diff --git a/Fund/Data/Base/rfso2.csv b/Fund/Data/Base/rfso2.csv
new file mode 100644
index 00000000..89b7ff49
--- /dev/null
+++ b/Fund/Data/Base/rfso2.csv
@@ -0,0 +1,1053 @@
+# Base scenario
+# Generated by calibration/so2rf/fund_so2_rf_scenarios.ipynb
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diff --git a/Fund/Data/Base/so20.csv b/Fund/Data/Base/so20.csv
deleted file mode 100644
index 82a55940..00000000
--- a/Fund/Data/Base/so20.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-# FUND scenario
-USA,1.6924
-CAN,0.1204
-WEU,2.0556
-JPK,0.0897
-ANZ,0.0152
-EEU,0.2585
-FSU,1.3184
-MDE,0.0311
-CAM,0.055
-LAM,0.2071
-SAS,0.4361
-SEA,0.1586
-CHI,1.3663
-MAF,0.0416
-SSA,0.2097
-SIS,0.0152
diff --git a/Fund/Data/Base/so2carb.csv b/Fund/Data/Base/so2carb.csv
deleted file mode 100644
index cfcbb75b..00000000
--- a/Fund/Data/Base/so2carb.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-# FUND scenario
-1.02
diff --git a/Fund/Data/Base/so2dir.csv b/Fund/Data/Base/so2dir.csv
deleted file mode 100644
index 614c5ebb..00000000
--- a/Fund/Data/Base/so2dir.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-# FUND scenario
-0.03
diff --git a/Fund/Data/Base/so2inc.csv b/Fund/Data/Base/so2inc.csv
deleted file mode 100644
index 5d20d5db..00000000
--- a/Fund/Data/Base/so2inc.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-# FUND scenario
--0.45
diff --git a/Fund/Data/Base/so2ind.csv b/Fund/Data/Base/so2ind.csv
deleted file mode 100644
index 740054cc..00000000
--- a/Fund/Data/Base/so2ind.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-# FUND scenario
-0.08
diff --git a/Fund/Data/Base/so2pop.csv b/Fund/Data/Base/so2pop.csv
deleted file mode 100644
index 0f7c6f12..00000000
--- a/Fund/Data/Base/so2pop.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-# FUND scenario
-0.33
diff --git a/Fund/Data/SRES A1b/rfso2.csv b/Fund/Data/SRES A1b/rfso2.csv
new file mode 100644
index 00000000..9584524f
--- /dev/null
+++ b/Fund/Data/SRES A1b/rfso2.csv	
@@ -0,0 +1,1053 @@
+# SRES A1b scenario
+# Generated by calibration/so2rf/fund_so2_rf_scenarios.ipynb
+1950,-0.51408617
+1951,-0.53129057
+1952,-0.54697036
+1953,-0.56104313
+1954,-0.57557676
+1955,-0.5903168599999999
+1956,-0.60582542
+1957,-0.6218562999999999
+1958,-0.63529134
+1959,-0.64552054
+1960,-0.65584153
+1961,-0.67235667
+1962,-0.6907649299999999
+1963,-0.70532916
+1964,-0.71961857
+1965,-0.7329564199999999
+1966,-0.745741
+1967,-0.7605957000000001
+1968,-0.7751724
+1969,-0.7855831900000001
+1970,-0.78954408
+1971,-0.7945667000000001
+1972,-0.8097149299999999
+1973,-0.82805172
+1974,-0.8454669699999999
+1975,-0.8635462700000001
+1976,-0.8821087000000001
+1977,-0.8998159099999999
+1978,-0.9131963300000001
+1979,-0.9238604699999999
+1980,-0.9414344399999999
+1981,-0.9614159200000001
+1982,-0.977245
+1983,-0.9928104
+1984,-1.00594548
+1985,-1.01637515
+1986,-1.0266699400000001
+1987,-1.03836786
+1988,-1.04870941
+1989,-1.0559914099999999
+1990,-1.06097369
+1991,-1.0638445600000002
+1992,-1.0675982199999998
+1993,-1.06771175
+1994,-1.06369146
+1995,-1.05719786
+1996,-1.05121331
+1997,-1.05784857
+1998,-1.07289133
+1999,-1.08539651
+2000,-1.09887021
+2001,-1.1082937400000001
+2002,-1.11079324
+2003,-1.11420344
+2004,-1.11807236
+2005,-1.1174946399999999
+2006,-1.104675712
+2007,-1.091856784
+2008,-1.0790378559999998
+2009,-1.0662189279999998
+2010,-1.0534
+2011,-1.0629799999999998
+2012,-1.07256
+2013,-1.0821399999999999
+2014,-1.09172
+2015,-1.1013
+2016,-1.10528
+2017,-1.10926
+2018,-1.11324
+2019,-1.1172199999999999
+2020,-1.1212
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diff --git a/Fund/Data/SRES A2/rfso2.csv b/Fund/Data/SRES A2/rfso2.csv
new file mode 100644
index 00000000..fe8c56c9
--- /dev/null
+++ b/Fund/Data/SRES A2/rfso2.csv	
@@ -0,0 +1,1053 @@
+# SRES A2 scenario
+# Generated by calibration/so2rf/fund_so2_rf_scenarios.ipynb
+1950,-0.51408617
+1951,-0.53129057
+1952,-0.54697036
+1953,-0.56104313
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diff --git a/Fund/Data/SRES B1/rfso2.csv b/Fund/Data/SRES B1/rfso2.csv
new file mode 100644
index 00000000..80779dd2
--- /dev/null
+++ b/Fund/Data/SRES B1/rfso2.csv	
@@ -0,0 +1,1053 @@
+# SRES B1 scenario
+# Generated by calibration/so2rf/fund_so2_rf_scenarios.ipynb
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diff --git a/Fund/Data/SRES B2/rfso2.csv b/Fund/Data/SRES B2/rfso2.csv
new file mode 100644
index 00000000..7baafbc6
--- /dev/null
+++ b/Fund/Data/SRES B2/rfso2.csv	
@@ -0,0 +1,1053 @@
+# SRES B2 scenario
+# Generated by calibration/so2rf/fund_so2_rf_scenarios.ipynb
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+2915,-0.3318
+2916,-0.3318
+2917,-0.3318
+2918,-0.3318
+2919,-0.3318
+2920,-0.3318
+2921,-0.3318
+2922,-0.3318
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+2927,-0.3318
+2928,-0.3318
+2929,-0.3318
+2930,-0.3318
+2931,-0.3318
+2932,-0.3318
+2933,-0.3318
+2934,-0.3318
+2935,-0.3318
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+2939,-0.3318
+2940,-0.3318
+2941,-0.3318
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+2948,-0.3318
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+2950,-0.3318
+2951,-0.3318
+2952,-0.3318
+2953,-0.3318
+2954,-0.3318
+2955,-0.3318
+2956,-0.3318
+2957,-0.3318
+2958,-0.3318
+2959,-0.3318
+2960,-0.3318
+2961,-0.3318
+2962,-0.3318
+2963,-0.3318
+2964,-0.3318
+2965,-0.3318
+2966,-0.3318
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+2968,-0.3318
+2969,-0.3318
+2970,-0.3318
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+2984,-0.3318
+2985,-0.3318
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+2987,-0.3318
+2988,-0.3318
+2989,-0.3318
+2990,-0.3318
+2991,-0.3318
+2992,-0.3318
+2993,-0.3318
+2994,-0.3318
+2995,-0.3318
+2996,-0.3318
+2997,-0.3318
+2998,-0.3318
+2999,-0.3318
+3000,-0.3318
diff --git a/Fund/Fund.csproj b/Fund/Fund.csproj
index 04d24df2..5d4d3b02 100644
--- a/Fund/Fund.csproj
+++ b/Fund/Fund.csproj
@@ -526,6 +526,9 @@
     <None Include="Data\Base\resp90.csv">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
+    <None Include="Data\Base\rfso2.csv">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
     <None Include="Data\Base\rlbm.csv">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
@@ -589,24 +592,6 @@
     <None Include="Data\Base\smnl.csv">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
-    <None Include="Data\Base\so20.csv">
-      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
-    </None>
-    <None Include="Data\Base\so2carb.csv">
-      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
-    </None>
-    <None Include="Data\Base\so2dir.csv">
-      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
-    </None>
-    <None Include="Data\Base\so2inc.csv">
-      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
-    </None>
-    <None Include="Data\Base\so2ind.csv">
-      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
-    </None>
-    <None Include="Data\Base\so2pop.csv">
-      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
-    </None>
     <None Include="Data\Base\spbm.csv">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
@@ -712,6 +697,9 @@
     <None Include="Data\SRES A1b\n2oem.csv">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
+    <None Include="Data\SRES A1b\rfso2.csv">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
     <None Include="Data\SRES A1b\scenacei.csv">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
@@ -733,6 +721,9 @@
     <None Include="Data\SRES A2\n2oem.csv">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
+    <None Include="Data\SRES A2\rfso2.csv">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
     <None Include="Data\SRES A2\scenacei.csv">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
@@ -754,6 +745,9 @@
     <None Include="Data\SRES B1\n2oem.csv">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
+    <None Include="Data\SRES B1\rfso2.csv">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
     <None Include="Data\SRES B1\scenacei.csv">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
@@ -775,6 +769,9 @@
     <None Include="Data\SRES B2\n2oem.csv">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
+    <None Include="Data\SRES B2\rfso2.csv">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
     <None Include="Data\SRES B2\scenacei.csv">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
diff --git a/Fund/FundModel.cs b/Fund/FundModel.cs
index daaaf472..720509d4 100644
--- a/Fund/FundModel.cs
+++ b/Fund/FundModel.cs
@@ -61,15 +61,11 @@ public static Model GetModel(bool storeFullVariablesByDefault = true, int years
             m.AddComponent<ClimateSF6CycleComponent>("ClimateSF6Cycle");
             m["ClimateSF6Cycle"].Parameters["globsf6"].Bind("emissions");
 
-            m.AddComponent<ClimateSO2CycleComponent>("ClimateSO2Cycle");
-            m["ClimateSO2Cycle"].Parameters["globso2"].Bind("emissions");
-
             m.AddComponent<ClimateForcingComponent>("ClimateForcing");
             m["ClimateForcing"].Parameters["acco2"].Bind("climateco2cycle");
             m["ClimateForcing"].Parameters["acch4"].Bind("climatech4cycle");
             m["ClimateForcing"].Parameters["acn2o"].Bind("climaten2ocycle");
             m["ClimateForcing"].Parameters["acsf6"].Bind("climatesf6cycle");
-            m["ClimateForcing"].Parameters["acso2"].Bind("climateso2cycle");
 
             m.AddComponent<ClimateDynamicsComponent>("ClimateDynamics");
             m["ClimateDynamics"].Parameters["radforc"].Bind("climateforcing");
diff --git a/FundComponents/ClimateForcingComponent.cs b/FundComponents/ClimateForcingComponent.cs
index 3e6e993b..030aaa5e 100644
--- a/FundComponents/ClimateForcingComponent.cs
+++ b/FundComponents/ClimateForcingComponent.cs
@@ -41,15 +41,6 @@ public interface IClimateForcingState
         /// <summary>Pre-industrial atmospheric SF6 concentration</summary>
         double sf6pre { get; }
 
-        /// <summary>Atmospheric SO2 concentration</summary>
-        IParameter1Dimensional<Timestep, double> acso2 { get; }
-
-        /// <summary>Direct radiative forcing by sulphate aerosols</summary>
-        Double so2dir { get; }
-
-        /// <summary>Indirect radiative forcing by sulphate aerosols</summary>
-        double so2ind { get; }
-
         /// <summary>Radiative forcing from CO2</summary>
         IVariable1Dimensional<Timestep, double> rfCO2 { get; }
 
@@ -63,7 +54,7 @@ public interface IClimateForcingState
         IVariable1Dimensional<Timestep, double> rfSF6 { get; }
 
         /// <summary>Radiative forcing from SO2</summary>
-        IVariable1Dimensional<Timestep, double> rfSO2 { get; }
+        IParameter1Dimensional<Timestep, double> rfSO2 { get; }
 
         /// <summary>Radiative forcing</summary>
         IVariable1Dimensional<Timestep, double> radforc { get; }
@@ -104,9 +95,7 @@ public void Run(Clock clock, IClimateForcingState state, IDimensions dimensions)
 
                 s.rfSF6[t] = 0.00052 * (s.acsf6[t] - s.sf6pre);
 
-                s.rfSO2[t] = s.so2dir * s.acso2[t] / 14.6 + s.so2ind * Math.Log(1.0 + s.acso2[t] / 34.4) / Math.Log(1 + 14.6 / 34.4) - 0.9;
-
-                s.radforc[t] = s.rfCO2[t] + s.rfCH4[t] + s.rfN2O[t] + s.rfSF6[t] - s.rfSO2[t];
+                s.radforc[t] = s.rfCO2[t] + s.rfCH4[t] + s.rfN2O[t] + s.rfSF6[t] + s.rfSO2[t];
 
                 s.rfEMF22[t] = s.rfCO2[t] + s.rfCH4[t] + s.rfN2O[t];
             }
diff --git a/FundComponents/ClimateSO2CycleComponent.cs b/FundComponents/ClimateSO2CycleComponent.cs
deleted file mode 100644
index 4050b59b..00000000
--- a/FundComponents/ClimateSO2CycleComponent.cs
+++ /dev/null
@@ -1,41 +0,0 @@
-// FUND - Climate Framework for Uncertainty, Negotiation and Distribution
-// Copyright (C) 2012 David Anthoff and Richard S.J. Tol
-// http://www.fund-model.org
-// Licensed under the MIT license
-using System;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
-using Esmf;
-
-namespace Fund.Components
-{
-    public interface IClimateSO2CycleState
-    {
-        /// <summary>Global SO2 emissions</summary>
-        IParameter1Dimensional<Timestep, double> globso2 { get; }
-
-        /// <summary>Atmospheric SO2 concentration</summary>
-        IVariable1Dimensional<Timestep, double> acso2 { get; }
-    }
-    public class ClimateSO2CycleComponent
-    {
-
-        public void Run(Clock clock, IClimateSO2CycleState state, IDimensions dimensions)
-        {
-            // create shortcuts for commonly accessed data
-            var s = state;
-            var t = clock.Current;
-
-            if (clock.IsFirstTimestep)
-            {
-
-            }
-            else
-            {
-                // Calculate SO2 concentrations
-                s.acso2[t] = s.globso2[t];
-            }
-        }
-    }
-}
diff --git a/FundComponents/EmissionsComponent.cs b/FundComponents/EmissionsComponent.cs
index b8175d6a..4ca7f768 100644
--- a/FundComponents/EmissionsComponent.cs
+++ b/FundComponents/EmissionsComponent.cs
@@ -23,8 +23,6 @@ public interface IEmissionsState
         IVariable2Dimensional<Timestep, Region, double> emissint { get; }
         IVariable2Dimensional<Timestep, Region, double> emission { get; }
         IVariable2Dimensional<Timestep, Region, double> emissionwithforestry { get; }
-        IVariable2Dimensional<Timestep, Region, double> so2WithSeeiAndScei { get; }
-        IVariable2Dimensional<Timestep, Region, double> so2 { get; }
         IVariable2Dimensional<Timestep, Region, double> sf6 { get; }
         IVariable2Dimensional<Timestep, Region, double> reei { get; }
         IVariable2Dimensional<Timestep, Region, double> rcei { get; }
@@ -48,13 +46,11 @@ public interface IEmissionsState
         IVariable1Dimensional<Timestep, double> globch4 { get; }
         IVariable1Dimensional<Timestep, double> globn2o { get; }
         IVariable1Dimensional<Timestep, double> globsf6 { get; }
-        IVariable1Dimensional<Timestep, double> globso2 { get; }
 
