diff --git a/doc/source/nb/AnalyticFluence.ipynb b/doc/source/nb/AnalyticFluence.ipynb
index 15c2a3742..136ff5b98 100644
--- a/doc/source/nb/AnalyticFluence.ipynb
+++ b/doc/source/nb/AnalyticFluence.ipynb
@@ -9,41 +9,31 @@
     "This notebook demonstrates how to use the `Analytic3Species` class from `snewpy.models` to create an analytic supernova model.\n",
     "The neutrino spectrum of this model follows a Gamma distribution (see [arXiv:1211.3920](https://arxiv.org/abs/1211.3920)) with user-selected parameters.\n",
     "\n",
-    "In this notebook, we first create a model file, then visualize the spectral parameters and finally use SNOwGLoBES to determine the number of events expected in a detector."
+    "In this notebook, we first create a model file, then visualize the spectral parameters and finally use the RateCalcultor to determine the number of events expected in a detector."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using folder `/Users/shlok2223/.astropy/cache/snewpy/models/AnalyticFluence/`.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "\n",
     "from astropy.table import Table\n",
+    "from astropy import units as u\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib as mpl\n",
+    "mpl.rc('font', size=14)\n",
+    "\n",
     "import numpy as np\n",
     "\n",
-    "from snewpy import snowglobes, model_path\n",
+    "from snewpy.neutrino import MixingParameters\n",
     "from snewpy.flavor import ThreeFlavor\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW\n",
     "from snewpy.models.ccsn import Analytic3Species\n",
-    "\n",
-    "mpl.rc('font', size=14)\n",
-    "\n",
-    "SNOwGLoBES_path = None  # change to SNOwGLoBES directory if using a custom detector configuration\n",
-    "\n",
-    "model_folder = f\"{model_path}/AnalyticFluence/\"\n",
-    "os.makedirs(model_folder, exist_ok=True)\n",
-    "print(f\"Using folder `{model_folder}`.\")"
+    "from snewpy.rate_calculator import RateCalculator, collate, center"
    ]
   },
   {
@@ -67,21 +57,24 @@
     "rms_or_pinch = \"rms\"\n",
     "rms_energy = (12.8788, 17.8360, 24.3913)\n",
     "\n",
-    "# Make an astropy table with two times, 0s and 1s, with constant neutrino properties\n",
+    "times = np.linspace(0,10,101) * u.s\n",
+    "tau = 3 * u.s # timescale for exponential decay of total energies\n",
+    "\n",
+    "# Make an astropy table with 101 times between 0s and 10s\n",
     "table = Table()\n",
-    "table['TIME'] = np.linspace(0,1,2)\n",
-    "table['L_NU_E'] =  np.linspace(1,1,2)*total_energy[0]\n",
-    "table['L_NU_E_BAR'] = np.linspace(1,1,2)*total_energy[1]\n",
-    "table['L_NU_X'] = np.linspace(1,1,2)*total_energy[2]/4. #Note, L_NU_X is set to 1/4 of the total NU_X energy\n",
+    "table['TIME'] = times\n",
+    "table['L_NU_E'] =  total_energy[0] * np.exp(-times / tau)\n",
+    "table['L_NU_E_BAR'] = total_energy[1] * np.exp(-times / tau)\n",
+    "table['L_NU_X'] = total_energy[2]/4. * np.exp(-times / tau) #Note, L_NU_X is set to 1/4 of the total NU_X energy\n",
     "        \n",
-    "table['E_NU_E'] = np.linspace(1,1,2)*mean_energy[0]\n",
-    "table['E_NU_E_BAR'] = np.linspace(1,1,2)*mean_energy[1]\n",
-    "table['E_NU_X'] = np.linspace(1,1,2)*mean_energy[2]\n",
+    "table['E_NU_E'] = np.linspace(1,1,101)*mean_energy[0]\n",
+    "table['E_NU_E_BAR'] = np.linspace(1,1,101)*mean_energy[1]\n",
+    "table['E_NU_X'] = np.linspace(1,1,101)*mean_energy[2]\n",
     "\n",
     "if rms_or_pinch == \"rms\":\n",
-    "    table['RMS_NU_E'] = np.linspace(1,1,2)*rms_energy[0]\n",
-    "    table['RMS_NU_E_BAR'] = np.linspace(1,1,2)*rms_energy[1]\n",
-    "    table['RMS_NU_X'] = np.linspace(1,1,2)*rms_energy[2]\n",
+    "    table['RMS_NU_E'] = np.linspace(1,1,101)*rms_energy[0]\n",
+    "    table['RMS_NU_E_BAR'] = np.linspace(1,1,101)*rms_energy[1]\n",
+    "    table['RMS_NU_X'] = np.linspace(1,1,101)*rms_energy[2]\n",
     "    table['ALPHA_NU_E'] = (2.0 * table['E_NU_E'] ** 2 - table['RMS_NU_E'] ** 2) / (\n",
     "        table['RMS_NU_E'] ** 2 - table['E_NU_E'] ** 2)\n",
     "    table['ALPHA_NU_E_BAR'] = (2.0 * table['E_NU_E_BAR'] ** 2 - table['RMS_NU_E_BAR'] ** 2) / (\n",
@@ -89,17 +82,17 @@
     "    table['ALPHA_NU_X'] = (2.0 * table['E_NU_X'] ** 2 - table['RMS_NU_X'] ** 2) / (\n",
     "        table['RMS_NU_X'] ** 2 - table['E_NU_X'] ** 2)\n",
     "elif rms_or_pinch == \"pinch\":\n",
-    "    table['ALPHA_NU_E'] = np.linspace(1,1,2)*pinch_values[0]\n",
-    "    table['ALPHA_NU_E_BAR'] = np.linspace(1,1,2)*pinch_values[1]\n",
-    "    table['ALPHA_NU_X'] = np.linspace(1,1,2)*pinch_values[2]\n",
+    "    table['ALPHA_NU_E'] = np.linspace(1,1,101)*pinch_values[0]\n",
+    "    table['ALPHA_NU_E_BAR'] = np.linspace(1,1,101)*pinch_values[1]\n",
+    "    table['ALPHA_NU_X'] = np.linspace(1,1,101)*pinch_values[2]\n",
     "    table['RMS_NU_E'] = np.sqrt((2.0 + table['ALPHA_NU_E'])/(1.0 + table['ALPHA_NU_E'])*table['E_NU_E']**2)\n",
     "    table['RMS_NU_E_BAR'] =  np.sqrt((2.0 + table['ALPHA_NU_E_BAR'])/(1.0 + table['ALPHA_NU_E_BAR'])*table['E_NU_E_BAR']**2)\n",
     "    table['RMS_NU_X'] = np.sqrt((2.0 + table['ALPHA_NU_X'])/(1.0 + table['ALPHA_NU_X'])*table['E_NU_X']**2 )\n",
     "else:\n",
     "    print(\"incorrect second moment method: rms or pinch\")\n",
     "\n",
-    "filename = \"AnalyticFluence_demo.dat\"\n",
-    "table.write(model_folder + filename, format='ascii', overwrite=True)"
+    "filename = \"./AnalyticFluence_demo.dat\"\n",
+    "table.write(filename, format='ascii', overwrite=True)"
    ]
   },
   {
@@ -117,7 +110,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x30b0147a0>"
+       "<matplotlib.legend.Legend at 0x74ee53056180>"
       ]
      },
      "execution_count": 3,
@@ -126,7 +119,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x300 with 3 Axes>"
       ]
@@ -137,8 +130,7 @@
    ],
    "source": [
     "%matplotlib inline\n",
-    "filename = \"AnalyticFluence_demo.dat\"\n",
-    "model = Analytic3Species(model_folder + filename)\n",
+    "model = Analytic3Species(filename)\n",
     "\n",
     "fig,axes = plt.subplots(1,3,figsize=(16,3))\n",
     "plt.subplots_adjust(wspace=0.3)\n",
@@ -157,7 +149,9 @@
     "\n",
     "axes[2].set_ylabel(\"pinch parameter\")\n",
     "axes[2].set_xlabel(\"time [s]\")\n",
-    "axes[2].legend(frameon=False)"
+    "axes[2].legend(frameon=False)",
+    "\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -167,14 +161,6 @@
     "## Calculating Number of Events"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "May 2026: **The following two cells are BROKEN**\n",
-    "To be fixed in a new PR."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -184,86 +170,81 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Preparing fluences ...\n"
+      "Total events in Super-K-like detector (with smearing): 26689.245513969094\n"
      ]
     },
     {
-     "ename": "AttributeError",
-     "evalue": "module 'snewpy.models.ccsn_loaders' has no attribute 'Analytic3Species'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 22\u001b[39m\n\u001b[32m     20\u001b[39m \u001b[38;5;66;03m#first generated integrated fluence files for SNOwGLoBES\u001b[39;00m\n\u001b[32m     21\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mPreparing fluences ...\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m22\u001b[39m tarredoutfile = \u001b[43msnowglobes\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgenerate_fluence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_folder\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodeltype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransformation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutfile\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     24\u001b[39m \u001b[38;5;66;03m#run the fluence file through SNOwGLoBES \u001b[39;00m\n\u001b[32m     25\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mRunning SNOwGLoBES ...\u001b[39m\u001b[33m\"\u001b[39m)\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/opt/anaconda3/envs/snewpyDevelopments/lib/python3.12/site-packages/snewpy/snowglobes.py:132\u001b[39m, in \u001b[36mgenerate_fluence\u001b[39m\u001b[34m(model_path, model_type, transformation_type, d, output_filename, tstart, tend, snmodel_dict)\u001b[39m\n\u001b[32m    102\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mgenerate_fluence\u001b[39m(model_path, model_type, transformation_type, d, output_filename=\u001b[38;5;28;01mNone\u001b[39;00m, tstart=\u001b[38;5;28;01mNone\u001b[39;00m, tend=\u001b[38;5;28;01mNone\u001b[39;00m, snmodel_dict={}):\n\u001b[32m    103\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Generate fluence files in SNOwGLoBES format.\u001b[39;00m\n\u001b[32m    104\u001b[39m \n\u001b[32m    105\u001b[39m \u001b[33;03m    This version will subsample the times in a supernova model, produce energy\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m    130\u001b[39m \u001b[33;03m        Path of NumPy archive file with neutrino fluence data.\u001b[39;00m\n\u001b[32m    131\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m     model_class = \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msnewpy\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m.\u001b[49m\u001b[43mccsn_loaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    134\u001b[39m     \u001b[38;5;66;03m# Choose flavor transformation. Use dict to associate the transformation name with its class.\u001b[39;00m\n\u001b[32m    135\u001b[39m     NMO = MixingParameters(\u001b[33m'\u001b[39m\u001b[33mNORMAL\u001b[39m\u001b[33m'\u001b[39m)\n",
-      "\u001b[31mAttributeError\u001b[39m: module 'snewpy.models.ccsn_loaders' has no attribute 'Analytic3Species'"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpknelle/.local/lib/python3.12/site-packages/snewpy/rate_calculator.py:357: RuntimeWarning: divide by zero encountered in log\n",
+      "  return np.interp(np.log(E)/np.log(10), xp, yp, left=0, right=0)*E*1e-38 <<u.cm**2\n"
      ]
     }
    ],
    "source": [
-    "#set model type as \n",
-    "modeltype = 'Analytic3Species'\n",
-    "#set model file we just created above\n",
-    "filename = \"AnalyticFluence_demo.dat\"\n",
+    "# set up the model for the neutrino emission from the supernova\n",
+    "model = Analytic3Species(filename)\n",
+    "\n",
+    "# set the desired Oscillation scenario\n",
+    "transformation = NoTransformation()\n",
+    "\n",
+    "# set distance in kpc\n",
+    "distance=10 * u.kpc\n",
     "\n",
-    "#set desired Oscillation scenario, snowglobes default is\n",
-    "#NoTransformation, so that is used here to match the output of\n",
-    "#./supernova.pl livermore water wc100kt30prct\n",
-    "transformation = \"NoTransformation\"\n",
+    "# first we create the flux as a function of time and energy\n",
+    "energies = np.linspace(0,100,501) * u.MeV\n",
+    "flux = model.get_flux(t=times, E=energies, distance=distance, flavor_xform=transformation)\n",
     "\n",
-    "#set distance in kpc\n",
-    "distance=10\n",
+    "# then integrate the flux over the entire range of time for the model to make the rate calculation more efficient\n",
+    "fluence = flux.integrate('time')\n",
     "\n",
-    "#set desired detector\n",
+    "# set the desired detector - this is the SNOwGLoBES detector name \n",
     "detector='wc100kt30prct'\n",
     "\n",
-    "#Running the SNEWPY/SNOwGLoBES modules\n",
-    "outfile = \"Analytic3Species_demo\"\n",
+    "# set up the RateCalculator\n",
+    "rc = RateCalculator()\n",
     "\n",
-    "#first generated integrated fluence files for SNOwGLoBES\n",
-    "print(\"Preparing fluences ...\")\n",
-    "tarredoutfile = snowglobes.generate_fluence(model_folder + filename, modeltype, transformation, distance, outfile)\n",
+    "# Run the fluence through the RateCalculator - which does the same calculation as SNOwGLoBES \n",
+    "# We will include the detector effects of smearing and efficiency \n",
+    "# Note that when the detector_effects are include, the events returned by the RateCalculator have been integrated over each energy bin\n",
+    "# The collate function combines all the neutral current and electron elastic scattering channels in the tables returned by the RateCalculator \n",
     "\n",
-    "# #run the fluence file through SNOwGLoBES \n",
-    "# print(\"Running SNOwGLoBES ...\")\n",
-    "# snowglobes.simulate(SNOwGLoBES_path, tarredoutfile, detector_input=detector)\n",
+    "events = collate( rc.run(fluence, detector, detector_effects=True) )\n",
     "\n",
-    "# print(\"Collating...\")\n",
-    "# tables = snowglobes.collate(SNOwGLoBES_path, tarredoutfile, skip_plots=True)\n",
+    "# Compute number of events in all interaction channels of the detector including smearing\n",
+    "total_events  = sum([chan.integrate_or_sum('energy').array.squeeze().value for chan in events.values()])\n",
     "\n",
-    "# totalcounts = 0\n",
-    "# for i in range(1,6):\n",
-    "#     print(\"Number of events of type\",tables['Collated_'+outfile+'_'+detector+'_events_smeared_weighted.dat']['header'].split()[i],end=': ')\n",
-    "#     counts = sum(tables['Collated_'+outfile+'_'+detector+'_events_smeared_weighted.dat']['data'][i])\n",
-    "#     totalcounts += counts\n",
-    "#     print(counts)\n",
-    "# print(\"Total number of events: \",totalcounts)"
+    "print(\"Total events in Super-K-like detector (with smearing):\" , 0.32*total_events)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# for smear in [\"smeared\", \"unsmeared\"]:\n",
-    "#     energy = tables['Collated_'+outfile+'_'+detector+'_events_'+smear+'_weighted.dat']['data'][0]*1000.\n",
-    "#     nc = tables['Collated_'+outfile+'_'+detector+'_events_'+smear+'_weighted.dat']['data'][1]\n",
-    "#     escattering = tables['Collated_'+outfile+'_'+detector+'_events_'+smear+'_weighted.dat']['data'][2]\n",
-    "#     ibd = tables['Collated_'+outfile+'_'+detector+'_events_'+smear+'_weighted.dat']['data'][3]\n",
-    "#     nueO16 = tables['Collated_'+outfile+'_'+detector+'_events_'+smear+'_weighted.dat']['data'][4]\n",
-    "#     nuebarO16 = tables['Collated_'+outfile+'_'+detector+'_events_'+smear+'_weighted.dat']['data'][5]\n",
-    "#     plt.plot(energy,nc,label=\"NC\")\n",
-    "#     plt.plot(energy,escattering,label=\"escattering\")\n",
-    "#     plt.plot(energy,ibd,label=\"ibd\")\n",
-    "#     plt.plot(energy,nueO16,label=\"nueO16\")\n",
-    "#     plt.plot(energy,nuebarO16,label=\"nuebarO16\")\n",
-    "#     plt.legend()\n",
-    "#     plt.title(smear)\n",
-    "#     plt.xlabel(\"Energy [MeV]\")\n",
-    "#     plt.ylabel(\"Count per bin\")\n",
-    "#     plt.yscale('log')\n",
-    "#     plt.show()"
+    "# plot the event numbers in the energy bins \n",
+    "# use the center function from the rate_calculator module to get the energy bin centers\n",
+    "for channel in events:\n",
+    "    plt.plot(center(events[channel].energy.to('MeV')),events[channel].array.squeeze() ,label=channel)\n",
+    "         \n",
+    "plt.legend()\n",
+    "plt.title('Event rates in a 100kt WC detector')\n",
+    "plt.xlabel(\"Energy [MeV]\")\n",
+    "plt.ylabel(\"Count per bin\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -293,7 +274,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,
diff --git a/doc/source/nb/README.md b/doc/source/nb/README.md
index 4e02b194c..a8efe5cde 100644
--- a/doc/source/nb/README.md
+++ b/doc/source/nb/README.md
@@ -6,7 +6,7 @@ The Jupyter notebooks in this directory contain different examples for how to us
 
 These directories contain notebooks demonstrating how to use the core-collapse and pre-supernova models available through SNEWPY.
 
-## AnalyticFluence
+## Analytic3Species
 
 This notebook demonstrates how to use the `Analytic3Species` class from `snewpy.models` to create an analytic supernova model by specifying the luminosity, mean energy and mean squared energy for three neutrino flavors.
 
@@ -25,4 +25,4 @@ This notebook demonstrates how to use SNEWPY’s `snewpy.snowglobes` module to i
 ## `dev` Directory
 
