diff --git a/.gitattributes b/.gitattributes
index b634d85..77c1700 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1 +1,3 @@
 *.pdf filter=lfs diff=lfs merge=lfs -text
+h5m/*.h5 filter=lfs diff=lfs merge=lfs -text
+*.h5m filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
index 38d401f..8c6bbb9 100644
--- a/README.md
+++ b/README.md
@@ -1,49 +1,36 @@
 # msre
 [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
 
-> detailed cad model of the [msre](https://en.wikipedia.org/wiki/Molten-Salt_Reactor_Experiment) (molten salt reactor experiment), operated by oak ridge national laboratory 1965-69.
+detailed cad model and openmc benchmarks of the [msre](https://en.wikipedia.org/wiki/Molten-Salt_Reactor_Experiment) (molten salt reactor experiment), operated by oak ridge national laboratory 1965-69.
+
+[onshape cad model v17](https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/)
+cad model includes crude drawings of the insulation, thermal shield, and the reactor pit, but not detailes such as components or piping.
+all part names' begining correspond to it's material, e.g. graphite, salt, or inor-8 (hasteloy n).
+all core parts are thermally expanded to the operating temperature (currently to the temperature of the zero power experiments during commisioning). note that graphite thermal expansion coefficients are only given for room temperature and thus likely underpredicted and the control rod assembly temperature is unknow and like much higher than currently assigned.
+
+[core/msrecore.pdf](core/docs/msrecore.pdf) lists reference of the msre core design, documented in the old msre reports and located [here](https://github.com/openmsr/msr-archive/blob/master/README.md).
+
+individual parts or assemblies can be exported directly from onshape. step files of entire msre assembly and one control rod assembly can be found in [](step_files/).
+
+this work and the cad models are under the GNU General Public License v3.0
 
-## msre core
 ![](core/docs/msre.png)
-[core/msrecore.pdf](core/docs/msrecore.pdf) lists reference of the msre core design, documented in the old msre reports and located in the repository [github.com/openmsr/msr-archive](https://github.com/openmsr/msr-archive/blob/master/README.md).
 
-the work-in-progress cad model can be found [here](https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/w/11cb17d9ef25bb27f8ada6c0/e/72f417dd8eb3e2fa4f9ccb9e) on onshape.
+![](core/docs/msre-pit.png)
 
-note that this work and the cad model is under the GNU General Public License v3.0
+# openmc benchmark
+openmc benchmark include:
+- msre cad with settable control rods
+- msre isothermal temperature coefficient calculation
+- msre depletion analysis with fission products removal
 
-## msre heat exchangers
-
-open-access [master's thesis](https://ltu.diva-portal.org/smash/get/diva2:1546993/FULLTEXT01.pdf) produced by Malcolm Akner about simulations of the heat exchangers of the msre, titled: 
-
-> Validating results from the Molten Salt Reactor Experiment by use of turbulent CFD simulations
-> - A study of a modified U-tube shell-and-tube primary heat exchanger and radiator with molten salts
-
-### msre primary heat exchanger
-![](heatexchanger/docs/phexcadmodel.png)
-
-[onshape primary heat exchanger cad model](https://cad.onshape.com/documents/03be2f510296a2e264886390/w/8cfbca3b7b9682dd4e53a998/e/54728fd981a1b4f5594c73d6), open to copy and use freely. chapter 4.1 in the [thesis](https://ltu.diva-portal.org/smash/get/diva2:1546993/FULLTEXT01.pdf) mentioned above covers the cad construction details extensively with references to original msre reports. 
-
-![](heatexchanger/docs/phexflowpaths.png)
-
-[simscale primary heat exchanger simulation model](https://www.simscale.com/projects/MalcolmAkner/phex_-_final_version/). simulation results for primary heat exchanger can be viewed in chapter 6.2.2.1 of the [thesis](https://ltu.diva-portal.org/smash/get/diva2:1546993/FULLTEXT01.pdf), with comparisons to msre data in chapter 7.1.1. 
-
-![](heatexchanger/docs/phexreal.png)
-
-primary heat exchanger produced and installed in the msre. 
+prerequisites
+- [openmc](https://docs.openmc.org/en/stable/) automated source installation scripts for linux can be found [here](https://github.com/openmsr/openmc_install_scripts)
+- [CAD_to_openMC](https://github.com/openmsr/CAD_to_openMC) is an open-source package to convert CAD geometry (in the form of '.step' files) into an openmc-readable h5m file
 
 
-### msre radiator
-![](heatexchanger/docs/radiatorcadmodel.png)
+# msre heat exchangers thermohydralics
+open-access [master's thesis](https://ltu.diva-portal.org/smash/get/diva2:1546993/FULLTEXT01.pdf) produced by Malcolm Akner about simulations of the heat exchangers of the msre, titled:
 
-[onshape radiator cad model](https://cad.onshape.com/documents/bf944323ed6a82e05924078c/w/2a25d73c5a3a66824d2d5fbd/e/a83d5535602a053216fedff4) open to copy and use freely. chapter 4.2 of the [thesis](https://ltu.diva-portal.org/smash/get/diva2:1546993/FULLTEXT01.pdf) covers the cad construction details with origianl references to msre reports. 
-
-![](heatexchanger/docs/radiatorreal.png)
-
-radiator produced and installed in the msre.
-
----
-
-please contact [me](https://github.com/aslakstubsgaard) if you want to contribute.
-note that this work and the cad models are under the GNU General Public License v3.0
-
----
+Validating results from the Molten Salt Reactor Experiment by use of turbulent CFD simulations
+- A study of a modified U-tube shell-and-tube primary heat exchanger and radiator with molten salts
diff --git a/core/docs/msre-pit.png b/core/docs/msre-pit.png
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diff --git a/dynamic_model/README.md b/dynamic_model/README.md
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+# Model
+Non-linear dynamic model of the MSRE based on [Singh et al.](https://www.sciencedirect.com/science/article/pii/S030645491730381X). The model uses a "nodalized" approach, in which the reactor system is composed of coupled subsystems, each of which contain multiple nodes to represent the various dynamics contributing to neutron density (reactor power) and heat transfer. This model is a replication of the one-region nodal model implemented in Singh et al. A schematic of the nodes is shown below 
+
+![](figures/msre_one_region_diagram.png)
+
+## Kinetics 
+The neutron kinetics are expressed in terms of fractional power. As described in Singh et al., "The premise is that reactor power is proportional to neutron density, with all other parameters held fixed." In the model herien, kinetics are described by the following set of equations
+
+```math
+\begin{equation}
+\frac{{dn(t)}}{{dt}} = \frac{{(\rho(t) - \beta)}}{\Lambda} n(t) + \sum_{{i=1}}^6 \lambda_i C_i(t) + S(t)
+\end{equation}
+```
+
+where $S(t)$ is an external source term, and $C_i(t)$ is the concentration of the $i^{th}$ precursor group (six total for this model) with
+
+```math
+\frac{dC_i(t)}{dt} = \frac{\beta_i}{\Lambda}n(t)-\lambda_i C_i(t) - \frac{C_i(t)}{\tau_C} + \frac{C_i(t - \tau_L) e^{-\lambda_i \tau_L}}{\tau_C}
+```
+
+where $\rho(t)$ is the total reactivity such that 
+
+```math
+\rho(t)=\rho_0+\rho_{fb}(t)+\rho_{ext}
+```
+
+where $\rho_0$ is the reactivity necessary for steady state operation, found by solving the above two equations in the steady state, which yields
+
+```math
+\rho_0 = \beta - \sum_{i=1}^6 \frac{\beta_i}{1+\frac{1}{\lambda_i \tau_C}(1-e^{-\lambda_i \tau_L})}
+```
+
+and $\rho_{ext}$ is external reactivity, e.g. from a reactivity insertion, and $\rho_{fb}$ is the feedback reactivity due to temperature differences in the core nodes, expressed as 
+
+```math
+\rho_{fb}(t) = \alpha_f \sum_{i=1}^{n} I_{fi} (T_{f_i,0} - T_{f_i}(t)) + \alpha_g \sum_{i=1}^{n} I_{gi} (T_{g,0} - T_{g_i}(t)), \quad \text{where} \sum_{\text{regions}} I_{fi} = \sum_{\text{regions}} I_{gi} = 1.0
+```
+
+where $\alpha_f$ and $\alpha_g$ are the fuel and graphite temperature-reactivity coefficients respectively, and $I$ represents the weighted nuclear importance factor of each region.
+
+The kinetics are coupled to the heat exchanger node via the feedback reactivity, $\rho_{fb}(t)$, which depends on the temperature of the fuel and graphite nodes, which ultimately depend on the fuel inlet temperature. Note, the differential equation govenring neutron density, $\frac{dn(t)}{dt}$, is nonlinear due to the fact that $n(t)$ is multiplied with $\rho(t)$.
+
+## Heat Transfer 
+
+The equations governing heat transfer contain three terms; a source term and a sink term, occuring from either mass transfer (1D flow) or conduction via wetted interface, as well as a fractional power generation term, which is zero at all nodes outside of the core. The equations governing the two core fuel nodes are, for example
+
+```math
+\frac{dT_{f1}}{dt} = \frac{W_f}{m_{f1}}(T_{f_{in}}-T_{f1}) + \frac{K_1 P_0 (\frac{n}{n_0})}{m_{f1} C_{pf}} + \left( \frac{K_{g1}}{K_{g1}+K_{g2}} \right) \frac{hA_{fg}}{m_{f1}C_{pf}}(T_g - T_{f1})
+```
+```math
+\frac{dT_{f2}}{dt} = \frac{W_f}{m_{f2}}(T_{f1}-T_{f2}) + \frac{K_2 P_0 (\frac{n}{n_0})}{m_{f2} C_{pf}} + \left( \frac{K_{g2}}{K_{g1}+K_{g2}} \right) \frac{hA_{fg}}{m_{f2}C_{pf}}(T_g - T_{f1})
+```
+"Here, $W_f$ is the mass flow rate of fuel salt, $m_{f1}$ and $m_{f2}$ represent the mass of fuel nodes 1 and 2 respectively, $C_{pf}$ represents the fuel salt specific heat capacity, $K_1$ and $K_2$ are the fraction of total power generated in fuel nodes 1 and 2, $K_{g1}$ and $K_{g2}$ represent the fraction of power generated in the graphite transferred to each fuel node, $hA_{fg}$ is the product of area and heat transfer coefficient for the fuel-graphite interface, $P_0$ is the nominal power which multiplied with fractional neutron density n/no gives the instantaneous power, and the $T$s represent the temperatures of the various nodes. Note that the direction of heat transfer depends on the instantaneous temperature of the various nodes." (Singh et. al). 
+
+# Method
+The model herein uses [JiTCDDE](https://jitcdde.readthedocs.io/en/stable/), a numerical solver for delay-differential equations. Sample implementation is shown below:
+
+```python
+# instantiate jitcdde object
+DDE = jitcdde([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1,
+               T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho])
+
+# set initial conditions
+DDE.constant_past([T0_rp, T0_rs, T0_p1,T0_p2, T0_p3, T0_p4, T0_t1, T0_t2, T0_s1, T0_s2, 
+          T0_s3, T0_s4, n_frac0, C0[0], C0[1], C0[2], C0[3], C0[4], C0[5], 
+          T0_g1, T0_f1, T0_f2,rho_initial])
+
+# jitcdde solver parameters 
+t0 = 0.0
+tf = 1000.00
+T = np.arange(t0,tf,0.01)
+
+sol_jit = []
+for t_x in T:
+    sol_jit.append(DDE.integrate(t_x))
+```
+
+The [scipyODE_implementation](./scipyODE_implementation/) contains a manual implementation using the `dopri5` method of SciPy's ODE library. However, since SciPy's ODE library does not support delay differential equations, the delay terms are stored and handled manually. Since the 'dopri5' method uses adaptive time-stepping, linear interpolation is used for approximating the value of the delay terms near the closest timestep to the delay. 
diff --git a/dynamic_model/figures/msre_one_region_diagram.png b/dynamic_model/figures/msre_one_region_diagram.png
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diff --git a/dynamic_model/jitcdde_implementation/parameters.py b/dynamic_model/jitcdde_implementation/parameters.py
new file mode 100644
index 0000000..d92d5c5
--- /dev/null
+++ b/dynamic_model/jitcdde_implementation/parameters.py
@@ -0,0 +1,234 @@
+import numpy as np
+import pandas as pd
+import math
+pi = math.pi
+
+# Perturbations
+# SOURCE INSERTION
+# No source insertion
+sourcedata = np.array([0, 0, 0])
+sourcetime = np.array([0, 50, 100])
+# % 1 (n/no)/s for 10 seconds
+# sourcedata = np.array([0, 10, 0])
+# sourcetime = np.array([0, 10, 20])
+source = pd.Series(sourcedata, index=sourcetime)
+
+# REACTIVITY INSERTION
+# No reactivity insertion
+simtime = 10
+reactdata = np.array([0, 5E-4])
+reacttime = np.array([0, 2500])
+# Periodic 60 PCM for 50 seconds
+# simtime = 500
+# periodic = np.array([[0, 0], [50, 6e-4], [100, 0], [150, -6e-4], [200, 0], [250, 6e-4], [300, 0], [350, -6e-4], [400, 0]])
+# reactdata = periodic[:, 1]
+# reacttime = periodic[:, 0]
+# Step up 60 pcm
+# simtime = 1000
+# reactdata = np.array([0, 6e-3])
+# reacttime = np.array([0, 300])
+# # Step down -60 pcm for 10 sec
+# simtime = 100
+# reactdata = np.array([0, -6e-4])
+# reacttime = np.array([0, 50])
+# # Pulse 600 pcm for 0.1 sec
+# simtime = 30
+# reactdata = np.array([0, 6e-3, 0])
+# reacttime = np.array([0, 10, 10.1])
+
+react = pd.Series(reactdata, index=reacttime)
+
+ts_max = 1e-1  # maximum timestep (s)
+
+# NEUTRONICS DATA
+tau_l = 16.73  # ORNL-TM-0728 %16.44; % (s)
+tau_c = 8.46  # ORNL-TM-0728 %8.460; % (s)
+P = 8  # Thermal Power in MW ORNL-TM-1070, p.2
+n_frac0 = 1.0  # initial fractional neutron density n/n0 (n/cm^3/s)
+Lam = 2.400E-04  # mean generation time ORNL-TM-1070 p.15 U235
+# Lam = 4.0E-04;  # mean generation time ORNL-TM-1070 p.15 U233
+lam = np.array([1.240E-02, 3.05E-02, 1.11E-01, 3.01E-01, 1.140E+00, 3.014E+00])
+beta = np.array([0.000223, 0.001457, 0.001307, 0.002628, 0.000766, 0.00023])  # U235
+# beta = np.array([0.00023, 0.00079, 0.00067, 0.00073, 0.00013, 0.00009])  # U233
+beta_t = np.sum(beta)  # total delayed neutron fraction MSRE
+rho_0 = beta_t-sum(np.divide(beta,1+np.divide(1-np.exp(-lam*tau_l),lam*tau_c))) # reactivity change in going from stationary to circulating fuel
+C0 = beta / Lam * (1.0 / (lam - (np.exp(-lam * tau_l) - 1.0) / tau_c))
+
+# Feedback co-efficients
+a_f = -8.71E-05  # U235 (drho/°C) fuel salt temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -5.904E-05; % ORNL-TM-0728 p. 101 %
+a_g = -6.66E-05  # U235 (drho/°C) graphite temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -6.624E-05; % ORNL-TM-0728 p.101
+
+# CORE HEAT TRANSFER PARAMETERS
+# FUEL PARAMETERS - DONE
+vdot_f = 7.5708E-02  # ORNL-TM-0728 % 7.571e-2; % vol. flow rate (m^3/s) ORNL-TM-1647 p.3, ORNL-TM-0728 p.12
+rho_f = 2.14647E+03  # (partially enriched U-235)ORNL-TM-0728 p.8 2.243E+03; % (Th-U) density of fuel salt (kg/m^3) ORNL-TM-0728 p.8
+W_f = 1.623879934566580e+02  # 1.83085e+02;%vdot_f*rho_f; % 182.78; % calcd from m_dot*cp*delT=P; vdot_f*rho_f; % fuel flow rate (kg/s)
+# tau_f_c  = tau_c; % ORNL-TM-0728 % 8.45; % transit time of fuel in core (s) ORNL-TM-1070 p.15, TDAMSRE p.5
+m_f = W_f * tau_c  # fuel mass in core (kg)
+nn_f = 2  # number of fuel nodes in core model
+mn_f = m_f / nn_f  # fuel mass per node (kg)
+# cp_f     = 4.2*9/5; % (MJ/deg-C) total fuel heat capacity TDAMSRE p.5
+scp_f = 1.9665E-3  # specific heat capacity of fuel salt (MJ/kg-C) ORNL-TM-0728 p.8
+
+# Core Upflow - DONE
+v_g = 1.95386  # graphite volume(m^3) ORNL-TM-0728 p. 101
+rho_g = 1.860E3  # graphite density (kg/m^3) ORNL-3812 p.77, ORNL-TM-0728 p.87
+m_g = v_g * rho_g  # graphite mass (kg)
+cp_g = 3.6 * 9 / 5  # TDAMSRE p.5 graphite total heat capacity (MW-s/C) ORNL-TM-1647 p.3
+scp_g = 1.773E-3  # cp_g/m_g; % graphite specific heat capacity (MW-s/kg-C) ORNL-TM-1647 p.3
+mcp_g1 = m_g * scp_g  # (mass of material x heat capacity of material) of graphite per lump (MW-s/°C)
+mcp_f1 = mn_f * scp_f  # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C)
+mcp_f2 = mn_f * scp_f  # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C)
+hA_fg = 0.02 * 9 / 5  # (fuel to graphite heat transfer coeff x heat transfer area) (MW/°C) ORNL-TM-1647 p.3, TDAMSRE p.5
+k_g = 0.07  # fraction of total power generated in the graphite  ORNL-TM-0728 p.9
+k_1 = 0.5  # fraction of heat transferred from graphite which goes to the first fuel lump
+k_2 = 0.5  # fraction of heat transferred from graphite which goes to the second fuel lump
+k_f = 0.93  # fraction of heat generated in fuel - that generated in the external loop ORNL-TM-0728 p.9
+k_f1 = k_f / nn_f  # fraction of total power generated in lump f1
+k_f2 = k_f / nn_f  # fraction of total power generated in lump f2
+
+# New node for power deposited in fuel outside the core
+k_out = 1 - (k_g + k_f)  # fraction of power generated in fuel in external loop ORNL-TM-0728 p.9
+m_out = W_f  # (kg) Mass of node such that resident time is 1 sec (W_f needs to be defined)
+
+# Initial conditions - DONE
+Tf_in = 6.3222E+02  # in °C ORNL-TM-1647 p.2
+T0_f2 = 6.5727E+02  # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2
+T0_f1 = Tf_in + (T0_f2 - Tf_in) / 2  # 6.405952380952389e+02; in °C
+T0_g1 = T0_f1 + (k_g * P / hA_fg)  # 6.589285714285924e+02; in °C
+# T0_out = k_out * P / m_out / scp_f + T0_f2  # in °C (scp_f needs to be defined)
+
+
+# Heat Exchanger - DONE
+# Geometry
+d_he = 16  # (in) he diameter ORNL-TM-0728 p. 164
+h_he = 72  # (in) active height % 96; %(in) he height ORNL-TM-0728 p. 164
+od_tube = 0.5  # (in) coolant tube OD ORNL-TM-0728 p. 164
+id_tube = od_tube - 2 * 0.042  # (in) coolant tube ID ORNL-TM-0728 p. 164
+n_tube = 159  # number of coolant tubes ORNL-TM-0728 p. 164
+a_tube = 254 * 144  # (in^2) total area of tubes ORNL-TM-0728 p. 164
+l_tube = a_tube / n_tube / (np.pi * od_tube)  # (in) tube length
+v_tube = n_tube * np.pi * (od_tube / 2) ** 2 * l_tube  # (in^3) hx shell volume occupied by tubes
+v_cool = n_tube * np.pi * (id_tube / 2) ** 2 * l_tube  # (in^3) hx volume occupied by coolant
+v_he = (d_he / 2) ** 2 * np.pi * h_he  # (in^3) volume of heat exchanger shell
+v_he_fuel = v_he - v_tube  # (in^3) volume available to fuel in shell
+
+# Unit conversions
+in_m = 1.63871e-5  # 1 cubic inch = 1.63871e-5 cubic meters
+
+# PRIMARY FLOW PARAMETERS - DONE
+W_p = W_f  # fuel flow rate (kg/s)
+
+m_p = v_he_fuel * in_m * rho_f  # fuel mass in PHE (kg)
+nn_p = 4  # number of fuel nodes in PHE
+mn_p = m_p / nn_p  # fuel mass per node (kg)
+cp_p = scp_f  # fuel heat capacity (MJ/(kg-C))
+
+# SECONDARY FLOW PARAMETERS - DONE
+vdot_s = 5.36265E-02  # ORNL-TM-0728 p. 164 % 5.236E-02; % coolant volume flow rate (m^3/s) ORNL-TM-1647 p.3
+rho_s = 1.922e3  # coolant salt density (kg/m^3) ORNL-TM-0728 p.8
+W_s = 1.005793369810108e+02  # vdot_s*rho_s; % calcd from mdot*cp*delT; vdot_s*rho_s; % coolant flow rate (kg/s) ORNL-TM-1647 p.3
+
+m_s = v_cool * in_m * rho_s  # coolant mass in PHE (kg)
+nn_s = 4  # number of coolant nodes in PHE
+mn_s = m_s / nn_s  # coolant mass per node (kg)
+scp_s = 2.39E-3  # cp_s/m_s; % specific heat capacity of coolant (MJ/(kg-C) ORNL-TM-0728 p.8
+
+A_phe = 2.359E+01  # effective area for heat transfer (primary and secondary, m^2) ORNL-TM-0728 p.164
+
+ha_p = 6.480E-01  # heat transfer*area coefficient from primary to tubes (MW/C) ORNL-TM-1647 p.3
+ha_s = 3.060E-01  # heat transfer*area coefficient from tubes to secondary (MW/C) ORNL-TM-1647 p.3
+
+# Primary Side
+mcp_pn = mn_p * cp_p  # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C
+hA_pn = ha_p / nn_s  # 3.030; % (primary to tube heat transfer coeff x heat transfer area) in MW/°C
+
+# Tubes - DONE
+nn_t = 2  # number of nodes of tubes in the model
+rho_tube = 8.7745E+03  # (kg/m^3) density of INOR-8 ORNL-TM-0728 p.20
+m_tn = (v_tube - v_cool) * in_m * rho_tube / nn_t  # mass of tubes (kg)
+scp_t = 5.778E-04  # specific heat capacity of tubes (MJ/(kg-C)) ORNL-TM-0728 p.20
+mcp_tn = m_tn * scp_t  # mass*(heat capacity) of tubes per lump in MW-s/°C
+
+# Secondary Side - DONE
+mcp_sn = mn_s * scp_s  # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C
+hA_sn = ha_s / nn_s  # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C
+
+# Initial conditions - DONE
+# Primary nodes
+Tp_in = T0_f2  # in °C ORNL-TM-1647 p.2
+T0_p4 = Tf_in  # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2
+T0_p1 = Tp_in + (T0_p4 - Tp_in) / 4  # in °C
+T0_p2 = Tp_in + 2 * (T0_p4 - Tp_in) / 4  # in °C
+T0_p3 = Tp_in + 3 * (T0_p4 - Tp_in) / 4  # in °C
+
+# Secondary nodes
+Ts_in = 5.4611E+02  # in °C ORNL-TM-1647 p.2
+T0_s4 = 5.7939E+02  # in °C ORNL-TM-1647 p.2
+T0_s1 = Ts_in + (T0_s4 - Ts_in) / nn_s  # in °C
+T0_s2 = Ts_in + 2 * (T0_s4 - Ts_in) / nn_s  # in °C
+T0_s3 = Ts_in + 3 * (T0_s4 - Ts_in) / nn_s  # in °C
+# Tube nodes
+T0_t1 = (T0_p1 * hA_pn + T0_s3 * hA_sn) / (hA_pn + hA_sn)  # in °C
+T0_t2 = (T0_p3 * hA_pn + T0_s1 * hA_sn) / (hA_pn + hA_sn)  # in °C
+
+# Radiator Parameters - DONE
+
+# Initial conditions - DONE
+# Primary nodes
+Trp_in = T0_s4  # 5.933E+02; % in °C ORNL-TM-1647 p.2
+T0_rp = Ts_in  # in °C ORNL-TM-1647 p.2
+
+# Secondary nodes - DONE
+Trs_in = 37.78  # (C) air inlet temperature ORNL-TM-1647 p.2
+T0_rs = 148.9  # (C) air exit temperature ORNL-TM-1647 p.2
+
+# Radiator Geometry
+od_rad = 0.01905  # (m) outer diameter of tubes in the radiator ORNL-TM-0728 p.296
+tube_wall_thick = 0.0018288  # (m) thickness of tubes in the radiator ORNL-TM-0728 p.296
+id_rad = od_rad - 2 * tube_wall_thick
+n_rtubes = 120  # number of tubes in the radiator (rows times tubes per row) ORNL-TM-0728 p.296
+l_rtube = 9.144  # (m) length of tubes in the radiator ORNL-TM-0728 p.296
+v_rp = pi * (id_rad / 2) ** 2 * l_rtube * n_rtubes  # volume available to salt in the radiator
+# v_rtube = pi * (od_rad / 2) ** 2 * l_rtube * n_rtubes - v_rp  # volume of metal in radiator tubes *TUBES NOT MODELED
+
+n_tpr = 12  # number of tubes per row in the radiator matrix
+n_row = 10  # number rows in the radiator matrix
+tube_space = 0.0381  # (m) spacing between tubes and rows of matrix
+v_rs = (n_row * od_rad + (n_row - 1) * tube_space) * (n_tpr * od_rad + (n_tpr - 1) * tube_space) * l_rtube  # volume of air inside radiator
+
+# PRIMARY FLOW PARAMETERS - DONE
+W_rp = W_s  # coolant salt flow rate (kg/s)
+m_rp = v_rp * rho_s  # coolant salt mass in rad (kg)
+nn_rp = 1  # number of coolant salt nodes in the radiator
+mn_rp = m_rp / nn_rp  # coolant mass per node (kg)
+cp_rp = scp_s  # coolant specific heat capacity (MJ/(kg-C))
+
+# SECONDARY FLOW PARAMETERS - DONE
+vdot_rs = 94.389  # ORNL-TM-0728 p. 296; 78.82; % air volume flow rate (m^3/s) ORNL-TM-1647 p.2
+rho_rs = 1.1237  # air density (kg/m^3) REFPROP (310K and 0.1MPa)
+W_rs = vdot_rs * rho_rs  # air flow rate (kg/s)
+
+m_rs = v_rs * rho_rs  # coolant air mass in rad (kg)
+nn_rs = 1  # number of coolant nodes in rad
+mn_rs = m_rs / nn_rs  # coolant mass per node (kg)
+scp_rs = 1.0085E-3  # (MJ/kg-C) specific heat capacity of air at (air_out+air_in)/2 REFPROP
+
+A_rad = 6.503E1  # (m^2) surface area of the radiator ORNL-TM-0728 p.14
+h_roverall = P / A_rad / ((T0_rp + Trp_in) / 2 - (T0_rs + Trs_in) / 2)  # cald as: P/A_rad/((T0_rp+Trp_in)/2-(T0_rs+Trs_in)/2)  3.168E-4; % (MW/m^2-C) polimi thesis
+
+# Primary Side
+mcp_rpn = mn_rp * cp_rp  # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C
+hA_rpn = h_roverall * A_rad / nn_rs  # 3.030; % (primary to secondary heat transfer coeff x heat transfer area) in MW/°C
+
+# Secondary Side - DONE
+mcp_rsn = mn_rs * scp_rs  # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C
+hA_rsn = h_roverall * A_rad / nn_rs  # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C
+
+# Pure time delays between components - DONE
+tau_hx_c = 8.67  # (sec) delay from hx to core TDAMSRE p.6
+tau_c_hx = 3.77  # (sec) subtracted 1 sec for external loop power generation node resident time; delay from core to fuel hx TDAMSRE p.6
+tau_hx_r = 4.71  # (sec) fertile hx to core TDAMSRE p.6
+tau_r_hx = 8.24  # (sec) core to fertile hx TDAMSRE p.6
+
+first_val = (rho_0 - beta_t) * n_frac0 / Lam + lam[0] * C0[0] + lam[1] * C0[1] + lam[2] * C0[2] + lam[3] * C0[3] + lam[4] * C0[4] + lam[5] * C0[5]
\ No newline at end of file
diff --git a/dynamic_model/jitcdde_implementation/simulation_study.ipynb b/dynamic_model/jitcdde_implementation/simulation_study.ipynb
new file mode 100644
index 0000000..193eaf3
--- /dev/null
+++ b/dynamic_model/jitcdde_implementation/simulation_study.ipynb
@@ -0,0 +1,913 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from jitcdde import jitcdde, y, t\n",
+    "from parameters import *\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define System"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# delays\n",
+    "taus = [tau_c, tau_c_hx, tau_hx_c, tau_hx_r, tau_l, tau_r_hx]\n",
+    "\n",
+    "# dT/dt for radiator nodes\n",
+    "T_out_rc = (W_rp/mn_rp)*(y(11,t-tau_hx_r)-y(0)) + (hA_rpn/mcp_rpn)*(y(1)-y(0))                  # T_out_rc: y(0)\n",
+    "T_out_air = -((W_rs/mn_rs)+(hA_rsn/mcp_rsn))*y(1) + (hA_rsn/mcp_rsn)*y(0) + (W_rs/mn_rs)*Trs_in # T_out_air: y(1)\n",
+    "\n",
+    "# dT/dt for heat exchanger nodes\n",
+    "T_hf1 = -((W_p/mn_p)+(hA_pn/mcp_pn))*y(2) + (hA_pn/mcp_pn)*y(6) + (W_p/mn_p)*y(21,t-tau_c_hx)  # T_hf1: y(2)\n",
+    "T_hf2 = (W_p/mn_p)*(y(2)-y(3)) + (hA_pn/mcp_pn)*(y(6)-y(2))                                    # T_hf2: y(3)\n",
+    "T_hf3 = -((W_p/mn_p)+(hA_pn/mcp_pn))*y(4) + (hA_pn/mcp_pn)*y(7) + (W_p/mn_p)*y(3)              # T_hf3: y(4)\n",
+    "T_hf4 = (W_p/mn_p)*(y(4)-y(5)) + (hA_pn/mcp_pn)*(y(7)-y(4))                                    # T_hf4: y(5)\n",
+    "T_ht1 = (2*hA_pn/mcp_tn)*(y(2)-y(6)) + (2*hA_sn/mcp_tn)*(y(10)-y(6))                           # T_ht1: y(6)\n",
+    "T_ht2 = (2*hA_pn/mcp_tn)*(y(4)-y(7)) + (2*hA_sn/mcp_tn)*(y(8)-y(7))                            # T_ht2: y(7)\n",
+    "T_hc1 = -((W_s/mn_s)+(hA_sn/mcp_sn))*y(8) + (hA_sn/mcp_sn)*y(7) + (W_s/mn_s)*y(0,t-tau_r_hx)   # T_hc1: y(8)\n",
+    "T_hc2 = (W_s/mn_s)*(y(8)-y(9)) + (hA_sn/mcp_sn)*(y(7)-y(8))                                    # T_hc2: y(9)\n",
+    "T_hc3 = -((W_s/mn_s)+(hA_sn/mcp_sn))*y(10) + (hA_sn/mcp_sn)*y(6) + (W_s/mn_s)*y(9)             # T_hc3: y(10)\n",
+    "T_hc4 = (W_s/mn_s)*(y(10)-y(11)) + (hA_sn/mcp_sn)*(y(6)-y(10))                                 # T_hc4: y(11)\n",
+    "\n",
+    "# dn/dt\n",
+    "n = (y(22)-beta_t)*y(12)/Lam+lam[0]*y(13)+lam[1]*y(14)+lam[2]*y(15)+lam[3]*y(16)+lam[4]*y(17)+lam[5]*y(18) # n (no source insertion): y(12)\n",
+    "\n",
+    "# dC_i/dt (precursor concentrations)\n",
+    "C1 = y(12)*beta[0]/Lam-lam[0]*y(13)-y(13)/tau_c+y(13,t-tau_l)*np.exp(-lam[0]*tau_l)/tau_c  # C1: y(13)\n",
+    "C2 = y(12)*beta[1]/Lam-lam[1]*y(14)-y(14)/tau_c+y(14,t-tau_l)*np.exp(-lam[1]*tau_l)/tau_c  # C2: y(14)\n",
+    "C3 = y(12)*beta[2]/Lam-lam[2]*y(15)-y(15)/tau_c+y(15,t-tau_l)*np.exp(-lam[2]*tau_l)/tau_c  # C3: y(15)\n",
+    "C4 = y(12)*beta[3]/Lam-lam[3]*y(16)-y(16)/tau_c+y(16,t-tau_l)*np.exp(-lam[3]*tau_l)/tau_c  # C4: y(16)\n",
+    "C5 = y(12)*beta[4]/Lam-lam[4]*y(17)-y(17)/tau_c+y(17,t-tau_l)*np.exp(-lam[4]*tau_l)/tau_c  # C5: y(17)\n",
+    "C6 = y(12)*beta[5]/Lam-lam[5]*y(18)-y(18)/tau_c+y(18,t-tau_l)*np.exp(-lam[5]*tau_l)/tau_c  # C6: y(18)\n",
+    "\n",
+    "# dT/dt core nodes\n",
+    "T_cg = (hA_fg/mcp_g1)*(y(20)-y(19)) + k_g*P*y(12)/mcp_g1                                             # T_cg: y(19)\n",
+    "T_cf1 = W_f/mn_f*(y(5,t-tau_hx_c)-y(20)) + (k_f1*P*y(12)/mcp_f1) + (hA_fg*k_1*(y(19)-y(20))/mcp_f1)  # T_cf1: y(20)\n",
+    "T_cf2 = W_f/mn_f*(y(20)-y(21)) + (k_f2*P*y(12)/mcp_f2) + (hA_fg*k_2*(y(19)-y(20))/mcp_f2)            # T_cf2: y(21)\n",
+    "\n",
+    "# rho y(22)\n",
+    "rho = (a_f/2)*(T_cf1 + T_cf2) + (a_g)*(T_cg)\n",
+    "\n",
+    "# initial reactivity \n",
+    "rho_initial = 0.000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Solve with JiTCDDE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating, compiling, and loading C code.\n",
+      "Using default integration parameters.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/mnt/envs/thesis_env/lib/python3.9/site-packages/jitcdde/_jitcdde.py:795: UserWarning: You did not explicitly handle initial discontinuities. Proceed only if you know what you are doing. This is only fine if you somehow chose your initial past such that the derivative of the last anchor complies with the DDE. In this case, you can set the attribute `initial_discontinuities_handled` to `True` to suppress this warning. See https://jitcdde.rtfd.io/#discontinuities for details.\n",
+      "  warn(\"You did not explicitly handle initial discontinuities. Proceed only if you know what you are doing. This is only fine if you somehow chose your initial past such that the derivative of the last anchor complies with the DDE. In this case, you can set the attribute `initial_discontinuities_handled` to `True` to suppress this warning. See https://jitcdde.rtfd.io/#discontinuities for details.\")\n",
+      "/mnt/envs/thesis_env/lib/python3.9/site-packages/jitcdde/_jitcdde.py:795: UserWarning: You did not explicitly handle initial discontinuities. Proceed only if you know what you are doing. This is only fine if you somehow chose your initial past such that the derivative of the last anchor complies with the DDE. In this case, you can set the attribute `initial_discontinuities_handled` to `True` to suppress this warning. See https://jitcdde.rtfd.io/#discontinuities for details.\n",
+      "  warn(\"You did not explicitly handle initial discontinuities. Proceed only if you know what you are doing. This is only fine if you somehow chose your initial past such that the derivative of the last anchor complies with the DDE. In this case, you can set the attribute `initial_discontinuities_handled` to `True` to suppress this warning. See https://jitcdde.rtfd.io/#discontinuities for details.\")\n",
+      "/mnt/envs/thesis_env/lib/python3.9/site-packages/jitcdde/_jitcdde.py:792: UserWarning: The target time is smaller than the current time. No integration step will happen. The returned state will be extrapolated from the interpolating Hermite polynomial for the last integration step. You may see this because you try to integrate backwards in time, in which case you did something wrong. You may see this just because your sampling step is small, in which case there is no need to worry.\n",
+      "  warn(\"The target time is smaller than the current time. No integration step will happen. The returned state will be extrapolated from the interpolating Hermite polynomial for the last integration step. You may see this because you try to integrate backwards in time, in which case you did something wrong. You may see this just because your sampling step is small, in which case there is no need to worry.\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "# instantiate jitcdde object\n",
+    "DDE = jitcdde([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1,\n",
+    "               T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho])\n",
+    "\n",
+    "# set initial conditions\n",
+    "DDE.constant_past([T0_rp, T0_rs, T0_p1,T0_p2, T0_p3, T0_p4, T0_t1, T0_t2, T0_s1, T0_s2, \n",
+    "          T0_s3, T0_s4, n_frac0, C0[0], C0[1], C0[2], C0[3], C0[4], C0[5], \n",
+    "          T0_g1, T0_f1, T0_f2,rho_initial])\n",
+    "\n",
+    "# jitcdde solver parameters \n",
+    "t0 = 0.0\n",
+    "tf = 1000.00\n",
+    "T = np.arange(t0,tf,0.01)\n",
+    "\n",
+    "sol_jit = []\n",
+    "for t_x in T:\n",
+    "    sol_jit.append(DDE.integrate(t_x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vary temeprature feedback coefficients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating, compiling, and loading C code.\n",
+      "Using default integration parameters.\n",
+      "Generating, compiling, and loading C code.\n",
+      "Using default integration parameters.\n",
+      "Generating, compiling, and loading C code.\n",
+      "Using default integration parameters.\n",
+      "Generating, compiling, and loading C code.\n",
+      "Using default integration parameters.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# solution vectors\n",
+    "sols = []\n",
+    "sols = [sol_jit]\n",
+    "\n",
+    "# feedbacks\n",
+    "factors_f = [1/2, 1/4, 1/8, 0]\n",
+    "factors_g = [1/2, 1/4, 1/8, 0]\n",
+    "\n",
+    "for i in range(len(factors_f)):\n",
+    "\n",
+    "    # reinstantiate jitcdde objects\n",
+    "    import sys\n",
+    "    sys.modules.pop('jitcdde')\n",
+    "    from jitcdde import jitcdde, y, t\n",
+    "\n",
+    "    # rho y(22)\n",
+    "    a_f_i = factors_f[i]\n",
+    "    a_g_i = factors_g[i]\n",
+    "    rho = a_f_i*(a_f/2)*(T_cf1 + T_cf2) + a_g_i*(a_g)*(T_cg)\n",
+    "\n",
+    "    # instantiate jitcdde object\n",
+    "    DDE = jitcdde([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1,\n",
+    "                T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho])\n",
+    "\n",
+    "    # set initial conditions\n",
+    "    DDE.constant_past([T0_rp, T0_rs, T0_p1,T0_p2, T0_p3, T0_p4, T0_t1, T0_t2, T0_s1, T0_s2, \n",
+    "            T0_s3, T0_s4, n_frac0, C0[0], C0[1], C0[2], C0[3], C0[4], C0[5], \n",
+    "            T0_g1, T0_f1, T0_f2,rho_initial])\n",
+    "    \n",
+    "    sol_i = []\n",
+    "    for t_x in T:\n",
+    "        sol_i.append(DDE.integrate(t_x))\n",
+    "    sols.append(sol_i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, '$\\\\rho$')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1200 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,axs = plt.subplots(2,3,figsize=(18,12))\n",
+    "\n",
+    "# fuel temps\n",
+    "#axs[0,0].set_xlim([0,20])\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[0]],label=\"core 1\") \n",
+    "axs[0,0].plot(T,[s[21] for s in sols[0]],label=\"core 2\") \n",
+    "axs[0,0].plot(T,[s[2] for s in sols[0]],label=\"hx 1\")  \n",
+    "axs[0,0].plot(T,[s[3] for s in sols[0]],label=\"hx 2\")\n",
+    "axs[0,0].plot(T,[s[4] for s in sols[0]],label=\"hx 3\")\n",
+    "axs[0,0].plot(T,[s[5] for s in sols[0]],label=\"hx 4\")  \n",
+    "axs[0,0].legend()\n",
+    "axs[0,0].set_title(\"Fuel Node Temperatures (C)\")\n",
+    "axs[0,0].tick_params(\n",
+    "    axis='x',          # changes apply to the x-axis\n",
+    "    which='both',      # both major and minor ticks are affected\n",
+    "    bottom=False,      # ticks along the bottom edge are off\n",
+    "    top=False,         # ticks along the top edge are off\n",
+    "    labelbottom=False) # labels along the bottom edge are off\n",
+    "\n",
+    "# coolant temps\n",
+    "#axs[0,1].set_xlim([0,20])\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[0]],label=\"hx 1\") \n",
+    "axs[0,1].plot(T,[s[9] for s in sols[0]],label=\"hx 2\") \n",
+    "axs[0,1].plot(T,[s[10] for s in sols[0]],label=\"hx 3\")  \n",
+    "axs[0,1].plot(T,[s[11] for s in sols[0]],label=\"hx 4\")\n",
+    "axs[0,1].plot(T,[s[0] for s in sols[0]],label=\"r 1\")\n",
+    "axs[0,1].legend()\n",
+    "axs[0,1].set_title(\"Coolant Node Temperatures (C)\")\n",
+    "axs[0,1].tick_params(\n",
+    "    axis='x',          # changes apply to the x-axis\n",
+    "    which='both',      # both major and minor ticks are affected\n",
+    "    bottom=False,      # ticks along the bottom edge are off\n",
+    "    top=False,         # ticks along the top edge are off\n",
+    "    labelbottom=False) # labels along the bottom edge are off\n",
+    "\n",
+    "# tube node temps\n",
+    "#axs[0,2].set_xlim([0,20])\n",
+    "axs[0,2].plot(T,[s[6] for s in sols[0]],label=\"hx 1\") \n",
+    "axs[0,2].plot(T,[s[7] for s in sols[0]],label=\"hx 2\") \n",
+    "axs[0,2].legend()\n",
+    "axs[0,2].set_title(\"Tube Node Temperatures (C)\")\n",
+    "axs[0,2].tick_params(\n",
+    "    axis='x',          # changes apply to the x-axis\n",
+    "    which='both',      # both major and minor ticks are affected\n",
+    "    bottom=False,      # ticks along the bottom edge are off\n",
+    "    top=False,         # ticks along the top edge are off\n",
+    "    labelbottom=False) # labels along the bottom edge are off\n",
+    "\n",
+    "# precursor concentrations\n",
+    "#axs[1,2].set_xlim([0,20])\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[0]],label=\"C1\") \n",
+    "axs[1,2].plot(T,[s[14] for s in sols[0]],label=\"C2\") \n",
+    "axs[1,2].plot(T,[s[15] for s in sols[0]],label=\"C3\")  \n",
+    "axs[1,2].plot(T,[s[16] for s in sols[0]],label=\"C4\")\n",
+    "axs[1,2].plot(T,[s[17] for s in sols[0]],label=\"C5\")\n",
+    "axs[1,2].plot(T,[s[18] for s in sols[0]],label=\"C6\")\n",
+    "axs[1,2].legend()\n",
+    "axs[1,2].set_xlabel(\"t (s)\")\n",
+    "axs[1,2].set_yscale(\"log\")\n",
+    "axs[1,2].set_ylabel(r\"concentration (1/cm$^3$)\")\n",
+    "axs[1,2].legend(loc=\"right\")\n",
+    "axs[1,2].set_title(\"Precursor Concentrations\")\n",
+    "\n",
+    "# multiplication factor temp\n",
+    "#axs[1,0].set_xlim([0,20])\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[0]],label=\"n\") \n",
+    "axs[1,0].set_xlabel(\"t (s)\")\n",
+    "axs[1,0].set_title(r\"$n$\")\n",
+    "axs[1,0].set_ylabel(r\"$\\frac{n}{n_0}$\")\n",
+    "\n",
+    "# reactivity\n",
+    "#axs[1,1].set_xlim([0,20])\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[0]],label=\"n\") \n",
+    "axs[1,1].set_xlabel(\"t (s)\")\n",
+    "axs[1,1].set_title(r\"$\\rho$\")\n",
+    "\n",
+    "#plt.figure(figsize=(8,6))\n",
+    "#plt.plot(T,[s[12] for s in sols[0]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[1]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[2]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[3]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[4]],label=r\"$(\\alpha_f,\\alpha_g) = $ (0.0,0.0) pcm\")\n",
+    "#plt.xlabel(\"t (s)\")\n",
+    "#plt.ylabel(r\"$n/n_0$\",rotation=0)\n",
+    "#plt.legend()\n",
+    "#plt.ylim([0,20])\n",
+    "#plt.xlim([0,500])\n",
+    "#plt.title(r\"Reactor Response vs $\\alpha$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Feedback"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, '$\\\\rho$')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x1200 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,axs = plt.subplots(2,3,figsize=(18,12))\n",
+    "\n",
+    "t_stop = 125\n",
+    "\n",
+    "# fuel temps\n",
+    "#axs[0,0].set_xlim([0, 500])\n",
+    "#axs[0,0].set_ylim([600, 1000])\n",
+    "axs[0,0].set_xlim([0,t_stop])\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[0]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[1]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[2]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[3]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[4]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\")\n",
+    "axs[0,0].legend()\n",
+    "axs[0,0].set_title(\"Fuel Node Temperatures (C)\")\n",
+    "axs[0,0].tick_params(\n",
+    "    axis='x',          # changes apply to the x-axis\n",
+    "    which='both',      # both major and minor ticks are affected\n",
+    "    bottom=False,      # ticks along the bottom edge are off\n",
+    "    top=False,         # ticks along the top edge are off\n",
+    "    labelbottom=False) # labels along the bottom edge are off\n",
+    "\n",
+    "# coolant temps\n",
+    "#axs[0,1].set_xlim([0, 1000])\n",
+    "#axs[0,1].set_ylim([500, 1000])\n",
+    "axs[0,1].set_xlim([0,t_stop])\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[0]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[1]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[2]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[3]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[4]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\")\n",
+    "axs[0,1].legend()\n",
+    "axs[0,1].set_title(\"Coolant Node Temperatures (C)\")\n",
+    "axs[0,1].tick_params(\n",
+    "    axis='x',          # changes apply to the x-axis\n",
+    "    which='both',      # both major and minor ticks are affected\n",
+    "    bottom=False,      # ticks along the bottom edge are off\n",
+    "    top=False,         # ticks along the top edge are off\n",
+    "    labelbottom=False) # labels along the bottom edge are off\n",
+    "\n",
+    "# tube node temps\n",
+    "#axs[0,2].set_xlim([0, 500])\n",
+    "#axs[0,2].set_ylim([500, 1000])\n",
+    "axs[0,2].set_xlim([0,t_stop])\n",
+    "axs[0,2].plot(T,[s[8] for s in sols[0]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "axs[0,2].plot(T,[s[8] for s in sols[1]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n",
+    "axs[0,2].plot(T,[s[8] for s in sols[2]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n",
+    "axs[0,2].plot(T,[s[8] for s in sols[3]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n",
+    "axs[0,2].plot(T,[s[8] for s in sols[4]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\")\n",
+    "\n",
+    "# tube node temps\n",
+    "axs[0,2].plot(T,[s[9] for s in sols[0]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "axs[0,2].plot(T,[s[9] for s in sols[1]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n",
+    "axs[0,2].plot(T,[s[9] for s in sols[2]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n",
+    "axs[0,2].plot(T,[s[9] for s in sols[3]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n",
+    "axs[0,2].plot(T,[s[9] for s in sols[4]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\")\n",
+    "\n",
+    "axs[0,2].legend()\n",
+    "axs[0,2].set_title(\"Tube Node Temperatures (C)\")\n",
+    "axs[0,2].tick_params(\n",
+    "    axis='x',          # changes apply to the x-axis\n",
+    "    which='both',      # both major and minor ticks are affected\n",
+    "    bottom=False,      # ticks along the bottom edge are off\n",
+    "    top=False,         # ticks along the top edge are off\n",
+    "    labelbottom=False) # labels along the bottom edge are off\n",
+    "\n",
+    "# precursor concentrations\n",
+    "#axs[1,2].set_xlim([0, 500])\n",
+    "#axs[1,2].set_ylim([1e1,1e3])\n",
+    "axs[1,2].set_xlim([0,t_stop])\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[0]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[1]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[2]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[3]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[4]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\")\n",
+    "axs[1,2].legend()\n",
+    "axs[1,2].set_xlabel(\"t (s)\")\n",
+    "axs[1,2].set_yscale(\"log\")\n",
+    "axs[1,2].legend(loc=\"right\")\n",
+    "axs[1,2].set_title(\"Precursor Concentrations\")\n",
+    "axs[1,2].set_ylabel(r\"concentration (1/cm$^3$)\")\n",
+    "\n",
+    "# multiplication factor \n",
+    "#axs[1,0].set_ylim([0,20])\n",
+    "#axs[1,0].set_xlim([0,500])\n",
+    "axs[1,0].set_xlim([0,t_stop])\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[0]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[1]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[2]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[3]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[4]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "axs[1,0].set_xlabel(\"t (s)\")\n",
+    "axs[1,0].set_title(r\"$n$\")\n",
+    "axs[1,0].set_ylabel(r\"$\\frac{n}{n_0}$\")\n",
+    "\n",
+    "# reactivity\n",
+    "#axs[1,0].set_ylim([0,0])\n",
+    "#axs[1,0].set_xlim([0,500])\n",
+    "axs[1,1].set_xlim([0,t_stop])\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[0]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[1]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[2]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[3]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[4]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "axs[1,1].set_xlabel(\"t (s)\")\n",
+    "axs[1,1].set_title(r\"$\\rho$\")\n",
+    "\n",
+    "#plt.figure(figsize=(8,6))\n",
+    "#plt.plot(T,[s[12] for s in sols[0]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[1]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[2]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[3]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[4]],label=r\"$(\\alpha_f,\\alpha_g) = $ (0.0,0.0) pcm\")\n",
+    "#plt.xlabel(\"t (s)\")\n",
+    "#plt.ylabel(r\"$n/n_0$\",rotation=0)\n",
+    "#plt.legend()\n",
+    "#plt.ylim([0,20])\n",
+    "#plt.xlim([0,500])\n",
+    "#plt.title(r\"Reactor Response vs $\\alpha$\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating, compiling, and loading C code.\n",
+      "Using default integration parameters.\n",
+      "Generating, compiling, and loading C code.\n",
+      "Using default integration parameters.\n",
+      "Generating, compiling, and loading C code.\n",
+      "Using default integration parameters.\n",
+      "Generating, compiling, and loading C code.\n",
+      "Using default integration parameters.\n",
+      "Generating, compiling, and loading C code.\n",
+      "Using default integration parameters.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# redefine system and get new solution set \n",
+    "\n",
+    "# delays\n",
+    "taus = [tau_c, tau_c_hx, tau_hx_c, tau_hx_r, tau_l, tau_r_hx]\n",
+    "\n",
+    "# adjust delay term \n",
+    "#tau_hx_c *= 0.5\n",
+    "tau_c_hx *= 0.5\n",
+    "#tau_r_hx *= 0.5\n",
+    "\n",
+    "# dT/dt for radiator nodes\n",
+    "T_out_rc = (W_rp/mn_rp)*(y(11,t-tau_hx_r)-y(0)) + (hA_rpn/mcp_rpn)*(y(1)-y(0))                  # T_out_rc: y(0)\n",
+    "T_out_air = -((W_rs/mn_rs)+(hA_rsn/mcp_rsn))*y(1) + (hA_rsn/mcp_rsn)*y(0) + (W_rs/mn_rs)*Trs_in # T_out_air: y(1)\n",
+    "\n",
+    "# dT/dt for heat exchanger nodes\n",
+    "T_hf1 = -((W_p/mn_p)+(hA_pn/mcp_pn))*y(2) + (hA_pn/mcp_pn)*y(6) + (W_p/mn_p)*y(21,t-tau_c_hx)  # T_hf1: y(2)\n",
+    "T_hf2 = (W_p/mn_p)*(y(2)-y(3)) + (hA_pn/mcp_pn)*(y(6)-y(2))                                    # T_hf2: y(3)\n",
+    "T_hf3 = -((W_p/mn_p)+(hA_pn/mcp_pn))*y(4) + (hA_pn/mcp_pn)*y(7) + (W_p/mn_p)*y(3)              # T_hf3: y(4)\n",
+    "T_hf4 = (W_p/mn_p)*(y(4)-y(5)) + (hA_pn/mcp_pn)*(y(7)-y(4))                                    # T_hf4: y(5)\n",
+    "T_ht1 = (2*hA_pn/mcp_tn)*(y(2)-y(6)) + (2*hA_sn/mcp_tn)*(y(10)-y(6))                           # T_ht1: y(6)\n",
+    "T_ht2 = (2*hA_pn/mcp_tn)*(y(4)-y(7)) + (2*hA_sn/mcp_tn)*(y(8)-y(7))                            # T_ht2: y(7)\n",
+    "T_hc1 = -((W_s/mn_s)+(hA_sn/mcp_sn))*y(8) + (hA_sn/mcp_sn)*y(7) + (W_s/mn_s)*y(0,t-tau_r_hx)   # T_hc1: y(8)\n",
+    "T_hc2 = (W_s/mn_s)*(y(8)-y(9)) + (hA_sn/mcp_sn)*(y(7)-y(8))                                    # T_hc2: y(9)\n",
+    "T_hc3 = -((W_s/mn_s)+(hA_sn/mcp_sn))*y(10) + (hA_sn/mcp_sn)*y(6) + (W_s/mn_s)*y(9)             # T_hc3: y(10)\n",
+    "T_hc4 = (W_s/mn_s)*(y(10)-y(11)) + (hA_sn/mcp_sn)*(y(6)-y(10))                                 # T_hc4: y(11)\n",
+    "\n",
+    "# dn/dt\n",
+    "n = (y(22)-beta_t)*y(12)/Lam+lam[0]*y(13)+lam[1]*y(14)+lam[2]*y(15)+lam[3]*y(16)+lam[4]*y(17)+lam[5]*y(18) # n (no source insertion): y(12)\n",
+    "\n",
+    "# dC_i/dt (precursor concentrations)\n",
+    "C1 = y(12)*beta[0]/Lam-lam[0]*y(13)-y(13)/tau_c+y(13,t-tau_l)*np.exp(-lam[0]*tau_l)/tau_c  # C1: y(13)\n",
+    "C2 = y(12)*beta[1]/Lam-lam[1]*y(14)-y(14)/tau_c+y(14,t-tau_l)*np.exp(-lam[1]*tau_l)/tau_c  # C2: y(14)\n",
+    "C3 = y(12)*beta[2]/Lam-lam[2]*y(15)-y(15)/tau_c+y(15,t-tau_l)*np.exp(-lam[2]*tau_l)/tau_c  # C3: y(15)\n",
+    "C4 = y(12)*beta[3]/Lam-lam[3]*y(16)-y(16)/tau_c+y(16,t-tau_l)*np.exp(-lam[3]*tau_l)/tau_c  # C4: y(16)\n",
+    "C5 = y(12)*beta[4]/Lam-lam[4]*y(17)-y(17)/tau_c+y(17,t-tau_l)*np.exp(-lam[4]*tau_l)/tau_c  # C5: y(17)\n",
+    "C6 = y(12)*beta[5]/Lam-lam[5]*y(18)-y(18)/tau_c+y(18,t-tau_l)*np.exp(-lam[5]*tau_l)/tau_c  # C6: y(18)\n",
+    "\n",
+    "# dT/dt core nodes\n",
+    "T_cg = (hA_fg/mcp_g1)*(y(20)-y(19)) + k_g*P*y(12)/mcp_g1                                             # T_cg: y(19)\n",
+    "T_cf1 = W_f/mn_f*(y(5,t-tau_hx_c)-y(20)) + (k_f1*P*y(12)/mcp_f1) + (hA_fg*k_1*(y(19)-y(20))/mcp_f1)  # T_cf1: y(20)\n",
+    "T_cf2 = W_f/mn_f*(y(20)-y(21)) + (k_f2*P*y(12)/mcp_f2) + (hA_fg*k_2*(y(19)-y(20))/mcp_f2)            # T_cf2: y(21)\n",
+    "\n",
+    "# rho y(22)\n",
+    "rho = (a_f/2)*(T_cf1 + T_cf2) + (a_g)*(T_cg)\n",
+    "\n",
+    "# initial reactivity \n",
+    "rho_initial = 0.000\n",
+    "\n",
+    "# instantiate jitcdde object\n",
+    "DDE = jitcdde([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1,\n",
+    "               T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho])\n",
+    "\n",
+    "# set initial conditions\n",
+    "DDE.constant_past([T0_rp, T0_rs, T0_p1,T0_p2, T0_p3, T0_p4, T0_t1, T0_t2, T0_s1, T0_s2, \n",
+    "          T0_s3, T0_s4, n_frac0, C0[0], C0[1], C0[2], C0[3], C0[4], C0[5], \n",
+    "          T0_g1, T0_f1, T0_f2,rho_initial])\n",
+    "\n",
+    "# jitcdde solver parameters \n",
+    "t0 = 0.0\n",
+    "tf = 1000.00\n",
+    "T = np.arange(t0,tf,0.01)\n",
+    "\n",
+    "sol_jit = []\n",
+    "for t_x in T:\n",
+    "    sol_jit.append(DDE.integrate(t_x))\n",
+    "\n",
+    "sols.append(sol_jit)\n",
+    "\n",
+    "# feedbacks\n",
+    "factors_f = [1/2, 1/4, 1/8, 0]\n",
+    "factors_g = [1/2, 1/4, 1/8, 0]\n",
+    "\n",
+    "for i in range(len(factors_f)):\n",
+    "\n",
+    "    # reinstantiate jitcdde objects\n",
+    "    import sys\n",
+    "    sys.modules.pop('jitcdde')\n",
+    "    from jitcdde import jitcdde, y, t\n",
+    "\n",
+    "    # rho y(22)\n",
+    "    a_f_i = factors_f[i]\n",
+    "    a_g_i = factors_g[i]\n",
+    "    rho = a_f_i*(a_f/2)*(T_cf1 + T_cf2) + a_g_i*(a_g)*(T_cg)\n",
+    "\n",
+    "    # instantiate jitcdde object\n",
+    "    DDE = jitcdde([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1,\n",
+    "                T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho])\n",
+    "\n",
+    "    # set initial conditions\n",
+    "    DDE.constant_past([T0_rp, T0_rs, T0_p1,T0_p2, T0_p3, T0_p4, T0_t1, T0_t2, T0_s1, T0_s2, \n",
+    "            T0_s3, T0_s4, n_frac0, C0[0], C0[1], C0[2], C0[3], C0[4], C0[5], \n",
+    "            T0_g1, T0_f1, T0_f2,rho_initial])\n",
+    "    \n",
+    "    sol_i = []\n",
+    "    for t_x in T:\n",
+    "        sol_i.append(DDE.integrate(t_x))\n",
+    "    sols.append(sol_i)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0xffff3c777e50>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x1200 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "fig,axs = plt.subplots(2,3,figsize=(18,12))\n",
+    "\n",
+    "t_stop = 30\n",
+    "vert = 10.555\n",
+    "\n",
+    "# fuel temps\n",
+    "#axs[0,0].set_xlim([0, 500])\n",
+    "#axs[0,0].set_ylim([600, 1000])\n",
+    "axs[0,0].set_xlim([0,t_stop])\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[0]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='b')\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[1]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\",color='g')\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[2]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\",color='r')\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[3]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\",color='y')\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[4]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\",color='m')\n",
+    "axs[0,0].legend()\n",
+    "axs[0,0].set_title(\"Fuel Node Temperatures (C)\")\n",
+    "axs[0,0].tick_params(\n",
+    "    axis='x',          # changes apply to the x-axis\n",
+    "    which='both',      # both major and minor ticks are affected\n",
+    "    bottom=False,      # ticks along the bottom edge are off\n",
+    "    top=False,         # ticks along the top edge are off\n",
+    "    labelbottom=False) # labels along the bottom edge are off\n",
+    "\n",
+    "# coolant temps\n",
+    "#axs[0,1].set_xlim([0, 1000])\n",
+    "#axs[0,1].set_ylim([500, 1000])\n",
+    "axs[0,1].set_xlim([0,t_stop])\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[0]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='b')\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[1]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\",color='g')\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[2]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\",color='r')\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[3]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\",color='y')\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[4]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\",color='m')\n",
+    "axs[0,1].legend()\n",
+    "axs[0,1].set_title(\"Coolant Node Temperatures (C)\")\n",
+    "axs[0,1].tick_params(\n",
+    "    axis='x',          # changes apply to the x-axis\n",
+    "    which='both',      # both major and minor ticks are affected\n",
+    "    bottom=False,      # ticks along the bottom edge are off\n",
+    "    top=False,         # ticks along the top edge are off\n",
+    "    labelbottom=False) # labels along the bottom edge are off\n",
+    "\n",
+    "# tube node temps\n",
+    "#axs[0,2].set_xlim([0, 500])\n",
+    "#axs[0,2].set_ylim([500, 1000])\n",
+    "axs[0,2].set_xlim([0,t_stop])\n",
+    "axs[0,2].plot(T,[s[8] for s in sols[0]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='b')\n",
+    "axs[0,2].plot(T,[s[8] for s in sols[1]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\",color='g')\n",
+    "axs[0,2].plot(T,[s[8] for s in sols[2]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\",color='r')\n",
+    "axs[0,2].plot(T,[s[8] for s in sols[3]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\",color='y')\n",
+    "axs[0,2].plot(T,[s[8] for s in sols[4]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\",color='m')\n",
+    "\n",
+    "# tube node temps\n",
+    "axs[0,2].plot(T,[s[9] for s in sols[0]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='b')\n",
+    "axs[0,2].plot(T,[s[9] for s in sols[1]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\",color='g')\n",
+    "axs[0,2].plot(T,[s[9] for s in sols[2]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\",color='r')\n",
+    "axs[0,2].plot(T,[s[9] for s in sols[3]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\",color='y')\n",
+    "axs[0,2].plot(T,[s[9] for s in sols[4]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\",color='m')\n",
+    "\n",
+    "axs[0,2].legend()\n",
+    "axs[0,2].set_title(\"Tube Node Temperatures (C)\")\n",
+    "axs[0,2].tick_params(\n",
+    "    axis='x',          # changes apply to the x-axis\n",
+    "    which='both',      # both major and minor ticks are affected\n",
+    "    bottom=False,      # ticks along the bottom edge are off\n",
+    "    top=False,         # ticks along the top edge are off\n",
+    "    labelbottom=False) # labels along the bottom edge are off\n",
+    "\n",
+    "# precursor concentrations\n",
+    "#axs[1,2].set_xlim([0, 500])\n",
+    "#axs[1,2].set_ylim([1e1,1e3])\n",
+    "axs[1,2].set_xlim([0,t_stop])\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[0]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='b')\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[1]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\",color='g')\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[2]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\",color='r')\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[3]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\",color='y')\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[4]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\",color='m')\n",
+    "axs[1,2].legend()\n",
+    "axs[1,2].set_xlabel(\"t (s)\")\n",
+    "axs[1,2].set_yscale(\"log\")\n",
+    "axs[1,2].legend(loc=\"right\")\n",
+    "axs[1,2].set_title(\"Precursor Concentrations\")\n",
+    "axs[1,2].set_ylabel(r\"concentration (1/cm$^3$)\")\n",
+    "\n",
+    "# multiplication factor \n",
+    "#axs[1,0].set_ylim([0,20])\n",
+    "#axs[1,0].set_xlim([0,500])\n",
+    "axs[1,0].set_xlim([0,t_stop])\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[0]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='b')\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[1]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='g')\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[2]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='r')\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[3]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='y')\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[4]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='m')\n",
+    "axs[1,0].set_xlabel(\"t (s)\")\n",
+    "axs[1,0].set_title(r\"$n$\")\n",
+    "axs[1,0].set_ylabel(r\"$\\frac{n}{n_0}$\")\n",
+    "axs[1,0].legend()\n",
+    "\n",
+    "# reactivity\n",
+    "#axs[1,0].set_ylim([0,0])\n",
+    "#axs[1,0].set_xlim([0,500])\n",
+    "axs[1,1].set_xlim([0,t_stop])\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[0]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='b')\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[1]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='g')\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[2]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='r')\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[3]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='y')\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[4]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='m')\n",
+    "axs[1,1].set_xlabel(\"t (s)\")\n",
+    "axs[1,1].set_title(r\"$\\rho$\")\n",
+    "axs[1,1].legend()\n",
+    "\n",
+    "#plt.figure(figsize=(8,6))\n",
+    "#plt.plot(T,[s[12] for s in sols[0]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[1]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[2]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[3]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[4]],label=r\"$(\\alpha_f,\\alpha_g) = $ (0.0,0.0) pcm\")\n",
+    "#plt.xlabel(\"t (s)\")\n",
+    "#plt.ylabel(r\"$n/n_0$\",rotation=0)\n",
+    "#plt.legend()\n",
+    "#plt.ylim([0,20])\n",
+    "#plt.xlim([0,500])\n",
+    "#plt.title(r\"Reactor Response vs $\\alpha$\")\n",
+    "\n",
+    "\n",
+    "#######################################################################################################\n",
+    "\n",
+    "# adjusted delay term \n",
+    "\n",
+    "# fuel temps\n",
+    "#axs[0,0].set_xlim([0, 500])\n",
+    "#axs[0,0].set_ylim([600, 1000])\n",
+    "axs[0,0].set_xlim([0,t_stop])\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[5]],linestyle='-.',color='b')\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[6]],linestyle='-.',color='g')\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[7]],linestyle='-.',color='r')\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[8]],linestyle='-.',color='y')\n",
+    "axs[0,0].plot(T,[s[20] for s in sols[9]],linestyle='-.',color='m')\n",
+    "axs[0,0].legend()\n",
+    "axs[0,0].set_title(\"Fuel Node Temperatures (C)\")\n",
+    "axs[0,0].tick_params(\n",
+    "    axis='x',          # changes apply to the x-axis\n",
+    "    which='both',      # both major and minor ticks are affected\n",
+    "    bottom=False,      # ticks along the bottom edge are off\n",
+    "    top=False,         # ticks along the top edge are off\n",
+    "    labelbottom=False) # labels along the bottom edge are off\n",
+    "axs[0,0].axvline(x=vert, color='black', linestyle=':')\n",
+    "# coolant temps\n",
+    "#axs[0,1].set_xlim([0, 1000])\n",
+    "#axs[0,1].set_ylim([500, 1000])\n",
+    "axs[0,1].set_xlim([0,t_stop])\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[5]],linestyle='-.',color='b')\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[6]],linestyle='-.',color='g')\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[7]],linestyle='-.',color='r')\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[8]],linestyle='-.',color='y')\n",
+    "axs[0,1].plot(T,[s[8] for s in sols[9]],linestyle='-.',color='m')\n",
+    "axs[0,1].legend()\n",
+    "axs[0,1].set_title(\"Coolant Node Temperatures (C)\")\n",
+    "axs[0,1].tick_params(\n",
+    "    axis='x',          # changes apply to the x-axis\n",
+    "    which='both',      # both major and minor ticks are affected\n",
+    "    bottom=False,      # ticks along the bottom edge are off\n",
+    "    top=False,         # ticks along the top edge are off\n",
+    "    labelbottom=False) # labels along the bottom edge are off\n",
+    "\n",
+    "# tube node temps\n",
+    "#axs[0,2].set_xlim([0, 500])\n",
+    "#axs[0,2].set_ylim([500, 1000])\n",
+    "axs[0,2].set_xlim([0,t_stop])\n",
+    "axs[0,2].plot(T,[s[8] for s in sols[5]],linestyle='-.',color='b')\n",
+    "axs[0,2].plot(T,[s[8] for s in sols[6]],linestyle='-.',color='g')\n",
+    "axs[0,2].plot(T,[s[8] for s in sols[7]],linestyle='-.',color='r')\n",
+    "axs[0,2].plot(T,[s[8] for s in sols[8]],linestyle='-.',color='y')\n",
+    "axs[0,2].plot(T,[s[8] for s in sols[9]],linestyle='-.',color='m')\n",
+    "\n",
+    "# tube node temps\n",
+    "axs[0,2].plot(T,[s[9] for s in sols[5]],linestyle='-.',color='b')\n",
+    "axs[0,2].plot(T,[s[9] for s in sols[6]],linestyle='-.',color='g')\n",
+    "axs[0,2].plot(T,[s[9] for s in sols[7]],linestyle='-.',color='r')\n",
+    "axs[0,2].plot(T,[s[9] for s in sols[8]],linestyle='-.',color='y')\n",
+    "axs[0,2].plot(T,[s[9] for s in sols[9]],linestyle='-.',color='m')\n",
+    "\n",
+    "axs[0,2].legend()\n",
+    "axs[0,2].set_title(\"Tube Node Temperatures (C)\")\n",
+    "axs[0,2].tick_params(\n",
+    "    axis='x',          # changes apply to the x-axis\n",
+    "    which='both',      # both major and minor ticks are affected\n",
+    "    bottom=False,      # ticks along the bottom edge are off\n",
+    "    top=False,         # ticks along the top edge are off\n",
+    "    labelbottom=False) # labels along the bottom edge are off\n",
+    "\n",
+    "# precursor concentrations\n",
+    "#axs[1,2].set_xlim([0, 500])\n",
+    "#axs[1,2].set_ylim([1e1,1e3])\n",
+    "axs[1,2].set_xlim([0,t_stop])\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[5]],linestyle='-.',color='b')\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[6]],linestyle='-.',color='g')\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[7]],linestyle='-.',color='r')\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[8]],linestyle='-.',color='y')\n",
+    "axs[1,2].plot(T,[s[13] for s in sols[9]],linestyle='-.',color='m')\n",
+    "axs[1,2].legend()\n",
+    "axs[1,2].set_xlabel(\"t (s)\")\n",
+    "axs[1,2].set_yscale(\"log\")\n",
+    "axs[1,2].legend(loc=\"right\")\n",
+    "axs[1,2].set_title(\"Precursor Concentrations\")\n",
+    "axs[1,2].set_ylabel(r\"concentration (1/cm$^3$)\")\n",
+    "\n",
+    "# multiplication factor \n",
+    "#axs[1,0].set_ylim([0,20])\n",
+    "#axs[1,0].set_xlim([0,500])\n",
+    "axs[1,0].set_xlim([0,t_stop])\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[5]],linestyle='-.',color='b')\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[6]],linestyle='-.',color='g')\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[7]],linestyle='-.',color='r')\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[8]],linestyle='-.',color='y')\n",
+    "axs[1,0].plot(T,[s[12] for s in sols[9]],linestyle='-.',color='m')\n",
+    "axs[1,0].set_xlabel(\"t (s)\")\n",
+    "axs[1,0].set_title(r\"$n$\")\n",
+    "axs[1,0].set_ylabel(r\"$\\frac{n}{n_0}$\")\n",
+    "axs[1,0].axvline(x=vert, color='black', linestyle=':')\n",
+    "\n",
+    "# reactivity\n",
+    "#axs[1,0].set_ylim([0,0])\n",
+    "#axs[1,0].set_xlim([0,500])\n",
+    "axs[1,1].set_xlim([0,t_stop])\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[5]],linestyle='-.',color='b')\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[6]],linestyle='-.',color='g')\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[7]],linestyle='-.',color='r')\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[8]],linestyle='-.',color='y')\n",
+    "axs[1,1].plot(T,[s[22] for s in sols[9]],linestyle='-.',color='m')\n",
+    "axs[1,1].set_xlabel(\"t (s)\")\n",
+    "axs[1,1].set_title(r\"$\\rho$\")\n",
+    "axs[1,1].axvline(x=vert, color='black', linestyle=':')\n",
+    "\n",
+    "#plt.figure(figsize=(8,6))\n",
+    "#plt.plot(T,[s[12] for s in sols[0]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[1]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[2]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[3]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n",
+    "#plt.plot(T,[s[12] for s in sols[4]],label=r\"$(\\alpha_f,\\alpha_g) = $ (0.0,0.0) pcm\")\n",
+    "#plt.xlabel(\"t (s)\")\n",
+    "#plt.ylabel(r\"$n/n_0$\",rotation=0)\n",
+    "#plt.legend()\n",
+    "#plt.ylim([0,20])\n",
+    "#plt.xlim([0,500])\n",
+    "#plt.title(r\"Reactor Response vs $\\alpha$\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(sols)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "thesis_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/dynamic_model/msrDynamics_implementation/__pycache__/parameters_U233.cpython-311.pyc b/dynamic_model/msrDynamics_implementation/__pycache__/parameters_U233.cpython-311.pyc
new file mode 100644
index 0000000..df09c81
Binary files /dev/null and b/dynamic_model/msrDynamics_implementation/__pycache__/parameters_U233.cpython-311.pyc differ
diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_insertion.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_insertion.csv
new file mode 100644
index 0000000..399c383
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_insertion.csv
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_magnitude.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_magnitude.csv
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_magnitude_theoretical.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_magnitude_theoretical.csv
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_phase.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_phase.csv
new file mode 100644
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_phase_theoretical.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_phase_theoretical.csv
new file mode 100644
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_insertion.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_insertion.csv
new file mode 100644
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--- /dev/null
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_magnitude.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_magnitude.csv
new file mode 100644
index 0000000..96fe61a
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_magnitude.csv
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_magnitude_theoretical.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_magnitude_theoretical.csv
new file mode 100644
index 0000000..c07967a
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_magnitude_theoretical.csv
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_phase.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_phase.csv
new file mode 100644
index 0000000..84303c8
--- /dev/null
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_phase_theoretical.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_phase_theoretical.csv
new file mode 100644
index 0000000..7790771
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_phase_theoretical.csv
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_insertion.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_insertion.csv
new file mode 100644
index 0000000..e57f835
--- /dev/null
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_magnitude.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_magnitude.csv
new file mode 100644
index 0000000..17ff279
--- /dev/null
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_magnitude_theoretical.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_magnitude_theoretical.csv
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_phase.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_phase.csv
new file mode 100644
index 0000000..283564c
--- /dev/null
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_phase_theoretical.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_phase_theoretical.csv
new file mode 100644
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--- /dev/null
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diff --git a/dynamic_model/msrDynamics_implementation/data/frequency_response_results_1_MW.csv b/dynamic_model/msrDynamics_implementation/data/frequency_response_results_1_MW.csv
new file mode 100644
index 0000000..5a8c4c0
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/data/frequency_response_results_1_MW.csv
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diff --git a/dynamic_model/msrDynamics_implementation/data/frequency_response_results_5_MW.csv b/dynamic_model/msrDynamics_implementation/data/frequency_response_results_5_MW.csv
new file mode 100644
index 0000000..b2b5bd5
--- /dev/null
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diff --git a/dynamic_model/msrDynamics_implementation/data/frequency_response_results_8_MW.csv b/dynamic_model/msrDynamics_implementation/data/frequency_response_results_8_MW.csv
new file mode 100644
index 0000000..a4a69d7
--- /dev/null
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diff --git a/dynamic_model/msrDynamics_implementation/data/simulink_msre_1MW_U233_insertion.xlsx b/dynamic_model/msrDynamics_implementation/data/simulink_msre_1MW_U233_insertion.xlsx
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diff --git a/dynamic_model/msrDynamics_implementation/data/simulink_msre_8MW_U233_insertion.xlsx b/dynamic_model/msrDynamics_implementation/data/simulink_msre_8MW_U233_insertion.xlsx
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diff --git a/dynamic_model/msrDynamics_implementation/frq_script.py b/dynamic_model/msrDynamics_implementation/frq_script.py
new file mode 100644
index 0000000..837b318
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/frq_script.py
@@ -0,0 +1,168 @@
+# Imports
+from parameters_U233 import *
+import numpy as np
+import matplotlib.pyplot as plt
+from jitcdde import t
+from msrDynamics.system_objects import Node, System
+import pandas as pd
+import sympy as sp
+from concurrent.futures import ProcessPoolExecutor
+from scipy.signal import find_peaks
+
+f_range = np.logspace(-2, 1, num=100)
+# tau_l = 2 * tau_l
+# tau_c = 2*tau_c
+# tau_c_hx = 2 * tau_c_hx
+# tau_hx_c = 2 * tau_hx_c
+def process_frequency(f):
+
+    MSRE = System()
+
+    # radiator
+    T_out_rc = Node(m = mn_rp, scp = mcp_rpn/mn_rp, W = W_rp, y0 = T0_rp)
+    T_out_air = Node(m = mn_rs, scp = mcp_rsn/mn_rs, W = W_rs, y0 = T0_rs)
+
+    # heat exchanger
+    T_hf1 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p1)
+    T_hf2 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p2)
+    T_hf3 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p3)
+    T_hf4 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p4)
+    T_ht1 = Node(m = m_tn, scp = scp_t, y0 = T0_t1)
+    T_ht2 = Node(m = m_tn, scp = scp_t, y0 = T0_t2)
+    T_hc1 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s1)
+    T_hc2 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s2)
+    T_hc3 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s3)
+    T_hc4 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s4)
+
+    # core 
+    n = Node(y0 = n_frac0)
+    C1 = Node(y0 = C0[0])
+    C2 = Node(y0 = C0[1])
+    C3 = Node(y0 = C0[2])
+    C4 = Node(y0 = C0[3])
+    C5 = Node(y0 = C0[4])
+    C6 = Node(y0 = C0[5])
+    rho = Node(y0 = 0.0)
+
+    # add reactivity input
+    r = 1e-5
+    def rho_insert(t):
+        return r*sp.sin(f*t)
+
+    rho_ext = MSRE.add_input(rho_insert, T)
+
+    T_cg = Node(m = mcp_g1/scp_g, scp = scp_g, y0 = T0_g1)
+    T_cf1 = Node(m = mn_f, scp = scp_f, W = W_f, y0 = T0_f1)
+    T_cf2 = Node(m = mn_f, scp = scp_f, W = W_f, y0 = T0_f2)
+
+    MSRE.add_nodes([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1,
+                T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho])
+
+    # dynamics 
+
+    # radiator
+    T_out_rc.set_dTdt_advective(source = T_hc4.y(t-tau_hx_r))
+    T_out_rc.set_dTdt_convective(source = [T_out_air.y()], hA = [hA_rpn])
+
+    T_out_air.set_dTdt_advective(source = Trs_in)
+    T_out_air.set_dTdt_convective(source = [T_out_rc.y()], hA = [hA_rsn])
+
+    # heat exchanger
+    T_hf1.set_dTdt_advective(source = T_cf2.y(t-tau_c_hx))
+    T_hf1.set_dTdt_convective(source = [T_ht1.y()], hA = [hA_pn])
+
+    T_hf2.set_dTdt_advective(source = T_hf1.y())
+    T_hf2.dTdt_convective = T_hf1.dTdt_convective
+
+    T_hf3.set_dTdt_advective(source = T_hf2.y())
+    T_hf3.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_pn])
+
+    T_hf4.set_dTdt_advective(source = T_hf3.y())
+    # T_hf4.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_pn])
+    T_hf4.dTdt_convective = T_hf3.dTdt_convective
+
+    # T_ht1.set_dTdt_convective(source = [T_hf1.y(),T_hf2.y(),T_hc3.y(),T_hc4.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])
+    # T_ht2.set_dTdt_convective(source = [T_hf3.y(),T_hf4.y(),T_hc1.y(),T_hc2.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])
+    T_ht1.set_dTdt_convective(source = [T_hf1.y(),T_hf1.y(),T_hc3.y(),T_hc3.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])
+    T_ht2.set_dTdt_convective(source = [T_hf3.y(),T_hf3.y(),T_hc1.y(),T_hc1.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])
+
+    T_hc1.set_dTdt_advective(source = T_out_rc.y(t-tau_r_hx))
+    T_hc1.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_sn])
+
+    T_hc2.set_dTdt_advective(source = T_hc1.y())
+    T_hc2.dTdt_convective = T_hc1.dTdt_convective
+
+    T_hc3.set_dTdt_advective(source = T_hc2.y())
+    T_hc3.set_dTdt_convective(source = [T_ht1.y()], hA = [hA_sn])
+
+    T_hc4.set_dTdt_advective(source = T_hc3.y())
+    T_hc4.dTdt_convective = T_hc3.dTdt_convective
+
+    # core
+    n.set_dndt(r = rho.y()+rho_ext, beta_eff = beta_t, Lambda = Lam, lam = lam, C = [C1.y(),C2.y(),C3.y(),C4.y(),C5.y(),C6.y()])
+    C1.set_dcdt(n.y(),beta[0],Lam,lam[0],tau_c,tau_l)
+    C2.set_dcdt(n.y(),beta[1],Lam,lam[1],tau_c,tau_l)
+    C3.set_dcdt(n.y(),beta[2],Lam,lam[2],tau_c,tau_l)
+    C4.set_dcdt(n.y(),beta[3],Lam,lam[3],tau_c,tau_l)
+    C5.set_dcdt(n.y(),beta[4],Lam,lam[4],tau_c,tau_l)
+    C6.set_dcdt(n.y(),beta[5],Lam,lam[5],tau_c,tau_l)
+
+    T_cg.set_dTdt_convective(source = [T_cf1.y()], hA = [hA_fg])
+    T_cg.set_dTdt_internal(source = n.y(), k = k_g*P)
+
+    T_cf1.set_dTdt_advective(source = T_hf4.y(t-tau_hx_c))
+    T_cf1.set_dTdt_convective(source = [T_cg.y()], hA = [k_1*hA_fg])
+    T_cf1.set_dTdt_internal(source = n.y(), k = k_f1*P)
+
+    T_cf2.set_dTdt_advective(source = T_cf1.y())
+    T_cf2.dTdt_convective = T_cf1.dTdt_convective
+    T_cf2.set_dTdt_internal(source = n.y(), k = k_f2*P)
+
+    rho.set_drdt(sources = [T_cf1.dydt(), T_cf2.dydt(), T_cg.dydt()], coeffs = [a_f/2,a_f/2,a_g])
+
+    MSRE.solve(T)
+
+    i_out = [i for i in range(len(T)) if T[i] >= 500]
+    n0 = n.y_out[i_out[0]-25]
+    n_out = np.array(n.y_out)[i_out]
+
+    # calculate output amplitude
+    peaks, _ = find_peaks(n_out)
+    troughs, _ = find_peaks(-n_out)
+    amplitude = (np.mean(n_out[peaks]) - np.mean(n_out[troughs]))/2
+
+    # calculate Gain
+    input_amplitude = r  
+    gain = amplitude / (input_amplitude*n0)
+
+    # calculate Phase Shift
+    peak_times = T[i_out][peaks]
+    input_period = 2 * np.pi / f
+    input_signal = [i[0] for i in MSRE.input.get_state(T)]
+    input_peaks, _ = find_peaks(input_signal)
+    time_differences = [abs(T[input_peaks[i]] - peak_times[0]) for i in range(len(input_peaks))]
+    closest_peak_index = np.argmin(time_differences)
+    closest_peak_time = T[input_peaks[closest_peak_index]]
+    
+    # calculate Phase Shift
+    phase_shift = 360*(closest_peak_time-peak_times[0])/input_period
+
+    if (phase_shift>180):
+        phase_shift = -360+phase_shift
+    elif (phase_shift<-180):
+        phase_shift = 360-phase_shift
+           
+    return f, gain, phase_shift
+
+with ProcessPoolExecutor() as executor:
+    results = list(executor.map(process_frequency, f_range))
+
+# Process the results
+results_df = pd.DataFrame(results, columns=['Frequency', 'Gain', 'Phase Shift'])
+
+# Write to CSV file
+# csv_filename = f"frequency_response_results_{P}_MW_double_tau.csv"
+csv_filename = f"frequency_response_results_{P}_MW.csv"
+results_df.to_csv(csv_filename, index=False)
+
+print(f"Results written to {csv_filename}")
\ No newline at end of file
diff --git a/dynamic_model/msrDynamics_implementation/model_frq_response.ipynb b/dynamic_model/msrDynamics_implementation/model_frq_response.ipynb
new file mode 100644
index 0000000..7c69724
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/model_frq_response.ipynb
@@ -0,0 +1,297 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from parameters_U233 import *\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from jitcdde import t\n",
+    "from msrDynamics import Node, System\n",
+    "import pandas as pd\n",
+    "import math\n",
+    "import matplotlib.patches as mpatches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "P = 8\n",
+    "\n",
+    "# unpack ORNL data\n",
+    "df_ORNL_mag = pd.read_csv(f\"./data/ORNL_msre_{int(P)}MW_U233_magnitude.csv\",names=['f','mag'])\n",
+    "df_ORNL_mag = df_ORNL_mag.sort_values(df_ORNL_mag.columns[0])\n",