         IVariable1Dimensional<Timestep, double> cumglobco2 { get; }
         IVariable1Dimensional<Timestep, double> cumglobch4 { get; }
         IVariable1Dimensional<Timestep, double> cumglobn2o { get; }
         IVariable1Dimensional<Timestep, double> cumglobsf6 { get; }
-        IVariable1Dimensional<Timestep, double> cumglobso2 { get; }
 
         IParameter1Dimensional<Region, double> taxmp { get; }
         IParameter1Dimensional<Region, double> sf60 { get; }
@@ -66,7 +62,6 @@ public interface IEmissionsState
         IParameter1Dimensional<Region, double> n2opar2 { get; }
         IParameter1Dimensional<Region, double> gdp0 { get; }
         IParameter1Dimensional<Region, double> emissint0 { get; }
-        IParameter1Dimensional<Region, double> so20 { get; }
 
         IParameter2Dimensional<Timestep, Region, double> forestemm { get; }
         IParameter2Dimensional<Timestep, Region, double> aeei { get; }
@@ -81,9 +76,6 @@ public interface IEmissionsState
         IParameter2Dimensional<Timestep, Region, double> income { get; }
         IParameter2Dimensional<Timestep, Region, double> population { get; }
 
-        double so2pop { get; }
-        double so2inc { get; }
-        double so2carb { get; }
         double sf6gdp { get; }
         double sf6ypc { get; }
         double knowpar { get; }
@@ -119,7 +111,6 @@ public void Run(Clock clock, IEmissionsState state, IDimensions dimensions)
                     s.energint[t0, r] = 1;
                     s.energuse[t0, r] = s.gdp0[r];
                     s.emissint[t0, r] = s.emissint0[r];
-                    s.so2[t0, r] = s.so20[r];
                     s.emission[t0, r] = s.emissint[t0, r] / s.energuse[t0, r];
                     s.ch4cost[t0, r] = 0;
                     s.n2ocost[t0, r] = 0;
@@ -140,7 +131,6 @@ public void Run(Clock clock, IEmissionsState state, IDimensions dimensions)
                 s.cumglobch4[t0] = 0.0;
                 s.cumglobn2o[t0] = 0.0;
                 s.cumglobsf6[t0] = 0.0;
-                s.cumglobso2[t0] = 0.0;
 
                 //SocioEconomicState.minint[t0]=Double.PositiveInfinity;
                 var minint = double.PositiveInfinity;
@@ -163,17 +153,6 @@ public void Run(Clock clock, IEmissionsState state, IDimensions dimensions)
                     s.emissint[t, r] = (1.0 - 0.01 * s.acei[t, r] - s.rcei[t - 1, r]) * s.emissint[t - 1, r];
                 }
 
-                // Calculate so2 emissions
-                foreach (var r in dimensions.GetValues<Region>())
-                {
-                    s.so2[t, r] = s.so2[t - 1, r] *
-                                Math.Pow(1 + 0.01 * s.pgrowth[t - 1, r], s.so2pop) *
-                                Math.Pow(1 + 0.01 * s.ypcgrowth[t - 1, r], s.so2inc) *
-                                Math.Pow(1 - 0.01 * s.aeei[t, r] - s.reei[t - 1, r] - 0.01 * s.acei[t, r] - s.rcei[t - 1, r], s.so2carb);
-
-                    s.so2WithSeeiAndScei[t, r] = s.so2[t, r] * (1 - s.seei[t - 1, r] - s.scei[t - 1, r]);
-                }
-
                 // Calculate sf6 emissions
                 foreach (var r in dimensions.GetValues<Region>())
                 {
@@ -344,7 +323,6 @@ public void Run(Clock clock, IEmissionsState state, IDimensions dimensions)
                 double globch4 = 0;
                 double globn2o = 0;
                 double globsf6 = 0;
-                double globso2 = 34.4;
 
                 foreach (var r in dimensions.GetValues<Region>())
                 {
@@ -352,20 +330,17 @@ public void Run(Clock clock, IEmissionsState state, IDimensions dimensions)
                     globch4 = globch4 + s.ch4[t, r];
                     globn2o = globn2o + s.n2o[t, r];
                     globsf6 = globsf6 + s.sf6[t, r];
-                    globso2 = globso2 + s.so2WithSeeiAndScei[t, r];
                 }
 
                 s.mco2[t] = globco2;
                 s.globch4[t] = Math.Max(0.0, globch4 + (t.Value > 50 ? s.ch4add * (t.Value - 50) : 0.0));
                 s.globn2o[t] = Math.Max(0.0, globn2o + (t.Value > 50 ? s.n2oadd * (t.Value - 50) : 0));
                 s.globsf6[t] = Math.Max(0.0, globsf6 + (t.Value > 50 ? s.sf6add * (t.Value - 50) : 0.0));
-                s.globso2[t] = globso2;
 