 This directory contains notebooks which may be under development or illustrate usage of internal/undocumented APIs.
-They are not recommended for general users.
\ No newline at end of file
+They are not recommended for general users.
diff --git a/doc/source/nb/dev/Detector_demo.ipynb b/doc/source/nb/dev/Detector_demo.ipynb
index 2e8006698..b53b05b8e 100644
--- a/doc/source/nb/dev/Detector_demo.ipynb
+++ b/doc/source/nb/dev/Detector_demo.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "0107cb95-e9f6-497f-a99f-6116a86ead8a",
    "metadata": {},
    "outputs": [],
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "3ee6ab13-fa76-4889-85af-7c07446b7866",
    "metadata": {},
    "outputs": [],
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "11004f41-f61a-4d2c-862f-1f1601647063",
    "metadata": {},
    "outputs": [],
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "485947db-d7a3-449b-bb7a-5a3acb663124",
    "metadata": {},
    "outputs": [],
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "c6d0dae4-90b3-42eb-870a-0098f8e48739",
    "metadata": {},
    "outputs": [],
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "86744938-3d21-48c6-9f92-78c477eb61b2",
    "metadata": {},
    "outputs": [],
@@ -109,17 +109,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "6ef1506c-fb97-408d-b7d2-8f1df89d2b9d",
    "metadata": {},
    "outputs": [],
    "source": [
     "#a helper function to calculate total rate\n",
     "from snewpy.flux import Container\n",
+    "from snewpy.flavor import ThreeFlavor\n",
+    "\n",
     "def sum_rates(rates:list):\n",
-    "    res = sum([rate.array for rate in rates])\n",
-    "    rate = rates[0]#take first as an instance\n",
-    "    return Container(res,rate.flavor, rate.time, rate.energy, integrable_axes=rate._integrable_axes)"
+    "    res = sum([rate.array for rate in rates])    \n",
+    "    return Container(res,ThreeFlavor.take([0,-1]), rates[0].time, rates[0].energy)"
    ]
   },
   {
@@ -132,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "54dbcde6-4aac-4f96-9dbe-ced31225919a",
    "metadata": {},
    "outputs": [],
@@ -155,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "0e7889dd-f442-4841-819b-ce7e31cd9a85",
    "metadata": {},
    "outputs": [],
@@ -176,10 +177,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "312c67d9-2556-4932-88b9-20ae7d042405",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['wc100kt30prct',\n",
+       " 'wc100kt15prct',\n",
+       " 'ar40kt',\n",
+       " 'scint20kt',\n",
+       " 'halo1',\n",
+       " 'halo2',\n",
+       " 'novaND',\n",
+       " 'novaFD',\n",
+       " 'wc100kt30prct_he',\n",
+       " 'ar40kt_he',\n",
+       " 'icecube',\n",
+       " 'km3net',\n",
+       " 'ds20',\n",
+       " 'argo',\n",
+       " 'lz',\n",
+       " 'xent',\n",
+       " 'pandax']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#list available detectors\n",
     "list(rc.detectors)"
@@ -190,17 +218,78 @@
    "id": "389865fa-25e9-430a-9736-365a299fc7f9",
    "metadata": {},
    "source": [
-    "### Read the detector you need"
+    "### Read the detector you need: You may get a bunch of warnings about efficiency"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "c565f89f-e855-47b1-a591-da1561dd0bf5",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=ibd. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nue_e. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nuebar_e. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=numu_e. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=numubar_e. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nutau_e. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nutaubar_e. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nue_C12. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nuebar_C12. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nc_nue_C12. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nc_nuebar_C12. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nc_numu_C12. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nc_numubar_C12. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nc_nutau_C12. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nc_nutaubar_C12. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nue_C13. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nc_nue_C13. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nc_numu_C13. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nc_nutau_C13. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nc_nuebar_C13. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nc_numubar_C13. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n",
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:398: UserWarning: Efficiency not found for detector=scint20kt, channel=nc_nutaubar_C13. Using 100% efficiency\n",
+      "  warn(f'Efficiency not found for detector={name}, channel={ch.name}. Using 100% efficiency')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Detector(name=\"scint20kt\", mass=20.0 kt, channels=['ibd', 'nue_e', 'nuebar_e', 'numu_e', 'numubar_e', 'nutau_e', 'nutaubar_e', 'nue_C12', 'nuebar_C12', 'nc_nue_C12', 'nc_nuebar_C12', 'nc_numu_C12', 'nc_numubar_C12', 'nc_nutau_C12', 'nc_nutaubar_C12', 'nue_C13', 'nc_nue_C13', 'nc_numu_C13', 'nc_nutau_C13', 'nc_nuebar_C13', 'nc_numubar_C13', 'nc_nutaubar_C13'])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#read the detector\n",
     "det = rc.read_detector('scint20kt')\n",
@@ -217,12 +306,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "a88a95fd-0e24-43e3-8205-41ac030a8632",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ibd': DetectionChannel (flavor=NU_E_BAR, smearing=True, weight=0.1429),\n",
+       " 'nue_e': DetectionChannel (flavor=NU_E, smearing=True, weight=0.5716),\n",
+       " 'nuebar_e': DetectionChannel (flavor=NU_E_BAR, smearing=True, weight=0.5716),\n",
+       " 'numu_e': DetectionChannel (flavor=NU_MU, smearing=True, weight=0.5716),\n",
+       " 'numubar_e': DetectionChannel (flavor=NU_MU_BAR, smearing=True, weight=0.5716),\n",
+       " 'nutau_e': DetectionChannel (flavor=NU_TAU, smearing=True, weight=0.5716),\n",
+       " 'nutaubar_e': DetectionChannel (flavor=NU_TAU_BAR, smearing=True, weight=0.5716),\n",
+       " 'nue_C12': DetectionChannel (flavor=NU_E, smearing=True, weight=0.07066404999999999),\n",
+       " 'nuebar_C12': DetectionChannel (flavor=NU_E_BAR, smearing=True, weight=0.07066404999999999),\n",
+       " 'nc_nue_C12': DetectionChannel (flavor=NU_E, smearing=True, weight=0.07066404999999999),\n",
+       " 'nc_nuebar_C12': DetectionChannel (flavor=NU_E_BAR, smearing=True, weight=0.07066404999999999),\n",
+       " 'nc_numu_C12': DetectionChannel (flavor=NU_MU, smearing=True, weight=0.07066404999999999),\n",
+       " 'nc_numubar_C12': DetectionChannel (flavor=NU_MU_BAR, smearing=True, weight=0.07066404999999999),\n",
+       " 'nc_nutau_C12': DetectionChannel (flavor=NU_TAU, smearing=True, weight=0.07066404999999999),\n",
+       " 'nc_nutaubar_C12': DetectionChannel (flavor=NU_TAU_BAR, smearing=True, weight=0.07066404999999999),\n",
+       " 'nue_C13': DetectionChannel (flavor=NU_E, smearing=True, weight=0.00078595),\n",
+       " 'nc_nue_C13': DetectionChannel (flavor=NU_E, smearing=True, weight=0.00078595),\n",
+       " 'nc_numu_C13': DetectionChannel (flavor=NU_MU, smearing=True, weight=0.00078595),\n",
+       " 'nc_nutau_C13': DetectionChannel (flavor=NU_TAU, smearing=True, weight=0.00078595),\n",
+       " 'nc_nuebar_C13': DetectionChannel (flavor=NU_E_BAR, smearing=True, weight=0.00078595),\n",
+       " 'nc_numubar_C13': DetectionChannel (flavor=NU_MU_BAR, smearing=True, weight=0.00078595),\n",
+       " 'nc_nutaubar_C13': DetectionChannel (flavor=NU_TAU_BAR, smearing=True, weight=0.00078595)}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#list all the channels\n",
     "det.channels"
@@ -230,10 +351,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "710bb21d-a36c-4291-846c-9a5cc962908c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:363: RuntimeWarning: divide by zero encountered in log\n",
+      "  return np.interp(np.log(E)/np.log(10), xp, yp, left=0, right=0)*E*1e-38 <<u.cm**2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#plot all the channels cross-sections\n",
     "E = np.linspace(0,50,101)<<u.MeV\n",
@@ -257,23 +397,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "635edf6e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "flux"
+    "rates = det.run(flux)\n",
+    "sumrates = sum_rates(list(rates.values()))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "948ca075-66eb-449b-b310-a9e599d1e2e9",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "rates = det.run(flux)\n",
-    "plot_rate(sum_rates(list(rates.values())), axis='energy', label='Total', lw=2, color='k')\n",
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_rate(sumrates, axis='energy', label='Total', lw=2, color='k')\n",
     "for chan,rate in rates.items():\n",
     "    plot_rate(rate, axis='energy', label=chan)\n",
     "#plt.yscale('log')\n",
@@ -294,16 +445,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "af612683-0bc4-4f2e-8262-8b678b82d84e",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "from snewpy.neutrino import Flavor\n",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Detector(name=\"scint_det\", mass=20.0 kt, channels=['ibd', 'nc_C12'])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from snewpy.flavor import ThreeFlavor\n",
     "from snewpy.rate_calculator import Detector, DetectionChannel\n",
     "#we can load cross-section from SNOwGLoBES to reuse\n",
-    "xsec_ibd=rc.load_xsec('ibd',Flavor.NU_E_BAR)\n",
-    "xsec_nc_c12=rc.load_xsec('nc_nue_C12',Flavor.NU_E)\n",
+    "xsec_ibd=rc.load_xsec('ibd',ThreeFlavor.NU_E_BAR)\n",
+    "xsec_nc_c12=rc.load_xsec('nc_nue_C12',ThreeFlavor.NU_E)\n",
     "# we can define smearing matrices \n",
     "e_true  = np.arange(0,50,0.1)<<u.MeV\n",
     "e_smear = np.arange(0,50,0.1)<<u.MeV\n",
@@ -325,14 +487,14 @@
     "                                        )\n",
     "#create the detector with the same IBD channels but with three different smearings\n",
     "det = Detector('scint_det', mass=20<<u.kt,\n",
-    "               channels={\n",
-    "                   'ibd':DetectionChannel(flavor=Flavor.NU_E_BAR,\n",
+    "                channels={\n",
+    "                   'ibd':DetectionChannel(flavor=ThreeFlavor.NU_E_BAR,\n",
     "                                          xsec=xsec_ibd, \n",
     "                                          efficiency=1., \n",
     "                                          smearing = m_smear_ibd, \n",
     "                                          weight=0.1429 #mass fraction of free protons in scint\n",
     "                                         ),\n",
-    "                   'nc_C12':DetectionChannel(flavor=list(Flavor),#you can provide list of flavors\n",
+    "                   'nc_C12':DetectionChannel(flavor=list(ThreeFlavor),#you can provide list of flavors\n",
     "                                                 xsec=xsec_nc_c12, \n",
     "                                                 efficiency=1., \n",
     "                                                 smearing = m_smear_nc,\n",
@@ -344,49 +506,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "53304732-d047-44e8-86e4-6a4b82af19d0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ibd': DetectionChannel (flavor=NU_E_BAR, smearing=True, weight=0.1429),\n",
+       " 'nc_C12': DetectionChannel (flavor=NU_E,NU_E_BAR,NU_MU,NU_MU_BAR,NU_TAU,NU_TAU_BAR, smearing=True, weight=0.07)}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "det.channels"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "df3bd402-0e37-4b7b-8d65-f0d47849ccef",
-   "metadata": {},
-   "source": [
-    "**May 2026: the following cell is BROKEN, with the error**\n",
-    "```\n",
-    "ValueError: Data array of shape (1, 1500, 501) is inconsistent with any valid shapes [(6, 1500, 501), (6, 1500, 500), (6, 1499, 501), (6, 1499, 500), (5, 1500, 501), (5, 1500, 500), (5, 1499, 501), (5, 1499, 500)]\n",
-    "```\n",
-    "**This needs to be investigated in a new PR.***"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "499c21ce-d1d7-45ab-8a2b-92f2894e9340",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# #plot the event rate in the detector\n",
-    "# rates = det.run(flux)\n",
-    "\n",
-    "# plot_rate(sum_rates(list(rates.values())), axis='energy', label='Total', lw=2, color='k')\n",
-    "# for chan,rate in rates.items():\n",
-    "#     #integrate over time\n",
-    "#     plot_rate(rate, axis='energy', label=chan)\n",
-    "# #plot the total rate\n",
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot the event rate in the detector\n",
+    "rates = det.run(flux)\n",
     "\n",
+    "#plot the total rate\n",
+    "plot_rate(sum_rates(list(rates.values())), axis='energy', label='Total', lw=2, color='k')\n",
+    "for chan,rate in rates.items():\n",
+    "    plot_rate(rate, axis='energy', label=chan)\n",
+    "    \n",
+    "plt.legend()\n",
+    "#plt.yscale('log')\n",
+    "#plt.ylim(1e-3)\n",
+    "plt.ylabel(f'Events per {rate.energy.diff()[0]}')\n",
+    "plt.xlim(0,50)\n",
     "\n",
-    "# plt.legend()\n",
-    "# #plt.yscale('log')\n",
-    "# #plt.ylim(1e-3)\n",
-    "# plt.ylabel(f'Events per {rate.energy.diff()[0]}')\n",
-    "# plt.xlim(0,50)\n",
-    "# plt.show()"
+    "plt.show()"
    ]
   },
   {
@@ -399,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "c531d8f8-d08f-418b-98d9-2fe260be9bbc",
    "metadata": {},
    "outputs": [],
@@ -412,33 +584,32 @@
     "det.channels['nc_C12'].efficiency = lambda e: 0.9*(e> 15*u.MeV)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "9a9ed4f5-e2e1-4ebe-a5f1-0e96d0c9e477",
-   "metadata": {},
-   "source": [
-    "**May 2026: the following cell is BROKEN, with the error**\n",
-    "```\n",
-    "ValueError: Data array of shape (1, 1500, 501) is inconsistent with any valid shapes [(6, 1500, 501), (6, 1500, 500), (6, 1499, 501), (6, 1499, 500), (5, 1500, 501), (5, 1500, 500), (5, 1499, 501), (5, 1499, 500)]\n",
-    "```\n",
-    "**This needs to be investigated in a new PR.***"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "7ae1ce70-4acd-4e48-953e-48c71407080a",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# #plot the event rate in the detector\n",
-    "# rates = det.run(flux)\n",
-    "# plot_rate(sum_rates(list(rates.values())), axis='energy', label='Total', lw=2, color='k')\n",
-    "# for chan,rate in rates.items():\n",
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot the event rate in the detector\n",
+    "rates = det.run(flux)\n",
     "\n",
-    "#     plot_rate(rate, axis='energy', label=chan)\n",
-    "# plt.legend()\n",
-    "# plt.show()"
+    "plot_rate(sum_rates(list(rates.values())), axis='energy', label='Total', lw=2, color='k')\n",
+    "for chan,rate in rates.items():\n",
+    "     plot_rate(rate, axis='energy', label=chan)\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -451,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "818f56cc-fc42-4ef2-814e-f6735bb51351",
    "metadata": {},
    "outputs": [],
@@ -459,34 +630,33 @@
     "det.mass = 100<<u.kt"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "ffa6c76d-b9de-4038-a8a4-7088268a722b",
-   "metadata": {},
-   "source": [
-    "**May 2026: the following cell is BROKEN, with the error**\n",
-    "```\n",
-    "ValueError: Data array of shape (1, 1500, 501) is inconsistent with any valid shapes [(6, 1500, 501), (6, 1500, 500), (6, 1499, 501), (6, 1499, 500), (5, 1500, 501), (5, 1500, 500), (5, 1499, 501), (5, 1499, 500)]\n",
-    "```\n",
-    "**This needs to be investigated in a new PR.***"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "1ec39528-b29a-4e5c-9a19-8bbfd76684ee",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# #plot the event rate in the detector\n",
-    "# rates = det.run(flux)\n",
-    "# for chan,rate in rates.items():\n",
-    "#     #integrate over time\n",
-    "#     plot_rate(rate, axis='energy', label=chan)\n",
-    "# plt.legend()\n",
-    "# #plt.yscale('log')\n",
-    "# #plt.ylim(1e-3)\n",
-    "# plt.show()"
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot the event rate in the detector\n",
+    "rates = det.run(flux)\n",
+    "\n",
+    "for chan,rate in rates.items():\n",
+    "     plot_rate(rate, axis='energy', label=chan)\n",
+    "plt.legend()\n",
+    "#plt.yscale('log')\n",
+    "#plt.ylim(1e-3)\n",
+    "plt.show()"
    ]
   }
  ],
@@ -506,7 +676,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.13"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/doc/source/nb/dev/FluxContainer_demo.ipynb b/doc/source/nb/dev/FluxContainer_demo.ipynb
index 72aea5245..da46769ee 100644
--- a/doc/source/nb/dev/FluxContainer_demo.ipynb
+++ b/doc/source/nb/dev/FluxContainer_demo.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "97774e5c-1aa9-4837-a0e0-d7779899e42f",
    "metadata": {},
    "outputs": [],
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "60602bc2-e42e-4a6b-851b-e3ef18ecee47",
    "metadata": {},
    "outputs": [],
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "f3f749f5-b82f-42c1-b1cf-8606005c5e76",
    "metadata": {
     "editable": true,
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "54e252f0-969b-4b40-a9c9-db60df9dc46f",
    "metadata": {
     "editable": true,
@@ -104,10 +104,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "56fa3f27-ce67-459f-a2dd-7f5e32713ab5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[1;31mInit signature:\u001b[0m\n",
+       "\u001b[0mContainer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0mdata\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mastropy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munits\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquantity\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQuantity\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0mflavor\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msnewpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflavor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFlavorScheme\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0mtime\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mAnnotated\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mastropy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munits\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquantity\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQuantity\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mUnit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"s\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0menergy\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mAnnotated\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mastropy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munits\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquantity\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQuantity\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mUnit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"MeV\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0mintegrable_axes\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msnewpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflux\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAxes\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0mflavor_scheme\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0msnewpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflavor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFlavorScheme\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+       "\u001b[1;31mDocstring:\u001b[0m     \n",
+       "base class for internal use\n",
+       ":noindex:\n",
+       "\u001b[1;31mInit docstring:\u001b[0m\n",
+       "A container class storing the physical quantity (flux, fluence, rate...), which depends on flavor, time and energy.\n",
+       "\n",
+       "Parameters\n",
+       "----------\n",
+       "data: :class:`astropy.Quantity`\n",
+       "    3D array of the stored quantity, must have dimensions compatible with (flavor, time, energy)\n",
+       "\n",
+       "flavor: list or a single value of :class:`snewpy.neutrino.Flavor`\n",
+       "    array of flavors (should be ``len(flavor)==data.shape[0]``\n",
+       "\n",
+       "time: :class:`astropy.Quantity`\n",
+       "    sampling points in time (then ``len(time)==data.shape[1]``) \n",
+       "    or time bin edges (then ``len(time)==data.shape[1]+1``) \n",
+       "\n",
+       "energy: :class:`astropy.Quantity`\n",
+       "    sampling points in energy (then ``len(energy)=data.shape[2]``) \n",
+       "    or energy bin edges (then ``len(energy)=data.shape[2]+1``) \n",
+       "\n",
+       "integrable_axes: set of :class:`Axes` or None\n",
+       "    List of axes which can be integrated.\n",
+       "    If None (default) this set will be derived from the axes shapes\n",
+       "    \n",
+       "flavor_scheme: a subclass of :class:`snewpy.flavor.FlavorSchemes` or None\n",
+       "    A class which lists all the allowed flavors. \n",
+       "    If None (default) this value will be retrieved from the ``flavor`` arguemnt.\n",
+       "\u001b[1;31mFile:\u001b[0m           c:\\users\\jpknelle\\appdata\\roaming\\python\\python312\\site-packages\\snewpy\\flux.py\n",
+       "\u001b[1;31mType:\u001b[0m           type\n",
+       "\u001b[1;31mSubclasses:\u001b[0m     d2FdEdT, dFdE, dF, d2PhidEdT, dNdT, dNdE, N"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from snewpy.flux import Container\n",
     "\n",
@@ -129,10 +180,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "9875d016-6ce3-4870-aec9-7b845956e7f4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "d2FdEdT (6, 9850, 501) [1 / (MeV s m2)]: <6 flavor[<enum 'ThreeFlavor'>](0;5) x 9850 time(-0.34462761827 s;8.350202515285 s) x 501 energy(0.0 MeV;50.0 MeV)>\n"
+     ]
+    }
+   ],
    "source": [
     "print(flux)"
    ]
@@ -147,10 +206,152 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "14e70ce5-1eaf-4a06-8c0d-4f53d85e5f9e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$[[[0,~4.1433464 \\times 10^{-9},~1.566693 \\times 10^{-6},~\\dots,~3.8754761 \\times 10^{-28},~3.2338573 \\times 10^{-28},~2.6983682 \\times 10^{-28}],~\n",
+       "  [0,~941181.5,~32301940,~\\dots,~1.4098028 \\times 10^{-19},~1.1878837 \\times 10^{-19},~1.0008757 \\times 10^{-19}],~\n",
+       "  [0,~2848083.5,~92885529,~\\dots,~9.4678615 \\times 10^{-18},~8.0345105 \\times 10^{-18},~6.8180117 \\times 10^{-18}],~\n",
+       "  \\dots,~\n",
+       "  [0,~1.6165443 \\times 10^{9},~9.7828181 \\times 10^{9},~\\dots,~347685.16,~332386.3,~317757.21],~\n",
+       "  [0,~1.6125536 \\times 10^{9},~9.762873 \\times 10^{9},~\\dots,~346759.13,~331498.68,~316906.43],~\n",
+       "  [0,~1.6123214 \\times 10^{9},~9.7618772 \\times 10^{9},~\\dots,~345903.76,~330679.17,~316121.29]],~\n",
+       "\n",
+       " [[0,~0,~0,~\\dots,~0,~0,~0],~\n",
+       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
+       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
+       "  \\dots,~\n",
+       "  [0,~3.578509 \\times 10^{9},~1.7450588 \\times 10^{10},~\\dots,~3217311.8,~3095026.8,~2977361.5],~\n",
+       "  [0,~3.5692882 \\times 10^{9},~1.7414106 \\times 10^{10},~\\dots,~3205388.5,~3083526.4,~2966269.2],~\n",
+       "  [0,~3.5609773 \\times 10^{9},~1.7382425 \\times 10^{10},~\\dots,~3189138.6,~3067852.9,~2951151.9]],~\n",
+       "\n",
+       " [[0,~0,~0,~\\dots,~0,~0,~0],~\n",
+       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
+       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
+       "  \\dots,~\n",
+       "  [0,~3.2886782 \\times 10^{9},~1.6617385 \\times 10^{10},~\\dots,~2355337.8,~2263522.6,~2175265.5],~\n",
+       "  [0,~3.2796591 \\times 10^{9},~1.6580394 \\times 10^{10},~\\dots,~2346462.3,~2254970.3,~2167024.8],~\n",
+       "  [0,~3.2724002 \\times 10^{9},~1.6551805 \\times 10^{10},~\\dots,~2334129,~2243087,~2155575.3]],~\n",
+       "\n",
+       " [[0,~0,~0,~\\dots,~0,~0,~0],~\n",
+       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
+       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
+       "  \\dots,~\n",
+       "  [0,~3.2886782 \\times 10^{9},~1.6617385 \\times 10^{10},~\\dots,~2355337.8,~2263522.6,~2175265.5],~\n",
+       "  [0,~3.2796591 \\times 10^{9},~1.6580394 \\times 10^{10},~\\dots,~2346462.3,~2254970.3,~2167024.8],~\n",
+       "  [0,~3.2724002 \\times 10^{9},~1.6551805 \\times 10^{10},~\\dots,~2334129,~2243087,~2155575.3]],~\n",
+       "\n",
+       " [[0,~0,~0,~\\dots,~0,~0,~0],~\n",
+       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
+       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
+       "  \\dots,~\n",
+       "  [0,~3.2886782 \\times 10^{9},~1.6617385 \\times 10^{10},~\\dots,~2355337.8,~2263522.6,~2175265.5],~\n",
+       "  [0,~3.2796591 \\times 10^{9},~1.6580394 \\times 10^{10},~\\dots,~2346462.3,~2254970.3,~2167024.8],~\n",
+       "  [0,~3.2724002 \\times 10^{9},~1.6551805 \\times 10^{10},~\\dots,~2334129,~2243087,~2155575.3]],~\n",
+       "\n",
+       " [[0,~0,~0,~\\dots,~0,~0,~0],~\n",
+       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
+       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
+       "  \\dots,~\n",
+       "  [0,~3.2886782 \\times 10^{9},~1.6617385 \\times 10^{10},~\\dots,~2355337.8,~2263522.6,~2175265.5],~\n",
+       "  [0,~3.2796591 \\times 10^{9},~1.6580394 \\times 10^{10},~\\dots,~2346462.3,~2254970.3,~2167024.8],~\n",
+       "  [0,~3.2724002 \\times 10^{9},~1.6551805 \\times 10^{10},~\\dots,~2334129,~2243087,~2155575.3]]] \\; \\mathrm{\\frac{1}{MeV\\,s\\,m^{2}}}$"
+      ],
+      "text/plain": [
+       "<Quantity [[[0.00000000e+00, 4.14334641e-09, 1.56669299e-06, ...,\n",
+       "             3.87547612e-28, 3.23385731e-28, 2.69836819e-28],\n",
+       "            [0.00000000e+00, 9.41181503e+05, 3.23019399e+07, ...,\n",
+       "             1.40980276e-19, 1.18788371e-19, 1.00087570e-19],\n",
+       "            [0.00000000e+00, 2.84808350e+06, 9.28855290e+07, ...,\n",
+       "             9.46786151e-18, 8.03451049e-18, 6.81801166e-18],\n",
+       "            ...,\n",
+       "            [0.00000000e+00, 1.61654430e+09, 9.78281808e+09, ...,\n",
