**5.12** *Plutella xylostella* **(L.) (Lepidoptera: Plutellidae)**

**Identification:** Diamondback moth is small greyish with three white triangular spots along the inner-margin of the forewings. At rest the triangular markings of opposite wings appear as diamond shaped markings and hence the name. The larvae are pale green, body tapering slightly at both ends (TIFP, n.d.-b).

**Life cycle and damage**: females lay eggs on the lower leaves. The first instar larvae make tiny, irregular holes in the leaves. Diamondback moth is the most destructive pest of brassicasea throughout the world. Caterpillars feed on the foliage and make the leaves a withered appearance. Moths of later generations lay eggs higher on the plant and the larvae feed first on leaves, moving later to buds, flowers and developing seedpods. In later stages larvae bore holes in the leaves and the leaves may be eaten up completely. They also bores into pods and feeds developing seed (TIFP, n.d.-b).

**Monitoring:** monitor the presence of *P. xylostella* by pheromone traps or yellow sticky traps*.*  Traps should be checked weekly and count the number of moths (TIFP, n.d.-b).

**Economic and action thresholds:** economic threshold is when 25-30 larvae observed per square foot or 1-2 larvae per plant, and there is significant evidence of damage to flowers or pods (Knodel et al., 2010).

**Control**: bio-control agents such as *Cotesia plutellae* (Kurdjumov), *Diadegma mollipla*  (Holmgren), *Oomyzus sokolowski* (Kurdjumov)*,* and *Apanteles* sp. can control diamondback moth. When the threshold exceeded application of malathion, Trichlorfon, Endoslfan 35 EC, Diazinon 20 EC or triazophos is recommended (Manyangarirwa et al., 2009; TIFP, n.d.-b).


**Trends on Educational Gamification: Challenges and Learning Opportunities**

Editors

**Jos ´e Carlos Pi ˜nero Charlo Mar´ıa Teresa Costado Dios Enrique Carmona Medeiro Fernando Lloret**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Jose Carlos Pi ´ nero Charlo ˜ University of Cadiz Spain Fernando Lloret University of Cadiz

Mar´ıa Teresa Costado Dios University of Cadiz Spain

Enrique Carmona Medeiro University of Cadiz Spain

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

Spain

This is a reprint of articles from the Special Issue published online in the open access journal *Education Sciences* (ISSN 2227-7102) (available at: https://www.mdpi.com/journal/education/ special issues/Trends Educational Gamification).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-3540-1 (Hbk) ISBN 978-3-0365-3539-5 (PDF)**

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The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.


**12. Recommendations**

The curriculum specialists need to motivate and encourage science teachers by organizing seminars, workshops, and educational conferences to share science content knowledge, strategies for assessment, and resources to use practical activities.


**10. Conclusions**

Clearly, there is a large gap between the extremely simplified dynamics in this paper and more realistic dynamics based on, say, ordinary Newtonian physics or quantum mechanics. This is, for better or for worse, both the strength and the weakness of the combinatorial method presented here: extreme simplification may be the price we have to pay in order to see the forest in spite of all the trees.

In any case, the few simple examples in this paper should only be considered as a first step toward more realistic models. And in fact, when the object of study is something as enormously large as the multiverse, one should not expect a single method of attack to give all the answers. Rather, it can be expected that future developments will have to combine computer computations, heuristics, and exact mathematical methods in completely new ways.


**5. The adequacy of VR resources to equip students with required skills**

The lecturers were requested to explicate their experiences on whether or not there were adequate VR resources to equip pre-service teachers with required skills? Some of the lecturers' views are presented below:

Although globally, 4IR tools are increasingly being integrated into teaching and learning in education [1], the findings in theme 1 above indicate that this is not the case at the institution involved in this study. The lecturers stress that there is a lack of adequate VR resources, hence they are not yet ready to use these tools in their teaching and learning environments.


Meet the editor

Dr. Boris I. Lembrikov is a senior lecturer at the Holon Institute of Technology (HIT), Holon, Israel. He is a co-author of the book *Electrodynamics of Magneto-active Media*. He has published 44 papers in peer-reviewed international scientific journals, 15 chapters in scientific books, and 36 papers in conference proceedings. He is the editor of two books. Dr. Lembrikov's fields of research interest are nonlinear optics, optical communications, quantum

dot lasers and optical amplifiers, and plasmonics.

**Preface III**

Introduction **1**

**Chapter 1 3**

Mathematical Methods of Nonlinear Optical Phenomena Analysis **13**

**Chapter 2 15**

**Chapter 3 35**

**Chapter 4 53**

Optical Generation Processes **71**

**Chapter 5 73**

**Chapter 6 89**

*Gerardo J. Escalera Santos, Sergio Mendoza Vázquez, Elizeth Ramírez Álvarez,*

Three Solutions to the Nonlinear Schrödinger Equation for a Constant

Hydrodynamic Methods and Exact Solutions in Application to the

Polarization Properties of the Solitons Generated in the Process of

Towards Enhancing the Efficiency of Nonlinear Optical Generation

*by Ariel Flores Rosas, Orlando Díaz Hernández, Roberto Arceo,*

Pulse Breakup in Twisted Fiber Pumped by ns Pulses

*Christian I. Enriquez Flores and Evgeny Kuzin*

*by Padma Nilaya J. and Dhruba J. Biswas*

Introductory Chapter: Nonlinear Optical Phenomena

**Section 1**

**Section 2**

*by Jing Huang*

Potential

**Section 3**

*by Gabino Torres Vega*

*by Boris I. Lembrikov*

Contents

Nonlinear Schrödinger Equation

Electromagnetic Field Theory in Medium *by Sergey G. Chefranov and Artem S. Chefranov*


**2. CFD modeling**

A Reynolds-averaged Navier–Stokes Equations (RANSE) model, implemented in ANSYS Fluent, was used to solve the governing equations that can model turbulent flow over rough walls using modified wall function. The governing equations consist of averaged continuity and momentum equations, which for an incompressible flow without body forces, are given using tensor notation as described in Eqs. 5 and 6, respectively. Where *i*, *j* = 1, 2, 3, *Ui* is the mean velocity component, *P* is the mean pressure, *ρ* is the fluid density, *ν* is the kinematic viscosity, *u*0 *i* is the fluctuating velocity component, and *ui* 0*uj* 0 is the Reynolds stresses [37].

$$\frac{\partial U\_i}{\partial \mathbf{x}\_i} = \mathbf{0} \tag{5}$$

$$\frac{\partial U\_i}{\partial t} + \frac{\partial \left(U\_i U\_j\right)}{\partial \mathbf{x}\_j} = -\frac{1}{\rho} \frac{\partial P}{\partial \mathbf{x}\_i} + \frac{\partial}{\partial \mathbf{x}\_j} \left[\nu \left(\frac{\partial U\_i}{\partial \mathbf{x}\_j} + \frac{\partial U\_j}{\partial \mathbf{x}\_i}\right)\right] - \frac{\partial \left(\overline{u\_i u\_j}\right)}{\partial \mathbf{x}\_j} \tag{6}$$

The realizable k–ε with standard wall function turbulence model, which relates the Reynolds stresses to the mean flow properties, was used to close the system of Eqs. (5) and (6). The turbulence model is a two-equation model representing the transports of turbulence kinetic energy k and turbulence dissipation rate ε [38].

The roughness function (*ΔU*þ) model used in this study is that proposed by Cebeci and Bradshaw [39], whose model follows Nikuradse's uniform sand-grain roughness data [19]. Therefore, the roughness height utilized in this study is referred to as equivalent sand-grain roughness height *ks*. The generalized Cebeci and Bradshaw's roughness function model is given in Eq. (7), where *A* = 0, *k*þ *s*; *smooth* = 2.25, *k*þ *s*;*rough* = 90.00 and *Cs* = 0.253. In which the power *a* is given in Eq. (8).

$$
\Delta U^{+} = \begin{cases} 0 & \to k\_{s}^{+} \le k\_{s;smooth}^{+} \\\\ \frac{1}{\kappa} \ln \left[ A \left( \frac{k\_{s}^{+} / k\_{s;smooth}^{+}}{k\_{s;rough}^{+} - k\_{s;smooth}^{+}} \right) + C\_{s} k\_{s}^{+} \right]^{a} & \to k\_{s;smooth}^{+} < k\_{s}^{+} \le k\_{s;rough}^{+} \\\\ \frac{1}{\kappa} \ln \left( A + C\_{s} k\_{s}^{+} \right) & \to k\_{s}^{+} > k\_{s;rough}^{+} \end{cases} \tag{7}
$$

*The Phenomenon of Friction Resistance Due to Streamwise Heterogeneous Roughness with… DOI: http://dx.doi.org/10.5772/intechopen.99137*

$$a = \sin\left[\frac{\pi}{2} \frac{\log\left(k\_s^+/k\_{s;smooth}^+\right)}{\log\left(k\_{s;rough}^+/k\_{s;smooth}^+\right)}\right] \tag{8}$$

The boundary conditions were set for this study, as illustrated in **Figure 1a**. The inlet was velocity inlet where the free stream velocity prescribes the flow velocity *U*∞. The outlet was the pressure outlet set to be hydrostatic to ensure no upstream propagation of disturbances. The no-slip condition is applied on the plate's surface while the top and side boundaries are modeled as free-slip walls. The boundary conditions, governing, and turbulence modeling equations are discretized using a finite volume second-order method. These sets are then solved using a finite volume solver, utilizing a SIMPLE algorithm in which gradient calculations are carried out using the least-square cell-based method. The residual is set at 10�5 as a convergence criterion. For all simulations in this study, the plate length, fluid properties, and free stream velocity are kept constant. The plate length *L* is 30 m. The fluid is seawater with mass density ρ = 1025 kg/m3 and dynamic viscosity μ = 0.001077 kg/(ms). Finally, the free stream velocity is set at *U*∞ = 9.77 m/s (19 knots).

