**7. References**

The following draws on the research reviewed to describe a series of steps which is likely to

1. Create a structure which includes key stakeholders to formulate a service-strengthening

2. Combine actions to strengthen delivery of disease specific programmes with actions to

3. Use the constraints-based list below to guide national and local information gathering on the nature, severity and possible solutions for national and local constraints to:

Management development and management system development especially for

4. Consider from this report and others which evidence about constraint-reduction actions

5. Combine this information with the national information described above to formulate sub-strategies to reduce each of the constraints. Consider which actions would produce

6. Consider which actions in each sub-strategy are the same or similar and then prioritise

7. Sequence the strategy in relation to priorities, funding availability and your assessment

8. Create a structure for implementation involving stakeholders, and with systems to allocate the resources to each sub strategy, and to monitor and to regularly review the

This report was commissioned by the MoPHP, Primary Health Care Sector to help inform future strategy to strengthen health services and health systems in Yemen. The grant is from

Al Serouri, A.W. (2004). Towards Quality Health Care In Yemen Republic: I- Quality

Cooley, L., Kohl, R. (2005). *Scaling-up – a conceptual and operational framework.* Washington

from Clients' perspective. The Yemeni Journal of Medical and health Research, 2004,

D.C., In: *Management Services International,* accessed 30 March 2010, Available from: <http://www.vibrantcommunities.ca/downloads/SSI\_downloads/kohl\_scaleup.pdf>

**5.5 Practical steps for developing a strategy**

strategy for the country and each region

 Necessary changes to organisation Drug supply and better prescribing

management information and use

these in the overall strengthening strategy.

GAVI for Health System Strengthening (HSS).

strategy.

**7. References**

3: 1-6.

**6. Acknowledgment**

Finance for health services

result in an effective strengthening strategy which is implementable:

strengthen health services and the health system overall.

Human resources and management and planning

Employee and provider motivation and payment systems

Cross-sector interventions which strengthen health services

significant short term results and which are longer term actions.

of the absorptive- and change-coping capacity of the system.

is most applicable to your country, and the strength of the evidence.

Quality and performance improvement programmes and methods

Good governance including community participation in health services

**17**

*Italy*

**Performance Measurement Features of the**

A growing number of factors among which rising costs, technological advancements, aging population, health market failure and medical errors, led many industrialized countries to manage their health services and goals through performance measurement (Arah et al, 2003; Kelley & Hurst., 2006; Smith, 2002). In this context it became a commonplace for countries to formally assess the performance of their healthcare system (Mc Loughlin et al., 2001). Since the 1980s the introduction of "New Public Management" (NPM) principles has promoted a number of reforms in order to drive a more efficient, effective and accountable public sector (Hood, 1995a; Lapsley, 1999; Saltman et al. 2007). OECD countries have

Among the NPM principles, the one asking the public sector to adopt more explicit and measurable standards of performance measurement, has motivated countries to create

In the Italian health sector, the development of PMS can be traced back to the 90s reforms that introduced managerial tools and devolved the organization and assessment of healthcare services to Regions. This devolution, enforced by the recent federalist reform of 2009, has led Regions to shape their own organizational structures and relationships among health system actors (Censis, 2008; Formez, 2007). As a consequence of these reforms, Italy

On the basis of these considerations the Italian health sector provides with an interesting scenario in order to detect and analyze the differences and similarities in PMS adopted by

This chapter attempts to provide a cross sectional analysis of the Italian Regional PMS

As a consequence of NPM reforms, especially those concerning PMS, academics and international organizations such as the World Health Organization (WHO) and the Organization for Economic Cooperation and Development (OECD) developed conceptual frameworks and models in order to help countries in building effective tools (Arah et al.,

applied these principles in different ways with different emphasis (Hood 1995b).

has now 21 Regional Health Systems with significant differences from each other.

characteristics using evidences of an empirical study carried out in 2008-2009.

2006; Kelley et al., 2006; Murray & Evans, 2003; Smith, 2002; Veillard et al., 2005).

different performance measurement systems (PMS).

**1. Introduction**

the Regional governments.

**2. Theoretical frameworks**

**Italian Regional Healthcare Systems:**

*Istituto di Management - Laboratorio Management & Sanità,*

**Differences and Similarities**

Milena Vainieri and Sabina Nuti

*Scuola Superiore Sant'Anna, Pisa*


**3. Numerical discretization procedures**

æ ö - = ç ÷ - è ø B ,L C,L d

is the latent heat at *T*B,L and *T*C,L is the critical temperature.

C,L B,L

The source terms *S*, due to interactions between continuous phase and dispersed phase, are expressed by summating total droplets existing in one computation cell, denoting as *N*c,

> = - å( & ) c d

=- + å( & ) c

=- + + ç ÷ è ø å &

1 for fuel

( )

*m*

Δ *F i N S F mu*

1

c

*N*

*k Y N*

ì ï- = í ï î

molecular weights of the *k*th species and fuel, respectively.

*S V* d

Δ

*Q*

d d d,

æ ö

0 for other species

where *m*d and *m*˙ d denote the droplet mass and d*m*d/d*t*, respectively, *u*d,*i* is the droplet velocity, Δ*V* is the volume of the computational grid for the gas-phase calculation, and *h*V,sf is the evaporated vapor enthalpy at the droplet surface. *F*d and *Q*d are the drag force and the convective heat transfer, respectively, described later. For the combustion reaction model, a one-step global reaction is adopted for the reaction of hydrocarbon-air mixtures. The source term, *S*combustion,*k*, is expressed using the combustion reaction rate per unit volume, *R*F, as follows:

= - combustion, F

*k n W S R*

F F *k k*

where *nk* and *n*F are the molar stoichiometric coefficients of the *k*th species and the fuel for the one-step global reaction (positive for the production side), respectively. *Wk* and *W*F are the

d, d, d d V,sf 1 ( ) Δ 2

*i i*

*u u S Qm h V* (33)

*N S m*

1 Δ *m*

*T T* (30)

*V* (31)

*V* (32)

å & (34)

*n W* (35)

*T T*

V V,

*L L*

where *L* V,*T*B,L

**2.4. Two-way coupling models**

36 Modeling and Simulation in Engineering Sciences

*T*

A finite difference methodology is used to solve the above governing equations. An explicit Runge-Kutta time-integration methodology is applied, obtaining a third-order time-accurate computation. For example, for a semi-discrete equation of d*Uj* = *L j* (*U* )d*t*, the iteration from *n* to iteration *n*+1 is performed as

$$\begin{aligned} \mathbf{U}I\_{j}^{(1)} &= \mathbf{U}I\_{j}^{n} + \Delta t L\_{j} \left(\mathbf{U}^{n}\right) \\ \mathbf{U}I\_{j}^{(2)} &= \frac{3}{4}\mathbf{U}^{n}\_{j} + \frac{1}{4}\mathbf{U}^{(1)}\_{j} + \frac{1}{4}\Delta t L\_{j} \left(\mathbf{U}^{(1)}\_{j}\right) \\ \mathbf{U}I\_{j}^{n+1} &= \frac{1}{3}\mathbf{U}^{n}\_{j} + \frac{2}{3}\mathbf{U}^{(2)}\_{j} + \frac{2}{3}\Delta t L\_{j} \left(\mathbf{U}^{(2)}\_{j}\right) \end{aligned} \tag{36}$$

where *Uj* is the conserved variable at the direction *j*.