+       "             3.47685161e+05, 3.32386299e+05, 3.17757211e+05],\n",
+       "            [0.00000000e+00, 1.61255359e+09, 9.76287300e+09, ...,\n",
+       "             3.46759127e+05, 3.31498679e+05, 3.16906426e+05],\n",
+       "            [0.00000000e+00, 1.61232142e+09, 9.76187718e+09, ...,\n",
+       "             3.45903765e+05, 3.30679175e+05, 3.16121289e+05]],\n",
+       "\n",
+       "           [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "            ...,\n",
+       "            [0.00000000e+00, 3.57850901e+09, 1.74505885e+10, ...,\n",
+       "             3.21731179e+06, 3.09502677e+06, 2.97736153e+06],\n",
+       "            [0.00000000e+00, 3.56928822e+09, 1.74141061e+10, ...,\n",
+       "             3.20538852e+06, 3.08352639e+06, 2.96626922e+06],\n",
+       "            [0.00000000e+00, 3.56097725e+09, 1.73824251e+10, ...,\n",
+       "             3.18913860e+06, 3.06785289e+06, 2.95115193e+06]],\n",
+       "\n",
+       "           [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "            ...,\n",
+       "            [0.00000000e+00, 3.28867822e+09, 1.66173852e+10, ...,\n",
+       "             2.35533785e+06, 2.26352262e+06, 2.17526554e+06],\n",
+       "            [0.00000000e+00, 3.27965908e+09, 1.65803938e+10, ...,\n",
+       "             2.34646226e+06, 2.25497028e+06, 2.16702481e+06],\n",
+       "            [0.00000000e+00, 3.27240025e+09, 1.65518049e+10, ...,\n",
+       "             2.33412904e+06, 2.24308702e+06, 2.15557527e+06]],\n",
+       "\n",
+       "           [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "            ...,\n",
+       "            [0.00000000e+00, 3.28867822e+09, 1.66173852e+10, ...,\n",
+       "             2.35533785e+06, 2.26352262e+06, 2.17526554e+06],\n",
+       "            [0.00000000e+00, 3.27965908e+09, 1.65803938e+10, ...,\n",
+       "             2.34646226e+06, 2.25497028e+06, 2.16702481e+06],\n",
+       "            [0.00000000e+00, 3.27240025e+09, 1.65518049e+10, ...,\n",
+       "             2.33412904e+06, 2.24308702e+06, 2.15557527e+06]],\n",
+       "\n",
+       "           [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "            ...,\n",
+       "            [0.00000000e+00, 3.28867822e+09, 1.66173852e+10, ...,\n",
+       "             2.35533785e+06, 2.26352262e+06, 2.17526554e+06],\n",
+       "            [0.00000000e+00, 3.27965908e+09, 1.65803938e+10, ...,\n",
+       "             2.34646226e+06, 2.25497028e+06, 2.16702481e+06],\n",
+       "            [0.00000000e+00, 3.27240025e+09, 1.65518049e+10, ...,\n",
+       "             2.33412904e+06, 2.24308702e+06, 2.15557527e+06]],\n",
+       "\n",
+       "           [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "            ...,\n",
+       "            [0.00000000e+00, 3.28867822e+09, 1.66173852e+10, ...,\n",
+       "             2.35533785e+06, 2.26352262e+06, 2.17526554e+06],\n",
+       "            [0.00000000e+00, 3.27965908e+09, 1.65803938e+10, ...,\n",
+       "             2.34646226e+06, 2.25497028e+06, 2.16702481e+06],\n",
+       "            [0.00000000e+00, 3.27240025e+09, 1.65518049e+10, ...,\n",
+       "             2.33412904e+06, 2.24308702e+06, 2.15557527e+06]]] 1 / (MeV s m2)>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "flux.array"
    ]
@@ -165,10 +366,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "73c1a777-426e-460d-bdeb-31ddf9db68df",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$[0,~0.1,~0.2,~\\dots,~49.8,~49.9,~50] \\; \\mathrm{MeV}$"
+      ],
+      "text/plain": [
+       "<Quantity [ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,\n",
+       "            1. ,  1.1,  1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,\n",
+       "            2. ,  2.1,  2.2,  2.3,  2.4,  2.5,  2.6,  2.7,  2.8,  2.9,\n",
+       "            3. ,  3.1,  3.2,  3.3,  3.4,  3.5,  3.6,  3.7,  3.8,  3.9,\n",
+       "            4. ,  4.1,  4.2,  4.3,  4.4,  4.5,  4.6,  4.7,  4.8,  4.9,\n",
+       "            5. ,  5.1,  5.2,  5.3,  5.4,  5.5,  5.6,  5.7,  5.8,  5.9,\n",
+       "            6. ,  6.1,  6.2,  6.3,  6.4,  6.5,  6.6,  6.7,  6.8,  6.9,\n",
+       "            7. ,  7.1,  7.2,  7.3,  7.4,  7.5,  7.6,  7.7,  7.8,  7.9,\n",
+       "            8. ,  8.1,  8.2,  8.3,  8.4,  8.5,  8.6,  8.7,  8.8,  8.9,\n",
+       "            9. ,  9.1,  9.2,  9.3,  9.4,  9.5,  9.6,  9.7,  9.8,  9.9,\n",
+       "           10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9,\n",
+       "           11. , 11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9,\n",
+       "           12. , 12.1, 12.2, 12.3, 12.4, 12.5, 12.6, 12.7, 12.8, 12.9,\n",
+       "           13. , 13.1, 13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9,\n",
+       "           14. , 14.1, 14.2, 14.3, 14.4, 14.5, 14.6, 14.7, 14.8, 14.9,\n",
+       "           15. , 15.1, 15.2, 15.3, 15.4, 15.5, 15.6, 15.7, 15.8, 15.9,\n",
+       "           16. , 16.1, 16.2, 16.3, 16.4, 16.5, 16.6, 16.7, 16.8, 16.9,\n",
+       "           17. , 17.1, 17.2, 17.3, 17.4, 17.5, 17.6, 17.7, 17.8, 17.9,\n",
+       "           18. , 18.1, 18.2, 18.3, 18.4, 18.5, 18.6, 18.7, 18.8, 18.9,\n",
+       "           19. , 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7, 19.8, 19.9,\n",
+       "           20. , 20.1, 20.2, 20.3, 20.4, 20.5, 20.6, 20.7, 20.8, 20.9,\n",
+       "           21. , 21.1, 21.2, 21.3, 21.4, 21.5, 21.6, 21.7, 21.8, 21.9,\n",
+       "           22. , 22.1, 22.2, 22.3, 22.4, 22.5, 22.6, 22.7, 22.8, 22.9,\n",
+       "           23. , 23.1, 23.2, 23.3, 23.4, 23.5, 23.6, 23.7, 23.8, 23.9,\n",
+       "           24. , 24.1, 24.2, 24.3, 24.4, 24.5, 24.6, 24.7, 24.8, 24.9,\n",
+       "           25. , 25.1, 25.2, 25.3, 25.4, 25.5, 25.6, 25.7, 25.8, 25.9,\n",
+       "           26. , 26.1, 26.2, 26.3, 26.4, 26.5, 26.6, 26.7, 26.8, 26.9,\n",
+       "           27. , 27.1, 27.2, 27.3, 27.4, 27.5, 27.6, 27.7, 27.8, 27.9,\n",
+       "           28. , 28.1, 28.2, 28.3, 28.4, 28.5, 28.6, 28.7, 28.8, 28.9,\n",
+       "           29. , 29.1, 29.2, 29.3, 29.4, 29.5, 29.6, 29.7, 29.8, 29.9,\n",
+       "           30. , 30.1, 30.2, 30.3, 30.4, 30.5, 30.6, 30.7, 30.8, 30.9,\n",
+       "           31. , 31.1, 31.2, 31.3, 31.4, 31.5, 31.6, 31.7, 31.8, 31.9,\n",
+       "           32. , 32.1, 32.2, 32.3, 32.4, 32.5, 32.6, 32.7, 32.8, 32.9,\n",
+       "           33. , 33.1, 33.2, 33.3, 33.4, 33.5, 33.6, 33.7, 33.8, 33.9,\n",
+       "           34. , 34.1, 34.2, 34.3, 34.4, 34.5, 34.6, 34.7, 34.8, 34.9,\n",
+       "           35. , 35.1, 35.2, 35.3, 35.4, 35.5, 35.6, 35.7, 35.8, 35.9,\n",
+       "           36. , 36.1, 36.2, 36.3, 36.4, 36.5, 36.6, 36.7, 36.8, 36.9,\n",
+       "           37. , 37.1, 37.2, 37.3, 37.4, 37.5, 37.6, 37.7, 37.8, 37.9,\n",
+       "           38. , 38.1, 38.2, 38.3, 38.4, 38.5, 38.6, 38.7, 38.8, 38.9,\n",
+       "           39. , 39.1, 39.2, 39.3, 39.4, 39.5, 39.6, 39.7, 39.8, 39.9,\n",
+       "           40. , 40.1, 40.2, 40.3, 40.4, 40.5, 40.6, 40.7, 40.8, 40.9,\n",
+       "           41. , 41.1, 41.2, 41.3, 41.4, 41.5, 41.6, 41.7, 41.8, 41.9,\n",
+       "           42. , 42.1, 42.2, 42.3, 42.4, 42.5, 42.6, 42.7, 42.8, 42.9,\n",
+       "           43. , 43.1, 43.2, 43.3, 43.4, 43.5, 43.6, 43.7, 43.8, 43.9,\n",
+       "           44. , 44.1, 44.2, 44.3, 44.4, 44.5, 44.6, 44.7, 44.8, 44.9,\n",
+       "           45. , 45.1, 45.2, 45.3, 45.4, 45.5, 45.6, 45.7, 45.8, 45.9,\n",
+       "           46. , 46.1, 46.2, 46.3, 46.4, 46.5, 46.6, 46.7, 46.8, 46.9,\n",
+       "           47. , 47.1, 47.2, 47.3, 47.4, 47.5, 47.6, 47.7, 47.8, 47.9,\n",
+       "           48. , 48.1, 48.2, 48.3, 48.4, 48.5, 48.6, 48.7, 48.8, 48.9,\n",
+       "           49. , 49.1, 49.2, 49.3, 49.4, 49.5, 49.6, 49.7, 49.8, 49.9,\n",
+       "           50. ] MeV>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "flux.energy"
    ]
@@ -185,10 +450,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "9c163ad6-ba5f-4ed3-90bf-7df55114659e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "d2FdEdT (1, 9850, 501) [1 / (MeV s m2)]: <1 flavor[<enum 'ThreeFlavor'>](0;0) x 9850 time(-0.34462761827 s;8.350202515285 s) x 501 energy(0.0 MeV;50.0 MeV)>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#get the flux for specific flavor\n",
     "flux[Flavor.NU_E] "
@@ -196,10 +472,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "33d054eb-38af-48f5-8463-095a865a6f3d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "d2FdEdT (6, 1000, 501) [1 / (MeV s m2)]: <6 flavor[<enum 'ThreeFlavor'>](0;5) x 1000 time(-0.34462761827 s;0.395847790315 s) x 501 energy(0.0 MeV;50.0 MeV)>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Or a trim the time or energy dimensions, \n",
     "#here we take first 1000 points in time\n",
@@ -218,10 +505,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "fae0f38e-3918-4744-9c61-22dea8171c26",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "d2FdEdT (1, 9850, 501) [1 / (MeV s m2)]: <2 flavor[<enum 'ThreeFlavor'>](0;5) x 9850 time(-0.34462761827 s;8.350202515285 s) x 501 energy(0.0 MeV;50.0 MeV)>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Sum over all flavors\n",
     "flux.sum('flavor')"
@@ -229,12 +527,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "2f5ebc14-eb05-4c10-ad9e-a69521147804",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ValueError: Cannot sum over time! Valid axes are {<Axes.flavor: 0>}\n"
+     ]
+    }
+   ],
    "source": [
-    "#trying to summ over time or flavor will raise an exception\n",
+    "#trying to sum over time or flavor will raise an exception\n",
     "with raises(ValueError):\n",
     "    flux.sum('time')"
    ]
@@ -250,10 +556,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "a05eecb0-e29f-42c8-9574-41a5803e54d1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Container[1 / (s m2)] (6, 9850, 1) [1 / (s m2)]: <6 flavor[<enum 'ThreeFlavor'>](0;5) x 9850 time(-0.34462761827 s;8.350202515285 s) x 2 energy(0.0 MeV;50.0 MeV)>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Integrate over the full range, if limits are not provided\n",
     "flux.integrate('energy')"
@@ -261,10 +578,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "e7731b3c-7c78-44f6-80c8-a6b28759e4a1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dFdE (6, 1, 501) [1 / (MeV m2)]: <6 flavor[<enum 'ThreeFlavor'>](0;5) x 2 time(0.0 s;1.0 s) x 501 energy(0.0 MeV;50.0 MeV)>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Integrate over the first second of the flux\n",
     "flux.integrate('time',limits=[0,1]<<u.s)"
@@ -272,10 +600,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "4b0f2ff2-9679-4c34-a47a-f14dcfcbfadf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dFdE (6, 3, 501) [1 / (MeV m2)]: <6 flavor[<enum 'ThreeFlavor'>](0;5) x 4 time(0.0 s;3.0 s) x 501 energy(0.0 MeV;50.0 MeV)>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Integrate over the several time bins\n",
     "flux.integrate('time',limits=[0,1,2,3]<<u.s)"
@@ -311,10 +650,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "7efae0e2-973d-4957-a300-2a2ce8e3c81d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "d2FdEdT (6, 9850, 501) [1 / (MeV s m2)]: <6 flavor[<enum 'ThreeFlavor'>](0;5) x 9850 time(-0.34462761827 s;8.350202515285 s) x 501 energy(0.0 MeV;50.0 MeV)>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "flux"
    ]
@@ -329,10 +679,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "4c7e58cb-9a2d-43bf-830c-4aca0fce8d53",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Can integrate over {<Axes.time: 1>, <Axes.energy: 2>}\n",
+      "Can sum over {<Axes.flavor: 0>}\n"
+     ]
+    }
+   ],
    "source": [
     "print(f'Can integrate over {flux._integrable_axes}')\n",
     "print(f'Can sum over {flux._sumable_axes}')"
@@ -348,10 +707,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "557dd45d-5a0a-41d6-bc1b-6ccc99d22672",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Can integrate over {<Axes.energy: 2>}\n",
+      "Can sum over {<Axes.flavor: 0>, <Axes.time: 1>}\n"
+     ]
+    }
+   ],
    "source": [
     "fI = flux.integrate('time',limits=[0,1,2,3]<<u.s)\n",
     "print(f'Can integrate over {fI._integrable_axes}')\n",
@@ -360,10 +728,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "c5fdfc31-35cd-440e-bb6d-799d2255eca3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dFdE (6, 1, 501) [1 / (MeV m2)]: <6 flavor[<enum 'ThreeFlavor'>](0;5) x 2 time(0.0 s;3.0 s) x 501 energy(0.0 MeV;50.0 MeV)>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#after we integrated over time we can sum over the time bins\n",
     "fI.sum('time')"
@@ -371,10 +750,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "e1c7219f-533d-4247-9253-ee25a6ea886a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ValueError: Cannot integrate over time! Valid axes are {<Axes.energy: 2>}\n"
+     ]
+    }
+   ],
    "source": [
     "#but cannot integrate over time again\n",
     "with raises(ValueError):\n",
@@ -402,7 +789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "0f1c6cf1-7fc1-4068-9318-8a4eaea7fed4",
    "metadata": {},
    "outputs": [],
@@ -438,7 +825,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "27151730-3463-40a2-89ed-1a62d92e3473",
    "metadata": {},
    "outputs": [],
@@ -449,10 +836,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "c993b25d-1f03-4946-b277-ea7d2428e9d8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jpknelle\\AppData\\Roaming\\Python\\Python312\\site-packages\\snewpy\\rate_calculator.py:363: RuntimeWarning: divide by zero encountered in log\n",
+      "  return np.interp(np.log(E)/np.log(10), xp, yp, left=0, right=0)*E*1e-38 <<u.cm**2\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "dNdT (1, 9850, 200) [1 / s]: <1 flavor[<enum 'ThreeFlavor'>](1;1) x 9850 time(-0.34462761827 s;8.350202515285 s) x 201 energy(0.00025 GeV;0.10025 GeV)>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#calculate time differential rate \n",
     "rates = rc.run(flux, 'icecube')\n",
@@ -479,10 +885,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "d7b56248-b368-4712-9c50-208cc2f58415",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "N (1, 20, 200) []: <1 flavor[<enum 'ThreeFlavor'>](1;1) x 21 time(0.0 s;2.0 s) x 201 energy(0.00025 GeV;0.10025 GeV)>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#calculate time integral rate \n",
     "fluence = flux.integrate('time', np.arange(0,2.1,0.1)<<u.s)\n",
@@ -500,10 +917,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "32a3957b-9e5c-4f76-9e18-75b7b9cd774c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dFdE (6, 20, 501) [1 / (MeV m2)]: <6 flavor[<enum 'ThreeFlavor'>](0;5) x 21 time(0.0 s;2.0 s) x 501 energy(0.0 MeV;50.0 MeV)>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Container can be saved to a file \n",
     "fluence.save('fluence.npz')\n",
@@ -532,7 +960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "95972dcf-7a45-4df8-9a36-826d1ec39f26",
    "metadata": {},
    "outputs": [],
@@ -564,7 +992,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "57954135-8721-400c-898e-3a36f7b4bf37",
    "metadata": {},
    "outputs": [],
@@ -591,10 +1019,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "78b18cba-8d8c-44d2-bfd9-1e7a0dd418f3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zLiLiDTam2pL0fsnRJMeF8fNfsDe3zMNRibjG6NGj2b17Nzt27KCiosIxTM6VlKTLES3als3afQUEBxi5a/Thq0h2Vw/uwKx1aWxIKeDF+Vt5cWw/N0UpIuJZrVu3pnXr1p4OQ0TEbf6oq6T3aRdDbFggALtzSo/2EpEWLSIigv79+7vt/bRPuhzRGwu2A3DNkA7HrIwbjQYeO78nAN+sTWVzepHL4xMRERER99uba0vIu7aOoEN8eL3HRKT5lKTLYW1OL2LdvgICTQZuHnFcg14zsH0s5/ZNwmqFdxbtcHGEIiIiIuIJGYUVACRFh9IxwTavKCW/nOpaiyfDEvEZStLlsL76PQWAM3q2plVkcINfd+eptrb4/23cr7YnERERER9TWVNLTkkVAEnRIbSODCEk0EitxUpafrmHoxPxDUrS5RAV1bXMWpsKwOWD2jfqtT2SohjdPRGLFd5bvNMV4YmIiIiIh2QVVQIQHGAkJiwQo9FAhzhby/setbyLOIWSdDnEj5szKaqooV1MKMO6JDT69beNsrXHz16XRmFZtbPDExEREREP2e9odQ/BYDAA0DbGNrvI3gYvIs2jJF0OMX9TBgAXDWiLyWho9OtP7BBL9zaRVNZYmFlXkRcRERGRlm9/oa2lvU30gaHC9p8zipSkiziDknSpp6K6lkVbswAY06tNk85hMBi4cnAHAKav3IvVanVafCIiIiLiOfsPGhpnlxhpS9Iz61rhRaR5lKRLPct35lBaVUtSdAh92kU3+TwX9W9LWJCJndmlrNyd58QIRUTcx2AwHPUmIuJv7C3th6ukZ6qSLi2QN17rlaRLPT/+mQnAmT1bN+v/lJEhgZzfty0As9emOSU2ERF3s1qtR72JiPgbe7t70kFJeuso205AStKlJfLGa72SdHGwWKz8/Fddkt7EVveDXTywHWDbjq2iurbZ5xMRERERz7JX0ltHHZykq5Iu4kxK0sVhS0YxOSVVhAWZGNQxrtnnO6ljHO1iQimurOGXLVlOiFBExP1mz57Nn3/+6bj/xx9/8PPPP3swIhERz8ktte2R3ioy2PFYm7okPaekiqoai0fiEmmO3377jQULFjjuZ2VlMX36dFXSxfOW78wB4KROcQQFNP//GkajgQv721reZ6nlXURaqClTpvDxxx877peXl3PppZdSWakBSSLifwrqtteNDQtyPBYbFkSgybZMMrtEvxul5Zk5cyb//Oc/HfdjYmK45557WLZsmUfiCfDIu4pXWrbDlqSfclzj90Y/kosGtOOdRTtZsi2b4opqIkMCnXZuEWnhrFaoLvPMeweGQQPnbpjNZlJTD2wnOXDgQIqKitiyZQv9+vVzVYQiIl6nsqaWksoaAOIOStKNRgOJkSGkFZSTWVRBu5jQI51C/E0LvdYHBQXRq1cvVq5cybBhw1wV4REpSRcAqmstrKqbwj60S7zTztu1dSSdW4WzK7uUhVuzuaBfW6edW0RauOoyeMZDvxMeSoeg8AYdajab2bhxo+N+eno6AKGh+hAqIv7FXkU3GQ1EhtRPIxIigkgrKCevpMoToYm3akHX+tzcXMrKyggLCwNs13tPXevV7i4AbEgpoLSqlrjwIHq0iXLquc+qG0I3f1OGU88rIuIOZrOZlJQUx/2XX36ZQYMG0bVr13rHVVXpg6mI+La8uvXosWGBGI31K5Rx4bbKem6p2t2l5TGbzQCOavp3331Hamoql156ab3j3HWtVyVdAPhtZy4AgzvHHfJLt7nG9GrDO4t2snBrFhXVtYQEmpx6fhFpoQLDbN9ye+q9Gyg5OZn9+/dTW1vLd999x8cff8yKFSscz7/55ps888wz5OXlccoppzB37lzHt/AiIr4k35GkBx3yXHyEbZCcfbCcCNCirvVgS9IDAwO58cYbefPNN2ndujUAixYt4u6772b79u0kJSXx3Xff0bt3b5eEDaqkS501+/IBnDLV/e/6mqNJig6hrKqWpdtznH5+EWmhDAZbG5onbg1cowa2b9dramr49NNPuemmm/j222/p1asXADNmzGDGjBn8+uuvpKSk0KlTJ2bNmuWq/2IiIh6Vf5ihcXbx9kq62t3lYC3kWp+UlERAQADLli3jzDPP5M477+TGG28EYM+ePUyYMIHXX3+d9PR0Hn74YV566SVX/RcDVEkXbPujr9tXAMAJHWKdfn6DwcCYXm34ZPke5v2Zwek9Wzv9PUREXMXeAvfPf/6Tb7/9lpEjRzqe++STT/jzzz858cQTHY+NGjXK3SGKiLhFXlldJT380EHA8RH2JF3t7tLyGI1G2rRpw+OPP87zzz/PAw884Hjuq6++Iisri4svvtjx2N/b4J3N40l6QUEB69atIyAggL59+xIdHX3M11itVn7//XcyMzPp3bs3HTt2dH2gPmxXTgmF5dWEBBrpkeTc9eh29iT9578yqam1EGBSE4eItAydOnXiv//9Lz179qRz5871nrNarbz11lucc845BAQEEBAQQEREhIciFRFxLXu7u339+cHiw9XuLi3bjBkzCA4O5qSTTqr3uNVq5ZprruHRRx91LGeLiYlxaSwey5SsVit33XUXPXv25Mknn+Rf//oX7du359NPPz3q64qKihg+fDgXXHABL7/8Mr169WLy5Mluito3rd1bAEDfdjEEuih5HtQxltiwQArKqvl9b75L3kNExBWCgoI477zzDknQAV544QU++OADevToQceOHbnllls8EKGIiHvkHWVNelyE2t2lZRs+fPghCTrArbfeSl5eHieeeCIdO3Z06Vp0O49V0q1WK926dWP37t0EB9u+eXvrrbe46aabGD16tGPx/t9NnjyZzMxM/vrrL2JiYli8eDGjRo1i9OjRjB492p1/BZ+xtm49+kAXtLrbBZiMjOzaim/Xp7NoazaDOztvmzcREU8ZMGAAv/76q6fDEBFxi/yyI1fSE+oq6XmqpIuPiY6O5quvvnLre3qskm40GrnzzjsdCTrAuHHjqK6urrcf7cGsViuff/45N9xwg6PFYOTIkQwaNIjPP//cHWH7JEeS3j7Gpe8zqlsiAIu2Zrn0fURERETE+eyD42IOO939wBZsVqvVrXGJ+BqPr0k/2C+//ILBYKBHjx6HfT41NZX8/Hz69u1b7/G+ffuyfv36I563srKSysoDQyyKioqcEq8vKK6oZntWCQAD2ruukg4womsrDAbYklFMRmEFbaJDXPp+IiIiIuI8BXWV9JjQQwfH2avr1bVWiipqiD7MMSLSMF4zvWvv3r3cc8893HzzzXTq1OmwxxQWFgIQG1s/mYyPj6egoOCI53722WeJjo523I7USu+PNqcXYbVC2+gQWkUGH/sFzRAXHkRfcwwAi7epmi4iIiLSkhRX1AAQHXZoAh4SaCIi2Fb/U8u7SPN4RZK+f/9+zjzzTE444QTeeOONIx5nb40vKyur93hJSQkhIUeuyk6aNInCwkLHLSUlxTmB+4BN6baugl7tjj1V3xlGdW0FwOJt2W55PxERERFxjqJyW7t7ZMjhm3G1DZuIc3g8Sc/IyGD06NF06tSJWbNmERR06BoXu+TkZAICAti3b1+9x/ft23fYqbt2wcHBREVF1buJzZ9ptu6E3m3dlKR3syXpv27PoabW4pb3FBERz0pLS+Pxxx9nxIgRvPvuu4167c6dOxk5ciRXXnmli6ITkYawWq2OSnpkyOFb2e0t7zma8C7SLB5N0jMzMxk9ejQdOnTg22+/PWw1fM2aNfz0008AhISEcOqpp/LNN984ns/NzWXBggWcffbZbovbl/xZV0nv3c49X1z0NccQGxZIcUUNa/cVuOU9RUTEc5YsWcLQoUOxWCykpKSwZ8+eBr+2qqqK8ePHk5OTw5o1a1wXpIgcU2WNhaq6AkvUkSrpmvAu4hQeGxxXWVnJaaedRm5uLg899BBz5851PGffgw5g6tSprFixgk2bNgHw3HPPMXz4cK677jqGDBnCBx98QNeuXbn++us98ddo0cqratmeVQxAbze1u5uMBkZ0bcWc9eks3pbFSZ3i3PK+IiLiGQMGDGDnzp0EBAQwa9asRr32wQcfpFevXrRp04Zvv/3WNQGKSIMUVdha3Q0GCA86fAqRoHZ3EafwWJJeVVVF9+7d6d69+yEX3tjYWEeSfuKJJ9ZrTx84cCBr1qzh/fffZ/HixVx66aXccccd9bZyk4bZklGExQoJEcEkunho3MGGdUlgzvp0lu/Mddt7ioiIZ0RGRjbpdf/73//47rvvWLduHU8//bSToxKRxioqr2t1Dw7AaDQc9hh7u3uuKukizeKxJD0yMrJe2/qR3HrrrYc81r17d1555RVXhOVXHEPj2kZhMBz+l60rnNIlAYA/Ugsprqg+4romERHxT/v37+eGG25g1qxZDU7ytd2qiGsVV9iHxh35c1t8hK3ooyRdpHk8PjhOPGdzum1oXK+27h2k1zYmlE4J4dRarKzclefW9xYREe9msVi48sorueWWWxgyZEiDX6ftVkVc68DQuCPX+NTuLuIcStL92NYM23r07knun3Y/5Lh4AJbtzHH7e4uIiPfavXs3CxcuZN68eQwbNoxhw4Yxffp09u3bx7Bhw1i+fPlhX6ftVkVcy74mPSr0yJV0e7u7BseJNI+SdD9ltVrZllkCQLfWTVsv2BynHGdreV++Q+vSRcS79e/fv96a6C+++IKBAwd6MCLf1q5dO3799VdeeuklnnvuOZ577jlGjhxJq1ateO655+jRo8dhX6ftVkVcy15JP9Jkdzgw3T1HlXRpYe677z5OP/10x/3U1FTi4uJIS0vzSDweW5MunpVeWEFJZQ0BRgOdEsLd/v72SvrWzGKyiytp5cbBdSLiHaxWK+U15R5579CA0AbP4oiNjWXXrl2O+2eddRZXXHEFu3btonPnzq4K0a8sWrSIyZMnM3v2bFq1asWwYcPqPT937lxCQ0MPeVxE3KeovCFr0m2V9PyyaiwW6xEHzIn/aCnX+oSEBLZv3+64bzab6dSpE99//z0333yzq0I8IiXpfmpbXav7ca0iCApwf0NFXHgQPZOi2Ly/iN925XJBv7Zuj0FEPKu8ppyTZ5zskfdeecVKwgLDGnSs2WwmNTXVcT8kJASA/Px8l8Tma/Ly8rjgggsAWyv79OnTWbp0Kb169eK9994DICcnh2XLltUb/CYi3qUhlfTYMFuSXmuxUlheTWxd+7v4r5Z0rU9PT8disWA02nKjkJAQj13rlaT7qa2ZtiS9axv3t7rbDT0uns37i1i+I0dJuoh4LbPZzJo1axz3v/32W2JjY+ndu7cHo2o5IiMjee655w55/OB29FNPPZVff/2VxMTEw57jtttuY9y4cS6LUUSOrSHT3YMCjESFBFBUUUNuaaWSdGkxzGYzNTU1ZGRk0LZtW9LS0li9ejUvvviiR+JRku6n7EPjurWO8FgMp3RJ4MOlu7VfuoifCg0IZeUVKz323g1lNpsdQ8iysrJ44IEHeOyxxwgODqampoYtW7Y4EvbMzExqampo166dS+JuiQIDA4/Zph4fH3/UYzp06ECHDh2cHZqINEKRvZIeevT0ISEi2Jakl1TR5fDfu4kfaUnXeoCUlBTatGnDzTffzFlnncXQoUMB2LhxIz169CAgIIDKykp27NhBr169XBI3KEn3W/YkvasHhsbZndAxFqMB9uWVkVlUQeuoEI/FIiLuZzAYGtyG5klms5mSkhJSUlK47LLLOPPMM7nnnnsAWLNmDa+++ipffvklAM899xwnn3wy48eP92TIIiJO15BKOtjWpe/KKdVe6QK0nGu9fdvOlJQUpk+fTkpKCosXL3Y8f9FFF7Fjxw7ANkfliy++4JNPPnFZPJru7odqai3syLZNdu/exnPTb6NCAulZt0f7qt3aL11EvJP9wj1ixAh69+7NRx995Hhu1apVnHLKKY77y5Ytq3dfRMRXFJUfe590OLANm5J0aUnCwsKIjY3lX//6F7/++iu//PILsbGxAGzfvp3u3bs7htC541qvJN0PpeSXU1VjISTQiDm24W0grnBSR9uUdyXpIuKtOnbsyLnnnstNN93ERx995BgoA7B69WpHK1xBQQH5+fmOpF5ExJcUNbiSbtuxJ1fbsEkLM378eEaPHs2SJUtISEhwPH7wtR5g4cKFLt9tRO3ufmhnlq2K3jkhwuNbY5zUKZZpy3YrSRcRrxUXF8fcuXMP+9zatWv57rvvKCgo4N133yU+Pp6cnJx6F3cREV9QUtmwSnp8XSU9T5V0aWHeeeedwz6+atUq9u7dy4IFC1i5ciUrV67EarW6NBZV0v3QzrpW9+MSPTc0zm5QxzjANm0+X7/MRaQFKSwsJC4ujsrKSt5++21uv/12EhISqKrS7zIR8T1lVbUAhAc1LEnPLdHvQvENv//+OxdeeCEvvPACvXv3Zvz48ezdu9el76lKuh9yJOmtwj0cia0lqktiBDuySvh9bz5n9Gzt6ZBERBpk9erV9O7du972YqeeeqoHIxIRcR17JT082HTU4+Ls7e6laneXlq+mpoaCggImTJjAhAkTALjgggtc/r6qpPuhXdmlABzXyvOVdICTOtmq6at2ays2EWk5srKylJSLiF+orrVQVWMBICL4GFuwqZIuPiQlJYUxY8a4/X1VSfdD9kp6Zy+opAOc1DGOGSv3aV26iLQoV1xxhadDEBFxi7LKWsfPYcdod4+L0HR38R2dOnXi5Zdfdvv7qpLuZ/JKq8gvs03n7JzgXZX0TelFlNa1UomIiIiIdyipsn0+CzIZCQo4evoQH25rd88vq6LW4trhWiK+Skm6n9lVV0VvFxNKaNDR1xS5S9uYUMyxodRarKzdl+/pcERERETkIKUNXI8OEBtm26LNarUl6iLSeErS/Yy3tbrbHViXrpZ3EREREW9iT9KP1eoOEGAyOhJ1bcMm0jRK0v3MTi8bGmd3cl2SvnKXknQRERERb1Jatyb9WEPj7OLqhsfllGjCu0hTKEn3M7tzbEm6t1XS7fulb0gtcEwPFRERERHPK61bkx7WgHZ3sG2xC6qkizSVknQ/szfXlqR3iPeuJL1TQjixYYFU1ljYvL/I0+GIiIiISB17u3tDK+kJEdqGTaQ5lKT7EavVyr68MgA6xIV5OJr6DAYDA9vHArBmr4bHiYiIiHgLx+C4BqxJhwPt7tqGTaRplKT7kaziSiqqLZiMBtrFhno6nEMM7GBL0jXhXUS8hcFgOOpNRMQflFbZ1qQ3uN29bhu2XK1JlxbAG6/1Dfs6THzC3lxbFb1dTCiBJu/7fsZeSV+rSrqIeAmrVXv8iog0tt09vq7dXWvSpSXwxmu992Vq4jJ7HOvRvavV3a5fcjQmo4H9hRWkF5R7OhwRERERAUoc+6Q3MEl3VNKVpIs0hZJ0P7KvrpLe3svWo9uFBQXQIykSUMu7iHiPPXv2UFBQ4Lifk5PD/v37PReQiIibldVtwRYe1LB2d8cWbKVqd5eWISsri/T0dMf9srIydu7c6bF4lKT7kb32oXFeWkkHOEHD40TEy5xzzjm88MILjvsrVqxg0KBBXtkeJyLiCiVVjaukJ6jdXVqYp556iosuushxv6Kigr59+7J582aPxKM16X7EW7dfO9jADrF8+tte1u4r8HQoIuJiVqsVa7lnlrYYQkMbPAzGbDaTmprquH/qqaeSlpbGli1b6NGjh6tCFBHxGmWNbHe3V9ILyqqprrV45SwkcY+Weq2Pi4ujb9++LFiwgJ49e7oqxCNSku5H7IPjvLmSbh8e92daIRXVtYQENqytSkRaHmt5OVsHnuCR9+62dg2GsIb9LjSbzezevdtxv6qqqt6fIiK+rtTR7t6w1CEmLAijASxWyC+rIjEyxJXhiRdrSdf6zMxMqqurCQwMBGzXeU9d6/W1lp8oLKumsLwa8N416QDm2FASI4OpsVj5I7XQ0+GIiBzy7frHH39M+/bt6dOnjwejEhFxnwOD4xpWPDEZDcSG1e2VruFx0gKYzWYsFotjXfqGDRtYt24d5557rkfiUSXdT+zNs7W6t4oMJqyB34J6gsFgYGD7WOb9mcHaffmc1CnO0yGJiIsYQkPptnaNx967oZKTkx1J+saNG3n88ceZPn06RqOR6upqLr74YubOnQvADz/8wMaNG/m///s/l8QtIuIJZY1ckw62bdhyS6u0Lt3PtaRrPUBKSgrx8fFcc8013HPPPXTv3h2AG264gSlTpmA2m8nPz+eGG25g1qxZLokblKT7jT11re4dvbjV3e6EDrYkXcPjRHybwWBocBuaJ5nNZioqKli5ciVjx47l8ccf5/zzzwds37RHREQ4jv3xxx85+eSTPRWqiIhLlDSy3R3s27CVkFOiCe/+rKVc69u1a4fBYGD79u089dRTdOrUyTE01mq18uuvv2I2mwFYvnw5cXGuLSSq3d1P7KsbGtc+znuHxtkN7GBbl75uX76mJ4uIx9kvymeccQYPPPAA999/v+O51atXM2zYMMf9ZcuW1bsvIuILSuva3SMaUUmP04R3aUGCgoJo1aoVd911F1FRUXz99deOtek7duyoNzzOHdd6VdL9REsYGmfXu10UQSYjOSVV7Msr8+pp9CLi+zp16sTDDz/MkCFDDlmbtmrVKs4++2zA9s16VlaWI6kXEfEFtRYr5dW2SnpYA9ekAySEa026tCyTJ08G4I477sBoPFDLXrVqFYmJiQDk5OQwY8YMfvzxR5fGoiTdT7SEPdLtggNM9G4Xxdp9BazZm68kXUQ8KiIigqeeeuqwz23atImdO3fy4IMPMnr0aNLT05kzZw4XXnihm6MUEXEN+3p0aGQlPTwYgFxV0qWFuOuuuw77+KpVq9i5cyft27enR48eBAcHM3XqVF599VWXxaIk3U/sdbS7e3+SDrat2OxJ+iUDVZUSEe9TXFyM0Wjkp59+Ij8/nzZt2vD000/TunVrT4cmIuI0ZVW2KrrRAMEBDV8pGx9hr6RrTbq0bKtXr+b777+nuLiY5ORkCgsLCW3EULqm0Jp0P1BZU0tmke0XZItJ0h3r0gs8G4iIyBGsWbOGXr16ERwcTJs2bQCUoIuIz7En6WFBARgMhga/Lt7e7q5KurRgNTU15ObmEhsbS/v27TEYDMTExBAcHOzS91Ul3Q/sL6gAICTQSFzdL0xv1z85BoCtmcWUV9USGtTwNVAiIu7Qv3//eoNkRER8kb3dvbGfxeIjbEmMBsdJS/fDDz+4/T1VSfcDqfnlALSLCW3UN6CelBQdQmJkMLUWK5vSCz0djojIIWJiYhyDZEREfFW5o5LeuCTdXhjSFmzSkgUEBNC5c2e3v6+SdD+QVmAbGmeObRmt7mDbU7FfXTV9Q0qBR2MRERER8Vf2dvfQwMYl6Ql1a9KLK2qoqrE4PS4RX6Yk3Q+k2Svpsa4dcOBs9pb3dUrSRURERDyirImV9KiQQExGWwenWt5FGkdJuh9ILTjQ7t6SDKhL0tdreJyIiIiIR5RX29akhwU1bpSV0WhwtLznlqrlXaQxlKT7AXsl3dzCKul9zNEYDJBWUE52sX65i4iIiLibo929CUN8HRPeS1RJF2kMJel+4ODBcS1JZEggXVpFAFqXLiIiIuIJTR0cBwf2Sle7u0jjKEn3cTW1FjKKbFuwtaTBcXaO4XGpBR6NQ0RERMQfNXVNOkB8uG0bNk14F2kcJek+LrO4klqLlUCTgcTIYE+H02j24XHrVUkXERERcbsD090btyYdDmzDpkq6SOMoSfdx9vXoSdGhGI0tY4/0gx2cpFssVs8GIyIiIuJnyqvsg+MaX0m3b8OmNekijaMk3cel5tv2SG9p69HturWJJDjASHFFDbtzSz0djoiIiIhfac7guLi6dndNdxdpHCXpPq6lTna3CzQZ6dMuGtBWbCLiGUVFRVRWHviAWV5eTmmpvjQUEf9QVm1L0sObMTguR5V08XJlZWX1ru3V1dUUFhZ6LB4l6T4uzb5HegtN0uHA8DitSxcRTxg0aBBPPfWU4/73339P7969PRiRiIj7HJju3vg16Y52d1XSxcv961//4vTTT3fcz83NpXXr1uzcudMj8ShJ93GOJL2FtrvDgXXpmvAu4lusVivVlbUeuVmtDZ9xkZycTEpKiuP+mWeeyZ49e9i2bZsr/rP4pJUrVzJhwgTat2/Ps88+e8zjCwoKePrppxk2bBg9e/Zk7NixrF271g2RisjfldWtSW9Ku3tCRN1092JV0v1VS73Wt2nThr59+zJ//nxX/Gc5psZ/JSYtir3dvSVX0u1J+l/7i6ioriUksPEXCRHxPjVVFt6/Z7FH3vvm10cSGNyw3yVms7nehdtOLe8NM2/ePB599FFuvfVWli9f3qD2wVtvvZWuXbvy3HPPERcXx9SpUxk2bBgrVqygb9++bohaROyas0+6PUkvr66ltLKG8GClHv6mJV3r9+/fT01NDQEBB/5/6qlrvSrpPsxisZJaV0lPboF7pNuZY0OJDw+iutbK5v1Fng5HRPyM2WwmNTXVcX/GjBmOb9jl2M444wxWrVrF9ddfT1BQUINeM336dJ544glHJf3NN9+kbdu2zJgxw8XRisjfNWdwXHhwAKF1xZXsYrW8i/cym81YLBb2798PwLZt21izZg1nnnmmR+LR11k+LKe0kqoaC0YDtIkO8XQ4TWYwGOifHMOCLVms31fAwPaxng5JRJwgIMjIza+P9Nh7N9TBlfSdO3fy0EMP8eabb2Iymfj0009588036x1/+eWX869//cup8bZkJlPjP9j//TVWq5Xq6moCAwOdFZaINFBZM9akAyREBpGSV05OSSUdE8KdGZq0AC3pWg+QkpJCYmIiEyZM4Nprr6Vfv35s27aNK664ot7xycnJzJ4926nxHkxJug+zt7q3jgoh0NSymyb62ZN0DY8T8RkGg6HBbWielJycTHl5OX/++SeXXHIJt912G1deeSUAl1xyCWPGjGHixImMGjWKs88+m8jISA