*The flow domain used in the simulations with three plate segments at the bottom boundary (a), and the hexahedron mesh with an exponential cell height gradation near the wall bottom boundary (b).*

In CFD modeling, the distance of the frame against the wall is set to decrease exponentially as it moves typically towards the wall., as shown in **Figure 1b**. A hexahedron-type mesh is chosen, and it is arranged manually with adjustable grid size. It is crucial to determine the mesh size near the wall to obtain an appropriate value for the dimensionless normal coordinate *y*þ, defined as *y*þ ¼ *uτy=ν*, where *y* is the outward wall-normal coordinate. To model the roughness effects correctly, the *y*þ value for the first cell center above the wall must be larger than the local roughness Reynolds number *k*þ *s* . To ensure this condition is always satisfied, ANSYS Fluent will virtually shift the wall if *y*þ <*k*þ *s* . For the roughness cases considered in this study, a blockage effect of 50% of the roughness height is assumed, and the corrected *y*þ value for the first cell center above the wall is given as *y*þ ¼ *y*þ þ *k*þ *s =*2. In this way, the singularity issue is avoided, and fine meshes can be handled correctly.

### **3. Surface roughness modeling**

In this study, we will investigate just single parameter variations, namely the roughness height *ks*. This section will explain details of the *ks* set up and their possible combinations. Four surface roughness with different *ks* values are considered in this study, namely, smooth wall (S), small roughness height (P), medium roughness height (Q), and high roughness height (R). All three combinations of roughness P, Q, and R are considered to form either homogeneous or inhomogeneous rough walled turbulent boundary layer flow. For example, a three-surface combination of PPP, QQQ, or RRR forms a homogeneous roughness, while a combination of PQR, PRQ, QPR, etc., forms an inhomogeneous roughness, where those are described in **Figure 2**. These *ks* values are specifically chosen so that the average height of three different *ks* of P = 81.25 μm, Q = 325.00 μm, and R = 568.75 μm will give an average height equal of Q, i.e., (81.25 μm + 325.00 μm + 568.75 μm)/ 3 = 325.00 μm. The selected *ks* values are also designed to simulate the various stages of ship-hull biofouling growth, ranging from light slime [16] to about small calcareous fouling [15].

#### **Figure 2.**

*Combinations of three plate segments resulting inhomogeneous (including fully smooth) and inhomogeneous rough surface conditions.*

*The Phenomenon of Friction Resistance Due to Streamwise Heterogeneous Roughness with… DOI: http://dx.doi.org/10.5772/intechopen.99137*


*2.2.2. New computational method - computational method of simulated annealing evolutionary computations*

The computational methods used to build the new MM-Models will be simulated annealing evolutionary computations (SAECs), where SAECs were got from the hybrid algorithms of (Abbass et al., 2003) by simply replacing the DG method by the SA algorithm of (Bagirov and Zhang, 2003) and numerical computational results show that SAECs can successfully pass the test of more than 40 well-known benchmark global optimization problems (Zhang, 2011c).

#### *2.2.3. New MM-models*

The atomic-resolution X-ray structure of 3NHD.pdb is a steric zipper, with strong vdw interactions between β-sheets and HBs to maintain the β-strands (Fig. 2).

By observations of the 3rd column of coordinates of 3NHD.pdb and Fig. 2, G(H) chains (i.e. β-sheet 2) of 3NHD.pdb can be calculated from A(B) chains (i.e. β-sheet 1) by Eq. 1 (where T is Transpose of column vector) and other chains can be calculated by Eqs. 2~3:

$$\mathbf{G(H) = ((-1, 0, 0)r, (0, 1, 0)r, (0, 0, -1)r)}\text{ A(B) + (-20.5865, 9.48, 0.0)r}\tag{1}$$

$$\mathbf{K(L) = G(H) + (0.0, 0.0, 9.59)^\gamma, I(f) = G(H) + (0.0, 0.0, -9.59)^\gamma} \tag{2}$$

$$\mathbf{C(D) = A(B) + (0.0, 0., 9.59)^{\mathrm{r}}}, \mathrm{E(F) = A(B) + (0.0, 0.0, -9.59)^{\mathrm{r}}}.\tag{3}$$

Basing on the template 3NHD.pdb from the Protein Data Bank, three prion AGAAAAGA palindrome amyloid fibril models - an AGAAAA model (Model 1), a GAAAAG model

Computational Potential Energy Minimization Studies on the Prion AGAAAAGA Amyloid Fibril Molecular Structures 301

300 Recent Advances in Crystallography

2011a).

2011c).

*2.2.1 New material*

*evolutionary computations*

*2.2.3. New MM-models*

SA-SDCG methods as in (Zhang, 2011a).

Gradient (DG (Bagirov et al., 2008)) method. Then the Models were optimized using SDCG-

Zhang et al. (2011, 2011d) used 3NHC.pdb, 3NVF/G/H/E.pdb templates to build several MM-Models (Figs. 9~11 in (Zhang et al., 2011b), and Figs. 5~8 in (Zhang, 2011b)). These Models were built in the use of canonical dual global optimization theory (Gao et al., 2012; Gao and Wu, 2012; Gao, 2000) and then refined by SDCG-SA-SDCG methods as in (Zhang,

This Chapter uses a suitable pdb file template 3NHD.pdb (the GYVLGS segment 127-132 from human prion with V129 (Apostol et al., 2010) from the Protein Data Bank to build MM-

The computational methods used to build the new MM-Models will be simulated annealing evolutionary computations (SAECs), where SAECs were got from the hybrid algorithms of (Abbass et al., 2003) by simply replacing the DG method by the SA algorithm of (Bagirov and Zhang, 2003) and numerical computational results show that SAECs can successfully pass the test of more than 40 well-known benchmark global optimization problems (Zhang,

The atomic-resolution X-ray structure of 3NHD.pdb is a steric zipper, with strong vdw

By observations of the 3rd column of coordinates of 3NHD.pdb and Fig. 2, G(H) chains (i.e. β-sheet 2) of 3NHD.pdb can be calculated from A(B) chains (i.e. β-sheet 1) by Eq. 1 (where T

Basing on the template 3NHD.pdb from the Protein Data Bank, three prion AGAAAAGA palindrome amyloid fibril models - an AGAAAA model (Model 1), a GAAAAG model

G(H) = ((-1, 0, 0)T, (0, 1, 0)T, (0, 0, -1)T) A(B) + (-20.5865, 9.48, 0.0)T, (1)

K(L) = G(H) + (0.0, 0.0, 9.59)T, I(J) = G(H) + (0.0, 0.0, -9.59)T, (2)

C(D) = A(B) + (0.0, 0., 9.59)T, E(F) = A(B) + (0.0, 0.0, -9.59)T. (3)

interactions between β-sheets and HBs to maintain the β-strands (Fig. 2).

is Transpose of column vector) and other chains can be calculated by Eqs. 2~3:

*2.2.2. New computational method - computational method of simulated annealing*

*2.1.3. Computational method of canonical dual global optimization theory*

**2.2. New material and method, and new MM-models**

models of AGAAAAGA amyloid fibrils for prions.

**Figure 2.** Protein fibril structure of human V129 prion GYVLGS (127–132) (PDB ID: 3NHD). The dashed lines denote the hydrogen bonds. A, B ... K, L denote the chains of the fibril.

(Model 2), and an AAAAGA model (Model 3) - will be successfully constructed in this Chapter. Because the template is a segment of 6 residues, the three shorter prion fragments are selected. This Chapter does not perform calculations on the full AGAAAAGA. Chains AB of Models 1~3 were respectively got from AB chains of 3NHD.pdb using the mutate module of the free package Swiss-PdbViewer (SPDBV Version 4.01) (http://spdbv.vital-it.ch). It is pleasant to see that almost all the hydrogen bonds are still kept after the mutations; thus we just need to consider the vdw contacts only. Making mutations for GH chains of 3NHD.pdb, we can get the GH chains of Models 1~3. However, the vdw contacts between Chain A and Chain G, between B chain and H chain are too far at this moment (Figs. 3~5).

**Figure 3.** At initial state, the vdw contacts between AB chains (β-sheet 1) and GH chains (β-sheet 2) of Model 1 are very far.

Seeing Figs. 3~5, we may know that for Model 1 at least 3 vdw interactions B6.ALA.CB-H3.ALA.CB-B4.ALA.CB-H5.ALA.CB should be maintained (their distances in Fig. 3 are 7.82, 8.36, 9.04 angstroms respectively), for Model 2 at least 3 vdw interactions G4.ALA.CB-

**Figure 4.** At initial state, the vdw contacts between AB chains (β-sheet 1) and GH chains (β-sheet 2) of Model 2 are very far.