The higher-resolution numerical scheme applied for the resolution of supersonic flow is essential to the reliability of simulation. The nonviscous flux *f* ^ *j*+1/2, for the interface at *j*+1/2, is evaluated using a fifth-order hybrid Compact-Weighted Essentially Non-Oscillatory scheme [14] for the resolution of turbulent field and shock-capturing calculation in the supersonic flow. The numerical flux of the hybrid scheme is calculated as

$$
\hat{f}\_{\backslash \ast 1/2} = \sigma\_{\backslash \ast 1/2} \hat{f}\_{\backslash \ast 1/2}^{\text{CU}} + \left(\mathbf{1} + \sigma\_{\backslash \ast 1/2}\right) \hat{f}\_{\backslash \ast 1/2}^{\text{WENO}} \tag{37}
$$

which is constructed by hybridizing a fifth-order Compact Upwind (CU) scheme, *f* ^ *j*+1/2 CU ,and a fifth-order WENO one, *f* ^ *j*+1/2 WENO, through a smoothness indicator *rc*. *σ j*+1/2 is the weight of the numerical flux,

$$\sigma\_{\boldsymbol{\gamma}^{\*1/2}} = \min \left( \mathbf{1}\_{\boldsymbol{\gamma}} \frac{r\_{\boldsymbol{\gamma}^{\*1/2}}}{r\_{\boldsymbol{\varepsilon}}} \right) \tag{38}$$

A sixth-order symmetric compact difference scheme is applied for the viscous diffusion terms,

$$\frac{1}{3}\hat{F}\_{j-1} + \hat{F}\_j + \frac{1}{3}\hat{F}\_{j+1} = \frac{28\left(\hat{f}\_{j+1} - \hat{f}\_{j-1}\right) + \left(\hat{f}\_{j+2} - \hat{f}\_{j-2}\right)}{36\Delta x} \tag{39}$$

where *F* ^ *j* is the difference approximation for ( ∂ *f* ^ ∂ *x* ) *j* at the node *j*. Because of the stiffness of the equation due to the reactions, the time integration of the equations is performed by means of the point-implicit method [27]. For instance, *U* is supposed as the unknown variable at time step *n*+1, and the below equation is solved to obtain *U*n+1=*U*n +Δ*U*

$$
\left(I - \left(\frac{\partial \mathcal{S}}{\partial U}\right)^{\
u} \Delta t\right) \Delta U = \left(-\mathcal{C}^{\
u} + V^{\
u} + \mathcal{S}^{\
u}\right) \Delta t \tag{40}
$$

where *C* is the convection term, *V* is the viscous term, *S* is the source term, and *I* is the unity matrix.

In order to obtain the physical quantities of the fluid at a droplet position, the fluid velocities computed on the Eulerian mesh were interpolated at the droplet locations. A fourth-order Lagrangian interpolation was used for this purpose [28]. When the droplet is located at (*x*d, *y*d, *z*d) in a cell whose coordinates are (*x*0, *y*0, *z*0)…(*xl* , *yl* , *zl* ), the fluid velocity at the droplet position is obtained via the Lagrangian interpolation, which is expressed as

$$\mu\left(\mathbf{x}\_{d},\mathbf{y}\_{d},\mathbf{z}\_{d}\right) = \sum\_{l=1-n/2}^{n/2} \sum\_{j=1-n/2}^{n/2} \sum\_{k=1-n/2}^{n/2} \times \left(\prod\_{\substack{l=1-n/2\\l\neq s\neq l}}^{n/2} \frac{\mathbf{x}\_{d}-\mathbf{x}\_{l}}{\mathbf{x}\_{l}-\mathbf{x}\_{l}} \prod\_{\substack{l=1-n/2\\(l\neq s)}}^{n/2} \frac{\mathbf{y}\_{d}-\mathbf{y}\_{l}}{\mathbf{y}\_{l}-\mathbf{y}\_{l}} \prod\_{\substack{l=1-n/2\\(l\neq k)}}^{n/2} \frac{\mathbf{z}\_{d}-\mathbf{z}\_{l}}{\mathbf{z}\_{l}-\mathbf{z}\_{l}}\right) \mathbf{u}\_{i,j,k} \tag{41}$$

where the subscripts *i, j, k*, and *l* represents the cell indexes and *n* = 4.

In the Lagrangian droplet-tracking method, the droplet equation of motion was integrated with the third-order Adams-Bash-forth scheme in direction, *i*, for droplet position,

$$\mathbf{x}\_{d,l}^{n+1} = \mathbf{x}\_{d,l}^{n} + \Delta t \left(\frac{\mathbf{23}}{1\mathbf{2}} \boldsymbol{\mu}\_{d,l}^{n} - \frac{16}{1\mathbf{2}} \boldsymbol{\mu}\_{d,l}^{n-1} + \frac{5}{1\mathbf{2}} \boldsymbol{\mu}\_{d,l}^{n-2}\right) \tag{42}$$

The equations of velocity and temperature of the droplet are solved using the same integration method, as Eq. (42).

#### **4. Numerical examples**

#### **4.1. Taylor-Green vortex (TGV) case**

Taylor-Green vortex problem is an idea test for a numerical scheme whether it has the ability to simulate the transition from laminar to turbulence, since it contains the stages of laminar, transition, and finally turbulence as time develops. The three-dimensional Taylor-Green vortex was first introduced by Taylor and Green, and its initial condition is given as follows:

$$\begin{aligned} u &= \mathcal{U}\_0 \sin x \cos y \sin z \\ v &= -\mathcal{U}\_0 \cos x \sin y \sin z \\ w &= 0 \\ p &= p\_o + \rho\_o \mathcal{U}\_0^{-2} (\cos 2z + 2)(\cos 2x + \cos 2y) / 16 \end{aligned} \tag{43}$$

The computing domain is 0, 2*π* × 0, 2*π* × 0, 2*π* . The parameters are set as *p*0 =100, *ρ*0 =1, *U*0 =1, then the Mach number is about 0.08 and the flow is nearly incompressible. The boundary conditions in the three dimensions are all periodic. Three numerical schemes are used here, WENO [7], WENO-compact upwind [9] (denoted as WENO-CU), and WENO-linear upwind [10] (denoted as WENO-LU), respectively. Two sets of computation grid are taken as 643 and 323 for different flow Reynolds numbers.