9H7Hveffdd0tPTGTt27BGPqaysrDeFv6hInVcizlBe3fR2d7C1vNuTdPE/LeVab0/Sd+/ezeuvv05wcDBvv/02AB07dmTu3LnMmzePX375hRdeeKHBnWFN1bIzNzmq/YUVALRtwUPj7DQ8TkQ8xX7hHjlyJOPHj+fpp592PBcZGUmbNm3YunUro0aNok2bNoSHq1LkTD/++CP33nsvr7zyylGXGDz77LNER0c7bsnJyW6MUsR3OQbHNXEmkH1dera2YRMvFhISQkJCAvfccw95eXl8//33hIbacqigoCDatGnDnj17GDJkCG3atCEuLs6l8ShJ92H2JL0lt7rb9TPHALA3t4y8Uv2SFxH36dSpE7fccguvvPIKU6ZMOeR5i8XCvn376NSpkwei822//PILF110EU888QR33XXXUY+dNGkShYWFjtvhhv2JSONYLFYqqi1A0yvprSLtE95VSRfvdtddd3HHHXfwv//9j7CwQ+d5rVu3jgEDBrglFrW7+7CMQlu7e1JUy0/So8MC6ZwQzq6cUjakFHBq90RPhyQifiIqKop33333iM+XlpZiMplISUlR9daJFi5cyPnnn88jjzzCxIkTj3l8cHAwwcHBbohMxH/YW92hGWvS7duwqd1dvNyjjz561OdzcnIoLS11y4wUVdJ9mC9V0kEt7yLinSIjI7nvvvt45plnPB1Ki/W///0Ps9lMRkYGAEuWLOG8885j8uTJTJo0ycPRifgve5JuMEBIYNPShlYRtrW7StKlpXvssceYNm2a41rlSkrSfZgvrUkH6GuOBmCDhseJiJe5//77mTp1qqfD8ErZ2dmYzWbMZjNbt27lnXfewWw2c8EFFziOKSsrIy0tjZoa29rXBx54gIqKCt5++23Ha81mM7fccoun/hoifsm+R3pooAmDwdCkcxyopGu5orRsZ555JtOnT3dL15za3X1Yho9V0vs5KumFWK3WJl8sRETEfeLj41mxYsUhjx/cmn7uueeSkpJCUlISAHPnzqWq6tAP9IdbIygirmOvpDd1aBxAQqTa3UUaS0m6j6q1WMkssiXpST6SpPdIiiLQZCCvtIrU/HKS4/RhTUTE2xmNRseE/CMJDQ2td0xiouaOiHgDeyU9pDlJeoQGx4k0ltrdfVRuSSU1FitGA7SK8I1BOiGBJnokRQFaly4i4g7l5eVs27aN9evXk56e7ulwRMTNHJX0Jk52B0ioW5NeWlXrSPpF5OiUpPso+3r0xMgQAky+8z+z1qWLiLhWVVUVn332GaeeeioxMTF069aNAQMG0K5dO5KTk7nzzjvZvHmzp8MUETc4eE16U0UEBxAcYPssqpZ3kYbxnexN6tlft/2ar6xHt7Pvl74hpdCzgYiI+KB58+bRo0cPnnjiCYYMGcLMmTNZv349W7duZfny5Tz88MNkZmZy4okncv3111NQUODpkEXEhZxRSTcYDI6W92wl6SINojXpPurAZHffStLt27BtTCukptbiU10CIiKetnDhQt555x3GjBlz2OeHDBnCrbfeSn5+Pi+99BKbN29m6NChbo5SRNzFGZV0sA2PSyso17p0kQZSku6jHJPdo3xj+zW7zq0iiAgOoKSyhh3ZJXRvE+XpkEREfMbzzz/foONiY2N5+umnXRyNiHiaM6a7w8F7pWsbNpGGUBnSR9kr6b4y2d3OZDTQu13d8DitSxcRFzMYDEe9iYj4sgontLvDwXulq5Iu3scbr/VK0n2Ur+2RfjD7funrtS5dRFzMarUe9earKisrmTdvHrNmzSInJ+eQ57/++mv27dvngchExJ3KnLAFGyhJF+/mjdd6tbv7qPS6wXG+VkkH6F83PO4PbcMmIuJ0ZWVljBo1itWrVwMQGRnJ+++/z/jx4x3HvPfee8TGxtK+fXtPhSkibmBvdw9rdiXd3u6uJF2kIVRJ90EWi5XMorp29xjfWpMOByrpWzKKHW1YIiLiHO+99x4VFRXs3LmT/Px87rrrLq666ireffddT4cmIm7mzMFxADnFWpMu0hBK0n1QbmkV1bVWDAZIrPul6EuSokNIiAim1mLlz3S1vIuIaw0dOpSXXnrJcf8///kPI0aM8GBErrVixQomTpxI586diYmJ4emnn+aLL77gnnvu4Z133vF0eCLiRlqTLv5i4sSJXHDBBY77qampmM1mMjIyPBKP2t19kH09equIYAJ9cIsyg8FA/+Rofv4ri/UphZzQIc7TIYlIE1itVmoqPfOBLSA4uMHDYAIDA9m6davj/umnn8748ePZs2cPHTt2dFGEnmM0GjGZ6n8gHzt2LEFBQVx++eUamCfiR5y9Jl37pPuflnKtj4iIYOPGjY77ZrOZVq1aMXfuXG688UZXhXhEStJ90H4fXo9u188cw89/ZWldukgLVlNZyRvXXuaR9777028IDGnY70iz2UxKSorjfnh4OAC5ubk+maQPHz6cpUuX1luDDnDhhRfy9ddfM3bsWEIa+N9ORFo2523BZkvSiytqqKiubXbSLy1HS7rWp6WlYbFYMBptRc7w8HByc3NdGeIR+V6ZVRzbr/niZHc7+7p0bcMmIq6WnJxcL0mfO3cuUVFR9OzZk+rqanJycurdCgtb9jKcCRMm8Msvv5CXl3fIc+effz4zZ86koqLCA5GJiLtVOGlwXFRoAEF13Z1qeRdvlJycTHV1NZmZmQBkZWWxevVqhgwZAti+mP/79d6Vk99VSfdBB/ZI972hcXZ9zdEA7Mkto6CsipiwIA9HJCKNFRAczN2ffuOx924os9lMamoqAHl5eTzwwANMmjSJ0NBQ5s+fz8SJE8nLy6OyspKkpCRGjBjBG2+84arQXS4sLIzNmzcf8flzzz2XsrIytb2L+IFyJ7W7GwwG4iOC2F9YQU5JFebYMGeEJy1AS7rWg20teps2bbjtttsYMWIEw4cPp6CggNNOO43q6mp2795N165dCQkJ4bfffnNV6ErSfVFW3WT31lG+W0mPCQuiY3wYe3LL2JBayMiurTwdkog0ksFgaHAbmieZzWaKiorIyMhg3LhxnHzyyUycOBGAMWPGMGbMGKZMmUJiYiK33Xabh6N1D3sroIj4tnInDY4D27r0/YUV5BSrku5PWsq1Pjk5GYCUlBRmzpzJpk2bWLp0KQaDgZiYGNavX8+yZct45ZVXmDlzpsvjUZLugzKL7Um67012P1i/5Bhbkp5SoCRdRFzG/u36qFGjGDBgAJ999tkhVeQ1a9bw8MMPeyI8lyosLGTx4sVkZGRgsVjqPXf++efTrl07D0UmIu7grC3YQHuli3cLDw8nOjqahx56CKPRyKJFi2jVqn5+sWbNGgYOHOiWeJSk+6DMItsvP1+upINteNyc9ekaHiciLtWxY0dGjhzJ0KFDeeqppw5bRd60aRN9+vTxQHSus3HjRkaNGkVlZSWtW7c+5IuJvn37KkkX8XHOGhwH2oZNvN+FF15IRUUFU6dOJS7u0N2jNm7cWG+bNldSku6DMov8pZJuW5e+PqUQq9Wq9ZEi4hIJCQksWrToqMcYDAY++ugjbr75ZoIbsQbOm7388stcdNFFvPvuuwQGBno6HBHxAKe2u0fak/SqZp9LxBU+/fTToz5vMBj44Ycf6NOnj8t3d9GiMh9TVlVDcUUNAIk+Xknv1TYak9FATkkl6YWaNCwinjN//nyioqIICvKdIZb5+flccMEFStBF/Jij3d1Ja9JBe6VLy/Xiiy8ybNgwt1zrlaT7mKy6VvfQQBORwb7dKBESaKJ7m0hAW7GJiGd16dKFa6+91qc6ekaOHMmCBQs8HYaIeEitxUpljW0WhVPXpGtwnLRQ0dHRXHHFFbRt29bl7+XbWZwfyiq2r0cP9qkPi0fSLzmGP9OL2JBawDl9kjwdjoiIz7jzzjsZPXo0F110ESeddNIhlYNx48bRvn17D0UnIq5m3yMdnJOkt9KadJEGU5LuY+zr0X291d2unzmaGStVSRcRcbZFixaxYsUKYmNj2b179yFf/A4fPlxJuogPKz8oSQ8OaH7zrdakizScknQfk+kHe6QfrF9yDAAbUwuptVgxGX2/e0BExB0+/PBDbr75Zt566y3tiy7ihw7efs3ohM9X9kp6YXk1VTUWgpyQ+Iv4Kv3r8DGOdvdI35gufCzHJ0YSFmSitKqWndklng5HRI7BarV6OgSPaWl/d4PBwJgxY5Sgi/ipCidOdgeIDg0koC7Zzy1Vy7uva2nXPGdyxt/d41fevXv3MnnyZC677DL+/PPPYx7//vvvc9lll9W73X///W6ItGU40O7uH0m6yWigdzvbVmxqeRfxXvYJ4WVlZR6OxHOqqmwtniaTcz7wutrgwYP54YcfPB2GiHhIWZXz9kgHMBoNxDuGx6nl3VfZr3H2a54/sn/Wac7uKB5td3/ppZd45513uOCCC5g5cyZ33nnnMV+zdu1a0tLS+Oc//+l4LDo62pVhtij+1u4OtnXpq3bnsSG1gLEnJns6HBE5DJPJRExMDFlZWQCEhYX5xXBLO4vFQnZ2NmFhYQQEtIyVZjExMfz73/9m9+7dnHzyyYcMjrvqqqtcvk+siHiOfU16SKDzanoJEcFkFlVqeJwPCwgIICwsjOzsbAIDA/2qG8tqtVJWVkZWVhYxMTHN+lLeo58Uxo4dy/333096ejqvv/56g1/Xrl07LrvsMhdG1nLZt2BLjPSjJL1uXfqGlELPBiIiR9WmTRsAR6Lub4xGI+3bt28xX06sW7eOvn37UlhYyI8//njI82PGjFGSLuLD7El6WJDz0gXtle77DAYDSUlJ7N69m71793o6HI+IiYlxfOZpKo8m6R06dGjS6zZt2sQ111xDdHQ0w4cPZ9y4cU6OrOU6UEn3j3Z3gH7mGAD+2l9ERXUtIU5qyxIR57JfuBMTE6murvZ0OG4XFBTUoioKb7zxhqdDEBEPqnByuzscSNJVSfdtQUFBHH/88X7Z8h4YGOiUZW0to+fuIAEBAZx88smMHDmStLQ0br/9dr744gtmz559xNdUVlZSWXngl0FRUZE7QnW7ksoaSut+ofrLFmwA5thQ4sODyC2t4q/9RQxoH+vpkETkKEwmU4tZly0i4q/sa9JDnDQ4DiAhUmvS/YXRaCQkxH/yEWdrcUn6k08+SWzsgSTsvPPO44QTTmDOnDlceOGFh33Ns88+y5QpU9wVosfYq+gRwQFEBLe4/2mbzGAw0NcczcKt2WxIKVCSLiIiItJM9nb3UCeuSW+lSrpIg7Scvrs6ByfoAAMGDKBDhw6sXr36iK+ZNGkShYWFjltKSoqrw/QIx3p0P2p1t3OsS0/VunQRERGR5nJswaZ2dxG3a/HlVqvVSnFx8VFbJ4ODgwkO9v3ENau4bj26Hw2NszswPK7Ao3GIiIiI+IJy+5p0FwyOU5IucnReX0l/9913HfugV1dXM3369HrPv/zyy+Tl5XH++ed7Ijyv4o9D4+zsw+N25ZRSWO5/A6lERFxh7969VFTYri2lpaW8/PLLvPTSSxQXF3s4MhFxtXJXVNLta9JLtCZd5Gg8WklftGgRb731FuXl5QA89thjtGrVinHjxjkmtv/++++sWLECsA0b+vnnn3nkkUfo1q0bKSkp7N+/n48++ogTTzzRY38Pb5FZ1+7uT3uk28WFB5EcF0pKXjkbUwsZdnyCp0MSEWnRNmzYwC233MLSpUsBuPnmm1m4cCGhoaH8+OOPh92WTUR8R5mjku7cfdIB8kqrqK61EGjy+nqhiEd4NEnv1KkT48ePB+C6665zPN6zZ0/Hz7fddhuXX345YJsS+PHHH7N//37++OMPYmNj6dWrF+Hh4e4N3EvZK+n+NNn9YP3MMaTklbMhtUBJuohIM73//vtcf/31BAQEUFJSwuzZs9m6dSsJCQl06NCBvXv3NnkrVRHxfq5Ykx4bFoTRABarLVH3x8KSSEN4fJ/0Y13gTzjhhEMeS0pKIikpyVVhtViOwXGR/tfuDtA/OYa5f+xnvdali4g0W0pKCmeddRYAy5Yto1evXiQnJwPQtWtX0tLSlKSL+DB7u3uIE5N0k9FAXHgwOSWVZBdXKkkXOQL1mPiQzLrBcf6apNuHx/2RWuDROEREfMFxxx3HzJkzKS8v54MPPuCMM85wPLdjxw66dOniwehExNXsg+PCnDg4DiAhwr4uXcPjRI5ESboPyS723zXpAL3aRmE02NbmZxRWeDocEZEW7a677uLHH38kLCyMZcuWcfvttwMwZ84chgwZQmJioocjFBFXcgyOc+KadIBWdcUk++dWETmUknQfUVpZ4xjw0cpPK+lhQQF0bR0JoJZ3EZFm6ty5Mzt27GDdunVs374ds9kMQMeOHZk6daqHoxMRV3NswebEdnc4UEzKUpIuckRK0n2E/dvIsCAT4cEeHTXgUf3t+6Wr5V1EpNGys7Pr3Q8LC6N///5EREQ4HuvXrx9t2rShurqawsJCd4coIm7iijXpAEnRtiR9f2G5U88r4kuUpPuI7Lp1Pf5aRbfTunQRkaa76aabuPrqq1m9evURjyksLOS9996je/fubNq0yY3RiYg7uWKfdDhQSc8oVCVd5Ej8t+TqY+yV9FYR/p2k9zVHA/BHSiEWixWj0eDhiEREWo7p06fzzDPPcNppp5GQkMDgwYNJTk4mJCSE3NxcNmzYwOrVq+nbty/Tpk3jlFNO8XTIIuIiFS4aHGevpGcUqZIuciSqpPsIR5Lu55X0rq0jCQk0UlxZw66cUk+HIyLSooSHh/P000+TmprKww8/jNFoZPHixXz77bds2bKFYcOGsWjRIlatWsXIkSMbff7Kykpqamoa9RqLxdLo9xGR5itz0eA4VdJFjk1Juo9Qkm4TaDLSu62tmr5Bw+NERJokKiqKG264gc8//5wVK1awYcMGfv75Z5599lkGDx7cqHNVVVUxffp0hg0bRkhICJMnT27Q6+bPn0/Pnj0JDAwkKSmJl156qSl/FRFpIvvgOFetSc8pqaSqRl/CiRxOo5P0srIyZs2axb333stll13GZZddxn333cfs2bMpKytzRYzSAGp3P6CfhseJiHiNxYsX8/333/PMM8/Qq1evBr1m69atXHjhhVxzzTUUFRXx0Ucf8cgjj/DJJ5+4NlgRAcBisVJZl0A7e016XHgQQSZbCpJVrC1zRQ6nwUl6UVERDz74IO3atWPChAmsW7cOo9GI0Whk7dq1XHPNNZjNZiZNmkRRUZErY5bD0OC4A+zr0jekauqwiIinnXHGGcyYMYMRI0Y0+DVvv/02HTt25MEHHyQ8PJxzzjmHK6+8kldffdWFkYqIXUVNrePn0CDnJukGg4HEKNvn1YxCJekih9PgSRA9evRgxIgRzJw5k5EjR2Iy1f8HW1tby6JFi/jwww/p0aMHaWlpTg9Wjkzt7gfYt2H7K72IyppaggOce3ERERHX+u233w5Z837qqacybdo0SktLCQ8P91BkIv6hrOpAkh7igs9RSdEhpOaXk1GkJF3kcBqcpP/4449HbVMzmUycdtppnHbaafz5559OCU4aTkn6Ae3jwogJC6SgrJot+4sd7e8iItIyZGZm0qpVq3qPJSYmYrVaycrKolOnToe8prKyksrKA4Oo1NUn0nQH1qMbXbJTzoHhcUrSRQ6nwe3uDV1H1thjpfksFis5and3MBgM9DPHAFqXLiLSWFOnTuW7777z+JyZv091t983GA6fMDz77LNER0c7bsnJyS6PUcRXVbhoj3Q7xzZsStJFDkvT3X1AQXk1NRYrAPHhStIB+tnXpadoXbqISGNUVVXxwAMPEB8fzznnnMM777zDvn373BpDUlISWVlZ9R7LysqyrWVNTDzsayZNmkRhYaHjlpKS4o5QRXxSuYuTdEclXe3uIofVqCS9traWKVOm0Lt3b0455ZTDTlmNiIhwVmzSQPZW99iwQIIC9L0LaMK7iEhT3XPPPWzbto0NGzZw+umn880339ClSxf69u3LQw89xPLly52+d7nFYqGi4sCHdft+7AdbsGABAwYMICws7LDnCA4OJioqqt5NRJrG0e7u5KFxdknRoYAq6SJH0qiM7sUXX+S1117jzDPPpHPnztx4443ceOON9S7WpaWlTg9Sjk7r0Q/Vt67dfWd2CUUV1Z4NRkSkBeratSv3338/v/zyC9nZ2UyePJnU1FQuvPBCWrduzTXXXMNXX33VoLb4iooKKioqsFqt1NbWUlFRQVVVleP5WbNmERoaSmpqKgB33HEHGRkZPPjgg+zfv58vvviCGTNmMHHiRJf9fUXkgLK6SnqYi5L0NtF1091VSRc5rEYl6dOmTePrr7/mlVde4bPPPmPJkiV89913XHfddU7/Vl0aLrvE9gtOSfoBrSKDaRcTitUKm7QVm4hIs0RHRzNu3Dj+/e9/k5mZyZw5czCbzTz11FP89ttvR31tZmYmMTExxMTEsHPnTt58801iYmI444wzHMeYTCaCg4Md6807d+7M/PnzWbJkCd27d2fKlCm8/fbbjBs3zqV/TxGxqahybbt7m7pKemZRBZa6JZsickCDp7sDpKamMnToUMf9oUOHsmTJEkaPHs2ECRMO2/4urueopEcoST9Yv+Ro0grK2ZBayNAuCZ4OR0TEJxiNRoYOHcrQoUN55plnjnl869at67WyH87FF198yDGnnHIKy5cvb1asItI09jXpIS5K0hMjgzEYoLrWSm5plQpNIn/TqEr6cccdx65du+o91r17dxYtWsSCBQu47rrrnBqcNIza3Q/PMeE9pcCjcYiIiIi0JGUurqQHmowk1n1uTS8od8l7iLRkjUrSr7rqKj7//PNDHu/atasjURf3U5J+eBoeJyIiItJ49sFxrlqTDmCOtQ2BTM1Xki7yd41K0m+//XY6d+582OeOP/54Fi9ezMMPP+yUwKThsrVH+mH1aReN0QD7CyvI0mASERERkQZxVNKDGrUytlHaxdjWpacVHHv4pIi/aVSSHhkZyc0333zE54877jieeuqpZgcljXNgTXqIhyPxLuHBAXRJtG0JuEHD40REREQapNzF090BzLG2JF2VdJFDaVNtH6B29yPTunQREeebOnUq27Zt83QYIuIi5VU1gOvWpAO0q0vS05SkixyiSUl6ZmYmN910Ez169KBNmzaH3MR9qmos5JfZ9gFXkn4orUsXEXG+mTNnsm/fPk+HISIucqDdXWvSRTyhSQtNrr32WnJzc7njjjuIiYlxckjSGLmltip6gNFATGigh6PxPv3tSXpKAVar1bEHr4iIHN7y5cvJyso66jHZ2dluikZEPKHMje3uaQXl+owm8jdNStKXLFnCrl27VDX3AvZW94SIYIxG/XL7u25tIgkKMFJUUcOe3DI6JYR7OiQREa/26KOParcWET/njunu9sFxJZU1FJZXExMW5LL3EmlpmtTunpiYSG1trbNjkSbQevSjCzQZ6dU2CtC6dBGRhujTpw9PPfUUxcXFR7yNGjXK02GKiAuV2deku3C6e0igiYQI2+dXtbyL1NekJP3+++/nzjvvJCMjw9nxSCMpST82+/C49UrSRUSO6ZZbbuGzzz4jIiLiiDeTyXXVNRHxPEcl3YWD4+DA8Dgl6SL1NSlJHz16NIsXLyYpKYnw8PBDLt7iPge2X1OSfiT9NTxORKTBunfvzm233XbUdedPPPEE/fr1c2NUIuJO7tiCDeqvSxeRA5rUwzJhwgR69+7N9ddfr8FxHpZdokr6sfQ1RwPwZ3oR1bUWAk3aeVBE5Gjuueeeoz4/dOhQN0UiIp5gn+4e4uokPcZeSS9z6fuItDRNStI3bdpESkoKrVq1cnY80khqdz+2jvHhRIUEUFRRw9aMYnq3i/Z0SCIiIiJeyx2D4+BAJV3t7iL1Namk2LFjR8rL9Y/JGyhJPzaj0eDYL13r0kVERESOrsyxJt11g+NAe6WLHEmTkvTbb7+dW265hd27dzs7Hmkktbs3jH14nCa8i4iIiByZxWJ1rEkPdVslXe3uIgdr0tdj999/P7W1tXTu3Bmj0YjBUH9/7pqaGqcEJ8d28D7pcmT2dekaHiciIiJyZBU1B7ZZdn27u62SXlxRQ2FZNdFhgS59P5GWoklJ+ty5c50dhzRBWVWNox1JlfSjs094355VQkllDRHBrm3fEhEREWmJ7J8tAUJdvAVbaJCJhIggckqqSMkvIzpMc4NEoIlJ+llnneXsOKQJckuqAAgOMBLu4m86W7rEqBCSokPYX1jBprRCBneO93RIIiIiIl7HPjQuJNCI0Wg4xtHNZ44NI6ekitT8Mg33FamjvahasJySA63uf19yIIfSunQRERGRo3OsR3dxFd3Ovi49JU/D40TslKS3YDl1lfSEiCAPR9Iy9E3WunQRERGRo3FMdg9yz9LA5Dj7hHcNjxOxU5LeguWWtLChcVYr1FSCxeKRt+/vqKQXeuT9RUR8QXV1NRYP/R4XEdcrq7INgHb1ZHc7RyVd27CJOGh6Vgtmb3eP9+ZKevo6WP8F7F0O2X+BpQaMgRDTHjqeAt3Pgy6ng9H1F4Le5mgMBkgrKCe7uFLD9kREmuDss89m27ZtPPvss1xxxRVabiXiY8odlXT3JOnt6yrpe3NL3fJ+Ii1BoyrpZ555Jl999RVVVVWuikca4UC7uxcmm5mb4dML4P1RsOo9yNxoS9ABLNWQtxPW/htmjIM3T4ANX7q8wh4VEshxrSIA+EMt7yIiTXLvvfdy22238cEHH7B06VJPhyMiTmZvd3fXmvSO8eGAbU16rcXqlvcU8XaNqqQHBgZyxRVXEBMTw9VXX82NN95Ir169XBWbHMOBSroXJelWK/z2Fvz0GFhrwRQEPS6AnhdAUn8IiYbKYsjeAtt/gj++hPzdMPsWWP0RXPwuxB/nsvD6mqPZkVXChpQCTuvR2mXvIyLiq8477zzOO+88Jk2a5OlQRMQF3F1JT4oOIdBkoKrWQkZRBe1iQt3yviLerFFJ+vfff09aWhqffPIJH3/8Ma+99hqDBw/mhhtuYPz48URERLgqTjmMA9PdvaTdvbYavr0NNn5tu9/9PBjzNMR2rH9caAzEJMPxZ8Dpj8Gq92HJS5C6Ct4dDhe8AX0uc0mI/ZNjmLU2jQ2pWpcuIvJ3f/31F4WFDf/92LNnT6KiolwYkYi4m326u7sGxwWYjCTHhbEru5Q9OaVK0kVowpr0du3a8fDDD/PQQw+xaNEipk2bxt133819993HuHHjuPHGGxkyZIgrYpW/yfWmdveaKvh6Amz93rbm/Ozn4MQb4FhrFYPCYdh90GcszL4V9vwKM2+A3B0wcuKxX99Ijm3YUguwWq1aSykicpC77rqLBQsWNPj4n376idNPP92FEYmIu5U59kl3TyUdbC3vu7JL2ZNbyildEtz2viLeqsnT3Q0GA6eeeiqfffYZ+/fv57nnnmP27NkMHTrUmfHJUeR4y3R3qxX+e48tQTcFw/gZMOjGxiXY0Wa4Zg4Mvdt2f9GzMP9h27mdqHtSJEEmIwVl1ezL01YfIiJ/99///pfi4uJj3kaNGuXpUEXEBcrrpru7q90doEO8fXicPpuJgBOmu+/bt8/R/l5QUMCpp57qjLjkGGpqLeSXVQNeMN3915dhwwwwmGD8dFsbe1MYTXDmk7bJ7/97AFa8bXt8zNNOq6gHB5jokRTJhtRC1qcU0KFuWImIiMApp5xC+/btG7R8bcSIESQmJrohKhFxpzI3r0kH6JRg+zy2J0cT3kWgiZX0yspK/vOf/3DmmWfSqVMn3nvvPa644gp27NjBL7/84uwY5TDySm2t7kYDxIZ5MEnf/Sv88pTt53NebHqCfrCTboLzXrX9vOJtWPhM8895kH7JMQD8oXXpIiL1TJkyhb59+zr9WBFpOcrq1qS7a590OHgbNlXSRaCRlfR169Yxbdo0ZsyYQVFREeeeey7ffvst55xzDiaT+/4hy4Ht1+LCgzAZPbSuuiwPZt0MWGHAVTDoBued+8Trba3u398PS16AuM7Q/x9OObVtXfpe1qcUOOV8IiK+qry8nJSUFMrKykhMTKRt27aeDklEXMzd090BR2fjvrwyzQwSoZFJ+sCBA+natSv/93//x7XXXkubNm1cFZccg1esR5//EBSnQ/zxcPYLzj//oBugMAWWvgrf3WWbCN9xWLNPO6B9DAAb0wqprKklOEBfMImI2FVVVfGf//yHadOmsXz5cqqqqhzPmc1mLrzwQm6//XZ69uzpwShFxFXK6takh7ppujtAu5hQjAbbZPns4koSo0Lc9t4i3qhR7e6LFy9m69atTJw4UQm6h+WW2vdI91Cr+86FsOELwGDb2zzIRWu7Rz8KPS8ESzV8dS0U7W/2KTslhBMbFkhVjYU/04ucEKSIiG+YN28ePXr04IknnmDIkCHMnDmT9evXs3XrVpYvX87DDz9MZmYmJ554Itdffz0FBQWeDllEnKy82gJAmBunuwcFGGlbt/XaXg32FWlckj5ixAjHzxaLhQ0bNjB79mzHY5WVlc6LTI4qp9iD26/VVNna0AFOuhnMJ7ruvYxGuPg9aN0HynLgm+uhtqZZpzQYDJzQIRaAtXvznRGliIhPWLhwIe+88w7bt2/nmWee4bzzzqNfv3507dqVIUOGcOutt/L111+TlpZGUlISmzdv9nTIIuJk5Y5Kuns7DTXhXeSAJg2O279/P0OGDGHgwIFccskljscvueQS5s+f77Tg5MhySj3Y7r76A8jbBRGtYfRk179fYCiM/QSCImHfclj4dLNPOaB9XZK+T0m6iIjd888/z5gxY455XGxsLE8//bS2XRXxQfbp7u5O0tvH1a1Lz9WEd5EmJen33XcfXbt2pbCw/nTsBx98kGeece4kbjk8eyXd7e3uZXmwuG79+akPQ0iUe943oQtc8Ibt56WvwO4lzTqdvZK+Zm8+VifvxS4iIiLSUjkGx7mx3R0OqqSr3V2kaUn6ggULePnllw/ZR7Vfv36sWrXKKYHJ0eV6qpK+/A2oKIDEnraJ7u7U+xIYeK3t52/vgIqmryfvZ47BZDSQWVRJemGFkwIUEfENlZWVzJs3j1mzZpGTk3PI819//TX79u3zQGQi4moH9kl33+A4gI51SfoetbuLNC1JLysrIyjIVsE9eIuE7OxsQkI0jdEdDkx3d2MlvTwfVn1o+3n0ZDB6YCr6mKchpgMU7rNNl2+i0CATvdraugDWaF26iIhDWVkZw4cP5+yzz+bSSy+lc+fOfPnll/WOee+999i2bZuHIhQRVyrz0Jr0jgm2dvfd2SXqchS/16QkfdiwYXz88cfAgSS9qqqKhx56iFGjRjktODmy3BIPDI5b+T5UFUNiL+h6tvve92DBkXDRVMAA6z6D7T81+VQD22t4nIjI37333ntUVFSwc+dO8vPzueuuu7jqqqt49913PR2aiLhBebX790kH6FC3Jr2ooob8smq3vreIt2lSH8uLL77IqFGjmD9/Plarleuuu46FCxdSVFTE8uXLnR2j/I3VanUk6fHuStIrS2DlVNvPw++3TV33lI6nwODbYcXbtinzt6+EoLBGn2Zgh1g+Wb5HlXQRkYOsWLGCiRMn0rlzZwCefvpp+vfvz1VXXYXFYuH222/3cIQi4irVtRaqa21VbHcn6aFBJpKiQ9hfWMHunFLiwj20zbCIF2hSkt63b182bNjAW2+9RXV1NXv27OHSSy/lnnvuoX379s6OUf6mqKKGqlrbHpbx7voF9vs0W7t73HHQ62L3vOfRnPoQbJ4DBftg8fNwxpRGn8I+PG7z/iLKqmrcvvZKRMQbGY1GTKb6H87Hjh1LUFAQl19+eb1lbiLiW+xVdIAQNw+OA+gYH87+wgr25JQ6PqeJ+KMmZyXJyck8//zzzoxFGsi+Hj0yOMA9v0Bra2BFXRV92H2eWYv+d8ERcM6L8OU/4Le3oO/l0Lpno07RNjqENlEhZBRV8EdqIYM7x7soWBGRlmP48OEsXbqU8ePH13v8wgsv5Ouvv2bs2LGaPyPio+yT3Y0GCA5wf9dkx4RwftuVyx5twyZ+rlFJ+qZNmxp0XO/evZsUjDSMYz16pJta3bf+D4rTISwB+o5zz3s2RPdzoPt5sGUuzL0XrpvXqDZ8g8HACR1i+X7jftbszVeSLiICTJgwgRNPPJG8vDzi4uLqPXf++eczc+ZMLr30Ug9FJyKudPBkd090zXRKsC1f3J2jJF38W6OS9D59+jToOE1kdC17Jd1tre6r6ya6D7wGAty85duxnP0C7FoEKSth/ee2GBthQPsYvt+4n3X7tC5dRAQgLCyMzZs3H/H5c889l7KyMrW9i/ggT012t0uOtSXp6QXlHnl/EW/RqCTdZDJhNpu57rrrOPfccwkI0BpeT8gtceMe6dnbYPdiMBjhxOuOefi2VStZM3c+xRk5WGpqwWAgNDoCc9/ujPjHPwgMdnLM0e1g1CT48WFY8AT0vAhCohr8cvt6pzV787FarfrQKSLSAMYmDA+trq4mLS2NhIQEIiIiGvSayspKMjIyiIuLIzIystHvKSKNU17lmcnudkkxoQCkF1R45P1FvEWjsux9+/bxySefMG3aNKZOncq1117L9ddfT7du3VwVnxxGtmOyuxsq6b9/ZPuz61kQc/ihgBXlZcx66gWydu2i1pJ3yPOlJZCTton1P/yXkOBW9D3vVIaPu9x5MZ50M6z5GHJ3wK8vN2qIXK+20QQFGMkvq2Z3TimdWzXsg6OIiD8oLCxk8eLFZGRkYLFY6j13/vnn065duwad5+OPP+a+++4jKCiIwsJCbrrpJt54440jJvtVVVXceuutfPHFFyQmJpKdnc2gQYP497//TYcOHZr99xKRw7O3u4d6YGgcQNsY27yLzOIKqmstBJo8uJuQiAc1Kklv27YtDz30EJMmTWLhwoV89NFH9O/fnxNPPJEbbriBCRMmuChMOZjbKulVZbB+hu3nQTcc9pB577/PX78swWItcDxmNMQQEBCKyWTCarFSU1NJjaUQqKSiMpVVMz9j/ZyfGHbd5Qw4/YzmxxkQBGc+DV9cDivegRMmQFynBr00KMBIP3M0q/fks3ZfgZJ0EZE6GzduZNSoUVRWVtK6detDOo369u3boCR99erV3HjjjUyfPp3x48fz559/MmzYMDp16sQ///nPw77mtddeY9asWfzxxx8cf/zxFBUVccYZZ3D77bfz/fffO+XvJyKH8tQe6XYJ4cEEmYxU1VrILKrAHNv4LXZFfEGT+tUNBgOjR49m9OjR7Nu3j8suu4zrrrtOSbqb5DiSdBdX0rd8D5VFENMBOo+u91RNTQ3Tbr+f4sJddY8EER7RluHXjaPXsBGHnKq8pIQf3nqH1D+2Ul2bSVVNBr988DZrv53HNS8/0/w2+K5joPOpsGsh/PQoXP5Zg186sH0sq/fks2ZvPpedYG5eHCIiPuLll1/moosu4t133yUwMLDJ53n//ffp16+fY1p8r169uOaaa3j33XePmKTv3buXbt26cfzxxwMQFRXFyJEj+d///tfkOETk2Ozt7p5ak240GkiKCWFvbhnpBUrSxX81uYdk6dKlXHfddfTs2ROj0ch7773nzLjkKHLs091dXUnf8IXtz37j601NLy0qYOq1tzoS9KCAJC6cNJlbP3rrsAk6QGhEBJc8+H/cPeMjBl4wHpMxHqihIHsr70y4hT0bNzYvVoMBxjxjWzv/13ewZ2mDXzqwbl362r0aHiciYpefn88FF1zQrAQdbJX0wYMH13ts6NCh7Nixg8LCwsO+5uabb2bXrl28/vrrrF+/npkzZ/L5558zceLEZsUiIkd3oN3dc3On2kbb16VreJz4r0Yl6fv37+f555+nW7duXHzxxcTGxrJy5UpWrFjBzTff7KoY5W/s7e7xrkzSizNsVWmw7UFepyS/gI9u/SdVNRmAkbikntw1/QO69B/Y4FOfeuVV3PHJ+8QmdgcCqLHkMOvpp1k4Y3rzYm7d09bqDrZqegN3GRjY3pakb8sqpqiiunkxiIj4iJEjR7JgwYJmnyc3N5eEhIR6j9nv5+bmHvY1/fr1Y+LEiTz44INccMEFXHnllZx++ulccsklR3yfyspKioqK6t1EpHHs09091e4O0LZueFyaknTxY436miw5ORmz2cyECRO44IILCAoKwmq1HrJ/uvZJd60DlXQXtrtv/AasFjCfBPHHAbYW94/v/BfVtZmAiQ79hnDZQw826fSBwcFc/+ZLLPnyS37/9lus1hLWzvmaooxsLrz/3qbHPWoSbPgS0tbY9k/vcf4xX9IqMpgO8WHszS1j3b4CRnZt1fT3FxHxEXfeeSejR4/moosu4qSTTiIoqP41Z9y4cbRvf/iBogczmUxUVVXVe6yy0vZl85F2iXnmmWd47bXX2LBhA127dqWwsJALLriASy+9lHnz5h32Nc8++yxTpjR8cKiIHMrT090B2sXakvTU/DKPxSDiaY1K0mtra9m7dy9Tpkw56oVQ+6S7TkV1LSWVtm85XVpJ3/Cl7c9+B6roH916H1U1+wFDsxL0g40YP54OfXvz7ZMvUWPJYcfKhXzzdDmXPTypaSeMSITBt8OvL8GCJ6HbOWA89oXmhPax7M0tY82ePCXpIiLAokWLWLFiBbGxsezevfuQwXHDhw9vUJKenJzM/v376z2WkZGByWSiTZs2h33Nl19+yeWXX07Xrl0BiI6O5u677+ayyy4jPz+f2NjYQ14zadIk7r//fsf9oqIikpOTjxmfiBxQVu3ZNekAHeNt69B355R6LAYRT2tUkr6xueuGpdnsQ+OCTEaiQly0XijzT8jcCMZA6GVrLfzmmecoKd4NQHxSD6ck6HYdevbmmjdf4LO7H6S6Nou9f/zG108+w9hHHmraCU+527Z1XM5W25cNA6485ktO7BjHrHVprNpz6BZyIiL+6MMPP+Tmm2/mrbfeatK+6HajRo3io48+ora2FpPJ9sH/+++/Z+jQoY7qfHFxMWlpaXTp0oWAgABiYmIOaYXPyckhICCA8PDww75PcHAwwc0dQiri57yhkt4xwfZvfE+OKunivxqV5amN3fNyD2p1/3tVw2k2zbT9efyZEBbH5t+WsXfD7wCEBLdjwmsvOP0tYxMSueHdV5h22wNU1WSwb9NK5r75JufddVfjTxYSDcPus61LX/Qs9LkMAo7+we2kTraqzLp9BVTVWAgK0L6cIuLfDAYDY8aMaVaCDnDXXXfx3nvvcc0113DnnXeyaNEi5syZw/z58x3HzJ8/n7Fjx5KSkoLZbObGG2/k+uuv54QTTuDUU09l+/btPP7441x99dWHtN2LiPMcSNI9NziuU7wtSc8oqqC8qtajVX0RT2nwlXfSpEkUFBQc87j8/HwmTWpiq7IcU46rh8ZZrbB5ju3nXhdTU1PDz29+BFRgNMRwxStPuuZ9gfCoGK6f+hKBpkSglq1LF/LLZw3fSq2ek26GyCQoTIHfpx3z8ONaRRAXHkRljYWNaYefNiwi4k8GDx7MDz/80OzzJCYmsnTpUqxWK7feeiuLFy/m+++/57TTTnMcExUVRbdu3RyT5K+55hpmzpzJkiVLuOGGG/jwww/5v//7P6ZOndrseETkyOzt7iGBnkuMY8ODiA61/S7Ym6eWd/FPDf6arKCggI4dOzJ27FjOP/98TjjhBBITEwHb2rLVq1fz3XffMXv2bP7xj3+4LGB/l+vqoXFZf0HuDjAFQdcxfDl5CtW1WYCRQWMvJDYh0TXvWyc8KoarXn2Gf987kVpLLuu//y9Jxx9Hj8FDG3eiwFAYORHm3gtLXoQBV0Fw5BEPNxgMnNghlh83Z/L7njxO6HDoekcREX8SExPDv//9b3bv3s3JJ598SAX7qquuomPHjg06V9euXZkxY8YRnz/zzDPZsmVLvccuvPBCLrzwwkbHLSJNV+4F093B1vK+IaWAPTmldG8T5dFYRDyhwZX0qVOnsnTpUmpra7nqqqswm80EBQURFBRE+/btue666wgICGDZsmW8++67rozZr2W7upL+13e2P48bzb49qWTu/guAiKgODLt0rGve82/iWrfhsimTMBqisFrLmP/aVPIyMxp/ogFXQdxxUJYLK4/9/8mTOsUBsFrr0kVEWLduHX379qWwsJAff/yRuXPn1rtlZ2d7OkQRcbIyL1iTDtCpbnjcnlytSxf/1Og16dOmTeODDz7gjz/+ICUlBYPBgNlspm/fvo6BMOI6ByrpLkrSN9cl6T0u4Pvn3sLW5h7FlS+4rs39cMxdu3PK1f/g139/Qq01n+n3T+a2T9894nY9h2UKtG3JNutG+O1tOPnWo1bTT+xoT9LzsVisGI0uWvMvItICvPHGG54OQUTczJ6kh3qw3R2gfZwtSU/JU5Iu/qlJ02BMJhMDBgzgggsu4Pzzz2fAgAFK0N3EvibdJe3uuTsh608wBrA2PYyy8lQAkvv2JSI2xvnvdwwnnXs+XU4eDhipqsng3/c1YaJ870sg/ngoz4dV7x/10F5towgNNFFYXs32rJKmBS0iIiLSQnnD4DgAc12Svk9JuvgpjbBuYXJL7Um6Cyrp9oFxHYezbMb3gIUAYwIX/d8Dzn+vBrrw/nuJijsOgPysbfz88ceNO4HRBCP+Zft5+VtQeeTkO9BkZGCHGABtxSYiIiJ+p6zatibd0xPVVUkXf6ckvYWxt7vHhbugkr7lewB+TD+eqhrbGvA+55zWuBZzF7juzRcJNLUGLPwxfz5pO7Y37gS9L7WtTS/Pg9UfHPXQQfaW991K0kVERMS/lHtZu3tqfjm1FqtHYxHxBCXpLUyOq9akl2RD2hoAtvyRC0BwYBKjr77aue/TBAEBAVz06D8xGMKxWkuY+ehz1NTUNPwEpoCDqulvHrWaflJdkv67KukiIiLiZ0orbUl6RLBnCzSto0IIMhmpsVjZX1ju0VhEPKFRSXpJidbpepLFYiWv1EVr0nf8BFhZXD68bss1GHjZOc59j2Zo370nvU47AzBSXZvJ9P+b3LgT9BkLcZ1tk95//+iIh/VvH0OA0UB6YQWp+WqxEhH/tnfvXioqKgAoLS3l5Zdf5qWXXqK4uNjDkYmIs9VarJTX7ZMeHuzZSrrJaKBdbCigdeninxqVpCckJDBmzBjefPNNdu/e7aqY5AgKyquxd/zEOrvdfdt8AP5IaQ1AUEAbhl50sXPfo5nG3HQjkbGdAchJ28raH+c1/MWmABhet7Z+2RtQVXrYw8KCAujVLhrQVmwi4t82bNjA5Zdf7ljydPPNN/Pyyy8zdepULr30Ug9HJyLOVlp1oEsx3MOVdIBke8t7nirp4n8alaRv3ryZc845h++++47u3bvTq1cvJk6cyK+//kptba2rYpQ6uXWT3WPCAgk0OXGlQm017PyF3ws7UlVXRe81Zrjzzu9E17z6DCZjPFDNko+/oLqysuEv7ns5xHaEshz4fdoRDzupYywAq3bnNy9YEZEW7P333+f6668nICCAkpISZs+ezcqVK9m0aRPr169n7969ng5RRJyotNKWpAcYDQQHeH5FbPs4VdLFfzXqX2Dnzp255557+Omnn8jJyeGJJ54gKyuLsWPHkpiYyJVXXskXX3xBfr6SG1ewr0ePd3YVfd8KqCxiZeZAwEqAsRWjr7nWue/hJCGhYQy7djwQQK0ll0/undjwF9erpr8O1Yf/ZtYxPE6VdBHxYykpKSQlJQGwbNkyevXqRXJyMqGhoXTt2pW0tDQPRygizmRP0sODAzAYDB6O5sDwuL1K0sUPNflrssjISC699FI+/vhj9u/fz//+9z86d+7MCy+8QGJiIiNHjnRmnMKB7dfinT00bvt80isiqai1fbnScVA/557fyU4862zik7oCUJS3i2WzZzb8xf3GQ0x7KM2GdZ8f9hB7kr4jq4S80qpmxysi0hIdd9xxzJw5k/Lycj744APOOOMMx3M7duygS5cuHoxORJytxEuGxtklx9onvCtJF//jlF4Wg8HAySefzJNPPsm6devYs2cPV1xxhTNOLQfJdUx2d/Z69B+Zlz4MqMFoiObcu+907vld4KqXniHA2AqwsPqr7xre9m4KhKF3235e9oat1f9vYsODOD4xAtCUdxHxX3fddRc//vgjYWFhLFu2jNtvvx2AOXPmMGTIEBITEz0coYg404FKumeHxtmZY+17pWtNuvgflyw4adeuHbfccosrTu3X7GvS48OdWEkvSKEmcyuFVbb/K0QltPH4vugNERAQwKhbrsLe9v55Y6a9D7gKwltB4T7YNOuwh5yolncR8XOdO3dmx44drFu3ju3bt2M2mwHo2LEjU6dO9XB0IuJsJQe1u3uD5Lo16TkllVRUa/aV+BePToXIz8/ntddeo1u3bhgMBhYtWtSg17366qt06NCBkJAQBg0axNKlS10bqJfIdsUe6bsW8UP2iVisRUAAZ917q/PO7WL9Rp1GdIJt2ntexk7+XLqkYS8MDIXBt9l+XvoqWCyHHHJSp7rhcXs0X0FE/Ed2dna9+2FhYfTv35+IiAjHY/369aNNmzZUV1dTWFjo7hBFxEXslXRvaXePDg0ksi4WtbyLv/Fokv7qq6+ye/du3nrrrQa/5qOPPuLhhx/m7bffJi0tjdGjR3PWWWexb98+F0bqHRyVdGe2u+9ayJ7C9gAEByXSrsvxzju3G1z10lOYDLFAFQve+bThLxx0IwRHQfZfsO3Qrdzs69I3pRU6LloiIr7upptu4uqrr2b16tVHPKawsJD33nuP7t27s2nTJjdGJyKuZP+8ExbkHe3uBoMBc93wuJR8tbyLf/Fokv7EE0/w+uuv06NHjwa/5sUXX+TGG2/kvPPOIz4+nueee46YmBi/aL3LLXXymnSLhd3r11FlyQWgx2lDnXNeNwoJDWPAhWcDRqprM/nysScb+MJoGHSD7eelr4DVWu9pc2wY5thQai1Wft+rarqI+Ifp06fTvn17TjvtNDp37swVV1zBxIkTeeyxx7jzzjsZPnw4rVu35qOPPmLatGmccsopng5ZRJzEPjjOW9rdAcyxtpb3VE14Fz/j9CS9oKDA2ad0yMvLY+vWrYwaNcrxmMFgYNSoUSxfvtxl7+stDlTSndTunrmJhfu6ARaMhhhOmzDBOed1s5H/uILw8GQA0rduJH3Xjoa98OTbwBQMqathz6FLJgZ3jgdgxa5cp8UqIuLNwsPDefrpp0lNTeXhhx/GaDSyePFivv32W7Zs2cKwYcNYtGgRq1at0i4uIj7G29rd4cA2bNorXfyN0/8VxsbGYv1bVdJZMjIyAGjVqlW9xxMTE4/amldZWUnlQdO/i4qKXBKfq+U6e5/0XYsoqgoEIDK21TEO9m6Xv/AYn9x5DxZrMXOeeIXbPnnn2C+KbG0bIvf7R7a16Z2G13t6cOd4vlmTqiRdRPxOVFQUN9xwAzfccIOnQxERNymt8q7BcQAd421J+u4cJeniXzza7u4sVqsVg8FwxOefffZZoqOjHbfk5GQ3RuccFdW1FNd9w+msSvqKn3+l1poPGBhx3XinnNNTYhMSSe7TF4Cy8lSWzvy6YS885W4wmGDnAkhfX++pwZ1t69L/SC10TDwVERER8UXeWEnvEB8OwN7cUg9HIuJejUrS+/fvf8ybK7Vp0wY4dPpsdnY2rVu3PuLrJk2aRGFhoeOWkpLi0jhdIa9uPXqgyUBUiBN+eVZXsH67bZ1PgDGBried3PxzethlD08i0JQIWFjzzffU1DQgsY7tCL0vtf289NV6T5ljw0iOq1uXrq3YRERExIeV2teke8ngOIBOCXVJel4ZFotrOnVFvFGjkvRt27YxdOhQTj/99CPeXCkuLo5u3bqxcOFCx2NWq5VFixYxdOiRh54FBwcTFRVV79bSHGh1Dz5q10BD1exZTlmN7ZxxHdo1+3zeYug1YwETNZYcvnrsqYa9aNh9tj83z4Gc+uvZB3eyr0tXki4iIiK+y9v2SQdIig4h0GSgqsZCRlGFp8MRcZtG/Su8/PLLOfHEE7n++uuPeMzLL7/c7KAOduONN7JixQrHNi8PPPAA99xzD2eddRZDhgzh+eefp6CggFtvbTn7ezdFTqlzt1+b/9lMrNYSIJBz7rnNKef0BieedTa/f/09pSV7yNi5mazUFBLNx1je0LondD0btv0Av70J57/ueGrIcfF8vSaV37QuXURERHyYN7a7B5iMJMeGsSunlN05pbSNCfV0SCJu0ahK+m233ca777571GP69evX4PN98sknGAwGxxrxU089FYPBwOOPP37E19x44408+eST3HbbbSQlJbFgwQLmzZtHhw4dGvy+LZGjku6k9eh7d9r2mwwyxRGf5DuVdIBxz0zGYIjAai1j1qPPNexFp9xt+3P9F1CS5Xj45LoJ75vSCimuqHZ2qCIiXmfq1Kl89913lJVpUJOIP/HGSjpA51a2lved2SUejkTEfRqVpJ900kn89ttvRz1m/fr1DT7fhAkTsFqth9wOTtI//PBDRxXd7v7772ffvn1UVlby+++/M2zYsMb8NVok+/ZrCU6Y7F5TUUpFrW0AR2LnNs0+n7eJa92Gtt17AVBamsLKud8d+0Xth0C7E6G2Ela973i4XUwo7ePCtF+6iPiNqqoqHnjgAeLj4znnnHN455132Ldvn6fDEhEX88bp7gDHJUYAsCNLSbr4j0ZPdzeZvGeYhD/JLbVX0pufpC+Y+hpWaykQwBl33tns83mjyyZPIsDYCrCwcsa3x36BwXCgmr76Q6g6MEV0iH2/9J1qeRcR33fPPfewbds2NmzYwOmnn84333xDly5d6Nu3Lw899BDLly/HYrF4OkwRcTLH4Lhg7/qs36WVknTxPz6xBZs/yCmxr0lvfrv7rnV7AFure1ybpGafzxsFBAQw6PILACPVtVnMfrEBsxK6nwdxnaE8H9ZNdzw8+DjbVmzaL11E/EnXrl25//77+eWXX8jOzmby5MmkpqZy4YUX0rp1a6655hq++uortcWL+AhHu3uQd1XSu6iSLn5ISXoLcWC6e/Mq6TU1NZRX2X7Jxbf27eEbQy+6mNCQtgDsWbOOivJjfJA0mmDIHbaff3sLam0Xq5PrJrxv1Lp0EfFT0dHRjBs3jn//+99kZmYyZ84czGYzTz311DGXwYmI96uutVBVY+uQ8abBcXAgSc8qrqSwXJ/DxD94179COaLcuunuCc2spP/6xQys1mLAxOnXXuqEyLzbOQ/cwcynHsNiLeDLh6Yw4dXnj/6CflfAwmegYC/89R30voS2MaF0iA9jb24Zq/fkMbp7a/cELyLihYxGI0OHDmXo0KE888wzng7nmP7v6w0EhUV4OgyvUGu1UlltobKm9sCfNRbbrfrAz9W1FgwGMBoMGLD9Sd19owEMdX+C/f6BYw0Gg+O+/di6l9uOqXuOv903HHTevx8bFGAkwGgg0GQkMMBI4GF+DjAZCTId+DnQZCA0yER4UABhQSbCggIICzYRdtBjUaGBBJpUr4IDk93B+9akR4YEkhgZTFZxJbtzSumfHOPpkERczrv+FcoRHZju3rxK+tZfbBWPQGMsiX1HNTcsr9exTx+i4zpQmLedvP27yNy7m9YdOh35BUFhMOgmWPwcLH8Del0MBgNDOsezN7eMFbuUpIuItCT/25SBMTjM02GIl4oKCSAuPIiYsCDiwoOIDQsiPiKIpOgQ2saE0i4mlLYxocSGBWKo+3LBFxVX2JL0oAAjQQHe98VF51bhZBVXsjOrREm6+AUl6S2A1Wp12hZs5eXFAMREAkbv+yXsCuOfeYwPbrsNi7WY2VNe4tZpbx/9BSfdBMteg/R1sGcpdBrO4M7xfLk6hd80PE5EpEWZeFY3QsMjPR2GVzAYICTQRHCAkeCAuj8DjYc8ZrKVybFawWK1YqXuT6vtM8nB9w88DlasWOqOsViBuvsWy4HXYMV2zEHHOl5r4ZD3qrVaqam1UlVroabWSnWtpe529J+raiyUV9dSXlVLaVVN/T8raymvtg1JK6qooaiiBnKPviQuMiSA4xMjOD4xkuNbR9C1dSR9zdHEhDV/oK83sCfpUSGBHo7k8Dq3imDFrjx25WhduvgHJektQHFlDVW1tnVCzVmTvunXxVisBQAMHXWUarKPiYiNIal7T9L+WklpaSobFi2g36jTjvyC8ATofyX8/pGtmt5pOEOOq9svPb2QgrIqn7koi4j4uquHdCQqKsrTYYiXqbVYKSyvJr+sivzSKvJKq8gvqyKvtJrs4kr2F5aTXlBOWkEFOSWVFFfUsHZfAWv3FdQ7T+dW4QxsH8uwLgkMPz7BKQN+PcE+cycqxDtTg+PqJrzvzCo9xpEivsE7/yVKPfYqekRwACGBTd8WY/U33wNgMsTSZdQFTomtpbhs8iTeuvoGai25LPngi6Mn6WAbIPf7NNj+I2T9RevEHhyfGMH2rBJ+25nL2X18cyq+iIiIPzAZDcSF21rcaXX0Yyuqa9mTW8r2zBK2ZxazPauEv/YXsSe3jF3ZpezKLuWbNakYDNCnXTTn9Eni/H5taRfTcgb0FtVV0iO9NEm3D4/bmlns4UhE3MM7/yVKPbmO7deaV70tzs4BIDTQAK37NDuuliQgIIA+Y05l/Q/fUFWTwbz33+esm28+8gvij4Me58Ff/4Xlb8FFb3NKlwS2Z5WwdEeOknQR8WtTp07ltNNOo2vXrp4ORcTlQgJNdG8TRfc29Tsy8kqr2JBSwIrduSzZlsNf+4v4I7WQP1ILee6HLZzUKY4JQztyZs/WBHj5gDpHJT3UO9vde7W1/bffnVNKcUU1kV7ali/iLE79jfHggw/y008/OfOUAuQ4Yfu1/JwsqmvzADi+fRWY/O/7mdMmTCA40LYl25aFS6mpqTn6C4beY/vzj/9A0X6GdUkAYNmOHFeGKSLi9WbOnMm+ffs8HYaIR8WFB3Fq90Qmnd2DH+4ZzqqHTuOZi/swuHMcBgOs2p3H7dPXMuqlRXy8bLdjizNvVOzllfSEiGCSokMA2Jxe5OFoRFzPqf8SP/nkE1555RXOPPNM5s6d68xT+zX79mvNWef0ywcfAzUYDGGMGN3fOYG1QKPvuJYfXnuRWkse3zz5LOOnPHLkg5MHQfshsO83WPkuJw+fjMloYE9uGSl5ZSTHaVqwiPie5cuXk5WVddRjsrOz3RSNSMuRGBXCFSe354qT25NeUM4Xq/bx+Yq9pOaXM+W/m/lk+R4mnd2DMb1ae92k+KK6/ccjg723Qt27XTT7CyvYlF7EyZ3jPR2OiEs5NUnPyMigqKiIH3/80Zmn9Xv2NekJzWh3z/hrNwBBxnACOg12SlwtUc8hp7Dkwy8oLdnD/q1/UV5SQmjEUfbPHXq3LUn//WMiRzxA/+QY1uzNZ/nOHC6Pa+++wEVE3OTRRx9lwYIFng5DpEVrGxPKP8/sxu2jujBzbSqvL9jO3twybv18DYM7x/HS2H6YY73ny/7iun3So0K9s5IO0LttND9tzuTPtEJPhyLick5fIBMVFcVll13m7NP6Ncea9PCmVdJramqorCoAoFVYDrQ7wVmhtUgXPHwvEILFWsSXk584+sFdz4L446GyENZ8yil1Le9Ld2grNhHxTX369OGpp56iuLj4iLdRo0Z5OkyRFiE0yMRVgzuw6IFR3D26C8EBRlbsyuPs13/lvxvSPR2eg31Nujev9e7dzrYufVO6knTxfY1K0ufNm1fvfkFBwSHHvPvuu80KSA6VU2rfI71plfR1P/6I1VoCGDm1exEE+/d+sW07dyEmoQMA+Rm7yUpNOfLBRiMMvdP284qpDO8cDcDyHTlYbJvAioj4lFtuuYXPPvuMiIiII95MpqbvNCLij8KDA7j/zG78dN9IBrSPobiihru+WMe/vt5ARd2e7Z5UVO7da9LB1u4OsCOrhLKqY8wVEmnhGpWkn3322fXux8bGHnLMbbfd1ryI5BAHprs3rZK+ad5CAEyGGBK7DXBaXC3ZuCcfwmCIwGotZ/aUF45+cN/xEJ4IRakMKFpIWJCJ3NIqtmRoGxAR8T3du3fntttuO+q68yeeeIJ+/fq5MSoR39A+PoyvbhnCXaO7YDDA12tSuerDleTVFWQ8pcixT7r3VtJbR4XQKjIYixX+2q/PYOLbvHs/CAEOWpPexOnuxTm2qe6hAVZIPtlpcbVkkXHxJHY8HoCSolR2rF975IMDQ+Bk23ZtASve4uSOti+nNOVdRHzVPffcQ6tWR948eujQoUd9XkSOLNBk5J9nduOz608mMiSA3/fmM/7938gurvRYTN4+3d2ud91WbH+q5V18nJL0FiDX0e7e+Ep6RXkZ1bX5ALSPTIHkk5waW0t2+ZTJGA0xQDU/vvr+0Q8+8QYIDIfMjYyL2wnAUiXpIuJjSktLj709ZROOFZFDDTs+gVm3DaV1VDDbMks8mqi3