diff --git a/notebooks/merge_climate_datasets_exercise.ipynb b/notebooks/merge_climate_datasets_exercise.ipynb
index 9be1adf..bb34bbd 100644
--- a/notebooks/merge_climate_datasets_exercise.ipynb
+++ b/notebooks/merge_climate_datasets_exercise.ipynb
@@ -28,7 +28,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 5,
       "id": "0a656265",
       "metadata": {
         "tags": [
@@ -69,18 +69,75 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 7,
       "id": "3270985f",
       "metadata": {
         "tags": [
           "exercise"
         ]
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(<xarray.Dataset> Size: 62MB\n",
+              " Dimensions:  (time: 1464, lat: 73, lon: 144)\n",
+              " Coordinates:\n",
+              "   * lat      (lat) float32 292B 90.0 87.5 85.0 82.5 ... -82.5 -85.0 -87.5 -90.0\n",
+              "   * lon      (lon) float32 576B 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
+              "   * time     (time) datetime64[ns] 12kB 2020-01-01 ... 2020-12-31T18:00:00\n",
+              " Data variables:\n",
+              "     air      (time, lat, lon) float32 62MB dask.array<chunksize=(8, 73, 144), meta=np.ndarray>\n",
+              " Attributes:\n",
+              "     Conventions:                     COARDS\n",
+              "     title:                           4x daily NMC reanalysis (2014)\n",
+              "     history:                         created 2017/12 by Hoop (netCDF2.3)\n",
+              "     description:                     Data is from NMC initialized reanalysis\\...\n",
+              "     platform:                        Model\n",
+              "     dataset_title:                   NCEP-NCAR Reanalysis 1\n",
+              "     _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
+              "     References:                      http://www.psl.noaa.gov/data/gridded/dat...\n",
+              "     DODS_EXTRA.Unlimited_Dimension:  time,\n",
+              " <xarray.Dataset> Size: 379MB\n",
+              " Dimensions:  (time: 366, lat: 360, lon: 720)\n",
+              " Coordinates:\n",
+              "   * lat      (lat) float32 1kB 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n",
+              "   * lon      (lon) float32 3kB 0.25 0.75 1.25 1.75 ... 358.2 358.8 359.2 359.8\n",
+              "   * time     (time) datetime64[ns] 3kB 2020-01-01 2020-01-02 ... 2020-12-31\n",
+              " Data variables:\n",
+              "     precip   (time, lat, lon) float32 379MB dask.array<chunksize=(16, 360, 720), meta=np.ndarray>\n",
+              " Attributes:\n",
+              "     _NCProperties:                   version=1|netcdflibversion=4.4.1.1|hdf5l...\n",
+              "     Conventions:                     CF-1.0\n",
+              "     version:                         V1.0\n",
+              "     title:                           CPC GLOBAL PRCP V1.0 RT\n",
+              "     dataset_title:                   CPC GLOBAL PRCP V1.0\n",
+              "     Source:                          ftp://ftp.cpc.ncep.noaa.gov/precip/CPC_U...\n",
+              "     References:                      https://www.psl.noaa.gov/data/gridded/da...\n",
+              "     history:                         Updated 2021-01-02 23:31:03\n",
+              "     DODS_EXTRA.Unlimited_Dimension:  time)"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
         "# TODO: load the air temperature and precipitation datasets.\n",
         "# Example: air = xr.open_dataset(..., chunks={\"time\": 8})\n",
-        "raise NotImplementedError(\"Assign datasets to `air` and `precip`.\")\n"
+        "air = xr.open_dataset(\n",
+        "    TEMP_URL,\n",
+        "    chunks={\"time\": 8}  # 6-hourly NCEP reanalysis; chunk by a small number of timesteps\n",
+        ")\n",
+        "\n",
+        "precip = xr.open_dataset(\n",
+        "    PRECIP_URL,\n",
+        "    chunks={\"time\": 16}  # daily CPC precip; chunk a couple of weeks at a time\n",
+        ")\n",
+        "\n",
+        "air, precip\n",
+        "\n"
       ]
     },
     {
@@ -103,10 +160,83 @@
           "exercise"
         ]
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(<xarray.Dataset> Size: 182kB\n",
+              " Dimensions:  (time: 120, lat: 13, lon: 29)\n",
+              " Coordinates:\n",
+              "   * lat      (lat) float32 52B 50.0 47.5 45.0 42.5 40.0 ... 27.5 25.0 22.5 20.0\n",
+              "   * lon      (lon) float32 116B 230.0 232.5 235.0 237.5 ... 295.0 297.5 300.0\n",
+              "   * time     (time) datetime64[ns] 960B 2020-06-01 ... 2020-06-30T18:00:00\n",
+              " Data variables:\n",
+              "     air      (time, lat, lon) float32 181kB dask.array<chunksize=(8, 13, 29), meta=np.ndarray>\n",
+              " Attributes:\n",
+              "     Conventions:                     COARDS\n",
+              "     title:                           4x daily NMC reanalysis (2014)\n",
+              "     history:                         created 2017/12 by Hoop (netCDF2.3)\n",
+              "     description:                     Data is from NMC initialized reanalysis\\...\n",
+              "     platform:                        Model\n",
+              "     dataset_title:                   NCEP-NCAR Reanalysis 1\n",
+              "     _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
+              "     References:                      http://www.psl.noaa.gov/data/gridded/dat...\n",
+              "     DODS_EXTRA.Unlimited_Dimension:  time,\n",
+              " <xarray.Dataset> Size: 1MB\n",
+              " Dimensions:  (time: 30, lat: 60, lon: 140)\n",
+              " Coordinates:\n",
+              "   * lat      (lat) float32 240B 49.75 49.25 48.75 48.25 ... 21.25 20.75 20.25\n",
+              "   * lon      (lon) float32 560B 230.2 230.8 231.2 231.8 ... 298.8 299.2 299.8\n",
+              "   * time     (time) datetime64[ns] 240B 2020-06-01 2020-06-02 ... 2020-06-30\n",
+              " Data variables:\n",
+              "     precip   (time, lat, lon) float32 1MB dask.array<chunksize=(8, 60, 140), meta=np.ndarray>\n",
+              " Attributes:\n",
+              "     _NCProperties:                   version=1|netcdflibversion=4.4.1.1|hdf5l...\n",
+              "     Conventions:                     CF-1.0\n",
+              "     version:                         V1.0\n",
+              "     title:                           CPC GLOBAL PRCP V1.0 RT\n",
+              "     dataset_title:                   CPC GLOBAL PRCP V1.0\n",
+              "     Source:                          ftp://ftp.cpc.ncep.noaa.gov/precip/CPC_U...\n",
+              "     References:                      https://www.psl.noaa.gov/data/gridded/da...\n",
+              "     history:                         Updated 2021-01-02 23:31:03\n",
+              "     DODS_EXTRA.Unlimited_Dimension:  time)"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
         "# TODO: subset both datasets using `sel`, handling coordinate ordering as needed.\n",
-        "raise NotImplementedError(\"Create `air_us` and `precip_us`.\")\n"
+        "# Helper to standardize longitudes into 0–360 range\n",
+        "def to_0_360(ds, lon_name=\"lon\"):\n",
+        "    if ds[lon_name].min() < 0:\n",
+        "        ds = ds.assign_coords({lon_name: (ds[lon_name] % 360)})\n",
+        "        ds = ds.sortby(lon_name)\n",
+        "    return ds\n",
+        "\n",
+        "# Helper to subset spatially (handles ascending vs. descending latitude)\n",
+        "def sel_conus(ds, lat_name=\"lat\", lon_name=\"lon\"):\n",
+        "    ds = to_0_360(ds, lon_name)\n",
+        "\n",
+        "    lat = ds[lat_name]\n",
+        "    if lat[0] > lat[-1]:\n",
+        "        \n",
+        "        lat_slice = slice(LAT_RANGE[1], LAT_RANGE[0]) \n",
+        "    else:\n",
+        "        lat_slice = slice(LAT_RANGE[0], LAT_RANGE[1])  \n",
+        "\n",
+        "    lon_slice = slice(LON_RANGE_360[0], LON_RANGE_360[1])  \n",
+        "\n",
+        "    return ds.sel({lat_name: lat_slice, lon_name: lon_slice})\n",
+        "\n",
+        "# Subset both datasets in space and time\n",
+        "air_us = air.pipe(sel_conus).sel(time=TIME_RANGE)\n",
+        "precip_us = precip.pipe(sel_conus).sel(time=TIME_RANGE)\n",
+        "\n",
+        "air_us, precip_us\n",
+        "\n"
       ]
     },
     {
@@ -128,10 +258,653 @@
           "exercise"
         ]
       },
-      "outputs": [],
+      "outputs": [
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+              "\n",
+              ".xr-var-list > li:nth-child(odd) > div,\n",
+              ".xr-var-list > li:nth-child(odd) > label,\n",
+              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+              "  background-color: var(--xr-background-color-row-odd);\n",
+              "  border-color: var(--xr-background-color-row-even);\n",
+              "}\n",
+              "\n",
+              ".xr-var-name {\n",
+              "  grid-column: 1;\n",
+              "}\n",
+              "\n",
+              ".xr-var-dims {\n",
+              "  grid-column: 2;\n",
+              "}\n",
+              "\n",
+              ".xr-var-dtype {\n",
+              "  grid-column: 3;\n",
+              "  text-align: right;\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-var-preview {\n",
+              "  grid-column: 4;\n",
+              "}\n",
+              "\n",
+              ".xr-index-preview {\n",
+              "  grid-column: 2 / 5;\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-var-name,\n",
+              ".xr-var-dims,\n",
+              ".xr-var-dtype,\n",
+              ".xr-preview,\n",
+              ".xr-attrs dt {\n",
+              "  white-space: nowrap;\n",
+              "  overflow: hidden;\n",
+              "  text-overflow: ellipsis;\n",
+              "  padding-right: 10px;\n",
+              "}\n",
+              "\n",
+              ".xr-var-name:hover,\n",
+              ".xr-var-dims:hover,\n",
+              ".xr-var-dtype:hover,\n",
+              ".xr-attrs dt:hover {\n",
+              "  overflow: visible;\n",
+              "  width: auto;\n",
+              "  z-index: 1;\n",
+              "}\n",
+              "\n",
+              ".xr-var-attrs,\n",
+              ".xr-var-data,\n",
+              ".xr-index-data {\n",
+              "  display: none;\n",
+              "  border-top: 2px dotted var(--xr-background-color);\n",
+              "  padding-bottom: 20px !important;\n",
+              "  padding-top: 10px !important;\n",
+              "}\n",
+              "\n",
+              ".xr-var-attrs-in + label,\n",
+              ".xr-var-data-in + label,\n",
+              ".xr-index-data-in + label {\n",
+              "  padding: 0 1px;\n",
+              "}\n",
+              "\n",
+              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+              ".xr-var-data-in:checked ~ .xr-var-data,\n",
+              ".xr-index-data-in:checked ~ .xr-index-data {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              ".xr-var-data > table {\n",
+              "  float: right;\n",
+              "}\n",
+              "\n",
+              ".xr-var-data > pre,\n",
+              ".xr-index-data > pre,\n",
+              ".xr-var-data > table > tbody > tr {\n",
+              "  background-color: transparent !important;\n",
+              "}\n",
+              "\n",
+              ".xr-var-name span,\n",
+              ".xr-var-data,\n",
+              ".xr-index-name div,\n",
+              ".xr-index-data,\n",
+              ".xr-attrs {\n",
+              "  padding-left: 25px !important;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs,\n",
+              ".xr-var-attrs,\n",
+              ".xr-var-data,\n",
+              ".xr-index-data {\n",
+              "  grid-column: 1 / -1;\n",
+              "}\n",
+              "\n",
+              "dl.xr-attrs {\n",
+              "  padding: 0;\n",
+              "  margin: 0;\n",
+              "  display: grid;\n",
+              "  grid-template-columns: 125px auto;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dt,\n",
+              ".xr-attrs dd {\n",
+              "  padding: 0;\n",
+              "  margin: 0;\n",
+              "  float: left;\n",
+              "  padding-right: 10px;\n",
+              "  width: auto;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dt {\n",
+              "  font-weight: normal;\n",
+              "  grid-column: 1;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dt:hover span {\n",
+              "  display: inline-block;\n",
+              "  background: var(--xr-background-color);\n",
+              "  padding-right: 10px;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dd {\n",
+              "  grid-column: 2;\n",
+              "  white-space: pre-wrap;\n",
+              "  word-break: break-all;\n",
+              "}\n",
+              "\n",
+              ".xr-icon-database,\n",
+              ".xr-icon-file-text2,\n",
+              ".xr-no-icon {\n",
+              "  display: inline-block;\n",
+              "  vertical-align: middle;\n",
+              "  width: 1em;\n",
+              "  height: 1.5em !important;\n",
+              "  stroke-width: 0;\n",
+              "  stroke: currentColor;\n",
+              "  fill: currentColor;\n",
+              "}\n",
+              "\n",
+              ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+              ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+              ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+              "  color: var(--xr-font-color0);\n",
+              "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+              "  stroke-width: 0.8px;\n",
+              "}\n",
+              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;air&#x27; (time: 30, lat: 13, lon: 29)&gt; Size: 45kB\n",
+              "dask.array&lt;stack, shape=(30, 13, 29), dtype=float32, chunksize=(1, 13, 29), chunktype=numpy.ndarray&gt;\n",
+              "Coordinates:\n",
+              "  * lat      (lat) float32 52B 50.0 47.5 45.0 42.5 40.0 ... 27.5 25.0 22.5 20.0\n",
+              "  * lon      (lon) float32 116B 230.0 232.5 235.0 237.5 ... 295.0 297.5 300.0\n",
+              "  * time     (time) datetime64[ns] 240B 2020-06-01 2020-06-02 ... 2020-06-30\n",
+              "Attributes: (12/13)\n",
+              "    long_name:     4xDaily Air temperature at sigma level 995\n",
+              "    units:         degK\n",
+              "    precision:     2\n",
+              "    GRIB_id:       11\n",
+              "    GRIB_name:     TMP\n",
+              "    var_desc:      Air temperature\n",
+              "    ...            ...\n",
+              "    parent_stat:   Other\n",
+              "    valid_range:   [185.16 331.16]\n",
+              "    dataset:       NCEP Reanalysis\n",
+              "    actual_range:  [188.6 324.1]\n",
+              "    level_desc:    0.995 sigma\n",
+              "    _ChunkSizes:   [  1  73 144]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'air'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 30</li><li><span class='xr-has-index'>lat</span>: 13</li><li><span class='xr-has-index'>lon</span>: 29</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4f2c768d-1ae4-469b-bb90-89a489d940be' class='xr-array-in' type='checkbox' checked><label for='section-4f2c768d-1ae4-469b-bb90-89a489d940be' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 13, 29), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 44.18 kiB </td>\n",
+              "                        <td> 1.47 kiB </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (30, 13, 29) </td>\n",
+              "                        <td> (1, 13, 29) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 30 chunks in 95 graph layers </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"246\" height=\"172\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
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+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"52\" style=\"stroke-width:2\" />\n",
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+              "\n",
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+              "  <line x1=\"80\" y1=\"70\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"125\" y1=\"0\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"10.0,0.0 125.99999999999999,0.0 196.58823529411762,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"80\" y1=\"70\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"80\" y1=\"122\" x2=\"196\" y2=\"122\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"122\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"196\" y1=\"70\" x2=\"196\" y2=\"122\" style=\"stroke-width:2\" />\n",
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+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"80.58823529411765,70.58823529411765 196.58823529411762,70.58823529411765 196.58823529411762,122.58823529411765 80.58823529411765,122.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"138.588235\" y=\"142.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >29</text>\n",
+              "  <text x=\"216.588235\" y=\"96.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,216.588235,96.588235)\">13</text>\n",
+              "  <text x=\"35.294118\" y=\"107.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,107.294118)\">30</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></div></li><li class='xr-section-item'><input id='section-b32ffaed-c509-4864-b0ba-489f5f8c7b78' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b32ffaed-c509-4864-b0ba-489f5f8c7b78' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>50.0 47.5 45.0 ... 25.0 22.5 20.0</div><input id='attrs-334f5086-c7ff-4409-acff-cb4743ac79d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-334f5086-c7ff-4409-acff-cb4743ac79d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3979bce2-1213-40dd-b9ba-ad6f7cbca10b' class='xr-var-data-in' type='checkbox'><label for='data-3979bce2-1213-40dd-b9ba-ad6f7cbca10b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>actual_range :</span></dt><dd>[ 90. -90.]</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([50. , 47.5, 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5,\n",
+              "       20. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>230.0 232.5 235.0 ... 297.5 300.0</div><input id='attrs-041de43e-4303-490b-bbc3-10825a656a39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-041de43e-4303-490b-bbc3-10825a656a39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acff98b1-49a2-480d-b78d-723f6a3afef2' class='xr-var-data-in' type='checkbox'><label for='data-acff98b1-49a2-480d-b78d-723f6a3afef2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0.  357.5]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5, 250. , 252.5,\n",
+              "       255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5, 275. , 277.5,\n",
+              "       280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5, 300. ],\n",
+              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-06-01 ... 2020-06-30</div><input id='attrs-262f6e76-635b-4278-bf15-7be8a07d92e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-262f6e76-635b-4278-bf15-7be8a07d92e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7aad92bb-1c68-476b-a853-929b43acb281' class='xr-var-data-in' type='checkbox'><label for='data-7aad92bb-1c68-476b-a853-929b43acb281' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-00-00 06:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[1928472. 1937250.]</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-06-01T00:00:00.000000000&#x27;, &#x27;2020-06-02T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-03T00:00:00.000000000&#x27;, &#x27;2020-06-04T00:00:00.000000000&#x27;,\n",
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+              "       &#x27;2020-06-15T00:00:00.000000000&#x27;, &#x27;2020-06-16T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-17T00:00:00.000000000&#x27;, &#x27;2020-06-18T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-19T00:00:00.000000000&#x27;, &#x27;2020-06-20T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-21T00:00:00.000000000&#x27;, &#x27;2020-06-22T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-23T00:00:00.000000000&#x27;, &#x27;2020-06-24T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-25T00:00:00.000000000&#x27;, &#x27;2020-06-26T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-27T00:00:00.000000000&#x27;, &#x27;2020-06-28T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-29T00:00:00.000000000&#x27;, &#x27;2020-06-30T00:00:00.000000000&#x27;],\n",
+              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fde581f0-e035-429b-bf83-4b345e9787af' class='xr-section-summary-in' type='checkbox'  ><label for='section-fde581f0-e035-429b-bf83-4b345e9787af' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-e672e544-c6d6-480a-8c29-cfe2b9b1a965' class='xr-index-data-in' type='checkbox'/><label for='index-e672e544-c6d6-480a-8c29-cfe2b9b1a965' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([50.0, 47.5, 45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5,\n",
+              "       20.0],\n",
+              "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6dd410e3-2178-461a-8bcc-61e82d30473b' class='xr-index-data-in' type='checkbox'/><label for='index-6dd410e3-2178-461a-8bcc-61e82d30473b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5, 250.0, 252.5,\n",
+              "       255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5, 275.0, 277.5,\n",
+              "       280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5, 300.0],\n",
+              "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-cb9c2642-145d-4eaa-93cb-45f1d4aa8d50' class='xr-index-data-in' type='checkbox'/><label for='index-cb9c2642-145d-4eaa-93cb-45f1d4aa8d50' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-06-01&#x27;, &#x27;2020-06-02&#x27;, &#x27;2020-06-03&#x27;, &#x27;2020-06-04&#x27;,\n",
+              "               &#x27;2020-06-05&#x27;, &#x27;2020-06-06&#x27;, &#x27;2020-06-07&#x27;, &#x27;2020-06-08&#x27;,\n",
+              "               &#x27;2020-06-09&#x27;, &#x27;2020-06-10&#x27;, &#x27;2020-06-11&#x27;, &#x27;2020-06-12&#x27;,\n",
+              "               &#x27;2020-06-13&#x27;, &#x27;2020-06-14&#x27;, &#x27;2020-06-15&#x27;, &#x27;2020-06-16&#x27;,\n",
+              "               &#x27;2020-06-17&#x27;, &#x27;2020-06-18&#x27;, &#x27;2020-06-19&#x27;, &#x27;2020-06-20&#x27;,\n",
+              "               &#x27;2020-06-21&#x27;, &#x27;2020-06-22&#x27;, &#x27;2020-06-23&#x27;, &#x27;2020-06-24&#x27;,\n",
+              "               &#x27;2020-06-25&#x27;, &#x27;2020-06-26&#x27;, &#x27;2020-06-27&#x27;, &#x27;2020-06-28&#x27;,\n",
+              "               &#x27;2020-06-29&#x27;, &#x27;2020-06-30&#x27;],\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f6ff91a-affe-4034-9b70-9cb317c8265d' class='xr-section-summary-in' type='checkbox'  ><label for='section-5f6ff91a-affe-4034-9b70-9cb317c8265d' class='xr-section-summary' >Attributes: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>valid_range :</span></dt><dd>[185.16 331.16]</dd><dt><span>dataset :</span></dt><dd>NCEP Reanalysis</dd><dt><span>actual_range :</span></dt><dd>[188.6 324.1]</dd><dt><span>level_desc :</span></dt><dd>0.995 sigma</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  73 144]</dd></dl></div></li></ul></div></div>"
+            ],
+            "text/plain": [
+              "<xarray.DataArray 'air' (time: 30, lat: 13, lon: 29)> Size: 45kB\n",
+              "dask.array<stack, shape=(30, 13, 29), dtype=float32, chunksize=(1, 13, 29), chunktype=numpy.ndarray>\n",
+              "Coordinates:\n",
+              "  * lat      (lat) float32 52B 50.0 47.5 45.0 42.5 40.0 ... 27.5 25.0 22.5 20.0\n",
+              "  * lon      (lon) float32 116B 230.0 232.5 235.0 237.5 ... 295.0 297.5 300.0\n",
+              "  * time     (time) datetime64[ns] 240B 2020-06-01 2020-06-02 ... 2020-06-30\n",
+              "Attributes: (12/13)\n",
+              "    long_name:     4xDaily Air temperature at sigma level 995\n",
+              "    units:         degK\n",
+              "    precision:     2\n",
+              "    GRIB_id:       11\n",
+              "    GRIB_name:     TMP\n",
+              "    var_desc:      Air temperature\n",