Because of the stiffness of the equation due to the reactions, the time integration of the equations is performed by means of the point-implicit method [27]. For instance, *U* is supposed as the unknown variable at time step *n*+1, and the below equation is solved to obtain *U*n+1=*U*n

Δ Δ Δ

where *C* is the convection term, *V* is the viscous term, *S* is the source term, and *I* is the unity

In order to obtain the physical quantities of the fluid at a droplet position, the fluid velocities computed on the Eulerian mesh were interpolated at the droplet locations. A fourth-order Lagrangian interpolation was used for this purpose [28]. When the droplet is located at (*x*d, *y*d,

> , *yl* , *zl*

=- =- =- = - = - = -

*nnn nnn*

*xx yy zz ux y z u*

with the third-order Adams-Bash-forth scheme in direction, *i*, for droplet position,

+ - - æ ö =+ - + ç ÷

1 1 2 ,, , , , 23 16 5 Δ 12 12 12

*nn n n n*

ååå ÕÕÕ /2 /2 /2 / 2 / 2 / 2

d dd , , 1 /2 1 /2 1 /2 1 /2 1 /2 1 /2

ç ÷ --- = ´ ---

*i nj nk n ln ln ln il i l il*

In the Lagrangian droplet-tracking method, the droplet equation of motion was integrated

The equations of velocity and temperature of the droplet are solved using the same integration

Taylor-Green vortex problem is an idea test for a numerical scheme whether it has the ability to simulate the transition from laminar to turbulence, since it contains the stages of laminar, transition, and finally turbulence as time develops. The three-dimensional Taylor-Green vortex was first introduced by Taylor and Green, and its initial condition is given as follows:

è ø

( ) ( ) ( )

*l i l j l k*

æ ö

ddd

*di di di di di x x tu u u* (42)

*l ll*

è ø

¹¹¹

*U* (40)

), the fluid velocity at the droplet position

*xx yy zz* (41)

*ijk*

( ) æ ö æ ö ¶ ç ÷ - ç ÷ =- + +

*S n nn I tU CV S t*

è ø ¶ è ø

*z*d) in a cell whose coordinates are (*x*0, *y*0, *z*0)…(*xl*

38 Modeling and Simulation in Engineering Sciences

( )

method, as Eq. (42).

**4. Numerical examples**

**4.1. Taylor-Green vortex (TGV) case**

, ,

*n*

is obtained via the Lagrangian interpolation, which is expressed as

where the subscripts *i, j, k*, and *l* represents the cell indexes and *n* = 4.

+Δ*U*

matrix.

**Figure 1** shows the results of the total kinetic energy integrated in the entire flow field. For Re = 30,000, the kinetic energy decays very slowly at the beginning and it has almost no change in the first few seconds. However, when the time is at about 4 s, the sub-grid scales are produced and the kinetic energy begins to decay due to the numerical dissipation, which also can be taken as the implicit sub-grid dissipation. The results of Re number equalling 3000 are similar, since in both cases, the numerical viscosity is larger compared to the physical viscosity. For the results of Re = 300, there is only slight difference between the curve of WENO-LU and WENO-CU, since in this case the physical viscosity is dominated.

**Figure 1.** Comparison of the total kinetic energy. (a) Re = 30,000; (b) Re = 3000; (c) Re = 300.

**Figure 2** shows the energy spectrum of the TGV with Re = 30,000. As the time increases, the slope of energy spectrum also changes. If the time is longer than 4, the turbulence develops and the kinetic energy builds up a Kolmogorov inertial range. The slope is then approximately decayed with the −5/3 law. It implies that it can predict the characteristics of turbulence.

**Figure 2.** Energy spectrum of TGV with Re = 30,000.

Since the passive scalar transportation is very important in many fields, especially in the chemical-reacting process, we also solved the conserved passive scalar transport equation

$$\frac{\partial \left(\rho f\right)}{\partial t} + \nabla \cdot \left(\rho f \vec{L}\right) = \nabla \cdot \left(\rho \mathcal{D} \nabla f\right) \tag{44}$$

Here, only the WENO-LU scheme is used since it has less dissipation and costs less computing time. Equation (44) presents the initial condition for *f* and it is a function of the initial *Q* distribution, where *Q* is the value of *q*-criteria. Different Schmidt numbers are taken in the simulations, 1, 0.1, and 0.01, corresponding to different diffusivity coefficients, respected *D* =*μ* / (*ρ*Sc)

$$f(t=0) = \frac{1 + err\{C\_0Q\}}{2}, C\_0 = 100\tag{45}$$

Here, erf is the error function. **Figure 3** shows the initial distribution of *f*. As we can see, in most regions, *f* equals 1 or 0 and there are large gradients of *f* initially.

**Figure 3.** Initial distribution of passive scalar *f*.

**Figure 2** shows the energy spectrum of the TGV with Re = 30,000. As the time increases, the slope of energy spectrum also changes. If the time is longer than 4, the turbulence develops and the kinetic energy builds up a Kolmogorov inertial range. The slope is then approximately decayed with the −5/3 law. It implies that it can predict the characteristics of turbulence.

Since the passive scalar transportation is very important in many fields, especially in the chemical-reacting process, we also solved the conserved passive scalar transport equation

(44)

¶( ) +Ñ× =Ñ× Ñ

+ = = = 0

1 () ( 0) , 100 2

0

*erf C Q f t C* (45)

r () ( ) *ρf ρfU ρD f t*

Here, only the WENO-LU scheme is used since it has less dissipation and costs less computing time. Equation (44) presents the initial condition for *f* and it is a function of the initial *Q* distribution, where *Q* is the value of *q*-criteria. Different Schmidt numbers are taken in the simulations, 1, 0.1, and 0.01, corresponding to different diffusivity coefficients, respected

¶

**Figure 2.** Energy spectrum of TGV with Re = 30,000.

40 Modeling and Simulation in Engineering Sciences

*D* =*μ* / (*ρ*Sc)

**Figure 4** shows the variance of the passive scalar for the Re = 30,000 flow in different Schmidt numbers and different grid levels. Similar to the kinetic energy decaying, the scalar variance decays very slowly in the first 4 s, and it can be concluded that the passive scalar mixing goes on with the laminar diffusion. However, the significant decaying can be observed due to the initial large gradients of passive scalar. For the time longer than four, the turbulent mixing occurs and the decaying of passive scalar variance is larger than that in the beginning stage.

**Figure 4.** Variance curves of passive scalar.

**Figure 5** shows the passive scalar contours for Re = 30,000 and Sc = 1.0 in the plane of *z* = π. During the stage of laminar diffusion, the interface between the high and low *f* region is clear while the interface is wrinkled in the stage of turbulent mixing, which enhances the mixing process.

**Figure 5.** Passive scalar field in the plane of *z* = π. (a) *t* = 0 s; (b) *t* = 3 s; (c) *t* = 6 s; (d) *t* = 10 s.

#### **4.2. Reactive H2/air spatially developing mixing layer**

The schematic of the supersonic mixing layer is shown in **Figure 6**.

**Figure 6.** Schematic of computation model of spatially developing mixing layer.

The inlet conditions are set as follows. The average velocity profile in the stream-wise direction is a hyperbolic tangent, and the average velocity in other directions is set to zero

$$\overline{\mathcal{U}} = \frac{\mathcal{U}\_A + \mathcal{U}\_F}{2} + \frac{\mathcal{U}\_A - \mathcal{U}\_F}{2} \tanh(\frac{y - 0.5l\_y}{2\delta\_0}) \tag{46}$$

where *δ*0 is the inlet layer thickness given as 0.4 mm, and *l*y is the length of computational domain in the *y* direction. *U*A is the hot-air inflow velocity, 2000 m/s, *U*F is the cold fuel inflow velocity, 1000 m/s. The temperatures of both streams are *T*A = 1600 K and *T*F = 390 K. The reactive system pressure is specified as *p*A = 1 atm.

The stream-wise and transverse domain lengths are *lx* and *ly*, respectively. By setting *lx*= 4*ly*, the transverse domain size is large enough to have minimal influence on the main flow region of the droplet-laden shear layer. The computation grid in the two-dimensional case is taken as 512 × 128 equally spaced computational cells in the stream-wise and the transverse directions, respectively, after a series of grid independence tests.