hDXpcKDlfWOqknTxbUrSvVxNrYX8sqavSV/8+QygCghiVHIqxHV2boAtWGBwMB1PsLX/l1ek8/u8H458cFgcDLwagBE5MwDb/qeVNZ5fRyYi4iwXXnghixYtcvqxInJ4x7eO5D83D6FtdAg7s0u57pNVlFS6/8uvogrvn+4OByXpmvAuPq7R/xJ///33o94X58ovq8ZqBYMBYsMan6TvXf0HAIHGKEI7nmA7kThc/K9/8vo/NlFjyea3z2Zy4llnH/ngwbfDqg8IT/2VoeEXsLy0Hev2FTBYe3WKiA+ZM2cOW7ZsOeZxqampbohGxPd1TAhnxk2DuXTqcjalFXHb52v4eMIgAkzuq6UVt4A16QAD2scAsDWzmLzSKuKauBRUxNs1OkkfNGjQUe+Lc+WW2tqe4sKCMBkbn2CXFxcBEBFUAWb9b3U4fc4Zzbq5/6GqJoMFn3zCaRMmHP7A2A7Q6yLYNJP7w+ezvPR6lu3IUZIuIj6jffv2/PTTT/z0008NOj48PLzB587IyGDLli0kJSXRrVu3Br3GarXy559/UlxczIABAwgJCWnw+4m0JB0TwvlowiD+8f4Kft2ewwvzt/LQOT3c8t5VNRYqqi2A969JT4wMoXubSLZkFLNsRw7n92vr6ZBEXKJR/xI3btzoqjjkCOxD45rS6p6VmkKNxTY0rlf0Nki+3amx+YrRV1/Nn/OWUFWzn03zFx05SQcYejdsmsnA4oW043yWbIvmn2c27MOmiIi3mzZtmkvOO2XKFJ577jn69OnDtm3bGDZsGN98881Rk+4//viDf/zjHxQVFdGlSxfS09P54IMPGDFihEtiFPG0/skxvDKuH7dNX8v7S3YxIDmGs/skufx97VV0gIhg707SAU7pksCWjGJW7s5Vki4+q1F9NL17927QTZwnx779Wnjjh8Ytnf4lYMFgCOeEmN3QdqCTo/Mdw64bBxipseQw64WXjnxg2/7QaSRGay3XB8zjj7RCj2+bIiLiCpMnT2bDhg3NPs/PP//ME088wfz581m1ahV//fUXa9eu5emnnz7ia3JzcznzzDM55ZRT2LNnDwsXLuTXX3+lrKys2fGIeLOz+yRx8wjb/KD/++YP0gvKXf6e9snu4UEmt7bYN1W/5BgANqYVeTYQERdq8L/EgoKCBt/EeXLs269FNj5Jz9yyB4AgQxgBbXtBcIQzQ/MpA04/g9CQdgDsXbue6sqjTFc95W4ArghYSKS1hF+3H3kvYRGRlmrTpk3s3bu32ef55JNPGDJkiKMCnpSUxIQJE/jkk0+O+Jr33nuP8vJyXnvtNUwmEwCJiYmcddZZzY5HxNv935huDGgfQ3FlDf/3zR9YrVaXvt+B7de8ez26XZ+64XF/7S+iutbi4WhEXKPBSXpsbGyDb+I8uY5KeuPb3SsrigGIDC4Gs7ZeO5az/3kLEITFWsCXjz555AOPOw1a9yaUCq4y/czirUrSRcT3nHHGGcycObPZ51m/fj0DBgyo99iAAQNITU0lLy/vsK9ZuHAhI0eOxGQysXTpUtavX0/l0b48BSorKykqKqp3E2mJAkxGXh7bj5BAI0t35DB95T6Xvl+RfWicl092t+sQF0ZkcABVNRa2Z5Z4OhwRl2jwv8Z169Y5fl68eDFPPfUUEydOdAyOW716Nc8//zyPPPKI86P0Y/Y16QmNXJOelZpCraUAgD6xW6Hdxc4Ozed06tufyOhkigt3kr13B8V5uUTGHWYonMFgW5s++2auC5jPhdsuwmLph7EJg/1ERLxVVFQUs2bNYseOHQwfPpyIiPrdWFdddRUdO3Y85nkKCgqIi4ur91h8vO13a35+/iHPgW3IXFJSEn379iU+Pp7s7GzKy8v5/PPPGTVq1GHf59lnn2XKlCkN+8uJeLnOrSKYeFZ3pvx3My/M28LZvdsQH9H4rsqGKCxvGZPd7YxGA73bRfPbrlw2phXQs22Up0MScboGV9L79+/vuH3++ed88803PPDAA4wcOZKRI0fywAMP8M033/D555+7Ml6/Y5/u3thfzMu++Ar7evS+EfugndajN8SlU/4PgyEMq7WErx555sgH9r4Ea1Q7WhkKGVHxC5v3q2IjIr5l9erV9OrVi9raWhYtWsTcuXPr3bKzG9ZFFBQURHl5/XW19rXlQUGH/wI6KCiIhQsX8u9//5vly5ezbds2zj33XMaPH09tbe1hXzNp0iQKCwsdt5SUlEb8bUW8zzVDOtIzKYqiihpenL/VZe+TX2YrCMW2oO3M+pi1X7r4tib1tWzevJn+/fsf8ni/fv3YvHlzc2OSg9jXpDe23T3zr10ABBrCCAgJh4SuTo/NF8UntSOuTUdy92+mIGcv6bt20LZzl0MPNAViGHIHzH+Im0zf88OWm+ldt0ZKRMQXvPHGG045T8eOHQ9JmFNTUwkKCiIp6fCTqzt16kRlZSUnn3wyAAaDgauvvpr333+fnTt30rXrode04OBggoNdU2kU8QST0cATF/bisnd/4z+/p3DFye3pa45x+vsUlNkq6bFhLaOSDjg+c2l4nPiqJo1wbNeuHe+9994hj7///vu0a9eu2UHJAU2tpFeU29boRAaX2CaSG03ODs1nXf7UoxgNUUAF3z392pEPHHgNVQGRHGfcT9nG/7orPBGRFuWss87ixx9/rDeZfebMmZx++ukEBNhqBZmZmcybN8+x7vycc84hIyODiooKx2t2796NwWCgTZs27v0LiHjQiR3juGRAO6xWeGGea6rp9l1qYsNaUCVdw+PExzUpSX/55ZeZPHkyJ510EnfccQe33347J510Eo888givvPKKs2P0a01Zk567P41aSz4APaN3QNsBx3iFHCw0IoKkbj0AKC1JZduqlYc/MDiSiv4TADgt7z+ONV0iIr5iy5YtTJgwgUGDBrFixQoA5syZw6pVqxp8jttuu424uDjOO+88vvjiC2655RaWLVvGk08eGND566+/cvbZZzta6K+++mo6derERRddxNdff81bb73FP//5T+69916iorT+VPzLfWd0JdBkYOmOHFbuynX6+e3t7jEtKEnvEBdGdGggVTUWNqQUeDocEadrUpJ+/vnns2XLFoYPH86uXbvYvXs3w4cPZ8uWLZx77rnOjtFvlVXVUFZlW3vXmEr6rzPs69HDGBi1S+vRm+CyRyZhMsQBNfz01rQjHhc18k6qCOAE4zY2r/jRfQGKiLjYzp07GTRoEFarlYKCAkpKbB1anTt35vbbb2/weSIjI1m+fDmDBw/myy+/xGAwsHLlSgYOPHBtatOmDWPGjCEkJASAwMBAFi5cyIgRI5gxYwZr167l7bffViFA/FJyXBjjTkwG4OWftjl9SzZ7u3tceMtpdzcaDQw/PgGARdplR3xQk/da6Ny5My+//LIzY5G/sVfRgwOMhAc1vF09Y/NOAAIN4QSYgLZK0hsrICCA44YOYtuy+VRUprPiuzkMvuDCQw+MbMOmhHMYmPMdUWvfgVP1JZWI+IY333yTO+64g+eee44zzjjD8XifPn3Iy8tjy5YtdO/evUHnatWqFc88c+RhnMOGDWPevHn1HouIiOChhx5qWvAiPubO0V34ek0qq3bn8duuXIYel+C0c7fESjrAqG6JzP1jP8t35gDdPB2OiFM1qpJ+0UUX1bu/aNEiJ4Yif5dbam91D8ZgaPj2XpVltmpHRFAZhMZBbEdXhOfzzr/7LgJNiYCV1f858prz2pPvAKBH8TKs2a6bvioi4k47duzglFNOATjkGpSQkNDg6e4i0nxJ0aFcXldN//DX3U4994HBcS0rST+xQywAm9KLqKrRunTxLY1K0ufMmVPv/qmnnurUYKS+3BL70LiG/9IsLymhxmLbjqJz5F5bq3sjEnypr9/5pwNQVZPB/A8+POwxffoPYoH1BIxYKVzwqjvDExFxmTZt2rB1q+2Lx4OT9PT0dP788086derkqdBE/NL1wzphMMAvW7LYmV3itPMeGBzXctrdATrEhxETZluXviVDU97FtzRpTbq4R24Ttl/7bfZsoBoI4uSYrWp1b6aR/7iC4EDbFkFbfllKTU3NIceEBJr4rc1VAERu/QaKM90ao4iIK1x//fW88MILzJw5k8rKSvLy8vj+++8566yzGDVqFGaz2dMhiviVTgnhnNa9NQDTljqnml5rsVJUUVdJb0H7pIPty8N+dVvSaXic+Bol6V4spwnbr+1ZtQGAAGMUIQEWDY1zguE3jgdM1FhymPPi4ecwJPcbzRrL8Zis1bDq0O0JRURamqFDh/Laa69x6623snjxYi6//HLOO+88kpKS+PTTTz0dnohfumGYrYNl5tpUCurWkjdHYXk19jl0MaEtq5IO0C85BoB1StLFxzQ6Sf/9998dt7/fP/hxaT5HJb0R7e6leQUAhJhsU+FVSW++fqNOIzS0LQD7Nmykum4f34Od1iOR92vOA8Cy6kOoLHZrjCIirnDFFVeQlpbGqlWrWLBgAbt27WL+/PkkJDhvaJWINNzgznF0bxNJRbWFOevTm30++9C4yJAAAkwtr3Y3oC5JX68kXXxMo/81Dho0yHH7+/2DH5fms69JTwhveCW9uta2RikxLAui2kFka5fE5m/O+eftQBAWawFfPPLEIc+bY8NIaTWSnZYkjJWFsPYz9wcpIuICQUFBDBo0iNGjR2sduoiHGQwGLh9kGyD3n9UpzT5fvmM9estqdbfra44GYFd2KYV1A/BEfEGjtmDbuHGjq+KQw7BPd29oJX3zb8uwWksAAyfH/QltR7owOv/SsU8fIqPNFBfuImffDorzcomMi693zKk9k/hwyTk8a/wIVrwDJ90EppbXOiYiYrdixQpmzZpFRkYGFkv96cmTJk2iV69eHopMxH9d1L8dz/5vC5v3F7EprZDe7aKbfK58x2T3lvl5JT4imE4J4ezOKWXZzhzO6ZPk6ZBEnKJRlfTevXs36CbOkeNod29YJX399z8DYDRE0zakGNoOcFls/ujSKRMxGMKwWkv5z+SnD3n+tB6tmVU7nFxrNBSmwJ+zPRCliIhz/PDDDwwdOpRVq1ZhMBgICAiod2vM1qAi4jyx4UGc2cvWKdncarq93b2lDY07mP2/xfcb93s4EhHnaXAlvaCgoMEnjYmJaUIo8neOLdga+IuzICUDgCBj3fFJ/VwSl7+KT2pHXFInctP/pDB3L2k7ttOuy/GO5/ubY4iMiGBa+Rj+FfgVLH0Vel8Gxpa3xktE5LPPPuPxxx/n0Ucf9XQoIvI3lw9K/n/27ju+ifp/4Pgrq2m6J2W07L1lyxBkKwIOQBEUBBwgONGvE+WHW3EB4gBEGQKCA2TI3iCjQNmbQoHSvdu0Se73x7WB0gKltE3avp+PRx4kd5/cve8acnnfZ/FP2GX+2n+Rt/s0wNWgK9R2cgafK63N3QHub1yJHzadYf3RKDItNlz08rtLlH4F/hT7+voW+CHunM2m2OetDChgTXqmORUAL6M6TzoVmxRLbOXZo5PeRavxAsws++jrXOu0Wg331qvAHGt3zFo3iDoCJ/91SJxCCHGnFEWhWTO52SuEM+pQK4BK3q4kZ1jYdCK60NvJae7uU0qbu4PaL93HzUB6llXmSxdlRoGT9H379tkfX3/9NQEBAXz++eds3LiRjRs38vnnnxMQEMA333xTnPGWG0kZWVhs6pwYfgWoSY+7EolVSQCgkc8JcA8EDxk0rqiZPDyoVL8BAKmpFzm6c3uu9d0aBJGEB0u0vdQFWyZjn9tECCFKka5du7J8+XJHhyGEyIdWq7H3v/4nrPDNvKOT1VabgZ4FH6TY2Vw7X7qM8i7KigI3d2/evLn9+dNPP83ixYvp3PnqwGSdO3emdevWvPbaa7zwwgtFGmR5lNMf3ctVX6BmO9sX/Q7Y0GjcaOpxHip2BekvWCwGvPMmU58YgdUWx/ppv9CgXXv7uk51AnDRafkqpQePua9AG7Ebzm2FGp0cGLEQQtw+k8nEb7/9xrlz52jXrh0uLrlvGA8dOpTq1as7JjghBA80rcTMrWdZd/QK6ZlWTC633+T9SlIGAEGerkUdXolqFuLDphPR7L+QwJN3OzoaIe5coTptHDlyJFfSnqNZs2YcOXLkTmMSXDP9WgGbul86dAoAvcYdvQ5p6l6M9Ho9tdurUw1mZF5i+19XB4hzN+q5u5Y/0fhwJKifunDLZEeEKYQQd2TXrl00atSIpKQkVq9ezT///JPrER1d+Ca2Qog71zzEh2BfE2mZVtYfiyrUNqKS1N+bFbxKb006yHzpouwpVJJepUoVfvjhhzzLf/zxR6pUqXLHQYnbn34tI1mdH91kzG5aXbFpscQlVA+MG4dBVwFQ2LPon1zrujeoAMC0zD6g0cGZDXAx1AFRCiFE4X377bfs3Lnzho/WrVs7OkQhyjWNRkOfpjlN3i8VahtXkrNr0r1Kd036XVV90GjU+dJzWgcIUZoVKkmfPHky77zzDm3atOH5559nzJgxtGnThnfffZcvv/yyqGMsl66O7H7rO5sWiwWLTR0oo7Jb9pe01KQXu+b9egKQZb3CymtuWnVtoI4F8O9FF8wNHlEXbpX/F0IIIYQoWn2bVgZgw/Eo0jOtt/XejCwrCdkDx5X25u4+bi40zZ4vfsvJGAdHI8SdK3Cf9Gv17duXY8eOMW3aNHvz9k6dOrFw4UJq1KhRpAGWV1fnSL91TfqxHdtQlHRAQ2ufQ6A3gX/tYo5Q3PPYY4T9sx5z1iWOb9xOj5Ej0ev1VPEx0aCSF0cvJ7GxwhB6HVkER5dB9HEIrOfosIUQ4oZmzpzJyZMnGTVqFJs2beLkyZM3LDtq1Chq15ZrjRCO1KiyF8G+JiLi09lyMpqejSoW+L05g8YZ9Vq8TIVKCZxKxzoBHIhIZOvJaAa0DHZ0OELckUJPJFizZk0mT57MypUrWblyJZMnT5YEvQjFpmbXpBegT/qhtZsB0Gq8qeCSCkENQVu4+TLF7ek06lFAh9UWy1+ffm5f3rOhWpv+e7gH1H9AXbj165IPUAghbsPx48fZs2cPycnJ9uc3eiQnJzs6XCHKPY1GQ8+GamK++siV23qvfdA4L1c0ZWCw4Y61AwHYdjoWRWbWEaVc6b9tVkbFpuTMkX7rmvT485EAGHTZCb00dS8xzbp0Y/vsJaSln+fCwUNkpKfhanLjviYV+WbdSTafjCZ95AuYjv0DYQuhyxvgW83RYQshRL4+++wz+/O77rrLgZEIIQqqZ6MgZm1TR3m3WG3odQWrg7uSPWhcUCkfNC7HXVV9MOg0RCebiYhPJ8TPzdEhCVFoha5JF8UrJ0kvSJ/0zIxUADxM2X2RJEkvUfe/PgZwwaYksmjCBwDUC/Kkur8bmRYb65JDoGYXUKywfYpDYxVCCCFE2dKqmi8+bgbi07LYEx5f4Pfl1KRXKOWDxuVwNehoWMkLgH0yyrso5SRJd1Ix9ubuN69JzzKbsdgSAajmFaEulJHdS1S1ho3x9AkBIObCaeJjotBoNPRurI64uvJQJHR8RS28bw6kFG6aFCGEcIQrV66wfft21q5dm+sRFxfn6NCEEIBep6VbfbWb3erDBW/yHpXdJ720Dxp3reY5U7GdT3BoHELcKUnSnVRMcs486TdP0nf9swzIAvS0c9sLaKBCw2KPT+Q24P/eRKNxQ1FSWfzuxwD0bqz2EdtwLIqM4A5QpRVYMmDHNEeGKoQQBWKxWBg6dCgVK1akQ4cO9OjRI9cjNFSmlhTCWfRslJ2kH4kscH/sKHuf9LLR3B3grqq+AOy7UPAWBUI4I0nSnZDZYiUpwwJAwC0Gjju9Yy8AOq03Jr0N/GuB0aPYYxS5+QVVxK9STQCS4s5z/tgRmgV7U8nblbRMK1tOxUKnV9XCu2dAmtRACSGc2/fff8+hQ4c4deoU3bp1Y9WqVRw9epSHHnqIAQMG0L17d0eHKITIdk+dQFwNWiLi0zl6uWCDOubMkV6hDCXpLaupSXpYRKLMly5KNUnSnVBOf3SDToO3yXDTsslR6p1CF312jbv0R3eYRye9g1bjDZj555MpaDQaemVPhbLqUCTUuw+CmkBmCuz8zrHBCiHELWzdupVXX32VWrVqodFoUBSF+vXrs2DBArZu3Up4eLijQxRCZDO56OhURx3dfPWRyAK9JzIxuya9DDV3D/Fzo011P6w2hYW7Lzg6HCEKTZJ0JxRtb+puvOWUGFmZ6qBx3p5qzbsk6Y5j8vAgpKl6/tPTL7J7xQruy27yvvboFbJsCnR+XS383w+QLk2xhBDOKzY2lkqV1LE1/P39uXJF7evq4uJC9erVOX/+vCPDE0JcJ2f61zUFmIrNZlO4EJ8OQBVfU7HGVdKGtKsKwMLdF2QqNlFqSZLuhGJSribpN5McF4tVUQeNa+Ang8Y5gwdfH49eGwjY2DF3Ca2q+xHg4UJiehY7z8Sqc6ZXaAjmJNj5vaPDFUKIG7r2x23Lli2ZMWMG58+fZ82aNezbt4/q1as7LjghRB7dGgSh1cDhS0lExKfdtOzFhHQyLTYMOg3BvmVrqrJejSri5qLjYkI6By8mOjocIQpFknQndDVJv/mgcduX/AnYAFeaaveoC6Um3aH0ej3N+/UANGRZr7By2lR6NFRr01ceigSt9mpt+s7pkJ7gsFiFEOJmunfvbq9Jf/rpp8nKyqJatWr06tWLV199lZCQEAdHKIS4lp+7C62r+wG3rk0/G6O2xKzm745Oe/NWm6WNq0HHvfUqALDiYMGa/gvhbCRJd0LXNne/mYgDRwHQaz3Ra23gHggeQcUen7i5zoMfx9WlMgCntu2iez11EJPVh69gtSnQoD8E1gdzIuz60ZGhCiHEDY0aNYratWsD4OPjw44dOzhz5gyRkZG8/PLLmM1mB0cohLhej4YFm4otJ0mvGeBe7DE5wn1N1AqSP0IjyMiyOjgaIW6fJOlOKCZ74LhAz5sn6WkJSQC4ul4zaNwt+rCLktHjxZGAAasSz8VfpuPlqicmxczuc3Fqbfo9r6kFd0yDjCSHxiqEEPl57LHH2LJli/21RqOhRo0aVKhQIc86IYRz6Jndem/XuTjiUzNvWO5MdAoANQLLZpLeo2EQlb1diUo289suGT9DlD6SpDuh6AL2SbdY1P5Gfj7ZdwilqbvTqNuqDZ7e6sAlMedP0ruqHoBlBy6pBRo9BAF1ISNBatOFEKVOWloabm5lqx+rEGVBVX836lf0xGpTWH8s6oblzpTxmnSjXsfzXdWWQN9tPC216aLU0Ts6AJFXTE5z95vUpMddicSmqDWwDQMiIQsZNM7JDJj0JrNffBFFSaXa1j+hwn2sOHiZ9/s1wqDTqbXpfzwNO6ZC22fB6OnokIUQgtmzZ3Pq1ClOnz7N7Nmz2bhxY671kZGRhIWFUa9ePccEKIS4qZ6NKnIsMpnVRyJ5pGVwvmXORGcn6YEeJRlaiRrYMoTvNpzmYkI6c3eGM6pTTUeHJESBSU26E4ouwMBxe5evABTAlXrWUHWh1KQ7Fb+gilSoXheAjNQImikRxKdlse1UjFqg8SPgX1udim33DAdGKoQQVx08eJCtW7eSkJBgf57z2L59O8nJyfz+++/4+/s7OlQhRD5ypmLbfCIm3xrkjCwrlxLV6ddqlNGadAAXvZax2bXp3286Q3qm1KaL0kOSdCeUU5Ne4SY16RfDjgGg13qgt6WC3qQmfMKpPPbBe+g0foCFrhHqCPxLc5q8a3XQabz6fPsUMKc4JkghhLjG5MmT2bhxIy+//DJz585l48aN9sf69etZuHAh9913n6PDFELcQKPKXlTxMZGeZWXryZg868/FpqIo4Omqx9/95jMJlXYDWgYT4mciJsXM3J3hjg5HiAKTJN3JmC1WkjIswM37pKfGqfM+uhiyv1yDGqpJn3Aqer2euvfcDYDFEkn7xFBWH75y9c52k4HgVxPSYuE/mTddCOE8JkyYQJMm0kJLiNJGo9FcHeX9SN4pyMIi1N+QdYM80ZTxAYcNOi1j71UrsebsDEdRFAdHJETBSJLuZGKzR3Y36DR4mww3LJeVpTZT8vDK/nKVpu5O6/4xo3HRVwQU2sSfJiXNzIacwVx0eujypvp8+7cyb7oQwqns3LmT119/nSeffJKhQ4fmehw+fNjR4QkhbiCnyfvao1Hq9K/X2J7d7a5dTb8Sj8sR+jarjMmg43xcGgeyb1AI4ewkSXcyOXOk+7sbb3h3MyM9DatNHTSuZkCCulCSdKfWYdgAQItViWFQ7EaWhV26urLxIxDYADIS1UHkhBDCCaxcuZL27duza9cuNBoNer0+16Os18AJUZq1ruGHt8lAXGome8Pj7csVRWHb6VgAOtQKcFR4JcrNRW9vWfDv4bwtC4RwRjK6u5OJyR407mZzpO9fswawAHpamsIgHRnZ3cm16NmbXQuWkZoaTsW0S8wLO0XyI03xdDWo3RTufQsWPQE7p0Pb58C9fFw4hRDOa86cObz//vtMmDDB0aEIIW6TQaelW/0K/LHvIv8ejqRNDbXW/FRUCtHJZox6LS2q+To4ypLTsXYASw9cYt/5+FsXFsIJSE26k4kpwMjup/9TR3PXaTxxTb8EaKBCw5IIT9yBhye+jkbjhqKkMChiA2uOXLm6skFfqNQcMlNg61cOi1EIIXIoikKzZs0cHYYQopB6N64IwO97LpCYngVgn2GmVXVfXA3lZyyjZiE+AByMSMzT/F8IZyRJupOJye6TfrNB45IuqV+wBr2rusC/FhjL7jyXZUWFkGoE1agPgN4SyeaV/15dqdFA13fV57tnQNJlB0QohBBXde3aleXLlzs6DCFEIXVrEESdCh4kZViYtfUsNpvCioNqc+/25aSpe47aFTxwc9GRmmnldLTMpiOcnzR3dzI5fdIDbtLcPTMjDQA39+x7LNIfvdR4dNIEpj4xEqstluqHthObMhT/nBsytbtBSDu4sBM2fw4PfOnYYIUQ5ZrJZOK3337j3LlztGvXDheX3C28hg4dSvXq1R0TnBDilnRaDS91r8vz80OZufUslxLS2XUuDqNeywNNKzk6vBKl02poFuzDjjOx/HcmlrpBno4OSYibkpp0JxOd0yf9JjXpVkW9A1jRP0NdIEl6qaHX62l6f3dAg9V2hXmfTr66UqOBbtm16aG/Qvw5R4QohBAA7Nq1i0aNGpGUlMTq1av5559/cj2io6MdHaIQ4hbua1yRu6r6kGK28PveCAAm9G1INX93B0dW8jrWUVsPbMln7nghnI3UpDuZmFvUpJ/Y9R+Kkg5oaON7ElKRQeNKma5PPMGhf7eQlXUJ68nDpKekYPLI7q5QvSPUvBfObIBNn8GD3zk2WCFEufXtt986OgQhxB3SajXMG9WW7zacZsbWM/RpUpnH21R1dFgO0alOAJ//e5ztp2OxWG3odVJXKZyXfDqdzK0Gjju4biMAWo0n/mlH1YVSk17q3PvSs4ARm5LIr69fN3JyTt/0A79B9IkSj00IIYQQZYebi57xvepxeGJvvhjYtNxOn9i4sjdernpSzBaOXk52dDhC3JQk6U4mp0/6jZq7x569CIBeawLFBu6B4BFUYvGJotGkVUsUjyoApMSe42zY/qsrg1tCvfvVv+/6SY4JUAghALPZzJQpUxg6dCgPPPAA48aNY/fu3Y4OSwhRCDqtptwm6KC2KmiZPe3c7nNxDo5GiJuTJN2JmC1WkjIswI3nSTenqoPGGU3ZPRUqNlH7MotSp/Hzr6DV+ACZrPj8umbtXd8FjRaOLoUL8oNYCFHyYmNjadasGe+//z4ZGRkEBQWxb98+2rZty+eff37b29u+fTszZsxg+fLlZGZmFvh9ZrOZ6dOnM2/evNvepxBCXKtVdXW++D3hkqQL5yZJuhOJzZ5+zaDT4G0y5FvGYk0FIMDXqi6Qpu6lVrdmVQn3rQ5ARuYlVs+ccXVlUENo/rj6fM0EUGROTyFEyfr6668JDAwkPDycxYsXM3PmTLZu3cqyZct47733iIsr2I9cRVF44okn6N+/P+vXr+eVV16hdevWxMbGFuj9r7/+OuPHj2fSJGlZJIS4M63sNenxKPLbSjgxSdKdSE5/dH93Y77NkaIuhGNT1D40TQIvqQtl0LhSy6DT4t/ncXTaigAcXrORjPS0qwW6vAV6Vzi/HY6vdFCUQojyKjQ0lBdeeAGPnIEts/Xp04f69etz5MiRAm3n999/Z+HChWzZsoX58+ezd+9e0tPTmTBhwi3fu2zZMtauXcuoUaMKdQxCCHGtZiE+GHQaopPNXIhLd3Q4QtyQJOlO5Ooc6fkPGrdn2QoANBo36lj2qwulJr1Ue6RFMH9XaAm4YFMSmPfau1dXeleBdqPV52vfB6vFESEKIcopb29vzp07l2e52Wzm8uXLeHl5FWg7Cxcu5N5776V+/foAeHh4MGzYMBYuXHjT9128eJFnn32WefPmYTKZbjt+IYS4nqtBR5Mq3gDskn7pwolJku5EYm4xR3rk0dMA6DTukJUKehP41y6x+ETRa1jZC1PNxqQbKwOQEH2GIzu2XS3Q8WUw+UHMcdg/10FRCiHKo0cffZT333+fKVOmcObMGWJiYti+fTv9+/fH39+fJk0KdpP4yJEjNGjQINeyBg0aEBsbS1RUVL7vsVqtPP7447zyyis0b968QPsxm80kJSXlegghxPVaZ/dL33W2YF1uhHAESdKdSEx2n/SAGyTpaQlqU3ejMbumPaghaHUlEpsoPo+0DGZ2UDe0Gl8gi3VTf7660tUb7nlNfb7hY8hMdUiMQojyp3///nzxxRe8//771KpVi8DAQDp06IDVamXp0qUFHiU6OTkZHx+fXMt8fdV+oTdKpCdNmoRer+fVV18tcLwff/wx3t7e9kdISEiB3yuEKD/a1fQHYOcZqUkXzkuSdCdytbl7/km6JUvtr+zlnT3QhTR1LxMebF4ZjYuRfd51AA2Zlkj++PSakZNbjwSfqpASCTu+u+F2hBCiqI0ePZqLFy8SFhbG5s2biYiIYM2aNdSsWbPA2zCZTCQn556TOCc5d3Nzy1P+8uXLfPDBB7Rp04Zp06YxdepUQkNDSUhIYOrUqfk2wQd48803SUxMtD8uXLhQ8AMVQpQbrar7otNqOB+XRkR82q3fIIQDSJLuRKKzm7vnV5OenpKCVVF/1NQOSFAXSpJeJvh7GOnVqCKbfVuiN6jN3s/tCyXuSqRaQG+Ebu+pz7d9Ayn5Nw8VQoji4OrqSpMmTejUqRNVqlS57ffXqVOHs2fP5lp25swZ3N3dqVixYp7yBoOB5557juTkZI4dO8axY8eIjY3FbDZz7NgxUlPzb1FkNBrx8vLK9RBCiOt5uhpoHuIDwIbj0Y4NRogbkCTdicRk16TnN0f6nhXLAStg4C6XMHWhjOxeZjzetioAi6t0RKNxQ1GSWfTWB1cLNHoYKt8Fmcmw/oMbbEUIIe5camoq9evXJz4+Pt/1H3/8MR98UPDvoX79+rF69Wp7/3Or1cpvv/1G37590WrVnyGnT59m6tSppKamEhAQwNSpU3M9evToQVBQEFOnTqVRo0Z3fpBCiHKtW4MKAKw9csXBkQiRP0nSnUiMvSY97+juZ/eoiblO64Uh/TKggQoNSzI8UYzurulPzQB3Lmj8cKmkDgaYmnKenUv/VgtotdDrY/V56K9wOcxBkQohyrqff/6Z3r172/uNX2/MmDFMmzaNrKysAm1vxIgRNGvWjC5dujBp0iTuu+8+wsPD+fDDD+1l9u3bx7hx4254Y0AIIYpSz4ZqK55tp2KIzf79LYQzkSTdieQMHJff6O4pUergFgZ99jr/WmD0yFNOlE4ajcZem76iag/02kDAxs7f/iDLnH3xqHa3WqOOAqveBEVxWLxCiLJr7969tGjR4obrvb298fHxydOE/UZcXFzYsGED48ePJyEhgZ49exIWFparX3vt2rV5/vnn88zJnqN9+/YMHTr09g5ECCFuoHYFD5oGe2OxKXyx+gRZVpujQxIiF0nSnYTZYiUxXa2VyK9PepY5HQAPz+w/mfRHL3MeaRGMi17LkStp1HiwH6DHaotl7uvvXC3UYyLoXSF8Kxxd5rBYhRBlV1RUFAEBATctExAQwJUrBW8m6uLiwogRI5g8eTLjx48nKCgo1/rmzZszderUPKPA5+jXrx/vvPNOvuuEEKIwRneuBcBvu87T8dP1nIpKcXBEQlzl8CTdZrOxY8cO/vrrL06dOnXL8nv27GHBggW5HqtWrSqBSItXzsjuLnotPm6GXOssFgsWmzoybrB/9iiUkqSXOb7uLvRpUgmAbbpaeAeotUxxkac4tGWTWsinKrQfpz5f/Q5kZTgiVCFEGVapUiUOHjx4w/UWi4WjR48WahA5IYRwFvc1qcRXjzbDoNNwJcnMN+tOOjokIewcmqQnJibSoUMHBgwYwLRp02jWrBlvvPHGTd8zY8YMXnvtNf766y/7Y926dSUUcfG5kpQ9aJyHMc/cs0e3bQXMgJY2PsfVhTJoXJmU0+R96YFL9PvgfXQaPyCL9dN/wWKxqIU6vASelSAhHP6b7rBYhRBlU58+ffj222+5dOlSvuu/+OILAgMDb2saNiGEcEYP3RXMH6M7APBP2CW2n4pxcERCqPSO3Pnbb79NXFwcR44cwdvbm61bt9KpUyd69OhBt27dbvi+tm3bsmDBghKMtPhFJ6s1ohW88jZ1P7ppGwBajReeKVvUhVKTXia1quZLnQoenIxKYdWxOFoN7Mt/i+aQZY1iwTsTGfrJJHUsgu7vw5/PwuYvoNnj4Bl0y20LIURB9O/fn6lTp9KwYUNefPFFWrZsiY+PD+fOnWP+/PmsWbOGlStXOjpMIYQoEk2CvXmsdQgLdl/g7b8OseqlThj1OkeHJco5h9WkK4rCvHnzGDlyJN7e3gB07NiRNm3aMHfu3Ju+Nz4+nqVLl7Jp0yYSEhJKINriF5Xd3L1CPtOvxZ9X58s26FxBsYF7IHhIUlYWaTQahmTXps/ZEU6Hhwfg4VUNgCtnj3I2bL9asMkgqNISMlNg3f85KFohRFmk1WpZtmwZjz/+OJ9++in9+/enc+fODBs2jHPnzrFixQp69uzp6DCFEKLIvN2nAYGeRs7GpDJ72zlHhyOE45L0iIgIEhISaNy4ca7lTZo0uWlfOIADBw7w/fff88ILL1CtWjVmz5590/Jms5mkpKRcD2cTlZSTpLvmWWdOU/uhu7plN3yo2ASuaxIvyo6HWwbj7qLjZFQK207FMuSzSWg13kAG/3w6TS2k1ULvT9Xn++fC+f8cFq8Qouxxc3Pju+++Iy4ujt27d7Np0yZOnTrFsWPH6NWrl6PDE0KIIuXpauC1XvUAmL39HFabzKAjHMthSXpiYiIAfn5+uZb7+/vftHb8scce48KFC6xYsYIDBw4wceJEnn766Zsm9h9//DHe3t72R0hISJEcQ1GKym7uHpRPc3eLLRWACn7ZfZKlqXuZ5uVqYEDLYAB+3nYWD18fGvW4F9CQabnM4g8+UQuGtIa7sqckWv4qWC2OCVgIUWa5ubnRqlUr7rnnHmrVquXocIQQotj0a1YZb5OBy4kZ7Dgd6+hwRDnnsCTdaFST0ZSU3NMdpKSk4OqatzY5R5cuXTCZTPbXL730Et7e3jftH/fmm2+SmJhof1y4cOEOoy96V5u75z72iBPHUBT1HDWvcFFdKIPGlXnD2lcHYP3xKM7FpNJz5ChMJjVxP38olPPHjqgFu08EVx+4chB2z3BMsEIIIYQQpZyrQUe/ZpUBWLTH+XIFUb44LEmvWrUqer2e8+fP51oeHh5+2yPGenp6Eh0dfcP1RqMRLy+vXA9nk9PcPfC6mvTQFasB0Gg8qJpxQF0oNellXs1AD+6tF4iiqM2uAB79+B20Gk8UJY2/P/hKLegeAN3fU59v+BCSIx0TsBBCCCFEKfdoa7W17YqDl4lMlGluheM4tCa9W7du/P777/ZlMTExrF+/nj59+tiX7d692z4Pus1mIyoqKtd29u7dS3h4OK1bty6ZwItJTnP36weOizpxDgC91g2yUkFvAv/aJR2ecIDhHWoAsHhvBMkZWfhXqkL9e7sAGjKzLvPbuxPVgi2GQeUWYE6C1e86KlwhhBBCiFKtcRVvWlf3xWJT+Hv/RUeHI8oxh86T/sknn7BlyxaeeOIJvvvuO3r27EmDBg0YPny4vcwPP/zA+PHjAbBarXTu3JlXXnmFmTNn8t5779GjRw/69OnDI4884qCjuHMWq43Y1Ewgb3P3jKRkAIxGF3VBUEPQyrQQ5cE9dQKoFehOitnC4r0RANz37LO4u6ujv186EcaJPbvUz0OfyYAGDi6Cs1scGLUQQgghROnVr3kVAP7afwlFkQHkhGM4NElv3rw5oaGhVKpUif/++4/HH3+czZs32/urA7Rp04b77rsPAIPBwJ49e6hduzY7d+4kPT2dX375hWXLlqHTld7ENSYlE0UBnVaDv7tLrnVZlnQAfH2zvySkqXu5odFo7LXpP2+7OtLo0MkfotX4AGZWffk9FosFqrSAViPUN64YD9YsxwQthBBCCFGKPdCkEq4GLUcvJzF902lJ1IVD6B0dQN26dfnss89uuP6ZZ57J9drd3Z0xY8YUd1glKqepe6CHEa326tRqyXGx2BR1urh6gXFgRpL0cuaRFlX4cvVxzselsfLQZR5oWhkPXx+a97uP0L8XkmWNYt7/3mXY5I+h27tw5G+IPgbbv4VOrzo6fCGEEEKIUsXX3YUxXWrz5ZoTfLbqOF6uBoa2q+bosEQ549CadKGyz5F+3aBxu//5B7ABRhppw9SFMrJ7ueLmoufJu6sD8MOmM/a7ufc+PgRPH7WWPSbiGPvXrwWTL/T6SH3jxk8h5pQjQhZCCCGEKNXGda3NKz3qAvB/y46weG8EmRabg6MS5Ykk6U7g6vRruZP0CweOAaDXeqJPjwQ0UKFhSYcnHGxY++q4GrQcvJiYa97OJ7/+GJ3WD8hi009zyTKboekgqNUNrGZY9gLY5IIihBBCCHE7NBoNY++tzT11A8m02hj/+wHe+CMsT7m1R67Q6oO1NJu4mufm7CUxTbobiqIhSboTuJKU3dz9ukHjUmPiATAYsvup+9cCo0eJxiYcz8/dhUdbqVOCTN902r7c1eRG+6EDAT0WWwyzX/wfaDTwwFdgcIfwbRD6i4OiFkIIIYQovbRaDZ890pRW1XwB+CP0IgcjEgFITMvi1UUHGPXrHmJSzCSmZ7HqcCQvLtznyJBFGSJJuhO4UU16ZqY6aJynZ/afSfqjl1ujOtVEq4EtJ2M4fCnRvrxNn774V1KbYyXFn2HNrFngW03tnw6wZgIkXXJEyEIIIYQQpVpFb1cWj25Pj4ZBAPSdupW+U7bS7ctNLAmNQKOBJlW8+XJQMzQa2Hg8mq0nYxwctSgLJEl3AtE5c6Rf0yc9y2zGalMHjasWlKYulCS93Arxc6NP08qA2jf9WkO/+AiDLgiwcWj1Gq6En4U2z0CVlurc6cvHg4xMKoQQQghRKK/3qoePmwGAgxcTiUkxUzPQncXP3c2ycR15uEUwQ9qqU+S+vGg/cdlTKwtRWJKkO4GcmvSga5q771+3DsgCdLT0UPumy6Bx5dtznWsC8E/YJc5Ep9iX6/V6HpzwKhqNOzYlmd/f/kidO73fFNDq4fhyddR3IYQQQghx2+oEebLzzW4sGd2eMV1qMXlgM1a9eA8tq/nZy7x1fwOCfU1EJ5t5aeF+mbpN3BFJ0p1AfqO7n9qxGwCdxgv35JPqQqlJL9caVfamW/0K2BSYtuF0rnVV6zekXsd7AA3mrMvM+d87ENQIOr6iFljxGqTG5t2oEEIIIYS4JVeDjpbVfHm9d30eaRmMiz53GuXmouebx5pj0GnYfCKapQeku6EoPEnSHcxmU4hJyemTfrUmPfFSNAB6vSsoNnAPBI8gh8QonMcL3eoA8Nf+i4THpuZa12fs83h4Vgcg6txhdq9YAfeMh8D6kBoFy1+RZu9CCCGEEMWkZTU/xnVVf6tN+ucIUdmDQwtxuyRJd7C4tEwsNgWNBgI8XOzLM9PVfuhu7np1QcUm6sjdolxrFuJDl3qBWG0K0zbknQddnZbNH8hi26+/kZyUAg99rzZ7P/IXHFpS4jELIYQQQpQXz3auSb0gT2JSMhkzLxSLVabDFbdPknQHy5l+zd/dBb3u6p/DYlP7HFcMyJ5vUZq6i2w5tel/hF7kQlxarnUmDw+6PfcUYMSqxPPrS29D5bvgntfUAstfhaTLJRyxEEIIIUT5YNTr+P6Jlni66tkTHs+36/NWqghxK5KkO1jOoHHXzpF+an8oiqImXy0DLqgLZdA4ka1FVV861QnAcoPa9Cadu1CtaSsAMswRzH3jXej0KlRqDhkJsHSsNHsXQgghhCgmNQLceb9vIwC+XXeSVxcdkKbv4rZIku5gVxLV/7CVvK8m6WGr1wOg1XgSlHZQXSg16eIaL2bXpi/eG5GnbzrAgLffxN29GgBXzh5k219/w0M/gM4Ip9bC3tklGa4QQgghRLnycIsqPJs9M8+S0AiemLkLq00qSUTBSJLuYJezk/Qgr6tJeuyZCAD0WjfISgW9CfxrOyQ+4ZxaVfejc91ALDaFL9ecyLfM8Gmfo9cGABZ2/f4HUWYTdJugrvz3bYg7W3IBCyGEEEKUIxqNhjfva8CS0Xfj7qLj+JVkvll30tFhiVJCknQHy+mTfm1Nekay2h/daDKoC4IaqvNeC3GN13rVA2DpgUscuZSUZ72ryY37XxuLRuOGTUli4RuTsLR6Bqp1UG/+/PE0WLNKOmwhhBBCiHKjZTU/3nmgIaA2fV+4+7yDIxKlgSTpDpZTk17xmiTdYlX7o/v5ZjeJkabuIh+Nq3jzQNNKKAp8sfp4vmXqtGhFvXu6ABoyLZH88sqb8OB0MHpDxG7Y8FGJxiyEEEIIUd4MblOV+xpXBOCtPw+x4XiUgyMSzk6SdAeLvK5Peuzli9iUZACaBsWohWTQOHEDr/ash06rYf2xKHafi8u3TJ8xY/D2U7tLJESfYOnsv6DfN+rKrV/BmY0lE6wQQgghRDn13ZAW9G1WGatNYfTcvRyLzNsKUogckqQ72OXEdOBqkr7nn5WAgkZjoi4H1EKVmjkoOuHsagS4M6hVCACfrjyGcoNR24dP+RwXQyVA4eTOzey77AEthgEK/PEMpESXXNBCCCGEEOWMRqNh8sBmtKjqQ0aWje82nHZ0SMKJSZLuQKlmC0kZFuDqwHGXDqnNlnUaD0iNBo0Ogho5LEbh/F7sVgejXsue8Hj+PRyZbxm9Xs+QyZPQaXwBMxtn/EJUo+chsD6kXIG/RoPNVrKBCyGEEEKUIy56LRP7NQZg1eFIYlPMDo5IOCtJ0h0oMnvQOA+jHk9XdZC4tDi16YuLi4taKKAuGEwOiU+UDhW9XXnmHnWKj49WHMNsseZbzi+oIt2fH4VGY8KmJLDg3c+wPPgj6F3h1Br4b3pJhi2EEEIIUe40ruJF02BvMi02Xlq4n4ys/H+3ifJNknQHisxn0LisLLX5u5e3Rl1QSfqji1t7rnMtKngaOR+Xxi/bz92wXONOnal3z72AlizrFWa+PwN6ZQ8et+Y9iNhTIvEKIYQQQpRHGo2GSf0bYzLo2HIyhonLDjs6JOGEJEl3oOsHjctIT8OqqDXpNQKzB5OQQeNEAbgb9YzPnpJtyrpTN20+1WfMGPwr1QcgJeks8/+6AA37gy0LFj0p/dOFEEIIIYpRsxAfpg25C4Dfdl1go4z2Lq4jSboD5TR3r5jdH33vqpWABdDTwnRQLSQ16aKABrQIplFlL5LNFr5ae+KmZYd//Rkm12AALp/ax4ro1mrXiqSLsPgpsFpKImQhhBBCiHKpa/0gutavAMD/loSRapbfXuIqSdIdKGdk95zm7ud2qaO567ReuKacVwvJHOmigLRaDe8+0BCA+f+d58ilm0/tMeL7LzHoKgBWjm7exO6g58HFA85tgfWTSiBiIYQQQojy6+vHmlPFx8SVJDPfb5LR3sVVkqQ70PV90pMi1XnRDfrsPuo+VcHk65DYROnUrqY/9zepiE2Bd/8+hM2W/5RsAK4mNx77bGL2iO8ZbF2ymvDm76ort30NR5aWSMxCCCGEEKWWxQybP4fl4+HQEshMLfBbvVwN9gqWHzafYdfZuOKKUpQykqQ70OXr+qRnZqg16+4e2X8W6Y8uCuHdBxri5qJjb3g8i0Mjblq2QnAIvV4ajUbjjk1J4s85O0hu+oy68q8xEH3zZvNCCCGEEOXWhd3wQQVY/wHs/gkWj4CPKsOvD0JswWrGezUKokfDIDItNsb9FkqWVabEFZKkO5S9Jt3LhMViwaokA1DZX11OpWaOCk2UYpW8TbzcvS4AH684Snxq5k3LN2jXnlYPPgS4YLXFMntJPFnB7SEzGRY8DukJxR+0EEIIIURpcf4/dbDdOQ9dXVa909UWsGc2wLS2cGDBLTel0Wj49rG7CPAwciXJzL+HI4spaFGaSJLuIGaLldjs5KmStysndv+HomQAGlr5ZtdeSk26KKThHapTN8iD+LQsPvv3+C3L3/PYY9Ro2R7QkmmJ5MfNVbG4V4HYk/D7cLBmFXvMQgghhBBOSVFg72yY0R1mPwCz+8CRv9UKDYCnVsHwf+ClQ/DA12D0VmfN+fNZWPrCLWvVTS46Hm8TAsDY+ftk7nQhSbqjRCWpU2QZ9Vp83AwcWb8ZAK3GC7+UY2ohGdldFJJBp+WDB9VBBxfsPs/e8Fv3cXr49fEE1VBbb2SYLzLzWBcwuKt3g1f+T71ACSGEEEKUFxd2wY/3wqfVYNmLELFbHWDXlgW+1eGBr+CtS1DtbrW80QNaPQX/OwedXlWXhf4CU1rCmgk33dXQu6th1Kup2dO/7im+YxKlgiTpDnL5mkHjNBoNseGXAdDrTKBYwT0QPCs5MkRRyrWp4cfAlsEoCry2OKxAd2WHfjIJ3wrZc6innGfmhYcADeyZCbt+LOaIhRBCCCGcRFaG2qT9UihkJILBDVo8Cfd9Dk/+DeNCodUIcHHP+16tFrpNgGHLoE5PQIFt36jj/dxgmtsKnq5MerAxAFtOxrDpRHQxHpxwdpKkO4h9+rXsOdLNKepIkK4mvVqgYlPQaBwSmyg73unTkEBPI2eiU/l23ckCvWfElC/w8KwBQEJ8OHNiB6srVr0BJ9cWV6hCCCGEEM4hM1VN0JMvg0cQPLMJXjsF/aZA22egZhfQ6m69nRr3wJDfr9aq758H8wbcMFEf1CqEltXUfu3PztnD2ZiCjxQvyhZJ0h0k8rqR3S22NAACfLP7/kpTd1EEvN0MfJB9V/aHzWc4dDGxQO8b+f1XmFyDAYiKusii+EdAscHip+DKkWKLVwghhBDCoQ7/Bd80g5P/glYP/aZC5eb515gXVLcJ0PsT9fmZDbBwiDp1Wz5+eKIlVXxMZGTZ+Hnb2cLvU5RqkqQ7yNXm7iYunjqJkj2ye4vAC2oBGTROFJFejSrSp2klrDaF8b8fKNDUHnq9nqd//AYXfSXAxoXIGBbH9gFzEsx9BBIuFH/gQogyw2KxMH/+fN555x2mTZtGXNytx8k4ceIEU6dO5f/+7/9YsmQJVqsMpCSEKEaZqbBuEiwZBanR4OIBQ/+Auj2LZvvtRsNjv4HeFU6sgv9+yLdYgIfRPnf64r0RpGXmX+suyjZJ0h3kUoLa3L2Styt7/1kJgEbjTrXMA2oBmX5NFKGJ/Rrh62bgWGRygZu9G4xGRv3wFS76ioCN8KhU/ojuDsmXYO7DkHbrH9lCCGGxWOjRowfvvfceNpuNRYsW0aRJE86fP3/D97zxxhs8+OCDHDt2DIvFwltvvUWbNm1ISUkpwciFEOWGzQp/j4UtX6iDwlVuAa8eh5qdi3Y/9e+H+z9Xn2//FrLS8y3Wo2EQvm4G0jKtHLmUVLQxiFJBknQHuZidpAf7mog6rjZl0WvdwZIBLp7gW8OR4YkyJsDDaB/tfdqGUwUa7R3A5OHBiOlfYNAFAVbOxlj4M6ozxJyA+YPUu85CCHETs2fPZteuXWzevJmPPvqIdevWERQUxNtvv33D9wwcOJBDhw7Za9J37tzJmTNn+P7770swciFEuZAzQNzhP9TXbZ6BYUvVkdqLQ7PB4F1Vra3fMyvfIjqthpbV/AAIiyhYV0VRtkiS7iA5SXoVXxPpyWrNgNFoUFdWbKKOCilEEerTtBIPt6iCTYGXFu4nxVyw5lPuXj6MmDYZg64CYOVMrJa/r3RQpyH5/SmZQ10IcVN//fUX3bt3p1IldcYSvV7PkCFD+Pvvv1FuMLVjy5Yt0V5zHfT19aVatWpERESUSMxCiHLkn5fg2D+gc4GBv6g13UbP4tufzgCdXlafb/gIUmPyLdY02BuAgwUcT0iULZIJOkCK2UJCmprYVPExYbGog8b5+WT3t6vSwlGhiTLu/X6NqOJj4kJcOhOXHi7w+zx8fXhq6ufotYGAhVNxLvxxqaM6qMrSF8B2637uQojy6fjx49SqVSvXslq1apGcnExkZGSBtrF//34OHjxIp06dbljGbDaTlJSU6yGEEDcVsRcO/AZo4PFF0OjBktlv86FQoSFkpsC2r/Mt0kSS9HJNknQHuBiv1qJ7uerJjI/Cpqg/JBr5Z9cQVL7LUaGJMs7L1cBXjzZHo4Hf90bwT9ilAr/X08+fJ6d8ak/UzybqWBDRGQ7MhxWvwg1qxIQQ5VtaWhpeXl65lnl7qz8+U1Nv3WUmJiaGgQMH0rt3bx5++OEblvv444/x9va2P0JCQu4scCFE2aYosDq7202zwVDr3pLbt94Fevyf+vy/HyDmVJ4i9YLU2vxzMalYCjDoryhbJEl3gIsJas15sK8bu5ctBxQ0GlfqWvaqBaQmXRSjNjX8eL5LbQDeWHKQM9EFH4jJN6ACI7/7KruPuo2LyQpzwrurfapWvSGJuhAiDw8PDxISEnIty3nt6XnzJqVxcXH06NGDihUrsmjRIjQazQ3LvvnmmyQmJtofFy7ILBRCiJs49g+c3wF6E3R7t+T3X7s7VL0brJmw/JU8qyt6ueJq0GKxKUTE5z/AnCi7JEl3gJya9Cq+Ji4fUkfa1mk80ZMJJl8ZNE4Uu5e616FtDT9SzBbGzAslPbPgUxt5+Prw9I/fYDRUAhSi0sz8fLYX/Pc9rHlXEnUhRC4NGzbk+PHjuZYdO3YMX19fgoKCbvi++Ph4evTogbu7OytXrsTd/eZzFBuNRry8vHI9hBAiXxYzrM5OzNuPA6/KJR+DRgN9vwWtAc5ugrNbcq3WajVU91e/987EyMwW5Y0k6Q4QkTNonI+J1Hi1qbuLS/agcZXvUv/TClGM9DotUwbfRYCHkWORyUz4+9Btvd/k4cGzM6fiagwGIC4jjR9P98WyZQqsnySJuhDCbuDAgaxfv55Tp9TmnGlpafz6668MHDjQXubgwYOMHz+exES172VCQgI9evTAZDKxatUqPDyKaZRlIUT59N8PEH8WPCpChxcdF0dgXWjxpPr8n5fUqeCuUTe7yfvRy8klHJhwNEnSHSCnyUqwrwlLltr03dsze6TtytLUXZSMCl6ufDu4Odrs/umL9txe01CD0cizs6bi5lYNgOTMBH44/RAZG76CDR9Koi6EAOCxxx6jX79+dOrUiZEjR9K2bVsAJk2aZC9z/PhxJk+eTHKy+kP0ySefZN++fTRu3Jj333+f8ePHM378eH799VeHHIMQogw5slStUADoNqH4plorqG7vgtEbYk/B5i9yrWpQSW0RdPSyDIRZ3kiS7gA5zd2DXKxYFbXWoI7PRXWl9EcXJah9rQBe6VEXgHf/OsTB25yLU6/XM/rnaXj7q9vIsMbw4+n+xK2dAv++JYm6EAKtVsvvv//OvHnzaN68Oe+99x6hoaFUqFDBXqZp06Z8/vnn+Pj4ADBo0CA+/fRTateuTcWKFe0PX19fBx2FEKJM2PQZLHpC7Qde/wF1wDhHM/nCvW+qz//7Xp23PVuDSjk16ZKklzca5UaTlJZhSUlJeHt7k5iY6JA+a60/XEt0spkvalwhfP0fgAsv1tuAXmuDV46BV6USj0mUXzabwqhf97D+WBRBXkaWju1IkJfrbW9n/tvvc/nUPsCKTuNH/5Ct1OjUBx74GrS6Io9biLLE0delskjOqRAilwML4c9n1Od3j4Vu76mjrDsDmxW+aQaJF6DfFHsT+CtJGbT9aB1aDRz5v964GuT3VGlX0GuT1KSXsIwsK9HJZgCSj50AQKf1VBN0z0qSoIsSp9Vq+Oax5tSp4MGVJDNP/7rntgaSy/H4h+9Tr2N3wAWrEsdfF9qyc+12WDIKrFlFH7gQQgghREFEHoRl2X3P73kNen3oPAk6qJUZbZ5Wny8dB6kxAFTwNOLlqsemwLnYW09ZKcoOSdJL2KXsQeNMBh3pcQkAuOR8SUh/dOEgnq4GZg5rjZ+7C2ERiYz//QA22+03snlg3DjaDhqMRuOOTUlm25Vq/LHmMiwcCplpxRC5EEIIIcRNpCeov0Ms6eq0Z13edHRE+WvxpDqQHcDntSAzFY1GQ60Kap/501GSpJcnkqSXsPA4NVGp6udGZqaasHu6Z9cyVrnLUWEJQVV/N74f2hKDTsPyg5f5et3JQm2n4yMDeeCVV9Bp/YBMziYamLFKh2VWH/udYSGEEEKIErF+EsSfA5+q8PBPztsFz+QLD3539fVHleHcNmoHqkn6qSiZhq08kSS9hJ2PzU7SvXRYbeogXdU9LqkrpSZdOFibGn589FATAL5dd5L5/50v1HbqtmnLyGlf2edSTzQn8f3m6qR81wNiTxdhxEIIIYQQN3AxFHbPVJ/3/w7c/Bwbz63U7qb2lc+x+h0aZQ8et/54lIOCEo4gSXoJC89O0mtF7gMsgJ7WrrvVlZWlJl043sBWIYy9tzYA7/x1kJUHLxdqO55+/jw3ezpefrUBDWZrNDP2NuHQ54/B+f+KMGIhhBBCiOvYrLD8FUCBJoOgRqebFrdYLOxavowN8+ay+INPWDhxEtv/+rNkYr1Wp1dgyBL1+aVQHghJx6DTcOBCAmERCSUfj3AIvaMDKG/Ox6n9SdwvniMT0Gm9cdXbwLe689/dE+XGqz3rEpuayW+7zvPigv14mwy0rx1w29vR6/U8Pf1rFn/0CeEHdmFV4vj3bDVOfPg+D7/wDDR6qBiiF0IIIUS5t/dnuLQPjF7Q84N8i6QmJbD862lEnzxPZlYKNiX3VLQRR/5jx2+/otN6YjC4YfR0o1b7FnQcMBCD0Vh8sdfpDlXbw/ntBMTsplv9Jqw6HMmzc/ay481uxbdf4TQkSS9hOTXpJCUD4KI3qK+rtHJQRELkpdFo+ODBxiSkZbLyUCRP/7qHBc/cTZNg70Jtb8Bbb7B7xQq2/jofm5LA2QQdP3z8ByOeCMPQ413QSqMeIYQQQhSR1BhY93/q867vgGcQACnxCWxesIArx8+QFptIRmYMYL7uzXo0Ghc06LEpyUAWVlscVnMcGWYIXXqC0KV/oNd64+btTbXWjbnn8cdxNbkV7TFU7wjnt8O5rYy590FWHY7kcmIGX689wUvd6xbtvoTTkXnSS3DuVJtNocGEVZgtNl46twCrEk9FX1+GVFwK939xdeoFIZyE2WLlqZ93s/10LD5uBuaNakujyoVL1AHirkQyf/x7mDMvAqDX+NOnbQa1R/8IroXfrhClnczpXfTknApRji17EfbOhopN4OmNpKdnsODticRfOYeipOcqqtG44moMwKtSIPcMH0zV+g3t6y6eOsmZ0FDO7T1IypU4Ms3pWGzxqF1Wr6VDp/HCt0ow9708hgrBIXd+DGc2wa/91CmaXznKiwv38/f+S/bVC55pR7ua/ne+H1GiCnptkiS9BC/ckYkZtPt4HR62NJ4KnwPY6BwSQyuPo/DsZqjUrMRiEaKgkjOyeGLmLvZfSMDHzcDckW1pXOXOEupfx79J9IWjgAWNxkQ1PxuP/N8nEFCnaIIWopSRhLLoyTkVopy6HAY/3AMo8NQqVq89zOE167EpSdkFjOi1Xuj1LlRuWpfeo5/F5OFR4M2nJiWwffEfhO8+SEp8DFYl/roSRkyuFej/7itUqX0Hv2uy0uHjELBlwbhQsnxqUOftlbmK1Ap0Z/kLnXA1OOmI9SIPSdJvwlEX7v/OxPLojzvpm7qX6lG7ACMv1l2L3tUd3jgPOul9IJxTUkYWw2btYt/5BLxNao36nSbqm36bT+jfy+39v0y6AAaP7YFv+yFFEbIQpYoklEVPzqkQ5ZCiwOw+EL4NS/0H+XmTH0lxpwEb4IpPQDUe/eBdPHx9imyXcVciObJ1C0f+3UJKUhSKkjNVmgse3sEM+ugdfAMqFG7jM3vBhZ3wwNfQ6ikURaHGmyvyFHutVz