+              "    ...            ...\n",
+              "    parent_stat:   Other\n",
+              "    valid_range:   [185.16 331.16]\n",
+              "    dataset:       NCEP Reanalysis\n",
+              "    actual_range:  [188.6 324.1]\n",
+              "    level_desc:    0.995 sigma\n",
+              "    _ChunkSizes:   [  1  73 144]"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
         "# TODO: use xr.resample to create daily means.\n",
-        "raise NotImplementedError(\"Create `air_daily`.\")\n"
+        "air_daily = air_us[\"air\"].resample(time=\"1D\").mean()\n",
+        "\n",
+        "air_daily"
       ]
     },
     {
@@ -146,17 +919,688 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 10,
       "id": "52eb7321",
       "metadata": {
         "tags": [
           "exercise"
         ]
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
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+              "</defs>\n",
+              "</svg>\n",
+              "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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+              "    --jp-border-color2,\n",
+              "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
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+              "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
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+              "  --xr-background-color: var(\n",
+              "    --jp-layout-color0,\n",
+              "    var(--pst-color-on-background, white)\n",
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+              "  --xr-background-color-row-even: var(\n",
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+              "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
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+              "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
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+              "}\n",
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+              "body[data-theme=\"dark\"],\n",
+              "body.vscode-dark {\n",
+              "  --xr-font-color0: var(\n",
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+              "    --jp-content-font-color2,\n",
+              "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
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+              "    --jp-content-font-color3,\n",
+              "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+              "  );\n",
+              "  --xr-border-color: var(\n",
+              "    --jp-border-color2,\n",
+              "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+              "  );\n",
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+              "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
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+              "  --xr-background-color: var(\n",
+              "    --jp-layout-color0,\n",
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+              "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+              "  );\n",
+              "}\n",
+              "\n",
+              ".xr-wrap {\n",
+              "  display: block !important;\n",
+              "  min-width: 300px;\n",
+              "  max-width: 700px;\n",
+              "}\n",
+              "\n",
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+              "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              ".xr-header {\n",
+              "  padding-top: 6px;\n",
+              "  padding-bottom: 6px;\n",
+              "  margin-bottom: 4px;\n",
+              "  border-bottom: solid 1px var(--xr-border-color);\n",
+              "}\n",
+              "\n",
+              ".xr-header > div,\n",
+              ".xr-header > ul {\n",
+              "  display: inline;\n",
+              "  margin-top: 0;\n",
+              "  margin-bottom: 0;\n",
+              "}\n",
+              "\n",
+              ".xr-obj-type,\n",
+              ".xr-array-name {\n",
+              "  margin-left: 2px;\n",
+              "  margin-right: 10px;\n",
+              "}\n",
+              "\n",
+              ".xr-obj-type {\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-sections {\n",
+              "  padding-left: 0 !important;\n",
+              "  display: grid;\n",
+              "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+              "}\n",
+              "\n",
+              ".xr-section-item {\n",
+              "  display: contents;\n",
+              "}\n",
+              "\n",
+              ".xr-section-item input {\n",
+              "  display: inline-block;\n",
+              "  opacity: 0;\n",
+              "  height: 0;\n",
+              "}\n",
+              "\n",
+              ".xr-section-item input + label {\n",
+              "  color: var(--xr-disabled-color);\n",
+              "  border: 2px solid transparent !important;\n",
+              "}\n",
+              "\n",
+              ".xr-section-item input:enabled + label {\n",
+              "  cursor: pointer;\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-section-item input:focus + label {\n",
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+              "}\n",
+              "\n",
+              ".xr-section-item input:enabled + label:hover {\n",
+              "  color: var(--xr-font-color0);\n",
+              "}\n",
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+              "  color: var(--xr-font-color2);\n",
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+              "  padding-left: 0.5em;\n",
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+              "  color: var(--xr-font-color2);\n",
+              "}\n",
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+              "  width: 15px;\n",
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+              "  grid-column: 1 / -1;\n",
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+              "}\n",
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+              "}\n",
+              "\n",
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+              "  grid-column: 2;\n",
+              "}\n",
+              "\n",
+              ".xr-var-dtype {\n",
+              "  grid-column: 3;\n",
+              "  text-align: right;\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-var-preview {\n",
+              "  grid-column: 4;\n",
+              "}\n",
+              "\n",
+              ".xr-index-preview {\n",
+              "  grid-column: 2 / 5;\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-var-name,\n",
+              ".xr-var-dims,\n",
+              ".xr-var-dtype,\n",
+              ".xr-preview,\n",
+              ".xr-attrs dt {\n",
+              "  white-space: nowrap;\n",
+              "  overflow: hidden;\n",
+              "  text-overflow: ellipsis;\n",
+              "  padding-right: 10px;\n",
+              "}\n",
+              "\n",
+              ".xr-var-name:hover,\n",
+              ".xr-var-dims:hover,\n",
+              ".xr-var-dtype:hover,\n",
+              ".xr-attrs dt:hover {\n",
+              "  overflow: visible;\n",
+              "  width: auto;\n",
+              "  z-index: 1;\n",
+              "}\n",
+              "\n",
+              ".xr-var-attrs,\n",
+              ".xr-var-data,\n",
+              ".xr-index-data {\n",
+              "  display: none;\n",
+              "  border-top: 2px dotted var(--xr-background-color);\n",
+              "  padding-bottom: 20px !important;\n",
+              "  padding-top: 10px !important;\n",
+              "}\n",
+              "\n",
+              ".xr-var-attrs-in + label,\n",
+              ".xr-var-data-in + label,\n",
+              ".xr-index-data-in + label {\n",
+              "  padding: 0 1px;\n",
+              "}\n",
+              "\n",
+              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+              ".xr-var-data-in:checked ~ .xr-var-data,\n",
+              ".xr-index-data-in:checked ~ .xr-index-data {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              ".xr-var-data > table {\n",
+              "  float: right;\n",
+              "}\n",
+              "\n",
+              ".xr-var-data > pre,\n",
+              ".xr-index-data > pre,\n",
+              ".xr-var-data > table > tbody > tr {\n",
+              "  background-color: transparent !important;\n",
+              "}\n",
+              "\n",
+              ".xr-var-name span,\n",
+              ".xr-var-data,\n",
+              ".xr-index-name div,\n",
+              ".xr-index-data,\n",
+              ".xr-attrs {\n",
+              "  padding-left: 25px !important;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs,\n",
+              ".xr-var-attrs,\n",
+              ".xr-var-data,\n",
+              ".xr-index-data {\n",
+              "  grid-column: 1 / -1;\n",
+              "}\n",
+              "\n",
+              "dl.xr-attrs {\n",
+              "  padding: 0;\n",
+              "  margin: 0;\n",
+              "  display: grid;\n",
+              "  grid-template-columns: 125px auto;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dt,\n",
+              ".xr-attrs dd {\n",
+              "  padding: 0;\n",
+              "  margin: 0;\n",
+              "  float: left;\n",
+              "  padding-right: 10px;\n",
+              "  width: auto;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dt {\n",
+              "  font-weight: normal;\n",
+              "  grid-column: 1;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dt:hover span {\n",
+              "  display: inline-block;\n",
+              "  background: var(--xr-background-color);\n",
+              "  padding-right: 10px;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dd {\n",
+              "  grid-column: 2;\n",
+              "  white-space: pre-wrap;\n",
+              "  word-break: break-all;\n",
+              "}\n",
+              "\n",
+              ".xr-icon-database,\n",
+              ".xr-icon-file-text2,\n",
+              ".xr-no-icon {\n",
+              "  display: inline-block;\n",
+              "  vertical-align: middle;\n",
+              "  width: 1em;\n",
+              "  height: 1.5em !important;\n",
+              "  stroke-width: 0;\n",
+              "  stroke: currentColor;\n",
+              "  fill: currentColor;\n",
+              "}\n",
+              "\n",
+              ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+              ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+              ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+              "  color: var(--xr-font-color0);\n",
+              "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+              "  stroke-width: 0.8px;\n",
+              "}\n",
+              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;air&#x27; (time: 30, lat: 60, lon: 140)&gt; Size: 1MB\n",
+              "dask.array&lt;transpose, shape=(30, 60, 140), dtype=float32, chunksize=(1, 60, 140), chunktype=numpy.ndarray&gt;\n",
+              "Coordinates:\n",
+              "  * time     (time) datetime64[ns] 240B 2020-06-01 2020-06-02 ... 2020-06-30\n",
+              "  * lat      (lat) float32 240B 49.75 49.25 48.75 48.25 ... 21.25 20.75 20.25\n",
+              "  * lon      (lon) float32 560B 230.2 230.8 231.2 231.8 ... 298.8 299.2 299.8\n",
+              "Attributes: (12/13)\n",
+              "    long_name:     4xDaily Air temperature at sigma level 995\n",
+              "    units:         degK\n",
+              "    precision:     2\n",
+              "    GRIB_id:       11\n",
+              "    GRIB_name:     TMP\n",
+              "    var_desc:      Air temperature\n",
+              "    ...            ...\n",
+              "    parent_stat:   Other\n",
+              "    valid_range:   [185.16 331.16]\n",
+              "    dataset:       NCEP Reanalysis\n",
+              "    actual_range:  [188.6 324.1]\n",
+              "    level_desc:    0.995 sigma\n",
+              "    _ChunkSizes:   [  1  73 144]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'air'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 30</li><li><span class='xr-has-index'>lat</span>: 60</li><li><span class='xr-has-index'>lon</span>: 140</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3d6da5da-7f6f-4c7b-95c5-2dd87f864799' class='xr-array-in' type='checkbox' checked><label for='section-3d6da5da-7f6f-4c7b-95c5-2dd87f864799' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 60, 140), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 0.96 MiB </td>\n",
+              "                        <td> 32.81 kiB </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (30, 60, 140) </td>\n",
+              "                        <td> (1, 60, 140) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 30 chunks in 113 graph layers </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"204\" height=\"126\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+              "  <text x=\"12.440090\" y=\"83.868661\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.440090,83.868661)\">30</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></div></li><li class='xr-section-item'><input id='section-7ba77b3b-0d16-4611-b1af-3e1e749b82df' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7ba77b3b-0d16-4611-b1af-3e1e749b82df' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-06-01 ... 2020-06-30</div><input id='attrs-a7623e04-a744-49b0-8744-b5b20e0adf39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7623e04-a744-49b0-8744-b5b20e0adf39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81f713a1-7805-4dfe-9205-de628e5f897d' class='xr-var-data-in' type='checkbox'><label for='data-81f713a1-7805-4dfe-9205-de628e5f897d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-00-00 06:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[1928472. 1937250.]</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-06-01T00:00:00.000000000&#x27;, &#x27;2020-06-02T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-03T00:00:00.000000000&#x27;, &#x27;2020-06-04T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-05T00:00:00.000000000&#x27;, &#x27;2020-06-06T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-07T00:00:00.000000000&#x27;, &#x27;2020-06-08T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-09T00:00:00.000000000&#x27;, &#x27;2020-06-10T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-11T00:00:00.000000000&#x27;, &#x27;2020-06-12T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-13T00:00:00.000000000&#x27;, &#x27;2020-06-14T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-15T00:00:00.000000000&#x27;, &#x27;2020-06-16T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-17T00:00:00.000000000&#x27;, &#x27;2020-06-18T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-19T00:00:00.000000000&#x27;, &#x27;2020-06-20T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-21T00:00:00.000000000&#x27;, &#x27;2020-06-22T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-23T00:00:00.000000000&#x27;, &#x27;2020-06-24T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-25T00:00:00.000000000&#x27;, &#x27;2020-06-26T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-27T00:00:00.000000000&#x27;, &#x27;2020-06-28T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-29T00:00:00.000000000&#x27;, &#x27;2020-06-30T00:00:00.000000000&#x27;],\n",
+              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>49.75 49.25 48.75 ... 20.75 20.25</div><input id='attrs-dff007cc-7d0f-4747-918f-d6f4fe752a97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dff007cc-7d0f-4747-918f-d6f4fe752a97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6daadf56-d744-4eba-9254-ec36eb9e752b' class='xr-var-data-in' type='checkbox'><label for='data-6daadf56-d744-4eba-9254-ec36eb9e752b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>actual_range :</span></dt><dd>[ 89.75 -89.75]</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([49.75, 49.25, 48.75, 48.25, 47.75, 47.25, 46.75, 46.25, 45.75, 45.25,\n",
+              "       44.75, 44.25, 43.75, 43.25, 42.75, 42.25, 41.75, 41.25, 40.75, 40.25,\n",
+              "       39.75, 39.25, 38.75, 38.25, 37.75, 37.25, 36.75, 36.25, 35.75, 35.25,\n",
+              "       34.75, 34.25, 33.75, 33.25, 32.75, 32.25, 31.75, 31.25, 30.75, 30.25,\n",
+              "       29.75, 29.25, 28.75, 28.25, 27.75, 27.25, 26.75, 26.25, 25.75, 25.25,\n",
+              "       24.75, 24.25, 23.75, 23.25, 22.75, 22.25, 21.75, 21.25, 20.75, 20.25],\n",
+              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>230.2 230.8 231.2 ... 299.2 299.8</div><input id='attrs-5559dc68-f576-462e-94a9-ad1d0e41a161' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5559dc68-f576-462e-94a9-ad1d0e41a161' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2002765-6980-47bd-9c3c-279853c25f77' class='xr-var-data-in' type='checkbox'><label for='data-f2002765-6980-47bd-9c3c-279853c25f77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>actual_range :</span></dt><dd>[2.5000e-01 3.5975e+02]</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([230.25, 230.75, 231.25, 231.75, 232.25, 232.75, 233.25, 233.75, 234.25,\n",
+              "       234.75, 235.25, 235.75, 236.25, 236.75, 237.25, 237.75, 238.25, 238.75,\n",
+              "       239.25, 239.75, 240.25, 240.75, 241.25, 241.75, 242.25, 242.75, 243.25,\n",
+              "       243.75, 244.25, 244.75, 245.25, 245.75, 246.25, 246.75, 247.25, 247.75,\n",
+              "       248.25, 248.75, 249.25, 249.75, 250.25, 250.75, 251.25, 251.75, 252.25,\n",
+              "       252.75, 253.25, 253.75, 254.25, 254.75, 255.25, 255.75, 256.25, 256.75,\n",
+              "       257.25, 257.75, 258.25, 258.75, 259.25, 259.75, 260.25, 260.75, 261.25,\n",
+              "       261.75, 262.25, 262.75, 263.25, 263.75, 264.25, 264.75, 265.25, 265.75,\n",
+              "       266.25, 266.75, 267.25, 267.75, 268.25, 268.75, 269.25, 269.75, 270.25,\n",
+              "       270.75, 271.25, 271.75, 272.25, 272.75, 273.25, 273.75, 274.25, 274.75,\n",
+              "       275.25, 275.75, 276.25, 276.75, 277.25, 277.75, 278.25, 278.75, 279.25,\n",
+              "       279.75, 280.25, 280.75, 281.25, 281.75, 282.25, 282.75, 283.25, 283.75,\n",
+              "       284.25, 284.75, 285.25, 285.75, 286.25, 286.75, 287.25, 287.75, 288.25,\n",
+              "       288.75, 289.25, 289.75, 290.25, 290.75, 291.25, 291.75, 292.25, 292.75,\n",
+              "       293.25, 293.75, 294.25, 294.75, 295.25, 295.75, 296.25, 296.75, 297.25,\n",
+              "       297.75, 298.25, 298.75, 299.25, 299.75], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3729e9bd-7dce-4052-8459-ed45d0e352ac' class='xr-section-summary-in' type='checkbox'  ><label for='section-3729e9bd-7dce-4052-8459-ed45d0e352ac' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ad920042-bb48-448e-bec3-550df35fe614' class='xr-index-data-in' type='checkbox'/><label for='index-ad920042-bb48-448e-bec3-550df35fe614' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-06-01&#x27;, &#x27;2020-06-02&#x27;, &#x27;2020-06-03&#x27;, &#x27;2020-06-04&#x27;,\n",
+              "               &#x27;2020-06-05&#x27;, &#x27;2020-06-06&#x27;, &#x27;2020-06-07&#x27;, &#x27;2020-06-08&#x27;,\n",
+              "               &#x27;2020-06-09&#x27;, &#x27;2020-06-10&#x27;, &#x27;2020-06-11&#x27;, &#x27;2020-06-12&#x27;,\n",
+              "               &#x27;2020-06-13&#x27;, &#x27;2020-06-14&#x27;, &#x27;2020-06-15&#x27;, &#x27;2020-06-16&#x27;,\n",
+              "               &#x27;2020-06-17&#x27;, &#x27;2020-06-18&#x27;, &#x27;2020-06-19&#x27;, &#x27;2020-06-20&#x27;,\n",
+              "               &#x27;2020-06-21&#x27;, &#x27;2020-06-22&#x27;, &#x27;2020-06-23&#x27;, &#x27;2020-06-24&#x27;,\n",
+              "               &#x27;2020-06-25&#x27;, &#x27;2020-06-26&#x27;, &#x27;2020-06-27&#x27;, &#x27;2020-06-28&#x27;,\n",
+              "               &#x27;2020-06-29&#x27;, &#x27;2020-06-30&#x27;],\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-47467dc5-8844-411c-bb51-d29255fc58f4' class='xr-index-data-in' type='checkbox'/><label for='index-47467dc5-8844-411c-bb51-d29255fc58f4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([49.75, 49.25, 48.75, 48.25, 47.75, 47.25, 46.75, 46.25, 45.75, 45.25,\n",
+              "       44.75, 44.25, 43.75, 43.25, 42.75, 42.25, 41.75, 41.25, 40.75, 40.25,\n",
+              "       39.75, 39.25, 38.75, 38.25, 37.75, 37.25, 36.75, 36.25, 35.75, 35.25,\n",
+              "       34.75, 34.25, 33.75, 33.25, 32.75, 32.25, 31.75, 31.25, 30.75, 30.25,\n",
+              "       29.75, 29.25, 28.75, 28.25, 27.75, 27.25, 26.75, 26.25, 25.75, 25.25,\n",
+              "       24.75, 24.25, 23.75, 23.25, 22.75, 22.25, 21.75, 21.25, 20.75, 20.25],\n",
+              "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-80f6c720-d0aa-43bd-b49f-b393b44c9cbb' class='xr-index-data-in' type='checkbox'/><label for='index-80f6c720-d0aa-43bd-b49f-b393b44c9cbb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([230.25, 230.75, 231.25, 231.75, 232.25, 232.75, 233.25, 233.75, 234.25,\n",
+              "       234.75,\n",
+              "       ...\n",
+              "       295.25, 295.75, 296.25, 296.75, 297.25, 297.75, 298.25, 298.75, 299.25,\n",
+              "       299.75],\n",
+              "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=140))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-34e31875-0e8e-47e6-8211-3c068bcb0ed1' class='xr-section-summary-in' type='checkbox'  ><label for='section-34e31875-0e8e-47e6-8211-3c068bcb0ed1' class='xr-section-summary' >Attributes: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>valid_range :</span></dt><dd>[185.16 331.16]</dd><dt><span>dataset :</span></dt><dd>NCEP Reanalysis</dd><dt><span>actual_range :</span></dt><dd>[188.6 324.1]</dd><dt><span>level_desc :</span></dt><dd>0.995 sigma</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  73 144]</dd></dl></div></li></ul></div></div>"
+            ],
+            "text/plain": [
+              "<xarray.DataArray 'air' (time: 30, lat: 60, lon: 140)> Size: 1MB\n",
+              "dask.array<transpose, shape=(30, 60, 140), dtype=float32, chunksize=(1, 60, 140), chunktype=numpy.ndarray>\n",
+              "Coordinates:\n",
+              "  * time     (time) datetime64[ns] 240B 2020-06-01 2020-06-02 ... 2020-06-30\n",
+              "  * lat      (lat) float32 240B 49.75 49.25 48.75 48.25 ... 21.25 20.75 20.25\n",
+              "  * lon      (lon) float32 560B 230.2 230.8 231.2 231.8 ... 298.8 299.2 299.8\n",
+              "Attributes: (12/13)\n",
+              "    long_name:     4xDaily Air temperature at sigma level 995\n",
+              "    units:         degK\n",
+              "    precision:     2\n",
+              "    GRIB_id:       11\n",
+              "    GRIB_name:     TMP\n",
+              "    var_desc:      Air temperature\n",
+              "    ...            ...\n",
+              "    parent_stat:   Other\n",
+              "    valid_range:   [185.16 331.16]\n",
+              "    dataset:       NCEP Reanalysis\n",
+              "    actual_range:  [188.6 324.1]\n",
+              "    level_desc:    0.995 sigma\n",
+              "    _ChunkSizes:   [  1  73 144]"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
         "# TODO: interpolate the resampled temperature field onto the precipitation grid.\n",
-        "raise NotImplementedError(\"Create `air_interp`.\")\n"
+        "# Interpolate daily air temperatures onto the precipitation grid\n",
+        "air_interp = air_daily.interp(\n",
+        "    lat=precip_us[\"lat\"],\n",
+        "    lon=precip_us[\"lon\"],\n",
+        "    method=\"linear\"\n",
+        ")\n",
+        "\n",
+        "air_interp\n"
       ]
     },
     {
@@ -171,17 +1615,49 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 13,
       "id": "b766aff5",
       "metadata": {
         "tags": [
           "exercise"
         ]
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<xarray.Dataset> Size: 2MB\n",
+            "Dimensions:          (time: 30, lat: 60, lon: 140)\n",
+            "Coordinates:\n",
+            "  * time             (time) datetime64[ns] 240B 2020-06-01 ... 2020-06-30\n",