**Figure 5.** At initial state, the vdw contacts between AB chains (β-sheet 1) and GH chains (β-sheet 2) of Model 3 are very far.

A3.ALA.CB-G2.ALA.CB-A5.ALA.CB should be maintained (their distances in Fig. 4 are 7.16, 7.43, 9.31 angstroms respectively), and for Model 3 at least 3 vdw interactions A1.ALA.CB-G4.ALA.CB-A3.ALA.CB-G2.ALA.CB should be maintained (their distances in Fig. 5 are 3.45, 7.16, 7.43 angstroms respectively). For Model 1, fixing the coordinates of B6.ALA.CB and B4.ALA.CB, letting the coordinates of H3.ALA.CB and H5.ALA.CB be variables, we may get a simple Lennard-Jones (LJ) potential energy minimization problem just with 6 variables (see Eq. 9). Similarly, for Model 2 fixing the coordinates of A3.ALA.CB and A5.ALA.CB, letting the coordinates of G4.ALA.CB and G2.ALA.CB be variables, we may get a simple LJ potential energy minimization problem just with 6 variables (see Eq. 10); for Model 3, fixing the coordinates of A1.ALA.CB and A3.ALA.CB, letting the coordinates of G4.ALA.CB and G2.ALA.CB be variables, we may get a simple LJ potential energy minimization problem with 6 variables (see Eq. 11).

The vdw contacts of atoms are described by the LJ potential energy:

$$\mathbf{V}\_{\rm t}(\mathbf{r}) = 4\varepsilon \left[ (\boldsymbol{\sigma}/\mathbf{r})^{\rm z} - (\boldsymbol{\sigma}/\mathbf{r})^{\rm s} \right] \tag{4}$$

where ε is the depth of the potential well and σ is the atom diameter; these parameters can be fitted to reproduce experimental data or deduced from results of accurate quantum chemistry calculations. The (σ/r)12 term describes repulsion and the -(σ/r)6 term describes attraction. If we introduce the coordinates of the atoms whose number is denoted by N and let ε=σ= 1 be the reduced units, the Eq. 4 becomes into

Computational Potential Energy Minimization Studies on the Prion AGAAAAGA Amyloid Fibril Molecular Structures 303

$$\mathbf{f}(\mathbf{x}) = \mathbf{4} \sum\_{\mathbf{i} \sim \mathbf{1}^{\mathsf{N}}} \sum\_{\mathbf{j} \sim \mathbf{1}^{\mathsf{N}}} \mathbf{1} \left( \mathbf{1}/\mathsf{t}\mathbf{i}^{6} - \mathbf{1}/\mathsf{t}\mathbf{i}^{3} \right),\tag{5}$$

where tij = (x3i−2 – x3j−2)2 + (x3i−1 – x3j−1)2 + (x3i – x3j)2, (x3i−2, x3i−1, x3i) is the coordinates of atom i, N≥2. The minimization of LJ potential f(x) on Rn (where n = 3N) is an optimization problem:

$$\min \text{ f(x)} \text{ subject to} \ge \text{€} \to \text{ $\mathbb{R}^{\mathbb{N}}$ .} \tag{6}$$

Similarly as Eq. 4, i.e. the potential energy for the vdw interactions between β-sheets:

$$\mathbf{V}\iota(\mathbf{r}) = \mathbf{A}/\mathbf{r}^{\text{r}2} - \mathbf{B}/\mathbf{r}^{\epsilon},\tag{7}$$

the potential energy for the HBs between the β-strands has the formula

302 Recent Advances in Crystallography

Model 2 are very far.

Model 3 are very far.

**Figure 4.** At initial state, the vdw contacts between AB chains (β-sheet 1) and GH chains (β-sheet 2) of

**Figure 5.** At initial state, the vdw contacts between AB chains (β-sheet 1) and GH chains (β-sheet 2) of

A3.ALA.CB-G2.ALA.CB-A5.ALA.CB should be maintained (their distances in Fig. 4 are 7.16, 7.43, 9.31 angstroms respectively), and for Model 3 at least 3 vdw interactions A1.ALA.CB-G4.ALA.CB-A3.ALA.CB-G2.ALA.CB should be maintained (their distances in Fig. 5 are 3.45, 7.16, 7.43 angstroms respectively). For Model 1, fixing the coordinates of B6.ALA.CB and B4.ALA.CB, letting the coordinates of H3.ALA.CB and H5.ALA.CB be variables, we may get a simple Lennard-Jones (LJ) potential energy minimization problem just with 6 variables (see Eq. 9). Similarly, for Model 2 fixing the coordinates of A3.ALA.CB and A5.ALA.CB, letting the coordinates of G4.ALA.CB and G2.ALA.CB be variables, we may get a simple LJ potential energy minimization problem just with 6 variables (see Eq. 10); for Model 3, fixing the coordinates of A1.ALA.CB and A3.ALA.CB, letting the coordinates of G4.ALA.CB and G2.ALA.CB be variables, we may get a simple LJ potential

VLJ(r) = 4ε [(σ/r)12 - (σ/r)6], (4)

where ε is the depth of the potential well and σ is the atom diameter; these parameters can be fitted to reproduce experimental data or deduced from results of accurate quantum chemistry calculations. The (σ/r)12 term describes repulsion and the -(σ/r)6 term describes attraction. If we introduce the coordinates of the atoms whose number is denoted by N and

energy minimization problem with 6 variables (see Eq. 11).

let ε=σ= 1 be the reduced units, the Eq. 4 becomes into

The vdw contacts of atoms are described by the LJ potential energy:

$$\mathbf{V}\_{\rm TH}(\mathbf{r}) = \mathbf{C}/\mathbf{r}^{12} - \mathbf{D}/\mathbf{r}^{10},\tag{8}$$

where A, B, C, D are given constants. Thus, the amyloid fibril molecular modeling problem can be deduced into the problem to solve the mathematical optimization problem Eq. 6. Seeing Fig. 6, we may know that the optimization problem Eq. 6 reaches its optimal value at the bottom of the LJ potential well, where the distance between two atoms equals to the sum of vdw radii of the atoms. In this Chapter, the sum of the

**Figure 6.** The Lennard-Jone Potential (Eqs.4 and 7) (This Fig. can be found in website homepage.mac.com/swain/CMC/DDResources/mol\_interactions/molecular\_interactions.html).

vdw radii is the twice of the vdw radius of Carbon atom, i.e. 3.4 angstroms. The optimization problem Eq. 6 is a nonconvex complex optimization problem. By the observation from Fig. 6, we may solve its simple but equal convex-and-smooth least square optimization problem (or the so-called distance geometry problem or sensor network problem) with a slight perturbation if data for three atoms violate the triangle inequality. The following three optimization problems for Models 1~3 respectively are:

$$\begin{array}{ll}\min f(\mathbf{x}) = \mathbb{I}\{ (\mathbf{x}\_{11} + 16.359)^2 + (\mathbf{x}\_{12}, 9.934)^2 + (\mathbf{x}\_{13} + 3.526)^2 - 3.42\}^2 + \mathbb{I}\{ (\mathbf{x}\_{21} + 9.726)^2 + (\mathbf{x}\_{22}, 9.530)^2 \} \\\ (8.530)^2 + (\mathbf{x}\_{23} + 3.613)^2 - 3.42\}^2 + \mathbb{I}\{ (\mathbf{x}\_{11} + 9.726)^2 + (\mathbf{x}\_{12}, 8.530)^2 + (\mathbf{x}\_{13} + 3.613)^2 - 3.42\}^2 \text{with} \\\ \text{initial solution (-12.928, 12.454, 3.034; -6.635, 14.301, 2.628), \end{array} \tag{9}$$

min f(x)= ½{ (x11+8.655)2 + (x12-8.153)2 + (x13-1.770)2 -3.42}2 + ½{ (x21+8.655)2 + (x22- 8.153)2 + (x23-1.770)2 -3.42}2 + ½{ (x21+2.257)2 + (x22-6.095)2 + (x23-3.078)2 -3.42}2 with initial solution (-13.909, 12.227, -0.889; -7.439, 14.419, -2.033), (10)

min f(x)= ½{ (x11+15.632)2 + (x12-9.694)2 + (x13-0.687)2 -3.42}2 + ½{ (x11+8.655)2 + (x12- 8.153)2 + (x13-1.770)2 -3.42}2 + ½{ (x21+8.655)2 + (x22-8.153)2 + (x23-1.770)2 -3.42}2 with initial solution (-13.909, 12.227, -0.889; -7.439, 14.419, -2.033). (11)

We may use any optimization algorithms or packages to easily solve problems Eqs. 9~11 and get their respective global optimal solutions (-13.062, 9.126, -3.336; -12.344, 6.695, -2.457), (-11.275, 6.606, 3.288; -5.461, 7.124, 2.424), (-12.149, 8.924, 1.229; -9.256, 11.007, 3.517), which were got by the SAEC algorithms in this Chapter. Input these global optimal solutions into Eq. 1, take average and tests then we get Eq. 12:

$$\mathbf{G(H) = ((-1, 0, 0)^r, (0, 1, 0)^r, (0, 0, -1)^r)}\text{ A(B) + (-20.2788, -0.0821, 0.5609)^r.} \tag{12}$$

By Eq. 12, we can get close vdw contacts in Figs. 7~9.

