju6QI3ARc8NhMv/ZaBIx22MZjyzal87EsrxZpZI7EgSob/7Lpo02PzEvIQ6O6MhhYNqhtVqGxQdtpSYopaa7/A8vqtkQMm2AL6/bWTR3jg2L9nYFdKMb49nW91kQoPF/37wWToS7eF+YgQ4StGTIDrkEsXWKVQ4r970i2eI78xzAsf3BVjp6OiBgO6D7cHA3UfLiEE094+jIIa63LLtnd7rB8iZCKLkwq0GeElAJ/DAp/Dho4QXCyqgzOPbXHiexceG27OHBTaKDVhe6tnjsC6hBCbP68jabQ6HMwoN2z1cnFig8VkoLJBiYp6JSoaWsBhMREiFWKklxCjvF0Q7CFAkIcAXkLekM9ipNLosPVUHt4/dBUNZi4U5HNYuCnSB0vi/DA+WDLkf4cDBd2HS9nUxSK5TYItoC/Q/ci0GzAvQtrldgipyAmzw73hL+Gjol6Jc/k1aFHrwGIycKnEOF0fi8lAoMQZdU3mz8V6uHDhzGWhoMb2wXZhtAxrZ4+y+fM6GpvFxLxIH8yLHHjZsPraP5cr8PJvGWaNsgD66Yel4/yxdnYIPIU8Ox8dNVjQgDtI9WafYEc6Anzw1zU8On24yee9e0IAyuuVhkovkb4iKDW6LnPFanXE4oVPLCbDUCDclkZ4ueCNWyNpz2QQ0mh1OH6tCuODJXDmsqHR6nCxWA6ZmI/iuiZUNqgg4LHw5bFcs6ZZAH1yj9nh3lg5ORhhPgNnxIvqH2jAHYQIIfgjvdSmz7kvrRRrZ4/CU3NCIHJi41KJHHVNGsQEuKJJpcX2MwWI8hOjoUWNtOLuE9Bbo7xeCXmzGjH+rkgtrLPJc44PluCTu8f265WylHWuVTTg2d3prYURGAhwd0ZlvRINSg14bKZVi8xuG+uH5xaEwdXZuvrSFEW/aQahy2UNyLeyLmhXtDqCtT+cx8rJwdiVUoSssgaEy0R4/Y/LYDCAuEA3nMu3rkiCuVrUOqQW1mF8sBuS82qhJfpUf0HuzsitarQol65U5ISvV4yjWzYGoRa1Fnd9cQaVDfoEFRodQU7l9aFiS4PtxGESPDUn1GRZQIqyBP22GYQySmzfwwT0hez/uVyBw1f0w29MQwJ5y5Me9EZSbi1GebugRa2FC4+DlII6eAp5GOElwLWKrufgRHw26pv1i2FeXBROg+0AVF7fgqLaJoz0Fposmdek0uCx7SmGYGstLouJRWNkWDEpqMt8yxRlKfqNMwhVKXr3ZdOVw1cqjdLjtb0Om6kv/eZIWeUKMBmAjugXUVU2KFHTqMKEYAmS82vQsRMzWibC1QoFxge5QcTnYE4/zI9MdY0Qgu+SCvDKb5loVmsxWibCxjuiESoVorCmGeeL6pBeLMelEjlOXLO+lKSXkIfEiYG4a0KAoQQgRdkKDbiDkKsFSQks1baJjM9hGaqi+LrykW+H1cM96bhNUqsjOJNbg0B3Z/DYTMOiLQ6LgZpGFVQaHQiAzxPj6CKpAUKt1SG1oA7bz+Tjl3Y5oC+V1GPe+8daL7p6/zrR/q64b3IQ5kX42GVxHkUBNOAOSmym/b8wVFodxga4gsVkgBD0ScDtSn51ExgM/aKolPwaxPi74WJxHW6KlGLNrFG0Oks/06TS4I0/LkPmykdWeQMOZpRDJubDmcdCerG8U3rS9noTbD2FPMwK9TIUFKAoe6MBd5BRaXT46J9rdn8drY4gpUMlov6EEH11Fx+xE1ILa/HFvXGYHuLV14dFmfDdmQJs7VDw/kqLfYpURPqKMTPUC7PCvBAhE9OLL8qhaMAdZLafye9UjGAoK5W3INrflQbbfszcPbDWcOIwMWWEJ2aFeWFGiBek4qGVppLqX2jAHUTyqxvxfg+FCYYaPoeFN2+L7OvDoLqg0eqQ3IvtZE4cJhaP8UWguwDNKg3qW0tKDvMUIMxHhDAfEV2NTvUbFk/2HT16FAsXLoRMJgODwcCePXu6bPvwww+DwWDgvffeM7pdqVRi9erV8PDwgEAgwKJFi1BUVGTUpra2FomJiRCLxRCLxUhMTERdXZ1Rm4KCAixcuBACgQAeHh544oknoFIZb09JS0vDtGnTwOfz4evri5deegkDNX10fnUjfjxXiH+uVEDeYc/p92cLMHPTEYvzEw9mPDYTzy0IR6iUZgTqbwghSCuS418/XECTSmvx4z1cuHjyxpE49u+ZeOO2KDw6fTjWJoTgxUWj8eKi0bg3PgjjgiQ02FL9isV/jY2NjYiOjsbKlStx2223ddluz549OHPmDGSyzgWmn3zySezduxc7d+6Eu7s71q1bhwULFiA5ORkslr782rJly1BUVIT9+/cDAB566CEkJiZi7969AACtVov58+fD09MTx48fR3V1NZYvXw5CCD788EMA+gTVs2fPxowZM3D27FlkZWVhxYoVEAgEWLdunaWn3mdqG1V45Ntko3J1vq58iPkc6AiBVkdwrVKBAXodYTdrbhyJZRMC+vowqA7Si+VYszMV2ZWWT32M8HLBY9OHY0GUjK4mpgYciwPuvHnzMG/evG7bFBcXY9WqVfjzzz8xf/58o/vkcjm++uorbNu2DTfeeCMA4Ntvv4W/vz8OHTqEOXPmIDMzE/v378fp06cxYcIEAMAXX3yB+Ph4XLlyBSEhIThw4AAyMjJQWFhoCOqbNm3CihUr8Oqrr0IkEmH79u1oaWnBli1bwOPxEBERgaysLLzzzjtYu3btgNgaUlDdhBWbkzolVS+ua7a6FNtgJ3JiY36UD+6bHNzXh0J1UN+ixgPfnENZfYtFjwvzEeGRacMwZ7TUrjWRKcqebH6JqNPpkJiYiKeeegqjR4/udH9ycjLUajUSEhIMt8lkMkRERODkyZMAgFOnTkEsFhuCLQBMnDgRYrHYqE1ERIRRD3rOnDlQKpVITk42tJk2bRp4PJ5Rm5KSEuTl5dn0vG1t2+l87EgqwC2fnDC7ggml98LC0Xj91ij6xdwPbT9dYHawZTEZSAj3xt5VU/D7E1Nw8xhf+p5SA5rNJzjefPNNsNlsPPHEEybvLysrA5fLhZub8b43b29vlJWVGdp4eXVeVerl5WXUxtvb2+h+Nzc3cLlcozZBQUGdXqftvuDgzj0gpVIJpfJ6pqb6evukSewOIQTvHcxyaLrEwWJWqBduHevb14dBdSG1oOcFUj5iJ9w3ORgLon3gI+Y74KgoyjFsGnCTk5Px/vvvIyUlxeLhWkKI0WNMPd4WbdoWTHV1fK+//jo2bNhg0bHb2rUKBQ22VhA5sfEaLbXXrxXWdj8N4sJj472lYzBhmLuDjoiiHMemQ8rHjh1DRUUFAgICwGazwWazkZ+fj3Xr1hl6mlKpFCqVCrW1xle6FRUVht6nVCpFeXl5p+evrKw0atPWk21TW1sLtVrdbZuKigoA6NQ7bvPMM89ALpcb/hUWFlr4W7CeVkfw3qEszH3/mMNeczB5YeFoeIvoPsv+rKDa9PQIgwEsHiPDX+um0WBLDVo2DbiJiYm4ePEizp8/b/gnk8nw1FNP4c8//wQAxMbGgsPh4ODBg4bHlZaWIj09HZMmTQIAxMfHQy6XIykpydDmzJkzkMvlRm3S09NRWnq97uuBAwfA4/EQGxtraHP06FGjrUIHDhyATCbrNNTchsfjQSQSGf2zt8NXKnClrAHLvjiN9w5dhdYWyWGHkKfmhOCZeaF0KLmfU2q0aDSxBWiMvyu+fyge790ZQy+YqEHN4iFlhUKBa9eupw7Mzc3F+fPnIZFIEBAQAHd346tTDocDqVSKkJAQAIBYLMb999+PdevWwd3dHRKJBOvXr0dkZKRh1XJYWBjmzp2LBx98EJ999hkA/bagBQsWGJ4nISEB4eHhSExMxNtvv42amhqsX78eDz74oCFILlu2DBs2bMCKFSvw7LPP4urVq3jttdfw/PPP94thR0II3j2YhQ/+tn8qxv6OyQBmh3vjv/PDAQC/p5Vi08EsqLqpXerhwsOzN4XilhjffvF+Ut0rlxtXsRofLMHqmSMwZYQHff+oIcHigHvu3DnMmDHD8PPatWsBAMuXL8eWLVvMeo53330XbDYbS5YsQXNzM2bNmoUtW7YY9uACwPbt2/HEE08YVjMvWrQIH330keF+FouFffv24bHHHsPkyZPB5/OxbNkybNy40dBGLBbj4MGDePzxxxEXFwc3NzesXbvWcMx9bV9a6ZAPtmwmAxtuHo3bxvoZrUB9eNpwRPu74s7PT5t8HJMBfHhXDOKH0+HHgaKotgkeLlzcEuOLG8O8MT5YQgMtNaQwyEBNu+Qg9fX1EIvFkMvlNh9efuCbsziUWWHT5xxofMROOPzUdPDYprd7PLj1HA5mGM/nh3gL8c7SaIyW0cLgA0luVSM8XLgQmigcT1GOZM/v9e7QvGd9aPXMkUM64LKYDHz/UHyXwRYAbhjpYQi4Id5CfH5vLGSufHBYNMvQQBPsIejrQ6CoPkW/tfpQtL8rxvi79vVh9BmtjoDH6f5P8O/L+guSEG8hvntwAgLdBTTYUhQ1INFvrj62fFJgXx9Cn+GwGN2mp/zyWA7+uVKJUKk+2Lq78LpsS1EU1d/RgNvHXJ25fX0IfYYQYLini8n79l4owSv7MuEl5GHb/TTYUhQ18NGA28f+SCvtudEgxGYyWrfzdL7vVHY11v1wAUwG8MFdMfAU0mBLUdTARxdN9bGXbo5AtL8r/m93el8fikPdEeeH12+NAgDodAQNLRrUNauQX92Ex79LgUqrw/qEUZhIsw5RFDVI0IDbx5w4LNw9IRD708tw7GpVXx+OXcnETmAwGBjp7YL/zA013H7TB8dwuazBqO3UkR54bPoIRx8iRVGU3dCA20/4uTn39SHY3cRh7th4RzTqmtWGuWulRtupXJu3iId3l44Bk0mTIlAUNXjQOdx+YrBnTApyd8aYAFfoCIFEcH2hGI/NwiuLIww/O3NZ+PCusfCgi6QoihpkaA+3nxDyBt9bIRFwMS9CiqkjPTErzKvL/bMLomT481I5ssoa8N6dYxDm47jMLxRFUY4y+L7lB6iT2YNr/jZUKsS2+yeYvcL49Vsj4cxh0WFkiqIGLRpw+4nj16r7+hBs6t74IIu287gMwh4+RVFUe/Rbrh+oViiRWVrf14fRa94iHkZ6CRHsIcAdcX59fTgURVH9Cg24/cCpnIHdu2UwgDvH+ePpeWEQ82klGIqiKFNowO0HmAO4Jmi0nxgvL45AlJ9rXx8KRVFUv0YDbj8wdaQHWEwGtLqBUZqYy2JCrdNhzayRWDVjBNi0eg9FUVSPaMDtB05lVw+YYDsjxBNv3BYFoRMbzlz650NRFGUu+o3ZD9wwyhOhUmGn9IZ9LcJXhNpGNYrrmuHMZeG5BeG4c5w/GAN4CJyiKKqv0IDbD3BYTDx7Uxju/Tqprw/F4M5x/nj91kjIm9X49nQ+FkbLEOgu6OvDoiiKGrBowO1j7xzMwpYTueCyWX19KAD0CSs2LYlGqFQEBoMBV2cuVs0c2deHRVEUNeDRgNtHcqsaodURfHokGyqNDoCmrw8JXkIevn1gAs1jTFEUZQc04PaBA5fK8K/vz0OtI63Btn948/YoGmwpiqLshAZcB/vyWA5e2ZfZ14fRyV3jAzAjxKuvD4OiKGrQohsoHahJpcFnR3P69BhMLTD2l/Dxf/PDHH8wFEVRQwjt4TrQ18dzUdmgdOhrTh3pgfmRPnDmscFjM7HvYil+vVBiuH+YhwBv3R5FiwdQFEXZGf2WdaCcykaHvdaiaBkenzECIVKh0e2zw7xx53h/bPg1A4W1TXj91kjEBUkcdlwURVFDFQ24DiJvVuNMbo1DXstTyMPbd0SBZ2KrEZPJwKThHtjx0ETw2EwIaM+WoijKIei3rQMQQrDquxQU1zXb/bXYTAa23jfeZLBtTyLg2v1YKIqiqOvooikHYDAYuFBYZ/fX4bKYeGVxBMJ8RHZ/LYqiKMoytIfrIAHuzkgvtl+ReScOE7+umoJR3sKeG1MURVEOR3u4DhJu517nszeF0WBLURTVj9GA6yBTRnra5XldeGw8Mm047pkQaJfnpyiKomyDDik7yLRRnnAXcFHdqLLocU4cJqaM8ESkrxiZpfU4klUJLxEPT80JgY/YCaO8hRA6cex01BRFUZStWNzDPXr0KBYuXAiZTAYGg4E9e/YY7lOr1fjPf/6DyMhICAQCyGQy3HvvvSgpKTF6DqVSidWrV8PDwwMCgQCLFi1CUVGRUZva2lokJiZCLBZDLBYjMTERdXV1Rm0KCgqwcOFCCAQCeHh44IknnoBKZRzQ0tLSMG3aNPD5fPj6+uKll14CIY4v9i7mc/D6rZFmtw9yd8Zbt0Uh9bkEfLk8DmtuHIlPE2OR8txs/PnkDVgQJUNsoIQGW4qiqAHC4oDb2NiI6OhofPTRR53ua2pqQkpKCp577jmkpKTg559/RlZWFhYtWmTU7sknn8Tu3buxc+dOHD9+HAqFAgsWLIBWqzW0WbZsGc6fP4/9+/dj//79OH/+PBITEw33a7VazJ8/H42NjTh+/Dh27tyJXbt2Yd26dYY29fX1mD17NmQyGc6ePYsPP/wQGzduxDvvvGPpadtEwmgppo706LHd/CgfHFo7DUvG+YPPNd7ew+ey4MTpH6X8KIqiKAuQXgBAdu/e3W2bpKQkAoDk5+cTQgipq6sjHA6H7Ny509CmuLiYMJlMsn//fkIIIRkZGQQAOX36tKHNqVOnCABy+fJlQgghv//+O2EymaS4uNjQZseOHYTH4xG5XE4IIeSTTz4hYrGYtLS0GNq8/vrrRCaTEZ1OZ9Y5yuVyAsDwnL21O6WIBP7nN5P/Il7YT+a8e4RUNbT0/EQURVGUVWz9vW4uuy+aksvl+kLmrq4AgOTkZKjVaiQkJBjayGQyRERE4OTJkwCAU6dOQSwWY8KECYY2EydOhFgsNmoTEREBmUxmaDNnzhwolUokJycb2kybNg08Hs+oTUlJCfLy8ux1yt1aFC3DwmgZmK1FBKQiJ9wbH4iHpw3DxRcS8NvqKXCnJfIoiqIGHbsummppacHTTz+NZcuWQSTSb4spKysDl8uFm5ubUVtvb2+UlZUZ2nh5dS4V5+XlZdTG29vb6H43NzdwuVyjNkFBQZ1ep+2+4ODgTq+hVCqhVF4vMFBfb9u9s0wmAx/eFYPXb41EZmk9ovzERlmh2CwT5XwoiqKoAc9uPVy1Wo0777wTOp0On3zySY/tCSFgtKsdxzBRR84WbUjrgilTjwWA119/3bBQSywWw9/fv8djt4YLj41xQZIeUzBSFEVRg4NdAq5arcaSJUuQm5uLgwcPGnq3ACCVSqFSqVBbW2v0mIqKCkPvUyqVory8vNPzVlZWGrVp68m2qa2thVqt7rZNRUUFAHTqHbd55plnIJfLDf8KCwstOXWKoiiKMsnmAbct2F69ehWHDh2Cu7u70f2xsbHgcDg4ePCg4bbS0lKkp6dj0qRJAID4+HjI5XIkJSUZ2pw5cwZyudyoTXp6OkpLSw1tDhw4AB6Ph9jYWEObo0ePGm0VOnDgAGQyWaeh5jY8Hg8ikcjoH0VRFEX1mqWrrBoaGkhqaipJTU0lAMg777xDUlNTSX5+PlGr1WTRokXEz8+PnD9/npSWlhr+KZVKw3M88sgjxM/Pjxw6dIikpKSQmTNnkujoaKLRaAxt5s6dS6KiosipU6fIqVOnSGRkJFmwYIHhfo1GQyIiIsisWbNISkoKOXToEPHz8yOrVq0ytKmrqyPe3t7krrvuImlpaeTnn38mIpGIbNy40ezz7avVbBRFUZR99NX3usUB959//iEAOv1bvnw5yc3NNXkfAPLPP/8YnqO5uZmsWrWKSCQSwufzyYIFC0hBQYHR61RXV5O7776bCIVCIhQKyd13301qa2uN2uTn55P58+cTPp9PJBIJWbVqldEWIEIIuXjxIpk6dSrh8XhEKpWSF1980ewtQYTQgEtRFDXY9NX3OoOQPki7NIDU19dDLBZDLpfT4WWKoqhBoK++12nxAoqiKIpyABpwKYqiKMoBaMClKIqiKAegAZeiKIqiHIAGXIqiKIpyABpwKYqiKMoBaMClKIqiKAegAZeiKIqiHIAGXIqiKIpyALvWwx0M2hJx2bouLkVRFNU32r7PHZ1okQbcHjQ0NACA3eriUhRFUX2juroaYrHYYa9Hcyn3QKfToaSkBEKhsMui9f1FfX09/P39UVhYOOjyPtNzG5jouQ1Mg/ncAEAulyMgIAC1tbVwdXV12OvSHm4PmEwm/Pz8+vowLDKY6/jScxuY6LkNTIP53AD997tDX8+hr0ZRFEVRQxQNuBRFURTlADTgDiI8Hg8vvPACeDxeXx+KzdFzG5jouQ1Mg/ncgL47P7poiqIoiqIcgPZwKYqiKMoBaMClKIqiKAegAZeiKIqiHIAGXIqiKIpyABpw+5FPPvkEwcHBcHJyQmxsLI4dO9Zt+48//hhhYWHg8/kICQnB1q1bje7/4osvMHXqVLi5ucHNzQ033ngjkpKSjNq8+OKLYDAYRv+kUmm/P7ctW7Z0Om4Gg4GWlpZevW5/OLfp06ebPLf58+cb2jjifTt69CgWLlwImUwGBoOBPXv29PiYI0eOIDY2Fk5OThg2bBg+/fTTTm127dqF8PBw8Hg8hIeHY/fu3Z3a2Pt9s8e59ZfPmz3Orb983uxxbg79vBGqX9i5cyfhcDjkiy++IBkZGWTNmjVEIBCQ/Px8k+0/+eQTIhQKyc6dO0l2djbZsWMHcXFxIb/++quhzbJly8jHH39MUlNTSWZmJlm5ciURi8WkqKjI0OaFF14go0ePJqWlpYZ/FRUV/f7cNm/eTEQikdFxl5aW9up1+8u5VVdXG51Teno6YbFYZPPmzYY2jnjffv/9d/J///d/ZNeuXQQA2b17d7ftc3JyiLOzM1mzZg3JyMggX3zxBeFwOOSnn34ytDl58iRhsVjktddeI5mZmeS1114jbDabnD592tDGEe+bPc6tv3ze7HFu/eXzZo9zc+TnjQbcfmL8+PHkkUceMbotNDSUPP300ybbx8fHk/Xr1xvdtmbNGjJ58uQuX0Oj0RChUEi++eYbw20vvPACiY6Otv7AzWCPc9u8eTMRi8U2fV1rOOJ9e/fdd4lQKCQKhcJwmyPet/bM+XL797//TUJDQ41ue/jhh8nEiRMNPy9ZsoTMnTvXqM2cOXPInXfeafjZEe9be7Y6t4766vPWnq3Orb983tqz1/tmz88bHVLuB1QqFZKTk5GQkGB0e0JCAk6ePGnyMUqlEk5OTka38fl8JCUlQa1Wm3xMU1MT1Go1JBKJ0e1Xr16FTCZDcHAw7rzzTuTk5PTibIzZ89wUCgUCAwPh5+eHBQsWIDU1tVevaylHvW9fffUV7rzzTggEAqPb7fm+WePUqVOdfhdz5szBuXPnDOfWVZu235cj3jdrmHNuHfXF580a5p5bX3/erGHN+2bPzxsNuP1AVVUVtFotvL29jW739vZGWVmZycfMmTMHX375JZKTk0EIwblz5/D1119DrVajqqrK5GOefvpp+Pr64sYbbzTcNmHCBGzduhV//vknvvjiC5SVlWHSpEmorq7u1+cWGhqKLVu24Ndff8WOHTvg5OSEyZMn4+rVq1a/bn85t/aSkpKQnp6OBx54wOh2e79v1igrKzP5u9BoNIZz66pN2+/LEe+bNcw5t4764vNmDXPOrT983qxh6ftm788brRbUj3Qs/0cI6bIk4HPPPYeysjJMnDgRhBB4e3tjxYoVeOutt8BisTq1f+utt7Bjxw4cPnzYqIc1b948w/9HRkYiPj4ew4cPxzfffIO1a9fa6Mxsf24TJ07ExIkTDY+ZPHkyxo4diw8//BAffPCBVa9rLXu+b1999RUiIiIwfvx4o9sd9b5ZytTvouPt5vy+HPG+Wcqcc2vT1583S/V0bv3p82YpS943e3/eaA+3H/Dw8ACLxep0JVhRUdHp6qwNn8/H119/jaamJuTl5aGgoABBQUEQCoXw8PAwartx40a89tprOHDgAKKioro9FoFAgMjISMOVa2/Z+9zaMJlMjBs3znDc1ryupex9bk1NTdi5c2enq21TbP2+WUMqlZr8XbDZbLi7u3fbpu335Yj3zRrmnFubvvy8WcOSc2vTF583a1hybo74vNGA2w9wuVzExsbi4MGDRrcfPHgQkyZN6vaxHA4Hfn5+YLFY2LlzJxYsWGBU4/Htt9/Gyy+/jP379yMuLq7HY1EqlcjMzISPj491J9OBPc+tPUIIzp8/bzju3ryuuex9bj/88AOUSiXuueeeHo/F1u+bNeLj4zv9Lg4cOIC4uDhwOJxu27T9vhzxvlnDnHMD+v7zZg1zz629vvi8WcOSc3PI563Xy64om2hbUv/VV1+RjIwM8uSTTxKBQEDy8vIIIYQ8/fTTJDEx0dD+ypUrZNu2bSQrK4ucOXOGLF26lEgkEpKbm2to8+abbxIul0t++ukno+XsDQ0Nhjbr1q0jhw8fJjk5OeT06dNkwYIFRCgUGl63v57biy++SPbv30+ys7NJamoqWblyJWGz2eTMmTNmv25/Pbc2U6ZMIUuXLjX5uo543xoaGkhqaipJTU0lAMg777xDUlNTDds8Op5b2xaMf/3rXyQjI4N89dVXnbZgnDhxgrBYLPLGG2+QzMxM8sYbb3S5Lcie75s9zq2/fN7scW795fNmj3Nr44jPGw24/cjHH39MAgMDCZfLJWPHjiVHjhwx3Ld8+XIybdo0w88ZGRlkzJgxhM/nE5FIRG6++WZy+fJlo+cLDAwkADr9e+GFFwxtli5dSnx8fAiHwyEymYzceuut5NKlS/3+3J588kkSEBBAuFwu8fT0JAkJCeTkyZMWvW5/PTdC9IEZADlw4IDJ13TE+/bPP/+Y/PtZvny5yXMjhJDDhw+TmJgYwuVySVBQEPnf//7X6Xl//PFHEhISQjgcDgkNDSW7du3q1Mbe75s9zq2/fN7scW795fNmr79JR33eaHk+iqIoinIAOodLURRFUQ5AAy5FURRFOQANuBRFURTlADTgUhRFUZQD0IBLURRFUQ5AAy5FURRFOQANuBRFURTlADTgUhRFUf3K0aNHsXDhQshkMjAYDOzZs8fi5yCEYOPGjRg1ahR4PB78/f3x2muv2f5gLUCrBVEURVH9SmNjI6Kjo7Fy5UrcdtttVj3HmjVrcODAAWzcuBGRkZGQy+VdllJ0FJppiqIoiuq3GAwGdu/ejcWLFxtuU6lU+O9//4vt27ejrq4OERERePPNNzF9+nQAQGZmJqKiopCeno6QkJC+OXAT6JAyRVEUNaCsXLkSJ06cwM6dO3Hx4kXccccdmDt3rqFc3t69ezFs2DD89ttvCA4ORlBQEB544AHU1NT06XHTgEtRFEUNGNnZ2dixYwd+/PFHTJ06FcOHD8f69esxZcoUbN68GQCQk5OD/Px8/Pjjj9i6dSu2bNmC5ORk3H777X167HQOl6IoihowUlJSQAjBqFGjjG5XKpWGovI6nQ5KpRJbt241tPvqq68QGxuLK1eu9NkwMw24FEVR1ICh0+nAYrGQnJwMFotldJ+LiwsAwMfHB2w22ygoh4WFAQAKCgpowKUoiqKonsTExECr1aKiogJTp0412Wby5MnQaDTIzs7G8OHDAQBZWVkAgMDAQIcda0d0lTJFURTVrygUCly7dg2APsC+8847mDFjBiQSCQICAnDPPffgxIkT2LRpE2JiYlBVVYW///4bkZGRuOmmm6DT6TBu3Di4uLjgvffeg06nw+OPPw6RSIQDBw702XnRgEtRFEX1K4cPH8aMGTM63b58+XJs2bIFarUar7zyCrZu3Yri4mK4u7sjPj4eGzZsQGRkJACgpKQEq1evxoEDByAQCDBv3jxs2rQJEonE0adjQAMuRVEURTkA3RZEURRFUQ5AAy5FURRFOQANuBRFURTlADTgUhRFUZQD0IBLURRFUQ5AAy5FURRFOQANuBRFURTlADTgUhRFUZQD0IBLURRFUQ5AAy5FURRFOQANuBRFURTlADTgUhRFUZQD/D+dcKs/cG2T6gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gdf.