+            "  * lat              (lat) float32 240B 49.75 49.25 48.75 ... 21.25 20.75 20.25\n",
+            "  * lon              (lon) float32 560B 230.2 230.8 231.2 ... 298.8 299.2 299.8\n",
+            "Data variables:\n",
+            "    air_temperature  (time, lat, lon) float32 1MB dask.array<chunksize=(1, 60, 140), meta=np.ndarray>\n",
+            "    daily_precip     (time, lat, lon) float32 1MB dask.array<chunksize=(8, 60, 140), meta=np.ndarray>\n"
+          ]
+        }
+      ],
       "source": [
         "# TODO: build datasets and merge them into one object named `merged`.\n",
-        "raise NotImplementedError(\"Create `merged`.\")\n"
+        "# Give clear names\n",
+        "air_ds = air_interp.rename(\"air_temp_daily_mean\").to_dataset()\n",
+        "precip_ds = precip_us[\"precip\"].rename(\"precip_daily\").to_dataset()\n",
+        "\n",
+        "# Merge into a single dataset\n",
+        "merged = xr.merge([air_ds, precip_ds])\n",
+        "\n",
+        "merged\n",
+        "\n",
+        "# Convert to datasets with names that match step 7\n",
+        "air_ds = air_interp.rename(\"air_temperature\").to_dataset()\n",
+        "precip_ds = precip_us[\"precip\"].rename(\"daily_precip\").to_dataset()\n",
+        "\n",
+        "# Merge them\n",
+        "merged = xr.merge([air_ds, precip_ds])\n",
+        "\n",
+        "print(merged)\n"
       ]
     },
     {
@@ -196,14 +1672,30 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 14,
       "id": "70410d84",
       "metadata": {
         "tags": [
           "validation"
         ]
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<xarray.Dataset> Size: 2MB\n",
+            "Dimensions:          (time: 30, lat: 60, lon: 140)\n",
+            "Coordinates:\n",
+            "  * time             (time) datetime64[ns] 240B 2020-06-01 ... 2020-06-30\n",
+            "  * lat              (lat) float32 240B 49.75 49.25 48.75 ... 21.25 20.75 20.25\n",
+            "  * lon              (lon) float32 560B 230.2 230.8 231.2 ... 298.8 299.2 299.8\n",
+            "Data variables:\n",
+            "    air_temperature  (time, lat, lon) float32 1MB dask.array<chunksize=(1, 60, 140), meta=np.ndarray>\n",
+            "    daily_precip     (time, lat, lon) float32 1MB dask.array<chunksize=(8, 60, 140), meta=np.ndarray>\n"
+          ]
+        }
+      ],
       "source": [
         "# The assertions below should pass once you have completed the exercise.\n",
         "assert set(merged.data_vars) == {\"air_temperature\", \"daily_precip\"}\n",
@@ -228,6 +1720,163 @@
       "id": "d1c320a1",
       "metadata": {},
       "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "407e475a",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "c:\\Users\\tnaut\\anaconda3\\envs\\xarray-climate\\Lib\\site-packages\\cartopy\\io\\__init__.py:242: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip\n",
+            "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n",
+            "c:\\Users\\tnaut\\anaconda3\\envs\\xarray-climate\\Lib\\site-packages\\cartopy\\io\\__init__.py:242: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_cultural/ne_50m_admin_0_boundary_lines_land.zip\n",
+            "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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QCATEUUcdJdLT08X69esj+1966SUBiNmzZ0eV+dnPftapaUglkX653W6RlpYmsrKyxOOPPy4++eQTccsttwi73S7OPffcLtva8by88sorUWn33nuvAMTmzZs7LatNt5quuO6668QFl/nEgvUlCf+9/Um+8Hq9O9X+0UcfLS666CIRDodFYWGhSeLQ1tYmMjIyIt+btbW1wul0itdeey2SZ/PmzcJms4n3339/p/rR0+gZvb2AZ555hpSUFM4++2wAUlNTOfPMM/n8889jrgpUOf3003e6H//73/8IBoNceOGFBIPByJ/H4+HII49k5syZpvyGYXDiiSdGth0OB4MGDaKoqIixY8dG9mdnZ5Ofn28yk+7gvPPOM21PnDiR0tLSiOnwyy+/pKamhosuusjUp3A4zJQpU5g7dy5NTU2mOjo7F42Njfz2t79l0KBBOBwOHA4HqampNDU1sXTp0oTPVTyo/UjmWGQ2b94MdG6GtMLKbNFZWn5+Pps2bbKsMxwOm44hkb9EVvXW1NRw4oknIoTgtddeizI9Jnps0G7KufTSS/n888/5xz/+Qd++feMuK+9Xj0tI0QPi7Vc4HKahoYEnnniCK6+8kkmTJnH33Xdz1VVX8fLLL0fM/er5Vt1FJHMeNJp4EMIgnORfIvzud7/j888/Z+3atSxcuJBbbrmFmTNnct5552EYBtdeey333nsv06dPZ9GiRVx88cV4vV7OPfdcADIyMrj00ku54YYb+Pjjj/n22285//zzGT16NJMnT94Vp2a3oQd6vZyVK1fy2WefcdJJJyGEoLa2ltraWs444wwA/v73v8dVT1FR0U73paKiAoDx48fjdDpNf6+99lrExLkDr9eLx+Mx7XO5XGRnZ0fV7XK5OjUh7ZiSV/dVV1eb+nTGGWdE9emBBx5ACEFNTY2pfGfn4txzz+Xxxx/nsssu43//+x9ff/01c+fOJS8vj5aWFqvTkjRqP5I5Fpkd/VTPuRU5OTm0trZ2qvesqanp9Fp5PJ4uz8mdd94ZdQzx/t15551x9X3btm0ce+yxbNq0iQ8//JCysrKdPjYhBJdddhkvvvgizz33HKeddlpUnUDk/rOqUz2uHTrURPq1o73jjz/elO+EE04A2t2mQIc0Y8ffMcccE1d/DcMgMzMzKk2jiZfkTbeJDfQqKiq44IILGDp0KMcccwxfffUV77//PsceeywAv/nNb7j22mu54oorGDduHJs2beKDDz4gLS0tUsfDDz/M1KlTOeusszj00EPxer385z//wW63d+cp2e3oEGi9nL///e8IIXjjjTd44403otKff/557r777i5v1O741Z6bmwvAG2+8QWlp6U7XFw+q5mLHvkGDBpn69Nhjj3HwwQd3Woeqv1DPRV1dHe+88w7Tpk3jpptuiuxva2uzHFipeDyeThe5bN26NdJPq34kcyydlU+kzzt0YgsXLmTChAmR/Vu2bGHr1q2d+mysqanp9Hhkfvazn3HyySfH3Q+Z4uLiLvNs27aNyZMns2bNGj7++ONONWiJHtuOQd6zzz7LM888w/nnnx9V544yCxcuNM1W79gn1zl37lxT+oABAxLu15gxYzp9BnbMDu6Ywbz99tv51a9+FUnf8eU2cOBAUlJSWLhwYVQdCxcuZNCgQQn9MNBoVMIYhJKYUwonWGbHgsRY7FiIaOXn1ePx8Nhjj/HYY48l1Paejh7o9WJCoRDPP/88AwcO5G9/+1tU+jvvvMOf/vQn3nvvvaS/VBPh+OOPx+FwsGrVqm4xBcfDSy+9ZGrryy+/ZN26dVx22WUAHHrooWRmZrJkyRLTF10iGIaBECJKBPy3v/0tygS2I09nM1r9+/dnwYIFpn3Lly9n2bJlXQ6MYOePZfjw4UC7AF/F7XZ32ucpU6bg8Xh47rnnTIOOHauep06dGlVm9erVlk67oX2wFs+ALRl2DPJWr17Nhx9+aJIByCRybEIILr/8cp599lmeeuqpqBWxOygpKeGggw7ixRdf5MYbb4z8wJozZw7Lli3j2muvjeQdN27cTvfr9NNP54MPPuC9996LmKAA/vvf/2Kz2Rg/fjzQfu/tWEkt43A4OOWUU3jrrbd48MEHIwPA9evXM2PGDK677rpO+6jRxIsgcTMsJB4ZQxMbPdDrxbz33nts3ryZBx54oFMP9aNGjeLxxx/nmWee2S0Dvf79+3PnnXdyyy23sHr1aqZMmUJWVhYVFRV8/fXX+Hw+7rjjjm5t85tvvuGyyy7jzDPPZMOGDdxyyy2UlJRwxRVXAO16xccee4yLLrqImpoazjjjDPLz86mqquL777+nqqqKv/zlL5ZtpKenc8QRR/CHP/yB3Nxc+vfvz6effsozzzwTZdbaMcB5+umnSUtLw+PxMGDAAHJycrjgggs4//zzueKKKzj99NNZt24dDz74IHl5eXEd684eS58+fSgrK2POnDlcffXVprTRo0fz6quv8tprr1FWVobH42H06NFkZ2dz6623ctttt5Gdnc1xxx3H3Llzuf3227nssssYMWKEqZ7q6mpWrFiRtLuPnaWlpSXizueRRx4hGAwyZ86cSHpeXl7EZU4ix3b11VfzzDPPcMkllzB69GhTnW632zSYfOCBBzj22GM588wzueKKK6isrOSmm25i1KhRMQeIMon066c//SlPPfUUV1xxBVu3bmXEiBF89NFH/N///R9XXHFFXDPrd9xxB+PHj+fkk0/mpptuorW1ld///vfk5uZyww03mPJ+8803kagv9fX1EWsCtEs2dtdMvqb3sMN0myjhJMpoYtAjS0A03cLUqVOFy+WK8l4vc/bZZwuHwyG2bNkihIi96jYZp8exyv7rX/8SkyZNEunp6cLtdovS0lJxxhlniI8++iiS56KLLhI+ny+qziOPPFKMHDkyan9paak46aSTotr+4IMPxAUXXCAyMzNFSkqKOPHEE8WKFSuiyn/66afipJNOEtnZ2cLpdIqSkhJx0kkniddffz2SZ8eq26qqqqjyGzduFKeffrrIysoSaWlpYsqUKWLRokWdrqR95JFHxIABA4Tdbo9EDhGiPSrFgw8+KMrKyoTH4xHjxo0Tn3zyScxVt3LfEj2WWNx2220iKysryrv82rVrxXHHHSfS0tIEIEpLS03pjz76qBgyZIhwuVyiX79+Ytq0acLv90fV/8wzzwin0xm533Y3a9asiXKwKv91tuo5nmMrLS2NWad6roQQ4oMPPhAHH3yw8Hg8Ijs7W1x44YWdOl22It5zXl1dLX7+85+LgoIC4XQ6xZAhQ8Qf/vAH0wrjrvjmm2/EMcccI7xer0hPTxdTp04VK1eujMq3Y7V8Z3877nONZgfXXXedOOvSDPHZmoEJ/73wUd+dXnWraccQQugJUk2v47nnnuOnP/0pc+fOjWkC00SzefNmBgwYwD/+8Q9+8pOfdHv9hx9+OP369eOll17q9ro1Gk3v4vrrr2dj/bNceWt8VguZdav8/PLUKktPApr40KtuNZp9iOLiYq699lruueeehNyUxMNnn33G3Llzueuuu7q1Xo1G03sJk9yqW2267T60Rk+j2ce49dZb8Xq9bNq0qVMfcMlSXV3NP/7xjyg3JhqNZt9FCINQEi57Q0ks4NB0jjbdajQajUaj6Xauv/561tU9xy9uTTyE2PpVbVx9WoU23XYDekZPo9FoNBrNLkEkaYYV2nTbbeiBnkaj0Wg0ml2CSNJhcjJlNJ2jB3oajUaj0Wh2CQKS0ugl42RZ0zlxD/RaW1vx+/27si8ajUaj0Wi6GZfL1WOh7NpNt7s+BJomNnEN9FpbW8nIzMff1rCr+6PRaDQajaYbKSwsZM2aNT0y2AsLI6kVtHrVbfcR10DP7/fjb2tgwqSbsTvabxRbqGOxrhE0L9w1Qh3+uWwBs68uIxhGCEFN3WoWrP5np+2VZO7HsKwjzEHd29pnEyubV7Ng2wcUe4bgD7WyNbA+xoG5SLNlMcY+ka3hcupEFUMcBxAOBDsyie71I9adGA6nadvmcXVsOM1phhyDNcUcj1V4Ox7skNdlSgv5Oi5/MMVuSgv4zL+mAt6O7aDX3NdgSuefAUIpHfdG2GW+T4S87TbHjLU5O7ZtDuUesikLxaXbxGaY02xSXqOb3htqG6auJJAmpBdZKGw+37KLO6G88GSThgiZ00IBu5RmrlMEOraNgLmcEVS2wx3btgAx8xpBJS3c+WcAIxQ7DWnbZr4VouuJsw2b0jeb9J5Sj8kWEJ1+BrCb0swN2tqk+9SvpAXMB2K0BaTPSueCUof85jTR1tbp5/btDiuLUOIua5JnCxv5gXkMYX+K6Tysm81lfp8aKeZB1I73cjDsZ0nTLKpbNhLCj8vmJSOliIyUEgygJdxEi38b9S1bCISaMQwHOZkDKcoeQ1beYAyj47kV9o7PYafyzrBL7wWH9G4JtvL1R/fi9/t7ZKCXrEZPz+h1Hwlp9OwODw7n9oGe9KVloAz0pLevTRlMGdu3C3JGMtJ/Mss2f0gwbH55balfyqicyaYbHFv7jVucOpzypmVsbl3OmPRjMAyDKv+6qL4G8bMtXMFWWzmLQ+1xKUcYEwibvu334IGeoQz0DOmlYlMGejY5TRno2Tu2DYc5zXBIl99pHugJ9SXikl425mpM20J5jwiPPJhLYKDnij3QsyU00OsouycP9FAGevJASx3oGdJ2WBnoCXmgF1QGeg5poOfoYqAn1Wsz3xqmvOpgymowt0sGempeeaCnfE/I941NuRfs0jvMprzPTGnK+8wmDa5sigNqW9h8cgzpS9qwKydVvv5Kx4XUb6HeQ4acpr8YuwMhBCtZQCF96ctA84SDhE15RxuGMvDb/l522FwcUHAaYRFiW+smKptXsbFpCdXNaxlZOIWy/CMj7Ta1baWyeRXl1QtZuOp1PJuy6Ft0MEWFB+B0pJgHeg7lHe3ofKDX0wgMwlqj16MkNNATDiNyA4WlN6U6w2L6krIrX2DOjpdhUb8J5JccwKaKb1i+9r+R/TabnbkVbzKo6GgyfX3ay3k6HqoxrqnM3/wWC+o/Jt1dQN/U/aj1b6Yt2EyedwCbGhcBkGLPYHGwfZDntvkoT6mkmvWMTD0cu+GI/gUsuxRU0oT8Eg9ZDBDVbxCL0YWhfhNJL3/DZX6JmGbxHPaYacJjLhd2d2zLgzUwvyjkX4MAwqZuS5+VQzKlqc+zvK2Ws0s/FuzKAE0a3DmiBnrmbbv8BW6RZjUI2x2oAwjZ5YAtrKRJz5A62yc/X2pwGyHVE1LqNOTzuP1ChVvbCDe3YE9JMw/85b4oF1x+3C29cKrXWx6Qq5NP8phXSUp2+KL2TUQffse29EgJ9bVgur+Vg5K3o54Z5bpJz7dQHm9DxD6phkUEE/kdIhQNdVhrquNGCEEbLbTQxDa2EiRAMQNiDvIAUJ6XqOdH3rbbsWEnJ3UAOakDGOKYzOLy91i05X0aQ7UUZY4k1ZNPqicPb3oR/QsPo65xE+ur5rBy7QesXvcxOVmDKS4aT3bWIAzDwBZUJ1GkH4DyjHywZyc0BMmtoNUzet1Hj6+6tdudlBSOo7m1mnCwjc1bvyMQbKGmcS1fr/g7w/ucSFHWGJzSRXfaPQzOOZzWYAMVjcsob1pKUPhx2jw4bW4OL7yAOn8lNW2bcDCAtU3f0RZuYmHtRwCMTD28pw5Xo9mjaPxsDq3LVxNuaCbzRyfhGTigp7uk0exyhBCUs45tVNFALc00EqZjhN+HgWQRHZ81IPzUU9Oe328jiJ/WcDOtognaDFw2L157Gim2NFJENpnuQpy2aHOp3eZkVPFJOCrcbNj6DWsqv8DnzqEgYxiZmWWk+YpJ9xUzKutMBvunsKliLpU1S/lu0XNkZZYxZsR52O0pUfXuiWiNXs/T4wM9aL/ph5WdjBEIk5sxlOr6lfjb6qmqX8HSjf8lHA7SP7sjcH1rsJGvN75M/6zxjC3+EWG/n3p/BRVNy1lfv4DK5pWUeEdQ6B1ErrMPYUKsb1oIwCDvgdj0LwWNBmjXdaWfMIlwXQuBTeV6oKfZqwmLMJVsYjWLaaYRgBLKKKIUH2mk4MOFB+d2M2xYhKihiio2U0MFLbRHaTAwsAecOAwnHsOLx/BhsztpCzdT0baVlnAjoimMgY0cd1/Kcg4mO8UcbtBm2BlReDxD+0yhunE1W2qXsqF6PqsrvwDAbnPhTcklzVdIiiebvKxhgGBb7WrWrJ/JoMEn7L4Tt1PoVbc9zR4x0JMpyB5BQfYIwm2tzP7hKZr9NZRvW0hl3Q+0+usRhGkN1AOQ6soBwGbYyHQXkekuok/aaOZteYsV9bMBSHfkUerbH2gf6K1snkd9sJp0R47JzNWOtK2mWZhWTBhKssWPkiizgKyx8cc260aZh2URlaLbEM6OS6yKd2XNVtipCP6V7ZBTTkPJG1+aUMyzwhGf6VbV5KkmWHlbTTOfqp413aq3gtwbVYcnb6umU/OtqFw3ycyrLtQwNbg9rXXFGjzDB+Pu34+aOd+QdsREy2PQaHorbaKFBcymjhpSySCfPgzngMigDtpn+pppoEJsoIYqqtlCiCAp+MiliHSySScLL6nYfT5T/baUFKmeMC1OP1Wta9nUtISvN79GUepwhudOwmU3r2az2ezkpQ8mL30wQggaw3U0tlTQ0lZLo38rDU1bqKr5AZvNgc3mJD2thNzsobv2ZHUjYZKLdas1et1HQgO9sN2IaLnkMUmUBli6QIZD1ZtIX0RORWMgrYozPHYOPuhaqretYNWaD4AgOVmD2Vg5F4DC7FEU9dm+uELSIKTgY5j9eOava1/RWx+sYm3r99htLpw2Dw6bi0r/WoTDzoDs8djlhQ3y4E7VxcQ70FOx0nhEDfSkz3bl0sgjFlXILeuLnOZyYZc00HObL1RI2g6pAzuPOW8wpSM9qCzGCEkWhJCaJi3GCLuUcyotzrC5zMJ1p7QYw+lQVi8qgzmHpMuzd9NijF0RAdpqMKeaKeTwP6Fw7HJBZcGFrOcLKWlheTtkAyFIGTscZ98ibMKBPSeD1tUr8QwZaBoTqtJGYfo9pOj35HwWctWwmmaxYDQhzZ78mKo6POkZEsqPB3OaWk7+rGgp5QUuIeVdp+pe5bxRAkLpuVXTpPdS1C1sehEr10JerRk0LzPel1boBkWAZXzHFtZjw85YDifHMMderRfb2MxaKtmInzYMDNLIpJQh5FOCj3QMwzB5QzAU7weydtrAjtfto9SdhcPuZmH1B5Q3LqU5WMshfS9AxHgZGYaBLyUXX0ouYL5ntlccQajaO+mhsks/8qLyafY59rgZPRnDsJGbPZTc7KHYAmGaWrZGBnrp3mJa/XWkuDOjyuWlD2ZI4WSWb2nX5OWlDiEYamZLwzIa/VvxONKoal6F15nJ8NxJHQVNAz3lRRjeBQM9q4UbqrBXFl2rizHkZfUuZaAnLc4IKS5UQp6O7aDb3Jeg1/yCCfg60gOKNCQo/bANqWleaaDnVl44HmnWzmMe6LncHdsupzlNneFzSsp+u5JmNw304r9u6qCsO4iemYs9mAubBnqq6xXpWgSVayq5VAkGzGnhgPzFr3yBBCF98hFsffZVXMULcRYU4MjJxpGTjSs7L3rhkEbTC2gVzXzHF7TQxCBGU0x/nIYLIQR1VFPFZqoop5kGXHgopB85FJBBLg6je74eXZKWri3Y2C119iYEJBXrNpkyms7Zowd6Kr6UXMaPvIz1m79kxaaPWb7xA3yeXLJTB5CbNpDstAGRGbrS3PGsrJhJWATZVPs9baEOZ887lsXXtZX3yHFoNHsijuxMCq7/OcHqbQQ3VhKsrqHlhxUEyytx9SnBO3I4gaoqmuZ9Lw38bKQMGEjqfmNxpKf3aP81ezdi+w9sy5WwEq2ima/5BBs2xjOJVCMDgGqxhRUspJE6XLjJpYghjCGbAtMsaHeRlzKAYwdex2dr/0q+b1C317+nI0SSplut0es2EjPdOoyIrx7T5IiVZC3KF0fHR5uiITKZbsPm2YgdZhFf2iBGFAxkkL+Jed88QSDsp7x2CRu2zsVmc5KZUUpGal9SvQWUFI5nQ/ls0yAPOswvta2bCWdK01Emm5R6HBYzepaavQR+lUh5o6b25Vm7KAdhUleU2b6Qu2M7pPrGk/0uRblXIfa2pWnLnGbyxaHo8LBLM3p28wmXzbVuh3lGz2FXXah0bDsVvx0uu+TnTJnRs9KA7Iq0KD2dxaydvK26ZQlZfBnJbajn1CRBRZ1d7PjsKMjCmZPTsSNo0Pz9QlpWrsSek03+Ty/Ett0ZrGgJ0LpiBVvfeQvD6STv1DMiaZa3/k78WJePyuqrQJXZyuZhS/OsMmEuu1uJek4kc21UmuJaCtm064j9DjGE8lqWT2SUA8COzhlqe7K7pqC5zt7oimUJ8yhnbeQ+9pJKMf0pohS3YTYlBEWABczePsg7GgcONok1VLCBGirJIo8DOJws8uMeOAIYsjRGPd+duFdpDTZQ3vgDNa0baQs1kuPr3349LW5c03eNKiGS+6re3zEsFrZgz5rpk3WYHNIzet1Gr5rRkwmHA7S21gBgGHYcDi+GAU1NldTVbyAUanfCnOLJwW5zkOotojB3DFtrfmBj5deRerbULKIwe1SPHING01vw7jca9hsNgE12mOx04h0xAu+IETQtWkTFy/9AiDCGYSOlbDCZhx6xS8y+waYGGlcvxV+1BWdaFhnD9sfhTev2djR7Dn0pax/obaeZRlayiJUsAgEppDKM/QkRYiULaaOVAzmCWrayjG/x00YGOYziIArom9AArzNCov0HqL0TE68Qgvq2Cr7Z/DohESTTU8yI/GMpSB2yU232RgTJLazQizG6j1470NvxkA4cdCIiFKSubh01NSsRNGMYduzbvZKHwwFcTh91DevwuNMRijfUBWveJD9rxC6Zstdo9iW8I0di9/jw9C0Fw6Bx3jdsfvYp0g88iNRR+2EYDvxVlWz7ciahujpEOEz2kZMBsDvcIAShlma8fcqwKSEAAUQoSMPKJdQv/hbDMEgbPJrMUQfhr6li88fTMQzIGjGe1H5DtaZwLyTdyGYyZyCEIEiAEEEq2MgKFgDQQiPfMguADLIZxQQ2sJJy1pFPCYMZQ4rhs2oiJkERYCOr2NSyjjRbFg6cVLZsBMIUu4eQ7ywl29MfMNjauo6V9V9RH6jC58xmQsk5uFyp3XMSeiFhHQKtx+m1Az2HIwXDsGG3OehTcigAbf4GamvX4G+pJxBsJhhsIRBoJhhqw5OawbrNsxCdxLddtXkGg0uO2d2HoNHsVRiGQUr/ssh2+oEHkTp6Pxq+nUf5y89BGJyZWWROOAxPQTFtFeU0LVsCNqM9Zqtojy5Q88UMvH0HEPa3YfN4MQT4a6oINtaTOnA4RceejsPb/sVpC4Enp5D0waMJNNVTu/Brqr6Zia9PGdnDJuBKzeyZk6HZZRiGgRMXTlyUMoRShiCEYDNrWMp8BjEKJ26+ZRZhgoxgHEWUJj2D5xdtfMZ/2jcEtEhSoCxHERVta1jfuhijwYbNsBMSATKcBYwtnEqOpx/zt0zH5fSxf9Fp3XH4vZBkQ6DpgV53kWAItA4vACaJXiIaBznKmBrD0xQLU3XLoqalkJk3mNVrP8KVlU9W/hBsZJOdm61o/To+DwkHadi2nu++edpUd3rBYAIZHkWHp3TcpKGxPsZY5brE4jxazmLL2j4lxqGsyxOqrzzZLYuqS1I1TEbsNCzSrMrZJM2eXQlzJq+0dSurbq10eC4rjZ5FCDKVsIUfO6s0q/qt6gkqM8ohaUZK1e8FLCQ3IUnTE30LS26P1B88ysURkhLOUNPMD7/aitQZAyPFTfrEiaRPnGh6hkUIXEVFuIqK2tuQ0tLHHECwqRGby024pRlCkLH/QTjS2hd7GKGOVuSjsKenkzdhMrkHHU3ThpVs+fIdwsEAWSPGk95/OEIOQabGCJbjySZwD5vcskRp9JRzo7rKiBPTAEV9R8j3u0XINYJKUGJFT2aTtoVfccWiuGbZU6miHDcppJDKQuZQSD8GMjLpWTwAw+HEKewMDLfLFgpSyqgIrmdrYAP14Rq2BcvJc5UyOPUgqsUWypuWERJB6gOVtIWaWFX7FTWtG7C3Oc2aPqvvTPm7JupZt3C1EwNbD7vSaZ/R06tue5JeO6MHMOSAs1k2/xUWf/0sY4+4Gl96kWX+2po1LJz3jGlfRkZ/snP2Pd2ERrOn4szMxpmZHdk2ghaZO8EwbKT2G0J6yRCCLY1sW/oNa/71NL78/mQPn4A7PbvrSjS9hpAI8gPfspVy8ilhEV+RRwkjGb/TOjxo90dYZm/XcdvsHtLs2Qxy74/wONnSuooF9R9jb3JSG6ykNVQfKbek6sPI5wE5B+90P2QCwRbqmjaRmpKPx7Vnr3YXIrnZud6o0bPb7ZSXl5Ofn2/aX11dTX5+PqEeGnT36oGe0+1jxEEXM/+TP7Jp9ecM2f+smHnrtq2NGuS1/ybq2WgJGo1m1+FISSXvgKPIHXskLevXsGXu/wj7W8guO5DM0lEYNnvXlWj2WJpFAwuYQyN1OHFTySaKKGU4B3bLIM8Km2GnOGUI9cGtbGldTYankHGZU0l15RAItzFjw1OERZixRT+iILN7IlmEwyFWbPqYdRVfApCbMZgDBp/XLXXvKkSSM3q9cdWtiGHBa2trw+VydZq2O0jMdGtIZgyTKxCLQhZRmKKWh0vvXEOV0gk5Ta7URVbxSKo3LSDoZLv3cslEFRYIEea7r58EwJdeRNnIU1g4+2lA4A+2dESBkNtI1nTbw+PGKPORdC7CFqYlobhtiLqmRnxpUchpaigzaVt2kQLgtHCZ4lHcrcjmWo9dTevYdij1yL8yo8yqcZt1bbHTEjDd+hV3QkHJXKuadeVzFQhZDVQUlxqWbmHUPZLJ30p+EJUkmxmVxFCMfF1hcYhRUTtkOYIpzcDXpwxfnzKCrc3ULZ3Hyv/9FV9BKTnDDyHFldFRTn0W5DpVi7d0isOK02s1Mob5wUlghsPCdGtyqaK6+5DNtapZN8pFU3zXY08y464WS1nDEtyk4MGLn1aGsB99GYRhGIREkJUswoWbvgzCYUQv8OkU6XkzXOYyhkcK/bP9i3tYzlEM4yiQ0mqbNhIWIQ4ZcAnpKYXRZnzZAb6aFmOA2tS6lS8WPR7ZLs7Zj7KiIzpu9FjuwUI9+6UkktToiV6k0fvzn/8MtI8//va3v5Ga2rH4JhQK8dlnnzFs2LCe6l7vntHbQU7JaMpXfk71xu/J7bt/VHo41PHCa6ov3z7I25HWtju6qNFo9hAcHi95ow4nd+RhNJavYvNX/4FgmNwhE0gvHkxCgzBNj7BOLGM1iymilDqqaaaRMRxCvlECtK+SXchX9GMQW9hAM42kiUyaaaSBWqrYRAA/uRTRh4Hd6nWhpnk9iyr+i8+VQ5qnoOsCcbJk7X8inw8bfTVeV1a31a3ZOR5++GGg/Ufxk08+iV3Sx7pcLvr378+TTz7ZU93bOwZ66bkDyCoczsZln3Q60LM7XOx3xFV8/9ljpv2ZOYMYMia2uVej0ey9GIZBWvEg0ooHEaqtY+vyr9iycAaZRcPJGzgeh9vbdSWa3UpQBFjHctawlHz6UEc1YcIcxDGkG1mRPAuZQxkjyDBycIsUVrIIQZgUUkkni4GMIgUf5azjO2YxWIwhzcjcub6F/czf/CY1LRsAGFZ0XMx8dsOdsGnZH2yKfHY7e4/PyGQjY6iLNfdk1qxZA8CkSZN46623yMraswbhe8VADyCraDirv32L2W/dRF7fAyjb77R231zbcadkkpbVj6C/mXA4RG7RKMqGnoih/edpNPs8Lm86xfsfiwgfTf3apayZ8xp2l5f8wYeQkd63p7u3zxMWYVaxiHWsYIdeoIrNePByAEfgNdpNZeogDyDVyGB/Du203mL6kyMK+Z4vGCeOwmYkr9ls8ldHBnkA32+czujiUyjOHL39GEKsqvic1VVfMLb/WeRnJKbbO2TEL/EHm3A5fNhs9k49OoTCAVZunkFV7XL65o6jNL97F4EkQ7KxbkUv1OjNmDGjp7vQKQkN9IywpJ2TxTFRmi1J+5VkxxLSgQmDjML2h0aIEJXr51I89Ei86QWRgk5vOmOOugqbrFcQ5v4ZcjijqOblY1K0ZqZKLPq5izC5f7A6bzvRt6R/XHWDPMSh6PccioBT1ujJmjyAFHuHpihao5fcQZm0fcpJDcWp31PL+kPmRzEolQ0q7lVkPZ89SswaGzkEnF/5QlMXtlrVKkwx95RE+RBDlg+tRcEussqPsPo7TU5TJXIWofoi+lW7g8z+o8jsP4rW+ioql81m07b3GXToeThc3qhK5WdfqCEGo0JUSdph5Z62yRo59bzJeZVySKv4dmbxgcm1lCv+h3Z3aPaaRSNf8r5pX3v4swH0ZWAkMkVA+FnIVwxkJBlG/CurPc40vMF0wnYbdsOJLcUTSTNp8gDc0rbbLK5P9/TliJQraAnU09xWw+LN/yU9rRicdkJGmO/XTKeqbhkALaFGxA53O8p1i+WuzLA78Nhir7Ctb97MorX/ptm/jXA4wIbqefTLm9DV4e9yBEnO6PVSCcXGjRv597//zfr16/EroQUfeuihHunTXjOj50nNwZtRRHNdOYPG/WT7IE+j0WiSw5OeR7/xp7Jt5bdUr/uOgsETe7pLez1hEWI9K2lgG1vZQkj5CZJJLmWMJItc08B2x0zeIEZHTLiJYMMW9QM+GVJcmaS4MiMzg5u3LWJw4SR+2PQ+VXXL8LgyaPXX4XZ2b6SM+uZyvl72HD5PDhOGXkJbWwPzV79MdcNqMn19urWtRGl3r7JvhED7+OOPOfXUUxkwYADLli1j1KhRrF27FiEEBxxwQI/1a68Z6AFkFg6lua6cNd/9i/zScT3dHY1GsxeQ1WckP3zyNJnFI0hxZSJEmNamGjzeLPTCje4hKAJ8xUe00BSVloKPgYyigD4xZy13uFVJZpAHYGAzOQnfWTK9JZRk78+ayi/ISe1PQ/MWAFr9dQB8v/qfDCw6kmDYT4avhAxfH1LcmVH1CBEmFA4gRBiH3dPpvHcoHGDhmun4PDkcNPRS7DYHtQ0bAfC5c7rtmJIl+RBovW+gd/PNN3PDDTdw5513kpaWxptvvkl+fj7nnXceU6ZM6bF+7VUDvbrKlQAYtr3qsDQaTQ9iszspO/gnrJ7zKg5HCiIcJOhvoaDsYApLDuzp7vVqwiLMcr5nI6si+/oyiCJK8eDFZbgtSnewjSoGMjLpfhiG0S0zejIj+5xMVf0KttQuYf+BZ9PUWsn3a94kEGwGYFX5pxi0t2sYNrJSS3E5vARCbbQFGmj11xEMtZrqPHz0tVEDwh/Wv0dL2zYOHnY5dpuDsAizpvILCjJHkOLOJNjDniXa3avsGzN6S5cu5ZVXXgHA4XDQ0tJCamoqd955J6eddhq//OUve6RfiWn0Qh0uhmTfUtFBkOQwLV34Z4vZmEU4MFvnD2RqTl+atm0EBEu+aHeO7EsroN+IKdjsju1lzT72TPVK/owMxfeQSYan+u6ycB2WLAnd4yY/W7HrsdQsJaKJ3AWol9uQTqRNOalRmj1Je+e2qxq9Do2EU9U3xYlN+aUvB9tWV4ZZhkRTdCoBabtN+XESlHR4/rA5zSU6jrfViP8RNhK4OeWzGHXWTDJX5Zd6nD4VVR2SOTydlfNNRRenNh+OnWal0ZPfZ2HlgI2wgTszj8FHXwqtbTg9qdSWL6dp20bCDqsDVnzXSZ2zKX7swtJBqevDTNq+qBBosVs35Nd7V/o9k59Q9QUnOv+sVhGnXi8kQlSxiWoqKGddZH8OhezPoQlrDdeLFbhw4yYldiblpNok7Z3hdmNrdoHbg83uw5C1d4oOD8nprXArfipdyrbdTl7mUDZunY8rJYPs9AEU542lsmYpLW01AIwZ/BN83nwqaxazcsPHkbJ98sfjcWfQ0LyFiupF7X22OQkRjjw7BrChci6bqr9lZP9TSU3JA6CuaSOt/lr8gUZWbP6YkpyeMxm2Y5jemfGSTJmexufz0dbWPrAuLi5m1apVjBzZ/gNk69atPdavvWrqy5fVrkUI+pup3fIDALVbfmDTik8ZuP+PKSw7pCe7p9FoejF2hwubp92BbmpOX8qXziA88EhEKEAw0EKKK7NnO9gLCAg/n/MuYcl7dj8GM5gxSS0mCQg/NVSwXxIDRBkb9m413e5gcN/jqGlYw+pNM1m9aSYADrsHjysTAxvfr3gVm+EgLMw/UIf2P5Fl6/5LRfUivJ4c+hcfTlHuftikXyjLN37I2i1f0DdvPMU5YwmF/NQ0rOHbVa8CEBZB1lR+wZrKL7r9uBIhLJJzldIbZ/QOPvhgvvjiC0aMGMFJJ53EDTfcwMKFC3nrrbc4+OCeWwG9Vw308gdOYPXc1wEoHX0SntRcKtd8zbYtS7G7LH7taTQaTQI4XCkUDDmUpbP+hsOVgs3uxCZsDBp75i4PvdVbaRMtfM67ke0+DKQfg/AayfuE20YVuRTv9Dk3DJtp8Nld2GwODhx6MTaHg2CwhY2V81i/ZTbBUCvD+59MqreA+qbNlG9dQCDYEpnpW7HhQzZWzAVg4n5XdbgB225pam6tYe2WLxhUcjT98g9m3vJ/UNOwxtT2UaNuYFvjeloD9Szb9L9uP7Z42ZdMtw899BCNjY0A3H777TQ2NvLaa68xaNCgiFPlniAx062QzCbyM6FeDzlkkOqKxMK2E+91japDMnu4vJnklY6jePgkoD1qRkdfOv7dUZOpGtmUq4YEktPUl4pkzthl96aVKdsUWckinM7u+ALqJtO1zcp0m4B7Fbe07VbSrFyTyKZEq3yq2wBZQByVptwcAcle2KaYZ+U01XTbJrliUc+F1XmzxRsCSiFp1ytRFUkmSOVGMYVnU021qplVTrcwz0alyU0o7tLk0GZqSKqw/OxLlWSWjia7T3uwe39LAytnvUDYYcQcdNikDoWVa2OWKigFrVxZWVm3ZJOvmmYV1k6tJu6cShOS65e2cFNkkNefoQxkVLcMiNtowRYjRp5NMrMabrPWTw1lZg95CLoNDLcXnNJz4jY/M8LZ0ZZqqg0r29gMVq/7mLUbP6OkYDzpqcX0KZrApsp5hMJ+1pTPIsWTjduZytCyk0j1FVJVvZTVG2ayccvXAGSmlRJGIMIBNlZ8TTDQwsA+R9MaqAWgIGsEm6u/ixrkAdQ1bybT14fFG/4TlbY7EUTLVuIq1wsHemVlZZHPXq+XJ554ogd708FeNaMH20W1ovt/mWk0Gk0sGreuBaC2YhmZBUP1rB7trlKWi+/ZSjmt21fTZpPPIGN0FyXjo1HUU8Vm9ovhDDkRAuG2iD++7iIUClBZvQSATRVz2VQBkybcxqSDb6OppYo1G2YSFiHqGzcxd+HTtA+pOwbgA/ocRX72CFZvnMnmyvn4Aw0AeD3ZOJ0+AGobNuB1d/gMtNmchMMBygqOoC3QyJwNfyMc7tnvQ4FBKImfC8mU6a1kZ8fv9xHaxznz58+ntLQ0rvx73UDP5c2irbm2p7uh0Wj2IbL7jiYjq4wtq2ezefmn5PbZj/ySA7Dbk5tF7a3UhCsAsOPge/E5bbRE0rIpYCyH7XQbQghWs4QmGhjOgdh3IprFDvI8pSyr+wKfI4scX38KfUOS7FuYuoYNrC+fTWXN4sh+l9PHfsPOx25vn2X0peQxasiZkTKbK+cTDodwu9Jobq1h5br/sWbjTNZsnInD7iE/ZyT9iw/nh9X/ZvHqf0XqXbzu36aBXjjcviBmdcVnAORnDGNw0dF88cOeMbOk6Zza2loeeeQRMjIyuswrhOCKK64gFIp/AL/XDfQCbY2kpOb1dDc0Gs0+htPto+/wyYSHHMXWjd+xdM6zpGX1o2jgYbjc3esgd08kJILMC30SM30MB3fLTGctW2mlmTFG94nbcz2l5Lj7sbFpMZsbl3Q50Ktt3kRzYz1F2e2zk6FwgPWbv2bd5ln4Aw047GZNeGnxYbS01VDfuBGbzUGKJ5tQyE9YhHA70yjIHY3D3mFOzskcSDgcoqW1hrys4ZEfDAcMu5CG5nLC4SDb6tayctPHBEIdg+mCzJE4HW7SU0rITi3F687uefcqyTpM3odm9ADOPvts8vPz48p71VVXJVR3giHQRIeOTRKSRKk9LCQl8btXsSgX1aAUdirYhsPj7dDqWEVaSiBcmKn5qDSr8E0JiNbi1eFFlYudL95yO/VMJanLkw9X1ZPJmiUrHRqAU3avoujwvJJ7FbfN7P7BaXSUs6Pq2cJSmoVGD1WHJ7leUU5qtHuVjpmI1rAzZpqq33NJrlhaQrtmxkh+MUdpZaTtkHJ/G9K5ilevB5h0veozo35JyN6VovR7Nos0WTusehCR08Kx06LvhE5eTE4HuYPGkdfvQOqrVrHiuzfIKRlFQd8OJ+421YWKdNsKJVEO2xjlXSXJgZNlKQv3KqpLKhmn3c5I/0QWt31p2m/DTrFtAE67t6NKK1csyoVrNZoxMNgmKmmika2UM8g2BsPmxJAFjQ7zc2IoLlTMnZXybtfyGUCmvZSaugqErz2/UOoUTjuV25by/faVrU5vJpXVi6msXkQg0EJh4ViKC8fR2FzBsuX/ipRbsc56MURO1hD2H3VhZDs1vRiAjJA5qoVhs5GeWoIQgrlL2l2ItfvlM9h/4E/Iz9wePzeU5Et5F9C+GCMZjV7vc6+SLGHVp1MXNDQ0JJR/r5rRCwVa8TfXkZIe36hYo9FodhWGYZCRP4j0vDKWfvF300Bvb6M5VM8XDW+ZVq6mG9mMcR6O15ZmWpiRCI2iluXie1LwkWpk4MLNcNuBZNhyu6vrJuyGg9ZAPT+Uf0h9SwVuVzoFmcNJcWVS3bCakBFkTfnnkfzzF/8dtyudwtz9KO4zAa+3vV8Ohwe3O4O2trqYbWWkl+JypRIOBRhSdmJc/RMiTH3jJrbULI6EU9uegsfVtdmvJxAYSc3O9eYZPb/fz5o1axg4cCAOR3zDrE2bNlFSUmKZ56WXXuK8885LuD971UCvua5dH+LNKOrhnmg0Gk07hmHDZncSCvqxO1xdF+glCBGmIVTD8ua5bA1tjOzv7xxFuj0bV9hNK820hpshHDbPmYugRSSKji/49WI5I+0H4TG2zwaq07TdjNeVxag+J1PXXM666rnQDFtqF5ny5GYMprTwMFzeNEIhP2m+onb3LE4bjY1bWLfhUyoqF+J0ptCnZCLZ2YNpbalh+cr21a9l/SfTv++RHS5TurD6hEIB6ho3UFO7ivKq72jz1+NyppLuK6G0cCL5me2mXZfNs0vOyc4ikvSjtwdNSsZNc3MzV111Fc8//zwAy5cvp6ysjKuvvpri4mJuuummmGWPPfZYvvjiC7KyOg/j9/LLL/PTn/50zxzoWV1fq2gMUZ7rLU237TTXbgbDwJNd2OEiQY0GELSoyCLChLytetsQVm5ZknUnExXFQjKlWZ43xVwoWfbCqtsI6eqr7iZCrvjzmratjsnC4X4obC4YDMnuRcwNupRV1QEpPajkldNkUy1A2ORCxZwmm2vVcvG6W+kqvqNsnlXbkE25qunYbhHhQj7+oN3cflDuW9icFlLMhUF7R3/CyrWR3Y+oZs6wfDOoEWwsIlOY5B5RYVKIuR1Vj1UbFpFgTFEzlPtbnlmwKTex6fCjzKgdeX25famv30BG/qDtec05TTIG9RmWo2ZEnRsp2gYWdGXitYq+Ye4c/lALaxrmUx+oJN2Rg9/YHgnAM4RUexYGBm34abN1yCaEvb2fkZgOEVOVeaVp+66OI+lnH4XXVSC1r7jEkgd+DuXCyW5SXIrEQdqWXaYApDjzcXozYCM4HSkMKT0BtyOVrPRSQjaBw9E+oNoRFUUAdfUbWLv8U7ZuXYrbk8ngoadQWDI+oq0zhKCgz4GsWvke+cVjEU5H5KijI5GA399EXf16tm5dQmXVIkKhNhyOFApyR1GQN4aM9L7YlRvV5AFMMgWKYM+aQPcl0+3NN9/M999/z8yZM02xbSdPnsy0adMsB3r5+flMmTKFTz75BJ/PZ0p79dVXufjii3nggQeS6tdeNaPXUleJOzUH2z620k2j0ezZpOUNoGHLqo6BXi9ECMGGxoVUtKxiYPp4hmRMxAiFYIe/Y1VnJI08oky38rY60LHv/CraZAmG/FQ3rSe0ffVqINjK4lVvkeYt4uDRv8BQBpOhUIBPZ90e2R424gwKCvfHZrNHhflzONwMHTYVIQSI9vNZX7+BVG8egUALVVWLaW2rZdu21TQ1bQHA48mib59Dyc8dic+Xj03+hdJLprySXozRC/3o/etf/+K1117j4IPNC49GjBjBqlWrLErCO++8w1FHHcVpp53Ge++9h3P7D5V//vOfXHjhhdx7771cd911SfVrrxroGTZb9BSDRqPR9DCp2X0oX/JpT3cjadpCTSyp/oQsZxHjck/rlX4CK5pW4HNm43NmgxD4Q804bW5CoSCrK78gFA6wsWY+4e0WA7czjb4FE1i58aMoNzlt/gY+/8I8uzJ0+I8oKj4wZvtVlYupKP+W6upl5OePprT/JObP+0sk3TDseDyZZGSUUtr3cDIy++NxZZjPdSKL+/YQ9iWNXlVVVacrZ5uamrp8ZlJTU3nvvfc44ogjOPvss3njjTd44403OP/887nrrru48cYbk+7XXjXQC7Y1Y3d5u86o0Wg0uxGb3YkIBxEibDY39gK2tW5iZe1shmUfRZqtc/3QnkxIBFlS+TGbGtq1dgMyx7Om9hvahyC2SIxbp91LTtpABvU5BrczFYfNheFykZXenzRvh/m4qbmK2d8+GtkeMewssrMH4fTGDuVWV7uORQtejGxv2fItVVXtzpR93nwGDDiGrKxBOJ0pZkt2LxzYdUYys3O9MTLG+PHjeffddyPuT3YM7v76179yyCGHdFk+Ly+PDz74gMMOO4zJkycza9Yspk2bxm9/+9ud6ldCAz1hMyIaMCtNiynUUFRILjktdjkr9ypRHlO2p7XUV2BzupFlWuooWnbbYSjaI1kmZajuPqTjiJZoxamfi0pTtE9W51TW6Fm4lAhH6eekcqr2yEJ3p3j0ICRpyMPu2HmFQ9XbEBMR6kiUNXkALYGOSg3DLDJWXxryttULJaAcpJXrFRlVP2eFnNfZRexMk/bO4rtf/WUr6+ucyqDBIbmacSimNNlNTdT9rWzbJX2dzaY+Cxb+kyz1ZDE+K0S5L1L6JpvEoqqJ031S1PMlfVY9iMhnWL0WpkO06CeAJ7OAppYqvJmFqMF7bLJ2OOraxGhP7Z163kyuZmxKmrLtkDrkaE+raVzLuq1zcdo87N//JzhsLggqHZfvMXVQEpbftWo5iwGMVRhHFfkgFU3qDhcqdc3rI4M8gDW1c6Wq7QgRZkTpyfTJa18VHZY0e2GHQXpO//bPQH3zFuZ++3gk/eCJN5LizWlPt8fud/mW+QCUDpxMbsFIDMNGdeVSDJuN4r4HRxwpq28MCzlutLRRPv/SZQkGe9bKJURyGr3eaLq97777mDJlCkuWLCEYDPLoo4+yePFiZs+ezaefWs/oL1iwIPL5D3/4AxdeeCE/+tGPOOWUU0xpY8aMSbhfveunpQVCCJprNpHZb2RPd0Wj0WiiSMvrHwmVticjhGBt1Vesr57HyD4nMar4pPZBXi9lzdY5eJwZFGeNwa7Eex5dehqT9r8pMsjrCoezwxHy+IOviQzyYhEOh1i78kPKN36Nx5tDYZ/xpKYX40srpN/ASfQdcGRkkLe30r4YI4m/BE239913H+PHjyctLY38/HymTp3KsmXLTHkqKiq4+OKLKS4uxuv1MmXKFFasWGHKc9RRR2EYhunv7LPPjqsPEydO5Msvv6S5uZmBAwfywQcfUFBQwOzZsznwwNhmfYD999+fsWPHsv/++3POOefg9/v55z//Gdm3Iz0Z9hrTbaitGREK4vJl9nRXNBqNJorUvP5s+O6/5A/qvogO3U0w1Maidf8iN62M/fr9uH1WtodnhBJBiDAVTStw2300NzXQHNhGVdMqhhUdR376UEQ4RL/ccaT6ihFC4LC7IjOY8ZDiyaJPyUS2Vi8lNbXQMm8o2MYPC1+nunIJJaWHMnDoSRi23bvQRAhBW1tiznW7vQ8kp7cTCZb59NNPufLKKxk/fjzBYJBbbrmF4447jiVLluDz+RBCMHXqVJxOJ2+//Tbp6ek89NBDTJ48OZJnB5dffjl33nlnZDslJaWzJk0EAgF+9rOfcdttt0XcqyTCmjVrEi4TLwmabjvMhCZP8spUu9l0G11HBCvzrIXJNwpDbPcODg6vz9rdh6ULFWmVmDJtLJtrLbxrRB+v1XlS8spm1yhTqoUJVliVszIHW5VT7gzhiJ0WdkrnzcKMr9rkhNS5UMBcsM2If+V0/KZbJYqFNKEdUtLs9o5jUt2kWJlk7SZ3F9ZuUZxCCoeg3lNSk1bBvYPKCXdK7ctmXACbFLTdYbOONiKbS63SDNWsKz9DiZhcdwFRt4J8GS1c/ahvRVnhER2ZQs6nvDOUc2NPT8ff1kDI2UlEC9N5M6fJVlb1tRg2vTOViBry+Vavt/ouCtsIixALl71N/z6HkZnWD0H7aTKk1Z1CGfSZzIVWejKLaBtRWJpqO0/b1rSBhtYqlm78r5LdRknuWIqLD8JuczIq/az25h02DLY/duq5ML1rFZmIw4Yvo4iNm2bT1FqNOyUT2/ZINXJef1sjC7/+Ky3N1Qw/4DxyikdFzmcnB2W5GS+dvfo2r/6S1Qv/lVyFewj19fWmbbfbjVuNdAK8//77pu1nn32W/Px85s2bxxFHHMGKFSuYM2cOixYtYuTIdsvfE088QX5+Pq+88gqXXXZZpKzX66Ww0Hogr+J0Opk+fTq33XZbQuV2UFpamlS5eNhrZvTCgXZfTjZn9A2g0Wg0ewJOj49ASwNuZ2zhfk+xct2HFOSOITOtX093JW7C4RALN7zNlrolpv02w8G4IReS4euDYRhRg+CdITN7ICD4etYfcbnSGHvwFXhSshAiTOWmb6mv3UBV+feAYP+JV5CaUZJslMidJr/P/mzdtID6mtU91IMO022ihIVBIBAgI8Mc8WPatGncfvvtXZavq6sDIDs7G4C2tvYxgsfTofm22+24XC5mzZplGui99NJLvPjiixQUFHDCCScwbdo00tK6fmZ/9KMf8a9//Yvrr7++y7yxkPV4MoZh4PF46NevX6cDXSv2moFe3cYfAANnSnpPd0Wj0Wg6JTV/AI2Va3GXjO72uoUQNNZvorZmNaGQH0I7DGYGCNHxmY4