**Figure 7.** After LJ potential energy minimization, the vdw contacts of Model 1 become very closer (the distances are illuminated by the overlap of border of CB atoms' surface).

**Figure 8.** Fig. 8: After LJ potential energy minimization, the vdw contacts of Model 2 become very closer (the distances are illuminated by the overlap of border of CB atoms' surface).

From Figs. 3~5 to Figs. 7~9, we may see that the Optimization algorithm works and the computational experiences show us we had better at least define two sensors and two anchors in order to form a zipper between the two β-sheets. Next, in order to remove very close bad contacts, we relax Figs. 7~9 by a slight SDCG-Optimization in the use of Amber 11 (Case et al., 2010) and we get the optimized MM-Models 1~3. The other CDEF and LKJI chains can be got by parallelizing ABGH chains in the use of mathematical Eqs. 2~3. The new amyloid fibril models are useful for the drive to find treatments for prion diseases in the field of medicinal chemistry. The computational algorithms presented in this Chapter and their references therein are useful in materials science, drug design, etc.

**Figure 9.** After LJ potential energy minimization, the vdw contacts of Model 3 become very closer (the distances are illuminated by the overlap of border of CB atoms' surface).

Because Eqs. 9~11 are optimization problems with 6 variables only and these optimization problems are to minimize fourth-order polynomials, the proposed SAEC method and other computational methods can easily get the same optimal solutions to optimize the above three models.

#### *2.2.4. The practical LBFGS quasi-Newtonian method*

304 Recent Advances in Crystallography

Eq. 1, take average and tests then we get Eq. 12:

By Eq. 12, we can get close vdw contacts in Figs. 7~9.

distances are illuminated by the overlap of border of CB atoms' surface).

min f(x)= ½{ (x11+8.655)2 + (x12-8.153)2 + (x13-1.770)2 -3.42}2 + ½{ (x21+8.655)2 + (x22- 8.153)2 + (x23-1.770)2 -3.42}2 + ½{ (x21+2.257)2 + (x22-6.095)2 + (x23-3.078)2 -3.42}2 with initial solution (-13.909, 12.227, -0.889; -7.439, 14.419, -2.033),

min f(x)= ½{ (x11+15.632)2 + (x12-9.694)2 + (x13-0.687)2 -3.42}2 + ½{ (x11+8.655)2 + (x12- 8.153)2 + (x13-1.770)2 -3.42}2 + ½{ (x21+8.655)2 + (x22-8.153)2 + (x23-1.770)2 -3.42}2 with initial solution (-13.909, 12.227, -0.889; -7.439, 14.419, -2.033).

We may use any optimization algorithms or packages to easily solve problems Eqs. 9~11 and get their respective global optimal solutions (-13.062, 9.126, -3.336; -12.344, 6.695, -2.457), (-11.275, 6.606, 3.288; -5.461, 7.124, 2.424), (-12.149, 8.924, 1.229; -9.256, 11.007, 3.517), which were got by the SAEC algorithms in this Chapter. Input these global optimal solutions into

G(H) = ((-1, 0, 0)T, (0, 1, 0)T, (0, 0, -1)T) A(B) +(-20.2788, -0.0821, 0.5609)T. (12)

**Figure 7.** After LJ potential energy minimization, the vdw contacts of Model 1 become very closer (the

**Figure 8.** Fig. 8: After LJ potential energy minimization, the vdw contacts of Model 2 become very

From Figs. 3~5 to Figs. 7~9, we may see that the Optimization algorithm works and the computational experiences show us we had better at least define two sensors and two

closer (the distances are illuminated by the overlap of border of CB atoms' surface).

(10)

(11)

Energy minimization (EM), with the images at the endpoints fixed in space, of the total system energy provides a minimum energy path. EM can be done using SD, CG, and LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno). SD is robust and easy to implement but it is not most efficient especially when closer to minimum. CG is slower than SD in the early stages but more efficient when closer to minimum. The hybrid of SD-CG will make SD more efficient than SD or CG alone. However, CG cannot be used to find the minimization energy path, for example, when "forces are truncated according to the tangent direction, making it impossible to define a Lagrangian" (Chu et al., 2003). In this case, the powerful and faster quasi-Newtonian method (e.g. the LBFGS quasi-Newtonian minimizer) can be used (Chu et al., 2003; Liu and Nocedal, 1989; Nocedal and Morales, 2000; Byrd et al., 1995; Zhu et al., 1997). We briefly introduce the LBFGS quasi-Newtonian method as follows.

Newton's method in optimization explicitly calculates the Hessian matrix of the secondorder derivatives of the objective function and the reverse of the Hessian matrix (Dennis et al., 1996). The convergence of this method is quadratic, so it is faster than SD or CG. In high dimensions, finding the inverse of the Hessian is very expensive. In some cases, the Hessian is a non-invertible matrix, and furthermore in some cases, the Hessian is symmetric indefinite. Qusi-Newton methods thus appear to overcome all these shortcomings.

Quasi-Newton methods (a special case of variable metric methods) are to approximate the Hessian. Currently, the most common quasi-Newton algorithms are the SR1 formula, the BHHH method, the widespread BFGS method and its limited /low-memory extension LBFGS, and Broyden's methods (http://en.wikipedia.org/wiki/Quasi-Newton\_method). In Amber (Case et al., 2010) and Gromacs (van der Spoel et al., 2010), LBFGS is used, and the hybrid of LBFGS with CG - a Truncated Newton linear CG method with optional LBFGS Preconditioning (Nocedal and Morales, 2000) - is used in Amber (Case et al., 2010).

## **2.3. New thinking about the construction of 3D-structure of a protein**

If a NMR or X-ray structure of a protein has not been determined and stored in PDB bank yet, we still can easily get the 3D-structural frame of the protein. For example, before 2005 when we did not know the NMR structure of rabbit prion protein, we could get its homology model structure using the NMR structure of the human prion protein (PDB id: 1QLX) as the template (Zhang et al., 2006). We may use the homology structure to determine the 3D-structural frame of a protein when its NMR or X-ray structure has not been determined yet. The determination is an optimization problem described as follows.

"Very often in a structural analysis, we want to approximate a secondary structural element with a single straight line" (Burkowski, 2009: page 212). For example, Fig. 10 uses two straight lines that act as the longitudinal axis of β-Strand A (i.e. A chain), β-Strand B (i.e. B chain) respectively. Each straight line should be positioned among the Cα atoms so that it is closest to all these Cα atoms in a least-squares sense, which is to minimize the sum of the squares of the perpendicular

**Figure 10.** The 3D-structural frame of AB chains of Model 1 in Fig. 3 with two β-Strands.

distances (*di*) from the Cα atoms to the strand/helix axis:

$$\mathbf{S}^\* \mathbf{S}^\* = \min \mathbf{S} = \sum\_{l} \mathbf{N}^{\parallel} \parallel \mathbf{d}\_l \parallel \mathbf{2}. \tag{13}$$

Define the vector *w***=(***wx***,** *wy***,** *wz***)T** for the axis. Then *di* represents the perpendicular vector going from Cα atom *a* to the axis:

(16)

$$\begin{array}{rcl} \mid \mid d\_{l} \mid \mid \, ^{2} = \mid \mid \, a^{\otimes} \mid \mid ^{2} \sin \theta \rangle = \mid \mid \, a^{\otimes} \mid \mid \, ^{2} \left( \mathbf{1} \cdot \mathbf{cos} \, \theta \right) = \mid \mid \, a^{\otimes} \mid \mid \, ^{2} \left\{ \mathbf{1} \cdot \left( \mathbf{a}^{\otimes \top} \, \mathbf{w} \right) \neq \left( \mid \mid \, a^{\otimes} \mid \mid \, ^{2} \\\mid \mid \, \mathbf{w} \mid \mid \, ^{2} \right\} \right\} = \left\{ \mathbf{a}^{\otimes} \, ^{2} + \mathbf{a}^{\otimes} \, ^{2} + \mathbf{a}^{\otimes} \, ^{2} \right\} \left\{ \mathbf{1} \cdot \left( \mathbf{a}^{\otimes} \, \mathbf{w} \, \mathbf{z} + \mathbf{a}^{\otimes} \, \mathbf{w} \, \mathbf{y} + \mathbf{a}^{\otimes} \, \_{z} \mathbf{w} \, \mathbf{z} \right) \neq \left\{ \left( \mathbf{a}^{\otimes} \, \mathbf{z}^{\perp} + \mathbf{a}^{\otimes} \right) \neq \left( \mathbf{1} \cdot \mathbf{a}^{\otimes} \right) \right\} \right\}. \\ \mid \, \mathbf{a}^{\otimes} \mid \, ^{2} \left( \left( \mathbf{w} \, \mathbf{z}^{\perp} + \mathbf{w} \, \mathbf{y}^{\perp} + \mathbf{w} \, \mathbf{z}^{\perp} \right) \right) \}. \end{array}$$

According to Eqs. 13~14, for the β-Strand A – β-Strand B of AB chains, we get the following two optimization problems for Model 1 respectively:

min SA = ( (-16.196)2 + 8.3152 + 1.0612 ) { 1- (-16.196*wx* + 8.315*wy* + 1.061*wz* )2 /[( (- 16.196)2 + 8.3152 + 1.0612 ) (*wx* 2 + *wy* 2 + *wz* 2)]} +

306 Recent Advances in Crystallography

squares of the perpendicular

Quasi-Newton methods (a special case of variable metric methods) are to approximate the Hessian. Currently, the most common quasi-Newton algorithms are the SR1 formula, the BHHH method, the widespread BFGS method and its limited /low-memory extension LBFGS, and Broyden's methods (http://en.wikipedia.org/wiki/Quasi-Newton\_method). In Amber (Case et al., 2010) and Gromacs (van der Spoel et al., 2010), LBFGS is used, and the hybrid of LBFGS with CG - a Truncated Newton linear CG method with optional LBFGS

If a NMR or X-ray structure of a protein has not been determined and stored in PDB bank yet, we still can easily get the 3D-structural frame of the protein. For example, before 2005 when we did not know the NMR structure of rabbit prion protein, we could get its homology model structure using the NMR structure of the human prion protein (PDB id: 1QLX) as the template (Zhang et al., 2006). We may use the homology structure to determine the 3D-structural frame of a protein when its NMR or X-ray structure has not been determined yet. The determination is an optimization problem described as follows.