In order to stimulate the flow instability, the inlet velocity perturbation is given as

while the interface is wrinkled in the stage of turbulent mixing, which enhances the mixing

**Figure 5.** Passive scalar field in the plane of *z* = π. (a) *t* = 0 s; (b) *t* = 3 s; (c) *t* = 6 s; (d) *t* = 10 s.

The schematic of the supersonic mixing layer is shown in **Figure 6**.

**Figure 6.** Schematic of computation model of spatially developing mixing layer.

system pressure is specified as *p*A = 1 atm.

The inlet conditions are set as follows. The average velocity profile in the stream-wise direction

tanh( ) 22 2

where *δ*0 is the inlet layer thickness given as 0.4 mm, and *l*y is the length of computational domain in the *y* direction. *U*A is the hot-air inflow velocity, 2000 m/s, *U*F is the cold fuel inflow velocity, 1000 m/s. The temperatures of both streams are *T*A = 1600 K and *T*F = 390 K. The reactive

0 0.5

*δ* (46)

is a hyperbolic tangent, and the average velocity in other directions is set to zero

+ - - = +

*AF AF y UUUU y l U*

**4.2. Reactive H2/air spatially developing mixing layer**

process.

42 Modeling and Simulation in Engineering Sciences

$$
\sigma' = (\mathcal{U}\_A - \mathcal{U}\_F) G(y - 0.5 \mathcal{l}\_y) \; A \sin(2\pi f\_\circ t + \xi) \tag{47}
$$

where *G* is the Gauss function, *A* is the fluctuation amplitude, *f*o is the maximum disturbance frequency, and *ξ* is the random phase. The nonreflecting conditions are used in the transverse directions. The outflow is treated by an extrapolation of each primitive variable. A 19-step detailed H2/O2 reaction mechanism is used, which can be referred in [29].

**Figure 7.** Instantaneous contour distribution of reactive flows. (a) temperature; (b) H2; (c) H2O.

Instantaneous distributions of temperature and mass fraction of H2 and H2O at *t* = 2 ms are shown in **Figure 7**. At about *x* = 0.4 m, the mixing layer loses stability, the large-scale vortices begin to form, and efficient mixing occurs inside the vortices. Due to the relatively high temperature of air, fast reaction occurs and a large amount of water concentrates inside the vortices.

**Figure 8** shows the flame structures in the mixing layer flow. The flame structures were obtained by means of plotting all the combustion status in the whole computation domain within the time interval 2<*t*<2.5 ms, that is, recording and plotting the temperature or species fraction at each calculation node in the observing time interval. Here, the mixture fraction is defined as

$$Z \equiv \frac{\mathcal{W}\_{\rm H}}{\mathcal{W}\_{\rm H} + \mathcal{W}\_{\rm O}} \tag{48}$$

where WH and WO are the mass fraction of element H and element O, respectively. For the stoichiometric ratio, *Z*st = 0.111. It can be seen that the peaks of temperature and water are both near *Z*st. The flame structure is similar with that in the flamelet model, since the non-premixed flame dominates in the current case.

**Figure 8.** Flame structure. (a) temperature distribution and (b) water distribution.

#### **4.3. Non-premixed combustion of** *n***-decane droplets in turbulent mixing layers**

The considered flow configuration is that of a spatially developing shear layer, as depicted in **Figure 9**, which is formed between a stream of hot air moving at velocity *U*A and a spray carried by cold air moving at velocity *U*S. *x* and *y* refer to the stream-wise and transverse directions, respectively. The inflow parameters of the two streams are listed in **Table 1**.

**Figure 9.** Schematic of a fuel spray in a two-dimensional turbulent shear layer.

**Table 1.** Inflow parameters.

Instantaneous distributions of temperature and mass fraction of H2 and H2O at *t* = 2 ms are shown in **Figure 7**. At about *x* = 0.4 m, the mixing layer loses stability, the large-scale vortices begin to form, and efficient mixing occurs inside the vortices. Due to the relatively high temperature of air, fast reaction occurs and a large amount of water concentrates inside the

**Figure 8** shows the flame structures in the mixing layer flow. The flame structures were obtained by means of plotting all the combustion status in the whole computation domain within the time interval 2<*t*<2.5 ms, that is, recording and plotting the temperature or species fraction at each calculation node in the observing time interval. Here, the mixture fraction is

where WH and WO are the mass fraction of element H and element O, respectively. For the stoichiometric ratio, *Z*st = 0.111. It can be seen that the peaks of temperature and water are both near *Z*st. The flame structure is similar with that in the flamelet model, since the non-premixed

*W W* (48)

º + H H O

*W Z*

vortices.

defined as

flame dominates in the current case.

44 Modeling and Simulation in Engineering Sciences

**Figure 8.** Flame structure. (a) temperature distribution and (b) water distribution.

**4.3. Non-premixed combustion of** *n***-decane droplets in turbulent mixing layers**

The considered flow configuration is that of a spatially developing shear layer, as depicted in **Figure 9**, which is formed between a stream of hot air moving at velocity *U*A and a spray carried

The initial Mach numbers of the two streams equal 1.2. The cold-air stream is nonuniformly laden with droplets at the cold-air inlet. For the combustion reaction model, a one-step global reaction model of *n*-decane is used [30]. In this model, the chemical reaction is simplified as

$$\rm{C}\_{10}\rm{H}\_{22} + \rm{\eta}\_{\rm{O}\_2}\rm{O}\_2 \Rightarrow \rm{\eta}\_{\rm{CO}\_2}\rm{CO}\_2 + \rm{\eta}\_{\rm{H}\_2\rm{O}}\rm{H}\_2\rm{O} \tag{49}$$

where *ni* is the molar stoichiometric coefficients of each species. The global reaction constant, *k* (mol/cm3 /s), is given by

$$k = A \cdot T^{\
u} \exp\left(-\frac{E}{RT}\right) \left(\frac{\rho \cdot Y\_{\text{p}}}{W\_{\text{F}} \times 10^{6}}\right)^{\circ} \left(\frac{\rho \cdot Y\_{\text{O}\_{2}}}{W\_{\text{O}\_{2}} \times 10^{6}}\right)^{\circ} \tag{50}$$

where *A* is the coefficient of frequency and *E* is the activation energy. In the reaction of *n*decane, these parameters are given as follows: *A* = 3.8 × 1011, *E* = 1.256 × 105 J/mol, *a* = 0.25, *b* = 1.5, and *n* = 0. Therefore, the combustion reaction rate per unit volume (g/cm3 /s) is *R*F = *W*V*k* × 103 .

In all simulation cases, the fuel droplets are initially located in the cold air and cannot vaporize until they traverse into the hot air associated with the rolling-up process of vortices. The hotair stream provides the heat needed for droplet vaporization. The fuel vapor diffuses into the air stream, and it begins to react with the local oxygen as it reaches the high-temperature regions.

