Repository: EnergyAssetOptimization/EAO (language: python)

Issue:
New feature: Specific starting ramp for CHP
Idea for a new feature: Specific start up ramping curve.

Background: Due to heating. a start up may require a very specific start up curve (below minimum power)

We could try with the restrictions as in following paper: https://www.researchgate.net/publication/3267153_Modeling_of_Start-Up_and_Shut-Down_Power_Trajectories_of_Thermal_Units


![IMG-0556](https://user-images.githubusercontent.com/23403303/179460283-db0b6a30-581b-42d8-8652-3c54f5c2adca.jpg)


Feature/eaopack
bugfix -- > deserialization now converts stored ns dates back to numpy date64


Discussion / hints:

die Versionnummer muss noch hoch gezählt werden.

Fix patch:
diff --git a/doc/source/samples.rst b/doc/source/samples.rst
index b8d45bd..ddb0e4d 100644
--- a/doc/source/samples.rst
+++ b/doc/source/samples.rst
@@ -23,7 +23,7 @@ Multi commodity optimization
 
    samples/cross_commodity/cross_commodity_sample
 
-Combining Heat and Power
+Combined Heat and Power
 ----------------------------
 
 .. toctree::
diff --git a/doc/source/samples/combined_heat_power/chp_sample.ipynb b/doc/source/samples/combined_heat_power/chp_sample.ipynb
index cbe771e..a9a0fb5 100644
--- a/doc/source/samples/combined_heat_power/chp_sample.ipynb
+++ b/doc/source/samples/combined_heat_power/chp_sample.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Combining heat and power\n",
+    "# Combined Heat and Power\n",
     "The CHPAsset class represents an asset that can generate both heat and power at the same time and models starting procedures, minimum loads, ramping and a minimum runtime."
    ]
   },
@@ -15,6 +15,7 @@
     "## Technical Details\n",
     "### Input Parameters\n",
     "Possible input parameters of the CHPAsset are\n",
+    "\n",
     "- Ramp $r$\n",
     "- Conversion efficiency $\\alpha$ from heat to power.\n",
     "- Maximum share heat $\\beta$\n",
@@ -22,8 +23,16 @@
     "- Maximum capacity $u_t$ for every timestep $t \\in \\left\\{0,\\dots, T-1\\right\\}$\n",
     "- Dispatch power $x_{-1}$ from the previous timestep\n",
     "- Dispatch heat $w_{-1}$ from the previous timestep\n",
-    "- Number $k$ of timesteps the asset has already been running\n",
-    "- Minimum runtime $m$"
+    "- Number $k_{on}$ of timesteps the asset has already been running\n",
+    "- Number $k_{off}$ of timesteps the asset has already been of\n",
+    "- Minimum runtime $m_{run}$\n",
+    "- Minimum downtime $m_{down}$\n",
+    "- Start ramp specification:\n",
+    "    - Minimum capacity $l^{start}_i$ for timestep $i \\in \\left\\{0,\\dots, k_{start}\\right\\}$ after starting\n",
+    "    - Maximum capacity $u^{start}_i$ for timestep $i \\in \\left\\{0,\\dots, k_{start}\\right\\}$ after starting\n",
+    "- Shutdown ramp specification:\n",
+    "    - Minimum capacity $l^{shutdown}_i$ for timestep $i \\in \\left\\{0,\\dots, k_{shutdown}\\right\\}$ before shutting off\n",
+    "    - Maximum capacity $u^{shutdown}_i$ for timestep $i \\in \\left\\{0,\\dots, k_{shutdown}\\right\\}$ before shutting off"
    ]
   },
   {
@@ -32,10 +41,12 @@
    "source": [
     "### Output variables\n",
     "The following variables are computed for a CHPAsset that runs for $T$ timesteps:\n",
+    "\n",
     "- Dispatch power $x_t \\in \\mathbb{R}$, for every timestep $t \\in \\left\\{0,\\dots, T-1\\right\\}$\n",
     "- Dispatch heat $w_t \\in \\mathbb{R}$, for every timestep $t \\in \\left\\{0,\\dots, T-1\\right\\}$\n",
     "- \"On\"-variable $a_t \\in \\left\\{0,1\\right\\}$, for every timestep $t \\in \\left\\{0,\\dots, T-1\\right\\}$\n",
     "- \"Start\"-variable $s_t \\in \\left\\{0,1\\right\\}$, for every timestep $t \\in \\left\\{0,\\dots, T-1\\right\\}$\n",
+    "- \"Shutdown\"-variable $d_t \\in \\left\\{0,1\\right\\}$, for every timestep $t \\in \\left\\{0,\\dots, T-1\\right\\}$\n",
     "\n",
     "When they are not needed to enforce a minimum runtime, start costs or similar, the \"on\" and \"start\" variables are removed."
    ]
@@ -46,10 +57,12 @@
    "source": [
     "### Variable bounds\n",
     "The output variables are within the following bounds in every timestep $t \\in \\left\\{0,\\dots, T-1\\right\\}$:\n",
+    "\n",
     "- $0 \\leq x_t \\leq u_t$\n",
     "- $0 \\leq w_t \\leq \\beta u_t$\n",
     "- $0 \\leq a_t \\leq 1$\n",
-    "- $0 \\leq s_t \\leq 1$"
+    "- $0 \\leq s_t \\leq 1$\n",
+    "- $0 \\leq d_t \\leq 1$"
    ]
   },
   {
@@ -59,32 +72,56 @@
     "### Constraints\n",
     "The following constraints are used in the asset\n",
     "\n",
-    "- **Ramp**: Ensure that the increase/decrease of the virtual dispatch $\\tilde{x}_t = x_t + \\alpha w_t$ is bounded by $r$, i.e.   \n",
-    "\t\\begin{align*}\n",
-    "\t\t\t\t\t-r \\leq \\tilde{x}_t-\\tilde{x}_{t-1} \\leq r,\n",
-    "\t\\end{align*} \n",
-    "&emsp;&nbsp;&nbsp;&nbsp; for $t \\in \\left\\{0,\\dots, T-1\\right\\}$.\n",
+    "-  **Minimum and maximum capacity**: The virtual dispatch $\\tilde{x}_t = x_t + \\alpha w_t$ is 0 when $a_t = 0$, i.e. the asset is \"off\", it is bounded by the start or shutdown specifications during the start and shutdown, and otherwise it is between minimum and maximum capacity, i.e.\n",
+    "\\begin{align*} \n",
+    "\\tilde{x}_t &\\geq& l_t \\left[ a_t - \\sum_{i=0}^{k_{shutdown}-1}d_{t+i+1} - \\sum_{i=0}^{k_{start}-1}s_{t-i} \\right] + \\sum_{i=0}^{k_{start}-1}l^{start}_i s_{t-i} + \\sum_{i=0}^{k_{shutdown}-1}l^{shutdown}_i d_{t+i+1} \\\\\n",
+    "\\tilde{x}_t &\\leq& u_t \\left[ a_t - \\sum_{i=0}^{k_{shutdown}-1}d_{t+i+1} - \\sum_{i=0}^{k_{start}-1}s_{t-i} \\right] + \\sum_{i=0}^{k_{start}-1}u^{start}_i s_{t-i} + \\sum_{i=0}^{k_{shutdown}-1}u^{shutdown}_i d_{t+i+1}\n",
+    "\\end{align*} \n",
+    "for $t \\in \\left\\{0,\\dots, T-1\\right\\}$. It should be noted that the term $\\sum_{i=0}^{k_{start}-1}s_{t-i}$ is 1 if timestep $t$ is part of the start ramp and 0 otherwise. Analogously, $\\sum_{i=0}^{k_{shutdown}-1}d_{t+i+1}$ is 1 if timestep $t$ is part of the shutdown ramp and 0 otherwise. <br>\n",
+    "If $k_{on} > 0$ and $k_{on}<k_{start}$, i.e. the asset is in the starting process at timestep $0$, the first $k_{start}-k_{on}$ timesteps are instead bounded by\n",
+    "\\begin{align*} l^{start}_{k_{on}+i} \\leq \\tilde{x}_i \\leq u^{start}_{k_{on}+i} \\end{align*}\n",
+    "for every $i \\in \\left\\{0, \\dots, k_{start}-k_{on}-1\\right\\}$.\n",
     "\n",
-    "-  **Minimum and maximum capacity**: The virtual dispatch $\\tilde{x}_t = x_t + \\alpha w_t$ is 0 when $a_t = 0$, i.e. the asset is \"off\", or between minimum and maximum capacity, i.e.\n",
-    "\\begin{align*} a_t l_t \\leq \\tilde{x}_t \\leq a_t u_t, \\end{align*} \n",
-    "&emsp;&nbsp;&nbsp;&nbsp; for $t \\in \\left\\{0,\\dots, T-1\\right\\}$.\n",
+    "- **Ramp**: Ensure that the increase/decrease of the virtual dispatch $\\tilde{x}_t = x_t + \\alpha w_t$ is bounded by $r$, unless during timesteps that belong to the start or shutdown ramp, i.e.   \n",
+    "\\begin{align*}\n",
+    "\\tilde{x}_t-\\tilde{x}_{t-1} &\\geq& -r \\left[ a_{t-1} - \\sum_{i=0}^{k_{shutdown}-1}d_{t+i} \\right] - u_{t-1}\\sum_{i=0}^{k_{shutdown}-1}d_{t+i} \\\\\n",
+    "\\tilde{x}_t-\\tilde{x}_{t-1} &\\leq& r \\left[ a_{t} - \\sum_{i=0}^{k_{start}-1}s_{t-i} \\right] + u_{t}\\sum_{i=0}^{k_{start}-1}s_{t-i} ,\n",
+    "\\end{align*} \n",
+    "for $t \\in \\left\\{0,\\dots, T-1\\right\\}$.\n",
     "\n",
-    "- **Start constraints**: When the \"on\" variable changes from \"off\" to \"on\", the start variable has to be 1, i.e.\n",
+    "\n",
+    "- **Start constraints**: (Only applicable when there is no start- or shutdown ramp specification, otherwise the start variables are defined simultaneously with the shutdown variables, see \"start and shutdown constraints\") \n",
+    "When the \"on\" variable changes from \"off\" to \"on\", the start variable has to be 1, i.e.\n",
     "\\begin{align*}s_t \\geq a_t - a_{t-1},\\end{align*} \n",
-    "&emsp;&nbsp;&nbsp;&nbsp; for $t \\in \\left\\{1,\\dots, T-1\\right\\}$. <br>\n",
-    "&emsp;&nbsp;&nbsp;&nbsp; If $k = 0$, i.e. previous state $a_{-1}$ is \"off\", we need the additional condition \n",
+    "for $t \\in \\left\\{1,\\dots, T-1\\right\\}$. <br>\n",
+    "If $k_{on} = 0$, i.e. previous state $a_{-1}$ is \"off\", we need the additional condition \n",
     "\\begin{align*}s_0 \\geq a_0.\\end{align*}\n",
     "\n",
-    "- **Minimum runtime**: Ensure that the asset follows the minimum runtime $m$, i.e.\n",
+    "- **Start and shutdown constraints**: (Only applicable when start or shutdown ramp are defined)\n",
+    "Ensure that the start and shutdown variables are set correctly, i.e\n",
+    "\\begin{align*}\n",
+    "s_t - d_t &=& a_t - a_{t-1} \\\\\n",
+    "s_t + d_t &\\leq& 1\n",
+    "\\end{align*}\n",
+    "for $t \\in \\left\\{0,\\dots, T-1\\right\\}$.\n",
+    "\n",
+    "- **Minimum runtime**: Ensure that the asset follows the minimum runtime $m_{run}$ and is on during the start and shutdown ramp, i.e.\n",
     "\\begin{align*}a_t \\geq s_{t-i}\\end{align*} \n",
-    "&emsp;&nbsp;&nbsp;&nbsp; for $t \\in \\left\\{1,\\dots, T-1\\right\\}$ and $i \\in \\left\\{1, \\dots, m-1\\right\\}$. <br>\n",
-    "&emsp;&nbsp;&nbsp;&nbsp; If $k>0$ and $m > k$, we need the additional condition \n",
+    "for $t \\in \\left\\{1,\\dots, T-1\\right\\}$ and $i \\in \\left\\{1, \\dots, m_{run}+k_{start}+k_{shutdown}-1\\right\\}$. <br>\n",
+    "If $k_{on}>0$ and $m_{run}+k_{start}+k_{shutdown} > k_{on}$, we need the additional condition \n",
     "\\begin{align*}a_i \\geq 1,\\end{align*}\n",
-    "&emsp;&nbsp;&nbsp;&nbsp; for all $i \\in \\left\\{0, \\dots, m -k-1\\right\\}$.\n",
+    "for all $i \\in \\left\\{0, \\dots, m_{run}+k_{start}+k_{shutdown} -k_{on}-1\\right\\}$.\n",
+    "\n",
+    "- **Minimum downtime**: If the asset turns off, it has to stay off for the minimum downtime $m_{down}$, i.e.\n",
+    "\\begin{align*}a_t \\leq 1 - a_{t-i-1} + a_{t-i}\\end{align*} \n",
+    "for $t \\in \\left\\{1,\\dots, T-1\\right\\}$ and $i \\in \\left\\{1, \\dots, m_{down}-1\\right\\}$. <br>\n",
+    "If $k_{off}>0$ and $m_{down} > k_{off}$, we need the additional condition \n",
+    "\\begin{align*}a_i \\leq 0,\\end{align*}\n",
+    "for all $i \\in \\left\\{0, \\dots, m_{down} -k_{off}-1\\right\\}$.\n",
     "\n",
     "- **Maximum share heat**: Bound the heat dispatch by a percentage of the power dispatch, i.e.\n",
     "\\begin{align*}w_t \\leq \\beta x_t,\\end{align*} \n",
-    "&emsp;&nbsp;&nbsp;&nbsp; for $t \\in \\left\\{0, \\dots, T-1\\right\\}$."
+    "for $t \\in \\left\\{0, \\dots, T-1\\right\\}$."
    ]
   },
   {
@@ -105,6 +142,8 @@
     "import datetime as dt\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
+    "import sys\n",
+    "import os\n",
     "myDir = os.path.join(os.getcwd(), '../..')\n",
     "sys.path.append(myDir)\n",
     "addDir = os.path.join(os.getcwd(), '../../../..')\n",
@@ -134,8 +173,8 @@
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\n"
      },
      "metadata": {}
     }
@@ -162,11 +201,15 @@
     "                               freq='h',\n",
     "                               conversion_factor_power_heat=0.5, \n",
     "                               max_share_heat=0.5,\n",
-    "                               ramp=20, \n",
-    "                               start_costs=1., \n",
+    "                               ramp=10, \n",
+    "                               start_ramp_lower_bounds=[3,4],\n",
+    "                               shutdown_ramp_lower_bounds=[1, 7],\n",
+    "                               start_costs=0., \n",
     "                               running_costs=0., \n",
-    "                               min_runtime=0, \n",
+    "                               min_runtime=0., \n",
     "                               time_already_running=0., \n",
+    "                               min_downtime=0.,\n",
+    "                               time_already_off=1.,\n",
     "                               last_dispatch=0., \n",
     "                               start_fuel=0)\n",
     "\n",
@@ -201,8 +244,8 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "               Values\nParameter            \nstatus     successful\nvalue      254.108917",
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qMHLkSCxduhRHHHFExo6lQ3kAHEKIMAAngG39faO3JYBLnl6R0t/XYycOT/lvdK6MwbnCONT3EZc/uxJHjU/9fUSuPEcHGqMkHB3w77b6d7rYbkN5cQHufHMdhpcU4OAxbizb0oJf/PML3H3W9EGP0R4Ko6K4IDFfSClx2ukLcO73zsejTz4NAPhy9RfYUr8Voz2VvR5farehNRjBqPKixFyRjTkvl6gJwnPPPRcAMHXqVDQ0NKC+vh5VVVUaR5c+aT3P8tUCz50HzL0aiHYDGxcNPpDDf6o8dvx8YP1b5h4jnTFMXgisex0460lgzLzBP556GzNP+R0+fz4w7nh9P7/SMYb6+Pn/AFrCQNEIQEog1NbvQ6YNt+GKZ1fgwbMOxOFjyvBRkx1X/HsNHjy9CmjZMqgx9iSdFVh46gm44Nzv4Ll7rweKRuCLrzdiR/1meDyjARnrPV6kCwjnKV+LhvGH3/0GZ377BLz1+ku45EcXYfWiF3rFEYlE8Phjf8XKjz8cOK7C4crP4HQDQR/gqgEKigf9c2RDfn4+jjvuODz//PM477zzNI0l5QRhT0KIGgAzAXwKYER8UgOA7QBG9POYiwFcDMBQE7rWRpU60NEVxiurlHPbO/6zbshjPfz+Jvzs2AN4wmdit772FdZuG3hCGF5cgO8/tgwjSgqwo60LBwwvwv2LNuD+RRv6/P5Jo0pw87cn9zvee++9B5vNhksvvTTxtenTpwNQTgwGa968edi4ceNeX3/33Xcxa9Ys5OUpfwaPPvpoHHLIIXjvvffg9/vx2GOPYe7cuQiFQrjsssuwfPly5OXl4Z577sExxxyDYDCIiy66CF988QU8Yw5AR2cAsZjE9tYQPvrgXfz5njvQ3d0FT/UY/OaPD8JZWNRnfCcdNg3zT12AJe8tQoHdgf97/ClMGjUFtbW1+MEPfoDm5mZUVFTgiSeewOjRo3HAAQdg8+bNaG1txbBhw/Dee+9h3rx5mDdvHh577DGMGjUKP/3pT7FmzRqEw2HccsstOP300/Hkk0/i3//+Nzo6OhCNRvHBBx9gwYIFePbZZ5kgjJNSbhVC3A2gHkAQwFtSyrd6fk/POSt/vwPgCEVS+vuajr/RuTIG5wpjOXxsOUrstpx5fmV6jEdPGwlrc6fy8eLN2NLUOeBYZQ4bLn56BdxOG1oCYXhcDtz79vp+v39MRSF+PHd/AIA/0A0A2N66e3nwp0s/RBQWHLvwPGyJx1E06gAUjQK83np0R2KJ728JdKPAZkFBH5Xm2ZjzJk6ciGAwmBj7rbfews0334yuri6MHTsWTzzxBIqK+p7zAOBPf/oTXnvtNYTDYfzzn//ExIkT0dnZ2efcVVtbi/PPPx+dncrv5MEHH8Thhx+Oa6+9Fl9//TVmzJiBCy64AD//+c/x7W9/G8899xyuueaafo+tZ6meZ80eaQG6wsCiW4YexJf/Um45RnpiWPkUcOgVTA6mw5h5QDScG8+NXBmjuxOQhUD7Ntz6YSvWNg1cvDDcCXz/6S8xotCCHZ0xHODOw/2Lt+F+AMDeS3InVdhw87z+q/HfW7IMtjwrLv3uyYCMAu3bML3SCcCJWm+9kvRs6VF5H2wBLCHla13tQMcOoH0b5h0yAxs31yXGUL37wceYdeD+yGurAwAcfeaPccjMKXjvo+Xwt7bjsT/+GnMPmYVQqAuXXfc7LF/9NfJsBbjn/j+ldV574403cPXVV6OwsBBHHHEENm/ejNdffx3Lli3DlVdeiVAoBIfDgSeeeAITJkzAV199hYsuugjd3d2IxWJ48cUXMW7cOCxYsADXXXedcRKEQogiAC8CuEpK2dazW4qUUgohZF+Pk1I+AuARAJgzZ06f30PJkVLi8mdXIhYDFs4cjXe+3oGr50/A1NHJLfn6cmsrbn31K0waVYJnPq3HoWOH8cSP+lXqsGFESQG2+kMYXWZHqSO1Crg1a9Zg9uzZ/d6/adMmzJgxI/H59u3b8Ytf/GKv73vttdcwderUvb6+dOnSvcaPRCJYtmwZ3njjDdx6661YtGgRHnroIQgh8OWXX2LdunWYP38+1q9fjz//+c9wOp34eMUXWLR0Gb570tGwWAQKIp14+s/34u2330ZhYSHuufsPeP3Zv+K6G27s8+fIs1gwcvgwvPTOx3jln//A9df8Av/v9dfw05/+FBdccAEuuOACPP744/jZz36Gl19+GRMmTMDatWuxZcsWzJo1C4sXL8YhhxwCr9eLcePG4frrr8exxx6Lxx9/HH6/HwcffDCOP/54AMDKlSuxevVquN1uAMCcOXNw4419x2VG8T2cTgcwBoAfwD+FEN+TUj6jfk/POWu/sZNliT1vyH9f73nrG8yfvB/e+mq77se49dWvMHFkMecKA/loUzMafAFUD3PC19mt++fovsbIa9uGsRXKm/9Shw2O/N7Jt2hMIhyNwWqxIBqLwV2Yj+5IDNtaQxhVakdFccGAMZQ6bBhV5sCOthBK7Da0hcIYUWKHI57ke3PrJhx2yEGJGHrF1unE1vpanHvyUYhEYwCA5qad+PnPr97rezM953399ddYvXo1Zs1Slrs1Nzfjtttuw6JFi1BYWIg777wT99xzD37961/3+7soLy/HypUr8fDDD+Puu+/GX//6V9x+++19zl3Dhw/H22+/Dbvdjg0bNuCcc87B8uXLcccdd+Duu+/G66+/nhh3zpw5uOOOOwyZIEzLedYBwyXsduCYG4FRM5ILYNsq4L3bgImnKhVvZh4jXTEsugUIdwKrngYmnMAkYarWvaH8PkfPBnZt1O/zKx1jqI/PLwSEFSgeCdgFYOsY8GGlNmBEOIStrV0YXZKP0gIBWKxALApY8wHLHrvT2YuA8nH9jrem7j+YPW0y4HADoVYlDnVpb0cBNtVtxYyTLkh8//YdO/CLq64EyscD9hKgZCRQUonXXn8KUydN2P2zxMdY+tULmH3YPOX7AcDmQMRWgmWffoo3/vMmbr3/T1h0ynfx0N13QwgLvnz3X1i3sQ7zz/0+1m/YkJZ5LRQK4ZJLLsGHH36IMWPG4JxzzkncN3HiRCxevBh5eXlYtGgRrr/+erz44ov4y1/+giuvvBLnnXceuru7EY1GAQBTpkzBZ599NuD/UVZIKVP+B8AG4E0AV/f42jcARsY/Hgngm32NM3v2bEmpu+31r2T1r16Xt73+lZRSyqUbm+TM37wll25sGvQY6mOO/cN78nt//WRIY5C+rV27NqnvV58jf3xzXVqeK/fff7+86qqr+rxvy5YtcvLkyb2+dvPNN8s//OEPUkopL7jgAllTUyOnT58ujz/+ePnll1/uNcaPf/xj+Y9//CPx+VFHHSWXLFkipZRy+/btcuzYsVJKKRcsWCDfeeedxPcdeeSR8osvvpCnn366fP0/b8rVDX75ZYNfzpgxU36w5GP50JPPS/ewYXL69Oly+vTp8sADD5Q/+MEP+v05q6qq5X8/WiV9nV1y+eadsszlll9tbZXuYcNkd3e3lFLK7u5uOWzYMCmllLfddpt8+OGH5S9/+Uv54osvyhNPPFEuXrxYnnXWWVJKKWfPni0nT56cOL7H45Fr166VTzzxhLzwwgt7Hbu7u1u63e5+Y8umvp5vAJbLNMxRg/0H4CwAj/X4/PsAHu7v+2fPnp3S31f1MUYY41v3vC+/85ePOFcYxNKNTXLGrW/Kml+9Lv/45jrNn1/ZGGOgOa892C2/2toq24Pdic///kmdnHHrm4Oe8/oao+fn+5rzDpw0SX61tVX6OrvkF16fvPpX18tf3PRb2R7sztqc1/PrM2fOlJ999pl87bXX5LAk5rzq6mrZ0NAgpZTyk08+kccdd5yUsv+5y+/3y+9973tyypQpcvr06dLhcEgppXzvvffkKaec0mvs9evXy1mzZvV77HTK5pyV1vOszR9IeecY5Xaw9nyMmcdIZwwrn5Xy5hIp374l+TGot80fSPl7j/L7/Opl/T6/0jFGj+9fu3atlKE2KRtXK7f7kDiXe2O1nHnLG3Lp1/XKHUmMkRBqk/f/9lfyqp9e3ucYgzqXq66W0ydPkMcfe4wyr+0xxqDmtVCbXHDisfKdl/8u5daVUgZb5ZEHz5RffPZRWua1zz//XM6bNy/x+SuvvJKYm+rr6+WCBQvk5MmT5ZQpU+SECROklFI+++yzctKkSfKOO+6Q69ev7zXeqFGjZFtbEr/nQUpmzkpHF2MB4DEAX0sp7+lx16sA1JTwBQBeSfVYtG872kJ46uM6TBpZgutPPhDA7r2EVje0Dnqc1Q2tePDcmZg4qgTelsCQxiDz6Lnn4NXzJ+DBc2f22hR+KCZPnowVK1YM+fF/+MMfsGrVKrz99tt7bYILAA6HA6FQ7+6PBQVKBYjVah3UJrFtQWXfqJGldggBOAvyUF5kw7yjj8WqVauwatUqrF27Fo899li/Y0hIjHY5UOqwQUBACIEqtwPop5563rx5WLx4MZYtW4aTTz4Zfr8f77//PubOnauMJyVefPHFxPHr6+tx4IHK34LCwsJeY6kl75RQD+BQIYQzPrcdB+DrgR6Qyt9XtcrOCGNMGlmCBl+Qc4VBrG5oxa+/PQkSQKXbqfnzS+sxAuFor71hV29txV1vrsPtC6cMes7bcwxlD1sHAmGlcmBfc15MAlXu+FwRrx4rc9gSj8/GnNcXKSW+9a1vDXrO6++4/c1d9957L0aMGIEvvvgCy5cvR3d3d7/jGnFOS/t5lrpX29Yk9qLcurL3HnlmHiOdMUw5Q/nc5kh+DOpt60rg0MuVj8uq9fv8SscYez6+oFjZc687MODDep3LHTkcD549BVf8c50yrw1yjF66A5g8+wisWPVlUnH09Iff3ohVKz7D2++8q8xre4wxqHmtO6BUHNric4M1H8izA+Hej+tpKPNaX2666SYcc8wxWLNmDV577bVErOeeey5effVVOBwOnHzyyXj33XcTj+nq6oLdbk/6WOmUji7GRwA4H8CxQohV8X8nA7gDwLeEEBsAHB//nDJISokbX14DAeDh82ah5/KDw8eW49Kjxg56rEuPGovDx5bD43Jiqz+IaEwmPQaZRzpOwPZ07LHHoqurC4888sju46xejcWLF6ccLwAceOCBfe7TtKe5c+fi2WefBQCsX78e9fX1mDBhAo44ci7+9c/nUZifh21b1ic2hD9m3pFYseyTxNidnZ1Yv77/faksQuD1l/8NIQQW/b+XMGvOwSiy23DEEYcnmrM8++yziQTgwQcfjI8++ggWiwV2ux0zZszA//3f/2HePOWNwAknnIA//elPaoUBPv/8836PvX79+j5PJM1KSvkpgH8BWAngSyhz5CMDPghD//tqpDGq3E40tgbRHYlxrjCAS48ai4oi5Q2qx6Us5dH7czSVMYYX23s1jlrd0IqHzpuFk6eOSoy3rzlvzzEAJUk4vFj5Pe9rzsuzCBTZleRgvtWCaEyiwGZNPH5fUp3z5s2bl+gavGbNmsScd+ihh2Lp0qWDnvP609/c1draipEjR8JiseDpp59OLMUqLi5Ge3t7rzEMOqel/zxrzDzgyKsGH8GRV+29/NWsY6QzBpsdKNoP8NUlPwb1duRVynJaAHBVK7d6fH6lY4y+Hl9QDBT3uU1pQq9zueIROHyip/e8NogxeikegWNPPLX3vFZQjNVbdgz+XM5RtnczkR5xDGpeKx6BuUcdg2df+DcAYP26tahv2IoJsw5Py7w2YcIEbN68ObE//vPPP5+4r7W1FaNHjwYAPPnkk4mvb968Gfvvvz9+9rOf4fTTT08cd9euXSgvL4fNlrlGlYORcoJQSrlESimklNOklDPi/96QUu6SUh4npRwnpTxeStmSjoCpf298uR1vr92B/50/HjXlhft+wCBUuZ0IRyW2t/WfZSdKxwnYnoQQeOmll7Bo0SKMHTsWkydPxnXXXYf99tsv1XABACeddBI+/PDDfX7f5ZdfjlgshqlTp+Lss8/Gk08+iYKCApz23QsQ6OzEKfMOws0335zY26miogJPPvkkzjnnHEybNg2HHXYY1q0beFN9n8+HadOm4ZnH/oLrfvN7AMom7k888QSmTZuGp59+Gvffr2wRXFBQAI/Hg0MPPRSAcjLX3t6e2HPqpptuQjgcxrRp0zB58mTcdNNN/R73vffewymnnLLvX5aJSClvllJOlFJOkVKeL6Xs0jomPah0OxGTwDZ/cN/fTLpQ36Jcoa8a5tQ4ktyj9ZyXn6ckCJOR6px32WWXoaOjAwceeCB+/etfpzTn9aW/uevyyy/HU089henTp2PdunWJSvhp06bBarVi+vTpuPfeewEYc07jeZbBuWqU7tKUOl8tYC8FHC6tI9Elree1oUhqXoPA1OO+g7O/d0Fa5zWHw4GHH34YJ554ImbPno3i4mKUlip7HF9zzTW47rrrMHPmzF5V+i+88AKmTJmCGTNmYM2aNfj+978PIHfmMKFeqcsFc+bMkcuXL9c6DF3yB7px/D0fYGSpAy9dfjjyrOkoDgUWb2jC+Y8tw3MXH4pD9x+WljFJH77++uvE0lSjWrhwIe666y6MG9f/Brt9aQ2GUberE/uV2DG8JLUy8JqaGixfvhzl5eVo8AXQFoxg0qiSlMYcrHnz5uGVV16By6X9m6m+nm9CiBVSyjkahbRPnLN2+3jTLpzz6Cd4+ocHY+64Cq3DoTS487/r8NfFm7HutyfBahH7foDO6WnO2+oLwh/sxuRRyTVrGeqcpwddXV046qijsGTJkkSn5kzinEVp8e+LgbqPgJ+v0ToS/XvmTKXr7aXpWWlkBHqa14Zq0POalEDjF0BhBVA6Oq0xdHR0oKioCFJK/OQnP8G4cePw85//POlxzjjjDNxxxx0YP358WuMDkpuz0pNFIs399vWv4Q+Ecef/TEtbchDYvbTI25LEngNEOnHHHXegsbExqcdEYjFs8wdht1lRvo+ulcnKt1oQicWSrgwZiqamJlx99dU5kRwk/fO4lb1dvC2sIDQKb0sAo8ocpkgO6k1+nkA0JhGJxZJ63FDmPL2or6/HHXfckZXkIFHalFUDbVuBaFjrSPTPX7d7eTGZxqDnNSGAvHwgmv6FQY8++ihmzJiByZMno7W1FZdccknSY3R3d2PBggUZSQ4mi7OoAXy4vgkvrmzAFccckPbKo1FlDggBeH086SPjmTBhAiZMmJDUY7a3hhCJxlA9rAgWMfgT54ULF2LLli29vnbnnXcm9qwAlGVjABCOxmC1WJOKK1kVFRVYsGBBRo9B5jGy1IE8i4DXx4tJRuH1BRMXCSm35McvBIcjMeTlD/6i8FDmvKHqb8474YQTMnK8cePGGbIykgzOVQ3IGNDqBdz7ax2NfkkJ+OuBcfO1joSyLKl5zVoARPpvdLUv/c1rP//5z4dUMdhTfn5+Yqmx1pgg1LnOrgiu+/eXGFtRiCuOPSDt4+fnWTCyxI4GVhASoSMURktnNyqKC+DMT+7P50svvbTP71FP+rojMdhtmU0QEqWT1SIwqszBanMDaWgJYP7kJDYkp6xRLyZ1R2PI1Z69g5nziEyvLF7x5qtjgjAVHTuASEjZ05GoP9Z8oLtzyA83y7zGJcY6d/db32BbaxB3/s+0jCUUKt1OVoWQ6cViEg3+IAryLBgxyK6RybL1OOkj0huP28Fqc4Po7IpgV2c3KllBmJNsiYtJubOPOBENgbok1l+nbRx654v//sq4xJgGkJcPyCgQi+z7e02MCUIdW1Hnw5Mf1eL7h1ZjTo07Y8fxuJzcV4pMb0dbCN2RGEaXOWHJ0J5ceRYBixDojjBBSPrjcTlZbW4QDfFEr8fNBGEuyrNaYLUIXkwi0ruS0YAlb3eCi4ZGTbByD0IaiDW+d3wKy4zNgAlCneqKRHHti6sxssSOX544MaPH8rgd2NEeQlckmtHjEOWqQHcEzR1dcBfmo8ieuZ0ZhBDIz7MwQUi65HE7sauzG51dvDKrd+pScY8rVxewUr6VcwWR7lmsQGklKwhTlaggrNI2Dspt1nzlNsoE4UCYINSph9/bhA07O3D7GVNRVJDZrSQ9LiekBLZy6RiZUExKNPiCyLNaMLI0M0uLe8q3WlgVQrpUGU8mNXCu0D11WxFWEOYuXkwiMoiyalYQpspfCxSNAGy8qEUDyFMThOnvZGwkTBDq0Dfb2/Hw+xuxcOZoHDNheMaPp54gcG8p6teS+4AtH/b+2pYPla+n4Pbbb8fkyZMxbdo0zJgxA59++ikAIBwO49prr8W4ceMwa9YsHHbYYfjPf/6T0rH609TehVA4itFlDlgtqf3JbGxsxKmnnjrg9+TnWRCOxCDlwHtLPfnkk7jiiitSiifbvvvd72LDhg1ah0EZkpgruMxY97wtQThsVgwrzNc6lNyUA3Pe4nffRjja91xRVFSUUhyqVatW4Y033uj3/s8//xw//OEP03KsW265BXfffXfSj7vwwgvxr3/9Ky0x/O53v0t83N3djXnz5iESYUU0ZZirmhWEqfLVcf/BVOXAvJapc7kESx4grIklxsFgEEcddRSi0dRXSb7//vv7PMfLpAcffBCPP/54WsZiglBnojGJa15cjRK7DTedOikrx/S4lasxPOmjfo2eBfzzwt0Ty5YPlc9HzxrykB9//DFef/11rFy5EqtXr8aiRYvg8XgAADfddBMaGxuxZs0arFy5Ei+//DLa29tT/zn2EApHsbO9C2UOG0octqQe29dJxT333IMf//jHAz7OZrUgKiWiMWNtPh+NRnHZZZfhrrvu0joUypCqxMUkzhV65/UF4HE7IERm9lvVvRyY80LBTsSkRCSDc8W+EoS/+93v8LOf/Sxjx8+2ngnC/Px8HHfccXj++ec1jIhMoawa6GxKqbuq6fnruP9gqnJgXsvEudxerPmJJcaPP/44zjjjDFitmWn0mk0/+MEP8Kc//SktY2V2bSql3RNLt+ALrx8PnDMT7ixd2R9RbEe+1cIEoZn951pg+5cDf0/xSODphcpteyNQMRF4/07lX1/2mwqcdEe/wzU2NqK8vBwFBcqGsuXl5QCAQCCARx99FFu2bEncN2LECHznO9/pd6yioiL8+Mc/xltvvYX99tsPzz33HCoqKrBq1SpceumlCAQCGDt2LB5//HGEw2GcdNJJWL58Od5esgynHXs4Nm7eAgwrxNixY/Hll1+is7MTl156Kerr6wEA9913H4444gjccsst2LRpEzZv3oyqqir84x//6BXHiy++iNtuuw2AUgH46quvIhAIYNOmTVi4cCHuuusu5OdZ8J+X/4Vz/nIfBIBTTjkFd96p/A6feOIJ/P73v0dZWRmmT5+e+Pmbmpr6jKcvaowbN25Ec3MzrrnmGvz4xz+GlBLXXHMN/vOf/0AIgRtvvBFnn302fvKTn+CEE07AaaedhoULF8LlcuHxxx/H448/jk2bNuH222/HM888gwceeADd3d045JBD8PDDD8NqtaKoqAiXXHIJFi1ahIceeghz587FhRdeiEgkgrw8Tj9GM6wwHw6bFfWcK3TP2xJIJHxNSQ9z3lnfQe2uTnRHYomuxj3dcMMNeP311+FwOPDKK69gxIgR/c4Vy5Ytw5VXXolQKASHw4EnnngCY8aMwa9//WsEg0EsWbIE1113Hc4+++zE+O3t7Vi9ejWmT58OQJlb6uvrsXnzZtTX1+Oqq65KJA/vueeeRGXDj370I1x11VUAlMqSp556CsOHD4fH48Hs2bMBAJs2bcJPfvITNDU1wel04tFHH8XEif3vt/3hhx/innvuwfbt23HXXXfhzDPPBAD84Q9/wAsvvICuri4sXLgQt956KwBgwYIF8Hq9CIVCuPLKK3HxxRfj2muvRTAYxIwZMzB58mQ8++yzWLBgAa677jqcd955/R6bKGWuGuXWVweMyE7xh6FEw0BrAzDt7H1/r5npYV4b4Fzusccew5133tnrHOjBBx/Ea6+9httuuw3d3d0YNmwYnn32WYwYMQIffPABrrzySgDKHu8ffvghiouLlWXGkRAA4Nlnn8Xf//53AEoF4C233ILy8nKsWbMGs2fPxjPPPAMhBN555x384he/QCQSwUEHHYQ///nPKCgowH//+19cddVVcDqdOPLIIxOxdnZ24qc//SnWrFmDcDiMW265BaeffnqfP1cgEMCFF16INWvWYMKECdi2bRseeughzJkzB5dddhk+++wzBINBnHnmmYk57Nprr8Wrr76KvLw8zJ8/H3fffTecTidqamqwbNkyHHzwwf3+HgeDFYQ6Ur8rgD++tR7HTRyOb08bmbXjWiwClS4Hq0JoYPYyZUJp9Sq39rKUhps/fz68Xi/Gjx+Pyy+/HB988AEAYOPGjaiqqkJJScmgx+rs7MScOXPw1Vdf4aijjkr8gf3+97+PO++8E6tXr8bUqVNx6623Yvjw4QiFQqhtbMZHSxdj5qzZ+OSjpairq8Pw4cPhdDpx5ZVX4uc//zk+++wzvPjii/jRj36UONbatWuxaNGivZKDW7ZsgcvlSkyEgFKd8fzzz+PLL7/E888/D6/Xi+Yd23Hf72/BK//vTaxatQqfffYZXn75ZTQ2NuLmm2/G0qVLsWTJEqxduzYxzkDx9GX16tV499138fHHH+M3v/kNtm3bhn//+99YtWoVvvjiCyxatAi//OUv0djYiLlz52Lx4sUAgK1btyaOu3jxYsybNw9ff/01nn/+eSxduhSrVq2C1WrFs88+m/i9H3LIIfjiiy9w5JFHwmKx4IADDsAXX3wx6P870g8hBDxuB7ve65yUEt6WACpdJk4QDobGc15+nvIWvq89azs7O3HooYfiiy++wLx58/Doo48C6H+umDhxIhYvXozPP/8cv/nNb3D99dcjPz8fv/nNb3D22Wdj1apVvZKDALB8+XJMmTKl19fWrVuHN998E8uWLcOtt96KcDiMFStW4IknnsCnn36KTz75BI8++ig+//xzrFixAs8991yiSvGzzz5LjHPxxRfjT3/6E1asWIG7774bl19++YC/u8bGRixZsgSvv/46rr32WgDAW2+9hQ0bNmDZsmVYtWoVVqxYgQ8/VCpjHn/8caxYsQLLly/HAw88gF27duGOO+6Aw+HAqlWrEnPYlClTesVFlBFqgpDLjIemtQGQMS4xToccPZfbtm0bfvvb3+KTTz7B0qVLsW7dusR9Rx55JD755BN8/vnn+O53v5tYqXT33XfjoYcewqpVq7B48WI4HPH9Ka0FQKQb3V1d2Lx5M2pqahJjff7557jvvvuwdu1abN68GUuXLkUoFMKFF16YOF+LRCL485//jFAohB//+Md47bXXsGLFCmzfvj0xzu23345jjz0Wy5Ytw3vvvYdf/vKX6Ozsu0L44Ycfhsvlwtq1a/Hb3/4WK1as6DXO8uXLsXr1anzwwQdYvXo1du3ahZdeeglfffUVVq9ejRtvvDHx/XPmzEmcs6WCJRw6IaXE9S99CatF4LaFU7K+7KfS7eRJn5kNcHUoQS1Fn3cNsPwx4OhfAWPmDfmQRUVFWLFiBRYvXoz33nsPZ599Nu644w7MmpV8qbvFYkmc3Hzve9/DGWecgdbWVvj9fhx11FEAgAsuuABnnXUWAODQQw/Df9/5AKs/+wQ33nA93nzzTUgpMXfuXADAokWLeiXo2tra0NHRAQA47bTTdk9CPTQ2NqKioqLX14477jiUlpYCACZNmoS6ujo0NTVjzmFHosQ9DHl5eTjvvPMSJzVHH310Yoyzzz4b69evHzCe/vahOv300+FwOOBwOHDMMcdg2bJlWLJkCc455xxYrVaMGDECRx11FD777DPMnTs3MVlOmjQJPp8PjY2N+Pjjj/HAAw/gqaeewooVK3DQQQcBUPbzGD5c2RvVarXif/7nf3ode/jw4di2bVuiUoSMxeNyooEXk3TNFwijsztq7gYlOpjz8uNVg301KsnPz0/shTR79my8/fbbAPqfK1pbW3HBBRdgw4YNEEIgHA7vM96+5rRTTjkFBQUFKCgowPDhw7Fjxw4sWbIECxcuRGFhIQDgjDPOwOLFixGLxbBw4UI4ncrz7LTTTgMAdHR04KOPPkrMxwDQ1TXwhvILFiyAxWLBpEmTsGPHDgBKgvCtt97CzJkzE+Nu2LAB8+bNwwMPPICXXnoJAOD1erFhwwYMGzZsr3GtVivy8/PR3t6uVJ4QZYKa2GKjkqFRE6tcYjwwHcxr/Vm2bBmOOuoouN1uAMBZZ52VOAdqaGjA2WefjcbGRnR3d2PMmDEAgCOOOAJXX301zjvvPJxxxhmorKxUBsvLByDRvHM7ysrKeh3n4IMPTnzfjBkzUFtbi+LiYowZMwbjx48HoJwvPvTQQzj66KMxZswYjBs3DoByfvnII48AUOafV199NbGvbigUQn19PQ488MC9frYlS5YkKh2nTJmCadOmJe574YUX8MgjjyASiaCxsTFxLma32/HDH/4Qp556aq99D4cPH94reTpUTBDqxD9XNGDJxmbctmAKRpZmv0OTx+XA6gZ/1o9LOqFOKGc9qUwkY+b2/nyIrFYrjj76aBx99NGYOnUqnnrqKXznO99BfX092trakqoi7GmgBLuUEpNnH4qVyz5GU2MDFixYgLvuugtCCJxyyikAgFgshk8++QR2+95djdWToD05HA6EQqFeX+tZTWi1WhGJRGCxCFjQ90lffwaKpy97/vwD/T5Gjx4Nv9+P//73v5g3bx5aWlrwwgsvoKioCMXFxZBS4oILLsDvf//7vR5rt9v32tdDXcJGxuRxO/HJ5l2QUnL/Op1StxPxuPg67VcOzHkWi0CeVWlqtSebzZZ4/alzC9D/XHHFFVfgmGOOwUsvvYTa2locffTR+4x1sHNasmKxGMrKyrBq1apBP6bncdWmLVJKXHfddbjkkkt6fe/777+PRYsW4eOPP4bT6cTRRx+918/RU1dX16DnVqIhKSwHbE5WEA6VmlhlBWFqcmBeG4qf/vSnuPrqq3HaaacllgkDyjLcU045BW+88QaOOOIIvPnmm8pWFVZlizaHzZKROQxQ5p8XX3wREyZMGNoPBWXl2d13343PPvsMLpcLF154IUKhEPLy8rBs2TK88847+Ne//oUHH3wQ7777LoD0nWNxibEO7GwL4bbX1+LgMW6ce3CVJjF43E74A2G0h/Z9VZlMaOvK3hPImHnK51tXDnnIb775plfH21WrVqG6uhpOpxM//OEPceWVV6K7W9lktqmpCf/85z/7HSsWiyW6HP7973/HkUceidLSUrhcrkQp9tNPP42jjjoK/kAYB848GP95+QWMHz8eFosFbrcbb7zxRmJ/ifnz5/faCHYwJzLjx49HbW3tPr/v4IMPxvJPP8L2HTsRjUbxj3/8A0cddRQOOeQQfPDBB9i1axfC4XCvnzfZeF555RWEQiHs2rUL77//Pg466CDMnTsXzz//PKLRKJqamvDhhx8m9rA49NBDcd9992HevHmYO3cu7r777kQ15XHHHYd//etf2LlzJwCgpaUFdXX9v8ldv379XsvSyDgqXQ50dkfhC3Cu0Ct1OxFTVxDuS47MeflWS59LjPvT31zR2tqK0aNHA1D2x1UVFxf3u2n8gQceiI0bN+7zmHPnzsXLL7+MQCCAzs5OvPTSS5g7dy7mzZuHl19+GcFgEO3t7XjttdcAACUlJRgzZkxijpNSDmlbihNOOAGPP/54orp/69at2LlzJ1pbW+FyueB0OrFu3Tp88sknicfYbLZe1ZO7du1CeXk5bLbkmpQRJUUIJbnFCsKh8dcpnWlLRmsdib7lyLzWl4MOOggffPABfD4fIpEIXnzxxcR9Peevp556KvH1TZs2YerUqfjVr36Fgw46aHdlnVVJArpKChGNRge8QAQAEyZMQG1tbWK+U88XJ06ciNraWmzatAkAem0tdcIJJ+BPf/pT4oLV559/3u/4RxxxBF544QUAyjZVX36p7BPZ1taGwsJClJaWYseOHYkOz2rV/8knn4x777231/yYrnMsJgh14OZXv0IoEsMdZ0yFxaJNRYYnvhcRlxlTn468au+rS2PmKV8foo6ODlxwwQWYNGkSpk2bhrVr1yauCt12222oqKjApEmTMGXKFJx66qkDXoEqLCzEsmXLMGXKFLz77rv49a9/DUCZSH75y19i2rRpWLVqFa674UZsaw1i3Nj9IQDMm6f8TEceeSTKysrgcrkAAA888ACWL1+OadOmYdKkSfjLX/6yz5+nsFBpcrKvE6qRI0fiVzfdinMXnoLp06dj9uzZOP300zFy5EjccsstOOyww3DEEUf0KlNPNp5p06bhmGOOwaGHHoqbbroJo0aNwsKFCzFt2jRMnz4dxx57LO666y7st99+AJQTvEgkggMOOACzZs1CS0tLIkE4adIk3HbbbZg/fz6mTZuGb33rW2hsbOzzuDt27IDD4UiMS8ajJpXY1Eq/1HmeCcIB5Micl59nSaravL+54pprrsF1112HmTNn9qqYOOaYY7B27VrMmDFjr26+EydORGtr6z67Ts6aNQsXXnghDj74YBxyyCH40Y9+hJkzZ2LWrFk4++yzMX36dJx00kmJbSoAZeP4xx57DNOnT8fkyZPxyiuvDPpnVM2fPx/nnnsuDjvsMEydOhVnnnkm2tvbceKJJyISieDAAw/Etddei0MPPTTxmIsvvhjTpk1LNCV57733EisHiDLKVc0KwqHy1QGllYCVCyNTkiPzWl9Gjx6N66+/HgcffDCOOOII1NTUJLZouuWWW3DWWWdh9uzZiSYogNKES12ya7PZcNJJJyl3xCsIEe3G/PnzsWTJkgF/BrvdjieeeAJnnXUWpk6dCovFgksvvRR2ux2PPPIITjnlFMyaNSuxvRKgdGgOh8OYNm0aJk+ejJtuuqnf8S+//HI0NTVh0qRJuPHGGzF58mSUlpZi+vTpmDlzJiZOnIhzzz030Xyyvb0dp556KqZNm4YjjzwS99xzT2KspUuX4lvf+taAP89gCDWzmQvmzJkjly9frnUYOeW/axpx6TMr8asTJ+Kyo8dqFsfqBj9Oe3Ap/u/82ThhMk/uzeDrr7/uc68EPSoqKkpUEQykflcnWkMRjBteBLst/S3vX3rpJaxYsSLRybg/ja1BNHd0Y8qokrQv07zllltQVFSEX/ziF2kddzDuvfdelJSU4Ic//OFe9/X1fBNCrJBSzslWfMninLW3tdvacPIDi/HguTNx6rRRWodDQ3D9S1/iP1824vNfz9c6lKzS45y3vTWEpvYuTBmd/rliMO69914UFxfvszGWXp1xxhm44447EntP9cQ5i9LqjWuAVX8HrvMqFYU0eH89HrA5gAte0zqSnKPHea0/6t7qkUgECxcuxA9+8AMsXLhwaINt/xKwl2Dl5l2499578fTTT6c32CREo1GEw2HY7XZs2rQJxx9/PL755hvk5+cnNc7nn3+Oe+65p9+fJZk5ixWEOaw1EMZNr3yFyaNK8OO5YzSNZXcFIatCyJjagmH4g2EMLy7ISHIQABYuXNirW1Z/8q0WSCkRjubOBZx0KCsrwwUXXKB1GJRBHrey9wmrzfXL2xJg9aBO5OcJSEiEk1hmnE6XXXZZrz2bjKS7uxsLFizoMzlIlHauaqC7HQj6tI5Ef3x13H/QBG655RbMmDEDU6ZMwZgxY7BgwYKhDxbvZDxr1iwcc8wxiEajaYszWYFAAEceeSSmT5+OhQsX4uGHH046OQgAzc3N+O1vf5uWmHKyFvejTc1Y3dCKS48aXMXcXz7YhGmVpTh87O6yUiOMcfsba9HS2Y2fHz8Of12yZdBjZEKZ04aigjw0+HjSNxS5+Pwa6hg720Nw2qwosu/ek6cjFEYgHMXw4sFt5J2pMQ466GAEu7qQ12Mp/tNPP91v9aA6hiPfiq3+IOw2K5z5VuxsDw06jmQNptIiP0+5dhOOxhIfJ+uJJ57A/fff3+trRxxxBB566KEhjZcOF110kWbHpuwotttQ5rQl9rEj/WnwBTFpZGqbhlN2nHD0XLQHgsjPs8ASrzp6+umnMXXq1Kwc32634/zzz8/KsW6//fa99qc666yzcMMNN2TkePn5+fj+97+fkbGJ9pLoZFwLON2ahqIr3QGgcyc7GBvIIYccslfn+qeffjrRETgt8vKB7k4AwA9+8IP0jTuAN998E7/61a96fW3MmDF46aWXkI7K7nQsLVblVIKwOxLDy59vxc2vfoVbT5uM+l2DO8HYr8SOy55ZiVtPm4xZVS6srPfpfoyLDq/BC8sbcNr0Ubj7rfV48NyZg3p8pgghUOlysIJwiKZVluKKv3+OP313JqqGObGiLvefo5GYRCgc2WvZks1qQV1LAKPKHHDmWxHojmKbP4hRZQ50RQZ3BSZTYzz5ytuJz3vqb0x1DGd+HiLRGIYXO+BtCaLKrW33znyrkhTsjsRQOMTijIsuukg3Cblc2uqCUlfldnKu0KlYTGKrL4j5k0doHYom9NZ9e+nHH2Pd9nZUuhxwD3Wy0IkbbrghY8nAZHHOorRTE1z+OmD0LG1j0RN/vXJbVqNpGLlMb/Pap59+mvmDWPOBqA+QMmtL+k844QSccMIJWTnWnpKds3IqQfjNjnZc9fwqAEjcJmPPx+h5jPve2YAyhw2LNzThofNm9ar40orH7UTdrk6tw9Clw8eW48FzZ+KHTy5HMLw7WZXLz9Eb5g1Dd95W5Dn73ttozwTAUBICuTJGeygMZ34edrR1ocrt6FWVqAVbvGowme6UeiWlxK5du2C3Z6Zik7LP43JibWOb1mHQEOxoD6E7GktsK2Imdrsdu3btwrBhw3RzMmWzWiAgkmpUQqnhnEUZkaggZKOSpKiNXVhB2Cc9zmtZ0aNRCfKMfXFtKHNWTiUIAeBbB47AiVOG1gTjv2u24+2vdxhqjJ8de0BOJAcBpSpkyYZm3V2JyBWHjy3H8JIC1O0K5Mzza6AxLIihK9ABGWzr8+JKsDuKYDgGh80CR/7Q9uzLmTHCMezojqLEngdvq7bJQdWu1hA68ixoKUx+Hwq9sdvtqKys1DoMSpNKtwNvr92BaEzCauFcoSfq3pFVJtyDsLKyEg0NDWhqatI6lKQ0t4bQlmeBzwRzRa7gnEVpZy8BHG52Mk6WmlB11WgaRq7S67yWcZEQ0LET2AUgz/gXe5Kds3IqQTi8uAAr6n246MiapJNiH21qxop6H3527AF45tN6Q41x6NhhOZEk9LgcCIajaO7oRkWxsbPtmfDRpmZ4WwIYN6LIEM/zn/39c3zvkCo882k9Hjx3pjHGeGdoY2TCzX/5GADwwqXTNY6EKDkelxPd0Rh2tIUwqkzb5fqUnPp4BbYZm5TYbDaMGaNtQ7ih+O2jnyAYjuKly4/QOhQiSoWrWtmDkAbPVwvYnEBhhdaR5CS9zmsZ56sD7j8a+PYDwFQ2T9xTTnUxHlFix4PnzsQVf/8cH21qHvTjPtrUjCv+/jkePHcmrp4/wfRjZIp6wsDN55P30aZm/OTZlYhJ4NSpo3T9/OIY2VHpdvC1RrqUmCu4D6HueFsCEAIYVWb8K+pG4XE52TWcyAjKqrnEOFn+OqCsKmv7yJFBlIwGhJUVu/1IS4JQCPG4EGKnEGJNj6/dIoTYKoRYFf938mDGUvdqW93QOujjr25o7VX1Y/YxMoUnfUO3uqEVvzxhAgBg3IgiXT+/OEZ2eFxObG8LDbppC6WHEKJMCPEvIcQ6IcTXQojDtI5JbzwupWrQy673uuP1BbBfiR0FeUPbroGyz+N2oLmjC8FuzhVGlM5zLMpxrmqg1QvEuKfooPnqdu/fSDRY1jygtJIJ+X6ka4nxkwAeBPC3Pb5+r5Qy6Z7Uh48tT2qJ36VHjeUYWVAZP+lr4Elf0i49aixe/nwrAOCA4UUA9Pv84hjZ4XE7ISWwzR/CmPJCrcMxk/sB/FdKeaYQIh+A+dZapmi0ywEheDFJjxpagqZsUKJn6sXbBl8A40YUaxwNZcCTSOM5FuWwsmqlaUJ7I1A6Wutocp+USgVYNa/j0hC4qllB2I+0VBBKKT8E0JKOsSh3OfPzUF6Uz5O+Idq4swNWi0DNMCZ7aN8SVVh8vWWNEKIUwDwAjwGAlLJbSunXNCgdKsizYkSxnUvkdcjrC6DSzX0j9aTSxe1fjIznWCaiduJl0mJwgj6gq40VhDQ0XNLfr0zvQXiFEGJ1vDze1dc3CCEuFkIsF0IsZ4ed3FfpcvJN6BBt3NmB6mFO5Ofl1NaflKO456cmxgBoAvCEEOJzIcRfhRC9MvqcswbH43aggfui6UpXJIrtbSFWEOqMx61eTOLrzWT2eY4FcM7SlbIa5ZZJi8FRE6kuJghpCFzVQOdOoJvnWXvKZKbizwDGApgBoBHAH/v6JinlI1LKOVLKORUV7ECU6zxuboY9VBt2tuOAiiKtwyCdGFFih80q+HrLrjwAswD8WUo5E0AngGt7fgPnrMHx8GKS7mzzhyClOTsY61lFUQHsNgurzc1lUOdYAOcsXSnzABCsIBwsNZHKCkIaCjUh76/XNIxclLEEoZRyh5QyKqWMAXgUwMGZOhZlj8flwDZ/ENGY1DoUXQlHY6jbFcC4EUwQ0uBYLQKjy9jJOMsaADRIKT+Nf/4vKAlDSpLHzSY7eqMmmNTtDUgfhBBc3WEyPMcyqLwCoHgkKwgHixWElAou6e9XxhKEQoiRPT5dCGBNf99L+uFxOxGJSTS2sqopGXW7OhGJyUSDEqLB8LidaGBVSNZIKbcD8AohJsS/dByAtRqGpFs9m+yQPqgJJlYQ6o/H5UA9q81Ng+dYBsbGCYPnqwPsZYC9VOtISI/UylMm5PeSli7GQoh/ADgaQLkQogHAzQCOFkLMACAB1AK4JB3HIm2pexN5W4KJjbFp3zbs6AAAHFDBDoM0eJUuJ97ctl3rMMzmpwCejXcw3gzgIo3j0aWeTXbYhVsfvC1B2KwCI0rsWodCSfK4nVhe64OUEkIIrcOhNOI5lsmUVQO1S7SOQh/8dawepKErGg7kOZiQ70NaEoRSynP6+PJj6RibcktVj8YJh2GYxtHox8adSoJw7HCeKNPgVbmdaOnsRkdXBEUFaflzTfsgpVwFYI7Wcegdm+zoj9cXQKXLCauFCSa9qXI70d4VQWswjDJnvtbhUBrxHMtkXDXA6ueBSDeQx9fygHx1wIhJWkdBeiWEkmD21WodSc5hO1VKysgyOywC3Aw7SRt2dmB0mQPOfCZ5aPB2d6fk6430RW2yU8/nrm54WwKo5P6DulTZY3UHEemYqxqABFq9WkeS22IxpfKLDUooFWXVXGLcByYIKSk2qwUjSx1MWCRp484O7j9ISdu9pJ+vN9IXtclOAxMWuuFtCXD/QZ1KXExixS6RviX2RavVNIyc17EdiHZziTGlRt3zU7L5ak9MEFLSPG4HvD6e9A1WNCaxqakD45ggpCTtXqbJ1xvpj8fNzqp60dEVgS8QTlyUIH1JzBW8mESkb+ysOjhq1VdZjaZhkM6VVQNdbUDQp3UkOYUJQkqax+Xkm9AkbPUF0RWJsYKQkuZy2lCYb+XrjXSpknOFbqj/T2olGulLid2GUoeNCXkivSseCVhsXPa4L2oClRWElAom5PvEBCElzeN2Ymd7F0LhqNah6MLGpnYAwLgRTBBScoQQ8LidaOBJH+mQx+2ALxBGR1dE61BoHxIJQlYQ6pbH7eAehER6Z7ECZR4mLPZFTaCWerSNg/QtsaSfr7eemCCkpKkVBg1c9jgoagfjAyqKNY6E9EipwuJrjfSHe2jqh7qNAfcg1C+Pi0v6iQyBjRP2zV+nVFva7FpHQnrGCsI+MUFISUuc9PGN6KBs2NGB8qIClDptWodCOqTs+RmA5Aa6pDPcF00/vC0BFOZb4eI8pVtKtXkQsRjnCiJdUxsnUP987GBMaWAvBexlTMjvgQlCSpp60tfAk75B2cgGJZQCj8uJQHcULZ3dWodClJQqNtnRjQaf0sFYCKF1KDREHpcD3ZEYdrZ3aR0KEaWirBoI7AK6OrSOJHf567j/IKUHE/J7YYKQklZRVID8PAtP+gZBSomNOzrYoISGjJ2MSa/YZEc/vC1BVHL/QV2rdHN1B5EhcNnjwKJhoG0rKwgpPbikfy9MEFLSLBaBSpeDJ32DsLO9C+1dESYIacjUPT/5eiO9YZMdfZBSwusLsIOxznHPTyKDKKtRbpm06FurF5AxVhBSeriqAX89EItpHUnOYIKQhoSbYQ+O2qCES4xpqLjnJ+kZm+zkvpbObgS6o+xgrHOVLvViEl9vRLrGCsKBqYlTVhBSOpRVA9EuoGOH1pHkDCYIaUiq3E7U72LCYl827GgHAFYQ0pAVFuRhWGE+q0JIlzxuB+pb2GQnl9XH/7ZUsYOxrtltVowoKeDFJCK9cw4D8osAX63WkeQm9ffiqtEyCjIK1xjllq+3BCYIaUg8bgfaQhG0BsNah5LTNjZ1oNieh4riAq1DIR2rdLMKi/TJ43IiGI5iF5vs5Cx1f1MPE4S653E5eTGJSO+E4L5oA/HXARYbUDJK60jICFixuxcmCGlIuNfN4GzcqXQwZmdISoXH5WBVCOlSoskO54qcpf7fqEtUSb+UPT95MYlI99hZtX++OqC0ErBYtY6EjKDUo9wyIZ/ABCENiXrSx83nB7ZxJzsYU+o8bie2+YOIxrhMk/Ql0WSHSYuc1eALYFhhPgoL8rQOhVLkcTnQ2BpEOMrN1ol0Ta0g5PYce/PXsUEJpY/NDhSPZEK+ByYIaUh2VxDypK8//kA3mju6MW54sdahkM55XE6EoxLb20Jah0KUFFab5z5vSxCVXF5sCJVuJ2IS2ObnezMiXXNVA+FOILBL60hyj6+ODUoovbikvxcmCGlISp02FNvzuOxxAGoHY1YQUqoSVVhMspDOFBbkwV2Yz2rzHOb1BeDh8mJD4MVbIoNQE2BMWvTW1QEEmllBSOnFJf29MEFIQ8bNsAe2gQlCShNWYZGeedhkJ2dFYxLb/EE2KDGI3Uv6OVcQ6VqicUKtpmHkHH+9cssKQkqnsmqgbSsQZfNVgAlCSoHH7eC+UgPYuLMDdpsFo8tYmUGpGVXmgBDcx430iU12ctf2thDCUZm4CEH6NrLUgTyLQD0vJhHpGysI+6ZWeblqNA2DDMZVDcgY0OrVOpKcwAQhDZnH5USDLwDJDXT7tGFnB8ZWFMFiYQdjSk1+ngUjS+xo4Ekf6RCb7OQutSpZrTwjfbNaBEaVOVhtTqR3BUWAcxiXPe5JTZiygpDSiQn5XpggpCHzuJ0IhWNo6ujSOpSctIkdjCmNKt1OVmGRLrHJTu5KJAhZQWgYXN1BZBBsnLA3fx1gcwKF5VpHQkaSWNLP1xvABCGlYHfjBL4R3VNnVwRb/UGMY4KQ0kTZ85OvNdIfNtnJXV5fEEIo2xiQMXhcTlabExkBGyfsTe1gLLg6i9KoZDRgyWNCPo4JQhqyKjcbJ/RnUxMblFB6Vbmd2N4WQigc1ToUoqSo1WncFy33eFsCGFXqQH4e3w4ahcftxK7ObnR2RbQOhYhS4aoB/F4gxvd9Cb5a7j9I6WexAqUe5flFTBDS0FWys2q/NrKDMaWZWoW11c8qQtIXtckOq5pyj7clgEoXqweNRO1I3cBlxkT6VlYNxMJA2zatI8kNUioVlS7uP0gZwIrdhLQkCIUQjwshdgoh1vT4mlsI8bYQYkP81pWOY1HusNusqCgu4L5ofdi4swN5FoHqYYVah0IG4WHFblYIIaxCiM+FEK9rHYtRqE12uC9a7vH6Aom/LWQMHheX9BsJz7FMjPui9RZoAbo72KCEMoN7fiakq4LwSQAn7vG1awG8I6UcB+Cd+OdkMB6Xg/ui9WHDzg7UlBfCZmWRLqWHukyTSZaMuxLA11oHYTSVbicTFjkmFI5iR1sXG5QYTOJiEi/eGsWT4DmWObGzam/+WuWWFYSUCa5qINAMdHVoHYnm0pK9kFJ+CKBljy+fDuCp+MdPAViQjmNRbvGws2qfNu3sYIMSSqvhxQXIz7NwmWYGCSEqAZwC4K9ax2I0Hhfnilyjblegbl9AxjCsMB8Om5UXbw2C51gmVuoBIFhBqFITpawgpExQn1f+em3jyAGZLG8aIaVsjH+8HcCIvr5JCHGxEGK5EGJ5U1NTBsOhTPC4nGhsDSESjWkdSs7oikRR1xLg/oOUVhaLQGWZg0mWzLoPwDUA+v2DxjlraDxuB3a0dbHJTg5RKzq5xNhYhBDwuDlXGNygzrEAzlm6lpevdFdlBaFCTZSygpAyQW1+w4R8dpqUSCklANnPfY9IKedIKedUVFRkIxxKI4/bgWhMorE1pHUoOaO2OYBoTDJBSGmnLNNkVUgmCCFOBbBTSrlioO/jnDU0atd7NtnJHep2BVxibDweF5f0m8VA51jx+zln6RkbJ+zmqwMcbqCgWOtIyIi4pD8hkwnCHUKIkQAQv92ZwWORRjzsZLwXdjCmTPG4WBWSQUcAOE0IUQvgOQDHCiGe0TYk42CTndzT0BJAfp4Fw4sLtA6F0swT3/NTyR2RAfEcyyzYOGE3djCmTCosB2xOJuSR2QThqwAuiH98AYBXMngs0gg3w97bhp3tEAIYW8EEIaWXx+2EPxBGeyisdSiGI6W8TkpZKaWsAfBdAO9KKb+ncViGwSY7ucfrC6CyzAGLRWgdCqVZpcuBzu4ofAHOFQbFcyyzcFUD7Y1ApEvrSLTnq+P+g5Q5QjAhH5eWBKEQ4h8APgYwQQjRIIT4IYA7AHxLCLEBwPHxz8lgRpbaYbUILnvsYePODlS6HLDbrFqHQgazu2KXrzfSFzbZyT3eliAquf+gIbFi1zh4jmVyZdUAJOD3ah2JtmIxoNXLCkLKLC7pBwDkpWMQKeU5/dx1XDrGp9yVZ7VgZKmdFYQ9bNzZgXHDuT8GpZ/abdTrC2DSqBKNozEuKeX7AN7XOAxDYZOd3OP1BTCtslTrMCgDdlfsBjDdU6ZtMJQSnmOZnJoQ89cC5QdoGoqm2huBaDcrCCmzyqqB2qWAlEpFoUllpUkJGVuVm5thq6Ixic3Nndx/kDKiilUhpGNsspM72kJh+APhxN8UMpbExSS+3oj0Te2savZlj4kOxjWahkEG56oButuBoE/rSDTFBCGlzONyop5vQgEoiZvuSAwHcP9ByoBShw3FBXlMEJIueVwO1PO5mxPUvyEeJggNqdhug8tpY8Uukd4V7QdYCwBfrdaRaEv9+ZkgpExSK3Z9W7SNQ2NMEFLKPG4Hmju6EOyOah2K5hIdjEcwQUjpJ4RQqrDY6IF0yON2ojUYRhub7GhOrSxTl6KS8Xi4uoNI/ywWoMzDfdF8dQAEUFqpdSRkZOoSdpNX7DJBSClTKxAaeKUaG9QEIZcYU4Z4XA6e9JEu7W6yw+ev1tT5Wl2KSsbjcTnRwItJRPrHzqpKgrRkFJBXoHUkZGSJPT/N/XpjgpBSVtljM2yz27izAyNKClBit2kdChmUx62c9EkptQ6FKCncFy13eFsCKC7IQ6mDc5VRVbod2OoLIhrjXEGka+ysqiRI2aCEMq2gGHC4TZ+QZ4KQUsaTvt027mxn9SBllMflQDAcRXNHt9ahECWlitXmOcPrC6LS7YQwcZc+o/O4nOiOxrCjLaR1KESUirJqpWlCqE3rSLTjr9td3UWUSUzIM0FIqasoKoDdZjH9sjEpJTY1dbJBCWWUuqSfFbukN2yykzu8LQF4XFxebGQedr0nMgazL3uMdAFt21hBSNnBJf1MEFLqhBCodDlNn7DY3hZCR1cEB4wo1joUMjCe9JFesclObpBSosEXZAdjg1MTwHy9Eemc2RsntDYAkKwgpOxwVQOtXiAW0zoSzTBBSGmhNE4w95vQDTviDUpYQUgZVBk/6ePm86RHbLKjveaObgTDUVYQGtxolwNC8GISke65apRbs1YQ+mqVW1YQUjaUVQPRbqC9UetINMMEIaWFx80Kwo3sYExZ4MzPQ3lRPk/6SJfYZEd73kQHY1YQGllBnhUjiu2mf29GpHsOF5BfbN4KQjUxygpCygazL+kHE4SUJh6XE+2hCFoDYa1D0czGpg6UOW0oL8rXOhQyOC7pJ71ikx3tqRcXmCA0Po/bgQaTr+4g0j0hzN04wVcHWGxA8UitIyEzKKtRbs2akAcThJQmbJwAbNzRgQMqitgVkjKuyu00/ZJ+0ifOFdpTtyfwuJggNDqu7iAyCFeNeRMW/jqgrAqwWLWOhMygzANAmDchDyYIKU08bmUvo3oTL3vc2NTB5cWUFR63A1v9QUSi5t1Al/SJTXa0V78rgPKiAjjyebJldB6XE9vbQuiKRLUOhYhSURavIDTj9hy+Wi4vpuzJKwBKRu3e+9KEmCCktDD7Sd+uji60dHYzQUhZ4XE5EY1JNLaGtA6FKClqkx2zzhW5wOsLJC7qkbF53E5ICWzzc64g0jVXNRAOAJ1NWkeSfb46Niih7CqrNm/FLpggpDQpsdtQ6rCZdikLG5RQNnGZJunV7iY7XCKvFa8vwOXFJuFhQp7IGNQEmdmSFl3tQLCFFYSUXWbe8xNMEFIaedwO0570bWxSEoTjRhRrHAmZgXpyz83nSY+4L5p2ItEYtvlDrCA0CV5MIjIIs3ZWVROirCCkbCqrBtq2AZEurSPRBBOElDYeE3dW3bCjA858K0aV2rUOhUxgZJkdFsGTPtInM88VWmtsDSEak6wgNIkRJXbYrMLU+0MTGUJZlXJrtn3R1IQoKwgpm1zVACTQ2qB1JJpggpDSxuN2osEXRCxmvg10NzV1YCw7GFOW2KwWjCx1cNkY6ZLH7cA2f4hNdjSgJmbVyjIyNqtFYHSZg9XmRHqXXwgUVpi4grBG0zDIZBJL+ms1DUMrTBBS2nhcDnRHYmjqMF857sadHRjH/QcpizxuB7w+nvSR/rDJjnbURBErCM2DS/qJDMKMjRP8dUB+EeB0ax0JmYlZl/THMUFIaVNp0k7G7aEwGltDGMsEIWWRx+U03WuNjIH7omnH6wvAIpRtCsgcKjlXEBmDGRsnqB2MuUKLsql4JGCxmS8hH8cEIaWNWpFgtpO+TU2dANjBmLLL43ZiZ3sXQuGo1qEQJYVNdrTjbQlgZKkDNivf/pmFx+2ALxBGR1dE61CIKBVl1cqeaDETve/z13H/Qco+ixUo85gvIR/Hd4iUNpUupSui2ToZb9wZ72DMBCFlkdqFtIHLjEln2GRHO15fkB2MTSZx8ZZVhET65qoGYhGgbavWkWSHlLsrCImyzYxL+uOYIKS0sdusGF5cYLo3oRt2tiPfakEVN32nLDJrxS7pH5vsaMfbEuD+gybjMen2L0SGk2icYJKkRWAXEO5kBSFpw4xL+uMyniAUQtQKIb4UQqwSQizP9PFIW1VuJ+pN9iZ0084O1JQ7kcclW5RFVTzpSzshhEcI8Z4QYq0Q4ishxJVax2RUHrfDdHOF1kLhKHa2d/Filskk5gpWmxsOz7FMxlWj3Jqls6r6c6o/N1E2uWqUJHVXu9aRZF22MhrHSClnSCnnZOl4pBGP22m6JY9KB+NircMgk6koLkBBnoUJwvSKAPhfKeUkAIcC+IkQYpLGMRmSx+VkwiLLGuLVxh4mCE3F5bShMN/KucK4eI5lFqWVgLCYp6pJTRByiTFpwWwVuz2w5InSyuNyoLE1iHA0pnUoWREKR1HfEmAHY8o6IQQqXQ7T7fmZSVLKRinlyvjH7QC+BjBa26iMyeN2oolNdrJK/VvBPQjNRQgRv3jLBCGRrlltQEmleRIWaiK0rErbOMic1KXtZknI95CNBKEE8JYQYoUQ4uIsHI80VOl2IiaBbX5zJC22NHciJtmghLThcTu5B2GGCCFqAMwE8KnGoRiSuuyRSYvsUf9WcA9C86l0mW/7F5PgOZbZmGlfNF8d4CwHCniORRooq1FuzZKQ7yEbCcIjpZSzAJwEZbnWvJ53CiEuFkIsF0Isb2pqykI4lEm7u+WZI0G4Id7B+AAmCEkDHpeTy8YyQAhRBOBFAFdJKdv2uI9zVhqoVWxmmStygbclgII8CyqKC7QOhbLM41aqzaWUWodC6TXgORbAOctwzNRZ1V/HBiWkHacbyC8yT0K+h4wnCKWUW+O3OwG8BODgPe5/REo5R0o5p6KiItPhUIYlTvpMUhWycWcHLAIYU16odShkQh63A22hCFqDYa1DMQwhhA1KcvBZKeW/97yfc1Z6sAt39nlbgqh0OSCE0DoUyjKPy4lgOIpdnd1ah0JptK9zrPh9nLOMxFUNdGwHwia4uOar4/6DpB0hzJWQ7yGjCUIhRKEQolj9GMB8AGsyeUzS1shSB/IswjRVTZt2dqDK7YTdZtU6FDKh3RW75ni9ZZpQMiePAfhaSnmP1vEYGZvsZJ/XF2CDEpPysOu94fAcy6TUhJnfq20cmRaLAq0NrCAkbZlpSX8Pma4gHAFgiRDiCwDLAPw/KeV/M3xM0pDVIjCqzGGa7pQbdrZzeTFpxsN93NLtCADnAzhWCLEq/u9krYMyIjbZyT5vS4D7D5rU7tUdfL0ZCM+xzMgsjRPatgGxMCsISVtqBaHJtufIy+TgUsrNAKZn8hiUe5S9boyfsIhEY9jS3IljJg7XOhQyKbPt+ZlpUsolALj+MkvYZCd7WoNhtIUi7GBsUqw2Nx6eY5mUmjDz1WoaRsapCVBWEJKWXNVAuBMI7AIKy7WOJmuy0aSETMbjcpqioqm+JYBwVGLc8GKtQyGTKnXaUGzPY5KFdIlNdrJH/T2zgtCcCgvy4C7MN8V7MyJDKxoBWAuMX0Go7vvGCkLSUiIhb/DX2x6YIKS087idaO7oRqA7onUoGcUOxpQLmGQhvUo02QmwyU6mqYkh7kFoXh4u6SfSP4sFKKsyfsLCXwdAAKUerSMhM0ss6a/VNIxsY4KQ0m73ZtjGfiO6MZ4gHFvBDsaknSq3E/VMEJIOsZNx9qh/I5ggNC8u6ScyCFeN8ZcY+2qB0kogL1/rSMjMzLKkfw9MEFLaeVzxzbANnrTYtLMDI0vtKLbbtA6FTMzjdqDBF4Q02Qa6pH/srJo93pYgSux5KHVwvjIrj9uJrb4gojHOFUS6ZobOqr46Li8m7RUUAc5y41fs7oEJQkq7xEmfwa9Ub9jZweXFpDmP24muSAxN7V1ah0KUFLPMFbnA6wuwetDkPC4nIjGJxlZjr+4gMryyaiDUCgT9WkeSOf46Niih3GCGhPwemCCktBtWmA+HzWroJcaxmMSmJiYISXtcpkl6VeqwocSeZ+i5Ild4WwJsUGJyagdrvt6IdC6xL5pBkxbhENDeyApCyg1l1awgJEqVEAIet8PQCYttrUEEuqNMEJLmeNJHesZ90TJPSokGXzDxt4LMiReTiAzC6J1VW73KLSsIKRe4qoHWBiAW1TqSrGGCkDLC6J1V1QYlB1QwQUjaqnRxHzfSL6PPFbmgqb0LXZEYlxib3KgyB4QAGvh6I9I3o1cQqolPVhBSLiirBmJhoG2b1pFkDROElBEet9PQjRPUBOG4EcUaR0JmZ7dZUVFcwKoQ0iU22ck89W8DlxibW36eBSNL7PD6WG1OpGsOF1BQatwKQn+tcssKQsoFRk/I94EJQsqISpcDHV0R+ANhrUPJiI07O+AuzIe7MF/rUIjgcTm4xJh0iU12Mk/928AlxlTpZsUukSG4qoybsPDVAdYCoGg/rSMhMv6S/j4wQUgZYfTulBt3dnB5MeUM7uNGesV90TJPTQhVsoLQ9DwuzhVEhmDkxgn+OqDMA1iYpqAcUOoBIIybkO8DX3mUEYmTPgNWNUkpsWFnBw4YwQQh5QaPy4nG1hAi0ZjWoRAlhU12Ms/rC6CiuAB2m1XrUEhjHrcDO9q6EAqbZ7N1IkNy1QD+esCI23P46rj/IOWOvHygZLRxE/J9YIKQMiJx0mfAK9XNHd1oDYZZQUg5w+N2IBqTaGwNaR0KUVLYZCfzvC1BeFxcXky7L95u9TMhT6RrZdVAJAh07NQ6kvTz13H/QcotrmpWEBKlqthug8tpM+RJ3+4GJUwQUm5ILOk34OuNjI1NdjLP6wugih2MCUDVMM4VRIbgqlFujZa0CLUCQd/un48oF7hqWEFIlA4etxP1BnwTunFnOwDggOFMEFJuUKtCjPh6I+OrMuhckQvC0Ri2+YOJiwhkbrv3/GQFIZGuqRV2vlpNw0g7NQnDJcaUS8qqgfZtQNgcK7WYIKSM8bicaDDgm9CNOztQVJCH/UrsWodCBAAYWWqH1SJYhUW6xC7cmdPoDyEmdyeGyNyGFxcgP8/CCkIivSurUm6NVtWkVkRyiTHlEvX52OrVNo4sYYKQMqbS7cBWXxCxmLE20N3Y1IGxw4sghNA6FCIAQJ7VglFldiZZSJc8bicaW4MIs8lO2qkXDSrd3IOQAItFoLLMwQQhkd7ZHEDRCMBfq3Uk6cUKQspF6vPRaAn5fjBBSBnjcTnRHY1hR7uxynE37OhggxLKOR6XkxWEpEselxMxqVS7UXqpiSBWEJKq0s25gsgQyqqNl7Dw1wEFJYDDpXUkRLupFYRGS8j3gwlCypjdjROMU9XUFgpjZ3sX9x+knONxOQ31WiPzqDRw13uteX0BWC0CI0u5JQYpuKSfyCCM2FnVV6ckPrlKi3JJ0X6AtcB4Cfl+MEFIGeNxxU/6DLSUJdHBmAlCyjEetwPNHV0Idke1DoUoKYnGCQaaK3KFtyWIUWV25Fn5do8UHrcTrcEw2kJhrUMholSUVQOtW4FoROtI0sdfx/0HKfdYLECZx3gJ+X7wHSNlzGiXA0IYqypk4w4lQcgKQso1asVug4Feb2QObLKTOV5fgMuLqRcm5IkMwlUNyCjQ1qB1JOkhJeCv5/6DlJuMuKS/H0wQUsYU5FkxothYjRM2NnUgP8+SSMYQ5YpK9aSPSRbSGTbZyRxvS5AJQurFoy7p5+uNSN+M1jihswkIB1hBSLnJiEv6+8EEIWWUx+0wVMJi484O7F9eCKuFe2NQbuFJH+kZm+ykX7A7iuaOrsTfBiJgdwUhq82JdC7ROMEgSQt2MKZcVlYNBH1AqE3rSDKOCULKKI/LiQYDLWPZsLOdy4spJ1UUFcBus3DZGOkSm+ykn5oAYsU79VTmtKGoII9zBZHelVQCwmqcCkI10ckKQspFRkvIDyDjCUIhxIlCiG+EEBuFENdm+niUOz75200Y27kSjW0hdEdiAIA1S1/DJ3+7Kakx1ix9rdfXkhkj1cf3HCPYHUWDL4hxw4uTHoMokz7520346qPXUdmjCivbr7VMjpFNnLOy75O/3YT9O1b0arKj57kiV8ZQ/xZUupycswhAz7nCAa9PScjr/Xmei2NkC+cr8/rkbzdhzSf/AUpHJxIWufL8H/IYvlrlC2VVnLMop3zyt5uwsb5e+cRnkNfbADKaIBRCWAE8BOAkAJMAnCOEmJTJY1LuKBp7MM6p+zUOFV9hqz+INUtfw+i3L0fR2IOTGmP025cnnsjJjpHq43uOsXTRvyElMMr3WdJjEGWS+hw9THyF+hZtXmuZGiObOGdpo2jswThry004zPIVvL6A7ueKXBmjbvl/AQCRje9zziIAu58bh4ivUN+izWvN6GNkC+crc1Ofdx2iCPDV5szzP5UxWtYtBgqHY81n73LOopxSNPZglH96l/KJQV5vA81ZQko56AGTJYQ4DMAtUsoT4p9fBwBSyt/39f1z5syRy5cvz1g8lH1vvPIPHL3yStRaq7F/tBZfFR2GcNGopMawdWzD5I6PUWcbg+rwlqTHSPXx6hiTOj7GsthETLVuwbZv/RlTjvh2UmMQZdKapa+h8q1LsEmOwmRRp8lrLd1jjH8o0FHf3Fmc1INTwDlLO2++/gLmfnaFIeaKXBljUsfHeD82E4dav8bWbz3MOYsAqHPFpdgkRxpmrsiVMbI5ZyU7XwGcs4xmzdLXMO7tiyAgEZMWzZ//qY4xo+NDtNuGQUa6OWdRzlmz5FVMevt8+CxuFMY6dP96G2jOynSC8EwAJ0opfxT//HwAh0gpr+jxPRcDuBgAqqqqZtfVGX9dt5m0b1mJ4qeOAQB0SyvCsA1pHBvCyBfRIY+R6uMBoADdyBMxLN/vbMy59JEhjUGUSRvvOAIHhNZo+lpL5xiHP9qK5duiWesIxDlLO50Na1D41yMA6H+uyJUx7OiCVUh8UvlDHPqje4Y0BhnThjvmYlxotebPUaONkc05azDzVfzrnLMM7OPfnYTDuj/Kied/usbgnEW5asPNkzFONOTMayVTc1bekEZMIynlIwAeAZQrWxqHQ2nm+/xlFANYPfx0eHa+N6QrQmr57MrKszCh4Z9Jj5Hq49Uxqt++GMUIYML217Bm6Wu8skU5Zc3S1zA+tA4hazGCUaHJay3dY+zs7upI6oFZwDkrM5qX/xuFANYM/zZG7/xA13NFrowx9u0fwoEuTGh4AWuWHsM5iwDEq45CX6PL4kQglqf753kujcE5i7JpzdLXMKHrSzxi+R+chbc1f/6nOsaoty7DPzEf32n4J+csyjlfLH4VlWjFv4rOxXEdr+v+9TbQnJXpBOFWAJ4en1fGv0YmsGbpa9h/9V8AANMuuh9rVi5R1rwDg34iq0/grd96GIce8W2sWXpMUmOk+vheY8z7AyZ++BO0j/+fpMcgyiT1ORopqYZzWCU2HvDjrL/WMjGG99bTvhnUg9KHc5YG1ix9DfuveggAMOX792DNF5/qe67IkTGap/wQnjUPo/mwGzhnEYAec0XZGBSWlGPDhMt1/zzPpTGyPGdxvjIx9Xl3c8Ev0TnqcBw+bqHmz/9Ux3ja8xv8act+OPzEhajknEU5ZM3S1+B553L8JPwznHnMudgaOln3r7eB5qxMdzH+DMA4IcQYIUQ+gO8CeDXDx6Qc0bFpGYKeeUBBKeBwYcoR38bWbz2Mjk3LkhqjZ2Y82TFSfXzPMSYecx5gL8OoElvSYxBlkvocdUbbAFe1Jq+1TI2RZZyzNNCxaRkCVccANidQWKH7uSJXxvAccgYAYFzNGM5ZBGD3c6Mw2mG4uSJXxsgizlcm1rFpGbYe/zDe7ZoIj9uZM8//VMYYNvU4hKMSw6YezzmLckrHpmVYMuNufBybbJjX20BzVkb3IAQAIcTJAO4DYAXwuJTy9v6+l5vnGtCzZwHt24FLF2sdSXo8Nh+w5AEXvaF1JES9dXcCvxsFHPdrYO7/ah1NWgghVkgp52T5mJyztPDcecCuTcBPPtE6EuPoaALuPgA46S7gkEu0joZyRaQLuG0EcPS1yj9Km2zPWcnMVwDnLKPxdXZj5m/fxk2nTsIPjxyjdTgpW7yhCec/tgzPX3woDtl/mNbhEPXyzCd1uPHlNfjkuuOwX6ld63DSor85K+N7EEop3wDAbIpZ+eqAivFaR5E+5eOBb/6jdRREe/PXK7dl1drGoXOcszTiqwNcfO6mVWG5UpXpY1MC6sHvBSA5VxgA5ytz8/oCAACPy6FxJOnhcTkBAF5fEIdoHAvRnry+APLzLBheXKB1KBmX6SXGZGZSAv46Y70JrZgIBJqBzl1aR0LUm69WuXXVaBkFUfKkVJ6/RporcoEQyu9U/dtABPSYK/h6I9Kz+pZ4gtDt1DiS9BhV5oAQu38uolzibQmgsswBiyUrjeo1xQQhZU7HDiASMlbComKCctuc7d4JRPugVgkxyUJ6E/QB3e1MWGSCq1q5UEek8tcqt5wriHTN2xIEYJwEYX6eBSNL7GhggpBykLcliEqDvNb2hQlCyhwjJizUBGETE4SUY/x18SYP5VpHQpQctaLJSHNFriirVubiDO83TTriqwOs+UDxSK0jIaIUeH0BuJw2FBVkfMewrKl0OxNLp4lyidcXMMxy/n1hgpAyR61aMFJVSEmlkoRpXq91JES9+eLL+YXxS9/JYIw4V+QKV7VSnRn0aR0J5Qp/HVDqASw8BSDSM29LwDDVgyqPy5mojCTKFe2hMPyBsOFeb/3huwPKnEQFYZW2caSTxQKUjwOa1mkdCVFvfjZ5IJ0yYrV5rlB/p9yHkFRsCERkCA2+YKKxh1F43A7saA+hKxLVOhSihMRyfoO93vrDBCFljr8WKBoB2AxWjlsxEWhiBSHlECl3VxAS6Y2/DnC4AHuJ1pEYj5oI4j6EpDJa8zgiE4rFJLb6gqh0G+scy+NyQkpgq49VhJQ7Eh3DDfZ66w8ThJQ5Rk1YlI8H2hqArnatIyFSsMkD6ZlR54pckKggZIKQAITalPmCcwWRru1oD6E7GjNcRZO6hNPLBCHlEK/aMdxgr7f+MEFImWPUJY+JTsasIqQcwSYPpGdGnStygb1Eqc5kBSEBu58HnCuIdM1oHYxVaoWWl52MKYc0+IIoKshDmdOmdShZwQQhZUY0ArRuNeab0IqJyi2XGVOuYJMH0qtYDPDXG3OuyBVqJ2Mi9XngqtE0DCJKze6KJmMteRxRbEe+1cJOxpRTvC0BVLocECZpBMkEIWVGWwMgo8Z8E+oaA1hsbFRCuYNNHkivOrYD0W5jzhW5wlXDCkJS+JkgJDICry8AIYDRBksQWiwClS4HGtjJmHKI1xdAlcGqdQfCBCFlhs/AFU3WPGDYWC4xptzhrwMcbjZ5IP1Rl8cbca7IFa5qpUozFtM6EtKarw7IL1aWnRORbnlbgtivxI6CPKvWoaRdpdvJCkLKGVJKeFuChlvOPxAmCCkzjL4nWsUEoOkbraMgUvhqmWAhfUpUv9ZoGoahlVUrVZrtjVpHQlpT5wqTLJMiMipvS8CwDRM8LgfquQch5Yjmjm4Ew1HDLecfCBOElBn+OkBYgZLRWkeSGeUTAN8WIBzSOhIidoEl/fLXARBAmUfrSIxLvXjAZcbk51xBZAReXwCVbmMmLDxuJ/yBMNpDYa1DIUpUs7KCkChVvjqgtFJZjmtEFRMAGQNaNmkdCZldLAa0ellBSPrkqwOKRwJ5BVpHYlxqdSYblZiblMpSc84VRLrWFYlie1vIwBWEys/l5T6ElAMSDYGYICRKkb/O2G9CKyYot1xmTFprb1SWD7IqhPTI6HNFLijzABCsIDS7ziYgHOBcQaRz2/whSGnchIUnXhnJfQgpFzT4lER1JZcYE6XI6Esehx0AQDBBSNrzG7ghEBmf0eeKXJBXoFRpsoLQ3IzcPI7IRBIVTQZNWOyuIGSCkLTnbQmgvCgfznyDrorsAxOElH7dAaBzp7HfhNocgKsGaGaCkDTGJg+kV5FuoG2rseeKXOGqZgWh2an//0zIE+ma0fdEK3PaUFSQl6jcItKS1xdApUGX8/eHCUJKP3+9cmv0hAU7GVMuYJMH0qtWLwDJhEU2lFWzgtDsfLXKbVmVpmEQUWq8LUHYrAIjSuxah5IRQghUuhysIKSc4G0JGjYZ3x8mCCn9zLLksXw8sGsjEI1oHQmZGZs8kF6ZZa7IBa5qpVoz0q11JKQVfx3gLAcKirSOhIhS4PUFMLrMAatFaB1KxnjcTu5BSJqLxiS2+YOGXc7fHyYIKf18JlnGUjFRaQ7BZVukJTZ5IL0yy1yRC8qqAch41SaZkq9O2RqFiHStoSVg+Iomj8sJb0sQUkqtQyETa2wNIhKThn+97YkJQko/fx2Q5wCKhmsdSWYlOhmv0zYOMjc2eSC98tUCFhtQMkrrSIxPvYjAC1rmxYtJRIbg9QUNvyeax+1AMBzFrk5WvZN2vC3KPpgeg7/e9sQEIaWfr1Z5EyqMW/oOACgfp9xyH0LSSqLJQ43WkRAlz1+n7J1psWodifGpfyO4D6E5RSNAawMvJhHpXEdXBC2d3agyeEWT+vNxH0LSkrrM3eivtz0xQUjpZ5aKJnspUDwKaF6vdSRkVmqTB1aFkB6ZZa7IBcUjlWpNtVEFmUvbViAW4VxBpHNqwszjNvaeaOqSznomCElD3pYALAIYWWbMhkD9YYKQ0ktKcy1jqRjPJcaknURXSpO83shYzDRXaM1iVao1ucTYnPzc75PICBIJQoMveayMN4Vo8AU1joTMzNsSwMhSB2xWc6XMMvbTCiFuEUJsFUKsiv87OVPHohwS9AFdbeZ5E1oxEWjeoCRGibKNXWDTRgjxByHEOiHEaiHES0KIMq1jMrSuDiCwyzxzRS4oq+YSY7Pyca4wEp5jmZc3njAzetMEZ34eyovyucSYNOX1BQ1frduXTKdD75VSzoj/eyPDx6JcYLaERfl4oLtDWb5DlG2+OmXZYPFIrSMxgrcBTJFSTgOwHsB1GsdjbGabK3KBq5oVhGblrwOEBSj1aB0JpQ/PsUzI2xJAYb4VLqdN61AyrtLlTOwBR6QFb0vA8NW6fTFXvSRlns9ky1jYyZi0xCYPaSOlfEtKGYl/+gmASi3jMbzEXFGjaRimUlatVG12dWgdCWWbrw4oGQ1YjZ9UIDKyBl8AHrcTwuiNIKFUSapdZImyLRSOYmd7l+GrdfuS6QThFfHlWo8LIVx9fYMQ4mIhxHIhxPKmpqYMh0MZZ7aqkIqJym0TG5WQBtjkIVN+AOA/fd3BOStNzDZX5AL1d80qQvPxc64woH2eYwGcs4zG2xJEpUkqmjwuB7b5g4jGuI0TZV9DYjk/lxgnRQixSAixpo9/pwP4M4CxAGYAaATwx77GkFI+IqWcI6WcU1FRkUo4lAt8dYC9TOnwawaF5YDDzQpC0gabPCRlH3OW+j03AIgAeLavMThnpYmvDrAVAs5hWkdiHmq1JvchNB8f5wq9Scc5FsA5y0iklPD6AqZJWHjcTkRiEo2trCKk7FOXt5txiXFeKg+WUh4/mO8TQjwK4PVUjkU6YcaERcUEoJkVhJRlbPKQtH3NWUKICwGcCuA4Kdl5KKPUucIEy6RyBisIzSkcBDq2A64arSOhJPAci/bU0tmNQHfUNAkL9ec0U9Uk5Y4GtWM4lxinjxCi5675CwGsydSxKIeYccljxQSlgpD5BMomLtFMKyHEiQCuAXCalJK7Ymear9Z8c4XWnMOUqk1WEJqL36vc8vVmGDzHMiezdDBWqZWSbFRCWvD6gsjPs6CiqEDrULIupQrCfbhLCDEDgARQC+CSDB6LckEsBvjrgQknah1JdpVPAII+oLMZKOLyDcoSNnlItwcBFAB4O7759ydSyku1DcmgpFSev2PmaR2JuQjBTsZmxItJRsRzLBPyJiqazLHEeFSZAxaxu5KLKJu8LQFUuhywWMy30iVjCUIp5fmZGptyVMcOINplvmUsFeOV2+ZvmCCk7Emc9NVoGoZRSCkP0DoG0wjsAsKdfO5qwVXDCkKz8dUqt6wgNAyeY5mT2fZEs1ktGFnqSFROEmWT1xdAlUmqdfeU6S7GZCaJN6E1WkaRfYlOxt9oGweZi68WyC8CnG6tIyFKTqL6lQmLrCurVv52cEsM8/DVAtYCoGiE1pEQUQq8LQEMK8xHYUEmFwDmFo/bgXpWEJIG6ncFTJOM3xMThJQ+Zl3GUjJaSdQwQUjZpO73ySYPpDf+WuXWbHNFLnBVK9WbgV1aR0LZ4q8DyqoAC9/yE+mZtyWISpNVNHlczsTSaqJsaQ2G0RaKmGY5/574boHSR60KKfVoG0e2CQGUj1eWGBNlixk7hpMxsIJQO+rvnMuMzcPHuYLICLy+ADwucyUsPG4ndrZ3IRSOah0KmUhiv09WEBKlyF8HFI8EbHatI8m+iglA03qtoyCzUJs8MMFCeuSvUzrqFhRpHYn5qIkitYqTjM/PuYJI76IxiW3+oGk6GKvUCq4G7kNIWdSg7vdpstebiglCSh8zJyzKxwPt24BQq9aRkBkkmjyY9PVG+mbmuUJrrCA0l6BfeV/CuYJI17a3hRCOStNVNKk/r9qghSgbvC1KQtpsrzcVE4SUPmZe8qg2KmneoG0cZA5cokl6Zua5QmsFRUr1pp8JQlPwc64gMoLEkkeT7YmmVnA1cB9CyiKvL4Biex5KnTatQ9EEE4SUHtEw0LbVvG9CKyYot03rtI2DzIFNHkivYlHA7zXvXJELyqpZQWgW6v+zq0bTMIgoNWbdE62iqAD5eRZ4ucSYssjbYt4OxgAThJQurV5AxsybsCirBqz57GRM2cEKQtKr9kYgFjbvXJELXNWsIDQL9f+ZrzciXfP6ghACGFVmrgpCi0Wg0uVgJ2PKKq8vaLpq3Z6YIKT0MHvCwpoHDBsHNLNRCWUBmzyQXvlqlVuzzhW5oKxaqeKMsSuk4fnqgIJSwOHSOhIiSkFDSwAjS+zIzzPfqbvH5eQehJQ1Uko0+FhBSJQ6XqUGKsZziTFlB5s8kF5xyaP2XNVKFWd7o9aRUKb56wBXldZREFGKvL4AKk3aUdXjdiSaRhBlWlNHF0LhmGk7GANMEFK6+OoASx5QMlrrSLRTPkH5PYQ5iVGG+euYYCF98tcBEECpR+tIzEv928F9CI2PF5OIDMHbEkSVSRMWVW4nWoNhtIXCWodCJqAmo836egOYIKR08dcpJ3wWq9aRaKdiAgAJ7NqodSRkZGqTBzNX65J++eqUC0l5+VpHYl5qwoj7EBqblLyYRGQAoXAUO9pDpl3yqP7c3IeQsqHBZ86O4T0xQUjp4atlwiLRyZiNSiiD2rYpywNZFUJ65K/jXKG1Ug8AsXs/SDKmjh1AJMS5gkjntvqDkNK8CQt1qScThJQN9buU51mlSRPyABOElC5cxgIMOwAQFiYIKbO43yfpGecK7eXlK1WcXGJsbD7OFURGoCbGzLon2u4KQm7hRJnn9QVQUVwAu828qyKZIKTUdXUAgWa+Cc0rAFxj2KiEMsvsHcNJvyJdSmMMs88VucBVzSXGRufnXEFkBF6fkhgz6xLjUqcNxfY8djKmrPC2BOFxmbNaV8UEIaXOX6/c8k2ossy4eb3WUZCRsckD6ZXfC0ByrsgFZdWsIDS6xMUkdjEm0rOGlgDy8ywYXlygdSia8bicXGJMWeH1BUxbratigpBSl1jyWKNpGDmhYgKwaxMQZactyhA2eSC98tcqt6wg1J6rWqnmjHRpHQllir8WKBwO5Jv7RIdI77y+ACrLHLBYhNahaMbjdiQqKYkyJRKNobHVvA2BVEwQUuq45HG38glKA4mWLVpHQkbFJg+kV5wrckdZNQAZr+okQ/KxgzGREXhbgqg0eUWTx+VEgy8AKaXWoZCBNbaGEI1J0zYEUjFBSKnz1wE2J1BYrnUk2qsYr9w2s1EJZQibPJBe+WoBaz5QPFLrSEi9yKBWdZLx8GISkSF4fQHT74nmcTsRCsfQ1MGqd8qcREMgVhASpUhNWAjzlr4nlMcThOxkTJnAJg+kZ/46Ze9MC996aE69yMB9CI0pGgFat/JiEpHOtYfC8AfCpt8TTa3oYidjyiS1EY7ZX298l06p41Xq3QqKgZJKJggpM9jkgfTMx7kiZxSPVKo52cnYmNoaABnl641I59SEmNkrmtSfv4GdjCmDvC1BWC0CI0vtWoeiKSYIKTVScp+bPVWM5xJjyoxEk4caLaMgGho/54qcYbEo3W1ZQWhM3O+TyBDUiqYqk1c0VcYThOxkTJnk9QUwqsyOPKu5U2Tm/ukpdUEf0N3ON6E9VUwEmjcAsZjWkZDRqCd9rAohvQm1KfMF54rcUVbNCkKj8nOuIDKCxJ5oJm+a4Mi3oqK4gEuMKaO8LQHTV+sCKSYIhRBnCSG+EkLEhBBz9rjvOiHERiHEN0KIE1ILk3KWL96tl29CdysfD4QDQCu7Q1Ka+WoBawFQtJ/WkRiWEOJ/hRBSCMGuS+nEhEXucVUrf1PIeHy1gLAqW56QbvE8i7wtARQX5KHUYdM6FM15XA7Us4KQMqi+JcgEIVKvIFwD4AwAH/b8ohBiEoDvApgM4EQADwshrCkei3IRl7HsrWKictu8Xts4yHj8dUAZmzxkihDCA2A+gHqtYzEczhW5p6xaqeoMtWkdCaWbrw4oHQ1Y87SOhFLD8yyT8/qCqHQ7IdgIEh63M7Hkmijdgt1RNHd0mb5aF0gxQSil/FpK2ddma6cDeE5K2SWl3AJgI4CDUzkW5ShWheytYoJy27RO2zjIeNSO4ZQp9wK4BoDUOhDDScwVNZqGQT2o8zaXGRuPn3OFEfA8i5Qlj0xYAEqjksbWECJRbuFE6dfADsYJmSpDGQ2g5/rKhvjXyGh8dYDDrXTvJYXTDTjL2cmY0o8dwzNGCHE6gK1Syi+0jsWQfHVAfjHgcGkdCanUBBIblRgPO4YbHc+zTEBKiQZfkAmLOI/bgWhMorE1pHUoZEBqdWollxhjn2sPhBCLAPS14dUNUspXUg1ACHExgIsBoKqqKtXhKNuYsOhbxUQuMab0YpOHlA00nwG4Hsry4n2NwTlrKNS5gsukcodazckKQmPpDgCdO1mtqxM8z6L+NHd0IxiOsoIwztOjkzGTppRuagMcLjEeRIJQSnn8EMbdCsDT4/PK+Nf6Gv8RAI8AwJw5c7isS298dcB+U7WOIvdUjAfWvAhIyRNiSg8u509Zf/OZEGIqgDEAvojv81MJYKUQ4mAp5fY9xuCcNRS+WsA9VusoqCeHS6nqZAWhsfjjW6iW1WgaBg0Oz7OoP14ueexF/T1wH0LKBG9LAHabBRVFBVqHorlMLTF+FcB3hRAFQogxAMYBWJahY5FWYjGlUy8TFnsrnwCEWoGOnVpHQkbBJg8ZI6X8Uko5XEpZI6WsgbJca9aeyUEaIimVpAXnitwihPJ/wgpCY+HFJDPgeZYJeFuYIOxpZKkdVotIVHoRpZPXF0Cliw2BgBQThEKIhUKIBgCHAfh/Qog3AUBK+RWAFwCsBfBfAD+RUkZTDZZyTHsjEO1mwqIvaqOSZu5DSGnCJg+kV51NQDjAuSIXlVWzgtBoeDHJMHieZW4NPiURVsklxgCAPKsFI0vtrCCkjPC2BLmcP26fS4wHIqV8CcBL/dx3O4DbUxmfchyvUvcv0cn4G2DMPG1jIWNgk4esiVcRUrr4OFfkLFc1sPk9bodhJP46IM8BFA3XOhJKEc+zzM3bEkB5UT6c+SmdrhuKx+VMVFYSpZPXF8CcGp5jAZlbYkxmkLhKXaNpGDmpeCRQUMJOxpQ+bPJAeuVnRVPOKqtWqjs7m7WOhNLFVwuUVXGuINI5dckj7eZxO+D1cYkxpVdrIIz2UCTRCMfsmCCkofPXARBAmWef32o6QgDl47nEmNLHV8flxaRPvlrllhWEuYedjI1HvZhERLrmbQmiivsP9lLldqKpvQvBbq6op/RhQ6DemCCkofPVAiWjgDx2++lTxQRWEFJ6SKmc9LECi/TIXwcUVgD5hVpHQntSE0lqEpf0TUrlYhLnCiJdi0Rj2OoPwuPmnmg9qQmcBu5DSGlUn2gIxNcbwAQhpYJvQgdWMQHo2AEEfVpHQnqnNnlgVQjpEeeK3FVWpdwyQWgMQR/Q1ca5gkjnGltDiMYklzzuQV1yzUYllE7sGN4bE4Q0dFzGMrBytVHJem3jIP1jV0rSM84VuSu/UKnu5BJjY+B+n0SGwCWPfVMrvLwt3IeQ0sfrC6DUYUOJ3aZ1KDmBCUIamkgX0LaNb0IHUjFeueU+hJQqdgwnvYpGgNYGzhW5rKx690UI0rdEx/AaTcMgotQ0xBNgrCDsraKoAHabhZ2MKa28LVzO3xMThDQ0rQ0AJBMWAymrBvLs3IeQUqcu/1OXAxLpRdtWIBbhXJHLXNWsIDQKXkwiMgSvLwCLAEaW2bUOJacIIVDpcnKJMaWV1xdgMr4HJghpaBIJC74J7ZfFCgwbxwQhpY5NHkivuOQx95VVKxf9YuwKqXu+OsBeBthLtY6EiFLgbQlgZKkDNitP1ffkcTm4xJjSJhaTaPAFuZy/B/7VoaHhVerBqRjPJcaUOjZ5IL3yca7Iea5qpcqzbavWkVCquN8nkSF4fVzy2B+PmxWElD5NHV3ojsTgcfH1pmKCkIbGVwdYbEDxSK0jyW0VEwG/F+ju1DoS0jOe9JFe+esAYQFKPVpHQv1RLz5wH0L948UkIkPwtnDJY388LifaQxG0BsJah0IGoO5nWckKwgQmCGlo/HVAmUdZRkv9Kx8PQALNG7SOhPQqFmWTB9IvXx1QMhqwsjNczlIvPnAfQn2LxQB/PS8mEelcKBzFzvYuLnnsR6KTMasIKQ0SHcOZkE9ggpCGhlepB6dionLbvF7bOEi/2OSB9MzPuSLnlXqUKk9WEOpbxw4g2sXXG5HONfjiHYy5xLhPlfFEDjsZUzqo+1lWcolxAhOENDT+OsBVo3UUuc+9PyCsQNM6rSMhvUrs4VajaRhEQ+LjXJHzrDagpJIVhHrn51xBZARqRVMVKwj7VDUsniBkBSGlgbclgBElBbDbuCpSxQQhJa+rHQjsYkXTYOTlK0lCdjKmoWLHcNKrcBDo2M65Qg9c1bv/1pA+ca4gMgS1Mo5LHvtWYreh1GFDPSsIKQ3qud/nXpggpOSpFU18Ezo4FRO4xJiGLtHkoVLrSIiS4/cqt5wrcl9ZNZcY613ivVmVtnEQUUq8LQEU5FlQUVygdSg5y+N2JJaGEqWiwRfkfp97YIKQkpdYxsKTvkGpmADs2gREurWOhPTIV6cs/2OTB9IbzhX64apWqj3DPOHSLX8dULQfYLNrHQkRpcDbEkSlywEhhNah5CyPy8klxpSycDSGxtYgPNx/sBcmCCl5iavUNZqGoRvlEwAZBVo2ax0J6ZG/jgkW0icuedQP9f9Irfok/eF+n0SG4PUFWNG0Dx63Ew2+IGIxqXUopGPb/EHEJFDJ11svTBBS8vx1QH4R4HRrHYk+VExQbpu5DyENATuGk175agFrAVA0QutIaF/UixBsVKJfvJhEZAhe7om2Tx6XA92RGJo6urQOhXRMXabO11tvTBBS8tSEBUvfB6d8nHLLRiWULDZ5ID3z1yn7oVn4ViPnqRch2KhEn6JhoG0rLyYR6VxrMIy2UAQeN5c8DkSt+PKyUQmlQF2mztdbb3zXTsnjVerk5BcCpVVMEFLy2OSB9MzHuUI3ikYo1Z6sINSnVi8gY3y9EekcOxgPjvr74T6ElApvSwB5FoGRpUwQ9sQEISVHSi55HIqKCVxiTMljkwfSMz/nCt2wWJRqT3Yy1qfE3tB8vRHpWUOiookJwoFUxptKsJMxpcLrC2JUmQNWC1dF9sQEISUnsAsIdzJhkayKCUDzBiAW1ToS0hM2eSC9CvqBUCvnCj1xVbOCUK94MYnIELgn2uDYbVYMLy7gEmNKibclwOXFfWCCkJLDq9RDUzEBiIQAf73WkZCe+OvY5IH0yc+5QnfKqllBqFe+OsCSB5SM1joSIkqB1xdAsT0PpU6b1qHkPI/bySXGlJIGHxsC9YUJQkqOv1a5ddVoGYX+lMc7GXMfQkqGuocbmzyQ3qiJJs4V+uGqAUJ+pfqT9MVfB5RWAhar1pEQUQq8LQFUcXnxoFS5nVxiTEMW6I6guaOby/n7kNJZpxDiLCHEV0KImBBiTo+v1wghgkKIVfF/f0k9VMoJiSWPVZqGoTsV45Vb7kNIyfDVsgIri4QQPxVCrIvPa3dpHY+uccmj/qj/V1xmrD+cKwyJ51nmU9/CiqbB8rgcaGwNIhyNaR0K6VBiOT8ThHvJS/HxawCcAeD/+rhvk5RyRorjU67x1QHOcqCgSOtI9MXhUpaJNq3XOhLSE38d4DlY6yhMQQhxDIDTAUyXUnYJIYZrHZOu+eqAglLlbx/pg5pg8tUBI6drGwslx1cHTDxZ6ygo/XieZSJSSjT4gjh2It9+DEal24mYBLb5g6geVqh1OKQzuzuGcw/CPaWUIJRSfg0AQrDzi2n461gRMlTl44GmdVpHQXqhNnlgVUi2XAbgDillFwBIKXdqHI+++esAFyvNdYUVhPrU1QEEmrmc34B4nmUuTe1d6IrEWNE0SGqlpbeFCUJKnpcdw/uVyY2txgghPhdCfCCEmNvfNwkhLhZCLBdCLG9qaspgOJQWvjomLIaqYgLQvB6QUutISA+4RDPbxgOYK4T4ND5vHdTXN3HOGiQuedQfh0up+mSjEn1Rm5/x9WY2PM8ymETCgkuMB0XtPstGJTQU3pYgHDYrhhXmax1KztlnBaEQYhGA/fq46wYp5Sv9PKwRQJWUcpcQYjaAl4UQk6WUbXt+o5TyEQCPAMCcOXOYOcllsSjQ2gBMXqB1JPpUMRHoagPatwMlI7WOhnIdO4an3UDzGZT50A3gUAAHAXhBCLG/lL0z+pyzBkFKJWkxbr7WkVCyXFWsINQbPxsC6RnPs0i1e080LnkcjJGlDuRZRGKpKFEyvL4APG4HK7T7sM8EoZTy+GQHjS/RUpdprRBCbIJSnbE86Qgpd7RtA2JhJiyGqjzeqKRpHROEtG+sIEy7geYzIcRlAP4dTwguE0LEAJQDYMlFsjp2AJEQ5wo9KqsGmjdoHQUlgxeTdI3nWaRSE12VrCAcFKtFYFSZA14fOxlT8rxsCNSvjCwxFkJUCCGs8Y/3BzAOwOZMHIuyiAmL1FRMUG6b2aiEBoFNHrLtZQDHAIAQYjyAfADNWgakWz7OFbrlqlGqP7kVhn746wCbEygs1zoSyhKeZxmT1xdARXEB7Dar1qHohsftYAUhJU1tCMT9B/uWUoJQCLFQCNEA4DAA/08I8Wb8rnkAVgshVgH4F4BLpZQtgx54y4fAkvsGH8iS+5THcIzM4lXqoVtyH9D0DWAvVW4B/T6/OEZ2sMlDtj0OYH8hxBoAzwG4YM/lxTRIfs4VulVWDUSCQAd79OiGujc0l0kZTsbOsygneVuC7KiaJI/LiQbuQUhJ8gfC6OiKoJKvtz6llCCUUr4kpayUUhZIKUdIKU+If/1FKeVkKeUMKeUsKeVrgx50y4fAPy8ERs8afCCjZymPUU/0zT5GpvjrAAig1KN1JPozehbwr4uAopFKglDPzy+OkR1sCJRVUspuKeX3pJRT4vPWu1rHpFuJi0lMcOsOOxnrj7+O1boGlZHzLMpZyp5orGhKhsftRHNHNwLdEa1DIR1hB+OB7XMPwqzatgp46tuAxQY8c2Zyj5Ux5bHCCsioMcYYfRDg2wyc9SQwZl5yY2SCrw4oGQ3ksdtP0sbMU/4fnz0TaF4HPLVE++cXx+gxxmnA1DOBTe/mxust0eThW9rGQTQU/lqgcDiQzzdeuqNelPDVAZ6DtY2F9k1K5f+q+gitIyGiFESiMTS2hrgnWpLUCrAGXxDjRxRrHA3pRaIhEF9vfcqtBCEkUHkIUH3Y0B5e9zHQ8Kkxxvj6NWDrZ8DUs7RPVqj8deySl4ox84CZ5wOf/VX75xfH6G3FU8CX/wQOvzI3Xm8dO5Vlfny9kR75OFfollr16a/VNAwapKAP6G5nBSGRzjW2hhCNSVSxoikp6u/L2xJggpAGbXcFIZcY9yW3EoTF+wEtG4Hjbkz+JH3Lh8DnTwPzrgGWP2aMMRxuYM2LSpJw/AnJjZEJvlpg7LFaR6FfWz4Evnopd55fHGP3GCueUD5e9n/AuOO1TxL6apVbLjEmPfLXAZ5DtI6ChiLfCRSN2P03iHKbb4tyy7mCSNfq1Q7GTFgkRV0iWs9GJZSE+pYAXE4biu02rUPJSRnpYjxkxSOV5X099wQbDHXfsLOeBI69wThjnPMPZfnjC99PboxMCIeA9ka+CR2qXHx+cYzdY5z9DHDo5UAkBDx3nvavN3YMJ72KhoHWrZwr9Kysevc+kpTbEh3DazQNg4hSo3bi5ZLH5AwrzIfDZk0sGSUaDG8L9/scSG4lCIHde7VtXTn4x2xd2XvfMKOMUXUocNCPlaTF2lcHP0YmtHqVWyYshiYXn18co/cYx96oLK8rKAK8ywY/RiawyQPpVWuDsh8o5wr9clWzSYle8GISkSF4fQFYLQIjS+1ah6IrQgh43I7EklGiwWjwBZmMH0BuLTFWjZmX3BK/I68y7hjH3wx88x+gdjEQ6QLyCgY/VjolEhZ8Ezokufr84hi7P84vBL59P/D0QqC7c/BjZIK/VlnmZ+NSE9IZP+cK3SurBtb8G4hGAGtuvk2kOF+dsh1NAffeItIzb0sQo8rsyLPmXu1OrvO4nIkKTKJ9icUktvqCmD95hNah5Cz+Fcp1BcXAt+8DmtYBi+/RLg51w3JepSYjG3ssMOM8YOn9QONq7eLw1THBQvrkY0WT7rmqlSrQtgatI6F98dfxtUZkAF5fgBVNQ+RxO9HgC0JKqXUopAM72kPojsb4ehsAE4R6MO5bwLSzgcV/BHas1SYGXx1gLQCK9tPm+ETZMv82wDkMePUKpYJGCzzpI73y1wHCCpRUah0JDZV6cYL7EOY+XkwiMgRvC5c8DlWly4GOrgj8gbDWoZAOqPtVcg/C/jFBqBcn/B6wlyhJi1g0+8f31wFlHsDCpwwZnNMNnPwHoPEL4OMHs3/8aIRNHki/fHVA6WguTdUz9eIE9yHMbbGYsj80LyYR6VqwO4rmji542MF4SNRED/chpMHY3RCIr7f+MNujF4XDgJPuArauAD79S/aPz6vUZCaTTgcmngq8/3tg16bsHruNTR5Ix/ycK3SvpFKpAmUFYW5rbwSi3Xy9EelcQzyxxYqmoVErL9nJmAbD6wtACGA0E4T9YoJQT6b8DzD+RODd24CWLdk9Npc8kpkIAZx8t7Ks/tWfKZUa2cKGQKRnPs4VumfNU6pAWUGY29jBmMgQ1Mq3Si4xHhK18pIVhDQY3pYgRhTbUZBn1TqUnMUEoZ4IAZxyj3Jl//WrgGxtxhpqA4I+JizIXEpGAvN/C9QtAVY+mb3j8qSP9Ko7AHTuBMpqtI6EUlVWzQrCXJe4mFSjaRhElJrde6Kxomkoiu02lDlt7GRMg+L1Bfha2wcmCPWmdDTwrVuBze8Dq57NzjGZsCCzmvV9oGYu8PbNQNu27BzTxyYPpFP+euWWc4X+uapZQZjr/HUAhLI/NBHplrclALvNgoqiAq1D0S2Pywmvj0uMad8aWtgxfF+YINSj2RcB1UcAb14PtO/I/PF8tcqtqybzxyLKJUIApz0ARMPA61dnp2rXVwuUVrLJA+lP4mJSjaZhUBq4aoCOHUpVKOUmXy1QPBLIY1KBSM/q4wkLIYTWoehWldvJCkLap65IFI1tIe73uQ9MEOqRxQJ8+wEgHALe+EXmj8c90cjM3PsDx94ArP8P8NW/M3887vdJeqVeTOJcoX/qslW1KpRyj6+OyXgiA/D6gkxYpKjS7cBWXxCxWJa23yJd2uYPQUo2BNoXJgj1qvwA4Ohrga9fBda+mtlj+euAghLA4crscYhy1SGXAaNmAm9cAwRaMnssdgwnvfLVAXkOoGi41pFQqtSLFFxmnLt4MYlI96SU8SWP3BMtFR6XE93RGHa0h7QOhXKYWmXK19vAmCD8/+zdd3hc1bX38e9WnVG3JHfZliybYmzZBvcKhN4JJWCa4V6IAw6B3FySvLk3lJCbRhJCTEJIAFMTWiBA6GCKOwIbV4wt925JltXr7PePMyPLtrpGmjPS7/M8ekZz5sw+62hmtGfWrL13OJvyXeiX41QRVhzsvOMEEhYqfZeeKjIKLpoHlUXw9o877ziBRR70oU/CUdE2SBmsvqI7CHxJoYVK3Km2ypkXV18miYS1QxU1lFTVqqKpgwJ/v8CCLyKNCax0rddb85QgDGeR0XDxPCjLh3f/t/OOo2+pRaDfSJh2J6z6B2x8v3OOERjOp1UpJRwdVF/RbST0capBVUHoTod2AlavN5EwF0hoZWjRhA4JVIRpHkJpzo7CCqIjDX2TPKEOxdWUIAx3/Uc7lYQrnnZWNg42a52khb6lFoEZ/w3px8Ebd0BVSfDb14rhEq6s9VcQ6rnbLRjjVIMG5pUUd9F8nyLdwuGKJg157IiBvbwYc/jvKdKYHQfLGZjiJTJCI12aowRhd3DqjyA1G17/HlSXBbftsgNQU66EhQg4q0Ve9EeneuODnwW/fS0IJOGq4iBUFauv6E56DVEFoVvpyySRbqF+TjQNeeyQ2KhI+iZ6NMRYmrWzsFyvtVZQgrA7iPY6SYuDW2HB/wW3bSUsRI40eBJMuBmWPwrblwW37SIt8iBhqkh9RbeTMgQOahVjVzq4DSKiIbF/qCMRkQ7YcbCcZG80SZ7oUIcS9galelVBKM3acbBCw/lbQQnC7iJzKoy7CZb+CXZ+Hrx29S21yLG+8VNIzoDX5kJNEFdMO7hVizxIeDqovqLb6TUEqg517iJo0j5F2yBlEEREhjoSEemAHYUVGl4cJIN6xbFTcxBKE8qqaiksq9brrRWUIOxOzrgXEvo5SYva6uC0WT/PzeDgtCfSHcQmwgUPQv7X8OkDwWtXCwJJuFIFYfejlYzd66Dm+xTpDnYcLGeQKpqCIiM1jj3FlVTX+kIdirhQ/Xyfer21SAnC7sSTBBf8Dvavg0UPBqfNom0Q3xti4oPTnkh3MfwMyLkKFv4e9q7peHvW6kOfhK+D28CTDN6UUEciwRL4skLzELqPvkwSCXs+n2XnwQrNiRYkg3p5sRZ2F2keQjlWYH5Kvd5a1qEEoTHmN8aYr4wxq4wxrxhjUhrc9mNjzCZjzAZjzNkdjlRa5/hzoe9I+PhXsP+rw9u3fAILH2x9OwsfdO5zcCv0ymxfGyLdXa8hEB3nVO3W1Trb2vtaq1/kIVOvtRAwxowxxiw1xqw0xuQaYya0+s7tfcy7UxtF29RXdCcLH4Ti3c7vgZEE4f4cDfc2AvevKoHyAvUVPUBnfc5anJfPIx/ntXr/Rz7OY3FevtoIcgz7S6qorvUxKDWuzW3IkR75OI/iyhoAtvuHGYfj8ysYbbghBje2EXheDNbrrUUdrSB8Dxhprc0BvgZ+DGCMGQFcBZwEnAP8yRijiVK6yswfgq8OXrjBudzyCbw4Gwae3Po2Bp7s3Gf/BqeiqT1tiHR3mdPA+mD3Clj254691ta96lyvLtNrLTR+DdxrrR0D/NR/vWUdecwDyYLu0Mb+deorupOBJ8O/boPoeKc6NNTPL7Vx+P5r/ulcry7Xa637C/rnrMV5+cx9bgU5GcmtDiInI5m5z62o/6Dek9sIZgxvrdkDQGllTZvbkCPlZCTzxw82Ac5Q0nB9fgWjDTfE4MY2lm8pID4mkq/2FOv11gJjrQ1OQ8ZcClxurb3GGPNjAGvtL/y3vQPcY61d0lwb48aNs7m5uUGJp8d7/15Y+DvwpDjfNidntH2YcHWZUxUyaBIUbIQr5kPWjM6IViR8bf4Ynr3MScZj2v9aO7TDSTZ6kuFbz+i1BhhjPrfWjuuiY70DPG6tfd4YczVwobV2VnP3GZcRa3Nvju/AY74TvL2c6tFwb6NoG2SMh8LN6iu6iy2fwNOXgIl0/r+F+3O0O7ShvsLVOrPPCsbnrPiBx9l+1z/IgBQPcTFRbTp+eXUtu4sqSYmLpqi8pke3EawYdhVV4LOQ7I3mz9eezJTs9Da1IUdauDGfax9bhjc6gupaG7bPr2C04YYY3NbGzoMVpMbHYIF5s8bq9UbTfVbb/rrNuwl43v/7QGBpg9t2+rc1FtgtwC0AgwdrIYyg+cZPYedy2LoQ0odD7xPa1060B3YshRl36U2oSGOGzoTxNzsriHfktRYVA/kbYfx/6rUWGncA7xhjHsCprp/S2E4N+6xT+kdA2tCOP+bBeN6EvA0P7PxMfUV3kjUDjjsPvnrdBc8vtXHM/cepr+hhOvw5K6bfMDLT4xjeJ7FdAcRERZB3oIzs3vE9vo1gxnDdpCFKVgTBtOHpTM5OY0leQdg/v4LRhhticGMbt58+TK+3FrSYIDTGvA/0a+Smn1hr/+Xf5ydALfBsWwOw1j4KPApOBWFb7y9N2Pop7F/vfFjLfQwm3NL2N5KB4S+BNrKm682oyNG2fAKrng/ua23oqXqtdYLm+jPgG8Cd1tqXjTFXAo8BZxy94xF91vEDLeWFwXnMu1Mb6iu6hy2fwPbF7nt+9eQ2jr5/9ql6rYW5rvycNei4kfZgeQ3XT2l7QiowtO/204fxzLLtPbqNzohhyrA0JS06aHFePhv2loT98ysYbbghBje3MSlbr7dmWWs79APMBpYAcQ22/Rj4cYPr7wCTW2rrlFNOsRIEmz+29ldZzmVj17uqDZHuTq+1TgXk2g72Ua39AQ5xeNoNAxS3dJ9TTjkldI95d2pD3Mctzw21EdwYpFN1Rp8V7M9ZizYdsGPve9cu2nSg1ed19H16chtuiEGO5ZbHxQ1tuCGG7tZGd9VUn9XRTuscYB3Q+6jtJwFfArFAFrAZiGypPSUIg+TT3x/7hnHzx872rmxDpLvTa61TdXGCcD1wqv/3bwCft3Sf+j4rFI95d2pD3Mctzw21EdwYpFMFu8/qrM9ZizYdsH/+aFOrz+vPH2065sN0T23DDTHIsdzyuLihDTfE0N3a6K6a6rM6tEiJMWaTv3Mq8G9aaq2d47/tJzjzZdQCd1hr32qpPS1SIiIiAV28SMk04A84U29UArdaaz9v7j7qs0REJCDYfZY+Z4mISGfplEVKrLXDmrnt58DPO9K+iIhIV7DWLgROCXUcIiIioM9ZIiLS9SJCHYCIiIiIiIiIiIiEjhKEIiIiIiIiIiIiPZgShCIiIiIiIiIiIj2YEoQiIiIiIiIiIiI9mBKEIiIiIiIiIiIiPZgShCIiIiIiIiIiIj2YEoQiIiIiIiIiIiI9mLHWhjqGesaYEmBDqOMIkmTgUKiDCJLuci7d5TxA5+JG3eU8wD3ncry1NjHUQTRFfZZrdZdz6S7nAToXN+ou5wHuORf1WV3HLY95R3WX8wCdixt1l/MAnUtnaLTPigpFJM3YYK0dF+oggsEY86i19pZQxxEM3eVcust5gM7FjbrLeYB7zsUYkxvqGFqgPsuFusu5dJfzAJ2LG3WX8wD3nIv6rK7jlse8o7rLeYDOxY26y3mAzqWT4mi0z9IQ487zeqgDCKLuci7d5TxA5+JG3eU8oHudi7ROd3rMu8u5dJfzAJ2LG3WX84DudS7SOt3lMe8u5wE6FzfqLucBOpcu47Yhxrnd5ZstERHpGLf3CW6PT0REuo7b+wS3xyciIl2nqT7BbRWEj4Y6ABERcQ239wluj09ERLqO2/sEt8cnIiJdp9E+wVUVhCIiIiIiIiIiItK13FZB6FrGmHOMMRuMMZuMMT/yb3vMGPOlMWaVMeYlY0xCE/f9sf9+G4wxZzfXZojOwxhjfm6M+doYs94Yc3sT973BGLPR/3NDg+2nGGNW+9t8yBhjQngupxtjvjDGrDHGPGmMaXQhHjedizHmcWPMfmPMmgbbfmOM+cr/3HrFGJPSxH0bfQ4ZY7KMMcv82583xsR09nn4j9vYudxjjNlljFnp/znP7efSxHmMMcYs9Z9DrjFmQhP3dc1zy3/cQcaYBcaYdcaYtcaY7/m3X+G/7jPGNDnkyE2Pi7ROE/8bw66/auZcjFGfFbJzaeL/Y9j1WU2cR9j1V82cS9j1WUb9VY/UxP9G9VmHt4fitdgt+iv/cdVnueg8mjkX9VmHt4fkcalnrdVPCz9AJJAHDAVigC+BEUBSg31+B/yokfuO8O8fC2T524lsqs0QnceNwFNAhH+/Po3cNxXY7L/s5f+9l/+25cAkwABvAeeG8DHZARzn3+c+4D/C4FxmACcDaxpsOwuI8v/+K+BXrf0b+G97AbjK//sjwHe66LXS2LncA/ygPY9nqM6lifN4N/B8AM4DPnL7c8t/3P7Ayf7fE4Gv/a+VE4HjgY+AceHwuOinVY93t+ivWjgX9VnqszrrPO4hzPqrZs4l7Pos1F/1uJ9m/jeqzwrRa7GZ8wi7/sp/XPVZLjqPZs5FfZZL+qxOrSBs4tuHVmVEjbu+EZoAbLLWbrbWVgP/AC621hb7YzKAF2hsvPbFwD+stVXW2i3AJn97jbYZivMAvgPcZ631AVhr9zdy37OB96y1hdbag8B7wDnGmP44nfhS6zyLnwIu6eTzaOpcLgOqrbVf+/d5z7/taK46F2vtJ0DhUdvetdbW+q8uBTIauWujj6f/+Xg68JJ/vyfpmsek0XNpJVedSxPnYYEk/+/JwO5G7uqq5xaAtXaPtfYL/+8lwHpgoLV2vbV2Qwt3d9Xj0tm6SZ/VXfqrJs8F9Vnqs4Kgu/RX0H36LPVXrddN+itQnxXgptdit+mvQH0WLjsPUJ/l57rHJaDTEoTGmEjgYeBcnEzq1caYEThZ+t9ba4cBB4H/aOS+I4CrgJOAc4A/GWMim2mzsw3E+dYkYKd/G8aYJ4C9wAnAH/3bLjLG3NfCfZtssxM1dcxs4Fv+ct63jDHDAYwx44wxf2vhvgP9vx+9vbM1Fk8/IKpBKe/lwCBw/bm05Cacb0Iwxgwwxrzp397UeaQBRQ06Pjecx1zjlPE/bozpBWF5LncAvzHG7AAeAH4M4fXcMsZkAmOBZc3sE26PS1B0oz6ru/RXzcWjPstF/1caEe59VnforyDM+yz1V03rRv0VqM9y42uxJ/VXoD7LLedxB+qzXPG4dGYFYVPforQmI+q2b4SaZK29ERiAkzH+ln/ba9ban4YyrjaKBSqts8z1X4HHAay1udba/wxpZG1jcd70/N4YsxwoAeogLM8FAGPMT4Ba4FkAa+1ua22j80u42J9x3hyNAfYAv4WwPJfvAHdaawcBdwKPQfg8t4wzf8/LwB2Bb+YbE4aPS7B0+z6rm/RXoD7LtbpBn9Vd+isI4z5L/VWLun1/BeqzXKbb9VegPstl1Ge5RGcmCJvKijaaEXX5N0K78H9L4pfh3waAtbaOw+XXrb1vs212kqaOuRP4p3/bK0BOG+67iyPLsrviPJqMx1q7xFo73Vo7AfgEZy6AVt2X0J1Lo4wxs4ELgGv85dJHa+o8CoAUc3jy4JCeh7V2n7W2zjpDK/6K8yb0aOFwLjdw+HXyIm07j5A+t4wx0Tgd17PW2n+2tH8D4fC4BEt36bO6S3/VXDzqs1zwf+Vo3aHP6kb9FYRpn6X+qlW6S38F6rNaum8oXovdvr8C9Vm46Dz81Ge55HFxzSrGLv9G6DNguHHm9ojB+QblNWPMMKifH+Mi4KtG7vsacJUxJtYYkwUMx5lEs9E2Q3EewKvAaf59ZtL4P/x3gLOMMb385ctnAe9Ya/cAxcaYSf6/w/XAvzr5PKDpx6QPgDEmFvghzsSebj+XYxhjzgHuAi6y1pY3sVujfwN/J7cAp/wfnH+4ITkPAOPMCxFwKbCmkd3C4Vx247w+wPmWfmMj+7juueU/3mPAemvt79p493B4XELCxX1Wd+mvmjwX1GeF+lyO0V36rG7UX0EY9lnqrzqHi/srUJ8V4KbXYrfur0B9Fi47Dz/1WW55XGznreoyGecBC1z/sf8nn8OrBh2xz9H7Nrj+jn/fRtvsrHM4KqbzcP6h5wE/wUmuLgJW47wYn8W/4hZOR3Zfg/v+xH+/DTRYUefoNkNxHv5tKcC//eeyBBjt3z4O+FuD+96EMxRhE3Bjg+3j/H+DPGAeYEJ4Lr/BGYqwAafMF7efC/B3nLLwGpxvGf/DH9cOYKX/5xH/vgOAN1t6DuGsiLTc386LQGwXPSaNncvT/ufWKpw3Sv3dfi5NnMc04HOcVaaWAae4/bnlP+40nKEhqxo8n87DeSOxE6gC9uH/3+rmx6WT/07dps86+jEjTPurpo6L+iz1WZ13HmHXXzVzLmHXZ6H+qrV/p27TXzX2uKE+yw2vxW7RX/mPqz7LRefRzLmozwrx4xL4Mf4ggs44ZZFfA9/AKYv8DJiFsxz3y9bafxhjHgFWWWv/dNR9TwKewyktHQB8gPOtkGmsTWvt2k45CRER6RHUZ4mISDhQfyUiIp2l04YYW2cOjLk430ytB17wdzI/BL5vjNmEs0rLY3Dk/Bj+/V4A1gFvA7dZZ3x9U22KiIi0m/osEREJB+qvRESks3RaBaGIiIiIiIiIiIi4n2sWKREREREREREREZGupwShiIiIiIiIiIhID9YpCUJjzDnGmA3GmE3GmB/5t831X7fGmPRm7nuqMeaNzohLRETkaOqzREQkXKjPEhGRzhL0BKExJhJ4GDgXGAFcbYwZgbNc/RnAtmAfU0REpD3UZ4mISLhQnyUiIp2pMyoIJwCbrLWbrbXVwD+Ai621K6y1W9vSkDFmgjFmiTFmhTFmsTHmeP/22caYfxpj3jbGbDTG/Dr4pyEiIj2A+iwREQkX6rNERKTTRHVCmwOBHQ2u7wQmtrOtr4Dp1tpaY8wZwP8Bl/lvGwOMBaqADcaYP1prdzTejIiISKPUZ4mISLhQnyUiIp2mMxKEwZQMPGmMGQ5YILrBbR9Yaw8BGGPWAUM4ssMUERHpSuqzREQkXKjPEhGRI3TGEONdwKAG1zP82xpljHnHGLPSGPO3Rm7+GbDAWjsSuBDwNLitqsHvdbg/2SkiIu6jPktERMKF+iwREek0nfHP/jNguDEmC6fDugqY1dTO1tqzm2krmcOd3uxgBSgiIuKnPktERMKF+iwREek0Qa8gtNbWAnOBd4D1wAvW2rXGmNuNMTtxvula1cQ3WeAkLQPfWv0a+IUxZgX65kpERIJMfZaIiIQL9VkiItKZjLU21DEcwRjzPWCgtfauUMciIiLSHPVZIiISLtRniYhIc1z1bZEx5jFgJHBlqGMRERFpjvosEREJF+qzRESkJa6rIBQREREREREREZGu0xmrGIuIiIiIiIiIiEiY6NQEoTFmkDFmgTFmnTFmrX/eC4wxqcaY94wxG/2XvfzbrzHGrDLGrDbGLDbGjG7Q1uPGmP3GmDWdGbOIiPRM6rNERCRcqM8SEZFg6+wKwlrgv6y1I4BJwG3GmBHAj4APrLXDgQ/81wG2ADOttaOAnwGPNmhrPnBOJ8crIiI9l/osEREJF+qzREQkqDo1QWit3WOt/cL/ewmwHhgIXAw86d/tSeAS/z6LrbUH/duXAhkN2voEKOzMeEVEpOdSnyUiIuFCfZaIiARbl81BaIzJBMYCy4C+1to9/pv2An0buct/AG91TXQiIiKHqc8SEZFwoT5LRESCIaorDmKMSQBeBu6w1hYbY+pvs9ZaY4w9av/TcDquaV0Rn4iISID6LBERCRfqs0REJFg6vYLQGBON02k9a639p3/zPmNMf//t/YH9DfbPAf4GXGytLejs+ERERALUZ4mISLhQnyUiIsHU2asYG+AxYL219ncNbnoNuMH/+w3Av/z7Dwb+CVxnrf26M2MTERFpSH2WiIiEC/VZIiISbMZa2/Je7W3cmGnAp8BqwOff/P9w5sd4ARgMbAOutNYWGmP+Blzm3wZQa60d52/r78CpQDqwD7jbWvtYpwUvIiI9ivosEREJF+qzREQk2Do1QSgiIiIiIiIiIiLu1mWrGIuIiIiIiIiIiIj7KEEoIiIiIiIiIiLSgylBKCIiIiIiIiIi0oMpQSgiIiIiIiIiItKDKUEoIiIiIiIiIiLSgylBKCIiIiIiIiIi0oMpQSgiIiIiIiIiItKDKUEoIiIiIiIiIiLSgylBKCIiIiIiIiIi0oMpQSgiIiIiIiIiItKDKUEoIiIiIiIiIiLSgylBKCIiIiIiIiIi0oMpQSgSBMaYTGOMNcZEddHxSo0xQzsalzHmHmPMM8GPUEREupvW9j0iIiIiEn6UIJQexRiz1RhT4f+Qc9AY829jzKBOPubbxpj7Gtl+sTFmb3uSitbaBGvt5uBEKCIiPdVR/eI+Y8x8Y0xCY/uq7xERERHpvpQglJ7oQmttAtAf2Af8sZOP9yRwrTHGHLX9OuBZa21taxvqqgpFERHpUQL94snAOOB/Gt6ovkdERESk+1OCUHosa20l8BIwIrDNGJNsjHnKGHPAGLPNGPM/xpgI/20R/uvbjDH7/fslt+JQrwJpwPQGx+kFXAA8ZYyZYIxZYowpMsbsMcbMM8bENNjXGmNuM8ZsBDY22DbM//v5xpgVxphiY8wOY8w9jcRwkzFmt7/9HzQVqDFmkjFmsT+WL40xpza4bbYxZrMxpsQYs8UYc00rzl1ERMKEtXYX8BYwshV9j9cY81t/n3jIGLPQGOP136a+RERERCTMKEEoPZYxJg74FrC0weY/AsnAUGAmcD1wo/+22f6f0/y3JwDzWjqOtbYCeMHfVsCVwFfW2i+BOuBOIB2YDHwDuPWoZi4BJtIgmdlAmb/tFOB84DvGmEuO2uc0YDhwFvBDY8wZRzdijBkI/Bu4H0gFfgC8bIzpbYyJBx4CzrXWJgJTgJUtnbuIiIQP/5Qb5wEr/Jsuoem+5wHgFJz+IBW4C/CpLxEREREJT0oQSk/0qjGmCDgEnAn8BsAYEwlcBfzYWltird0K/BZnKDDANcDvrLWbrbWlwI+Bq1o59OpJ4HJjjMd//Xr/Nqy1n1trl1pra/3H/AtOcrKhX1hrC/3JxiNYaz+y1q621vqstauAvzdy/3uttWXW2tXAE8DVjcR4LfCmtfZNf1vvAbk4HxYBfDhVJV5r7R5r7dpWnLeIiLhfoF9cCHwM/J9/e6N9j7+y/ibge9baXdbaOmvtYmttFepLRERERMKSEoTSE11irU0BPMBc4GNjTD+cCr5oYFuDfbcBA/2/D2jktiigb0sHtNYuBPKBS4wx2cAE4DkAY8xxxpg3/AuWFON8MEs/qokdTbVtjJlojFngHxZ9CJjTwv23+c/laEOAK/xDwor8HxanAf2ttWU41ZZzgD3+xV1OaOm8RUQkLFxirU2x1g6x1t7aICHYVN+TjtOH5jVym/oSERERkTCkBKH0WP6Kh3/iDPGdhpPAq8H5cBMwGNjl/313I7fV4ix00hpP4VQOXgu8Y60N3O/PwFfAcGttEvD/gKMXNLHNtPsc8BowyFqbDDzSyP0brtQ82H8uR9sBPO3/kBj4ibfW/hLAWvuOtfZMnMVdvgL+2vzpiohImGuq78kHKoHsRm5TXyIiIiIShpQglB7LOC4GegHrrbV1OHMF/twYk2iMGQJ8H3jGf5e/A3caY7KMMQk4lX7Pt2EV4qeAM4Cb8Q8v9ksEioFSfyXFd9p4KolAobW20hgzAZjVyD7/a4yJM8achDOn4vON7PMMcKEx5mxjTKQxxmOMOdUYk2GM6WuMudg/f1QVUIozTExERHoYa60PeBz4nTFmgL/PmGyMiUV9iYiIiEhYUoJQeqLXjTGlOEm5nwM3NJgD6bs4i35sxpmL6TmcD0H4L58GPgG24FRPfLe1B/XPL7gYiMep+Av4AU5SrwSnkqKx5F1zbgXuM8aUAD/FSXIe7WNgE/AB8IC19t1G4tsBXIxTwXgApwrkv3H+T0TgJEt3A4U4cxy2NZEpIiLdxw+A1cBnOP3Cr4AI9SUiIiIi4clY29zIRREREREREREREenOVEEoIiIiIiIiIiLSgylBKCIiIiIiIiIi0oMpQSgiIiIiIiIiItKDKUEoIiIiIiIiIiLSg0WFOoCG0tPTbWZmZqjDEBERF/j888/zrbW9Qx1HU9RniYhIgNv7LBERkZa4KkGYmZlJbm5uqMMQEREXMMZsC3UMzVGfJSIiAW7vs0RERFqiIcYiIiIiIiIiIiI9mBKEIiIiIiIiIiIiPZgShCIiIiIiIiIiIj2Yq+YgFBERERFpr5qaGnbu3EllZWWoQ5FuyuPxkJGRQXR0dKhDERERCSolCEVERESkW9i5cyeJiYlkZmZijAl1ONLNWGspKChg586dZGVlhTocERGRoNIQYxERERHpFiorK0lLS1NyUDqFMYa0tDRVqIqISLekBKGIiIiIdBtKDkpn0vNLRES6KyUIRUREREREREREerB2JwiNMY8bY/YbY9Y0cbsxxjxkjNlkjFlljDm5tW2vWfQ6S5/631bH8sjHeSzOyz9i2+K8fB75OE9thLANOWzpU//LmkWvH7FNz/Pwb8MNMchhxhiPMWa5MeZLY8xaY8y9jewTa4x53t83LTPGZDa47cf+7RuMMWd3arALH4Qtnxy5bcsnznYRCUv7Syopraw5YltpZQ37SzQcVURERNyvIxWE84Fzmrn9XGC4/+cW4M+taXTNotcZ+N6tJGRPaHUgORnJzH1uRf2H7MV5+cx9bgU5GclqI4RtyGEJ2RMY+N6t9UlCPc+7RxtuiEGOUAWcbq0dDYwBzjHGTDpqn/8ADlprhwG/B34FYIwZAVwFnITTt/3JGBPZaZEOPBlenH04SbjlE+f6wFZ/lxYSSmhLl+iiBHqwn89x0ZFsL6xg4qTJgJMc3F5YQVx0x/+VbN26leeee65V+86ePZuXXnqp2X3mz5/P7t27OxxXa5x33nkUFRV1ybFERESk/Yy1tv13diov3rDWjmzktr8AH1lr/+6/vgE41Vq7p6n2cgZ47Uc3p/FQ6k/Ym9r6xAlAQVkVK3cUMTg1ju2F5YwZlEJafKza6GAbX+44xBXjMnhrzV7mzRrLlOz0NrUhh61Z9DqD37uFDZHHkV2bp+d5N2kjmDEc3y+RXQcrePiak/VaA4wxn1trx7XzvnHAQuA71tplDba/A9xjrV1ijIkC9gK9gR8BWGt/cfR+TR1j3LhxNjc3tz3hOfI+hOe+BcefB1s/hSvmQ9aM9rfXBQIJ7N9ekcO04b35bGshc59bof5BgiuQMA+8Jo6+3oz169dz4okntuowgedz4Pl79PX2KK6oYXthOanxMRSV1zA41UuCJ7pdbTX00Ucf8cADD/DGG2+0uO/s2bO54IILuPzyy5vc59RTT+WBBx5g3Lh2/YttFWst1loiIsJ/RqO6ujoiIw8neht7nnWkzxIREXGDqE5seyCwo8H1nf5tRyQIjTG34FQYckr/CN4yM1jiOwnyy9p8wCRPNHkHykiLj6G4opbiilq10cE2qut8PLtsO7efPkwf/jpo5NQL2fjhAMbXreSv5jI9z7tRG8GIITYqgjW7irl24mC91jrAX/X3OTAMeLhhctCvvm+y1tYaYw4Baf7tSxvsF+izjm6/vs8aPHhwx4LNmABx6bDuVTjpm65PDgJMyU7nl98cxX88mUuiJwqfhb9cd4qesxJcWTPgrJ87CfQJt8CKp9uVQL/39bWs213c7D59EmO5/rHl9E2KZV9xFcP6JPCH9zfyh/c3Nrr/iAFJ3H3hSY3eVl1bx97iSiYcN5ClG3ayceUybn3gF6Snp7NmzRpOOeUUnnnmGYwxZGZmcuWVV/LWW2/h9Xp57rnnGDZs2DHJvYSEBEpLS/nRj37E+vXrGTNmDDfccAN33nln/XGttXz3u9/lvffeY9CgQcTExNTfdt999/H6669TUVHBlClT+Mtf/sLLL79Mbm4u11xzDV6vlyVLlvCb3/zmmP2aW4xj/vz5vPLKKxw6dIhdu3Zx7bXXcvfdd7N161bOPvtsJk6cyOeff86bb77JzJkzyc3NJT09naeeeooHHngAYww5OTk8/fTTHDhwgDlz5rB9+3YAHnzwQaZOndroce+55x7y8vLYtGkT+fn53HXXXdx8881Ya7nrrrt46623MMbwP//zP3zrW9/itttu4+yzz+aiiy7i0ksvpVevXjz++OM8/vjj5OXl8fOf/5xnnnmGhx56iOrqaiZOnMif/vQnIiMjSUhI4Nvf/jbvv/8+Dz/8MNOmTWv2uSQiIhLuOjNB2CrW2keBRwHGDYi0F/ARoydczcipF7apncC3vrefPoxnlm3n7otGtPkDi9o4to1Zf13G2EEpPLNsO5Oy0/QhsAPWLHqd4bVbwcDlvMvkCZfqed4N2ghWDLc89TkAr325m/Ny+uu11k7W2jpgjDEmBXjFGDPSWtvoXLntbP9wnzVuXPtL8AF2fwG1lZDYH9b+E/qNhOn/FYwwO421lhc/3wnAIX8i/PUv9zByYDJJQaiSEmH3Slj4O1j3GmBh0YMw465OS6Ane6PpmxTLrqJKBqZ4SPa273lcVlXLtoJyfA1G5hyqqGHFihWsXbuWAQMGMHXqVBYtWlSfaEpOTmb16tU89dRT3HHHHc1WB/7yl79ssoLwlVdeYcOGDaxbt459+/YxYsQIbrrpJgDmzp3LT3/6UwCuu+463njjDS6//HLmzZt3RAVhY/tdeOGFPPLIIwDMmTPnmOMuX76cNWvWEBcXx/jx4zn//PNJT09n48aNPPnkk0yadOQMD2vXruX+++9n8eLFpKenU1hYCMD3vvc97rzzTqZNm8b27ds5++yzWb9+fZN/i1WrVrF06VLKysoYO3Ys559/PkuWLGHlypV8+eWX5OfnM378eGbMmMH06dP59NNPueiii9i1axd79jh1Cp9++ilXXXUV69ev5/nnn2fRokVER0dz66238uyzz3L99ddTVlbGxIkT+e1vf9tkLCIiIt1JZyYIdwGDGlzP8G9rVsG47ztztUGrkydHDwmZlJ3W5iEiaqPxNlLjoxnWJ4H/Pud4DSPrgMCcg1ujhnJ83dfsn/5zPc+7QRvBjOEHZx/PPa+tZc6p2XqtBYG1tsgYswBnPsGGCcJA37TTP8Q4GSignX1WuwWGTF75JPTLgb+eDh/cByYCpt3Z4t1D5Zml23hv3T7iYiK5YXImTyzewj+Wb+fDr/Zx/yWjOHNE31CHKOFq22L49Lew6X2ITYJRl8P616C2CpY9AlnT25wkbKrSr6Gjv+T53hnD2/y/t6i8mh0HK4iKMBggwkD/ZC8WGDH6ZFLS+xIREcGYMWPYunVrfYLw6quvrr9sWBHYVp988glXX301kZGRDBgwgNNPP73+tgULFvDrX/+a8vJyCgsLOemkk7jwwmPfdzS1X2OJwYAzzzyTtLQ0AL75zW+ycOFCLrnkEoYMGXJMchDgww8/5IorriA93fn7pqamAvD++++zbt26+v2Ki4spLS0lISGh0eNefPHFeL1evF4vp512GsuXL2fhwoX1f4O+ffsyc+ZMPvvsM6ZPn86DDz7IunXrGDFiBAcPHmTPnj0sWbKEhx56iCeffJLPP/+c8ePHA1BRUUGfPn0AiIyM5LLLLmv2by8iItKddOakIK8B1/tXM54EHGpu/sGAocNHsuvMP1Gat7zVB1q189ARH6anZKczb9ZYVu08pDY62EbvBA/FlTXtakMOK81bzq4z/+R8+AeOHzVBz/Nu0EYwY5g2zGljYIpXr7V2Msb09lcOYozxAmcCXx2122vADf7fLwc+tM5kvK8BV/lXOc7CWWCr9S/Qttr1xeEhk94U+M/3oddQ+OBnkLeg0w7bEV/tLebe19cRHWn42/Xj+OG5J/D47PEkeqKIiYzg5qdymfvcF+SXVoU6VAkX1sLG9+Dxc+CJc53qwW/cDd981Jmj88pnoFcmxCTAizccu3BJBzX8kuf7Zx3PvFljj1g0quXwLfuKK9leWE5cdCS94mMYkhoHQHpCDEmeKKJjYigoqwachFNt7eEpKBoO4Q38HhUVhc/nA8Dn81FdXd3u86usrOTWW2/lpZdeYvXq1dx8881UVh67onJr9zva0UOQA9fj4+PbFKfP52Pp0qWsXLmSlStXsmvXriaTg80dtzEDBw6kqKiIt99+u76i8IUXXiAhIYHExESstdxwww31x96wYQP33HMPAB6P54h5B0VERLq9wATCbf0B/o4zn2ANzlxN/wHMAeb4bzfAw0AesBoY11Kbp/SPsHblP6y4xxV/Xmy/9ZfFoQ6j29h274nW3p1k7Vb9TeVI+4or7JAfvmGfWrwl1KG4BpBr29Yv5QArgFU4VYM/9W+/D7jI/7sHeBHYhJMAHNrg/j/x91kbgHNbOt4pp5wS3BMuK7D2T1Os/Vkfa/M+Cm7bHVReVWvP+O1HdsT/vmXfWr37iNsWbTpg53240f7h/a/t8P/3ph197zv2n1/ssD6fL0TRiuvV1Vq7+mVr/zzV6RN/O8LapX+xtqrMuf3T31u7+WPn97yPnH1emO1sb8G6detaHcafP9pkF206cMS2RZsO2D9/tKnlU6jz2W0FZfbLHQft9oIyW9fg+R4fH2+ttfaDDz+0p515tl21s8iWVNbY2267zT7xxBPWWmuHDBlif/GLX1hrrX366aftBRdcYK219mc/+5m96667rLXWvvLKK9Z5q25tbm6unTFjRqOxvPzyy/ass86ytbW1dvfu3TYlJcW++OKL9uDBg7ZPnz62vLzclpSU2JNOOsnefffd1lprL7jgAvvhhx9aa22z+zXliSeesP3797cFBQW2vLzcjho1yn722Wd2y5Yt9qSTTjpi3yFDhtgDBw7YNWvW2OHDh9v8/HxrrbUFBQXWWmuvvvpq++tf/7p+/xUrVjR53LvvvtuOHj3aVlRU2Pz8fDto0CC7a9euI/4G+/fvt4MHD7Z79uyx1lp7ww032EGDBtmNGzfapUuX2oyMDHvHHXdYa61du3atHTZsmN23b199TFu3brXWHn4cG9PY86ytfZZ+9KMf/ehHP277afcQY2vt1S3cboHb2txwZVE7I5LOkOSNZndRRajD6DbifP5FSSpVHSZHCszfVlzZ9gVOxGGtXQWMbWT7Txv8Xglc0cT9fw78vNMCbElcKlz/L3jyQvj7VXDNi5Dpjknxf/bvdWw6UMrTN01k2vAjh19OyU6vr6I9d2Q/fvjyKu58/kv+tXI3P790FANTvKEIWdyothpWPQ8Lfw+FeZA2HC7+E4y6AqIOL6zBtDsO/z50Jpx8Pax4BqZ8N6jhzJmZfcy2hs/nptTW+dhaUE55dS39kjz0ToxttIotwhi80ZHEREawraCMOt+R05YePHiQnJwcYmNj+fvf/w7AzTffzMUXX8zo0aM555xz6qvxcnJyiIyMZPTo0cyePfuIIcmXXnopH374ISNGjGDw4MFMnjwZgJSUFG6++WZGjhxJv3796ofRgrPS8Zw5c+oXKWlqv+bmIJwwYQKXXXYZO3fu5Nprr2XcuHFs3bq1yb/bSSedxE9+8hNmzpxJZGQkY8eOZf78+Tz00EPcdttt5OTkUFtby4wZM+qP25icnBxOO+008vPz+d///V8GDBjApZdeypIlSxg9ejTGGH7961/Tr18/AKZPn867777LsGHDGDJkCIWFhUyfPh2AESNGcP/993PWWWfh8/mIjo7m4YcfZsiQIU0eX0REpLsyTh7PHcYNiLS5f/8FzLwr1KGI3/dfWMmyzYUs+tHpLe8szbI+HzX39ibG1MKlf4HRV4U6JHGZ4//nLW6Yksn/O+/EUIfiCsaYz62140IdR1PGjRtnc3Nzg99w6QF48gIo2gHXvgxDJgf/GG3w1uo9fOfZL5gzM5sfnXtCi/vX+SxPLdnKr9/eQISBH517AtdMHEJERNPDAKWbWfggDDz58JyB1WXw3t1OcrCqGPqPdhbkOeECiGjFEM6KIvjTJIhLg5sXHJlMPMr69es58cTO+x9aWVPH1oIyaussg3p5SY5rOpaAqto68vaXEhkRQXbveKIiI8jMzKxf2TcczZ8/n9zcXObNm9elx73nnntISEjgBz/4QZce92iNPc/c3meJiIi0pDPnIGw7E+m8CRTXSPZGU1xZE+owuoWqynInOQiqIJRGJXujKa7Q663HS+gN178GSQPg2cthR+dNhdiSnQfL+eHLqxg9KIX/Ouu4Vt0nMsJw49Qs3r1zBicP6cX//mst33p0CXkHSjs5WnGNgSc7i/B8/Q588hv47Qnw2V8hZZCT9L7lYxhxceuSg+DM03n+72DfGlj0h86MvFkllTXk7S/FZ2Fo7/hWJQcBYqMiGZwWT3Wdj+2FR650LCIiIuIWnbmKcdtFRCpx4jJJnmhKKmup81kiVf3RISVF+XgCV5QIl0YkeaM5pAShACT2hRteh/nnw9PfhOtfhYyuLUyprfNxxz9W4rPw0FVjiI5s23eKg1LjeOqmCbz8xS5+9sY6zvr9J1x+Sgb3XzKyvq3Fefms2nmo0aGeEsayZjiL8DxzGdRVQ2Q0nPMrmNT0irgtOuE8OOmb8Mmv4cQLoU/L1azBVFBaxe6iSmKjI8hMiycmqm2vh4TYKAameNl5sJzdRRVs2bKl2cU13G727NnMnj2709p/4okn+MMfjkwGT506lYcffrjTjikiItLTuauCMCJScxC6TLLXmRetRFWEHVZ2qMGqiHqeSyOSlSCUhpL6w+w3ID7dSRLu+qJLD//Qh5vI3XaQn186kiFpbVuVNMAYw+WnZPDe92cwbkgKz3+2gzN++zFrdh2qX0E2JyM5yJGLK6QNgzr//7Opd3YsORhw7q+dFY1fmwu+uo631wrWWnYXVbCrqIJETxTZvRPanBwMSI2PoU9iLIVl1eSXtn914p7gxhtvrF9ZOPCj5KCIiEjncleC0KiC0G2S/AnC4gotnNBRlcWFDa7oeS7HSvJEaUi/HClpgJMk9KbA05fA7pVdctilmwuY9+FGLjs5g4vHDOxwe30SPTz/7SncecZwth8s55KHF3Hbs18wb9bYFheEkDD1/r2AhQm3QO5jsOWTjreZ0BvO/RXs/AyWP9rx9o6yv6SS0gb/g+t8lrwDZeSXVpGeEMuQtLgOj6bom+Qh2RvNnkMVmlJCREREXMVdCcIIzUHoNoEKQlU1dVxlaYMEoZ7n0ghVEEqjkjOcJGFsspMk3Lu6Uw93sKyaO59fyZC0eO69+KSgtv29M47jqvGDqPVZRg5MVnKwu9r0obMgycBxcN5vnOHGL84OTpJw1BUw/Gz44D4o3NLx9hqIi45ke2EFpZU1VNf62LivhPLqWtITYhmQ4g3KkGBjDIN6xeGNjmR7YTkV1V1TCSkiIiLSEpclCKM09NJlkjzONJWqauq42rKDzmVcX1UQSqOSvNGq1pXGpQyGG16D6Hh48iLYt7ZTDmOt5YcvryK/tIqHrhpLQmxwpypenJfPO2v30TcplsWb8lm8Kb/lO0n4WfksYOHUHzvXA3MSBmOYvDFwwe+cUSevfw+CuOBHgieawalethWW8/W+EqrrfPRP9jAgxRu0YwBERBgy0+OJjDBsLSijps4X1PZFRERE2sNdCUINMXad5DhVEAZLIEFoew1RIlwaFVg13OfTCpfSiNQsJ0lYVwOPnwP7vzp825ZPYOGDHT7EM8u28+66ffzwnBMYFeS5AQNzDs6bNZbvn3kcdRbmPPM5i/OUJOx2Du2E1KGQffrhbVkzYNodwWk/OQPOvBe2fAwrnm5/OwsfPKaqMWHPEvp8+Qg+a0mLj6F3oqfx+3ZQdGQEmWlx1Pks2wrK9H9fREREQs5dCcKISKgq7rKJp6VlSZ7AHIRKEHaU9Se/I3sNUSJcGpXsjcZaKK1WFaE0IS3bGbJZVQKPnw0HvnYSHC/OhoEnd6jpr/YW87M31jHzuN7cNDUrOPE2sGrnofo5By/IGUB8TCSjB6Wwaqf+H3Yre1bBjqUw/j8hohPfZp5yIwyZBu/8DxTvaV8bA08+cujzlk/wvTCb4rRRxMVEcaii9og5CVtrypQprdrPGxPFoNQ4yqvr2HGwHNtMNeTWrVt57rnnWtXu7Nmzeemll5rdZ/78+ezevbtV7XXUeeedR1FRUVDauueee3jggQeC0taDDz5IeXl5UNoSERHpDoI7dqijIiKdy8pDEJca2lgE0ByEwWQqiyizHuLj0zUHoTQqkJA/VF5T/7vIMcZc7Qyr/Net8LczIDLKGb6ZNaPdTVZU13H731eQ5InmgStGE9HBhRgaM2dmdv3v8bFRXDx2IP/8YifzZnUssSku89lfIToOxszq3ONERMBFD8Gfp8C//wuuetYZftzQWz9qec7OxP7w9KWQ2B9bsoeqlOH0/eIPxK2Zh7WWylofdVERRAaSnf1Gwbm/bLbJxYsXt/o0kr3R9E/2sOdQJTsPVjAoNa7+ttLKGspr6uiT6KlPEM6aFZy/6/z58xk5ciQDBgwISnuNsdZireXNN9/stGN0xIMPPsi1115LXFxcyzuLiIj0AO6qIDSBBGFRSMOQw+JiIomKMJqDMAgiqoopMQnOaqTVJVCnKjE5Uv2q4Xq9SUvGzoITL4CqQzDsjA4lBwHu//c6vt5Xyu+uHE3vxNggBdm8WRMGU1nj418rd3XJ8aQLlBfCqhch50rw9ur846Vlw2k/gQ3/hrWvtK8NT4qTJDy0A198P2xsMpEREGEMkREReKIiqGvj6N+EhAQAPvroI0499VQuv/xyTjjhBK655pr6KsHMzEzuuusuRo0axfnfmEHB7u0cLK/mW7Ou46WXXqK0sobthRUM7e8s5POjH/2ITz/9lDFjxvD73//+iONZa5k7dy7HH388Z5xxBvv376+/7b777mP8+PGMHDmSW265BWstL730Erm5uVxzzTWMGTOGioqKRvdrzvz587n44os59dRTGT58OPfeey/gVDoef/zxXH/99YwcOZIdO3aQmZlJfr4zlcBTTz1FTk4Oo0eP5rrrrgPgwIEDXHbZZYwfP57x48ezaNGiZo+9bt06Tj31VIYOHcpDDz1Uv/2ZZ55hwoQJjBkzhm9/+9vU1Tkjkr7zne8wbtw4TjrpJO6++24AHnroIXbv3s1pp53Gaaed1uzxREREegr3VhCKKxhjSNLKqkERXX2IiogE8Pjn9aoqVqWsHCHJ6/xL1utNWrTlE9i2CCJiYN2rMPbadicJ316zh2eXbefbM4Yy47jewY2zGSMHJjNyYBLPLdvOdZOGBGWFWAmxlc9CbQWMv7nrjjnpVlj7T3jzvyFr5pG3tVDpBxweoj/jLiI+e4w9Y79Hyohv4E1wEuWR/p/2WrFiBWvXrmXAgAFMnTqVRYsWMW3aNACSk5NZvXo1Tz31FA/c9/948LG/U1lTR35pFdsLKxicenhxlF/+8pc88MADvPHGG8cc45VXXmHDhg2sW7eOffv2MWLECG666SYA5s6dy09/+lMArrvuOt544w0uv/xy5s2bxwMPPMC4ceOa3O/CCy/kkUceAWDOnDnHHHf58uWsWbOGuLg4xo8fz/nnn096ejobN27kySefZNKkSUfsv3btWu6//34WL15Meno6hYWFAHzve9/jzjvvZNq0aWzfvp2zzz6b9evXN/k3/eqrr1iwYAElJSUcf/zxfOc732HTpk08//zzLFq0iOjoaG699VaeffZZrr/+en7+85+TmppKXV0d3/jGN1i1ahW33347v/vd71iwYAHp6VpNXUREBNxWQRhIEGr4paska2XVoIipLaEiKtGpVgBVysoxAkP69XqTZgUSGlc8CaMud6rvG86j1ga7iiq466VV5GQk819nHR/0UFty9YTBfLW3hJU7irr82BJkPh989jcYPAX6jey640ZGwUXznD71nR+37b71r6X5cPpPKDjvLwz58DZS9i0NWngTJkwgIyODiIgIxowZw9atW+tvu/rqq+svly5ZQlZ6PMZAWVUtqfExJLRyqolPPvmEq6++msjISAYMGMDppx9eHGbBggVMnDiRUaNG8eGHH7J2beMroDe135w5cxpNDgKceeaZpKWl4fV6+eY3v8nChQsBGDJkyDHJQYAPP/yQK664oj4hl5rqfEn6/vvvM3fuXMaMGcNFF11EcXExpaWlTZ7v+eefT2xsLOnp6fTp04d9+/bxwQcf8PnnnzN+/HjGjBnDBx98wObNmwF44YUXOPnkkxk7dixr165l3bp1Lf1JRUREeiR3JQiNKgjdKMkTpYqmIPDUlVAVlXi4glCJcDmKFgWSVtn1xeE5B3OuhJpyp2Jr1xetuvsjH+exOC+f2jofd/5jJXU+y41TMnl80ZbOjbsRF40eQFxMJP9YvqPLjy1Btul9OLgVJnRh9WBAv5Ew/b9g1fNQU9H6+zV4LfmsZX/aRPLP/QuRe1YELbTY2MND9iMjI6mtPfwFUMOqWWMMlTV1REZG4fP5KCirori8iurq6nYfu7KykltvvZWXXnqJ1atXc/PNN1NZWdnu/Y52dNVv4Hp8fHyb4vT5fCxdupSVK1eycuVKdu3aVT9MuzGN/U2ttdxwww31bWzYsIF77rmHLVu28MADD/DBBx+watUqzj///Fadm4iISE/krgRhhH/EsyqrXEVDjIMjvq6U2ugkZw5C0PNcjpEcp0WBpBWm3XF4OHHWDEjoB/vWONtbIScjmbnPreCul1axfGshN0zJ5Gf/Xk9ORnKnhdyURE80F40ewGtf7qZEc2+Gt+WPOs/FEy8MzfGn/xf0PgEqDoKvrnX3afBaKqmspdbnI+6401r9Wuqo559/vv5y/MSJbC+s4MTjslm3eiXJnmie/MfL1NQ4r4vExERKSkoabWfGjBk8//zz1NXVsWfPHhYsWABQnwhLT0+ntLT0iJWNG7bX3H7Nee+99ygsLKSiooJXX32VqVOnNrv/6aefzosvvkhBQQFA/RDjs846iz/+8Y/1+61cubJVx2/oG9/4Bi+99FL9/IuFhYVs27aN4uJi4uPjSU5OZt++fbz11lv192nubyoiItITuSxBqApCN0ryRmvRhCCIt6XUxSYdriDU81yOkhAThTFapETaICISRl4GG991EiOtMCU7nR+fewL/XLGLE/ol8o/PdjBv1limZIdmHq6rJgymoqaOf63cHZLjSxAU5MGm92DcjRAZohXYo2Lh4ofBVwvFbX8uFZVXExURQaKn66bnPnjwIDk5OfzhD3/gnp//msGpXr576xxWLFvMGdMnsnntF8T5q/FycnKIjIxk9OjRxyxScumllzJ8+HBGjBjB9ddfz+TJkwFISUnh5ptvZuTIkZx99tmMHz++/j6zZ89mzpw5jBkzhtjY2Cb3e+SRR+rnITzahAkTuOyyy8jJyeGyyy6rn8+wKSeddBI/+clPmDlzJqNHj+b73/8+4CwYkpubS05ODiNGjGjyeM0ZMWIE999/P2eddRY5OTmceeaZ7Nmzh9GjRzN27FhOOOEEZs2adUQS85ZbbuGcc87RIiUiIiJ+pqVVyrrSuHHjbO5FW2DK7XDG3aEOR/z+3yureXftXnL/58xQhxK26mpribw/jSWDb2HyZXfA70fABQ86H6ZEGhh977tcPGYA913chXN4uZQx5nNrbfOfOENo3LhxNjc3N9RhwO4V8OipcOEf4JTZrbrLb9/dwB8/3ATA7acP4/shmH8wwFrLeQ8tJMLAv2+fHrI4pAPe+QksewTuXAuJ/UIayvrPPuLEgcmQNgxiE52NVSVQXQ6JfRu9T22dj/V7SkhPiKF/irfRfYItMzOT3NzcRhfIyC+pYvehCo7rm4gnuiNLpHSe+fPnk5uby7x580IdSkisX7+eE0888Yhtbu+zREREWuKuCkJwFnDQ0EtXSfI4i5S4KZkcbkoPOcNpjCdZFYTSrCRvlOYglLbpPwbSj4NVL7T6Li9/sZOoCMPtpw/jmWXbWZyX33nxtcAYw6wJg1i7u5jVO/V/MexUl8GKp2HExSFPDgIQm+RcHtzmLJxSVeLMjRgT1+RdiipqsFhS4mO6JsYWaLoJERERCQUXJgiTlThxmWRvNNV1PiprfKEOJWyVHnI+fEfG9YKYeGe+TSXCpRHJmvNT2soYGHUlbFsERS0v9vFi7g52F1Vy7aQhfP+s45k3ayxzn1sR0iThxWMH4omO4Lnl20MWg7TT6hed923jQ7A4SWOivZA0AHw1ULjJSQ72yjxcTdiIg2XVeKMj8XZhtd7WrVsbrR4EiI6MID42iqLyGtd+OTt79uxOrR584oknGDNmzBE/t912W6cdT0RERNyYIPSmaHVXl0nyOvPxaF609isvdibijo7v5XyY96QoES6NSvZGU1xZ2/KOIg2Nuty5XNPy4gKvfenMz/adU7MBZ07CebPGsiqE1XtJnmguyBnAayt3UVal53/YsBaW/xX6joLBk0IdzWEJfSEyxqlujEtrNjlYUV1HRU0dvVxSPRiQ4o2mqraOytqe+eXsjTfeWL8iceDn4YcfDnVYIiIi3Zr7EoSqIHSdZK+GunRUpT9BGJOY6mzwJCsRLo1K8qiCUNohNQsyJsCqF1vcNb+0mlOG9KJvkqd+25TsdObMzO7MCFt09YTBlFXX8fqXWqwkbGxf6qygPeFm58svt6gqcRYrASjLd6434WB5NcYYUrwhWlylCUneaAxwqLw61KGIiIhID+HCBGGKhl66TCBBqHnR2q+mzEkQepPSnA3eFCXCpVHJ3mi91qR9cq6E/Wth75omd9maX8b6PcWcO9IFc8Ud5eTBKRzXN4G/a5hx+Fj+qPOF16grQh3JYbWVzrDilMHOdU+yc72RJKHPWorKa0jyRBEV6a63xPXDjCvcO8xYREREuhd3vRsCVRC6UJJHFYQdVVt2EIC4pAYVhEqESyOSNAehtNdJ33TmN13d9GIlb63ZC8A5LkwQGmO4esJgvtx5iDW79D7A9Yr3wPrXYOx1zS4A0uVqq505B729IDoOaiuc69Xlx+xaWllLrc9Hrzh3DS8OSImLprrWR0VNXahDERERkR7AfQnCwByE+rbUNeorCDUHYbv5/MOJE1P8E5JrDkJpQrI3mqpaH5X6QChtFZ8G2d+A1S87q7c24u01e8jJSCajl4sSOg1cOnYgsVER/OMzVRG63ufzwVcH424KdSRH8iQdnnPQkwI1FRAZC4l9j9n1r6se46uiFSR4ouq3Ld+znMfXPN5FwTYvyRONwehLIxEREekS7ksQepKdledqKkIdifglBeYgLNcb1PayFUXU2Ei8cYEPLZqDUBqX5NGiQO1ljBlkjFlgjFlnjFlrjPleI/v8tzFmpf9njTGmzhiT6r9tqzFmtf+23K4/gyDIuRKKd8L2xcfctPNgOV/uPMS5I/uHILDWSYmL4bxR/fnXit2UV2uxEteqrYbPn4DhZ0JaaOeubJYn2bls5Au52jofA+OO56HVPyV372eAkxz8wcc/YGTayA4ddsqUKR26f0BUZAQJnigOldewZcsWnnvuuVbdb/bs2bz0UvMLFs2fP5/du7tmvs/zzjuPoqKiLjmWiIiItF9Uy7t0MU+Kc1lZ5K4hKz1YIGFxqEIf1torouoQJSae1Ah/Tt6b4jzHrXXXxO4SckkN5vzsk+hpYW85Si3wX9baL4wxicDnxpj3rLXrAjtYa38D/AbAGHMhcKe1trBBG6dZa/O7NOpgOv48iEmAVS9A5rQjbnrbP7zYjfMPNnT1hMG8smIXb6zaw5XjBoU6HGnMV69D6T4Yf3OoI2nWr1b8ga/2fQEYiPYecVtNnY/qWh994vrw7fe+Te+43hwoP8DQlKH8+cs/8+cv/9xomyeknsAPJ/yw2eMuXnxsgr69UrzR7KisYcvGzTz33HPMmjUrKO3Onz+fkSNHMmDAgKC01xhrLdZa3nzzzU47hoiIiASP+yoIvSnOpaqrXCMqMoL4mEhVNHVAVHUxZSbh8AZPsrPCYs2xcyJJz1ZfsauEfJtZa/dYa7/w/14CrAcGNnOXq4G/d0VsXSYmDk64ANa9CrVVR9z09pq9nNAvkcz0+NDE1krjM3uR3Ttei5W42fK/OvP6DTsj1JG0LCIKbN0xU9fU+iwRxpAcm0TvuN7sKdtD77jeJMUkdfiQCQlOf//RRx9x6qmncvnll3PCCSdwzTXX1C84kpmZyV133cWoUaOYMGECmzZtAo6t/hvUNxVjDD/5yY/59NNPGTNmDL///e+POJ61lrlz53L88cdzxhlnsH///vrb7rvvPsaPH8/IkSO55ZZbsNby0ksvkZubyzXXXMOYMWOoqKhodL/mzJ8/n4svvphTTz2V4cOHc++99wKwdetWjj/+eK6//npGjhzJjh07yMzMJD/f+d7lqaeeIicnh9GjR3PdddcBcODAAS677DLGjx/P+PHjWbRoUZPHPXDgAGeeeSYnnXQS//mf/8mQIUPq277kkks45ZRTOOmkk3j00UcBqKurY/bs2YwcOZJRo0Yd87cTERGRw1xYQdj0cBAJnWQtnNAh0TXFVEQ2TBCmOJcVRRDj7g/r0rW0anhwGGMygbHAsiZujwPOAeY22GyBd40xFviLtfbRRu53C3ALwODBg4McdZDkXAGr/gEb34UTLwRgX3EludsO8v0zjwtxcC0LLFZy/7/X89XeYk7o1/GEjQTR3tWwfQmc9XOIcN/3zA39cMIPoboM8r92VjWOSwOgsqaOr/eVMCDZy+bSL/nBxz/g2znf5oUNL/Cd0d9hQv8JQYthxYoVrF27lgEDBjB16lQWLVrEtGlOdW9ycjKrV6/mqaee4o477uCNN95otI3E2Ci+96O7efGJPze6zyuvvMKGDRtYt24d+/btY8SIEdx0kzM35Ny5c/npT38KwHXXXccbb7zB5Zdfzrx583jggQcYN25ck/tdeOGFPPLIIwDMmTPnmOMuX76cNWvWEBcXx/jx4zn//PNJT09n48aNPPnkk0yaNOmI/deuXcv999/P4sWLSU9Pp7DQKd7+3ve+x5133sm0adPYvn07Z599NuvXr2/0b3Hvvfdy+umn8+Mf/5i3336bxx57rP62xx9/nNTUVCoqKhg/fjyXXXYZW7duZdeuXaxZ46zurqHOIiIiTXPfO7uGQ4zFNZK80UpYdEBsbQmVUYmHNygRLk3QokAdZ4xJAF4G7rDWFjex24XAoqOGF0+z1p4MnAvcZoyZcfSdrLWPWmvHWWvH9e7dO+ixB0XWqRDf2xlm7PfO2vAYXhzwzZMziImM4B/Ld4Q6FDna8r9ClBfGXhPqSFonOg4ioo/obw+WV2MwbCxewQ8+/gEPzHyAuWPn8sDMB/jBxz9g+Z7lQTv8hAkTyMjIICIigjFjxrB169b6266++ur6yyVLljTZRkpcNLU+S62v8aq+Tz75hKuvvprIyEgGDBjA6aefXn/bggULmDhxIqNGjeLDDz9k7dq1jbbR1H5z5sxpNDkIcOaZZ5KWlobX6+Wb3/wmCxcuBGDIkCHHJAcBPvzwQ6644grS050F21JTUwF4//33mTt3LmPGjOGiiy6iuLiY0tLSRo+5cOFCrrrqKgDOOeccevXqVX/bQw89xOjRo5k0aRI7duxg48aNDB06lM2bN/Pd736Xt99+m6QkfeEgIiLSFBcmCJU4caMkVRB2SFxdCTXRDd6UBobSKxEuR0nyBIYY6/XWHsaYaJzk4LPW2n82s+tVHDW82Fq7y3+5H3gFCF4ZUVeKjIKRl8PX79RP1/HW6r1k945neN/E5u/rEqnxMZwzsh///GInFdVa0ds1Kg46ieecK8Dbq+X93cAY571lZQn46rDWcrC8hkRPFOsPruOBmQ/UVwxO6D+BB2Y+wJqCNUE7fGxsbP3vkZGR1NYenj7CNJiDOPB7VFQUPv8q5D6fj+rqahI90UTgLKzSFpWVldx666289NJLrF69mptvvpnKysp273c0c9QcyoHr8fFtGxnh8/lYunQpK1euZOXKlezatat+mHZrffTRR7z//vssWbKEL7/8krFjx1JZWUmvXr348ssvOfXUU3nkkUf4z//8zza1KyIi0pO4L0EYeMOpOQhdJckTTXGl5kRrrzhbRm3DeY3qK2WVCJcjJXn9qxgrQdhmxvl0+hiw3lr7u2b2SwZmAv9qsC3ev7AJxph44CwgeFmCrpZzBdRVwfrXKCitYtmWAs4b5d7Vixtz9YTBFFfW8ubqPaEORQJWPAu1Fa5fnOQYnmTAB1UllFTWUlvno1d8DDeNvOmY4cQT+k/gppE3dUlYzz//fP3l5MmTAWduws8//xyA1157jZqaGiIjDL1TUzhUXNLo3IAzZszg+eefp66ujj179rBgwQKA+iRfeno6paWlR8xtmJiYSElJSYv7Nee9996jsLCQiooKXn31VaZOndrs/qeffjovvvgiBQUFAPVDjM866yz++Mc/1u+3cuXKJtuYOnUqL7zgVEe/++67HDx4EIBDhw7Rq1cv4uLi+Oqrr1i6dCkA+fn5+Hw+LrvsMu6//36++OKLVp2biIhIT+S+OQhj/UkUJU5cJdkbzfo9TY3Uk+ZYn49EW4ovNvnwxkClrBLhcpTYqEg80RGqIGyfqcB1wGpjzEr/tv8HDAaw1j7i33Yp8K61tqzBffsCr/grYKKA56y1b3dF0J1iwMmQmg2rXuDd2lPxWTgnTIYXB0wamkpWejz/+Gw7l52SEepwxOeDz/4GgydD/5xQR9M2sQlgIqHyEAd90URFGBI9oX8LfPDgQXJycoiNjeXvf3cKmm+++WYuvvhiRo8ezTnnnFNfjTdp/MmYiAhyRo/mphtv5M4776xv59JLL+XDDz9kxIgRDB48uD7ZmJKSws0338zIkSPp168f48ePr7/P7NmzmTNnDl6vlyVLljS5X3NzEE6YMIHLLruMnTt3cu211zJu3LgjhlAf7aSTTuInP/kJM2fOJDIykrFjxzJ//nweeughbrvtNnJycqitrWXGjBn1xz3a3XffzdVXX83TTz/N5MmT6devH4mJiZxzzjk88sgjnHjiiRx//PH1Q5x37drFjTfeWF+V+Ytf/KLFx0VERKSnMi2tUtaVxo0bZ3Nzc+H/MuDk6+AcdeJucd/r63gxdwer7z071KGEnYqyEry/yWDp0NuZdP3PnI3lhfDrLDjnVzCp8bl9pOea+H/vc+pxffjV5WH2ITzIjDGfW2vHhTqOptT3WW710S/ho1/yvQHPsfKQl49+cOoxQwLd7i8f5/GLt77ivTtnhM3w6G5r43vw7OVw2WMw6vJQR9Ok9evXc+KJJx57w8Gt2Mpi1voGkxofy4AUb9cH10BmZia5ubn18/G1xOezrN9TTLI3mozUuE6OrmXz588nNzeXefPmdelxq6qqiIyMJCoqiiVLlvCd73yn2YrDztLY88ztfZaIiEhL3DfEGPxzxaiC0E2SvFGUVNVS18QE2dK0kqJ8AExg3kFoUClb1OXxiPsleTTnpwTBqCsAS7/tb3DOyH5hlxwEuOyUDKIjDX/XYiWht/xRSOgLJ14U6kjax5OCsXXE2Up6xUWHOpo2i4gwznzQlTX4XPTlflfbvn0748ePZ/To0dx+++389a9/DXVIIiIi3Ubox1c0xpuioZcuE1hZtaSyhpS4mBBHE17Ki525dqLiUw9vjIyCmEQlwqVRyd5orWIsHZeWTUFKDhcXLqR65P+FOpp2SU+I5awR/fjnip3cdc7xeKIjQx1Sz1S42akgnPlDiArT9wCxifgw9Iosd8XzqLmhuE1J9kZzsLya0spakryhTXLOnj2b2bNnd1r7TzzxBH/4wx+O2DZ16lQefvhhVqxY0WnHFRER6cncmSBUBaHrNFxZVQnCtqnwJwhjElKOvMGTrES4NCrJG82+4pZXkBRpyTsR05kV8TA2ZjeQEupw2uXqCYP59+o9vLN2LxePGRjqcHqmzx6DiEg4ZXaoI2kVa+0xFbOVdVBtvSSZcsKvltaR4IkiMsJwqKIm5AnCznbjjTdy4403hjqMRrlpeiYREZFgcukQ4xQNvXSZQAWhhj22XVWJs0qfJzHtyBu8KXqeS6OSvRpiLB1XUlnDQ/ty8BGJWf1iqMNptynZaQxOjeO5ZdtDHUrPsvBB2PIJVJfDiqfhxAuhYKOz3cU8Hg8FBQXHJHEOlldTTDyRtgZqKkIUXcdEGEOyJ5riihp8mvIlJKy1FBQU4PF4Qh2KiIhI0Lm3glCVVa4S+Ka6uKI2xJGEn5qygwB4E1OPvEGVstKEJE8UxUoQSgd9+NV+9tYlUpw5nZTVL8Hp/wsR7vxesDkREYarJgzi129vIO9AKdm9E0IdUs8w8GR4cTaMvtrpqwZNdK5fMT/EgTUvIyODnTt3cuDAgfpt1sLe4kpiIy0ltfmwr8rpg8NQZU0d+aXVVOXH4I0J/VDpnsjj8ZCRoZXVRUSk+3FngtCbosSJy6iCsP3qyp0EYULKUSsVelLg4NYuj0fcL9kbTUlVLT6fJSIiXAfDSai9tXovfRJjSZowC165BXYsgyGTQx1Wu1x+Sga/e/drnv9sB//vvEZWqJXgy5rhJAOfugTie8Mnv3GuZ80IcWDNi46OJisr64htH399gJteXc6frzmZqbn/AxUH4dYlIYqwY2rrfEz8vw+YlJ3Gw7NODnU4IiIi0o24s5TAkwLVJVCnajW3SPI6uWQtnNB2tsJJdickH1VBqES4NCHJG421UFKl/4HSPuXVtXz09X7OPqkfESecD9FxsOr5UIfVbn0SPZxxYl9e+nwnVbV1oQ6n50gZArYOyg7AuP9wfXKwKS99vpOUuGhOP7EPnHAB7F8HBXmhDqtdoiIjOHdUPz5cv5/yavURIiIiEjwuTRD6h31UFYc2DqmnCsL2M5VFlFovUdFHLe7iSdYchNKow0P69XqT9vl4wwEqa3ycO7IfxCbACefDulehtjrUobVbkjeawrJq3l27r37b4rx8Hvk4PBM9YWH5o87luJsg9zFnTsIwc6iixlngZvQAYqMindcCwIY3QxtYB1yQM4CKmjo+WL8/1KGIiIhIN+LOBKE3xbmsOBjSMOQwb3QkURFGCYt2iKwuptTEH3uDJwWqS1UpK8dQQl466s01e0mNj2FClr9yOedbTp+66f3QBtYBF48eQIShPiG4OC+fuc+tICcjPOeSc70tnzgJQm8qnP87Z3jxi7PDLkn471V7qK71cdkp/jnjeg2BfqNg/RuhDawDxmem0icxlte/3B3qUERERKQbcWeCMFBBqOGXrmGM0cqq7RRVfYjyiEYm1dfzXJqQ5FEFobRfZU0dH67fx1kj+hIV6e/mh54Gcemw+oXQBtcBU4enc9nJGazdXcxPX13D3OdWMG/WWKZkp7d8Z2m7nZ9DZAwcdw4Yc3hOwl1fhDqyNnnp8x0c1zeBUQMbJJJPuMCZk7M0PCvwIiMM5+f056OvD1CiqV9EREQkSNqdIDTGnGOM2WCM2WSM+VEjtw82xiwwxqwwxqwyxpzX6sY9Kc6lhl+6SrI3muJKVbu1VUxtCRVRicfeEKiU1fNcjhKoINScn9IeCzfmU1Zdx7mj+h/eGBkFI78JG96CyvCdvuP7Zx0HwFNLt3HtxMFKDnamoTOdKvfs0w5vy5oB0+4IWUhtlXeglC+2F3H5KRkY02DBpxPOB6zzeghTF+QMoLrWx3vr9rW8s4iIiEgrtCtBaIyJBB4GzgVGAFcbY0Yctdv/AC9Ya8cCVwF/avUBVFnlSomqIGwXb20J1Y0lCOuf50VdGo+4X2BRIL3epD3eXLOHJE8Uk4emHXnDqCuhthLWvx6awIJgS34ZkQYGpXp5Ztl2Fuflhzqk7mvzAudy6KkhDaMj/vnFTiIMXDJm4JE39B3pLMDyVfgOMz55cAoDU7waZiwiIiJB094KwgnAJmvtZmttNfAP4OKj9rFAkv/3ZKD172Dq5yAsamd40hmSvdEa8tgOcb4yamMamSOrvlJWiXA5Un0FYYUqdqVtqmt9vL9uH2eM6EtM1FFdfMY46JUVtsOMA3MOnjGiL/uLq/jdlaOZ+9wKJQk7S94CJ5GW0CfUkbTJIx/nsTgvnzqf5Z9f7GLmcb3ZdKD0yMVsjHGGGW/+CKpKQhZrRxhjuCCnP59uzKeoPHwXHxIRERH3aG+CcCCwo8H1nf5tDd0DXGuM2Qm8CXy3sYaMMbcYY3KNMbkHDhxwNqqC0JWSPFFKELZDgi2lLibp2BsCz3MlwuUo8TFRRBhVEErbLdlcQHFlLeeN7H/sjcZAzpXOIhPFe7o+uA5atfMQ82aN5YpTBlFV6yMmMoJ5s8ayaqfeKwRddbkzR18YVg/mZCQz97kV/O3Tzew5VElORkrji9mccD7UVYf1wj0X5Ayg1md5Z+3eUIciIiIi3UBnLlJyNTDfWpsBnAc8bYw55njW2ketteOsteN69+7tbIyOg4hoDb10GS1S0na1NdUkmApsoFqwIc1BKE2IiDAk6fUm7fDW6j3Ex0QybXgTc/ONuhKsD9a83LWBBcGcmdlMyU5n4tBUIiMMi/LymZKdzpyZ2aEOrfvZtthJnjWcfzBMTMlOZ96ssfz23a+JjYrgqSVbG1/MZvAkiEsL69WMRw5MYkhaHK9/GX4JfxEREXGf9iYIdwGDGlzP8G9r6D+AFwCstUsAD9C62cSNcaqrVFnlKkneaIora7DWhjqUsFF6qBAA421siLEqZaVpSZ5oLVIibVJb5+Pddfs4/cS+eKIjG98pfRgMGBu2w4wBEj3RjM5IZuGmglCH0n1tXuCsYDx4SqgjaZfxmalYa6mq9XHdpCGNL2YTEQnHnwsb34Xa8Byia4zhwpwBLM7LJ7+0KtThiIiISJhrb4LwM2C4MSbLGBODswjJa0ftsx34BoAx5kScBOGBVh/Bm6LEicske6OpqbNU1NSFOpSwUVrkzI0VGdfr2BsDlbJKhEsjVLErbbV8SyGFZdWcN7Jf8zuOuhL2fAkHvu6awDrBtGHprN5ZpNdIZ8lb4FTYxcSFOpJ2eXbZNmp8lvNG9mt+MZsTLoSqYtj6SdcGGEQXjO6Pz8JbazTMWERERDqmXQlCa20tMBd4B1iPs1rxWmPMfcaYi/y7/RdwszHmS+DvwGzbltIzT4qGXrpMkkcLJ7RVRYlTQRgd30iC0BglwqVJWhRI2uqtNXvxREcw8/jeze9YXgiYI6sIt3wCCx/szPCCauqwdHwWlm5WFWHQleyD/WthaPgNLwZnMZtfvbUBgJ9fOop5s8Y2vZjN0JkQHQ9f/buLowye4/smMqxPglYzFhERkQ5r9xyE1to3rbXHWWuzrbU/92/7qbX2Nf/v66y1U621o621Y6y177bpAJ5kJU5cJrCyqio2Wq+yxPnwGpuY2vgOnmQlwqVRSd4ovdak1Xw+y9tr93LqcX2Ii4lqfuehMyAiCj5/Cqx1koMvzoaBJ3dJrMEwdnAvvNGRLNqkFYyDbvNHzmUYzj8IzmI2WenxjOifRK/4mPo5CRtdzCbaC8O+AV+9CT5f1wcbBIFhxp9tLWRfcWWowxEREZEw1pmLlHSMN0VDL10myet86NS8aK1XXVoEgKfJBGGKEuHSqGRvNMWVqtaV1vl8+0EOlFRx7qgWhhcDZM2AyXOhbB/8a66THLxivrM9TMRERTAhK1UJws6weQF4U6Hf6FBH0i6zp2Sy6UApU4el1W9rdjGbEy+E0r2w6/MuijD4LhjdH2vh36u0WImIiIi0n3sThKogdJ36CsJyJQhbq7bMGWIcn9zE+jxajEeakOTRHITSem+t3ktMZASnn9CndXeYertzufIZGPcfYZUcDJg2LJ28A2XsOVQR6lC6D2ud+QeHzoQI975FbM4X2w5SXetrfGGSxgw/06mo/Sp8VzPO7p3Aif2TeH2VhhmLiIhI+7n33V9gDkKtmOsagQShKghbz/qTf4kpTXxQ0RyE0oQkbzTVtT4qtSiQtMBay9tr9jB9eDqJ/rliW7RvDZhISB4EuY85w4zDzBR/hdgirWYcPAe+cqrpwnT+QYBFeflERhjGZzVRuX80by/InBbW8xA+8nEeORnJrNhexM6D5YAzF+MjH+eFODIREREJJy5OECaDrxZqykMdifgFFilRVVPr2coiqm0kHm984ztoDkJpQlIgIa/Xm7Tgy52H2H2oknNH9W/dHQJzDp54IZTuh0sfda6HWZLwxH5JpMbHsFjDjIMnb4FzGabzDwIszitgdEYyCbEtzMXZ0AkXQMFGOLCh8wLrRDkZybztX8X436v2sDgvn7nPrSAnIznEkYmIiEg4cW+C0JviXGr4pWskevxzEGoV41aLqCqmxCRgmhqqFZiDUJWychRV7LadMWaQMWaBMWadMWatMeZ7jexzqjHmkDFmpf/npw1uO8cYs8EYs8kY86Oujb793lqzh6gIw5kn9m3dHXZ94cw5OPpqqKuCqBjn+q4vOjPMoIuIMEzOTmPhpnys/ocGx+YFkJoNKYNDHUm7lFTWsGrnIaYOa+Xw4oATzncuw3SY8ZTsdP587clERhj+tnAzc59bwbxZY1s/zFpEREQENycIPf5vPTX80jWiIiNIiNXKqm0RVX2IctNE9SAcrpStLuu6oCQsJPkT8nq9tUkt8F/W2hHAJOA2Y8yIRvb71Fo7xv9zH4AxJhJ4GDgXGAFc3cR9XcUZXryXKcPSSY5r5fDiaXc4cw4OmQwmArZ86lyfdkdnhtoppg1LZ39JFXkHSkMdSvirrYati8K6evCzrYXU+SyTs9Na3rmhpAEw8JSwHmY8JTudSUNTOVBSzcWjByg5KCIiIm3m4gRhinOp4Zeu4qysqoRFa0XXlFAemdj0DoFKWSXC5Sj1FYSq2G01a+0ea+0X/t9LgPXAwFbefQKwyVq72VpbDfwDuLhzIg2edXuK2VZQzrkjW7F68dE8ydB/DGz9NOhxdZVp/kqxhRs1zLjDdi6HmrLwnn9wUwExURGcPLhX2+98wvnOSsbF4bnQx+K8fFbvdN5LvPD5Dhbn6TUhIiIibePiBKG/glBDjF0l0aMKwrbw1BZTFdVMgrC+UraoS+KR8FG/arheb+1ijMkExgLLGrl5sjHmS2PMW8aYk/zbBgI7Guyzk0aSi8aYW4wxucaY3AMHDgQ77DZ7e81eIgycNaKVw4uPljUDduaGbRXzoNQ4BqV6WaiFSjoub4GzcE3W9FBH0m6L8woYN6QXnujItt/5hAucyzCsIgzMOfjna04h2RvN+CG9mPvcCiUJRUREpE3cmyBUZZUrJXujlbBoA29dKTXRSU3vEKiUVSJcjpKkBGG7GWMSgJeBO6y1xUfd/AUwxFo7Gvgj8Gpb2rbWPmqtHWetHde7d++gxNtWj3ycV//B/83Ve5iYlcaGfSXtW7E0azr4amD70iBH2XWmDUtn2eYCaut8oQ4lvG1e4Ayz9YTnwhaFZdWs31Pc9vkHA3ofD2nDwzJBuGrnIebNGsvU4elMzEplc34582aNZdVOvYcWERGR1nNvglBDjF0pyRutVVXbIN6WUhvTTIJQiXBpQmDVcL3e2sYYE42THHzWWvvPo2+31hZba0v9v78JRBtj0oFdwKAGu2b4t7lOTkYyc59bwYu5O8g7UMbx/RLbv2LpoEkQERXWw4ynDkunpKqWVbv0f7TdKg7C7hVhPf/g0s1OFWmb5x9s6ITznddCxcEgRdU15szMrp9zcNLQNLYXljMkLZ45M7NDHJmIiIiEExcnCLVIiRslK0HYatbnI9GW4Ytt5kO7hhhLE2KiIvBGR6qCsA2MMQZ4DFhvrf1dE/v08++HMWYCTj9YAHwGDDfGZBljYoCrgNe6JvK2mZKdzrxZY/npv9YC8OqKXe1fsTQ2waka2xK+CcLJQ52E0CLNQ9h+Wz4B6wvz+QfzSYiNImdgByogT7jAWThs43vBC6yLBRKkS/M07F5ERETaxr0JwohIiE3S0EuXSfJEU1ypRRNao6K8hGhTh2luuFZ9pawS4XIsLQrUZlOB64DTjTEr/T/nGWPmGGPm+Pe5HFhjjPkSeAi4yjpqgbnAOziLm7xgrV0bipNojSnZ6fSKd6pMr588pGMrlmZOd6rHqkqCFF3XSkuIZUT/JBZpvrX2y1sAMYmQMS7UkbTbkrwCJmSlEhXZgbe2A0+BhH6w/vXgBdbFju+bSEpcdH1FpYiIiEhrRYU6gGZ5kpU4cZlkbzSlVbXU1vk69ia8BygpyicOiIhrZjVFLcYjzUjyalGgtrDWLgRMC/vMA+Y1cdubwJudEFrQfbRhP7uLKhk7KIVnlm1nUnZa+5OEWTPg0wdg2xI47qzgBtpFpg5L48nF26iorsMb044FKnq6zQsgcxpERoc6knbZc6iCzfllzJo4uGMNRUTACefBl89DTQVEe4MTYBeKiDBMzEpliRKEIiIi0kbuzvB4UjT00mWSvU5OuURVhC0qLy4EICq+mQRhoFJWiXBphDOkX681OdLivHy++/cVANx22jDmzRrbsRVLB02AyBjY8nEQo+xaU4elU13n47OthaEOJfwUboGDW8N6/sEl/uG0HaqkBVj4IKQMgZoy2Ox/PWz5xNkeRiYPTWPnwQp2FJaHOhQREREJIy5PEKqC0G20smrrVRQ7H1ii41Ob39GTrES4NCrJo1XD5Virdh7irBF9MQbGZ6XWz0nY7hVLo72QMSGsFyqZkJVKdKRh0SYNM26zzQucy7Cef7CAXnHRnNAvsWMNDTwZFj8E0XHw1etOcvDF2c72MDLJPw/hsi1KmIuIiEjruTtB6E3R0EuXSfYnCDUvWsuqS51VED1JLSUIU5QIl0ZpDkJpzJyZ2ewqqmBE/6T6/8lTstM7tmJp1nTYsyrsVm8NiIuJYuzgXixUgrDt8hZA0kBIHx7qSNrFWsuSvHwmZ6cREdHsDAMty5oBV8x3FipZ/bKTHLxivrM9jBzXJ5FecdH1lZUiIiIireHuBKEqCF1HFYStV1PqfHMf12KCMFmJcGlUklcVhHKsypo6VmwvYpJ/9d6gyJwOWNi2OHhtdrFpw9JZt6eYwrLqUIcSPnx1TpXc0NPAdDC5FiLbCsrZfaiy48OLA7JmwLAzoLYCTrwo7JKD4MxDOGlomhYqERERkTZxeYIwRUMvXaa+glDzorWozp/0S0hu4UOLN0WJcGlUkn9RIJ/PhjoUcZEvdxRRVetjYlYLXz60RcY4iPLClvAdZjx1WDrWoqqptti90nmfFcbzDy6un38wSAnzLZ/A9iXO76tecK6HoUlD09hVpHkIRUREpPVcniBMhupSqFMFjVskeVRB2Fq2PkHYwocWJcKlCcneaKzVokBypGVbCjHGmXcvaKJiYfDEsE2GAIzOSCYhNkrDjNti84fOZdbM0MbRAYvy8umX5CErPb7jjQXmHLzyKUgeBANGO9fD8HURqDDWasYiIiLSWu5OEHpTnMvK4pCGIYcla4hxq5nKIkqsl8ioqOZ31BBjaUKSx3nu6PUmDS3dXMAJ/ZJIiYsJbsOZ02H/WigLzwRbVGQEk4amtn81554o7yPoNwoSeoc6knbx+SxL8wqYkp2GCcYQ6V1fHJ5zcMhUyN8Ilz/hbA8zx/VNIDU+RsOMRUREpNXcnSD0pDiXqq5yDU90BNGRRgsntEJkVTFlJqHlHb0pUFOmSlk5hhYFkqNV1/r4YvvB4A4vDgjMtbZ1YfDb7iJTh6WzraBcwypbo6oUdiwL69WLv95fQkFZNVOGBWn+wWl3HH4dZE6FsgOQ2M/ZHmaMMUwamsqyzYVYq2kqREREpGUuTxAmO5dKELqGMYZkLZzQKlE1xZRFtiJBWP881zyEciQtCiRHW7WziMoaX3AXKAkYMBZiEmBreM9DCKiKsDW2LQZfTXjPP7jJqY6bHKz5BxsaMtW5DOOE+eF5CCtCHYqIiIiEAXcnCANDjDX80lWSPNEUK2HRotjaEipblSBMcS6VIJSjHF4USK83cQSGCwZ1/sGAyGgYPDmsFyoZ3ieB3omxLNykYZUt2rwAImOdxzxMLc7LJzMtjoEp3uA3njoUEvrBtkXBb7uLTPZ/kaBhxiIiItIa7k4QqrLKlZJUQdgq3toSqqOSWt4x8DxXIlyOogpCOdqyLYWc0C+R1Pggzz8YkDUd8jdAyd7Oab+TGWOYmp3G4k35Wv27JXkLYMhkiO6E5FoXqK3zsWxzIZOzgzS8+GjGOMOMty2GMB2iO6xPAmmah1BERERayeUJwhTnUkOMXSXZG02xVlVtkddXSk1Mcit2THEu9TyXo2gOQmmops5H7tZOmn8wIHO6cxnGwyqnDkunoKyaDftKQh2KexXvgQPrw3r+wTW7iympqmVKZwwvDhgyFUr2QOHmzjtGJ3LmIUxjyeYCzUN4tIUPHrs69ZZPnO0iIiI9lMsThKogdKMkr4YYt0aiLcUX24YKQiUI5SjxMZFERhhVEAoAq3YeoqKmrnPmHwzoPxpik4/94BxGAvMQLtqkeQibtPkj5zKc5x/0zzPZKfMPBmROcy7DeJjxpOw09hyqZLsW7jnSwJPhxdmH/9dt+cS5PvDkUEYlIiISUu5OEEZ7ITJGQy9dJtkbpQRhC2prqok3ldhAFWxzNAehNMEYQ5IniuIKVexKJ88/GBAR6QyrDOOFSgakeBmaHs9CJQibtnkBxKVD31GhjqTdluQVcEK/RNITYjvvIOnHQXxv2Bq+CcLJQ53/FxpmfJSsGXDFfHj+WvjXXCc5eMX8w6tYi4iI9EDuThAa41RXKXHiKkkeZw5CDVdpWkmR80bceFoxxFhzEEozNOenBCzbUshxfRNI68yECDjDjAs3w6GdnXucTjR1WDrLtxRSXesLdSjuY61TQTh0JkS4+21gU6pq6/hsa2HnVg+C8z50yJSwriDM7p1AekIsS/KUIDxG1gxI7AcrnoZxNyk5KCIiPZ773xl6UjT00mWSvdHU+iwVNXWhDsW1yg45b8Sj4nu1vHOgUlaJcGmEM+enEoQ9nTP/YCETszo5IQLOQiUQ1qsZTx2WTnl1HSt3FIU6FPfZvw5K94X1/IMrthdRWeNjSmctUNLQkKlwaAcc3Nb5x+oEzjyEqSzdXKgvdo+2+WM48DX0GQG5j4f11AoiIiLBEAYJwmRVVrmMVlZtWXlxGxKExigRLk1KVgWhAGt2HaK8upPnHwzocxJ4U8N6mPHkoWlEGDTMuDF5C5zLsJ5/sIAIAxOHduJw+4AhU53LMK4inDQ0jb3FlWwr0DyE9bZ8Ai9cD1iYcIszvLjhnIQiIiI9kPsThN4UVVa5TLIShC2qKnEShJ6EVn54USJcmhAYK9JnpgAAhlZJREFU0i8929LNhUAnzz8YEBHhzEMYxhWEyXHRjBqYzGIlCI+1eQGkDYfkjFBH0m5L8vIZlZFCkie68w/WZwR4e4V1gjAwFHuJ5iE8bNcXcPJ1zu9Dph6ek3DXFyENS0REJJTcnyBUZZXrBN6Qa+GEplWXHQTAk9TKD/NKhEsTnFXD9Vrr6ZZtKWBYnwR6J3by/IMBWTPh0HY4uLVrjtcJpg5LZ+WOIkqr9PqpV1vlLLgRxtWDZVW1rNhexJTOnn8wICICBk8J64VKhqbH0zsxVguVNDTtDijLdxbrSR/ubMua4WwXERHpocIgQahFStxGFYQtq/UnCOOTWzk/kidZiXBpVJJ/1XDNHdVz1db5+GxLIRO7onowIDMwD2H4DrebOiydWp9l+RYlRertWAa1FWE9/+BnWwup9dmuSxCCU1F7cAsU7+66YwaRMw9hGks3F6gvaWjbImcRGmNCHYmIiIgruD9B6E1xhl7qDY1rJHmjAChWgrBJvgonqZ2Q3MoPMJ4UJcKlUcneaKrrfFRpNdYea+3uYsq6av7BgN7HQ3yfsB5mfMqQXsRGRbBwoxKE9fIWgImEzGmhjqTdluQVEBMZwbghXZgwD8xDGMZVhJOHprGvuIot+WWhDsUdinZA0fbDj62IiIiEQYLQkwy2Dqr1hsYtVEHYChVFVNsoPN741u2vOQilCYEh/Xq99VzL/BVwXbIgQ4AxThJp66dh+wWdJzqScZm9WKR5CA/bvAAyxoMnKdSRtNvivALGDk7BGxPZdQftNwpik2Dbwq47ZpBN8v//CMxn2uNtX+JcDpkS2jhERERcJAwShCnOpYZfukZiYA7CSiUsmhJRfYhik4CJaOVLLDAHYZh+EJfOE0jIq2K351q6uZCh6fH0SfR07YGzZkDJHijI69rjBtHUYels2FfC/pLKUIcSeuWFsHtlWM8/WFRezZrdh5iS3crpO4IlIhIGTwrrCsKs9Hj6aB7Cw7YtdpK+fU8KdSQiIiKuEQYJwmTnUsMvXSMywpAYG6WKpmZEVR2iPKKV1YPQoFK2tPOCkrCUpIrdHq3OZ535B7tyeHFA1gzncmv4zkM4bZiTSFqSp6QIWz4GbFjPP7h0cyHWwpRhIXg9DJkKBRuhZF/XHzsIjDFMzk5jieYhdGxb7CR9I7qwElVERMTl3J8g9KY4lxp+6SpaWbV50bUlVEQktv4O9ZWySoTLkeorCFWx2yJjzCBjzAJjzDpjzFpjzPca2ecaY8wqY8xqY8xiY8zoBrdt9W9faYzJ7droG7dudzElVbX1wwO7VOpQSBwQ1guVnDQgmSRPFAs3apgxeQuciqmBp4Q6knZbkpdPXEwkozNSuv7ggXkbt4VvFeGkoWkcKKlic0+fh7D0AORv0PBiERGRo7g/QVhfQVgU0jDkSEneaFU0NcNTW0JVVFsShP7nuRLhchTN+dkmtcB/WWtHAJOA24wxI47aZwsw01o7CvgZ8OhRt59mrR1jrR3X+eG2rH7+wawQVEwZA1nTYevCsJ3+IDLCMCU7nUWb8nt21ZS1zvyDmdMhMirU0bTb4rwCxmemEhMVgrev/UdDdLxTeRamAgsd9fhhxvXzD2qBEhERkYbCIEGY4lyqsspVkr1RqmhqhreulOroNkwCH6iU1fNcjpLkCawarordllhr91hrv/D/XgKsBwYetc9ia+1B/9WlQEbXRtk2SzcXkJkWR7/kLp5/MCBzOpQdgANfheb4QTB1eDq7D1WytaA81KGETuFmZ8XWMJ5/cH9xJRv3lzIlOwTJcoDIaBg8MawrCDPT4uiX5NFCJdsWQ5QX+o8JdSQiIiKuEgYJQlVWuVGSJ1qLJjQjwZZSF9OeIcZFnRGOhDHNQdg+xphMYCywrJnd/gN4q8F1C7xrjPncGHNLE+3eYozJNcbkHjhwIGjxNqbOZ1m+pbC+6ickAvMQbvk0dDF00FR/QqlHr2a8eYFzGcbzDy7xV711+QIlDQ2ZCvvXQVl4VuAZY5g0NJUleT18HsJti2DQeIiKCXUkIiIirhI+CUJVVrlKsoYYN8n6fCTaUnyBpF9rKBEuTYiOjCAuJlKvtzYwxiQALwN3WGuLm9jnNJwE4Q8bbJ5mrT0ZOBdnePKMo+9nrX3UWjvOWjuud+/enRD9Yev3FFNcWcvEUMw/GNBrCKQMDuuFSrLS4xmQ7Ol5CcKFDx6ePzJvASQPguJdzvYwtHhTAcneaEYMaEN1frAF5iHcHt7DjPNLq8g70EPnIaw8BHtXa3ixiIhII9yfIIyIdCbVVmWVqziLlChh0ZjysmKijA8TSPq1hoYYSzOS9XprNWNMNE5y8Flr7T+b2CcH+BtwsbW2vhTIWrvLf7kfeAWY0PkRN23ZFmcYYEjmH2woc4YzD6HPF9o42skYw5Rh6SzOK6DO14OqpgaeDC/OdpKDWz6F3ifASzc628PQ4s35TBqaSmSECV0QA8ZClAe2hu8w48nZPXwewu3LAKsFSkRERBrR7gShMeYcY8wGY8wmY8yPmtjnygarST7X7ig9KUqcuEyyN5qy6jpq6sLzA2NnKilyqlQi4nq1/k6x/ooIJcKlEUkeVey2hjHGAI8B6621v2tin8HAP4HrrLVfN9geb4xJDPwOnAWs6fyom7Z0cwGDU+MYkOINZRjOQiUVB2FfSP8cHTJtWDqHKmpYt7vRgtLuKWsGXDEfXrwBqg45CzNcMf/wsPEwsqOwnB2FFaEdXgwQFQsZ42HbwtDG0QGDU+Pon+ypH7Ld42xbBBHRMNAV61CJiIi4SrsShMaYSOBhnGFYI4Crj14p0hgzHPgxMNVaexJwR7uj9CZr6KXLBBZOKKnUwglHqyh23nRHxaW0/k4RkRCbrES4NCrZG61FgVpnKnAdcLoxZqX/5zxjzBxjzBz/Pj8F0oA/+W/P9W/vCyw0xnwJLAf+ba19u8vPwM9XP/9gCIcXB2ROdy63hu88hFOGOVVTC3vaMOOsGc7quwAnzw7L5CDA4jzncQvZAiUNZU6DvWucpHkYcuYhTGPZ5h46D+G2xU4VbUxcqCMRERFxnfZWEE4ANllrN1trq4F/ABcftc/NwMOB1SL9Q7baRxWErpMcp4UTmlJR7AwLjElo4wd7jxLh0rgkbxSHtIpxi6y1C621xlqbY60d4/9501r7iLX2Ef8+/2mt7dXg9nH+7ZuttaP9PydZa38eynP5am8JhypqQj+8GCB5IKRmh/VCJX0SPRzXN6HnzUO45ROnctCbBqv+fnhOwjCzOK+A3omxDOuTEOpQ/HPXWf9Q1fA0eWga+aXV5B0oDXUoXau6HHZ/AYMnhzoSERERV2pvgnAgsKPB9Z3+bQ0dBxxnjFlkjFlqjDmnncdyEicaeukqyf6VVTUv2rGqS52qAk9SGz/Ye1VBKI3TnJ89z7ItTiVySBcoaShrujM0ry48E9WPfJzH0PR4PttaSGVNHeBUpT3ycV6II+tEWz5x5iCMiIYRF/qHG88OuyShtZbFeQVMyU7DmUUgxDLGQWRMWA8zDqyMviSvhw0z3pULvlotUCIiItKEzlykJAoYDpwKXA381RiTcvROxphbjDG5xpjcAwcONN6SKghdJ8mjCsKm1JQ5FYTexDYmCD0pSoRLo5I8ShD2NMs2F5LRy0tGL5cMg8ucDlXFsPfLUEfSLjkZySzcVEBVrY8vth1kcV4+c59bQU5GGxaTCje7voDTfwo15U5CJDAn4a4vQh1Zm2zaX8qBkip3DC8GiPY689eF8UIlg1K9DEj2sHRzYahD6VrbFgMGBk8MdSQiIiKu1N4E4S5gUIPrGf5tDe0EXrPW1lhrtwBf4yQMj2CtfdRaO85aO653796NH82boqGXLlNfQah50Y5RV14EQEJKGydT96iCUBqX7I2mpKq2Z63A2oP5fJZlWwrqq3xcITAPYZgOM56Snc4frhoDwO/e+5q5z61g3qyxoV/0ojNNuwNqK53fA0Mqs2Y428PIYn+Vm6seq8ypsOdLqCoJdSTtYoxhUnYaS3vaPITbFkG/Uc77LRERETlGexOEnwHDjTFZxpgY4CrgtaP2eRWnehBjTDrOkOPN7TqaJxlqyqBOySi3SPKqgrAp1p/MTkhqwyrGoES4NCmQkC9RQr5H2Li/lIPlNUzMcsnwYoDEvtD7hLBeqOQbJ/alX1IsudsOcu3Ewe5KOHWW7YsheTCkDGp5X5danJfPoFQvg1JdUk0LMGQK2Lqwnodw0tA0Csqq2bi/h8xDWFsNOz7T8GIREZFmtCtBaK2tBeYC7wDrgRestWuNMfcZYy7y7/YOUGCMWQcsAP7bWtu+yU48Kc6lqqtc4/AchOE5H1VnMlWHKCaOyKiott1RQ+mlCUl6vfUoSzc7XaWrKgjBqSLctiRsv6xbnJdPUUUNBnh66bb6lXG7LWudIZVDwndBhjqfZenmQqYMdVkyd9BEiIgK63kIJ/v/vwT+33R7e1ZCbYWT3BUREZFGtXsOQv/KkMdZa7MDqz1aa39qrX3N/7u11n7fWjvCWjvKWvuPdkcZGAqg6irXiI2KICYyQhWEjYisOkQZ8W2/oydFlbLSqGRV7PYoy7YUMDDFS0Yvb6hDOVLWdOd/1O4VoY6kzQJzDt7+jeFY4DunZjP3uRXdO0lYkAdlB8I6IbJ+TzGHKmqYMsxlyfKYeBgwNsznIYxjYIq35yQIt/kfqzB+PYiIiHS2zlykJHi8Kc6lqqtcwxhDkjdaCYtGRNcUUx6Z2PY7KhEuTUjyONWoer11f9Zalm0uZGJWqjtWbG1oyDTncsvHoY2jHVbtPMS8WWO5dtIQjIGyqjrmzRrLqp3d+H1FfUIkfIdULtrkJHAnu62aFpy/6+4voLos1JG026ShaSzdXIivJ8xvu20xpB8P8S6rRhUREXGR8EgQBhInlQdDG4ccIckbpUVKGhFTU0JlZELb76hEuDQhOU6LAvUUm/aXUlBW7b7hxQDxadB3ZFguVDJnZjZTstNJ8kQzon8Sy7cUMiU7nTkzs0MdWufZthjie0PasFBH0maPfJzH4rx8FucVMLxPAn2SPCzOy+eRj/NCHdphmdPAVws7Pwt1JO02aWgqhT1hHkJfHWxfqupBERGRFoRJgjDFuVTixFWSvdEUq6LpGN66Eqqjk9p+x/pEeFFQ45Hwl+TREOOeIjDcb+JQFy1Q0lDWDNixDGqrQh1Ju03ISuWL7QeprvWFOpTOtX2xs3qx2ypRWyEnI5nbnv2CpZsLmJKdVj9EPCfDRavPDpoIJiKshxlP6inzEO5bA1XFYV1NKyIi0hXauIpCiAQqqzT00lWSPNEUlVeHOgzXifOVcqBdCcIU51IJwhbV1NSwc+dOKisrQx1Kl/BZy18v6k9ydBHr14fvcLameDweMjIyiI6ODnUoIbd0SyH9kz0MdtOKrQ1lToelf4KduZAZnh+2J2Sm8sSirazedYhThrRxtflwcWgnFG2HSbeGOpJ2mZKdzve+MZx7Xl/HgdIq5j63gnmzxrpr5WlPEvQffXgodxgalBpHRi8vS/IKuGFKZqjD6TzbFjuXYbxgj4iISFcIjwRhfWWVKgjdJNkbzbaC7pes6KhEW4ovth1VDpqDsNV27txJYmIimZmZ7punrRNYa/HtKqZ3Ygz9kl22cEUHWWspKChg586dZGVlhTqckHLmHyxg2rB09z6vh0zxV019GrYJwvFZTnXm8i2F3TdBuG2JcxnGQyrLqusAeHP1Xm4/fZi7koMBQ6bC8r9CTSVEe0IdTbtMGprGB+v34fNZIiJc+n+no7YtgpTBkJwR6khERERcLTyGGEd7ITJWlVUu48xBWBvqMFylprqKOFOF9bQjQag5CFutsrKStLQ09yZRgswYQ2SEoa4bTiRvjCEtLa3HVIM2J+9AGfmlLp1/MMCbAv1yYMsnoY6k3dITYsnuHc/yLd14WOW2RRCb5MwZGabeWbuXSGO4/fRhPLNsuztXnB4yFeqqYFduqCNpt8lD0zhYXsPX+0tCHUrnsNZJmGt4sYiISIvCI0EITnWVEieukuxfxdja7pe0aK+SIucDTEQg2dcWmoOwTXpKcjAgMgLquumUaT3tsWzK4fkHXZwgXPggpGY5CzPUVDjbtnzibA8jE7JSyd12sFsm3QHYvsSZIy8iMtSRtMunGw+wauchTj+hD98/63jmzRrL3OdWuC9JOGQyYMJ6HsLAfKdL8rppwjx/I5Tnh3U1rYiISFcJnwShN0VDL10m2RtNnc9S7h8GJFBWXAhAZFw7hq3VV8oqES7Hioww1CkZ360t21JI36RYMtNcOv8gwMCTYdMHUFftLFay5RN4cbazPYxMyEqlpLKWr/YWhzqU4CvLhwNfhXVC5IP1+wG4cMwAwJmTcN6ssaza6bL+0dvLqdLctjDUkbRbRq84BqV6u+9CJYE5IlVBKCIi0qLwSRB6klVZ5TJaWfVYFcXOG+zohJT2NaBEuDQhwnTPIcbiCMw/ODHL5UPns2bAZX91fl/wCyc5eMV8Z3sYmZDlVGku31IY4kg6wfbwn38wo5cz1+qEzMOreU/JTmfOzOxQhdS0zKmw4zOoDd9F2yYPTWPZlkJ83bGP2bYYEvpC6tBQRyIiIuJ6YZQgTFFllcske50EYXGlEoQBVSXOh83YhHYOEdRQ+qB75OO8Y4alLc7L55GP8zrU7t69e7nqqqvIzs7mlFNO4bzzzuPrr79m69atjBx55Lxf99xzDw888AAAs2fPJisrizFjxnDyySezZMmSRtt/8MEHeeqppwA49dRTWfvlig4nCOfPn8/cuXM71EZDBw4c4Jxzzglaez3Zlvwy9pdUuXv+wYDjzoHEAbBjKYz7j7BLDgIMTPEyMMXLZ1u7YYJw2xKI8sCAsaGOpN2WbylkSFoc/ZLDYOGPIVOhtgJ2rwh1JO02aWgaReU1fLW3m81DaK1TQThkCrj5ixcRERGXCKMEYbIqq1wmyZ8gPFSuBGFAdanzYdObmNrCnk3wpKhSNshyMpKPmLtqcV4+c59bQU5GOxaS8bPWcumll3LqqaeSl5fH559/zi9+8Qv27dvXqvv/5je/YeXKlfzyl7/k29/+9jG319bW8vjjjzNr1qz6bREuXKSkd+/e9O/fn0WLwnf+LbdY5q9kC8wH5mpbPoGKg85qxrl/C9sFSyZkpbJ8S2H3m0d32yIYOA6iYkMdSbv4fJbPthYeUT3oaoFKzTAeZhz4YqLbDTMu2g7FuzS8WEREpJWiQh1Aq3lTVFnlMoEKQg0xPqy27CAA8cnp7WvAkwxlB4IYUfd37+trWbe7+XnE+iTGcv1jy+mbFMu+4iqG9UngD+9v5A/vb2x0/xEDkrj7wpOabG/BggVER0czZ86c+m2jR48GYOvWra2OfcaMGWzatOmY7R9++CEnn3wyUVGH/0W/8sLfWfjpd4gylscff5wJEyZQWFjITTfdxObNm4mLi+PRRx8lJyenye0t2bp1KzfddBP5+fn07t2bJ554gsGDBzN79mySkpLIzc1l7969/PrXv+byyy8H4JJLLuHZZ59l6lR9AOuIpZsLSE+IZWh6fKhDaV5gzsGZ/w0f3AfTfhDGw4xTeWXFLjbnl5HdOyHU4QRHVQnsXQXTfxDqSNpt04FSDpbXMCErTBKE8enQ+wRnoZLp/xXqaNplQIqXIWlxLN1cwE3TskIdTvBsW+xchvFwexERka4UXhWElYec4QLiCoE5CIsra0MciXv4/FWuCSntHCaoRHinSPZG0zcpll1FlfRNiq1PbrfXmjVrOOWUU5q8PS8vjzFjxtT/PPLII43u9/rrrzNq1Khjti9atOiY9isrK3jhnU+ZN+9hbrrpJgDuvvtuxo4dy6pVq/i///s/rr/++ma3t+S73/0uN9xwA6tWreKaa67h9ttvr79tz549LFy4kDfeeIMf/ehH9dvHjRvHp59+2qr2pXHO/IOFTBqa6u75BwF2feEkA8f/J2CgutS5vuuLEAfWduP9FWqfdad5CHcsA+vzr64bnuqrabPCYLh9wJCpzt++LjzfDz3ycR5ZafFHzEMYjKk4Qm7bImdkRu8TQx2JiIhIWAifCkJPCtg658NIbGKooxFUQdioyiKqbDSeuHZWo2gxnjZrrtIvIDCs+PbTh/HMsu1874zhTMluZ5VnK2RnZ7Ny5cr66/fcc88Rt//3f/83999/P7179+axxx475v579uzhxBOP/EBz2RVXAjB12nSKi4spKipi4cKFvPzyywCcfvrpFBQUUFxc3OT2lixZsoR//vOfAFx33XXcdddd9bddcsklREREMGLEiCOGUvfp04fdu3e32LY0bVtBOXuLK5kYDvMPTrvj8O/9RjnDKk/9YdhVDwJk944nLT6G5VsKuWrC4FCHExzbloCJhIwJoY6k3ZZvKaRfkodBqd5Qh9J6mVMh9zHY8yVkNP3lkVvlZCTzxw83UlZVx/q9xRyqqGHucyuYNyt857EEnArCIVMgInzqIUREREIpfHpMb4pzqXkIXSPB4+SXi5UgrBdRVUyJ6cAQwcBiPKqUDZpAcnDerLF8/6zjmTdr7BFzErbHSSedxOeff97u+wfmIHzvvfeOWdAEwOv1UllZecS2KP8HnDr/c6OrK81iYw/PZ9ZwzrbKykq83jD6IO9Cy7Y4835NDof5BxsaEt6rtxpjmJCVWl+x1i1sWwwDxkBseA6ZttayfEsBE7LCoJq2oSHTnMswnYdwSnY6v77MmYbil299Vd9nduYXaZ2uZC8U5sHg8K2mFRER6WrhkyD0+BcU0PBL14iMMCR6olRB2EB09SHKIjrwwcyT7AwPq+pmKwmG0Kqdh474oDMlO515s8ayamf7/5ecfvrpVFVV8eijjx4+zqpVQRtqe+KJJx4zN+Gr/3wJgE8//ZTk5GSSk5OZPn06zz77LAAfffQR6enpJCUlNbm9JVOmTOEf//gHAM8++yzTp09v8T5ff/11o0lOab2lmwtJT4gJv3nwhkxxVm/dszLUkbTb+MxUdhVVsKuoItShdFxNJezKDeuEyPbCcvYVV4XP/IMBiX0hbZgzD2GYOj9nAMneKD7dmM+1EweHd3IQGsw/qPlxRUREWiu8hhiDhl+6TJInmuJKJQgDomtKqOxIgjBQKVt5CDwtJ3SkZXNmZh+zbUp2eoc+/BhjeOWVV7jjjjv41a9+hcfjITMzkwcffLADkR527rnnct111x2xzePxcOU5M4iwdcx/4gnAGbp80003kZOTQ1xcHE8++WSz21vyxz/+kRtvvJHf/OY39YuUtGTBggWcf/75bTxDCXDmHyxgYlZaeFVMweGJ/7cuhEHhOaQ1kIj6bEshA8cODHE0HbT7C6irDuuEyOH5B8MsQQjO333tq+Crg4jIUEfTZovz8qmo8REbFcEzS7czKTstvJOE2xZDdDz0b3mBLhEREXGEUYJQFYRulOyN1hDjBjx1JZRHpXSggcDzvAgYFISIpLMMGDCAF154odHb1qxZc8T1hnMQzp8/v8W2hwwZQlpaGhs3bmT48OF89NFHVNXUsWFfCYN6xdErPgaA1NRUXn311WPu39T22bNnM3v27GaP++GHHx6z/eiYS0tL639/7bXX+Ne//tXiOUnjdhRWsPtQJXPCbXgxHF69ddtimP79UEfTLif2TyIxNoplWwq5JNwThNv81WuDJ4U2jg5YvqWQ1PgYhvUJs2pagMz/396dx0dV3/sff30n20xIZkIgLLKGAFFAdkEREGhdu1htq1Xbamuv9V5ta3tbu/3aem1t7e5ttfXaqtjWva2ttrYuKKCALCKyyhLCvoaQjZD9+/vjTEKABMhkZs6cmffz8ZjHZM6c8z2fCTPM5DOf7+c7HVY+BvvXQv9xbkfTJa2tOD57YSEPLijh7iuLvT/NePti54uLtO4tSiYiIpJKvDPFWD0IE5KTIPTmqn2xEGiuoTEzFPkAbZWySoSnunvvvZe9e/e23U7zOdVlzQnUn/LgwYN85StfoWfPnm6HAoAxZpAx5nVjzHpjzDpjzJc62McYY35ljNlijFltjJnY7r4bjTGbw5cbYxnrLc//lEfffoW3wv0Hzx/Wi0fffoVbnv/paY99ZO0jLNu77Lhty/Yu45G1j5zRubtz/EnHDpnGsn0reGTN770Vd/jYx9Y/yqShPVm+rfM+hIkYd4fHbl8CfUZBdr634m6/T2k55w3tyaPrHo37ubt7LEOm8Ugol2Xrj//iKOHjBuaue5QvXAE3THUW66lvauELVzjbEznuTo9feT8cWH/KatrunltERCQZeSdBeFxllSSKYEA9CNvrYWtozuzG1GAlwiWsuLiYmTOPrQzbliBs6X6C8J577mH8+PHHXe65554uj1NQUMBHPvKRbscTRU3Af1trRwHnA7cZY0adsM/lwIjw5RbgtwDGmHzge8BUYArwPWNMzDKfFwwYzy/e/Q5PrH6N/B6ZLNr5Fr949ztcMGD8aY8d02sMX13w1bY/bpftXcZXF3yVMb3OrBdkd44/6dj8s/hqfjZjzOkXqkmouNsdO6Uwny0HaiirqfdU3MdpboKdS4/rP+iJuNvZV1nHjvJaphT2ivu5o3EsoYGMyejJV3f901txA5877yIe3fx99tavZUBegJe2LOLRzd/nc+ddlNBxd3p8kwXssTYIMTi3iIhIMjI2gapRJk+ebFesWNHxnS0tcHc+XHQnzP5WfAOTTt3553d5Y3MZS775PrdDcZ1taaH5f3qxfOCNXPAf90U2yOFt8L/j4MrfwIQbohleUtmwYQPnnHOO22HE3drdleT3yOSsvORbNbijf1NjzNvW2smRjmmM+Ttwv7X2lXbb/g+Yb619Mnx7IzCr9WKt/XxH+3XklO9ZZ+DRt1/h56vvJM3m0uKrZGCPQvrlnllOsqqhiq0VWynILuBg7UGG5Q0j2IUvJ7pz/PHHHmBY3VGCeUMgePopuokT97Fjq+uaWLenkpF9c8kPT9/3QtzHaaiBPaugoBh6FHgn7nYO1TSw+UA15w4I0SMrPa7njtaxlG2mqq6crZlZ3oq73fHpNkSdPczI/OGeivu4448cgqq9znR703ktROux5/Q6h13Vu/jZRT9jSv/I+6l29z1LRETEbd6pIPT5ICuoyqoEE/RnqIIw7EhNJemmBROIxhTjimiEJEkmzWeiUkGYCowxQ4EJwNIT7hoA7Gx3e1d4W2fbTxz3FmPMCmPMioMHD3YrxhvGvY+slgG0pB2mR1r+GScHAYKZQQqyC9h7ZC8F2QVd+oO6u8cff2wfgmmZUFflsbiPHZuTlY7PGKrrOm+XkYhxH6e1LYU/1PVj3Yy7naq6RtJ8huys9LifO1rH4g8SbGqkICvPW3G3O76OQ7Q0BskwPTwV93HH11VBVs4pk4Ptj11TtoZriq/pVnJQREQkKVhrE+YyadIke0q/HGPtX2459T4SV796dZMd8vV/2IamZrdDcd3e7Zus/V7QLv3zLyMfpLnZ2u+FrJ33g2iFlZTWr1/vdgiu2LivypYerHE7jJjo6N8UWGEjeC8BcoC3gas7uO8fwPR2t+cBk4GvAv+v3fbvAF891XlO+551Go+seNmOeXiqveHPd9kxD0+1j6x4+YyPXbpnqZ3x5Az765W/tjOenGGX7lnapXN35/iTjn32emvvHer8/+WluNsde+3/LbYf+NVCz8Xd5snrrb1vrPfibufiX8y3Nz5ybJ94njtax9ryUrv0RwV2xh8neyvudsd//81f2FG/n2p/OO95T8Xddvz2+dbe1dPaV++O+blPFOl7li666KKLLrokysX1ANpfTvvH1m8vtPbxa069j8TV3EWldsjX/2HLquvcDsV1JWvesvZ7Qfv2vx7t3kA/GmTtP78WlZiSVaomCLccqLZbDlS7HUZMRCtBCGQALwFf6eT+/wOua3d7I9AfuA74v8726+jSnQRha3KwNSl44u1Taf2jtvWP2RNvx/L4Do/943l26Y8KrN23zltxt7v985c32sJv/MNWHW3wVNzWWmtbWpwE7XP/6a242zlUU2+HfP0f9oHXN8f93NE61lprl778NTvjkdF26RNXHTv+8fPt0pdP/57uatzt9m9pabETf/obO2HuBYn/++7o+D9Ndf4/2vxqTM/dESUIddFFF1108frFO1OMwZl+qdVdE0ookAGgacbA0SpnNdLMnPzuDeQPaYqxdCjNaIrxqRhjDPAwsMFa+4tOdnse+HR4NePzgUpr7V6cpOIlxpie4cVJLglvi4klu1fxlXHf5zOTLgbgM5Mu5ivjvs+S3atOe+zaQ2uP65U1pf8UfnbRz1h7aO0Znbs7x3d47NT/x9qsTNi+yFtxtzt2amE+LRbe3n7YU3EDcHAjHC0/boEST8TdTusq0lML8+N+7mgdC7A2K4ufHTzMlO0rwFqm1NXxs/0HWZuVldhxtzveGMO0AeeTdvDTrClb45m4247veZ7z+x506unC3T23iIhIMvLOIiUAT38SyrbAbW/FLyg5pdfe289n567gb7ddyPhBeW6H46p3Xv4TExbfxpar/snwcdMjH+jBGRA8C65/OnrBJZkuLVLy5n0wYCIUHlsRmNKFsHslTL8j4hj27dvHHXfcwfLly8nLy6Nv377cd999ZGZm8sEPfpC1a4/9kXHXXXeRk5PDV7/6VW666SYWLFhAKBTC5/PxwAMPcMEFF5w0/n333Ud+fj6f/vSnmTVrFj/72c/oO2wUR+qbOLt/ZCtlz507lxUrVnD//fef8TGrVq1iz549XHHFFV0617Zt21i8eDHXX389AGvWrOHnP/85c+fO7XD/aCxSYoyZDrwBrAFawpu/BQwGsNY+GE4i3g9cBtQCn7HWrggf/9nw/gD3WGsfPdX5urtISdKwFn45GgZNhY+f8leWsGobmhh718vcMnMYd152ttvhdM2KR+AfX4YvrIReRW5HE5Hv/2M9f3prO2vuupTMdG99d32Sl74NS+6HyTfD+r/Bx+ce//7jAX98azvf+dtaFnxtFkN6nXkvwoTw6BXQWAu3zI/7qbVIiYiIeJ23PoX5Q6ogTDCqIDym8YhTeZId7N29gfwhLcYTTQMmwrM3OUlBcK6fvcnZHiFrLVdddRWzZs2ipKSEt99+mx/96Efs37//jI7/6U9/yqpVq7j33nv5/Oc/f9L9TU1NPPLII23JtVZuLFKyatUqXnzxxS4d09TUxLZt23jiiSfatp177rns2rWLHTt2RDvENtbaN621xlo71lo7Pnx50Vr7oLX2wfA+1lp7m7W2yFp7bmtyMHzfI9ba4eGLNzNdbjAGhlzoVBAm0JeOXZGdmc6YASGWlZa7HUrXbV8MOX0hf5jbkURsWWk5EwbneT85CDD5s871ioedJKHHkoMA54crOZdu9djrobEOdq1w/j8SERGRLkt3O4Au8edp6mWCCfqdBGGVEoS0hJN6PUK9ujdQIM+plJUz869vwL7TTIPK7Q9/vMq5rt4LBWfD/B87l470Oxcuv7fT4V5//XUyMjK49dZb27aNGzcOcCrnztTMmTPZsuXkf+vXXnuNiRMnkp5+7L/oP/7xj8x7fT519Q08/oe5TJ06lfLycj772c+ydetWsrOzeeihhxg7dmyn20/n2Wef5X/+539IS0sjFArx6quv8t3vfpejR4/y5ptv8s1vfpPCwkK+9KUvUVdXRyAQ4NFHH6W4uJi5c+fy17/+lZqaGpqbm6mvr2fDhg2MHz+eG2+8kS9/+ct86EMf4qmnnuLOO+8849+ReMSQabDmGSjf6tkqtqmF+TyyqJS6xmb8GWluh3NmrHUShEOmOYlaD6qua2TdnkpunzPC7VCio3KXs3pu75FOkrBwhueShMP75NCrRyZLS8u55rxBbodz5vashOZ65/UgIiIiXeatr2r9ec60gaYGtyORMFUQHmOPVtBiDbmh7vYgzFMiPNr8eU5ysHKnc+3P69Zwa9euZdKkSZ3eX1JSwvjx49suDz74YIf7vfDCC5x77rknbV+0aNFJ49fW1jJ/8TK+fc/PuPnmmwH43ve+x4QJE1i9ejU//OEP+fSnP33K7adz991389JLL/Huu+/y/PPPk5mZyd133821117LqlWruPbaazn77LN54403eOedd7j77rv51re+1Xb8ypUr+fOf/8yCBQu49957mTFjBqtWreLLX/4yAJMnT+aNN944o1jEY1ordk7ThzCRnTc0n8Zmy6qdFW6HcuYqdkDVbk9XTL29/TAt9lj/QU8rXQh//gwMnQG15fCxR4+vYPcIYwxTCvNZWnrI7VC6pvX/n8Ent+0QERGR0/NWBWEgz7muq4ScAldDEUcwnCCsqlOC0NRVUmOyCaZ1s/JEU+m75hSVfm1apxXPvNOp6Jj19ZhWdBQVFbFq1aq223fddddx93/ta1/jBz/4AQUFBTz88MMnHb93796T+vFdd911pBnDpPMvpLKqioqKCt58803+8pe/ADBnzhwOHTpEVVVVp9tP58ILL+Smm27immuu4eqrr+5wn8rKSm688UY2b96MMYbGxmOv/Ysvvpj8/M7/yO/Tpw979uw5bRziQb1HQI8C2LYIJp5ZQjrRnDc0H2Oc6a7nD+tmJXi8bF/sXHs4IbKstJx0n2HC4Dy3Q+m+3SudnoPlW+GFLzlfSH18rrPdY1WEUwrz+dfafeyuOMqAvIDb4ZyZ7UugzyjIToJks4iIiAs8VkEYcq5VXZUw/BlpZKb7VEEIpDVUUmOi0Mw7kKdK2WhqTQ5+fC7M+bZz3c2KjtGjR/P2229HfHxrD8JXXnmFMWPGnHR/IBCgrq7uuG3GGNJ85rjb0fbggw/ygx/8gJ07dzJp0iQOHTq5euQ73/kOs2fPZu3atbzwwgvHxdmjx6mf/63TkiUJGeNM62tNWHlQKDuD4r653upDuGOx89mozyi3I4nYstJyzh0YIjvTW99Zd2j6HU4icOgM5/a2N5zb3VgQyy1TC50k+dKtHqkibG6CnUs1vVhERKQbPJYgzHOuVV2VUEKBDKqONrkdhusyGqup9eV2fyA9z6OrtaKjtXqjcOaxio4IzZkzh/r6eh566KG2batXr47a9NlzzjnnpN6ETz/9NGk+w8plSwgGg4RCIWbMmMHjjz8OwPz58+nduzfBYLDT7adTUlLC1KlTufvuuykoKGDnzp3k5uZSXV3dtk9lZSUDBgwA6HRFYuCk4wA2bdrUYUJUksSQC6FyhzPt1aOmFOazcsdhGptbTr9zIti+2Kke9Hnr41yrusZm3t1VwZRkmF7cXv4wyD0Ltr3pdiQRO7tfLqFAhncWKtm3GhpqlCAUERHpBm99omytINQKrwkl6E/XIiVAVmMV9Wk53R+oLUFY0f2x5FhFR3vdrOgwxvDcc8/x6quvUlRUxOjRo/nmN79Jv379uhVqq8svv5yFC4+vcPT7/Vw49Tx+8M2v8Kvf/B/gTF1+++23GTt2LN/4xjd47LHHTrn9dL72ta9x7rnnMmbMGKZNm8a4ceOYPXs269evZ/z48Tz99NPceeedfPOb32TChAk0NXX+xcDYsWNJS0tj3Lhx/PKXvwScxV0+8IEPRPIrES9o60Po3SrCKYX51DY0s27P6afku67mABza4umEyDs7KmhstsnRf7A9Y2DodCdB6NGVvX0+w3lD81m2zSMJwrbp9t59PYiIiLjNW/M52noQVrgZhZwgFMhQD0LA31zD4cDgKAzUOpVeFYSJ7KyzzuKZZ57p8L61a9ced7t9D8JTVd21GjJkCL169WLz5s2MGDGC+fPnA9DQ1MJ7+6oY0NOZppufn8/f/va3k47vbPtNN93ETTfd1Ol5//rXv3Y41vLly4/btmnTpraff/CDH3Q4dkZGBq+99lrb7fr6elasWMF9993X6fnF4/qMcv7/2r4Ixn3C7WgiMmWok6haVnqI8YPy3A3mdJIgIbKstBxjYNKQJEsQgpMgXPMMlG2GgpFuRxOR84fl8+qG/RyoqqNP0O92OKe2fbFTuRns73YkIiIinuXNCkIlCBNKMJChHoRAj5ZqmjKiMMW4NRGuStmUdu+997J3797jtrX2IGxu8V5Fyo4dO7j33ntJT/fW91LSBT6fk6zycAVhn6Cfob2yWVZ62O1QTm/HEsjIhv7j3I4kYsu2HeKcfkFC4QXPksrQ6c71Nm+tYNxe69TvtxK9L2dLi9OP08PVtCIiIonAYwnCPOdalVUJJaQEIQA59ggtWaHuD6REuADFxcXMnHn81GifAYPpdoLwnnvuYfz48cdd7rnnnm6NeTojRoxg1qxZMT2HJIAh05xpr9X73I4kYlMK81m+rZyWRE/Eb18EA8+D9Ey3I4lIQ1MLb28/nHz9B1slQR/CUf2D5GSlJ/5CJQffg6OHPV1NKyIikgi8VcqR4Ye0LFVWJZigPyPlexA21NeRbeqx/mgkCPOcayUIT8laG5OVfBNZ60rG3U1cfPvb3+bb3/52lKLqPuvRHl3SgaHt+hCOudrdWCI0pbAXz6zYxaYD1Zzd7/SL+7jiaAXsWwuzvuF2JBFbu6eSusaW5Os/2MoYKJwBJa85fQg9+H6VnuZj8tCeibuy95v3wYCJcHCjc3vINChd6CxC5sGVo0VERNzmrQpCcKZfqoIwoTg9CJtS+o/86ooyAHzZPbs/mBbjOS2/38+hQ4dS8jmX5ut+BWEisdZy6NAh/P4E728lZ6bfOMjo4elpxq19CJcnalIEYOcywHp6SmVr0um8ZE0QgjPN+MhBKNt0+n0T1NTCXmw+UMOhmnq3QznZgInw7E2w/u9OtWbFDuf2gIluRyYiIuJJ3qogBKe6SpVVCSUYSKe5xXKkoZmcLO89paKhtuoQvYC01v6B3ZHhh3S/EuGnMHDgQHbt2sXBgwfdDiXuDlTX4zNw5ECW26FEjd/vZ+DAgW6HIdGQlg6DpzrTXz1qUH6AfkE/S0vL+dQFQ90Op2PbF4EvAwZMdjuSiC0rLaeooAe9c5Ln/7KTtPUhfAMKit2NJUKtU8CXlZZz+bkJtgBI4Uz42KPwhyuh4Gz482fg43Od7SIiItJl3svm+ENKnCSY1ubilUcbUzdBWOn058nIiVIlhBLhp5SRkUFhYaHbYbjiR48so/JoI3+/bbzboYh0bMiF8Nr3obYcsr1XHWaMYUphPm9tPZS4rQx2LIGzJkBmttuRRKS5xbJ8WzkfHHuW26HEVs9CCA6A0jfgvM+5HU1Exg4MEchIY2kiJggBgmcBFg5ugJl3KjkoIiLSDd6cYqyplwmlNUGYyn0I62ucFS+zcqOVIFQiXDoW9KdTncKvNfGAIeE+hDuWuBtHN5xXmM+B6np2lNe6HcrJGo86PdaGXOB2JBF7b18V1XVNydt/sJUxMHSGs1CJR1tiZKT5mDSkJ28l6kIlyx92rs/7D1jxsNODUERERCLivQShP6TKqgQT9B+rIExVDUecXkqBaCUIlQiXTmjVcEl4AyY6C4pt8+4049bE1dJE7EO4awW0NB5LxHpQa//BpF3BuL2h06G27NhCGh40tTCfjfurqahtcDuU45UudJKC2b3hip8604ufvUlJQhERkQh5MEGYp8qqBBNUBSHNRyoA6JHXOzoDqoJQOhEMZFBV15iSC7SIR6RnwcDzPN2HcHhBDj2zMxJz9dbtiwEDg6a6HUnElm8rZ2DPAGflBdwOJfba9yH0qKnDemEtLN922O1QjrfrbUjLhJGXhleNnukkCXevdDsyERERT/JggjCcOGlpcTsSCWvfgzBVtRx1PjTnhHpFZ0D1IJROhAIZNDZbjjY2ux2KSOeGXgj7VkNdlduRRMTnM0wems/ybYmYIFwEfcc4leYeZK1lWWl5alQPAvQcCsGBzjRjjxo7MERmuo+liTbNePgcaKg5vu9g4UyYfodrIYmIiHhZxAlCY8xlxpiNxpgtxphvnGK/jxpjrDEmOkvtBfLAtjgfCCQhtFUQ1jW5HImL6iqpsxn4Az2iM54qCKUTrVP6q46m8OtNEt+Qac579c5lbkcSsamF+Ww/VMu+yjq3QzmmuRF2LXd+vx61tewIZTUNyd9/sJUxThWhh/sQ+jPSmDAoL/Gm3LdOJdbCJCIiIlERUYLQGJMGPABcDowCrjPGjOpgv1zgS8DS7gR5HH/IuVZ1VcLIzUrHmNSuIEyrr6TGRCk5CE4iXJWy0gFV7IonDDwPfOmw3btVU60VbssSqYpw77vQWOvpBUpap22fNzRFEoQAhTPCfQjfczuSiE0d1ot1eyqpqkug957ShdBrRHglYxEREemuSCsIpwBbrLVbrbUNwFPAlR3s933gx0D0vn735znXqq5KGD6fITcrPaV7EKY3VHLElxu9Af2hcKVsdfTGlKQQDKQDShBKgsvsAWdNDPfL86ZR/YP0yExjeSJVTbX+Pgd7t4JwWWk5vXOyKOwdxS/VEl1bH0LvJszPL8ynxcLb2xOkD2Fzo/N6UPWgiIhI1ESaIBwA7Gx3e1d4WxtjzERgkLX2n6cayBhzizFmhTFmxcGDB09/5tYKQq3wmlCCgYyUThBmNFZz1JcTvQGVCJdOhLQokHjFkGnOYgENtW5HEpH0NB8Th/RMrIVKti+G/CLI7et2JBFbVlrO1MJ8jDFuhxI/eUMgNMjTC5VMGNyTjDTD0q0J8nrYvdJpNzTsIrcjERERSRoxWaTEGOMDfgH89+n2tdY+ZK2dbK2dXFBQcPrBW5tyK3GSUEKBjJSuaPI3VVOfEeUKQlAiXE6iKcbiGUMuhJZwzzyPmlqYz8b91Rw+0uB2KE7LiR1LPN1/cNfhWnZXHE2dBUpaJUEfwkBmGmMH5rG0NEEWKildABgYOsPtSERERJJGpAnC3cCgdrcHhre1ygXGAPONMduA84Hno7JQSVtlVUW3h5LoCfozEqsvTZwFWmpozAhGccA851qJcDlB2yIlKfx6E48YPBWMz9PTjKcUOivTr0iEaZUHNziffYZc6HYkEWutxky5BCE4CcLaQ97uQ1iYz5pdldQ2JMAiWaULod+5kJ2CzyUREZEYiTRBuBwYYYwpNMZkAp8Anm+901pbaa3tba0daq0dCrwFfNhau6LbEbctUqLESSJJ9QrCHFtDc2YUE4RKhEsncv3qQdgZY8wjxpgDxpi1ndz/NWPMqvBlrTGm2RiTH75vmzFmTfi+7r9XifN+3e9c2L7I7UgiNnZgiMw0H8sSoWqqNdHq8QVKgv50ivtGseLeK1or3Uq9O8146rBeNLVY9/sQNh6FnUvVf1BERCTKIkoQWmubgNuBl4ANwDPW2nXGmLuNMR+OZoAnyQoCRlMvE0wokEHV0QT4RtkFtqWFXHuEltakXjQoES6dSE/zkZOVnrKvt9OYC1zW2Z3W2p9aa8dba8cD3wQWWGvbN9SaHb6/+9Xu4hhyoTPFuKne7Ugi4s9IY/ygvMToQ7h9MQQHOP3sPGpZaTlTCvPx+VKo/2CrnkMgNNjTfQgnDelJms+4/3rY8RY0N0Ch+g+KiIhEU8Q9CK21L1prR1pri6y194S3fdda+3wH+86KSvUggM8H/qAqqxJMMJCeshVNNdUVpBmLaU3qRUPrFGMlwqUDqV6x2xlr7ULgTP9yvQ54MobhCDj98prqYM87bkcSsSmF+azdU8WReheT8tY6CcLBFzj97DzoQHUdW8uOpOb04lZDpzsVtS0tbkcSkZysdMacFXR/oZLSheBL93Q1rYiISCKKySIlMefPU2VVggkFMjja2ExDkzc/9HbHkUpn6llads/oDZqZCxg9z6VDuf509SDsBmNMNk6l4V/abbbAy8aYt40xt7gTWRIaHF5Qw8PTjM8rzKe5xbJyhwvTKt+8z0mGHC6Fmn1OwrV0obPdY5aXOr+/1r6OKSkZ+hAO68WqnRXUNTa7F0TpAhgwCbJScKq6iIhIDHk0QRhSZVWCCQZSd+GE1gRheo+86A3q8znPc1XKSgdUQdhtHwIWnTC9eLq1diJwOXCbMabD5lbGmFuMMSuMMSsOHjwYj1i9rUcvKDgHtnk3QThpSE98BnemVQ6YCM/eBCvmOrfTs5zbAybGP5ZuWlZ6iOzMNEafFcV+vV4zdLpzve1Nd+PohqmF+TQ0t/DOjgp3AqirdCqSNb1YREQk6ryZIAzkqbIqwYRaE4QpmLSoq3b+aMzIifK0KX9Iz3PpUDCQkZKvtSj6BCdML7bW7g5fHwCeA6Z0dKC19iFr7WRr7eSCgoKYB5oUhkxzFhRo9mbfzJysdMYMCLHUjQRh4Uz4+FxY+iCk++GV7zq3Pbg4w9LSciYN6UlGmjc/ekZFzyGQNxi2LXQ7kohNHpqPcSthDs6XDbbFk68BERGRROfNT2mqrEo4Qb+TIEzFqqaGGudDsj/aCcJAniplpUMhJQgjZowJARcBf2+3rYcxJrf1Z+ASoMOVkCUCQy+EhhrYt9rtSCJ23tB8Vu2soL7JhWmVQ2dAWobTy3HyzZ5MjFTUNrBxfzVThqZw/8FWQ2c4SS6P9iEMBTI4p1+QpW6t7F260EmWD+rwOxwRERHpBo8mCPNUWZVgWqcYp2KCsPGIkyDMDvWO7sBKhEsngn5NMe6IMeZJYAlQbIzZZYy52RhzqzHm1na7XQW8bK090m5bX+BNY8y7wDLgn9baf8cv8iSXBH0IpxTm09DUwupdLnz2WPW4k2AdcSmseNhJkHjMim2HsZbUXqCk1dDpcLQcDm5wO5KITR2Wz8odh93pO126EAaf70y3FxERkajyaIJQPQgTTSiQDkBVnTenkHVHS20FAD1CUW68rkS4dCIUyOBIQzNNzd6sQIkVa+111tr+1toMa+1Aa+3D1toHrbUPtttnrrX2Eycct9VaOy58GW2tvSf+0SexYH/IH+aswutBDy4oafu5dVrl4pKy47bHTOlCePFrzs8f+LkzvfjZmzyXJFy2rZzMNB/jBuW5HYr7hlzoXHu6D2Ev6hpbWL2rIr4nrjkIB9Z5sopWRETEC7yZIAzkQdNRaKp3OxIJS+UKQo5W0GINucEormIMmmIsnUrlhLx41JALnQShB6dVjh0Y4pt/XcOAPD/LSstZXFLG7U+8w9iBodiffPdKKCiG3iMhb9CxnoS7V8b+3FG0tLSc8YPy8GekuR2K+9r6EL7hdiQRa60EjXtfztbejYWz4nteERGRFOHNBKE/z7lWdVXCaO1BmIp90Ux9JTUmG19alP/w0SIl0olgCi8KJB415EKnZcKB9W5H0mXTinpz//UTKKtpYHFJGbc9vpL7r5/AtKIot5XoyNTPw4ENUDTn2LbCmTD9jtifO0qO1Dexdnelphe3N3SmU0HowYQ5QH6PTIr75sY/Qbh1AWQFof+4+J5XREQkRXg7QajqqoThz0gjK92XkgmLtPpKakxO9Af256lSVjoUSuWKXfGmIa19CL05zXhaUW9mF/ehsdnyvnP6xic5CM7vq6kOit4Xn/PFwModh2lusUoQtjd0Ohw97MmEeasphfm8va08vq0uShc6v7u09PidU0REJIV4M0EYyHOuVV2VUEKBDKrqUi9hkd5YTa0vFgnC8PQ1Pc/lBG0VhCn4ehOPyhsMwYGeXahkcUlZ26qt/1i9h8UlZfE5cclrkJbprATtUctKy0nzGSYOiXIbDi8bmgR9CIflc6ShmbV7quJzwoodcLhU/QdFRERiyJsJwrbESYWrYcjxgoHUXFk1q6mKuvTc6A8cCP8xpUpZOYEqCMVzjHGSItsXg7VuR9MlrT0HH7hhIhMH5zEgFOD2J96JT5Kw5DUYfAFk9oj9uWJkaWk5Y84KkpOlqq82eYMhb0hy9CHceig+J2xdmKfwovicT0REJAV5NEGY51yrsiqhhAIZVB1NvUUTAs01NKQHoz+wKgilE8d6fqbe6008bMg0OHIADm1xO5IuWb2rsq3n4EUj+7D10BF+eNUYVu+K8f/NVXucKajDvTe9+MEFJSwuKaO+qZlVOyuYUpgfv5WfvWLoDKei1qN9CPvk+hlW0CN+fQhLF0J2b+hzTnzOJyIikoI8miAMJ06OHnY3DjlO0J+ekhVN2S01NGXGoIKwLRFeEf2xxdNUQSieNCQ8rdJj04xvvaiorefgrOICrIW6xhZuvagoticuec259mD/wbEDQ9z+xDs8sXQHDU0t5Poz4rfys1cUzvB8H8Kphfks31ZOc0uMq4KtdRYoKZzpVCOLiIhITHg7QajKqoSSqj0Ic+wRWrLyoj+wnufSCX+Gj4w0k5KvN/GwXsOhRx/PLlQCcO6AEPk9Mpm/8UDsT7ZlHuT0hb6jY3+uKGtd+fkn/94IwCOLSuO38rNXtCbMPTzNeGphL6rrmtiwN8Z9CMs2Q80+GKbpxSIiIrHkzQRhhh/S/aqsSjCp2IOwob6ObFOP9cdginHrYjyqlJUTGGMIpeDrTTzOGGea8bZFnutD2MrnM8wc0ZuFm8toiWXVVEszbH0diuZ4tmJqWlFv8ns41c6fPn+IkoMnyhsEPYd6fqESIPbTjEsXONdaoERERCSmvJkgBGf6pSqrEorTg7Axtn80JZiqwwcB8AVisDqjFuORUwj6lSAUDxpyIVTtclYk9ahZxX0oP9LAmt0x/Ayyd5Xz5ZAHpxe3emX9fnZX1DF5SE/+tHRH/FZ+9pKh050EoUf7EPYPBRicnx37hUpKF0BoMPQsjO15REREUpyHE4Qhre6aYIL+DFosHGlInYUTaqucD8Vp2XnRHzw9C9IDSoRLh4LhhLyIZ7x5nzMDAI5NMy5d6Gz3kBkjemMMzN94MHYn2fIaYKBoduzOEUOLS8r48tOrAPjG5Wdz//UT4rfys5cMneF8CXhgnduRRGxKuA9hzL4cbmmB0jfUf1BERCQOvJsgDOQpcZJgUnHhhNYEYUZOfmxOEMhTIlw6FFKCULxmwER49S7I6AHb33SSg8/e5Gz3kF45WYwdEGLBphj2ISyZB/3HQQ9vTstdvauSCYPzyMvOYMLgnm09CWO+8rPXDJ3uXHt5mnFhPodrG9l8oCY2J9i32kmiqv+giIhIzHk3QejP09TLBBMMpANQdTR1Kggbqp3+gFk5MZhiDE6lrBLh0oFgIIOqutR5rUkSKJwJH58LLY2w4R9OcvDjcz3ZV+yi4j6s2llBRW1D9Aevq4Sdy2C4d6cX3zJjGOv3VHHRyALSfE7V17Si3rFf+dlrQgOdabOl3l2o5PxhvQBYWhqjacalC53roTNiM76IiIi08XCCUFOME00wBSsIG444jbkDwRhVeSgRLp0IBdJT6rUmSaJwJgyZ7vy/NuajnkwOAlw0soAWCws3x2DKbOlCsM3OAiUe9e6uCg4daWDO2X3cDiXxDZ0O2xd5tg/hwJ4Bzgr5Wbo1RguVlC6A3iMh2D8244uIiEgb7yYINcU44bROMa6qS52kRXNtBQA9Qr1icwJVEEongn5nirH16GqwkqJKF8Kelc7P7zx+rDrIY8YPcqbPLohFH8KS1yAzBwZOif7YcfL6ewfwGSeRKqfR2odw/1q3I4mIMYapw3qxtPRQ9N+Pmhpg+xIo1PRiERGRePBugrA1ceLRb1yTUdCfehWENpwgzM2LUYJQPQilE6FABk0tltqGZrdDETkzrT0Hr/2jUxHUq8i57cEkYZrPMGNEAQs2HYzu4gzWwpZ5TmVlemb0xo2z1zYeYOLgnuRle/cxxI3H+xA+uKCEXjmZlNU0sLXsCOAsUvPggpLuD75nJTQe8WylsYiIiNd4OEGYB1hoqHY7EgkLZYcrCFMoQUhdBUdtJln+7NiMrwpC6UQwBSt2xeN2rzzWc7D4cjiwHq58wNnuQReNLKCspp71e6uiN2j5VqjY7unpxQeq6li7u4rZml58ZkIDIH+YZxOEYweGeHbFLgCWbi1ncUkZtz/xDmMHhro/+NYFgDmWRBUREZGY8nCCMPzBQ9VVCSMnMx1jUitB6KuvpMb0iN0J/HmqlJUOpeKq4eJx0+84Vgk08nJoaYLGo852D2qdPrtgUxSnGW+Z51x7eIGS1zc6qzur/2AXDJ3urOzd4r2K8GlFvfntDRMxBuYuLuX2J97h/usnMK0oCr2ZSxdC/7GQnd/9sUREROS0vJsgDOQ516quShg+n3H6oqXQyqrpjVUc8eXE7gT+EKqUlY60TulPpVXDJYkMmgKBfNj4L7cjiVhBbhZjBgSZH06IRUXJPOg51Kko86jX3jtA/5Cfs/vluh2Kdwyd4Xye9WgfwmnDezOiTw6b9tdw/ZTB0UkONtTCrmXqPygiIhJH3k0QtlYQaoXXhBJMsZVVMxurqEuL4R9BrYlwVcrKCVRBKJ7mS4ORl8Lml6HZu0nui0YWsHJHRXReh00NUPoGFHm3erC+qZk3N5cx++w+GGPcDsc7hlzoXHt0mvHikjL2VNQB8NiSbSwuicLq3jvfguYGJQhFRETiyMMJwjznWhWECSUUyEiphIW/qZr69BgmCNue5xWxO4d4khKE4nkjL3P+b9u51O1IIjaruA/NLZZFW6KREFnqLMjg4enFy0sPc6ShmTnFml7cJR7uQ9jac/B/PzGezDQfM0f05vYn3ul+knDrAvClw+DzoxOoiIiInJaHE4TqQZiIgv6MlOpBGGipoTEjlgnC1kpZJcLleMFAOpBaPT8lyRTNAV8GbPLuNOMJg/LI9adHZ5pxyTwnITJ0RvfHcslr7x0gM93HtOG93A7Fe4bOgO2LPNeHcPWuSu6/fgLvO6cv04b3Yu2eKu6/bgKrd3Xzc0vpQhh4HmTFsI2LiIiIHMe7CUL1IExIqVZBmGNraM6Mwkp9ndEUY+lErl8VhOJx/iAUzvB0H8L0NB8zRvRmwaaDWGu7N9iWeTBoqvN78ajXNx7ggmG9yM5MdzsU7/FoH8JbLypq6zl4yah+bD9US6+cLG69qCjyQY9WwN5VxxY1EhERkbjwboIwMxcwmnqZYJxFSlIjYdHS3EyOraXFH8MEoSoIpRNpPkNuVnrKvN4kSY28HA5tgbItbkcSsVkj+7C/qp739nVjMamaA7BvtVNV6VGlZUcoLTui1YsjNTTch7D0DXfj6Ib3j+qDMfDyun3dG2j7IrAt6j8oIiISZ95NEPp8TvJElVUJJZSdOhWENdUVpBmLaa3yiwX1IJRTCKZYxe7pGGMeMcYcMMZ0WIJjjJlljKk0xqwKX77b7r7LjDEbjTFbjDHfiF/UKa74Mufaw9OMZ44sAGD+xoORD1LyunPt4QTha+8506yVIIxQ8CzIL/JkH8JWfXL9TBiUx8vr93dvoNKFkB6AgZOjE5iIiIicEe8mCMGZfqnKqoQSCmRQ19hCfZO3euhE4kil04Dbl90zdifJzAHj0/NcOhQMZFB11LsrwMbAXOCy0+zzhrV2fPhyN4AxJg14ALgcGAVcZ4wZFdNIxZE3GPqM9vQ0434hP2f3y2XBpm70ISyZB9m9oP/4qMUVb6+/d4DhfXIYlJ/tdijeNXQ6bF/suT6E7V0yuh9rdleyp+Jo5INsXQBDLoD0rOgFJiIiIqfl7QShP6TKqgQT9LcunJD8SYvaykMApMcyQahKWTmFUCBdi5S0Y61dCJRHcOgUYIu1dqu1tgF4CrgyqsFJ54ovhx1vQW0k/3SJYVZxH1ZsO0x1JFP+W1qcCsJhs53/8z2opr6JpaWHVD3YHW/eBz36QH0l7FvjbCtd6Gz3kEtG9QXglUirCGsOwMEN6j8oIiLiAm9+Em3lz1NlVYIJBpyFE1KhL1pd9WEAsnLyY3sif0jPc+lQKvX8jKILjDHvGmP+ZYwZHd42ANjZbp9d4W0nMcbcYoxZYYxZcfBgN6aUyjHFl4Nthi2vuh1JxC4aWUBTi2VxyaGuH7x/LRw5AMPfF/3A4uTNzWU0NltmFytBGLEBE2HF752ft73pJAefvcnZ7iHDCnIoKujBy+sj7ENYutC5VoJQREQk7jyeIFRlVaJpTRCmQl+0+hqn2sWfG8MKQggnwitiew7xpFRbNTwKVgJDrLXjgF8Df+vqANbah6y1k621kwsKCqIdX2o6a6JTOeXhacaThvQkJys9sj6EJfOcaw/3H3z9vQPk+tOZPDTG74fJrHAmXPMHp63I8t85ycGPz/VkouyS0f14a2s5lbURvD+VLoCskKen24uIiHiVtxOE6kGYcEKtFYQpkLRoqnUqCLNDvWJ7IlUQSiecHoTJ/1qLFmttlbW2Jvzzi0CGMaY3sBsY1G7XgeFtEg8+H4y81KkgbGpwO5qIZKb7mFbUiwUbD2Ct7drBW+ZB3zGQ2y82wcWYtZbXNx5g5ogCMtK8/bHSdYUzYcBkOLwNxl3vyeQgONOMm1ssr22MYJpx6UKnF6MvLfqBiYiIyCl5+5OcehAmnKA/dSoIW8IJwh6h3rE9USBPlbLSoVAggyMNzTQ2t7gdiicYY/oZY0z45yk474GHgOXACGNMoTEmE/gE8Lx7kaag4suhvgp2LHY7kojNKu7Dnso6thyoOfOD6muc/otFs2MXWIyt21PFgep6Zqv/YPeVLoSyjc7Pbz96bLqtx4wbmEef3Kyu9yE8vN1Jjg67KCZxiYiIyKl5PEGYB0110FjndiQSlkoVhPZoBc3WkJObF9sTaYqxdCKVXm9nwhjzJLAEKDbG7DLG3GyMudUYc2t4l48Ba40x7wK/Aj5hHU3A7cBLwAbgGWvtOjceQ8oaNgvSsmDjv92OJGIXFTtTzrs0zXj7ImhphCLv9h987b0DGAOzijXlvltaew5e80foWQi9hju3PZgk9PkMF4/qy/yNB6lr7MKKzOo/KCIi4iqPJwhDzrWmXyaMYCC8inFd8q9i7KurpMZk40uL8TQYTTGWTqTS6+1MWGuvs9b2t9ZmWGsHWmsfttY+aK19MHz//dba0dbacdba8621i9sd+6K1dqS1tshae497jyJFZfZwkoQbX4SuTtFNEAPyAozok8OCTV1IEG6ZB+kBGHxB7AKLsdfeO8DYgXn0zslyOxRv273S6Tk47CIYc7WzkvGH/tfZ7kGXjO5HbUMzi0vKzvyg0gVOP9KCs2MXmIiIiHTK2wnCQLgZtpInCSMrPQ1/hi8lphinNVRSY3Jif6JAniplpUOhFFoUSFJA8WVQsR0Ovud2JBGbVVzAstJyjtSfYdK+ZJ7Tby3DH9vAYuRQTT3v7qpgjlYv7r7pdxyrnBt9tbOy95GDznYPumBYL3Kz0nl53RlOM7bWqSAsnAlOJwgRERGJM28nCNsqCCtcDUOOF/SnxsIJGY3VHPXFIUGoSlnpRGvPz1R4vUkKGHmZc+3h1YxnFfehobmFJSWHTr/z4e1waAsM9+704vkbD2ItzFH/wejqOxp6jYC1f3U7kohlpvuYdXYfXt2wn+aWM6gKLtsENfs1vVhERMRFHk8Q5jnXWsAhoYQCGSlR0ZTVVE1dem7sT9T6PFciXE6gCkJJKsGzoP842OTdPoSTh/YkOzPtzKYZl8xzrovmxDaoGHpt4wEKcrMYfVbQ7VCSizHONOPti6A6gpWAE8Qlo/pSVtPAOzsOn37nrQucay1QIiIi4hpvJwgDec61KqsSSiiQQVVd8ics/M3VNGTE4Y+itgShnudyvGDrIiUp8HqTFFF8BexcBjVd6OOXQLLS05hW1Iv5mw5gT9dLccs8CA6E3iPjE1yUNTa3sHDTQWYXF+DzaUpo1I2+GmwLrP+725FEbFZxARlphpc7W834zfuOLUxSugDyBkPFDme7iIiIxF3ECUJjzGXGmI3GmC3GmG90cP9XjDHrjTGrjTHzjDFDuhdqBzTFOCEFU6SCsEdLDU3xSBC2JsJVKSsnUAWhJJ2RlwEWNr/sdiQRu2hkATvLj7K17EjnOzU3OYmR4XM822/t7e2Hqa5r0vTiWOlzNvQZBeueczuSiOX6M7igqDcvrdvXccJ8wERnpeaS+bDtDehd7NweMDHOkYqIiAhEmCA0xqQBDwCXA6OA64wxo07Y7R1gsrV2LPBn4CfdCbRDShAmpFAgg6qjyb+qao49QktWKPYnUg9C6URWuo/MNF9KvN4kRfQfB7lnwSZv9yEEWLDxFFWQu1dAfRUUebf/4OvvHSAjzTB9RIHboSSv0VfBjiVQtcftSCJ2yai+bD9Uy+YDNSffWTjTWbn52U87n3F2LHFuqw+hiIiIKyKtIJwCbLHWbrXWNgBPAVe238Fa+7q1tjZ88y1gYORhdiI9C9IDqqxKMEF/etJXNNXX1RIwDcem/8aSehBKJ4wxKVOxKynCGGc14y2veXbl9kH52Qwr6MH8U/Uh3DIPjM/T/dZee+8AUwrzyclKdzuU5DX6asDCur+5HUnELh7VF4CX1+3reIfCmVBwjvPzxBuVHBQREXFRpAnCAcDOdrd3hbd15magw3IAY8wtxpgVxpgVBw9G0HMokKfKqgQTCmRQXddIy5msWudR1RXOCpUmOy/2J1OlrJxCMJCuHoSSXEZeDo1HYNubbkcSsYtGFrB06yHqGps73qFkHgyYDIGe8Q0sSnaWOxVhs4s1vTimeg+HfufCOu+uZtw36Gf8oDxe6awPYcl82LUM8ofB6qeO9SQUERGRuIv5IiXGmE8Ck4GfdnS/tfYha+1ka+3kgoIIpqn4Q0qcJJhgIIMWCzUNyTvt8UilkyBMi0eCMD0TMrJVKSsdcqb0K0EoSaRwpvN/nsenGdc3tbBk66GT76wth90rPb168esbDwCo/2A8jL4adi13Fu/wqEtG9+XdXZXsrTx6/B2lC+GZTzmLsbzvu+HpxjcpSSgiIuKSSBOEu4FB7W4PDG87jjHm/cC3gQ9ba+sjPNep+fNUQZhgWldWraxN3qTF0aoyADJ75MfnhP48JcKlQyFNMZZkk+F3kmcb/w2nWwk4QU0tzMef4eu4D+HW+YCF4d7tP/jaewcY2iubYQU5boeS/EZf5Vx7eJrxJaP6AfDqiVWEu1c6fUf9ec4K5q09CXevjHuMIiIiEnmCcDkwwhhTaIzJBD4BPN9+B2PMBOD/cJKDB7oX5in4Q6qsSjBBv5MgTOZpj/U1hwHIzI3T9DB/SIlw6VDQrwpCSUIjL4OqXbBvjduRRMSfkcb5w3qxoKM+hCXznP/Tz/LmSq1HG5pZUnKI2aoejI/8QjhrgqenGQ/vk8Owgh68fGKCcNJNsHMZnPtxp684OEnC6XfEO0QREREhwgShtbYJuB14CdgAPGOtXWeMudsY8+Hwbj8FcoBnjTGrjDHPdzJc96gHYcIJtVYQJnHSorGmHIDsYK/4nDCQp0S4dEgVhJKURl4KGNj0b7cjidiskQWUlh1h+6EjxzZa6yzAMmwWpHlzcY/FJWXUN7VoenE8jb4a9rwD5VvdjiRil4zqx5KSQ8e/X639CzTXw4Qb3AtMRERE2kTcg9Ba+6K1dqS1tshae09423ettc+Hf36/tbavtXZ8+PLhU48YIfUgTDjBgPNHT9XR5O1B2FxbAcQxQagKQumEs0hJE9ajUzFFOpTTBwZMgo3e7UN4UXgBj+OqCA++B9V7oMjb04uzM9OYUhinFhsCoz/iXHt5mvHovjS1WOZvbDepaNUT0GcU9B/vWlwiIiJyTMwXKYk5fx7UVUFLi9uRSFhrBWEyT3tsOepMMc7Ni1eCME+JcOlQKJBBc4vlSEMnq6WKeFXx5bBnJVTtdTuSiBT27sGQXtnMb9+HcMs859qjC5RYa3n9vQNMH96brPQ0t8NJHXmDYeB5np5mPH5gHgW5Wby8LjzN+OBG2L0Cxt8AxrgbnIiIiADJkCAM5AEW6qvcjkTCWhcpSeYehKauiqM2kyx/dnxOqApC6URbz88kTshLiiq+3Lne/JK7cXTDrJEFLC4po64xnMAvmQe9R0LeoFMfmKA27q9mT2Wdphe7YfTVTk/Osi1uRxIRn8/w/nP6Mn/jAef1sOpxMGkw9hq3QxMREZEw7ycI/SHnWtVVCSMnMx2fSe4ehL76CqpNHFdvDOSpUlY6lAo9PyVF9RkFocHOasYedVFxAXWNLSzfVg6NR2H7Ys9PLwa0QIkbRn8EMJ6uIrxkdF+ONDTz1ub98O5TTq/RHD2XREREEkUSJAjznGtVVyUMn88QDCT3yqrpDdXU+uKYIPSHUKWsdCSYAlP6JUUZ41QRbn0dGmrdjiYiFwzrzX9l/IPS5f92koNNdTD8fVC6EN68z+3wuuz19w4w+qwgfYN+t0NJPcGzYPAFsO45tyOJ2LSiXvTITGPbshegZj+Mv97tkERERKSdJEgQhisItcJrQgn6k3tl1cymKo6mxTNBmOdcq1JWTqAKQklqxZc5SbXSBW5HEpFAZhpN/cbz4c3fgpV/gLQssMCzN8GAiW6H1yUVtQ28vf2wphe7afRVcGA9HHjP7UgikpWexqyz+zBw+3PY7F4w4lK3QxIREZF2vJ8gDOQ516ogTCihQAZVdcm7irG/qZr69GD8TqjnuXSibVGgJH69SQobMh0ycz29mnGfsRfzn/VfwG54AXL7wd8+Dx+fC4Uz3Q6tSxZsOkiL1fRiV426EozP09OMPzA8ixktyzkw9MOQnul2OCIiItKO9xOE6kGYkIKB9KSuaMpuqaEpIzd+J1SlrHSidZGSZH69SQpLz4Thc2DTS57twTqruA9lNoSxzVCxHSbf7LnkIDjTi/N7ZDJuYJ7boaSu3L4w5EJY+1ew1u1oIjK7cSFZpol/+Ly5kreIiEgyS4IEYZ5zrcqqhBJK8h6EPWwNzVmh+J1Qz3PpRK4/HWPUg1CSWPEVULMP9r7jdiQReWX9Pu7KehILcMEXYMXDrF30Ag8uKHE7tDPW3GJZsOkgs0YWkOYzboeT2kZfBYc2w/51bkcSkcDap9iWUcSftgWxHk1yioiIJCvvJwgzc5zpFqqsSijJ3IOwpbmZXFuLjWuCUJWy0jGfz5CTldwVu5LiRlzivM97dDXj2c1LmGbfYV7LZBredzdrL/xfBrzyX0xPX+92aGds1c7DHK5t1PTiRDDqSjBp3pxmvH8d7F3FwaKPUVp2hJKDNW5HJCIiIu14P0Ho8znJE1VWJZRQIHkThNVVh/EZe6wvYDy0nkuJcOlAslfsSorLzodBU2GTN/sQnr3zaTBwd+MNfP0vq/n0a352X/wbxljvVBC+9t4B0nyGmSML3A5FevR2pqh7cZrxqifAl8HgWTcC8NK6/S4HJCIiIu15P0EI4QRhhdtRSDvBQAb1TS3UNTa7HUrUHak8BIAvOy9+J83McSoGlAiXDgT9GVTVKUEoSaz4cti3Bip2uh1J19TXwL41NBd/iD2mH8+9s5tPTh3MmAs/BNPvcDu6M/baeweZNKRn26JI4rIxV8PhUti7yu1IzlxzI6x+Goovo2+/AYwbGOLl9UoQioiIJJIkSRDmqbIqwQTbVlZNvqRFbWUZABk98uN3UmOUCJdOJXPFblcYYx4xxhwwxqzt5P4bjDGrjTFrjDGLjTHj2t23Lbx9lTFmRfyiljMy8nLnepPHphmvehzqKlhfeCPpaU7vvseWbGdxSZnLgZ25vZVH2bC3ijmaXpw4zv4g+NJh3XNuR3LmNr8CRw7C+E8CcMnofry7s4J9lXUuByYiIiKtkiNBGMhTZVWCCfrTAag62uRyJNFXV1MOQGaPnvE9sabSSyeCgfSkfK1FYC5w2SnuLwUustaeC3wfeOiE+2dba8dbayfHKD6JVO8RkD/MWwnC5iZY8gBVBZO46RX4xTXjyEz3MWFQHrc/8U7CJwkfXFDC4pIyXn/vIABzzu7D4pIyTy2ukrSy82HYbCdB6JVpxqsehx59YPj7AbhkVF8AXtmgKkIREZFEkRwJQlVWJZzWaUjJWNXUGE4Q+oO94nviQJ4qZaVDqiB0WGsXAuWnuH+xtfZw+OZbwMC4BCbdZ4yzmnHpQmfarhe89wJUbGdx3+u5//oJXHHuWVx33iDe3FLGXR8ezepdif2Fz9iBIW5/4h3+/PYuBuQFKKuu5/Yn3mHswDgu0CWdG3M1VOyA3W+7Hcnp1Rx0kvvjroU05wvk4X1yKOzdg5fX7XM5OBEREWmVJAnCPFVWJZhQEk8xbjpSAUCPUJwThKoglE6oB2FEbgbar3phgZeNMW8bY27p7CBjzC3GmBXGmBUHDx6MeZDSzsjLoLkBSl5zO5LTsxYW/Qryi7js6s8wrag3AJ+/qAhjYHlpObdeVORykKc2rag3P7xqDCt3HCa/Rya3P/kO918/oe2xiMuKr4C0TG9MM17zLLQ0wfgb2jYZY7hkVF/e2npI718iIiIJIkkShCFVViWYth6ESVjV1FzrFCDFP0GYp0pZ6VAokEFtQzONzS1uh+IJxpjZOAnCr7fbPN1aOxG4HLjNGDOzo2OttQ9ZaydbaycXFGhF17jatQwyehw/zbh0Ibx5n2shdWrHEtizEi74L/CltW0+Ky/AxyYN5OkVO9lflfi9197aWo4B1uyu5JNTBys5mEgCeVD0PidB2JLA//db60wvPmsi9DnnuLsuGd2XxmbL/I36skVERCQRJEeCMJAHzfXQmPgftlNFKIkThLaukmZryMnNi++J1WtTOhHKTt7XW7QZY8YCvweutNYeat1urd0dvj4APAdMcSdC6dTA86ClETa8AC3NTnLw2ZtgwES3IzvZ4l9DIB/GXX/SXf950XCaWyy/W7jVhcDO3M7yWv741jYy0318cc5w/rR0R8L3TUw5Y66Gqt1O8jxR7VsN+9fC+JNfC+MH9aR3TpamGYuIiCSI5EgQ+sP9cFRdlTCC/uTtQeirq6Da9MD44vzyUaWsdCKZX2/RZIwZDPwV+JS1dlO77T2MMbmtPwOXAB2uhCwuKpwJ074E9VXw99ud5ODH5zrbE0nZZtj4Ikz5D8jMPunuwb2yuXLcWTy+dAeHaupdCPDMfOu5NTS3wM+vGcdXLinm/usneGJxlZRSfDmk+2HtX92OpHPvPO5MhT73YyfdleYzXDyqD/M3HqS+qdmF4ERERKS9JEkQ5jnXqq5KGJnpPgIZaVTVJd/KqmkNVdSYnPif2J+nSlnp0LGen8n3eusKY8yTwBKg2BizyxhzszHmVmPMreFdvgv0An5jjFlljFkR3t4XeNMY8y6wDPintdZDy+WmkAu/CGlZ8O4TMPmziZccBFhyvxPjef/R6S7/NXs4dU3NPPxmaRwDO3Mb91XzxuYyPji2Px8cexbg9CS8//oJCb+4SkrJyoURF8P6vztVtYmmqR7WPANnfwACPU+6+8EFJQzqmU1NfRNLSpyCbq2ULSIi4p50twOIitYEoaqrEkowkE5lbfJVNGU0VnHU50aCsF2lbEa/+J9fElYw4PxXnuoVhNba605z/+eAz3WwfSswLlZxSRTtXQW+dOfLkqUPOgnCREoS1hyEVU/C+Osgp/MelcP75HDFuf35w5LtfH5mUVubgETx05c2kutP5wcfGXPc9mlFvdWHMNGMvsqZdr9jCQyd7nY0x9v0bzh6GMZ/ssO7xw4McdvjK/Gn+3h5/X4y033c/oSzGI6IiIjEX3JUEAbynGtNMU4ooUBGUiYsshqrqUvPjf+JW5/nSoTLCVorCJPx9SbSprXn4Cceh4JzICPbuV260O3Ijln+Oyd5ecHtp9319tnDqalv4tHFiVVF+Pb2cl7dsJ9bLyoiLzvT7XDkdEZe5rwWEnGa8aonILc/FM3u8O5pRb154IaJtFjL397Zze2Pa6VsERERNyVHgrCtskrTXhJJ0J9BVV3yJSwCLTU0ZriQINTzXDrR2oNQi5RIUtu90uk5WDQbLvsR1OyH4iuc7YmgoRaW/c6JqfeI0+5+Tv8gF4/qy6OLtlGdIO+V1lp+/K+N9M7J4jMXDnU7HDkTmT1g5KXONOPmBGozUb0fNr8C4z5x3EreJ5pW1Js5Z/eltqGZSUN7KjkoIiLioiRJEOY516qsSijJWkHYo6WapsxQ/E/sD/fvUaWsnCCoCkJJBdPvODaduGg2nP1Bp2pq7LWuhtXm3SfgaDlM+8IZH3L77OFUHm3kT2/tiGFgZ27+poMs21bOl943nOzM5OhCkxJGXw21ZbD9TbcjOWb102CbYfwNp9xtcUkZy0rL6RfM4tX1+/n32r1xClBEREROlCQJQlVWJaJgIDkrCHPsEVoyg/E/sZ7n0gl/RhqZ6b6kfL2JdOqS70NLI7x6l9uROAtELHkABkyCwRec8WHjBuUxc2QBv39jK7UN7lZ/tbRYfvLvjQzOz+ba8wa7Got00YiLITMncaYZWwurHoeBU05ZTbu4pMzpOXjDBP5w81R8Pvjik6u0UraIiIhLkiNBmJ7p9F9RZVVCCQUykm6RkrqjR/CbRmxrP8B4Ug9COYVQIENTjCW15A9zev2tfgp2Lnc3lo0vQvlWp3rQmC4d+sU5wzl0pIEnl+2MUXBn5oXVe9iwt4r/vmQkmenJ8fEwZWQEoPhy2PA8NCfA+8CelXDwPRh//Sl3W72rsq3n4Mi+udw2ewQNzS08987uOAUqIiIi7SXPJ0B/nhKECSYYyKC6vomWFut2KFFTU3kIAF+gZ/xPrgpCOYWgP52qownUf0okHmZ8BXL6wb/uhJYW9+JY/GvIGwJnf6jLh04ems/5w/J5aGEJdY3NMQju9BqaWvj5y5s4p3+QD409y5UYpBvevA96FzsrBm9d4GwrXehsd8M7j0O6H8Zcfcrdbr2o6Lieg7fNLqKooAeLtxziSL3ez0REROItiRKEIVVWJZigPx1roTqJPuQdqXCmvaT1yIv/ydMyIKOHEuHSoWTt+SlySlm5cPH/OBVLq59yJ4YdS2HnUrjgNkiLrG/fF+aMYH9VPc++vSvKwZ2Zp5fvYEd5LXdeVozP17UKSEkAAybC0t86s2nWPXdsxe8BE+MfS2MdrP0znPOhY19snqGs9DTu/ehYdlcc5ecvb4pRgCIiItKZ5EkQBvJUWZVgQoHkW1n1aHU5AJk98t0JIJCnBKF0KJSkPT9FTuvca2DAZKcXYX11/M+/5NfOLIbTLMZwKtOKejFxcB4Pzi+hsTm+lZC1DU3877wtTCnMZ9bIgrieW6KkcKazwndLM6x5Fp650bnduqhPPG38p/N5PMLXw3lD8/nk+YOZu7iUVTsrohubiIiInFLyJAj9ISVOEkwyrqxaH04QZuW4MMUYVCkrnQqqglBSlc8Hl/8EavbDwp/F99yHSmDDP+C8myErJ+JhjDF8Yc4IdlccjXv/tUcXbaOspp6vX1aM6WL/REkghTNh3LXQXA+ZPbq0WE5UrXoCggO7lZy887KzKcjN4ht/WR33hLmIiEgqS6IEYZ4qCBNMWwVhElU1NR45DEB2qJc7Aeh5Lp3QIiWS0gZOgnHXw1u/cZJ28fLWb5z2D1Nu6fZQs4oLGDMgyG9e30JTnJIih4808OD8Et5/Tl8mDXGpMl6io3QhvPdPKP4AVO6EZz4d/xiq9kDJazD+OvClRTxM0J/B968cw3v7qnlo4dYoBigiIiKnkkQJwhAcVeIkkQT9yTfFuLk2nCAMupUgVKWsdCzoz6Cqrglrk2dRIJEuef/3IC0TXv5/8TnfkUPOYgxjr4Hcft0ezhjD7bNHsO1QLf9cszcKAZ7egwtKqGlo4muXFsflfBIjrT0HPz4XrnsCRl/trKz9r2/EN453nwLbAuOu6/ZQl4zuxxXn9uN/521m68GaKAQnIiIip5M8CcJAHtRXOv1XJCGEspNvinFLOEGYm9f7NHvGSCBPiXDpUCiQQXOLpSaJFgUS6ZLcfjDzq05iZMu82J9vxcPQdBQuuD1qQ14yqi8j++Zw/2tbaGmJbbJ/b+VR5i7exlUTBlDcLzem55IY273y+J6DH/2905dz6YNO8jCW3rzPOYe1sOpxGDwNqnZHZQXluz40mqx0H9/865qYvx5EREQkmRKErSul1Ve5G4e0Cfqd1RyrjiZPwsLUVVJrs8jM8rsTgD+kKcbSoWAg/HqrS57Xm0iXnf9f0LMQ/v1NaI7hl1ONdbD0/2DEJdDnnKgN6/MZbps9nM0Hanhp3b6ojduRX83bjLXw5fePjOl5JA6m33F8zz9fGnzqOSgodqYal8dwmu6AiU714rLfwaEtMHBy1FZQ7hP08+0rzmFpaTnPrNjZ7fFERETk1JIoQZjnXGsBh4SRk5WOzyRXBaGvvpIa08O9APx5qpSVDrX2/KysTZ7Xm0iXpWfBpT+Eso2w/PexO8/qp6C2DKZ9IepDf3DsWRT27sGvX9sSs5YBJQdreGbFLm44fzCD8rNjcg5xmT8I1z3p/PzEJ6AuRl+gF850KhZf/jb40uGdP0V1BeVrzxvE1MJ8fvjiBg5U1UVlTBEREelYEiUIwxWEqq5KGMYYgoGMpFqkJL2xmlpf5CtVdpsqZaUTbT0/k+j1JhKR4suhaA68/iM4Uhb98VtaYPH90H8cDJ0R9eHTfIb/mlXE+r1VvL7xQNTHB/j5yxvxp/u4bfbwmIwvCSJ/GFzzBygvgb98LjZfLh45BG/8ApoboKUJzvtc1JKD4HyW/NHV51LX1MJdL6yL2rgiIiJysuRJEAbynGst4JBQQoGMpKogzGqs4miai72aWp/nqpSVEwQDydfzUyQixsClP4KGGnjtB9Eff/NLcGgzTPuic64Y+MiEAQzIC/CredGvInx3ZwUvrtnH52YMo3dOVlTHlgRUOBMu/7HzvJ33P9Ede99a+N0s2PEWZObAzDud3pxR7ns4rCCHL71vBC+u2cfLMZ56LyIiksqSJ0HYOsVYFYQJJRTISKpVjLOaa6jPCLoXgJ7n0onWKcbJ9HoTiVifs2HKLfD2XNi7OrpjL/41hAbBqCujO247GWk+/nNWEat2VrBoy6Gojv3TlzaS3yOTz80ojOq4ksDO+5xzWfS/sOrJ6Iy54QV4+BKoq4bMbGc685xvO9OLn70p6knCW2YO4+x+uXz37+uojkKl/IMLSlhccnyF8eKSMh5cUNLtsUVERLwqiRKE4amXqqxKKEF/clUQZrdU05jhYgVh21T6CvdikISkCkKRE8z6OgR6wr+/4aywGqnWVVoBdr0N2xc5i5MseSAqYXbm45MH0jeYxa9f2xy1Md/cXMabW8q4bfZwcsNtCSRFXHavMyX+hS/CzuWRj9PSAvN/DE9/0knEn3czXPunY9OKC2c6ScLdK6MSdquMNB/3fnQsB6rr+Mm/N3Z7vLEDQ9z+xDu8umE/q3ZWsLikjNufeIexA0NRiFZERMSbkidB2DbFWJVViSQUyEiqVVVz7BGaM1388KjnuXQiNysdY7SKsUibQE9433echN76v0U+TusqraULYcmvIaMHrHsuKqu0nkpWehqfn1nE0tJylpWWd3s8ay0/eek9BuQFuGHq4ChEKJ6SluH0IwwOgKeuh8pdXR+j4Qg8eyPM/yGM/QTc9KLzGjux52DhTGdl5SgbPyiPm6YV8se3trNiW/deE4Pzszm/MJ/PPbaCG373Frc9vpL7r5/AtKLeUYpWRETEe5InQZiZAyZNlVUJJhhIT5qKppbmZnJsLdb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UndiV30jYG2vro119vqXbiL0Wdn4l54qNOOsEYWtKqShgNLABCG0a1ACOAKHtPOc24DbgjG/Gn/l+J7sOl532MSHertz8zkZCfVzJL6ulf4gXry7fz6vL95/y8UP7+PDUpcPabW/lypU4Oztzxx13tNw2cuRIwHxh0VGJiYkcOHDgpNtXrFhBXFwcTk7m/4qpU6cyfvx4Vq5cSUlJCe+88w6TJ0+mpqaGO++8k5SUFJycnHjppZeYNm0a1dXV3HLLLWzbto3BgwdTXV0NgLPRwJqVv/Kfl+Zjaqhj8MABLFq0CC8vr1PGFxUVxTXXXMNPP/2Eu7s7S5YsoX///mRmZvK73/2OgoICgoODWbRoEWFhYfTv35+DBw9SWlpKYGAgK1euJDExkcTERN555x369OnDvffeS1paGvX19Tz99NNcfvnlLF68mK+++oqKigoaGxtZtWoVV1xxBR999JEkCIUQutI0LVcp9SKQBVQDv2ia9kvrx9jbmOVkNFBrgpKqOjQNSqrqySioJGxwHOkH0skoqGzz+ORVK4gZPILsklqglluvvoTho+PZlLyG8rJSXn19IUMvPL/TYxbAL7/8wlNPPUVtbS0xMTGnHbMA/v3vf/P9999TX1/P559/zuDBg6msrDzl2JOZmclNN91EZaX591mwYAEJCQk88sgj7N69m1GjRjF37lwefPBBLr30Uj755BMefvjhdo8tes6w3r5U1Tbwbao5Vz//pz1dbste2vgsJYe5EyMlOehguvs6yya4B0BNKez4wvzz8qe73tbZtrH1A4ieIgkP0ZaLl/X0UWuIAWDT2+YkoZwrNsFiCUKllBfwJfCApmllrXfn0zRNU0ppp3qepmlvAW8BjBkz5pSP6Qxfd2dCfVzJLakhzM8NX/ezm8WQlpZGfHx8u/enp6czatSolp+PHDnCn/70p5Me9/333zNixIiTbl+7du1J7Tc0NLBx40Z+/PFHnnnmGZYvX87rr7+OUoodO3awZ88eZsyYwb59+3jjjTfw8PBg9+7dbN++nbi4ODRNI3V/Nm+99iJvfvw1np5efL3odV566SWefPLJdn8XX19fduzYwfvvv88DDzzADz/8wL333svcuXOZO3cu7777Lvfddx/ffPMNgwYNYteuXWRkZBAXF8fq1asZP3482dnZDBgwgMcee4xzzz2Xd999l5KSEsaNG8f5558PwJYtW9i+fTsBAQEAjBkzhr/+9a+n/X8QQoju1lTD6XIgGigBPldK3ahp2ofNj7G3MetwXh43/eEefN2dUQr8PV2ICfbiqy+XMnzEcGKC2yboPt61lckJ41pud3M24m5ULPnhVzasWs7zf3+W86dN4c1OjFkABQUFPPvssyxfvhxPT0+ef/75M45ZQUFBbNmyhf/85z+8+OKL/Pe//+W555475dgTEhLCsmXLcHNzY//+/Vx//fWkpKQwf/58XnzxRX744YeWdseMGcP8+fMlQWglnv95N/UmjQuH92LtgQIemjGIEWGdW1a7I7eUl37Zy4xhvfhl5xGbbWNHbikvLN1DZV0jX27J5YLhvSRJ6CCs5TpLd8ufMn+NvQ72/QTT/gp9RnWujcOpsPJZGHwJ7Pmh8200P9+rF2SsgpX/gGmPdi4GYb9+fQZQMPI68wxTPftoV59vyTZWPAtGJ9j+mTmhPvqGzrUhepxFEoRKKWfMg9ZHmqZ91XRzvlKqt6ZpeUqp3sDRsz3O6WZNNGteonXfuf35cEMW958/oFvfPMXExJCamtry89NPP93m/j//+c88++yzBAcH884775z0/Ly8PIYMGdLmttmzZwMQHx/fMuNjzZo13HvvvQAMHjyYyMhI9u3bR1JSEvfddx8AsbGxxMbGUlhRy/6d68k4sI/fX3URDY0m6uvrmZQw8bS/y/XXX9/y9cEHHwRg3bp1fPWV+b/0pptuarlgmjx5MklJSWRkZPDoo4/y9ttvM2XKFMaOHQuYZ4J89913LfUYa2pqyMrKAmD69OktyUGAkJAQDh/utlV8QgjRUecDGZqmHQNQSn0FJAAfnvZZ7bD2Mauipp4/P/YERqOByEBPPF2dePKxR3jlhfmEhoSw6N138XRt+zah8Fg+I0cMa3N7wvkX4eZsJHZUHEcP55BVVM1vq5J48IH7gTOPWQDr169n165dLTPJ6+rqmDjx9GNW67GyeZxqb+zp06cP99xzD6mpqRiNRvbt29duuzImWY81+wv4ZGM2g3t588aN8W2W4Xf0PElOL+C1X/ez8KZ481L+uDCbbKP5+f+8Kpa7l2zl6vjwTscgbFNPXWdZvd0/mJc3DpwJs988XtOsM8sWM5Jg1Xy49kPzczKu61wbrZ8fPg7emmL+2SMIxv+h67+bsA87v4WDv8HgS2HWQv37aFeeb+k2rvsQ/CLhzUT47h5w9Yahl3WsDaELS+xirIB3gN2apr3U6q7vgLlN388Fvj3bY51J6zeOD80YxII5o9sUdO6KYcOGsXnz5i4//4UXXiA1NZVly5adtKEJgLu7OzU1bXd7dHV1BcBoNNLQ0NCp42lASXUDzkYDF8yYzo5tqfy6diNfr1jP3/614LTPbf1pZOvvTyUxMZHVq1ezceNGLrroIkpKSvjtt9+YPHmyOQ5N48svvyQ1NZXU1FSysrJaEqGenp5t2qqpqcHd3b1Tv6cQQnSDLGCCUsqjaWw7D9jdXQfTe8wqrjLXEPR2NW8o4mw0MH/+8yxfs6HDY5ZJ0+gb5IObsxGTpmhsbKBvgDuNWucmqmiaxvTp01vGjF27dp3yQ7XWTjVWtjf2vPzyy4SGhrJt2zZSUlKoq6trt10Zk6zHV1tzMGnwwPkDgeM1CbfnlHa4je05pW2SaLbaRvPzLxzeGxcnA85Ohk7HIGyPNV1n6W7zu+av5z9j/tpc7y23E3Xcc7e0TXJ0to3Wz3d2g3n/M88kXP4kFGV0PA5hnzYvMn+dbiV9VK8YTmzDPxJu/AqUEyx9BOqrz/h0oR9L7GI8CbgJOFcpldr07yJgPjBdKbUf86yM+RY41mlZ4g3cic4991xqa2t56623jh9n+3ZWr1591vECDBky5JS1CU80efJkPvroIwD27dtHVlYWgwYNIjExsWUHxh07drBj+3YUcMl5iaxdu5b09HT6+Lrh1FjHjp27KWsqKn8qn376acvX5pkbCQkJLYXuP/roo5YE4Lhx40hOTsZgMODm5saoUaN48803SUw0/yG54IIL+Pe//43WdJG4devWdo+7b9++U16ICiFET9I0bQPwBbAF2IF5jHzrtE86C3qPWfWNJgxK4dZqx2E3FydCvN3abf/EMcvZaMDD1QkXo4F6kwkwb35y/rSpZxyz0tLSWjbumjBhAmvXrm1pu7Ky8rSz/NrT3thTWlpK7969MRgMfPDBBzQ2NgLg7e1NeXl5mzZkTLIeh0uqCfNz5/whIS23JcQEcceUmA63cceUmJNm2NliG83PNxgU4f7uZBdVdToGYZOs5jpLV431kL8b+k2FkMHHb49OhHMe6Hg75zxw8gyozrRx4vM9g2DeD2B0gSXXQHVxx2MR9qWhFvJ3woALILDV32W9+6geMZyqjfB4uPpdKDsMX90GTe8ZhfU56wShpmlrNE1TmqbFapo2qunfj5qmFWqadp6maQM0TTtf07QiSwR8OpZ4A3cipRRff/01y5cvJyYmhmHDhvHoo4/Sq1evsw0XgAsvvJCkpKQzPu6uu+7CZDIxYsQIrr32WhYvXoyrqyt33nknFRUVDBkyhMf++gRDRowiwNOZ8D69WLx4Mddffz0jR47kukvPJzcznazCKqrrGk95jOLiYmJjY3n11Vd5+eWXAXMR+EWLFhEbG8sHH3zAq6++CphnbkRERDBhwgTAnMAsLy9vqbP4xBNPUF9fT2xsLMOGDeOJJ55o93dbuXIlF198cadeNyGE6A6apj2ladpgTdOGa5p2k6Zptd11LD3HrJr6RipqG3B3MZ5xxnhr7Y1ZLk4GNMyz2KFjY9aTTz7ZUi8xODi4ZcyKjY1l4sSJ7NnT+Q0c2ht77rrrLt577z1GjhzJnj17Wmayx8bGYjQaGTlyZMu4J2OSddidV8b6g0XcPDESJ6MlPs+2H30DPMgqqtI7DNEDrOk6S1e7v4fywzD+jjM/tqcFDYDrlphnEH56EzS0P0Nd2LGd30DlURh/u96RWK8hl8KMZ2H3d/Dr03pHI9qhtE4uA+pOY8aM0VJSUtrctnv37pNq9NmbWbNm8c9//pMBAwZ0uY2GRhP78itwNir6h3id8oKvvtHEgaMVAPQP9sLZ6fgb7qioKFJSUggK6vk6NomJiXz77bf4+/v3+LFP5Aj9TQhboZTarGnaGL3jaI8tj1m5xdUUVdUxuJc3zp1MvpxqzKqoaeBgQQXRQZ54u53dRit6qq2tZcqUKaxZswYnJ4vt49YuW+kvenjky+18k5rL+kfPw8/DRe9wrMoT36TxbWou25++QO9QRCu2OGbZjHcugIojcO8WMBjP/Hg9bPsUvr4NRl4PV7wBnfjwTdg4TYO3p0FdJdy9Uf7vT0fT4H9/hJR34JJXYMwtekfksNobs+QjWSswf/588vLyzqqNvNIaGk0a4f4e7c4GcTYaiAr0pNGkkVlYSaNJ/+TwsWPHeOihh6wiOSiEEI6gwWSiuKoOP3fnTicH4dRjlouTedypa7DtJSNZWVnMnz+/R5KDon1FlXV8vTWX2XHhkhw8hYgAd8pqGiitbr9sjBB24/BWyF4P42633uQgwMhrYepjsO1jSHpB72hET8rZZO6n42+X5OCZKAUX/hMGzDAnCg8s1zsicQJ5B2wFBg0axKBBg7r8/PKaeoqr6gjxdsPd5fQD55xrryL94EHqGsy1p1ycDDz//PMtuyX3tODgYK644gpdji2EEI6ouLIek6YR5NW1xMupxixnowGFoq7RsgnCWbNmkZHRtvD7888/zwUXdM/MqQEDBpzVbH5hGZ9syqK2wcS8hCi9Q7FKEf4eAGQXVeEb5qtzNEJ0sw1vgrMnjL5B70jObMrDUJwBK58D/yiIvUbviERP2LAQXH0h9jq9I7ENRie46l1490L4bB78bin0ktrP1kIShDau0aSRW1yNq5OREG/XMz7+66+/BqCwopbckmoCvVzp49t+QXohhBD2Q9M0Citr8XRxwt3Fcm8BlFI4OynqLTyDsHnMEo6jodHEB+sOMal/IANDvfUOxypFBJgThDnFVQyXBKGwZxVHIe1LiJsLbjbQ15WCS1+D0hz49m7wCYOoSXpHJbpT2WHY9a25Pqarl97R2A5Xb5jzKfz3PPMGP7//FXx66x2VQJYY27z8shrqGk2E+7tjMHR8SnOglyvB3q4UVtRSUCHFdIUQwhGU1zRQ12Dq8uzB03ExGqhr1L90hbBtv+zKJ6+0hlsSovUOxWodn0FYrXMkQnSzzYuhsc62Nn5wcoFrPwC/SPhkDhTs1zsi0Z1S3gVTI4z9vd6R2B7fMJjzGVSXmJOEtRV6RySQBKFNq6xtoKCilkAvVzxdOz8TpJePG77uzuSVVksdGyGEcAAFFbU4Gw34uFt+IxEXJ4PN1yAU+lu0NoO+AR5MGxyidyhWy9fDGW83J7KLZSdjYcca6mDTO9D/fPNOwbbE3R9u+BwMTvDR1VBZoHdEojvU10DKIhh0EQTIh1pd0jsWrl4M+Wnw5e/NyVahK0kQ2iiTZl5a7Gw00Muna0uElVJE+Hvg4eJEdlEVVXUNFo5SCCGEtaipb6SitoFAL5d2N7M6Gy5GAw0mk1VsgCVsU1puKZsyi7l5YiTGTqyKcEQR/h5kF0mCUNixXd+ady4ef4fekXRNQDRc/wmU55lnEtbX6B2RsLS0L6GqwLZmuFqjgTPMG5fs+wl+fkzvaByeJAht1LHyWmoaGgnzdz+rN9EGgyIy0AMngyKzoIq6BsnaCyGEPSqoqMWgFAHdtCusi5P5LUW9hTcqEY5jcXImHi5GrhkboXcoVi8iwJ3sYlliLOzYhoUQ2B9iztM7kq6LGAuz3oTsDfDNnWCS8dFuaJq5j4YMhehEvaOxfeP+ABPvMb+m6xfqHY1Ds68E4ZpXICOp7W0ZSebbz8Jzzz3HsGHDiI2NZdSoUWzYsAGA+vp6HnnkEQYMGEBcXBwTJ07kp59+OqtjdURNfSNHy2vx93DBx63zy8Ty8vK45JJLWn52NhqICvJEQyOzsIrGTgxeixcv5p577ul0DHq67rrr2L9f6oEIIXTWg2NWQ6OJY6VVvPHC3xgyeFCHxiwvr84V23Y2mt9SnLjMODU1lR9//LHd523dupVbb721U8dqz9NPP82LL77Y6efNmzePL774wiIx/P3vf2/5vq6ujsTERBoaZIb+mRRU1PJd6mGuig/v0nsbR9M8g1DTZMausEM5KZCbAuNuB4ONX64OuwLOfwZ2fgUrn9U7GmEpWevgyHbz7MFuWJXhkKb/DQZfAksfgT3tv28U3cvG/+KeICwOPp93/IIrI8n8c1hcl5tct24dP/zwA1u2bGH79u0sX76ciAjzJ9tPPPEEeXl5pKWlsWXLFr755hvKy8vP/vc4DU3TyCmuxqgUvTuw+/CpLkpeeukl/vCHP7S5zc3ZSGSAB7X1Jg4VVmGy0zecjY2N3Hnnnfzzn//UOxQhhKPrwTGrqKqOf7/wLKWFx7ptzGqeQVjX2LkE4d///nfuu+8+i8Wht9YJQhcXF8477zw+/fRTHSOyDR9vyKKu0cTchCi9Q7EJEQEe1DaYOFZeq3coQljehoXg6gOjrtc7EsuYdD/Ez4PV/4ItH+gdjbCEDQvBzQ9GXKN3JPbDYITZb0Of0fDlrXB4q94ROSTbShD+9Agsurj9f789D9694YNZ8PJw81fv3ubb23vOT4+c9pB5eXkEBQXh6uoKQFBQEH369KGqqoq3336bf//73y33hYaGcs017f+R8PLy4sEHH2TYsGGcd955HDt2DDBfPE2YMIHY2FhmzZpFcXExR48eJT4+HoBt27ahlCIrK4uCijqmjR2Bn4uJ4qJCrrzySsaOHcvYsWNZu3YtYJ5BcdNNNzFp0iRuuummk+L48ssvmTlzJmCeATh79mxmzpzJ6BFDeftff6OitoHDxdUsWbKEESNGMHz4cP7yl7+0PH/RokUMHDiQcePGtRwT4NixY6eM51SaY5w4cSIDBgzg7bffBswJ0D//+c8MHz6cESNGtFxU3X333Xz33XcAzJo1i9/97ncAvPvuuzz++OMAfPjhh4wbN45Ro0Zx++2309jY2PK6//GPf2TkyJGsW7eOyZMns3z5cpnRIYToXlYyZvXu3ZvcoyV8/fH7/Of1BR0eswAef/xxRo4cyYQJE8jPzwfa/1u/JWUTN10+g6kJ40hISGDv3r3U1dXx5JNP8umnnzJq1KiTEmXl5eVs376dkSNHAuax4Xe/+x1Tp06lX79+vPbaay2Pfemllxg+fDjDhw/nlVdeabn9ueeeY+DAgZxzzjns3bu35fb09HRmzpxJfHw8kydPZs+ePaf9XZOSkkhISKBfv35tZhO+8MILjB07ltjYWJ566qmW26+44gri4+MZNmwYb731FgCPPPII1dXVjBo1ihtuuKHlcR999NFpj+3o6htNfLD+EFMGBhMT3LmZq46qb0DTTsayUYmwN2V5sPNrGH0juHrrHY1lKAUX/cu8XPqHByB9pd4RibNRkg27f4D4ueDioXc09sXFw1y70yMIllxrfq1Fj7KtBGFHuPmZL7BKs81f3fzOqrkZM2aQnZ3NwIEDueuuu1i1ahUABw4coG/fvvj4+HS4rcrKSsaMGcPOnTuZMmUKzzzzDAA333wzzz//PNu3b2fEiBE888wzhISEUFNTQ1lZGatXr2bMmDGs+G0VW3ftIzgkhN6Bvtx///08+OCDbNq0iS+//JLf//749uq7du1i+fLlfPzxx21iyMjIwN/fv+UCEcwJyk8//ZQdO3bw7VdfUF96jD0HD/Hnh//CihUrSE1NZdOmTXz82Rfs2J/BU089xdq1a1mzZg27du1qaed08ZzK9u3bWbFiBevWreNvf/sbhw8f5quvviI1NZVt27axfPly/vznP5OXl8fkyZNZvXo1ALm5uS3HXb16NYmJiezevZtPP/2UtWvXkpqaitFobLkgq6ysZPz48Wzbto1zzjkHg8FA//792bZtW4f/74Q4lYWr0klOb7szXXJ6AQtXpesUkbA5PTBmlVXXk37wABERnR+zJkyYwLZt20hMTGz5IKe9v/VDhgzh4+9+5ocVyfztb3/jsccew8XFhb/97W9ce+21pKamcu2117Y5RkpKCsOHD29z2549e/j555/ZuHEjzzzzDPX19WzevJlFixaxYcMG1q9fz9tvv83WrVvZvHkzn3zyScssxU2bNrW0c9ttt/Hvf/+bzZs38+KLL3LXXXed9vfNy8tjzZo1/PDDDzzyiDkR+8svv7B//342btxIamoqmzdvJinJPOPz3XffZfPmzaSkpPDaa69RWFjI/PnzcXd3JzU1tWUMGj58eJu4xMl+3JHH0fJa5k2K0jsUmxER4A5AdpHUIRR2ZvMi806mY09/HWFzjE7m3VrdA82blhzdffw+C5QXET1o038Bzf76qLXwDoUbPoPqUlh8CdSUHb9PzpVu56R3AJ1y4fwzP6Z5iVbiw5DyDkz9y1kVDvXy8mLz5s2sXr2alStXcu211zJ//nzi4jq/BMxgMLRcHN14443Mnj2b0tJSSkpKmDJlCgBz587l6quvBiAhIYG1a9eSlJTEo48+yuff/MDIkmqmTUlEKcXy5cvbJOjKysqoqKgA4LLLLsPd3f2kGPLy8ggODm5z23nnnYevry8AQ4cOparoCOkZucRNmESjizdOTk5cec11LF3+GwBTp05taePaa69l3759AO3G014dq8svvxx3d3fc3d2ZNm0aGzduZM2aNVx//fUYjUZCQ0OZMmUKmzZtYvLkybzyyivs2rWLoUOHUlxcTF5eHuvWreO1117jvffeY/PmzYwdOxaA6upqQkJCADAajVx55ZVtjh0SEsLhw4dbZmkK0RWx4b7cs2QrC+aMJiEmiOT0gpafhbCWMeuhx59m0NDYTm9o5eLi0lKvNj4+nmXLlgHt/60vLS3lwTvuIiM9HVdnI/X19Wc8xqnGpIsvvhhXV1dcXV0JCQkhPz+fNWvWMGvWLDw9PQGYPXs2q1evxmQyMWvWLDw8zJ/gX3bZZQBUVFSQnJzcMp4C1NaefinmFVdcgcFgYOjQoS2zJX/55Rd++eUXRo8e3dLu/v37SUxM5LXXXuPrr78GIDs7m/379xMYGHhSu0ajERcXF8rLy/H2tpPZMBa2ODmT6CBPpgwIPvODBQDh/k0zCGUnY2FPGmoh5V0YeAEExugdjeW5+cCF/4AvboX3LoU71kLBXvP7gKsX6x2d6Ii6KtjynrlWnl9fvaOxXyFD4NzHYdkT8P5lcOsyc91HOVe6nW0lCM+k+ULr6sXmC6zoyW1/7iKj0cjUqVOZOnUqI0aM4L333uOaa64hKyuLsrKyTs3IaE2doaBpYmIiq1ev5tChQyROv4inn/0Hrs5GrrzCfAFkMplYv349bm4n1yJsvog6kbu7OzU1NW1uaz2b0Gg00tjYSIiPG0alOFpeQ4PJRFFlHd5uTri7tN9lThfPqZz4+5/u9QgLC6OkpISlS5eSmJhIUVERn332GV5eXnh7e6NpGnPnzuUf//jHSc91c3PDaDS2ua2mpuaUCVQhOiMhJoiXrhnJH95PISbIi+ziKl6/IY6EmCC9QxO2oAfGrEFDhvLmO4u4tgtjlrOzc8vfZaPR2FKWob2/9ffccw/nJE7lX299iGddEdOmTTvjMToyJnWlHITJZMLPz4/U1NQOP6f1cZs3ftA0jUcffZTbb7+9zWN/++03li9fzrp16/Dw8GDq1Kkn/R6t1dbWdnhsdDSp2SVszSrh6UuHYuhkEtuRuTkbCfZ2lSXGwr6kfQWVx8wbP9ir4VdCTSn88CC8mQim+rMe90UP2vE5VBfD+Dv0jsT+TbrP/Pcg+TV4ZwaUHJJzpQfY1xLj3C1tO010ovnn3C1dbnLv3r1tdrxNTU0lMjISDw8Pbr31Vu6//37q6uoAc12mzz//vN22TCZTS12jJUuWcM455+Dr64u/v3/L8tkPPvigZTbh5MmT+fDDD4np35/88loCAgJYsexnzjnnHMC8lOzf//53m9jOZODAgWRmZp7xcRPGj2frxmRKioo4VlbNsu+/Yvp50xg/fjyrVq2isLCQ+vr6Nr9vZ+P59ttvqampobCwkN9++42xY8cyefJkPv30UxobGzl27BhJSUmMGzfOHNOECbzyyiskJiYyefJkXnzxRSZPngyYZ0F+8cUXHD16FICioiIOHTrU7rH37dt30rI2ITpj5+FSnvo2jfs/SaWytpHtuaWMiQqQ5KDouB4Ys9Zv2kKfsL6EBfl1esxqT3t/60tLSwkLC8OkaSxatLjlfm9v73Y3QxkyZAgHDhw44zEnT57MN998Q1VVFZWVlXz99ddMnjyZxMREvvnmG6qrqykvL+f7778HwMfHh+jo6JbfT9O0LpWVuOCCC3j33XdbZufn5uZy9OhRSktL8ff3x8PDgz179rB+/fqW5zg7O7eZPVlYWEhQUBDOzrIz76ksXpuBl6sTV42J0DsUmxPh7y5LjIX90DTzxg9Bg6DfmT9gsmljfgcDZ0LFEfNXSXjYhuY+GjoCIhP0jsYxzPg/6DUCDm+BuLlyrvQA+0oQnvPAyZ0mOtF8exdVVFQwd+5chg4dSmxsLLt27eLpp58G4NlnnyU4OJihQ4cyfPhwLrnkktPOzPD09GTjxo0MHz6cFStW8OSTTwLw3nvv8ec//5nY2FhSU1Nbbo+KikLTNEbET8CkwblTJuPn54e/vz8Ar732GikpKcTGxjJ06FAWLlx4xt/H09OTmJiYM16Q9e7dm6f/9iy3XnMp11wwmQHDYjnvgovMtz/9NBMnTmTSpEkMGTKk5TmdjSc2NpZp06YxYcIEnnjiCfr06cOsWbOIjY1l5MiRnHvuufzzn/+kV69egPkCsaGhgf79+xMXF0dRUVFLgnDo0KE8++yzzJgxg9jYWKZPn05eXt4pj5ufn4+7u3tLu0J0VGl1PR+sy+SSf6/m4tfW8PGmbIb28cHbzQlXJwOr9h07qSahEO3q5jFrRNOY9dhf/4qT0dDpMas97f2tf/jhh/n7M09wzcxEalslyKZNm8auXbtOuUnJ4MGDKS0tPeNuynFxccybN49x48Yxfvx4fv/73zN69Gji4uK49tprGTlyJBdeeGFLmQmAjz76iHfeeYeRI0cybNgwvv32207/rjNmzGDOnDlMnDiRESNGcNVVV1FeXs7MmTNpaGhgyJAhPPLII0yYMKHlObfddhuxsbEtm5SsXLmSiy++uNPHdgRHy2r43448rh4TjperfS1q6QkRAR4yg1DYj+yNkJdqnj14hlVWNi8jyfz7gnlDlowkfeMRHZO5Go7uggl32H8ftRYZSVCcaf5+03/lXOkBqnkZjTUYM2aMlpKS0ua23bt3t0lC2TIvL6+WWQgdVVpdx6HCKnr5uhHibZnlSV9//TWbN2/m2WefbfcxFTX1ZBVV4+3mRElVPZGBHuQUV9M3wB0vt7OfBfH000/j5eXFn/70p7Nuq7NefvllfHx8uPXWW0+6z576m7AMk0ljfUYhn23K5qe0I9Q2mBjS24drx4QT5ufOX77awYI5o/lg3SE2ZRRhgpaahOLsKKU2a5o2Ru842mPtY9bRshqOlNUwMNQbN2fjmZ9gAdV1jew/Wk7fAA/8PFw69JyXX34Zb2/vM25sZatmz57N/PnzGThw4En3WVN/0cPLy/bx2or9rPzjVKKCTl0aRbTvxZ/38saqdPb+30ycjPb1mb8tssUxy6p8Pg/SV8BDu8HFjv8etC4v8vNjoAHlh2XppC345AZzHbwHd4GzlA3pds3nyuX/MW/sM+IqOLBczhULaW/MkncTVqyh0URuSQ3uzkaCvVzP/IQOmjVrFlFRUad9TFV9I30D3PH1cEZDw6Cgb4A7VfWNFotDL35+fsydO1fvMISVO1Jaw4IV+5n64m/MeXsDv+45ytVjwvn+nnP48b5zmDcpmvSCypZkYEJMIAWVdTxxyRC255TqHb5wcCZNo7CyDi9Xpx5LDgK4OJk/Ua9rNHX4OXfeeWeb+n/2pK6ujiuuuOKUyUFHV9vQyEcbsjh3UIgkB7soIsCdRpNGXmn79S+FsAmlubDrO4i72b6Tg9C2vEj0FCjYB7PePKvyIqIHFGfC3h8hfp4kB3tK87kyaCb0GW3+PzjLUjzizGQ9hwUdLa/Bw9nIeVPOadkt0aRpmDT4+KMPOzR7sLkNLzdnjpTW0NioEertyrGKWovNIATOOFOj+VgNTRd5VXXmjUs6O3tw0aJFvPrqq21umzRpEq+//nqn2rGkW265Rbdj623hqnRiw33bzG5LTi9ge04pd0zp2G5x9txGXYOJN347wLeph8ksrMSkwYR+ATw4fQAXDu99UqKl9XEmNrVRU2/q8PGF6C5l1fXUN5oI8zvzZkzjx48/aYffDz74gBEjRnT6uEaDAaNBUdfQ8QShm5sbN910U6eP1RXPPffcSXUXr776ah5//PFuOZ6Liws333xzt7Rt6/63PY+CilrmTYrSOxSbFdFqJ+OIAA+doxHiLKS8A2gw9g96R9L9WpcRiU6EdQvA6HJW5UVED9j4NqBgzMkr0EQ3OfFcSX7NnCiU2YPdyiYShJqmnXHHX2vg4Wwkq6ia5avW4OJkoKqukcMl1fTxc8fDxUhtw5ln3zkbDRwqqiLIy5Wiqjr83F3IL6ulb4A+O+46GQ24ORmprOvazMFbbrnFZhJy1rTcvrvEhvtyz5KtvHrdKCIDPNmSVcxT3+3kmcuGkVXYsTpGvXzcuPPDLTxz2TDi+vrbRRt/mTmIjIJKPt2UTVlNA/4eztw5NYZrxkQQGdixT7Jjgj0J9XFl7YECrh/Xt0PPEfbJGsasgoo6XJwMeLudeZjfsGGDRY/t4mToVIKwJz3++OPdlgzsLEcYc9qjaRqL1mbSP8SLc/pLOYauak4KZhVVIeXyhc2qr4aURTDoIvCP1DuantV3IigjZKyCflP0jka0p7YCtnwAQy8H3zC9o3FM0Ymw5iU4tA4GztA7Grtm9QlCNzc3CgsLCQwM1P2C60y83JzpGwAZBVVoHH/jn13U+QLS+WU1GA2Kitp6+gZ4WKTuX1d5uBopra63iove7qJpGoWFhbi52feU8YSYIP593WjmLtpIg+l4H33g09ROt3Xic2y5jce+TkMBTkbFny8YyO2JMZ2u56SUIiEmiKR9x+z6XBGnZw1jVlVdA1V1DfTxddclBhejgZp660wQWgtHGXPasyWrhB25pTx7xXD5W3kWevu6YTQo2ahE2LYdX0B1EYy/Q+9Iep6bD4TFy8YL1m77p1Bb6ph91Fr0nWCeaZuxShKE3czqE4Th4eHk5ORw7NgxvUPpkOr6Rgor6gBwdzbg7tK12k/VdY1U15vwcXMiu1S/5CBAZW0DxVX1NBa54mzHRbDd3NwIDw/XO4xuV1xd15IcnD4klJnDu7ab89K0IyzbnW8Xbfyy6wg/78znzikx3D1tQJfaAJgYE8jXW3PZl1/BoF7eXW5H2C5rGLOKKuuoqW/EWObGMR2SL6XV9VTUNtBY5C6b/J2Go4w5p7JobQbebk7MjpOZGGfDyWigj58b2UXVeociRNdoGmx4E0KGQdQ5ekejj+hEWPMy1JSZE4bCujT30d6jIGKc3tE4Lmd3iBgvyfQeYPUJQmdnZ6Kjo/UOo0MaGk0k/nMlR8pquGtqDEs2ZndpN9Pk9ALuW7KVG8f35cNfs3TfETX9WAVX/WsVz185gmvHytJJW1bfaOL/ftiFUSnunBrDko1Z3HJOVJf66OasYu47tz8fbrD9NjZlHm9jQkxgl8+3hJjAljYlQeiY9B6zjpbXcNn8FcwZ15dnJg7VJYYP1x/ir9+mkfzIufTpQA1E4VjySqv5Ke0Iv5sUhYeL1b8NtXoR/h4yg1DYrkPJkL8DLn0Nh/1EKToRVr9o3h134AV6RyNOdHAlFOyFKxY6bh+1FtGJsPLvUFUEHgF6R2O37Hc6mA6eX7qHw6U13H/eQP50wWAWzBnNPUu2kpxe0OE2ktMLuGfJVhbMGc1DMwZ1qQ1L6xfkiZ+HM5sPFesWg7CMv/9vN/lltTw0YwB/uqBr/csSfdSe2mgt3N+DvgEeJKcXdun5QpytjzdkU9+ocXNClG4xNNdF60p5DWH/PlqfhaZp3DwxSu9Q7EKEv4fMIBS2a8NCcPeHEVfrHYl+IsaB0VVmRlmrDW+CZzAMn613JCI6EdAgc43ekdg1iyQIlVLvKqWOKqXSWt32tFIqVymV2vTvIkscy1pV1zXy8cYs+od4ct95/QFzvbcFc0azPae0w+1szyltM2OwK21YmlKK+L7+kiC0cZW1DXyaks3gXt7cNVXfPmpPbZwoISaQ9QcLaTQ57gYEtkwp5aeU+kIptUcptVspNVHvmDqqrsHEhxsOMXVQMDHBXrrFEeFvnjWYXSxJC9FWTX0jSzZmcf6QUNl110IiAtwpqKiluoubyQnrZtfXWCVZsOcHiJ8HLg7898DZ3ZwkzFildyTiRIXpsO9nGPM7cHLVOxrRJw6cPSWZ3s0stbZjMbAAeP+E21/WNO1FCx3Dqi1KzqCitpHnrhjRpuB2QkxQp5Yr3jEl5qTbOttGd4iL9OfXPUcprqzD39NF11hE1/x3dQZVdY38fbb+fdSe2jjp+f2D+GRTNmm5pYyM8OtyO0I3rwJLNU27SinlAtjMVcuPO/I4Vl7LPB1nDwKE+ZtrD8oMQnGi77YdpqiyjnmTovQOxW40J1pziqsYECqlLezQYuz1GmvTfwEFY27VOxL99ZsCK56FykLwDNQ7GtFs49tgcDInCIX+nFwgMkGS6d3MIjMINU1LAoos0ZYtKqmq443f0jlvcAjj+9nnH/X4SH8AtmbLLEJbVFBRy1tJ6cwc1ou4vv56h2PXJvZrrkMoy4xtjVLKF0gE3gHQNK1O07QSXYPqhEXJmfQL8iRxQLCucbg6GQn1dpO6aKINTdNYvDaTQaHeLX8nxdkL929a0i/nm12y22usuirY/B4MuQT8IvSORn/RU8xfM1frG4c4rrYctn4Iw2aBd9c2QRTdIDoRCvZBWZ7ekdit7q5BeI9SanvT9PhTZiWUUrcppVKUUim2slPxif7zWzoVtQ08PHOw3qF0m5HhfhgNSpYZ26gFKw5Q02DizzMH6R2K3Qv2dmVgqJeudUNFl0UDx4BFSqmtSqn/KqU8Wz/AWsesrVnFbMsuYd6kKAwG/YtoRwS4kyN10UQrmzKL2ZVXxi2TotrMYhdnJyKgaUm/nG+O5ozXWGC9YxbbP4WaEhh/p96RWIc+o8HFS5ZOWpPUj6GuHMbfoXckorXoRPNXSaZ3m+5MEL4BxACjgDzgX6d6kKZpb2maNkbTtDHBwfrOeuiK3JJqFidncmVcuF3vWuruYmRYHx9JENqgrMIqPtpwiGvGROhal8yRJMQEsSmziLoGk96hiM5xAuKANzRNGw1UAo+0foC1jlmL1mbi7erE7LhwvUMBZGdVcbJFazPw83Dm8lFheodiV4K9XHFzNsiSfsfSoWsssNIxS9PMGz/0ioW+E/SOxjoYnZuWTkqC0CqYTOYNdMLGQHi83tGI1nqNADc/WWbcjbotQahpWr6maY2appmAt4Fx3XUsPb28bB8AD04fqHMk3S+urz/bskupb5Skhy3517K9GA2KB84foHcoDmNiTCA19SZSs0v0DkV0Tg6Qo2nahqafv8CcMLRq+WU1/Lgjj6vHRODlaqnSwmcnIsCDI2U11DbIxgnC/GHqzzuPcN3Yvri7GPUOx64opQiXhLxDsflrrIwkOLbbPDNLZhMfF50Ihfuh7LDekYj0X6EoXWYPWiODEaLOkWR6N+q2BKFSqnerH2cBae091lbtOVLGl1tymJcQRZifu97hdLv4SH+q6xvZk1eudyiig9JyS/k29TC3nhNNqI+b3uE4jAn9AjEoZJmxjdE07QiQrZRqXot/HrBLx5A65KP1h2jUNOYmROodSouIAA80DQ6X1OgdirACH6w7hFKKmyZaTx+1JxH+7mTJEmOHYfPXWBveBI8gGH6l3pFYl+Y6hJL40N+GheDVC4Zernck4lT6TTXvgl6UoXckdskiCUKl1MfAOmCQUipHKXUr8E+l1A6l1HZgGvCgJY5lTV5YuhcvVyfumnryTqj2qHmjks2H7K9Wsr16fuke/Dycuf0Uu/WK7uPr7szwMF+SD8hGJTboXuCjprFrFPB3fcM5tYWr0klOL6CmvpGPNmRx3uAQckuqWbgqXe/QAHPCAmQnY0fW3Eer6xr5eGMWFwwL5VBhpdX0UXsSEeBBTlEVmqbpHYqwMLu5xlrzijnxVZQBe3+EMbdAzkbz7cIsdDi4+0uCUC/NffTYPjiwHMbeCtnrpY9ao+Y6hHKudAuLrEXSNO36U9z8jiXatlYbM4r4dc9RHp45CD8PF73D6RF9/Nzp7evG5qwS5k3SOxpxJmv2F7B6fwF/vXgIPm7OeofjcCbGBPLumgyq6hrwcLGOZZ/izDRNSwXG6B3HmcSG+3LPkq1cMyaCwso6xkUFcM+SrSyYM1rv0ABzwgJkZ1VH1txHZ8eFUVpdz5hI6+qj9qRvgAfltQ2UVtc7zHtSR2E311hhcfD5PIiabF4iGDLM/PPVi3UOzIoYDObXJyPJXKdRll/3rOY+GjEBjC4QPFj6qLUKGgheoeZzJX6u3tHYne7exdguaZrG/J92E+rjyi0J0XqH06PiI/3ZIhuVWD2TSeP5pXsI83PnxgmypEsPCTFB1DdqpGTK+SIsLyEmiAVzRvPf1QcJ8HBmYdJBFswZTUJMkN6hARDq44azUZElMwgdVnMfXbQ2kyAvFxasPGBVfdSehPs3JeRlmbGwVtGJMPu/sOtbCOwPP/7RnHhpngkkzKIToTQbimXpZI+LToRZb8Le/0HQAPjhAemj1kop8/9LczJdWJQkCLvgl135bMkq4cHzBzpcse34SH9yS6rJK5U3odbsfzvy2JFbykPTB+Lm7Fh91FqMjfLH2ahITpdlxqJ7DOvjS4NJo6iqnhvH97WqxIvRoAjzcydHEhYObWCoN40mjYKKOqvro/YkIqBpSb/M2BXWzNkd0ODYHhhzqyReTkXqEOrL2LTiKn+n9FFrF50IlUfh2F69I7E7kiDspIZGE/9cuoeYYE+uig/XO5wed7wOocyKslZ1DSZe/GUvg3t5c8XoML3DcVgeLk6MjvBnnWxUIrrJe8mZAFwZF8aHG7KsblOciADZWdXRvb8uE4Crx4RbZR+1Fy1L+mXGrrBmW943f514L6S8I0mwUwkaYN4cQ14bfWz90Pw14X7po9ZO6hB2G0kQdtIXm3NIP1bJwzMH42R0vJdvSG8f3JwNkiC0Yp9syuJQYRV/mTkYo0Hql+hpYkwgO3JLKa2u1zsUYWeS0wt4feUBnI2Kv88ewYI5o7lnyVarSsCE+3tIwsKBJacX8Oaqg7g5G/jHLOvso/bCx80ZX3dnScgL65WRBDs+g4AYuOBZ89LNz+fJxf2JlIJ+U2TppB4ykmDn1xA0CGb8TfqotfOPAr9IyFildyR2x/EyXGehuq6Rl5fvI66vHzOGhuodji6cjQZGhvtJHUIrVVnbwGu/7md8dABTBwXrHY7DS4gJxKTBhoOyzFhY1vacUkK8XZnQLxBXJ2NLvbftOaV6h9YiIsCd4qp6Kmob9A5F6GB7Tin+Hi5MignCyWiwyj5qTyIC3KUGobBeWRvMXwdfZP4anWhOwORu0S0kqxWdCJXH4OhuvSNxLIfWgckEQy4x/yx91PpFJ0LmajA16h2JXZEEYScsSs4gv6yWRy4cgnLgnaXiI/3ZebiM6jo5Ga3Nf1dnUFBRx18uHOzQfdRajOrrh5uzQeoQCou7Mi6c7OLqNjXdEmKCuGNKjI5RtRXhL8seHdllI/twpKyGhP7W20ftSYS/LOkXVix8DJgaIHrq8duiE+GcB3QKyIrJ0kl99BkFmI7XgQTpo9YuegrUlMKR7XpHYlckQdhBJVV1vPFbOucNDmFcdIDe4egqPtKfBpPG9pwSvUMRrRRU1PJWUjozh/Uirq+/3uEIwNXJyNioANZJglBY2PqmWakJMYE6R9I+qYvm2Jr/7llzH7UnEQEe5BRXYzLJskRhhTKSwOAEfSfoHYn18+trXj4pCcKelZEERleIGKd3JKKjoiebv8q5YlGSIOyg//yWTkVtAw/PHKx3KLob3ZR82pwly4ytyYIVB6hpMPHnmYP0DkW0MjEmkL355Rwrr9U7FGFHktML8HZzYlgfH71DaVff5gRhsSx7dERr0wsI8HRhUKi33qE4hAh/d+oaTByVsUZYo4xVEDYGXL30jsQ2RCdC5hpZOtmTDq4yJwed3fWORHSUdy9zzUhJEFqUJAg7ILekmsXJmVwZF86gXvJGN8DThX7BnlKH0IocKqzkow2HuGZMBDHB8ubLmkxqWgK6XuoQCgtKTi9kfHSgVW+W5e/hjKeLUWYQOiBN01iXXsjEfoEYZLOsHhHekpCX801YmZpSOLz1+NJZcWbRU6C2FPK26R2JY6gshPwdbZcXC9vQb4q5fmRDnd6R2A3rvbKwIi8v2wfAg9MH6hyJ9Yjv68/mQ8VossOWVfjXL/swGhQPnD9A71DECYb18cHbzUl27hQWk1NcxaHCKqtfuqmUalr2KAkLR5NZWEVeaQ0TrbyP2hOp+Sms1qFk0EySIOyMljqEskNrj8hcbf4qfdT2RCdCfSXkbtY7ErshCcIz2HOkjC+35DAvIYowP5ly3Cw+0p/iqnoyCir1DsXhpeWW8t22w9x6TjShPm56hyNO4GQ0MD46UDYqERbTUtutv/UnX8L9PWRnVQfU/IGItSex7Um4v/k9qpxvwupkJIGTG4SP1TsS2+EVAsFDZOlkT8lIAhcvCIvTOxLRWZGTACXnigVJgvAMXli6Fy9XJ+6aKrvutRYf2VSHUJYZ6+75pXvw83DmdtkZ0molxARyqLBKZlIJi1iXXkigjdR2iwhwJ6uoSmabO5jk9EJ6+7oRHeSpdygOw83ZSKiPqywxFtYnI8m8OYmzfIjdKdGJsnSyp2QkQWQCGJ31jkR0lkcA9I6VBKEFSYLwNDZmFPHrnqPcOTUGPw8XvcOxKjHBXvi4ObFFNirR1Zr9BazeX8A90/rj4yaDmrVqnukluxmLs6VpGmvTC5gYE4hS1l/bLcLfg+r6Rgor5QLHUZhMTfUHbaSP2pMIfw9ZYiysS8UxyE+TpZtdEZ0IDdWQm6J3JPat7DAU7pc+asuiEyFnI9TJ+GcJkiBsh6ZpzP9pN6E+rtySEK13OFbHYFDERfrLDEIdmUwazy/dQ5ifOzdOiNQ7HHEag0K9CfR0kQShOGsHCyrJL6sloWnzG2sXESB10RzN3vxyiirrbKaP2hNzzU9ZYiysSEttN9n8odOiJoEyyMyo7tb8+kqC0HZFT4HGOsjeoHckdkEShO34ZVc+W7JKePD8gbi7GPUOxyrF9/VnX34FpdX1eofikP63I48duaU8NH0gbs7SR62ZUoqJMeY6hLLUUpyN5lqWtlLbLSKgqS6aJC0cRnMflQ1Kel6Evzt5pdXUN5r0DkUIs4wkcPGG3qP0jsT2uPtD75FwUDYq6VYZSebXOnSE3pGIruo7EQxOsqmPhUiC8BQaGk38c+keYoI9uSo+XO9wrFZzHcKtssy4x9U1mHjxl70M7uXNFaPD9A5HdEBCTBBHymo4KBv7iLOwLr2APr5uRAZ66B1Kh8jOqo5nXXoBUYEesrGbDsIDPDBpcLhEEvLCSmQkmWfCGZ30jsQ2RSdCziaok/eO3ULTmvroZDBIWsRmuXpB2BiZbWshciY0WbgqvWXXvS8255B+rJLLR4Xx3zUZOkdmvUZG+GFQsEWWGfeI1n30k01ZHCqs4rKRfXh79UGdIxMd0TzjS3YzFl3VXNstoX+QzdR283R1IsDTRTbocRANjSY2HCwiob8sL9bD8YS8JAiFFSjNgaJ0WV58NqITwVQPWev1jsQ+FWdAabYsL7YH0YlweCvUlOodic2TBGGT2HBf7lmyld/2HOXl5fsYEOLJ4uRMYsN99Q7Nanm6OjGktw+bZQZhj2juoyv25PPar/sZ3Mub/67JkD5qIyIDPejj68a6piSvEJ2150g5xVX1NrO8uFlEgIckLBxE2uEyymsbbK6P2ovjS/olIS+sQEZz/UFJvnRZy9JJmRnVLVrqD0oS2+ZFJ4JmgkPJekdi8yRB2CQhJogFc0Zz50dbyC+r5UhZLQvmjJYi22cQH+lPalYJDVLvpts199G7P9pKQUUdh0uqpY/aEHMdwiDWpRdiMkkdQtF5zTOIba22W4S/uyQsHMTaA+Y+OqGfbfVRe9Hb1x0ngyJLlvQLa5CxCjwCIWSo3pHYLhdPCB8rCcLucnAVePWCoAF6RyLOVvhYcHKTc8UCJEHYSkJMEG7O5pfkloQoSbx0QHykP5V1jezNL9c7FIcwsV8gLk7mpYXzpI/anEn9AymuqmfPETlfROclpxfSL8iT3r62VdstIsCDwyXVNEpi3O6tSy9kcC9vgrxc9Q7FIRkNij5+7lLzU+hPartZTvQUyEuF6hK9I7EvzX00OhFspGyLOA1nN+g7QRKEFiB/sVv5IiWb4qp6pgwM5sMNWS2zNUT7mjcq2Sx1CHvEJxuzKa1uYNqgEOmjNmhiSx1C+X8TnVPfaGLDwUKbmz0I5rpo9Y0aR8pq9A5FdKPahkY2ZRbZZB+1JxEB7rJruNBf0UEoy5XlxZbQsnRyrd6R2Jeju6GqAPrJ8mK7EZ0I+WlQcUzvSGyaJAibJKcX8MS3OwF4/spYFswZzT1LtsqF/BmE+bkT6uMqCcIekJxewDM/7EIBL1wtfdQW9fZ1p1+Qp2xUIjptR24plXWNNjlruKUumsxqsmtbs0qobTDZZB+1JxH+HuTIuSb0lrHK/FVqu5298DHg5C4zoyytpf6gJLHtRvPfm8zV+sZh4yRB2GR7TimhPq7E9fWjl69bS7237TmyE87pKKWIj/SXBGEP2J5TSpCXM+OiAwjycpU+aqMmxgSy4WAh9VK3U3TCuqaksi3OzmreWVXqotm35PRCDArG9wvQOxSHFhHgQWFlHZW1DXqHIhxZRhL4hEFgjN6R2D4nV1k62R0yksA/Cvz66h2JsJTeo8DFW86VsyQJwiYXDe9NZmEVM4f3arktISaIO6bIwHYmcX39ySmuJl+Wj3Wr6UNDySmu4ULpozYtISaIyrpGduRKYld0XHJ6AUN6+xDg6aJ3KJ3Wx88dpZBZTXZuXXoBI8L98HFz1jsUhxYRYE7I58gyY6EXk8m8g7HUdrOc6EQ4ugsqjuodiX0wNULmGpk9aG+MThA1SRKEZ8kiCUKl1LtKqaNKqbRWtwUopZYppfY3ffW3xLG6y9KdeQBcOLy3zpHYnuY6hFtkFmG3Wpp2BIALWiUIhe1pngG2TpYZWyWllFEptVUp9YPesTSrqW8kJbOYBBucPQjg4mSgt4+b1EWzY5W1DWzNKrHZPmpPIvxlSb89sclrrKO7zLXdJPliOf1k6aRF5aVCbaksgbdH0VOgKB1Kc/SOxGZZagbhYmDmCbc9AvyqadoA4Nemn63W0rQjDOvj0/LJq+i4YX18cXEyyDLjbrY07QijIvxsbgdT0VaApwtDevtI7UjrdT+wW+8gWtuSVdxU2812ky/hAR6SsLBjmzKLaDBpNt1H7UXz+9jsYjnf7MRibO0aq3n2TtRkfeOwJ71GgquvzIyyFKk/aL+a/08zJJneVRZJEGqalgQUnXDz5cB7Td+/B1xhiWN1hyOlNWzJKmmzdFN0nIuTgZHhvmzOkgRhd8kuqmJHbqn0UTuREBNISmYxNfWNeociWlFKhQMXA//VO5bW1qUXYjQoxkXbbm23CH8PSVjYsXXphbgYDYyJtN0+ai8CPV1wdzaSXSQzdu2BTV5jZSRBQD/wi9A7EvvRvHTy4Cq9I7EPGUkQPAS8QvSORFhayFDwCDy+UZLotO6sQRiqaVpe0/dHgNBTPUgpdZtSKkUplXLsmD5bUv+807x0c6YkX7osLtKftNxSSXh0E+mj9iUhJpDaBhNbJKlubV4BHgba3UFGjzErOb2Q2HBfvG24tltEgDv5ZbUyRtip5PRCRvf1w93FqHcoDk8pRUSAuyTk7VuHrrFAhzGrsQEOrZWlm90hOhGKM6AkS+9IbFtDHRxaJ7MH7ZXBYJ69nJEEmqZ3NDapRzYp0TRNA075P6Rp2luapo3RNG1McHBwT4RzkqVpR+gf4kX/EG9djm8P4vv6U9+okSYbL3SLn9KOMKS3D5GBnnqHIixgXHQARoOSOoRWRCl1CXBU07TNp3tcT49ZFbUNbMu2/dpufZuWPeaWyKwme1NaVU/a4VISYoL0DkU0ifCXJf2O4nTXWE339+x1Vt42qC2T5Et3kKWTlpGbAg3V0kftWXQilOVC0UG9I7FJ3ZkgzFdK9QZo+mqV2y4VVtSyIaNQlm6epbimjUqkDqHlHS2rYfOhYumjdsTbzZkRYb4kS4LQmkwCLlNKZQKfAOcqpT7UNyTYlNFc2822ky8tddEkaWF31h0sRNMgob9tJ7HtSURTzU9NZk/YK+u9xsr4zfxV6g9aXvAQ8AiSOoRn6+AqUAbzkm1hn5pnMMsy4y7pzgThd8Dcpu/nAt9247G6bNmufEwaXDBMki9nI8jLlahAD0kQdoPm5cWSILQvk/oHsi27hIraBr1DEYCmaY9qmhauaVoUcB2wQtO0G3UOi+T0AlyMhpbd4m1VhH/zxgkyg9DerEsvwN3ZyMhwP71DEU3C/d2prGukuKpe71BE97Dea6yMJAgZBl76rAqzawaDeWaULJ08OxlJ0HskuNv2+ypxGoEx4BMmyfQuskiCUCn1MbAOGKSUylFK3QrMB6YrpfYD5zf9bHV+SjtCRIA7w/r46B2KzYuL9GdLVrF8Ym1hP6UdoV+wJ/1DvPQORVhQQkwQDSaNTZkn1h4X4rjk9ELiIv1wc7bt2m4h3q64OBnIkRmEdic5vZCx0QG4OPVI1RrRATJj137Y1DVWQy1krZelm90pOhHKD0PhAb0jsU11lZCzSfqovVPqeDLd1G5ZcdEOS+1ifL2mab01TXNumoHxjqZphZqmnadp2gBN087XNM3qroJLq+tJTi/gwuG9UUrpHY7Ni4/0p6Cijix5Q2oxRZV1bMgo4sLhvaSP2pn4SH9cjAaSDxToHYo4gaZpv2madonecRRX1rErr8zmlxcDGAyKcD/ZOMHeHC2vYf/RCpuvkWlvjs/YlfPN1tnUNVbOJmiokeRLd2qpQyhLJ7skaz2Y6qWPOoLoRKgqhKO79I7E5jj0x70r9uRT36jJzrAWEi91CC1u+a58Gk0aFw7vrXcowsLcnI3ERfpJHULRrg0Z5tpuk+yktlt4gAfZRbLE2J40b7Q0yQ6S2PYkIsAdQM430bMykqS2W3cL6Ac+4bJ0sqsyksDgBH0n6h2J6G7NdVDlXOk0h04Q/rTjCKE+roySujkWMSDEG29XJ0kQWtBPaXmE+8sSeHuVEBPErrwyiivr9A5FWKHk9EI8XIzE2skYFeHvLjPM7cy69EJ83JwYKmOUVfF2c8bfw1lmEIqelZEEfUaDm6/ekdivlqWTq2XpZFdkJEH4WHDx1DsS0d38IswJdUkQdprDJgir6hpYte8YM4f1wmCQpZuWYDQoRvX1kwShhZTV1LPmQAEzh8nyYns1qX8gmmaeKSbEidYeKGBcdADORvsYqiMCPCitrqesRjZOsBdr0wuY0C8Qo7yPsjrNOxkL0SNqK6S2W0/pNwWqi+DoTr0jsS3VJZCXKn3UkURPgUNroVE2hOwM+7jq6ILf9h6jtsHETFm6aVHxkf7szS+nXC4Az9qK3Uepb9S4cIQsgbdXseF+eLgYZZmxOEl+WQ3pxyrtqrZbS100SVrYheyiKrKLqu2qj9qTCH8PcmTXcNFTstaDqUGSLz1Blk52zaG1oJnMSSPhGKITobYM8rbpHYlNcdgE4U9pRwjwdGFslGxxbknxkf5oGqRml+gdis1bmmZeAj86QvqovXI2GhgXHSAJQnGS5tpu9rBBSTOpi2ZfWvpof/vpo/YkPMCd3OJqGk2a3qEIR5CxCgzOEDFB70jsn28YBPaHg7JRSadkJIGTO4SP0TsS0VNakum/6RqGrXHIBGFNfSMrduczY2goTnaydMtajIrwQynZqORsVdU18Nu+o1wgS+DtXkJMIAeOVpBfVqN3KMKKJKcX4OvuzJDe9lPbrW+AeQZhjtRFswvJ6QUEebkyIMRL71DEKUT4e1DXaJKxRfSMjCSIGAcuHnpH4hiiE5uWTsqKrQ7LSIK+E8DJVe9IRE/xCoaQYTLbtpMcMju29kABlXWNsntxN/B2c2ZQqLckCM/Sqr3HqKk3MXOY9FF71zxDbJ3MIhStJKcXMtHOarv5ujvj7eokS4ztgKZpJKcXkhATKDVyrVREgCzpFz2kuti8hE+Wbvac6ESoq4DDqXpHYhsqjsLRXbIE3hFFJ5pLIDTU6h2JzXDIBOFPaUfwdnOyq6Vb1mRMlD9bs0pkWctZWLrzCP4ezoyLDtA7FNHNhvb2wdfdmeT0Ar1DEVYiq7CKnOJqEvrbV203pRThAR5kS100m5d+rIKj5bVSf9CKRfg3LemX8010t8w1gCbJl54U1fRaZ8gy4w5pnkEmSWzH028KNNSYN1ESHeJwCcL6RhPLduVz/pBQXJwc7tfvEfGR/lTUNrAvv1zvUGxSbUMjK3YfZcbQXrIE3gEYDIqJ/QKlDqFo0ZwstsfkS4S/u8xosgPJdlgj096E+bujlMwgFD0gIwmcPSAsXu9IHIdnIISOkKWTHZWRBK6+0Huk3pGInhaZAMog50onOFz2Yf3BQkqr62V5cTeK72ue9SbLjLtm7YECymsbmCm7FzuMhP6B5BRXy4WcAMzJl2BvV2KC7a+2W0SAeWdVTZMZ5rYs+UAhYX7uLRvPCOvj6mQk1NuNbKn5KbpbRhL0nQhOLnpH4liiEyF7A9RLndEzykiCqElgdNI7EtHT3Hyhz2jZ1KcTHC5BuDTtCB4uRqYMDNY7FLsVEeBOkJcrWyRB2CU/7TiCt6uTXc4eEidbuCodD2cjcHzmWHJ6AQtXpesZltCJvdd2i/B3p7q+kYKKOr1DaWPhqvSTlvnLeXhqJpPGuoP220ftSUSAOzmya7joTuX5cGyPLC/uaWteAY+ApqWTG823ZSSZbxdtlWRBcYb0UUe15hXwj4bcFKitMN8m58ppOVSCsNGk8fPOfKYNCsGt6YJcWJ5SivhIPzZnSYKwsxoaTSzbnc95Q0JwdZI+6ghiw335+0978HV3Zu2BQpLTC7hnyVZiw331Dk3o4MDRCgoqaplkp0s3WzZOsLJZTbHhvtyzZGubJL2ch6e2K6+M0up6JvW3zz5qTyICPKzuXBN2JnO1+Ws/qe3Wo8LiYN3rQNPSyYwk+Hye+XbRVkZTH5UEoWMKi4P9v4CpwbxZiZwrZ+RQCcLNh4opqKjlAlle3O3iI/05VFjFsXLZMagzNmQUUVJVz8zhvfUORfSQhJggFswZTXVdI8t25XPPkq0smDNaans5qObabhPtdAaxte6s2nwe3vPRVv7y5XY5D09jnZ33UXsS4e/BkbIaahsa9Q5F2KuMVeYlfL1i9Y7EsUQnwjXvgcEAqUvMCY+rF0sS7FQyksAjCIKH6B2J0EN0Ilz5rvn7Vc/LudIBDpUgXJp2BBcnA+cODtE7FLsXH+kPwBaZRdgpP6Xl4e4sS+AdTUJMEJMHBlFd38h5Q0IkKeHA1h4oICLAvSWRZm/Cm3dWtbIEIZjPw1F9/fh0UzZTBgbLediOtekFxAR7Eurjpnco4gwiAjzQNDhcIjXKRDc5uAqiJoNBVr30uOhECB8LZbkw4lpJeJyKppmT2NGJ5mSqcEwDp4NvhHk5fvzv5Fw5A4c5UzRN4+edR0gcEISXqxQo7W7D+vjiYjRIHcJOMDUtgZ86KBh3F3mj5UiS0wvYcqgYZ4Pi29TDJ9VCE46h0aSx/mAhCf3sNzHl4eJEkJcL2VZYFy05vYCkfccA+GG7nIenUt9oYmNGkSRPbUSEFSfkhR0ozoSSQ3KxrZeMJDi62/z9lkWyS+upFB6A8jzpo44uIwmqm3ISG9+Uc+UMHCZBuD2nlNySai4YJsuLe4Kbs5HhYT6yk3EnbMkq5lh5reyw7WCaa529fkMc143ri8mkcddHWyQ54YB2HS6jrKaBhP72vXTTGuuiJacXcOeHW2gwafT2daO+UePOD+U8PNH2nBKq6hplEy0bYa01P4WdkNpu+mmuo3btBzDwQjA4m3+WxEdbGU0710ofdVzN58o1H4BXKAQOkHPlDBwmQfhT2hGcDIrpQ0P1DsVhxEf6sz23VGrfdNBPaUdwMcoSeEezPae0pdbZTRMjaTBpXDi8F9tzSvUOTfSw5mTUxH72nXyJ8Le+BOH2nFLOHRyMQcGbN8XjZFAkxATKeXiC5AOFKAUT7LyP2otQHzecjYosmUEoukNGEniGQPBgvSNxPLlbjtdRm3AH1JbB6JvMt4vjMpLAJxwC+ukdidBL87nS/1wYcysc3gwznpNz5TQcIkGoaRpL0/KYGBOIn4eL3uE4jPhIf+oaTOw8XKZ3KFbP3EePcM6AILzdnPUOR/SgO6bEtCzXGxjqzfjoAFbvL+APk+XNjKNJTi9kQIgXIXZe2y0iwJ3DJTU0NJr0DqXFbZP7sSmzmEn9g4gN9+OiEb1Zs7+AGydE6h2aVUlOL2Robx/8PeW9lC0wGhRhfu7kWOGSfmHjNM2cfIlOBKX0jsbxnPPA8Vlx0VPMSdr0FTDpfl3Dsiomk3mWq/RRx9b6XBlzi3m27eEt5tvFKTlEgnBvfjmZhVWydLOHxfVt2qhElhmf0Y5c8xJ46aPi5olR5BRX89veo3qHInpQXUNzbTf7n5kV4e9Bo0kjr9R6Nk7YmFlETnE1V8aFAzBvUhTltQ18tSVH58isR019I5uzih2ij9oTa1zSL+xAwT6oOCJLN62BUjD+djiyHbLW6x2N9chPg+oi6DdF70iEtfAKgeFXmnf+rpEVIu1xiAThTzuOoBSyvLiHhfi4ERHgLnUIO2Bp2hGMBsX0IdJHHd2MYaGEeLvy/rpDeocietC2nBKq6xuZ6ACbP1hjXbQvN+fg6WJsqVM8OsKPkeG+LE7OxGTSdI7OOmw+VExdg0k2KLEx4f4eskmJsLzm+l2SILQOsdeCmy9sWKh3JNajuY9GTdY3DmFdxt8OdRXmJKE4JYdIEP688whjIwMI8bbvZVvWZuGqdPr6e5ByqBhNM19gJacXsHBVus6RWZfm5cUT+wXKsi2Bs9HA9eP6smrfMQ4VVuodjughx2u7BegdSreL8DcnCK1l2WN1XSM/7sjjohG9W3aQV0pxy6RoDh6rJGn/MZ0jtA7J6QUYDYqx0fbfR+1JRIA7xVX1VNQ26B2KsCcZq8C3L/hH6R2JAHDxhLi5sPt7KJWZ74A5QRjYH3zD9I5EWJOwOIgYDxveBJPsk3Aqdp8gzCioZM+Rclm6qYPYcF+2ZpdwrLyWnOLqlt1aY8N99Q7NquzLr+BgQSUXSB8VTeaM74uTQfHheplF6CiS0wsY1sfHIerk9vZzw6CsZwbhzzuPUFnXyJXx4W1uv2hEb4K9XVmcnKlPYFYmOb2QkeG+eLk66R2K6ITmhLzMIhQWI7XdrNPY3wMabPqv3pHor7EeDq2VGa7i1MbfDsUZsH+Z3pFYJbtPEP6UlgcgyRcdJMQE8cTFQwF4/Osd3LNka8tureK4n9LyUAouGCbLi4VZqI8bFwzrxWcpOVTXyadb9q66rpGtWSVMcpC/jc5GA7193a0mYfHllhzC/NwZF9V2ZpyLk4Ebxvflt73HOHisQqforEN5TT3bc0qZ1N8x+qg9aVnSbyXnm7AD+TugpkRqu1kb/0gYdBFsXgz11jFDXzeHU83LSCVBKE5lyGXg3UeW5Lej2xOESqlMpdQOpVSqUiqlu493oqVpRxgZ7kuYn3tPH1oA142LoI+vG0n7C7h2TLgkB09hadoRxkT6yxJ40cZNEyMpra7n+22H9Q7FYSilIpRSK5VSu5RSO5VSPbId4OZDxdQ1mpjoQJs/RAS4k2UFCYsjpTWsPVDAlXFhGAwnz4S5YXwkzkbl8DVBN2UW0WjSHKqP2ou+LTU/HTxhYId0u8aS2m7Wa8KdUF0MOz7XOxJ9Zawyf42SBKE4BaMzjL0VDq6Eo3v0jsbq9NQMwmmapo3SNG1MDx0PgNySarbnlDJzeO+ePKxoZd3Bwpa6N4uTD5GcXqBzRNYls2UJvPRR0db46AAGhnrx/vrMlhqeots1AH/UNG0oMAG4Wyk1tLsPuja9ACeDYmyU49R2i/D3sIqExTepuZg0mBUXfsr7g71duTS2D5+nZFNWU9/D0VmPtQcKcXEyENfXX+9QRCf5ezjj6WKUGYT2q+evsQ6ugqCB4CPvXa1O5CQIHQ7rF4Ijv3fMWAWhI8BTPtQS7YifB0ZX2Pim3pFYHbteYrw07QiA1B/USXPNwYU3xTNjaCgaGnd9tEWShK381NRHZXmxOJFSipsmRJKWW0Zqdone4TgETdPyNE3b0vR9ObAb6Pbq1snphYyK8MPTgWq7RQR4cKy8lpp6/ZbQa5rGl5tziI/0JzrIs93HzZsURWVdI1+kOG7h9+T0QsZE+uPmbNQ7FNFJSikiAjzIsZKan8LGNdbDoWRZummtlDLXVzu6EzLX6B2NPuprIGuD9FFxep5BMOJq2PaJedataNETCUIN+EUptVkpdVsPHK/F0rQ8BvfyPu0bf9F9tueUttQcfHjmIOoaTEyMDmR7TqneoVmNpWl5xIb7Et5URFyI1mbFhePl6sQHDr68UQ9KqShgNLChO49TVlPPjpwSEhxs6Wbzskc9kxZpuWXsP1rB7LjT54Bjw/2Ij/TnvXWZmEyONyOjqLKO3XllDtdH7Um4v4dVLOkXFtfz11i5W6C+UpIv1mzE1eAe4Lj11XI2QmOt9FFxZuNvh/oq2Pqh3pFYlZ5IEJ6jaVoccCHm5Vptzlal1G1KqRSlVMqxY8csdtCj5TWkHCqW2YM6umNKTEvNwf4h3lwdH8HyPflcPEKWJIB5Cfy2nFLpo6JdXq5OzI4L44fteRRW1OodjsNQSnkBXwIPaJpWdsJ9Fh2zNh4swqTBRAerzxoRYK4LnF2k3zLjL7fk4OJk4JIRfc742HkJURwqrGLl3qM9EJl1WX+wEHC8PmpPIgLcyS6qlnIV9ue011jQDddZGUmAkvqD1szZ3bx8cu+PUOyAHzBnJIEyQmSC3pEIa9c71rwsf+NbYJJNIZt1e4JQ07Tcpq9Hga+BcSfc/5amaWM0TRsTHBxsseP+sjMfTYMLpbab1Xhg+gAMSvHSsn16h2IVfm5eAj9MEoSifTdOiKSu0cRnDry8sScppZwxJwc/0jTtqxPvt/SYlZxeiKuTgbhIv7Nuy5ZE+DdvnKDPrKa6BhPfbTvM9CGh+Ho4n/HxM4f3ItTHlcXJmd0fnJVJTi/Ay9WJkeG+eociuijC34Pq+kYKK+v0DkVY0JmusZrus+x1VsYq6DUCPBynZq5NGnsroGDT23pH0vMykiAsDtx89I5E2ILxt0NJFuz9Se9IrEa3JgiVUp5KKe/m74EZQFp3HrPZ0rQjRAd5MjDUqycOJzqgt687t0yK5pvUXHYelmXGS9OOMCjUm37B0kdF+waGejOhXwAfrj9EowMub+xJSikFvAPs1jTtpZ44ZnJ6AWOjAnB1cqzabsHerrg6GXTbOOG3vUcpqqzjyviOlZh0Nhq4aUIkq/cXsD+/vJujsy7JBwoZFx2Ak9Guy1bbtYjmnYxlmbHd0OUaq74asjfK0k1b4BsOQy+DLe9DXaXe0fSc2nLI3Sx9VHTcoIvBN8Jxl+SfQne/2wsF1iiltgEbgf9pmra0m49JcWUd6w4WMnN4L8zXe8Ja3DklBh83Z/65dK/eoejqaHkNmw4VyfJi0SE3T4wit6SalXscb3ljD5sE3AScq5RKbfp3kaUPsnBVOsnpBRRU1LLnSDkTYwJJTi9g4ap0Sx/KaimlCPd3122J8VdbcgnyciFxQMdn1Fw/ri8uTgaHmEXY3EfzSqs5WFBJggP2UXvSsqTfCnYOFxbTc9dYa14xz8rK3tBU222K+ec1r3TL4YSFjL8DakrNmzDYu+Y+emgdmBrMCULpo6IjjE4w9veQuRqO9Mg8NqvXrQlCTdMOapo2sunfME3TnuvO4zVbvjufRpPGhZJ8sTq+Hs7cPS2GVfuOkXzAcXczXraraQn8COmj4symDw0l1MeV99c7YC2ZHqRp2hpN05SmabGapo1q+vejpY8TG+7LPUu28v66TAC83Zy4Z8lWYh1sCWdEgIcuS4yLK+v4dU8+l48K69SsuEAvVy4f2YevtuRSWlXfjRHq73gfNf/N8XAxOmQftRctS/plBqHd6NFrrLA4+HyeuZC/MgIm889hcd12SGEBEeOh90jY8CbYe/3R5j6a+hEYXcy7bUsfFR0VdzM4ucPGN/WOxCrY5XqRpWlHCPNzZ0SYvJG1RjdPjKKPrxvPL93jsAWzl6YdISrQg0Gh3nqHImyAs9HA9eP6krTvGJkFDrRUxE4lxATx2IWD+c/KdJyNipeX7WvZ8d2RRPh76JKw+H77YeobNa6MC+/0c+dNiqK6vpHPUrK7ITLrkRATxF9mDubtpIO4Ohl48WfH7KP2wtPViQBPF113DRc2LGoyTHkY0r4Er1D45i64erEs47R2SplnERbshYO/6R1N94qaDJP/BLu/M/fRr2+XPio6ziMAYq+B7Z9BVZHe0ejO7hKE5TX1rN5fwAXDZHmxtXJzNvLg9IFsyynlp6aNOhxJSVUd69ILmTm8t/RR0WFzxvXFyaD4UGYR2qzK2gY+3ZTF7P+s5U9fbKfRpFHfqHHThEiHTLxEBLhTVtPQ47PxvtySy+Be3gzt0/kC5sP6+DIuOoD31mXaZU3Q8pp6lmzI4vLX1/KXL7dj0jRqG0zcOKGvQ/ZRexKh45J+YaMqjsLaV2HBWPjpL+bZg+WHYcytknixFcOvBM9g8yxCe1R+BFa/BP+Oh58fBeUEpdnSR0Xnjb8DGmpgy3t6R6I7u0sQrthzlLpGkyzdtHKz48IZGOrFCz/vpb7RpHc4PWr57qM0yBJ40UkhPm5cMLwXn6VkU13XqHc4ooM0TWPzoSIe/mIbY59bzl++3EFpdT1zxkfg6+HMfef258MNWSSnO17JBT12Mj5wtIJt2SVcFd/52YPNbkmIIqe4muW78y0YmX40TWNjRhF//Gwb4577lce+3kFVbQM3ju+Lj7tj91F7oteSfmFjGhtg71L45AZ4aQgse9I8uybhPnD1hsSHIeUdc303Yf2cXCH+Fti3FIoO6h2NZTTWw57/wZLr4KWh8Osz4BUCCfeDq5f0UdE1oUPNSeWN/zX/HXRgdpEgbC6mDfDzziMEe7tSV2+SYtpWzGhQPHzBYDIKKvl0k30v1YK2fXRpWh59fN2orG2QPio65eYJkZTVNPDdtly9QxFnUFBRy1tJ6Ux/OYkr31jHD9vzuCS2N1/eOZH/u3w4S9Py+c8NcTw0YxAL5ozmniVbHS4Bo8fOql9tycFoUFw2qk+X25g+NJQwP3cWr820XGA6OFpew8JV6Zz3r1Vc8+Y6lqblcfmoPnx1VwLPXDaMH9OOOHwftScRAR7kFlfb5cxXYQGF6bD8GXhlOHx8rXlDkgl3wt0b4dy/mmu7XfMenPu4eenm5/MkAWMrxvwODEbY+LbekZydggOw7Cl4eRh8Mse8W3HCPXBPCkx7DFI/lD4qzs74O6AsB/b8oHckunLSOwBLaC6m/dLVI1m55xgJMYHc+8lWFswZrXdo4jTOGxLC2Ch/Xlm+n1mjw/B0tYvueErNffTFq2NJ2l/AuYOCuedj6aOic8ZFBzAw1Iv31x3imjERskTdyjQ0mkjaf4xPN2Xza9NM4bi+fjx/5Qguju2DV9PfuIWr0tvUc0uICWLBnNFszyl1qGWcLQnCHprV1GjS+HprLokDggjxdutyO05GAzdNjGT+T3vYc6SMwb06v1RZLw2NJn7be4xPU7JZsecojSaNMZH+3DE1hotH9G4Zh6WP2p8Ifw8aTBp5pdWEN83eFQ6urspcs23LB3BoDSgD9J8OF70AA2eC0dn8uL0/ta3nFp1o/jl3iyzjtAU+vWHYLPMmM9MeM88EtRV1lbDrW3MfzUo2L3MfeAGMvhEGzDjeR/f8T/qoOHsDZ4JfpHlJ/rAr9I5GN3aRkUmICeLla0dy54dbqK5vZGNmEW/eFC9vYq2cUopHLhzMlW+s4901Gdx73gC9Q+o2CTFBvHSNuY/WNZhYm14ofVR0mlKKmyZG8cQ3aWzNLiGur7/eIQng6605fLYpm4MFleSX1RLo6cItk6K4ZkwEA06xEdEdU2JOui0hJsjh/h74ujvj4+bUY3XR1h8sJK+0hscuGnLWbV03NoJXlu9j8dpM5l8Za4HoLGfhqnRiw33b9KevtuTwWUo2B49VcrS8liAvF35/TjRXj4mgf4jXSW1IH7U/EQHuAGQXSYLQoWkapLwLqUugYB/UloF/NJz7BIyaAz6nmF19zgMn3xadKIkXWzL+DtjxOaR+DONv0zuatta8Yt5tuLk/aRpsege2LYFj+6CuHAJi4PynYeT14H2KEk3SR4UlGIww7jb45XHI22beBdwB2XSCsLS6nt/2HuWXXfms2nuMqqa6XDdPdMyC77YoPjKAGUNDeTPpIHPG9yXQy1XvkCyqpKqOlXuP8svOfJL2HaO63txH506Mkj4qumTW6DCe/2kPH6w7JAlCnZVU1XPhq0nszitHAVMHBfPMZRGcOzgUFye7qODR7XqyLtqXm3PwdnNi+tDQs27Lz8OFWaPD+GpLLn+ZORh/TxcLRGgZLasqrhlJYUUdb68+yJ4j5j567uAQrhkbwbmDQ3A2Sh91JK1rfk4kUOdohC4qj8Fro6A4EwzOMHw2jL4JIieBQf4e2LXwMRAWDxvfhLG/t67/77A483LgS/8NJYdgw0LzV4MLjLiyqY8mmHdlFqK7jb4RVv7dPIvwiv/oHY0ubC5BmFtSzbKdR1i2O58NB4toMGkEebkyLtqfjRnF3Dwxko83ZjOpv3zSbSsenjmIGS8nsWDlAZ66dJje4Zy17KIqlu3KZ9mufDZmFtFo0gjxdmVCv0A2ZBRx88RIlmzMIqF/oPRR0Wlerk5cGRfGxxuz+evFQ+wuqW5LsourMB0p55ox4Tw4fSC9fd31DsnmRPh7sP9oebcfp7K2gZ/SjnDF6D64ORst0ua8hGg+3pjNJ5uyuXPqyTPu9NI8Y/3376XQYNIwKLh2TAQPzRhIqE/Xl1YL29bHzx2lIKcHa34KK1OaA6XlMP5OmPoIuPvpHZHoSePvhK9+D+krYMD5ekdzXHQiXPQv+OxG0ExgcIIJd5n7qJuv3tEJR+PuB6Ouhy3vw/nPgFew3hH1OKtPEGqaxq68spaEy87DZQDEBHvyh8R+TB8aSnVtI/d+spW3bjYv2TxnQBD3LNnapn6OsF79Q7y5ZkwEH64/xO8mRbfUpbIVmqax83AZvzT10d155j46IMSLO6b0Y/rQXlTWNEgfFRZz44RI3lt3iE9Tsrlran+9w3Fod0/tzx8vGKR3GDYrIsCdlXuPomlat9bU/CntCNX1jVwZ1/Xdi080qJc3CTGBfLAukz9MjsbJSmbkmUwaX2zOoaFpM4q7p/XnjzOkjzo6FycDvX3cyC7umSX9wkqd80fzJg7C8Qy93Lx0csMb1pUgrK2ANS+Z6wtqJjjnIemjQl/jboNN/4XNi2HKn/WOpsdZZYJw9f5j/Lg9D1dnI8t25ZNbUo1SEN/Xn0cvHMz0oaH0Cz5eM0eKadu+B84fyNdbc/nXL3t55Trr2rjjVPWcVu8/xv+25+HqZGDZrnwOl9ZgUDAmMoDHLxrC9KGhRAV5tmlD+qiwlAGh3kzsF8hH67O4PTEGo0GWXeghxNuVjzZmMVFmA3dZRIAHtQ0mjpXXEtKNs9u+2pJDZKAH8ZGWXZY/LyGK2z7YzC+78rloRG+Ltt1VL/6ylx+25+HubOQPk6P5cEMWE2OkjwoID/Do0V3DhZXx7gUp70D0ZKnN5oicXGDMrfDb36FgPwRZQe13UyN8+Xs4sgNcvWD8g9JHhf6CB0HMueYk4TkPHN8Mx0FYVYKwtLqeG95eT3J6IRrg6mRg8oAg7juvP+cODiXY+9RL6aSYtu3r5evG786JZuGqdP6Q2I9hfaxnSnlzPadXrh1FWU09SzZksa6pj7o5G5g8IJgHpg/kvMEh7S73lD4qLO3miZHc+dEWVuw5apGaaqLzQn3ceG3OaJkNfBZa10XrrgRhbkk16w4W8sB5Ay0+S/G8IaFEBLizeG2mVSQIP92UxX9+S8fVycA7c8eQ0D+ICTGB0kcFYD7f1hw4pncYQi/eveHql8z13pp2fK2vrycnJ4eamhq9oxM9IfhiuGA0ZB2DYw0AuLm5ER4ejrOzDkmQnx+DfT+Biydct6RpY5HJbfqoELoYfwcsuca8i/aIq/SOpkdZVYIwq6iKhvRCzhkQxI0TIpk8IAgPF6sKUXSjO6bEsGRDFv9cupf3fjdO73BaJMQE8eLVsfxu8SYaTBoKSBwYxA3jI5k8IBh3F8vUsxKiM6YPDSXUx5UP1h+SBKGOZDbw2Wm9s2p8ZPcc45utuWgazI4Ls3jbRoNi7sQonv3fbtJySxkept+HW2v2F/D412lEBXrwf1cMJ6G/zFgXbUUEuJNfVktNfaPFanEKGxOdaE685G6B6ERycnLw9vYmKiqqW8s8CCtS7A01JRA6AE0ZKSwsJCcnh+jo6J6NY/1C84YkfSfCtMeOJwNP6KNC6KL/dAjoZ96sxMEShNZRMKeVu6fF8MGt47lgWC9JDjoYX3dn7p4Ww6p9x0g+UKB3OC2KKut4Zfl+GlvVc3rvd+OZMayXJAeFbpyMBuaMiyRp3zEyCir1DsehJcQEnXKWsDiz8OYZhN207FHTNL7cnMO46IBuq2979ZgI3J2NLE7O7Jb2O2LvkXLu/HAz/UO8+P7ec5g8oG1RbemjAo7P2M0tkTqEDi060bxsDqipqSEwMFCSg47EM9hc66+qCKUUgYGBPT+DdM+PsPQRGHwJzPvfyYnAVn1UCF0YDDDudsjZCLmb9Y6mR1lVgjDE25UlG7NJTree5JDoWTdPjKKPrxvzl+5B0zS9wyGvtJqrFyaz+3AZnq5O3Hduf5ZszJI+KqzC9eMicDIoPlx/SO9QhOgSN2cjwd6uZBd3T4Jwa3YJBwsqubIbZg8283V35sr4ML5LPUxBRW23Hac9R8tr+N3iTbi5GHln3li83RyrVo7ouL6B3ZuQF7ZJkoMOxsUDnD2h8hh08wZhp3R4K3x5K/QZBbPfAoNMthBWatQccPE2zyJ0IFaVIAz1cWNBUz0nScA4JjdnIw9OH8j2nFJ+3HFE11gOHqvgqjfWcbikGjcXI2/dHM9DMwZJHxVWI8THjZnDe/F5SjbVdY16hyNEl/QN8CCrmxIWX23Jwc3Z0O31AeclRFHXaOKTjVndepwTVdU18Pv3UiiqrOPduWMJ83Pv0eML23K85qfMIBTCoXkFQ2Md1Jb17HFLsmHJteARCNd/aq49KIS1cvOB0TdA2ldQnq93ND3GqhKE0LZWjnBMs+PCGRjqxQs/76G+0aRLDGm5pVy9cB019Y1cO7Yvb94Uf8odiIXQ280ToyiraeC7bbl6hyJEl0T4u5NdZPmERW1DI99vy+OCYb26fVZd/xBvJg8I4oP1h3ps3Go0adz/SSo7ckv59/WjGRFuPZt7CesU4u2Ki5NBZhCKLlm4Kv2kD8eT0wtYuCr9rNo9cuQI1113HTExMcTHx3PRRRexb98+MjMzGT58eJvHPv3007z44osAzJs3j+joaEaNGkVcXBzr1q07ZfuvvPIK77///lnF2Gzq1KmkpKRYpK3OqqurIzExkYaGhrNvzM0XDM5Q0YObFtWUmTd9qK+GGz4Hb6mfLWzAuNvAVA8p7+odSY+xugQhSK0cR2c0KP4yczCZhVV8sim7x4+/MaOI699aj5uzkc/vmMjTlw07qbC79FFhLcZG+TMo1Jv31x2yimX5QnRWRIAHeaXVFk+srdh9lNLqembHhVu03fbcMimK/LJafkrrmdnvf/9xN8t25fPkJUM5XzYqEh1gMCjC/dwlQSi6JDbct80KmuT0Au5ZspXYs/hwQtM0Zs2axdSpU0lPT2fz5s384x//ID+/Y7N1XnjhBVJTU5k/fz633377Sfc3NDTw7rvvMmfOnC7HaC1cXFw477zz+PTTT8++MWUAzyCoKzcn7LpbYz18PhcK9sE170PIkO4/phCWEBgDA2aYE4QNPV9GRg+yC4iwSucODmFcVACvLt/P7NFheLr2TFddsSefOz/cQri/Ox/cOp4+slxLWDmlFDdNjOSv36SxJauE+Eh/vUMSolMi/D0waZBXUtNSI80SvtySQ4i3K+f075mde6cODCEq0IPFazO4bGSfbj3W++syeWdNBvMSorhlUg/vPClsWniAR7fV/BS27Znvd7Lr8OmXnIZ4u3LzOxsJ9XElv6yW/iFevLp8P68u33/Kxw/t48NTlw5rt72VK1fi7OzMHXfc0XLbyJEjAcjMzOxw7ImJiRw4cOCk21esWEFcXBxOTubriKlTpzJ+/HhWrlxJSUkJ77zzDpMnT6ampoY777yTlJQUnJyceOmll5g2bRrV1dXccsstbNu2jcGDB1NdfTyZ9ssvv/DUU09RW1tLTEwMixYtwsvL65Tx/fjjjzz00EN4enoyadIkDh48yA8//MDGjRu5//77qampwd3dnUWLFjFo0CB27tzJLbfcQl1dHSaTiS+//JIBAwZwxRVX8Oijj3LDDTd0+LVpl0cQlB+Bym4umaRp8L8/QvoKuOzfEDOte48nhKWNvwM+nA07v4GR1+odTbezyhmEQiil+MuFgymoqOXdNRk9csxvU3O57f3NDAz15rPbJ0pyUNiMWaPD8HZ14oN1mXqHIkSnhQeY/9ZaMmlRUFHLb3uPMWt0GEZDzxRgf2v1QRIHBrMlq4Rt2SWAZZbfnWjFnnye/m4n5w8J4YlLhlq0bWH/umtJf2d011JV0f183Z0J9XElt6SGUB9XfN3PrnxDWloa8fHx7d6fnp7OqFGjWv4tXLjwlI/7/vvvGTFixEm3r1279qT2Gxoa2LhxI6+88grPPPMMAK+//jpKKXbs2MHHH3/M3Llzqamp4Y033sDDw4Pdu3fzzDPPsHmzeTfTgoICnn32WZYvX86WLVsYM2YML7300iljq6mp4fbbb+enn35i8+bNHDt2fFnv4MGDWb16NVu3buVvf/sbjz32GAALFy7k/vvvJzU1lZSUFMLDzTPhhw8fzqZNm9p9vTqlqhBcvaC6yLyrMUBGEqx5xTLtN1v7Kmx5D855COJutmzbQvSEIzvAJxw2vGFOeEP3nCtWQmYQCqsVH+nPgBAvXv/tAHPG9yXQyxUwv4ncnlNq0SW+76/L5KnvdjI+OoC3bx4ju0AKm/LB+kMk9A/kxx1H+OsltQR5uXbLeSJEd2jZOMGCyx6/Sz1Mg0nrseXFYF5+9+aqdNycDSxOzuTqMeHcs2QrC+aMttgx0nJLuWfJVob28eHV60b3WPJT2I+IAA9Kq+spq6nHR6f3Os1LVV+7bjQT+gWwMbPI4ueK6LzTzfRr1rys+L5z+/PhhizuP3/ASWV4LCkmJobU1NSWn59++uk29//5z3/m2WefJTg4mHfeeeek5+fl5TFkSNvlrLNnzwYgPj6+ZZbimjVruPfeewFz0i4yMpJ9+/aRlJTEfffdB0BsbCyxsbEArF+/nl27djFp0iTAXB9w4sSJp/wd9uzZQ79+/YiONs/2vv7663nrrbcAKC0tZe7cuezfvx+lFPX19QBMnDiR5557jpycHGbPns2AAQMAMBqNuLi4UF5ejre395lfwNNx8YCKfHNysK7CnPD4fB5cvfjs2m1t59ew/CkYNhvOfcJy7QrRk8LioKYYynIgZxM01Fj+XLEiMoNQWLXbE/tRU2/i8a93AJapd9Kapmm89ut+nvx2J+cNDmXxLeMkOShsTmy4L+sPFlHXaOLTTdkWP0+E6E69fd0wGpRFZxB+tTWH4WE+DOp1lhdQnZAQE8TrN8ShafD11lzmvruR302KYlxUgEXazyut5tb3NuHn7sw7c8f2WOkNYV+6IyHfWb193ZkUE8RN725gyJNLuWXRJh48fwAT+wXqFpM4s+b3FgvmjOahGYNYMGd0m5qEXTFs2LCWWXld0VyDcNmyZSdtaALg7u5OTU1Nm9tcXc0TDoxGY5c3/NA0jenTp5Oamkpqaiq7du06ZYLyTJ544gmmTZtGWloa33//fUusc+bM4bvvvsPd3Z2LLrqIFStWtDyntrYWNze3LsXdhqs3BEQDCqpL4cOrYMrDEDX57NsGyN4IX90OEePhijfAIGkHYaOiE+GqRYCC9y+Hj66BC/5uvt0OyZkqrNpVYyKYNiiYpTvzuXphMnd8sJkF14+2yKeVJpPG//2wm5eW7WN2XBgLb4zDzdlogaiF6FkJMUG8cWMcTgbFghX7+f17KTx1ydBu/VRfCEtxMhro4+dmsWWPe4+Uk5ZbxpU9OHuwWUJMEPMSogBwMihe/GUfCfNX8PzSPWQUVHa53YraBn63OIXK2kbevWUsoT4WuDgUDimieUl/Dy8zrqpr4IvNOVyzcB3TXvyNH9PyiAr0oL5Ro8Fk4olvdzLzldW8syaDosq6Ho1NdMz2nFIWzDn+HjwhJogFc0azPae0y22ee+651NbWtsyoA9i+fTurV68+63gBhgwZcsrahCeaPHkyH330EQD79u0jKyuLQYMGkZiYyJIlSwDzcujt27cDMGHCBNauXdvSdmVlJfv27Ttl24MGDeLgwYMtsxVbbzJSWlpKWFgYAIsXL265/eDBg/Tr14/77ruPyy+/vOW4hYWFBAUF4exsockMrt7mzUrQzDMJf/oLvD7OvCy44mjX2y3KgI+vB58+cN0ScJYxS9i4gRfAqDlQXwUN1fD17bDoIkj9GOrsq66vJAiF1Xv+qljC/d3ZlFlMWU0Df/x8G098k0bSvmPUNXRt18uGRhN//mI7767N4JZJUbx41UicjHI6CNuVEBPElfFhVNebqKpr5P5PU5n5ShIv/bKXHTmlssOxsGoR/pbbOOGrLTk4GVS3bxRyKsnpBXy+OYf7zu2Pm7ORh6YPZESYeenxtBd/45o31/Hl5hyq6xo73GZDo4m7P9rCvvxy/nNDHIN7+XTjbyDsXfMMwpwe2KhE0zS2ZZfw2Nc7GPfcr/zp820cLa/h4ZmDeO26UZRWN3Dfuf3xcXPm1nOicHM28H8/7GL835dz90dbWLXvGI0mGbusxR1TYk764DEhJuisSpkopfj6669Zvnw5MTExDBs2jEcffZRevXqdbbgAXHjhhSQlJZ3xcXfddRcmk4kRI0Zw7bXXsnjxYlxdXbnzzjupqKhgyJAhPPnkky31DIODg1m8eDHXX389sbGxTJw4kT179pyybXd3d/7zn/8wc+ZM4uPj8fb2xtfXvMLj4Ycf5tFHH2X06NFtZjN+9tlnDB8+nFGjRpGWlsbNN5tr961cuZKLL774bF+W42rLoboY3HzNycKE+8DdH5Y9CS8NgU9ugL1LobETMy2riuCjq8HUADd83pSAFMLGZSTBvqWQ+LD5HBl9E5TnwTd3wL8GwfcPQO7m4zUKbZiypovGMWPGaCkpKXqHIaxM85KGK+PCWLIhiyG9fUg7XEpNvQlvVyemDg5h+tBQpg4K7lA9nZr6Ru79eCvLduXz0PSB3Htuf5SSOk7CtjWfJzeO78t76w5xaWxv9h2tICWzCJNmXsZ5/pBQpg8NZUK/QFycrD8hrpTarGnaGL3jaI+MWZbzly+28+ueo6T89fyzaqeh0UTC/BXEhvvx37k923VaL79LiAlq83NMsBdfbM7h85RsMgur8HZ14tJRfbh2TASx4b7tjkGapvHXb9L4aEMW/5g9guvH9e3R30nYH03TGPH0L1wZF8Yzl5+8JNMSiivr+HprLp+lZLPnSDluzgYuGtGba8dEMC46gHUHC9s9VwI8Xfh0UzZfb82lpKqePr5uXDUmgqvjw4kIsNwu593BFses3bt3n1Sjz97MmjWLf/7zny11/PRQUVGBl5cXmqZx9913M2DAAB588MFOtzN79mzmz5/PwIEDzz6o2nIozgT/KHYfzGGI27HjddW8QmHrB7DtE6g8Bt69YeT1MPpGCDxNQrihDj6YBdkb4OZvIWrS2ccphN5a1+eMTjz+81WLwGCELR/Arm/NMwtDhkHcTRB7LXhYpsRMd2lvzOr2BKFSaibwKmAE/qtp2vz2HisXW+JE7V1wvXT1SOpNGst2HeHX3UcprKzD2aiY0C+QGUNDOX9oKL193Vn//hN4xYxj+KRLASivqee5BW/hW7yDPhc/wtympWBC2LLTJSYG9/Lh1935LNuVT9L+Y+0m1k88VwDS1n5PRfpGJtz8fx2Kw9Jt6HGxJWNWz1v//hNsM/XjH3tC2P23mbi7GLvcbwqDJzD33Y28cUMcEWUpnWrjbC1clU5suG+bGTYnbhakaRobMor4bFM2P6blUVNvYnAvb64ZE8Gs0WHs/fL/2pxDbycdZMXSL7gk8Ag3/PHUO2QK0VHN58mfNvnSx8+dd+eNtdjf+fL0jdRPuI9PU7JZtjOfukYTI8N9uWZsBJeO7NPmA9yOnCu1DY0s25XPp5uyWXPAXONuUkwQ14yNYMbQUFI/ftrhx6zOjFfguAnCvXv3kp+fT2KifvXCXn75Zd577z3q6uoYPXo0b7/9Nh4enUt419XV8cknn7TMJjxr5fnmjUpcvY/3g4wkyN0C5zxgfkxjvXnW1JYP4MAy8zLkyHPMCZAhl7H+k38cP4c0Db6+A7Z/wv6QGQy463PLxCmE3ta8Yt6opHXNwRPPlZpS2PGFObF+eCsYXWDwxeaZhv2msf7Dp2xmzOrWCtdKKSPwOjAdyAE2KaW+0zRtV3ceV9iP09U7uWNKDNOHhtJo0tiaVcwvu8xJkCe+3ckT3+5kRJgvF3n349pld5KmQZ/RF/DsvxfyePU/WRY7n+skOSjsxOnOk4SYIK4eE8HVYyKormtkzYGClsT699sOtyTWz3cbwKXL7iQNGD7pUtLWfk/YsrvInf6fDsfhFTOOsGV3WayNniZjlj68YsZx9S938pvhPrKLE6k78FuX+803QY/j696PPiWbCPv17k61cbZOtcwuISaoTRJEKfP5NqFfIE9fPozvUg/zWUo2f/thF/N/2sONvcK4Z9mdpJkgx38MK5Z+wevOr3F43Bs99nsI+9V8noz3f4y1RUMt8nd+7bKvGLLmPh41/pGfd23Ez8OZGyb05dqxEe0uh+/IueLqZOSS2D5cEtuHnOKqphm4Odz38VZ83Z25uXdfbnHgMUvGq44bNGgQgwYN6pFjzZo1i4yMjDa3Pf//7N13WJRX9sDx76U3BQErqCD2XrBhSzfVlI2JqZpkkzXZZJNsSdndtN3NL1tMNrtr6qaYmJiYuum9aBQVu2KNCAhYQZog/f7+uAOiAaXMzPvOzPk8zzww7c554b5c5sy59/7tb9x9991tqhhsLCgoyHnJQYAOXX96W+LU45Mg/oEw6CJzKdkLGxbB+tfM+muf3kP/zskEfPUi6VoztGYbbHqTowRTOcKJcQphtfokYGMnnishkTD2JnPZn24ShZsWm528I3vSJyaZ4K9e8ogxy6UVhEqpicDDWuvpjuv3A2itH2vq8VKNIdpLa03GoSN8ufUAX245wIacIqb5bWB+4H/YpPozSGewdMQ8LrnsKqtDFcJSJybWM/PLOMdvNU8EPkNuQG8SajLZEjGR6ojWreMWeGQvQ46sIDswkd7V7Wuj/1PlR/bkl7ltG1oZs6zzxcdvMWX17WT596ZPbVab+83gIyv4JPQizqz4iryznz7uU1Y727avhMWrc/jfhjyGVazl6cB/sUYPYITKYM+ZTzNy6gyrQxReIn35R8R/OZcM3Z0hKrtdf6PT/QeQVJvJbdV3EpA0jSvH9uTswV0JDnDNhm91dZrUjAIWr8nhi/T9TNFp/DvwaXIDe7d7vPG0Mau14xX4bgWhOLVW9YO6OshebhIgWz+AmgpqtR/+qo5KAvnxrJcYOlnGLCGoqYTtH5sK3N3fo9HUaH92B/a19Zjl0gpCIA7IaXQ9Fxjf+AFKqVuAWwB69ZK1dUT7KKXo26UDfcMrua3jCio7foR/5hIC6iqYxEYKAztzyXnnWh2mEJbz91MkJ0ST3KGQ+yM3UZH+EcH71uBHHQNrd1CFPwOPrIIjrW/bj1oG1LS/jS5BFRGtf2a7yJhlkckjBxO2ppLBdTvb1W9CVRWXV7zLyvibmOAhyUGAQd078vB4xR8DvqF2w2KCq45yutpAZodkSQ4Kpxo66SJ+/OHvjKnYRJVu39/o5LrNaAUv90slZFx/GDAKXJQcBPDzU0xOimay/xaq/N7Gf8dH+NdVOWW88cAx65TjFciYJVzAzw+dMJk0PZgPKq8jaNt7zFXv0Y0ivo++gumSHBTCCAimcuAlfF2XwtcVa+i95z3m+H9u+zHL1QnCU9JaPw88D+aTLYvDEZ6sIAO2fwI7PoU9KwFNcMd4CuKmEpSzjH3R4+l3+DuOPjWF0F9803RpvRDerq4O9q4z58r2TyB/BwoI7TqMg30uJmT3l2yNu5IBee+0qQKrvtx9XfxMBuS+3a42DlZVtmHIcy0Zs1wjf817hAPpXS4i7uCSNvebgV9dT0VgJwbkvk368tPtX0FYUQLp75pKjLy1BPgFUhY7iqqD6VSGdiOxdA173v0DvX72qNWRCi+Rvvwj+lVso9IvjPK6gHb9jU7vNp1B+z/Af/8GeHs2hMXA8FlmfbIuTq5KK85zTG9cCEXZBIVEUhA3jcCcVLbGz2r3eCNjlhCndrCkgnfWmen+mfllRAQHcH33wQQdeJsX/C7n0oIP+frT0znr/JlWhyqEpXbsL22YGXK4rIrukWH07TOFuj1fsTL+JluPWa5OEOYBPRtdj3fcJkTLnGxR0JRfORIdH8P2TyF/h7m/2zCYdi8MPJ/0jGzivv4l2Wc/z9BJF5H5wWMkrP8rlU9PJviWr6BTghVHJYRznWrx3OoKc33HJ7DjcziyH5S/2V0u+UYYcB7pWzcR99Vt7Dn7OSZMuoj05Wcct85FSzReC8O0cXq72sh5ZMaO1v4o2knGLAukL/+IPhueAmDo9U+QvnFVm/vNwQHX0GPHQnZNeKzVbbiN1rBnhWPXu/9BdTl0HgTT/49tFVF0W3IfOWc/z9BxZ1M2fwo9N88nIySGpAt+bXXkwsPVnyc1UYmEd4zlxwG3tetv9OhJF5G+/GLivrqVvaN+R0LlTkh7HlY+BXHJJlE49GcQ3MZZtzVVsPMzc65kfGM2SEicCmc8wNYiP7p/e1ejMat9440HjlkyXgm3qK6t47vtB3lrTQ7f7ThEbZ1mXEI0vzy9L0lH1pLw3Z/IO/sZZgw7m98/+Qz/t+puUoMCSDnrUqtDF8KtSiuq+WjjPhavyWFjThGB/oqzB3fliuSexBxcRfw3D5F39jO2H7NcnSBcDfRTSiViBq1ZwNUufk3hTeJGH7+t+I9fwztzoHcKrHymyUQHnXo3PP3IV8dXQSVefD8ZASH0WvsYvHQuXPe+8z/lFsLdTjxPMpfCW7NhzBx463rY9Q1UHYGgCOh7Jgy4APqdDWHRDU0cyXjhuHNl6KSLSAeOZKRBCwedIxlpTm/DzWTMssCRjDTKe51O2L4lEN65ff1m5ER4/A36+u0l/eynW9WGy5UegI2OBd4LdpnzcdhMGH09xI0BpSh+9QFqG/X/8Fs+p3L+RHqt/RtMvASi+1h7DMKjNZwnK++BTmOc/nc+4fpXoSwfNr5pKv0+uhM+vx+GXGr6ec/xoNSpX+TgdvP8jW9CeT506AFTfgMjr4HoRABKXn2AOt8es2S8Ei61+9ARFq/J4b11eRwqraRzh2BuntKHK5Lj6dPZzExc+epzx/X/X9/yc379VC1jUr9m2OQL6dBo93IhvJHWmtVZhSxencOnm/dxtLqW/l0j+OMFg7h0VBwxEcEArFzl/PHGVWOWSzcpAVBKnQ88CfgDL2mtm50nIwu+iybtXgJvXg0de0D+TnPbSRIdLXJgKyy8FGor4Zp3IP4nO3wL4Vkyl5opXt1HQeYSM5WYOojoahLnAy4wycPAEKsjbTGl1FqttVtPThmzLPLmNWaZiF+ubH9bb1wNeWvg7q3g78aVVJqq5M34zuxiV1EMO78AXQs9J5hkyZBLICj81O0WZMALZ0JYLNz0ZevHOyEaq6mEv3SF0+4zF1fRGnLXwPpXIf098yFVTD9TVVhZ+tMdIHd+Dutfh9J9kLsa/ALM2DXqevP/np/r1jZ0FnePWa0Zr8AJm5ScarZCGz366KMsWrQIf39//Pz8eO655xg/fjzV1dU88MADvPvuu3To0IHg4GAefPBBzjvvvDa/VmsdPXqUc889l2+//RZ///b1we+//5558+bx8ccfOym61pk/fz5hYWHceOONABwsrSAs0J+IkMCGfvDd9gO8vTaXQ6WVrM4qxN9PcfqALlw5tienDehMoL/fKV9n6c5D3LBgNZP6xvLS7GQCWvAcIezs2SUZDI+PJCUptuG2Tzfv5a3Vuew5XM7u/DLCg/yZMbIHVyT3ZGTPKFRLPgyzWHNjlsv/c9Zafwp86urXEV6sKNv8Y5m/E7qPgDMegIQp7Ut0dB0MN34OCy+BV2bArNch6XSnhSyE2yVOhU6JZhpWaAyMuR4GXgg9RoOf/HPWUjJmWaQw+7jq73YZMctMp9/9PfQ7yzlttkTjSt6OcfDto7DlPUBDeGeY+EsYdR107t+6dmOSYNYb8OoMWHytqXwPCHbBAQifUJQDaIhy0vnWHKWg51hzmf6YmU6/biF89SAoP1j+L7McTMJkWPIPyPjaPC92AJzzF7OWYURn18bo4dw+XjU1W6H+ehutWLGCjz/+mHXr1hEcHEx+fj5VVVUAPPDAA+zbt4/09HSCg4M5cOAAS5YsccaRtNhLL73EZZdd1u7koB3ceOONTJo0qSFBGBboz57DR+kVDVU1ddy4II1vtx8CIDE2nHvPHcjPRsfRpWPr3m9N7d+ZRy8Zyn3vbebBD7fw6CVDPSJZIkRzhsdHcvui9fxr1kgqqut45vsM1u0pBCC5dyfmnpbEBcO6Ex5s+fYeTuEdRyG8V1U5fPWw+SR50t2w9iXzxsgZVVDRiXDjF7DwMlh0BfzsRRgsO28JD7XzC/MpfpdBcOQgJJ0hlbHCM2gNhVlm6Qhn6D8dQjuZ6bzuTBAmTjVvlN+4ynyoBRA/FibdZWLyb8dUq94T4ZJn4N2b4MNfwaXPtmyqphAnKswyX52VkG+J4AgYda25HNpppg+vewW+/fOxx/Q9G6bdY84Z6dvW+Ow+2L/55I/p0N3MwOnQ3VR7dh4I3//NXJrSbRic99dmm9u3bx+xsbEEB5sPPWJjTYVOeXk5//3vf8nMzGy4r2vXrlxxxRXNtvXiiy/yt7/9jaioKEaMGEFwcDDz58/no48+4i9/+QtVVVXExMTw+uuv07VrV5YsWcKdd94JgFKKpUuX0qHD8etlvv766yxatAgwFYAPP/wwsbGxpKenM2bMGF577TWUUnzzzTf89re/paamhrFjx/LMM88QHBzM559/zl133UVYWBiTJ09uaLesrIw77riD9PR0qqurefjhh7n44oubPK7y8nLmzJlDeno6AwYMYO/evTz11FMkJydz6623snr1ao4ePcrll1/OI488AsB9993Hhx9+SEBAAOeccw7z5s0jLCyMhIQE0tLSGDduHBEhgfSKhqyCcg6WVvLt9kNM6RfL7af3ZVxidLuSerPG9SL7cDnPfJ9BQkwYt0xNanNbQlgtJSmW+VeN4vqX0qip0yjgwuHdueus/vTt4s7N691DykqEvX1+HxwtgLP/DGf+0bz5enuO+dTSGTp0gxs+ge4jzfTMdQud064Q7pS5FN65AdBw0b+df54I4UpHC6Gq1HkJi4BgszHC9k/M1F53SpgCAY4PsMbfBj//GgZd2L7kYL1hl8Ppf4RNb8L3zb/hFuKkirLMV1dXEDanc38458/wuwyzNiGYJPq170DPcZIctLuQKJMcLM4xX0Oi2tXcOeecQ05ODv379+e2225rqBDctWsXvXr1omPHji1qZ+/evfz5z39m5cqVLF++nO3btzfcN3nyZFauXMn69euZNWsWf//73wGYN28eTz31FBs2bOCHH34gNDT0uDarqqrYvXs3CQkJDbetX7+eJ598kq1bt7J7926WL19ORUUFc+bMYfHixWzevJmamhqeeeYZKioquPnmm/noo49Yu3Yt+/fvb2jn0Ucf5YwzziAtLY3vvvuO3/3ud5SVlTV5bE8//TSdOnVi69at/PnPf2bt2rXHtbNmzRo2bdrEkiVL2LRpEwUFBbz//vts2bKFTZs28cc//rHh8cnJyfzwww8N10OD/KlfbewXU/uw8KbxjO8T45SKv9+dM4ALhnfn/z7dzmeb97W7PSGsFODvR02dOVluOz2J+VeP9srkIEgFobCz8sOwcbH5NHnibea2+gqNvHXHr4HSHqGd4Pr/malbH94OFUWQcodz2hbCHfLWQVhns4FBffWFs88TIVylvqLJmQmLEVfD6hdgy/9gzGzntXsqK+abTRX6nwebF8PA85x7Dk79LRRmwpK/QqcEGHmV89oWvqEwG/yDTHLHSntWmA+xpt4Da1406wzKeGWtk1T6NaifVlz/ezvt3nb93iIiIli7di0//PAD3333HVdeeSV//etfGT16dKvaSUtLY9q0aURHmzVaZ86cyc6dZt3y3NxcrrzySvbt20dVVRWJiWajm0mTJvHrX/+aa665hssuu4z4+Pjj2szPzycqKuq428aNG9fwuJEjR5KVlUWHDh1ITEykf3+zfMTs2bN56qmnOO2000hMTKRfv34AXHvttTz//PMAfPnll3z44YfMmzcPgIqKCvbs2dPkepDLli1rqHQcOnQow4cPb7jvrbfe4vnnn6empoZ9+/axdetWBg8eTEhICDfddBMXXnghF154YcPju3TpclzydH9xBRpNeLA/b6/NZdqAzsets9Yefn6Kx2eOYF/RUe5avIGukSGM7tXJKW0L4W7zvtiOAuZOS+KNtBwm9Y112rliN1JBKOxr2RNmE5GL/nX87YlT27UYcpOCwuGqxebT7C//CN/8CVy8gY8QTtNznKkKGXvzseoLV5wnQrhCUbb56swpj3GjIba/2QXVXTKXmrEjuKNJ0LuiklcpuPBJc35/eAdk/nDKpwhxnKJsiOxp7dq0jdeuO+MPUvXuKVz0e/P39+e0007jkUceYf78+bz77rv07duXPXv2UFJS0u6w77jjDm6//XY2b97Mc889R0VFBWCm4b7wwgscPXqUSZMmHZc4AwgNDW14bL366c71cdfU1LQpJq017777Lhs2bGDDhg3NJgdPJjMzk3nz5vHNN9+wadMmLrjgAioqKggICCAtLY3LL7+cjz/+mHPPPbfhORUVFQ2VkqUV1RwuqyIk0J9OYUHMv3oUty9aT2pGfpuOqSkhgf789/pkunYM4eZX1pBzuNxpbQvhLh9u3EtaViEXDO/OvecNdMm5YieSIBT2VJQDq56HEVdB1yHuec2AILMO4Zg58MPj8PHdUFfrntcWoj3S/gshkTBsptWRCNF6hY4EoTMrCJUym5XsSYXDmc5r92R2fQO1VTDuFrNObuOKd2cKCIIrFpqK4cXXmDXdhGgpZ24I1FZ5645tdAGuO1eEc7ng97Zjxw5+/PHHhusbNmygd+/ehIWFcdNNN3HnnXc2bFpy6NAh3n777SbbGTt2LEuWLKGwsJCamhrefffdhvuKi4uJi4sD4JVXXmm4PSMjg2HDhnHvvfcyduzYnyQIO3XqRG1t7U+ShCcaMGAAWVlZ7Nq1C4CFCxcybdo0Bg4cSFZWFhkZGQC88cYbDc+ZPn06//nPf9COYoT169c32/6kSZN46623ANi6dSubN5t1IktKSggPDycyMpIDBw7w2WefAXDkyBGKi4s5//zz+ec//8nGjRsb2tq5cydDhw4FoKi8Gg106WCSnilJscy/ehSbcp27NEdMRDAv3zCWmjrNnJfTKC6vdmr7Qrja6yuzUcD955skvqvOFbuQBKGwp+8fM19P/717X9fP31RnTL4b1r4M7/4caqrcG4MQrVG6H7Z9CCOvhaAwq6MRovWKss1SDyEtW2uqxYZfCSj3VRHW1YDyh7E3HbvNVZW8oVFwzVtmquiimVDmnZ9iCxcoyrZu/cF6k+/66bRUqXq3Pxf83o4cOcLs2bMZPHgww4cPZ+vWrTz88MMA/OUvf6Fz584MHjyYoUOHcuGFFza7JmFcXBy///3vGTduHJMmTSIhIYHIyEgAHn74YWbOnMmYMWMaNkEBePLJJxum7AYGBnLeeef9pN1zzjmHZcuWnfQYQkJCePnll5k5cybDhg3Dz8+PuXPnEhISwvPPP88FF1zA6NGj6dKlS8NzHnjgAaqrqxk+fDhDhgzhgQceaLb92267jUOHDjF48GD++Mc/MmTIECIjIxkxYgSjRo1i4MCBXH311UyaNAmA0tJSLrzwQoYPH87kyZN54oknGtpavnw5Z599NgDVtXUE+vvRMfTYGrkpSbHMneb8DUWSOkfw3HVj2HO4nLmvraWqps7pryGEKxytqmX7/lLOHdqNuKhj65S66lyxA6VtNI0yOTlZr1mzxuowhNUObIVnJ8GE22D6o9bFsfxf8NWD0PcsU60hyRdhR9//Db7/P7hjHcR410CllFqrtbbtVswyZjnJwsugvAB+scT5bb8yw6xxeOdG125+UHkEnhhs1lGb+bLrXudEuWtgwQVmp9DZH0Fg6KmfI3xXRQn8tSec9bD5IFQ4lSeOWdu2bWv11Fa7OnLkCBEREdTU1HDppZdy4403cumll7arzXXr1vHPf/6ThQut28SwtraW6upqQkJCyMjI4KyzzmLHjh0EBQW1qp3169fzxBNPsHDhQiqqa9l5oJRuHUPo0jHEbf3g/fW53L14Iz8bHc+8mcOdshmKEK70Rtoe7n9vM4tvmcD4PjFWh+NUzY1Z9qwgzFwKy560OgphlW/+BEEdYMpvrI1j0p1mR9hd38B/z4CjRcfukz4q7KC22lS6Jp3hdclB4UOKXDjlceTVpv09K1zTfr1Nb0JlMUy41bWvc6L4ZLjseZMofH8u1ElVhjiJIhdM5xfCJh5++GFGjhzJ0KFDSUxM5JJLLml3m6NHj+b000+ntta6JYfKy8uZPHkyI0aM4NJLL+Xpp59udXIQzKYrf/7zn833RyrxU4ro8Na30x6XjornrrP68e66XP7z7S63vrYQraW1ZsHyLAZ378i4xGirw3Eb++1i3HgBXOF7slNh52dw5kMQZoMTccxsKNlrdox8/jS48QvI3yF9VNjDjk+hdB9c8MSpHyuEHdXVQdEeGHC+a9ofeCEEhsPGN6B3imteo64OVj0HPUaZXcTdbfDFcPaf4KsH4JsEOPsR98cgPEP9ep+dEiwNQ4j2GD9+PJWVlcfdtnDhwoYdgZ3txhtvdEm7J/riiy+49957j7stMTGR999/H2fMVqifWlxTW0dReTVRoYEE+Lu/VujOM/uxp6CcJ77aSa/oMC4ZFef2GIRoiRUZBew4UMrfL/etald7JQj3b4bXZ5p/bntPsjoa4W5aw1cPQYfuMH6u1dEcc/r9ZnrxVw/Cf0abjUvO/jP0nmx1ZMLXpf3X7EbZf7rVkQjRNkf2m409XJWwCI4wCbQt/4Pz/u6aKbi7v4P8nXDpc66dxnwyKXdAYSYsfxKiE81mW0KcqEgShMLzrVq1yuoQXGL69OlMn+76/+cKy6uo05qYiOBTP9gFlFI89rNh5BUd5Z53NtEjKtSnqrOE53g5NYvo8CBmjOhhdShuZa8pxnU1UFMJn90Ljw+AD26HHZ9B9VGrIxPusP0TyE2D0+6z33p/k+6EUddB1RGoqYDPfgtPDIQPfwU7v4Dqk+9wJoTTHdwOWT9A8g1mcx0hPFFhlvnqyl1VR8yCyhIzxrjCqucgvAsMad9aV+2iFJz3D7Nm7se/NktjCHGiwmyzhEtoJ6sjETZip/XohWtprSk4UkV4cAChQf4Nt7lbcIA/z103hvjoUG5ZuIbdh464PQYhTibncDlfbzvAVeN6EhLoW++z7JUg7NDN/NMy5XdmV6ytH8Abs+DvfeDNa2DDIigrsDpK4Qq1NfDNIxDTz+zGajeZS810zqn3OProb6DXREh/FxZdYfro4uvMbpnlh62OVviCNS+aHUxHz7Y6EiFrkrZd/ZTHqATXvUbCFOgYb6YZO1tBBvz4BSTfCAHWVGM08A+Ay1+G8FjzP9OBLcfukz4qwCTkO/W2rtJV2EJqRj7PLskAzA68BQUFkiT0ESUV1VTV1hHrqB7UWlNQUEBISIjbY4kKC+LlOWOprK7jqv+u5HBZVcN9jfuoEFZ4JTULP6W4bkKC1aG4nb2mGHfoDlc8cWx9t0ueNRUyOz6F7Z/C9o9B+ZnEzIDzYeD5EN3H/NMbN9okFetlLoW8dTD5LmuORbTOhtfNFK0rXzNvcuyk8bqYiVMhccqx65c9b+7f/ompdt32ISh/s9bVwAtMP+3UW/qocK7KUtjwhqlYCo+1OhrfJuvmtk9RNqAgqqfrXsPPD0ZcCcv+CaX7zYeRzpL2PPgFmkpeOwjpCOf+Fd69yezgPHcZFPwofVQYRdkQLRta+bLUjHxuX7Se+VePAiA+Pp7c3FwOHTpkcWTCHQ6VVlJbpwkoCWav44OCkJAQ4uPjLYmnd0w49543kIc/3MKs51fw4e2TWben8Lg+KoS7lVXWsHhNDucN7Ua3SPcnz61ms0wMJoEyc4FJnCROhb5nmsv582Dv+mPJwi//YC5dBkPXobDsCZi5EJKmyRs2T1NVDt8/ZhZ3H3ih1dH8VN66Y8lB+Gkf7Xe2uVzwhKOPfmIShp/fZy5dh0LXIaaPXrEQ+kgfFe20aTFUlcLYm62OxLft3wyLroSLnz4++S9arjDbfDjo6uq7EVfBD4/Dprdg0q+c02ZFCax/3STqnZl0bK+hl0FFMXx8Fzw1zizfcsmz0kd9ndZmQ6CkM6yORFhk274Sfv7KGv591ShSksyHi4GBgSQmJlocmXCHrXtLuP6VH7j/vIGcPtg+HxTMSUmgsKyKf33zI1P+9i3lVbX85+pjfVQId3tvfR6lFTXcMCnB6lAsYb8EITiqtE74R1YpU4EVNxrO+CMczjyWLEx/B3QdvHaJqdw6uO34hI6wt7TnzE6sP3vRntNemqrwa6qP+vlB/BhzOfNBM/Wsvo9uftv00YWXmOluB9Klj4q20RrSXoBuwyE+2epofFtdDVSXw7s3wqY3zTql/aeDf6DVkXmOomzXrj9YL7YfxCWbacYpdzhnrNmwyCTqJ9hoU616yTdAzqpj06rfuQE2TTd9tN859qvUF65Xdsj8vYpyw/kmbKmmTlNeVcvti9Zx/rDuXJnck3GJ0T61O6cvW5CaSWigP7PG9rI6lJ+4++z+bN9fwhdbDgDwy9fXc8Hw7lw5tifJvTtJHxVuU1enWbA8k+HxkYzu5Zvr9dprDcLWiE6Eib+EGz6B3+6CS56BjnGQtcx8ei6JF89Qfhh++Cf0PxcSvGzn6pgk80b0xs/gtz/CxU+ZSpnMJTBspvRR0TbZqXBoG4y72Z4JdV/SoRuERMGQy2DvBlh8DTwxGL58AA7ttDo6z1CY7b6Excir4OBW2L+p/W3V1ZkPt+LHQtyY9rfnbJlL4ccvzbq5IVFmJ+fcNfDmVfDPwfDVQ5C/y+oohTvVr/fpjoS8sKUuHYLpGBJASlIsX245wJXPr+SMx5fw9Pe7OFgim+15s8NlVfxvw14uHR1HZJj9PsRMzchndVYhd5zRl44hAUzoE81nm/cx89kVnPnEEp5dksHBUumjwvWW7con41AZc1ISfDYx7bkJwsbCYyAy3uwwC2bKT+ZSa2MSLbPsCbO75JkPWR2Ja4XHQlQvqCoz19ctlD4q2mb1f80b/qGXWx2J6NAdrlwIu7+DS5+Fq940CaMVT8FTY+HF6bD+NaiU3fmaVFMFJXnuS1gMucxs7LPxzfa3tetrOLwbxtuwerDxEhZn/MH00cwlcNlzMGsR9BgNqf+B+WPgpfMclZBlVkctXK2ofkMgSRD6qq4dQ3j2ujFsyCniP1eNZN7MEXSOCObvn+9g4l+/5eevrObLLfuprq2zOlThZG+k7aGqpo45KQlWh/ITjdfF/M05A3j2ujFszC3mP1eP4u+XDyc6LIi/fradiY99y82vruGbbQeokT4qXGRBahaxEcFcMLy71aFYxjsShPX/DF/xKnQfYZKFb8+RBIzdFeXAqudh5NXQdbDV0bhWfR+9cqFZNzM6QfqoaL3S/bDtIxh1LQSFWR2NgGNrku7bCAPOg6sWwa+3wVmPQHk+fPBLeHwAfHgH5Kw2U8SXPfnTc98Xd5ktzgG0+xIWYdGmWn3TW1Bb3b62Vj1rEsSDL3ZObM7U3Lq5+zaZzbOufhPu3mI+mDuyH/53K8wbAB/dCblrpY96q8Is8zXKftMLhfukJMUy/+pR7DhwhMvHxPPW3Il8+5tp3DylDxtzi7ll4VomPvYtj322jd2HzIdbzy7JIDUj/7h2ZJdZz1FdW8drK7OZ3DeW/l07WB3OT2zKLWZ+ozUH6/vozgNHuCK5J+/cmsLXv57Gzycnsn5PITe9soaUv37L3z/fTla++XBL+qhwhsz8Mr7dfpBrxvciOMDf6nAs4x0Jwsb/DA+aYXbDPX+euV3Y1/ePma+n3W9tHO5wXB+9CA5shQuflD4qWmftArPuXfKNVkciGkucevxapR26muu3r4EbPjdJpM3vwItnwdMTzEYBb80+loCp/wAhbrQFwVuoyIIpjyOvNonbXV+3vY1DOyHjG0i+yZ7rTU6+66dLWJzYRzt2hym/hjvWwZxPYdCFsHExvHAGPDPJfIAnfdS7FGVDWCwER1gdibBYSlIsc6cd26SiT+cI7jtvIKn3ncF/r09mZM8oXvghkzMeX8LMZ1MpLKvil6+va0jA1Fd8DY+PtOoQRCt8sWU/+4orbFk9CDB3WtJPNiQ5sY/27RLB/ecPYsX9Z/LcdWMYGhfJs0syOG3e91z53AqKj1ZLHxXt9kpqFoH+imvG+/YHad6xSnXjf3oHXwzf/tksxtzU5hLCHg5sMdOaJv4SonpaHY3rNe6Lg2bAkr+ZN6nSR0VL1VabBGHSmWZ9S2F/SkHvieZy7l9hy3tmeYE1L4Lyh9d+ZnbX3f6xb25aVGjBlMe+Z5kkycY3TMVnW6Q9Z6Yqj5nj1NAsoZRZ/zdhEpz3N5PIXr8Q1rwAfgGmj468BrZ96Jt91JsUZkOnBKujEDYW6O/H2YO7cvbgrhwsqeDddXm8tSaH55buJiTQjxteXs0143vxvw17j6v4Eva2YHkWvaLDOH1gF6tDabdAfz+mD+nG9CHd2F9cwbvrcnlrTQ7PfJ9BqKOPXju+F+9LHxWtVFpRzTtrc7lgWHe6dAyxOhxLeUcFYWOx/aDzINj6odWRiJP55k8Q3BGm/MbqSNyv6xCI7mOmigrRUts/Mbt9j7vZ6khEW4R0NAmlm7+B21Y61q5TsO4VU4nmi4mXwizwC4SOPdz3mv6BZpOoHZ+ZTbJa62gRbHjDtBHR2enhWSokEsbeBLd8D3OXw9ifAwrWvuy7fdSbuGvHcOEVunQM4dbTkvj2N9N46xcTuWBYD6pr63hpeRbXju8liRcPsTm3mDXZhVw/sTf+ft614UK3yBB+eXpfvvvNabxx8wTOHdqd6to6XpQ+Ktrg3bW5HKmsYc6kRKtDsZz3JQjBTOHckwpHDlkdiWhK1nLY+bmpnguLtjoa91PKVBFmLoWjhVZHIzzF6hcgshf0O8fqSER7dRkEA841FVpgNp7xxfVIi7JNBbmfm9d5GTELaqtgy/utf+7616C6DMbd4vy47KTbULNeYf3vxlf7qLeorYHiXNmgRLSaUopxidH8bEwcAf5+hAT48drKPT9Z703Y08upmYQF+XPFWO+dreXnp5iYFMPM5HgC/PwIC/LntVXSR0XL1dVpXlmRzaheUYzsGWV1OJbzzgTh4Bmg68y0LWEvWsPXD5nF3e24+6O7DJph1pLb8ZnVkQhPcHAbZP0AyTe4P5kinK9+PbdzHeuwjp7tm5sWFWZbk7DoPsJsFrXxjdY9r64W0p6HXhOhx0iXhGYb9X30nD+b68k/980+6i1K8sz/HFJBKNqgfj23myYnUlFTx2+n9+f2ReslAWNzh0or+XjjPi4fE0/HEBuul+tE9X10dkpvyqtq+f15A6WPihZbsvMQmflltl2n0928M0HYdSh0SpQpnHa0/RPIXW02JvHlXVjjRkPHeOmjomVWv2jWPBt9vdWRCGeo37Ro1HUQFgNHDpjrvrZpkVVTHpUyVYS5qyF/V8uft/MLE7MvfLhV30dHz4HgSCg76Jt91FsUWbDep/Aa9bvMXj/R9J/yqlrmXz2KTbnFFkcmTuaNtD1U1dYx2weSHvV99LoJCQCUV0sfFS33cmoWXToEc97Q7laHYgsuSxAqpR5WSuUppTY4Lue76rWaeHFTRZi5RKZw2kltDXzzCMT2N4ue+zKlzFT4Xd9AZanV0Qg7qyyFjW/CkMsgXNZTcRWl1D+UUtuVUpuUUu8rpaJc9mL1u8z6+UHCFFOVlTDFtzYtqjwC5QXWJSyGXQHKr3VVhKuegY5xMPBC18VlF/V91D/AbGCSufSnOyELz1FowY7hwmXc/R6rfpfZ7pGh9IkNJzWj4Ce7zAp7qaqpY+HKbKb170xSZ+/fuby+j/aMDiUuKpTUXdJHRcvsOniEpTsPcd2E3gQFeGftXGu5+qfwT631SMflUxe/1vEapnB+7taXFSex4XXI3wlnPmjedPi6QRdBbSX8+KXVkQg72/gmVJU6NgwQLvQVMFRrPRzYCdzvlldNnGqm/x3e7ZaXs40iixMWHbtDn9Nh02Koqzv14w9sNUmysT/3vfErcSoUZkLRHqsjEW1VlG0S4pHeuw6ZD7LkPdbEpBhW7S6gurYFfzeFZT5L38eh0krmTEqwOhS3UkqRkhTDit0F1NVpq8MRHuCV1CyC/P24anwvq0OxDe9Nk/YYbT7plymc9lBVDt8/BvFjfaP6oiV6TYDwzrLjtmie1mZzku4jID7Z6mi8mtb6S611jePqSiDeLS/c5zTzNXOJW17ONuormqISrIth5NVQnAPZy0792LTnICDE7ETtaxKnma+ZP1gbh2i7wmzzP7G/d69DJlxvUt9Yyqpq2ZwnUzft7OXlWfSJDWdav85Wh+J2k/rGUny0mq37SqwORdhc8dFq3l2Xy0UjehAbEWx1OLbh6gTh7Y7pWi8ppTo19QCl1C1KqTVKqTWHDjlx12E/P1OhlfGNmcokrLXqWSjdB2c9YqbXCrPZxMAL4cevoPqo1dEIO8peDoe2w9ib5bxxrxuBJncQcvqYFd3HvHHf7WMJQqsrCAEGnA9BHUyV7smUH4aNi2H4FRAW7Z7Y7KTLIAiL9b0ktjcpsmhDIOFKp3yPBc4fsyb0iQEgdZds/mBX6/cUsiGniNkpCfj5+d7/jhOTHH1UNigRp/D2mhzKq2q5wccqbU+lXQlCpdTXSqn0Ji4XA88AScBIYB/weFNtaK2f11ona62TO3d28qccgy6CmgqZwmmVZU+aKVnlh833/c8FXWu+F8bgGVBdBhnfWh2JsKO0/0JIFAz9mdWReIVTjFn1j/kDUAO83lQbTh+zlDJTOLN+aNlUV29RmA2B4WaTFqsEhcGQS2DrB1BV1vzj1r0KNUdh3C/cFpqt1PfRzKWmqll4nkKLNgQSbeaM91jg/DErOjyIQd07kppR0O62hGssSM0iIjiAn41xz0QIu+naMYSkzuHSR8VJ1dZpXlmRxdiETgyNi7Q6HFtpV4JQa32W1npoE5cPtNYHtNa1Wus64L/AOOeE3Aq9JpopnNtkCqcl4kbD23Pg47uhssRUy709x9wujIQpJgEk04zFiUr2wfaPYdS1vr3jtxOdbMwCUErNAS4ErtHajZmQxKlmw46DW932kpar38HY6srYEVdB1RHY9nHT99fWmGn+CVOg21D3xmYniVPNLICCVuz6LOyh+igc2Q+dEqyORLSCnd9jpSTFsCa7kIrqWne+rGiBAyUVfLJpHzOT44kI9rH1chtJSYolLfOwrJUpmvXt9oPkHD7KnJREq0OxHVfuYtx4n+hLgXRXvVaz/Pxh4AWw80uornD7y/u8xKlw0b9g6/+g6xD4+iGYucDcLgz/QDPNbcdnUFNldTTCTtYuMBstJd9odSQ+QSl1LnAPMENrXe7WF0+YYr5mLnXry1qqMMseUx57TTRxbFzU9P07PjHrFI6f69647KZ+3JZpxp6nKMd8tcP5JpzC6vdYKUkxVNXUsW5PoTtfVrTA6yuzqdWa2RMTrA7FUilJMZRX1bIpt8jqUIRNvbw8k+6RIUwf0tXqUGzHlWsQ/l0ptVkptQk4Hbjbha/VvEEyhdNS2vHJzYF0SL5JkoNNGTwDKoshy4eSA+LkaqtNgrDvWRCTZHU0vmI+0AH4Sim1QSn1rNteOaqnWYvQVxKEWttnyqOfH4yYZdaALM796f2rnoPIXjDgPPfHZifRfaBjvO/0UW9ih/U+hbNZ+h5rXGI0/n6KFTKF01Yqa2p5fdUezhjQhYTYcKvDsdSEPjEoBam7pI+Kn9qxv5TUjAKum9ibAH/v3bO3rVz2E9FaX6e1Hqa1Hq61nqG13ueq1zqphCkQEinTjK2S/p75OvnXsOZFeXPRlD6nQ1CETDMWx2z/2EwJG3uz1ZH4DK11X611T631SMfFvSVjidPMpjS1Nad+rKcrLzAf3NllyuOIWYCGTW8df/u+TeZ3Mu5mMyPBlykFfaaZnYx9aa1Mb1CYZb5KBaHXsPo9VoeQQIbHR8oabzbz8cZ9FJRVMUc2XKBTeBCDu3dkuWxUIpqwIDWL4AA/Zo3tZXUotuT9KdOAIMcUzk9NVY5wn8ylJtHRZTCc5Zhe/PYcSRKeKDAE+p0D2z+BOlnPRQBpL5iqpX5nWx2JcJfEqWat1n0brI7E9QodFU12SVhE94GeE2DjG8dvwrHqOQgMg9HXWRebnSROhaOHzYwA4TkKs8A/GCJkGpVwnpSkGDbmFHGk0gc+1PIAWmteTs2kb5cIJveNtTocW0hJimFddpGslSmOU1Rexfvrc7lkZBzR4UFWh2NL3p8gBDPNuKJYElPulpNm3mzVJzkSp5okYd46S8OypcEzoDwfslOtjkRY7cBWyF4GY2+UqiVf0rAOoQ+s8VaUZb7aacrjyKsgfyfsdYxPZfmw+W1TXRjaydrY7MIX18r0BkXZENXLTKcXwklSkmKpqdOszjxsdSgCWJtdSHpeCXNSElBWb/5lEylJsVTV1rE2W9bKFMe8uTqHiuo6qbQ9Cd/4byHpDAgMl2nG7pYwGXQt9Bx/7LbEqTD5LstCsq2+Z0NACGz7yOpIhNXWvGiqPUZdb3Ukwp0iOkOXIb6RfLFbBSHAkEvNebfhDXN97QKorYRxv7A0LFuJjIOYvr7RR72JXdb7FF5lTO9OBPn7kSpTOG3h5dQsOoQEcNnoOKtDsY2xjrUypY+KejW1dSxckc2EPtEM6t7R6nBsyzcShIEh0F+mcLpdzirzNX6ctXF4guAIsyHFto9kfSdfVlECG9+EoZdBeIzV0Qh3S5wKe1ZCTaXVkbhWUTaExZi/e3YREgkDL4D0d6CqDFa/aNaH7TLQ6sjsJXGqY61MWbLFYxRl2ysZL7xCSKA/o3tHyTqENrCv+Cifp+9n1tiehAUFWB2ObUQEBzBC1soUjXy97QB5RUeZk5JodSi25hsJQjDTjMsOmTdfwj1y0szaThGdrY7EMwy6CEr3Qt5aqyMR7rTsyWMVOZsWQ9URiBtjbhe+pc80qKmA3NVWR+JahTZMWCx7EroNh6OF8OEd5m9xwmQ5D0+UOM38jdq7wepIREscLTJL7EgFoXCBSUmxbN1XQmFZldWh+Jxnl2Q0VMYtXJGN1pqhPSJ5dkmGxZHZy6S+sWzKLaa0Qj7U8lWNz5WXlmcRFxVKRHCAnCsn4TsJwn7nmOlDMs3YPbQ2FYSNpxeLk+t/LvgFwrYPrI5EuFPcaLN5z+4lkPZfiE6C7x8ztwvf0jsFlJ/pC96syIZTHuNGw4r/QEgnSH8XIrrByqflPDxRwzqE31sahmihIhtO5xdeI6VvDFrDyt1SoeVuw+MjuX3Rer7fcZA30vYwulcnHvl4K8PjI60OzVYmJsVQW6dJk7UyfVb9ubJoVTZpmYeZNiCWX725Xs6Vk/CdBGFwBPQ9U6ZwukthpqnY7CnTi1ssNMpUEG376PidNIV3q9+8Z/G1kL8Djhww1xOnWh2ZcLeQSOgxyrvXeKurhaIc+yUs6s/DmqPmemWpnIdNCY+BrsO8u496k/r1PjslWBqG8E7D46MIC/KXKZwWSEmKZf7Vo/jl6+soLK9mx4FS5l89ipQk2cG4sdG9OhEU4Cd91IfVnyuPfLSVAD/FZ5v3y7lyCr6TIAQzzbgk79guhcJ1ctLMV6kgbJ1BM6AwC/ZvtjoS4U6JU6GzY62zsT+XpIQvS5wKeWug8ojVkbhG6T6oq7ZfBSGYn/2YOeb7cXIeNitxKuxZBdUVVkciTqW+gtCO55vweIH+foxLjJZNICySkhRLt8gQAOakJEjCowkhgf4k9+4kCUIfN7FPDH5KUVOnuW5CbzlXTsG3EoQDzgW/AJlm7A45qyC447Gkh2iZgReYKYbSR31L5lLYuxYie8L6hVKd48sSp0Jdjfeul1uYZb7arYIQzHm3+W2Yeg+sf03Ow+YkTjU7POemWR2JOJXCbAiOhNBOVkcivFRKUgwZh8o4UCIfGLjb8h/z2X2ojEHdO/D6qj2SqG1GSlIM2/aVcFjWyvRZ76zN5Wh1LWcO7MJrcq6ckm8lCEM7mQW2t34oUzhdLScN4pPBz9/qSDxLeCz0nmSmGQvfkLkU3pptpl6Ous5Ma3x7jiQnfFXPCeAfBJleug6hXac8Zi41593MBXDGH+Q8PJneKaD85WfjCYqyoVMvq6MQXqy+EmeFVGi5VWpGPre+vhYN/GJqEvOvHsXti9ZL4qMJE6WP+rTUjHwe+nALAA/PGCLnSgv4VoIQzE6xhZlwIN3qSLxXRQkc2CLTi9tq0Aw4tB0O7bQ6EuEOeetg/FzzfeLUY2uh5clSCD4pKAzix3lv8qUoG1CmWtZO8tYdv+agnIfNC+loNm/x1j7qTey4Y7jwKoO7dyQyNFDebLvZptxiLhzRHTAbcdSvs7Ypt9jiyOxnRHwkEcEB0kd91KbcYgZ370jP6FB6RofJudICvpcgHHihYwqnVGi5TN4aQMsGJW016ELzVXYz9g2T74LyfAgMg7gx5rbEqeZ24ZsSp8K+jVDuhbvuFWZDxzgICLI6kuNNvuunaw7Kedi8xKmQt9Zs5iLsSWtHBWGC1ZEIL+bnp5jYJ4bluwrQMjvLbeZOS2JvUQVJncPp2tGsQ5iSFMvcaUkWR2Y/AY61MqWC0DfdPKUPOw+UktLn2LqDcq6cnO8lCCM6Q68UM81YuEZOGqAgLtnqSDxTxx4QP1b6qC/JXAq9JtovaSKskTgV0JC93OpInK8oWzZM8Ab1a2Vmr7A6EtGcIwegpkIqCIXLpfSNIa/oKDmHj1odis+orq0jLfOwbLbQQilJMezOL2NfsfRRX7N1bwklFTWk9I2xOhSP4XsJQoDBM+DQNsj/0epIvFPOKug6xExDEm0zaAbs33RsQX/hvUoPmCnlfaZZHYmwi7gxpqLUG6dwypRH79BzvHevlekNCmUHY+EeKUnmjbdM4XSfTblFlFfVNvzsxclNdPycpIrQ99T/XZrYR86VlvLNBOFAxxTOrTKF0+nqaiF3jUwvbq/BM8xXmQrv/bJ+MF9PnN4ofFdAkKko9bYEYU0llO6ThIU3CAw1SUJv66PepMiRIJSEvHCxpM4RdO4QTKokX9wmdVcBSsEESXq0yKBuHYkKC2T5LumjviY1o4B+XSLo4piKL07NNxOEkXFm+qskX5zv0HaoLJENStqrUwJ0Gy7TjH3B7u8hJNL8voWolzjV/D0tPWB1JM5TlANoSVh4i8SpsH+zd66V6Q3qKwijZBdj4VpKKVKSYkjNkHUI3WV5Rj6Du3ekU7gsTdMS9WtlrsjIlz7qQ6pq6qfiSyK9NXwzQQimQmvfhmP/QAnn2LPSfJUKwvYbNANy06Bkr9WRCFfKXAoJU8DP3+pIhJ3UTzn3pgqtoizzVSoIvUPiNEAfq4IW9lKUBeFdzM7oQrjYpKRY8o9U8uPBI1aH4vUqqmtZl10kSY9WSukby97iCrILyq0ORbjJxtwijlbXMlHW6mwV300QDrrIfJUqQufKSYPwztAp0epIPF/9NOPtn1gbh3CdwiwzDUymF4sTdRtuKku9aY23Qpny6FXiRkNguHclsb1JoexgLNynfo231F2yDqGrrc0upKq2TjYoaaVja2XKNGNfcWwqfrTVoXgU300QRveBrsMkQehsOavM9GKlrI7E83UeALEDZK1Mb5Yp6w+KZvj5m8pSb0q+FGaZjS06dLc6EuEM/oHQO8W7+qg3kR3DhRv1jA6jZ3SoJF/cIDUjH38/xdhESXq0Rp/YcLp2DJbNdHxIakY+Q3p0JCpMpuK3hu8mCMFUaOWsgtL9VkfiHY4chMJMWX/QmQbPgOzlUCaDmVfKXGqmgHUeaHUkwo4Sp5o3+d6ym3lRNkT2BD/f/tfDqyROhfydULLP6khEY7U1UJwn1brCrVL6xLJydwG1dbLGmyulZhQwIj6SiOAAq0PxKGatzFhWZBRQJ33U6x2tqmX9niKptG0D3/4vfdAMQEsVobPkpJmvkiB0nkEXga6TacbeSGszfTRxqlTciqbVV5Zmeskab4VS0eR16vuorENoLyW5oGvlfBNuldI3hpKKGrbuLbE6FK9VWlHNptxiJvWVpEdbTEyKoaCsip0HS60ORbjYmuzDjqn4slZna/l2grDzAIjpB9tkp1inyFllpo91H2F1JN6j23BTASBJbO+TvxOOHJDpxaJ5nQeaClNvmcJZJGuieZ1uwyEkyrvWyvQGst6nsEDDOoQyhdNl0jIPU1unG37WonUa1iHcJVPhvV1qRgEBfoqxCTIVv7V8O0GolJnCmbUcyuQPRbvlpEH3kRAYYnUk3qO+j+7+Ho4WWR2NcKb6pI8kCEVzlDL9I3OJqTj1ZBUlcLRQEhbexs8PEqfA7qWe30e9SZEjQSgVhMKNunQIoV+XCJbLOoQuk5pRQFCAH6N7dbI6FI8U3ymM3jFhslamD0jNKGBkzyjCZSp+q7UrQaiUmqmU2qKUqlNKJZ9w3/1KqV1KqR1KqentC9OFBs0w0zB2fGp1JJ6tphL2roee46yOxPsMuhjqqmHnF1ZHIpwpcwlE9pKKKptRSv1GKaWVUvaYv5M41VSa5u+0OpL2kYSF90qcBsV7vGetTG9QmAXKHzrGWx2JaAdPfJ+VkhTD6szDVNXUWR2KV0rNKCC5dydCAv2tDsVjpSTFsGp3ATW10ke9VUlFNZtzi2R6cRu1t4IwHbgMOG7+k1JqMDALGAKcCzytlLLnX7LuIyCql0wzbq99m6C2UtYfdIW4MWbXT+mj3qOuzqwr10fWH7QTpVRP4Bxgj9WxNGhYh9DDpxnLlEfv5S191JsUZkNkHPhL5YSH87j3WROTYjlaXcvG3CKrQ/E6h8uq2LavRJIe7TQxKZbSyhrSZa1Mr5W2+zB12vyuReu1K0Gotd6mtd7RxF0XA29qrSu11pnALsCepWVKmSrC3d9DRbHV0XiunFXmq1QQOp+fn9msZNfXUFVmdTTCGQ5shooiU3kj7OSfwD2AfeZKdkowlaaevsZbQwVhgqVhCBeI7Q8RXSVBaCdF2ZKM9wKe+D5rQp9olJI13lxhhWNabIpsUNIuE/vIWpnebnlGPsEBfozuHWV1KB7JVWsQxgE5ja7nOm6zp0EzoLYKdn5pdSSeK2eV+We0QzerI/FOg2ZATQX8+JXVkQhn2O1I9iRMsTYO0UApdTGQp7XeaHUsx2lYh/AHU3nqqQqzIagDhMq6SV6noY/KOoS2ITuGezvbvs+KCgtiSI+OknxxgdSMfCKCAxgeF2l1KB6tc4dg+neNaEi4Cu+zIqOAsQnRBAfYorDa45wyQaiU+lopld7E5WJnBKCUukUptUYptebQoUPOaLL14sdCRDfY9oE1r+/ptDYJQple7Dq9UyAsVqYZe4vMpabqpmN3qyPxKacYz34PPNiCNtw/ZvWZZipOD2x2z+u5QpEjYSFT6r1T4jQoOwiHmip2Em5VVW5+F1Kt6xG88X3WpKRY1u8p4mhVrVtez1esyChgXGI0Af6+vceoM6QkxbI66zCVNdJHvU3+kUq27y+Vnb7b4ZR/YbTWZ2mthzZxOVk2LQ/o2eh6vOO2ptp/XmudrLVO7ty5c+uidxY/Pxh0IfwoUzjbpGiPWURfphe7jp8/DDzfbFRSXWF1NKI9aqshO1V2L7ZAc+MZsBtIBDYqpbIwY9Y6pdRPSqItGbPqK013e/A048IsmfLozRrWIfTgPuotihxLqEYlWBqGaBlvfJ81MSmGqto61mQfdsvr+YJ9xUfZnV8m6w86SUpSDBXVdWzYU2R1KMLJVu52TMWXc6XNXPURxIfALKVUsFIqEegHpLnotZxj0AyoOQq7vrE6Es+T4/jVSgWhaw26GKqOmPUyhefKWwfVZZIgtBGt9WatdRetdYLWOgEzXWu01nq/xaEZHbubilNPXeNNa5O0kCmP3qtTb5MA9tQ+6k1kx3BfYOv3WWMTognwU6TKFE6nqZ8OK1VRzjG+Twx+CumjXig1o4CI4ACGyVT8NmtXglApdalSKheYCHyilPoCQGu9BXgL2Ap8DvxSa23vGt7ekyA0WqZwtkXOKgiKgC6DrY7EuyVOheBI6aOeLnMpoGT9QdE6iVNN5WlttdWRtF7ZIagulwpCb5c4FbJ+gDp7/7vn9WTHcK/hqe+zwoMDGNkzSpIvTpSaUUCnsEAGdetodSheITI0kKFxkbJWphdakVHAeJmK3y7t3cX4fa11vNY6WGvdVWs9vdF9j2qtk7TWA7TWn7U/VBdbMR/ixpgpnDWV5rbMpbDsSUvD8gg5q8zPzj/A6ki828qnIW40bP/kWJJA+qjnyVwC3YZBWLTVkYhmOCoJ7fVfY+JUU3mat87qSFqvUCqafELiNKgohv2brI7EtxVlQ0AoRHSxOhLRTp78PislKYbNuUWUVHjgh1o2o7UmdVc+E5Ni8POTdXydZWJSDOv3FFFeVWN1KMJJ9hYdJTO/THb6bidJrdaLGw05K6GyxKzzlLkU3p5jbhfNqzwCB9JlerE7xI2GvDVms4KsZdJHPVH1UZNQl+nForUSpgDKM6dwFklFk09oWIfQA/uoNynMgqhesiGQsFRK31jqNKTtlnUI2yu7oJy9xRVMTJKkhzNNSoqlpk6zOqvQ6lCEk9RXLcv6g+0jCcJ6iVNh5iuAgm//ZBIvMxfIG/lTyVsLuk4ShO6QOBUuf9l8//XD0kc9Uc4qqK0ylTZCtEZYtKk89cRNIAqzzFepIPRuHbpC54GevZmON6jfMVwIC43qFUVwgB/LZQpnu0nSwzWSEzoR6K9kmrEXSc3IJzo8iAFdO1gdikeTBGFjfc+EroNh/2YYcbUkXlqifoOS+GRr4/AV/c42b8D2bYCR10gf9TSZS0H5Q++JVkciPFHiVJNkrj5qdSStU5QN4Z0hKNzqSISrJU6FPSugpsrqSHyT1mZKv1TrCosFB/gzNiG6YXMN0XapGfl07RhMn1gZQ50pLCiAUT07SR/1ElprVmQUMLGPTMVvL0kQNpa5FIrzzPer/yvTZFoiZyV0HgShUVZH4hsyl0KpY2PVNOmjHidzqVmvM1g+2RJtkDjNVKDmrLI6ktaRhIXvSJxqNqTJW2t1JL7paKFZKkcqCIUNTEyKYfv+UvKPVFodiseqT3qkJMWiZNkAp5uYFMPmvGKKy2WtTE+XVVDOvuIK2enbCSRBWK9+PbcrF8Lo680mEG9dLwmYk6mrg5zV0HOc1ZH4hsZ9dPgsqKuRPupJKkrMBhN9ZHqxaKPeE00Fqqed8zLl0Xf0noTHrpXpDWS9T2Ej9VNiV+6WCq222nGglIKyKkl6uEhKUgxaw8pM6aOebvkuM1VcpuK3nyQI6+WtO7ae22n3g38gdB/hmTtGukv+Dqgshl4TrI7ENzTuo6f/3ixAHjdG+qinyE4FXSvTwkXbBXcw57wnJV9qa6A4VxIWviIsGroP96w+6k0adgxPsDQMIQCGxUXSITigYQ090Xqpu2T9QVca2SuKkEA/mWbsBVZkFNA9MoREmYrfbpIgrDf5rmNv3Dv2gAm3moW2+55paVi2Vj/NTTYocY/GfbRTbxh7M2R8C/3PtTQs0UKZS8E/GOKl4la0Q59p5kOBihKrI2mZkjxT7SwVhL4jcRrkpkFVudWR+J76CkI534QNBPj7Mb6PrEPYHqkZBfSOCSO+U5jVoXil+rUyZaMSz1ZXp1mxu4CJSTEyFd8JJEHYnEl3QUgkfP2I1ZHYV04ahMVAdB+rI/FNU34DQRHwzZ+sjkS0ROZS6DUeAkOsjkR4ssSpphI1O9XqSFpGpjz6noa1MldaHYnvKcyGkCjz/6sQNjAxKZbM/DL2FnnY5lo2UFNbx6rdBVI96GIpSbHsPHCEQ6WyVqan2nGglMNlVaQkxVodileQBGFzQqNMAmbXVzJVpjk5q0z1oGTqrREeA5PuhB2fwB55I2ZrZQVwYLNMLxbtFz/OVKJ6yrhUKBVNPqfXBPAL8Jw+6k1kvU9hM/XJLZlm3Hpb9pZQWlnDREl6uNSxPipVhJ6q/u+LrNXpHJIgPJlxt0DHePjqIdDa6mjspawACnbJBiVWm3ArRHSTPmp3WT+Yr4myQYlop8AQU4nqKcmXomxQfhDZ0+pIhLsER0Bcsuf0UW8iO4YLmxnQtQPR4UGSfGmD5Y6f2cQ+kvRwpSE9OtIhJECmwnuw1F35JMSEERcVanUoXkEShCcTGGI2g9i7DrZ+YHU09pKbZr7K+oPWCgqH0+41U7l2fGZ1NKI5mUsgqAP0GG11JMIbJE41FallHvDPbGE2dIwzG38J35E4Ffauh4piqyPxHXV1ULRHKgiFrfj5KSb2iWFFRgFaPshulRUZBQzo2oHOHYKtDsWrBfj7MT4xRqpcPVRNbR2rMg+T0lcqbZ1FEoSnMmIWdB5k1nmrrbY6GvvIWWWmEPUYZXUkYtR1ENMXvnnE7Bgq7CdzKfROAf8AqyMR3qC+ErW+MtXOiqSiySclTgVd5zlrZXqDIwegtlLON2E7E5Ni2FdcQVaBbFzUUpU1tazOOixTJt0kJSmGPYfLyTksfdTTbM4r5khljazV6USSIDwVP38462E4nAHrXrU6GvvISYPuIyBQSnkt5x8IZz4Ih7bDxjesjkacqDjPTMeX9QeFs/QYbSpSPWEKZ2E2dEqwOgrhbj3HQUCIZ/RRb9Gwg3GCpWEIcaJJjsoemWbcchv2FFFRXSdJDzep76MrdksVoaepr/ycIFPxnUYShC3Rfzr0SoElf4OqMqujsV5tNeStlenFdjJoBsSNge/+D6plpzhbaVh/UBKEwkn8A0xFauYSqyM5ueqjcGS/THn0RQHBZrOS3Tbvo96kMMt8lQpCYTMJMWF0jwwhdZckX1oqNaMAPwXjJenhFv27RhATHkTqLklie5oVGQUM7NaB2AiZiu8skiBsCaXg7EfM9I2VT1sdjfX2b4KaCtmgxE6UgrP/BKV7Ie15q6MRjWUuhdBo6DrU6kiEN0mcaipTi/OsjqR5RTnmqyQsfFPiVDi4BY4csjoS31C/Y3hUL2vjEOIESikmJsWwYncBdXWyDmFLrMgoYGhcJJGhsn6vO9T30VRZK9OjyFR815AEYUv1HAcDL4Rl//KMheFdKcexQUm8JAhtJWEy9DsHfngcjhZaHY0As7N05lJInAJ+8udWOFF9Raqd1yFsmPIoCUKf5ElrZXqDomyI6GY22BPCZlKSYjlcVsWOA6VWh2J75VU1rM8plKSHm6UkxXKwtJKMQzJb0FOsyy6isqaOlCTZoMSZ5B1ra5z5IFSXwQ/zrI7EWjmrILInRMZZHYk40ZkPQUUJLPun1ZEIgMO7oThHphcL5+s61FSm2nmNN5ny6Nu6j4Tgjvbuo95E1vsUNla/lp7sFHtqq7MKqa7VTJKkh1vV99EVslamx1iRke+Yih9tdSheRRKErdF5AIy6FtL+e+yNjy/KSZPpxXbVbajZeXvls1Cca3U0ov6NcX0ljRDO4udnKlMzl5pKVTsqzAL/YIjoanUkwgr+AdB7kiQI3aUoW6p1hW31iAolMTZcki8tkJqRT6C/Ijmhk9Wh+JTeMWHERYVKEtuDpGYUMCw+io4hMhXfmSRB2Fqn3W92Nv7u/6yOxBrFuVCSJxuU2Nnpvwc0fP+Y1ZGIzKXQoTvE9LU6EuFtlj0JHXqYCtXDu81tmUvN7XZRlG3WQ5Pp9b5p2ZPQMQ4OZxz7wMpufdRb1Fab/82kWlfY1LNLMugTG86q3Yepqa0DTCLs2SUZFkdmPysyChjVsxNhQQFWh+JTnlu6m6Qu4cetlSl91L7KKmvYkFMkO327gPzX3lode8CEW2HTW7Bvk9XRuF/OKvNVKgjtK6oXjL0ZNiyCg9usjsZ3Naw/ONVsIiOEM8WNho1vmO8zl5rL23PM7XZRKBVNPi1uNKS/Y763ax/1FsU5oOvkfBO2NTw+klWZhymtrGFzXjGpGfncvmg9w+MjrQ7NVorLq0nPK5b1By0wPD6StdmFFJVXs3VfifRRm1uddZiaOi0JQheQBGFbTLoLQiLhm0esjsT9ctIgMEx2ZLW7Kb+BoAj45k9WR+K7Dm6D8nxZf1C4RuJUuOJVUH6w4imTeJm5wF79rShbKpp8WeJUmPkqoGD5v+zZR71Fww7Gcr4Je0pJimXezOEAPPbpdm5ftJ75V4+SzQVOsDKzgDqNJD0skJIUy99/Zvroo59skz5qc6kZBQT5+5HcW9YfdDZJELZFaJRJwOz6GnYvsToa98pZBXFjwF/m+ttaeAxMuhN2fArZK6yOxjc1rD8ob4aFi/SZZv4eF/wIw2baq68dLYKKYqlo8nVJ00zF4KHtMHyWvfqoN5Edw4UHOHdod3pGh5KWdZirxvWUxEsTVmQUEBLox6hesv6gFS4Y3oO4qBBW7C7gaumjtpaakc+oXlGEBvlbHYrXkQRhW427BTrGw9cP23eBeGerKjPTqmV6sWeYcCtEdIOvH/KdPmonmUugU6KZ8i2EK2QuhYJd5vu1r9hrM4giqWgSOPqoY/2mtS/Zq496k8Js8Aswaz4KYVOpGfkUllUD8EpqNqmyYclPpGbkMzYhmqAAeYtuhdSMfIqP1gCwQPqobRWVV7Flb4kkcF1E/vq0VWCI2Qxi7zrY+oHV0bhH3jrQtbJBiacICofT7jNVnzs+szoa31JbA1nLpFpGuE79em5XvAoDLzTJgbdn2ycBUz/lsVOCpWEIC9X30SsXQr9zzI7Wb8+xTx/1JkXZEBlvNtETwobq13N77toxJMSE0SMqhNsXrZcETCOHSivZeeCIJD0sUt9Hn7l2NHFRofSMDpU+alMrdx9Ga0jpK1PxXaFdCUKl1Eyl1BalVJ1SKrnR7QlKqaNKqQ2Oy7PtD9WGRsyCzoPMOm+11VZH43r1G5TEj7U2DtFyo64zO+h+84hJWgn32L8RKkskQehhlFJ3KKW2O8a1v1sdz0nlrTu2ntv4uVBVCiOvMbfbgUx5FCf20YoiGD3bPn3UmxRmSbWuF/Km91mbcouZf/UoJvWLZXZKAjsPHOHXZ/djU26x1aHZxordBYCsP2iV+j46pV9nrp/Ym237Srln+gDpoza0IiOf0EB/RsRHWR2KV2pvBWE6cBnQ1MfBGVrrkY7L3Ha+jj35+cNZD8PhDFj3qtXRuF5OGsQOgDBZDNRj+AfAmQ+Z9Z/qdzwVrifrD3ocpdTpwMXACK31EGCexSGd3OS7jvWvhMnQZQhkfGfWHrWDwmwIjoRQWUfJZzXuo0lnQGx/2G2jPupNZMdwb+U177PmTktqqIy7fEw84UH+rMsuYu60JIsjs48VGfl0CAlgSI+OVofikxr30SvH9iQk0I/1e6SP2lFqRgFjE2Uqvqu066eqtd6mtd7hrGA8Uv/p0CsFvv8rVB6xOhrXqauD3DRZf9ATDboI4pLhu/+D6qNWR+MbMpea6uKILlZHIlruVuCvWutKAK31QYvjaTmlYPwv4EA6ZC+3OhqjKBs6yfqbwkEps3bz3vWQu8bqaLxL5REoz5fp/F7IW99ndQgJZGZyTz7atJeDpRVWh2Mby3cVMD4xhgB/SXpYLSosiEtHxfO/DXkcLquyOhzRyMGSCn48eEQqbV3IlX+BEpVS65VSS5RSU5p7kFLqFqXUGqXUmkOHDrkwHBdRCs5+BMoOwspnrI7GdQp2wdFCWX/QE9X30dK9sOo5q6PxfjWVZufoPtOsjkS0Tn9gilJqlWPcanItBduOWcOvMNV6dhmHZMqjONGIq0xV6Sqb9FFvUbTHfJXzzdd49Pus6yf2prpWs2jVHqtDsYWcw+XsOVzOJFlTzTZumJRAZU0db66WPmon9VPxJ8lanS5zygShUuprpVR6E5eLT/K0fUAvrfUo4NfAIqVUk/XSWuvntdbJWuvkzp07t+0orNZznFkkfvm/oMxLFzKtX39QEoSeKWGyWSR+2RNQftjqaLxb7hqoOSrTi23oFONZABANTAB+B7yllFIntmHbMSswFMbMgR2fHtsgxCpam6SFVDSJxoIjYPR1ZmO3kr1WR+M9imRDIE/mq++z+nSO4PQBnXlt5R6qauqsDsdyx9YflKSHXfTv2oFJfWNYuCKb6lrpo3aRuquAjiEBDJap+C5zygSh1vosrfXQJi7Nbt2rta7UWhc4vl8LZGCqM7zXmQ9CdRkstfeyVW2Ws8pUp8T0tToS0VZnPgQVJbDsn1ZH4t0yl4Lyg96TrI5EnOAU41ku8J420oA6wLP+U0++CVCw+gVr4zhyAGoqpKJJ/NTYn0NdLax5yepIvEf9BwJyvnkkX36fNWdSIvlHKvl08z6rQ7HciowCYsKD6N81wupQRCNzUhLZV1zBl1sOWB2KcEjdnc+EPjH4+/3kM3zhJC6ZYqyU6qyU8nd83wfoB+x2xWvZRucBMOpa88asMMvqaJwvJw3ix4GfrIvhsboNNTtvr3oOinOtjsZ7ZS6F7iMgNMrqSETr/A84HUAp1R8IAjyrJDyqJwy6ENa9AlVl1sVRKDsYi2ZEJ8KA82DNy1Ata485RVE2BIZBuGd9niHazlveZ03pG0ufzuG8vDwTrbXV4VhGa01qRj4Tk2JoYuKCsNAZA7vQMzqUBamZVociMFPxcw4flfUHXaxd2R6l1KVKqVxgIvCJUuoLx11TgU1KqQ3AO8BcrbX3z2sMd5Tuf/d/x27LXArLnmx5G8uePLYDalvbcLbyw5C/QzYo8QYduoGuhe8fO3abFX3Um9porKoMclfL9GLP9BLQRymVDrwJzNae+I5l/K1QUQybFlsXQ5FUNImTGD/XbKqR/q7VkXiHwmxzrkliwet4+/ssPz/FDSkJbMwtZn1OkdXhWCbjUBkHSiplerEN+fspZk9MYHVWIel5xVaH4/NSM8zn9il95VxxpfbuYvy+1jpeax2ste6qtZ7uuP1drfUQrfVIrfVorfVHzgnX5vqcBn4B5o3Zvk0m0fD2HIgb3fI24kab59QnLdrShrPV7zgo6w96vqQzTB9d/zoc3GZdH/WmNhrbsxLqqiFRNijxNFrrKq31tY6pXaO11t9aHVOb9JoA3YabSmGr8psNUx5lF2PRhMSpZpf3Vc9a10e9SVG2VOt6KV94n3XZ6Hg6BAewYHmW1aFYZkV90kOqomxpZnJPwoL8edmH+6hdpGYUEBsRTL8uMhXflQKsDsCrJE6Fy1+CN6+G56YCGvwC4bXLW9eOroNXLoK4sVC4G2YusLYiKWcVKH9rk5TCORKnws9egsXXwNMTaXcfVf6mItEb2ojtD6X74YqFbT/fMpeYGHpNaNvzhWgvpUyF1ge3mf7Y5zT3x1CUBeFdICjM/a8t7E8pGP8L+Pgu86FK74lWR+S5tDYJeVnzVnio8OAArhjbk1dSs/jDBYPo2jHE6pDcLjWjgLioUHrHyJhpR5GhgfxsdDyLV+dw//kDiY0Itjokn2Sm4heQIlPxXU4ShM428HwYeQ1seA3ix7f9H99tH0Heahg20/rpijmroNswCAq3Ng7hHIMugJFXwYZF7euj2Ssgd5V3tLH7O9i30Xz/wW0w8mqzpmhrd4XMXArxY+VcEdYa+jP46kFY+aw1CcLCbNlRVZzc8Cvh64dh1TOSIGyPo4VQVSoVhMKjzZ6YwEvLM3ltZTa/OWeA1eG4VV2dZsXuAs4a1FWSHjY2OyWBhSuzeWPVHu44s5/V4fikjENHOFRaKZW2biAJQmfLXAo7P4Op98CaF+HMP7Y+wZe5FNYvhNBos0bPsJnQf7pr4j2V2hrIWwujrrPm9YXzZS6FnV84p496UxuTfwNpz0NEV7Mb+dJ/mKnCo6+HgRdC4Ck+1T5aaJKMU+9p3esL4WyBIZB8g+nHh3dDdB/3vn5RtixJIU4uKAzGzIbU+VCUYzbYEa1X6Fg4X9b7FB6sV0wYZw7syqJVe/jl6X0JCfS3OiS32ba/hKLyakl62FzfLhFM7d+ZhSuz+cW0JIICZNNOd0vNKACQtTrdQHq3M9WvXzZzAZzxB/O18fpmrW3jqjfM9Me3rm9dG850IB2qy2WDEm/h7D7qTW2c9SBctcgkNy5/EU7/g3nz9e5N8PgA+PR3Zm3R5mSnmvPV6opfIQCSbwI/f0h7wb2vW1sNxXmSsBCnNvbngDYf7oi2adgxPMHSMIRorxsmJVBQVsXHm/ZZHYpbpe4ySY+JkiC0vRtSEjhYWsln6b7VR+1i+a584qJC6RkdanUoXk8ShM6Ut+749QITp5rreeva1kavCTD2ZqipgK0fuiDgk6jf3TUnzVzvOd763ZRF+zm7j3prG0U5MO0e+NVGuP4D6HsWrH0Fnpti1hdN+y8cLTp+F+TMpRAQas5XOU+E1Tp2h8GXmOrYylL3vW5xrlkPVKY8ilOJ6mWqs9cugKpyq6PxTPU7hsv5JjxcSlIM/btG8PLyTLSXb1707JKMht1YUzPy6RMbTmZ+Gc8uybA4MnEy0/p3JjE2nAWpWVaH4jPqz5XaOs3K3YdJSYphxe4COVdcTBKEzjT5rp9WDyVONbe3tY2zHoKO8ZD1A9RUOiHIFqrf3XXbR9Chh5mmZvVuyqL9XNFHvbkNPz+zhtvlL8JvtsN5f4e6Ovj0t6aqMOM7ePMayFgCu5dAl4Hw3s1yngh7GD8XKktg45vue836hIVUEIqWGD/XLM+w+W2rI/FMhdlmOZrgDlZHIkS7KKWYk5LIlr0lrMkutDoclxoeH8nti9bzw4+HSMs8TJ/O4dy+aD3D4yOtDk2chJ+fYvbE3qzfU8SGnCKrw/EJ9efK4tV7KD5aTZcOwXKuuIEkCO0uuANc9CQc2g4/POG+102cCpe/DNnLTAzv3GD9bspCWCks2uy8OfcHuOV7sxnR3vUmAfPaJXBoGxzaIeeJsI/4ZOgxGlY9ZxLb7lAoFU2iFXqnQNdhpo96edWQSxRly7kmvMYlo3oQGRrIguVZVofiUilJscy/ehS/fH0dZVW1rNx9mPlXj5K11TzAz8bEExEcwILlmVaH4hPqz5U/f7INgNdX7ZFzxQ0kQegJ+p1tdvz74XE4sNV9r3sg3ayplr/DrGclSQ8hQCnoMQoufMJUFV76PHSMM/eNmCXnibAPpWDCrVDwI2R8657XLMoG5W8q34U4FaVgwlw4uMXMlBCtU5gt1brCa4QFBTBrbE8+37KfvUVHrQ7HpUb36kRYkNmM5apxPSXh4SE6hAQyMzmeTzbv42BJhdXh+ISencIaPkC8fmJvOVfcQBKEnmL6YxDSET68HepqXf962z6CL34P/kEw5XdmEXGrNkoRwq6Cwsxab9XlMPV3sPUDOU+EvQy+xOzMvepZ97xeYTZExoF/gHteT3i+oZdDWIypIhQtV1cHxTlSQSi8ynUTe6O1ZuHKbKtDcZm6Os3sl9LYX1LJ+UO78e66vIY1CYX9zZ6YQE2d5vVVe6wOxesVl1dz5XMrqKiu49oJvXht1R45V9xAEoSeIjzGrH+Wt9b1b/Ty1sLbN4JfAFy5CM78Y9t2iBXC2x23k7KcJ8KGAoIg+UbY9RXk73L96xVJRZNopcAQGDMHdnwKhVlWR+M5SvdBbZWcb8KrxHcK45zB3XgjbQ8V1W4oiLDAnW9uYFXmYa4e35Onrx3D/KtHcfui9ZL48BAJseGcPqALr6/KprLGO/uoHVTV1HHVf1eyt7iCP144iL9cMkzOFTeRBKEnGfoz6H8ufPsXOOyitQ8Ks2HRLFMZdfnL0P9sc3tbdogVwts5YydlIVwt+UbwC4Q0N1RoFcqaaKINxv4cUGaHeNEysoOx8FJzJiVQVF7N/9bnWR2K072RtoePNu3lzIFdePSSYcCxddY25RZbHJ1oqRsmJZB/pIpPNu2zOhSvpLXm/vc2s3VfCbeelsRNk/sAcq64iyQIPYlScMETZn2nj+9y/oLeR4tg0RVmt+SbvoTBM46/v7U7xArh7Zyxk7IQrhbRxXzAtGERVLjwn6qqcig7CFEJrnsN4Z069oDBF8O6hVB5xOpoPEP9hkByvgkvMz4xmoHdOrAgNQvtRZsXLd15iD/+L51p/Tvz3HVjUEo13JeSFMvcaUkWRidaY3LfWPp2ieDl5d7VR+1i/re7eHddLnee2Y97zx143H1yrrieJAg9TWQcnP0I7P4eNrzuvHZrq+Ht2VCwC65cCJ0HOK9tIYQQ1hr/C6g6YpKErlLkWI9HKppEW4yfC5XFsGmx1ZF4hqJsQEFUT6sjEcKplFLcOCmR7ftLWbn7sNXhOMX2/SXc9vo6+nWJYP7Vowjwl7fgnkwpxeyUBDbnFbNuT5HV4XiVDzbk8fhXO7lsVBx3ndXP6nB8kvx18kRjboDek8wmIqUH2t+e1vDx3SbpeNG/oc+09rcphBDCPuJGQ8/xZiOIujrXvEbDlMcE17QvvFvPcWaH+FXPOX+GhDcqzIIO3SEg2OpIhHC6GSN70CkskAWpLlpSyY0OllRw48urCQ/25+UbxtIhJNDqkIQT/Gx0HB1CAnh5uef3UbtIyzzM797exPjEaB772bDjqmyF+0iC0BP5+ZlEXnUFfPrb9re37AlYv9Dswjrqmva3J4QQwn7G/wIKM+HHL13Tfv0GE7JpgmgLpUwVYf4O2P2d1dHYX2G2JOOF1woJ9Oeqcb34ausBcg6XWx1Om5VX1XDTK2soOlrNi7PH0j0y1OqQhJOEBQUwa2xPPkvfz77io1aH4/F2HzrCLQvXEB8dynPXjSE4wN/qkHyWJAg9VWxfOO0+2PYhbP2w7e2kvwvf/AmGzYTT/+C8+IQQQtjLoBmm4mjVs65pvzAbAkLNmodCtMWQSyG8s6kiFCdXJBsCCe927YTeKKVYuDLb6lDapLZO86s31rNlbzH/uWoUQ+MirQ5JONn1ExOo05rXV+6xOhSPdrisihsXrMZPKV6eM5aosCCrQ/JpkiD0ZCl3QLfhporwaGHrn79nJbx/K/SaCBc/ZT69F0II4Z38A2HsTaY66+B257dflA1RvWQsEW0XEGx23d75BRRkWB2NfdVUQsleqdYVXq1HVCjnDu3Gm2l7KK+qsTqcVvvLJ1v5ettBHrpoCGcO6mp1OMIFekaHcdagrixK20NFda3V4Xikiupabnl1DXuLK/jv9WPoHRNudUg+TxKEnsw/EC6eD2X58OUDrXtuQQa8cRVExsOsRbKGjRBC+IIxN4B/MKS5oEKrUCqahBMk3wh+AZD2X6sjsa/iXEDL+Sa83g0pCZRU1PDeujyrQ2mVBcszeXl5FjdOSmR2SoLV4QgXumFSAofLqvhw416rQ/E4dXWa372ziTXZhTxxxQjG9I62OiSBJAg9X/cRppJw/UKzyUhLlB+G12ea7695G8LkZBRCCJ8QHmuWlNj4Ztsqz5ujtaOCUBIWop06dDNTjde/BpWlVkdjT7Lep/ARY3p3YmhcRxakZqE9ZPOir7ce4E8fb+XswV35wwWDrA5HuNjEPjEM6NqBBcs9p4/axRNf7eSjjXu559wBXDi8h9XhCAdJEHqD0+6D6CT46E6oKjv5Y2sq4c1roDjHVA7GJLknRiGEEPYw/hdQXW4SMM5ytBAqS6SiSTjH+LlQVQob3rA6Entq2DFczjfh3ZRS3JCSyK6DR1i+q8DqcE4pPa+YO95Yz5Aekfxr1kj8/WTJDW+nlGLOpAS27ithdZYTP3j1cm+tzmH+d7uYNbYnt06TfISdSILQGwSGwoz/mE+Uv/u/5h+nNXxwO+xJhUuegd4T3RaiEEIIm+g+HHpPgrTnoc5Ja+bUJyykokk4Q/wYiB9rNtSpq7M6GvspzAa/QLPpkBBe7sIR3YmNCOLl5ZlWh3JSe4uOcuOC1USHB/Hi7GTCggKsDkm4ySUj44gKC7R9H7WLZT/m8/v3NzOlXyx/vmQoStauthVJEHqLhElm3Z6VT0Pu2qYf8/1jsPktOOMBGHa5e+MTQghhH+N/AUV7YMdnzmmvUCqahJONnwuHMyDjG6sjsZ+ibIjqCX7+VkcihMsFB/hz9bhefLvjIFn5p5gpZZHSimpuXLCao1W1vDRnLF06hlgdknCj0CB/Zo3txRdb9pNXdNTqcGxt54FSbn1tLUmdI3jqmtEE+ks6ym7kN+JNznoEIrrBh7dDTdXx9214A5b8DUZdC1N+Y018Qggh7GHABRDZ01RoOYNUEApnGzTD/E/jrD7qTQplvU/hW66Z0Bt/pXh1RbbVofxEdW0dv1y0nh8PHuHpa0czoFsHq0MSFrhuYm+UUiy0YR+1i4OlFdzw8mpCgvx56YaxdAwJtDok0QRJEHqTkI5w4RNwcCssf/LY7Zk/wId3QOI0uPBJkDJeIYTwbf4BMPbnkPUD7E9vf3uF2RASCaFR7W9LCICAINNHd30Nh3ZaHY29FMmO4cK3dO0YwgXDu/P2mhyOVNZYHU4DrTUPfbiFpTsP8eglQ5nSr7PVIQmLxEWFMn1IV95I28PRKict3+JFjlbVcvMrazhcVsWLs5OJiwq1OiTRjHYlCJVS/1BKbVdKbVJKva+Uimp03/1KqV1KqR1KqentjlS0zIDzoOtQ+P6vcHA7HNoBi68xuwL2TgF/ydQLIcSJlFIjlVIrlVIblFJrlFLjrI7J5arLwS8I0p47dlvmUlj2ZMvbWPakeU5RNnRKaFsbQjSntgr8Asx6mfXa2kcb88Q26p9fWQrlBeZ8k3PNq8n7rONFhgZSWlnDu2tzG25Lzcjn2SUZLW7j2SUZpGbkH3dba9o48fnPL93NolV7mNAnmlnjerU4DuGdOoUFUXy0mv9tyGu4zeo+akUMJ7ZRW6e5a/F6NuYWc96wbgyPj2pxO8L92ltB+BUwVGs9HNgJ3A+glBoMzAKGAOcCTyulZKEUd5l2L+g6WHwtvD4TUFB1xCQIhRBCNOXvwCNa65HAg47r3i1hMvgpswRF+WGTbHh7DsSNbnkbcaPNcw5uNVMe29KGEM1JnALKH9YthIri9vXR+uScp7ZR//z098z1qnI517yfvM9q5Nyh3fD3Uzz7fQZ1dZrUjHxuX7Se4fGRLW5jeHwkty9a35C4aG0bjZ//6eZ9PPbZdoL8/bjj9H5tOibhXS4Y1h1/P8XT3+1Ca+v7aFue74o2Hvt0G19sOUBYkD+Xj4lvcRvCGkpr7ZyGlLoUuFxrfY1S6n4ArfVjjvu+AB7WWq84WRvJycl6zZo1TonH5339CCx7AlQABIfDla9B4lSroxJCiBZTSq3VWie76bW+AF7SWi9WSl0FXKS1vvpkz/GKMWvdQrNubXBHqCqDyHgICm9dG1VlpoIwfiwc3g0zF8h4I5xnzUvw8d3t76PFuRDaCY4Wem4bVWVQnGM+BA6JlP/tbMaVY5a8zzLmfbGd+d9lEBUWSOnRGnpEhbR6t+Dyqhr2FlUQFRZIUXl1q9uof75G46cUL8xO5rQBXVp7KMJLPfbpNp5buptOYYGUWNxH2/p8Z7aRV3SUOg3BAX68PGcsKX1jW9WGcJ3mxixn7r9+I7DY8X0csLLRfbmO25oK7BbgFoBevaQ022nOfBBy0yBrGYz7hfwDKYQQJ3cX8IVSah6mur7JkmuvG7NGXwcb34TsZRDbDzoPbFs7ASGQuxqm3iPjjXCu5Bth09uwJ7WdfTQI8n/0/Dbqn5/8cznXfIu8zwJ+dWZ/vt95iPS8EpI6h9OvS9s2BAkK8CPjUFmb26h//o2TEiQ5KI7z63P6s2xXPlv22qOPWhlD4zZunpIoyUEPccoEoVLqa6BbE3f9QWv9geMxfwBqgNdbG4DW+nngeTCfbLX2+aIZWT/AwW3mzdqaF800HflHUgjhw042ngFnAndrrd9VSl0BvAicdeIDvW7MylwKhxqNFeNuaf1YUT9VUsYb4QqZSyF/h3P7qKe2ceLzk06Tc83Dyfus1lmTfZi9RRX86oy+vLZqD9en9CYlqXVJh/rpkm1t48TnnzGoS6tjEN5rbXYh+4rt1UetiKGpNlL6xsq54gm01u26AHOAFUBYo9vuB+5vdP0LYOKp2hozZowWTrB7idZ/SzRfm7ouhBAeAFij2zlGtfQCFHNs2Q0FlJzqOR4/ZjljrJDxRriSXfqoHdqQc832XDFmyfusY5bvOqRH/elLvXzXoSavu6MNZ8QgvJe39FG7tCFcq7kxq727GJ8L3APM0FqXN7rrQ2CWUipYKZUI9APS2vNaohXy1h2/BlTiVHM9b52VUQkhhJ3tBaY5vj8D+NHCWNzDGWOFjDfClezSR+3QhpxrPkfeZx1vU24x868e1VCBlJIUy/yrR7Ept9htbTgjBuG9vKWP2qUNYY12bVKilNoFBAMFjptWaq3nOu77A2a9jBrgLq31Z6dqzxsWzxVCCOEcbt6kZDLwL8zSGxXAbVrrtSd7joxZQggh6jl7zJL3WUIIIVzFJZuUaK37nuS+R4FH29O+EEII4Q5a62XAGKvjEEIIIUDeZwkhhHC/dk0xFkIIIYQQQgghhBBCeDZJEAohhBBCCCGEEEII4cMkQSiEEEIIIYQQQgghhA+TBKEQQgghhBBCCCGEED5MEoRCCCGEEEIIIYQQQvgwSRAKIYQQQgghhBBCCOHDJEEohBBCCCGEEEIIIYQPU1prq2NooJQqBXZYHYeTRALFVgfhJN5yLN5yHCDHYkfechxgn2MZoLXuYHUQzZExy7a85Vi85ThAjsWOvOU4wD7HImOW+9jld95e3nIcIMdiR95yHCDH4gpNjlkBVkRyEju01slWB+EMSqnntda3WB2HM3jLsXjLcYAcix15y3GAfY5FKbXG6hhOQcYsG/KWY/GW4wA5FjvyluMA+xyLjFnuY5ffeXt5y3GAHIsdectxgByLi+JocsySKcau85HVATiRtxyLtxwHyLHYkbccB3jXsYiW8abfubcci7ccB8ix2JG3HAd417GIlvGW37m3HAfIsdiRtxwHyLG4jd2mGK/xlk+2hBBCtI/dxwS7xyeEEMJ97D4m2D0+IYQQ7tPcmGC3CsLnrQ5ACCGEbdh9TLB7fEIIIdzH7mOC3eMTQgjhPk2OCbaqIBRCCCGEEEIIIYQQQriX3SoIbUspda5SaodSapdS6j7HbS8qpTYqpTYppd5RSkU089z7Hc/boZSafrI2LToOpZR6VCm1Uym1TSn1q2aeO1sp9aPjMrvR7WOUUpsdbf5bKaUsPJYzlFLrlFLpSqlXlFJNbsRjp2NRSr2klDqolEpvdNs/lFLbHX3rfaVUVDPPbbIPKaUSlVKrHLcvVkoFufo4HK/b1LE8rJTKU0ptcFzOt/uxNHMcI5VSKx3HsEYpNa6Z59qmbzlet6dS6jul1Fal1Bal1J2O22c6rtcppZqdcmSn34tomWb+NnrceHWSY1FKxizLjqWZv48eN2Y1cxweN16d5Fg8bsxSMl75pGb+NsqYdex2K85FrxivHK8rY5aNjuMkxyJj1rHbLfm9NNBay+UUF8AfyAD6AEHARmAw0LHRY54A7mviuYMdjw8GEh3t+DfXpkXHcQPwKuDneFyXJp4bDex2fO3k+L6T4740YAKggM+A8yz8neQA/R2P+RNwkwccy1RgNJDe6LZzgADH938D/tbSn4HjvreAWY7vnwVuddO50tSxPAz8ti2/T6uOpZnj+LK+PwDnA9/bvW85Xrc7MNrxfQdgp+NcGQQMAL4Hkj3h9yKXFv2+vWK8OsWxyJglY5arjuNhPGy8OsmxeNyYhYxXPnc5yd9GGbMsOhdPchweN145XlfGLBsdx0mORcYsm4xZLq0gbObThxZlRJW9PhEaB+zSWu/WWlcBbwIXa61LHDEpIBRoar72xcCbWutKrXUmsMvRXpNtWnEcwK3An7TWdQBa64NNPHc68JXW+rDWuhD4CjhXKdUdM4iv1KYXvwpc4uLjaO5YfgZUaa13Oh7zleO2E9nqWLTWS4HDJ9z2pda6xnF1JRDfxFOb/H06+uMZwDuOx72Ce34nTR5LC9nqWJo5Dg10dHwfCext4qm26lsAWut9Wut1ju9LgW1AnNZ6m9Z6xymebqvfi6t5yZjlLeNVs8eCjFkyZjmBt4xX4D1jloxXLecl4xXImFXPTuei14xXIGMWNjsOkDHLwXa/l3ouSxAqpfyBp4DzMJnUq5RSgzFZ+n9qrfsChcBNTTx3MDALGAKcCzytlPI/SZuuFof51KReruM2lFIvA/uBgcB/HLfNUEr96RTPbbZNF2ruNZOAKx3lvJ8ppfoBKKWSlVIvnOK5cY7vT7zd1ZqKpxsQ0KiU93KgJ9j+WE7lRswnISileiilPnXc3txxxABFjQY+OxzH7cqU8b+klOoEHnksdwH/UErlAPOA+8Gz+pZSKgEYBaw6yWM87ffiFF40ZnnLeHWyeGTMstHflSZ4+pjlDeMVePiYJeNV87xovAIZs+x4LvrSeAUyZtnlOO5Cxixb/F5cWUHY3KcoLcmI2u0ToWZprW8AemAyxlc6bvtQa/2glXG1UjBQoc021/8FXgLQWq/RWv/c0shaR2P+6fmnUioNKAVqwSOPBQCl1B+AGuB1AK31Xq11k+tL2NgzmH+ORgL7gMfBI4/lVuBurXVP4G7gRfCcvqXM+j3vAnfVfzLfFA/8vTiL149ZXjJegYxZtuUFY5a3jFfgwWOWjFen5PXjFciYZTNeN16BjFk2I2OWTbgyQdhcVrTJjKjNPxHKw/EpiUO84zYAtNa1HCu/bulzT9qmizT3mrnAe47b3geGt+K5eRxflu2O42g2Hq31Cq31FK31OGApZi2AFj0X646lSUqpOcCFwDWOcukTNXccBUCUOrZ4sKXHobU+oLWu1WZqxX8x/4SeyBOOZTbHzpO3ad1xWNq3lFKBmIHrda31e6d6fCOe8HtxFm8Zs7xlvDpZPDJm2eDvyom8YczyovEKPHTMkvGqRbxlvAIZs071XCvORa8fr0DGLGx0HA4yZtnk92KbXYxt/onQaqCfMmt7BGE+QflQKdUXGtbHmAFsb+K5HwKzlFLBSqlEoB9mEc0m27TiOID/Aac7HjONpv/gfwGco5Tq5ChfPgf4Qmu9DyhRSk1w/ByuBz5w8XFA87+TLgBKqWDgXszCnnY/lp9QSp0L3APM0FqXN/OwJn8GjkHuO0z5P5g/uJYcB4Ay60LUuxRIb+JhnnAsezHnB5hP6X9s4jG261uO13sR2Ka1fqKVT/eE34slbDxmect41eyxIGOW1cfyE94yZnnReAUeOGbJeOUaNh6vQMasenY6F716vAIZs7DZcTjImGWX34t23a4uEzG/sPrr9zsu+RzbNei4x5z42EbXv3A8tsk2XXUMJ8R0PuYPegbwB0xydTmwGXMyvo5jxy3MQPanRs/9g+N5O2i0o86JbVpxHI7booBPHMeyAhjhuD0ZeKHRc2/ETEXYBdzQ6PZkx88gA5gPKAuP5R+YqQg7MGW+2P1YgDcwZeHVmE8Zb3LElQNscFyedTy2B/DpqfoQZkekNEc7bwPBbvqdNHUsCx19axPmH6Xudj+WZo5jMrAWs8vUKmCM3fuW43UnY6aGbGrUn87H/CORC1QCB3D8bbXz78XFPyevGbNO/J3hoeNVc6+LjFkyZrnuODxuvDrJsXjcmIWMVy39OXnNeNXU7w0Zs+xwLnrFeOV4XRmzbHQcJzkWGbMs/r3UX5QjCKdTpixyJ3AmpixyNXA1Zjvud7XWbyqlngU2aa2fPuG5Q4BFmNLSHsA3mE+FVFNtaq23uOQghBBC+AQZs4QQQngCGa+EEEK4isumGGuzBsbtmE+mtgFvOQaZe4FfK6V2YXZpeRGOXx/D8bi3gK3A58AvtZlf31ybQgghRJvJmCWEEMITyHglhBDCVVxWQSiEEEIIIYQQQgghhLA/22xSIoQQQgghhBBCCCGEcD9JEAohhBBCCCGEEEII4cNckiBUSp2rlNqhlNqllLrPcdvtjutaKRV7kueeppT62BVxCSGEECeSMUsIIYSnkDFLCCGEqzg9QaiU8geeAs4DBgNXKaUGY7arPwvIdvZrCiGEEG0hY5YQQghPIWOWEEIIV3JFBeE4YJfWerfWugp4E7hYa71ea53VmoaUUuOUUiuUUuuVUqlKqQGO2+copd5TSn2ulPpRKfV35x+GEEIIHyBjlhBCCE8hY5YQQgiXCXBBm3FATqPrucD4Nra1HZiita5RSp0F/B/wM8d9I4FRQCWwQyn1H611TtPNCCGEEE2SMUsIIYSnkDFLCCGEy7giQehMkcArSql+gAYCG933jda6GEAptRXozfEDphBCCOFOMmYJIYTwFDJmCSGEOI4rphjnAT0bXY933NYkpdQXSqkNSqkXmrj7z8B3WuuhwEVASKP7Kht9X4v9k51CCCHsR8YsIYQQnkLGLCGEEC7jij/2q4F+SqlEzIA1C7i6uQdrraefpK1Ijg16c5wVoBBCCOEgY5YQQghPIWOWEEIIl3F6BaHWuga4HfgC2Aa8pbXeopT6lVIqF/NJ16ZmPskCk7Ss/9Tq78BjSqn1yCdXQgghnEzGLCGEEJ5CxiwhhBCupLTWVsdwHKXUnUCc1voeq2MRQgghTkbGLCGEEJ5CxiwhhBAnY6tPi5RSLwJDgSusjkUIIYQ4GRmzhBBCeAoZs4QQQpyK7SoIhRBCCCGEEEIIIYQQ7uOKXYyFEEIIIYQQQgghhBAewqUJQqVUT6XUd0qprUqpLY51L1BKRSulvlJK/ej42slx+zVKqU1Kqc1KqVSl1IhGbb2klDqolEp3ZcxCCCF8k4xZQgghPIWMWUIIIZzN1RWENcBvtNaDgQnAL5VSg4H7gG+01v2AbxzXATKBaVrrYcCfgecbtbUAONfF8QohhPBdMmYJIYTwFDJmCSGEcCqXJgi11vu01usc35cC24A44GLgFcfDXgEucTwmVWtd6Lh9JRDfqK2lwGFXxiuEEMJ3yZglhBDCU8iYJYQQwtnctgahUioBGAWsArpqrfc57toPdG3iKTcBn7knOiGEEOIYGbOEEEJ4ChmzhBBCOEOAO15EKRUBvAvcpbUuUUo13Ke11kopfcLjH0heSwAA+vlJREFUT8cMXJPdEZ8QQghRT8YsIYQQnkLGLCGEEM7