"Very often in a structural analysis, we want to approximate a secondary structural element with a single straight line" (Burkowski, 2009: page 212). For example, Fig. 10 uses two straight lines that act as the longitudinal axis of β-Strand A (i.e. A chain), β-Strand B (i.e. B chain) respectively. Each straight line should be positioned among the Cα atoms so that it is closest to all these Cα atoms in a least-squares sense, which is to minimize the sum of the

Preconditioning (Nocedal and Morales, 2000) - is used in Amber (Case et al., 2010).

**2.3. New thinking about the construction of 3D-structure of a protein**

**Figure 10.** The 3D-structural frame of AB chains of Model 1 in Fig. 3 with two β-Strands.

Define the vector *w***=(***wx***,** *wy***,** *wz***)T** for the axis. Then *di* represents the perpendicular vector

S\* = min S = ∑i=1 N ||*di*||2. (13)

distances (*di*) from the Cα atoms to the strand/helix axis:

going from Cα atom *a* to the axis:

 ( (-12.977)2 + 6.4602 + 1.9082 ) { 1- (-12.977*wx* + 6.460*wy* + 1.908*wz* )2 /[( (-12.977)2 + 6.4602 + 1.9082 ) (*wx* 2 + *wy* 2 + *wz* 2)]} +

 ( ( -9.178)2 + 6.7452 + 1.4482 ) { 1- ( -9.178*wx* + 6.745*wy* + 1.448*wz* )2 /[( (- 9.178)2 + 6.7452 + 1.4482 ) (*wx* 2 + *wy* 2 + *wz* 2)]} +

 ( ( -6.455)2 + 4.1122 + 1.5582 ) { 1- ( -6.455*wx* + 4.112*wy* + 1.558*wz* )2 /[( (-6.455)2 + 4.1122 + 1.5582 ) (*wx* 2 + *wy* 2 + *wz* 2)]} + (15)

 ( ( -3.006)2 + 5.7502 + 1.7822 ) { 1- ( -3.006*wx* + 5.750*wy* + 1.782*wz* )2 /[( (-3.006)2 + 5.7502 + 1.7822 ) (*wx* 2 + *wy* 2 + *wz* 2)]} +

 ( ( -1.226)2 + 2.7502 + 0.2332 ) { 1- ( -1.226*wx* + 2.750*wy* + 0.233*wz* )2 /[( (-1.226)2 + 2.7502 + 0.2332 ) (*wx* 2 + *wy* 2 + *wz* 2)]},

min SB = ( (-0.959)2 + 2.9502 +(- 4.817)2 ) { 1- (-0.959*wx* + 2.950*wy* -4.817*wz* )2 /[( (-0.959)2 + 2.9502 +(- 4.817)2 ) (*wx* 2 + *wy* 2 + *wz* 2)]} +

 ( (-3.465)2 + 4.9992 +(-2.846)2 ) { 1- (-3.465*wx* + 4.999*wy* -2.846*wz* )2 /[( (-3.465)2 + 4.9992 + (-2.846)2 ) (*wx* 2 + *wy* 2 + *wz* 2)]} +

 ( (-7.213)2 + 4.4122 +(-3.340)2 ) { 1- (-7.213*wx* + 4.412*wy* -3.340*wz* )2 /[( (-7.213)2 + 4.4122 + (-3.340)2 ) (*wx* 2 + *wy* 2 + *wz* 2)]} +

 ( (-9.954)2 + 7.0782 +(-3.168)2 ) { 1- (-9.954*wx* + 7.078*wy* -3.168*wz* )2 /[( (-9.954)2 + 7.0782 + (-3.168)2 ) (*wx* 2 + *wy* 2 + *wz* 2)]} +

 ( (-13.660)2 + 6.2412 +(-3.137)2 ) { 1- (-13.660*wx* + 6.241*wy* -3.137*wz* )2 /[( (-13.660)2 + 6.2412 + (-3.137)2 ) (*wx* 2 + *wy* 2 + *wz* 2)]} +

 ( (-16.702)2 + 8.5072 +(-3.074)2 ) { 1- (-16.702*wx* + 8.507*wy* -3.074*wz* )2 /[( (-16.702)2 + 8.5072 + (-3.074)2 ) (*wx* 2 + *wy* 2 + *wz* 2)]}.

We solve Eqs. 15~16 (taking the average of the coordinates of Cα atoms as initial solutions), getting their optimal solutions **w1** = (-10.751, 6.428, 1.411)T, **w2** = (-7.960, 4.579, -2.256)T respectively (Fig. 10). We may use the vectors **w1, w2** and Eq. 12 to construct Chains GH and then build an optimal Model 1 (Aqvist, 1986; Abagyan and Maiorov, 1988; Orengo et al., 1992; Young et al., 1999; Foote and Raman, 2000). In (Burkowski, 2009: pages 213-216), *wx* 2 + *wy* 2 + *wz* 2 =1 (i.e. *w* is a unit vector) is restrained and Eq. 13 becomes into a problem to seek the smallest eigenvalue (S\*) and its corresponding eigenvector **w** of the following matrix:

((∑i=1N(ay(i))2 + (az(i))2, -∑i=1Nax(i)ay(i) , -∑i=1Naz(i)ax(i))T, (-∑i=1Nax(i)ay(i) , ∑i=1N(az(i))2 + (ax(i))2, - ∑i=1Nay(i)az(i))T, (-∑i=1Naz(i)ax(i), -∑i=1Nay(i)az(i), ∑i=1N(ax(i))2 + (ay(i))2)T).

This matrix is symmetric and positive definite, and its eigenvectors form an orthogonal basis for the set of atoms under consideration. In physics, it is called the inertial tensor involving studies of rotational inertia and its eigenvectors are called the principle axes of inertia. Furthermore, we may also notice that Eq. 13 can be rewritten as

$$\min \left( \sum \mathbb{L}^{\perp\_1 \mathbb{N}} \mid \lvert \mathbf{d} \rvert \mid \mathbb{L} \right) \mathbb{z} \quad \text{subject to} \ \mathbf{w}^{\tau} \mathbf{w} \mathbf{w} \mathbf{1}, \tag{17}$$

where ||*di*||2 = (*a(i)x* 2 + *a(i)y* 2 + *a(i)z* 2) (*wx* 2 + *wy* 2 + *wz* 2) – ( *a(i)x wx* + *a(i)y wy* + *a(i)z wz* )2 . Thus, Eq. 17 can be easily solved by the canonical dual global optimization theory (Gao et al., 2012; Gao and Wu, 2012; Gao, 2000), by the ways of solving the canonical dual of Eq. 17 or solving the quadratic differential equations of the prime-dual Gao-Strang complementary function (Gao et al., 2012; Gao and Wu, 2012; Gao, 2000) through some ordinary or partial differential equation computational strategies.


**2.3 The process definition**

The definition of the high-level process map gives the team an eye of bird's view about the project. One of the most used high-level map is the SIPOC which details the Supplier, Input, Process, Output and Customers. By completing the SIPOC, the detailed map (Swimlane Map) can be developed after a series of Gemba walk and several discussions with the teamwork.

*Enhancement of Textile Supply Chain Performance through Optimal Capacity Planning DOI: http://dx.doi.org/10.5772/intechopen.96292*

**Figure 3.** *SIPOC diagram.*

The process definition of the SIPOC diagram, as detailed in **Figure 3**, ties the different steps of the new proposal solution to create the added value. In the current case, a new approach based on two level integrated productiondistribution plan and composed of two level of strategies (tactic and operational) is developed. Knowing that textile and apparel manufacturers are currently dealing with unpredictable and short-term ROs when production for the next season is already ongoing. The company uses two types of sub-contractors: overseas specialized subcontractors who offer products at very low prices but with long delivery times, and - local capacity subcontractors that the company uses in case of production saturation but who offer prices 20% higher than the internal production costs. In addition to this flexibility provided by subcontracting, the company can resort to overtime with higher costs than production in regular hours.