JxB1xuv3+RmyGncLc0dH+8PZgNtTMY0vdEvpkjyU1pYBQ2E+/vIOx2xygLo7oJlJSshk+6idUViygvnY9yxdPp++AI9mwdibbtq7A480hJ384A0achMudukv6EC8Ol5cRB1/MnP/+vsf6sDN+9JxOJ1u3bjXtj2eQI4Tg+uuv57DDDmPUqFEADBs2jNLSUm6++WaeeuopfD4fDz30EFu2bKG8vDxS9rzzzmPAgAEUFhayaNGiiBPkDz/8sMt2Bw0axF133cWXX37JgQceGOX4+Oqrr+6yjv3339/SFYvT6eQnP/kJTz31lGnQasVeMdAT4RCb572HL68fLl9GZ5HHNRqNZo8gHGzr1vpCoQAbV39KXc0aUtOKyc4dgsPhwSatajdCoe2zSu3OetvNsZIJXhik+XpX6MjG1q2srZqNzbBTln8YHs/uc/1SUDSW/JKxVJZ/z9IFr7CtejkA6Vn92f+QX7ZnioqStG8i2Dn3KunpiU/e/OpXv2LBggXMmjUrss/pdPLmm29y6aWXkp2djd1uZ/LkyZxwwgmmspdffnnk86hRoxg8eDDjxo1j/vz5HHDAAZbt/u1vfyMzM5N58+Yxb948U5phGHEN9KZPn85vf/tbfv3rX3PQQQchhGDu3Ln86U9/Ytq0aQSDQW666SZuvfVW/vjHP8ZzOhLU6Nk7NFiW7j6S1ehZhfmymHnfOPsdAMLhIGG7obgjiN9VgexCJWrpuuwqQJWbmNw2xNbhWbk+ad+W8qo6oThdoQi1nJzX4lpEu3NR+2qhwzPpEKPW/Hf2cXvnpI9B87kISLemWkzVT4o4NXpWgmCbw/zrwCOJpqPqlDbVcGgm9ypG0JSmhjILSa4hwsrFkV2oeJR6wlI5h+L6xdK9iqTTUnV3dlXDJQ8C1GdI/qxeVEv3JqY4Z8TEQrurFrVKs3KvEvUsxGtbs9DIqe7xjJByn4b8VK/9jsEn/DzKu4j87lOvjXxQ6vHajPYQadWVS9jvkF+aYukaQekaKlEUorbDokNDpjz8pnef+iM63hOXiO0yjjFBY3Mlc1c9i8eVwSFjfobLkxX1+96ktbPQakfpwU1h9BSNnt308JPXbyxpuf0J+BsJiSBpWf06XJCZHxRz3yzuU8tyViH+Ymjcg4HduwBERYhkTbfJtXfVVVfx73//m88++4w+ffqY0g488EC+++476urq8Pv95OXlMWHCBMaNi73q+oADDsDpdLJixYouB3rdsaDinnvu4dFHH+X444+P7BszZgx9+vThtttu4+uvv8bn83HDDTfsmoHenogQgrq1C/EVDmDgCT/r6e5oNBpNp1Qu+oy8EYe2r7zsxvBVdoeb3D77U7dtLdl5Q7qt3j0VIcIsW/MuLqeP8WN+Fok921N4vFl4vFnRAy8NkPyq20TLCCG46qqrmD59OjNnzmTAgAEx8+7Q/a1YsYJvvvmGu+66K2bexYsXEwgEKCraPTPeCxcu7HRhRmlpKQsXLgTazbuyubkrev1Ar6VqI/7GbfQ78ifY9UIMjUazBxLyt9K4ZRUF+x2zS+p3ODz4W+u7zrgXsHTVv9lWt5r9hp3f44M8TTwkN6OXaGSMK6+8kpdffpm3336btLQ0tmxpjxeckZERcY/y+uuvk5eXR79+/Vi4cCHXXHMNU6dO5bjjjgNg1apVvPTSS5x44onk5uayZMkSbrjhBsaOHcuhhx7aZR8uueQSy/S///3vXdYxbNgw7r//fp5++mlcrvYZ+kAgwP3338+wYcMA2LRpEwUFBVbVmOieyBgJmW5jm3wto2bE+LVUuegznL4MfH0GduSxmPo2m4+UNkTn2dR+W5luo02usd2iJObCpPPPal7VdGs2z4qYaahplu5slDZM59sirIB63kymLdV0Kbu6MR+U1QsgkReKbJ5028zmUTmKhupexVyHarrtqMelmFVVU67s0iWs2P1MZl3lHnYadumz2kbHts2IbY5VzYM25eJYRsYwRb8gZppVlBhLs6qKxf2mtm9lEovXPbyVOdjCAhhl1pQvacPmNaSVDke4jHYTqWIStFmZtS3MwzvyZpXux7IvniU1vz8pqXntdcqRMYJKMXWhgvTySyQagyVxPoqJfJ8H/M2Ub/2e/oOOI7N0FLHj2ah9ib8RU39UeU2c323tbcZOs/z+tDAdxyu3kcuF/D2rFUx6MUaCM3p/+Ut73OCjjjrKtP/ZZ5/l4osvBqC8vJzrr7+eiooKioqKuPDCC03uUFwuFx9//DGPPvoojY2N9O3bl5NOOolp06Zht3dtAt+2bZtpOxAIsGjRImprazn66KPjOo7/+7//49RTT6VPnz6MGTMGwzBYsGABoVCId95pl6mtXr2aK664Iq76YC+Y0atf/wNZg/bf7Y4oNRqNJl4aN64ke7C1Z/xkCbQ1sXXDtxiGDdteHmVhW81KRDhIdu7eb6LeW9gZ9yoJtROHXvTqq6+2XBDRt2/fLkOVWTF9+vSofeFwmCuuuIKysrK46pg4cSJr167lxRdfZPny5QghOOOMMzj33HMjLl4uuOCChPrV6wd6WYPHUr30KwoOPM4yqLRGo9H0BKG2FlqqNuKZcFK31RkOBti2eTE169p9buX03Y8Rh14Wcd67t5KZ1a67mj/ncYaNPouCYmtxvKbn2Rn3KnsDNpuN6667jqOOOorf/OY3cZVJTU3lF7/4Rff1odtq6iHy9j8KEQzQXLGup7ui0Wg0Jho2LGftf5+l6JATMaLsrokhwmHqtixn1VevsfzLfxD0t1B2wOkMPeRCcvvst9cP8oQIs2rZuwDYbA58XYQg0+wZCAyESPyvx7xM7wJWrVpFMBjsOuN2XnjhBQ477DCKi4tZt659bPPwww/z9ttvJ9V+Ym8Go0MHELfGwGJ5uKW7Dwvdn0x4ezgqw+Pu0CxYLl2XtHbqe9ek0VMFZXKakiS7UHGYG4zb9QmKDs9lkaZq9FySLsoRW2unavRM578LjV636HbUayg1ItSoYpLfClWroT4ubZad60DVpcnuRtwhc60p9g71T0ARw8SrHYnS7ymh02TXLAFDDWXWsW1TwsHJ2jvVvYt8jGrINZPrFaVctBui2Ho+h72jrPqrOyxdN0N1vGuK5RVbLxr1XIbVmzHGZ3VbfYcQH1G3qVVm+RClgv76Gmq++5JAYy39p16GzeGKvsflNmQXQQiEEARbG2mtq8K/rZLmbZtprasivWAQxfsdhzu13du/LSQiXTAs9FxqyLNotyi7YPbEwi2MZdMxTviK+W9SWf4dfYccQ7/hx2MYhkUwQixdv1i9MuJ1ixKtMY99/qP02FbuyUzuqsxpplCYFt8nch2hvXv8v0ehRsQQQlBeXs67777LRRddFFcdf/nLX/j973/Ptddey913300o1H6XZ2Vl8cgjj3Daaacl3K9efws4vOlgs9G2rZK0PoN7ujsajWYfJ9DUwMb/vULhISfiLepv6eV+B611VdSu+Z7W2kpCrU0AODypeNLzSEnPp2DY4bjTc7Gpg959iHCo/cfXhuUfA1A6YgrQ/mUazznW9By7w73KnsC3335r2rbZbOTl5fGnP/2pyxW5O3jsscf461//ytSpU7n//vsj+8eNG8eNN96YVL96/UDP7nKTWjKIqm9nkt5/BK60rJ7ukkaj2Ydp2rCcrJET8BXH9uMVyVuxjqoFn2EzHOQMHU/usENwOr2mPDbLaat9h6Hjz2PgmB+xccUMNiz/mJziUdidHuZ9+AAABxzza7xp+T3cS41KspExeqNGb8aMGTtdx5o1axg7dmzUfrfbTVNTU1J1JjTQC9vBiETG6NhvvazcnGQdGSPOcgp9jj6TFa89wuYv/0PpCRdaul+QzULqzL48nW/pw0ed9reYoo83ogV0YZ41mXWFkiaZbp1K50wRLRTznLytutBAQVikyuarqKgVsepQ6zeXky2LUQE1FNte0IjvNrYZZj+LLnvHN6jHbj7hLaGO7TYlzS9poaLckkgHrLpX2R2/UGVzrdo3m5U5VomMIUfKUNNC0vNuV82FsmlJmX0SYYt7MSyXw4y6oF46x+qXgS2GKRXij5qRbLSNHd2yuVJoqdwY072MEGEa1/1A9eIvcWfmUXzIybi9HT9QVROv1WrCsIUURVYDRJuNY9+Llo5/rV6LlubZBO595XhNnmfcPvKGHMymFTNZ8PkTjDt1Gg63j2BbE/M//gMTTn8A23YPDIblOzw+dzJR7k1MUhg1b+yyUe9+k5nVQu5jFQVJNevGKBfqYSV+RHOXRLnextFHH81bb71FZmamaX99fT1Tp07lk08+6bKOAQMG8N1330U5TX7vvfcYMWJEUv3q9TN6AK6MHJxpmbTVVPR0VzQazT5O6oBh1P4wj8qvPyJnv8Owu9ud+oZDQWqXf0vtD9+QVjKYfseci8OzffZOz9p1StO2Taye/RqZRUPJ6bcfvqw+ONw+CgcfxpYVs1i/6H0Kyg5m09J2c25zXTmpWX26qFWzO9nZWLe9iZkzZ+L3+6P2t7a28vnnn8dVx69//WuuvPJKWltbEULw9ddf88orr3Dffffxt7/9Lal+7RUDPYDUPoPY+u2ntGzdTEpOcU93R6PR7KMYho0+J5xP46rFbHjvBWxOF9hshJoayRi8P/1PuQyHssBG0znVa7+luXYzzbWbCfqbSc3pz+qvX4ukb1kxy5Q/FGjZ3V3UdEWyM3q9SKO3YMGCyOclS5ZEonIAhEIh3n//fUpKSuKq66c//SnBYJDf/OY3NDc3c+6551JSUsKjjz7K2WefnVT/9pqBXtHEk9m29BvqVnynB3oajaZHMQyD9IGjSB84inCgDSHAbnN2uFjRM3hxsW3T0sjnylVfUbnqK1zeLPzN7REIUnNKSc3uiyc1F6c7jfS8gT3VVU0M9gWN3v77749hGBiG0WkEjJSUFB577LG467v88su5/PLL2bp1K+FwmPz8ndOeJhYCzd5h+zfpD9TrYXKvotRhocOzLGd1ze2A3U5KYV9atm4y6xpU3YSq/5GI14WItXsVJc1ukWal0VPTJF1eWNHhCbd0UBYaPZuitbJJrlhsdmt3G7LbjLAi+hAmfZWqtZO2ozRbUj713Evh0aLS1HBhpq3Yt7R6TC5Hhw8bj93sXqVFijDQqggm26Rtu3JOnaKjnlCU2Ctm13YJiYRAU3V4TmkFQMhmPt8OWYcXpcmU7pOo692JoC2SJm2rz2iUJlbWpakug+Q2lGri1CpF329SHRYavVgyNJurXRsqSzaj+mKhZZVPlWV4tqg6pfPUxZteWLx7LY8/Rr6o+mVXKyJMW10VNqcbEQoSbG2itbYCDIPW2gqCLY0YNjttdVW0NlTi9GUiggHyRx6B25fFms9fjtQ16MgLad62meUzniG3bBxZg/bv6I78zg6rJ1V2A2TRb1WHZ9KfW7gOQ333x9bhWYbCVNtPolxPa/QQlp5uYhfrRX701qxZgxCCsrIyvv76a/Ly8iJpLpeL/Pz8uEKoqeTm5nZL//aaGT0RDtGyZQPZow/u6a5oNBrNPs3m+e9T/u2H7H/BPdhd7RpFf1Mt21Z9x7Y139Fctb7Tcq7ULByeNEQogDsjj74HnUb+sEMxtv/YaN2uw3Z6M8gbfBBhEWL5jGcA2Lr6G8omnrMbjk6TCAIjqYVovWicF1k4EVZ9h8bJ2LFj43YRNH/+/ITr32sGem21Wwm1NpHaT/vS02g0mp4kHGgXpNesmo/Ll0nlD7Op37gUw2YntbCMfoefhd2VQqC5jo2z/xUpJ4QgHGxjyJRf4PSmA+aZuZTMAsZf9KfIthGGUSfdQPO2zXjStWuVPRFBcgsreuNijB0sWbKE9evXRy3MOPXUUzvNP3Xq1Mjn1tZWnnjiCUaMGMEhhxwCwJw5c1i8eDFXXHFFUv1J0L2KgbHDbBPvVL9VhAvLiBoWdXbiSV3s8B9gt5nrUV2oyG3GDn4Rhcn1itq8bLpNcqodlKl+xQRrcqGiuFfB1fErwnCq5lkpioJinpW3nQ5r0VBQmv8Phc0XTjblqmbdcFAy2agX3MKsazLfdekkVuqbkiKkk6zW0iodc6vTfDFM7lWUCydHyoiOmiH3ZdfbTKJcqEgnTjXPyttqZAy1HtmU67Sbz6r8AlZdQ5hMt1Zm3WifOdJnc5KlGyTVPGzxfBsWrles3ksm2YbFeymqTrV9q3eYHBlEKSfPhqhmxvjN0WrkG4vzlojcJsaj6cjOAWD9l28C7S5nnGnZOFJSaapcR8Om5Z2WCzTVEgACjiA2T3vlpmNWjt8Ig6uoBFdRCUZYmJ5/uZwRJSmRPlu6V1G243Udhtlcm5BMJ860KNcrcpokywmr8obdjBDGXq/R28Hq1av50Y9+xMKFCzEMI+Iaacds3Y4oFyrTpk2LfL7sssu4+uqrueuuu6LybNiwIal+9bT1vttY/5/nAUgp7NvDPdFoNJp9FxEKsemzt0z7vAV98Rb0xZWWSc7oifiKyzDs5lFM1rDxDPzRlez3yz/hTs/ZnV3W7GKESO6vt3HNNdcwYMAAKioq8Hq9LF68mM8++4xx48Yxc+bMuOp4/fXXufDCC6P2n3/++bz55ptJ9WuvMN02l6/Dv60Km8uNzenuXcZ9jUaj2Uto3VbFyjf/bNqXM/IQsNtprS4n1NpE3coFgMCTW4K3sB8ZA0aR1ndoRIdntThCo9mTmT17Np988gl5eXnYbDZsNhuHHXYY9913H1dffXVUiLTOSElJYdasWQwebJahzZo1C4/Hk1S/9oqBXs2C2QAMvvi3PdwTjUaj2XdZ/+GLkVi9O6heMgd3Rh6enEJcGTnkjj2KtH7DcPqiNXiavY+kNXq9yI/eDkKhEKmpqUD7itnNmzczdOhQSktLWbZsWVx1XHvttfzyl79k3rx5HHxw++LSOXPm8Pe//53f//73SfUrafcqcevwrPQeCWj7rDR6/m1VZAwfiyMjM3oyz0KHl8gLRsTcUI7JKgSammalsVB0eKbQZm6znd/m6th2uMxpVjo8edvjMLsXUfEHOzrrD5k7HpDSgjZ1CXnHdkgVZplES0pSKPZDbukFR7lxZJ1HQNGqtAY6LkhLwOxCpdURW6Mnu1txKmHOQvGKpnoA2d1KV+5VZF1YUFlJ5rDH1nPK59uu6ucsw6OFpc/mc6jqq+LV+UaLaWOXM50OC/1c1LNv8a5T9XxWoRmt3n1yVqt3poo5bxeuQORtVWtmkab2x51XSEtlu44odeBISo4/C3uKFyPqvSDpaaOeffO2zUKjJ8cBVt8ZZo2eGnIvdnsyVucpyr2Kqs+WXZy4zGmW4S6lvNZhMpXvCEmXZ9LoRfkr2s2IJBdW9EKN3qhRo1iwYAFlZWVMmDCBBx98EJfLxdNPP01ZWVlcddx0002UlZXx6KOP8vLL7a6Ehg8fznPPPcdZZ52VVL/2ihk9d14xDcsX0Fq1GU+edpas0Wg0PUHxlLPJP/wk1r72f4T9rTh8aT3dJU0PI9h3FmPceuutNDW1z2jffffdnHzyyRx++OHk5OTw2muvdVG6g7POOivpQV1n7BUDvdyDJtG8cTWrX3yEkhPPI2Pofj3dJY1Go9nnMAwDe4qXQEMdmaPG93R3NHsC+4DD5B0cf/zxkc9lZWUsWbKEmpoasrKy4vaTtyvYc+1MCeDKyKbs/GtIGzSaje+8QMPKxT3dJY1Go9nrEELQvGU9i/50A6tf7Tyk09pXHkME/XhL+u/ezmn2SHZo9BL/6+meJ0YwGMThcLBo0SLT/uzs7C4HednZ2WzdujXutvr168e6devizp+YRs8maRTi9AmViKYkfr1JdJrhclFy8rmIfwfZ8O/nGXjJb3Bl5UbrZizCCcWLpbbQQqdhFSKnfVvSWKhtSGkoWjNDSlN95ck6PLeiw3M7O7bdSggwVcNlt3XcKvagmtaxHVDS2qTP6oNrmpq3K4lB+aQSPxa6rChdmNR+UNX2SRc1bJEWUm4wq3KqXz27pJ1R08Jx/gaz8jhvtzhxUeHRVJ97UllVv2fyo6folOTQaWHFx15YEnupIfcsw6OpN45VOEK5WqsHXNVlWZ1uK21frHxRiV346rN4n1p+R1i9h+PUSqtlo95L2/MGmxtZ/liHGDwsQoTdSl4haNnSrtFzFBcSUtI7Q72GqmZOhCzSHLHT4vajZ6XRi3Eu1M+dbcsa7LCq0ZN1eKpGz0qHJ22HHcqJk7edphcfPcr2gVvCxXqZ6dbhcFBaWhrTV54VtbW1vPfee2RkZMSVv7q6OqF29grT7Q4Mm52Sk85jxZN3sW3BHAqOPLmnu6TRaDS9lmBLEzaPG2y2yCDPU9yP/Ekn4+03MCp/3cJvADDsdhze1N3aV82eiWDf8Xh26623cvPNN/Piiy+SnZ2dUNmLLrpoF/VqLxvoAdicLnylg2nZtLanu6LRaDS9knAwQM38z6n49B0ABv3sd5G0ARdf22mZio/epuarTwEoPPnsXd5HTe9hX3Gv8uc//5mVK1dSXFxMaWkpPp/PlB4rTm2yMXLjpVtMt1ZEmVbkNIuQQVHVW4QvUgnUbcOZntml+5SE7j0r03GcIYK6q71EMKSTYFdMvnIYLE8XpltTmvL7zNSGYhKU01qVeuQW1dvc1EKUm47YpmtDDfcjbavmQptkkuwqJFi8yO5VVHNslOsVK8uilFddfaaahLsDy3BpqusVOZSZchAOyVwbUkxGIdmFitK+ZXg0i5iDal6b6VwpiRYue6zMnHHTxS1j6pnVMVq5d1GLxWm67crMKLdRt+w7Nk3/hyk57OqorOqrj8k6+EhsDkekjea1q6j5+jMA+l/5W9x5BaZDsnr3RZlu1cdENp92EgKts89R2xZh9Kyum9pvKylOlFsaOfylYp6VTc6hKFdacnuxTbfCobxrJdOtIb3rjGDipsRuJdkpvV44DSjHrN2T2Otm9AAwDML+tq7zaTQajQaAsL+N1c8/jL+60rTfkZGFw5dKn59cysbXnmHrx+9SN38O6fuNI33UWEJtrWx47gnsvlT6XPBz3HkFPXQEmj0Rwb6h0QNzzNo9ib1yoJc1+iA2/+91Ao11OFPjEzdqNBrNvozhdEUN8kovvIqU0gEApA4ZiSu/CLvHAxhUz/wf1TP/hz01HVduPqU/uw6by9VJzZp9nX3FvQq0L6x44403WLVqFb/+9a/Jzs5m/vz5FBQUUFJS0iN92ivcq6h4+w4EBG3KS0uj0Wg0HYhwGH9dDUCnLiAMxY7qKxtCa/kmCk4+k6yJkwAItzRRfNZFepCn2edZsGABQ4YM4YEHHuCPf/wjtbW1AEyfPp2bb765x/qV2IyewS4dGppcn1hEy+pKQ+P0ZWD3eNky41/0+9GluDLiXP2SrBsDC3cyViGSdofWVD5vhiKGcUqhrFxKWCsrzZZf0WzZpe2QXXEhomrmJNqkOgNKmqkF1TWCWqcha1NEzLw2JU3um90W292I6ookWaJcpkjdsdLdRWn9ZPcuUa5fbJ3mA7Cbjim2OxU13aY8jLJmL0o/KLlbUbV9Iekei/KYYo/9gIWiMssvCnNSWEqzWflWUp89Wb+n3rLxvvO6yQ1QVJg1q3rjdPcRS09WOeN9qr/8iCE33Y/N6SKlXxkt61dHsjmKCkz6suzJU2havYyNLz9N6vBR+IaNJOuIY3AWFyYdaCvKK43aV/myWYh5o2TFcaZZucWxPKdqVDflnjKHvzQ3Ygp3qaRZ6vAs9Mg2Ka/pPRjoafcqSS7G6IWm2+uvv56LL76YBx98kLS0jqgwJ5xwAueee26P9WuvnNGzOV30P/tX7ZqTFx+hpWJTT3dJo9Fo9igaVy2l+suP2jeEwL+tmmBDnSlPzRefmOIQ29xuii+4jGDtNppXr6T4/EtJ6dd/N/Za0/sw2n+8JfPXy5g7dy4///nPo/aXlJSwZcuWpOqsqqoiEFCnQhJjrxzoAXhyCyk771qcaZmse/1J6pbOR/S040iNRqPZAwi1trDh1b8CkHv4cdhcblY/fg+BbdW4cvIj+Wo+/4i2inJT2XBLCwD+inJEEs5hNfsWQiT/19vweDzU19dH7V+2bBl5eXmWZZ9++mna2toXkQohuPfee8nKyqKwsJDMzEyuv/76pN2wJO9exZQQu0zU0vnYVhdzPnUwb2HWjbLQbP/fmZJK6Rk/Z/P7r7LxnRfJ31ZN3sRjLVqNXaeVCTZu86xVuc7KxshraWG2MLmq5knZtOayhZQ083ZQ8nGgmuTkssGwarqN78ZUp+ll1ytCcYsRbbqNnWbEefzqManHLxOv65VQF79IzWbW2NE3egJbnGZe1eQvE7KZj0l2vaJebyszTXREFTnN3IYhZbZ+T1i9YBI49xbmQcu7xMrtVAJfcJaRdyxMt00b1kY+b/38A+qWfBvZ3rEgI6X/QFJHjaFp7XIceTnYPB6EDRx9ijryNtXizMmJv8NxkKx5Nll3HFHXLY4oIe1pqm8fJa8cGcMiioWVmxTVJZT8flNlKnJUJDlfyLkHDMZ74aAtGU477TTuvPNO/vnPfwLtutf169dz0003cfrpp1uW/eUvf8nUqVPJz8/n6aef5t577+XOO+/k4IMPZv78+dx6662UlZXxq1/9KuF+7ZWrbmUcKT76/ehStsx4m8ov3iPU2kzh0af1dLc0Go2mx3ApLlAC1VVReVrWrqJl7SoA3EUleAcNAaB5xfL2DIaBIytr13ZU0/tJWqO3C/qyi/njH//IiSeeSH5+Pi0tLRx55JFs2bKFQw45hHvuuceyrBzq8ZlnnuGuu+7iuuuuA2DixIl4PB4ee+wxPdCzIv/wE6me9ynV8z4l79Dj8NdUsfGdFwm1NuNITadt6xY8BX2wud0EGxvod+aluLKsp1o1Go2mN+LMzGbw7X9i7Z/vJVBT3Wme0mtvYsNfHibc1kbrxvV4Bw0hULuNipeeA8Ddtx+Gba9V/2i6k33EYXJ6ejqzZs3ik08+Yf78+YTDYQ444AAmT54cV/kdK9/XrFnDMcccY0o7+uijIwO/RNlnBnqylmTLjH9j2Oz4a7cCEGptBqC1YmMkT/VXMymacubu7aRGo9HsJgzDIO/409j8yt8BcJf0RQQC+CvbRePOzGz6/3oaq+/+HTUf/BdXTh61X3+B8PsBKL70Fz3Wd03vQSQ5o9cbF2Ps4Oijj+boo49OuNz7779PRkYGKSkptGzXwu6gpaUFW5I/rBLT6MkLYWTdhIXcxQpLLYqlgC+B5rbntbncFE0+g2BzA9VzZxIOdETOKD37ClxZuYRDAUQwgDM9C8PjNt9nVi5UrFwc9PC9KmvU1DBf8rbLZg6B5lZCooWk9GDYLFwJS9or1d2GK6TE/okTOVxaWNXoqZpMC7cZpuO3x3YLY6VDswoHpyJr7VR3KlaaPTXNHAJN0e9ZhEdLFqvjV/WLsh8NVUsou1dxKvo908ve4s2jfinYFQ2TyS2N0r5pwZVy3mStZ7TOMrZ7FctwWfKzH1s62mnRWG1YvhatQnJZ6cliuFfxjRpF/xtvZe0f76Zt0wa8Q0fgHTGC2pmfULdgLukTDiH9sMOpn/U51R++S2Br+4/j/PPPx0h1EU5EUBiDLrWNyX5PmBqJP810jtUQZCb9nrVGz1RW1ehJ7yLV7ZPsJsVuodGLemZjuIvqtRq9XjijB/Dxxx/z8MMPs3TpUgzDYNiwYVx77bVxzepddNFFpnomTJgQ2Z49ezYDBw5Mqk/7zLy7YRhk7z+R/EOPZ8gvfm9Kq/jkbRzeNNzZeXjyi7F7UqIchWo0Gs3eiDMrm343tDtzbV62hNqZn7Tvz8tvd6i8aSM2r5dgXT3uvn0pvv56UseO7ckua3oVxk789S4ef/xxpkyZQlpaGtdccw1XX3016enpnHjiiTz++OOWZcPhsOnvd7/7nSm9sLCQ++67L6l+7TOmWxm7J4XsA4+gZl57EO7Wyk1Uf/MpuYckPtWq0Wg0vR27J4WCcy6g4pUXIvucOTk0LviO1jVrcBUV4S8vJ/PYY/H0UBgnTS9mH5nRu++++3j44YdNCyauvvpqDj30UO65556kFlLs4OSTT066bNLuVSyjWMhllDq6YaY/imQsxymFfQHI2n8i2777kvrl35MzURroJeCqwDLNyrSimgXkJfdOc5rhlKb6nebpfKc0Ne9SpulTnB2OFn1Ovykt3dVhIE1ztprSvHazg8ZAOLYpMWBhZpTNw1FRFKRtNU023wUM84lTXcjEm6aaQWTzhmrWtkI2F0abVWNHrQgI8+MmR9yIMvPGOdkeZb6RTJd2NdqFbI5Vjjes2MPlngaVvsimXNWdjjlNkQpYuGIRJtcr1qYmq9V4YTruFaEcoxGUrptyvOYIMkqlJvc9SrmwnEbMtO2di028kxdWshHl/RI2vXuizYzB+nrCTU3YfD6aViyh+p+v48jLJVRbhwgEqF84j5YflgHgr6jAd8D+uIeXEXZ1w0s8TlN1txGn6yoAZBdNUSZv6R2tunKKOsexXaHI5lrVhYq8rbqEku/N6DTZdCu3rf3H7i7q6+uZMmVK1P7jjjuO3/72t3HVsXr1ambNmkV5eTl2u50BAwZw7LHHkp6ennS/9skZPYCMYWPZMvPf2FO8DLsuuelQjUaj6a1sfuwxgjU1pn3Bqq2Rz46sLFpXrAQg84TjyDx+cm+0pml6GkGvnJ1LhlNPPZXp06fz61//2rT/7bff5pRTTrEs29TUxMUXX8ybb74JtMvN8vPzqaqqIiUlhfvvv58rr7wyqX7tswM9w2bDk1dE7cKvyRhxAO7cwn3lXtRoNBryzzuP2o8+onnp0uhEm42UYcMim2kTJ0Tn0WjiZR9ZdTt8+HDuueceZs6cySGHHALAnDlz+OKLL7jhhhv485//HMl79dVXm8pef/31lJeX8+233+LxeLjlllsYOHAg06ZN49VXX+Wqq64iKysrqZi5++xAD6DkpHNZ98+nWPPSY6QNHIGnqC+ZYydic+zTp0Wj0ewDePr3p/CyywAItjRT8de/0rZufXtiOEz162+Q85MzcRYXYpcCtGs0CZFkOLPe6DD5mWeeISsriyVLlrBkyZLI/szMTJ555pnItmEYUQO9t956i/fff5/99tsPgL/+9a8UFxczbdo0LrnkElpaWvjDH/6w6wd6wt6hA5EvglXImihditUgPckLG1UszrBjjrR0+p97BVWz/kdL+Qbqln6Lv3Yrhcf/2FprZ6HRCytn1KS7U9NUjZ5LOlkeRU/m7nBv4nab9XM+d4f2LtXdZkrLdHdo79IVHV6msznyOcfZZEpzKu5WZC1aQBEDBSQxkJrmsLk76lAuhqzvUvVssrsRK91dIkSFQJNu3Citm7St6gflvoaUYzK7V4mdFlVPp7EFd/RF0WQaHRq2kCIMC5hC1Zm1bnJfuwqxFpQfcOUZljV7qusV2RWOmibrLoWidXPE6XpFRfG8Y9LJhZU2hN0mfVYKBqU09fmW+mMo8kGTRk9NUztninNGTCwnMhRdmOV7SX6/RGn7FH2Z10P+dT+n6i/P0rqs3Vzb9P0Cmr79jqwzTsI9rG/n9cj6xe5wg6JWujNYdUh2kxKlyZT0bRYavWgtp6rDk0OSYZEW+92jtmHl6imqP3sS+8hijDVr1iRdNhgMmnR4qampBINBmpqa8Hq9HHfccdx4441J1b3PT13ZU3wUHvtjADb++x80rVnewz3SaDSa3Y/N7SLvlz+lbfUanCVF2FN9bLr5Hra98S5pk4+IeO3XaBLC5IA3wXL7EOPHj+fRRx+NuGF59NFHycvLIy+vPUJXY2MjqampSdW9zw/0dtC0biX1S74n9/Bje7orGo1G0yPY3C5SRgyNbIfqGwBoW7UWz6ABPdUtTS8nGaPMrvDQsasRQvDGG28wY8YMKisrCSueB956662YZe+//36OPfZY3nzzTVwuF1u2bOH555+PpH/55ZeceOKJSfUradOtbLKIGnjL5gy1Dgsv85Y/GK3MHlaezVXkKXtpOr2ttgoQ5Bw+ud2NjJWXeSs3BopblLBTNt0qB6xuuyV3Hx6zeTZF2pZNtQAZno5QKZkus3k2wymlSaZagCzJXJttV0y3ih1KNsm2CmfMtLawOU02NagRNYLOju2garqVzLrRUQzMdGWG3IFTMSVaRcawbM/kFiZ21Aq/Yqt3Km5DbJb+NmIjm3LtFmZd1YVIQu7R5WrVclKaleuVeK+LSlfXQjblB5WQQCHJPBu2K2lByRyvhhKSJRZBpd+SCVao5tiwJDFQLqelpMXC7Bfv+wsU86yV6VY11drUl69Fm8C2N9+haNqVqC9tw2LL3BmLynfVF3qc5zE6m2y6jX2eVElJlHsd6VmIiuZjEeFCrtcqrbskLbucfWjV7TXXXMPTTz/NpEmTKCgoSGgW/IADDmDRokW88847tLW1cfTRRzNixIhI+pVXXqlX3e4sKUXtGpTKj98hf/KpYE/kW1Gj0Wh6LyIcpnXJSlJGDQEg3NJKzYv/jqT7V2/oqa5p9gb2EdPtiy++yFtvvZX0zFtRURGXX355N/dKD/QieAr7kH/8j6j84F8EardRfNaFGHZ71wU1Go2ml9P0xXyqn3mDvGsvIrC5ktp/vhedZ+5CfONH9UDvNL2efWQxRkZGBmVlZTtVxyeffBLlMPnUU09l8ODBSdepp60ksg86nJKzLqFx+WIq35uO6I3ruzUajSZBHLlZAFQ98rxpkJdxSke0oK2Pv4QIJyc30OzDiJ3462Xcfvvt3HHHHbS0tHSdWaGyspIJEyYwefJk7rzzTp5++mnmzJnDH//4R4YPH85vfvObpPuVtEbPJGxI4Nk36VZUvUmy+j0LopfOW7Rvg9RhIyk4+Qwq/vNPXIVFZB50aHuaNLkXVvV7kg4vrGj05DShhC4zlG2Hp8OlSYqi0Uv1dLhNyfaYtXbZ7o7tbJdZa5cl6fIy7OabL9vR2PHZ3mhKU7VfrZL2rlW4YqY1G+Y0WU8WVN2ySHo2VaMnu15RtSjCIpSaipxXDcHliFOjp+rwZDcpUa5mpG2nMLuoiXKhInXbSs9mVx4wOVyaqqU0uVux0Nap+j1LPZ/6fMsuTBTNmnweo8LKWfyslK9xVyHv7NIxOlQdnqSZC4YUTais33Oo+j0pTdXvhY3OPwNCPsSQmmbx0rLS6KnnKU53H6oOzya7AlE1ekr77v7ZGCluREv7e8aenU6opp66/3xiylf72jvkXnwSnWL5HKo7YmbdNagaOUv9XmwdnFW5KM2clNdSh2fpXsVKo2duLhGd8W5nH5nRO/PMM3nllVfIz8+nf//+OJ1mzfr8+fNjlr366qspLi6mpqYGt9vNr3/9axoaGvjmm2/45JNPOOussygpKeGaa65JuF/adNsJGWMn0Lp5PZXvvIkzJw/fwCE93SWNRqPZZTiy0hjw7G0IISIC8son3qTxs29J2X8ILd8tB6eDhhnfkHPhCRjqYFij0XDxxRczb948zj///IQXY7z33nt8+eWXZGZmAvDAAw+QlZXFY489xtFHH80jjzzC3XffrQd63YVhGOSffDptWzaz6fknyTr0KHJOOEX7kdJoNHsVocZmmr9dhm/CCIQ/SOOXC0g9dD/sqSmkjOhP87wfaFuxHnt2OvbcDPzLNxDa1oAjJ6Onu67pLexDfvTeffdd/ve//3HYYYclXNbtdpvGGDabjVAoRDDYbhmaOHEia9euTapfiZluJbcj8jVQf9vFPeOayAp/0enHzgvGGRnD0qxrt9PnZ1dT++WnbP3ff2jZtJ7CCy7G7kuNcpNijn4RO9pFlKnWbTbtuaVt2VQLZnNtrsdsni1w13ekOc0m2Ax7R7lMu2Lylcy1mYpZVzUXNkkmWdW9SrMU/cITdpvSbCHJJKeEDQlI5jPV9Yrf0bGtmiRksy6YzbOqCVQ266qmRLleKxcuavQLuc6oiB5S3oDiXiWguFeR+2odGcPcNyfBGDkhJEeDUCynsnk2yjyp1mnhXsUc/UIJBxGnWxY1zWbhUkKN/uGUolqEwqrpVoo2opjq/ZIpNxGzbjgkRc1Qzqlsno0y1aovGPmwLMyzqinPsDDBWplnTedUcS2041ZoWLyGqifeoOqJjrS6tz8l9+yjsBsC4ffj7l9IyXU/Zstf38MPOI1WXG5v+yHJ0U6U4zUpcay+tLvLRJeIWTW+Ygm5MLFsw8LMal0uftNxrHI97YbFIEmfeAmWue+++3jrrbf44YcfSElJYeLEiTzwwAMMHdrhF7KiooLf/va3fPDBB9TW1nLEEUfw2GOPmRY6tLW1ceONN/LKK6/Q0tLCMcccwxNPPEGfPn267EPfvn1N0S0S4bDDDuP3v/89zz//PC6Xi9/97neUlZWRnZ0NQFVVFVlZWUnVreffLTBsNrIOm0TR2RfTunY1jQsX9HSXNBqNpttIGzeYvHOOMu0L1jSw5cl32PKXd0gdN5R+d19EOBik6dv20GjOPD2bp0mA3bQQ49NPP+XKK69kzpw5fPjhhwSDQY477jiamtonRoQQTJ06ldWrV/P222/z7bffUlpayuTJkyN5AK699lqmT5/Oq6++yqxZs2hsbOTkk08mFArFajrCn/70J37zm98kNfP2xz/+ke+++47MzEx8Ph/PPfccf/nLXyLpS5cu5eKLL064XtCm27jwDh2Bzetj63+mI/xtZBx9VE93SaPRaHaa2pkLaJi7nKGv3ETT92vYeP9r5F1wDFlTxhNqaGbbB/NY/pN78ZQVAeAqycVwaLdTmt1HfX29advtduN2u6Pyvf/++6btZ599lvz8fObNm8cRRxzBihUrmDNnDosWLWLkyJEAPPHEE+Tn5/PKK69w2WWXUVdXxzPPPMMLL7zA5MmTgXbfeH379uWjjz7i+OOPt+zr+eefT3NzMwMHDsTr9UYtxqipqYlZtqysjAULFvDFF1/Q1tbGwQcfTG5ubiQ92UEe6IFeXNgcDvr86lq2/ns61e+9Q/pRR2gxskaj6fUEq+tpXbmZZefcH9kXqm0iUFGLPcNLsLIWgNxzjiJlUDGOzORibWr2bZINgRYIBMjIMM8gT5s2jdtvv73L8nV1dQAR02dbW7scyuPxRPLY7XZcLhezZs3isssuY968eQQCAY477rhInuLiYkaNGsWXX37Z5UDvkUceiefQYuL1ejn22O4Pw5q4Rm/7+MbKu4r10nWLgtLNoHpmsIygYyWNSVK/p6Y5s3Pw9C2lZc0q6/ajwuBIOhm7ohGzm49Kdv/htps1U15Hh7uVdIcS5szRoa+TXaaAWaOXo7hQybRJ5Wxmdy525fg9khuPJsVtiF3yraWG9ZLdjTTbza5XmsMd2yl2c/ut9o5fQqr7FIeiN5I1e2peeVvVfkW5/4gT2b2J2p6sQ3TaY4eRUwkloKIwawuVEGhIbVpUaVP8XdiUV4HcRls49tNnU7SVpr4paQ6Tqx01dJysH7R2nyOXtbreqn7PJfUnoGr0pLzBkLmcvB0Ox+63qtELW7hXsdLhqfeprK+zR2n0YofZkkP82dU6t2+nnjuO0KYtVH+yOJJW85851PxnDu7CTMY8+0uEOLXjmDA/pzJWbo/Uc2Gp2dsFdJfWLrE2kysXr5uUntbexY0gycUY4HQ62bp1q2l3Z7N5UUWF4Prrr+ewww5j1Kh2J9/Dhg2jtLSUm2++maeeegqfz8dDDz3Eli1bKC8vB2DLli24XK4oLVxBQQFbtmzpst2LLroo3qPbrehpqQRw5uYi2toINzV1nVmj0Wj2QEQozIr73qb2m9Vs+NsMHGkeco8bE/XjdsANJ/dMBzV7Hzuh00tPTzf9xTPQ+9WvfsWCBQt45ZVXIvucTidvvvkmy5cvJzs7G6/Xy8yZMznhhBOwdxEFS3Y71BWrVq3i1ltv5ZxzzqGyshJoNysvXry4i5K7Dj3QSwJ/RUVPd0Gj0WiSItjYSs2nS1l26z/Z8p/51H+7juZVFTgyfZE8o568jLRRfXuwl5q9it20IAPgqquu4t///jczZsyIWil74IEH8t1331FbW0t5eTnvv/8+1dXVDBgwAIDCwkL8fj/btm0zlausrKSgoKDLtj/99FNGjx7NV199xVtvvUVjY7sVbcGCBUybNi25A+oGtEYvAVxFxThz8yh/+insPh+h5mZsHg+eQWW4+pQg2vy4hw/AM3yI1vBpNJo9jkBtE2sfbQ9x5i7KZPiD5+LOSycsDIQQNC3dhLMgA3dOWg/3VLO3YIjkNXqJIITgqquuYvr06cycOTMyeOuMHbq/FStW8M0333DXXXcB7QNBp9PJhx9+yFlnnQVAeXk5ixYt4sEHH+yyDzfddBN33303119/PWlpHc/QpEmTePTRRxM7oG4k+RBoMgno4EySIjVN9pWnhqyxuOhW/vAspQGqHz95W5FvGWFw5xbQ91fXUzfvK8KtLdhTvISammhetYz6VWsAqPvoE5zFheScczrusv4IyedYWNH+qHEjrXQrZj9j5nJuSV/nNswaGp+twx9fms3smy9TKpdpUzRaysWx2zp0efawX+lczG6bfO55beYpd4/cb5tZ9yf7Z3PYzDddVMgzW+w0WXulavusfLdZtReSdWCKj7ewyY+eud925YaTdXmq38JY/VRRQ6BZtSfru1RffGo9bZLWUNV3yXnbDPN9I4e5CynXVPaVaOXvUA05p2IV8k6uV80n+9Hzh2Nr9AJKmsk3n+q3z0ITmIhGT9bTqT4d5etmj/KH15Gmak5NabYwtYs3s/h3/wabwajbTqBg0pDt5qjWyLlKH5u7/bP5XbGDKB2exfk2+bdMKK3TpjvFKnSgjJWfzETonT7z9wAt327owpVXXsnLL7/M22+/TVpaWkRTl5GRQUpKCgCvv/46eXl59OvXj4ULF3LNNdcwderUyOKLjIwMLr30Um644QZycnLIzs7mxhtvZPTo0ZFVuFYsXLiQl19+OWp/Xl4e1dXVMcupK4utSMZPn57RSxCby03GxMNACIztdv3UloOx+3xgt9OyYTU1r75J5VPPkXvRObhH9cdQllhrNBrN7mThXf+l4pPlZI4uZswdJ+PK8vZ0lzT7CsmaYRMss8Pn3FFHHWXa/+yzz0Zck5SXl3P99ddTUVFBUVERF154Ibfddpsp/8MPP4zD4eCss86KOEx+7rnnutTxAWRmZlJeXh41m/jtt99SUlJiWa4rDeAOnWA8/vxU9EAvCTY+9jD+zZsw3G6cefn4N26IpGWcMJm8yy+k6ul/UPl/f8NwOXEU5CECAZxFueRcdjr2NJ9F7RqNRrPziND2GT7DoOKT5QCMvv0kPcjT7JWIOKaCr776aq6++mrLPB6Ph8cee4zHHnss4T6ce+65/Pa3v+X111/HMAzC4TBffPEFN954IxdeeGHMcjNmzEi4rURI2HS7w6ohDz6jrEdxujCJGsDK18nK5KvSReShuLG4T2SrSMa4g6mfN4e2TZvwb9yAzZdKuKlddOnMycOVW0DxzTcSKN9C84qlBLfWYDgcNH7xNTX/+A95V5zTfkhJuhxwKuYbj2Su9Snm2TSjwxWLTzHrpkk6wlTFrGqLinsluXRRTHKhcEe9YeWitkr9aVDal03Oaigtl7TtV443ynQXlj/GNutGua2IUwSimhLl9oOKeTYQjm2CU0+p7AolrCQ6jdhhzqzMvPLhh1FN1bHLBZSH2C7ldSqh21qNjhnq6PB0HeWiQsdJ903UOY3TBJcIoSgzvhQCTWnfL5mq1RB7JtcrVvdClFk3tgnabhGOz0piYBnGzxAIIdg8czWr/vk925ZUIELm61NQaAfM7pnA2gRrymfhQsXSZY6V6xULs24ifUvEZUtPuyaJt69Wxxtv/eE9wA3L7tDo7Qncc889XHzxxZSUlCCEYMSIEYRCIc4991xuvfXWmOWOPPLIXdovPaOXBBmHHErGIYfS9MMSQs3N+MbtHzHjhl3td6dhGLiKi3CWdazUMZwG9e9/zqbVG3CV9SH3oik4spOLi6fRaDQyTeX1zL/7Y6q/LydvfB/GXHUodq8TEQrjTnVReHC/nu6iZl9EGEn60et9gkin08lLL73EXXfdxfz58wmHw4wdO9YUSzcePv/8c5566ilWr17N66+/TklJCS+88AIDBgzgsMMOS7hfeqC3E/iGjQAgbI/vp4f3oNH4N1Vg2Azalq1l403/R9bUI0k/boIOK6TRaJKmdWsTs678F4bdxsSHTyF/fF/FsXYvnB7R7D3sBo3ensCdd97JjTfeSFlZGWVlZZH9LS0t/OEPf+D3v/99l3W8+eabXHDBBZx33nnMnz8/EtGjoaGBe++9l//+978J90v7ANlNNMz4ki33PEXKfsPIu+5iiu68Cu/YIVS/+D6b73qGYHVdT3dRo9H0Qqq+2cCsK95CCMHhT0wlf7z2f6fZgxAdLlYS/ett3HHHHRHfeTLNzc3ccccdcdVx99138+STT/LXv/7VFCt34sSJzJ8/P6l+JajRE4gds1dWK0Ti1dMl4npF3lZvgO6a4bUIwWaFsT1kU/OK5bRVbSTt0EOwbffeHXGvYtggFGLbi//GsNlIm3QweT8/g7RJ46n882tsvOn/6HfL2XiHtb+kVc2SrK9yKHoq2d2Fx6a6V+lwhZKmaH9SjY6Yf1GaPIVUW0dek14PCElthpQQSa2io321bx5Jh6a6V3HK7lWUi2Glw7PS71lp9Kz0ayqyviykCIADdMzMqmHGop4L+ZQr11S+HjYsXKgo/Tbp9xJ4LvzC/CowuVAJm1eNy7NDqluWgOQKJ0qXhXzerMOcmdpTrpvdNFMVW7MWFZ5O0lMGFRcqASHr8BT3KiYdnuKWxRSOzRYzTUWdYZM1qlH3KSJmWuPGWmZf928Apr54Cjl9DKCp0zaskJ+p6GtjEXLOQj8XFvFpG630e2rZrvLGm5Ys8ep6u2rfFDovTndBXdVpqkfqp7Al8GW2q9hHZvRiRdD4/vvvIzF3u2LZsmUcccQRUfvT09Opra1Nql/adNuN1M/+gqbFC8GwkTHJfKHSDj8Ew2un+ZtFOPJzIvs9Q0opufdKKh56iY0P/JOyh36OI0sHDtdoNF3TUt0RrzpnaHxfJBrN7sQgycUY3d6TXUdWVhaGYWAYBkOGDDEN9kKhEI2NjfziF7+Iq66ioiJWrlxJ//79TftnzZplMgcngh7odSN5Z56Nb8IBeAYPikpr+PRLAtuqyL8mOuixPd1H/rXnsPnmx6h8ZQbFV5yyO7qr0Wh6KY3ljXz75Dw2fr4ed4abKU9O6ekuaTSds5v86PUkjzzyCEIILrnkEu64445I5A0Al8tF//79OeSQQ+Kq6+c//znXXHMNf//73zEMg82bNzN79mxuvPHGuDR+nZF8ZAzTEF0NYxE7qVvMs1YRLRIgqpwcmcMiTbXy7bBeOVwp+EaNad/YYYkJtxds/Goe/jXrCVbUkPurCzEMAyG5V7Gnp5Nx9P7Uffwt4V+cGtVX2WSguteQTaI+wxy1wieZR72G2ezkNpJz5Gwy42J2KaK66Wi2cP0iu1dRzbqyedqlRlhQTWKye5UEXNfHa9pSzSey2xCbakqSImWokTE6qTiCXYmUEDbdcBauTywiY6hmTXMd5vbcwnz+26SIJur9JrfZqph8A+HYrxSzeTB+ebBqnpbbV03uVudDvh6qmVVOU/tmMvkmYNZV3cvIqFFL5ONQzYMmM3pDgOaaVmb/fi4VS7Zx0IVDOOiiwaRkOoBobVBHe+bzFMLClIrqJiY+82wgyjwrmVyVcyFHH7Ey1ar1JGICjpWvJ0jE5C276VHfIFYmX/ndJ+fraVcywF4/0LvoovYJnAEDBnDooYficCQ/h/ab3/yGuro6Jk2aRGtrK0cccQRut5sbb7yRX/3qV0nVqWf0dhP5P/8pG2+6g5bvlhDaWoMjLycqjz01hVB9M6GmVkjS60owIKjbFsLtMUhN1yt5NZq9iefO+pD68mY8GS5+/OdDGXho14HWNZoeZTfFut0T6C5/ePfccw+33HILS5YsIRwOM2LECFJTk5d06YHebsKelkqfv9yNaG3Dnt75BXOV5AKw8f5XKHjkDMKtAQL1LQTrWyn3V9FW34a/ro0NTdtoqW2jpc6PvymI19aGYbTPQma4/VRXhjj8+FTO/lnW7jxEjUbTjfibA9RuaCQz38XC6WvZ/H019eXN5JSlc/YzR+LNctOrpj00Gk2XPP/885xxxhn4fD7GjRvXLXXqgd5uxOZygiu2uTR1/FDSDx9N/ewlLL7lLexeF86MFJzpKdhyBa4MD6l90hmQ7yIlw4Unw4U71cFQb0Wkjv7OrZx75FpCIUFtdZDC/N1xZBqNprsQQvDV00uY8+SiyD7DbtBnbC5H/2Y/9j9zIHan9ozVG/A3tOJIcYJdf9Vq4uPGG2/kiiuu4JRTTuH8889nypQpO2UKhkQHenbR/gcIUww05VelJJMRimbKsNDaxVgdvn2HRTmr0GUJ/OC1knHI9UR1zUq/J+nwhBryTN3GRtbUw6j/fCE5Rwwn/7hRkbR+adsin4u8W2N3FHjqP335emYzj91ZRagpwMETXUw+zkOfgSmW5ZIl1XBFPocVHZ7sXqU2bNYPyqHbVG2V00KzpGrrrFxqyNtqPWadjOoaQ3JpIcwX1aTFUr9v5awJfBfbheomRNI9KrowWW+lngtZz6bq8EzlotyyWLgCUfSDrRbnJixpQq00aur5tiL63gjGTJOPXz03Jh2ehUYtOk1yC6NqzeLU/alYXbeWuoBpkJeW5+by5yaQ29+3XdvXHLNeGavzryJrK6M0qRbuTeRzZaVfjHJ1Y+GGJzqsYHx6vkTuKZlE9HuJuFdZ8NRcfnh+PiMuG8+wn5pnZqxC58nupKzcq8QbVs6wxdat7jb2co1ed1JeXs7777/PK6+8wtlnn01KSgpnnnkm559/PhMnTkyqTv0zYw/DVVpA2qGjWPnQe7hyU8k8oH/CdaSl2znm1DSOOTWN3FADc75s429PNXHv+gbGHeDilCle9h/t7F3r1zWafQSXt2NgUzIygzPvH0Nuf18P9kiTKMHWAD883+7ctuTIAT3cm54laefHvWygFwwG8Xg8fPfdd4waNarrAjFwOBycfPLJnHzyyTQ3NzN9+nRefvllJk2aRJ8+fVi1alXidSbdG80uwTAMCq85nfCWSra8PZ+MsaWdOmCMF5fb4IhJHo6Y5KHQcPDNt36m/6eZaff6GT7IzWlTUjni4BScTj3q02j2BD788zIAJpxdyim3jkrImbdmz8DhcXLaB5dgdzuwOazWv+8j9LJBWzI4HA5KS0sJhbpvBtXr9XL88cezbds21q1bx9KlS5PrWyKZhQGR2XGTvdKIyhdJCseODqAuDze9z5QnwzQrn4B7FdWsa5nXaqyT7DjIKtqGYroNh3YcpI3cEw9g3WPvs+TWtxhww8m0eju0fbLrC4BWKXLBXx6oZltViCvvKCDFa0M+/Ta7wUEHujnowPaoHetWGrz9fiN/+ss28nLsnHq8j+Mn+Uj1JWYCcRodt1GqcnGajI4oGl7F9YvsUkU1wVl9uVmZb1QziMkdg3oryqYOW/wXOGxlWjG5OFBcWqhtyF21+iZQLodsglVdj8gPivr1Yrc4RNkc2hV2C7OyldsU1SQaLy6lb/K9YmXWtUI181n1TTZBBhR3MvL1V6OLJOrSo7kuwD/vWs68dysZd0oB508rA5otpQmmflocQ/TxmuuR3ftEmadls67yrpejv6guXOQoMbLbIbWcekxqRBnZ1ZJq1nWYzJW7V7fY1QDck2ED2u9HK/cqqlsek1k3AXcysSKI2Ow9bLrdB/zo7eDWW2/l5ptv5sUXX4w7EkZn7JjJe+mll/joo4/o27cv55xzDq+//npS9ekZvT2U/BPHEqhpZPNLs9j6wQL6XzI8rnLvv15HXXWID96s4/gzM7jlN27yCzrXCg0b7GLY4Gx+exVs3hLk3/9r5MJfbcFmgylH+zjlOB8Febv3Fpnx11W0NYVoCHnafz8Y0CZckTBzhs2IfKEZhoFhg4IjB5PaJ8OiVo1mz+fL1zcz791K8kpTOOv3Q3q6OxpNt7GvuFf585//zMqVKykuLqa0tBSfzyy5iCdW7TnnnMN//vMfvF4vZ555JjNnzkxam7cDPdDbg7F722fePMXxu0l56r8DOHvCSjBg9keNnPh+Pdf/No1Tf5zCuTdWUVRg59ILUxkx1GUqV1zo4BcXZfKLizKpqw/x3ifN3DBtK9vqQhx9mJfTpvgYNMAVo9XuY8WX1Zxy83Aq29IQCBBQ73ez/SOEBYGwDSEEhCEUCDH//pkcdNexeLK8u7x/Gs2uIBQM8/afVgNwzT/GkpKqX82avYh9ZEZv6tSpO12HYRi89tprHH/88Tu92nYH+m2yB5N12FA2PPMJdd+sRhxXEJnVsmLZ9+2xL//vX6UU9HXx6oMbufPWeu68tR4Ab4rBX59v5G9/zuGi092d1pGRbufsqWmcPTWNtrYwM75o4aEna1m1NsBBY9t1fQfu594p7WAsXCl2ioakEW7LjOxzBswDOL9i6jB8XubdPYNxvz8Ge5oe7Gl6H0tn1QCQWegmI7/z51Kj6Y0kuxijN87oTZs2bafrePnllyOfW1tb8Xg8FrnjIzGNnk0gdmg5TPoA8xWRv/+FMjiRZUuGosUwpymNS3o2Nc1Sh2eRd5fcSFZ1qu5UQrE0eu1aM3tuFn1/dQIbHvsv850BRt0wCcMwaAuZL1urpNnbvCmE02VQOsqHYRjc8WAWF1yaylez/Rw1xs0B+7s49rQK7vpjHeGAnWGDXBww2o3b3bm+xe22MeVoH1OO9hEOC+Z+18b0/zZyy33VDBno5MiJKbicRuS8CoHpc9N29ypCQFUoYDr/lcFQ+8wcUBVsH6AaCJxGKCE3BumD8xh22UF8c9fHZO3flwFnjY0Miv2h2C4u4tXlRemi7JIOTJXpWMmEEtHhSQ+D6sJEDp+l6plkPZsaykr2e6TqqVRXLDu0RRCtRZK1UKpGLpSgLi1SJ7G1ha4E3KvIx28V4k7VVloha99UDaLcb79QtVbx6QAbagK89Lt2kfVNzwwmwx7bfUp0eLLYYcbktJARW+sFEJC+CmwW+j1VkymfYyv9XpRbFqNzPZlaTk13Ks9CwMK9SncsYrF6D6lh7FRCVmHOZFc/aohH2Z2Mci2CcbuakXSlPa3Rg31mRq87CIfD3HPPPTz55JNUVFSwfPlyysrKuO222+jfvz+XXnppwnXqGb09nNzjx2LYbKx/5B2y9yuh5NihlvlrtwbJzHGYZtsGDXUyaKiTPvb2l8Tvb8rkjvtquey6SgAy0m3c+ZscfnVppmXdNpvBhAM8TDig/RfGspV+vpjbQjjcPrg3uVbc/rlFejE3C5spzR8KRz6HQ+1P9eRfJOeKIGtYPgc/eBIr31zCt7e/z5ibjsHh3fWmZo1mZ5k/o5bGbUGcLoO+Q7yE9tEvOM1eSrKLMXoJ2dnZLF++nNzcXLKysiwtXTU1NV3Wd/fdd/P888/z4IMPcvnll0f2jx49mocfflgP9PZWco7dj8YZ89kyc2WXAz23x0ZdTZCt5QFyizqPwnH0ER6OPqIQW6uLRcv8HHLiBh55eluXAz2VoYNcDB1kPZjaFu6YnVgXNP/iXhvoWJW0zp+XUNudYRgGpT8aQ9qgXObd+l/2+91kyMrc6Xo1ml3Jhy+0R7b53QvDergnGs2uYW823T788MOkpaUB8Mgjj+x0ff/4xz94+umnOeaYY/jFL34R2T9mzBh++OGHpOpMMDLG9j8w2UBVa43sNiXKvYqFmxTZtBltjpXaU6NPRJlnZduxkjccM8nS5GtuQNm0iqhhioyhmLhV021QWlYfMg+KUkpzKH9/CRs/W0PJFPOy7TbJvcox5+bzzovbeOaBKq55qNRk2lDxem2M379dD9S3OHZotp3B+//tnXd4FMUbxz97NZfeSSAQegldqkqTbkEEFVRU7Eovlp/YG2BHsSsKKigqgqAiRUEQFKUoXXqHAIH0dm1/f5zkdveSzV1IgIT5PE8euNuZ2dl2Nzfvd76v5G03RFJnzVDbq6jDF76hRC/aEI3WjiC8WRKNx4Xw9wtLqXX75UQ2T/KU03GZ992HYps2+qsInbs1rvOqDAua0LBbJ5SkPV6HWyf7hSKU5hvW9b7WhjX19qcNlxpUh699iM4+FKQX4tX2TXtvmBUhaL2wrrbfgcgBSkJ7zyjDtT5hVb0ME/9tc9hl9m/Lw2qTaN/OAOTqhoe1501p96INDSvDrD42OGjDnCWHZ906H4bK8Kj2uVTef77HJJW4TS90qz3HJh27EXU//b/2ep89eo54Ppl3dPrtUNhA+ZxvRSjXJ0uI6nzr2Ux56533zBhV3F5l6NChxf5fy8mTJ/1q78iRI9SvX9/nfbfbjcPhKKZG6YiEiZWEenddRky7ZDY9+QO7F+8rsVxohIleN8WwblkmTod/T4rVKtGvT9Vy3g9JiqL1xH6kLtrCng9WYM/wL22UoPJy6oT/XoAXCqmH7MgyFOZXkm81gUDgN7Iss3DhQgYOHEhSUpJfdZo2bcpvv/3m8/4333xD69aty9QPMdCrJFgibLR4vh+h9eM4tPqIbtkmbUPIy3GTerBQtxx4wp2N65tZ8mte0cKIqoIp2ELjh/sS06Eu/770E6k/bT7fXRJUEL8tyeH6Dvv49rMsn225OW62/lOIvfDCu7/jqpsxWyQu7R12vrsiEFQcchn/Kil79+7liSeeoFatWgwZMoTg4GBmz57tV92nn36akSNH8tJLL+F2u5k7dy733nsvkyZN4qmnnipTf8RArxIhSRK2xAjy0vRnp+qk2LAESaz9OdOvdp9+KIalK/J4/zP/ylc2IlvVpPnEgWRvP0bG3wfPd3cEZ0Fetos92wp8ZqsXfeMZ4H31sfcelmWZf7cU0qPZQe6+7hifv59J+ikXP3yTw21XHaNzo4Pc2P0IJ4+fv5nAIJuBLleHc7oSzkYKBP5wxl6lLH+ViYKCAmbOnEm3bt1ISUlh48aNHDt2jN9++42ZM2cyYMAAv9rp168fX331FQsXLkSSJJ566im2b9/O999/T69evcrUt8DsVYwystFz9lUXQbvKRKmn0w4lZWUxTQo03fxkyv+XbNni6ZtyJ5p2lGW17SitV0rRAfqNqk2tvYqmqEK3odXoOf6zCQlvmsSe939l0UO/0nVSdwwmgyoFmkM2YQ03EV3NQma6G5cfl7h/31DuviWcxyedYuBVoeWaDcOq0OhZFenQQK218kmBFkB2SJVGz63Vwni1Kkn3dGfXC98R3Ly2Z2WUQv5i18pYlNIYzTZdWxa9tGbaZ8Fd8nk2GJX2KhqNno71ip4uSs9RRJtKTWu/UmI/y/qzW9OXUyecPD/8CCePOejUO5R7H43FGuTp/98rs/luZiZZ6W62byrAYYeoGAOR0Ubyct0gw/FjLoJDJI4ccPLlW2n89Uchf/2uTrn3weuZfPC6+sfMgT1O/nf3Cb75LpqcHJmdOxx0aG/BaFTqq7xodXdK2a2PJk+bSkxpd6K4GZo3M/DbwgJC3bmYTFJAFi7KNGPKzwEAo8oyRZuezKQpq5NyULFNa1mi0pdJWr2qwjJHN6WhRtun+WBU7kPPpkUPvePTtVDR9FvvfteW1bO+cUgl6yeVr51udZv+poNzKvpiMJZN11WuVGGNHsDw4cOZPXs2jRo14tZbb+Xbb78lJiYGs9mMwRD4fFqfPn3o06dPufVPrLqtZNQY0JpoWz5rp/xJ2raTxLeoVmy5/BwX1uCSPeS0TH48lm++z+HtjzN4/tHY8uruBYUpxEp4q2TS1+wm+tIG57s7gv+QZZlJo49w7KCdy/uE8d1nGcz7NAOAHv3DWLEwm9h4E41bWhk+IZb6DYyMvu046afc9Lzaxs8/ejwY83I93wxvv5Zd1LbBAG43dO5mpesVVqolGmiSYmbjBgfz5+Xz+2+FbNns4JJmJ8j/Tyf305IYGjepmMVJxdGkhQV7ocyOzYU0bX325qgCwYVGVV51C/Dhhx/yv//9j0cffbRoBe6FhBjoVTIko4F61zRg7ZQ/yT2WAyUM9Go2srF2STqOUTGYLaWbw8ZEG7mxXyjf/pBTZQd6AAn9WrP7lR/FQC9AsjNdhEX4/8MhEE6fdLFlbT616luwBkkYjOD6L5L5y3zPoC31iMdn7n8vxjPjjTSuHBhCXq7M6AnhREQaOHzQSdMWFo4fcdK9VxCNUszUq2tQeVopJ2JrJZvoN8AGwI7tDr6elcfnn3okEXv3ukoc6OXluXnuiSz+Xm8n9ZibHn2sDB8TRt16Zf8obdHGSkyckeU/5YmBnqDqUcn1dv7w2WefMX36dBITE7n66qu57bbb6Nu3r9/1S/PfU+KPF5+WwD6dDLLnD439iaZ/ujYlKusT9UbVDL6OZYquZQuoM1BoZ+yVfdXanSgjvtqTrgr56rSph8+50LxW7MOlE4LMs3t2eGDVERJ7NibP7fWyy3Z7viiuHVeXl2/ZxLDb03jp40SCbAbyZLW2L1Sz+45tg/jkyyy27SgkpVH5p2GyaM6p0hrDJ0SiuBi+mRlKtknRnjfla5dbAqsNV74Dp8uguqeMBvU+lNucmm0m2Y0sy0iS5NMXk+ImtmitV7Q3isJGweQT5vNeb20oyyckWwa0oTxtaEsZovpw4nHmfJzOy5/XoE2nEHVmDp8MG97X9kIZSaLUHxohCfD27ASmv5XBH0uzufwKGyuX5vuUu+WuEJxZBXz1STbdegUx9eMYLLiZODm8qIxZed8AyodMEY3FqDjfl6SYaTsxgvGjQmnd9gSjhmeQ/nQ4993jfUJcLpn53+cz8cVsDh1ycdutwVSLNzDzizyuWXCSu+8O4YmnQlUf1lrjY7fifnco7xsTXNrJwsY/8ggzhPpYIinDvGbNNmVWHN/sKooOlBKJ982iUvz+A/nsU9qyaIfNqqwRPjY4mntKIU/Qs2nR4m+YVS+s7GvR439ZJT7hWZ3QrfIYHQb1NmW41qH5rHOr6inuGaNawnBeqOKh21tuuYVbbrmF/fv3M336dEaMGEFeXh5ut5tt27aRkpKiW788/Pf0EDN6lRBrlI3kPg04uuoA+afyoBhnlDotwhj7STOm3LmZUYOPMurJGBpcpt/u4P5hvP5eBveMP8HqH5IqJJfthUBw/QQyN+wlum1gWTjcdifZu05wbOFm8lOzsEYGE143CkuYFclspOB4NvFtaxDbunqlOndZ6U5S9+Sx8Y9crrg2guq1vT8cjh2yM+fjdADefPIE9ZpYsRe4KSyQqdvYymVX2GhzuQ2DYrrM5ZJ5e+Jp5n+RTXI9M1deH8o1g8MIDil5MNGqg403O3hm2IzI/Lu5kPdeTufP3zy6zt79bAy6PYy9Oz16oxPHy98bLCHByJ6d1ajX8DhPPJ2F2SwRG2NgwY/5rF5tJ+2UmyaNTdxwvY3JE8MxGiWGDQ/lk09ymTgpm4hIidFjtD+f/KPtpVYWzs8nI91FSKRYIyeoWlSeT8Ozo3bt2jz77LM888wzLF68mE8++YRbb72VsWPHMnDgQKZOnVpsPT3/vfJADPQqIZIk0Xx4B46vPcziW7/BProJLQbUwWBUP06N2kfw6oxE3n/pNONuPUrkR1H06BGEwyGzc5eTqMYyZrO3TkiwgamT4uh14xFeeiudR0dHa3ddJUi86XJ2PzuH0DpxWGL8+2J25TvY8uwPhDdJoMaVKUS1qIEjuwD7kVM4cgpx292YQywcWbab7H2nqXt98wo+ivLjuXv2s/Mfzwza9r/zefTNGuzbls9PX2WyapFX7xYVayI3201QkERktIGVi3L4dnoGdRqa6TMgjBq1zcTHG1i7Op+vP8kiNEziyAEH70w6zaZ1BTwzNZ70NBdxCaV/7CTWNBUN8i7vFsS9oyMAeGGCJ2wx6fWo8j4NgMdEPCHBQGqqm/89pl648dKkCG6/PVj1XlCQxPDhoRTaZV59JYfkZCP9r7MFvN+uPYIwGGH+N3nccm/EWR2DQHBBUcUNk4tDkiT69u1L3759OX36dFFo93whBnqVlOC4EHp/diMb31nD4uf/ZtO8/Vz5TBvqNZNVs0ltLg/m3Tk2nhyWym13pDNyRAh16hgZ/2AWbVpks+TrGkQqtFfdOwXz5PhoHp98ik4dbHTqEPiX1oWO0WYheWQf9rzyPVGXNqBavzalGg0d/u4fEq9qRnyXBpj+C+Waw4IISUlQlat5RR22vr+GDZOW0Xp0B6zhF77mSqm9W7cilxta7QQgqY6Fm4bFUL2WifZdQ4iI9pQ7E6KSZZlt6/OY/VEmn7yR7uNTl5Pted3msiB+/SmPrvX3AzBtfnWatPSVBqxbnc/YW1Np2NRCp+7e+27i1BjCIjwX6J7R4YQFQ92GFbdYYs2qeBYvKWTvXicvveoZ6H7wbhQD+ttwleA1OXZMKHv2OnnowUwSqxtp3z6wPMsxcUb69rPx5ae5DL4rXLXqVyCozEhU/cUYekRHRzN27FjGjh173vogyX645GZlZREREUHNd5/BYPvvi0uhMfFpQccKxX/9nk6bbp02NWW1libqbTr70LQp6RyTcqNGboFsKtlq5oxVTRE2RWqnULWuIjLUq1mqFpKt2mbcvYvlT60m63AOMbWCGfxiC5JbRgLQKOgY4Amn/TZtH2+9lk2zlp5VhwBtW1lY9E0iUaHeLyZZlolpvJc2Laws+Kw6Nlv5hJKOuNT93uvwrk7aZVcvKtlX4M19e9wertqW7VAPnrIc3kFDrkP9BZtr977W6vck2c3JH9aRt+MISXd0xRrvnUlRRl4NspPtj31NkxcHI0mSSs9nNar9zyxGzzVM33KMvZ/+SfvJV2G0eG6KIG1ZhUYvxKQ2t7YpLBGCDep7IVihufHZZigs9v+gTjkXoti2amEmk0ceVpV9c3YSLdoHIUmSrg7vzKDP7ZbJTHdz+qSTvBw3tRuY+ePXAn5ZmMvdoyKQgQVfZvPtrFxat7cy7Zt44L/B4iY7mRluIiIN3H7tCbTYbBKdulnpfIWV629SawTNmodfeYW1mlDlNqPPNoVeUyfQ5NJ8MCj1ZYWFMoOHnGL9BjuTno/g5pttqh9dyrNo13xoOpDYssnBwKvTePmtSK7u7505VOrZCmT1b3Olnk+l19O81lqvODTtKHV4enYfWo2csp5eCjIt2hRsSrR2K1qbGr2ySsqqp9Ovp9T2+T8S0Z4bdeq6kvW5PudCLvk6qbTKivNbmOPgjcsXkJmZSXi4+rO0ohk/fjyfL99A9S79A65bcPo4h+e+RW5ubgX07OJCiEHOIa6cXDJ/Xo7jVOCrZvRIaBHHDbOvoe+UboREmfn4vnXsXnNKVcZolLh3ZBgvvRnFxg0OEqp7Lv26f+xMfC1DVTbtlIs6tUwsW5XPnWOOl2tfLyQkg0T8te2Iv649R2f+xs4nZpO2ZBOyS/1Bn7XlMLZaMQHp7qKaJVL3+uasfWwhBWkX7gfVvn/zeeN/R4teP/dhIr/sa0jLDraAjtdgkIiKMdKgiYWW7YKIiDTS97oQXvkwnsbNrTRpbqVDJ88A/e+/CnljYgarl+fz6rMZ3H7tCUbdnsYjw05Ro5aRq64L5uY7QjH+9z2Wny+zb4+Tpx7J5ImH0nFrF2NdIFitErNnxjD4xmAefCSTseMzyQsgtVmzFma6drfy/ps5uLQrOQSCysxFlhnjQkMM9M4hOX/8Sfr8H0l9451yb9scZCK5cxJ3f9iOWi0i+OT+dWz52XeQduW1Nu4eFsqJVDetW3hmu6a8n6lKf/ba+xls3+WZ/Vm6suqlRtMS3KA6dcZfQ/2nbkB2udj5xGzS1+xCdstkbznEsW//ovoN7QNut9pltWk6qhMbnl+KI7v0dHTnGpdL5q3HjpGf6xnYTvkqict7VZwHVPcrbbwzM44BN4ewcF4uo+9IY/b0HO4fF860OfGkHnFx5KCLXlfb+N+zkazZUYOHn/bMsu7e6Zn9/H5ufpHf3YVIUJDEqy9F8vabkSz4Pp9+16Zx9Kj/C0dGjQtl724nC+f7rjoWCASCsiA0eucQW0pj0hcsxJWRWWGDp6BQE3e+24bZEzbxxYP/UCu4Nu26q6frHxgTyvQPcti+w0GvbkGcTFPPYP3+Vz79eofw6+95pJ1yM3HKaR4fF12pVpKWBYPFRNyVrYnumsLJHzew84f1GIOtNHi0H8bgstnNhCVHUeuaFI4s20XjgY3Lucdl59CeQj587hi7NuUzamIi/QaFYjRV7PWVJImOnYPo2DkIWZY5sNeJvVCmYYrnB8dHX8fhcMBlnTzn2myWGHJXGB+/nc3pU24kCd6dEU2IzurdC4VBNwSTkmLi1ttPM2xkOnO/iUHyQ3fXopWFnn2DeGViFp2vCCIy6sI/VoFAlzKmM6va3zb6rF27lm+++YaDBw9it6vlOXPnzg24vQB99PDOASoGKroX0ccPT/HCR2unk7pMpXHQ+O9p9XSKdmWDrLOt5L5qj0n10meHik1aHZ5imzmpOuG9u+POL/C4t/rsRKHn0Zwbu0vho+dU622ynV7N2mlnCEjQ+/lLKSj8k5eG72fEeyk06xxFtCHHU8gKwybE8OHLp1j6awGTJ4arBnEZWW42bc8jO8dN18tsPP3KaY6dcPHOi/GcDXrRKK3nlcpHr5Q5fD0fPWUqOadLrWlR7sOpTKVkMRE9sBPRAzsBYJcARRYhpUZPuz+3rCj439MV27kB6x5ZQM3eDTAHezWDJoXPnlaLo6d3UmqIzJJa93dGP5e6L5/kZINq8GaRXKSfdPDt+yf4aWYa8dXNPPdxTdp2DcUiOVHe5UpdnkWbnk5x3iyUvE2LShclQeP6Z47Z08ZlHc3/lVO3+dW8GAxGSEg0YjZLGo2edh8Kg2Tt/pXmyZqvEXU9zTZlvQB+oLVOCeK1yVHcfMcpdm5106Kl97n19TH0tvu/R0Pp1S2NR0edZsasaI2GS5sTV6H10votKs5AqanCdDRj2nOsruf9r944VqvJM2jzCirxSaWm07CfowH9+1JHk6ej1wsk/Z9b8yNZ6Y2pPTclae0825Rp9LR+osVf4wLTBZBH+SJZdVu7dm3uuusu7rjjDmrVqlWmNmbPns3tt99O7969Wbp0Kb1792bXrl2kpqb6nS9Xi/i5eI6JuvYqYgYPrPD9GM0Grn25AymdInn7gW388tlR1fbB90SydHEs7dqZefHlbPLzvR9ocz5O5No+IbRuZuXDV+OZ/HgMH36eyc8r87S7EfiBwWKi/h3t2fzGqnOyv32bsnmy73pub7KWSbf/yxsjdjGswwZeuGcvD1zxLz9/c5qh4+P4YHFd2nYtm+/buSS5jomatUwqK6DKQnzcfyuVA0gqcmC/54v5t5V2flggQriCys2ZVbdl+atsPPjgg8yfP5+6devSq1cvZs+eTWFhYLKdSZMmMWXKFH744QcsFgtvvvkm27dvZ9CgQWUePIqBXhXGaDYw7K0mdLslkdkT97J5nfpLo1FDM2+8Fklmpszjk09x+KhnNqphPQufvZ3AuqW1qF/HwoPDoujZJZgBdxxl1Z/ii6csRLdKwhIZROrq/RW+r+oNvCs2t/6Rxdol6WSddrJueTZX3RbDtFUpDHogFotVPP4VTX6B59vKFuT/IDWlqXfmb8yITDIzSklrIRBcyJR1IUYlHOiNGjWK9evXs379elJSUhg9ejSJiYmMHDmSDRs2+NXGnj17uPrqqwGwWq3k5uYiSRLjxo3jww8/LFO/AgvdljTM9vkMKznNmaq61vlElWZMJ6zrU1H7UhFW1oRAlaFcX1sWRVgX7baS96fqjkHn7izlJ4oqcq1N5aUIQRZoQrfpGRJp6w8TWiuSsCZq37ssUzg9H27OplWZfPlZHgmtveHXSGMBYbUt3H5vKG9+lMGHn2dyYHNNQkMMWCXvPoxGiTkfJ9K1/2G6XneYKy638f3n5We7Uhz+pigC/dCtW5kCzaU5p372RdJaeCiusRzALFNCzyYcXLCF6I71ALAo0hspUxYBKK+iNuykSkGm2XZkaybPDNxc9LpmgyAO7Sqg//0J3DE+tiiU6xMq14SvlOFa7T6U4VqzNl2Vqm+UiK69hbasTnhWr66ehYpZsxd1WFfPsqPETcWyY1seJhPUrGZFOdbTHoMyfJicqN6Wm+EiNvK/93yeBUVYTtNt5b2vlyqsNOyKS+Vzbkp5Ns+gFx4tlTJ23d8UaL719EK5/oeA9SxkXEqZjm45HRsaPRsgZSpG4/kP3V5sPnotW7bkzTff5NVXX+Xdd9/lf//7H++99x7NmjVjzJgx3HnnnSVq3qOjo8nO9liR1ahRgy1bttC8eXMyMjLIyytbVE38pK8CpK0/zNrHf2LjK78Wu12SwJ7nIiaheBPXh56M4KtP4skvkFm2svgZu5BgA38uqsk30xL5Y30Bj05MK6/uXzSE1Y2h4GQOpzceKfe2c9Id/LPkJM9ev1n1fqM2ocze1ZabH0qq8MUWAl++/i6X3lfYiAgP7KN24ACv7ja5tlgzJ6jEXEQzemdwOBx8/fXXXHvttTz44IO0bduWadOmMWjQIB5//HGGDBlSYt3OnTuzdOlSAAYNGsSYMWO49957ufnmm+nRo0eZ+iM+QaoA++dvBSB7f/H+fHv+PE1magGtu9QosY2retmokWhkxeoCrr2ymOS5eGb2Bl4dytHUWMY8cZJLWgQxdNC5NeCszEiSRMvHerH1jRXkn8ih0VWB5drV49HLVvusEXpyZkOadhDX53yRmeXmrw2FvPNKTMB1J0+OYO48Twq4rEw34RHiN7mgElOJB22BsGHDBqZPn86XX36J0WjktttuY8qUKTRu7HVc6N27N126dCmxjbfffpuCAs+zP2HCBMxmM6tWrWLgwIE8+eSTZeqXGOhVARK71iVj+3ESu9Tz2Zabbueju9cC0KxjycJ7o1GibWsrS38tXYM34q4INm0r5K4xx/l+cQ5NGliICDfSpqWVy9vZsFjEzFFJmEOttJzQk/VPLqR6syjCakWeVXuOQhcZx+2qQd6wKfVpf1UsNsP5D9lczPz+VwGyDMMfOsWc+bm8/EIUjRv4l7otNNRARIREZqbMqBEZfDqzauadFlR9yrqwojKGbtu1a0evXr147733uO666zCbfZ/3lJQUbrrpphLbiI72PusGg4FHHnmERx555Kz6FdBAT5JkpOI0aHoWKtrvfB2tmzJkLWtkEso2fYYRPl1Sau107FV8qvl3Z+m4ovigI7HQRXv8DqdXz1XgVF+22Ctb0/nK1uyd8QcftJ2JLT6E8ORIQhJC2fv9jqJy6W61Ge5plzdjQ5ohmyYtDSz6xcm6vTm0raseFJol7/4NkoEPXo2nZVMr02ZlsvCXPAySR3j+6KgoJj4WW1TWrdCt6Kl0tJoZg44OTQ+XRpOptFfRavSUBGJraNDRYSr1fEobFgDnGUsFg4FGI7ux/rWldJzUF1OwWTeVkxazQj/31p0b2bMhq+h1uz5RXHp1DCD7aBtVqct0toG+hYpZZe+iPhdmXV2U4v9+6uyKb0evbsnblLo8X/2eQfF/rb2KodhypbFpk3egvey3Aq4ceILULXX/a8ehKmuQlZpIz/ld8kM8HTofZ+UKO648mZAQg4+20S0rny+NLYnk3YdeGjFPXaVth1Z3qdT6aT+Ylee0bDo8PU1caQSimVPtswK0hXp9cfmkzfSvTT2dn7/kGf037K4wLhJ7lb1795KcnKxbJiQkhOnTp5fa1okTJzhx4gRut/oebNGiRcD9EvGAKoQpxGM0m38iF9ktk77rFGHVvLL+v348qVv/hhtt1Khh5PoBp1n+m/7MniRJjLgrkr9/SSZ7Tz0Wf+UJC/fuFqxbT+DBlhhO4zva8MeEn0jfoX9d9GhyWZTq9cipDc62a4JyQvssdLnUVkLJ4qlX10STRp4fda+9nl1KaYHgAkWWkcr4V9kobZDnD+vXr6dZs2YkJibSokULWrVqVfTXunXrMrUpBnpViJo3tKbDk10xh5gpOJVHm/GXMXzJVdz0UWdqtYsjvlaQbv34eCNz5kZTr56RqwcfZ+yEU7rlz2A0ShQUeh7K2JgADMMucmJbVafd0z35d8Z6jv+d6nc9WZY5ud8zG9u6t1f/9djcS6p89pLKRMc2Nt58IQ6A1s2szHizWsBtPPV4BADvfSA8LAWCC5GoqCiio6P9+vOHO++8k4YNG/L777+zd+9e9u3bV/S3d+/eMvUxsNCtQRG6LWN4VlYVLjl2K/lU9C8cC2gsTjT2Ksq6PhYu+IVOpNg3jKtsVDus1jlvsuaYlJkylGFcgDyHdzVtTM8WXFo/iY2TlvLz/Qswvt6dWp3q0OfdOthspziuiBhZFaGdogwLkfDqlyF89VYab0/J5q7xwURHGwhSHEeQxmXfjIGml8iEhUpM+eg077ziDd0Wyt7wVYEmPGmXFeFgTYgkyODtm1WjNbNoXpsUIVKTUROCVNrpaEKuynMsay6capv2Wijb0Vp/GJThUfX+TJpwkcXgwhARRsN7LuXAwo3EtUr07kMnJHZoUwZv3LyOxAYhhIR6OhCfbKVhMysWxTXVZrQwK21RdMKxoA7X6tm7aEOJetkRypq1oqzoh4e1mRq8ZY2SdtvZ/x4eOjic4GBvO0ophA+KU3pldxtmMzgc8NcaB207qjU/ytC5NuRqV2YQ0cma4WmnbHpO5XOrF7rUUtawqn4WCz3LnrJlvwhk/7qU0++vQLJxnMFsEKHbiuSNN94o1/b27dvH3LlzqV+/frm1KRZjVEFCa0dz2Xs3svr+r9n42RZim8Rgi9afzVMiSRKDbwnm/bdz+Pj9HB6aEFbqB1WwzcBTEyJ4+PEM4mKNPDomgqAgMWHsD/bMAiSj/+eqZlOP1vLYLq/G8pEZTcu9X4KzZ8RdESxYksMHn2Uy/M4IjH7ku1UiSRKPPhTG85Oz6X9DGkcOJ5ZeSSC4gDiTGSNgKslAb+jQoeXaXo8ePdi4caMY6AlKRzIaqH97WzY+v5hZfb8holY4t7zSghpNI/yqH1/NyD33h/LeWzls3eJg+idR2Gz6X1J3Dw0hPweeeSmDN9/PpEObIO4aGkz/a4RuT4/oFonsn/Wn3+WNZgMTFnbk66f/Zc/aDABia1grqHeCs0GSJCZOiKXjlYf4fE42dwwO3O7mvrvDeGVKNmFhBpxOGZPwQxRUJsrqiVdJBnpZWVmEh4cX/V+PM+X0mDZtGkOHDmXLli00a9bMZ+XutddeG3AfxUCvCpPQpT6Nvwvh+KaTbPx0C+8NXkXyJVEMfKElMbU8XnlpRwtZNS+N8WNNPvqusQ+HEhQEU17JYdWqQjp1suoO9iRJ4qFRkfTrG8zSX/P56NNsxv4vnX5X2TAEmlLgIkKSJCQksg5mEl7Lv4F4tTohRMSLwV1loF2rIG7oF8r/nkvjWKqTT77M4plHorh5YFjplQGbTWLOl7FcMyCNFSsK6dHD/9n5M2z9u4BnRx+nYTMrl3UPpn3vSELDhZ5WcG4ok71K+XejQoiKiuLYsWPEx8cTGRlZrE5almUkScLlKj2M/vvvv7Nq1Sp++uknn23+tqHlLDR6Sn2TtjNKsZmmET39nlL+oB0YKHYi6enu0Gj2fDRzJXVG3yZFt6BUsmZL9VpPv6fZrtWMKa1BtBq9fIN3xJ9pUH8JWMLjCesUT/uW9Ulf/DdbvtrB9OEb6PRwWxavP8lvn3jEnd1uakt4jLedU8YcADrcGk3NeXu4+84MbCESr85Mok1r9f6DFXq6IKmQkLpwXd0gaqRIDLnxNHN+yaNr9yAy3Oq+OSj5iybY4E0EHWFSrwAudKtv2wKXt992s7pNpb1KHpYSt8ka/aDy/MuukvV7Lo3uzy55++aTOk3S6KsUtgdtJnRjzbPL6PRqX0xBZl07jDP2KkGKQbdRdmIwSCptY5BkV9VTava0+j2t9kipy9NLV6btp0HxnAYYpSwX9HR5SrQ2IQade7E8eGtSHO36HOKJFz0LnA4eDkwT17G9lYQEA2vX2umlGOipUr79d5327nGycnkB1w0Jx2gEgwE+ejmN1MNOUg87WbkoFx45yderaxNf/b97Uk9zrYNeBsDy0tPpaeh0tX5lTIGmbr9sur+y7q9CMJzHfcM5m9GbPHkyc+fO5d9//8Vms3HZZZfx0ksv0ahRo6IyOTk5PProo3z33XecOnWK2rVrM3r0aIYNG1ZUplu3bqxYsULV9uDBg5k9e3ax+122bFnRQovly5cH1uliGD16NLfddhtPPvkk1aoFvoCrOMSM3kWCJcxKq9uakNypOt/dvZQfRiwjLM6K0SThcspsXpnB5QPifOoFhxq5/5nqPHHrPvJzZcYMOsSM76tTv3Hx6dSUtO1gocNlFkbck05UtAGTxcATr8XSqkNgNhMXA7a4EOpe14R9P+6gwfXN/KrT6NIofp/jWa17aEc+yU1EiPxCJT7WxO8/1OS6O46yYVMh73+ayaZtdma+F+/3Sum6dUwcPlz8r/n0dDdX902jc7cgDux38ufvdl58Tm3Jcu1NoVx3cxgvPnaKnVvtDLp8P1O+rEHrjuK+EVQs58IwecWKFYwYMYJ27drhdDp5/PHH6d27N9u2bSMkxBPBGjduHMuXL2fmzJnUrl2bJUuWMHz4cKpXr07//v2L2rr33nt57rnnil7bbCV/Z3Xt2rXY/5eVU6dOMW7cuHIb5IGwV7noiKoTweA513Ddx714+JcePPP3lTTvm8hnz+7lj++L93Nr1Mr7ReB0wNcz9HUIZ5Akibc/jGLMQ2EMHhJMQpKJMbem8vtyYRVRHNU7JZP6xyG/yze61Ouht2Tm8YrokqAcqZFo4miqZ8b18FEXX8/PCcgOx2qRKCwsfluwTeLYUTdff5HHn7/biy3TtJWVlJZWPvuxOp37/PfFd/MR5kxPD+xABIJAOEe5bhctWsQdd9xB06ZNadmyJdOnT+fgwYOsX7++qMwff/zB0KFD6datG7Vr1+a+++6jZcuWrFu3TtVWcHAwCQkJRX8REf5Jas6Ql5fHv//+y6ZNm1R//jBw4MBymRlUEtCMnsEgF9lVyMp4rTaUqbJCUaO0TZE1MQKl/YWuV6JmeKoXyvX9GFXGR3X2oUUV2ig55Kobng0klOUT1vZWdjpLDjNprTDSiwsZ2CKgUTUOFXrCs20mdMNt+I0PHtzNmt8c9HykOQmRCv2QAV79I5yNv6RxbHceC2Yc4eoHGxIa6bl9wgze0Gq4oUC9rxDod79HgNr3ASOP3HKAD1/LpGnnKArwhjK1oRxlCDLUqG4zz6ieTbRbvLexUxOCdbmVrv7qC1Bg9+7f7So5dItORg23pt9OxWuDQX2djJowb6FL02+rldDkKDZ+sI567SKofkk1jBajTwgq6D8LFWs0RMSYyDzl5NevTzJqUg2VTYZZa6+iu02bDaFsSmhVKz4Pv+LcnAMLlUBwq72N1BsVuzhbq5XUE962O7YtXmOpzdrh/u9ePHbMxeWXW1Xbz1ynYJvExp0J3NgvjZ07nPS7LoguvWx07BxERKSBAvnMveYpP+m9OG7tZefAHgcLZmZyw50K423NdVOGHQ2arwxlRo2KCrmqMrqUMZQamIWKzj5Ukgb9Nv19hgLJ/FMWpPMcui3rqtszdbQLHKxWK1Zr6frkzMxMQJ1SrFOnTixYsIC77rqL6tWr8+uvv7Jz507efPNNVd1Zs2Yxc+ZMqlWrxpVXXsnTTz9NWFjpmtqTJ09y5513FquvA/zS1zVs2JAJEyawatUqmjdv7rMYY/To0aW2oUWEbgWYg81cO6ktdTrGs3jSRrYuOkzzHnH0GlGPuNqeX/6hUWYuvyGR08cK+OXTI/z+wyl63xrY1LLJLJGR5uToAQdzPjzF1ffVqIjDqdQ0G3EpaX8f5fTuw/wzfStNBzci9BIL4dV8QweSJNHrphjmvOOZzctOdxIU41NMcAFxYH1tPpqZRdppJ98syCkSaZeG0ymzZ5+ToUNDSixjs0n88LNXfqH1rVRiMEicPO4Z+LvdlWR5o6ByIhNYjklFPYfD4TOb9vTTT/PMM8/oV5Vlxo8fT6dOnWjWzCuFmTp1Kvfeey9JSUmYTCYMBgPTpk2jU6dORWWGDBlCnTp1SEhIYMuWLUyYMIGNGzeydOnSUrs8duxY0tPTWbNmDVdccQXz5s3j+PHjvPDCC7z22mt+Hfa0adMIDQ1lxYoVPlpBSZLEQE9QdiRJokX/ZJLbx7F14SH++XovM0b+w9g5HTEHeWemohODaNklgp9nHScixkyHK/1Ptr58QSZHDzgwWyTqNQ185eDFgCRJxF1Sg7hLw2nUrx57lh7g5zf3Ihkk2t9Um4RG4SjXlFx3XzzfTz9JYZ6bv1dm0XuAfys5BeeHpOpmnn0khgVLsnn/0yy273SQ0qh0vev+A04cDmhQr3w+sg/ssZOX4/nyffnj8tMCCQTFUdZVt2azmbS0NNX7/szmjRw5kk2bNrFq1SrV+1OnTmXNmjUsWLCA5ORkVq5cyfDhw0lMTKRnz56AR593hmbNmtGgQQPatm3Lhg0buOSSS3T3u2zZMubPn0+7du0wGAwkJyfTq1cvwsPDmTx5MldffXWpfd+3b1+pZQJFaPQEKiISg7ns7kbc8+ElnD6cz6Kpu33KtOwWybF9BUwdvZvZr/ivKZs1NY1Le4WyYHsj2nQOLc9uV0mCIqw0vaEhAye1puOQOvy7PJWvH1rP1Ae28ca925g75QD2Apmhj3pMdA/tLiilRcGFQo/ONpKTTNz34An2HXSUWv7kKU/4LSHh7D+yZVnmwaFeTWdyvdIHmgLBWXEWGr3w8HDVX2kDvVGjRrFgwQKWL19OUlJS0fv5+fk89thjvP766/Tr148WLVowcuRIBg8ezKuvvlpie5dccglms5ldu3aVepi5ubnEx8cDnpDxyZMe3Xvz5s3ZsGFDqfUrisB+HkqywjJCka5MM1z3V7/nM52ra72ikzpNR7PnY71S0v6K62uJFbXbdH6u+JseDVUGOJ8dKi0+tOmLVKdb5xi0+jW7yztTl+1UPzwR1UO45L48fnt3I7UGtSGxutfoMS84C7frAACmmnEcsHvjhRFG9UILVSqzUBPWiCAy3aH/9dUbttLqXYINxQvKAQpNas2CQ5FKTal7A3BavMesPX6lvYpDm5JKeU419ipKuY9sULfpdnorOjUavULNpbEo7FWU10KLUXKTlBJGUopntq6WJQ23W2bX+mzeeWQ/WaedmC0SUVFSkX4PUP3f81qp0dOmPCubjscn6ieV/JxWBdc2X1uWsg28bDYDM96O59Zhx2nY4SAd2lj58qN4qieYir0Sz0326I1CQ9T7U2oUfdLRKV4rU9ohweMvxzB26HHaXW7zlNNJgaZn9aMkEDuTQLR2eho9PX2brr1LGfVzevV8+1Zyu/rHXzZKOlr3+bR2OYfIssyoUaOYN28ev/76K3Xq1FFtdzgcOBwODJrPbKPRiNtd8jnaunUrDoeDxMTSs9I0atSIHTt2ULt2bVq1asUHH3xA7dq1ef/99/2qDzB+/Phi35ckiaCgIOrXr0///v39zp0LInQr0CHlhob89fY/HP7zGIkDvHq6eh1iMZol6raPoc2AmoB/q2ir1Qxi54YcUg8U8MePp/lqylEmzm1CveYl644ExWMwSDRqF85llyZz6riD2y/dSUw1c+kVBRcMnTrY2LyyFvMW5nD3mJNM/SCTF5/2FVkePeZizV92zObymdEDaHuZjfmrkwgJF0EdQcUiyeofx37XC7DOiBEj+OKLL5g/fz5hYWGkpnqspyIiIrDZbISHh9O1a1cefvhhbDYbycnJrFixgs8++4zXX38dgD179jBr1iyuuuoqYmNj2bZtGw8++CCtW7fm8ssvL7UPY8eO5dixY4BHS9inTx9mzZqFxWJhxowZfh3H33//zYYNG3C5XDRq1AhZltm1axdGo5HGjRvz7rvv8uCDD7Jq1SpSUlL8alM85YISsYZaCIq0cmzDCdX7obFWeo5qxO4/0lg85V/1DK4O19xTjVOpdsb23MJXU44CsOizE37XFxTPsnkZmMwSl3QWA+bKRliogdsHhfPso1G88UEWK37P9ynzwSfZREcZ2PF39XLNMBMTbyrKR52e5mLMkGNsWS/C/4Jy5hzZq7z33ntkZmbSrVs3EhMTi/6++uqrojKzZ8+mXbt2DBkyhJSUFF588UUmTpzIAw88AIDFYuGXX36hT58+NGrUiNGjR9O7d29+/vlnjMbSYxJDhgzhjjvuAKB169bs37+ftWvXcujQIQYPHuzXcfTv35+ePXty9OhR1q9fz4YNGzhy5Ai9evXi5ptv5siRI3Tp0oVx48b5fW7OIjOGYoPPBSk5fKNyKfGxRVFu0wnP+oR1dXavWdGmjGz6fGSWwzL30sKz6m16DWleKmxCZE28TBla1NqEuBUd0mbUKHR4L3+2Qx26zTB7VnlGXpLM0e2nOZCnCM+a80m6MYoGu5389vFu6vapQ3zDSACizOpFFmEKu5W4lGBeWNyW5bOOsvD9wwD89t0p6rYKo89NMRgUqRRCFJkxtCGYAmPJoVunW32MSksVu7vk4y/QWJ8opQKSU3Oh9EK3iqLaatrrrQzXasPKbp0Vk8qsFgWn8/nmvTSuGhxOXJSsCtdqw7PK19pQrVlznyrDdXqLMn3Cen5mxtA+soGEdf21VCkvCxeX4gPGKFXMb+NHRkXww+I8xj52iqXzqxEZ4d3P1q1OmqeYiYky4vA5c0rLGjXKa+rSfKCYUds8LPo2m3WrC1i3+hiXdbfx6IuxxMR7ng+D4l7U2iC5dELHyhBsQKFbn3Cpf6HbQMKqeijH0oGEWLX3u959qrw3tXY6/t63bp2+uRTPoSsAr8aKokxfrQHW8WfCICEhgenTp5e4vWbNmj4rXc+G4ODgUhdwaHnllVdYunSpKi9ueHg4zzzzDL1792bMmDE89dRT9O7d2+82xYyeoERyD2dwau0BLFG+zvkGk4HLH26PNcLClvn7/W4zMt7CgHG1mbq+Ix9vaUfHa2L49Jn9PHz1Vv5dl116AwIVn73pSak1dKzwVanMSJJE7Vomtu90kNTkMNt22Plnk51HnkrnlxUFrFhdWKaZ76xMN9u2Okqt27Gzjdse8Hyx/L4sn2vbH2LFotwyHYtAoEKWy/5XicjNzeWpp56iWbNmhIaGEhYWRosWLXjuuefIy/M/SUBmZiYnTpzwef/kyZNFnoKRkZHY7SXr2LWIgZ6gRPZ/tQFHdiHmsOKtUExBJhpf15DN3+1n+08HObbltN9t20JNmC0Ghr9en2fmNCU4zMgzN+/gpgbrWDxThHP9ISfTycKvshh8fxSRMUJuW9lJiPfOa7a/IpVOfVP5cLrnx4/RSEBZNM7wzOOZXNc3jbsGpZGb45kZ27fHwU/f5bL0x7yi56xhUwujJkSxelctHns5FoDHHjjB6l9EFhvB2XHGMDngv/Pd8QCw2+107dqVl19+mQYNGjBq1ChGjBhBnTp1mDhxIj169MDhKH11PXhCt3fddRfz5s3j8OHDHDlyhHnz5nH33Xdz3XXXAfDXX3/RsGFDv/snvh0EJXJkyb8ARDUvebVQi1tTOLBsHwufXIvbKXPXtHbUvzQ2oP3UbRHKM182ZkgTT6qa6c8epEGKmZS2woJFjzUL03G5ZK4cFHG+uyIoBx4dE4nJKGG0yNSvY2LzNjvvfOTJXrN7Y/Uytdmgoecjfv2fduZ/k8eAm4K5oc8JHP9NBnzybTwtFRk6zBaJqwd5VndPeiSNH7/JpmN38RwKzoIy6O2K6lUS3nvvPQ4fPszGjRtp1KiRatu///5Lt27deP/99xk1alSpbX3wwQeMGzeOm266CafTsxreZDIxdOhQpkyZAkDjxo2ZNm2a3/0LTKOHYpStCLr7pDLTqO3UyMX870xJndRpyv25NW1qd2FQbipZI1huk0ZK3V8gNiy6ZbVvlNxZlaWKRsLjVuj37FoLEQUujSbMUeDGXeC5yU5uPsHpfi0wmDxllLo/goPpOaUX3w6aD8C2v3KJautd1u4wem+xME0qM5dC3xZmknllUXMe7rsZgFp1LUUaH6VFC/haryhfF5jUyUAdiuMq0KSSyTF5v+B8bBp03Hwkpd2KJqONrBDmuTV6Lo1LC4UmhUbQqX4UC93e19prc4ZNqzJpdkkQcXHGok6WaKmBWpdn1eqgNH0zKB4O7eOm0uWdh5lXpd5MTwel1TDpaZ/Utinq8+1vvbNNjxYTbWTSk1Fs3VnIv7ucfD47l6Agie3rEomO8jy72uM1Kz5wtDo8p8vNmt/t1G9gJLmOiY/fzubGQUE8NzmCjRsdtO9opX1bI5LkwqG4jtkZDiY94jGpvaJXEEEGr/WKW8e+KRCrE+V9WpqFib6ez79yPvX8LKenu/PV6PmnyfNtp2xzV3oaPeU5NV8AU2NnkwKtMjB37lyefPJJn0EeeAZljz/+OHPmzPFroBcaGspHH33ElClT2Lt3L7IsU69ePUJDvT+4WrVqFVD/ROhWUCyFJ716ubRf/2X9g3NxZBW/Ii+sRhjWcAv1r6rHJUMalHmf1evZmPx9MwxGWDjrVJnbuRhwu2W2/ZVN60t99ZOCykN+vpu3pmUQVH0fcxbkYKuxn7ZXHGPchFO880oMx3fUKhrkBconH+Xxx2o7994fwoSnw8nNlRnzQAYDbgzmyRci6XONrdhwcNoJ7w+EFpdYKChwM/F/p1j0ndDsCcpIFdfobdu2jW7dupW4/YorrmDbtm0BtRkaGkqLFi1o2bKlapBXFkToVlAsthpRNHp6ADuenYfBaiJ90xFW3jSdVs9fTXhHdcqkzINZFGQUUu/KOtgiSk9Po0etxsG06xPNnPdOcvyQg9adQ6nZ0EZyI99crxczB//NJyfDRetLxXmpjKSdcjHuyTRmz8spem/VmgJiogx072Lj47diMZk8gzBHGb7vNm92MPkFz4+1jAyZpJomho0O5Z03Sl/wVL+RhfFPRvLGxAyiY42cTHUxf3YO82fn0LxNEIlJ4mtD4D9nNHdlqVdZyMjIICam5AVxMTExZGZmlrh94MCBzJgxg/DwcAYOHKi7r7lz5wbcv8BCt8rMGIop/HK7Hoor62O9otiJNhOHdl5SZbei8Z1ShXK1YYiy3llSCf/37KTEbT7HoZeYw0+bFkljE2IwyMUVA9RhGG1IxuEyEt6xMbG9W5C2ZBMArjw7GyYswPlwd2r0blxUNiguBqPVxLF/s0nqWIPcE7lYI4IwWY24lPvQOUBlePbOifWJidnDws9P8cu36QDM+DOFyFhP6FVr1WBV1NWGdZ0m72xIrlO9zWL0hqRMRnWbyvOolzBFGypXhXW1oVtNVYfZ27cCpzqsXODyvs5zq1NUFchm/vkjF7NVon6rEOyK0K5ZJ8OBKsylOSazNrSkik5r7imdsK6qnOa1XtirrGj75jeaanphN9X+NF5OZbVbkd0SEWFGHh4RSUpDK316WomKNPDGpFifkJyqbz4ffd5tymvYqqmZ7t2tLFtWyOQXsklMMLDkx3zcbjDLbsyaz0WlFYtbhvvutXHvPUFIkpu4CImHnwznleezuKHrYTbsrV7sTGBFhVz1zrCehY+6L2WLX5ZXeLa8UN4b2nu/pFCuXoj3nFHFNXput1vXZ89gMOByab8BvERERBQ9UxER5a+5Fj/NBLrUGXc1SXd2Y+/z35C17RiRzZPYOHEpR5ftImVkZ0KSIjEFmUnokMTub7cSV8vGssdWEFothNb3tWLlM6voO6kjja9M9nufQSFG7nsmCZNZYsEnHq3QhhXZdL/e/5QvVZ39W3Op3TQUi1WoLyoj1eJMvPtSfNFrh1zyAL0sGAwSn38WzZo1hVx/w2lGj8wkKAhqJRtZsriQ3lcWv5JeiXIwd8e9obzyfBYuFwy7/RTvfx7YgivBRUxZZ/TKvycVhizL9OjRA5Op+CHVmUUVJaH09tPz+SsrYqAnKBVzZAitp96CPSMPS2Qw2X9sY/s7q1g5dBZ1BrfmkvsvodWYy/jl/u/45X+/ApB9NIffnl8NwOqpmwIa6J3hrsdrcPXQON548CBTHznEjr/zuOPRRBDRSk4dKSQ26ezC5IKqT8eOVqZNi+SeezIoKIDdu1wMuy+DVX/FkZgYmPZv4muRPP5gBr+vKOTfrXYaN/XONO/818Hs6TmMGBdGfEJVyGosEPjP008/XWqZ66+/3q+28vPzkWWZ4GCP/vrAgQPMmzePlJSUgEySlYiBnsBvLJGeGy+hS33iOtRm75cb2P3ZWgqPnKb9E1fQ94tBbHr1V/JPFdDz1Sv45ZFfSf07labX1Sml5ZKplmRh4pf1WPTFKT576Rh//5bN2A+bUL3+xb0I4dTRQhq1Dy+9oOCi58q+Nq6/oYBv53gXU63+rZAbBgX2DDVsbKJrzyAaNDZRM1n91THx8Qz+XmvnpttDxEBPoEaW9dPrlERZ6pwn/Bno+Uv//v0ZOHAgDzzwABkZGbRv3x6LxUJaWhqvv/46w4YNC7jNchnoafVjsq6oyc96mm261is+/heKNrU7VWlTArmR/NTI6ejutNt85D0689vKunpyIt99yCVuU+LWiK1cGg2PQ5E+rMBlApOJmrddRlDdamybuJCld82j4xv9aftMHwAKgUtfvorVD/7A5nn7qXd9M4xx6nCRWZHKK9it1qjZDd79WQwurro1lku6hDHp/v1MvGUrT3/dlITanvaUab8cRrW9ilIXmKexVwkyeXPDGvU0eujg6z1SIrLGJsVp9x5jocZeJU+h2VNarQDku0xknnIQEhuEQ5M8TGXFonOfaDV5Zs3NqNT1aGSfuvo9VbkKsI0IBH1bFM0+VI5Q2jRjBsX/KiagpNL6+eR0VF5T9TaVtlbnGr4yOZKfFh7n1iHBfP99Pnt3uggqyZpFe0v/p8ls28LEjOmRZzqJ8ma/pJUJt0OmaT0DQf/1UU8/Z9QRNgciRvBXW1leqFKXlXIvVNS9UhLKtGfKu8RxIaxereIavTNs3bqVpk2bFrtt0aJF9O3bt9Q2NmzYUOSXN2fOHBISEvj777/59ttveeqpp8o00BMCH8FZEXd5PTpPG4wz38Hq4d9yYPEu3K7/PuitJjo/cRmFWYX8M33zWWe7SKhlZdLseoRGmnhr1C7sBdovxIuDwjw3Loesyg0sEOhhs0l07WLhw49yOZbqpnkzc6l1ZFlm4Y8FZKR7nzOXS2bZLwW4XOpn+Ymnwvnu+xiCbOKeFGiQy5gZoxIO9Nq2bctbb72leq+wsJCRI0cyYMAAv9rIy8sjLMxjWr5kyRIGDhyIwWCgY8eOHDhwoEz9EgM9wVkTWiuKS6cOIKxONH8+u4xl98/HkeeZZQuvGU79K+uy9YttfH3PrzgLdaa8/NlXhInRbzXg+IECPnhkDy5nJfw0OEvsBZ5zGBFnKaWkQODl2afDmfh8OC88F84115S+GGPDBgcP3J9B6xYnWPBdPgA7dji5+44Mbhnsf7pDgaCq++idYdasWTz77LNceeWVpKam8s8//9C6dWuWLVvG6tWr/Wqjfv36fPfddxw6dIjFixcX6fJOnDhBeHjZ5DoB2qt4w3+64dkyRkdV2S90rE98rFe07ShjTdoIjZ+TQP7amfiU1W7TCZ0atDExP0Oyugk1tKFbP0O+2vPtdKl/A9gpWXfjliWIjyPlheups/0ga8fPY9PnW2hwV0fcskTTsd2Ibl+XPx5fzJ9f7qPlrSkAWBUh1wKjWdOmd/8ujYdJ7cY2Rrxej6mjdzPjyb3cPsnrRq61V1FaN+QZ1QOjIIW9itmgHoCq7iG966K9v5T2KtrLqwnzuhXWL4UO9aNY4PK+znepz01amqdha3QwDlmdNUSZjcPlY/fg7ZBZkyUlSPPaoXhQtJkL9MK6JeeXODe4ZP+uW8VYX6jv07PNlOFtR1K9UqLMemCQda6TJFO3loX77yr9x8GZei2bWLj37hA++jiXMaMySUt188C9HuPWv/50kH1CJkGhxwskVFkR57+sBCI/0DvGiggjuzTXVNlXrfzAXUK5MlsQlRMSlXN2riwMHDiQjh07MnToUJo1a0Zubi533nknr732Gjabf6sIn3rqKW655RbGjRtHjx49uPTSSwHP7F7r1q3L1C8xoycoV6KaV6fWgBbsnbmW/OMec1bJaCDx8mTMwWZcBWc3o3eGtr2iuPv52qz4No2Nyy+u2YWcU57BbFismNGrjJythOFcERpiYOJzEfz1ezyXX2qh7SUWzGaJt96IBOCGm0T2GoEfyGfxVwlxuVzY7XZcLhcul4uEhASsVv8dEm644QYOHjzIunXrWLRoUdH7PXr0KNLuBYpYdSsod+rd3p4Dc/7h5J/7iRvYCLfTzY5Zf2PPtlOtVVy57afL9bGs+ek0n0zYxci3m9CgrViFKrjwyMp28e2POXz1XQ5bdxSSesJFXIyRWjVMNG1sJSHOyJ4DdpxOeGxcFHsP2snLl3E4ZPp0D6Za3PldxVo72cS8Ob6+eTt3OVm/wU6bS8QPDoEeMlIZftyUpc75Zvbs2QwbNozOnTuzc+dO/vnnH+68804WL17M559/Tt26df1qJyEhgYSEBNV77du3L3O/xEBPUO6YQ61ENk3kxKq9NOhdm7UTf+Xoqv20GNKE6m2qld6An0iSxPBX6vL6yD28cscWRr/fhPqXx5desZITFOZ5bAuyy9dkV3B2yLLMnxsKWbwsjz37HRw+4uTgESeHjzlxu6F7Jxt33hRBYoKRtFMu9h10sHlbIctPuzhw2HMt5y1U55OtlWTi8fGR1Ek2UVgo07G9lZDg8xuISWnilRJc2S+NH76LpX07MdgTlICMTwYhv6iEa+3uvvtuXn311aKVsb169WLz5s3cf//9tGrViqysrPPSrzIP9HT1dOWAvkZOK4ySSn6t+VWg1+9AdHmqTX7q8HzSk5VRT6etV1IboH9t9FKg4VZ/mTiUmzXjC2XdMzYssX1bsuvln1hw1QwMFhOXvHA1Kd2rkaeI3NqMXn1ZodZeRfbOYmjTehkVnwCR0UYe/7QhU4bv4t2R/zJ+mkSjdt6ZPYuibrZRLUAPNnn1fNoUaMrr5tJeJ530f6qievnBALfRe44dTvWsTaFCo6e1VzEUeOoV2A2q8wS+ujxVPcX/zRrVhkmjwTQotH4OWR1u12q/StpWXmh1SmWu5+/HlM9F1VEeym