*Edited by Anil Akdogan and Ali Serdar Vanli*

It is always hard to set manufacturing systems to produce large quantities of standardized parts. Controlling these mass production lines needs deep knowledge, hard experience, and the required related tools as well. The use of modern methods and techniques to produce a large quantity of products within productive manufacturing processes provides improvements in manufacturing costs and product quality. In order to serve these purposes, this book aims to reflect on the advanced manufacturing systems of different alloys in production with related components and automation technologies. Additionally, it focuses on mass production processes designed according to Industry 4.0 considering different kinds of quality and improvement works in mass production systems for high productive and sustainable manufacturing. This book may be interesting to researchers, industrial employees, or any other partners who work for better quality manufacturing at any stage of the mass production processes.

Published in London, UK © 2020 IntechOpen © Kwangmoozaa / iStock

Mass Production Processes

Mass Production Processes

*Edited by Anil Akdogan and Ali Serdar Vanli*


4. Implementation in MATLAB

The above equations can be implemented in MATLAB. The flow chart of the MATLAB program is shown in Figure 4. In the first step, the physical constant and parameters are defined, and the time array and droplet size are determined by the user.

function PWR\_Fission\_Product % MATLAB Program for In-containment Fission product program by Khurram Mehboob % Date : 08-07-2017 %================================================% clear; clc; clear all; %================================================ Global Hi Lr V S vd dec r Rr neu EI h Klcm Kgcm d Ea fr H y00 Q y t I Ac D Core\_I Cont\_A QQ f x fc B wx YY Sorc wx1 tn = input('Enter end time = tn = '); h = input('Enter stepsize = h = '): t = (0:h:tn); % time array for d1=100: 100: 1000; % particle diameter (microns) %=======Control Variables==================== d = d1\*1e-4; % particle diameter (cm) k=d1/100; % Droplet control Factors for printing fx = 0.20; % activity immediately available in the containment air fc = 0.35; % core damage fraction. H =10000; % partition coefficient for iodine Rr = 4.719; % Recirculation flow rate Lr = 14.15; % leakage rate wx = 0.01; % mixing rate

In the second step, the fixed variables are loaded from an input text file. The input text file contains the output data from the ORIGEN2.2 code that contains data for 84 different FPs.

load 'input.txt'

%=======Fixed variables==============

Figure 4. Flow diagram of computer program.

No\_iso = input2(5,1); % no of isotopes

V = input2(1,1); % free volume of the containment S = input2(2,1); % free surface of the containment

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Hi = input2(6,1); % height of spray system 40.0 m fr = input2(10,1); % Recirculation filtration efficiency Numerical Simulation of Fission Product Behavior Inside the Reactor Containment Building Using MATLAB http://dx.doi.org/10.5772/intechopen.70706 51

Figure 4. Flow diagram of computer program.

been adopted: (1) evaluation of activity in the core just before the accident and (2) kinetic quantification of airborne activity under confined conditions. The core activity has been evaluated at for one complete fuel cycle to get maximum core activity. The behavior of airborne FP activity has been quantified for loss of the coolant accident (LOCA) under NUREG-1465 [8] and regulatory guide 1.183 [32] assumptions. The developed model uses subroutine functions containing coupled ODEs and Runge–Kutta (RK) method. The ODEs (Eqs. (1)–(12)) are implemented in MATLAB. The system of ODEs (Eqs. (1), (3), (7), (8)) is coupled and solved numerically using the Runge–Kutta (RK) method in this

The RK numerical provides efficient time-domain solution, yielding static as well as dynamic values of FPAs corresponding to about 84 different dominant FPs. The computational cycle starts with the initialization of the variables with t = 0. In the time loop, the values of FPAs inside the containment building are calculated using RK scheme for each next time step. The

The above equations can be implemented in MATLAB. The flow chart of the MATLAB program is shown in Figure 4. In the first step, the physical constant and parameters are

% MATLAB Program for In-containment Fission product program by Khurram Mehboob

Global Hi Lr V S vd dec r Rr neu EI h Klcm Kgcm d Ea fr H y00 Q y t I Ac D Core\_I

tn = input('Enter end time = tn = '); h = input('Enter stepsize = h = '): t = (0:h:tn); % time array

In the second step, the fixed variables are loaded from an input text file. The input text file contains the output data from the ORIGEN2.2 code that contains data for 84 different FPs.

program allows performing these calculations for spray system operation.

defined, and the time array and droplet size are determined by the user.