                 s.cumglobco2[t] = s.cumglobco2[t - 1] + s.mco2[t];
                 s.cumglobch4[t] = s.cumglobch4[t - 1] + s.globch4[t];
                 s.cumglobn2o[t] = s.cumglobn2o[t - 1] + s.globn2o[t];
                 s.cumglobsf6[t] = s.cumglobsf6[t - 1] + s.globsf6[t];
-                s.cumglobso2[t] = s.cumglobso2[t - 1] + s.globso2[t];
             }
         }
     }
diff --git a/FundComponents/FundComponents.csproj b/FundComponents/FundComponents.csproj
index ca3e39a2..f6a762a7 100644
--- a/FundComponents/FundComponents.csproj
+++ b/FundComponents/FundComponents.csproj
@@ -84,7 +84,6 @@
     <Compile Include="ClimateN2OCycleComponent.cs" />
     <Compile Include="ClimateRegionalComponent.cs" />
     <Compile Include="ClimateSF6CycleComponent.cs" />
-    <Compile Include="ClimateSO2CycleComponent.cs" />
     <Compile Include="EmissionsComponent.cs" />
     <Compile Include="GeographyComponent.cs" />
     <Compile Include="HotellingTaxationComponent.cs" />
diff --git a/calibration/so2rf/RCP85_MIDYEAR_RADFORCING.xls b/calibration/so2rf/RCP85_MIDYEAR_RADFORCING.xls
new file mode 100644
index 00000000..dc4714fd
Binary files /dev/null and b/calibration/so2rf/RCP85_MIDYEAR_RADFORCING.xls differ
diff --git a/calibration/so2rf/fund_so2_rf_scenarios.ipynb b/calibration/so2rf/fund_so2_rf_scenarios.ipynb
new file mode 100644
index 00000000..c40e04df
--- /dev/null
+++ b/calibration/so2rf/fund_so2_rf_scenarios.ipynb
@@ -0,0 +1,408 @@
+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pandas"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "FUND SO2 scenarios"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "I. Introduction"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The SO2 radiative forcing scenarios are exogenous. Historical forcings are based on numbers from RCP85 up until 2005. After that time the FUND scenarios is based on the RCP85 scenario, and the SRES scenarios on the IMAGE 2.2 interpretation of these scenarios."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "II. Data sources"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "1) IMAGE 2.2"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This data was obtained from http://themasites.pbl.nl/tridion/en/themasites/image/projects/downloads/index-2.html on 11/18/2013 by Anthoff.\n",
+      "\n",
+      "Steps:\n",
+      "\n",
+      "- Download and install the User Support System (USS)\n",
+      "- Run Start_SRES_viewer.bat in the install directory\n",
+      "- Using Tools->Scenario Manager, load all the relevant scenarios, then select them one by one to export the data\n",
+      "- Select Aggregated Indicators (IPCC Regions) View -> State -> Other State Indicators -> Climate -> Relvative Contributions to Radiative Forcing -> Negative Contributors\n",
+      "- Right click the area around the graph and select \"Save selection as Excel...\" and pick a filename\n",
+      "- Open each of the saved files with a newer version of Excel and save it as an xlsx file"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "image22_scenarios = ['A1b', 'A2', 'B1', 'B2']\n",
+      "data = {}\n",
+      "\n",
+      "for image22_scenario in image22_scenarios:\n",
+      "    temp_df = pandas.read_excel('main.DELTQNEG_' + image22_scenario + '.xlsx', 'main.DELTQNEG')  \n",
+      "    temp_df['t'] = temp_df['t'].astype(int)\n",
+      "    # Filter out non sulphur forcings\n",
+      "    temp_df = temp_df[(temp_df['Contributor (a)']=='Sulph. direct') | (temp_df['Contributor (a)']=='Sulph. indirect')]\n",
+      "    \n",
+      "    # Aggregate direct and indirect forcings\n",
+      "    temp_df = temp_df['Negative~Contributors'].groupby(temp_df['t']).sum()\n",
+      "    \n",
+      "    # Negate sign so that forcing is negative\n",
+      "    data['IMAGE 2.2 SRES ' + image22_scenario] = -temp_df"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df_image22 = pandas.DataFrame(data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df_image22.loc[2005:2100].plot()\n",
+      "xlabel('year')\n",
+      "ylabel('W/m^2')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "<matplotlib.text.Text at 0x92d6a20>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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w19RkU1TVJD8fiIwsFYsS4RAIgI4ducAEJa+2tkCjRizWlhhMSBjKxCf3y5gY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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7e93e80>"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "2) RCP"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Data obtained from http://www.pik-potsdam.de/~mmalte/rcps/ on 11/15/2013 by Anthoff."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "temp_df = pandas.read_excel('RCP85_MIDYEAR_RADFORCING.xls', 'RCP85_MIDYEAR_RADFORCING',\n",
+      "                            skiprows=59)\n",
+      "temp_df.index = temp_df['v YEARS/GAS >'].astype(int)\n",
+      "temp_df.index.name = 'year'"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df_rcp = pandas.Series(temp_df['TOTAER_DIR_RF'] + temp_df['CLOUD_TOT_RF'], name='RCP85')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df_rcp.loc[1950:2500].plot(figsize=(10,10),label='RCP85')\n",
+      "xlabel('year')\n",
+      "ylabel('W/m^2')\n",
+      "legend()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 7,
+       "text": [
+        "<matplotlib.legend.Legend at 0x7e832e8>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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gzlR895307rvS4MG2KykpqJn7GZmbR+bmkbnbaOQcx5Wr0bVkidSsGVerAgDc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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7528320>"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "III. FUND scenarios"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here we construct scenarios for FUND."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df_fund = pandas.DataFrame(index=range(1950,3001))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "1) FUND scenario"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This constructs an emission scenario that has emissions as RCP85 from 1950-2500 and then stays constant at the 2500 emission level for the rest of the time."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "new = df_rcp.reindex(range(1950,3001)).interpolate()\n",
+      "df_fund['Base'] = new"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "2) SRES scenarios"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This constructs emission scenarios that have historical emissions as in RCP85 from 1950-2005, followed by emissions as in the IMAGE 2.2 SRES scenarios for the years 2006-2100, and then stays constant at the 2100 emission level for the rest of the time."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "switch_year = 2006"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for scenario in image22_scenarios:\n",
+      "    new = df_image22['IMAGE 2.2 SRES %s'%scenario].reindex(range(1950,3001))\n",
+      "    new.ix[1950:switch_year-1] = df_rcp.ix[1950:switch_year-1]\n",
+      "    new = new.interpolate()\n",
+      "    df_fund['SRES %s'%scenario] = new"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "3) Figures"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df_fund.plot(figsize=(10,10))\n",
+      "xlabel('year')\n",
+      "ylabel('W/m^2')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 12,
+       "text": [
+        "<matplotlib.text.Text at 0x943b8d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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OSUnBihUrkJ+fD71e7/D6wYMH49e//jU+++yz1uf0ej3mzp2L/v3748033+x0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\nAABsikIOAADApijkAAAAbOr/A1Rly2SMXtqIAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x9429668>"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df_fund.loc[2000:2020].plot(figsize=(10,10))\n",
+      "xlabel('year')\n",
+      "ylabel('W/m^2')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 13,
+       "text": [