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot: >"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdwAAAG0CAYAAACYBJbCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAADyXUlEQVR4nOydeXhU5dn/P2f2ySQz2ZfJwk5Ywg6yKq6gBZXWVi2Vli52EfRV7Obb5e1r31bbn9pFW23VaisqrihuCAKCCGEJCSSQBAKE7PtkJjOZfc7vj8jAkG0mG0Sfz3Xluphz7vOcZwKc77mf514kWZZlBAKBQCAQDCqKiz0BgUAgEAi+CAjBFQgEAoFgCBCCKxAIBALBECAEVyAQCASCIUAIrkAgEAgEQ4AQXIFAIBAIhgAhuAKBQCAQDAFCcAUCgUAgGAKE4AoEAoFAMAQIwRUIBAKBYAiISHAfeugh5syZQ0xMDMnJyaxYsYLS0tIQG7vdztq1a8nIyECv1zNx4kSefPLJEBu3283dd99NYmIiBoOBm266iaqqqhAbi8XCqlWrMJlMmEwmVq1aRWtra4hNRUUFN954IwaDgcTERO655x48Hk+ITWFhIYsXL0av15Oens6DDz6IqGYpEAgEgqEmIsHduXMna9asITc3l61bt+Lz+ViyZAkOhyNoc99997F582bWr19PcXEx9913H3fffTdvv/120Obee+9l48aNbNiwgd27d2O321m+fDl+vz9os3LlSgoKCti8eTObN2+moKCAVatWBc/7/X6WLVuGw+Fg9+7dbNiwgTfeeIP7778/aGOz2bjuuuswm80cOHCAxx9/nEceeYTHHnusT78sgUAgEAj6jNwPGhoaZEDeuXNn8NjkyZPlBx98MMRu5syZ8i9/+UtZlmW5tbVVVqvV8oYNG4Lnq6urZYVCIW/evFmWZVk+duyYDMi5ublBm71798qAXFJSIsuyLL///vuyQqGQq6urgzYvv/yyrNVqZavVKsuyLP/973+XTSaT7HK5gjYPPfSQbDab5UAg0J+vLhAIBAJBRKj6I9ZWqxWA+Pj44LFFixaxadMmvvOd72A2m/n44485fvw4f/nLXwDIy8vD6/WyZMmS4DVms5mcnBz27NnD0qVL2bt3LyaTiblz5wZt5s2bh8lkYs+ePWRnZ7N3715ycnIwm81Bm6VLl+J2u8nLy+Oqq65i7969LF68GK1WG2LzwAMPUF5ezqhRozp9J7fbjdvtDn4OBAK0tLSQkJCAJEn9+XUJBAKB4BJAlmXa2towm80oFEMXytRnwZVlmXXr1rFo0SJycnKCx//6179y5513kpGRgUqlQqFQ8Mwzz7Bo0SIA6urq0Gg0xMXFhYyXkpJCXV1d0CY5ObnTPZOTk0NsUlJSQs7HxcWh0WhCbEaOHNnpPmfPdSW4Dz30EP/7v/8bya9CIBAIBMOQyspKMjIyhux+fRbctWvXcuTIEXbv3h1y/K9//Su5ubls2rSJESNGsGvXLu666y7S0tK49tprux1PluUQD7Irb3IgbOTPAqa681YfeOAB1q1bF/xstVrJysqisrISo9HY7fwFAoFAMDyw2WxkZmYSExMzpPftk+DefffdbNq0iV27doW8HTidTv77v/+bjRs3smzZMgCmTp1KQUEBjzzyCNdeey2pqal4PB4sFkuIl9vQ0MCCBQsASE1Npb6+vtN9Gxsbgx5qamoq+/btCzlvsVjwer0hNme93fPvA3Tyjs+i1WpDlqDPYjQaheAKBALB54ih3iaMaPFalmXWrl3Lm2++yfbt2zstyXq9Xrxeb6c1caVSSSAQAGDWrFmo1Wq2bt0aPF9bW0tRUVFQcOfPn4/VamX//v1Bm3379mG1WkNsioqKqK2tDdps2bIFrVbLrFmzgja7du0KSRXasmULZrO501KzQCAQCASDSiQRVj/60Y9kk8kkf/zxx3JtbW3wp729PWizePFiefLkyfKOHTvkU6dOyc8995ys0+nkv//970GbH/7wh3JGRob80UcfyYcOHZKvvvpqedq0abLP5wvaXH/99fLUqVPlvXv3ynv37pWnTJkiL1++PHje5/PJOTk58jXXXCMfOnRI/uijj+SMjAx57dq1QZvW1lY5JSVF/vrXvy4XFhbKb775pmw0GuVHHnkk7O9stVplIBj5LBAIBILhzcV6rkckuECXP88991zQpra2Vl69erVsNptlnU4nZ2dny48++mhIGo7T6ZTXrl0rx8fHy3q9Xl6+fLlcUVERcq/m5mb5G9/4hhwTEyPHxMTI3/jGN2SLxRJic+bMGXnZsmWyXq+X4+Pj5bVr14akAMmyLB85ckS+/PLLZa1WK6empsq/+c1vIkoJEoIrEAgEny8u1nNdkmVRdqknbDYbJpMJq9Uq9nAFAoHgc8DFeq6LWsoCgUAgEAwBQnAFAoFAIBgChOAKBAKBQDAECMEVCAQCgWAIEIIrEAgEAsEQIARXIBAIBIIhQAiuQCAQCARDgBBcgUAgEAiGACG4AoFAIBAMAUJwBQLBsEGWZZ795ABlDc0XeyoCQcT0uR+uQCAQDDXvHSnl0Q938+iHu5lkTubm6RP50tQJJERHXeypCQS9IgRXIBAMG/adqgz++VhNA8dqGvjj5l1cPWEM3140i2mZaUPe41QgCBchuAKBYNhQWFXX6Zg/ILP1WBlbj5WRaoxm5dzpLJ82gdTYmIswQ4Gge8QerkAgGBY43J5e927rbHbeLShm6e+eZc2zb7GtqAyv3z8o8wkEZDYXlHKitmlQxhd8/hAerkAgGBYU1zQQCKObqC8QICDL7Co+za7i0yQZDXzlshy+Om/KgHq9v9u4naLKOv75/VsGbEzB5xshuAKBYFiQX1ETlp3nAo+20ebgHx/t4+lt+5k2Io3bF07juinjUKuU/ZrPuNRErpo8BpVCQb3VToopul/jCT7/CMEVCATDgoNnqsOy684JDsgy+eU15JfX8FDUDpbPmsiNsyYyMT054kCrTQeP8dzHB2mw2hmRFEtVs5UVl+Xw3atmkxY3dA3NBcMLSZbDWKP5AmOz2TCZTFitVoxG8R9JILgY+PwB5v/+SRxuT6+2CVF6WlucYY89Ojmem+dM4qZZk0g0Gnq1P17bxD3PvU11i63TOYUk8fq6OxiXlhj2/QVDz8V6rgsPVyAQXPJ8XHoqLLEFiNSDONXQwp/e281fP/iUKyeN4Stzc1gwfgQqZdcxpRZ7e5diCx1e9E/Wv0dqrJFvXjGTBdkjIpyN4POMEFyBQHDJ88r+I2Hb6tVqrITv4Z7FH5DZVlTGtqIyEmKiuHHmRL46bwojkuKCNgdOVvH8zrwexzlZ38LJ+hYSovVCcAUhCMEVCASXNB6fn7ww928Bkg0G6ujaAw2X5rZ2nt+Zx7935TFjpJkVc3K4OmcM7+YVs7ukPKwxSmoakWVZFOIQBBGCKxAILmmOVNXi8vrCtldJA1deQJbh0OkaDp2u4TevSWgiiGw+XtvER4VlXDd13IDNRzC8EYUvBALBJc355RzDwe8PDMo8ArIckfADbCsqG5S5CIYnQnAFAsElTaSCq1JdOo+1KZmpF3sKgkuIS+dfpkAgEFyA0+OloLI2omv8l1Cm4+mGlos9BcElhBBcgUBwyfJp2Rl8ES4RX0qlBbYVlREIXDrzEVxchOAKBIJLlqLq+oivUSr691hLH8BKUU1t7fz85Q/Yd6LiknoREFwcRJSyQCC4ZPm45FTkF/VT15rt7f0b4AI+yC/lg/xSshJj+fJlk1k+c6JoHfgFRXi4AoHgkqTaYuV4feSt72wOV8hnnVrFl2ZMCPv6SCORw6WiqZW/vP8pS3/3LD96ZiO7ik/jD3Qsl5+qb6Y9zEpaguGL8HAFAsElSW6E0ckAWbEmyurO9cwdmRTHj5bM4819RQM5tX4RkGV2l5Szu6ScJKOBZTMmUNHcyp7SMyyeNJql08Yzb1wWMXrtxZ6qYIARzQt6QTQvEAguDg9u2saGCEo6Asw2p5N/KvyqVJcqerWKa6aMZUH2SK6aPJponRDfgUQ0LxAIBILzKKyqi8heq1JyvLox4vuoFAp8gcEplhEOOSNSiI8xcKisCrurY1nZ6fXx7qES3j1Uglal5MrJY7hq8hiumTIWnVo8tocr4m9OIBBcchyva+JoTUNE1xi1WrKSYqlpsdFgtYd1zeg4E1pJQXGLpS/T7BejUuJRR6vIa65HssIvb7qSjw6coN3rISpKg0cOYPd5kJCodbbxyv4jvHGwkESDgTljMrh2yjjiDPohn7eg70QUNPXQQw8xZ84cYmJiSE5OZsWKFZSWlnayKy4u5qabbsJkMhETE8O8efOoqKgInne73dx9990kJiZiMBi46aabqKqqChnDYrGwatUqTCYTJpOJVatW0draGmJTUVHBjTfeiMFgIDExkXvuuQePJzTwoLCwkMWLF6PX60lPT+fBBx8U4fkCwSXOW/nHIr6m0dFOXm0NtW47sfF6Jo1MYebYdEYlx6FUdN1AICG/nqjA0DUXUEgSY1ITmDreTImrhcLmjpcKGfjn3oOsuDyHw/ZG9jZUk9dYS6mlmRJLEwcaasltqmZ3QxVvnSrl6f0HWffSe/zq1S18VHhCBFwNEyLycHfu3MmaNWuYM2cOPp+PX/ziFyxZsoRjx45hMHQ0bj558iSLFi3iu9/9Lv/7v/+LyWSiuLgYnU4XHOfee+/lnXfeYcOGDSQkJHD//fezfPly8vLyUCo7ioOvXLmSqqoqNm/eDMD3v/99Vq1axTvvvAOA3+9n2bJlJCUlsXv3bpqbm/nWt76FLMs8/vjjQMc6/XXXXcdVV13FgQMHOH78OKtXr8ZgMHD//ff3/7cnEAgGHK/fzzsFxf0ao7ndSXP7uRZ9kgZmxCcS1eLhmOTG6nSTEx9H5WPvkTktrb9TDovs9CTqAg6OOZvBBVyg8x6/H2U4nYUkKLdZKbdZoQ6KmhpYvyefxCgDy2dO4MrJYwZl/oL+06+gqcbGRpKTk9m5cydXXHEFALfffjtqtZoXXnihy2usVitJSUm88MIL3HbbbQDU1NSQmZnJ+++/z9KlSykuLmbSpEnk5uYyd+5cAHJzc5k/fz4lJSVkZ2fzwQcfsHz5ciorKzGbzQBs2LCB1atX09DQgNFo5Mknn+SBBx6gvr4erbYj6ODhhx/m8ccfp6qqKqy2WSJoSiAYWnaUnGTN+k39GkOvVnH9lGyO1dTjCwRobHMwVm2g/ufvgkIiKsWI2qjDWlpP0v8tp7BlcEswjk9P4rirGXcPVbNSYqJZOWsaf/r40z7dQy1J3H35fH54xdy+TvMLw8V6rvcrD9dqtQIQHx8PQCAQ4L333mP8+PEsXbqU5ORk5s6dy1tvvRW8Ji8vD6/Xy5IlS4LHzGYzOTk57NmzB4C9e/diMpmCYgswb948TCZTiE1OTk5QbAGWLl2K2+0mLy8vaLN48eKg2J61qampoby8vMvv5Ha7sdlsIT8CgWDo2NRP7xbg+e9+jd99ZQkb167iqVUrkJAodtrQxOohINNea8Va2lHFqvnBzcwqtpFuGvhiFGlxMR3Lx86exRZgTlY6lZbWPt8rLjqK22ZPBeDTU2doOc/DF1wa9FlwZVlm3bp1LFq0iJycHAAaGhqw2+08/PDDXH/99WzZsoUvf/nLfOUrX2Hnzp0A1NXVodFoiIuLCxkvJSWFurq6oE1ycnKneyYnJ4fYpKSkhJyPi4tDo9H0aHP281mbC3nooYeC+8Ymk4nMzMyIfi8CgaDvWJ0udvSlutR5qBQKctI7/p9XNLey6pnXsDpdOL0+zLfN7GQf8Pio/qCIEVY/CxQGJsTHdbKJFK1axbTxZsoDbRxoqMEbRhT0/JFZ/RLJB65djEGj5rX8Iu5+412cHm+fxxIMDn0W3LVr13LkyBFefvnl4LHAZ/+obr75Zu677z6mT5/Oz3/+c5YvX85TTz3V43iyLIcs8Xa13DsQNmdX0LtbTn7ggQewWq3Bn8rKyJPvBQJB37A5XXx97jSM/Sj6sHrRLCRJQpZlfv76ZuqsbedO9tBA/tRjH3Hqka0owtlH7YXRqfGcsFk67dP2xLyRmTQ6HH2+p0Gr4em9B/nFe1uxuz1c9cSzXPbok/x+684+jykYWPokuHfffTebNm1ix44dZGRkBI8nJiaiUqmYNGlSiP3EiRODUcqpqal4PB4sltAw/IaGhqD3mZqaSn1956LljY2NITYXeqkWiwWv19ujTUNDR1TghZ7vWbRaLUajMeRHIBAMLrIs8+DXHuGJrz9OVkELL37vVgxaTcTjJMUYuPua+QC8mXc0pLXfLGM8p/+2q9cxoitsTMvqXyBVcWUDcouXecnpaMJopmDQqGn3eiiuizyPGGByajKLRo/gQEVo0Y9Wp4u3C/u/RC8YGCISXFmWWbt2LW+++Sbbt29n1KhRIec1Gg1z5szplCp0/PhxRowYAcCsWbNQq9Vs3bo1eL62tpaioiIWLFgAwPz587Farezfvz9os2/fPqxWa4hNUVERtbXn/kNt2bIFrVbLrFmzgja7du0KSRXasmULZrOZkSNHRvLVBQLBILL3nYN88sY+Dm09wr9+8RLOE408tWoFK2ZMIiE6Kuxxfrz0ctRKJZuLjvPrt889Y7QqFbbHd/fqcKo0Kk6qAjTYwsvj7Qmnx0d9Y1tYRTXmZGVg0ukx93Ef+faZU1EpFJi6qEj15akdDpDX78fhEelDF5OIBHfNmjWsX7+el156iZiYGOrq6qirq8PpPLfv8JOf/IRXXnmFp59+mrKyMp544gneeecd7rrrLgBMJhPf/e53uf/++9m2bRv5+fnccccdTJkyhWuvvRbo8Iivv/567rzzTnJzc8nNzeXOO+9k+fLlZGdnA7BkyRImTZrEqlWryM/PZ9u2bfz4xz/mzjvvDHqlK1euRKvVsnr1aoqKiti4cSO///3vWbduXVgRygKBYPBxO908ed/zIccS0uMpWZ/L6pFj2fnT77P+zltZmjOux+Veo07LsqkTkGWZZ3Yd4Pz8iyl6I+3Vrb3OZezSKbjUCmpb23q1DYcaiw1zdO+rZPNHZXG4upamPnYqcvs7Gi4U14d6yAaNmm/NmQ505AAbNJGvGggGjogE98knn8RqtXLllVeSlpYW/HnllVeCNl/+8pd56qmn+OMf/8iUKVN45plneOONN1i0aFHQ5k9/+hMrVqzg1ltvZeHChURFRfHOO+8Ec3ABXnzxRaZMmcKSJUtYsmQJU6dODUk1UiqVvPfee+h0OhYuXMitt97KihUreOSRR4I2JpOJrVu3UlVVxezZs7nrrrtYt24d69at69MvSyAQDDyvPfIOdadDq0r9+39ewdpkY82cn/OtcWv56H82cl2bjv989UYmpnUEVMbpdSHX2Fxuvvnsq6zPLeDYBVWqNKW9dx3SRes4mhXdbZGM85k5Ip3stMRe7QAyo3r3WiemJGFxOvnBwjlhjXmWGRlpHP7ZWlbOmkaby82ZltaQ8zdPmYRCoaCgqrbffYIF/Uc0L+gFkYcrEAwedeUNfHfSvXhc4UXU6qK0PHjod/zPO9tR/m0fURlx5F/ROaPhQmYZ46n9+bs9Liln37GA3UaI1muwyj0vveakJlNc3kB6kpHE+GiO1zcF6yBfyMxx6ext7Lmhwn9ft5j86lo+OHa8t68Swkd3fZuMOBMPvLOFD4tP0O4N/T1GazXcMHE8Cknit8uujWjszzOieYFAIPjC8Y8f/ydssQVwtbuZmJFK4gtFVJY301bejOrK1F73SbVOX49ia842szdGBiRUSgVzMjKQP+tkf9YjkWUIyB33aW3t2EarbrRR3WgjSqdmxggzhytqCVzgw7Q7e983PVRVQ31bz/vGMVotbW43AD9aeBlOr5es+Fg+OHacjUe6LoVpd3t4raCIpRPG9joHweAjBFcgEFwUSvafYPeb+yK6JjrWgFavoa6kBujIupkSE0u+tetKUQpJYkGbmtN/3dztmMbEGOzXjsMvdwi/w+nhUElVt/Zd0e7ycqS0hjHmeNoCXurO2wMurW4kKTWKRmf3+7N5ldV8dVoO+VW1GDRqpppTOVJTh+O8XNqxSfGMiIvl7cJiJqYmcfX4MfgDAR7ftbfHucVotXxzzoyIvo9gcBCL+gKB4KKg7CEntjsyss2cLCjH6/EFjzX/z2bm6WPRKDuPN08Vw+m/ftzteGMWjMd2+wxOyl40KiUpsdF4/QH6GlN5qqaFthYns0amBwO8ZBnGxnxWTKObDbxGezvT0lN5dMUN3HvlAv72tZsYk5gQYpNfVUur08VDNy5BrVSiUSr5oPgEZU1dv2xkxBr54cLLeOt732DOiIwubQRDixBcgUBwUdjy/McR2SsUEt97+BuUFZSHHPe7fFT+6n3SHz/Igkofc6NiUSkUxGg1NDy1p9M4iVkJ6AxaRt15BbnTEmgMdIh3VlJsMHtBiqRixQW4PD4KSqoZn5AQTGkqr7Ewy5TKTFPX+f8AP3x1E9FaDWlGIw12OxNTkjrZfFx2mp0ny7lq3OgevdvvzZ/FtjXfYd1VC8mMM4U990OVNTi9okLVYCGWlAUCwZDj9/sp+Lgoomu+8cuvMvWKSfzzJ103RvG0OjnzQkfu/oQpZqInp1FZH1oLffy1ORwabyJaoWR/wMf5paDKapsBUEh02oftCyeqmojRa5mWlcbhilpaPkv5ycgwUmXvuka7pd1JrF5Hi6O9233Z7cdPcu+b73HN+DGcbu66j++MDHNEqY8BWcbj8zMz09y7saDPCMEVCARDzrb1n1BeFH7Z1PRxaXzjl7ew+819HD94sld7S2ENlsKa4OeEzASSZ41kV5oGkHEGfN1eG2vQ02IfmML/bU43hcdrmT42jRONzTjcHjJ0Md0KrtXlJjPORElDE8nRBqqsne3cPj+bi0+wpaSs2/uOTwovZeksA/GCIegdIbgCgWBI8bg8PP/rDRFds/DmOQQCAf71i5civl9siomamyZSEvAT1ubsANfEkYDCslqSTAbMqTGUVNRjMKlxdLF022i3MydrJtPS02h2tFNU28DOstNdjtudSOpUqpBlZEu7k7gofY9zVCkUqESe7qAjfsMCgWBIeftvH9JY2Ry2fcqIJK771mL++K0nqDpeS8b4NNRadYhNlFHPlMsnMmn+eEZPGxE8rlBI6L4yHZscCE9s6SgDORg0Wh2cPtPMZHMKU+O6zh3ecKiQRrsDjVLJ3VfM55+33cwfb1oa0TvA2KSEkIpc3Yltm8uNP4yyk4KBQwiuQCAYMuytDl7+/Rth20uSxE//vZbdb+7n41f2EJtsIi41Fq/7nHcYm2xkzLSRFH5SzLG9x3HaXcFzySOSaJAiWy6N0g1e+UNZhvySahwtbjKjOwczaZRKGu0O9pVXUtVqRZIkVkydxOq559oKrpw1rcd7zMwIbbzQXW2j+jZ7p8pUgsFFCK5AIBgyNjy8kTZL7y3oomL0RMXoue1nK1CqlLzwv6+iVCnImpBO6f4ylColBlNHBHBiejyFn3R0xBkzfSS1Jzs6jWn0GuKyEqiXI4u61WsGf6ettsmGw9u5IMYfb76en236kFXrX2dTYUnw+H1XLmRcUgJfnTaZq8eP7nbcq8eN5hdLrgx+/vTUGW7518td2o5NSmBkQhzvFpXw5O793QqzYOAQe7gCgWBIOF14htcfezcsW4/LwxP7H8Zg0vOzJf9HICBDQMZhaycpM4H4tDiK9x5n7MxRlB06t8d55lgVUy6fiMflxdpoxToxGaTIBFfdRT7vQDNmTBK5zZ3LPf5gw1v4PxM+zXl5yjq1it8tu46p6am81U30MsDC0SOQJIlaa0eXojRTDP/v5uu7tVdIEq8VFLG3vBKHx8OPr17Ura2g/wgPVyAQDDp+v5/Hvv8P/D5/WPbGhBhMiTHcPe8X1JSd62l9sqAcWYZje0rxef0hYgvg8/go/KSY0gNl6BJjOCxF3o7O6fEybVQaauXgPB5HpsaR29R1bWW/LKNVKbnnivmdqkNNz0hDIUk9loCclWmmztbGt196k09OlTM6IZ4xifE9zuds8NU/9xzg/WOlPdoK+ocQXIFAMOi8/cRmSvadCNv+pjXX8/7T22htsHY6V1NWh9/Xe7CP9bKssAOlzqekqhGlUoHXP/ABRTF6LW3R/h4joe9aNI+1V8xDp+56AbLW1rl1YGpMND+5ehHZyYm8cfgop5pbcHnPpT51l68LodHO//P+NuoGoBewoGuE4AoEgkGlrryB537R9T5iVxgTYlhx9w1UHq/p3bgbxl01ieOE591KUocQno/b232ebn8YmR6PJPf8EvD8/kO0udzdnm91ukI+Z8WZeO07X+fOBXNQKhSMjO8oI3l+b1yXz8eppmbKWzoL7/mC6wsE2FwcWcciQfgIwRUIBIOGLMv8+Yf/xNXevYBcyDd+eQtnjlWx69XOZRnDpWVS7y37ziLLcN+XL8dwXnSythvvsj9Myzazz15LhaOz134+lnYnrx8+2u35B65dHPJ5UmoyKTHRwc83TBrP+KQE9p6uCHq5E1OSGJ2YEBTj81ErOvaKTTotu+/9fkhEtGBgEYIrEAgGjY9e2EXelsNh26eOTGLusln87NoHOwKl+kD6pAxKAuELPMCDL30Usmfb0tZ9Z5++0iCHX71qRA/1j6M0alLPE9htpSexnuf1KiSJNZfPo8Hu4Ffvf9RrFakxSR17vNMzzBg0g5cSJRCCKxAIBomPX/mUP/3gHxFds/q3X2fT3zZH5BFfiH7uyD7t3U4fk47us5SgysbWkOIRA0GVvWfP9nxONVu6TdMx6XU8ddvN6D/zwgOyjOqCAK9rxo9Go1TydmExP37rgx7vFafvKIzRVbclwcAiBFcgEAwKf/zWEyEFKnpjzPSRTF6UzfvPfNTne0aZosjX980zTjZFMz69o0PPhIzkAa0vHB+jxx1BVac/bvuE6i7qKJ9lUmoyv1u+BIB7Fi/o5JlWW9vw+Dsiwt87WkphTV2nMc7yrctmoFYoGBkfG/b8BH1DCK5AIBgUIu13+92HvsFff/Q0LkffvdsRS3Jwddd0themjEylsbUjQrfJ5iDJZOjzPC7EcEFQVjg0O3pe1l42aTxXjh3F9+bP6nTuk1PlwT/LwP9u3tHjC4Q3EGB2VnrEcxREhhBcgUAwKOiidWHbzrhmCi21Fg5sLujz/dRaNUXJkQvbWZ5451NqLR0pNxZ7Oy7PwEUqG/SR74121Q/3fCRJ4rEvf6lToQ6v38+fdoQGnB2pqaPkvKjl86lq7VjqTjcZI56jIDKE4AoEgkFh/OzuSxBeyM1rruepdf/u1/3GXDuZFjm8whpdUd96Lv90rDmJNmffPe0L0Wojj3pu6sXDBYjWdhZylUJBVBflKbvrrzspNZlvzJ7G77fuFG36BhkhuAKBYFCYND87LLsRkzI4sDkfe6uDEdNHEvvDxaSOS2XclZOY+OXZTFg+o1N3oLMoFOcCm06PHDgPrd0VeYWqnpCUkQdgvVZQ1Ld7SRI/ufrykGMKSeKr03O6tf/v6xaTaIjieENTn+4pCA9RS1kgEAwKrfXhReVe/fVFrP+/NzDEGmicN4JKvxeuDPWOk789j+wyC6UfhYrQ2Ksnczw7jngUlEbYpKA7jFHaTlG//cUlhe95qxQKrs0e0+e0KICbp0zk6b0HOdHY0QYxVq8jO7n7pvRqpZJHVtzQ5/sJwkMIrkAgGHBkWWb/B4d6tYtLiyVtbCpet5fYby+gxN+1aDb4/TSMMpL6wytIDkjovAE0Ti/qVBONnjYa6ftS8vkoJDBG6Sir7blfr06jIiU2BpVSgV6jpriyodvesmqlgqLWhrDn4AsEaGiz8/CNS7s8X2tt4/1jpay6bEaPqTzLJmXz550de7kt7U4+Kj3Jtdljwp6HYOARS8oCgWDAOXOsiprP2uT1xHX/czOb/7Wd8VdP7lZsz6fO7+OI7GW/ys/uGAXW9IEN9Jk2Op2qpnOeeaxBz9RRacwcm86UkamMSonHZNDh8vg402DhZG0zRWfqmJSV3G3ebmqCEU+Ejd4PVdWy53RFl+fSTDHsOHGaih562UqSxA8WzglpXPDEJ7kRzUEw8AjBFQgEA86nG/f3apM8Kombb1/M6SNn8IzufrmzJ3wRCllPKBUSzTYHeq2aManxTB6RQqvDyZHTtRwqq6awvI7T9S1YHa5O1xaW1zEhMxn9Z3vN2RlJjEqOY+r0dLQJfavedLSu+xeW9d/8GmOTEnq8XiFJXD9xXLBPQnmzpVsvXDA0iCVlgUAw4Hz6du+Ce8OapdQVVKIzRZFP3/ZfHe6BC27yB2T0WjVOt5eTdS0RX3+sop4RybEdS9I1TTg9PsabtRyxhL+cfBa1UklmbPflHcNBkiT+a/ECGu0OXs0vQqGQCMgyop7UxUN4uAKBYEBpqGziRN6pHm1i4qNZced1vPboJozzRhPoQxXFMeYETvSy1xoJU0elUVbTv/HONLRSWF6H87McXq3UN3lLM0bzlWmT+zWXs1wxZhRalZJHV9zQKWdXMLQIwRUIBAPKtvWf9Gpz+