3GdKmFRqMp9HGIklHQa5N0BC1B9jnSs+eK1ms90euA4FaSoAun5uVq4NcRbWhR1YfE9Cwe/2nnHY8w2nnw4wz7ZjImYxUA0q0xzP52BRs+fBayMoogaiGEEEKUK4eWQPg2zpkr8d3fOpLiTgI2DLogBk78P0ZOm1ykCTqAX1BFOj4ykGd+/JbRM2YQ3LAtWo03kElK4hl+HvsCCydOwmK5vol8AdTurv574l9AHTPo3Cd92PtOd1pX97UX+/zf43T7chNRyfL7qayQJL0E5cyR7pKh/mvMaZoS0tZRIQlRYJ6uBn4d2ZZW1XxJyrAw+MedbD0Zc0fb9AuqyJhff6Ba0w5oNCZsSiKhYZf5YeRzpIYfKKLIhRBCCFHmZSTBv2/zT2Qr/jjblCxbFKDDL6gBY379IVdf8+Ji8vDg0ffeZewvswiqcRcajRuKkkLEkf/47slnCT9y6PY2WDt7JPfzO9RWAtn8PYwsevZufnyipX3ZmehU2ny4jtcXHyDFXIgbAsKpSJJegnJq0m02dcAKP7fsaR4kSRelhIdRz+wRbehQ25/UTCtPzd7F3/sv3vF2B7z9Jn1f/x8GXQXARoo5hh//9znrv3z3zoMWQgghRJm3e/qbTNnbnuPxJhQlDa3Gkxb9BvLUt5+j15dsi1WD0cjQTyYx9PPPcXOrBmjJsl5hyf9NYvl33xV8Q0GNQecCGQkQfzbXKo1GQ89GFdn9dncGtAzGy1U9xkV7Imj83r88Mn07Z6JT8tmoKA0kSS9Bp6NS8M2Msw9c0dwjTF0RLNOvidLDw6hn1vDW9G1WmSyrwosL9jNjy5k73m6dFq0Y8+uPVKjWGDBiUxLY998Bvn9yBMkRJ+88cCGEEEKUORdPnWTaE8+weecFMm3RABgNlRny+SfcO2SoQ2OrEFKN0T9Po/l9D6PVeKIoqRzbtJIfn3mBjPS0W29A76KOUA9wMTTfIoGeRr4Y2Ixdb3enV6Mg+/K94fF0nbyJ6m8s5/XFB4hJuX6quZxycSzZG3HbxyaKlwwcV0KDydhsCg3fW0X3yM3USDqIRuPOK/VXQWADeH5nicQgRFGy2RQmLT/Cz9vOATC8fXXe6dMAve7O7/2d2LWTVV//SJY1CgCtxpNq9ary8MRP73jbQjgjGeSs6Mk5FaLsW/HddI5t3oiipAKg1wbQ5P5udH3iCQdHlteV8LMseusjMi2XATDognj6x29uPbL8qjdh53fQZCA8MuPW+0nK4LXFYew/H09SRt5m70vHdqBpsI/9dfU3lgPw55j23FXVN095UbRk4DgnczEhnYwsG1XTEwAwaN3VFSFtHBeUEHdAq9Uw4YGG/K93fQBmbz/H8J93k5CWecfbrtumHS/Mn0Wl6g2z+6onc/bYYaY8PoJD61fd8faFEEIIUXqlp6Tw/cixHN20AkVJRaPxoI6vhRe//9ApE3SAoGo1GP3LdAKCmwA6sqxX+OmZF4m9fItugw36qv8e/B3ObLz1frxc+XVEGw6815NXe9TNs77f1G30nbKVbpM38k/Y1XnXz8Wm3sbRiOImSXoJOZ3dJ8TFqo666GbMvrNVtZ2jQhLijmk0GkZ3qcX3Q1vi5qJj66kY+k/bxskryUWy/cc//YxH33sfk6EioCHTGsW/P3zPT6NGk5p4/bykQgghhCjr9qxayQ9PjyE15Ryg4KIN5LHqW+n3xP3gXcXR4d2UXq9n2OSPCWnUhpxEfc4rb3Lu4MEbv6lae6jRWX3+a3/IvKaZfFa6+jrhPPz3I+z6CbJnkdJoNIzrVodzn/Th7Mf30795ZfvbDl5M5HR0KmPn70OHFQ/S0CDTtzkTae5eQk3gZmw5w4dLwxh7/lcgk2YBiXQPDIOXDoFPSInEIERxOno5iad/3UNEfDoeRj2fDWjK/U0qFdn2N0z/nAObDmJV4gDQaryoUrc2D0+YUOIDwghR1KRpdtGTcypE2WKxWJj/xrtEXzgGZAEuBAX4MTTwd7X76HNbQGdwdJgF9s+UKRzfugHIRKvxovOIJ2nRs3f+ha8cgel3F3zjFRqpFYEVGoBnRajTE/RG0jOtHL6UyF/7LzJ353kAlru8SSNtOOv77aBri+IfAb+8K+i1SZL0Erpwv/nHQc6uWUbruO2AnjF1NmEKrAovyjRTouyIS81k9Ny9/HdWTaSfaFeNt/s0wDVnusE7lJWSyPzXXic2PhZFUVul6LT+NOrWkR6jni6SfQjhCJJQFj05p0KUHSdD97Dyi+lkWa8A6rW/y6BuNA97ERQbDF8O1Ts6OMrbt3XJ7+z6fXF2k30TzXr3odvw4fkX3vsLLHuhaHbc7T1o/DD/xbnTdk5tAA60/oxmfZ4tmu2LG5I+6U7mdFQKDVPVfh96rTcmvQ2qd3JwVEIULT93F+aOasvoLrUAmLMznIe/K7opQAwe3gyb/gOPjh+Nm0sFQIfVFkvYmr+Z8vhI9q1ZXST7EUIIIYRz+GvyVyz77NPsBF2Lp3dNnpv5Hc0TF6kJesMHS2WCDtDxkYH0Gvs8Wo0PipLO/pV/s+j/Psy/cMth8MK+3MtcPKDJIOg0Ht6NUR8Pfg93PQG1u4PelP+21k2Eb5rZE3SAywkFGG1elBipSS+hu+stJ61h6JGF2GyRuLsE8FytP+Hhn6DpoBLZvxAlbePxKF5ZdIC41EzcXHS83acBj7epikZTRH2eFIUDcz5l88rj9ilXQIurS0U6PzOExp06F81+hCgBUutb9OScClG6ZZnNzBr3GimJZwEFrcabZn16qwPDHVkKi54AnRHG7gbfao4O945cPHWSxe9+iMWm9id30Vek3ZCHaX3//Xe2YUWBlCiIOQ6bP4ezm9XlRi8wJ+UqOjrzRaZ/9H93tj9xS1KT7kTiUzOJTc20Tw8R4JqdUEhNuijDutSrwMoXO9Guph9pmVbe/vMQT87axaWE9KLZgUZDsyffYNzsb7mraTX0Gn/ARkbmJf6dOplpTz7LoS2bimZfQgghhCgx5w4e5Lvhz5GSeAZQMBoq8+RXn6kJelYGrH5HLdjhhVKfoANUqV2Hkd99jck1BNCQaYlk8y/f892w0UScOFb4DWs04BkENe6BYcvg/UT18eYF+F84tB9nL+qpSaMc1t06LUnSS8DJqBSqpZ9HUdQRr9v4HQL/2uBVdINqCeGMgrxcmTeqHe/0aYBRr2XLyRh6fbWZRbsvFN2FwOhJ17en8fyUd6kd7ItO4wfYyDBftCfrBzdtLJp9CSGEEKJY7Vu7hj8//AiLLRrQEVClMWPn/oh/peyR23dMhYRw8KwEHV5yZKhFysPXhzG/TOeuPgPQawMBG+kZF1g04X2O7txe9Ds0+UDPD+wvPzP8xO5zMnOOs5AkvQQcvZxE2+STAGg13lQ1JUgtuig3dFoNozrVZMWLnbirqg/JZguvLwnj0R92cqKIpmoD0AfWpv/kOYz98GlqV3LPlayv/u5Lpgx5mg3z5hbZ/oQQQghRtDbMm8uGGT9iU5LRaNxp0W8gw7785GqBpEuw5Uv1eY9JYPRwTKDFqOuTw3jxt5+p3rwTYERRUlj59df8+9OMYt/3nB3nin0fomAkSS8Bhy8lEpSRPU+61qgurCFJuihfagV6sPi59rx5X31MBh27zsVx/zdb+HjFUVLNliLbj75WJ/p/tYCx7w2hdpCrvRl8puUyoUsX8M3g4fzx2RdYLEW3TyGEEELcmb+//JrQpX+gKOloNd70Gvs89w4ZmrvQmgmQlQoh7aDJAMcEWkIeefN/tOj/MFqNF4qSxqG1y/hu2HNsXrCgaHfUfIj9afzRTWRZbUW7fVEoMnBcCQwm88CULXTb9gM2JYGK7gaGVF0P40+CR4Vi37cQzigiPo3/W3aE1UfUqVSCvIy80qMuA1qGoNMW0cByoA6YcnQpa2bO5NglfzJtMYD6lafVeOMTVJk+48dSIaT092cTpZsMclb05JwKUXrMf/t9Lp8KBWzotP4MmPgmwXXr5y50ah3MfRjQwDMboPJdjgi1xF0JP8vCNz+wTz93lQuuxkA8Av0Iad6QewYPRq/X3/4OLGb4QM1J1lnvwuXJ3+lUJ/DOAxf5knnSb6IkL9xZVhs9xv9M30t/AtA35Dh161SGZ2VAKyHWHb3Ce0sPExGvDiZXL8iTN+6rT5d6gUU3CjyAzQYn/2XfgmnsOB5AujUByKlJN+BqDKJZ/250fGRg0e1TiNsgCWXRk3MqROkwa9x44qPUwdEMuiCe+PZjfAOuq8jKTIXv7lb7ord9Du771AGROo7FYmHp5K+IOHAsn2RdpdGYcNH7EFC7Kj2eHXG1D39BxJ7GNqUlWhSm3LWUcf1lhpziIkn6TZTkhfvo5SRmv/U+FdJOoNV48XL95XDP69D17WLdrxClhdliZc6OcKasP0ViehYA7Wv582rPurSs5le0O1MUCN/G+b8mszrURHKmgk252i9ep/XHL7gKvV8cTYXgkKLdtxA3IQll0ZNzKoRzs1gszHj2JVJTzgHgaqzCyB++wtXklrfwyjfgv+ngFQzP7wSjZ8kG60RO7Q/lxPb/iAg7jiU9A7M5BZuScF0pPXqtLyZvb6o0rUv7QQPy3vi4TtzXHfFLOMg0zxd5/lWZiq24SJJ+EyV54V6yN4LwyRNRrJdx1QXwfN0/YeQaCGlTrPsVorRJTMti2sZTzN52jszs/lDta/kzrmsd2tX0K9qadYDLB8jaOp1/1oZzISmILFssOU3hwYDREEjVlo24b8xzGIzGot23ENeRhLLoyTkVwnlFRVxg4f8mkmmJBMDDqwYjp3+Vf3Pto//Awux+04//DnV7lmCkpUNyXCzrZ8/h4oHjZJjj7NM+X6VFp/XB6OqBX/VKtOzfh9rNW+QqkbBiEj67vuBfWxs6v7sKV4Ou5A6gHJEk/SZK8sI96Z8jeMz9AJuSRGUPLYPrhsFrp0ErH3wh8nMhLo2p60+xJDQCi039empd3Zcx99amS90ibgYPkBoLob9wcNUfbA+vQaolC0VJsa/WaNxxcw+kyf1d6PBI2R6kRjiOJJRFT86pEM5p65Lf2f37X9iURECLf6X6DP/6s/wLx52FHzqDORHuHgu9PizRWEsji8XCloULOLV1D+mJyVhsiShKRp5yWo0nBr3aIsEj0Jdhrw5CM6MryYqJw0P20a6uTBVdHCRJv4mSvHCPmTiDWkf+AuCRaoep3qYrDJhZrPsUoiyIiE/jh01nWLj7gr1mvVagO8M71ODhu6rgbizE4Cg3Y7XA8RVYds3i351JnE2qhtmaCGTai2g13rh5+NGwZ0c6DXq0aPcvyjVJKIuenFMhnEt6Sgrz/jeBxJjTgBWNxpW6HTrzwLhx+b8hMw1+7g2XD0BwG3hqBegMJRpzWWCxWAj9dyXHNmwn6UocWVkp2TdIcnM1VuGh6muprI1hSePpPDLgcQdEW/ZJkn4TJXXhzrTY+HTEi7iZz6LT+PFS/b9h4Gxo9FCx7VOIsuZKUgY/blaT9ZTsqdo8XfU81jqEIW2rUT3Aveh3mhgBB34jbscCVh6vSky6NxZbHHB1WhKtxguTux91u7Qt/IiqQmSThLLoyTkVwnks+3YKp7bvtCeHem0g9738HHXbtM3/DTYbLB4OR/4Gkx88twW8g0su4DIu/Mghdv/xD9GnL5CWFm5frtF4EORmpkXwSaq/cwCTi7T8LWqSpN9ESV2494bHs+WNV7HaovAw+PJs/dVqU3ejR7HtU4iyKjkjiyV7I5i9/RznYtPsy9vU8OPRViHc16Qibi5FnCgrCoRvhwPzOb9nAxsjGpKQ4UHWdQm7RuOG0eBLYJ0QuowYJoPOidsmCWXRk3MqhONtW7KYvUtWXjMiuRG/ijV5YvLHN7+5ve7/YMtk0Bpg2FKo1r5E4i2PIk4cY/F7H2G1pZDTelCn8cVFp8W7shcPv/EWJn9p+l5UJEm/iZK6cE9bHkrGr/8HWLgrIJ6unarDY/OKbX9ClAc2m8KmE9H8suMcm09Ek91tHQ+jngeaVqJfs8q0qeGHXqct2h1bMuHMBjj0BxdDN7LuYmMSMjzJsiVwdTo3AB16rR/ufr7U7dSG9gMGSC27uCVJKIuenFMhHGf7X38Sungl5qzLqIOyanB1qUyf18dSvUmTm79553RY9Yb6/MHvofng4g5XoA7m9+f7n5GSHAFkXbPGgE7jiVEH5spVue/pEdSrW9NRYZZ6kqTfRElduP/vxXdxj9yHRmPihTqr0Q/4EZpJP1YhisrlxHSW7I1g0Z4IzsddrV0P8HChd+OK9GmiJuw6bREPNpeVAafXweE/iT+0kfURdYhMDcJsy8g16ByARuOKQeeDZ6AfjXvfS6ve9xVtLKJMkISy6Mk5FaJkxV6+yNofZxF57AwWW7R9uUEXRItHetPxkYG33kjoHFg6Vn3e5S3o8r9iilbcyIk9u1j91Q+YLfnPxw5adBofbFojl129sDbvwIhHutGgklfR/94qgyRJv4mSuHDbbArfDH0Wm/USRl0gYxssh/EnwORTLPsTojyz2RR2nYvj7/0XWXkokoS0q3eAAzyM9GhYgS71KtCxdkDxDDh34T84sQrL0RXsPKXlaEJd0rL0WJREcteyq/29XPSeuAf4UqtdC+5+6CGZ4k1IQlkM5JwKUbxS4hPY9Nt8Lu4/RnpyMhZbPNde8wy6CtTt0o7ezzxTsA0e+gOWjATFpo7k3vMDKOoZXUSBrV28GL2nJ5lrp3Ixyo2UTDeylFQUJS1PWa3GEzTupBhcSWrQiru63MPDrarJNG75kCT9Jkriwn3w7GXWvDkORcmgupeVR3pWhIE/F8u+hBBXZVlt7Dgdy/Kwy6w6HEli+tWE3UWnpU0NP+6tX4HOdQOpFehe9FO6xZ6Gk6vh9AaST+5iY2QtLiZXIcMKViWBa/uyZ0eFXuuNq7sHFepVp81D/ahSu07RxiScniSURU/OqRBFK+pCONsWLiHq2FkyUpOx5OnupU5b6mr0o9Wg+2nTp2/BN75/Pvz9vJqgtxgGfb+RBN3ZJF3CcmAJ+/+YwZGEOiSZPcmymbEpSXmK5iTtqXoj50x+7PaoR6KLLwNbBjO6Sy1qBpbf8bkkSb+Jkrhwf/vWB2Sd3gkYea7ORtyHL4Ta3YtlX0KI/GVZbWw/HcuGY1GsPxaVq0k8QKCnkXY1/bm7pj/tavpRI6CIk3ZrFlwMhbOb4Mwmok4eZGtMI6JSAzFbNfnWtANoNJ4YdO4YPdwJqBVCi/t63boPnyjVJKEsenJOhbgzGelprJ0xkwt7j2LOSM6+0Xx92uCKQeeNu58PtTu0pMPAQbc/Dsuun2DFePX5XUOh77eglRpYpxe+A37uzbk0P/bENyAmLYAMq3KDz4n620arMWHRGEg2GLlk9OKQe3UuG9VB6b4f2pLOdQNxNWiLvgLFiUiSfhMlceH++vGnsVov46oL4PlWu+Clg/KFI4QDKYrCmZhUNhyLYsPxKHafiyfTkrtWu4KnkTY1/Lirqi/NQ7xpVNm7aJtqZaaqSfuFnXBhF+lnd7PjchXOJYeQmmUiS0nP06c9h0bjjl7rgYvJhGcFP4Kb1qdFr154+vkXXXzCYSShLHpyToUonP/+WUro4lWkZ0ShKBm51mk07hh0nrj7+VCnU2vufviRwg+OqijqCO7rJ6mv246GXh+BtogHfhXFL+4MfHsXAJcyPNkc04KEdB/MNi1WJSXP5+haGo0JLe5YtS6k6NV+7idcgzljrAI69TfYtMdb0KVeYNF3WXQASdJvorgv3BcvRbLg5ecBM7V9zPR/tDN0fafI9yOEKLyMLCv7LySw80wsO8/EEno+IU/SrtdqqF/Jk2bBPjQL9qFhZS9qV/AousTdZoOYE/aknYuhnDsfzb742kSnB2K2GLAoGdiU5BtsQINW44lOa7In75Ub16VR584yDVwpIwll0ZNzKsTtWf/rLxxatYksa9Q1S424GPzwDa5Is/u60aRzl6LZWVYGLHsRwhaor+95De59W5q4lwU2q/q33TfHvuhkaiBHkmoQkx5AhsWIxaZgJT3f/u1XGdBq3NFoXMjSGkjSu3LR6E2YRy1iXALoVr8CM4a1KnW17pKk30RxX7injX2NjOijaDRujK63CdP4MPAMKvL9CCGKTk7SvudcHPsvJLL/QgIxKeY85XRaDTUC3Klf0ZMGlbyoX9GTukGeVPExoS2KUU0z0+DKYbi8P/txgIhzFwmNr01UWiAZVhcsNhtW5ep8pvnRaNzQakzodUZc3FzxCPSjYr0a1O/Ygco1a995nKJISUJZ9OScClEw2//6k72/LyfTEmlfZtBVoGL9WvR95UVMHkXcfzg5EhYMgYt7QKOD+z6FNk8X7T6E87CY4cS/sPVLuLQv16orGR7sS6xNZHoQaZluZNkUrGRkJ+83TlE1Gje0uKFoDKTqXbjo6sNej1qs/+RJPJy8tl2S9Jsozgu3xWJhytDh2JQEPA0+PPOovzr4hRCiVFEUhUuJGew/n8CBiATCIhI4Fpmca+T4axn1Wqr7u1M9wI0aAR7UsP/rToCHy53d6c3KgNiTEHUMoo9C1DEskYc5FmHhRHJVYtN9MVtdsNisWJU0IO/Nhdxc0Grc0Gld0OkNGNxMuPt64RtSiapNGlK7VWtcTW6Fj1fcNkkoi56cUyFu7sDGdWyZsfCauczBRV+Jto/3u71B327HseWwdBykxYKrDwz6BWp2KZ59CeelKGqFxPcd8l2dnGngYEo1LqZVIsHsRabVhSybDRvpKEr6DTer9nt3I8VgokG3DjwybHBxHUGhSZJ+E8V54V705Tdc+G8NoOPhagep8fpqCJBaKyHKAkVRiEo2c/RyEscikzmW/e/p6BSyrDf+KnVz0VHZx0RlHxNVfExU8XGliq+Jyt4mqviaqOjlil5XiD54Welqc/no42p/sOxH+Plojsd4E5XhT2qmB5lWPVbFio20m/YLu0qT3UfMiFZjQKvXo3dxwcXNFTc/b7wqBlKpTi2qN2+Gb0CF249b5CEJZdGTcypE/o7s2MbG7+eQnnGJnBlHDLoKNHmgG/c+PqR4dhofDmvfg8N/qq+DGsOgX8G/VvHsT5RO5hT45yU4+PsNi0Ske3M4qTpXMiqQmulGlk2LRUlHUVLzlNVqvDDp9NTqeBf3Pj2u8OMnFKFSk6QfO3aMn376iStXrtCkSROef/55PG7RrKYw77lWcV64vxn8FBZbNEZdAGMf84R+U4p0+0II52Ox2riUkMGZmBTOxaRyNiaVM9n/XkxI51bfsloN+LkbCfTMfnhc8/ya137uLni56guW0KcnQPxZNXGPPQOJ5yExgiuXojh5GSJTvUnO9MRsMZJl02Un8eZbNjHLywWNxogWPRqNHq1Oj06nR2c04OJqxOjlgXt2Yh9UozoVa9bCyz/AKS6UzqSsJ5RJSUlMnTqVI0eOUKlSJZ555hnq1Ln5VIOFec/17y/L51SI23Vg4zq2zlpEhjkSsAKg1wZQt8vd3Pfss8Wz09RY2DEVdkwDqxnQQPtx6lhNemPx7FOUTTb1M8vGj2Hz53lWh6f5sTehHtFpgWRYtFiUeK6d9laj8cBFZ6LO3U3p9pzjEvZSkaSHhobSqVMnBg0aRLt27ZgxYwYWi4WdO3diNOb/H7cw77lecV2453/wMZcPbgO0tK8Sw92TloG7jLwsRHmWkWXlcmIGF+PTuZSQTkSC+u+lhHQuJqRzOSGDTOv1c6ffnJerHh83F3zdDHhn/+tjuua5mwEPowEPo159uOpxN+rwNBrUqU0A0uMh6SIkRlx9JF0iI+Eypy5kcDlOQ3yakTSLG2aLEYtNh1UBm2LFRmZ2jby1kGdFi5rcG9CgQ6vRo9Fo0Wp16sOgR+eix+BqxMXNhNHDhNHDDZOnJyYfb7z8/fAKDMQvqDIevj6FjMG5lOWEMi0tjTZt2uDl5cXw4cPZtGkTy5YtY/v27TRu3LjI3nO9snxOhSio2MsXWfnN98SGR2CxxZBzE1an9aNai2b0ffnFok9WFEUdU2XfXNg3DyzZzZOrd1JHb6/UtGj3J0SOpEvq527Dh1xM92J9dGvi073IssVz7W8WjcYdvcYdF1c9PsH+tOzTizrtupRMiKUhSe/Vqxd6vZ7ly5cDEBsbS0hICF9++SXPPfdckb3nesVx4T4Ruod/PvscRUnFpAtg9Kv3omk5rEi2LYQou2w2hZhUMzHJmUSnmIlOvuaRYiY6OYPoZDNRyWaSM/LOqX67dFoN7i46PF0NuBt1eBj1uBv1uLnoMBl0uF7zcNcp+JCAry0eb0scntY43LNiccuKR58eS0JCKleiLSSlakhN15GeZSDTasRq02FVNNgUsCk2FCzYlEzUvvJFfcnRQnYtvgYdoEOj0aFBnWdVfajPtVotGq0GjVaLVq9Fq9eh0+vRGQzojC7ojS7oXY3qzQGTCRd3EyYPD1w93HHz8MTo4Y7J0xM3D29cPT2K9IdtWU4ov/rqKyZOnMiFCxfw9PQEoEuXLvj4+PDXX38V2XuuV5bPqRA3ciX8LHuWreBi2HHSk5Ox2OKBq9cOvTaA6q2b0+eFsUX3HaYoanIUsUudqeT4Cog/d3V9pWbQ+X9Q734ZvV2UuIsxcRgmN2HtldbEpvuSed3/iauM6DRuaDV6tDodOhcdRh83vKpUonrz5jS6uwMmN/c7jsfpk3Sz2YyHhwc//vgjTz31lH1537590Wg0LF26tEjek5+ivHBnZWaycvbPnF6/BZuSgFbjycP3Gqn2zM/yRSSEKFIWq43E9Czi07JITM8kIU19npCmPk9Iz1TXpWWRYraQYraQaraQkmEhJdNyy2b3d0qLDS9S8dWk4EMKftoU/LWp+GrT8dJm4G5LwSXLjDbTipJpQ8nSYLVosVp16sOmPmyKBpuiQUHJTvJtgBUFK4piQb243l7rg+KhBbTc1as3XUcU7CbxjZTlhLJHjx74+fmxcOFC+7Jp06Yxfvx4UlJS0OnyTmlYmPdcL+ecntq7Dc+iHp36pgr/H025zVY1RbHPQsvepVUpbIsax1Fst3GenWToJktWJinxiSTHJ5KWlEJSTALJMfFkJKWTlZFJVqYZq5KWb79cjcYDNzc/mva+m/b39ch/B4oCNgtYM6/+a83KfmSCLQvMyZAWB+lx6r/JkRB3Wu1SZU7MvT29Cer2hNaj1Bp0+U0snECmxUbozk0c+n0x5vgMrFk2rDed6vYqV2NFnv91xh3HUNDrvcM6BZ4/fx6LxULVqlVzLa9atSqbNm0qsveAmtybzVdHO05KSrqDyK+a++5bXDlxhKt3Y1xpWFtDtVE/yZeREKLI6XVa/D2M+Hvcfj8+m00hPctKitlCckZ28p79PMVsIT3LijnLSnqmlQyLlfRMGxkWKxn211YysmykZ1nJsD/UMlkWG1lWhUwrJOBJgqLWfGIl/xbxem5w9VEwkoUn6Zg0GZjIxIQZkyYTV8z2124aM0ZLOq6WDFysWeitVnRW0FhAYwNsGhSbDsWmQVG0KIoGFFDQgKJB4ervbgUFRVFQ1KXZNwQUFKyADUXJOYj8DsQG2Miw3HkLh7LszJkzNG/ePNeyqlWrkpGRweXLlwkODi6S99zoWr/gg4m4Ggx3fiBClBIajQm9xh13g5maXufo6LsVg06BI/PgSHHtVAdBjSCkDVTvCHV6gsud1zoKUZRc9FradbyXdh3vtS8zW6yE7dnDqY2rSbkYiSUlA2umgs2mYFMs2eP1pKN3Ldm02WFJes6F1M0t9zQ/Hh4eZGTkP/pwYd4D8PHHHzNx4sQ7CTdfvpUrcOVEGKDBoPWnSeca3PvsBEnQhRBOR6vV4J7dtD2omCpqFUUhy6qQZbWRabGp/1qzE/hrX1tyltvItChkWm1YrDasNkV9KOq/FquCTVGw5Cy35Ty3YbVBps1Gmk29kFps2WWtV7dhsSlYrepzRVHjsyoKtuznNkUBqxWdLROdkpX9byY6WxZ6JQudkoXWloVWsaK1mNHazOiyzOgsZvSWTLS2LHRWC9U7dS+eE1pGmM3mfK/bwE2v97f7nuK61gvhPDSAIbtLjwEtBrQaLXqtDaPeTAVTDE28z1DNLeG69xVw9hCtHnQuoDNkP1xyL3NxBzd/MPmBmy+4B4JfTfCrBX41wGAq4uMVovgZ9Tpat2tL63Zt8y+gKCRcicBSwg34HJake3t7AxAfH59reWxsLD4+PkX2HoA333yTV155xf46KSmJkJCQQkSdW8dBQ9B7eNGmew98K9359oQQojTTaDS46DW46LW4y6C9Ipu3t3e+123gptf7233Pja71Qz762t6vvcTcwb16jfbWTfnzf6PjKgg0hZlC0sH0hT3PDqLR6Ip8LAwhRAFoNPhULPk8z2H/04ODg/H19SUsLIz777/fvjwsLIymTfMf9bEw7wEwGo0FHvn9dnj7B9LriRFFvl0hhBCirGjatClhYWG5loWFhVGxYkUCAgKK7D03utb7Va5S5vr5CyGEKNscdutTo9EwZMgQZs6cSUJCAgCbNm1i9+7dDB061F7u+++/t98ZL+h7hBBCCOEchg4dytatW9m1axcAMTExzJ49myeeeMJeZufOnQwYMIC4uLgCv0cIIYQoqxzaPunDDz+kQoUKNGjQgK5du3L//ffz1ltv0bVrV3uZPXv2sHr16tt6jxBCCCGcQ58+fXjppZfo2rUr3bp1o1GjRtSqVYsJEybYy0RERLBkyRLS0tIK/B4hhBCirHLoPOmgDt6ze/durly5QuPGjalRo0au9Xv37iUuLo4ePXoU+D23UpanuhFCCFH6lIfr0unTpzly5AiVKlWiZcuWaK7pQ33x4kV27NhBnz59MJlMBXrPrZSHcyqEEKJ0cfp50h1JLtxCCCGciVyXip6cUyGEEM6moNem0jccpxBCCCGEEEIIUUZJki6EEEIIIYQQQjgJSdKFEEIIIYQQQggnIUm6EEIIIYQQQgjhJCRJF0IIIYQQQgghnIQk6UIIIYQQQgghhJOQJF0IIYQQQgghhHASkqQLIYQQQgghhBBOQpJ0IYQQQgghhBDCSUiSLoQQQgghhBBCOAlJ0oUQQgghhBBCCCchSboQQgghhBBCCOEkJEkXQgghhBBCCCGchN7RATiCoigAJCUlOTgSIYQQ4ur1KOf6JO6cXOuFEEI4m4Je78tlkp6cnAxASEiIgyMRQgghrkpOTsbb29vRYZQJcq0XQgjhrG51vdco5fC2vc1m49KlS3h6eqLRaO5oW0lJSYSEhHDhwgW8vLyKKMKyTc7Z7ZNzdnvkfN0+OWe3ryjPmaIoJCcnU7lyZbRa6YlWFApyrW/dujW7d+8u0PZuVvZG6/Jbfv2ya1/n97woP2dyvIUrV9DjvZ3XrVu3Zt26dfK3LSQ53sKVK8hx5bdMPstF97ct6PW+XNaka7VagoODi3SbXl5e8sP2Nsk5u31yzm6PnK/bJ+fs9hXVOZMa9KJVkGu9Tqcr8N/uZmVvtC6/5dcvu/b1jZ5D0XzO5HgLV66gx3s7r699Ln/b2yfHW7hyBTmu/JbJZ7lo/7YFud7L7XohhBBClEvPP/98kZS90br8ll+/7NrXN3peVOR4C1euoMd7O6+L+njlb1u4smXteAvzWb7dY73+tXyWi0e5bO5elJKSkvD29iYxMVFqnwpIztntk3N2e+R83T45Z7dPzpkoCeXtc1aejrc8HSvI8ZZl5elYoWSOV2rS75DRaOS9997DaDQ6OpRSQ87Z7ZNzdnvkfN0+OWe3T86ZKAnl7XNWno63PB0ryPGWZeXpWKFkjldq0oUQQgghhBBCCCchNelCCCGEEEIIIYSTkCRdCCGEEEIIIYRwEuVyCjYhhBBCiLImLS2NS5cu2V/7+PgQEBDgwIhKRnp6OhcvXqRWrVpoNBpHhyPEbYuMjCQlJQVAPselXGxsLPHx8QCEhIQUut+61KQXUlZWFtOmTeOJJ55g7Nix7Nq1y9EhOZ3Q0FCef/55BgwYQEZGRr5lNm3axOjRo3nyySeZNWsWNputhKN0HhEREXz88ccMGzaM//3vf+zfvz/fcn/++ScjR45k5MiR/PnnnyUbpJOJjo7m888/Z9iwYbz88sts2LAhTxmbzcasWbN48sknGT16NJs2bXJApM4nKSmJoUOHMmzYsDzr0tPT+eqrrxg6dCgvvfQSBw4ccECEzmHLli0MGDAgzyMtLS1Xubi4OCZNmsSQIUN44403OHfunGMCFuXa+vXradmyJb1796Z3795MmzbN0SGViNGjR9OoUSNSU1MdHUqxURSFr7/+mooVK+Ln58f//vc/R4dUrE6ePEnPnj0xmUw0bdqUrVu3OjqkYjVx4kR69+5NvXr1yuTneOLEiQQEBBAcHMz8+fMdHU6x+vnnn+nduzdNmjTh4MGDhd6OJOmF1L9/f7755hvuvvtuDAYDHTp0YMWKFY4Oy2k8/vjjjBw5kuTkZJYsWYLFYslTZu7cufTo0QN/f39atWrFhAkT8k0YyoM1a9Zw7733kpycTNeuXcnMzKRNmzbMmzcvV7mJEycyfPhw6tatS7169Rg2bBgTJ050UNSOdezYMTp37kx8fDzdu3fHw8ODPn368NFHH+UqN2zYMN577z1atWqFn58f3bt3Z+7cuQ6K2nk8++yzbNu2jb///jvXcqvVSo8ePfj555/p2LEjZrOZNm3asGXLFgdF6ljh4eGsW7eOxx57LNfDYDDYyyQmJtK2bVvWrVtHly5dOHXqFC1atODUqVMOjFyUVw8//DCbNm3ixIkTvPfee44Op9j98MMPdOvWDZPJ5OhQilV0dDTR0dEcOnSIgwcP8ueff7J9+3ZHh1VsFixYwOuvv058fDzPPPNMic5P7QjTp0/n1KlT+Pr6OjqUIrd161bmzZvHgQMHWL58OS+//DJRUVGODqvYjB8/nlOnTtGyZcs725AibtuKFSsUQDl+/Lh92TPPPKPUq1fPgVE5lzNnziiKoih//vmnAijJycm51mdlZSkVKlRQJkyYYF+2efNmBVB2795dorE6gytXrihmsznXshdeeEGpVauW/XVkZKRiMBiUX3/91b5s1qxZisFgUK5cuVJisTqLxMREJT09Pdeyd955RwkJCbG/3rVrlwIo27dvty976623lKCgICUrK6vEYnU2P/30k3L33XcrX3zxheLt7Z1r3fz58xW9Xq9cvHjRvmzQoEFK27ZtSzhK5zBnzhwlKCjopmX+7//+TwkKClLS0tIURVEUq9WqtGrVShkyZEhJhChKEbPZrCxZskSZPn26YrFY8i1z8eJFZe7cucpvv/2W57s9Pj5eOXnyZJ7HhQsXFEVRlPXr1yu1a9dWqlSpovj7+ytLly4t9mO6mUuXLikzZsxQVq1adcMyO3fuVH7++Wfl33//zfO9fP78+XyPNyEhQVEURTl48KDywgsvKIqiKN7e3nl+a5S08+fPKytXrlQiIyNvWCYsLEzZuHGjEhsbm2ddVlZWvo/ry5w7d05p0KBBrt+hjrBr1y7l33//veH61NRUZcuWLcquXbvyfN6tVmu+x2q1WvNsZ8eOHcq9995b5PHfjvPnzys//vijsn79+huW2bp1qzJr1ixl7dq1eY43PDw8389yYmJirnL+/v4O/xzn5+zZs8rKlSuV6OjofNfbbDZl3759ysaNG5X4+Phc6yZMmKBMnDjR/vrRRx9Vfv/99+IM946kpKQo8+fPV2bPnn3DMqdPn1Z++eUXZfHixfbvo+t16NDhjnIaSdILYezYsUrTpk1zLVu7dq0CKKdPn3ZQVM7pRkn6zp07FUAJCwvLtbxSpUq5/iOXZ5999pni5+dnfz137lxFq9XmOpdJSUmKVqtV5s2b54gQnc7gwYOVe+65x/76/fffVypXrpyrzP79+xVA+e+//0o6PKdw5MgRpWLFisrp06eVr776Kk+SPnTo0FznUFEUZfHixQqgxMTElGCkzmHOnDmKu7u7MmrUKGXUqFHKlClT7Ml4jnbt2ikjRozIteyTTz5RfH19SzJU4eS++OILJTg4WGnatKkC5LnJqCiK8scffyhubm5Kr169lHvvvVfx8PBQVq9ebV8/Y8YMpVatWnkeDz74YJ5trV+/XgkICFBsNluxHld+EhISlIEDByrBwcFKcHCw0r9//zxlbDabMnToUMXf318ZOHCgUrNmTaVly5a5fuD37ds33+P9+eeflbS0NOW+++5TDh48qJw8eVLx9PRUDhw4kG+SV9xCQ0OV/v37K8HBwQqQbwISGxur3H333UpAQIDSokULxWQyKd9//719/bFjxxSdTpfvIyfpT0hIUHQ6nQIo48aNK7Hju96sWbOURo0aKRUrVlR0Ol2+Zf7991/F19dXadSokVK1alWlZs2ayrFjx+zrJ0yYkO+x9uzZM9d2IiMjlY4dO+b5vVhSIiMjlX79+inVqlVTgoKClGHDhuUpY7FYlIcfflipUKGCMmjQIKVq1apKhw4dcv1e69q1a76f5UWLFuXalrMl6f/9959y//33K1WqVFEAZeXKlXnKREZGKi1atFCCgoKUu+66S3F3d1d++eUX+/oxY8Yo3333nf31uHHjlOnTp5dI/Lfr9ddfVypVqqQ0aNBA8ff3z7fM9OnTFZPJpPTt21dp27atEhAQoISGhuYpd6dJujR3L4QzZ85QtWrVXMtyXp85c8YRIZU6Oefp+vMYEhIi5xC1T/CMGTPo1auXfdmZM2fw8/PDw8PDvszT0xMfH59yfc7effddHnroIRo2bEh8fHyuvk7yfzW3jIwMHn30UT799FNq1qyZb5mbnbOzZ88We4zOqF27dtx11100adKE6dOn07RpUxISEuzrb3TO4uPjc5UT5VuVKlXYs2cP7777br7rk5OTGTlyJG+88QarVq1i/fr1jBgxguHDh5OVlQXAyJEjOXXqVJ5HfuOT3HvvvVitVpKSkor1uPKTlZXFgAEDOHPmDG3bts23zMKFC1m0aBFbt25l0aJFhIaGEhcXl6sL19KlS/M93uHDh3PkyBFOnDjBgw8+SO/evUlNTeWhhx4iMTGxpA7T7vz58wwbNoxjx47dsMzLL79McnIyZ86cYe/evfz000+MGTOGw4cPA1CvXj0sFku+j6CgIAC8vb2xWCxERUVx5MgRFi5cWCLHd73o6Gh+++03Pv7443zXJyYmMnjwYMaMGcOhQ4c4e/YsDRo0YOjQofYyEydOzPdY//33X3uZU6dO8cgjjzBt2jSaNGlS7MeVn6ysLEaMGMHp06dp3LhxvmV+/vlnVq9ezc6dO1m4cCGhoaGcPXuWTz/91F5m3bp1+X6WBw4cWFKHUijh4eGMHj2a0NDQG5YZM2YMWq2Ws2fPEhoayuTJkxk1apT9N0NgYCBXrlyxl79y5QqBgYHFHnthNGzYkCNHjvDMM8/ku/78+fO8+OKLTJs2jaVLl7Jz5046d+7MiBEjijwWSdILwWw24+bmlmtZTuJ0owHSRG5msxkg3/NY3s+hzWZj2LBhpKam8uWXX9qX5/e5Azln3bp1Y9CgQfTr148dO3bk+rEq/1dze/nll2nQoAFPPvnkDcvIOcutT58+rF27ljFjxvDCCy+wY8cO0tLS+PDDD+1l5JyJgnjsscfsyVZ+/v33XxITExk9erR92bhx47h06RKbN28u0D6io6M5deoUhw4d4o033iAkJARvb+87jv12BQQEMGjQoFxjN1xvwYIFdOvWjfr16wNqAvrkk0+yYMGCAu2jZcuWuZIdT09PDhw44JA+vf379+ehhx5Cp9Pluz4jI4NFixbx/PPP4+npCcCQIUMIDg4u8Bgpq1evZtasWSQkJJCWlkZmZiaZmZlFdgy34/XXX79p0rxs2TKSkpJ49dVXAdBqtbz++uvs2bPHflPiVnbu3Mmjjz7KjBkzaNiwIVartUhiv13BwcH079//hn9bUD/L9913HzVq1ADA39+fwYMHF/izDOqI4KdOncJms3HmzJlcSa0jDRw4kAceeACtNv+UMSEhgb///psXX3zRPi7EqFGj8PHxsVea9OzZkzlz5nD69Gl27drFhg0b6Ny5c4kdw+0YNmwYPj4+N1z/xx9/YDKZct1wGjt2LPv37+f48eOAOjDvqVOnyMjIICIiggsXLhQqFknSC8Hb29s+tH6O2NhYgJv+YcVVOT8a8juP5fkc2mw2Ro4cyZYtW1i3bh0VK1a0r8vvcwdyzrp06cLgwYP55JNP+PTTT3n11VftNSnyf/WqtLQ0vv/+e2JjY+0jlM+ePZu0tDQGDBjAunXrADln17v+B7+Xlxddu3Zl9+7d9mU3OmcajcYhCZIonQ4fPkxgYGCuKdNq166Ni4tLgRObr776it69ezN48GAiIiL466+/iinaO3f48GEaNmyYa1nDhg2JjIwkLi7utrdXs2bNGyYSjnbs2DEyMjK46667ci1v3rx5gWfPuOeee9i/fz+1a9emY8eOtGnThscff7w4wr1j+/fvp3r16rm+P3OOvaDH+/nnn3PgwAEaN26Mq6vrTW9wOdqNPsunT5+2V0rdyk8//UTv3r3x8/Pj4YcfvmErBWdz6NAhrFZrrs+2TqejSZMm9r91+/btGTVqFPfccw+DBw9m6tSppXZqyMOHD1O7du1cNyBz/vY539P//vsvvXv3JiEhgfHjxxd60EOZJ70QmjZtyg8//ICiKPZ5DMPCwtDpdHn+k4r8NW3aFFDPW/fu3eIQOYwAABOPSURBVAG1ifepU6du2MSkrFMUhaeffpqVK1eyYcMG6tWrl2t906ZNSU5O5ty5c1SvXh1Qm9mmpqbaz2d5V79+fTIzM4mMjMTb25umTZsyb948MjIycHV1BdTPHOCwpnOOYjQa+f3333MtW758OSdPnuSxxx6jVq1agPo5W7lyZa5yYWFhuLq6UqdOnRKL15klJibmqlVp2rSp/XOVIywsjNq1a5f5EadF0UlKSsr3RpiPj0+Bm6x/9NFHeWa4cFb5HW9OUpeUlISfn99tbe9mzXEdLafby/XH5O/vz9GjRwu0DVdXV7799lu+/fbbog6vyCUkJOQ5Vk9PT1xcXPKtbMjPkiVLiiO0YnGjz7KiKCQnJxdonuw33niDN954o5giLD43+2xf+7d+8803efPNN0sytGJxq+8tUFsfFEU3Bue85ejkHnvsMaKiouzNOMxmM1OmTOH++++/7YtKeVWrVi3atWvH119/bZ8bffr06SiKwsMPP+zg6Eqeoig888wzrFixgg0bNtCgQYM8Zbp06UKVKlWYPHmyfdkXX3xBcHAwXbp0KcFoncOaNWu4dOmS/bXFYmHGjBlUrlzZnnA+/PDD2Gw2fvjhB0BtqfDVV19x991328uUFzqdLs9c382aNcNgMDBgwAD7jZ8hQ4Zw/Phxli9fDkBqairTp09n4MCBBfqhUdYsWLAgV5P1LVu2sGrVKvr3729fNnToUNauXWtP1C9fvsz8+fNzNYcT4lZMJhPJycl5licnJ+fb1am0y+94c37klrXjdXFxAdTKiGulpaXZ15UlLi4ueY7VYrGQmZlZJo+3PH2Wr1fePtsl+beWmvRCqFevHlOmTOHpp5/mhx9+4MKFC7i6uuappSrPZs6cycqVK+1J1BNPPIFOp+PNN9+0zxs4e/ZsevXqRYMGDQgICODAgQPMmDEjVxPv8mLOnDnMmDGDFi1a5BlUaO7cubi6umI0Gpk/fz4PPfQQ27ZtA9QBPf78888y+UV4KwaDgZ49e+Lh4YGfnx9hYWF4e3uzePFi9Hr1q61SpUrMnDmTp59+mkWLFhETE4PZbM41MI3IrVWrVnz66acMHDiQNm3acPr0aQIDA3PdHCpPrly5Qv369alVqxYZGRmEhoYybtw4xo4day8zaNAgNm7cSIcOHWjTpg0HDhygdevWvP766w6MXJQ2derUITo6mtTUVNzd3QGIjIwkPT2d2rVrOzi6olenTp08g1GePXsWLy8vpx1UqrByboJeuHAhV8u3iIiIMnnDuHr16ixcuDBXi9OIiAj7urLmRp/lSpUqlfkk/drP9rUD0kZERNChQwcHRVV86tSpw6pVq3J9tnP+9kX9PS1JeiGNHj2aBx98kL179+Lt7c3dd99tTwyEOqBLfn0xq1SpYn9er149Tpw4wfbt20lLS6NNmzalto/KnerQocMNb/Jc2+/lnnvuITw8nO3btwNqP59rR3svT7p06cL+/fs5cOAAUVFRhISE0KhRI/uXZo7BgwfTo0cPdu3ahZubG+3bty+XNzXy88ADD+Q7yvvrr7/O4MGD2b9/P/7+/rRr185p+3oWtxdffJGnnnqK0NBQNBoNDRs2zJNAaDQapk+fzssvv8zx48cJDg7O0/dUiFvp1asXGo2G33//neHDhwPqTVoPDw/uvfdexwZXDPr168c777xDXFwcfn5+WK1WFixYQN++ffN8j5d2lStXpkmTJvz111/06dMHgEuXLrFz585C91d1Zr169eJ///sfmzdvtg8QtmTJEjw8PMpk4tavXz+++uorkpKS8PLyIjMzk0WLFtGvXz9Hh1bs6tatS40aNfjrr7/sf+tTp05x4MABJk2a5ODoil7fvn155513WLdunb277ty5cwkJCaF58+ZFui+NoihKkW5RCCGEEELksmXLFg4fPsy+ffv48ccfmTJlCnq9nj59+hASEgLAp59+yqRJkxg7dixZWVlMnTqVb775hueee87B0d++n3/+GbPZzI8//ojNZuO5557DaDTy1FNPAeqI5506dSIzM5PHHnuMjRs3sn//fnbu3GkfJbu0iI6OZu/evWRlZdGvXz/efvttOnbsSLVq1ezd11atWkXfvn159dVXady4MV9++SUGg4Ft27aVukqesLAwLl26xJo1a/j666/t3aPatWtn7687bNgw1q9fz3vvvUdiYiLvvvsuH374IS+//LIDIy+c77//HoBvvvkGHx8fhg0bhqenJ0OGDAEgJSWFu+++G6PRyCOPPMLq1as5efIku3btonLlyo4M/Y5FRkayf/9+kpKSePTRR/nggw9o2bIlNWvWpG7duoB6A+axxx7jrbfeolatWnz22WcEBgayfv36UnfDbeXKlYSHh7N+/XpWrFjBF198AcCjjz5q73s+btw4FixYwJgxY7hy5QozZ85k8eLFubrBFQVJ0oUQQgghitn8+fPznUrthRdeyDXo7Pr161m5ciVarZb+/fvTvn37kgyzyLzyyiukpaXlWubm5pZratGMjAxmz57N0aNHqVy5MsOHD3fqUbxvZNeuXUyYMCHP8n79+jFmzBj7623btjFz5kzi4uJo1aoVL730UqlsDff555/bZwS51uTJk2nUqBGg9kH/6aefWLt2LUajkYEDB/LQQw+VdKhFIr+bZP7+/rmm4kxNTWX27NkcP36cqlWrMnz48DLROnTTpk35jjT/6KOP2m+4AWzYsIHZs2eTmJjI3XffzQsvvFAqB0797rvv8gwECzBhwoRcN1z+/vtvNm3ahJubG4MGDSqWAZwlSRdCCCGEEEIIIZxE+exkKIQQQgghhBBCOCFJ0oUQQgghhBBCCCchSboQQgghhBBCCOEkJEkXQgghhBBCCCGchCTpQgghhBBCCCGEk5AkXQjhUImJiWzdupXQ0NA86xRFYdu2bWzduhWLxVKg7V24cIEdO3bccP3x48fznV5DCCGEEEIIZyBJuhDCoXbv3k2nTp1o27YtkZGRudatWbOGjh070qlTJxISEgq0vTNnztC+fXsOHDiQZ53NZqNHjx7MnTu3KEIXQgghhBCiyEmSLoSTsdlsHD58mMOHD5OZmZlrndlsZuvWrWRkZJCamsqBAweIjo6+o+3Exsaya9euXNu5cOECYWFhZGRkkJCQwPbt2wFIS0tj69atpKen59peeno6W7duJTU1tdDH3axZM3799ddcy2bOnEmrVq3yLZ+enk5oaChnzpzBZrPZl3fu3Jk6deowc+bMPO9Zs2YNFy5cYOTIkYWOUwghhCgN/vjjDyIiIhwdhhCiECRJF8KJbN68mRo1atCvXz8ee+wxKleuzOLFi+3rL1++TKdOnXjhhReoXbs2w4YNIzg4mI8//rhQ2xk3bhwNGjTgpZdeYt++fSiKwqhRo6hduzZDhw4lJCSEsWPH0rVrVwAMBgOPPPII8+fPz7W/efPm8eCDD2IwGAp97CNHjmTWrFn217GxsSxdupSnnnoqT9lp06ZRuXJlhg8fTteuXWnSpAkHDx60rx8xYgTz5s3DbDbnet/MmTPp2LEj9erVK3ScQgghhLNZsmQJFy9ezLVsxIgR7Ny500ERCSHuhCTpQjiJyMhI+vfvzyeffMLp06c5ePAgc+bMYfjw4YSHh+cqGxUVxblz59i/fz8LFixgwoQJXL58+ba3Ex4eTnh4ONu3b6dnz54sWrSIBQsWsGfPHsLCwjhw4ABbt261lzcYDAwbNixXMg0wa9YshgwZgouLS6GPv3///sTGxrJt2zYAfv31V9q3b0/NmjVzlVu5ciXvvfceW7duJSwsjLNnz9K/f38GDx6MoigADB8+nKSkJP7++2/7+2JjY/n7778ZNWpUoWMUQgghnNGwYcPYvXt3rmWPPPIIISEhDopICHEnJEkXwknMmzcPHx8fatSowY4dO9i+fTve3t74+vqyfv36XGVff/11jEYjAH379sVms3H06NFCbcdkMtlfz5kzh0GDBtGkSRMAKleuzOjRo3O9Z9SoUezYsYPjx48DcOzYMXbs2MGIESPu6PhdXFwYOnSovZn6zJkz822W/t1339G5c2eSk5PZsWMHO3bsoG3bthw+fJhz584BULFiRe6///5cTd7nzJmDq6srAwcOvKM4hRBCCGeybNkyLBYLW7duZcGCBfYb1H369KFKlSr2cosWLSIyMpKIiAhWrFjBjh077De3T506xd9//33DgVUzMjLYsGEDy5cvz3PDvzAURWHXrl0sW7aMkydP3vH2hChr9I4OQAihOnHiBImJiYwfPz7X8mrVqqHT6XItq1Chgv25Xq/HYDCQlpZ229upWrVqrtdnz56lY8eOuZbVrVs3z+tOnToxa9YsPv30U2bNmkWLFi1o1qzZbRxt/kaOHEm7du0YPHgwFy9e5OGHH2bjxo25ypw4cYLMzMw8x9ehQwdSUlLsr0eNGsWDDz7IhQsXCAkJYdasWQwePBg3N7c7jlMIIYRwFqtXr8ZqtfLff/8RERGBj48P/fv3Z8SIEcyYMYMBAwYA8Pjjj9O+fXsuX75MvXr12Lx5M927d6devXr88ccf1KlTh40bNzJ+/Hjef/99+/a3b9/OgAEDCA4OpkKFCmzfvp1Ro0bx2WefFSrelJQUunXrRnR0NE2bNuXEiRO0adOG2bNnF8HZEKJskCRdCCdhMpkICQnJ1by8uLej1eZuTOPp6Zkr0QXyvAY1AX799deZOHEic+bM4d13372jmHM0btyYRo0a8eSTTzJkyBBcXV3zlDGZTPTu3Ztvvvnmptu6//77qVixIrNnz6Z3794cPHgw38HkhBBCiNJsypQp/Pzzz7z66qs8+OCDNy1rNBo5cuQIBoOBtWvX0qNHDx544AEOHz6MXq/nr7/+4tFHH2X8+PF4eHiQkpLCQw89xKeffsrw4cMBdXDZZs2a0bVrV3r37n3b8f79/+3dX0hTbxzH8c9sS6U/bFGSgaAMuigorFWUFOe2P2SjYJlBlEQyqCCiqG7qKuifYWyNchhEFKwlQpsYkdCEItEsRAgarJvdVUoIIiu7EA+dVmDLX7+TvV8guOc5PH6PV/s+5/t8T3u7stms3r59a1YFtrW1/fI6wExGuTtgE4ZhaGBgQP39/Zbxz58/a3R09I+s4/P59PjxY8vY958ladeuXRodHdXhw4c1NDSkPXv2WOa7u7uVzWanHPO3jh07Jq/Xq4MHD/5w3jAMPXjwwKwcmPT9ZsKsWbO0b98+tba2qqWlRStWrNCaNWsKigkAgJngwIEDZpPX9evXm2NOp9McGxsbM4+PJZNJDQ8Pa86cOYrFYorFYnr27JmqqqrU1dVVUAylpaUaGRlROp02x/x+/2/cFTDzkKQDNlFbW6utW7dq8+bNCofD6uzsVHNzs6qrq3/p/NfvrHP8+HENDAyooaFByWRSZ86cUTwel8PhsFxXWlqq+vp6tbS0yO/3y+12m3Pj4+PauHGj4vH4L93/pEAgoO7u7p+Wz58+fVoul0uGYeju3btKJBI6e/ZsXpm+NFE+n8lkFI1GaRgHAPjneTwe8/fJp9g/Gpvc1M9kMnK5XIrH4+bPZGl8VVVVQTHs2LFDe/fu1bp167R8+XIdPXrU7KsDYALl7oBNOBwOtbW16datW0okEmpvb9eyZcsUi8XMV4aVlJSopqbG0uxNmjiPvWDBgt9ep7KyUqlUShcvXtS1a9e0cuVKXblyRadOncqLt66uTqFQKK9hXF9fn+bOnTvlBm1ut1s1NTU/fX2bx+OxzJeVlam3t1ehUEi3b9+W0+nU2rVrf7ij7/V61dDQoDdv3qi+vn5K8QAAgAnz589XLpfTnTt38vraFKqoqEjNzc26dOmSXrx4odbWVq1evVqDg4OqrKyclr8B/O0c45NtHQFAE7vn354FDwaDGhwczGvgduHCBUUiEaXTacuT9nv37unDhw8KBoN/KmQAAP5pixcv1tWrV7V7925zzO12WxrHOZ1OPXz40DxHnsvl5HK51NXVJcMwJElDQ0PyeDzq6emRz+dTOp3W0qVLdfPmTcum/NjYmIaHh7Vo0SJJUk9Pj96/fz+lM+rZbFbl5eXmd4cvX76ouLhY8Xhc27dvn5b/B/C340k6AIstW7aorq5OFRUVevLkiaLRqKV0/d27d3r16pUuX76sc+fO5ZXCf/sFAQAA/Pd8Pp+uX7+uXC6nefPmqba2dlrW9Xq9On/+vBobG/Xy5UtVV1crk8no/v37ikQiZpIeDof1+vXrKSXpnZ2dCofD8vv9WrJkiZLJpMrKyrRhw4ZpiRmYCTiTDsAiEomov79fTU1N+vjxo1KplLZt22bOd3R0qKmpScFgUIcOHfofIwUAAJIUjUZlGIYePXqkjo4OSdLOnTtVUVFhXhMIBFReXm5+LioqUiAQsLzWdfbs2QoEAuYROkk6ceKEUqmUSkpK9PTpUxUXFyuRSGjTpk3mNc+fP9eRI0emFOv+/ft148YNjYyMKJVKadWqVerr69PChQsLvn9gpqHcHQAAAEBBPn36pJMnTyoUCuVV1wEoDEk6AAAAAAA2Qbk7AAAAAAA2QZIOAAAAAIBNkKQDAAAAAGATJOkAAAAAANgESToAAAAAADZBkg4AAAAAgE2QpAMAAAAAYBMk6QAAAAAA2ARJOgAAAAAANkGSDgAAAACATZCkAwAAAABgE18BJ0f6sHHNel4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#plot the neutrino flux \n",
     "fig,ax = plt.subplots(1,2, figsize=(12,6))\n",
@@ -611,10 +1050,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "0dab79aa-b9a1-4b73-bf4b-86707ffc253e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "for ch, rate in rates.items():\n",
     "    l = plot_projection(rate, 'time', integrate=False)\n",
@@ -627,10 +1077,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "c5b932d5-7d31-49eb-a1ab-c44afc50c89f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "for ch, rate in ratesI.items():\n",
     "    l = plot_projection(rate, 'time', integrate=False, step=True)\n",
@@ -658,7 +1119,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/python/snewpy/flavor.py b/python/snewpy/flavor.py
index ac261b207..d37fccb6a 100644
--- a/python/snewpy/flavor.py
+++ b/python/snewpy/flavor.py
@@ -46,7 +46,7 @@ def __getitem__(cls, key):
         return np.array(list(cls.__members__.values()),dtype=object)[key]
 