+    "df_ORNL_phase = pd.read_csv(f\"./data/ORNL_msre_{int(P)}MW_U233_phase.csv\",names=['f','phase'])\n",
+    "df_ORNL_phase = df_ORNL_phase.sort_values(df_ORNL_phase.columns[0])\n",
+    "df_ORNL_mag_t = pd.read_csv(f\"./data/ORNL_msre_{int(P)}MW_U233_magnitude_theoretical.csv\",names=['f','mag'])\n",
+    "df_ORNL_mag_t = df_ORNL_mag_t.sort_values(df_ORNL_mag_t.columns[0])\n",
+    "df_ORNL_phase_t = pd.read_csv(f\"./data/ORNL_msre_{int(P)}MW_U233_phase_theoretical.csv\",names=['f','phase'])\n",
+    "df_ORNL_phase_t = df_ORNL_phase_t.sort_values(df_ORNL_phase_t.columns[0])\n",
+    "df_jit = pd.read_csv(f\"./data/frequency_response_results_{P}_MW.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,axs = plt.subplots(2,1,figsize=(6,12))\n",
+    "\n",
+    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown']\n",
+    "\n",
+    "# function to update the style of each axis\n",
+    "def update_axis_style(ax, title, x_label='', y_label='', x_ticks=True):\n",
+    "    ax.set_title(title,fontsize=20)\n",
+    "    ax.set_xlabel(x_label,fontsize=14)\n",
+    "    ax.set_ylabel(y_label,fontsize=14)\n",
+    "    ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
+    "    ax.tick_params(axis='x', which='both', bottom=x_ticks, top=False, labelbottom=x_ticks)\n",
+    "    ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)\n",
+    "\n",
+    "update_axis_style(axs[0],\"Magnitude\")\n",
+    "axs[0].plot(df_jit['Frequency'],df_jit['Gain'],label=\"msrDynamics\")\n",
+    "axs[0].plot(df_ORNL_mag_t['f'],df_ORNL_mag_t['mag'],label=\"ORNL: theoretical\",color=colors[1],linestyle=\"--\")\n",
+    "axs[0].scatter(df_ORNL_mag['f'],df_ORNL_mag['mag'],marker=\"2\",color=colors[2],label=\"ORNL: measured\")\n",
+    "axs[0].set_yscale(\"log\")\n",
+    "axs[0].set_xscale(\"log\")\n",
+    "axs[0].set_xlim([9e-3,1e0])\n",
+    "axs[0].set_ylim([1e2,3.5e3])\n",
+    "axs[0].set_ylabel(r\"$\\frac{\\delta n}{\\delta \\rho n_0}$\")\n",
+    "axs[0].legend()\n",
+    "\n",
+    "update_axis_style(axs[1],\"Phase\",x_label='Frequency (rad/s)',y_label=\"Phase(deg)\")\n",
+    "axs[1].plot(df_jit['Frequency'],df_jit['Phase Shift'],label=\"msrDynamics\")\n",
+    "axs[1].plot(df_ORNL_phase_t['f'],df_ORNL_phase_t['phase'],label=\"ORNL: theoretical\",color=colors[1],linestyle=\"--\")\n",
+    "axs[1].scatter(df_ORNL_phase['f'],df_ORNL_phase['phase'],marker=\"2\",color=colors[2],label=\"ORNL-TM-2997\")\n",
+    "axs[1].set_xscale(\"log\")\n",
+    "axs[1].set_xlim([9e-3,1e0])\n",
+    "axs[1].legend()\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_adj = pd.read_csv('frequency_response_results_8_MW_double_tau.csv')\n",
+    "\n",
+    "\n",
+    "fig,axs = plt.subplots(2,1,figsize=(10,10))\n",
+    "\n",
+    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown']\n",
+    "\n",
+    "# function to update the style of each axis\n",
+    "def update_axis_style(ax, title, x_label='', y_label='', x_ticks=True):\n",
+    "    ax.set_title(title)\n",
+    "    ax.set_xlabel(x_label)\n",
+    "    ax.set_ylabel(y_label)\n",
+    "    ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
+    "    ax.tick_params(axis='x', which='both', bottom=x_ticks, top=False, labelbottom=x_ticks)\n",
+    "    ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)\n",
+    "\n",
+    "update_axis_style(axs[0],r\"Magnitude, $\\tau = \\tau_l + \\tau_c$\")\n",
+    "axs[0].plot(df_jit['Frequency'],df_jit['Gain'],label=r\"$\\tau = \\tau_l + \\tau_c$\") \n",
+    "axs[0].set_yscale(\"log\")\n",
+    "axs[0].set_xscale(\"log\")\n",
+    "axs[0].set_ylabel(r\"$\\frac{\\delta n}{\\delta \\rho n_0}$\")\n",
+    "axs[0].set_xlim([1e-2,2e0])\n",
+    "p = tau_c+tau_l\n",
+    "f_dip = (2*math.pi)/(p)\n",
+    "f_dip2 = (2*math.pi)/((p)/2)\n",
+    "f_peak = (2*math.pi)/(2*(p))\n",
+    "f_peak2 = (2*math.pi)/((2/3)*(p))\n",
+    "axs[0].axvline(x=f_peak,linestyle=\"--\",label=r\"$f = 2\\pi / (2\\tau)$\",color=colors[1])\n",
+    "axs[0].axvline(x=f_dip,linestyle=\"--\",label=r\"$f = 2\\pi / \\tau$\",color=colors[2])\n",
+    "axs[0].axvline(x=f_peak2,linestyle=\"--\",label=r\"$f = 2\\pi / (2\\tau/3)$\",color=colors[4])\n",
+    "axs[0].axvline(x=f_dip2,linestyle=\"--\",label=r\"$f = 2\\pi / (\\tau/2)$\",color=colors[3])\n",
+    "axs[0].legend()\n",
+    "\n",
+    "update_axis_style(axs[1],r\"Magnitude, $\\tau = 2\\tau_l + 2\\tau_c$\")\n",
+    "axs[1].plot(df_adj['Frequency'],df_adj['Gain'],label=r\"$\\tau = 2(\\tau_l + \\tau_c)$\") \n",
+    "axs[1].set_yscale(\"log\")\n",
+    "axs[1].set_xscale(\"log\")\n",
+    "axs[1].set_ylabel(r\"$\\frac{\\delta n}{\\delta \\rho n_0}$\")\n",
+    "axs[1].set_xlim([1e-2,2e0])\n",
+    "p2 = 2*(tau_c+tau_l)\n",
+    "f_dip = (2*math.pi)/(p2)\n",
+    "f_dip2 = (2*math.pi)/((p2)/2)\n",
+    "f_peak = (2*math.pi)/(2*(p2))\n",
+    "f_peak2 = (2*math.pi)/((2/3)*(p2))\n",
+    "axs[1].axvline(x=f_peak,linestyle=\"--\",label=r\"$f = 2\\pi / (2\\tau)$\",color=colors[1])\n",
+    "axs[1].axvline(x=f_dip,linestyle=\"--\",label=r\"$f = 2\\pi / \\tau$\",color=colors[2])\n",
+    "axs[1].axvline(x=f_peak2,linestyle=\"--\",label=r\"$f = 2\\pi / (2\\tau/3)$\",color=colors[4])\n",
+    "axs[1].axvline(x=f_dip2,linestyle=\"--\",label=r\"$f = 2\\pi / (\\tau/2)$\",color=colors[3])\n",
+    "axs[1].legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_adj = pd.read_csv('frequency_response_results_8_MW_double_tau.csv')\n",
+    "\n",
+    "\n",
+    "fig,axs = plt.subplots(2,1,figsize=(10,10))\n",
+    "\n",
+    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown']\n",
+    "\n",
+    "# function to update the style of each axis\n",
+    "def update_axis_style(ax, title, x_label='', y_label='', x_ticks=True):\n",
+    "    ax.set_title(title)\n",
+    "    ax.set_xlabel(x_label)\n",
+    "    ax.set_ylabel(y_label)\n",
+    "    ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
+    "    ax.tick_params(axis='x', which='both', bottom=x_ticks, top=False, labelbottom=x_ticks)\n",
+    "    ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)\n",
+    "\n",
+    "# Filling between ranges where inc_dec is True\n",
+    "inc_dec = np.ones(len(df_jit))\n",
+    "for f in enumerate(df_jit['Frequency']):\n",
+    "    delay_input = np.cos(f[1]*((-p))) # t = 2pi-tau\n",
+    "    if (delay_input > 0):\n",
+    "        inc_dec[f[0]] = 0\n",
+    "\n",
+    "# Find start and end points of True and False segments in inc_dec and fill between them\n",
+    "start_true = None\n",
+    "start_false = None\n",
+    "for i in range(len(df_jit['Frequency'])):\n",
+    "    if inc_dec[i]:\n",
+    "        if start_false is not None:\n",
+    "            # Fill the False segment before starting the True segment\n",
+    "            axs[0].fill_between(df_jit['Frequency'][start_false:i], 0, df_jit['Gain'][start_false:i], alpha=0.23, color='red',interpolate=True)\n",
+    "            start_false = None\n",
+    "        if start_true is None:\n",
+    "            start_true = i  # Mark the start of a True segment\n",
+    "    else:\n",
+    "        if start_true is not None:\n",
+    "            # Fill the True segment before starting the False segment\n",
+    "            axs[0].fill_between(df_jit['Frequency'][start_true:i], 0, df_jit['Gain'][start_true:i], alpha=0.23, color='green',interpolate=True)\n",
+    "            start_true = None\n",
+    "        if start_false is None:\n",
+    "            start_false = i  # Mark the start of a False segment\n",
+    "\n",
+    "# Check if there is a segment that goes until the end\n",
+    "if start_true is not None:\n",
+    "    axs[0].fill_between(df_jit['Frequency'][start_true:], 0, df_jit['Gain'][start_true:], alpha=0.3, color='green',interpolate=True)\n",
+    "if start_false is not None:\n",
+    "    axs[0].fill_between(df_jit['Frequency'][start_false:], 0, df_jit['Gain'][start_false:], alpha=0.3, color='red',interpolate=True)\n",
+    "\n",
+    "update_axis_style(axs[0],r\"Magnitude, $\\tau = \\tau_l + \\tau_c$\")\n",
+    "axs[0].plot(df_jit['Frequency'],df_jit['Gain'],label=r\"$\\tau = \\tau_l + \\tau_c$\") \n",
+    "axs[0].set_yscale(\"log\")\n",
+    "axs[0].set_xscale(\"log\")\n",
+    "axs[0].set_ylabel(r\"$\\frac{\\delta n}{\\delta \\rho n_0}$\")\n",
+    "axs[0].set_xlim([1e-2,2e0])\n",
+    "\n",
+    "red_patch = mpatches.Patch(color='red', alpha=0.3, label=r\"$\\frac{d\\rho_{in}}{dt}(t-2 \\pi /f) \\cdot \\frac{d\\rho_{in}}{dt}(t) > 0$\")\n",
+    "green_patch = mpatches.Patch(color='green', alpha=0.3, label=r\"$\\frac{d\\rho_{in}}{dt}(t-2 \\pi /f) \\cdot \\frac{d\\rho_{in}}{dt}(t) < 0$\")\n",
+    "\n",
+    "axs[0].legend(handles=[axs[0].get_lines()[0], red_patch, green_patch])\n",
+    "\n",
+    "######################################################################################\n",
+    "# Double tau\n",
+    "######################################################################################\n",
+    "\n",
+    "update_axis_style(axs[1],r\"Magnitude, $\\tau = 2\\tau_l + 2\\tau_c$\")\n",
+    "axs[1].plot(df_adj['Frequency'],df_adj['Gain'],label=r\"$\\tau = 2(\\tau_l + \\tau_c)$\") \n",
+    "axs[1].set_yscale(\"log\")\n",
+    "axs[1].set_xscale(\"log\")\n",
+    "axs[1].set_ylabel(r\"$\\frac{\\delta n}{\\delta \\rho n_0}$\")\n",
+    "axs[1].set_xlim([1e-2,2e0])\n",
+    "axs[1].legend(handles=[axs[1].get_lines()[0], red_patch, green_patch])\n",
+    "\n",
+    "# Filling between ranges where inc_dec is True\n",
+    "inc_dec = np.ones(len(df_adj))\n",
+    "for f in enumerate(df_adj['Frequency']):\n",
+    "    delay_input = np.cos(f[1]*(-p2)) # t = 2pi-tau\n",
+    "    if (delay_input > 0):\n",
+    "        inc_dec[f[0]] = 0\n",
+    "\n",
+    "# Find start and end points of True and False segments in inc_dec and fill between them\n",
+    "start_true = None\n",
+    "start_false = None\n",
+    "for i in range(len(df_adj['Frequency'])):\n",
+    "    if inc_dec[i]:\n",
+    "        if start_false is not None:\n",
+    "            # Fill the False segment before starting the True segment\n",
+    "            axs[1].fill_between(df_adj['Frequency'][start_false:i], 0, df_adj['Gain'][start_false:i], alpha=0.23, color='red',interpolate=True)\n",
+    "            start_false = None\n",
+    "        if start_true is None:\n",
+    "            start_true = i  # Mark the start of a True segment\n",
+    "    else:\n",
+    "        if start_true is not None:\n",
+    "            # Fill the True segment before starting the False segment\n",
+    "            axs[1].fill_between(df_adj['Frequency'][start_true:i], 0, df_adj['Gain'][start_true:i], alpha=0.23, color='green',interpolate=True)\n",
+    "            start_true = None\n",
+    "        if start_false is None:\n",
+    "            start_false = i  # Mark the start of a False segment\n",
+    "\n",
+    "# Check if there is a segment that goes until the end\n",
+    "if start_true is not None:\n",
+    "    axs[1].fill_between(df_adj['Frequency'][start_true:], 0, df_adj['Gain'][start_true:], alpha=0.3, color='green',interpolate=True)\n",
+    "if start_false is not None:\n",
+    "    axs[1].fill_between(df_adj['Frequency'][start_false:], 0, df_adj['Gain'][start_false:], alpha=0.3, color='red',interpolate=True)\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "thesis_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/dynamic_model/msrDynamics_implementation/model_step.ipynb b/dynamic_model/msrDynamics_implementation/model_step.ipynb
new file mode 100644
index 0000000..81c67f5
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/model_step.ipynb
@@ -0,0 +1,283 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from parameters_U233 import *\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from jitcdde import t\n",
+    "from msrDynamics import Node, System\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# unpack ORNL data\n",
+    "df_ORNL = pd.read_csv(f\"./data/ORNL_msre_{int(P)}MW_U233_insertion.csv\",names=['t','dP'])\n",
+    "df_ORNL = df_ORNL.sort_values(df_ORNL.columns[0])\n",
+    "df_simulink = pd.read_excel(f\"./data/simulink_msre_{P}MW_U233_insertion.xlsx\")\n",
+    "i_trans = [i for i in range(len(df_simulink)) if df_simulink['time'][i] >= 2500]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "T = df_simulink['time']\n",
+    "MSRE = System()\n",
+    "\n",
+    "# radiator\n",
+    "T_out_rc = Node(m = mn_rp, scp = mcp_rpn/mn_rp, W = W_rp, y0 = T0_rp)\n",
+    "T_out_air = Node(m = mn_rs, scp = mcp_rsn/mn_rs, W = W_rs, y0 = T0_rs)\n",
+    "\n",
+    "# heat exchanger\n",
+    "T_hf1 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p1)\n",
+    "T_hf2 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p2)\n",
+    "T_hf3 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p3)\n",
+    "T_hf4 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p4)\n",
+    "T_ht1 = Node(m = m_tn, scp = scp_t, y0 = T0_t1)\n",
+    "T_ht2 = Node(m = m_tn, scp = scp_t, y0 = T0_t2)\n",
+    "T_hc1 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s1)\n",
+    "T_hc2 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s2)\n",
+    "T_hc3 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s3)\n",
+    "T_hc4 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s4)\n",
+    "\n",
+    "# core \n",
+    "n = Node(y0 = n_frac0)\n",
+    "C1 = Node(y0 = C0[0])\n",
+    "C2 = Node(y0 = C0[1])\n",
+    "C3 = Node(y0 = C0[2])\n",
+    "C4 = Node(y0 = C0[3])\n",
+    "C5 = Node(y0 = C0[4])\n",
+    "C6 = Node(y0 = C0[5])\n",
+    "rho = Node(y0 = 0.0)\n",
+    "\n",
+    "# add reactivity input\n",
+    "t_ins = 2500\n",
+    "def rho_insert(t):\n",
+    "    if (t<t_ins):\n",
+    "        return 0.0\n",
+    "    else:\n",
+    "        return inserted\n",
+    "\n",
+    "rho_ext = MSRE.add_input(rho_insert, T)\n",
+    "\n",
+    "T_cg = Node(m = mcp_g1/scp_g, scp = scp_g, y0 = T0_g1)\n",
+    "T_cf1 = Node(m = mn_f, scp = scp_f, W = W_f, y0 = T0_f1)\n",
+    "T_cf2 = Node(m = mn_f, scp = scp_f, W = W_f, y0 = T0_f2)\n",
+    "\n",
+    "MSRE.add_nodes([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1,\n",
+    "               T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho])\n",
+    "\n",
+    "# dynamics \n",
+    "\n",
+    "# radiator\n",
+    "T_out_rc.set_dTdt_advective(source = T_hc4.y(t-tau_hx_r))\n",
+    "T_out_rc.set_dTdt_convective(source = [T_out_air.y()], hA = [hA_rpn])\n",
+    "\n",
+    "T_out_air.set_dTdt_advective(source = Trs_in)\n",
+    "T_out_air.set_dTdt_convective(source = [T_out_rc.y()], hA = [hA_rsn])\n",
+    "\n",
+    "# heat exchanger\n",
+    "T_hf1.set_dTdt_advective(source = T_cf2.y(t-tau_c_hx))\n",
+    "T_hf1.set_dTdt_convective(source = [T_ht1.y()], hA = [hA_pn])\n",
+    "\n",
+    "T_hf2.set_dTdt_advective(source = T_hf1.y())\n",
+    "T_hf2.dTdt_convective = T_hf1.dTdt_convective\n",
+    "\n",
+    "T_hf3.set_dTdt_advective(source = T_hf2.y())\n",
+    "T_hf3.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_pn])\n",
+    "\n",
+    "T_hf4.set_dTdt_advective(source = T_hf3.y())\n",
+    "# T_hf4.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_pn])\n",
+    "T_hf4.dTdt_convective = T_hf3.dTdt_convective\n",
+    "\n",
+    "# T_ht1.set_dTdt_convective(source = [T_hf1.y(),T_hf2.y(),T_hc3.y(),T_hc4.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])\n",
+    "# T_ht2.set_dTdt_convective(source = [T_hf3.y(),T_hf4.y(),T_hc1.y(),T_hc2.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])\n",
+    "T_ht1.set_dTdt_convective(source = [T_hf1.y(),T_hf1.y(),T_hc3.y(),T_hc3.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])\n",
+    "T_ht2.set_dTdt_convective(source = [T_hf3.y(),T_hf3.y(),T_hc1.y(),T_hc1.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])\n",
+    "\n",
+    "T_hc1.set_dTdt_advective(source = T_out_rc.y(t-tau_r_hx))\n",
+    "T_hc1.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_sn])\n",
+    "\n",
+    "T_hc2.set_dTdt_advective(source = T_hc1.y())\n",
+    "T_hc2.dTdt_convective = T_hc1.dTdt_convective\n",
+    "\n",
+    "T_hc3.set_dTdt_advective(source = T_hc2.y())\n",
+    "T_hc3.set_dTdt_convective(source = [T_ht1.y()], hA = [hA_sn])\n",
+    "\n",
+    "T_hc4.set_dTdt_advective(source = T_hc3.y())\n",
+    "T_hc4.dTdt_convective = T_hc3.dTdt_convective\n",
+    "\n",
+    "# core\n",
+    "n.set_dndt(r = rho.y()+rho_ext, beta_eff = beta_t, Lambda = Lam, lam = lam, C = [C1.y(),C2.y(),C3.y(),C4.y(),C5.y(),C6.y()])\n",
+    "C1.set_dcdt(n.y(),beta[0],Lam,lam[0],True,tau_c,tau_l)\n",
+    "C2.set_dcdt(n.y(),beta[1],Lam,lam[1],True,tau_c,tau_l)\n",
+    "C3.set_dcdt(n.y(),beta[2],Lam,lam[2],True,tau_c,tau_l)\n",
+    "C4.set_dcdt(n.y(),beta[3],Lam,lam[3],True,tau_c,tau_l)\n",
+    "C5.set_dcdt(n.y(),beta[4],Lam,lam[4],True,tau_c,tau_l)\n",
+    "C6.set_dcdt(n.y(),beta[5],Lam,lam[5],True,tau_c,tau_l)\n",
+    "\n",
+    "T_cg.set_dTdt_convective(source = [T_cf1.y()], hA = [hA_fg])\n",
+    "T_cg.set_dTdt_internal(source = n.y(), k = k_g*P)\n",
+    "\n",
+    "T_cf1.set_dTdt_advective(source = T_hf4.y(t-tau_hx_c))\n",
+    "T_cf1.set_dTdt_convective(source = [T_cg.y()], hA = [k_1*hA_fg])\n",
+    "T_cf1.set_dTdt_internal(source = n.y(), k = k_f1*P)\n",
+    "\n",
+    "T_cf2.set_dTdt_advective(source = T_cf1.y())\n",
+    "T_cf2.dTdt_convective = T_cf1.dTdt_convective\n",
+    "T_cf2.set_dTdt_internal(source = n.y(), k = k_f2*P)\n",
+    "\n",
+    "rho.set_drdt(sources = [T_cf1.dydt, T_cf2.dydt, T_cg.dydt], coeffs = [a_f/2,a_f/2,a_g])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating, compiling, and loading C code.\n",
+      "Using default integration parameters.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/luke/git/envs/onion_env/lib/python3.11/site-packages/jitcdde/_jitcdde.py:795: UserWarning: You did not explicitly handle initial discontinuities. Proceed only if you know what you are doing. This is only fine if you somehow chose your initial past such that the derivative of the last anchor complies with the DDE. In this case, you can set the attribute `initial_discontinuities_handled` to `True` to suppress this warning. See https://jitcdde.rtfd.io/#discontinuities for details.\n",
+      "  warn(\"You did not explicitly handle initial discontinuities. Proceed only if you know what you are doing. This is only fine if you somehow chose your initial past such that the derivative of the last anchor complies with the DDE. In this case, you can set the attribute `initial_discontinuities_handled` to `True` to suppress this warning. See https://jitcdde.rtfd.io/#discontinuities for details.\")\n",
+      "/home/luke/git/envs/onion_env/lib/python3.11/site-packages/jitcdde/_jitcdde.py:795: UserWarning: You did not explicitly handle initial discontinuities. Proceed only if you know what you are doing. This is only fine if you somehow chose your initial past such that the derivative of the last anchor complies with the DDE. In this case, you can set the attribute `initial_discontinuities_handled` to `True` to suppress this warning. See https://jitcdde.rtfd.io/#discontinuities for details.\n",
+      "  warn(\"You did not explicitly handle initial discontinuities. Proceed only if you know what you are doing. This is only fine if you somehow chose your initial past such that the derivative of the last anchor complies with the DDE. In this case, you can set the attribute `initial_discontinuities_handled` to `True` to suppress this warning. See https://jitcdde.rtfd.io/#discontinuities for details.\")\n",
+      "/home/luke/git/envs/onion_env/lib/python3.11/site-packages/jitcdde/_jitcdde.py:792: UserWarning: The target time is smaller than the current time. No integration step will happen. The returned state will be extrapolated from the interpolating Hermite polynomial for the last integration step. You may see this because you try to integrate backwards in time, in which case you did something wrong. You may see this just because your sampling step is small, in which case there is no need to worry.\n",
+      "  warn(\"The target time is smaller than the current time. No integration step will happen. The returned state will be extrapolated from the interpolating Hermite polynomial for the last integration step. You may see this because you try to integrate backwards in time, in which case you did something wrong. You may see this just because your sampling step is small, in which case there is no need to worry.\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "sol_jit = MSRE.solve(T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_199186/112360540.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "  duration = df_ORNL.iloc[-1][0]-df_ORNL.iloc[0][0]\n",
+      "/tmp/ipykernel_199186/112360540.py:8: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
+      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
+      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
+      "\n",
+      "df[\"col\"][row_indexer] = value\n",
+      "\n",
+      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "  df_ORNL['t'][i] += delta\n"
+     ]
+    }
+   ],
+   "source": [
+    "duration = df_ORNL.iloc[-1][0]-df_ORNL.iloc[0][0]\n",
+    "i_insert = [i for i in range(len(T)) if (T[i] > t_ins) and (T[i] < t_ins + duration)]\n",
+    "ref_P = P*n.y_out[i_insert[0]-100]\n",
+    "dP = [(k*P)-ref_P for k in n.y_out]\n",
+    "\n",
+    "delta = t_ins - df_ORNL['t'][0] - t_ins\n",
+    "for i in range(len(df_ORNL)):\n",
+    "    df_ORNL['t'][i] += delta\n",
+    "\n",
+    "ref_P_simulink = df_simulink['Mux(4)'][i_trans[0]-100]*P\n",
+    "i_window = [i for i in i_trans if df_simulink['time'][i]-t_ins <= duration]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown']\n",
+    "\n",
+    "# function to update the style of each axis\n",
+    "def update_axis_style(ax, title, x_label='', y_label='', x_ticks=True):\n",
+    "    ax.set_title(title,fontsize=20)\n",
+    "    ax.set_xlabel(x_label,fontsize=14)\n",
+    "    ax.set_ylabel(y_label,fontsize=14)\n",
+    "    ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
+    "    ax.tick_params(axis='x', which='both', bottom=x_ticks, top=False, labelbottom=x_ticks)\n",
+    "    ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "update_axis_style(ax,f\"Step Reactivity Insertion Response: {int(inserted*(10**5))}pcm, {int(P)}MW, U233 Fuel\")\n",
+    "ax.plot(T[i_insert[0]:i_insert[-1]+1]-t_ins,dP[i_insert[0]:i_insert[-1]+1],label=\"msrDynamics\",color=colors[0])\n",
+    "ax.plot(df_simulink['time'][i_window]-t_ins,df_simulink['Mux(4)'][i_window]*P-ref_P_simulink,color=colors[1],linestyle=\"--\",label=\"Simulink, Singh et al.\")\n",
+    "ax.plot(df_ORNL.iloc[:,0],df_ORNL.iloc[:,1],label=\"ORNL-TM-2997\",color=colors[2],linestyle=\"--\")\n",
+    "ax.set_ylabel(r\"$\\Delta P$\")\n",
+    "ax.set_xlabel(r\"$t$(s)\")\n",
+    "ax.legend()\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "thesis_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/dynamic_model/msrDynamics_implementation/parameters_U233.py b/dynamic_model/msrDynamics_implementation/parameters_U233.py
new file mode 100644
index 0000000..423b850
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/parameters_U233.py
@@ -0,0 +1,151 @@
+import numpy as np
+
+# domain
+t0 = 0.0
+tf = 5000.00
+T = np.arange(t0,tf,0.01)
+
+# REACTIVITY INSERTION
+inserted = 1.39e-4 # 1MW
+# inserted = 1.96e-4 # 5MW
+# inserted = 2.48e-4 # 8MW
+
+# NEUTRONICS DATA
+tau_l = 16.73
+tau_c = 8.46
+# P = 0.1
+# P = 5
+P = 8    
+n_frac0 = 1  # initial fractional neutron density n/n0
+Lam = 4.0E-04
+lam = np.array([1.260E-02, 3.370E-02, 1.390E-01, 3.250E-01, 1.130E+00, 2.500E+00])
+beta = np.array([0.00023, 0.00079, 0.00067, 0.00073, 0.00013, 0.00009])
+beta_t = np.sum(beta)  # total delayed neutron fraction MSRE
+rho_0 = beta_t-sum(np.divide(beta,1+np.divide(1-np.exp(-lam*tau_l),lam*tau_c))) # reactivity change in going from stationary to circulating fuel
+C0 = beta / Lam * (1.0 / (lam - (np.exp(-lam * tau_l) - 1.0) / tau_c))
+
+# Feedback coefficients
+a_f = -11.034E-5
+a_g = -05.814E-5
+
+# CORE HEAT TRANSFER PARAMETERS
+vdot_f = 7.5708E-02
+rho_f = 2.14647E+03
+W_f = 1.623879934566580e+02
+m_f = W_f * tau_c
+nn_f = 2
+mn_f = m_f / nn_f
+scp_f = 1.9665E-3
+
+# Core Upflow
+v_g = 1.95386
+rho_g = 1.860E3
+m_g = v_g * rho_g
+scp_g = 1.773E-3
+mcp_g1 = m_g * scp_g
+mcp_f1 = mn_f * scp_f
+mcp_f2 = mn_f * scp_f
+hA_fg = 0.02 * 9 / 5
+k_g = 0.07
+k_1 = 0.5
+k_2 = 0.5
+k_f = 0.93
+k_f1 = k_f / nn_f
+k_f2 = k_f / nn_f
+
+# Heat Exchanger
+d_he = 16
+h_he = 72
+od_tube = 0.5
+id_tube = od_tube - 2 * 0.042
+n_tube = 159
+a_tube = 254 * 144
+l_tube = a_tube / n_tube / (np.pi * od_tube)
+v_tube = n_tube * np.pi * (od_tube / 2) ** 2 * l_tube
+v_cool = n_tube * np.pi * (id_tube / 2) ** 2 * l_tube
+v_he = (d_he / 2) ** 2 * np.pi * h_he
+v_he_fuel = v_he - v_tube
+in_m = 1.63871e-5
+W_p = W_f
+m_p = v_he_fuel * in_m * rho_f
+nn_p = 4
+mn_p = m_p / nn_p
+cp_p = scp_f
+vdot_s = 5.36265E-02
+rho_s = 1.922e3
+W_s = 1.005793369810108e+02
+m_s = v_cool * in_m * rho_s
+nn_s = 4
+mn_s = m_s / nn_s
+scp_s = 2.39E-3
+A_phe = 2.359E+01
+ha_p = 6.480E-01
+ha_s = 3.060E-01
+mcp_pn = mn_p * cp_p
+hA_pn = ha_p / nn_s
+nn_t = 2
+rho_tube = 8.7745E+03
+m_tn = (v_tube - v_cool) * in_m * rho_tube / nn_t
+scp_t = 5.778E-04
+mcp_tn = m_tn * scp_t
+mcp_sn = mn_s * scp_s
+hA_sn = ha_s / nn_s
+
+# Initial conditions
+Tf_in = 6.3222E+02
+T0_f2 = 6.5727E+02
+T0_f1 = Tf_in + (T0_f2 - Tf_in) / 2
+T0_g1 = T0_f1 + (k_g * P / hA_fg)
+Tp_in = T0_f2
+T0_p4 = Tf_in
+T0_p1 = Tp_in - (Tp_in - T0_p4) / 4
+T0_p2 = Tp_in - 2 * (Tp_in - T0_p4) / 4
+T0_p3 = Tp_in - 3 * (Tp_in - T0_p4) / 4
+Ts_in = 5.4611E+02
+T0_s4 = 5.7939E+02
+T0_s1 = Ts_in + (T0_s4 - Ts_in) / nn_s
+T0_s2 = Ts_in + 2 * (T0_s4 - Ts_in) / nn_s
+T0_s3 = Ts_in + 3 * (T0_s4 - Ts_in) / nn_s
+T0_t1 = (T0_p1 * hA_pn + T0_s3 * hA_sn) / (hA_pn + hA_sn)
+T0_t2 = (T0_p3 * hA_pn + T0_s1 * hA_sn) / (hA_pn + hA_sn)
+
+# Radiator Parameters
+Trp_in = T0_s4
+T0_rp = Ts_in
+Trs_in = 37.78
+T0_rs = 148.9
+od_rad = 0.01905
+tube_wall_thick = 0.0018288
+id_rad = od_rad - 2 * tube_wall_thick
+n_rtubes = 120
+l_rtube = 9.144
+v_rp = np.pi * (id_rad / 2) ** 2 * l_rtube * n_rtubes
+n_tpr = 12
+n_row = 10
+tube_space = 0.0381
+v_rs = (n_row * od_rad + (n_row - 1) * tube_space) * (n_tpr * od_rad + (n_tpr - 1) * tube_space) * l_rtube
+W_rp = W_s
+m_rp = v_rp * rho_s
+nn_rp = 1
+mn_rp = m_rp / nn_rp
+cp_rp = scp_s
+vdot_rs = 94.389
+rho_rs = 1.1237
+W_rs = vdot_rs * rho_rs
+m_rs = v_rs * rho_rs
+nn_rs = 1
+mn_rs = m_rs / nn_rs
+scp_rs = 1.0085E-3
+A_rad = 6.503E1
+h_roverall = P / A_rad / ((T0_rp + Trp_in) / 2 - (T0_rs + Trs_in) / 2)
+mcp_rpn = mn_rp * cp_rp
+hA_rpn = h_roverall * A_rad / nn_rs
+mcp_rsn = mn_rs * scp_rs
+hA_rsn = h_roverall * A_rad / nn_rs
+
+# Pure time delays between components
+tau_hx_c = 8.67 #+2.145 
+tau_c_hx = 3.77 #+2.145
+tau_hx_r = 4.71
+tau_r_hx = 8.24
+
diff --git a/dynamic_model/msrDynamics_implementation/parameters_U235.py b/dynamic_model/msrDynamics_implementation/parameters_U235.py
new file mode 100644
index 0000000..918de9b
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/parameters_U235.py
@@ -0,0 +1,201 @@
+import numpy as np
+import math
+pi = math.pi
+
+# domain
+t0 = 0.0
+tf = 1000.00
+T = np.arange(t0,tf,0.01)
+
+# NEUTRONICS DATA
+tau_l = 16.73  # ORNL-TM-0728 %16.44; % (s)
+tau_c = 8.46  # ORNL-TM-0728 %8.460; % (s)
+P = 8.0  # Thermal Power in MW ORNL-TM-1070, p.2
+n_frac0 = 1.0  # initial fractional neutron density n/n0 (n/cm^3/s)
+Lam = 2.400E-04  # mean generation time ORNL-TM-1070 p.15 U235
+# Lam = 4.0E-04;  # mean generation time ORNL-TM-1070 p.15 U233
+lam = np.array([1.240E-02, 3.05E-02, 1.11E-01, 3.01E-01, 1.140E+00, 3.014E+00])
+beta = np.array([0.000223, 0.001457, 0.001307, 0.002628, 0.000766, 0.00023])  # U235
+# beta = np.array([0.00023, 0.00079, 0.00067, 0.00073, 0.00013, 0.00009])  # U233
+beta_t = np.sum(beta)  # total delayed neutron fraction MSRE
+rho_0 = beta_t-sum(np.divide(beta,1+np.divide(1-np.exp(-lam*tau_l),lam*tau_c))) # reactivity change in going from stationary to circulating fuel
+C0 = beta / Lam * (1.0 / (lam - (np.exp(-lam * tau_l) - 1.0) / tau_c))
+
+# Feedback co-efficients
+a_f = -8.71E-05  # U235 (drho/°C) fuel salt temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -5.904E-05; % ORNL-TM-0728 p. 101 %
+a_g = -6.66E-05  # U235 (drho/°C) graphite temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -6.624E-05; % ORNL-TM-0728 p.101
+
+# CORE HEAT TRANSFER PARAMETERS
+# FUEL PARAMETERS - DONE
+vdot_f = 7.5708E-02  # ORNL-TM-0728 % 7.571e-2; % vol. flow rate (m^3/s) ORNL-TM-1647 p.3, ORNL-TM-0728 p.12
+rho_f = 2.14647E+03  # (partially enriched U-235)ORNL-TM-0728 p.8 2.243E+03; % (Th-U) density of fuel salt (kg/m^3) ORNL-TM-0728 p.8
+W_f = 1.623879934566580e+02  # 1.83085e+02;%vdot_f*rho_f; % 182.78; % calcd from m_dot*cp*delT=P; vdot_f*rho_f; % fuel flow rate (kg/s)
+# tau_f_c  = tau_c; % ORNL-TM-0728 % 8.45; % transit time of fuel in core (s) ORNL-TM-1070 p.15, TDAMSRE p.5
+m_f = W_f * tau_c  # fuel mass in core (kg)
+nn_f = 2  # number of fuel nodes in core model
+mn_f = m_f / nn_f  # fuel mass per node (kg)
+# cp_f     = 4.2*9/5; % (MJ/deg-C) total fuel heat capacity TDAMSRE p.5
+scp_f = 1.9665E-3  # specific heat capacity of fuel salt (MJ/kg-C) ORNL-TM-0728 p.8
+
+# Core Upflow - DONE
+v_g = 1.95386  # graphite volume(m^3) ORNL-TM-0728 p. 101
+rho_g = 1.860E3  # graphite density (kg/m^3) ORNL-3812 p.77, ORNL-TM-0728 p.87
+m_g = v_g * rho_g  # graphite mass (kg)
+cp_g = 3.6 * 9 / 5  # TDAMSRE p.5 graphite total heat capacity (MW-s/C) ORNL-TM-1647 p.3
+scp_g = 1.773E-3  # cp_g/m_g; % graphite specific heat capacity (MW-s/kg-C) ORNL-TM-1647 p.3
+mcp_g1 = m_g * scp_g  # (mass of material x heat capacity of material) of graphite per lump (MW-s/°C)
+mcp_f1 = mn_f * scp_f  # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C)
+mcp_f2 = mn_f * scp_f  # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C)
+hA_fg = 0.02 * 9 / 5  # (fuel to graphite heat transfer coeff x heat transfer area) (MW/°C) ORNL-TM-1647 p.3, TDAMSRE p.5
+k_g = 0.07  # fraction of total power generated in the graphite  ORNL-TM-0728 p.9
+k_1 = 0.5  # fraction of heat transferred from graphite which goes to the first fuel lump
+k_2 = 0.5  # fraction of heat transferred from graphite which goes to the second fuel lump
+k_f = 0.93  # fraction of heat generated in fuel - that generated in the external loop ORNL-TM-0728 p.9
+k_f1 = k_f / nn_f  # fraction of total power generated in lump f1
+k_f2 = k_f / nn_f  # fraction of total power generated in lump f2
+
+# New node for power deposited in fuel outside the core
+k_out = 1 - (k_g + k_f)  # fraction of power generated in fuel in external loop ORNL-TM-0728 p.9
+m_out = W_f  # (kg) Mass of node such that resident time is 1 sec (W_f needs to be defined)
+
+# Initial conditions - DONE
+Tf_in = 6.3222E+02  # in °C ORNL-TM-1647 p.2
+T0_f2 = 6.5727E+02  # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2
+T0_f1 = Tf_in + (T0_f2 - Tf_in) / 2  # 6.405952380952389e+02; in °C
+T0_g1 = T0_f1 + (k_g * P / hA_fg)  # 6.589285714285924e+02; in °C
+# T0_out = k_out * P / m_out / scp_f + T0_f2  # in °C (scp_f needs to be defined)
+
+
+# Heat Exchanger - DONE
+# Geometry
+d_he = 16  # (in) he diameter ORNL-TM-0728 p. 164
+h_he = 72  # (in) active height % 96; %(in) he height ORNL-TM-0728 p. 164
+od_tube = 0.5  # (in) coolant tube OD ORNL-TM-0728 p. 164
+id_tube = od_tube - 2 * 0.042  # (in) coolant tube ID ORNL-TM-0728 p. 164
+n_tube = 159  # number of coolant tubes ORNL-TM-0728 p. 164
+a_tube = 254 * 144  # (in^2) total area of tubes ORNL-TM-0728 p. 164
+l_tube = a_tube / n_tube / (np.pi * od_tube)  # (in) tube length
+v_tube = n_tube * np.pi * (od_tube / 2) ** 2 * l_tube  # (in^3) hx shell volume occupied by tubes
+v_cool = n_tube * np.pi * (id_tube / 2) ** 2 * l_tube  # (in^3) hx volume occupied by coolant
+v_he = (d_he / 2) ** 2 * np.pi * h_he  # (in^3) volume of heat exchanger shell
+v_he_fuel = v_he - v_tube  # (in^3) volume available to fuel in shell
+
+# Unit conversions
+in_m = 1.63871e-5  # 1 cubic inch = 1.63871e-5 cubic meters
+
+# PRIMARY FLOW PARAMETERS - DONE
+W_p = W_f  # fuel flow rate (kg/s)
+
+m_p = v_he_fuel * in_m * rho_f  # fuel mass in PHE (kg)
+nn_p = 4  # number of fuel nodes in PHE
+mn_p = m_p / nn_p  # fuel mass per node (kg)
+cp_p = scp_f  # fuel heat capacity (MJ/(kg-C))
+
+# SECONDARY FLOW PARAMETERS - DONE
+vdot_s = 5.36265E-02  # ORNL-TM-0728 p. 164 % 5.236E-02; % coolant volume flow rate (m^3/s) ORNL-TM-1647 p.3
+rho_s = 1.922e3  # coolant salt density (kg/m^3) ORNL-TM-0728 p.8
+W_s = 1.005793369810108e+02  # vdot_s*rho_s; % calcd from mdot*cp*delT; vdot_s*rho_s; % coolant flow rate (kg/s) ORNL-TM-1647 p.3
+
+m_s = v_cool * in_m * rho_s  # coolant mass in PHE (kg)
+nn_s = 4  # number of coolant nodes in PHE
+mn_s = m_s / nn_s  # coolant mass per node (kg)
+scp_s = 2.39E-3  # cp_s/m_s; % specific heat capacity of coolant (MJ/(kg-C) ORNL-TM-0728 p.8
+
+A_phe = 2.359E+01  # effective area for heat transfer (primary and secondary, m^2) ORNL-TM-0728 p.164
+
+ha_p = 6.480E-01  # heat transfer*area coefficient from primary to tubes (MW/C) ORNL-TM-1647 p.3
+ha_s = 3.060E-01  # heat transfer*area coefficient from tubes to secondary (MW/C) ORNL-TM-1647 p.3
+
+# Primary Side
+mcp_pn = mn_p * cp_p  # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C
+hA_pn = ha_p / nn_s  # 3.030; % (primary to tube heat transfer coeff x heat transfer area) in MW/°C
+
+# Tubes - DONE
+nn_t = 2  # number of nodes of tubes in the model
+rho_tube = 8.7745E+03  # (kg/m^3) density of INOR-8 ORNL-TM-0728 p.20
+m_tn = (v_tube - v_cool) * in_m * rho_tube / nn_t  # mass of tubes (kg)
+scp_t = 5.778E-04  # specific heat capacity of tubes (MJ/(kg-C)) ORNL-TM-0728 p.20
+mcp_tn = m_tn * scp_t  # mass*(heat capacity) of tubes per lump in MW-s/°C
+
+# Secondary Side - DONE
+mcp_sn = mn_s * scp_s  # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C
+hA_sn = ha_s / nn_s  # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C
+
+# Initial conditions - DONE
+# Primary nodes
+Tp_in = T0_f2  # in °C ORNL-TM-1647 p.2
+T0_p4 = Tf_in  # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2
+T0_p1 = Tp_in + (T0_p4 - Tp_in) / 4  # in °C
+T0_p2 = Tp_in + 2 * (T0_p4 - Tp_in) / 4  # in °C
+T0_p3 = Tp_in + 3 * (T0_p4 - Tp_in) / 4  # in °C
+
+# Secondary nodes
+Ts_in = 5.4611E+02  # in °C ORNL-TM-1647 p.2
+T0_s4 = 5.7939E+02  # in °C ORNL-TM-1647 p.2
+T0_s1 = Ts_in + (T0_s4 - Ts_in) / nn_s  # in °C
+T0_s2 = Ts_in + 2 * (T0_s4 - Ts_in) / nn_s  # in °C
+T0_s3 = Ts_in + 3 * (T0_s4 - Ts_in) / nn_s  # in °C
+# Tube nodes
+T0_t1 = (T0_p1 * hA_pn + T0_s3 * hA_sn) / (hA_pn + hA_sn)  # in °C
+T0_t2 = (T0_p3 * hA_pn + T0_s1 * hA_sn) / (hA_pn + hA_sn)  # in °C
+
+# Radiator Parameters - DONE
+
+# Initial conditions - DONE
+# Primary nodes
+Trp_in = T0_s4  # 5.933E+02; % in °C ORNL-TM-1647 p.2
+T0_rp = Ts_in  # in °C ORNL-TM-1647 p.2
+
+# Secondary nodes - DONE
+Trs_in = 37.78  # (C) air inlet temperature ORNL-TM-1647 p.2
+T0_rs = 148.9  # (C) air exit temperature ORNL-TM-1647 p.2
+
+# Radiator Geometry
+od_rad = 0.01905  # (m) outer diameter of tubes in the radiator ORNL-TM-0728 p.296
+tube_wall_thick = 0.0018288  # (m) thickness of tubes in the radiator ORNL-TM-0728 p.296
+id_rad = od_rad - 2 * tube_wall_thick
+n_rtubes = 120  # number of tubes in the radiator (rows times tubes per row) ORNL-TM-0728 p.296
+l_rtube = 9.144  # (m) length of tubes in the radiator ORNL-TM-0728 p.296
+v_rp = pi * (id_rad / 2) ** 2 * l_rtube * n_rtubes  # volume available to salt in the radiator
+# v_rtube = pi * (od_rad / 2) ** 2 * l_rtube * n_rtubes - v_rp  # volume of metal in radiator tubes *TUBES NOT MODELED
+
+n_tpr = 12  # number of tubes per row in the radiator matrix
+n_row = 10  # number rows in the radiator matrix
+tube_space = 0.0381  # (m) spacing between tubes and rows of matrix
+v_rs = (n_row * od_rad + (n_row - 1) * tube_space) * (n_tpr * od_rad + (n_tpr - 1) * tube_space) * l_rtube  # volume of air inside radiator
+
+# PRIMARY FLOW PARAMETERS - DONE
+W_rp = W_s  # coolant salt flow rate (kg/s)
+m_rp = v_rp * rho_s  # coolant salt mass in rad (kg)
+nn_rp = 1  # number of coolant salt nodes in the radiator
+mn_rp = m_rp / nn_rp  # coolant mass per node (kg)
+cp_rp = scp_s  # coolant specific heat capacity (MJ/(kg-C))
+
+# SECONDARY FLOW PARAMETERS - DONE
+vdot_rs = 94.389  # ORNL-TM-0728 p. 296; 78.82; % air volume flow rate (m^3/s) ORNL-TM-1647 p.2
+rho_rs = 1.1237  # air density (kg/m^3) REFPROP (310K and 0.1MPa)
+W_rs = vdot_rs * rho_rs  # air flow rate (kg/s)
+
+m_rs = v_rs * rho_rs  # coolant air mass in rad (kg)
+nn_rs = 1  # number of coolant nodes in rad
+mn_rs = m_rs / nn_rs  # coolant mass per node (kg)
+scp_rs = 1.0085E-3  # (MJ/kg-C) specific heat capacity of air at (air_out+air_in)/2 REFPROP
+
+A_rad = 6.503E1  # (m^2) surface area of the radiator ORNL-TM-0728 p.14
+h_roverall = P / A_rad / ((T0_rp + Trp_in) / 2 - (T0_rs + Trs_in) / 2)  # cald as: P/A_rad/((T0_rp+Trp_in)/2-(T0_rs+Trs_in)/2)  3.168E-4; % (MW/m^2-C) polimi thesis
+
+# Primary Side
+mcp_rpn = mn_rp * cp_rp  # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C
+hA_rpn = h_roverall * A_rad / nn_rs  # 3.030; % (primary to secondary heat transfer coeff x heat transfer area) in MW/°C
+
+# Secondary Side - DONE
+mcp_rsn = mn_rs * scp_rs  # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C
+hA_rsn = h_roverall * A_rad / nn_rs  # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C
+
+# Pure time delays between components - DONE
+tau_hx_c = 8.67  # (sec) delay from hx to core TDAMSRE p.6
+tau_c_hx = 3.77  # (sec) subtracted 1 sec for external loop power generation node resident time; delay from core to fuel hx TDAMSRE p.6
+tau_hx_r = 4.71  # (sec) fertile hx to core TDAMSRE p.6
+tau_r_hx = 8.24  # (sec) core to fertile hx TDAMSRE p.6
+
+first_val = (rho_0 - beta_t) * n_frac0 / Lam + lam[0] * C0[0] + lam[1] * C0[1] + lam[2] * C0[2] + lam[3] * C0[3] + lam[4] * C0[4] + lam[5] * C0[5]
\ No newline at end of file
diff --git a/dynamic_model/pump_transient_benchmark/__pycache__/parameters_U235.cpython-312.pyc b/dynamic_model/pump_transient_benchmark/__pycache__/parameters_U235.cpython-312.pyc
new file mode 100644
index 0000000..63c0dfa
Binary files /dev/null and b/dynamic_model/pump_transient_benchmark/__pycache__/parameters_U235.cpython-312.pyc differ
diff --git a/dynamic_model/pump_transient_benchmark/data/ornl_spindown.csv b/dynamic_model/pump_transient_benchmark/data/ornl_spindown.csv
new file mode 100644
index 0000000..a7275cc
--- /dev/null
+++ b/dynamic_model/pump_transient_benchmark/data/ornl_spindown.csv
@@ -0,0 +1,73 @@
+0.0, 205.26315789473685
+0.9046563192904662, 206.3397129186603
+1.9157427937915745, 208.4928229665072
+2.926829268292683, 186.96172248803828
+3.884700665188471, 171.88995215311007
+4.8957871396895785, 144.97607655502395
+5.906873614190688, 142.82296650717706
+6.917960088691797, 130.622009569378
+7.929046563192904, 119.4976076555024
+8.886917960088692, 104.42583732057417
+9.951219512195124, 99.04306220095697
+10.90909090909091, 88.99521531100478
+11.92017738359202, 86.84210526315792
+12.931263858093129, 83.61244019138758
+13.942350332594234, 81.45933014354068
+15.059866962305989, 79.66507177033495
+15.964523281596453, 71.77033492822966
+16.922394678492243, 64.23444976076556
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diff --git a/dynamic_model/pump_transient_benchmark/data/ornl_spinup.csv b/dynamic_model/pump_transient_benchmark/data/ornl_spinup.csv
new file mode 100644
index 0000000..739f1ae
--- /dev/null
+++ b/dynamic_model/pump_transient_benchmark/data/ornl_spinup.csv
@@ -0,0 +1,48 @@
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diff --git a/dynamic_model/pump_transient_benchmark/data/spindown.csv b/dynamic_model/pump_transient_benchmark/data/spindown.csv
new file mode 100644
index 0000000..1066b41
--- /dev/null
+++ b/dynamic_model/pump_transient_benchmark/data/spindown.csv
@@ -0,0 +1,29 @@
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diff --git a/dynamic_model/pump_transient_benchmark/data/spinup.csv b/dynamic_model/pump_transient_benchmark/data/spinup.csv
new file mode 100644
index 0000000..349665d
--- /dev/null
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diff --git a/dynamic_model/pump_transient_benchmark/model.ipynb b/dynamic_model/pump_transient_benchmark/model.ipynb
new file mode 100644
index 0000000..9f7af70
--- /dev/null
+++ b/dynamic_model/pump_transient_benchmark/model.ipynb
@@ -0,0 +1,348 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from parameters_U235_pump_transient import *\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from jitcdde import t\n",
+    "from msrDynamics import Node, System\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MSRE = System()\n",
+    "\n",
+    "# radiator\n",
+    "T_out_rc = Node(m = mn_rp, scp = mcp_rpn/mn_rp, W = W_rp, y0 = T0_rp)\n",
+    "T_out_air = Node(m = mn_rs, scp = mcp_rsn/mn_rs, W = W_rs, y0 = T0_rs)\n",
+    "\n",
+    "# heat exchanger\n",
+    "T_hf1 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p1)\n",
+    "T_hf2 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p2)\n",
+    "T_hf3 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p3)\n",
+    "T_hf4 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p4)\n",
+    "T_ht1 = Node(m = m_tn, scp = scp_t, y0 = T0_t1)\n",
+    "T_ht2 = Node(m = m_tn, scp = scp_t, y0 = T0_t2)\n",
+    "T_hc1 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s1)\n",
+    "T_hc2 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s2)\n",
+    "T_hc3 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s3)\n",
+    "T_hc4 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s4)\n",
+    "\n",
+    "# core \n",
+    "n = Node(y0 = n_frac0)\n",
+    "C1 = Node(y0 = C0[0])\n",
+    "C2 = Node(y0 = C0[1])\n",
+    "C3 = Node(y0 = C0[2])\n",
+    "C4 = Node(y0 = C0[3])\n",
+    "C5 = Node(y0 = C0[4])\n",
+    "C6 = Node(y0 = C0[5])\n",
+    "rho = Node(y0 = rho_0)\n",
+    "\n",
+    "T_cg = Node(m = mcp_g1/scp_g, scp = scp_g, y0 = T0_g1)\n",
+    "T_cf1 = Node(m = mn_f, scp = scp_f, W = W_f, y0 = T0_f1)\n",
+    "T_cf2 = Node(m = mn_f, scp = scp_f, W = W_f, y0 = T0_f1)\n",
+    "\n",
+    "MSRE.add_nodes([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1,\n",
+    "               T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho])\n",
+    "\n",
+    "\n",
+    "# dynamics \n",
+    "\n",
+    "# radiator\n",
+    "T_out_rc.set_dTdt_advective(source = T_hc4.y(t-tau_hx_r))\n",
+    "T_out_rc.set_dTdt_convective(source = [T_out_air.y()], hA = [hA_rpn])\n",
+    "\n",
+    "T_out_air.set_dTdt_advective(source = Trs_in)\n",
+    "T_out_air.set_dTdt_convective(source = [T_out_rc.y()], hA = [hA_rsn])\n",
+    "\n",
+    "# heat exchanger\n",
+    "T_hf1.set_dTdt_advective(source = T_cf2.y(t-tau_c_hx))\n",
+    "T_hf1.set_dTdt_convective(source = [T_ht1.y()], hA = [hA_pn])\n",
+    "\n",
+    "T_hf2.set_dTdt_advective(source = T_hf1.y())\n",
+    "T_hf2.dTdt_convective = T_hf1.dTdt_convective\n",
+    "\n",
+    "T_hf3.set_dTdt_advective(source = T_hf2.y())\n",
+    "T_hf3.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_pn])\n",
+    "\n",
+    "T_hf4.set_dTdt_advective(source = T_hf3.y())\n",
+    "# T_hf4.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_pn])\n",
+    "T_hf4.dTdt_convective = T_hf3.dTdt_convective\n",
+    "\n",
+    "# T_ht1.set_dTdt_convective(source = [T_hf1.y(),T_hf2.y(),T_hc3.y(),T_hc4.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])\n",
+    "# T_ht2.set_dTdt_convective(source = [T_hf3.y(),T_hf4.y(),T_hc1.y(),T_hc2.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])\n",
+    "T_ht1.set_dTdt_convective(source = [T_hf1.y(),T_hf1.y(),T_hc3.y(),T_hc3.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])\n",
+    "T_ht2.set_dTdt_convective(source = [T_hf3.y(),T_hf3.y(),T_hc1.y(),T_hc1.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])\n",
+    "\n",
+    "T_hc1.set_dTdt_advective(source = T_out_rc.y(t-tau_r_hx))\n",
+    "T_hc1.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_sn])\n",
+    "\n",
+    "T_hc2.set_dTdt_advective(source = T_hc1.y())\n",
+    "T_hc2.dTdt_convective = T_hc1.dTdt_convective\n",
+    "\n",
+    "T_hc3.set_dTdt_advective(source = T_hc2.y())\n",
+    "T_hc3.set_dTdt_convective(source = [T_ht1.y()], hA = [hA_sn])\n",
+    "\n",
+    "T_hc4.set_dTdt_advective(source = T_hc3.y())\n",
+    "T_hc4.dTdt_convective = T_hc3.dTdt_convective\n",
+    "\n",
+    "# core\n",
+    "n.set_dndt(r = rho.y(), beta_eff = beta_t, Lambda = Lam, lam = lam, C = [C1.y(),C2.y(),C3.y(),C4.y(),C5.y(),C6.y()])\n",
+    "C1.set_dcdt(n.y(),beta = beta[0],Lambda = Lam,lam = lam[0],t_c=tau_c,t_l = tau_l, flow = True)\n",
+    "C2.set_dcdt(n.y(), beta = beta[1],Lambda = Lam,lam = lam[1],t_c=tau_c,t_l = tau_l, flow = True)\n",
+    "C3.set_dcdt(n.y(),beta = beta[2],Lambda = Lam,lam = lam[2],t_c=tau_c,t_l = tau_l, flow = True)\n",
+    "C4.set_dcdt(n.y(),beta = beta[3],Lambda = Lam,lam = lam[3],t_c=tau_c,t_l = tau_l, flow = True)\n",
+    "C5.set_dcdt(n.y(),beta = beta[4],Lambda = Lam,lam = lam[4],t_c=tau_c,t_l = tau_l, flow = True)\n",
+    "C6.set_dcdt(n.y(),beta = beta[5],Lambda = Lam,lam = lam[5],t_c=tau_c,t_l = tau_l, flow = True)\n",
+    "\n",
+    "T_cg.set_dTdt_convective(source = [T_cf1.y()], hA = [hA_fg])\n",
+    "T_cg.set_dTdt_internal(source = [n.y()], k = [k_g*P])\n",
+    "\n",
+    "T_cf1.set_dTdt_advective(source = T_hf4.y(t-tau_hx_c))\n",
+    "T_cf1.set_dTdt_convective(source = [T_cg.y()], hA = [k_1*hA_fg])\n",
+    "T_cf1.set_dTdt_internal(source = [n.y()], k = [k_f1*P])\n",
+    "\n",
+    "T_cf2.set_dTdt_advective(source = T_cf1.y())\n",
+    "T_cf2.dTdt_convective = T_cf1.dTdt_convective\n",
+    "T_cf2.set_dTdt_internal(source = [n.y()], k = [k_f2*P])\n",
+    "\n",
+    "rho.set_drdt(sources = [T_cf1.dydt, T_cg.dydt], coeffs = [a_f/2,a_g])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finalizing integrator...\n",
+      "integrating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Integration progress:   0%|          | 0/500000 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating, compiling, and loading C code.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Integration progress:   0%|          | 1/500000 [00:00<111:03:32,  1.25it/s]/home/luke/miniconda3/envs/onion_dynamics_py312/lib/python3.12/site-packages/jitcdde/_jitcdde.py:820: UserWarning: The target time is smaller than the current time. No integration step will happen. The returned state will be extrapolated from the interpolating Hermite polynomial for the last integration step. You may see this because you try to integrate backwards in time, in which case you did something wrong. You may see this just because your sampling step is small, in which case there is no need to worry (though you should think about increasing your sampling time).\n",
+      "  warn(\"The target time is smaller than the current time. No integration step will happen. The returned state will be extrapolated from the interpolating Hermite polynomial for the last integration step. You may see this because you try to integrate backwards in time, in which case you did something wrong. You may see this just because your sampling step is small, in which case there is no need to worry (though you should think about increasing your sampling time).\")\n",
+      "Integration progress: 100%|██████████| 500000/500000 [00:01<00:00, 255515.48it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "populating nodes objects solution vectors...\n"
+     ]
+    }
+   ],
+   "source": [
+    "sol_jit = MSRE.solve(T, max_delay = 30.0, abs_tol = 1e-14, rel_tol = 1e-12, populate_nodes = True)\n",
+    "\n",
+    "# T, sol_jit = MSRE.equilibrium_search(dT = 0.01, \n",
+    "#                                      max_delay = tau_l, \n",
+    "#                                      populate_nodes = False, \n",
+    "#                                      md_step = 0.0001, \n",
+    "#                                      abs_tol_eq = 1.0e-12, \n",
+    "#                                      rel_tol_eq = 1.0e-10,\n",
+    "#                                      show_conv_metrics = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fig, axs = plt.subplots(3,2, figsize = (8,8))\n",
+    "\n",
+    "# axs[0,0].set_xlim([0,20])\n",
+    "# axs[0,0].plot(T, T_hf4.y_out, label = 'core inlet')\n",
+    "# axs[0,0].plot(T, T_cf2.y_out, label = 'core outlet')\n",
+    "# axs[0,0].plot(T, T_cg.y_out, label = 'graphite')\n",
+    "# axs[0,0].set_ylim([647,649])\n",
+    "# axs[0,0].legend()\n",
+    "\n",
+    "# axs[1,0].set_xlim([0,20])\n",
+    "# axs[1,0].plot(T, T_cf2.y_out, label = 'hx fuel inlet')\n",
+    "# axs[1,0].plot(T, T_hf4.y_out, label = 'hx fuel outlet')\n",
+    "# axs[1,0].plot(T, (T_ht1.y_out+T_ht1.y_out)/2, label = 'hx tubes')\n",
+    "# axs[1,0].legend()\n",
+    "\n",
+    "# axs[2,0].set_xlim([0,20])\n",
+    "# axs[2,0].plot(T, T_out_rc.y_out, label = 'hx coolant inlet')\n",
+    "# axs[2,0].plot(T, T_hc4.y_out, label = 'hx coolant outlet')\n",
+    "# axs[2,0].plot(T, (T_ht1.y_out+T_ht1.y_out)/2, label = 'hx tubes')\n",
+    "# axs[2,0].legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x750c0c9332c0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(3,1)\n",
+    "\n",
+    "axs[0].set_ylim([0,20])\n",
+    "# axs[0].set_xlim([0,100])\n",
+    "axs[0].plot(T, P*n.y_out*(1e6), label = 'Power (W)')\n",
+    "\n",
+    "# axs[1].set_ylim([600,700])\n",
+    "# axs[1].set_xlim([0,100])\n",
+    "axs[1].plot(T, T_hf4.y_out, label = 'Core Fuel Inlet')\n",
+    "axs[1].plot(T, T_cf2.y_out, label = 'Core Fuel Outlet')\n",
+    "axs[1].plot(T, T_cg.y_out, label = 'Graphite Temp')\n",
+    "axs[1].plot(T, T_hf2.y_out, label = 'Hx Fuel Temp')\n",
+    "axs[1].plot(T, T_hc2.y_out, label = 'Hx Coolant Temp')\n",
+    "axs[1].legend()\n",
+    "\n",
+    "axs[2].plot(T, rho.y_out, label = 'feedback')\n",
+    "axs[2].legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fig,axs = plt.subplots(2,3,figsize=(18,12))\n",
+    "\n",
+    "# # Set a professional color scheme\n",
+    "# colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown']\n",
+    "\n",
+    "# # Function to update the style of each axis\n",
+    "# def update_axis_style(ax, title, x_label='', y_label='', x_ticks=True):\n",
+    "#     ax.set_xlim([t0,tf])\n",
+    "#     ax.set_title(title)\n",
+    "#     ax.set_xlabel(x_label)\n",
+    "#     ax.set_ylabel(y_label)\n",
+    "#     ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
+    "#     ax.tick_params(axis='x', which='both', bottom=x_ticks, top=False, labelbottom=x_ticks)\n",
+    "#     ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)\n",
+    "\n",
+    "# # Applying the updated style to the subplots\n",
+    "# # Fuel temperatures\n",
+    "# sol_jit = np.array(sol_jit)\n",
+    "# update_axis_style(axs[0, 0], \"Fuel Node Temperatures (C)\", y_label=r\"$^\\circ$ C\")\n",
+    "# axs[0,0].plot(T,[s[20] for s in sol_jit],label=\"core 1\",color=colors[0]) \n",
+    "# axs[0,0].plot(T,[s[21] for s in sol_jit],label=\"core 2\",color=colors[1]) \n",
+    "# axs[0,0].plot(T,[s[2] for s in sol_jit],label=\"hx 1\",color=colors[2])  \n",
+    "# axs[0,0].plot(T,[s[3] for s in sol_jit],label=\"hx 2\",color=colors[3])\n",
+    "# axs[0,0].plot(T,[s[4] for s in sol_jit],label=\"hx 3\",color=colors[4])\n",
+    "# axs[0,0].plot(T,[s[5] for s in sol_jit],label=\"hx 4\",color=colors[5]) \n",
+    "\n",
+    "# # Coolant temperatures\n",
+    "# update_axis_style(axs[0, 1], \"Coolant Node Temperatures (C)\", y_label=r\"$^\\circ$ C\")\n",
+    "# axs[0, 1].plot(T, sol_jit[:, 6], label=f\"hx 1\", color=colors[0])\n",
+    "# axs[0, 1].plot(T, sol_jit[:, 7], label=f\"hx 1\", color=colors[1])\n",
+    "# axs[0, 1].plot(T, sol_jit[:, 8], label=f\"hx 1\", color=colors[2])\n",
+    "# axs[0, 1].plot(T, sol_jit[:, 9], label=f\"hx 1\", color=colors[3])\n",
+    "# axs[0, 1].plot(T, sol_jit[:, 0], label=f\"r 1\", color=colors[4])\n",
+    "\n",
+    "# # Tube node temperatures\n",
+    "# update_axis_style(axs[0, 2], \"Tube Node Temperatures (C)\", y_label=r\"$^\\circ$ C\")\n",
+    "# axs[0,2].plot(T,[s[6] for s in sol_jit],label=\"hx 1\",color=colors[0]) \n",
+    "# axs[0,2].plot(T,[s[7] for s in sol_jit],label=\"hx 2\",color=colors[1]) \n",
+    "\n",
+    "# # Precursor concentrations\n",
+    "# update_axis_style(axs[1, 2], \"Precursor Concentrations\", x_label=\"t (s)\", y_label=r\"concentration (1/cm$^3$)\")\n",
+    "# for i in range(6):\n",
+    "#     axs[1, 2].plot(T, sol_jit[:, i+13], label=f\"C{i+1}\", color=colors[i])\n",
+    "# axs[1, 2].set_yscale(\"log\")\n",
+    "\n",
+    "# # Multiplication factor temp\n",
+    "# update_axis_style(axs[1, 0], r\"$n$\", x_label=\"t (s)\", y_label=r\"$\\frac{n}{n_0}$\")\n",
+    "# axs[1, 0].plot(T, sol_jit[:, 12], label=\"n\", color='tab:blue')\n",
+    "\n",
+    "# # Reactivity\n",
+    "# update_axis_style(axs[1, 1], r\"$\\rho$\", x_label=\"t (s)\")\n",
+    "# axs[1, 1].plot(T, sol_jit[:, 22], label=\"n\", color='tab:orange')\n",
+    "\n",
+    "# # Adding legends\n",
+    "# for ax in axs.flat:\n",
+    "#     ax.legend()\n",
+    "\n",
+    "# plt.tight_layout()\n",
+    "# plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "onion_dynamics_py312",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/dynamic_model/pump_transient_benchmark/parameters_U235_pump_transient.py b/dynamic_model/pump_transient_benchmark/parameters_U235_pump_transient.py
new file mode 100644
index 0000000..0dc743f
--- /dev/null
+++ b/dynamic_model/pump_transient_benchmark/parameters_U235_pump_transient.py
@@ -0,0 +1,217 @@
+import numpy as np
+import math
+pi = math.pi
+
+# domain
+t0 = 0.0
+tf = 10000.00
+T = np.arange(t0,tf,0.01)
+
+# NEUTRONICS DATA
+tau_l = 16.73  # ORNL-TM-0728 %16.44; % (s)
+tau_c = 8.46  # ORNL-TM-0728 %8.460; % (s)
+# P = 8.0  # Thermal Power in MW ORNL-TM-1070, p.2
+P = 1.0e-5 # 10 W
+n_frac0 = 1.0  # initial fractional neutron density n/n0 (n/cm^3/s)
+Lam = 2.400E-04  # mean generation time ORNL-TM-1070 p.15 U235
+# Lam = 4.0E-04;  # mean generation time ORNL-TM-1070 p.15 U233
+lam = np.array([1.240E-02, 3.05E-02, 1.11E-01, 3.01E-01, 1.140E+00, 3.014E+00])
+beta = np.array([0.000223, 0.001457, 0.001307, 0.002628, 0.000766, 0.00023])  # U235
+# beta = np.array([0.00023, 0.00079, 0.00067, 0.00073, 0.00013, 0.00009])  # U233
+beta_t = np.sum(beta)  # total delayed neutron fraction MSRE
+rho_0 = beta_t-sum(np.divide(beta,1+np.divide(1-np.exp(-lam*tau_l),lam*tau_c))) # reactivity change in going from stationary to circulating fuel
+C0 = beta / Lam * (1.0 / (lam - (np.exp(-lam * tau_l) - 1.0) / tau_c))
+
+# Feedback co-efficients
+# a_f = -8.71E-05  # U235 (drho/°C) fuel salt temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -5.904E-05; % ORNL-TM-0728 p. 101 %
+# a_g = -6.66E-05  # U235 (drho/°C) graphite temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -6.624E-05; % ORNL-TM-0728 p.101
+
+# low power coefficients 
+a_f = (-4.1e-5)*5/9
+a_g = (-4.0e-5)*5/9
+
+# CORE HEAT TRANSFER PARAMETERS
+# FUEL PARAMETERS - DONE
+vdot_f = 7.5708E-02  # ORNL-TM-0728 % 7.571e-2; % vol. flow rate (m^3/s) ORNL-TM-1647 p.3, ORNL-TM-0728 p.12
+rho_f = 2.14647E+03  # (partially enriched U-235)ORNL-TM-0728 p.8 2.243E+03; % (Th-U) density of fuel salt (kg/m^3) ORNL-TM-0728 p.8
+
+# W_f = 1.623879934566580e+02  # 1.83085e+02;%vdot_f*rho_f; % 182.78; % calcd from m_dot*cp*delT=P; vdot_f*rho_f; % fuel flow rate (kg/s)
+
+W_f = 1200*(1/264.172)*(1/60)*(rho_f) # gpm -> kg/s
+
+# tau_f_c  = tau_c; % ORNL-TM-0728 % 8.45; % transit time of fuel in core (s) ORNL-TM-1070 p.15, TDAMSRE p.5
+m_f = W_f * tau_c  # fuel mass in core (kg)
+nn_f = 2  # number of fuel nodes in core model
+mn_f = m_f / nn_f  # fuel mass per node (kg)
+# cp_f     = 4.2*9/5; % (MJ/deg-C) total fuel heat capacity TDAMSRE p.5
+scp_f = 1.9665E-3  # specific heat capacity of fuel salt (MJ/kg-C) ORNL-TM-0728 p.8
+
+# Core Upflow - DONE
+v_g = 1.95386  # graphite volume(m^3) ORNL-TM-0728 p. 101
+rho_g = 1.860E3  # graphite density (kg/m^3) ORNL-3812 p.77, ORNL-TM-0728 p.87
+m_g = v_g * rho_g  # graphite mass (kg)
+cp_g = 3.6 * 9 / 5  # TDAMSRE p.5 graphite total heat capacity (MW-s/C) ORNL-TM-1647 p.3
+scp_g = 1.773E-3  # cp_g/m_g; % graphite specific heat capacity (MW-s/kg-C) ORNL-TM-1647 p.3
+mcp_g1 = m_g * scp_g  # (mass of material x heat capacity of material) of graphite per lump (MW-s/°C)
+mcp_f1 = mn_f * scp_f  # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C)
+mcp_f2 = mn_f * scp_f  # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C)
+hA_fg = 0.02 * 9 / 5  # (fuel to graphite heat transfer coeff x heat transfer area) (MW/°C) ORNL-TM-1647 p.3, TDAMSRE p.5
+k_g = 0.07  # fraction of total power generated in the graphite  ORNL-TM-0728 p.9
+k_1 = 0.5  # fraction of heat transferred from graphite which goes to the first fuel lump
+k_2 = 0.5  # fraction of heat transferred from graphite which goes to the second fuel lump
+k_f = 0.93  # fraction of heat generated in fuel - that generated in the external loop ORNL-TM-0728 p.9
+k_f1 = k_f / nn_f  # fraction of total power generated in lump f1
+k_f2 = k_f / nn_f  # fraction of total power generated in lump f2
+
+# New node for power deposited in fuel outside the core
+k_out = 1 - (k_g + k_f)  # fraction of power generated in fuel in external loop ORNL-TM-0728 p.9
+m_out = W_f  # (kg) Mass of node such that resident time is 1 sec (W_f needs to be defined)
+
+# Initial conditions - DONE
+Tf_in = 648.85  # in °C ORNL-TM-1647 p.2
+T0_f2 = 648.85  # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2
+T0_f1 = Tf_in + (T0_f2 - Tf_in) / 2  # 6.405952380952389e+02; in °C
+T0_g1 = T0_f1 + (k_g * P / hA_fg)  # 6.589285714285924e+02; in °C
+# T0_out = k_out * P / m_out / scp_f + T0_f2  # in °C (scp_f needs to be defined)
+
+
+# Heat Exchanger - DONE
+# Geometry
+d_he = 16  # (in) he diameter ORNL-TM-0728 p. 164
+h_he = 72  # (in) active height % 96; %(in) he height ORNL-TM-0728 p. 164
+od_tube = 0.5  # (in) coolant tube OD ORNL-TM-0728 p. 164
+id_tube = od_tube - 2 * 0.042  # (in) coolant tube ID ORNL-TM-0728 p. 164
+n_tube = 159  # number of coolant tubes ORNL-TM-0728 p. 164
+a_tube = 254 * 144  # (in^2) total area of tubes ORNL-TM-0728 p. 164
+l_tube = a_tube / n_tube / (np.pi * od_tube)  # (in) tube length
+v_tube = n_tube * np.pi * (od_tube / 2) ** 2 * l_tube  # (in^3) hx shell volume occupied by tubes
+v_cool = n_tube * np.pi * (id_tube / 2) ** 2 * l_tube  # (in^3) hx volume occupied by coolant
+v_he = (d_he / 2) ** 2 * np.pi * h_he  # (in^3) volume of heat exchanger shell
+v_he_fuel = v_he - v_tube  # (in^3) volume available to fuel in shell
+
+# Unit conversions
+in_m = 1.63871e-5  # 1 cubic inch = 1.63871e-5 cubic meters
+
+# PRIMARY FLOW PARAMETERS - DONE
+W_p = W_f  # fuel flow rate (kg/s)
+
+m_p = v_he_fuel * in_m * rho_f  # fuel mass in PHE (kg)
+nn_p = 4  # number of fuel nodes in PHE
+mn_p = m_p / nn_p  # fuel mass per node (kg)
+cp_p = scp_f  # fuel heat capacity (MJ/(kg-C))
+
+# SECONDARY FLOW PARAMETERS - DONE
+vdot_s = 5.36265E-02  # ORNL-TM-0728 p. 164 % 5.236E-02; % coolant volume flow rate (m^3/s) ORNL-TM-1647 p.3
+rho_s = 1.922e3  # coolant salt density (kg/m^3) ORNL-TM-0728 p.8
+W_s = 1.005793369810108e+02  # vdot_s*rho_s; % calcd from mdot*cp*delT; vdot_s*rho_s; % coolant flow rate (kg/s) ORNL-TM-1647 p.3
+
+m_s = v_cool * in_m * rho_s  # coolant mass in PHE (kg)
+nn_s = 4  # number of coolant nodes in PHE
+mn_s = m_s / nn_s  # coolant mass per node (kg)
+scp_s = 2.39E-3  # cp_s/m_s; % specific heat capacity of coolant (MJ/(kg-C) ORNL-TM-0728 p.8
+
+A_phe = 2.359E+01  # effective area for heat transfer (primary and secondary, m^2) ORNL-TM-0728 p.164
+
+ha_p = 6.480E-01  # heat transfer*area coefficient from primary to tubes (MW/C) ORNL-TM-1647 p.3
+ha_s = 3.060E-01  # heat transfer*area coefficient from tubes to secondary (MW/C) ORNL-TM-1647 p.3
+
+# Primary Side
+mcp_pn = mn_p * cp_p  # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C
+hA_pn = ha_p / nn_s  # 3.030; % (primary to tube heat transfer coeff x heat transfer area) in MW/°C
+
+# Tubes - DONE
+nn_t = 2  # number of nodes of tubes in the model
+rho_tube = 8.7745E+03  # (kg/m^3) density of INOR-8 ORNL-TM-0728 p.20
+m_tn = (v_tube - v_cool) * in_m * rho_tube / nn_t  # mass of tubes (kg)
+scp_t = 5.778E-04  # specific heat capacity of tubes (MJ/(kg-C)) ORNL-TM-0728 p.20
+mcp_tn = m_tn * scp_t  # mass*(heat capacity) of tubes per lump in MW-s/°C
+
+# Secondary Side - DONE
+mcp_sn = mn_s * scp_s  # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C
+hA_sn = ha_s / nn_s  # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C
+
+# Initial conditions - DONE
+# Primary nodes
+Tp_in = T0_f2  # in °C ORNL-TM-1647 p.2
+T0_p4 = Tf_in  # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2
+T0_p1 = Tp_in + (T0_p4 - Tp_in) / 4  # in °C
+T0_p2 = Tp_in + 2 * (T0_p4 - Tp_in) / 4  # in °C
+T0_p3 = Tp_in + 3 * (T0_p4 - Tp_in) / 4  # in °C
+
+# Secondary nodes
+hx_f_temp = 648.85-(3e-4)
+Ts_in = hx_f_temp  # in °C ORNL-TM-1647 p.2
+T0_s4 = hx_f_temp  # in °C ORNL-TM-1647 p.2
+T0_s1 = Ts_in + (T0_s4 - Ts_in) / nn_s  # in °C
+T0_s2 = Ts_in + 2 * (T0_s4 - Ts_in) / nn_s  # in °C
+T0_s3 = Ts_in + 3 * (T0_s4 - Ts_in) / nn_s  # in °C
+# Tube nodes
+T0_t1 = (T0_p1 * hA_pn + T0_s3 * hA_sn) / (hA_pn + hA_sn)  # in °C
+T0_t2 = (T0_p3 * hA_pn + T0_s1 * hA_sn) / (hA_pn + hA_sn)  # in °C
+
+# Radiator Parameters - DONE
+
+# Initial conditions - DONE
+# Primary nodes
+Trp_in = T0_s4  # 5.933E+02; % in °C ORNL-TM-1647 p.2
+T0_rp = Ts_in  # in °C ORNL-TM-1647 p.2
+
+# Secondary nodes - DONE
+Trs_in = 37.78  # (C) air inlet temperature ORNL-TM-1647 p.2
+T0_rs = 148.9  # (C) air exit temperature ORNL-TM-1647 p.2
+
+# Radiator Geometry
+od_rad = 0.01905  # (m) outer diameter of tubes in the radiator ORNL-TM-0728 p.296
+tube_wall_thick = 0.0018288  # (m) thickness of tubes in the radiator ORNL-TM-0728 p.296
+id_rad = od_rad - 2 * tube_wall_thick
+n_rtubes = 120  # number of tubes in the radiator (rows times tubes per row) ORNL-TM-0728 p.296
+l_rtube = 9.144  # (m) length of tubes in the radiator ORNL-TM-0728 p.296
+v_rp = pi * (id_rad / 2) ** 2 * l_rtube * n_rtubes  # volume available to salt in the radiator
+# v_rtube = pi * (od_rad / 2) ** 2 * l_rtube * n_rtubes - v_rp  # volume of metal in radiator tubes *TUBES NOT MODELED
+
+n_tpr = 12  # number of tubes per row in the radiator matrix
+n_row = 10  # number rows in the radiator matrix
+tube_space = 0.0381  # (m) spacing between tubes and rows of matrix
+v_rs = (n_row * od_rad + (n_row - 1) * tube_space) * (n_tpr * od_rad + (n_tpr - 1) * tube_space) * l_rtube  # volume of air inside radiator
+
+# PRIMARY FLOW PARAMETERS - DONE
+W_rp = W_s  # coolant salt flow rate (kg/s)
+m_rp = v_rp * rho_s  # coolant salt mass in rad (kg)
+nn_rp = 1  # number of coolant salt nodes in the radiator
+mn_rp = m_rp / nn_rp  # coolant mass per node (kg)
+cp_rp = scp_s  # coolant specific heat capacity (MJ/(kg-C))
+
+# SECONDARY FLOW PARAMETERS - DONE
+# assume only convection airflow 
+# vdot_rs = 94.389  # ORNL-TM-0728 p. 296; 78.82; % air volume flow rate (m^3/s) ORNL-TM-1647 p.2
+vdot_rs = 1.0
+rho_rs = 1.1237  # air density (kg/m^3) REFPROP (310K and 0.1MPa)
+W_rs = vdot_rs * rho_rs  # air flow rate (kg/s)
+
+
+
+m_rs = v_rs * rho_rs  # coolant air mass in rad (kg)
+nn_rs = 1  # number of coolant nodes in rad
+mn_rs = m_rs / nn_rs  # coolant mass per node (kg)
+scp_rs = 1.0085E-3  # (MJ/kg-C) specific heat capacity of air at (air_out+air_in)/2 REFPROP
+
+A_rad = 6.503E1  # (m^2) surface area of the radiator ORNL-TM-0728 p.14
+h_roverall = P / A_rad / ((T0_rp + Trp_in) / 2 - (T0_rs + Trs_in) / 2)  # cald as: P/A_rad/((T0_rp+Trp_in)/2-(T0_rs+Trs_in)/2)  3.168E-4; % (MW/m^2-C) polimi thesis
+# h_roverall = 3.0
+
+# Primary Side
+mcp_rpn = mn_rp * cp_rp  # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C
+hA_rpn = h_roverall * A_rad / nn_rs  # 3.030; % (primary to secondary heat transfer coeff x heat transfer area) in MW/°C
+
+# Secondary Side - DONE
+mcp_rsn = mn_rs * scp_rs  # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C
+hA_rsn = h_roverall * A_rad / nn_rs  # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C
+
+# Pure time delays between components - DONE
+tau_hx_c = 8.67  # (sec) delay from hx to core TDAMSRE p.6
+tau_c_hx = 3.77  # (sec) subtracted 1 sec for external loop power generation node resident time; delay from core to fuel hx TDAMSRE p.6
+tau_hx_r = 4.71  # (sec) fertile hx to core TDAMSRE p.6
+tau_r_hx = 8.24  # (sec) core to fertile hx TDAMSRE p.6
+
+first_val = (rho_0 - beta_t) * n_frac0 / Lam + lam[0] * C0[0] + lam[1] * C0[1] + lam[2] * C0[2] + lam[3] * C0[3] + lam[4] * C0[4] + lam[5] * C0[5]
+
diff --git a/dynamic_model/pump_transient_benchmark/pump_transients.ipynb b/dynamic_model/pump_transient_benchmark/pump_transients.ipynb
new file mode 100644
index 0000000..25b110d
--- /dev/null
+++ b/dynamic_model/pump_transient_benchmark/pump_transients.ipynb
@@ -0,0 +1,338 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MSRE Pump Transient Benchmark"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook simulates pump transients for the MSRE at zero power (10W). A dynamical system representing the MSRE is built using the [msrDynamics](https://github.com/LukeLabrie/msrDynamics) tool. The system is run at steady-state until $t = 2500$, where the fuel and coolant pumps are spun down at the rate defined by the ORNL data. After allowing the system to settle to a new equilibrium, at $t = 7500$, the pumps are spun up again. The model includes a reactivity response, `rho_control`, which models the response of the MSRE control rod to maintain constant power. It is modeled as a pure integrator of $\\frac{dn}{dt}$ from the point-kinetics equations with a coefficient of -1.0, i.e. it tries to exactly cancel reactivity changes introduced by flow changes, with the caveat that changes are limited to $\\pm$29 pcm/s, which was the maximum rate achievable by the MSRE control rods. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from parameters_U235_pump_transient import *\n",
+    "from jitcdde import t\n",
+    "from msrDynamics import Node, System\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "from scipy.interpolate import interp1d\n",
+    "import sympy as sp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting Style "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plotting style \n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.rcParams[\"font.family\"] = \"monospace\"\n",
+    "\n",
+    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown']\n",
+    "\n",
+    "# function to update the style of each axis\n",
+    "def update_axis_style(ax, title = None, x_label='', y_label='', x_ticks=True,fsl = 12, fsb = 12, fst = 14):\n",
+    "    if title:\n",
+    "        ax.set_title(title,fontsize=fst)\n",
+    "    ax.set_xlabel(x_label,fontsize=fsb)\n",
+    "    ax.set_ylabel(y_label,fontsize=fsl)\n",
+    "    ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
+    "    ax.tick_params(axis='x', which='both', bottom=x_ticks, top=False, labelbottom=x_ticks)\n",
+    "    ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sytem Definition using `msrDynamics`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up system \n",
+    "\n",
+    "MSRE = System()\n",
+    "\n",
+    "# define flow events\n",
+    "df_spindown = pd.read_csv(os.getcwd() + '/data/spindown.csv', names = ['time', 'pct'], header = None)\n",
+    "df_spinup = pd.read_csv(os.getcwd() + '/data/spinup.csv', names = ['time', 'pct'], header = None)\n",
+    "spindown_fit = interp1d(df_spindown['time'], df_spindown['pct'])\n",
+    "spinup_fit = interp1d(df_spinup['time'], df_spinup['pct'])\n",
+    "t_spindown = 2500\n",
+    "t_spinup = 7500\n",
+    "\n",
+    "# defining flow events\n",
+    "def flow_fac(t):\n",
+    "    if (t <= t_spindown):\n",
+    "        return 1.0\n",
+    "    elif (t < (t_spindown + 20.0)):\n",
+    "        return max(spindown_fit(t-t_spindown)/100.0, 0.02)\n",
+    "    elif (t <= t_spinup):\n",
+    "        return 0.02\n",
+    "    elif (t < t_spinup + 8.20):\n",
+    "        return max(spinup_fit(t-t_spinup)/100.0, 0.02)\n",
+    "    else:\n",
+    "        return 1.0\n",
+    "\n",
+    "flow_pct = MSRE.add_input(flow_fac, T, max_anchors = 5000, input_tol = 32)\n",
+    "\n",
+    "# define nodes\n",
+    "\n",
+    "# radiator\n",
+    "T_out_rc = Node(m=mn_rp, scp=mcp_rpn/mn_rp, W=W_rp, y0=T0_rp, name='T_out_rc')\n",
+    "T_out_air = Node(m=mn_rs, scp=mcp_rsn/mn_rs, W=W_rs, y0=T0_rs, name='T_out_air')\n",
+    "\n",
+    "# heat exchanger\n",
+    "T_hf1 = Node(m=mn_p, scp=mcp_pn/mn_p, W=W_p*flow_pct, y0=T0_p1, name='T_hf1')\n",
+    "T_hf2 = Node(m=mn_p, scp=mcp_pn/mn_p, W=W_p*flow_pct, y0=T0_p2, name='T_hf2')\n",
+    "T_hf3 = Node(m=mn_p, scp=mcp_pn/mn_p, W=W_p*flow_pct, y0=T0_p3, name='T_hf3')\n",
+    "T_hf4 = Node(m=mn_p, scp=mcp_pn/mn_p, W=W_p*flow_pct, y0=T0_p4, name='T_hf4')\n",
+    "T_ht1 = Node(m=m_tn, scp=scp_t, y0=T0_t1, name='T_ht1')\n",
+    "T_ht2 = Node(m=m_tn, scp=scp_t, y0=T0_t2, name='T_ht2')\n",
+    "T_hc1 = Node(m=mn_s, scp=mcp_sn/mn_s, W=W_s*flow_pct, y0=T0_s1, name='T_hc1')\n",
+    "T_hc2 = Node(m=mn_s, scp=mcp_sn/mn_s, W=W_s*flow_pct, y0=T0_s2, name='T_hc2')\n",
+    "T_hc3 = Node(m=mn_s, scp=mcp_sn/mn_s, W=W_s*flow_pct, y0=T0_s3, name='T_hc3')\n",
+    "T_hc4 = Node(m=mn_s, scp=mcp_sn/mn_s, W=W_s*flow_pct, y0=T0_s4, name='T_hc4')\n",
+    "\n",
+    "# core \n",
+    "n = Node(y0=n_frac0, name='n')\n",
+    "C1 = Node(y0=C0[0], name='C1')\n",
+    "C2 = Node(y0=C0[1], name='C2')\n",
+    "C3 = Node(y0=C0[2], name='C3')\n",
+    "C4 = Node(y0=C0[3], name='C4')\n",
+    "C5 = Node(y0=C0[4], name='C5')\n",
+    "C6 = Node(y0=C0[5], name='C6')\n",
+    "rho = Node(y0=rho_0, name='rho')\n",
+    "\n",
+    "rho_control = Node(y0=0.0, name='rho_control')\n",
+    "\n",
+    "T_cg = Node(m=mcp_g1/scp_g, scp=scp_g, y0=T0_g1, name='T_cg')\n",
+    "T_cf1 = Node(m=mn_f, scp=scp_f, W=W_f*flow_pct, y0=T0_f1, name='T_cf1')\n",
+    "T_cf2 = Node(m=mn_f, scp=scp_f, W=W_f*flow_pct, y0=T0_f1, name='T_cf2')\n",
+    "\n",
+    "MSRE.add_nodes([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1,\n",
+    "               T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho, rho_control])\n",
+    "\n",
+    "# dynamics\n",
+    "\n",
+    "# radiator\n",
+    "T_out_rc.set_dTdt_advective(source = T_hc4.y(t-tau_hx_r/flow_pct))\n",
+    "T_out_rc.set_dTdt_convective(source = [T_out_air.y()], hA = [hA_rpn*flow_pct])\n",
+    "\n",
+    "T_out_air.set_dTdt_advective(source = Trs_in)\n",
+    "T_out_air.set_dTdt_convective(source = [T_out_rc.y()], hA = [hA_rsn])\n",
+    "\n",
+    "# heat exchanger\n",
+    "T_hf1.set_dTdt_advective(source = T_cf2.y(t-tau_c_hx/flow_pct))\n",
+    "T_hf1.set_dTdt_convective(source = [T_ht1.y()], hA = [hA_pn*flow_pct])\n",
+    "\n",
+    "T_hf2.set_dTdt_advective(source = T_hf1.y())\n",
+    "T_hf2.dTdt_convective = T_hf1.dTdt_convective\n",
+    "\n",
+    "T_hf3.set_dTdt_advective(source = T_hf2.y())\n",
+    "T_hf3.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_pn*flow_pct])\n",
+    "\n",
+    "T_hf4.set_dTdt_advective(source = T_hf3.y())\n",
+    "T_hf4.dTdt_convective = T_hf3.dTdt_convective\n",
+    "\n",
+    "T_ht1.set_dTdt_convective(source = [T_hf1.y(),T_hf1.y(),T_hc3.y(),T_hc3.y()], hA = [hA_pn*flow_pct,hA_pn*flow_pct,hA_sn*flow_pct,hA_sn*flow_pct])\n",
+    "T_ht2.set_dTdt_convective(source = [T_hf3.y(),T_hf3.y(),T_hc1.y(),T_hc1.y()], hA = [hA_pn*flow_pct,hA_pn*flow_pct,hA_sn*flow_pct,hA_sn*flow_pct])\n",
+    "\n",
+    "T_hc1.set_dTdt_advective(source = T_out_rc.y(t-tau_r_hx/flow_pct))\n",
+    "T_hc1.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_sn*flow_pct])\n",
+    "\n",
+    "T_hc2.set_dTdt_advective(source = T_hc1.y())\n",
+    "T_hc2.dTdt_convective = T_hc1.dTdt_convective\n",
+    "\n",
+    "T_hc3.set_dTdt_advective(source = T_hc2.y())\n",
+    "T_hc3.set_dTdt_convective(source = [T_ht1.y()], hA = [hA_sn*flow_pct])\n",
+    "\n",
+    "T_hc4.set_dTdt_advective(source = T_hc3.y())\n",
+    "T_hc4.dTdt_convective = T_hc3.dTdt_convective\n",
+    "\n",
+    "# core\n",
+    "n.set_dndt(r = rho.y() + rho_control.y(), beta_eff = beta_t, Lambda = Lam, lam = lam, C = [C1.y(),C2.y(),C3.y(),C4.y(),C5.y(),C6.y()])\n",
+    "C1.set_dcdt(n.y(), beta = beta[0],Lambda = Lam, lam = lam[0], t_c = tau_c/flow_pct, t_l = tau_l/flow_pct, flow = True)\n",
+    "C2.set_dcdt(n.y(), beta = beta[1],Lambda = Lam, lam = lam[1], t_c = tau_c/flow_pct, t_l = tau_l/flow_pct, flow = True)\n",
+    "C3.set_dcdt(n.y(), beta = beta[2],Lambda = Lam, lam = lam[2], t_c = tau_c/flow_pct, t_l = tau_l/flow_pct, flow = True)\n",
+    "C4.set_dcdt(n.y(), beta = beta[3],Lambda = Lam, lam = lam[3], t_c = tau_c/flow_pct, t_l = tau_l/flow_pct, flow = True)\n",
+    "C5.set_dcdt(n.y(), beta = beta[4],Lambda = Lam, lam = lam[4], t_c = tau_c/flow_pct, t_l = tau_l/flow_pct, flow = True)\n",
+    "C6.set_dcdt(n.y(), beta = beta[5],Lambda = Lam, lam = lam[5], t_c = tau_c/flow_pct, t_l = tau_l/flow_pct, flow = True)\n",
+    "\n",
+    "# control rod response\n",
+    "rho_control.dydt = -sp.Min(29.0e-5, sp.Max(-29.0e-5, n.dydt))\n",
+    "\n",
+    "\n",
+    "T_cg.set_dTdt_convective(source = [T_cf1.y()], hA = [hA_fg*flow_pct])\n",
+    "T_cg.set_dTdt_internal(source = [n.y()], k = [k_g*P])\n",
+    "\n",
+    "T_cf1.set_dTdt_advective(source = T_hf4.y(t-tau_hx_c/flow_pct))\n",
+    "T_cf1.set_dTdt_convective(source = [T_cg.y()], hA = [k_1*hA_fg*flow_pct])\n",
+    "T_cf1.set_dTdt_internal(source = [n.y()], k = [k_f1*P])\n",
+    "\n",
+    "T_cf2.set_dTdt_advective(source = T_cf1.y())\n",
+    "T_cf2.dTdt_convective = T_cf1.dTdt_convective\n",
+    "T_cf2.set_dTdt_internal(source = [n.y()], k = [k_f2*P])\n",
+    "\n",
+    "rho.set_drdt(sources = [T_cf1.dydt, T_cg.dydt], coeffs = [a_f,a_g])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finalizing integrator...\n",
+      "integrating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Integration progress:   0%|          | 0/1000000 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating, compiling, and loading C code.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Integration progress: 100%|██████████| 1000000/1000000 [05:19<00:00, 3128.32it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "populating nodes objects solution vectors...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# solve \n",
+    "sol_jit = MSRE.solve(T, max_delay = 30.0, abs_tol = 1e-12, rel_tol = 1e-10, min_step = 1e-13, populate_nodes = True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x75e806ae3d40>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_ornl_spindown = pd.read_csv(os.getcwd() + '/data/ornl_spindown.csv', header = None, names = ['time', 'pcm'])\n",
+    "df_ornl_spinup = pd.read_csv(os.getcwd() + '/data/ornl_spinup.csv', header = None, names = ['time', 'pcm'])\n",
+    "\n",
+    "fig, axs = plt.subplots(1,2, figsize = (12,5))\n",
+    "\n",
+    "T_compare_spindown = T[(T >= 2500.0) & (T <= 2572.0)] -2500\n",
+    "rho_compare_spindown = 200+(rho_control.y_out[(T >= 2500.0) & (T <= 2572.0)])*1e5\n",
+    "\n",
+    "T_compare_spinup = T[(T >= 7500.0) & (T <= 7547.0)] -7500\n",
+    "rho_compare_spinup = 200+(rho_control.y_out[(T >= 7500.0) & (T <= 7547.0)])*1e5\n",
+    "\n",
+    "update_axis_style(axs[0], title = 'MSRE Pump Coastdown Test')\n",
+    "axs[0].plot(T_compare_spindown, rho_compare_spindown, label = 'simulated, msrDynamics')\n",
+    "axs[0].scatter(df_ornl_spindown['time'], df_ornl_spindown['pcm'], marker=\"2\",color=colors[2],label=\"measured, ORNL\")\n",
+    "axs[0].set_xlim([0,72])\n",
+    "axs[0].legend()\n",
+    "\n",
+    "update_axis_style(axs[1], title = 'MSRE Pump Startup Test')\n",
+    "axs[1].plot(T_compare_spinup, rho_compare_spinup-rho_compare_spinup[0], label = 'simulated, msrDynamics')\n",
+    "axs[1].scatter(df_ornl_spinup['time'], df_ornl_spinup['pcm'], marker=\"2\",color=colors[2],label=\"measured, ORNL\")\n",
+    "axs[1].set_xlim([0,47])\n",
+    "axs[1].legend()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "onion_dynamics_py312",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/dynamic_model/scipyODE_implementation/main.py b/dynamic_model/scipyODE_implementation/main.py
new file mode 100644
index 0000000..1f6af37
--- /dev/null
+++ b/dynamic_model/scipyODE_implementation/main.py
@@ -0,0 +1,253 @@
+from parameters import *
+import numpy as np
+from scipy.integrate import ode
+import matplotlib.pyplot as plt
+import copy 
+
+def dydtMSRE(t,y,delays,rho_ext):
+
+    '''
+    Returns derivative of state vector y; y' or dy/dt, of the MSRE system.