+        "<matplotlib.text.Text at 0x9416128>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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envj444/N/bZ0G65tRsZirtgWtVaLrMuXse7iRTx+4gSG7N8Pn19+wewTJ7BH\npUJ/Dw+s6NsXF0eNwrnERPwnNhZ/CQ/HPf7+CDbDpAjmC4lBlGVSwsPDcfLkSbi7u6OhoQEDBgzA\nr7/+2m6f+vp6xMfH44cffoC7uzvuuusuPPDAA1i+fLlR9wdsb5kUR2YNnwkb58lYzBXrdamlpV2/\nXL5Gg9LmZsS2TX5IkMmQIJUizssLHs7O3RIT84WMZRPLpEycOLHTRXTfeOMNo4L38vLC2rVrMX/+\nfKhUKsTFxRmGEsUuBMg+8QuYjMVcEZ8gCPi1qckw6SG/raBr0OsNkx/u8/fHS336INrDAy4iTn5g\nvpAYLFbgff/9911u27JlC4qKipCQkICioiIMHz680/0mT55s6PmaMWMGkpKSAADDhw836v4AMHv2\nbPTp0wfAb0OXQzhV3WpdGca48mXI67zO67w+fvx46AQB/9i9G6cbG9EUH48CjQb7s7LQQyJB4q23\nYqhMhhHnzuEPHh6YcdddkEgkhvsPsoL4eZ3Xu7p+5d/FxcUwN1GGaFetWoULFy5g1apVePbZZ9G3\nb188++yzHfa7dOkSAgMD8cMPP+Cpp57CsWPHTLo/h2hthzV8JpkcRiEjMVcsp0mnw9H6esPwaoFa\njaP19Qh2c/ttiPXKRSZDrx49xA7XKMwXMpZNDNFeS1paGh555BFER0dj6NCheOuttwD8trBuamoq\ndu3aBQB48MEHcenSJchkMnz00UfXvT8REdmOOq0Wh343xHqmsRGRHh6GIm5mYCAGS6Xw7uT0jUTU\nNZ6LlqwCPxMi+1Z+ZbHgq878UN7SgrirjsoNlckwyNMT7t00+YHI2pjzbyELPLIK/EyI7IMgCDh/\nZfLDVWd+aNLr2xVyCVIpojw94cz15IgMWOAZiQWe7bCGz4R9MmQs5spvtHo9TjQ0tJvJekijgdTZ\nGQkyGYZe1S8X5uZmF2e8uRHMFzKWzffgERGRbWnU6VBYX2/olSvQaHCsvh4KN7ffCjmZDM+HhyNB\nKkWAjUx+ILJnPIJHVoGfCZH1uNza2qFf7mxTE2I8PdvNZB0slULGyQ9EZmPOv4XirfxIncrPz0dq\naip69+4Nf39/jB07FgcOHAAAZGRkwNnZGTKZDEFBQZg+fTp+/PFHw32Li4vh5OTU7jRlMpkM27dv\nBwBUVFRg2bJliI2Nhbe3N+Li4rBly5ZO48jMzGz3WOPGjcPGjRsN2ysrKzFz5kwoFAooFArMnTsX\nhYWFFnyQrTieAAAgAElEQVRniMjcBEFAaXMzdlVX4/XiYkw9ehQROTkI3bcPL5w/j3ONjRjv44Ot\nAwbg8tixKLjlFmyOicHC0FCM9fFhcUdkxfh/p5VJSUnBY489htWrV6NHjx7IysoynMEDAMaMGYM9\ne/ZAo9Fg9erVSEtLw6lTp9o9hkqlgpNTx9r9zTffRHV1NXbv3o2QkBAcOXKk07ONXKFQKHDhwgUA\nwMGDB3HHHXdg4sSJ6N+/PzQaDUaOHIm1a9fCy8sLa9asQWpqKnJycsz0TnQ/9smQsWwxV/SCgHON\njR3O/KATBMPpux4MCMAbffsikpMfzMoW84VsHws8K3Lq1CmcOHECCxYsgEvbL+OJEye22+fKoVup\nVIq5c+di+fLlOHHiBGJiYq77+J999hk+/fRThIaGAoBJZ/UYNmwYRo8eja+//hqLFi1C3759sXjx\nYsP2559/Hm+99RZOnjyJ6Ohoox+XiMyvVa9HUdvkhyuF3GGNBt4uLobh1XkKBRKkUoQ68OQHInvG\nAs+KREVFQaFQYMaMGZg9ezbGjRsHb2/vTvdVqVRYv349AgICEBUV1W5bV+P3kyZNwpIlS7Bw4ULc\neeedhkLvevR6PXJzc7Fv3z6sWLGi030OHToEAAgJCTHqMa0Rf2GTsawpVxp0Ohy5ctaHtn654w0N\nCHd3NxRzk/39MUQqhZyTH0RhTflCjoMFXicyJZlmeZzxwniT7/Pzzz9jw4YNmDdvHmpra5GSkoKX\nXnoJfn5+AICcnBz4+vpCo9HA3d0d+fn5HYZj5XJ5u+s5OTmIjo7GunXrkJGRYRhOnTx5MpYvX47Y\n2NhOYyktLYWvry90Oh00Gg2WLVuGhISEDvupVCrMmjULr7/+OmQymcmvmYiMU3Nl8sNVM1mLm5ow\n4MrkB5kMjwcFId7LC1L2xxE5NM6itWK5ubmYM2cOpk6dildffRUZGRn48MMPkZWVhbKyMqSmpsLD\nw8MwiaK4uBgRERHQarWd9uBdraSkBEuWLEFlZSX27NnTYXtmZiYeffRRQw/emTNnsGjRIkRGRuKd\nd94x7NfQ0IBJkyZh0KBB2LRp0w2/Vmv4TNgnQ8aydK4IggBlJ2d+qNFqMVgqbbe+3ABPT/S4zv/v\nJC5+t5CxuA6egxg5ciRmzpyJ7777rsO24OBgbN68GVFRUcjKysK4ceNMeuywsDAsWbIEd955J5qb\nm9tN5OhM//798cgjj+C5554zFHjNzc1ITk5GeHj4TRV3RI5MLwg4c2XyQ9uRuXyNBk5Au/OxroqI\nQD8PDzixX46IjMACz4qcPHkSu3btwvTp0xEUFITDhw9j27ZteOaZZzrdPzAwEKmpqVixYgW++eYb\nw+1dVf9Lly7FrFmzEB0djZKSEqxbtw7Tpk27bnEH/HZ08OOPP8bkyZMBAK2trXjwwQfh6enZ5VIr\ntoa/sMlYN5orLXo9jtfXt5vJeqS+Hv6uroZ+uYUKBYbKZAju0YOTH+wEv1tIDCzwrIhMJkNubi7W\nrFkDtVqNhIQEPPzww5g1axaA3w7d/v4Lf/HixejXrx+OHDmCnj17AgB8fHza7fPaa69h8eLFaGxs\nRHJyMsrKyhAREYEJEyZg4cKFncYikUhQWlpq6KkLDQ3FXXfdhaVLlwIAsrOzsWvXLnh6erZ7vt27\nd2PMmDHmeUOIbJhGq8WR35354URDA/pemfwgkyFZLscQqRS+rq5ih0tEdoY9eGQVrOEzYZ8MGev3\nuVLV0tKhX+5CczMGenm165eL8/KCp7OzeIGTKPjdQsZiDx4RkUiadDr8olIh8/x5Q0Gn0moNRdzd\nfn54PiwMMZ6ecOXkByISCY/gkVXgZ0K24tkzZ/Dz5ctI8vMzFHV93d05+YGIbhqP4BERiUCj1eKj\n8nIcHDYMfTw8xA6HiKhLHD8gapOZmSl2CGTlPq6owDhvbxTn5oodCtkQfreQGFjgEREZQRAE/F2p\nxEIjT/FHRCQm9uCRVeBnQtbup9paLDx9GkeHD+f6dERkEezBu0m+vr78grYyvr6+YodAdE3pSiUW\nKhT87iAim+CQQ7Q1NTUQBIEXK7rU1NSInRbsk6EuFTc2Ys/ly3ikVy8AzBUyDfOFxOCQBR4RkSk2\nlJbisaAgSF0cctCDiGyQQ/bgEREZq0GnQ9i+fcgdNgz9uDQKEVmQOesWHsEjIrqGTyoqMMrbm8Ud\nEdkUFnhEbdgnQ78nCIJhcsXVmCtkCuYLiYEFHhFRF7JUKjTr9ZjAWd5EZGPYg0dE1IUHjx7FeB8f\nLODixkTUDdiDR0RkYReamvDT5ct4LChI7FCIiEzGAo+oDftk6GobS0vxaK9ekHWyNApzhUzBfCEx\ncFEnIqLfadLp8EFZGX5JSBA7FCKiG8IePCKi38koK8O/KivxTXy82KEQkQNhDx4RkYUIgoB1nSyN\nQkRkS1jgEbVhnwwBQHZdHdQ6HZL8/Lrch7lCpmC+kBhY4BERXSX94kUsUCjgJJGIHQoR0Q1jDx4R\nUZvS5mbE7t+P84mJ8O5k9iwRkSWxB4+IyAI2lZZiZmAgizsisnks8IjasE/GsTXr9XivtBQLjJhc\nwVwhUzBfSAws8IiIAHx26RLipVIM8PISOxQiopvGHjwiIgAjDh7Ei+HhmCyXix0KETko9uAREZlR\nbl0dqlpbcY+/v9ihEBGZBQs8ojbsk3Fc6y5exHyFAs5GLo3CXCFTMF9IDCzwiMihlTc34+uaGswJ\nChI7FCIis2EPHhE5tOXFxShrbsam6GixQyEiB8cePCIiM2jR6/GukUujEBHZEhZ4RG3YJ+N4/lNZ\niRhPT8RKpSbdj7lCpmC+kBhY4BGRw1qnVGIhj94RkR1iDx4ROaQDdXWYeuwYzo4cCRcn/tYlIvGx\nB4+I6CalK5WYr1CwuCMiu8RvNqI27JNxHJdaWvBVdTVSgoNv6P7MFTIF84XEwAKPiBzO+2VlmCqX\nw9/VVexQiIgsgj14RORQWvV69M3Jwa74eAw2cfYsEZElsQePiOgG7aiqQoSHB4s7IrJrLPCI2rBP\nxjGkK5VYdJNLozBXyBTMFxIDCzwichiH1GoUNzXhfrlc7FCIiCyKPXhE5DBSTpxAPw8PPB8eLnYo\nREQdmLNucTHLoxARWbmqlhb8p7ISp0eOFDsUIiKL4xAtURv2ydi3D8rKcL9cjoAePW76sZgrZArm\nC4mBBR4R2T2tXo8NpaVYGBoqdihERN2CPXhEZPc+r6zE6gsXsHfoULFDISLqEtfBIyIyQbpSiYU3\nuTQKEZEtYYFH1IZ9MvbpiEaDUw0NmBoQYLbHZK6QKZgvJAYWeERk1/6uVGJuSAhcnfh1R0SOgz14\nRGS3alpb0S83FydGjEAvM8yeJSKyJPbgEREZYXNZGe7z92dxR0QOhwUeURv2ydgXnSBgfWmpRSZX\nMFfIFMwXEgMLPCKySzurqxHo6ooRPXuKHQoRUbdjDx4R2aUJhw5hdlAQHgkKEjsUIiKjsAePiOga\njtfX41hDA6YFBoodChGRKFjgEbVhn4z9+LtSiT8GB8PNQkujMFfIFMwXEoOL2AEQEZnT5dZW/PPS\nJRwbPlzsUIiIRCPKETy1Wo0pU6YgLCwM999/PzQaTaf7ffLJJ7jtttswaNAgfPDBB4bbX3nlFYSG\nhiIhIQEJCQnYvXt3d4VOdmz8+PFih0Bm8FF5OZL8/BDi5max52CukCmYLyQGUQq8jRs3IiwsDKdP\nn0ZoaCg2bdrUYR+VSoXly5fjiy++QG5uLt577z3U1dUB+K0J8emnn0ZBQQEKCgqQlJTU3S+BiKyQ\nXhCwXqnEIp53logcnCgFXl5eHlJSUuDm5oY5c+YgNze3wz7Z2dkYOnQofH19IZVKcfvttyM7O9uw\nnbNjydzYJ2P7vqmpgY+LCxItvDQKc4VMwXwhMYhS4O3fvx8xMTEAgJiYGOTl5XXY59Zbb0VeXh7O\nnz+PsrIyfP3119i3b59he3p6OhITE/HWW29BrVZ3W+xEZL3SL17EwtBQSCQSsUMhIhKVxSZZTJw4\nEeXl5R1uf+ONN4w6+ubl5YW1a9di/vz5UKlUiIuLg1tbT01aWhpeeukl1NXV4U9/+hPeffddPPvs\ns50+zuzZs9GnTx8AgI+PD4YMGWLoh7jyq4rXef2KzMxMq4mH1027vnX3buSePo0v5s2z+PONHz9e\n9NfL67ZznfnC611dv/Lv4uJimJsoCx1PnToVL7zwAhISEnDw4EGsXLkS//73v695nxkzZuC5557D\n0KFD291++PBhzJs3D3v37u1wHy50TOQ4Fp4+jZ7OzngjIkLsUIiIbojNL3Q8cuRIbN68GY2Njdi8\neTMSExM73e/SpUsAgB9++AGFhYWG4q6srAwAoNVq8cknn+Cee+7pnsDJrl39i4psS51Wi20VFUgL\nCemW52OukCmYLyQGUQq8tLQ0lJSUIDo6GkqlEk8++SQAoLS0FPfee69hvwcffBAxMTH485//jI8+\n+shw+9KlSxEfH4/ExES0trYiLS2t218DEVmPLeXlmODri1B3d7FDISKyCjwXLRHZNL0gYEBeHj6I\njsY4Hx+xwyEiumE2P0RLRGQu39XUwMPJCWO9vcUOhYjIarDAI2rDPhnblK5UdvvSKMwVMgXzhcTA\nAo+IbNaZhgbkqdV4ODBQ7FCIiKwKe/CIyGYtOXMGbhIJ3uzXT+xQiIhumjnrFostdExEZEkarRZb\ny8uRf8stYodCRGR1OERL1IZ9MrZla0UFbvPxQbgIS6MwV8gUzBcSAws8IrI5giDg70olFikUYodC\nRGSV2INHRDbnh5oaLDl7FkduuaVbZ88SEVkS18EjIoeWrlRioULB4o6IqAss8IjasE/GNpxrbMRe\nlQp/6NVLtBiYK2QK5guJgQUeEdmUDUolZgcFwcvZWexQiIisFnvwiMhm1Ot0CN+3D/uHDUNfDw+x\nwyEiMiv24BGRQ9pWUYEx3t4s7oiIroMFHlEb9slYN0EQDJMrxMZcIVMwX0gMLPCIyCZkXr4MnSDg\nTl9fsUMhIrJ67MEjIpvwwNGjmOjrizQrOIJHRGQJ7MEjIofya1MT/nf5Mh4VcWkUIiJbwgKPqA37\nZKzXRqUSs4KCIHVxETsUAMwVMg3zhcRgHd+WRERdaNTp8GF5OfYlJIgdChGRzWAPHhFZtQ/LyrCj\nshI74+PFDoWIyKLYg0dEDkEQBKRfvIiFoaFih0JEZFNY4BG1YZ+M9flFpUKjXo+JVrY0CnOFTMF8\nITGwwCMiq5WuVGKBQgEniUTsUIiIbAp78IjIKl1oasLgAwdQnJiInlYye5aIyJLYg0dEdm9TaSke\n6dWLxR0R0Q1ggUfUhn0y1qNJp8MHZWVYYKVnrWCukCmYLyQGFnhEZHX+VVmJBKkUUZ6eYodCRGST\n2INHRFZFEAQMP3gQy/v2xb3+/mKHQ0TUbdiDR0R2a19dHS5rtbjbz0/sUIiIbBYLPKI27JOxDraw\nNApzhUzBfCExsMAjIqtR2tyMb2tq8HhwsNihEBHZNPbgEZHVePn8eVS2tmJDVJTYoRARdTv24BGR\n3WnW6/FuaanVLo1CRGRLWOARtWGfjLi2X7qEOKkUA728xA7lupgrZArmC4mBBR4RWYV0pRILefSO\niMgs2INHRKLLq6vD9OPHcWbkSDhb8exZIiJLYg8eEdmVdKUS80NCWNwREZkJCzyiNuyTEUd5czN2\nVlcjxYaWRmGukCmYLyQGFnhEJKr3ysrwUEAAfF1dxQ6FiMhusAePiETTotejT04Ovo2PR5xUKnY4\nRESiYg8eEdmFzysrEe3pyeKOiMjMWOARtWGfTPdLVyqxyAaXRmGukCmYLyQGFnhEJIqDajUuNjdj\nsr+/2KEQEdkd9uARkShmFxVhgJcXloaFiR0KEZFVYA8eEdm0ypYWfFldjSdsaGkUIiJbwgKPqA37\nZLrP+2VleEAuh7+NLo3CXCFTMF9IDCzwiKhbter12FhayvPOEhFZEHvwiKhbbb90CelKJfYkJIgd\nChGRVWEPHhHZrHSlkkfviIgsjAUeURv2yVjeYY0G5xobcb9cLnYoN4W5QqZgvpAYWOARUbdJv3gR\n8xQKuDrxq4eIyJLYg0dE3aK6tRX9c3NxasQIBPToIXY4RERWhz14RGRzPigrwxR/fxZ3RETdgAUe\nURv2yViOVq/HBqUSC0NDxQ7FLJgrZArmC4mBBR4RWdx/q6sR6uaGYTKZ2KEQETkE9uARkcXdcegQ\n/hgcjBm9eokdChGR1WIPHhHZjEKNBicbGjA1IEDsUIiIHAYLPKI27JOxjL8rlZgbEmJXS6MwV8gU\nzBcSg4vYARCR/aptbcVnlZUoGj5c7FCIiBwKe/CIyGJWX7iAQxoN/jFggNihEBFZPfbgEZHV0wkC\n1vO8s0REomCBR9SGfTLmtau6GgGurhjRs6fYoZgdc4VMwXwhMbDAIyKLSOfROyIi0bAHj4jMrqi+\nHnccPozixES42dHsWSIiS2IPHhFZtb8rlfhjcDCLOyIikfDbl6gN+2TMQ6XV4p+XLmFuSIjYoVgM\nc4VMwXwhMbDAIyKz+qisDJP8/BDi5iZ2KEREDos9eERkNnpBQHReHrbExGC0t7fY4RAR2RT24BGR\nVdpdUwNvZ2eMssOlUYiIbAkLPKI27JO5eelKJRaGhkIikYgdikUxV8gUzBcSgygF3vbt2zFo0CA4\nOzsjPz+/y/327NmDAQMGIDIyEunp6Ybb1Wo1pkyZgrCwMNx///3QaDTdETYRXcPJhgbkq9WYHhAg\ndihERA5PlAIvLi4OO3bswK233nrN/Z566im8++67+OGHH7B+/XpUV1cDADZu3IiwsDCcPn0aoaGh\n2LRpU3eETXZu/PjxYodg09YrlXgiOBjuzs5ih2JxzBUyBfOFxCBKgRcTE4OoqKhr7qNSqQAAt956\nK8LDw3HXXXchJycHAJCXl4eUlBS4ublhzpw5yM3NtXjMRNQ1tVaLbRUVSLPjpVGIiGyJ1fbg7d+/\nHzExMYbrAwcONBR4V2+LiYlBXl6eKDGSfWGfzI3bUl6OO319EeruLnYo3YK5QqZgvpAYXCz1wBMn\nTkR5eXmH21esWIHJkyff0GNeadzm0idE1kMvCEhXKvFBdLTYoRARURuLFXjff//9Td1/+PDh+NOf\n/mS4fuzYMSQlJRm2FRUVISEhAUVFRRg+fHiXjzN79mz06dMHAODj44MhQ4YY+iGu/KridV6/IjMz\n02risZXrzfHx8HBygragAJkSiejxdMf18ePHW1U8vG7d15kvvN7V9Sv/Li4uhrmJutDx7bffjr/+\n9a8YNmxYp9sTEhLwzjvvICwsDElJSfjll18gl8uxatUqXLhwAatWrcKzzz6Lvn374tlnn+1wfy50\nTGR59x05guSAAKQEB4sdChGRTbP5hY537NiB3r17IycnB/feey/uvvtuAEBpaSnuvfdew35r167F\n3LlzMWHCBMybNw9yuRwAkJaWhpKSEkRHR0OpVOLJJ58U42WQnbn6FxUZ52xjI/LUajwcGCh2KN2K\nuUKmYL6QGHiqMqI2mVcNz5Jxnj5zBj0kErzZr5/YoXQr5gqZgvlCxjJn3cICj4huiEarRXhODvJv\nuQXhDjJ7lojIkmx+iJaIbN8/Kipwm48PizsiIivEAo+oDftkjCcIAv6uVGKhQiF2KKJgrpApmC8k\nBhZ4RGSyny5fhpNEgvE+PmKHQkREnWAPHhGZbEphIe7198cfeWoyIiKzYQ8eEYnmfGMj9qpU+EOv\nXmKHQkREXWCBR9SGfTLG2VBaitlBQfBydhY7FNEwV8gUzBcSg8VOVUZE9qdep8NHZWXI6+LsM0RE\nZB3Yg0dERnuvtBS7qqvxZVyc2KEQEdkd9uARUbcTBAHpDrw0ChGRLWGBR9SGfTLX9r/Ll6ETBNzp\n6yt2KKJjrpApmC8kBhZ4RGSUdKUSCxQKSCQSsUMhIqLrYA8eEV1XSVMTEg4cwK+JiZC6cG4WEZEl\nsAePiLrVBqUSs4KCWNwREdkIFnhEbdgn07lGnQ6by8sxn2etMGCukCmYLyQGFnhEdE3/vHQJw2Uy\n9Pf0FDsUIiIyEnvwiKhLgiBg6MGDWNm3L5L8/cUOh4jIrrEHj4i6xS8qFep1Otzl5yd2KEREZAIW\neERt2CfT0ZWFjZ24NEo7zBUyBfOFxHDNAu/HH3/ESy+9hLy8vHa3v/766xYNiojEd7GpCT/U1uKx\noCCxQyEiIhN12YP34osv4sCBA5gwYQK2bt2KCRMmYPXq1QCAhIQEFBQUdGugN4I9eEQ37oVz56DS\n6ZAeGSl2KEREDqFbevC+/fZbfPnll3jmmWeQl5eH2tpaTJ06FU1NTWZ5YiKyXk06Hd4vK8MCnneW\niMgmdVng1dXVoUePHgAANzc3bN68GfHx8ZgwYQI0Gk23BUjUXdgn83/+VVmJBKkU0VwapVPMFTIF\n84XE0GWBN2zYMHzzzTftbnv55Zcxe/ZsFBcXWzouIhKJIAhIv3gRC0NDxQ6FiIhuENfBI6J29qlU\neLSoCKdGjuTsWSKibtSt6+BptVqzPBER2YZ0pRLzuTQKEZFNu2aBp1arMWXKlO6KhUhU7JMBSpub\n8U1NDR7n0ijXxFwhUzBfSAxdFnhlZWW488478cc//rE74yEiEb1bWoqZgYHwcXUVOxQiIroJXfbg\n9evXD2+//TYeeOCB7o7JbNiDR2S8Fr0e4Tk5+HHwYAz08hI7HKtV9WUVyreWo0evHugR1MmlVw84\nufEkQURkOnPWLS5dbfD19YVSqTTLkxCR9dteWYlBnp4s7q6h9sdanEw9iYhVEdDX69FS3gL1fjVa\nylv+71LRAmepc+fF3+8urv6ukDiz15GIzK/LAi8zMxMPPfQQnJycMH/+/O6MiUgUmZmZGD9+vNhh\niGbdxYt4Pjxc7DCslrpAjeMzj2PQ9kE4JBzqMlcEvQBtrbZ90dd2qS+sb3dde1kL1wBXo4pBZ5kz\nJJz4YpMc/buFxNFlgSeVSvHVV1/hySef7M54iEgEeXV1uNTaivv8/cUOxSo1nmtE4X2FiNoUBZ/b\nfIDMrveVOEng6u8KV39XeA269tFQfaserZdaOxSCjacbocpSoaW8Bc1lzWitaIWgE7ocEm53VLCX\nK5zdnc37BhCRzeE6eESER4uKMNjLC8+GhYkditVpqWhBwdgChD4TCsWT4p26TavRorWiYzHY4VLR\nAmcvI4eI5RwiJrIm5qxbjC7w6uvr0dzcbLju5+dnlgAsiQUe0fWVNzdjwP79ODtyJPw4e7YdrVqL\nQ+MPwf8+f/Rd3lfscIxyrSHilor217W1WrjKjRwi7skhYiJL69YC7/PPP8fLL7+M2tpauLZ9+Usk\nEpw7d84sAVgSCzwyhaP2ybxaXIyLzc14Lzpa7FCsir5Fj8J7C+Ee4Y6oTVHtiht7yRV9qx6tlb87\nKljWsRBsKW+B0NrFEPHvjwpyiLgDe8kXsrxumUV7xSuvvIKdO3cinM3XRHanRa/HptJSfBsfL3Yo\nVkXQCzgx+wScvJwQuT7Sbo9cObk6wS3EDW4hbtfdV1ev67TwUx/sZBaxJ4eIicR23SN4SUlJ2Lp1\nKwIDA7srJrPhETyia/u0ogKbSkuRmZAgdihWQxAEnH3mLNT71Yj/Lh7OHjwaZQpBuMYQcTmHiImu\npVuHaM+fP4+77roLo0aNgre3tyGAdevWmSUAS2KBR3RtY/Lz8Uzv3nggIEDsUKxGydslKN9SjoSs\nBLj6sifRkgyziDs5MsghYnJE3TpE+/jjj2PcuHEYNWoUevToAUEQ+CuK7JKj9ckcVKtxsbkZ/49L\noxiUby2H8u9KJOy9dnHnaLliKU6uTnBTuMFNYd9DxMwXEsN1C7zKykqeKJnIDqVfvIi0kBC4OPG0\nWgBQ/U01zj53FkN+HgL3UHexw6HfcfZyhkeEBzwiPK6537WGiOuP1bebRMIhYrJn1x2ife211wAA\nf/jDH+Dj42O4ncukENmuypYWRObm4szIkZD36CF2OKKry61D4X2FiP0qFt6jvMUOh7pJp7OIOURM\nIurWHrw+ffp0+NXCZVKIbNuKX3/FmcZGbI6JETsU0TWcbEDBbQWI/iAa8vvkYodDVqqrIeJOF5q+\naojYNdAVEtff/oYa/pZK2i74v/9abNvvbuvWbTcRY7v7mPp4Jj6XNW3rNa2X5XvwSktLERISguLi\nYrM8EZG1c5Q+mVa9HhtLS/FVbKzYoYiuubQZR5KOIGJlhEnFnaPkCv2fGx4ivtSCvYV7MTpmdNsO\nbZe2fQ23WWLb1du7edvVtwuCYFqMv7vt949ni++HSa/bTLos8FJSUlBTU4Pbb78dSUlJGDt2LFxc\nrtuyR0RW7ouqKvRxd0eCTCZ2KKLSqrQ4cvcRBKcGI/jxYLHDITshkUjg6ucKVz9XeA387VzEfkF+\nCBofJHJkZBPM2OZ5zSHaxsZGZGZm4ptvvkF2djZ69+6Nu+++G0lJSQizgXNWcoiWqKNbCwqwQKHA\nQza4tqW56Jp0OJJ0BNJ4Kfq/05/N80RkFbqlB+9vf/sbxowZg6FDhxqO3J07dw7ffPMNdu/ejYqK\nCuTl5ZklCEthgUfU3mGNBvceOYLziYlwddDZs4JOwLGHjkHiIsHAfw6ExInFHRFZB3PWLV1+w1+8\neBGLFy9GQEAAbrvtNjz//PM4fvw4ZsyYgf/+97/IysoySwBE1sIRlgNKv3gRaQqF4xZ3goDTC09D\ne1mLAVsH3HBx5wi5QubDfCExdNlUt3r1agBAc3MzDhw4gH379mHz5s1ITU2Fj48PioqKui1IIrp5\n1a2t+E9VFU6OGCF2KKL59fVfUbevDkP+NwRObo5Z5BKRY7jurInGxkbU1dVBpVJBpVIhJCQE8Twx\nOdkhe58V+UFZGf6fvz8CHXTdu9L3S1H+UTkSshPg0vPmJozZe66QeTFfSAxd9uClpqbi+PHjkMlk\nGC75sPkAACAASURBVDFiBEaNGoXExET4+vp2d4w3jD14RL/R6vXol5uL/wwahFt69hQ7nG5X9WUV\nTj15CkP2DIFnpKfY4RARdapbevBKSkrQ3NyMoKAgKBQKKBSKdmeyILI39twn89/qaoS4uTlkcXf5\nl8s4+cRJxP431mzFnT3nCpkf84XE0OU4xbfffgu9Xo9jx45h3759WLNmDQoLC+Hv74/ExES8+uqr\n3RknEd2EdKUSixQKscPodpqjGhybegwDtg1Az1scr7glIsd13VOVAcCFCxeQnZ2NvXv3YufOnaiu\nroZKpeqO+G4Kh2iJgKMaDe46cgTFiYno4UCzZ5tKmlAwtgARKyPQ6w+9xA6HiOi6umUdvHfeeQfZ\n2dnYt28fXFxcMHr0aIwZMwajR49GbGwsnJ2t/2TKLPCIgLknTyLEzQ0v9+kjdijdprWmFQVjCxD8\nRDB6P91b7HCIiIxizrqlyyHa4uJiPPTQQ/jb3/6GkJAQszwZkTWzx/OL1ra24rPKShQNHy52KN1G\n16BD4X2F8L/X32LFnT3mClkO84XE0GWB97e//a074yAiC9hcXo57/PwQ5OYmdijdQq/V4/j04/Do\n54GItyLEDoeISDRG9eDZKg7RkiPTCQIic3Pxz4EDMdIBZs8KgoCTT5xEs7IZcf+Ng5Or4/QbEpF9\n6JYhWiKybbuqqyF3dXWI4g4Azr9wHvWF9Rj802AWd0Tk8PgtSNTG3taqSlcqsdBBlka5mH4Rlf+u\nRNyuOLhILf+71d5yhSyL+UJiYIFHZIeK6utRqNHgocBAsUOxuEvbL6HkrRLE745HjwDHPA0bEdHv\nsQePyA7NP3UK/q6ueLVvX7FDsajan2txfPpxDP5+MKSDpWKHQ0R0U9iDR0RdUmm1+OTSJRyz86VR\n1AVqHJ9+HAM/G8jijojodzhES9TGXvpkPiorwyRfX4TY8dIojecaUXhfISI3RMJ3vG+3P7+95Ap1\nD+YLiYEFHpEd0QsC1peWYmFoqNihWEzLpRYcmXQE4X8JR+CD9t9jSER0I9iDR2RHvq6uxgvnz+Pg\nsGGQSCRih2N2Wo0Wh28/DL8kP/R9zb77C4nI8ZizbuERPCI7kq5UYpFCYZfFnb5Fj2NTj0E6RIo+\nr/YROxwiIqvGAo+oja33yZxsaMBBtRoz7HBpFEEv4MScE3Byd0LkxkjRC1hbzxXqXswXEgNn0RLZ\nifVKJZ4IDoa7s7PYoZjd2efOoqm4CYO/GwwnF/4uJSK6HvbgEdkBtVaL8JwcHL7lFvR2dxc7HLMq\n+WsJyj8qR0JWAlz9XMUOh+j/s3ffcVVXbwDHP2w3ThQFRw5QhMSJM3eOHGXDzJ0jNeuXOVNLG25b\nmmauzLQyV8Ny5cqBC5Sl4hZZoiJL1r33+/vjKEUu0AvfCzzv14tXXe6X730uneDhnOc8R4gcI33w\nhBCZrIqKol2pUvkuuYtaHUX4gnC890tyJ4QQ2SFrHULckVfrZEyaxsJ8eO7sja03OD/2PF5/elHI\n1bIS17w6VoQ+ZLwIPeiS4P388894eHhgY2ODn5/fA6/bt28ftWvXpmbNmixYsCDj89OmTcPFxQVv\nb2+8vb3ZunVrboQthEXaERuLg7U1LR0d9Q7FbOKPxnO632nqbqpL0TpF9Q5HCCHyHF1q8E6fPo21\ntTXDhw9n/vz51K9f/77XeXt788UXX1ClShWeffZZDhw4QJkyZZg+fTrFixdnzJgxD30dqcETBcFz\nAQH0LFuWIRUr6h2KWdwOvc2JZ05Q65talO1WVu9whBAi1+T5Pnju7u7UqlXrodfExcUB0KpVK6pU\nqULHjh3x9fXNeF4SNyHgfHIyhxMS6FO+vN6hmEVqZCoBzwZQ7eNqktwJIcQTsNgavKNHj+Lu7p7x\nuE6dOpkSvAULFuDj48Ps2bNJSEjQI0SRz+TFOpmvwsMZXKECRfJBaxRDnIGAzgE4D3HG+XVnvcN5\nqLw4VoR+ZLwIPeRYgtehQwc8PT3v+fjtt9+e+N4jRozg4sWLbNu2jfPnz7NkyRIzRCxE3pJoMLAq\nKoqR+WBzhTHFSFDPIBxbOFL5vcp6hyOEEHlejrVJ2bFjxxN9faNGjRg3blzG4+DgYDp16gSA051O\n/Y6OjowaNYqRI0cyduzY+95n4MCBVK1aFYCSJUtSr149WrduDfzzV5U8lsd37dmzx2LiedTjqZs2\nUTsxkSotWlhEPI/7+JmWz3C632n8Tf5Ufb4qtaxqWVR893vcunVri4pHHlv2Yxkv8vhBj+/++6VL\nlzA3XRsdt2nThnnz5tGgQYP7Pn93k0XlypXp1KkT+/fvp2zZskRGRuLs7IzBYGDy5MmUKFGCyZMn\n3/P1sslC5FeaplH36FEW1KxJ21Kl9A7nsWmaxtnRZ7kdchuvP72wdrDWOyQhhNBNnt9ksWnTJlxd\nXfH19aVr16507twZgIiICLp27Zpx3eeff87w4cNp3749I0eOpGxZVXQ9YcIEvLy88PHxIT09nREj\nRujxNkQ+8++/qCzdrlu3sALalCypdyhP5MqMK8Ttj6Puprp5KrnLS2NF6E/Gi9CDHFUmxB17/rU8\na+l6BgbSuUwZhufh1igRyyK4MuMK3ge8cXB20DucbMlLY0XoT8aLyCpz5i2S4AmRx1xMTqbh8eNc\nadqUonl09+z1X68TOjyUenvrUaRWEb3DyZZzN88RlRhFi8ot9A5FCJHP5PklWiHE41sUEcHAChXy\nbHIXdyCOM6+foe6vdfNUcqdpGiv9V9J0eVMGbB7As98/y8mok3qHJYQQ9yUJnhB35IU6mdtGIysj\nIxmVR1ujJAUnEfRCELW/r02JRiX0DifL4lPj6bupL/MPzWfPgD0s8VhC91rdefb7Zxm4eSBhcWF6\nhygsWF742SLyH0nwhMhD1kRH08zRkacKF9Y7lGxLCUshoHMANT6tQelnS+sdTpYdizhG/SX1KWFf\ngqNDj+Lh5IGtjS2jGo8idHQoriVcqbekHhN3TuRWyi29wxVCCEBq8ITIMzRN4+ljx5hfvTodSued\nBAkg/WY6/i39cR7sjOu7rnqHkyUmzcRnhz5jzsE5LOqyiF51ej3w2vD4cKbtmcYvZ37hvZbvMaLh\nCBxs89bGESGE/mSTRRZJgifykz2xsYw4e5aQRo2wsrLSO5wsM942crLDSUo0LUGNeTX0DidLriVd\nY+DmgdxKucXaXmupWrJqlr4u6FoQE3dO5NT1U8xoO4OXPF7C2urJFkpMJkhNBVtb9ZGH/tMLIbJJ\nErwskgRPZIeltzLoFRRE21Kl8lT9nclgIviFYGxK2FD7u9pYWVt+dvLXhb8YsHkA/Z/uz/TW07Gz\nsbvnmkeNld0XdzNuxzisrayZ02EOras++Nq7NA0iIyEwEIKC1EdgIJw6pZI8g0F92NiAnZ1K9nLj\nn7n5Wra26v3ltyTW0n+2CMthzrwlx44qE0KYz5WUFHbfusW37u56h5JlmqYR+kYopjQTHis8LD65\nM5gMfLD7A749+S2req6i/VPtH/tebaq14cjQI6wLXsfgXwbj4eTBrHaz8HDyACA29p8k7m4iFxSk\nkpy6ddVH8+YwfDjUqQMl7uxH0TQwGiE9XX0YDOb957/ve/dzaWmQlGT+1/r3a/779Uwm8ySKuZGM\nZvXa+HhISPjncX5MYoXlkRk8IfKASRcukGw08nnNmnqHkmUXp17k5rabPL3raWyLWfbfkpdvXabP\nxj6UcCjBqp6rcCrqZJb73r4NJ4NSWeC7iM03ZlI6pgfGndNJiqqIh4dK5Dw9/0nqnMzzsnnav2cr\nzZVI5lRympXXuN/zRuP9Z2KzmkRmN9nU675SUnB/BoP62XC/jzZtZIk2SyTBE/lBstFIFV9fDnh7\nU7NI3ugbF/5VOFe/uIr3AW/sy9nrHc5DbQjZwIgtIxjffDxjmo55rJo5gwHOns08GxcUBGFhUKuW\nSuKqe8QSXGoWf91axqjGIxnffBwlHPJOqxhhPpp2b9KX3cTU3J973Hv8+/F/n79bUpAbCWlO/Tvc\nPxFLSnpwkvao54xGKFoUihS592PvXknwskQSPJEdllonsyIykvUxMfzh5aV3KFlybf01zr19Du/9\n3hSuZrntXJLTkxmzbQzbL2znh14/0LhS4yx9XUICLFy4Byur1hkJ3dmz4OLyz0zc3Zm5GjXUL4t/\nuxJ3ham7p7Lt3Dbef+Z9htYfet86P5F/WOrPlpz275KC7Ca02XnenPf67+fgwcnYgz7/qOfs7R88\nsyk1eEIUEJqmsSA8nBnVqukdSpbE7o7l7MizeG33sujkLiQmhFfWv0Jdp7r4D/fP0kyawQBLl8KH\nH0LFitCmDXToAO+8A7Vrqx/cWVHZsTKreq7iRNQJxu8Yz+e+nzOr/Syed38+T+2OFuJRrKwyz4SJ\n3CUzeEJYsP23bjH4zBlON26MtYX/8k88mcjJDiep81MdSrUppXc496VpGsv9lzPpr0nMbj+bQfUG\nPTKp0jT4/XcYPx4qVYJ586BePfPFtP38dsbvGE8RuyLM7TCX5pWbm+/mQog8RdqkZJEkeCKveyU4\nmOaOjrzl4qJ3KA+VfDEZ/5b+1Pi8Bk4vWuZOgbiUOIb9PozT10/zY68fqV2u9iO/5vhxGDsWrl1T\niV2nTjlTNG40GVkTuIYpu6bQsGJDZrabiVtZN/O/kBDCopkzb5GjyoS4w9LOi7yaksKO2FgGVqig\ndygPlRaTRsCzAVSZVMVik7vDVw/jvcSbckXKcXjI4Ucmd5cvQ9++0K0b9OkDJ09C587/JHfmHis2\n1jb0f7o/Z948g4+LDy1WtmDUllFEJ0ab9XWEPiztZ4soGCTBE8JCfR0RQR8nJ0pYcAGLIdFAYNdA\nyr1cjkqjLK8Bs0kzMefAHLr/2J35HeezsMtCCtkWeuD1cXEwcSLUrw/Vq0NoKAwdmns1RIXtCjO+\n+XhOjzqNvY09dRbV4aO9H5GUlpQ7AQgh8g1ZohXCAqXcaY2yt1493IsW1Tuc+zKlmQjsHoiDiwNu\nS90sboNAdGI0/Tf3JyktibW91lLZsfIDr01Ph6+/ho8/VrN2dzdS6O1C7AXe++s9/r7yN9OemcYg\n70HYWltuwi+EeDKyRCtEPrcuJoZ6xYpZbHKnmTTOvH4Ga3tran1dy+KSux3nd+C9xJsmlZqwZ+Ce\nByZ3mgabNoGHB2zZAjt2wLJllpHcATxV6il+fPFHNr+ymTWBa/Ba7MVvZ36TP1yFEI8kCZ4Qd1hK\nnYymaXx59SqjLfjM2QsTLpB8Ppk6P9bB2tZyfoykG9OZuHMig34ZxJoX1vBhmw8fOON