 class FlavorScheme(enum.IntEnum, metaclass=FlavorEnumMeta):
-    """Configurable enumeration for different flavor schems (2, 3, 4, ... flavors).
+    """Configurable enumeration for different flavor schems (1, 2, 3, 4, ... flavors).
     """
 
     def to_tex(self):
@@ -89,14 +89,18 @@ def lepton(self):
 
     @classmethod
     def from_lepton_names(cls, name:str, leptons:list):
-        enum_class =  cls(name, start=0, names = [f'NU_{L}{BAR}' for L in leptons for BAR in ['','_BAR']])
+        if leptons != None:
+             enum_class =  cls(name, start=0, names = [f'NU_{L}{BAR}' for L in leptons for BAR in ['','_BAR']])
+        else:
+             enum_class =  cls(name, start=0, names = [f'NU_{BAR}' for BAR in ['','_BAR']])
         return enum_class
         
     @classmethod
     def take(cls, index):
         return cls[index]
 
-#- Define 2, 3, and 4-flavor schemes for the module.
+#- Define 1, 2, 3, and 4-flavor schemes for the module.
+OneFlavor = FlavorScheme.from_lepton_names('OneFlavor',None)
 TwoFlavor = FlavorScheme.from_lepton_names('TwoFlavor',['E','X'])
 ThreeFlavor = FlavorScheme.from_lepton_names('ThreeFlavor',['E','MU','TAU'])
 FourFlavor = FlavorScheme.from_lepton_names('FourFlavor',['E','MU','TAU','S'])
diff --git a/python/snewpy/flux.py b/python/snewpy/flux.py
index 53ce54331..fae45d616 100644
--- a/python/snewpy/flux.py
+++ b/python/snewpy/flux.py
@@ -108,7 +108,7 @@ def __init__(self,
                  *,
                  integrable_axes: set[Axes] | None = None,
                  flavor_scheme: FlavorScheme | None = None
-    ):
+                ):
         """A container class storing the physical quantity (flux, fluence, rate...), which depends on flavor, time and energy.
 