6On3TxxZwcPpudzfZdDmKjDTSqb6FWDTOXt7dRJ9lM767B1EoqeaWrGze79zk4eNhBixQrIcESqSddjHjkJA88mKb6GIuNNvD19HiQJTq294aEDNpHWPGclL7G1n86NDeRc7QWX87J5d7Rp/h0ej7d24ciyzLTPs+mWWMLl7ZXP2tnYzVS1ntKq5lUorw3yqq7OxsNpl7f/EVPv6fMj1yWXMnliWcF7cUxo7dhwwYaNWqkei8qKoqvv/6azz///Dz1SszoCSqIuJ5NQQZDdhaJPRsRFBcK5JZaryyYzAZGT63PK/fs5KXbt3LPS/W57NryCxFfaPz5zSGiathIbBwOpJ3v7lyUyLLMol/ymTErh4U/52EySVx3ZQivPx/LFZ1sGI0Skizx/ZJcduy2M+eHHIwGMJkkDEYwGSWSEk306hqMyeQZTNSvY6Z+He+QrE4tAwtnV+d4moNTp104nDDq0TT+WFtIzwGpuN1gMMDef2oSH3vuw7s33xDC8eNuunX2DOrSM9yMedSjl23UwESQReKlZ6PpcplIZXPRc5H46GkHebIsF6USvO22285HlwAx0BNUEJIkEd+7GWGWwtILlwNWm5EJnzbmw8f389Eju6mWHES9lmHnZN/nmj1/naZ572qYLGIt1fmgoMDN3aPTmPdDHq2bW3jtuRgGXRdCXLR3kHbkmJPHXjjNF3OzCQv1XCeXS8bl9vx7JvXlVT2D+f7zGrr7i40xEhvjGcgtn1+dA4cdDL7rBP9sseN2w/ufZPHUI1GA54vl2wV5HE11Ihllena10ahBec7nqRk/IqLo/9FRRu65PYxpn2WzY5fnAH9eUSAGeoKLFqvVysaNG2nSpMl57YcY6AmqDEaTxF2T6rF3Uw7z3zrM+Gnn9+GqKGzhJux55bN6WRAYDofMoLtOsGpNIbM+jOP6a0J9yqzfWMg1txwFJD55sxpDB6kXCX2/JIev5+fwxdxsfl9b4FmFG0BkMznJzDfTq3Hl4GMcPOwkO8fN1/NyKLTLIMP945WrYTMYPDCYV56PIjqq4mf9pr4Yw8QnIklodAhZhpNp6vv05bcymPZ5Nj26BDHyngiaNhbaviqPTKX0xAuE8ePHF/u+y+XixRdfJCYmBoDXX3/9XHariDL46J15pdCT6Xje+X6AVYR/klziKz3PPV/POZ1dqPRzJdfT6vCUZbV6Jq2PnkF3HyXr9/Q0ez7aO2U9pd1gKXpFvX0o67o02j67y/vlYtdozZyKtGoOWevjZlL83/9BjdXo5rphibz74F6O784hUZETN1iTHs1icCrqqXWABoVmTzZpdGiKrvqcFllvm+YcOxXnzaE+/gKHdxYmx6HWOyU0j2XP+jQynTYyTep8pRZFWjllajgAh0Jc6evBpX3tPX4fnVI5fGbr6a7KqskLBD1/NO3xKrVY3y/J4+dfC1j4VQLdO9uKUpfZ7TJ2l5tJb6Tz2nsZtGxqZeGsGsREa65rgZvrhh4DYMBVITw6KhpJkpCQWL46j2qxRho3Us/AGX1yJULtJAtrl9Rk0hvpfPBpFu9My1Ztj4sxkJhgZNNWB1/NzWPtejsLZiVQv27ps3vF7a8ktDq1fQcd1O9wqOj1lGfisSo8Gj+bncPBw06mf5HD9C9yWP1DEh3bqGf8tPo15Rl0+WuE+l/vStyiuMR6ejk9HV4gukPt/a5sV2//pT2nlYWy+OhVJu+9N954g5YtWxIZGal6X5Zltm/fTkhISFEI93wgZvQEVY76LT2zLJmnHCTWP8+dqQBqtIjmn2/3e7KMlJ7gQFCOpJ12YTBAu9ZWjh13cv+4NI4cc7L/kJPcPBlJgifGR/HQ8EhCg31n0IKCDCz8ojq3jzzOvIW5LPk1j2CbgQ5tgvhhiUfDmrW3DjabAbdbZtlv+dSupdbunSE0xMCkx2N4/tFo8gtkTGaZtNMu1qwrZMaX2Sxelk+NRCNHjrnYe8BJs06H2bwqiQZ+DPbKSveBhwFokWJh0ewaRISrz8EfP9ZkzwEH/+6yM/fHHOolV1xfBBcKctlm9CrRLODEiRP56KOPeO211+jevXvR+2azmRkzZpCSknIeeycGeoIqiNvt+YDQrkKsKoTFeUZ3677cQ4MHos5zby4O/t5UyLsfZzHrG89gbM26Aub/lMfvawu446ZwEqsZCQ01cGnbIFo3t+JyyRw87KBGogmjJidxnytC2PZbMguW5JCV5SY9083UaRlF26Ma7EOSKNLxAWxeWZPGDYoPcxqNEqEhEi7ZTWI1EwOuNjHg6hD+2VzIq29nMuf7XIxGcLmgeafDDLsznCkTY8r9HAG8NTmOIKuBnl2Ci90eHWUkOspIu1ZB3Hajr+/lseNOUtMctGzqv8Gs4MJGkjUL1/2tV4nsVSZMmEDPnj259dZb6devH5MnT8ZsvnB+xAQ00DNIclEIz62YtvaxJVHaT+ilCyu3Abv2G10u5n9n9u9ni7oWJiWX1Qt5lhYOVYV5AwjrGitgjlu3bwFsMxl0LA4U100bus1ze7/UjKU88QZN6CMkyoI12MDK707RsL1XLG6W1CFgqzJ0a1KHbq1m72u7RWMvorRNcWluBmVRzTafy6TottupLlugSImWYVdP28XVSQJg2ZStdLz5Ksw2b1mXXHKoSZUCjQLVtjDN/eZviqpAwlcVkYopkLRTSksZbb+V7SitZmRZpv+Q4yz5NV9V/sa7TlBQIDPm3khef069wtvhkOk96Cgr1+RjsUiMvz+SiY+pDYdjoo3ceZP33nxsTDRGI+zZ7+CXVXkAWMwShYUyr76bTt/Bx5j0WAwDrg7GZlMeh+L/mpBruxYmvvowhJ9+yeXmYcfIzvac/2/m52AxGXjl6VifQejZck0vX81iabhkFx/PyqZ+HTPjnjzJlu0Oru8Xwq03hHFN7xC/w7VlDWsGYpOib8WibkfZb2298pAuVJpxUFk1epVoRg+gXbt2rF+/nhEjRtC2bVtmzpx5XsO1SsSyPUGlxO2S+XlmKjvW+hpQhkSYGPJYMivnnGTX39nF1K7cBMd6NU2T2i/EUU5p5QTFczrD85Xat7t3lurBYZEsm1udV572zRixcWshK9fkM/6BSEbeFcGLb6Xzy295uvuwWCSMRomG9SwMGxrJsKGR3H1LBMPvjGTV9zVpkWJh6Kjj1Gx1gLvGnODr+TmkZ/h33a/sEcLWFbW57UbPICzttJs3P8qg321H2X/IUUrtiufwURfDHj5JrxuO0qie5wfet9/nMmBoKrv3nf/+CcoBvTRnJf1VQkJDQ/n000+ZMGECvXr1wuW6MD6bRehWUCk5ebiAmc/tA+Ddv9oQGqm+lbvcEMfiGalMf3If979cj+SUkPPRzQqjxe3N2PTZFgAmtfuRR9dchTXk4nmc7XaZhT8V8O92JwUFMn/8acdsglatLLS5xEzHDhaOHnXz5DOZ/P2Pg759gpj6chRxAfrNSZLEHz8lIcsydjss+TWPzh2DiIzwtFPcDM/ENzxectt22vn+8+pMn53F1GkZ9OhcfDizNKonmPhhZg127bXz6deZLFicx+dfZ2MwQMc2QfToHIzLJbP5Xzu79zpo19rKlOfiivoIUCPRxIypiUx+3Mny1Xk8++ppFi/Po177/bz5Qhwj744sU9/KwsJfcomONBQtwkiqbmTiY9Hk5smMvT+CD16L48t5ObhcUDfZhFxZv/UFHuSLJwXaGW666SY6derE+vXrSU5OPt/dEQM9QeUkprqVhDpBZJ50YDT5ftkaDBL3TK7Lu+N28+R1W3jiyxRiWwUeVrpQqX9V3aKBHsDxnZnUal0xuqsLgcJCmQMHnezd62LbdgfffJPP/gMualQ3YjBAbKyB5Nomlv9awMfTfY25Fy0uoN/ek8z4MIbGDQPXzkiShNUK/fqU/oPhh6We/S9alkdK5wOkZ7iJizl7a5MGdS0892gMzz0aw6EjThYvz2Pxsjze/zQToxFapFjpcqmNr+Znc+y4i5++rO4TOkqsZuKWgeG43TB01HEAxjxxkk3bCnnpyViiIivWgmXTtkL63XoUANexBoDn3D4ySq01fWCoN6x9LlZhCyqYiyB0qyUpKYmkpKTz3Q0gYHsVuUiPpYr5agLAbneJm9S/zbSuDWVMpSb5KvG8//NJCabTjl6aMx3LGJVGT6eenrZP+1qrdVPq1IxaPZVCB6enLfSxd9H5paybDk4Hn32ojkmtKikuddoZCmTvl7HBrW7TIjnBZOTZH9tQkOfCFGIsSvOjPE91WoYz5u36PHHdVgySrEqdBuoUbMEmdYgoSKHRKwhS6/fsCo2e7FTf4ZLijvfJwOXWvla041K3U6jQ6GUXqjV6J02hUCOUpiMvZ+vbqz1vJieRWmhWXVOttjFI8qZ8Czaoj9eosbAx69iNaNOnKSlrqiulZik93c2PCwv4d4eDvXtd7N3n5NAhV9HnSni4RLcuVj6bHu1jRWJAIj3dza8rC/hzrZ3f1xRyRdcgbroxmG69TzDukXQWfBVPcJC6n0pdnlZrFWiqq5Pb6vLca6f5e3MhtWuZeO/leLqVk2nwmb4k17Bw360W7rs10qdMj842Bt2byqZt9hIXNdx6QzhWq0R4qIE16wt47rXTfPxFFsPuiKB5EyvtWllp1cyKoZxXNAXboEFdMyPv8u33GfQ0eefaXiQQTZ7e9sBsYaoYMhdNrtsLFTGjJ6i0GIwSwWEm9AQdJrPng/oC0cSWK8n9mxYN9CwhF84Kr7Jy6LCTt9/NYfbX+djtMnXrmqhb18g1VwVRr66JunVM1KtrIj7eoCtyjooyMKB/MAP6q0Oll7a3sOoPO9M+zWH0/b4rPsuLyAijzwKNc0m/3qFYrRIrfs/TXb16Yz9P5pjunYIJCTawa6+dL+Zmk5ObicsFzRpbuO+2CGokmggNMdClow2LJbAHKTvHzRsfpvPxrCzq1Taz/5CD/YecOJ2Ve7ZG4D8SF1/o9kJDDPQEVZo1P55GMkBs9apn12AKMtNhdGvWvruRrMPZhCdV3pRvc7/LY/zDmQQHS4wcEcLttwUXpf0KZGWtHvXqmtm81UH3rlXbfNBikWje2MIPP+dy760RqlW6xWE2Szw03BM6/eDVahQUuFm9toBX3kln7JMni2ZSWzez8vuPNQMa7D3xYhpvf5xJ9042duxxcOSYZ3a8d7ey6RUFlZCLIDPGhU5g9ioGucj2Qy88q3zDrZ1+1QnPKsOFgdwWsmZ6Xd/CRccWpvhivpu0IVe9bYqNetkltGW1YTdluNZo0G5TZjFQb1PuU2t1ogqrap3bNX1VhlndOidHm4nDpGON4lZYgRTK6hmpPJd3YGbU9M2tDSUqbFOUx79q7gm+e/cog8YnEZ1gIdtVsr1KkFEdygw2e8Oc+RZ131yKMKt2TZXy80xyaZ+MkjNMyBp7Fafd+2jmmtT+aekmbxiwWv82WGbtYNV722n3VA/MiuMP0oRngw3eYwozqO1VLBq7Favi+muHRcp7w6wTvvJXW+V0yjzxVBZdOlt5d2okthD/BxI+NimK18ptdrvMz8sKGHhtCM0aW1ShWlCH2QIN1V6IPDQiiluHp9Ks6wHmzahOixT/f+gEBRno0TmYHp2DcTplTme46HjlIf7eUkhGlov4WP+/NkL+G2QuW+W1p3n5qVgaNzLrZoTwl7JmtVCX8/9+CyRrSFlRhqcrwpJIcPEhZvQEVZKs0w6mPboHgITaVXcGxxRkptHQNmyc8hvNhnWAmue7R4Gz+vdC0k65GTsqlNBQQ4WI7194JZPUEy6G3V11FuTocWO/MFo1tXLz/al06neI+26LICHeREy0gZgoIyHBBpo1tlAtTv8rwGSS2LHbwYHDnh9F783IJD7WiNsNjepbaN3M6pPmTcmkx2Pp2TWYnFw30ZFGGtUzExdrKpdBnqCyUPUzY1zoiIGeoEoSFmXi3pfq88MHh3n3wT207BJRZdOFVe9Sh81vrWbnrH+o92jz892dgFm0tICaSUZatyp/naHbLfPqm1m8+W42t98Uwl/r7DRpaK6YlNsXGA3qWlgxP4knJp/im+9zyMl1k5GpHmAVZ/isJbGakdo1TRQUykz5IIO8fDdGo4TdLmM0wm03hjP58ZgSZ/q6dxJh2osasRjjvCMGeoIqh9st89WLB/j16+MU5rmpnRKMJchAVbVetcWF0HzEZWx8YxVrYyXa3dPsfHcpII4cddGggalCXOQffCyd6TNzqVfHxGezc/lsdi6DBgRjvkjGHiHBBqY8H8eU5z2DObtdJiPLRWaWm/dmZPLmRxl0aBPE4P4l6zvr17Gw5686Ra9lWcbthr0HHPywJNdjCL0yj4VfVCelUdXTwgrOErmMCyvEjF65UeYUaHpOKHrWK7Li4unaqQRitaKjfdPV72mb0dumU07PQkUvdZl+ujR1O0odntngKnGbxajeptTI6aUn09PSgb5Gz+k2FFtOuw+fNpUp0NwaexHJe2saJbVGLVhzTymPyyUb2LMxm8UzjtGgTTjX3BFHm+4RmIwyZrf63Cg1bCEmu2pbiEKjl2dR79+p0N7ZNcfrUqZHK0XOo7RXkTQ2LS6793WBUT3TlWnwTk2e0VZGXtOB2pkyf763mkKjjcZDWmAzqo8p1OjV4WUb1dObQZLaQga8r7Up9vTm3VS6KB87neKfL5MRnC7Z75CtUlOll6pt/g/5fPJ5LhOfiuTx5zIAePzBSEKDTWdtoVJZsVgk4mNNxMXIdGgTBB/BwcOB/QSSJAmj0TNjOO4BC4P6hzHwzqM073aQnl2CqVPLxEtPxhIR7p8nX2nWI0rNWiAhX2VZ7fU9G9uUktrU278egUgVKq2noBjonVcujk83wUVFneah1G0ZisEIHfpGYbJcHLd58pCOpNzRmn/e/pPd87af7+74TX6BjC2ofGfz8vLdjPnfaQb0C2bkvaE0qGvimj42JoyLKL1yFefIMScD7zzGLQ+kcv01obqedv5QI9HEbwtq8vJTnnRwH83MYvqXvqkJlTgcMvn5IjZ3USDLZf8TlAsBzei58gq9/1eavWpmNZSvtTM8ymundx0DMU/Wux0CaafsM3rF/x9ADmBGD4OyrPpDUFLM1EmaGT1ZMWtm0MzoKfevndFT1pMraEZPuQ+jZmWrXTHjZNJsMyteGzTbDAb17JNb8Vr+b2aq7ZWxzHllPycOFRalR8uX1fUK7d527QXq2S9Hvve18r4HcBUoZhgK1efbXaiYxSjQzGjYSx5wun1mNZyKbep+u2Rv35xudd8aD2lOQXoe615dRe3ky6ne0puLNd/pbSfXrO63zajev/J+cGkMul2Km9yuueGVR6id0XAojlG1stDlme3Lzi7+HtTaqxhKWFmrZPnKfNIz3IwbHkZuHqz4KQEDBnJyPYk0zT5tXhxfKnsPOOh+/WGsFolP36rGtX1CcDhlHNkunE7IynZzPM3J4SNOcgvchId6Fm9UTzAVZfeQZU8as46X2Fi2KpefV+bzz5ZC0tJdnE53Y7VC8xQLWdnF5/n8fkkut4/0ZOXodlkQn74bp2sBU9YZPSXa1vVn9PxtU/+eUd3jmudbuc2hs01rEK1+htS4VG1638/JuQDu7TIN2i6AflcRJFku/QoUFBRQp04dUlNTz0WfBAKBQCAQlBMJCQns27ePoKBzuyJt/PjxzP10NY0TegZcN6cwjb+PzSQ31zeloSAw/JrRCwoKYt++fdjt9tILCwQCgUAguGCwWCznfJDnpayZMSqgKxcpfodug4KCzuONIhAIBAKBoNJR1swYQqNXbgh7FYFAIBAIBBWDLINbDPTOJ2KgJxAIBAKBoOIQM3rnlYvDd0IgEAgEAoHgIkTM6AkEAoFAIKgYyuqJJ2b0yg0x0BMIBAKBQFBxiIHeeUUM9AQCgUAgEFQMMmVbjFGWOoJiEQM9gUAgEAgEFYQMpeQzLr6aGOiVF2KgJxAIBAKBoGIoc95aMdArL8RATyAQCAQCQcUgQrfnHTHQEwgEAoFAUEGIVbfnG+GjJxAIBAKBoGI4kwKtLH8BMHnyZNq1a0dYWBjx8fFcd9117NixQ1UmJyeHkSNHkpSUhM1mo0mTJrz33nuqMoWFhYwaNYrY2FhCQkK49tprOXz48NmehfOKGOgJBAKBQCCoIMo4yAtwQm/FihWMGDGCNWvWsHTpUpxOJ7179yY3N7eozLhx41i0aBEzZ85k+/btjBs3jlGjRjF//vyiMmPHjmXevHnMnj2bVatWkZOTwzXXXIPL5SqvE3LOEaFbgUAgEAgEFYMMuMuy6jawOosWLVK9nj59OvHx8axfv54uXboA8McffzB06FC6desGwH333ccHH3zAunXr6N+/P5mZmXz88cd8/vnn9OzZE4CZM2dSs2ZNfv75Z/r06RP4cVwAiBk9gUAgEAgEFyRZWVmqv8LCQr/qZWZmAhAdHV30XqdOnViwYAFHjhxBlmWWL1/Ozp07iwZw69evx+Fw0Lt376I61atXp1mzZvz+++/leFTnFjHQEwgEAoFAUEGUNXQr43A4iIiIUP1Nnjy59D3KMuPHj6dTp040a9as6P2pU6eSkpJCUlISFouFvn378u6779KpUycAUlNTsVgsREVFqdqrVq0aqamp5XtaziEidCsQCAQCgaBiOLMYI+B6MmazmbS0NNXbVqu11KojR45k06ZNrFq1SvX+1KlTWbNmDQsWLCA5OZmVK1cyfPhwEhMTi0K1xXdFRpKkwI/hAkEM9AQCgUAgEFQMsnxWPnrh4eEBVRs1ahQLFixg5cqVJCUlFb2fn5/PY489xrx587j66qsBaNGiBf/88w+vvvoqPXv2JCEhAbvdTnp6umpW78SJE1x22WWBH8MFggjdCgQCgUAgqCBkZNkd+F+Ay25lWWbkyJHMnTuXZcuWUadOHdV2h8OBw+HAYFAPe4xGI+7/Fou0adMGs9nM0qVLi7YfO3aMLVu2VOqBnpjREwgEAoFAUDGco8wYI0aM4IsvvmD+/PmEhYUVaeoiIiKw2WyEh4fTtWtXHn74YWw2G8nJyaxYsYLPPvuM119/vajs3XffzYMPPkhMTAzR0dE89NBDNG/eXDe0e6EjBnoCgUAgEAgqiHOTGeOM8fEZ65QzTJ8+nTvuuAOA2bNnM2HCBIYMGcLp06dJTk5m4sSJPPDAA0Xlp0yZgslkYtCgQeTn59OjRw9mzJiB0WgM/BguECRZFnlGBAKBQCAQlC/jx49n7jtLaBzUPuC6Oa4M/pYXqwyPBWVDzOgJBAKBQCCoGMqQzqyonqBcEAM9gUAgEAgEFYMsI5cpM4YMldfR5IJCDPQEAoFAIBBUHGWd0RMDvXJBDPQEAoFAIBBUDGfjoycM4MoFcRoFAoFAIBAIqihiRk8gEAgEAkEFIYNcFo1eGeoIikUM9AQCgUAgEFQIsgxyGUK3YtFt+SEGegKBQCAQCCoGWczonW/EQE8gEAgEAkHFUOYZPTGlV16IgZ5AIBAIBIIKQcZdptk5WczolRti1a1AIBAIBIJyp02bNpzkKC7ZFXDdVA5iyQuugF5dfIiBnkAgEAgEgnLn5ptvxoyFQ+wOqF6BnM9BdrFozQ8V1LOLCzHQEwgEAoFAUO4YDAbm/TyH/fyLXS70u95ethJHIh06dKjA3l08SLJQPAoEAoFAIKggYqVEggmlkdSq1LI5ciZ/8Qs7d++kXr16Fd+5iwAxoycQCAQCgaDCWL5pCUfYS56cU2rZXWyiBnXFIK8cETN6AoFAIBAIKpQaUh1cOGkudSyxzCn5OJtZQ2raMWJiYs5h76o2YkZPIBAIBAJBhfLX4VWc5BiZ8qlit8uyzC42U5vGYpBXzoiBnkAgEAgEggqlRo0aJNOAXWwu1gw5lYM4sbM5/6/z0LuqjRjoCQQCgUAgqHA2Zv1FLtmc5JjqfZfsYjdb+OjzDwgKCjpPvau6iIGeQCAQCASCCicsLIw33nmd3WzGrch8cYhdWLByyy23nMfeVV3EQE8gEAgEAsE54d577wVkjrIPALtcyH52MO/nORgMYkhSEYizKhAIBAKB4JxgNpuZNe9z9rINp+xgH9uJIIYePXqc765VWYS9ikAgEAgEgnOGLMtEG+IIIoQTHGbj5o00a9bsfHeryiJm9AQCgUAgEJwzJEli0ZofSeUgCdQSg7wKRszoCQQCgUAgOOesWrWKlJQUoqOjz3dXqjRioCcQCAQCgUBQRRGhW4FAIBAIBIIqihjoCQQCgUAgEFRRxEBPIBAIBAKBoIoiBnoCgUAgEAgEVRQx0BMIBAKBQCCoooiBnkAgEAgEAkEVRQz0BAKBQCAQCKooYqAnEAgEAoFAUEX5P2JcFSL7/lY/AAAAAElFTkSuQmCC",
+            "text/plain": [
+              "<Figure size 800x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "first = merged.isel(time=0)\n",
+        "\n",
+        "if ccrs is not None:\n",
+        "    proj = ccrs.PlateCarree()\n",
+        "    extent = [LON_RANGE_180[0], LON_RANGE_180[1], LAT_RANGE[0], LAT_RANGE[1]]\n",
+        "\n",
+        "    # Air temperature\n",
+        "    fig = plt.figure(figsize=(8,5))\n",
+        "    ax = plt.axes(projection=proj)\n",
+        "    first[\"air_temperature\"].plot(ax=ax, transform=proj, robust=True, cbar_kwargs={\"shrink\":0.8})\n",
+        "    ax.coastlines()\n",
+        "    ax.add_feature(cfeature.BORDERS, linewidth=0.5)\n",
+        "    ax.set_extent(extent, crs=proj)\n",
+        "    ax.set_title(f\"Air Temperature (t0) — {str(first.time.values)[:10]}\")\n",
+        "    plt.show()\n",
+        "\n",
+        "    # Daily precip\n",
+        "    fig = plt.figure(figsize=(8,5))\n",
+        "    ax = plt.axes(projection=proj)\n",
+        "    first[\"daily_precip\"].plot(ax=ax, transform=proj, robust=True, cbar_kwargs={\"shrink\":0.8})\n",
+        "    ax.coastlines()\n",
+        "    ax.add_feature(cfeature.BORDERS, linewidth=0.5)\n",
+        "    ax.set_extent(extent, crs=proj)\n",
+        "    ax.set_title(f\"Daily Precip (t0) — {str(first.time.values)[:10]}\")\n",
+        "    plt.show()\n",
+        "else:\n",
+        "    # Fallback: simple xarray plots without Cartopy\n",
+        "    first[\"air_temperature\"].plot(robust=True)\n",
+        "    plt.title(f\"Air Temperature (t0) — {str(first.time.values)[:10]}\")\n",
+        "    plt.show()\n",
+        "\n",
+        "    first[\"daily_precip\"].plot(robust=True)\n",
+        "    plt.title(f\"Daily Precip (t0) — {str(first.time.values)[:10]}\")\n",
+        "    plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "89019fba",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Pearson r (t0): 0.022\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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99NP79Jcjv6DRLhnC30lq3bp1YV9XKBRJ/Ty32w2tVhtywWo0GgB9L+LecnJycMEFF+C1117D8uXLsWbNGkgkElx22WUh+82cORMzZ86Ex+PBd999h9WrV2PhwoUoLCzEb3/724TSPWnSpMBol0jCPc3m5eVBKpXi73//e9j3+I+Zn58Pj8cDjUYTUwHdmz8Pe28TiUTIysqK+D7/5+t0upg/a8KECQCAH3/8EVOnTg35vK6urkAtVDCdThc1/2688Uace+65MacjmP9mEU5+fj4A4OjRoygvLw+7j//ca2tr6/Naa2trv2kPfm/vwCL4vcHpiMRgMOCjjz7CkiVL8Mc//jGw3eFwxPwb+T9v9erVEUdo+DtocxwX8dyJxv+9/cFO7/eHq/2IZbST/7iJpqu/fXuXNbm5uRF/cwBRz9mBEggEWLRoERYtWgS9Xo8NGzbg/vvvx5lnnokjR44kfbRcJqCajwxx7rnnQqvVwuPx4IQTTujzL3gERLK88cYbIX/7h3jGMnnWtddei9bWVnzyySf4xz/+gQsvvBDZ2dlh9+Xz+Zg6dSr++te/AkCgqnUwnXvuuTh06BByc3PD5q+/gJ43bx4A38iTRKxduzaklslkMuE///kPZs6cCT6fH/F9/uaxQ4cO9XlNLBaHfXo766yzIJFI+kya5O/9P3/+/D7vOXz4cNRhwiUlJWHzKJZ//QUfc+fOBZ/P7zdvTz75ZEilUvzjH/8I2X706FF8/vnnOP300yO+19/U1vu927dvx759+wLvnT59OlQqFV544QUwxsIei+M4MMYCT89+L7/8cp/ms0hP2DNmzEB2djb27t0bMb9EIlFgVFqkcyeaqVOnQiwW45///GfI9m3btsXU5BDJmDFjUFxcjLfeeiskn5qamrB169aYj7Nnzx7s2rUrZNubb74JhUKB4447DgBw+umnY+/evX3Khtdeew0cx+G0005L+HsEi3QtBcvOzsbFF1+MW2+9FTqdLiWTx2UCqvnIEL/97W/xxhtv4Oyzz8add96Jk046CUKhEEePHsWmTZtwwQUX4MILL0za54lEIjzxxBMwm8048cQTsXXrVixfvhzz5s3DKaecEvX9c+fORVlZGW655RZoNJo+wz1feOEFfP755zjnnHNQUVEBu90eqHU444wzAvvV1tYCAA4ePJi07xbOwoUL8d577+HUU0/FXXfdhYkTJ8Lr9aK5uRnr16/H3XffjalTp2LmzJm48sorsXz5crS3t+Pcc8+FWCzGDz/8AJlMhttvv73fz+Hz+ZgzZw4WLVoEr9eLlStXwmg0YtmyZf2+r6ysDDU1Ndi2bRvuuOOOkNcmTJiAt99+G//85z9RU1MDiUSCCRMmQK1W48EHH8TixYuhVqsDk4wtXboU119/fZ8gQ6vV4sCBA1G/Q6pUVVXh/vvvx0MPPQSbzYbLLrsMKpUKe/fuRVdXF5YtW4bs7GwsXrwY999/P6666ipcdtll0Gq1WLZsGSQSCZYsWRLx+GPGjMGNN96I1atXg8fjYd68eWhsbMTixYtRXl6Ou+66CwCQlZWFJ554Atdffz3OOOMM3HDDDSgsLMTBgwexa9cuPPvss1AqlTj11FPx+OOPIy8vD1VVVdi8eTP+9re/9Qmy/TVML774IhQKBSQSCaqrq5Gbm4vVq1fj6quvhk6nw8UXX4yCggJ0dnZi165d6OzsDARiDz30EM466yzMmTMHd999NzweD1auXAm5XB61pkWtVmPRokV45JFHkJOTgwsvvBBHjx7FsmXLUFxcDB4vsWdUHo+Hhx56CNdffz0uvPBC3HDDDdDr9Vi6dGlczS4lJSU4//zzsXTpUhQXF+Mf//gHPvvsM6xcuTJQo3DXXXfhtddewznnnIM///nPqKysxMcff4znnnsON998M+rq6hL6Dr1FupbOO+88jB8/HieccALy8/PR1NSEp556CpWVlRg9enRSPjvjDGVvV5K43qNdGGPM5XKxv/zlL2zSpElMIpGwrKwsdswxx7CbbrqJHThwILBfZWUlO+ecc/ocEwC79dZbQ7b5R2EED2vzf/bu3bvZ7NmzmVQqZWq1mt18881hh21Gcv/99weGl3o8npDXvv76a3bhhReyyspKJhaLWW5uLps1a1bIqBv/d4ml13ykoba9zZo1i40bNy7sa2azmT344INszJgxTCQSMZVKxSZMmMDuuusuptFoAvt5PB725JNPsvHjxwf2O/nkk9l//vOfkM8JN9pl5cqVbNmyZaysrIyJRCI2ZcoU9t///jfq92PMNww1JyeH2e32kO2NjY1s7ty5TKFQMAB98uvpp59mdXV1geHTS5YsYU6ns8/x//a3vzGhUBjyXYfCa6+9xk488cTAOT5lyhS2Zs2akH1efvllNnHixED+X3DBBWzPnj0h+4QbauvxeNjKlStZXV0dEwqFLC8vj11xxRXsyJEjfdLxySefsFmzZjG5XM5kMhkbO3YsW7lyZeD1o0ePsl//+tcsJyeHKRQKdtZZZ7GffvqJVVZWsquvvjrkWE899RSrrq5mfD6fAQj5Pps3b2bnnHMOU6vVTCgUstLSUnbOOeewd955J+QYH374YeA7V1RUsEcffTTsdwzH6/Wy5cuXB867iRMnso8++ohNmjSJXXjhhYH9/CNaen928Gu9R+28/PLLbPTo0UwkErG6ujr297//nV199dUxj3Y555xz2LvvvsvGjRvHRCIRq6qqYqtWreqzb1NTE1uwYAHLzc1lQqGQjRkzhj3++ON9yhZEGO3SeyRbuO8T6Vp64okn2PTp01leXl4g/3/3u9+xxsbGqN9xpOIYi1BvSEgE11xzDd59991+O0CS+DQ2NqK6uhqPP/44/vCHPyR0jNbWVlRXV+O1117Db37zmySn0NcHp6Kiok9zG8lMDQ0NOOaYY7BkyRLcf//9Q5KGqqoqjB8/PjABIskc1OxCSIYoKSnBwoUL8fDDD+OSSy5JuLo8nC1btmD79u149dVXk3ZMkj527dqFt956C9OnT4dSqcT+/fvx2GOPQalU4ne/+91QJ49kIAo+CMkgDz74IGQyGVpaWiKOCEmEVqvFa6+9hpqamqQdk6QPuVyO7777Dn/729+g1+uhUqkwe/ZsPPzwwxGH2xIyENTsQgghhJBBRUNtCSGEEDKoKPgghBBCyKCi4IMQQgghg2pEdDj1er1obW2FQqGIaVpgQgghhPgwxmAymVBSUpK0UXQjIvhobW1Nas9/QgghZKQ5cuRI1AUVYzUigg//ompHjhyBUqlM6rEZYzAYDFCpVFSrEgHlUWwon6KjPIqO8ig6yqPogvPIZDKhvLw8qQuUjojgw39yKZXKlAQfjDEolUo6iSOgPIoN5VN0lEfRUR5FR3kUXbg8SmZeUYdTQgghhAwqCj4IIYQQMqgo+CCEEELIoKLggxBCCCGDioIPQgghhAwqCj4IIYQQMqgo+CCEEELIoKLggxBCCCGDioIPQgghhAwqCj4IIYQQMqgo+CCEEELIoKLggxBCCCGDioIPQgghhAyqEbGqLSGEEBIPncUJo80FpVQItVw01MnJOBR8EEIIIUF0Fie2HdbCYHNBJRViWk0uBSBJRs0uhBBCSBCjzQWDzYWqXDkMNheMNtdQJynjUPBBCCGEBFFKhVBJhWjUWqCSCqGUCoc6SRmHml0IIYSQIGq5CNNqcqnPRwpR8EEIIYT0opaLKOhIIWp2IYQQQsigouCDEEIIIYOKgg9CCCGEDCoKPgghhBAyqCj4IIQQQsigouCDEEIIIYOKgg9CCCGEDCoKPgghhBAyqCj4IIQQQsigouCDEEIIIYOKgg9CCCGEDCoKPgghhBAyqCj4IIQQQsigouCDEEIIIYOKgg9CCCGEDCoKPgghhBAyqCj4IIQQQsigouCDEEIIIYNqyIOPlpYWXHHFFcjNzYVMJsPkyZPx/fffB15njGHp0qUoKSmBVCrF7NmzsWfPniFMMSGEEEIGYkiDj+7ubsyYMQNCoRCffvop9u7diyeeeALZ2dmBfR577DGsWrUKzz77LLZv346ioiLMmTMHJpNp6BJOCCGEkIQJhvLDV65cifLycqxZsyawraqqKvB/xhieeuopPPDAA7jooosAAK+++ioKCwvx5ptv4qabbhrsJBNCCCFkgIY0+Pjwww9x5pln4pJLLsHmzZtRWlqKW265BTfccAMAoKGhARqNBnPnzg28RywWY9asWdi6dWvE4MPhcMDhcAT+NhqNAHzBDGMsqd/Bf8xkHzeTUB7FhvIpOsqj6CiPoqM8ii44j1KRT0MafBw+fBjPP/88Fi1ahPvvvx/ffvst7rjjDojFYlx11VXQaDQAgMLCwpD3FRYWoqmpKeJxH3nkESxbtqzPdoPBkJLgw2w2AwA4jkvqsTMF5VFsKJ+iozyKjvIoOsqj6ILzKBXdHIY0+PB6vTjhhBOwYsUKAMCUKVOwZ88ePP/887jqqqsC+/U+ORhj/Z4w9913HxYtWhT422g0ory8HCqVCkqlMqnfwR/MqFQqOokjoDyKDeVTdJRH0VEeRUd5FF1wHvF4ye8eGnfwwRjD5s2b8eWXX6KxsRFWqxX5+fmYMmUKzjjjDJSXl8d8rOLiYowdOzZk27HHHov33nsPAFBUVAQA0Gg0KC4uDuzT0dHRpzYkmFgshlgs7rOd47iUnGj+49JJHBnlUWwon6KjPIqO8ig6yqPoUplHMYczNpsNK1asQHl5OebNm4ePP/4Yer0efD4fBw8exJIlS1BdXY2zzz4b27Zti+mYM2bMwP79+0O21dfXo7KyEgBQXV2NoqIifPbZZ4HXnU4nNm/ejOnTp8eadEIIIYSkkZhrPurq6jB16lS88MILOPPMMyEUCvvs09TUhDfffBO/+c1v8OCDDwY6jkZy1113Yfr06VixYgUuvfRSfPvtt3jxxRfx4osvAvBFXQsXLsSKFSswevRojB49GitWrIBMJsOCBQvi/KqEEEIISQcxBx+ffvopxo8f3+8+lZWVuO+++3D33Xf32yHU78QTT8T777+P++67D3/+859RXV2Np556Cpdffnlgn3vuuQc2mw233HILuru7MXXqVKxfvx4KhSLWpBNCCCEkjXBsBIw1MhqNUKlUMBgMKelwajAYqONSPyiPYkP5FB3lUXSUR9FRHkUXnEcmkynp99Ahn16dEEIIISMLBR+EEEIIGVQxBx+MMSxevDiVaSGEEELICBBT8NHZ2Yk5c+ZAr9enODmEEEIIyXQxBR8vvPAC5s6di9WrV6c6PYQQQgjJcDENtb399ttDlrknhBBCCElUTDUfFHgQQgghJFkSXliuu7sbf/vb37Bv3z5wHIdjjjkG1113HdRqdTLTRwghhJAMk9BQ282bN6O6uhrPPPMMuru7odPpsHr1alRXV2Pz5s3JTiMhhBBCMkhCNR+33norLr30Ujz//PPg8/kAAI/Hg1tuuQW33norfvrpp6QmkhBCCCGZI6Gaj0OHDuHuu+8OBB4AwOfzsWjRIhw6dChpiSOEEEJI5kko+DjuuOOwb9++Ptv37duHyZMnDzRNhBBCCMlgCTW73HHHHbjzzjtx8OBBTJs2DQCwbds2/PWvf8Wjjz6K3bt3B/adOHFiclJKCCGEkIyQUPBx2WWXAfAtdx/uNY7jwBgDx3HweDwDSyEhhBBCMkpCwUdDQ0Oy00EIIYSQESKh4KOysjLZ6SCEEELICBFz8PHhhx9i3rx5EAqF+PDDD/vd9/zzzx9wwgghhBCSmWIOPubPnw+NRoOCggLMnz8/4n7Uz4MQQggh/Yk5+PB6vWH/TwghhBASj4Tm+SCEEEIISVRCwccdd9yBZ555ps/2Z599FgsXLhxomgghhBCSwRIKPt577z3MmDGjz/bp06fj3XffHXCiCCGEEJK5Ego+tFotVCpVn+1KpRJdXV0DThQhhBBCMldCwUdtbS3WrVvXZ/unn36KmpqaASeKEEIIIZkroUnGFi1ahNtuuw2dnZ341a9+BQDYuHEjnnjiCTz11FPJTB8hhBBCMkxCwcd1110Hh8OBhx9+GA899BAAoKqqCs8//zyuuuqqpCaQEEIIIZkloeADAG6++WbcfPPN6OzshFQqRVZWVjLTRQghhJAMlfA8H263Gxs2bMDatWvBGAMAtLa2wmw2Jy1xhBBCCMk8CdV8NDU14ayzzkJzczMcDgfmzJkDhUKBxx57DHa7HS+88EKy00kIIYSQDJFQzcedd96JE044Ad3d3ZBKpYHtF154ITZu3Ji0xBFCCCEk8yRU8/HVV1/hf//7H0QiUcj2yspKtLS0JCVhhBBCCMlMCdV8eL3esCvXHj16FAqFYsCJIoQQQkjmSij4mDNnTsh8HhzHwWw2Y8mSJTj77LOTlTZCCCGEZKCEml1WrVqFX/3qVxg7dizsdjsWLFiAAwcOIC8vD2+99Vay00gIIYSQDJJQ8FFaWoqdO3fi7bffxvfffw+v14vf/e53uPzyy0M6oBJCCCGE9BZ38OFyuTBmzBh89NFHuPbaa3HttdemIl2EEELIoNNZnDDaXFBKhVDLRdHfQBISd/AhFArhcDjAcVwq0kMIIYQMCZ3FiW2HtTDYXFBJhZhWk0sBSIok1OH09ttvx8qVK+F2u5OdHkIIIWRIGG0uGGwuVOXKYbC5YLS5hjpJGSuhPh/ffPMNNm7ciPXr12PChAmQy+Uhr69duzYpiSOEEEIGi1IqhEoqRKPWApVUCKVUONRJylgJBR/Z2dn49a9/ney0EEIIIUNGLRdhWk0u9fkYBAkFH2vWrEl2OgghhJAhp5aLKOgYBAkFH34dHR3Yv38/OI5DXV0dCgoKkpUuQgghhGSohDqcGo1GXHnllSgtLcWsWbNw6qmnorS0FFdccQUMBkOy00gIIYSQDJJQ8HH99dfjm2++wUcffQS9Xg+DwYCPPvoI3333HW644YaYj7N06VJwHBfyr6ioKPA6YwxLly5FSUkJpFIpZs+ejT179iSSZEIIIYSkiYSaXT7++GP897//xSmnnBLYduaZZ+Kll17CWWedFdexxo0bhw0bNgT+5vP5gf8/9thjWLVqFV555RXU1dVh+fLlmDNnDvbv308L2BFCCCHDVEI1H7m5uVCpVH22q1Qq5OTkxHUsgUCAoqKiwL/8/HwAvlqPp556Cg888AAuuugijB8/Hq+++iqsVivefPPNRJJNCCGEkDSQUM3Hgw8+iEWLFuG1115DcXExAECj0eD//b//h8WLF8d1rAMHDqCkpARisRhTp07FihUrUFNTg4aGBmg0GsydOzewr1gsxqxZs7B161bcdNNNEY/pcDjgcDgCfxuNRgC+gIYxFlf6ovEfM9nHzSSUR7GhfIqO8ig6yqPoKI+iC86jVORTQsHH888/j4MHD6KyshIVFRUAgObmZojFYnR2duL//u//Avvu2LEj4nGmTp2K1157DXV1dWhvb8fy5csxffp07NmzBxqNBgBQWFgY8p7CwkI0NTX1m75HHnkEy5Yt67PdYDCkJPgwm80AQFPOR0B5FBvKp+goj6KjPIqO8ii64DwymUxJP35Cwcf8+fOT8uHz5s0L/H/ChAk4+eSTMWrUKLz66quYNm0agL4nBmMs6sly3333YdGiRYG/jUYjysvLoVKpoFQqk5L24PQAviYnOonDozyKDeVTdJRH0VEeRUd5FF1wHvF4CfXQ6FdCwceSJUti2u+tt96CxWLpM/16JHK5HBMmTMCBAwcCAY5Gowk07QC+uUV614b0JhaLIRaL+2z3j6hJtuDROiQ8yqPYUD5FR3kUHeVRdJRH0aUyj5IfzgS56aab0N7eHvP+DocD+/btQ3FxMaqrq1FUVITPPvss8LrT6cTmzZsxffr0VCSXEEIIIYNgQDOcRhOtf8Uf/vAHnHfeeaioqEBHRweWL18Oo9GIq6++GhzHYeHChVixYgVGjx6N0aNHY8WKFZDJZFiwYEEqk00IIYSQFEpp8BHN0aNHcdlll6Grqwv5+fmYNm0atm3bhsrKSgDAPffcA5vNhltuuQXd3d2YOnUq1q9fT3N8EEIIIcPYkAYfb7/9dr+vcxyHpUuXYunSpYOTIEIIIYSkXEr7fBBCCCGE9EbBByGEEEIGVUqbXSorKyEUClP5EYQQQsiQ0VmcMNpcUEqFUMtFQ52cYSOhmo9rrrkGW7ZsibrfTz/9hPLy8kQ+ghBCCElrOosT2w5r8fVhLbYd1kJncQ51koaNhIIPk8mEuXPnBoa/trS0JDtdhBBCSFoz2lww2FyoypXDYHPBaHMNdZKGjYSCj/feew8tLS247bbb8M4776Cqqgrz5s3Du+++C5eLMp8QQkjmU0qFUEmFaNRaoJIKoZRSN4NYJdzhNDc3F3feeSd++OEHfPvtt6itrcWVV16JkpIS3HXXXThw4EAy00kIIYSkFbVchGk1uTi5JhfTanKpz0ccBjzapa2tDevXr8f69evB5/Nx9tlnY8+ePRg7diyefPLJZKSREEIISUtquQhVeXIKPOKUUPDhcrnw3nvv4dxzz0VlZSXeeecd3HXXXWhra8Orr76K9evX4/XXX8ef//znZKeXEEKGPZ3FicYuC3VQJCNWQkNti4uL4fV6cdlll+Hbb7/F5MmT++xz5plnIjs7e4DJI4SQzOIfIWGwuaCSCqm6noxICQUfTz75JC655BJIJJKI++Tk5KChoSHhhBFCSCYKHiHRqLXAaHNR8EFGnISCjyuvvDLZ6SCEkBGBRkgQEkfwcdFFF8V80LVr1yaUGEIIyXT+ERI0KyYZyWIOPlQqVSrTQQghI4ZaLqKgg4xoMQcfa9asSWU6CCGEEDJC0Kq2hBBCCBlUMdd8HHfccdi4cSNycnIwZcoUcBwXcd8dO3YkJXGEEEIIyTwxBx8XXHABxGIxAGD+/PmpSg8hhBBCMlzMwceSJUvC/p8QQgghJB7U54MQQgghgyqhScY8Hg+efPJJ/Otf/0JzczOcztD1CXQ6XVISRwghhJDMk1DNx7Jly7Bq1SpceumlMBgMWLRoES666CLweDwsXbo0yUkkhBBCSCZJKPh444038NJLL+EPf/gDBAIBLrvsMrz88sv405/+hG3btiU7jYQQQgjJIAkFHxqNBhMmTAAAZGVlwWAwAADOPfdcfPzxx8lLHSGEEEIyTkLBR1lZGdra2gAAtbW1WL9+PQBg+/btgeG4hBBCCCHhJBR8XHjhhdi4cSMA4M4778TixYsxevRoXHXVVbjuuuuSmkBCCCGEZJaERrs8+uijgf9ffPHFKCsrw9atW1FbW4vzzz8/aYkjhBBCSOZJKPjobdq0aZg2bVoyDkUIIYSQDBdz8PHhhx/GfFCq/SCEEEJIJDEHH73Xc+E4DoyxPtsA3yRkhBAy0uksThhtLiilQqjloqFODiFpI+YOp16vN/Bv/fr1mDx5Mj799FPo9XoYDAZ8+umnOO6447Bu3bpUppcQQoYFncWJbYe1+PqwFtsOa6GzOKO/iZARIqE+HwsXLsQLL7yAU045JbDtzDPPhEwmw4033oh9+/YlLYGEEDIcGW0uGGwuVOXK0ai1wGhzUe0HIT0SGmp76NAhqFSqPttVKhUaGxsHmiZCCBn2lFIhVFIhGrUWqKRCKKXCoU4SIWkjoeDjxBNPxMKFCwMTjQG+WU/vvvtunHTSSUlLHCGEDFdquQjTanJxck0uptXkjqhaD53FicYuCzU1kYgSanb5+9//jgsvvBCVlZWoqKgAADQ3N6Ourg4ffPBBMtNHCCHDllouGlFBB/BLXxeDzQWVVDjiAi8Sm4SCj9raWuzevRufffYZfv75ZzDGMHbsWJxxxhmBES+EEEJGHurrQmKR8CRjHMdh7ty5mDt3bjLTQwghZBijvi4kFkmZ4ZQQQggBfunrQvObkP7EFXx4vV7weAn1USWEEDJCjMS+LiQ+MUcSHR0dmDNnTirTQgghhJARIKbgY/v27Zg2bRp+//vfpzo9hBBCCMlwMTW7bNu2Da+++ipmzpyZ6vQQQgghJMPFFHzcfvvtqU4HIYQQMmzRIoLxSaveo4888gg4jsPChQsD2xhjWLp0KUpKSiCVSjF79mzs2bNn6BJJCCGEBKFFBOOXNsHH9u3b8eKLL2LixIkh2x977DGsWrUKzz77LLZv346ioiLMmTMHJpNpiFJKCCGE/CJ4YjWDzQWjzTXUSUp7aRF8mM1mXH755XjppZeQk5MT2M4Yw1NPPYUHHngAF110EcaPH49XX30VVqsVb7755hCmmBBCCPGhidXilxaTjN16660455xzcMYZZ2D58uWB7Q0NDdBoNCGzqIrFYsyaNQtbt27FTTfdFPZ4DocDDocj8LfRaATgC2YYY0lNu/+YyT5uJqE8ig3lU3SUR9FRHkUXKY8S7beRIxNiarU68N4cmXDY539wHqXiu8QcfCxatAgPPfQQ5HI5tmzZgunTp0MgGHjs8vbbb2PHjh3Yvn17n9c0Gg0AoLCwMGR7YWEhmpqaIh7zkUcewbJly/psNxgMKQk+zGYzANC6NhFQHsWG8ik6yqPoKI+iC5dHBpsLP7UYYHK4oRALML5UBVUcNRh8ADlCAG43DAZbClI9uILzKBXdHGKOHlavXo17770Xcrkcp512Gtra2lBQUDCgDz9y5AjuvPNOrF+/HhKJJOJ+vS8gxli/F9V9992HRYsWBf42Go0oLy+HSqWCUqkcUJp78wczKpWKLvQIKI9iQ/kUHeVRdJRH0YXLo26XBUaPFZWF2WjSWuAVSKFSyYcymUMqOI9SMbN5zMFHVVUVnnnmGcydOxeMMXz99dch/TOCnXrqqTEd8/vvv0dHRweOP/74wDaPx4MtW7bg2Wefxf79+wH4akCKi4sD+3R0dPSpDQkmFoshFov7bOc4LiUXo/+4dKFHRnkUG8qn6CiPoqM8iq53HqlkIqhkIjTprIH/j/T8S+V5FHPw8fjjj+P3v/99YDjshRdeGHY/juPg8XhiOubpp5+OH3/8MWTbtddei2OOOQb33nsvampqUFRUhM8++wxTpkwBADidTmzevBkrV66MNemEEEJIv8ItiEdzd6ROzMHH/PnzMX/+fJjNZiiVSuzfv3/AzS4KhQLjx48P2SaXy5GbmxvYvnDhQqxYsQKjR4/G6NGjsWLFCshkMixYsGBAn00IIYQEC14Qzz93h8HmgkoqxLSaXApAkijuHqNZWVnYtGkTqqurk9LhNJp77rkHNpsNt9xyC7q7uzF16lSsX78eCoUi5Z9NCCFkZAqeu6NRa4HR5qLgI4kSih5mzZoFj8eD9957D/v27QPHcTj22GNxwQUXgM/nDyhBX3zxRcjfHMdh6dKlWLp06YCOSwghhMSK5u5IrYSCj4MHD+Kcc87B0aNHMWbMGDDGUF9fj/Lycnz88ccYNWpUstNJCCEjGvU/GFzh+oCQ5Elo/Mwdd9yBmpoaHDlyBDt27MAPP/yA5uZmVFdX44477kh2GgkhZESjtUOGhlouQlWenAKPFEio5mPz5s3Ytm0b1Gp1YFtubi4effRRzJgxI2mJI4QQQv0PSOZJqOZDLBaHnfHMbDZDJKILghBCkon6H5BMk1Dwce655+LGG2/EN998E5j3fdu2bfj973+P888/P9lpJISQEc3f/+Dkmlwa8kkyQkLBxzPPPINRo0bh5JNPhkQigUQiwYwZM1BbW4unn3462WkkhJARj/ofkEySUJ+P7Oxs/Pvf/8bBgwexb98+MMYwduxY1NbWJjt9hBBCCMkwA5olrLa2lgIOQsiIRkNgCYlf6qcoJYSQDEVTcBOSmOSvk0sIISNE8BBYg80Fo8011EkiZFig4IMQQhJEQ2AJSQw1uxBCSIJoCm5CEpNQzce6devw1VdfBf7+61//ismTJ2PBggXo7u5OWuIIISTd0RBYQuKXUPDx//7f/4PRaAQA/Pjjj7j77rtx9tln4/Dhw1i0aFFSE0gIIYSQzJJQs0tDQwPGjh0LAHjvvfdw7rnnYsWKFdixYwfOPvvspCaQEEIIIZkloZoPkUgEq9UKANiwYQPmzp0LAFCr1YEaEZL+dBYnGrsstEImIYSQQZVQzccpp5yCRYsWYcaMGfj222/xz3/+EwBQX1+PsrKypCaQpAbNT0AIIWSoJFTz8eyzz0IgEODdd9/F888/j9LSUgDAp59+irPOOiupCSSpQfMTEEIIGSoJ1XxUVFTgo48+6rP9ySefHHCCyOCg+QkIIYQMlYSCDz6fj7a2NhQUFIRs12q1KCgogMfjSUriSOrQ/ASEEEKGSkLBB2Ms7HaHwwGRiG5iw4VaLqKggxBCyKCLK/h45plnAAAcx+Hll19GVlZW4DWPx4MtW7bgmGOOSW4KCSGEEJJR4go+/H06GGN44YUXwOfzA6+JRCJUVVXhhRdeSG4KCSGEEJJR4go+GhoaAACnnXYa1q5di5ycnJQkihBCCCGZK6E+H5s2bUp2OgghhBAyQsQcfCxatAgPPfQQ5HJ51PVbVq1aNeCEEUIIISQzxRx8/PDDD3C5XIH/R8Jx3MBTRQghhJCMFXPwEdzUQs0uhBBCCElUQtOrE0IIIYQkKqEOpxaLBY8++ig2btyIjo4OeL3ekNcPHz6clMQRQgghJPMkFHxcf/312Lx5M6688koUFxdTPw9CCCGExCyh4OPTTz/Fxx9/jBkzZiQ7PYQQQgjJcAn1+cjJyYFarU52WgghhBAyAiQUfDz00EP405/+BKvVmuz0EEIIISTDJdTs8sQTT+DQoUMoLCxEVVUVhEJhyOs7duxISuIIIYQQknkSCj7mz5+f5GQQQgghZKRIKPhYsmRJstNBCCGEkBGCJhkjhBBCyKBKqOaDx+P1O7eHx+NJOEGEEEIIyWwJBR/vv/9+yN8ulws//PADXn31VSxbtiwpCSOEEEJIZkoo+Ljgggv6bLv44osxbtw4/POf/8Tvfve7ASeMEEIIIZkpqX0+pk6dig0bNiTzkIQQQgjJMEkLPmw2G1avXo2ysrJkHZIQQgghGWhA06v7/+Xk5EChUODvf/87Hn/88ZiP8/zzz2PixIlQKpVQKpU4+eST8emnnwZeZ4xh6dKlKCkpgVQqxezZs7Fnz55EkkwIIYSQNJFQn4+nnnoq5G8ej4f8/HxMnToVOTk5MR+nrKwMjz76KGprawEAr776Ki644AL88MMPGDduHB577DGsWrUKr7zyCurq6rB8+XLMmTMH+/fvh0KhSCTpBIDO4oTR5oJSKoRaLhrq5BBCCBlhOMYYG+pEBFOr1Xj88cdx3XXXoaSkBAsXLsS9994LAHA4HCgsLMTKlStx0003xXxMo9EIlUoFg8EApVKZ1PQyxmAwGKBSqfodfpwudBYnth3WwmBzQSUVYlpNbsoDkOGWR0OF8ik6yqPoKI+iozyKLjiPTCZT0u+hMdd8NDc3o6KiIuYDt7S0oLS0NOb9PR4P3nnnHVgsFpx88sloaGiARqPB3LlzA/uIxWLMmjULW7du7Tf4cDgccDgcgb+NRiMAX2YmO9byHzPNYriIDFYnDFYnKnPlaNJaYLA6kSMTRn/jAAy3PBoqlE/RUR5FR3kUHeVRdMF5lIp8ijn4OPHEE3H++efjhhtuwEknnRR2H4PBgH/96194+umncdNNN+H222+Petwff/wRJ598Mux2O7KysvD+++9j7Nix2Lp1KwCgsLAwZP/CwkI0NTX1e8xHHnkk7HwjBoMhJcGH2WwGgGERQfPcLij5LjS3d0EpFoDntsFgcKf0M4dbHg0VyqfoKI+iozyKjvIouuA8MplMST9+zMHHvn37sGLFCpx11lkQCoU44YQTUFJSAolEgu7ubuzduxd79uzBCSecgMcffxzz5s2L6bhjxozBzp07odfr8d577+Hqq6/G5s2bA6/3PjEYY1FPlvvuuw+LFi0K/G00GlFeXg6VSpWSZhcAw6b6TqUCshTKQe3zMdzyaKhQPkVHeRQd5VF0lEfRBecRj5f8lVhiDj7UajX+8pe/YPny5fjkk0/w5ZdforGxETabDXl5ebj88stx5plnYvz48XElQCQSBTqcnnDCCdi+fTuefvrpQD8PjUaD4uLiwP4dHR19akN6E4vFEIvFfbZzHJeSE81/3OFyEudmiZGb1Td/Umm45dFQoXyKjvIoOsqj6CiPoktlHsU92kUikeCiiy7CRRddlPTEAL5oy+FwoLq6GkVFRfjss88wZcoUAIDT6cTmzZuxcuXKlHw2IYSQ5KGRdSSShIbaJsv999+PefPmoby8HCaTCW+//Ta++OILrFu3DhzHYeHChVixYgVGjx6N0aNHY8WKFZDJZFiwYMFQJpsQQkgUQzGyjgwfQxp8tLe348orr0RbWxtUKhUmTpyIdevWYc6cOQCAe+65BzabDbfccgu6u7sxdepUrF+/nub4IISQNGe0uWCwuVCVK0ej1gKjzUXBBwkY0uDjb3/7W7+vcxyHpUuXYunSpYOTIEIIIUmhlAqhkgrRqLVAJRVCKU3tkH4yvAxp8EEIIWT4iaUvh1ouwrSaXOrzQcKi4IMQQkjM4unLoZaLhmXQQR1lUy/h4KO+vh5ffPEFOjo64PV6Q17705/+NOCEEUIIST+Z3peDOsoOjoSCj5deegk333wz8vLyUFRUFDIGmOM4Cj4IISRDZVpfjt61HJkeXKWLhIKP5cuX4+GHHw5MBEYIIWRkyKS+HDqLE9806EJqOTItuEpXCQUf3d3duOSSS5KdFkIIIcPAcO3L0Vu4Wo6qPHnGBFfpLKEJ2y+55BKsX78+2WkhhBBCBk2kWg61XISqPDkFHimUUM1HbW0tFi9ejG3btmHChAkQCkOrpe64446kJI4QQghJlUxqQhpuEgo+XnzxRWRlZWHz5s0hK9ACvg6nFHwQQggZDjKlCWm4SSj4aGhoSHY6CCGEEDJCJNTnIxhjDIyxZKSFEEIIISNAwsHHa6+9hgkTJkAqlUIqlWLixIl4/fXXk5k2Qggh/dBZnGjsskBncQ51UgiJS0LNLqtWrcLixYtx2223YcaMGWCM4X//+x9+//vfo6urC3fddVey00kIISQIzcRJhrOEgo/Vq1fj+eefx1VXXRXYdsEFF2DcuHFYunQpBR/DCK1hQMjwRDNxkuEsoeCjra0N06dP77N9+vTpaGtrG3CiyOCgJydChi+aiZMMZwn1+aitrcW//vWvPtv/+c9/YvTo0QNOFBkcwU9OBpsLRptrqJNECImRf46Kk2ty6cGBDDsJ1XwsW7YMv/nNb7BlyxbMmDEDHMfhq6++wsaNG8MGJSQ90ZMTIcMbzVFBhquEgo9f//rX+Oabb/Dkk0/igw8+AGMMY8eOxbfffospU6YkO40kRWh2P0IIIUMhoeADAI4//nj84x//SGZayBCgJydCCCGDLebgw2g0QqlUBv7fH/9+hBBCMheNliOJijn4yMnJQVtbGwoKCpCdnQ2O4/rswxgDx3HweDxJTSQhhJD0QqPlyEDEHHx8/vnnUKvVAIBNmzalLEGEEEJSI5k1FcGj5fa0GrC31YCxJSoKQEhMYg4+Zs2aFfh/dXU1ysvL+9R+MMZw5MiR5KWOEEJIUiS7psI/Wm5PqwEagw0cBxjtbqoBITFJaJ6P6upqdHZ29tmu0+lQXV094EQRQghJrmTP6+MfLXdMkQLF2VKMLVbRfEEkZgmNdvH37ejNbDZDIpEMOFGEEEKSKxXz+qjlIowtUcFod2fMfEHUiXZwxBV8LFq0CADAcRwWL14MmUwWeM3j8eCbb77B5MmTk5pAQgghA5eqeX0yab4g6kQ7eOIKPn744QcAvpqPH3/8ESLRLz+KSCTCpEmT8Ic//CG5KSSEEJIUqZrXJ1PmC6LF+gZPXMGHf5TLtddei6effprm8yCEkGGKmhf6oiUnBk9CfT7WrFmT7HQQQggZJNS8EF4mNSGlu4SnV9++fTveeecdNDc3w+l0hry2du3aASeMEEJIagx288JwqmXJlCakdJfQUNu3334bM2bMwN69e/H+++/D5XJh7969+Pzzz6FSqZKdRkIIIUk0mM0L/lqWrw9rse2wFjqLM/qbSMZLKPhYsWIFnnzySXz00UcQiUR4+umnsW/fPlx66aWoqKhIdhoJIYQkkb954eSa3JQ3uSR7fhGSGRIKPg4dOoRzzjkHACAWi2GxWMBxHO666y68+OKLSU0gIYSQ5FPLRajKk6e8iYE6cZJwEurzoVarYTKZAAClpaX46aefMGHCBOj1elit1qQmkBBCyPBFnThJOAkFHzNnzsRnn32GCRMm4NJLL8Wdd96Jzz//HJ999hlOP/30ZKeREELIMEadOElvCQUfzz77LOx2OwDgvvvug1AoxFdffYWLLroIixcvTmoCCSFkJEmHkSHpkIZ0QXmRGgk3u/jxeDzcc889uOeee5KWKEIIGYnSYf6NdEhDuqC8SJ2EOpzy+Xx0dHT02a7VasHn8wecKDKy6CxONHZZaAgeGfHSYWRIomnIxOs4HX6PTJXwqrbhOByOkPVeSOZKVlWk/8miVW+DgM9h5uh8jMrPSmJKCRkaiVwj6TAyJJE0ZGoNQTr8HpkqruDjmWeeAeBb1fbll19GVtYvNwmPx4MtW7bgmGOOSW4KSdpJZkFjtLnQqrfBZHejWWcFGJAjo85pZHhL9BpJh5EhiaRhuC/IFilQTIffI1PFFXw8+eSTAHw1Hy+88EJIE4tIJEJVVRVeeOGF5KaQpJ1kFjRKqRACPodmnRWVuTJ4GBt2BRchvQ3kGkmHkSHxpiGeGoJ068AZLVBMh98jE8UVfDQ0NAAATjvtNKxduxY5OTkpSRRJb8msilTLRZg5Oh9ggIcxFKukVLVJhr2RVl0faw1BOjbPDPdam+EqoQ6nmzZtSkrg8cgjj+DEE0+EQqFAQUEB5s+fj/3794fswxjD0qVLUVJSAqlUitmzZ2PPnj0D/mySuGRPzTwqPwvnTy7F6ccUBo6XiZ3XyMgxmNOXJyrZ11gsM6amYwfOkRYopouYaz4WLVqEhx56CHK5HIsWLep331WrVsV0zM2bN+PWW2/FiSeeCLfbjQceeABz587F3r17IZfLAQCPPfYYVq1ahVdeeQV1dXVYvnw55syZg/3790OhUMSafJJkya6KDD5eOj4dERKvdK6uH6prLB1v9NSvY2jEHHz88MMPcLlcgf9HwnFczB++bt26kL/XrFmDgoICfP/99zj11FPBGMNTTz2FBx54ABdddBEA4NVXX0VhYSHefPNN3HTTTTF/Fhk+qBqUjESD2RdiqK6xdL3Rp3OgmKliDj42bdoU9v/JZDAYAPwyiVlDQwM0Gg3mzp0b2EcsFmPWrFnYunUrBR9DLFWFZTo+HRGSSoNdEzGU1xjd6AmQ4DwfwY4cOQKO41BWVjag4zDGsGjRIpxyyikYP348AECj0QAACgsLQ/YtLCxEU1NTxGM5HA44HI7A30ajMfAZkeYoGUi6U3HcdOcvLP3BR3+FZbx5lCMTYmq1OnDsHJlwROTvSD2X4pGpeWSwOmGwOlGZK0eT1gKD1YkcWWIBQSx5NFKvMb9MPY+SKTiPUpFPCQUfbrcby5YtwzPPPAOz2QwAyMrKwu23344lS5ZAKIz/orntttuwe/dufPXVV31e692Uwxjrt3nnkUcewbJly/psNxgMKQk+/HkQT5PTcNeut8FkMqJUJUWrwYj2Lg58tzTsvonkER9AjhCA2w2DwZakVKe3kXouxSNT84jndkHJd6G5vQtKsQA8tw0GgzuhY8WaR+GuMYPNBYvDDblYAFUG1zhm6nmUTMF55F/FPpkSCj5uu+02vP/++3jsscdw8sknAwC+/vprLF26FF1dXXHP9XH77bfjww8/xJYtW0JqUIqKigD4akCKi4sD2zs6OvrUhgS77777QjrFGo1GlJeXQ6VSQalUxpW2aPzBjEqlGlEnsUcghcLI0GJxQalQojBPDVU/NR/AyMujeFE+RZepeaRSAVkKZVKaMRPNI53FiZ86rT1pYJhWo8zY5pFIeaSzOHsmO2SoyO1/5E6mC84jHi+hgbH9Sij4eOutt/D2229j3rx5gW0TJ05ERUUFfvvb38YcfDDGcPvtt+P999/HF198gerq6pDXq6urUVRUhM8++wxTpkwBADidTmzevBkrV66MeFyxWAyxWNxnO8dxKSmw/MfNpMIwmtwsMU4elRdzYRlvHqXbRESDZSSeS/HK1DzKzRIjN6tvuZWIRPLIZHfDaHejKi8LjVoLTHZ30tKTjnrnkc7ixMZ97dh51AAwhsnlOThjbOGIKn96S+W1llDwIZFIUFVV1Wd7VVVVXGu73HrrrXjzzTfx73//GwqFItDHQ6VSQSqVguM4LFy4ECtWrMDo0aMxevRorFixAjKZDAsWLEgk6SSJUtVxjIbaEjL4RnpHb6PNhQ6TA3wOcHgYGmikXUolFHzceuuteOihh7BmzZpADYPD4cDDDz+M2267LebjPP/88wCA2bNnh2xfs2YNrrnmGgDAPffcA5vNhltuuQXd3d2YOnUq1q9fT3N8ZIhwNRzJGAY4UmtOCElUug6DHSxKqRByER+Huyxwub0Q8XnwUIfUlEko+Pjhhx+wceNGlJWVYdKkSQCAXbt2wel04vTTTw/MyQEAa9eujXicWDp/chyHpUuXYunSpYkklaSxSDUcA30Co5oTkq7SPSgeycNg1XIRRhUocLDTgoocKQQCHvgpatpL9/NgMCQUfGRnZ+PXv/51yLby8vKkJIiMHJFqOAb6BEaTlJF0REFxetNZnNAY7GCMoanbhtoCOfRWJ3SW5AYIdB74JBR8rFmzJtnpICNQfzUcA3kCG+lt1yQ9UVCc3ow2FzyM4fhKNXY0dUNndmKfxoRWgz2pAQKdBz4DnmSMkESlqo15pLddk/SU6qDYX5WvkAjAT+qRU6u/JojBbJ5QSoXgc8DWQ11oN9rRoudQnZ8VWAAvWZ9PD0c+MQcfxx13HDZu3IicnBxMmTKl36E3O3bsSEriSOZLVRvzSG67JukplUFxcFW+UiLA+HwhVKqkHT5l+muCGOzmCbVchCKlBGa7C24PQ5vejv/+2IYzxhYmNUCghyOfmIOPCy64IDCyZf78+alKDyGEZCx/UOxfzj5ZN5+QqvwuMyyO4TEHSn9NEEPRPKGUiSAS8iBw8lCSI0W+UoK6QkXSP5cejuIIPpYsWRL2/4QQQmKXiif64Kp8pVQIuXh4tKj31wQxFM0TFWoZplTk4IembogEPEwqy0ZFrjzlnzsSJXSGbt++HV6vF1OnTg3Z/s0334DP5+OEE05ISuIIISTTpOKJPrgqXyERgO8e+HpIg9Hfor8miKFonlDLRZg/uQzHVahpivUUS2jC9ltvvRVHjhzps72lpQW33nrrgBNFSH/8VdY6izMtjkNIPFL1RK+Wi1CVl5ybpb925uvDWmw7rE3pNdJfupP5neJJT4VahmwZBR2plFDNx969e3Hcccf12T5lyhTs3bt3wIkiI0MiT1bJqrKmsfZkqKRTh8NI1+BIHg5KZcPgSKjmQywWo729vc/2trY2CATDo62RDK1En6yCC0X/ELhEJOs4hCQi1if6VNbO9XcNjuThoFQ2DI6Ego85c+bgvvvug8FgCGzT6/W4//77MWfOnKQljmSuRC/wZBWKI7lwJcNDqps++rsG/bUzJ9fkjrgnfyobBkdC1RRPPPEETj31VFRWVgaWut+5cycKCwvx+uuvJzWBJDMleoEnq8o6naq+CQkn1U0f0a7BkToclMqGwZFQ8FFaWordu3fjjTfewK5duyCVSnHttdfisssug1BIUSKJbiAXeLIKxZFauJLhIdVP4HSTjYzKhtRLuIOGXC7HjTfemMy0kBEmeDKh4L8JIYMTHKRq0jNCokmozwcAvP766zjllFNQUlKCpqYmAMCTTz6Jf//730lLHMlsgzmcL140DJekg8EYaprO12GmoPKkr4SCj+effx6LFi3CvHnz0N3dDY/HAwDIycnBU089lcz0kQyWrr3KqTAmI0mk65BumMlB5Ul4CQUfq1evxksvvYQHHnggZGjtCSecgB9//DFpiSOZJ7hAS9de5ekaFBESK4PNFXPgEO46jOWGOZDgpPd7MznQofIkvIT6fDQ0NARGuQQTi8WwWCwDThTJTDqLE9806EIm70nHDm/pGhQREgudxYmfWgwweqxQyURRh8qG61vS2GXpd6TNQCbi6v3eMUUK7NeYMnZSLypPwkso+KiursbOnTtRWVkZsv3TTz/F2LFjk5IwknnCDR0c7KmTY0GjAMhwZrS5YHK4UVmYjSadNaYhur1Hd0S7YQ5kGHDv97bpbRk9myqVJ+ElFHz8v//3/3DrrbfCbreDMYZvv/0Wb731Fh555BG8/PLLyU4jyRDD6QmAhtqR4UopFUIhFqBJa4FKJkroOot2wxzItex/755WAwR8DjKRYNiUC4mi8qSvhIKPa6+9Fm63G/fccw+sVisWLFiA0tJSPP300/jtb3+b7DSSDEFPAISknlouwvhSFbwCKVSyxG96/d0wBzpPz5giBTqMdni8DBqjHWOKFOBzHDyM0dD7ESLu4MPtduONN97AeeedhxtuuAFdXV3wer0oKChIRfpIhqEnAEJSTyUVQqWSg+O4fvdLZHFHv4Fcy3yOg1jIDzS18DkOSqmQFnQbQeIe7SIQCHDzzTfD4XAAAPLy8ijwIGkhk3vMk+En3c/HoRwCGq7ZJt1GhaT77zfcJdTsMnXqVPzwww99OpwSMlRoGWySTtLhfNRZnDDZ3RFrNVK9dkx/IjXbpEvfD//v12awgc9xmFmXj1H5WUOWnkyUUPBxyy234O6778bRo0dx/PHHQy6Xh7w+ceLEpCSOkFjFW5AOpLqZkGiG8sYO+Ob5+KnTCqPd3Sf48Z/7HsaG9Gbfu9lmKPqEhQvQdBYn9rYacLDDDC9jaNJaAQ7IGUD/GdJXQsHHb37zGwDAHXfcEdjGcRwYY+A4LjDjKSGDJZ7e9+nwVEoy21CP7LI43D1D2bNCgp9wc2z4+1ukwzUwmH3CwgVoALDtsBatehuO6CywOj2oLVDA7WEZNwR4qCU8yRjJTMO1RiCep6ahfiolmW+oR3bJxQIopaxP8NP73OdzHKry5FGOlpnCBWiALygZV6KCzeWG082gkAhQki3NyCHAQymh4IP6emSm4V4jEOtT01A/lZKRYShHdvmuX2WfJoXhcO4P1gNQpADNnz+FCimKVBIoJQJU5KbfZIjDXULBBwDs378fq1evxr59+8BxHI455hjcfvvtGDNmTDLTRwZRfzUCw7VGJJyhfiolI9NgX0NquQi5WeI+29L53B/MB6BIAdq0mlw0ay2obzehRW+DWSpERe7IrB1KpYQWlnv33Xcxfvx4fP/995g0aRImTpyIHTt2YPz48XjnnXeSnUYySCI9FaXrqozJGArXbaXhdCS8ZA61TPY1NJC0qeWitFzWABj8RdjC5YVaLkK2TAQPQ9oM+81ECdV83HPPPbjvvvvw5z//OWT7kiVLcO+99+KSSy5JSuLI4Ir0VJSOfSSSsbBVq94GjcGG4mwpilXSYdfMRFIn2U/gsV5DsdSOREqb/70KiQD8GL5fOtZ+pEuzULqkI5MlFHxoNBpcddVVfbZfccUVePzxxwecKDJ0wrVTp9OF6C809T2Fea5chPp2E0qypXEvbJUrF2NPqxGVuXIc6jSjRCVJq4KYDJ1kB9yxXEOxBjzh0gYg8F6lRIDx+UKoVOHTks59u9KlWShd0pHJEgo+Zs+ejS+//BK1tbUh27/66ivMnDkzKQkj6aO/C3Ewn6CCC00+x8HmcuOLegPAGOo1JlSoZXF1OG3V2yAX8bDzSDfEQgHq2019OpZF+n7p+uRIkiPZAXe0m5l/bok2gw1ji1X9BjzRZgdt7DLD4og8rXo61mQGi9ZRd7CuPVoKIrUSCj7OP/983Hvvvfj+++8xbdo0AMC2bdvwzjvvYNmyZfjwww9D9iXDX7gLsb8nqOACIkeWnJqS3oVmsVICi8ODukJF4PNiKSyCbwTNOgV2H9WjrlCBZp0Ve1sNGFuiCjsnQnD19oa9GnSYHChQiHHG2CIqpDJMKp58I93MejcDAkCxKvLQzmizgyqlQsjFkYv2dKrJjFc619qQ+CQ8wykAPPfcc3juuefCvgaAJhzLcJGeoHoXEFOr1VHboGPRu9CsK1KAgYPO4oy7EPXfCJRSIcwON5p1VmgMNnAcYLS7A4V7uO/XrLNi51ED5CI+Wg121BUpqQDMQIP15Os/z8aV+NpJxhQqAgFwrGkLDkgUEgH4blvgtd41BcO5SSHda21I7BIKPrxeb7LTQQZJMqssIz1BhSsgcpLwcBWp0GzT+zqNJvJ9/Mfc22oAxyGkyjviEyJjvn+9/09GhGRX+wefZyXZ0qiBRyT+wIIxBkNPDUqkmoLh2qQwnGttSKiE5/kgw0+yqywjBQNhCwi3OynfIbjQ1Fmc2K8x+YbC2d0Jr72gloswtkQFo90dkuZI368iV47J5TloN9kxukBBcwCMIKmo9k9lTUSm1RQM51obP+ov5kPBxwiSioIo3BNU7wIiRyYMPIklk//7qP0jXsKMVon1Qo9UqEX6fmeMLaQCZARK1c08GTUR4YbaDlVNQbw32Hj2H661NgD1WQlGwccIMpgFUXABwVLULKGUCsHnOGze3wFwXJ/RKvFe6PEUasO5ACSJS9dq/+BzPXio7VDUFMR73UWbtySTAvxMq4kaCAo+RpB0qLJMZoGilotQV6RAh8mOukIFtL1GvMRzoWdiQUeSLx2uoXD6G2o72IFyvDfYaPOWZFINQboGr0OBgo8RZiif2HUWJ75p0AXm6agrUsQ8N0ckFWoZavKzoA0z4iXWC52qQkk8goPb4L+HUvC5Hm2o7WCmJZYbbNR5SzKohiBdg9ehkPAkY9dddx0uueQSSKXSZKeJZKjgPhqb93egw2RHTX7WgG72kS5mf03GmCIF+BzX74VutLnQZrBBLROhzWDLmIKOpMZQBKvRaub6G2o72OK9wUabtyTTagioydYnoeDj+OOPxz333IPbb78dl156KX73u98FJhsjI1e0AtL/hFPfbgI4LmxTSSJ6X8zx3hw8jKFNb8NPLUbkyoWBxbpG+pMJCS/ep/KBNunFej6HG2oby7EjBe4D6Swa6QYb6di9R7HF+uCQTqjpNj4JrWr7xBNPoKWlBa+99ho6Oztx6qmnYuzYsfjLX/6C9vb2uI61ZcsWnHfeeSgpKQHHcfjggw9CXmeMYenSpSgpKYFUKsXs2bOxZ8+eRJJNUiiWVTv9Tzgza/MwuUwVtqkkkc/tvbpnvCtj8jkORSopTh2dD6VUiN1H9SHfI5mrm5LhL55mhWSsZpuqlV7Dpa2/9Ia7DuL5frHse6jTjA93tmDjz+3YrzGFXdYgOA2puDYTOWa6rvydzhIKPgCAz+fjggsuwAcffICWlhYsWLAAixcvRnl5OebPn4/PP/88puNYLBZMmjQJzz77bNjXH3vsMaxatQrPPvsstm/fjqKiIsyZMwcmkynRpJMk8l+ozTprTAWkWi7C5IocnDG2CCfX5A6oyjrSBZ9Im3NJthR2twcCHg+dJgdy5SIYbC4066zYsFeDj3a3YsNeDRUqJBBEx3L+JiNwSFUnxXBpa9ZacKjTDHXP+e9Pb6RrLZ7vF21fncWJLw90YtdRA0x2d6AJNPj14DQc6jQn/YafaBCRqgAxkw24V9K3336LNWvW4K233kJBQQGuueYatLW14bzzzsPNN9+Mv/zlL/2+f968eZg3b17Y1xhjeOqpp/DAAw/goosuAgC8+uqrKCwsxJtvvombbrppoMknA9B7oTc+h5gLyGS0e0aq/k60zblZa8EPzd042u3EF/WdqC2Q44DGiG8adFDLRTSVOgmI9fxNRuCQqk6KvdPmYQz17Sa0Gexo09swuTyn31mL1XJRXN8v2r5GmwtuD0OFWoYmrRWTylTwMIbGLkugE2qr3oZcuRitehuUEkHSO6VGGnkTLe9pFEv8Ego+Ojo68Prrr2PNmjU4cOAAzjvvPLz99ts488wzwXG+IV6XXnop5s+fHzX46E9DQwM0Gg3mzp0b2CYWizFr1ixs3bqVgo8h1vtCPbZIgWyZqE/7scHqBM/tirjEd6LCFZ7+gire4EYtF8Foc0EiEmDWmALsOqKHzeFBp8kJjdEOmVBAU6mTmAW3/ycjcEhFJ8XeQY3R5oKHAbPr8lHfbkJdkSLwmZFurvEERtH29ddAtuptmFSuwsTS7MAMxiqpEEVKCTQGG/a0+vpmHVeRk/QbfrgyJdb+NjSKJT4JBR9lZWUYNWoUrrvuOlxzzTXIz8/vs89JJ52EE088cUCJ02g0AIDCwsKQ7YWFhWhqaor4PofDAYfDEfjbaDQC8NWkJHvCK/8xUzWRVjpTSARQSgRo7DJDKRWiPGjYLGMsUDNitDqh4Lsgz1IgN0uctM/PkfkWrfMVmgw/txlDCvzebcXRCgb/99GZHShQiODxMowqyMIRnQl2lwtji5UoV8tS9luP5HMpVsMhjwLnfdC5WJkrA5C6CfeCxZNHObJfVp1mjEEpEUBrdqA6T47yHGngGMHXmn/W4uDXgo8Rz+f1fi34M4w2F/ZrjKjMlaNJa4FCzEexSoJxxUrorE7kyISozJWFTVOieRQuDQarM5AGQ8/nxvvdhqPgPErF90ko+Ni4cSNmzpzZ7z5KpRKbNm1KKFG9+WtT/BhjfbYFe+SRR7Bs2bI+2w0GQ0qCD7PZHDadmY4PYHy+EBYHB7nYN7wvuJd9u94Gk8mIEqUEGp0F7V06CDyypKbBbHPB5nDD5HDDZLKgVCVFq8GI9i4OfLdvGLjB5sJPLQaYHG4oxAKML1VBFfSUZLC5YHG4IRcLAt/HyxiatFZoOnUokQJZEg5ZPDfMJiP47tRUqY7kcylWwyGP/Od9uHNxMCSaR9GuZz7gWyDS7e4zmib4GlINoAYi+DN4bheUfBea27ugFAuQIxSiSMpgsltQJBWA57aB73ZHTFN/+suj/tLAc9tgMCRnnap0F5xHqehjmVDwES3wSJaioiIAvhqQ4uLiwPaOjo4+tSHB7rvvPixatCjwt9FoRHl5OVQqFZRKZVLT6A9mVCpV2haGqdRfU4pHIIXCyNBqcUIhk6MwTw1VEms+dBYnfuq0wtjT58TKhNjd7kCBQur7rJ4ajm6XBUaPFZWF2WjSWuAVSKFSyfscQyllmFaTi4oi3/uK853Y22qAyduNsmw5tBZHyHuTbaSfS7EYDnnkP+9bLC4oFcqQc3EwxJNH/hpBD2O+Ya0KaeD8j9WhTjO+OqSDhzEUqaSYVhNfv6hItZIqFZClUIa8VpyfnOGsseaRPw3NWgvAcchSyAb1txxKwXnE4yU8NiWimIOPKVOmxHyx79ixI+EEBauurkZRURE+++wzTJkyBQDgdDqxefNmrFy5MuL7xGIxxOK+NzmO41JSYPmPm66F4VDJzRLj5FF5PX0+bMjNEic1j0x2N4x2N6rysrCn1QCHywvGcQDHC/k9VDIRVDIRmnTWwP/9rwUfo1FrgcnuDjQN5WaJUZIjw+b6Thzu6kKuXAgvUvvETedSdOmWR71vnv7zfqA3yYHMG9E7jyLN5/FNgw6tehs0BhuKs6UoVknjGoGmszjx1cEu7Gox+pqWjPaQayiW9/tnPfb3qQB+6eCZmyUOOVbvvwci1vOI4zi0GR0w2FxoM9hD8icZ86Sks1ReazEHH/Pnz0/6hwOA2WzGwYMHA383NDRg586dUKvVqKiowMKFC7FixQqMHj0ao0ePxooVKyCTybBgwYKUpIckl1ou6lnVNvlVlcGdwwR8DhzHx9hiVZ+e7/11BovWS53PcSjOlmJciQo6qxP8NLnhkfRwqNOML+s74WEs5MY90A6iyZxFNdKx/B3Gc+Vi7Gk1YlyJKjBMNNbPCjdCJZ6On707rTfrrGjV29JqqYNII33C5SuQmWvSpELMwceSJUtSkoDvvvsOp512WuBvf3PJ1VdfjVdeeQX33HMPbDYbbrnlFnR3d2Pq1KlYv349FApFStJDho/goMLDGPZrTBGDiEg3g1h64BerpDDYXChWSWkIXQZK9Ek1eF4KX4fS5E3Nn8y1TaINk/UNXxVCZ3XGfY73HqEyc7Rv8EHwqLNo7w8O/sFYStd08f/WCokA/Bj2U/aUJeFG1emtzrDDcjNxTZpUGPKF5WbPnt1vJ1CO47B06VIsXbp08BJFho3goCJHJkroJtLfUyoNoctsA6lhGOhTf3+SOW9Ef0PSg4P3RKYyD54jBz21gvHkZ+/rCwBaDfaYZ4+Ndxp4f9qUEl/n8nB91sKdE70fcnxzG/lqRnunleb7iE3MwYdarUZ9fT3y8vKQk5PTbxuQTqdLSuJI5kpFu2iqFmyihaAy10BqGMI99SfzXE5W0BuuhjD4xlqVN/AO1K0GOww2FxxuDzxeFrb5s7/0Be8Ty/dOJGgM+a27zLA4wt/Dwp0TVXlyqOUi7GzuxqFOM+oKFdBZnDi2WIHsniDD//n0sBKbmIOPJ598MtDU8dRTT6UqPWQEGC5L2PcOkDKpIxnxGUgNQ6prxZIZ9PqP1dhlSemsoHtaDRDw+tYGJJLWWD8z1u8R/FsrpULIxeFvf5HOCZ3F2WcG2IqguY3iST+JI/i4+uqrw/6fkHglsz07VUKnjodvdkWjHR6GtA6YSHziCSDiWb01EbEEtwMNgFMxDXjwMUuypYOyGm0i3yP4t1ZIfPOYRNsv+Dv0NwMsid+A+3zYbDa4XKGL6CR7Lg2SWYbDOgi/jAQQ4Yv6TkiEJthdXsyuy4e25wZABU9miCWAiFZbN9CgIJbawGTUGKaitmYo+kUl+pn+35ox1u+kZOHOCX+5pbU4UZOfhQp19AkTqbY0soSCD4vFgnvvvRf/+te/oNVq+7zu8XgGnDCSuYZDJ05/QVPfbgIYw7hiFb5v0qG+3YSa/Ky0DJhI6vRXW+cPClr1Ngj4HGaOzseo/KykHT+efWLhv7H6V6ROxjUYXDsQ/HcqDXbzRrzl1nBpXh4qCQUf99xzDzZt2oTnnnsOV111Ff7617+ipaUF//d//4dHH3002WkkGSiegiORXu3R9j/UaUab3jexUrgbhb+gKcmWol5jgtPjxegCBYpUkrDVrfSEk9nC1db5f3O91YlWvQ0muxvNOivAfCOv4jkPYqkNTGaNYaw1LcGjUPo7v0fKjTaecms4NC8PpYSCj//85z947bXXMHv2bFx33XWYOXMmamtrUVlZiTfeeAOXX355stNJRqh4C7VY9j/UacZb3zRBa3EhVy7EZVMrIwYgarkIFWoZmrUW1LeboLe5sF9jCrm5jJSCdyQLNyQ0uE+Q0+1Fs86KylwZPIzFfaOJ5ak6mTWG0W6Mvfs8ARw8jEU8v4fqRpvOQf9waF4eSglN2K7T6VBdXQ3A17/DP7T2lFNOwZYtW5KXOjIs+atzdRbngI8VXKj5Z18c6P5tehvaDHaUqCSBnuvRmHsWrwt33ODPbDPYsLfVkJTvTtKLWi4KDLkM/s09DBhfqsKkMhUUEkHCk9EFH38g+8Qi2o0x+Pt1mBxoN9n7vaZSfaMNV6b4A6SvD2ux7bA27a45f7B4cs88IekWHA21hGo+ampq0NjYiMrKSowdOxb/+te/cNJJJ+E///kPsrOzk5xEMpyEqwWItAR1LPqbICncxRxLISgTCWBzuvG/Q1qoZQLIRJEvg+D2fI3BBovDA6VEAE/QxHj+z9zbZkCb3gbGAKPdTQVOBut9no0vVWF8qSptn8J7C1eLEmlWzwKFGED/w2dT2Y8r2vTwya5tSWZtCg27jSyh4OPaa6/Frl27MGvWLNx3330455xzsHr1arjdbqxatSrZaSTDSLgCYSDBR7QJksKNsY+l+npKZQ7EfD4cHk+/hYP/+4wrUcHqdMNoc8Hu8mDL/k6Y7O7AOP9pNbnY22oAY8C4ktgnWCLDU3/n2WB2uhyI4Btjf7N6xtLno/fxkina9PDJrG3RWZzYsLcd7SY7ChUSnDG2MJCG4RBUDicJBR933XVX4P+nnXYafv75Z3z33XcYNWoUJk2alLTEkeEnFQVCvBMkRSsElVIhRuUrYLC5UCaV9ZvG4O/D53HotjogEwqx64genWYHxpeqAkHQ2BIVjHY3tfGOEL3Ps3Tt+xPLk7zR5upZ40WMVr0tMKun/zWlVJiUmVATEalMUctFGFOkCHQcT0ZeN2st2HmkG3KxAG16G4qUYpidnrT7TTNBUtZ2qaioQEVFRTIORYa5cE+E/a3dE494m2DiSWMs+zbrrGjR2+HweODyelGikoSsAjochhCT1EnH0Q2xBkQexqAx2LCn1Yhcue/aSpdgKtJ1pbM4AzWhRrs77hFGveksTrQZbHC4vZCLAXAcLD2BR+/F41J1jadzB9pkizv48Hq9eOWVV7B27Vo0NjaC4zhUV1fj4osvxpVXXtnvmi9kZEhV9Wu8TTCRBA+zjeU9/n2UEkFPJ1UbKnOk0FmdUPYEQcH7ZnqhQcJLx9ENsQZEfI5DcbYU40pU0Fmd4HNcWgVT4a6rZKbvl75ddkiFfIj4PIwuy0JdkSJktWwPYykLyNIl2BsscQUfjDGcf/75+OSTTzBp0iRMmDABjDHs27cP11xzDdauXYsPPvggRUkl6WIoo/NITTDNPf/AcWHXW/CLdZhtMH87cIPWgk6jHYUqCWRCHjiOg8fL+gy9JSNTOtZ8xRoQ+Ve11VmdIaN10i2YCpbMYC+4bxcAHFOkwNgSFdRyUWC1bA9jPSPlbCjPkaG+3YSSJDX3BKchHYK9wRBX8PHKK69gy5Yt2LhxI0477bSQ1z7//HPMnz8fr732Gq666qqkJpKkj3SJzv0Fz55WA5xuL/53sBMtejvAcZhcpsIZY4vCpqtNb4PGYEdlrhxNWgva9LaowYe/HdjtZWjWWTGpPBt6mwscQ1yrd5LMl241X7EERP7mC7eXQcDnMCZoEr1UBlMDfYhJZrDXe30af+Dh/xwAgVFvjV1m7G01QizgoV5j6vdhJ9E0pGOwl2xxzfPx1ltv4f777+8TeADAr371K/zxj3/EG2+8kbTEkfQT77wbqeLvbCbgcbA43PhZYwafx0Eu4qPD5Aikq/f8ADKRANaeYbZWpzvsMNs+cwpwHMBxEAt4EAp40Bh8PeELFOIRU1CQvpI5n00qRZsbJPipXyzgg59g03k8+ZGsOTqSNe9JtDk5gvNIxOeDxwHHV6oDE8olw0ibFySumo/du3fjsccei/j6vHnz8Mwzzww4USR9pVN0zuc4iIV8TCrPhsZoh97qhEIqwuiCrMD01+FqaSZX5EAiEMDudve5wMO9p0Itw+QyFTpMDtTkyTGlUh1YVCqdqtjJ4PE1xWnQYXKgQCGOWNM2HPS3hPy2w1q0GWzgcxxm1oVfs0ZncaJZZ0W9xtTvLKjB0rGJob9aq+C5fJweLzxehu+buzG5TJXUMjDdas5SKa7gQ6fTobCwMOLrhYWF6O7uHnCiSPpKdbt2PFWxHsbgcHlwpNuKqdVqFCklUMpEgWrQcENzlVIhagv8w2z7zkQZrlCsypPjjLFFYdM1UgoKEqpZZ8XOowbIRXy0GuyoK1IO23Mh0jVttLnQZvCtWdOktQJc3zVr/AHK4U4zWvU2zBpTELiGow13T5eHmFj4a1qtTjcKlGKMKcrHziN6KCRJGTA6IsWVcx6PBwJB5Lfw+Xy43e4BJ4qkt1RF5/H0J+ndTn18lbrPU1mkAq5EJUFJtjRsW21/cwoM15sLSQHGfP96/z9O6TK0Mtz5rZQKwec4NGmtqFDL4Pb0XbPGH6zXFSrQarCjvt2EUTGs+hwu4BmKvNBZnDDZ3VE/M2RYr82F/RojXG4PWg12bDusHRHNJMkW92iXa665BmKxOOzrDocjKYkiI1M8VbHBbbCNWkvYdmp/AecfBdNtdYYMz/U3nYR7TzrcEEj6qsiVY3RBFo7qbRhdkIWK3Pgm4NJZnIHFCk0Od7/NGoOl981fLRdhZl0+wAFuD0NJdt+aQn+wrrU4MblMhbpCBSpy+/bBCBdYBAc8g92RXWdx4qDGiCajAR5w4HOImHbgl/JmbLFvNIxKKoRcLKAO5wMQV/Bx9dVXR92HRrqQRMVTFdtfO3XvQq7VYIfB5oLD5YHby6JOf061HKQ//j4OAIdsmQhycWxNBv5z0z9HzaFOMxq7LMjLEqHD5OzTrBGpJiAVNQSRbv6j8rMCQ00jpWNMkQJ8jut3NE20wGIw+4D403OkXY9ms6/j6PdN3Wg3OTDKYA+bvuDyplglxZhe83+ke7NROoor+FizZk2q0kFIwjOPBlfb9i7kggu1vW0GCPiRF8hKtFBPl6pzknr+jqb7NSbobU7MHVccUx+HQ51mfFnf6ZtPg+ebH6auUIF6jQkNnRYcW6IKadY41GnGlwc6AzUO/ht8rJPrGWwudLssUMU4/0x/N39/MO4fzeK/bmKtqYglsBjMPiDGnqaTSrUczSYz9rYYAMZQopLiUKcZJSpJn/SFK28iBWUkNtRbhqSVeGodelfb7m01oM1gQ5l/AiCVBBW58j5PLMFPaf7ZTmUiATRGe9zVvv091Q3mkysZHP6OpnwOaDc6sOuIHuNL+x/xcKjTjA9+aMGhTjPqChXg8wAeONS3m3BMkQIcB4gE/ECzhs7ixJf1ndh11IAKtQyHOk3oMNkhFvDhcHvg8bJ+q/t1Fid+ajHA6LFCJRPFdC5Hu/n3Ps9LsqUx11TEGlj01xcrmfyr9hpMNkwuy0FRthSHOsz4vkkHcL7fJVzzC9WIJhcFH2TYC172/uc2I749rEOWRAC5mA8AEauFg2c79Xg9yM2SYFpNbkxPsn6RnuoiBSXpMknbSJO0gK+nc6lEJEChUowJpUpMjdIx+sv6ThzuNAMMqG83YVyxElIxH1aHB1IxH7X5CiglgsANr7HLAg9jqMyVoUlrRVmOFG4Pw5hCOfa0GiDg9b+8vdHmgsnhRmVhNpp01pjO5Wi1jr3Pc+b1oq3bik6TA7UF/XcwjXbs3sOWFRJBSoNzf3rauzgU5qmRmyWGUiqExelGXaEC2hiuf7qOB46CDzLs+QvGcrUM3zZoYXF6UKSUYE+LERaHB6Pys8IWDm16G7QWF2ryZPj85w6YbW44XB5MLs+Judo30lNdpKAkHec3yHTRbhTxBCYVuXJMLs9Bu8mO0QUKTK3JA4CICxz6+3iU5ciwX2NEbUEWJpRlo0VvQ1m2DF/UdwbOUX+nVaVUiGKVFIANk8pUmFjmm8fGP/tmcBOMf4Kr4M9VSoVQiAVo0vqaXWI9l/t7svcwBofbgz2tBvA5Dnta9NBZXZCL+Diltv88iJa/wcOWD3dZ0GV2IEcuTulNXS0Xge+WQtVz7Aq1DDX5WdBanFGbffy1rK16W7/9x6iGs38UfJBBEW8bdDz8BePOI3pkSYSozpPjcKcFEiEfdYWKiDUZxdlS5MqF2N1ihFwswOxjCmG0u1AXNL20X6SCJNJTXaSgZLjNbzCYUlVY9xfwxfsEq5aLcMbYwkA6gf77PvgDgb1mIxQSIUqzpSjNkcLscKO+3QQw1uccjWUYan/pVstFGF+qglcgTcr15h9m6vEyCHgc5GIB9FYXqnuWKGg32vo0Wfrz3d9HpVVvg4DPYeboMCN6goYqu9weWBxuTKlQD2pwHmt/s+CJ1zQGGwCEHQVENSPRUfBBUi6RNuh4ju0vGBViPgpKlPAwQCUVQSrkQdfPk8yo/CxcNrUS9Roj2ox2CAU81ORn9RmCG60gCffEGKkwS3Qob6Y/RaWysO4v4IsWmEQKAKryfLUU4Say631u1BUq0G5yBIIMPsdhWk0ussQC2F0e7D6iR2WuLCRdvc+p3n9Hq0FTSYVQqeRxrzIe7jwLHmbaqLVAJuQFlihQywTwMsBoD1rkUWdFq94WGGFmtLvg8fpqOMD6TlQWXJtUopJCLuYPSXAeS5+O3kNuxxQqQtaB6b0f1XBGRsEHSblE2qDjOXZwwXhssRLZUt/S1yabK+oqtzkyEY4tVqGuSBlxuGCiBUmkwizejmsj4SkqlYV1fwFftKnFDTYX+ByHIpUEGoM9sPprXZECFWpZTDVZFblyjDLY+wTCGoMdrXobXF6GvKz4vmt/6W7SWuC0mlArkCI3K/ycTOH0HmHjP896f1ZJthSTK3LAvAx6mxsKiRAc90s/FDAWMsLM5WE42m1DZa4s0FTUO7DqXZuUroF2cF4oxAJkicPfQmOt4cz0h4r+UPBBUi7RNuhYjx18kftrLYJvHH7hmlJi6QvgX69iqJpKRsJTVKqbo/oLBCNNLW7oyef1e9rAcRwYY5g+Kh/fN+nQYbKjpqcvUbSaLLXcNzV3m96G4p4l2Bu7LGg32QPBgcXpiet3jdQ0s2GvBjuP6iFjDjQZgTPGxbbmTO8RNq16W0gzUHD6c2QiFCkl2HnUN0RVY7Dj+KocmOxugDEogn7LYpUUU8pzsPuoHh7GUKzq20QR7vdJ1xpBf77717LZpzGhNczcILHUcI6Eh4r+UPBBUi5cG3QshUYs+4S7yP1V4Wq5CJv3d4TcKKJVXfu3955PYUyRAj+3GtBpsuN/BzqRIxehOFsacUbKZBaKI6GfSKLNUcn67N6f58/zXUf0aDc6UKySQmO0Y0dTN8BxIaMieq+qGq65JjA1t92NnJ4AvFAhQZveBnBcYDHEYNHOoXBNMR0mB+RCPiTgo91kj2vUVvAIm0nlqpDalO8bdegwOdCqt+GMsUV9mpJMdjfqNSa0m3wrPh9flRNSk1iVJx+0idGCXx/IZ/bXz8ufX/09EESr4RwJDxX9oeCDDIrgNuhYIv54C5ZwVen17aY+N4r+qtw9jGHD3na0m+zgcYBEyMfYYhX2thnQYbRj4752dJmdMDucGJWnQG2hr89I7wAkXNqBxKuSh/LGPJjibY5KJX+ec2BoN9ogEfAhFwkwukAOmVgQMipiR1M3DneZUZOXhao8eb8T3YUuVliIusKssE2DidxYlVIhChRitBpsYMyD8gJJXKO2gkfYzBydHzhuuEX0FD21mUe7rShWSWG0OvFNQxf4PA6NXWbUFWZhckVOSH4m+7dNZkfi3qK9PxkPBCPhoaI/FHyMIPE8CSTy1BDre2KJ+AdSsPhvHCUqCerbTYEbhYexkOGAvW/qzVoLdh7phlwsgM7iQE1eFva2GdCmt6HDZMeRbhsq1DK0Ge1QSoXQWlxo09v6BB+9096stQSmePfXovQ3HXU46XRjHizB5xPgy1eFRAD+IH2+Wi7C1Jo8aAx27Diih1Dgax6ZWJ4T6AvR2GXBs5/XQ2d1Qy0T4MxxxWjUWVGikgSaLiLdZPr7TYPPoT2tBuxtNQQ6NkY6/319J4owulDh6/NRVhi1VtHfh0XZc14qJYK+NXpBo1HsTjcOaIxg4AKLOo4pUqCl24Z2owMAB4DBaHeHfM5AazjDSbQjcSxi6YicyHUcbKQ8VERCwccIEe+KsbHUTAQXXpGmfdZZnDBYneC5XVD5OojHFPEPpGDxp60iV46KXDmatRYY7W5836jrGQkTWmAHP+Ghp4+IWCjAhLJs8DhfuVuuluNnjRkt3VYoxDwYbS7UFmahOFsaNe3guJAbSYfRHii4ww49JH06fAIMHgYoJQKMzxfCI4htNdL+jh9Loa+WizCqQIGDHRa4vF7sPGqA1eXBnLFFUEqFONxlhs7qxugCOXYe0ePz/R3QW5zYbHehUi3DcRU5qMqT9+nzEY3/HNrTaoDGYAPHAUa7O2JNiv+YarkIOTIhDAYuMIdF8Cy+arkocK22GWxo09tQpJJCKRHA4vDA7HSjVW8PGZHiH43SoLVAJhKgUWtFt9WJWWMKcLTb6ms6AlCoFIPP48Hj9UIpESSlhrM/4frS9M6/RGsVYumInIx+GiPxocKPgo8RIp4ngUj79l4YK7jwEvC5PtM+Az0dP61OKPkuZCmUyM0SxxTx97dPfwVL8BoaxSopipQSbDvchZZuO+xuD6aPyou4fkOFWobJZSp0mBwYXZCF8aW+aMlod8Ngc+GCScXIkgihlonC9vkIvqEFpx0AWvU2NGotEPA5mOwuuD2/DD0EAD7HDepTfboLPge/a9KBg28BsMYuMzRGD7o6ff0nUlGl3rvGRWOwo9PsQL3GhGy5AFqTHYwBVbkyMAZIBBy2HdaCAeg0OcADYHN50aSzYdvhLqhkQnzf2I12kx0H2k0w9QTG/fV98p//e1sN4DiEXFfB5z+fA/RWJ3SW8NfRoU4z1nx1GEe6bfB4vDipJg8CHmB2eFCskkBr8eXxriN6dFmcvpo9vQ11hVkhAc0ZYwuxt9WA/e0mlOf4JkfbfVQPh8sDxgClRIjxJSqYnR4UKiSBoN8/pXykuXaMNhcOdpghEfLQaXJgbLEy5t8yeHSOvy9NcJoHUqsQ6f3xDM0m/aPgY4SI50lAKRWCzwHfN+lQoPBNPRxcYPvXl1DLRPihqRsFCglMLjcUYt+TmoD/y+yLBpsLlbly1B9px95WA8aVZvepcYgk0j69CwYAgSmpvzzg67FfmSuD1WnCjiYddh81QCzkw2J3wWx3I1chDqzfACCkBuf4KnWfqtRwhZC/oNFZnCFV4ZEmU/IfI5DGI740Gu1ufHmgE2IBP/BU768hCv6cZBdo6V5QBp+vhQoJAIZGbc+iZszd028iK6lV6sHL3Bvtbgj4HPLlIjRoLciWCuD0emF3Mbg8Hgj5HHYeNaBYJUFpthQmhxsFCgl0Zjt0djd4HAelTACLw416jRE7j3TD5fXicIcZR7ptOKFKHVgZtb/mw7ElKhjt7pDr1v/EX68xQmOwRxxxobM48dWBTuxtNUIpFaJJZ0WZ2gqz3Q2OA9qNdgg4hh1H9LDYXdDbXChQiAGO831mzzXlvx78adFanJhcpoJCIsCBDjNy5WJoLQ6cUJmD7KDRbPXtJrQZ7GjT2zC5PKdPs6c/jTubdYFmq1Nq8wJzqPSnv9E5wfmX7FqJwaoRGQko+Bgh+nsSCNeEAnA9D+Ucuq1OtOltgemE/etLHOm2wep0Y+dRPUpUEkwoyUaj1gKP11czMqZIAZVUiL2tBujNdnh6CqO6QgUUUmHM7aXhbpT+giEkKHJ50G60QykR4kC7CTX5WfAyQCERwOn2QiYWIE8hwqy6fGgtzsBkSK1632yFxdlSFKukgQ6ivQtKf20O0HdWS6PNhVa9DSa7O+xkSn0KMoZAfgfW7WjRQw5HoIYoFQVa8A3W3wQ10LbrVAgXYPr7fJhNRjQZbfiuSYdCReROleH6jCh7bhi9byD+p+h2owPtRjuyxHw0a63gOA4GmxNGqxNCDihViWF2etBlcgLwrYT601HflOM8DsiRiyAVCWC0u8AYkKsQQy4Wwmh3oaXbBq3ZAavTgzaDDUqJIGptZLjr1j965lCnBW0GO2b3nM/B7zfYXPip04pGrQUWpxsOjxcOlxd7WoxQyoSYO7YIRrsLRQoxNCYHSlRSbD3UCRGfh7IcKTQGGw50mAI1m/5p3f2LvykkArR029BhdKChy4pcuRAKaUHI5GseBsyuy0d9uwlFKkmfQAsADneZwePxMGNULhq0Flid7pjOD6PNV/Ml4nPYdaQbJ1XnJtxhM55APJEaERIeBR8jSLhI3jc3QDsatBaYbC5U58t9N0QvwwmVauxpNQSaMYKnE5YJ+djR3I1smRBTKnLhcHtgc7lhcrj7zOS4p4WPQ147inNk2HygC41aK5xuT8jNPtH+J/6bfq5cjGadBV0WBywOD+QiPo6vyEGHyQG91QmXl+GYwizkKyRo1lkh4HMwWp1o1dvg9QJtBjvGlahgsLlCZmj035yDC86SbCkOdZog5vPRZbZjbLESSqkQAj6HZp014mRK/u/D5zjMrMsP6Svjb9sXeDlYoMXJo/ISKtD6K0j9eXmo0xy4aR3ptgZWTE23J7be56taLgJjDGYTADD4euewsO+N1GfE/x17Bzb+p2iFhI/GLhOMVjey5SKYbC5IhQLIxUK4vV4UZcswqkCO2nyFb5Iwgw1ZEgGmKHPQarChJEcOq9ODKgEHHsfhuPIceBlg7ZnHQyUVQWd2gF+kgEwkgMPtwfZGLZQ9HaJjyQd/c0aJSoo2vQ31PYF28M3X4vDVDk2ryUOLztYzp4gIowrk0Fud+OmoHmNLlDiuSh04t6dW56GuSAEA2NdmhFomwk8tRowryUabwYaGLjO8DODB1zXK7PDA5vLgmMIsGO1u3zwfPfy1p/XtJl/tqUSAFr0t0O/pm8NdMNnd0Bgd8Hq9aNBaUKKShO1DFY6HMXQY7TjQYQLHAV7mjel9veksTnzToIsrwO/9e+gsTuh7zrOROnIlERR8jEC6nqd+9Nwgdx7phtvL8HObr8lEKuSjUCUJ9FFoN9qRIxVDKODhmJ5Cc+2OI9jbZoLZ4YbD7cUJlbloM9qxv82IHU06nFD5y+JsWRIh5EIBDvSsZVGskmJHc3fgZh88v0bvm2azzorDPe3GvZ/udBYn9rQasPuIHjaXB3w+hyKlBBNLs6Ex2MHj+WaiLFKKoZSJUKGWodvqq671eBkOdpnR2GWGzuqC3elba0MhEeCAxgiD3R1YNKqtJxBRy0Wo72mz/6GpO1BVPGNUPqry5Jg5Oj9QoxFuMqVIgVSOTIRvDneh2+JAoUIEfU+exNpU1rsvjv+GW1ek8PUj6anVMNpcaDPYkC0VYr/GgG8atFDLRBAL+RhTmLontmQ38VgcbnhYTx+QXvOz+D8jOCit7zBBLuKjWCXF/w50wWJ34/Sxhb2e0hkKFGJ8ub8dersbbi9DtlwMAY+D1e2GSiJClkCIyeXZOP3YwpBmmkKFGCaHG4UqMTpNDvzYaoDN6UGhQowjOiu0PQFnmdrXR2RMsRJVuXLsPuqbQ6TLbEe5Wo79GhMAhNRC9c67Q51mbD3UhcYu33k5ukCBKZU5fYbqysUCKKUMOosTkyqy0WV2QsTn4XCnGWa7B4ADhzotOHVM32ZFncUXlLcZbJCL+KjvMIEHQGP0dVrd22qAQiJEXaESTrcHP7UZoBAL8b8DnTjQbkKhQoxStQzBtaf+icf8QXa31Yluqwuz6/Jhc7qhkglxUlVuv52vg/OCz/mOWVuggFjAA8AldO4OtMYiNMgFji1SROzPk+5NnYONgo8RxF9Y7jjSjYMdFoAxqOUiONxeuD0edJkc2HqwC2U5UkytzkVVnhyNXRZs2d+BTrMDcrEAU6tyYXW60WF0oFglQZfZCZlIgCKVBAc6zLC7vdAYHKhvN6OxywKN0Y42vRVehwNV+bkoUEphtLuQKxdCZ3WiWCWFh7GwN+VDnWb872AnGrrMaOiyYFS+PPB0eKjTjP/+1IZdRwxoNVihkAgg5PPRrDXjYIcJAo6PIzoragsVqC3IwvgyWeCmJBbyUZUrx/dNOqhkIhxfqcauowZozQ4c6bYC8DWHWJ1u1Bb4gi2d2YHvGrUQCwUwWp3g91QVNwZVFY/Kz0KO7JcJiPw3RX/Bs7fVgDaDLaTzoL8QMtnd6La58H2TGXVlhYECKp5ZEh0uD9xehnK1DJv3d6BJa4bD7Q1UmxcpfZNatRrsMNncKFAAUjEfWaLkzTXQu6kjFU08/htr8Pwsvc8ff03dnlYj5CI+ODD8c3szOkwO7Dyiw88aIxZM883R4p/j4mCHGUanBxwHCDgevIxhcmU2WrvtaDc64GEcGrss6LY6Q/otVeT6JtDSW534z+5WKMR8CHi+9U62N+iQp5SgJj8L+zVGjC7MwmljCrC7RY9dRw1QSX2LtI0u5OFghzmkFqp3jduYIgX++5MGPzTrUaKS+mpcKnMwuTy7Tx758kEJk90d0kFcZ3HAYHOiJl8OjdERMlQ8+HydVpOLn1oM6LY4wRgDx+MAcHC4vfDCN918q94KmZCPLJkAleosfLK7FUa7GzIRH5PKslGSI8UJPQEin/M9GDTrLBDyeZhYlo3N+zuw60g3XB4vBDweNEZ7oA9VuPMq+DceU6RAda4cBqsTHoZA37R4DXRUTO/gJVvWtzk40m+Zbk2dg42CjxHAX9NRrzGhUWvBz21G5ClEUElF8DJgVL4ch7ssyMsSo7ZQgYYuK/a2GTCxPBs2lwcOtxd5WWJ0mR34vrkbp40pQIFSjPoOM4Q8HsYWq1BXpMC+VgO6zE6MLVHA5vLicJcZBpsLJrsb2m4bhFIHZtYVgN8z0Vi70QYvY76F3Qx2lOXIUN9uQolKAsBXFX6wwwKFRACNwQ6zw4PvG7vR0m3Djy167Gr21XiIBQJ0W9wYUyhFfYcRVqcXIj6HdhMPOVkiyA18NPesKePrZ+HrTCsT832dBK1OWBwudJoc8DCAYwCPB7jdXhQpJdAY7bA4PXB6vJhem4OD7Wbw+Rzq203IlgkhE/1yGfkLknAFj79vCYCQmhFfuoBZo/PR2NaJ0T1V3zuP6APTVQffGIL5C79cuQi7euYo8U+uVpQtw46mbowryYbB5oJSIkCRSooChRS7jnZjQqkKdrc3UNWOCNX+8ZxnwU+BAId2kz3pTTzBN9bgGh21TISDHeae520OSqkI40pU0FmdEPA4gAOkQh60Zhe+PNAFqYiPk6pze746Q5aID4WYD6lQgE6zA7UFcpw5rgTfHO6CSua7MdZ3+PqG9O7PA/hGneTIhGDgYLa7UaaWgcfnIOABDZ1m2F0eiAU88DjA7WHIzxJjT6sedpcHu47oIRPxUa6WYUyRFPXtJnBgMNjcgRtbvcaIgx0meBnDPo0Rs8fkhyyC6A/8/KOm1HIROI4LzElRopKgRWdDp9nX1FqZK4fbw7ClvhM/tRggEvCglAgCfbIOd5nRorejMlcGPs9XTpgdHoj4HBQSIaxOL/h8Hpwub6CTrpDv6yOzp9UIHo8Dx/nO9R+P6vHJj22wOT3g8QCpSIDJ5TngOKChy4IKtSzsRIB+vW/yfI7rd5K2/s7R3nkU62q2sfYfCpfeeo0pUIObzk2dg4mCjwwWHHR0mOw43GlGlkQAvdUJrdkOtVyMcaUqTKvJw/jSbLzjcmNHczdsTg827GuHlzFw4GB3edGit6O2QA631wur040zji3C6EIL8uQinFzrmw1x9jGFaOu5UZeoJKjJy8Luo3o06ayozZbA7fmlQ+u2w13Y22ZEt9UFuYgPl5sBYFBniVGoEKNFb8OBdjMUEgHa9DZIRHwUKiX48kAnlFLfaqBF2RL8rDFBIRagNFsMPo+DWMBHjkyM/e1G5GeJ0dptg0IswGd7NWAMyM8SA2BgALJEQhxflYM2vQ0Whxs5chG2HdICAE4elQeVXASr0+0bwcLjoDP7ppmWifiBoCRLLMC2w10wO9wYX6oK1K4EBwSdJt8EY+U5clid7j4rYfoLMJ3FibIcGRQSQc8aHQbYnW7IRAJU58uhEPtuDP5qXf97+RzwRX0nwBjysiQYVZCFQoUNJocbuXIhtBaHr5+OSOBrRjPbIeBx2N6oQ1mOFF4vCyyJ3mqwx/1U5i+Y9VZnoHnq2wYtRAI+JpdnB/olZIkFgc61ifZhOdRpxlFNN8qK+Kgt8AVN/g7RO5q6YbS5UN9ugEggAA8MDrcHlWo5aguy8H2THi3dVvA4Xyfkn1oMqG83w2B19gQmfPA4DnanG9W5cpw9vgTjS1XQGGxo1ll9/RuKlL6hnUFpDx7yWaCQ4twJxfjqYCc8zNdE5JWL0GVxQsTnsPOIb7IwpUSInc2+5k6xkI+ybClMdjecbi827++Aw+2Fw+WBVMhDl9mBQoUEcrEQEpHAt6S9zYXxPSPH/PnkD/yC50IJ7s+QJRag0+JArlwEu8sLqZCP75p0aNJa0W6045giJTRGG7Y36FCYLYFYwOsJkIyYXKbCmeOLA9dvvcboGy3D4+FItxWl2RKoZAK0dPs667q8HlicLuitfBRkifHxT234qdWIUpUEUiEfJdkSTKnIwfeN3dBbnfiivhOTy1QRax7CDS9WSoUhs6jGcp72ziOVKvqomHBBtX+9p0hr+/ROr38RwVaDHWXZEogEqW3qHA4o+MgQwUM/2412GG1OaC1OWBxuaIy+WoUOkx06Cw/jy7JxuMMMi9ONA+1m5MpFuGBKGdqNhWjVOyDO7nma0ZgwukiJ8yeXYvP+dhRkSWCyubFhXwfcHi+q8+WwOD1o1lrQ2OVrfjhnQgkEfC6wAJXZ4YbF4YKccyE/WwqdxYlN+zvw4xE9HB5fIGO0+iaMEot4qFDL0NBlRUtPLQEH9NzUhfiuUYt2kwM5cgUADlliIU6qUqNSLcOoAgXMDjc+3NmC3S16eL2A0+0Fnwd09zyxKiVCHOSZUVuQhZmj87Gn1RCYoKjW7gsyTq3LB+AbtVCs8t2w97cZ8V1TN+wuN/QWJ8YUKzGlIgddJgesLg++2N+JfRoTNAbfuhf+gOC/ezTQGOyQi82wOT348agB2TIhJpZmh4yc8T+Z8gDw3DZ4OQ5NWivcHi88Xt+QyDHFSmzY245NP3dgfKkSl02tChSavdfZqFDLML5UFajpMdl9eXyww4R2gwP1GiOMdl8H4S6zAwAgEvADI5k6jHaIhbE9lQUPMXa6fXM+/HhUD4fbC6nQiyM6KyaX5wT6n+zvqX0LV8Udro08uAapSCnB2h1HoNcbYPRocM7EEpxQnYs2vQ1KqRAFCin+d6gLMpEAjAHNWjtMDo9vjpCqHFwzvQpKiQANXZaeueR8/Zt0jMHl8sLjZbC7vOCBg0TIR4fJDgA4Y2wRGAO+a9KBz/d1uPYPG9VZnPh0dysOdpoxulAJm8uNYpUUY4pUsLncaOy04KjO5mvqyMuCwe4Cr2eVXI4DCpUS7G01YoulC2q5CDIRHxIhDwI+hx9bjJAIeajNz4LZ6QKYGLUFclgdHtTkyQND4NVyUcicGkd1FsjhgMoVOpqGA4OQx0HA42CyuWAQ8VHfboZKJgCPB2w91Amzw4NCpQSHusyYUp4Dvc0JiZCDVOybgUZvdQIcB7nYt5LtgQ4jGrUWtHRbUKCUoipX2jOxHqCQCHG02wa91Qmbw4NSlQQtBjsmlChRk5eFNr0NJocLs8YUoL7dhLpCRcRzzV9D8fWhLmz6uQPfNmoxrjgbF0wpjfnGHVIb0WWGxcFFfxNC+53Vt5vAgEBzUnBtSLj0Gm0uNGst2NViwPGVarQabIH5UMJdByOpXwgFH8NQ7yrAn47q8VOrASaHu6cjpBN2lxcqqQDHFivR2GXBvjYjAKBQIQWfx0EmEcBu9qBVb8WGfS4UqSQYlZ+F6jw5fmjWAxxDcbYEWSIBRAIezhpfAg4MXx/WwuMFDrSbwOdxMDvc+LndiMZOC6QiAYpVElw2tRIA8OGuFrg9DFIRH/kSX5vv7qN6NHSaIRBwaDc5YHJ44HB6IRFy4BgPu1sMKFJK4PYyHF+pxu6j3ShXy1CoFENrdqEkW4oOkxOjC+QYX6KCxmiHmwEao++JfVypCoe7zJAK+eDgS5+Qz4NUyPNNdiYRIEvMx3dNWjRrrbA6Pai1u1GklASmlvb321BKhWjWWWGyu+DyeMEY0NRthc7mQkOnFWIhh8OdZngZg1wswM8aI0qypSjuGYrIwKCQCuDyeGF3e5CnEENvdeHjn1oxplAJpUSITpMNWqsL5dkynD+5BHy3G4f0ThzptuJotxUCPg+VOVJ8c6gLPxzphlTIx6EuC6rysnDOxBIc6jTjQLsJTpcH+zXGwA0J8HV8bem24dsGLdr0dugsDkhEfBzussLh7unbwOdwuNOCmnw5thzoQKveBqmwJxBpMaCkZ/RBpAKx9xDjPLkQ2TIRJpdn40i3tU8tDwDUa3wdQHuf0737bfSepKrTZMfuo3qIPHb8rHOjWWfDMfs7eybLciBLLER+lhB6qwtNWt/igl1mIdq6rZCK+DhrfDFq8mRoM9jh8Xjg9XphsDnh9TI43F6YHW5IRXyIhXxky0XoMDl8zXVWJ75r1KLFYIfW4kRFjgxf1nfCaHfjYIcRBzrM8HgYNCY7KtUy6CxOaM0OdJmc6LI4oLf6zh+v14zxZQrUFSlgsrkgEvBgdXrA44CSHAlKlDJ4OYbWbhsOdvhqKbstDFVqGX5qMcLi8E3gVZuvwKEOEz7bq4FMyMfJo/Kw9VAXvjmsxZf1nRDxgUMKDk5OD4lIgGadb/l3m8MFHs/Xl0nI56CWi2B3edDU5XtgsTrcMNtdEPoe72GwuSARCjC9Ngct3Ta8/U0z2oxWOD0M5TkySAU8tBnsPbU0TkiEfEwoy8bM0flo6LJgT6uh5zs4IeBzkHB8jC9V4tTR+dh9VA+TwwWD1ReEj8rPCsy707uc8wfQ+9uMWLvjKA52+iYla+j09QM7dUxBTOVmcG2EUiqEXBz59hf82fU9TaatBl/tb3AfKX9/o3Dz+/jPeY3RHjLfyfiy7LDX1EibK2TYBB/PPfccHn/8cbS1tWHcuHF46qmnMHPmzKFOVsqFexrcsFeDJq0FdpcXHDg0d1vRZXZAKuTQ0GmGVCyEzeFrzjjUYQEAyEQCeBng8npRmSOF0+XFDz3TjbfqrfjkxzbMHF2AE6vUMDvcKFSIIRP7qmoBhsKefhgcx6HDZIPWYsf/DnZCLRcjT54NndWNGcUqtBrsPVWyDnzXqIOAz4Pe4sBxJRJ0Ovgw2j2oLVTgYLsJdUVKGKxOaM1OHNXboLe7IBcIIOTxYHe7cbjLArmIhz0tBhjtMjg9HoDjoyxbgvElKihlIrQY7MiVi7DziL7nacYNIY8HjvPiqM4KpVMEk9UFi8sDBkAs5EFncaDd6IDJ7oZUJEC31Qm5iA/GALmYjzPHF2NUfhZ0Fid+aOpGu8kRuEm5PAxGrwtmmwtKiRAWlwd2lxstehs6jDaIhXx09hz7UKdvNJCIz4NaJkK7wQGRgENjpxW1+Qp809CFg+1mSER87GruRn6WEFUqHjbvN8Du9KBCLYeHeVCglOL75m443F7IRAJoTQ58sb8dALDp5w406azweL3IV0hQkyfH2vYj8DJAa3FCb3HiaLcFfB4PNpcXPABCAQe91QUBz9csIBcJoZQI8f0RHQwWF+xOD3Yf6UaWVASHy4MStRRSoSDiAlsCPocDHb4hlQI+HwVKMbQWX2fi4MDDvzpqYCl2owO1BVmwOt3wellgdEqr3hboqBpcaOdlicG8XlhdXoj4PNhcHuxpNaC12wKLk6EkW4LRhQq43F7wOB6YF9BaHLA4edjeoEOLzoath7XQWx1gDJCK+FDLRFBniSER8Xsm0PPC4nQjVybCMcUK1GtM2HZYi28atOCBg8HuRku3FaMKFMhXiHvmuvGCeRkEHl9fDwGPB6PVjU6TA51mO8R8Pvg8Dl4vg4jHh8nmgpf5mnm6zC6oZSLwweFotwUujxeHuyzQW339EqQiPnYe1QMMyJUJsb/NiByZEO1GJzxg6DT4ZlE90m2H2e6CxeEGBy/aszi0WTjkKSRQyYQQ8/noMDsAxuBweaCUCbFPY0ShQgKrww3GGORSIfQ2F7RmX7BwqMMCmYQPg80Jl4ehrdsGo90FcIBGb0NVnhw8AB6PFw43cFRvg0oqQuHxvqaV7xt1ONxlhtfrG/01ukCBU2rz0KizBCYCFPJ/6VwL/DK3DoDATb2xywK9zYmGTjM0Bge8ADxeBrPDg3ajo898PJEE10YoJALw3baIZW7vCRX9tTO1eVlQykSBvlj1GiO+OtAFq9MDvdUJq8ON355UGTL/R/B8J3VFobU7wX25RtpcIcMi+PjnP/+JhQsX4rnnnsOMGTPwf//3f5g3bx727t2LioqKoU5eyvgDjQ6TAzIxH7X5CrQb7dhS3wmDzfd0J+LzkS0Xwup0o9PkG4Lo9TIAPDg9Xij5gIgngMHugtPthdnuxp42I0R8X9UuH4DR5kWX2YFvGrqQIxXB5HCj3ehb2XX3UT3cXi9UEhEKlGJf4eX2okglgUoiBo/PodPsAJgXPxzpRn7W/2/vzKOjqvI8/n3v1Xu1p7KHBEKFHRpEAbUFlIAC0XZYZtTGZVhGGmRYpF3oHh1tWj1n0OYwYzvtAjRL43hkxgZtWVpFZBHoFiFECUsIISiGLIQsVZWqevWW3/zxUkUqqZACQkByP+fUSeq+++677/d+9e7v/u7v3muOLNQUVDUUnq4BzwE9XDw4SUGCxdhbIifVjvK6ILxBFb6gCpfFBE9QhUoET9D40SuaERxaVOlBVpIVdYEQeI6HR+DxTVkdeHCo9oXwXU0DQqoGm2jCnX3SYJU8KKuTYeIFuJNtKDhTB4LhBj5cVofj5V6AA7okSDh1zockm4SAqsEqmsA3uuPv6JmCw2V12HWiyui1EhDSCIoOgDPWFPArGlSNwHEcNB0oqw3gQGlNZIGlBlmBwyKiS4IEm2TCmVof/LIOjgf+fEAGAahtUJBoF6GohL+X1qDUouObcqMXeqzCCxMHnKjwQVZ0mHgO5XXGPh+7jp9DUYUPqqaja5INNQ1GwKxdElBYVg+B51HjC0IQePhkFYpGkAQBPM/BJpjAQYEOgq4BFpHH6fM+VNQFkeqQcM4nw6mL0HUd+WfqcKbWj/GDMvF1aQ3OeYNwJ9txzmvE9lgbV44NyCqOemU4zQJ+ktkNN3VLbDHjxxMwtnwXOEDWCH8rqcYXRZUIhDToOsEmCbCZRaTYRXiCTmM6bfckHC2vRxeXBYO6Gt6YQyfPoiqoQNEM701JdQMkEw9JMNbTMNZ8IPgVFYqqwy8b61ZUeYMg3TDY64MqFF2DJ6DibH0QdjMPDhy6p9ihaIT0BAuckgkV3gA8gRCqvTJCmrFtWqlirGZqlK0iIKtwWkxwWU0IL8pXEwghqOpQNIJXDhnLkdgoMrTRJ8OBDJcFw9wp+Kq02ogLCiqoqA/ivM9oYGv9IaTaneiX4cTOoioUVXoRUnWYeIDnObgsEmRNM7wzQQ0AoMOoY0Ah+BWCToRqr2x4PGQNoolDfdBoEAkE3UHwykYwtc8fAschMtznDSqwSBzK64KNcWIyfLIGi8jDKgrwBlTYzAL8IRNCmuHJOd8QwvtffRcZtqptCIEHB6dFxQ81fnx+rALVXhkJNhGHvquFDiPCW9eB8FosxlYDAk5W+aA3elmdVhPMogCrJKAuEIIkmJDlsuK7mgZ4ZCVmPFQswkOVRIT6+gBqGi7sEQQYxkBdIHpTPxNvBMg7JAGFZ+uh6kBI1RAMafi+tgHHy41OT7rTjKNnPcYyBriwcrLLKuJ8Qwg90xyRAOFYXo5LWTb/RoAjusIQ9w7gpz/9KYYOHYq33347kjZgwABMnjwZS5YsafN8j8cDl8uF+vp6JCQktGvdDCWuh8vlAte4KVmscbu2xvLC02DDkdu1/hA+LSzH16U1SGlcLyDRKoEAnK72wR8yFvhJsJjA8zwkgUe/DCfq/CGYRR4NsgZPIIQ0pwWyarw1y2qD4GD0GuwWE2p9MoKajoCioXeqA1lJVqQ5LOB54HS1H9+db4DNLCCk6FB0HXJIgyQKSLaJaAjpkATAZjY8Kg1B1egx2s1wWkWoOqHaa/TGJBOH7k4ezgQnbuqaiFSHBV8cr0TBmVrwPA9PQwg6RwgphmeCGqe6io0Np9nEI8NlBUeAw2qCN6AiJ9WGKq+Mam8Qmg7YRAGSyMMs8qisC8KvahFPhaoDPGcsjqQQIPHGSzrJJkJWdFhMPPyKBlHgkWSV0D3FClUH6oMhnDkfQEgzetvN4Ro/OoyydQBC+C8HqASIHGC3GD1fb0AFz3PQVALxgFXiEQwZ5aY6RHRPssEMGT/4OJytC0AjQBSMxapMAiAreuTlrAOwSwIkEw9R4MEBaAipAHHwyQrMJh4aEVSNoGhGnWyiMfvALAjwh9TI9EmHxCPJbkaVV4aiaFB0gsNigqoTHGYTQooGnuOgEeC0ipAVtbEuBEngkWCVEFSNJcUVlTAkOxFTbu+O0zUNUDWKrI7pDarYW1yFfadq4PGHEFQJvqACnYxYizSnGQ8O6wZF19ElwYpj5R6U1RqLeP20RzKG5STjwKnz2Fl4GgfOBtAQ0qHrRoMFGLKySAIEALV+FToZ920CwAuA2cRB142GuekzM3HGolmaDogmwNm4AVui3Ywav4wanwKPX4Ha+LwBIN0p4ac9kuAJajjvk1FebwTxOsyGAWXiOXiDqmEYG3YBBA7QCchOtsIscKDGNWGCIR2qrkPWNARDOkK6oaMEINNlQUDRjKGbVtbRMpZQa/qd0NUOlDUAHDiIhvMyogc8AFPjb0DkeRAMz0XzhsDEAZLJKD2gRB9PsPBIsVkAItQFFfhkY4qtXRIQ1AxPUENQQyj8bHijLEW98Ht0WQSYJcOjluE0w2kTkWSRUHi2HjazgICsQdZ0Iw6H4xBQNJCuG8NiNhPSHBYIAo8UhxmV9UH0THfgpq6umAHTzd+/RIQjp8tx4GwQXlmFwBnxPxoB5XUByKoOk8BD5IHe6Q44LCJ2Hq9C4VkPVF1DUNEj+zH5ZA1+2Si7W5INuf3S0SCrsDSuLdO8PuFp98crvMhONmb43dwtEd2TbZEhphMV3ojh0iXBAn9Ijewl1VFxIU3bNq/X2+5t6HVvfIRCIdhsNnzwwQf4x3/8x0j6woULUVBQgF27drVZxtUwPmoaQigsq8epSg8EPYikxERwAI6We3H8rAdWs4B0pxmeYHj/D2Nq2tn6ALomWpGT4sCZ8w1okDUk2kSopBvjp4pmDFX4FdT4ZMgaIc0hQVYJaU4JVklASZUP9QHj5SqJQNdEGzJdFqQ7LEiyS6jzKzh8tg7ltQGYeB7gOdhF3piA2PgjlgQeDY3ubh4czBKPId2T0DfDifzvalFyzodASIXDLMIbVBBQ9PCGr+jiskAUeCiajlq/jJBCEE0cAiGCVTQaQ3/IqB8ISLAISLcSzvoBI1DUhAZFhV/WoOnGSzCMyBkGQlMkwXgRhlTjRavhwotca5LPajJesGqjgaHp0cfbgofxUrRKAjTd8Lp4Q5e3cuLl0LTRoEgz17kQYTRyTRfZtomArBjPksmobTqTjAQYhhRgDCdquuFFIQL4RkPSaRXBkTFMUx9QYDLxcFkEJJk0lHo0CLwxAGAxGd7gOr8CIoKGsOFqzG4JKRd/n3Aw3jsWkYes6bCIAhxmE4a5kzCiVxpu7ZEMb0Ax1lmq9OFUlS/SiUh1SEhNMLxpSTYJZbV+pLusqKgP4odaY0VouyRiUDcXdJ0a3+XAT3ukoGuSFd6AEum4Aq3HaF0KV9v4uO6HXaqrq6FpGjIyMqLSMzIyUFFREfMcWZYhy3Lku8djBFsSEdrD1qppCOHjQz9g0+GzKDvfgHQroU45C1Un1AcUhDSCJAAaAap2occQ7iV/c6a+RW9FbHxHaBTdGAPAD7UB8BxQHzB2zCQOABkKGFKA8roGNAQUnBEbkOIw45w3CE9QRSCkAZwR6+APAglWE3iOR0AOQeaNPUVMPAdZJ4QCwNGyOmNNgmoP6hoUoxHQdOjU2Lsko9Gv8ckQeA4N8gVXr9LYxQkqGvzKhZ8oB8CvKJBNgF8GtMYgUAEXGpimr0eV0OJ1qWiA0ixNpZbnhld35oBIL/FSXr0EQ/4++ULT15Gvbq6xqbixm4uLE0snwhODDNkwGbVFZ5KRDiDcPwiFmr3bFUBWFFQ3RL89ZE2DX1bB29Ho0TG0ziujBQqAiGsNbcs0pAOhxveirKqoD6goqyvH3pPnMNSdgq6JVnz7Qy28QWNoGwC6J1tR6fHDUSOisj4IVTOG6ySRh6oRgqoGs8CjPqjg6NlacI2LvPlkDUfL6pDiMINAMIsm9E4zAmI1osju2pdrgITby/ZqN5tz3RsfYcJDGmGIqEVamCVLluCll15qkV5fX98uQqysC+B8XR0SOAW6DUgUCUQqVAJsje5bDhcaUp670FiamvzfFIG7sMZTYzsfwcQZLuVEqwSvrEASeNQG1IgBYxaARBvAcQSXqCFk0uCyAz7ThfKcFmN2QYZTRH3AkKc/ZKwrQODgNJugkQ5JCyFFJLhchmdD4BtdtwKP+qAGs4mDReRhMfGo9hk/svD9iFxjr7XZ/SVaBNh5rXGc2ZCJwMU2tFoj7CK/keEApDdubXFduyOvIUxGbcNk1DYdKSMOgFXUEPR7kZYhwUoh6JyKHAcHhQguQYPOA5ocgDvBBI/f6DAm20043xCCrgMa6bACyEkRUekNQtRldLEJSBY1BBu8SHNa4BCBmpo6hKwifpLpwtl6DyqrOQhqfPvlNIeI4PP5AABer7fd5BHmujc+UlNTIQhCCy9HVVVVC29ImOeeew5PP/105LvH40F2djZcLle7uIw0kxUpiX54zvhR7g9Bs3KoV40xcm9QR0hrHCulC2Ol4YmFGi40wE2V3iZyRo9dM8ajdd04LgpAgtkEjoBAAHBYrEhPsqHqhzoEVMO/aOVNCMk8JEGA3WGBxSagNqAgxBvj8IGQBlk2dqsce3M2Dn5fg+IKH0wWCRaBN4JDZcBhMYEzWxDiQjgXCELVeKQ4zZB4HgEQNEFDl1Q7kuwSvAEVpz11jTNuAIskwCSZkOIwo6I+gHNeGboO2CQeA7KTEQr4UFMnQ9UJLosxjnnOF0K1T454KRwiB4vZBE/j+DYP4/zExmBFX1CLxFNwvOERiQUHwCwCAscjENJBMIZuuiRYcc4bgD/Gxpnh58HBMPZMvGFENR8WupqETWnDXc6IBZNR2zAZtU1HyyjFLiDH5oQqWJGUlIDacw3wNa72Ktoc6JFsR5kngPI6P7y6CRxxIIWH2WqDP6SiQdYgE4+zAQC8FYJZggpAFsywmAV4dIKsXPB8lDUQEpwJyEhNhusKPB8A4HK5wPN8+wmjkeve+JAkCcOGDcO2bduiYj62bduGSZMmxTzHbDbDbDa3SOc4rlVvyaWQ4jBj0tBs9MpIaBHzcfq8H7V+BV0TLUh1mPFVaQ3Oe2Xc1iMZOhEOflcLmyigS6I1EvOR4bKgTxcnbKKxS2qtXwHpBI7nkWQTkZNig0/WEGxcrTG858p3NQ2wmAQ4LCb4ZRU2swldk2zQdWpcutyY4VFeF0BQ1XBT10QMdSfh9l6pOFHhgd0somuSFcfLPaj2yVFlH6vwQNd13NQtCbpOqG6QkWo3IyfVHjXv/nyDjBS7GQ6LMVPEmD6rYOu3ZyFrOm7PScagri5UVtfg2HkVNQ0h9M9MQP/MBJTV+PFVaQ3O1BgLFN3ZOxUNIRXHy73QyZhWmu40VuwsrvTiSFkdRBOPZLsEuySiqNJYYyHZJqF3ugPnvDIAQnqCFWlOM6yigBMVXnhlBUO6J2FAlgtlNQ3Yd/I8Cs/WwSIa47F2s7GKqqzoMEs8Ml0WpDosqPYZ8/MtIg9NB3Ycq0SFN2hsYOe0oovLAofZmKobUjUk2czGapv1AWiaDkEQAF2HwyYhxS5CbbRiEswmBEI6zjfI8AYUyJoRRGcWABsXglMhOKwWuKwiLI1BqSFNh00U0CCrqPUrxvRIzgiidFklmATOmKKsaPCHNIDjYJUEWEUBvqCCGl8IOgCLyEEQjKBMnufQoCggDRB4I6ZGjtPSEnFhKCz8Eom1GTrf+AEuBOJSowcsbDuGDXOTCDhEASFNi7jA7SKQYjfjXIMMWbngFTQ+XOQaEm8sia+oF+olwTBSm95TuC4WYx0sBBszm7jwcCYgCICmGecm2UQEFAV+2Vh2X2+8z/B9EQCb2ZhGG2ysn4kDmo8AhOEAWARjOFZplt6ejWBzGcWDCYBkQkzjvDnhUjvSuLEJQFay1VhFWSbwANIdEtKcEnQYQ76egAJF1aGSDhNnrNnitJqQZBNRH1RRXh8ADyDRYkKWnYPVxsMqGWt+JFhE6KRHVnFVNQJxQEhVUevXkGA2QdM11PhV+GUFlV4l0jFxSEC3JBssogCLZIIvEIKsAUmN04gHdU3ErT2SG8vOQlltAJX1AeOdnWhF9xQ7av0hnKjwNMbKEXieR0aCJbJQo6oRZE1Hqt1sdMj8ISTYJDgtpqsS8wFcaDPbo91sUfb1HnAKGFNtp06dinfeeQfDhw/HihUrsHLlShw5cgRut7vN8zt6tgsjGiaj+GByahsmo7ZhMmobJqO26fQBpwAwZcoUnD9/Hi+//DLKy8sxaNAgbN26NS7Dg8FgMBgMxvXFj8L4AIC5c+di7ty517oaDAaDwWAwrpD2jyJhMBgMBoPBuAjM+GAwGAwGg9GhMOODwWAwGAxGh8KMDwaDwWAwGB0KMz4YDAaDwWB0KMz4YDAYDAaD0aEw44PBYDAYDEaHwowPBoPBYDAYHQozPhgMBoPBYHQozPhgMBgMBoPRofxolle/EsJ753k8nqtStsfjuWo7/90IMBnFB5NT2zAZtQ2TUdswGbVNUxl5vd5IWnvRKYyPsOCys7OvcU0YDAaDwfhxEt7dtj3gqD1NmesUXddx9uxZOJ3OdrdyPR4PsrOzcebMmXbbavhGg8koPpic2obJqG2YjNqGyahtmsrI6XTC6/UiKysLPN8+0RqdwvPB8zy6det2Va+RkJDAlLgNmIzig8mpbZiM2obJqG2YjNomLKP28niEYQGnDAaDwWAwOhRmfDAYDAaDwehQmPFxhZjNZixevBhms/laV+W6hckoPpic2obJqG2YjNqGyahtrraMOkXAKYPBYDAYjOsH5vlgMBgMBoPRoTDjg8FgMBgMRofCjA8Gg8FgMBgdCjM+GAwGg8FgdCjM+IjBkiVLcNttt8HpdCI9PR2TJ09GUVFRVJ7wngDNP0uXLo3kGT16dIvjDz/8cEffzlUhHhn5fD7Mnz8f3bp1g9VqxYABA/D2229H5ZFlGQsWLEBqairsdjsmTpyIH374oSNv5arRXjLq7HpUWVmJGTNmICsrCzabDffeey+Ki4uj8nR2PYpHRjeyHgHA22+/jcGDB0cWxRo+fDj++te/Ro4TEX77298iKysLVqsVo0ePxpEjR6LKuJH1CGgfGbWbHhGjBXl5ebRmzRoqLCykgoICuv/++6l79+7k8/kiecrLy6M+q1evJo7jqKSkJJInNzeXZs2aFZWvrq7uWtxSuxOPjH7xi19Qr169aMeOHVRaWkrLly8nQRDoo48+iuSZM2cOde3albZt20b5+fk0ZswYuvnmm0lV1WtxW+1Ke8moM+uRrut0xx130F133UX79++n48eP0+zZs1vIsTPrUbwyupH1iIjo448/pi1btlBRUREVFRXR888/T6IoUmFhIRERvfrqq+R0OmnDhg10+PBhmjJlCmVmZpLH44mUcSPrEVH7yKi99IgZH3FQVVVFAGjXrl2t5pk0aRLdfffdUWm5ubm0cOHCq1y764NYMho4cCC9/PLLUfmGDh1KL7zwAhER1dXVkSiKtH79+sjxsrIy4nmePvnkk46peAdyOTIi6tx6VFRURAAiL0ciIlVVKTk5mVauXElETI/ikRFR59KjMElJSfTHP/6RdF2nLl260Kuvvho5FgwGyeVy0TvvvENEnU+PwlyKjIjaT4/YsEsc1NfXAwCSk5NjHq+srMSWLVswc+bMFsfee+89pKamYuDAgXj22WcjO+zeaMSS0Z133omPP/4YZWVlICLs2LEDJ06cQF5eHgDg4MGDUBQF48ePj5yTlZWFQYMGYd++fR17Ax3A5cgoTGfVI1mWAQAWiyWSRxAESJKEPXv2AGB6FI+MwnQWPdI0DevXr0dDQwOGDx+O0tJSVFRUROmI2WxGbm5uREc6mx5djozCtIseXbH5coOj6zpNmDCB7rzzzlbzvPbaa5SUlESBQCAqfcWKFbRt2zY6fPgwvf/++5STk0Njx4692lXucFqTkSzLNG3aNAJAJpOJJEmidevWRY6/9957JElSi/LGjRtHs2fPvur17kguV0ZEnVuPQqEQud1ueuihh6impoZkWaYlS5YQABo/fjwRMT2KR0ZEnUOPvv32W7Lb7SQIArlcLtqyZQsREe3du5cAUFlZWVT+WbNmdTo9uhIZEbWfHnWKXW2vhPnz5+Pbb79t0YNoyurVq/HYY49F9TwAYNasWZH/Bw0ahD59+uDWW29Ffn4+hg4detXq3NG0JqM33ngDf//73/Hxxx/D7XZj9+7dmDt3LjIzMzF27NhWyyMicBx3tavdoVyJjDqzHomiiA0bNmDmzJlITk6GIAgYO3Ys7rvvvjbL6yx6FK+MOoMe9evXDwUFBairq8OGDRswffp07Nq1K3K8uT7EoyM3mh5dqYzaTY8u23zqBMyfP5+6detGp06dajXP7t27CQAVFBS0WZ6u6y3GFH/stCYjv99PoijS5s2bo9JnzpxJeXl5RES0fft2AkA1NTVReQYPHky/+c1vrm7FO5ArkVEsOpMeNaWuro6qqqqIiOj222+nuXPnEhHTo6a0JqNY3Ih61Jx77rmHZs+eTSUlJQSA8vPzo45PnDiRpk2bRkSdR4+acykyisXl6hGL+YgBEWH+/PnYuHEjvvjiC/To0aPVvKtWrcKwYcNw8803t1nukSNHoCgKMjMz27O614S2ZKQoChRFAc9Hq5ggCNB1HQAwbNgwiKKIbdu2RY6Xl5ejsLAQI0aMuPo3cZVpDxnFojPpUVNcLhfS0tJQXFyMAwcOYNKkSQCYHjWlNRnF4kbSo9YgIsiyjB49eqBLly5ROhIKhbBr166IjtzoetQalyKjWFy2Hl2ajdQ5+Nd//VdyuVy0c+fOqOlEfr8/Kl99fT3ZbDZ6++23W5Rx8uRJeumll+jrr7+m0tJS2rJlC/Xv35+GDBlyQ0zbikdGubm5NHDgQNqxYwedOnWK1qxZQxaLhd56661Injlz5lC3bt3o888/p/z8fLr77rtvmKlt7SEjpkdE//d//0c7duygkpIS+uijj8jtdtM//dM/RZXT2fWoLRnd6HpERPTcc8/R7t27qbS0lL799lt6/vnnied5+uyzz4jImEbqcrlo48aNdPjwYXrkkUdiTrW9UfWI6Mpl1J56xIyPGACI+VmzZk1UvuXLl5PVao05x/n777+nUaNGUXJyMkmSRL169aInn3ySzp8/30F3cXWJR0bl5eU0Y8YMysrKIovFQv369aNly5aRruuRPIFAgObPn0/JyclktVrpH/7hH+j777+/BnfU/rSHjJgeEf3+97+nbt26kSiK1L17d3rhhRdIluWocjq7HrUloxtdj4iIHn/8cXK73SRJEqWlpdE999wTaVSJjOGBxYsXU5cuXchsNtOoUaPo8OHDUWXcyHpEdOUyak894oiILs1XwmAwGAwGg3H5sJgPBoPBYDAYHQozPhgMBoPBYHQozPhgMBgMBoPRoTDjg8FgMBgMRofCjA8Gg8FgMBgdCjM+GAwGg8FgdCjM+GAwGAwGg9GhMOODwWDc8OzcuRMrVqy41tVgXCXWrl2Lzz777FpXg3EJMOOD0S7s3LkTHMehrq7uWleFEQdTp07Ff/zHf7RrmWvXrkViYmK7lhkPFRUVGDduHOx2e6vXv+2227By5UqsXbv2sq7BcRw++uijy64j4+pyxx134Be/+AWOHDnS4thtt92GjRs3XoNaMS5KO6zYyugk7N27l3iej7njqizLVF5eHrV0ejzs2LGDAFBtbW071fL6BgB9+OGH17QO33zzDSUlJUXtadEe+P1+qqysjHxfvHgx3Xzzze16jVj86le/ooEDB9KJEyeirt+ciooKGjhwIH3zzTeXfI3y8nIKBoOXXcc1a9aQy+W67PN/TJSWlhIAOnToUIde98svv6Sbb765hV7/5S9/oT59+pCmaR1aH8bFYZ4PRtysXr0aCxYswJ49e/D9999HHZMkCV26dAHHcTHP1TTtoju1/pi5FvcWCoUu+9w//OEPeOihh+B0OtuxRoDVakV6enq7lhkPJSUlGDZsGPr06XPR62dkZKCwsBCDBw++5Gt06dIFZrO51eOKolxymT82rsU9Xoqe33nnnSgoKGih1/fffz/q6+vx6aeftnf1GFfCtbZ+GD8OfD4fOZ1OOn78OE2ZMoVeeumlqOPNPRjhnt6mTZtowIABJAgCnTp1KuqccA+p6Wf69OlEZGxw9Nprr1GPHj3IYrHQ4MGD6YMPPmhxvU8++YRuueUWslgsNGbMGKqsrKStW7dS//79yel00sMPP0wNDQ2R83Jzc2nevHk0b948crlclJycTP/+7/8e5bGRZZkWLVpEWVlZZLPZ6Pbbb6cdO3ZEjrd2b/v376exY8dSSkoKJSQk0KhRo+jgwYOR89xud9S9ut1uIiKaPn06TZo0KUo2CxcupNzc3Bb1fuqppyglJYVGjRpFRERHjhyh++67j+x2O6Wnp9M///M/07lz51p9jpqmUWJiIm3evDkqHTE8Mi6XK7J5WfhZbdiwgUaPHk1Wq5UGDx5M+/btayGX8P/Nn224rMWLF1N2djZJkkSZmZm0YMGCVutLRPTWW29Rz549SRRF6tu3L61bt65VmYb151Jo67k1l09YFv/7v/9Lubm5ZDabafXq1a2WH9bVpp/FixcT0aXpWt++fclqtdIDDzxAPp+P1q5dS263mxITE2n+/PlRu4q63W56+eWX6ZFHHiG73U6ZmZn0xhtvRNWrrq6OZs2aRWlpaeR0OmnMmDFUUFAQOR72XK1atYp69OhBHMeRruv017/+lUaOHBn5/dx///108uTJKFk1/YT1ODc3lxYuXBhVh0mTJkU9M7fbTa+88gpNnz6dEhISaNq0aURkeF3vuususlgs1K1bN1qwYAH5fL5WZd6cGTNm0NSpU+POz7j6MOODERerVq2iW2+9lYiINm3aRDk5OVENdizjQxRFGjFiBO3du5eOHz/e4mWhqipt2LCBAFBRURGVl5dHdgh+/vnnqX///vTJJ59QSUkJrVmzhsxmM+3cuTPqenfccQft2bOH8vPzqXfv3pSbm0vjx4+n/Px82r17N6WkpNCrr74auWZubi45HA5auHAhHT9+nP7nf/6HbDYbrVixIpLn0UcfpREjRtDu3bvp5MmTtHTpUjKbzXTixImL3tv27dvp3XffpaNHj9LRo0dp5syZlJGREXEDV1VVRRrh8vJyqqqqIqL4jQ+Hw0GLFi2i48eP07Fjx+js2bOUmppKzz33HB07dozy8/Np3LhxNGbMmFaf46FDhwgAVVRURKXHa3z079+fNm/eTEVFRfTggw+S2+0mRVEicgkbH36/n5555hkaOHBg1BbwH3zwASUkJNDWrVvpu+++o6+++ipK9s3ZuHEjiaJIb775JhUVFdGyZctIEAT64osvIjK999576ec//3mU/lwKbT235vIJyyInJ4c2bNhAp06dorKyslbLl2WZXn/9dUpISIjIwuv1ElH8ujZu3DjKz8+nXbt2UUpKCo0fP55+/vOf05EjR2jTpk0kSRKtX78+ck23201Op5OWLFlCRUVF9MYbb5AgCJEdTHVdp5EjR9KECRPo66+/phMnTtAzzzxDKSkpkR1KFy9eTHa7nfLy8ig/P5+++eYb0nWd/vznP9OGDRvoxIkTdOjQIZowYQLddNNNkWGN/fv3EwD6/PPPqby8PFJevMZHQkICLV26lIqLi6m4uJi+/fZbcjgc9F//9V904sQJ2rt3Lw0ZMoRmzJgR9zN+6623KCcnJ+78jKsPMz4YcTFixAh6/fXXiYhIURRKTU2lbdu2RY7HMj4ARPWkYhEr5sPn85HFYonqVRMRzZw5kx555JGo8z7//PPI8SVLlhAAKikpiaQ98cQTUTEqubm5NGDAgCjD6de//jUNGDCAiIhOnjxJHMe1aEzuueceeu655y7p3lRVJafTSZs2bYqkxWrk4zU+brnllqg8L774Io0fPz4q7cyZMxFjLhYffvghCYLQIjYnXuPjj3/8Y+T4kSNHCAAdO3aMiFrGNcSK+Vi2bBn17duXQqFQzPo1Z8SIETRr1qyotIceeoh+9rOfRb43b8CulLaeW1gW4d9DPMSK+bgUXWvqWXjiiSfIZrNFDBgiory8PHriiSci391uN917771R5U6ZMoXuu+8+IjIMroSEhBZxLL169aLly5cTkfH8RFGMGMmtETaqw1uvtxbzEa/xMXny5Kg8U6dOpdmzZ0elffnll8TzPAUCgYvWLcxf/vIX4nmexX1cR7CYD0abFBUVYf/+/Xj44YcBACaTCVOmTMHq1asvep4kSZc1vn706FEEg0GMGzcODocj8lm3bh1KSkqi8jYtPyMjAzabDT179oxKq6qqijrnjjvuiIpNGT58OIqLi6FpGvLz80FE6Nu3b9S1d+3aFXXtWPdWVVWFOXPmoG/fvnC5XHC5XPD5fC3iYy6XW2+9Ner7wYMHsWPHjqh69u/fHwBayClMIBCA2WxuNTanLZrec2ZmJgC0kO/FeOihhxAIBNCzZ0/MmjULH374IVRVbTX/sWPHMHLkyKi0kSNH4tixY5dY89a53OfW/HlcKvHqms1mQ69evSLfMzIykJOTA4fDEZXW/DkMHz68xfew3A4ePAifz4eUlJSoa5eWlkZd2+12Iy0tLaqckpISPProo+jZsycSEhLQo0cPALiqer527dqoeubl5UHXdZSWlsZVptVqha7rkGW5XerIuHJM17oCjOufVatWQVVVdO3aNZJGRBBFEbW1tUhKSop5ntVqvaxGLhy8uWXLlqhrAmgR9CeKYuR/juOivofTLiUYVNd1CIKAgwcPQhCEqGNNX/ax7m3GjBk4d+4cXn/9dbjdbpjNZgwfPrzNoDme50FEUWmxgvvsdnuLuk6YMAGvvfZai7xhw6A5qamp8Pv9CIVCkCQpks5xXFx1aC7vcD3iJTs7G0VFRdi2bRs+//xzzJ07F0uXLsWuXbtaPLvm1wlDRJdtPMXicp9b8+dxqcSra7F0+nL1vOkzy8zMxM6dO1vkaTpdOdY9TpgwAdnZ2Vi5ciWysrKg6zoGDRp0VfX8iSeewJNPPtkib/fu3S96zTA1NTWw2WywWq1x5WdcfZjxwbgoqqpi3bp1WLZsGcaPHx917IEHHsB7772H+fPnX3b54QZQ07RI2k9+8hOYzWZ8//33yM3NveyyW+Pvf/97i+99+vSBIAgYMmQINE1DVVUV7rrrrksq98svv8Rbb72Fn/3sZwCAM2fOoLq6OiqPKIpR9woAaWlpKCwsjEorKChotTEOM3ToUGzYsAE5OTkwmeL7Kd9yyy0ADO9S+P9wHcrLyyPfi4uL4ff74yqzNSRJanGvgGG4TZw4ERMnTsS8efPQv39/HD58GEOHDm2Rd8CAAdizZw+mTZsWSdu3bx8GDBhwRXVrSjzP7UqJJYsr0bV4iKXnYc/Y0KFDUVFRAZPJhJycnLjLPH/+PI4dO4bly5dH6rxnz56oPLF+00BLHdM0DYWFhRgzZsxFrzl06FAcOXIEvXv3jruezSksLIypX4xrBxt2YVyUzZs3o7a2FjNnzsSgQYOiPg8++CBWrVp1ReW73W5wHIfNmzfj3Llz8Pl8cDqdePbZZ/HUU0/hT3/6E0pKSnDo0CG8+eab+NOf/nTF93TmzBk8/fTTKCoqwvvvv4///u//xsKFCwEAffv2xWOPPYZp06Zh48aNKC0txddff43XXnsNW7duvWi5vXv3xrvvvotjx47hq6++wmOPPdaip5WTk4Pt27ejoqICtbW1AIC7774bBw4cwLp161BcXIzFixe3MEZiMW/ePNTU1OCRRx7B/v37cerUKXz22Wd4/PHHYzb6gNEADB06tEWDcffdd+MPf/gD8vPzceDAAcyZM6dN46ctcnJyUFpaioKCAlRXV0OWZaxduxarVq1CYWEhTp06hXfffRdWqxVutztmGYsWLcLatWvxzjvvoLi4GP/5n/+JjRs34tlnn72iujUlnud2peTk5MDn82H79u2orq6G3++/Il2Lh7179+J3v/sdTpw4gTfffBMffPBBRM/Hjh2L4cOHY/Lkyfj0009x+vRp7Nu3Dy+88AIOHDjQaplJSUlISUnBihUrcPLkSXzxxRd4+umno/Kkp6fDarXik08+QWVlJerr6wEYOrZlyxZs2bIFx48fx9y5c+NalPDXv/41/va3v2HevHkoKChAcXExPv74YyxYsCBuWXz55ZctOk+MawszPhgXZdWqVRg7dixcLleLYw888AAKCgqQn59/2eV37doVL730Ev7t3/4NGRkZES/KK6+8gt/85jdYsmQJBgwYgLy8PGzatCkyvnwlTJs2DYFAALfffjvmzZuHBQsWYPbs2ZHja9aswbRp0/DMM8+gX79+mDhxIr766itkZ2dftNzVq1ejtrYWQ4YMwdSpU/Hkk0+2WHdi2bJl2LZtG7KzszFkyBAAQF5eHl588UX86le/wm233Qav1xvV02+NrKws7N27F5qmIS8vD4MGDcLChQvhcrnA863/tGfPno333nuvRb2ys7MxatQoPProo3j22Wdhs9narMPFeOCBB3DvvfdizJgxSEtLw/vvv4/ExESsXLkSI0eOxODBg7F9+3Zs2rQJKSkpMcuYPHkyfv/732Pp0qUYOHAgli9fjjVr1mD06NFXVLemxPPcrpQRI0Zgzpw5mDJlCtLS0vC73/0OwOXrWjw888wzOHjwIIYMGYJXXnkFy5YtQ15eHgBj+GXr1q0YNWoUHn/8cfTt2xcPP/wwTp8+jYyMjFbL5Hke69evx8GDBzFo0CA89dRTWLp0aVQek8mEN954A8uXL0dWVhYmTZoEAHj88ccxffp0TJs2Dbm5uejRo0ebXg/AiDPatWsXiouLcdddd2HIkCF48cUXWx1abE5ZWRn27duHf/mXf4krP6Nj4Kj5IByDcQMzevRo3HLLLXj99devdVWuGcFgEP369cP69etbBCUybgxycnLwy1/+Er/85S+vdVWuOYsWLUJ9fT3b2+c6g8V8MBidDIvFgnXr1rV7XAODcT2Snp7ersN0jPaBDbswGJ2Q3NxcTJgw4VpX44bhvvvui5oK2vTT3hv4MS6NRYsWXXQoiXFtYMMuDAaDcYWUlZUhEAjEPJacnIzk5OQOrhGDcX3DjA8Gg8FgMBgdCht2YTAYDAaD0aEw44PBYDAYDEaHwowPBoPBYDAYHQozPhgMBoPBYHQozPhgMBgMBoPRoTDjg8FgMBgMRofCjA8Gg8FgMBgdCjM+GAwGg8FgdCj/DxQTmMFv8A+cAAAAAElFTkSuQmCC",
+            "text/plain": [
+              "<Figure size 600x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "first_stacked = first.stack(points=(\"lat\", \"lon\")).dropna(dim=\"points\")\n",
+        "\n",
+        "x = first_stacked[\"air_temperature\"].values\n",
+        "y = first_stacked[\"daily_precip\"].values\n",
+        "\n",
+        "plt.figure(figsize=(6,5))\n",
+        "plt.scatter(x, y, s=4, alpha=0.25)\n",
+        "plt.xlabel(\"Air temperature (units of `air_temperature`)\")\n",
+        "plt.ylabel(\"Daily precipitation (units of `daily_precip`)\")\n",
+        "plt.title(\"Temp vs. Precip (t0) — colocated grid points\")\n",
+        "plt.grid(True, alpha=0.2)\n",
+        "\n",
+        "# Quick correlation for reference\n",
+        "if x.size > 1 and y.size > 1:\n",
+        "    corr = np.corrcoef(x, y)[0,1]\n",
+        "    print(f\"Pearson r (t0): {corr:.3f}\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "067501a1",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Wrote merged_conus_june2020.nc\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Add a couple of helpful attrs (optional)\n",
+        "merged.attrs.update({\n",
+        "    \"title\": \"CONUS merged daily air temperature and precipitation (June 2020)\",\n",
+        "    \"source_temp\": TEMP_URL,\n",
+        "    \"source_precip\": PRECIP_URL,\n",
+        "    \"spatial_domain\": \"20–50N, 230–300E\",\n",
+        "    \"time_range\": \"2020-06-01 to 2020-06-30\",\n",
+        "})\n",
+        "\n",
+        "# Compression & dtype to keep size manageable\n",
+        "encoding = {\n",
+        "    \"air_temperature\": {\"zlib\": True, \"complevel\": 4, \"dtype\": \"float32\"},\n",
+        "    \"daily_precip\":    {\"zlib\": True, \"complevel\": 4, \"dtype\": \"float32\"},\n",
+        "}\n",
+        "\n",
+        "out_path = \"merged_conus_june2020.nc\"\n",
+        "merged.to_netcdf(out_path, encoding=encoding)\n",
+        "print(f\"Wrote {out_path}\")\n"
+      ]
     }
   ],
   "metadata": {
diff --git a/notebooks/merged_conus_june2020.nc b/notebooks/merged_conus_june2020.nc
new file mode 100644
index 0000000..11cd624
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