%================================================%

%================================================

for d1=100: 100: 1000; % particle diameter (microns) %=======Control Variables==================== d = d1\*1e-4; % particle diameter (cm)

fc = 0.35; % core damage fraction.

Lr = 14.15; % leakage rate wx = 0.01; % mixing rate

H =10000; % partition coefficient for iodine Rr = 4.719; % Recirculation flow rate

k=d1/100; % Droplet control Factors for printing

fx = 0.20; % activity immediately available in the containment air

4. Implementation in MATLAB

50 Numerical Simulations in Engineering and Science

Cont\_A QQ f x fc B wx YY Sorc wx1

function PWR\_Fission\_Product

% Date : 08-07-2017

clear; clc; clear all;

program.

In the third step, the fixed variables for Eqs. (9)–(12) are solved for droplet by using the data listed in Table 3. The values of parameters (T, x, Ml, ul Sc, Re) are the constant variable for the static containment atmosphere.

%Initial Condition

end

if t <=700

else F= 0.35;

end

end

y00 = fx\*fc\*Ac/V; y0 = [y00, 0, 0];

[t,y] = odeRK4(@diffeq, tn, h, y0);

function dy = diffeq(t,y)

dy(2)= vd\*y(1)- r\*y(2); dy(3)= Q - ((EI\*H\*F)/V)\*y(3);

neq = length(y0);

h2=h/2; h3=h/3; h6=h/6;

y(1,:) = y0(:)';

for j=2:nt

%================================================

%================================================

%================================================

Cont\_A(i)= y00; % immediate released activity in containment

condition for containment spray is controlled in subroutine function here.

F = 0.0; % input2(8,1); % spray flow rate (0.1-2.0 m3/s)(950 m3/h=0.264m3/s)

global Lr V S vd dec r F EI H Rr fr fx Ac fc wx B wx1 Q Sorc

The subroutine function containing Eqs. (1), (3), (4) and (7) or (8) is depicted below. The

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dy(1)= -dec\*y(1)-(Lr/V)\*y(1)-vd\*(S/V)\*y(1)+r\*(S/V)\*y(2)-fr\*(Rr/V)\*y(1)-((F\*H\*EI)/V)\*y(3)+((1-fx)

The Range–Kutta fourth-order method is implemented by calling the odeRK4 subroutine function. The function is capable of solving N number of coupled ODEs at separate time steps.

YY(:,i)= y(:,1) ; % YY=y(length(y),:); I(i)= input2(i+10,1); % Atoms number D(i)= input2(i+10,2); % decay constant Core\_I(i)= input2(i+10,4); % Activity in the core

Q = ((1-fx)\*B\*Sorc\*(exp(-wx1\*t)- exp(-wx\*t)));

The implementation if RK4 method is shown below.

% implementation of Range kutta 4thorder method.

t = (0:h:tn); % time vector with spacing h nt = length(t); % no. of elements in vector t

y = zeros(nt, neq); % prelocation of y for speed

k1 = zeros(neq,1); k2= k1; k3 = k1; k4= k1; ytemp = k1;

\*B\*Ac\*(fc/V)\*((exp(-wx1\*t))-exp(-wx\*t)));

function [t,y] = odeRK4(diffeq,tn ,h, y0)

Next, the fixed variables are used to determine the dynamics variables (DL, Dmix, KG, KL). The Eqs. (9)–(12) are solved using the static data calculated above. Also, the kinetic release of FP is Q(t) that is determined as the function of time.

Next, the initial conditions for airborne and surface activities are implemented, for example, mv (t) = fx � ff � fp � fc � Ac/V g.m�3 and ms(t) = 0.0. The kinetic and static parameter values are implemented in coupled equation written as subfunction diffeq, and Rang–Kutta fourthorder method is implemented by calling odeRK4 subroutine function.

```
%Initial Condition
```
wx1 = wx/10.0; % mixing rate of fission products in coolant

In the third step, the fixed variables for Eqs. (9)–(12) are solved for droplet by using the data listed in Table 3. The values of parameters (T, x, Ml, ul Sc, Re) are the constant variable for the

c1 = T-281.615; c2 = (T-281.615)^2; c3 = 8078.4+ c2; c4 = c3^0.5; C = 2.1482\*(c1+c4)-120; ul = (100/C)\*2.41908832931\* 14.8816390000001; %conversion of Centipoise to g/cm.s

Next, the fixed variables are used to determine the dynamics variables (DL, Dmix, KG, KL). The Eqs. (9)–(12) are solved using the static data calculated above. Also, the kinetic release of