bevoKXaQu47edRF9W2hTatT9dhXNlySY6PJzkjiyOnagV9y7WMsV3+F0RcI0GR3ALBkwli2r/0uV40LPy9aMDgIwRUIBANGIBBg20s9C27y6GRSVuSw67W9RJmiOO2LfFk4NT6Go5W9B2X1hASY440EAgFKq7quwtRfvK6+VavqqR9uOKgUChKjz5WmTIoeuDKVgr4jBFcgEAwYhz4qRDfeyKgFXXtTCRnxnPnBeKITo6kvbyBtYjpyH7rypCYa+1gx+RwzxqRTa7HRZBv4VnxnUWv65qk22B20ewYmr1hw6SAEVyAQDAiBQIBnH3iRwCQDB3+uYsz/XkZ8+rmG5zqDlvo1k6nR+kiKMmC3tqNJ79wQvTeitGqKq/vn3abERnO0op5BrWQoQVOgc0RzuJS3WAZwMoJLASG4AoFgQNj56l7K8k8jS4BCYv8sK+V/SyH7x3MwxEaR9ZWpnNF3eG3tXi86g7ZPe7ATRiQHg5L6Slx0FG7vwDUn6Iqp09M52RJ5tPNZTjcLwf28EZHgPvTQQ8yZM4eYmBiSk5NZsWIFpaWl3dr/4Ac/QJIk/vznP4ccd7vd3H333SQmJmIwGLjpppuoqqoKsbFYLKxatQqTyYTJZGLVqlW0traG2FRUVHDjjTdiMBhITEzknnvuweMJ3Q8qLCxk8eLF6PV60tPTefDBB7tt7CwQCPqG1+Pl+V+9DECr+VzgkVcls+dKG61Pj8ZxY1rw+BlrK9GxBuT2yPdvm63tRHVTWzkcJmelUFIVeapOJKy8cjoH6mr6Ncb6gwXiWfU5IyLB3blzJ2vWrCE3N5etW7fi8/lYsmQJDoejk+1bb73Fvn37MJvNnc7de++9bNy4kQ0bNrB7927sdjvLly/H7z8X0rdy5UoKCgrYvHkzmzdvpqCggFWrVgXP+/1+li1bhsPhYPfu3WzYsIE33niD+++/P2hjs9m47rrrMJvNHDhwgMcff5xHHnmExx57LJKvLRAIeuGDZ7ZTc7IeQ5yB02mdnwc2vZeGmHMpN6daLRgTYij94DCxUmQLbQmmKNojaGx/IdouOukMND5Zxt9PscyrrOm2w49geBLRv7zNmzeHfH7uuedITk4mLy+PK664Ini8urqatWvX8uGHH7Js2bKQa6xWK88++ywvvPAC1157LQDr168nMzOTjz76iKVLl1JcXMzmzZvJzc1l7ty5ADz99NPMnz+f0tJSsrOz2bJlC8eOHaOysjIo6o8++iirV6/md7/7HUajkRdffBGXy8Xzzz+PVqslJyeH48eP89hjj7Fu3TqkPgRrCASCznz8yqcApP9gEhVqW5c2cd4koA6ASmsrt1+dw/oHX0OvUNDqDy8dZ9KIFA6equ60FC1JMDXbjF+Wcfg8mNQ6CkqqQ2xMBh0qhWIgMol65e29RRjNWmz9jDZ+79jxAcvHFVx8+rWHa7V21ByNjz9XrzMQCLBq1Sp+8pOfMHly538oeXl5eL1elixZEjxmNpvJyclhz56OQtt79+7FZDIFxRZg3rx5mEymEJucnJwQD3rp0qW43W7y8vKCNosXL0ar1YbY1NTUUF5e3uV3crvd2Gy2kB+BQNA9bqebsvzTjL9hAnsXdf3/JVYRQ37FudZvLU4npiQjIy8bS60//L1Urbbr3NvUOCPHmhs5VF9LaXMzJ60tTJtgJmNkXPCnPcqPUqno9/5vOLg9fhaPGtnvcb43b1b/JyO4ZOiz4MqyzLp161i0aBE5Oef6LP7hD39ApVJxzz33dHldXV0dGo2GuLjQ6MSUlBTq6uqCNsnJyZ2uTU5ODrFJSUkJOR8XF4dGo+nR5uznszYX8tBDDwX3jU0mE5mZmd3+DgQCAbz55/dJHJvIsW93v2CW7MrA5TsndM3t7fh9ATSx+l7HTzIZmDI2jYwkE9Y2J8ouVqZqW2ykRcecG9/p5EBdDadaLcGfGI2WhlY7zbbOS96DwShDbL/HUCoUBGSZD0tOcOtzGyis6fq5JRge9Flw165dy5EjR3j55ZeDx/Ly8vjLX/7C888/H/FyrSzLIdd0df1A2JwNQuhufg888ABWqzX4U1lZGdH3EAi+SFgarLz+p3eo/mk81qiu91XNyiQOVIQKRYvTSXJWIrh7LsUUG60nNcvEkYpaKixWZJWEMUrXyS45NpqyXiKCzZ8Jsi/M5ev+UtdgY/7I/r2w/+dAPsv/8R/ufv1dCqpreeOw2NMdzvRJcO+++242bdrEjh07yMjICB7/5JNPaGhoICsrC5VKhUql4syZM9x///2MHDkSgNTUVDweDxZLaMh7Q0ND0PtMTU2lvr5znl1jY2OIzYVeqsViwev19mjT0NARnXih53sWrVaL0WgM+REIBF2z/sHXSJ2XSaOxm71KWUK2mDoVqfAE/ChUCrz27vNUU+KiUSWrOVBXQ9qIWKaNN1Pf2kZyfDQJxqgQ28RYAyplzy/5No+bsWOTSE2I6dFuoCg6U8fKWdP6NcaWkjLKms69SOSWCwdgOBOR4MqyzNq1a3nzzTfZvn07o0aNCjm/atUqjhw5QkFBQfDHbDbzk5/8hA8//BCAWbNmoVar2bp1a/C62tpaioqKWLBgAQDz58/HarWyf/+5jiP79u3DarWG2BQVFVFbWxu02bJlC1qtllmzZgVtdu3aFZIqtGXLFsxmc/AFQCAQRI7H5eHY3lLyPz1K6R1dV1OKlvRMUoynuKGbhgBKBS1VXZ/LSDbhi5WoauvYEy5vbeVAYw0WnYfCtkZqpXYyR8UzIzudRKOBY+X1JBuie5xzmaWFY02NxCTqMUZpe7QdCMrrLczJ6Jyl0R9ONbdgdfa9mIbg4hKR4K5Zs4b169fz0ksvERMTQ11dHXV1dTidTgASEhLIyckJ+VGr1aSmppKdnQ2AyWTiu9/9Lvfffz/btm0jPz+fO+64gylTpgSjlidOnMj111/PnXfeSW5uLrm5udx5550sX748OM6SJUuYNGkSq1atIj8/n23btvHjH/+YO++8M+iVrly5Eq1Wy+rVqykqKmLjxo38/ve/FxHKAkE/aLPYKdhxlEd/+BRlvzTREt11Lq2hNZ09J7rPd5UUEshwYbmnzORYzkhtVNvbOl0TtJTgpKWFffXV1EgO4jOiqbF1tu+KAxXV/PiWK8Oy7S9HKwc+3/dE48B1SBIMLREJ7pNPPonVauXKK68kLS0t+PPKK69EdNM//elPrFixgltvvZWFCxcSFRXFO++8g/K8DhkvvvgiU6ZMYcmSJSxZsoSpU6fywgsvBM8rlUree+89dDodCxcu5NZbb2XFihU88sgjQRuTycTWrVupqqpi9uzZ3HXXXaxbt45169ZFNF+BQNCBLMs0Vbfw5x/+A+XtZuz6riN+kxRxlPYgDFkxJrx+Pyr1uf/zSkXH40gRp8QbSdceCers9rCrVjk9XirsrVwzbWz49+gDS2aOx6XoY7ugHvifD7ZhH8A+wIKhQ5JFKZMesdlsmEwmrFar2M8VCIDak++x/p95vHyFuyMBtgsmMYE9Zd3XO041RLPp9jv4+fxfcWh+R2BR3MhojFotpS2D78FJEvxq2dUcLKrgo4KyAR8/Wq9l+++/z0/f+ZD3jx0f8PGz4kwkGgz8cNFlLB4zUqzYRcjFeq6LWsoCgSBs5EALpuj/Yeb3a7oVW4BGS8+VoLIMsUhI6EwdwU+jzfHUOuxDIrbQsYr94LvbyR6Xwn/dvIgk08C2r/vm1TPRqFWcaWkd0HGTojt+XxUWK4eqavj+hrf4xn9ew+7uX4ENwdAgBFcgEISNx/YH5EAj+Y6R3dqkKBM40dx9ik5GjJH8qho0KiWx5o58fGNc7/m4g8FfPtpDXkMt/7N6CY9+bznzJmSFdd2krBTUqq6DxSaPSOHO6zuK9mTGmQZsrtDxonB+5LNaqeSGSePxBcRC5XBACK5AIAgLOdCGz/U2Vd5sXq3uvoes0dm5aM35JKii8MsyKiTaLA4SjFEcsQxuM4HuCMgy24tP8v1/b+RPO/dww8KJ/PL2a3q97mRtE0tmjOPmeZP56sIpKBXnvP0JGcnBJd6+RBQrJIlR8XFouxD0Jkc7KoXETTkTALjninmsmjOdWH3n3GTBpcfgV/EWCASfC7z2J0F28mTd9fjkrj3Y0aoMDpR1Xw1pckIyhTX1xGi1aFRK/GPiaI5y4/IOfHBRpJxqbOE3b3/EJz//AZ8UnWZn0alubd1eP+8dKAEgLd7Ij7+ymFc+OUx5vYUrp44J2oW7pJwVZ+JLk7KZkZHG9PQ04qL0+AMBzlhaKaqpZ2dZOe8c7bjfqWYLay+fx/yRWdwyXdRZHk4IwRUIBL0S8JXjdTxNvvMqdjV1LbYxiigqKnpY2pTB5eyIao7V6bA1teEvacAVH9fjfvBQ4vb5eeTDT/ifb1zHV3//Ai1t3XvyZ6ltsfHyzgLu+/IVrN9+iIUTRwIdEd3eQPcvEgpJIjs5kfuuWthl4JNSoWB0QjyjE+K5acpEZmWZ+e3mHew+dQZfIMC/v3FLv76rYOgRgisQCHrFY3sQv+zjieocoDXknCxDsjKOOHcq+9u7924lID5aT6vLRW1bGyqTjqoDp7kmzci2UcpLRnR3HS/n1zdew4s/+TrHqxupbrbh8nhZMnM8u4pO88fXP+50zYTMZOZNyCJGr0Xx2fKyJEncOn0Kf9u9r5P9P29fwWVZGURpwu/ru3LWNDJiTdzz+rvkllfy5O79fGfeLHRq8RgfLog9XIFA0CM+13b87m28Y1vJ8bbWTucnqcZyukzBgTM9F9aXJTjQUM3IxFh8gQBH6urJmpSBu7wF1ZA0zQuPOmsbh85UkxZvZPGUMay8cgbfWXIZGYmxXJEzqstrstOT0GvUzB6XEXL89llTUSlCH7MGjZrFY0ZGJLZnuWLMSNZ/82tMS0+lqK6eG576N49u302tNbyiH4KLixBcgUDQLbLsxmN7kBZ/Gk+UdxYIDSpOVDrxBQKd6iV3hVpSUNvaIQ65lZVc9stl7L4qCd+lo7cAvHO4pMvjicauy0fWtnTdljAlJprrJ44LOTY6Mb5febM5aSm89u2v8/ev3cQ/b7+ZbcdPcvUTz/Lr9z+ipd3Z53EFg49YixAIBN3idfwL2X+a/PZbcAc6V5Uay1j2tHdf4CI92og34CdJbcCnCBCr0XGgoqMx/H8OFbDxjq/zUtkJKlus/ZqnUadldFI85lgj5tgYEqINnGm24A8EsLs9bD1WFlGXoD1lZ7o8rlIq0GvUOD0decZn//z6p4XMGJPOsssmdrrmW5fN5N2jpcHPYxMTIvx23TMuKZHXv7OSZ/Ye5LWCIjYcKiTdZORXS6/k6vFjeh9AMKQIwRUIBF0S8Nfjtf8VgP1t6YClk01Zrb3b61WShMIr0Wp30RToCD46v5etw+PhqX0H+M3N1/Ld597o11y/tXAmP7pqXrfnPy45xV3r3w57vMUTRnd5XKVU8PC3b2Br/gkkSeKemxbyz837MOp1VDV3/dIwLT2V1759OycamylramF6emrY8wiHKI2aexbP54ZJ4/jm+jeottrYWnpSCO4liCjt2AuitKPgi4q79af4nK9w1LWANSVj8XfxqNDXZ1Lb1rXoToxPoqyuucvrzqJWKNj5g+/x5w8/5a38vvV6/fsdNzNvTFavwUO/2riVN/KKeh1Pq1Kyed13SOlm+fhCAgE5GCh1sbG7PRxvaGJqemqnvWPBOURpR4FAcMkQ8Bbjc76KLMM/aud0KZoa1LR7ui/hWNzSSHZyYo/38QYCvF50lO8vvqxPQcrLpmZz5YTRQbFtcXSfxvOT6y8nOcaATq1i3uhMVs2fwfU545mYloxKee5RuPaaBWGLLXDJiC1AtFbDzEyzENtLFLGkLBAIQpBlGbftd4CMJMG3U4/Q7s+h9III5bHSaPa4u68QNSEukWN1jb3eb1vZSe6aN5d/3flV9ldWkRVrwtHm4f/e3dHjdbFROr6/+LLg52qLlev/9Bw/u2Exd8yf0cneqNfx4vdvw6DVEhsVWpnp358e4g8f7GR0UjzfWjCz1zkLBH1BCK5AIAjB7/6YgOeT4OcZ+h38c8wOtttv4amqZOpdHUvI5Ypy/meezD+KEqmzXxAdK0PAG95u1ZHaOqwuF/e88x7N7R0e6vXjx/G9K2bzzK6D3V73X9cuZFzKOQ+6oc2BPyDj7SE4Kr2b2sZ3zJ/OjBFmJl3g7Q41ddY2EqKjUCu7rtMsGN6IdQeBQBBEln142v6v03GFBNfGvMH67H/zwxEajGodt5qVfDXzWd5Z8jj3zfRjUJ9LG5qZbKashwYGIfcE9p6pDIotwObjJ3jj5DGumtR18NJXZk7ma7OnhBzLjDfxn+9+jdULu/ZQewpXUSoUTM1Ivahiu734JDf99T9865nXgqlTgs8XImiqF0TQlOCLhNexHo/tF73atfkTUOFCr3QEjzW5M3ii5A42n/ISjYbGHvZTL+TJFTfyo7fe6fLc0pFjySutwubqaEG3YsYkfvvl64IN63uipd3JHz/ZhcPjxe7x8MSNyzFoNGHPa7A509xKZpyJk43N3PrkS7h9HaUgTXodD391KYuzu37hEPSPi/VcF0vKAoEA6OgG5LE/FpZtjDK0b61SdxNphjR+M+1PmBT38vzh8MUWYEpq96kyu6rL+fi+71FS04gv4OeK8aO6LRyx8dBRUozRLBg7AoDf7tjBppJzRSy+8eprPPuVL5MQFRXR/PpDICAjI3d6QXB5fXx07AQv7MnH7vYExRY6ugz96IW3+c6iWZTWNfGDxZcxe1QG/kAgOM6lFB0tCA8huAKBAPisG1Cgbw3g/a5NqGKfoMG3jJeP9tx8/kKi1GreLSnt9rzT6+OD48e5Y8b0Xsf68sxz3XOsLhcfHD8ecr6wvp6vvPgSKyZNZOGIEUxPS0MziPulT+3Yx/N78rjzisv47uWzQ869euAIj364u8fr/7U7D4DcUxWMSUrgTLOFBWNHkBRj4NMTZ8iIMzElM5Wbp09kdFLPFaw8Pj8/f30zh85UE6PXcsusHL61YGa/ql4JIkMIrkAgIOCvxut4JkxrBUrdDfjdH4N8bknZ6/gnj+cvw+0L37s16bQ8cOViHv3k0x7tPik/E5bgns/xpia8gc4BVFU2G0/k7uOJ3H3EaLX88soruWXypD4JT5vLjValQnNe79rckxXsKDnF+JRE/rFzH26fn4KKmk7X3n7ZNJ7ZdYAme++/L39A5nh9EwA7Ss61DaxutbHvdCXP7DpARpyRP399OZPMKV2OoVRInG6y0NDmoKHNwR8/2EVAlvnOotld2gsGHiG4AoEAj+2PgDtM6wB+13udjpY2q9lUGp7Y6lQq1syfxzdnTmfj0WM0Ohzd2iYZDNx/+cIw53aO5Ojec2nb3G5+9uGHfHSyjP+77joSI1xq/t5zb9DqdPFf1y4k2Whg27GT/HvPoU52BZW1yLIcIuoqhQKtauAewVUWG4cr63oQXAW3XTaFBzdtDx77x8f7WTV/hoiKHiKE4AoEX3D8ngL8rrf6Pc5fD98A9B5dG6fX89o3bmdkXBxtbjdP7+8+9SctJoYXbvsqI+PiIp7PiNhYxsTHc7Kl92jprWUnaWl38tJtt0ZUNMLh8VLZYuXHr77fo12zvZ1qi42M+HNpSf/Ze4jq1q6bHvSVVFPPLxnW9tCXqjaXm4+OlXHDlOwBnYega0RakEDwBcdr/3O/xzjQtIRdZ8JLZbl7wTxGxsVhcTpZ9crrVNu6Fp2sWBMbvn5bJ7Ft93qpsdkIhJFg8cDiK1gxsXNDga7Iq6nhz3v2hGV7ljvmTw/bdv/pquCf3z1cwh8/2BXRvcLhRH0z/s+W0WVZpt3jpabVxsZDR/mvl97h1QNHOl3zu3d3RNTYQdB3hIcrEHyBCfhO4Xf3XNGp1zFkeDTvMsLxbvVqFbfkTEaWZW5Z/xIVrd13CfrnV1aQbgpN2Whqb+d7b26ksL6eMfHxvPfNVaiVSjYfP87VY8Z0CoC6avRopqSk8HZxcVjtA5/at58FWVksyMoKwxqitVokCcJJrvzb9r2MSY7H6/ezYd/hsMaPlFcPFOJwe5BlmVcPFAZTqXqixeGkqLqO6VnmQZmT4BxCcAWCLzBex/ORXSBFISnTUShHIQeaCHgPUeq8nub2zq37umJZdjYGjYYNh4/0KLZzMzMYm3CujZ0vEODVwiL+sX8/VZ95xCdbWliz6R2+M3sWa955l2mpqfx1+TIyTKHVpBINBuZmZpJbWdnr/GTgZHNL2IJrjo0JS2wBaq1tfP0fG8Iz7iM1rTae3nUg4uue+ng/T31zxcBPSBCCEFyB4AuKHLDic74WwRVqopIPIikMAHjbX6LdWkqzqoz7ryuktObLvHZYT5vb0+XVcXo9v7j6SmpsbTz0cc/LqbdPmxry+f92fMwLBQWd7LadOoUMpEZHc7iujhtfWM+6hQu5dUpOSEDS+q99lUqrlfzaWo41NFDc0MjhujrsnnNzlYDlE7K5cvSoXn8Tbq+PkrpGfv/ex73aXspoVUqWTB7HwnEjL/ZUvhAIwRUIvqB47I+DHH4Kj6QaHRRbAEmKoVKag8t/GJUSJme+yJiUFPad/DLvl7jwnZeSE6vT8aurryRGq+XZA3k4PF2LMkCcXseScWODn1vanbx0uPsl2O2nzqXJ2NxufrN9Oza3mzXz5p43V4ms2FiyYmO5+bM9Xa/fz/GmZvRqFSadDpNOh0qhoN3dfR6x1eniSGUd/9i5j0NnOqf6DDe8/gBfmzOF2SMzLvZUvhCIoCmB4AuI31OAz/FsRNdIKJADHcu5suzFbn+OcmdJiI1OU8/iiU/x6xs+4coxHfuvGSYj76xexU2TOoRO3Uu94ltyJge9U6/fz3/y83vsqXshRq2WpecJdneolUompyQzOj6ehKgoVAoFtRYbS373DIUVdV1eE6VR4/b5KK1rCns+lzIBWeanr35Aa7uzd2NBvxGCKxB8wZBlN27rT4DIIlMDvmKcjUsI+Mrwtb/ISV80frnroByD7iQ3Tn+CB64t4+8rlpAWExM89705s/mfa67m8pEjOuV/jo6PY838eQBU22zc8O//8HhubljzSzIYyDSZePLmm0L2f8NFlmV+9+Z2rO0uPj52qksbtVLJtZPGsnrhTIx6bcT3uBSps9n5xZtbemzu0BVuX3j79oJziCVlgeALhtfxDLLveO+GXSAHanE130GblEKVs6JX+0TTHkbHf7bnG7DjC9jRq1JZNXM6q2ZOx+py8astH/F+6XHGJybw2je+Hmwu8K+8PE5bLGHN6yuTJ/GDOXOostn454ED2N0erh07ptfrck9UoJAkLhubydYjJ9hZfJrrpoxl2cwJPV635ur5zB2dyf5TVTTZHZxutLDvdO9BWZcqO0pO8WJuQZd9hC/EFwjw39u3kFtVyfM338LouPghmOHnAyG4AsEXBNnfhBxowWv/Z0TXScoMZP+5HFI5UMvJwCgIK9EGml15pEcvob59FxpFPHrVuUYFJp2Ov9y4jOvGjWVaWmpQbP954ADPH8oPa/xojYa18+ax/D8v0O7t2H9tsDuYnpZKosHQ47Uvf1rA9qKT3DRrIrtLzxClVfPgbUuI1vXuvc4emRHc+3z3cMmwFlyA/7f5E2aOSGeSOblHu3ePl/D6saMAfPW1l/nn8hXMNqcPxRSHPWJJWSD4giDLLnzuj0BujexCSYekOFcu0KKYQqO7OOzL69s7CvSnRl1JctS8zsNLEjdOnEBWbCxev58/7vqEP+z6JOzx7R4P+yorg2ILUNzYyKrXX6fVGbo3Kcsy/2/TTn749Jss+b9n2F50EoBNecW02Ntpd3u557lNlNY0hn1/gJGJsRHZX4p4/X7+sHUnnh6WimVZ5qmD+4OfW10u7njzNd4oPjoUUxz2CMEVCL4AyLIXn3NjR0egMFHH/BxQI/vKkAP1AARkBcc9kRX5b3F3RBirFOfqFK8vKOCmF9aHRB/X2Gws+Mc/+ceByPJI1QoFD27vXLzD0tbOmnfeDalIJcvw+r5CPi09022T9wMnq7j9zy/x1w8+xe0Nb58yxRjN6KThs7Salmhk+mQzMjIqpYIp41LJyI7lE2sFx1u67xh1pL6u03lPwM9Ptm7mD5/uCqv61xcZIbgCwRcAOdCM35MLcni1eyVFGir9Ci4MrKpTzKfNG9nSqVEdGjFsdbn4f5/s5mhDA7/+aBsv5Bew49Qp7n3vfVqckUfLegMBnBd4ZRNiEvBX+5gUlcj5rwcKhUR2WlKvY/oCAZ7etp+vPrae/NPVvdonxUSz5urO3vulRFyMnumTzCSPjeG0opXcxiqm5KShTldzwFpDmaWj5vTh+q4jtAG2njrZ7bl/5B3gB+++RatLRDx3R0SC+9BDDzFnzhxiYmJITk5mxYoVlJae62Pp9Xr52c9+xpQpUzAYDJjNZr75zW9SUxOar+Z2u7n77rtJTEzEYDBw0003UVVVFWJjsVhYtWoVJpMJk8nEqlWraG1tDbGpqKjgxhtvxGAwkJiYyD333IPngvy+wsJCFi9ejF6vJz09nQcffDDiaDyBYLgj+04Q8Hauo9stkoKAJw9t7ONIyo5m7h5MlLmqermwM6mGxSGfd5w6FSw4IQO/2b6d7218i7yagctrjVXqcHl9fHL4FK4LvNTRKeF7ouWNFr7191d57N1dBAI9Pzeaw2izN9To1Eqmjk8jIzuWWr2D3KYqKmznKnwdbKjBcoFAvlx4uFtPVa/uOexn2+lTLHvpBUqbPx9pUwNNRIK7c+dO1qxZQ25uLlu3bsXn87FkyRIcn7XWam9v59ChQ/zqV7/i0KFDvPnmmxw/fpybbropZJx7772XjRs3smHDBnbv3o3dbmf58uX4/f6gzcqVKykoKGDz5s1s3ryZgoICVq1aFTzv9/tZtmwZDoeD3bt3s2HDBt544w3uv//+oI3NZuO6667DbDZz4MABHn/8cR555BEee+yxPv2yBILhhix7kD2HCQRsYXu3ALK/Grf1pwR8J5D99UiqcZxiOp5ApN1tFKREXR5yZMORwgjHiJzc1mqmT0yn2mLjrQOh+4tjUyNLGZJleO7jPG7/y0scq6rvxkbm7fxjfZ7vQJMSF830HDNKs5r9rdVB7zUcjjU18uHJE12eu2Hs+F6vr7W38bXXXmbb6e694S8qktwPd6+xsZHk5GR27tzJFVdc0aXNgQMHuOyyyzhz5gxZWVlYrVaSkpJ44YUXuO222wCoqakhMzOT999/n6VLl1JcXMykSZPIzc1l7tyOajG5ubnMnz+fkpISsrOz+eCDD1i+fDmVlZWYzR1Ftzds2MDq1atpaGjAaDTy5JNP8sADD1BfX49W2xF1+PDDD/P4449TVVUVVsNpm82GyWTCarViNBp7tRcILiVkdy74SvB5DuJxvdPncRzSKPa224g0dzcr+mbSo6/nQP2PmZv6Z+J1s5n457+EVKEaTKaYkslJTuayjAwqG1tpdbg4UddE3qnel4m7QqmQuPOay/j+NXNRq0JziG/403OcaW4dgFn3Db1GxQhzPLIBDjd2vywcDqNi43j366vQq9Wdzn35lRd7XHY+n/vmLWDNnHkownjWDiUX67nerz1cq7VjaSI+vvslGqvViiRJxMbGApCXl4fX62XJkiVBG7PZTE5ODns+a421d+9eTCZTUGwB5s2bh8lkCrHJyckJii3A0qVLcbvd5OXlBW0WL14cFNuzNjU1NZSXl3c5X7fbjc1mC/kRCIYjcsAB/jPI7p0Uu9z4lJP7PNapgJlIxTbdcD0T4+8mv/HX+GQHJu0EFJLEt2fOJPOCBgODRaG1gZdPFPFafiFPbd3Hhj2H+yy2AP6AzFNb9/HQW6FBWu8eLrmoYgvgDwRwaL39FluA060W/u+Tjzsd/+hUGVW27ptOXMifcvdw/5YP8Jy3evlFps+CK8sy69atY9GiReTk5HRp43K5+PnPf87KlSuDbxF1dXVoNBriLuhxmZKSQl1dXdAmOblzLlhycnKITUpKSsj5uLg4NBpNjzZnP5+1uZCHHnoouG9sMpnIzMzs8fcgEFyKyHIAZAeyrwQ8eZQ6K9lkbaY0sICAcmREY7VKOdS7IlsuNWmymZb4a/Ibf43L34hBlYlK6ohS/vniK3j967dHNF5/ccqRP/Blus80vmpyaFENVS/lKocCjy9Ae60bvWpgyiu8XHSEzWWhS8vbT5+iOcLAtrdLi1n5xis0tjsGZF7DmT7/K1m7di1Hjhzh5Zdf7vK81+vl9ttvJxAI8Pe//73X8WRZDlni7Wq5dyBszq6gd7ec/MADD2C1WoM/lWG09BIILjlkJ7i3QaANG0m4/FZA4mj7ad6yuqnkclD0XOAAOvYvT3g7Lyv2hISKWckPU+/cRYNzDzplEnNSHkGSlHj9fuweD68WFfXxi/WNFm/kkbPTJpmZMalzQYcF40ewMHtkyLHKi+zdnqXZ1o5Rqxuw8X6+7UOq286t8sVo+1bO8lBdLTe9vJ791ZEH3X2e6JPg3n333WzatIkdO3aQkdG5y4TX6+XWW2/l9OnTbN26NWSNPDU1FY/Hg+WCkm0NDQ1B7zM1NZX6+s7BCY2NjSE2F3qpFosFr9fbo01DQwNAJ8/3LFqtFqPRGPIjEAw7fMdAMiKpxlDgy+JCX+2AvYx323Q0KC4noByLrEjtcpgGxTys3vKIbj3GtBKDOoOSlr8hoeLy9P9g0mbj9HpZ8vzzXPPsv3h096d9/GJ9o7LdRmZiZMvY7nYfRU2NTBmXFnJ8z/Ez/PrVLby4Oz9YJOKO+TOIN+gHbL59RUYe0LQcm9vNfR++H9xzTzFE93mseoedb258nZeLjnxhM0UiElxZllm7di1vvvkm27dvZ9Sozn0jz4rtiRMn+Oijj0i4oIj4rFmzUKvVbN26NXistraWoqIiFixYAMD8+fOxWq3s33+uosm+ffuwWq0hNkVFRdTW1gZttmzZglarZdasWUGbXbt2haQKbdmyBbPZzMiRIyP56gLBsEEOtEKgBVSjaHTspNLZdVUoj+xlt62Mt6wO3mlT06i4AqQYFOrpSMpR+GUNJ92tEd07SmVmfNydnLG9icNXSVbMTUSp0th28iSXPfkUFa1WmtqHPn0mgExMUmSCWFreQEpAR6WrjemT0lEqOlbFrpw8mt/etoT547J47N1PqG6xoteo+ersKYMx9YgwRulwD/B+6cGaau58ZyN5tdX9ElzoKJLxi+1b+elHH+Lydd8G8fNKRFHKd911Fy+99BJvv/022dnZweMmkwm9Xo/P5+OWW27h0KFDvPvuuyFeZHx8PJrP6qT+6Ec/4t133+X5558nPj6eH//4xzQ3N5OXl4fys+4hN9xwAzU1NfzjH/8A4Pvf/z4jRozgnXc6Ii39fj/Tp08nJSWF//f//h8tLS2sXr2aFStW8PjjjwMdAVvZ2dlcffXV/Pd//zcnTpxg9erV/PrXvw5JH+oJEaUsGG7IzvchUEPAncdWu5VGd9edb7rCoDBwWUwWcXIhlfJEStsjS+GZn/okKoWBT2vvRCnpuCZzI5WtMre89HJIs/eBJEqlwuXzdQrpStLqGauPxyn7UEgStnonNS2RB0GOTIvnpLuVsYnx/P5LS5iQlhTcs3V5ffxr+wHuWjqfI1V13P5UxxabRqXk+pzxROu0lNQ2DFnv3BFpcZT6u68U1V/SY4whS8z9YUxcPA9ds+Si1GG+WM/1iAS3u33P5557jtWrV1NeXt6l1wuwY8cOrrzySqAjmOonP/kJL730Ek6nk2uuuYa///3vIQFKLS0t3HPPPWzatAmAm266iSeeeCIY7QwdhS/uuusutm/fjl6vZ+XKlTzyyCMhUcmFhYWsWbOG/fv3ExcXxw9/+EN+/etfh5USBEJwBcML2VcGnv0QaOGMfQ+ftIUvtudjUsYQrx1FizsPmfA8prGmbzHa9HU+rvo6noCFmUn/R0b0MpY89zynwuz60xfmpKfT5nFT0niu2IJakhjjjaWisXVA7jF9Qjr76qpZNHoET9++AqXi3OKg1+cPpgh9XHKKxjYHs0dl8PC2new4cZovT5nEh/mleHyDH6lrTjLSonfR6nIN+r0GAo1SyVPLbubKkV3rxmAxLAT3i4gQXMFwQZb94CsFz14s9lf4yA7uiAtVhJKkzcKkVNHg7jlK2agZzxXm9Rxs+Al17TsxG65jdvIfaWpvZ95T/+jXHHoiLSaaRkc709PSOFh9Lt1noSmD/JK+p/9ciAxkjonnZHML665ayA8XXtat7anmFr6/4S0qLB3pMwaNmplJaew92Xs7w4EgeWxMSDWpSx2VQsFvr7yG23KmDtk9h2UerkAguIRofxECjXja3+Rjh6bfYgswQatkrLST6VFJxGnGdmkjoWJm0oPUO3dT174TnTKJaYm/RJIk/r5vX7/n0BNGrQ5fIECV9ZzATIhJ4HDpwC7hSoDe2/G4/MvHezhY0bWYH6qs4bbnNgTFFsDh8TIlo+ugtIFGRh526Te+QIAHtm/lt7t24B+igigXCyG4AsHnBe1VyNZfUOg14/D3v5Ztoioeg38vAFGBY0yQ9pITNYIYdWhu+tTEnxOlzqCw6WEAZiQ9iEZpYmtZGf/JL+j3PLpjTno6pU0d37PObkf7WfyHzqEYlK41JyqamGNOxy/L3PXaJs60tAbPybLMtuMn+fZLb2B1uTtdm2rqX7BRuEgSTIxLYmpi11kYA41aoUCjUPZuGAbPFRziu5s2dmqp+HlCNKAXCD4HyP5a8HxKqxxFiWNgavpON6jhvEhSSQJj4CA5CglL1CJOuxsxG5Yw0vhVCpv+gMvfwCjj10mOmk+NrY2fffjhgMyjOwpqazHpdFg/26+cnJJMFCoO5Q1eruex0louG5dOg9vBV559ia/NyEGpkNhVVk5pQ9cvOVlxpqFrbCBLHDtaT/aI3jsiRcLVI0ezvbxzPEBAlnlkyfX8esc22jydXzTCJVkdRbYygcajVr559BX+/N0bGR1hzevhgPBwBYJhjix7wLWVQNsTHHAnhR3k1BMjdOmofAe7PCdJMvGBT5itVzMp7r+wuIo4ZXsFnTKJSfH34AsEuO/997v09AaSsQkJQbEFOFRTi9w2uCEpPn+AwyXV1JW3YtRp+VduHk/vOdit2ALMH5k1ZFHKZ7E5BuZ3n6DXs+GW29Aou/Zi/bKM1+/nh7Pn9Pke46Li4bSPgmPVVDVaOdNo4ZY/vMA/t+zrtUPTcEMIrkAw3PHXI3v2Ui6PosE9EB1aZCZoWnux0WAyPYikUJDf+BsgwKT4e1Ep9Dy+d29IANNgoe1CBIbq8SzLkBEVE5bt5WNGUFBZ27vhAJA+PhZ1hpo6jX3Axvzl9q1s7qZ7EMBPP/qQ5wvy+zS2BgW+Ci9OT2gLxYAs87f39/Crlz7E5//87OsKwRUIhjGy7EdSZeJDQ0F7+C3YukOJklnRY5H8JT1YSRjjn0WtvYzjlqdp85Yx2vgNMmOWsbeigr/lDm6g1Fkqu2gsIg3hE62iuqXXLjhZcSYkPzg9Q1PkQZKgzePG6fP1bhwGzU5nWK39+hSoJcOMQApNtu6vffdgMT/7z3t4hyClaigQgisQDGdkK4G2RznhctDer4IHHb7h7JiRJMkf92ip1S1Dq7uaFlcBJ1r/RUrU5eQkrKPZ0c669z9ArVQQqxu4er5dMSExkeYuKlbZGbrqReaUWAyanutMf3nqJLaV9S0Xui9opIEJYBoKYlQaSisae7X76HAZ9z67iea24RV93RVCcAWC4UygBWQXld6+B+UYFFFcb1QxJ2YMcfKhHm0lyYjB+ACyHOBA/U8waScwK/khJEnJ73fuZGRcLFNT0/AFAsxJT+/zAyZWp+Oy9HTGJSSgU6lIMhiYmprC7PR0JiQm4vB2LayF1gYSjYY+3jUy3Ao/be6eq2cpJAVvFRUzfbS5R7uBQqW4eI/0SKKVx+rjiK1RhZ0GtLu4nGW/fY59x4cml3mwEFHKAsFwJtCKy1dDo/t0n4eYHZMEvt0kUN6rbbTptyhVI3H5mknSz2dq4gOoFHr2VlTwVnFozeYD1dVkGI2YdDqOftY05HySoqLIio0NWZb1+P1Y3S4qWq3sP28f2OXz0ejo3cMJIJOZFtvjMuVAYVD03kWpwW4nIMvk19SQHGPA5fWiUiixtA9O6ku4FfQGA28gvGVfo1KLqh7qHZFVw3J6vPzXM5t4/M6bmTNueLZNFYIrEAxnJC01nnb6Gi6UqE5A78sNy1aruxGt/hYAdKoEZiY/GDz3/KGug2aqbDaqbDampqZQZbXR8lmO5cTkJMqam2msGfjoXY9i8Pf7ZKCqva1Xu7LGjmV+P2CXvLTJHuYkmbGcGQTBlWTs3sGpVx0O4f4LnKpM4nBT3/7enR4vh8trh63giiVlgWA449lHf2JzkzVGwnnvVihSiY59qEsPqspqJbeXvtFH6upx+nzMSU9nXEI8FZZWvIMUfVrp6l0Ie0IG9NqefycSkGHoPUq5pL6R5JiOJe42jwckqGvt3/y6ItagI2NcHCdb+x84N5gkq6MoOl7Xu2EPDOeoZSG4AsEwRfbXgDKTEWo/ij4uVh1znGavawySIrEHKyUxcX9BoYjrdKbN7ebpAweZ20Vf7Atxer0cqK7mZIul2z3YgaDOZSd7QjIpsd1Xd5KBxFgD40ckMW1COlMmmRk7Phldqo5AvAJNkrZDVbtAoYCJo5Kxh9Fezupy8915s4ON26PUKmqt/RPcW+dM4UtTskOOtTpcJKqi+jXuUJCtTuh3+UaLY/hWohJLygLBcCVgB8mAQ9YRoO9pICnaWOTA0W7P66K+jka7qMtzG48dY/3hwxHdbzDKLl5IvrUejUHBnLR0nO1elCoFaq0St+zH4fVQ3+ag2u2g2uqALur8N9gdTDQncKq6Y0lYBiaOTEYdpaLM0kKBpfOedHc43B5MOi1ev49UTTQVtPbru/n8gS7zevOPVTM+O4HjlsFrz9df2lr638WozjIw7QEvBkJwBYJhiwtkD7Z+Nhwfraqnu+JUCkUaBuMvu722ynrpPvw8gQCftlYxK8HMoarIC0/ERGtJSzSSkhxDpd3G4dYG+qKVh2vqsHs8TElOIf9U//es3zzU9cuRhESsum/pWGqFAq1KxRhjPFUOG83OgS9FOc2QwqnTvacB9YbFLjxcgUAw1HiPQsBGW6Dvkakzo8eA/+NuzxuM/41C0f1eZZt7cMs3DgQqVd92zkpam7H53JTX9O+lwuPz4XB7aHcOfo6wQo7s34IEzE3M4GhBDQopQJm/gexxyTQzsII7w5BK7fFWBmJxo909dLnWA43YwxUIhiu660HSYO+j4GolLSl0vxysUJrR6ld0e/6RT3bzalFR2PeL1+sjmd6AYfO6mJGZRmacKbLrBuhlYm95Jd5AAGv74DaFj9ZrOG7vfTnZoFYzNSGFeaZ00q0GCg/VEAicC0YqO9mI6bM9534jw+W6TMoKG3D0krMcLq2DlFI1FAjBFQiGK95jgMRUZT4TDDkopN7zQs9ndkw6smzp9rxaMx+ph1qJ205FVrf5mjGju4tDGlSKW5o42FQzZC3yumJGUiqSSyY7NoE5GekDPr5eo8KUGRVMu7qQaLWGOUlmZqhTUJ4OcDK/kcLCWizWzvb+gMwEY09BdGEiwyJNBgXHBrautqXNOWybGoglZYFguKKIQbb/BS0OZil2MSk6nuNyDsfby/EEei5eb1IaifH3nH+rVk/v9ty/8vI42XwuBUUhST0GQ6XFxFBYXz9kzQW6otbZxpyR6RTXNjI2NQGfHAAfFNXUD/q9tQolDVY7DVY7cYaB9/THZCVwsDV0nzpKrWacKQG1Q+JEWSPH/OGn49jqXNCfKpEyLNJmcLhk4POsfYEAFoeThJhLPyr7QoTgCgTDEFl2gq8KlFng66jwpKeFadIuJhmiOC7P4lh7BZ5A1ykoM6ON4OtlybSHqkVtbjd+WWZ0XByrZkwnM8ZEs8vJS4cPc7iu84N9YVYW75aWhv8FB4EzNitVNhsoIa+hQwiUksSU9BQKqwdXdBWKc79LfyDQEfb82aHxiQno1WrKmptxXNDkQClJ+MPY+LS2nfu7nJaYisoOJ081UeYLP5r6fM5Ut5A52URlWxch3GFwuSGTgqLB6xjl62eg4MVCCK5AMCzRgu4qcL0fFNyzqGlnsvQJ4wzRHAvMoqT9OH753AM5XZOCttfqUhpkuXtBPm2xcNfcuVwzajT/t30nBTW1GDRqvjplMj+67DI2nzhBnd1OrE6Hy+fj9aPdpx0NJX7kYJ0QtULBuLiEbnu9RsrYlHi0KhVHqztE7vaZU5icmsLJ5ha2l5xEqZDwB2TsLg9jzPGcbG5hTno6RWfq8Pj8KCSJUQmxxMdEoVBKyAGZojN16JVKorQalAoFaqWSKK0ajVqJSqFAkkChUCABibooqqtbKavom8iej4REhjqGyq5ypnpAicRcg5nCo4PXjtAUpSN+GHq3IARXIBieyO3gPQSB7hufa7AzXbGT7OgEivw5nGgvRsbPZL0XfD17TaaEl1Aqs7o9f+fsOWw7cZJbX3wluJTs8Hj5d14BLxw6zPXZ4/j9dddRYW3l0d2f9u07DiKSDBMTkjhe3dSnildKhUR2WhJ6nQpkiTavm+KmRiYlJQFw5dhR/O8N1yBJEv9v2ydUWK2kRUXRYm8nIMvYbW4uy0jnyOna4P0DskxFUysVTa0h9/L6A536xQ4FFadaIDaya+YZ0jlSOPDLyOez+urZqAfoJWmoEYIrEAxLfCDpQe498lVPM3OUu8iOyeSoPwuFb0uv17S1/hca7TVERX8fpWpUyDmP38/Ok6f566dde8kBWeb9kuNMSkmize3hgSuu4HhzM7/Zvj28rzYEyBIcbWxAinBl0hwbQ1p8DKUtTRS2dF6GPtHSjFqpQKdWBctgphijUQI257m/q6Y2B02XeLs5i82JMi68JW2A8VHxlJb037vuiYwEE9+8atag3mMwEYIrEAxHAhbwN4YluGcxUsl4CUoDXyJbmQty93V31ZrZaPU3dxJbgDaXmxcOFfR4L4NGTbvHyz9yD/Bm4VHeXv0NNp840WvN5f6QERVDeiAaf0Cm3eUhWq/Br5eQkVEi0Sq7ONFqCe6d+pG5bGQ6eeW9eWQyORmpeBV+jjU2UlnbfV6u1x9gfHISn5w8Q0ObneSYaI7VdojQcKsBnJoYQ62yPaxm9unaGJynXbi9g+uJ33vjIlTK4ZtcIwRXIBhmyL4K8B0HzXSQNBFdayORd1ss7FTm8LUEJXGBXXRVZkqWXag1M7oc42BVNY2OngsjdCwvd3QQanS081+b3ueH8+YMmuCqJAmDVUlhU/eRuCmZRuLRkxoTTYxeS5mtmf311cwdmcHB8tAAn4nmJBx4kWUZq8vF4ebwI3w1OhUOj4e/797Hb264hhprG35gfEo8p+ov7eYCZzFF62gwuXCGIaBj9HHIVT4aI2y3FynZ6UlcO23coN5jsBm+rwoCwRcUSZUFmnngLQZfWUTXNgU68ivb/F7+1eBio/1qXMq5new8ro+Arj2yUfGdmxh0xfkRtwcqqzlUVcvk5OSI5hsuc43pVDb1HuBjdbkobWji4JlqXG0+5iZnIJ+XrDQqMY6xGQkUWRo4bbFQ3tqKxRWZkBQ11GOOjeHdo6V4fD4yYo0AaI2RvRxdTKx2F5O0vefijtSZcJ9202gd/OXxO6+77KL2+x0IhOAKBMMQ2foAsu1BiLBpQa0vNAf0lMvO3+oVbHFej1c5/bwzfqwt30OWO3u/4xITSIk2RDznv+/dx7emz2BKSkrE1/bEmOg4Ck/0HhVr1IRWT3L5fBwsr8Zh8zA3wczM9DQCGpnS5u4D0cLBGwigM6hx+by8kl/EuqsWcs34MRQ01pEzYmC/+2BSdKyWWYnmbs+P0sUSqPANegUtgGxzEtdMHd7eLQjBFQiGHbLsBNkBgciXJ2u6aYtX6LDy13o9+zxfQlaMBMDr/hi//0wn2756GTLw4Ecf871Zs7lvwQISo/qf2qFEQt0a3v6o3B5qE63WMC0miabaNgpO1lBSUkesWjcg1bBOtDQzyhzP77bsoMHu4O9fu5HrJ46jMeBEMUy8NAmJ00ebyDJ2Lok5OzqN9pPOIWskcM/yhSG5zMMVIbgCwbBDhyL+30jRayK6yiPrqXX13K1lt83CnxvMlASWISvHgRTfpV18H8XS4fFw76b3eWL3PkYZ4/jKhEn84orFzM3svZ9uV8wzpVPR2BqWbZ2ljVmmVCZq40n16vE1eSipaqTN2ZFv7PfLlByu47L4gSm9eKyxkZmj0vm/Dz9mz+kKDBoNFW02po5OG5DxhwK32wfVAUaZzm0jTEtMRdemGLImApOzUlg4ceSQ3GuwEUFTAsFww7OHgHMjeA5EdFlZYBZeuffONwFk3mtpIV83k3tSYru08fWzibgvEOBgVQ0Hq2rYSDF/+/JyDlXX4I1g3IXGDPJLwq9mZHd5KDrTe/BTTXUrDFD1xfyGWmJlLd9+6c3gseK2JuIM+mHTSL3Z4kC2yCToNKQmxhBrUVNWP7g9d81xRuJi9MwYlc7XFkwZ9nu3ZxGCKxAMI2RZBu9hJN1SZGUKOJ4O+9pTvngg/FZzidruvc7Hb17Gv/Py2XSspFM5wr5QVNfA7VOn8kJBQe/GMiyMjUxsI6Gp1UFaUjS19p7rUYeDx+9nZFo8zac65nr9xHFUW21IRvBW+bG7BqaDzmAjIaFXqnG3ejnUOvC/97S4GK6eMpY54zKZOjJtWNZJDgchuALBsEIG7dXgOwXO9yO68pQrMo8qSdv90upjn+xhTEI880dk8dGJyLoGdcXbR4v5921f4fWiop7zPmVYaBo8sYUOccnSmKil/4ILUGW3IiMzK8PMX29ZTkObnRXPvIjXCGNi4zlZNzxShSwOJ7OyzNS3DszvJUavZeGEkdyyYAqzx2R8LvZoe0MIrkAwjJAkBagngHoCsvtDcIUnPK1yGnXuyAr0J2m7jlDddaqcLccjS0fqjRpbGyUNTTz6pRv4f5/sZln2eF4sOBySkmNUa8jRJpFfOnhie5bCY9WMm5TAiZb+L53WtLUxNSOVotoG3D4fyTHRGHU6TjlaiErWQvgpvhedgVjanZedxeqrZzNnbOawLmLRF4TgCgTDCFn2g3sbBBrBWxj2dSX+KUT6ZO/Kw3V5ffzqw20RjRMuzx08xKt33E68Xs+33ngDty80JSlDb6SgdHDr9AaRJfz1XrQ6Je4B6EzjVwbw+P3Ut9nJiotF/ZnQnGgdHt7tWSotVmINOlr7UOQizqDnN1+/jitzxgzCzIYHEb1ePPTQQ8yZM4eYmBiSk5NZsWIFpRe03JJlmd/85jeYzWb0ej1XXnklRy/oFOJ2u7n77rtJTEzEYDBw0003UVVVFWJjsVhYtWoVJpMJk8nEqlWraG1tDbGpqKjgxhtvxGAwkJiYyD333IPHE7onUlhYyOLFi9Hr9aSnp/Pggw927IMJBMMSGRRxgAb84Xusx1yRFV1QSipSdJmdjj+9/yDVtvD3gSPhUHUth2vqKG1q6iS2AMdsTWRnJA3KvbuirqmNGabUARnraEMDWfEmNh45hizL1Fg72iZaPW7M8cYBucdQUG+zk5rQt/nGxei/0GILEQruzp07WbNmDbm5uWzduhWfz8eSJUtwOM5VGfnjH//IY489xhNPPMGBAwdITU3luuuuo63tXF/Oe++9l40bN7JhwwZ2796N3W5n+fLl+M97k1y5ciUFBQVs3ryZzZs3U1BQwKpVq4Ln/X4/y5Ytw+FwsHv3bjZs2MAbb7zB/fffH7Sx2Wxcd911mM1mDhw4wOOPP84jjzzCY4891qdflkBw0ZFdyI5/Ibs2AeEFK7nlKM44I/Nu03QjUSnUIcdqbDb+kRtZZHSkfO/1jVRbuxF0GeTood3nO3y0mpzEgamOlRRr4Ok9B/nzx3toc59rfZhoiryIyFBz1iNHBpVCgUEbedWsU3UtnKob3OjmSx1J7oe719jYSHJyMjt37uSKK65AlmXMZjP33nsvP/vZz4AObzYlJYU//OEP/OAHP8BqtZKUlMQLL7zAbbfdBkBNTQ2ZmZm8//77LF26lOLiYiZNmkRubi5z53aUncvNzWX+/PmUlJSQnZ3NBx98wPLly6msrMRs7thr2rBhA6tXr6ahoQGj0ciTTz7JAw88QH19PVptR5WZhx9+mMcff5yqqqqw9iNsNhsmkwmr1YrROHzeRAWfT2R3LrLlm73aFfivwijZGK3I45Dval5viax60tXJX2VJ6u3BzwFZ5tuvvMmnZyoinnO4pEQb8Msyf735S3z91deCx+M0OiZEJdBQ10atpa2HEQYHg16DL1WKuMTjhagVCsZEx3PigpSaecnp5B8f/H3pvqBRKpiWnkZNow1dtBqVpOBETd8rcf30y1fyjcVd1+geSi7Wc71fO9ZWa0ft0vj4juT406dPU1dXx5IlS4I2Wq2WxYsXs2fPHgDy8vLwer0hNmazmZycnKDN3r17MZlMQbEFmDdvHiaTKcQmJycnKLYAS5cuxe12k5eXF7RZvHhxUGzP2tTU1FBeXt7ld3K73dhstpAfgeCSwXukVxOXHMN7rW6ebfLxvGM5H/chqDQ7JvSh+GHpiUEV269NzcHp9dHkaOfZ/YdYaMxggSGdKXIC3goPh4trLorYAjicHkZIpn5XoPIGAlS5bIxKDK1F7ZH9TM5KIWdEKjkjU4mPHqAk4D6QEB3F9Iw0JGBGZhomhY5Dx6ups7QRr9P3S2wBjpQPXmP64UCfBVeWZdatW8eiRYvIyckBoK6uY9kq5YJaqSkpKcFzdXV1aDQa4uLierRJ7qLIeXJycojNhfeJi4tDo9H0aHP281mbC3nooYeC+8Ymk4nMzM77WALBxUL2Hu7VZov7Khx+OzIyxx0VNHkie0hGKaPJjBobcmzrAKT+dEemyUhFayu2z5ZZ29s85JdWU1BWw8m6lmCD+4vJ8VMNzB2AClR2j4d6n4PRSeeef4ca6zjltiJpFUiAtd3d/QCDRHK0gdmZ6bS1ODl6so4Mg5EjJ2ppaTvXFaqkogFjlK5f9zlyRghun1i7di1Hjhzh5Zdf7nTuwqVaWZZ7Xb690KYr+4GwObuC3t18HnjgAaxWa/CnchD7dwoEEeMrBmUmKLouuVjin0+urbxft5hgnIVCUoYcy60YnP8HOanJGHU69lWcC5pU+y7NfMzDR6uZldz/soxtLjexBh1j4+KZl5rOeH0cmcpoCo/XUFheh7+fVbzCJUarIceczJTkZFqaHeSfqMYfkAnIMjXNnVf22t1exiZ3/e8uXGpabDS39dza8fNMnwT37rvvZtOmTezYsYOMjHPVaFJTOyL6LvQeGxoagp5lamoqHo8Hi8XSo019fecIzMbGxhCbC+9jsVjwer092jQ0dDSDvtDzPYtWq8VoNIb8CASXArK/ESnm50hx/wCp879Lq5zKm639z2vMMc4L+dzkcNBgH6D2azKMM8SxMKbDWzTpdLgu6Lla2N7EiLTwWgAOJRISKm//XgZGRceSo0qk7HgjZypbyCur5nSjBUOUtveLB4CE6ChmZ6WTHZ+Az+Gn+FQDxyoaCHcR4fCpGsxx/XsmFlUMo8TjASai/52yLLN27VrefPNNtm/fzqhRo0LOjxo1itTUVLZu3Ro85vF42LlzJwsWLABg1qxZqNXqEJva2lqKioqCNvPnz8dqtbJ///6gzb59+7BarSE2RUVF1NaeW6LYsmULWq2WWbNmBW127doVkiq0ZcsWzGYzI0eOjOSrCwQXHdn+BHLrWuSmL4G/vNP5aBoZrU/o1z1GGyYzwTgr5Ngbhcf6NeZZ5hrTyGmPpb7QQuHhakZFmahvs3OyJTQX1eJ0oksYGgGKlD4XfpBhfnwGjWfaKKtrxukJfckormlg5th0EgZw/zY+SodJr2NkQiyzs9IZbYyjtamd/OPVlNU04+ki9ao3AgFIMUX3a16FYdSz/rwSkeCuWbOG9evX89JLLxETE0NdXR11dXU4nR0l4yRJ4t577+X3v/89GzdupKioiNWrVxMVFcXKlSsBMJlMfPe73+X+++9n27Zt5Ofnc8cddzBlyhSuvfZaACZOnMj111/PnXfeSW5uLrm5udx5550sX76c7OxsAJYsWcKkSZNYtWoV+fn5bNu2jR//+MfceeedQa905cqVaLVaVq9eTVFRERs3buT3v/8969at+9wUwxZ8gXDv6PG0UvJzvf4gSZrQXFW1pCFDZ2aOcTQ3Jk7EqIrt8vrMqHHckvEjFFLoY6G0sX+BMnONaSxQpVF3spWK+tbg8QyiSTQYiFKrO11zutVCzBB5fRHRhwWEcTHxTNcmk19a3W3Th3aPl4Pl1YxIjXzJVoKO3oefkRITzUxzGvZWN3aLi8rqVvKPV3OmwdLdEBFR1Wjt1/WFX+DAqYgqTT355JMAXHnllSHHn3vuOVavXg3AT3/6U5xOJ3fddRcWi4W5c+eyZcsWYmJigvZ/+tOfUKlU3HrrrTidTq655hqef/55lMpz+0Yvvvgi99xzTzCa+aabbuKJJ54Inlcqlbz33nvcddddLFy4EL1ez8qVK3nkkUeCNiaTia1bt7JmzRpmz55NXFwc69atY926dZF8bYHgoiPLHgj07hnESjV8z+ThOdsMJut1ZEcZSNcmo9DMBPV0CNThVB6kxnkatUJLgiaVZG06Zv0oknWhzQpcXh86tYqy5v5VQ9J4FRzuIu2lvtZGrc/NlyaM5/XCc8VxopVqRuiM1Mr9e7APBgFf+AFc42LiifFqOHYq/AIlhdX1jDMnhhcNLMPsEelU1lkwxemRkNBJKo6dqaepcWDqHXdFk9XBqPR4Ttf37d/FwbIqHC4PBl3kubzDnX7l4X4REHm4gksBWZaR66cB4eWC+pU5qEwPIGnm9Ol+B85Ucd9b7/PbL13L33P3k1/Td69kUUw6Rw53nWeaOS2RtNgYHFY3CqdMU7OD2qaOgJ0ZE9LZX1XNgHSEHyAmjEvhkL3zi0+iVk+yPhqv30+0SoPKLfV56XRmlpmCU72XsJyTlc6hz15k1EoFXv/QBFsBzBiXzqHTfc8d3vm7HxJruHjpTxfruS5qKQsEwwHZSnhiqwDtlahi/4ok9c2D8AcC+GUZl9fH/W99wI8uv4zyllYsEXYbOotH6l4IKg83UUnX3lx+STUzx6ZxrLkBVx/2GwcDl/NcdS+9UsUUUzLuNh8na5s55e/f0vtZfHLXvy+lBONTkyiuaex4CTnPVRpKsQUor+1fxSiNStm70eeQL1arBoFguCIZQIoFRWIPRmrQ34KkWw6Brpf7LtxDlGWZ8hYLueWVPLErF6fXS+CzYxNTkrC7PehVakbHxfZ56s3+vjdaLyyrJRE9o+L7fv+BpLLGwvSkVC4zpTFLl8qR0lpKaxrxDaDgHamsY9R56TdxUXrmZKYTr9Rz/HQjIxNjgY4uRBcLi93FyKS+R5J/0boEnUV4uALBMECS1JD8CXLz7RDowpNSJCBFfeez1n0zkBTnIkmdXi+/+WA76SYjByuqUSsVtLk91LfZsblcIQ3kD1RUcc/i+dw+cyrtHi/7zlRhaXdi1Gg7PKo+LO/WuxzMyEnnyNHqsNNPQq632NHYlUwfnUZBP5a2BwJ/QOb4kY492fHmnl5++kdU1LlAsvTYGA6dOLd8G6+LIs6sp/wi9tGdNsZMwZm+d26yOlwk9TPaeTgiBFcgGCZIkhb0X0FuO3rBiTjQ3Qi6a0A5Aum8ohWyawdPfNLGxiPhVYrKLa/kG7OnI8syY5MSMBtjeHxXLmZjDJelp7O/uus9VbUkYVBpaPV2VElK10czSmViv70Wu9/LJ+3VZBp0WO19q0fs8fo5WlrLrAlm8qqHpkWfDERpVBijdMRG63F7fTRY7LR/9oJSWtPE5BEpHK2OrM9wOBRW1ZMeF0OtpY02V2gHtMMnh6hFYQ9Y+lm8otHm+EIK7hfTrxcIhitR3wDNwnOfFelI8c8iRf8IJF2o2PrOUFf3J9YfOhP28DKw4dARrC43R6rrmJWZzrikBGpsbRw8U81l5vSQvUOAWLWOSf54zI4oopQqsqPjodpHwbFqRjtjyIrqCEqJjel/kIxiiCKoxqQmoNEpcQR81NrtFNc1cqrZgj3gZXJWR9Gc6aPS0KkHz2cZk5jAnMx0kqMvvW5CZxpayUw09fn6C4udfFEQHq5AcBGQXR8h6a6N+DpJUkDMz0G2gaQD1WSQ7UgKI3BuT02W/cjeYh4vuBGnN7IUkTpbG79+/yOiNRokCerbzl1/sKKauSPS2VfTscSpABIsKk5aOoJoRqfGU13ZGgziqWq0MkmfzKhYEzWt/c8DPVxazbRxqRyuHdziCSaDDndT14FaWo2KEWlx5FcO7vK2y+Ml/8Sl2UVIApKN0VQ29S11q8k2QJXLhhlCcAWCi4DsOQiSHkm7sHfjC5DU2Rcc6CKtwV9LfesR3joWefGIVqeLzcUnAFBIUqfmAUeq61ApJHyyTABC9mW72lcsO9OEzzcwQUWy3NEAYLDxB7rfbD5YPjQieKrV0uXv/1Kh3RleP+aucAsPVyAQDBUK488HbeyA3wL2P/PS0QX4AuEvJ5+lyXFuf66rh32iwUCmyUig2U9ji5361p6jZaeM6Sj47/b58PoDOOweavroGQE019mZnJnM0bqGPo/RG7J08UWuydHO5PQkjlc1XuypdIlO23f5SOtnPebhihBcgeBzhuTNw8MI3j46MHmhF1JtteH0enGdCi/dJ+9UqEeoVauYOs7M4RM1fdqRdbg8nChr4LLsdA5UV1+4pdxvYqP1FFRcGuUHDYZLtxpTfzzv1LiY3o0+h4igKYHgc4TsOYzsyWVr5Xzq2gZvn6yl3YlW07fiBW6vj4LyGqaMS0Or7tsYstxRGGOCKZFozcCKUqvdyZT0rruJDTWFDfXoNZemX3SispEorZqJGcnMHJUeURBVfzsODVcuzb9JgUAQRJY94NnXsVerntpj4w1ZMkGgmX2n8gZ9XiMyEwjIMiqlgpITkafGHCmvJS0+Bi3KkKYGkRAIBHAM0J5utFbDuOQEAl4Z5SXSk9fp9ZEzIp2CSzB4yunxYVLrKCnvWNofnR5+44Uvau8YIbgCwSWO3PZ7aH+p44NyFMTci6S7oUtbyfspJ6xj+eDE4JdCLHB0PGjjo/qe7lPb0oZGpWT6eDP5xyNfYvb5ZczGGKpt/au6ND4lkaYmO0dOdCwlZ/Qj5WWgqbRbL9ngKaujI686yWTgVEP4hTj8ARmV8ounumJJWSC4hJE9h6H9lXMH/KeRW/+LQNtjnW1lLwRaWV+Uhc3lRqkYmgdaS7uTKH3nFnvh4vH5OXS6hpyxqRF3kKmot9DW5GK6ObVvN5dhVqaZ8srmoHgAVDVZB3ypuq/UtdmZPOrSWOLujoyU2IiqiPm7aVP4eUcIrkAwyMiyn0DjNciOZ5G7KUzf9XUyctvDQBfeqmdPF8f20e6N4p2jZ9BplGhHS0yblkx2VgKdqlUMAHF6HROSEpmdZkY1AMXoC8/UYTBqyUqNjeg6p9uL2x5eisrE9OTgnxOio5iZnkbBiZou04BG9KN+9EAQpVEzc5SZcemJNPmdKC7RddiUuGiOnIksyKyntKvPM0JwBYJBRpKUoLseue0x8EewF+d8Fbzd7MXK3gs+liK7PuTpglQcHi/ZIxNp9bg4YKmikFqix6nIGZvU9Vh9xOw3UFHYxLH8GmxtfSvZeCF1ljaqWm3MyE6PaJ/vdK2lR480xRRNeryRR1ctY1JGMjMyzTgsLg6f6l4oolR999rDQoJxqQkhr0JKpcTEjGSmjUrDjZ+8yhqO1zdxxmpl8ug+evGDTEp8TMTNG5yevufwDmfEHq5AMAQoYn6CrP8akiozLHvZsx/Z9r9dn1SOQor9yzlbfx2B9rf4a958ntxTAoBT5wnp5tfidpLsG9hiA1HRg7Pk6vMHyDtVTfaIJGrqrDhcvQdF+QMBJiYmdNu3947LZ9Du9rK7tJxMk4lth070ugQqR9Bsvi/MGmXmYEUNE9ISCcgyMQYdJ+qbONZNfnG1uw1Jok8NIAaLrORYDkfo3QLYXW4SYqIGYUaXNkJwBYIhQlKNDMtOlv3I1p8DFwikpEcy/AgM3wnpdVvr0POzt9PYV1EKQJIpiqOtoVHDWQYTJ4tbkAayFrFWYsyoRPRRGvz+AMUlA1tusbS6kWRTNMnx0Zyu6T0gx9vDvmBhRR1ZibF8mHecIydqwhKt9jCEvj802ToKjJy2tOL2+eimLXCQOrudKRlJlFZeGoUwZCBK37eXrvpWOyP60d5vuCIEVyC41PCXg78q9Jh6FlLso0hKcyfzXx3cylFtKzOnpmKTXaglJdWtrSE2ZoWJOvrel7YrDtR+1rXGCaPiB+fh2WC1Y0iJZ0Z2OodKq3t8XXC0ulBK4O9CTLccOcHsTDMFJ8LvtFPV2IqkGeDdbwkmpCWhVCko/syTdUew8iDpL51dwGmj0zjcxwIhBu0gL9dfolw6f3sCgaADRRpo5p/7rPsyUvy/O4mt/JmbNjUhjRa3k9zWCo5ZGzjc2vkh2Gbr3VuTkRmbEX4u5fnUtbUNQlhWBwadmrxT1eSMTe2xUEZVo5WZ5vSODzJMy+jY84zV65iUlkx+BGILHXmm6aaBKdCQHm9k1ph04ox6ihsaKaqp71PgUGFDPemXQMqSJEFNq63P1287UjaAsxk+CMEVCC4xJEUUivh/IyVuR0r6GEXsH0KWkGVZ5tljB/jN/o+QZRmbp/eApRJFPdOnpRAfo+vWZuLIJE6oGpgyLYmZOaloI4g8dnp9JMVF1kZOkiAxMZrx45KJNelQKiSSk2MYOzYpJM1I81kLvMIzdSQmxZCW2L0Ilp6qJ9GgZ3pmGkVldczJSkfpkThR3rdl2KSovu8zRms7/s7GpSVQZbNx8Ew1LY7+rTLIQFLixW/XN9acSGM/Ov5cqhHXg40QXIHgEkVSZXS9hLxvK1sry/jpzMU8UvAJzxYf7HUsvyyz31JJa2I7M6alkmTq/ND2xnjxBgLkWaqp99jxq2BiRjKzR6YzIj62Vw82MQIhyMqMw5mloFxv55CrAUcy2DPglLaNAncjI0cnBm3l8zzBysZWLC4nE0d2nZfq9PjIjDJx5rM930PHq2l1OPtcNEItKUkz9twoPducRHx0R/EPg0bN9JFpTMxKxiP50aqUnKhrJsEwcAFCxZamIcux7o5oQ+RdqM4nZ8SlGXE92AjBFQiGCc2udp45tp9Xy45Q227jL4c/5W+FeyMaw+X3sc9SwewpqUzLTiEuusPjHZsRT7H1nBeYrIzB4/dztL6B/dXVnG5rJdaoY2pWGrNGmEk3GTsJcFSsjpTkGNSq3h8rLS0OfOcFOVlcLvzniaJCd26MC5uVt7u9HKupZ/r49OCx8+sNt7W7sbW7e51DOOQfr8Zt8QY3cruS7Wi9hmaXkxEpcbTLPvKrajlW04Db52dyVioTMpJoPq8DU39pc3sYnxl+itfXF08nfiAjgiWotfR9ORnA6xv8SmiXIiJoSiAYBjS72rnqrX9i83QIyZm2Vv55bH+fxorT6il3tHLYWYs2ScmM0enYZCd81jFPiURtY+em9RanC4vz3P5wcpyBTKMJn8+PSq0kv6mONq0H0kCrVBKj1RKt0RCt0qBTKlHKCiQZpAAE/DIN1tpuPU/XecU+ukoLkmU4dLqaGePNWNtcOHwejL6OqFm5/zU4QmhzuklLjyYtwUSTw4HH7cPj8xOt02KK0mFzdSzplzdbOl2bVzE4NZAlXfi+0oJJIzlR00RL28CI/rTRZgrORLYffiGXUGbTkCIEVyAYBhQ11wXFtr9clzmWV8sKAXD7/exrrQg5P8WQRlFt7639GuwOGuxd7+O5/X7c7e00tfftId/kaGfyiBTUSiUSUNHY2qVd/ukaRqfE4/fI1Nsd0Pk9YUCIQ8fhqhp8ATmYC9vsckLX0xp0ihrriTPoQspRdsfhUzXUtPTPIz2LUiFR18+61dDRovGLiFhSFgiGASesA9fb1uPveTmvwFHDmDGxTE4b2MpUkaBG4uiZegpO1ZB/qmdv6lR9Cy6PlykjB29f8ER1E1nGWODSKDzhC8iMSk8Iy9br93PXsvlkJsX2+75TR5ups/RPcBWSxOTMS7s29GDxxXzNEAiGGVMT0vp8bYxay02jJuL2+4jT6nmhNL/Xa0rbO3JE00bEkCHHkV8xsEUtekOvjCxP0+7ycKou/G41fcGk7V+g0EDjvrAwSjf4/AHa3V6qm6x9vtfI1HhMBi0VXSybR8q87CwSjRc/0vpiIARXIBgGXJaSyRXmUeyqOR3RdQpJ4qqM0bx4vKBP9631tOFVB5AY2n03gyLywggOl4ckkwGL3Yk53ohGpaSstnnA5qRhgDeH+8kJSwvReg12Z8851mqlknf2HetzpPbM8enknarutRJWuHxp1oSBGWgYIpaUBYJhwpOLV/CDyZdFdM3XxkxhS8WJft3X4m3HpOs+f3cwUPaxBKUkScgBmYrG1rBqMEeC03lpFdxv93oZZe59Wfn5jw5SWN73FQqrfWAaU5xlbFpi70afU4TgCgTDBINawwOzruKZq24hRt378qZJowNZxuXvX9MCPzJNcTYyRhmYPdpMYj8azoeLoo/udEOrPZheVGtpG9DgnJom6yUXXqtUD/4j/GRNM5PMyQO273o2Z/mLiBBcgeASx+51s6PqZPDztZlj2bTsm2THhnoKN2SND/m8Im0iW08PXAm9cpeFXGc5uniJ6XIii4zp5BiT0EgD/xhRyANT2GHyiBTGpw+MR2VrdzMn2cycxM7FSC4WXnlo8lmLKxrQDdDLS6xBCK5AILhEiVZreebYAaznlXAcZYznz5ffiOozsfv2xFksPU9w9So1hhYNU5wDHw1a57dzsrqJgqPVlB9tJLpGYoovnllSMgt0aSyKSWd+bP9ESe6qA0EfOFRWjV47cG0ED5+oobi8fiB7LvUL5SC87HTLAPyVGLSaL2xKEIigKYFgWHC5eRSf1pbzpRHnAk5GRMeiVaqYHJvAAzOv4tGCT1iUNoKvjpnCrOQM3DYvH//jJOPHJ3LcP3BpRS58ZJniaWjtSHr1+Pycrg2NENaolczLSkWpViIpwa+Q8RDAI/tp8jipdfacMOvzRtbQvCcsA1Tw4Swenx+z0Ui1bWByW4cL8gAorskwtLEAlxpCcAWCYcBV6aP5V/HBEMF9v6IUpULiiStuRqNUMj0xjZ/PXMx7B4q5/5V3WDBxBA0WOyOq4qDvWUVdEherDwpuV3i8fopOdh2oM3W8mdoeKlREK9W0tA6cSFY3WdGqVbi9/dvLPp8UveGSENxSSxMmgw5buwuNSkW0ToNGrcJib0eWZWINetRqJSaDDrVKiUIhEZBl/AEZfyBAm9ON3eVGo1aREB2FTq3C75dpd3mwtLXTaGsPRqjXW/tfVcQY9cUW3IjXI3bt2sWNN96I2WxGkiTeeuutkPN2u521a9eSkZGBXq9n4sSJPPnkkyE2brebu+++m8TERAwGAzfddBNVVaH9Py0WC6tWrcJkMmEymVi1ahWtF/T4rKio4MYbb8RgMJCYmMg999yDxxMamVhYWMjixYvR6/Wkp6fz4IMPBtuaCQTDhfGxiZywNiHLMrIss6++kmeOHuDRhcvIjO5o1zY3PhMAu9PDqbpmnv/oIL5AYFCia7XR/ehnKsvoFF2k2ARgrimN2CYVtc0DJ2aTR6SEiG2yKbrfD371JbIb1+b2YNG4USSoaDP4qFW2cyZgwxblY+K4VOrsdiotVoqq6skvryHvVDX5p2s4cqaWo5X1VDS10mJ3Umdp42hlPXmnqik4U8Px+iYa29tRahTEx0WRmRpL9QBUq0ruomnGF4mI/9U4HA6mTZvGE0880eX5++67j82bN7N+/XqKi4u57777uPvuu3n77beDNvfeey8bN25kw4YN7N69G7vdzvLly/GfVwFn5cqVFBQUsHnzZjZv3kxBQQGrVq0Knvf7/SxbtgyHw8Hu3bvZsGEDb7zxBvfff3/Qxmazcd1112E2mzlw4ACPP/44jzzyCI899likX1sguKhIksRi82gePLiNJZueZcOJw/xu3hKuyxwXtNlx5CSPbdzFLYum8NTaWwBQKRXYxw5COou27y+tR07UojrjJ6FORVarnmynieviRjDJG4vWpaDV3r8WdhdS29JRGUmnUTFrbDrNbQ5Gp/Wt7+9Z7PZ+lNmUYWxCfJ/3REfGxTLXnE5WbMeLlk+Wcfp8qBRSR2DTZ+MGfP1flvcHAjS3tVPZ1NrvsQCOlNdiax/YNKPhhCT3w92TJImNGzeyYsWK4LGcnBxuu+02fvWrXwWPzZo1iy996Uv89re/xWq1kpSUxAsvvMBtt90GQE1NDZmZmbz//vssXbqU4uJiJk2aRG5uLnPnzgUgNzeX+fPnU1JSQnZ2Nh988AHLly+nsrISs7kjQGPDhg2sXr2ahoYGjEYjTz75JA888AD19fVoP6sS8/DDD/P4449TVVWFFEZPRpvNhslkwmq1YjQOTDNqgaAvyLLMocZqMqNjSY6KDjkuSRIuj4+/vfspheV1jE1L4PVPO+ol54xP5WBsDa4wKxP1hgKJRYEsjhzs3Oi+L0wdlcaR0x1jKRUSY82JlFb1rX9tT/c4U2/B2u7CGKVlbFoih072rbGAWqlgXEYSBdb6Pl0/PjGB0+XNxEfrSY03otOq8BDA5fPS2N5Ok72dnqKypsWncKyinpnj09lX0/EdLjOnU1LegNPjRa9RkxYfQ5PVgc05MPW3B5IPfv1dzPEX91l6sZ7rA74usmjRIjZt2kR1dTWyLLNjxw6OHz/O0qVLAcjLy8Pr9bJkyZLgNWazmZycHPbs2QPA3r17MZlMQbEFmDdvHiaTKcQmJycnKLYAS5cuxe12k5eXF7RZvHhxUGzP2tTU1FBeXt7l/N1uNzabLeRHILgUkCSJWckZIWLbbHPwrcde4cb/fY73DxZz9bSxHD5dExRbgKLjdYw/E0+GouPBMl2ZRoJ0rl1bptJEqqLnnq/ns0DOHDCx5f+3d97hUVXpH/9MyUz6pJdJT4AQSAgh9CK9SREbKIrAWte+gLuWXV1de19d249FWRVFUUERxYB06SEBAoEQ0nvvbTJzf3+EDAzpYVIg5/M8eSD3vvfec2Zy7/eec94CnErNYaC3GwB6g0RGQSlezhoUMpnZ6r6eSM6moqaWYUFeDZWGOim2AAP7ubdbbOUyGb7ODibbssrKUSkVFFVUczotl2PnMok7l01icgGluVXY1ikJtHIgwsWDCBcPhrhf9DS3kMtJyi5EIZeRW11JPycnRnpoiUnIpLquYSajuk5HUk5RrxTboQGePS62PYnZBfe9995j0KBBeHt7o1KpmDVrFh9++CHjx48HICcnB5VKhaOjo8lx7u7u5OTkGG3c3NyanNvNzc3Ext3dNOTB0dERlUrVqk3j7402l/PKK68Y1401Gg0+Pj4d/QgEgm4hMauAO99cz4nkbNLzS3h7417+8cVvzSbXT8stQR+jZ4LBj6LjFYTWXChMIIFbujWyGImRMu9W1ybHevgBoJeZ1wdCb5BIzSvCz80BaEjRqDcY8HVzJMzfPN5eYf4euGpsOXY+k/IrFCKl1P7H5ogAb+aFm6YyrKirI7iFerbBWleevmUK90wbyVA/LTIJrGRKwtzdsVer6e/sTI2unmBvN+rKdKSmFRGTeGWl8rqT2ORsMsw0PX01YnYv5ffee4+DBw/y008/4efnx549e3jwwQfx9PRk2rRpLR7XOC3WSHPTveawaZxBb2k6+amnnmLFihXG38vKyoToCnoFqbnFVNTUMtjPA0mS+ON0Cu4OtuSXVFBvMFBRXUtFK2JSXavj5IWR6fHj2UyM8EeSS5zIaNiWcCiP/i5OKPorOKk3HcGFu3gaw0LKFeZNmQhgY6nC2c6G1LwSAHKKy9HYWJKcW8SwIK9Oj0j7a13QGwxXlNqwkRBfN5TWClLL218E4GRGDuP7+zXZXidvur76/r03UF5dywe/7iezsAy5TIadlRqVhYL80oYyiNmVDdc+nda56ezewJboM9w/c3RPN6NHMKvgVldX8/TTT7Nx40bmzJkDwJAhQ4iNjeXNN99k2rRpeHh4UFdXR3FxsckoNy8vj7FjxwLg4eFBbm7TP6j8/HzjCNXDw4NDhw6Z7C8uLkan05nYXD6SzctrqIJy+ci3EbVabTIFLRD0Bs5nF3Lfe99RVl3LEzdPxM/NkU+jjqCxsUSplFNf13EHmeMxTUdGWQVlUAATh/qzW5UCwEAHV7xt7PG3d6KktoZ8M4SHXE5dvb6JqDbWej12PpPBfu6cSm2/yHg42uFib01cB45pjfCBWo7kZEEHV5iq6nS42Jp65jpYW3ImLx8XO2sKL8QIB2tdOZGSzepth412Bkmi9DIHowoz54fuCb7aE8OyKcP7ZAIMs04p63Q6dDodcrnpaRUKBQZDwwMhMjISCwsLtm3bZtyfnZ1NXFycUXDHjBlDaWkphw9f/OM7dOgQpaWlJjZxcXFkZ19cS4qKikKtVhMZGWm02bNnj0moUFRUFFqtFn9/f3N2XSDoMrKLyrjn3xsoLK9CV6/n5W928MB/vqe0qoa0/BJq6swXX9pIamKx0ds1ubyYo3mZ+Nk5MMbDl1x9JSql+SrnNBZ0b43ErELjlHNLBHu5MqyfF+EBnlTU1JpNbAEUehnXDQigHX6WrRLo6oSNSkW9JOHncXHAMTrYl5PdXAKxpyiprCEx23yJWK4mOvyKUVFRQWLixfysycnJxMbG4uTkhK+vLxMnTuSJJ57AysoKPz8/du/ezeeff24MxdFoNNx9992sXLkSZ2dnnJycWLVqFWFhYcYp55CQEGbNmsW9997LJ598AsB9993H3LlzCQ4OBmDGjBkMGjSIJUuW8MYbb1BUVMSqVau49957jV5nixcv5vnnn2fZsmU8/fTTnDt3jpdffplnn322XR7KAkFPU6er54k1Wyi+LFSmq0PJSyqq8VM6oLeS2Hz9UmwsVFTV13G6KA9k4OpoQ35JBXUtZIRysLHE3toSSwslKgtFg0DLZEiShF5voKaunoqaOkqraqisqWtzXbVWV095VS0ejnZIkkTuZUk3wgM8OZGS3WWfy7Fzmax74nYqa+uITu3Y9HZZTS12lmrKa2pJLSzGw94OgGrDxRel0cF+bD4Sb9Y292bSC0oZ7OvR083odjosuEePHmXy5MnG3xvXO5cuXcratWtZv349Tz31FHfccQdFRUX4+fnx0ksv8cADDxiPeeedd1AqlSxcuJDq6mqmTp3K2rVrUSguvjWvW7eORx991OjNPH/+fJPYX4VCwZYtW3jwwQcZN24cVlZWLF68mDfffNNoo9Fo2LZtGw899BDDhw/H0dGRFStWmKzRCgS9mbc27iEutWdGPm6SDf08XbBTqXk5eidfnD2GzmAg0MIJOycrnJ1tOXGm6bR045prSaV54y2LLrx0eLs0xJ+GB3gil8uprK7leLKp17SFUo7ODHGol/L2pj1MGx7UYcE9l1NAuI8H+86lojdIaB3sySwpI6+y0mjj4WBLUYV5U1D2ZjIL278Ofi1xRXG4fQERhyvoKYorqrn15c8pKOuZB3F4uJbAoc5ore15KXonTgorgstdOHm+Yao2yM2J1NSipscFajme1LWes7ZWKpxsrUnLL2myb1CQO2nlZdjWK8gpMt96s1wm47kl09kUF8+hpPR2HzdI68aUgUH8Z8cBAO4aG8Hn+2NAAmepIS3jv++Zz8vf7Wgycr9WmRURzGtLr++x618zcbgCgcA8ONpa8dVf72BoYM+Ug6ssquWz+GhePbYLgAp9Hdn5FwUhu7S82WRJ9fVdXzJuiL9niw5EWbllyOUyNC7mTSNokCTe/WEvowN8eGhK+71sz+bk08/9YqF4gyTh46QBGXg5NzzsT6XlEuLdNBTyWmVffLIxbrgvIQRXIOilnEnP4+GPNuHu0P6kFObEQqVEjsxY0N0gSWjd7Yz7q2p1aF3ssbVU4WJvjbeLhiBP5y73Pg3vp6VKVk9RC1WAvD0dGpymsnIZ3M+864TFFdUcjEtlgLsrzrbWbR9AQ5yx7pKXkHM5hUzo7w+AlVVDTuq+IrhezvY8MHM0m/++HCvVFeTjvkrpe37ZAkEbSJJEYXkVLvY9k2i9pq4eS5WSl7/dQUJmPgmZ5k1z2F7iz+cywM2Zc/pCRii8yU+p5ESJ6Xqyo50V2QVl3RauEjHAi8OZmSCDiP6exJ27uHYrAREhXhxJv7jGmlBUiIezHTmF5WZrw4QB/kwf3I8gNye+j45DkkApl/HfvUdbPKa8phaNlSWl1TUk5hfy0V0L2HU2mRpDgxCfSs9h4fghZmtjb8HOSs3c4SH017pw3aAAXDU98/LYWxCCK+jTHDqbxs7j59l18jwKuQx/dydOp+VSVVvH/NGDmR0ZzLB+3t3Wng9+3o/GxpIQHzdGD/Q15hjuKfzqHNBlGYgraT7ERqXunkeIXCZjcH8PDmdlGvMMny7Ix8fDgfScElQWcvoFuZmILTTEwbo626Aqk7foUd0aD1w/mq92xVBWddGLekSwL9AQ4vPErOsoq65h0cdft3qeU1l5jO/vz5YTZyisqOJMdh7/XXYT7/62j8WLZ5JWUIKXs4aZEQP4LSahw+3sbagtFNw4Ooz7ZozC2a59MwF9ASG4gj5JYlYBn/8ezU+HTptsz7ykLNyGvSdwtLXqNsE9EJ/K6q0NyVwCPZzor3Xplut6OWuM09YlldUk5Vx0hKos1bXqyNMd0XX21mpc3e04lmX68lGrq6fcUkegtzM1Cj3HM5r35k4tLCE8yJNTZ7JbqwnQLEcSMojs583OE+eN26pqTUfzu84mk1pY0up5TmbksPHhO/nr7OsA0FipUSmVvLN4LpIkUVxZjb2VJa8vncPksH68tOH3K05B2VPcPmEo9wqhbRYhuIJW2Xcqmch+3lipLSgsq8RKbYG1WtXTzWqCrl5PXb0eXb2ec1kFnM3MJy2vhGlD+zEy2JfMwlJiz2dxPruQk6k5HElon5fpVztj8HN1ZEiAJz6uDl3ah2H9vJg9fCC/Hj1DUk6RifB1Ba72Nni5aDiRnG0M04gI0mKlUmKQJOytLLFRtrHO1sUxDj6uDlQq6jmT33yihPzySvKpbHbfpRzPyGb4IC9iT2e2KrpjQ/zYH59q/D0hM5+Xl802EVy93nSkPDUkiFGBPq16LiflF6HT63G1M12mkMlkyGQynC/ZPntYMMMCvfjn+ij2n0m9/FS9nhkRA4TYtoAIC2qDvhgWdCA+lahjCcQkZZKeX8I7983nutBAHv5oEym5RXy+8jaceskNVVpZww/7T7I1+iyJWQXoDU3/nAf5uJGSV0xVbee9IscM9OOjh2+6kqa2SEllNf/+aR8aK0t2n0zC0dbqiqrZtIWN2oIBXq6cSMlBbzAVD4Vcjt5gINDDifT8Eur0Bmyc1C1Wnhnu50Xsma5pa4CHE9l1FZTXmW99eIR36+19bfn1uGhs+GDzfoK9XRk90JexIf48tfYXRg/0Y0JoYLNObPV6Az8fjye/vJLymloKK6sprKgiKb+QeUND8LC344aIQR3K0CVJEhv+OMFbP+3pkmxinWWInyf1Bj2n0/Oa3b/7pQdwsLHq5lZ1jJ56rgvBbYO+JLj1egPrdh7jnU17UchlRvG6Z+ZI9p1KISmnkLp6PdOG9uPV5XNQKnrOyT2zoJQvdx1j0/5T3RZe8MKdM5g/erBZzylJEqu+2MIfZ1MZ6OZCzLmsDk97dgRXexsslAqyitqfFHhgP3fiMptfwx3i48HphK5JzDG0v5Yj2eaN57VQyLl1aCjFpVVsj01ssn+AlyvfPnWn2a5XVl2DvZXlFZ0jo7CUf2/eS1TsOTO16soZoHUhyMOZrTFnTbJ7aR3t+fW5u3uuYe2kp57rYkpZAEBidgH5JZVsPBAHYDJS/O9vh01st8cmsulAHAq5HI2NJafTcnng+jEo5LJOp8ysqK7l6LkMknIKieznjaOdNV7O9ijkTUX90Y83sScuuVPXuRLWbj/K3JGDkJupRivAr7FniTrR8CCNTs8iIljLyYQsk4eYRKv1yFtE62RvIqxBns4UlFWSX9b2FOylWClbfkykF5VipVZSXWueEVh/LxdsbdScScmlWjL/qE6nN7A/NZ1vHr6dyH7evPH9bgyXfNiNXuEDvJovn9dRrlRsAbydNbyxbC6LkzJ5Y9NuTvWCSkEJWQWUV9cyb8Qgfjp80Q/CzaFnPPuvFoTgCtDp9by+YRcnUrLxcta065gX1/9u8vvXu2MJ8/fgjbvnYmupapfwJucUoVTISc8v4fMd0Rw8k2bcZ2mhBBksnTqcBWMG4+ZgS2JWIRv3x5mssXUnSTlFxKXmMCTAPDVaAU5nmE7LxaRmMSjQjYKCCgpLq0AG4UGenEjsmLdyeIAnJ1NyGNbPi2OJmYT6eZCQmU9dJ5JSVFW1PKVbXFnNUH9PTp7tuENSc9jaqMmrrsTJ3Za4nOanLK+UlIJi/v5dFEvGD+OeOSP5v59Nq45tORJvNsE1JxGBXnz5+O1sjTnLvzfvI6fEfKFOnSG7uLzhpdva0ljVqF4vJkxbQwhuHycxq4Cn1v7KuawGp5Tz2YWdOk9lTR0Hz6RxID6Vd3/ci4+LA4uuC2fEAB/q9QaUCjm6ej3JuUWUVtZgqbLg5W9+p7KmrklifoAaXcPoJupYAp/8ehClXE69wby5cTtDYnaBWQXXRt3UKel0Zh6R/l4UllYREezVYb8kX1cH4tPzMEgSxxIzCXB3uqJ8zJn5ree9jU3LJnKAluMJnZ/+HeTnjtpSSW5lBaklXZ9nd1tcItviEpk2uF+TfbGdTEtpMEhU1NZ2alSbml+Mn6tjm3ZyuYzrIwcyJawfX++NYc22I5TX9Jw3828xZ5k7PIRv/zgB0MQnQGCKENw+TJ2unr9+usWs3rBPr/2VeoOBrMIyDp1NY/Wjt/Dr0TPkl1aiVMiN3p4WSoVJ9p2WSM5taFtvEFuApGzzfVZ6g4Efj55udt/x9GwiBnpxIiMH7wtpANujvK72NtTU1ZuMZBs/w85SUV2Hh4ctOa3UwT2ZnUv4AC0GSUJ+YWlBQqKmSsfZtLYTd6itlBzO6DpHsZZwtbMxGaFZqSxYseC6Dp/nTFY+/9r0O0l5RTw6cxwLR4U1uxxyMi2HlPwi5g4LMZkFeuLLX3hryRx8XBzadT1LlZLlU0dw4+hQVkcdYv2+49Tru/8eUSrkJssf8Rl55JaU4+5g1/JBfRghuH2YH/bHmT305HJhfOyTH43ewcpLHkDtEdveiGTGOJi98clktuC8VK83cDSlQYCS8oqIDPYi5kzrIS0KuQxbK/UVC2xzuNm1Lrh19Xqi05uODIf7ebXvAj3057Dh8EkWjgnjux3HUSoUvHv/fMI7kLu6sraO/2w7wLr9MUa/hxd/3IGzrTUzwvo3sa+u0/H0179xPCWbZ26aYhRdmQyOp2W3W3AbcbCx4okbJ3HbhKG8v+WPbk+aEeLtRnphCXdNjmSwjzsqpYKTqTlCcFtACG4fpbKmji2Hmx9dmZNLQ3F6yyj1SjDnKGLdvth220anZBLaz530zGIqqptfUw0P8OTYefNX6RkQ5MbZ3M4VDG/v64mhmXCu7uJwSgZLZg1npL83oy5kkWoLSZLYFpfIq5t3kVtm+iKilMsZ3c+n2eMGXlgb/ubACZDJuH1sON8dOklSbhF74pOZHta/U7mofVwceH3pHJZMiuSdn/YQ3YVhZZcyxM+T8AAtI/r5YKlS8t7P+0jLL2FaeNOXDYEQ3D6Jrl7Pqo83k55VykCtK2eyeiZX79VInc48Q7Hs4jIOnktr2/AS4jJy0TraN+Qtvkyfhvh3jdhCg1Ndra5zHsOGZqIO+3u5YGutBjnokdDp9eTW9ExZuiXjI7hx+GBkyAh0c2r3cVEnz7Hiqy3N7hsZ5N3iOq69lSVeTvZkFpXxzf7jfLP/uHHfrzFnCXB14s8z2l+J6HLC/DxY8/CtvLt5L2t3RHf6PO3By9me6UMHEHyJg9nsYQNx1QhP5ZYQgtvHyC+p4B+fbeXwmYasOBVptQzv70V0cmZXJw26JqitN0+oypaYM506Lqu4jIFeLpzLMB1xFpRV4qqxIb+0YyE/raFQyBjSX8uJzM47XOkMTV9QbG3VDQUIehgrCyV/um44Tu2s+nMp3x052fw5VRbcPXGE8ffCiio2H4vnVFoOj80ej7ezhhAvtxaXEuLSrzymWSaT8fi8CUwO68cTa7eQ18pSQGdw09hy34xRLBg9GAuFaSKP7kpHerUiBLeP8f7GfUaxhYYRyLGETIJ9XMmrKKeooqYHW9f7qevkSO9SJEnixyOdn85PLCoiItiLY2cvrulqXeyJSTSfiPXzdqZMX8eR1Cs7Z129Hhd7G5ztrVGrlSjkMip0HcscNaafL7llFSTlmW9t2t5SzbtL5nVKbLOKyziQ2PzsxOq7byLCT0tpVQ3Pfr+NXfFJ1BsMDPZyMyaKaU0AH509Dmj4G0krKGmX53JzyGQyhgZo+fD+G3lszY8mOcI7S5CHM7dPGMr8kYO6vATjtYr41PoQeoOB32OaZtcBSEjPx8HGkhAvN85k5onRbgsoFe1PzdcSCdkFpOQXd/r4eoOBo6kNa7qpGUUE+7txKDmDyH5enDiX1ew0bnuxtrQg0M+F2LQrr1KksbKktK6GHEMl2SWdH3lnFJeycvYEUgqK+fj3Q8aQsc6idbDn4z8tIMjNuW3jZjiVmcvwAG9qdPXU1Omoqa+npq6e4YHeRPhpkSSJpzf8xq74JOMxkwcF4eFgR3FlNSdbGcU2FkbYdyaFB9ds4oWF07lxZGin2gkNI86fn/kTmw6f4vn12zp1Dhd7ax6aPZYbRg1u1vNa0H6E4PYhUnOKqW4ln3BJZQ0llTX4uzuilze8YQtMMUfd1x1xzb/0dJS4jFzsLNUN3swyiE7LZJC/G+lZnc8bbaFQmEVsoaFEm7eDplXv5vaQXljK41/+zKwhA/jsvlv5vx2H2HmJmHWEQFcn7p08otNiCzA9tD/TQ02dgiRJ4qv9sVTV6bBWWZhU+rG3VHPnuAgAjiVl0Nz7kLezhpyScl7ZtIuIAC1D/Ro8pV/6YQcTQgJwsev8uqhcLuPGUYNx19iydsdRDp9rX+EOO0s1y6cO5/brIrBuJl5c0HHE60ofIqud00qpucXk5JURGeDV5dVgrjaKK6qu+ByJOZ1LLtIclyc9OJ2dx0A/t06fr7SqxiR860qoqtdx9AqnpC9l64kEHvhsI7eNCef9JfPx7GDoiYVCwWf33cL8YYPaZR+TmsWh803Fqa6+ns/2HEVvMLBm9xH2nk3m/W0H+PHYadQXihPcO/niOu7SCZHYWaqRJIkdLbwoeDnZM9jbnfjMPL7aF0tmcSkqpYLaej0J2S17iBdXVhtjiFtDJpMxLsSf1Q/dwof330igR+sOYj4uGn58Zil3Tx8pxNaMCMHtQ+S1Utf0cur1BmISMgn1dsfWsveV4+spLi1E3lnyyrrWIzc6LZOIfu2Mf70cCXzamd6zLTTW5q8YU1pVw0Nrf6Sqro6fVizlnkkj2vWC4OOs4YOlN7RrpKjT63kvaj93ffwtz2z4jcpL6t82TBdH8eYve5nyymo+2XGYBz7bxCc7DvHU/MnGWNzxA/wZ6OmKvZWaO8YNBRryk6taWJIY5OXGqP4XQ5LOZuXz1/kTsbNS88aPu5s95nRGLq9u2sX4Zz9i0bvrONvOaINxIf5seGIJz9wyBcdmqvo42Fjywf03mpQMFJgHIbh9iNzijudePZ2Si43Cgv6enZ+Cu5Yor6q54ljcPDN6EjeHATiakUl4fy3hAZ5EdlB8HdWWRPpoGeDkTKRvJ4WbhiLrXUG9wcDfvtnKt4dO8PjMcXz/2J2MCPRu0T7IzYlvH17MuAF+bZ47Jb+YOz/6hk92HMIgSWSXlHPgXBpZJWVklZSxMz6JX4+fBaCgvMooxpYWSlas+5mZr69h5+nzyGQy7pk0gqXjG0a3AFFx5zjfguNXjU7PiKCLffB1diA2JQtXOxsScwub5E0+kZrN/f/3A79c8HY/k5nP0aSMNvvXiFIhZ+H4cDb/fTk3jTZdI/7ogZs67awlaB0huH2IjoxwLyW/pJLzaYUM9fVs9o24L1FSWUPMFSQVkCSJ3NJuSDovQVJBEQqlnGPnMwnxcUNj3b4cvyfOZXM8IYukzEJKSquw76RwWllYGKdYu4I3tuzh5Z924e/iyGf33sKjM8Y2a7cgcnCb+Y0lSeLbQye45b0vicswrcYzwNOFBz7dyLy3/scbW/Y0e3yNrp68skryyip5Yv2vFFVUMSOsP0vGRxhtkvOKjF7lQ3w9TI4/lZ5jMkW+bl8s900dZVzRic9sKOSgNxh46qtfuefj7yi5MJWsVioYM8CPg+fSMBgkqmrrOJacSV07QtjsrNQ8c+tUk4LxLvZiZNtVCMHtQ1xpNp8T57PRVdcTGeCFGSvUXXV8/MvBTn+WMlnnSxh2hOF+Xkg1BjLyS5EkiE/PQ6mQE+TRsZmK1JxiPCw79wCOTsrE19mhU8e2l68OxPLoFz9RVl3L/VNG8e875yG/7PP1cmq73um2uESe3/g71Zd5QNtbqvFx0jCmny8Tgv1JKyxp81zVdTp2xSehkMuxUTcsx5zLKeDD3w8ik8kI1rpy7rJ1/JKqGvxcHRnq74nW0R61hZLs0nKujwgGMH6Oh86l8/OxMybtrK3XE6x14e0lc9l28hx3/ucbln7wLat/P0x+O5Yv6urrTQqImCP0TdA8QnD7EBqbK6/NWVWrIyYhE2+Nps9OM0cnZvDV7phOH29l0bVOKAPcnTmRmEVFdZ3JrEZheRUpecUMC+rYNHF+cSWDvNwY7udFsEfHEhvYqrp+/X/3mWRu+vcXbDh8ksmDAvnTxOEAaKwtuT48mPED/Ns8x87T55vdHuzpikwm46n5k1G3UhfY3lLNolFDuH/KKP57903cNMJ0mvbYhbzYEhK6ej0udqbxv2kFJRRXVlNYXsXkwYH8/o97GTvAD2c7a9w1tvi6OgCQ24zH9wBPFx6dPQ4LpYIfj5xGV6/HTWPLx9sOMePFNXz9R2yrfU/MLjSGkq1aMBHvDuZzFrQfERbUh7Axo/NTxoWSbYP93KjQ1ZGaX2K2c18NfPzLQeaNHNSplxgrlbLLSqq52tlQWFBldN65HL3BwLHzmYQHenI6NRddO9ajy6trKU9smNIcGtz+xP6A2Tye2yKntIJ//rCdjUdP8eItM3h4+pgmWZBaol5vYM/Z5Gb3XToNn91M/dkwHw8WjRrC7PDghhrOLXAm+4JDk9RQjELVzFT7mz/tJr2wlN/jzrMj7jy+Lg68ddcc1uw4QnWdDgsrRROhBrg+YiAKuZwTqdnsPWPaj3qDgfTC1ssdJuUXMcDXlVnhA1gwajCSJHXLLExfRIxw+xDVdZ2LzWyN+NQ8MrJLiAzwQtGH5pl9XTTGzEEdpTPZjdqDQibDQammpLJpfeHLOZ6UzWA/jzbtLqequv1/QwFujtTpu7cM0PG0bJb934YOJcc4npZlXA+9nMYpYYBq3cWqVzcPD2XDI4tZ/9Dt3Dh8cKtiCxjFX7owkrxv2qgmNhlFpYwL9ienpJzsknKikzKpqtUxfmAApzMaXnjGBvvhfYkX+fQh/fFyssdgkHjxhx3NXnvd3hg+3XmkxbYdPJeGm8aWKWH9mPvaWr4/FNdqXwSdRwhuH2KQn3uXnFeSICYhEz9HR9w1tl1yjd7G6fQ8Zv59NemdGNl3iQeoBEO9PDtUrzcpp9Ak8Xx7KKtoW8wbcbK15njGlecG7iiFFVV8/Puhdts3l0TD19mBp+dP5pkbJnMqI5cH124iOa+IcQP8+PEvd/HCLdMZ5NW++6myto4Jwf5AQyrVccF++DfzN5CYU8g/bp6Ck601NmoVL90+E09He+YOG8j53IY1X4Vczm1jw43HaB3tmRbWnxNp2UbHqssxSBL7zqQYs1hdyv6zqcRn5LFq3nX8+b8bKaqoIr0d69SCziGmlPsQIwb6Mn/sYE6n5pKcXYiXi4a0vBKznT8lpwgbSxVhvh6cSs+hByuudQsumoYcwR0l3M+T346bt25ppK+W2HMdqxZUVlVLRXUBw/p5caydeZjzSipQ2ShMCty3RGcrDLUHKwtlEwenS/nyjxhuGjG4XRmldsUnIZPBYC93xvb3Y1x/P4b5eyG/MGPT38OZeyaN4PYx4Ywf4N/qdGu93sDKr7agVMh5a/EccksrmPn6p9hdWM4xGCT+On8i9/7fDybHKeQyfn5yOY42VjhYW3LTqFCujxgIwFB/rcmoNtzP0/j/yto6lAo5n+062mofj5zP4Kej8dw27qJYl1bV8PpPu3jxtpms/GKLsaBCc1PnAvMgRrh9CEdbK/65dAbfPruEfe89zIeP3WT2a1TW1HHqfA6DtF0zmu5N5BSVcya9+VFFa0wN7WfWdvRzc+bk+c6NJA2SxLHETAb7uWPTjoxCBgPtTjNYWlWDn5NDp9rVFi72Nni0MptSbzDw6ubdxinclqjR1fPw9DHs+/sDfPPwYh6bOY7hgd5GsQVQKZUM8/diQnCAUWy/PXSC3WeajoxzSsvZfiqR+yaPBMDeSs2MsP4UXZjm1xsk3vt1f5MCBnqDxLcHTqA3GNAbDMQkm748XfqZh/t5MvxC3HGgmxNHEtPZeap5p69LWf37IU5fEvKkq9fz7C3TOJaUScUlqShzOhGvL2gfQnD7CJIk8cPek+yKPU90QgZqCyVujnZdlratplZ3zWeoqtHV88jHP3Y4vlnrZM/YdiRhaA8O1laUFlejN1xZMo5TqbnY21ji7dx2CI1DGzGt0LCe7GxjjYuNedarZTKwvWQ9Nb2wlKF+WlxbEf/951LZebr1nMuWFkpmDQnG4ZL48vZ8lk421nz5RwySJLHujxje3boPSZI4lZmLTAbu9g0vA1YqC15bNIubhzd4LYd4ubJwzBD+tWgGVpet+/5n634mPPcxqQUlHE9tENzmpoFlMhlaJ3us1RbMHjqAJ7/e2mx+5svJK6tkyX++MVZdcrG3YViAF0snRRL193vY/c/7+eTem0xGwQLzIgS3jxB7PosXv9zOio9+4pOfDwKw/1RKl10vKbsIZytrkwfZtUhlTR1bjsR3+Li/zp94xU5mCrkMD0sbisqvPL8zQHZROYXlVYT5t+5MZd1GWFOEnycWCgXHU7OpNEOxBxu1ilcXzmoihFtPJHDryLBWHZZe27Krw1PbX+yLIauk5bzjeoOBnNJyrFUqUgqKeXvrPr78IwaZTMaxlCwkCRa8+wVRJ8+RmFvAz7Fn8HVxwNJCyZ8mjWBssB8LRgxm/eOLmR0RbJJhqrHoQUF5FV/ti+WB1RubbcMgbzf+ftMUdHpDh+rd2lmpkS5Z69Hp9Tz7bRSPrf2JVV9s4ZVNO/n2wAlOmaEur6ApQnD7CPpLwj98XDXU6up545udna4q0x7S80qwUViYZLExJzaWKtw1tjjZWLXpJdqV/Bbd8fXYIA9nE+eXzjBU68m5rJYT23eG6rp6TqbktJggIyzIA0tV65+1JF1cvy1uh8d0223S4evi2Oya7f/2HWNeREiLx2YUlbF2b3SHrldRW2symr6cH46e4pXNuziclM5fvvwZpVyOlcqCWl09G4+eAiC/vJK/rPuZG975gsLyKmaG9efZBVPxvSTGNdDdmdfvuJ4JAwOavU5/TxduHtV8ab47xkcwd1gI72zZ1+5+hXi5sf6xxSbfbZ1Oz49HTrMj7jxHzmeQkl9MdFImX/1xHL3B0OqUfHWdjp2nzlNQ3rWpSq8lOiy4e/bsYd68eWi1WmQyGZs2bWpiEx8fz/z589FoNNjZ2TF69GjS0i4WbK6treWRRx7BxcUFGxsb5s+fT0aGaR7Q4uJilixZgkajQaPRsGTJEkpKSkxs0tLSmDdvHjY2Nri4uPDoo49SV2f6Rn3y5EkmTpyIlZUVXl5evPDCC22u61zr3DYlgg27jpNZcOVFqdsiu7AMNQqczDTSVSrkDAvQEuzuQnV5HfkFFZSUVEOdRJC7EwO1rvT3dCYywIth/l4MD2j4afhd2+CA0o7MQx0hKaeQDXuPd/i4P88Yg0M70y1eToS3J7GJHXOS6gh21s2nc1RaKCi8REQVchlejvaEerszPMCLCD8t2cUX/67yyyqbTJ12lAg/bYvZjypr6ziXU0Ckf8vJPFbvPNwhR6AhPp5siW3Il2wwSE2O3XMh1rWsupZzuYVU1NZhpbLAQqGgRtf0BXaIrwc+zg7cENl8laI7r4swiu4to8OM25NyCxnq33Lcc73ewK1jhvDGndczPzKkxb8lSwslyycNZ+2DC/G4rMKSjaWKfy+b1yRl609HTzPzpTX86aPvSMkvNimMUFlTx5d7j3HD6//j0c9+YuE769h64fMStE6H74TKykrCw8NZvnw5N998c5P958+fZ/z48dx99908//zzaDQa4uPjsbS8+Mfw+OOPs3nzZtavX4+zszMrV65k7ty5REdHo7gQr7Z48WIyMjLYunUrAPfddx9Llixh8+bNAOj1eubMmYOrqyv79u2jsLCQpUuXIkkS77//PgBlZWVMnz6dyZMnc+TIERISEli2bBk2NjasXLmy45/WVUyjN3I/LxfcHGyJScxCLpNdUbHy9pJTVI6nkx2RAV7EJGfSrtVGCZzsLNFYW2OlssDKQolOpyctt4TYhKZCU1evJzmj/SExg/3c0EkGErIK4ArDh+vq9bz0zQ7srS2ZGRnc7uM01pY8Mmss/2ohfrIlglydiE/ObdvwCrh0uluhkOHqYIuTnTU6g4G0/GIGerhQVFlNQXklWUVlZBW1/PLmobEjrbCECB8thVVVJBcUt6sN8yNC6OfhzJ1jI3j7170t2sWmZfPJn27k8S82NzsKrtbV89Yve3hz8Zx2XXdCsD/z3v4fC0eFcSwlCydbUzEa6OnKjssyU/Vzd0Yul+FkY03+JSM+pULO4EvChz7beQQnW2tuGDEYgKKKKpxsrXlo1hh0ej1TQ/vx3cGTAGQUlnLevqhJGNmhc2kM9deSXVLOl3uPUVRRxYggH75bcScrPv+ZE2kXp4OD3J34+N6bjEIrSRIf/HaAw4npuNrbMDzIm9vHDWXDCnf+9d12dsdfTJyRW1qBm70ti9/7mvLqWtw1ttw2Npwv9hwzOoFBw0vV+1v3M2PIABNnM0FTOiy4s2fPZvbs2S3uf+aZZ7j++ut5/fXXjdsCAwON/y8tLWXNmjV88cUXTJs2DYAvv/wSHx8ftm/fzsyZM4mPj2fr1q0cPHiQUaMaAsRXr17NmDFjOHv2LMHBwURFRXH69GnS09PRahveAt966y2WLVvGSy+9hL29PevWraOmpoa1a9eiVqsJDQ0lISGBt99+mxUrVvSpbCqZBQ3ZZlzsrdl7IomdsYkND9VuGuxnF5WTXVSOj5sDro42RqGXXVA7vd6Arl5PTV091bU6CssqKSmuoaS47VqfnSE+tcG7eICXM0qVktPpuVcsvP/6ejsTBgdg3QFnsVtGDyE+K9/4kG0LBytLyktq2pUh6krIKChl1GBfEvIKKKyoIrOynMzKiyO9s63UaL0cS7mSQR5uHEvKZKi/J83ndGqKvZWauyeOoLpOx4/HWl8nP5qUwV9mT+Dln3Y2u//XEwncP2UU/duRmlImk/HXORNRyOVE+GmbJDj589TRFFVWs/5gw6zGAA8XXlk4k1+PnzUp5QcQonVDfWGEvys+if/8th9PB3sKyqu4e8oIHli9kQ/vXkCwpysKuZzkS6oJfbP/OP0um9ovLK8ixNuNM1n53Pd/3xuXhGJTsjmQkMp7y2/g/tU/cC67gP6eLvz3/ptNEq0kZBfwyfaLMcpRJ84xxNeDwT4eDPHzNBFcgOT8IiourMPnllbw71//aPYzSysoYdfpJKaEBrX5+fZlzLqGazAY2LJlCwMGDGDmzJm4ubkxatQok2nn6OhodDodM2bMMG7TarWEhoayf/9+AA4cOIBGozGKLcDo0aPRaDQmNqGhoUaxBZg5cya1tbVER0cbbSZOnIharTaxycrKIiUlxZxd7/U0PjRqdPVkXBDfltL/dSXpeSUcO5tJbEIWsQlZxCRkEpOQyYnz2cSn5pGcXUROUTm6+q4VlEYSMws5k5xLsIcLQ/w8sLRQolLK8XN1IMTLDbd2Vk7xd3XE086O17/ZRXk7CoI3IpfLuPuSYuVt4WJr06XxrY3kllRQp9NTUF7VLg/Y1jiblc+p9Fz8XR2Jzchu93Ff7o/lx+jTHE3OaJIK8/J35XqDgdtHhzMi0Bu1UsG0wf147bbZHPrng/yyajlfP3hbu8S2kZFBPgDNZhOTy2VMG9wgLF6O9vzfn27k2e+3s+rrX6i6LJtbuO/FmNm3f91Lbb2B1IISY3KJ/7vvJhxtrVAq5Lx911zW7buYo7taV28ynb3pyCme+zYKeytLvthzrIn/RVx6Lo//bzOTBwUxUOvKmgduaZLV7Kejp5v052Raw2xJYzarS6nogNNbY6lAQcuYVXDz8vKoqKjg1VdfZdasWURFRXHjjTdy0003sXt3QxHlnJwcVCoVjo6m0yTu7u7k5OQYbdzc3Jqc383NzcTG3d001tPR0RGVStWqTePvjTaXU1tbS1lZmcnPtcBgfw8UchmxiVkMH+CNm0PfyAjVXs5lFBCXmENdVT22CjUZ2SWcTcmjoLCSACdHwv08m62Q5OFoR6jWnfTsYs5nFfLT/lN8t+dEh67tZGvV7jVuuVzWpY5uJpj5nSclv5ihvh3Lxfz8xu042Vjz1LxJxm1qpYKov93NvZMuvqhoHe2Ry2W8e8dc9v7jAf69ZB5zhw7E1lKNn4sDQ3w9kSSJ/LKKdiXtaMsJz8/FkdtGh7P67ptwsbNpMu3cyNBLBNfbQYMMGKh15fYLRekdbKxQyOXo9HpWfP6zMfkENMTYzotscAgzGCSOJKbz4m0zgYYRZXOEeLlx//SRfPrnW5usy67bF8Pne46ZbLNWWzAtrCEuPKv4yp51vx1P4Mcjp67oHNc6Zh/hAtxwww385S9/YejQoTz55JPMnTuXjz/+uNVjL0+Y3dx0rzlsGh2mWppOfuWVV4yOWhqNBh8fn1bbfbVw3ZBA3rh/Hiqlgi+3H+ORG8f3dJN6JdZqC7a+eg8PLxhn3JaaW8zJxGxcrWyIvOCENVjrxvAAL4qLKjmdmmsyCvz695gOlTizVqtYNX+iSd7e5nCytqSosLJb1t2h+RjQK+VkSg7B7u0fadbW63nsy81cHx7MotFDjNv2J6Ty+Kzx/LJqOWvuuZnJIQ0jTgcbK5PPsbK2jh2nz/PCxt+Z+fqnTHp5NR/9frB9bU3PISGn+alzraM9/1gwBT8XR2QyGc8umMo9l7wA+Ls48vxN05g6+OIU61PzJ7Hz2fv49i93EKxtmlLz3CXXGtXPh/eWz8fV3oYfDsVxICGVFxbNwNZSzfmcQgZ5Nx2QqJQKxvT3RaVUYndZDeOkvCJe3bSryTFTQ/vhYm9DXmkFZ7I6nsTlcv7+TRQPrdmErh0vNX0Rs8ZSuLi4oFQqGTTI1BsvJCSEffsa3Nc9PDyoq6ujuLjYZJSbl5fH2LFjjTa5uU2dQvLz840jVA8PDw4dMs2XWlxcjE6nM7G5fCSbl9fwR3X5yLeRp556ihUrVhh/Lysru2ZEd9LQIP52+2T+9cV2Hrt5PDIZVzxdeK1hY6Uiq7CMorKm4Sz5pZXkl7YdAlFQVkVOcQW+bg7tvu68yBCmhgaxJeYMCVkFF5fWL3xBSblFoJMw6CV0ej0V1eYXwyZ0wd+G3mAgr6gCT3tbsi/Uap07dCCu9jZ8tqf58J3sknJWfvUL7901j6mD+qFWKnDX2JJdUo5cBm72tpTX1FKcWUVZdS2ZxWXEZeRwMj2XhJz8JksnGw6f5IEpo4xrq81RVacjwNWxzZegRmQyGX+ZNR5PBztcbG2YMiioiQORTytl7ywUCpOlgusGBVJUUcXyjzaQX1bJPVNGMG5gQ2GDito6lk8azkCtG4cT05BoiK9dMGIwwwJMvbXPZOZhZ6XG19kBb2cNGZdVDroupME72katQi6ToTfDA2FPfDKv/bSbv9805YrPda1hVsFVqVSMGDGCs2dNXcQTEhLw82vIrBMZGYmFhQXbtm1j4cKFAGRnZxMXF2d0tBozZgylpaUcPnyYkSMbUqQdOnSI0tJSoyiPGTOGl156iezsbDw9G6ZtoqKiUKvVREZGGm2efvpp6urqUF2oyxkVFYVWq8Xf37/ZPqjVapM132uNBeNCScwswMfVAa2zfbeEBl1NGAwSr6/fSWJWYdvGLRA5wLtDYtuItVrFrRdGcZdTVaPj6Ll0iiqqSc8vIS41h1pdPbFJXRca1Fa8bWcpra7BWm1HqIc75woK8Hd15IEpo6isrePbQ807jx1OSmf0Pz80y/WLK6v55fhZbhw+uEWbw+fTmRQSaHLMX9f/glwmZ+GoMCYODGx2ffe20Z2Lra6oqTVxuBoR5E1+WSX5ZQ0veGcuhOV4ONgZPY79XB1bzQq1Nz6ZJ778hb/fPIWckvImYutqb8O4C0UVbCxVhPtpOZbcvpzabbHz1HkhuM3Q4TuqoqKCxMRE4+/JycnExsbi5OSEr68vTzzxBIsWLeK6665j8uTJbN26lc2bN7Nr1y4ANBoNd999NytXrsTZ2RknJydWrVpFWFiY0Ws5JCSEWbNmce+99/LJJ58ADWFBc+fOJTi4IexixowZDBo0iCVLlvDGG29QVFTEqlWruPfee7G3b4izXLx4Mc8//zzLli3j6aef5ty5c7z88ss8++yzfcpD+VJkMhmrFk7iUHyaENtmKCyrorAsrW3DVrjluuZF80qwtrTgurCLAqDT69kRm4iTnTVHEtKNGYrMiULZdXlxsi+UoAv1dec/UQfIL6vkqXmTScor4qiZHvqtsW5/LAsiBzX7HCirrqH0Ese3xNwC/vzZj8bsU/sSUnCzt+HmEaHcPCIMz8tiWzvDB78doP6C5/nM8AGEeLlhpboYPhWdlEmtrr7VUfmlbDh4gpd+2MGM8AGE+njwwne/m+x309gS4OpIUUWVcfo5WOtqNsGt72Iv+quVDt9RR48eJSIigoiICABWrFhBREQEzz77LAA33ngjH3/8Ma+//jphYWH897//5fvvv2f8+Itrhu+88w4LFixg4cKFjBs3DmtrazZv3myMwQVYt24dYWFhzJgxgxkzZjBkyBC++OIL436FQsGWLVuwtLRk3LhxLFy4kAULFvDmm28abTQaDdu2bSMjI4Phw4fz4IMPsmLFCpMp476ITCYjp0gkKO8KPJ3tmTLMvMUJmsNCoWBmZDCvLJvNlPAg+mvbvy7aIbp4ySEuLZcR/l58c+gE727dxzt3zMXX2QGVUsGUQUGsnD2h2WLtV0p8Vh7HUpufHTAYJII9L66xemjsCHAzdfLMK6vko98PMeO1NXy4vX1rws0hSRLJeUVU1+mMGdnsLwigv6ujMRuZraXKRGzj0nLYGnuW2JQsfj+ZSFl1DXmlFZxKz+GfG7bxwne/c+PIUP42fxKPr/3JWAv7upAAvnh4ES/fNpMj5zPYdzbFeM7OJmFpjv6ebVdp6ovIpL6edqkNysrK0Gg0lJaWGkfO1wIvfrmdH/a2L/ZT0H6evH0KCyd1b/L3Ol09f/vsF8qqaohOzMTeWo2vqwNxqVeeHMPKXkVFXdevFw8J8CQ2PZs/Tx3FfZNHUlevJy4jlxCtG8dSMnnsy81mD2ObNWQAb7WQDON4WrZJSI9Or+fFH3fw3eGmxdlfWzSLua2kl2wOvcHA9pOJfLrzCKcz8vB3dSQlv2FEu3LuBJZNGg40VFy66c3PiQjw4s0lDW1Nzivi5re+RKdv2TFp0uBA3l9+A39d9wu/xlxc4nt32TzGB/tzzyffEZuSTaiPO18+chuSBGez83lw9UaTpBad5c4JEfzthklXfJ6uoqee66Iebh/EYJBIuoI1SkHzONtbM39sy+uCXYXKQsnrd8/hL/+3mSBPZ2QyiEvNJTzAk+PJ7Y97bcTRxgo3B1usLS0orK2moqBrBddGrSK3rGHG5aPfD2GlsuDuiSNYs/soCTn5fPHAIl5eOIu/rf/VrNfdcfo8ZdU12DdT/cjPxXREa6FQ8M8bp+Hj5MA7Wy/mL74uOIDJg1pO9iBJElW1OmwuJEPR6fU8vOZHTmXkmkxbN4qtg7UlU8MuzpBorC1Z//hik9rScek5rYotwI0jQtkRd95EbAH+sT4KJ1srUi+EFcWl53Ljm1+QU1xGf60rVioLMIPgdlvo2lWGENw+yN6TScSe7zpnm77KHdOGdZmjUVtYKBS8umw2i1//irT8EqDl0LfmcNXYorFRk1dSSXFltbHogIdH17/9V9bW0V/rTPaFqjdv/7oPK5UFLy+cye0ffM1/dx3hhZuncz63kP/bedhs162r1xN18hy3jAxrsq+56VWZTMY9k0bg5WhPTGoWd4wd2kSYGzmVnkPUiXPsOpVEakExSyYMY+W86/hqbyz7E1Kb2CvlciaHBvHn6aPxcXYw2edqfzFmXqfXM7KfL24a21arBDnZWvHHJdPFjZTX1DZJItKY3epEasdfzlri8rAkQQNCcPsY5dW1vPPdnnbbKxVyJg/tR35JhRDpVrCzVneJs1RHsLVS8/fbpnI4IZ3K2jpScooZFuTFsfPNO8KolQpqL8RLllfVkN/MA7ywousqwSjkMm4fN5SNh08Rl5ZLoJsTSfkND/+XftyJlYUFHy1bwP2fbqSqTsf9U0ax8egpk1zFV8rmmPhmBbc1ZocHMzu85ZzZsSlZ3PXBNyYhdxsOnuSR2WMZ7OPOdSEBaKwtGeTtxsSQQFw1tuj1BuMouDXW7opm+8lz/GXOeJ76amuLdin5xaReePHqalzsrJk0OIjs4jLq6vVkFJay/cQ57pwQ0aRYQl9HCG4fwmCQeO6z34yFDNrDv5bP4sOf9pNVUNq2cR9m+awR2PaCt/qRwb6MDPZFkiRe27CL9XtiieznRXSiqegGeDhRUFrJYD8PdHo9JZU1pDfzgFYqFNBFSQz0Bokpg4NQyuWs3R1NVWUt9pZqyi6MwJ79fhuv3zabF2+dwZmsPIb5e7Hsukje2NL+F8a2OJqcSVZxGVpH84zkdfV6nvt2m1FsfV0cyCwqpbK2jlveXsdH9yzgg7sXND2w9RLDQEMmp+8PniSzuIzC8ipc7KwpaKEW8ts/7zVLacTWUMhlTB/SnydvmNykBKckScJTuRlEPdw+xP+ijrDr+Pm2DS/g4+ZAcXkV6XklPZJ3+WphXKg/d00f3tPNaEI/rTNaZ3uiEzNNisprrC0pr6qlvLqWY+czOZmS06zYArjatS+XdGfZHZ/MrxdKu+WVVeJlb4/iwlS4QZJ48puGUdywC+X3bowcbPbaxz/HmuYALq2qYdknG1i7N7rDeauVCjm19fXYqFVYqSz47/03G0vvJecVMffVtTzz9Vbq6tt/3sOJ6by/dT9//fIXJMDHWcO7v+xrNc9xV4vt1NB+RD1zD2/cOafZetcymQyLLvAuv9oRI9w+QkxiJh9s2t+hY2SA1kUDQJDWmWnD+hOflseeE0ld0MKrEw8nO/61fFavK0smk8kID9BSU9fwYM8pLkchlxEeqKWkopqknPaVMtRYWjbUvNXY42hjhUquoL7egEGSOJ7dfD7yjqDT640J/2WyhjzDtpYqjqRmgqyhKMFjX2zm8/sXEuLlhsbakpuGh/LVgdgrvnYjm4/Fc9/khgQ7kiTx1/W/ciQ5gyPJGXy0/SC3jgpj1fXXtetcMpmMX578ExINIzy1hZLYlItLMfUGA7/GJvD0jVNoz3L/2l1H+fcvf1B/IW1uVnEZ9lZqJEmi5gqKWNhbqRk30J+hflrcHWyxsrCgRldPXHoOpzPyKKmsRm8woFIqsFAqUCkVqC2U2Fuq6efhwtKJkb3ub/5qQAhuH6BOV88za37tcA7etLwSgrTOvPinWUT09+KR9zaSlN3+mrPXOkqFnNfum4NDC4nre5pXvt1B0YUpx/zSSvprXTiW2LHEBolp+ch1kFlVSiYXlxU0NuaJ2TyXXcD9U0fx7cETRAZ6sTXmLNW6eoYHenH0guhW1elYtz+WF29tqDBm7gd9Un4RdfV6Y7zvyYyLLxJVdTo+2xPNrSOH4NdKasZLaWifDIW8YQLR09Gekks8kscM8G1zvbZeb+CNzbv5al9sk31lnUxy4uVkz5TQfkweFMjQAC0WiqYjUFFer2sRU8p9gPi0vE4nukjJKeJsej63/+tLIbaX8dAN4wgL8GzbsId4eN5YJg25+AA9l9X+GraNVNfVN/uiVlZVc6Xlg4GGOrYB7k6seeAWNh4+ZSwgfywpkxH+F/MCF1xw3tIbDOyON51hUchlTcr1dZTG42UyGUFuTgDYqlU8c8NkADZFd74Kzp8mD8fTwQ5bSxVWFkr+MqflwiGSJPHtgRP8+b8bmxXbjuLtrOHeqSPZ8Jc7+PWpP/HX+RMZ0c+nWbEVdD1ihNsHcGxmjaU9LJ0xnKH9vEjLK2GQnztHz2YYp7b6OsMHeHPn9GE93YxWCfBwJrYFD+UrRZLA3sqS0ur21/5tia//iOXR2eNYNDacb/YfN26PPp/J8EAt9rZWvHhLw+j2091HSS8qZXpoP8pravFzceTxmeP4IyGVVV//0uk2KGQXxx7j+vujdbBnemh/PttzFMCY07gzzBoazKyhLXs1Q8OI9kBCKiVVNfzr+99btW0NDwc7wnzcCfX1INTHg2EBXs3mfBb0DEJw+wB21h33nvVxc+CRG8fz8PsbORyf1m0l4a4GbK3UPL98pnHKsLfyyrc7KKm8ckFsCYUZhriu9raUVtVw7yff8+riWSaCa2WhRI6MuydEci6ngJyyCtRKBev+vIihfqZ1dWcNGcB7UftJu1DYvaNcOk39wNRR6A0GntnwG7FpDbGpwwO9Wjr0ikjJL2bfmWTW/3HcmIyivaiUCob4ejIs0KtBZH08cLHvWic3wZUhBLcPkNGJeDxJktAbDPi4ajh4WojtpTx5+2Q8nXp/ms8bRg+morqWwwnpDNC60E/rwtHEDLIKzVO0or2J9NvCIJMo1dXwzm/7uH3CUHJKyimvqSW/opIT2bnc8cm3Rtu375jTRGzhgldsJ0dyl48ADQaJ53/4nc0xDd7LzrbWTB1s3vzYkiRxLDmTZR9uaPcxWkd7ZkcEMyLIm/4eLrjY2QjHpasMIbh9gJNJHc8gk5Ffyjc7Y1k0aSgbdp/oglZdfchk8MTCSVw/qmN5c3uKcYP8GTfIH51ej4VCQU1dPZOf/NjERqVUoDcYOhX2ZW9pacwOZadW4eZgS4CbE8eSMrl9/FDcNY0ZkmRU1daRVliClcqCWl09RRXV5JVWYG1lwdGUTBRyBWeyCziT3fo687tb/2BGaH9jFq2z2fkEujlhoVB0urbzn64zDek6np7N90cv5kxeOXsCdpbmibHOKirjl5gzbDl2hsTcttOrOtlac31Ew5T0EF+PPlvl7FpBCG4f4EQnBBfg/34+yOxRAxkR7MORs+lmbtXVhYVSwYt/msX0yAE93ZQO0+ggk1VUaqwaA+Bib81NY8PYHZfE2Yx84/ZJQ4JIyytuM3QoOb0QZytLnOytSc0t5l/3zyC1oASNtSUDPF0J83U3SUvYGpIkUV5TS2FFFUUVVRRUVFFYUUVhecO/Y/r7cj63kE92Hqaoshpn2wa/hBPpOTz25WaWTYjkttFDeHnzrg59Nv3dnfnz1FEm2wZ7uWNnqaa8ppZh/lrmD2v5BUuSJPadSeF0Zh7uGlsi/LX4ujiYCOP2k+eISc4iLj2HY8ltZ2tTKxVMCe3H3MgQxgzwFQ5O1xBCcK9x8ksr2B59DqVc3mGHp4qaOr7ddZzbpgzt04Jra6ni7QfnMzzYp6eb0iySJPHjwdOcSM5i1EBfdp9MwsnWmgeuH22S/SrA3YmIIC0xF1J0erlo+L+th3C0teLGMaHsiUvi7pkjScsrYdeJthOk6A0SJZU1xnXiZ776jeSChiT83x9qGCG6a2yNI11Jgtr6emp19dTo6qmr1/PmkjmM7OeDTCbD3soSeytLAlydmr9gGNw+ZqhJnuNbRoRio1bx0o87TEJv2oNCLuPlhTNRKU0fgyqlgokDA/jl+FmemT+lxVFldnEZz3+3nT/OmuZGtrJQIr9kff/SwvKtEe7nyYIRg5k1dAC2ZhpRC3oXQnCvcQx6ibuuG8qpczkMCHAjMMCVyloddbp6UnOL2XzgdKvHO9paMW6wPwq5rM9mm3ph+axeKbYnU7J5d9NeYpOyjN/ND/svToWu23WMQA9nwvw9CPXzIMzfk9f/NIe/ffoLbg627D7ZEF5TXFHNjwdPMWPYAFZvPURxReeyFDk0U3Unt7SC3FaS7Be2kJqwJRxtTGOeZTIZ14cHMyrIhxd/3EHUyXMm+wNdnVg4KoyD59PZezYZJxtr+ns4M8THkzH9fBnk5d7sdRZEDmLKoCAGal2b7JMkiZ+OnuaVTbuaFdPqDiSk8HK0Z25kCPMiQ/Bzbb4QguDaQQjuNc7PO0/y9c/RAJw4m0l/P1c+ffFOo6NIgIcT723ch6OdFYunDENja0lGfgkJ6fmMCw1A66LhthfX9Umx9XS2Z/bIgUwa2vuSAeSVVLD87W9bnbWQJDifXcj57EI2HWiII7WxVDExLJDMQtPpZYMksTX6bEunahfKToT1mysFobOtNe/cMZffTiTw6s+7GOqn5bbR4YwM9EYmk3HnuAgkqf1JM8b092uxvf/6bjvbTiZ2uq1OttZMHhzInGEhRAZ4CcenPoQQ3GucuETT9Vtvd0eUCjlrvt+Pi6MtEf6erP/7naRkFLJh6zHGRQRyy3VDOJWSi0wGz6z5lcpWcrZey4wO8eXhBeN6uhnNUlpZ06mY6MqaOn45cqZtw05Q04kaqCVmzvk7c8gApof2byJiMtmVJ8eITsrgr1/+Ql4nYnKVCjlzh4Vw48jBhPt59vqQMkHXIAT3Gqa8soajcWkm2/y9nCivrOG7qFhKyhsedj4ejqTnNKy9xZ3LJsjXhZfW/U5FJ1PImRNrlQWBdvZY26opq63jTE7HsyV1hkduHMeymSO65VqdIdDTCXtrNWVVPf8dNVJU3jHx9HNxYFZE6wkhOoM5R4zVdToScwrZdTqJNTsOd3imx85KzaIxQ7h93FDcNO1zIBNcuwjBvYZJzixsUiLLy03DsdPpRrEFjGLbyMff/ME9s0fw7g/7uqWdzWGjsmCQpyt5OWWcS7noQRs20JOTmbldeu2nFk/h1onhXXqNK0UhlzNtaH+TNdvuxMvZngAPZyRJolZXT51Oj4SEo4s13s4aQr09cLS1wsdZQ0p+MYUVVZRV1VBWXUtVrY5QXw9uGRWKtbrtGrA9xes/7Wbd3phOJX3xdtZwx/ih3DgitF11bgV9AyG41zBOGhvuXzgOGysVdTo9NXX1hPbXsub71qsGJaUXkJ9dxqerFvJ/Ww5yMD6tVXtzoFYq0DrY42BtiVKScT45jxNxGU3srLowREIuk/HsXdOZP3Zwl13DnDy1aAozI4N59ONN1Oq6pmYtNIjrYD8PQnzcGOTrToiPG/bW7S9eEBHQNVmaupKMwlK+3hfbYbGdHRHMguGDGdXfR0wbC5ogkySRs681ysrK0Gg0lJaWYm/f+7MLXUpRaSX/+/EwQwd6obGzQm2h5L/f7+fg8ZR2He9ob83dN49BYaXghS+2m7VtrnY2+Gvsqayso6SsisKS9q2LhQZric668rJwzfHskuksGB/aJefuSvJLK/j5cDzf7j1BdlHnskg521nj7apBfiE8Z4C3KyE+bgwJ8Oxz6QILy6t4Zv1v/HE2pUPHTRoUyPt/uqFrGiUwKz31XBeC2wZXq+BKksStf1lDZl5p28ZtMOe6wfQb4M7r3+zq1PGBro5YqyywVCoxSBKnsnKxtFBRk9exkBBrKxXWrtZkFpsnNeGl3HP9KB68YazZz9ud1OsN7Dpxnp8ONXokq1HIZRSWVZFfWkFBWSUWCgVBWmeCPBt+/Nwc8XFxwFVj0+ezGOnq9Xz1RywfbzvYanH3S7GyUDI9fAA3jhhMZKBXn/8MrxZ66rkuppSvUeKTcs0itgC/7j3NunkjmDqsH78faz4cws3BlonhQXi52FNQWklsYha62nos6uBcUp6JrUIuIyDQnvgOCK6jxhoLBxXpnRzBtcbMEcH8ef4Ys5+3u1Eq5EyL6M+0iP493ZSrjj3xybzx025S8ovbNqZh+eHGkYN5eNZYXOz61gyAoPMIwb1G2XU4wWznMkgSn248yPK5I5oV3FuuG0J+aQUbdh8HSSLU2x19pY6U9OZzxeoNEvGJHZsWVsjlXZLiLsDTiX/cOU2MTK5B6vUGDp5LY1iAF9ZqC+r1Bk5l5OLhYEd2cRmF5VVYqS34fPexdk8fO1hbMjk0iDvGRxDcTFIMgaA1hOBeg0iSxI7D59o27AA7Dp7l3lvG8vCCcdhaqTmTlktZVS1hAZ5U1+n4bs8JQr3cqCmt5dwZ86+xFhRXoK6sIbyfO8fTzeOlPKy/F6/fNxdr4UV6zZGUW8gL3/9OdFImSrkcb2cNBeWVVNTUoVYqqK3vuJPZ/OGD+Ov8iWg64DAmEFyKENxrkPPpBWTmlpj1nHqDxAsf/cqiWcP4Zc9pkjIKCAx2598/7EUmg2G+npw61TXFzhupravnzOlsRg725lh6NvWShJONJf4aB5JKSjqUS9fNwZZ/P7xAhGxcg9To6rn74+8ouJA2st5gMJkq7qjYjgjy5tHZ4xjq37QsoEDQEYTgXoMkpOS1bdQJ7Kwt+SMmmQPHkwHQZzRM8UoSlBV1zAHqSjh+KoP+Ps5UyPVoZBacOpuFs4MNTq6OJLWyBmdvpabsQjKPJxZNEmJ7FZJXWkFWcRlB7s7YWTVN8F9Vq2PlFz8bxbazWCgUXB8RzB0TIgjxcruicwkEjQjBvQYpKu0a8TtwPNkkPV5BRUMoj1IuI9tMDlrtJSm9ELlMRt4FJ/vCkkoUZVWMHOTVMPq9LCNQiNaV9PMFDB+oxUZjyZQI8xYUF3QtkiSx4eBJ3vxpN9W6ekK83PjXohkM8HQho6iUuLRcTmfkEp+Vx6Fzna9s5Wpvw6Kx4dwyKgxnO2sz9kAgEIJ7TWJv23VrTI1BZJYqJaUXki1oHe3JLW2fd6c5uTwpgd4gcTwuA193B+R2ShJzG+q5qhRyKgqq0NXrkesk3vrzfOEkdZWg0+s5kZrNtwdO8kvMxRzQ8Zl53PL2l8hlsk5lgrqcMB8P7pgQwYwh/bFQivqzgq5BCO41SGMloK5EV68nMsATlDJkesil+wW3JTJzS5DlwcgQb45lZjPUy4Mz53KYPHIAd988RlRn6WVU1ep4Z8tePB3tOJ9TyM5TSbg72GKtVnE6I7dJetJLuRKxdbGzZuKgQG4cGUq4n2enzyMQtBchuNcYuno9azcd6vLr6A0ScWezuvw6nUWS4PjpDDyd7Th1JovXVy1gTHhATzdL0AwbDp5g/f7jJtvKc7qmKMMgbzcmhgQycVAAIV7u4uVL0K0Iwb3G2Lj9eJNiBH2ZvMJyBgV5CLHtxXQ0hWJHsLRQMnqALxNDApkQEoC7qNgj6EGE4F5DZOSWsOaHAz3djF6FpVrJU/fO6OlmCFqgXm8gNiW7bcMWsLRQMmfYQHycHaiu01FeU4sM8HN1JFjrSrCnq/BGF/QaOrzYt2fPHubNm4dWq0Umk7Fp06YWbe+//35kMhnvvvuuyfba2loeeeQRXFxcsLGxYf78+WRkmFaGKS4uZsmSJWg0GjQaDUuWLKGkpMTEJi0tjXnz5mFjY4OLiwuPPvoodXWmOVBPnjzJxIkTsbKywsvLixdeeIGrNX10Rm4JW3bHcSA2mbIK05jTn3ae5LaVnzbZ3pdRWSh57M7J9PMVGYF6G5IkcSojl6e/3kp1XccL1zvZWvPgjNH8+vSf+Oet07l7yggenjWWpxZM5skFk7l93FCGBXgJsRX0Kjo8wq2srCQ8PJzly5dz8803t2i3adMmDh06hFbbNFj88ccfZ/Pmzaxfvx5nZ2dWrlzJ3LlziY6ORnEhfd/ixYvJyMhg69atANx3330sWbKEzZs3A6DX65kzZw6urq7s27ePwsJCli5diiRJvP/++0BDgurp06czefJkjhw5QkJCAsuWLcPGxoaVK1d2tOs9Rml5NU+9+xMx8RdfSjxc7LCzsUSSQK83kJJVyFX6HtFl3H3zaBZMHdLTzRBcRnxmHn9b9yvJeUUdPjbQzYl7po5gVniw8CYWXHV0WHBnz57N7NmzW7XJzMzk4Ycf5rfffmPOnDkm+0pLS1mzZg1ffPEF06ZNA+DLL7/Ex8eH7du3M3PmTOLj49m6dSsHDx5k1KhRAKxevZoxY8Zw9uxZgoODiYqK4vTp06SnpxtF/a233mLZsmW89NJL2Nvbs27dOmpqali7di1qtZrQ0FASEhJ4++23WbFixVURGpKZW8KK138gLdt0XTanoJycgvIealXvxs5azZRRA1g0K7KnmyK4jPLqWh7+9EfySis6dFyw1pU/TR7O1NB+qC3ESpjg6sTs8SMGg4ElS5bwxBNPMHhw00Le0dHR6HQ6Zsy4uK6m1WoJDQ1l//6GwugHDhxAo9EYxRZg9OjRaDQaE5vQ0FCTEfTMmTOpra0lOjraaDNx4kTUarWJTVZWFikpKWbtt7n5YVssP+44wb3PfdVEbAWt8/hdk3ny3hmoVeLB3Nv49sCJdoutQi5jSmgQ6x9fzIa/3MH1EQOF2Aquasz+1/vaa6+hVCp59NFHm92fk5ODSqXC0dHRZLu7uzs5OTlGGze3punU3NzcTGzc3d1N9js6OqJSqUxs/P39m1yncV9AQFPP1draWmprL4YklJWZvxxcW0iSxH+/309xWXW3X/tqZ1xEILMnDOrpZgha4ERa2w5S7hpblkwYxsyhA/BwsOuGVgkE3YNZBTc6Opp///vfHDt2rMPTtZIkmRzT3PHmsGl0mGqpfa+88grPP/98h9publIyi4TYdgI7azV/u2f6VbFU0FfJbKOesY1axauLZzM8yLubWiQQdB9mnVLeu3cveXl5+Pr6olQqUSqVpKamsnLlSuNI08PDg7q6OoqLTadJ8/LyjKNPDw8PcnOblmDLz883sWkcyTZSXFyMTqdr1SYvryGx/+Wj40aeeuopSktLjT/p6Z3Py9pR9AYDa77fz5In/9dt17yWePyuybg6ijjL3kx6YUmz22UymDNsID/9dakQW8E1i1kFd8mSJZw4cYLY2Fjjj1ar5YknnuC3334DIDIyEgsLC7Zt22Y8Ljs7m7i4OMaOHQvAmDFjKC0t5fDhw0abQ4cOUVpaamITFxdHdvbFKaqoqCjUajWRkZFGmz179piECkVFRaHVaptMNTeiVquxt7c3+elqDhxP5nx6AY+8tIH/fn8AvUG4G3eEBxaN56HbrxNTyb2cuvp6qmqbhgAN8fXgsz/fyquLZ+MmElMIrmE6