i8gpCpVQgZtB6XWv9nuPmA0qp7o77uwMHGz1+OPACcLHWusDV8QkhhBD1ZMwSQgjhKWTMEkII4Uyu3sVYAS8C27TWTzS660NgtuP72cAHjsf3At4DrtNa73RlbEIIIURjMmYJIYTwFDJmCSGEcDaltT71o9rauFKTgR+AzUCd4+bfY9bHeAvoBWQDV2itDyulXgB+5rgNoEZrnexo6w3gNCAWOAA8pLV+0WXBCyGE8CkyZgkhhPAUMmYJIYRwNpcmCIUQQgghhBBCCCGEEPbmtl2MhRBCCCGEEEIIIYQQ9iMJQiGEEEIIIYQQQgghfJgkCIUQQgghhBBCCCGE8GGSIBRCCCGEEEIIIYQQwodJglAIIYQQQgghhBBCCB8mCUIhhBBCCCGEEEIIIXyYJAiFEEIIIYQQQgghhPBhkiAUQgghhBBCCCGEEMKHSYJQCCGEEEIIIYQQQggfJglCIYQQQgghhBBCCCF8mCQIhRBCCCGEEEIIIYTwYZIgFEIIIYQQQgghhBDCh0mCUAgnUEolKKW0UirATa93RCnVp71xKaUeVkq95vwIhRBCeJuWjj1CCCGEEMLzSIJQ+BSlVJZS6qjjTU6hUuoTpVRPF7/m50qpPzVx+8VKqf1tSSpqrSO01rudE6EQQghfdcK4eEAptUApFdHUY2XsEUIIIYTwXpIgFL7oIq11BNAdOAD8x8Wv9wpwrVJKnXD7dcDrWuualjbkrgpFIYQQPqV+XBwNJAN/bHynjD1CCCGEEN5PEoTCZ2mtK4B3gMH1tymlIpVSryqlDimlspVSf1RK+Tnu83Ncz1ZKHXQ8LrIFL/U/IAaY0uh1OgEXAq8qpcYppVYopYqUUvuUUvOVUkGNHquVUr9USv0I/Njotr6O7y9QSq1XSpUopXKUUg83EcONSqm9jvZ/21ygSqkJSqlURywblVKnNbpvjlJqt1KqVCmVqZS6pgXHLoQQwkNorfOAz4ChLRh7QpVSjzvGxGKl1DKlVKjjPhlLhBBCCCE8jCQIhc9SSoUBVwIrG938HyAS6ANMA64HbnDcN8dxOd1xfwQw/1Svo7U+CrzlaKveFcB2rfVGoBa4G4gFJgJnAred0MwlwHgaJTMbKXO0HQVcANyqlLrkhMecDvQDzgHuVUqddWIjSqk44BPgL0A08FvgXaVUZ6VUOPBv4DytdQcgBdhwqmMXQgjhORxLbpwPrHfcdAnNjz3zgDGY8SAauAeok7FECCGEEMIzSYJQ+KL/KaWKgGLgbOAfAEopf2AWcL/WulRrnQU8jpkKDHAN8ITWerfW+ghwPzCrhVOvXgEuV0qFOK5f77gNrfVarfVKrXWN4zWfwyQnG3tMa33YkWw8jtb6e631Zq11ndZ6E/BGE89/RGtdprXeDLwMXNVEjNcCn2qtP3W09RWwBvNmEaAOU1USqrXep7Xe0oLjFkIIYX/14+IyYAnwf47bmxx7HJX1NwJ3aq3ztNa1WutUrXUlMpYIIYQQQngkSRAKX3SJ1joKCAFuB5YopbphKvgCgexGj80G4hzf92jivgCg66leUGu9DMgHLlFKJQHjgEUASqn+SqmPHRuWlGDemMWe0EROc20rpcYrpb5zTIsuBuae4vnZjmM5UW9gpmNKWJHjzeJkoLvWugxTbTkX2OfY3GXgqY5bCCGER7hEax2lte6ttb6tUUKwubEnFjOGZjRxn4wlQgghhBAeSBKEwmc5Kh7ew0zxnYxJ4FVj3tzU6wXkOb7f28R9NZiNTlriVUzl4LXAF1rr+uc9A2wH+mmtOwK/B07c0ESfpN1FwIdAT611JPBsE89vvFNzL8exnCgHWOh4k1h/Cdda/xVAa/2F1vpszOYu24H/nvxwhRBCeLjmxp58oAJIauI+GUuEEEIIITyQJAiFz1LGxUAnYJvWuhazVuCjSqkOSqnewK+B1xxPeQO4WymVqJSKwFT6LW7FLsSvAmcBN+OYXuzQASgBjjgqKW5t5aF0AA5rrSuUUuOAq5t4zANKqTCl1BDMmoqLm3jMa8BFSqnpSil/pVSIUuo0pVS8UqqrUupix/pRlcARzDQxIYQQPkZrXQe8BDyhlOrhGDMmKqWCkbFECCGEEMIjSYJQ+KKPlFJHMEm5R4HZjdZAugOz6cduzFpMizBvgnB8XQgsBTIx1RN3tPRFHesLpgLhmIq/er/FJPVKMZUUTSXvTuY24E9KqVLgQUyS80RLgF3AN8A8rfWXTcSXA1yMqWA8hKkC+R3m74QfJlm6FziMWeOwtYlMIYQQ3uO3wGZgNWZc+BvgJ2OJEEIIIYRnUlqfbOaiEEIIIYQQQgghhBDCm0kFoRBCCCGEEEIIIYQQPkwShEIIIYQQQgghhBBC+DBJEAohhBBCCCGEEEII4cMkQSiEEEIIIYQQQgghhA8LsDqAxmJjY3VCQoLVYQghhLCBtWvX5mutO1sdR3NkzBJCCFHP7mOWEEIIcSq2ShAmJCSwZs0aq8MQQghhA0qpbKtjOBkZs4QQQtSz+5glhBBCnIpMMRZCCCGEEEIIIYQQwodJglAIIYQQQgghhBBCCB8mCUIhhBBCCCGEEEIIIXyYrdYgFEIIIYQQoq2qq6vJzc2loqLC6lCElwoJCSE+Pp7AwECrQxFCCCGcShKEQgghhBDCK+Tm5tKhQwcSEhJQSlkdjvAyWmsKCgrIzc0lMTHR6nCEEEIIp5IpxkIIIYQQwitUVFQQExMjyUHhEkopYmJipEJVCCGEV5IEoRBCCCGE8BqSHBSuJP1LCCGEt5IEoRBCCCGEEEIIIYQQPqzNCUKl1EtKqYNKqfRm7ldKqX8rpXYppTYppUa3tO305R+x8tUHWhzLs0sySM3IP+621Ix8nl2SIW34eBsvpb9E2r60425L25fGS+kvtTiGla8+QPryj467TfqotGGXGJzVhjPOFasppUKUUmlKqY1KqS1KqUeaeEywUmqxY2xapZRKaHTf/Y7bdyilprs02GVPQubS42/LXGpuF0J4pIOlFRypqD7utiMV1RwslemoQgghhLC/9lQQLgDOPcn95wH9HJdbgGda0mj68o+I++o2IpLGtTiQ4fGR3L5ofcMb5NSMfG5ftJ7h8ZHSho+3MTRmKL9d8tuGxEfavjR+u+S3DI0Z2uIYIpLGEffVbQ1JQumj0oadYnBWG844V2ygEjhDaz0CGAmcq5SacMJjbgIKtdZ9gX8CfwNQSg0GZgFDMGPb00opf5dFGjca3p5zLEmYudRcj2vxZ2mWcEYyWohTclMC3dn9OSzQnz2HjzJ+wkTAJAf3HD5KWGD7/5RkZWWxaNGiFj12zpw5vPPOOyd9zIIFC9i7d2+742qJ888/n6KiIre8lhBCCCHaTmmt2/5kU3nxsdb6J+8glVLPAd9rrd9wXN8BnKa13tdce8N7hOrvb47h39F/YH90y5MvAAVllWzIKaJXdBh7DpczsmcUMeHB0oaXtLExt5g5KQm8szaX+VePIiUptsXPT9uXxq+X/JqEjglkl2Tz+LTHGde9df0rfflH9PzqF2T5J9CzZo/0UWnDVjE0bmNYXCRZBeWtPk/AnCt3fXcXwzsPZ2vBVuZNm9fqc8WZlFJrtdbJbXxuGLAMuFVrvarR7V8AD2utVyilAoD9QGfgPgCt9WMnPq6510hOTtZr1qxpS3hGxrew6EoYcD5k/QAzF0Di1La35wb1yefHZw5ncr/OrM46zO2L1repvwnRrPqEef05ceL1k9i2bRuDBg1q0cvU9+f6/nvi9bYoOVrNnsPlRIcHUVReTa/oUCJCAtvUVmPff/898+bN4+OPPz7lY+fMmcOFF17I5Zdf3uxjTjvtNObNm0dycpv+xLaI1hqtNX5+nr+iUW1tLf7+xxK9TfWz9oxZQgghhB0EuLDtOCCn0fVcx23HJQiVUrdgKgwZ092Pz9RUVtQNgfyyVr9gx5BAMg6VERMeRMnRGkqO1kgbXtBGRU0tVTV1PL90N786o2+r/2kf130cIzuPZEnuEi7vd3mbEh5DJ13Elu//zojqTbyqLpI+Km3YLgaAQH8/1u0p4hdT+7Tpze247uPoEdGD5XuX84vhv7A0OdhWjqq/tUBf4KnGyUGHhrFJa12jlCoGYhy3r2z0uPox68T2G8asXr16tS/Y+HEQFgtb/wdDLrN9chAgJSmWv142jJteWUOHkADqNDx33RhJDgrnSpwK5zxqEujjboH1C9uUQH/koy1s3Vty0sd06RDM9S+m0bVjMAdKKunbJYJ/ff0j//r6xyYfP7hHRx66aEiT91XV1LK/pIJx/eNYuSOXHzes4rZ5jxEbG0t6ejpjxozhtddeQylFQkICV1xxBZ999hmhoaEsWrSIvn37/iS5FxERwZEjR7jvvvvYtm0bI0eOZPbs2dx9990Nr6u15o477uCrr76iZ8+eBAUFNdz3pz/9iY8++oijR4+SkpLCc889x7vvvsuaNWu45pprCA0NZcWKFfzjH//4yeNOthnHggULeP/99ykuLiYvL49rr72Whx56iKysLKZPn8748eNZu3Ytn376KdOmTWPNmjXExsby6quvMm/ePJRSDB8+nIULF3Lo0CHmzp3Lnj17AHjyySeZNGlSk6/78MMPk5GRwa5du8jPz+eee+7h5ptvRmvNPffcw2effYZSij/+8Y9ceeWV/PKXv2T69OnMmDGDSy+9lE6dOvHSSy/x0ksvkZGRwaOPPsprr73Gv//9b6qqqhg/fjxPP/00/v7+RERE8Itf/IKvv/6ap556ismTJ5+0LwkhhBCezpUJwhbRWj8PPA+Q3MNfX8j3jBh3FUMnXdSqduo/9f3VGX15bdUeHpoxuNVvWKQNe7bxzpocfvvOJqYP6cprq/YwISmm1RWEafvNtMnPsz7nvMTz2lRBmFS1HRRczHeMHnel9FFpwzYx1Ldx8yummm3x6hymDejcpgrC3cW7TRs7FjOu2ziPSxJqrWuBkUqpKOB9pdRQrXWTa+W2sf1jY1ZycttL8AH2roOaCujQHba8B92GwpTfOCNMl9Fa8/baXACKHUnsjzbuY2hcJB2dUCUlBHs3wLInYOuHgIblT8LUe1yWQI8MDaRrx2DyiiqIiwohMrRt/bissobsgnLqGs3MKT5azfr169myZQs9evRg0qRJLF++vCHRFBkZyebNm3n11Ve56667Tlod+Ne//rXZCsL333+fHTt2sHXrVg4cOMDgwYO58cYbAbj99tt58MEHAbjuuuv4+OOPufzyy5k/f/5xFYRNPe6iiy7i2WefBWDu3Lk/ed20tDTS09MJCwtj7NixXHDBBcTGxvLjjz/yyiuvMGHC8Ss8bNmyhb/85S+kpqYSGxvL4cOHAbjzzju5++67mTx5Mnv27GH69Ols27at2Z/Fpk2bWLlyJWVlZYwaNYoLLriAFStWsGHDBjZu3Eh+fj5jx45l6tSpTJkyhR9++IEZM2aQl5fHvn2mTuGHH35g1qxZbNu2jcWLF7N8+XICAwO57bbbeP3117n++uspKytj/PjxPP74483GIoQQQniV+vL/tlyABCC9mfueA65qdH0H0P1k7Y3p7qczPvqHPvxQvN687EPdUst3HdKj/vSlXr7rUJPXpQ3PbmPEI1/o3vd+rF9NzWx1G6v2rtJT3pii71t6nx66YKh+ZsMzesobU/SqvataHMPmZR/qww/F6w2PnaH1Qx31rk+elD4qbdgmhsbP+eeXO3Tvez/Wi1Zmt7qN+nPl8g8u10MXDNVfZX3V6nPF2YA1un1j1IPAb0+47QtgouP7ACAfUMD9wP1NPa65y5gxY9p+cLuXaP23RPO1vFDrf43S+qGOWv/wRNvbdINXUzN173s/1oMe+Ez/9dNtesAfP9UJ936sxz36lf5yy36rwxOeLGu51gsvM+fB/8Vr/c5NWv+587Hru5e0qJmtW7e26mXr/34+/sX2Vv/drFdYVqk35RbprXuLdXpekQ4PD9cHSyr0C299pCdMOU2XHq3SWms9d+5cvXDhQq211r1799YZGRlaa62rqqp0dHS01lrr2bNn67fffruh7fDwcK211t99952+4IILmnz9O++8U7/44osN1y+99NKGNt555x09btw4PXToUN2jRw/92GOPaa21njZtml69enXDc5p7XHNefvllfd111zVcf+CBB/Q///lPnZmZqRMSEo57bO/evfWhQ4f0v//9b/373//+J2117txZjxgxouHSo0cPXVpa2uTrPvTQQ/qBBx5ouH7dddfp999/X991113H/QyuvfZa/cEHH+jc3Fw9fvx4vWXLFj179mw9Y8YMvXfvXj1gwABdUlKi//Of/+ju3bs3vHb//v31Qw89pLXW2t/fX9fU1DQZR1P9rL1jllzkIhe5yEUuVl9cuSjIh8D1jt2MJwDF+iTrD9br028oeWc/zZGMtFM9tMGm3OLj1otJSYpl/tWj2JRbLG14QRtPXjkSgJKKmla3kV6Qzrxp8+gQ1AGAbuHdmDdtHukFLS8oOpKRRt7ZT1PrHwpA0oBh0kelDdvE0LiNsYnRACR2Dm91G/XnSv2Usn6d+rX6XLGaUqqzo3IQpVQocDaw/YSHfQjMdnx/OfCt1lo7bp/l2OU4EbPBVstP8tbKW3dsymRoFPz8a+jUB775M2R857KXbY/t+0t45KOtBPorXrg+mXvP+3/27ju+zfpa/Pjnq2FLHpLjkenEdpyQ5Tjb2QMKlFlKGRfSAoFbIC1pS1to6Y/bQinc0pLbUhoKl15IgEILhEIpZTdhZJNFJpA4znaG7XhbliV9f388lmInsi3JQ1Jy3q+XX7IePc+jE0ePLR+f7znDeWbeJFJtFhLMJm59bj0LXtxIWW1jtEMV8UJr2PU+PHMRLL7YqB78yn3wjaeMHp3X/gV65UJCCrxy0+mDSzqpZc/BH104jEVzx7Ua+NRx+Jqj1S72V9STZDXTKzmBnPQkADJTEnDYLFgTEiivcwNgNpvxeE62j2i5hNf/ucViwefzAeDz+XC73RH/+1wuF9/97ndZunQpW7du5dZbb8XlOn2icqj7nerUJcj++8nJyWHF6fP5WLNmDZs3b2bz5s0cOnSIlJSUsJ83mAEDBlBZWck777wTqCh8+eWXSUlJITU1Fa01N910U+C5v/jiC+6//34AbDZbq76DQgghxBkv0swi8FeMfoJNGL2a/hOYD8xvflwBjwPFwFZgYkfnnNDPpPXmv2khTnXOvW/ph/4VXlVASz/9+Ke6YEmBfnbbsxGfY9tDM41Khq2vRnwOIbrT1oOVOuenb+p3tpVGfI6vLv2qLlhSoLcc29KFkUWGMKsxgEJgE7AF2Ab8onn7A8DXmj+3Aa8AuzESgINbHH9v88+sL4CLO3q+TlUQBlNXrvWfpmn9q95aF3/YtefupPpGjz7/fz7UI3/+tn576+FWj63cfVwvWrZL/+GDL/XQ//eWHvPLd/XfNx7QPp8vStGKmOf1GD9Ln5hu/Fz9n5Far/lfrRvrjMc/+f3JisHiD419Xp5nbO9AOBWET3y4+7SKwZW7j+snPtzd8T/B69P7yuv0ZwdO6P3lddrb4vXur/r797Jl+twLvqq3HKzUNa4mfccdd+jFixdrrY2qOn+V3vPPP68vu+wyrbXWv/rVr/RPfvITrbXWr732mjbeqmu9fv16PWvWrKCxvPrqq/rCCy/UHo9HHz58WKelpelXXnlFnzhxQvfu3VvX19frmpoaPWrUqEB13GWXXaaXLVumtdbt7teWxYsX6379+uny8nJdX1+vR48erT/99FNdUlKiR40a1WpffwXhtm3b9NChQ3VZWZnWWuvy8nKttdbXX3+9/u1vfxvYf9OmTW0+73333afHjBmjGxoadFlZmR44cKA+dOhQq6/BsWPH9KBBg3RpqfGz8KabbtIDBw7Uu3bt0mvWrNHZ2dn6zjvv1FprvX37dj1kyBB99OjRQEx79+7VWp/8fwxGKgjlQz7kQz7k40z8iLgHodb6+g4e18AdYZ/YVRlhROJM5rRbqW5oivj4GneNcdtUE/E5Er21xieu0KuyhOhJ/t5ZnblWqt1GQ3//NRNPtNZbgHFBtv+ixecu4Jo2jn8IeKjbAuxIUjrc+A949nL463XwzVcgNzaa4v/qXzvYfbyW52+ZzIyhrXtbTsvPDFTAXlzQl5++uoUfvvQZ/9h8mIeuHM2ANHs0QhaxyOOGLS/Bit9DRTFkDIUr/gSjrwHLycEazLjz5OeDZ8P4G2HTX2Da97o0nPmz80/b1vL13BaP18fe8nrq3R76OmxkpSYGrWIzKYXdaibBbGJfeR1eX+u2pSdOnKCwsJDExET++te/AnDrrbdyxRVXMGbMGC666KJANV5hYSFms5kxY8Ywb968VkNKrrzySpYtW8bIkSMZNGgQU6dOBSAtLY1bb72VgoIC+vbty6RJkwLHzJs3j/nz5weGlLS1X3s9CIuKirjqqqs4ePAg3/rWt5g4cSJ79+5t8+s2atQo7r33XmbPno3ZbGbcuHEsWbKExx57jDvuuIPCwkI8Hg+zZs0KPG8whYWFnHvuuZSVlfHzn/+c/v37c+WVV7J69WrGjBmDUorf/va39O3bF4CZM2fy3nvvMWTIEHJycqioqGDmzJkAjBw5kgcffJALL7wQn8+H1Wrl8ccfJycnp83nF0IIIc5UysjjxYaJ/c16/V9/DbN/Eu1QRIw5/3cfMbR3Ck98a0JEx9/w1g1sPr6ZucPn8rPJP4voHId/OZT++hicfz/M+GGH+wvR06rqmxjzwHv816Uj+PbMwWEf79M+xj43Fo3mkVmPcFHeRd0QZeiUUhu01hOjGkQ7Jk6cqNevX9/1J649Ds9eBpUH4FuvQs7Urn+OMLy9tZTvvLCR+bPzuefi4R3u7/Vpnlu9l9++8wUmBfdcPJxvTs7BZGp7GaA4w6x4FAaMPzlYxF0H799nJAcbq6HfGGMgz/DLwBTCEs6GSvjTFEjKgFuXt04mnmLnzp2MGDGiS/4ZwbiavOwtr8Pj1QzsZceZ1HYsfo0eL8XHajGbTORnJWMxm8jNzQ1M9o1HS5YsYf369SxatKhHn/f+++8nJSWFu+66q0ef91TBXmex/jNLCCGE6Eh39iAMnzIbbwKFOIXTbqXa1QUVhJ2oikrRdcYnUkEoYlSqzYJSRr/OSNQ21aIx/mjkryQUUZCSBTe+AY7+8MLVcKD7WiF25OCJen766hbGDEzjxxeeE9IxZpPi5ul5vPfDWYzP6cXP/7Gd/3hqNcXHa7s5WhEzBoyHV+bBl+/Cx4/A/wyHT/8MaQONpPdtH8HIK0JLDoLRp/PS38HRbbDyD90ZebtqXE0UH6vFp2FwVnJIyUGARIuZQRnJuL0+9le0nnQshBBCCBErIl5i3C1MZkm+iKAcNgvHO9H43p/siDTp4fN6SdH1RmdNSWKLGGUyKVISLREvMa5uPHl9SIIwylL7wE3/hCWXwvPfgBtfh+yeLUzxeH3c+bfN+DQ8dt1YrObw/qY4MD2J524p4tWNh/jVmzu48Pcfc/WEbB78ekHgXKuKy9hysCroUk8Rx/JmGUN4/nIVeN1gtsJFv4Eppy9TDdnwS2DUN+Dj38KIy6F3x9WsXam8tpHDlS4SrSZyM5JJsIR3PaQkWhiQZufgiXoOVzZQUlLS7nCNWDdv3jzmzZvXbedfvHgxf/hD62Tw9OnTefzxx7vtOYUQQoizXWxVEJrM0oNQBOW0W6nqgr5qkSY9aqoqMKnmv/jLa1TEsM5cKy2vD0kQxgBHP5j3JiRnGknCQxt79OkfW7ab9ftO8NCVBeRkhDeV1E8pxdUTsnn/R7OYmJPGS58e4Pz/+Yhth6oCE2QLs51dHLmICRlDwNv8vWj6DzuXHPS7+LfGROM3FoDP2/nzhUBrzeHKBg5VNpBqs5CflRJ2ctAvPTmB3qmJVNS5KauNfDrx2eDmm28OTBb2f0hyUAghhOhesZUgVFJBKIJz2K1UN0S2bLLR20ij16g+jHSJcV1V+ck78hoVMcxhi3ygT8vrIx6HlJyRHP2NJKE9DZ7/Ohze3CNPu2ZPOYuW7eKq8dlcMXZAp8/XO9XGS7dP44fnD2X/iXq+/vhK7nhhI4vmjutwIISIUx/8EtBQdBusfxpKPu78OVOy4OLfwMFPYd1TnT/fKY7VuKht0c7E69MUH6+jrLaRzJREcjKSMHeyl2Yfhw2n3UppVUOnBkoJIYQQQnS12EoQmqQHoQjO34PQ5wu/b0/LREfLJZThqK8uO3lHXqMihnVZBWGE14roBs5sI0mY6DSShEe2duvTnahz88OXNpOTkcwvrxjVpef+wfnncN2kgXh8moIBTkkOnql2LzMGkgyYCJc8Yiw3fmVe1yQJR18DQ78K/34AKko6f74Wkqxm9lc0UOtqwu3xsetoDfVuD5kpifRPs3fJkmClFAN7JWG3mtlfUU+Du2cqIYUQQgghOhJjCUKLLN8UQTlsVrSGWnf4VYT+pEeWPYuapsiqolw1FQC47b2lglDENIfdEvFAH38yPcueJRWEsSZtENz0BliT4dmvwdHt3fI0Wmt++uoWymobeey6caQkdm2r4lXFZby7/Sh9HIms2l3Gqt1lHR8k4s/mFwANc35m3Pf3JOyKZfJKwWW/M1ad/PMH0IUDP1JsVgal29lXUc+XR2twe330c9ron2bvsucAo19sbmYyZpNib3kdTV5fl55fCCGEECISsZUglCXGog1OuxWAqvrwEx/+Sqjs1GwaPA00ecM/h7vWSBB6HDmSxBYxrVMVhC2uFelBGIPS84wkobcJnrkIjn1+8rGSj2HFo51+ir+s3c97O47y04uGM7qLewP6ew4umjuOH11wDl4N8/+ygVXFkiQ841QdhPTBkH/eyW15s2DGnV1zfmc2XPBLKPkINj0f+XlWPHpaVWNK6Wp6f/YkPq3JSE4gK9XWuVjbYDWbyM1IwuvT7Cuvi2iFhBBCCCFEV4qtBKHJDI3VPdZ4WsQPh92oYomkMspfCTUgxeijFUniw1NXCYDuNchIYndhxYIQXcnoQRhZv85qdzUmZaJvUl+pIIxVGfnGks3GGnjmq3D8SyPB8co8GDC+U6f+/Eg1v3pzB7PPyeKW6XldE28LWw5WBXoOXlbYn+QEM2MGprHloPxh8IxSugUOrIFJ3wZTN77NnHAz5MyAd/8LqksjO8eA8a2XPpd8jO/leVRnjCYpwUJVg6dVT8JQTZs2LaT97AkWBqYnUe/2cuBEPbqd9xZ79+7lxRdfDOm88+bNY+nSpe3us2TJEg4fPhzS+TrrkksuobKyskvOdf/997Nw4cIuOdejjz5KfX19l5xLCCGEOBN07dqhzjKZjVtXFSSlRzcWEVMc/grCCCqj/AnBlgnCDHtGWOfwNZwAwJKeCz4PuOsgMSXsWITobk67lYYmL26PL+xJm9XualITUnEkOqSCMJaNvd74I8U/vgv/dz6YLcbyzbxZEZ+ywe3l+3/dhMNmZeE1YzB1chBDMPNn5wc+T060cMW4Afx940EWze1cYlPEmE//DNYkGDu3e5/HZIKvPQZPTIN//Riue8FYftzS2/d03LMztR88fyWk9kPXlNKYNpQ+G/9A0rZFaK1xeXx4LSbM/mRn39Fw8cPtnnLVqlUh/zOcdiv9nDZKq1wcPNHAwPSkwGO1ribqm7z0TrUFEoRz53bN13XJkiUUFBTQv3//LjlfMFprtNa89dZb3fYcnfHoo4/yrW99i6SkpI53FkIIIc4CsVVBqPwJwsqohiFij3+JcSSVUadWEEZSGaUbqvBqRUL6IGODLIUXMcqZ1HytRFht60hw4EhwUOOuabeaRUTZuLkw4jJorIIh53cqOQjw4L928OXRWn537RiyUhO7KMj2zS0ahKvJxz82H+qR5xM9oL4CtrwChdeCvVf3P19GPpx7L3zxL9j+WmTnsKUZScKqA/iS+6ITnZhNYFIKs8mEzWLCG+a3wpQU4w+IH374IXPmzOHqq69m+PDhfPOb3wx8X83NzeUnP/kJo0eP5tKvzKL88H5O1Lv5j7k3sHTpUmpdTeyvaGBwP2OQzz333MMnn3zC2LFj+f3vf9/q+bTWLFiwgGHDhnH++edz7NixwGMPPPAAkyZNoqCggNtuuw2tNUuXLmX9+vV885vfZOzYsTQ0NATdrz1LlizhiiuuYM6cOQwdOpRf/vKXgFHpOGzYMG688UYKCgo4cOAAubm5lJUZrQSee+45CgsLGTNmDDfccAMAx48f56qrrmLSpElMmjSJlStXtvvcO3bsYM6cOQwePJjHHnsssP0vf/kLRUVFjB07lttvvx2v11iR9J3vfIeJEycyatQo7rvvPgAee+wxDh8+zLnnnsu5557b7vMJIYQQZ4vYrSAUogWHzZ8g7JoKwnCZGquoUcmk2dOMDa5KcA4I+zxCdDf/tVLV0ERmSniJHn8FYWpCKl7tpd5TT7I1uTvCFJ1V8jHsWwmmBNjxOoz7VsRJwne2lfLC2v3cPmsws87J6to421EwwEnBAAcvrt3PDVNyumRCrIiyzS+ApwEm3dpzzznlu7D97/DW3ZA3u/VjHVT6ASeX6M/6CaZPn6Z03A9IG/kV7M3fP83NH5HatGkT27dvp3///kyfPp2VK1cyY8YMAJxOJ1u3buW5555j4QP/j0ef/iuuJi9ltY3sr2hgUPrJ4SgPP/wwCxcu5M033zztOV577TW++OILduzYwdGjRxk5ciS33HILAAsWLOAXv/gFADfccANvvvkmV199NYsWLWLhwoVMnDixzf0uv/xynnzySQDmz59/2vOuW7eObdu2kZSUxKRJk7j00kvJzMxk165dPPvss0yZMqXV/tu3b+fBBx9k1apVZGZmUlFh9Hf+wQ9+wA9/+ENmzJjB/v37+epXv8rOnTvb/Jp+/vnnLF++nJqaGoYNG8Z3vvMddu/ezUsvvcTKlSuxWq1897vf5YUXXuDGG2/koYceIj09Ha/Xy1e+8hW2bNnC97//fX73u9+xfPlyMjNlmroQQggBsVZB6E8QNlRGNQwRezpbFWUz28i0Zwbuh8vsrqZWpUAgQShJbBGbTlbbdq6C0H9fxCB/QuOaZ2H01Ub1fcs+amE4VNnAT5ZuoTDbyY8vHNbloXbk+qJBfH6khs0HKnv8uUUX8/ng0/+DQdOgb0HPPa/ZAl9bZPzh7t2fhXds4FpaAufdS/kl/0vOsjtIO7qmy8IrKioiOzsbk8nE2LFj2bt3b+Cx66+/PnC7ZvVq8jKTUQrqGj2kJyeQ0vwHn458/PHHXH/99ZjNZvr37895550cDrN8+XImT57M6NGjWbZsGdu3B5+A3tZ+8+fPD5ocBLjgggvIyMjAbrfzjW98gxUrVgCQk5NzWnIQYNmyZVxzzTWBhFx6utFO6IMPPmDBggWMHTuWr33ta1RXV1NbW9vmv/fSSy8lMTGRzMxMevfuzdGjR/n3v//Nhg0bmDRpEmPHjuXf//43e/bsAeDll19m/PjxjBs3ju3bt7Njx46OvqRCCCHEWSm2EoRKKghFcCkJFpSKvAehvyoKTk5qDYfVXUWDKQVszVM9JYktYpR/oE9XXCtVjfK9OCYd2niy52DhtdBUb1RsHdoY0uFPflTMquIyPF4fP/zbZrw+zc3TcnlmZUn3xh3E18b0JynBzN/WHejx5xZdbPcHcGIvFPVg9aBf3wKY+WPY8hI0NYR+XItryac1xzImU3bx/2Iu3dRloSUmnqzkNpvNeDwnW6W0rJpVSuFq8mI2W/D5fJTXNVJd34jb7Y74uV0uF9/97ndZunQpW7du5dZbb8XlckW836lOrfr1309ODq/y3OfzsWbNGjZv3szmzZs5dOhQYJl2MMG+plprbrrppsA5vvjiC+6//35KSkpYuHAh//73v9myZQuXXnppSP82IYQQ4mwUWwlCU/OKZ+lBKE5hMikcNmtkSY/GaqMqKtGoiopkibHNU4PLkmr0KgJ5jYqY5ezMQJ8uuFZED5hx58nlxHmzIKUvHN1mbA9BYbaTBS9u4idLt7BubwU3TcvlV//aSWG2s9tCbkuqzcrXxvTnjc8OUxNBhbiIIeueMl6LIy6PzvPP/DFkDYeGE+DzhnZMi2upxuXB4/ORdM65IV9LnfXSSy8FbidNnsz+igZGnJPPjq2bcdqsPPu3V2lqMq6L1NRUamqCV3XPmjWLl156Ca/XS2lpKcuXLwcIJMIyMzOpra1tNdm45fna268977//PhUVFTQ0NPD6668zffr0dvc/77zzeOWVVygvLwcILDG+8MIL+eMf/xjYb/PmzSE9f0tf+cpXWLp0aaD/YkVFBfv27aO6uprk5GScTidHjx7l7bffDhzT3tdUCCGEOBvFWIJQKghF2xx2S8TLJlMTUkk0J5JgSoho2aTNV0uTNfVkBaG8RkWMCvTrdEU20MeR4AhUEMoS4zhgMkPBVbDrPSMxEoJp+Zn87OLh/H3TIYb3TeVvnx5g0dxxTMuPTh+u64oG0dDk5R+bD0fl+UUXKC+G3e/DxJvBHNqy2C5nSYQrHgefB6rDfy1V1ruxmEyk2nquPfeJEycoLCzkD3/4A/c/9FsGpdv53nfns2ntKs6fOZk92zeS1FyNV1hYiNlsZsyYMacNKbnyyisZOnQoI0eO5MYbb2Tq1KkApKWlceutt1JQUMBXv/pVJk2aFDhm3rx5zJ8/n7Fjx5KYmNjmfk8++WSgD+GpioqKuOqqqygsLOSqq64K9DNsy6hRo7j33nuZPXs2Y8aM4Uc/+hFgDAxZv349hYWFjBw5ss3na8/IkSN58MEHufDCCyksLOSCCy6gtLSUMWPGMG7cOIYPH87cuXNbJTFvu+02LrroIhlSIoQQQjRTsTSlcuLEiXr910pg2vfh/PuiHY6IMZf98ROyUhJZfHNRWMdd+89ryUrK4vGvPM65L5/L7OzZ3D/t/rDOUXZ/DnvSZ1K04Fl4IB1m3wPnhtnrSIge4GryMvzn73D3V4dxx7lDQj/O42LSC5P4wfgf8NXcr3LJ3y/hV9N/xdeHfL37gu2AUmqD1rr93zijaOLEiXr9+vXRDgMOb4Kn5sDlf4AJ80I65H/e+4I/LtsNwPfPG8KPotB/0E9rzSWPrcCk4F/fnxm1OEQnvHsvrH0SfrgdUvtGNZSdn37IiAFOyBgCicYfO2isAXc9pPYJeozH62NnaQ2ZKQn0S7MH3aer5ebmsn79+qADMspqGjlc1cA5fVKxWTszIqX7LFmyhPXr17No0aJohxIVO3fuZMSIEa22xfrPLCGEEKIjsVVBCMYSTlm+KYJw2KwRVUX5+6oBpCakRrRsMlXX4UtwGNU6iQ6pIBQxy2Y1k2AxhV1t668WTLWmypCSeNNvLGSeA1teDvmQVzcexGJSfP+8Ifxl7X5WFZd1X3wdUEoxt2gg2w9Xs/WgfG+NO+462PQ8jLwi6slBwPgZDXBinzE4pbHG6I2YkNTmIZUNTWg0ackJPRNjB/yD2SJpFSGEEEIIEakYTBA6JfkignLaI+xB6K4OJDwcCY6wE4Su+loSVRPaP8FYktgixkVyrfivC0eigxRrSqttIsYpBaOvhX0robLjYR+vrD/A4UoX35qSw48uHMaiueNY8OKmqCYJrxg3AJvVxIvr9kctBhGhra8Y79smRWE4STBWOzj6g68JKnYbycFeuSerCYM4UefGbjVj78Fqvb179watHgSwmk0kJ1qorG8illb6tDRv3rxurR5cvHgxY8eObfVxxx13dNvzCSGEECIWE4T2NJkQK4Jy2KxhV0X5tI9ad22rCsJwq6JqK41m2qZAglCS2CK2OWwWqsMc+BCoIExIxWwyk2JNkQrCeDL6auN2W8fDBd74zOjP9p05+YDRk3DR3HFsiWL1nsNm5bLC/ryx+RB1jeFXioso0RrW/Rn6jIZBU6IdzUkpfcCcYFQ3JmW0mxxscHtpaPLSK0aqB/3S7FYaPV5cHl+0Q4mKm2++OTCR2P/x+OOPRzssIYQQ4owWewlCSb6INjiTwq+Kqm2qRaNbVxA2hlcVVVdlVNWYk3sZGySJLWJcpyoIO3GtiChKz4PsItjySoe7ltW6mZDTiz4OW2DbtPxM5s/O784IO3R90SDq3F7++ZkMK4kb+9cYE7SLbjUqWWNFY40xrASgrsy434YT9W6UUqTZozRcpQ0OuxUFVNW7ox2KEEIIIc4SMZggTJPlmyIop91Ko8eHq8kb8jH+CqiWSY+apvCqohpqKgBISE43NkgSW8Q4p91KdUN4VVinJQgTHVJBGG8Kr4Vj2+HItjZ32VtWx87Sai4uiIFecacYPyiNc/qk8FdZZhw/1j1l/EwcfU20IznJ4zKWFacNMu7bnMb9IElCn9ZU1jfhsFmwmGPrLXFgmXFD7C4zFkIIIcSZJbbeDYEkX0SbHDYLQFhLJ/0VUP6kh3+JsU+HvmSnsdZYYpyY0lxBKElsEeMckVQQNl8rnR3oI6Jo1DfAZIGtbQ8reXvbEQAuisEEoVKK64sG8dnBKrYdkvcBMa+6FHa+AeNuaHcASI/zuI2eg/ZeYE0CT4Nx311/2q61Lg8en49eSbG1vNgvLcmK2+OjIYw/jAohhBBCRCr2EoT+5Zvy11JxCkfz8p9wKqMCFYTNUw2diU582kd90+m/KLSlqbYSgCRnhrHBniZJbBHTnHZrxD0IOzPQR0RZcgbkfwW2vmpMbw3inW2lFGY7ye4VQwmdFq4cN4BEi4m/fSpVhDFvwxLweWHiLdGOpDWb42TPQVsaNDWAORFS+5y265+3PM3nlZtIaf4DJMC60nU8s+2ZHgq2fQ6bFYWSacZCCCGE6BGxlyC0OY3Jc00N0Y5ExBh/gjCcN8r+BEfLqqiW20PhrT8BQJKjOUFoc4K7Frzyhl3EJv9AH58v9D+0VLursVvsWM3GdRaPFYRKqYFKqeVKqR1Kqe1KqR8E2edupdTm5o9tSimvUiq9+bG9SqmtzY+t7/l/QRcovBaqD8L+Vac9dPBEPZ8drOLign5RCCw0aUkJXDK6H//YdJh6twwriVkeN2xYDEMvgIzo9q5sl81p3Ab5o57H62NA0jAe2/oL1h/5FDCSg3d9dBcFGQWdetpp06Z16ng/i9lEis1CVX0TJSUlvPjiiyEdN2/ePJYubX9g0ZIlSzh8uGf6fV5yySVUVlb2yHMJIYQQInKWjnfpYbY049ZVGVtLVkTUOQMVhOEnCFtWRfm396d/SOfwNQ8kSU3LNDYEXqNVkJwZcixC9BSn3YpPQ63bg8MWWuP9and1IIEOzf06468HoQf4sdZ6o1IqFdiglHpfa73Dv4PW+hHgEQCl1OXAD7XWFS3Oca7WuqxHo+5Kwy6BhBTY8jLkzmj10DvNy4tjsf9gS9cXDeK1TYd4c0sp104cGO1wRDCf/xNqj8KkW6MdSbt+s+kPfH50I6DAam/1WJPXh9vjo3dSb25//3aykrI4Xn+cwWmDeeKzJ3jisyeCnnN4+nB+WvTTdp931arTE/SRSrNbOeBqomTXHl588UXmzp3bJeddsmQJBQUF9O8f2nuhSGit0Vrz1ltvddtzCCGEEKLrxF4FoT3NuJUpseIU/kRHOEsn/QmOUysIw0l8KFcV9TqRhMTmiZ/tVCQIEQsc9uZ+nWEk02vcNYEEOhjXSoOngaY4qpTVWpdqrTc2f14D7AQGtHPI9cBfeyK2HpOQBMMvgx2vg6ex1UPvbDvC8L6p5GYmRye2EE3K7UV+VrIMK4ll6/5s9PUbcn60I+mYyQLae1rrGo9PY1IKZ6KDrKQsSutKyUrKavV9MFIpKSkAfPjhh8yZM4err76a4cOH881vfjMwcCQ3N5ef/OQnjB49mqKiInbv3g2cXv03sE86SinuvfdnfPLJJ4wdO5bf//73rZ5Pa82CBQsYNmwY559/PseOHQs89sADDzBp0iQKCgq47bbb0FqzdOlS1q9fzze/+U3Gjh1LQ0ND0P3as2TJEq644grmzJnD0KFD+eUvfwnA3r17GTZsGDfeeCMFBQUcOHCA3NxcysqMv7s899xzFBYWMmbMGG644QYAjh8/zlVXXcWkSZOYNGkSK1eubPN5jx8/zgUXXMCoUaP49re/TU5OTuDcX//615kwYQKjRo3iqaeeAsDr9TJv3jwKCgoYPXr0aV87IYQQQpwUgxWEknwRwTkjWGJc1ViFSZlIthq/EAcqCBtDXzppaqyiViUTqGeVJLaIcS2vlexeoR1T7a5u9Ytxy2rbDHtGl8fY3ZRSucA4YG0bjycBFwELWmzWwHtKKQ38r9b6qSDH3QbcBjBo0KAujrqLFF4DW/4Gu96DEZcDcLTaxfp9J/jRBedEObiO+YeVPPivnXx+pJrhfTufsBFd6MhW2L8aLnwITLH3d+aWflr0U3DXQdmXxlTjJON7mavJy5dHa+jvtLOn9jPu+ugubi+8nZe/eJnvjPkORf2KuiyGTZs2sX37dvr378/06dNZuXIlM2YY1b1Op5OtW7fy3HPPceedd/Lmm28GPUdqooUf3HMfryx+Iug+r732Gl988QU7duzg6NGjjBw5kltuMXpDLliwgF/84hcA3HDDDbz55ptcffXVLFq0iIULFzJx4sQ297v88st58sknAZg/f/5pz7tu3Tq2bdtGUlISkyZN4tJLLyUzM5Ndu3bx7LPPMmXKlFb7b9++nQcffJBVq1aRmZlJRYVRvP2DH/yAH/7wh8yYMYP9+/fz1a9+lZ07dwb9Wvzyl7/kvPPO42c/+xnvvPMOTz/9dOCxZ555hvT0dBoaGpg0aRJXXXUVe/fu5dChQ2zbZkx3l6XOQgghRNti751dyyXGQrQQaVVUijUFkzJe6pH0ILQ2VVNvSjm5IZDErgz5HEL0pEC1bZgDfVouMY6k2jZWKKVSgFeBO7XWbV3slwMrT1lePENrPR64GLhDKTXr1IO01k9prSdqrSdmZWV1eexdIm8OJGcZy4ybvbs9PpYX+31jfDYJZhN/W3cg2qGIU637M1jsMO6b0Y4kNNYkMFlb/eH5RL0bhWJX9Sbu+uguFs5eyIJxC1g4eyF3fXQX60rXddnTFxUVkZ2djclkYuzYsezduzfw2PXXXx+4Xb16dZvnSEuy4vFpPG30lf3444+5/vrrMZvN9O/fn/POOy/w2PLly5k8eTKjR49m2bJlbN++Peg52tpv/vz5QZODABdccAEZGRnY7Xa+8Y1vsGLFCgBycnJOSw4CLFu2jGuuuYbMTKM9S3p6OgAffPABCxYsYOzYsXzta1+jurqa2traoM+5YsUKrrvuOgAuuugievU6+Vewxx57jDFjxjBlyhQOHDjArl27GDx4MHv27OF73/se77zzDg6H/MFBCCGEaEsMJgilglAEl2gxY7Oawh5S0qoqKvFkVVSoEpqqaTCfTJxIElvEuogG+jS2vlacicb34jgcVGLFSA6+oLX+ezu7Xscpy4u11oeab48BrwFdV0bUk8wWKLgavnw3UOn89tYj5GclM7RPavvHxoj05AQuKujL3zcepMHtjXY4wq/hhJF4LrwG7CGWJ0ebUsZ7S1cN+LxorTlR30SqzcLOEztYOHthoGKwqF8RC2cvZFv5ti57+sTExMDnZrMZj+fkH26UUqd9brFY8DVPIff5fLjdblJtVkwYg1XC4XK5+O53v8vSpUvZunUrt956Ky6XK+L9TtUy/pb3k5PDa2Pg8/lYs2YNmzdvZvPmzRw6dCiwTDtUH374IR988AGrV6/ms88+Y9y4cbhcLnr16sVnn33GnDlzePLJJ/n2t78d1nmFEEKIs0nsJQj9bzhl+aYIwpjOGnlVVIo1BYUKqyrK5q2l0dIyQShJbBHbAgN9wuzXGe8VhMr47fRpYKfW+nft7OcEZgP/aLEtuXmwCUqpZOBCoOuyBD2t8BrwNsLONyivbWRtSTmXjI7d6cXBXF80iGqXh7e2lkY7FOG36QXwNMT8cJLT2JyADxprqHF58Hh99EpO4JaCW05bTlzUr4hbCm7pkbBeeumlwO3UqVMBozfhhg0bAHjjjTdoamrCbFJkpadRVV0TtDfgrFmzeOmll/B6vZSWlrJ8+XKAQJIvMzOT2traVr0NU1NTqamp6XC/9rz//vtUVFTQ0NDA66+/zvTp09vd/7zzzuOVV16hvLwcILDE+MILL+SPf/xjYL/Nmze3eY7p06fz8stGdfR7773HiRMnAKiqqqJXr14kJSXx+eefs2bNGgDKysrw+XxcddVVPPjgg2zcuDGkf5sQQghxNoq9HoTNFV6SfBHBOO3W8CsIE09WRZmUiZSElLCqopK8NVQkDDm5QXoQihjnCHPit9fnpaapptW10rIHYRyZDtwAbFVKbW7e9v+AQQBa6yebt10JvKe1rmtxbB/gteYKGAvwotb6nZ4Iulv0Hw/p+bDlZd7zzMGn4aI4WV7sN2VwOnmZyfzt0/1cNSE72uEInw8+/T8YNBX6FUY7mvAkpoAyg6uKEz4rFpMi1Rb9t8AnTpygsLCQxMRE/vpXo6D51ltv5YorrmDMmDFcdNFFgWq8KZPGo0wmCseM4Zabb+aHP/xh4DxXXnkly5YtY+TIkQwaNCiQbExLS+PWW2+loKCAvn37MmnSpMAx8+bNY/78+djtdlavXt3mfu31ICwqKuKqq67i4MGDfOtb32LixImtllCfatSoUdx7773Mnj0bs9nMuHHjWLJkCY899hh33HEHhYWFeDweZs2aFXjeU913331cf/31PP/880ydOpW+ffuSmprKRRddxJNPPsmIESMYNmxYYInzoUOHuPnmmwNVmb/+9a87/H8RQgghzlbRf3d0KrMFElJl+aYIymm3hl0V1Tupd6ttjgRHWFVRydThTXCe3GC1gzlRktgiZqUmWlAq9ARhbZPR6ynYkJJ4qiDUWq8AVAj7LQGWnLJtDzCmWwKLBqWg8Fr48GHWuLeRk5HEyH7x1XtLKcV1kwby67c/Z9fRmrhZHn3GKv43nCiB8/4r2pGET5nA5kC7qqjxOUlPTsSkOvxWETF//7w5c+YwZ86cwPZFixa12u/uu+/mN7/5Tattffr0CVS/AYHH01PsLH75nzjtVrLTk1odo5Q67dx+Dz74IA8++OBp26+66iquuuqqDvdrq/8gQHZ2Nq+//nqrbbm5uYGBIH4tk4Y33XQTN910U6vHMzMzA9WUHXE6nbz77rtYLBZWr17Np59+GljG/fbbbwc9RqoGhRBCiNDE3hJjaO4VI8kXcTpHJBWECa1/KXYkOEKuivJ6PDioR9ucrR+wOSWJLWKWyaRITbSEfK34r4dgS4zjrIJQtDT6GkDTd/+bXFTQ97R+YfHgqgnZWM2Kv8qwkuhb9xSk9IERX4t2JJGxpaG0lyTtoleSNdrRhM1kUsZ7IFcTviDLjM8W+/fvZ9KkSYwZM4bvf//7/PnPf452SEIIIcQZI/YqCMFYwinLN0UQTruVXcdCr2iqcdcETRCGWhVVW30CJ6D8y4r97GmSxBYxzZlkpdoVWr9O//XQ8lqxWWwkmBIkQRjPMvIpTyvkiooVuAv+O9rRRCQzJZELR/bl75sO8pOLhmGzmqMd0tmpYg/seh9m/xQsCdGOJjKJqfhQ9DLXx8TrqL2luG1x2q2cqHdT6/IEWklEy7x585g3b163nX/x4sX84Q9/aLVt+vTpPP7442zatKnbnlcIIYQ4m8VmglAqCEUbHDYLVfWhVUU1ehtp9Da2qooCozKqpKokpHPUVpbhBEynJghtTklii5jmsIVebRusgtB/v7pREoTx7F3TTOaaHkcnHAbSoh1ORK4vGsS/tpby7vYjXDF2QLTDOTt9+jSYzDBhXrQjCYnW+rSKWZcX3NqOQ9V33IcgRqXYLJhNiqqGpqgnCLvbzTffzM033xztMIIKNihGCCGEOBPE6BLjNFm+KYJy2q3UNHrw+Tp+c+ZPbJxWQZgY+hLj+uoyAKzJvVo/IK9REePCGejTFdeKiD01riYeO1qIDzNq6yvRDidi0/IzGJSexItr90c7lLPLikeh5GNw18Om52HE5VC+y9gew2w2G+Xl5aclcU7Uu6kmGbNugqaGKEXXOSalcNqsVDc0hfQ+SHQ9rTXl5eXYbLZohyKEEEJ0uditIJTqLBGEw25Fa6hp9ODs4K/n/mWTp1VFWVNDXmLcWFMBQEJKeusHbE6oKA4xaiF6nsNmpfh4bUj7BltiDMa1E09DSkRryz4/xhFvKtW5M0nbuhTO+zmYYvPvgu0xmRTXFQ3kt+98QfHxWvKzUqId0tlhwHh4ZR6Mud5Y1TFwsnH/miVRDqx92dnZHDx4kOPHjwe2aQ1Hql0kmjU1njI42mj8HI9DriYvZbVuGssSsCdEf6n02chms5GdLZPVhRBCnHliM0Eo/d1EG/xLaqobmjpMEPornxyJp1dFubwu3F43Ceb2eym5a40Eod2R0foB6ZMpYlxYFYRtXSsJDipcFV0em+gZb289Qu/URBxFc+G12+DAWsiZGu2wInL1hGx+996XvPTpAf7fJSOiHc7ZIW+WkQx87uuQnAUfP2Lcz5sV5cDaZ7VaycvLa7Xtoy+Pc8vr63jim+OZvv6/oOEEfHd1lCLsHI/Xx+T//jdT8jN4fO74aIcjhBBCiDNIbJYS2NLAXQPe0Brsi7OHw2YkBUNJfLTXV63l4+3x1FcCkOQIUkHoqjLKEoSIQQ67hWpXaAnCGncNZmUmyZLUartUEMavereHD788xldH9cU0/FKwJsGWl6IdVsR6p9o4f0Qflm44SKPHG+1wzh5pOaC9UHccJv5nzCcH27J0w0HSkqycN6I3DL8Mju2A8vhcBWAxm7h4dF+W7TxGvVveJwshhBCi68RogrB52Yc0xxencLaoIOxIoCoqyBTjlo+3x1d/AoCUtMzWD9jSjF+a3KEt4RSipzntVlxNvpCSKdXualITUk9r6u9IkB6E8eqjL47javJxcUFfSEyB4ZfCjtfB4452aBFz2K1U1Ll5b/vRwLZVxWU8+VF8JnriwrqnjNuJt8D6p42ehHGmqqHJGHAzpj+JFrNxLQB88VZ0A+uEywr709Dk5d87j0U7FCGEEEKcQWIzQeifGNtwIqphiNjjsBur4kOpjGqzB2Hz/VAqo7SrCo82kZxySq8ifxJblsKLGHVyOX7HFSb+BOFp50hwUOOukYmNceitbUdIT06gKK+5+rnwP4yfqbs/iG5gnXDFmP6YFIGE4KriMha8uInC7PjsJRfzSj42EoT2dLj0d8by4lfmxV2S8F9bSnF7fFw1oblnXK8c6Dsadr4Z3cA6YVJuOr1TE/nnZ4ejHYoQQgghziCxmSCU5Itog7+CMKQlxs0VqM6E1r88BioIQ6hQNbkqqVHJqFMb+weS2JUdnkOIaAjrWnFXn1ZpC8a14tM+6prqujw+0X1cTV6W7TzKhSP7YDE3f+8afC4kZcLWl6MbXCdMH5rJVeOz2X64ml+8vo0FL25i0dxxTMvP7PhgEb6DG8CcAOdcBEqd7El4aGO0IwvL0g0HOKdPCqMHtHgvMPwyoydnbXxW4JlNiksL+/Hhl8epCbGVhBBCCCFERyJOECqlLlJKfaGU2q2UuifI44OUUsuVUpuUUluUUpeEfHJbmnHrqow0PHGGcoZRFVXjrsFusWM1tx5m4h/EEEoFodldQ50KMjEz8BqVJLaITYEKwhCrbYMmCMO4VkTsWLGrjDq3l4tH9zu50WyBgm/AF2+DK36Xjf/ownMAeG7NPr41eZAkB7vT4NlGG438c09uy5sFM+6MWkjhKj5ey8b9lVw9Ibt1C4XhlwLauB7i1GWF/XF7fLy/42jHOwshhBBChCCiBKFSygw8DlwMjASuV0qNPGW3/wJe1lqPA64D/hTyE0gFoWhDcoIFkwq9KirVGnzZpP/xjiQ0VdNgDpYg9L9GKzs8hxDRENZAn8bgS4zDGegjYsdb20px2CxMHXzK9PXR14LHBTv/GZ3AukBJWR1mBQPT7fxl7X5WFZdFO6Qz157lxu3gOVENozP+vvEgJgVfHzug9QN9CowBLJ/H7zLj8YPSGJBml2XGQgghhOgykVYQFgG7tdZ7tNZu4G/AFafsowF/SYoTCP0djCzfFG0wmRQOuzX0qqjE06uiwulBmOipwWU+PXESeI1KElvEqHAG+rR1rYSTTBexwe3x8cGOo5w/sg8JllN+xGdPhF55cbvM2N9z8PyRfThW3cjvrh3Dghc3SZKwuxQvNxJpKb2jHUlYnvyomFXFZXh9mr9vPMTsc7LYfby29TAbpYxlxns+hMb4rJBWSnFZYT8+2VVGZX38Dh8SQgghROyINEE4ADjQ4v7B5m0t3Q98Syl1EHgL+F6wEymlblNKrVdKrT9+/LixUSoIRTscNmvoFYRBqqISzYkkmhNDSnrYfTU0BalCDLxGJYktYlRgoE8H14rWus1rRSoI48/qPeVUuzxcUtDv9AeVgsJrjSET1aU9H1wnbTlYxaK547hmwkAaPT4SzCYWzR3HloPyXqHLueuNHn1xWD1YmO1kwYub+L9P9lBa5aIwOy34MJvhl4LXHdeDey4r7I/Hp3l3+5FohyKEEEKIM0B3Dim5Hliitc4GLgGeV0qd9nxa66e01hO11hOzsrKMjdYkMFll+aYIymkPPUEYrK8aGJVRoSQ9kn21NCUEmZCZ6ASUvEZFzAp1ibHL66LJ19TmkBIIbaCPiA1vby0lOcHMjKFt9OYbfS1oH2x7tWcD6wLzZ+czLT+TyYPTMZsUK4vLmJafyfzZ+dEO7cyzb5WRPGvZfzBOTMvPZNHccfzPe1+SaDHx3Oq9wYfZDJoCSRlxPc24YICDnIwk/vlZ/CX8hRBCCBF7Ik0QHgIGtrif3bytpf8EXgbQWq8GbEBo3cSVMiq0pDpLBOGwW0JeNhmsKgqMyqiOlhhrn49UXYcvyNJLTCZIdEiVq4hZNquZRIuJalf7A33810GwBGE4y/FF9Hm8Pt7bcZTzRvTBZjUH3ylzCPQfF7fLjAFSbVbGZDtZsbs82qGcufYsNyYYD5oW7UgiMik3Ha01jR4fN0zJCT7MxmSGYRfDrvfAE59LdJVSXF7Yn1XFZZTVNkY7HCGEEELEuUgThJ8CQ5VSeUqpBIwhJG+css9+4CsASqkRGAnC4yE/gz1Nki8iqJArCBs7qCDsoCrK1VBHgvKcnFh8KrsksUVsc9qtVNW3f634r4O2EoQKJUuM48S6kgoq6txcUtC3/R1HXwuln8HxL3smsG4wY0gmWw9WhvSzQESgeLlRYZeQFO1IIvLC2n00+TSXFPRtf5jN8MuhsRr2ftyzAXahy8b0w6fh7W2yzFgIIYQQnRNRglBr7QEWAO8COzGmFW9XSj2glPpa824/Bm5VSn0G/BWYp7XWIT+JLU2Wb4qgHDZrh1VRPu2jtqm23QrCjpIetVVGdYrJP5DkVDanJLFFTAtloE9Nk1EdGOxaMSkTKdYUqSCME29vO4LNamL2sKz2d6yvAFTrKsKSj2HFo90ZXpeaPiQTn4Y1e6SKsMvVHIVj22Fw/C0vBmOYzW/e/gKAh64czaK549oeZjN4NliT4fN/9XCUXWdYn1SG9E6RacZCCCGE6LSIexBqrd/SWp+jtc7XWj/UvO0XWus3mj/fobWerrUeo7Ueq7V+L6wnkOSLaEMoFYQ17ho0uu0KwsSOexDWVRm/TFiSewXfQZLYIsaFcq20V0EIoV0rIvp8Ps07248w55zeJCVY2t958CwwWWDDc6C1kRx8ZR4MGN8jsXaFcYN6YbeaWblbJhh3uT0fGrdx2H8QjGE2eZnJjOznoFdyQqAnYdBhNlY7DPkKfP4W+Hw9H2wX8C8z/nRvBUerXdEORwghhBBxrDuHlHSOPU2Wb4qgHHYrbo8PV5O3zX38FU9tVhBaO+5B6KquAMDaZoJQktgitjlslg4rCP3Jv8706xTRt2H/CY7XNHLx6A6WFwPkzYKpC6DuKPxjgZEcvGaJsT1OJFhMFOWlS4KwO+xZDvZ06Dsm2pFEZN60XHYfr2X6kIzAtnaH2Yy4HGqPwKENPRRh17tsTD+0hn9tkWElQgghhIhc7CYIJfki2uCwG9NZ2xtU4k96OIINGGneXuOuwafbrhhw1RoJQpsjI/gOksQWMS6kCsKOrpUQJ36L6Hp76xESzCbOG947tAOmf9+43fwXmPifcZUc9JsxJJPi43WUVjVEO5Qzh9ZG/8HBs41hXHFo474TuD2+4INJghl6gVFR+3n8TjPOz0phRD8H/9wiy4yFEEIIEbnYfffnX74ZRttCcXZw+hOE7VRGtTeZ1b9do6lrqmvzHJ66EwDY20oQ2tIkiS1imtNupbohtCnGbVUQOhIcUkEY47TWvLOtlJlDM0m1WUM76Og2UGZwDoT1TxvLjOPMtOYKsZUyzbjrHP/cqKaL0/6DACuLyzCbFJPy0kM7wN4LcmfEdR/CJz8qpjDbyab9lRw8UQ8YvRif/Kg4ypEJIYQQIp7EcILQCT4PNNVHOxIRYxw2o79We5VRgaqodhKELfcLxp8gTHa08UuGLQ2a6sArUzRFbPIPKfH52v5DS7W7GrvFjtUUPLGUmpDa4cRvEV2fHazicJWLi0f3C+0Af8/BEZdD7TG48injfpwlCUf0dZCenMAqWWbcdYqXG7dx2n8QYFVxOWOynaQkdtCLs6Xhl0H5Ljj+RfcF1o0Ks5280zzF+F9bSllVXMaCFzdRmO2McmRCCCGEiCexmyD0T46VJZziFIEKwnYqo0KpIGy5XzC6uTowNa2NZUr+16hUEYoY5bRb0Rpq3e1fK21dJ9BcQdgUHxWESqmBSqnlSqkdSqntSqkfBNlnjlKqSim1ufnjFy0eu0gp9YVSardS6p6ejT5yb28rxWJSXDCiT2gHHNpo9Bwccz14G8GSYNw/tLE7w+xyJpNian4GK3aXoWW1QdfYsxzS8yFtULQjiUiNq4ktB6uYPiTE5cV+wy81buN0mfG0/Eye+NZ4zCbF/63Yw4IXN7Fo7rjQl1kLIYQQQhDLCUJb8189JfkiTuHvQdhuBWFjx4MXWu4XjHJVUq8TsSYkBt/B/xqVJLaIUY7m5aZV9e1fK21dJ2BcKw2eBprio1LWA/xYaz0SmALcoZQaGWS/T7TWY5s/HgBQSpmBx4GLgZHA9W0cG1OM5cVHmDYkE2dSiMuLZ9xp9BzMmQrKBCWfGPdn3NmdoXaLGUMyOVbTSPHx2miHEv88bti7Mq6rBz/dW4HXp5ma30ZrkLY4+sOACXG9zHhafiZTBqdzvMbNFWP6S3JQCCGEEGGL4QRhmnHrqoxmFCIGhdKDsNpdjUmZSLYmB33cP5ChvQpCU2M1NSql7UACr1FJYovY5AilX2dTBxWEiR0vx48VWutSrfXG5s9rgJ3AgBAPLwJ2a633aK3dwN+AK7on0q6zo7SafeX1XFwQwvTiU9mc0G8s7P2ky+PqKTOaK8VW7JJlxp12cJ3RNiOe+w/uLifBYmL8oF7hHzz8UmOScXV8DvpYVVzG1oPG+5GXNxxgVbFcE0IIIYQITwwnCKU6SwQXUlWU26iKUkoFfTxQQdhO0sPqrqLe1F6C0F/leqKDiIWIDoc9hH6djdXtJghDuVZikVIqFxgHrA3y8FSl1GdKqbeVUqOatw0ADrTY5yBBkotKqduUUuuVUuuPHz/e1WGH7Z1tRzApuHBkiMuLT5U3Cw6uB3fbA5ti2cD0JAam21khg0o6r3i5Mbgmb2a0I4nYquJyJub0wmY1h3/w8MuM2zisIvT3HHzimxNw2q1MyunFghc3SZJQCCGEEGGJ3QSh9HcTbUiwmLBbzR0OKemor5p/vzafx1NNg7ntpZfSJ1PEupP9Oju4VhI7d63EGqVUCvAqcKfW+tTANwI5WusxwB+B18M5t9b6Ka31RK31xKysrC6JN1xPflQc+MX/ra2lTM7L4IujNZFNLM2bCb4m2L+mi6PsOTOGZLJ2Tzkery/aocS3PcuNZba2+BxsUVHnZmdpdfj9B/2yhkHG0LhMEG45WMWiueOYPjSTyXnp7CmrZ9HccWw5KO+hhRBCCBG62E0QyhJj0Q6H3dL+skl3Tbt91ZKtyShUu0kPm7cWt7WdBKH0yRQxzl9t29FAn/aulVAG+sQSpZQVIzn4gtb676c+rrWu1lrXNn/+FmBVSmUCh4CBLXbNbt4WcwqznSx4cROvrD9A8fE6hvVNjXxi6cApYLLE9TLj6UMyqWn0sOWQfC+OWMMJOLwprvsPrtljVJGG3X+wpeGXGtdCQ3ytDJg/Oz/Qc3DK4Az2V9STk5HM/Nn5UY5MCCGEEPEkhhOEknwRbXParZ2qIDQpE6kJqe0OKUn21tJkbfscksQWsc4/tKKta8Xr81LbVBtatW0710qsUEZPgaeBnVrr37WxT9/m/VBKFWH8HCwHPgWGKqXylFIJwHXAGz0TeXim5WeyaO44fvGP7QC8vulQ5BNLE1OMqrGS+E0QTh1sJIRWSh/CyJV8DNoX5/0Hy0hJtFA4oBMVkMMvA58Hdr3fdYH1MH+CdE2xLLsXQgghRHhiN0FoMkOiQ5ZviqAcNmunqqLA6K1W09R2VVSyrsXbTuIEqw3MiZLEFjErJcGCUm0PKaltMia/djTFGOKmgnA6cANwnlJqc/PHJUqp+Uqp+c37XA1sU0p9BjwGXKcNHmAB8C7GcJOXtdbbo/GPCMW0/Ex6JRsJ4Bun5nRuYmnuTKN6rDEu/o9Pk5GSyMh+DlZKv7XIFS+HhFTInhjtSCK2uricorx0LOZOvLUdMAFS+sLOf3ZdYD1sWJ9U0pKsgYpKIYQQQohQWaIdQLtsTkm+iKCcdiulVa42H+9o8AIYlVFtVUV5PR5SVQPaXyXYFnuaJLFFzDKZFA5b29W2/tf/GTTFeAUQfDLRyX0WAYvaeOwt4K1uCK3LffjFMQ5Xuhg3MI2/rN3PlPyMyJOEebPgk4WwbzWcc2HXBtpDpg/J4NlV+2hwe7EnRDCg4my3ZznkzgCzNdqRRKS0qoE9ZXXMnTyocycymWD4JfDZS9DUAFZ71wTYg0wmxeS8dFZLglAIIYQQYYrdCkIwlnDK8k0RhNNu7bAHYXuDF8BIfLRVFVVbZbyxVv5BJG2xpUkSW8Q0p93a5pCS6qaOE4SJ5kQSzYnxUkF4VlhVXMb3/roJgDvOHcKiueM6N7F0YBGYE6Dkoy6MsmdNH5KJ2+vj070V0Q4l/lSUwIm9cd1/cHXzctpOVdICrHgU0nKgqQ72NF8PJR8b2+PI1MEZHDzRwIGK+miHIoQQQog4EuMJQqkgFME52ulB6PK4cPvcoVUQtlEVVVtl/KJtTkprPxCbU5LYIqY57JYOKwhDWY4fDxWEZ4stB6u4cGQflIJJeemBnoQRTyy12iG7KK4HlRTlpWM1K1bulmXGYduz3LiN6/6D5fRKsjK8b/vfyzo0YDysegysSfD5P43k4CvzjO1xZEpzH8K1JZIwF0IIIUToYjtBKMs3RRscdiu1jR58Pn3aY/5Kp1AShG1VRdVXG2+qrcnp7QdiT5MktohpRrVt8H6dgWulo2rbdpLpoufNn53PocoGRvZz4LQbS0Kn5Wd2bmJp3kwo3RJ301v9khIsjBvUixWSIAxf8XJwDIDModGOJCJaa1YXlzE1PwOTqd0OAx3LmwXXLDEGlWx91UgOXrPE2B5HzumdSq8ka6CyUgghhBAiFLGdIJQKQtEGh82C1lATJPHhT2R0pirK1ZwgTEjp1X4gNqcksUVMa7cHobvjJcYgFYSxxtXkZdP+SqY0T+/tErkzAQ37VnXdOXvYjCGZ7CitpqLOHe1Q4ofPa1TJDT4XVCeTa1Gyr7yew1Wuzi8v9subBUPOB08DjPha3CUHwehDOGVwhgwqEUIIIURYYjxBmCbLN0VQ/qqZYH0Iw6kgbPQ20uhtPO2xpjojQWh3dFBBKD0IRYxrrwdhV1Tbip732YFKGj0+Jud18P0pHNkTwWKHkvhdZjx9SCZaI1VT4Ti82XifFcf9B1cF+g92UcK85GPYv9r4fMvLxv04NGVwBocqpQ+hEEIIIUIX4wlCJ7hrwdv2MApxdnI0JwiDVUaFU0EIBE18eOqMZXZ2Rwe/cPirXPXpS52FiAXt9eusdldjVmbslvYndaYmpLY58Vv0vLUlFShl9N3rMpZEGDQ5bpMhAGOynaQkWmSZcTj2LDNu82ZHN45OWFlcRl+HjbzM5M6fzN9z8NrnwDkQ+o8x7sfhdeGvMJZpxkIIIYQIVWwnCP0TZF3yi6lozdlOgrCq0ajoC6UqCgia+PDVGwnC1LQOlizZ00B7oVGqq0RsctqtNHp8uJq8pz1W3ViNI8GB6mBpofQgjC1r9pQzvK+DtKSErj1x7kw4th3q4jPBZjGbmDI4PfJpzmej4g+h72hIyYp2JBHx+TRrisuZlp/R4fexkBzaeLLnYM50KNsFVy82tseZc/qkkJ6cIMuMhRBCCBGy2E4Q2tKMW1lmLE7hsDUvMQ6SIPRXBIZaQRgs8aFdVXi0iaTk9pOM2JzGrSwzFjHKYbMAbS/H7+g6AeNaqW2qxad9XR6fCI/b42Pj/hNdu7zYz99rbe+Krj93D5k+JJN95fWyrDIUjbVwYG1cTy/+8lgN5XVupg3pov6DM+48eR3kToe645Da19geZ5RSTBmczto9FWhZ5SCEEEKIEMR4gtCffKmMahgi9jiTOl5i3GEFYfPk1mAJQlNjFTUqBWXq4BKRJLaIcf7l+MGS6dXu6g6vEwBnohOf9lHXVNfl8YnwbDlYiavJ17UDSvz6j4OEFNgb330IAakiDMW+VeBriu/+g7uN6ripXdV/sKWc6cZtHCfMT/YhbIh2KEIIIYSIA7GdIPQvMZYpseIUHVVF2S12rGZru+dorwehxV1NnQqhn5FUEIoYd7Jf5+kTv8OpIPTvL6LLv1ywS/sP+pmtMGhqXA8qGdo7hazURFbslmWVHdqzHMyJxv95nFpVXEZuRhID0trvoxqR9MGQ0hf2rez6c/eQqc1/SJBlxkIIIYQIRWwnCCX5ItqQkmjBbFJtVhCGkvQI9CAMUkFobaqm3tzxOSSJLWKds6MKwsSOKwjbu1ZEz1pbUsHwvqmkJ3dx/0G/vJlQ9gXUHOme83czpRTT8zNYtbsMn0+WVbareDnkTAVrNyTXeoDH62Ptngqm5nfR8uJTKWUsM963Km4HkQ3pnUKG9CEUQgghRIhiPEGYZtzK8k1xCqUUDpuF6jaqokJZNunfJ1hVVKKnBpclhARh4DUqSWwRmwIJwiDVtqEuMW7vWhE9p8nrY/3ebuo/6Jc707iN42WV04dkUl7n5ouj8nptU3UpHN8Z1/0Htx2upqbRw7TuWF7slzMdakqhYk/3PUc3MvoQZrB6T7n0ITzVikdPn05d8rGxXQghhDhLxXiCUCoIRdscdmubFYShJD0SzAnYzLagU4yTvDU0hZQglD6ZIrb5B/qceq1orUOutg0M9AlyrYies+VgFQ1N3u7pP+jXbwwkOk//xTmO+PsQrtwtfQjbtOdD4zae+w8295nslv6DfrkzjNs4XmY8JT+D0ioX+2VwT2sDxsMr805+ryv52Lg/YHw0oxJCCCGiKrYThFY7mBNk+aYIymm3ttmDMJQEIRiVUTVNp1eZJOk6PInOjk+Q6ACUJLFFzHLYm/t1npIgdHldeHye0CoI2xnoI3pOt/Yf9DOZjWWVcTyopH+ancGZyayQBGHb9iyHpEzoMzrakURsdXE5w/umkpmS2H1PknkOJGfB3vhNEE4dbHy/kGXGp8ibBdcsgZe+Bf9YYCQHr1lycoq1EEIIcRaK7QShUkaFliRfRBAOWxsVhI2hVUWBURl1alWU9vlI1bX4EkJIEJpMYHNIElvErESLGZvVdNq14n/dh1VBKAnCqFpbUsE5fVLI6M6ECBjLjCv2QNXB7n2ebjR9SCbrSipwe3zRDiX2aG1UEA6ebfwMi0ONHi+f7q3o3upBMN6H5kyL6wrC/KwUMlMSWV0sCcLT5M2C1L6w6XmYeIskB4UQQpz1Yv+doS1Nlm+KoJx2a9DBCzXumpAGL4BRGXVqXzVXQx0JyntyAElHbGmSxBYxzbhWWvfr9L/uQ7lWUqwpKJT0IIwio/9gBZPzujkhAsagEojracbTh2RS7/ay+UBltEOJPcd2QO3RuO4/uGl/Ja4mH9O6a0BJSznToeoAnNjX/c/VDYw+hOms2VMhfQhPtecjOP4l9B4J65+J69YKQgghRFeIgwShU6qzRFAOu4WqU5IeXp+Xmqaa8CoIT6mKqqk0lqWZ7CFUEEJzlWtlaPsKEQXBqm39r3uHteMEoUmZSElIkQrCKNp2qIp6dzf3H/TrPQrs6XG9zHjq4AxMCllmHEzxcuM2rvsPlmNSMHlwNy6398uZbtzGcRXhlMEZHKl2sa9c+hAGlHwML98IaCi6zVhe3LInoRBCCHEWiv0EoT1NqrNEUI7mCsKWfxGvbao1HgujB+GpSY+6KuMXSktyr9ACsadJElvENGeQgT6BBGGo1bZBrhXRc9bsqQC6uf+gn8lk9CGM4wpCZ5KV0QOcrJIE4en2LIeMoeDMjnYkEVtdXMbo7LTAEKZu1Xsk2HvFdYLQvxR7tfQhPOnQRhh/g/F5zvSTPQkPbYxqWEIIIUQ0xX6CUJYYizY4bFbcXh+NLXpM+RMYnakgdNUYv4gnhJoglD6ZIsY5ggz08S8XDvVacSScvhxf9Jy1JeUM6Z1CVmo39x/0y5sNVfvhxN6eeb5uMH1IJpsPVFLb6Ol457OFp9EYuBHH1YN1jR427a9kWnf3H/QzmWDQtLgeVDI4M5ms1EQZVNLSjDuhrswY1pM51NiWN8vYLoQQQpyl4iBBKMkXEZzTblQOtKyMClRFhVFBWOuuxadPJhkbmxOEiakh/vIhSWwR49qtIAyn2rZRKgijweP18WlJBZN7onrQL9ffhzB+l9tNH5KJx6dZVyJJkYADa8HTENf9Bz/dW4HHp3suQQhGRe2JEqg+3HPP2YWMPoQZrNlTLn0IW9q30hhCo1S0IxFCCCFiQuwnCP3LN+UNjTiFozlB2HJQSbhVUakJqWh0YGkyQFPdCQDsjhB/GZcktohxDpvltIE+/gRhSkJKSOdITUiVCsIo2X64mrqe6j/olzUMknvH9TLjCTm9SLSYWLFLEoQBxctBmSF3RrQjidjq4nISzCYm5vRgwtzfhzCOqwinDs7gaHUjJWV10Q4lNlQegMr9J/9vhRBCCBEHCUKbE7QX3PKGRrQWtIKwMfyqqJbHAXjrjQRhijPE6Yj2NGiqB487tP2F6GFOu5WaRg8+38k/tFQ3VpNkScJqCq2HlyNRehBGy9rmCrgeGcjgp5SRRNr7Sdz+gc5mNTMxtxcrpQ/hSXuWQ/YksIX2MzIWrSouZ9ygNOwJ5p570r6jIdEB+1b03HN2sSnN3z/8/UzPevtXG7c506IbhxBCCBFD4iBBmGbcyhJOcQp/grBlbzV/hZMzMbQJxP4BDS0ro3SDUQ2YmhbGEmOQKkIRsxx2K1pDTYtebDXumpAHlID0IIymNXsqGJyZTO9UW88+cd4sqCmF8uKefd4uNH1IJl8creFYjSvaoURffQUc3hzX/Qcr691sO1zFtPwQ/4DXVUxmGDQlrisI8zKT6S19CE/at8pI+vYZFe1IhBBCiJgRBwnC5kSPJF/EKRw2CxC8B2E4gxdaHgegXJXUaRsWa0JogUgSW8S4YMvxq93VIV8nYFxTLq8Lt1cqZXuS16eN/oM9ubzYL2+Wcbs3fvsQzhhiJJJWF0tShJKPAB3X/QfX7KlAa5g2JArXQ850KN8FNUd7/rm7gFKKqfkZrJY+hIZ9q4ykr6kHK1GFEEKIGBf7CUJ7mnHbUBnNKEQMClQQNrSuijIrM0mWpJDO4U8QtqyMMrurqVGh9WUDTr5GJYktYlSw5fg17pqQl+JD8GR6rFFKDVRKLVdK7VBKbVdK/SDIPt9USm1RSm1VSq1SSo1p8dje5u2blVLrezb64HYcrqam0RNYHtij0gdDav+4HlQyqr8Th83Cil2yzJji5UbF1IAJ0Y4kYquLy0hKMDMmO63nn9zft3Ff/FYRThmcwfGaRvac7X0Ia49D2ReyvFgIIYQ4RewnCAMVhJVRDUPEHkcbU4xTE1JRIU6k81dQtUx6WNxVNJjCSBD6X6OSxBYxymHrmgpC/3ExzAP8WGs9EpgC3KGUGnnKPiXAbK31aOBXwFOnPH6u1nqs1npi94fbsUD/wbwoVEwpBXkzYe+KuO1DaDYppuVnsnJ32dldNaW10X8wdyaYLdGOJmKrisuZlJtOgiUKb1/7jQFrslF5Fqf8g47O+mXGgf6DMqBECCGEaCkOEoRpxq1UZ4lTWM0mkhLMpyU9IqmKallBmOCpocESeuJElhiLWNdWv87OXiuxRmtdqrXe2Px5DbATGHDKPqu01iea764Bsns2yvCs2VNObkYSfZ093H/QL3cm1B2H459H5/m7wPShmRyucrG3vD7aoURPxR5jYmsc9x88Vu1i17FapuVHIVkOYLbCoMlxXUGYm5FEX4dNBpXsWwUWO/QbG+1IhBBCiJgSBwlCqc4SbXPYrEErCEOVZE3CpExUNZ5MQNs9NTSGlSCUKlcR2xz24P06w0kQBioIG2O6gjBAKZULjAPWtrPbfwJvt7ivgfeUUhuUUre1cd7blFLrlVLrjx8/3mXxBuP1adaVVASqfqLC34ew5JPoxdBJ05sTSmf1NOM9y43bOO4/uLq56q3HB5S0lDMdju2AuviswFNKMWVwOquLz/I+hPtWwsBJYAmx17QQQghxloifBKFUEIognPbWCcKaxvCqokzKRGpCaqtlk0m+WjzW0M8hSWwR607tQejxeahrqguvgjAx9nsQ+imlUoBXgTu11kEDVkqdi5Eg/GmLzTO01uOBizGWJ8869Tit9VNa64la64lZWVndEP1JO0urqXZ5mByN/oN+vXIgbVBcDyrJy0ymv9N29iUIVzx6sn9k8XJwDoTqQ8b2OLRqdzlOu5WR/cP4+dzV/H0I98f3MuOy2kaKj5+lfQhdVXBkqywvFkIIIYKI/QShyWw01ZbqLBGEw25ptWwy3ApCgFRraqtlkym6Dm9iGL+AWG1gsUkSW8Ss5AQLJnVyoE+tuxYIfdo3xMcSYwCllBUjOfiC1vrvbexTCPwfcIXWOlAKpLU+1Hx7DHgNKOr+iNu2tsRYBhiV/oMt5c4y+hD6fNGNI0JKKaYNyWRVcTle31lUNTVgPLwyz0gOlnwCWcNh6c3G9ji0ak8ZUwanYzaF1mO4W/QfZ/y83xu/y4yn5p/lfQj3rwW0DCgRQgghgog4QaiUukgp9YVSardS6p429rm2xTTJFyOO0pYmyRcRlFFBeHKKcbW7OlDpFCpHoiNQFeVpcpOiGtD+voKhsqVJElvELJNJ4WhRbet/vYdzrcTDkBJlTCd6Gtiptf5dG/sMAv4O3KC1/rLF9mSlVKr/c+BCYFv3R922NXvKGZSeRP80ezTDMAaVNJyAo1H9cnTKjCGZVDU0seNw7L5+u1zeLLhmCbxyEzRWGYMZrllyctl4HDlQUc+BioboLi8GsCRC9iTYtyK6cXTCoPQk+jltgSXbZ519K8FkhQExMYdKCCGEiCkRJQiVUmbgcYxlWCOB60+dFKmUGgr8DJiutR4F3BlxlHanLN8UQTls1sCQEq01Ne6a8CsIE05WENZWGRU7yr9sOFQ2pySxRUxz2KyBalv/6z3VGvq1kmhOJNGcGOsVhNOBG4DzlFKbmz8uUUrNV0rNb97nF0AG8Kfmx9c3b+8DrFBKfQasA/6ltX6nx/8FzXyB/oNRXF7slzvTuN0bv30Ipw0xqqZWnG3LjPNmGdN3AcbPi8vkIMCqYuP/LWoDSlrKnQFHthlJ8zhk9CHMYO2es7QP4b5VRhVtQlK0IxFCCCFiTqQVhEXAbq31Hq21G/gbcMUp+9wKPO6fFtm8ZCsyUkEo2uCwn0wQurwumnxNYfVVA2PppL8qqrbS+CXEnNQrvEDsaZLEFjGtZb/OKrfx/TTsatsW10os0lqv0ForrXWh1nps88dbWusntdZPNu/zba11rxaPT2zevkdrPab5Y5TW+qFo/ls+P1JDVUNT9JcXAzgHQHp+XA8q6Z1q45w+KWdfH8KSj43KQXsGbPnryZ6EcWZVcTlZqYkM6Z0S7VCae9fp5qWq8Wnq4AzKat0UH6+Ndig9y10PhzfCoKnRjkQIIYSISZEmCAcAB1rcP9i8raVzgHOUUiuVUmuUUhdF+FzN1VmVER8uzlxOu5WaRg9enw5UNkWSIPQf21BjVBBaU8Ks2pEktohxzhbJ9K64VkT3WltiLP+L6oCSlvJmGkvzvJ6O941BT35UzODMZD7dW4GryQsYVWlPflQc5ci6UcnHRg9CkxVGXt683Hhe3CUJtdasKi5nWn4GRheBKMueCOaEuF5m7J+Mvrr4LFtmfGg9+DwyoEQIIYRog6Wbzz0UmANkAx8rpUZrrStb7qSUug24DWDQoEHBzyTJF9EGR/N01hpXE9WNzX3VIqkgbD7WVWO8WU5ICbOC0OaEsi/CO+YM1dTUxMGDB3G5XNEORbQwf6wNj9fHzp07yWjK4NGRj9J4uJGdR3aGfI4fD/oxCsXOnaEfEwqbzUZ2djZWq7VLzxvP1u6pILuXnexeMbIMLncmbFgCRz6DAROiHU3YCrOdLFq2m0aPj437ToCCBS9uYtHccdEOrfsc2gjn/QLe/IGREPH3JDy0Ma6WGu8+VsvxmsbYWF4MYLUb/evieFDJwHQ7/Z021uyp4IapudEOp+fsWwUoGDQ52pEIIYQQMSnSBOEhYGCL+9nN21o6CKzVWjcBJUqpLzEShp+23Elr/RTwFMDEiRODN0OR5ZuiDc7mBGF1g4cab4RVUYkO3D43jd5G3LWVANgdYf4iYk+TJHazgwcPkpqaSm5ubmxUewgADp6op9rlYUQ/B2UNZRytO8rw9OGYTeaQz7Gveh8en4f8tPwui0trTXl5OQcPHiQvL6/LzhvPfD7N2pJyvjKiT7RDOcnfh7Dkk7hMEE7Lz+QP143lP59dz+/e/5I9ZXUsmjsu+kMvutOMO2HNk8bn/iWVebPiKjkIxvJiILb+r3Knwye/g8YaSAyv73EsUEoxJT+Dj744jtb67PlZvW8l9B1t/FFXCCGEEKeJdInxp8BQpVSeUioBuA5445R9XseoHkQplYmx5HhPRM9mc0JTHXibIgxXnKkcNiPHXdVwsoIw7CElzYMaqhur8dQZS4yTHOEuMW4eUuLzhXfcGcjlcpGRESNLwUSA2aTw+oy/wXh9XhQKkwrvR4BZmfFqb5fGpZQiIyNDKk5b2HWslhP1TUzOi5HlxQCpfSBreFwPKvnKiD70dSSyft8JvjV5UGwlnLrL/lXgHARpAzveN0atKi5jYLqdgekxUk0LkDMNtDeu+xBOGZxBeZ2bXcfOkj6EHjcc+FSWFwshhBDtiChBqLX2AAuAd4GdwMta6+1KqQeUUl9r3u1doFwptQNYDtyttY6s2YktzbiVCi1xikAFoaspMDwh7MELzfvXuGvQzZWqqWlh/uJoSwPtA/dZ8ka7A5IcjD1mpdBa4/NpvNqLyWQK+//JrMz4uiEJLq+X1tbsMX5U+vuExYzcmbBvddz+sW5VcRmVDU0o4Pk1+wKTcc9YWhtLKnPidyCD16dZs6eCaYNjLJk7cDKYLHHdh3Bq8/cX//ebM17pZvA0GMldIYQQQgQVaQUhzZMhz9Fa5/unPWqtf6G1fqP5c621/pHWeqTWerTW+m8RR+lfCiDLjMUp/D0IqxpOJgjDriBs3r/aXY12VdKkzdiTwlwy5H+NyjAdEaPMJiMJ59VGgtCsQl9a7GcymfBqL1oH7wYhusbaknIGpNnJ7mWPdiit5c00qvkPb4p2JGFbVVzGghc38f2vDEUD35mTz4IXN53ZScLyYqg7HtcJkZ2l1VQ1NDFtSIwlyxOSof+4OO9DmMSANPvZkyDc1/x/FcfXgxBCCNHdIk4Q9ih7mnErFYTiFM4uSBD6exZWu6sxNVZRo5JRpjAvDf9rVJLYIkYFEoQ+jU/7IkoQ+o/xaVlK31201qzdU8HkvPTYq6zMmWHclnwU3TgisOVgFYvmjuNbU3JQCuoavSyaO44tB8/g9xWBhEj8LqlcudtI4E6NtWpaML6uhzeCuy7akURsyuAM1uypwOc7C/7os28VZA6D5BirRhVCCCFiSHwkCAPVWSeiG4eIOY7AkJImatw12C12rKbwJqG2rCC0uKupUynhBxJ4jZ7Bv2x2gyc/Kj6tgmdVcRlPflTcqfMeOXKE6667jvz8fCZMmMAll1zCl19+yd69eykoKGi17/3338/ChQsBmDdvHnl5eYwdO5bx48ezevXqoOd/9NFHee655wCYM2cO69ev71S8AEuWLGHBggWdPo/f8ePHueiiiwL3WyYIvT5vWMNJAudoThB2dR9CcdLuY7WU17ljb3kxQHIG9CkwBpXEmfmz85mWn4nDZmVkPwfrSiqYlp/J/NldN3An5uxbBclZkDEk2pGEzf+zYVVxOUN7p9DbYeuSnw1dKncG+Dxw8NOO941RUwanU3E29CH0eWH/GqkeFEIIIToQJwnCNONWki/iFMkJZswmFRhSEu4EY2hRQdhYjbWpmgZzJAnCNONWlhiHpTDb2WqZn38ZYGF25BMGtdZceeWVzJkzh+LiYjZs2MCvf/1rjh49GtLxjzzyCJs3b+bhhx/m9ttvP+1xj8fDM888w9y5cyOOsSdkZWXRr18/Vq40qojMqkWCUHvDHlBinEMShN3Nv9xv8uAYGlDSUt4sOLAWPI3RjiRiRXnpbNx/ArfnDK+E3b/KmF4ca5WoISjMdnLHCxtZs6ecafkZXfKzocsNnAzKFNfLjKecLX0Ij26Dxuq4rqYVQggheoIl2gGERJZvijYopXDYLFS7mqhJqAl7eTGcTBDWuGuweWpwWcI/h1QQBvfLf25nx+HqdvfpnZrIjU+vo48jkaPVjQzpncIfPtjFHz7YFXT/kf0d3Hf5qDbPt3z5cqxWK/Pnzw9sGzNmDAB79+4NOfZZs2axe/fu07YvW7aM8ePHY7Gc/Pb5/PPP8+1vfzuQPCwqKqKiooJbbrmFPXv2kJSUxFNPPUVhYWGb2zuyd+9ebrnlFsrKysjKymLx4sUMGjSIefPm4XA4WL9+PUeOHOG3v/0tV199NQBf//rXeeGFF5g+fTqmLuhBGFhiLNO6u82akgr6OW0MiqWJrS3lzoQ1f4KD6yE3Pn/ZLspNZ/HKvWw9VMWEnF7RDqd7VB2Eyv0w5bvRjiQi0/Iz+cFXhnL/P3dwvLaRBS9uYtHccbE1edrmgH5jTi7ljkMD05PI7mVndXE5N03LjXY43WffKuM2jgf2CCGEED0hTioIJfki2ua0W6lq8FDtjqyC0Gq2YrfYqXZXY/fW0mQN/xySxI6c026ljyORQ5Uu+jgSA30lI7Vt2zYmTJjQ5uPFxcWMHTs28PHkk08G3e+f//wno0ePPm37ypUrTzt/fX09mzdv5k9/+hO33HILAPfddx/jxo1jy5Yt/Pd//zc33nhju9s78r3vfY+bbrqJLVu28M1vfpPvf//7gcdKS0tZsWIFb775Jvfcc09g+8SJE/nkE2M5aKsehL7IehCamntzSgVh9zD6D5bHZv9Bv5xpzVVT8bfM2G9SnlGdua6kIsqRdKN9ze0R4nhJZZ3b+D7z1tYjfGvyoNhKDvrlTDeS5U2uaEcSsSmDM1hbUn5m9yHctxLSBoEzO9qRCCGEEDEtPioIrXYwJ8ryTRGUw26luqGJuuQa+iX3i+gcqdZUatw1JOtaPBEkGUlIBZQksU/RXqWfX2C66HlD+Mva/fzg/KHd+otgfn4+mzdvDty///77Wz1+99138+CDD5KVlcXTTz992vGlpaWMGDGi1bbrr78eMKoOq6urqaysZMWKFbz66qsAnHfeeZSXl1NdXd3m9o6sXr2av//97wDccMMN/OQnPwk89vWvfx2TycTIkSNbLaXu3bs3hw8fBk4mCD0+LxotS4xjUPHxOspqY7T/oJ89DfoWQsnHMOeeDnePRZkpieRnJbOupJzvzDlDexDuWwmJDqNnZJx6d/sRzEpxx7n5/GXtfqbkZ8RekjBnOqxeBIfWGz0J49DUwRks3XCQL4/VMLxvBO9/Yp3WRsJ86AXRjkQIIYSIefFRQQhGFaEkX0QQRgWhMcXYkRjZm1tHooPqxipSdR2+xAh6HJlMza/Ryoie/2zlTw4umjuOH104jEVzx7XqSRiJUaNGsWHDhoiP9/cgfP/9908baAJgt9txuVpXi5xa7dXT1V+JiYmBz7U+WQXicrmw2+0AmJTCpBQen5HckyElsedk/8EYThCueBTS84zBDE0NxraSj43tcaQoL531+07gPVOrpvavNnrkRXCdx4JPdh1ny8Eqzhveu8t+NnSLnKmAius+hP5+p6uLz9A+hGW7oL4srqtphRBCiJ4SPwlCe5os3xRBOexWowehuyaiJcZg9CGsdFViVV6Uf+BIuOxpksQO05aDVa36Sk3Lz2TR3HFsORj51/G8886jsbGRp5566uTzbNkSWGrbWSNGjDitN+FLL70EwIoVK3A6nTidTmbOnMkLL7wAwIcffkhmZiYOh6PN7R2ZNm0af/vb3wB44YUXmDlzZofHfPnll62SnGaTwutPEEayxFjJEuPutLakgj6ORHIzYrT/IMCA8bD73+B1G8NKSj6GV+YZ2+NIUV46NS4Pnx/puHo37tSVwfHP4zoh8u+dxwC4fGx/oGt+NnQLey+jSnPfimhHErHsXkkMTLefuYNK/D0iZUCJEEII0aH4WGIMUp0l2uSwWalqaMTdVBvRkBKA1IRUDlbuB0D5+wmGy+aUJHaY5s8+fXnftPzMTi0jU0rx2muvceedd/Kb3/wGm81Gbm4ujz76aCciPeniiy/mhhtuaLXNZrMxbtw4mpqaeOaZZwBj6fItt9xCYWEhSUlJPPvss+1u78gf//hHbr75Zh555JHAkJKOLF++nEsvvTRwv7MJQqUUZmWWISXdwN9/cMrgjNjtPwjGFOOr/gwv/gcs/zWU74Jrlhjb40hRnlGlua6kglH9Y2gyblfYH//9B7N7GZXPRbknp3l39mdDt8mdDhueBY8bLAnRjiYiUwdn8N6Oo/h8OjDQ6oyxbxWk9IH0wdGORAghhIh5cZQgTDOWCAhxCqfdSk1jLYnQqQrCGncNAJbk9A72boMtTSoIY0T//v15+eWXgz62bdu2Vvdb9iBcsmRJh+fOyckhIyODXbt2MXToUD788MOg+6Wnp/P666+HvH3evHnMmzev3eddtmzZadtPjbm2tjbw+RtvvME//vGPwH2zUni1D1RkCUIwBpVIBWHXKymr41hNY2z3H/Q75yJI7Q8H1sCsn8RdchBgQJqdAWl2Pt1bwc3T86IdTtfatxosNug/LtqRRGxdSQU5GUn0ddqiHUrHcqbD2ifh8CYYNDna0URkyuAMXl5/kM+P1DCy/xnUh1Bro4IwZxrE8h9ehBBCiBgRP0uMpTpLtMFht9BEHUCnKghrPUZiJSElLbJApMr1rPHwww9TWloa7TDadfz4cX70ox/Rq1evwDazSQWSe/6JxOEyK7MkCLvB2uaJuv5+YDGt5GNoOGFMM17/f8b9OFSUl866kopWfTvPCPtWwoCJYEnseN8Y5PNpPt1b0ap6MKb5KzXjeJmx/w8TZ9wy48r9UH1IlhcLIYQQIYqfBKH0dxNtcNqtKLPRLD/iCsJEB/U+F17AlhphBY/0yTxrDBs2jFmzuqdq6qGHHmLs2LGtPh566KGwz5OVlcXXv/71VtvMJoVPR77E2H+cJAi73po95WSmJDI4MznaobTP33Nw9t2gfTDjLuN+HCYJi/LSKat1s6esLtqhdJ3GGjiyJa6XF+8+XsuJ+iaK8uIkQZicCVnD43pQSf80OzkZSWdegnDfKuM2jq8HIYQQoifF0RLj5inGWssyAdGKw2ZFmYwEYcQVhFbjuFqTwp4a6RJjmbQtOu/ee+/l3nvv7ZZzm00KjdE/MOIEocmMx+PpyrDOekb/wQqmDE6P7f6DAIc2Gj0H+42Bf/8K3LXG/UMb426p8aTmCrVPSyrIz0qJcjRd5MBaI3GbMzXakUQsUE2bFwfL7f1ypsOWl8DrAXP8vLX2e/KjYvIykllbUhHoQ7iquIwtB6uC9gqOG/tWGu1fskZEOxIhhBAiLsRPBaEtDbTX+GVEiBZaVRAmRl5BCFBtMpGSFmETdFsaeBrA0xjZ8UJ0M1NzgtCkTBEnoqSCsOvtK6/nSLWLyfHQf3DGnUYi0OaEvqONZZV5s4ztcSY/K5mM5ATWNSekzgj7VoMyQ3ZRtCOJ2LqSCvo6bAxMt0c7lNDlTjfen5Z+Fu1IIlKY7eTTfRVUNTSx80g1q4rLWPDiJgqz43yAz75VRvVghC01hBBCiLNN/PzE9E+WlSWc4hQOuxVldhmfR7jE2F95WGMykeLsRAUhSBWhiFlmpQAfpgirBwFMSoaUdLW1Jcayvqnx0H+wpZzpcOBTY3prHFJKUZSXHqhYOyPsWwX9x0JifFZEaq1ZV1JOUV4cVNO2lDPDuI3TPoTT8jP57VWFADz89ucseHETi+aOi82p0aGqOQIVxTAofqtphRBCiJ4WPwlCSb6INjjtJ5cYd2aKMcBRZcdiTYgsEHvzMAhJYosYZTYpUEYFYcTnUGa01vi0rwsjO7ut2VNBZkpC/C1zzZlmVE2Xbo52JBGblJvOocoGDlU2RDuUzmtywaH1cZ0Q2V9Rz9HqxvjpP+iX2gcyhsR1H8JLC/vjtFv4ZFcZ35o8KL6Tg9Ci/6AMKBFCCCFCFUcJwjTjVqbEilM4bBYwN6AwYbdEtiTJnyAsM3diSZMksUWMM5v8FYSdSxACUkXYRYz+g+VMzsuIr4opONn4f298Vk0BgUTUp2dCFeHhjeB1x3VC5GT/wThLEILxdd+/Bnzx+b1xVXEZDU0+Ei0m/rJmP6uKy6IdUufsWwXWZOhXGO1IhBBCiLgRRwlCSb6I4BzNPQgTTSkR/4LtTxCWW2yRByJJ7PCtePT06aclHxvbO+HIkSNcd9115OfnM2HCBC655BK+/PJL9u7dS0FBQat977//fhYuXAjAvHnzyMvLY+zYsYwfP57Vq1cHPf+jjz7Kc889B8CcOXNYv359p+IFWLJkCQsWLAjrmM2bN/PWW2+FvL9RQag5tP8wL774YmD71q1bmTdvXojnMBKEPp9UEHaFAxUNHK5yMTnelhfDyemt/kqdODSin4PURMuZscx4X3P12qAp0Y2jE9aVVJCenMCQ3nFWTQuQOwMaq+DotmhHEjZ/z8FbpufR6PHx04uHseDFTfGdJNy3CgYWgdka7UiEEEKIuBE/CULpQSjaYDWbsFoasZAc8Tn8Q0oqLYmRB+J/jUoSO3QDxsMr804mCUs+Nu4PGB/xKbXWXHnllcyZM4fi4mI2bNjAr3/9a44ePRrS8Y888gibN2/m4Ycf5vbbbz/tcY/HwzPPPMPcuXMjjrGrhJ0gVAqPx31agnD06NEcPHiQ/fv3d3gOf/VhLFYQKqUGKqWWK6V2KKW2K6V+EGQfpZR6TCm1Wym1RSk1vsVjNymldjV/3NSdsd72xiMs3vA+a5r7D04ZnMHiDe9z2xuPdHjsM9ueYV3pulbb1pWu45ltz4T03J05/rRjc6ax7sh6ntn6f/EVd/Oxz+5YzITcXny6t+0EYSzGHfTYfauh90hISo+vuFvuU1LBpNxeLN6+uMefu7PHkjONZ5yprNvxcnzFDSzZvpjvXQLfnDwIgEaPj+9dYmyP5bjbPH7jIji2o91q2s4+txBCCHEmip8EYaCCsDKqYYjYZLG6MBH58uAkSxImDdWd+Uuz/zXacCLyc5xp3r4HFl/a9seHv4HUfvD8lfD7AuM2tZ+xva1j3r6n3adcvnw5VquV+fPnB7aNGTOGmTNnhhX6rFmz2L1792nbly1bxvjx47FYLIFtzz//PGPHjqWgoIB164xfOCoqKvj6179OYWEhU6ZMYcuWLe1u78grr7xCQUEBY8aMYdasWbjdbn7xi1/w0ksvMXbsWF566SXWrVvH1KlTGTduHNOmTeOLL74AjOrEr33ta1zy1Qu49aqb+e0vH+aTTz5h7Nix/P73vwfg8ssv529/+1uHccT4EmMP8GOt9UhgCnCHUmrkKftcDAxt/rgNeAJAKZUO3AdMBoqA+5RSvbor0KkDxvK7z37Oi1uWkZ6cwMoDa/jdZz9n6oCxHR5bkFHAXR/dFfjldl3pOu766C4KMgo6OLLzx592bHp/7kpPokB1/P03puJucWxRXjq7j9VSVht8An2sxt2K1wMH1rbqPxgXcbdwpMrF/op6ivIyevy5u+JYnNkUWHtx18F/xVfcwLcnzWbxrl9R2riNAWl23t29ksW7fsW3J82O6bjbPN6jAX2yDUI3PLcQQghxJlJa62jHEDBx4kTd5lI9nw8eSIfZP4Fz/1/PBiZi3vinLyfJksyKmzpOcLRl+jMFTGhK57HbP+5452A8jfBgbzjv5zDrrojjiHc7d+5kxIgRxp2374EjWzs+qHIfVB0A50BIy2l/376j4eKH23z4scceo6SkJJD4amnv3r2MGDGCYcOGBbYdOXKEu+66i7vuuot58+Zx2WWXcfXVV/PKK6+wcOFC1q5d2+oc9913H5mZmXzve98DjCXGQ4cO5c9//jMff/wx3/3ud9m2bRvf+973yMzM5L777mPZsmX86Ec/YvPmzW1uX7JkCevXr2fRokVB/12jR4/mnXfeYcCAAVRWVpKWlnbaMdXV1SQlJWGxWPjggw944oknePXVV1myZAn/9V//xcbNmznOUbau+Zy/PLmYN998M3D+lStX8vDDD/PPf/6z3S9/o6eR3ZW7GZA6gLTEtHb3DUer100zpdQGrfXESM+plPoHsEhr/X6Lbf8LfKi1/mvz/S+AOf4PrfXtwfYLpt2fWSFYvOF9/mfLTzDrVHymKrKT8+ibGlpOstpdzZ7KPWQlZXG8/jiD0waHNaSpM8e3PvYYg10NONJywDEgjuI+eWyNy8P2w1Wc0yeV9OTgQ6piMe5W3LVweDNkDYPkrPiJu4XyWje7jtUweoCT5ERLjz53Vx1L2S6qXRXsSUiMr7hbHG/RTlz6BOekD4mruFsdX1cO1aXGcvt2eu76jx2RMYKDNQdZOHshRf2KQn7uU3X2Z5YQQggRbZaOd4kRJhMkOmSJsQhKmRrQ3s5N3HP4vNRZOlFUa0kEi12qXFtqJ5EX4F9WPOsnsP5pmPNTyJvVbSHl5+ezefPmwP3777+/1eN33303Dz74IFlZWTz99NOnHV9aWnpaIuv6668HjKrD6upqKisrWbFiBa+++ioA5513HuXl5VRXV7e5vSPTp09n3rx5XHvttXzjG98Iuk9VVRU33XQTu3btQilFU1NT4LELLriA9PQ0jlccBX16r87evXtz+PDhDuMwmZqXGMd4I36lVC4wDlh7ykMDgAMt7h9s3tbW9lPPextG5SGDBg3qVIzfHPMVFm0egNuyj2RTZsjJQTD6pmYlZVFaV0q/5H5hT3DvzPGnHdtUCq7qkBKEMRV387EpiRZMSlHj8rSZIIzFuFvxt7bwV7LHS9wtVLuaMJsUSYmWHn/urjoWmwNH7VGyUvvEV9ynHO9rSsOqQm/bEitxB44vL4HElHaTgy2P3Vq2ldsLb+9UclAIIYQ4E8RPghDA7pT+biIon6keT2PkA0aa3I04fV7qEjo5RdSeJknscPiTg9csMZKCeTNb34/AqFGjWLp0acQhPfLII1x99dVtPm6323G5XK22nTocpzum0T755JOsXbuWf/3rX0yYMIENGzacts/Pf/5zzj33XF577TX27t3LnDlzAo8lJycHlgXrIAlCl8uF3d7xMlH/EmOfjt0hJUqpFOBV4E6tdcfZ1zBorZ8CngKjgrAz53rhs3/jVmWMSbmaz6reZlbWXG6ecEFIx/qXw91eeDsvf/Ey3xnznbB+ue3M8acd6yykqHgVfGuV8ce8eIm7xbHXPbWa2goPi78ZvBVBrMYd8LdvQuNRuPSF+Iq7hQt//xET0+wsuaiox5+7q47lxF7W/W8Rd6WkxVfcLY7/jyG38NfPX2JM8jX87LzL4ybuwPEj51G09kqY8UP4ys/DOraob5EkCYUQQpzdtNYx8zFhwgTdriema/3Cte3vI846Pp9Pj14yRo9f9IOIz1Fx7LD+9hP5+mvPzu5cMIuKtP7btzp3jji3Y8eO0Hf+5Pda7/mo9bY9HxnbI+Tz+XRRUZH+3//938C2zz77TH/88ce6pKREjxo1qtX+9913n37kkUe01lrfdNNN+pVXXmn3/E888YS+9957A/dnz56tb7/9duOf88knuqCgQGut9fe+9z39wAMPaK21Xr58uR47dmy72xcvXqzvuOOONp939+7dgc8nTpyoN23apJcuXapvvPHGwPavf/3reunSpYF/V05OTqtz17vr9bbj2/TrH3ygZ82a1er8S5cuDfw7OrK9bLsurS0Nad9QBXvdAOt1mD9HACvwLvCjNh7/X+D6Fve/APoB1wP/29Z+wT46/JnVjmfWv6cLnp6sn1n/XtD77Vl7eK2e+deZeu3htUHvd+fxQY99fpJe++ssrY9sj6+4W9z/n/e+0Hn3vKmrG9xxFbfWWmufT+uHc7V+7TvxFXcL5bWNOuenb+rHl+/q8efuqmO11nrte3frmc+M0mtfvPLk8S9M0Wvfuzu2426xv8/n0+Mf+ZMet2Rq7H+9gx3/l8nG96NdH3TrcwcTyc8s+ZAP+ZAP+ZCPWPqInyElALY0qSAUp2nwNKDx0tgY+QTi2qoyHF4f9aqTyyZtabLEOBwz7jy9UjBvlrE9QkopXnvtNT744APy8/MZNWoUP/vZz+jbt2+nQvW7+OKL+fjj1n0qbTYb48aNY/78+YFlyffffz8bNmygsLCQe+65h2effbbd7R25++67GT16NAUFBUybNo0xY8Zw7rnnsmPHjsCQkp/85Cf87Gc/Y9y4cXg8ntPO4a8gPGfkKMxmM2PGjAn0aly+fDmXXnppSLGYlTkmh5Qoo3TzaWCn1vp3bez2BnBj8zTjKUCV1roUI6l4oVKqV/Nwkgubt3WL1Yc286MxvwpUDN484QJ+NOZXrD60ucNjt5Vva9Urq6hfEQtnL2Rb+baQnrszxwc9dvJ/sS0xAfatjK+4Wxw7OS8dn4YN+04fMhXLcQNw/AtoqGg1oCQu4m7BP0V6cl56jz93Vx0LsC0xkYXHT1C0bz1oTZHLxcKjx9mW2PH7k6jG3eJ4pRTTBkzBfPxGtpZ13EM4VuIOHN9rkvH1Hth+JWBnn1sIIYQ4E8XPkBKAl74FZbvhjjU9F5SIeUfrjnL+0vNxlV7Jzrvvx2IOP+/95caPeHHlPD5I68OKGzrx+nrhWqg9ApEOOjkDBBs2caa58sor+e1vf8vQoUOjHUpYqhqrOFhzkARfP4b2Tg9sb2xsZPbs2axYsaLVdOa27D6xm0RzIgMdA7sstq4YUqKUmgF8AmwF/Gug/x8wCEBr/WRzEnERcBFQD9ystV7ffPwtzfsDPKS1Xtze83V2SMkZQ2v4/SgYOBmuafdLFrPq3R4K73+P22YN5icXDY92OOFZ/wy8+UP43kbIyI92NBH51Zs7+MuafWy9/6skdKYXcCx4915YvQgm/ifseL1TLTOi5fk1+/j569v46O455GSE3oswJiy+BJrq4bYPe/ypZUiJEEKIeBdfPQht0oNQnK7abbQY0147NS4PvdpoMt+exppyHD4fddqF1jryHnL2NDj+eWTHirjx8MMPU1paGncJQn/Vn/eUHoT79+/n4YcfDik5CGA2xWYFodZ6BdDuxauNv4rd0cZjzwDPdENoZzalIGc6lHxkJAu7oQdnd0tKsFAwwMm6kopohxK+fasgpQ+kD452JBFbV1LBuEFp8Z8cBJh4i5EgXP+0MXwrzpKDAFOaKznX7qmIrwRhkwsOroeiW6MdiRBCCBGX4uudmCzfFEHUuGsA0D4b1a6mDvYOzl13AofPh0d7afQ2Rh6MJLHPCsOGDWPWrO75pe+hhx5i7NixrT4eeuihLjm3f/Kwz9c6gTN06NBWA006YlKmmEwQiijKmQa1R6FiT7QjidjkvHQ+O1iJqymOXttaGwnCnGlxmZgFqHE1sf1wFUV5GdEOpWtUHTSm52YNN5KEJfG3omBI7xQykhNYG28J88MbwdtoXA9CCCGECFv8JQib6sHjjnYkIoa0rCCsaogsQehpThC2PF9E/H0yfbE74VXEtnvvvZfNmze3+rj33nu75Nz+ycNen6Iz7SVitQehiKKc6cZtB30IY9mk3HSavJrNByqjHUroKvdD9aGTX/84tGHfCXz6ZP/BuFbyMSy9GXJnQn0FXL0YXpkXd0lCpRRFeemsLSmPdijh8X//OaUfpxBCCCFCE18JQnuacSsVWqKFQAWh1051w+mDGULha6gKJAj954uIPQ3Q0JlzCNFNvNqLSZmbp1RFfh6zMgeqEYUAIHMoJGfB3vhOECpFfC0z3rfKuI3jhMi6kgosJsW4QWnRDqXzDm00eg4WfAPqjkFqP+P+oY3RjixsRXnpHDzRwKHKhmiHErp9q6H3SEg6A5LNQgghRBTEV4LQ5jRuZZmxaCFQ8eeLvIKQhkps3lPOFwn/a7ShMvJzCNFNvNqLav627+1MBaHJjE/7OlWFKM4wShnL+vwJqzjkTLIyrE9qfCUI968yfu70HhntSCK2rqSC0dlOkhLiqy12UDPuNHoO5s407u/9xLg/485oRhWRyc1LvtfuiZMqQq8HDqyV5cVCCCFEJ8RZgjDNuJUKQtHCySXGkfcgNLmrsPhsQCcrCOU1KmKYT/swKzMAXl/kyT2TMgXOJ0RAznSo2m8se41TRXnpbNx/giZvnLy2960yqgdN8fV2zs/V5OWzg5UUnQnLi1tKHwyp/WHvimhHErHhfVNx2q2s3RMnCfMjW8BdKwlCIYQQohPi6x2lVGeJIKobq0myJAHmiCsILY1VmLWRIKxq7ERyT6pcRQzz+owlxsbnnetBCEgfQtFaoA9h/FYRFuWlU+/2sv1wJyrJe0rtMSjfHdcJkU37K2ny6jOj/2BLSkHuDCNBGKeV1iaTYlJuOuv2xkmCMLDcPn6vByGEECLa4itBGOhBWBnNKESMqXHX4EhwYDEpqiNMEFo9NZhVSuB8EZM+mWF5ZtszrCtd12rbutJ1PLPtmU6d98iRI1x33XXk5+czYcIELrnkEr788kv27t1LQUFBq33vv/9+Fi5cCMC8efPIy8tj7NixjB8/ntWrVwc9/6OPPspzzz0HwJw5c1i/fn2n4gVYsmQJCxYs6NZjvNrLv996n+IvPw+aIAz29Qkm0gThddddx65du8I6RsSR3iONP5LE8aCSolwjUbUuHoYznAEJkXUlFSgFE3LOsAQhGAnCumNQFr/f86YMTqekrI5j1a5oh9KxfauMyk1Hv2hHIoQQQsSt+EoQSnWWCKLaXU1qYioOuzXiCkKbpwaLKTVwvohJlWtYCjIKuOujuwJJwnWl67jro7soyOg4SdUWrTVXXnklc+bMobi4mA0bNvDrX/+ao0ePhnT8I488wubNm3n44Ye5/fbbT3vc4/HwzDPPMHfu3IhjjBav9vL+W++zZ9cXne5BCIQ9qOQ73/kOv/3tbyN+XhHjTCYjWRXHFYS9HTZyM5JYV3Ii2qF0bP9qsCZBvzHRjiRi6/aWM6KvA6fdGu1Qul7uDON2b3xNMG7Jv/R7Taz35fT5jH6ccVxNK4QQQsSC+OoILf3dRBDV7mocCcYvGJEmCJO8NdTYs7FbajuZIEwzbiWJDcBv1v2Gzys+b3efrKQsbn//drKSsjhef5zBaYN54rMneOKzJ4LuPzx9OD8t+mmb51u+fDlWq5X58+cHto0ZY/wCvXfv3pBjnzVrFrt37z5t+7Jlyxg/fjwWy8lvn88//zzf/va3A8nDoqIiKioquOWWW9izZw9JSUk89dRTFBYWtrm9I6+88gq//OUvMZvNOJ1OPv7Y+KXz8OHDXHTRRRQXF3PllVcGEnApKSnU1tYCsHTpUt58803Ov/Z8Pnj7fVZ9spbFj/+Of7z2dyorK7nlllsAuPDCCwPP53K5+M53vsP69euxWCz87ne/49xzz+XSSy/l/l/dT9KgJKYVTePqb1zNL37xC37xi18wcOBAhg4dyv33309mZibbtm1jwoQJ/OUvf0EpxcyZM5k3bx4ej6fV10+cQXKmwZdvQ80RSO0b7WgiUpSXzrvbj+LzaUwmFe1w2rZvJWRPAktCtCOJiNvjY8O+E1w3aVC0Q+keLfsQTvp2tKOJyMh+DlISLazdU87XxvSPdjhtO/45NJyI62paIYQQIhbEVwWh1QbmRKnOEq3UuGtITUjFYbNQ7fJEdI5kXYcnwUFqQmrnlhgnpIAySRI7DI4EB1lJWZTWlZKVlIUjwdGp8/mTUm0pLi5m7NixgY8nn3wy6H7//Oc/GT169GnbV65cedr56+vr2bx5M3/6058Cybb77ruPcePGsWXLFv77v/+bG2+8sd3tHXnggQd49913+eyzz3jjjTcC2zdv3sxLL73E1q1beemllzhw4EDQ47XWjCsax1cv+So/uvcB/r1iLfn5+dx888388Y9/5LPPPmu1/+OPP45Siq1bt/LXv/6Vm266CZfLxcyZM1m1chU11TVYzBZWrjSWk37yySfMmjULgE2bNvHoo4+yY8cO9uzZE9jHZDIxZMiQ055LnEFyz4Q+hBlUNTTx5bFO/Czobg2VcGRbXFdMbTtchavJd+b1H/RTCvJmxnUfQovZxMTcXrE72XvFo1Dy8cm2BjnTjPsrHo1mVEIIIUTcir8SDnuaJF9EK9XuaoYnDI94ibH2+UjVtfgSnTgSqqlu7EQFoclkLDOWJDZAu5V+fv5lxbcX3s7LX7zMd8Z8h6J+Rd0WU35+Pps3bw7cv//++1s9fvfdd/Pggw+SlZXF008/fdrxpaWljBgxotW266+/HjCqDqurq6msrGTFihW8+uqrAJx33nmUl5dTXV3d5vaOTJ8+nXnz5nHttdfyjW98I7D9K1/5Ck6nsbR95MiR7Nu3j4EDB552vMb4BVUphULh9WkqKyuprKwMJPZuuOEG3n77bQBWrFjB9773PQCGDx9OTk4OX375JTNnzuQPf/gDCZkJnH/R+az8cCX19fWUlJQwbNgwSktLKSoqIjs7G4CxY8eyd+9eZswwltv17t2bw4cPt5vEFXGs7xiwJhsJwoJvdLx/DPL3Ify0pILhfTv3B4tuc2AdoOM6QehPOk06UxOEYCwz3vISlH0JWcOiHU1EJudl8JsvPqe8tpGMlMRoh9PagPHwyjyj/2lqf2OC+tKb4Zol0Y5MCCGEiEvxVUEIxhJOWb4pWvAPKXHYrdREkCCsr6vGonwomxNHgoOapk5WjdicksQOkT85uHD2QhaMW8DC2Qtb9SSMxKhRo9iwYUPEx/t7EL7//vtBB3bY7XZcrtYN25VS7d7vCk8++SQPPvggBw4cYMKECZSXG0MUEhNP/sJmNpvxeDynxeByudDNFSwKhckU+RTjSZMmsWHDBjas2cDkaZMZN24cf/7zn1sl/NqKyR+L3W6P6LlFHDBbYNDkuB5UMjDdTl+HjbWxWjUFxtfXZIUBE6MdScTWlVSQn5VMZqwlnbpSoA/hJ9GNoxP8fQhjsoowbxZcvdio0rQ5TyYH82ZFOzIhhBAiLsVhglCSL+Ikj89DXVNdp3oQ1lSWAWBK6oUjwdG5CkKQJHYYtpVvY+HshYGKwaJ+RSycvZBt5dsiPud5551HY2MjTz31VGDbli1b+OSTrvkFbcSIEaf1JnzppZcAo+rO6XTidDqZOXMmL7zwAgAffvghmZmZOByONrd3pLi4mMmTJ/PAAw+QlZXV5lJivz59+rBz5058Ph+vvfZaoILQkeqgoa4Or0+TlpZGWloaK1asAAjEBbSK88svv2T//v0MGzaMhIQEBg4cyPv/fJ+xRWOZOXMmCxcuDFQhduTLL78MaVKyiGM50+HYDqiPwYRCCJRSFOWls66kIpBYjzn7V0P/cZCQFO1IIuL1aT7dW0FRXka0Q+levfLAMQBK4jdBWJjtxG41x27C3NEf0HB8J0z8T0kOCiGEEJ0QfwlCe5os3xQBtW5jCIMj0UgQVruawv6FrqHaqMSyJPfCkejoXA9CkGXwYbil4JbTlhMX9SviloJbIj6nUorXXnuNDz74gPz8fEaNGsXPfvYz+vbtmoEJF198cWBAiJ/NZmPcuHHMnz8/sCz5/vvvZ8OGDRQWFnLPPffw7LPPtru9I3fffTejR4+moKCAadOmBQavtOXhhx/msssuY9q0afTr1y9wXVz7H9fy9BN/4JJzp1FcXMzixYu54447GDt2bKtr57vf/S4+n4/Ro0fzH//xHyxZsiRQGThz5kwyMjNItCUyc+ZMDh48yMyZMzv8Nxw9ehS73d5l/xciRuU09yHcvzq6cXTCpLx0jtU0sr+iPtqhnK6pAQ5thJyp0Y4kYp8fqabG5Tlz+w/6KQW58d2H0Go2MSGnF2v2lEc7lOA+bW4FMulWWP+00YNQCCGEEBGJvx6ENqfRy0UICEwcNoaUWGnyahqavCQlhP7Sbqg2/ipuTU4nNSG1c1OMwXiNVpd27hyiU/r378/LL78c9LFt21pXJ7bsQbhkyZIOz52Tk0NGRga7du1i6NChfPjhh0H3S09P5/XXXw95+7x585g3b16bz/v3v/+9w2PefPPNwOdXX301V199deB+pauSQ7WHmDFjBh+s3IDb6yO/TypAq6Eh/inINpuNxYsXB43lV7/6FfN+PA+v9pLdP7tVYnHOnDnMmTMncH/RokWBz1988UVuv/32Nv+N4gwxYLwxUGzvShh+abSjiYg/cbW2pIKcjOQoR3OKg+vB13QyERuH/MtVi870BCE09yH8Gxz/AnoPj3Y0EZmcl87vPviSyno3aUkxNDW75GMjKZiUCZc8AiO/ZvQklGXGQgghRETir4LQlibVWSLAX+3nX2IMUN0Q3iRjd+0JAGyOdBwJDmqbavH6vJEHJa/RM97DDz9MaWl8JYG92nhNm5QJs0nhi7AHoZ9ZmQPnDFVaWho33XRTp55XxAFLImRPius+hEOyUuiVZI3Nvmv7VgEKBk6OdiQR+3RvBdm97PRPOwv6kZ4BfQgnD85Aa/h074loh9LawQ1gToBzvto8NXqWkRw8tDHakQkhhBBxKQ4ThM09CH2+aEciYkCV20jEpSak4rAbVYPh9iFsqjN+AUxyGBWEALVNtZEHZXNKD8Iz3LBhw0LuuReuhx56iLFjx7b6eOihhzp9Xn8yz6zMmE0q4iElfmaTOexE+s0334zFEn+F6yICudPhyBZwdbIiO0pMJsXE3HQ+3RuLCcKV0KfAaGcRh7TWrCupODuqBwF65YIj21hmHKcKs50kWEysjbVlxkPOA3dt62rBvFkw486ohSSEEELEs4gThEqpi5RSXyildiul7mlnv6uUUlop1TWj9uxpoH3GGwJx1gtaQegKL0Hora8EIMWZiSPBGBbRqWXG9jTwuKDJ1eGuZ6qYbewfB+699142b97c6uPee+/t9Hm92otJmVBKGQlCrTv1/2RWZny6a/5QI6+XM1DONONn9YHIJ5JH2+S8dPaV13OkKoa+l3ub4OCnxtc3Tu0pq6Os1n3m9x/0U8qoIozjPoQ2q5lxA9Nib1CJv9+gLCcWQgghukRECUKllBl4HLgYGAlcr5QaGWS/VOAHwNrOBNmKzWncSoWW4PQehABV9eElCHXz0JsUZ0aggrBTCcLAa/TsXGZss9koLy+XpE+M8fl8mJUZAJNSAJ2qIjQpU9hLjIPRWlNeXo7NZuv0uUQMyZ4EJgvsi9+qKX+F27pYqiIs/Qya6uN6QIl/2fak3LMkQQiQNxPqy+D459GOJGKTB2ew/XBV2H+E7VYlH0PG0OZJxkIIIYTorEjXehUBu7XWewCUUn8DrgB2nLLfr4DfAHdHHOGpbGnG7VmafBGtVTcaiTxHggOX3Uh2hLvEWLkqqdF2Ui2WkxWEjZ1JEKYZt65KSO0T+XniVHZ2NgcPHuT48ePRDkW0UOGqwOvz4j3qpd7toaKuCVWZiMUcWSF5jbvGqOA9akyO7gybzUZ2dnanziFiTEIy9B/f3C8vPo3s5yA5wcynJRV8bUyMJCD8X89B8VtBuK6kgsyURPIyY2z4S3cK9CFcAb1HRDeWCE3JS+cxDRv2neDcYb2jHY5RTbtvFRT+R7QjEUIIIc4YkSYIBwAHWtw/CLTqlq2UGg8M1Fr/SynVZoJQKXUbcBvAoEGDOn5mf3VWc9WXOLvVuGuwKAt2ix2H3UgMhvvXbbO7mjqVQioEKgj9S5cjcpYnsa1WK3l5edEOQ5ziprdvwqRMLL5oMe9tP8Jtb2zgnwtmMCLbGdH5Xvr8JR7c/CDLrllGVlJWF0crzgg502D14+Cuh4SkaEcTNovZxPicXrE1qGTfKkjPj+s/Pq0rqWByXnqn/7AQV9JywDnQGFRSdGu0o4nIuEG9sJoVa/dUxEaC8NBGo93Q4NnRjkQIIYQ4Y3TLkBKllAn4HfDjjvbVWj+ltZ6otZ6YlRXCL5n+ptxnafJFtFbtrsaR6EAphcMW2ZASa1M1deYUAJyJzsB5I+Z/jUoSW8SQand1oELW368z3GulJUdiF/TrFGe2nOnga+6ZF6cm56XzxdEaTtS5ox2KMZxt/+q47j948EQ9hyobzp4BJX5nQB9Ce4KZwuw01pbEyKCSko8ABbkzox2JEEIIccaINEF4CBjY4n528za/VKAA+FAptReYArzRJYNKWi7fFGe9GndNoOrPYjaRnGCmusET1jkSmmpwNScIu6aC8OzuQShiU8trxRHhQJ+WuuRaEWe2QZNBmeJ6mXFRXgYA6/ediHIkwPGdxnufnOnRjiRi/mrMsy5BCEaCsL48vvsQ5qWz9WAV9e7w3md1i5KPoe9oSDoLX0tCCCFEN4k0QfgpMFQplaeUSgCuA97wP6i1rtJaZ2qtc7XWucAa4Gta6/WdjliSL6KFllVRYFRGhVsVZffW4LYY50iyJGFW5k4OKUkzbiWJLWKIv9oWuqiCsCsmfncxpdQzSqljSqltbTx+t1Jqc/PHNqWUVymV3vzYXqXU1ubHOv+zShg/r/uOhn0rox1JxAqznSSYTayLhaopf6I1zgeUOGwWhvVJjXYoPe//t3fncXJVZf7HP6e6qnpJd3Wns5O100kaEgwJhAAhZEENgVEQXNh0RPGHjESHmfGnzqLyc0Zn1HFkRnQYBAYcWYRRFBRZDJAACQkhCSE76XRWyNLZqvfq7jq/P25Vp5P0UnvV7fq+X696VfetW6fO7dyb6n7qOc8TzXSrezW780jCRROH0BG2vJXtgHl7C+xdpe7FIiIiKZZQgNBa2wEsAZ4HtgBPWGs3GWO+Y4y5OpUTPENhADBavimAk73UPUAYKPbFnRVVEm6k3e8Eno0xBPyBFGUQHk98DJEUag+309LRcsYS4+AACxACDwGLe3vQWvtDa+0Ma+0M4G+BZdba7gXmFkYeTz7bXRzjL3WWGHe0ZXsmCSnyFTBjbEVu1CHcvQICo516di61uu4os6sq8XjyqP5g1ODxUD7OqUPoUheMH0yBx2T/etjzBnSGoEr1B0VERFIp4RqE1tpnrbVTrLXV1trvRrZ9y1r7dA/7LkhJ9iCAxwNFAQVfBHCCE9GljuAECOPNiiqzjYQLTwYZy/xlyXUx9vrBV6IgtuSMaMA7eq2U+Aso8JikMgijYyV1raSYtXY5EOtfrjcCj6VxOgJOvbyOVnhvXbZnkrDZVZVsfC9IU1sWl1Va6wQIx13i1LNzoUMNreysb8rP5cVRE+Y6GbXhcLZnkpDSQi/nnhVg1c4sBwjrloPH6+psWhERkVyUliYlaVdUoSXGApyZQVhe7IsrK6o91EaJacNGlwXjZEYF25MMeugclRwSDRBGrxVjjHOtJFGDMDqWG2sQGmNKcDINf91tswVeMMa8ZYy5LTszG4DGRRpquHiZ8YVVlXSGLWv3ZGFZ5Wt3O8GQY3XQeMAJuNYtd7a7zJt1zs8vWtcxLw2EOoQTh7B+73Fa2zuzN4m6ZTD6AijMw6XqIiIiaeTSAGG5srMEay3BttMyCIviCxA2HK8HwESXBeNkRjW0JRn0KCpXlqvkjGiW3ynL8Yu8nIizoU93vgIfxd7iXFtiHKuPAq+ftrx4rrX2fOBK4A5jTI/FrYwxtxlj1hhj1hw+fDgTc3W3QUNg2Dmwy70BwgvGD8ZjyM6yytHnw5O3wJqHnO+9hc73o8/P/FyStLruCCX+AqadFeh/54Fqwlznftdr2Z1HEi6qqiTUGWbdnuPZmUDrCScjWcuLRUREUs6dAcLiCmVnCS0dLXTYjq7GCxDJIGyNPejRFHT+4PMOGty1LVAYSD7ooXNUckhXBuHp10oSS4whEkx3YQYhTmOtU5YXW2v3R+4PAU8Bs3t6orX2PmvtLGvtrGHDhqV9ogPC+DlOQ4HOHOh8moDSQi/nji5nVTYChFXz4JMPwap7wVsEL37L+d6FzRlW1R3lgvGD8RW481fPlBg8HirGwa7l2Z5JwmZNqMRkK2AOzocNNuzKa0BERCTXufO3NGVnCSebI5xag9BLY1sHHZ2x1fdpCTqdKbsHCMv8ZckHCJXlKjmk61rxJVev83QBfwqC6RlmjCkH5gO/67ZtkDGmLPo1sAjosROyJGDCpRBqhAMbsj2ThF04oZL1e4/T1pGFZZUTLoMCn1PLcdatrgyMHG8Ose1gA7Mn5HH9wagJlzlBLpfWISwv9nHOyACrstXZu265Eywf2+NnOCIiIpIElwYIK5SdJV2BidNrEAIxZxG2RgKERaUn/2iJBj2stYlPrqhCQWzJGV3XSrcMwkAKMghzLUBojHkMWAnUGGP2GWNuNcbcboy5vdtu1wIvWGubum0bAbxmjHkbWA38wVr7XOZmPsANgDqEs6sqCXWE2bAvC797rH/ECbBOvgLWPOAESFxmza5jWEt+NyiJmjAXWo7C4S3ZnknCLppYydo9xwh1ZCHIWbccxl3sLLcXERGRlHJpgFDZWXJmZ1ZwahACMQc+2pucoulFgZN/tJT5y+gId9Da2Zr45IrKFcSWnNFjtm1Rck1KouPl0hJja+2N1tpR1lqftXaMtfYBa+291tp7u+3zkLX2htOet9Nae17kNs1a+93Mz34AC4yCyolOF14XundZbdfX0WWVK2rrT9meNnXL4dn/63z9Zz9ylhc/eYvrgoSrdx3FX+DhvLEV2Z5K9o2/1Ll3dR3CIbS2h9mw73hmX7jxMBza5MosWhERETdwZ4CwuAI6WqCjLdszkSyKNl4o959sMBLNIIx16WRHJEA4qHxo17ZoRmJ0/IQUV0Br0LVLiGRgCYaC+Dw+igqKuraVR5YYJ5MpG/AHkrtOJH+Mv9QJELrw/8TpY8r529+8w+iKIlbXHWVFbT1LHl3H9DHl/T85WfvXwrAaGDoFKsaerEm4f236XzuFVtUdZcbYCop8BdmeSvZ11SF8NdszSVg0EzTjdTmjtRurFmT2dUVERPKEOwOERRXOvTK08lpDew8ZhF1LjGMLEIYjmail5UNOjhEJECaVGVVUDlhQ8ERyQEOogTJ/GcaYrm2BYi/tnZbW9sQDNrmWQSg5bPylTtmFQ5uzPZO4zakeyj03zaS+McSK2nrueGQt99w0kznVQ/t/crIu+iIc2gLVl5/cVjUP5t6Z/tdOkaa2DjbuP6Hlxd1NmOdkELowYA5QOchPzYiyzAcIdy6DwgCMOi+zrysiIpIn3B0g1DLjvBbNXOqpBmHMzRdajxOyXoqKB3Vt6sogTKa2WlcQ+3jiY4ikSLAteMp1AglcKz0IFAZoaG+gM5yFxg3iLuOjdQjducx4TvVQFtYMp73T8sFzRmQmOAjOz6ujFao/mJnXS4O1e47RGbYKEHY3YS60HHNlwDxqdlUlb+06GnNTuJSoW+787Aq8mXtNERGRPOLOAGFxhXOvDMK8Fs1cKvWXdm3ralLSEluTEk9bkKApxXhOXgrRRg5JZUbpHJUc0hBqOKVBCXRv6JNEgDASdGxsb0x8cpIfKsZBYIxrG5WsqK3v6tr6+w3vsaK2PjMvXPsSFPidTtAutbruKAUew/njB2d7KrljwgCoQzixkqZQJxvfy9BKieN74Fid6g+KiIikkTsDhEWRuj/KzsprwVCQQb5BeD0nP0kOFDtfx5oV5QudoNkz6JRt0SXLyWUQRs5RZblKDgiGgqcsxYeTDX2SySBMybUi+cEYJyiyewUk0yE+C6I1B3968/mcP66C0eXFLHl0XWaChLUvwbhLwD+o/31z1Kq6o5x7VoDSQmV9dakYBxXjB0Ydwp1HMvOC0cY8VfMz83oiIiJ5yKUBwgrnXtlZeS0YOnPZZLGvAF+BiTkrytfeQIvntMBJSpcY6xyV7GsINfS6xDjWjt89SUm9Tskf4+dA0yE4siPbM4nLhn0numoOzp8ynJ1HmvjeteeyYV+a/38PvucsQZ3kvuXF9y6rZUVtPW0dnazfe5zZVZWZ6/zsFhMuczJqXVqHcHhZEROHDcpcHcK65VAyFIafk5nXExERyUMuDRBGs7OOZXceklU9ZUUZYwgU+WLOiirqbKDNe+oY0SXLKckgVJar5ICegumBFNQgVAahxGV8ZFmly5YZ3z6/uqvm4IKaYVgLre1hbp9fnd4Xrn3JuXdh/cHpY8pZ8ug6Hl21h1BHmLIiX+Y6P7tF1WWur0N4UVUlb+46Smc4zVnB1joNSqrmOdnIIiIikhbuDhAqOyuv9ZQVBU5mVKxZUcWdjYR8p47h8/go8ZaoBqEMCNZaZRBKbhgyCQYNd22jEoAPjC6ncpCfV7YdSv+L7VgKpSNgxLT0v1aKRTs//+C5bQA8+Hpd5jo/u0U0YO7iZcYXVQ2hobWDLe+n+UOi+neh8QBM1PJiERGRdHJngNBXBN4iZWfluZ4yCAHKimPPIBxkG+nsaQx/WVeX5IT4S8EUqAahZF1zRzOdtvOMa6WsKFqvM7aGPj3pWo6fzLUi+cMYZ5nxrtddV4cwyuMxzJs8lOXv1hNOZ9ZUuBN2vgzVl7s2Y2pO9VAqBzkfRPz5xeMVHDxdxVgYPMH1jUqA9C8zrlvm3KtBiYiISFq5M0AITo03ZWfltWDbmcsmIfYMQhsOU2abCEfrBXYTKAwkt2zSGCfTVUFsybJo8O70a8VX4GGQvyCpJcbRzshaYiwxG38pBPc5HUldakHNcI42hXhnfxp/B3l/vbP81IXLi6Ne3HyQ/cdbmTV+ML9ctSdznZ/dZMJcJ0Do0jqEo8qLGVdZkv5GJXXLoHwcDK5K7+uIiIjkORcHCMuVnZXnGkINPWYQBoq8BFv7z4pqajyB14QxRWfWRCrzlSW/bLKoXEFsybpo8K7Ha6XYF3NDn56UeEsoMAVaYiyxee1uZwUAnFxmXLfc2e4il00eijHwyrbD6XuRHS8BBqoXpu810mhFbT1/9av1AHzjyrO556aZmev87CYTLnM+SDy0KdszSdjsSB3CtGXUhsNQ96rqD4qIiGSAewOExRUKvuSx9nA7zR3NXRlM3ZXHuMS48bjzh4qnZPAZjyWdQQjOOaogtmRZ9DxO5lrpjTHGWY6vDEKJxejz4U93gW8Q7H7NCQ4+eYuz3UWGlBYyfXQ5y7ansQ5h7VIYdR4Mcuey3A37TjBzXAUVJT5mjhvcVZMw7Z2f3WbCXOfezcuMqyo51tzOu4ca0/MCBzY4QVTVHxQREUk79wYIiyq0fDOPNYacX0R7WmIciCwxtv3UuGoOOjVzvCUVZ47hDyiDUAaE6Hncc7Zt7A19eqMAocSsah588iEIt8OW3zvBwU8+5Mq6YvNrhrN+73GON4dSP3jrCdi7Gia5d3nxbZdNZPN7QeZPGUaBx8n6mlM9NP2dn92mfIyzbLbOvY1KLp44BIBVdWlaZly33LmfcFl6xhcREZEuLg4QaolxPuvKiuqlBmFH2NIc6uxzjJag88usv7TyjMcC/hRkECqILTmgr2slkGQGYXRcBQglZlXzYPxc5//Gcz/uyuAgwPwpwwhbWP5uGpbM1i0H2+k0KHGpt/cd50hTiMvPHp7tqeS+CXNh9+uurUM4ZnAxZ5UXsWpnmhqV1C2DoVMgMCo944uIiEgX9wYItcQ4r0WzonoLEAL91lYLNTq/zBYFhpzxWMAfoKm9iY5w4h1edY5KLujvWmmIoV5nX1KSbSv5o245vLfW+XrdIyezg1xmxlhn+eyydNQhrH0J/KUwZnbqx86Ql7cewmOcQKr0I1qH8ODGbM8kIcYYLpo4hFV1R/pduRG3jhDsXglVWl4sIiKSCe4NEEaXb7r0E1dJTrQza2/LJoF+M6Pam44BUFx2ZoAwOm50KXNClOUqOSCa3VfqKz3jsUCxN+kMwjJ/Wdf1KNKnaM3B6//HyQgaUu1878IgYYHHcNnkYSzbfji1zRmshR1LncxKrz9142bYS9sOcf64wVSUuPcYMsbldQjvXVbLkFI/9Y0hdtY3AU6TmnuX1SY/+Htrob3JtZnGIiIibuPiAGEFYEGZK3kp2N73EmOAYEvfmVHhSPCutOLMIvDRhg5JZUYVVUBnG7S3Jj6GSJIaQg2U+coo8BSc8Vh5sY/Gtg46OhP/oCVQqAxCidH+tSdrDtZcCYc2wzU/dba70Pwpw6hvbGPz+ykMkB/dCcd3u3p58aFgKxv3B1mo5cWxKR8NlRNdGyCcPqacJ9fsA2DVzqOsqK1nyaPrmD6mPPnBdy4DzMkgqoiIiKSViwOEkV88lKGVl/rMICz2Av1nENpogDBwZhfjMp8zblK11aLnqOoQShYF24I9XidwMts2mWXG0SYlKV9aJgPP3DtPZgJNuRLCHdDe4mx3oejy2WXbU7jMeMdS597FDUpe3uZ0d1b9wThMmOt09g73XTs5F82pHsp/3nw+xsBDK+pY8ug67rlpJnOqU9CBu245jJoOJWfWihYREZHUc2+AsLjCuVeNt7zUVVetsK8Mwr4DhKb1BEFKKPB6z3gsOm5SAUKdo5IDGkINPV4nEHu9zr4E/AHaw+20dbYlPIbkobGzobgStv0x2zNJ2LCyQs4dHeCVSEAsJWqXwuAJTkaZS7209RCjyos4e2TPH0xIDyZc5vyu4NI6hHMmDWXy8FK2H2zkptnjUhMcDDXDvtWqPygiIpJB7g0QKjsrrwVDQbweL0UFRWc8FmsNwoLQCZoY1ONj0YyrlGQQKstVsigY6iODsDi2a6Uv0WX+6mQscfEUwJQr4N0XoDO5RjnZNH/KMNbuOZ50LU/AachQ9ypUuzd7sK2jk9ferWfh2cMxxmR7Ou4x/lLn3qXLjFfU1vPecaecysMrd7GiNgXdvfe+AZ0hBQhFREQyyMUBwgrnXtlZeSkYChLwB3r8A6SsKLYlxr5QkOaCXgInqQh6FEWWLiuILVkUvVZ6Up7KAKEalUi8pix2/n/cuyrbM0nYgprhdIYtr+9IRUBkldOQwcXLi9+sO0ZTqJPLa7S8OC4urkMYrTn47zfMwF/gYd7koSx5dF3yQcKdy8DjhXEXp2aiIiIi0i8XBwiVnZXPGkINvQY9vAUeSgu9/S6bLOxooLXgzM6ucDLokVyTkmiWq4LYkj19ZxA6wfT+Gvr0JTp2Q7salUicqi8Hjw+2u3eZ8cyxFZQVeVOzzLh2qRMQmXBZ8mNlyUtbD+H3epgzaUi2p+I+Ey6D3a+7rg7hhn0nuOemmXzwnBHMmTSEje8FuefGmWzYl+TvPnXLYcyFUNjz72kiIiKSeu4NEKq+W14LtvWeFQVOZlR/WVHFnQ2EfD2PUewtxmu8yWVFRc9RBbEli/oKpiuDULKqKABVl7m6DqG3wMNlk4eybPvh5Bv17FgKYy9yfi4u9fK2Q1wycQgl/jNr+0o/XFqH8Pb51V01BxdNHcnuI80MKS3k9vnViQ/achzeX3+yqZGIiIhkhHsDhP4ywGj5Zp5qCDX0mhUFzjLj/rKiSsKNtPcSIDTGUOYvUwahuFp7ZzstHS39djFOpklJSup1Sv6aciUc2QH1O7I9k4QtmDKcg8E2th5I4v2i8RAc2OBkVbpUXX0TdfVN6l6cqAmROoR1r2Z3Hkn40NThGAMvbDqQ3EC7XwcbVv1BERGRDHNvgNDjcQIwys7KS33VVQMnM6q/LsaltolwYXmvjwcKA8kFPQp84BukILZkTfT87e1aKfEX4PWY5DIIU9HxO0WMMQ8aYw4ZY3pMwTHGLDDGnDDGrI/cvtXtscXGmG3GmB3GmG9kbtZ5rmaxc+/iZcbzpgwD4JVthxMfpPZl597FAcKXtjrLrBUgTFDgLKisdmUdwqjhZUXMHFvBC5sPJjdQ3XLwFsOYWamZmIiIiMTEvQFCcJZwKjsrLzWEGroCEz0pL/b1mRUVamulxLRhi/oIEPoDyWUQQuQcPZ7cGCIJip6/vV0rxpiYgul96apBmOy1khoPAYv72edVa+2MyO07AMaYAuCnwJXAVOBGY8zUtM5UHBXjYPg0Vy8zHllexNkjy1i2PYk6hLVLoWQIjJqRsnll2stbDzFpeCljK0uyPRX3mjAXdq9wXR3C7hZNG8k7+0/w3vGWxAfZuQzGXwLewtRNTERERPrl7gBhUbmCL3nIWttn4wWAQD81CBuOO931PNE6gT0o85clnxWlLFfJov4yCKH/a6U/Po+PYm9xTmQQWmuXA0cTeOpsYIe1dqe1NgQ8DlyT0slJ72quhD1vQHMi/3S5YUHNcNbsOkZDIsv1w2Eng3DiQmd1hAs1tnWwqu6IsgeT8drdMGg4tJ2AA+842+qWO9tdZNHUEQC8mGgWYeMhOLxF9QdFRESywJ2/iUYVVSiDMA+1dLTQaTuTWmLcHDwCQEHJ4F73SUkGoc5RyaKuDMJ+AoTB1sS7GEfHz5EMwlhcYox52xjzR2PMtMi20cDebvvsi2w7gzHmNmPMGmPMmsOHk1hSKifVXAm2E3b8KdszSdj8KcPoCFtW1B6J/8kHN0LTIZj0wdRPLENee7ee9k7LwhoFCBM2+nxYc7/z9a7XnODgk7c4211k4rBSqocN4oXNCdYhrFvu3CtAKCIiknEuDxAqOysfRTOV+swgLPLRFOqkvTPc4+PNJ5w/4nylFb2OkbIMQmW5SpbEdq14k8ogjI7vki7Ga4Hx1trzgJ8Av413AGvtfdbaWdbaWcOGDUv1/PLTWec7mVMuXmZ8wfjBlBZ6E6tDWLvUuXdx/cGXtx6irMjLrAm9f+gm/aiaB5/6BRgPvPlzJzj4yYdcGShbNG0kb+w8yonmBN5b6pZBYbmrl9uLiIi4lbsDhKpBmJdiWTZZXuwFoKGXzKi2xmMAFJYO6XWMgN9pUmKtTXSqOkclq2LJICwv9tGQZIAw4A/Q0J77GYTW2qC1tjHy9bOAzxgzFNgPjO2265jINskEjwemXOFkEHaEsj2bhPi9HuZUD2HZtkPxv2fsWAojzoWykemZXJpZa3l52yHmTR6Gr8Ddv1ZmXdU8GD0Lju2C825yZXAQnGXGnWHLS9sSWGZct9ypxegpSP3EREREpE/u/k1O2Vl5KZqp1F8NQqDXzKhQk1PrqrisstcxyvxldIQ7aOlIotB2UTm0KEAo2RFTBmGSNQghEkx3QQahMWakMcZEvp6N8x54BHgTmGyMqTLG+IEbgKezN9M8VHMltAVhz4pszyRhC2qG896JVnYcaoz9SW2NTv3F6oXpm1iabXovyKGGNhaq/mDy6pZD/Tbn67f+++RyW5c5b0wFw8sK469DeGy3ExydOD8t8xIREZG+uTxAWAEdrdDemu2ZSAZ1ZRD208UYeg8QdjY5GYSDyof2OkZ0/KSWGRdVOAXHXdyRUNwr2BbE7/FT5C3qdZ/ySIAwmUzZQGEgJ5qUGGMeA1YCNcaYfcaYW40xtxtjbo/s8glgozHmbeA/gBusowNYAjwPbAGesNZuysYx5K2JC6CgELY9l+2ZJGx+jbPkPK5lxrtfh3A7VLu3/uBLWw9hDCyo0ZL7pERrDn7qf2BwFQyZ5HzvwiChx2P48NQRvLLtMK3tcfz+o/qDIiIiWeXyAGG5c68lnHmla9mkr+/GC0CvjUrCkdqVpRW9LzGOZl0l1Xwheo66ILtKBp7+un2DU6+zI2xpieePuNOU+ctyokmJtfZGa+0oa63PWjvGWvuAtfZea+29kcfvsdZOs9aeZ6292Fq7ottzn7XWTrHWVltrv5u9o8hT/kFOkHDbs5BMWYcsGl1RzOThpSzbHkeAcMdS8BbDuEvSN7E0e2nrIaaPqWBoaWG2p+Ju+9c6NQcnzodzr3M6GX/0353tLrRo2kiaQ52sqK2P/Ul1y5x6pMPOTt/EREREpFfuDhAWR4phK0CYV1KRQWhajtNmfRSVlPY6RrRuW1KZUcUVzr2a6UgWBEPBPq8T6P9aiUXAH6CxvZFOZcpKMmoWw/HdcHhrtmeSsAU1w1hdd5Smthg7g9cudeqt+XrP8s1lRxrbeHvfcS5X9+Lkzb3zZObctOuczt5Nh53tLnTJxCGUFXp5YVOMy4ytdTIIq+aBUwlCREREMszdAcKuDMLjWZ2GZFY0U6nU10dwryiSQdjac9DDEwrSYAb1+TrRAGFKMggVxJYsaAg19J9BGGnoE2yJMaDRg+hrNLbHUXtN5HRTFjv3Lu5mvKBmOKHOMCtrj/S/87HdcGQHTHLv8uJXth3GWrhc9QdTa8Q0GDIZNv4m2zNJmN/rYcHZw/nTloN0hmPICq7fDo0HtbxYREQki1weIKxw7pWdlVeCoSClvlIK+uhw119WlDd0giZP7wFGSFEGYfQcVRBbsiAYCvbZwRhSl0EIuKJRieSwwFkw6jzY7t46hLMmDKbEXxDbMuPapc599eXpnVQavbTtEMPKCpl2Vt//z0icjHGWGe9+HRoS6AScIxZNHUF9Y4h1e471v/POZc69GpSIiIhkjbsDhNHlm8rOyisNoYZ+gx5FPg/+Ak+vWVG+9gZaPP1kVqUig1DnqGRRLNdKeT/1OmPRFSBsV4BQklRzFexdDY1x1PHLIYXeAuZUD+GV7Yf6b/yzYykExsDQKZmZXIq1d4ZZvv0wC2uG4fFoSWjKTbsObBg2/y7bM0nYgpph+AoML/TWzfi1u082JqlbBhXj4PgeZ7uIiIhkXMIBQmPMYmPMNmPMDmPMN3p4/K+NMZuNMRuMMUuNMeOTm2oPtMQ4LwXb+m+8YIwhUOztNSuqqKOBNm/fGYSl/tKu10tY9BxVlqtkQaxNSiC5DMLoayiDUJI2ZTFg4d0Xsj2ThM2fMoy9R1vYWd/U+06dHU5gZNLlrq239tbuYzS0dmh5cboMPxuGT4VNT2V7JgkrK/JxSfVQnt90oOeA+ejznU7Nta/ArldhaI3z/ejzMzxTERERgQQDhMaYAuCnwJXAVOBGY8zU03ZbB8yy1k4H/hf4QTIT7ZEChHkplsYL4HQy7q0GYXG4kXZ/eZ/P93q8DPINStESY2UQSmZZa+PLIOzlWolF9HrMhU7G4nKjzoOys2C7u+sQAizb1kcW5P41Tnf7avfWH3x56yF8BYa5k4dleyoD17RrYc9KCL6X7ZkkbNHUEew+0sy7h3qoUVs1z+nc/OSfO78n7VnpfK86hCIiIlmRaAbhbGCHtXantTYEPA5c030Ha+3L1trmyLdvAGMSn2YvvIXgLVZ2Vp4JhoKU+frOigInM6q3ZZOltpHOfgIn4GRGJRUg9A8CU6AgtmRcU3sTYRvuN0BYVuQ0KUlJDcJkrhURcLLpahbDjpegvTXbs0nI2MoSJg4bxCt91SHcsRSMx9X11l7aeojZVZWUFnqzPZWBa9p1gIVNv832TBL24akjAHhh04Ged6iaB8POcb4+/7MKDoqIiGRRogHC0cDebt/vi2zrza1Aj+kAxpjbjDFrjDFrDh9OoOZQcYWys/JMQ6ghpgzC8uKeA4Q2HKbMNhEu7DuDEJzAR1JZUcboHJWsiJ63/V0r3gIPpYXepLoYp6Rep0jUlCuhvQl2vZbtmSRs/pRhrNp5hNb2zp53qF0Ko2dB8eDMTixF9h51MsIW1mh5cVoNnQQjPwCb3NvNeESgiBljK3ixtzqEta/AvtVQORE2PH6yJqGIiIhkXNqblBhjPg3MAn7Y0+PW2vustbOstbOGDUtgmUpRubKz8kwsddXAWWLcU1ZUY8NxCozFFPcfIEw6gxCcc1RZrpJh0fM2pmulqPd6nbEo9hZTYAqUQSipUTUPfCWuX2bc1hFm5c4jZz7YfBT2r3V19+KXtx0CUP3BTJh2Hex702ne4VKLpo3g7X0neP9Ey6kP1C2HJz7jNGP54Lciy41vUZBQREQkSxINEO4Hxnb7fkxk2ymMMR8C/h642lrbluBr9a2oQtlZeaQ93E5LR0u/yyYByou9BFvPzIpqOlEPgCeGzI2AP5CCAGGFgtiScdHzNpZrpbdgeqyMMc61oiYlkgq+Iid4tu056K8TcI66qKqSIp+n5zqEO18BLExyb/3Bl7YeYsKQEiYO67vZl6TAtGudexcvM140dSQAfzo9i3D/WqfuaFGF08E8WpNw/9qMz1FEREQSDxC+CUw2xlQZY/zADcDT3XcwxswE/gsnOHgouWn2QdlZeSW6hDG2rCgn6HF657ymE0cB8A6q6HeMMn9Z8ssmi8oVxJaMiyuDsI+GPrFKybUiEjVlMQT3wYF3sj2ThBT5Crh44hCW9VSHsHap875wljs7tbaEOllZe4SFyh7MjMoqOGumq5cZTxpeysRhg3jh9ADhBbfA3tXwgU86dcXBCRLOvTPTUxQRERESDBBaazuAJcDzwBbgCWvtJmPMd4wxV0d2+yFQCjxpjFlvjHm6l+GSo/pueSWaoRRbBqGPzrClKXRqDajWoLPky19a2e8YKcmKKq5QEFsyLt5rpbeGPrFKSbatSNSUKwAD25/L9kwStmDKMOrqm9h9pOnkRmudBiwTF0CBO5t7rKitp60jrOXFmTTtOnhvHRzdme2ZJGzR1JGsrD1yarb6xl9DZxvMvDl7ExMREZEuCdcgtNY+a62dYq2tttZ+N7LtW9bapyNff8haO8JaOyNyu7rvEROkGoR5pavxQozLJoEzAh+hpmMAFJXFFiBs7mimI5x4AwdlEEo2xJttm2yAUBmEklKlw2H0BbDNvXUI50caeJySRXh4KzS8B9XuXl5c4i9gdlX/76GSItM+5ty7eZnxtBF0hC2vbOu2qGj9ozB8KoyakbV5iYiIyElpb1KSdkUV0BqEcDjbM5EM6KqrFmMXY+CM2mrtTc4S45LA0H7HiL5OUoGPaA1Cl9bSEncKhoIYTEwBwvIkaxCCc60og1BSquZKeG8tBN/P9kwSUjV0EOOHlPBK9zqEO5Y69y5tUGKt5eWth5g7aSiF3oJsTyd/VIyDMRe6epnxjDEVDCsr5IVNkWXGh7fB/jUw42YwJruTExEREWAgBAiLKwALKo6fF7qyonyxZUXBmRmE4ebjAAwqH9LvGNHgSnIBwnLoDEFHa+JjiMSpIdRAqa8Uj+n/v/lAsZemUCcdnYl/0JKSjt8i3dVc6dy/+3x255GEBVOGsaK2ntb2SKmL2qUwdApUjO37iTlq28EG3jvRquXF2TDtOqcmZ/2ObM8kIR6P4UPnjOCVbYec62H9I2AKYPqnsj01ERERiXB/gLCo3LnXMuO8kIoMQlqOE7aGsvLYlhh3f92EFFd0va5IpgRDwZiuEzh5rfTU9TtW0RqEpzcFEknY8KlQPs7pZuxS82uG0doe5s1dR6G9BXavcP3yYkANSrJh2scA4+oswkXTRtAU6uSNdw/C2487tUZLdS6JiIjkigEQIKxw7lXjLS90BQhjbLwAZwY9TNsJGk0JnoL+l0elJECoc1SyoCHUENN1At2ulSSWGQf8ATrCHbR2KlNWUsQYJ4tw58sQas72bBJyycShfMn3e+refM4JDna0wqQPQt1yeO3ubE8vbi9vPcS0swKMCBRleyr5J3AWjLsENj2V7ZkkbE71EAb5C9i1+hloPAgzbsr2lERERKSbARAgjGQQKjsrLwRDQXweH4UFhf3uGyh2OkSenkFY0HaCRjMopteLLjFOLkCoLFfJvGAoGFP9QTi5HD+ZOoRd14rKPUgq1Sx2gmp1y7I9k4QU+wvoGDmDq9/9O1j7CygoBAs8eQuMPj/b04vL8eYQb+0+puXF2TTtWji0GQ5tzfZMElLoLWDB2cMZs/spbMkQmHxFtqckIiIi3bg/QBhdvqnsrLwQzYoyMRS0LuulBqG3vYFmT4yBE38KmpToHJUsiCuDsCSabZtEBmEqGvqInG78XPCXubqb8fDpH+Yv2r6M3fIMlI2E334RPvkQVM3L9tTismz7YcJWy4uzauo1YDyuXmb8Z5MKuSz8JocmXA1ef7anIyIiIt24P0Co7Ky8EmyLPSuqwGMoK/SekRVV2BGkraA0pjFSkhUVXWKsLFfJoHiulVRkEAZ8KViOL3I6rx8mXQ7bn4dw4k10smlBzXDqbTnGdsLx3TDrVtcFB8FZXlw5yM95YyqyPZX8VTYCxl8KG38DLq33urB9OYWmg9973NnJW0REZCAbAAHCCude2Vl5oSHUEHPjBYBAse+MrKjizkbafLGNUewtxuvxJtnFuMK51zkqGdTQnkgNwiSalCiDUNKl5ipoPADvr8v2TBLy4uYD3FX4GBbgki/DmgfY+Poz3LusNttTi1ln2LJs+2EWTBlGgaf/DH5Jo2nXwpF34eCmbM8kIcUbH2eXr5pf7gqoqZWIiEiOcX+A0F/qLLdQdlZeiKeuGkQChKdlRZWEG+nwxTaGMaarO2vCiiJBGmW5Soa0d7bT0tESewZhL/U645GSep0iPZm8yHmfd2k344WdK5lj17E0PIvQB7/Dxkv/ndEvfom53s3ZnlrM1u89xrHmdi0vzgVTrwFT4M5lxgc3wfvrOVz9Cerqm6g93JjtGYmIiEg37g8QejzOMmNlZ+WFYCgYc1YUQHnxmUuMy2wj4cLymMdIOkBY4HMC2QpiS4acCDn/H8aabVvsK8BXYJJbYpyKjt8iPSmphLEXwXZ31iE8e++vwMB32m/m67/ewJ+/VMT+D/+Mc617Mghf2nqIAo9h3pRh2Z6KDBrqLFF34zLj9Y+Cx8e4BZ8F4PlNB7M8IREREenO/QFCiAQIj2d7FpIB8TReAKe2Wvdlk22tzRSbELYo9gBhmb8s+WWTCmJLBkXP11gzCI0xzrWSRJOSUr9T11MBQkmLmivhwDtwfG+2ZxKftkY48A6dNR/lPTOSp9bt59MXjePcSz8Kc+/M9uxi9tLWw1wwfnBXOQLJsnOvg2N18P76bM8kdp3tsOFXULOYESNHc96Ycl7YrAChiIhILhkgAcIKZWflAWtt3AHC8mLfKVlRDcePAOApGRzzGAF/ILkmJeCcowpiS4ZEg3TJXCvx8nl8lHhLkr9WkmCMedAYc8gYs7GXx282xmwwxrxjjFlhjDmv22O7ItvXG2PWZG7WEpMpVzr32122zHj9I9B6nM1Vn8Vb4NTue3jlblbU1md5YrF7/0QLW94PcrmWF+eOsz8CHi9seirbM4nduy9C02GY8WkAFk0bydt7j3PgRGuWJyYiIiJRAyNAWFyh7Kw80NzRTKftjL8GYbesqOagEyAsKK6IeYwyfxkN7cogFPeIZhDGEyAs66FeZ7xSkm2bnIeAxX08XgfMt9Z+APhH4L7THl9orZ1hrZ2VpvlJooZOhsqJ7goQdnbAyp8SHHYBt7wI//ap8/B7PcwcW8GSR9flfJDw3mW1rKit5+WthwG4/OzhrKitd1VzlQGrpBImLnQChG5ZZrz+ERg0HCZ9CIBFU0cA8OIWZRGKiIjkioERINQS47wQzUyKNyuqOdRJe2cYOBkg9JVWxjxGSjIIiyuU5SoZk+i1kmyAMFCYZL3OJFlrlwNH+3h8hbX2WOTbN4AxGZmYJM8Yp5tx3XJn2a4bbH0Gju9mxYibuOemmVz1gbO48cKxvLajnruunsaGfbn9odH0MeUseXQd//vWPkZXFFPf0MaSR9cxfUzsJTokjc69Do7vgf1vZXsm/Ws87AT3z7seCpymWJOGl1I1dBAvbDqQ5cmJiIhI1AAJEFYoOysPdC2bjLHxAtBVLyka+Ag1OLGBwrI4AoSFARpCDdhkPqXXOSoZ1JVBGOe1Emzt6H/HPgT8gWxnEMbjVqB71wsLvGCMecsYc1tvTzLG3GaMWWOMWXP48OG0T1K6mbIYOkNQ+1K2Z9I/a+H1/4DKahZf9znmVA8F4IvzqzEG3qw7yu3zq7M8yb7NqR7K9649l7V7jlE5yM+Sx9Zxz00zu45FsqzmKijwu2OZ8TtPQrgDZtzctckYw6KpI3hj55Gk6t+KiIhI6gyQAGG5srPyQDRAGN8SY+eT6mhttVCTk1xUHEeAsMxfRoftoKWjJebnnEFZrpJBCV0rRWd2/I5Xmb/MFU1KjDELcQKEX++2ea619nzgSuAOY8y8np5rrb3PWjvLWjtr2DB1dM2ofavBN+jUZcZ1y+G1u7M2pV7tWQnvrYVLvgSegq7NZ1UU84kLxvCrNXs5GMz92mtv7DyKAd7Zf4JPXzROwcFcUlwB1R90AoThcLZn0ztrneXFZ50Pw8855aFF00bQ3ml5ZZs+bBEREckFAyNAWFwBnW3Qnvu/bEviEqmr1pVBGMmM6mw+DsCgitj/yIm+XlKBj+IKaAtCuDPxMURi1BBqoLCgkMKCwpifE11inEymrBsyCI0x04H7gWustUei2621+yP3h4CngNnZmaH0asyFEG6HLc84/5fWLYcnb4HR52d7Zmda8RMoroTzbjrjob+YP4nOsOXny3dmYWKx23u0mf95Yxd+r4evXD6JX67ak/N1E/POuddBcL8TPM9VBzbAwY0w48xrYcbYwQwtLdQyYxERkRwxMAKERZF6OMrQGtASy4pyAoTRzCgbCRCWlg+JeYzo6yUVIOw6R7XMWNIvGArGdZ2A09CnI2xpDiUexA74s1uDsD/GmHHAb4DPWGu3d9s+yBhTFv0aWAT02AlZsqhqHsz5S+fDlt8tcYKDn3zI2Z5L6t+Fbc/C7P8D/pIzHh43pIRrzjuLR1bt4UhjWxYmGJu/e+odOsPwo0+dx18vquGem2a6orlKXqm5ErxFsPE32Z5J79Y94iyF/sAnzniowGP48NThvLLtMG0d+gBVREQk2wZIgLDCuVfwZUBLKoMwunSy9Tit1kdR8aCYx4i+XlKZUTpHJYOCoWBc1wl0z7ZNfJlxwB+gqb2JjnBytQwTZYx5DFgJ1Bhj9hljbjXG3G6MuT2yy7eAIcDPjDHrjTFrIttHAK8ZY94GVgN/sNa6qF1uHrn0K1BQCG8/CrM+n3vBQYCV9zhzvPD/9LrLlxZOorWjkwdeq8vgxGK37UADr75bz0emj+Ij088CnJqE99w0M+ebq+SVwjKY/GHY/LvcXKHQ0QbvPAFn/xkUDz7j4XuX1TJ2cAmNbR2srHUSutUpW0REJHu82Z5ASkSDL6pDOKBFM5NKfaUxPydQfGoGoaftBI1mEEVxvG7XEuNkOhkry1UyKKEMwm7ZtqPKixN63ehrNoYaqYj+v5xB1tob+3n8C8AXeti+EzgvXfOSFHp/PXi8TlmRVfc6AcJcChI2Hob1j8GMG6G09xqVk4aXctUHRvGLlbv54rxqykt8GZxk/374/DbKirz808fOPWX7nOqhqkOYa6Zd6yy737MSJszN9mxOtf05aDkGMz7d48PTx5RzxyNrKfJ6eGHzQfxeD0sedZrhiIiISOYNjAzC4grnXsGXAS3YFqTMV0ZBt4Lv/Sk/LUDoDQVp8sQZOElVDUJQEFsyItiWeAbhieYkMggLU3CtiPQmWnPwhkdg2DngK3G+r1ue7Zmd9ObPneDlJUv63XXJwkk0tnXw3ytyK4vwrd1H+dOWg9w+v5qKEn+2pyP9mbLYuRZycZnx+kehbBRUL+zx4TnVQ/npzecTtpbfrtvPkkfUKVtERCSbBkaAUPXd8kJDqCHurKhCrwd/gadr2aS/I0iLJ/YMRDiZFZXcEmOdo5I5iVwr0Y7f0YY+iSjzpeBaEenN/rVOzcHqhbD4n6HxINRc5WzPBaFmWP1zZ05DJ/e7+zmjAnx46gj++/VdNCSxtD+VrLV8/4/bGFpayOcunZDt6Ugs/INgyhXOMuPO7JR36FHDQXj3RTjvhlM6eZ9uTvVQLj97BM2hTi6YMFjBQRERkSwaIAHCCude2VkDWjAU7MpQipUxhkCkOytAUUcDbb74Aiel/tKu109YVw3C44mPIRKjZGoQRrNtExG9Pk+EFAiXNJh758nlxNUL4eyPOFlT06/P6rS6vP0otByFOV+O+SlLFk7iREs7v3xjTxonFrtXth9m9a6j/OUHJ1HiHxhVaPLCtOuguR52v5btmZy04VdgO2HGzX3utqK2ntV1RxkZKORPmw/y3Mb3MzRBEREROd0ACRAqOysfJJIVBU5mVLDF+VS9ONxIuy++wInX42WQb5AyCMUVwjZMY3tjwjUIg0kECFOSbSsSq0X/COF2+NNd2Z6J0yBi5U9h9AUw7pKYn3be2ArmTRnG/a/upDmU3eyvcNjyg+e2Ma6yhOsvHJfVuUicJn8Y/KW5s8zYWlj/CIyZ3Wc27Yraeqfm4M0z+cWtF+HxwFceW69O2SIiIlkyMAKEXr9Tf0XZWQNaIllR4GRGRbOiSm0jnQmMEfAHkssg9A9yCusry1XSrKm9ibANU15YHtfzTm/ok4iU1OsUiVXlRKfW34bHYe+b2Z3Ltmfh6E4ne9CYuJ76lcsncaQpxGOr96ZpcrF5ZsN7bHk/yN8smoLfOzB+PcwbvmKouRK2PA2dObBc/b21cHgrzLipz9027DvRVXNwyogy7lg4mVBnmKfW7c/QREVERKS7gfMbYFGFAoQDXDIBwmBrO+HOTkptM+EEuqsmHSA0JnKOKoNQ0iuavRfvtVLgMZQVervqdSYi+prKIJSMueyvoXQk/PFrEA5nbx4rfgIV4+Hsj8b91FkTKrl4YiX3La+ltb0zDZPrX6gjzI9e2M45owJ8dPpZWZmDJOG1u2FojdMxeOcyZ1vdcmd7Nqx7BLxFcO51fe52+/zqU2oO3rGwmuphg1ix4whNbTlUT1FERCRPDKAAYbmyswa4hJcYFzkZhI0NxykwFlMUX2YVOEsng21JZkUVlSuILWkXDWQnthzfl1QGYbG3GK/xJn+tiMSqsAw+/P+cjKUNj2dnDntWwd5VcMkdUJBY3b4vXz6Zg8E2nnxrX4onF5tfvbmHPUeb+driGjye+DIgJQeMPh9W/aezmmbTUyc7fo8+P/NzaW+Fjf8L53z0ZHmVGBV6C/iXj09n//EWfvTC9jRNUERERHozcAKExRXKzhrA2jvbaeloSTyDsKWdphNOTZuCksFxjxHwB2hoTzIrSueoZECiGYRApKFP4lkbxhgChQFlEEpmfeBTMHqWU4uwLQvn3sqfOBni/TRj6Muc6iGcP66Ce1+ppb0zs5mQzaEO/n3pDmZXVbJgyrCMvrakSNU8p8N3uBPeeRKe+KzzfbSpTyZt+4Pzu06C18OFEyr59MXjeGhFHev3Hk/t3ERERKRPAydAqOysAS25rCgvwdYOmk4cAcA7qCLuMVKWQagsV0mz6HmaWLatN6kmJdHXVQ1CySiPB678ATQehOX/mtnXPlILW34PF94KhaUJD2OM4cuXT2b/8ZaM11/779d3Ud/YxtcX12DirJ8oOaRqHpx3PXS2OXWP42iWk1LrH4XAmKSCk19bfDbDygr5xq83ZDxgLiIiks8GUICwQtlZA1hXVlRhYhmEnWHLiaNOBqGvtDLuMVKSFaVzVDIgGpxL9FpJpgYhRLJtlUEomTbmAjjvJnjjZ07QLlPe+BkU+GD2bUkPtaBmGOeODvCzl3fQkaGgyLGmEPe+UsuHzhnBBePjf2+UHFK3HLb+AWr+DE7shSf+PPNzCL4HtS/BjBvBU5DwMIEiH/94zblsPdDAfct3pnCCIiIi0pcBFCAshxYFXwaqrqBHIssmi5zurMePHQagKIEAYZm/jOaOZtrDSQRPlOUqGZDNGoTR11UGoWTFh74NBX544R8y83pNR5xmDNM/BWUjkx7OGMOShZPZdaSZP7zzfgom2L97l9XSGOrg/15Rk5HXkzSJ1hz85ENw46Mw7Tqns/Yfv5HZebz9ONgwnHdj0kMtmjaSqz4wkn9f+i47DzemYHIiIiLSn4ETICyugLYTTv0VGXCSCRCWFzsBwsbjTgZhSfnQvnbvUUq6sxZXOEuMrU18DJF+BENBDIZSX/zLHctTECBMuuO3SKLKRsK8rzqBkR1L0/96ax6Ajha4ZEnKhlw0dQRTRpRyz0s7CIfT+17x/okWHlqxi2tnjqZmZPwfKEgO2b/21JqDH7/fqcu56l4neJhOr93tvIa1sP4RGDcHgvtT0kH5ro9Oo9Dr4W9/807arwcREREZSAHCaKc0dc8ckKKBuUSzogBaG44CMKh8SPxjpCJAWFQO4XZob0l8DJF+NIQaKPWX4jHx//ceKPLRHOpMquZTmb9MS4wley7+Egyuguf+FjqTC3b3qb0VVv0XTF4Ew89J2bAej+GOhZN491Ajz286kLJxe/IfS9/FWvirD01J6+tIBsy989Saf54C+MxTMKzGWWp8NI3LdEef72Qvrv45HNkBY2alrIPy8EARf3/VOayqO8oTa/YmPZ6IiIj0bQAFCCucezWBGJCijReSySDsbDpG2BrKAol1Me4+j4REz1EtM5Y0CoaCCV0nAOXFXmeMJLIIA/4AwbYgVpmykg3eQrjie1C/Dd68P32vs+FxaK6HOV9O+dAfmX4WVUMH8ZOXdqTtOqo93MgTa/Zx88XjGFtZkpbXkCwrCsCNjzlfP3oDtKbpA/SqeU7G4gt/Dx4vrPtlSjsoX3/hWC6qquR7z27hULA1JWOKiIhIzwZQgDCSQagmEANSQ3sSGYSRGoS29TiNpgRPQfyFs6Ovm3QGIegclbRqCDUkHCCMZtsGWzsSfv0yfxkdtoOWDmXKSpbUXAnVl8PL/wxN9akfPxyGFffAqPNgwmUpH77AY/jSgmo2vx/k5W2HUj4+wI9e2EaR18MdCyelZXzJEZUT4VO/gKO18OsvpKcMT9MRePXfoDME4Q648AspCw6CU5vzn6/7AK0dYe56ZlPKxhUREZEzDZwAYXGFc6/srAEp2BbE7/FT5C2K+7nRDMKScCONJv66bNAtgzCZ2mrRc1RZrpJGwbZkMgidayWZOoTR7smqQyhZYwxc8c8QaoSX/in147/7PBx5F+Z8xXmtNPjYzNGMrijmP5amPovw7b3HefadA3zhsokMLS1M6diSg6rmwZXfd87bpf8vtWMf2Ag/XwB73gB/Kcz7mlObM8V1DycOK+UvPziZZ985wAtpXnovIiKSzwZOgLBr+aayswaiYCjYFXiIV1mRF2MgQDPNngQDhKkIeugclQxoCDUkfK1EA4TJLjGOzkMka4afDbNvg7cegvc3pHbsFT+B8rEw9ZrUjtuNr8DDXyyoZv3e47y+40hKx/7h89uoHOTnC5dVpXRcyWEXfsG5vf7vsP6x1Iy55Rl4YBG0NoC/xFnOfPnfO8uLn7wl5UHC2+ZN5OyRZXzrd5toaE2+vui9y2pZUXtqhvGK2nruXVab9NgiIiJuNYAChJHlm8rOGpCCoWBCy4vBKfpeWuil3DTR6k1sjOhrJxcgjC4xPp74GCL9SOZaCaQggzAl14pIKiz4OhQPhue+kVz3+GiXVoB9b8Hu153mJCt/mpJp9uaTs8YwIlDIT156N2VjvvZuPa/tqOeOhZMoi5TfkDyx+F+cJfHPfAX2vpn4OOEwvPJ9+NWnnUD8hbfC9b88uay4ap4TJNy/NiXTjvIVePiXj0/nUEMrP3huW9LjTR9TzpJH1/GnLQdZv/c4K2rrWfLoOqaPKU/BbEVERNxp4AQIu5YYKztrIEqmrho4mVEBmgh5ExujqKAIn8eXXFZUcaQ5is5RSaNkrpWuDMIksjPK/eVd8xDJquLB8MFvOgG9zb9NfJxol9a65bDyJ+AbBJueSkmX1r4Uegv44rxqVtUdZXXd0aTHs9byg+e3MrqimJsvGpeCGYqrFPiceoSB0fD4TXBiX/xjhJrgyc/CK9+D6TfALc8619jpNQer5jmdlVNsxtgKbplTxf+8sZs1u5K7JsZVlnBxVSVfeHgNN//8De54ZC333DSTOdVDUzRbERER9xk4AUJ/KZgCZWcNUMlkRYHTqKTcNNGR4BjGGMr8ZcllRUWXfSrLVdIk1BmitbM18QzCImUQygBz/mdhxAfghW9CqDmxMarmwccfhMdvdgKDNgyfejiljRh609TWQaDIe0oWYaLLIJ/beIAN+05w54cmU+SLv1mXDAAllXDj49DeAo/d6AT8YnV8DzxwBWz9PSz6J7j2XvDFXxc6WX+zaAqjK4r5+q830NYRf9OV9XuPs+TRtcz/4Ss8v/kgU0aU0hTq5DMXj1dwUERE8t7ACRAa4yzhVHbWgJSaDMJmwoUVCY8R8AeSy4oq8IK/TOeopE00KJfotVLk8+Av8BBsSbyLsWoQSk7xFMCV/wIn9jq1A+NxfC+89TA88Vkna6otEvS+4JaMBAcBLpgwmPZOy6vv1se9DLJ7jbWOzjA/fGEboyuKONzYlu5pSy4bfjZ84kE48A789kuxLb/fvQLuW+AECW96AuZ8OW0NevozqNDLd689l9rDTfzs5dgC5Z1hy3MbD/DJe1fwsZ++zrJth7l1bhV3Xz+D+sYQX7l8Er9cteeMmoQiIiL5xpvtCaRUUbmyswaoZDMIK4vCFJsQtijxIGPAHyDYlmRWVFG5slwlbaIBwkSvFWMMgWJvUhmEpX6nEVDS14pIqkyYC1M/Bq/9GGbcBBVje96vrdFZjlz7EuxY6nQqBig7C8bMcjq1XvA5ePtROPuqjAQJ51QP5ac3z+TWh5xlkKHOMJdNHsqqnUfZVd/MiEAhw8uKGBEoZEhpIQWek0GbaI21e26ayd6jzew83ERpoZcZYyvSPm/JcVMWwYe/Ay9+E5b9wKnX2Zs1/w3PfhUGT3CyD4dOztg0e7OgZjgfm3EWP3tlB382fRRTRvT8ntcc6uDJNft48PU6dh9pZnRFMd/8yFSuv3AsG/Yd77o+5lQP5eLqIad8LyIiko8GVoCwuELBlwEobMNJZxCO8LUC4InWAUxAWWEZx5M9v4orFMSWtIkG5ZK5VgLFvqS6GHs9Xgb5BmmJseSWyioId8Cfvu1kTwHULoNtf4CykU5QcM8bEG4Hb7ETVJz1eai+HBoPwv9+zunSWjXPCa48eYvTiCEDQcLLzx7BR84bxTNvv8+wUj8b9gV5edvhMxK/PAaGlRUyIlDUFTS8/Ozh/J+H12AMeD2G+z5zgYIf4pjzZTi02aknOPzsM7tyd7Y7DX7evB+qP+hcN9F63zngmx+ZyvObDnDHI2/x/J3z8USC4ytq61mx4wid1vLoqj2caGlnxtgKvnbF2VwxbQTeAmfx1IZ9J04JBs6pHso9N81kw74TukZERCRvJRwgNMYsBv4dKADut9b+y2mPFwK/AC4AjgDXW2t3JT7VGGiJ8YDU3N5M2IaTCnoM8zoBwoKSioTHCPgC7A3uTfj5gM5RSavost5k63Um06Qk+vrZCBAaYx4EPgIcstae28PjBud96yqgGbjFWrs28thngX+I7PpP1tqHMzNryYjqy2HVvbDx1zDsHNj1aqQzcSTKNvIDcMmXnP3GXnxqbbXtz50aDOzepTUDAcIVtfW8vuNI1zLIe26awYUTKqlvbONQsI2DwVYONrRxKNjqfB1sY9+xZtbuOcbRplDXONedP5o5kxT4kAhj4CN3w5FaeOp2GFwFo6Y7jzUdcZbV73oVLlniZBt6cqtu5ZDSQm65tIr/fKWW7/x+M3ddPY1HV+3mrmc20xkOYy1cMW0kX7isigvGV57x/NvnV5+xbU71UAUHRUQkryVUg9AYUwD8FLgSmArcaIyZetputwLHrLWTgB8D3491/NXvr+bBjQ/GPJ8Hn/k8q9fdD0UVXdlZq9fdz4PPfD7+MbrPQ2PkxBjL1/0XAIFIk494xnjjF99k4+vPMNTbAoCvtJKNrz/DG7/4Zuxz2Pggq99fTaAw0BX0SOocjWQhuvnfRGPkzhy6j9FVg7AwEPcY0WulvNjXtcQ43mvlrodv4Fcv3u0sx4/M5Vcv3s1dD98Q9xgJeghY3MfjVwKTI7fbgP8EMMZUAt8GLgJmA982xiSebiy5p2oefPIXYDzw8j85wcGJC+Da++BvtsPtrzlBkIkLzmy8MPfOjHVpPV205uA9N83krxfVcM9NM1ny6Dre3HWUUeXFnDe2gkXTRvKZi8fzN4tq+MEnzuPhz8/muTvnsfabH+bhz19IRbGPz1w8nle2HVaNNTmVrwiu/yV4vPA/10LjITi4CX6+wMmonXYtXPHdnAsORn3tiho+MDrAQyt2Mff7L/F3T23EAJ+5eAKvfHUh//npC3oMDoqIiEjPEs0gnA3ssNbuBDDGPA5cA2zuts81wF2Rr/8XuMcYY6ztvRpyc0czj255lHvW3cOSmUtYf2h9TJMpGjyBO9ffzRLvSKa2H2Xzc3dwz/vLWDJqPus3PhrbGL5iZ4yD65g65lI273tdY+TIGP9v83+Dx1AWPMDql77FV3f9mn+d8HHYu7rf5w8bMphhL95G89ArAGg5uIOarT9j/4d/FtPrA5w75Fy+uuyrXHLWJTSEGpI7Rz3DmNpWr3N0gIyRC3PoPsal5VMAqF19L/+49w8xXydw8lpZELiFFS1jeePxV5m69T/YfPZX2Prmn2IaY4JvJPfs/TmlBZXsbmnie7+8hWdCb/JR/4X8btn9/Q8A+AuKuHvvz6n95fqY9u/OWrvcGDOhj12uAX4ReR96wxhTYYwZBSwAXrTWHgUwxryIE2h8LO5JSO6asghm3+ZkEs77Klz+D/0/J8uSWQa5oraev/rV2/zs0+czp3ooV35gpGqsyZnKRjjB8d/fCfd/CJrqocAP/hJnmX0OM8bws5sv4PJ/fYV9x1qYUz2E//z0BZQX+7I9NREREVcyfcTren+SMZ8AFltrvxD5/jPARdbaJd322RjZZ1/k+9rIPr1+fF1cVWwn3TUp7vlIfvhYQyPLSor510P1zG5NrAvjCUrZ++F7OffSj8b1vNXvr2bJS0to6WhJ6HVFMsVjLYFwmB8lcZ0kY3VRIbePHE57Cjpcvvd3taEj+5sL43lOJED4+16WGP8e+Bdr7WuR75cCX8cJEBZZa/8psv2bQIu19l/7eq1Zs2bZNWvWxDM9yaa65U7twFm3wpoHMlZDMFvuXVbL9DHlpwQDV9TWs2HfiR6XV0qee/l7sOz7UDoSOtvgU79wxfWxoraeOx5Zy00XjeOx1XuzGgA3xrxlrZ2VlRcXERFJgaw3KTHG3Iaz1IuiCUVcVXUVV1dfndBYT7/2Tzzbuo+rCiq5uuaTiY2x7Ume7TyqMXJsjD9u/zW/LYMv+s5i9if+I+7nb37uPqbWP8+WMddzcZzBQYDZo2Zz8zk3c/879+sc1Rg5OYfuY1xfNDah6wRg0x//i2lHXmBN2QfxX3Bz3M8vAj645W6eKznORa2lLBj3sYTm8cqe3/KI3/oTenIadX/PGjduXJZnIzGLBgejQcGqyzLaaCQbVGNN4rLw75zswTUPwLyvueK6iC7B/+nNTpbspZOGKktWREQkCYkGCPcDY7t9Pyayrad99hljvEA5TrOSU1hr7wPuAxg3dZxd+d5KPj7548weNTuuCa1edz8rm/fyxYpzeeL4Rj7uLWL2zC/EP0b7EY2Rg2MsDx3uGmN2w664xtj4+jOMrl/JG2NupWbfk2x8fWFCGYS/3v5rvjj9izyx7Qmdoxojp+bQ0xjxXicQuVaOvNF1rez3fS7ua+VXL97NysKjfKSzmld9tXzYW8j1H74z7jG2+oKUhEyo/73j0tt7136cLMLu21/paYDu71mzZs2KPwVfsmP/2qw2GhHJeXXLYfNvneDgmgecIHqOXxvqRCwiIpJi1tq4bziBxZ1AFeAH3gamnbbPHcC9ka9vAJ7ob9wLLrjArnpvlb3sscvsqvdW2VitWvtze9mD0+yqtT/v8XuNkb9jvPPa0/bot8fYd157usfvY5rDaeekzlGNkUtzSNUYqbhWHn/hx/bSB6bax1/4cY/fxzsGsMbG//40AdjYy2N/BvwRMMDFwOrI9kqgDhgcudUBlf291gUXXBDzcYmI5Kydy6z9fpVz39P3EpNE3rN000033XTTLZduiT8RrgK2A7XA30e2fQe4OvJ1EfAksANYDUzsb8zoH1ur3ltlH3jnARurB57+3Bl/CK9a+3P7wNOf0xh5PsbKh//hjADHO689bVc+/A+xz+GdB84IBuoc1Ri5ModUjZGKa+XbD11/RjDw8Rd+bL/90PUJjRHvH1s4TUXeB9qBfcCtwO3A7ZHHDfDTyPvWO8Csbs/9fOT9agfwuVheTwFCERkQXv3xmcHAncuc7RIzBQh100033XRz+y2hJiXpooLvIiISlesF3/WeJSIiUbn+niUiItIfT7YnICIiIiIiIiIiItmjAKGIiIiIiIiIiEgeU4BQREREREREREQkjylAKCIiIiIiIiIikscUIBQREREREREREcljChCKiIiIiIiIiIjkMQUIRURERERERERE8pgChCIiIiIiIiIiInnMWGuzPYcuxpgGYFu255Ei5cCJbE8iRQbKsQyU4wAdSy4aKMcBuXMsNdbasmxPojfGmMPA7hQMNRSoT8E4+UQ/s/jo5xU//czil+8/s/HW2mHZnoSIiEiivNmewGm2WWtnZXsSqWCMuc9ae1u255EKA+VYBspxgI4lFw2U44DcORZjzJpsz6EvqfpD0BizZqC892WKfmbx0c8rfvqZxU8/MxEREXfTEuP0eSbbE0ihgXIsA+U4QMeSiwbKccDAOhYRERERERHphwKEaWKtHTB/YA+UYxkoxwE6llw0UI4DBtaxiIiIiIiISP9yLUB4X7YnICIiOSNf3hPy5ThTST+z+OjnFT/9zOKnn5mIiIiL5VSTEhEREREREREREcmsXMsgzFnGmMXGmG3GmB3GmG9Etj1gjHnbGLPBGPO/xpjSXp77t5HnbTPGXNHXmFk6DmOM+a4xZrsxZosx5iu9PPezxph3I7fPdtt+gTHmnciY/2GMMVk8lsuNMWuNMRuNMQ8bY3psxJNLx2KMedAYc8gYs7Hbth8aY7ZGzq2njDEVvTy3x3PIGFNljFkV2f4rY4w/3ccRed2ejuUuY8x+Y8z6yO2qXD+WXo5jhjHmjcgxrDHGzO7luTlzbkVed6wx5mVjzGZjzCZjzF9Gtn8y8n3YGNNrUflc+ncRERERERGRNLHWpu0GLAa2ATuAb0S2VQGrItt+Bfh7ee7fRvbZBlzR15jpvgEFQC0wEfADbwNTgUC3ff6tp/lE9nsbKIwce21kvB7HzNJxfA74BeCJ7De8h+dWAjsj94MjXw+OPLYauBgwwB+BK7P4b7IXmBLZ5zvArS44lnnA+cDGbtsWAd7I198Hvh/rzyDy2BPADZGv7wX+IkPXSk/Hchfw1UT+PbN1LL0cxwvR8wG4Cngl18+tyOuOAs6PfF0GbI9cK+cANcArwCw3/LsMtBtZeD9z+w3YBbwDrAfWZHs+uXgDHgQOnfb/VyXwIvBu5H5wtueZS7defmZ3Afsj59p64KpszzNXbsBY4GVgM7AJ+MvIdp1nuummm2666ebiW9oyCI0xBcBPgStx/hi90RgzFSfY8WNr7STgGHBrD8+dCtwATMP5A+pnxpiCPsZMt9nADmvtTmttCHgcuMZaG4zM1wDFQE/rta8BHrfWtllr63D+EJzd25jZOA7gL4DvWGvDANbaQz089wrgRWvtUWvtMZxf/BYbY0bhBErfsNZanEDjx9J8HL0dy8eBkLV2e2SfFyPbTpdTx2KtXQ4cPW3bC9bajsi3bwBjenhqj/+ekfPxcuB/I/s9TGb+TXo8lhjl1LH0chwWCES+Lgfe6+GpOXVuAVhr37fWro183QBsAUZba7dYa7f18/Sc+ncZSLL4fjYQLLTWzrDW9pr5mucewvndqbtvAEuttZOBpZHv5aSHOPNnBs7vqzMit2czPKdc1gH8jbV2Ks4HX3dE/v/SeSYiIuJi6Vxi3FswKpY/KnMpqAYwGiczLWpfZBvGmP8GDgBnAz+JbLvaGPOdfp7b65hp1NtrVgPXR5ZN/tEYMxnAGDPLGHN/P88dHfn69O3p1tN8RgLebsslP4HzKXeuH0t/Po+TcYYx5ixjTPSPlN6OYwhwvFuAMReOY0lkufSDxpjB4MpjuRP4oTFmL/CvOFnOrjq3jDETgJk4Wdy97eO2fxe3ytb7mQxwvXzAcQ3O71yggP4ZkvhwKy/19sETOs9ERERcLZ0Bwt7+sOzxj8ocDqr1yVr7OeAsnF+Oro9se9pa+61szitOhUBrJBvj5zhLbbDWrrHWfiGrM4uPxck8/bExZjXQAHSCK48FAGPM3+N8Uv8IgLX2PWttjzX8cth/4gShZwDvAz8CVx7LXwB/Za0dC/wV8AC459wyTo3UXwN3RrOfe+LCfxe3yrn3M5ewwAvGmLeMMbdlezIuMsJa+37k6wPAiGxOxkXO+HBLTnXaB086z0RERFwsZ5qU5HhQbT+RTLSIMZFtAFhrOzm5xDXW5/Y5Zpr09pr7gN9Etj0FTI/jufs5dflrJo6j1/lYa1daay+z1s4GluPUW4vpuWTvWHpkjLkF+Ahwc2RZ6ul6O44jQIU52aAlq8dhrT1ore2MLGH/OU7m1OnccCyf5eR18iTxHUdWzy1jjA8nOPiItfY3/e3fjRv+XSS/zLXWno+zNPsOY8y8bE/IbSLvJz29p8ipevxwS07q64MnnWciIiLuk84AYW9/WMbyR2UuBdUA3gQmR7p2+nGy1J42xkyCrhqEVwNbe3ju08ANxphCY0wVMBmnWUGPY2bjOIDfAgsj+8yn56Da88AiY8zgyKfoi4DnI58UB40xF0d+Dn8O/C7NxwG9/5sMBzDGFAJfx2mekOvHcgZjzGLga8DV1trmXnbr8WcQ+aX8ZZwl1uAEtrJyHACR+ntR1wIbe9jNDcfyHs71AU6phHd72Cfnzq3I6z0AbLHW/lucT3fDv4tbZev9zNWstfsj94dwPtDqsZu4nOFg9P/iyH1PtYalmxg/3MpbvXzwpPNMRETExdIZIOwtGBXLH5W5FFQjsiR6Cc4f/1twunduAR42xryD01FxFE7X3FOWS1trN0X23ww8B9wR+YXzjDEj+2b0OCKv+S/AxyPH8s/AFyLH0VVbzVp7FPhHnH+DN3GamkTr9XwJuB+nVmQtkXp5WTqW/2uM2QJsAJ6x1r6U68dijHkMWAnUGGP2GWNuBe7B6Tj7ojFmvTHm3si+XfXh+jmHvg78tTFmB069uAfSfRx9HMsPjDHvGGM24ASi/yrXj6WX4/g/wI+MMW8D3wNui+ybs+dWxKXAZ4DLI+fSemPMVcaYa40x+4BLgD8YY56PHE/O/rsMMFl5P3MzY8wgY0xZ9GucAHxPHzjImZ7G+Z0LFNCPSYwfbuWlPj540nkmIiLiYqbnlYspGtyYq4C7gQLgQWvtd40xE3GW41YC64BPW2vbjDFXA7Oiy4yNU3vt8zj11+601v6xtzHTdgAiIiJpovez+ER+f3gq8q0XeFQ/szNFPuBYAAwFDgLfxlkp8AQwDtgNfKrbhxd5r5ef2QKc5cUW2AV8sVt9vbxmjJkLvIrzAXk4svnvcOoQ6jwTERFxqbQGCEVERERERERERCS35UyTEhEREREREREREck8BQhFRERERERERETymAKEIiIiIiIiIiIieSwtAUJjzGJjzDZjzA5jzDci25ZEvrfGmKF9PHeBMeb36ZiXiIiIiIiIiIiInCrlAUJjTAHwU+BKYCpwozFmKvA68CGcrmYiIiIiIiIiIiKSA9KRQTgb2GGt3WmtDQGPA9dYa9dZa3fFM5AxZrYxZqUxZp0xZoUxpiay/RZjzG+MMc8ZY941xvwg9YchIiIiIiIiIiIy8HnTMOZoYG+37/cBFyU41lbgMmtthzHmQ8D3gI9HHpsBzATagG3GmJ9Ya/f2PIyIiIiIiIiIiIj0JB0BwlQqBx42xkwGLODr9thSa+0JAGPMZmA8pwYmRUREREREREREpB/pWGK8Hxjb7fsxkW09MsY8b4xZb4y5v4eH/xF42Vp7LvBRoKjbY23dvu4k94OdIiIiIiIiIiIiOScdQbU3gcnGmCqcwOANwE297WytvaKPsco5GVy8JVUTFBEREREREREREUfKMwittR3AEuB5YAvwhLV2kzHmK8aYfTgZhRt6yRgEJ2gZzQ78AfDPxph1KENQREREREREREQk5Yy1NttzOIUx5i+B0dbar2V7LiIiIiIiIiIiIgNdTmXlGWMeAM4FPpXtuYiIiIiIiIiIiOSDnMsgFBERERERERERkcxJRxdjERERERERERERcYm0BgiNMWONMS8bYzYbYzZF6gtijKk0xrxojHk3cj84sv1mY8wGY8w7xpgVxpjzuo31oDHmkDFmYzrnLCIiIiIiIiIikk/SnUHYAfyNtXYqcDFwhzFmKvANYKm1djKwNPI9QB0w31r7AeAfgfu6jfUQsDjN8xUREREREREREckraQ0QWmvft9aujXzdAGwBRgPXAA9HdnsY+FhknxXW2mOR7W8AY7qNtRw4ms75ioiIiIiIiIiI5JuM1SA0xkwAZgKrgBHW2vcjDx0ARvTwlFuBP2ZmdiIiIiIiIiIiIvnJm4kXMcaUAr8G7rTWBo0xXY9Za60xxp62/0KcAOHcTMxPREREREREREQkX6U9g9AY48MJDj5irf1NZPNBY8yoyOOjgEPd9p8O3A9cY609ku75iYiIiIiIiIiI5LN0dzE2wAPAFmvtv3V76Gngs5GvPwv8LrL/OOA3wGestdvTOTcREREREREREREBY63tf69EBzdmLvAq8A4Qjmz+O5w6hE8A44DdwKestUeNMfcDH49sA+iw1s6KjPUYsAAYChwEvm2tfSBtkxcREREREREREckDaQ0QioiIiIiIiIiISG7LWBdjERERERERERERyT0KEIqIiIiIiIiIiOQxBQhFRERERERERETymAKEIiIiIiIiIiIieUwBQhERERERERERkTymAKGIiIiIiIiIiEgeU4BQREREREREREQkj/1/GE0QbQgJxVQAAAAASUVORK5CYII=\n"
      },
      "metadata": {
       "needs_background": "light"
diff --git a/eaopack/assets.py b/eaopack/assets.py
index 6269344..47153dd 100644
--- a/eaopack/assets.py
+++ b/eaopack/assets.py
@@ -621,15 +621,12 @@ def setup_optim_problem(self, prices: dict, timegrid:Timegrid = None, costs_only
         ## if it's zero, one variable is enough
 