+
+    The y vector contains the following:
+
+        T_in_rc = Inlet temperature (°C) of radiator coolant, will be equal to 
+                  the outlet temperature of the heat exchanger (T_hc4) plus 
+                  relevant time delay
+
+        T_out_rc = Outlet temperature (°C) of radiator coolant
+
+        T_in_air = Inlet temperature (°C) of air in radiator
+
+        T_out_air = Outlet temperature (°C) of air in radiator
+
+        T_in_hf = Inlet temperature (°C) of heat exchanger fuel, will be equal 
+                  to the outlet temperature of the core (T_cf2) plus relevant 
+                  time delay
+
+        T_hf* = Temperature (°C) of heat exchanger fuel node *
+
+        T_ht* = Temperature (°C) of heat exchanger tube node *
+
+        T_hc* = Temperature (°C) of heat exchanger coolant node *
+
+        T_in_cf = Inlet temperature (°C) of core fuel, will be equal to the 
+                  outlet temperature of the heat exchanger (T_hf4) plus 
+                  relevant time dela
+
+        S = neutro source perturbation term 
+
+        rho_fb = feedback reactivity (from fuel and graphite temperatures)
+
+        rho_ext = external reactivity (reactivity insertion)
+
+        rho_tot = total reactivity = rho_0 + rho_fb + rho_ext (rho_0 = 
+                  steady-state reactivity, constant)
+
+        n = neutron density n(t)
+
+        C* = precursor concentration of group *
+
+        T_cg = Temperature (°C) of core graphite node 
+
+        T_cf* = Temperature (°C) of core fuel node *  
+
+        *_delay = Parameter at time t = t - delay
+
+    Other parameters are defined in parameters.py
+    '''
+
+    # unpack state variables 
+    T_out_rc, T_out_air, T_hf1, T_hf2, T_hf3, T_hf4, T_ht1, T_ht2, T_hc1,     \
+    T_hc2, T_hc3, T_hc4, n, C1, C2, C3, C4, C5, C6, T_cg, T_cf1, T_cf2 = y
+    
+    # delay terms
+    T_out_rc_delay, T_hc4_delay, T_cf2_delay, T_hf4_delay, C1_delay,          \
+    C2_delay, C3_delay, C4_delay, C5_delay, C6_delay = delays
+
+    # reactivity
+    rho = (a_f/2)*((-T0_f1+T_cf1)+(-T0_f2+T_cf2)) + a_g*(-T0_g1+T_cg) + rho_ext
+
+    # derivatives 
+    dydt = [ 
+    (W_rp/mn_rp)*(T_hc4_delay-T_out_rc) + (hA_rpn/mcp_rpn)*(T_out_air-T_out_rc),                  # T_out_rc                                                                                   # T_out_rc
+    -((W_rs/mn_rs)+(hA_rsn/mcp_rsn))*T_out_air + (hA_rsn/mcp_rsn)*T_out_rc + (W_rs/mn_rs)*Trs_in, # T_out_air
+    -((W_p/mn_p)+(hA_pn/mcp_pn))*T_hf1 + (hA_pn/mcp_pn)*T_ht1 + (W_p/mn_p)*T_cf2_delay,           # T_hf1
+    (W_p/mn_p)*(T_hf1-T_hf2) + (hA_pn/mcp_pn)*(T_ht1-T_hf1),                                      # T_hf2
+    -((W_p/mn_p)+(hA_pn/mcp_pn))*T_hf3 + (hA_pn/mcp_pn)*T_ht2 + (W_p/mn_p)*T_hf2,                 # T_hf3
+    (W_p/mn_p)*(T_hf3-T_hf4) + (hA_pn/mcp_pn)*(T_ht2-T_hf3),                                      # T_hf4
+    (2*hA_pn/mcp_tn)*(T_hf1-T_ht1) + (2*hA_sn/mcp_tn)*(T_hc3-T_ht1),                              # T_ht1
+    (2*hA_pn/mcp_tn)*(T_hf3-T_ht2) + (2*hA_sn/mcp_tn)*(T_hc1-T_ht2),                              # T_ht2
+    -((W_s/mn_s)+(hA_sn/mcp_sn))*T_hc1 + (hA_sn/mcp_sn)*T_ht2 + (W_s/mn_s)*T_out_rc_delay,        # T_hc1
+    (W_s/mn_s)*(T_hc1-T_hc2) + (hA_sn/mcp_sn)*(T_ht2-T_hc1),                                      # T_hc2
+    -((W_s/mn_s)+(hA_sn/mcp_sn))*T_hc3 + (hA_sn/mcp_sn)*T_ht1 + (W_s/mn_s)*T_hc2,                 # T_hc3
+    (W_s/mn_s)*(T_hc3-T_hc4) + (hA_sn/mcp_sn)*(T_ht1-T_hc3),                                      # T_hc4
+    (rho-beta_t)*n/Lam+lam[0]*C1+lam[1]*C2+lam[2]*C3+lam[3]*C4+lam[4]*C5+lam[5]*C6,               # n (no source insertion)
+    n*beta[0]/Lam-lam[0]*C1-C1/tau_c+C1_delay*np.exp(-lam[0]*tau_l)/tau_c,                        # C1
+    n*beta[1]/Lam-lam[1]*C2-C2/tau_c+C2_delay*np.exp(-lam[1]*tau_l)/tau_c,                        # C2
+    n*beta[2]/Lam-lam[2]*C3-C3/tau_c+C3_delay*np.exp(-lam[2]*tau_l)/tau_c,                        # C3
+    n*beta[3]/Lam-lam[3]*C4-C4/tau_c+C4_delay*np.exp(-lam[3]*tau_l)/tau_c,                        # C4
+    n*beta[4]/Lam-lam[4]*C5-C5/tau_c+C5_delay*np.exp(-lam[4]*tau_l)/tau_c,                        # C5
+    n*beta[5]/Lam-lam[5]*C6-C6/tau_c+C6_delay*np.exp(-lam[5]*tau_l)/tau_c,                        # C6
+    (hA_fg/mcp_g1)*(T_cf1 - T_cg) + k_g*P*n/mcp_g1,                                               # T_cg
+    W_f/mn_f*(T_hf4_delay-T_cf1) + (k_f1*P*n/mcp_f1) + (hA_fg*k_1*(T_cg - T_cf1)/mcp_f1),         # T_cf1   
+    W_f/mn_f*(T_cf1 - T_cf2) + (k_f2*P*n/mcp_f2) + (hA_fg*k_2*(T_cg - T_cf1)/mcp_f2),             # T_cf2
+       
+    ]
+    return dydt
+
+def get_tIdx(t,tao,timeVec):
+    '''
+    Returns index of time t = t-tau
+    '''
+    td = t-tao
+    diff_min = 999999.9999999
+    idx = 0
+    for t in enumerate(timeVec):
+        diff = abs(td-t[1])
+        if (diff<diff_min):
+            diff_min = abs(td-t[1])
+            idx = t[0]
+    return idx, timeVec[idx]-td
+
+def main():
+    '''
+    Sets initial conditions and calls the solver 
+    '''
+
+    # initial conditions
+    y0 = [T0_rp, T0_rs, T0_p1,T0_p2, T0_p3, T0_p4, T0_t1, T0_t2, T0_s1, T0_s2, 
+          T0_s3, T0_s4, n_frac0, C0[0], C0[1], C0[2], C0[3], C0[4], C0[5], 
+          T0_g1, T0_f1, T0_f2]
+
+    # initial delay terms 
+    d0 = [T0_rp, T0_rs, T0_f2, T0_p4, C0[0], C0[1], C0[2], C0[3], C0[4], C0[5]] 
+
+    # solver
+    backend = 'dopri5'
+    r = ode(dydtMSRE).set_integrator(backend,max_step=0.10)
+
+    sol_interim = []
+    def solout(t, y):
+        sol_interim.append([t, *y])
+    r.set_solout(solout)
+
+    # timing parameters
+    t0 = 0.0
+    t_start = t0
+    t_stop = 500.00
+
+    # solution 
+    sol = []
+    
+    # step-reactivity insertion
+    t_insert = 2500.00
+    insert = 1.0e-4
+    if (t_start>=t_insert):
+        rho_ext = insert
+    else:
+        rho_ext = 0.0
+
+    # delay parameters
+    d_terms = []
+    derivs = [dydtMSRE(t0,y0,d0,rho_ext)]
+
+    # takes one step at a time and then accounts for delay terms
+    i = 0
+    while (t_start < t_stop):
+        # take one step
+        if (i == 0):
+            t_start = t0
+            r.set_initial_value(y0,t0).set_f_params(d0,0.0)
+            r.integrate(1.0)
+            sol.append(sol_interim[0])
+            sol.append(sol_interim[1])
+        else:
+            t_start = sol[-1][0]
+            if (t_start>=t_insert):
+                rho_ext = insert
+            else:
+                rho_ext = 0.0
+            r.set_initial_value(y_next,t_start).set_f_params(d_new,rho_ext)
+            r.integrate(t_start+1.0)
+            sol.append(sol_interim[1])
+            derivs.append(dydtMSRE(t_start,y_next,d_new,rho_ext))
+
+        # account for delays, linear interpolation for time differences 
+        # core fuel inlet
+        d_new = [0]*10
+        idx_cf_in = 0
+        dt_cf = 0.0
+        if (t_start > tau_hx_c):
+            idx_cf_in, dt_cf = get_tIdx(t_start,tau_hx_c,[s[0] for s in sol])
+        d_new[3] = sol[idx_cf_in][6] + dt_cf*derivs[idx_cf_in][5]
+
+        # heat exchanger fuel inlet
+        idx_hf_in = 0
+        dt_hf = 0.0
+        if (t_start > tau_c_hx):
+            idx_hf_in, dt_hf = get_tIdx(t_start,tau_c_hx,[s[0] for s in sol])
+        d_new[2] = sol[idx_hf_in][22] + dt_hf*derivs[idx_hf_in][21]
+
+        # heat exchanger coolant inlet
+        idx_hc_in = 0
+        dt_hc = 0.0
+        if (t_start > tau_r_hx):
+            idx_hc_in, dt_hc = get_tIdx(t_start,tau_r_hx,[s[0] for s in sol])
+        d_new[0] = sol[idx_hc_in][1] + dt_hc*derivs[idx_hc_in][0]
+
+        # radiator coolant inlet
+        idx_rc_in = 0
+        dt_rc = 0.0
+        if (t_start > tau_hx_r):
+            idx_rc_in, dt_rc = get_tIdx(t_start,tau_hx_r,[s[0] for s in sol])
+        d_new[1] = sol[idx_rc_in][12] + dt_rc*derivs[idx_rc_in][11]
+
+        # precursors
+        idx_c = 0
+        dt_c = 0.0
+        if (t_start > tau_l):
+            idx_c, dt_c = get_tIdx(t_start,tau_l,[s[0] for s in sol])
+        d_new[4] = sol[idx_c][14] + dt_c*derivs[idx_c][13]
+        d_new[5] = sol[idx_c][15] + dt_c*derivs[idx_c][14]
+        d_new[6] = sol[idx_c][16] + dt_c*derivs[idx_c][15]
+        d_new[7] = sol[idx_c][17] + dt_c*derivs[idx_c][16]
+        d_new[8] = sol[idx_c][18] + dt_c*derivs[idx_c][17]
+        d_new[9] = sol[idx_c][19] + dt_c*derivs[idx_c][18]
+
+        d_terms.append(d_new)
+
+        # initial condiiton for next step
+        y_next = sol[-1][1:]
+
+        # empty interim solution
+        sol_interim = []
+
+        # display progress
+        #print(f"{t_start}")
+
+        i += 1
+
+    # plot single parameter
+    #of_interest = 13
+    #ti = [s[0] for s in sol]
+    #oi = [s[of_interest] for s in sol]
+    #print(type(ti[0]))
+    #plt.plot(ti,oi)
+    #plt.show()
+
+    # check delay behavior
+    # for i in range(len(sol)-1):
+    #    print(f"t: {sol[i][0]}, hf4: {sol[i][6]}, c1_delay: {d_terms[i][3]}")
+
+    # write output data
+    output_filename = f"sim_out_{t_stop}_{P}"
+    results = open(output_filename,'w+')
+    for k in range(len(sol)):
+        for col in range(len(sol[0])):
+            results.write(f"{sol[k][col]} ")
+        results.write("\n")
+    
+    return None 
+
+main()
\ No newline at end of file
diff --git a/dynamic_model/scipyODE_implementation/parameters.py b/dynamic_model/scipyODE_implementation/parameters.py
new file mode 100644
index 0000000..d92d5c5
--- /dev/null
+++ b/dynamic_model/scipyODE_implementation/parameters.py
@@ -0,0 +1,234 @@
+import numpy as np
+import pandas as pd
+import math
+pi = math.pi
+
+# Perturbations
+# SOURCE INSERTION
+# No source insertion
+sourcedata = np.array([0, 0, 0])
+sourcetime = np.array([0, 50, 100])
+# % 1 (n/no)/s for 10 seconds
+# sourcedata = np.array([0, 10, 0])
+# sourcetime = np.array([0, 10, 20])
+source = pd.Series(sourcedata, index=sourcetime)
+
+# REACTIVITY INSERTION
+# No reactivity insertion
+simtime = 10
+reactdata = np.array([0, 5E-4])
+reacttime = np.array([0, 2500])
+# Periodic 60 PCM for 50 seconds
+# simtime = 500
+# periodic = np.array([[0, 0], [50, 6e-4], [100, 0], [150, -6e-4], [200, 0], [250, 6e-4], [300, 0], [350, -6e-4], [400, 0]])
+# reactdata = periodic[:, 1]
+# reacttime = periodic[:, 0]
+# Step up 60 pcm
+# simtime = 1000
+# reactdata = np.array([0, 6e-3])
+# reacttime = np.array([0, 300])
+# # Step down -60 pcm for 10 sec
+# simtime = 100
+# reactdata = np.array([0, -6e-4])
+# reacttime = np.array([0, 50])
+# # Pulse 600 pcm for 0.1 sec
+# simtime = 30
+# reactdata = np.array([0, 6e-3, 0])
+# reacttime = np.array([0, 10, 10.1])
+
+react = pd.Series(reactdata, index=reacttime)
+
+ts_max = 1e-1  # maximum timestep (s)
+
+# NEUTRONICS DATA
+tau_l = 16.73  # ORNL-TM-0728 %16.44; % (s)
+tau_c = 8.46  # ORNL-TM-0728 %8.460; % (s)
+P = 8  # Thermal Power in MW ORNL-TM-1070, p.2
+n_frac0 = 1.0  # initial fractional neutron density n/n0 (n/cm^3/s)
+Lam = 2.400E-04  # mean generation time ORNL-TM-1070 p.15 U235
+# Lam = 4.0E-04;  # mean generation time ORNL-TM-1070 p.15 U233
+lam = np.array([1.240E-02, 3.05E-02, 1.11E-01, 3.01E-01, 1.140E+00, 3.014E+00])
+beta = np.array([0.000223, 0.001457, 0.001307, 0.002628, 0.000766, 0.00023])  # U235
+# beta = np.array([0.00023, 0.00079, 0.00067, 0.00073, 0.00013, 0.00009])  # U233
+beta_t = np.sum(beta)  # total delayed neutron fraction MSRE
+rho_0 = beta_t-sum(np.divide(beta,1+np.divide(1-np.exp(-lam*tau_l),lam*tau_c))) # reactivity change in going from stationary to circulating fuel
+C0 = beta / Lam * (1.0 / (lam - (np.exp(-lam * tau_l) - 1.0) / tau_c))
+
+# Feedback co-efficients
+a_f = -8.71E-05  # U235 (drho/°C) fuel salt temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -5.904E-05; % ORNL-TM-0728 p. 101 %
+a_g = -6.66E-05  # U235 (drho/°C) graphite temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -6.624E-05; % ORNL-TM-0728 p.101
+
+# CORE HEAT TRANSFER PARAMETERS
+# FUEL PARAMETERS - DONE
+vdot_f = 7.5708E-02  # ORNL-TM-0728 % 7.571e-2; % vol. flow rate (m^3/s) ORNL-TM-1647 p.3, ORNL-TM-0728 p.12
+rho_f = 2.14647E+03  # (partially enriched U-235)ORNL-TM-0728 p.8 2.243E+03; % (Th-U) density of fuel salt (kg/m^3) ORNL-TM-0728 p.8
+W_f = 1.623879934566580e+02  # 1.83085e+02;%vdot_f*rho_f; % 182.78; % calcd from m_dot*cp*delT=P; vdot_f*rho_f; % fuel flow rate (kg/s)
+# tau_f_c  = tau_c; % ORNL-TM-0728 % 8.45; % transit time of fuel in core (s) ORNL-TM-1070 p.15, TDAMSRE p.5
+m_f = W_f * tau_c  # fuel mass in core (kg)
+nn_f = 2  # number of fuel nodes in core model
+mn_f = m_f / nn_f  # fuel mass per node (kg)
+# cp_f     = 4.2*9/5; % (MJ/deg-C) total fuel heat capacity TDAMSRE p.5
+scp_f = 1.9665E-3  # specific heat capacity of fuel salt (MJ/kg-C) ORNL-TM-0728 p.8
+
+# Core Upflow - DONE
+v_g = 1.95386  # graphite volume(m^3) ORNL-TM-0728 p. 101
+rho_g = 1.860E3  # graphite density (kg/m^3) ORNL-3812 p.77, ORNL-TM-0728 p.87
+m_g = v_g * rho_g  # graphite mass (kg)
+cp_g = 3.6 * 9 / 5  # TDAMSRE p.5 graphite total heat capacity (MW-s/C) ORNL-TM-1647 p.3
+scp_g = 1.773E-3  # cp_g/m_g; % graphite specific heat capacity (MW-s/kg-C) ORNL-TM-1647 p.3
+mcp_g1 = m_g * scp_g  # (mass of material x heat capacity of material) of graphite per lump (MW-s/°C)
+mcp_f1 = mn_f * scp_f  # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C)
+mcp_f2 = mn_f * scp_f  # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C)
+hA_fg = 0.02 * 9 / 5  # (fuel to graphite heat transfer coeff x heat transfer area) (MW/°C) ORNL-TM-1647 p.3, TDAMSRE p.5
+k_g = 0.07  # fraction of total power generated in the graphite  ORNL-TM-0728 p.9
+k_1 = 0.5  # fraction of heat transferred from graphite which goes to the first fuel lump
+k_2 = 0.5  # fraction of heat transferred from graphite which goes to the second fuel lump
+k_f = 0.93  # fraction of heat generated in fuel - that generated in the external loop ORNL-TM-0728 p.9
+k_f1 = k_f / nn_f  # fraction of total power generated in lump f1
+k_f2 = k_f / nn_f  # fraction of total power generated in lump f2
+
+# New node for power deposited in fuel outside the core
+k_out = 1 - (k_g + k_f)  # fraction of power generated in fuel in external loop ORNL-TM-0728 p.9
+m_out = W_f  # (kg) Mass of node such that resident time is 1 sec (W_f needs to be defined)
+
+# Initial conditions - DONE
+Tf_in = 6.3222E+02  # in °C ORNL-TM-1647 p.2
+T0_f2 = 6.5727E+02  # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2
+T0_f1 = Tf_in + (T0_f2 - Tf_in) / 2  # 6.405952380952389e+02; in °C
+T0_g1 = T0_f1 + (k_g * P / hA_fg)  # 6.589285714285924e+02; in °C
+# T0_out = k_out * P / m_out / scp_f + T0_f2  # in °C (scp_f needs to be defined)
+
+
+# Heat Exchanger - DONE
+# Geometry
+d_he = 16  # (in) he diameter ORNL-TM-0728 p. 164
+h_he = 72  # (in) active height % 96; %(in) he height ORNL-TM-0728 p. 164
+od_tube = 0.5  # (in) coolant tube OD ORNL-TM-0728 p. 164
+id_tube = od_tube - 2 * 0.042  # (in) coolant tube ID ORNL-TM-0728 p. 164
+n_tube = 159  # number of coolant tubes ORNL-TM-0728 p. 164
+a_tube = 254 * 144  # (in^2) total area of tubes ORNL-TM-0728 p. 164
+l_tube = a_tube / n_tube / (np.pi * od_tube)  # (in) tube length
+v_tube = n_tube * np.pi * (od_tube / 2) ** 2 * l_tube  # (in^3) hx shell volume occupied by tubes
+v_cool = n_tube * np.pi * (id_tube / 2) ** 2 * l_tube  # (in^3) hx volume occupied by coolant
+v_he = (d_he / 2) ** 2 * np.pi * h_he  # (in^3) volume of heat exchanger shell
+v_he_fuel = v_he - v_tube  # (in^3) volume available to fuel in shell
+
+# Unit conversions
+in_m = 1.63871e-5  # 1 cubic inch = 1.63871e-5 cubic meters
+
+# PRIMARY FLOW PARAMETERS - DONE
+W_p = W_f  # fuel flow rate (kg/s)
+
+m_p = v_he_fuel * in_m * rho_f  # fuel mass in PHE (kg)
+nn_p = 4  # number of fuel nodes in PHE
+mn_p = m_p / nn_p  # fuel mass per node (kg)
+cp_p = scp_f  # fuel heat capacity (MJ/(kg-C))
+
+# SECONDARY FLOW PARAMETERS - DONE
+vdot_s = 5.36265E-02  # ORNL-TM-0728 p. 164 % 5.236E-02; % coolant volume flow rate (m^3/s) ORNL-TM-1647 p.3
+rho_s = 1.922e3  # coolant salt density (kg/m^3) ORNL-TM-0728 p.8
+W_s = 1.005793369810108e+02  # vdot_s*rho_s; % calcd from mdot*cp*delT; vdot_s*rho_s; % coolant flow rate (kg/s) ORNL-TM-1647 p.3
+
+m_s = v_cool * in_m * rho_s  # coolant mass in PHE (kg)
+nn_s = 4  # number of coolant nodes in PHE
+mn_s = m_s / nn_s  # coolant mass per node (kg)
+scp_s = 2.39E-3  # cp_s/m_s; % specific heat capacity of coolant (MJ/(kg-C) ORNL-TM-0728 p.8
+
+A_phe = 2.359E+01  # effective area for heat transfer (primary and secondary, m^2) ORNL-TM-0728 p.164
+
+ha_p = 6.480E-01  # heat transfer*area coefficient from primary to tubes (MW/C) ORNL-TM-1647 p.3
+ha_s = 3.060E-01  # heat transfer*area coefficient from tubes to secondary (MW/C) ORNL-TM-1647 p.3
+
+# Primary Side
+mcp_pn = mn_p * cp_p  # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C
+hA_pn = ha_p / nn_s  # 3.030; % (primary to tube heat transfer coeff x heat transfer area) in MW/°C
+
+# Tubes - DONE
+nn_t = 2  # number of nodes of tubes in the model
+rho_tube = 8.7745E+03  # (kg/m^3) density of INOR-8 ORNL-TM-0728 p.20
+m_tn = (v_tube - v_cool) * in_m * rho_tube / nn_t  # mass of tubes (kg)
+scp_t = 5.778E-04  # specific heat capacity of tubes (MJ/(kg-C)) ORNL-TM-0728 p.20
+mcp_tn = m_tn * scp_t  # mass*(heat capacity) of tubes per lump in MW-s/°C
+
+# Secondary Side - DONE
+mcp_sn = mn_s * scp_s  # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C
+hA_sn = ha_s / nn_s  # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C
+
+# Initial conditions - DONE
+# Primary nodes
+Tp_in = T0_f2  # in °C ORNL-TM-1647 p.2
+T0_p4 = Tf_in  # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2
+T0_p1 = Tp_in + (T0_p4 - Tp_in) / 4  # in °C
+T0_p2 = Tp_in + 2 * (T0_p4 - Tp_in) / 4  # in °C
+T0_p3 = Tp_in + 3 * (T0_p4 - Tp_in) / 4  # in °C
+
+# Secondary nodes
+Ts_in = 5.4611E+02  # in °C ORNL-TM-1647 p.2
+T0_s4 = 5.7939E+02  # in °C ORNL-TM-1647 p.2
+T0_s1 = Ts_in + (T0_s4 - Ts_in) / nn_s  # in °C
+T0_s2 = Ts_in + 2 * (T0_s4 - Ts_in) / nn_s  # in °C
+T0_s3 = Ts_in + 3 * (T0_s4 - Ts_in) / nn_s  # in °C
+# Tube nodes
+T0_t1 = (T0_p1 * hA_pn + T0_s3 * hA_sn) / (hA_pn + hA_sn)  # in °C
+T0_t2 = (T0_p3 * hA_pn + T0_s1 * hA_sn) / (hA_pn + hA_sn)  # in °C
+
+# Radiator Parameters - DONE
+
+# Initial conditions - DONE
+# Primary nodes
+Trp_in = T0_s4  # 5.933E+02; % in °C ORNL-TM-1647 p.2
+T0_rp = Ts_in  # in °C ORNL-TM-1647 p.2
+
+# Secondary nodes - DONE
+Trs_in = 37.78  # (C) air inlet temperature ORNL-TM-1647 p.2
+T0_rs = 148.9  # (C) air exit temperature ORNL-TM-1647 p.2
+
+# Radiator Geometry
+od_rad = 0.01905  # (m) outer diameter of tubes in the radiator ORNL-TM-0728 p.296
+tube_wall_thick = 0.0018288  # (m) thickness of tubes in the radiator ORNL-TM-0728 p.296
+id_rad = od_rad - 2 * tube_wall_thick
+n_rtubes = 120  # number of tubes in the radiator (rows times tubes per row) ORNL-TM-0728 p.296
+l_rtube = 9.144  # (m) length of tubes in the radiator ORNL-TM-0728 p.296
+v_rp = pi * (id_rad / 2) ** 2 * l_rtube * n_rtubes  # volume available to salt in the radiator
+# v_rtube = pi * (od_rad / 2) ** 2 * l_rtube * n_rtubes - v_rp  # volume of metal in radiator tubes *TUBES NOT MODELED
+
+n_tpr = 12  # number of tubes per row in the radiator matrix
+n_row = 10  # number rows in the radiator matrix
+tube_space = 0.0381  # (m) spacing between tubes and rows of matrix
+v_rs = (n_row * od_rad + (n_row - 1) * tube_space) * (n_tpr * od_rad + (n_tpr - 1) * tube_space) * l_rtube  # volume of air inside radiator
+
+# PRIMARY FLOW PARAMETERS - DONE
+W_rp = W_s  # coolant salt flow rate (kg/s)
+m_rp = v_rp * rho_s  # coolant salt mass in rad (kg)
+nn_rp = 1  # number of coolant salt nodes in the radiator
+mn_rp = m_rp / nn_rp  # coolant mass per node (kg)
+cp_rp = scp_s  # coolant specific heat capacity (MJ/(kg-C))
+
+# SECONDARY FLOW PARAMETERS - DONE
+vdot_rs = 94.389  # ORNL-TM-0728 p. 296; 78.82; % air volume flow rate (m^3/s) ORNL-TM-1647 p.2
+rho_rs = 1.1237  # air density (kg/m^3) REFPROP (310K and 0.1MPa)
+W_rs = vdot_rs * rho_rs  # air flow rate (kg/s)
+
+m_rs = v_rs * rho_rs  # coolant air mass in rad (kg)
+nn_rs = 1  # number of coolant nodes in rad
+mn_rs = m_rs / nn_rs  # coolant mass per node (kg)
+scp_rs = 1.0085E-3  # (MJ/kg-C) specific heat capacity of air at (air_out+air_in)/2 REFPROP
+
+A_rad = 6.503E1  # (m^2) surface area of the radiator ORNL-TM-0728 p.14
+h_roverall = P / A_rad / ((T0_rp + Trp_in) / 2 - (T0_rs + Trs_in) / 2)  # cald as: P/A_rad/((T0_rp+Trp_in)/2-(T0_rs+Trs_in)/2)  3.168E-4; % (MW/m^2-C) polimi thesis
+
+# Primary Side
+mcp_rpn = mn_rp * cp_rp  # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C
+hA_rpn = h_roverall * A_rad / nn_rs  # 3.030; % (primary to secondary heat transfer coeff x heat transfer area) in MW/°C
+
+# Secondary Side - DONE
+mcp_rsn = mn_rs * scp_rs  # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C
+hA_rsn = h_roverall * A_rad / nn_rs  # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C
+
+# Pure time delays between components - DONE
+tau_hx_c = 8.67  # (sec) delay from hx to core TDAMSRE p.6
+tau_c_hx = 3.77  # (sec) subtracted 1 sec for external loop power generation node resident time; delay from core to fuel hx TDAMSRE p.6
+tau_hx_r = 4.71  # (sec) fertile hx to core TDAMSRE p.6
+tau_r_hx = 8.24  # (sec) core to fertile hx TDAMSRE p.6
+
+first_val = (rho_0 - beta_t) * n_frac0 / Lam + lam[0] * C0[0] + lam[1] * C0[1] + lam[2] * C0[2] + lam[3] * C0[3] + lam[4] * C0[4] + lam[5] * C0[5]
\ No newline at end of file
diff --git a/dynamic_model/scipyODE_implementation/plot_results.py b/dynamic_model/scipyODE_implementation/plot_results.py
new file mode 100644
index 0000000..b2133dc
--- /dev/null
+++ b/dynamic_model/scipyODE_implementation/plot_results.py
@@ -0,0 +1,94 @@
+import matplotlib.pyplot as plt
+from parameters import *
+
+# 1: radiatior coolant inlet temp
+# 2: radiator coolant outlet temp
+# 3: radiator air outlet temp
+# 4: heat exchanger fuel inlet temp
+# 5: heat exchanger fuel node 1 temp
+# 6: heat exchanger fuel node 2 temp
+# 7: heat exchanger fuel node 3 temp
+# 8: heat exchanger fuel node 4 temp
+# 9: heat exchanger tube node 1 temp
+# 10: heat exchanger tube node 2 temp
+# 11: heat exchanger coolant inlet temp
+# 12: heat exchanger coolant node 1
+# 13: heat exchanger coolant node 2
+# 14: heat exchanger coolant node 3
+# 15: heat exchanger coolant node 4
+# 16: core fuel inlet temp
+# 17: k
+# 18: C1
+# 19: C2
+# 20: C3
+# 21: C4
+# 22: C5
+# 23: C6
+# 24: core graphite temp
+# 25: core fuel node 1 temp
+# 26: core fuel node 2 temp
+
+filename1 = "sim_out_1000.0_1.txt"
+filename5 = "sim_out_1000.0_5.txt"
+filename8 = "sim_out_1000.0_8.txt"
+
+sol1 = []
+k_file = open(filename1, 'r')
+for k in k_file.readlines():
+    sol1.append(k.split())
+k_file.close()
+
+sol5 = []
+k_file = open(filename5, 'r')
+for k in k_file.readlines():
+    sol5.append(k.split())
+k_file.close()
+
+sol8 = []
+k_file = open(filename8, 'r')
+for k in k_file.readlines():
+    sol8.append(k.split())
+k_file.close()
+
+sol1 = [[float(j) for j in s] for s in sol1]
+sol5 = [[float(j) for j in s] for s in sol5]
+sol8 = [[float(j) for j in s] for s in sol8]
+
+k = 10000
+test_pow1 = [(1*s[13]-1) for s in sol1]
+test_pow5 = [(5*s[13]-5) for s in sol5]
+test_pow8 = [(8*s[13]-8) for s in sol8]
+tidx1 = [t[0] for t in enumerate(sol1) if (t[1][0] >= 500.00 and t[1][0] <= 800.00)]
+tidx5 = [t[0] for t in enumerate(sol5) if (t[1][0] >= 500.00 and t[1][0] <= 800.00)]
+tidx8 = [t[0] for t in enumerate(sol8) if (t[1][0] >= 500.00 and t[1][0] <= 800.00)]
+t1 = [s[0] for s in sol1[tidx1[0]:tidx1[-1]]]
+t5 = [s[0] for s in sol5[tidx5[0]:tidx5[-1]]]
+t8 = [s[0] for s in sol8[tidx8[0]:tidx8[-1]]]
+
+# Create a figure and a 3x1 grid of subplots
+fig, axs = plt.subplots(3, 1, figsize=(6, 12))
+
+# Plot data on the first subplot
+axs[0].plot(t1, test_pow1[tidx1[0]:tidx1[-1]])
+axs[0].set_title('1 MW')
+#axs[0].set_xlabel('x')
+axs[0].set_ylabel('dP')
+axs[0].set_xticklabels([])
+axs[0].set_xticks([])
+
+# Plot data on the second subplot
+axs[1].plot(t5, test_pow5[tidx5[0]:tidx5[-1]])
+axs[1].set_title('5 MW')
+#axs[1].set_xlabel('x')
+axs[1].set_ylabel('dP')
+axs[1].set_xticklabels([])
+axs[1].set_xticks([])
+
+# Plot data on the third subplot
+axs[2].plot(t8, test_pow8[tidx8[0]:tidx8[-1]])
+axs[2].set_title('8 MW')
+axs[2].set_xlabel('x')
+axs[2].set_ylabel('dP')
+
+plt.tight_layout()
+plt.show()
\ No newline at end of file
diff --git a/h5m/.gitattributes b/h5m/.gitattributes
new file mode 100644
index 0000000..79f8bd1
--- /dev/null
+++ b/h5m/.gitattributes
@@ -0,0 +1 @@
+*.h5m filter=lfs diff=lfs merge=lfs -text
diff --git a/h5m/msre_control_rod.h5m b/h5m/msre_control_rod.h5m
new file mode 100644
index 0000000..3e3453d
--- /dev/null
+++ b/h5m/msre_control_rod.h5m
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2548e13bd352401752934a9cbe4afefd15248cfa47c4cca2eac69ae27193aade
+size 277319556
diff --git a/h5m/msre_full.h5m b/h5m/msre_full.h5m
new file mode 100644
index 0000000..09932c1
--- /dev/null
+++ b/h5m/msre_full.h5m
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f079e740a395ca078b36b829df68afca92282b09d71b36024e5bc1ea0ac3094
+size 323881764
diff --git a/heatexchanger/hx.md b/heatexchanger/hx.md
new file mode 100644
index 0000000..30239f1
--- /dev/null
+++ b/heatexchanger/hx.md
@@ -0,0 +1,23 @@
+
+### msre primary heat exchanger
+![](heatexchanger/docs/phexcadmodel.png)
+
+[onshape primary heat exchanger cad model](https://cad.onshape.com/documents/03be2f510296a2e264886390/w/8cfbca3b7b9682dd4e53a998/e/54728fd981a1b4f5594c73d6), open to copy and use freely. chapter 4.1 in the [thesis](https://ltu.diva-portal.org/smash/get/diva2:1546993/FULLTEXT01.pdf) mentioned above covers the cad construction details extensively with references to original msre reports.
+
+![](heatexchanger/docs/phexflowpaths.png)
+
+[simscale primary heat exchanger simulation model](https://www.simscale.com/projects/MalcolmAkner/phex_-_final_version/). simulation results for primary heat exchanger can be viewed in chapter 6.2.2.1 of the [thesis](https://ltu.diva-portal.org/smash/get/diva2:1546993/FULLTEXT01.pdf), with comparisons to msre data in chapter 7.1.1.
+
+![](heatexchanger/docs/phexreal.png)
+
+primary heat exchanger produced and installed in the msre.
+
+
+### msre radiator
+![](heatexchanger/docs/radiatorcadmodel.png)
+
+[onshape radiator cad model](https://cad.onshape.com/documents/bf944323ed6a82e05924078c/w/2a25d73c5a3a66824d2d5fbd/e/a83d5535602a053216fedff4) open to copy and use freely. chapter 4.2 of the [thesis](https://ltu.diva-portal.org/smash/get/diva2:1546993/FULLTEXT01.pdf) covers the cad construction details with origianl references to msre reports.
+
+![](heatexchanger/docs/radiatorreal.png)
+
+radiator produced and installed in the msre.
diff --git a/msre_docker/.gitattributes b/msre_docker/.gitattributes
new file mode 100644
index 0000000..01a72f7
--- /dev/null
+++ b/msre_docker/.gitattributes
@@ -0,0 +1,4 @@
+msre_simple.h5m filter=lfs diff=lfs merge=lfs -text
+dagmc_controlrods.h5m filter=lfs diff=lfs merge=lfs -text
+msre_control_in.h5m filter=lfs diff=lfs merge=lfs -text
+msre_control_out.h5m filter=lfs diff=lfs merge=lfs -text
diff --git a/msre_docker/.gitignore b/msre_docker/.gitignore
new file mode 100644
index 0000000..2dc56f7
--- /dev/null
+++ b/msre_docker/.gitignore
@@ -0,0 +1 @@
+notebooks/
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diff --git a/msre_docker/Dockerfile b/msre_docker/Dockerfile
new file mode 100644
index 0000000..8a8dd9a
--- /dev/null
+++ b/msre_docker/Dockerfile
@@ -0,0 +1,59 @@
+FROM debian:11
+
+ARG compile_cores=8
+
+#update system
+RUN apt-get --allow-releaseinfo-change update
+RUN DEBIAN_FRONTEND=noninteractive && apt-get --yes update && apt-get --yes upgrade
+RUN DEBIAN_FRONTEND=noninteractive && apt-get --yes install git sudo bash wget apt-utils xz-utils python3 python3-pip
+
+#RUN git clone git@github.com:openmsr/openmc_install_scripts
+
+RUN groupadd -g 1000 usr
+RUN useradd -rm -u 1000 usr -G sudo -g usr -s /bin/bash \
+ && sed -i '/%sudo.*ALL/a %sudo ALL=(ALL) NOPASSWD: ALL' /etc/sudoers
+USER usr
+WORKDIR /home/usr
+
+#clone the install scripts and run them
+#RUN git clone git@github.com:openmsr/openmc_install_scripts.git
+#this is done step by step to avoid invalidating the docker cache.
+COPY openmc_install_scripts/Debian11/nuclear_data-install.sh .
+RUN ./nuclear_data-install.sh
+ 
+COPY openmc_install_scripts/Debian11/embree-install.sh .
+RUN ./embree-install.sh "$compile_cores"
+
+COPY openmc_install_scripts/Debian11/moab-install.sh .
+RUN ./moab-install.sh "$compile_cores"
+
+COPY openmc_install_scripts/Debian11/double_down-install.sh .
+RUN ./double_down-install.sh "$compile_cores"
+
+COPY openmc_install_scripts/Debian11/dagmc-install.sh .
+RUN DEBIAN_FRONTEND=noninteractive && ./dagmc-install.sh "$compile_cores"
+
+COPY openmc_install_scripts/Debian11/openmc-install.sh .
+RUN DEBIAN_FRONTEND=noninteractive && ./openmc-install.sh "$compile_cores"
+
+#clean up a bit
+RUN rm *-install.sh
+RUN rm *-install.sh.done
+RUN rm $HOME/openmc/nuclear_data/*.xz
+RUN rm $HOME/openmc/nuclear_data/*-install.sh.done
+
+RUN sudo pip install --no-cache-dir requests jupyterlab
+
+#Here should be added COPYING in MSRE-data directories - probably needs meshes and h5m-files as well
+#include neither cubit nor onshape can be distributed like this.
+RUN mkdir msre
+COPY msre_simple.h5m msre/
+COPY msre_control*.h5m msre/
+#COPY msre_*.py msre/
+RUN mkdir example_notebooks
+COPY MSRE.ipynb example_notebooks/
+ENV OPENMC_CROSS_SECTIONS=/home/usr/openmc/nuclear_data/mcnp_endfb71/cross_sections.xml
+
+#we are now ready to run the msre
+EXPOSE 8888
+ENTRYPOINT ["jupyter","lab","--ip=0.0.0.0","--allow-root"]
diff --git a/msre_docker/MSRE.ipynb b/msre_docker/MSRE.ipynb
new file mode 100644
index 0000000..0bfd77d
--- /dev/null
+++ b/msre_docker/MSRE.ipynb
@@ -0,0 +1,632 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a28f447d-a699-4495-bf71-bace1a4c0833",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Notebook for running MSRE calculations directly from a CAD drawing\n",
+    "This notebook show an example of running computations of a model of the Moten Salt Reactor Experiment of MSRE in short.\n",
+    "The model itself has been generated from tehe original drawings from Oak Ridge National lab \n",
+    "\n",
+    "The CAD-model is available on github at https://github.com/openmsr/msre, which in turn is generated from a long list of documents which have been compiled at https://github.com/openmsr/msr-archive\n",
+    "\n",
+    "The simulation backend is run using the Open Source Monte Carlo particle transport code OpenMC (https://openmc.org), through its' python interface.\n",
+    "\n",
+    "**Important: If you want your work to be available after you shutdown the docker, you must copy your notebooks to a location mounted on your local machine.**\n",
+    "\n",
+    "If you started the docker using the supplied ```run_docker.sh```-script, the ```notebooks```-directory has been mounted like this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb624881-ed0a-4962-b528-fb1ee5141f35",
+   "metadata": {},
+   "source": [
+    "## The (obvious) 1st step is to import the OpenMC python interface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85306562-ad94-4072-9cd4-1734fc7b0ab0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openmc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51730852-f7a5-4491-8b1d-de7609dce96f",
+   "metadata": {},
+   "source": [
+    "Next we define a set of materials objects that form the core of the MSRE, graphite,  hastelloy N / inor-8, inconel, the fuel salt, and helium. Lastly these are exported to am OpenMC-xml control file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6df036db-7b00-4a73-a4e0-dd9a407d7395",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graphite=openmc.Material(name='graphite')\n",
+    "graphite.add_element('C',1.0,'ao')\n",
+    "graphite.set_density('g/cc',2.26)\n",
+    "\n",
+    "#Hastelloy N / INOR-8 nominal material composition from\n",
+    "#ORNL-TM-4189\n",
+    "inor=openmc.Material(name='inor')\n",
+    "inor.add_element('Ni',0.72)\n",
+    "inor.add_element('Mo',0.16)\n",
+    "inor.add_element('Cr',0.07)\n",
+    "inor.add_element('Fe',0.05)\n",
+    "inor.set_density('g/cc',9)\n",
+    "\n",
+    "# LiF,BeF2,UF4,ZrF4 [0.67,0.23,0.05,0.0079] mol % @ 33% enrichment \n",
+    "molar_comp={'LiF':0.67,'BeF2':0.23, 'ZrF4':0.05, 'UF4':0.0079}\n",
+    "enrichment=0.3333\n",
+    "salt=openmc.Material(name='salt')\n",
+    "salt.add_element('F',molar_comp['LiF']*1/2+molar_comp['BeF2']*2/3+molar_comp['ZrF4']*4/5+molar_comp['UF4']*4/5,'ao')\n",
+    "salt.add_nuclide('Li7',molar_comp['LiF']*1/2,'ao')\n",
+    "salt.add_element('Be',molar_comp['BeF2']*1/3,'ao')\n",
+    "salt.add_element('Zr',molar_comp['ZrF4']*1/5,'ao')\n",
+    "salt.add_nuclide('U235',enrichment*molar_comp['UF4']*1/5,'ao')\n",
+    "salt.add_nuclide('U238',(1-enrichment)*molar_comp['UF4']*1/5,'ao')\n",
+    "salt.set_density('g/cc',2.2)\n",
+    "\n",
+    "# The natural isotopes have been used for this alloy\n",
+    "# The density is set to that of Ni.\n",
+    "inconel=openmc.Material(name='inconel')\n",
+    "inconel.add_element('Ni',0.72,'ao')\n",
+    "inconel.add_element('Cr',0.20,'ao')\n",
+    "inconel.add_element('Fe',0.08,'ao')\n",
+    "inconel.set_density('g/cc',8.9)\n",
+    "\n",
+    "helium=openmc.Material(name='helium')\n",
+    "helium.add_nuclide('He4',1.0,'ao')\n",
+    "helium.set_density('g/cc',1.0e-4)\n",
+    "\n",
+    "materials=openmc.Materials([helium,salt,graphite,inconel,inor])\n",
+    "materials.export_to_xml()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e21759c-4d3f-44f4-8b61-eb1a519d7d68",
+   "metadata": {},
+   "source": [
+    "As a control we can inspect the materials object. Notice how OpenMC has in the revant cases expanded our material definition to consist the naturally occurring  isotope concentrations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82b7ea1e-28d6-450d-9bad-e5f5262c5272",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "materials"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a5f8f08-ec15-4ab4-b091-c3e0b2147490",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#geometry\n",
+    "h5m_filepath=\"../msre/msre_simple.h5m\"\n",
+    "dag_univ = openmc.DAGMCUniverse(h5m_filepath)\n",
+    "geom = openmc.Geometry(root=dag_univ)\n",
+    "geom.export_to_xml()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be73fe16-ea81-4efa-939f-3934bc5b77bc",
+   "metadata": {},
+   "source": [
+    "We can now plot our geometry to verify that this is in fact the geometry we want. We plot two slices (xz and xy) through the centre of the MSRE core, and color the geometry by constituent material."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dde16460-008e-4e6f-ac81-32e82dd066d4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "xwidth = 350\n",
+    "yheight = 350\n",
+    "material_colors={salt:'red', inor:'lightblue', inconel:'blue',helium:'white',graphite:'gray'}\n",
+    "#xz plot\n",
+    "p1 = openmc.Plot()\n",
+    "p1.background='white'\n",
+    "p1.basis = 'xz'\n",
+    "p1.width = (xwidth,yheight)\n",
+    "p1.origin=(0,0,125)\n",
+    "p1.pixels = (800, 800)\n",
+    "p1.color_by = 'material'\n",
+    "p1.colors=material_colors\n",
+    "#xy plot\n",
+    "p2 = openmc.Plot()\n",
+    "p2.background='white'\n",
+    "p2.basis='xy'\n",
+    "p2.width=(xwidth, yheight)\n",
+    "p2.pixels = (800,800)\n",
+    "p2.origin=(0,0,100)\n",
+    "p2.color_by='material'\n",
+    "p2.colors=material_colors\n",
+    "\n",
+    "plots=openmc.Plots([p1,p2])\n",
+    "openmc.plot_inline(plots)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d02c4c6-2f47-44ac-989c-1ecaf9441450",
+   "metadata": {},
+   "source": [
+    "Now we need to define som settings for our calculations.\n",
+    "\n",
+    "First of all - we need to some neutrons to kick-start or chain reaction. In OpenMC this is doen by defining a source region. Here this is simply defined as being a region that encloses the MSRE core.\n",
+    "\n",
+    "Next we define some settings for the Monte Carlo-computation, such as how many particles we would initially run with. \n",
+    "\n",
+    "After the members of the settings python object have been filled to our desires, we export this to a settings xml-file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a17a765-10dd-4666-9f66-2d7f7931bb3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a neutron source for kick-starting\n",
+    "source_volume=openmc.stats.Box([-125,-125,0],[125,125,500], only_fissionable=True)\n",
+    "source = openmc.Source(space=source_volume)\n",
+    "source.angle=openmc.stats.Isotropic()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee524ba8-d65f-48ab-8024-e05f3409a6ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Finally we build a settings object for OpenMC where we define parameters for the run.\n",
+    "settings = openmc.Settings()\n",
+    "settings.source = source\n",
+    "settings.batches = 20\n",
+    "settings.inactive = 5\n",
+    "settings.particles = 20000\n",
+    "settings.export_to_xml()\n",
+    "\n",
+    "openmc.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfadbeb7-077b-42c6-8714-c92829536998",
+   "metadata": {},
+   "source": [
+    "Now that we have a running model let's try to do some more useful work with and extract some data from the model. To do this we need to specify what information we want to extract before \n",
+    "starting simulations. In many Monte Carlo particle transport codes, we add objects known as tallies to our models. In this respect OpenMC is no different.\n",
+    "\n",
+    "We will add tallies to monitor the neutron flux, and the fission sites in volumes along the geomtrical slices through our reactor that we plotted earlier.\n",
+    "\n",
+    "A tally needs to know what to measure and where to measure that. In OpenMC the \"where\" is known as a filter and the \"what\" is known as a score.\n",
+    "In or case we'd like to spatially resolve the flux so we first generate mesh object as filters and then add that to tally objects. In addtion we assign a list of scores to the score-member of the tallies. Lastly (as always) we export this to an xml-file which will be read by OpenMC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3383b0ba-4cec-4733-a271-a032079d0b00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mesh1=openmc.RegularMesh()\n",
+    "mesh1.dimension =  [400,400,1]\n",
+    "mesh1.lower_left = [-125, -125, 90]\n",
+    "mesh1.upper_right = [125, 125, 110]\n",
+    "mesh1_filter = openmc.MeshFilter(mesh1)\n",
+    "\n",
+    "mesh2=openmc.RegularMesh()\n",
+    "mesh2.dimension =  [400,1,400]\n",
+    "mesh2.lower_left = [-175, -10, -50]\n",
+    "mesh2.upper_right = [175,  10, 400]\n",
+    "mesh2_filter = openmc.MeshFilter(mesh2)\n",
+    "\n",
+    "t1 = openmc.Tally(name='flux1')\n",
+    "t1.filters =[mesh1_filter]\n",
+    "t1.scores = ['flux','fission']\n",
+    "\n",
+    "t2 = openmc.Tally(name='flux2')\n",
+    "t2.filters =[mesh2_filter]\n",
+    "t2.scores = ['flux','fission']\n",
+    "\n",
+    "tallies=openmc.Tallies([t1,t2])\n",
+    "tallies.export_to_xml()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e62c1ebc-75bf-44eb-8dde-90fef06e0030",
+   "metadata": {},
+   "source": [
+    "We have to re-run our simulation after generating the ```tallies.xml``` file.\n",
+    "\n",
+    "Note that we forcibly remove the _old_ datafiles first."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e624e13-f734-4e58-9307-f3b4d56151a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.system(\"rm -f summary.h5 statepoint.20.h5\")\n",
+    "openmc.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "430aff88-5238-4a68-8d7a-2d0de99eb77d",
+   "metadata": {},
+   "source": [
+    "After the run has finished the data we are after resides in the \"statepoint\" file that OpenMC saves.\n",
+    "In the below code, we will open that and extract the mean values for neutron flux."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7bebead-b04a-4a38-8802-0d01239e2c57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp=openmc.StatePoint('statepoint.20.h5')\n",
+    "\n",
+    "tl1=sp.get_tally(name='flux1')\n",
+    "tl2=sp.get_tally(name='flux2')\n",
+    "\n",
+    "flux1=tl1.get_slice(scores=['flux'])\n",
+    "flux2=tl2.get_slice(scores=['flux'])\n",
+    "\n",
+    "flux1.mean.shape=(400,400)\n",
+    "flux2.mean.shape=(400,400)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8205bf93-0351-458e-9d44-f8b43b0509ac",
+   "metadata": {},
+   "source": [
+    "The last 2 lines are necessary to reshape the flux maps into a 400x400 grid.\n",
+    "\n",
+    "In the end we plot the maps using matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84b1be81-188c-467a-9da5-74e7054792d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "fig,(ax1,ax2)=plt.subplots(ncols=2,figsize=(20,16), constrained_layout=True)\n",
+    "\n",
+    "ax1.set_xticks(np.arange(0,401,399/4))\n",
+    "ax1.set_xticklabels(np.arange(-175,176,350./4))\n",
+    "ax2.set_xticks(np.arange(0,400,399/4))\n",
+    "ax2.set_xticklabels(np.arange(-175,176,350./4))\n",
+    "ax1.set_yticks(np.arange(0,400,399/4))\n",
+    "ax1.set_yticklabels(np.arange(-175,176,350./4))\n",
+    "ax2.set_yticks(np.arange(0,400,399/4))\n",
+    "ax2.set_yticklabels(np.arange(-175,176,350./4))\n",
+    "ax1.set_xlabel('X / cm')\n",
+    "ax1.set_ylabel('Y / cm')\n",
+    "\n",
+    "ax2.set_xlabel('X / cm')\n",
+    "ax2.set_ylabel('Z / cm')\n",
+    "im1=ax1.imshow(flux1.mean)\n",
+    "fig.colorbar(im1,ax=ax1,shrink=0.4)\n",
+    "im2=ax2.imshow(flux2.mean)\n",
+    "fig.colorbar(im2,ax=ax2,shrink=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1099e99a-e4a8-46c8-b3e4-00259de64c82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Similarly we can plot fission reactions:\n",
+    "\n",
+    "fission1=tl1.get_slice(scores=['fission'])\n",
+    "fission2=tl2.get_slice(scores=['fission'])\n",
+    "\n",
+    "fission1.mean.shape=(400,400)\n",
+    "fission2.mean.shape=(400,400)\n",
+    "\n",
+    "fig,(ax1,ax2)=plt.subplots(ncols=2,figsize=(20,16), constrained_layout=True)\n",
+    "\n",
+    "ax1.set_xticks(np.arange(0,401,399/4))\n",
+    "ax1.set_xticklabels(np.arange(-175,176,350./4))\n",
+    "ax2.set_xticks(np.arange(0,400,399/4))\n",
+    "ax2.set_xticklabels(np.arange(-175,176,350./4))\n",
+    "ax1.set_yticks(np.arange(0,400,399/4))\n",
+    "ax1.set_yticklabels(np.arange(-175,176,350./4))\n",
+    "ax2.set_yticks(np.arange(0,400,399/4))\n",
+    "ax2.set_yticklabels(np.arange(-175,176,350./4))\n",
+    "ax1.set_xlabel('X / cm')\n",
+    "ax1.set_ylabel('Y / cm')\n",
+    "\n",
+    "ax2.set_xlabel('X / cm')\n",
+    "ax2.set_ylabel('Z / cm')\n",
+    "im1=ax1.imshow(fission1.mean)\n",
+    "fig.colorbar(im1,ax=ax1,shrink=0.4)\n",
+    "im2=ax2.imshow(fission2.mean)\n",
+    "fig.colorbar(im2,ax=ax2,shrink=0.4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd6f56f4-6ade-4e4f-b751-e67df40b3d8c",
+   "metadata": {},
+   "source": [
+    "Suppose we now would like to see what the energy spectrum of the neutrons generated in our reactor is. To explore this we will add another talliy to our simulation. This time however, instead of a spatial regular mesh, the tally will have an energy filter. Furthermore, we restrict the tally to neutron flux and fission events within the fuel salt, by means of a material filter. Unfortunately to fill the new tally we have to re-run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e440a7a0-3f4d-4067-ba47-09245d82400e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define a lograrithmic binning from 1keV to 10 MeV\n",
+    "ef=energyrange=openmc.EnergyFilter(np.logspace(3,7,200))\n",
+    "te = openmc.Tally(name='energy')\n",
+    "\n",
+    "sf = openmc.MaterialFilter(salt)\n",
+    "te.filters =[ef,sf]\n",
+    "te.scores = ['flux','fission']\n",
+    "\n",
+    "tallies.append(te)\n",
+    "tallies.export_to_xml()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa856b84-144c-49fc-8184-1c35eedc0449",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.system(\"rm -f summary.h5 statepoint.20.h5\")\n",
+    "openmc.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f431317-75ca-4960-ba3a-fae1c13e643b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp=openmc.StatePoint('statepoint.20.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4920fa40-9468-4fdc-a7e6-4cfe1a88612d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tl=sp.get_tally(name='energy')\n",
+    "\n",
+    "e1=tl.get_slice(scores=['flux'])\n",
+    "e2=tl.get_slice(scores=['fission'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22a6b009-f2d9-441b-ac98-4cb71c6c99a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig,ax=plt.subplots(figsize=(7,3.5),squeeze=True)\n",
+    "ax.plot(ef.values[:199],e1.mean[:,0,0])\n",
+    "ax.plot(ef.values[:199],e2.mean[:,0,0])\n",
+    "ax.set_xscale('log')\n",
+    "ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9aefede-8fdf-4494-b0c0-d19e41fb6c4f",
+   "metadata": {},
+   "source": [
+    "A reactor with a k_{eff} significantly higher than 1 is likely not what you want. Therefore we need to modify our initial model to also include a set of control rods. In the MSRE these rods consisted of three sets of cylindrical elements made from a Al_2O_3/Gd_2O_3-mixture. These absorb neutrons to \"dampen\" the nuclear process - something also known as poisoning.\n",
+    "The cylindrical elements were stacked to form the control rods (~=80''), which can be inserted into the reactor core in 3 of the 4 voids visible in the centre of the XZ-geometry of the reactor.\n",
+    "\n",
+    "To run our reactor model with control-rods we will now simply point openmc at a different geometry-file, and append the missing materials to the materials list. To visualize we also add tallies to track the absorption."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57d11f47-2c4a-4549-8944-ef1aacf81c7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#geometry\n",
+    "os.system('rm -f materials.xml geometry.xml tallies.xml')\n",
+    "h5m_filepath=\"../msre/msre_control_in.h5m\"\n",
+    "dag_univ = openmc.DAGMCUniverse(h5m_filepath)\n",
+    "geom = openmc.Geometry(root=dag_univ)\n",
+    "geom.export_to_xml()\n",
+    "\n",
+    "b_w_conc={'Al2O3':0.3,'Gd2O3':0.7} # Wt. (D. Shen et.al. Nucl. Sc. & Eng., v. 195, pp. 825, 2021)\n",
+    "A_w={'Al':26.9815385,'Gd':157.25, 'O':15.99} #g/mol, (Webelements.com)\n",
+    "rho={'Al2O3':3.987,'Gd2O3':7.07} # g /cc, (Wikipedia: Aluminium_oxide & Gadolinium(III)_oxide)\n",
+    "b_mol_w={'Al2O3':2*A_w['Al']+ 3*A_w['O'],'Gd2O3':2*A_w['Gd']+3*A_w['O']}\n",
+    "bush_mol_comp={'Al2O3':(b_w_conc['Al2O3']/b_mol_w['Al2O3'])/( b_w_conc['Al2O3']/b_mol_w['Al2O3'] + b_w_conc['Gd2O3']/b_mol_w['Gd2O3'] ),\n",
+    "                'Gd2O3':(b_w_conc['Gd2O3']/b_mol_w['Gd2O3'])/( b_w_conc['Al2O3']/b_mol_w['Al2O3'] + b_w_conc['Gd2O3']/b_mol_w['Gd2O3'] )}\n",
+    "\n",
+    "bush=openmc.Material(name='bush')\n",
+    "bush.add_element('Al',2/5*bush_mol_comp['Al2O3'],'ao')\n",
+    "bush.add_element('Gd',2/5*bush_mol_comp['Gd2O3'],'ao')\n",
+    "bush.add_element('O',3/5*bush_mol_comp['Al2O3']+3/5*bush_mol_comp['Gd2O3'],'ao')\n",
+    "bush.set_density('g/cc',b_w_conc['Al2O3']*rho['Al2O3'] +b_w_conc['Gd2O3']*rho['Gd2O3'] )\n",
+    "\n",
+    "t1 = openmc.Tally(name='flux1')\n",
+    "t1.filters =[mesh1_filter]\n",
+    "t1.scores = ['flux','fission','absorption']\n",
+    "\n",
+    "t2 = openmc.Tally(name='flux2')\n",
+    "t2.filters =[mesh2_filter]\n",
+    "t2.scores = ['flux','fission','absorption']\n",
+    "\n",
+    "tallies=openmc.Tallies([t1,t2])\n",
+    "tallies.export_to_xml()\n",
+    "\n",
+    "\n",
+    "materials=openmc.Materials([helium,salt,graphite,inconel,inor,bush])\n",
+    "materials.export_to_xml()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea8d4900-f24e-4f0e-b5af-39ad3342bebc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xwidth = 350\n",
+    "yheight = 350\n",
+    "material_colors={salt:'red', inor:'lightblue', inconel:'blue',helium:'white',graphite:'gray',bush:'purple'}\n",
+    "#xz plot\n",
+    "p1 = openmc.Plot()\n",
+    "p1.background='white'\n",
+    "p1.basis = 'xz'\n",
+    "p1.width = (xwidth,yheight)\n",
+    "p1.origin=(0,0,125)\n",
+    "p1.pixels = (800, 800)\n",
+    "p1.color_by = 'material'\n",
+    "p1.colors=material_colors\n",
+    "#xy plot\n",
+    "p2 = openmc.Plot()\n",
+    "p2.background='white'\n",
+    "p2.basis='xy'\n",
+    "p2.width=(xwidth, yheight)\n",
+    "p2.pixels = (800,800)\n",
+    "p2.origin=(0,0,100)\n",
+    "p2.color_by='material'\n",
+    "p2.colors=material_colors\n",
+    "\n",
+    "p3 = openmc.Plot()\n",
+    "p3.background='white'\n",
+    "p3.basis='xy'\n",
+    "p3.width=(xwidth/10, yheight/10)\n",
+    "p3.pixels = (800,800)\n",
+    "p3.origin=(0,0,100)\n",
+    "p3.color_by='material'\n",
+    "p3.colors=material_colors\n",
+    "\n",
+    "plots=openmc.Plots([p1,p2,p3])\n",
+    "openmc.plot_inline(plots)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c024f9cd-3ca8-4f8d-a5d0-60eca95cac3c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.system(\"rm -f summary.h5 statepoint.20.h5\")\n",
+    "openmc.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "046eb354-e11f-4dfa-90e8-59768cc4c063",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Plot maps of the absorption\n",
+    "sp=openmc.StatePoint('statepoint.20.h5')\n",
+    "\n",
+    "tl1=sp.get_tally(name='flux1')\n",
+    "tl2=sp.get_tally(name='flux2')\n",
+    "\n",
+    "abs1=tl1.get_slice(scores=['absorption'])\n",
+    "abs2=tl2.get_slice(scores=['absorption'])\n",
+    "\n",
+    "abs1.mean.shape=(400,400)\n",
+    "abs2.mean.shape=(400,400)\n",
+    "\n",
+    "fig,(ax1,ax2)=plt.subplots(ncols=2,figsize=(20,16), constrained_layout=True)\n",
+    "\n",
+    "ax1.set_xticks(np.arange(0,401,399/4))\n",
+    "ax1.set_xticklabels(np.arange(-175,176,350./4))\n",
+    "ax2.set_xticks(np.arange(0,400,399/4))\n",
+    "ax2.set_xticklabels(np.arange(-175,176,350./4))\n",
+    "ax1.set_yticks(np.arange(0,400,399/4))\n",
+    "ax1.set_yticklabels(np.arange(-175,176,350./4))\n",
+    "ax2.set_yticks(np.arange(0,400,399/4))\n",
+    "ax2.set_yticklabels(np.arange(-175,176,350./4))\n",
+    "ax1.set_xlabel('X / cm')\n",
+    "ax1.set_ylabel('Y / cm')\n",
+    "\n",
+    "ax2.set_xlabel('X / cm')\n",
+    "ax2.set_ylabel('Z / cm')\n",
+    "im1=ax1.imshow(abs1.mean)\n",
+    "fig.colorbar(im1,ax=ax1,shrink=0.4)\n",
+    "im2=ax2.imshow(abs2.mean)\n",
+    "fig.colorbar(im2,ax=ax2,shrink=0.4)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/msre_docker/README.md b/msre_docker/README.md
new file mode 100644
index 0000000..999ed05
--- /dev/null
+++ b/msre_docker/README.md
@@ -0,0 +1,61 @@
+# docker-msre
+This repository is created for the purpose of creating a docker conatiner which includes not only support for OpenMC with DAGMC/MOAB, embree, and double_down libraries, but also a Jupyter notebook server. The "raison d'etre" for this is to run a virtual version of The Molten Salt Reactor Experiment (MSRE). The experiment itself was performed physically in the '60s at Oak Ridge National Lab (ORNL), Tennessee,  US.
+
+# Getting started:
+1. Install the docker engine on your system (for windows and MacOS: docker-desktop, for Linux: docker server)
+  Instructions for how to do this may be found on the Docker website at: [Docker installation](https://docs.docker.com/engine/install/)
+2. Run the msre docker container.
+    - Linux:
+        1. Open a terminal, navigate to a directory where you want to run.
+        2. Download the msre-docker runscript, and run it:
+        ```{bash tidy=false}
+        wget https://raw.githubusercontent.com/openmsr/msre_docker/main/run_docker.sh
+        chmod u+x run_docker.sh
+        ./run_docker.sh
+        ```
+        The script contains a call to ```docker run``` with some options preset. Among other things it sets up a subdirectory called notebooks which is shared between the conatiner and the host so that you can keep data between runs.
+        Feel free to inspect the runscript if you prefer to run your docker manually. If you'd prefer for instance to run commands in the docker from a shell
+        you may do so by adding the option ```--entrypoint /bin/bash``` to the docker run command.
+        3. If you used the run-script "as is" you should now be able to open a browser and point it to "https://127.0.0.1:8888" and be treated with a login page to Jupyter.
+        Please enter "docker" in the password box, which should provide you with the base Jupter Lab screen.
+        4. In the example_notebooks folder open the MSRE.ipynb file. You are now ready to run!
+      
+      ## Troubleshooting:
+        - If the wget command fails with a message complaining about certificates you may bypass this by adding ```--no-check-certificate``` to the command.
+        - If you get an error message saying: "docker: Cannot connect to the Docker daemon at unix:///var/run/docker.sock. Is the docker daemon running?." that likely means that you need to start the docker engine. This can be done using the command:  
+        ```sudo dockerd```  
+        - If you get an error similar to: "Got permission denied while trying to connect to the Docker daemon socket...", it is likely that your user needs to be added to the "docker" group. To check which groups your user belongs to you may use the groups command. To add your user to the docker group you may use:  
+        ```sudo usermod -aG docker <username>```  
+        where \<username\> is your unix username. 
+
+  - Windows:
+    There are two options for running the docker on a windows system:
+    1. Install the docker-desktop application.
+    Once you have downloaded and installed the application in the normal manner from docker.com, you may proceed by:
+        1. Open the command prompt and issue: docker pull copenhagenatomics/msre:0.1.0. This will look like this ![docker pull](.images/cmd_prompt_pull.png)
+        3. Open the docker-desktop application. The msre docker image should now be visible on the "images"-tab. ![docker images](.images/image_dld_and_run_button.png)
+        4. Click on the run-button and then on options. Here we need to set a few parameters to mimic the run_docker.sh script. You should now have a dialog: ![docker-desktop options](.images/container_opts_annotated.png)
+        4. Once the docker has been downloaded and started, you should see among many other messages something about a Jupyter Server at "http://127.0.0.1:8888/lab?token=...". ![docker log](.images/cont_run_log_and_URL.png) Please copy this entire URL and paste it into the adress field of your browser. You should now see the Jupyter lab interface and may proceed.
+  
+    2. Install Windows Subsystem for Linux (WSL) and run the docker from the command-line there.
+    N.b. In fact the docker-desktop application also relies on WSL. 
+    WSL is a native windows system that enables users to run anyh and all Linux applications natively on Windows 10 and 11, including parallelization and even access to acceleration through GPUs.
+    Please note that the process may require updates to Windows to be installed and also rebooting your system. Furthermore, it may also require you to edit settings in the BIOS to enable virtualization on your system.  
+    First you must install a Linux kernel in WSL. Open a command prompt and run the command  
+    ```wsl --install --distribution debian``` (or shorter ```wsl --install -d debian```)
+    ![install WSL](.images/Debian.png)
+    
+    Once this is done wsl will start and you can proceed proceed to get the run-script using wget:  
+    ```wget --no-check-certificate https://raw.githubusercontent.com/openmsr/msre_docker/main/run_docker.sh```![wget run_docker.sh](.images/wget.png)
+    Set the executable bit on this script and run it to start the docker as if on Linux:
+    ```chmod u+x run_docker.sh```![chmod](.images/chmod.png)
+    At this point you should be able to run the script ```./run_docker.sh``` and point your browser to "127.0.0.1:8888" or "localhost:8888" to get into Jupyter notebook.
+    
+    ## Troubleshooting:
+    Should you run into problems, please ensure that the following options have been set.  
+    First, we need to tell docker to use the debian kernel in WSL. This is done in the setting pane under resources ![docker_wsl_kernel](.images/docker_2.png)  
+    Second, a number of windows features need to be turned on:![windows features](.images/WSL_2.png)
+    
+    Should you still have problems - please contact us (for instance by opening an issue in this repo) and we'll help you out.
+3. Jupiter notebook 
+ The default password for the jupyter notebook is 'docker'.
diff --git a/msre_docker/build_docker.sh b/msre_docker/build_docker.sh
new file mode 100755
index 0000000..1b71377
--- /dev/null
+++ b/msre_docker/build_docker.sh
@@ -0,0 +1,2 @@
+#!/usr/bin/env bash
+docker build -t copenhagenatomics/msre:0.1.1 .
diff --git a/msre_docker/msre_control_in.h5m b/msre_docker/msre_control_in.h5m
new file mode 100644
index 0000000..ec95820
--- /dev/null
+++ b/msre_docker/msre_control_in.h5m
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a8303e7b1aee5e8f8cbfb11ca706105df9136f0081794d57f6a5049519a25f14
+size 487008884
diff --git a/msre_docker/msre_control_out.h5m b/msre_docker/msre_control_out.h5m
new file mode 100644
index 0000000..902df1d
--- /dev/null
+++ b/msre_docker/msre_control_out.h5m
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3595689c7c1a42bbaa1bee63749150b31e5a846ae4ceb10a697d05f41c5493ca
+size 489986000
diff --git a/msre_docker/msre_simple.h5m b/msre_docker/msre_simple.h5m
new file mode 100644
index 0000000..c64e211
--- /dev/null
+++ b/msre_docker/msre_simple.h5m
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:856f320312259f84818cf26de6d7d6969ae0951f58675eca2a396d0e8efb2671
+size 255476120
diff --git a/msre_docker/run_docker.sh b/msre_docker/run_docker.sh
new file mode 100755
index 0000000..bb3be71
--- /dev/null
+++ b/msre_docker/run_docker.sh
@@ -0,0 +1,12 @@
+#!/usr/bin/env bash
+PWD=`pwd`
+
+mountdir=$1
+if [ "a$1" == "a" ]; then
+	mountdir=notebooks
+fi
+if [ ! -d $mountdir ]; then
+	mkdir $mountdir
+fi
+
+docker run -it -p 8888:8888 -e JUPYTER_ENABLE_LAB=yes -e JUPYTER_TOKEN=docker -v ${PWD}/${mountdir}:/home/usr/notebooks copenhagenatomics/msre:0.1.1
diff --git a/msre_docker/run_docker_daemon.sh b/msre_docker/run_docker_daemon.sh
new file mode 100755
index 0000000..e9886c0
--- /dev/null
+++ b/msre_docker/run_docker_daemon.sh
@@ -0,0 +1,12 @@
+#!/usr/bin/env bash
+PWD=`pwd`
+
+mountdir=$1
+if [ "a$1" == "a" ]; then
+	mountdir=notebooks
+fi
+if [ ! -d $mountdir ]; then
+	mkdir $mountdir
+fi
+
+docker run -d --restart unless-stopped -p 8888:8888 -e JUPYTER_ENABLE_LAB=yes -e JUPYTER_TOKEN=docker -v ${PWD}/${mountdir}:/home/usr/notebooks copenhagenatomics/msre:0.1.1
diff --git a/openmc_notebooks/README.md b/openmc_notebooks/README.md
new file mode 100644
index 0000000..1378e78
--- /dev/null
+++ b/openmc_notebooks/README.md
@@ -0,0 +1,7 @@
+# OpenMC MSRE notebooks
+[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
+
+<img src="images/lat.png" width="700" height="500"/>
+
+`OpenMC` simulation examples of the Molten Salt Reactor Experiment (MSRE), operated at ORNL in the 1960s.