Next, the initial conditions for airborne and surface activities are implemented, for example, mv (t) = fx � ff � fp � fc � Ac/V g.m�3 and ms(t) = 0.0. The kinetic and static parameter values are implemented in coupled equation written as subfunction diffeq, and Rang–Kutta fourth-

DI2= (((7.4)\*(10^-8))\*((x\*Ml)^0.5)\*T)/(ul\*(v^0.6)) ; %diffusion coefficient if I2 (cm2/s) DIMix = 0.00035\*0.258064; %diffusion coeffi of I2 in steam ( cm2/s) Kgcm = (DIMix/d)\*(2.0+0.6\*(Re^(1/2))\*(Sc^(1/3))); % liquid phase trans coefficient ( cm/s ) Klcm = ((2/3)\*(3.1416^2)\*(DI2))/(d); % gas phase trans coefficient (cm/s)

vt = ((Re\*2387.4)/(d1))\* 0.508; % terminal velocity (cm/s) te = (40\*100)/(2\*vt); % exposure time (s)

EI = (1 - exp(-6\*(A/BB))); % FP removal rate (s-1)

Q = ((1-fx)\*B\*Sorc\*(exp(-wx1\*t)- exp(-wx\*t))); % kinetic source

order method is implemented by calling odeRK4 subroutine function.

Ac = input(i+10,4); % fission product activity in the core.

K = (wx\*wx1)/(wx-wx1); % normalization constant

for i = 1:No\_iso; % loop to read isotope data

%===================================%

52 Numerical Simulations in Engineering and Science

Ac = Ac\*input(i+10,j); % Ac\*ff\*fw

static containment atmosphere.

x = 2.6; % H2O

A = Kg\*(te); BB = d\*(H+(Kg/Kl));

Sorc= Ac\*fc/V;

dec = input2(i+10,2); % decay constant vd = input2(i+10,08); % deposition velocity r = input2(i+10,09); % resuspension rate neu = input2(i+10,10); % heap filter efficiency

T = 80+273.15; % Temperature (K)

Sc = 1.742; % Schmidt number. Re = 1.95; % Reynold number.

FP is Q(t) that is determined as the function of time.

Ml = 18.01528 ; % molar weight of solvent (g/mole ) v = 71.5; % molar volume of diffusing substance (I2)

for j = 6:7

end

%================================================ y00 = fx\*fc\*Ac/V; y0 = [y00, 0, 0]; %================================================ [t,y] = odeRK4(@diffeq, tn, h, y0); %================================================ YY(:,i)= y(:,1) ; % YY=y(length(y),:); I(i)= input2(i+10,1); % Atoms number D(i)= input2(i+10,2); % decay constant Core\_I(i)= input2(i+10,4); % Activity in the core Cont\_A(i)= y00; % immediate released activity in containment end

The subroutine function containing Eqs. (1), (3), (4) and (7) or (8) is depicted below. The condition for containment spray is controlled in subroutine function here.

```
function dy = diffeq(t,y)
global Lr V S vd dec r F EI H Rr fr fx Ac fc wx B wx1 Q Sorc
if t <=700
 F = 0.0; % input2(8,1); % spray flow rate (0.1-2.0 m3/s)(950 m3/h=0.264m3/s)
 else
 F= 0.35;
end
Q = ((1-fx)*B*Sorc*(exp(-wx1*t)- exp(-wx*t)));
dy(1)= -dec*y(1)-(Lr/V)*y(1)-vd*(S/V)*y(1)+r*(S/V)*y(2)-fr*(Rr/V)*y(1)-((F*H*EI)/V)*y(3)+((1-fx)
*B*Ac*(fc/V)*((exp(-wx1*t))-exp(-wx*t)));
dy(2)= vd*y(1)- r*y(2);
dy(3)= Q - ((EI*H*F)/V)*y(3);
end
```
The Range–Kutta fourth-order method is implemented by calling the odeRK4 subroutine function. The function is capable of solving N number of coupled ODEs at separate time steps. The implementation if RK4 method is shown below.

```
% implementation of Range kutta 4thorder method.
```

```
function [t,y] = odeRK4(diffeq,tn ,h, y0)
t = (0:h:tn); % time vector with spacing h
nt = length(t); % no. of elements in vector t
neq = length(y0);
y = zeros(nt, neq); % prelocation of y for speed
y(1,:) = y0(:)';
h2=h/2; h3=h/3; h6=h/6;
k1 = zeros(neq,1); k2= k1; k3 = k1; k4= k1; ytemp = k1;
for j=2:nt
```

```
told = t(j-1); yold = y(j-1,:)';
  k1= feval(diffeq,told, yold);
  for n= 1:neq
     ytemp(n) = yold(n) + h2*k1(n);
  end
  k2= feval(diffeq,told+ h2, ytemp);
  for n= 1:neq
     ytemp(n) = yold(n) + h2*k2(n);
  end
  k3= feval(diffeq,told +h2, ytemp);
  for n= 1:neq
     ytemp(n) = yold(n) + h*k3(n);
  end
  k4= feval(diffeq,told+h, ytemp);
  for n= 1:neq
     y(j,n)= yold(n)+h6*(k1(n)+k4(n))+h3*(k2(n)+k3(n));
  end
 end
end
```