         # Make vector of single min/max capacities.
-        max_cap = self.make_vector(self.max_cap, prices)
-        min_cap = self.make_vector(self.min_cap, prices)
+        max_cap = self.make_vector(self.max_cap, prices, convert=True)
+        min_cap = self.make_vector(self.min_cap, prices, convert=True)
 
         # check integrity
         if any(min_cap>max_cap):
             raise ValueError('Asset --' + self.name+'--: Contract with min_cap > max_cap leads to ill-posed optimization problem')
-        # need to scale to discretization step since: flow * dT = volume in time step
-        min_cap = min_cap * self.timegrid.restricted.dt
-        max_cap = max_cap * self.timegrid.restricted.dt
 
         # Make vector of extra_costs:
         extra_costs = self.make_vector(self.extra_costs, prices, default_value=0)
@@ -689,7 +686,7 @@ def setup_optim_problem(self, prices: dict, timegrid:Timegrid = None, costs_only
             return OptimProblem(c = c, l = l, u = u,
                                 mapping = mapping)
 
-    def make_vector(self, value:  Union[float, Dict, str], prices:dict, default_value: float = None):
+    def make_vector(self, value:  Union[float, Dict, str], prices:dict, default_value: float = None, convert=False):
         """
         Make a vector out of value
         Args:
@@ -719,6 +716,9 @@ def make_vector(self, value:  Union[float, Dict, str], prices:dict, default_valu
             vec = self.timegrid.restricted.values_to_grid(value)
             if default_value is not None:
                 vec[np.isnan(vec)] = default_value
+
+        if convert:
+            vec = vec * self.timegrid.restricted.dt
         return vec
 
 
@@ -1119,7 +1119,14 @@ def __init__(self,
                  running_costs: Union[float, Dict, str] = 0.,
                  min_runtime: float = 0,
                  time_already_running: float = 0,
+                 min_downtime: float = 0,
+                 time_already_off: float = 0,
                  last_dispatch: float = 0,
+                 start_ramp_lower_bounds: Sequence = None,
+                 start_ramp_upper_bounds: Sequence = None,
+                 shutdown_ramp_lower_bounds: Sequence = None,
+                 shutdown_ramp_upper_bounds: Sequence = None,
+                 interpolate_ramp_frequency: bool = True,
                  start_fuel: Union[float, Dict, str] = 0.,
                  fuel_efficiency: Union[float, Dict, str] = 1.,
                  consumption_if_on: Union[float, Dict, str] = 0.
@@ -1165,9 +1172,42 @@ def __init__(self,
             ramp (float): Maximum increase/decrease of virtual dispatch (power + conversion_factor_power_heat * heat) in one timestep. Defaults to 1.
             start_costs (float): Costs for starting. Defaults to 0.
             running_costs (float): Costs when on. Defaults to 0.
-            min_runtime (int): Minimum runtime in timegrids main_time_unit. Defaults to 0.
+            min_runtime (int): Minimum runtime in timegrids main_time_unit. (start ramp time and shutdown ramp time do not count towards the min runtime.) Defaults to 0.
             time_already_running (int): The number of timesteps the asset is already running in timegrids main_time_unit. Defaults to 0.
+            min_downtime (int): Minimum downtime in timegrids main_time_unit. Defaults to 0.
+            time_already_off (int): The number of timesteps the asset has already been off in timegrids main_time_unit. Defaults to 0.
             last_dispatch (float): Previous virtual dispatch (power + conversion_factor_power_heat * heat). Defaults to 0.
+            start_ramp_lower_bounds (Sequence): The i-th element of this sequence specifies a lower bound of the
+                                                virtual dispatch (power + conversion_factor_power_heat * heat) at i timesteps
+                                                after starting. If interpolate_ramp_frequency is False, it is assumed
+                                                that the ramp is given in the timegrids freq, otherwise it is assumed
+                                                that the ramp is given in the timegrids main_time_unit and will be
+                                                interpolated accordingly. Defaults to None.
+            start_ramp_upper_bounds (Sequence): The i-th element of this sequence specifies an upper bound of the
+                                                virtual dispatch (power + conversion_factor_power_heat * heat) at i timesteps
+                                                after starting. If it is None, it is set equal to start_ramp_lower_bounds.
+                                                If interpolate_ramp_frequency is False, it is assumed
+                                                that the ramp is given in the timegrids freq, otherwise it is assumed
+                                                that the ramp is given in the timegrids main_time_unit and will be
+                                                interpolated accordingly.
+                                                Defaults to None.
+            shutdown_ramp_lower_bounds (Sequence): The i-th element of this sequence specifies a lower bound of the
+                                                   virtual dispatch (power + conversion_factor_power_heat * heat) at i timesteps
+                                                   before turning off. If interpolate_ramp_frequency is False, it is assumed
+                                                   that the ramp is given in the timegrids freq, otherwise it is assumed
+                                                   that the ramp is given in the timegrids main_time_unit and will be
+                                                   interpolated accordingly. Defaults to None.
+            shutdown_ramp_upper_bounds (Sequence): The i-th element of this sequence specifies an upper bound of the
+                                                   virtual dispatch (power + conversion_factor_power_heat * heat) at i timesteps
+                                                   before turning off. If it is None, it is set equal to shutdown_ramp_upper_bounds.
+                                                   If interpolate_ramp_frequency is False, it is assumed
+                                                   that the ramp is given in the timegrids freq, otherwise it is assumed
+                                                   that the ramp is given in the timegrids main_time_unit and will be
+                                                   interpolated accordingly. Defaults to None.
+            interpolate_ramp_frequency (bool): If this is False, it is assumed that the start and shutdown ramp specification
+                                               are given in the timegrids freq, otherwise it is assumed that they are given
+                                               in the timegrids main_time_unit and the ramps will be interpolated with
+                                               a piecewise linear function. Defaults to True.
 
             Optional: Explicit fuel consumption (e.g. gas) for multi-commodity simulation
                  start_fuel (float, dict, str): detaults to  0
@@ -1195,13 +1235,35 @@ def __init__(self,
         self.start_costs          = start_costs
         self.running_costs        = running_costs
         self.min_runtime          = min_runtime
+        assert self.min_runtime >= 0, "Min_runtime cannot be < 0. Asset: " + self.name
         self.time_already_running = time_already_running
+        self.min_downtime = min_downtime
+        self.time_already_off = time_already_off
         self.last_dispatch = last_dispatch
+        self.start_ramp_lower_bounds = start_ramp_lower_bounds
+        self.start_ramp_upper_bounds = start_ramp_upper_bounds
+        if self.start_ramp_upper_bounds is None:
+            self.start_ramp_upper_bounds = self.start_ramp_lower_bounds
+        assert self.start_ramp_lower_bounds is None or len(self.start_ramp_lower_bounds) == len(self.start_ramp_upper_bounds), "start_ramp_lower_bounds and start_ramp_upper_bounds cannot have different lengths. Asset: " + self.name
+        self.start_ramp_time = len(self.start_ramp_lower_bounds) if self.start_ramp_lower_bounds is not None else 0
+        assert np.all([self.start_ramp_lower_bounds[i] <= self.start_ramp_upper_bounds[i] for i in range(self.start_ramp_time)]), "shutdown_ramp_lower_bounds is higher than shutdown_ramp_upper bounds at some point. Asset: " + self.name
+        self.shutdown_ramp_lower_bounds = shutdown_ramp_lower_bounds
+        self.shutdown_ramp_upper_bounds = shutdown_ramp_upper_bounds
+        if self.shutdown_ramp_upper_bounds is None:
+            self.shutdown_ramp_upper_bounds = self.shutdown_ramp_lower_bounds
+        assert self.shutdown_ramp_lower_bounds is None or len(self.shutdown_ramp_lower_bounds) == len(self.shutdown_ramp_upper_bounds), "start_ramp_lower_bounds and start_ramp_upper_bounds cannot have different lengths. Asset: " + self.name
+        self.shutdown_ramp_time = len(self.shutdown_ramp_lower_bounds) if self.shutdown_ramp_lower_bounds is not None else 0
+        assert np.all([self.shutdown_ramp_lower_bounds[i] <= self.shutdown_ramp_upper_bounds[i] for i in range(self.shutdown_ramp_time)]), "shutdown_ramp_lower_bounds is higher than shutdown_ramp_upper bounds at some point. Asset: " + self.name
+        self.interpolate_ramp_frequency = interpolate_ramp_frequency
+
         if len(nodes) >= 3:
             self.fuel_efficiency      = fuel_efficiency
             self.consumption_if_on    = consumption_if_on
             self.start_fuel           = start_fuel
 
+        if self.min_downtime > 1:
+            assert (self.time_already_off == 0) ^ (self.time_already_running == 0), "Either time_already_off or time_already_running has to be 0, but not both. Asset: " + self.name
+
         if len(nodes) not in (2,3):
             raise ValueError('Length of nodes has to be 2 or 3; power, heat and optionally fuel. Asset: ' + self.name)
 
@@ -1218,29 +1280,57 @@ def setup_optim_problem(self, prices: dict, timegrid: Timegrid = None,
         Returns:
             OptimProblem: Optimization problem to be used by optimizer
         """
-        if self.freq is not None and self.freq != timegrid.freq:
-            raise ValueError('Freq of asset' + self.name + ' is ' + str(self.freq) + ' which is unequal to freq ' + timegrid.freq + ' of timegrid. Asset: ' + self.name)
-
-        # convert min_runtime and time_already_running from timegrids main_time_unit to timegrid.freq
-        min_runtime = self.convert_to_timegrid_freq(self.min_runtime, "min_runtime", timegrid)
-        time_already_running = self.convert_to_timegrid_freq(self.time_already_running, "time_already_running", timegrid)
-
         op = super().setup_optim_problem(prices=prices, timegrid=timegrid, costs_only=costs_only)
 
-        # Check that min_cap and max_cap are >= 0
-        assert np.all(op.l >= 0.), 'min_cap has to be greater or equal to 0. Asset: ' + self.name
-        assert np.all(op.u >= 0.), 'max_cap has to be greater or equal to 0. Asset: ' + self.name
+        if self.freq is not None and self.freq != self.timegrid.freq:
+            raise ValueError('Freq of asset' + self.name + ' is ' + str(self.freq) + ' which is unequal to freq ' + self.timegrid.freq + ' of timegrid. Asset: ' + self.name)
+
+        # convert min_runtime and time_already_running from timegrids main_time_unit to timegrid.freq
+        min_runtime = self.convert_to_timegrid_freq(self.min_runtime, "min_runtime")
+        time_already_running = self.convert_to_timegrid_freq(self.time_already_running, "time_already_running")
+        min_downtime = self.convert_to_timegrid_freq(self.min_downtime, "min_downtime")
+        time_already_off = self.convert_to_timegrid_freq(self.time_already_off, "time_already_off")
+
+        # Convert start ramp and shutdown ramp from timegrids main_time_unit to
+        # timegrid.freq IF self.interpolate_ramp_frequency is True, otherwise leave as is
+        start_ramp_time = self.start_ramp_time
+        start_ramp_lower_bounds = self.start_ramp_lower_bounds
+        start_ramp_upper_bounds = self.start_ramp_upper_bounds
+        shutdown_ramp_time = self.shutdown_ramp_time
+        shutdown_ramp_lower_bounds = self.shutdown_ramp_lower_bounds
+        shutdown_ramp_upper_bounds = self.shutdown_ramp_upper_bounds
+        if self.interpolate_ramp_frequency:
+            if self.start_ramp_time:
+                start_ramp_lower_bounds = self._convert_ramp(self.start_ramp_lower_bounds)
+                start_ramp_upper_bounds = self._convert_ramp(self.start_ramp_upper_bounds)
+                start_ramp_time = len(start_ramp_lower_bounds)
+            if self.shutdown_ramp_time:
+                shutdown_ramp_lower_bounds = self._convert_ramp(self.shutdown_ramp_lower_bounds)
+                shutdown_ramp_upper_bounds = self._convert_ramp(self.shutdown_ramp_upper_bounds)
+                shutdown_ramp_time = len(shutdown_ramp_lower_bounds)
+        if start_ramp_time:
+            start_ramp_lower_bounds *= self.timegrid.restricted.dt[:start_ramp_time]
+            start_ramp_upper_bounds *= self.timegrid.restricted.dt[:start_ramp_time]
+        if shutdown_ramp_time:
+            shutdown_ramp_lower_bounds *= self.timegrid.restricted.dt[:shutdown_ramp_time]
+            shutdown_ramp_upper_bounds *= self.timegrid.restricted.dt[:shutdown_ramp_time]
+
+        min_runtime += start_ramp_time + shutdown_ramp_time
+
+        # scale ramp and last dispatch in case timegrid.freq and timegrid.main_time_unit are not equal
+        ramp = self.ramp * self.timegrid.restricted.dt[0] if self.ramp is not None else None
+        last_dispatch = self.last_dispatch * self.timegrid.restricted.dt[0]
 
         # Make vectors of input params:
         start_costs = self.make_vector(self.start_costs, prices, default_value=0.)
-        running_costs = self.make_vector(self.running_costs, prices, default_value=0.)
+        running_costs = self.make_vector(self.running_costs, prices, default_value=0., convert=True)
         max_share_heat = self.make_vector(self.max_share_heat, prices, default_value=1.)
         conversion_factor_power_heat = self.make_vector(self.conversion_factor_power_heat, prices, default_value=1.)
         assert np.all(conversion_factor_power_heat != 0), 'conversion_factor_power_heat must not be zero. Asset: ' + self.name
         if len(self.nodes) >= 3:
             start_fuel = self.make_vector(self.start_fuel, prices, default_value=0.)
             fuel_efficiency = self.make_vector(self.fuel_efficiency, prices, default_value=1.)
-            consumption_if_on = self.make_vector(self.consumption_if_on, prices, default_value=0.)
+            consumption_if_on = self.make_vector(self.consumption_if_on, prices, default_value=0., convert=True)
             assert np.all(fuel_efficiency != 0), 'fuel efficiency must not be zero. Asset: ' + self.name
 
         # calculate costs:
@@ -1249,85 +1339,127 @@ def setup_optim_problem(self, prices: dict, timegrid: Timegrid = None,
         else:
             c = op.c
         c = np.hstack([c, conversion_factor_power_heat * c])  # costs for power and heat dispatch
-        if len(self.nodes)==2:
-            include_start_variables = min_runtime > 1 or np.any(start_costs != 0)
-            include_on_variables = include_start_variables or np.any(self.min_cap != 0.)
-        else:
-            include_start_variables = min_runtime > 1 or np.any(start_costs != 0) or np.any(start_fuel !=0.)
-            include_on_variables = include_start_variables or np.any(self.min_cap != 0.) or np.any(consumption_if_on != 0.)
+
+        include_shutdown_variables = shutdown_ramp_time > 0 or start_ramp_time > 0
+        include_start_variables = min_runtime > 1 or np.any(start_costs != 0) or start_ramp_time > 0 or shutdown_ramp_time > 0
+        include_on_variables = include_start_variables or min_downtime > 1 or include_shutdown_variables or np.any(self.min_cap != 0.)
+        if len(self.nodes) >= 3:
+            include_start_variables = include_start_variables or np.any(start_fuel != 0.)
+            include_on_variables = include_on_variables or include_start_variables or np.any(consumption_if_on != 0.)
 
         if include_on_variables:
             c = np.hstack([c, running_costs])  # add costs for on variables
         if include_start_variables:
             c = np.hstack([c, start_costs])  # add costs for start variables
+        if include_shutdown_variables:
+            c = np.hstack([c, np.zeros(self.timegrid.restricted.T)])  # costs for shutdown are 0
         if costs_only:
             return c
         op.c = c
 
+        # Check that min_cap and max_cap are >= 0
+        min_cap = op.l.copy()
+        max_cap = op.u.copy()
+        assert np.all(min_cap >= 0.), 'min_cap has to be greater or equal to 0. Asset: ' + self.name
+        assert np.all(max_cap >= 0.), 'max_cap has to be greater or equal to 0. Asset: ' + self.name
+
         # Check that if include_on_variables is True, the minimum capacity is not 0. Otherwise the "on" variables cannot be computed correctly.
-        if np.any(op.l == 0) and include_on_variables:
+        if np.any(min_cap == 0) and include_on_variables:
             print("Warning for asset " + self.name + ": The minimum capacity is 0 at some point and 'on'-variables are included" 
                   ". This can lead to incorrect 'on' and 'start' variables. "
                   "To prevent this either set min_cap>0 or set min_runtime=0 and start_costs=0 and start_fuel=0"
                   " and consumption_if_on=0.")
 
         # Prepare matrix A:
-        n = len(op.l)
+        self.n = len(min_cap)
         if op.A is None:
-            op.A = sp.lil_matrix((0, n))
+            op.A = sp.lil_matrix((0, self.n))
             op.cType = ''
             op.b = np.zeros(0)
 
+        # Define the dispatch variables:
+        op = self._add_dispatch_variables(op, conversion_factor_power_heat, max_cap, max_share_heat)
+
+        # Add on-, start-, and shutdown-variables:
+        op = self._add_bool_variables(op, include_on_variables, include_start_variables, include_shutdown_variables)
+
+        # Minimum and maximum capacity:
+        op = self._add_constraints_for_min_and_max_cap(op, min_cap, max_cap, time_already_running,
+                                                       conversion_factor_power_heat, include_on_variables, start_ramp_time,
+                                                       start_ramp_lower_bounds, start_ramp_upper_bounds, shutdown_ramp_time,
+                                                       shutdown_ramp_lower_bounds, shutdown_ramp_upper_bounds)
+
         # Ramp constraints:
-        if self.ramp is not None:
-            variables = op.mapping[["asset", "node", "var_name"]].drop_duplicates()
-            for i in range(len(variables)):
-                I_past = None
-                for t in range(self.timegrid.restricted.T):
-                    I_curr = np.where((op.mapping["asset"] == variables.iloc[i]["asset"])
-                                      & (op.mapping["node"] == variables.iloc[i]["node"])
-                                      & (op.mapping["var_name"] == variables.iloc[i]["var_name"])
-                                      & (op.mapping["time_step"] == self.timegrid.restricted.I[t]))
-                    if I_past and I_curr:
-                        a = sp.lil_matrix((1, n))
-                        a[0, I_curr] = 1
-                        a[0, I_past] = -1
-                        op.A = sp.vstack([op.A, a])
-                        op.cType += 'L'
-                        op.b = np.hstack([op.b, -self.ramp])
-                        op.A = sp.vstack([op.A, a])
-                        op.cType += 'U'
-                        op.b = np.hstack([op.b, self.ramp])
-                    I_past = I_curr
+        op = self._add_constraints_for_ramp(op, ramp, conversion_factor_power_heat, time_already_running,
+                                            include_on_variables, max_cap, start_ramp_time, shutdown_ramp_time, last_dispatch)
 
-            # Initial ramp constraint
-            a = sp.lil_matrix((1, n))
-            a[0, 0] = 1
-            op.A = sp.vstack([op.A, a])
-            op.cType += 'L'
-            op.b = np.hstack([op.b, -self.ramp + self.last_dispatch])
-            op.A = sp.vstack([op.A, a])
-            op.cType += 'U'
-            op.b = np.hstack([op.b, self.ramp + self.last_dispatch])
+        # Start and shutdown constraints:
+        op = self._add_constrains_for_start_and_shutdown(op, time_already_running, include_start_variables, include_shutdown_variables)
+
+        # Minimum runtime:
+        op = self._add_constraints_for_min_runtime(op, min_runtime, include_start_variables, time_already_running)
+
+        # Minimum Downtime:
+        op = self._add_constraints_for_min_downtime(op, min_downtime, time_already_off)
 
+        # Boundaries for the heat variable:
+        op = self._add_constraints_for_heat(op, max_share_heat)
+
+        # Reset mapping index:
+        op.mapping.reset_index(inplace=True, drop=True)  # need to reset index (which enumerates variables)
+
+        # Model fuel consumption:
+        if len(self.nodes) >= 3:
+            op = self._add_fuel_consumption(op, fuel_efficiency, consumption_if_on, start_fuel, conversion_factor_power_heat, include_on_variables, include_start_variables)
+
+        return op
+
+    def _convert_ramp(self, ramp, timegrid=None):
+        ramp_duration = len(ramp)
+        old_timepoints_in_new_freq = [self.convert_to_timegrid_freq(i, "ramp", timegrid, round=False) for i in
+                                      range(ramp_duration)]
+        new_timepoints = np.arange(self.convert_to_timegrid_freq(ramp_duration, "ramp_duration", timegrid))
+        ramp_new_freq = np.interp(new_timepoints, old_timepoints_in_new_freq, ramp)
+        return ramp_new_freq
+
+    def _add_dispatch_variables(self, op, conversion_factor_power_heat, max_cap, max_share_heat):
+        """ Divide each dispatch variable in op into a power dispatch that flows into the power node self.nodes[1]
+            and a heat dispatch that flows into self.nodes[2] """
         # Make sure that op.mapping contains only dispatch variables (i.e. with type=='d')
         var_types = op.mapping['type'].unique()
-        assert np.all(var_types=='d'), "Only variables of type 'd' (i.e. dispatch variables) are allowed in op.mapping at this point. " \
-                                       "However, there are variables with types " + str(var_types[var_types != 'd']) + " in the mapping." \
-                                       "This is likely due to a change in a superclass."
+        assert np.all(
+            var_types == 'd'), "Only variables of type 'd' (i.e. dispatch variables) are allowed in op.mapping at this point. " \
+                               "However, there are variables with types " + str(
+            var_types[var_types != 'd']) + " in the mapping." \
+                                           "This is likely due to a change in a superclass."
+
+        self.heat_idx = len(op.mapping)
 
         # Divide each dispatch variable in power and heat:
         new_map = pd.DataFrame()
         for i, mynode in enumerate(self.nodes):
-            if i>=2: continue # do only for power and heat
+            if i >= 2: continue  # do only for power and heat
             initial_map = op.mapping[op.mapping['type'] == 'd'].copy()
             initial_map['node'] = mynode.name
             new_map = pd.concat([new_map, initial_map.copy()])
         op.mapping = new_map
         op.A = sp.hstack([op.A, sp.coo_matrix(conversion_factor_power_heat * op.A.toarray())])
 
+        # Set lower and upper bounds
+        op.l = np.zeros(op.A.shape[1])
+        if max_share_heat is not None:
+            u_heat = max_share_heat * max_cap
+        else:
+            u_heat = max_cap / conversion_factor_power_heat
+        op.u = np.hstack((max_cap, u_heat))
+
+        return op
+
+    def _add_bool_variables(self, op, include_on_variables, include_start_variables, include_shutdown_variables):
+        """ Add the bool variables for 'on', 'start' and 'shutdown' to the OptimProblem op if needed """
         # Add on variables
         if include_on_variables:
+            self.on_idx = len(op.mapping)
             op.mapping['bool'] = False
             map_bool = pd.DataFrame()
             map_bool['time_step'] = self.timegrid.restricted.I
@@ -1341,133 +1473,345 @@ def setup_optim_problem(self, prices: dict, timegrid: Timegrid = None,
             # extend A for on variables (not relevant in exist. restrictions)
             op.A = sp.hstack((op.A, sp.lil_matrix((op.A.shape[0], len(map_bool)))))
 
-        # Add start variables
-        if include_start_variables:
-            map_bool['var_name'] = 'bool_start'
-            op.mapping = pd.concat([op.mapping, map_bool])
+            # set lower and upper bounds:
+            op.l = np.hstack((op.l, np.zeros(self.timegrid.restricted.T)))
+            op.u = np.hstack((op.u, np.ones(self.timegrid.restricted.T)))
 
-            # extend A for start variables (not relevant in exist. restrictions)
-            op.A = sp.hstack((op.A, sp.lil_matrix((op.A.shape[0], len(map_bool)))))
+            # Add start variables
+            if include_start_variables:
+                self.start_idx = len(op.mapping)
+                map_bool['var_name'] = 'bool_start'
+                op.mapping = pd.concat([op.mapping, map_bool])
 
+                # extend A for start variables (not relevant in exist. restrictions)
+                op.A = sp.hstack((op.A, sp.lil_matrix((op.A.shape[0], len(map_bool)))))
+
+                # set lower and upper bounds:
+                op.l = np.hstack((op.l, np.zeros(self.timegrid.restricted.T)))
+                op.u = np.hstack((op.u, np.ones(self.timegrid.restricted.T)))
+
+            # Add shutdown variables
+            if include_shutdown_variables:
+                self.shutdown_idx = len(op.mapping)
+                map_bool['var_name'] = 'bool_shutdown'
+                op.mapping = pd.concat([op.mapping, map_bool])
+
+                # extend A for shutdown variables (not relevant in exist. restrictions)
+                op.A = sp.hstack((op.A, sp.lil_matrix((op.A.shape[0], len(map_bool)))))
+
+                # set lower and upper bounds:
+                op.l = np.hstack((op.l, np.zeros(self.timegrid.restricted.T)))
+                op.u = np.hstack((op.u, np.ones(self.timegrid.restricted.T)))
+
+        return op
+
+    def _add_constraints_for_min_and_max_cap(self, op, min_cap, max_cap, time_already_running,
+                                             conversion_factor_power_heat, include_on_variables, start_ramp_time,
+                                             start_ramp_lower_bounds, start_ramp_upper_bounds, shutdown_ramp_time,
+                                             shutdown_ramp_lower_bounds, shutdown_ramp_upper_bounds):
+        """ Add the constraints for the minimum and maximum capacity to op.
+
+            These ensure that the virtual dispatch
+            (power + conversion_factor_power_heat * heat) is 0 when the asset is "off",
+            it is bounded by the start or shutdown specifications during the start and shutdown ramp,
+            and otherwise it is between minimum and maximum capacity"""
         # Minimum and maximum capacity:
-        A_lower_bounds = sp.lil_matrix((n, op.A.shape[1]))
-        A_upper_bounds = sp.lil_matrix((n, op.A.shape[1]))
-        for i in range(n):
+        start = max(0, start_ramp_time - time_already_running) if time_already_running > 0 else 0
+        A_lower_bounds = sp.lil_matrix((self.n, op.A.shape[1]))
+        A_upper_bounds = sp.lil_matrix((self.n, op.A.shape[1]))
+        for i in range(start, self.n):
             var = op.mapping.iloc[i]
-            on_variable = np.where((op.mapping["asset"] == var["asset"])
-                                   & (op.mapping["var_name"] == "bool_on")
-                                   & (op.mapping["time_step"] == var["time_step"]))
 
             A_lower_bounds[i, i] = 1
-            A_lower_bounds[i, n + i] = conversion_factor_power_heat[i]
-            A_lower_bounds[i, on_variable] = - op.l[i]  # has no effect if no on variables found
+            A_lower_bounds[i, self.heat_idx + i] = conversion_factor_power_heat[i]
+            if include_on_variables:
+                A_lower_bounds[i, self.on_idx + var["time_step"]] = - min_cap[i]
 
             A_upper_bounds[i, i] = 1
-            A_upper_bounds[i, n + i] = conversion_factor_power_heat[i]
-            A_upper_bounds[i, on_variable] = - op.u[i]  # has no effect if no on variables found
-        op.A = sp.vstack((op.A, A_lower_bounds))
-        op.cType += 'L' * n
-        op.b = np.hstack((op.b, np.zeros(n)))
-
-        op.A = sp.vstack((op.A, A_upper_bounds))
-        op.cType += 'U' * n
+            A_upper_bounds[i, self.heat_idx + i] = conversion_factor_power_heat[i]
+            if include_on_variables:
+                A_upper_bounds[i, self.on_idx + var["time_step"]] = - max_cap[i]
+
+            for j in range(start_ramp_time):
+                if i - j < 0:
+                    continue
+                A_lower_bounds[i, self.start_idx + i - j] = min_cap[i] - start_ramp_lower_bounds[j]
+                A_upper_bounds[i, self.start_idx + i - j] = max_cap[i] - start_ramp_upper_bounds[j]
+
+            for j in range(shutdown_ramp_time):
+                if i + j + 1 >= self.timegrid.restricted.T:
+                    break
+                A_lower_bounds[i, self.shutdown_idx + i + j + 1] = min_cap[i] - shutdown_ramp_lower_bounds[j]
+                A_upper_bounds[i, self.shutdown_idx + i + j + 1] = max_cap[i] - shutdown_ramp_upper_bounds[j]
+
+        op.A = sp.vstack((op.A, A_lower_bounds[start:]))
+        op.cType += 'L' * (self.n - start)
+        op.b = np.hstack((op.b, np.zeros(self.n - start)))
+
+        op.A = sp.vstack((op.A, A_upper_bounds[start:]))
+        op.cType += 'U' * (self.n - start)
         if include_on_variables:
-            op.b = np.hstack((op.b, np.zeros(n)))
+            op.b = np.hstack((op.b, np.zeros(self.n - start)))
         else:
-            op.b = np.hstack((op.b, op.u))
+            op.b = np.hstack((op.b, max_cap[start:]))
 
-        # Start constraints:
-        if include_start_variables:
-            myA = sp.lil_matrix((self.timegrid.restricted.T-1, op.A.shape[1]))
-            for i in range(self.timegrid.restricted.T-1):
-                myA[i, 2 * n + i + 1] = 1
-                myA[i, 2 * n + i] = - 1
-                myA[i, 2 * n + self.timegrid.restricted.T + i + 1] = -1
-            op.A = sp.vstack((op.A, myA))
-            op.cType += 'U' * (self.timegrid.restricted.T - 1)
-            op.b = np.hstack((op.b, np.zeros(self.timegrid.restricted.T-1)))
+        # Enforce start_ramp if asset is in the starting process at time 0
+        if time_already_running > 0 and time_already_running < start_ramp_time:
+            for i in range(start_ramp_time - time_already_running):
+                # Upper Bound:
+                a = sp.lil_matrix((1, op.A.shape[1]))
+                a[0, i] = 1
+                a[0, self.heat_idx + i] = conversion_factor_power_heat[i]
+                op.A = sp.vstack((op.A, a))
+                op.cType += 'U'
+                op.b = np.hstack((op.b, start_ramp_upper_bounds[time_already_running + i]))
 
-            if time_already_running==0:
+                # Lower Bound:
                 a = sp.lil_matrix((1, op.A.shape[1]))
-                a[0, 2*n] = 1
-                a[0, 2*n + self.timegrid.restricted.T] = -1
+                a[0, i] = 1
+                a[0, self.heat_idx + i] = conversion_factor_power_heat[i]
                 op.A = sp.vstack((op.A, a))
+                op.cType += 'L'
+                op.b = np.hstack((op.b, start_ramp_lower_bounds[time_already_running + i]))
+
+        return op
+
+    def _add_constraints_for_ramp(self, op: OptimProblem, ramp, conversion_factor_power_heat, time_already_running, include_on_variables, max_cap, start_ramp_time, shutdown_ramp_time, last_dispatch):
+        """ Add ramp constraints to the OptimProblem op.
+
+            These ensure that the increase/decrease of the virtual dispatch (power + conversion_factor_power_heat * heat)
+            is bounded by ramp, except during timesteps that belong to the start or shutdown ramp"""
+        # Ramp constraints:
+        if ramp is not None:
+            for t in range(1, self.timegrid.restricted.T):
+                # Lower Bound
+                a = sp.lil_matrix((1, op.A.shape[1]))
+                a[0, t] = 1
+                a[0, self.heat_idx + t] = conversion_factor_power_heat[t]
+                a[0, t - 1] = -1
+                a[0, self.heat_idx + t - 1] = -conversion_factor_power_heat[t]
+                if include_on_variables:
+                    a[0, self.on_idx + t - 1] = ramp
+                for i in range(shutdown_ramp_time):
+                    if t + i >= self.timegrid.restricted.T:
+                        break
+                    a[0, self.shutdown_idx + t + i] = max_cap[t - 1] - ramp
+                op.A = sp.vstack([op.A, a])
+                op.cType += 'L'
+                if include_on_variables:
+                    op.b = np.hstack([op.b, 0])
+                else:
+                    op.b = np.hstack([op.b, -ramp])
+
+                # Upper Bound
+                a = sp.lil_matrix((1, op.A.shape[1]))
+                a[0, t] = 1
+                a[0, self.heat_idx + t] = conversion_factor_power_heat[t]
+                a[0, t - 1] = -1
+                a[0, self.heat_idx + t - 1] = -conversion_factor_power_heat[t]
+                if include_on_variables:
+                    a[0, self.on_idx + t] = -ramp
+                    b_value = 0
+                else:
+                    b_value = ramp
+                for i in range(start_ramp_time):
+                    if t - i < 0:
+                        if time_already_running > 0 and time_already_running - t + i == 0:
+                            b_value += max_cap[t] - ramp
+                            break
+                        continue
+                    a[0, self.start_idx + t - i] = ramp - max_cap[t]
+                op.A = sp.vstack([op.A, a])
                 op.cType += 'U'
-                op.b = np.hstack((op.b, 0))
+                op.b = np.hstack([op.b, b_value])
+
+            # Initial ramp constraint
+            a = sp.lil_matrix((1, op.A.shape[1]))
+            a[0, 0] = 1
+            a[0, self.heat_idx] = conversion_factor_power_heat[0]
+            for i in range(shutdown_ramp_time):
+                a[0, self.shutdown_idx + i] = last_dispatch - ramp
+            op.A = sp.vstack([op.A, a])
+            op.cType += 'L'
+            if time_already_running == 0:
+                op.b = np.hstack([op.b, last_dispatch])
+            else:
+                op.b = np.hstack([op.b, -ramp + last_dispatch])
 
-            # Minimum runtime:
+            a = sp.lil_matrix((1, op.A.shape[1]))
+            a[0, 0] = 1
+            a[0, self.heat_idx] = conversion_factor_power_heat[0]
+            if include_on_variables:
+                a[0, self.on_idx] = -ramp
+            op.A = sp.vstack([op.A, a])
+            op.cType += 'U'
+            if not include_on_variables:
+                op.b = np.hstack([op.b, last_dispatch + ramp])
+            elif time_already_running > 0 and time_already_running > start_ramp_time:
+                op.b = np.hstack([op.b, last_dispatch + max_cap[0] - ramp])
+            else:
+                op.b = np.hstack([op.b, last_dispatch])
+        return op
+
+    def _add_constrains_for_start_and_shutdown(self, op: OptimProblem, time_already_running, include_start_variables, include_shutdown_variables):
+        """ Add constraints that ensure that the 'start' and 'shutdown' variables are correct """
+        if include_start_variables:
+            if not include_shutdown_variables:
+                # Define just start constraints
+                myA = sp.lil_matrix((self.timegrid.restricted.T - 1, op.A.shape[1]))
+                for i in range(self.timegrid.restricted.T - 1):
+                    myA[i, self.on_idx + i + 1] = 1
+                    myA[i, self.on_idx + i] = - 1
+                    myA[i, self.start_idx + i + 1] = -1
+                op.A = sp.vstack((op.A, myA))
+                op.cType += 'U' * (self.timegrid.restricted.T - 1)
+                op.b = np.hstack((op.b, np.zeros(self.timegrid.restricted.T - 1)))
+
+                if time_already_running == 0:
+                    a = sp.lil_matrix((1, op.A.shape[1]))
+                    a[0, self.on_idx] = 1
+                    a[0, self.start_idx] = -1
+                    op.A = sp.vstack((op.A, a))
+                    op.cType += 'S'
+                    op.b = np.hstack((op.b, 0))
+            else:
+                # Simultaneous definition of start- and shutdown constraints
+                myA = sp.lil_matrix((self.timegrid.restricted.T - 1, op.A.shape[1]))
+                for t in range(self.timegrid.restricted.T - 1):
+                    myA[t, self.on_idx + t + 1] = 1
+                    myA[t, self.on_idx + t] = - 1
+                    myA[t, self.start_idx + t + 1] = -1
+                    myA[t, self.shutdown_idx + t + 1] = 1
+                op.A = sp.vstack((op.A, myA))
+                op.cType += 'S' * (self.timegrid.restricted.T - 1)
+                op.b = np.hstack((op.b, np.zeros(self.timegrid.restricted.T - 1)))
+
+                if time_already_running == 0:
+                    a = sp.lil_matrix((1, op.A.shape[1]))
+                    a[0, self.on_idx] = 1
+                    a[0, self.start_idx] = -1
+                    op.A = sp.vstack((op.A, a))
+                    op.cType += 'S'
+                    op.b = np.hstack((op.b, 0))
+                else:
+                    a = sp.lil_matrix((1, op.A.shape[1]))
+                    a[0, self.on_idx] = 1
+                    a[0, self.shutdown_idx] = 1
+                    op.A = sp.vstack((op.A, a))
+                    op.cType += 'S'
+                    op.b = np.hstack((op.b, 1))
+
+                # Ensure that shutdown and start process do not overlap
+                myA = sp.lil_matrix((self.timegrid.restricted.T - 1, op.A.shape[1]))
+                for t in range(self.timegrid.restricted.T - 1):
+                    myA[t, self.start_idx + t] = 1
+                    myA[t, self.shutdown_idx + t] = 1
+                op.A = sp.vstack((op.A, myA))
+                op.cType += 'U' * (self.timegrid.restricted.T - 1)
+                op.b = np.hstack((op.b, np.ones(self.timegrid.restricted.T - 1)))
+
+                # Ensure that shutdown and start at timestep 0 are correct:
+                if time_already_running == 0:
+                    op.u[self.shutdown_idx] = 0
+                else:
+                    op.u[self.start_idx] = 0
+
+        return op
+
+    def _add_constraints_for_min_runtime(self, op: OptimProblem, min_runtime, include_start_variables, time_already_running):
+        """ Add constraints to the OptimProblem op that ensure that every time the asset is turned on it remains on
+            for at least the minimum runtime. """
+        if include_start_variables and min_runtime > 1:
             for t in range(self.timegrid.restricted.T):
                 for i in range(1, min_runtime):
                     if i > t:
                         continue
                     a = sp.lil_matrix((1, op.A.shape[1]))
-                    a[0, 2 * n + t] = 1
-                    a[0, 2 * n + self.timegrid.restricted.T + t - i] = -1
+                    a[0, self.on_idx + t] = 1
+                    a[0, self.start_idx + t - i] = -1
                     op.A = sp.vstack((op.A, a))
                     op.cType += 'L'
                     op.b = np.hstack((op.b, 0))
 
+            # Enforce minimum runtime if asset already on
+            if time_already_running > 0 and min_runtime - time_already_running > 0:
+                op.l[self.on_idx:self.on_idx + min_runtime - time_already_running] = 1
+        return op
+
+    def _add_constraints_for_min_downtime(self, op: OptimProblem, min_downtime, time_already_off):
+        """ Add constraints to the OptimProblem op that ensure that every time the asset is turned off it remains off
+            for at least the minimum downtime. """
+        if min_downtime > 1:
+            for t in range(self.timegrid.restricted.T):
+                for i in range(1, min_downtime):
+                    if i > t:
+                        continue
+                    a = sp.lil_matrix((1, op.A.shape[1]))
+                    a[0, self.on_idx + t] = 1
+                    a[0, self.on_idx + t - i] = -1
+                    if t > i:
+                        a[0, self.on_idx + t - i - 1] = 1
+                    op.A = sp.vstack((op.A, a))
+                    op.cType += 'U'
+                    if not t > i and time_already_off == 0:
+                        op.b = np.hstack((op.b, 0))
+                    else:
+                        op.b = np.hstack((op.b, 1))
+            # Enforce minimum downtime if asset already off
+            if time_already_off > 0 and min_downtime - time_already_off > 0:
+                op.u[self.on_idx:self.on_idx + min_downtime - time_already_off] = 0
+        return op
+
+    def _add_constraints_for_heat(self, op: OptimProblem, max_share_heat):
+        """ Add constraints to the OptimProblem op to bound the heat variable by max_share_heat * power. """
         # Boundaries for the heat variable:
         if max_share_heat is not None:
-            myA = sp.lil_matrix((n, op.A.shape[1]))
-            for i in range(n):
-                myA[i, n + i] = 1
+            myA = sp.lil_matrix((self.n, op.A.shape[1]))
+            for i in range(self.n):
+                myA[i, self.heat_idx + i] = 1
                 myA[i, i] = - max_share_heat[i]
             op.A = sp.vstack((op.A, myA))
-            op.cType += 'U' * n
-            op.b = np.hstack((op.b, np.zeros(n)))
-
-        # Set lower and upper bounds for all variables
-        op.l = np.zeros(op.A.shape[1])
-        if max_share_heat is not None:
-            u_heat = max_share_heat * op.u
-        else:
-            u_heat = op.u / conversion_factor_power_heat
-        op.u = np.hstack((op.u, u_heat, np.ones(op.A.shape[1] - op.u.shape[0] * 2)))
-
-        # Enforce minimum runtime if asset already on
-        if time_already_running > 0 and min_runtime - time_already_running>0:
-            op.l[2*n:2*n+ min_runtime - time_already_running] = 1
+            op.cType += 'U' * self.n
+            op.b = np.hstack((op.b, np.zeros(self.n)))
+        return op
 
-        # Reset mapping index:
-        op.mapping.reset_index(inplace=True, drop=True)  # need to reset index (which enumerates variables)
+    def _add_fuel_consumption(self, op: OptimProblem, fuel_efficiency, consumption_if_on, start_fuel, conversion_factor_power_heat, include_on_variables, include_start_variables):
+        """ In case there is an explicit node for fuel, extend the mapping.
+
+            Idea: fuel consumption is  power disp + conversion_factor_power_heat * heat disp.
+            To realise this, the mapping in the same way as in the simpler asset type 'MultiCommodityContract'."""
+        # disp_factor determines the factor with which fuel is consumed
+        if 'disp_factor' not in op.mapping: op.mapping['disp_factor'] = np.nan
+        new_map = op.mapping.copy()
+        for i in [0, 1]:  # nodes power and heat
+            initial_map = op.mapping[
+                (op.mapping['var_name'] == 'disp') & (op.mapping['node'] == self.node_names[i])].copy()
+            initial_map['node'] = self.node_names[2]  # fuel node
+            if i == 0:
+                initial_map['disp_factor'] = -1. / fuel_efficiency
+            elif i == 1:
+                initial_map['disp_factor'] = -conversion_factor_power_heat / fuel_efficiency
+            new_map = pd.concat([new_map, initial_map.copy()])
+        # consumption  if on
+        if include_on_variables:
+            initial_map = op.mapping[op.mapping['var_name'] == 'bool_on'].copy()
+            initial_map['node'] = self.node_names[2]  # fuel node
+            # initial_map['var_name'] = 'fuel_if_on'
+            initial_map['type'] = 'd'
+            initial_map['disp_factor'] = -consumption_if_on
+            new_map = pd.concat([new_map, initial_map.copy()])
+        # consumption on start
+        if include_start_variables:
+            initial_map = op.mapping[op.mapping['var_name'] == 'bool_start'].copy()
+            initial_map['node'] = self.node_names[2]  # fuel node
+            # initial_map['var_name'] = 'fuel_start'
+            initial_map['type'] = 'd'
+            initial_map['disp_factor'] = -start_fuel
+            new_map = pd.concat([new_map, initial_map.copy()])
 
-        # in case there is an explicit node for fuel, extend mapping
-        # idea: fuel consumption is  power disp + conversion_factor_power_heat * heat disp
-        # mapping extention equivalent to simpler asset type "MultiCommodityContract"
-        if len(self.nodes) >= 3:
-            # disp_factor determines the factor with which fuel is consumed
-            if 'disp_factor' not in op.mapping: op.mapping['disp_factor'] = np.nan
-            new_map     = op.mapping.copy()
-            for i in [0,1]: # nodes power and heat
-                initial_map = op.mapping[(op.mapping['var_name']=='disp') & (op.mapping['node']== self.node_names[i])].copy()
-                initial_map['node']        = self.node_names[2] # fuel node
-                if i == 0:
-                    initial_map['disp_factor'] = -1./fuel_efficiency
-                elif i == 1:
-                    initial_map['disp_factor'] = -conversion_factor_power_heat / fuel_efficiency
-                new_map = pd.concat([new_map, initial_map.copy()])
-            # consumption  if on
-            if include_on_variables:
-                initial_map = op.mapping[op.mapping['var_name']=='bool_on'].copy()
-                initial_map['node'] = self.node_names[2] # fuel node
-                #initial_map['var_name'] = 'fuel_if_on'
-                initial_map['type'] = 'd'
-                initial_map['disp_factor'] = -consumption_if_on
-                new_map = pd.concat([new_map, initial_map.copy()])
-            # consumption on start
-            if include_start_variables:
-                initial_map = op.mapping[op.mapping['var_name']=='bool_start'].copy()
-                initial_map['node'] = self.node_names[2] # fuel node
-                #initial_map['var_name'] = 'fuel_start'
-                initial_map['type'] = 'd'
-                initial_map['disp_factor'] = -start_fuel
-                new_map = pd.concat([new_map, initial_map.copy()])
-
-            op.mapping = new_map
+        op.mapping = new_map
         return op
 
-
 class MultiCommodityContract(Contract):
     """ Multi commodity contract class - implements a Contract that generates two or more commoditites at a time.
         The main idea is to implement a CHP generating unit that would generate power and heat at the same time.
diff --git a/eaopack/serialization.py b/eaopack/serialization.py
index 4c1161c..8826793 100644
--- a/eaopack/serialization.py
+++ b/eaopack/serialization.py
@@ -49,6 +49,8 @@ def json_serialize_objects(obj) -> dict:
     elif isinstance(obj, Asset):
         res = obj.__dict__.copy()
         res.pop('asset_names',None)
+        res.pop('start_ramp_time', None)  # only relevant for class CHPAsset
+        res.pop('shutdown_ramp_time', None)  # only relevant for class CHPAsset
         # res.pop('timegrid', None) # not to be serialized
         res['__class__']  = 'Asset' # super class Asset
         res['asset_type'] = obj.__class__.__name__ # store child class
diff --git a/setup.cfg b/setup.cfg
index 921adfe..24c45bd 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,6 +1,6 @@
 [metadata]
 name = eaopack
-version = 1.2.1
+version = 1.2.2
 author = The EAO development Team
 description = A Framework for Optimizing Decentralized Portfolios and Green Supply
 long_description = file: README.md


Test patch:
diff --git a/tests/test_asset.py b/tests/test_asset.py
index caa400d..6246a61 100644
--- a/tests/test_asset.py
+++ b/tests/test_asset.py
@@ -586,7 +586,36 @@ def test_minruntime(self):
         start_variables = res.x[3*timegrid.T:]
         self.assertAlmostEqual(on_variables.sum(), 15., 4) 
         # 10 times full load, 10 time min load, NO start!
-        self.assertAlmostEqual(res.value, 10*10*100. - 5*1 + 15*(-5), 4) 
+        self.assertAlmostEqual(res.value, 10*10*100. - 5*1 + 15*(-5), 4)
+
+    def test_mindowntime(self):
+        """ Unit test. Setting up a CHPAsset and check min down time restriction
+        """
+        node_power = eao.assets.Node('node_power')
+        node_heat = eao.assets.Node('node_heat')
+        Start = dt.date(2021, 1, 1)
+        End = dt.date(2021, 1, 2)
+        timegrid = eao.assets.Timegrid(Start, End, freq='h')
+
+        a = eao.assets.CHPAsset(name='CHP',
+                                price='price',
+                                nodes=(node_power, node_heat),
+                                min_cap=1.,
+                                max_cap=10.,
+                                min_downtime=5,
+                                time_already_off=1)
+        prices = {'price': -1. * np.ones(timegrid.T)}
+        prices['price'][10] = 100
+
+        op = a.setup_optim_problem(prices, timegrid=timegrid)
+        res = op.optimize()
+        on_variables = res.x[2 * timegrid.T:3 * timegrid.T]
+        self.assertAlmostEqual(on_variables.sum(), timegrid.T-9, 4)
+        # Asset is off for the first 4 timesteps, and off again for min_downtime=5 timesteps around hour 10 when price
+        # is 100, otherwise on because price is negative
+        # T-9 times full load at price -1
+        self.assertAlmostEqual(res.value, (timegrid.T-9)*10, 4)
+
 
     def test_gas_consumption(self):
         """ Unit test. Setting up a CHPAsset with explicit gas (fuel) consumption
@@ -713,6 +742,229 @@ def test_freq_conversion(self):
         new_value = eao.assets.convert_time_unit(old_value, old_freq='min', new_freq='h')
         self.assertAlmostEqual(new_value - old_value / 60, 0, 5)
 
+    def test_freq_conversion2(self):
+        """ Unit test. Setting up two CHP Assets with different freq and checking that both give the same results
+        """
+        np.random.seed(42)
+
+        node_power = eao.assets.Node('node_power')
+        node_heat = eao.assets.Node('node_heat')
+        node_fuel = eao.assets.Node('node_fuel')
+
+        Start = dt.date(2021, 1, 1)
+        End = dt.date(2021, 1, 2)
+        prices = {}
+
+        timegrid1 = eao.assets.Timegrid(Start, End, freq='h', main_time_unit='h')
+        a1 = eao.assets.CHPAsset(name='CHP',
+                                 price='price1',
+                                 nodes=(node_power, node_heat, node_fuel),
+                                 extra_costs=1,
+                                 min_cap=5,
+                                 max_cap=20,
+                                 conversion_factor_power_heat=0.5,
+                                 max_share_heat=0.8,
+                                 # ramp=6,
+                                 start_costs=0.4,
+                                 running_costs=0.3,
+                                 min_runtime=4,
+                                 time_already_running=0,
+                                 min_downtime=2,
+                                 time_already_off=1,
+                                 last_dispatch=0,
+                                 start_fuel=0.7,
+                                 fuel_efficiency=0.8,
+                                 consumption_if_on=0.2
+                                 )
+        price1 = 20 * np.random.rand(timegrid1.T) - 10
+        prices['price1'] = price1
+        op1 = a1.setup_optim_problem(prices, timegrid=timegrid1)
+        res1 = op1.optimize()
+
+        timegrid2 = eao.assets.Timegrid(Start, End, freq='15min', main_time_unit='h')
+        a2 = eao.assets.CHPAsset(name='CHP',
+                                 price='price2',
+                                 nodes=(node_power, node_heat, node_fuel),
+                                 extra_costs=1,
+                                 min_cap=5,
+                                 max_cap=20,
+                                 conversion_factor_power_heat=0.5,
+                                 max_share_heat=0.8,
+                                 # ramp=6,
+                                 start_costs=0.4,
+                                 running_costs=0.3,
+                                 min_runtime=4,
+                                 time_already_running=0,
+                                 min_downtime=2,
+                                 time_already_off=1,
+                                 last_dispatch=0,
+                                 start_fuel=0.7,
+                                 fuel_efficiency=0.8,
+                                 consumption_if_on=0.2
+                                 )
+        price2 = np.vstack([price1] * 4).T.reshape(-1)
+        prices['price2'] = price2
+        op2 = a2.setup_optim_problem(prices, timegrid=timegrid2)
+        res2 = op2.optimize()
+
+        self.assertAlmostEqual(res1.value, res2.value, 4)
+
+    def test_start_and_shutdown_ramp1(self):
+        """ Unit test. Setting up a CHPAsset and checking start and shutdown ramps where the upper and lower bounds are equal
+        """
+        node_power = eao.assets.Node('node_power')
+        node_heat = eao.assets.Node('node_heat')
+        Start = dt.date(2021, 1, 1)
+        End = dt.date(2021, 1, 2)
+        timegrid = eao.assets.Timegrid(Start, End, freq='h')
+
+        a = eao.assets.CHPAsset(name='CHP',
+                                price='price',
+                                nodes=(node_power, node_heat),
+                                min_cap=5.,
+                                max_cap=10.,
+                                time_already_off=1,
+                                start_ramp_lower_bounds=[1, 2, 3, 4],
+                                shutdown_ramp_lower_bounds=[1, 4, 6, 7],
+                                time_already_running=2)
+        prices = {'price': -1. * np.ones(timegrid.T)}
+        prices['price'][15:] = 100
+
+        op = a.setup_optim_problem(prices, timegrid=timegrid)
+        res = op.optimize()
+        # Asset starts out with the last 2 steps of the startramp, then runs at maximum capacity for 9 steps and then
+        # follows the shutdown ramp
+        self.assertAlmostEqual(res.value, 3 + 4 + 9 * 10 + 7 + 6 + 4 + 1, 4)
+
+    def test_start_and_shutdown_ramp2(self):
+        """ Unit test. Setting up a CHPAsset and checking start and shutdown ramps where the upper and lower bounds are different
+        """
+        node_power = eao.assets.Node('node_power')
+        node_heat = eao.assets.Node('node_heat')
+        Start = dt.date(2021, 1, 1)
+        End = dt.date(2021, 1, 2)
+        timegrid = eao.assets.Timegrid(Start, End, freq='h')
+
+        start_ramp_lower_bounds = [1, 2, 3, 4]
+        start_ramp_upper_bounds = [2, 3, 5, 5]
+        shutdown_ramp_lower_bounds = [1, 4, 4.5, 5, 5]
+        shutdown_ramp_upper_bounds = [2, 4, 5, 8, 10]
+        min_cap=5
+        max_cap=10
+        min_runtime = 10
+
+        # Case use all lower bounds:
+        a = eao.assets.CHPAsset(name='CHP',
+                                price='price',
+                                nodes=(node_power, node_heat),
+                                min_cap=min_cap,
+                                max_cap=max_cap,
+                                time_already_off=1,
+                                start_ramp_lower_bounds=start_ramp_lower_bounds,
+                                start_ramp_upper_bounds=start_ramp_upper_bounds,
+                                shutdown_ramp_lower_bounds=shutdown_ramp_lower_bounds,
+                                shutdown_ramp_upper_bounds=shutdown_ramp_upper_bounds,
+                                time_already_running=1,
+                                min_runtime=min_runtime)
+        prices = {'price': 1. * np.ones(timegrid.T)}
+
+        op = a.setup_optim_problem(prices, timegrid=timegrid)
+        res = op.optimize()
+        power_variables = res.x[0 * timegrid.T:1 * timegrid.T]
+        heat_variables = res.x[1 * timegrid.T:2 * timegrid.T]
+        on_variables = res.x[2 * timegrid.T:3 * timegrid.T]
+        start_variables = res.x[3 * timegrid.T:4 * timegrid.T]
+        shutdown_variables = res.x[4 * timegrid.T:]
+        # Asset starts out with the last 3 steps of the startramp, then runs at maximum capacity for min_runtime steps and then
+        # follows the shutdown ramp
+        disp_res = power_variables + a.conversion_factor_power_heat * heat_variables
+        disp_true = start_ramp_lower_bounds[1:] + min_runtime * [min_cap] + list(reversed(shutdown_ramp_lower_bounds)) + [0.] * 6
+        self.assertAlmostEqual(abs(disp_res - disp_true).sum(), 0, 4)
+        start_variables_true = np.zeros(timegrid.T)
+        self.assertAlmostEqual(abs(start_variables_true - start_variables).sum(), 0, 4)
+        shutdown_variables_true = np.zeros(timegrid.T)
+        shutdown_variables_true[len(start_ramp_lower_bounds) - 1 + min_runtime + len(shutdown_ramp_lower_bounds)] = 1
+        self.assertAlmostEqual(abs(shutdown_variables_true - shutdown_variables).sum(), 0, 4)
+
+        # Case use all upper bounds
+        a = eao.assets.CHPAsset(name='CHP',
+                                price='price',
+                                nodes=(node_power, node_heat),
+                                min_cap=min_cap,
+                                max_cap=max_cap,
+                                start_ramp_lower_bounds=start_ramp_lower_bounds,
+                                start_ramp_upper_bounds=start_ramp_upper_bounds,
+                                shutdown_ramp_lower_bounds=shutdown_ramp_lower_bounds,
+                                shutdown_ramp_upper_bounds=shutdown_ramp_upper_bounds)
+        prices = {'price': 10 * np.ones(timegrid.T)}
+        prices['price'][5: 5 + len(start_ramp_lower_bounds)+ len(shutdown_ramp_lower_bounds)]=-1
+
+        op = a.setup_optim_problem(prices, timegrid=timegrid)
+        res = op.optimize()
+        power_variables = res.x[0 * timegrid.T:1 * timegrid.T]
+        heat_variables = res.x[1 * timegrid.T:2 * timegrid.T]
+        on_variables = res.x[2 * timegrid.T:3 * timegrid.T]
+        start_variables = res.x[3 * timegrid.T:4 * timegrid.T]
+        shutdown_variables = res.x[4 * timegrid.T:]
+        # Asset follows start ramp, then immediately shutdown ramp at highest possible load while the price is
+        # negative, otherwise asset is off
+        disp_res = power_variables + a.conversion_factor_power_heat * heat_variables
+        disp_true =[0.] * 5 + start_ramp_upper_bounds + list(reversed(shutdown_ramp_upper_bounds)) + [0.] * (timegrid.T-len(start_ramp_lower_bounds)-len(shutdown_ramp_lower_bounds)-5)
+        self.assertAlmostEqual(abs(disp_res - disp_true).sum(), 0, 4)
+        start_variables_true = np.zeros(timegrid.T)
+        start_variables_true[5] = 1
+        self.assertAlmostEqual(abs(start_variables_true - start_variables).sum(), 0, 4)
+        shutdown_variables_true = np.zeros(timegrid.T)
+        shutdown_variables_true[5 + len(start_ramp_lower_bounds)+ len(shutdown_ramp_lower_bounds)] = 1
+        self.assertAlmostEqual(abs(shutdown_variables_true - shutdown_variables).sum(), 0, 4)
+
+    def test_start_and_shutdown_ramp3(self):
+        """ Unit test. Setting up a CHPAsset and checking interpolation of start and shutdown ramps when timegrid.freq
+            and timegrid.main_time_unit are different
+        """
+        node_power = eao.assets.Node('node_power')
+        node_heat = eao.assets.Node('node_heat')
+        Start = dt.date(2021, 1, 1)
+        End = dt.date(2021, 1, 2)
+        timegrid = eao.assets.Timegrid(Start, End, freq='15min', main_time_unit='h')
+
+        start_ramp = [1, 2, 3, 4]
+        shutdown_ramp = [1, 3, 5, 5, 5]
+        min_cap=5
+        max_cap=10
+
+        a = eao.assets.CHPAsset(name='CHP',
+                                price='price',
+                                nodes=(node_power, node_heat),
+                                min_cap=min_cap,
+                                max_cap=max_cap,
+                                start_ramp_lower_bounds=start_ramp,
+                                shutdown_ramp_lower_bounds=shutdown_ramp)
+        prices = {'price': 10 * np.ones(timegrid.T)}
+        prices['price'][5: 5 + len(start_ramp)*4+ len(shutdown_ramp)*4]=-1
+
+        op = a.setup_optim_problem(prices, timegrid=timegrid)
+        res = op.optimize()
+        power_variables = res.x[0 * timegrid.T:1 * timegrid.T]
+        heat_variables = res.x[1 * timegrid.T:2 * timegrid.T]
+        on_variables = res.x[2 * timegrid.T:3 * timegrid.T]
+        start_variables = res.x[3 * timegrid.T:4 * timegrid.T]
+        shutdown_variables = res.x[4 * timegrid.T:]
+        # Asset follows interpolated start ramp, then immediately interpolated shutdown ramp at highest possible load while the price is
+        # negative, otherwise asset is off
+        start_ramp_interpolated = [(start_ramp[i]+(start_ramp[i+1]-start_ramp[i])/4 * j)/4 for i in range(len(start_ramp)-1) for j in range(4)] + 4*[start_ramp[-1]/4]
+        shutdown_ramp_interpolated = [(shutdown_ramp[i]+(shutdown_ramp[i+1]-shutdown_ramp[i])/4 * j)/4 for i in range(len(shutdown_ramp)-1) for j in range(4)] + 4*[shutdown_ramp[-1]/4]
+
+        disp_res = power_variables + a.conversion_factor_power_heat * heat_variables
+        disp_true = [0] * 5 + start_ramp_interpolated + list(reversed(shutdown_ramp_interpolated)) + [0] * (timegrid.T-len(start_ramp_interpolated)-len(shutdown_ramp_interpolated)-5)
+        self.assertAlmostEqual(abs(disp_res - disp_true).sum(), 0, 4)
+        start_variables_true = np.zeros(timegrid.T)
+        start_variables_true[5] = 1
+        self.assertAlmostEqual(abs(start_variables_true - start_variables).sum(), 0, 4)
+        shutdown_variables_true = np.zeros(timegrid.T)
+        shutdown_variables_true[5 + len(start_ramp)*4 + len(shutdown_ramp)*4] = 1
+        self.assertAlmostEqual(abs(shutdown_variables_true - shutdown_variables).sum(), 0, 4)
+
 
 class MultiCommodity(unittest.TestCase):