{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### **Description**\n", "\n", "This recipe shows how to calculate regional sea ice area and sea ice extent using monthly-mean outputs from the National Oceanography Centre Near-Present-Day global eORCA025 configuration of NEMO forced using JRA55-do from 1976-2024.\n", "\n", "For more details on this model configuration and the available outputs, users can explore the Near-Present-Day documentation [here](https://noc-msm.github.io/NOC_Near_Present_Day/).\n", "\n", "---\n", "### **Background**\n", "Sea ice is a critical component of the climate system owing to its ability to mediate the exchange of heat between the atmosphere and ocean.\n", "\n", "Two of the most visible indicators of anthropogenic forcing on the climate system are Arctic and Antarctic sea ice area / extent, where:\n", "\n", "* Sea ice area represents the total region covered by sea ice.\n", "\n", "* Sea ice extent is the total region with at least some minimum threshold percent sea ice cover, where this threshold is often taken to be 15% for satellite observations.\n", "\n", "Observational products typically report total sea ice extent, which gives larger values than sea ice area since:\n", "\n", "$$\\int \\int_A simask(\\lambda, \\phi) \\ dA > \\int \\int_A siconc(\\lambda, \\phi) \\ dA$$\n", "\n", "where $siconc(\\lambda, \\phi) \\in [0, 1]$ is the fractional sea ice concentration and $simask$ is given by the indicator function:\n", "\n", "$$\n", "simask(siconc_i \\geq siconc^*) =\n", "\\begin{cases}\n", "1 & \\text{if } siconc_i \\geq siconc^* \\\\\n", "0 & \\text{otherwise}\n", "\\end{cases}\n", "$$\n", "\n", "where $siconc^*$ is the threshold sea ice fraction to be considered ice covered.\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# -- Import required packages -- #\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "import matplotlib.pyplot as plt\n", "import xarray as xr\n", "\n", "from nemo_cookbook import NEMODataTree\n", "\n", "xr.set_options(display_style=\"text\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Using Dask**\n", "\n", "**Optional: Connect Client to Dask Local Cluster to run analysis in parallel.**\n", "\n", "**Note:** Although using Dask is not strictly necessary for this simple example using eORCA025, if we wanted to generalise this recipe to eORCA12 or eORCA36 outputs, using Dask would be essential to avoid unnecessary slow calculations using only a single process." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# -- Initialise Dask Local Cluster -- #\n", "import os\n", "\n", "import dask\n", "from dask.distributed import Client, LocalCluster\n", "\n", "# Update temporary directory for Dask workers:\n", "dask.config.set({'temporary_directory': f\"{os.getcwd()}/dask_tmp\",\n", " 'local_directory': f\"{os.getcwd()}/dask_tmp\"\n", " })\n", "\n", "# Create Local Cluster:\n", "cluster = LocalCluster(n_workers=4, threads_per_worker=3, memory_limit='5GB')\n", "client = Client(cluster)\n", "client" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Accessing NEMO Model Data**\n", "**Let's begin by loading the grid variables for our eORCA025 NEMO model from the [JASMIN Object Store](https://help.jasmin.ac.uk/docs/short-term-project-storage/using-the-jasmin-object-store/)**. \n", "\n", "**Alternatively, you can replace the ``domain_filepath`` below with a file path to your domain_cfg.nc file and read this with xarray's ``open_dataset()`` function.**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 10GB\n",
       "Dimensions:       (y: 1206, x: 1440, nav_lev: 75)\n",
       "Coordinates:\n",
       "  * y             (y) int64 10kB 0 1 2 3 4 5 6 ... 1200 1201 1202 1203 1204 1205\n",
       "  * x             (x) int64 12kB 0 1 2 3 4 5 6 ... 1434 1435 1436 1437 1438 1439\n",
       "  * nav_lev       (nav_lev) int64 600B 0 1 2 3 4 5 6 7 ... 68 69 70 71 72 73 74\n",
       "Data variables: (12/49)\n",
       "    atlmsk        (y, x) int8 2MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "    bathy_metry   (y, x) float32 7MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "    bottom_level  (y, x) int32 7MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "    e1f           (y, x) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "    e1t           (y, x) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "    e1u           (y, x) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "    ...            ...\n",
       "    top_level     (y, x) int32 7MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "    umask         (nav_lev, y, x) int8 130MB dask.array<chunksize=(1, 603, 720), meta=np.ndarray>\n",
       "    umaskutil     (y, x) int8 2MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "    vmask         (nav_lev, y, x) int8 130MB dask.array<chunksize=(1, 603, 720), meta=np.ndarray>\n",
       "    vmaskutil     (y, x) int8 2MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "    wmask         (nav_lev, y, x) int8 130MB dask.array<chunksize=(1, 603, 720), meta=np.ndarray>\n",
       "Attributes:\n",
       "    CfgName:    orca\n",
       "    CfgIndex:   25\n",
       "    Iperio:     1\n",
       "    Jperio:     0\n",
       "    NFold:      1\n",
       "    NFtype:     T\n",
       "    VertCoord:  zps\n",
       "    IsfCav:     0\n",
       "    file_name:  mesh_mask.nc\n",
       "    TimeStamp:  08/11/2022 13:15:21 -0000

" ], "text/plain": [ " Size: 10GB\n", "Dimensions: (y: 1206, x: 1440, nav_lev: 75)\n", "Coordinates:\n", " * y (y) int64 10kB 0 1 2 3 4 5 6 ... 1200 1201 1202 1203 1204 1205\n", " * x (x) int64 12kB 0 1 2 3 4 5 6 ... 1434 1435 1436 1437 1438 1439\n", " * nav_lev (nav_lev) int64 600B 0 1 2 3 4 5 6 7 ... 68 69 70 71 72 73 74\n", "Data variables: (12/49)\n", " atlmsk (y, x) int8 2MB dask.array\n", " bathy_metry (y, x) float32 7MB dask.array\n", " bottom_level (y, x) int32 7MB dask.array\n", " e1f (y, x) float64 14MB dask.array\n", " e1t (y, x) float64 14MB dask.array\n", " e1u (y, x) float64 14MB dask.array\n", " ... ...\n", " top_level (y, x) int32 7MB dask.array\n", " umask (nav_lev, y, x) int8 130MB dask.array\n", " umaskutil (y, x) int8 2MB dask.array\n", " vmask (nav_lev, y, x) int8 130MB dask.array\n", " vmaskutil (y, x) int8 2MB dask.array\n", " wmask (nav_lev, y, x) int8 130MB dask.array\n", "Attributes:\n", " CfgName: orca\n", " CfgIndex: 25\n", " Iperio: 1\n", " Jperio: 0\n", " NFold: 1\n", " NFtype: T\n", " VertCoord: zps\n", " IsfCav: 0\n", " file_name: mesh_mask.nc\n", " TimeStamp: 08/11/2022 13:15:21 -0000" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define directory path to ancillary files:\n", "domain_filepath = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca025-jra55v1/domain_cfg\"\n", "\n", "# Open eORCA025 NEMO model domain_cfg:\n", "ds_domain = xr.open_zarr(domain_filepath, consolidated=True, chunks={})\n", "\n", "ds_domain" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Next, we need to import sea ice concentration stored at T-points in a single dataset.**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 321GB\n",
       "Dimensions:               (time_counter: 577, y: 1206, x: 1440, ncatice: 5,\n",
       "                           axis_nbounds: 2)\n",
       "Coordinates:\n",
       "  * time_counter          (time_counter) datetime64[ns] 5kB 1976-01-16T12:00:...\n",
       "    time_centered         (time_counter) datetime64[ns] 5kB dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    nav_lat               (y, x) float64 14MB dask.array<chunksize=(1206, 1440), meta=np.ndarray>\n",
       "    nav_lon               (y, x) float64 14MB dask.array<chunksize=(1206, 1440), meta=np.ndarray>\n",
       "  * ncatice               (ncatice) float32 20B 1.0 2.0 3.0 4.0 5.0\n",
       "Dimensions without coordinates: y, x, axis_nbounds\n",
       "Data variables: (12/54)\n",
       "    albedo                (time_counter, y, x) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "    hfxcndbot             (time_counter, y, x) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "    hfxcndtop             (time_counter, y, x) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "    hfxsensib             (time_counter, y, x) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "    isig1                 (time_counter, y, x) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "    isig2                 (time_counter, y, x) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    utau_ai               (time_counter, y, x) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "    utau_oi               (time_counter, y, x) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "    vfxice                (time_counter, y, x) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "    vfxsnw                (time_counter, y, x) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "    vtau_ai               (time_counter, y, x) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "    vtau_oi               (time_counter, y, x) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "Attributes:\n",
       "    name:         OUTPUT/eORCA025_1m_icemod\n",
       "    description:  ice variables\n",
       "    title:        ice variables\n",
       "    Conventions:  CF-1.6\n",
       "    timeStamp:    2024-Dec-07 20:28:41 GMT\n",
       "    uuid:         b43a126e-39e5-4c6f-92aa-9fde45db2d07

" ], "text/plain": [ " Size: 321GB\n", "Dimensions: (time_counter: 577, y: 1206, x: 1440, ncatice: 5,\n", " axis_nbounds: 2)\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 5kB 1976-01-16T12:00:...\n", " time_centered (time_counter) datetime64[ns] 5kB dask.array\n", " nav_lat (y, x) float64 14MB dask.array\n", " nav_lon (y, x) float64 14MB dask.array\n", " * ncatice (ncatice) float32 20B 1.0 2.0 3.0 4.0 5.0\n", "Dimensions without coordinates: y, x, axis_nbounds\n", "Data variables: (12/54)\n", " albedo (time_counter, y, x) float32 4GB dask.array\n", " hfxcndbot (time_counter, y, x) float32 4GB dask.array\n", " hfxcndtop (time_counter, y, x) float32 4GB dask.array\n", " hfxsensib (time_counter, y, x) float32 4GB dask.array\n", " isig1 (time_counter, y, x) float32 4GB dask.array\n", " isig2 (time_counter, y, x) float32 4GB dask.array\n", " ... ...\n", " utau_ai (time_counter, y, x) float32 4GB dask.array\n", " utau_oi (time_counter, y, x) float32 4GB dask.array\n", " vfxice (time_counter, y, x) float32 4GB dask.array\n", " vfxsnw (time_counter, y, x) float32 4GB dask.array\n", " vtau_ai (time_counter, y, x) float32 4GB dask.array\n", " vtau_oi (time_counter, y, x) float32 4GB dask.array\n", "Attributes:\n", " name: OUTPUT/eORCA025_1m_icemod\n", " description: ice variables\n", " title: ice variables\n", " Conventions: CF-1.6\n", " timeStamp: 2024-Dec-07 20:28:41 GMT\n", " uuid: b43a126e-39e5-4c6f-92aa-9fde45db2d07" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define directory path to model output files:\n", "icemod_url = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca025-jra55v1/I1m_3d\"\n", "\n", "# Construct NEMO model T-grid dataset, including sea ice concentration and sea ice volume (m3):\n", "ds_icemod = xr.open_zarr(icemod_url, consolidated=True, chunks={})\n", "\n", "ds_icemod" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Creating a NEMODataTree**\n", "\n", "**Next, let's create a NEMODataTree to store our domain and icemod variables for the eORCA025 model.**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.DataTree 'NEMO model'>\n",
       "Group: /\n",
       "│   Dimensions:               (time_counter: 577, axis_nbounds: 2, ncatice: 5)\n",
       "│   Coordinates:\n",
       "│     * time_counter          (time_counter) datetime64[ns] 5kB 1976-01-16T12:00:...\n",
       "│       time_centered         (time_counter) datetime64[ns] 5kB dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│     * ncatice               (ncatice) float32 20B 1.0 2.0 3.0 4.0 5.0\n",
       "│   Dimensions without coordinates: axis_nbounds\n",
       "│   Data variables:\n",
       "│       time_centered_bounds  (time_counter, axis_nbounds) datetime64[ns] 9kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│       time_counter_bounds   (time_counter, axis_nbounds) datetime64[ns] 9kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│   Attributes:\n",
       "│       nftype:   T\n",
       "│       iperio:   True\n",
       "├── Group: /gridT\n",
       "│       Dimensions:               (time_counter: 577, j: 1206, i: 1440, ncatice: 5,\n",
       "│                                  axis_nbounds: 2, k: 75)\n",
       "│       Coordinates:\n",
       "│           time_centered         (time_counter) datetime64[ns] 5kB dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│         * j                     (j) int64 10kB 1 2 3 4 5 ... 1202 1203 1204 1205 1206\n",
       "│         * i                     (i) int64 12kB 1 2 3 4 5 ... 1436 1437 1438 1439 1440\n",
       "│           gphit                 (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│           glamt                 (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│         * k                     (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n",
       "│       Dimensions without coordinates: axis_nbounds\n",
       "│       Data variables: (12/58)\n",
       "│           albedo                (time_counter, j, i) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "│           hfxcndbot             (time_counter, j, i) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "│           hfxcndtop             (time_counter, j, i) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "│           hfxsensib             (time_counter, j, i) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "│           isig1                 (time_counter, j, i) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "│           isig2                 (time_counter, j, i) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "│           ...                    ...\n",
       "│           vtau_ai               (time_counter, j, i) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "│           vtau_oi               (time_counter, j, i) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>\n",
       "│           e1t                   (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│           e2t                   (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│           tmask                 (k, j, i) bool 130MB dask.array<chunksize=(75, 603, 720), meta=np.ndarray>\n",
       "│           tmaskutil             (j, i) bool 2MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           name:         OUTPUT/eORCA025_1m_icemod\n",
       "│           description:  ice variables\n",
       "│           title:        ice variables\n",
       "│           Conventions:  CF-1.6\n",
       "│           timeStamp:    2024-Dec-07 20:28:41 GMT\n",
       "│           uuid:         b43a126e-39e5-4c6f-92aa-9fde45db2d07\n",
       "│           nftype:       T\n",
       "│           iperio:       True\n",
       "├── Group: /gridU\n",
       "│       Dimensions:       (j: 1206, i: 1440, k: 75)\n",
       "│       Coordinates:\n",
       "│         * j             (j) int64 10kB 1 2 3 4 5 6 7 ... 1201 1202 1203 1204 1205 1206\n",
       "│         * i             (i) float64 12kB 1.5 2.5 3.5 ... 1.438e+03 1.44e+03 1.44e+03\n",
       "│           gphiu         (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│           glamu         (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│         * k             (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\n",
       "│       Data variables:\n",
       "│           e1u           (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│           e2u           (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│           umask         (k, j, i) bool 130MB False False False ... False False False\n",
       "│           umaskutil     (j, i) bool 2MB False False False False ... False False False\n",
       "│       Attributes:\n",
       "│           nftype:   T\n",
       "│           iperio:   True\n",
       "├── Group: /gridV\n",
       "│       Dimensions:       (j: 1206, i: 1440, k: 75)\n",
       "│       Coordinates:\n",
       "│         * j             (j) float64 10kB 1.5 2.5 3.5 ... 1.204e+03 1.206e+03 1.206e+03\n",
       "│         * i             (i) int64 12kB 1 2 3 4 5 6 7 ... 1435 1436 1437 1438 1439 1440\n",
       "│           gphiv         (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│           glamv         (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│         * k             (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\n",
       "│       Data variables:\n",
       "│           e1v           (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│           e2v           (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│           vmask         (k, j, i) bool 130MB False False False ... False False False\n",
       "│           vmaskutil     (j, i) bool 2MB False False False False ... False False False\n",
       "│       Attributes:\n",
       "│           nftype:   T\n",
       "│           iperio:   True\n",
       "├── Group: /gridW\n",
       "│       Dimensions:       (j: 1206, i: 1440, k: 75)\n",
       "│       Coordinates:\n",
       "│         * j             (j) int64 10kB 1 2 3 4 5 6 7 ... 1201 1202 1203 1204 1205 1206\n",
       "│         * i             (i) int64 12kB 1 2 3 4 5 6 7 ... 1435 1436 1437 1438 1439 1440\n",
       "│           gphiw         (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│           glamw         (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│         * k             (k) float64 600B 0.5 1.5 2.5 3.5 4.5 ... 71.5 72.5 73.5 74.5\n",
       "│       Data variables:\n",
       "│           e1w           (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│           e2w           (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "│           wmask         (k, j, i) bool 130MB False False False ... False False False\n",
       "│           wmaskutil     (j, i) bool 2MB False False False False ... False False False\n",
       "│       Attributes:\n",
       "│           nftype:   T\n",
       "│           iperio:   True\n",
       "└── Group: /gridF\n",
       "        Dimensions:       (j: 1206, i: 1440, k: 75)\n",
       "        Coordinates:\n",
       "          * j             (j) float64 10kB 1.5 2.5 3.5 ... 1.204e+03 1.206e+03 1.206e+03\n",
       "          * i             (i) float64 12kB 1.5 2.5 3.5 ... 1.438e+03 1.44e+03 1.44e+03\n",
       "            gphif         (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "            glamf         (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "          * k             (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\n",
       "        Data variables:\n",
       "            e1f           (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "            e2f           (j, i) float64 14MB dask.array<chunksize=(603, 720), meta=np.ndarray>\n",
       "            fmask         (k, j, i) bool 130MB False False False ... False False False\n",
       "            fmaskutil     (j, i) bool 2MB False False False False ... False False False\n",
       "        Attributes:\n",
       "            nftype:   T\n",
       "            iperio:   True

" ], "text/plain": [ "\n", "Group: /\n", "│ Dimensions: (time_counter: 577, axis_nbounds: 2, ncatice: 5)\n", "│ Coordinates:\n", "│ * time_counter (time_counter) datetime64[ns] 5kB 1976-01-16T12:00:...\n", "│ time_centered (time_counter) datetime64[ns] 5kB dask.array\n", "│ * ncatice (ncatice) float32 20B 1.0 2.0 3.0 4.0 5.0\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables:\n", "│ time_centered_bounds (time_counter, axis_nbounds) datetime64[ns] 9kB dask.array\n", "│ time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 9kB dask.array\n", "│ Attributes:\n", "│ nftype: T\n", "│ iperio: True\n", "├── Group: /gridT\n", "│ Dimensions: (time_counter: 577, j: 1206, i: 1440, ncatice: 5,\n", "│ axis_nbounds: 2, k: 75)\n", "│ Coordinates:\n", "│ time_centered (time_counter) datetime64[ns] 5kB dask.array\n", "│ * j (j) int64 10kB 1 2 3 4 5 ... 1202 1203 1204 1205 1206\n", "│ * i (i) int64 12kB 1 2 3 4 5 ... 1436 1437 1438 1439 1440\n", "│ gphit (j, i) float64 14MB dask.array\n", "│ glamt (j, i) float64 14MB dask.array\n", "│ * k (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables: (12/58)\n", "│ albedo (time_counter, j, i) float32 4GB dask.array\n", "│ hfxcndbot (time_counter, j, i) float32 4GB dask.array\n", "│ hfxcndtop (time_counter, j, i) float32 4GB dask.array\n", "│ hfxsensib (time_counter, j, i) float32 4GB dask.array\n", "│ isig1 (time_counter, j, i) float32 4GB dask.array\n", "│ isig2 (time_counter, j, i) float32 4GB dask.array\n", "│ ... ...\n", "│ vtau_ai (time_counter, j, i) float32 4GB dask.array\n", "│ vtau_oi (time_counter, j, i) float32 4GB dask.array\n", "│ e1t (j, i) float64 14MB dask.array\n", "│ e2t (j, i) float64 14MB dask.array\n", "│ tmask (k, j, i) bool 130MB dask.array\n", "│ tmaskutil (j, i) bool 2MB dask.array\n", "│ Attributes:\n", "│ name: OUTPUT/eORCA025_1m_icemod\n", "│ description: ice variables\n", "│ title: ice variables\n", "│ Conventions: CF-1.6\n", "│ timeStamp: 2024-Dec-07 20:28:41 GMT\n", "│ uuid: b43a126e-39e5-4c6f-92aa-9fde45db2d07\n", "│ nftype: T\n", "│ iperio: True\n", "├── Group: /gridU\n", "│ Dimensions: (j: 1206, i: 1440, k: 75)\n", "│ Coordinates:\n", "│ * j (j) int64 10kB 1 2 3 4 5 6 7 ... 1201 1202 1203 1204 1205 1206\n", "│ * i (i) float64 12kB 1.5 2.5 3.5 ... 1.438e+03 1.44e+03 1.44e+03\n", "│ gphiu (j, i) float64 14MB dask.array\n", "│ glamu (j, i) float64 14MB dask.array\n", "│ * k (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\n", "│ Data variables:\n", "│ e1u (j, i) float64 14MB dask.array\n", "│ e2u (j, i) float64 14MB dask.array\n", "│ umask (k, j, i) bool 130MB False False False ... False False False\n", "│ umaskutil (j, i) bool 2MB False False False False ... False False False\n", "│ Attributes:\n", "│ nftype: T\n", "│ iperio: True\n", "├── Group: /gridV\n", "│ Dimensions: (j: 1206, i: 1440, k: 75)\n", "│ Coordinates:\n", "│ * j (j) float64 10kB 1.5 2.5 3.5 ... 1.204e+03 1.206e+03 1.206e+03\n", "│ * i (i) int64 12kB 1 2 3 4 5 6 7 ... 1435 1436 1437 1438 1439 1440\n", "│ gphiv (j, i) float64 14MB dask.array\n", "│ glamv (j, i) float64 14MB dask.array\n", "│ * k (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\n", "│ Data variables:\n", "│ e1v (j, i) float64 14MB dask.array\n", "│ e2v (j, i) float64 14MB dask.array\n", "│ vmask (k, j, i) bool 130MB False False False ... False False False\n", "│ vmaskutil (j, i) bool 2MB False False False False ... False False False\n", "│ Attributes:\n", "│ nftype: T\n", "│ iperio: True\n", "├── Group: /gridW\n", "│ Dimensions: (j: 1206, i: 1440, k: 75)\n", "│ Coordinates:\n", "│ * j (j) int64 10kB 1 2 3 4 5 6 7 ... 1201 1202 1203 1204 1205 1206\n", "│ * i (i) int64 12kB 1 2 3 4 5 6 7 ... 1435 1436 1437 1438 1439 1440\n", "│ gphiw (j, i) float64 14MB dask.array\n", "│ glamw (j, i) float64 14MB dask.array\n", "│ * k (k) float64 600B 0.5 1.5 2.5 3.5 4.5 ... 71.5 72.5 73.5 74.5\n", "│ Data variables:\n", "│ e1w (j, i) float64 14MB dask.array\n", "│ e2w (j, i) float64 14MB dask.array\n", "│ wmask (k, j, i) bool 130MB False False False ... False False False\n", "│ wmaskutil (j, i) bool 2MB False False False False ... False False False\n", "│ Attributes:\n", "│ nftype: T\n", "│ iperio: True\n", "└── Group: /gridF\n", " Dimensions: (j: 1206, i: 1440, k: 75)\n", " Coordinates:\n", " * j (j) float64 10kB 1.5 2.5 3.5 ... 1.204e+03 1.206e+03 1.206e+03\n", " * i (i) float64 12kB 1.5 2.5 3.5 ... 1.438e+03 1.44e+03 1.44e+03\n", " gphif (j, i) float64 14MB dask.array\n", " glamf (j, i) float64 14MB dask.array\n", " * k (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\n", " Data variables:\n", " e1f (j, i) float64 14MB dask.array\n", " e2f (j, i) float64 14MB dask.array\n", " fmask (k, j, i) bool 130MB False False False ... False False False\n", " fmaskutil (j, i) bool 2MB False False False False ... False False False\n", " Attributes:\n", " nftype: T\n", " iperio: True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define dictionary of grid datasets defining eORCA025 parent model domain with no child/grand-child nests:\n", "datasets = {\"parent\": {\"domain\": ds_domain, \"icemod\": ds_icemod}}\n", "\n", "# Initialise a new NEMODataTree whose parent domain is zonally periodic & north-folding on T-points:\n", "nemo = NEMODataTree.from_datasets(datasets=datasets, iperio=True, nftype=\"T\", read_mask=False)\n", "\n", "nemo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Visualising Sea Ice Concentration**\n", "\n", "**Now we have constructed our `NEMODataTree`, let's creat a `NEMODataArray` storing the Arctic sea ice concentration for March only:**\n", "\n", "Here, we first define a geographical mask using the latitude defined on scalar **T**-points before applying this mask to only the time-slices of the sea ice concentraction (fraction of unity) for March each year." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "

\n", " \n", "

\n", " \n", " NEMODataTree 'NEMO model'\n", "

\n", "\n", "

\n", " NEMODataArray 'siconc'\n", "

├─ Domain: '.'

\n", "

├─ Grid Path: 'gridT'

\n", "

└─ Grid Type: 'T'

\n", "

\n", " \n", "

<xarray.DataArray 'siconc' (time_counter: 48, j: 396, i: 1440)> Size: 109MB\n",
       "dask.array<where, shape=(48, 396, 1440), dtype=float32, chunksize=(1, 396, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time_counter   (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n",
       "    time_centered  (time_counter) datetime64[ns] 384B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "  * j              (j) int64 3kB 811 812 813 814 815 ... 1203 1204 1205 1206\n",
       "  * i              (i) int64 12kB 1 2 3 4 5 6 ... 1435 1436 1437 1438 1439 1440\n",
       "    gphit          (j, i) float64 5MB 29.73 29.73 29.73 ... 50.07 50.02 50.0\n",
       "    glamt          (j, i) float64 5MB 73.0 73.25 73.5 73.75 ... 73.0 73.0 73.0\n",
       "Attributes:\n",
       "    standard_name:       sea_ice_area_fraction\n",
       "    long_name:           Sea-ice area fraction\n",
       "    units:               \n",
       "    online_operation:    average\n",
       "    interval_operation:  1800 s\n",
       "    interval_write:      1 month\n",
       "    cell_methods:        time: mean (interval: 1800 s)

\n", " \n", " " ], "text/plain": [ "\n", " \n", "\n", " Size: 109MB\n", "dask.array\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n", " time_centered (time_counter) datetime64[ns] 384B dask.array\n", " * j (j) int64 3kB 811 812 813 814 815 ... 1203 1204 1205 1206\n", " * i (i) int64 12kB 1 2 3 4 5 6 ... 1435 1436 1437 1438 1439 1440\n", " gphit (j, i) float64 5MB 29.73 29.73 29.73 ... 50.07 50.02 50.0\n", " glamt (j, i) float64 5MB 73.0 73.25 73.5 73.75 ... 73.0 73.0 73.0\n", "Attributes:\n", " standard_name: sea_ice_area_fraction\n", " long_name: Sea-ice area fraction\n", " units: \n", " online_operation: average\n", " interval_operation: 1800 s\n", " interval_write: 1 month\n", " cell_methods: time: mean (interval: 1800 s)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define time_counter mask to select only March outputs:\n", "month = 3\n", "time_counter_mask = nemo['gridT/time_counter'].dt.month.isin([month])\n", "\n", "# Define hemisphere mask to select only Northern Hemisphere (>30 N):\n", "gphit_mask = (nemo['gridT/gphit'].data > 30).compute()\n", "\n", "# Calculate March Arctic sea ice concentration:\n", "siconc_march = (nemo['gridT/siconc']\n", " .masked\n", " .sel(time_counter=time_counter_mask) # Select only March.\n", " .where(gphit_mask, drop=True) # Select only Arctic.\n", " )\n", "\n", "siconc_march" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Next, let's visualise the Arctic sea ice concentration climatology for March:**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -- Create a Figure with an North Polar Stereographic projection -- #\n", "fig = plt.figure(figsize=(7, 7))\n", "proj = ccrs.NorthPolarStereo()\n", "ax = plt.axes(projection=proj)\n", "ax.coastlines(resolution='110m', linewidth=0.8)\n", "ax.add_feature(cfeature.LAND, facecolor='0.1', edgecolor='0.1', linewidth=0.2, zorder=4)\n", "ax.gridlines(draw_labels=False, dms=True, x_inline=False, y_inline=False)\n", "\n", "# Plot eORCA025 JRA55v1 March-mean Arctic sea ice concentration:\n", "c_parent = ax.pcolormesh(siconc_march['glamt'],\n", " siconc_march['gphit'],\n", " siconc_march.mean(dim='time_counter'), # Average over all March outputs.\n", " transform=ccrs.PlateCarree(),\n", " cmap=\"Blues_r\", shading=\"auto\",\n", " vmin=0, vmax=1,\n", " zorder=1)\n", "\n", "# Add colorbar with label:\n", "cb = plt.colorbar(c_parent, orientation=\"horizontal\", pad=0.05, shrink=0.7)\n", "cb.set_label(\"Sea Ice Concentration\", fontsize=11, fontweight=\"bold\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Calculating Arctic Sea Ice Area**\n", "\n", "**Now we have constructed our `NEMODataTree`, let's calculate the total March sea ice area (`siarea`) for the Arctic each year:**\n", "\n", "$$siarea(t) = \\sum_{j} \\sum_{i} (e_{1t}(j, i) \\ . \\ e_{2t}(j, i) \\ . \\ siconc(t, j, i))$$\n", "\n", "Given that our sea ice concentration is a fraction of unity, we can calculate the total sea ice area using the `integral()` method to perform a discrete area integral.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "

\n", " \n", "

\n", " \n", " NEMODataTree 'NEMO model'\n", "

\n", "\n", "

\n", " NEMODataArray 'integral_ij(siconc)'\n", "

├─ Domain: '.'

\n", "

├─ Grid Path: 'gridT'

\n", "

└─ Grid Type: 'T'

\n", "

\n", " \n", "

<xarray.DataArray 'integral_ij(siconc)' (time_counter: 48)> Size: 384B\n",
       "dask.array<mul, shape=(48,), dtype=float64, chunksize=(1,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time_counter   (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n",
       "    time_centered  (time_counter) datetime64[ns] 384B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    standard_name:       sea_ice_area_fraction\n",
       "    long_name:           Sea-ice area fraction\n",
       "    units:               \n",
       "    online_operation:    average\n",
       "    interval_operation:  1800 s\n",
       "    interval_write:      1 month\n",
       "    cell_methods:        time: mean (interval: 1800 s)

\n", " \n", " " ], "text/plain": [ "\n", " \n", "\n", " Size: 384B\n", "dask.array\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n", " time_centered (time_counter) datetime64[ns] 384B dask.array\n", "Attributes:\n", " standard_name: sea_ice_area_fraction\n", " long_name: Sea-ice area fraction\n", " units: \n", " online_operation: average\n", " interval_operation: 1800 s\n", " interval_write: 1 month\n", " cell_methods: time: mean (interval: 1800 s)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define conversion factor from m2 to millions km2:\n", "m2_to_million_km2 = 1E-12\n", "\n", "# Calcuate Arctic sea ice area [millions km2]:\n", "siarea_march = m2_to_million_km2 * siconc_march.integral(dims=[\"i\", \"j\"])\n", "siarea_march" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Notice that the output above contains dask arrays, so we haven't actually computed the sea ice area time series yet. To do this, we need to call the ``.compute()`` method:**" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "

\n", " \n", "

\n", " \n", " NEMODataTree 'NEMO model'\n", "

\n", "\n", "

\n", " NEMODataArray 'integral_ij(siconc)'\n", "

├─ Domain: '.'

\n", "

├─ Grid Path: 'gridT'

\n", "

└─ Grid Type: 'T'

\n", "

\n", " \n", "

<xarray.DataArray 'integral_ij(siconc)' (time_counter: 48)> Size: 384B\n",
       "array([15.2408672 , 15.79595013, 15.52007057, 15.67490804, 15.4822803 ,\n",
       "       15.38406103, 15.53892779, 15.73057268, 15.10191968, 15.09060138,\n",
       "       15.16780209, 15.33381377, 15.42379346, 14.71695332, 15.18094565,\n",
       "       14.67146894, 14.76720599, 15.20104239, 15.03428544, 14.55060593,\n",
       "       14.42182747, 14.77674223, 14.98954958, 14.86408957, 14.63162772,\n",
       "       14.96846618, 14.81741809, 15.07512074, 14.73251504, 14.31403776,\n",
       "       14.01130097, 13.99332879, 14.6702956 , 14.55893887, 14.62526484,\n",
       "       14.00725094, 14.60984041, 14.31873289, 14.11679314, 13.73934657,\n",
       "       13.90021079, 13.76015308, 13.63476796, 13.71709969, 13.93802755,\n",
       "       13.91244292, 13.88787505, 13.86517418])\n",
       "Coordinates:\n",
       "  * time_counter   (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n",
       "    time_centered  (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n",
       "Attributes:\n",
       "    standard_name:       sea_ice_area_fraction\n",
       "    long_name:           Sea-ice area fraction\n",
       "    units:               \n",
       "    online_operation:    average\n",
       "    interval_operation:  1800 s\n",
       "    interval_write:      1 month\n",
       "    cell_methods:        time: mean (interval: 1800 s)

\n", " \n", " " ], "text/plain": [ "\n", " \n", "\n", " Size: 384B\n", "array([15.2408672 , 15.79595013, 15.52007057, 15.67490804, 15.4822803 ,\n", " 15.38406103, 15.53892779, 15.73057268, 15.10191968, 15.09060138,\n", " 15.16780209, 15.33381377, 15.42379346, 14.71695332, 15.18094565,\n", " 14.67146894, 14.76720599, 15.20104239, 15.03428544, 14.55060593,\n", " 14.42182747, 14.77674223, 14.98954958, 14.86408957, 14.63162772,\n", " 14.96846618, 14.81741809, 15.07512074, 14.73251504, 14.31403776,\n", " 14.01130097, 13.99332879, 14.6702956 , 14.55893887, 14.62526484,\n", " 14.00725094, 14.60984041, 14.31873289, 14.11679314, 13.73934657,\n", " 13.90021079, 13.76015308, 13.63476796, 13.71709969, 13.93802755,\n", " 13.91244292, 13.88787505, 13.86517418])\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n", " time_centered (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n", "Attributes:\n", " standard_name: sea_ice_area_fraction\n", " long_name: Sea-ice area fraction\n", " units: \n", " online_operation: average\n", " interval_operation: 1800 s\n", " interval_write: 1 month\n", " cell_methods: time: mean (interval: 1800 s)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "siarea_march = siarea_march.compute()\n", "siarea_march" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Calculating Arctic Sea Ice Extent**\n", "\n", "**Next we will calculate March sea ice extent from sea ice concentration. This is often used to compare model simulated sea ice to satellite-derived sea ice observations, such as NSIDC, HadISST, OISSTv2 etc.**\n", "\n", "To calculate sea ice extent, we must first mask our sea ice concentration field such that any T-point grid cell with a sea ice concentration exceeding a chosen threshold is treated as completely sea ice covered (i.e., `simask=1`), whereas those a sea ice concentration less than the threshold are considered sea ice free (i.e., `simask=0`).\n", "\n", "Typically, this threshold is chosen to be 15% sea ice concentration to be consistent with observational data products (e.g., [**NSIDC**](https://nsidc.org/sea-ice-today/about-data)).\n", "\n", "Note, often NEMO model `icemod` outputs include sea ice mask variables for a range of common thresholds.\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "

\n", " \n", "

\n", " \n", " NEMODataTree 'NEMO model'\n", "

\n", "\n", "

\n", " NEMODataArray 'simask'\n", "

├─ Domain: '.'