+All scripts are set up using the [h5m meshed files](https://github.com/openmsr/msre/tree/master/h5m) obtained with the open source meshing tool [CAD-to-OpenMC](https://github.com/openmsr/CAD_to_OpenMC) from a CAD version of the the MSRE, designed with the CAE tool `OnShape` and available for export here: [onshape msre model](https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/).  
diff --git a/openmc_notebooks/images/control_rod.jpg b/openmc_notebooks/images/control_rod.jpg
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diff --git a/openmc_notebooks/images/msre_core.jpg b/openmc_notebooks/images/msre_core.jpg
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diff --git a/openmc_notebooks/images/plot_1.png b/openmc_notebooks/images/plot_1.png
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diff --git a/openmc_notebooks/images/plot_3.png b/openmc_notebooks/images/plot_3.png
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diff --git a/openmc_notebooks/msre_criticality_test.ipynb b/openmc_notebooks/msre_criticality_test.ipynb
new file mode 100644
index 0000000..8c0714b
--- /dev/null
+++ b/openmc_notebooks/msre_criticality_test.ipynb
@@ -0,0 +1,1054 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "84287fea",
+   "metadata": {},
+   "source": [
+    "# MSRE CAD - Criticality test \n",
+    "\n",
+    "In this notebook the MSRE criticality test is simulated using a CAD design version developed by [Copenhagen Atomics](https://www.copenhaagenatomics.com) using the CAE tool OnShape and made available for export here: [onshape msre model]((https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/)).  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f8a4de08",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {
+      "image/jpeg": {
+       "width": 400
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(\"./images/msre_assembly.jpg\", width=400)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a9f360a",
+   "metadata": {},
+   "source": [
+    "Fig. 1: Section view of entire assembly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d951f7c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {
+      "image/jpeg": {
+       "width": 400
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(\"./images/msre_core.jpg\", width=400)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16d51fe0",
+   "metadata": {},
+   "source": [
+    "Fig. 2: Section view of reactor vessel and thermal shielding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "d39b1541",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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PcaXcRuXBTVIJGnjjUA4xIrTgDpk4hGOcqBXnvjLT73UNC8BxanL/AGzaTeKoLZFuGV1eMhf3Zbc4YHceSzdSDggqACT/AIJ+2j2fx3UyCM/aNCuZUZHDHb5sYwcHg5U8deea86BI+NfxBwet3fZ/8CxXrf7GWnppv7XHiuziijhitoNSiWOEYRFW4QAKOwGMCvIYJFm+MnjuVWDCS5vX47ZuxR0I+0zo7Q/va3bXoKwrUZmP1rdtegoLK/h24Fr8ffAErfdE0oJ9AYyCf1r76uf+PaX/AHD/ACr4E8PWwvPjx4FgbgSG4TPpmFq+77C6a90RJX/1hiIcejDIb9Qa8zFfGjtofCz5+/ZI/wCPr4kf9jLdfzWvddS/ealpcXbzHlP/AAFD/VhXhX7JH/H18SP+xluv5rXur/vfEUY7Q2zH8WYD/wBlNc1b42ddL4EaVZviIn+x7hB1k2xD/gTBf61pVm6187WEP9+6Q49ly3/stYGpogBQAOg4oZgilj0AyaWqWty+Ro96/cQtj644oAj8PKRotqx6uvmH/gRLf1rRqG0h+z2sMX9xFX8hU1AGbpY33upzf3pwg/4Cij+eaZ4q/wCRb1L/AK4P/Kn6AfMsGl/56zSyfm5x+mKZ4q/5FvUv+uD/AMqpboHseR/sTkH4F6dg9Li4z/39evOPhx/yfF4s/wCvZv8A0GCu5/YhmMXwhtoGPEkksyf9/XVv5L+dcJ8NZFl/bg8WMjBl+zOMj2WAV3fbqfM8/pA9X+Fem3Tft/3EdleGwkfw28iyCNZODOuRg+pya+8Psniq2+5qOmXg/wCm9q8Z/NXP8q+GvhhqNrpn/BQETXlxFaxHwuy75nCrnzxxk19/W2qWV7j7PeQT5/55Sq38jXNV+z6HVS2fqcV45v8AxNB4O1lZ9JsbhPssmXtrwrjj0ZP61+V/7K6LP8UvgBHIA6fbNUbaemQHINfrj49/5EvW/wDr0k/9Br8iP2YI7iX4mfARLWZbe4N1quyV03heH7ZGfzreh8Ev66Myr/FH+uqP158eceC9a/69JP8A0Gvz7/Yn/wCSFab/ANfFx/6NevuPxzo3iH/hDtZMniNCotZMrHYIM8epY18MfsRziX4H2aD70d1OCPrIx/rWf/Lt+qB/Gj5d+PZ834nfEu1QF7m5vLGOGJeWkbavCjua+tfiZvh/Zo8SWkoKy2umTQsD1AEZK/8AjpFfKXxR5/agvx2/4SLTf/QVr65/aAH2T4U+L16Jd6LI4/30Qg/oR+VdFR/w0YQXxHe/s4Xken/s7+DrmU/u4tIhc++IxxXonhyyktNMVrj/AI+p2M83+83OPwGB+FeVfs+gX/wQ+HOmjlZNOgnmH/TNEU4P1YqPzr2muOfxM7o/CgrP16+fT9LmkiGbhsRwr6yMcL+prQrFuP8AiZeJIIOsNgnnv6GRshB+A3H8RUFHgn7b2nppX7NF9ax8iKa2BJ7nz0JP4nJrlPjF8TdB8B6T4Rh1e5eB57NmQLEz5A2Z6A+ortv29P8Ak3rVf+vm2/8ARyV8v/ttf80//wCwfL/7SrspRUlFPzOOq7Nv0Puqs7X+dMePvK6Rf99OB/WtGs3V/wB5Pp0P9+5Dfgqlv6CuA7jSpk0ghhkkPRFLH8BT6z9fkMei3hH3mjKD6ngfzoAPD8Rh0WzU/eMYY/U8/wBat3P/AB7S/wC4f5U6KMRRIg6KoUfhTbn/AI9pf9w/ypgeBfsgf6/4k/8AYy3X81r6IuIFubeSFxlJFKH6EYr5y/Y2k85fiG/97xFct+e2vpGt6vxs5IfCfO37Xc7Tfs7XgkP72KeCKT/eWZVP8s17V4V/5FvTf+uCfyrxH9sv/RfhF4gt+iSz2twg9zKit+oB/GvbvCv/ACLem/8AXBP5Upfw16v9C4fGx2tfvGsIf+el0hI9lBb/ANlrTrMvP3uuaen/ADzSWU/kFH/oRrTrA6ClrUvkaReOOohbH1xxVi0h+z2sMQ6Iir+QxVLX/msFi/57TRx/m4z+ma0qACszw/8APYPN/wA9ppJPzc4/QCrt3MLe1mlPREZvyGag0WH7PpFnGeoiXP1xzQBdrMs/3uuag/Xy0jiH5Fj/AOhCtOsvQxvOoSn/AJaXT4PsuF/pQBxv7RP/ACRXxf8A9g6b/wBANVf2bT9m+GHhaM/dudJt5V/3lRVb9CtWv2if+SK+L/8AsHTf+gGqnwN/0f4J+A7zp9ntIA5/2GQKf5g/hXQv4XzOeX8T5HkPxI/5O6X/ALAa/wDo1q9GPSvOfiR/yd0v/YDX/wBGtXozdK9al/DR5s/iZi6p0P8An0ryL4qn/iktY/693/8AQa9f1IZBz/npXkvxXix4Q1g/9O7/APoNakGx+zYcfsZfGf8A7ef/AElSvH/2UNO0+9+OngJbxkmjfUpnmt5sFF8qAvE5B77i2D/s8V6/+zZ/yZl8Z/8At5/9JUqT/gnNbaFqHibxjpyPCnjS60YnSZ7lAqxMrtvRX81GDSAxjcuNvBzkU30Ijuz73u/jh8PZLYMPGOixKQMCe8SIjPThiCK53xBc6D4usyUk0/WbZhwVKToR+orjr74L+MNTt8NBaazbopWS207xRLLK56bdh1iNVGOMEE9fYDyT4jfA/UfD0cUqeG9Q02BSJGlubP7S8b9MiSK2nYDpyZ1Hqe9Is6HxN8PNL04tJoL3HhmcMXD6RIYUJPUmP7jduqmvG/ihoMsnhq8i8VXcUGnWkq6pa67p+22mW5G1AHQdXIxhlHOMcEDLhq/iCwLLpniGeWNWLPaXBFwIVGchj5k5UYxgM0P4VyHxp8Tal4t8DaBa6jCNNivtYjtJmtpFlSaPG4SIRkYyQcAnletAHa/8E87521/4oavdzC5vvsCTyTHli7Gcscjg5Izx6ivn3T4zbfFzxpETgpPdoffFyP8ACvbP+Ce2JLzx7bKTm6hsIORj70km79M14lfymL42+N8fxajfL/5M0iFuzrrQ/vfxrctjlRWHp43EH/Pat62GAKZZF4S/5OD+H/8A10n/APRRr7htf9GuNVtOi/8AHwn0cHP/AI8D+dfD3hL/AJOE+H//AF1n/wDRRr7d8Q3CaXNBfudsWx4JT7EZX9V/WvMxXxo7aHws8J/ZI/4+viR/2Mt1/Na90sf3ms6lJ2URxA/RSx/9Crw39j+J7vSfGWtRqf7P1XXbm5tJD/y0jJAzjqOQevpXueifOt7N/wA9bqQg+wO3/wBlrmrfGzrpfAjSrMu/3uu6fH/zzSWU/kFH/oRrTrMi/e+Ibhu0NuifizE/0FYGpp1meIPnsFh/57TRx/gXGf0BrTrM1T95f6XD6zNIf+Aof6kUAadRXc32e1mlPREZvyGalrO8QsV0a6A6uvlj/gRC/wBaAJNEh8jSLND1ES5+uOareKyB4a1Mnj9w/wDKtRFCKFHQDArgPj9dzWPwc8WT28rwTJp8xWSNiGU7DyCKuKvJIUnZNnnP7I2bb4G6HeDgJdXMTn/ZeVx/6EFryD9mQk/tX+JyeSX1D/0pWvpP4B6Nbj9njw/b20SQ+fpiynYMZkZdxb67jmvmj9lqUzftS+IZCMF/tzEembha7k7uozz39g+l/hRBHcf8FBCksayIfCrZVwCP+Phe1fdVz4M0G8JMuj2TN/eECqfzAr4N+G0l9H+37u0+GCe4HhdvknkKKR54zyAeenavuf8At7XoP9f4aaQDqbW8jf8ARtprlq/Z9DrpbP1MTxz4D0m38Hay9qt1ZstrIR9nu5VHT03Y/Svyq/ZQul/4Wz8CA7BBFeaoCScDBRjn9a/VTxz4xA8Haytxo2rWpNrINzWu9Rx6oWr8p/2WtOt734q/AdLiJJ4ZrnU1eORcq2BIeR+P6VtQ+CX9dGZVvij/AF1R+uHj7WdPXwbrSm+tgxtZAAZlz0+tfnr+xS32b4UaUOiXTXA/4Gkzn+TH8q+/vHnhPQ7bwRrjR6PYIVs5SCLZMj5T7V8AfsjAxfs/6Tdj71rezS8f3fOcN/46TUf8u36oH8aPnT4o/wDJ0N//ANjFpv8A6CtfYH7ScBf4G+IZlHzw2Mh/Boyp/nn8K+P/AIoc/tQXxHI/4SLTf/QVr7M/aE/5IT4r/wCwXL/6LNb1N6ZjD7Rufsm2rP8ABbwxfy/ek063giB/hREA/Vsn8q9mry79mEAfAPwRjj/iVwf+gCvUa5J/Ezsj8KI55ktoJJZG2xxqWYnsAMmszw1C/wBga7mXbcXrm4cHqAfur+CgCmeIybz7Jpanm8k/eY7RLy/58D/gVbIAAAAwB2qCz5x/b0uov+FFXFgHBvL29toraAfflfzVO1R1JwCcD0r5m/beiaGTwCjqVdbCUEHt/qq+hv24OR8Mh2/4Si1/9mrwj9vz/kO+Df8Ar1n/APaVd9HRx+Zw1vtfI+2azLn97r9kn/PKKSQ/jtUfzNadZlv+98QXbf8APKCOP8SWY/0rzT0DTrN1357e2h/563MSn6Btx/8AQa0qztQ/eanpsfYO8p/BCP8A2agDRqlrU32bR76XONkDtn6Kau1zvxCcp4M1UDq8XljH+0QP601uDPEP2MpfLuviBAeUbW7hoz2OCoP81r6br5w/Y/Rbb4J6FfgAMl9OsreqvKykn8dv5V9H1vV+NnJD4UfPf7b9uW+DFxOvVLiJG/3TIh/mor2Xwr/yLem/9cE/lXkn7bH/ACQrUv8Ar4t//RqV634V/wCRb03/AK4J/KlL+GvV/oXD42Oi/e+Ibhu0NuifizMf6CtOs3S/3l9qc3rMIx9FQf1JrSrA6DM1X95faZD6zmQ/RUJ/mRWnWbL+98Q269ord3/FmUf0NaVAGd4hYrot0B1dfLH/AAIhf61oKoRQo6AYFZut/OLKH/npdR5HsMt/7LWnQAhIUEnoOaoaAP8AiUWzHrIDKf8AgRLf1qTWZ/s2k3kvdYmx9ccVNaQC2tYYR0jRU/IYoA8//aJ/5Ir4v/7B03/oBpfgBare/AXwrbt0l0uJM+mYxSftE/8AJFfF/wD2Dpv/AEA1Y/Zz/wCSJeD/APsHQ/8AoAroX8L5nPL+J8jwLxhcteftUWsr/wCsOgqr/wC8JWDfqDXqRGRXmHjy3+zftdzAfcbSN6j6yEn9c16fXrUv4aPOqfEzNvovlryT4xult4L1ZncIGgZRk9SRgD9a9gvSNpryb4wXOnWvhS9fU4vOtiuNgHJYkYx+OK1MzE+GDfEHwf8AAjxZ4Xs/B63uneJYml+2y30UTxK8Kof3ZOeMdDg/SuN/Z40mHV/Ffw9ttKV7XxXL4qMi36yFFFnFFE8sZwc5OWxx3YZGTn0Dwl+xZct4DGt6/q1xpGq3MLXFkloQYoXAyiSZGSe+Qw9M9zX/AOCfPhXQPFXximh1HUZLfxPbWM9x4YsS22O7vPLcFcl4wThQMF1BG7JG3K+dhcdQxspxoy5uV2f9ddjtxGDq4RRlVjbm/r5H6PeJPg34H1p3utS8JaNdX0hMkty9lH5jueWYtjJJOTnPevONe+Eei6dI0mjXWr+H2GcLpepzxRj/ALZlin/jteXXf2waeYX1jU/Cp2Awy3F09ntAPG4S60vBAOMIeoPscHUPFvjnS5Xgs/Eo1fy1Mr3H2gXVuF6fMy2zqp4PBnHr716JxGr4z8N6/CCJrrT/ABfbr92LXrVEuVOcjZcRAFSPXbn3rxD4uaV5/wAP7g2FtdaVfeH9STWJIdTAff8AJsAWVf8AWA+WSGYluMHHGO+vPjPrKO/2/S7fVbZWIa60lg2QO4CvIo/4G6f0rzz9oHxIfH/hDw1p3h2V521m82i32fO6oWDZGCcKygkjjjOcUAXv+CdlwkHizxU0gJiht4bl8eiJPj9WFeK6VNbeKfGvi/xFAzrHcXzzQo3HyzSPJgj1G0D8+tdB+zn4i1vwbaa/qeheIPDOjyX0bWU8OvTtG2wAMGjAOT94j8Kyvh9oB0tPE1q1zb3H2d7N/Ngk3pKGSUgocDP3s/QGkmthcrTudNpoxj/PpW/b/dFYtgADj3rag6CmMg8Jf8nCfD//AK6z/wDoo19s+P7c3Pg3V1X7627On+8vI/UV8U+CInvv2ifA0dujTPAZ5ZQgzsQxkbj6DPGa+4PFn/Is6n/17v8AyrzcT8aOyj8LPEf2MGCfAvT2PQT3BP8A39evaPDyFdFtCerp5h/4Ed39a8L/AGSJvI/Z2icdQ1yB9TK4FfQFrCLe2hiHREC/kMVyVv4kjtp/AiWszS/3l9qk3rMIx/wFB/UmtOszw/8APp7Td5ppJPzc4/TFYmhp1mS/vfEUC9obZ3/FmA/oa06zLP8Ae63qL/3FiiH5Fj/6EKANOszW/nWyh6+ZdRgj2Hzf+y1p1m3v7zWtNj7IJJT+ACj/ANCoA0q84/aKIHwU8X5/6B8w/wDHDXo9eYftLy+X8EfFfq1m6j8q0h8SJn8LLv7Pn/JCfCn/AGC4v/RYr5b/AGYV/wCMrfEwA4DX/Tt/pK19Sfs+f8kJ8Kf9guL/ANFivAP2SnEPx2+IzsBh72SIE9iZpmH/AKDXXF2VQ4mr8h7p8A4ZPE37eet6jYIZrPRvD4s72XoI5XlDouDycqCcj0r79r4E/Y/0Gx139qb40LewmURrYlCsjIVzG+cFSD2r7Y/4QiCL/j21TVrQdhHeswH4Purnq7peSOml8LfmS+Pf+RL1v/r0k/8AQa/Ij9mC5e0+JnwEmjt5Lp1utVIhixubh+mSBX6l/E2zvfDnw/8AEF9c+LJIbG3spZJXvoImVVCknLALjivzB/ZatJrT4r/s+iaJ4i9xqkihxjKlXIP0I5reh8Ev66Myr/FH+uqP1E+IviTVpPAfiDHhm8RPsMuXkuIRgbTzjca+H/2L4km+AdjHJzG81yrfQyvmvtD9qrW7zw5+zx4+1Cwl8i7g0i5aOQAHafLbBwa+VP2Z9HtdH+A/hxbVNgnshcPyTl3G5j+bGs/+Xb9Qfxo+M/GExvf2g5rhG82JfEmn25mXlWdAFYZ9crX2z+0H/wAkJ8V/9guX/wBFmviDP/Fce/8Awn3/ALVr7Y/aPv7ew+BHic3EqQiTT3iQucbmZdqge5JA/GuiqrSgjCDupM7j9mH/AJIJ4I/7BcH/AKLWvUa80/Ztsp9P+Bngu3uoXt500yAPHIpVlOwcEHpXd67ftp2lzSxjdO2I4V9XY4X9TXHP4md0fhRV0n/iY6vf6geY0P2SA+ynLn8W4/4DW1VXS7BdM063tUOREgUt/ePc/icmrVQUfLH7dTOlr8N2j5ceJbYr9cPivCf29J1udW8EzIcpJZzMPofKr3v9uAj/AItiO/8AwlFr/wCzV87ftwAwat4UtD/y7RXKL/ukxMv6MB+Fd9HeHzOGr9r5H3XWbpP7y61Kb+9cbB9FVR/PNaPSszw382kxynrM7yn8WJ/lXmnoGpWaT5niJR1ENqT9CzD/AOJrSrL0/wDeazqkvUAxxD8Fyf8A0KgDUrmviAd2gPF/fJP4KjN/QV0tcx41/eQOn/PO0nlP5BR/M1S3B7Hl37HFoL/9ny1tzx5ktyoPofNfB/OveNKuzfadbztw7IN49GHBH5g14f8AsT/8kK03/r4uP/Rr17PpX+jX2oWh4CyCdP8Adfk/+PBq2q/Gzkp/Ajxj9tj/AJIVqX/Xxb/+jUr1vwr/AMi3pv8A1wT+VeQftr3EZ+DM1iGDXd5d28VvAOXlfzFO1R1JwDwPSvW9FZrHwnas4KtFagkHthaUv4a9X+hcPjZN4f8An09pu800kn5ucfpitOqWjQ/Z9Js4z1WJc/XHNXawOgzbT97rmoP/AM80iiH6sf8A0IVpVmaL876hN/fumAPsoC/0rToAzb395rWmx/3BLKfwAUf+hVpVmp+98RSntDbKv4sxP/sorSoAzPEK+Zpvk95pY4/zcZ/TNadZuqHfe6ZD/enLn6KjH+eK0qAPOf2if+SK+L/+wdN/6Aasfs5/8kS8H/8AYOh/9AFcj+2FqtzpXwO1k20nlmdooHOAco0iqw/EEivU/hzo1r4f8DaHp9mnl21vaRxxqSTgBQBya6NqXzOaX8T5HzF4vu0179rLUZrPMkWnaWlrct02SFi4HPX5TnivTGOBXlOjLu/aT+JHs9v/AOiq9WYcV69NWgjzp6yZm3r4U5rw/wDaDl3+Crkf9NI//Qlr2rUm2qf8+leF/Hp93g25/wCukf8A6EtaEHb2Pxj+LGu/A648SyJ4dPhaNGgMcglS4kVX2YXkjcSMDnrivMP2O4fs3x5+G19G7pcNqd5FuDHhVtQRj05kb867JFZ/2BNqgknUMYHf/Tab+xh8KNavrpfijPe2Vh4Q8GXE880k/Ekk0kSRhF3FFwSyKCWADA545HzWTQpxeI5IqNqko6K2i2v56nvZpKclR55N3gnr5n6O6j4e8b2KD7H8XfGCKv3Y7s2l0n0IeA15j460nxtrEyPrV/4Z8ZxRcoNb0NUmU84KyoxCnk8hO5rmNb+OHjvTRKqanod5MpG+2vv7OjlUYH8C6mHGev3W6+mKzNY+OXiTTVi/trwq0yEfvpbCK6Xy/f5oTHyOf9bj3r6U8E4HxdosEEjvq/h7UdGnVi66hY3J1W1i4IOFfE0Qx/zz2kYHNee+NlttLu/AnjMzjXNH0f7XHqctg7O+Jdyq+GO7ABAYElgBznGa9ZuPjR4d1tmEkkmn4coz3BR4gwOCDLGzxg/Vq8W/ab8QWvh3QjYaPHHDea/8128Ee7zYY1YlvTPzDLdcE+nCaurDWg79kX4TeG/iZ4A1uPxBYJdRtqDWonBKyQFo4ypVhyATlT67hXAp4Qs/h5r/AIu8NSDdqmmX6IlwGJ82BlZkB6DcAckgCug+Bfg/xBe634207wH42l03RbS3gu98tsJPtCuhdCVbG1hjBIA6D0FcB4S8Q6h4t1LxBq+q3Bu9RuXtmmnbblyEkGflAHQDtXy9GFX+0K/728U4vl1um7W8tVe9vI+hqzpfUqK9naXva6apXv56O1r+Z2WnnOD/AJ7VtwdBWHp4zj/PpW5b/dFfUnzprfs+gH9psZ5/4k7/APoxa+xvFn/Is6n/ANe7/wAq+NvgDMsX7TsSn/lppLqPrvB/pX2T4s/5FnU/+vd/5V5eI/iI7aPwHgH7Jvz/AAA0qH/ntfPH/wCTDE/oDX0VXzp+yCfM+EHhmHsby6lP/AXk/qRX0XXNW/iSOyn8CIbyb7PaTy/3EZvyGag0WH7PpFnGeohXP1xzUXiJiNGuVHWRRGP+BEL/AFrRVQihR0AwKxNBazdF+dr+X/npdPz7Lhf/AGWtEkKCT0FZ/h4H+x7dz1kBlP8AwIlv60AaNZqfvfEUp7Q2yr+LMT/7KK0qzNM/eajqkvbzViH/AAFB/UmgDTryn9ppt3wh16L+9aTufosLn+eK9Wryf9o4+b8NfEy9otGun/ErgfyNXD4kTP4Wa37Pn/JCfCn/AGC4v/RYr59/ZciZ/in8WHjGZYL0zp9VnmP64I/GvoL9nw/8WJ8Kf9guL/0WK8N/Y/w3xl+KfcG8b/0dPXX0qf11OLrA9m/Y51o2H7UPxlnjsbu/jljsD/oiByo8tiCQSPXtX23/AMJ5psX/AB9Q39if+nmxlUfmFIr4t/YWgNr+058aIT/yzSxRf90JIB+mK+9awq/F8l+R00vh+88B/a28Y6Jqf7NnxChttUtpJn0e5CxeYFcny24wea+Jv2O9JfxL+0X4AtpLpofsHgtLm1fy1k8mQsqMyhgQCVYjI5xX3b+2RbQyfs0fEN3iRnXR7khioJB8tq+G/wBiG/ttO/aU8HzXdxFbRf8ACCRjfM4Rc+YnGTW9L+GzGr/ER9m/tdeHL63/AGbviDNN4gv7pU0i5JiZYlRv3bcHCZx+NeHfs+f8kJ8Kf9guL/0WK94/a/8AF+iXX7N3xBt4NWs5p5NIuVSOOZWLHy24GDXg/wCz5/yQnwp/2C4v/RYrP/l38x/b+R8Pbh/wnu3Pzf8ACeZx7ebX1f8Atn8fAK9/6623/o1K+XPANoNQ/aRgtjg79f1MDPY7WwfzxX07+2Jd/bv2ebqc8M8ttuHo3moCPzzXVUf7yCOeC9yTPqLwcAPCulADA+zJ/KkugdR8R20HWCyT7Q/oZGyqD8BuP5UnhSRYfCWmSOQqLaozE9gFp/hvdcWkt+4Ie9kMwB6hOiD/AL5AP4mvOe56K2Neiiq2pX8emWE91L9yJSx9/QfjQB8pftqzvea58PSCfItfEtnAPQudzN+Q2j868O/b0uLZ/GXhqKKZGuUsWaWIH5lBKhSR77SP+A17p+2DaSWelfCwT/8AHzJ4otppj/tsXY/lnH4V8z/tsf8AJZYv+wXB/wChSV30FrH5nDW+18j9AdUn+zabdS5xsiZv0NJpUP2bTLSLoUiUH64FQeIvm0qSMdZXSL/vpgP61ogYGK809AWsvQPnhu5v+et1Iw+gO0f+g1ozSCKJ3PRVLH8KpaBGYtFswfvNGHP1PJ/nQBoVzHiX97HrbdorAJ+LFif5Cunrl9V/eaR4mm9d0Y+ioB/PNUt0D2PN/wBif/khWm/9fFx/6NevZ77/AEXV7G46LLutnP1+Zf1BH414x+xP/wAkK03/AK+Lj/0a9e2a5A0+lz+X/rYwJY/95TuH8q2q/Gzkp/CjwH9r/mb4bjt/wktr/Nq9s1gkeHZFHV41jH/AsL/WvD/2tp1uh8MZk5STxFaOPocmvcNQ+e102Lr5k8WR7L83/stE/gj8zSn8UjVVQqhR0AxQSACT0FLVTV5vs2l3cvdYmI+uK5zcg8Og/wBj27nrJulP/AmLf1rSqCwh+zWNvF/cjVfyFT0AZumfvNR1SXt5qxj/AICg/qTWlWZoHz2Uk3/PaeWT8NxA/QCtOgDMm/e+IbZe0Vu7/iSoH8jWnWZa/vddv3/55xxRD9WP8xWnQB4F+2xJs+CV0v8AfuoB/wCPg/0r2zwTcC68I6PKvR7ZD9OK8K/bbbd8JJYx/DKkh/7+xj/2Y17T8PG8rQIrQ/8ALFVdf91xuH6lh+FdH/Lter/Q5n/EfofMOgf8nJfEr/ftv/RVepN9015boH/JyXxL/wB+2/8ARVeov0r2IfAvQ82XxMw9XOFP+fSvC/js3/FHXI/6aR/+hLXueq9P8+1eF/HcY8IXP/XSP/0Jask3f+bA/wDuIf8At5XQ/wDBPbxHqFt411Xwo9npur6br+gS3Q07UbeM2ryxTlI/Owhc/MM7gcjqMkAjnv8AmwP/ALiH/t5Vj9g64v7D4g3HiqPTHm0nQNBntJ7maZIITPJceZHGZHIA3bgO+CRxyM/OZOk3iX/09n+h7mZt/uP+vcf1PvDVdK8RWxaR/hv4fVcgkeH/ABnq2mfXCIAo+gxXjnj/AMP2V1qU99deDPE/h64bLPeWs9trRY8DmWTN0fXO7PH0rudT/a10q3ZIb3QpI5ZCQsdrrGnzOQDj7hnVs+2PpWZqHx28LahCj3L3+k+aCV+32MqJx1/eBTH/AOPV9GeGeAX3hrTdauGj03VLDX7pVZls75Ht75MjkmO6ZnbHPCSxda8j+PHhw+FNP8HOIJLO1+w6pAVuIinlyMrgIeTy2eOTnrlup+qPEUnhvx5ZsI5NP1y03YLxOkwU/UZwf1rw741Q+G/C/hptL8QXs2qWlw6Np1lcSFprdlyHKSfeIwwxuPHTcAaANn9hmygb4V+Or4xqbtpjCZiPmKLChVc+g3HA96+ZfhYcW2rf78P8pa+ov2Gip+DXjjaCP9KfOTn/AJYR/wBc18s/C5iE1RexMR/R6+Py28swx1/5ofkfTY+ywWEt/LM9K07qP8+lbcB+UViad0H1/wAK24B8or7A+ZJ/gyGH7RLyxjMkGjtMo/3ZUJH5Aj8a+0/FEiy+FtRdTlWtmYH1GK+Ov2flD/tM7SMg6NICD/10Wvq3W9Qg0rwBrJvJlhisIZYpJJDgBVBIJJ/2cV5eI/iI7qPwHjX7GI834X6QP+eIuW/Fp2H/ALLX0VXz9+xXZTW/wkgkmieIs7BN4xlTI7Aj2IYH8a+ga5qv8SR10/gRm618/wBhh/56XSZHsuW/9lrSrMvf3ut6dH/cWSU/kFH/AKFWnWJoU9Yn+zaVdy91iYj644qWxh+z2NvF/wA841X8hiqfiEeZp3kjrPLHF+bDP6ZrToAKzNA+ezlm/wCe08sn4biB+gFXrmYW9vLKeiIW/IZqtocXkaPZIevlKT9SMn+dAF6vJvj9+9+GXj5/+eelNEP++HY/zFes14D+1Rqtzp3wT8Uz20nltdXa27nAOYy6RsOfUbh+NaU1eaRFR2i2dD8Crx1/Z00pAxWa30xcEHnBjDA/rj8K8K/YAmkufFfjCWWRpZZIYHd3OWYlpSSSepr6Q0jR7bwr4As7GzTyrN9HEKJknBjXjk/7JP5V82f8E+v+Rm8Wf9e9v/OSutO8Js43pKJ9Ffsf6dc6h+1N8aPs2pT6c6LYndCqMG/dv1DA19sf2X4lt+Ytdtbn/ZurHH6ow/lXxR+x7run6H+1P8aGv7qO0WRbEK0pwDiN+/4190WviTSb7H2fU7OfPaOdSf51z1fi+S/I6aPw/eeH/tcyeJI/2b/iAt3Dpk1udIuQ8lvJIjKPLbkKQQfzr42/Yctobr9pfwck0STJ/wAIJGdsihhnzE9a+5f2xGDfsyfEQggj+xrnkf8AXNq+EP2I766j/aP8IS2NmL6YeB0TyjKI+kqgnJHt+tb0v4bMav8AER91/tg2NtB+zL8RDHbxRkaNc4KIB/yyavnT9neRZfgP4UK8gabGv4hAD/Kvc/2vdT1+X9m34gC40W3trc6TcB3F7vZQY25ACc/nXz9+za5i+DehWx6f2bBOn0aIA/8Ajyn86z/5d/Mf2/kfInwu/wCTobH/ALGLUv8A0Fq+i/2wv9H+CviG07JeW8yf7ryof/Qt1fOnwu/5Ohsf+xi1L/0Fq+kP23oTH8IbmdRxJJFC/wD39Rl/k35101P4sPkc8PgkfRtoxuPCGhaahIe+ijjbHURgZc/lx+NdfGixIqIAqqAAB2Fch8P3/tKzt7vGYrW2S0iP+1gGQ/ntH4V2Nee9z0OgViawo1LVbHTcbolP2qcf7KnCA/Vv/Qa2iQBk8Csbw4DeG71Rut3J+6z2iXhPz5P40DPnb9t51aX4YRAgyHxPakKOuPm5/Wvlf9tj/kssf/YLg/8AQpK+nf2klEv7TXwfRxuT/TDtPTPlrXzN+3B/yW0/9g6H/wBDkr0aOjivJnBW6n3rq/7y402H+9chj9FUt/MCtKsy4/e6/Zp/zyhkkP4lVH9a068s9Ez9elMWjXhHUxFR9TwP51dhjEMMcY6IoUfgKoa989tbw/8APa4iQ/TcCf0FaVABXL3Z3eCtTmP/AC2WeXP1ZsfpiuhvZvs1nPL/AM842b8hmvOfjNLPpPwH8QPbyvBPFpMhEkbFWU+WeQR71cVeSQpOybOc/Yn/AOSFab/18XH/AKOeveq8k/ZYhhtPgv4ft4o1iZbaOVtoxu8xQ+4/Us35V63WlR3m2c0FaKPmD9qL9wfh9Znra+KrdAP9gksv6ED8K9/k/eahpMf9yN5T+ChR/wChV4N+15AYde+HUij5JtftN3+8rNj9G/SveLP97rQPaGzRfxZif/ZRTn8EfmXT+KRr1meIfm00xd5pI4vzcA/pmtOszVv3l3pkP9643n6KrH+eK5zc06iuZRBbyynoiFvyFS1neIXKaLdgfedPLH1Y7f60AP0OIwaPZIevlKT9SMmr1NRBGiqOAowKUnAJPQUAZ2jfvJNQm/v3LAH2UBf6GtKs3w6CdIgc9ZS0p/4ExP8AWtKgD52/bOPm/C7WD2iFqv4tOp/9lFe1+HP9FttCl6Jc2ggb/eA3L/7NXif7X/7z4PeJZu3222jH/AXj/qTXt1lGzeB9PlQZkt4Y50+q4J/MZH410P8Ahr1f6HM/4jPmDQP+TkviX/v23/oqvUX6V5Z4ckWX9o74kOpyrG2IPqPKr1N+lexD4F6Hmy+JmFqzfKf8+leFfHY/8Uhc/wDXSP8A9CWvdNVXAP8An0rwT493kUHhloHOJJpUVBjqRg/yBqyTpf8AmwP/ALiH/t5W1/wTn8aXuh+NPFvhW8F43h7X/D8140VmGknWaBwIpYUVhlw+fl/iKKDx1d4q8Ojw7+wRp6CUyi6eC85H3fMuFk2/hux+FYP7FetWcHxe8I3Md4mkro2h30+q3bEv58X2h2EW3+EruVvlyT0xXzWSTU1iZR2dWf6HvZtFxdCL3VOP6n2b4o03wxPMbe88d+MbTzVGJtZ8La01shxgZWO9SPj3UA+9eZeO/BehPqNvF4c8aeF/EGoKhkCs1vp06N/0zXy2lycYOZgema+ldS+M/gKaElfGOhrlQ22S/jRgCQBkMQRyQMH1rjfFMHh/xnYvvTTtbtGHUhJ0P8xX0p4J8l+KvAN94auFub9bywutjML23kYLzyFM5dwR/vXEfbjGQPJ/jjb3lyvhF9XlGpzPp2psJwFIcorBW+Xj5dqnIznGQT1P1JrngCHQ5TJ4Zv7zw5ODgJays1uRnODCxKY+gBr59/aQtP7P8D6bLqa21nrVncS2tm2npsiuIphmU7Odmdz5HqMg80Adl+xlrFroHwH+IOoXsnlWtvcSySPjOFEEZP8AKvnL4Y6bLHp+oXrFPKeZYVw2TuVST+HzrXsnwA/5NN+LP/bx/wCkyV5V8Nf+ROk/6/pf/RcVfJ5fHlx2LkvtTS+6Nz6PGy5sJh4vpBv75WOx0/8Ar/hW3B0FYendv8+lbkA+UV9YfOGt+z5/yc2P+wM//oxa+gf2iP8ARPhj42Xol3pEsg/3kQg/oV/Kvn79nr5/2miV5C6O+cdv3iV9D/tOwFvgv4lnUfPDZy/98sjKf5g/hXm1/wCKvkdtL+Gyr+zGmPgt4bfGPMtoz+SKv9K9UrzX9nCPyvgl4RHrYRt+ag/1r0quGfxM7ofCjNj/AHviGYjkQ2yr+LMT/wCyitKszS/3l/qk3YzCMf8AAUH9Sa06gozdV/eXumQ+s5kP0VSf5kVpVmy/vfENuvaK3d/xZlH9DWlQBneIXKaLd46unlj6t8v9avxoI0VR0UACs7XPnSzi/wCel1GPyO7/ANlrToAQnAya+d/2sBn9na7c9ZbiKU/8CnU/1r37VZvs+mXcucFImI+uDXhf7YUP2b9n65i/uPar+UiVtS/iR9TOr8DPTtajP/CrIp1Hz21osv4bcN/46TXy3/wT7O3xJ4tJ4At7fJ/GSvrN0EnwxZWGVbTsEf8AAa+cv2BbC3Hg3xVeiFBdNd+UZcfMUEakLn0BZvzNbw/hz+Ryy+KJ7p+w7EdT/aR+NepwRmfTWeztxcquYzIiOHTd0yDjI7ZFfbN14a0i+z9o0uzmJ7vApP8AKvir/gncNZFt8VX0z7Ayf8JXdB1uw4YnCdGX/CvsT+1fEtvxLoNtc/7VrfAfo6j+dY1vjsdFL4DyH9rvwToth+zh8Qbm1sVtpY9IuWUwuyDPlt/CDg/lXxp+wbD5P7TvhX0fwPG4/F48/rmvsf8Aa98Uzn9m7x/BeaJqFiZdJuEEjiN4wTGwGWVj/KvjP9ivTG1H9pLwfAbm5sj/AMILE2+2fY/+sTjODxW9L+GzGr/ER90ftott/Zg+IZ/6hUw/8dNfMXwNItPg74FuicI+mJbyH/ejBX9Rj8a9W/4KBQTeDf2ZPEtzbalqN1JcmG0dbq7d02SSojfKMA8Ma4DS/DMXh/4F22lWru62emJ5TufmJRQQf0rP/l2vUPt/I+L/AIVsJP2ntPdSGVvEOpEEdD8rV9PftsgH4F6jkdLi3I/7+pXy18AVA+I3wtbHLXF6SfXh6+pP22XUfA2/QkbmuIAo7n96ldVVfvYGEP4cj6Q+Htkun+CdFgHJFshY/wB5iMk/nmuirI8IAr4X0oEYIt0/lWvXns9BGT4lncWC2kLbbi9cW6EdQD95vwXJrSt4EtoI4Y12xxqEUegAwKyYP+Jl4kmm6w2CeSnoZGwXP4DaPxNbVIZ8pftH/wDJz3wf+l7/AOi1r5l/bh/5Laf+wdD/AOhyV9KftNXAtf2l/hBKfug3efYFFBNfNf7cP/JbT/2Dof8A0OSvRo/FH0/U8+r9r1PvaAeZr12//POCOP8AElif6Vo1naUN9zqU39+42/gqqP5g1o15Z6Rmaj+81TS4uwd5T+CEfzatOs0/vfES9xDak/izf/Y1pUAZviIkaNcqOsgEY/4EQv8AWuK/aHUL8E/FwHAGnTAf98Gu01r5zYQ/89LpMj2XLf8AstcZ+0T/AMkV8X/9g6b/ANANaQ+JEz+FlT9nj/Rfhh4Ifol1pMUR/wB5UDL+hb8q9bryX4JIR8AvB9wgzJa2NvOB/uqM/wDjua9ZRxIispyrDIPqKqfxMwj8KPnT9r//AF/w2/7GW1/m1e16J+8ur+XtujiH/AUH9WNeJ/tfMGvPhrGDmQ+JLUhe+MmvbfDA3aYZf+e0rv8AhuIH6AVU/gj8yqfxSNasyf8Ae+ILVf8AnlBI/wCJKgfyNadZtr+916+f/nnFFEP/AB5j/MVzm5pVma588dnD/wA9bqMEewO4/wDoNadZl9+81nTI+y+ZKR9F2/8As1AGnVTVpvs+l3cvdImI+uKt1meIfm0tou80kcX5uAf0zQBb0+H7NYW0X9yNV/IVYoqK6m+z200p6Ihb8hmgD57/AGs/n+AGqzf89r5JPw+0KB+gFe+eFtp8L6buxt+zLnPpivnv9qlpJvAPgzw2xCW2t6paWly4++qsdxK++QOua+jLK1Wx0OK3QkrHAFGf92uh/wAOPzOZ/wARnx54AAn+O3xFmhbz7WOaG3WZeVJRCpGfUY5r15zgV5D8DP8AkYfH57/2/df+hV65Lwpr2Yq0UjzG7tsxNVICn/PpXzl+0NhtP08ZC5uBye3yGvoXVm4P+fSvnf8AaE50ywP/AE8f+yGqEe3fFR1T9g/w6pYZa0sMD1+aI10f/BPjxbovjbwd438EpoOhWnjL7Lby2eomb7FLdw+f++V2jCnCrnnOCJAD3z5X8e5ZE/ZM+FCI5UP9nBAOAf8ARn61a/YBsre8+OGjW88Mc8E/hi7SaKRQyyKbwghgeCCOMHtXy/D9Pkw9SXepN/jb9D6DOZ81aC7Qivwv+p9Wa38CvG2ryTmOCy1WC3cxta6d4h+1yofQpJqwTIHrHnnha8j8afCC/wBI1SN5fDV/pbJwpjsXlO7/AG5IbRcDIHP2j1NfV3iP4KeAb1zNJ4Q0dJ/+esNmkTj/AIEoBrzjXfhbYaSzvoWra54fcjhbHU5TGP8AtnIXT9K+oPnz5ivtc8V+FrZpo9Ya+SH5pra/AdYBnhTIZHXd1BDXAI4+WvNvj/Ivi7XNEl1GddMiXQrjULV4T5onnEe/yQcDbyo+YgfgTX0X4y0DXIpi91Bp/iltpUXjxix1RCc5ZZ48KT07L05zXz/8fbf7T4a0jWLKNlg0cXun3kF/GIbhZbjnBCqFYjzd24cN97JJJoA2PgEFH7JnxY2kn/j46jH/AC7JXk/w0b/ikpR2F7If/Icder/s/jP7JvxZx/08f+kyV5P8NVK+FZgQQReyAg/7kdfMZf8A73if+vn/ALYe/jf92of4P/bjstP6j/PpW7bn5awtO6j/AD6VuQdBX054B2X7HiK/xa8euwBZYrYAnqOHr3f9o3n4JeMP+wdN/wCgGvCv2Ov+Sr+P/wDrla/yevdf2jP+SJeMP+wdN/6Aa8qr/G+47af8Mr/s7f8AJFfCH/YOh/8AQBXo1ec/s7f8kV8If9g6H/0AV6BeTfZ7OeX/AJ5xs35DNcs/iZ3Q+FFPw/8APYNN/wA9ppJPwLnH6YrSqno0P2fSbOM9ViXP1xzVysyjMs/3uuahJ/zzSOIfkWP/AKEK06zNDPmrez9RLcuQfYYUf+g1p0AZl9+81nTY+oXzJSPooUf+hVp1mr+98ROe0NsB+LN/9jWlQBm+Ifm0t4u8zpF/304H8s141+2l/wAkN1L/AK7wf+jUr2TV/wB5daZD/euN5+iqx/nivG/20v8Akhupf9d4P/RqVtS/iR9TOr8DPVv+aaf9w/8A9lr5w/YGudvhDxRAejXJdfwjQH+a19HkgfDPJOP+Jf8A+y18z/sPf6P4E1q7HSLVCjn/AGXhjU/rtP4VtH+HL5HM/jifSv8AwTYdRZfFddw3f8JZdHGeeiV9qV8Nf8E7vDWl67ZfFV7+yiuXTxXdKrsPmAwnAI5r7B/4QLTYubWfULE/9O99KAPwLEVlW+NnRS+BHzx/wUudl/ZZ1vBIzdWgOO/+kR14l+xZe6S37TPjrVtcuEiuNJ0qysdPY5ASJ03uoC9fmVTk16n/AMFHdAuNN/Zk1iT+2b+7hF1agwXRRwf38ffaD+tcL/wT8AP7QvxZ4/5dNN/9FPW8P4L/AK7HPU/ir+u53n/BR7xfpOsfsx6zbWd0Z5WurUgCJwOJ4z1IxVH/AJpp/wBw/wD9lrp/+CmP/JrGtf8AX3af+lEdcx/zTT/uH/8AstZv4I+v+Q/tP0Pgr4Bf8lE+Ff8A13vf5PX0z+1sBJffDKNuUfxPaBlPQjJr51/ZFtYdY+MHhqC8zIljY3FxbLnG2QyFSeOvyk9a+i/2s/8AkJfDD/saLP8A9CNddV/vkYQX7tn1taALawgDACDj8Kj1O+TTNPuLp+ViQtj1PYfieKltf+PaH/cH8qytX/4mGq2GnDmNT9qnH+yp+UH6tj/vmvNPRRZ0GxfT9LiSXm4fMszesjHLfqcfhWjRRQB8mftN2wvP2k/hLA3Akjvkz6ZiFfLv7Y979u+LcTlgZV02FJR6OHkDD86+oP2irtZv2qvhNAvJhW6LH3MY4/ID86+Of2l0CfHTxcB3uUP5xIa9KiruL8v1PPrPdef6H6U+Hjv0xZO8skkn5uSP0xWlVPSIfs+lWcfQrEoP1xzVyvKPSMyw/eavqcnZTHEPwXP/ALNWnWbofzw3U3/PW5kYfQHaP/Qa0qAMy8/e65pyf3EllP5BR/6Ea4z9on/kivi//sHTf+gGu0j/AHviGdu0Nsi/izE/+yiuL/aJ/wCSK+L/APsHTf8AoBrSHxImfwsn/Z2QSfA/wgrDKtpsIIPcbBXcaA7DThA5zJbO0DZ/2Tx+mDXEfs5/8kS8H/8AYOh/9AFdrD/ouvXEfRLqISr/ALy/K36FaqfxMwj8KPnz49OF/aN+FZf5o0W8faenEQP9K990KIwaNZIevlKT9SMn+dfPP7RTEftA/DIDq0d6g+piA/rX0nGgjjVB0UACnU+GPp+pVLeXqOrN0f8AeS6jN/fuWUH2UBf6GtEnAzWb4bBOjwSHrKWlP/AmJ/rWBuadZq/vfETntDbAfizH/wCJrSrM0395qeqS9vMSIf8AAUB/mxoA06zdW/eXWmQ/3rjefoqsf54rSrNn/e+ILRe0UEkn4kqB/WgDSrO8QuV0W7A6unlj/gR2/wBa0azdb+dLOH/nrdRgj2B3f+y0AeEftaII5/hqo6DxJaAfm1fRf/MO/wC2X9K+c/2vZRDJ8OpG+6niO2Y/hur6LBzpv/bL+ldD+CPzOZ/Gz42+Bn/Iw+P/APsP3X/oVeuyj5TXkXwM/wCRg8f/APYfuv8A0KvXZBla9pbI8x7nM6uvB/z6V89ftBrjR9P/AOvj/wBkavo3VIsqc/56V88ftERn+ytOUAljcjAH+61MR1nx7v8Ayv2YfhLbhEk/cRMwcZAzbuoP1+8R7gV6z+x38LfC8/wH1/xNpuqyTeNUl+watEiNLLYWMkjqFRNyZDsFBK7vvMf4cr4d8YdUtNU/Zl+Hzw3Mc0sUtvbFFYEoI7Zw2R/vFhXsH/BOXRpJPE3j7TbHVNR0X7TpelSm80yfyrhGZGkJV8HGSSOnQkV4OTQ5KEl/en/6Uz2M0lzVk/7sfyOuuNFurFpIrXxPqHh9Y+WupybRSQSFwW1UEqcgcR9SBgkDOZfeLPHOjSMkXiC4vra3jJknukMsD9QCHe2RcYx/y8HoTmvrrV9C8c2sRjtvi54te26G2v8A7JdoR774c/rXinjbwV4jubhp7618IeJpVO5Li40o6fdg+08LEg/QCvePHPF5PjPqNugn1/SFFqOTf2Dq0W3u3DOnpwJS3XivIv2l7pvGWteH4tLZ7+0h0+bVZ4FLKBAqK+9hjK5UMASOpx3r2HxNpNjBcf8AEwstT8MajnC3s8v9oWh6nBuhidB/vMB6g15D8Wr5fCvi2/vZIbqTTdR8G/2Z9us03wNcOpxmQADaWUcgDO4cAHhPbQaO4+G2m2+i/sQeLb6NQL3Uxd+eytuDsC6cYOPup244rwX4ZknwvMMcC7c5/wCAR1718OSbn9izUoP4YLDUrhvqWkVf5t+VfP3wzlA0KePv9pY/+OpXy2UJ+3xTe/tH+Vl+B9Dmb/dYdL/n2vzud5p/b/PpW1B0FZFgnArZgA219UfOnYfsdyAfFvx8mfmMNsQPYBv8RXu/7Rn/ACRLxh/2Dpv/AEA188/snubb4u+Mrj+FTaxv/uurj/0LbX0J+0fIsfwR8X7mC50+YDPc7DXlVf433HdD+GQ/s7f8kV8If9g6H/0AV2niJiNFulHV1EY/4EQv9a4z9nhSvwW8IAjB/s6Hr/uCuz1r5/sMP/PS6TI9ly3/ALLXLP4mdsPhRoqoVQB0AxSO4jRmPRRk06qOuS+To96w6+Uyj6kYH6msyiLw0hTQ7Qt951Ln/gRJ/rWnUdvEIII4x0RQv5CpKAM3Tf3mp6pL28xIh/wFAf5sa0qzdB+e1nm/57XErj6biB+gFaVAGVMfN8SW0faG3eT8SQo/rXjv7aX/ACQ3Uv8ArvB/6NSvY7NfN17UJf8AnmkcI/Lcf5ivD/2xbiS98MeGfD2Qlrres21nO+PmVS2cr75UVtR/iIyq/AztvivdzWP7OetzW8rwTLozlZI2Ksp8s8gjpXj37C1qL74Y+L4D/wAtLx1B9D5KYP51638YMD9m7xAnOY9IljbPXcqEH9Qa8s/YG/5J94o/6/2/9FR1vH+FL1OV/wARHtn/AATk1m9stL+J7R6Rc36P4ouWdrd0BVtqZG1mBNfZH/CbwRf8fWl6taDuZLJ2A/FN1fKH/BNfjT/iv/2Nl1/JK+0qxrfGzqpfAj47/wCCjvi7SdY/Zj1m2tbwPcG6tSInRkbiePPDAV5p+wnJqiftA/Fb+y4bSVza6dv+1SMgA8psY2g5r2L/AIKYAH9lrWTjkXdp/wClEdeW/wDBPz/k4T4tf9emm/8Aop63h/Bf9djnqfxV/Xc7z/go83iA/sx6z/aCaatt9qtci2aQvnz48feAFUf+aaf9w/8A9lrpf+CmZx+y1rHveWg/8jx1zKsH+GQZTkHT8g/8BrN/BH1/yH9p+h8K/sjlovirY3CAlrfSpZsD0FwN3/jua+j/ANrFg+ofC5lOQfE9mQR9TXz9+xfGsvxhskYZVtGnUj1HnivcP2lJGJ+FsLnMlt4qtYG/4CxA/MYP4101P4yMYfwz7Jtf+PaH/cH8qzdCUXU19qPX7TKUjP8A0zT5Rj6kMfxputXktrokcdv/AMfdyEgh/wB5h1/AZP4VpWVolhZwW0f+riQIv0AxXnHodCemSyrBE8jsFRAWYnsB1p9YniVnuxa6XEfmvHxLjtCvLn8eB+NMD5X+NUT/APDR/wAILmYYuLtr64de6ho12j8FAH4V8k/tNf8AJdfFv/XxH/6KSvsTxao1P9uGxguv38Nl4e862jk5WJzKVLKOxI4yO1fHf7TX/JdfFv8A18R/+ikr0qG6Xl+p51bW78z9OAAoAHQUkjiKNnPRQSadVDXZTDo16w6+Uyj6kYH868o9Mb4fQx6LZ56tGHP1bn+taNR28QggjjHRFCj8BUlAGbpf7y/1Sb1mWMf8BQf1Jri/2if+SK+L/wDsHTf+gGuz8P8Az2DTf89ppJPzc4/QCvJv2wdVudK+B2sm1k8sztHA5wDlHkVWH4gkVpTV5pEVHaLZ137Of/JEvB//AGDof/QBXa63/o5tL3/n3mG4/wCw3yt/MH8Kz/hzo9roHgbQ9Ps08u2t7SOONck4AUAcmt2+tVvbOe3b7sqFPpkU5O8mzGOiR82fH2Lzv2kPhMnUF7jP02rX0fXzV8WLk3v7Qnwfkk/1m27Vwf7yxgH9Qa+laqp8MfT9SqW8vUq6pN9m027l6FImYfkadYQfZrG3ixjZGq/kKq+Ifn0t4h1mdIh/wJwK0qwNwrM0H57Sab/ntcSv+G4gfoBV+eUQQSSHoilj+AqpoURh0azU9fKVj9SMn+dAF+s22/e69ev/AM8oo4x+O5j/ADFaVZmj/vJ9Sm/v3JUH2VQv9DQBp1mX37zWdNj/ALvmSn8FC/8As1adZqjzPETntFaqPxZz/wDE0AeEftdxiWb4cowyreI7UEe3zV75oczS+HI1kOZYYjC/1X5f6Z/GvBf2t/8Aj6+G/wD2Mtr/ADavdrP/AEW5vYOiTwLcIPfbtb+Sn8a6H8ETm+3I+SPgZ/yMPj//ALD91/6FXr7DIryD4Gf8jB4//wCw/df+hV6+3SvaWyPMe5k6mAUP+fSvBPjnELe10vUJIYrqCyvI55baVtomUdU/HOK941OQKD/n0rwT4sahpNh4y8H3eshLrTIdQje7spQWR4Ay+YSo5OB/PGDnFDV1ZgnbVGB8T/DjxeBE1y4+F154VW7KfZtREknlgAqCGj2hVyBjJAySCAeTX0R+xl4V8TeCvh3qvxAi1K3tp/EJi06wihhimdY7MmOSRvMlRMY+XqfvAjrxqftfftC+CPiZ+ztLpnhi/sXMj2zRwtMiT4EiEhYc7hjBzkDoaqfsDeI/FHiLS/HPg29aw8VWEUGn6lY6d4h3ta28soZ3KlPnjOxiPkI78YZs506UKK5YN2823+Y5VZ1nzTSv5JL8j1PW/jl45tZVFvcaPqcmQZLSdbGOXYeQVWPUmk5B/wCeZ7Vz2rftB6nHMq6h4XkkhYAtLZ/aVKk9v30CRk/SSvata07Xre3jhPwz8P2cEKgL/wAIx4u1LT2bGOqAKp6Z+bPPXqTXhni/wzp1vqN3eSeHfFnhWeU5luUjg1NWwBz50BF0QMf3/WtSTMh+LfhvxNPEsV2baSU4VLpNqlv7okGUY+ysa+Z/2o9VTT9RsPC2kwtZ6ZaxNeTQQriImSQkHjtu3HHTLD2x7DrfhKDxElxPpt3p/ig7PLu0td6XSgnOHjkcTjj/AKat0+41eH/HjT/7H19oApVH8L2WFkJ3J+/j+X5gpJGMcgH1A6UAdn8L/it4Z079mDxR4Sur8Q+ILmzuUggdGAfJcqqtjBJMnQHNeW/D2xltdCjnfGy5lZ0x6AhTn8VP6V6he+BviV4W/Z+0bxTLoeieIfCtxBHMI5bbz3s4/Lwku3apHy/eOW5OeBk1xXhrRNW0bwtpK6jbxxQTK09pJG4ffGzZ5IJGcg9OxFePhMHPD1as1H45cz1v09FbWx6WJxUK9OnBy+GNlpbr6vzOmsR8q/StWH7tZViflFasP3a9g806j9k60+3/ABG+I8A4Z4LYKfRsPg/nivRf2xL03v7PV3ODgyyWxYD181Mj864T9jrn4reP/wDrna/yeuw/bB/0f4J+IbPtHewSIP8AZeVG/nurzJ/7wvkdsf4TPbPCMaxeGNLVFCqLdAAO3FS3n7zWtOj/ALiyyn8AF/8AZqZ4V/5FvTf+uCfyp6fvfEUp7Q2yr+LMT/7KK4Hud62NKs7XV8yySL/nrPEn4bwT+gNaNZ2pNuv9Mi9Zmc/8BRv6kUhmjUc8oggkkPRFLH8BUlZviOUxaJeEdWTYP+Bcf1oAfoMRi0ayU9TErH6kZP8AOr9MijEUSIOigAU5mCqSTgDkmgDN0T5/t03/AD0unwfZcL/7LXhf7W//AB9fDf8A7GW1/m1e7eHlI0a2Y9ZFMh/4ES39a8J/a3/4+vhv/wBjLa/zat6Pxoyq/Azsvjl/o3wc8d2vRW02S4QezRkN/wCPA/nXlv7A5C/D3xQScAXzf+io69U/aSQw/B3XbkdDpk8D/RoyR+qj868//ZF0e20/9nbUL6BCtxevcSTNknLKSg+nCLWsf4T9Tmfxp+R67/wTs0R9WsPilcQ6nfWI/wCEqugptZFCNwhyQQQetfYf9ha9B/qPEjSAdBdWUb/qu2vlv/gnB4p0nRf2aNKjvJHtpmu7pmkMDlW/fvzvC46Y719XWvjLQrwgQ6xZM390zqD+ROaxrfxGdNL4EfKf/BR2PX4v2ZNYW/m0+4tftVrl4I3jkz58eOCSK8z/AGD9GtPEHx++Kn2hp/LW001l8md4s5ibrtIzXsH/AAUquYbj9ljWTFKkgN3aYKMDn/SI68Z/YF1eDw78ePiot0lw+bbT0QQQPKcCN8ZCg44xXRD+C/67GFT+Kv67np3/AAUl8KaZo37M+o3FtFKs/wBttV3SXEj8eavZmIrH0abPwqkhc/NbWjQnPoF+X/x0irP/AAUe8ax+IPhX4d8JW9neQf2/rtraG6urZ4liG4tkBgNxyo44rj/jH5/hj4ReJ47ed4nfRHdJUJUh0jIJBHQ4K/lUW92K8w+1I+W/2K/+Syaf/wBgef8A9HivcP2qozbeKvh0v8E/iWxmH+8CVb9NteTfsn28Vt8a9BWJFjDeGQ7BRjLGRST+Jr2L9r2RLe/+GdwwyYvElseOuMkn+QrpqfxkYw/hs+pbQf2jrsbdYdPgVR7yuAT+S4/76rerI8K2ktrodubj/j6mHnSn/abnH4DA/CtevOPQCsXSP+Jhq1/qJ5jU/ZID/sqfnP4tn/vmrWvXz6fpcrxc3D4ihX1kY4X9Tn8Km0yxTTNPt7VOViQLn1Pc/ieaAPlXX/8Ak+kf9i0P/R5r46/aa/5Lr4t/6+I//RSV9i6//wAn0j/sWh/6PNfHX7TX/JdfFv8A18R/+ikr0qPxL0POq7P1P05rN1757SGH/ntcRJ+G4E/oDWlWZqX7zU9Li7eY8p/4ChH82FeUemadQ3c32e1mlPREZvyGamrN8ROV0a6UdZF8sfViF/rQBLosP2fSLOM9REufrjmvFf20v+SG6l/13g/9GpXuyqEUKOgGBXhP7aX/ACQ3Uv8ArvB/6NStqX8SPqZ1fgZ7D8P7g3PgzSS330gWN/8AeXg/yroK5fwL/o1hFb9Fkt4rlB9V2t+qg/jXUVL3Mz5f+L8Rtv2ovhrFj5DJdzL/AMCiGf8Ax4N+dfS1fO3xuA/4aa+FBH3sXYP/AH7FfRNaVPhj6fqVS3l6mbq/7y402H+9chz9FVm/mBWlWZcHzfENmnaKCST8SVUf1rTrnNjP8QSGPRbzHVoyg+rcf1q7EgijRB0UACqGu/PDaw/89bmNSPYHcf8A0GtKgBOlZ3h35tJikPWVnl/76Yn+tWNTm+zabdS5xsiZv0pdNg+zadaxf3IlX8hQBZrOsP3mpalJ6OkQ/BAf/ZjWjWboXzwXM3/PW5kYfQNtH/oNAHhX7W//AB9fDf8A7GW1/m1e66uRbW9hedFjHlOf9l1x/wChba8G/aQZvEfxS+F3hZcQiXUzqBuDzjyRu2498nnPFe7+M7XzvBeqwZ5+ysAfQgcH8xXTL4Io5vtyZ8l/Apg+u+PXU5RteuSGHQjdXrkpwK8i/ZiQD4c2745aWQk+vztXrk4ypr2VojyzA1Z8qf8APpXzB+0N+81jSwCOI5Dycf3a+l9YcgH2/wDrV8xftAtnVdL/AOucn/stMD2D9r74Y6H4O+GHhXV7PTrS01W/u1E8lrEqDaYpGI4HPIBJPU13X7EDa78Mf+Es8X6z4burHTrm30zTrU6lLHYi4kjjKuEaZlBO3LDHXGOOccN+3hqNyLD4d2Qlb7K1lLIYv4Sw8oBvqATz7n1r6U+AnxQ8NfF74babod9P4z07WfAuWvbW2sEmvrsXLLtcBgThQp4GOATjGK+a4ddSeXQqVJXbcvuu1+h7WcqEcbKEFZJL8l/mdVqf7Vmhw3CWl/o91BducFLXULG4wCflIAnDsCMHhT171l6r8ZfDF35iXd3NokoGSusW0loMZI++4CHoejGtfxIfBrT27XfjHxXFbsQ0kPifwtrMsUZHqqXiQDjHG0jrnNeI+IPBmn6lqU7eHdZ8J65cykuG064t7a6QH+FYQsMqjno05Pqe9fSnim34p0XQfGTLK6W94Qcx3ds48yM9ikinKn6GvmX9o2CCY6b4dm1QalrsEjSwXFwiRuICrbYnctySVOGA5O0EAnJ7rXfC2peErtLg/wBo+Gb5kCxzMEAmPPLgkq/JwFM8rd9vQV5n8WNGsJ9W+Lt1qpMus6a+mwafvcnyw7Ay7RnGMDr7n1NJuw9z6S+Cf7ZngTQvgTpXhLxLp+pWRttMWximmtGeC9dV2MI2Td0IHUDrXyx4WuZ5/DlhlytgWmktLY8/Z0aVxs3dTypOT616K1pC/wCwIk5iQzJqBCyY5GbznmvOfCH/ACJ2if8AXGT/ANHy1wYHGvGqrdW5JSj62tqdGJwiwrp2d+eKl6XudXY/dWtReIz9Ky7A/Iv0rUH+qb6V6BzHpf7DtrFJZ+Nb50VrxtVeEzEfMUVV2rn0G4/nW3+29EYvhFdTqPllkhhf6iZGX/2b86yv2Gv+QF4z/wCw1L/6CldH+2yoPwL1Ekci4tyP+/qV5cv4/wAztX8I9c8K/wDIt6b/ANcE/lTtM/eajqk3bzVjH/AUH9Sab4V/5FvTf+uCfyp+gfPZSTf89p5ZPw3kD9AK4Xud62NKsyX974ht17Q27v8AizAf0NadZlp+913UHP8AyzSOIfqx/wDQhSGadZevfPFZw9RLdRqR7A7j/wCg1qVmX483WNNj/u+ZKR9F2j/0KgDTqnrEhh0m9cdVhcj64NXKz9dOdP8AL/56yxxfm4B/TNAFqzh+z2kEX9xFX8hivn79rf8A4+vhv/2Mtr/Nq+h6+eP2t/8Aj6+G/wD2Mtr/ADat6Pxoyq/Az0L9omJZvgP4qVug02RvxCE/0rz79lP/AJNlP/b3/wCjHr0T9oP/AJIT4r/7Bcv/AKLNea/sm3Al/ZsuYujRG5/Iu5z/AD/KtY/wvmcz+P5H0h/wTP8A+TWdF/6+7v8A9KJK+obnSrK9z9os7efP/PWJW/mK+Rf+CcVvrp/Zk0eTT7yxWA3V1iC6t2JB8+TPzKw/lX1F9s8U23+s03Tbwf8ATC7eM/kyH+dY1v4kjqp/Aj4+/wCCkWhadpmgfDmC0sYLaC+8TW0N1FEgVJkw52sBwRkA4PpS/wDBOTSY9Z8WfFrxfKSl7Lq40vyY+I1jgUBCB1yQ/PPbtUH/AAUZ1O/vdN+F8d7pMmnbfFFsQ5mSRW+V+AVOfzFL/wAE5Ytak0z4of2ZcWUEX/CU3O77TCztnanTDDiuhfwf67nNL+KbH/BSn/kH/Cf/ALG20/k9cj+0pAW+B3iCdR88NjJ/3y0ZU/zB/Ctr/golbaxDafCptSvrW6Q+K7UKlvbmPBw/OSxzVL9oTn4E+K/+wXL/AOizU9IA95Hy9+yn/wAlt0D/ALFhf/Q1r2L422q63+0l8ItKuyZtPee4na3flGdEDKSOmQR1968r/Yn09dd+JF/qU7FZNJ0e2tYVXoVcbiT75X9a9b+K3/J1fwf+t5/6Kreo/wB6/T9DKHwL1PrkAKAAMAdqWio550toJJpG2xxqXY+gAya889Aybj/iZeJIIesNgnnP6GRshB+A3H8RW1WT4agcWDXcy7bi9c3Dg9QD91fwUAVrUAfI/iSZYv264Q3/AC08OBR9fOY/0r49/aZIPx08W45/0iP/ANFJX1n8Q1cftoXEsX+tg8MCZMeqzk/yBFfEXxLvJNQ+Inia4lled31K4+d2LEgSMAMnsAAB7CvTor3k/I82rt8z9ZKznHma/F/0ytmP4sw/+JNaNZtkfM1rUW7IsUQ/It/7NXknqGlWXrv7w6fD/wA9LpMj2XLH+ValZl2PN1zT0/55pLKf0UfzNAGnXhH7aX/JDdS/67wf+jUr3evBv21HC/A+/H965gH/AJFU/wBK2pfxI+pnV+BnrWkf6NpHhy76ARrA/wDuuBj/AMeC11Nc/pNob7wRaQrw7WqlD6MBlf1ArY0+7F9YwXA48xA2PQ45FQ9zNbHznqiDU/20bOG6/fxWegma3STkROZdpZR2JBxkV9FV87Tf8nr/APcu/wDtavomtKv2fQqls/UzLYebr99J/wA8oo4h+OWP8xWnWbo/7ybUZv79yyj6KAv9DWlWBsZuofvNW0yPqAZJT+C4/wDZq0qzv9Z4g9RFbfkWb/7CtGgDN8RfNpUkY6yskQ/4EwH9a0ulZmsfvLjTYf71yGP0VS39BWnQAyWQRRO56KpY/hVLQIzHotmD95ow5+p5P86TxDMYdEvGX7xjKj6nj+tXYIhDDHGOiKFH4CgD55+Mv/JzPwn/AO3v/wBFivf/ABZ/yLOp/wDXu/8AKvAPjL/ycz8J/wDt7/8ARYr3/wAWf8izqf8A17v/ACrqltD0/U5ftS/rofI/7Maj/hWFoe/my/8Aoxq9Xm+7XlP7Mf8AyS60/wCusv8A6MavVJ/umvZPMOZ1zhTj/PSvl/4+nOq6Z/uP/wCy19P6wcq3+fSvmL4/jGraZ/1zf/2WgD2X9uK+hsl8AF7OK6eTSbmNDMTiMsIRvAB5IGcZ6Eg9q9h/YU8c+G9D0z4h+J9c1CGCbUfEL28d7IGkd4xGGjXIBOwAtjPAzjuK8S/bwGT8ORnH/Evm5P8A2yr379i24tPir+zNa6Rb6Zpmna74f1trOESakbG31BWiEpeQEgPL2AzzlzwCcfNcOL/hLpP/ABf+lM9rOX/t1T5fkj6H1P4x+BLtvKj8Y6EZiMiM6jEr4xn7pbPSuE8a6H4f8YWW+5sbDVraQbklaNJVYdirf1FZXi/9n/xp4g1Fp3023vNkmGh0rW4pQi54UpLqxQD/AHYf+AnpXhHjnwNe+BNQvHeyvdCuU+RnRWiibAKgmWOK2Qn389gc9819KeKdRrPgmfSWkj8P6tc2MbZzYXkjXVm4/umOQnaP90ivB/j3o+lO9vr2sWtzZeKplkkkttOPmwyyQq5jeTjG3CjnhtrH047RPGHirw8sNwbqDWdPJCBbuX5pDjpFNja/r8skx9Aa8F+M91eR/ELxZfi9uInhuIdP2hsjyZYH3rg9vlPHue/NAHvfjDw/D4a/YD0tYHaT7V9lvG8zBw0k6SEdOgLEfSvD/CBB8H6Lg5xFJn/v/LXunxOS8uP2GNAlkbyLOK0sFjiH3pDuiBZvb0H414R4Lbf4S0oYA2o4z6/vpD/WvleHW3RxDk7t1Z/oe9nNlUopKyUI/qdfYfdX/PpWqP8AVN9Ky9PHyr/n0rUA/dN9K+qPBPUf2Gv+QF4z/wCw1L/6CldJ+2x/yQrUv+vi3/8ARqVzX7DTD+xPGgzyNZkOP+ApXS/tsEf8KL1Hn/l4t/8A0aleVL+P8ztX8I9W8PTC38JWUp6JahvyXNXNDi8jR7JD18pSfqRk1j2zlPAMAHDParGPq2F/rXSIgjRVHRRgVxPc71sOrN0X52v5v+el04B9lAX/ANlrRJwCfSs7w6D/AGPbuesm6U/8CYt/WkM0qzE/e+IpD2htlH4sxP8A7KK06zNN/ealqkvbzEiH/AUH9WNAGnWbq/7y502L+9chj9FVj/MCtKsy5/e6/ZJ/zyhkkP4lVH9aANOvnj9rf/j6+G//AGMtr/Nq+h6+dP2kHbxF8Uvhd4WGIRLqf9oG4POPJG7bj3yRnPFb0fjMqvwM6L9r7VbrSvgFqhtZTEZ/JgcgA5RnVWHPqCR+Nch+yyTa/AKF/wCC5ivIj/vLK7L+hb8q6b9tNPL+A1+vXbNbj/yKlc7+zZEW/ZaEqjL28lxMPoJXz/47mtl/B+Zy/wDLz5H0j/wTRvrZf2YNGhNxEJhd3eYy43D/AEiTtX1l1r5C/wCCcHhvStX/AGXdGe9061upDdXY3yxKzf8AHxJ3xmvps/D/AEROYIJrNvW1uZY8fgGxWFb+JI66fwI+TP8Agph/yB/hZ/2NVt/6DJS/8EzyBo3xTycf8VVc/wDoMdZX/BRnQRpGmfC9lv766RvFFsPLu5/NVflfkZGf1p3/AATj8MaXrumfFCW/s47p08U3KqXzwNqds10L+D/Xc5pfxTV/4KTyo9h8KArqxHi20yAc44esD9oP/khPiv8A7Bcv/os1e/4KK+G9L0O1+FL2FhBaO/iu1VmiXBIw/Bqj+0H/AMkJ8V/9guX/ANFmp6QB7yPnb9gxgPGHilc8mwsjj/gDf416j8Vv+Tq/g/8AW8/9FV5B+w7Kbbxxrk2fkNrZQv8ARo2x/wCPBa9f+K3/ACdX8H/ref8Aoqtp/wAZ+n6GcP4a9f1PrqsXxGftn2TS1PN5J+8x2iXl/wA+B+NbVY2lA3+sX9+33EP2SH6KfnP4tx/wGuA7zYAAGBwKWikJABJOAO9AHyP4pAb9t+UHkf8ACLkEf9tjXwb4+iEHjrxHGOQmpXKj8JWr7g+HN1/wmP7VHxL1i9Jlm0+1htrNumyFlLYwOuSAea+I/iL/AMlB8T/9hS6/9GtXqUd7eSPMq/qz9Z6zdF+c383/AD0unwfZcL/7LWizBVJPAHJrO8OgjRrZj1kBkP8AwIlv615B6ppVmxfvfENw3aK3RPxZmJ/kK0qztNXdfanL6zKg/wCAov8AUmgDRr58/bckx8G5Ix3uYmP0DqP619B183/tjzvqFj4U8PE7bXV9TgtJ3H3lV5Acr75j71tS/iIzq/Az6F8J/wDIs6Z/17p/KnaN/o8t9Z9oZS6D/Yf5h+u4fhVjSbVbHTLW3QkrHEqjP0qvc/6LrlrN/BcI0Df7w+Zf/Zqhma2Pn+b/AJPX/wC5d/8Aa1fRBOBmvneb/k9f/uXf/a1e/wCqTfZtMu5f7kTMPyNaVfs+hVLZ+pX8OjOkwyHrKWlP/AmJ/rWlVbTofs2n20XTZEq/kKs1gbGbYfvNX1OTspjiH4Ln/wBmrSrN0P54rqb/AJ63MjD6A7R/6DWlQBmXP73X7JP+eUMkh/Eqo/rWnWbb/vdfvG/55Qxx/iSzH+laVAGdri+bbQQH/lrcRr+Abcf/AEGtGs6/zJqmmxjorPKR9FI/m1aNAHzn8Z3CftMfCYscDN2PzjAr6B8Wf8izqf8A17v/ACr51+PUby/tEfC4R/60LeMn+8IwR+or6E8Q3C3fhC+nT7klqzj8VzXVLaHp+py/al/XQ+Tv2Y/+SXWn/XWX/wBGNXqk4ypryv8AZj/5Jdaf9dZf/RjV6rOflr2TzDl9Y4B/z6V8xftAHOq6X/1zf/2Wvp7WhhSf89q+afjTYTa14p0PT7UB7q4JhiQnG52KhR+J4pNpK7Gld2R6J+3A15L/AMK+mulWIPYTeXCOSi/uup9T+lfQv7FGi6bN41+KnhhbG013wjb6hDewXF1AsyC6eM+agJG3KYC4AyM89a8N/b7tmsrr4ewSj5o7GZWAP/XKvZf2dvh14fvf2WvDOs6c2q3B1PUbm61vdbXFx/pURWJdzRMERTuYjcVZgowc7q+c4efNllGS/vf+lM9jOFy46on5fkj6T1/4N+Bzc/a4vC+m2l2vS4soBbyD6NHtI/OvN/EfgGbT5pJ9F8UeIdInJzj+0Xuoz9UnLjH0xXmms6VdaNexC18R3ejQyA+W63UtoHIGMKH1Fy59hCfb2x9S8a+OdG+0TNrbXFhCm7dqcKqjnpjfNDbDBJHSVj9a+kPGDxjpWtwXl3dahYW+vSzqRJqej5sNQAxgFwGKTfQ8cdDXzB+0pbND4zutUQk2viPyNRjRwUki2RldrKfXeD69RivpS2+LV1Jqtpaa5o0lhJdukcNxC4aGRmOBgtgHk4Owvj1xzXyr8dXvde8c+INZJNxplrdRadHPj5Vfyy+wEcEgq+e4yKAPpj4qyKv7BHhxSwDNaaeAPX5oq+efBsMlv4W00So0ZZGYB1wSpkcg/Qgg5969T/aAmkj/AGRvhMqOyq32cMAcZ/0Z64lT/wASjRB6aZZ/+iEP9a+W4cp8mFqyvvUm/wAbfoe5nM+avTj2hH8v+CaVh91fp/hWqP8AVN9Ky7AfKv8An0rUA/dN9K+pPDPQv2H5jBH4sUn5LjVZk/4EqoR+hb8q6L9sVjPa+ArCQlrO88Q20VxAT8kqEn5WHcdOD6VzP7HIMfhXxjdKMta6883HoFQN/wCOk10n7XxDTfDYg5B8SWpB/Fq8t/x2dy/hI9qv4kXT9NtkUKrzQqFHoPm/9lrZrIm/eX+jx9lV5T+CY/8AZq164TvKuqT/AGXTbqbOCkTMPril02D7Np9tFjGyJV/ICq3iH5tMaLvNJHF+bgfyrSpAFZuhfPBczf8APW5kYH2DbR+i1fmkEMTyHoqlj+FUtAjMWi2YP3jGHP1PJ/nQBoVmW/73xBeN/wA8oI4/zLE/0rTrM0j95c6lN/euCg+iqo/nmgDTr5p+PTOn7RPwtkj5eMXcgHrtjBx+lfS1fN/xjcP+1F8K4zyAlySPrGf8K3o/F8n+RlV+H7jd/bSlWf4C38iHKPNbsD6gypWX+yqAf2YnDY2kXYOf+uj1L+1yTH+z9qtqxy1reQw8/wB0TIV/8dIrN8K6WfCP7FFzJY3EyTTaPNd+aGwytIrOcEYxgtx9K3jrSS8zlelRvyPoD/gnFqmrWX7Mejrb6K1/bfaroiWO5RWP7+TPytj+dfUP/CYyQ/8AHzoOrwEdStuJQPxRjXmX7FWlWmlfsx+AFtLeO3WbTIZ5BGoXdI6hnY47liST6mvcK5qrvNs7KatBHwp/wUZ8S2Ot6b8L4rczrMnii2Zo57d4iBtf+8BTv+CcmunStM+KEa6bf3xbxTctutIg6j5U4JJHNaX/AAUxYLo3wtJOAPFVsSf+AyU7/gmd/wAgf4p/9jVc/wDoMddK/g/13OWX8Uyv+Ciuuy6mnwnjk0u909R4ot2D3aqA2M9MMfWq/wC0H/yQnxX/ANguX/0Wauf8FGbg+JfG/wAIPB1opXUJ9VfUFlbhAkKgsM9c/Nxx2rhf2r9eurX9ne/ntpPKe5WGCXAB+VnVXXn2LCklfkX9bif2meM/sR2pvdc8YxJ/rDp9kUPowRiv6gV6p8R7oX37T3wZuF4Ei3bY9MwjiuJ/YwsotO+Jnji1gXbDFbWaKCc4ARq6zx6htv2qvhVa9BDPfFB/stEGH8yPwrWf8Z+n6EQ/hr1/U+v9Z1AaXps9zjc6rhF/vOeFH4nFO0qy/s7Tre2zuaNAGb+83c/icms6/jGq+IbS23Zhsl+0yr2Lk4jH4YY/lW5XAd4VleJLgrp/2VG2z3rC2THUbuGb8FyfwrVrEgUap4kmuDzDp6+TH6GRhlz+A2j86APlj4WQpb/tOfGGKNQsaJZqoHYCE14P8L/CWj+KfiD8Sv7W0+3v/I1Q+V58YbZulnzjPrgflXvfwy/5Oi+Mv+7af+iTXjfwQ/5KD8Uf+woP/RtxXetOb0R573Xqz7X1ib7PpN5J3WJsfXHFS2UP2azgi/55xqv5DFUvER3WCw/895o4vzYZ/TNaleaekFZ2hHzLF5f+es0r/hvOP0Aq3ezfZ7OeX+5GzfkKr6HF5Gj2SHr5Sk/UjJoAvV80/tWnzPEHgVv7niKxj/8AQ2/9mFfS1fMP7VEj7/Bk6DLnxZBt99o2j9Vrej8aMqvwM+pLb/j2i/3B/Kqevxs2mvLGMy25E6fVTn9RkfjVnT5luLC2lQ5R4lYH2IFTsAwIIyDwRWZCPmoyLN+2msiHKP4cDA+oM1e8+Ifm0t4h1mdIh/wJgP5V88aSrQ/tki3Yktb6A0OT6Cf5f/HcV9D6v+8udNh/vXAY/RVY/wAwK0q/Z9CqWz9TSpsjiKNnPRQSadVDXZTDo16w6+UwH1IwP51gbDfD8Zj0Wzz1aMOfq3P9a0ajt4hBBHGOiKFH4CpKAMzSP3lzqU3964KD6KoX+YNadZvh75tLSU9ZneU/8CYn+VaVAGZzJ4l9orT9Wf8A+xrTrOsf3mq6lJ/dMcQP0Xd/7NWjQB86/GX/AJOZ+E//AG9/+ixXteoEW/hXXLMnH2ZZVXP9wjcv6HH4V4l49LeKv2tPA+nWo2yaLZT307PwGRx5YC++QPTrXoP7Qt3Po/w58VXNtK8D3GkzqJIyQQ6ISCD64J/Kupq/Iv63OW+smeEfsyf8kutP+usv/oxq9UuOFNcP8DbWK1+GWhiKNYw9sjttGMsRkn8ya7i4+6a9k8w5bWW+U/59K+ctdDSfH7wtgE4vrHp2/fivorXOFPf/ACK+c9dG34/eFf8Ar+sf/R4rnxH8Gfo/yNaP8SPqj1//AIKLf8h/wR/16XH/ALSr2f8A4J66FcS+BvHel6dr2r+H4rPxPKyTaTdeTKwWJFCs2DkdMg9cCvnD9vnW7q8+LOm6dKwa2tNLjeFccqXZw357F/Kvo39nTwz4u+EvwN0LWdBu4rq78XynXbi6uYoxEQybWgUs4BZSEZuVIJXnDEDw+HKbp5VQi+1/vbZ6uczU8fVa7/kkj6S1nSfHdnlY/in4h1G1H/LnrcNtexH2OY1Y/wDfVeG+M/BmuJdz3Uug+EdalY8XNlbSaPeAe0sRc5/EU7U/jd8RTeeXFDoWpNEv7+1WS084Hv8ALHfvID/2z/CsHWPj9e6fJcJr3hx7eOLhri2eSNT64+0xxA9R91mr6Q8U891u303Sr1cR33h/VfNV1i1kqbaVwMDbdRL87bgmPP3jg5Tmvnf9oOyl8PeMvEmlxI9vpOo6jBqNnFk+U+InV3HYHc+D36dq+trfx54e8ZSfZ4p0Y3KcWl5GUaRSMnAYYcYI5XI5618h/GrU7e88e6ppl+ssOlaLaCz0yO1X5I38pSiuTzg4I/4CPegD1b9oQY/ZJ+En/bv/AOkz1xELh9K0fHI/s60H5QJXVfHq+kuv2VfhUiwMttF5C+a/G9/s75Cj0HPNcjZj/iT6R/14Wv8A6JSvnshi44Saf88//SmevmrTxEWv5Y/kja088L9P8Kv3UhjsZmXghCQfwrOsDwv+fSr97/yD5/8Armf5V9CeQe3/ALE+kW3/AApx74pm51O7nkuWJ++28p07cKOlZP7UErPafDOKQ5lt/E1tA/1UsP1GD+NdH+xP/wAkK03/AK+Lj/0a9c5+1rGbbX/ASfwT+I7OZf8AeG5W/kv515X/AC/Z3/8ALtHvlr+91pO4hs1H4s3/ANjWvWPoZ827v5ewMcQ/4CgP82rYriO4zdV/eXmmQ/3py5+iqx/nitKs2b974htV7RW8j/iSoH8jWlSAz/EEhj0a72nDMmwfVvl/rV6KMRRoijCqAAKz9c+eK0h/563MYI9gdx/9BrSoAKzfD4zpaSd5neU/8CYmrWoz/ZtPuZf7kTN+Qpulw/Z9MtIu6RKp/IUAWq+Z/iw3mftVfDZuoVrmP8oAf/Zq+mK+Y/iSfM/aU+GMvaS61Aj6BAo/9Brej8Xyf5GNX4fuNr9tRfs3wo1YdEumt2/4GkyA/ow/Ko/+bJf+5d/9o1o/tuwCX4H3kn8Ud1AQfrIo/rWd/wA2S/8Acu/+0a2j8EfU5pfE/Q+tP2RvCMF1+zf8P7iO/wBSs5JNItmb7PeOq58tf4TkD8q9f/4RrVYP+PbxNegelzDFL/7KD+teQfsjeMbLTv2b/h/DdQX8ITSLZfNNnI0Z/dryGUEYr2ODx54enbaNXto2/uzP5Z/JsVzT+JnbD4UfC37fM2r3Xxb+FnhzWtQi1HSnN5fiKKDyT50UYKMTk9Cx6cc9K7H/AIJweE9WvPhJrXiq11tdPTxFrN1em1W1WQxHfsI3Mef9X6DrXH/t731tf/tA/CZ7a4iuEFpqXzROGH+qX0r1P/gmvqllZ/st6KtxdwQN9ru+JJVU/wDHxJ6mut/wV/Xc5P8Al6/67HnP7YVje2P7U3wWF7qT6izLfbWaFI9v7tOgUVw37YP+j/BPxDZnpHewSIP9l5UP891egftm6laaj+1R8FTa3UNyES/DGGQPj92nXFcH+29EYvhHdTqPllkhhf6iZGX/ANm/Okvip/11E9pnG/sff8lY8e/9cLP/ANAau1aCHXv23NLj1E77fSNBe8txnAjcuUZjjr8pPWuV/Yn09tT1vxv4jLhBLcR2QgAzjy1+9n33jj2roDC95+22LNMj7V4eETsOyeflv/HQR+NXUf7yXoTT+GPqfV/huMyW01+4PmX0hmGeoj6Rj/vkD8zWvTY41ijVEAVFAUAdgKdXCdxV1S/XTNPuLpxkRIWC+p7D8TgVDoVg2naXDHKcztmSZvWRjlv1NVdW/wCJhq9hp45jQ/a5x7KfkH4tz/wGtqgD5A+GX/J0Xxl/3bT/ANEmvG/gh/yUH4o/9hQf+jbivVfBt21p+0j8bnjbbKsFs6/hD/8AXFeIfstXs+p33jK8uZDLc3EltLLIQAWdjMSePUk16CWkn5I897r1Z90at+91HSoexmaQ/wDAUP8AUitWsuQed4jhHaC2ZvxZgP8A2U1qV5h6RmeJHKaJdBfvOojH/AiB/WtGNBGiqOigAVna2PN+wwf89blMj2XLf+y1p0AITgEnpXzP+0qM2Pwwc9ZfEtvKf+BO5/rX0dqs32fTLuXOCkTEfXBr57/anh+zRfC6Lps8QWa/lmt6Pxoyq/Az6C8MHytPNoetq5jH+795f0I/Ktesi3/0TWIeyXluB/wNP/sW/StesyT5o+GUQ139qz4i395+9udNgtra2bpsjZNzDjryo6171cfvdfs17RQySH6kqo/rXhHwa/5OZ+LH/bp/6LNe8QDzNeu3/wCecEcf5lif6VpV+Jei/IdH4TSrM1/57SGH/ntcRJ+G4E/oDWnWZqX7zU9Li7eY8p/4ChH82FYGxp1X1Gf7Np9zL/ciZvyFWKzfERzpE0Y6ylYh/wACYD+tAFjS4fs2mWkXQpEqn8hVqkAwMUkjiONnPRQSaAM/Q/niu5u0tzIw+gO0f+g1pVneHkKaLaZ6sm8/Vuf61o0AfOdtJv8A22pB/c8P4/8AIoP9a9F/aatzP8EvFLL9+Kzkcf8AfDA/oTXmOkOZf22bt+x0RgPwlC/0r1f9oz/kiXjD/sHTf+gGut/FD5HH0keOfBX/AJJnoH/XpH/6DXZXGSprjfgr/wAkz0D/AK9I/wD0Gu0lHFeweccnrKEq3+fSvnTxOywfHzws7kKq3tkzE9h59fS+rICp/wA+lfOfifQ5tZ+M0L2+p6XpEthbw3q3Osz+TbApMMBjg5ySOOM+tZ1YudOUV1TLhJRkpPobH7bOpR3nxiSdYZE36VAqCUYDpumy2Oo5wR9M19TfsMpq/if4Bvo7aB4f8Q2mga3dRaXPrsc6RPBIA8rRvEwOTLjkcYUjqM18c/tBXfiW9+IWka74xg069ieCJY7nSJhLbXcCOGJXkkcShecV9rfsr+LdS+B/wD8JaXrXh6SO5v8A7VeMl1ewWjxxNKWjbbIw+8GPUqRtI55xw5bh54TCU6E1ZxVu51Y2tHEYidWD0bueseIrHUP7Ne3vPhRoh2lgo8O+LdQtI8MMEmFsRsc9nBH614Rqvh7w/pVxcTzaX4o8HTORuL2EM9ucHPM1iFlxnHLOBgc16lqn7VPh19RjsbnT7hZpiAhsr2yvOuBysU7Pn2CmsbVfjD4WvmMc2oPpUx4CarbyWeSewMqqG/AmvSOI8El+H1p4m1Wxu0uNP1+wnvoxdXemXGXQbgMyIqxumOeXMp56rjdXzt8TLiy1OPxZO827xDL4laJrOL7qWsEUiiQD73UnJ6fL2r7D8VeDdG8Q3Et7EPsd+V3R6nYNsmU9Qdw+8PY5Br4v+N/ie11nxvKtgiCazjNrd30UPkvdzgbZXIB6HGMfXqKTGfQv7WnhtvB37Nvw00eSQXL2ksMLSbSuSLeQZxnj1ryiwcSaNpJH/PjbD8olFe5/t3kt8GfAxPJN5Gf/ACDJXhOkS79F0oelnAOn/TNa+U4XnKpl3PLdyk/xZ72exUMbyx2UV+SN2wGAPp/hV69/5B8//XM/yqpYrwtXL4Y06f8A3D/KvrDwD3v9if8A5IVpv/Xxcf8Ao16y/wBsCMG8+Gr/AMQ8R2yj6En/AAFan7E//JCtN/6+Lj/0a9ZX7YsnlN8OXPRfEdsf/Qq8r/l8zv8A+XaPbfCw3aY03/PaV5B9M4H6Ctis3w3EYNA09D18hCfqRn+taVcR3Gba/vddv3/55xxRD/x5j/MVpVm6N+8k1Cb+/csAfZQF/oa0qQGZf/vNY0yPspklI+i4H/oVadZo/e+ImPUQ2oH0LN/9jWlQBm+IctpM0YODKViH/AmA/rWiBgYrO1fDzadF/fuVb/vkFv5gVpUANkcRxs56KCTXzN8REKfH/wCDeerJdufq0Yb+tfRuuSmHRr1h18pgPqRgfzr598axHxH+1X4C0m0Gx9CsJrydn4DIw8sBffIrej8T9GY1fh+4g/b51W6s/hzpFnDKUt7vUFWZAB84COwH/fSqfwq7BMJ/2ImI/h8Psh+oiIrH/wCCgX/Ij+HP+wj/AO0pKt2bGD9ju+tz92Tw4twn4wbW/VQfxroX8OHqcr+OXofcX7HX/Jsvw6/7A1t/6LWvYJ7aG5XbNEkq+jqCP1rwL9kbTdeP7N/w/ls9ahSJtItisFxZhwo8teNwYGvX9/iyDrFpF4B/deWEn8wwrkn8TO6Hwo+Gf29tNtNP/aF+FP2W1htvMtdRL+TGE3ful64616f/AME2PD+mX37MOjT3OnWlxObq6BklhVmP+kSdyK8p/bru9Quv2gfhV/aFgli62uo7dk4lD/ul74GPxFem/wDBOHWNStP2Y9HhtdCnvoxdXWJlnjRT+/k/vHP6V1v+Cv67nJ/y9f8AXY5H9s6wtbD9qf4KLa20NsrJf5EMYUH92npXH/tsqD8C9RJHIuLcj/v6ldN+2Ff317+1P8F/tumtpzKl9tUzLJuBjX+706VzX7bH/JCtS/6+Lf8A9GpSXxU/66kvaRy/7Cf/ACLni/8A7DEn/oCV0mggH9un6eGj/wCjxXN/sJn/AIpzxf8A9hd//QErpNA/5PpP/YtH/wBHinL+JP0CHwxPrSkJABJOAKWsnxLO4sFtIW23F64t0I6gH7zfgoJrjO0j8OA3n2vVG63kn7vPaJeE/Pk/jW1UcEKW0EcMa7Y41CqB2AGBSzOY4XYdVUmgD4i0ljb/ALRfxmuB91Baxv8A7rwFf57a8b/ZM/5mr/t0/wDa1e1fCW2Pi/Xfjd4juztvp9Qm0/YnCBIF2ofXPIzz2rxX9kz/AJmr/t0/9rV6K+GXyPOe6+Z942X7zWtSk7II4h+ALH/0KtOs3RPnW9m/56XUmD7D5f8A2WtKvLPTMy8/e63pyf3FllP5BR/6Ea06zIv3viGdu0Vsi/izE/0FadAGZ4h+bS2i7zPHF+bgH9M14X+1v/x9fDf/ALGW1/m1e6at+8utMh/vXG8/RVY/zxXg/wC0gzeI/il8LvCy4hEupnUDcHnHkjdtx75POeK3o/GZVfgZ79qw8rTLW7H3rVklOP7vRv8Ax0mtcHIyOlRNbrJaGB/mRk2N7jGKq6FM0mmRJIcyw5hf6qcf0z+NZknz18Gv+Tmfix/26f8Aos17zpXz3Oozf3rjaPoqqP5g14N8G/8Ak5n4sf8Abp/6LNe8eHzv0xZT1lkkk/NyR+mK0q/F8l+Q6Pw/eaVZrfvfEUY7Q2xP4swH/sprSrNsf3ms6lJ2Xy4h+Clj/wChVgbGlWbrH7yXTof79yrEeygt/QVpVmXX73XrFP8AnnFLKf8Ax1R/M0AadUNdlMOjXrDr5TAfUjA/nV+szX/ns4of+e08SfhuBP6A0AX7eIQW8UQ6IoX8hUnSiq2pz/ZtOupc42RM35A0AfN3holv2wopD1l8PPJ/31cE/wBa9c/aM/5Il4w/7B03/oBryrR4hbftjWkR4K+GFXHuJa9V/aM/5Il4w/7B03/oBrrfxQ+Rx9JHjnwV/wCSZaB/16R/+g120n3a4n4K/wDJMtA/69I//Qa7K4baK9g84w9WIAP+fSvDP+EL0/4r/F/+y79TZWunxLLcOoPm3Cei452g9SOme3Wva9YmwpP+e1fP2pS+IJPjzpll4Xuns9W1FIbNJo41coryfMSpHIAGT7Ck5wpJzqbLVhyuXux3YftF/DTR/AHifw/B4bieWLUo5VEBmZgznavDZzzv6Zxx7mvuf4L/ABFb41fAXwFrvi4eJRqtul1p8994S0uO6LwRTYjEiuTsbG4gqOSzZ6KB8KfG4eJPC/xf0TR/GN3Y3M+kTxP9ttEKCSMyAF3B4Dfu+cDA5619pfsN/Efw34F/Zs8Pxa7q0OnyXNxeGJXDNkLO2c7QcfeGM4z26GlGvTxMVVou8Zar5jdOVN8k90dF4sPg5dNa1i8WeIY4Rw8HjbQdWMXI4w32kxIODk+WR06d/FG+HdrcWckmg3PhrXp7kyfaIfD13EWVc/dVB9lfpjBZmII79a+sr/4teCtXlMFr4r0ae4IJEC30fmf98k5/SvOvG/hTw54nhMtzpljfK3zLOI1LfVXHI+oNUI+YtN8OX3hvxfaWkD3ehz3FyitaNFxPGSBtZSEznu6ibGSS45r5p8YWFq2t3H2mQWZl1++huLjZvMcYaLkqOTt3OcfWvtPW/DOsaIFk0LUpL+K1kEyaVrEnnxEqcjy5Gy8bA4wckD0r5N8e2OhzfHWO006T7ZY3GqxG6jdt0YleUeaoz2PGfxHsBK7A73x7daT8Q/Ad0t98Z4tc/sO2Nza6Tc6X9jeeTym2KjNgsc8HAPX3FczoEKx6VpoSQSA2sJyvYmNSR+ByPwr1r9on4N+FdI8Bf8JDpFlYaXfadsfaiqkc65AKMoGGJ7e+K8w064S7ihlSIwI4BEZGNvtjtV/VKeEhyU0orslbcl1p15c022/M6CyGFWrF/wD8g+4/3D/KoLToKnv/APkH3H+4f5VBR71+xP8A8kK03/r4uP8A0a9c1+0876v8YPhnodwxfT5Jp7kwjjMqIDG2evBJ/Otn9iCcj4O29ux/5bSyp9DK6n9V/Wsz4+xed+0h8Jl7F7kn/vlTXlf8vZfM718EfkfRkUSwxJGowqKFH0FOJwCT0paqatN9m0u7l7pExH1xXCdxB4dBOkQOespaU/8AAmJ/rWlVfT4fs1hbRdNkar+QqxQBmad+81TVJewdIh/wFAf5sa06zdB+e1nm/wCe1xK/4biB+gFaVAGbcjzdcsl7RRSSH6nao/ma0qzoP3mu3bdo4Y0H1JYn+laNAGZr/wA9nFD/AM9p4o/w3An9Aa8Jh/5PX/7l3/2tXu2p/vNS0uLt5rSn/gKH+rCvn68uDaftqwS/wDQ1V/o023+ZFb0vtehjV2XqUf8AgoF/yI/hz/sI/wDtKSrmoRmL9jWwuR28PtC/+60HH/jwX86p/wDBQL/kR/Dn/YR/9pSVc+NVjdeA/wBj600qG5Ejpa2trJKExvUsitwc4yM/nXTHWEF5nK9JSfkfaP7HXiDSx+zb8Prc6jaidNItlaIzKGU+WvBGc17mkiyruRg6+qnIrgvhT4B0DQvhh4Z0m30u2eztbCGKNZoxIQoQActknpW8/wAP/D7MWTTY7dvW3Zoj/wCOkVxTd5NndFWikfEH/BQL/k4T4Tf9empf+ikr1b/gmd/yaxov/X3d/wDpRJXjP7deiw6L+0D8KkgluZEe11E7bidpdv7pem4kivTf+CcOgXOo/sx6NKmt39lEbq6AhtvLCj9/J3Kk/rXY/wCCvl+pyf8AL1/12OU/bXbP7WPwWX0hvT/44v8AhXG/tsf8kK1L/r4t/wD0albH7UdsL39sz4aaTaahd6pd6ZYT3d19rYExRuGVSCAByVPAzXJftuajN/wg+haKuFt9W1WG2mfHzKuS2V98qKIq8of11Je0jn/2F5jDaeK4yfkn1KUL/vKqH+TH8q7HQP8Ak+k/9i0f/R4rjv2PIhZeHvGE6Z/0LxA7Z77NqK3/AI6T+VdjoHP7dB/7Fo/+jxRL+JP0CPwxPrSsW3/4mXiSefrBYJ5CehkbBc/gNo/E1pahdiwsLi5I3CKNn2+uBnFV9BsX0/S4Y5ebh8yzN6yMct+p/SuM7TQqK6/49pv9w/yqWsvxNePZaFdvCMzunlRD1dvlX9Tn8KAPkH9nc50v4xEcj/hIL7+leefsT+ErPVfD/ie+mmdZmuoYCmQFCqjEHp1y5/IV337NsJt9B+LkTNuKa7eqW9cYGa4n9jjSLrUPAWsvaSGNl1MhyD1HlR4/rXdJ2U/kcCV3H5n154eUro1qT1dfMP8AwIlv61o1FaQi3tYYh0RAv5Cpa849EzNL/eX+qTdjMsY/4Cg/qTWnWb4f+ewab/ntNJJ+bnH6AVpUAZlx+98QWi/88oJJPxJUD+teEfGX/k5n4T/9vf8A6LFe72373Xr5/wDnlFHGPx3Mf5ivCPjL/wAnM/Cf/t7/APRYrej8T9GY1fh+4+lKzLT/AEXW7yDos6rcIPf7rfyU/jWnWZq/+j3Nhedo5fKc/wCy/H/oW2sxHzt8Kpvs37Rfxhl6bI7ZvyhNfQ+kwfZtLtIu6xKD9cV83fDwn/hoH4wIOshsoh/wJNv9a+mwAAAOgrSr8XyX5Do/D94tZuifOl5N/wA9bqQg+wO3/wBlrQdgiMx6AZNUPDyldFtCerp5h/4Ed39awNjRrMg/e+ILpu0UEafiSxP8hWnWZpP7y71Ob+9cbB9FVR/PNAGnWZqX7zUtLi7eY8p/4Ch/qwrTrNf974ijHaG2Y/izAf8AspoA0qzfEXzaVJGOsrJF/wB9MB/WtKszWP3k+mw/37kMfoqlv6CgD571GY2n7Z8VwOFh0JS3+6Z9p/Rs/hXrP7Rn/JEvGH/YOm/9ANeW6VAvi39r/XJLQGW10/QxZXb9PLkd9yjnr8vPHpW9+1br95afs8alJDIYppTHaz8A5BkVJBz6/N+ddjV5wXocTekmcj8Ff+SZaB/16R/+g1110cLWR4C0+HSvCem2tuuyGKBVUZzgAVq3oO3ivXPPOU12TCHH+eleKfCbUpbb9rXRLj5ppI7l1iQ85b7M+0D8T+tey65naf8APpXkPwLAf9sTw6rcr9uZse4tnxXmZo0sBXb25Jf+ks6cLrXp+q/M1P26tJg0/wCPtnZ3c/lxjSIPOn2liWMkxZsD1Y19sWvhnTPH2ieG9S8BeIPC1r4H+xrBpMNx4gk0x/lRRKPJCJyJQdw8zOCgJGAF+Hf2tPEr+I/2tblLtESPTruzs1PYxjZJz+MjfhivsT9gvw1o/iP9mLQ01fS7LU1jubvyxeW6ShMzvnG4HGcDp6CufI1KOV4dT35U/vVzTHNPFVGtrss+JvgDr08qTX2jzaqUUGM6NqUV1FIpzhsXGqyjac5z5P4CvBvFfhXUPAd1KHe/8O30j+Y6rIsFt1GDwlorHgjrJ9DxX194g+D3gpJxcW3hyx0+5RSqXGnobWRQc8BoipHU9+5rzDxH4FvtK3NovivW7Ic5gvbo38DD0Kzljj6MK9s4jwbw9408RQ6l9kvp4NVtZklcSMwWZVVGbcvyp5iZAGVVgM53mvl/xj4entNA8P8AjKO5dZ/El3f3SwR5H2fyZgBhhyTlmOeMcV9iX8tz4W1M32raFBJCFkFxqPh/dCjbkZS01rkgkA/eG48+ma+R/jLZah4Uv9P8Gz3cd3peiCZ7BlGHCTSF2DnoWyO3YCk79BnpX7V3w5uPhFY+FrKPxLrWswa3ZPNPFqN15iKyNERgY9z79PfOTanBTj/Oa9H/AOChkytN8M4wcuulysR6A+Vj+Rrze3PzR/5714eQYmti8rp1q8uaTvdv/FJL8Dsx9OFLFShTVkv8kb1n0FQ+Kbx9P8N6hcRgF44WYZ6ZxU1nyBVLxwMeENU/64P/ACNe6cR9N/szaBD4X+EPgqeB2ZbuItKW9Zv3mPoGOB9a5v44DP7S/wAJ/wDt7/8ARYrvvgXatefs/wDhaNP9Z/ZkLJ/vBFK/qBXnfxiulvf2jPhDcJ92VLpx+MQryI61JfM9H7MfkfR1ZniH5tMaLvNJHF+bgH9M1p1mat+8utMh/vXG8/RVY/zxXGdhp1HPKIYZJD0RSx/AVJWf4gkMei3mPvNGUH1PH9aAF0GIxaNZqfvGIMfqeT/Or9MijEUSIOigAU4nAyeBQBnaV891qU3ZrjaP+Aqq/wAwa0qzfD2X0qOU9ZneU/8AAmJ/rWlQBmv+98RRjtDbM34swH/spr5/vLT7d+2RPb9DJ4aKg+h87g/nX0BY/vNZ1KTrtEcQ/AFj/wChV4RD/wAnr/8Acu/+1q3pfa9DGrsvU5v9u67+3fDbwpOeGe+G4ejeVICPzzXT/tWf8myj/t0/9GJXIft2j7P4R0u06KmrCZP914pD/wChbq6/9qz/AJNlH/bp/wCjErojtT9TllvL0PvbwNqPiW28HaMDo9neJ9ljwYL0oSMejJ/Wtz/hKr6D/j68N6lH7weXMP8Ax1s/pU3gL/kS9E/69I//AEGt6uF7netj81P2wPHVt4i/an05NWSfT9L8NeHLi/i82Bo5S77lfcDnI2oMYHWvef8AgnnqNj4Q/Zh8PW+r39pZXE0k9ykTzqW8t5ndCQDxlWBx15r5j/bzhM37THi3H3k8Dsw/7+vn9M19y/sc2dv/AMM0fDt/Ij3nR7Yltgyf3a13T0pr5HGtajPk/wCNGsWupft4f2hYXCXUVv4Zjk3xnIO24O4f98k1y/7bbA6V4GIOR/bkJB/4C9d/8YoUk/4KBNEVGxvCiqQPQztXlv7YlyY/CXgcS8va65HFJ6nYHH6gA/jTj/Eh6ES+CRofsVwrdaJ49icZSTWp1b6FVrX+AcUni39qvxrqd+xW58P6dBpsKpwHVvmYtnqdy+3Bqt+xDps3/CC67rTYW31bVZriFc/Mq8Lg++VNbH7N6hf2nfjAAMD/AEM/+Q2pT+Ob/roVDaKPpXWpjdX9hpsZ+aSQTze0aHPP1baPzrZrE0BRf3V7qrDPnv5UJ9IkOBj6tk/lW3XGdgVz+oyNqOuC2UZt7GIzyHsZGUhF/AEn8q3LidLW3kmkbbHGpdj6ADJrI0S3dNGmuZhi4vN1xID23D5V/BcCgD5I/Z3/AOQZ8Y/+xgvv6VhfsJf8k+8Q/wDYU/8AaSVH8N9duPDXw8+O2pWgQ3FvrWoyJ5gyuQMjNSfsJf8AJPvEP/YU/wDaSV3TXuzfocC3R9X1DdzfZ7SaX+4jN+QzU1Z3iFiNGulHWRRGP+BEL/WvOPRJNFh+z6RZxnqIlz9cc1dpFUIoUdAMCjpQBnaP+8n1Gb+/clR9FUL/AENeA/G+4Fr+0n8J5G+6GugT6Aooz+te/eHfm0mKQ9ZWeU/8CYn+teAfHK2F5+0b8LYCcCRL1c+mYhW9H4n6P8jKr8P3H03VPWLb7XpV3EPvNE20+hxwfzxS6Rdm9023mbiQrhx6MOG/UGp7n/j2l/3D/KsyT5Y/Z0L+KPHXjrxJdHbf3evLZvEvCBIFJU4659a+oa+Z/wBlNd/iPxyg6R+Ir+TH/fCj/wBCNfTFaVvjZVL4EUdbm8jR7xx1ETAfUjAqzawi3toYh0RAv5DFUPEJ3WUUP/PaeOP8NwJ/QGtPcKxNRazPD3zaaJe80kkv5ucfpird/cC2sbiX+5GzfkKj0iL7NpdpFjlYlB+uKALlZlj+81nUpOoURxA/QFj/AOhVpbqzdEbet7N/z0upCD7A7f8A2WgDTrMuf3uv2Sf88oZJD+O1R/M1pbqzLdhLr94//PKGOP8AMsx/pQB4X8EP+Tl/ix/25/8Aos1L+2OPs3we8RWx4V7m2uE+jSoG/VT+dRfBA/8AGS/xY/7c/wD0Wat/tvwn/hTlxOo/5bRRN9DKhH6r+tdn/L2PyOH7EvmL4ZTZoNj/ANclqzd8LUHhwj+wrH/rktTXpBSvWOA43Xmyh/z6V5H8CP8Ak8fw7/1+v/6TPXrWukYP+fSvHvgreR2H7XWh3UpxHFdSO30Fq5ry80TeAxCX8kv/AElnVhdMRT9V+ZR/avaxtv2lPGM1zA98qX0DyW6yeUDGIYSylsE/NkrkYxjPNfb83g7RdH8FeDv+ETs74eDZ9Ih1LSG021vZHRZh+98yQtGiyF1YlfMzt2k4LV8P/G7wzfeMf2nfFemCFp9SuzJMkCsFJlFl5qrk8cEAfhX6E/soNJ4x+AfgLU9H8Y+I9GOn6dFYvBpV80EJkiJVtyEEE7gwJ7gCnla5cBQje9oR/JBita9R+b/M8a1DUtUt9XkX/hKdUsSy5itp9TaJ5GBHCodQuiT3/wBUM4I9qxdR+JfjTRA5v9Qt7uCR8QnUbby2OT0y62vr/Crk8dc19ZeILPx/DFLD/wALP1jV7J87rHxBa297Cw/unCoxH/Aq8F8S+EdRsZpGu/DHhy+jdiZLrw4smj3Y64O1WKue3zMAe/pXpnKcHafFQXNy9lqOmz6fqMiSNCdreVNtUsShZVbGBnO3HbJr5E+2P4u1C6tbqxifVfFV3BJY30kxJtSbiSMqTj7pzg47IvHp9aabDokPiFbZ7ufTHImA0/WLYQSs7owBR4mEMhDHk7S3U59flLQdFvNP+LXhHw9qKJDc6dqdtZOY3yGDXW8MD7+bx9OcVlVm6dOU10TLguaSR7j+3tZyQy+ALi5BM1xZ3BTnAWNfJCdvQE/8CrhYP+Wf4V67/wAFM41i1/wAiKFRbG4AA6Afua8htz/q/oK+e4Xqe0yajPvf/wBKkd+Zx5cZNf1sjfsjgCqXjn/kUdU/693/AJGrll91apeOP+RQ1T/rg/8AKvpjzT7B/Zz/AOSJeD/+wdD/AOgCvJPiqv2b9pX4Y2h4EE17sH+w0YYfzx+Fet/s5/8AJEvB/wD2Dof/AEAV5j8bYFX9p/4Vyj7zrdKfwj/+vXkR/iP5no/ZXyPoys2f974gtF7QwSSH6kqB/WtKs2A+br92c8RQRp+JLH/CuM7DSrK8QfPDaRZ/1t1GuPUA5P8AKtWsy/XzNY0xOoXzZD+Cgf8As1AGnVXU5DFpt24OCsTnP4GrVZ3iDnSZkHJlKxgeu5gMfrQBPpcP2fTLSLBGyJQQfpVqkAAAA4Aps0ghhkkPRFLH8KAM7QWE0V3cDkTXLsD7D5R/6DXhUP8Ayev/ANy7/wC1q938OQmHRLNW+8U3n6k5/rXg3h5v+Eh/bK1a7sgZbfStFW1un6bJGfeo565Xnit6X2vQxq7L1OC/4KC6rKt/4W04BRC6yzMccll2hf8A0Y3516F+1Z/ybKP+3T/0YleYf8FBf+Rm8J/9e9x/OOvTf2p5Fl/ZgjdTlWFoR/38SupbU/U5HvM++fA3gTSj4O0Z4Dd2bm1jJa2vJU7em7H6Vuf8Indw82viPVIj6TNHMP8Ax5c/rWF4G8ZJb+DtGW40jVoAtrGC/wBkMi9PVCa3l+IOgZAk1AWzelzE8WP++gK897noLY/M79tW2urT9pDxjHeXn26X/hBZT5piEZx5j8YHFfaf7I2oeIE/Zu+H6Wuj2skI0i2CSy3u0sPLXnaEOK+MP229QtdT/aS8YT2dxFdQnwJIBJC4Zc+Y/cV92fsc/wDJsvw6/wCwNbf+ilrun/DicUf4jPlf4lSX0v7f27UIYbe4/wCEXX5IJC6489sckDn8K8x/bjQ21n4cT+CfVYZl/wB4I6t/Jfzr1n4vHP8AwUI+nhVf/R7V5r+3dGD4f8HP/ENWRR9Cjf4CnH+JD0JfwSOq/Yn/AOSFab/18XH/AKNes/4J3z2f7RnxiWH/AI+rg2MEP++0bAH8OT+FaH7E/wDyQrTf+vi4/wDRr1B+zxZLcftV/Fed+RALUqv+0YiAfyz+dRL4qn9dS4fYPrGztY7G1ht4hiOJAij2AxU1FFcp1mL4iP2xrPS1/wCXuTMuO0S8t+fA/GtW5GLWUDpsP8qytH/4mGq3+onmMH7LAf8AZU/MR9Wz/wB8ireu3n9n6Ne3O3f5ULNt9eKEB8E+GGH/AAqb9oEZ5GrakcfhWt+wl/yT7xD/ANhT/wBpJXPeCEkuf2efiR4mc7E8TtqN35A58lhuwue/AbnArof2Ev8Akn3iH/sKf+0kr0J/BL1PPXxRPq+s3W/nFlD18y6TI9ly3/staVZl7+81rTY/7gllI/AKP/Qq8w9I06q6nP8AZtNupemyJm/Q1arN8RfNpUkY6yskQ/4EwH9aALOmwfZtOtYv7kSr+Qr5++Mv/JzPwn/7e/8A0WK+iulfOfj4t4q/az8D6dajbJotlPfXDPwGRx5YC++R7VvR+J+jMavw/cfQemf6NqGoWp4G8XCfR+v/AI8G/Or9z/x7S/7h/lVDUP8ARtWsLnorlrZz/vcr+ox+NX7n/j2l/wBw/wAqzEfM37I8ZbxB8S37Lr1yo/F//sRX0nXzr+yOgF98Sn7nxHcj8iP8a+iq0rfGyqXwIzNUHm3+mRdvNaU/8BQ/1YVqRoCMnmsuU+Z4hiXtFbM34swH/sprVj+56VkjRlHXkB0mdABmTbH/AN9MB/Wr4jUAAKMCs3VSzPZx7s7rleDgcKC39KvmUfKc4GSDg9aYhzhEUsQMAZNUdBhC6RbEqMuvmHjuxz/Wl1q6+zaXdtwGELHk9yMCrVugt7WJBjCIFGTjoKAJNi+grP0qNZLjUZdo+afb+CqB/jV8PujLD3rN0B2bTInb78sjSH3DMSKAPJvhPaQx/HX4gSJCiyP5G91UAthTjJ71qftUWsNz8KbqOaJJYzPFlXUMD869jWX8K5x/wvHx7gA/6rHPP3TWr+1HL/xa66GOk8OPf51rq+2vkcn2WY+hRRjR7MBFx5Y7VNcwxspyi/lUOjEjR7LkgeWMkCpp2JGF+bgZOa9Q4Tktagj2nMan/gP0rgPhDodjd/tFaDvtYdxvSS4jG44iJxnr2xXf63N+7YkYGcDJ9xXGfBNzJ+0VoG1cn7W+eeBiFu9ebmTawVZr+WX5M6sMr14eq/MT4w3TeH/2mdd1Wxjs4dRS8VEublAFXfCqEs3UABjzmvqbwp4Q8UfAbwlpfhPRLmGO1s4jcT3QtozDcSTAShYyzquBv67l/iznHPyZ+0HE998c/FkcYGRIZTk9kgVj/KvtT4ZvrHjj4UeBb2TRPCviIW+iwwfa9eFx9t3AMdgmiYHYCc4J4JY96WWf7lQ/wR/JBiv40/V/mcNqPxf8a3F9NDBDo2pLCpaWJDCJRg8riK8lIPXgrn2rndR+NQV2XV9FeyG7ZvSUgk+oSZInP/AQa9Q8T6VfXcXk3/w3tYoT+7Z9C8U3RVlz1MFx+7J47143qui6XpryLG2seGA7EmDVLJUtWx0zLZ7V7n724eoOK9M5CPVdW8N+MdKvYJRb6ghjZns7hdsgGP7pAYfWvkNdVGt/H7w5fvA8F1c67Zyv02FfMhC7eAeNrA9eR25r6csfB5n1G3v0jt54Gim232nyxzQlvJfligQAngYMYPP3z0Py/Y3EU3xl8AJGwZ4b2ySQD+FvtZbB/BlP41zYn+DP0f5GtP416o+gv+Cmxz4j8Bf9eVx/OGvH7TlYvoK6/wDbl8ST+Jfj3o9jPiXTba1gS3UjqplZXPuCU/QVyox5q4GB6V43DVCWHybDxlvb823+p15lNTxlRr+un6G1Z/dFUvHH/Ioap/1wf+VXbL7oql44/wCRQ1T/AK4P/KvpDzj7B/Zz/wCSJeD/APsHQ/8AoArzPxcG8VftheHtPuTsg0TSpL6DZ1Z3byyG9sY9K9M/Zz/5Il4P/wCwdD/6AK80m/5PX/7l3/2tXjr45fM9HpFeh9E1maT+8u9Tm/vXGwfRVUfzzWnWb4e+fTRL/wA9pZJPzc4/TFch2GlWaMy+IWPVYbYAexZjn/0AVpVn2Hz6lqUnUB0jz9EBx+bGgDQrN1n530+L+/dKceygt/StKsy8YSa7p8Y5MaSSkenAUfzNAGnVDXpDFot6R1MTKPqRgfzq/Wbr3z2sEP8Az2uIk/DcCf0FAF+GMQxIg6KoUfhXz18EP+Tl/ix/25/+izX0RXzv8EP+Tl/ix/25/wDos1vT+GXp+phV3j6nmX/BQX/kZvCf/Xvcfzjrv/2k3K/svzWzfetprePn+6ZUZf0I/KuL/bv0ybWvHfgbTrfaLi7EtvHvOF3M8SjPtk13H7T2nzSeE/CXhBdsZ8TX9jpr3PUQuJBh8d+wxkdK6ltTOV7zP0k8Bf8AIl6J/wBekf8A6DW6yq6kMAwPYjNcZ4e8I61o+hWFrbeJpmWGBFAuLSOQcD2Cn9a0fJ8WW/C3Ok3g/wCmkMkJP5Mw/SvPe56K2PzX/bjgjt/2mPGKxRpGp8CSEhFAGfMfmvsv9kXwjDffs3fD+aTUtUXfpFsfLivXRF/drwAOgr4s/bVkvpP2kPGJ1CGGC4HgWUbYJC6keY/OSAa+0/2RfG+jWH7N3w/t5rp/Pj0i2VkS3kfB8tfRTXdP+HE4Y/xGfOPxG06PSv8AgoDJbxSTyovhlTm4laRuZvVjmuF/bt/5Fvwh/wBhdP8A0B67XxnrFt4s/b91W+0tnubWz8Px29xJ5bL5UhcMFYEDBIOea88/bZ1BtT1rwT4cCBBJPJfGcnOPKT7uPfcec9qqC/eR9CJfBI7b9if/AJIVpv8A18XH/o16f+zh/wAnPfGD6WX/AKLaqX7EFwT8GbeBuqzyyL/umVx/NTV39nD/AJOe+MH0sv8A0W1Zy+Kp/XU0j9j+uh9W1na9fPYaXK8XNw+IoR6uxwv6nP4Vo1iz/wDEy8SQw9YbBPOf0MrZCj8BuP4iuU6zQ02xTTNPt7VOViQLn1Pc/ieao+MP+RW1X/r2f+VbFY/jD/kVtV/69n/lTW4mfC3gG28/9hzWGX78Ud44P/A5Af0JrsP2G/CzW3wlub8zhhqF/JKEC42BcR468/cJ/GuY+Gn/ACY7r/8A17X3/oUlejfsSuG+BengHlbmcH6+ax/rXZUdoy9ThhvH0PcqzE/e+IpT2htlX8WYn/2UVp1m6Z+81HVJe3mrGP8AgKD+pNeeeiaVZur/ALy402H+/chj9FUt/QVpVmXP73X7JP8AnlDJIfxKqP5mgDTr52h/5PX/AO5d/wDa1fRNfO0P/J6//cu/+1q3pfa9DGrsvU+htbtmutLnWP8A1qjzI/8AeU7h+oqRbhbvTfPT7kkW8fiM1ZrI0z9xZ31mePszuqj/AGGG5f0OPwrMR4L+yR/x9fEj/sZbr+a19D188fskf8fXxI/7GW6/mtfQ9aVvjZVL4EZdp+81rUX/ALixRA/gWP8A6FWtF92snRT5ovZu0l0+D7DC/wDstaIZlzg1kjRlW6jEus2SZPyLJKfrhVH8zV/yh3ye/NZUUzy6/cHjEVui/izMf6CtHzX9qdxWKeuR7rOOIMQZZY48+o3DP6ZrSZd2OcYOayNTmd7/AEyHjmVpD9FQ/wBSK0fNf2pXCwXTi3s53zwqM36ZqPTbYQafaRnIMcSj9Kp+ILiRNHuQMbnAjH/AiF/rV8ysq9gAKLhY8c+FKbfjj49wTj9zx2+6a0/2oYlHwsn6/LPF/wChrXE/BnxDPqf7QfxAkiCSaWs0VolzGCVMoQllLZxkFSMds1f/AGw/Et5pfhbw7psIjMWq6rDbSs6kkKcnIweuVFdiV6iXocb0g2aGiDOj2fJH7sdKkuIwF4JGMDjvVfTZWgsIIxghUA6Uy7u5AvG38q9OxxXOc11cQsATxzn8qo/siET/AB6lmnbJS1uG3tjjDoP5Ua/eyiJ+F6en0rh/2RfEl/rPx+v9JEUSJLbXMZeMEMEEqbuc/wB0MPxrw88jfLa93ZcrO7Av/aYepofEy81O7+PXiS+0u1nub13fyooI/Mfa8IRTtxz99e1fVvwv8TP8Evh1oXhu+8O3Mc0dnFcXJmvIYtk0hZmU72AGOG+9nDjIAwT8CfFr4taxN8cvEuo6JBFaz2OpSGHjLR/ZiRuHzYIAi3dPbnv+jieL9K+J+maF42vH8cRQ61plndSLomjW91HEPKCbdwIffhMnO7G4YyOB3YCPLhKStb3Y/kjDESvVl6v8zC1D9o3QLnUPsRsbxpWb5Psk9tdk5/2YZWb9Kxbz4ieHNYm8mLUBBOc7be9je3fPcBZApP4ZqTxOnhB9Iumn8farDEWKmLxX4bv45cjByXluJIwMHGRFg84ryTUPAU91bO+kjTvEejEktLoV5HPvB6Fo4vs+w9ONstd+pznQal4U021un1K1L6fPG4kle2JVZlBBKuo4YEDH418YXd4usftC6fdWEa263Gt2hhVex3x4OPfrXv8Ao51bTtdk0y3mlhtzbzGawlikGMRuR99UK4K9fLVT0DMTg/O/g+5N18TfhzebUjnur20eby+AzC+ZQT+CL+QrnxGlGb8n+RpT1mvU9a/bcso9N/aK0G1iGI4tMsUHviWTmuc/5bD611X7dv8AyctpP/YOsv8A0bJXK4/fD615mQNyyjDN/wAv+R049WxdT1NmyGFFY/xIvjY+Dr4hd3mKIuvTccZ/Wtiz6Cud+K//ACJlx/vp/wChCvcOE+4/ghpDeHfhnomkSS+dLYQLbs+MbsKMHHuCK8nm/wCT1/8AuXf/AGtXtfhH/RIdPHRLuyRh/voMH9CPyrxSb/k9f/uXf/a1eLB3cvRnpP7J9A3832exuJc42Rs35Co9Hh+z6VZx91iUH645qDxGT/Y1wg+9JtiH/AmA/rWkAFAA6DiuY6xazdB+e1nm/wCe1xK/4biB+gFX5X8uJ3/ugmqWgJs0a0PUugkP1b5j+poA0KyrdvO8R3Z7QQJH/wB9Esf5CtWszSUD3mpzg5Dz7Af91QP55oA06zdS/eanpcXbzHlP/AUI/mwrSrPf95r8f/TK2Y/99MP/AIk0AaFfN/wWufJ/ak+J8Z+7KLZQf9oRZH6A19IV8zfC5Sv7RHxYuFGXtXspwB6LGd3/AI7mt6fwy9P1MKu8fUxP2wf+SzfCz/r8X/0dBXc/tPSpb6t8I5ZDtSPxTYMzYzgBsmuF/a/YP8ZPhWynKm7Ugjv++grvf2l/+Q38IP8Asa9P/wDQq6Y/Y+ZzPeR+g2l+N9AubWBU1izDbF+V5Qh6ehxW5b3tvdjME8Uw9Y3DfyqvHYWt5ZQCe2hnHlrxIgbt71QuPAvh+5bc+j2it/ejjCH81xXAegfmD+1frkXiz4+/GPU7l0sE0Dw9HoyKzZ+0NKpkU54wcttxzmv0A/ZI0+50v9m/4fWt5BJa3Mej2weKVSrKfLXgg8g1+Yf7T9rBpvxO+PVpApSNLnS9ilixA+TuTnvX6s+BR4r/AOEN0YRro0Uf2WPbuMrHGPwruq6QS/rZHDDWTPijwsR/w2d8awfvbrLH/fgV5n+2D/yVbwH/ANe95/6Atdv4XkurX9sz4tS3jRNMZrOGYwAhPmg4wDz1Cj8a4H9s+9i074l+CbmZtsUVresxxnjYtVT/AIy9P0Il/Dfr+p2f7HT/AGL4O6Dd/wALzXVu/wCMrsv6qR+Nbf7I0DeI/ih8WPFzEQibUxpothzjyF27s++4cY4xVL9lzQryx/ZxsVmi8uZxLeQDIOQZGkQ8eox+daX7DUomh+Jci/dfxNdMPodtZy3mzSH2T6huJ0tYJJpG2xxqXY+gAyazfDUDrp5upl23F65uHB6jP3V/BcCjxCou4rbT8/8AH3KFfH/PNfmb8wuP+BVqgYGBwK5TrFrA8e3aWHgzWZ5PupbPx6nGAPzrfrkPiIBfaXNY54SCS7lH+yowo/FiD/wGmtxHxt8NP+THdf8A+va+/wDQpK6r9jvWo/D/AMG7FpgTFcSzMuP7wlkDfptrgfCWvw+H/wBhnU3mR5PtJuLVQmOGkmZAT7AtmvUP2ONJttU+BWk/aE3+XcXO32zK2f5V2VV7svU4ofEvQ98rO0Nc2s0n/PW4lf8ADeQP0Aq9NIIYnkPRVLH8KqaGmzR7IHqYlY/UjJ/nXnnoF6sq1PneIb9u0MUcY/HLH+latZejLvm1GfvJcsufZQF/oaANSvnaH/k9f/uXf/a1fRNfOSzrH+21GpPL+H9g+vmk/wBK3pfa9DGrsvU+l65/Xrv+xrie625Sa0cEeroCR+hb8q6Cua+IsZPhC/nUZe3QzDHoOG/8dJqFuSeM/scWrXngrXPEjEKdd1a4vfIH/LLL7due/wB3rgda9/dxGjMeijJrwr9i3/khum/9d5//AEa9e065KYNHvXHXymA+pGBVVv4jNKXwIi8OIV0S1LD5nUyH/gRLf1rS21HaxCC2iiHREC/kKlrI0MvSl8y91Ob1nEY+ioB/MmtLbVDQhusWl/57TSyfm5x+mK0aAMuRfN8QwjtDbM34swH/ALKa0ttZ1l+81vUX/uLFEPyLH/0IVp0AZmtLv+ww/wDPS6TI9ly3/stXblf9Hl/3D/KqN63m67p0X/PNZJj+W0f+hGr9z/x7S/7h/lQB84/svwNJa/E2WMZmg8S3MyeuVKnH6EfjTv2z5Fm0vwBKhyj65bsp9QVar/7IIBm+JIPIPiS6/mtYP7W+YtF8D2rfetfEcUPP93DFf/HSK7l/HPPf8I7G0A+yxf7oqO6UbTxzUtp/x6xf7opLgfLXqHEcjr0Y8lzjsf6V5P8AsieLNG8E/tD6xqeu6jbaXYm1vYlnupVRd5lTAyT1xn8jXrutgNEwPcY/lXz94V8JeLNS8XazonhXwtpnjKUTPeObm3D/AGcPjguzKq9BxnJxmvPzChRxOEqUa8+SElZvt950Yec6dWM6cbtPY8/+IF5FL8SfE17ZX0T295f38kU8B3hkeSVQP+BDv6ODX6v/AAY+J3hDwt8G/BWn395b+HZzpFuw0+eRmaEFMbWYjgjaQc4PHPUV+Xdjq918MfjtZanq+nQaPPpGpwyXunW8asgRceaiqMr8y5HXgv17j9RvGXghfFPjDUJ9A8Q+GbeORVuo4dR8U/YJxviVhGYECsCoIVMyAEKpLAHA6qKjGnFQd1ZWfddDKd3J3VmXrv4p+DvETtDYeJtJu5W4MC3aeZ+KE5/SvM/G3gHwzrbNcTaRaG4GSl1AgjlU+okXDA/jVbxl8B/EV5BJcz6LJqF2YyqXFneQ31s+CTyZr+7Oeg3CLOOg7V4Vc6He+FZVht9Ru/Dl+XJMLqLdC3HBjYWwf/gMD9+orYg7DUdD1bSbq1uhqq6vptqzbo9YXfPbxMhVjFMBuzgnhs5zjcOtfIvw40hPEHx48MWvhuG4nsDrcJtFmOCIklEj4J7BdzY6468mvdvEXjDxHceCPFVlfsknk6XNOLyM+VNG3CgMmEb5slg2xBxgZ614h8GvEqfC34weHNbkguL6x05Evpkhj3sgltRubA6KDIMn0UdSBXHjPaPDVFSV5crsu7tobUeX2keZ2V1c9T/bnuI5/wBpmxRSd8FpZwtxxkSuTz7ZA/OuaA/fD61S/aO8d6J8RfjDpGseH5nu7GO2tYJp2idMzea7N94A87v84q6j75QRXJk1KVDLKFKaaaVtdzXGTU8TUlF3TZsWYOBXOfFf/kTLj/fT/wBCFdLZ/dFc18WDnwbcf76f+hCvXOQ+9rD9x4U0S8HW1WJ2P+wRtb9Dn8K8Um/5PX/7l3/2tXvPh23W68I2ML/cktVQ/QrivnjSp3n/AGxIDJ/rU8OeW/8AvLPtP6ivFhvL0Z6b+z6n0JrP7yTT4f79ypI9lBb+grTrMu/3uu2Cf8845ZT/AOOqP5mtOuY6jP1+UxaNdlThjGUBHqeB/OrsMQhiSNRhUUKAPQVQ1757WCEdZriNR+DBj+imtKgA6Vm+Hvm0qOQ9ZWeU/wDAmJ/rVnUZfI0+5kyF2RM2T9KbpUYh0uzQDG2FBj8BQBbrNsj5mtai3ZFiiH4At/7NWlWZonz/AG+b/npdPg+y4X/2WgDTr52+CaLL+0p8WkYZVhaAj1HlGvogkKCTwBXzv8A2/tD9ob4r6hb/AL6yMltCLhOULqhDLnpkHqO1b0/hl6fqYVd4nlX7SPiKS++PfgDRJIgraTcW6mXdy5a5C9PpEp/4FXsf7TokOrfCPyiol/4Smw2FxkA7jjPtXiH7TEKxftYeHWH/AC0l09j9fPI/pXuX7TJC618ISTgDxVYZJ/3q69uT0OXfmPv+zuPFdvaQFrPSrxdi/wCqnkiJ491I/Wpv+Ei1iD/j48M3JHra3MUv6Eqa3LFg1lbkEEeWvI+lT15x6J+MX7UszX3xV+OV0IZbfN3pitFMu11BVOo57qK/YHwF/wAiXon/AF6R/wDoNfkb+1Z/yVX4/f8AX3pX8kr9UfA3gDQ5vB2jPLbSys1rGTvupT2/3q7qvwx/rojgp/E/66s+G9OtWu/2ufjpGn+szZMn+8IQV/UCvIP25rpb3xH4PuF+7Lp1449sxrXsvgeyh079sH40W1unlwxtZBV3E4Hk+prw79tlfs/izw/aHgQW9/sH+wyKw/mR+FVT/jL0/QmX8N+v6n01+z5/yQnwp/2C4v8A0WK539huMRRfEtF4VfE90o+g210X7Pn/ACQnwp/2C4v/AEWK5P8AY3vTp+mfFadRmRfEt2I1/vOdoUfiSKye0zWO8T6T05m1LX728P8AqLYfZYfc5BkP5gD8K26p6Rp40vTbe2zuZF+dv7zHlj+JJNXK5jqEJCgknAHUmuMvA974Z8QarKObqFxDntCoO388k/jW74lmc2KWcLbZ71xbqR1AP32/BQaxPirrtr4K+F/iDUpYna1sbCR/LjHO1UPAz9Ka1aE9EfA//Niv/cQ/9u696/Yn/wCSFab/ANfFx/6NevFNW0ebR/2FrUSsrG4uEuF2nor3IYA++CK9r/Yn/wCSFab/ANfFx/6Neu2rrTl6nDD416HsGvyGLRrwj7xjKj6ngfzq7DGIYUjHRVCj8Kz9e+e3t4f+e1zEp+m4Mf0FadeceiITgEnoKzvDoJ0eBz1l3Sn/AIExb+tWNVlMGmXcg6pC7D8jTrCH7NY28X9yNV/IUAWK+XryRn/bKvpoxl7bQxKo/wB2VSR+WRX1DXzN4KH/AAkf7YOu3tovnW2n6W1tdueAjvMWVeeuV54rel9r0MavT1PqKN1lRXU5VgCD6isnxcofwvqqsMg2zgj8Kl0BitgbZjl7V2gOfQH5f/HcUzxZ/wAizqf/AF7v/KoW5LPE/wBi3/khum/9d5//AEa9ey6/89lHD/z2nij/AA3gn9Aa8a/Yt/5Ibpv/AF3n/wDRr17Lqf7zUdLi7ea0h/4Ch/qRVVv4kjSn8CNKorqUW9tLKeiIW/IVLWd4hcrot2B1dPLH1Y7f61iaEmix+TpFkh6iFM/XHNXaaiCNFUdAMClZgqknoBmgDN0T5/t03/PS6fB9lwv/ALLWnWb4dUjRrZj1kBkP/AiW/rWlQBmRp5niOaTtHbIn4lif6Cr1z/x7S/7h/lVTTSJL3UpP+mwjH/AUX+pNW7n/AI9pf9w/ypgeBfsgf6/4k/8AYy3X81rG/bRhMF14JYfcuNZt2P8AvKGB/Qj8q2f2QP8AX/En/sZbr+a1T/bWA+xeAzjka9CM/g1d0f4557/hs6C04tYv90VHcyYHvT7X/j1j/wB0VWveBXqHEcx4gkAhk+n+FYX7Kn7QvhT4NaJ41stauXt9Yv8AV5ntmW3kkAXCLuJVT0OTjrWh4hlwjj2P9K57/gnzp9tqf7VOtRXUCXEQ02+bZIuQD50fP615eZYGlmOGdCvfl0enlqdeFrzw1VVKe54v8XPEmk+M/iJ491bRp3utKlt4ntpZVZSxD2qM2GwQSQa/XjSvBXh7X/COlyanoem6jK1rHue6tI5GPygDJYE9MV+Tln4b8Na3+07rWn+LNS/sTwg/ia6g1C4SRYwkXnylV5PQsig46DJyMZr74+Jfwzm0bx9rCNb6iNL3xm11GOyvpraSHy1EJ88NHGz+WqEgSnaSRxtwvZQpxo040ovSKS+5dTCpJzm5vrqdd4j+CfgeNpJLTw9a6VMefO0vdZyf99RFTXm/ifwnq+mWjW+leKb9rdQcWesquoQN7MZB5mP+B1xt/Pqmlz3NzB42uoI1A8rT5NVUzl+gCwLd3znvwIwOmcYrnr74n+MtFa3t9Qks9XeTJMMyeTclc8cEROf+AwGugzKut6HLd2usaJPoiaTcazp89il7prl7AMw3ljEcGFsoTgfKe5Jxjw74J/C7xB4z+NEnhrTfE1lp2pR6aVe+tEFxHsj8uLy8NjB4XP0969j8WfFH+0/AHilra3u9L1Sys3ZhNEdqEqdp3EcEkHCsFY7Txwa8M/Zm8XW/wu+L1pc6loN54ha5sPLisNNi82ZzKscqkLkZ+Trj/wCvXBj5V4YWpLDfxLO3r89Dow6purFVfhvqdD40+Fnj+x+JcfgfxTqNrGLeRr/T53hEa3ixhiPKKqMsc4Kk5Gc896lsoWQYIYdiO9dl8d/jFpnxT+I3hqw0jw9qPhO+0hrmS4N8piu1ZYd6oBvJVSBzkDPGK460BMuDzXJllfGV6aeM0lZaW2d5JvrvZddDXFU6NObVDVXev3fkbdp0H0rmviz/AMibcf76f+hCuntRhRXMfFn/AJE24/30/wDQhXtHEfoR4T/5FnTP+vdP5V88eFIf+Ej/AGxPFGoWo2W+kadHazh+CXkIkBX24P419D+E/wDkWdM/690/lXgHwa/5OZ+LH/bp/wCizXix2k/I9LrFHvEP73xBct2it0T8WLE/yFaVZmlfvL3U5vWcRj6Kij+ea065jrM3Uf3upabCegd5j/wFcD9WFaVZrfP4iQdRHasT7FnH/wASfyrSoAzfEXOlSRjrKyRf99MB/WtHpWbrHzz6bF/fuQ34Kpb+YFadACMwVST0AzWd4dUjRrZj1kBkP/AiW/rU2sS+RpN5IOqwuR9cHFTWcP2ezgi/55xqv5DFAC3P/HvL/uH+VeBfsgD/AEj4knv/AMJJdc/ite+3P/HtL/uH+VeAfseOJH+Izjo3iO5P/oNbw+CXyMKnxRPHP2nmCftWeGmJwA1gSf8At4avbf2nIkn1f4RxyKHjfxTYKysMggscg14z+0jo9zr/AO1l4csbSMyzSrZnaCB8qzOzHn0UE/hXrnxkmufGPxE+C3hqxtzLq8msRagVZgqkW3zSrk9D1x9K6/5PQ5V9r1P0Mtfh9oP2WForI2rFF+a2mki7f7LCpf8AhDPJ/wCPbW9YtvQfavMA/Bw1R2/iq5tII47vw9qsTIoUmKNJhwP9lif0qT/hP9Hj4uHurJvS6tJY8fiVxXnHon5G/tPwSWvxL+PcUtw91It1pQM0gAZuE6gACv1Y8DeNrdfB2jKmmavMRaxjMdhJg8epAr8p/wBqC9g1H4mfHu4tpVmgkutKKyIcg8JX68+A+PBei/8AXpH/AOg13Vfhj/XRHBT+J/11Z+fvgK8Goftf/Gm4EUsIeSz/AHcyFHXEWMEduleP/t4QKvjbwzKOr6feA/gg/wAa9i8COJ/2wvjdKh3x/aLZdw6ZEZBFeL/tr36678SbHTYFKyaTo9zdzM3AKuNoA98r+tXT/jL0/QiX8N+v6n0l+z5/yQnwp/2C4v8A0WK4f9jGF9Q1nx/b8/Z7fxRd3UvuRtCD88n8K7j9nz/khPhT/sFxf+ixXPfsPoA3xPbHzHxPdAn2G3/E1i9pm0d4n1HRRVXVL9dM064umGREhYL/AHj2H4nArmOoz7b/AImXiO4uOsNin2eP08xsFz+A2j864j9p7/kgnjf/ALBc/wD6LavQdCsG07S4YpDmdsyTN6uxyx/M159+09/yQTxv/wBguf8A9FtVQ+JEz+FnyN49YwfsZafbH7rW2n3CfQtGG/Vf1r2X9jjSrrSvgXowuojEZ2lnQEg5RpGZTx6gg/jXkHxTtz/wxd4WuF6i0tIm/wB0tGf5qPzr6G/Zz/5Il4P/AOwdD/6AK6Zv9215nJH416HV6j+81XS4uwd5SPouP5tWnWZnzvEeOogtvyLMP/ia064TvM/XedNeP/nq6Rf99MB/WtCsvWW33GmwDrJcBvwUFv6CtSgBsjiONnPRQSa+bv2eVK/tCfE7d94xWTH6mIk/zr6F1yUw6PesOvlMB9SMD+deB/AxBH+0p8VkHRVswP8Av0a3p/DL0/Uwq7x9T6Bi/wBF1+ZOiXcQkH+8nB/Qr+VN8Wf8izqf/Xu/8qdrn7hbW8HH2aZSx/2G+Vv55/Cm+LP+RZ1P/r3f+VQtxPY8T/Yt/wCSG6b/ANd5/wD0a9eyv+98RRDtDbM34swH/sprxr9i3/khum/9d5//AEa9eyWX7zWtSk/uCOIH6Asf/Qqqt/EkaU/gRp1ma586WcP/AD1uowR7A7v/AGWtOsy+/eazpkfZfMlI+ihR/wChViaGnVLWZvs+k3kg6rE2PrjirtZniD59PEPeaaOP83Gf0zQBds4fs9pBF/cRV/IYqaiorqYW9tNKeiIW/IZoApeH387TfOPWWWR/zc4/TFXbn/j2l/3D/Kq+iwfZdJs4j1WJc/XHNWLohbaYk4Gw/wAqYHgX7IH+v+JP/Yy3X81qp+2t/wAePgP/ALD0H8mq1+x6RI3xGkXmN/El0VYdCPlrJ/a+vm1bxl8PPDYUIJL1r4z5zjyh93HvuPOe1d8Veuee/wCEdVa/8esf+6Kqag+Af8+lXYF2W6L1wMVR1HhTXpnEef8Aiycw2k0i8lUJ5+grq/8AglRBFqvj74i6lcwxvdpZRGOTbynmNKXC+mSiZ/3R6Vx/jLmxuf8Armf5Cus/4JT30Om6z8TbmQNti062ZjnPGZ//AK1ZVfgZpT+JHyV8UbFLv4o/E2V87rXU7yZMepvgn8nNfsDpHhfVbzQtP1Gx8deLdCkmtYv3GmaoUgTCBfljZWAzjJ9SSe9fk94F8JP+0R+0ZPoOl3Rsbfxbq1xJ5rKN6Qs73G0gnrhAM9iB1HX9BNW8UfE/SZ2fTbkW2iptgtrae0iRAiqAGJmEeGbaxI80hSSM8caLYhnceKrD4hC2mt1+JupavbsMfZvENjb3kZ4xg7VjJ/PvXgHibwpeaPG0dz4Usbm3IJlk8LXklgXOec2rt5TD2JPNazfHvxpcTzLLZ6PrEakACy2MxH8XNtcXOf8Avn8D0rG1H48WEgSPVtLu9LuJFLbAyyHOM4EZ2zH/AL9joaYjzXxHoGma54K8WeHbCf7XM1j5ttYXMD2l1DNHygEeREVPOTGqjKjrzXnX7KuuXHij9sXwLfX6sbtphDOJECkvHZNGxKjgcp0r1z4m+OdEt/Ad94ksZ7W8vbFGW1nALPDM6lACB8y53YIOOteRfse2q6f+1F8OIp0vIdZN7cm/gvotrKTbylGXI3YZWyc85GemKiezKjujS/aLAj/ba8ahQFAkk4HA/wCQeKw7Q/vj/nvWx+0t/wAnreNv+urf+kArHs/9cfrWFNfvG/KP6lzelvN/obtr0Fcz8UEa80GDToRvu724iggj6bnLDAz0H4101r0Fc/4viM2v+DowcF9YtlB9MvXS9EZo/QXw5bva6Dp8Mgw6QqpHvivnz4Nf8nM/Fj/t0/8ARZr6G0O7+3aPZznhniXcPRsYI/PNfOXwom+z/tG/GCXpsS1b8oia8WPwy9P1PS+1E9+8PfPp3m95pZJfzc4/TFadU9Hh+z6VZxnqsSg/XHNXK5zqMyx/e6zqUg+6vlxfiAWP/oQrTrN0P5o7yTu91IfyO3+laVAGbc/vdeso+0UUkpHv8qj+ZrSrMg/e+ILtv+eUEaD8SxP9K06AM3xAc6eIu80scX5uM/pmtKs3Vf3l7pkPrOZD/wABRj/MitKgCtqUog066kPRImb8ga+f/wBjEFYPiAD1HiC4z+S17r4ncp4fvwOrxGMf8C+X+teFfsezquo/Eq26FfEFy6+4yAf6fnW8Pgl8jCp8UTkPiP8A8nxeE/8Ar2X/ANBnr0TWYzbftqfCGD+D7RezJ9Hgyf8Ax4NXnfxH/wCT4vCf/Xsv/oM9el+OLJb39sP4MReZLAZGvFMkD7HGISeD2rq6x9DmXX1R+jtMlcRRO56KCTXP/wDCL6hB/wAeviXUU9rhYph+q5/Wobyw8UW1pP5erWF4NjcXFmyHp6q/9K889A/JD4/XqeLda+O3im2Hk2M+uWumxxScSGS3dEc4HGD1Ffqt4T03xOfAum51mygUWS4VbEkj5fUvX5OoryePBHcBGL/FkCVV+4T5vPXtX7KNdW8GjYaaKMCDoXA/hruraJL+uhwU92z86P2W42/4ST4sPMwluP8AhK71XlxjcQ4/LvxXin7VRx8bfEH/AGK7f+htXt37LrB/EfxaKkMD4tviCD1G8V4Z+1lcRW3xs10yuEDeGtiljjLGRgB+JrSn/HZnP+GfS/7PuprD+z74fnkBP2fTlDAeyAj9CKqfsKWUt14M8U+JMBbPXtcub22Q/fVCwXDds5U9M1F8JLSfR/glpdpcxPA8+hRSGOQFSrrGFYYPfBX8q1v2C/8Ak3rSv+vm5/8ARz1g/hk/M6I/FH0PoqsXVv8AiY6xYaeOY4z9rn+inCD8W5/4DWySACScAdzWN4cBuxd6ow5vJP3ee0S8J+fJ/wCBVzHSbVeXftPf8kE8b/8AYLn/APRbV6jXl37T3HwD8b/9guf/ANFtVw+JES+Fnyx8S/8Akx3QP+vax/8AQo696/ZyIPwR8H4Of+JdD/6AK+d/ixrNrYfsW+EbKZytxfQ2ccKgE7ioVz9PlRq96/ZimP8Awprw5bsfmis4WH+60asP1yPwroqfB82csPi+R2+l/vdX1absHjiH/AVyf1atWsrw8N1tczf897mSQfTdgfoK1a4TvMq6xN4jsU/55QyS/nhf8a1ay7ZfN8Q3snaKGOIfjlj/ADFalAGZr/z2cUP/AD2niT8NwJ/QGvCvgh/ycv8AFj/tz/8ARZr3XU/3mpaXF28x5T/wFD/VhXhXwQ/5OX+LH/bn/wCizW9P4Zen6mFXePqfRt5bLeWs0D/dlQofxFcp4s8Q2+lfDLVNT1B/LjtrOTz2wSQUBDcD6GuwryT4+f6L8J/iBbdFfT5blB/vIwb9Rn8amKvJIl7MxP2PtKudK+B2jC6j8sztJOgyDlHkZlP4gg161onzi9m/56XUmD7Lhf8A2WuK/Z4IX4KeESeg06H/ANAFdr4dUjRrVj1kUyH/AIES39aKjvNs2grRRpVmr+98RSHtDbAfizH/AOJrSrM0395qeqS9vMSIf8BQH+bGsizTrM1X95faXD6zmQ/8BQ/1IrTrNl/e+Ibde0Vu7/izKB/I0AaVZ3iFyui3YHV08sf8CO3+taNZuufOlnD/AM9bqMEewO4/+g0AaIAUADoOKyvFb+X4a1Ns4xbuc/hWtWB49l8nwXrTd/skgH1IwKa3B7Hjv7ELCH4TW0ZH/HxJNMD6kTOrfptrA/afXd8c/hqP9i6/9AWt39j/AP0f4I+H7zoIr2eNz/sPK6/z2n8KxP2m/wDku3w0/wBy7/8AQFr0If7w/medL+EjtFXC46Vnan9w/wCfStKs7UeVP+fSvSOM828Xn/QLj/rmf5Ct/wD4JiKXf4sqOp0q3A/8j1geMuLC4/3D/IVu/wDBMdgG+LQzz/ZMBH/kesqvwM0p/Ej5++C2v+Mfglf3vxC8NnTPtWiW9vPKbgCcotyzRxodpG0sDuIBzjbnuK/UEeD5p7eLUdE+Hfw0isrmGOZALGewuNxQbtz2zL/FuwcdMelfld8GNK8RePNM8S/D3wvo7atqviM2bkiVUEEcEpZnOevLqMDtn6V+mh/aBTwpa2+mT+HLySwtdtmNQM6RI7qq7+ZNqbQSfmDkEYOcGqjGzb7/AORDd7GF470e+1PT3ttW+G02xW3Y0Txhc3kJ4Iz9mvmMWcH+7+NeF+ItH0GziaKW/wBQ0KRRhbTxJp32aAA56SQf6OSDjlo3Ht0r2y6/ac8MaxdGAWt+rEctbeTegHOMEW0kh9O3fHXisG8+IXhrxOTBZ6pBLNJkC1nBilbHUeW4DenbuKsR82fFXwI2n/CTxbdW1isKl7OWP7GUkhkjDksytG20heSSI4xz93vVf9mbVLn4h/tseG9dhQXMz6lctILVCUjt47YxxyHrgEYGehP1r0XxjaaF8MtPn8QCe407TQ2LvTrZd8NzuyANhyFJZgdy471Z/Y9u/Dcn7ZOg3XhX7J/ZF94dlJW1hEflymQMyOBxvHy574K1lU0i2XDWSR5H+0ugH7avjQ7gSZn4Gcj/AEBf8/hWNaD98f8APetX9pltv7avjUjA/fkc/wDXitZFoczf59ain8XyX6lT/Vm9a9BWJ4n/AORo8Ff9hu1/9DFbdr0FYnib/kaPBP8A2G7X/wBDFbvZma3PvrQP9He6tOyMsyf7rjP/AKEGr5v+H7EftAfGBFOGk+xRD/gSY/rX0hJ/ouoaZcdFmjNs598bl/UEfjXzh8Mx5n7TnxJi677mxJHsImb+grxYfDL0/U9J/FE+mwAoAHQcUMwVSScADJNLVLWpvs+kXkg6iJsfXHFc51EXh5SNGtmP3pAZD/wIlv61pVDaQ/Z7SGIfwIq/kMVNQBnaYfMvdTkzkeeEH4Iv9Sa0azPD/wA9g03aeaSUfQscfpitOgDNf994hjU9ILcuPqzY/kprSrNtD5muag//ADzSKIH8GY/+hCtKgDI8S/PaQRf89Z0BHsMt/wCy14F+ymTbat45uf4f+Eou4H+j7cf+PBfzr33WSHv7JCeESaU/gu3/ANmrwj9lW1OoWHxPWPkyeIrpomHTcCpUj8QK3h8EvkYVPiiedeIvELa/+3PpamERCwkFoDuzvAgd8+3+sI/CvZ/HN5Hp/wC2D8F7ibf5cbXpby0Ln/U+gBJr56srkXn7b0E44El6Hx6Zs6+jPFf/ACeX8E/9+9/9EV1veP8AhOWPX1PvSPx/4ekba2qQwt6XAMR/8eAq8+taffWc/wBnv7afMbf6qZW7exrQkhjmXbIiuvowyKxtW8JaJeWk5m0mydtjfMYFz09cV5x6J+PEscdx8Q3hkAdH+LO1lPcGbFfr4ngTw7HpORotkT5HVoQf4fevx42/2f8AE3EUTNCvxTwsUYyfkm4A/Bq/YH/hLLx9IxH4a1Vv3HVxEn8Pu9d1focFLqfn3+y662njf4q26qEhk8T34jVRgApIOB+DfpXgf7av/JZb/wD7BEH/AKPNe6fs7SMusfE+/KGJ7fxjeSujclVLhXBx6Bj+VeF/tqf8llv/APsEQf8Ao81pT/jMif8ADR9launk/DazulGPItQG/wBxl2n+YP4VkfsF/wDJvWlf9fNz/wCjnrpbiFbn4WyRN0fTiv8A45XNfsF/8m9aV/183P8A6OeuZfw5ep0L416HuniWWQ6etrCSs17ILZWH8IOdx/BQxrSghS2hjijXbHGoVQOwAwKzVcX3iRgOY7GHB/66Sc/oq/8Aj9a1YHQFeOftZXL/APCkPE9nFkNNYXEjn0RIyT+u0fjXsdeC/tQa9ZW3wY8d6ldSFYXs30u0YDIZ3+U4wO7soz7VcPiRMvhZ8n/Hb/k1r4Wf9sP/AEnevof4N21+nwc8Ez6fndJpcKSY/wBlQV/9CNeB/tGaTdaH+zV8MrG8j8m5haFZEyDtIgfI4r6h/Zz/AOSJeD/+wdD/AOgCumr/AA16s46ek/kdd4fjMei2YPUxhz+PP9a0Kjt4hBBHGOiKFH4Cnk4Ga4D0DO0f95NqM39+5ZR9FAX+hrSrN8OjOkwyHrKWlP8AwJif61pUAZr/AL3xFGO0Nsx/FmA/9lNeEfBBwP2m/iumfmItCB7CP/64r3ayO/WNTlPRBHFn6KWP/oVfPPwYl8/9oz4laxbZlsBdW1o1wnKFvLKsuemQ23I7VvT+GXp+phV3ifUdeT/tNoYfhJ4iuR0/s+4gf6NGSP8Ax5R+desV5r+0jEsvwQ8YBug0+VvxCkj+VKHxImWzM74GzfZ/gB4bkHVdJjI+vljFel2kP2e0hi/uIq/kMV5d8FDu+Avg+L/nrZ2kf57M/pmvV6mfxM3h8KCs3QfntJpv+e1xK/4biB+gFXbqb7PazSnoiFvyGaraJD9n0izQ9fKUn6kZP6msyi9Wbafvdc1B/wDnmkUQ/Isf/QhWlWbovztfzf8APS6cA+ygL/SgDSrMvv3ms6bH/d8yU/goX/2atOs1f3viKQ9obYD8WY//ABNAGlXN/EU/8UbqSd5ECD8WFdJXMePz5mj+R3YPIfoqMf54prcHseW/sc2gv/2e7W3PHmS3Kg+h818H865D9oa7N98ZfhZO3DtFdbh6MEUEfnmu4/Yn/wCSFab/ANfFx/6NeuB/aHjNt8fvANv/AAh7uVPo6KT/AOPbq9CH+8P5nnS/hHop6Vm6iflNaR6VmakMqf8APpXpHGedeMsHT7j/AHD/ACFWf+CaV35GvfEmI/dn0UKP94eYw/QNVfxguNPuf+uZ/kKZ/wAE7j5Fz4/ux/yxgtUJ9pFuY/5sKyq/AzSn8SPBf2cfF+teAvjT4L1zRL2awmj1i0tppYmADxySgNGwPUMobr6A8EA1+pviaDw3DqV+1rY/ELQLdrmVyumeHbCWzRdx2iD7OIpPKH8OSzY25OQSfyi+Heo2kmi6t4fSML4i1i/01NJvT8otJVmbdJv6pwwGRzzX612/x58EaVYRabqHiEJfWg+zzPLbThC68Nhym1hkcMCQeoJFaGZ4Z4s0bwjPausfxBmmuZGYuPF+iXlg4+st5Jcx/wDjgH1rzbW/hvNNpKbLK21TTYt7o2h3QurcEhcErFtC9DyLZz09K+rrzx/4W8SNJBY67pt9Lj5reO5RpBn+8mcjqOCO9eT+Mvh14eubiW8g09NOvyOL3TibaYHsdyYJ/GmB8q/Eiwurb4R+KYzqUl9aWtxZokM0u94cyHk55GcdCqEbfuDjO1+wpHFpX7WF7JAojgsRMVVeix/a4UIHttY11fxP0eS28D6/beKtXivdDmt1YX0iBLzzUYeUrFRiTknHGc8c5rhv+CekT6h8ctaDM0kr+Hb19xOSXDxMD9cisqnwM0h8SM39qFtv7aXjQ/8ATyB/5JrWdaD99U3xQ1qx+JX7T/j7xBbtJHDG0jopGCXjEdsQfYncRj29xUVp/rqmnJNtLpZfr+oTRuWvQVieJv8AkaPBP/Ybtf8A0MVt2vQVieJv+Ro8E/8AYbtf/QxWz2ZC3Pv3VYmk0FnT/Wwosyf7y4Yfyx+NfN3wHlXxL+0N8RteswZNMaVIUlPGZI0COMdeC1fTvXTv+2X9K+aP2QbYRaz8Sdv3Y9duEX/vr/6wrxY/BJnpfaifStZuvjzLBYRyZpo4/wA3Gf0BrSrN1Q77/S4R3maQ/RUP9SK5zqNKoL6b7PZXEv8AcjZvyGanrO8QsV0W6A6uvlj/AIEQv9aAJdHh+z6VZx/3YlH6VcpqKERVHQDApSQASegoAztG+d9Qm/v3TgH2UBf6VpVm+HQf7Hgc9Zd0v/fTFv61pUAcx4nJcaqwP+p04j6Fif8A4mvNP2JkUfA2wcAbmuJyx7n969el6x+90vxNN22GIA/7Kf4sa82/Yn/5IVpv/Xxcf+jXroj/AA36o56nxr5nz/aW4tf24xGv3RqLED0BtSQP1r6E8cx3Ev7YPwXW1mSC4LXuyR03qD5PcZGfzrwWG2luP26n8qNpNl9vbaM7R9k6n0HI/OvfdXuYtX/bY+FltBLGr6RBdXl40riNYonjKKctgHLcYHNdUt4/4Tmj19T72x4sg/i0e8A9RLCT/wChCobzWvEENpOJ/DqyjY2Wtb5G7ejBa6SC6huow8MqSoRkMjBgfxFJe/8AHnP/ANc2/lXnHon4xQOZfiFHI0ZjZviyCUbGVzL0OK/Zn/mD/wDbD/2WvxmlV3+ITrG/lOfi0Qr4ztPm8HHev12Ph7Wn0jMnii5A8jpHawr/AA/7prur9DgpdT4C/ZjhW91f4uRPykviq/U/QsK+b/2upnn+KNwZP9amjQxv/vLcFT+or6N/ZLBXUfieGcykeKLwGQ9WORzXzj+1rLFqfxg8TzWZMkNlZ28FwcYCSNIGxz1yDnitKf8AGZE/4Z91f800/wC4f/7LXH/sNXken/s22NzL/q4prpz74mfiuw/5pp/3D/8A2WvOv2KY21L4F+GNPAzCby5uLjH9xJ32g+xY/pXL/wAu5eqOiPxr0PpHw3ZS2mmh7n/j7uGM8x/2m7fgMD8K1aKKxOgztfvnsdLlaHm5kIhhHq7HA/nn8K+fP239Pj0r9mq+tIvuRT2wye589Mn8Tmvepv8AiZeJYousOnp5rehlcYUfguT+Irw/9vT/AJN61X/r5tv/AEclaUv4i9TOp8DPD/203Enwe8DupyrXEZB9vJevoL9n20msfgz4RhuIngmXToQ0cilWU7BwQelfN/7Xkp/4U14Pt2+9bX4i/wCA+U5X/wAdIr688JADwzpgAwPs6fyrWb/dRXqc8V77LlVdUm+zaZdy/wByJmH5GrVZviH5tLeIdZnSIf8AAmA/lmuM7izp0P2bT7aLpsiVfyFWaQAAADoKR2CIzHoBk0AZWm/PaalN/wA9J5SD7D5R/wCg14V+yrZ/b7b4owj77+IrsKfRsqQfzxXu2joV8OxE9XiaQ/8AAst/WvE/2QP9f8Sf+xluv5rW8Pgl8jCp8UT6D027F/YW9x0MiAkeh7j864D9oz/kiXjD/sHTf+gGu10f/R7i/s+gil8xB/sv83891cV+0Z/yRLxh/wBg6b/0A0ofEiZbMxPgZmT4RfD2AfxWsUh+ixZ/nivWa8m/Z7/e/DbwSO0OjI34sEH9DXrNTP4mbw+FGf4gcpo12F+86eWPq3yj+dXokEUaIOigAVn63862UP8Az0uowR7A7v8A2WtKsyhCQASegrO8PLjR7dz1kBlP/AiW/rUutXH2TSbuXusbY+uOKmsYfs1lbxf8841X8hQBPWZpn73UNTn7GVYh/wABUZ/UmtOszw/81i8n/PSeV8+vzmgDTrl/GH7xLpe0enzN+JKj+hrqK5fxEfMg8QOekVksY/EMx/mKpboHseb/ALE//JCtN/6+Lj/0a9cf+00gHx5+Gr4+Yx3QJ9go/wATXYfsT/8AJCtN/wCvi4/9HPXE/Hy8HiH9pLwfplupWXSbOa6mZ+hV/kAHvlf1rvh/HfzPOl/CR3p6Vm6h901pHpWdfjKmvSOM8u+JV+dM8O6hdBN5ihZgucZwBXef8E+fCFs/wK+LfiRN/wBvUpBgH5dsCrMDj1zI35CvPPi/ER4M1c/9O7/+g17B/wAE+dZ07w9+yt8XNQ1Kcw2EM9w88jL91RaRFuBn3rGt8BrS+I+WP2LPGOi+Cv2lvBF54hsrW90ia8+ySi5hMnltKNqMuOQ2/YM9gzeua/QOb4SRa5qt1bpqHw/GrrJILiZ/FyLI8u45Mgjit5S27Oc4OeuK/M/4QxEeLfDZcneviXSlQZyCvmy5x6jI7V+ymr+BPDOsWIN/oGl3pkXc5uLOOQsx5JJI65rUyPlfxx8ANfs9Oe5uPDrX82XP2iC9jv4jkAcKbi/Y9e8Y9R7ePnTda8P3At7LU59GuwnmfYvIkjMvt5DhWP1S1xzwelfWXiD4K+DLfe2n6OuiS9RLpE0lm4+hiZa818Y+GNbgtJIIdfOtWYAA0/xJbR3kRHGRvCrICRxks30NMD5q+LvibWtd+E3iG21q3hjnsrq3TzYRt3ZcHDLk7SMDrgnPKrxnf/4J06eunftU6xY7jKlvpV9AGfqwWWJRn8BVjx94bk1nwV4j8NjSG0C9ureK8jPnefZEW8gJEbn51yZB8p6ZJA65p/8ABNq/fUv2m9QuplXz7jR72V2XI+ZpYSe/Tr+dZ1PhZcPiR5Jp9ubT4wfESAnBimvEP4XqCt+0OZjWPcsR8dfiYB0N9fg/+Bq1sWYxKfrWdJWlP1/RDnsv66s3LXoKxtdje98aeB7SBWmuW1i3kESDLFVcFjj0A5NbNr0FM8JgH9oP4f55Hmz/APoo1tJ2i2THdH3iQV08g8ERf0r50/ZITF98Sm9fEdyPyI/xr6Puf+PaX/cP8q+cv2SP+Pr4kf8AYy3X81rxo/w5HpL44n0PWax83xEg6iG2J/FmH/xNaVZtgC2r6nJ2BjjH4Ln/ANmrnOk0qzdaHmGxh6iS5TI9QoLf+yitKs+7PmaxYR/3FklP5BR/6EaANCqerzfZ9KvJO6xMR9cVcrM8Q/NpjRDrNJHEB9XH9M0AXLCH7NY28X9yNV/IVPRUdxKIIJJD0RSx/AUAc3d/P4O1af8A5+BNKPoSQP0Arzf9if8A5IVpv/Xxcf8Ao169P1OH7P4Gnj6FbLB+u3mvMP2J/wDkhWm/9fFx/wCjXroj/CfqjnqfGvmeT+FQw/bR8YTIMvb25nUf7q25P6ZH417l8JY4L7/goABIiTwv4UYgOoYEeevY1458OlD/ALcHi5WGVNqwIPf5IK9O+DM19Zft3LHaWq3txbeGZIDG8vl5VbgYOcHtt/OuiW//AG6YQ/U+/J/Afh+4kLtpNsjk5LRL5Zz6/LjmqGteErDTdGvJ4tQ1PT44oWYtDeyMAMH+FiR+narf/CTanB/x9eGb5R620sUw/wDQgf0rC8c+N7NfB2srd2eo6crWsgWS6tGVCceoyB+OK4Fud7PyZ0gi71fQdejkMml6r8T0vLS6lODLEZfvkcY6HOQK/XTxX4v0m08D6gyazZRzLZNt23Kbgdvbmvyd/Zos4dd8ffATSdQjW80yTUNSme0mG6JnTeyMVPBIIyK/WrxtoOmW3gvWfK060i22kmNkCjHy/Su6tukcFLZs+Bv2KYUf4NxX7jfeXl5cS3E7cvK/msNzHqTgDk+lfLnx+/5KH8U/+vix/wDQVr6m/Yn/AOSFab/18XH/AKNevln4/Ef8LF+KS55a5sgB6/KtbUv40jKf8OJ9z61rFroHwguNQvH8u2t9MMkjYJwAmTwKw/2E/D93o/wK065vIzG147yQgkH915jsp/HeTVL4rTi5/Zk1mUcbtEckeh8s5Fej/swgD4B+CMcf8SuD/wBAFcf/AC7fqdUfj+R6jUVzcJaW8s8rbY41LsfQAZNS1i+IT9tez0tf+XqTdL7RJy358D8axOgk8NW7pp32mZdtxeObiQHtu+6PwXA/CvC/29P+TetV/wCvm2/9HJX0SBgV87ft6f8AJvWq/wDXzbf+jkrSl8cTOp8DPA/20oTB8OfB7gfJPJEx/wB5YpB/Jh+VfXfhP/kWdM/690/lXyV+2xfWw+GPgezMqfa2lEqxZ+YoImBOPQFl/MV9a+E/+RZ0z/r3T+VaT/hx+ZhH438i3Wbq/wC8udNh/vXAc/RVY/zArSrMuP3viCzT/nlDJIfxKqP61yHaadUdbm8jR7xx1ETAfUjAq9WZr3z21vD2muI0I9Ruyf0BoAtCH7PpoiHRItv5DFeDfsgf6/4k/wDYy3X81r325/49pf8AcP8AKvAv2QP9f8Sf+xluv5rW8Pgl8jCp8UT3m7/0XW7Ofok6NbufcfMv8mH41wf7TF/b2HwQ8WG4lSESWUkSFzjczKVUD3JIH413+vRNJpkrxjMsBE6fVTn+hH414l+2lMs/wEv5UOUea2YH1BlQ0U1eaRE9ItnQ/s7Wc1r8ONDS5ieGaHTLSEpIpDKfL3EEH/eFepVzHw8XPh2OX++3H0VQv/stdPWUneTZ0xVkkZl9+81rTY/7gklP4AKP/Qq06zU/e+IpT2htlX8WYn/2UVpVIzM8Q/Pppi7zSxxfm4z+ma06ztUUS3WmxetxvP8AwFGP88Vo0AR3EoggkkPRFLH8BVTQojDo1kp+95SsfqRk/wA6b4hdk0W628F18vPpuIX+tX40EUaoOigAUAOryz4zXc1p8IvHt7byvDN5FwEkjYhhtj28Ee4NepMwRSxOABkmvKPjOCf2f/FMhGDLYXMp/wCBBj/WtIfEiZ/CzT/Znsbex+CHhMW8SRCSyjlcIMZdlBYn3JJNeK/Ej/k7pf8AsBr/AOjWr2T9mKYn4MeG4GPzQ2cJH+60asP5kfhXjfxH/wCTul/7Aa/+jWrto/xn8zgqfw0ej1Tu1ytWzxVG8kwtekcZ5f8AGJf+KI1nH/Ps/wD6DXLfDK81rw78GdZ8P+H/AIgeEINL8WWzDUtK1a48qe2YxhDtIYnceRyP4RxzXZfEjVRpnhjUroxiXyoWbYehwK9F/Z7/AGXfClt8CIPFur6Fa+J7rV9PF5M4Yx3FrkFl8rnAIDAZXacrmuetSqVUlTny28rlxqRp6yV/wPAv2IPCvhLxF+0VB4b8QQQX19KJv7CuzcyC3F7EGaMsyLyhClt+ONgIGSK+r9T8N3tk7XL2+t3k0pZnlk0rUlk5JZ3lLNaRhckknIGT0718YfsjDd+074Jvra2ays5tQuRAgkL+WBA52bjydoZeT1r9V5vA2raOu/SfiN4401l5GNaacZ+kwfP0rdbEHx5eahr2ixTtY+LbuZgMx2UOorLJEcdDEJb9j0HGBjn1rHk+KXi2xeS31KC31vy1BkRcRXUfPVo1HmHjt5C/WvpbxzovjnUVmN343i8TK+Q0PifRre4VgcgjMQi7EjoeteGeLfDR0+I2+oeE2i07GGbw1fGWJeev2K4Ux8cdAxpgeS/GTxzL488DadpGjI9veaxqsWkSJLJt8qQ7W2sVzkHKg+zHIyCK1v8Agn4kunftT3el3dva2lzpGhXmnTm24SR4p41LnPUk9T3696peMbaG68NaRq2k3Da3P4c8Rxard24heG5EChWKeVIflbgnaML/AHcdB5n4G8UeHtS8W+KvEF74p8SeDLjUb+eZZdHGwJBK5kTzQh3McsRxnGB2xXNiJ+zg5NN+iu/uRtSV5JX+/Qq3aFfjv8SskHN9qB4IP/L8BWva5841zWnwaRbePdTn0/xIfEwvbGS5fUHt2gaSdrgFgUPIOAW565+ldNZ/NJmihJTUpK+r6prou4pq1kzbtRwKpaPM1v8AHXwJMvJikmkIHcCMk/oDV+2+6MVB4WQS/tAeAUYZVnuAR6jymrafwsiPxI+9Z2D2sjKcgoSCPpXzn+yR/wAfXxI/7GW6/mte+aS7HRXhc5ktg8Df8ByB+mD+NeB/skf8fXxI/wCxluv5rXjx/hy+R6X20fQ9Z2jOJo7ub/npcyfodo/RRV93EaMx6KMms/w4u3RbUnq6mQ++4k/1rnOk0qz4z5mvTf8ATK3Qf99Mx/8AZRWhWbpo3anqkn/TRE/JAf8A2agDSrN1b95d6ZD/AHrjefoqsf54rSrMn/e+IbVe0UEj/iSoH8jQBp1n+IJDHot5j7zRlB9W4/rWhWT4hbMVlF/z1u4lI9QDk/yoAZ4jiEHhS+jHRLVlH4LXkf7E9yp+BdoO8Vxcbh/20Y/1r2DxV/yLepf9cH/lXh37GLfZvhBYg8JdC5x/vpM/9G/SuiP8N+qMJ/GvmedfBbXYvEf7Zviq+hR442juIgr4zmNooyfxKE/jXvHwfiWP/goTIR1fwuzH6+eg/pXzZ+zH/wAnXeJv9+//APSha+i/htpcOrft/eRM8yL/AMIuzBoJWjYHzx3Ug10zVpNeRy09bep+hdYPj3/kS9b/AOvST/0Gov8AhD5of+PbxBq8B7B5llH/AI+prD8c6R4gt/B2slNfjuEFrJlbmyXJGPVSP5VwLc9F7H5U/swTzW/xM+AkkFu11Mt1qu2FWCluH7ngV+rHjnWtfk8HayD4b8tTayZZ7+Pjj0ANfld+yl/yVb4Af9feq/yev1y8e/8AIl63/wBekn/oNdtf4l/XU4KXws/Pv9if/khWm/8AXxcf+jXr5g+KaiT9p7UEYblbxDpoIPQ/KtfSv7EFwW+DFvA3VLiV1/3TI4/mpr5r+KP/ACdDf/8AYxab/wCgrWlP+LP5mc/gifXPxqH2b4HeObLoIbGZ0H+w8ZYfruH4V6J+zD/yQTwR/wBguD/0WteeftGA23wm8QzD7txo88DfURll/wDZq9D/AGYf+SCeCP8AsFwf+i1rl/5d/M6o/H8j1GsXR/8AiYapf6ieUB+ywH/ZU/MR9Wz/AN8irOv3z2GlyvDzcSYihHq7HC/zz+FWNNsU02wt7VOViQLn1Pc/ieawNyzXzr+3p/yb1qv/AF823/o5K+iq+bv29r2JfgZLp+7de397bw20C8vK/mK2FHUnCnpW1L40Z1PgZ8zfttf80/8A+wfL/wC0q+0/h9cGfwZpO7/WRwCJ/qvH9K+Lv23omhk8Ao6lXWwlBB7f6qvsHwtqEOh6bAk7bIp4Ipk+pXa3/oIP41rV/hw+Zz0/jkdPWZbfvdfvX7RQxxj8SzH+YrTrN0j95calN/fuSo+iqq/zBriO40qzNSy+qaVH1HmO5H0Q/wCNadZkn73xFCO0NszfizAf+ymgC9c/8e0v+4f5V4F+yB/r/iT/ANjLdfzWvfbohbaYk4Gw/wAq8B/Y+YPL8R2U5VvElyQR0IyK3h8EvkYVPiifRpAIIPINfOX7XJMX7P8Aq1ox+a1vIYef7vnIV/8AHSK+ja+c/wBtVfs3wo1Xsl01u3/AkmQfyYflTpfHEzn8LPX/AIdoV8E6OT1eASH/AIESf610dY3gyPyfCekR/wB21jX8lFbNYPc6lsZumfvNR1SXt5qxj/gKD+pNaVZmgfPZyzf89p5X/DcQP0ArTpDM2b974gtU7RQPJ+JKgfyNaVZlr+916/f/AJ5xRRD/AMeY/wAxWnQBm6989tbxf89bmJf/AB4H+laVZuofvNW0yLqAZJiPouB/6FWlQBV1Sb7Npt3LnBSJmH5GvOvjvD9m+AviaLps0qRfyjNd74h50xo+pkkjQA+7iuL/AGif+SK+L/8AsHTf+gGtIfEiZ/Cyp+zx/ovwx8EP0S60mKI/7yoGH6FvyryH4j/8ndL/ANgNf/RrV6/8ElK/ALwfcIMva2NvOAPRVG7/AMdzXj3xFYP+1ujKcqdCUg+v7xq7aP8AGZwVP4aPR2OBWZqD/Ka036Vi6o20H/PpXpHGeX/FuTd4N1kf9Oz/APoNY/wTHxEufgR408UaD49vdK07RI3gl0qVftETxxwqwEe8kR/eYcD044q78VZd3hHWB/07v/6DVn9nq8ns/wBkX4ugx+ZZTfaIy6jmN/syYJ9jnHsceteHnGJrYXDxnQdpc0V8nJJr7jtwlKFWo41FdWf5EX7DPwa8WeLPGFn40t7eSHw54Waa+85bOW6e5llQwmOOOPlsbMnptAyeCSPrW78c/EbTAsOp69LaeUq+bNqmk29qgz0JeZ4ARnuMf0r42/Ye+JHizwr8a/AljpviG8ttIur+4il02OQrE+yB3BYDg8zMfqPpX6FS+DNY0OzeG2+HXwrkG4t51hZ3Ol3B9zLAd2fxr3NOhw69TwWX4/eLi8pvbDS9TjG7y00+KR2l9MNbSXS49yaxr346aVfyCDVLC90a7KbzFNH5hHOPur+8Ud8ui8c16N468PHVDDLr/wAPNTmNszeWmleJn1WFAcciK/bAxjoF/OvFPEukaRqDyWT6wLKYn93pniS1ezbcOixlzJbn0+WP8e9Azkf2i/FkNj4LgvfD4gmvNZdbL+0bTDSGLlioYAk524wD+oFdl/wTd8P6Zct40ur24hn0a4NvaR218QHaUITI2zJAB3qAc5wOeleX/FbQI/CvhXw3dXVuLDPimOWYN90oIl/eA73G0heoOOOi8isb9kX4PaZ8WH8Yve3FxaXmkW0NzaXFtO0TRuTJyCOh+VSCQcEfWuPF42ll9GWJrfDG1/m7fqaU6EsTJUobv/hzqfjT4L0f4MfGXxj4T0q1i/szVLa31Sy8lQPshEhVoweSVPz8ZAGRgVylh1B/z2rkdJvrvxN4m1vVLnWbzWYbIiytbi+kMs0kLyO6FmJ9FPH+1jjGK6/ThwP8+ldvNzJMxiuW6Ny3+6Kg8Jf8nCfD/wD66z/+ijU9uMAVB4S/5OE+H/8A11n/APRRrOfws0j8SPt+aVdP1HUA7BIp4PPBPADKNrfptrwf9kIG4g8e38YLWd54hupbecD5JUJHzKe468j0r2r4hqYfD012OsCsrH/ZcbT+pB/CvJP2LQP+FHad/wBd5/8A0a9eOv4cvkektZo9t1aUQaXdyHosTH9DT7CA21jbwt96ONUP4DFVNfYmxSIdZpo48eo3An9Aa065zpCszQf3ltPcd555H/AHaP0UVfuJRBbyynoilvyFVNCj8vRrId/KUnHqRk/zoAv1mWv73Xr5/wDnnFFEP/HmP8xWnWdpPzz6jLjG+5KjPoqqv8waANGsy9Xztb09G5VEklwfUbQP/QjWnWbxP4g6/wDHvb/q7f4J+tAHLfHPVbnRPhL4pvbOTybmGwmaN8A7SEODzXMfATRrbQf2f/CtzaIY9lul5LySSZBmQ/8Aj7Gtz9on/kivi/8A7B03/oBpfgFCtx8A/C8TjKPpMakexjFdC/hfM55fxPkfL37Mf/J13ib/AH7/AP8ASha+i/htqS6V+3957wXE6/8ACLsCttEZGH78c4HOK+cP2WI3T9qTxCr8uovgx9/tC5r6b+CKtq37f1/cWatcQWPhow3MsY3LC7TKyqxHQkcgHtXVU+J+hzU9l6n24PiDoanE11JaN6XVvJFj/vpRVHxf4m0jUvBusra6naXDtaSYSOdSx+U9s5rsCARgjIrk/iNoOm3vgzWzPp9tKfssh3PEpOceuK89bnovY/I/9lmGS8+K3wNtRPLbFrvU2WaHG5QVfpkEdVP51+r3j3wi8fgrW5H17WJNtpIcG5UA/KfRRX5U/su3Vvp3xP8AgLdXMqQQpc6pvkc4AGHxk/jX6p/Ef4geHm8B+IEj1WCRzYyhQmWydp9BXbX+Jf11OCl8L/rofB37Gh+y/CLw/P0Sae6tnPv5rsv6gj8a+dPij/ydDf8A/Yxab/6CtfR37IsLS/s62jJ/rYp55k/3lmdh/Kvmvx9exav+0bdahaMJrOTxJpyLMnKllADLn1BGCK0p/wAWZE/gifZn7R1uLj4D+J/7yae7g+mEOf0zXb/sw/8AJBPBH/YLg/8ARa1x37Qf/JCfFf8A2C5f/RZrr/2ZpFh+AHgqRyFRdKgZiewEYrl/5d/M6I/H8jvblF1DX7WPduSyQzOn+23yp+gc/lWtWP4ajaS0lv5VImvpDNg9QnRB/wB8gfma2KxOgK+R/wBtad7zxB8PNpJtrbxHZwkdjIdzH9NtfVmr6gmlabc3b8iJCwHqew/E4FfK/wC2DZtYaP8ACsTcXD+JrWWdj1LtuLE/57VrS+NGVT4GeNft+f8AId8G/wDXrP8A+0q+udP8Pwa34b0YzZBitxjHuB/hXzD+2HaQ3/xT+FttcRrNbzTCOSNxlWUywAgj0INfYVjEsNnAiKFRUUADtxWlR/u4Iwh8cmR9KzfD3zaVHIesrPKf+BMT/WrOpz/ZtOupemyJm/IGjTIPs2nWsWMbIlX9K4zuLNZll+81vUX/ALixRD8ix/8AQq06zdF+f7dN/wA9Lp8H2XC/+y0AM8Ukr4c1Ig4Igf8AlXiP7FQFr8K9LfGBePcBj6ukzkf+Ok/lXt3ir/kW9S/64P8AyrxH9kYGP9n7SrpRlrW8mm4/uiZw3/jpNdEf4T9UYT+NfM+ja8A/bdgEvwPvHP3o7mAj8ZFH9a9/BDAEHIPevBf22P8AkhWpf9fFv/6NSil8cfUzn8LPW/Cv/It6b/1wT+VaNzKILeWU9EQt+QrO8K/8i3pv/XBP5VJ4hkKaLdgHDOnlj6tx/WsHudS2H6HEYNGskPXylJ+pGT/Or1NjQRxqg6KABSk4GTSGZ2j/ADy6jN3e5ZfwUBf6GtKs3w6CdIhkPWUtKf8AgTE/1rSoAzR+98RMRyIbYA+xZv8ABa0qzdOJk1TVJM/KHSIfggJ/9CrSoAztUXzbrTouqmfef+AqxH64riv2if8Akivi/wD7B03/AKAa7WZvM162Qc+XBI59slQP5GuK/aJ/5Ir4v/7B03/oBrSHxImfwsn/AGdkWT4H+EEYZVtNhBHqNgr5/wDFRZf2qYYXOXg0TySfXbMwB/LBr6B/Zz/5Il4P/wCwdD/6AK8E8VzRaz+1rqtxZfvI7HTUgumxjZITuA56/Lg8V20f4zOCp/DR6e/SsHVhgH/PpW8/SsLV+h/z6V6RxnjHxhvlsPCWollLeYnlDHq2FH869G+BXhlNK/YR+IuqLMX/ALUhvJTGf+WZSPy8Z9/Lz+NeWfHUf8Ufcn/ppH/6Eteu/DC2l0/9gLxBeW8oeGe11CK4hJ4P7yVVdc9xxkdx7ivj+J2/q9CKdr1YfPVu34Hr5Yl7SbfSMjxj9i/TNT1r4z+FJdP0u5vbbQrm71G/niChIYntwgLMxAHKHqR3PY4+/pP2jtSjtTPrHg1rS3z88ttqkTLGNpILGURBeh6kdK+Mf2IPFtz4X8Xaf4U1fw1Bc+F/iFBLYvfsyvNutjJIssayb48xyEYV4yCQCdwyD9q2eheEdC0Vw0nxU0RUUxtB/YVqzFT3BsVi9TyGzx9K+wPIORn/AGjvDWs24ma01O1gbcPOEK3UYIIGN1u0g/L8cVzer+JfDvjBJbe1v7DVR1e3DrIwz/eQ8j6EVi+M9B8H3Vy8tv4w065kbASLxPYT6bOR/ddr4XSkn1AFefeJ/hddraNqM8GLAFVjm0p0uIFHPIKGTZjA5WCMdMstAFX4s+ENP0XwdratqBTSbuErHp1y0ZjguCRtkjaRl2gf3M4z0wapfsAItnffFG1aaKSRdPjUNE4ZX2mYEqe46c+9ZOreGoPFfjb4VaRrN1Nqmj3iahK4nnDeYYg5XcVdgSNoX7xzz0yRVj9grTU1GH4kFGaG6h0+CSKYH7rfvv0I4Ir5fiazyqspf3f/AEpHp5bf61Brz/JniPwoTdouvH0ntR/47PXfad0H1/wrgPhSxGk64oPyma2JH/AZ677Tuo/z6V9U+h5S3ZuQD5RUHhD5/wBobwAF5IknJA7fumqeDoK1fgAiyftNpuAbbo7kZ7HzFrGo7QZrDWSPsLxrALnwjq8TdHtXH04rxj9i3/khum/9d5//AEa9e2eLP+RZ1P8A693/AJV4n+xb/wAkN03/AK7z/wDo168hfwn6o9GP8Reh7NqQ8y90yP8A6bGQ/wDAUb+pFaNZbfvfEyjnENqT7ZZv/sa1K5zpM7xC5TRbvH3nTyx9W+X+tXYIlghjiX7qKFH0AqjrnzxWkP8Az1uYwR7A7j/6DWlQAVm+H/n07ze80skn5ucfpirGqTfZtMu5ehSJmH5GnadB9m0+2i/uRKv5CgCxWZpv7zU9Ul7eYkQ/4CgP82NadZuhYe1nlH/LW4lf/wAfIH6AUAcX+0T/AMkV8X/9g6b/ANANSfs+f8kJ8Kf9guL/ANFio/2if+SK+L/+wdN/6Aak/Z8/5IT4U/7BcX/osV0L+H8znl/E+R4V+yAIz8bfihuAMgu3KnuB582f6V7Z+x/Y3t7+1P8AGj7FqJ0+REsct5Kyhh5bcEH+hrwb9lKQ23xt+I1x/CuomJ/915px/wChba99/Y41ix0n9qn40fbbyC0Ei2IQzyBA2I3zjNb1N5+i/Qwp/Z9T7U+y+Krf7moaXeD/AKbWzxH81Y/yrmPib4h1vw58P/EF7q1pphsYrOV5Z4rwxCJQpJJ3rg8e46V6DbalZ3ozb3UE4/6ZSBv5GvI/2xT/AMYy/EXH/QGuf/RbVxwV5JHdJ2TZ+bf7LljLbfFX9n1biFoxJcapIokXG5SrkHnsQQa/TP8Aao1i68Nfs8ePtQ06QW93BpFy0cgUHafLbBwa/Pv9ky2ufEn7QvwytzbR6h/Y3hFb6yhllMKxytiNnyAc/KcYORX2z+1zceJJP2bviD9qstNgtv7Iud5S5d3A8ts4+QDNdtR3nH+upww+BngH7NGj2uj/AAH8OLbJsE9kLh+Scu43MfzY18WRRrD4zSNei+PQB9BJX3D+z5/yQnwp/wBguL/0WK+H8/8AFd4/6n3/ANq1pR+KZnU2ifbP7R9/b2HwI8Tm4lSESae8SFzjczLtUD3JIH4103wStLm3+A3gPRZopLa6urCCKWN1KsiCMGTIPTgY/GvLv2z/APkgN7/11tv/AEalfQXw+X7fZW95j91b2yWkP1ABkP54H/Aa43/DXqdUPjfodiqhFCqAFAwAO1LRSEhQSTgDkk1kbmLq6jVNXstOI3Qx/wClTjsQOEB+rc/8Br50/boiM9t8NowcF/EtsufTIevo3w2DdJdamw+a9k3JntEvCfpk/wDAq+dv24Onwx/7Gi1/9mrWj8aM6vwM8f8A2pLv7d8Tvg/OeGaePcPRhNACPzzX2Rbf8e0X+4P5V8X/ALUWIPjR8NrUHhL5JVHorywH/wBCDV9oW3/HtF/uD+VXU+CBhH4pGP4i+bSZYx1lZIh/wJgP61o9KztZ+aTTo/710px64Bb+laVch2iMwVST0AzWd4dUjRrZj1kBkP8AwIlv61LrU/2bSbuQdRGQMep4H6mp7SD7Naww/wDPNFT8higCh4q/5FvUv+uD/wAq8h/YtiWf4C2EbjKPNcKR6gyvXr3ir/kW9S/64P8AyryT9if/AJIVpv8A18XH/o166I/wn6o55/Gvme1aDKz6ZHHIcywEwP8AVTj9Rg/jXif7bLqPgbfoSNzXEAUdz+9SvabX/Rdcu4eiXCLcKPcfK3/sv514N+2CN7/DiNuUbxJahlPQ/eopfGiKnws9t8LAr4c00EYIgT+VP1z547SH/nrcxgj2B3H/ANBq7agLbQgDA2D+VUb/APeaxpkfZfMlI+i7R/6FWD3OtbGnVTVZvs2mXcvQpExH1xVus3xD82lvF3mdIh/wJwP5ZpAWtPh+z2FtF/ciVfyAFWKKZNIIYZJD0RSx/CgDP0H57a4m/wCe1zK/4biB+grTrP0CIxaNZg/eMQY/U8n+daFAGZaHzddv3A4jjiiz7/Mx/RhXk37YOq3OlfA7WTayeWZ2jgc4ByjyKrD8QSK9X0JjLJqUp6PduB9FAX+leO/tpf8AJDdS/wCu8H/o1K1pfxImdX4GevfDnR7XQPA2h6fZp5dtb2kcca5JwAoA5NfLGjLu/aT+JHs9v/6Kr618J/8AIs6Z/wBe6fyr5P0D/k5L4lf79t/6Krsw2s2cVb4Ueov0rD1X7p/z6VuP0rA1fOD/AJ9K9I4zw347Nnwhcj/ppH/6Etdna3+P+CdEyQTFZYtTCuEOCM3oIrifjp/yKVz/AL8f/oS12VtC1j/wT9ttWtJWtryHUgN6fxAX+VyPZgCPx9a+Xz5JrCX/AOf0P1PTwP8Ay9/wS/Qxf2ZtQtL34l/BLTreSWTUNHl1STUo3BC2iOX2BieFzz+LD1FfoTJ+0T8PtRUj/hIFsyf+f+2mtR+ciKK+Ff8Agn/4mi1fXfiF4I1O9ht7bWtCmv01e7nSEW1xHtLeZI8UuIpNwWRirYRWIGTmvrGy+FkGsRJb6ZqHgoBpCTc6b40jRi+PvEwpasT17E819OlY81u5tX3jHwz4yilTTNY0vWkA+ZLW4jnwPcAnH41474o+HejRXMlzpccugXrNu+06TKbck+pVflb/AIEDWV44+B+uaVY/8THw3eXkjpg3UbrfbcNnKbDftn3Yqf0rzcS+I9FupItM1ecSQ9bG+jeTPHG9AXkRenJjiHv1piK3xts7iDwk8uvarBFqtk2NF1a2k+yzvI4bcjYIC543EHBBJwMZqX9hmxl8KeIfihoupKYL5bGKIocHDgSnbkcZwensa4H41X7eMLvwc3iEz2tnJaX9zPHp7qwYwoxVkyWU52epwGIzXtn7DNlF4o+EvxL1fUx9r1S6u2Et6/8ArTtiRl+bqMFiR6Zr5LimpCnlVRTW7iv/ACZHrZXFyxUWul/yPk74VgpputAgg+bbHn/dmxXf6ccn/PtXnvwrYiw1lccGW3OfT5Zq9B008/j/AIV9c+h463ZuwdBWt+z5/wAnNj/sDP8A+jFrJg6Cr3wJuPs37Tdux+42lsjfjIoH6kVjV+Bm1P4kfZ3iz/kWdT/693/lXif7Fv8AyQ3Tf+u8/wD6NevbPFn/ACLOp/8AXu/8q8T/AGLf+SG6b/13n/8ARr15K/hP1R6Ef4i9D2W0O/XdQb+5HEn/AKEf61pVm6SC9zqUxH37gqD7KoX+YNaVc50mZf8A7zWNMj7KZJSPouB/6FWnWan73xFKeohtlA9izEn/ANBFaVAGb4iOdGuE7yARj6swH9a0RwKztV/e3umQdmmMh+iqT/PFaVADJpBDC8h6KpY/hVPQY/K0azB4JiDH6nk/zpNfkMWi3hHUxlB9TwP51dhjEMSRjoqhR+FAHnn7RP8AyRXxf/2Dpv8A0A0z4E3S2P7P3hm4cErHpMbED/rmKf8AtE/8kV8X/wDYOm/9ANUPg24k/Zr0IqcgaOg/ERgGumP8P5nNP4/keEfsbXSeLPHXxLu4VaNb8rcRK/3l3STOufcZFfQv7D/lat+0x8ZZJoUkWSHT2KOoIB8p8jBr5x/4J9f8jN4s/wCve3/nJX0J+xxFqlp+0/8AGldJS0kYCzZkuiyggo5ABXp171vV0lP0/wAjCltH1Pue58GaDeD97o9kT13LAqt+YGa8b/a78JadYfs1+P5LRZ7Ux6PcHZDcyKjYjbhl3YP4ivW/7X8SQcS6BBcY/itb4fydV/nXkH7XPiiSb9m74gW91o+o2MsmkXKhniEkY/dt1dCQPxrjh8SO6fws+O/2Ff8Ak5rwf/2Icf8A6NSvuP8AbG/5Nl+Iv/YGuf8A0U1fCH7FOmJq37SPg+CSa4gX/hBYm3W0pjb/AFicZHOK+0f2ufBlhZfs3fEG4WW+llj0i5ZTNeyuM+W3UFsGuup/ERxQ+Bnh37Pn/JCfCn/YLi/9FivibwZsl/aItop0E1qniW+naJuVJQlg2PUYyDX2z+z5/wAkJ8Kf9guL/wBFivjD4aRLP+05aRuMo/iDU1I9irVdPeZE/sH1B+2tj/hRGo7enn2+Mf8AXVK+kPh7ZDT/AARosA5K2yFj6sRkn8ya+YP2upmk/Z3vUkOZYbiCF/qsyD9cZ/Gvqjwf/wAitpX/AF7J/KuV/wANer/Q6Y/GzYrI8SzObJLKJts984t1I6hTy7fgoNa9Ytr/AMTLxFcXB5hsV+zx+nmNgufwG0fnWRua8MSW8KRRjaiKFUDsB0r5g/bg6fDH/saLX/2avqKvlz9t51aX4YRAgyHxPakKOuPm5/WtqXxoyq/Az54/aUJP7UvhPJzj+zgP/Ahq+8Lb/j2i/wBwfyr4K/adLr+054aKf6wJYbfr57Yr7x02dbrTrWZOUkiVh9CAa0rfDD0MKfxSMy7Al1qwTP8Aq0klx/3yo/8AQjWjWbH+98RTntDbKv4sxP8A7KK0q4juM3X/AJ7KOIfelniQD1+cE/oDWlWZqX73U9MhPK+Y8pH+6px+rCtOgDH8YuI/CurOei20h/8AHTXk/wCxP/yQrTf+vi4/9GvXp3xDk8vwRrJzgtbsn/fXH9a8x/Yn/wCSFab/ANfFx/6OeuiP8N+qOefxr5ns+s/6PNY3naGXY5/2H+U/rtP4V4N+1/8A6/4bf9jLa/zavoTUbQX9hcW5/wCWiFQfQ9j+dfOH7VV2b61+F054dvEVpuHowLAj8wadL40RP4T6Itv+PaL/AHB/KqSfP4ilP/PO1Uf99Mx/9lq7bf8AHtF/uD+VUtN/eapqkvbzEiH/AAFAf5sa5zrNKszV/wB5daZD/euN5+iqx/nitOs25/ea/ZKOfLhlkPtkqB/WkBpVn6/IY9FvSDgmJgPqRj+taFZmvfPbW8Paa4jQ/Tdk/wAqANCGMRQxoBgKoGKf0oqrqc5ttNupVxuSJmGfUA0AVvDibdHgc9Zcyn/gRJ/rXjf7aX/JDdS/67wf+jUr3CxiEFjbxjokaqPwFeH/ALaX/JDdS/67wf8Ao1K2pfxI+pnV+BnsPw/uDc+DNJLf6xIFjf8A3l4P8q+WtA/5OS+Jf+/bf+iq+nfAp+zWMdseFkt4rlB9V2t+q5/GvmLQP+TkviX/AL9t/wCiq6sL8bOKt8KPUX6Vg6sflP8An0refpWDq33T/n0r0zjPCvjqf+KRuf8Afj/9CWusi1GKT/gnjNZnKTx6iH2t/EpvfvD1GciuV+Oy48IXJ/24/wD0Ja66W0im/wCCdMM7IDLDqZ2P3Gb3BH0/wFfMZ7y/7Jzf8/ofqengb/vbfyS/Qo/sjRt4W+KHwk1G3ENz/wAJJbanpl3bOu4CGOR33EepPHORha/R/wARfDzwlq9syXfhvSLuMjH7yyibP/jtfA3/AAT18O+HvFNv468l7uL4jaXprTaUUuJAxs2K+f5KRhmaRMO4VY3JZ0ADFsV61qHwssdCsFlsLHUrOSOM+Z5ulXsD8dyW+xovPTkDt1Iz9MjzT0vX/hT4dsbcxaVFeaCqncg0i+mtVU+yo4X9K8r8ceH9b+yGN7q08WJE26KDXrdfNXnok8e0qffaa5q51nxLosKjS/F17qBYfPDC5vBC2/kcfbeMDoXXnuBWXN8Xdfg81r3T01q1h2mSW0CpNGCM5ZEd8AeriPgg+tMRxXxV8JXHxH0i1js4r/RtZ8OxSJJb6nN5omguC+4LMdxbhWCnk4wCM13H7BerS6d8GvHJnUtYS3jxmX/nk/kRAbv9k8DPYgeteSftB+PLzxCPDEmizkadcQ3N29vOqlZPI5IYcg42v0OD+Rr2H9g26tLL9n34kTX0kcVolzKZXlIChfs8ec59q+M4tt/ZbTV/ej/6Uj2cp/3lej/I+SvhY3+j6svq0J/ST/GvRtPH8/8ACuE+GWk3EGj6hqDgLDJcC2AOdwdFJbI/4Gv613ennp9f8K+0PFRt24woq58E7Vrz9oieJP8AWHRJCh9GEqFf1Aqpb9BWt+z5/wAnNj/sDP8A+jFrGr8DNIfEj688Q30dz4KvbosER7RnJPb5ea8d/YwBj+BemswwPPuDz6ea9dP8cWNr8FvHtmDt8iznKY/uOhYfqSPwqD4IlbX9nzw8yKFLaVGOP7zIB/M15O1J+p6Uf4nyPSNDGdKt3PWVfOP1Y7j/ADq/UVrCLe1hiA2hECgemBUtc50GXpZM2oapNwB5qxAZ/uqP8a1Ky/DqlrF5z/y8TPMPoW4/QCtSgDNuP3viCzX/AJ5QSSH8SoH9a0qzLb97r96/aKGOIfiWY/0rToAzdcO+G2h6+dcxqR7A7j+i1pVm3/7zV9Mj7KZJSPouB/6FWlQB4z+1xrs+hfA/XGtwhNyEtm3jPyyOEb8cMa6DwRocXhL4TvoMLvJDa6cjRPJ95kMeP5qfzrjf20jj4Haj73EH/o1K9Evv9G8F2Nx0WTT2tnP1Tcv6gj8a6V/DXqc0vjfofL3/AAT6/wCRm8Wf9e9v/OSvpr9ij/k6n42f7lh/6LevmX/gn1/yMvi0/wDTvb/zkr6K/Y70Ox8Q/tRfGlrmNpo4xY7HjkZCp8t88qR6V0Vfin6f5GFLaPqffdeNfti/8my/EX/sDXP/AKLavQv+ENaD/jy1zVbNR92PzxKo/CQMf1ryH9rvS9ct/wBnH4gvLrkd1bjSLndC9mqkr5bcBgeD74rih8SO6fws+Lv2KdWttG/aR8H3F27JF/wgsS5SNnOfMTsoJr7Q/a88a6bffs2/ECCBbx3k0m4UMbOVVGY26sVAFfGn7BUxl/aZ8JhvvJ4IVT/39Qj9CK+5P20G2/swfEM/9QqYf+Omuup/ERxQ+Bnzf+zhc/aPgR4Z/vR2CIR9EGP0xXx18Lv+TobH/sYtS/8AQWr65/Z2b7J8JvDsbHCXGjQTLnpuWMK36ba+RfhWwk/ae091IZW8Q6kQR0PytV095kz+yfRn7Zo+y/CTXoeiTzWtwo9/NRW/kp/Gvqvwf/yK2lf9eyfyr5e/bftt/wAFridesdxCp/3TIn9QtfUPg/8A5FbSv+vZP5Vyv+GvV/odEfjfyLuq366Zp1xdMNwiQkL/AHj2H4nAqLQrBtO0uGKQ7pzmSVvV2OWP5mq2rL/aGrWFiCDHGTdTj2X7gP1Y5/4Ca2ayNwr5R/aSUS/tNfB9HG5P9MO09M+WtfV1fJX7RN7HP+1T8J7dDl4Fu9/sWiBx+X862pfF8n+RjV+H7jw79pzj9q7wz/v2H/pQ1fbPhk+TYvaHg2zlAP8AYPzL+hx+FfI/xf0i21z9tTwpaXaF4DDDKVBI+ZDM69P9pRX1P4hu7nQr1JrSIutxGFYDsU6H8mH5VdV+7D0Mqa1kW9L/AHl/qk3rMsY/4Cg/qTWnWboB32DTY5mmlk+uXOP0ArSrjO0z3Hma7F/0yt2P/fTD/wCJNaFZll++1nUZT/yzEcA/Abj/AOhVp0AeYftJXk1l8HPEL28rQzG3fa6HDDCM2Qf+A1P+zFa21t8DvCn2WJIlezR3CDGXIyxPuTk/jWf+0qd/wt16P+7pt3KfwjK/+zVZ/Zkcw/CXw7bHodPt50+jRgH9VP510L+F8znl8fyPWa+Yf2qgbbU/A1qeFHim1mT/AHX3E/8Ajwavp6vmz9sC3/4mvw0nHB/4SG2Rvf5iR/X86KXxoifwn0Jbf8e0X+4P5VS0H57Web/ntcSv+G4gfoBVpZRDYCQ9Ei3H8BUGgxGHRrNT1MSsfqeT/OsDrL9Ztt+9169f/nlDHGPx3Mf5itKszR/3k+pTf37kqPoqhf6GkBp1man+81PS4u3mPIR/uof6kVp1mn994hUdoLYn8Xb/AAWgDSrO18eZpckWcGZkiz/vMB/WtGs3WP3k2nQ93uVb8FBb+goA0eleE/tpf8kN1L/rvB/6NSvd68I/bS/5IbqX/XeD/wBGpW1L+JH1M6vwM9Z0f/RtI8OXY4AjWB/91wMf+PBfzr5i0D/k5L4l/wC/bf8AoqvqTS7Y3fga1iBw5tFKn0YDIP5gV8p+B71Na+PPxD1O3/eWsr26eavK7xFhlz6g8GurC/GzirfCj1l+lYmpDr/n0rcYZFZd/FlTXpnGeB/H6WOHwjKjOFeSVAik8k5B4/AGu78R6Zc+F/8Agn7aafegbb6aG/tJl6Oklysm0+jAMCR/hXnH7SkJOlaWq/ea6AGTj+Bq7f4hfHbwh4h/Y20XwLbXrnxHZ2tmrQSQOo3RvHu2sVweAeh6Zr5bO4VqlTBxpwcoqrFu3S3X0PTwUoRjWcnZ8rS8yn/wT90yOb442sMU1zaNf+GrtZZ7aVopFJuAm5HBypCquCOhGa/RjV/CevWxaXTfiT410yTHHlar5iD/AIBIrL+lfCP7Dvw0v/8AhFdd+JOh6097rFhFJoMNhBZSyfYWkYyiZysch28BgxjK7mCt14+g9a+I3xA0W3aTUdXaCEhhHJqGmWtujYP96ea3B4xn6/jX1J5hoeO/CPizUyzajr+leMwT88finRYZXcf9dYthB99prxXxbpcemoo1fw9caPEjsyTaJINRsYOPvC3lXdFnJ/1QGOx9OluPjv4qhtjNqumabfR5/wCXIS7iv97MH2mMcc8uB7isZ/jZoWsOVulm0yRTiTzgJIkPP3pEyq8c/NigD59+PEMNnoXhTVbCRNWsYLXULK4vbRmZPMmDKm7eSwPzc7iSMYJJxnp/2eheQfsi/F2eA+bbt9oimh7qDbJh1+meR6fSsD9qHxFDFPpuhad5X2C/J1C/WzVWeQALh/T7qE5/2OuM11/7OJT/AIY2+MoRixxcEgjBH+ip7+ufyr5fiGXLhIafbh/6Uj08vV6r/wAMvyPGfhuf+KBA9dSuP/RUFdTp/UfX/CuU+G7A+BQvcajOfzig/wAK6zT/AOv+FfVS3PKjsbcHQVrfs+c/tN/9wZ//AEYtZNv92uv/AGQ7WK7+MXje5mQST28FvHDIwyUVgxYA9gSo/Kues7U2bU9Zo9o/aTH2b4X+KJR9y50m4hb/AHlRmX9N1Vvgr8/wL8Ew/wDPa2tEP0wpP6CtT9pi3+0fBDxZ/ejspHB+inP6ZrL+BX7z4T/DuPP/AC5xyEfSH/69eU/4XzPRj8fyPWar38/2axuJs48uNmz9BVis3xESdInQdZSsQ/4EwX+tc50E+kwfZtLtIsYKxKD9cc1bpAMAAdBQzBFLHoBk0AZujnzZ9Rn7PcFR9FVV/oa06zPDkezRrdj96UGU/Vjn+tadAGcP3niA+kNt+rN/9hWjWZp37zVNUl9HSIH/AHVz/NjWnQB4B+21Ls+Cs6d3u4fyDj/61ep6pbm5+E7hf9YlgJE/3lGR/KvJP22W3/CedP7jRyH/AL/RAf1r2b/mmn/cP/8AZa6V8EfV/oc0vjfofOX7Athbr4N8VXohQXbXflGbHzFBGpAz6As35mvdv+Cd+p3GnR/FWWPSLnUQ/iq6DzWzJuXhOMMwJ/8Ar14h+wKR/wAIB4oGeftxP/kJK+hf+CbH/IP+LH/Y2XX8krarvP5GVL7J9X/8J5pkB23yXelN6XtsyD/vrBX8jXlP7XvinRrv9mv4hxQ6rZyynSbhAiTqSSYjgAZ56ivea+S/+ClWn2tv+y7rDxW0MTrdWqqyRgEA3EeQDj3P51y01eaR11HaDZ8wfsUQX8X7SnhL+zhbpdP4GiY/aw23G9B/DzngV9Tft/eIPFXhr9mTxO80+lvBeeVZypDBJu2SSKjYJbrhj2ryD9iXTE8V/tU+MNYuD5Unh3RbTT7WKLhWjlHmEt7gpxjHBr2H/gpiR/wyzrQzz9rtP/SiOuuWtWPyOJaU2eaafoEXhH4O6Lb2ru8en2SQ73PJRk2HP/jp/Cvi/wCAQH/Cxvha2OWuL0k+vD194XUAufhZJE3R9OI/8cr4P+AX/JRPhX/13vf5PVUdYzZNTeJ9Sftsuo+Bt+hI3NcQBR3P71K+m/CAK+F9KBGCLdP5V8s/tbASX3wyjblH8T2gZT0Iya+o9Xu3tdFhgt22XVyEt4cdQW4LfgMn8K5ZaU4/M6YfGyXRVjuZr7UEO77TLsU/7EfyDHtkMfxrVqGztI7G1it4l2xxKFUD0FTVkbjJpkt4nkkYIiAszHsK+QfjfbGL9pH4Q3MilZ7t764cHqN0alV/BcD8K+pfEq/2ibXSVJH2ti0pHURLy35naPxr5j/ahkNr+0b8I5Ix/qVvH2j0VFJH5CtaXxfJ/kZVfh+48/8AiP8A8nxeE/8Ar2X/ANBnr7AeNZMblDY9RXx78RHWT9uDwkykMptVII/3J6+xKurtH0Mae79TH0SHyNIs0PURLn64yavUgAUAAYA4AFJI4jjZz0UEmuQ7TP0T94l5N2luZCPoPl/9lrSrN8OAjRbUn+NS/wCZJ/rWlQB5P+0N+9+G/jM9odFlH4sG/wDian+An+jfCX4f3XRW0+K3f6Milf8Ax4D86g+O/wC8+E/xDl7fYpIh/wABhz/NjV34GWzXf7P3haNP9aNMheP/AHlRSv6gV0L+F8znl/E+R6rXzn+1+f8ASPhsO/8Awklrx+LV9DWdyt7aQzp92VA4/EV85fHxf7Q/aG+FGn3H76yMlzMbd+ULogKtjpkHoe1Ol8RE9rHuuryGPw7Pj7zQbB9SMf1rRijEUSIOigAVn62Abe1gHSW5iXHsDuP6LWnXOdYVm+Hfm0qOTvK7yH/gTk1av5/s1jcTHjZGzfkKj0iH7PpVnH3WJQfrjmkBcrNsP3mr6nIOQDHFn6Lkj/x6tKszQvnguZv+etzIw+gbaP8A0GgDTrMuv3uvWKf884pZD+O1R/M1p1n27+brl5/0zhiT8SWJ/pQBoV8+ftiXD3vhnwx4dJCWmt61bWdw4++qFs5X3yo9a+g6+c/2uXI1T4Zr2PiK3P5N/wDXraj8aMqvwM+h7K1Wx0OK3QkrHAFGf92vjv4FKF8QePgBgDXrkD/vqvsv/mHf9sv6V8a/Az/kYfH/AP2H7r/0KuzC7s4q+yPXyaztQYBTV9+lY+oSbVNeich4v8eYUGgwagl01reWFwk9uyjOZBjaMfjWh4t8MfGNvg9qt/43sNJ1LTxbtMltqMEa3lugUqJ4WRApKhix+YnA7Gsf4vazFoms+EtSuEMttZ6vBcyxjHzKh3Ec4HIHc169+0F+2novj/4Galodn4e1LSLzVLeOOzfVrPEc8ZIEjRsrEZCE4Oe4607paNkNN7I4X/gncupz/EbxDo1rBYato2r6C7X9jqtiLux89Jh9m89D12sC4AKnuCCAR9yw+EdV8LzrdR/DX4YX9xECElsorvTJB9Cm8CvkP9grRfFnw08PeM/Go8NSXtjci2tbN5JdscsizyRyglA7LtyTnYR8uThcsPpW7/aZc2jSaj4YFm6qzvEur24YKM5IEvlknA6YFIs4Lxv4VF9dRS654N8SWcakljo+tQ6zFyQfu3qNJjIB+UcYGOleWa1o+meIZZLKPWlupY/MeHTdTjks7wNggbVuS6nDYz5LRDHQivbZf2hPDetWyzyW+pafDIAVlmtvNjweh8yEyLj3zjiuZ1y68PePrMmN7HXLEsVJUrKqt6ex6e9AHyx8b7M+Gn8J3EwaxmuNG1KxlRwUbCRtHHG2SSeq4yW65y33iz4PeJ9W0X4G+NvDlj4U8Qajd60ZolurGxaW3KNEIyrkc5UqTwD3Hetj9o/T30/SNI8LWDpfCedp4n1G7j82ADaFjjLkHHPck8kdMAavwu+B/wAR/EfwBtvG/grxxqEX2QtJHogkeNcxSOGCnzCpzjgFcHABxk1zYjC08XFQqq6TT+53RcK0qD5o77feeXfD+xnsvCM8M8bRSxancROjgqysscIYEHkEe9dRYL0+v+FQadZGHwTpeqJqr6oNaurm9ujJEI2jusqJAQPbb36hiMCrVh0FdklZ2MYO6ua0AwBXafsdf8lX8f8A/XK1/k9cbD92uy/Y6/5Kv4//AOuVr/J65a/8NnRS+NHuv7Rn/JEvGH/YOm/9ANYn7Pf734beCQf+WWjRv+LBB/Q1t/tGf8kS8Yf9g6b/ANANYv7NY8z4YaBIf4NLtIh/373H/wBCFeW/4XzPQh/E+R6xWbrXz/YI/wC/dJ+mW/8AZa0qzr395rGnR9lEkp/BQv8A7NXOdBo1V1SdbbTbqRuixseme1WqzPEXzaW6d5JI0A9cutAFrToGttPtYW4aOJVPOeQBVmk6Uy4k8q3lfIG1Scn6UAUdCG63uJeoluZWB9txUfoK0qoaDGYtGslPXylY/iM/1q/QB87ftlnzfhZrjdovsifiZwT/AEr1rW9ZtfD/AMH7nUL1zHa2+mGSRgCSAEyeBXkn7Xn7z4MeJZv71/Ag+ivGP55rt/jD/wAm2a7/ANgZ/wD0Wa6o6wivP/I5Z6TfoeO/sFzGDwv4hUn5Li6dR/vLFGR+hb8q9/8A+Cd2iSanafFWSLU77T3XxXdLi1dQp4TkqykGvnz9iQGP4Z+JLpR81rqfncf3REgb/wAdJr6A/wCCd/i3SdAtvinDqF6ltJL4rumQMCcjCeg9q1q7z+RFL7J9jf2Hr8H+p8SeaB0F1ZI36qVr5d/4KOjX4v2ZNYTUG0+4tjdWuZbdXjdT58ePlJIP5ivrW18Q6VfY+z6laT56COdW/ka+Yf8Agpc6v+yzrW0hv9LtOh/6eI65qX8SPqdNX4GePfsJ6DY69+0D8VkvoTMsdrpxUCRl6xN/dIz0r0z/AIKPeD9H0f8AZi1m5s7FIZ1urUCTcxIzPGD1NeZ/sJ6ymj/tA/FZmtbu6L2unALaQGUjETdcdK9M/wCCj3iQ6n+zHrMI0rUrUG6tT5t1b+Wg/fx985/Sul/xV8jkX8N/Mo/800/7h/8A7LXw5+yDYxa38X/D0V2DJHp+n3F1brnGyQy7SeOvDHrX3H/zTT/uH/8AstfDv7GlwLf4zaTn7r6XMhP1m4/XFOl8ExT+KJ9D/tZ/8hL4Yf8AY0Wf/oRr6gsEF/rXnFg0djAsKr6SMAzH/vnaPxNfLP7X8/2W4+G02M+X4ltXx64LGvqzwtZSWWi2/nf8fMw8+Y/7bckfhwPwrnn8EfmdEPika1FFZ3iC9ex0uUw83MpEMI9XY4H5Zz+FZGxW0b/iYalf6keULfZoD/sIfmI+rZ/IV81ftJIsn7TfwhRhlWW9BHqPLWvqbTrJNNsYLWP7kSBAfX3/ABr5b/aOOf2n/g+O4F7x/wBs1ral8Xyf5GVX4fuPnyxnuZf22rWK5meVra8+zpvYnaq2pAA9B1P4mvvqvgrp+3V/3EP/AG0r71rSv9n0MKXX1KtUtakEWkXrk4xC/T6GrtZniD57BYu000cRx6Fhn9K4juLlhD9msbeLpsjVfyFT0VHcSiCCSQ9EUt+QoA8q+NXz/ArxrN/z2trt/wAMMB+gFbf7Of8AyRLwf/2Dof8A0AVkfGuIw/s+eIlP3v7JkJ+pjJP861/2c/8AkiXg/wD7B0P/AKAK6F/C+Zzy/ifI7XQ/3Au7I8fZ5jtH+w3zL/Mj8K8A+N//ACcv8J/+3z/0WK9/m/0XX4H6JdRGI/7y/Mv6FvyrwD43/wDJy/wn/wC3z/0WKdL4vk/yJn09Ue7ah+81bTI+oDSSkfRcf+zVpVmf63xGe4htfyLN/wDY1p1zHUZfiU50l4gcNO6Qj33MAf0zWmAFAA6Cs3WVEtxpkWMk3If/AL5Vj/StOgBksgiidz0UEmqXh+Mx6LZg9WjDn6nn+tLr0ph0a8YfeMTKPqRgfzq3BEIYI4x0RQo/AUASVmaT+8u9Tm/vXGwfRVUfzzWnWZ4e+bTFl7zSSS/m5I/TFAGnXzV+1tJ5niT4cIP+WevWuf8AgTn/AOJr6Vr5h/axZhrHg+aMZdPEdki+5Csf/ZhW9H40ZVfgZ9Qf8w7/ALZf0r41+Bn/ACMPj/8A7D91/wChV9jQTLcaRHKhyjwBgfYrXxz8DP8AkYfH/wD2H7r/ANCrrwm7OKvsj1yU4Wud1eXap/z6V0Mwyprl9ZOAf8+leich8/8A7QkvmaPp/wD18D/0Bq7j9oSKNf2UPhC4RRIUh+YDnH2dv/rVwXx//wCQRYf9fA/9Bau7/aHbH7K3wcX1ijP/AJL/AP16+fzL/esJ/jf/AKSz2MF/u+J/wr/0pHrH7HvxJn1/S734YeMfDAaBtLfxF4bn0+wsZrq3ieXfIkImRo/3vyrh1OeSSCFI9yk8P+FtFsLhYZPiZ4ciUGRxN4fi2seARnTHtyRyTj0HtXzL+w74g02T4j6n4s1u+l8jw54e07Sbf7PE8pKybQQUUMxKuuDjgDJPQmvtC6+PfgG/iyPEtraBlLj+0Fe04yRz5qrjp0PsehFe/dvVnj6LY+bfFvhnwt4h1RHtfFfh69u2IWPT9XtJNOmkPODm9Fy5c5A2hl+6PmGTXmni34fX+iyDUWsriwCsSb6y4gC87TG4klCr06yQLx26V9baxrXh3xlayGzvdN1u3xk+RLHcJg5HYkdj+RrxnXPANnp9xI3h67uvDcz540+QiEn/AGoTlD+QpgfJ3x7sXPiSSS+m+23I8M2dwtwygFmadAXwCQCQzdCep617R+zz8f8Ax18JP2fXFh4BTWvDdkbmR9UOoJGMGRy/y4J4OR3zivK/2orJbYaDdanMg8TSQta3EVllYHt42JRgD05Knb2yRzgEewfDuYQ/8E/NeJbbmG7Uc+szjH6142Z46pgIU5Ukm5TjHXsz0sDhKeMlONR6Ri5ad0eA6Brq+I7nW9QjiMEF5q91dxwk/wCrEhVtvpxwK66wX5R/n0rz74d2c1toCTSJtjuJneI/3gNqk/mpH4V6DYH5V/z6V7TPMRqxdK6r9j+4Efxh8cxH/lpDBg+4Dcfln8q5WLpW/wDssEwfE3x1djrbGzkOP7uHVv0Jrnr/AMNm1L40fQ/7RpA+CXjDJ/5h03/oBrI/ZiX/AIs14ekPV7aL8hGi/wBKyv2z7ya0+BWqmCV4vMlhjYo2NymVQQfYgkfjXoHwusYdO8A6LBbxLBCIAVjQYABJIAH0xXlPSmvU9CHxv0OqrNH7zxExHIitQD7Fm/8Asa0qzNN/eapqkvbzEiH/AAFAf5sawOg06zNY+efTYf79yGP0VS38wK06zLn97r9kn/PKKSQ/jtUfzNAGnWfr0hj0e7x950Ma/VvlH6mtCs3Xv3lrBCOTNcRpj1G4Mf0U0AX4YxDEiDoqhR+FPoqC/m+z2NxL3SNm/IUAfN/7V1+1x8MtI0YL83iLWI4TN/zzzIWDY78IBj3r0v4324tP2ePEcKtvVNIkAb1HlmvOP2oYBFZ/CyI8ga/ZA+/BzXoHxlcj9nrxXA5y9tp08Bz/ALKHH/juK61tD1OSfxSPKf2C4lm+HPiuNxlXvWUj2MKV79/wTVso5NF+KUVxGk+3xVcqfMUMD8sfrXgf7A3Hw+8Uf9f7f+io69w/4J3nWlsviq+lixki/wCEqu9yXW8MThOjLx6dRWlXefyIpfZPsy68FaBe587R7Jie4gVT+YFfKf8AwUb8GaRof7MusXNham1kFzarhJX2kG4j6gnB6/y9K+pP7f1yD/X+GpHA6m1vI3/Rtpr5i/4KP6zPefsvauk+lXtiXu7Uq0wRlx58Z5KscfQ1zUv4kfU6qvwM4D/gn5/ycJ8Wv+vTTf8A0U9ep/8ABTI4/Za1n3vLT/0fHXjn7CdtqNz+0D8Vhp19FYsLXTt7Swebn902MDcMd69H/wCCkmn6xa/sz6i97rK3kJvbUGFLRYwT5q98k10v+Kvkci/hv5lb/mmn/cP/APZa+F/2RLd7n4o26x/60aNO8f8AvLcAj9QK+47a4W4+FccwIAfTgfp8tfFP7Ff/ACWTT/8AsDz/APo8U6X8OYp/FE95/armj1CX4VygZjm8SWbYPoSeK+vrX/j2h/3B/KvjX9pf9xf/AA5szx9m8XWqqP8AYZiy/ocfhX2Va/8AHtD/ALg/lWEvgj8zoh8UiWsWX/iZeJYo+sOnp5rehlcYUfguT+IrVubhLS3lnlO2ONS7H0AGTWf4ct3Swa5mXbPeSG5de67vuqfouB+FYmxq18leLl/tT9uGyguybiGy8PGa3jk5WJzKVLKOxI4yK+ta+S9f/wCT6R/2LQ/9HmtqfX0Mamy9TwK8uBa/txmU/dGork+gNqATX33Xw7pPhb/hKv23dajMwiW1L3DfLncPs6R49v8AWA/hX2jpmpLLpltJMcSFdr/7y8N+oq632V5GNLr6k9Zmq/PfaXF1zOXI/wB1GP8APFadZc58zxHap1EVvJJ9CSo/xrjO41Kz/EDlNGuwv3nTy1HqW+UfzrQrN1v5lsU677qPj1wd39KAPI/2vdSuNF+BWrLaSeV5pit3OAcozqrD8QSK9V+HOj2ugeBtD0+zTy7a3tI441yTgBQBya8h/bS/5IbqX/XeD/0ale1eEHEnhfSmU5VrZCD+FdH/AC7Xqzml/EfoTa+jDTzcIMyWrrOAP9k8/wDjua+ffja4k/aV+ErKcqwuyD6jyhX0k6CRGRhlWGCPUV8u/FF2H7Q3wlgc5ktmvYG/4DGAP0wadL4vkxS2Xqj6G0795q2qS9g0cQ/Bc/8As1adZuhfPb3M3/PW5lYfQNtH6LWlXMdRmXX77XbGPtFHJKfrwo/ma06zIf3niG6YciK3jT6Esx/wrToAzNfO60giwT51xEmB6bgT+grTrM1QeZqOlR5/5bNIR/uof6kVp0AQ3cogtJpD0RGb8hUGixGDSLJD1WFM/XAqLxCxGjXKqfmkAjHuWIXH61oqoRQoGABgCgBa+Zv2nPnufAsh53+L4efXBK/0r6YYhQSeg5r5n/aVUix+Fzn70niS2lP/AAJmb+tb0fjRlV+Bn0RoR8rRZrQ/etd0Q/3cbl/8dI/Kvkn4Gf8AIweP/wDsP3X/AKFX1qSLO+JJ2x3dp1/20H/xLfpXyT8CmD6749dTlG165IYdCN1deE3Zx19keuTfdrltZHB/z6V1E5wprltZPyn/AD6V6Jxnz38fCrabp6FgpNwOT2G0816H+1FpN1oH7O3wl0u9j8q6tYIhKmc4JhIx/wCOmvJfj7mTVdMCgnEchwP+A5r6A/bt5+Hngsf3DAPzjn/wr5vMptY7Bw6Xk/uVv1PcwMU8JiZeUfz/AOAdZ+xD4r0zxT+zz4i8IPeaBpOoaLrEF1Pea3cfYreWykD7VklaCdWzOcAbVPyqdwOA3rd/8J5Nas5I9Iv/AAfdyZ2rPpHiqOeUqewCS2Jyceg6dK8V/wCCbkFvdeNvG8c8UMkDaVpQaNgGQnyW6g8Zz+tfZvir4d+EtZtJIrvw3pF1E+CVksoyD6H7vua+kPDPk7xx8GNY0+WKa58P6mdiAG8RYWk3nKh08gXTADI+9MDwSxwcV5zc694h8PXjrZa/FqFpA7HN05liP+yzszlTnj55Y8dQpHX6d1/4U6HpgdtGl1Lw+w6f2VqM8Kf9+92w/Qqa8i8YeG9bE6vdCy8XogI33kQtdQA6fJcRbRnHqBnHWgD5p+O+iz+K/FfjTWVbyY/DFraW86t8rPJLLgYHPy4ds89h1rvRO8H/AAT8TY7IW1LGVOP+Xwn+lcp+0hBqE1x/wkVl5tra+IIEstQspk8uV5oXLB3VcKWzHjOO3y8Nx0t4RH+wDaKGDA6sRkd/9Kf/AAr57N1f6tf/AJ+w/U9nLXb2/wD17l+h5z4UTHg3w+c9YZD/AOTEtdTYfdX6f4Vy/hN9/gvQRjG2CQfX/SJj/WuosPur/n0r6OW54kdjWi6Cut/ZCgW5+J/xEhcZSSC2Vh7FXFcipxGT7V6P+xDp8Ny3jfWJFLX76kbVpM/8s0UFRjpxuNcuIdqbN6Xxo3/2u52m/Z3vBJ/roriCGT/eWZVP8s/jXsngmPyfCGjJ/dtYx/46K8X/AGzP9F+EmvwdEmmtbhB7+ait/JT+Ne3eFP8AkW9M/wCuCfyry5fw16v9D0IfGzVrM0A+Zazzf89biV/w3ED9AK0JpVgieRvuopY/QVS0CJodHtQ33mTeR6buf61gdBoVm2oLa9fP1VYokBx3+Y4/UfnWlWbpP7y61Kb+9cbB9FVR/PNAGlWdfkNqmmJk53SSY7cIR/7NWjWW+ZfEkY7Q2pP4swH/ALLQBqVm+Iif7InQdZNsQ/4EwX+taVZms/PJp8P9+6Ukeygt/QUAeFftaKEuPhqqjAHiS0AH4tXZ/H7/AEX4UeMk6JdaNJKP95UKt+hX8q439rf/AI+vhv8A9jLa/wA2ruP2lISfghr9wo+aGwkB/wB1oyp/mD+Fda2gckvikee/shaVBpX7PN9qFsDHdXklxLK4Y8spKDHpwi9K+i/+CZltar+zfa3wVDqN9f3U13Nx5kz+c67nPUnCqMn0FfP/AOyn/wAmyn/t7/8ARj17n/wTj8JaPrP7MOjT3mnwzzm6uh5pGH/4+JO45qqu0/UVLePofYdfKX/BTAgfstayM8m7tP8A0ojr6OTwXZW0bJaXWoWKn+GC9kwPoGJFfHX/AAUZsbrS/DXw70+XV77UNMvvE9tHcWl2yssi4Y7WwoLDgcHIrCj/ABEb1fgZzf8AwT2vYj8evirNLIkQaz0wnewHPlPmu+/4KW+I4NR+FPhzwlbSwyP4g121s2mSQMYRktu2jryoHbrXJf8ABPrwVp/i7x78WfF13aW8kB1NdMispYg4j8leXBP97eOMcY71s/8ABRrQ9O0qx+FLWVhbWjN4stAWgiVCRh+DgV0/8vV/XQ5f+XbOW+KDT+HfgP4vs4J3instPlMcsZ2sFaMsCCOnJI/CvnH9k+3itvjXoKxIsYbwyHYKMZYyKSfxNfSP7RX+jfCfxHJ/Bc6PPC3+8sZZf03V84fsqf8AJbdA/wCxYX/0Naql/CkTU+OJ63+1jB5fij4ZSr0m8RWYYe6ucfox/KvsO1/49of9wfyr5H+Mka6n+0x8IdOuh59iZrmc278oZEjDI2OmQeQe1fXgAUAAYA7Vzz0jFHTDdsxvEWb17PS1P/H0+6X2iTlvzO0fjWz0rG0tBe67qV8WYiI/Y416AAAM3/jxP5VtVgbBXyXr/wDyfSP+xaH/AKPNfWlfHkt42o/tvyXJ/wBVJ4fPle6CfAP44z+Nb0+voY1Nl6nFfDj/AJPi8Wf9ezf+gwV9NXlxHpt/c28rCNGfz4y3cOOf/HlP518y/Dj/AJPi8Wf9ezf+gwV9L+OPD/8AbT2bLJ5bRhwSO4OMf1/Oqq7x9EZU9n6nQ1mWSh9d1GQ9UWKMfTBb+tadZmikO2oSg5V7p8H6AL/SuQ7TTrNv/wB5q+mR9gZJT+C4/wDZq0qzP9d4i9oLb9Xb/BKAPG/20v8Akhupf9d4P/RqV658OpCfCNhA337ZBEc+gGV/8dIryP8AbS/5IbqX/XeD/wBGpXrPhH/RIdPXol3ZRsP99Bg/oR+VdD/hr1f6HM/4j9DqK+Y/jVD9l/ah+GUvSOQXUh/3hEAf0Ar6cr5d8eOda/aztbe8YyQ6ToEt1Zp02SuxQnjrkEDmnS0bfkyZdF5n0P4eUrotoT1dPMP/AAI7v61o1HbQi3t4ol+6iBR+AxUlcx1mbpX7y+1Sb1nEY/4Cij+ZNaVZugfNp5mPWaWSX8Cxx+mK0qAMxx53iOMdoLYt+LNj/wBlNadZlh+81jU5OoXy4h+C7j/6FWnQBm638/2GPu91H+mW/pWlWZe/vNa02P8AuCSU/gAo/wDQq06AKeszfZ9JvJO6xNj644r56/aDibWviN8JvCC4iBvxem4POPIUHbj3yec8V7/rw8yySDODPNHGM9/mBP6A14P8Zf8Ak5n4T/8Ab3/6LFb0fi+8xq/Ce2+P4jB4RuZ0Pz2ib8j+7ja36E18p/sxIB8Obd8ctLISfX52r688YRiXwrqyMMq1s4P0xXyT+zGo/wCFYWh7+bL/AOjGrtwmzOKv0PVpx8lcvrC5U/59K6ic/Ka5bWTwf8+ld5ynzH8fxjVtM/3H/wDZa9c/bp1qcN4H0wndZtp6zOg4JdeAc/R2H415F8fv+Qrpn/XN/wD2WvUP24pkGqeDwULSf2a0asGxt4hOffgsMe9eBjEnmOEuv5/yR7GGbWCxFv7v5s9U/ZW+HmjeLP2azfeHNPur7VLrVnsfFEUNrcX1w7IpltiIorScGEcOzFQEbZ8zEsD3GtfDq48LaAZtJutc00RRFXE9pfWY4x/Ez2KqOuMjjuawf2B/DNnDqPxV8Mf2lqejXC3kGLPTL6WHy4B80UqSK2Qz4O4huQACAOK+pNX8KeItPtxHpXxN8a6eEG1Q+pi5XGSeVlRs9e/PboBXv7njnyGvibxdptuI9O8TS6tsbcUk33cRBHIaUJP0OODOO/NZMXxl1GJpLjWtOS6sFfab/TACsY9ZAryIo+sgPbbmve/HugeK9RiI1G98P+Lxzv8A7d0kLPJzkHzo2+UjjkJ2rxHxfaWls8L6ppV34YkRDH54lbUbBee025biFTjopRcY4oA8Z+NnxBk0f4n6Rqmkl7ojTg7Wd2XWOeKbKbAgIOSGJII/hHpite+fH7A+nL66sx/8mW/xrlvj/wCDtTj8Q6Vr2l2y3mnWWj2zPd22ZYmkiOHwQOnIbJwMAntXpHj7QIfDX7DXheO3dpPtIjvm8zBw0k0bkdOgLkV89m0kp4WD3dSP4Jns5dFuOIl2g/zR5D4QP/FHaJ/1xk/9Hy11Vh91f8+lcp4OYN4P0YDqsUgP/f8Alrq7D7q/T/CvomeKjVH+qb6V6j+w1/yAvGf/AGGpf/QUry4f6pvpXqP7DX/IC8Z/9hqX/wBBSuXE/wANm9L4zb/bfti/wXuJ16pcRK3+6ZE/qBXsnhX/AJFvTf8Argn8q8k/bY/5IVqX/Xxb/wDo1K9b8K/8i3pv/XBP5V5kv4a9X+h3w+Nkuvy+Tot4e5jKD3J4H86uxRiKJEHAUACqGvMfscSD70lxEo/77B/pWlWB0BWb4e+bS1l7zPJL+bk/yxVy8mFvZzynokbNx7CoNFhMGkWcZ6iJc/XHNAF2s2y+fW9SfsqxR5/Ak/8AoQrSrM0X52v5v+el04B9lAX/ANloA06zLv8Ae67YJ/zzjllP6KP5mtOs2H974huW7RW6J+LMxP8AIUAeFftb/wDH18N/+xltf5tXe/tIX9tYfAfxObmVIVk09okLkDc7LtUD3JIH41wX7W//AB9fDf8A7GW1/m1af7Z//JAb3/rrbf8Ao1K7Iq/Ijjno5Gb+yn/ybKf+3v8A9GPXuP8AwTi1u+0/9mTR0TRLq9txdXWJraSM5/fyZ+VmBrwL9kucj9na8t29LiVPoZHB/Vf1r6W/4Jn/APJrOi/9fd3/AOlElOptP1ClvH0Pon/hOLaL/j603VrMdzLYuwH4puFfHH/BRnxNpuuaZ8L47K5EsqeKbZmjKMjAbX7MAa+6q+KP+CmA/wCJR8LP+xqtv/QZKxo/Gjet8DM3/gnLrNzpumfFCODSbvUd3im5O63KBV+VOCWYVJ/wUS1W91C0+FS3OkT6dGPFdqVeaWNtxw/GFJrQ/wCCZ3/IH+Kf/Y1XP/oMdSf8FKf+Qf8ACf8A7G20/k9dH/L3+uxy/wDLs5f9o62+0fAjxP8A3o9Pdwfohz+ma+c/2JtPXXPiRf6lOxWTStItrWFV6FXG4k++V/WvpP8AaD/5IT4r/wCwXL/6LNfO/wCwZ/yN3ir/AK8bL/0BqKbtRkKX8SJ6h8Vv+Tq/g/8AW8/9FV9XavfjS9NuLkjcY1+Vf7zHhR+JIFfKPxW/5Or+D/1vP/RVfT2ohtU1+zs15t7UC6nHq3/LMfmCfwFY1Phj6fqdFPeRe0SwOmaXBA53SgbpW/vOTlj+ZNXqKKxNjK8RXMi2sdnAdtzev5CN/cBB3N+Ayfyr5P8AFTLo37bNqkI2wx+G0iA9B5pUfrivquE/2h4mmkAzFYxeSD/00fBbH0AX86+V/Hlqb39tC7hU4dvCpKH0YTEj9QK1p9fQyqbL1PJf2dtTudY/a68UXV3KZp2+2oWIA+VZkVRx6KoH4V9q6x/yx/H+lfDn7Lcvn/tTeIpMY3/bmx6ZuFr7j1j/AJY/j/Sta/xr0Oel8LJicDNZ3h0f8SeBs58zdJx/tMT/AFqxqk32fTbuTONkTN+hpdMh+zadaxYxsiVf0FcR3lmsyw+fWNUfsDHH+S5/9mrTrN0P547ub/nrcyEH2B2j/wBBoA8a/bS/5IbqX/XeD/0ales2H7jwpol4OtqsTk/7BG1v0Ofwryb9tL/khupf9d4P/RqV7P4dt1u/CFjA/wByS1VD9CuK6H/DXq/0OZ/xGbVfLviBfN/bMSHtLocaH6faAT+gNfSmiXDXOmQGT/WoDHJ/vKdp/UV85ahF5n7blt6LoW7/AMiNRT6+gpbr1PpGoL6XyLK4kzjZGzZ+gqes3xExGjXKjrIojH/AiF/rXOdRNo8Rh0mzQjBWFAfrgVcpFUKoUdAMUMwRSx4AGTQBnaINyXkv/PS6kP5Hb/7LWlWb4dUjRrZiMGQGQ/8AAiW/rWlQBmp+98RSntDbKv4sxP8A7KK0qzNM/eajqkvbzVjH/AUH9Sa06AM3VPnvdLi7mcvx6Kjf4ivB/jL/AMnM/Cf/ALe//RYr3eTEviOFTz5Nszge7MB/Q14R8Zf+TmfhP/29/wDosVvR+J+jMavw/ce/+LP+RZ1P/r3f+VfJH7Mf/JLrT/rrL/6MavrDxrfW9l4Z1EzzJDugcLvbGTivkz9mueK1+FNrLNKkMayykvIwUD943c124TZnFX3R6vP901zGs/cP+fSpbr4g6KWMdpcPqsvQJp0LXHPoWUFR+JFc7qXiHUbqMsnh+7iU9PtMsKHt2DEiu85T5++P3/IV0z/cf/2WvSP23zu1Lwg3/TrIv5RW/wDjXmXx0llmvtKaeEQSmOTdGH3Y5XvXpv7a5zN4Mf1juOvstuP6V4eL/wCRhhf+3/yR62H/ANyxH/bn5s95/Z08BeJ9F+GsnxI0PXf7Y13x4YoHn0+zkaPSxAvMbNFBcKsg3bAsiDcFYjlDnsvEfj74k+DY9+qa9FcqygRG+srSFZGI/wCmktsx6HoOcdK4f9hTQ79vhx458CfZ/Dl7fWPiMXc2neKtL+2w7Ps4jWVYSylWbB2v/d3DnNfQ2oeGNW0mGOEfDr4eTQod3naK15pFyDjna8bEj6bgOnXFe2vI8k8KvPjn4mhgRtZ8OW8qMebi1mZE24+8SoliX/gUwrLs/i54d8TXRj+1JZzFvKMVy6FSxz8okUmNjx0DE+1b/jjw1YPfJdaj4K13Qpo/mN7p88Gug4/vNcBp/wAhXlniLwra+IIJorLUbXXmhjLCJVeO/hX18idhMSRwQJwOCAp6UwPMfi/8VrLw0PFvg3RNCFrb3UiG5ufMbYZm2FsKDhQ0YZeCM88d69L+Lzb/ANifweuPu6Xat/5EthXgPx8jhsvH3jezRw7DUbORPUoLdwW/N1/MV738Vju/Yt8NjP3dHsP1mh/+Jr5jOP8AeMG/+ni/I97Lf4OJ/wADPC/BjZ8JaUOOEccf9dpK6+w+6v0/wrjPCLiw8MaXHcn7O8qM0ay/KWUuxBAPUYIP412WntlVwcj/APVX1DPARrD/AFTfSvUf2Gv+QF4z/wCw1L/6CleXD/VN9K9N/YZmU6V41i/iXV3b8Cqj+lcuJ/hs3o/GdR+2x/yQrUv+vi3/APRqV634V/5FvTf+uCfyrxX9siV7vRvBuiux/s/Vddtra7iH/LSMknGeo5A6ele7adAlrYW8MYwiRqAPbFebPSnH5nfT+NlXU/3upaZD1HmtKR/uqcfqRWnWa373xCg7Q2xJ+rMP/iK0q5zoM7xCxXRrlR1kAj/76IX+taCqEUKOABgVnavmSXT4Bn57lWI9lBb+YFaVACEhQSegrO8Og/2Pbuesm6U/8CYt/WptYm+z6VeSZwViYj644qazgFraQQjpGip+QxQBNWZpX7y91Ob1nEY+ioo/nmtOszw/8+neb3mlkk/Nzj9MUAeEftNOfEHxA+GHhm3XF5Lq634d+ECQjcw9c4PHFa/7aSGP4C36nqs1uP8AyKlYvxqlWH9pb4TM3A3XS/mij+tbv7bH/JCtS/6+Lf8A9GpXbHR0/wCupxT+2cv+zH/o37PFjcjhWN5bv9Gkcr/48o/OvoP/AIJxeHZL/wDZk0e4i1fUbFzdXQ2W8q+X/r5P4WUivBf2Zbc3X7LEqL98fanT/eWVyP1Fe9f8E4vF2l6P+zJo9ve3D27i6ujuaF9nM8n8QGP1oq7T9R0t4+h9Rf2R4is/+PbXYrsf3b+0B/WMr/Kvjj/go0utLpnwv/tOSxkj/wCEpttptEdWB2vwdxPFfadt4v0O8A8nWLFyei/aFDflnNfHv/BSuaOfRfhY8TrIh8VW3zIcj7slY0f4iN63wMz/APgnJpV9qGmfFBrXV59OjHim5DJDFG247U5ywNP/AOCiWkXOm2vwqe41a71Hd4rtQEuAgVeH5G1RUn/BN3W9P0rSfiit7fW1ozeKbkgTyqhI2pzyaZ/wUa8RaZq6/CeGxv7e8dfFNuzLBIHwPmGePrXR/wAvf67HL/y7KX7Qf/JCfFf/AGC5f/RZr5y/YPnCeNvEsR6vp9mR+CH/ABr6N/aD/wCSE+K/+wXL/wCizXzL+xOxt/FPiG7HH2e3sGb/AHCjq36Nn8KUP4MhS/iRPYfiuwX9qn4QsTgD7aST2/dV9ReGlNxb3OoMMNfTGVc9RGAFQfkM/jXyl8bbaS8/aa+E1vEdrS/bIy3opjAY/lmvsOKJYYkjRQqIAqgdgOlZT2j6fqdFP7XqPqvqF6mnWM91J9yJC598dqsVi61/xMNQsNNHKM32mcf7CHgH6tj8jWJsT+HbOSz0uPzxi6mLTTf77HJ/oPwr5T8H58R/tafE2+v2M1xpVpb2do3Ty4nTey8dfmGea+v6+QPhl/ydF8Zf920/9Emtqe0vT9TGpuvU8X/ZhUL+1Z4lAGAGvwB/28LX3BrH/LH8f6V8V/su6ZNP+034uv1Ki3guLyFyTzuedmXH4Rt+lfamsEDyfx/pWlf416GFL4Stry+ZpjxZ/wBc8cX4M4B/Qmr/AErO1U77vTYezz7z/wABUn+eK0q4jvGTSCGF5D0RSx/CqegxmPR7TPDNGHb6tyf1NJr8hi0W8I+8Yyg+p4H86uwxiGJIx0VQo/CgD5//AGxLh73wz4Y8PEhbTW9atrO4cfeVC2cr75Udc19BaTarY6Za26ElY4lUZ+lfPP7W/wDx9fDf/sZbX+bV9G23/HtF/uD+VdEvgj8zmfxsoWH+i6vf23RZNtyg+vyt+oz+NfP8wz+2x9PDv/tavoDVP9Gv9PuxwokMD/7r9P8Ax4L+dfP83/J6/wD3Lv8A7Wop/a9BS3j6n0TWbrXz/YYf+el0mR7Llv8A2WtKsy9/ea3pyf3FllP5BR/6FXOdRp1S1qbyNIvHHURNj644q7WZ4g+ewSH/AJ7TRR/m4z+gNAF20hFvawxDoiBfyFTUVHcyiC3llPRELfkKAKGgfPZyzf8APaeWT8NxA/QCtOqOhxeRo9kh6+UpP1IyavUAZtr+912/f/nnHHEP/HmP8xXgPxvuBa/tJ/CeRvuhroE+gKKP619AaT87Xsv/AD0uW/8AHQF/9lr5++OVsLz9o34XQHjzEvVz6ZiFb0fifozKr8P3GT+1zYr4j+Knw+0O8klbTLkXLS26yMqsQq4PB6jnn3PrWto3w18O6HpdnYRaZDNb2bO0C3C+bsLHLEbs9ax/2g7s3vxl+FczcO0V0HHowRQ36g16BXq0P4aPMq/Gyr9nSGMJGixoOAqjAH4Vg6vDlT/n0rpXHGKxtSi3A/59K3Mj5a+PFjLd+INGtbeMy3EquiRr1ZjtwB7nsO9ek/tlabNd3nw+sxtjnn+1xDzDtUMZIlGT2Fcf4vj0eT4vka9rsvh+K1sFuLK+SBpxFcq4ZMooOQdpzx2Fc78YvEUPizTtNvJPHb+LLm1meEWz6cbXy1fLM6scbhlV6jjIrzK+GqVcXRrprlhzX76qx3Uq8KeHq0ne8remjufYf7M9l4x+EvhfU/HWo+FJ9c8Q+MbwSwtFJLJD9hjjG1mlgilRSC4A3Bdwb72RivS9Q/amMENxJq/hqOxSIfMItZt/MzxkbJvKPr+X1x5/+yX4lfx/+zvYaBr2l6/fXPgzVrvTrYaNFp93MscqrIci+jcKqFUjAjYfw5yPu+n3th4RtLXff6p8R9EEXS0m8OskR68/8SwxgnOMknoMY9PTOE5uf47+G9as47mRNQ02CYEpNd2b+Scf9NUDR/8Aj3euQ8R6V4e8fJHdA2+oIvMF7aSjeh9UkQ5B+hrE1HwnpOt6xNJp/ibwzqF1tysd072V4pweiTo8vOennL2+YV5/4l8G3nhi5/tCKO+0C/fAF2yyEPITgEMXdGHopuDnJwhOBQB5F+0TJph1qOxjvXvtX06No7i6nRFeVS6mNSRyzKjAZwM/Mc5yD3/x21G4t/2YvhrBFKyQy2dikqDo48mZgCPqAfqAe1eW/HPThD478Z+aii6i1O0iDEjgNBITz6Hav5CvRfjnNHN+zP8ADnY6uUSxQ4OcEWspI/WvCzBXxGG/xfoetgnajX/w/qc7NHHdaJoaSRI6rplouGUH/lih/rS2Ph+1AUw+baH1t5Sg/IcfpU0KBtH0Yg5/4l1n/wCk6VpWMeAte/Ldnjx+FDIrbUrJkK3f263Jw6TqquoPcMoGcehHPrXqv7FDG2tvEk/RJNamt3/FEK/quPxrzu4cw2crjqqE16r+x5p1s3wh1C+juA2q6jey3rQ7h8ro+BtHXHyD8648T/DOmj8Zp/tf/wCv+G3/AGMtr/Nq99tv+PaL/cH8q+ff2tbhbtfhjMnKSeIrRx9Dk19BW3/HtF/uD+VedP4I/M76fxSKVh+81fU5OymOIH6Ln/2atKs3RMPFdTDkS3MjA/Q7f/Za0q5zczbo+ZrtigP+rilkP/jqj+ZrSrNh/e+ILpu0VvGn4ksT/IVpUAZniH59N8rvLLHGPxcf0zWnWbqv7y80yH+9PvP0VWP88VpUAQXs32aznl/55xs35DNRaPD9n0mzj7rEoP1xzUPiEn+xrlR1kAj/AO+iF/rWiAFAA6DigD5t+O9u11+0V8Lok4dkvNp9G8oY/XFbf7ZF2L79ny7uBx5kts2PQ+amRWb8Zf8Ak5n4T/8Ab3/6LFT/ALX/APo/wP1+zPSG9gdB/sPKjD9dw/Cu2O9P+upxz+3/AF0Kngq1k8CfsYzXem3DrcvpMt6JGAJV3VpOOMYBbj6V9i/sO+HLHw5+zD4GjsYjGt1YJeS5YnMko8xzz6sx4r5C/wCbJf8AuXf/AGjX1p+yNa+I0/Zv+H72d/YPAdItysNzbMCo8teNyvz+VTUfuv1HS+Jeh7tdaPYX2ftFlb3Gf+esSt/MV8Sf8FH/AA7pmj6f8MJrGwgtJH8U2ysYUCZG1+wr7E+2+Kbb/WaXp14PW3u2jP5Mn9a+Gf2+vE+pan8XfhRo+rafJZ6TFLc6i9qZEkEkkKKysGXnjLccdazo/GbVfgZ1v/BNXT7W70r4pPPbQzOPFVyA0kYYgbU9aof8FFLy2vPG/wAH/C9rbrFdtqx1MyBAEEcIBccc5w3p2q7/AME5odeg+HPizxHpujx3Wn+Idfub21aW6WMom4IQy4POUaue/bHk1DUv2nvg1FqthHZlor9cRTeYGBiUHsMf/Xro/wCXr/rocv8Ay7Rj/tg6ndaN8BtWFtL5bS+TbyEAHKM6qw59QSPxryn9jvSoIfiF4906NdtuLO0iAJzgeWwrvv2u52m/Z3vBIf3sVxBDJ/vLMqn+Wfxrjf2Pv+SsePf+uFn/AOgNTh/AkTL+KjqfHdw11+0x8GGk/wBaovY5P95Ytp/UV9kV8f2enrrf7b2n2l7mW10zSJNQs484EcrsEduOuRng19gVhU2ivI6afV+YVi+H/wDTp73VDyLh/Lh/65JkD8zuP4ipvENzNHZpb2xK3V2/kRv/AHCQSW/AAmr1nax2NrFbwqFjiUKoHoKxNiavkD4Zf8nRfGX/AHbT/wBEmvr+vkD4Zf8AJ0Xxl/3bT/0Sa2htL0/VGNTePqed/stJn4r/ABVmAy1vfeeAP9meYkfiMj8a+jPiVqs9s+mvaysqSRs2VPXpivAf2QUWT4yfFVWAZWu2BB6EedPXs3jqZ4IdOttx32xlgPuAV2/+OkVpU/i/12MYfAd9Onm65af9MoZHP4lQP61o1m27GXXrw/wxQxp+JLH+orSriO4zddHmQW8PXzriNcewbcf0U1pVm6h+81XS4uwaSU/guP8A2atKgD53/a4IFz8NyeAPElr/ADavo62/49ov9wfyr5t/bAiMx+HkYOC/iK2UH0zur6J0O7+3aPZz9GeJdw9Gxgj8810S+CPzOZ/Gx2rWhvtOuIV4dl+Q+jDkfqBXzfa3Qvv2yoLgceZ4aDY9D53Ir6br5i+Glp/bv7Vnji9uG2totrFZ2yJwDHITKc+4PT2op7SfkJ7peZ9J1mQjzfENy/8Azxt0j/Elif5CtOs3Sj5l3qcvrcbB9FVR/PNc51GlWbqf73UNMh7GVpT/AMBU/wBSK0qzX/e+Ioh2htmb8WYD/wBlNAGlWd4gcpo10F+86eWPq3yj+daNZut/MLGP+/dR8euMt/SgDQjQRoqjooAFKSFBJOAOSaWqWtTGDSL2QdVhbH1xQBH4eydIgdushaX/AL6Yt/WvBPHxbxV+1n4H061G2TRbKe+uGfgMjjywF98j2r6HtIhBawxgABECgD2FfPcP/J6//cu/+1q3pbt+RjV2S8zm/wBoiM237QHgGD+Dddyp9HRSf/Hg1ei1xH7TKAfHr4aNj5il0Cfoi/4129erQ/ho82r8bGN1rPvlGKvyNisy9l+WtzI8k8OeH9O8TftIy2GqWcN9Zvo5LQzoHUkSjsa3P2pPhP4U8OfC6fUtK0S00+8t54iktrEIz8zqpBxjIwe/tWb8M7mK6/aT1C8EiLbWWmCCZ3YLtdmDAc9eM9K+i/EcGmeJNNhsrlYL6yuZlSSN8MrrySCD16V6FKKdF9zmk2qhxX7BnxO8N+FvDXxBk1fV7S2abW57tI/MG+RPLU5jT7z/AHTgKCT0AJr226/ao+FGtRO9p43087TgiUSRMPwZQa+Kv2ivAnhzwp4x8JQ+G9NTS9TvJzIz23yRGJOWGBxk7h27VfudGsApkaxtjK5LM/lLliepJxXdg8qeKjJ81uUwrYv2TWl7n0b4s+JPwy8TwMl14l8M38bcbbm7gb9GNeeHQ9OuoZj4N8QRQWrqf9DgnW7sJOvBiJIAP+yRXhmsaRprBt1jb/8Afof4Vx15OfD+q2J0myt5tRu5ktYftQLqpZhjAPQ5xyKmvljoJuU9PQdPFe00UST47RQ658Ungt4bu41WdYv7UhsQ0qG48vrECAWwueoGR6c1c8d3Hw1j0i70yxj8aaTd28LS22m6gY/skU2wgZRizDLHOevzdRXqHw7+F3i3XPiDoXiXxHpraddWkkkkihYvLdShUEsrFi5yvUYAXrk8eo/tBabaN8PdfkFvH5qaZcyb9vOdoArzPq/uuT6HT7TWyPnLSZbe70PRzbF2VbC2SQuMfOIlDAe3T9a2rWMqBXO+CFz4esM/88k/9BWuriUACsqitJpGkfhQ8oHjKsMgjBFY3wu0qHwn+0H4MTS2ktI7uScTIjkK48tjgjPTODj2HpW5VHwl/wAnC/D/AP66T/8Aopqwn8LNI/Ej1n9qL9w3w+sz/wAu3iq3Rf8AcJLL+hx+FfR0TiOzRz0WME/lXzx+15AYte+HUqj5ZtftN3+8rNj9G/Sve9Ul8nw9cMOv2cgfUjA/nXjT+CPzPUp/FIXw5G0eiWm/lnTzD/wIlv61pVHbxCC3jjHRFC/kKeSACT0FYG5m6T+8u9TmPU3GzPsqgf41p1m+H1J0xJW+9MzzHHQ7mJ49sYrSoAzJf33iGBe0Fuz/AIsQB/6Ca06zLL97reoydkWOH8QCx/8AQhWnQBma185sIf8AnpdJkey5b/2WtOsy8/ea3pyf3EllP5BR/wChGtOgD51+Mv8Aycz8J/8At7/9FirP7bwFv8JrqTIAuHhhI9SsyMv6b6rfGX/k5n4T/wDb3/6LFY//AAUCJ/4Qbw6M8f2kDj/tlJXfBXlT/rqcU3bnNz/myX/uXf8A2jX2V+x1fW3/AAzX8O4vtEXmjRrbKbxu/wBWvavjX/myX/uXf/aNfXf7I3hLRdQ/Zp+H0lzpVnNJJpFszSNCu4nyl5zjNZ1PhfqXS+L5Hvdfn7/wUEQSftBfCdWGVaz1IEf9slr7f/4V/oqc28VxZN62t1LHj8A2K+Ev269IGkftA/CpBeXl2r2uokfa5vMK/ul6E81FD4/kzSt8B7N/wTO/5NY0X/r7u/8A0okrz/8AbZ/5Op+CX+5f/wDotK6f/gnD4ZTVf2Y9Hlm1DUY4zdXQ+zwXJijH+kSc/Lg/rXEfthaLFov7U3wWjhnupY3W+IW5naXb+7ToWyefrW//AC9l8zn/AOXa+Rw37ZY+zfCLxBB0WWe1uEH1lRW/UA/jXOfsT6e2p65438SFwgluI7IQAZx5a/ez77xxjtXWftv2xb4MXE69UuIkb/dMiH+aisH9hP8A5Fzxf/2GJP8A0BKIu1B+pL/io6TQP+T6T/2LR/8AR4r60r5L0D/k+k/9i0f/AEeK+r7u5jsrWW4lO2OJC7H2AzWNT7PodFLZ+plQ/wCn+KJpB80VjD5IJ6eY+C2PooX862qzPD1nJZ6XGZv+PmYmeb/fY5I/Dp+FadYmwV8Y/D/U1T9ov42XluQ4SO2KnscREf0r6/1y6a10ucxttnkHlQ/9dGO1f1Ir4u8DRLpPx/8AjJYDGPsluuR3YQZP58mtqe0v66oxqdDlf2ENTm1rx3451G42i4uxFcSbBhdzPKxx7ZNe3/Gu8/sXW7OTbuS5iLY/2lIBP5bfyrwX/gn1/wAjN4s/697f+clfR/xmsoby50rzV3bUkx+a1rV/iswp/wAM7TR8SyX9wOfNuGA+igJ/NTWlWb4dJbR4XIAMheTA/wBpyf61pVwneZqHzvEMvcQW6gexZiT+iitKs3TCZdQ1OYnI81Yh9FUf1Y1pUAfO/wC1uR9r+Gw7nxJan9TXvegf6O13adkZZk/3XGf/AEINXz3+1zKTrvwzj7DX7dj/AN9V9CNm11PTZx9yeI27k+oG5f5N+ddEvgj8zm+3I16+a/g1/wAnM/Fj/t0/9FmvpSvmv4Nf8nM/Fj/t0/8ARZoh8MvT9RP4on0VWZ4e+fTRL3mkkl/Nzj9MVb1Cf7NYXMo6xxs3PsKj0iH7PpVnH3WJQfrjmuc6i5WZY/vNa1KTsgiiH4Asf/Qq06zdE+db2b/npdSEH2Hy/wDstAGlWZf/ALzWNMj6hfMlI+i7R/6FWnWav73xE/pDagfizH/4mgDSrN8Q/PpphH3p5EiH4sM/pmtKs3VP3l/pcPrMZD/wFD/UigDSr52h/wCT1/8AuXf/AGtX0TXztD/yev8A9y7/AO1q3pfa9DGrsvUw/j3djxD+0n4P0y3UrLpNnNdzM/Qq/wAgA98r+td7XnPxG/5O6H/YCX/0a1ejV61HSmjzKmsmV7g4zWLqEmEOK25kyKyL6HcprYzPCfij4O0m48QWEosyL6/WRpJo5CuRGFHIHXO4c+3ftzsPh3V9LjEOn65qtjbISyQwXLBUJ6kDPufzrpdT1PxR4t8eanp+haFBqi6QFjI3qkoEg3E7m7fLjHsKZf2/jXQpk/tTwZeeW44FoVnYfUL0HB5r38GsJ7FKtvfs/wAzzq3tuf3NjAaTVxrGmat4j1W51a30hHWFXUb1UjBy3U8YJzzxXpXiO/Gn6ZPdBDKI0LhF6sew/GuKfUf7cn/sS90+70O7u1/djUITH5iAjft9SFzXaeIbEX+mz2ocx702qw6qex/CvqMFGEKM/q7un+f9WPKrtucfab/ocfeeEbi6tZLjUJ5Zrp1JCJIyRxnsFUHt69a8quNTvfsInDO17ps0c0c2OVYHIJHqMd695tfFunvarBq8sWm3yDDiU7Uk/wBpGPBBx06ivPtZs7G98SS3ujoWilMcTugykrFxvYD024yfx964MfhoTUZUpeq6/wDDo6MPVkm1NenY+lvDP7UHgS+isbSbWBDcugVmmidEBA5yxAArL+MvxR8Ka/4H8S2ljr1jdXEmlSxxRxTqzOxDEgAHn7o/OvGLvwjbXMZR4UkQ9iuRWVdfDzT5oiv2OMD1Vdp/SuCpl+Ita6Z0RxFO9yx4MJTw5p3/AFxT/wBBFdPA+QKxdJZX0q3KIsSgbFRRgKFO0f8AoNa1t0FfN1VapJLuenD4UXQciqXhL/k4T4f/APXWf/0UatqeKg+HlpNrX7Rng+K0TzWsVmuZ+cbEKFQfzIH41zz+FmsfiR7R+1//AK/4bf8AYy2v82r2vVfn0u1h/wCe0sKf+PAn9Aa8P/aPZvEPxj+FnhmY7LGS+e/Z0+/vhUMoz0wcnPFe53ozqOl2wyVUtKceirgZ/FhXj1Pgij1KfxSZqVW1GZbfT7mRvurGxP5VZrM8Rkf2RKmeXZEA9csBiuc3LGkxmHS7OM9VhQH8hVukAAGBwKCcAmgDO0T51vZv+el1IQfYYX/2WtKs3w4CNGtmPVwZP++mJ/rWlQBmx/vfEM57Q2yL+LMT/wCyitKszTMvqeqydvNRB+CD/GtOgD5z+Jbf2/8AtV/DyxsgZrjS7e4u7penlxuuxTz1+YY4rJ/4KBf8iP4c/wCwj/7SkrZh/wCT1/8AuXf/AGtWN/wUC/5Efw5/2Ef/AGlJXoQ+OC8jgn8Mmallcef+xJOp+/FoTIwP/XHI/Qivrz9kbV9ZtP2b/h+qaCbu3GkW+2WG7QMw8tcHa2MfnXxyh+y/siSL0S78Lhx/vpDg/oR+Vfcf7HX/ACbL8Ov+wNbf+i1rGp8L9TWn8XyPQv8AhMmh/wCPnQtYt8dSLYSgfijGvhL9uvXbXW/2gfhU1sZQY7XUQ6zQvEwzEvZgPSv0Qr8//wDgoF/ycJ8Jv+vTUv8A0UlTQ+P5M0rfAeg/8E3vFWj6V+zBo8F5qVtazLd3WY5ZArczyHpXC/th69p+u/tUfBdtPu4rtY0vgxiOQMxrj+Vek/8ABNG0gk/Zc0V2hjZzd3fzFQT/AMfEneuA/bVCr+1h8FlUBcRXpwB/sLW//L2XzOf/AJdr5HHftsf8kK1L/r4t/wD0alcv+wn/AMi54v8A+wxJ/wCgJXUftsf8kK1L/r4t/wD0alcr+wlIp0HxjH/EurOx+hVf8KS/gP1Jf8VHTaB/yfSf+xaP/o8V9MeIHN9d2WkqMrcN5s59IkOcH/eOB+dfM+gkL+3OxJwB4ZJJP/XcV9M+HUa9a51aQkm7OIgR92FSdv59fxrGp9n0Omls/U26KKZLKsMTyOwVEBZiewHWsjUxdQYXuvRRcvHYQm5cZ+XzDxHkdyAGNfHPwxR/EXiH4x+Mbhtt+mryWbInCeVCDGD652uc89q+yNFhY6Xc30v+uvs3B/2VK/Iv4Lj8c18i/s+xCfRvjNG33X1+/U498VtT+GXyMKm6PP8A/gn1/wAjN4s/697f+clfS3xd/wCPnTP9yT+a183/APBPm0m/tzxZc+U/2fyoEEu07SwMhIz68jj3FfSHxd/4+dM/3JP5rW1X+MzCn/DO20y2Nnp1tAescaqceoHNWaKZLIIo3c9FBJrgPQM/QMtaTSH/AJaXErZ9fnI/pWnWf4fjMei2eerRhz9W5/rWhQB8zftEs3if41fDfwwuIg18l55/XHkguVx7huueK+kdatnbR28rmaALLGf9pef6EfjXzb8Uj5n7Unw4k7Ge7X8oEH+NfUnWuifwxOb7chkEy3MEcqHKSKGU+xGa+bvg1/ycz8WP+3T/ANFmvoHQf3NtNZnraytEM/3eq/oR+VfP3wa/5OZ+LH/bp/6LNEPhl6fqJ/FE958QZOlSxjjzWSEn0DMFP6GtAAKAB0FZ2sjzHsIRzvuVJHsoLf8AstaVc51DXYIrMegGTVDw8pXRrUnq6+Yf+BEt/Wn63N5GkXbj73llV+p4H6mrNrD9mtoYh0jQL+QxQBLWZpn73UtTm/h8xYh/wFef1Y1p1maB89lJL2mnlkH03nH8qANOs2X974ht17Q27v8AizAf0NaVZtn+81zUH/uJFEPyLH/0IUAaVfO0P/J6/wD3Lv8A7Wr6Jr5zWVY/22YlP8fh/aPr5pP9K3pfa9DGrsvU534jf8ndD/sBL/6NavRq85+I3/J3Q/7AS/8Ao1q9Gr16X8NHlz+JkbjOap3Me4Grp71E44rUg83/AGexs+NnxDXvttv/AEBq+golWbWbosAwSKNOR6lj/hXzXZanb/Dn9oQavqUv9maJqWn+S9yx2xPMGyN56Z2jgmvonRdYsb+8vpILuGXfIoXa4OQEX/GvToNOCXmclRWbZ4F+1BZ/2d8QfAmr3DR2mmJJLbyXcnCRs6jbuwCccHnHaql/bQXOnm4sfG/g++cHBhtJryRgcZ6mBB+tfR+u+HtJ8V2JstUs7fUbUkMYpkDrkHIODXmk/wCzZ4C1++u7mXRkgIlMapbO0S4GOykDrmvVoYqphrxirpu/bock6Uatm90eEala6nPG2yLT7sDvHOwB/NK4zWVvbrU9E06TzdMa81CK2cwyDdtZgMqR9f0r6QuP2RdAFzI1hrWr6dbMcrbQXHyJ9Mgn3696j+H/AOzNZ6V4nj1nVNVn1q0s5WNjb3IB2MGPzsf4jkcelFfFOtHlSab81/lcKdJQd73Riyfsr69CxGneM5UtCA0aXNssrjIyctkZ5z2rjPEfw4+Ivg3w/qmqzXGlXdhpokLSEMJJFTqcAYzgdM19lABQAOgryD4965FovwM1+WVWf7WkkK7f70jlQT7fMK5HUqU/hm1p3NlGMt0jwHRLtNT0mzuUhW3WWMP5S9FLcnH4k1sQrgCsvwrZNZ6BYwOQZIo1jbHqAAa21TivHqtupJve7O2GkVYOgrR/Z5/5OZ/7g7/+jFrPxitD9nogftND/sDv/wCjFrlq/AzaHxI9L+N//Jy/wn/7fP8A0WK92Cl/EROMiO1Az6Fn/rt/SvCfjf8A8nL/AAn/AO3z/wBFivddPzNq2pTY+VSkIP8Augk/+hV5NT4Y+n6np0vtepp1la/GZhp8Y6tdxnj0GSf0FatZl1+/12yjH/LGN5m/HCj+ZrnNzTqlrU5ttJvJAcMsTYI9ccVdrM8QfPYJD/z2mij/ADcZ/QGgC7ZwC2tIYQMCNFXj2FTUVV1Sf7Npt1LnGyJm/Q0AVvD+ZLOWc/8ALeeSQfTOB+gFadVtNh+zadaxf3IlX8hVmgD5wtpwf23JFJ5GgbB9fMBrM/4KBf8AIj+HP+wj/wC0pKZ5vlftky3ikkQ6Q0jYHO0XG1v/AB3NO/4KBHPgbw4R0/tH/wBpSV3w/iQ9Dgn8Mif4tWt94E/Y+0yzfymu4rKC0lIyV2uoifHTsx/IV96/Bj4S2HhD4WeF9KsNU1eGO1sIYw6XjDdhAM7fu9uwr4g/as/5NlH/AG6f+jEr798B+KoYvBei/aLDUrbFrGMtZu46eqBhWM3eF/Nm1P4n6I1v+Eb1aD/j28TXePS6gil/XaD+tfCX7ddvqVv+0D8Kl1G8hvD9l1HY8UBiOPKXORuPtX3h/wAJ94fVir6pDCwO0rNmM/kwH0z68V+fX7YfjOw8SftT2IvLyFtF8MeGbjUoZ7U7yzuSjhiM5AVQcDmlQ+L5FVvhPY/+Cb0mvf8ADMujLYwaebX7XdfvLiVw/wDx8SZ+UL/WvPv2p7i+8R/tmfDbTS9tcXmkWE93cpbIyCKJwUUksfmyy9q9p/4JzaDfaD+y14bW+hMDXLz3UQJB3RySu6Nx6qwP414r8ZLhYf8AgoVCD/y08MKmffznP9K2/wCXkn6mH2Ir0OO/bc1Gb/hB9C0VcLb6tqsNtM+PmVclsr75UVg/sVD+z18U4/1UmtT2xJ9QqFf5N+daf7bhxpfgf/sOQ/8AoL1n/sgxtJ4Y8eyRjMsOuyzp9VCN+uCPxqv+Ycn/AJemy7St+2rJbwDM114b8hfbdN8x/Bcn8K+xIIVt4Y4kAVEUKoHYAYFfJnwWjXxr+13431q5bDaHp9vZ2qoOGSUeYSffIP4Gvrauepul5I6KWz9QrG8SMbmK201CQ17JsfHaIcufy4/4FWzWPYKL7Xr68zuS3AtI+Oh4Zz+ZA/4DWRsaVwoS0kVQAAhAA7cV8Z/s7/8AIM+Mf/YwX39K+zbr/j2m/wBw/wAq+Mv2d/8AkGfGP/sYL7+lbw+F/Iwqbox/+CflwB4X8SQEcm83qfX5EB/mPzroP2s/jD/wrXVvD9uNPN6bmKZs+Zt24Kex9a5T9hM/ZvCGqXfZNW8p/wDdeKMf+hbawP8AgoMf+Kk8JD/phcfzireylXaZz3tSuj7UqlrU3kaReOOoibH1xxV2s3Xl82yjh/56zxofpvBP6A15x6Jeto/Jt4owMBVC4+gqSiorub7PazSnoiFvyFAHzR8RTv8A2ivhTN/z2m1CT8CvH6Yr6mr5e+JcJt/2g/g3GRhhFc5z6+UM19Q10z+GPocq+KRmH/RPEA7Jdw4/4Gh/+Jb9K+fvg1/ycz8WP+3T/wBFmvoDxETb2K3oGWs5BNx/dHDf+Ok18/fs3q3iT4pfFHxUSIhLqQ08QDnHkjbuz75HGOKUPhk/63G/iie8Xf7zXNPQf8s0llP5BR/6Ea06zYwZPEE7dorZF/FmYn+QrSrnOkzNd+aC2iPSW5iU/QNn+ladZmqfvL/S4fWZpD/wFD/UitOgCK5lEFvLKeiIW/IVW0OIwaPZoevlKT9SMmmeIXKaLdgdXTyx9W+X+tX40EaKo6KABQA6s3Rvna/l/v3TjPsuF/8AZa0SQoJPQc1neHQf7Gt3PWQGU/8AAmLf1oA0q+ZY2fXv2uda1HT1M0Wh6PHHcv02SeYJAvPXKZ6V9NV86/BDbN+0p8WgRlT9kUj/ALZkGt6e0n5GFX7Pqcz8QnWT9rdHU5VtBUg+o8016RXlPikNH+1UkDnLW+ieTk9wszBf0xXqpOK9el/DR5tT4mNPeo2p5NROa1Mzi/Emn22v+Iv7K1CFLrT3sTK0EqhhvEgAPPQ8n/IFec6L8MNJufEWuWujzahoM+mSxxi6s7xg0ium4jnoMivcPBnwx8WfFT4k3tn4W0f+1JLLSUlnY3MUKxh5iFzvYE5KnpmuZXwXq/w9+KXj7Qtet0tdWtrm1M0McgkC77ZJFG4cH5XXp3oA5KPwb4w0CYvonjnUkEgxIL7FxnHTG7p1NOstZ+K3haWT7LrdhrSTEswvoNmxs5yNnXOe/pXfyHNU5qtTktmS4p9DnbP46/EfRTJDqXhiz1ZydyzWdx5SgehDZOf8auaJ+1DJpVgtrq3g/V472MneLWMSx8kkYbIz1qxJgmqc0ankqD+FaKvUTvcn2cTorH9rLwXLak3zXml3YyGtbi2fevpnaCOeD1715z8Zfi/oHxM+HUPhXw7JNe6nO8W4eSyrGFYMzMSBxx+tL4qC2Oi395AiJcxRM6SFAcMBwcEYNcrNrKzWOlymTy727uIS7eX5ZkBcFscAYPtwR7U5V5yVmJU0jbtbc29rDGxyyqAT6mpadIc02sG23dmuwh6U74ITG1/aUhuM4WLTCX/3TKqn/wBCz+FJVn4E2q3v7R81u33ZdElQn0zItY1fgZpT+JHp/wAb/wDk5f4T/wDb5/6LFe66H80d5L2kupCPoDt/9lr59+LF2br9oP4QSycOFvFkz2ZYwG/UGvoLw0jLolqW5ZwZD/wJif615NT4Y+n6npUt5epp1mxfvfENw3aK3RPxZmP9BWlWZpf7y+1Sb1mEY/4Cg/qTXObmnWXq4Mt7pcA6GfzD9FUn+ZFalZkn73xFAvaG2dvxZgP/AGU0AadZ3iBj/ZFxGDh5gIV9yxwB+taNZ2rr5sunR+t0pP0VWb+goAvooRQo4AGBSkhQSeg5paoa65j0a9K9fJYfmMUAfOPh+BZ/2t4I5BlJvDJyPZpyf5Gsn9uWdpfhp4XSQ5mh1Ewv65WORc/pn8a6ezjEP7aMcY6L4bCj8Jq4j9szQbjxF8TPCWgpdPb2t+s8wAyUEm1Ru25wT8v/AI8fWvQpq9SHocE/hl6ne/tWf8myj/t0/wDRiV+jHgL/AJEvRP8Ar0j/APQa/I3xnoPxP8R+DpfCl3rNjq+kyNHIJriIxSptIIUbRjHyjr6mvZ/C/wC2t8ZvBGjW+k3vhex1qSzXyxdRyoiOo6YGM8Dj8Kc8PPlSXcKdaKk2z9J2iR/vIrcY5GeK/LH9uoRWP7TXitI40iSTwRICEUAZLyc/oK9b0/8A4KaXdvaRLqvgLUBeoAJ0t7clNw+8Fbf0z/Fj8K+U/wBoz9oHQvjF8WNc8Tw20+nxXnhY6YtvKjF0ud5baeBjrjPT3qaNKcJaourUhKOjP0h/ZF8D6LqH7N3w+uLmzM0smkWzMWmkwT5a9t2K+ZvjRo1to/7dcsWnW6wLD4XS4VEzyy3BP6gYr139lT9qf4aeHfgb4E8O6h4kt7fWLfT7a0e1JBcy7FXaADnOeK8k+IvirTfEH7ei31vIVtH8MrErTrs3Hzz2P1qUmpS+ZLekfkch+25dRt4f8D3KndEdZhkBHcbHNX/2ItMlPgTXdZcKLbV9UmuIU/iC8LhvfKmuZ/bMkSPwn4Xsw4Y2uuKg5ydhR2X9CB+Fd7+xP/yQrTf+vi4/9GvVP/d16k/8vWQ/sv25tf2kfi5CekYs0X/dCMB+mK+tq+Uv2cP+TnvjB9LL/wBFtX1bWFX4vkvyOil8P3lDXNQOmaVcXCjdIq4Qerk4X9SKfpNgNM0+G3zudVy793c8sx+pyaoXjDVPEFtaKQYrIC5mx/fOQin9W/AVt1kbEV1/x7Tf7h/lXwv8K/ELeFfAvxz1VYRO1rreoyCMttDEDOM9ulfdF1/x7Tf7h/lXwB4Y/wCSSftAf9hfUv8A0Guilqn8jnq7r5mx+whafb/ht4rg6M98Qp9G8qPB/PFcb+3nd/btW8F3HQyWk5YehzHkfnmu8/4J+/8AIj+I/wDsI/8AtKOvPP28IzbeKvD9sfuotzIn+65jb+e6t4/7wznf8E+5azdRPmanpcXbzHlI/wB1CP5sK0qzW/eeIk9IrUn8WYf/ABNeaekaVZviFj/Y9wg6y4iH/AiF/rWlWbq/72fT7fvJcBz9EBb+YFAHz38d5Gtf2iPhXKgJ8kXT49Qsak/oDX0+rB1DA5BGQRXzV8aI1l/aW+FKMMqwvAR6jyhX0LoEjHTUhc5kt2aBv+AnA/MYP410T+GPp+pzfakWr+JZ7G4jcZR42Uj2Ir5w/Y+Ro/8AhYaOcsviK5BPv8tfSVz/AMe0v+4f5V85fskf8fXxI/7GW6/mtEf4cg+2j3XS/wB5fanN6zCMfRUA/mTWlWb4f+fTzN3mmkk/Nzj9MVpVznSZkn73xFAvaG2dvxZgP/ZTWnWZZ/vNb1F/7ixRD8ix/wDQhWnQBm63+8Szg/563MYP0B3H/wBBrSrNvQZNa01OyiWU/gAv/s1aVAFPV5vs+lXkndYmI+uOKlsYfs1lbxf8841X8hiqfiH59N8rvNLHF+bjP6ZrToAK+bf2fZfO/aM+K8nZntiPptbFfRtzMLe2llY4WNCxP0Ga+cf2eIjD+0H8TUYYYRWOQfXyjmt6fwy9P1MKu8fU5rx1PBc/tdXb28iyCLSFScqchH3Z2n0OMGuk1X4n+HNMjdjqK3ZXg/ZFMoB9Cw+Venciqv7Yfw10zw9oM/xD0cy6Z4jjnhSW4t5ComUsFww6Hqv/AHyO1aeh+G9JuYYNUk021fUJ0DyXDRKWJ9cnp+FerRkpQVjzqiak7nOR/FiTUnKaR4Y1XUW7OAgiP/A1ZlqR9f8AHV2hMXhWzsiehuNQEmPqFA/nXoBAUAAYA7ComrcyOf8Ahh8SPjJ8JvE+qa54fbw7a3Wo2cdlKJ42kUIjs4IB3c5Y81ieMJviN478b634t1bWdNj1nWDC149vCoRjFEsSYXyxj5EUfhXavVWSgDz5/DvjVvveKraMf7Ono3+FVp/D3jJSdviqCQejaein9M138vQ1TloA4V7LxjboAt9plyw7zIy5/wC+QKomfxtE58620eZOwhaRT+pru5qozd6APPfEc/iXUdHu7L+xIt08ZTzEul4z3wf8ag1zxZdvo+nWU/hy5sUtp4mPluJFCJ6HGPTqa7ubvWdcdKAOXg8XaZdFgJmjKfe3ocL9WGR+taFvfW94u63ninX1jcN/KkvNLs74n7RaxTH1dAT+dZl14Rsrhy6tLFJjAbcJNv03g4/CgDa3Vo/s9nP7TQ/7A7/+jFrjFtdS0OQbr4z2OPvzRl/L4/ix8wH+0N2P7td1+z3pl1F+0vOWVJTb6ORO9u3mJGzOpClsemcdMgVlV+Blw+JHU/HZWsf2jfACjhSt7Oh/3oef/HgT+NfTdpEILWGMdEQL+Qr5r+K+7xd+1V4W0oAQf2RYNdb+pl81xFtx2x1zzX01XkVNoryPUpfa9QrM8P8Az2Lzf89ppJM+oLHH6AVfnlEMMkh6IpY/gKqaDEYdGs1PUxKx+p5P86wNi/Wbarv12+k5wkUUY9P4mP8AMVpVmaN88uozf37ph/3yAv8ASgDTrPu2zrNgmeAkr7cdwFGf1P51oVmD974jJ7Q2oH4s3/2NAGnWZ4g+ewWDALTyxxqCM/xAn9Aa06zNW/eXmmQ/3p95+iqx/nigDwjS3Gp/to3k1r+/is9BENw8fIicy7grHsSDnBrI/ahG745/DQf7N1/6CtbPwQGf2mPiwTycWf8A6LNY/wC09/yXX4Z/7t1/6CtelT/ixXl+h50/gb8zqHtQUHHas6507fmt4KCg+lRPCDXonGchc6GrEnYPy/8ArVjXnhS3cNm3Q565Xr+legy2wIPFU5bMHPA/KgDx+9+FuivEyf2ZAgIxlI9pH0IHFYU3wj0qGQTW6XFvdoQY7iOZ96MOhBz1Fe3T6eGz8v6VnzaYv939P/rUAeN6l4Bu9VmgfVNb1XVFhcOsd3cM6hgDg8+laHgTxH8QvhHaJZeHtUt7rTEZ9lleQfIu5txbK85z745Nei3GmDH3f0/+tWfNpi4+7+n/ANapcU1ZoabTujmfh18XfHHwv+IfiDxdNo9lrM2uqi3VvDKYtpQYUgsDgYzkc9a9l0b9via1nkTxH4IvrVWA8k2Ei3BJ7g/dx29a8xm0wD+H9P8A61cL4xikt57oI7JstA67TjDFyMj3461nKjCW6NI1Zx0TPqrwd+274BSPUH11dQ0G+muWlMF1auSVwApG0NxgY+oNeg+G/wBrD4X+JbeWaHxTZ2ojfYVvG8hjwDkB8EjnrXx94l0iOPw2bsorTLabxIVBOdmc9Oa48+F7G9iimntIpJGjXLMg9PpWLwsOhqsRLqfo3d/FfwzdeD77W9O1iy1C1jhkZWgnVg5GRgEZ7jFfFPw7lOofs1fFPXJnVJ9ce91Dyc/c3b1I9+UP515fJ8P9LlZiYGRW6ojsq/kDil/4QuWHTp7C21nUrTTXUq1nFcMIsHkjb6E5P40Rw/KrJilW5t0e8f8ABP3/AJEfxH/2Ef8A2lHXFf8ABQSMDxV4UfHzNbTg/QGP/E13v7B1uLXwr4rjQHyk1Z0UnuAiCuE/4KC/8jN4T/697j+cdZx/3hlP+EfatZtj+91nUpOuzy4R+C7j/wChVpVm6H88d3N/z1uZCD7A7R/6DXmHpGlWZdfvNesFH8EUrn/x0f1rTrNiHmeILhu0VuifizMT/IUAeC+Pi3ir9rPwPp1qNsmi2U99cM/AZHHlgL75HtX0Hb/6LrtzF0S5jWdf95flb9NtfP8AD/yev/3Lv/tavoDWv9HeyvOggmCuf9h/lP6kH8K6am0V5HLHd+pfuf8Aj2l/3D/Kvmz9lWb7OvxPl/55+Ibxvywa+k7n/j2l/wBw/wAq+Yf2an26f8Ux1L+IrqMAdyxVR/OlH+HIf20fRejQ/Z9Js4z1WJc/XHNXaai7EVfQYpScCuc6TO0X5zfTf89Lp+fZcL/7LWlWZ4c50a2cjBk3SH/gTE/1rToAzkYS69J/0xtwP++mz/7LWjWZpn7zUtUl/wCmqxj/AICg/wATWnQBl6x+9utMgA5a48z8FUn/AArUrNm/e+IbZe0Vu7/iSoH8jWlQBna+5XSLhF+9KBCPqxC/1rwj4HDH7S3xXHoLP/0Wa911khpNOi/v3Sn/AL5Bb+grwr4If8nL/Fj/ALc//RZren8MvT9TCrvH1Nj9tj/khWpf9fFv/wCjUqr4c/5AVj/1yWrX7bH/ACQrUv8Ar4t//RqVV8Of8gKx/wCuS16OF+D5nBW+I0G61E1St1qJq6zAheqslWXqtJQBUlqnLVuU1TmoApzVRm71elqjN3oAozVnXHQ1oT96z5+9AFBvvGkpW+8aSgBCQAc8Cut/Y6Gfit4+PX91a4PttevL/iFqR0vwnfSAsrsnlqyHBBbgH9a+sP2a/hNpPw58A2F1bWsa6tqVtFLe3CsWMj7egJ6AEnjpya5cRJKFu5vRi3K55/q0fm/ttWi9v7EVj+EpP9K+lK+dZ1DftsAnt4eyP+/1fRVebV+z6HoUtn6mf4gk8vRrvBwWQoPq3A/nV6NPLjVeu0AVna788dnD1826jBHsDuP/AKDWnWBsHSs3w782lRyHrKzyn/gTE/1qxqc/2XTbqb+5EzfpS6bb/ZdPtof+ecar+QoAs1m6ePM1TU5c9GSED/dXP82NaVZuhfPDdTf89bmRh9Ado/8AQaANKsyc+Z4htUP/ACzt5H/ElR/jWnWZB+98QXTf88oI0/Elif6UAeFfBD/k5f4sf9uf/os1j/tPf8l1+Gf+7df+grWx8ECP+GmPiwM84tOP+2ZrH/ae/wCS6/DP/duv/QVr0af8Zen6HnT+B+v6nbJ9xfpSOcUq/cH0rM1jXdP0aLzNQv7ayj7NcTKgP5mvSOMtu49qrSyqK5G6+LPhSHP/ABO4JDjOIg0hx9FBrPl+K+gzAmGa8nPpHYT8/mlAHZzTqPSqU1wnqK4e4+JsDlhBpOtXA7bLJlz/AN9EVQl8f3T5KeHtXA/20jU/+jKAO5mnT2qnNIntXDy/EKUZ3aDrCj1EKH+T1Wk+IsIIElhqcX+/aNx+WaAO0mZCD0rz3xsFkvL5V6/ZIVH4yPVmT4j6aF+drqP/AHrWX/CuT1vxdY6lqVyYJyxeO3RQ6MuSJGJHIHqPzoA9X8ZKsfgmf/ZsCP8AyHXOwwAW8Q44QD9Ks+Odct/+EUvIRcRmQWbLsDjP3PTNU4LoGNBn+EUASGEZqG/i22M5H9w1aSQNUWokGwuMf3DQB7x+xNGq/A3T2CgM1xOWPr+9evG/2+m+3+L/AA1b2wa4ngtpmljiG4oGZNpIHTO1vyNew/sRTiX4H2cfeO6nGPrIxz+v6V85/ttvAPjYfOjkk/4lsONkgXHzy+qmvOp/x38zsl/CR9/yOI0Zj0UEmqHh5Cmi2merp5h+rfN/Wn65KYNHvXHXymA+pGB/OrNtEILeKIdEQL+QrzT0SWs3TD5l7qcvrMIx9FRf6k1pVmeH/nsHm/57TSyfm5x+gFAHhMP/ACev/wBy7/7Wr6Mv7Vb6ynt26SIVz6ZHWvnOH/k9f/uXf/a1fSddNT7Pocsd36mfY3ZvdESZv9YYiHHowGG/UGvmz9mP57rx1D/z08XT5HspDf8AstfQr3S6QmtI4PlxqbpQP7rA5/8AHgfzr5+/ZIt31Gz1bXgAtprGsXuowIfvKmQg3e+c+tKP8OXyH9tH0rVPV5za6Xdyg4ZImI+uOKuVmeIPn08Q95pY4/zcZ/TNc50lyxtxa2UEIGBHGq/kKnoqK6l8i2ml6bELc+woApaD+8tJZ+888kn4biB+gFaVUdEh8jSLND1ESk/UjJq9QBmWv73Xb9/+eccUQ/8AHmP8xWnWbo37yTUJv79ywB9lAX+hrSoAzNQO/V9NQ/dXzJj+C4/9mrwf4CTJf/tF/Fa7tz51qz28azJyhZEKuufUEgEV7pcfvNanPUQ2ePoWY/8AxNeA/srH7Pqfjm77L4puoXP+y+0f+hBa3h8EmYVNXFGr+2zqbH4e6ToKoP8Aic6nBa+aT/q/m3Zx3+5j8auaTB9l0u2izu2RgZrP/bZ0+VfBWga2MG20jVoLmdf4iuSvy++WFLpfgbUvEsMc82v362ko3R2tmqQBVPQFwC5wO+4Zr0sNb2ZwVvjL2oana6bCZbu5htoh/HM4QfmTWJ/wnvh5vua3YS/9crhX/ka7bSvg34M0O4iuNZudMsZmIUT6rciSdyTwAZCWJJ9K9f8AD/w28GSKD5PiS9AOCdN8KalcLnpwUtyP1rqMD5kn8daOoytxLNzj9xayy/8AoKmq/wDwmVpOMw2upy/TTLgfzQV9t6f8JPCdzlYPCHj66cDPzeGZ7YH8Z9lVPEfwaFhGxs/hd4vuMDIMj6dCP/HrsUAfElz4kmH+q0TVp/8Adtwn/obLVZvEcgx5ukapD65t92P++Sa+k9f8CaxAzhfh5fWQAyDe6nZj8P3bvXmeuadrVk7h/BV3wcZgv7ZwfpudaAPK7jxbZRcSQ38f+9p8/wDRKqy+K9LyQ12I/wDrqjJ/MCu1urq5jP77wxq8H0EMn/oMhrFudctV/wBZpWsRfXTZW/8AQVNAHLy+KdHZto1WyLHgL9oTJ/WkN5DdKWhlSVfWNgw/StS71TRJFJmWaIYz/pFjKn80rHfw74b12RpLU2cswOC9s4SQH32kEGgCInJNFVbnwjd2RzZ6pdRgfwTETD/x4Z/WqLvrdmcPFaXo9UZoW/I7h+tAGJ8Wv+ROuP8AfT/0IV97fDxvJ8PwWZ/5YIrL/uONw/I5H4V+ffxI1I33g+8SS3ltZ43j3xyj1YYII4I4NfoF4d/0W20GbolxaCBv94Dcv/s351wYvaJ1UOp4rN/yev8A9y7/AO1q+ia+dpv+T1/+5d/9rV9E1xVfs+h20tn6mZf/ALzWNMj67TJKR9Fxn/x6tOs1f3viJz2htgPxZj/8TWlWBsZviH5tLeP/AJ6vHH/304FaVZusfvJ9Oh7vchvwVS39BWlQAyVxFE7nooJNUtAjMejWeerRhz9W5P8AOl16Uw6NesOvlMo+pGB/OrcEQggjjHRFCj8BQBJWZpP7261Kb+9cbB9FUD+ea0icAk9KzvD3zaYJe80kkp/FyR+mKAPnv4PzG2/aj+JUn8DPaQt/wKI4/VQPxpv7Xdm2j+Nfh14mDCQRXzWJgxjPmj72fbaePen/AAptmuv2hPi+kf8ArQtm8f8AvLGSP1Ap37ZVyt5o3w9nT7smt27j8VavQg7Vl6L8jz5fw3/XUzj4D1PWy0upeL9We1Zi0dtZlLXCHorMgBbjGTx+FUW8IeBPC+ow289vZnUZ8shv5PNlcjv85P6V6Bp3NjF/uD+QryOHwFo/xI/aabRddtzdWJ0XzdocqQwmGCCCCOp/OvRlLlV2caV3Y6u+1SC1Aj0/Tvtj9FS2eFB/48wqjMviS8H7nS7GLP8ADPfncPqFjYfrXoeo/sb+BJbf/iWC+0e8Ugpd2t2/mJ643EjkZHTvXNaj+ytqul6rbpoPjzVreaSNjuvyLhSAeQFOBnlTn2Nc6xNNmzoyOXbQPFM7YZNJhB9JZXx/44KcfAPiK5XJ1PT4Sf7tk7/zcVt3fwL+LXh+dTpniXTddSQfP9vgMPlkdMbM5zk9fSs7UdL+Mng4hr3QdO1yCQffsLgxiI5A+bf65HT0NaqrB9SHTkuhSPw28QrnGrWMg7BrB1/lIa5/xH4b1fQlVrm+0pd5wit5qO59FUBiT9Kv6v4j+L818um2PgeS0vUja4d5W82IoBwAwIG4njGfT6ji7nxjoV/PLcay19oOtS/8fljdWkku1jywzkNg5BCsSo/u9haknsyGmtzNfVNUmeVTFBbohAMwcvj67gqofZiD/smtSw+Hk1/5l3qOj6pd27A75YYl3PwRj5yrkY4+RFzgcmu08G+MPhzYwxXMd/A90mVWa/KrInb5UOBGPZVUV6Va+J9Gv7VJob63khkUMrK6kEGqEfL2taPpFh4d0m3nsVs9SzAs6XMDRSk7hvyWAJHXkVLB4dSIBtPvrqyHYRybo/8Avk8V9Pyx6bqMRVjDLG46NtINczq/wj8PX8Lta2yaZc4yk9jiMg+6gbW+hFAHicMviGyxhrXUkH97MTn+lWo/EL3iy2dxp9xZ3TRMyqwDK2FJOGHoAea0tV0q/wDC+rJp+pKvmShjb3MX+rnA6kDqpHcH8CaSeRobZ5kO2WNSUcdVOMHH4Ej8aAPc/wBixvsvwn0c/wAF09zGf99JnI/Qt+VfPX7cP/JbT/2Dof8A0OSvoX9kdSn7PmmXKjLWt5NPx6CZ93/jpNfPP7b5DfGzIOQdNhIP/A5K86n/AB5fM65/wkfe2v8Az2cUP/PaeKP8N4J/QGtKszU/3mo6XF281pT/AMBQ/wBSK06809IgvZvs1nPL/wA842b8hmotHh+z6TZx91iUH645qHxCx/se4QfelxEo9SxA/rWiAFAA6DigD53h/wCT1/8AuXf/AGtX0nXzHPcfZv22Ldj9xtCCN+MxA/XFfTldNT7Pocsd36nL+O/9G0+S5HCvBLbOf95cr+q/rXiv7FQ8z4WWTdohMv4tcSH/ANlFe5+P7c3Pg3V1X7627On+8vI/UV4h+xEv/FnUfH/LzIufo7H+tL/l2/VFR+Neh9CVmar+8vdMh9ZzIfoqE/zIrTrNm/e+Ibde0Vu7/izKB/I1znQaVZ3iFymi3YHV08sfVjt/rWjWZrnzx2cP/PW6jBHsDuP/AKDQBoogjRVHRRgUpOASegpaqatN9m0u7l7pExH1xQBB4dBOkQOespaU/wDAmJ/rWlVfT4fs1hbRdNkar+QqxQBkW/7y71mXsCsQ/wCApn+bGvCv2VbQ31r8UYB95/EV3tPo3ykH88V7rpXz6beTf89ppn/DJA/QV4p+yB/r/iT/ANjLdfzWt4fBL5GFT4onp3xS8Cw/GH4X3ujyMIpLyAPE5ziOUYKkgEdGA49q8c+GOpreXieD/Gkl9Y+INLjWJrVLt4IbtVJxKuwqXB7jpxjHr9HaP/o9xfWfaKXzEH+w/wA3891ed/Hz4RyfELw+t7ojJY+KtPYTWN6OGBByUJHZhxznr0rehV5HyvYwq0+bVbnrPw0s9D8Kz+Zpml2VhI7bnlggVXc9MswGSfqa+n/BHjhBCg8wZHvX55fBz4uHxDHJpmpRvp3iGwPlXllPw6sM8j1B7EV9B+HPGb2u35z+deqcB9pw+OYHiyWGa5fxT4ujnicb814TB8QHCD95+tUNS8ctKp/efrQBe8batHOZOc14n4lnV3euk13xCZ93zVwOr3vmM3NAHN6jgsaxLlAc1sXbbiaypx1oAyp481hatoOn6ng3ljb3RHQyxKxH0JHFdFOKzrgUAcZc+ELaEH7Hd3tjznEdwXX/AL5k3AD6Cse+0jVrcSGO5tr4D7qTRGJj9XUkf+O13FwM5rJuyBmgDxj4mXouvCN7G0MltcQyRiWGQcrlhgg9CDg4I9K/QKyiZvA9hLGMywQRzp9Vwf1GR+NfCnxcB1DSLfS7ZfNv7+5iggiHV2LDAz/jX6AeHbZ7bQLCCVcOkCqyn6V5+L6HVQ6nz0ZVm/bSSRDlH8NhgfUGavoyvmfwLDJr/wC1nr1zCNsGgaWunS+Z1YtIXQr7beK+mK46vT0O2ls/UzNO+fVdUk7B44/yQH/2atOszQf3lvcT9priRx9N20fotadYGxm3P73XrJP+eUUkh/Hao/ma0qzYP3viC7btFBHH+JLE/wBK0qAMzXvntIYf+e1xEn4bgT+gNadZmpfvNT0uLt5jyn/gKEfzYVp0AVNWm+zaXdy90iYj64p2mwC1062iHRI1H6VW8Q/NpjRd5pI4vzcD+Wau3A220gHGEP8AKmB8+fAwiT9pT4sOh3Jm0G4dM+Wax/2uv3GneDbM8fZvEkQUf7DBmX+ePwrd/ZBUNefEqQjMh8SXQLd8ZFZX7aluY5/Asy/dl1iBW+q7sfox/Ku6P8ZL+tjgf8NnTad/x4xf7g/kK86S/HgT9pjwzrVzj7Fq9s+mNK52rE+dy89CWOABxXoum/8AHlD/ALo/kK5L4ueDT4x8JXEVuTFqNti4tJlOGjlXlSDjI/D1r0pLmTRxJ2dz6pVgwBByDyDWZrv7iG3vBwbWVXY/7B+Vv0Ofwrhf2eviWPiZ8ObG8nITVbUfZb6E8MkycNkZJGeozzgivSLq3W7tpYH+7IhQ/QjFeHKLi7M9RNNXJahvbZb2zngb7siFD+Iqvolw1xpcBk/1qDy5P95TtP6ir1SMo6NP9s023mdR5uzY+RzuHDfqDRNoWnTyGSSygdz1YoMmotN/0XU9QtDwpYXEf0b73/jwP51p0AecN8GfBOp63qhv/DOm3Fw7iYSyWyFmVh6kdiDXOXP7IHw1uJ5Zf7IkjMjFtsdzKqrk54AbAHtXq99/our2Nz0WTdbOfr8y/qCPxrTq1UktmS4xfQ+adQ/ZB0+wu4IrHxZr9laTO6RRpd/LCcZRRx0wCPyrM1b9n34j+HdHuL628fJeyWkZlW3mslVZNvO0sCSAema+mdehaTTJXjGZYcTJ9VOf6Y/Gqniq6ibwhqE+8LE1qzBieMFa1jXqX3M3ShbY+IbfxC/jfwjpHiS5gCalcT/ZZ2Viy7UWX7oP3ckAnHtWF411eTQvDdzcxIrvwgDdBkgf1qr8N3a68G6PGGdEtjcZUdHLvwT9AP19hTfiqu3wdP8A76f+hCvYPPPs/wDZw+Htx8P/AIQ6Vo9/NHcTyI80vlg7R5jFtoz1xuxn2r4y/bEZ/wDhbccUhzJBp0ULfVZJRn8QAfxr9C/Cf/Is6Z/17p/Kvz//AG3IRF8cJmH/AC0sIWP13OP6V5lB81VtnbV0hY++X/e+Ioh2htmb8WYD/wBlNaVZlj+81rUpOyCOIfgCx/8AQq06889Azda+dtPiHV7pD+Cgt/StKs28/e63pyddiSyn8go/9CNR+KvEtn4P8O6hrWoMyWdlC08pVSTtUZOAPpTSvog2Pn+1t28W/tca9Np4Mkel6GttPJ08uYyCRBz14549K+mbG6W+soLhekqB8emR0r5q/ZdnvvGHxN8e+NxpV3p+jawLb7K92mwybUIOOeR0ORxz9a+idF/0d7yy6eRMSg/2H+YfzI/Cuiro0uyOSGqbG+LP+RZ1P/r3f+VeI/sVoF+B9gf71zOf/IrD+le3eLP+Ra1P/r3f+VeHfsV3MTfBSwhEimVJ5yyA8jMr0l/Cfqio/wAReh75WZafvdd1B/8AnnHFEP1Y/wAxWnWbo3zvqE39+6YA+ygL/Suc6TSrNvj5msabHnhfMlI+i7R/6FWlWav73xE57Q2wH4sx/wDiaANKszxD82mNF3mkjiH4uAf0zWnWZq37y60yH+9cbz9FVj/PFAGnUc8ohhkkPRFLH8BUlZ+vyGPRbwj7zRlB9Tx/WgCPSojF4dgU9TBuP1Iyf514j+yB/r/iT/2Mt1/Na97kjEVkyDosZUfgK8E/ZA/1/wASf+xluv5rW8Pgl8jCp8UT3m6/0XXLSbotwjW7fUfMv8m/OtOs/Xomk0ySSMZlgInT6qc/qAR+NXYZVuIUlQ5R1DKfY1mB4f8AtC/C2/vDaeNfCaLB4j0lvNmWP5TdwDl4ienuMg1ofDH4qWXjnSFuLdzDdRHy7i1l4khcdVYdq9idBIhVgGUjBB7ivmf4s/DrVPCtzB428F2kkmpWM/2fV7K35+126/xBP4nClSOc9evSu6hWt7kjlq07+8j3mLW3CAbqjm1pmH3jXi3g34++G/EwW3ku/wCztSDeW9lefupVcDLDaT256elegrqEc67kkVh6gg16Rxmnd6gXzzWLdT78806SbNU5mzQBVnbJqhNVyY1RmNAFGes6foa0JzWZdSBQecUAZl220GuM8WeJ4NDtxw09zI3lw20Q3PK56KB607xb43W11CLRdLhfUtdujsgtIBkljwM46Dvk9ga9k+C/7NsPhe+g8T+KLltW8Rum4RyHMNqT1Ea+uMDPt71lUqRprU0hBzehyXwC+B2p6x4pTxl4ytzZXenuUsdL3Z8olR87kdSQeB/Xp9T9KzCPsfiBccJdw4/4GnT/AMdJ/KtOvInN1HdnfGKgrI+a/g1/ycz8WP8At0/9FmvoW5mFvbSynoiFvyGa+evg1/ycz8WP+3T/ANFmvefELldFuwOrp5Y/4Edv9aKvxfJfkaUfh+8focJt9Is0P3vKUn6kZP8AOr1NRQiKo6AYFKTgEnoKwNjO0j95PqUx6vclQfZVC/zBrSrN8OgnSIHP3pd0pPruYn+taVAGaf33iJf+mFsT+Lt/9hWlWbYfvdW1KbqqlIR/wFcn/wBCrSoAzNW/eXWmQ/3rjefoqsf54q9c/wDHtL/uH+VUp/3viC0X/nlBJJ+JKgf1q7c/8e0v+4f5UwPAv2QP9f8AEn/sZbr+a1U/bW/48fAf/Yeg/k1W/wBkD/X/ABJ/7GW6/mtVP21v+PHwH/2HoP5NXdH+Oee/4TNvTf8Ajyh/3R/IVYcA8HkGq+m/8eUP+6P5CrDV6hxHm/gTWj8GfjqIHYxeG/FZ28nEcV2OnoBuHHck4r68VgwBByDyDXyT8a/Cs3ifwTcfYvl1KzYXVtIvDq6HI2nIwTjGfevevgh8Qbf4lfDjSNYhOJWiEc8ZJJSReGGSBnBB5rzsVCzU0dlGX2TqbD/RdWv7bosm25QfXhv1GfxrTrM1T/Rr+wuxwocwSH/Zfp/48F/OtOuA6jM1L/RtT0+66KzG3f6Nyv8A48B+dadU9ZtWvNMuI0/1m3ch9GHK/qBU1ldLe2cFwv3ZUD49MigCvrlu1xpc4j/1qASx/wC8p3D+VWrW4W7top0+5IgcfQjNSVmaF+4iuLM9bWZkUf7B+Zf0OPwoA0yAQQeQa+S/2nviNLdWlt8MtJmkW9eTN/IpK7LUMdoznqw2jGDxnPWvon4n+PbL4beC9S1y9kVRbxMY0JGZHx8qjJGSTxivhzw0b7Xb6+8T6y3mazqz+bMQMBV/hUDtgYFdmHp8z5n0OetOy5Ua9hp8Wn2sVvCgSONQoAGK5j4qKZfDH2aMF7ieaOOKJRlnYsMADua7PFcv4rZU8S+DGb7o1q2J+m+vTeiOJH6AeGImh8O6cjqVdYEBB7cV8A/tw/8AJbT/ANg6H/0OSv0Ntv8Aj2i/3B/Kvgv46+D7/wCMXxq8ULaSRwTaL5Fq/mHCtGyb1I687jJn22+9eXh3abbO2qrxSR9uaJ863s3/AD0upCD7D5f/AGWtKs3w9/yDF/66y/8Aoxq0q4TvMyL974inbtDbIv4sxP8AQV5Z+1xrs+hfA/XGtwhNyEtm3jPyyOEb8cMa9Tsf+Q1qf0i/9BNeN/tpf8kN1L/rvB/6NStaXxxM6nwM9X+Fnh6Dwt8PdB0y2ZnitrOOMO+NzYUDJx9K17j/AEXXraXolzG0Lf7y/Mv6bqb4T/5FnTP+vdP5U7XPv6b/ANfifyak9WZo0J4UuYXikUPG4Ksp6EV8qeCfCGnfDX9rm70Xw/G9jpdzoxupLVJG8suZuuCccDgDtk4r6ur5sm/5PX/7l3/2tWtN6SXkRLePqfRBIAJPQVneHQf7Ht3PWXdKf+BMW/rV25/49pf9w/yqtof/ACBbD/rgn/oIrmOsvVmab+81PVJe3mJEP+AoD/NjWnWbov3tQ/6+3/ktAGlWbP8AvfEFovaKCST8SVA/rWlWaP8AkY3/AOvQf+hmgDSrM175rW3j6iS5iUj1G8H+ladZus/f07/r7T/0FqALt0QttMScDYf5V4F+x8d7/EeReUbxJdFWHQ/dr3DxH/yAdQ/64t/KvF/2Iv8Akjy/9fk//oZroh/Dl8jCp8SPoQgEEHkGs3QCYrSS1Y/NayNDz/d6r/46RWlWZp3/ACGNW/34/wD0WKyA06zI1Fvrs8ZGY7uISAHpuX5W/Qr+VadZmof8hzSv+2v/AKCKAOT8d/ArwZ8Q45TqujW7XUgA+1xLsmGDnhxz1z3714r4k+B/jr4UoNQ8H+IZ/EGnqVEum6tJlzk4yr8Y6KADgck19U1meJ/+QDef7n9RW0K04bMzdOMtz5Yg+O8vhy8/s3xvot34av8AIAMqF4Xz3VxwQAVye2a7ax8faHq9qtxa6lbzRNnDJIpH861v2lv+RJ1n/rzm/wDQK/PIffk+tepSqe0V2cVSHI7H3xc+J9OjYq13EGHbeP8AGsDWfiN4f0kot1qltCz52h5VGcfjXx/4v/5GK7/4B/6AtZNp/wAfLf7tbGR9Tan8dvDNrcGJbz7QcZ3QIZF/Ncis608VeIfirfjSvA2mSzO2fM1G6jZLeHA5BPryOPel/Za/5Betf9d1/wDQRX1d8PP9TN/vH+QrkqV3G6SOiFJS1bOX+EPwZsfhQ9u1ww1LWdQQm7v5RlmmXkbfQYLAD2r1ys3WP+PjS/8Ar6H/AKA9aVeZKTk7s7UklZGbrwMVrHdgfNayrNx/d6N/46TWkCCARyDVTWP+QTe/9cH/APQTT9O/5B9t/wBcl/kKkZ85/Br/AJOZ+LH/AG6f+izXvGtqZEs4uokuYwfoDu/9lrwf4Nf8nM/Fj/t0/wDRZr3vUv8Aj603/r4P/ot60q/F8l+Q6Pw/eX6qatN9m0u7l6FImI+uKt1m+JP+QHef9c6wNi3YQ/ZrG3i/uRqv5Cp6QdKWgDN0P547yb/nrdSEH2B2j/0GtKs3w9/yCk/66S/+jGrSoAzbb97r16//ADyijjH47mP8xV25/wCPaX/cP8qpad/yFdV/66R/+i1q7c/8e0v+4f5UwPAv2QP9f8Sf+xluv5rVT9tb/jx8B/8AYeg/k1WP2P8A/j9+Jf8A2Mdz/MVX/bW/48fAf/Yeg/k1d0f4557/AITNvTf+PKH/AHR/IVO5xUGm/wDHlD/uj+QqaXpXqHEQyYdWU8g8GuF+DOun4SfGy+8NTts0PxMTdWeeiXAHzr3PI55IHAFdwa8k+M3/ACNvw/8A+w3b/wDoQrOpFSi0y4Plkmj7O1W0N9ptxCv32Q7D6MOQfzAp+nXYvrGC4HHmIGI9D3H51Ja/8e0X+6P5VQ8N/wDIIi/35P8A0Nq8M9M06zNE/wBH+12R4+zzHYP9hvmX+ZH4Vp1mWv8AyMF//wBcYf5vQBp1luRZa+GJ2x3UJB/305/9BJ/KtSsLxV9yz/35P/RT0AfH/wC0R8QD8WPiNHodmT/YPh2f99ICCs9xjsR/d5B56k8Vk2oCoAOAK4XSf+Q/qf8A2MN1/wCgSV3dt90V7sIqMUkeXJuTuyxXK+LohP4i8HRno+s2yn8Xrqq5jxP/AMjT4K/7Ddr/AOhiqezEtz9AfD1013oto7/6wIEf/eXg/qK+TdJ/5Lr8Vv8Ar4sf/RT19V+GP+PGf/r5k/nXyppP/Jdfit/18WP/AKKevHpfa9P1PRnuj//Z",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {
+      "image/jpeg": {
+       "width": 400
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(\"./images/control_rod.jpg\", width=400)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7ee2ce3",
+   "metadata": {},
+   "source": [
+    "Fig. 3: Section view of control rod bottom end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce7fc65d",
+   "metadata": {},
+   "source": [
+    "Teh CAD geometry has been surface meshed and converted into h5m files, readable by OpenMC, using the open-source meshing tool \n",
+    "[CAD-to-OpenMC](https://github.com/openmsr/CAD_to_OpenMC). \n",
+    "Two step files have been exported from OnShape:\n",
+    "- the entire vessel and shielding\n",
+    "- one of the the control rod \n",
+    "\n",
+    "**Note**: only one control rod has been meshed. The other two can be created directly within the OpenMC environment, as DAGMC universe duplicates of the imported one.\n",
+    "The reason to have a separate export of the control rods is to paramterize their position within Openmc directly.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "787fab93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import openmc\n",
+    "import numpy as np\n",
+    "from math import log10\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os \n",
+    "\n",
+    "os.system('rm *.xml *.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ceb51601",
+   "metadata": {},
+   "source": [
+    "Materials definition during the first criticality test comes from the MSRE benchmark evaluation [Fratoni et al](https://www.osti.gov/servlets/purl/1617123) and available in the International Reactor Physics Experiment Evaulation Project (IRPhEP) handbook. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "e1001bc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "salt_temp = 648.9\n",
+    "salt = openmc.Material(name=\"salt\", temperature = salt_temp + 273.15)\n",
+    "salt.add_nuclide('Li6', 0.0000131541127279649)\n",
+    "salt.add_nuclide('Li7', 0.263082254559299)\n",
+    "salt.add_nuclide('Be9', 0.118688703357854)\n",
+    "salt.add_nuclide('Zr90', 0.0105474637491834)\n",
+    "salt.add_nuclide('Zr91', 0.00230014661352455)\n",
+    "salt.add_nuclide('Zr92', 0.00351582124972781)\n",
+    "salt.add_nuclide('Zr94', 0.00356297220526352)\n",
+    "salt.add_nuclide('Zr96', 0.000574011632608622)\n",
+    "salt.add_nuclide('Hf174', 0.000000000838247756705461)\n",
+    "salt.add_nuclide('Hf176', 0.000000027557395001692)\n",
+    "salt.add_nuclide('Hf177', 0.0000000974463017170099)\n",
+    "salt.add_nuclide('Hf178', 0.000000142921242518281)\n",
+    "salt.add_nuclide('Hf179', 0.0000000713558402895524)\n",
+    "salt.add_nuclide('Hf180', 0.000000183785820657672)\n",
+    "salt.add_nuclide('U234', 0.000010347565467384)\n",
+    "salt.add_nuclide('U235', 0.00101016470221956)\n",
+    "salt.add_nuclide('U236', 0.00000422977321829143)\n",
+    "salt.add_nuclide('U238', 0.00216927473057667)\n",
+    "salt.add_nuclide('Fe54', 0.00000285638012142825)\n",
+    "salt.add_nuclide('Fe56', 0.0000448390593090724)\n",
+    "salt.add_nuclide('Fe57', 0.00000103552942297801)\n",
+    "salt.add_nuclide('Fe58', 0.000000137809956243416)\n",
+    "salt.add_nuclide('Cr50', 0.00000212334843928242)\n",
+    "salt.add_nuclide('Cr52', 0.000040946661076878)\n",
+    "salt.add_nuclide('Cr53', 0.00000464302267471169)\n",
+    "salt.add_nuclide('Cr54', 0.00000115574661884993)\n",
+    "salt.add_nuclide('Ni58', 0.00000586242358522768)\n",
+    "salt.add_nuclide('Ni60', 0.00000225819506936691)\n",
+    "salt.add_nuclide('Ni61', 0.0000000981621760803009)\n",
+    "salt.add_nuclide('Ni62', 0.00000031298397136929)\n",
+    "salt.add_nuclide('Ni64', 0.0000000797077903148755)\n",
+    "salt.add_nuclide('O16', 0.0000514608160260434)\n",
+    "salt.add_nuclide('O17', 0.0000000189357310970321)\n",
+    "salt.add_nuclide('O18', 0.0000000964845153990466)\n",
+    "salt.add_nuclide('F19', 0.594363006576997)\n",
+    "salt.set_density('g/cm3',2.3278)\n",
+    "\n",
+    "#moderator blocks170\n",
+    "graphite = openmc.Material(name='graphite',temperature= salt_temp + 273.15)\n",
+    "graphite.set_density('g/cm3',1.8492)\n",
+    "graphite.add_element('C',0.999992212250888) #endfb71 does not have C12 cross sections\n",
+    "graphite.add_nuclide('B10', 1.76873036539477E-07)\n",
+    "graphite.add_nuclide('B11', 7.1193229306592E-07)\n",
+    "graphite.add_nuclide('V51', 2.12200224802724E-06)\n",
+    "graphite.add_nuclide('S32', 1.77900760271203E-06)\n",
+    "graphite.add_nuclide('S33', 1.40462754188233E-08)\n",
+    "graphite.add_nuclide('S34', 7.95955607066653E-08)\n",
+    "graphite.add_nuclide('S36', 1.87283672250977E-10)\n",
+    "graphite.add_nuclide('O16', 1.85674385782835E-06)\n",
+    "graphite.add_nuclide('O17', 6.81675170610618E-10)\n",
+    "graphite.add_nuclide('Si28', 5.31911333979174E-07)\n",
+    "graphite.add_nuclide('Si29', 2.70215087309286E-08)\n",
+    "graphite.add_nuclide('Si30', 1.78336189959512E-08)\n",
+    "graphite.add_nuclide('Al27', 4.0117589478321E-07)\n",
+    "graphite.add_nuclide('Fe54', 2.31047953307142E-09)\n",
+    "graphite.add_nuclide('Fe56', 3.62695875239409E-08)\n",
+    "graphite.add_nuclide('Fe57', 8.37622947917593E-10)\n",
+    "graphite.add_nuclide('Fe58', 1.11472237523719E-10)\n",
+    "graphite.add_nuclide('Ti46', 1.12809907219147E-09)\n",
+    "graphite.add_nuclide('Ti47', 1.01734025419449E-09)\n",
+    "graphite.add_nuclide('Ti48', 1.008041983054E-08)\n",
+    "graphite.add_nuclide('Ti49', 7.39759512794646E-10)\n",
+    "graphite.add_nuclide('Ti50', 7.08309478054763E-10)\n",
+    "graphite.add_nuclide('Mg24', 8.53578076978748E-09)\n",
+    "graphite.add_nuclide('Mg25', 1.0806153652092E-09)\n",
+    "graphite.add_nuclide('Mg26', 1.18975751709533E-09)\n",
+    "graphite.add_nuclide('Ca40', 4.58305905846326E-09)\n",
+    "graphite.add_nuclide('Ca42', 3.05880815220158E-11)\n",
+    "graphite.add_nuclide('Ca43', 6.38236631448551E-12)\n",
+    "graphite.add_nuclide('Ca44', 9.86193787556798E-11)\n",
+    "graphite.add_nuclide('Ca48', 8.8407592652503E-12)\n",
+    "graphite.add_s_alpha_beta('c_Graphite')\n",
+    "\n",
+    "#inor-8\n",
+    "inor = openmc.Material(name='inor-8',temperature= salt_temp + 273.15)\n",
+    "inor.set_density('g/cm3',8.7745)\n",
+    "inor.add_element('Ni',(66+71)/2,'wo')\n",
+    "inor.add_element('Mo',(15+18)/2,'wo')\n",
+    "inor.add_element('Cr',(6+8)/2,'wo')\n",
+    "inor.add_element('Fe',5,'wo')\n",
+    "inor.add_element('C',(0.04+0.08)/2,'wo')\n",
+    "inor.add_element('Al',0.25,'wo')\n",
+    "inor.add_element('Ti',0.25,'wo')\n",
+    "inor.add_element('S',0.02,'wo')\n",
+    "inor.add_element('Mn',1.0,'wo')\n",
+    "inor.add_element('Si',1.0,'wo')\n",
+    "inor.add_element('Cu',0.35,'wo')\n",
+    "inor.add_element('B',0.010,'wo')\n",
+    "inor.add_element('W',0.5,'wo')\n",
+    "inor.add_element('P',0.015,'wo')\n",
+    "inor.add_element('Co',0.2,'wo')\n",
+    "\n",
+    "#helium\n",
+    "helium = openmc.Material(name='helium')\n",
+    "helium.add_element('He',1.0)\n",
+    "helium.set_density('g/cm3',1.03*(10**-4))\n",
+    "\n",
+    "#Control rods inconel clad\n",
+    "trace = 0.01\n",
+    "inconel = openmc.Material(name='inconel-600', temperature = 65.6 + 273.15)\n",
+    "inconel.add_element('Ni',78.5,percent_type='wo')\n",
+    "inconel.add_element('Cr',14.0,percent_type='wo')\n",
+    "inconel.add_element('Fe',6.5,percent_type='wo')\n",
+    "inconel.add_element('Mn',0.25,percent_type='wo')\n",
+    "inconel.add_element('Si',0.25,percent_type='wo')\n",
+    "inconel.add_element('Cu',0.2,percent_type='wo')\n",
+    "inconel.add_element('Co',0.2,percent_type='wo')\n",
+    "inconel.add_element('Al',0.2,percent_type='wo')\n",
+    "inconel.add_element('Ti',0.2,percent_type='wo')\n",
+    "inconel.add_element('Ta',0.5,percent_type='wo')\n",
+    "inconel.add_element('W',0.5,percent_type='wo')\n",
+    "inconel.add_element('Zn',0.2,percent_type='wo')\n",
+    "inconel.add_element('Zr',0.1,percent_type='wo')\n",
+    "inconel.add_element('C',trace,percent_type='wo')\n",
+    "inconel.add_element('Mo',trace,percent_type='wo')\n",
+    "inconel.add_element('Ag',trace,percent_type='wo')\n",
+    "inconel.add_element('B',trace,percent_type='wo')\n",
+    "inconel.add_element('Ba',trace,percent_type='wo')\n",
+    "inconel.add_element('Be',trace,percent_type='wo')\n",
+    "inconel.add_element('Ca',trace,percent_type='wo')\n",
+    "inconel.add_element('Cd',trace,percent_type='wo')\n",
+    "inconel.add_element('V',trace,percent_type='wo')\n",
+    "inconel.add_element('Sn',trace,percent_type='wo')\n",
+    "inconel.add_element('Mg',trace,percent_type='wo')\n",
+    "inconel.set_density('g/cm3',8.5)\n",
+    "\n",
+    "#Control rods bushing posion material\n",
+    "Gd2O3 = openmc.Material()\n",
+    "Gd2O3.add_element('Gd',2)\n",
+    "Gd2O3.add_element('O',3)\n",
+    "Gd2O3.set_density('g/cm3',7.41)\n",
+    "Al2O3 = openmc.Material()\n",
+    "Al2O3.add_element('Al',2)\n",
+    "Al2O3.add_element('O',3)\n",
+    "Al2O3.set_density('g/cm3',3.95)\n",
+    "bush = openmc.Material.mix_materials([Gd2O3,Al2O3],[0.7,0.3],'wo')\n",
+    "bush.name='gd2o3-al2o3'\n",
+    "bush.temperature = 65.6 +273.15\n",
+    "\n",
+    "#Concrete block\n",
+    "concrete = openmc.Material(name='concrete')\n",
+    "concrete.add_element('H',0.005,'wo')\n",
+    "concrete.add_element('O',0.496,'wo')\n",
+    "concrete.add_element('Si',0.314,'wo')\n",
+    "concrete.add_element('Ca',0.083,'wo')\n",
+    "concrete.add_element('Na',0.017,'wo')\n",
+    "concrete.add_element('Mn',0.002,'wo')\n",
+    "concrete.add_element('Al',0.046,'wo')\n",
+    "concrete.add_element('S',0.001,'wo')\n",
+    "concrete.add_element('K',0.019,'wo')\n",
+    "concrete.add_element('Fe',0.012,'wo')\n",
+    "concrete.set_density('g/cm3',2.35)\n",
+    "\n",
+    "#Thermal shielding as water and SS305 (50-50)\n",
+    "water = openmc.Material()\n",
+    "water.add_element('H',2)\n",
+    "water.add_element('O',1)\n",
+    "water.set_density('g/cm3',0.997)\n",
+    "\n",
+    "#stainless steel 304\n",
+    "ss304 =  openmc.Material()\n",
+    "ss304.add_element('C',0.08,'wo')\n",
+    "ss304.add_element('Mn',2,'wo')\n",
+    "ss304.add_element('P',0.045,'wo')\n",
+    "ss304.add_element('S',0.03,'wo')\n",
+    "ss304.add_element('Si',0.75,'wo')\n",
+    "ss304.add_element('Cr',19,'wo')\n",
+    "ss304.add_element('Ni',10,'wo')\n",
+    "ss304.add_element('N',0.1,'wo')\n",
+    "ss304.add_element('Fe',67.995, 'wo')\n",
+    "ss304.set_density('g/cm3',7.93)\n",
+    "shield = openmc.Material.mix_materials([water,ss304],[0.5,0.5],'vo')\n",
+    "shield.temperature = 32.2 + 273.15\n",
+    "shield.name='steelwater'\n",
+    "\n",
+    "# \"Careytemp 1600\" by Philip Carey Manufacturing Compamy (Cincinnati)\n",
+    "# http://moltensalt.org/references/static/downloads/pdf/ORNL-TM-0728.pdf\n",
+    "insulation=openmc.Material(name='insulation')\n",
+    "insulation.add_element('Si',1)\n",
+    "insulation.add_element('O',2)\n",
+    "insulation.set_density('g/cm3',0.16) #https://www.osti.gov/servlets/purl/1411211\n",
+    "\n",
+    "# sand water, not sure about this material\n",
+    "sandwater=openmc.Material(name='watersand')\n",
+    "sandwater.add_element('Fe',3)\n",
+    "sandwater.add_element('O',4)\n",
+    "sandwater.set_density('g/cm3',6)\n",
+    "\n",
+    "#Vessel anular steel\n",
+    "steel = openmc.Material(name='steel')\n",
+    "steel.add_element('Fe',1)\n",
+    "steel.set_density('g/cm3',7.85)\n",
+    "\n",
+    "mats = openmc.Materials([salt, graphite, inor, helium, inconel, shield, concrete,\n",
+    "                         steel, sandwater, insulation, bush])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31f8365e",
+   "metadata": {},
+   "source": [
+    "Let's now import the h5m mesh files: \n",
+    "- MSRE vessel + shielding \n",
+    "- Control rod \n",
+    "\n",
+    "We will create two `DAGMC` universes that will be used to fill the core and control rod cells, respectively. \\\n",
+    "As previously mentioned, we will create the other two control rods as conveniently translated replica of the first one. \\\n",
+    "All three control rods are now objects and the `translation` attribute value can be set accordinly using the `setattr()` function. \n",
+    "\n",
+    "All three control rods at set as fully withdrawn (56 inches from bottom end)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "6e9faef6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another UniverseBase instance already exists with id=1.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another UniverseBase instance already exists with id=2.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10000.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10001.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10002.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10003.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10004.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10005.\n",
+      "  warn(msg, IDWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# CAD h5m files\n",
+    "core_h5m = 'msre_full.h5m'\n",
+    "control_rod_h5m = 'msre_control_rod.h5m'\n",
+    "\n",
+    "#Geometry\n",
+    "core = openmc.DAGMCUniverse(filename = core_h5m, auto_geom_ids = True,\n",
+    "                            universe_id = 1)\n",
+    "control_rod = openmc.DAGMCUniverse(filename = control_rod_h5m,\n",
+    "                            auto_geom_ids = True, universe_id=2)\n",
+    "core_region = core.bounding_region()\n",
+    "cr_region = control_rod.bounding_region(boundary_type = 'transmission')\n",
+    "\n",
+    "_offset_xy = 10.163255 #cm, _offset_xy between cr1, cr2 and cr3 (from Onshape model)\n",
+    "_lower_rod = 61.728 # distance to lower limit (from OnShape model)\n",
+    "_upper_rod = 51 * 2.54 # escursion to upper rod limit (from ORNL)\n",
+    "start_rod = 56 * 2.54 # rod iniitial position (user input)\n",
+    "\n",
+    "# Create control rod regions w\n",
+    "cr1_region = cr_region.translate([_offset_xy/2, _offset_xy/2, _lower_rod + _upper_rod])\n",
+    "cr2_region = cr_region.translate([_offset_xy/2, -_offset_xy/2, _lower_rod + _upper_rod])\n",
+    "cr3_region = cr_region.translate([-_offset_xy/2, -_offset_xy/2, _lower_rod + _upper_rod])\n",
+    "\n",
+    "# Extend control rod region1 to include downloads translations\n",
+    "cr1_region = cr1_region | cr1_region.translate([0, 0, -_upper_rod])\n",
+    "\n",
+    "#Define cells\n",
+    "core_cell = openmc.Cell(region=~(cr1_region | cr2_region | cr3_region) & core_region ,\n",
+    "                        fill=core)\n",
+    "\n",
+    "cr1_cell = openmc.Cell(name='CR1', region=cr1_region, fill=control_rod)\n",
+    "cr2_cell = openmc.Cell(name='CR2', region=cr2_region, fill=control_rod)\n",
+    "cr3_cell = openmc.Cell(name='CR3', region=cr3_region, fill=control_rod)\n",
+    "\n",
+    "#translate cells to regions\n",
+    "setattr(cr1_cell, 'translation', [_offset_xy/2, _offset_xy/2 , _lower_rod + start_rod])\n",
+    "setattr(cr2_cell, 'translation', [_offset_xy/2, -_offset_xy/2, _lower_rod + start_rod])\n",
+    "setattr(cr3_cell, 'translation', [-_offset_xy/2, -_offset_xy/2, _lower_rod + start_rod])\n",
+    "\n",
+    "root = openmc.Universe(cells=[core_cell,cr1_cell,cr2_cell,cr3_cell])\n",
+    "geometry = openmc.Geometry(root)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fc3dcec",
+   "metadata": {},
+   "source": [
+    "Let's initialize settings and tallies:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "92335e2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "settings = openmc.Settings()\n",
+    "settings.temperature = {'method':'interpolation','range':(293.15,923.15)}\n",
+    "settings.batches = 50\n",
+    "settings.inactive = 20\n",
+    "settings.particles = 30000\n",
+    "settings.photon_transport = False\n",
+    "source_area = openmc.stats.Box([-100., -100., 0.],[ 100.,  100.,  200.],only_fissionable = True)\n",
+    "settings.source = openmc.IndependentSource(space=source_area)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "edfde381",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tally = dict()\n",
+    "tally['heating'] = openmc.Tally(name=\"heating\")\n",
+    "tally['heating'].scores.append('heating-local')\n",
+    "e_min, e_max = 1e-5, 20e6\n",
+    "groups = 500\n",
+    "energies = np.logspace(log10(e_min), log10(e_max), groups + 1)\n",
+    "energy_filter = openmc.EnergyFilter(energies)\n",
+    "particle_filter = openmc.ParticleFilter(['neutron'])\n",
+    "cell_filter = openmc.MaterialFilter([salt])\n",
+    "tally['flux']= openmc.Tally(name=\"flux\")\n",
+    "tally['flux'].filters = [energy_filter, particle_filter] \n",
+    "tally['flux'].scores = ['flux']\n",
+    "mesh = openmc.RegularMesh()\n",
+    "mesh.dimension = [500, 500, 1]\n",
+    "mesh.lower_left = [-100, -100, 50]\n",
+    "mesh.upper_right = [100, 100, 200]\n",
+    "mesh_filter = openmc.MeshFilter(mesh)\n",
+    "tally['mesh'] = openmc.Tally(name=\"Mesh\")\n",
+    "tally['mesh'].scores = ['flux','absorption','fission']\n",
+    "tally['mesh'].filters = [mesh_filter]\n",
+    "tally['mesh'].filters.append(particle_filter)\n",
+    "tallies = openmc.Tallies(tally.values())\n",
+    "\n",
+    "#Build the model\n",
+    "model = openmc.Model(geometry=geometry, materials=mats, settings=settings, tallies=tallies)\n",
+    "model.export_to_xml()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41ca034e",
+   "metadata": {},
+   "source": [
+    "We can now plot the geometry to see if the control rods are positioned as set and run the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "c2d50f58",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colors = {salt:'yellow', graphite:'black', inor: 'grey', helium: 'cyan', inconel: 'grey',\n",
+    "              bush: 'blue', concrete: 'brown', shield: 'red', insulation: 'green',\n",
+    "              sandwater: 'lightgreen', steel: 'grey'}\n",
+    "plot=openmc.Plot()\n",
+    "plot.basis = 'xy'\n",
+    "plot.width = (20,20)\n",
+    "plot.pixels = (200,200)\n",
+    "plot.origin = (0,0,220)\n",
+    "plot.color_by = 'material'\n",
+    "plot.colors = colors\n",
+    "openmc.plot_inline(plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "76287483",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot=openmc.Plot()\n",
+    "plot.basis = 'xz'\n",
+    "plot.width = (20,30)\n",
+    "plot.pixels = (150,200)\n",
+    "plot.origin = (0,-5,200)\n",
+    "plot.color_by = 'material'\n",
+    "plot.colors = colors\n",
+    "openmc.plot_inline(plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "b8bde700",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ca-ws:1462097] mca_base_component_repository_open: unable to open mca_accelerator_rocm: libamdhip64.so.6: cannot open shared object file: No such file or directory (ignored)\n",
+      "                                %%%%%%%%%%%%%%%\n",
+      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
+      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
+      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
+      "                #######################     %%%%%%%%%%%%%%%%%%\n",
+      "                 #######################     %%%%%%%%%%%%%%%%%\n",
+      "                 ######################     %%%%%%%%%%%%%%%%%\n",
+      "                  ####################     %%%%%%%%%%%%%%%%%\n",
+      "                    #################     %%%%%%%%%%%%%%%%%\n",
+      "                     ###############     %%%%%%%%%%%%%%%%\n",
+      "                       ############     %%%%%%%%%%%%%%%\n",
+      "                          ########     %%%%%%%%%%%%%%\n",
+      "                                      %%%%%%%%%%%\n",
+      "\n",
+      "                 | The OpenMC Monte Carlo Code\n",
+      "       Copyright | 2011-2024 MIT, UChicago Argonne LLC, and contributors\n",
+      "         License | https://docs.openmc.org/en/latest/license.html\n",
+      "         Version | 0.14.1-dev\n",
+      "        Git SHA1 | d93ac83697e7fdb4fd4cc0f2705795398bf804bd\n",
+      "       Date/Time | 2024-04-19 13:18:31\n",
+      "   MPI Processes | 1\n",
+      "  OpenMP Threads | 56\n",
+      "\n",
+      " Reading model XML file 'model.xml' ...\n",
+      " WARNING: Other XML file input(s) are present. These files may be ignored in\n",
+      "          favor of the model.xml file.\n",
+      " Reading cross sections XML file...\n",
+      "Using the DOUBLE-DOWN interface to Embree.\n",
+      "Loading file msre_full.h5m\n",
+      "Initializing the GeomQueryTool...\n",
+      "Using faceting tolerance: 0.01\n",
+      "Building acceleration data structures...\n",
+      "Using the DOUBLE-DOWN interface to Embree.\n",
+      "Loading file msre_control_rod.h5m\n",
+      "Initializing the GeomQueryTool...\n",
+      "Using faceting tolerance: 4.67638e-310\n",
+      "Building acceleration data structures...\n",
+      " Reading Li6 from /home/lorenzo/nuclear_data/endfb80_hdf5/Li6.h5\n",
+      " Reading Li7 from /home/lorenzo/nuclear_data/endfb80_hdf5/Li7.h5\n",
+      " Reading Be9 from /home/lorenzo/nuclear_data/endfb80_hdf5/Be9.h5\n",
+      " Reading Zr90 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr90.h5\n",
+      " Reading Zr91 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr91.h5\n",
+      " Reading Zr92 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr92.h5\n",
+      " Reading Zr94 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr94.h5\n",
+      " Reading Zr96 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr96.h5\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 1200K\n",
+      " Reading Hf174 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf174.h5\n",
+      " Reading Hf176 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf176.h5\n",
+      " Reading Hf177 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf177.h5\n",
+      " Reading Hf178 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf178.h5\n",
+      " Reading Hf179 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf179.h5\n",
+      " Reading Hf180 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf180.h5\n",
+      " Reading U234 from /home/lorenzo/nuclear_data/endfb80_hdf5/U234.h5\n",
+      " Reading U235 from /home/lorenzo/nuclear_data/endfb80_hdf5/U235.h5\n",
+      " Reading U236 from /home/lorenzo/nuclear_data/endfb80_hdf5/U236.h5\n",
+      " Reading U238 from /home/lorenzo/nuclear_data/endfb80_hdf5/U238.h5\n",
+      " Reading Fe54 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe54.h5\n",
+      " Reading Fe56 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe56.h5\n",
+      " Reading Fe57 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe57.h5\n",
+      " Reading Fe58 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe58.h5\n",
+      " Reading Cr50 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr50.h5\n",
+      " Reading Cr52 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr52.h5\n",
+      " Reading Cr53 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr53.h5\n",
+      " Reading Cr54 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr54.h5\n",
+      " Reading Ni58 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni58.h5\n",
+      " Reading Ni60 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni60.h5\n",
+      " Reading Ni61 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni61.h5\n",
+      " Reading Ni62 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni62.h5\n",
+      " Reading Ni64 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni64.h5\n",
+      " Reading O16 from /home/lorenzo/nuclear_data/endfb80_hdf5/O16.h5\n",
+      " Reading O17 from /home/lorenzo/nuclear_data/endfb80_hdf5/O17.h5\n",
+      " Reading O18 from /home/lorenzo/nuclear_data/endfb80_hdf5/O18.h5\n",
+      " Reading F19 from /home/lorenzo/nuclear_data/endfb80_hdf5/F19.h5\n",
+      " Reading C12 from /home/lorenzo/nuclear_data/endfb80_hdf5/C12.h5\n",
+      " Reading C13 from /home/lorenzo/nuclear_data/endfb80_hdf5/C13.h5\n",
+      " Reading B10 from /home/lorenzo/nuclear_data/endfb80_hdf5/B10.h5\n",
+      " Reading B11 from /home/lorenzo/nuclear_data/endfb80_hdf5/B11.h5\n",
+      " Reading V51 from /home/lorenzo/nuclear_data/endfb80_hdf5/V51.h5\n",
+      " Reading S32 from /home/lorenzo/nuclear_data/endfb80_hdf5/S32.h5\n",
+      " Reading S33 from /home/lorenzo/nuclear_data/endfb80_hdf5/S33.h5\n",
+      " Reading S34 from /home/lorenzo/nuclear_data/endfb80_hdf5/S34.h5\n",
+      " Reading S36 from /home/lorenzo/nuclear_data/endfb80_hdf5/S36.h5\n",
+      " Reading Si28 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si28.h5\n",
+      " Reading Si29 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si29.h5\n",
+      " Reading Si30 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si30.h5\n",
+      " Reading Al27 from /home/lorenzo/nuclear_data/endfb80_hdf5/Al27.h5\n",
+      " Reading Ti46 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti46.h5\n",
+      " Reading Ti47 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti47.h5\n",
+      " Reading Ti48 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti48.h5\n",
+      " Reading Ti49 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti49.h5\n",
+      " Reading Ti50 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti50.h5\n",
+      " Reading Mg24 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mg24.h5\n",
+      " Reading Mg25 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mg25.h5\n",
+      " Reading Mg26 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mg26.h5\n",
+      " Reading Ca40 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca40.h5\n",
+      " Reading Ca42 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca42.h5\n",
+      " Reading Ca43 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca43.h5\n",
+      " Reading Ca44 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca44.h5\n",
+      " Reading Ca48 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca48.h5\n",
+      " Reading Mo92 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo92.h5\n",
+      " Reading Mo94 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo94.h5\n",
+      " Reading Mo95 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo95.h5\n",
+      " Reading Mo96 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo96.h5\n",
+      " Reading Mo97 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo97.h5\n",
+      " Reading Mo98 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo98.h5\n",
+      " Reading Mo100 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo100.h5\n",
+      " Reading Mn55 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mn55.h5\n",
+      " Reading Cu63 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cu63.h5\n",
+      " Reading Cu65 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cu65.h5\n",
+      " Reading W180 from /home/lorenzo/nuclear_data/endfb80_hdf5/W180.h5\n",
+      " Reading W182 from /home/lorenzo/nuclear_data/endfb80_hdf5/W182.h5\n",
+      " Reading W183 from /home/lorenzo/nuclear_data/endfb80_hdf5/W183.h5\n",
+      " Reading W184 from /home/lorenzo/nuclear_data/endfb80_hdf5/W184.h5\n",
+      " Reading W186 from /home/lorenzo/nuclear_data/endfb80_hdf5/W186.h5\n",
+      " Reading P31 from /home/lorenzo/nuclear_data/endfb80_hdf5/P31.h5\n",
+      " Reading Co59 from /home/lorenzo/nuclear_data/endfb80_hdf5/Co59.h5\n",
+      " Reading He3 from /home/lorenzo/nuclear_data/endfb80_hdf5/He3.h5\n",
+      " Reading He4 from /home/lorenzo/nuclear_data/endfb80_hdf5/He4.h5\n",
+      " Reading Ta180 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ta180.h5\n",
+      " Reading Ta181 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ta181.h5\n",
+      " Reading Zn64 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn64.h5\n",
+      " Reading Zn66 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn66.h5\n",
+      " Reading Zn67 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn67.h5\n",
+      " Reading Zn68 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn68.h5\n",
+      " Reading Zn70 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn70.h5\n",
+      " Reading Ag107 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag107.h5\n",
+      " Reading Ag109 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag109.h5\n",
+      " Reading Ba130 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba130.h5\n",
+      " Reading Ba132 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba132.h5\n",
+      " Reading Ba134 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba134.h5\n",
+      " Reading Ba135 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba135.h5\n",
+      " Reading Ba136 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba136.h5\n",
+      " Reading Ba137 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba137.h5\n",
+      " Reading Ba138 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba138.h5\n",
+      " Reading Ca46 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca46.h5\n",
+      " Reading Cd106 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd106.h5\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cd106 at\n",
+      "          1200K\n",
+      " Reading Cd108 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd108.h5\n",
+      " Reading Cd110 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd110.h5\n",
+      " Reading Cd111 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd111.h5\n",
+      " Reading Cd112 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd112.h5\n",
+      " Reading Cd113 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd113.h5\n",
+      " Reading Cd114 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd114.h5\n",
+      " Reading Cd116 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd116.h5\n",
+      " Reading V50 from /home/lorenzo/nuclear_data/endfb80_hdf5/V50.h5\n",
+      " Reading Sn112 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn112.h5\n",
+      " Reading Sn114 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn114.h5\n",
+      " Reading Sn115 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn115.h5\n",
+      " Reading Sn116 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn116.h5\n",
+      " Reading Sn117 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn117.h5\n",
+      " Reading Sn118 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn118.h5\n",
+      " Reading Sn119 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn119.h5\n",
+      " Reading Sn120 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn120.h5\n",
+      " Reading Sn122 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn122.h5\n",
+      " Reading Sn124 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn124.h5\n",
+      " Reading Gd152 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd152.h5\n",
+      " Reading Gd154 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd154.h5\n",
+      " Reading Gd155 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd155.h5\n",
+      " Reading Gd156 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd156.h5\n",
+      " Reading Gd157 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd157.h5\n",
+      " Reading Gd158 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd158.h5\n",
+      " Reading Gd160 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd160.h5\n",
+      " Reading H1 from /home/lorenzo/nuclear_data/endfb80_hdf5/H1.h5\n",
+      " Reading H2 from /home/lorenzo/nuclear_data/endfb80_hdf5/H2.h5\n",
+      " Reading Na23 from /home/lorenzo/nuclear_data/endfb80_hdf5/Na23.h5\n",
+      " Reading K39 from /home/lorenzo/nuclear_data/endfb80_hdf5/K39.h5\n",
+      " Reading K40 from /home/lorenzo/nuclear_data/endfb80_hdf5/K40.h5\n",
+      " Reading K41 from /home/lorenzo/nuclear_data/endfb80_hdf5/K41.h5\n",
+      " Reading N14 from /home/lorenzo/nuclear_data/endfb80_hdf5/N14.h5\n",
+      " Reading N15 from /home/lorenzo/nuclear_data/endfb80_hdf5/N15.h5\n",
+      " Reading c_Graphite from /home/lorenzo/nuclear_data/endfb80_hdf5/c_Graphite.h5\n",
+      " Minimum neutron data temperature: 250 K\n",
+      " Maximum neutron data temperature: 2500 K\n",
+      " Preparing distributed cell instances...\n",
+      " Reading plot XML file...\n",
+      " Writing summary.h5 file...\n",
+      " Maximum neutron transport energy: 20000000 eV for Li6\n",
+      " Initializing source particles...\n",
+      "\n",
+      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
+      "\n",
+      "  Bat./Gen.      k            Average k\n",
+      "  =========   ========   ====================\n",
+      "        1/1    0.60880\n",
+      "        2/1    0.88232\n",
+      "        3/1    0.96334\n",
+      "        4/1    1.00418\n",
+      "        5/1    0.98436\n",
+      "        6/1    1.00206\n",
+      "        7/1    0.99280\n",
+      "        8/1    0.98498\n",
+      "        9/1    0.99798\n",
+      "       10/1    1.00342\n",
+      "       11/1    1.00202\n",
+      "       12/1    1.01708\n",
+      "       13/1    0.99542\n",
+      "       14/1    1.01309\n",
+      "       15/1    1.00049\n",
+      "       16/1    1.00931\n",
+      "       17/1    0.99363\n",
+      "       18/1    1.00903\n",
+      "       19/1    1.00821\n",
+      "       20/1    1.01369\n",
+      "       21/1    1.00872\n",
+      "       22/1    0.99225    1.00049 +/- 0.00824\n",
+      "       23/1    1.00850    1.00316 +/- 0.00545\n",
+      "       24/1    0.99733    1.00170 +/- 0.00412\n",
+      "       25/1    1.01087    1.00353 +/- 0.00368\n",
+      "       26/1    1.01232    1.00500 +/- 0.00334\n",
+      "       27/1    1.01345    1.00620 +/- 0.00307\n",
+      "       28/1    1.00635    1.00622 +/- 0.00266\n",
+      "       29/1    1.01336    1.00702 +/- 0.00248\n",
+      "       30/1    1.01235    1.00755 +/- 0.00228\n",
+      "       31/1    0.99936    1.00680 +/- 0.00219\n",
+      "       32/1    1.01775    1.00772 +/- 0.00220\n",
+      "       33/1    1.00712    1.00767 +/- 0.00202\n",
+      "       34/1    1.00815    1.00771 +/- 0.00187\n",
+      "       35/1    1.01133    1.00795 +/- 0.00176\n",
+      "       36/1    1.00568    1.00781 +/- 0.00165\n",
+      "       37/1    1.01406    1.00817 +/- 0.00160\n",
+      "       38/1    0.99515    1.00745 +/- 0.00167\n",
+      "       39/1    1.01624    1.00791 +/- 0.00165\n",
+      "       40/1    1.00469    1.00775 +/- 0.00157\n",
+      "       41/1    1.01875    1.00827 +/- 0.00158\n",
+      "       42/1    1.00781    1.00825 +/- 0.00151\n",
+      "       43/1    1.00939    1.00830 +/- 0.00144\n",
+      "       44/1    1.00363    1.00811 +/- 0.00139\n",
+      "       45/1    1.00444    1.00796 +/- 0.00135\n",
+      "       46/1    1.00185    1.00773 +/- 0.00131\n",
+      "       47/1    1.01499    1.00800 +/- 0.00129\n",
+      "       48/1    1.01605    1.00828 +/- 0.00128\n",
+      "       49/1    0.99991    1.00799 +/- 0.00127\n",
+      "       50/1    1.00888    1.00802 +/- 0.00122\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 3.7033e+01 seconds\n",
+      "   Reading cross sections          = 1.4467e+01 seconds\n",
+      " Total time in simulation          = 1.5251e+02 seconds\n",
+      "   Time in transport only          = 1.5208e+02 seconds\n",
+      "   Time in inactive batches        = 5.1562e+01 seconds\n",
+      "   Time in active batches          = 1.0095e+02 seconds\n",
+      "   Time synchronizing fission bank = 1.9172e-01 seconds\n",
+      "     Sampling source sites         = 1.5614e-01 seconds\n",
+      "     SEND/RECV source sites        = 3.4788e-02 seconds\n",
+      "   Time accumulating tallies       = 8.5964e-02 seconds\n",
+      "   Time writing statepoints        = 3.0541e-02 seconds\n",
+      " Total time for finalization       = 8.4758e-01 seconds\n",
+      " Total time elapsed                = 1.9094e+02 seconds\n",
+      " Calculation Rate (inactive)       = 11636.5 particles/second\n",
+      " Calculation Rate (active)         = 8915.68 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 1.00758 +/- 0.00121\n",
+      " k-effective (Track-length)  = 1.00802 +/- 0.00122\n",
+      " k-effective (Absorption)    = 1.00818 +/- 0.00096\n",
+      " Combined k-effective        = 1.00803 +/- 0.00101\n",
+      " Leakage Fraction            = 0.00001 +/- 0.00000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "results=model.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ab1fdd2",
+   "metadata": {},
+   "source": [
+    "We can now post-process the result file and plot the tallies:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "44bd9b3c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with openmc.StatePoint(results) as sp:\n",
+    "    heating = sp.get_tally(name=\"heating\").get_pandas_dataframe()[\"mean\"].sum()*openmc.data.JOULE_PER_EV\n",
+    "    t = sp.get_tally(name=\"flux\")\n",
+    "    flux = sp.get_tally(name=\"Mesh\").get_slice(scores=['flux']).get_pandas_dataframe()[\"mean\"]\n",
+    "    fission = sp.get_tally(name=\"Mesh\").get_slice(scores=['fission']).get_pandas_dataframe()[\"mean\"]\n",
+    "    absorption = sp.get_tally(name=\"Mesh\").get_slice(scores=['absorption']).get_pandas_dataframe()[\"mean\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "7232a2ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot neutron flux spectrum\n",
+    "power = 8e6 #Thermal power in W\n",
+    "volume = 1.65e6 #Fuel salt volume in cm3\n",
+    "\n",
+    "energy_filter = t.filters[0]\n",
+    "energies = energy_filter.bins[:, 0]\n",
+    "mean = t.mean.ravel()\n",
+    "uncertainty = t.get_values(value='std_dev').ravel()\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(energies, mean*power/heating/volume, drawstyle='steps-post')\n",
+    "ax.set_xlabel('Energy [eV]')\n",
+    "ax.set_ylabel(r'Flux [neutrons/cm$^2$-s]')\n",
+    "ax.set_xscale('log')\n",
+    "ax.grid(True, which='both')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "99831db8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "flux = flux.values.reshape(500,500)\n",
+    "fig, ax = plt.subplots()\n",
+    "pos = ax.imshow(flux*power/heating/volume, aspect='auto', origin='lower')\n",
+    "cbar = plt.colorbar(pos,ax=ax,label=r'Flux [neutrons/cm$^2$-s]')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "bdbb596d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fission = fission.values.reshape(500,500)\n",
+    "fig, ax = plt.subplots()\n",
+    "pos = ax.imshow(fission*power/heating/volume, aspect='auto', origin='lower')\n",
+    "cbar = plt.colorbar(pos,ax=ax,label=r'Fission [reactions/cm$^2$-s]')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "2ece2684",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "absorption = absorption.values.reshape(500,500)\n",
+    "fig, ax = plt.subplots()\n",
+    "pos = ax.imshow(absorption*power/heating/volume, aspect='auto', origin='lower')\n",
+    "cbar = plt.colorbar(pos,ax=ax,label=r'Absorption [reactions/cm$^2$-s]')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f35270b2-0a23-49c9-905d-70f6ddc1376f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/openmc_notebooks/msre_depletion_transfer_rates.ipynb b/openmc_notebooks/msre_depletion_transfer_rates.ipynb
new file mode 100644
index 0000000..7239204
--- /dev/null
+++ b/openmc_notebooks/msre_depletion_transfer_rates.ipynb
@@ -0,0 +1,2474 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fafca9f7",
+   "metadata": {},
+   "source": [
+    "# MSRE U235 Power Run\n",
+    "In the 1966 the MSRE started power operations with U235 as main fissile material. \n",
+    "In this notebook we will run a fully coupled transport-depletion simulation using a CAD design version developed by [Copenhagen Atomics](https://www.copenhaagenatomics.com) using the CAE tool OnShape and made available for export here: [onshape msre model]((https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/)).\n",
+    "\n",
+    "To effectively run OpenMC, the MSRE CAD geometry is meshed and converted into an h5m format readable by OpenMC, using the open-source meshing tool [CAD-to-OpenMC](https://github.com/openmsr/CAD_to_OpenMC). \n",
+    "\n",
+    "Furthermore, to model fission products removal we will use OpenMC [Transfer rates theory](https://docs.openmc.org/en/latest/methods/depletion.html#transfer-rates) capability available through the depletion solver and integrated into the main code from version 0.14.0. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "079fc782",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[ca-ws:1568059] mca_base_component_repository_open: unable to open mca_accelerator_rocm: libamdhip64.so.6: cannot open shared object file: No such file or directory (ignored)\n",
+      "rm: cannot remove '*.xml': No such file or directory\n",
+      "rm: cannot remove '*.h5': No such file or directory\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "256"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import openmc\n",
+    "import openmc.deplete\n",