**3. Grid code for wind farms**

32 An Update on Power Quality

shown in Fig. 4.

turbine is allowed to disconnected from the grid.

connected to the grid for voltage dips as low as 5%.

**2.9. IEC recommendation**

of the wind and the risk of instability due to lower degree of controllability. Many countries in Europe and other parts of the world are developing or modifying interconnection rules and processes for wind power through a grid code. The grid codes have identified many potential adverse impacts of large scale integration of wind resources. The risk of voltage collapse for lack of reactive power support is one of the critical issues when it comes to contingencies in the power system. The low voltage ride through (LVRT) capability, which is one of the most demanding requirement that have been included in the grid codes and

It defines the operational boundary of a wind turbine connected to the network in terms of frequency, voltage tolerance, power factor, fault ride through is regarded as the main challenges to the wind turbine manufactures. The wind turbine should remain stable and connected during the fault while voltage at the PCC drop to 15% of the nominal value i.e. drops of 85% for the part of 150 msec. Only when the grid voltage fall below the curve, the

Significant barriers to interconnection are being perceived already with the requirements of the new grid codes and there it is a need for a better understanding of the factors affecting the behavior of the wind farm under severe contingencies such as voltage sags. Wind farms using squirrel cage induction generators directly connected to the network will suffer from the new demands, since they have no direct electrical control of torque or speed, and would usually disconnect from the power system when the voltage drops more than 10–20% below the rated value. In general, fulfillment of LVRT by reactive compensation will require fast control strategies for reactive power in wind turbines/farms with cage induction generators. The LVRT requirement, although details are differing from country to country, basically demands that the wind farm remains

For consistent and replicable documentation of power quality characteristic of wind turbine, the international Electro-technical Commission IEC-61400-21 was developed and today, most of the large wind turbine manufactures provide power quality characteristic data accordingly.IEC 61400-21 describe the procedures for determine the power quality characteristics of wind turbines. It is a guideline for power quality measurements of wind turbine. The methodology of IEC standard consists of three analyses. The first one is the flickers analyses. IEC 61400-21 specified a method that uses current and voltage time series measured at the wind turbine terminals to simulate the voltage fluctuation on a fictitious grid with no source of voltage fluctuations other that wind turbine switching operation. The second one is regarding the switching operation. The voltage and current transients are measured during the switching operation of wind turbine. The last one is the harmonic analysis which is carried out by FFT algorithms. Recently harmonic and inter harmonic are treated in the IEC 61000-4-7 and IEC 61000-3-6. The method for summing harmonics and The Electricity Grid Code is a regulation made by the Central Commission and it to be follow by various persons and participants in the system to plan, develop, maintain, and operate the power system grid in the most secure, reliable, economic and efficient manner, while facilitating healthy competition in the generation and supply of electricity.

The first grid code was focused on the distribution level, after the blackout in the United State in August 2003. The United State wind energy industry took a stand in developing its own grid code for contributing to a stable grid operation. The rules for realization of grid operation of wind generating system at the distribution network is defined as - per IEC-61400-21.The grid quality characteristics and limits are given for references that the customer and the utility grid may expect. According to Energy-Economic Law, the operator of transmission grid is responsible for the organization and operation of interconnected system. The grid code also covers some of the technical standards for connection to the grid.

To ensure the safe operation, integrity and reliability of the grid is utmost important. It is mentioned that reactive power compensation should ideally be provided locally by generating reactive power as close to the reactive power consumption as possible. The regional entity except generating stations, expected to provide local VAr compensation/generation such that they do not draw VArs from the grid, particularly under low-voltage condition. Indian grid code commission mentions that the charge for VArh shall be at the rate of 25 paise/kVArh w.e.f.1.4.2010, for VAr interchanges.

The wind farms must be able to run at rated voltage at a specified voltage range. The voltage range depends on the level of the voltage on the transmission system, which varies from country to country.

The wind farms shall have a closed loop voltage regulation system. The voltage regulation system shall act to regulate the voltage at the point by continuous modulation of the reactive power output within its reactive power range, and without violating the voltage step emissions.

Voltage fluctuations at a point of common coupling with a fluctuating load directly connected to the transmission system shall not exceed 3% at any time. The flicker contributions Pst and Plt are defined in IEC 61000-3-7 (Electromagnetic compatibility).

The wind turbine generator (WTG) shall be equipped with voltage and frequency relays for disconnection of the wind farm at abnormal voltages and frequencies. The relays shall be set according to agreements with the regional grid company and the system operator. Following are the technical requirements to be fulfilled to integrate the wind generation system.

Power Quality and Grid Code Issues in Wind Energy Conversion System 35

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[2] R.C. Bansal, Ahmed F. Zobaa, R.K. Saket, "Some issue related to power generation using wind energy conversion system: An overview" *Int. Journal of Emerging Electric*

[3] J.Charles Smith, Michael R. Milligan, Edgar A. DeMeo, "Utility wind integration and operating impact state of the art., *IEEE Trans on Energy Conversion* Vol.22, No.3, pp 900-

[4] Z.Sadd-Saoud, N. Jenkins, "Models for predicting flicker induced by large wind turbines" *IEEE Trans on Energy Conversion*, Vol.14, No.3, pp.743-751, Sept.1999. [5] Weihao Hu*,* Zhe Chen*,* Yue Wang and Zhan Wang "Flicker Mitigation by Active Power Control of Variable-Speed Wind Turbines With Full-Scale Back-to-Back Power Converters", *IEEE Trans on Energy Conversion,* Vol. 24, No. 3, pp.640-648, Sept. 2009 [6] F.Zhou, G.Joos, C.Abhey. "Voltage stability in weak connection wind farm." *IEEE PES*

[7] S.W.Mohod, M.V.Aware, "Power quality issues & its mitigation technique in wind generation, "*Proc. of IEEE Int. Conf. on Harmonics and Quality of Power* (ICHQP), pp. 1-6,

[8] S.Z. Djokic, J.V. Milanovic, "Power quality and compatibility levels: A general approach.", *IEEE Trans on Power Delivery,* Vol.22, No.3, pp.1857-1862, July. 2007 [9] Helder J. Azevedo, Jose M. Ferreiraa, Antonio P. Martins, Adriano S. Carvalho, "An active power filter with direct current control for power quality conditioning." *Electric*

[10] Z.Chen, E. Spooner, "Grid power quality with variable speed wind turbines", *IEEE*

[11] Dusan Graovac, Vladimir A. Katic, Alfred Rufer, "Power quality problems compensation with universal power quality conditioning system." *IEEE Trans on Power*

[12] Juan Manuel Carrasco, Leopoldo Garcia Franquelo, Jan. Bialasiewicz, "Power electronic system for the grid integration of renewable energy sources: A survey." *IEEE Trans on*

[13] "A report on Indian Wind Grid Code" – *Committee draft*, Version 1.0, pp 5-42 July 2009. [14] J.F.Conroy, R. Watson, "Low-voltage ride though of full converter wind turbine with permanent magnet generator", *Proc. IET Renew. Power Generation.* Vol.2, No.2, pp.113-

[15] Bousseau, P.: "Solution for the grid integration of wind farms – A survey", *Proc. of*

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on sustainable development, Vol.4, Issue-3, pp-346-352, Sept-2010.

**5. References**

907, August 2007.

Sept. 2008

122, 2008.

**Voltage Rise (u)** -The voltage rise at the point of common coupling can be approximated as a function of maximum apparent power Smax of the turbine, the grid impedances R and X at the point of common coupling and the phase angle . The Limiting voltage rise value is < 2%

**Voltage dips (d) -** The voltage dips is due to start up of wind turbine and it causes a sudden reduction of voltage. The acceptable voltage dips limiting value is 3 %.

**Flicker**-The measurements are made for maximum number of specified switching operation of wind turbine with 10-minutes period and 2-hour period are specified. The Limiting Value for flicker coefficient is about 0.4, for average time of 2 hours

```
Harmonics The THD limit for KV is  132 3%.
```
**Grid frequency-** The grid frequency in India is specified in the range of 47.5-51.5 Hz, for wind farm connection. The wind farm shall able to withstand change in frequency up to 0.5Hz/sec. Thus the requirements in the Grid Code can be fulfill the technical limits of the network.


*2.8. Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI TOF MS) Analysis*

The molecule weight of OVM was determined by MALDI-TOF MS (4800 Plus MALDI-TOF/TOF Analyzer, AB Science, Framingham, MA, USA) according to the method of Liu et al. [21]. OVM samples were dissolved in distilled water at 1:100. The matrix solution was prepared by the mixing of sinapic acid (5 mg/mL) in 50% acetonitrile with 0.1% TFA. The OVM solution was mixed with the matrix solution at a ratio of 1:1. The mixtures (2.0 μL) were then dropped onto the MALDI target plate and allowed to dry at room temperature before analysis.

#### *2.9. High Performance Liquid Chromatography Orbitrap Tandem Mass Spectrometry (HPLC Orbitrap MS/MS) Analysis*

The OVM samples were digested by trypsin at 37 ◦C for 24 h. The digested sample solution was separated via a nanoliter flow HPLC system (UltiMate 3000RSLCnano, Thermo Fisher Scientific, Waltham, MA, USA). Solution A was an aqueous solution with 0.1% formic acid. Solution B was acetonitrile binary solution with 0.1% formic acid and 84% acetonitrile. The digested sample solution was injected into a RP-C18 column to remove insoluble or impure substances and then separated by another RP-C18 column at a flow speed of 300 μL/min. Gradient elution was then carried out.