1+DC0agUf\nfAALFsDWrZDVVoO5PVYaVWrE7gG7mdthLhP/mkibVW04Gn40V2MQj89SfraIgsVyfjILIQDwjY8n\n1mCgc5kyeodyX2GfhnFjyw08f/fEpoiN3uFkuBh7kZYrWxJ4LRD/4f60qdbm/tddhFdfhV69YNAg\n8PeHZ5/N5WAfg5WVFV1rdeXkGyfp69WXnj/1pPf63py/eV7v0IQQFkgSPCHusJRO8wvCwxlVqRI2\nFrbsCRC9Jpqrn1/Fa5sXdqUt5/SFdcHraLKsCa94vMLvr/5OuaLl7rkmNla1PGnYEOrUgTNnYPBg\ndZRSduk5VmytbRlSfwihb4ZS16kuTZY14X9b/8eN2zd0i0k8nKX8bBEFiyR4QliQyNRU/rx5k8EW\n2Brl5vabnBtzDq8/vSjk+uCdqLnpdvpthv02jMm7JvPna3/yTtN37qkHTE2Fzz4DNzd1zFhwMEyd\nqo4SysuK2hdlSqsphIwKwWAy4P6VO7P3zyY5PVnv0IQQFkASPCHusIQ6mSUREfR2cqLkfw8w1Vn8\n0XhO9T1F3Y11KephGZlR0LUgGi1tRLIhGb9hfjSo2CDT85oGP/+sZuv++gt274YlS8AcubMljJW7\nnIo6sbDLQg4MPsDRiKO4LXRj1YlVGE1GvUMTd1jSeBEFhyR4QliINJOJJZGRvGlhmytun71NUPcg\n3Ja54djcUe9w0DSNJceW0GZVGyY0n8Dq51dT3KF4pmsOHoTmzWHGDPjmG3XUmIeHmQI4dgyOHFEZ\npAWpVaYW619ez48v/sg3ft9Q/5v6bDu3Te+whBA6kT54QliINdHRrIiM5C9zHnT6hFKjUvFv5k/l\n9ypTcYj+vUNik2MZ+ttQzsee58deP95znNe5czBpEvj6wiefqNMorM31Z2xYmLr57t3g6KimAj/7\nDJ5+2kwvYD6aprH59GYm/jWRKo5VmN1+Nt7O3nqHJYR4BOmDJ0Q+tMDCWqMY4g0Edg6kwqAKFpHc\nHQw7iPcSbyoVr4Tv676ZkrsbN+B//wMfH3UKRWgo9O9vpuQuKUn1UqlXD6pVU7szAgLgpZegY0cY\nNgyiLetIMSsrK56v/TxBI4J43v15uqztQv9N/bl867LeoQkhcokkeELcoWedzNH4eKLS0uhWtqxu\nMfybKdVE0PNBlGhWgipTqugai9FkZMbfM3j+p+f5svOXfNH5CxxsHTKeX7UK3N3VaRQhIWqSrXBh\nM7ywyQTffad2Z5w9q/qpfPQRFCvGnv37YcQIlewVL67Wf+fMUTs6LIidjR0jGo0g9M1QqpWsRv1v\n6jN+x3hik2P1Dq1AkRo8oQdJ8ISwAJbUGkUzapzqdwq70nbU/LKmrqdURCZE8uz3z7Lt/DaODztO\nd7fuGc8lJsKAASqv2rULvvoKnJzM9ML790OTJrBoEaxfD2vXQuX7nIZRsiTMnw+HDqnCv9q1YcMG\ni6vPK+5QnOltphM0Ioi4lDjcFrrx6aFPSTVYVkIqhDAfqcETQmfRaWm4HznC+SZNKK3z7llN0zj3\n1jkSAxPx2uqFTSH9Ghn/efZPBv86mDcavMGUVlOwsf4nloAAePllaNZMnUJhtpYnFy/ChAmqiG/W\nLNUROTsJ7l9/wTvvQKlSqj6vfn0zBWZeITEhTNw5kcBrgcxoO4NX6r6CtZX8vS+E3syZt0iCJ4TO\nPrp0iSupqSx1c3v0xTns8ozLXPvpGt77vLF11OdQ+zRjGu/99R7rgtex+vnVPFP1mYznNE3tip0y\nReVPffua6UXj42HmTHXz//0P3n0XihR5vHsZjbB8uarb69xZ7fZwdjZToOa199Jexu0Yh0kzMbfD\n3Aee/iGEyB2yyUKIHKBHnUy6ycTXEREWsbkickUkkUsj8frTS7fk7vzN8zRf0ZzQG6H4D/fPlNzF\nx6sJtUWL1AqqWZI7oxGWLlV1dlFREBiouiA/Irl76FixsVEbL06fhnLlwNNTJXnJlteA+Jmqz3B4\nyGHGNx/PkN+G8Nza5wi6FqR3WPmO1OAJPUiCJ4SONl6/Ts3ChfEqVkzXOK7/fp0L713Aa6sXDhUd\nHv0FOeCHwB/wWe5DP69+/NL7F8oU+ecs3uPH1Wpn6dJq9dQsk527dqmbrl6tGuWtXAkVzbhb2NER\nZs9WPfP8/VV93k8/WVx9npWVFS97vMypUafoWL0j7b5rx+u/vE54fLjeoQkhnoAs0QqhoxZ+frzj\n6kqvcveenZpb4g7GEdQjCM/fPSnRpESuv35SWhKj/xzNgbAD/Njrx0z92jRN1dh9/DEsXKjq7p7Y\n2bMwbpwq5JszB3r1yl6d3ePau1fV5xUurNaXGzfO+dd8DHEpcczaP4tv/L5hRMMRjG8+nhIOuT8u\nhCiIZIlWiHzALyGBK6mp9ChT5tEX55CkkCSCng/CfbW7LsndyaiTNFzaEJNm4viw45mSu9hYeOEF\n1ank0CEzJHexsTBmDDRtqnZnhITAiy/mTnIH8MwzcPQovP469OypGvVdvZo7r50NjoUcmdl+JieG\nnyA8IZxaC2qx8MhC0oxpeocmhMgGSfCEuCO362QWhIczomJFbM121EL2pFxNIaBzANXnVadMp9xN\nMjVN46sjX9F+dXsmt5zMtz2/pZj9P8vUvr7g7Q1Vq8KBA1C9+hO8mMGgeqi4u6umxcHBMH48FCr0\n2Ld87LFiYwODB6v+ea6u6hSM6dPh9u3HjiWnuDq6srLHSrb3286Ws1vwWOTB+pD1siryGKQGT+hB\nEjwhdBCTlsbm69cZqtPuyvTYdAI6BVDpzUpU6FchV1/7ZvJNXlj3AitOrODg4IP09fpnt4TJBHPn\nQo8e8MUXaiXT4UlKArduVUnUpk2wYwcsWQLlyz/5m3hSxYurjRfHj8OpU6qo8Pvv1TfAwniV9+LP\n1/5kcdfFzPh7Bs1WNGP/lf16hyWEeASpwRNCBzMvX+ZscjIr3N1z/bWNyUYCOgZQvFFxqs+vnquN\njPdf2c9rG1+jV+1ezGw3M9OJFNevq8bFsbHwww9Q5UkO0Dh1SrU6OXcO5s2Dbt1ybyn2cezfr+rz\nrK3h88/VMrIFMmkm1gauZfKuyXhX8GZW+1m4l839MSxEfiU1eELkYQaTicU6tUYxGUyEvBqCQ2UH\nqs/LveTOaDLy0d6PeHHdiyzqsohPn/00U3K3b59akq1bV+1FeOzk7vp1ePNNaNVKnRMbFATdu1t2\ncgfQogUcPgyjRqkzbvv0gStX9I7qHtZW1vT16suZN8/QonILWq5syYjfRxCVGKV3aEKI/5AET4g7\ncqtOZvP161R2cMC7ePFceb27NE3j7MizmG6bcF/pjpV17iQ94fHhtF/dnl2XdnF82HG61uqa8ZzR\nqHbIvvyy6jE8ezY81mEeaWlqPbd2bZXMnT6tGhbb25vvjfxLjowVa2u18eLMGahZU2W8U6eqM9ks\nTCHbQoxtNpYzb56hqH1R6i6qy/Q900lMs7xYLYHU4Ak9SIInRC5bEB7OWy4uuf66lz64RIJfAh4b\nPLC2z53/9X8P/Z0G3zSgXbV27Oy3k0ol/pm1jIqCTp1Uadzx4+rQh2zTNPj1VzX1t2OHmv5bsAB0\n3Jn8xIoWVRsvTpxQR6e5ucG331pkfV7pwqWZ13Eex4YdI/RmKLUW1GLJsSUYTAa9QxOiwJMaPCFy\nUUBiIl0CArjo44NdLu6eDV8cztVPr+J9wBt7p5yZ1fq3VEMqE3dOZOPpjax5YQ0tKrfI9Pxff0G/\nfjBkCLz/Ptg+zsEZAQGqbi0qCubPV9lifuTrq95nerqapWzZUu+IHuh4xHHG7RhHZGIks9rNortb\n91yt8RQir5OzaLNIEjxhaYaeOUMVBwemVK2aa68ZsyGGs6PP4r3fm8JPFc7x1zt74yy9N/SmsmNl\nlndfTunCpTOeMxjgww/VUa3ffQft2j3GC0RHq6XLX35R570OG/aYGWIeomlq58nEieDjo9ayq1XT\nO6r70jSNree2Mn7neEoWKsncDnPxcfHROywh8gTZZCFEDsjpOpkb6emsj4lhmDmPw3qEW3tvEToi\nFM8tnrmS3H0f8D3NVjRjcL3BbHx5Y6bkLjxcJXSHDoGf32MkdykpKrHx8FBtRk6fhpEjdUnucr2m\nyspKbbw4fVqdbduwIUyapA7otTBWVlZ0rtmZE8NPMLjeYF5c9yIv/fwS526e0zs03UgNntCDJHhC\n5JLlkZF0K1MGpxwq/P+vxIBEgl8Kps6PdSjunbMbOhLTEhmweQAf7/uYnf12MqrxqExLc3/8AQ0a\nqI2tW7dmsxWdpsH69VCnjsoODx1SS7KlSpn/jVi6IkXU7GVAAERGqubNy5ap3SoWxsbahkHegwgd\nHYp3BW98lvnw1p9vEZMUo3doQhQIskQrRC4wahrVfX352cODRiVy/kiw5EvJ+Lfwp8b8Gji94pSj\nr+Uf6U/vDb1p4dqCLzt/SVH7ohnPpafD5MlqdXHt2scoHzt+XNWfxcfDp59C27bmDT6vO3ZMfX8S\nElR9Xps2ekf0QDFJMXy07yPWBq5lTNMx/M/nfxSxK6J3WEJYFFmiFSKP+e36dZwdHHIluUuLSSPg\n2QAqj6+co8mdpml84fsFHb/vyLRnprG8x/JMyd3ly6odXXAw+PtnM7mLiICBA1WD4v79VaInyd29\nGjZUTQQnT1ZHoL3wApw/r3dU91WuaDm+7PwlvkN8ORF1gloLarHCfwVGk+XNPgqRH0iCJ8QdOVkn\nsyA8PFcaGxuTjAQ+F0i5XuVweSvnWrFcv32dHj/24PvA7/F93ZdXPV/N9Pwvv0DjxtCrF/z2G5Qt\nm8Ub376tdmF4eoKzs6o5GzJEneFqQSyqpsrKSjVHPnVKfdObNIFx4yAuTu/I7qtG6Rqse2kd619e\nz8oTK6m3pB5/nP0jX6+2WNR4EQWGJHhC5LCgxERCbt/mxXLlcvR1TOkmgl8KpqhHUap9knM7LPde\n2ov3Em/cyrhxYPABqpeunvFcaqrqL/z22yrJGztW9e99dPAmWLNG1ZQFBamlx5kzIRdmPPONQoXU\nLtugIHXem5sbfP212rpsgXxcfNg3cB8ft/mYMdvG0H51e45HHNc7LCHyDanBEyKHvXHmDBXs7ZmW\ng20tNE3j9MDTpN9Ip+7muljbmv9vN4PJwMf7PmbJ8SWs6L6CzjUzdyY+dw5eeUUdM7Z8eTb2QBw6\npOrIjEZVR9aixaO/Rjyav7/6vt64ob6v7dvrHdEDGUwGlvstZ/re6bSp1oaP23xMtVKW2QZGiJwk\nNXhC5BGx6en8FBPD8BxujXJh4gWSQ5PxWOeRI8ldWFwYbVe1Zf+V/fgN87snufvpJ2jaFAYNgg0b\nspjcXb4Mr76qlhdHjlRnsUpyZz7e3rB7t1ryHj5cnckbGqp3VPdla23L8IbDCR0dSq3StWi4tCHv\nbnuXm8k39Q5NiDxLEjwh7siJOpmVUVF0KV0aZwcHs9/7rrDPwrjx6w08f/fEpoj5a9V+PfMrDZc2\npHONzmzruw3n4s4ZzyUnq9xhyhTYtg3efFOVhD1UYqL6gvr11TLimTNqI0UunuzxpPJMTZWVFTz/\nPISEqB0vzZqpWb3YWL0ju69i9sX4oPUHBI0IIik9CbeFbsw7OI8UQ4reoT2RPDNeRL6Sd36i9HNx\nvwAAIABJREFUCpHHGDWNr3J4c0X02miufnoVr21e2JWxM+u9UwwpvPXnW7z151tsemUTk1pOwsb6\nnwTy9GlVzx8frza51q//iBuaTLBypUrqLl9WZ61Om6bOXhU5y8FBFUSGhKis3N0dFi5UfWwskHNx\nZ75+7mv2DdzH/iv7cVvoxvcB32PSLO88XiEsldTgCZFDfrt+nQ8vX+ZI/fo5ch7nzR03OdX3FE//\n9TTF6hYz673PXD/DK+tfoUbpGizttpRShTOvuX73Hbz7rtoH8frrWZi127dPzRw5OMDnn6vdnkI/\ngYEwZow6XuTTTy3+HN+/L//NuB3jSDOmMafDHNo/Zbn1hEI8CTmLNoskwRN66njyJP3Kl6dfhQpm\nv3fC8QQCOgXgsdGDki1Lmu2+mqax6uQqxu0Yx0dtPmJ4g+GZktPERLUMe+SIqrvz9HzEDc+fh/Hj\n1RTf7Nnw8stZyAZFrtA02LJFZerVq6vTQWrX1juqB9I0jQ2nNjBx50RqlK7B7PazebrC03qHJYRZ\nySYLIXKAOetkTiclEZCYyMtO5m80fPvcbQK7BVJraS2zJncJqQn029SPOQfmsKv/Lt5o+Eam5C4g\nABo1UqVyR48+IrmLi1OJXZMm6oyyU6fUFtt8ktzli5oqKyt47jk1m9exIzzzDIwerXbdWiArKyte\nrPMiIaNC6FqzKx2/78jAzQMJiwvTO7RHyhfjReQ5kuAJkQMWhocztGJFHMy8cSA1KpWAZwOoOq0q\n5Xqar6/esYhjeC/xpohdEY4NO4Zn+X+yN02Db76Bdu3gvfdgxYqHlM0ZDLBkiarxunFDJQ/vvQeF\nC5stVmFm9vaqeeGpU+px7dpqGT0tTd+4HsDexp7RTUYT+mYolYpXot6SekzaOYm4FMts7CyEXmSJ\nVggzizMYqOrrS1CjRlQy4+5ZQ7yBE61PULZHWap+UNUs9zRpJj73/ZxZ+2exsMtCXvZ4OdPz8fEw\nbJj63b9undof8UA7dqi6rjJlVN81b2+zxChyWUiIWra9cEEt23btatEzr1fjr/LB7g/4/ezvvNfi\nPUY0GoG9jb3eYQnxWKQGL4skwRN6+OLqVQ7GxfGTh4fZ7mlKNRHQJYAitYpQc1FNs2zaiEmKYeAv\nA7lx+wY/9Prhnsayx4+rVdWOHdXv+QdOwp05o3ZonjoFc+dCz54WnRCILPrzT5XoubiojRh16+od\n0UMFRgcyYecEztw4w8x2M3mpzks5srlJiJwkNXhC5ABz1MmYNI2F4eG85WK+c2A1k8ap/qewLWlL\nzYXmSe52XdyF9xJvPJ08+XvQ35mSO02DL7+Ezp3VLtlFix6Q3N28qZb2WrRQ9VvBwarnWgH4pVog\naqo6d4aTJ1WD5HbtYMQIiInRO6oH8izvyR+v/cE3z33D7AOzabKsCXsv7dU7LKCAjBdhcSTBE8KM\ntt28SXEbG5qZ6QxVTdM498450qLSqL2mNlY2T5Y8GUwGpuyaQt+NfVnRYwWz2s/Czuaf/nk3b6oc\nbfVq8PVVh0zcIz0dFixQdXZpaWpJb+xY1QJF5C92dmrb9OnT6qzbOnVg3jx16LCFavdUO44OPcr/\nfP7HgM0D6P5Dd0JiQvQOS4hcJ0u0QphRl4AAXipXjkHOzo++OAsuz7rMtbXXqLevHnYln6yR8ZW4\nK/TZ0IcidkX47vnvqFAsc/uWQ4fUyWEvvACzZqna+0w0Df74QyVzrq55YtlOmNm/l+PnzYMePSx6\nxjbFkMKio4uYtX8WPd17Mr319EwnsQhhaWSJVggLFHr7NscSEuhtptYokd9GEvF1BF5bvZ44udt0\nahONljaiu1t3tvbdmim5M5lUi7qePdXS7Kef3ie5CwpSzXDffVf9Yt+2TZK7gsjNDX77DRYvhqlT\noW1bdSKJhSpkW4gxTcdw5s0zlCxUkrqL6/L+7vdJSE3QOzQhcpwkeELc8aR1Ml+Fh/O6szOFbZ78\nPNgbW25wYeIFvLZ64VDx8Zc+k9OTGbllJGO2j+GX3r8wvvl4rK3++d8+JkZtkvz1V9Xbrnv3/9wg\nJkbVXrVt+0/PNAvfVZkbCnxNVYcO4O+vduF06gRDh0JUlN5RPVCpwqWY02EOfsP8uBB7gVoLa7H4\n6GLSjblzVFuBHy9CF5LgCWEGCQYDq6OjGVGx4hPfK+5QHKcHnsbzF0+Kuj/+Oa2nYk7RZFkTrt++\njv9wf3xcfDI9v3ev6mTi7Q179kDlyv96MjVVzdTVqaOm806fVk1w7cx73q3Iw2xt4Y031NgoWVLN\n6M6aBSkpekf2QFVKVuH7F75nS58tbDi1gbqL67Lp1CYp5RH5ktTgCWEGX4WHszs2lvVPuGyZdCqJ\nE21O4L7SnTKdyzzWPTRNY4X/CibsnMDMdjMZUn9Ipp23RiN88olaZfv2W3j22UxfDJs3w7hxquHt\nvHmPaH4nxB3nzqnTS06cgDlzoFcvi57p1TSN7ee3M27HOIo7FGduh7k0c22md1iigJM+eFkkCZ7I\nDSZNo86RIyxxc+OZko9/dFhqeCp+zf2oNr0aFQY83vm1cSlxDP99OEHXgvjpxZ/wcMrciy8yEl57\nTf37mjWQaS+Iv79qVHz9uirE69DhMd+JKNB274Z33oESJVTD6wYN9I7ooYwmI6sDVjN191SaVGrC\njHYzqFWmlt5hiQJKNlkIkQMet05mZ2ws9tbWtHJ0fOzXTo9NJ6BTAJVGVHrs5O5I+BHqf1OfUoVK\ncXTo0XuSu+3b1e/a1q3VoRMZyV1kJLz+uup71ru3SvQkuXsoqal6iDZtVJfs/v1V3eagQRARoXdU\nD2RjbcPAegMJfTOUhhUb0mx5M978402uJV0z22vIeBF6kARPiCe0IDyctypVeuwGxMZkI0E9gijZ\nriSu412z/fUmzcTcA3N5bu1zzGk/h8XPLaaw3T+diQ0GdRzs4MGwdi28/z7Y2ADJyTBjhqqdKlNG\ntcAYPlzVVgnxJGxsYMgQNaYqVABPT/j4YzXmLFRhu8JMbDGR02+extbaljpf1eHjfR+TlJakd2hC\nPBZZohXiCZxPTsbHz4/LPj4UeYzds5pRI/jFYKwLW1P7+9pYWWcvSYxOjGbA5gHEp8azttdaqpas\nmun5sDDV2654cfjuOyhXDlVnt24dTJigpvTmzIHq1bMduxBZdvGiGm+HD6uNGL17W3R9HsD5m+d5\nb9d77L+yn+mtpzOw3kBsreWPH5GzpAYviyTBEzltzLlz2FpZMecxEiRN0wh9I5SUCyl4bvHE2j57\nE+o7L+yk/6b+DKo3iGmtp2U6kQJUu7IhQ1TrurFjwdoaOHJE1UclJ6v6qGeeyXbcQjy2v/9Wx9s5\nOKjx16SJ3hE90uGrhxm/czw3bt9gdvvZdKnZRc64FTlGavCEyAHZrZNJNBhYFRXFyMdsjXJp+iUS\njiXgsdEjW8ldujGdSTsnMWDzAFY/v5pP2n2SKblLS1N7JUaPhk2b1MZG64ir0K+f6mY8ZIhqeifJ\n3WOTmqrH1LKlGnvDh6sjU/r1g6tX9Y7qoZq4NGHPgD3MbDeTcTvG0fa7thyLOJate8h4EXrQJcH7\n+eef8fDwwMbGBj8/vwdeN3jwYMqXL4+np2emzyckJNCjRw8qV65Mz549SUxMzOmQhbjH99HRtHR0\npGrhwo+++D8ilkQQ/X00Xn94YVs868s+l25dotW3rTgRfQL/4f60e6pdpucvXIAWLeD8efDzg2ZP\nJ8G0afD001CliqqJGjToThGeEDqwtoYBA9RYrFpVjc1p0yDJcmvdrKys6ObWjYARAfSp24fuP3Tn\n1Q2vcjH2ot6hCfFAuiR4np6ebNq0iVatWj30ukGDBrF169Z7Pr948WIqV67M2bNncXFx4euvv86p\nUEUB0rp16yxfq2kaC8PDecvFJduvE7MphkvTL/H0tqexL//fM8Ee7Ofgn2m8tDEv1n6RLX224FQ0\n85Fo69eDj49qg7J5o4nSW1arHnZnzqhs7+OPVTGeeGLZGSviAYoVg48+Uru2Q0PB3R1Wr1Zn51ko\nW2tbhjYYytnRZ6lTtg6NljZizLYx3Lh946FfJ+NF6EGXBM/d3Z1atR7dZ6hly5aUKlXqns8fOXKE\n119/HQcHBwYPHszhw4cfeI8v/rfmiWIV4n5237oFQJts9r27te8WocND8fzdk8LVszbzl5yezBu/\nv8HEvyaypc8W3m32bqbjxlJSYNQoVcP+xx/wdsMDWPk0gYUL1WaKH35Qs3dCWKLKldX27p9+UmPW\nxwcOHtQ7qocqal+Uqc9MJXhkMKmGVNy/cmfOgTkkp1vuLmFR8OTJGryjR4/i7u4OqGTxyJEjD7y2\n4poKLPbYzKq5v+VWeCKPyk6dzJdXr/JmNlujJAYmEvxiMLXX1qZ4/azNpAVfC6bR0kbEpcbhP9yf\nRpUaZXo+NFT9PoyJAf9Nl2g49xW1Q/F//4NDh6CZdObPCVJTlQOaNVNj9u231Rm3vXvD5ct6R/VQ\n5YuV56uuX7F/0H58r/rittCN705+h0nLPAsp40XoIcf2fHfo0IGo+xw+PWPGDLp16/ZE987ODpP1\nrZcR45eE09RSbP7iZ5oMbMTEj0cD//xPd3f6XB4X7McnTpzI0vVVmzTh77g4hl+7xp6zZ7N0/5TL\nKaxquwrnEc40b9/8kddrmsbYb8ayzG8Zn73xGYPqDWLv3r2Zrp88eQ8LF8Jn0+szKGome1t8Bb16\n0frMGShSRPfvpzyWx4/1+LXXoGdP9owaBZ6etH7rLZg4kT3HjllGfPd57FbWjbfKv0UggXx97Gs+\nPfQpfYv3pWGlhhYRnzy23Md3//3SpUuYnaaj1q1ba8ePH3/oNRcvXtTq1q2b6XMvvPCC5ufnp2ma\nph07dkzr1avXfb/2328v4Nh5bWbLldqvxf/SZjf7TjuyJ/gJoxcF1bhz57QxZ89m+frUmFTN181X\nC/s8LEvXxybHai+te0nzWuylhVwLuef5pCRNGzxY02rXMmhX3l+qac7OmjZggKZdvZrlmITIE8LC\nNK1fP02rWFHTVqzQNKNR74geyWQyaRtCNmg1v6ypdVzdUfOP9Nc7JJGHmDMtszZ/ypjtBDPbX9Ok\nSRNWrFhBcnIyK1aswMfH55Ff49ngKSbuG0ix70tgY7AlslsUM9qt4NK5e2cZhXiQ20YjKyIjGVWp\nUpauNyYZCXwukLI9y+Ly9qM3ZPhe9cV7iTdORZ04POQwtcvVzvR8cDA0agQ1wnYT6NAA112r4Ndf\n4dtvIYsxCZFnuLioDt2bNsHSpWrw79und1QPZWVlxQu1XyB4ZDA93HrQeU1nqn9ZnX6b+rH46GIC\nogMwmox6hykKArOlitmwceNGzcXFRStUqJBWvnx5rVOnTpqmaVp4eLjWpUuXjOt69+6tOTs7a/b2\n9pqLi4u2YsUKTdM0LT4+Xuvevbvm6uqq9ejRQ0tISLjv6zzs7f24cJu2wGu9tr70Tu3j577R4m8l\nmfEdirxo9+7dj7zmm/Bw7bmAgCzdz5hm1E52OamFDAjRTCbTw681GbVZf8/SnOY6aRtDNt7zvMmk\nacuXa1rjUqHaZe8emqlqVU1bt049IXJdVsaKMDOTSdN+/FHTKlfWtF69NO38eb0jyhKjyah9u+lb\nbdnxZdqgzYM0twVuWomZJbQO33XQPtj9gbb93HYtLiVO7zCFhTBnWlbgT7JY/N467NYXoWiiA5fb\nXmHsygHY2slxNAXRnj17Muoj7kfTNJ4+dox51avTsXTph95L0zRODzpNekw6dTfXxdruwZPlUYlR\n9N/Un9vpt1nbay2VHStnej4hAd59/RbNd33Ea8ZV2E4YqzZRFCqUrfcnzOdRY0XkoORk+PRT9TF0\nqDpouUQJvaN6qP+Ol+u3r+N71ZcDVw5w8OpBjkccp3rp6jRzaUYz12Y0r9ycaiWryYkZBZAcVZZF\nWf1GGdINfDbieypsd8ZgayK+Ryxvf9YnFyIUecneW7cYfuYMIY0bY/2IH7znJ57n1p5b1PurHjZF\nH9xUeNu5bQz8ZSBD6w/l/Wfev+esyxPHDGzu8g1jEqZT5NUe2M78CMqXN8v7ESJPi4iAKVPgzz/h\nww9h8OA828A7zZjGyaiTHAg7wMGwgxwIO4DRZKSZa7OMjwbODXCwddA7VJHDJMHLoux+o1JT0pj/\n2nfU3FeN604JFBlow4BxT7bjV+QfLwYF0aZUqUfW31394irhi8Px3u+NfVn7+16TZkxjyq4prA1c\ny+rnV9OmWptMz2sa/D56GzW/HkNJt/JUWPuZ6vgvhMjs+HF1vnJcnDrftm1bvSN6YpqmERYfpmb4\nwg5y8OpBTl8/Tb0K9TJm+Zq5NqN8MfljL7+RBC+LHvcbFR1+k2WDN+Jx+CkuVY+h1rsudOnTPAci\nFJbkYctuV1JSqHfsGJd9fChu++Al/Ogfo7kw7gLe+70pVOX+S6gXYi/w6oZXKVukLN/2+JZyRctl\nej7+8CnO9XiXsrfOYvvZPCq+0R1kqcaiyBKthdE02LgRxo0DLy+YOxdq1tQ7qgzmGC+JaYkcCT+i\nEr6wgxy6eogyhctkmuXzKOeBjXXenMUUijkTPN130Vqi8pVKM3nbEKr/VZWUoskY30hlTvPVHN0b\nondoQieLIyLoV778Q5O7mztvcu7tc3j+4fnA5O6noJ/wWeZDb4/e/P7q75mTuxs3iH55NOnNWnGp\nZgecrgVTcUQPSe6EeBQrK+jVC0JCoGlT9TF2LNw5cSY/KGZfjLbV2jKl1RT+eO0Pboy/wS+9f6Fl\n5ZYcDj/MSz+/ROk5pXn2+2eZvmc6O87vID41Xu+whY5kBi8Ldv96DL9PzlLjdHmCG16iz5IuVK1R\nwQwRirwg2Wikiq8v+729qVWkyH2vSfBLIKBTAB7rPSjZ6t7jy26n3+btP99mz+U9/NjrRxpUbPDP\nk2lpaAu/Ivn9GfxgfIXyi6fx3MCyOfV2hMj/oqNh6lTVQuiDD9RmjIf8cZZfxCTFcOjqoYxZPr9I\nP2qUrpFplk82b1g2WaLNInN+owB++mo7Md8k4Hy1JKebX+Ct1a9R3PH+v/BF/rEyMpJ1MTH86eV1\n3+eTzyfj39Kfml/VpNzz5e55PjA6kFfWv0KDig1Y1GURxR3uHFOmafDbbxjfGcuJhOrMrTCfmb/U\noVq1nHw3QhQgJ07AmDFw7Zradduxo94R5ao0Yxr+kf4ZdXwHrhxAQ1PJ3p1avvrO9WXzhgWRBC+L\nzJ3g3XW3tUqRJAfC2obx7or+0lolH7hfnYymaTQ4fpxPqlWjc5ky93xNWnQafs39qDyuMhWHV7zn\na78+9jXv73mf+R3n0//p/v88GRAAY8Zw+1wEbyTNx2lAZ2bMAPv778kQFkZq8PIQTVMzeWPHgpsb\nzJsHd84yzy2WMl40TeNK3JWM3boHww4SeiNUbd741yyfU1EnvUMtsMyZt0hW8hhGzHgZw3TVWqXi\n9kqsdt9OfPdb0lolHzoQF0ei0ciz9+l7Z0gwENAlgPJ9y9+T3MUmxzLktyFciL3A/kH7cSvrpp64\ns3Skbd7Mdp8PeD15GF+vtOO553Lj3QhRAFlZQY8e0KkTLFwILVtCnz5q6fYR/SzzGysrK6qUrEKV\nklXo46l+X93dvHHgygGWHF/CoF8GUbZI2UyzfB5OHlhbScl+XiMzeE/o361VDHYmYksnkVQikbRS\nKTi42lOraVXa92pMkWLSlDYveiU4mGaOjrztkvmYMVOaicCugRSqXohai2tlqmk5cOUAfTb2oadb\nT+Z0mKOWP1JS4IsvYO5cbnTtz9ArU4kxlOKHH9RpTEKIXBITo5K79etVnd4bb4Cdnd5RWQyTZuJU\nzKmMfnwHww5yLekaPi4+GTN8TSo1+afURJiVLNFmUW4keHfduhHPLyv2EhUQgzFKo9CtIhSPL07p\nG4UplmDNjbIGbpWS5C8vCU9Npe7Ro1zy8cHxXwXamknjVN9TmFJMePzsgZWNSu6MJiOz9s/iyyNf\nsrTbUrq7dVfLQxs2wPjxJD7lyfSic1l1qBbjx6vDKApA3bcQlikoSNXnhYWp+rzOnfWOyGL9e/PG\ngbAD+Ef6U7NMzUwnb1RxrCKbN8xAErwsys0E72HCLlxj14bDD03+4komkegoyZ+e/lsnM/XiRWLT\n01lYq1bG5zRN49w750j0S8Rruxc2hVTPqYiECPpt6ofBZGDNC2twKeGS0YA1LSaOL6t9yqwj7Xj7\nbXj7bYs/WUk8gqXUVIknpGnwxx8q0atWDebPBw8Ps79Mfhsv/968cXeWD8iY4Wvu2hxvZ2/sbaSo\nOLukBi+PcX3K6YEnYtxN/uIDbmKM0ih8rSjFzxUnbX06fw87lDn5K5mCQ2VJ/nJDitHINxER7K1X\nL9Pnw+aEceuvW9T7u15GcvfH2T94/dfXeaPBG0xpNQWbqGh4ayDGP7fxU50PeTt6MMN72XB2DZQq\npce7EULcl5UVdO2qdtcuWgRt2sBLL8H06VBWWhU9iL2NPU1cmtDEpQnvNH0HTdO4HHc5Y+PG6oDV\nnL1xFm9n70wnb/y3qbvIWTKDZ8HCLlxjz4ajRAREZ5r5K3WjMMUTrLlWPo3rTre4XS4BR49idB7S\niuruFR99Y/FI30VF8X10NNv/dTxY1KooLn5wkfoH6uNQyYE0YxqTdk5iXcg61rywhlblGsL8+Zg+\n+5w9NYYy6Ox7vPR6CSZMgHLyc00Iy3fjhkrufvgB3nsPRo2Sre2PKSE1IePkjQNhB/C96otTUadM\nu3XrlKsjmzf+Q5ZosyivJ3gPE3bhGn+s3Mf1E7EUji5G6ZhSVIh04GYZA9edbpFQLo4itRxo278p\nTzeuoXe4eYqmaTT28+P9KlXoduev+Bt/3OD04NPU21OPou5FOXfzHK9ueBXnYs6s7LacMr9sxzhh\nEiHFfegfOZsW/aoxaRJUlHxbiLzn1Cl49104d04t2z73nJwo84RMmomQmJBMy7rXb19XmzfuzPI1\nrtS4wG/ekAQvi/Jzgnc/t27Es/mbPYQficYhqjClYkrhHFGEhOJGYirEEV82Drtq1jR90ZsWneTg\n+v+6WyfjGxdHn1OnONukCTZWVsQfjiewWyB1f62Lo48jawPX8vbWt3m/1fu8aWyA8e0xXIswMCzx\nM5xfbsmUKVClit7vRuSk/FZTJR5g61ZVn1exotqI8YBm548i4+X+riVd41DYoYyEzz/KH7cybplm\n+Qra5g1J8LKooCV493M7MYVfv93LhX1h2IbbU+p6KZzDi5HqYCLaOYH4srewqmKi3nN1aNezYYFu\n2Hz3h/BrISE0KF6cMa6uJJ1O4kTrE7gvd8ehgwOj/xzNwbCDbGjyKe5zvid5+9+8p31CfI9+TP3A\nmhoyWVogyC/sAsRggCVL4MMPoWdP+OgjcMpeI2AZL1mTakjFP8qfA1cOZJy8YWNtk6knX37fvCEJ\nXhZJgnd/hnQDf6w9SMj2sxBmS8nrJakQURwrDaIqJnKrbCwml3RqtX2KHgNbFaikLzI1lTpHj3Kx\nSRMKXzPh19yPqtOqEt0pmt7re/NMmQZ85lcJq0XLWWT1Jic6jmfSx0WpU0fvyIUQOSo2ViV3330H\nEybAW2+BgxzxlZM0TePSrUuZTt44d/Mc3s7eNHdtTjPXZjR1aZqvNm9IgpdFkuBlnSHdwN7f/Tj2\nSxCmS1Y43iiJU2QJCqVYE1UxidiysaS7puLVzY1OrzTNt0nftIsXiUpLY0H5pzjR8gROfZz4rd1v\nfLh7GhuTX8D78y1sS2/DluYzGT3Hlf9sshVC5HehoerYs+BgdexZz55Sn5eL4lPjM07eOHj1IL5X\nfSlftHxGe5Zmrs2oXa52nt28IQleFkmC9+QO7wrm73XHSTtrwPFaKSpddcRkpRHhEkd8+ViKeRbm\n+Tfb4/pU3j+7cMeuXfQvVIjttepi6HUe27q2TG0xlbLHQ/j4Z3sioovz7dOf03dBExo31jtaoSdZ\nchPs3AnvvANlysBnn4G39wMvlfGSc4wmIyExIZlm+W4k38jYvNG8cnMaV2pMMftieoeaJZLgZZEk\neOZ3d3k3aEso9mGFKBtVlgqRDkQ7pxJT/jpprsnU7VKTLn1b5LlZvikbN+JbpRqfz7DjRvoNptbt\nw8wtRagaksqyGnNou+QVWraSv9SF/MIWdxgMsHy5Ovqsa1f45BOoUOGey2S85K7oxOiMZO/g1YOc\niDqRsXnj7ixfZcfKFrl5QxK8LJIEL3eEXbjG5kV/kXDyNiWiS1Ex3BFrkxURLnHccoqliIc93Ua2\ntfgefT7HjjFtoS2pJ85zpehg+h7V+Kn8BGp9PYY2XQrLKowQ4v7i4lRyt2KFaq/yzjtQSBrRW4pU\nQyp+kX6ZZvn+vXmjeeXm1KtQzyI2b0iCl0WS4OnDkG5g+8+HOfnLKWyvFKJsdBmcIwpxrXwqMRVu\nkupyG7eO1egx+BmLmeU7Gh/Pd2P86bjhMg1S3+ZwsWcpNu8z2vdzlsROCJE1587B+PHg7w9z5sCL\nL0p9ngXSNI2Lty6qnnx3avnO3zxPfef6GbN8TV2bUrZI7p9mIgleFkmCZzkirlxn41c7iTuRSImo\nUjiHl8Qu3YoIl3hulb9Jodp2dBvZhhoeLjkWQ2pKGpFXb3I94hY3Y26RcCOJxLjbJMelcu68HWV+\nCaJV4XUkTVhG+7GNsM6bNboiF8iSm3ioPXvULF6xYvDZZ+xJTJTxYuHiU+M5fPVwRiPmw+GHqVCs\nQqYWLbmxeUMSvCySBM+ybd/gy/H1wdhcsadsVBkqhhcmximNmAo3Sa6YiJW9FVqaBulWkG6FtcEK\nK4M11kZrrI022BhtsDaof9oYbbA12GBrsMbmzj/t0qyxM1hhl2aFXTrYGiDNHtLtIN3eRLqthsHO\nhMHWRJpDGuerbeeT3xdgYyt/cYuHkwRPPJLRCN9+C1OnsqdqVVrPnw9Nm+odlcgio8l7UqXtAAAV\n1UlEQVRIcExwxgzfwbCD3Ey+SVOXphmzfI0qNTL75g1J8LJIEry85Xr0LX7+Yjs3/eIpFu2ItWaF\n0caI0daEydqIydaIycaEZmdCs9XATsPKzgorBytsHKyxK2KLXWE7HIrZU8SxEMVLFqGEUwlKlStB\nhUqlKeNUwmKWhIUQBURyMqxcCXPnqiNuJk2Cjh1l6TYPikqM4lDYoYxZvpPRJ3Ev654xw9e8cnNc\nS7g+0eYNSfCySBI8IYQQFsFggB9/hFmzwN4eJk6EXr3AxkbvyMRjSjGk4Bfpl+l8XTtru4xj1pq7\nqs0bdjZ2Wb6nJHhZJAmeyA5ZdhNZJWNFZEem8WIywZYtMHMmXL+uNmX06yenYuQDmqZxIfZCpoTv\nQuwFGlRskDHL18y1GWWKlHngPcyZt8h6lRBCCJFbrK2hWzd47jnYt08letOmwZgxMGyY2pgh8iQr\nKyuql65O9dLV6fd0PwDiUuI4HH6YA1cO8OWRL3lt42tULF4x0yyfW1m3HNm8ITN4QgghhJ78/NTS\n7e7dMGoUjB6tTsgQ+Y7RZCToWlCmWb5bKbdo6tqUZi7NmPLMFFmizQpJ8IQQQuQZZ8+q/nkbNsCA\nAappskvOtY4SliEyIZJDVw9x4MoBPu30qdnyFun0JcQde/bs0TsEkUfIWBHZkeXxUrMmLF0KgYFq\nKdfLC15/Hc6cydH4hL6cizvzQu0XmP/sfLPeVxI8IYQQwpJUqgTz56sZvSpVoGVLeOklOH5c78hE\nHiJLtEIIIYQlS0pSM3vz50Pt2qqXXuvW0ksvH5I2KVkkCZ4QQoh8Iy0N1qyB2bOhZEmV6HXrhpyr\nmH+YM2+RUSHEHVJXJbJKxorIDrONF3t7GDQIgoNh3Dj46CPw9ITvvoP0dPO8hsg3JMETQggh8hIb\nG3UKxtGj8PnnsGqV2qCxcCHcvq13dMJCyBKtEEIIkdcdPqx66R06pProjRqllnFFniJLtEIIIYT4\nR5MmsGkT7NoFoaFQvTpMmABRUXpHJnQiCZ4Qd0hdlcgqGSsiO3J1vNSpo5Zs/fwgOVk9HjECLlzI\nvRiERZAETwghhMhvqlSBL7+E06fVsWeNG8Nrr0FAgN6RiVwiNXhCCCFEfhcfD19/rTZl1K+vWqw0\nb653VOI/pA9eFkmCJ4QQQvxLSgp8+y3MnatOzJg0CTp1kqbJFkI2WQiRA6SuSmSVjBWRHRY1XgoV\ngjfeUOfbjhgBEyeCtzf8+CMYjXpHJ8xIEjwhhBCioLG1hVdfhRMn4JNPVA89Nzf45hs1yyfyPFmi\nFUIIIQTs3w8zZ4K/P7zzjprpK15c76gKFFmiFUIIIYR5tWgBW7bAn3+qNitPPQVTp0JMjN6Riccg\nCZ4Qd1hUnYywaDJWRHbkufHy9NPwww/qVIxr19TS7dtvw5UrekcmskESPCGEEELcq0YNWLIEgoPB\nwUFtxhg4EE6d0jsykQVSgyeEEEKIR4uNha++ggULVA+9SZOgUSO9o8pXpAZPCCGEELmrVCmYMkUd\ne9a6NfTqBe3bw86dIJMpFkcSPCHuyHN1MkI3MlZEduS78VK0KLz1Fpw/D/36wejR0KQJbNwIJpPe\n0Yk7JMETQgghRPbZ2cGAAapGb9IkmDULPDzUSRlpaXpHV+BJDZ4QQgghnpymwa5dKtE7cwbefReG\nDFEzfiJLpAZPCCGEEJbFygratYMdO2DDBti3T/XS++gjtUFD5CpJ8IS4I9/VyYgcI2NFZEeBHC+N\nGqkkb88etSmjRg0YNw4iIvSOrMCQBE8IIYQQOaN2bVi5Uh1/lpYGdevCsGFw7pzekeV7UoMnhBBC\niNxx/Tp8+SUsXqyWcydOhHr19I7KYkgNnhBCCCHynrJl4cMP1bJtw4bQtSt06QJ//613ZPmOJHhC\n3FEg62TEY5GxIrJDxst9FC8OY8eqXno9e8KgQdCiBfz+uzRNNhNJ8IQQQgihj0KFVE3emTOqYfKU\nKfD007B2LRgMekeXp0kNnhBCCCEsg6bB1q0wcyaEh6udtwMHqkSwADBn3iIJnhBCCCEsz4EDKtE7\nfhz+9z8YMQJKlNA7qhwlmyyEyAFSJyOySsaKyA4ZL4+peXNVk7dtGwQEqKbJkyfDtWt6R5YnSIIn\nhBBCCMvl5QVr1sCRI3DzJri7q3q9S5f0jsyiyRKtEEIIIfKOyEj4/HNYtky1WZkwATw89I7KLGSJ\nVgghhBAFk7MzzJ6tWqy4u6uGyT17wuHDekdmUSTBE+IOqZMRWSVjRWSHjJccUrIkvPeeaprcvj28\n/DK0bQvbt0svPSTBE0IIIUReVqQIvPmmOt924EB45x1o1AjWrwejUe/odCM1eEIIIYTIP0wm+O03\n1WIlNlbV6PXtC/b2ekf2SNIHL4skwRNCCCEKKE2DPXtUonfqFIwZA0OHQrFiekf2QHl+k8XPP/+M\nh4cHNjY2+Pn5PfC6wYMHU758eTw9PTN9ftq0abi4uODt7Y23tzdbt27N6ZBFASB1MiKrZKyI7JDx\nohMrK2jTRtXkbdoEBw+qXnrTp6t2K/mcLgmep6cnmzZtolWrVg+9btCgQfdN3qysrBgzZgz+/v74\n+/vTqVOnnApVCCGEEHldw4bw88/w999w5QrUqAHvvquOQ8undEnw3N3dqVWr1iOva9myJaVKlbrv\nc7L0KsytdevWeocg8ggZKyI7ZLxYEDc3WL4cTp5UtXqenjBkCISG6h2Z2eXZXbQLFizAx8eH2bNn\nk5CQoHc4QgghhMgrXF3hs89UYlepkjoW7eWXwd9f78jMJscSvA4dOuDp6XnPx2+//fbE9x4xYgQX\nL15k27ZtnD9/niVLlpghYlHQSZ2MyCoZKyI7ZLxYsLJlVU3exYvg4wPdukGnTmpzRh5fKbTNqRvv\n2LEjp26Nk5MTAI6OjowaNYqRI0cyduzY+147cOBAqlatCkDJkiWpV69exnT53f/p5LE8Bjhx4oRF\nxSOP5bE8lsfyOJceHzsG9evT+vx5WL2aPX37QokStJ41C557jj379uXI69/990s5cK6urm1S2rRp\nw7x582jQoMEDr7l06RLdunUjMDAw43ORkZE4OztjMBiYPHkyJUqUYPLkyfd8rbRJEUIIIUS2GY2w\nYQPMmgVpaTBxIvTuDbY5Ni8G5IM2KZs2bcLV1RVfX1+6du1K586dAYiIiKBr164Z17366qs0a9aM\n0NBQXF1dWblyJQATJkzAy8sLHx8f0tPTGTFihB5vQwghhBD5kY2Nqsk7fhzmz4dly6BmTVi0CJKT\n9Y4uS6TRsRB37NmzJ2P6XIiHkbEiskPGSz5x6JBqmnzkCLz9NowcCY6OZn2JPD+DJ4QQQgiRpzRt\nCr/+Cjt3QkiIapo8adL/27v7mCrLP47jn4MZW0JN7MFMQCLkUTtHRXOEopPKKEHI2YNOl//YstYf\nrbVZ0dbWhj2gbi1Xm1tLgynttLYWzc1RycaDog2BmkwDNSSoHA/VKrl+f5zjmfz01LnlHO7Dzfu1\n3X94OOfmuraP177c9/e+jtTba/fIrokreAAAAFadOSO9/bZUVSU98YT04otSSsqYTskVPAAAADul\npEjvvef7ntubb/Z9W8aGDdLJk3aPTBIFHhBw5WPrwL8hK7CCvDjcHXf4evNOn5ZycqRVq6Q1a3w9\nezaiwAMAABirW27xbady5oxvs+Qnn5QKCqTaWls2TaYHDwAAINz++UeqrvbtpXfjjb7ir6zMtwVL\nEOGsWyjwAAAAImVkRPriC99t3P5+6aWXpI0bpdjYq97KQxZABNAng1CRFVhBXia5mBjfd9zW10sf\nfijV1EipqdK770pDQ5H7tRE7MwAAAHxcLmn5cl9P3uefSw0Nvidxy8ulX34J/6/jFi0AAIANTp2S\nduzwfe/tpk1y7dzJLVoAAIAJLS3Nd9u2tdV3KzeMKPAAP/pkECqyAivIC/7TXXdJ77wT1lNS4AEA\nADgMPXgAAABRgG1SAAAAEBQFHuBHnwxCRVZgBXmBHSjwAAAAHIYePAAAgChADx4AAACCosAD/OiT\nQajICqwgL7ADBR4AAIDD0IMHAAAQBejBAwAAQFAUeIAffTIIFVmBFeQFdqDAAwAAcBh68AAAAKIA\nPXgAAAAIigIP8KNPBqEiK7CCvMAOFHgAAAAOQw8eAABAFKAHDwAAAEFR4AF+9MkgVGQFVpAX2IEC\nDwAAwGHowQMAAIgC9OABAAAgKAo8wI8+GYSKrMAK8gI7UOABAAA4DD14AAAAUYAePAAAAARFgQf4\n0SeDUJEVWEFeYAcKPAAAAIehBw8AACAK0IMHAACAoCjwAD/6ZBAqsgIryAvsQIEHAADgMPTgAQAA\nRAF68AAAABAUBR7gR58MQkVWYAV5gR0o8AAAAByGHjwAAIAoQA8eAAAAgqLAA/zok0GoyAqsIC+w\nAwUeAACAw9CDBwAAEAXowQMAAEBQFHiAH30yCBVZgRXkBXagwAMAAHAYevAAAACiAD14AAAACIoC\nD/CjTwahIiuwgrzADhR4AAAADkMPHgAAQBSgBw8AAABBUeABfvTJIFRkBVaQF9iBAg8AAMBh6MED\nAACIAvTgAQAAICgKPMCPPhmEiqzACvICO1DgAQAAOAw9eAAAAFGAHjwAAAAEZUuBd/DgQWVnZ2vK\nlClqaWm55nvOnj2rFStWKDs7WwUFBfrkk08CPxscHFRxcbGSkpJUUlKioaGh8Ro6HIw+GYSKrMAK\n8gI72FLgzZs3T16vV8uWLQv6nqlTp6qyslJtbW2qqanRK6+8Eijk3n//fSUlJenUqVOaPXu29uzZ\nM15Dh4OdOHHC7iFggiArsIK8wA62FHgZGRmaO3fuv75n5syZcrvdkqRbb71V2dnZam5uliQ1NTVp\ny5Ytio2N1dNPP63GxsaIjxnOd/HiRbuHgAmCrMAK8gI7TIgevM7OTrW1tWnx4sWSpObmZmVkZEjy\nFYtNTU12Dg8AACCq3BCpExcWFurChQtXvf7mm2/q0UcfDfk8g4ODWr9+vSorKzVt2jRJ4slYRMSP\nP/5o9xAwQZAVWEFeYAtjo4KCAnPs2LGgP//rr79MYWGhqaysHPV6aWmpaWlpMcYYc/ToUVNWVnbN\nz6emphpJHBwcHBwcHBxRf6SmpoatxorYFbxQmSBX44wx2rJli3JycvTCCy+M+tmSJUu0d+9e7dix\nQ3v37tV99913zXN0dnaGfbwAAADRzpYePK/Xq8TERDU0NKioqEirV6+WJP30008qKiqSJNXX12vf\nvn06fPiwPB6PPB6PamtrJUnPPPOMuru7lZ6ervPnz2vr1q12TAMAACAqOfqbLAAAACajCfEU7WXB\nNj/+t42Pd+/erbS0NGVlZenIkSOB1zs6OrRgwQLdfffd2r59+7jPBZEXzrwUFBQoIyMjcDW5v79/\n3OeDyLGalV9//VUrVqxQfHy8nnvuuVHnYm1xvnDmhbXF2axm5dChQ1q0aJHmz5+vkpKSUbuEWF5b\nwtbNNw56enrM8ePHjTHG9PX1mZSUFDMwMGAqKirMtm3bzJ9//mmeffZZ89ZbbxljjOnt7TXp6emm\nq6vL1NXVGY/HEzjX6tWrTXV1tenv7zd5eXmmubnZljkhcsKZl/96IAgTm9WsDA8PmyNHjpg9e/aY\nbdu2jToXa4vzhTMvrC3OZjUrx48fNz09PcYYY77++muTn58fOJfVtWVCXcELtvlxsI2PGxsb9dBD\nDykpKUnLly+XMSZQJf/www9av369ZsyYodLSUjZLdqBw5kUK/kAQJj6rWbnpppuUl5en2NjYq87F\n2uJ84cyLxNriZFaz4na7NXPmTElSfn6+Tp48qUuXLkmyvrZMqALvSldufhxs4+PGxkZlZmYGPpOe\nnq7GxkZ1dnbq9ttvD7yelZWlhoaG8Z0AxtVY8nLZpk2bVFhYqI8++mh8B49xFUpWLnO5XFd9lrVl\nchlLXi5jbZkcrGRFkqqqqrR06VJNmTLlutYW27dJuR5Xbn4cFxdn6a+fa/0H468nZwtHXvbv369Z\ns2apq6tL69atU3Z2thYtWhSpIcMmY8mKdPVawtribGPNi8TaMllYzUpra6tee+01HTp0SNL1rS0T\n7gre33//rbKyMm3cuFHFxcWSpNzcXHV0dEjyNSHm5uZK8u2X197eHvjs999/r9zcXN1zzz3q7e0N\nvN7e3h50Lz1MbOHIiyTNmjVLkpScnKwNGzbI6/WO5zQwDqxkJZi0tDTWlkkiHHmRWFsmA6tZOXfu\nnB577DF9/PHHSklJkXR9a8uEKvBMkM2PL298/Mcff4za+Hjx4sX66quv1N3drbq6OsXExCg+Pl6S\n75JodXW1+vv75fV6tWTJElvmhMgJV14uXboUeLJtYGBAXq9XDz/8sC1zQmRYzcqVn/t/rC3OF668\nsLY4n9WsXLx4UUVFRaqoqNDSpUtHncvy2jL2Z0TGz7fffmtcLpe59957jdvtNm6323z55ZdmYGDA\nrFmzxiQmJpri4mIzODgY+MzOnTtNamqqyczMNN98803g9ba2NuPxeMycOXPMyy+/bMd0EGHhysvQ\n0JBZuHChmT9/vrn//vtNRUWFXVNChFxPVpKTk01CQoKJi4sziYmJpqOjwxjD2jIZjDUvs2fPNh0d\nHWZ4eJi1xeGsZuWNN94w06ZNC7zX7Xabvr4+Y4z1tYWNjgEAABxmQt2iBQAAwH+jwAMAAHAYCjwA\nAACHocADAABwGAo8AAAAh6HAAwAAcBgKPAAAAIehwAOACBkZGbF7CAAmKQo8AJBUXl6uXbt2Bf69\nfft27d69WwcPHtQjjzyi/Px8ffDBB4Gfr127VgsXLtTKlStHfX9oXFycXn31VbndbjU0NIzrHADg\nMr7JAgAkdXV1qbS0VMeOHdPIyIjmzp2rAwcOqKKiQvv379fIyIgeeOABVVVV6c4779Rvv/2m6dOn\na2BgQAUFBWppaZEkxcTEaOfOnXr++edtnhGAyewGuwcAANEgOTlZM2bM0IkTJ3ThwgV5PB7V1dWp\nqalJubm5kqTh4WEdPnxYTz31lKqrq1VTU6Off/5ZZ8+eVWtrq+bNm6eYmBht3rzZ3skAmPS4ggcA\nfgcOHFB9fb16e3u1efNmtba26vfff1d5efmo950+fVolJSWqq6tTQkKCPB6Pdu3apWXLlik+Pl6D\ng4M2zQAAfOjBAwC/tWvXqra2VkePHtWDDz6oxx9/XJ9++qm6u7slSefPn1dfX596enp02223KSEh\nQfX19fruu+9sHjkAjMYtWgDwmzp1qlauXKnp06fL5XIpMTFRr7/+urZu3apz584pPj5e+/btU15e\nnpKTk5WZmamcnBytWrUqcA6Xy2XjDADAh1u0AOA3MjKiBQsW6LPPPtOcOXPsHg4AXDdu0QKApPb2\ndmVlZWndunUUdwAmPK7gAQAAOAxX8AAAAByGAg8AAMBhKPAAAAAchgIPAADAYSjwAAAAHIYCDwAA\nwGH+B2d60B236acMAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7e93dd8>"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "3) Output"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Write the input file for the model."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for scen in df_fund.columns:\n",
+      "    with open('../../Fund/Data/%s/rfso2.csv' % scen, 'w') as f:\n",
+      "        f.write('# %s scenario\\n' % scen)            \n",
+      "        f.write('# Generated by calibration/so2rf/fund_so2_rf_scenarios.ipynb\\n')            \n",
+      "        df_fund[scen].to_csv(f)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 14
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/calibration/so2rf/main.DELTQNEG_A1B.xlsx b/calibration/so2rf/main.DELTQNEG_A1B.xlsx
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index 00000000..2c1d7852
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diff --git a/calibration/so2rf/main.DELTQNEG_A2.xlsx b/calibration/so2rf/main.DELTQNEG_A2.xlsx
new file mode 100644
index 00000000..cf240241
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diff --git a/calibration/so2rf/main.DELTQNEG_B1.xlsx b/calibration/so2rf/main.DELTQNEG_B1.xlsx
new file mode 100644
index 00000000..999f9433
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diff --git a/calibration/so2rf/main.DELTQNEG_B2.xlsx b/calibration/so2rf/main.DELTQNEG_B2.xlsx
new file mode 100644
index 00000000..f7160430
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