+    "import numpy as np\n",
+    "from math import log10\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os \n",
+    "\n",
+    "os.system('rm *.xml *.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfca4d07",
+   "metadata": {},
+   "source": [
+    "For materials nuclear properties we will be using the same definitions used in the first criticality test derived from the [MSRE benchmark evaluation project](https://www.osti.gov/servlets/purl/1617123) and available in the International Reactor Physics Experiment Evaulation Project (IRPhEP) handbook. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4bc6b094",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define materials \n",
+    "salt_temp = 638.3 # in C\n",
+    "salt_density = 2.32556 # g/cm3\n",
+    "salt = openmc.Material(name=\"salt\", temperature = salt_temp + 273.15)\n",
+    "salt.add_nuclide('Li6', 0.0000131541127279649)\n",
+    "salt.add_nuclide('Li7', 0.263082254559299)\n",
+    "salt.add_nuclide('Be9', 0.118688703357854)\n",
+    "salt.add_nuclide('Zr90', 0.0105474637491834)\n",
+    "salt.add_nuclide('Zr91', 0.00230014661352455)\n",
+    "salt.add_nuclide('Zr92', 0.00351582124972781)\n",
+    "salt.add_nuclide('Zr94', 0.00356297220526352)\n",
+    "salt.add_nuclide('Zr96', 0.000574011632608622)\n",
+    "salt.add_nuclide('Hf174', 0.000000000838247756705461)\n",
+    "salt.add_nuclide('Hf176', 0.000000027557395001692)\n",
+    "salt.add_nuclide('Hf177', 0.0000000974463017170099)\n",
+    "salt.add_nuclide('Hf178', 0.000000142921242518281)\n",
+    "salt.add_nuclide('Hf179', 0.0000000713558402895524)\n",
+    "salt.add_nuclide('Hf180', 0.000000183785820657672)\n",
+    "salt.add_nuclide('U234', 0.000010347565467384)\n",
+    "salt.add_nuclide('U235', 0.00101016470221956)\n",
+    "salt.add_nuclide('U236', 0.00000422977321829143)\n",
+    "salt.add_nuclide('U238', 0.00216927473057667)\n",
+    "salt.add_nuclide('Fe54', 0.00000285638012142825)\n",
+    "salt.add_nuclide('Fe56', 0.0000448390593090724)\n",
+    "salt.add_nuclide('Fe57', 0.00000103552942297801)\n",
+    "salt.add_nuclide('Fe58', 0.000000137809956243416)\n",
+    "salt.add_nuclide('Cr50', 0.00000212334843928242)\n",
+    "salt.add_nuclide('Cr52', 0.000040946661076878)\n",
+    "salt.add_nuclide('Cr53', 0.00000464302267471169)\n",
+    "salt.add_nuclide('Cr54', 0.00000115574661884993)\n",
+    "salt.add_nuclide('Ni58', 0.00000586242358522768)\n",
+    "salt.add_nuclide('Ni60', 0.00000225819506936691)\n",
+    "salt.add_nuclide('Ni61', 0.0000000981621760803009)\n",
+    "salt.add_nuclide('Ni62', 0.00000031298397136929)\n",
+    "salt.add_nuclide('Ni64', 0.0000000797077903148755)\n",
+    "salt.add_nuclide('O16', 0.0000514608160260434)\n",
+    "salt.add_nuclide('O17', 0.0000000189357310970321)\n",
+    "salt.add_nuclide('O18', 0.0000000964845153990466)\n",
+    "salt.add_nuclide('F19', 0.594363006576997)\n",
+    "salt.set_density('g/cm3',salt_density)\n",
+    "\n",
+    "#moderator blocks170\n",
+    "graphite = openmc.Material(name='graphite',temperature= salt_temp + 273.15)\n",
+    "graphite.set_density('g/cm3',1.8492)\n",
+    "graphite.add_element('C',0.999992212250888) #endfb71 does not have C12 cross sections\n",
+    "graphite.add_nuclide('B10', 1.76873036539477E-07)\n",
+    "graphite.add_nuclide('B11', 7.1193229306592E-07)\n",
+    "graphite.add_nuclide('V51', 2.12200224802724E-06)\n",
+    "graphite.add_nuclide('S32', 1.77900760271203E-06)\n",
+    "graphite.add_nuclide('S33', 1.40462754188233E-08)\n",
+    "graphite.add_nuclide('S34', 7.95955607066653E-08)\n",
+    "graphite.add_nuclide('S36', 1.87283672250977E-10)\n",
+    "graphite.add_nuclide('O16', 1.85674385782835E-06)\n",
+    "graphite.add_nuclide('O17', 6.81675170610618E-10)\n",
+    "graphite.add_nuclide('Si28', 5.31911333979174E-07)\n",
+    "graphite.add_nuclide('Si29', 2.70215087309286E-08)\n",
+    "graphite.add_nuclide('Si30', 1.78336189959512E-08)\n",
+    "graphite.add_nuclide('Al27', 4.0117589478321E-07)\n",
+    "graphite.add_nuclide('Fe54', 2.31047953307142E-09)\n",
+    "graphite.add_nuclide('Fe56', 3.62695875239409E-08)\n",
+    "graphite.add_nuclide('Fe57', 8.37622947917593E-10)\n",
+    "graphite.add_nuclide('Fe58', 1.11472237523719E-10)\n",
+    "graphite.add_nuclide('Ti46', 1.12809907219147E-09)\n",
+    "graphite.add_nuclide('Ti47', 1.01734025419449E-09)\n",
+    "graphite.add_nuclide('Ti48', 1.008041983054E-08)\n",
+    "graphite.add_nuclide('Ti49', 7.39759512794646E-10)\n",
+    "graphite.add_nuclide('Ti50', 7.08309478054763E-10)\n",
+    "graphite.add_nuclide('Mg24', 8.53578076978748E-09)\n",
+    "graphite.add_nuclide('Mg25', 1.0806153652092E-09)\n",
+    "graphite.add_nuclide('Mg26', 1.18975751709533E-09)\n",
+    "graphite.add_nuclide('Ca40', 4.58305905846326E-09)\n",
+    "graphite.add_nuclide('Ca42', 3.05880815220158E-11)\n",
+    "graphite.add_nuclide('Ca43', 6.38236631448551E-12)\n",
+    "graphite.add_nuclide('Ca44', 9.86193787556798E-11)\n",
+    "graphite.add_nuclide('Ca48', 8.8407592652503E-12)\n",
+    "graphite.add_s_alpha_beta('c_Graphite')\n",
+    "\n",
+    "#inor-8\n",
+    "inor = openmc.Material(name='inor-8',temperature= salt_temp + 273.15)\n",
+    "inor.set_density('g/cm3',8.7745)\n",
+    "inor.add_element('Ni',(66+71)/2,'wo')\n",
+    "inor.add_element('Mo',(15+18)/2,'wo')\n",
+    "inor.add_element('Cr',(6+8)/2,'wo')\n",
+    "inor.add_element('Fe',5,'wo')\n",
+    "inor.add_element('C',(0.04+0.08)/2,'wo')\n",
+    "inor.add_element('Al',0.25,'wo')\n",
+    "inor.add_element('Ti',0.25,'wo')\n",
+    "inor.add_element('S',0.02,'wo')\n",
+    "inor.add_element('Mn',1.0,'wo')\n",
+    "inor.add_element('Si',1.0,'wo')\n",
+    "inor.add_element('Cu',0.35,'wo')\n",
+    "inor.add_element('B',0.010,'wo')\n",
+    "inor.add_element('W',0.5,'wo')\n",
+    "inor.add_element('P',0.015,'wo')\n",
+    "inor.add_element('Co',0.2,'wo')\n",
+    "\n",
+    "#helium\n",
+    "helium = openmc.Material(name='helium')\n",
+    "helium.add_element('He',1.0)\n",
+    "helium.set_density('g/cm3',1.03*(10**-4))\n",
+    "\n",
+    "#Control rods inconel clad\n",
+    "trace = 0.01\n",
+    "inconel = openmc.Material(name='inconel-600', temperature = 65.6 + 273.15)\n",
+    "inconel.add_element('Ni',78.5,percent_type='wo')\n",
+    "inconel.add_element('Cr',14.0,percent_type='wo')\n",
+    "inconel.add_element('Fe',6.5,percent_type='wo')\n",
+    "inconel.add_element('Mn',0.25,percent_type='wo')\n",
+    "inconel.add_element('Si',0.25,percent_type='wo')\n",
+    "inconel.add_element('Cu',0.2,percent_type='wo')\n",
+    "inconel.add_element('Co',0.2,percent_type='wo')\n",
+    "inconel.add_element('Al',0.2,percent_type='wo')\n",
+    "inconel.add_element('Ti',0.2,percent_type='wo')\n",
+    "inconel.add_element('Ta',0.5,percent_type='wo')\n",
+    "inconel.add_element('W',0.5,percent_type='wo')\n",
+    "inconel.add_element('Zn',0.2,percent_type='wo')\n",
+    "inconel.add_element('Zr',0.1,percent_type='wo')\n",
+    "inconel.add_element('C',trace,percent_type='wo')\n",
+    "inconel.add_element('Mo',trace,percent_type='wo')\n",
+    "inconel.add_element('Ag',trace,percent_type='wo')\n",
+    "inconel.add_element('B',trace,percent_type='wo')\n",
+    "inconel.add_element('Ba',trace,percent_type='wo')\n",
+    "inconel.add_element('Be',trace,percent_type='wo')\n",
+    "inconel.add_element('Ca',trace,percent_type='wo')\n",
+    "inconel.add_element('Cd',trace,percent_type='wo')\n",
+    "inconel.add_element('V',trace,percent_type='wo')\n",
+    "inconel.add_element('Sn',trace,percent_type='wo')\n",
+    "inconel.add_element('Mg',trace,percent_type='wo')\n",
+    "inconel.set_density('g/cm3',8.5)\n",
+    "\n",
+    "#Control rods bushing posion material\n",
+    "Gd2O3 = openmc.Material()\n",
+    "Gd2O3.add_element('Gd',2)\n",
+    "Gd2O3.add_element('O',3)\n",
+    "Gd2O3.set_density('g/cm3',7.41)\n",
+    "Al2O3 = openmc.Material()\n",
+    "Al2O3.add_element('Al',2)\n",
+    "Al2O3.add_element('O',3)\n",
+    "Al2O3.set_density('g/cm3',3.95)\n",
+    "bush = openmc.Material.mix_materials([Gd2O3,Al2O3],[0.7,0.3],'wo')\n",
+    "bush.name='gd2o3-al2o3'\n",
+    "bush.temperature = 65.6 +273.15\n",
+    "\n",
+    "#Concrete block\n",
+    "concrete = openmc.Material(name='concrete')\n",
+    "concrete.add_element('H',0.005,'wo')\n",
+    "concrete.add_element('O',0.496,'wo')\n",
+    "concrete.add_element('Si',0.314,'wo')\n",
+    "concrete.add_element('Ca',0.083,'wo')\n",
+    "concrete.add_element('Na',0.017,'wo')\n",
+    "concrete.add_element('Mn',0.002,'wo')\n",
+    "concrete.add_element('Al',0.046,'wo')\n",
+    "concrete.add_element('S',0.001,'wo')\n",
+    "concrete.add_element('K',0.019,'wo')\n",
+    "concrete.add_element('Fe',0.012,'wo')\n",
+    "concrete.set_density('g/cm3',2.35)\n",
+    "\n",
+    "#Thermal shielding as water and SS305 (50-50)\n",
+    "water = openmc.Material()\n",
+    "water.add_element('H',2)\n",
+    "water.add_element('O',1)\n",
+    "water.set_density('g/cm3',0.997)\n",
+    "\n",
+    "#stainless steel 304\n",
+    "ss304 =  openmc.Material()\n",
+    "ss304.add_element('C',0.08,'wo')\n",
+    "ss304.add_element('Mn',2,'wo')\n",
+    "ss304.add_element('P',0.045,'wo')\n",
+    "ss304.add_element('S',0.03,'wo')\n",
+    "ss304.add_element('Si',0.75,'wo')\n",
+    "ss304.add_element('Cr',19,'wo')\n",
+    "ss304.add_element('Ni',10,'wo')\n",
+    "ss304.add_element('N',0.1,'wo')\n",
+    "ss304.add_element('Fe',67.995, 'wo')\n",
+    "ss304.set_density('g/cm3',7.93)\n",
+    "shield = openmc.Material.mix_materials([water,ss304],[0.5,0.5],'vo')\n",
+    "shield.temperature = 32.2 + 273.15\n",
+    "shield.name='steelwater'\n",
+    "\n",
+    "# \"Careytemp 1600\" by Philip Carey Manufacturing Compamy (Cincinnati)\n",
+    "# http://moltensalt.org/references/static/downloads/pdf/ORNL-TM-0728.pdf\n",
+    "insulation=openmc.Material(name='insulation')\n",
+    "insulation.add_element('Si',1)\n",
+    "insulation.add_element('O',2)\n",
+    "insulation.set_density('g/cm3',0.16) #https://www.osti.gov/servlets/purl/1411211\n",
+    "\n",
+    "# sand water, not sure about this material\n",
+    "sandwater=openmc.Material(name='watersand')\n",
+    "sandwater.add_element('Fe',3)\n",
+    "sandwater.add_element('O',4)\n",
+    "sandwater.set_density('g/cm3',6)\n",
+    "\n",
+    "#Vessel anular steel\n",
+    "steel = openmc.Material(name='steel')\n",
+    "steel.add_element('Fe',1)\n",
+    "steel.set_density('g/cm3',7.85)\n",
+    "\n",
+    "mats = openmc.Materials([salt, graphite, inor, helium, inconel, shield, concrete,\n",
+    "                         steel, sandwater, insulation, bush])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a0355f1-a513-4fc5-8812-7dffb5138c11",
+   "metadata": {},
+   "source": [
+    "The MSRE geometry was produced with the CAE tool OnShape ([onshape cad model](https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/)) as step files and converted into OpenMC readable h5m files using the open source mesh tool [CAD_to_OpenMC](https://pypi.org/project/CAD-to-OpenMC/).   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "edfa5bd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10000.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10001.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10002.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10003.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10004.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10005.\n",
+      "  warn(msg, IDWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# CAD h5m files\n",
+    "core_h5m = 'msre_full.h5m'\n",
+    "control_rod_h5m = 'msre_control_rod.h5m'\n",
+    "\n",
+    "#Geometry\n",
+    "core = openmc.DAGMCUniverse(filename = core_h5m, auto_geom_ids = True,\n",
+    "                            universe_id = 1)\n",
+    "control_rod = openmc.DAGMCUniverse(filename = control_rod_h5m,\n",
+    "                            auto_geom_ids = True, universe_id=2)\n",
+    "core_region = core.bounding_region()\n",
+    "cr_region = control_rod.bounding_region(boundary_type = 'transmission')\n",
+    "\n",
+    "_offset_xy = 10.163255 #cm, _offset_xy between cr1, cr2 and cr3 (from Onshape model)\n",
+    "_lower_rod = 61.728 # distance to lower limit (from OnShape model)\n",
+    "_upper_rod = 51 * 2.54 # escursion to upper rod limit (from ORNL)\n",
+    "start_rod = 56 * 2.54 # rod iniitial position (user input)\n",
+    "\n",
+    "# Create control rod regions w\n",
+    "cr1_region = cr_region.translate([_offset_xy/2, _offset_xy/2, _lower_rod + _upper_rod])\n",
+    "cr2_region = cr_region.translate([_offset_xy/2, -_offset_xy/2, _lower_rod + _upper_rod])\n",
+    "cr3_region = cr_region.translate([-_offset_xy/2, -_offset_xy/2, _lower_rod + _upper_rod])\n",
+    "\n",
+    "# Extend control rod region1 to include downloads translations\n",
+    "cr1_region = cr1_region | cr1_region.translate([0, 0, -_upper_rod])\n",
+    "\n",
+    "#Define cells\n",
+    "core_cell = openmc.Cell(region=~(cr1_region | cr2_region | cr3_region) & core_region ,\n",
+    "                        fill=core)\n",
+    "\n",
+    "cr1_cell = openmc.Cell(name='CR1', region=cr1_region, fill=control_rod)\n",
+    "cr2_cell = openmc.Cell(name='CR2', region=cr2_region, fill=control_rod)\n",
+    "cr3_cell = openmc.Cell(name='CR3', region=cr3_region, fill=control_rod)\n",
+    "\n",
+    "#translate cells to regions\n",
+    "setattr(cr1_cell, 'translation', [_offset_xy/2, _offset_xy/2 , _lower_rod + start_rod])\n",
+    "setattr(cr2_cell, 'translation', [_offset_xy/2, -_offset_xy/2, _lower_rod + start_rod])\n",
+    "setattr(cr3_cell, 'translation', [-_offset_xy/2, -_offset_xy/2, _lower_rod + start_rod])\n",
+    "geometry = openmc.Geometry([core_cell,cr1_cell,cr2_cell,cr3_cell])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0abbe529-1d22-438a-aa58-b083f8faac87",
+   "metadata": {},
+   "source": [
+    "Let's set some generic settings "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ba99fb4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "settings = openmc.Settings()\n",
+    "settings.temperature = {'method':'interpolation','range':(293.15,923.15)}\n",
+    "settings.batches = 50\n",
+    "settings.inactive = 20\n",
+    "settings.particles = 30000\n",
+    "settings.photon_transport = False\n",
+    "source_area = openmc.stats.Box([-100., -100., 0.],[ 100.,  100.,  200.],only_fissionable = True)\n",
+    "settings.source = openmc.IndependentSource(space=source_area)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd3cd25c",
+   "metadata": {},
+   "source": [
+    "# Transfer Rates\n",
+    "The circulation of molten salt along the main fuel circuit of the MSRE made it possible to have a continuos separation of fission products.  \n",
+    "In particular it is reported that volatile noble gasses would bubble out in the off-gas system removal and noble metals plate out on the surface of the heat-exchangers. \n",
+    "\n",
+    "In this particular and simplified case, we can set removal rates as negative transfer rates from the main fuel salt with removal rate coefficients that are function of the time characteristic and efficiency of the removal methods for the two set of materials. \n",
+    "\n",
+    "This depletion capability has been added to OpenMC main branch from version 0.14.0.\n",
+    "\n",
+    "We will use the same removal rates as the one derived in this [ornl paper](https://info.ornl.gov/sites/publications/Files/Pub173113.pdf).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b9f8eada",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                %%%%%%%%%%%%%%%\n",
+      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
+      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
+      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
+      "                #######################     %%%%%%%%%%%%%%%%%%\n",
+      "                 #######################     %%%%%%%%%%%%%%%%%\n",
+      "                 ######################     %%%%%%%%%%%%%%%%%\n",
+      "                  ####################     %%%%%%%%%%%%%%%%%\n",
+      "                    #################     %%%%%%%%%%%%%%%%%\n",
+      "                     ###############     %%%%%%%%%%%%%%%%\n",
+      "                       ############     %%%%%%%%%%%%%%%\n",
+      "                          ########     %%%%%%%%%%%%%%\n",
+      "                                      %%%%%%%%%%%\n",
+      "\n",
+      "                 | The OpenMC Monte Carlo Code\n",
+      "       Copyright | 2011-2024 MIT, UChicago Argonne LLC, and contributors\n",
+      "         License | https://docs.openmc.org/en/latest/license.html\n",
+      "         Version | 0.14.1-dev\n",
+      "        Git SHA1 | d93ac83697e7fdb4fd4cc0f2705795398bf804bd\n",
+      "       Date/Time | 2024-04-22 09:14:24\n",
+      "   MPI Processes | 1\n",
+      "  OpenMP Threads | 56\n",
+      "\n",
+      " Reading settings XML file...\n",
+      " Reading cross sections XML file...\n",
+      " Reading materials XML file...\n",
+      " Reading geometry XML file...\n",
+      "Using the DOUBLE-DOWN interface to Embree.\n",
+      "Loading file msre_full.h5m\n",
+      "Initializing the GeomQueryTool...\n",
+      "Using faceting tolerance: 0.01\n",
+      "Building acceleration data structures...\n",
+      "Using the DOUBLE-DOWN interface to Embree.\n",
+      "Loading file msre_control_rod.h5m\n",
+      "Initializing the GeomQueryTool...\n",
+      "Using faceting tolerance: 0\n",
+      "Building acceleration data structures...\n",
+      " Reading Li6 from /home/lorenzo/nuclear_data/endfb80_hdf5/Li6.h5\n",
+      " Reading Li7 from /home/lorenzo/nuclear_data/endfb80_hdf5/Li7.h5\n",
+      " Reading Be9 from /home/lorenzo/nuclear_data/endfb80_hdf5/Be9.h5\n",
+      " Reading O16 from /home/lorenzo/nuclear_data/endfb80_hdf5/O16.h5\n",
+      " Reading O17 from /home/lorenzo/nuclear_data/endfb80_hdf5/O17.h5\n",
+      " Reading O18 from /home/lorenzo/nuclear_data/endfb80_hdf5/O18.h5\n",
+      " Reading F19 from /home/lorenzo/nuclear_data/endfb80_hdf5/F19.h5\n",
+      " Reading Cr50 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr50.h5\n",
+      " Reading Cr52 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr52.h5\n",
+      " Reading Cr53 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr53.h5\n",
+      " Reading Cr54 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr54.h5\n",
+      " Reading Fe54 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe54.h5\n",
+      " Reading Fe56 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe56.h5\n",
+      " Reading Fe57 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe57.h5\n",
+      " Reading Fe58 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe58.h5\n",
+      " Reading Ni58 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni58.h5\n",
+      " Reading Ni60 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni60.h5\n",
+      " Reading Ni61 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni61.h5\n",
+      " Reading Ni62 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni62.h5\n",
+      " Reading Ni64 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni64.h5\n",
+      " Reading Zr90 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr90.h5\n",
+      " Reading Zr91 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr91.h5\n",
+      " Reading Zr92 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr92.h5\n",
+      " Reading Zr94 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr94.h5\n",
+      " Reading Zr96 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr96.h5\n",
+      " Reading Hf174 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf174.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 1200K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Hf176 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf176.h5\n",
+      " Reading Hf177 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf177.h5\n",
+      " Reading Hf178 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf178.h5\n",
+      " Reading Hf179 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf179.h5\n",
+      " Reading Hf180 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf180.h5\n",
+      " Reading U234 from /home/lorenzo/nuclear_data/endfb80_hdf5/U234.h5\n",
+      " Reading U235 from /home/lorenzo/nuclear_data/endfb80_hdf5/U235.h5\n",
+      " Reading U236 from /home/lorenzo/nuclear_data/endfb80_hdf5/U236.h5\n",
+      " Reading U238 from /home/lorenzo/nuclear_data/endfb80_hdf5/U238.h5\n",
+      " Reading B10 from /home/lorenzo/nuclear_data/endfb80_hdf5/B10.h5\n",
+      " Reading B11 from /home/lorenzo/nuclear_data/endfb80_hdf5/B11.h5\n",
+      " Reading C12 from /home/lorenzo/nuclear_data/endfb80_hdf5/C12.h5\n",
+      " Reading C13 from /home/lorenzo/nuclear_data/endfb80_hdf5/C13.h5\n",
+      " Reading Mg24 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mg24.h5\n",
+      " Reading Mg25 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mg25.h5\n",
+      " Reading Mg26 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mg26.h5\n",
+      " Reading Al27 from /home/lorenzo/nuclear_data/endfb80_hdf5/Al27.h5\n",
+      " Reading Si28 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si28.h5\n",
+      " Reading Si29 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si29.h5\n",
+      " Reading Si30 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si30.h5\n",
+      " Reading S32 from /home/lorenzo/nuclear_data/endfb80_hdf5/S32.h5\n",
+      " Reading S33 from /home/lorenzo/nuclear_data/endfb80_hdf5/S33.h5\n",
+      " Reading S34 from /home/lorenzo/nuclear_data/endfb80_hdf5/S34.h5\n",
+      " Reading S36 from /home/lorenzo/nuclear_data/endfb80_hdf5/S36.h5\n",
+      " Reading Ca40 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca40.h5\n",
+      " Reading Ca42 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca42.h5\n",
+      " Reading Ca43 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca43.h5\n",
+      " Reading Ca44 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca44.h5\n",
+      " Reading Ca48 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca48.h5\n",
+      " Reading Ti46 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti46.h5\n",
+      " Reading Ti47 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti47.h5\n",
+      " Reading Ti48 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti48.h5\n",
+      " Reading Ti49 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti49.h5\n",
+      " Reading Ti50 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti50.h5\n",
+      " Reading V51 from /home/lorenzo/nuclear_data/endfb80_hdf5/V51.h5\n",
+      " Reading P31 from /home/lorenzo/nuclear_data/endfb80_hdf5/P31.h5\n",
+      " Reading Mn55 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mn55.h5\n",
+      " Reading Co59 from /home/lorenzo/nuclear_data/endfb80_hdf5/Co59.h5\n",
+      " Reading Cu63 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cu63.h5\n",
+      " Reading Cu65 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cu65.h5\n",
+      " Reading Mo92 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo92.h5\n",
+      " Reading Mo94 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo94.h5\n",
+      " Reading Mo95 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo95.h5\n",
+      " Reading Mo96 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo96.h5\n",
+      " Reading Mo97 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo97.h5\n",
+      " Reading Mo98 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo98.h5\n",
+      " Reading Mo100 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo100.h5\n",
+      " Reading W180 from /home/lorenzo/nuclear_data/endfb80_hdf5/W180.h5\n",
+      " Reading W182 from /home/lorenzo/nuclear_data/endfb80_hdf5/W182.h5\n",
+      " Reading W183 from /home/lorenzo/nuclear_data/endfb80_hdf5/W183.h5\n",
+      " Reading W184 from /home/lorenzo/nuclear_data/endfb80_hdf5/W184.h5\n",
+      " Reading W186 from /home/lorenzo/nuclear_data/endfb80_hdf5/W186.h5\n",
+      " Reading He3 from /home/lorenzo/nuclear_data/endfb80_hdf5/He3.h5\n",
+      " Reading He4 from /home/lorenzo/nuclear_data/endfb80_hdf5/He4.h5\n",
+      " Reading Ca46 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca46.h5\n",
+      " Reading V50 from /home/lorenzo/nuclear_data/endfb80_hdf5/V50.h5\n",
+      " Reading Zn64 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn64.h5\n",
+      " Reading Zn66 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn66.h5\n",
+      " Reading Zn67 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn67.h5\n",
+      " Reading Zn68 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn68.h5\n",
+      " Reading Zn70 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn70.h5\n",
+      " Reading Ag107 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag107.h5\n",
+      " Reading Ag109 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag109.h5\n",
+      " Reading Cd106 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd106.h5\n",
+      " Reading Cd108 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd108.h5\n",
+      " Reading Cd110 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd110.h5\n",
+      " Reading Cd111 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd111.h5\n",
+      " Reading Cd112 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd112.h5\n",
+      " Reading Cd113 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd113.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cd106 at\n",
+      "          1200K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Cd114 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd114.h5\n",
+      " Reading Cd116 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd116.h5\n",
+      " Reading Sn112 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn112.h5\n",
+      " Reading Sn114 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn114.h5\n",
+      " Reading Sn115 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn115.h5\n",
+      " Reading Sn116 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn116.h5\n",
+      " Reading Sn117 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn117.h5\n",
+      " Reading Sn118 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn118.h5\n",
+      " Reading Sn119 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn119.h5\n",
+      " Reading Sn120 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn120.h5\n",
+      " Reading Sn122 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn122.h5\n",
+      " Reading Sn124 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn124.h5\n",
+      " Reading Ba130 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba130.h5\n",
+      " Reading Ba132 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba132.h5\n",
+      " Reading Ba134 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba134.h5\n",
+      " Reading Ba135 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba135.h5\n",
+      " Reading Ba136 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba136.h5\n",
+      " Reading Ba137 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba137.h5\n",
+      " Reading Ba138 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba138.h5\n",
+      " Reading Ta180 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ta180.h5\n",
+      " Reading Ta181 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ta181.h5\n",
+      " Reading Gd152 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd152.h5\n",
+      " Reading Gd154 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd154.h5\n",
+      " Reading Gd155 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd155.h5\n",
+      " Reading Gd156 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd156.h5\n",
+      " Reading Gd157 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd157.h5\n",
+      " Reading Gd158 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd158.h5\n",
+      " Reading Gd160 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd160.h5\n",
+      " Reading H1 from /home/lorenzo/nuclear_data/endfb80_hdf5/H1.h5\n",
+      " Reading H2 from /home/lorenzo/nuclear_data/endfb80_hdf5/H2.h5\n",
+      " Reading Na23 from /home/lorenzo/nuclear_data/endfb80_hdf5/Na23.h5\n",
+      " Reading K39 from /home/lorenzo/nuclear_data/endfb80_hdf5/K39.h5\n",
+      " Reading K40 from /home/lorenzo/nuclear_data/endfb80_hdf5/K40.h5\n",
+      " Reading K41 from /home/lorenzo/nuclear_data/endfb80_hdf5/K41.h5\n",
+      " Reading N14 from /home/lorenzo/nuclear_data/endfb80_hdf5/N14.h5\n",
+      " Reading N15 from /home/lorenzo/nuclear_data/endfb80_hdf5/N15.h5\n",
+      " Reading c_Graphite from /home/lorenzo/nuclear_data/endfb80_hdf5/c_Graphite.h5\n",
+      " Minimum neutron data temperature: 250 K\n",
+      " Maximum neutron data temperature: 2500 K\n",
+      " Preparing distributed cell instances...\n",
+      " Reading plot XML file...\n",
+      " Writing summary.h5 file...\n",
+      "[openmc.deplete] t=0.0 s, dt=432000 s, source=8000000.0\n",
+      " Reading H3 from /home/lorenzo/nuclear_data/endfb80_hdf5/H3.h5\n",
+      " Reading Be7 from /home/lorenzo/nuclear_data/endfb80_hdf5/Be7.h5\n",
+      " Reading Ne20 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ne20.h5\n",
+      " Reading Ne21 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ne21.h5\n",
+      " Reading Ne22 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ne22.h5\n",
+      " Reading Na22 from /home/lorenzo/nuclear_data/endfb80_hdf5/Na22.h5\n",
+      " Reading Al26_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Al26_m1.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Na22 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Na22 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Na22 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Na22 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Na22 at 1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Na22 at 2500K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Si31 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si31.h5\n",
+      " Reading Si32 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si32.h5\n",
+      " Reading S35 from /home/lorenzo/nuclear_data/endfb80_hdf5/S35.h5\n",
+      " Reading Cl35 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cl35.h5\n",
+      " Reading Cl36 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cl36.h5\n",
+      " Reading Cl37 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cl37.h5\n",
+      " Reading Ar36 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar36.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 2500K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Ar37 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar37.h5\n",
+      " Reading Ar38 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar38.h5\n",
+      " Reading Ar39 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar39.h5\n",
+      " Reading Ar40 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar40.h5\n",
+      " Reading Ar41 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar41.h5\n",
+      " Reading Ca41 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca41.h5\n",
+      " Reading Ca45 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca45.h5\n",
+      " Reading Ca47 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca47.h5\n",
+      " Reading Sc45 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sc45.h5\n",
+      " Reading V49 from /home/lorenzo/nuclear_data/endfb80_hdf5/V49.h5\n",
+      " Reading Cr51 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr51.h5\n",
+      " Reading Mn54 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mn54.h5\n",
+      " Reading Fe55 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe55.h5\n",
+      " Reading Co58 from /home/lorenzo/nuclear_data/endfb80_hdf5/Co58.h5\n",
+      " Reading Co58_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Co58_m1.h5\n",
+      " Reading Ni59 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni59.h5\n",
+      " Reading Ni63 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni63.h5\n",
+      " Reading Cu64 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cu64.h5\n",
+      " Reading Zn65 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn65.h5\n",
+      " Reading Zn69 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn69.h5\n",
+      " Reading Ga69 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ga69.h5\n",
+      " Reading Ga70 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ga70.h5\n",
+      " Reading Ga71 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ga71.h5\n",
+      " Reading Ge70 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge70.h5\n",
+      " Reading Ge71 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge71.h5\n",
+      " Reading Ge72 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge72.h5\n",
+      " Reading Ge73 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge73.h5\n",
+      " Reading Ge74 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge74.h5\n",
+      " Reading Ge75 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge75.h5\n",
+      " Reading Ge76 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge76.h5\n",
+      " Reading As73 from /home/lorenzo/nuclear_data/endfb80_hdf5/As73.h5\n",
+      " Reading As74 from /home/lorenzo/nuclear_data/endfb80_hdf5/As74.h5\n",
+      " Reading As75 from /home/lorenzo/nuclear_data/endfb80_hdf5/As75.h5\n",
+      " Reading Se74 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se74.h5\n",
+      " Reading Se75 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se75.h5\n",
+      " Reading Se76 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se76.h5\n",
+      " Reading Se77 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se77.h5\n",
+      " Reading Se78 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se78.h5\n",
+      " Reading Se79 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se79.h5\n",
+      " Reading Se80 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se80.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Se79 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Se79 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Se79 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Se79 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Se79 at 1200K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Se81 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se81.h5\n",
+      " Reading Se82 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se82.h5\n",
+      " Reading Br79 from /home/lorenzo/nuclear_data/endfb80_hdf5/Br79.h5\n",
+      " Reading Br80 from /home/lorenzo/nuclear_data/endfb80_hdf5/Br80.h5\n",
+      " Reading Br81 from /home/lorenzo/nuclear_data/endfb80_hdf5/Br81.h5\n",
+      " Reading Kr78 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr78.h5\n",
+      " Reading Kr79 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr79.h5\n",
+      " Reading Kr80 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr80.h5\n",
+      " Reading Kr81 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr81.h5\n",
+      " Reading Kr82 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr82.h5\n",
+      " Reading Kr83 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr83.h5\n",
+      " Reading Kr84 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr84.h5\n",
+      " Reading Kr85 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr85.h5\n",
+      " Reading Kr86 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr86.h5\n",
+      " Reading Rb85 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rb85.h5\n",
+      " Reading Rb86 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rb86.h5\n",
+      " Reading Rb87 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rb87.h5\n",
+      " Reading Sr84 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr84.h5\n",
+      " Reading Sr85 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr85.h5\n",
+      " Reading Sr86 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr86.h5\n",
+      " Reading Sr87 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr87.h5\n",
+      " Reading Sr88 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr88.h5\n",
+      " Reading Sr89 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr89.h5\n",
+      " Reading Sr90 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr90.h5\n",
+      " Reading Y89 from /home/lorenzo/nuclear_data/endfb80_hdf5/Y89.h5\n",
+      " Reading Y90 from /home/lorenzo/nuclear_data/endfb80_hdf5/Y90.h5\n",
+      " Reading Y91 from /home/lorenzo/nuclear_data/endfb80_hdf5/Y91.h5\n",
+      " Reading Zr93 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr93.h5\n",
+      " Reading Zr95 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr95.h5\n",
+      " Reading Nb93 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nb93.h5\n",
+      " Reading Nb94 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nb94.h5\n",
+      " Reading Nb95 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nb95.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 2500K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 1200K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Mo93 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo93.h5\n",
+      " Reading Mo99 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo99.h5\n",
+      " Reading Tc98 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tc98.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 2500K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Tc99 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tc99.h5\n",
+      " Reading Ru96 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru96.h5\n",
+      " Reading Ru97 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru97.h5\n",
+      " Reading Ru98 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru98.h5\n",
+      " Reading Ru99 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru99.h5\n",
+      " Reading Ru100 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru100.h5\n",
+      " Reading Ru101 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru101.h5\n",
+      " Reading Ru102 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru102.h5\n",
+      " Reading Ru103 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru103.h5\n",
+      " Reading Ru104 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru104.h5\n",
+      " Reading Ru105 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru105.h5\n",
+      " Reading Ru106 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru106.h5\n",
+      " Reading Rh103 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rh103.h5\n",
+      " Reading Rh104 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rh104.h5\n",
+      " Reading Rh105 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rh105.h5\n",
+      " Reading Pd102 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd102.h5\n",
+      " Reading Pd103 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd103.h5\n",
+      " Reading Pd104 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd104.h5\n",
+      " Reading Pd105 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd105.h5\n",
+      " Reading Pd106 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd106.h5\n",
+      " Reading Pd107 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd107.h5\n",
+      " Reading Pd108 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd108.h5\n",
+      " Reading Pd109 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd109.h5\n",
+      " Reading Pd110 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd110.h5\n",
+      " Reading Ag108 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag108.h5\n",
+      " Reading Ag110_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag110_m1.h5\n",
+      " Reading Ag111 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag111.h5\n",
+      " Reading Ag112 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag112.h5\n",
+      " Reading Ag113 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag113.h5\n",
+      " Reading Ag114 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag114.h5\n",
+      " Reading Ag115 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag115.h5\n",
+      " Reading Ag116 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag116.h5\n",
+      " Reading Ag117 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag117.h5\n",
+      " Reading Ag118_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag118_m1.h5\n",
+      " Reading Cd107 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd107.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n",
+      "          250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n",
+      "          294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n",
+      "          600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n",
+      "          900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n",
+      "          1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n",
+      "          2500K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Cd109 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd109.h5\n",
+      " Reading Cd115_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd115_m1.h5\n",
+      " Reading In113 from /home/lorenzo/nuclear_data/endfb80_hdf5/In113.h5\n",
+      " Reading In114 from /home/lorenzo/nuclear_data/endfb80_hdf5/In114.h5\n",
+      " Reading In115 from /home/lorenzo/nuclear_data/endfb80_hdf5/In115.h5\n",
+      " Reading Sn113 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn113.h5\n",
+      " Reading Sn121_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn121_m1.h5\n",
+      " Reading Sn123 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn123.h5\n",
+      " Reading Sn125 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn125.h5\n",
+      " Reading Sn126 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn126.h5\n",
+      " Reading Sb121 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb121.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Sn123 at\n",
+      "          1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Sn123 at\n",
+      "          2500K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Sb122 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb122.h5\n",
+      " Reading Sb123 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb123.h5\n",
+      " Reading Sb124 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb124.h5\n",
+      " Reading Sb125 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb125.h5\n",
+      " Reading Sb126 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb126.h5\n",
+      " Reading Te120 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te120.h5\n",
+      " Reading Te121 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te121.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Te120 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Te120 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Te120 at\n",
+      "          1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Te120 at\n",
+      "          2500K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Te121_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te121_m1.h5\n",
+      " Reading Te122 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te122.h5\n",
+      " Reading Te123 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te123.h5\n",
+      " Reading Te124 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te124.h5\n",
+      " Reading Te125 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te125.h5\n",
+      " Reading Te126 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te126.h5\n",
+      " Reading Te127_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te127_m1.h5\n",
+      " Reading Te128 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te128.h5\n",
+      " Reading Te129_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te129_m1.h5\n",
+      " Reading Te130 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te130.h5\n",
+      " Reading Te131 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te131.h5\n",
+      " Reading Te131_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te131_m1.h5\n",
+      " Reading Te132 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te132.h5\n",
+      " Reading I127 from /home/lorenzo/nuclear_data/endfb80_hdf5/I127.h5\n",
+      " Reading I128 from /home/lorenzo/nuclear_data/endfb80_hdf5/I128.h5\n",
+      " Reading I129 from /home/lorenzo/nuclear_data/endfb80_hdf5/I129.h5\n",
+      " Reading I130 from /home/lorenzo/nuclear_data/endfb80_hdf5/I130.h5\n",
+      " Reading I131 from /home/lorenzo/nuclear_data/endfb80_hdf5/I131.h5\n",
+      " Reading I132 from /home/lorenzo/nuclear_data/endfb80_hdf5/I132.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide I131 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide I131 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide I131 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide I131 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide I131 at 1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide I131 at 2500K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading I132_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/I132_m1.h5\n",
+      " Reading I133 from /home/lorenzo/nuclear_data/endfb80_hdf5/I133.h5\n",
+      " Reading I134 from /home/lorenzo/nuclear_data/endfb80_hdf5/I134.h5\n",
+      " Reading I135 from /home/lorenzo/nuclear_data/endfb80_hdf5/I135.h5\n",
+      " Reading Xe123 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe123.h5\n",
+      " Reading Xe124 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe124.h5\n",
+      " Reading Xe125 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe125.h5\n",
+      " Reading Xe126 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe126.h5\n",
+      " Reading Xe127 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe127.h5\n",
+      " Reading Xe128 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe128.h5\n",
+      " Reading Xe129 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe129.h5\n",
+      " Reading Xe130 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe130.h5\n",
+      " Reading Xe131 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe131.h5\n",
+      " Reading Xe132 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe132.h5\n",
+      " Reading Xe133 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe133.h5\n",
+      " Reading Xe134 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe134.h5\n",
+      " Reading Xe135 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe135.h5\n",
+      " Reading Xe136 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe136.h5\n",
+      " Reading Cs133 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cs133.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Xe133 at\n",
+      "          2500K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Cs134 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cs134.h5\n",
+      " Reading Cs135 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cs135.h5\n",
+      " Reading Cs136 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cs136.h5\n",
+      " Reading Cs137 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cs137.h5\n",
+      " Reading Ba131 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba131.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cs136 at\n",
+      "          1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cs136 at\n",
+      "          2500K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Ba133 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba133.h5\n",
+      " Reading Ba139 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba139.h5\n",
+      " Reading Ba140 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba140.h5\n",
+      " Reading La138 from /home/lorenzo/nuclear_data/endfb80_hdf5/La138.h5\n",
+      " Reading La139 from /home/lorenzo/nuclear_data/endfb80_hdf5/La139.h5\n",
+      " Reading La140 from /home/lorenzo/nuclear_data/endfb80_hdf5/La140.h5\n",
+      " Reading Ce136 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce136.h5\n",
+      " Reading Ce137 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce137.h5\n",
+      " Reading Ce137_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce137_m1.h5\n",
+      " Reading Ce138 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce138.h5\n",
+      " Reading Ce139 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce139.h5\n",
+      " Reading Ce140 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce140.h5\n",
+      " Reading Ce141 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce141.h5\n",
+      " Reading Ce142 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce142.h5\n",
+      " Reading Ce143 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce143.h5\n",
+      " Reading Ce144 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce144.h5\n",
+      " Reading Pr141 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pr141.h5\n",
+      " Reading Pr142 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pr142.h5\n",
+      " Reading Pr143 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pr143.h5\n",
+      " Reading Nd142 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd142.h5\n",
+      " Reading Nd143 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd143.h5\n",
+      " Reading Nd144 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd144.h5\n",
+      " Reading Nd145 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd145.h5\n",
+      " Reading Nd146 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd146.h5\n",
+      " Reading Nd147 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd147.h5\n",
+      " Reading Nd148 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd148.h5\n",
+      " Reading Nd149 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd149.h5\n",
+      " Reading Nd150 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd150.h5\n",
+      " Reading Pm143 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm143.h5\n",
+      " Reading Pm144 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm144.h5\n",
+      " Reading Pm145 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm145.h5\n",
+      " Reading Pm146 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm146.h5\n",
+      " Reading Pm147 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm147.h5\n",
+      " Reading Pm148 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm148.h5\n",
+      " Reading Pm148_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm148_m1.h5\n",
+      " Reading Pm149 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm149.h5\n",
+      " Reading Pm150 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm150.h5\n",
+      " Reading Pm151 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm151.h5\n",
+      " Reading Sm144 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm144.h5\n",
+      " Reading Sm145 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm145.h5\n",
+      " Reading Sm146 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm146.h5\n",
+      " Reading Sm147 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm147.h5\n",
+      " Reading Sm148 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm148.h5\n",
+      " Reading Sm149 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm149.h5\n",
+      " Reading Sm150 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm150.h5\n",
+      " Reading Sm151 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm151.h5\n",
+      " Reading Sm152 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm152.h5\n",
+      " Reading Sm153 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm153.h5\n",
+      " Reading Sm154 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm154.h5\n",
+      " Reading Eu151 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu151.h5\n",
+      " Reading Eu152 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu152.h5\n",
+      " Reading Eu153 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu153.h5\n",
+      " Reading Eu154 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu154.h5\n",
+      " Reading Eu155 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu155.h5\n",
+      " Reading Eu156 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu156.h5\n",
+      " Reading Eu157 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu157.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Eu156 at\n",
+      "          1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Eu156 at\n",
+      "          2500K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Gd153 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd153.h5\n",
+      " Reading Gd159 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd159.h5\n",
+      " Reading Tb158 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tb158.h5\n",
+      " Reading Tb159 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tb159.h5\n",
+      " Reading Tb160 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tb160.h5\n",
+      " Reading Tb161 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tb161.h5\n",
+      " Reading Dy154 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy154.h5\n",
+      " Reading Dy155 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy155.h5\n",
+      " Reading Dy156 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy156.h5\n",
+      " Reading Dy157 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy157.h5\n",
+      " Reading Dy158 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy158.h5\n",
+      " Reading Dy159 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy159.h5\n",
+      " Reading Dy160 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy160.h5\n",
+      " Reading Dy161 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy161.h5\n",
+      " Reading Dy162 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy162.h5\n",
+      " Reading Dy163 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy163.h5\n",
+      " Reading Dy164 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy164.h5\n",
+      " Reading Ho165 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ho165.h5\n",
+      " Reading Ho166_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ho166_m1.h5\n",
+      " Reading Er162 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er162.h5\n",
+      " Reading Er163 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er163.h5\n",
+      " Reading Er164 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er164.h5\n",
+      " Reading Er165 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er165.h5\n",
+      " Reading Er166 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er166.h5\n",
+      " Reading Er167 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er167.h5\n",
+      " Reading Er168 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er168.h5\n",
+      " Reading Er169 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er169.h5\n",
+      " Reading Er170 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er170.h5\n",
+      " Reading Tm168 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tm168.h5\n",
+      " Reading Tm169 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tm169.h5\n",
+      " Reading Tm170 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tm170.h5\n",
+      " Reading Tm171 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tm171.h5\n",
+      " Reading Yb168 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb168.h5\n",
+      " Reading Yb169 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb169.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb168 at\n",
+      "          1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb168 at\n",
+      "          2500K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Yb170 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb170.h5\n",
+      " Reading Yb171 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb171.h5\n",
+      " Reading Yb172 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb172.h5\n",
+      " Reading Yb173 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb173.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb170 at\n",
+      "          1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb170 at\n",
+      "          2500K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb171 at\n",
+      "          1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb171 at\n",
+      "          2500K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb172 at\n",
+      "          1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb172 at\n",
+      "          2500K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb173 at\n",
+      "          1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb173 at\n",
+      "          2500K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Yb174 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb174.h5\n",
+      " Reading Yb175 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb175.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb174 at\n",
+      "          1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb174 at\n",
+      "          2500K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Yb176 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb176.h5\n",
+      " Reading Lu175 from /home/lorenzo/nuclear_data/endfb80_hdf5/Lu175.h5\n",
+      " Reading Lu176 from /home/lorenzo/nuclear_data/endfb80_hdf5/Lu176.h5\n",
+      " Reading Hf175 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf175.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb176 at\n",
+      "          1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Yb176 at\n",
+      "          2500K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Hf181 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf181.h5\n",
+      " Reading Hf182 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf182.h5\n",
+      " Reading Ta182 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ta182.h5\n",
+      " Reading W181 from /home/lorenzo/nuclear_data/endfb80_hdf5/W181.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Hf181 at\n",
+      "          1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Hf181 at\n",