After separation, the eluant was injected into an LTQ-Orbitrap Fusion Velos mass spectrometer (Thermo Fisher Scientific; Waltham, MA, USA) for analysis by tandem mass spectrometry (MS/MS) to identify protein modification forms and sites with a positive ion detection mode. The precursor ions were subjected to high-energy collisional dissociation (HCD) fragmentation to detect fragment ions. Twenty fragment maps (MS/MS2 scans) showing the mass-to-charge ratio of the polypeptide and polypeptide fragments were collected at each full scan. Raw files were obtained from the corresponding database using Proteome Discoverer 1.4. Some parameters were set as follows: Enzymes, nonspecified; Missed cleavage, 2; Modification, carbamidomethyl, oxidation, acetylation, phosphorylation, sulfonation, methylation, ubiquitination, nitro.

#### *2.10. Statistical Analysis*

The values were expressed as means ± standard deviation from three separate experiments. The analysis was performed using SPSS version 20.0 (SPSS Inc., Chicago, IL, USA). Statistical data were determined based on a two-tailed *t*-test using standard deviations.


Conventional and Unconventional Transformation of Cocoa Pod Husks into Value-Added Products

*Martina Francisca Baidoo, Nana Yaw Asiedu, Lawrence Darkwah, David Arhin-Dodoo, Jun Zhao, Francois Jerome and Prince Nana Amaniampong*


**Author details**

Angeliki Antonakou1 \* and Dimitrios Papoutsis2

1 Midwifery Department, School of Health Science, International Hellenic University, Greece

2 Midwifery Department, School of Health Science, University of Western Macedonia, Greece

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. 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.


**8. Inferior Vena Cava (IVC) filters in cancer patients**

Treatment of VTE typically includes initial anticoagulation with unfractionated heparin, LMWH or a pentasaccharide like fondaparinux, (Buller et al., 2004) along with vitamin K antagonists like warfarin. Occasionally, specific clinical situations present in which the risk of PE is very high or systemic anticoagulation might be associated with high risk of bleeding; in these instances, IVC filters are utilized to provide mechanical thromboprophylaxis to prevent PE, the life-threatening complication of VTE. Such filters are inserted using a relatively noninvasive technique to maintain central flow. Thanks to newer technology, the IVC filters are becoming a very attractive option and can function with anticoagulation to optimize the prophylaxis strategy. Inferior Vena Cava filters are usually utilized in many clinical situations detailed in table-2 (Schwarz et al., 1996; Saour et al., 2009).

However, many of such indications are subjective and consensus might occasionally be difficult to reach. In a community-based study, researchers at McMaster University reviewed 1547 local county residents with confirmed diagnosis of acute VTE and without a prior IVC filter. Following the VTE, 203 (13.1%) patients had an IVC filter placed. In reviewing the indications for IVC filter placement, panel members unanimously agreed that the use of IVC filter was appropriate in 51% of the cases and inappropriate in 26%; no consensus was reached in the remaining 23% of the cases (Spencer et al., 2010).

The clinical benefit of IVC filter placement was addressed in one prospective trial (the PREPIC study) in which 400 patients with proximal DVT who were at risk for PE, were randomized to receive IVC filter (200 patients) or no filter (200 patients). Both groups were anticoagulated with LMWH or unfractionated heparin. At day 12, two (1.1%) patients assigned to receive filters, as compared with nine (4.8%) patients assigned to receive no filters, had symptomatic or asymptomatic PE (odds ratio, 0.22; 95 percent confidence interval, 0.05 to 0.90). However, at two years, 37 (20.8%) patients assigned to the filter group,

Several clinical trials have addressed the issue of VTE prophylaxis in such patients. One study showed a benefit in reducing VTE events when low fixed-dose warfarin (1mg/day) was used for prophylaxis (Bern et al., 1990). However, two subsequent clinical trials failed to

Low molecular weight heparin was also tried in two large, double-blind clinical trials (Verso et al., 2005; Karthaus et al., 2006). The first trial failed to show beneficial effect of enoxaparin when used at a dose of 40 mg once daily versus placebo in a group of 385 cancer patients with CVC (Verso et al., 2005). In the second trial, dalteparin at 5,000 units once daily was tested against placebo in 439 cancer patients who were receiving chemotherapy through such catheters; clinically relevant VTE occurred in 3.7% and 3.4% in the dalteparin and placebo recipients, respectively (Karthaus et al., 2006). Nadroparin, another LMWH, showed no advantage when tested against low fixed dose of warfarin (1 mg/day) in a small

Given the results of these studies, thromboprophylaxis with anticoagulants for patients with

Treatment of VTE typically includes initial anticoagulation with unfractionated heparin, LMWH or a pentasaccharide like fondaparinux, (Buller et al., 2004) along with vitamin K antagonists like warfarin. Occasionally, specific clinical situations present in which the risk of PE is very high or systemic anticoagulation might be associated with high risk of bleeding; in these instances, IVC filters are utilized to provide mechanical thromboprophylaxis to prevent PE, the life-threatening complication of VTE. Such filters are inserted using a relatively noninvasive technique to maintain central flow. Thanks to newer technology, the IVC filters are becoming a very attractive option and can function with anticoagulation to optimize the prophylaxis strategy. Inferior Vena Cava filters are usually utilized in many clinical situations detailed in table-2 (Schwarz et al., 1996; Saour et al.,

However, many of such indications are subjective and consensus might occasionally be difficult to reach. In a community-based study, researchers at McMaster University reviewed 1547 local county residents with confirmed diagnosis of acute VTE and without a prior IVC filter. Following the VTE, 203 (13.1%) patients had an IVC filter placed. In reviewing the indications for IVC filter placement, panel members unanimously agreed that the use of IVC filter was appropriate in 51% of the cases and inappropriate in 26%; no

The clinical benefit of IVC filter placement was addressed in one prospective trial (the PREPIC study) in which 400 patients with proximal DVT who were at risk for PE, were randomized to receive IVC filter (200 patients) or no filter (200 patients). Both groups were anticoagulated with LMWH or unfractionated heparin. At day 12, two (1.1%) patients assigned to receive filters, as compared with nine (4.8%) patients assigned to receive no filters, had symptomatic or asymptomatic PE (odds ratio, 0.22; 95 percent confidence interval, 0.05 to 0.90). However, at two years, 37 (20.8%) patients assigned to the filter group,

consensus was reached in the remaining 23% of the cases (Spencer et al., 2010).

show any benefit [Heaton et al., 2002: Couban et al., 2005).

central venous catheters is not recommended.

2009).

**8. Inferior Vena Cava (IVC) filters in cancer patients**

randomized trial that involved 45 evaluable patients (Mismetti et al., 2003).

as compared with 21 (11.6%) patients assigned to the no-filter group, had recurrent DVT (odds ratio, 1.87; 95% CI, 1.10 to 3.20) ( Decousus et al., 1998). This study was updated 8 years later; patients with IVC filters experienced a greater cumulative incidence of symptomatic DVT (35.7%versus 27.5%; HR 1.52, CI 1.02 to 2.27; *P* = 0.042), but significantly fewer symptomatic pulmonary emboli (6.2%versus 15.1%; HR 0.37, CI 0.17 to 0.79; *P* = 0.008) (The PREPIC Study Group, 2005). The conclusion from this long-term follow-up was similar to the original report; that is, with an IVC filter there is an equivalent trade-off of fewer PE at the cost of more DVTs. There was no difference in long-term morbidity or mortality in both groups.

IVC: Inferior Vena Cava, GI: Gastrointestinal, GU: Genitourinary, GYN: Gynecological, CNS: Central Nervous System, VTE: Venous Thromboembolism

Table 2. Indications for IVC filter placement

Given the lack of long term benefits of IVC filters; temporary, retrievable filters had gained increasing interest. Many different retrievable filters had recently received approval for temporary insertion. Recent data suggest that the use of these filters may be associated with low rates of PE and insertion complications (Imberti & Prisco, 2008). Nevertheless; no randomized clinical trials have been performed. In one large retrospective study that included 252 evaluable patients who had retrievable filter placed for different indications; only 47 filters were successfully retrieved yielding a retrieval rate of 18.7% ( Dabbagh et al., 2010). Similar or higher retrieval rates were reported by others (Mismetti et al., 2007).

Regardless of the type of the filter placed, the most recent American Colleague of Chest Physicians (ACCP) guidelines recommend systemic anticoagulation, when possible, even with the filter in place (Kearon et al., 2008).