\n", "

├─ Grid Path: 'gridT'

\n", "

└─ Grid Type: 'T'

\n", "

\n", " \n", "

<xarray.DataArray 'simask' (time_counter: 48, j: 396, i: 1440)> Size: 219MB\n",
       "dask.array<where, shape=(48, 396, 1440), dtype=float64, chunksize=(1, 396, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time_counter   (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n",
       "    time_centered  (time_counter) datetime64[ns] 384B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "  * j              (j) int64 3kB 811 812 813 814 815 ... 1203 1204 1205 1206\n",
       "  * i              (i) int64 12kB 1 2 3 4 5 6 ... 1435 1436 1437 1438 1439 1440\n",
       "    gphit          (j, i) float64 5MB 29.73 29.73 29.73 ... 50.07 50.02 50.0\n",
       "    glamt          (j, i) float64 5MB 73.0 73.25 73.5 73.75 ... 73.0 73.0 73.0\n",
       "Attributes:\n",
       "    standard_name:       sea_ice_area_fraction\n",
       "    long_name:           Sea-ice area fraction\n",
       "    units:               \n",
       "    online_operation:    average\n",
       "    interval_operation:  1800 s\n",
       "    interval_write:      1 month\n",
       "    cell_methods:        time: mean (interval: 1800 s)