Large eddies rotate and entrain the hot air, providing heat for the evaporation of fuel droplets that preferentially accumulate in the high-strain vortex-braid regions adjoining the hot air. The fuel vapor generated by droplet evaporation diffuses toward the hot air and mixes with the surrounding oxygen, forming reactive pockets. Auto-ignition occurs when the fuel mass fraction is high enough, as depicted in **Figure 10(a)**. Under the instantaneous condition, the time *t*ig defines the moment that the incipient establishment of ignition kernels occurs. We normalize the gaseous temperature by *T*\* = *T*/*T*in and *T*in = (*T*A + *T*S)/2. The droplet diameters are normalized by *d*\* d = *d*d/dd0 and dd0 is the initial droplet diameter.

Large eddies rotate and the reaction kernels between two convection vortices are strained at time *t* = *t*ig + 0.25*t*L, as shown in **Figure 10(b)**. Here, the time *t*L is the large eddy turnover time and is defined as

$$t\_{\rm L} = \frac{\delta\_{\rm L}}{\left| \mathcal{U}\_{\rm A} - \mathcal{U}\_{\rm s} \right|} \tag{51}$$

where *δ*L is the length of the large eddy. Compared with the reaction rates of the ignition kernels at time *t*ig, the chemical heat release in the reaction kernels at time *t*ig + 0.25*t*L results in a larger temperature increase and facilitates the self-acceleration of the chemical reaction rate. At time *t* = *t*ig + 0.5*t*L, the reaction kernels are separated into two parts due to the stretch effects, as shown by **Figure 10(c)**. One part of the reaction kernels still exists in the high-strain vortexbraid zone and suffers the shear strain continuously. Evaporating fuel droplets segregate in a thin layer along this reaction kernel and feed fuel vapors for the chemical reaction. The other separated part is entrained in the inner of the large cold eddy. It is difficult forthe fuel droplets to penetrate into the high-vorticity core of the large eddy because of the large droplet inertia and fuel droplets surround far from this separated reaction kernel, forming a thick vaporiza‐

tion layer. However, the turbulence motion separates the vaporization layer and the flame kernel. Hence, the fuel vapor diffusion is difficult forfeeding the chemicalreaction in the flame kernel effectively. Here, we call it the weak-fueling mechanism due to the preferential concentration effects of droplets. If we track the separated reaction kernel in the inner of the cold eddy, it is found that the local chemical reaction rate of this flame kernel is decelerated, compared to that ofthe reaction kernel in the high-strain zone accompanied by the rich reactant supplement. **Figure 10(d)** depicts the extinction phenomena at time *t* = *t*ig + 0.75*t*L. Compari‐

where *ni*

regions.

and is defined as

*k* (mol/cm3

/s), is given by

46 Modeling and Simulation in Engineering Sciences

normalize the gaseous temperature by *T*\*

normalized by *d*\* d = *d*d/dd0 and dd0 is the initial droplet diameter.

is the molar stoichiometric coefficients of each species. The global reaction constant,

F O

2

*RT W W* (50)

= *T*/*T*in and *T*in = (*T*A + *T*S)/2. The droplet diameters are

*U U* (51)

J/mol, *a* = 0.25, *b* =

/s) is *R*F = *W*V*k* × 103

.

2

*b a*

F O 6 6

æ ö æ ö × æ ö × =× - ç ÷ ç ÷ ç ÷ ´ ´ ç ÷ è øè ø è ø

*n E ρ Y ρ Y k AT*

decane, these parameters are given as follows: *A* = 3.8 × 1011, *E* = 1.256 × 105

1.5, and *n* = 0. Therefore, the combustion reaction rate per unit volume (g/cm3

exp 10 10

where *A* is the coefficient of frequency and *E* is the activation energy. In the reaction of *n*-

In all simulation cases, the fuel droplets are initially located in the cold air and cannot vaporize until they traverse into the hot air associated with the rolling-up process of vortices. The hotair stream provides the heat needed for droplet vaporization. The fuel vapor diffuses into the air stream, and it begins to react with the local oxygen as it reaches the high-temperature

Large eddies rotate and entrain the hot air, providing heat for the evaporation of fuel droplets that preferentially accumulate in the high-strain vortex-braid regions adjoining the hot air. The fuel vapor generated by droplet evaporation diffuses toward the hot air and mixes with the surrounding oxygen, forming reactive pockets. Auto-ignition occurs when the fuel mass fraction is high enough, as depicted in **Figure 10(a)**. Under the instantaneous condition, the time *t*ig defines the moment that the incipient establishment of ignition kernels occurs. We

Large eddies rotate and the reaction kernels between two convection vortices are strained at time *t* = *t*ig + 0.25*t*L, as shown in **Figure 10(b)**. Here, the time *t*L is the large eddy turnover time

> = - L

A S *δ*

where *δ*L is the length of the large eddy. Compared with the reaction rates of the ignition kernels at time *t*ig, the chemical heat release in the reaction kernels at time *t*ig + 0.25*t*L results in a larger temperature increase and facilitates the self-acceleration of the chemical reaction rate. At time *t* = *t*ig + 0.5*t*L, the reaction kernels are separated into two parts due to the stretch effects, as shown by **Figure 10(c)**. One part of the reaction kernels still exists in the high-strain vortexbraid zone and suffers the shear strain continuously. Evaporating fuel droplets segregate in a thin layer along this reaction kernel and feed fuel vapors for the chemical reaction. The other separated part is entrained in the inner of the large cold eddy. It is difficult forthe fuel droplets to penetrate into the high-vorticity core of the large eddy because of the large droplet inertia and fuel droplets surround far from this separated reaction kernel, forming a thick vaporiza‐

L

*t*

**Figure 10.** Flame kernels at (a) time *t* = *t*ig; (b) time *t* = *t*ig + 0.25 *t*L; (c) time *t* = *t*ig + 0.5 *t*L; and (d) time *t* = *t*ig + 0.75 *t*L. Instantaneous distributions of dimensionless gaseous temperature (left) and reaction rate (right), with red isolines giv‐ en by *Ω* = [11, 15] (kgm−3s−1) in (a) and *Ω* = [15, 24] (kgm−3s−1) in (b)–(d). Here, the dots indicate fuel droplets in the figures, with colors corresponding to dimensionless droplet diameters in the right figures.

son with **Figure 10(c)** shows that the reaction kernels that have high chemical reaction rates in the high-strain region at time *t* = *t*ig + 0.5*t*L disappear. The other reaction kernel entrained in large eddies is distorted due to the turbulence motions.


**Author details**

José Luis Braga de Aquino\* and Vânia Aparecida Leandro-Merhi Postgraduate Program of Health Sciences, Pontifical Catholic University of Campinas, Campinas, SP, Brazil

\*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.

*Surgical Treatment of Esophageal Advanced Achalasia DOI: http://dx.doi.org/10.5772/intechopen.99944*


**5. Appendix A: Microalgae methodology**

*Chlamydomonas reinhardtii* **(Plantae, Chlorophyta)** (Förster et al., 2006). The figure correspond to the species mentioned.

This protocol is an adaption of the one described by Mathesius and co-workers (Grotewold, 2003) that are suited for plant material.

Proteomics in Seaweeds: Ecological Interpretations 39

2. Mix suspension with the same volume of sample buffer containing 0.3% w/v SDS, 1%

3. Denature solution at 100°C for 3 min, cool on ice and treat with DNase/RNase.

4. Wash the pellet with 100% acetone several times and then air-dry it at room

5. Determine protein concentration of the sample (e.g. with a Bradford assay or a BCA

*Gracilaria changii* **(Plantae, Rhodophyta)** (Wong et al., 2006). The figure correspond to the

In this study two protein extraction methods were analysed in two-dimensional gels. The best results were yielded by the phenol/chloroform method, which is detailed below.