         Parameters
@@ -149,7 +149,8 @@ def __init__(self,
             
         Nf,Nt,Ne = len(self.flavor), len(self.time), len(self.energy)
         #list all valid shapes of the input array
-        expected_shapes=[(nf,nt,ne) for nf in (Nf,Nf-1) for nt in (Nt,Nt-1) for ne in (Ne,Ne-1)]
+        expected_shapes=[(nf,nt,ne) for nf in (Nf-1,Nf) for nt in (Nt-1,Nt) for ne in (Ne-1,Ne)]
+
         #treat special case if data is 1d array
         if self.array.ndim==1:
             #try to reshape the array to expected shape
@@ -335,6 +336,7 @@ def integrate_or_sum(self, axis: Axes | str)->'Container':
     def can_integrate(self, axis):
         "return true if can be integrated along given axis"
         return Axes.get(axis) in self._integrable_axes
+
     def can_sum(self, axis):
         "return true if can be summed along given axis"
         return Axes.get(axis) not in self._integrable_axes
@@ -484,8 +486,7 @@ def project_to(self, axis='energy', squeeze=False):
         if squeeze:
             return x, fP.array.squeeze().T
         else:
-            return x, fP
-        
+            return x, fP        
         
     def plot(flux, projection='energy', styles=None, **kwargs):
         x, fP = flux.project_to(projection, squeeze=False)
@@ -511,6 +512,28 @@ def plot(flux, projection='energy', styles=None, **kwargs):
         plt.ylabel(f'{fP.__class__.__name__}, {x.unit._repr_latex_()}')
         return lines
 
+    @staticmethod
+    def _reconstruct(array, flavor, time, energy, integrable_axes, flavor_scheme):
+        return Container(array,
+                         flavor,
+                         time,
+                         energy,
+                         integrable_axes=integrable_axes,
+                         flavor_scheme=flavor_scheme,
+                         )
+
+    def __reduce__(self):
+        return ( Container._reconstruct,
+                      ( self.array,
+                        self.flavor,
+                        self.time,
+                        self.energy,
+                        self._integrable_axes,
+                        self.flavor_scheme,
+                       ),
+               )    
+
+
 #some standard container classes that can be used for 
 Flux = Container['1/(MeV*s*m**2)', "d2FdEdT"]
 Fluence = Container[Flux.unit*u.s, "dFdE"]
diff --git a/python/snewpy/models/model_files.yml b/python/snewpy/models/model_files.yml
index b76071258..a7126dce6 100644
--- a/python/snewpy/models/model_files.yml
+++ b/python/snewpy/models/model_files.yml
@@ -6,7 +6,7 @@ config:
     - &snewpy "https://github.com/SNEWS2/snewpy/raw/v{snewpy_version}/models/{model}/{filename}"
     - &ccsn_repository "https://github.com/SNEWS2/snewpy-models-ccsn/raw/v0.4/models/{model}/{filename}"
     - &presn_repository "https://github.com/SNEWS2/snewpy-models-presn/raw/v0.2/models/{model}/{filename}"
-    - &presn_repository_main "https://github.com/SNEWS2/snewpy-models-presn/raw/master/models/{model}/{filename}"
+    - &presn_repository_main "https://github.com/SNEWS2/snewpy-models-presn/raw/main/models/{model}/{filename}"
     
 models:
     ccsn:
diff --git a/python/snewpy/rate_calculator.py b/python/snewpy/rate_calculator.py
index 346f7e2eb..d81d9084a 100644
--- a/python/snewpy/rate_calculator.py
+++ b/python/snewpy/rate_calculator.py
@@ -6,10 +6,14 @@
 .. autoclass:: RateCalculator
     :members: run
 """
+import re
+
 import numpy as np
+
 from snewpy.snowglobes_interface import SnowglobesData, guess_material
-from snewpy.neutrino import Flavor
+from snewpy.flavor import ThreeFlavor
 from snewpy.flux import Container
+
 from astropy import units as u
 from warnings import warn
 from typing import Callable
@@ -131,11 +135,13 @@ def __init__(self, callable):
             A function of one parameter (energy). This can be an analitical function, or an interpolation of a (E,value) dataset
         """
         self.value = callable
+
     def __mul__(self, f:Container)->Container:
         e = f.energy #Define sample points
         if not f.can_integrate('energy'): #we have bins, let's use central values for sampling
             e = center(f.energy)
         return f*self.value(e)
+
     def __rmul__(self, f:Container)->Container:
         #same as multiplication from the left
         return self.__mul__(f)
@@ -150,14 +156,14 @@ def from_threshold(cls, e_min=1<<u.MeV):
 class DetectionChannel:
     """Description of a single detection channel in the detector"""
     
-    def __init__(self, flavor:Flavor, xsec:callable, smearing:SmearingMatrix=None,
+    def __init__(self, flavor:ThreeFlavor, xsec:callable, smearing:SmearingMatrix=None,
                  efficiency:FunctionOfEnergy=1., weight:float=1.):
         """
         Parameters
         ----------
         name:str
             channel name
-        flavor:Flavor
+        flavor:ThreeFlavor
             flavor of the interacting neutrino
         xsec:callable or FunctionOfEnergy
             crossection as a function of energy
@@ -184,7 +190,7 @@ def flavor(self):
         return self._flavor
     @flavor.setter
     def flavor(self, flavors):
-        if isinstance(flavors, Flavor):
+        if isinstance(flavors, ThreeFlavor):
             flavors = [flavors]
         self._flavor = flavors
     @property
@@ -208,6 +214,7 @@ def xsec(self, xsec):
     
     def __repr__(self):
         return f'{self.__class__.__name__} (flavor={",".join([f.name for f in self.flavor])}, smearing={self.smearing is not None}, weight={self.weight})'
+
     def calc_rate(self, flux:Container, apply_smearing=True, apply_efficiency=True)->Container:
         """Calculate the event rate in this channel
         
@@ -232,14 +239,15 @@ def _calc_interaction_rate(self, flux):
         """calculate interaction rate for given channel"""
         tgt_mass = 1<<u.kt
         Ntargets = tgt_mass.to_value(u.Dalton)
-        #sum flux over flavors
-        array_total = sum([flux[flv].array for flv in self.flavor])
-        #create a summary flux container
-        flux_total = Container(array_total, flavor=self.flavor, 
-                               time=flux.time, energy=flux.energy,
-                               integrable_axes=flux._integrable_axes)
-        rate = self.xsec*flux_total*self.weight*Ntargets
-        return rate
+        if len(self.flavor) > 1:
+           #sum flux over flavors
+           sumfluxarray = sum([flux[flv].array for flv in self.flavor])
+           #create a summary flux container
+           sumflux = Container(sumfluxarray, flavor=[self.flavor[0],self.flavor[-1]], 
+                               time=flux.time, energy=flux.energy)
+           return self.xsec*sumflux*self.weight*Ntargets
+        else:
+             return self.xsec*flux[self.flavor]*self.weight*Ntargets
 
 class Detector:
     """A detector configuration for the rate calculation. """
@@ -288,12 +296,12 @@ def run(self, flux:Container, detector_effects:bool=True)->dict[str, Container]:
         return result
 
 def _get_flavor_index(channel):
-    _map = {'+e':Flavor.NU_E,
-            '-e':Flavor.NU_E_BAR,
-            '+m':Flavor.NU_MU,
-            '-m':Flavor.NU_MU_BAR,
-            '+t':Flavor.NU_TAU,
-            '-t':Flavor.NU_TAU_BAR
+    _map = {'+e':ThreeFlavor.NU_E,
+            '-e':ThreeFlavor.NU_E_BAR,
+            '+m':ThreeFlavor.NU_MU,
+            '-m':ThreeFlavor.NU_MU_BAR,
+            '+t':ThreeFlavor.NU_TAU,
+            '-t':ThreeFlavor.NU_TAU_BAR
             }
     return _map[channel.parity+channel.flavor]
 
@@ -303,7 +311,9 @@ def _bin_edges_from_centers(centers:np.ndarray)->np.ndarray:
     edges = centers-0.5*np.pad(binw,(0,1),mode='edge') #get lower edges
     edges = np.append(edges,edges[-1]+binw[-1])
     return edges
+
 #--------------------------------------
+
 class RateCalculator(SnowglobesData):
     r"""Simple rate calculation interface.
         Computes expected rate for a detector using SNOwGLoBES data. 
@@ -340,13 +350,13 @@ def __init__(self, base_dir=''):
         """
         super().__init__(base_dir=base_dir)
 
-    def load_xsec(self, channel_name:str, flavor:Flavor)->FunctionOfEnergy:
+    def load_xsec(self, channel_name:str, flavor:ThreeFlavor)->FunctionOfEnergy:
         """Load cross-section for a given channel, interpolated in the energies"""
         xsec = np.loadtxt(self.base_dir/f"xscns/xs_{channel_name}.dat")
         # Cross-section in 10^-38 cm^2
         xp = xsec[:,0]
         #get the column to read from the file
-        column = {Flavor.NU_E:1, Flavor.NU_MU:2, Flavor.NU_TAU:3, Flavor.NU_E_BAR:4, Flavor.NU_MU_BAR:5, Flavor.NU_TAU_BAR:6}[flavor]
+        column = {ThreeFlavor.NU_E:1, ThreeFlavor.NU_MU:2, ThreeFlavor.NU_TAU:3, ThreeFlavor.NU_E_BAR:4, ThreeFlavor.NU_MU_BAR:5, ThreeFlavor.NU_TAU_BAR:6}[flavor]
         yp = xsec[:, column]
         def xsec(energies):
             E = energies.to_value('GeV')
@@ -425,4 +435,64 @@ def run(self, flux:Container, detector:str, material:str=None, detector_effects:
             dict[str, Container]
                 A dictionary with interaction rates (as instances of :class:`snewpy.flux.Container`) for each channel.
         """
-        return self.read_detector(detector,material).run(flux, detector_effects=detector_effects)
\ No newline at end of file
+        return self.read_detector(detector,material).run(flux, detector_effects=detector_effects)
+
+def collate(rates):
+    """Collates the event rates / numbers table returned by RateCalculator.run 
+       into distinct channels e.g. add all electron elastic scattering and NC channels
+
+    Parameters
+    ----------
+    dict[str, Container]
+                A nested dictionary with interaction rates (as instances of :class:`snewpy.flux.Container`) for each channel.
+
+    Returns
+    -------
+    dict[str, Container]
+                A nested dictionary with interaction rates (as instances of :class:`snewpy.flux.Container`) for the collated channels.
+    """
+
+    def aggregate_channels(rates,patterns):
+        for aggname, pattern in patterns.items():
+            #get channels in rates with names that contain the pattern
+            matches = [channel for channel in rates.keys() if re.search(pattern,channel)]
+            #sum over the matches
+            sumrates = sum([rates[channel].array for channel in matches])
+            #make a new entry with the aggregate 
+            if len(matches) > 0:
+                rates[aggname] = Container(sumrates,ThreeFlavor.take([0,-1]), rates[matches[0]].time, rates[matches[0]].energy)
+            #remove matching channels from rates
+            for channel in matches:
+                del rates[channel]
+        return rates
+
+    # make collated rate table
+    patterns = {'nc':'nc_',
+                'eES':'_e', 
+                'coh_helm_Ar':r'coh_helm.*_Ar', 'coh_helm_Ge':r'coh_helm.*_Ge', 'coh_helm_Xe':r'coh_helm.*_Xe',
+                'coh_klein-nystrand_Ar':r'coh_klein.*_Ar', 'coh_klein-nystrand_Ge':r'coh_klein.*_Ge', 'coh_klein-nystrand_Xe':r'coh_klein.*_Xe'                
+               }
+
+    collated_rates = aggregate_channels(rates,patterns)
+
+    return collated_rates  
+
+
+def aggregate(rates):
+    """Sum all the channels in the event rates / numbers table returned by RateCalculator.run 
+
+    Parameters
+    ----------
+    dict[str, Container]
+                A nested dictionary with interaction rates (as instances of :class:`snewpy.flux.Container`) for each channel.
+
+    Returns
+    -------
+    dict[str, Container]
+                A dictionary with interaction rates (as instances of :class:`snewpy.flux.Container`) summed over all channels for a given detector.
+    """    
+    sumrates = sum([rates[channel].array for channel in rates])
+    return Container(sumrates,ThreeFlavor.take([0,-1]), rates[0].time, rates[0].energy)
+
+
+
diff --git a/python/snewpy/test/simplerate_integrationtest.py b/python/snewpy/test/simplerate_integrationtest.py
index 46f94dd47..e3781b3fe 100644
--- a/python/snewpy/test/simplerate_integrationtest.py
+++ b/python/snewpy/test/simplerate_integrationtest.py
@@ -2,84 +2,49 @@
 """Integration test based on SNEWS2.0_rate_table_singleexample.py
 """
 import unittest
+
 from snewpy import snowglobes
-from snewpy import model_path 
+from snewpy.models.ccsn import Bollig_2016
+from snewpy.neutrino import MassHierarchy, MixingParameters
+from snewpy.flavor_transformation import AdiabaticMSW
+from snewpy.rate_calculator import RateCalculator
 
-from snewpy.models import ccsn
+import numpy as np
 import astropy.units as u
 
-def preload_model(name:str, **parameters):
-    #initialize the model with given name and parameters
-    model = ccsn.__dict__[name](**parameters)
-    return model
-
-
 class TestSimpleRate(unittest.TestCase):
 
     def test_simplerate(self):
         """Integration test based on SNEWS2.0_rate_table_singleexample.py
         """
-        # Hardcoded paths on GitHub Action runner machines
-        SNOwGLoBES_path = None
-
-        distance = 10  # Supernova distance in kpc
-        detector = "wc100kt30prct" #SNOwGLoBES detector for water Cerenkov
-        modeltype = 'Bollig_2016' # Model type from snewpy.models
-        model = 's11.2c' # Name of model
-        transformation = 'AdiabaticMSW_NMO' # Desired flavor transformation
-
-        # Construct file system path of model file and name of output file
-        model_file_path = f'{model_path}/{modeltype}/{model}'
-        outfile = f'{modeltype}_{model}_{transformation}'
-
-        #make sure the model files are loaded
-        preload_model(modeltype, progenitor_mass=11.2*u.Msun)
-        
+        model = Bollig_2016(progenitor_mass=11.2<<u.Msun) # SN model
+        transformation = AdiabaticMSW(MixingParameters('NORMAL')) # Desired flavor transformation
+       
         # Now, do the main work:
         print("Generating fluence files ...")
-        tarredfile = snowglobes.generate_fluence(model_file_path, modeltype, transformation, distance, outfile)
-
-        print("Simulating detector effects with SNOwGLoBES ...")
-        snowglobes.simulate(SNOwGLoBES_path, tarredfile, detector_input=detector, detector_effects=True)
-
-        print("Collating results ...")
-        tables = snowglobes.collate(tarredfile, skip_plots=True, smearing=True)
-
-        # Use results to print the number of events in different interaction channels
-        key = f"Collated_{outfile}_{detector}_events_unsmeared_weighted.dat"
-        total_events = 0
-        for i, channel in enumerate(tables[key]['header'].split()):
-            if i == 0:
-                continue
-            n_events = sum(tables[key]['data'][i])
-            total_events += n_events
-            print(f"{channel:10}: {n_events:.3f} events")
-
-        #Super-K has 32kT inner volume
-        print("Total events in Super-K-like detector:" , 0.32*total_events)
-
-        # Use results to print the number of events in different interaction channels
-        # with efficiency and smearing
-        key = f"Collated_{outfile}_{detector}_events_smeared_weighted.dat"
-        total_events_smeared = 0
-        for i, channel in enumerate(tables[key]['header'].split()):
-            if i == 0:
-                continue
-            n_events = sum(tables[key]['data'][i])
-            total_events_smeared += n_events
-            print(f"{channel:10}: {n_events:.3f} events (smeared)")
+        times    = model.get_time()
+        energies = np.linspace(0,100,501)<<u.MeV
+        distance = 10*u.kpc
+        #get the flux from the model
+        flux = model.get_flux(t=times, E=energies, distance=distance, flavor_xform=transformation)
+        fluence = flux.integrate('time')
+
+        print("Simulating detector effects ...")
+        detector = "wc100kt30prct"
+        rc = RateCalculator()
+        events = rc.run(fluence, detector, detector_effects=True)
+        
+        # Compute number of events in all interaction channels
+        total_events  = sum([chan.integrate_or_sum('energy').array.squeeze().value for chan in events.values()])
 
         #Super-K has 32kT inner volume
-        print("Total events in Super-K-like detector (with smearing):" , 0.32*total_events_smeared)
+        print("Total events in Super-K-like detector (with smearing):" , 0.32*total_events)
 
         # We do not use the SNOwGLoBES scaling factors but use other constants so we do not
         # expect the results to agree to 7 digits. Here sub-permille agreement is good enough.
-        sk_expected = 4491.783259
-        sk_expected_smeared = 4065.662374
+        sk_expected = 4065.662374
         sk_computed = 0.32 * total_events
-        sk_computed_smeared = 0.32 * total_events_smeared
         discrepancy = abs(sk_computed - sk_expected)/sk_expected
-        discrepancy_smeared = abs(sk_computed_smeared - sk_expected_smeared)/sk_expected_smeared
 
         assert discrepancy < 0.001, f"Number of unsmeared events computed for SK is {sk_computed}, should be {sk_expected}"
-        assert discrepancy_smeared < 0.001, f"Number of smeared events computed for SK is {sk_computed_smeared}, should be {sk_expected_smeared}"
+