\n", " \n", " " ], "text/plain": [ "\n", " \n", "\n", " Size: 219MB\n", "dask.array\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n", " time_centered (time_counter) datetime64[ns] 384B dask.array\n", " * j (j) int64 3kB 811 812 813 814 815 ... 1203 1204 1205 1206\n", " * i (i) int64 12kB 1 2 3 4 5 6 ... 1435 1436 1437 1438 1439 1440\n", " gphit (j, i) float64 5MB 29.73 29.73 29.73 ... 50.07 50.02 50.0\n", " glamt (j, i) float64 5MB 73.0 73.25 73.5 73.75 ... 73.0 73.0 73.0\n", "Attributes:\n", " standard_name: sea_ice_area_fraction\n", " long_name: Sea-ice area fraction\n", " units: \n", " online_operation: average\n", " interval_operation: 1800 s\n", " interval_write: 1 month\n", " cell_methods: time: mean (interval: 1800 s)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define sea ice boolean mask [1: siconc > 15% | 0: siconc <= 15%] for all months:\n", "nemo['gridT']['simask'] = (nemo['gridT/siconc'].data > 0.15)\n", "\n", "# Calculate March Arctic sea ice mask:\n", "simask_march = (nemo['gridT/simask']\n", " .masked\n", " .sel(time_counter=time_counter_mask) # Select only March.\n", " .where(gphit_mask, drop=True) # Select only Arctic.\n", " )\n", "\n", "simask_march" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Next, let's visualise the Arctic sea ice mask from a single March:**" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcQAAAIdCAYAAACjhg1lAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsnQV4k+fXxu9I3d1dgOLubhsMNjZg7j7mwlyY+/5T2PbNmW+MwWC4Q0tLBUppqUDd3S3Jd51T0rWlkrZJm6bPjytXSxp58yZ57ue4RKVSqSAQCAQCwSBH2t8HIBAIBAKBPiAEUSAQCAQCIYgCgUAgEDQhLESBQCAQCIQgCgQCgUDQhLAQBQKBQCAQgigQCAQCQRPCQhQIBAKBAIBc07NQW1uL+vp6cdIEAoFAMOAwNjaGqalp7wWRxNDPzw+5ubnaOjaBQCAQCPoMV1dXnD9/vlNR1EgQyTIkMczIyIC1tTUGKzk5OTh9+jQcHR0xfPhw3nEIBjbUubCurg6VlZV8+fC7HXBztICbgzlsLUwglUpQWV2P0qp6lNGlog5lVQ0orapDeWXTdaX0s7IO1XWKHh2DraURPnhgBh75+AhKKxu6fX9zExlsLE1ga2kMGwtjWFsa87HbWBjBxop+0v+NYWluDKVSxceeU1SNnMIqPHTzJbC0tOSLiYkJJBJJj16DQH+g9TouLg6FhYUYMWIE3NzcMNgpLy+Hl5cXn5teC6IaEsPBKIh0EmNjY5GXl4cpU6bAw8Ojvw9J0APhq66uRkVFBQvfJz/sgoeTBdwdLWBpZoT8khpkFVZBKjdFdHI5/gnLQWFpDQteo0KTdr8ySGSyHr0vUrkxzM3NIZWbQCLrviDVNAI1pY3ILW0EUN3h7eQyCYujo60Zv24PRwts2hXBP53tzFBZ04DswipkFVTh/hsXsUhaWVnxsQmhHFjMnj0bWVlZOHnyJH/eR40aBSMjo/4+LL2nW4I4GMnPz0d0dDRvBObOnQszM7P+PiSBBuJHi0BxcTHe/3ob/N2t4e5gztYeWUa06JdU1OH0+WJe/LOLqlDfoDT480rCXlRex5ez6aWt/mZsJIW7gwVvEkggv/59H4smWcpkVWYXVeNcdjkevW0p7O3tWSyFSOo3tHGn9yomJgb79u3D2LFj4ezs3N+HpdcIQewAhULBbof09HR2O/j4+IgFQE9pbGxESUkJC+BPWw4jyNMGJkYyJGeVo6K6Hn8ePIfM/Eq2AhVKMdylPWhDkJpbwZeWyKQSth49nSwQ4GGDnzbvR6CHNeoaFEjKLMN1y2fyomtnZwe5XCwn+gZt4MmrlZaWhvDwcHh7e3O4R9ZDb4ahIz7B7UCuNfrwSKVStgotLCz6/p0RdGj91dTUsPi99tlfCPayhY+rJVt8SRllSMwoxR8HziEtrwIKjVydgs6gDQTHG4uqEZFQwNfJZBL4uFhhiJcNvvnjAIK8bGBnZYK03Eo+/8/et4JFkhZjYUX2P/Qe+Pr6wsnJCZGRkThy5AgmTpzIrnBBa4QgtuMipQ+Nu7s7W4ZiJ9X/UNILvS8ff7cNw3ztYW1uxJaMUqXClqOpSMooZTegoG+gjQa5T+ny7/EMvs7B2gRBXrYI9rLBr1v2wdfVCuXVDRg51B8uLi7sqqOkHUH/QRv76dOnc2LgwYMHMX78eOFCbYMQxBaWR1JSEhITEzkATa4FQf+9F5QVds/zX2JcsBMCPKzZ+sgtrsHuE6d4IR4MMb+BBMcm4/IQFpfXHJOk2O1I/yyMDXJkKz4lqxyXL5rKAkkxeWE99j20wR89ejS7uMkLFhwcjKCgIPFeXEAIIoCGhgZOnCktLcWMGTNga2urPj+CPowDzrnhNV486WJuKucFdX9UFj747RS7RAUDB9qwJKSV8uX3/SnsUh0T6IDSyoMYFWCPqppGRCcV4pn7rmRXnog/9i204adNCYkirXuUcGMkslAhUdF2vAtot25jY4OysjKDK7ug1xYREcHxjgkTJojawj6kqqqKS1l++Gs/QnztUFRWh+ikAkQnFiI+rUTDcoeBj52VMdY/Nhv3vncQJRWG3w2Kyj+G+dhhbDBtfpzgYGOCM6kluHHFXLYeRcy+b0vKTpw4wXH5SZMmcZmNIaKphg1qCzE7O5stQ+rCM2zYMOE26APoi3fzE59gxig3Tu8n4TuVXITv/j3LiRsCw4c2OrHnivny/Y5ELu0gr8BPfx9goaR60EvnToanp6coc9Ix1Fxk6tSpiI+Px6FDh9hSpPyJwcqgFESlUskfgNTUVIwbN050cugDd+iSO97CzFFubAmO8LPH1qOpiDxbgJoedncRGA5NWazp2B6WDjMTGcYPcUJpRR2G+9mz5Xj4VA62/9+Twq2qIyiWGxISwnHFqKgoLmEiA4Gy7Acbg85lShmL5CKgn5R6bKgugv6GPlYFBQV4+/NNmDjUGXnF1bywHY3NFfHAQe4y1RSKO04f6cobKRd7c0Qk5GPt3U0xR5GQoxsqKio4hEQZwRRCMpTMYOEybQfa+dCbTTuhyZMnix2nDqBN0+Ovf4tpI5v6J9JO/4WvwpGeV6mLpxMYMJRI9c+xNL54u1hi1mg3bNt1gP92LDYH7z5zC2/UBdrDysoKM2fO5O42VJpBRgOtl4OFQeMypU4N1I906NChCAgIEDtMLULTUDIzM7F9Xxhc7My5X+aGzac5RtS1/0Eg6BraUG3clYQfdydhpL89W407du1FXkk1xxupcXNXo30EmmFkZMTWYUpKCo4ePYqRI0dyp67BgHwwuO6oBRtN6iCrkNwtAu1A6drnzp1raiKcXIBDMTkIT8hHXb2ICwp0A22wTqUU88VkWwImDXNGeVU9hnjbIvR0Hl546FpRNqUFJBIJAgMD2QKnEBP1BqY4o6G7quWGnjxDWaTU5ovcANSQWND7DQaNwSIhJEHcHpqKHcfTRYaooM+hjdfhkzl8cXc0xyWTvbFz976mDjphaTj40wsGv4DrGicnJ147Q0ND2RNEWaiGnGxjsEk1VGxP8UKqs6HmtsKd0vvzSY3OSQjpI/Pl3ye5aL6qlkYOCXqDSKrRHhZmcswb54FLJnmjUanE/JmTuAhdFJ33jtraWoSFhXGZBsUVB9r5HNRJNeo3jzKkqPOM6ILRc8hVQlOmKQZ75nwRtoelcZNnGgkkEOgb1AFn69E0bA9Nx8RhziirrIensyUORGfh/eduF0X/PcTU1JT7oJKRQXFFQzUy5IaYNkxi6ODggDFjxhi0ea8ryAKkadtkDWZkZuP4mTz8ezyde1EKBANlSgf1VaULjau6dIo3/t2xCzHJhXj49hW8Pgh3avcwMjJiIaQw1OHDh7mg39DCUAYliBQrPH78OGdEic4zPY8Pnj17ljvK/L4vEbsiMlAsJkkIBjA0F/PjP0/D3joJiyZ6Ycfu/Sgsq8Xq5fO5KYcQRs2RSqXczOTMmTMsipSoSKO+DAWDEcTc3FzOhqJMKH9///4+nAEnhDReibr3nM/Mx9+Hz2N/dJaYKCEwKGhj98veZGw6dA5zx3rA1vIIiivqcP2Vi3gMkhBGzaDzREOGqf/zsWPHuETD1dUVhoDckGoMaecymPvw9QTqJpOQkMCxwq+2nsbuExlCCAUGP4ljZ3gGb/oWTvCCk80hzpK+9ZoloiyrG5DhQXkaZIgYSq2ifKBbNjS/kApIybft6OjY34c0oNzLZBFS1tUPO+K5dKJW1A8KBpkwbgtNw97ITC7Z8Nx3EOezy3HvzVcYlBtQl3h4eLAo0hgpSmak+YoD2dKWD+Qaw1OnTvH4IMokHSjlIP0N1Q6SRZiansXNlGlBqBalE4JBDG0ENx8+j90RGVg61Qe79uznKSy/7U9B/PY3+vvw9B5HR0deg9W1ijRgfaCK4oCsQ6TpCZGRkTxPjzKdyJct6Po9JCGkWOFfh1J42kRFdYM4bXqAqEPUL6zMjbB8ui8WTfJCTFIhnlpzjV6se/pOTU0NiyLNs6S4okwmg75gsHWIJIaUSUoWIu1KqFBU0DHV1dXsGqXZj9tCUzlhprRSTFQQCDqCNorUM5W8J5fP9MPOXXsQHp+PdY/dBHNzc3HiOoAME1qTaX2m0reBOEBhQBXpKRQK9lWTUUuWoRDDzs8VlU/s27cPG7ce59FCNIRXiKFAoBn0XaHvzCOfHEOjQomt23bguofe4++WoPOBw7RG01o90M6VdKCJIf0ciDuPvoTiqvv378e/+8PxzOeh2PD3GRSV1fb3YQkEAxL67tB36LXvI7mZOH236DsmaB9am2mNbrlmDxQGhKrQCaWWQdRPk3YfA62PXl+6R6n8JPl8Bn7dm4w9kZli/JJAoCWSMsvwzBfHsWCCJ64uLEWgnxeXGwg3asddbSimSGs39T/Vp5jigBVEihWqJ9xPmzZNiGEHG4bk5GQkJSVhd0Q6ft6TJBJmBAIdQCmIuyMyuSXctQuCsLCgAEFBQTwqaSAs+H0tilOnTuXifVrDSRT1vZWmfCCIIWUvCTHsuEPP6dOncSopB99si+c2VQKBQPeJN19sOYN9UVm4bUkJRmZkYMSIEQbTsUVXokjZp/osinJ9FkMqrSA3IImhSKBpDZWckBAWFRXhs00n+YspptMLBH1LcmYZnv3yOOaP98S99fXcNJyEkUoPBE3Q2k1rOE3JoDV9/PjxeiuKcn0Vw6ioKG4nJsTw4nNDrlG67ApP496Mop5QIOg/aCO650QmT4W5Zn4gFl1wo9JFXxf+/hLFY8eO8dpObTb18dzonSBSui6NF6FCSpq/RW2BBE3QOaEP06nETHbXiHFMAoH+QBvTL7fGs7fmrmWlPDmGFn5R1N8EreVqS5HWeDo3+tbRRq6PYkjtxYQYtj4vNJuQCuw3HUzG7/tT0KgQA3oFAn2ENqrkRl01t5C9XDSKjhph69virw+iOHbsWL06L3J9WvRjYmK46TR1OzDEacw9gWKoZBWeScrAZ5vjcDa9tL8PSSAQdAFtWH/ek4yoxELcd0UFJ7/R4i9KNMBrOxk8R44c4TWfBrnriyjK9UUMT548yQkidKKEGDadk/T0dE6c+TcsFRt3JaJOTKMQCAYUtIFduyEMNy4qZs8X1S16eXnpjQD0tygePXqUhzToS0NwvRBEajFGTadnzpwpGnVT9/3aWt4gxCel4fMtZ7jBsEAgGJjQRvb//onHibMFuHt5FYYF5bBVNNjzI8zMzFgUDx8+zBowdOjQ/j6k/hdEsoIoPkZuUjG1AtyEm8TwYHQ6vvonAZU1YiKFQGAI0MZ27fpQ3L60lENDJIpubm4YzJiZmXFHG3KfkjvZ29t78AoiTWsnc5n63g32TCxqS0dt1+ITz+Gb7Qk4Gpvb34ck6AUmxjKYm8hhLJfCSC6FsZGMf/LvF342XWSwsWhqRTh9pCs3lG5oVPKl/sLPpt8VaGhouq66rlG4zwdwJur/fj+FiIR8PKpScSYquVEHcztKa2tr7mJDUzJIIJ2cnAafIFIJATV+Jd9xf54AfYB2i9TF4fjpTGz4Ow7F5XX9fUiCdqAQh7WFMewsTWBraQwbSxOeZcg/21xnatz01WoSNEUrcWsWuAuiJ70QOhkb5MTPoRbKlsLZ9HuTqBK19Y0oqahHWWUdi2hJZR3/3va68qp60bBBD6ENb3zaXtxz+XAUFhZyBxd7e3sMVpycnFgLSBModNZfBpK8v2JkNC8rICCg303k/k6cSU1NRVxcHL7ZfgbbQ9P7+5AE9KWQSeDqYA4PRwt4OFle+GkBdwdztvTIjV1KolPRJDz0e3JWGUoqSJSa/k9iVF3bqJEYqQcEf7IplgWtM0gwzU3lsL0gwjaWxrCzahJjTydLjPAzhi3/3wSWZkaob1Agu6gaWQVVyCqsQlZBJf/MLaoWpTv9DG1839gYhSVTilBfX88dbnx8fPQiuaQ/IC2grHrShlmzZvVLcqW8Pwb80gumFkdDhgzBYG7ITe7iU2eS8OHvsYhPK+nvQxp00Lrj5WwJPzcrFj33C+LnYmfGFlxWYTWyCyqRkV+BsLhc/n9BaQ1bdv0FCWxVTSNfSOQ6g6xJJ1vTZkGnn+OHOMHD0RzGchnySpqEMpuEsrAK53MqkJFfKSzKPn4/aRDx+ZxyPLiyGqNKSthSGqyNwocMGcJtKUkjKK+kr8f8SVRkpmjg3rSxsUFZWVmvTFn10EgSRWr4qo+te/oC2gXReYg4ncbxBOEi7RvIsgr0sEGwF11sEehpA9qLp+VWILPZgmr6WVxe22fCoLYQaYhzVxaitjYC9tathZJ++rpaQXWhP2diRikSM8rY8iVLV6B77K1N8MjqUZgw3AeTJk0atDWLSqWSx0aRGNJ50IbFrKmG9Zn8khhS0gh1biAf8WAVQyov2bX3IELj8vD9jrPCbaVD3BzMEeTZJH4kgp7OligoqeHFPiI+Hz/uTkRGfhWUysHV9YeEnobe0uVUSlHz9VKpBF7OFgj2bDpfM0e5wcnODJn5lSyOapHMLa7u1+M3VGhjvO6bE7j5kgqe8ENNsJ2dnTHYkEqlnGRD5RhUh01JR31FnwkilVZQSQH5hgfj5AraEFBD7sTERC6yPxiT09+HZHBQzGx0oAPGBTtipL8DTE1kOJddzos4tbtLzCzjGJ+gfWhjkJZbyZfdJzL5OopRBnvaIMjLFvPGe+COZcNQW6dA7LliRCUW4GRykSgN0nKHm6+2JbBlfr9CgeDgYG4SPtjiisbGxlyOQaJIljLlmxiMIJIQUh9OKsIcjG4AKqmgvn2n4s/hvV9PIjWnor8PyWDwdrFkAaQMzUBPa17Mo5MKsTM8hsVQ9HztHbSBiEgo4Is64cjf3RqjAx1x2TQf3LdiOJIzyxGdVMBtytLzKrXyvg52aMNM5/LRqyu5ww21fRtspRkWFhZckkfdbEg3+qJmU94XJQXUi5PMfzs7Oww2yHcdERGBYyfT8NEfsWI33UuMjaQY4WePscFOGBfkCAszOU6lFONATDbHYynTU6A7aIPR5D5tsropw3VMEG1IHHHFTD9O9olKKkR0YgFOny9GfUP/JSANdCjJ6enPj3OyTUVFBcfTrKysMJiws7Nj7aA5imRQ6VpDdCqIlC1ExZbU7X0wdmQgy5gsw9/3JeK3/Skie68XIjhhiDNmjHLFCH97jrWQu45qNik7V1iB/QdtQPZHZfGFrMcQXzu21m+6ZAgniZw+V4wjp3Jx4my+EMceQCU+VJqxOquMy9XIUnR3d8dgws3NjTWEtITyT3Q5fFmuSzchpc56enr2mf9Xn0hOTkZE1Ems/+t0s7tJoDkUMhnmY4eZo90wOcSFsz6PnMrBDzsTkVMkkjr0EdqYkLVOl+92nOWkpskhzrhyth/HHmmA7uGTObyJ6asMXkOAztWv+1KQkl2O+6pqMXHcaAQGBmIwERAQ0FyjOHv2bJ2VY8h1Ob2CCiup2HQwQa+dCu0jT57B2z81xbEEmkPp/5TdSNYgdWY5GpuDV749we4jwcCCNi6bD6fyheKO9L4+tGoUd+4hq/HwqZwuaykF/3EioQCvfheJtQ0KthaHDx8+qJJtRowYgWPHjvHIKHKj6uK160QQqfsKtSOaM2fOoHrDqNieXKSRsUl444co5JXU9PchDQhsLIwxbaQrL5ieThY8FYAam1NJgGKQlUQYKrQxpAtlWI8KcGDL/427JnP9JwnjsdhclFWJDGBNzuOLX0Xg6RsVzS7UwVLEL5FIWAgPHDjAGuPn56f/glhSUsIWEqXMDqa5huQipmL74ydT8NZP0SivElMquoJqBJdM8cbEYc48N25XRAaOn8lHTZ0oBDdUaINDWcB0MTORY0qIM4vj9QuDuDZ0e1g6kjLL+vsw9RraaL/wVTievK4BdXV1nGwzWDJQTU1Nue8ruU4pwcbW1lZ/BZH68VGTamq/4+joiMECFdF++f2fKK6o40xHMci3Y2RSCccESQjdHc2xLyobj35yDPnCmh500MZnf3Q2X5ztzLB4kheevnEcsguqWBgp5ig8BO1DG+6Xv4vEI6saeN0lA2SwjM9zdHRkjaHsfYonarOuXa7N2Bm5CykteDAFfCkdmtoMUS/CL7fGiy9wJ0Xz88d7YNEkL+4F+m9YOg7GZKO2XtG3b5hAL6ENESVM/XEgBbPHuGP1vABcvygIO8MzsC8yS5QrtQNtvN/5OYZdzeShonaYg6UsIzAwEEVFRaw52mrvplVBpKxK6hM3mOKGVGO5fede7urx276U/j4cvU2SuXSyN7vFyBX29bYELpkQWYaC9qipU2DH8QwWwnHBTrh0ijeumu2PQydzsON4ukjCaQNZ0Os308i4WuQXlWHJ4vmDYoyURCLBuHHjOJ6YkpKiNSNMK4JISn327FkunBwsbdlosCcVi1J94e6IpjZXgv+gkokrZvpimK8dz357/v/CRRcTgcbQhinybAFfqBsRCeMbd0/GmdQS/H04VUyHaQOVZVDIxtbKjBNPBkPdt7GxMfc8pU42FE+kCUr9LogU1KW4YUhIyKDpREMZTscjIvHpJlFj2BaaILF6bgACPaw5DvTZX3Eie1DQK6iF2ed/n8Eve5KxcKInnrh2NJKzynkzSpM5BE3Qxpzmc66pqcPkiePh6+tr8KfGzs6OtYc0iLyTJiYm/SeIFDckK4lMdF2kwOoj1KQ8NDyKffeUGSlogkYHrZobgOF+duzy+vCPU9zGSyDQFhQr++PAOfx7PB3LpvniuZvGIe58CX7bn8w9bAXgTN03q+rxmoUZj1Hy9/c3+NPi5+fHXkrSIoqj9iZk16sZTOQmpe4BY8aMGRRxw/PnzyMsIopbKQkx/C9G+PCqUVh3+0QeC/Tgh0fwy95kIYYCnUEbLfqM0WeNPnMv3z6JP4P0WRQACemlWPvZEV6raM0ydCQSCWsQaRFpUr9YiDTXjxJpqLfcYKiBITdpWHgk3tgYjZQs0X3Gxd4MK2f7Y/JwFxyIzsbDHx0VjbUFfV56QJmp/xxLw4pZfhxjPB6Xx1bkYG+KQWvUmxuj8fQFwTB096mRkVHzDEXyWPZ0jqS0p3V3ZJ7S4EaaQmzopKWlITQ8Em/+GD3oYxY0H+/OZcPwzn1T0ahU4bFPjnHmqJgyIegv6LNHn0H6LNJn8p01U/kzSp/VwQxldb/1UwyvXbSGGTo2NjasSaRNpFF9IogUN6QApqurK3x8fGDopKenI/T4Cbz9UzSPvBms0DR1yvT74P5psDA1wpPrwzjRoaC0tr8PTSBg6LNIn0n6bNJnlD6r9Jmlz+5ghUI7tHbRGkZrmaHj4+PD2kQaRVqlc5cp1XxQZikFLw2djIwMHAuL4ASahLTBm0Az1McWty0Zyl1m3v/tFI/0EQj0uak4dYyiUWG3XjoEc8e64+vtCYP2O0yvm9awtZIm96mXlxcMmZEjR/a4PlHe3a4sCQkJLIa6Gr+hL2RmZuJYWDje++Uk1z4NRmwtjXH9omBMGOKETYfOcRmFQiGabQsGBrRxW7shjNsEPnndWG4a/+OuRC5N6C/IalHVFEBiZMGXvoLWsHd/OYnHAUyXSHgsn6Eil8u56Tl1EHNxcelW955uqdqpU6c4OKuNAkh9JisrC0dDj+P9X0/y1O/BBlmCiyd7YeWcAMQkFeKxT4/xUF6BYKBBG7itR9O4OcQNi4Lx/gPT8cf+FO6E05d9UpW1xVAVx8GkLhPmJnJutVavMkKdiTuk1gGQmDtDIulV0r9GG4T3fz3Fv8+YJoGHhwcMFQcHB9Yqau1GiZ86EURqIjt06FAY+pT7I8eO43+/x/Kg08EGTTy/dclQHtD7/i+Dc0MgMDxoQ/fRH7EY4WePW5YMwdxxHpyIQ8OKdY2qvhKmOdthY20Nhdy02YqhVtxKVT7qi7NQl6dAg8t8SC16lh2pKTRSjdY2QIKZ06fA3d0dhsrQoUObXaeaZp12SxBHjx5t0K5Sasd2+GgYPvrjFFtGgwlzUzluvmQIJg1zxp8Hm4qfhXtUYGjQBu/JDWHcX3ftdWMQHp+P73acRXWt7ppIKAujYWZmwfNS20L5PqYmRjAxNkJ1wR5U102AzD4YuoTWNlrjiNkzp3ESiqG7Ts3NzTW6T7dsdENuGkuNug8dOYZP/zqNqMTBJYaUfPDOvVN5UC+5R6muS4ihwFChzzZ9xmnsGH3m6bNP3wFto2qohipjN6wasyDrYqUlj4yFuRnsak9CkXusRxmS3YHWOFrrKBuT1j5Ddp36+PhwuE8TDNfc6wZVVVU4fvw4ft6TzK2PBgvGRlJcuyAIc8a448fdSdhzQjQpFwyu+kWqLV4wwROPXT0aB2Ky8fOeJNQ3KHv92Ir8GJhXnYGNjRUaG026ZdU4IQ+Faduhcp3Grk2AxFHV1PH8AhJTu17HHGmt++ofmpRRg6suXwILC8Ps9DNs2DBuuakJg14QKS5K05c3H0riQPtgIcDDGvetGIGqmgY89XkY8ooHd2cPweCFNoKx54qwZsUIvHH3FG7afy67592oFEUJsK4/CxMLMzQ29swVa2taj8b8HRf+p66jpLoJ0kUlKi3GQubQ+3wO6jvsYmcOD5cwTj4xxGlFtMmgcJ9Gt8Ughnz64eHh2H/iHLeAGiwZpNTmatl0X44Vbj2aKmYTCgY9tCF88esI/l68eOsE/l78deh8tzNRlTVFMK+Igol576bXy6WA3LQpAac9qmqyKW1EK+/b9zvPwsnWlCdFUEmdTCaDoaFpuG/QCiL56GNiYnD81Dl88mfsoBAFd0cLrLlyBIxkErzwVbiYECAQtIDWgC1HUnEyuZCtxbHBTmwtZhdWaXSeVA1VMMrZDQvL3omhJhgpSqBos571dMACve6P/4yFnZUJzMzMePDuYBjW0B6DVhCpwUBUbBLe+SkGdVqIGeg7iyd5cbxwV3g6z5FrFAX2AkG70CipZ744znM9X79rMscV2wunkOtSVZULZcU5BNjWIi8vD9a21j12k2oKWXCNKgkk6dshU1RDinooGhVQBFwDibRngxZoDXz75xhYWxhzLNHQy+s6YlAKIjW6jToZxwF1mrFmyJgYSXHX8hCeYP/mxigeDSMQCDqHNow/7UnmbMwHV45EsJcNvthypnnzrKzIgknBIRgby2BuZoqKCgWn9utaDMmDW1JWB5XcCnYmNZBKyL1pxqOPapSNgNSIhVpZmQWpiS0kxpp3aSmrrOc1cZ2JnF+Lt7c3Bhu6bY2gh9DYqtjYWO5Co6krZCCPaHrljknsCnn68+NCDAWCbkIbSPru0HeIvkv0nVLWlcOk8CCsLE25frC9+kJdUV9XiwbrEPg6yriGUY1cbgRZ5jYosg7AOPVX2JQdgyr/eLuPoVIqoChORGPhGY55koCqySqowge/neQyhYKCAgw2BpWFWF5ejj37D+Hbf88afH/SccGOHC/cH9WUSt6XbaoEAkOCvEivfR/FIYfX75yMV197HRVWFjyRvitajiFqistxh23+nf6Zm5t2aFXSV5bv0UL4TCnRpiYWKWUmcLORNh+DsbERKEFUKi2D0qgpGcekqhANbWKcisKTMKtNhamJMZdtKItiUdfQiDqnOZBaNrVyiztfgo//OInyqlpcdukiWFtbY7AwaASxtraWyyu2h6bj8MkcGCr05blqtj+WTvVhF09oXF5/H9KgRiaTwNrcGMZyKYxaXIzlsqafRlIuDifGBjmipKIeDY1K1Dcq+Kf6Ut+oRHl1vWiY0E/QhnLjrkQkxMVAhoZOxbChUYHqOgXqbcdDatvUBabJClMCSgWZaIBK0SRQZeEwM2udTUpiWVFZjVq5E8wUBVyw3xJTExPIZUq+jVwmYwvV1MycLcaWxyWTKFDfWAtVXRkkRdEwUxZxo2uFrMXjyYwhkclR21Dd6jkOncyBs50ZHO3CMGvWrCYhHgQMCkGkDwmVV+wMS+FSA0PFwkyO+68cCVd7Mzz/VTgy8w3bJdzfWJkbwcPRAg42pjyM1s7ShCeE2FrRz6bfrcybxK5R0SRq9Q2thY4uahZO9IJUImktnEYyFlP5hVYnFdX1PK2htLIOpRV1/HtJZR3Hf4rKapFVWIWK6pZ2gUBbUCZn9JF/YGPRfuKKVCpFaXk1aq1GQuYWAlmLwvn2cjZVykYoS0L5d4USqKutQT0s0GA9DFIff0ilclTnn4CVNOsiAZbTZ8PYEmUVlagxHwJVdSIsLP4Turq6elTKXGCc8Q9M5Y0suiqVebvuXRWZou0k4/xx4ByLIonhjBkz+PUZOoNCEM+cOYOoM2n4cms8DBVvF0s8ds1oZORRhlw4aup0G9wfLJDF7WBtCg8nCxY/9U93Jwu2/EiECspqWJCo80lWYTXiUktaiVZ5NVkUHbus7ayMsf6x2TzIlSzE9qAht9bmRq3EVv1ziJctC7KTjRmLM1mS2QVVLI5ZLX4WldcOivIiXaGqzoOJrP0TSBubEpU9ZL5LIZdqtqxKpHI0KIDyqjrUmftB6j4cEmNLtKwClDqNRfH5c7C1vFiwSCStLMxh2pCKOpUKZZU1UEotoJBbAXZDIS2Nh60FPZqs01ZwNXUKSDtofv3l1nh4u1jB3v4MRowYAUPH4AWRplfExiXwwNCWu3FDYnKIM+69Yjg2H07F30fOi0Wvl1ZfsJctZxXSTz83K7bUqHBbLSx7o7L4JyVl1db3TUIFCWqTyJJgVnR4O1NjGdebqoV7mK8dtyajZBD6/J/PqUBiRikSM8r452CzJlWNtVBWZEBq7QuJrB2RqSuDsjwDMsdhkHAG539Iik/BxMS43TIIR1cvlCuofq97VpTCdxUUMmPIOqj7o2NodF+AsuzdsDA3grxlJs0FjIxkMDIy5+OorKxCtUIKmDtDVZrQqVvXzNQEOQiG1C+ow+NuaFTif7+dgrOtKRe3G/J0DIMXROpReuDwMXz+9xmDbU12yWQvXD0vkEfbDLam5L2F1iBPJ8tm8aOfLvbmLHRJGWU4FJONr7eVI6eoasDUbZJAU9uxtq3H5DIJ3BwsEOhhza/12gWBLJx5xdXN4kg/MwsqDXpDpco9BhtJIepLw3HJ4kXYHK2AxNQeipIkGFcmwlRSAxNjYxSXGLeaOqFS1MOksQASk4unJhgZG+PpJx/Hr/tSuBVad5DIu+5zKjW1R6PPlSjLPgBLlMDEpH2XLblDyTVqpGhAWfq/gLzjCQ+1tXUod1sBWRvRb4/c4mp8vuUMd1S9/LJLDbbnqUELIn04IiIicDAmm0e8GOJifu38QMwZ64FXv49ESlbPey8OJijOOjrAEWODHTEm0IGtv+TMMiRmluG7HXlIyixFVY3huZtJ0DPyK/myPzq7+VwEeTZtBKaOcMWNi4PZIohJLkJ0YiFOphQa1LmguYRmjbkwNjeDsZERjh07Btv6etTl18PKyhJK1o+mOJxReRyULQWxtpjbHtJmoa0xl1Flw1moa68fC3srE/y8N1nrmwq2Zr0WorwoAWblUbDuJMuVNj82xvUoKy8E7G07uI0U9Q1VgLFmGaTHz+RjqLcd3F0iuOepIbZ3M2hBpFrDiNNpPMXBEDMX71kegmBvW+6/mFPUOkNM0BpPZwuMC3bCuCBHBHnZICO/CtGJBXjn5xgkZ5V3Gt/ra2qiP4HZ2Pv75LlI7Gg2nnr2J8UpyYKkbNflM3yx5srhbCmT5yEqqUCvk7TovBHtnTv1OVXmh8PU7L/EExIUavxMl7biYiKpRVVtCaSmdlApG6AsT0GDQorqKsBcVgupTIbaBqDOIhgyl1H8OXrxqwg8fcM4rlncsOWMTjKCqaF3raU76rN3w8FShsZGFVtuhKmJvLmEg5JuOiuXkMhkvEGQaCiIxMbdiQj0tIatrS3GjBkDQ0Si0mDwFtXv2djYoKysbEDUpGRkZODgkTA89flxTnowJChG9MjqUdxiiTvtcExJ0BayeqaPdGUhpExPGgxLIkiLf1F5nV6dMHVSzb3vHewwqaY/cLA2wZggsqadeNI8ZbhGJRbgaGwuu1cHEjSb0Cz7by5hIGu5q/mEZAHl1loDFl4wLg6HhRmVOki4fKtc5g5YBUBm5XFRz09Kbnrq+rEor6rHB7+d0lmMmbvRlCZzpirlsFIMUNVYAeOaDJigptmtWd/QCKVKBVNjo1aiT4k8Db6rux3zdLAxxZt3T8bsGVPg5eWFgYKmGmZwFiK98KOh4dyU19DEkOrVnrx+LCprGrDumxN9ltAxUHCxM8PM0W6YMcoNlmZGCIvLw//9E89NGAZiQlVfWovtQRuHvZFZfCHXcoivHSYOdcba65o+g1TPe+RUDvJKBkB8XiJFlZEnqpQm3NbM3lwBI7ms05CLZWM2pJX5MLH8rwaPShCkDXkorXeAROJ50f1og/rytyfwyOrReOGWCXhLR+0hSchkdv+5dNUoMQHVjbWoLE8D6kuhMrLkzNXSunKYVsXB3JSSglRNlm0P5ikWldXy2kou1yU2NnplIGVmZiI0NBR79+5FUlISioqKWA8ol+Tpp58efBYiuQsOHjyIH7af4gC3IeFqb46nbxjLsa4Nf8eJAu0LUBxs6nBXzBrtBn93a3bvHT6Vw9ZgXyfCqN12RHeETF8txI4gS4ksb9p8jAl05AQeOuehcbkDIuZI1pUsddOFkoTuU1lVgzqP5Sw0nYU17r18OII8bfDGxmhOTOlvuO6xIBLyqnQofS6HRNbz2YdXzwvAjUtGYfbs2exy7muoA1BUVBQOHTqE3bt34+zZs2y9k2VMlnBLWaPbkkjSFI9BYyHSCTh58iSOx6bj9wOGVXxPCz25YdQTvQ05C1BThvvZYdFEL16Yz+c0Lchv/xTDlkt/0Z/WXF9CGw1KVKMLWeJTR7jwhuTmS4awS5UmQ2irNWJJRNMmw27i/Vq1rhpsx6K+OpobH3SHuoYG1FiNgawTMSQofvjpX6dx3YIgvHz7RA5v9GbosDagukeZy2SoMLndRgHdgdZYyla2tT3ZZ+OiaOr9b7/9hl9//RWpqalsrdNAY3XclP7ftvEAub5XrFiBgIAAjZ5DbkgTLE4npOCjP2P1KklCG2L47E3j8OeBc9gelo7BDLntpo1wxZIp3rCzNsG+yCw88VmoXuy+Byu0AdkdkckX8mLMGevOMe7i8jr+vB47ndsrdzUJIYmiWhjV1/UWqa0v6krD2xVEWtyp0wv1B211H6kUlQ3mkLoP0+g5aONKSX3USYi+w5SJ2t+iqC2UShWvtVTv6uDgAF9fX52L4YQJE2BpacmiR9M4iK6mi5DV+PrrryMuLk6j55F2V3Q08LD2OZWVlTh9+jTX4hlSkgkVhT9z4zj8sT9lUIshJSqsnOOPTx6Zicum+bAFcv8Hh/HL3uR+EcOWrlF9QF+Oh94Lek/WfHCY3yN6r+g9o/eO3sOeok3rUC2sVPCukF1cT0fLW1lFDUpNQ1BeWcMt1UjPK6urkVdrBXgu6LY1RP2TqQ0afZfpO20on4+yynpec2ntpTVYl5D2kAh2d7LIiy++yK5VTY9P3l2VLiwsxMiRI+Ho6Ai96S8YHY2tR88Z1AQLH1dLPHvTeGw6dA7/drPY11DwdbXCpVO82SV3+lwxPvkzFrHnivv7sPTONapvx1PfoMS+qCy+jPS3x5KpPlzGEXo6D/+GpSM1t+NOO5rQmbVIf+tKQNW3UbE9oGjlXisuq0aDx1LITazRYBuA0twjUMotIPUcC5m8dZPt7kCvm/rU0nf6le9O8BBiQ/h8nEkt4bU37nwh1t5/s85cp6WlpSyGmvZTNTExwVNPPQV/f38MHz682aLUalJNcXExC2JiYiKcnJz4ifq7a0FKSgr2HArHk+tDm4d3GkJf0uduHo+tR1Ox9WgaBhv0+lfNDcBIfwccOpnNi0l/1lrqOttzoCXV9IT8Q2/gyTe/w+wx7og9V4Tf96cgPa+y2wLYHVoKY3siKknfBjvTpvNNliC1sVN4XQaJkWaLZ09YPt0Xl033wavfRWr8+rsLTdGgAcaoy4dEUQ+J61RIeiHmmgwhf+veqVgwa5LGsbruUlFRgc8//xz//vsvEhISmhNo1AKpljES5KCgINx3333ce5UEkTY6miaG9ijLlA4mPj4eWVlZ/ITBwcH9kmlEZvC2f3fizY3RBjP8lorIX7h5AraHpXFv0sGEu6M5Vs4JwIQhTtgTmYm/j6QalAvc0AWxqTbuHIzKYtFg4gyZ2/SLbkOu08tn+GLBeE+cOFuAPw6kILuwWmsi2BGUeZieloaxq9/773jT/4WDaS0vosWVKii9lmjUSq23XDHTj+PgL393QuvNDhRlaTArPgoba8tmkSitqEa94yxIrZrmHeqCod62eOqGsVh66WKO8+kSel3p6ek4evQoaxMl05BFyCUxUil7L8ePH99qZJVOBbGlGUsdYaqrqxESEgJPT88+yTYi6LDf/uQ7nM+uwHc7zsIQoN6SL9wyHrsiMrHJgMdUtYVGzNAMR3KNHozJweZD5/SieL6z7ifaZKALoqqxDsriWJhUJsH6Qs1eYb0NpJ7zOi36v2KWP2aPcWNXKo1ly29Rz6htQXzisUfw848/oMrrmv+uzNgFc1URyo18IHOd0u0i9d5An/eFEz3x8reR3DtXG+uhsiAaVrVn251dWF9fjwrYQ2U3EhJzF52s0zdfMgR+7lY6dZ2297qp/pCSZshipHAeddLpl8J8emKak0WWIh3Q+fPn2Uylrui6huKZNP7ml71RMATcHMzx/M3juQh6sIihvbUJrpzlj1kXFsXHPw1ttSgO5qL4gQK1NZOlbYKzrQUUF8SQXFSqLlx0tOH56p94DguQOLy7ZioOxeRwzDxl739WnDbY8vdm/PLTRs5I5M4ujTVQlCRAYj0ElVI55Dq0nDqCNgDUG5W+81TI35uQgIqGDWfuh420GMYdDPKl8gQ7VSUaig+hLl+JOptxrZqXa+Mz/8veJLx971Rem3XlOm0JhfDIICOPJemOh8fFnYO6S6/9nHQAZBm6uroiOTmZG+a6ubmxxWjWom+gtl2lJ6JiuEDdEOKG1GGFvhjUiJxiK4YOFXYvneqDFbP8uIn0UxvC2nWb9RdCDDVHmRvWJIYtsv9oTVDJbTS6P22A1m+O47Fl5C5///5pOJecCF//QK0NpH3+madYEOgYJWlbYSataSqhsBvWaXG9rvltfwr3j33+lglY93VEjzv+KHOOwcWsCkpl+1Mw1NDkKJqUYWIC1FRGolIqg8z2P+Ey6+UGkNbiDX+fYbe4i4uLzlynVGhPM25zcnIQGBjIF22F7LQW+KMDGjp0KLy9vflgqTMAxRZpp6DNzuhkIsfExHDH/oS0gR83pMJm8r1TmzFKWTd0RgXY45ZLh3JtGnXwOKuHsV9hGXaMsraU26BR4omqrgzWiiwoFCYXbVilLt2zumhDRCn8Q7xt8d5D9sjKTMew4aPg6NT+4NruUFnVlLxCC7QZC7cZChoduyyu7wvoO29sJOU14Pn/i+h2YwllZQ5slNlQKrtX1mJmagpVWTiqpMaQWWuvJ2l8WgmvzR5uMZg+fXq7FltDQwO3WKOuYrfffrvGMxZpQ0NJlJTUSQbYvHnzNM4e1RStZ8LQAVIBJfWRI3OW6kcoG5WsRm34lckcP5OUgV/2JBlEofnj14zmbLMfdiXCkKGmwDctDsYIf3v8ti8Fu09k6lUDBVVjjU4z8QwBRel5WJWHNf1+wSI0tfwveUONiakpqroxRaEltEGaOmM2MtJScTIqAg6OThgSMgJmZj1f+Gpralp1MeH0fZfJ0Bd+2JkIB2tTXgte+yFK40YGdN7lBUdgbNGzGk8aEFxVmwdoURAJWptpsoyb23+uU3JXb9myBZ999hlOnTrFRhIZURs3bkR4eDjH9zp7nWQNUljOyMgIU6dO5WYAukBnqaF0wNTnjrKB6ASo44udvXDNXaVnBryrlPYG91w+nHsefrLptMG2YyP3KBVoU2YdzVR79ONjOml23BshVGUfhpkiH9XWE3i8jqCd86RUwLjkOEwtWseo2svJK6k16dXml9yZPn7+cHV3R2LCGRw9uA/+gcHw9QvgsUvdoa6urpU7l5JLyqwmQ9aLPp7ahk4htXmj7HJaEz7ZFKvReqCqzoeZvOfrYGGDLaQe46Ft6hqUPFCYXKe0EdmwYQO++eab5s8KiZo685fcn2qXJ31m1J8bdrurVGxgDRkyhH9Sv1JKoKH7UQIN5bA4Ozs3Vzr4+Phg8uTJveqao9NaCXpRdJBkEpOZS41YyaVKrlVKk+2Jq/RAdDab5QMdmnJPs8We/7/wATmJQdMRTPQFr2tQ4PUfovRqZFB11Mcw9poL88pTsLQwQ02NFBLjvu8iMlBQFkbDwrTzGBVBC1ujmZdWFhYTE1OMHD0Onl4+iI87xW7UEaPGwa6dpL2TMTHw8va+KKHvTFxccyySrJRX33gbN99yq1a732irmQHN53z5jolYPTdAo+EEkpJTMDMz7XBQcHs0NCg4jpir9IHMY7TOskHPpJbwWn02/it8++23HXY4o80KxXfbor49VTBQ4xU16gxa+ntJSQlfSCipwTdBky2mTZuGt956C8OGadZiryV9kmdMOwJym86dO5czgii+SL7g7ryR1Mz1THIGN7ce6Mwd54EFEzx5NEx5Vf81o9YVZPVeMz8Qz9w4nruVPPtluF6JIWHiOR22dfEshkSlWbBO67T0ja7aeakaa6GsyoWiKAGK3FCYVyfyyJ8uH7emBlJLzWJCmmJn74Ap02ezMJ44foytxpZrBy2OK1csw4rlS1pdv/GH77F86WK2Luj6NQ88zGLYm7IOspSVdWU8QkpRfBaKvAgoM/dCmvY35Od/gzRtc9N11flcl9kdyHNCa8LCiV68RnR6HIoGmDTkd2sNraqmlnQjUOCwDHLnMTovjfh5bzIqK2u6dYw9hTY7dCFDiwR05syZWLRoEQ986A59Wk1PQW0yafPz8zm+SCJHblTKSOoMcnMcCTuBb7efHfCuUkoqoXqdN3+M0qvMSm3h5WyJNVeO4EXquf87rpdT1qmLh019Eu9MyXrIrzaB1Ev7riN9prPEIWVtCUyytrJbis6RykgFhdRM482vxLT7ZVddtVyj98kvIAiOzi6IjYlCYUEeRo4eDytrayQlJrIQZ2Zk4L133sYDDz2Me++8Hfv3721+j4OGDMUTTz6l0XNSKYmqthSqulKgrgTS+mLIlVWQquoghRJSqYwtswYFDd6VQyFRu2TJglZBJstBVdE5NDQ2os7YHRL3ORqLD60J7/1ykueeFpXV4FRK+60KFSVJHKvVFBoUXGUxEvI+DAnU1Suw40BTzLkvIQEmYaTkzgULFrCl+NJLL+n3PEQ6aBJEasNDbg4Sxo7SdEk8N+0M5xEqA70l2Uu3TsBX2xJ46rghQd93ihVS6vy20DSus6IROPoGLfY2Rbt54aZ41LBhITiU59+nRdn6XphPhfZmmX90O4OPFv3JU6Zie0rnG9yW9MRSo7UjJeksUs+lIDB4CP7e/DfWf/YxWwi0ebazs0dtbU1z7JCuj4iO5XhTS9qKIVt/BVEwI2tYLoelhTlv7LrbULrtOSmotYTUa0G37jd9pCtuXzoUL359Ahn5F7d4k6b9BVuzzp+XzgV9zil3raRGDvhcplOrUFVfCVXOIRgry1FnO4FDEOaF+2Buprlw6wL1uSgoKOhSw/ptFaBdGwVDScGpXnH//v3cNZ1SctuKccLZJHy/c2BnYdpZmeDJ68Ziy9E0gxND6jTz4i0TMG+cB175LpKzSPVRDKnHo3PV4eaYBX0Gf9/0d7fFUF+mS+gKVW1xjzYItPCsuvpajW/fU7dlk8U3DBOnTENmRhrS0883jwEiy6CmprpVRuk77394kRiqn58K9RUVmWjMOQqjtN/hIstmITQ1aZqz1xsxJEhQTRvzun0/WiO2HkvjOai0drRFpqzt9P61dfUolnrzelpVVQ14zNeZGKpo05AfAdPsLbA3rYO1hQmsq6Jglr+Hyzv6Gzo+dSJPV/TftvgCtDiNHj2aM1JJvffs2cOWI70IupBI7jmRqZX2Rv0ZU3vs6tE4da4Imw+fhyExZbgL3rxnCtLyKrjAPjlTv2KFzQlZfzwBz9ojnNKn3vW//7+Pu53c1dLdqO32Yv0JWUdntjzLAmFUcIhdgj3JAp83v3uWUG+wtbPH2AmTEdMi6YJbmF2IWZGgXXv9jVh99TWtwy+HD+G5Z57C5AljYJr2K5xqIuBmXMQLuS7iXdZWmtc7qhqqoajI4PjjX4fOcyN0WjtoDWm+jVIBaYspHe1BoSW562SUOy1BndslOm1YrqrJh1XdOViYm3HxP2FibAQLC3P2HA0k9GZAMJmxlB2krjchUaTs1JS0LHa/DWRuWBjMHSm+3pYAQ4FeD00DnzvOHZ9uOo3IswXQRyg5RJa2GVMn/swueVrwaKGcNHkKll62rNOYUnvT2luKoKlcNxPd+4M755hi/OjhsJLJYGZt1iPLyMfHV2shFU0JPXaMa53bQsL3ymtv4pbbbm++7vy5c7js0oWcuUjNBWjxpkWb6K0l2BkU31Slb4fC1A1SCzdIzByhqinieKPUwrX5dsraYhhn74SFmRGqCxuglJrgq8/+wSP3343rFwTg+50XGneoOj9WmtxRZ+YDGamRsSUk0G0DAknxKXaL9kXyzKARRIJMehJBSrKhNnBxcWdQWFIDMxM5qmo7n4ysr0wOccbM0W545ovjBlNeYWNhjIdWjeQuO89+Ea63E+s5PJ61G1YWJqAESfrCmptb4JP1n2P2nLntilx7Vl9HlmDLj+RAFkY6T3/9+Xtzq8WeiAMVWi9fsQJ9ze+//sIu1JaLMfW0pIzSq6+9rvm6gwf246brr2lOsulLyAthgjoolKmQVJxDTX4tZHIZrwc1VqMgkRpB1VDBmbxWVk1xSysLdb1lA779agN3ZZngZYTwxHJI80M7bVWmVCqAFkKrS1SK+qZsV2PdWaB9Sb+7TDv6ctGF3KQZBVV4d800no9Hc7cGWsPuu5eHcM9VfWla3VsCPW3w+t2TOfmDaij1VQwJZXECrOR1LIbqDddrb77dSgy1zUB0o6qqcppjcD2FyqmWL1/Rp+eFiu537tjeSgxJ7K6++hq4ODugtKQpQ/Pz9Z/hhmtXs4j0pxVDn0M6PrJKTU1MYGNlAYeGJNjWxcHdKIdLgNrLcaT3ZteuXahID4dD+WE4Wao6dfVT/NOkKJTjohrkTPYKRUlit7Jd9R29shBbfrmo2PLr7Qlc4LkrIoNLFeaMdcdPu5MGRFIKtWV7eNUorsM7kaCf7sTuQrWTNywKxq/7knlor95TVwRjI1krN9qcuR2PJNIWmkxt1yeWjzVCaKgRVL0QCyp/GD5iRKevu6UVrQ2B/OP33zhZQr3o0yba188f99z/EDLSUxFx/BiOHj2Gb7/5ut3i7/6GxJka78gg0WhDQlmS5AXtyoKnv1tbmkFSkYJ65URAR115VMoGmJbHwujClBNDQC8FkYYPHz2VxWJIpGSV48WvIzBthCuuXxiExZO88O2/Z3Euuxz6yq1LhqK2XoGf9wz8ht0U0L9j6TCMDXbkwuGB0ilIoqiCStZUEkI889yLfRbjam/x10eRpAQNcidqmoXXHrTR+OOvra2yGDt7zdqyFj/84N1WFhA9P2UNkzD6+Prjg3ffRVjYMa0OFxhIKOVWkOiwRZ0yPwLmZrofqDyoBZGGDielnMfGNs2u6XNPluGJhHxcPsMPL9wyAWFxudwtvlTPpqrPGu3GU9+f+jwMCj1qYN0TTI1lePTq0bAyN8LTnx9HSUX/D+7VBEVZKuxk5ZBImnokUkbinXff0+fH0TY+qXeiqKzn89M2Dtcd7lnzAMaOG9eu2OnKhUzWYV5ubnMsjUTvtjvvbu6V/OzTT+Kff7ZgsEHvI20SKD6pgm5rDs1rUyE3N6yG+HoVlKM3korwt4emoaC0tsN0Ypoj9sRnx2BiLMP7D0zH8um+3ERaXzq13LZ0KDfoLdaDqe+9gZrz0saDWPfNiQEjhlRvaFp8rFXiwbpXXuvzZIqBQGn0VzgcGgELC8tuW1J0e3cPDzz9zHPoS6gzzSMPrmn1/lIPy9Wrm8orPvvkY/y08XsMJkgAKXs2r9oU+Q32KJYHo9FFN7FyFZUuZR/oVjnJQEGvLEQquUhJy8HmI6ld3pYE88PfYzHUx5bji/PGe7BV2Z/xOrKmHl49Cv8cS+uw5dJAwdXeHE/fOBaJ6aXYsOWMXhbad4Qy/wQnLKgtHipO1mUiTXfQRyvRy8sLHp4eSIiP75YYUqzq59829elGgxb9FcuXclJJS3cpTdehJJurr1qB0NCjvXIBDzSorKPCYgxkzv6QyXUfz1PmHIGTeQN0WKnSb+iNINKHmxJpaHI29cDTFBoSTCUN1CXlrmUhWDypAt/tONuqhyaZ9zCyaLdTg/pLpY0uDhTfLK2oG/B1kwEe1txV50B0Fjfo1efRVIriRMgrk9BoNQQyu8CmK2XGze8nLdwrrlrV6fRuWkh7UqDfFfTZqq+v48evr6OftVA0KrBkqjeM5VIYyWWcfGVp1vQ1XDHLHxXVDWhoVKC+Ucm7fvouUNNnss7LKutRXl3f6/dD7cakOsqyqjrceN013RJDihnectsdeOqZ57jfaV/yyYf/4/PYUgxJmOn9XjB3JrebG0xiSNB7UAElJN0QQ0VpKqBsgMw+qFvPpcg/CWtlzoW+rYaH3ghibm4u0rIKsDcyq9v3pe8G3S80Lg9XzvLH63dOxv7oLPy2+wyqEv+GmaQGjSoZak29OaYkbSiDTFUFmaIOEjRALpOhQSGFUmoMhdQcjXJLQG4DqZU7JCZ2UNWXAzQ3T9kAKBr4gySxcIXU5L/ZjiP87DFjlBvWrg/VawHpirFBjnhw5UjOJN1xPAP6DHX8Ny09AStLM9RVRqKciq2tfWBUnQmllVHzYhkff6bp9ioVZzC37HG59JKFSElOwq69BzFq9OhuHwM9VnVVJXdpqaqs4Et1VRUv2iQcPMRVbsSCa2xiwm6+J26eC6lMio1bwlj41HFmI5kED90wFz/9EwrjC2JJXgdyXdtamnDdp0KpRFlVA2+88oqrkVVYhayCKqSmxCO/XAWFsYNGQkikZeXhsksXoay08yQpsgDVFiFdPvxkPa68aiX6Gjqfn2/4tN3ryWVK51iXBfb6CnlC5HUkUiM0jq9blIVCKpGgXFELudNIze5Xng7bxkQYGRumGOqNIKqtwy1HU3tVvF5d28hu072RmVwe8MBl7vj2GxPU1zdZCxaqPF5oFFL1l4YysJqysP7bW1VDKq1FQ0M+6vJiueuDXEaLQlNyBmVV1dQ1QFkVjQqVHeA8GebWDrhreQh+3HW2w9jnQIAaCt+5rKluMiyu+/0X+2NGn62NJS+CJlR7VRqJuqoMONu0XhhjT8bw/zf9+QfHnswtLFBcVMRu1IL8PFhZWeGdt97ADz/90u7zbN3yN4oKCnDpZcu4JRXVt9GlvLwMNdXVLBYWllawtLSClbUNXN08uDaL5vmRCHYUm3t7TNOEjdqaahzctxsvPX4rTM3M8dnt/9fu7UkgqSmCrZUJ97ekOlcPRwvccMkIfPbZCbiNdsMVK5bhYGQaEjNLeeTW+cwi1BefQ+zeL1o1FKCNwZyZU6HopF+nWgSffe5FkGQnJJxBQ30DrlhxJfqDn3/c2Dw4lqDm7FWVldxQQNf1dvqOkYkMDZr2Vi06CnPLC4X0tWdQka+EzLnzzaBMKsH0YWaIj9YLydAZ/Tbtoq11uHPvITzwvyNa6ebC/QzzT8DNKA+NjbqbN0g758qqaoQMGwZTUxNERkai2n4GZDY9n9jcX0wd7oK7Lx+O93+NGTDxT3nqb7A2N2p+L3LrbGHZmAXzCx1XWsadps2YifCwUK5Hox21uleuOjGDFv7UzKb61vT0dHa92dnZ8iL84Qfv8W1pKveSJUsRMnwEbO3sYG1jyyJI4tcbl7taEGfPW8iC2F67uLa1e+r/7/h3Ox647+7maeOzZs3GY0+sRdLZBMTGnuIRODRRZvacOSgtKcW+fXu5ATedg7//+rPDzFKKu86bvxAffvJZc+Zmf0Ln39PVka1A9QaDRJ3EcDBahW1xcnLCmQITSJ0mdPlZVGTug5t5VXPtY11dPcqN/SCx8oNEbgLILSCR/reJG+lvj5suGYKs82fx/XdfYaCSnZ3dpYbJ9eGDTl/Y3lqHLWNKpmVRMDaSolGiW9OeW4GZmSI19XyzL19SEooqiRQya28MFCYNc2Yx/N9vJweMGCrrymEkoS9003tcWlENmUQGS8v/kmnU0AIRExXZaspFe2633379BWfiYrFt6xZuIXjbbbfB1saKF1z6IkVERKC2th433PJff0xd0VX5QuL2F3HF8iWIjoxslW25Z89ubN++rXkorpr9+/bxxoBe+47t//B17YkhiQ1ZXt/+8BNmzpoNfWH3rp187BQfXLX6GljZ2ODP339FUWFhfx+aXkBF+65yOXKzDkDqMbvTaSVS16koz9zUPN6LvCu2igwois5zc4ZGBVBjORTuwVNw0yVDMczXDn8cOIddf/4MG8P1lmpfEKmG8J9//sE333yDGTNmYN26dV3eJy8vD+nZBdh7IrNXz62sLoBKUctjR0ws+q9Y1NzcFCg+iqrGGkjtgnU+lbq3TBjqhPtWjMBHf5xCTDI1HB4Y8IwzhQyy+nrU1ytQ6zAT5sWHoFRe3FNR3aOzM+g2n3z4PifAFBcX82Xt2rW8i/YPCMCoMWN5asKlSy5Df0OF9LfefAPHvtv2tCTRMDW9uNEyCboaire1J4QkmDffejuee+Eljc5ZX/LFhvX8nq++5jq88tobfB0JouA/yP3tYlqOwrQtUNiOgtTaGxLpxUu8RG4GZRvHoJFMCqMWRfwu5vmY5V+K/NwMrP9xF8qrGyCvrzbUXBrtCiIlFKxcuRLh4eFsJZG7pavhonQbWnx+/HMHwmKzUVt98RBMRmoEiazjd4FH+RSehlXNaf7C0Be5v7uukyjKqk+iquIsVG5zeVCmPjIu2BH3XzkSH/8Zi6jEgbXTpnPa6Lca5bVFKD/5HWzG3cVxjt6QldU6oUvtUspIT8fufYfQn9BnOj0tDV98/hl++O5btna15SqkZvpXXXUVxk+YAF+/QE620Dc+3fAFwkKPYdr0Gc3XNTYMzIb/uv6c2JsD9VVRqC8JRY2xJ6TuMy+yGFVdlKBXV1djx44daFSoYGYkg6SuAcbNDccNF7k2TPW5c+dynFEtRiR0n3zySYe3py8fFeDb2dlhyJAh7Ioy6+DLzfFASKCCHCqpMRpkVlCaukEiM4WkKhUmDQUwlkuaG8z2txiqoeQLcxlQnr0VtVI7KK0COQNSl62UusMYziYdhU//0t/RTV3BcTMzR9hOeQzKulK2jHRBRUUF9u3d06ez/tTQd+n/vvgc/3v/HV6kaMOprb6c9F2hcpTnXnwZCxctRl5uNrIzM5GYEAcHRyd4eHrD2dWtlYuZRHjEsCDY29lj7vwFWLBwESZOmqzz8gsS7cuvaN08vLcNyQ0Z6uFrbGQOa2kpCtO2QuV1aau1R6lqGqDQ1aZKLpPw56RlT2BDpsdJNeQepRjLsWPHLtqt0hfo6aefxn333dfKity0aRNfr8kb0RnkJqqprWOXkR5uZtuFzkl5RSVqHGZDZu3Zr8cy3M8OT1w7Bus3x+H4mXwYAsraErjVHGXPgy6gx73jrnvwwksva/2x1Uk1x0KPY/7CRZg6bTo/H3Vc+ezTjyDrRVu19qDH9vD0wksvv4pFiy/hhhgff/g+TsfGYuv2nTxxPicrE5npaTyo1svXH55ePs1C7OPhwt9B9feYCsPJpfzAgw9j8SWXdjqaSJuMGBrExyro7L2mGk0VqupUaPRaxu5SgkrJFEWx8DIvg4WlJUqKiw0+U1eTpJoeC+Lvv/+Oe+65p8NdOe3eHnroIQwdOhTr169HVFTURYH+wUhFVQ3qPZb1mxvV09kC626biO/+PYtDJ6l2aWBDzalB9aRGNEdOiYlWp3H+/DmdfLntHRwQEXVKJ4L43ddf4t1332WBIcGiDZQ6I1ab0Pfyg48+xYorr8LxsDC88forOBF+nL079LxUm6iGzmFBXi5Sz59DeVkJ3D284O3rj1kzpqCstPSix6ZjnTFjFn74uW9ie999+w1eePapPhNgfYfcm3W11QgKCkJRcQl7Nkqlnpw1Kq0rBJwmQGrStH5T0uFl03yxfLoPwmLO4cdvP4MR9Ksn9IDIMqUkGBLCo0ePduqiog8pCaE6vb29QP9ghFqKFWXugNL3Ch4M2pfYWhpzB5rtoemGIYYqJZC+DXJFJRp9VnCHmsTEszpbIDvrdtNbCgsL+ftBbmC1Jabt7wtZdMNCQuDr64exo4ajpLiIszbpu0nfUaM2rlg6FnKZ0qW8rAynT0Vjx4ZPuKylPUEkMb/9rrvRV9x8y618XG+/+RocHZ1QVl6mw3bW+k9tbQ3qPFeg1FyG1x5biCfe3AgZqBmJ5KISq+sXUVeterz6fRSSM8ugUDrCQZKNXobhBzzdakL42WefYeTIkVxvp0l7JHW9V1voC+/r21Srp+9ZmNqGzomjjQkk6dugauy7ZtnUCH3tdWN5pNZAby2nRpV9iIel2ttaYoZbBn55+TK4ubnp5LlITEaPGQtdQTWOuh5TRDHIt975AFevvAIV5WW8cWj5/TTu4DtNGbePP/YIVq9aiQ3r17OLtS30PXZxdevznrEPPPQwMnIKEB17Bq+89iYGMw0qI05CzCw3xZHTBbj1ustbra9+blZ46dYJuPGSIfh1bwqe/yqcxZBQSemz0I8HP9AEkbp5fPzxxyyEPYn/0ZedYg0eHh546aWXEDT+UhSYTUBhXVOnkdraOm5jNRhml9HrdbKSQp6+Gcrai3fa2oa+Ew9eNRLVdY34YmtTG7OBDk20MG/I4g0Gnc/K8nIU5GWzpaULqAj89jsutn5KSkqwcsVyvPDcM5wwRqJDNY3dhdw4pjosdSAX7PSZs/Dk2sc63IRSWzhyo5JrSZ0c9/Ybr2PMiKE4sG9Pp63RSFiLiwpx/TWrkZmZyY+z+a9NfH76iiB/n0FdpG9lJodR+p9QFCXgt31JGOpti1EBDtzdiDppvXTbRJ5l+sjHR3H4VE6zACpzw2BTl6Q3E4P6E41jiMOGDevWl48u9OGkmicSwVtvvRU33HADC+u/O3bi4Y+OorCsttn1paotgaquDPKSSDhaD46ehLQwVVRWo8Zhhk4L+W9dMgTD/ezx4lcRqKK+XQaAIusg3MzKmzMNKcZNH2X6rOoC+pKcS7u4zy5lXFZWVPDnnY6FxJAyIk/Gne12Us2BA4fw55+/6+SzT9ag2gXbkStWKpVxZycSQnahGjX1YO2O65Y2tBS7opikugfqX1u2I2T4cOgK6s1KH+s7br0ZBw/sE6EZzlWoxfT5y3DNikthbCzH6XNF2LgrCfklNRedP9/avTr73gyqTjX0YadFnSw/Z2dn/n9RURFnoN5///3w8/PjTNSWMR1qJRWdVNgshgTVyEjMHKCSmUBV/N8X19ChRcfSwgwmFWEorS2E1Gms1l3IS6Z4Y3KIC57/v3DDEcOiBHhZ16C25r/XQx90cu3Z29vr5DlNO5iGMZ1awh0PQ0F+Pn/+6UJxdhK17no7Lrv8cvz884868ZJoUqKgVCpYxFoW5Xf3u0ivW12DzDXCCgUWL5iDT9Z/cVHZhLZQf6ypY5ShZ0pqipWFKRJjDuOnyhwoIMPhqHNQGdkAJnaQWvu1as1GZXNbt24dNOtuZ/RIEOlLQ7vIFStWYPz48Rg7dizGjBnT/Hf6ULa3sNOXIy0tDbvCL56iQPeRZf4DBxvzQWEdtoR24o6qVBRlFABuMzljUhtQD8LV8wLw8reRnTYdpykeyrJU6pbMadkSM+dWXxh9wt2iAh5WDUhJuXinq+3uKrSRo3Ih+ixfc/2N7d7m8y+/5p/5+fn4/ddfMGHiJPaCkKhRGUN2dhbGjZ8AR0fHLp9vzNhxXM9H7llDgb7XlDPw4Jp7EHUiHC+98rrO8ga+/OpbzJ4xZdCNf+oI+hydPn2af3c0JgOmGor6TBTk5kDmPrP5dl9uTYDbIMvl6LUgqgvv6cN2++2347HHHuMvfnt09IEnkzU1uwSnz7ful8k7yYJYWJs0uVkHI3TOXC3rUZa5GdVm/pA6je+0Q09X2Jqp8MCVw/HNtgScy+7CHZK+E7ZGTfVctEuk9PtGqSVn7NWbe0HqOLbfBZImPVw9LxDTQ+zw5huvXfR3tZtPm5C7/6df/0BQ8BDubdoZ5CFZ88CDzf8/sH8fbrh2dVMGJ4BNm//B6Babxva4/uqVLMBqd6UhQZuLjT98j/3793Fiz9Rp03q92FPMtpAuhXQpRF5uLr9XyUmJohSjHdRrq7UiCxU1RZCSV06lgEl5LBSW+tEwxOCmXZBr5tChQ/jrr7/YcqRU9RMnTnA3GldXVySlnMfZxHOc6aGQmKDROgSy8jOwM20cdBmnHUHWRUlZJWqtRkDmOBwSSTfFqCIN84aokJObg+SU86gz84XUteMFSFGeAVSkQK6gGZFVMDVq2tUTjQolKmqUUPpc3m9ddvzdrbFmxXBOClr/VxwyY/6Gs1l1KxcPfdm11c6MPCAUBxs+YiT2Hjjco8d4+aXnsfH775qH19ICHhgYhDfefu8iMTibkIANn36I7du3G3z3Fe5BW18PH19fvP7Wu5g5c1a7t6NknHia1BEfh4jwcMTHx/G0jqqqSk5covOqrtWkzYO6mw5dL9ymXb8HNBi6ztQHKkUd7KSF3MvU0NFpYX5baJ7hG2+8wf3v1MF0+ql++I4WqvqGRpiZDo4kmp58cMsrq1Fr5AaJ/XBIzJxabRpUinqo6isASpkuOQOTmlS+3tfbg+f9tbR0aj2ugMS46zo6rhvN3ANbeUVz1hkt5uXmoyF3DEFfQr1Jr5jph+UzfPHXwRT8fTgZSsihzNoPO1mpzrLiyNrcuecAho9oPXD1XEoKx6k8Pb3g7ePTaR0uieEr6164SODkRkY4m9z0PhH0uZ80bhTH3QcbdG7cPTzx6ONrOQYbEXEcCfFnkJuT0zzaiQSPbidETvvQXGr6jg2Wc5vdF+OfyPpbs2YNUlJSOJiuTgig3bsmQVpjI7kQww6gD6qVhRlsZRWoKtyHBoUSo0cOR3ihD1TVeTAtjWRXImXpGpuYQWbRtECrxZDeC54mbjcNcg3EkGDB9ZiH8rTNcLCUorq6BhUWoyFz0DzLWBu4Witwy0I/5Oacw+OPf4aKsiIYX/jiyo2MIe+FO7kraBGOCD/OmZGPPPQAT5SnLxIlz5AIkmBSEfvRsBMd1j2SYLZn7dFA4cUL58HayhpRkRE8m3AwiqHajZqfl4tnnny8OUuXoN/ViTm6asUnABfhDxYx1JReWYiUXUoFxSI7qe+gt6ukoga2lqbtzvVTQwtJRXUDzIwlUChVaJDZoMHcDzK7II1cn1TnJ0/fgnoLX8hcp6IvmRJsDnd5Fo4cOdJvLkR63pGjRiMmOopFkDYKLT/ntNmgcMCfm/+Bn79/q4kZzz21FocOHdDoeeg7RKUNnb2XAoFAD12mW7ZsYbco+fBpB0duDQpmC3dn39DQqEBDfT0XcHfWYoneF1rAjYxNWt2OFt3CigaovJez4F072Qi//70DDbbjILP1R030JzAbe3/rPqFUEkOlNdGtp5e0vJ02ICtXWpOHFTPcUVOShePHj6M/UY8n66xdG51POtevvfkObrr5FhbM6VMmoiA/T+OdN91fV1M6BAKBjgSRio3pgUQj3b6BXJ20INP5plR8+r2k3hRO5v8lH5GVUlZeAR9vL3h7e/PCevDgweZ6uPag69UF5LSpoc0Mj/QZMQKrVq1iC+etDZsoQAmJ8+RWccfqqI+bn7utILYUzLZ/o1insjwNqC0ApMZQSU2arFS6KBshrUiGtbwK3t5ePAG9J51e+hNraxtuHfbjxh/w7FNPDIpuSwLBoBZEmlohskD7PhuPZkamFJtAZhdMo66xKLgK+45EsmDVyWwhV9XDxQpwc3NBRkbmhZ4q3UedBKVuhUa/l5ZXoc48kDOCjWpzYCqp4szhb7/9Fp6ens3ip7Ys21qRXKTtNhpm9VkwNjHh5+CjU12Yc0n1qhdmtw3UWAa9xgOHQ+Hr58c1tpcsmNO8yWhrcdKGRXhTBAIDEMTutG4TaJe6+gbUNyhQZ+QMpbEd5LU5MFaUwtrKUufxW7W1o17I1T1paeJJSEgIL/IUA1vx6A+oS/j5v2Ouq8Pll1+OqJgYGBmgxaTesFBbMpo0r4Y6NS29ZAEnNrUUP3KNqssEBAJB3yME0cDgDkH1DTCS97/AtHSdkyhXVdegRmWOI7v/Yvft0qVLkZubaxAWEW0CKG5OqBNgyO28dfsujBg58qLbk/hde/VKhB072uxV4U5MMhnfXyAQ9D1CEAV9Ci3+ZBkSVENmCGJIkPhdtXI1gocM5dpCS2pQv3Mv/AMCOr0f9VYlK5IuZDFes/pKKNtxpwoEAt0jBFEg0BIkYjRc99Ily3Df/Q/0KLmMBHLp4vnsVhWiKBDonyCK4ieBQAPI3ZmUmIj3330Lt950fY9itzSJY//hUAwbPkIkqAkEeogQRIFAQ9RxwGPHjuKaVVdxI+7uQklIf/39D6ZMnd7sXhYIBPqBEESBoLuoVIiJjsS4USH45KMP2RXTrS+dVIqNP/+K5ZevaE7WEQgE/Y8QRIGgB6hrDT/+8H0WxtTz57v9GJ99/iWPQiL3q6GNexIIBiJCEAWCXmagUnatVRdj0Tripltuwa69B7F69WqNE3W4k49EIvqfCgRaRgiiQNBD1DWGtnZ2nDDTU7y8PDF9+nQu69CkmTm1ivv9z79FDFIg0DJCEAWCHtBkFdpgw5df4+TpBI2yRhWKRlRXV6GkuBiFBfnIz81BTnYmcnOaYpCffrYeX33zHQICArmjjbqdXluKi4vg6eUF1w5GTwkEgp4hWrcJBD2A3JbUqWbM2LGtrqd4YGVFOSorKlBZWYEqulRVoq62ttm9Sn1d5XIjyKRSSKm/q0rFMxetbWy4z6tCqeT/V1HCjUqF7JwcHD50iGeOVlRUwMHBAf/++y8SExNx1113o6q6ih+fmrWLUWwCQfuIwnyBQIeCmJFTgMbGBu5CU1pClxKUlZZCKpXA0soaFpZWPD6KfpqamsHE1ARGRsYXWZO1NdU4uG83Zs9bCFOzpsG4BAllQ0M96mrrUFCQj317dmHnjn9xxYoVcHVx4b9b29iyy7a2th6PPPwgUpKT2LoUhf8CQWuEIAoEOoKstI8/+ZQtOTNzc9jZOcDWzp4vFpaWGrlQyZrb9OcfiAgPg7WlBSqra/DGW+9q9PwkhlWVlSgtKeZLSUkRaqqrIZPJcSY+Hrt374apiSlCQ4+KhuICATQTxO73nxIIBBg5ciRc3dwxYvRYWFh0PES4I8iCW7J4PpKTklg8SeCoaF9TQaT7UE9Vunh6+/B15JotzM+Dja0thgwJhpWVFYZsC8avv/4iLEaBQAOEIAoE3YQSXYYMGwEfv86be3dGaWkpYqKj2aVKYkgW59x583v1XpAwW/hZ8nHR/MXszAwkJycJMRQINERkmQoE3WTZ5Svw+None3XeSAB37N4PZxcXrj+8++678dLLr2rtvaBC/8SkJISHh2vtMQUCQ0cIokCg6ZdFKoWHpyfeff9/WimKHztuHELDo3DydDw8PT213vDb3d2DLUWBQKAZQhAFAg0gYZEbGeHHX/4YMB1iaFyVjY0t1zMKBIKuGRjfbIGgj2lrrQ0LGY74xHPw8vIaMO8FCXdoRBTGjhvPgi6EUSDoHCGIAsGFeYfqonb6ndKzqdaQ4ntUUL/mgYcG5HmipJ1f//gLm7f+i6tWrebEm9raWiGOAkE7iCxTwaCFLCgSP5pLOG7cBDz6xJN4Zd0LmDJ1Ku64617k5uQgKSkRtra2WLT4Egxkxo0fzxcq60hPT8fVV12B/Py8/j4sgUCvEIIoGHQ4Ojqy1TdtxiyMGDESQcHBmD1nLovj9p17mm/n7u7OImJoeHt744GHH8WLzz0tWr0JBC0QgigYNFCpw7Rp0zFm7Hhce8ONMDExwWDl8itW4IlHH+JmAAKBoAkhiAKDhqw+Ozs7uLm5oVGhwNvvfyhamXERvwWGDhvWo8HGAoGhIgRRYFAxQcoOpUtNTQ3s7O3x8EMPwdPLG0NDRvLIJG3X+g1k6BwJBIL/EIIoMAiorGDV1dfyoF4arzRr5ixUV5bDydkFw0aMElmV7VBVWdX3b5RAoMcIQRQYBDQs970PPuTfafhuTFQEAgKD4esfKKzCDnjj7Xdx9x23CheyQHABUYcoMAiqq6r5JzW0jokMR8iIUfALCBJi2AlLll6G5198WTT/FgguIARRYBC4e7gjIz0VZ+JOYeyESXD3GDgdZfqTu+65F9fdcGN/H4ZAoBcIQRQMaNSF9ffedz/Oxsdh/MQpcHB07u/DGlC89sbbmDZ9xoDp0SoQ6ArxDRAMaCoqKrB5yzZYWZhh3ITJsLN36O9DGnBQ5u033/8IH1+/i5KP6G80zJh+il6oAkNHCKJgwBfblxQVYOyEybB3cOzvwxmQUA/XZUsWY+as2bCwtGwlfFKZDIdDI3DDTbdAcaHXq0BgqAhBFAxoJk6ciBGjx8DB0am/D0VvKS0txeuvvYKFc2fh/LlzF/39ZEwM4k7H4vtvv0ZgUDBUlKRUXc2uaOp9ShM+aHjxqtXXCLeqwKARgigY0BgZG8PN3bO/D0OveeetN/DF+k9x/vw5LJg7E1VVresP3dzdmwcJHw89huuuvxGx8UnIyCnAVStXNd/u0cfXImT4CBZKmgIiEBgaQhAFA5Jhw4ZhwoQJWLrs8v4+FL3niSef5mYFKpWKLbxdO3e0+ruLiwt8fHy44TkJY3VVFTdAb9vVx9nZGX//8y/efveD5uvo8WhMlkBgCAhBFAxIysvLMXvuAlx+xZX9fSh6D42v+vX3v1i4KF74159/4LqrV7G1WFxczMIXGhGN3zdtQeTJOLzz/v86fTwSV7WVedc9a1hIBQJDQKKibaMGiw/tyAWC/oQWdGrNRs2609LS2OJJzcwd8G9KbU01Du7bjdnzFsLUzFxnz7Pxh+/x3NNrYW1jg9oLfUxJDP/ash0hw4d3Kya5d89uXLZsOVuUwf7eMDMz09lxCwTaIDs7mwd/W1tbd3gbEQgQ6D20aJP4GRsb4+2334aRiTlqams5K1KgOTfceBOkEgmefXptc3IMndtF82dzycU9992PG2+6WSOLUx1bXP/Jxzw5gyxPgWCgI1ymggEBCSLFtXLz8jFvwUIsvWxZpzs9Qfssv2JFKxcnnVeaC5mbk41vvvqiW6ctIyMDn336kRBDgcEgLESB3kOF4e++/z80NtRBqRJ7uN5gaWmJRZdcisMHD1zUw9TO1l6jxyBrcOWK5TgeFsqPJxAYCmJ1Eeg95Cqtra7E4ksvw7XX33BRXODLzzdwcohAM55Y+/RFsxDJderq5qrR/dc9/zROxkTD1NRUJNQIDAohiAK9hWrdKJFm1apVGDNuAmzt7JGWmoppk8fj1ptvxNRJ4zBx7Ei8+forWHXl5cJ1pyFDhw3D+i++ai6XoHiig6Mjnnn+pS7vS9mlX3zRPdeqQDBQEC5TgV5Crrh7770XgUFBnAgyZGgIX5+UlIjcnBzk5TZll5KVQqSeP4dnnlqLN99+FwMBit2RIDU2NqC2uml0VXV1FSRSGVvEbWsAtQ1liA47HIr333uH6wufff5FjYrtT5082XzOBQJDQwiiQK9QF3q/9e4HmDtvHo4c2MtuuYT4OAQNGcYtxYyMjNp11f384w+YNHkKrrxqJfQJ6uxSWlKE0pISVFSUo6qyAnW1tc2F8hJJk6PmRHgYVEoli6GJqSksLK1gZWUNWzs72No5cPKLNgkIDMSn6z/v1n3OpSSzcIqsUoEhIuoQBXoDNZUmsft39354e3vjXHIi8nKzMWrsBCTGn0FxUQHKK6vw5BOPdzjUlurijoSd4Pv3J2Tt5WRlIj8vF+VlpbC8IGxW1jawtLSCmZk5F7jTa25Zh2hkbIL6ujrU1FSjsrICFeVlLKQkonRfZxdXuHl4wtzcot+mi0wcNwoNojuNYIAh6hAFeg0ldrSsYSPL0MXVFStXLGNL6cYbb8LKq6/FV19+yW66pZddhoQzp7F48WLs3buXLa+2kBV19VVX4L3/fYyRo0bBysqqz14PWXwkgOmp51BSXARHJ2d4+/rB0clFY+uOBNLM3JwvLad30GstLMhDXk42UpLOssXo4+fPAqlr92pL6Hy++vpbeHrtY/y+0UXMURQYCsJCFPQLZMm98NIrePH5Z2Bubs4LfnDwEKSmnmdhIaZOnQoXN3ds3vQnW4S33n4nNn7/LVzd3DB/3jz89NNP7VqK5NLjnpzV1ThwOJSTSHQJHW9uThaSExOgVCjg5esPT09vtgB10amGLMjMzHRkpJ7j8UyBwUPh6ubRZ8JIiTVBfl78vq26+lr8+vOPfSrKAoGuLEQhiIJ+wdHRCccjY3Dj9dcgOTER7u7uCA8/zgklanx9/ZCQEM/WIQkcXWgRbhpWK4ezsxN/yDsjITmV3bC6orS0BPGnT6G+rhb+QUPg4endbYupp63byDrLzsxgi5HEN2TEKNjY2qEvoJFRlPBELdtGDx/Cmw+BYKALoii7EPQ55BakLEci8WwCCgrycerUyVZiSL/HxZ1mlypBIqjul0kWmVKpQFZ2doeWCT3HTbfcpjMxJMs04UwsToQdhZOzC2bMWQAvb98+dR/Sc3l6+2DGnPl8DBFhR9ml3FF8VZuMHjOG34/MjDS8/PIr3M5NIBjoiCxTQZ9D7lEa20QxxIz0dF5Y2/aYJyGj69WLe9uSAI5fKRSQtJju3vK+9NhrHnhIJ8dfWVGBmKgIPqapM+bwlPn+RHbBbUpzIWNPRiH0yEGMGT+Rk3d0RVFhAYsvlY18//33vPMWCAY6wkIU9DmUPENDaaOjojpMNikoKIC8C+uOhK+tq44syzvuuAO//vYHzM1bT2DQxty+/NwchB07xMksk6bO6HcxbAkdCx0THVvY0UN8rLrIno2ODEdMZDg8PL1gY+eEiIjwizY0AsFARAiioE8tGbIO1SISeSKiQ0Hk7NMuXH9kJbZ0sxIkkNfddCssraxw9OB+JCWc4ZrF61avhI+HC95+840eH3962nmcionEyFFjETw0RC+zK+mY6NhGjBrLx0rHrA3IEkxMOMPn1MjImN20vv6BGDV6tEYF/QLBQEB8kgV9Bllojz7xJAIDg7Bt6xYcOrifE2XaoyuLg1ypJLAkiiSy9H9PT0+89uY7cHf34IuXtw+79TL27UJcXCwn5Hz0v/dww003cxJPd0g9l4yU5ESMnzQNdvaaNcHuT1zd3GFiYoqoE2FQNCrgFxDYo8eh9yE7K4M3FmbmFpg8bQasbWxbCfDUadMRfjxMi0cvEPQPQhAFfQYJ2LtvvcGxQbLaepOIQQs1ZZ+ue/UNjB49BsFDhjRbm/n5+VyLWFtXh0MHDyAuPhEeHh44e/YsW56PPvQAfvn9T42fKy31HIvhxMnTWomBvkPCTcccEXYMUpkUPr7+3bp/aUkxJw7V1dZhSMhIFtn2kpgeX/s0rlh26UXWukAw0BCCKOhT1JminP3Zw9o1skpcXd24bEMNWYi1tbUskvfedTsyMzN48Z4zdx527zuIxKQkvh0JcX5eDlJSkhGggdWUl5vD1tGEASaGauiYx0+aghPHj8HU1Awurm5d3qe2tobdoxSD9A0Igp9/AJe5dMT4CRNw59334osNn2m9vZxA0JfoXxBEMGisxVGjxnQ7/kSit3Dxpdj482+trn/phecQ4OOBtY8/yu47cqWSSBbk52HZpYtg18IazcvLw8G9u7l+r7MSBeo7GksxwzHjeNLGQIWOnV4DvRZ6TR1B54LOCfWPJQt8+uz5CAwa0qkYqt+TDZ99IsRQMOARgijoFyh22Kho7HZ2IlmA+bnZCAoObnX9n7//yn/77puvWrnuaJE/f/4c7BwcWYTVscx9+/expXj04D6eFt/2OMiSPBkZAR+/ALi4di/eqI/Qa6DXQq+pbWP0pk472ThycC9bheMnTcXosROarXk1lZWVePzRhxES7I8RQ4Pw/rtvc/zW1tIUL657tfn8CgQDFSGIgj6FFk2qEVx0yRKEh4V2WUROizVZINRUmlqG0c+Ro8dedDtzi6bMVXUnm7bi++BDj2BYyHD+nQTxltvuxJTpsxEQFIz4uFNc1F5e/l8tXWJCHHd/ofo+Q4FeC70mcoeqKS8r49dO54CswSkzZsPO3qHd9+HqlSvw919/sghS8/H1n36MUSHB+OX3TcjJ6bxjkEAwEBCCKOgzyI15862349SZRMRER2pkUZC4USKMnZ0dW4DkYv1n65aLbkdjn+i2FF9UW3vqsojNW//FyhXL8fc//2LT39swbvwEDB0Wwrf38PLBzNnzueXZ8aOHERd7kq0kaolGpQuG1KOTXgu9puzMdH6N9FqPHzvEr53OAZ2Ljl5v6LFjiDsd22rsE/1O1uYjD67B5+s/7ZMOOQKBLhGCKOgzyLK48eZbsWf3LhTk52u8gNLtaOHlSRBmZsjJzuJM0paMGDGylUt05OjRePrZFxB2IoaTPmobm9ygubk53NKNSjTUUAOAIcOGY9qsOaitqeHCc3KxmrZxGRoC9Jpoiga9RkqemTZrLr/2rpogfPrxBx22waONB1nmAsFAR2SZCvoEEqPnXljHcwovWfh8h7cjUSO3KP2kjEWyCMlqIWtE3eCbLMClixcgIvpU8/0S4s80CyzdNuzYMb68/NLz2H/oGPz8/XH5ZZci/kwcP4azswumz5jR6rktLCw5C7OivBTVVVU4dmg/hoSM4D6hhgAlGJ09cxpkP1ONoouLG79mTYiMjBRDgQUGj8aCKOaeCXrDE08+g/vuf4B/nzxpEnbu3MkCRvP1Wia0uLi44N9d+9jqyMhIR2FhIYqLipCZlYkfvv0GxsYSFsj6htZt2M7ExzU/DlkrLV1796+5B9v+3cVjo84mxHPSzWMPP4Cy8jKciI5tbiBO96GhxMHDhvM4pYz0VO72Ymtrj6Ehw3mC/UCEhgsnnIlDaWkxxxGpCTmPqzqbAHdPL4067kydOg1HDh8SoigwaDR2mYpehYKeQgI2fMSI5v9/88PPOB55EkuWXNbqc0Xu1INHjyMgMJAtulmz5+DKq1bijrvuxkvrXkHc2WTMm7+QrcZ1r7ze6jnc3TyafydhIyGdNXsu/AMC8fY77/P1d9+zprmrTVpaKluBlCmpJjc7CxKphJtkk0hQIfvMOQt4WO+xwwe4601HnXX0ETpWOmY6dnoN9FroNdFro9dIr5Vesybcefd9nGWqj+3qBIJ+sRBFWrWgp4JIi6kaUzng5eWFufMXIO7MaQQHBWH5ipVYffU1F903KjISd91+Cwvnhi+/xv998127z/HtDz/i4/99gC+/WI+ioiKs/+IrXHf9Da1uM2nyZERENyWG3Hz91SgrK+ckHzXpaefg7evfKrGErEmaM+jl44uEuFgcPrAHQUOGwbOTBJT+hs4VjWVKOhsPKytrzhylny2hY6fXSq+ZrMSu+OyTD5tb5QkEGOyCKMRQ0FOo4TZZdaWlpdyuLSOnAKtWLOOC719+/QMlxYVc+9bWunnx+Wfx/bdfc3YplV7ExpxAfn4eliy97CIxIsvlybWP4aFHH2vuc9oerq6u/HPHngN8H/XjVJSX8VgnD4/2xYEEhbrVFOTlIiE+Dhlp5zE0ZCQnqOgTxUWF3G6tsVGBESPHwMnFtUPhptdKXXio3MTa2qZDcV00bxbi4+NF4b3A4NFYEIWrRNBTKDP07jtuZaHavmsfrrp8KX+e6P9//PYLll9xRfNt6bq33nydC+xp2gUl1qjdqi+99BIL3eJLl2D95/930UL/xJNPI+38OSxYMB8LFy6Em5dfs0VKUKYp1TKGhR5jdyy5T9XZkZS5SuLRWbYlPZ+zqxscnZy5v2nUieP8+5Chw9kl2Z/UVFfjbHwcCgvzERAY3OQa7aKshV4rvWZym7YUxJMxMYiJjuJzFB9/BsnJyfweCutQYOiIgIBA55CgUco+ic+/W//iwnx1RujPP/+EDevXN9/2781/4f8+X4/GhoZWCzAJIz0GCenWvzfj8KGDzY/91NrHsPqqFdj4w3c4evQI1r38Mq686qrm+5IQ0oWgFmM3XX8NggN84O/tjlfXvcTX5+VmcyKNJpDQ+AUEce2eXCbH0UP72D3ZtgNMX0DPSc995OA+tsLpmOjYuhJDNfSa6bWrOXb0KJZesgCvvvwili1Z3PwcQgwFgwFRdjGIIAuHLCz62R/JIQqlEqGhoZzVqRZEHx8fLL5kSfNtSktKWhXXt4WOnSZbTJ8xk/+/49/t+PXnn/g+RnJ50+OqVJxUQ9agOoOU2L1rJz75+EN2wRIksFSXR0NvycJycHTq1usxMTXFiNFjm+KLZ04jKzOdZxFywoqO44t0fnKyM7nrjJmZOSZNnc4F9t2FXjO9djoH5Ga9+cZrefNBAkh1ilTOQo0Nrll9JRrq65stdlGELzBEhCAOEmiB/uKrb5GdlYXi4kLUVFViw4YNzdabLiwAeky6UKE8WRmff/kN1tx7J1uKJMx0/YwZM7Duxefx2acfY/iIkRg9Zgy78mjxbXnsJG5UokELsY+PX3OMkKxN+r3tIq1SKrF505+4/sab+P+xp07hlhuvaxZDuv2Sy5bjsSfWIisjnRtg93TQLQkRTaon1yO1fEtPPY+hw0fCtgcCpQmlpSWc4EOCFTx0ONzcPXoswPSa6bUXFxbiWGgo6mprmwvw6Rz97/13ccNNtyAh6Tyio6JwJu403nz9FSGIAoNEuEwHAbRYzpw5G/PmL+DhuFdccSUWLlqM5NRMbPzld66xI4tR21YNWW204JKVtmr1NVi6bBm++vYHODo6YdqMWXz9119/jYKCfF5oqUH38888daGlmhcvzCSExNqnnsWwkBGQSmUIGT68+Tk8Pb1gb+/AwkjPp7aAKT5IdYxq/t2+jRd4EmES5FvvuAsbvmiKQ5aVlfTIumoJPY6bhydPiKC44omwo4g9GcUCoy3osegx6bHpOWbMng93j95bo/TakxLPIjoqks85nSM6f3TuaSPz1Zef83mdMHEibrrlVqy65johiAKDRFiIBgotYGQx0aJGmZ0PPvJY89/q6+tgZGzMt5kxYyZmbNuBExEReOj+e5GVlan1jGJasHf8uw1/e2+Cp5cXu+cOHdyPtsu42kol6zA5MZH/Ty46uv+Gzz5m0abrJJKmfRwN/73+mlUsdBMmTMAtt9+J+DNnkJmRjhMnTmDXjn8xYeIkLFi4iAvy6Xa0wBcUFMDJ0alZSCrLy+Dt17OJ8m2hDQAVv3t4erO1SGUa/pTk4hfQ4/PKdZPnU7hpAHXNmT5rntaSeHJycvDh/z5g12hmZmZzqQkl2dC8STpXzs7Ore6z7uVX4evji+eeebLZ4hYIDAEhiAaETC7n0gFfP39ctmw5ZsychdFjxsLGpnVKfWNjA+Ty1tmUtPs/EhbBltSae+5kS0FbcaImEZPw4llYUMDXdWbTqOOHLTvIqB+DROXjD99n1+qzTz/ZPOrp9OnTKC0pxbPPv4ixo4ZzdxYqQXjlpRfw8AP3oaSkpDmjlKyfwKAg/p3axFE9YnV1DbQJCdbocRMvlEGcRmZ6GoaEDIezi5vGFh2dBxpRRdmj9H6NmzhFa2Ue9NjrP/0Eb7z2Mp+XtglB1MWHzq2HR/uJRt4+Ph32NhUIBipCEA0JlQo33nQL3nr3/c5vplS1m4VICzXV+IVGROPKyy/jmj+KxfUl5PYk9+dzL65ja++vP39nYWy5YNNC/Neff7DYECSSVOsIiQR79+zmnp1qy9IvIIDnIZIYW9vY8O1ef+tdzJw1G9esvBLHjh3BqFGjcOrUKQQHD8G6V9+4qMdpbyABmzpjNscpz8SeaoovhoyElXXrQvm2VJSXcz1hZUU5ArXcCIDE8LrVK3H8eCiLXlsxpPO54sqVnT5GYFAwW+yiPllgSEhUGvRkKy8vx7Bhw/rmiAS9wtnFFaHhkZ3ehsb+kKhQRmRH0CI5fcpEFBa0niqhTSwtLdmlSx9B7k9aX4/pM2axoFMTcLVLb/b0ya2sVXJL3n3v/Vh++RW487ZbkJefC5lUikZyEVdW8iIvkUoxbdoMPPLYE1g0fzbHIOfOm4+HH1yD5KREfn0kmnRpCQnmmLFjuZ+qtiEBIbdneuo5dqkGDhkKY2MTNDbUI+pEOMZNmASlSsU9RiljlTrJkLtV25YYnefhQwLaTaSiDQltlg4dPc59ZTuC3rPhQwNRX1cn2joKBgTZ2dkoK6MmFB1vRoUgGhi0eCYkp3Z6m9OnonnaAbUg64wXnnsGP/7wHS+SushCffLpZxAdeQLnUtNw9z33sXXaXkyKahMfuO9u/hsJI7k89+w/jLdefxWHDx9sd0Gm4/32h58we87c5utoyjsNtlVDItzefUmkj5+I4bIKdWcbbUKifTb+NHfqGT1uQqu/nYw6AYWiEUOGjYCFpWaTKHrCmbg4XLViWXO9Z0BAAMcLqYTki//7RqPXTedp6sRxqKr6ry2fQDCQBVG4TA0Icl+tvvraLm9HIqBJ56F71zzAbsaiwkLs2P4PL4C9iSuShURipp6fR1mvEydOxNjxk/jv9GH9+ceNSE09j9Tz51FQWIBp06Zj4aJLEB0bj4/+9z6++epLjJ8wkY9r9+6drSw8Ejiy8GiBnzhpCoYMGcpdaqgon+YnFhUVtprb11YM6bXR43EW7OTx/HdKILnznvugTUjoKB7Y3rmkusa+cENSpm70qTN479238fWXn+PhRx7B5KkzulWLSa7saTNmYM+uncJKFBgEwkI0IKjfZ9SpM526uohT0ZGwtLJid5ymUFxv2qTx3PeyN5AIkFBt/XcXx9XKy0rQoADXCHp4eiLxbEKrhB51vSL9/6lnnsfNt97G133w/rv49qsvm29HblRyg+7YvR9LL1nIVgs9zsm4s9xDlWr25s+e0WE/TnpMEuu2tYjkPow9k6iTbEq1q7ir6/qCw/v3IGTk6G43J0hJTsb8OTP4vImJOIKBbiF2qw6RUvUF+glZXVOmTu9SDAkVjYjt5qJLvSzJMusttHBWVJRfWPibMj6vWHYpuzIpttc2u1XdqJvigu+/+xbefuM1PpYD+/a2uh1ZhZ+s/4LHRpFok4DRfYYG+mLqxLGYNW0y7Owd+Plo46C2kOl4qFsOZaGqM1ZbQmeJ4m3kttUWZClT5mj86f8GHKuh6+hv5MrsSxTKjhuidwaN6qJ4K51XgWCgo7Eg0oLh7uVPiYwCPYQW+Vdff1Oj23JMsAeuz9lz53XLZUrPQwsliRwdH1mZN95yGw6HRrClRmUiNKqJBE5NV4///XffXBC11mUS9NhBQcE8LkptzdFjkYtUPTC4vKyUn5eETy2MdH1aWhrftj03Ml1PlucjD92PtNTOY7MajWVKT8ORA3v5WChe1xa6jv52+MBevm1fWV2KxkaOafaEYSEhCAwMFBmngsEjiLTgWLoORaNCzEPTN2ghv+W223lh0gSyirojbHRb6g16400344OPPu10vJKapg4wZXjn/Q85yScztxDn0rPx4ksvN9+XGmNTh5SuoNuTeKldp39t+hOuru6tbkNCR31NKysrLjo2ssjs7Oya3aHc95T6mJqaNs/5VDcyaA+6n1wmw8xpk9hF2BNKiosQduQgZ5kOGzGKR0lZXZgwQZuT5MQE/knX0d9oBiPdNvTIQb6vLiHB56xb0/bdyZqw+Z8duHzFVc0bDXqvBIKBRrdcpqZm1Euy7zv6CzqmabK7H55/8WWNTxNZAmQRaAp1sRk5LAgvvfAcli2/HOFRp7Bw8SWwtLTiBbA9cSTLhnz1Tz7+CKZMGMMZqzR2KSsrC2cTEnA8LAynTp1ERERElxmslMxjbGKCUaPH4Nlnn8X48RO45KLt87779huYNn3GRY3Lu9oA0N8cHBw6nRBBtyHL8uknH0d3IFcwZY5Ghofy6Kjps+fB1c29VZyQXn9K0tnm80B/c3Fzb74t3Zceo2WGrDYha7tpk9DzkAg1f3jnvQ8QG5+EsylpGCLKtAQDkG75SKirvpIFUcQS9QEShJARI/D7n393q1aNBKKty7EzgoKbkm9oWC+NT1Iv2mSVqYvm66kXKm2aLpRGEOrG4ZTO/+vPP+Knjd83N+Om6+k4vLy6ntaudoFSwk1cQCDuuf9h2NnbY+3jj7Ryt9LzUTG/m5sbW7TdQZPpH/T4B/bv0yjxhTaO51OSkXouGS6ubpgxZz5MTf87Vk2g80SJTzTRngb5Hj24D77+gfALIPek9hLEqyoqOPNVG8k8ZIkTPXHJCwQDykI0NhJVGvoElRi8/c4H3S7cZveghhbizz/9yFmE9By0YJIA0kU9sULtbvvfR5/imutu6NASU19PAsaF8+o+opWa17CRBbVl8ybs3PEvWyRtF3B63PT0tOaenLqAJm78+MP3nY9lysrEkQP7uKkBuT9HjhnfbTFsCd2XHoMeix6THptGP2krvkhJTpZWnXfO6S7p6f81VhcIDNNlKlfytAGBfkCiQLMBu4u6REETDh3Yj5Li4g5Fl1ym1F2GXKnRkRHdtsp6MpfxxIkIjlG1TYKh2JWpiWkroejIpdtT6DljY0+2+7eyslKEhx7hLFFqejB52kweraQt6LHoMemxz56J4+ei5+wtpSXFWh9VRR1sBAKDFkQTIwnMzXu+0xVoD7KO7rpnTY/uS+UzmiY9fLL+c4yfMKnDQn4SG2q1Zm1ujJtvvUOjgn813Um+UCfW0OuOCAtjK/Xjzz5nF6xa+Cjh5/Jll3IiDwktCSNd7+/vzxZqS/Glx+uJIJOVejY+4aKxTKdPRiP82BEeRUXuUXJz6qKekB6THpueg/qk0nPSc9fV9WzMFL0eEkQ7e+00DVfjH6Cd6SECgf5aiEZNs+YE/Q/Fc9c88GCPXXB1ncQQE8+excJ5s3HDtauRl5eHX//YhEmTp15kVZLIkChRHJC6wVy1ajUflyYWGQknFeJ3Vr9GnzUSPBJCei5yt9Jjh4Ud479fseJKTvCh+kPKZF25ajUfx/U33IgjYScwZux43PfAg9ix5wBSM3Px9XcbWUjJ1Rw0ZAjWrVvHgtxdCzIzK7M5TnY+JQmHD+7lCSLTZ89F0NCQHg8a7g70HGQp0nM2NDZwmQYdS3djd9QgnQYyU6MGbfLhx59h7vwF8PTy5g0Yvc/UE7k9y14gGJgWoplZ88BWQf9Bi+GTTz/b44WXBJEso44yhr/Y8BnOpSQj/HgYJo0bxW3PNv78K6bNmNlsUdGiFhgYxNmeakiwjh4/wVmvLRc9crOToKkL4ukxnF1ccPkVV3bYOYYgC4/E6o233+MFtaUrVP27p6cnLl2ylBuFt4Sag2/6eyvWvfxa83U0F/HbH37GY4+vxSeffo5b77wXYRHRGDoshDNiSSzJNdxSIGkT0FYwrawseSzTkUP7kZ2ViXETJmPM+EkwN28aV9WX0HNS6zs6BjoWOiY6Nk3ji/m5OXB2dtX6RpfGc/3f19/h4JFQJCSdx/ovvuL3iOK8uuiLKxD0uSCamZjwRAFB/0KLV2922bRjp/vX1rTvZpswaTILgdoqiz8TxzV+YceONscSSaDeeveDdpNOduzehwWLFvNtaPFbtPgStuCOR55kcdu2Yw8OHgnDbXfc1SpLtD1IMNc+9nAr8ac+pj0h9tQpLFuyCF98vh6zZ0zB9m3/wNfPD3/8tQVPP/M83njjDUyfOQu+vn58e9r8kYiTtKjFnMTx8ccex+lTMfDzD8S0mXO0NqOwN9Ax0LHQMdGxUakGjY7qDHp/c3Ky4ObhqfPji4mOEvMTBXpPt0wMExMZFBIxIVsf6I2lziUTpmZcetHeRIVrrr2Od/P33X0H/z87Kwtxcad5SK1UKuHM0BGjRmH8hNaTGtSQaKz//P/40hKy5sitqYaGz86ZNw+RERHcLaY9yNIhy40ESd3X9Jcff+2RRfP2m6/zY5EQkLDfc+dtuPra6/Dq629h1TXX4u3XX8axI4dZxMmKJWtm5KjR3AOxob4B11xzDWbMmAEfvwAEBA3RuwWezgl1unF19+C6RirqpzmKAcE0ZqqpVErd7FxtHdL12kz86YjszHStDZwWCPQjhmgsg1IiahD7GxKJ7pQrdFTb11kt4mXLluPo8Ui8/OobWHLZMoSHH4dSqWCxuPnW27Gjl/MCaWFWu1MpttQZJIRjxo1nMVzzwEPdGuBbXFyMaZPG4acfNyIyMqLVokyCtnnTnxg7KgS//vIzduzYwc9B1jH9jS7UX7WygrrfSLFlyxa8+uqrcHX31DsxbAkd29CQEZg6cw6qq6tw5MAensFI751aDOkzlHo+Bd4+fjrNCyAvAbV9XLxkmRBEgYFZiHIZjJQVFLnQ3REJNHsvOom9aYKZuTlqaMp8J1CyDLWEI2644Sa89MKzvJAuv3xFrxdRWphpgQ49dpQXzK6s4c+//Jotyu48Lz3+qisv5xjo02sf42Satp3u6TaUJfrcM0/ykOG2tEwkok0IJfncdfst+O3PzdB3qJPQ+ElTUZCfh4QzschMT8XIkSNhZeeE4qICFksPLx+dPT9NGQny82o1CYM2N7QZ09WMTYGgzwRRgkZYW1mKD3I/xg3Vk+W7ir1psliWll4sRPTYjz78AA4fOogrV67GCy+u4+e87Y474ermisLCIkyZOrVbz1VRUcEuTxIjEvKE+Hiea0hT4bO6KKKnBfS6G25id2t3ee+dt3m2InWkp0W5sxFOKqUSjRos0LSI79+3FwvmzMQTTz7NmZSUlXv2bALmzpsPe3vdux+7i5OzC491Sk89j/DwcJ76QcLuFxCkUWLWV19+gYiIcMycNZszeDWFPqNLli5DTHQkZytTMwf6fHG9qA7GaQkEfTYPkYrAFy5ciLi4uF4/qUAz1BmZ1GNz9uy5GDZ8OMxMzXgxWXHVSjg69jyZgzqe0KihmXMXXNSZhvqPqidUHDgc2ty6rSU0wPfuu26Dh7snj4Wikou2U9bJQnjgvnuwb+9uFhL6qL308qvcz3Tj99/ytAsSoo6g29PnLoym13fTIqbn8/V01alrk0oKzp9L4d/pedQLPsVfX33jbc5+1QQa9bR313bMX7SESyB0gTp2SC7Mk9ERKCkqgo+fPwKDh3JsuDMCfDyajrOxEV9+/R0nSWlCxPHjOHjwAMearayssWL5knZHbAkE+jIPUWML0dbWVohhHwoh7eAXX7oEd9x5DyZPmaL12i2qOyOXGS1yLa2E4cNHYNjwEcjOzMDUaTN43l17QnX7LTdwIkxyYiIOHzqA7du2YtuO3a1uR5MhSAxbWgNvvPYKgocM5dfTmRgSJMi79x3qkXuYrFqyjKjoXBdQLJImdbS01GsoI5UK3UtLOWHn5ltuw7pXX9er2l0zIwn3Lg0eGsKuVKpfDB4yDO6e3h0ep9qiI9GnzFxNBJFat121Yhm/d7Q5IUFcuOgS7N+3pzlpSW21i2Qbgb4g7auYlUAzaKGguX7Hjx/HV998j6nTpumkkNnEpGmBq6qkmPB/jBo9Gjt378OeA0cwb/4CnDp58qJEGBLR+Pj45rgQLXBxp2MvcqWPGz+emz2r6/howZ0zdz5SkpM0OkayuB5/7OEevT56rs82fNmj1nCaPn5ba6elwNNi/+PG77FyxXL0J2FHDuCF557mCRT0fkXFnISNrT03CZ84ZTqGDR+J5KSzCDt6iFv0tceQIUP5J91fk/dOpmpga1AthkRVVSV279rBsWsTU1NcunQZHn38SSxdugwhISGtxFjdAF4g0Ot5iALdIpFK8f2Pv2D7rr0ICAjo0WOoRavL55JIuKEzNXZWk5GRgffffQeTJ4zB2JHD8MZrL3PdXlhoKP+dXG70+CSk4yZMbPV4tPBRnV/b5zh49Di3dKPyhWeeeYaHDNPr1AQSs1defQM9hSzrseMn6GRxVdckdsXxsFCOMfYH9J4sXboU33/7DV5d9yLys9PZVTp81Bh+b+ji6uaBGbPnw9nFFZHhx3AqOhK1bb7rI0ePad6U5ebmdvm8OQUlyMnObrVBot9pA9FAMy0bG3Fg3x6s//QjREWdYCub4sw2tnYYMiwEX3z1LVvZ9g4OCAgK1rjvrkDQZ4JIcUR9TBgwFLi+7rdNmDN3Hv9fnR7flqNHjly0wJJIqYWwo/vR7v6Zp57AE489gpjoaP6/lbU1KsrKsO2frZg0fgzPLdyw/hMUFhTwIkWLGP08dvRI8+OoH/+hhx9t1Q2FXF//e++di56X5gw++/wL2Lx1O6677jq8986bnOSiyQbs901bMGLkSPSGDz/6tF+7K5GV++03resx+4o333iNn5/w9PRAbGwsRo4Zd5G3h4Sd6ipJGIkjB/dyHaPalXnsyKFmcdOkmbizszMuXXpZu0LWUiTp7zSZJDIykuM65MKlJhBU/0qfzfKyMjz40CNYufoakcgn0L+kmuuvvx6HDx8WOzYdQI26n3z6mU5vQ2J49corWKSo64t6g9KVGBIfvv8ePvzfexdmIdbyorhq9TUoKy3G9u3bO3TL0u1fef0trL76mlbX02IZHODTStwoYWP2nHk4HXsSEokUnt7enJk4adJkFqXHHn6ARVSTmBEdY2ZuoVbib7OmT+4ym1Vb0PG2/UpRogy1L+votegqqcbBxoLfZ9qs0Gbkjrvuha9/154HirvGx8Wivr4OAYFDMGnC2OZYKW3cMnIKNHr+rVv+xkMP3AejbkxXaQvd76lnnoOHpxceebBnzewFAk2TaroVnJo1a9ZFPSMFvYN354GBWPvU0x3ehurnvv7yC1x39VW8wNHCSvV16t02CWFnYkjT6d99502+n7pLC933999+4WL0zmKUtCC1zR5VH/c9997PwqWGusBEhIdxvR65YuPjTuOrLzbgjltvwv333tXcDk4T6EOrrWQUHx9f9BW0KaBzoo6D0bmlOseW56mvyMorwsaffsVdd92FIUOHcVapJlDnminTZ3EGalJiPCZOnNjKjU2fR02gkWCRMafRG2hD9ubrr3J887LlV4jG4AKd0i1B/P3339m3L9AuCoWy3TFIZxMScPedt2NooC/eeP2V5mxQEpW01PN4YM09XY5Popq5a1ataDfdXZPC6M5Eieru2jbCbit46v93J45HIhIUFARtiBP19bS10e7w286g80wWGZ0LajhgSi7n8Khe1432BErykcuAy5Zfjhtuvq1bGwy6rYenN2bOmc+9XFv2qqW4qKZQUhU1VNd0zFd70Of++WefgpOTEwuyPmXtCgapINIXPSlJs+xAgeaQYGSkp2FIgA8mjBmJy5YswiMP3Y/pkyfwpPr9e3c3W3QtIbfcrh3/YlRIMKfCt4UW5LWPP4qbrr+mV7tqui8V0bdn2VHMUBfZx7SIfvjJhh7fn85V6rlkblmWk52Fbdu2oa+hEANZzDfefCvc3d21Nt1eU8pKS3D82CEuPRk+simJpidQjWJl5X8xWHqcbVs2dyumR5nSq6+5rldCRt+Bz9d/yr/39bkUDB40jiHSjr0v5rwJ2o9DdQaJFS3Ao8eMxZSp03kHf/jwQW5Fpq32WPfd/xAee2Jt8//JbUYi3ttC65YuXILioi+tW4cly7rfHo7OGSVmnI0/zUJeXVOP2265sd+KwclNSlYNvZWFhQX83vzw0y/NiS66iiFSDemZ0yfZ5UmNyHtrUT245l6uM1V/JidMmIA1a+7nkg1HZxeNHp8suw8/eB+//LQReXm5GDZsGNcq0sZNPe9S3Xi9sw2caPkm0IsYohDDvqO7O2ByR1Lc7uiRw/jwg3dx9Mgh0BKlLTGkRWra9P8aau/Yvg2PPfygVtpvkRg6OTvzc9Di+NGnG/hxS4qLuvU41IA7MiIMp09GwcfXH1NnzMH/fflFv9XP0rmnxZvKCSorK/g1nToZjZdeeE5nz0miExsThYT40xg9biLXGmrDvThp8pRWLm/KCi0uKUXsySg+53TuNXmfH1/7JE7ExCIm9gyeeGIt/Pz8ONb6069/IOZ0ArZs29ncnrAjRP9TgS4RJp+BoYuaLRoRpe5fSnG5e+66vV03bnehhW/W7Ln48utv8duvv2DChIkIDApCUsIZnE9J1mjOYENDPZITz3Ljahp1NGrMeLYIKXZ67OjhfrMOaWFv+9x0vrpqqK4J51JSuEerv38Az3OkDVRebjYS4mJhaW3DcxFpCLS2GDV6DHsE1K+Hnu/xxx/F8RMxyM/NQuiRA/D09kVgMI3E6vp8p55L4WzX7bv2oaioCBnp6bjtputw8uTJ5nIfgaA/0H4LFIHB8cJLrzTv2n/9+SetiCFBj7n8ihUsHjSDkcSQ8Pb152kMnQ24pUU5I+08Du/fy912ps6YjWEjRvGiTYJx8w3X6lXfTLKwyMty1z339fgx6Jw/9cRj3CzhtptvwPQpEzBjygRuv0ZlEsFDh2P8xCm9FkO7ifdf1L3otTfebnWdVCLB/ffchWHDR/G5p/eA3gt6TzrzcNB7Su+tt68foiIjOVv68ssuwdmzZ1t1thEI+gMhiIJOoXjXqtVXN///yy82aLxoqWNBHcWEyMXn5eV90fXU2ot6a6YkJ7Z7v6LCAhw7fIAtjRGjx/KII+q6Q5CLkgRDn8SQXufc+QsRHRuPkaNG9fhxyDKnUhnK2FSPUaLOMdTmj7JB3T29tJaB2VYUb7rlVqx54GHIZP9lOoeGHsXfm//ic0/vAb0X9J7Qe0PvUXvQe+ri6o57774LVyy7lBPKaIMlEmUE+oAQREG70MJKwkcp8y0FLTMjXaMzRgs2ZTh+8803PG6IJna0hRbC9kZQEQGBwcjPzUV5i84o1Iw8OjKcL+4enpg+ay63HFOLAC2q11+7mgVIHxZYsnjIKnxx3av4/MuvLkqm6Q406Pjo4UP8e8sNCb3OtPSMLidW9EQI24riqquv4Z6kLd/jxx55kN2p9B7Qe0HvCb039B7FRIbze6aG3sv83BysfeJxHDl0gDct+vA+CQRaiSHSQtlycrtMbgQTY/2dJC7QHFqoxowZx6N71NBMO8oI1GRhp0Xyl9838RxDzpptx6qkx3Jxubjon6D6PXKrJSacwZjxk3A+ORGp58/Bzd2DrSFqTt6Wjz/8H07HnuKEIn2AsiafffZZzgA+GRXBTbWtuRzDEsYXGix0dO7r6+pYfLKzMjkWmp+Xzw0SyDosLCzkn+rxYLfc2jTEube0FcD2BkabW1hwHFSdrESu01dffhGvv9nUto82PjRn0d3DC0ln43H04H74+vnDLzAYCWdO4+8tW3DuXIqYcCEY2GUXlCat3hVSCUZYdAIaLIdBanuh+4WyEcqaIsiKo2AmrYG5manY/Q1w6IORkprZvHDv2b2Lxxq1/MjQgtx25iB9RhZfuhQff7oeGz77FO+89fpFj023WbhwMT79/MsOn59E4eD+3Vw+YmFpxWn+1ja2F92OjuH48TBct/oqnc4/7A70+rx9fLBzzwG2jIqLClFWWoqK8lLeCEilMhibGHMSCp1fug25HhsbG1BfVw+lUsGi+d0PPyIyPIxfI13ocakNHonjlGnT8fyLL7fbSUibYlgS8Umr/z/5xGP468/fm0WNEq1+/WMzps/4LxNZDfU+pWQfijGmpqbizTffFGIoGPjzEGkRpC8k1SDdc889OPnmfihb9guTGUNmZA5Ye6GqugC1uQdgZSaFXCa8sgMVsgSoVszHx4f/n5uTwwuyOpNVPdOuLfR3ijtSfOv1V9e1K1IkAu988GGn/TTJoiALhKyOCZOnXdTtJjoqCg8/eB+Sk5L4OfRpth6J3ncbf+FjJpcxXdTQMdbUVLPgUw0incfTp6LhHxDE8VMSQv+Fz6K2MBkOtdGtXNZ0X3qd1DGKppNoQwy7I5Ykjg898hgPeFaX3ZC1eO3qKzFp8lQ8vvYpdpPS8dGxrly1GtOnTcXevXt5oDUdL2WWkoUrEmgE+obGgkhtqP7991/MmTMHO3fuhJeLJRLS2m/jJjV3gtL3SpTkHoV1XbaYpTiAIDcfueRoUPCd99wHb+//kl4cHB2bFzFa6Glho8kGbUWIFrvJU6bi9VfWtStSdN/lV1zZruu1traGyy7ycnO4jm78pCkIDz2KtPMp8A8MbnXbD957B5kZGc0Ls76IIfHk08/Bw6Np0nxb6PVbWlrRlGb+f+MFQaSYq7owv6FRCamlB2pKQmFhcfF5IpGcNWM6XnjuGdx62x3w89esT2lHkNB15TIl6DZ024DAIO4CpP480IaE6ixJGGmTRNeT23z+goUoKSqA3MgYl1x2OYxNzACVAnn5+Xjt1Vf16j0TCKTd2fGOGzeOfyeT09u58ybfEqkMMvdZKDMdjtranvcxFPQNlD1Ii9pTTz2FmNh47N5/iCdctIxzOTu7NFuHtNj5+vpdtKDR7W+7/U7eBO3atbPdBY+u2/zXn60sBLruXHIijhzYC6VKhemz53OnFXIpDh85mv9W3WaM0/ARI3Q2ALg30Ou6576eT2ZQC5NEboJamUO7sUaFUomEhAR8981XWL50Mbb8vblXx9zd4/vtz81wc3dvZbWru8zQ66frp02fCVMTE5xPSeL30NjYBJcsXQZIjVg0FyxY0GfHLBBoQo/8mSSIXi5Nu9uukDuNRLnZcDEySo+hGNDM2bN5mC9ZGvV17U9moI4ydFta7K657gZUtcggJGghXLj4Erz48qv8//z8PL49QQsgCa7swvgpZycn/kyQKz43JxtHD+5DXk42p++PHjuhVTNsmr5ASRpxsTH8HIcOHsBtN9+Iq1auhpEelVcQFBtcsmSJ1oYSq1ymNJ/DlpC7lXqMUqYmbU7uvuPWdm/XHdrGCjuDXJ+vvPZmu99reu309x9/+Y3fM3rv6D1sWT7y0osvYvfu3b06XoFAL7JMNbEQWz2J0wiUFEpgVR0LM5Fso3dcvuIqfLr+c/69rqYKWRlp8PRuihu2RO0eJbfq8y+uw+ZNfzT/jSy1Bx9+jNtzqTkVdxbZWVnIy89Dfl4e8nJzebGk6QvU8Lq8vIyzL6n1V9DQYTxdoaPMy+ChITh0YA9uvG41z4UkgT169DDHOftjkkRH0AzBW++4R2uPJzWxRZnZaNjXn24lsrQxuPaGm3D06BEWxenTZ/Z5aGLM2HFcakH9Y+n9p2xSmsJiZmaOLdt3IScrk2OlYydManU/+vz0xzgsgUBngujlbNGt+8gch6Oi3AoVFedhWp8NW+umRr6C/oVcXFLpfyLk5uGJs/Fx3FFEXeyuhmJ1m/7ehgkTJ/LiS424K6uqMG7seNx6x50cL2q78AUFB/OlrXVzJvYksjIzuLRi7PhJXTa1pk4mjzz8cHNROlmWJKT0HPqUnEEWU8tSFW0gcxiKkuw82DTkNSco0YZg+oyZeHzt04iJjuRuQr3NJNUkhtjyseg+x46fwK+//Izg4CH8usla5SHQjQ1ITkzAxCnTWtVIknD2ZhSUQKB3gkhDgi3MjOFoY4rCMs13ejJrb8DaG/UN1SjI2Aln255P0hZoB3KzPf3sC83/p5idm7tncxeYtrRMrQ+POtWt5yLhSk87j5TEBNg5OGLarDlck6cJBw/s50bQLTdR1GOVR0noEdrY5LUnTlL32SjN2AMXaRWfRxId2pA89MijGj1mV2LXHTFseR861muvu775OkqUou90WEQY/AODWrlKibffeqPbzyMQ6HUMsSlLzhLeLpq7TVsiMTKHynMBysu77pIv0B3kngwIDLzI1UaNl3OyM3nSu7Ygt9qxQ/uRmZ7GkxjGTZissRgSJAJtLUFFOwOJ+8vKpo2FenOns02e3LT5HDRtLtI0ultPxK6nkFDTxBEqH6ECfbVVeMO1V+PHjT/g+2++0iuLXiBoSY+LBJvcpj0TREJibIUaWVNiRW9RL4rkRlInagi6hs5TVmYWxo4chrfefJ1dXz//9CO2btmC2rp6HDt6iHuD9gYqyI6KCMOpmEh4+fjxJAZHJ+duP871N97EY4jUsSea3K5XXX3GjUdaVh5ftNEcoL0EF6nrdBTAh12O9JzUMKMvxa492j4/ZQOXl5Vh9LgJzfWT/2zdgoMH9uG5Z55sVcYjEBhM6zZakLw1zDTtCInXQpSUpcG4OAwWJnLI5T3TZ9px0i6UhFE9VUCgGQpFIy/gX3/ZlFSDC43P5HIZ3NzcOLX/ZNxZODj8V1iuCfR+pCSd5ekHHl7emDl6QbcbblNHln+3b+P7LVy0GN/+8BPWf/oJ3nz9Fa1lcWoDEqeT0VGQKuu1mtiiFsXmMgyJBDLncSiv88epLa9izKp3u3yMvhBMtes0Py+HSywmT5vJJRZqzp8/x7FesqKTkpL06r0TCFrSY+Wg/ow9dZm2RGbjg0YrT5Slb4edrBEt8js6RamijL4GbnVFCSAUT9HGwNrBSltLnc4rtTm6+557uyWGJA6ZGWlIPhvPSTlTps+GVSetktpC1s+2f7biqy82IDo6imNS9JhN7c6kLN50nT64SttSp5DASMuNmdoTNMo8VYthd0oldAUdQ0lxMU5FR2LE6HGwsrZp9ffU800joTqaeiIQGIggWsHUWIba+t4tTlTEr/KYh+rMv2Fp0XUKfVV1LerrapomMpg4wsLq4mGsZKHQ34W12DNoFz9i5CjMnDmj2fLuCurXSe3WSExDRo5pNYlCU+6/7x7s3b2Tf6cNjjre1NclBd0lZPgInfRR7W72Z0v6yp1aUV6OqBNhCB42HK5u7jC9sKqoOzump6eJ5DnBgKDHWzaq/aJLsFfr3WBPkRhZoNpieJe3q6urR42RKxTGTrxIm0sqYW7aeiGqra3j+IoQwx6+FxIJT5P4+LPPeaNBbs/OoFpAqiekWKGruwdmzJoHF1e3bokhWRCHDx3E7p3/YiBBn0HafK1afU2/PH9/xxCdbE0RGX4Mvn4B8Pbxa/c22ZkZfX5cAkFP6FWwjVxpQ33scCqlGNpA5jQKhdklMK3PgrWVZau+mRRPqoYVlHYTIbXy5OvrVSrU1ZVCWZ4G45rzMFZVQ6EEqmvrYGvVvjuXRxGJpJtOoVjP3//s4EzioCEhiI2JhIeXz0UWELlZKWZEfUZd3Nwxc84Czi7sSRf6Ky67FHl5uQNuEyORSnHLbXfg7jtvg66cuD2xEvtCKF3tzbFh7QK4uLm16jPbsuc/UVhUpDcjuQQCnQpiiI8dtAWJlcRjDuqUDXj54Sn46KOPuPYspcIeMvdhkMjNWpm0fHtTO0hN7aDEGNQo6gGpnF9UeUUmpCVxsJCUQmZkDA83V46JUeNqsmwH2sLblzz86BPcJ5SgjFBLKxuknktG0JCmEWC0oaCyDJpVaGZqholTpsPGtuefg7fffB0lJcUD8z1RqeDm5g6FRHdjpzStIWybhKNLPJ0t8NxN49kjQF2EOvIGkPVMDRT0KStYINCZIAZ42MBILuXu/NpCIjXCfR9FUhk4YAbINezMJZEZX9QEoKqxFsqKDFx7yVxU1Tbi+60nICs62Gwp6moRJuuWxJwWgoFSd0WWuIeHJx59/Inm6+g8BQ8dhhPHj7FLjKZR0Hy7mtoaBA8dzgN7uxsnbGtl/v3XnwMy4YLeXzpufWou0Rdi6O9ujadvGIuxY0axZdjZ+59zYWSYQDAQ6NUqRKnUlAQT6KGdOKIukMhNIbMLQuT5OkwcFQCZlSfqVOb8JaVkEXKx6gJaKOk51Mk+lCWp7xYQuUp/+u3PixY46jZiZ++I8LAjPIrJ3tEJM2bPh7uHZ6/EkKCyioEohupEn+079+KW27Qzsb49uttOTddMHOaM528ej78OnUdA0JCL3v/YU6dw2aWLcP01q3heZXFRkd4MbRYIuqJXKzR9GWjo5zAfW8SnlUCfOZVSBFtLE3b1pNfOQXXuDhgby1FXW93uXD5tQFmS5DIioaFFgVy1pqZmyM3NYeuUFlR9KR8gK+ejTzdw7WFLlAoFUlPPobi4EEqFkhs1U/aoJgtyVyUBSYmJePThBzCQoLIQ+ryQ1R88ZChGjR6NwcLyGb5YMdMPn/51GicSClr9jYY0P/bog4iMiODPPa0Nly6axxuGYSEhOH/unN581gWCjui1yaJOrAE6z0Tsb8ileyqlEBOHOiMzvwpVZv4wamxqfaVQATIdRf1pEVCXDFDj44KCArh7eGDN/Q8hMysT33z1JWRSab8uFmTJzpo9B5dfsaL5OhLs/LxcnI0/zc2Zx0+cgqLCAi62p0G2asugp1ZJWGgorl55xYCzHmxsbHH/gw/z+bn2+hswGDAxkuKOy4YhxNce6745gdTci7sX3XrzDTzpgj7rdG7oQgOlR48Zg+82/oIxI4ayR0kg0Gek2hDEYC9byDStqO9HaFc7YYgT/y51noDKykq24Gpravrk+Un0aPdMbqTnn30K1pZWOBp2AtY2tv0aZyFR+nTDl83/r6go55jhmdgY7kc5dcZs2Ds4cryIzhc16O5Nw+j09HSsunI5u5AHQsYvHae6ZZxSqeDhv/euuR+2trYwdKj5xut3TYaDjSme/fJ4u2JI7NyzH+tefo2zbtVQC7eN33+H9Z9+LJJqBIPDQqSkEWtLM/i5WSE5qxz6TFRiIe6+PAQO1iYoKq+D0m81FIo61FechwUurpUikeKhqxIZ5Fo2ISm2+OH/3sOu3TuxbcduPHDfPTh86ADPCSSh1nWiBr0ucuFSEg2JIbkByR1II3toHiL1HR0zflIrC47OR8iIUTh86BDm3vZpj56XBPDWG6/j5x4oLjRyj5J1T68/IKipYbWh1xcSCyd44vpFwdh6NBWbDp3rdLAIfX5oBJiHlxfuuv2W5vj5qy+/OGCSygSCXgsiuc6obRq5TfVdECtrGnA2vRTjhzhhV0QmJMaWkMASKlN7FKdmwM6cXk/Twl9cXos6S39Ibd2grC6EvCYT8sZi2Flrt21YYkI8ZkydiJ9++QNffPUNjh09jMK8XLz88jqdWk/UnP1M4jn+nRastPPnkJyUAFtbe27AbWFp1eEC/cBVI3DT4mB8+Edsl8/TtiSApt2npCQPqMxDOj+08XNycsK6l183CDFUFJ6BSeVpKGEEldQYCpkFVKbOkDmEcLH9nctC4OFkgbd+jO4wP0Ddw7Ql1HN22vQZOLB/n97NqhQIukIraY9NiTV2+OeYZuNo+pMIcpsOdWZBbCnqCpvhaKw9BalUhuKKGihdZkFu2ZRgIjWn6QwhaFDUozDtbzjaaC8ZRv04K5YvQWhENJYsXYbYmChcd931+OWXn3VmRVG5BFFYkM/t1mj7P2rMeI4PdrVAf78zEe+tmYYJQ50uSq7oCPVjLAsqHDCu0pYt68iiffChRzF12rQBLYYqlQKKwjhY15yBqbm6HR4N7K1HRUU+Fly6AFfPD8bhk5n44LdTbBkrSpJgXJEAOZrcxqoWZfYBPn/B2sYGV161CnffuwbOzs746dc/eErKm6+/it9++UmIomDAoJV896bEGlu2rvSdEwn5CPG1g4W64eIFpLb+qKpVAioFTKQNUGXtabfOUem1BCXlVVovFSCX0wNr7uHfff0DMWPGdI7X6QLaAIwbOw5REcdxMuoEPL19MG3W3IvEkBbn9hbossp6fL0tAXctD4GdleY9RlUqJXbs2jUgxJBoef5JHKOiqDZWd3jPfVynj68oToRx6q+wrU+EqWnr941c4/evuRezRzpg7ROP4af1L6MxYSNM0n+HK5JhayGFpYU5X6wszJov6ljhD999g1nTJyM/P5+vKykpgY2tLbdQFAgGClJtud9sOI6o+VSD/qKgtJYTA6YMb734S6RyNHos4hZxtPhJPBZ02HO13p1EsZonbmgLXnBPRODI4cM8HcLB0Qm33HqrTlyL1Jh9/vy53GZt5pz53IeyrcB3ZakcO52Lk0mFuPeK4RpvhJSVWbA0HxgTSeitpViu+rzQ+zBu/IQBKYRqjCoSWMSMjWTtiv+PP3yHV156luPXZBFbWZiwAGri9qTPL5XoLJo3CyciIjB98nj83+fr+bMmEAwqQaRFw8XFpTmDU985GJ2NOWPd2x2r0xBwExoDb4b0gru0PaQmNmj0vAwVVfVaFSzapb/04rP8e1JSMoKDgpqzG7WJr68vJk2dgeEjR8O4F1Mkvt6eABd7M1w6WbOhr9KyswOmCH/YsBB8/OmGZjGgjdKMmbMGdPKMi23nERKaUtKbqSIkirm5udi7d3fziK6BkjglEBBaW52ooJtiSgOBo6dz4eNiBU+ni+uiNO28IjG2QqPXMhSUK9DQqJ0vPYnF0CFD+XdyX3333Xe4dMnSXneDaUtWVjaXUfR2ka6pU+DTTaexel5gl7MxVfUVMG3MHzDu0pzsLB5ETP1vKfN31Jix8A8I0Nrjd+SO1gV0zqN+WsOvpS8oLCgYMO+zQKATQaRguo+rNVzsNGw82o9U1zYiPD6/XSuxO0iMzKHyvQKlcn9U19SiurqmW+USVLNF/TDVw1PJGll2+QqOu5C1mJGRgSWXXsruO22SkZHeVE6iBRIzyjgt/8GVI3k2ZkdIcg7wBJOBAokHnaPAwECsXLUaW7ft6PXGRC2CfWkVqhT1MM7YjOXLL4ednfYa8XcEWZjkahaWoWBQCyIt4JSWPn6AWIkHorMxY5RbrxsK0CIpcx6HapfFqHZeiNIGS43cqFTzd9XK1TifkYOvv9uIOXPmcGeP6TNmIiE+nhcWnhRQVQn3Nu3Uegs9dljosXb/1pPF+q/D51FcXoc1V45oN56oKD0HC1lNvy6SlNlK51MTl626/tTe3gG333EnxowZjQN7diI+7hRPhu+u9dPXIkjQ+xDkaY0lQ6vh5miJ6uoq3nzpEjq3Dz78GCor+sYSFQi0jVYDOq6urtwabSAQl1rM7dzGBHXsOuwOErk5JGYOkLjPRll5ZaeLLS2nP/z0K9565z0Wpzlz5+HLb35AeORJnkFIZSxkaZKA7N27HytXrtSq25TEYc/uXRdd39NFW6lU4cM/TsHL2RJXzfa/6O9GZbEwMdZOi7aW8yw1OSe0SNOFRlpt27EHN95yK4yMjbmcQP1+0Pmg803xWtqUrLr6Wuw/dAwR0adw5z1rMGvuQoybOBmKRkp8CsPBvbtYHIuLCrtMOOlLIZTJJJxBffOlQ/DpIzNx1yVeSDgdxRabbp9XxufOysoaDzz0MNewatvNLxD0BXJtCyL1O7QyN0JFtW5KBrQFrakHY7Ixd6w7Is9qVkvXHsqaIqAgHObKYtSrjNHoMgc1Zv4wVWRf1N1GLYb/++hT7h3aER6enrxAk1jSzyFDhnK2XmlpKbQBLVYuHTTo7ilVNY149+cYvHzHJKTnVbJLWo1MSZZJz5M1WlrVU6ZOw51338uv4btvvsbxsGOc8EIbCbUFSgJIfyeL6O777sfjTzzJLfOIkaNG4YUXX2aX6MmYaISFhcLSwpKLyWkGZHu9Vemx7Owd+BKiHM1CmJeTzSUrCkUjX08xWbpYWds0W6G6FkMSQIqFkwiO8KPmGLaorVfw53n9X7FIP/Z/Ws9SVgsdnXMqt5owYRIWL1nKng1v76bkqo8+WY9PP/4IH37wbvNnWCAYdIJIqdq0cI8LdmKx0XfoGK94YDpsLI25tq67KHKOwLI+Axacmm4OmplRX7wPZUpb1CqqYdmimTFZIQqlEorGRtx71+0YcSy8wyQNWkTMLkzgoISOCZOnYtq0adi+fTu0Ae3mJ06aDG2TWVDFSTb3XzkCucXVLIxkzUnQ2GtBpEX1pXWv4va77m4hNE4oOZ/Bsdb33nkLqefOwdHJCc4uLjy09+prr+PYdnvQ55Q2JZ1tTNqDxI6GJtMlRKXi4bfFRQUoKizEueREFmVLK2tsOXAGiyd58XnIL6lBYVltj2eG0rxRRxtTONuZ8ZR6X1cr+LpZsUVe16BAfFopTiYXYuPuRG5cTyhKUuCkg5IdciW7u3vgnfc/xCWXLuHvfEvo/aaifDqv4cdDEXkiQuvHIBDoCq0P6KNs04lDB4YgUk0itaWaOcqtZ112FA1cy9fSbUb9G11ldSgro0QZGTeD5psqFLyY0N8p1koTLzpj6WXLsHnTnxgWMpwXX5pEER0dzQNXewP3Er39TkyeMgW6gKwTSrJ54toxeOGrCJRU1KHGxAeWKOzR45EAkaVXbhKAx7+MxeNf3t9u+7D/fdT5qCldQNYS1YzSxccvgM9tTXU1ysvLULYtBqMDHbDI3otboRnLqQNSHZfq0KDqqtoGTu6qb1RCfiGOfdPiIZBKJTA3lcPC1IibR1hZGMPeygT1jQr+vOZd2GhsPnweqTkVyC+lpKyLj226Xy0SE7X/munzW1JSjMcfeRAP3Hc3/vhrK6ZMncp/u/n6a7nkQj3hhW4rkmsEGOyCOCrAgUfG1DUoB0RyzYpZfj0TRJtANJaFwrhNfIytBEvLZjFUX0eZDvST4llqF15HvP3uB1h++QruDUlMmTYTDz30EJ577rkeu6DIfTZ9xiy8uO4V6JJNh87Dyc6Mp6qv+/YEKt1moDBzD5zNqjUq8iYLls4TWdUhw4fjeL4XZKYdT5bQ1DXZ1XxGbQikuYUFPOY82eZ6wMbCGE62ZhxOILFj0TMzYre6kUza3Gu3pl6BqpomsSTRpNBDQWkNyqrqO22uraYm+hOYjrwD0TExsNDRnE+1yJHoXX/NSoSdiOFNXuzp2FZ1jEIMBQMNrVdJkxDY2VpjZIADBgIU66LBwRSH6S5SS0+UGfmgvKrr4nkSMZp7SKJ08MD+Lm9Phc2LFl/SHLOxtLJCYNAQ3H333QgI+P/2zgO6rSrp4/+nXty74zhxeu89IQQISQglJEDCF3qvAZaldxZYeie0ZWkLCYEEQkJ6gfTee7cd23HvVi/vO3ONHNmWbcmWJVm6v3N0LEtPT09PT3fuzJ35T5cmDaprBHTr0d0nCQ9f/XEU+aUGPHXDIFaOIbQfzwxcU97gNdfNQFXi5TB2uB661P/Dzop+kDRiDNtCBwkyZGVVZpzMLmcdVzYeyMXKHVn4bf0Z/PLnadZJgqC/9Bg9R9vQtvQaeq0nia1v3tkXEeG1xdlbC/pOZ06/hk10Bg0ezNZ525JwO4fTqgaRBtvqsGnbyDaldZ1VO7Nw5eiOzSu5SBwBS9oMFMr6oEwvwmRuOplo9aoVHoU+aZChrMbCgjz07dsX/3r1ddx5933o0qUrM7TuGjhKAKGO5r7woCjz9OMFB2G12fHo9QMgk0mbPE4KOX61zsCUgARp6zQObm2j6O+2TWSYPv74Y595Z3T9paefwZtv/Jsp+3w0+/NWL+/gcFqLVtHRomzTwd3j2HpIW4Bm5JSl50q5xh0EQQppTHfY065BhXYwKnWGerNkWk+hAd8RCvznIw+5NbidzUjHpnVrWFbf6LEXo3uvPrBajHjk0X9i1Z/rcebsOUyZOs2tMCq99/0PNv2+3pxsvPPTPkRoFHhwWl8Ul7iuT6NzpdMZUKAYwDRlWxvnAvm6Bqy5Bs0ftYauwqUrVqxoFbm/pvh89sfo3KEdHpl1P/s+24pEH4fjTKuMPtQfMS46HH3SonHwTAkCHcowpRAVeYlfLDrSon1Jo7vCHJ6Cguw1iNOer5kjaH2lX78BGDpiBDSqxhV9iosKcezIQWYU+w4YzDpRONaozmVnIf30SXTt3pMNPu9/+AmGDhuBpx5/lBleV1BROhnO8ZdOgC8hebc35+zBS7cPw+BB/XGiTqYHfb4SgwxiyhRIFf5RsmmOUSRv2lU/QH+iHjQLQsbviNb4fiJK153ztcf7IHLaIq1iEGngbt++PcYNbNcmDCKxdGsm3rxvJH7+8zTLjGwJgkwNseOVKDi3HhEogPLv2jby0Hbu3I5LLp2ALl27YtPGDdBqw9i6a8e0NDag6HU6HD96iNW6denWA0q1ltW3OcKNNPMmUe4dWzchL68Ax44dZantFKYaNeYCHDp4EOVlpWwNkgYl2p5Crp99+V9cedUU+IMKnQWvfLsLN44aztoDUT0lGXI6rhJlf0gSu/m1kFu0W2AvOgBINRA08ZComxZrcBhNf3uFdT+HUqSyi9ZJpuFwgp1Wi09Rke6FA1Pw7bJjLM080DlXpMeBU8WYPCIVc9ecavH+2PpiykWoKNiFcNNpqJTVs2cq/P74w/eY50gGi0oxyFA++vgTuHrKFGSmpyMhMQl2QQaFSoNB/Xox40GF9F27dUN2dhZbf2yfksL2UVhYWCtc6gjVprRPRXRMDJPs+vrbH9gExZ9QluR3m2x4+b778Ntvv2Hn8QJY40dBKvNvOyjRaoKQtRTRKhGwCTBX7YM+dhwkYS3TufUHor4QMhfCAu7gUOvhcEKZVgv0sx6JkZEY3de7iiityR9bMnHp0PZQK72XJSdNGIpK7UAm/u3AIctG3ht1ynj+hZcwdPAglJWWYsToC/D8C8/hrttvwZABfaDVallGaXl5GfME8/PyIBEEnDt3jhnGuoMY7Ze2P3P6FO64826sXP2X28awtb0dvdGGl+acwqSpN+O5p55g4V+/k7MaMVoBMqmElUCoVSoIJQfOe44m76gD+QJRlwOZzHODSOH0xx57DEOHDmu1ptQcTlugVVe+yUsc18KOEr7k+NkyprZyyWDvelPSmB7Qx1yIyio9rDax1qz8hVfeRp8+vdCzdz8MGzkGEZFRyMrMZM9R2LMpSPeUBjHS5qQQJK1TkkrLpRMvQ+/efRBo0JriG3P2VU8Ebh2KqDDXa56+Qowdgoqq80lQFLlV2Yphz1oDZeYv0OQuha34GOyGIohZayHN+AW2vC2snVWgITPl4e+SRrehCMX83xbjngcexs+//o4nn36OJ8RwQpZWNYgpKSno0SGGSUy1JS/x8pEdWtwFoy6S8BSYO05HqTQNVruEJR717NkTu0+W4IJx45HcLqVmHe39j2a7Hb6i7FPaDwlXZ+UW4siJM9i0bSe++e5/bJ0yEDFbqrNPswuq8O97RjApMn8g2kwQzRUwxYxAUamuxhCQbF6CWse6xdOkJNKwH2GFaxCtqECkRo4Y5EF2diECCQqfy2yeG+mkpORaqkW//PwTT4jhhCytahBpvYxqElvad9CX7DpWwOS0RvVN9Pq+FUoVpl93LT7+6ENMmnor9pzWYeHGs/VKNC4ZfynKzRq3CpwpmYaMYu65bLQlyFP+/PfDWLk9Cy/dPpTJ/fka0WKAtnI3Iip3Qa04nxlJc6G6cnwqlZI9Xv28AJs8sIQnyGN1SMB5QufO5/V0qdv9qZOtoPfG4bQRWr1YiMKm08Z1Y8r8bQGqkliyJQPTxnbyah3liN4JeH/WaAzpEY/Xf9yDT5dkoFzTr+HjSB6Lisqm2/ZQzRkJLNNgSDqazcVf2ZKLN2fg04WH8MC0vphyQZpP35sUcKroOxAEj5R/6LqwRfZEICHqchEW5vmabH5Bfs39n3+ayzKeOZxQpdUNIq1x0XoYdcBoK5C+KYXPxg1oeWPejklhePG2Ibhtck8sWHcaz/93B+sy31hhN/2VKCOgV3WGxda49id5MvMW/I7UDh1xYN/uZoW7/F06sOtYIf717S5MGpbKOmWQ1JuvkMb1RYVF5dG6GYW2hapmaN+2IpKqjGap06SfOY0tmzez+3N++I7rj3JCmlY3iDR4kJd40cC2Eza12UX88ucpXHdxF9Z6pzmQiPNdV/bCK3cOZ3qU/5y9Gev35brUpHQ2SM73JYnDUWms1j9tCMpCnTzxEmTlnIPFbGYF+22RjLxKPPvVdpZk88a9vltXpEL2CJnBo4kEbRspFEF0Em/3J6Joh8LWvG4ilISVmZnBWmjl5OR4/dg4nLaET/SVUlNTMbRnAqLDW94k1ldsO5LPFGyop50nUGj48lEd8OHD1X0Wn/xsK35ac4plV3qKIEggpl6GsoqqRpMpSoqL8cpLL6D/oCHMIJaVtg0xhLrQ+f73D3uwYX8uXr5jmMfnvjnYwrqyzFJPhAFogqI3WQF7YNTt2avOQdHM+kNag75qytVYtuQPHi7lhDw+MYiUqUehU+o72FYgT+6nNScxdWwn1qrHHQZ2i8M7949i3vAHPx/Ae/P2s44PzT8GOyBVwiJrWjmFCvapW3uXbj1Z6NRqtbSJcKmr875wQzre/HEPpoxJw2P/NwBadevpm0rj+6EibgJKK93r0kDbFFWYYE29GoIsMCZ4QvkJlsDm8esEAWPGjGGGkDqw8MJ8TqjjMwVeFjZtQ9mmBMnOpedWsIG5MdrFafD0jYPw4LQ+WLH9LJ7+cjsOpbfcS7PnrIMq8ydIzIW16hddDWyUXEOSaGmdu0Cj0eLQ/n21dFTbGsfOluGpL7aB/DaaZFAyUmshUcXAlnYN8gzhLgvTaQ3caKTmvnoU6FWwd5wGQR4AogJ/RwgUlvyaDFhPIAP44MOPsvtHjhzy/sFxOG0MnxlEKr9IaxeNvp1j0JagcOdlIzq4DPdSR/NbJnXHm/eORF6JHv/4ZDNW7cxmrY+8gRDVi6X4R2hVTEXFFWQIu3XvgcVLVyI6OpoZRwqdlpeX4syptp1CTw1z3523Hz//eQr3T+2DR6b3Y412WwPqsiFtfzHKI0Yzb4uMX7nehiJzOM7Zu6AqYSLMaTdASJ3ok44c7mIvOw1lM5OQSCN37IXj2H3esonDaUUtU1ehpk6dOuGq0edwqI0IfhNnzlVg74lCXHdRZ9b0lqDlpksGp+D6S7pWJ4P8ZztTuPE2krBkVNlGo8qQD6mpDFK7nt2oYNwBlVycOH4Mw0eMqHlMoVBi0NAR2LFlI8IjIpCQmNwmwqUNQclI+04V47bJPfDerNH4YeUJrN93rlXeSxLeHnliChBV7XnTjDFQGxkxMe/SnVCGea4HSxmp/37jrZq1U3+Kq3M4gYJPf+tkEAf3SESHxLZV60QdMGj9MzlWg95p0cwjvGpMGr5cfASv/7CnVYyhA2lkGqRJI2CPGQgbpJDWKQ+gEN/jTz5db0CLiIhE3wGDcHDfHlRVVqCtQwk3H80/yIr5r7+kC569eTDaxbVO2JKVVbQBA2HL3wGNWtls7/CyyZfX/D+g/0De6Z4T8vjUIFKKN2WcXtWM7vT+hMKh248W4IVbh+DxmQOx6WAuHv90C3YfL/TJ+9sqzkJbtBaxGjvU6vPeAA3ad9x1T4NNf5OSU9AhrTP27toBi8WMYIDO+WOfbkVOoQ5v3jsCd17Rs9XCqIGM3VQBrSmzwVB6Y9Ca6MMP/6OW0f/y62+bVcPKGwFzggmfR4O6dOmCcYPaIzYiMDL0moLWZ2Zc0gXDeyUgQqvAfxYfwR+bMxtNcvE2giKsugTDKUmGBrPiKjuef/HlRl9LTYS1YeHYv2dX0GhUGkxWfL/iOEu6iQxT4oOHx2Dq2DQo5IEa3PQudlM55DlLERHevEgLXTvWOkX8UVFR+PaHuW53u6AlENp20uQrMGzYMBb94XDaOj4fQSjFOzExEZNHBraXSJNnCpN+MGs0C5O+/M1Otnb1f+O7NrtYv9nHooyGySqer4HT61GiE2Fvd2mTYS6WZDNwMIxGA04cO1JPFactk1usx/s/78c7c/dhWM8EfDBrDMtk9rYweyBh1xVAkbMU0RGaZqvK0MSosLCg3uNjx17IfptNQe/bp18/rFi9FtOvnYY777yLtSPjtM3QO+c8fplSd+3aFeOHpLhd3+druqZE4JU7hzHj98OqE3j5m11Iz63Eqp1Z0BksrDbRl9CPytLuMpQYFci1pcKQMhVi2jQm7+YO1DT27tdX4uCho7jp4XcQbBzNLGWSeHPXnGQlMiSKQEX9vp64tDa2iiyoCtcgIkzdIm+fDFp+/nkNU2fI42tskkXX4n33P4B/PPIIigvz0a1Hb/zw44+8j2IDrbWoFym1ZeOGsW3glxGDWh+lJCcwoxhIUGkFpfdTn779p4qZ3NrWQ+cHDopYfrXkKK4c3ZHVHvoSiSoa6HAFZAmDatXAuevp0Trom3P24tpxnTCuDcnouQt9N5sP5uGxT7fgx1UnmKf4yT8uwNUXpEGtDMyJlydQP0ZtyUaEadRe2V+hk6i3M1dedTUbwBsS2Hj00UcxduxYSGVKnDh5BtddMxWbNm4ImnC8t6C11dtvvx3btm3DihUr2HIHN4qBj9+m0OQlTh7RISC6YJAnQWtQ1I2C7tOgumDdGZgs9X/kGbmVWLsrm+mUBgruhkCphITq+m6/vCeG+qHdkq8M4/YjBXjmy+344vfDGNQtDp8+egFumtjdp5MYw97ZXt2fJH8ztE7lNi1ly+ZNOHmifp3qsOHDcfW0a9jSBnk4Dtq1a4eZN9yAiZOvwAsvPI+bbrgeb7/5b+Tn5/GB3gkyenTeRowYgddee409NmTIEKxfv776e/RARJ7je/z27SQlJaFDuzhc0C8J/oSSZd59cBRG9E7EW3P24uMFB1Fcbmz0Nb/8dRqJ0Rpc6IVuGL7mSEYpZv92kHWVoLXRYIZqF1/+dhfemrsP0eEKvHnfSNZ5ZEy/pGZlZ3qCepD31mjtlTnQSo1eVR6isOhlEy5G+pkz9R5//8NPsHzVWnzz7XcYP348C/vNuH4mbrjpNlw+aQKys7LYdlzqzbVQxtq1a7FgwQLWR9PZAXj++efZ+j8ncBFEN35lFRUViIyMRHl5OSIi3Fu3coeMjAwsWb0ZT3y2Fb6GaiFvvawHUuK1rM5w3d4cl50oGoIa2t59VW/8c/YWpqgSKJTurO+ZuPIeKWx662Xd8er3u9n6aChAHUguHNCOherDNHJs2JeLjQfOITOv6b6TrYlaKcW3z1yC29/406UIvK34KKLNR71uxMlbIYWa+2c9jCeefJo1TE7PzMK57CzIFXKkdkgDJDKUlZYiuV07XDh6OAuN8vBow5Bm8/79++s9/uGHH+L1119vluYsxztQ4ldTNsyviytUk9g1NZaJYu872bz2Nc0ZFGdc3IUZhJU7svDuvH3N6kSx81ghxg0qx40TurEC/UCBjJ8ro1gXUnqhc/HMTYPx0jc7WcZmsFOpt2Dp1kx2I++Y1Ib+dccwlFWZsf1IPgu1Ulg50BBUMRDMFL73bp9IMmxUG/zLT3OgUcrRr18/xCcmYeCQYYiOiT0fCk1Lw7VTp3Bj6AYDBw6s9xh5hdwY+g+6zlNSUtzKhPZrQJvCLp07d8bbD41v/feSCJg8IhUfPDQG0REqPPn5NsxZfbJZxtDBd8uOYVTfRPTsEIVApiEDuWRLJv7ck8MEB3ydJBQYoeNDuPvt9SwJJyZChedvHcIScW6a2I0ZzEDJUhVUUSwU11pUVVXh4MGDuOFfqzBg0FCmYhMz/KFakYVpfxtETuPj2UUXXVTv8R9++IFNPDj+gSIha9ascWtbv6ffUUHv6dOnW9VLHNA1lolwU0SU1ggPnC72yn6Lyo0s+eaeKb3xzJfbXCbhBLKXSMxbewp2UcRLtw/Dv/+3G2fz/Rs+9DUWqx27jhWyG4Uk+3eJZevKtMYappbjeFYZDqeX4FB6KfMevSXc7gmCVAm7l73DukhkcnYunI2g8zVEGZMffPAB66hCa4ekdsOp7wlee+21Lk8LZe7yc+YfYzhhwgS3k5n8flVTTL179+6YeWkx9p8q8mgdrylIe5SyC3t0iML8v05jza5s2Lw8oFH4jVoT3TypB/67pFr8u60ZxV/+PA2T2YYXbxvK+hCeygm8sKEvIPWhPSeK2M1x/VB3lr6dYnDFqI6QSiU4lV2OjNwKpOdVsoxjKmfxRZctu8RzAW9PPJv27ZKBg1UNXkO0Da2NzZv/Gx5+8D7m8TRXGCAYoQGXEpBcrU/de++90Gq1rGTFOdGG0/qQmtIdd9zh9vZ+N4hEWloaeqadxgX9krHxQG6L90d1Z1RvN3FYKv7aew5ffLKZrR+1BjQYfvrbIbx1/0hm0GltsS2yaFMGjGYbnr1lCFN+oWL3UIfWVem2emc2Uy7qmBSOrimR6JQcjitHdURqYhhsNhGZ+ZXIyq9CQZkBBaUGFJYZUVhmcOuao/KMmJGPNLmdRRpJEudoDSivLi6BMqZP1nvO2ShaIcN102dg4MBBuGjsKB4GdILCyY2tUel0Oo/LUyhMThMRMraOnqf0lzrccNyDzt3o0aPZskDAZ5k6c/bsWaz6awse/WRzs3VC6Xq7aFAKU5jJyq/E9ytOIKvANyFAWku84/KebG2ytPJ8/Za/ocHME5k2KminNksf/HKAiRNwGl+Xpo4bZCDbx2sRH61GfJQaCVFqpntLmqsUVifDqDdaoDNaoTdaofv7vtVWHWKntcpbJvXA/1YeZ2FLmVTCem2SkpNWJWf3FdDjh28+g87NH7a70ICrUqmw6s8N6HVVdd2cq2soMyMDLz7/DN54+z1Wk9ire2eYneoUOdUG7PDhw6ykzJmVK1fihhtuYOe5sYG7srKSGTtHeI8ETPbu3Ys9e/awSUvHjh1x8cUXs/GY0zR0HmlNl9Zw3bVhAeEhOjJOO6cmYMLQ9li+Pcvj11Niy62Te0CjlOGrP46wNSFfQoo2A7vGMaWbN36kCxgBgaeapev2nmPh00dnDMCnCw9h59H6mpecaij8ThMuV5MulUKKuCgV4iPV0KrPGzaNSo7YCBU6JMpqWnk5tFf7d45l+7TZ7dAZyHBakV+iZ38rS/NhdyNEabFYIZe7/7Mmz6Ug6rIGjaHD+7nh/6YjL/ccRg8fjK+/+wF9+/XHvj27eaJNnQG4qKionkEcPHgwunXrhjNnzrCC/ezsbOYAUBiVDB2Vvtxzzz149tln8eabb+L7779n55XWHOnv8OHD2X5o7bagoKBRw8o5D50vT8KlAeUhEnl5eWxGdOO/Vrid/RkXqWKlD5SU8/vGdCzbdpbNsv1VT0bF36t2ZLO1xbbM4O5xePi6fvh57almTVA43qtDJESrEersX5l8WmMDQLFJi2iFDgo3jSIlghjaXwtB1vAg+91z4/HPR2bVSxDhqivnoXNBnXzWrVvX4Hl0lm+j8OrcuXNx5MgRvPLKK0z8gCCVGzKgZFgJMqSrVq1CTk4OS9gpLS3lkxA3oTVumnzQtequDQuMvPK/IaX98PBwlsDQFEq5BNMv7oJ3HxwNs9XOCuRpHcxfxpCgwWz2r4dYu6iOSW2rCXJdKLHkte934+qxnXDnlb2CuoNEW4AMlgXKRkOfZRYtZGmTUWENc7vZLxlY0VjSqOTcTY98UKugnBJDuDGsDXl5b7/9duPfodMaIoWdH3/8cXzzzTc1xpCgZKUDBw7gP//5D0aOHIkTJ07gsssuY+tgJSUl3Bi6CV3/N910k8eZvQFlEOmC6d27NxPPbqzpK0lvvf/QGPTrHINXv9/FuqgHyrrdyexyLN6UgYeu7dfm+/NRtulz/9mObu0j8cxNg1joj+M/jVOTMrlBQ8cSLtqNq245lDwWFZXurTWysJyhqbViq1dl44IRWvsrLPTOMg19x1dffTUWLVqE3NxcvPvuu+xx/h24D0U+nnnmGXhKwI3YtJCc1qE9rhlXv8VS53bVbZkoRPrTmpNMYeV0AJYILNyYztaAqOSjrVNcYWLn2WC24bW7hrNSBI570AAmZiyCNOMXiJlLIWQthSq2a7NPnySic4MF+qXyHjVdUKihtF6W7NbsmMKsElPj68SCPJx7Jk1AE5HVq1fD25BxpDXEzz77jJUQ0Hc6aNAgtyMAoYhUKmXh5djY2LZvEIlevXph8sg0JEZXpxdHhilw79W9WZ3coTMleHT2Fmw6kBcwiSt1oeJtEtAm4XLKPm3rUJINNeKlBJtX7xrO6vI4TWMrOogIuQWRGjli1WZEK82Q2AzNPnUSbTLKNINRXmViIUv64dOaU6ElBtL4/jXbiVYDJAlDUV7hnkatzFreqCi5JLKT2x5nKK9XNbZ+2FKmT5+Ol156CdOmTWOJN3U1UXlNaG3vkM5VcwhIg0iLnhRXv358V9bwleTWKGvvic+2sE4TNEAHOlSLRtJg907pjbSkcLR1aPIxd80p/LDyOB6fOZA14OU0HBYVLTpoqg5BLj8/k9cbDLAlj2vRaZNGd4W103Tk2dOQZ45BVdJVkKZUh0oJe9U5qLIWwlaWDr0i1S1PQiJaG31ekMhgksXzdcMmSE9Px/Lly5GZmel1j5oUgqjs4umnn0bfvn2xZMkS9OjRA7Nnz0ZWVhaee+453qQZ1d7hxIkT2RptcwioLFMHdEh0UZEyxrkiHb7642ibLRSnBrWXDm2P577ajgpd4HTFaAmk/EMZqKdzyvHloiOsLIBTG2vuZiQrimtm7lRbW6wTIER1hyCVQ1BGQ6KJdzvL1B3ICCuzf0eYVoMicwSEpDGQZy1EhLbxNP1yvQ22tOsa3cZuKkN4wQpejN8IlGxEYU1KsKExLCoqCk8++STuu+8+tASKAlA/RRIwIVWvhvj5558xa9askFbDMZlM2LFjB9PIdqZNZpk6Dnzr1q04evQoyzo1WWxMT7KtQpmvlGhDdX2B0AzZGxw/W4anv9jG6uiozKR7KqmocJw9RKXpXO0wlmhDcpQEKdJ0RBkOQF5Q3TDWG4h2G2x52yDLXoqoyIi/vUWBZaaaYkbBaDI3vgPBDS9SGYUKSRJfu2oEKkchQ0h1gpRkQ4Pziy++iN9//x3NhTzNnTt3stwKKsFojOuvvx4LFy5EQkICM8yeKuMEA9Sxpa4x9ARJIF1MlG5MMyGy4JdeeimGDRuGvl2TMWnY+bTktsiXiw6zkC/1XwwWSH3lnZ/2YdnWTDx78xDmCYfg788lqj63Qo7a0QBHkTUNVHKZFFYmxeYd7GWnES/JRaRWXp0kI5HALlWxwVkamQYdYvC3KE6t0BIdExltG+RuGV3BquPJNR5Ca3133303Ro0ahVtvvZVljFL2KHUXoTCoqwBdcXExdu/ezeqyaTv6TqmtlDsG7oILLmC13EuXLmUeUSgl34iiiCeeeKJF+/B7Hj0NEtQo+NixY2wWRFI7VIvoYMCAAbjTZML2owUoqQiM0gpPoS4Y783bj9fvGYHMvEqs3Z2DYIGK9o+dLcMj0/ujT6cYputarmvCIwlybBWZUCgbDlMyQyRJ9tqPT1KVDji9HRm5GDEHlRm/Q+w0DWLSBdBlL0KE9nwdIxnmIklnSCLbQVDGoLGhVrRbIZxdiviw6oQxjmdQCJOUaehGiTc0cJP36OxR0prXmDFj2HhH64H0HVLpxXXXXYdx48Z5XE9HzgTtk8bVUEEikWDSpEkt2wf8CMkQ0QVCi9FDhgxhhajOxpCIj49nUkht3bsiTUvSB6WuGLQGF0yk51ay9leVejMTOSfVoFBGUERAIjRsOCj7UxrRwWvvJxXrl2KwekTYYNcVQJa1GDLBVsvDYMLRMT0h0SSyNc3GsFdmIUZj596hFyBvj4wdGTgSRSD5NvLi8vPzWWiVVGvoORoHySCSYWtMnaghTp48yTzFUOlhKZVKceONN7bYI/aLh0jK4ySCS/JEPXv2ZD0RG1O+oKwqumCoy7ujNU9bhBKD5qw6gUdn9GcF71TjFyxQMsgnvx7CuIHJeOjavux7+t+K463WZSSQkYSnoqp4Sy2PzAEJeutVnSBVeC85zSqlfdVeZyehaJOqPSKK1kIdRu6jghlBR6NaNnBIVS5Do2LuBsgsZcygmjVdYLfqIVFIQmZw9RfO5/f//u//2LgXF9e8ySV5mWRIQ6UcQ6fTsYSiluJTD5HCNGQIySukMAGtE5L+X1MyUPQjJgWbl+4czSTb2jKrd2Uz4fHH/m9gm/8srli/LxePf7qVfbb3Zo3GBf1rCx2HAuSJiRLXMmtVegMkMX29+n52ZVytkBoZO3P0EMhgrSUEbbbYWAZkmQGoCBtSb01KtJkhnP0DUZISRGmASI0UkeYTaKcsDipjSOMNjUWBzOLFi/HVV18167WkbvPnn3+GjDGUSqVsjZYaRLQUn4zIjjKKtWvXMnHasWPHsli5Y7bqDlSDQ5lb145rfgZRoPDt8mPQm6z45/XBk3nqDMnovf/zAVYuc+OE7njqxkFMhD2UoLZQrgbiqHAt1OcWw1p40GvvJVHHQKc31Jot71n+KdYtncMMoMPwWaRR0Le/FvaO0yCNrp2xKFr0kGQuQny4hLWfcl7/CraBlVLvKXQZyFDCDemc/vHHHx69jgz97bffzkKxoYBEImF2Yd68ed7ZH1oZEqSlzFESqe3fvz9bOKbsJ0+hHzUZ0WnjuiI1oW0LZ1NT2ffm7UOYRoEHp/UN2uxMUrZ5/NMtKKkw4p0HRuGyEalB+1mpTs9uLIFoqw6DM0+svBwmJ0/E4WVRKCtNfc5r2pSCJglGy3kPjgbD119/na3J6xMnoURnYwkcNhWtF9afhNpNFWydMVJbnXUazNB5IGPYFprsktf/8MMPu2286ZqbMGECi8IFusH3FjQBoG4gYWFhgW0Q6cvZtWsXtmzZguTkZFxyySUs66kltTFUjkHrjXdf1avND6y05vbmj3tYF3ZqLBysUNE+eYrvztuHy0Z0YNJvwZRUREbtngulrGg9sngN5BkLmIHJN0XBJqigbiAKQvW2oj7fK8cgSKQwx4ysMWb0d+nyFVANfBASdSzsHa9hiiYQ5EwwYNt3d9cYYxL2VuQsQXSEGsHe0IQGT9IBjY6ORluBrhPKTnVnO4q8Uc/FYApvNzW5+eWXX1j00Ft43SDSzOT48eMsPErGb/z48UxiyFv1MLSvAd2TccngFLR1KOHk9R/2YHD3eMy4uAuCmcPppXjy863YfbwQT984iCUWJcYE/iy9MUgzdHjEQfz6668s/E81Z1GRYehk3wFJ8mjIZUKDgxOFIoWS/V47FklER+iN55O0wjRqKDLmQcj8A7BU4uanv0eY8QhTzyEvQp7xM2zFR6HIXYWIMHVIDKJUtO0Il7aV+jw6zg0bNrh8btOmTfjwww9x8803s+gZZe0Hu4fvgKKMpFd64YUXwpt4LcuUZpzU9JLcdRocqH8X1RW2RhiBQq8PWK0sOaWt17wVlxvx7x/24OU7hqLSYMHybU3PBtsqZosdCzek4889OZh+URe8c/8orNmdjd/Wp6PKENhJDnWxlWdCWbwZGeVhtUKfZFhIXBjFG2EO74fKin2sWa9CLq8V1aDtlNYiGE0VgLLlHgtNPm0SWjcSa/4P16ogkdhRlLkYESo5lCoVGzDJcJM2tNV0CIoITUgYQzoftLY2ZcoUVptHKfrsewpw6Lt66qmnWFjwhRdeYI0PKDufev3t27ePjbWhEh51Pid33nknk7GjxsktjTx6XcuUFoBJUYEW8ykblLJ9Wls2iOSMFv25jxW8BwPU2ur5W4fg22XHsHF/LkKB9vFa3DixO+u3SIZy5Y6zTPMz0LltpI15hXU7Djgv9OdbEyFNGglRtDPB7UTT7nqGhyZ3VIJkDe+O/330dIu1TG2lpxGh2w2lovZxMeWaEDB6DUFjERkSEsR2JPLR+iqJYrcVyFMkA05jK2WRBmOyk7vngRo/UF4KnYcjR46wNXPy/kk7tiF8omVKdU1U/EmuO/WeojKKDh06+ERDj0IEYwd2wPghbT90Spw5V4H35+3HnVf0xJAe1aLPwU52oQ5vzdmLj+YfwIUDklmZxriB7SAN4MUsMnCU0daQMXRA7ZcI477PIA1vj3xJr3pJNDSzp7IIadXplh2T1QhbeQZEYz5UyvrCzqFsDGkApXFp5cqVtbLafdGkwJuQ8aPjp7AofaZQNYbx8fGs7yT9/sjW0JIc2R6yQWSLGuoX6i7NMoj0Azt16hRbJ6SF6osvvph5hp7KC7UEmiENHjwY903th3ZxwdG09lB6CT5deJgVtvdOazsL/y3l4JkSPP3lNvy67gzTRP3w4TGsvZQiAOs0BUHSaEYb/VBzzfGsZZJzf0FJdDdU6VwLMaiVTWuJumw+bDXAWrAP6uxfEWvYjQhLJu+qXgcaID/99NN6tc4kncZpW8YwKiqK1bA7l5TQ741sD9kgskVkk8g2NXcSKPP0R0iCs4cOHarp5EwW21/Qe1PW6axrSvHC1ztYOUMwlCp8q5TiiZkDWVNeMhahADlPG/bnYuOBXAzvlYCpYzvhmnGdmXj4qp3ZMJgCZ52kVGdHpLrh34ggq37SrsuHULwHdkUsWyeUSlz/SB3SXe5C+5UVbIBCMLPXKv+W9nIuwudUh4ppoHQMoPTdkLj26dOnWTcdV14+abVKGohQ0P5MZgu7VtWq0AxZ+gO6xuk7pOSiutKeDuh5skeFhYXMPpE+Nin9UMckTyKWHq0h0sIuhXlIbo1SXZtSmPEFdFF+8tVcHDxdzBrYBgtj+iXh7qt645NfD7LMzFBkQNdYZhg7JoZh5Y4sLN9+NjB6SmatRIzSdUIGdZUoEVIhTRwKe/afiFOUg/SwbTY7+2HKGhBiuPzyy7HggBpGs71RkW177iaEWXOhVnPj5w7kNWzevJn1ErzhhhvY2hOVhJExpKGPwpC0Dd3MNgFWKSmY21jTZIlgY5q0EpkSEtECkzQGYmQP1i5L1GUhzJwJrUbDvXIfQN0/SNPaHcg7JCEYSp4ir7JPnz7scXfWED0yiFRTSCLcgdaAknQbl61YxdoRUXp/sDC0ZzxmXdMX/1l8FFsO5SFUobpFCqVSN41th/Oxdnc2TmSV++VYqMYwonB5g78Bqo2qiJsIiSoasoxfEKFxLxxKA/ahwkiYNJ0anNFaCw8g2nKCtY/iuAedSwqb0uSdjB8Nd+Q1HLaNBKwGiJYqiFYThLAUjyf4rIYzb7VLzVqOZ98RnXtXHjf9npYvX46hQ6vX5D2BXkuTIbIPpAlLojBNGUSPQqZkaQPNGBLkRv+46iQemNaX1brpDIETXmsJVFZCEmj/vL4/0wb9a+85hCLUkPjtufvQPkGL8UPaMyk4KlehNlqbDuSy4n9fJdT0UR1CoUrlco2CftgkMPzm73979KL76xgU4lFT4kTZLlRa1RDbjYcgr702LtdnQK7mxrAxyBt3jng6vED2nN2OiiojiiL7QVAIgFxT7xx7AokeGMP7IwonQzpxqbkwzV2zGa+++ipLFqJuHyQsQKF/MpD0HIW43TWG9F2T80bLD1SOQdEAWj+m/b388su+K7sIBOhjvPj2f2Gzi6zNUjDRs2MUnpw5EPP/Os36D4Y6lGwzsnciM45pyeE+8xqfuCqG6Uu6+smQMSQFlJPSsSzxpiUeHYXvyqMvgkSTUKsLhTJzHsK0wZFA5g0o9OlIcKIkGb3JBqMkAhFCucv1VJ3eCEPiRGbIvIWtYDdibGfRRur8AwqLxYIVK1aw5EgHVFqyceNGzJ8/n9UZPvnkk03uh5by7rnnnmr1J2qKLZVWZ29LpTU1mvSXjK5XQ6aBbBAdP4qf5i/Cz3+ewrog86a6pETgmZsGY8mWDPy+McPfhxMwkK7tJUNSWNkGNZDefDAP24/kI7fY+0XX3e0b2eyTsqkdPxuLxQaT2QSjPBlCwnAIijBWBmHP2wq1JRfhYZ6vMdFAr0++CoLifAKBrTILERXboJT7vad3QFCpM8AYOxbSsoOwSTUQontBUCdAOLsEcdr6vRtpcMw1x0GWPNqrxyHJWIAoDbeGzYGMFmWEUv36gw8+yOQ9b731Vo/28cEHHzDd3qYil/T9U92pV0OmgQ6FRj7//TCTBTuWWYa8ksBXonCX0zkVeOW7XXj25sFQKWSYtzZ4EohaQlZBFb5ffhw/rTnJslNH9E7ENeM6Ia9Yj+1HCphxpHpHb3BcGIPlP03H5Bn3IUZeiVKjDLaIvpAkd4ZEcn5QtBuKEIkCKLRqj40hCxXRUoqsjidYcQZqpYKH5v5mwviLsexMMhCRCseZt+sLoRWqYLdXe4fO9XoV1JQ5dQK8vZ6sAilltW0JQn8glUoxc+ZMFlmhNT7q6kHCCeTUkLfnDhRqpZCoO8t47mYEB5WH6ODepz9G9w5ReOnrnSyEGkwkx2qYos2e44X4bvnxoPt83kClkGJQ9zgWVh3YLQ5FZQZmHLcdycfZ/KpWf39RtEGePg9atcbjUBolgFTFjIMkrN35/VmNUGT9jnCevFFDmUECe8era507MojK3OWwSzWwyGKgseayziI0+BbotRBSx7f4u635TswVEHLWIj4iNIvkWwpN/L7++mtMnDiR/U9ynMXFxcwgUvImhUsb45NPPsFrr73mUe07SYuGVMjUAV2gs//7E45mlOL7FccRbFBvwSduGIjyKjM+nH8Aeh8llbRFlAopBnaNZcZxUPd46I0WHE4vwaH0Uva3qLxlyhaNeQ+24kNQVh1nNVIyWW0t04agUtpSSwQk7ccz79Jeng556U7ERqj5wNuEQaypA3Wc6PSFUEsMsFhFmNtfCYnS87ZzLr+josPQVO1HeJiWe+zNhBJmaK2QSviIF198Ed99913NOiJ1JaFQ6LBhw2p9t9RB6a233mKv9VQIJmQNIkFx6YWLl7Hs0/X7gms90eEFPXRtPyTFavD23L3ILznfIJbjGrlMwnRT+3aKYSUctC5LBpEZyDMlOJxR4rU6R7uxFIpzKxDloXg282Yq7bBrUqHUnYJSIeHrhi4oNodBaN94CJSUfGC3AHItBME763y2stOI0u1uUrqP0/T4TJmgDtEEUqAhwXUHNKkho0lQSJTON/2OaH2dlsaak9Ub0gaRINWCP9dtxGv/241T2f6pW2tNaCJ844RuTP+TMmuPZARPDaavJhU9O0ajb6doZiA7JoYjt0SPjNxKpOdWICOvkt33tBOHXVcAZcEaRHiYUEPGsLDCBgEiYsOl3Pto6PyKQLGYzMTTfYm9Kheaor+g0fA1Q29kmFLWp7OBJNFuCnG3Fu4YxKBKqnEl7TZv7Un88/oBeO4/21FaGVz6hTTWkgecU6jDkzcMYuHhv/bk+Puw2gxGsw37ThaxGxGmlqNr+0ikJYUzT3LCsPZIjNagsMxQYxzpb36Jnj1mcupS7zxoqgr/QlidhBqLzQ671cI6tRtNFlgtJlhFGewSDRQKKe6+5Tp8OWcF7KkjoM35FXY7H3QbgspYRB9J+TF1oPJMSCpPQGMv48bQC9DEr0uX2v1fyVMkwQRKsPEnQW0Qid8+fxKP/utzPHb9APzru12wWIOvgJYK9vNLDSy7NiVOizmrTzBjyfEM8gSdDSShVcnQMSmc1Tt2SgrHiN4JSIjWMO+SenEWlhpQUGZgBrKguBLHNq6EQatm6xuVVVUQIECpVKDcEgZE90OFLhMIT4ZEkwRBVp0NKSilTEXjvxtMQGlujTA4p5HOD+Y8eDuVhRRrRHM5RFM5YC6D1FQApa0MSoUSchWFXLlcnje+u3HjxrF63rp07dqVdazwJ0EdMnX+Et7//EdWm0ZlGcEKdaAnT5FKDkgDlTwgTusQrpEjIVqNhCg14unG7qugEqqwY8tG1nIoOTkZVqsNVXoT9GYRepMVOoOF/XUWopdKBYzqk4TNe0/h2N7NOHL4AFsr4TTcF7BCMxDS2OqEjJZAJTLS/I2QiXqQzKxEKoNUJmcC3w3IznKaAa0JUgYpJcTccccdLrehBsjff/99qyWPhfwaYt10dira/2NLZlB3pdeoZPjH9P6IClOwdcXWKFDnNI5os0AqV0CjlDEPk74TrUpe/VcJWPL3Ijp1IAR5dUKBxFqJtGgzFi1axCadnMbT9fNKjZB2ndGivqssg7dwD7SGE9BwofRWn8BERUVh8eLFrFVTQ5A6zeOPP16jLuNtQn4Nsa4qwns/78cLtw5lxdyUVRiMUAnGm3P2Yub4rnj9nhH4dtkx1laJ4zsEKWXEiSwEWzchR7SZoMleD2H7BlQqOkNmzEWY3AI5V6BxC8ouVMvsMIM87OYZRNGig5CzGlEKK+TcGLpEoVCwMZOig54aQFoHTEhIYBqkkyZNYssBnTt3bnICQ7WH5Lj4sq9uXUJqseLQktdx9uxZ/MNmZ0k2tO4WjNBgPGf1SVZOQILnfTvH4Oulx2DiIVS/I0iVoGXscK0KGkkeRC15KyH1M2wxWrUCxqpzkIa39/i1orkK8uzFiI4Mg83GJdcaYuzYsRgxYgR+/vln1kaJoISwxrw3yhyl133xxRfMI/QUqkmkffjTIPq/oaGP6dChA6tLfHzmQJYYEczsO1WMp77YhpgIFd68dwQ6JjXc6Z3jXcgLoZo1a+4WCGf/gHh2JWwlx1ltnE1Q1Xg7nkq7caq9EEnZUc+/E7sVkuzliIrQcpGDRoiLi0O/fv2Yvig15aWm8H/++Scee+wxFvKk0giqEaRSCTJgBHl/P/74I+bNm9csY0hQfSF5lv4kJJJq6kID0b8//A4mi411pQ/2MYkiFdPGdsKUCzoxzU9qtstpXSRn5iM6XM6k9RztiKj0wmIysjo63rWiZZTpAXvaNLe3p2FOzFqFRK2JG8MGMBqNuOzKqzH1ysm46qqrXHYMqWsXqBEvrc2NHj26wW72nnD99ddj06ZN8NcaYsh5iI6F+adm3YR2sRrceUUvBDtk8H/bkI43f9yDKWPSWF2mVs3DdK0JdVqnQdi5N59cKmGza24MvdBHT+mZJ2GvzEG4UM6NYSP07dsXd9xyI66++uomjSFBhoU8SVon9IYxJGi90Z8qQCFpEB2LxrffeA0GdovF9ZfULhINVo6dLWMhVBqk37x3JLqnekfbkeOCv3sicrwPhecEVbxHr5GW7odKFXjNzQMJlUrl1ybwpCz2zjvvtFqWqTuE9K+WFolnTLuCNZq9fFQHhAKU9fjuvP1YujUTz90yBDdP6g6lPKQvg1bBrEmFTmdg3gzH+4iWCo8SaVRiGV+vbSK3YsiQIUhKSoI/oLXIyZMns8mOP9fVQ34kJFf/jR/34LpxnTFuYDJChRXbs/DMl9vQpV0E3n5gFPp1jvH3IQUVkoQRMLafyprSVlZxw+hNyIOQmMvc3t5WfMBrIb1ghdbXfvnlF6+t31Fx/c6dO1kyTnp6epPb33nnnaz5tr9baYW8QSSOLHsDl14yDrdf3hNDengWimnLnCvSMzm7JZszmezb/VP7MD1PjncQ5BrWod2cNgP5ejWstuCTDfQXWlsha8PUEORl2CqyIGQsQrQ92+8DbVuYZBgMBta0l9RiWqqURBmnV1xxBW655RbWwumzzz5r0PObPXs2Vq9eHRBi9h4ZREqzDWYh8Nm/HcJD1/ZF77RohAp0ja7elY3HP9sKrVqOdx8chVF9Ev19WEEFaZMK7cej3KTgIVQvoVarEGE4DFvetlqPi6IdttJTkGb8ihjddkRr7LxVkwfY7XY8//zz6NixI2666SZs3769yRAmrf3V1SClcg1ai6TXUjkFdben/dXl1KlTePnll1miYyDgUdnF8uXLWYfjQDn41oDSiLds24lXv9+N9Fz/Kq/7AzKGt13eEyezy/HN0qMoqQiuDiH+RLRbkFi2wq9JA01Bv21ax2krHhWdy0qLAoIgQrDbYTWWITYmdCa0rX0tmM1mlmxz3XXXsVKMuXPnYuXKlejRowfrWEEhUTKIVEw/YMAA5hlS14pt27ZhypQptSYjdE19+umnuPbaa2semzFjBtvWF9eb17VMSWuRTgTdgpnpD77DyhNe+mZnSGqBUtiUkm2G9UpgdYtrdmUHfa2mr7BVnEV4+RaolEoEGhTSrTDJYDbpEaOV+C3bsLmQkLperwuKWumALHUxm9k14TBeZDCdw5z0f2xsLOtqTwZy+PDhrAmwM2RuVq1axTxDWjN89tln3SrxCEiDSLJn+/fvZ0WYMTHBnYRx5MgRbNl5AC9/sxPFIeolUaLNXVf1Yvqoc1adxKH04NR/9TX2sysRrdBDGkCBFlreLDXKIXScDFGXh5iqbWwQLK6yQykxQRvgmp80jJXpqdmWBVFavg7uLwRBYLW2U6dOxdGjR5kHSbbDoWNKhpLEvsmokgGlPoi+ipi0SrcL6nJMWUMXXXRRUMfm6bQ88tJn6NkhiiWeVOg865oeLMhlEkwanoppF3bC8bNlmLv6JLILg3ct2ReIViOUWb+xJsKBAhm/PFM0pO3GsnU4ZC5jpQrGlKmwlx5BvBCYjadpgC0tr4QxYhAk0d0hZC5GTHUTEY6frye73c4MIRlAshX+LkFqFYNI6ctbtmxhNXyDBw9GMEOnZvfu3dix7zhe+98elFaGpqfo6P93zYWdMX5ICtbvz8WCv06zBrmc5iHN/A2R6sBquFdusMPWsXp9RxRtsFflMgFtlrF5biMi7edYgkQgQT32KuMnQjSWQVGygyWGyXzoepN3408xao6fpdvI4pMhJMHX7OxsBDP0WalY9URWOV6+YyjiowI7bNSaVOot+H7FcTz5+TZEauX48OExzGtU8KL+ZiGxB17EQRBt1d4hu/alNd0k6HcgiRvItC79Pcuvi8FkgSRvAyKqdiEyTOkzY+jwfGhdjRM8NOvqIe9w4MCBbD2RLopghi78D16+H6OG9MXLdwxDcqwGoUxeiR7v/3wAb83Zy2o2P3hoDC4ckMwExDnuIyDwMk3VSinseVtcptlLlBGwdpqJfJ0CJrN/jLkj+9VhlEks3WYxIVotQqmQ+y6rVWdCoSkC+riLYGkbybgcX3S72LdvH6qqqpgga0u6V7cF6DTd8cRHuGhQO7z+wx6cza9CqENf+ag+SZh5aVeWeLNwYzq2H8nnGalutCFSZc5jCQWBBhmcKqMdpvC+kMZ0ZzWU9bYpPQF5+SFIRRMksEGhUEEul7bqMemNFpilUbBrO0FVvhtarQZ6gx7t27VDWZn7qjUtpVxvg7XjtTXjnWi3wV6RgfCKHT7LluQE0Bqi885otrRu3Tqmg0fdjkOBEydOYOeeA0zu7XSOZ92kgxVKvLlkcAquGtMRFqsdizZlYOOBXNhsvFbDFXZTBbR5S1lxuS9gBsVggkIhh0pZXSzdFJQBWFqhg7X9FAiKsEaNu73sDOQVhyAX9cwoiJBC5iX7aDAaUaXuA2l8/+r/986Gtv/t6BueiRtmXI358+e7JQ3mLUpMWiB1Yr3HOxrWsE7xnBA2iATNzkj/jrzE6OjQKIi95r63MP3iLnj7p304lum72WmgI5UKuKBfEuu7qJJL8ceWDPy5Jwdmi/8lmQIJuy4P4aUbfBLmo593odAZ0vgBsFedg6T8KGSWUsREKJsshiajWFRphdhxKgSJ1C3hAdFYCru+EBJjLlSWAkSEa5styUXKWPrI4cxTJWi9mk28RqfBaLFBV5KDTz75xGciAjTBK5amQZZQP5nQVngQEaajPgvdcgLUIBInT55kCi/jxo0L6lIMZ+jzbtuxi62nHThdjFCAZufqQbOa3I6iScN6JrCkm9gIFZZty8SqndksrNpWoU739qJ9gCCDENWDrak1F1v5GSRZD7X6QE4GjdREtpX1qbWkIdosiMhb6NZvlfaRbwyHtP0lHr8/dZkQctdDJVZAo/HcG7baAZNRD1GTjMnTbsUVozujqNyI3zemY+exAgjpCxHloyX9mslB6uVk+WE7/iOkPW+FIDv/uWy5mxEvLfDNAXEC1yDSLkh+hy4aUicI9vVEB+Nvfg33TOmN2b8dxK5jhf4+nICkf5dYTB3bCWlJYcwoLt92tk2VazBDWLALGlMWIiLCajwXo10OszIFkEdAUGghyLQQVNEu19zqYs9agyStoVULkinUqJfEA0ljIMjq1zvactYjWV3h1jGYzVZUWRWwJ42FRNUMQY6sVYhRule7SgkzdScKEyZMQI+BF+L3TZm1Jp/2nHWIEPNZ70nyzFqrbRAlEVUiFkK7i5inbDv6LVQKKcydb6490aD3z1yCWE3bnfgFMz4ziASlH5Oga0pKCnr1Cv4u9A5yc3OxYdNWfPXHEWw+mOfvwwlYenSIwtUXpKFv5xjsPFqItbuzcSSjFIGMLXcTIqzZTHnDlTdHgzdNAqvX6IzMuJilkbBpuwAK+p1QqFBgXgQzSlIVC1uGl29uVek2o9GMSu0ASGN71nqc1RZWZEES0RGiuRLavCUsY9wd6HPSRKBKmgRJ4gjYDcUQddmQxPSGRBnV6GttedsRg3NuKfNQXWFMXCJUChlbY22X0h6n9Sk4me867EpJLbai/YiynoGihQuX9BlZ1rxECqlUxo6Buj6UKftAFt+3+rOUnkCkbi/KbeGQdry8/mfN34FY5LAm3Jy2ZxC9VlFKGnfkHW7cuJG9IRnGUCA5ORnvzduHf14/gK3L/LE509+HFJCQys3bc/exshVqyPyPGf1Rpbdg7e4cbNh/jtU5BhpKYzaU2obX2uhxx3OUrAK6wQKL+SgU9mqRbJpvUvNTujmUO1SalsX5KHHJZDJCrVKy2kDaJ/3+6KbXG1Gp6uHCGIpA9lqE2YugU4RDoo5Flbo7BONJt7Ij6djJeCrs5TDl/M4mCYJMQHn2aVg6zWzUMxbCOsBalgmpG+trarUGE8ZfDKMsFmu2HsP+M7EQhIbXIMljE1SxQNUZNBc6NSazCXq7Fvb48ewB0VwB0VIOSXQHyLQJLHnIlr8d4eazkMhlkFpMzBjXXVsVpVoopXL2fXPaHl7zEB1QwT6Ju44dO5a9JlSg5KKFf6zE/tPF+O+SozzD0o3MVFpnvHRoCrq2j8TOowXMOAaS12gtPIAkIT0g+rSRGkpFZSXMUMMS1hOSqK4QpBQmtANWI0SrnoV3BUECSVj9yaitcB+izKegVMqRb02ANGlU9eMVWVAVb0RURFiz1jT1egNMkkhYVYmAOgkSTTwEaW3vlwxHWM58t1Vu6LMWlRlhiRsJaUSqW4LpkZU7oGhm6Qd5hfrYcZD8LUTgCiFrOcKlOsj+9kKrdAaYOlwHQVpbAN1WkYGoyl2tWobCaQMhU2eOHz9ek2QTaFJPrQmFVz7/bgHMFhve/3l/QHo9gYjDa7xwYHJAeY22ikxEVu5s9kDrTcgLLJD3hzSqc7NeL8v4BRGaag+tXG+BLW1GreQX5K5DmETPDGZzoPCx3miCzWKGFXKY1GmQRPeuKdkg6bcYSZFHguYmkxlVQhzrJdkYlNWqyl8JpVLNlGo8TWEgg2hImcYaOrtCtJogz1qICK2y5rvIFztBljCg/rYWHdQ5i6DRBI5OLacVpdvcgWoSqQSDPMVAmF37CgopPXz3TFTozHjtruFIiQ+8wutAhFps/bjqBB58fyMWrDuDIT3i8Nk/L8STNwzEuIHJ0Kr9pBWpP1fjEfgbCsEJyuZFXGjO6ywVJxPNNRJtBBktoeOVqAgfiooqI6zNqB8l71Ipl7FQKhneJFke1OcWQ8z8A6LNjPCOo9G+Qyekpqa6v25JCTaSpifU5JWaUq9DmaovSk1K6A3kKQtuG3KTXQbU8Wod0LHLzv6GqPDzx1xaZYE0vp/L7QW5FlbvrURxfEyreIgEJRjQeiL1x+rfv7qoNlSgU3rbYx9i0ohUfDT/YMiUZXiTxGg1RvROxIjeCeiYFM5aT20/UoBdxwp85jlKMhcjSh0Y2lyUtFYRN6F5WZ50TWavRrxKX7OOWRw/1fV2NjPsxQeh1J2AWlldyE8JQ1Si4U6iCG1H17/BZIZcKmERohtvvx8D+3RFZl4lth3IwKal30AKW5OTZTJWFHWh7u233XYbHpu9ma19NoXdUAR53hrERJxPhmLrhCYTLDYRAmyQkD4rJdHYlKyUwrl8otb5oIlD9lpESErZZ6uq0sGYMB6SsOQG39+Wsw6JyvKQcgbaAn4LmTqHItavX4/evXuzizrUyMrKwpZtO1mT3ZU7svx9OG0WElUf3qvaOHZuF4GjmaXMONK6Y2uWcEgzfkWkJjCaFrJ6QMVASCOa9zuirNCI4jUs8YY6mh8VRze6fbUkWbUCDGXISgp2IFxpg1QmbdAw0toflSeMGnMhRgzogqKck0zJqsvAi7E7S47CMmP1vs1VkGYtQ2SY3O3wpqOP3scff4w73t7c5PZk2GkSECGr1lrWmwVY48cwQ8aGPHreZoCgCGdC5u6IKdA+6fWCpPGwMmWiRhkPsgkBJ3Dwu0EkioqKWI1iKDQVdkVJSQn+WL6GDeD/W3EcNjuXM2sJsRFKDO+VwLxHSsY5kVWG3ceLcCi9mOnLerMUTTi7FNGqwKiZJG+jWDME0oi0Zu/DWngQqqrDMEpjIOtQX36sMchTspeeBMylkOmzEKGR1VsPTEpKwrPPPY9T2eVM05ZuhRkHIAlPYYan1ucxVUBybjXClSLkHoSly3UWWNOmuxUSpaHNXnwUkMqrk5B8VB9NHmpY4RqoVKGTP9EWCAiDSJw5c4ZpgFKSjbvrB8EEzWz/++NvKK8y46P5B6Brw4otgUR0eLVxHNA1Fr06RrPuB0fSS3E4vYSFWGltsiXY8nYgSZ7nM2mwxii0xkGSfEGLBnUyQrFla1j4tTL6QkjC2jVrP0mRMiRa9kKwm9mYQDeC1g/LLFqUSzuwTNem5N6YkS3YCa3xDNQqVYPeos1evYZKXqvVaoGp/dRaSkHuKCi5q7LkDWhIlWXMZ23SOIFDwBhEegvqjEH7ueCCCwKup5ovoB/0qx98j6RYDd79aV+LB2tObaQSgYVT+3SKYcX/3VMj2VojM45nStjf4grPGjzbs/9CnMK/WrWUsVyOOEjajWu2MbRVZkNd+BcLu4aFhTFvk67H8pjxbq3JkVfuOK/0l5pFnzhbjkOn87Bz7S/QlRfUhFEdQgVF1lhIU8a5dXx2XQHk+X8hTKuAVGh4Ulml7QdpTM96pQ6BiLVgD2JsGZA19IE4oWsQCfqRbN68mbW8oQbDoSLv5gyd6ruf+hjjh7bHf/84ii2HuLJNa9Y59kiNQp9O0WwgJ2NZUGrEqZxyZORWIj23giV5NOStk/cip1KFv1PtfQX9LsxWGywmEyIiwpFnjoOQOLJFvxfRakRY7u/1SqDKdFbYO02v9ZhWJUNacjjSkiLY364pkUiIVrHOLg7PmxpmkziAY9+ys4sQrlXUWlssM0pg73C1B8dogCJrIcK1riNIZjLgSIA05SK0Bei8qLJ/hbaFIgycIDWIBKlqUJJNp06dQqZdVENyb2vXbWL6p98tPwYT7wbR6qiVMvTsEMUMY/WAH474KDUKSg3MOGbkVdYYyrIqM2xlpxGp2wOF3Lcp9JSdXaoaUL3uJvde2Q5lPiapKmqFfy1WG7qOu4+dj05/G8GEaDUKyww15+PMuQocO1sGg6nhMD/JwMmylyA6QsO8T/ISS6pssKdd49kxFh9DhOFAvY4RVqsNxZYIyDpMaFMTaVk6Tah42DRQCDiD6FB0IU+xb9++IZl56oDSyT/66mcWfqLSjKwC3nDY14Sp5TXGkRmE5AgmEkB1pH+uWYU1q5b79HhoKaFfv37481wnr+2TajjJ8McpdSg+vqZmvY8YMWIELr38Wmb80nMra4xglcHzshZr0WEk4RQziCU6EWLKxEb7KLqChiIxazWiZJWQyWpn7NC6Z3n4SEgj29CYkbkUsRpLq4mOcwJYy9RdoqKi2A+RMk+prqddu+Yt7Ld1KLnoyVm3MFWfuEg1flh5nCm0cHwHDfy0vkg3B0qFFKnRAsTs7T43hpV6M9aeTaYOU01Cx0nhTY1KhjCVHFq1nJWnJESpER+tZkYwIUoFjUrO9ltQqsP8vJ21DCLVCS87HgaBCZG3DIkxH3aFnZVHWLXtIPPQGBLM+0sdj4rMJYhXCrW8WSoX0ZRuhoFKQLQJboUsqbRCaEGbrpZi1aRCENK5QWxD+EVSIS4uDkOGDMHu3buZUYyPj0coQqEl6gxy07NzMevafmyt66s/jrbpvoFtHUNpLjKPr0GYRu2xBFhLoEliSs8xsOF8iE0iEaBWkuGT/238HH9lTKKM0BstbB2UjHtxuREFZQamB1tQeo6FPulmMNnYoKzIOF5rjY5K1EUL1eJ5w2hUrymywn9V83/PVBMopk5GYeZiaGRWSOUKyCQCqFqJvg7RUAS4MIjMu9TlAmVHoTBms+MgI2pIne7HJJzq4n9eoN92kPmzSwQp2OzYsYPVKJLUW6iye+GrTEXjjU9+xJv3jsTHCw7gVE6Fvw8rJBGKdzeY2NFaUKuh8ePHY+cZaiF1vu6RAm05hVZm9KqM1hrjRxMmncECvcnqdt0leV92yfnPxTK9ScLNVl0s3xxYnV95BmRl+yG3lrOWV9QiShLZdOZqo8cqVUDsdA2qTGUQdQUQDLkQZUpIUgZC+rfeKGs/ZTUyb1E0lkGSvwlRKhvkKjnsigi2Fqsz+K+GlM6NsuoIrNrAz4jlnMevonsdOnRg6d8UPqVyjPDw2sW7oQRlAL702B2YMetdvHDbUMz/6zSWbs30aqE5p3GYF2Wjbhu+NYiUbEYTxIXf/cm8OVc1dA5aUktnVpBnVcCMY55eA4T3gUTr+ZKFaNHDXnIISv1phGuUEDQCKqtkqNLpYFB1gkTW8sxK6tohkEwdk6qr3cqKkBZug0ZSBWuRlXnSKg3VMUrZZ6MOHDpFe0jSRvjFO7QbSyDJ34xIbgzbHH5Xoe3SpQtbMN+yZQtrGUXFvaEK/Zjnf/oESktLUVZlYiHUzxceblMd5ts0lkrUyeXwCVZ747FZhxF0NozNQZI0GoWF+yHR5UDS4eImC+cdkwRb8VEIpkKM7BmNvQeOQSZY2OTVpj2v/ykKMpg7TIPURwZIFBRQKhRMpJS8wfJKHRQyCQyIhJh8BaTNFEJv1rGw/onlsFfmQKrPRLhQAbVWDS5l2vbwu0EkevbsyYzi1q1bmacYSi2jXEHh49tvvA779+9nZQI/rDiBjQdy/X1YwY88DFY/iNJIGmmA60xLlVZowiVNGAiAbm5iMyGsai+bqJ4+XfJ3GYG8nnqPSN6ZD70xe2RvFBpyIES3h6BJoDgr9KZySFRRbK2x1d/fWAqx/ASgToGkdB+0QiXCw8JgVdH6v5KvG7ZRAsIg0g+V1hOpXRQZxTFjxrBkm1CGPv/QoUNZFq7NJmJM/yR8veRojUAyx/tQmM6k7QKp/hRUao1b3R28AXW+P3LkCNoyKpkdFWWnIY3q4pP3k0SkAHRzQlBFtfr7khcoK9oKtdSCMK0WxspMWGGFWqVmnqq70LaUkcsJLAJGjp2MImWeUmYYJdoEgn5kIEAG8cbrp7EMwrfvH4XJIyiV299HFbxIEkegKmxgveLw1r72A1bjVyKH0a5g0mkms6XBQVyjVgHG4G1zxkLH+duhLd2AKK2Uaa/SGEUT1+Ys81BEjDJQOYFFQH0jdIEMHz6cXWih1ly4MehH9/ErD+Kdn/Zh4vAOeOXOYWifwJsPtxpOzXNbG6vVjnKrlqk3BSJsnbHzdBhSZ6AyaizOWduj2KhGud6GCr0VVbpqTV6z2QJREsQZlWdXIMqWxQxhXZpTeE9jXWVlpZcOjuMtAs5npxnoyJEjsWnTJraGNnDgwDYl19Ta5Rk0Wbj76U/w77tHYMmWTCzccKZZHc45DSNYSmGX+MYoGkwmSHtc6pevg/r72Yr/DtVK5ZBGdgWo5MFQAEGdUOt3J0jlEMKSaxrjOuI31IHCUJUDVJ6BJCoVwYjdVAYtSiGXec+LJ03ngoICpgDmSaiVE2IGkaCw6ahRo5hRPHz4MPr06cONolP92Dfv/INJ4OUU/oGRvRPw5eIjTHCZ46X6MfM52GS+SewyS6OgEXwfqLFVnIWiaDMi1UqolHJW/lRYYoZEfw5aoRy6mAshhLd3y4OURnQA6BakiAW7oVB6ZgzpfDaWB0ETW0oeJFnMUM6sDzQCKmTqDK2pkFEk/bmDBw9y+SMXEniP3Hsj1u07h2dvHoLbL+8BlSL02mp5G9FYAoWk9T1uWiKnejl7eI9WKgOohL0qB9aiI7Dl72C1cayQ3lQOe9ZahJdvQWSYirUnIg+FQniqigOIUhqrJ59toMWSL7CVZyAchfWaITcFnVNad20sukWtuKgGlYwj6WvSd0DfEY+I+Y+ANYiOC4bKMCi0sHfvXr6mWAf6Af0y+wlcMXkiUuLD8N6Do3FB/ySedNMSbCaYbVKU6wGd3gCrzY5KnQEVOhOMRhOTEGsJtNxE4uHFss4wtJsCabR3szJFcxWEs39Am7cE4WVbEGM5jhgxB+GFq6DImAdt/jJEyyugqlPaRAMxhe+ou4fOHgEJlTK4SUvrIwMN0W5lSji28nQoi7fUO1fuTugpmuOsHetqeWjSpEmYNWsWm/RnZmbiu+++YxExMpSh2DfW3/i820VzO0NQ4T69N2Wi8uys+tDXOOHWf2PmpV1RrrPgx5UncDSTVFc4zUW06NigKI2itTUl09EU8jYhRkuZoc3bZ5XOAGO7K2t1fCe90m+fuQS3v+FaqabWMdHP1aqHaK6AaKoAzGWQWCtgU8Sx5zW6o9Bo1M0uGWENicOGQhqZhmCHpN9EUynshhImPEDyc1LRAIloY8aIziNNiFpSfkP7KS4uZuFTmuDXHW6ptGrRokX1XkdLIvPmzcMXX3yBrKws9lq+1hiE3S6aA822yFOkGkUqyRg2bBifPdWBwixr/vc8C7+cPn2atTE6kl6COatPIre4OhOQ4xnUj1AW1/f8/5p42KL7QWo77DJa4ShJoOdcPU8epiFmDKRudGBgA6elCnZjMTPEUlMRZPZKSEUTJILA1p3o/US5CJvEBpnMWP2+LdRhpQFcNOQDQWoQq/VX0yEv3QOFYIJUJodWo4ZFZoXAlvzOe4Ost2ML8/no90jLG+Tx0WBMv1NaO6TvjwxcQ3KV9Jr77ruP3dLT0/Hiiy9i+fLlbCzk2fchGjJ1hi4iKtin+h3SPuWzpYYHNGq+fM3VV6K4woS37hvJ1hep7yKn5UjC2zMBa2fIdlEUI1evwTlrB+RbYlFi0qLUpECZUYpyo4Byg4hKVc8m+/lRyNOauwVyCm/mLUGieT/ayXMRqzIgUiNjxeCOJAz6DTjqdem+NwZKWiuLtGRAzF6LgA9r6vJgLdgLW94WiDZTfcOnL4AtbyvsOesgWg2wlaVDmrEAsca9rJaQziPVm9K5a+1EdpVKxYwcJQx27doVt912GwYNGoRHH320yddSSc4PP/zAxj0Kp9LaJA+phnDI1Bm6eLdv385+/FSeEeqKNk1BtU5vf/4zeneMwu8bM7B8+1lYrLy+syXYCvYgynIGcrmUrSlW6sywJF8Kibp5XR4cIdNb7nsKMnMhS8n3tzBFpc4IS6eZCLQQpz1/OxSWQshEE1QqJZsoM+Nnt6PAHA2lJR92iQJSmwFqpaxGBpLGMPKuAmG5hSat1N3mzTffZIbR0yQaGoeLiopYOJXWHB3eJqflIdM2ZxAJGix27tzJZkmUiRrq2qfuUFhYiDm/rmBd4uetPYUth/J4J41mQt6G9fhcltXLwpSdZnjcHd4ZpViBWy6MYmtJgTKw0W9L32FmQGV7UoJLdGRYg5MFMjT+nkh4Ag295P3NnTsXHTs2HjloCEo47N27Nx8DQ9kgEn4JIMwAAB7/SURBVDQQUYNh8oConyKFJDiNQ1/1pNtex/+N74rSKhN+XHUCxzLL+GnzEDFrFeJUejYAUyZqpS0cQodJTAvVrWvXWAp7xRlIjYWQ2yqglIlQq1UBtTZEYWFjx5ludcRobexV56AtWe9SJaatQ9cQhUCffPJJPPbYY83KjaBQKr32xIkTzAumSQGF8MkbpgkWRdFofAyk68sfBLVBJOgL3rdvH0pKSphR5AWu7kE/kjNnzmD3voPIyK3A7xvTceB0SSt/W8EBpeNrC1YxA+aAMhHLzGqI4VRCIUIU7ZCo4yGo42qFw0jVxV6wA1pTOuuMEMjeDA2oeuo2L/HvkoQo2iBLX4DIsOCvi4yNjcWPP/7IGh00B1L2WrhwIUtApBsZQcq5IOH4Z599liUk0hgZyNddaxL0BpGgwz9w4ADy8/OZUaT0ZI77ahpkGHfsPoDCciMzjLuOFfBQaoPXmh1CxiJEa+qXXdjsDk3L6iesFhMsNhFGRTIQRhmbIuTFOxGmlkLmaZW3HyhEp79bRfkXEtROlOaFhHdDkycKVffo0QOvvPIKLrnkEq8W6Z88eRLPPPMM/vrrr5As4zgXCgaRoI9AsyCq1yGjGIjHGMjQD2PGrPdw5Zg0GE1W/L4pA1sO5sHW0ir0IMNWsAtR1gzIZe6HtegU2qw22CFCIZMFvGgClXLQgLyhoFtAKKbIMn5BRAhmSJMXl5iYiNmzZ7PG6d6ExsmpU6eyvIJQ8hbPhYpBJOhjUAydavCo2DUhwX2lDU41NAunH8vytVuYpNcfmzPw195zfslKJfWTljbE9XaoVFOwqrrNURBDBrzUrIGQOikgPHJVxlyWdRvKURxKvLn//vuZEaPSDW+Qk5PDyj5CIUtfJpOxSX9IGUQHZ8+eZSFUqtcJ1JY6gQ5dEnTxLFjyF6LCFFi29SxW78pqUkUlWKG1P2nGr4jUygPew/MGBoMRJoShV+d4HDL0gaBwXTzuizILdfavPDfg7+Sbqqoq9OzZE3fccQeuueYaxMTEtOj8LliwAA888EDQG0WJRIKPPvoI1157begZRIKkkmgBOSUlBX379g2I2qO2CF0alNb9/S8rkBKvxaodWVi1MwtlVWaEAuSh2EtPQVG+F1qVnHnNoQT9booq7RDTpvotfGrLWY9oSXHInXt3jOP48eMxf/78Fu3r119/ZWo4JBgQrFBi0bJly1iv3ZA0iI60cSrgpzRkCqEG+yyoNaFLhCYZs79bhL6dYrDnRCHW7s7BwTPFQZmAI1r0sJUeg1J3CmqFBAq5/0sP/G4YDXLYZJEQ5VEQlBEQJDIIxfsgt1UiMS4C+UUVECUy2AQ1bPJwiPJotp1EkwihBZ0zSJGm2jtXhIR37gnR0dE4dOhQi/ezYcMGXHfddayWMRiTl8gWUI4JOUghaxAd8fddu3axOh9StQnltQhvXlwPvvgfXDQoha0t/rknG+v2ngsKr5GKv6VlB6BGFRQK6lbg7yMKPMNI3gmtxxiMJigVro0UbeeogaMJabbmkhYZRWvBfsTYTnMv0YXn89NPP+HSS1veYJrG+GXLljHlG6rvJo+RogL0PVLiDUXaaJvs7OyaNlVtJSGHjpPyS9yxYUFtEAma8VCTYfoiyWWmWh+Od85rXl4e/vvTcvROa9teI5P+KtiOcHMmVMrgDR35AzKgBZUC0PEKt4UL6iJJX8C0Rzn1oUnHL7/8gnbt2rGEG9bCq4XhT7PZzEoz5syZg169eqFfv35s/yREnpuby/ILyHjSjWod3TAhfr3+brzxRlZuwg2iE6QYT4aRil47dAje7t7+8hqpl9vOvYdgtlR7jZSdWt4GvEZmDHP+QqRQzHoBcryPxWpDGRIgSRxFmTKAnerfRECQAoIMIDUcQepyndJWcZY1M25OT8JQgAZ8ioTRjTwhup7pMTJUVIRPy0Wvv/56sxIMMzMzWZ9G8g7T0up3P6HnSIuVjGQglOg0ZNyp/pK8Wm4Q60B1N6SBSrqBpP8XqF9iMHmNf+4hr7EE9gCtabTlbkaCrDCgZ7nBgJkGbbOZ/eYcNzrnjhtBA7nZLoVVGgabIh5QREFVthPhLWxpFcrQeSYh8ffeew+33HKLR3XdmZmZrNVefHx8o9tS2Jb2T04HFfwHSiiVricqVfn444/djnIGfci0LqR9Ssk25P5Ts2FHDztO63iN23cfhEwmwa5jhdh+JB+H0ktgs4kBU06hzPwZYXzADThoMDOaLVAp5AEzwLZl6BxSI4T//e9/DfZgdIRgHRrRlHfhifIXSWguWbKErUNS6RuNrWQ3CMqK9fX1Q2UppPNK3jI3iE240eQp0l/60mnhn9M60HyLfii07rBz7xEoFVLsPl5tHA+cLobVj8bRVnwMUcZDrI0ThxPskJGgG2Wmukow1Ov1zFmgNUjyDL1VikGRI5Kio3ZV5K2SoWztMg8KkZJhdwi0cIPoxpdEsxgK8VGyTUuLXDnuGcfS0lI8+eYPGNE7EWFqGXafKGLGcf+pYp8q4og2C+SZCxARFtzKMxxOXaN36tSpesmFVFa1c+dOJCUlsTyL1qrdpjGAvDYSFyCvsTXKPBxJQbT26YAbRDe/HIp7U7ycusx36xYY+o2hAJ17CsGThNSmHQeYIs6eE0XYdjgf+04VseSc1uT+ixVMqYOH4zihBBkgSoR54403an6HlHRCZQm9e/dmyTe+GAPpd3fXXXdh5cqVXn+/V199lRlcZ7hB9ADyWsi9pqyswYMH896KPoZ+lHTBPvzKNxjZOxFxUSqcOFvG1hsPpZfizLkKrybl2E0VCC9YxpuqckLWKJIxeu6557Bnzx7mNVI2apSXdFI9+d2TIACFab2V1Ea5C6THXHedlBtED6G0ZeonVlRUxIwiFwf3D/TDoFAKfQ9fzVuN3mnRkEolOJpRygzk4fQSZBVUtajW0Vp8FO2EUyHX/obDcUCqNC+88AKrMRwwYIDflLzWrVuH6dOns+PxhlGk9lkUkqVonzPcIDYD+kJIHJzqazp37syEdLkOamCEVslA/vj7evTqGAWTxY4jzHusvuWXGDzaJzeIHA5YYs2kSZPwwQcf+DXb/tChQ+w4vAElSB47dqze5+EGsQXQySPJNzqpFEqgUConcMI9FOJ++JXv0KdTDLqnRqFcZ8Lh9FKczilHRl4lMvMqmdFsCFvJMSTjJPcQOSEPTfgpwWbx4sV+FSyh96ZkGMo+bcm6PnU5WrVqVb3HuUFsIfSl0MyFkj4GDhzIpIs4gQeFPamsgzzI+cu3Ii0pHBFaBXKLdMw4pudWIiO3Eul5FdAZrDUGkUKmFCbncEIdMopUDvHFF1+wtlL+gN6f8jhWr16Nzz//vNmGkdZBSZGsLtwgegkyiPv27UP79u1ZGi/V8XACO8RK6wgUZqXb/xauR1pSBBKi1SgsMzADmZ5+BjvWzofB4FmolcMJ9snlZZddhmnTpqGsrIzdaLJJERka+yg7ldb6Wht6v3vuuQfr1693e22TxmWKHpFBJf3VunCD6EUoc4lCqHTCKYTamNIDJzChcAwZSKq3euf92di0frXP1TM4nEBH+veE31EK4ehqQV4kjX+PPPIIZs2a5ZNlpLVr1+LRRx9lTgm9X2M1i/Qc1VGS4+IKbhC9DJ3wo0ePsrpFKlxNTU3lNYttDBIhnjx5Mpv18vpDDsdzJBIJW2qgso0nnniiRpqtNTl+/DjGjRvXYHSOHp85cybeeuutBvfhrkHkreQ9uBBowZYkjShGTfFuintz2gaUik3ePTeGHE7LHAOpVMo0Ubt27Yp333231ZsK9+jRAwsXLmRRnrrQsdC64VNPPeWV9/JI3JsKHtu6uLc3oDUqSrihWDfFq6kTM1e4CTzoh0oZZ7RIT6U0vlj/4HBCjZSUFGYgWzvxkNYUqXaSQqgk2E1RHqoZ/+abb5oUUyEbRlE9r3S7IANAkj6k+8nhcDgcTluDdFppyasx4+mWQXQYRVcuK4fD4XA4gQ6VcjTlSbptEDkcDofDCWZ4Ug2Hw+FwONwgcjgcDodTDfcQORwOh8PhBpHD4XA4nGq4h8jhcDgcDjeIHA6Hw+FUwz1EDofD4XC4QeRwOBwOpxruIXI4HA6Hww0ih8PhcDjVcA+Rw+FwOBxuEDkcDofDqYZ7iBwOh8PhcIPI4XA4HE413EPkcDgcDocbRA6Hw+FwquEeIofD4XA43CByOBwOh1MN9xA5HA6Hw+EGkcPhcDicariHyOFwOBwON4gcDofD4VTDPUQOh8PhcADIAu0sGI1GmM1mfx8Gh8PhcFoZhUIBlUqFQEEWaMZQHR4LWPX+PhQOh8PhtDJJSUlIT08PGKMYUAaReYZWPZS9bwVkKkD4+wlBAghC/fto4PGG7kuE6v/Z427eh/PjdfbZwGuEv7djfx0vqfO44z49f/5x+rf+a1u2z/qP1zyHxl7jdApqvd/fp1LivE3d+64+T53P6eJ+vXPR2H5d3Wdfcf3HJbU+w/ljYpeD0/auXlvrccf5r/NauHi85gqt9xlcP+5Yu6i9Td39nn/t+e3pCnU+VqfP6bwfp898fhuh/n5rrjnX+3U+bnr+/H3X34Pj9S4/J5p6vPYx1LqWnN673ndaazs0/njNo39ffzUXClw+LjT38ZrdOh23m487HZJ7+zl/GOeHyjrbCw3sX6jzuPN34fjP6at18b24f24qKyvQrVMqG/e5QWwMqQKCVOGZsWv0vqT5BtHl/dYyiO691msGsdHXoNH7bhvEFt73dL8NGURXRtpbBtHVe7XEIEo8NYi1jrX+/ptjEBsytLX327RBFFrJIErcNohNGUrBu4avFQxiSx73pkEUvGwQnfcTKPCkGg6Hw+FwuEHkcDgcDqca7iFyOBwOh8MNIofD4XA41XAPkcPhcDgcbhA5HA6Hw6mGe4gcDofD4XCDyOFwOBxONdxD5HA4HA6HG0QOh8PhcKrhHiKHw+FwONwgcjgcDodTDfcQORwOh8PhBpHD4XA4nGq4h8jhcDgcTqA1CK7BZobIeg62zQbBdRq1NfC4yyZkTo3Nzr9WDMAGwSJvEMwbBPMGwbxBsNCyBsGBRkAZRFEUERYWhqoj3/v7UDgcDofTytB4T+N+oBBQBpE8kKqqKmRlZSEiIsLfhxPwVFRUIDU1lZ8vfr749RUA8N9j885XTZQqAAgog+iAjCE3iPx88esrMOC/R36+QgWeVMPhcDgcDjeIHA6Hw+EEoIeoVCrx0ksvsb8cfr749eVf+O+Rn69Qu74EMZBSfDgcDofD8RMB5SFyOBwOh+MvuEHkcDgcDocbRA6Hw+FwquEeIofD4XA4/jCIn332GTp16gSVSoUhQ4Zg48aNjW6/fv16th1t37lzZ3zxxRcIJTw5X7/99hsmTJiA+Ph4Vkw9atQorFy5EqGEp9eXg82bN0Mmk2HgwIEIJTw9XyaTCc899xw6duzIsgO7dOmCb775BqGCp+drzpw5GDBgADQaDZKTk3H77bejuLgYocCGDRtw1VVXoV27dkyN5vfff2/yNX4f70UfMm/ePFEul4tfffWVeOTIEfGRRx4RtVqtmJmZ6XL7M2fOiBqNhm1H29Pr6PULFiwQQwFPzxc9/9Zbb4k7duwQT5w4IT7zzDPs9Xv27BFDAU/Pl4OysjKxc+fO4sSJE8UBAwaIoUJzzteUKVPEESNGiKtXrxbT09PF7du3i5s3bxZDAU/P18aNG0WJRCJ+9NFHbCyj//v06SNOnTpVDAWWLVsmPvfcc+Kvv/5KlQziwoULG90+EMZ7nxrE4cOHi/fdd1+tx3r27Ck+/fTTLrd/8skn2fPO3HvvveLIkSPFUMDT8+WK3r17i//617/EUKC55+v6668Xn3/+efGll14KKYPo6flavny5GBkZKRYXF4uhiKfn65133mETLWc+/vhjsX379mKoATcMYiCM9z4LmZrNZuzevRsTJ06s9Tj9v2XLFpev2bp1a73tJ02ahF27dsFisSCYac75qovdbkdlZSViYmIQ7DT3fH377bc4ffo0KxAOJZpzvhYvXoyhQ4fi7bffRkpKCrp3747HH38cBoMBwU5zztfo0aORnZ2NZcuWsY4O+fn5WLBgAa644gofHXXbYmsAjPc+E/cuKiqCzWZDYmJircfp/7y8PJevocddbW+1Wtn+KCYfrDTnfNXlvffeg06nw4wZMxDsNOd8nTx5Ek8//TRbB6L1w1CiOefrzJkz2LRpE1vfWbhwIdvHAw88gJKSkqBfR2zO+SKDSGuI119/PYxGIxu3pkyZgk8++cRHR922yAuA8d7nSTV1W33QzKmx9h+utnf1eLDi6fly8NNPP+Hll1/Gzz//jISEBIQK7p4vGtxuuOEG/Otf/2KeTqjiyfVFEQd6jgb54cOH4/LLL8f777+P7777LiS8RE/P15EjR/Dwww/jxRdfZN7lihUrkJ6ejvvuu89HR9v2EPw83vtsWhwXFwepVFpvNlVQUFBvVuAgKSnJ5fY0m4+NjUUw05zz5YCM4J133on58+fj0ksvRSjg6fmiUDKFYvbu3YtZs2bVDPj0A6Tra9WqVbjkkksQrDTn+qIZOoVKIyMjax7r1asXO2cUGuzWrRuCleacrzfeeANjxozBE088wf7v378/tFotxo4di9deey2oI1zNIRDGe595iAqFgqXTrl69utbj9D+FFlxBZQN1t6eBitYx5HI5gpnmnC+HZ3jbbbdh7ty5IbVW4en5orKUgwcPYt++fTU3mrn36NGD3R8xYgSCmeZcXzS4nzt3jjXxdnDixAlIJBK0b98ewUxzzpder2fnxhkyqgSXkA7Q8d4factff/01S6v9xz/+wdKWMzIy2POUrXXzzTfXS8N99NFH2fb0ulAsu3D3fM2dO1eUyWTip59+Kubm5tbcqKwgFPD0fNUl1LJMPT1flZWVLEPyuuuuEw8fPiyuX79e7Natm3jXXXeJoYCn5+vbb79lv8fPPvtMPH36tLhp0yZx6NChLFs1FKisrBT37t3LbmRq3n//fXbfUaYSiOO9Tw0iQYN1x44dRYVCIQ4ePJj9qBzceuut4rhx42ptv27dOnHQoEFs+7S0NPHzzz8XQwlPzhfdpwuv7o22CxU8vb5C2SA253wdPXpUvPTSS0W1Ws2M4z//+U9Rr9eLoYKn54vKLKj0ic5XcnKyeOONN4rZ2dliKPDXX381Oh4F4njP2z9xOBwOh8O1TDkcDofDqYaLe3M4HA6Hww0ih8PhcDjVcA+Rw+FwOBxuEDkcDofDqYZ7iBwOh8PhcIPI4XA4HE413EPkuGT79u1M+o20K6kzOukMkqDzvffei8LCQp+fNRIqJ4FfupGYtK8hGS4SsiaZrqioKKjVanTt2pUJhK9btw7BCp13urnT7by50PdJ7/Hhhx/We+6iiy5i33laWlqrvT+H4yC0et5w3GLt2rWsDxl1hHBAvdzotnPnTjz00EOIj48PmbOZlZWFyy67jHUvcIb6KNKN9D2D1ShSNxDi1ltvxdSpU1vNIK5fvx4dO3bEP/7xj1Z5Dw7HHbiHyKnHu+++y4whdTWg5qfUy42MwtKlS5lHRB5jqEAdMK655poaY0gNTA8cOACTyYSzZ8/i448/Zt4zpxq6brzZzJUmGiQxmZGRwU8xp/XxqVAcp03QvXt3pjnYs2dP0W63N7n9mjVrxIkTJ4pRUVFMg5Be/+qrr4pms7lmGxI3JlHoLl26iOHh4Uy0l7QwSc/w7NmzTb4H6Yw6tBBJNNkBHR+JAI8cOVIMCwsTVSqV2KNHD/HNN9+stQ1pIpKwMokHk67kiBEjxJ9//rnJ9/39999r3rdDhw6i0Wist43Vaq25b7PZxE8++UQcOHAgex96v2HDhtU65rqfZ8OGDeI111zDhKJTUlLEZ555ptY+iX379okzZswQk5KS2LlLTEwUp0yZIpaUlNRss2vXLnHatGliXFwc24a0IB977DEmsuxKX5LOCWmRJiQksO9u+vTpYlFREduOjteVDqXz+XfWpvzoo4/ETp06iRKJhAk479ixQ7zqqquY7id9Lrou6PlZs2bVHHN6enqD7+HQu3To89J+nNm+fTvbf2xsbM1nffjhh8Xi4uKabZz3/8ILL4ivvfYau+bo+qPrlZ7ncJzhBpFTj4svvrhmIOnVq5f4+OOPi4sWLXLZNYMGR0EQXA5qV1xxRY1BbUjol26dO3d2aWjcMYh33nmny306iwbfdtttDb7322+/3ej73nfffTXbvvHGG01eLTfddFOD7/XEE0+4/DyRkZH1tv3yyy9rTTjIoLjap2NQX7VqVYPbDBkyRDQYDPW+B1fvO3PmTI8NYkxMTK3nySA29voxY8a0yCAuX76cGUFXr6PJWGlpab39u/qso0ePbvL75IQW3CBy6kHtVlwNNkqlUnzooYdEk8nEtiPPwzHQTJ48man4U+eD119/veY1S5YsYduSF7h06VLWjoo8RzKuzkZh4cKFHhtEaqfj7L1R54Gqqipx//79rOUOsXHjxpptnnvuObG8vJx5QeRJ0WPkUTq8IlfQ53L3GOn9HduOGjWKnY+TJ08yT5seo4nDsWPH6n0e8lypJc7u3bvZ8dBj1FHCQdeuXWu2feedd5iHlZOTwzzR/Pz8WttQBwZ6T5pg/PDDDzWvmz17dj2DGB0dzc5hXl6e2LdvX/YYGVXycmsGiEY6pjhfGy+++CIzRGSE6BxT+x56Lzo+i8XCzjG1iXI2mg4a8gJdPUcTLJpAOb67FStWsGuJWjE59v3ss8/WM4j0uRYvXszOHZ1bx+Oh0nmC4x7cIHJcQoMH9W1zZRhffvllts3KlSsbnOE7bhSSI2iApoGKjINj0He+OYc43TWIFFp0PDZnzhyXr3PepqEbfdaGuOyyy9w2iNTfre5EgPjqq69qHqd2QHU/D00UHDjOOXk6xPHjx2u2mzBhgsv3dd6moRuFZOsaRGeP9cknn6x5/Ny5cx4ZRPpO60LGkcKjZLxcea7UW7A5BpEmFI59UAjegU6nq/EaaVJQ1yBSONgBTZYcj2/ZssXlOeWEJjyphuOSq666ipVe5OTkYM6cObj00ktrnlu0aBH76075RUlJCfv7+OOP4/XXX8exY8dYkk5dDAaDx9+E8/v36tWryW2aOkZXOKf7Hz9+vNH9FBUV1dxPTU11ed/V8XTr1q3mvkqlYn8paae1P6Or93V+b3fp379/vcduvvlmzJ49G2fOnIHZbPbK993YOdZoNIiNjfXoHDfns3KCG24QOfWorKysud+uXTuWWbpixQpER0fXGlydSy/eeOMNlg1Y9/bNN9+w53/99Vf2lzIyjx49yrI3//jjjxadfef3J0Pb1DZbt26td3x0HFRS0BBUbuHg888/dzm4W61W9jcuLq7msezs7Jr7lKHrwHkbB3K5vOY+1dy15DNSnair7+HPP//06H09gWoy6xq75cuXs/t9+/Zl2bh0DJ988onL13vy3g2dY3rP4uLiett4+7NyghtuEDkuvcO77rqLDaLl5eXMo6PC7LKyMvZ8z5492V8qUqfSDOK9995j9Yu0Lc3iFy5ciEsuuQQbNmxgzzsMiVQqRXh4OPM833rrrRadfRIOcPDss89i06ZNrID+8OHDzHgRl19+ec02VONGz9Gx0CD92Wef1XyWxs7F0KFD2f3MzExWi3fo0CFWWkCGjsoubrzxxnrGk7xh+ozkIVFBv2MgprINT+jevXuNd7Nq1Sp88MEH7HvIy8tjn7GgoIBtQyIBxPfff48FCxZAp9Ox745eQ8f8448/ojk4JkGnTp1y26ujCYKjhpUMkVarZd71p59+2uh70HVDn6sx6LN26tSJ3acJ1Zo1a1BRUYHnn3++ptzD+XvgcDzC3zFbTuBBJQkNrUVRWv3q1atrtv3+++8bzDKlG61ZEbfffnu956gEw3Gf1tRaK8u0oW0ct6aghCDKtm3o9c7vRVmaTa2n1v08zun/rtbT3MkybWwb53PmvIbofB4bOh4qT6i7L0raaWp90TlT2dX37fzezklYjhutuzZ0PmjNVSaTufyclFzkqqzD+fpyzoB1XJ8cDsE9RE49XnvtNTzwwAMYMGAAC8fJZDLExMQw9RryOJzXE2+55RbmSZK3RtuQR9C+fXuMHz8eH330EQYPHsy2I8/mtttuY9uQR3DPPfcw76qlfPXVV/jvf/+LkSNHIiwsjK0P9ejRA5MnT25wGwrxdenSBdOnT8fcuXObfA9aq9q1axfzgmkfERERTJygc+fOmDlzJpMdc0CeGEmQ0bmjY6H3Ig/z66+/Zq9vDnQuaT13xowZSExMZN8H/Z0yZUqNh+7Yhj6TYxsKT19wwQUsnO18PjyBviOSTyOv3hPoPJBnSq9LSEjA008/jWeeecbltqR8RGuOrkKdriCvnyIPV155JbuW6LN26NABs2bNYmFxh8fJ4XiK8PdMj8PhcDickIZ7iBwOh8PhcIPI4XA4HE413EPkcDgcDocbRA6Hw+FwquEeIofD4XA43CByOBwOh1MN9xA5HA6Hw+EGkcPhcDicariHyOFwOBwON4gcDofD4VTDPUQOh8PhcLhB5HA4HA4HjP8H1xao/esIPWcAAAAASUVORK5CYII=", "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -- Create a Figure with an North Polar Stereographic projection -- #\n", "fig = plt.figure(figsize=(7, 7))\n", "proj = ccrs.NorthPolarStereo()\n", "ax = plt.axes(projection=proj)\n", "ax.coastlines(resolution='110m', linewidth=0.8)\n", "ax.add_feature(cfeature.LAND, facecolor='0.1', edgecolor='0.1', linewidth=0.2, zorder=4)\n", "ax.gridlines(draw_labels=False, dms=True, x_inline=False, y_inline=False)\n", "\n", "# Plot eORCA025 JRA55v1 example March Arctic sea ice mask:\n", "c_parent = ax.pcolormesh(simask_march['glamt'],\n", " simask_march['gphit'],\n", " simask_march.isel(time_counter=0), # Average over all March outputs.,\n", " transform=ccrs.PlateCarree(),\n", " cmap=\"Blues_r\", shading=\"auto\",\n", " vmin=0, vmax=1,\n", " zorder=1)\n", "\n", "# Add colorbar with label:\n", "cb = plt.colorbar(c_parent, orientation=\"horizontal\", pad=0.05, shrink=0.7)\n", "cb.set_label(\"Sea Ice Concentration\", fontsize=11, fontweight=\"bold\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now we have produced our binary sea ice / ocean mask, let's calculate the total March sea ice extent (`siextent`) for the Arctic each year:**\n", "\n", "$$siextent(t) = \\sum_{j} \\sum_{i} (e_{1t}(j, i) \\ . \\ e_{2t}(j, i) \\ . \\ simask(t, j, i))$$\n", "\n", "Given that our sea ice mask is defined as either `ocean=0` or `ice=1`, we can similarly calculate the total sea ice extent using the `integral()` method." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "

\n", " \n", "

\n", " \n", " NEMODataTree 'NEMO model'\n", "

\n", "\n", "

\n", " NEMODataArray 'integral_ij(simask)'\n", "

├─ Domain: '.'

\n", "

├─ Grid Path: 'gridT'

\n", "

└─ Grid Type: 'T'

\n", "

\n", " \n", "

<xarray.DataArray 'integral_ij(simask)' (time_counter: 48)> Size: 384B\n",
       "dask.array<mul, shape=(48,), dtype=float64, chunksize=(1,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time_counter   (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n",
       "    time_centered  (time_counter) datetime64[ns] 384B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    standard_name:       sea_ice_area_fraction\n",
       "    long_name:           Sea-ice area fraction\n",
       "    units:               \n",
       "    online_operation:    average\n",
       "    interval_operation:  1800 s\n",
       "    interval_write:      1 month\n",
       "    cell_methods:        time: mean (interval: 1800 s)

\n", " \n", " " ], "text/plain": [ "\n", " \n", "\n", " Size: 384B\n", "dask.array\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n", " time_centered (time_counter) datetime64[ns] 384B dask.array\n", "Attributes:\n", " standard_name: sea_ice_area_fraction\n", " long_name: Sea-ice area fraction\n", " units: \n", " online_operation: average\n", " interval_operation: 1800 s\n", " interval_write: 1 month\n", " cell_methods: time: mean (interval: 1800 s)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calcuate Arctic sea ice extent [millions km2]:\n", "siextent_march = m2_to_million_km2 * simask_march.integral(dims=[\"i\", \"j\"])\n", "siextent_march" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Notice that the output above contains dask arrays, so we haven't actually computed the sea ice area time series yet. To do this, we need to call the ``.compute()`` method:**" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "

\n", " \n", "

\n", " \n", " NEMODataTree 'NEMO model'\n", "

\n", "\n", "

\n", " NEMODataArray 'integral_ij(simask)'\n", "

├─ Domain: '.'

\n", "

├─ Grid Path: 'gridT'

\n", "

└─ Grid Type: 'T'

\n", "

\n", " \n", "

<xarray.DataArray 'integral_ij(simask)' (time_counter: 48)> Size: 384B\n",
       "array([16.32824789, 16.86941318, 16.5183107 , 16.72445344, 16.4576558 ,\n",
       "       16.29064754, 16.52695254, 16.76295723, 16.22850156, 16.16246814,\n",
       "       16.23776532, 16.44139358, 16.41876482, 15.75799642, 16.2668418 ,\n",
       "       15.6035572 , 15.87174656, 16.27217629, 16.04578136, 15.55259182,\n",
       "       15.56961209, 15.84498984, 16.13352913, 15.90625433, 15.69600352,\n",
       "       16.00316629, 15.90223515, 16.16833642, 15.87076961, 15.32602473,\n",
       "       15.06186523, 15.03758909, 15.68691232, 15.56774097, 15.64110223,\n",
       "       15.05039325, 15.62906747, 15.33490905, 15.12441622, 14.65957986,\n",
       "       14.86761508, 14.79781814, 14.59909284, 14.76745932, 15.03032672,\n",
       "       14.86457691, 14.91763625, 14.87652651])\n",
       "Coordinates:\n",
       "  * time_counter   (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n",
       "    time_centered  (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n",
       "Attributes:\n",
       "    standard_name:       sea_ice_area_fraction\n",
       "    long_name:           Sea-ice area fraction\n",
       "    units:               \n",
       "    online_operation:    average\n",
       "    interval_operation:  1800 s\n",
       "    interval_write:      1 month\n",
       "    cell_methods:        time: mean (interval: 1800 s)

\n", " \n", " " ], "text/plain": [ "\n", " \n", "\n", " Size: 384B\n", "array([16.32824789, 16.86941318, 16.5183107 , 16.72445344, 16.4576558 ,\n", " 16.29064754, 16.52695254, 16.76295723, 16.22850156, 16.16246814,\n", " 16.23776532, 16.44139358, 16.41876482, 15.75799642, 16.2668418 ,\n", " 15.6035572 , 15.87174656, 16.27217629, 16.04578136, 15.55259182,\n", " 15.56961209, 15.84498984, 16.13352913, 15.90625433, 15.69600352,\n", " 16.00316629, 15.90223515, 16.16833642, 15.87076961, 15.32602473,\n", " 15.06186523, 15.03758909, 15.68691232, 15.56774097, 15.64110223,\n", " 15.05039325, 15.62906747, 15.33490905, 15.12441622, 14.65957986,\n", " 14.86761508, 14.79781814, 14.59909284, 14.76745932, 15.03032672,\n", " 14.86457691, 14.91763625, 14.87652651])\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n", " time_centered (time_counter) datetime64[ns] 384B 1976-03-16T12:00:00 ......\n", "Attributes:\n", " standard_name: sea_ice_area_fraction\n", " long_name: Sea-ice area fraction\n", " units: \n", " online_operation: average\n", " interval_operation: 1800 s\n", " interval_write: 1 month\n", " cell_methods: time: mean (interval: 1800 s)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "siextent_march = siextent_march.compute()\n", "siextent_march" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Visualising the evolution of Arctic Sea Ice Area & Extent**\n", "\n", "**Finally, let's visualise the results by plotting the time-series of Arctic sea ice area and sea ice extent from 1976-2025 together:**\n", "\n", "As expected, we can see that Arctic sea ice extent is always larger than Arcitc sea ice area, but both time series capture almost identical interannual variability." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -- Plotting upper ocean heat content time series for Pacific Ocean -- #\n", "plt.figure(figsize=(8, 4))\n", "plt.grid(visible=True, lw=1, color='0.1', alpha=0.3)\n", "\n", "siarea_march.plot(lw=3, color='0.1', label='Sea Ice Area')\n", "siextent_march.plot(lw=3, color='coral', label='Sea Ice Extent')\n", "\n", "# Axes labels:\n", "plt.title('eORCA025 JRA55v1: Arctic Sea Ice Area vs. Extent', fontdict={'size':12, 'weight':'bold'})\n", "plt.xlabel('Time', fontdict={'size':12, 'weight':'bold'})\n", "plt.ylabel('Sea Ice Area | Extent (millions km$^{2}$)', fontdict={'size':12, 'weight':'bold'})\n", "plt.legend(loc='lower left', prop={'size':10, 'weight':'bold'})" ] } ], "metadata": { "kernelspec": { "display_name": "dev", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.12" } }, "nbformat": 4, "nbformat_minor": 2 }