1. Grind frozen seaweeds at -70°C into a fine powder with a mortar and pestle in liquid nitrogen. Put approximately 100 mg of the resulting powder into a 1.5 mL tube for a

2. Add 1 mL of TRI reagent (containing phenol and guanidine-isothiocyanate) to 100 mg

3. Store the homogenate for 5 min at room temperature to clarify phases. Reserve the

4. Cover the samples and shake vigorously for 15 seconds. Store the resulting mixture at room temperature for 2-15 min. Centrifuge mixture at 12,000 *g* for 15 min at 4°C. 5. Discard upper aqueous phase containing RNA, and retain interphase and lower red

7. Retain phenol/ethanol supernatant and add 3 volumes of acetone to precipitate

8. Store sample for 10 min at room temperature and sediment the protein precipitate at

9. Discard the phenol/ethanol supernatant and disperse the protein pellet in 0.5 mL of 0.3

10. Add another 0.5 mL aliquot of the guanidine hydrochloride/ethanol/glycerol solution to the sample and store for 10 min at room temperature. Centrifuge the proteins at

phenolic phase and add 200 µL of chloroform per 1 mL of TRI reagent.

proteins, mix by inversion for 10-15 sec to obtain a homogenous solution.

M guanidine hydrochloride in 95% ethanol + 2.5% v/v glycerol.

Precipitate proteins with 10% TCA in 100% Acetone at -70°C for 3 h.

w/v β-mercaptoethanol and 0.05 M Tris-HCl pH 8.0.

assay) and keep at –80°C until used for isoelectric focusing.

**6. Appendix B: Macroalgae methodology**

of seaweed powder and homogenize the mixture.

phenol-chloroform phase containing DNA and proteins. 6. Add ethanol to the reserved phases in order to precipitate DNA.

temperature for 5 min.

species mentioned.

single extraction.

12,000 *g* for 10 min at 4° C.

8,000 *g* for 5 min.

*Haematococcus pluvialis* **(Plantae, Chlorophyta)** (Wang et al., 2003). The figure correspond to the species mentioned.

In this study a number of key chemical reagents were evaluated, the protocol that yielded the best 2-DE results is detailed below.

*Nannochloropsis oculata* **(Chromista, Ochrophyta)** (Kim et al., 2005). The figure correspond to the species mentioned.

In this study only one method for protein extraction was performed, the details are shown below.

1. Collect *N. oculata* cells by centrifugation at 12,000 x *g* for 10 min. Suspend cell pellet in PBS buffer pH 7.2.

*Haematococcus pluvialis* **(Plantae, Chlorophyta)** (Wang et al., 2003). The figure correspond to

In this study a number of key chemical reagents were evaluated, the protocol that yielded

1. Collect *H. pluvialis* cells by centrifugation at 3,000 x *g* for 5 min and wash the pellet three

2. Resuspend cell pellet in one volume of 50 mM Tris-HCl buffer pH 8.0, 3 mM DTT, 5 mM MgCl2, 10% glycerol, 0.5% PVP, 5 mM Na2-EDTA, 1 mM PMSF, 5 mM benzamidin,

3. Disrupt cells by one passage through a pre-cooled French Press Cell at a pressure of

5. Dialyze the supernatant from the previous centrifugation step against 250 mL of 85% w/v sucrose solution at 4°C for 2 h. Precipitate the dialysate with 9 volumes of icecold 10% w/v TCA in acetone containing 0.07% w/v β-mercaptoethanol at -20°C

6. Centrifuge samples at 15,000 x *g*. Discard supernatant and wash the pellet with acetone

8. Resuspend pellet in solubilization buffer containing 2 M thiourea, 8 M urea, 4% CHAPS, 2 mM TBP, and 0.2% ampholytes (ones suited for the desired 2-DE gel). 9. Determine protein concentration of the sample (e.g. with a Bradford assay or a BCA

*Nannochloropsis oculata* **(Chromista, Ochrophyta)** (Kim et al., 2005). The figure correspond to

In this study only one method for protein extraction was performed, the details are shown

1. Collect *N. oculata* cells by centrifugation at 12,000 x *g* for 10 min. Suspend cell pellet in

5 mM acoproic acid and 1% v/v plant protease inhibitor cocktail.

4. Collect the supernatant and centrifuge at 100,000 *g* for 1 h.

containing 0.07% w/v β-mercaptoethanol to remove TCA.

assay) and keep at –80°C until used for isoelectric focusing.

7. Then, remove residual acetone by air-drying.

20,000 psi. Centrifuge cell lysate at 3,000 *g* for 10 min to pellet cell debris.

the species mentioned.

overnight.

the species mentioned.

PBS buffer pH 7.2.

below.

the best 2-DE results is detailed below.

times with cold deionized water.

#### **6. Appendix B: Macroalgae methodology**

*Gracilaria changii* **(Plantae, Rhodophyta)** (Wong et al., 2006). The figure correspond to the species mentioned.

In this study two protein extraction methods were analysed in two-dimensional gels. The best results were yielded by the phenol/chloroform method, which is detailed below.

Proteomics in Seaweeds: Ecological Interpretations 41

In this study a very simple method for protein extraction was performed, the details are

2. Homogenize seaweed material using a mortar-driven homogenizer in sample lysis solution composed of 7 M urea, 2 M thiourea, containing 4% w/v CHAPS, 1% w/v

7. Determine protein concentration of the sample (e.g., with a Bradford assay or a BCA

*Saccharina japonica* and *Ecklonia cava* **(Chromista, Ochrophyta)** (Yotsukura et al., 2010; 2012).

2. Freeze seaweed material at -80°C before pulverization. Homogenize the tissue in 99.5%

3. The protein pellet is rinsed in 99.5% ethanol and 100% acetone and resuspended in 0.1 M Tris-HCl buffer pH 8.0, 30% sucrose, 2% SDS, 5% β-mercaptoethanol and phenol.

\* If cell lysing is found to be difficult, used a bead beater in order to facilitate the process.

1. Freeze seaweed material at -80°C before pulverization.

DTT, 2% v/v ampholytes and 1 mM benzamidine. 3. Perform freezing and thawing steps for five times for 1 day\*. 4. Extract proteins for 1 h at room temperature with vortexing.

6. Retain soluble fraction and discard insoluble material.

assay) and keep at –80°C until used for isoelectric focusing.

5. Centrifuge mixture at 15,000 *g* for 1 h at 18°C.

The figures correspond to the species mentioned.

1. Remove the salt excess in Milli Q water.

cold ethanol and centrifuged.

shown below.

*Scytosiphon gracilis* and *Ectocarpus siliculosus* **(Chromista, Ochrophyta)** (Contreras et al., 2008). The figures correspond to the species mentioned.

In this method, a major extraction of proteins was obtained in comparison with pervious macroalgae methods described.

*Bostrychia radicans / B. moritziana* **(Plantae, Rhodophyta)** (Kim et al., 2008). The figure correspond to *B. moritziana*.