PKVcUVFBYmKi8ffk5GRiY2NxcnLC19cXZ2dnE3sLCws8PDwIDg4GQKPRcPfdd7Ny5UqcnZ1xcnJi1apVhIWFGb2WQ0JCmDVrFvfeey+ffPIJ0BAWNHfuXON5ZsyYwaBBg1iyZAlvvPEGRUVFrFq1invvvdcokosXL+b5559n2bJlPP3005w7d46XX36ZZ599tldMO0qSxOrv9vPZxoM93ZQeRy6TMWF4EI/dOQmA3w8msPq7P6jTtVy71EljzcOLJzJrfEiv+D4FrZN7mbNUZKAX900bxZj+vuL7E/QJOiy4R48eZfLkycbfV6xYAcDSpUtZu3Ztu87xzjvvoFQqWbhwIdXV1UydOpW1a9caY3AB1q1bx6OPPmr0Zp4/fz7/+c9/jPsVCgVbtmzhwQcfZNy4cVhZWbF48WLefPNNo41Go2Hbtm089NBDDB8+HEdHR1asWGFsc0/z+6GEPi+2CoWclcumMHvCICxVFsbtd84bwaB+Hjz0r2+bPU4uk/GvR+YybJBPdzVVcIVkFZXhZGvNvMgQJg0KJDLQSwitoE8hk67WtEvdRFlZGRqNhtLSUrNPLz/x5kb2HUsy6zmvNtycbNnwzt2oWgj3+Ntbm9gTfd5kW6CPC8/9eTYD/EVh8KuJ1PxinO2ssbVsWjheIOhOuvK53hoiqK0HWX7jmD4tuAq5jA//sahFsQUYOcTfKLiBPi68tuIGPJztUIosQ1cdfq6ObRsJBNcwXV84VdAig4I8GNyv79bh1BskVG0kp9gf0/BCEujjwn+euRVvdwchtgKB4KpECG4Pc8uMoT3dhB5DqZCTW9ByXObXW46yPzaZoAti62hv3Y2tEwgEAvMiBLeH0dha9XQTegwJ8NM6Nbtv+4EzvLduNy4ONrz39C1CbAUCwVWPENweZsfhhJ5uQo+gUMhbDOc5djqdFz7ailwm4/lH5uCksemBFgoEAoF5EU5TPcyqZVMZFOTB62u293RTupW51w3myQuF4Q0GiYqqWsoqa8jMLeHv7/2Mrl7P/QvHMSxEhP0IBIJrAyG4PYxapeTGqeHsOnyOwydTe7o5XYq7sx0ymYwAL2f+fPsE4/a7nvqc8+kFJrajwvy4a/6oy08hEAgEVy1CcHsJnq6anm5ClxMR4sM/HphFWUW1ce26TldPfrFpBiIXR1ueffB65HKRFEEgEFw7iDXcXkLkNZ4xydvdgcH9PDBIEg6XOECpLJQ88adpxt+t1Bb865E5OGmEk5RAILi2ECPcXoKNlaqnm2B2HOysmDxyACOH+DE+IrDF+Nlpo4PZc+Qc5zMKef6h6+nn69rNLRUIBIKuRwhuL+Ho6bSeboJZCfJx4b2nb2m3h/Hf7pmBldpCTCMLBIJrFiG4vYSjcdeW4N4yI6JD4TzX4ghfIBAILkUIbi+guKyKc6n5Pd2MK8bF0ZYALyd8PB2ZO3FwTzdHIBAIehVCcHsB0afTe7oJV4RMBvMnhfHQ4uuws7Hs6eYIBAJBr0QIbi9AfhXXBA0J9OCJP00lJNCjp5siEAgEvRohuL2AUWF+KOQy9IarozSxhVJBvV7P3TeNYemC0SgVIrpMIBAI2kIIbi8g+nT6VSO2Y4cG8OS9M7C1UmNladHTzREIBIKrBiG4vYBRQ/wJ8nFpkt6wpwn2d6O0ooacgjKs1BY8tmQS8yeHNVtwQCAQCAStIwS3F6BUynnkjok8/ur3Pd0UI/Mnh/HkPdMpq6xh4/bjTBszEG93h55ulkAgEFy1CMHtYVZ/9wcbtsZgYdF8Fqbupp+vK/94YBZBvi7IZDI0tlYsWzC6p5slEAgEVz1CcHuI9Oxi9AYDX24+Qp1O39PNAcDFwYZ/P3WLyGMsEAgEXYAQ3B5gz9FEnv/wF+r1hl4jtgBP3zdTiK1AIBB0EUJwu5mvtxzlvXW7e7oZTbhhyhDGDA3o6WYIBALBNYsIoOxGqmt0rNtytEfb0JyDsdZVwyN3TOz+xggEAkEfQoxwu5FvtkZTWFLZrdccFebHlNEDsFKrUFko+P1QAtv2nzHu9/V05On7ZoriAQKBQNDFCMHtRlKzirvtWtPHDmTpDaMI8nEx2T4hsh83TA7j7c93kp1XypP3zCA82Kvb2iUQCAR9FSG43UR5ZQ2xZzK65VrODjb8/f6ZqCyafr1yuYzIwb588MytqFRKrC3FyFYgEAi6AyG43YAkSfz9vZ/JKSjr8mspFHLeffLmZsX2UhzshTeyQCAQdCdCcLsBmUxG/PmcLr+OhVLBquVT6Ofr2uXXEggEAkHHEILbTWjdNJxNyeuy86tVSj598Q4CvV3aNhYIBAJBtyPCgrqJ/v5uXXr+hxdfJ8RWIBAIejFCcLuJkaF+XXJeaysVS+aN4KZpQ7vk/AKBQCAwD2JKuZsYHe6Po70VxWXVHTpOrVIyMsyP4AB3ElPzOXgiGWcHWx5YNB43J1uCvF2wsVZ3UasFAoFAYC46PMLds2cP8+bNQ6vVIpPJ2LRpk3GfTqfjb3/7G2FhYdjY2KDVarnrrrvIysoyOUdtbS2PPPIILi4u2NjYMH/+fDIyTENmiouLWbJkCRqNBo1Gw5IlSygpKTGxSUtLY968edjY2ODi4sKjjz5KXV2dic3JkyeZOHEiVlZWeHl58cILLyBJ3V/s3c7GkifvmdFue293B56+bwZbP3mQ11cu4O6bxvDKX+bz68cP8uVrdzFtdDBDBngJsRUIBIKrhA4LbmVlJeHh4fznP/9psq+qqopjx47xj3/8g2PHjvHDDz+QkJDA/PnzTewef/xxNm7cyPr169m3bx8VFRXMnTsXvf5iIv/FixcTGxvL1q1b2bp1K7GxsSxZssS4X6/XM2fOHCorK9m3bx/r16/n+++/Z+XKlUabsrIypk+fjlar5ciRI7z//vu8+eabvP322x3ttlm4bng/Roa1PbU8dfQAvn5zOfMmhWGptjDZZ6m2wFJl0cKRAoFAIOityKQrGO7JZDI2btzIggULWrQ5cuQII0eOJDU1FV9fX0pLS3F1deWLL75g0aJFAGRlZeHj48Mvv/zCzJkziY+PZ9CgQRw8eJBRo0YBcPDgQcaMGcOZM2cIDg7m119/Ze7cuaSnp6PVagFYv349y5YtIy8vD3t7ez766COeeuopcnNzUasbRoKvvvoq77//PhkZGciaSyx8GWVlZWg0GkpLS7G3t+/sR2Xkt33x/PPDX5rdZ2utxsPFnveevgVHEScrEAgEXYK5n+vtpcudpkpLS5HJZDg4OAAQHR2NTqdjxoyL06tarZbQ0FD2798PwIEDB9BoNEaxBRg9ejQajcbEJjQ01Ci2ADNnzqS2tpbo6GijzcSJE41i22iTlZVFSkpKV3W5VaaPHcj0McHIL4i9q5MtN08fyp3zRhC1+iE+e+lOIbYCgUBwDdKlTlM1NTU8+eSTLF682PgWkZOTg0qlwtHR0cTW3d2dnJwco42bW9MwGjc3NxMbd3d3k/2Ojo6oVCoTG39//ybXadwXENC0HF1tbS21tbXG38vKzJsdSi6X8cIjc/nbPXUkpuYREuRhkhVKqWh71C0QCASCq48uG+HqdDpuu+02DAYDH374YZv2kiSZTPE2N91rDpvGGfSWppNfeeUVo6OWRqPBx8enzbZ3BhsrFeEDvdtMwSgQCASCa4MuEVydTsfChQtJTk5m27ZtJnPkHh4e1NXVUVxsWjknLy/POPr08PAgNze3yXnz8/NNbBpHso0UFxej0+latcnLa8j2dPnouJGnnnqK0tJS4096enpHui4QCAQCQbOYXXAbxfbcuXNs374dZ2dnk/2RkZFYWFiwbds247bs7Gzi4uIYO3YsAGPGjKG0tJTDhw8bbQ4dOkRpaamJTVxcHNnZ2UabqKgo1Go1kZGRRps9e/aYhApFRUWh1WqbTDU3olarsbe3N/kRCAQCgeCKkTpIeXm5FBMTI8XExEiA9Pbbb0sxMTFSamqqpNPppPnz50ve3t5SbGyslJ2dbfypra01nuOBBx6QvL29pe3bt0vHjh2TpkyZIoWHh0v19fVGm1mzZklDhgyRDhw4IB04cEAKCwuT5s6da9xfX18vhYaGSlOnTpWOHTsmbd++XfL29pYefvhho01JSYnk7u4u3X777dLJkyelH374QbK3t5fefPPNdve3tLRUAqTS0tKOflQCgUAg6IX01HO9w4K7c+dOCWjys3TpUik5ObnZfYC0c+dO4zmqq6ulhx9+WHJycpKsrKykuXPnSmlpaSbXKSwslO644w7Jzs5OsrOzk+644w6puLjYxCY1NVWaM2eOZGVlJTk5OUkPP/ywVFNTY2Jz4sQJacKECZJarZY8PDykf/7zn5LBYGh3f4XgCgQCwbVFTz3XrygOty/QU/FaAoFAIOgartk4XIFAIBAIBEJwBQKBQCDoFoTgCgQCgUDQDQjBFQgEAoGgGxCCKxAIBAJBNyAEVyAQCASCbkAIrkAgEAgE3YAQXIFAIBAIugEhuAKBQCAQdAOiNlwbNCbiMnddXIFAIBD0DI3P8+5OtCgEtw3Ky8sBuqwurkAgEAh6hsLCQjQaTbddT+RSbgODwUBWVhZ2dnYtFq3vLZSVleHj40N6evo1l/dZ9O3qRPTt6uRa7htAaWkpvr6+FBcX4+Dg0G3XFSPcNpDL5Xh7e/d0MzrEtVzHV/Tt6kT07erkWu4bNDzfu/V63Xo1gUAgEAj6KEJwBQKBQCDoBoTgXkOo1Wqee+451Gp1TzfF7Ii+XZ2Ivl2dXMt9g57rn3CaEggEAoGgGxAjXIFAIBAIugEhuAKBQCAQdANCcAUCgUAg6AaE4AoEAoFA0A0Iwe1FfPjhhwQEBGBpaUlkZCR79+5t1f6DDz4gJCQEKysrgoOD+fzzz032r169mgkTJuDo6IijoyPTpk3j8OHDJjb//Oc/kclkJj8eHh69vm9r165t0m6ZTEZNTc0VXbc39G3SpEnN9m3OnDlGm+743vbs2cO8efPQarXIZDI2bdrU5jG7d+8mMjISS0tLAgMD+fjjj5vYfP/99wwaNAi1Ws2gQYPYuHFjE5uu/t66om+95X7rir71lvutK/rWrfebJOgVrF+/XrKwsJBWr14tnT59WnrsscckGxsbKTU1tVn7Dz/8ULKzs5PWr18vnT9/Xvr6668lW1tb6aeffjLaLF68WPrggw+kmJgYKT4+Xlq+fLmk0WikjIwMo81zzz0nDR48WMrOzjb+5OXl9fq+ffbZZ5K9vb1Ju7Ozs6/our2lb4WFhSZ9iouLkxQKhfTZZ58Zbbrje/vll1+kZ555Rvr+++8lQNq4cWOr9klJSZK1tbX02GOPSadPn5ZWr14tWVhYSN99953RZv/+/ZJCoZBefvllKT4+Xnr55ZclpVIpHTx40GjTHd9bV/Stt9xvXdG33nK/dUXfuvN+E4LbSxg5cqT0wAMPmGwbOHCg9OSTTzZrP2bMGGnVqlUm2x577DFp3LhxLV6jvr5esrOzk/73v/8Ztz333HNSeHh45xveDrqib5999pmk0WjMet3O0B3f2zvvvCPZ2dlJFRUVxm3d8b1dSnsebn/961+lgQMHmmy7//77pdGjRxt/X7hwoTRr1iwTm5kzZ0q33Xab8ffu+N4uxVx9u5yeut8uxVx96y3326V01ffWlfebmFLuBdTV1REdHc2MGTNMts+YMYP9+/c3e0xtbS2WlpYm26ysrDh8+DA6na7ZY6qqqtDpdDg5OZlsP3fuHFqtloCAAG677TaSkpKuoDemdGXfKioq8PPzw9vbm7lz5xITE3NF1+0o3fW9rVmzhttuuw0bGxuT7V35vXWGAwcONPksZs6cydGjR419a8mm8fPqju+tM7Snb5fTE/dbZ2hv33r6fusMnfneuvJ+E4LbCygoKECv1+Pu7m6y3d3dnZycnGaPmTlzJv/973+Jjo5GkiSOHj3Kp59+ik6no6CgoNljnnzySby8vJg2bZpx26hRo/j888/57bffWL16NTk5OYwdO5bCwsJe3beBAweydu1afvrpJ77++mssLS0ZN24c586d6/R1e0vfLuXw4cPExcVxzz33mGzv6u+tM+Tk5DT7WdTX1xv71pJN4+fVHd9bZ2hP3y6nJ+63ztCevvWG+60zdPR76+r7TVQL6kVcXv5PkqQWSwL+4x//ICcnh9GjRyNJEu7u7ixbtozXX38dhULRxP7111/n66+/ZteuXSYjrNmzZxv/HxYWxpgxYwgKCuJ///sfK1asMFPPzN+30aNHM3r0aOMx48aNY9iwYbz//vu89957nbpuZ+nK723NmjWEhoYycuRIk+3d9b11lOY+i8u3t+fz6o7vraO0p2+N9PT91lHa6ltvut86Ske+t66+38QItxfg4uKCQqFo8iaYl5fX5O2sESsrKz799FOqqqpISUkhLS0Nf39/7OzscHFxMbF98803efnll4mKimLIkCGttsXGxoawsDDjm+uV0tV9a0QulzNixAhjuztz3Y7S1X2rqqpi/fr1Td62m8Pc31tn8PDwaPazUCqVODs7t2rT+Hl1x/fWGdrTt0Z68n7rDB3pWyM9cb91ho70rTvuNyG4vQCVSkVkZCTbtm0z2b5t2zbGjh3b6rEWFhZ4e3ujUChYv349c+fONanx+MYbb/Cvf/2LrVu3Mnz48DbbUltbS3x8PJ6enp3rzGV0Zd8uRZIkYmNjje2+kuu2l67u27fffkttbS133nlnm20x9/fWGcaMGdPks4iKimL48OFYWFi0atP4eXXH99YZ2tM36Pn7rTO0t2+X0hP3W2foSN+65X67YrcrgVlodKlfs2aNdPr0aenxxx+XbGxspJSUFEmSJOnJJ5+UlixZYrQ/e/as9MUXX0gJCQnSoUOHpEWLFklOTk5ScnKy0ea1116TVCqV9N1335m4s5eXlxttVq5cKe3atUtKSkqSDh48KM2dO1eys7MzXre39u2f//yntHXrVun8+fNSTEyMtHz5ckmpVEqHDh1q93V7a98aGT9+vLRo0aJmr9sd31t5ebkUExMjxcTESID09ttvSzExMcYwj8v71hiC8Ze//EU6ffq0tGbNmiYhGH/88YekUCikV199VYqPj5deffXVFsOCuvJ764q+9Zb7rSv61lvut67oWyPdcb8Jwe1FfPDBB5Kfn5+kUqmkYcOGSbt37zbuW7p0qTRx4kTj76dPn5aGDh0qWVlZSfb29tINN9wgnTlzxuR8fn5+EtDk57nnnjPaLFq0SPL09JQsLCwkrVYr3XTTTdKpU6d6fd8ef/xxydfXV1KpVJKrq6s0Y8YMaf/+/R26bm/tmyQ1CDMgRUVFNXvN7vjedu7c2ezfz9KlS5vtmyRJ0q5du6SIiAhJpVJJ/v7+0kcffdTkvBs2bJCCg4MlCwsLaeDAgdL333/fxKarv7eu6Ftvud+6om+95X7rqr/J7rrfRHk+gUAgEAi6AbGGKxAIBAJBNyAEVyAQCASCbkAIrkAgEAgE3YAQXIFAIBAIugEhuAKBQCAQdANCcAUCgUAg6AaE4AoEAoFA0A0IwRUIBAJBr2LPnj3MmzcPrVaLTCZj06ZNHT6HJEm8+eabDBgwALVajY+PDy+//LL5G9sBRLUggUAgEPQqKisrCQ8PZ/ny5dx8882dOsdjjz1GVFQUb775JmFhYZSWlrZYSrG7EJmmBAKBQNBrkclkbNy4kQULFhi31dXV8fe//51169ZRUlJCaGgor732GpMmTQIgPj6eIUOGEBcXR3BwcM80vBnElLJAIBAIriqWL1/OH3/8wfr16zlx4gS33nors2bNMpbL27x5M4GBgfz8888EBATg7+/PPffcQ1FRUY+2WwiuQCAQCK4azp8/z9dff82GDRuYMGECQUFBrFq1ivHjx/PZZ58BkJSURGpqKhs2bODzzz9n7dq1REdHc8stt/Ro28UarkAgEAiuGo4dO4YkSQwYMMBke21trbGovMFgoLa2ls8//9xot2bNGiIjIzl79myPTTMLwRUIBALBVYPBYEChUBAdHY1CoTDZZ2trC4CnpydKpdJElENCQgBIS0sTgisQCAQCQVtERESg1+vJy8tjwoQJzdqMGzeO+vp6zp8/T1BQEAAJCQkA+Pn5dVtbL0d4KQsEAoGgV1FRUUFiYiLQILBvv/02kydPxsnJCV9fX+68807++OMP3nrrLSIiIigoKGDHjh2EhYVx/fXXYzAYGDFiBLa2trz77rsYDAYeeugh7O3tiYqK6rF+CcEVCAQCQa9i165dTJ48ucn2pUuXsnbtWnQ6HS+++CKff/45mZmZODs7M2bMGJ5//nnCwsIAyMrK4pFHHiEqKgobGxtmz57NW2+9hZOTU3d3x4gQXIFAIBAIugERFiQQCAQCQTcgBFcgEAgEgm5ACK5AIBAIBN2AEFyBQCAQCLoBIbgCgUAgEHQDQnAFAoFAIOgGhOAKBAKBQNANCMEVCAQCgaAbEIIrEAgEAkE3IARXIBAIBIJuQAiuQCAQCATdgBBcgUAgEAi6gf8H6kjZnU8uBa4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gdf.plot(column='rent2008')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "StdMean                   \n",
+       "\n",
+       "     Interval        Count\n",
+       "--------------------------\n",
+       "[   0.00,   68.04] |     3\n",
+       "(  68.04,  662.61] |     0\n",
+       "( 662.61, 1851.75] |    45\n",
+       "(1851.75, 2446.32] |     2\n",
+       "(2446.32, 2900.00] |     5"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "std = mc.StdMean(gdf.rent2008)\n",
+    "std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "StdMean                   \n",
+       "\n",
+       "     Interval        Count\n",
+       "--------------------------\n",
+       "[   0.00,   68.04] |     3\n",
+       "(  68.04,  662.61] |     0\n",
+       "( 662.61, 1257.18] |    33\n",
+       "(1257.18, 1851.75] |    12\n",
+       "(1851.75, 2446.32] |     2\n",
+       "(2446.32, 2900.00] |     5"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stda = mc.StdMean(gdf.rent2008, anchor=True)\n",
+    "stda"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1257.1818181818182"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = gdf.rent2008\n",
+    "y.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y1 = y.values\n",
+    "y1[0] = 5000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "StdMean                   \n",
+       "\n",
+       "     Interval        Count\n",
+       "--------------------------\n",
+       "(   -inf, -227.42] |     0\n",
+       "(-227.42,  551.24] |     3\n",
+       "( 551.24, 2108.58] |    45\n",
+       "(2108.58, 2887.24] |     5\n",
+       "(2887.24, 5000.00] |     2"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mc.StdMean(y1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "StdMean                   \n",
+       "\n",
+       "     Interval        Count\n",
+       "--------------------------\n",
+       "[   0.00,  551.24] |     3\n",
+       "( 551.24, 1329.91] |    35\n",
+       "(1329.91, 2108.58] |    10\n",
+       "(2108.58, 2887.24] |     5\n",
+       "(2887.24, 3665.91] |     1\n",
+       "(3665.91, 4444.57] |     0\n",
+       "(4444.57, 5000.00] |     1"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mc.StdMean(y1, anchor=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1329.909090909091"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y1.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-2.1144409159637743"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "68.04306411189191"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.mean() - 2 * y.std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0000007194253047"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(2446.321 - y.mean()) / y.std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-2.1144409159637743, 2.7630386718042783)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(z), max(z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1257.1818181818182"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-2, 2]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(map(int, (min(z), max(z))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ybar = y.mean()\n",
+    "ystd = y.std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bins = [ ybar + trim * ystd for trim in range(-2, 2+1) ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[68.04306411189191,\n",
+       " 662.6124411468551,\n",
+       " 1257.1818181818182,\n",
+       " 1851.7511952167815,\n",
+       " 2446.3205722517446]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bins"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-2"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(-2.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(2.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}