+      "          2500K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 600K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Hf182 at\n",
+      "          1200K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Hf182 at\n",
+      "          2500K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading W185 from /home/lorenzo/nuclear_data/endfb80_hdf5/W185.h5\n",
+      " Reading Re185 from /home/lorenzo/nuclear_data/endfb80_hdf5/Re185.h5\n",
+      " Reading Re186_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Re186_m1.h5\n",
+      " Reading Re187 from /home/lorenzo/nuclear_data/endfb80_hdf5/Re187.h5\n",
+      " Reading Os184 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os184.h5\n",
+      " Reading Os185 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os185.h5\n",
+      " Reading Os186 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os186.h5\n",
+      " Reading Os187 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os187.h5\n",
+      " Reading Os188 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os188.h5\n",
+      " Reading Os189 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os189.h5\n",
+      " Reading Os190 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os190.h5\n",
+      " Reading Os191 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os191.h5\n",
+      " Reading Os192 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os192.h5\n",
+      " Reading Ir191 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ir191.h5\n",
+      " Reading Ir192 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ir192.h5\n",
+      " Reading Ir193 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ir193.h5\n",
+      " Reading Ir194_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ir194_m1.h5\n",
+      " Reading Pt190 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt190.h5\n",
+      " Reading Pt191 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt191.h5\n",
+      " Reading Pt192 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt192.h5\n",
+      " Reading Pt193 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt193.h5\n",
+      " Reading Pt194 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt194.h5\n",
+      " Reading Pt195 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt195.h5\n",
+      " Reading Pt196 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt196.h5\n",
+      " Reading Pt197 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt197.h5\n",
+      " Reading Pt198 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt198.h5\n",
+      " Reading Au197 from /home/lorenzo/nuclear_data/endfb80_hdf5/Au197.h5\n",
+      " Reading Hg196 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg196.h5\n",
+      " Reading Hg197 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg197.h5\n",
+      " Reading Hg197_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg197_m1.h5\n",
+      " Reading Hg198 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg198.h5\n",
+      " Reading Hg199 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg199.h5\n",
+      " Reading Hg200 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg200.h5\n",
+      " Reading Hg201 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg201.h5\n",
+      " Reading Hg202 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg202.h5\n",
+      " Reading Hg203 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg203.h5\n",
+      " Reading Hg204 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg204.h5\n",
+      " Reading Tl203 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tl203.h5\n",
+      " Reading Tl204 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tl204.h5\n",
+      " Reading Tl205 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tl205.h5\n",
+      " Reading Pb204 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pb204.h5\n",
+      " Reading Pb205 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pb205.h5\n",
+      " Reading Pb206 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pb206.h5\n",
+      " Reading Pb207 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pb207.h5\n",
+      " Reading Pb208 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pb208.h5\n",
+      " Reading Bi209 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bi209.h5\n",
+      " Reading Bi210_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bi210_m1.h5\n",
+      " Reading Po208 from /home/lorenzo/nuclear_data/endfb80_hdf5/Po208.h5\n",
+      " Reading Po209 from /home/lorenzo/nuclear_data/endfb80_hdf5/Po209.h5\n",
+      " Reading Po210 from /home/lorenzo/nuclear_data/endfb80_hdf5/Po210.h5\n",
+      " Reading Ra223 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ra223.h5\n",
+      " Reading Ra224 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ra224.h5\n",
+      " Reading Ra225 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ra225.h5\n",
+      " Reading Ra226 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ra226.h5\n",
+      " Reading Ac225 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ac225.h5\n",
+      " Reading Ac226 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ac226.h5\n",
+      " Reading Ac227 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ac227.h5\n",
+      " Reading Th227 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th227.h5\n",
+      " Reading Th228 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th228.h5\n",
+      " Reading Th229 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th229.h5\n",
+      " Reading Th230 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th230.h5\n",
+      " Reading Th231 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th231.h5\n",
+      " Reading Th232 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th232.h5\n",
+      " Reading Th233 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th233.h5\n",
+      " Reading Th234 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th234.h5\n",
+      " Reading Pa229 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pa229.h5\n",
+      " Reading Pa230 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pa230.h5\n",
+      " Reading Pa231 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pa231.h5\n",
+      " Reading Pa232 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pa232.h5\n",
+      " Reading Pa233 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pa233.h5\n",
+      " Reading U230 from /home/lorenzo/nuclear_data/endfb80_hdf5/U230.h5\n",
+      " Reading U231 from /home/lorenzo/nuclear_data/endfb80_hdf5/U231.h5\n",
+      " Reading U232 from /home/lorenzo/nuclear_data/endfb80_hdf5/U232.h5\n",
+      " Reading U233 from /home/lorenzo/nuclear_data/endfb80_hdf5/U233.h5\n",
+      " Reading U237 from /home/lorenzo/nuclear_data/endfb80_hdf5/U237.h5\n",
+      " Reading U239 from /home/lorenzo/nuclear_data/endfb80_hdf5/U239.h5\n",
+      " Reading U240 from /home/lorenzo/nuclear_data/endfb80_hdf5/U240.h5\n",
+      " Reading U241 from /home/lorenzo/nuclear_data/endfb80_hdf5/U241.h5\n",
+      " Reading Np234 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np234.h5\n",
+      " Reading Np235 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np235.h5\n",
+      " Reading Np236 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np236.h5\n",
+      " Reading Np236_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np236_m1.h5\n",
+      " Reading Np237 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np237.h5\n",
+      " Reading Np238 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np238.h5\n",
+      " Reading Np239 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np239.h5\n",
+      " Reading Pu236 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu236.h5\n",
+      " Reading Pu237 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu237.h5\n",
+      " Reading Pu238 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu238.h5\n",
+      " Reading Pu239 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu239.h5\n",
+      " Reading Pu240 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu240.h5\n",
+      " Reading Pu241 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu241.h5\n",
+      " Reading Pu242 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu242.h5\n",
+      " Reading Pu243 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu243.h5\n",
+      " Reading Pu244 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu244.h5\n",
+      " Reading Pu245 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu245.h5\n",
+      " Reading Pu246 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu246.h5\n",
+      " Reading Am240 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am240.h5\n",
+      " Reading Am241 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am241.h5\n",
+      " Reading Am242 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am242.h5\n",
+      " Reading Am242_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am242_m1.h5\n",
+      " Reading Am243 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am243.h5\n",
+      " Reading Am244 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am244.h5\n",
+      " Reading Am244_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am244_m1.h5\n",
+      " Reading Cm240 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm240.h5\n",
+      " Reading Cm241 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm241.h5\n",
+      " Reading Cm242 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm242.h5\n",
+      " Reading Cm243 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm243.h5\n",
+      " Reading Cm244 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm244.h5\n",
+      " Reading Cm245 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm245.h5\n",
+      " Reading Cm246 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm246.h5\n",
+      " Reading Cm247 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm247.h5\n",
+      " Reading Cm248 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm248.h5\n",
+      " Reading Cm249 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm249.h5\n",
+      " Reading Cm250 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm250.h5\n",
+      " Reading Bk245 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk245.h5\n",
+      " Reading Bk246 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk246.h5\n",
+      " Reading Bk247 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk247.h5\n",
+      " Reading Bk248 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk248.h5\n",
+      " Reading Bk249 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk249.h5\n",
+      " Reading Bk250 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk250.h5\n",
+      " Reading Cf246 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf246.h5\n",
+      " Reading Cf247 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf247.h5\n",
+      " Reading Cf248 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf248.h5\n",
+      " Reading Cf249 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf249.h5\n",
+      " Reading Cf250 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf250.h5\n",
+      " Reading Cf251 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf251.h5\n",
+      " Reading Cf252 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf252.h5\n",
+      " Reading Cf253 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf253.h5\n",
+      " Reading Cf254 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf254.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " WARNING: Negative value(s) found on probability table for nuclide Cf250 at 250K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cf250 at 294K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cf250 at 900K\n",
+      " WARNING: Negative value(s) found on probability table for nuclide Cf250 at\n",
+      "          1200K\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Reading Es251 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es251.h5\n",
+      " Reading Es252 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es252.h5\n",
+      " Reading Es253 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es253.h5\n",
+      " Reading Es254 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es254.h5\n",
+      " Reading Es254_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es254_m1.h5\n",
+      " Reading Es255 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es255.h5\n",
+      " Reading Fm255 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fm255.h5\n",
+      " Maximum neutron transport energy: 20000000 eV for Li6\n",
+      " Initializing source particles...\n",
+      "\n",
+      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
+      "\n",
+      "  Bat./Gen.      k            Average k\n",
+      "  =========   ========   ====================\n",
+      "        1/1    0.60942\n",
+      "        2/1    0.89106\n",
+      "        3/1    0.97799\n",
+      "        4/1    0.99502\n",
+      "        5/1    0.99819\n",
+      "        6/1    1.00437\n",
+      "        7/1    0.99348\n",
+      "        8/1    1.00257\n",
+      "        9/1    1.01441\n",
+      "       10/1    1.01353\n",
+      "       11/1    1.00178\n",
+      "       12/1    1.01576\n",
+      "       13/1    1.01754\n",
+      "       14/1    1.00683\n",
+      "       15/1    1.00505\n",
+      "       16/1    0.99786\n",
+      "       17/1    1.01767\n",
+      "       18/1    1.00034\n",
+      "       19/1    1.00494\n",
+      "       20/1    1.01117\n",
+      "       21/1    1.00252\n",
+      "       22/1    1.00592    1.00422 +/- 0.00170\n",
+      "       23/1    1.01500    1.00781 +/- 0.00372\n",
+      "       24/1    1.01347    1.00923 +/- 0.00299\n",
+      "       25/1    1.01031    1.00944 +/- 0.00233\n",
+      "       26/1    1.00326    1.00841 +/- 0.00216\n",
+      "       27/1    1.00972    1.00860 +/- 0.00184\n",
+      "       28/1    1.01373    1.00924 +/- 0.00171\n",
+      "       29/1    1.00611    1.00889 +/- 0.00155\n",
+      "       30/1    1.01140    1.00914 +/- 0.00141\n",
+      "       31/1    0.99045    1.00744 +/- 0.00212\n",
+      "       32/1    1.00671    1.00738 +/- 0.00194\n",
+      "       33/1    1.01682    1.00811 +/- 0.00193\n",
+      "       34/1    1.00605    1.00796 +/- 0.00179\n",
+      "       35/1    1.00573    1.00781 +/- 0.00167\n",
+      "       36/1    1.00131    1.00741 +/- 0.00162\n",
+      "       37/1    1.00832    1.00746 +/- 0.00152\n",
+      "       38/1    1.01443    1.00785 +/- 0.00148\n",
+      "       39/1    1.00582    1.00774 +/- 0.00141\n",
+      "       40/1    0.98830    1.00677 +/- 0.00165\n",
+      "       41/1    0.99523    1.00622 +/- 0.00166\n",
+      "       42/1    1.00353    1.00610 +/- 0.00159\n",
+      "       43/1    1.00226    1.00593 +/- 0.00153\n",
+      "       44/1    1.01347    1.00624 +/- 0.00150\n",
+      "       45/1    1.00294    1.00611 +/- 0.00144\n",
+      "       46/1    1.01234    1.00635 +/- 0.00141\n",
+      "       47/1    1.01298    1.00660 +/- 0.00138\n",
+      "       48/1    1.00300    1.00647 +/- 0.00133\n",
+      "       49/1    1.01010    1.00659 +/- 0.00129\n",
+      "       50/1    0.99917    1.00635 +/- 0.00127\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 3.6500e+01 seconds\n",
+      "   Reading cross sections          = 1.4960e+01 seconds\n",
+      " Total time in simulation          = 7.9708e+02 seconds\n",
+      "   Time in transport only          = 7.9662e+02 seconds\n",
+      "   Time in inactive batches        = 6.4076e+01 seconds\n",
+      "   Time in active batches          = 7.3300e+02 seconds\n",
+      "   Time synchronizing fission bank = 1.9497e-01 seconds\n",
+      "     Sampling source sites         = 1.5892e-01 seconds\n",
+      "     SEND/RECV source sites        = 3.5173e-02 seconds\n",
+      "   Time accumulating tallies       = 1.2986e-01 seconds\n",
+      "   Time writing statepoints        = 1.1006e-02 seconds\n",
+      " Total time for finalization       = 9.3783e-05 seconds\n",
+      " Total time elapsed                = 8.3474e+02 seconds\n",
+      " Calculation Rate (inactive)       = 9363.88 particles/second\n",
+      " Calculation Rate (active)         = 1227.83 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 1.00599 +/- 0.00121\n",
+      " k-effective (Track-length)  = 1.00635 +/- 0.00127\n",
+      " k-effective (Absorption)    = 1.00602 +/- 0.00110\n",
+      " Combined k-effective        = 1.00598 +/- 0.00110\n",
+      " Leakage Fraction            = 0.00001 +/- 0.00000\n",
+      "\n",
+      " Creating state point openmc_simulation_n0.h5...\n",
+      "[openmc.deplete] t=432000.0 s, dt=432000 s, source=8000000.0\n",
+      " Maximum neutron transport energy: 20000000 eV for Li6\n",
+      " Initializing source particles...\n",
+      "\n",
+      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
+      "\n",
+      "  Bat./Gen.      k            Average k\n",
+      "  =========   ========   ====================\n",
+      "        1/1    0.59905\n",
+      "        2/1    0.89606\n",
+      "        3/1    0.95802\n",
+      "        4/1    0.99236\n",
+      "        5/1    0.99845\n",
+      "        6/1    0.99229\n",
+      "        7/1    0.98286\n",
+      "        8/1    0.97778\n",
+      "        9/1    0.99499\n",
+      "       10/1    1.01694\n",
+      "       11/1    1.00326\n",
+      "       12/1    1.01001\n",
+      "       13/1    0.98846\n",
+      "       14/1    1.01495\n",
+      "       15/1    1.00818\n",
+      "       16/1    1.00436\n",
+      "       17/1    0.99966\n",
+      "       18/1    1.00010\n",
+      "       19/1    1.00763\n",
+      "       20/1    1.00442\n",
+      "       21/1    0.98804\n",
+      "       22/1    0.99163    0.98983 +/- 0.00179\n",
+      "       23/1    1.00938    0.99635 +/- 0.00660\n",
+      "       24/1    1.01242    1.00037 +/- 0.00616\n",
+      "       25/1    1.00773    1.00184 +/- 0.00499\n",
+      "       26/1    0.99938    1.00143 +/- 0.00410\n",
+      "       27/1    1.00102    1.00137 +/- 0.00346\n",
+      "       28/1    1.00009    1.00121 +/- 0.00300\n",
+      "       29/1    0.98385    0.99928 +/- 0.00328\n",
+      "       30/1    0.99113    0.99847 +/- 0.00304\n",
+      "       31/1    0.99098    0.99779 +/- 0.00283\n",
+      "       32/1    0.99213    0.99731 +/- 0.00263\n",
+      "       33/1    0.99835    0.99739 +/- 0.00242\n",
+      "       34/1    0.99722    0.99738 +/- 0.00224\n",
+      "       35/1    0.98990    0.99688 +/- 0.00214\n",
+      "       36/1    1.00706    0.99752 +/- 0.00210\n",
+      "       37/1    0.99409    0.99732 +/- 0.00199\n",
+      "       38/1    0.99508    0.99719 +/- 0.00188\n",
+      "       39/1    0.99834    0.99725 +/- 0.00178\n",
+      "       40/1    1.00495    0.99764 +/- 0.00173\n",
+      "       41/1    0.99605    0.99756 +/- 0.00165\n",
+      "       42/1    1.01356    0.99829 +/- 0.00173\n",
+      "       43/1    1.00674    0.99866 +/- 0.00169\n",
+      "       44/1    1.01598    0.99938 +/- 0.00178\n",
+      "       45/1    1.00243    0.99950 +/- 0.00171\n",
+      "       46/1    0.99797    0.99944 +/- 0.00164\n",
+      "       47/1    1.01167    0.99989 +/- 0.00164\n",
+      "       48/1    1.00427    1.00005 +/- 0.00159\n",
+      "       49/1    1.01168    1.00045 +/- 0.00159\n",
+      "       50/1    1.00832    1.00071 +/- 0.00155\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 0.0000e+00 seconds\n",
+      "   Reading cross sections          = 0.0000e+00 seconds\n",
+      " Total time in simulation          = 1.1922e+03 seconds\n",
+      "   Time in transport only          = 1.1918e+03 seconds\n",
+      "   Time in inactive batches        = 2.9643e+02 seconds\n",
+      "   Time in active batches          = 8.9582e+02 seconds\n",
+      "   Time synchronizing fission bank = 1.6833e-01 seconds\n",
+      "     Sampling source sites         = 1.3534e-01 seconds\n",
+      "     SEND/RECV source sites        = 3.2420e-02 seconds\n",
+      "   Time accumulating tallies       = 1.2552e-01 seconds\n",
+      "   Time writing statepoints        = 1.0222e-02 seconds\n",
+      " Total time for finalization       = 1.2883e-04 seconds\n",
+      " Total time elapsed                = 1.1933e+03 seconds\n",
+      " Calculation Rate (inactive)       = 2024.11 particles/second\n",
+      " Calculation Rate (active)         = 1004.66 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 1.00028 +/- 0.00144\n",
+      " k-effective (Track-length)  = 1.00071 +/- 0.00155\n",
+      " k-effective (Absorption)    = 1.00027 +/- 0.00121\n",
+      " Combined k-effective        = 0.99976 +/- 0.00115\n",
+      " Leakage Fraction            = 0.00001 +/- 0.00000\n",
+      "\n",
+      " Creating state point openmc_simulation_n1.h5...\n",
+      "[openmc.deplete] t=864000.0 s, dt=2592000 s, source=8000000.0\n",
+      " Maximum neutron transport energy: 20000000 eV for Li6\n",
+      " Initializing source particles...\n",
+      "\n",
+      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
+      "\n",
+      "  Bat./Gen.      k            Average k\n",
+      "  =========   ========   ====================\n",
+      "        1/1    0.59951\n",
+      "        2/1    0.89383\n",
+      "        3/1    0.96249\n",
+      "        4/1    0.97631\n",
+      "        5/1    0.98446\n",
+      "        6/1    0.99669\n",
+      "        7/1    0.99834\n",
+      "        8/1    1.00950\n",
+      "        9/1    1.01054\n",
+      "       10/1    0.99328\n",
+      "       11/1    0.97327\n",
+      "       12/1    0.99273\n",
+      "       13/1    0.98182\n",
+      "       14/1    1.01219\n",
+      "       15/1    0.99945\n",
+      "       16/1    1.00978\n",
+      "       17/1    1.00249\n",
+      "       18/1    0.99919\n",
+      "       19/1    0.99317\n",
+      "       20/1    1.00091\n",
+      "       21/1    0.99973\n",
+      "       22/1    0.99912    0.99943 +/- 0.00030\n",
+      "       23/1    0.99789    0.99892 +/- 0.00054\n",
+      "       24/1    0.99658    0.99833 +/- 0.00070\n",
+      "       25/1    0.99600    0.99787 +/- 0.00071\n",
+      "       26/1    1.00965    0.99983 +/- 0.00205\n",
+      "       27/1    1.01420    1.00188 +/- 0.00269\n",
+      "       28/1    0.98691    1.00001 +/- 0.00299\n",
+      "       29/1    0.99490    0.99944 +/- 0.00269\n",
+      "       30/1    0.99755    0.99925 +/- 0.00242\n",
+      "       31/1    0.98216    0.99770 +/- 0.00268\n",
+      "       32/1    0.99123    0.99716 +/- 0.00251\n",
+      "       33/1    1.00182    0.99752 +/- 0.00233\n",
+      "       34/1    1.00869    0.99832 +/- 0.00230\n",
+      "       35/1    1.00672    0.99888 +/- 0.00222\n",
+      "       36/1    0.99972    0.99893 +/- 0.00207\n",
+      "       37/1    0.99412    0.99865 +/- 0.00197\n",
+      "       38/1    1.00012    0.99873 +/- 0.00186\n",
+      "       39/1    0.98822    0.99818 +/- 0.00184\n",
+      "       40/1    1.00183    0.99836 +/- 0.00176\n",
+      "       41/1    0.98764    0.99785 +/- 0.00175\n",
+      "       42/1    0.99374    0.99766 +/- 0.00168\n",
+      "       43/1    1.00587    0.99802 +/- 0.00164\n",
+      "       44/1    1.01337    0.99866 +/- 0.00170\n",
+      "       45/1    1.01178    0.99918 +/- 0.00171\n",
+      "       46/1    1.00958    0.99958 +/- 0.00169\n",
+      "       47/1    0.99599    0.99945 +/- 0.00163\n",
+      "       48/1    0.99515    0.99930 +/- 0.00158\n",
+      "       49/1    1.02132    1.00006 +/- 0.00170\n",
+      "       50/1    1.01018    1.00039 +/- 0.00168\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 0.0000e+00 seconds\n",
+      "   Reading cross sections          = 0.0000e+00 seconds\n",
+      " Total time in simulation          = 1.1960e+03 seconds\n",
+      "   Time in transport only          = 1.1955e+03 seconds\n",
+      "   Time in inactive batches        = 2.9597e+02 seconds\n",
+      "   Time in active batches          = 8.9999e+02 seconds\n",
+      "   Time synchronizing fission bank = 1.8753e-01 seconds\n",
+      "     Sampling source sites         = 1.4925e-01 seconds\n",
+      "     SEND/RECV source sites        = 3.7660e-02 seconds\n",
+      "   Time accumulating tallies       = 1.6143e-01 seconds\n",
+      "   Time writing statepoints        = 1.1208e-02 seconds\n",
+      " Total time for finalization       = 1.3444e-04 seconds\n",
+      " Total time elapsed                = 1.1971e+03 seconds\n",
+      " Calculation Rate (inactive)       = 2027.2 particles/second\n",
+      " Calculation Rate (active)         = 1000.01 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 1.00030 +/- 0.00176\n",
+      " k-effective (Track-length)  = 1.00039 +/- 0.00168\n",
+      " k-effective (Absorption)    = 1.00219 +/- 0.00116\n",
+      " Combined k-effective        = 1.00173 +/- 0.00101\n",
+      " Leakage Fraction            = 0.00001 +/- 0.00000\n",
+      "\n",
+      " Creating state point openmc_simulation_n2.h5...\n",
+      "[openmc.deplete] t=3456000.0 s, dt=2592000 s, source=8000000.0\n",
+      " Maximum neutron transport energy: 20000000 eV for Li6\n",
+      " Initializing source particles...\n",
+      "\n",
+      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
+      "\n",
+      "  Bat./Gen.      k            Average k\n",
+      "  =========   ========   ====================\n",
+      "        1/1    0.61123\n",
+      "        2/1    0.87158\n",
+      "        3/1    0.95155\n",
+      "        4/1    0.99351\n",
+      "        5/1    1.00301\n",
+      "        6/1    0.98470\n",
+      "        7/1    0.99388\n",
+      "        8/1    0.99495\n",
+      "        9/1    0.99603\n",
+      "       10/1    0.99768\n",
+      "       11/1    1.01248\n",
+      "       12/1    0.98342\n",
+      "       13/1    0.98357\n",
+      "       14/1    0.99239\n",
+      "       15/1    1.00545\n",
+      "       16/1    1.00092\n",
+      "       17/1    1.00569\n",
+      "       18/1    1.00036\n",
+      "       19/1    0.98902\n",
+      "       20/1    1.01742\n",
+      "       21/1    1.00107\n",
+      "       22/1    1.00187    1.00147 +/- 0.00040\n",
+      "       23/1    1.01306    1.00533 +/- 0.00387\n",
+      "       24/1    1.01023    1.00656 +/- 0.00300\n",
+      "       25/1    0.99202    1.00365 +/- 0.00372\n",
+      "       26/1    1.00845    1.00445 +/- 0.00314\n",
+      "       27/1    0.98179    1.00121 +/- 0.00419\n",
+      "       28/1    1.00492    1.00168 +/- 0.00366\n",
+      "       29/1    0.99114    1.00051 +/- 0.00343\n",
+      "       30/1    0.99859    1.00031 +/- 0.00307\n",
+      "       31/1    1.01276    1.00145 +/- 0.00300\n",
+      "       32/1    0.99187    1.00065 +/- 0.00285\n",
+      "       33/1    1.00283    1.00082 +/- 0.00263\n",
+      "       34/1    0.99591    1.00047 +/- 0.00246\n",
+      "       35/1    0.98841    0.99966 +/- 0.00243\n",
+      "       36/1    1.00272    0.99985 +/- 0.00228\n",
+      "       37/1    1.00289    1.00003 +/- 0.00215\n",
+      "       38/1    0.99722    0.99988 +/- 0.00203\n",
+      "       39/1    0.99767    0.99976 +/- 0.00192\n",
+      "       40/1    0.99660    0.99960 +/- 0.00183\n",
+      "       41/1    0.99972    0.99961 +/- 0.00174\n",
+      "       42/1    1.00816    1.00000 +/- 0.00171\n",
+      "       43/1    0.99989    0.99999 +/- 0.00163\n",
+      "       44/1    0.98888    0.99953 +/- 0.00163\n",
+      "       45/1    1.01053    0.99997 +/- 0.00162\n",
+      "       46/1    1.00289    1.00008 +/- 0.00156\n",
+      "       47/1    1.00280    1.00018 +/- 0.00151\n",
+      "       48/1    0.99904    1.00014 +/- 0.00145\n",
+      "       49/1    1.00821    1.00042 +/- 0.00143\n",
+      "       50/1    1.00049    1.00042 +/- 0.00138\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 0.0000e+00 seconds\n",
+      "   Reading cross sections          = 0.0000e+00 seconds\n",
+      " Total time in simulation          = 1.1738e+03 seconds\n",
+      "   Time in transport only          = 1.1734e+03 seconds\n",
+      "   Time in inactive batches        = 2.9980e+02 seconds\n",
+      "   Time in active batches          = 8.7403e+02 seconds\n",
+      "   Time synchronizing fission bank = 1.7583e-01 seconds\n",
+      "     Sampling source sites         = 1.3959e-01 seconds\n",
+      "     SEND/RECV source sites        = 3.5731e-02 seconds\n",
+      "   Time accumulating tallies       = 9.2453e-02 seconds\n",
+      "   Time writing statepoints        = 1.1671e-02 seconds\n",
+      " Total time for finalization       = 1.0126e-04 seconds\n",
+      " Total time elapsed                = 1.1750e+03 seconds\n",
+      " Calculation Rate (inactive)       = 2001.34 particles/second\n",
+      " Calculation Rate (active)         = 1029.71 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 1.00044 +/- 0.00132\n",
+      " k-effective (Track-length)  = 1.00042 +/- 0.00138\n",
+      " k-effective (Absorption)    = 0.99942 +/- 0.00116\n",
+      " Combined k-effective        = 0.99979 +/- 0.00108\n",
+      " Leakage Fraction            = 0.00001 +/- 0.00000\n",
+      "\n",
+      " Creating state point openmc_simulation_n3.h5...\n",
+      "[openmc.deplete] t=6048000.0 s, dt=2592000 s, source=8000000.0\n",
+      " Maximum neutron transport energy: 20000000 eV for Li6\n",
+      " Initializing source particles...\n",
+      "\n",
+      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
+      "\n",
+      "  Bat./Gen.      k            Average k\n",
+      "  =========   ========   ====================\n",
+      "        1/1    0.60920\n",
+      "        2/1    0.88238\n",
+      "        3/1    0.95497\n",
+      "        4/1    0.97401\n",
+      "        5/1    0.98079\n",
+      "        6/1    1.00531\n",
+      "        7/1    0.98860\n",
+      "        8/1    0.99611\n",
+      "        9/1    1.01045\n",
+      "       10/1    0.99669\n",
+      "       11/1    0.98725\n",
+      "       12/1    0.98875\n",
+      "       13/1    0.99812\n",
+      "       14/1    1.00441\n",
+      "       15/1    0.98289\n",
+      "       16/1    0.99336\n",
+      "       17/1    1.00776\n",
+      "       18/1    0.99696\n",
+      "       19/1    0.99040\n",
+      "       20/1    0.99449\n",
+      "       21/1    0.99139\n",
+      "       22/1    0.99801    0.99470 +/- 0.00331\n",
+      "       23/1    0.98397    0.99112 +/- 0.00405\n",
+      "       24/1    0.99908    0.99311 +/- 0.00349\n",
+      "       25/1    0.97751    0.98999 +/- 0.00413\n",
+      "       26/1    0.99818    0.99136 +/- 0.00364\n",
+      "       27/1    1.00552    0.99338 +/- 0.00368\n",
+      "       28/1    0.99768    0.99392 +/- 0.00323\n",
+      "       29/1    0.99227    0.99373 +/- 0.00286\n",
+      "       30/1    0.98219    0.99258 +/- 0.00280\n",
+      "       31/1    0.99263    0.99258 +/- 0.00254\n",
+      "       32/1    0.98981    0.99235 +/- 0.00233\n",
+      "       33/1    1.00244    0.99313 +/- 0.00228\n",
+      "       34/1    0.98570    0.99260 +/- 0.00217\n",
+      "       35/1    0.98772    0.99227 +/- 0.00205\n",
+      "       36/1    1.00208    0.99289 +/- 0.00201\n",
+      "       37/1    0.99180    0.99282 +/- 0.00189\n",
+      "       38/1    0.99222    0.99279 +/- 0.00178\n",
+      "       39/1    1.00119    0.99323 +/- 0.00174\n",
+      "       40/1    0.98831    0.99298 +/- 0.00167\n",
+      "       41/1    0.99386    0.99303 +/- 0.00159\n",
+      "       42/1    0.99413    0.99308 +/- 0.00152\n",
+      "       43/1    0.99594    0.99320 +/- 0.00146\n",
+      "       44/1    0.99504    0.99328 +/- 0.00140\n",
+      "       45/1    0.99611    0.99339 +/- 0.00134\n",
+      "       46/1    0.99503    0.99345 +/- 0.00129\n",
+      "       47/1    0.99561    0.99353 +/- 0.00125\n",
+      "       48/1    0.98118    0.99309 +/- 0.00128\n",
+      "       49/1    0.99013    0.99299 +/- 0.00124\n",
+      "       50/1    0.99924    0.99320 +/- 0.00121\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 0.0000e+00 seconds\n",
+      "   Reading cross sections          = 0.0000e+00 seconds\n",
+      " Total time in simulation          = 1.1641e+03 seconds\n",
+      "   Time in transport only          = 1.1637e+03 seconds\n",
+      "   Time in inactive batches        = 2.8967e+02 seconds\n",
+      "   Time in active batches          = 8.7445e+02 seconds\n",
+      "   Time synchronizing fission bank = 1.7591e-01 seconds\n",
+      "     Sampling source sites         = 1.4104e-01 seconds\n",
+      "     SEND/RECV source sites        = 3.4345e-02 seconds\n",
+      "   Time accumulating tallies       = 1.3762e-01 seconds\n",
+      "   Time writing statepoints        = 1.0267e-02 seconds\n",
+      " Total time for finalization       = 1.0079e-04 seconds\n",
+      " Total time elapsed                = 1.1652e+03 seconds\n",
+      " Calculation Rate (inactive)       = 2071.3 particles/second\n",
+      " Calculation Rate (active)         = 1029.21 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 0.99299 +/- 0.00117\n",
+      " k-effective (Track-length)  = 0.99320 +/- 0.00121\n",
+      " k-effective (Absorption)    = 0.99592 +/- 0.00103\n",
+      " Combined k-effective        = 0.99467 +/- 0.00090\n",
+      " Leakage Fraction            = 0.00001 +/- 0.00000\n",
+      "\n",
+      " Creating state point openmc_simulation_n4.h5...\n",
+      "[openmc.deplete] t=8640000.0 s, dt=15552000 s, source=8000000.0\n",
+      " Maximum neutron transport energy: 20000000 eV for Li6\n",
+      " Initializing source particles...\n",
+      "\n",
+      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
+      "\n",
+      "  Bat./Gen.      k            Average k\n",
+      "  =========   ========   ====================\n",
+      "        1/1    0.59395\n",
+      "        2/1    0.87478\n",
+      "        3/1    0.94880\n",
+      "        4/1    0.97846\n",
+      "        5/1    0.98156\n",
+      "        6/1    0.99089\n",
+      "        7/1    0.99707\n",
+      "        8/1    0.99833\n",
+      "        9/1    0.98616\n",
+      "       10/1    0.99305\n",
+      "       11/1    1.01009\n",
+      "       12/1    1.00400\n",
+      "       13/1    1.00343\n",
+      "       14/1    1.01125\n",
+      "       15/1    0.99997\n",
+      "       16/1    0.99101\n",
+      "       17/1    0.98479\n",
+      "       18/1    1.00612\n",
+      "       19/1    0.98939\n",
+      "       20/1    1.00571\n",
+      "       21/1    0.99917\n",
+      "       22/1    0.98535    0.99226 +/- 0.00691\n",
+      "       23/1    0.97807    0.98753 +/- 0.00619\n",
+      "       24/1    0.98580    0.98710 +/- 0.00440\n",
+      "       25/1    1.01019    0.99171 +/- 0.00574\n",
+      "       26/1    0.99703    0.99260 +/- 0.00477\n",
+      "       27/1    0.99612    0.99310 +/- 0.00406\n",
+      "       28/1    1.00438    0.99451 +/- 0.00379\n",
+      "       29/1    1.00792    0.99600 +/- 0.00366\n",
+      "       30/1    0.99877    0.99628 +/- 0.00328\n",
+      "       31/1    0.98893    0.99561 +/- 0.00304\n",
+      "       32/1    0.99071    0.99520 +/- 0.00281\n",
+      "       33/1    1.01313    0.99658 +/- 0.00293\n",
+      "       34/1    0.99102    0.99618 +/- 0.00274\n",
+      "       35/1    0.99006    0.99577 +/- 0.00258\n",
+      "       36/1    0.99945    0.99600 +/- 0.00243\n",
+      "       37/1    0.99126    0.99573 +/- 0.00230\n",
+      "       38/1    1.00254    0.99610 +/- 0.00220\n",
+      "       39/1    1.01296    0.99699 +/- 0.00226\n",
+      "       40/1    0.98790    0.99654 +/- 0.00219\n",
+      "       41/1    0.99339    0.99639 +/- 0.00209\n",
+      "       42/1    1.00031    0.99657 +/- 0.00200\n",
+      "       43/1    0.99268    0.99640 +/- 0.00192\n",
+      "       44/1    0.99117    0.99618 +/- 0.00185\n",
+      "       45/1    0.99900    0.99629 +/- 0.00178\n",
+      "       46/1    0.97923    0.99564 +/- 0.00183\n",
+      "       47/1    0.99626    0.99566 +/- 0.00176\n",
+      "       48/1    0.99515    0.99564 +/- 0.00170\n",
+      "       49/1    0.98663    0.99533 +/- 0.00167\n",
+      "       50/1    1.00867    0.99577 +/- 0.00167\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 0.0000e+00 seconds\n",
+      "   Reading cross sections          = 0.0000e+00 seconds\n",
+      " Total time in simulation          = 1.1668e+03 seconds\n",
+      "   Time in transport only          = 1.1664e+03 seconds\n",
+      "   Time in inactive batches        = 2.9723e+02 seconds\n",
+      "   Time in active batches          = 8.6962e+02 seconds\n",
+      "   Time synchronizing fission bank = 1.7613e-01 seconds\n",
+      "     Sampling source sites         = 1.3993e-01 seconds\n",
+      "     SEND/RECV source sites        = 3.5642e-02 seconds\n",
+      "   Time accumulating tallies       = 1.1405e-01 seconds\n",
+      "   Time writing statepoints        = 1.0900e-02 seconds\n",
+      " Total time for finalization       = 9.8751e-05 seconds\n",
+      " Total time elapsed                = 1.1681e+03 seconds\n",
+      " Calculation Rate (inactive)       = 2018.65 particles/second\n",
+      " Calculation Rate (active)         = 1034.94 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 0.99546 +/- 0.00162\n",
+      " k-effective (Track-length)  = 0.99577 +/- 0.00167\n",
+      " k-effective (Absorption)    = 0.99554 +/- 0.00093\n",
+      " Combined k-effective        = 0.99558 +/- 0.00097\n",
+      " Leakage Fraction            = 0.00001 +/- 0.00000\n",
+      "\n",
+      " Creating state point openmc_simulation_n5.h5...\n",
+      "[openmc.deplete] t=24192000.0 s, dt=8208000 s, source=8000000.0\n",
+      " Maximum neutron transport energy: 20000000 eV for Li6\n",
+      " Initializing source particles...\n",
+      "\n",
+      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
+      "\n",
+      "  Bat./Gen.      k            Average k\n",
+      "  =========   ========   ====================\n",
+      "        1/1    0.60126\n",
+      "        2/1    0.87245\n",
+      "        3/1    0.95624\n",
+      "        4/1    0.97141\n",
+      "        5/1    0.99008\n",
+      "        6/1    0.98539\n",
+      "        7/1    0.97347\n",
+      "        8/1    0.98761\n",
+      "        9/1    0.98760\n",
+      "       10/1    0.98178\n",
+      "       11/1    0.98329\n",
+      "       12/1    0.98290\n",
+      "       13/1    0.99169\n",
+      "       14/1    0.98385\n",
+      "       15/1    0.98329\n",
+      "       16/1    0.98600\n",
+      "       17/1    1.01095\n",
+      "       18/1    0.99684\n",
+      "       19/1    0.96558\n",
+      "       20/1    0.97788\n",
+      "       21/1    0.99445\n",
+      "       22/1    0.98070    0.98757 +/- 0.00688\n",
+      "       23/1    0.97964    0.98493 +/- 0.00477\n",
+      "       24/1    0.98686    0.98541 +/- 0.00341\n",
+      "       25/1    0.98554    0.98544 +/- 0.00264\n",
+      "       26/1    0.98777    0.98583 +/- 0.00219\n",
+      "       27/1    0.98990    0.98641 +/- 0.00194\n",
+      "       28/1    0.99333    0.98727 +/- 0.00189\n",
+      "       29/1    0.99888    0.98856 +/- 0.00211\n",
+      "       30/1    0.99786    0.98949 +/- 0.00210\n",
+      "       31/1    0.98268    0.98887 +/- 0.00200\n",
+      "       32/1    0.99090    0.98904 +/- 0.00183\n",
+      "       33/1    0.99132    0.98922 +/- 0.00170\n",
+      "       34/1    0.97072    0.98790 +/- 0.00205\n",
+      "       35/1    0.97238    0.98686 +/- 0.00217\n",
+      "       36/1    0.97156    0.98590 +/- 0.00225\n",
+      "       37/1    0.99956    0.98671 +/- 0.00226\n",
+      "       38/1    0.96919    0.98574 +/- 0.00234\n",
+      "       39/1    0.99332    0.98613 +/- 0.00225\n",
+      "       40/1    0.97164    0.98541 +/- 0.00225\n",
+      "       41/1    0.99085    0.98567 +/- 0.00216\n",
+      "       42/1    0.98472    0.98563 +/- 0.00206\n",
+      "       43/1    0.99120    0.98587 +/- 0.00198\n",
+      "       44/1    0.99101    0.98608 +/- 0.00191\n",
+      "       45/1    0.98239    0.98593 +/- 0.00184\n",
+      "       46/1    0.99074    0.98612 +/- 0.00178\n",
+      "       47/1    0.98124    0.98594 +/- 0.00172\n",
+      "       48/1    0.98247    0.98581 +/- 0.00166\n",
+      "       49/1    0.99072    0.98598 +/- 0.00161\n",
+      "       50/1    0.99106    0.98615 +/- 0.00157\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 0.0000e+00 seconds\n",
+      "   Reading cross sections          = 0.0000e+00 seconds\n",
+      " Total time in simulation          = 1.1755e+03 seconds\n",
+      "   Time in transport only          = 1.1751e+03 seconds\n",
+      "   Time in inactive batches        = 2.9584e+02 seconds\n",
+      "   Time in active batches          = 8.7965e+02 seconds\n",
+      "   Time synchronizing fission bank = 1.7278e-01 seconds\n",
+      "     Sampling source sites         = 1.3887e-01 seconds\n",
+      "     SEND/RECV source sites        = 3.3359e-02 seconds\n",
+      "   Time accumulating tallies       = 1.4017e-01 seconds\n",
+      "   Time writing statepoints        = 1.1532e-02 seconds\n",
+      " Total time for finalization       = 1.0987e-04 seconds\n",
+      " Total time elapsed                = 1.1766e+03 seconds\n",
+      " Calculation Rate (inactive)       = 2028.14 particles/second\n",
+      " Calculation Rate (active)         = 1023.13 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 0.98672 +/- 0.00143\n",
+      " k-effective (Track-length)  = 0.98615 +/- 0.00157\n",
+      " k-effective (Absorption)    = 0.98630 +/- 0.00088\n",
+      " Combined k-effective        = 0.98668 +/- 0.00086\n",
+      " Leakage Fraction            = 0.00001 +/- 0.00000\n",
+      "\n",
+      " Creating state point openmc_simulation_n6.h5...\n",
+      "[openmc.deplete] t=32400000.0 (final operator evaluation)\n",
+      " Maximum neutron transport energy: 20000000 eV for Li6\n",
+      " Initializing source particles...\n",
+      "\n",
+      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
+      "\n",
+      "  Bat./Gen.      k            Average k\n",
+      "  =========   ========   ====================\n",
+      "        1/1    0.59303\n",
+      "        2/1    0.86391\n",
+      "        3/1    0.93364\n",
+      "        4/1    0.96444\n",
+      "        5/1    0.97242\n",
+      "        6/1    0.97278\n",
+      "        7/1    0.98454\n",
+      "        8/1    0.97761\n",
+      "        9/1    0.98113\n",
+      "       10/1    0.98741\n",
+      "       11/1    0.98869\n",
+      "       12/1    0.98135\n",
+      "       13/1    0.98958\n",
+      "       14/1    0.98251\n",
+      "       15/1    0.97528\n",
+      "       16/1    0.98259\n",
+      "       17/1    0.99675\n",
+      "       18/1    0.98021\n",
+      "       19/1    0.98825\n",
+      "       20/1    0.96487\n",
+      "       21/1    0.97379\n",
+      "       22/1    0.98972    0.98175 +/- 0.00797\n",
+      "       23/1    0.98301    0.98217 +/- 0.00462\n",
+      "       24/1    0.98410    0.98265 +/- 0.00330\n",
+      "       25/1    0.97599    0.98132 +/- 0.00288\n",
+      "       26/1    0.97146    0.97968 +/- 0.00287\n",
+      "       27/1    0.98196    0.98000 +/- 0.00245\n",
+      "       28/1    0.98602    0.98076 +/- 0.00225\n",
+      "       29/1    0.97612    0.98024 +/- 0.00205\n",
+      "       30/1    0.98126    0.98034 +/- 0.00184\n",
+      "       31/1    0.99264    0.98146 +/- 0.00200\n",
+      "       32/1    0.97451    0.98088 +/- 0.00192\n",
+      "       33/1    0.98932    0.98153 +/- 0.00188\n",
+      "       34/1    0.98116    0.98150 +/- 0.00174\n",
+      "       35/1    0.97452    0.98104 +/- 0.00169\n",
+      "       36/1    0.98064    0.98101 +/- 0.00158\n",
+      "       37/1    0.97813    0.98084 +/- 0.00149\n",
+      "       38/1    0.98740    0.98121 +/- 0.00145\n",
+      "       39/1    0.99051    0.98170 +/- 0.00146\n",
+      "       40/1    0.98332    0.98178 +/- 0.00139\n",
+      "       41/1    0.98448    0.98191 +/- 0.00132\n",
+      "       42/1    0.97983    0.98181 +/- 0.00127\n",
+      "       43/1    0.97592    0.98156 +/- 0.00124\n",
+      "       44/1    1.00545    0.98255 +/- 0.00155\n",
+      "       45/1    0.99607    0.98309 +/- 0.00158\n",
+      "       46/1    1.00015    0.98375 +/- 0.00165\n",
+      "       47/1    0.97365    0.98338 +/- 0.00163\n",
+      "       48/1    0.98990    0.98361 +/- 0.00159\n",
+      "       49/1    0.98849    0.98378 +/- 0.00155\n",
+      "       50/1    1.00661    0.98454 +/- 0.00168\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 0.0000e+00 seconds\n",
+      "   Reading cross sections          = 0.0000e+00 seconds\n",
+      " Total time in simulation          = 1.2161e+03 seconds\n",
+      "   Time in transport only          = 1.2157e+03 seconds\n",
+      "   Time in inactive batches        = 3.0233e+02 seconds\n",
+      "   Time in active batches          = 9.1382e+02 seconds\n",
+      "   Time synchronizing fission bank = 1.9335e-01 seconds\n",
+      "     Sampling source sites         = 1.5557e-01 seconds\n",
+      "     SEND/RECV source sites        = 3.7010e-02 seconds\n",
+      "   Time accumulating tallies       = 1.3974e-01 seconds\n",
+      "   Time writing statepoints        = 1.3431e-02 seconds\n",
+      " Total time for finalization       = 1.3210e-04 seconds\n",
+      " Total time elapsed                = 1.2174e+03 seconds\n",
+      " Calculation Rate (inactive)       = 1984.6 particles/second\n",
+      " Calculation Rate (active)         = 984.875 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 0.98444 +/- 0.00153\n",
+      " k-effective (Track-length)  = 0.98454 +/- 0.00168\n",
+      " k-effective (Absorption)    = 0.98138 +/- 0.00115\n",
+      " Combined k-effective        = 0.98218 +/- 0.00120\n",
+      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
+      "\n",
+      " Creating state point openmc_simulation_n7.h5...\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = openmc.model.Model(geometry,mats,settings)\n",
+    "\n",
+    "#Depletion general settings\n",
+    "depletion_days = [5,5,30,30,30,180,95]\n",
+    "power = 8e6 #total thermal power [W]\n",
+    "salt_mass = 4590 * 10**3 #grams\n",
+    "salt_volume =salt_mass/salt_density #cm3\n",
+    "salt.volume = salt_volume\n",
+    "\n",
+    "# Initialize depletion operator\n",
+    "op = openmc.deplete.CoupledOperator(model, normalization_mode = \"energy-deposition\")\n",
+    "\n",
+    "# Initialize integrator object and start depletion calculation \n",
+    "integrator = openmc.deplete.PredictorIntegrator(op, depletion_days, timestep_units='d', power=power)\n",
+    "integrator.add_transfer_rate(salt, ['Xe','Kr'], 4.067e-5)\n",
+    "integrator.add_transfer_rate(salt, ['Se','Nb','Mo','Tc','Ru','Rh','Pd','Ag','Sb','Te'], 8.777e-3)\n",
+    "integrator.integrate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "41d076d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's store some results that will be used later for comparison\n",
+    "results = openmc.deplete.Results('depletion_results.h5')\n",
+    "t, k = results.get_keff()\n",
+    "n_xe = 0\n",
+    "n_kr = 0\n",
+    "for nuc,_ in openmc.data.isotopes('Xe'):\n",
+    "    n_xe += results.get_atoms(str(salt.id), nuc)[1]\n",
+    "for nuc,_ in openmc.data.isotopes('Kr'):\n",
+    "    n_kr += results.get_atoms(str(salt.id), nuc)[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "415f0334",
+   "metadata": {},
+   "source": [
+    "# Critical Factor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "826c26f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's convert time from sec to days \n",
+    "t /= (3600 * 24)\n",
+    "\n",
+    "plt.figure()\n",
+    "ax = plt.subplot()\n",
+    "k1, = ax.plot(t, [k[0] for k in k], 'o-', c='red', label='keff wo removal rates')\n",
+    "ax1 = ax.twinx()\n",
+    "n1, = ax1.plot(t, n_xe, '--', c='black', label='Xe wo removal rates')\n",
+    "n3, = ax1.plot(t, n_kr, c='black', label='Kr wo removal rates')\n",
+    "ax.set_xlabel('Time[d]')\n",
+    "ax.set_ylabel(r'$k_{eff}$', color='r')\n",
+    "ax.tick_params(axis='y', colors='red')\n",
+    "ax1.set_yscale('log')\n",
+    "ax1.set_ylabel('Nuclides [atoms]')\n",
+    "ax1.legend(handles=[k1, n1, n3])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc827041",
+   "metadata": {},
+   "source": [
+    "# Fission products\n",
+    "The removal rate for gaseous fission products has been tuned (see [here](https://info.ornl.gov/sites/publications/Files/Pub173113.pdf)) to obtain a Xenon poison fractions matching the measurements reported during the MSRE U235 operation, of 0.3%-0.4%. \n",
+    "\n",
+    "The Xenon poison fraction is defined as:\n",
+    "$FP = \\frac{\\Sigma_a^{135}Xe}{\\Sigma_a^{235}U}$\n",
+    "\n",
+    "Let's plot the same quantity and see if we obtain values that matches the reference :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dc07279e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Microscopic absorption cross section at 0.0253 eV\n",
+    "xs_xe135 = 2664214.0\n",
+    "xs_u235 = 686.006994850397\n",
+    "_, n_xe135 = results.get_atoms(str(salt.id), 'Xe135')\n",
+    "_, n_u235 = results.get_atoms(str(salt.id), 'U235')\n",
+    "# Poison fraction\n",
+    "pf = (xs_xe135*n_xe135)/(xs_u235*n_u235)*100\n",
+    "plt.figure()\n",
+    "plt.plot(t, pf)\n",
+    "plt.xlabel('Time [d]')\n",
+    "plt.ylabel('Xe posion fraction [%]')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d28beee",
+   "metadata": {},
+   "source": [
+    "# Inventory \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "aa32c623",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "inventory = dict()\n",
+    "for nuc,_ in openmc.data.isotopes('U'):\n",
+    "    inventory[nuc] = results.get_atoms(str(salt.id), nuc)[1] / openmc.data.AVOGADRO * openmc.data.atomic_mass(nuc) / 1000\n",
+    "\n",
+    "for nuc in ['Pu238','Pu239','Pu240','Pu241','Pu242']:\n",
+    "    inventory[nuc] = results.get_atoms(str(salt.id), nuc)[1] / openmc.data.AVOGADRO * openmc.data.atomic_mass(nuc) / 1000\n",
+    "\n",
+    "plt.figure()\n",
+    "for nuc, mass in inventory.items():\n",
+    "    plt.plot(t, mass, label=nuc)\n",
+    "plt.xlabel('Time [y]')\n",
+    "plt.ylabel('Mass [g]')\n",
+    "plt.yscale('log')\n",
+    "plt.ylim(1e-5)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7434b75d",
+   "metadata": {},
+   "source": [
+    "# Neutron absorption in the fuel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0c5bd844",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "import seaborn as sns\n",
+    "regex = re.compile(r'(\\d+|\\s+)')\n",
+    "\n",
+    "# All nuclides present in the fuel at last time-step\n",
+    "nucs = results.export_to_materials(-1)[0].get_nuclides()\n",
+    "\n",
+    "# Let's begin by making some useful groupings\n",
+    "gaseos = ['H', 'He', 'Ne', 'Ar', 'Kr', 'Xe', 'Rn'] #gaseous fission products\n",
+    "noble_metals = ['Se','Nb','Mo','Tc','Ru','Rh','Pd','Ag','Sb','Te'] # noble metals fission products\n",
+    "metals = ['Cr','Mn','Fe','Co','Ni','Cu','Zn','Hf','Zr','W',]\n",
+    "halogens = ['F','Cl','Br','I','At']\n",
+    "alkali_metals = ['Li','Na','K','Rb','Cs']\n",
+    "alkali_earths= ['Be','Mg','Ca','Sr','Ba','Ra']\n",
+    "lanthanides  = ['Y','La','Ce','Pr','Nd','Pm','Sm','Eu','Gd','Tb','Dy','Ho','Er','Tm','Yb','Lu']\n",
+    "m_a = ['Ac','Th','Pa','Np','Am','Cm','Bk','Cf','Es','Fm','Md','No','Lr']\n",
+    "# Get fissile nuclides in the fuel, based on Ronen's rule for determining fissile isotopes\n",
+    "fissile = []\n",
+    "\n",
+    "for nuc in nucs:\n",
+    "    elm = regex.split(nuc)[0]\n",
+    "    a = round(openmc.data.atomic_mass(nuc))\n",
+    "    z = openmc.data.ATOMIC_NUMBER[elm]\n",
+    "    if 90 <= z <= 100:\n",
+    "        ronen = 2*z -(a-z)\n",
+    "        if ronen in [41,43,45]:\n",
+    "            fissile.append(nuc) \n",
+    "\n",
+    "# Calculate totat absorption rate of fissile nuclides\n",
+    "tot_abs_rate = 0\n",
+    "for nuc in fissile:\n",
+    "    tot_abs_rate += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
+    "    tot_abs_rate += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
+    "\n",
+    "nuclides_stack = dict()\n",
+    "groups_stack = {'Gaseos':0, 'Noble metals':0, 'Metals':0, 'Halogens':0 , 'Alkali metals':0, 'Alkali earths':0, 'Lanthanides':0, 'MA':0, 'Others':0}\n",
+    "for nuc in nucs:\n",
+    "    \n",
+    "    if regex.split(nuc)[0] in ['U','Pu']:\n",
+    "        nuclides_stack[nuc] = results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
+    "        nuclides_stack[nuc] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
+    "        nuclides_stack[nuc] /= tot_abs_rate\n",
+    "        \n",
+    "        \n",
+    "    elif regex.split(nuc)[0] in gaseos:\n",
+    "        groups_stack['Gaseos'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
+    "        groups_stack['Gaseos'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
+    "    elif regex.split(nuc)[0] in noble_metals:\n",
+    "        groups_stack['Noble metals'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
+    "        groups_stack['Noble metals'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
+    "    elif regex.split(nuc)[0] in metals:\n",
+    "        groups_stack['Metals'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
+    "        groups_stack['Metals'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
+    "    elif regex.split(nuc)[0] in halogens:\n",
+    "        groups_stack['Halogens'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
+    "        groups_stack['Halogens'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
+    "    elif regex.split(nuc)[0] in alkali_metals:\n",
+    "        groups_stack['Alkali metals'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
+    "        groups_stack['Alkali metals'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
+    "    elif regex.split(nuc)[0] in alkali_earths:\n",
+    "        groups_stack['Alkali earths'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
+    "        groups_stack['Alkali earths'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
+    "    elif regex.split(nuc)[0] in lanthanides:\n",
+    "        groups_stack['Lanthanides'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
+    "        groups_stack['Lanthanides'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
+    "    elif regex.split(nuc)[0] in m_a:\n",
+    "        groups_stack['MA'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
+    "        groups_stack['MA'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
+    "    else:\n",
+    "        groups_stack['Others'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
+    "        groups_stack['Others'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
+    "\n",
+    "# Divide each array by the total absorption reaction rate of fissile nuclides\n",
+    "for g in groups_stack.keys():\n",
+    "    groups_stack[g] /= tot_abs_rate\n",
+    "\n",
+    "# Sort dictionary groups\n",
+    "groups_stack=dict(reversed(sorted(groups_stack.items(), key=lambda item: item[1][len(item)])))\n",
+    "\n",
+    "# Create red color palette for groups_stack\n",
+    "colors = list(reversed(sns.color_palette(\"Reds\", len(groups_stack))))\n",
+    "\n",
+    "# Order uranium series\n",
+    "u_series = {key:value for key,value in nuclides_stack.items() if key.startswith('U')}\n",
+    "u_series = dict(reversed(sorted(u_series.items(), key=lambda item: item[1][len(item)])))\n",
+    "# Create green color palette for Uranium isotopes\n",
+    "colors += list(reversed(sns.color_palette(\"Greens\", len(u_series))))\n",
+    "\n",
+    "# Order plutionium series\n",
+    "pu_series = {key:value for key,value in nuclides_stack.items() if key.startswith('Pu')}\n",
+    "pu_series = dict(reversed(sorted(pu_series.items(), key=lambda item: item[1][len(item)])))\n",
+    "# Create blue color palette for plutonium isotopes\n",
+    "colors += list(reversed(sns.color_palette(\"Blues\", len(pu_series))))\n",
+    "# Add uramium and plutonium series to the stack\n",
+    "groups_stack.update(u_series)\n",
+    "groups_stack.update(pu_series)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cc4b980e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,10))\n",
+    "plt.stackplot(t, groups_stack.values(), labels=groups_stack.keys(), \n",
+    "              edgecolor=\"black\", linewidth=0.5,colors=colors, alpha=0.8)\n",
+    "handles, labels = plt.gca().get_legend_handles_labels()\n",
+    "legend = plt.legend([handles[idx] for idx in list(reversed(np.arange(0,len(handles),1)))],\n",
+    "                            [labels[idx] for idx in list(reversed(np.arange(0,len(handles),1)))],\n",
+    "                            bbox_to_anchor=(1.05,1), loc='upper left',\n",
+    "                            borderaxespad=0, ncol=2,\n",
+    "                            fontsize=15)\n",
+    "plt.xlabel('Time [d]',weight='bold',fontsize=17)\n",
+    "plt.title('Neutrons absorption distribution per neutron absorbed in fissile isotopes',\n",
+    "                   weight='bold', fontsize=17)\n",
+    "plt.xticks(fontsize=13)\n",
+    "plt.yticks(fontsize=13)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0c7c200",
+   "metadata": {},
+   "source": [
+    "Here we can visualize the neutrons absorption for each nuclide present in the fuel per neutrons absorption by fissile isotopes. In other words we can see, out of the total neutrons generated, where and how many we end up losing.\n",
+    "\n",
+    "It is useful to group together those isotopes with similar characteristics that individually wouldn't represent a big contribution to capture.\n",
+    "\n",
+    "Due to the low burnup and relatively short simulation time, we are far from equilibrium. However, we can notice the quick increase of absorption in the Pu isotopes, created from neutron capture of U238, and in particular of Pu239, due to its higher absorption cross section than U235.\n",
+    "\n",
+    "**Note**: Neutrons lost to leakage out of the core and capture in other isotopes other than fuel are not represented. Thus, approximately 1 neutron is missing from the counting (we know that a fission event releases approximately 2.3 neutrons). This is simply due to the fact that we have defined the fuel salt as our only depletable material."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}