11. Discard the wash solution and perform two more washes in 1 mL each of the guanidine

12. Perform a final wash in 1 mL of ethanol containing 2.5% glycerol v/v. At the end of the

14. Resolubilize protein pellet in 40 mM Tris buffer pH 8.8 containing 8 M urea, 4% CHAPS

15. Determine protein concentration of the sample (e.g., with a Bradford assay or a BCA

*Scytosiphon gracilis* and *Ectocarpus siliculosus* **(Chromista, Ochrophyta)** (Contreras et al.,

In this method, a major extraction of proteins was obtained in comparison with pervious

3. Homogenize seaweed material using a mortar-driven homogenizer in sample lysis solution composed of 1.5% PVP, 0.7 M sucrose, 0.1 M KCl, 0.5 M Tris-HCl pH 7.5, 250 mM EDTA, protease inhibitor cocktail, 2% v/v β-mercaptoethanol and 0.5% w/v CHAPS 4. Equal volume of Tris-HCl pH 7.5-satured phenol is added and the mixture homogenized at 4°C. Then, by centrifugation the upper phase is removed and the lower

5. The proteins in the phenol phase are precipitated by means of ammonium acetate (0.1 M in methanol). The protein pellet obtained by centrifugation is washed in 80% ice-cold

6. Determine protein concentration of the sample and keep at –20°C until used for

*Bostrychia radicans / B. moritziana* **(Plantae, Rhodophyta)** (Kim et al., 2008). The figure

1. Remove the excess salt by rinsing in Milli Q water and 50 mM Tris-HCl pH 8.8.

2. Freeze seaweed material at -80°C before pulverization.

phase is re-extracted using the same volume of phenol.

acetone and cold acetone containing 20 mM DTT.

10 min of ethanol wash, centrifuge the proteins at 8,000 *g* for 5 min. 13. Discard the alcohol and air-dry the pellet for 7-10 min at room temperature.

assay) and keep at –80°C until used for isoelectric focusing.

Hydrochloride/ethanol/glycerol wash solution.

2008). The figures correspond to the species mentioned.

and 2 mM TBP.

macroalgae methods described.

isoelectric focusing.

correspond to *B. moritziana*.

In this study a very simple method for protein extraction was performed, the details are shown below.

\* If cell lysing is found to be difficult, used a bead beater in order to facilitate the process.

*Saccharina japonica* and *Ecklonia cava* **(Chromista, Ochrophyta)** (Yotsukura et al., 2010; 2012). The figures correspond to the species mentioned.

Proteomics in Seaweeds: Ecological Interpretations 43

This work was supported by FONDECYT 11085019 to LC. Additional funding cames from FONDAP 1501-0001 (CONICYT) to the Center for Advanced Studies in Ecology and Biodiversity (CASEB) Program 7. Finally, we are especially grateful to Nicole Ehrenfeld and Javier Tapia for image acquisition and Daniela Thomas, Alejandra Nuñez and Aaron

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Baker, C.J. & Orlandi, E.W. (1995). Active oxygen in plant pathogenesis. *Annu. Rev.*

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**8. References**

*Porphyra columbina* **(Plantae, Rhodophyta).** The figure correspond to the species mentioned.

This method is an adaptation of the method performed by Contreras et al. 2008, where the details of it are shown below\*.

1. Pulverize 3-5 g of frozen seaweeds at -80°C to a fine powder with a mortar and pestle in liquid nitrogen.

### **7. Acknowledgment**

42 Gel Electrophoresis – Advanced Techniques

4. The solution is vortexed and centrifuged at room temperature and the upper phase

5. The protein pellet obtained by centrifugation is rinsed in 0.1 M ammonium acetate in methanol and 80% acetone, subsequently dried in evaporator and preserved at –80°C

*Porphyra columbina* **(Plantae, Rhodophyta).** The figure correspond to the species mentioned. This method is an adaptation of the method performed by Contreras et al. 2008, where the

1. Pulverize 3-5 g of frozen seaweeds at -80°C to a fine powder with a mortar and pestle in

2. Resuspend pulverized tissue in 5-10 mL of buffer lysis containing 0.5 M Tris-HCl pH 7.5, 0.7 M sucrose, 0.5 M KCl, 250 mM EDTA, 1.5% w/v PVP, 0.5% w/v CHAPS and 2%

3. Add an equal volume of Tris–HCl pH 7.5-saturated phenol and homogenize for 15 min

4. Retain only upper phenol phase containing proteins being careful not to remove the

7. Retain newly only upper phenol phase containing proteins being careful not to remove

10. Centrifuge at 2,000 *g* for 40 min. Discard supernatant and wash pellet with 8 volumes of

13. The proteins pellet is washed in 80% ice-cold acetone and cold acetone containing 20 mM

14. The protein pellet is then washed in ice-cold acetone 80% and ice-acetone 60% in

15. Determine protein concentration of the sample and keep at -20°C until used for isoelectric

5. Add ½ volumes of Tris–HCl pH 7.5-saturated phenol and mix well by inversion.

the interphase and mix with the previously retained upper phase. 8. Add 5 volumes of 0.1 M ammonium acetate on methanol ice-cold.

9. Shake vigorously to mix the solution and leave to precipitate for 4 h at -20°C.

11. Shake vigorously to mix the solution and leave to precipitate for 30 min at -20°C.

v/v β-mercaptoethanol and homogenize for 15 min at 4°C.

at 4° C. Centrifuge the homogenate at 2,000 *g* for 30 min.

6. Centrifuge the homogenate at 2,000 *g* for 20 min.

0.1 M ammonium acetate on methanol ice-cold.

methanol to remove the majority of contaminants.

12. Centrifuge at 2,000 *g* for 30 min.

collected. Agitate solution in 0.1 M ammonium acetate and kept at -20°C.

until protein quantification.

details of it are shown below\*.

liquid nitrogen.

interphase.

DTT.

focusing.

\* Method not yet published.

This work was supported by FONDECYT 11085019 to LC. Additional funding cames from FONDAP 1501-0001 (CONICYT) to the Center for Advanced Studies in Ecology and Biodiversity (CASEB) Program 7. Finally, we are especially grateful to Nicole Ehrenfeld and Javier Tapia for image acquisition and Daniela Thomas, Alejandra Nuñez and Aaron Mann for text editing.

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**3**

 *Turkey*

**Gel Electrophoresis Based Genetic**

*1Istanbul Technical University, Environmental Engineering Department, Maslak, Istanbul 2Bogazici University, Institute of Environmental Science, Rumelihisarustu-Bebek, Istanbul*

Molecular tools in environmental microbiology have been applied extensively in last decades because of the limitations in culture-dependent methods (Amann *et al.*, 1995; Muyzer *et al.*, 1996; Head *et al.*, 1998). Despite isolation techniques are provided detailed knowledge about the single species in terms of morphology, biochemistry, and also genetic (Bitton, 2005), they have important drawbacks. The first one is to find the selective media favoring the desired microbial group. Additionally, isolated species cannot reflect their behaviors in the natural environment. Until today, 19.000 microbial species have been isolated (DSMZ, 2011; http://www.dsmz.de), however it is accepted that this number is only a small portion of real diversity (Amann *et al.*, 1995). Besides, using the molecular tools in natural and engineering systems, we can find the answer to the questions such as 'which species do exist?', 'which species are active?', 'how many microorganisms are there?', which

Microbial ecology studies need identification of species based on a comprehensive classification system that perfectly reflects the evolutionary relations between the microorganisms (Pace, 1996). Zuckerkandl and Pauling (1965) indicated that nucleic acids could document evolutionary history. Due to the pioneering studies, nucleic acids, especially 16S rRNA, are the ultimate biomarkers and hereditary molecules probably because of their essential role in protein synthesis, making them one of the earliest evolutionary functions in all cellular life-forms (Olsen *et al.*, 1986; Pace *et al.*, 1986; Woese, 1987; Stahl *et al.*, 1988). In particular, 16S rRNA and 16S rDNA have been used in

Genetic fingerprinting techniques are one of the most applied molecular tools based on 16S rRNA in microbial ecology studies. These techniques such as denaturing gradient gel electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE), amplified ribosomal DNA restriction (ARDRA) or restriction fragment length polymorphism (RFLP), terminal restriction fragment length polymorphism (T-RFLP), and single strand conformation polymorphism (SSCP), have been developed for estimation of diversity in ecosystems, screening clone libraries, following the diversity changes with respect to time

phylogenetic analysis and accepted as ideal evolutionary chronometer.