Cancer itself, or its treatment, might result in certain clinical complications that make systemic anticoagulation very risky (Abdel-Razeq et al., 2011). Venous thromboembolic disease is a frequent complication in patients with intracranial malignancies. Many of the primary brain tumors like gliomas or secondary metastatic tumors to the brain are either bulky or very vascular thus increasing the risk of bleeding with or without systemic anticoagulation (Ruff & Posner, 1983). Brain metastases from melanoma, choriocarcinoma, thyroid carcinoma, and renal cell carcinoma have particularly high propensities for

Venous Thromboembolism Prophylaxis in Cancer Patients 121

primarily by the malignant process (Schunn et al., 2006). Researchers at M.D. Anderson Cancer center concluded, in a study that included 308 cancer patients with VTE and IVC filters, that such filters are safe and effective in preventing PE-related deaths in selected patients with cancer. However, patients with a history of DVT and bleeding or advanced

Despite its proven efficacy, VTE prophylaxis in cancer patients is clearly underutilized. Strategies to improve prophylaxis rate in such high risk patients are highly needed (Abdel-Razeq, 2010). Establishment of "VTE prophylaxis multidisciplinary team" addressing this issue supported by hospital administration might help. Recently, many health advocacy groups and policy makers are paying more attention to VTE prophylaxis. The National Quality Forum (NQF) recently endorsed strict VTE risk assessment evaluation for each patient upon admission and regularly thereafter (National Quality Forum (NQF), 2011). Additionally, the Joint Commission has recently approved new measure sets that included VTE prophylaxis; this standard mandates that a VTE prophylaxis method is in place within 24 hours of hospital admission, otherwise, a risk assessment and contraindications for prophylaxis should be documented for each and every hospitalized medical or surgical patient (The Joint Commission Manual for Performance Improvement Measures, 2011). Recently, Maynard and Stein (2011) have published their experience and recommendations following their extensive efforts to better utilize VTE prophylaxis in high-risk patients. Such

disease had the lowest survival after IVC filter placement (Wallace et al., 2004).

recommendations are worth careful attention and are summarized in table-3.

**Support by hospital administration for better VTE prophylaxis initiative. Establishment of VTE Prophylaxis "Multidisciplinary Team"; this team should:**

Report regularly to hospital administration or a "Quality Council".

Provide clear link between risk level and prophylaxis choice. Provide guidance to manage patients with contraindications. **Continuous education and training of all health care providers.**

Table 3. Strategies to improve VTE prophylaxis in high risk cancer patients.

Simple, yet efficient in daily use; two to three VTE risk levels are enough!

In conclusion, though published guidelines are somewhat different; hospitalized cancer patients, in the absence of bleeding or absolute contraindications, should be considered for thromboprophylaxis. Certain cancers, like Multiple Myeloma when treated with drugs like thalidomide or other immune modulators may benefit from prophylaxis. However, current guidelines do not recommend prophylaxis for ambulatory cancer patients or patients with

Extended thromboprophylaxis with LMWH (21-28days) should be considered in cancer

Standardize the process of providing VTE prophylaxis

Facilitates implementation of guidelines.

patients undergoing major pelvic/abdominal surgeries.

Audit and monitor outcomes.

**Better guidelines:**

central venous catheter.

**9. Conclusions and future directions**

spontaneous hemorrhage while metastatic tumors from sites like lung and breast are less likely to bleed spontaneously (Mandybur, 1993). However, not all patients with intracranial malignancies are at higher risk of bleeding with anticoagulation. Complication rate of IVC filters in patients with brain tumors is higher than commonly perceived and may outweigh the risk of anticoagulation. Researchers at Brigham and Women's Hospital in Boston reviewed the records of 49 patients with intracranial malignancies and venous thromboembolic disease to determine the effectiveness and complications resulting from systemic anticoagulation or IVC filter placement. Of the 42 patients received IVC filters, a strikingly high percentage (62%) developed one or more complications; 12% developed recurrent PE, while 57% developed filter thrombosis, recurrent DVT, or post-phlebitic syndrome. These complications severely reduced the quality of life of affected patients. Only 15 (31%) patients were treated with anticoagulation, and seven of these received it because of continued thromboembolic disease. None of these 15 patients had proven hemorrhagic complications (Levin et al., 1993).

Many recent studies questioned the need to insert IVC filters in cancer patients particularly those with advanced-stage disease whose survival is short and prevention of PE may be of little clinical benefit and could be a poor utilization of resources. In one retrospective study performed to determine the clinical benefit of IVC filter placement in patients with malignancy, 116 patients who had such filters inserted were included. Ninety one (78%) patients had stage IV disease, 42 (46%) of them died of cancer within 6 weeks and only16 (14%) were alive at one year (Jarrett et al., 2002).

The benefits of IVC filter placement on overall survival, as measured from the time of VTE was addressed in a recent retrospective study that examined 206 consecutive cancer patients with VTE. Patients were classified into 3 treatment groups: anticoagulation-only (n= 62), IVC filter-only (n=77), or combination of both IVC filter and anticoagulation (n=67). Median overall survival was significantly greater in patients treated with anticoagulation (13 months) compared with those treated with IVC filters (2 months) or combination of both (3.25 months; *P* < 0.0002). IVC patients were at 1.9 times more risk of death than anticoagulation only (hazard ratio=0.528; 95% confidence interval=0.374 to .745). Multivariate analysis revealed that performance status and type of thrombus were not confounders and had no effect on overall survival (Barginear et al., 2009).

In another study, the survival benefit of IVC filters in patients with late-stage malignancy was evaluated in a group of 5,970 patients who were treated with a primary diagnosis of malignancy at a tertiary care facility. Retrospective analysis identified 55 consecutive patients with stage III or IV malignant disease and VTE who received IVC filters. In a case control study, 16 patients with VTE but without IVC filter were matched for age, sex, type of malignancy, and stage of disease. Filter placement prevented PE in 52 (94.5%) patients, however, four (7.3%) of patients had complications related to the procedure; 13 (23.6%) patients with late-stage cancer survived less than 30 days following placement of the filter; another 13 (23.6%) patients of this group, however, survived more than one year. Ambulatory status differed significantly (*P* = 0.01) between these two subgroups. Authors concluded that IVC filter placement conferred no survival benefit compared to the control group and that the survival of such patients with advanced-stage cancer was limited

spontaneous hemorrhage while metastatic tumors from sites like lung and breast are less likely to bleed spontaneously (Mandybur, 1993). However, not all patients with intracranial malignancies are at higher risk of bleeding with anticoagulation. Complication rate of IVC filters in patients with brain tumors is higher than commonly perceived and may outweigh the risk of anticoagulation. Researchers at Brigham and Women's Hospital in Boston reviewed the records of 49 patients with intracranial malignancies and venous thromboembolic disease to determine the effectiveness and complications resulting from systemic anticoagulation or IVC filter placement. Of the 42 patients received IVC filters, a strikingly high percentage (62%) developed one or more complications; 12% developed recurrent PE, while 57% developed filter thrombosis, recurrent DVT, or post-phlebitic syndrome. These complications severely reduced the quality of life of affected patients. Only 15 (31%) patients were treated with anticoagulation, and seven of these received it because of continued thromboembolic disease. None of these 15 patients had proven hemorrhagic

Many recent studies questioned the need to insert IVC filters in cancer patients particularly those with advanced-stage disease whose survival is short and prevention of PE may be of little clinical benefit and could be a poor utilization of resources. In one retrospective study performed to determine the clinical benefit of IVC filter placement in patients with malignancy, 116 patients who had such filters inserted were included. Ninety one (78%) patients had stage IV disease, 42 (46%) of them died of cancer within 6 weeks and only16

The benefits of IVC filter placement on overall survival, as measured from the time of VTE was addressed in a recent retrospective study that examined 206 consecutive cancer patients with VTE. Patients were classified into 3 treatment groups: anticoagulation-only (n= 62), IVC filter-only (n=77), or combination of both IVC filter and anticoagulation (n=67). Median overall survival was significantly greater in patients treated with anticoagulation (13 months) compared with those treated with IVC filters (2 months) or combination of both (3.25 months; *P* < 0.0002). IVC patients were at 1.9 times more risk of death than anticoagulation only (hazard ratio=0.528; 95% confidence interval=0.374 to .745). Multivariate analysis revealed that performance status and type of thrombus were not

In another study, the survival benefit of IVC filters in patients with late-stage malignancy was evaluated in a group of 5,970 patients who were treated with a primary diagnosis of malignancy at a tertiary care facility. Retrospective analysis identified 55 consecutive patients with stage III or IV malignant disease and VTE who received IVC filters. In a case control study, 16 patients with VTE but without IVC filter were matched for age, sex, type of malignancy, and stage of disease. Filter placement prevented PE in 52 (94.5%) patients, however, four (7.3%) of patients had complications related to the procedure; 13 (23.6%) patients with late-stage cancer survived less than 30 days following placement of the filter; another 13 (23.6%) patients of this group, however, survived more than one year. Ambulatory status differed significantly (*P* = 0.01) between these two subgroups. Authors concluded that IVC filter placement conferred no survival benefit compared to the control group and that the survival of such patients with advanced-stage cancer was limited

confounders and had no effect on overall survival (Barginear et al., 2009).

complications (Levin et al., 1993).

(14%) were alive at one year (Jarrett et al., 2002).

primarily by the malignant process (Schunn et al., 2006). Researchers at M.D. Anderson Cancer center concluded, in a study that included 308 cancer patients with VTE and IVC filters, that such filters are safe and effective in preventing PE-related deaths in selected patients with cancer. However, patients with a history of DVT and bleeding or advanced disease had the lowest survival after IVC filter placement (Wallace et al., 2004).