**2. Materials and Methods**

#### *2.1. Sample Preparation*

Tissue samples were dissected on ice, weighted and placed in three volumes of 140 mM NaCl containing 10 mM Hepes-NaOH pH 7.4, 1 mM EDTA and 1 mM dithiothreitol. The tissues were homogenized using a Teflon pestle tissue grinder (5 passes) and centrifuged at 3000× *g* for 20 min. The supernatant was collected, filtered on the 0.45 μm pore membrane and stored at −85 ◦C until analysis. A fractionation procedure was also studied based on salting-out methodology. The homogenate sample (300 μL) was mixed with 400 μL of saturated NaCl (5 M) and 300 μL of 100% acetonitrile (ACN) for 10 min and centrifuged at 3000× *g* for 5 min. Polystyrene NPs readily partitions in the upper ACN layer and was collected for chromatographic analysis.


**Role of Cysteine Residue of Mutant Cu, Zn‐Superoxide Dismutase (SOD1) in the Pathogenesis of Amyotrophic Lateral Sclerosis (ALS)**

Seiichi Nagano and Toshiyuki Araki

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/63160

#### **Abstract**

[51] Amadio M, Bucolo C, Leggio GM, Drago F, Govoni S, Pascale A. The PKCbeta/HuR/ VEGF pathway in diabetic retinopathy. Biochem Pharmacol. 2010; 80:1230–7.

[52] Pillai RS, Bhattacharyya SN, Filipowicz W. Repression of protein synthesis by miRNAs:

[53] Filipowicz W, Bhattacharyya SN, Sonenberg N. Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat Rev Genet. 2008; 9:102–14.

[54] Wang Q, Wang Y, Minto AW, Wang J, Shi Q, Li X, Quigg RJ. MicroRNA-377 is upregulated and can lead to increased fibronectin production in diabetic nephropathy.

how many mechanisms? Trends Cell Biol. 2007; 17:118–26.

FASEB J. 2008; 22:4126–35.

86 Update on Amyotrophic Lateral Sclerosis

Mutations of *Cu, Zn‐superoxide dismutase (SOD1)* gene have been identified in a subset of familial amyotrophic lateral sclerosis (ALS). Conformational change, that is, misfolding, of mutant SOD1 underlies its toxic gain of function for motor neuronal degeneration. Mutant SOD1 is prone to cause oxidative stress through the copper exposed on the protein by misfolding. The protein structure of SOD1 is critically affected by the redox state of cysteine residues, especially of Cys111. Oxidative modification of Cys111, which is enhanced in mutant SOD1, causes destabilization of the dimer interface to promote misfolding and aggregation of the protein. Substitution of Cys111 to serine alleviated the degeneration of motor neurons as well as the misfolding and aggregate formation of mutant SOD1 in the spinal cord of transgenic mice. It indicates that Cys111 is a crucial residue for the pathogenesis of ALS by mutant SOD1.

**Keywords:** amyotrophic lateral sclerosis, Cu, Zn‐superoxide dismutase, cysteine resi‐ dues, oxidative stress, monomerized SOD1

#### **1. Introduction**

Amyotrophic lateral sclerosis (ALS) is a fatal degenerative disease of motor neurons, which affects skeletal muscle strength of the whole body. About 10% of ALS cases are affected in a familial trait, 25–30% of which are caused by mutations of *Cu, Zn‐superoxide dismutase (SOD1)* gene [1]. Although many causative genes of ALS have been identified so far, *SOD1* is still the second frequent responsible gene for ALS next to *C9orf72* [2]. Since the identification of the gene in 1993, research emphasis for ALS has been placed on uncovering the pathogenic

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

mechanism of motor neuronal death by the disease‐causing mutant SOD1. We review the recent concept of neuronal toxicity by mutant SOD1 in relation to the posttranslational modification of SOD1 at cysteine residues, especially at Cys111, which is closely related to its conformational change, in ALS pathogenesis.