**1. Introduction**

species do utilize the specific compounds?'.

**Fingerprinting Techniques on**

Zeynep Cetecioglu1, Orhan Ince1 and Bahar Ince2

**Environmental Ecology**


*Article* **Inverse Molecular Design Techniques for Green Chemical Design in Integrated Biorefineries**

**Jamie W. Y. Lee 1, Lik Yin Ng 1,\*, Viknesh Andiappan 1, Nishanth G. Chemmangattuvalappil 2 and Denny K. S. Ng 1**

**Abstract:** Over the past decades, awareness of the increase in environmental impact due to industrial development and technological advancement has gradually increased. Green manufacturing is one of the key approaches that begin to address environmental issues. With the current global attention, methodologies to incorporate green manufacturing into the design of green products through the green process route are much needed. However, it is challenging for industries to achieve this, as there is no definite definition of green. This work presents a systematic approach that provides a clear and consistent green manufacturing definition with a measurement method in terms of both product and process. With the consistent green definitions, the developed approach designs a product that satisfies green property and other product performance properties. In addition, the developed approach synthesises the production process that fulfils green manufacturing definitions and financial considerations for the designed product. A case study on the design and production of green biofuel is solved to illustrate the efficacy of the approach. Green product design is obtained by identifying molecular building blocks that fulfil the targeted properties using an inverse molecular design technique. The goal is to design a chemical product that is environmentally friendly while fulfilling customer requirements. Moreover, a superstructural mathematical optimisation approach is used to determine optimal conversion pathways that have minimal environmental impact on the production of the identified green product. The utilisation of multi-objective optimisation allows the design of product and process to strike a good balance between operational and environmental performances.

**Keywords:** green manufacturing; integrated biorefineries; multi-objective optimisation; chemical product design; inverse design techniques


**Acknowledgements**

The first author thanks the German Research Foundation (DFG) for funding field work and logistical support, the Universidad Técnica Particular de Loja for support in finishing the manuscript. The authors thank MSc Galo Guamán for helping in GIS analyses. The authors thank Dr. Diana Szekely for the revision of English language.


**Author details**

Abdelrahman Mosaad Khattab Botany and Microbiology Department, Faculty of Science, Al-Azhar University, Cairo, Egypt

\*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.

*The Microbial Degradation for Pectin DOI: http://dx.doi.org/10.5772/intechopen.100247*


*Article* **Trend and Attribution Analysis of Runoff Changes in the Weihe River Basin in the Last 50 Years**

**Junjie Xu , Xichao Gao, Zhiyong Yang \* and Tianyin Xu**

Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China<EMAIL_ADDRESS>(J.X<EMAIL_ADDRESS>(X.G<EMAIL_ADDRESS>(T.X.) **\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** In recent years, the Weihe River basin has experienced dramatic changes and a sharp decrease in runoff, which has constrained the sustainable development of the local society, economy, and ecology. Quantitative attribution analysis of runoff changes in the Weihe River basin can help to illustrate reasons for dramatic runoff changes and to understand its complex hydrological response. In this paper, the trends of hydrological elements in the Weihe River basin from 1970 to 2019 were systematically analyzed using the M–K analysis method, and the effects of meteorological elements and underlying surface changes on runoff were quantitatively analyzed using the Budyko theoretical framework. The results show that potential evapotranspiration and precipitation in the Weihe River basin have no significant change in 1970–2019; runoff depth has an abrupt change around 1990 and then decrease significantly. The study period is divided into the base period (1970–1989), PI (1990– 2009), and PII (2010–2019). Compared with the base period, the elasticity coefficients (absolute values) of each element show an increasing trend in PI and PII. The sensitivity of runoff to these coefficients is increasing. The sensitivity of the precipitation is the highest (2.72~3.17), followed by that of the underlying surface parameter (−2.01~−2.35); the sensitivity of the potential evapotranspiration is the weakest (−1.72~−2.17). In the PI period, the runoff depth decreased significantly due to the combination effects of precipitation and underlying surface with the values of 6.18 mm and 13.92 mm, respectively. In the PII period, rainfall turned to an increasing trend, contributing to the increase in runoff by 11.80 mm; the further increase in underlying surface parameters was the main reason for the decrease in runoff by 22.19 mm. The significant increase in runoff by 8.54 mm because of the increased rainfall, compared with the PI periods. Overall, the increasing underlying surface parameter makes the largest contribution to the runoff changes while the precipitation change is also an important factor.


*4.1. Plant Materials and Field Experiments*

The HHZ/JZ1560 mapping population consisting of 280 RILs was developed using the single-seed descendent method by Ying et al. [29]. HHZ is an *indica* variety as the female parent with small grains and being widely cultivated in China, while JZ1560 is a *japonica* rice accession with very large grains (Figure S2). Three RH-derived F2 populations (WB01, WB02 and WB03) consisting 180, 137 and 115 individuals, respectively, were originated from 3 RH plants that were selected from one line of the F7 HHZ/JZ1560 RIL population. The three populations were used to validate the target QTL, *qGPC1-1*.

All the populations, together with the two parents, were planted with the spacing of 16.7 cm between plants and 26.7 cm between rows during the rice-growing seasons in the experimental field of China National Rice Research Institute, Hangzhou (120.2◦ E, 30.3◦ N), China. Eighteen plants per RIL were transplanted and the middle four plants were harvested in bulk at maturity for the measurement of GPC in 2016 and 2017. The three RH-derived populations were tested for one year in 2018. Field management was conducted according to the common practice in rice production. Fertilizer was applied for each cultivation year as follows: 375 kg/ha compound fertilizer (N-P2O5-K2O: 14:16:15) as the basal fertilizer, 52 kg/ha nitrogen at seedling stage, 69 kg/ha nitrogen at 5 days after transplanting and 150 kg/ha potassium at 20 days after transplanting.


**Abstract**

Miniature and highly efficient optical-based gas sensors have gained enormous consideration over the last few years. Materials based on the group-IV elements, namely silicon, germanium and their compounds, are deemed to be the potential candidates for the optical gas sensors. Optical gas sensors based on these materials offer appreciable sensitivity and high-density integration. Basically, these sensors paved the path for the flexible applications areas, namely internet of things (IoT), pointof-care testing, information and communication technology, etc. because of their potential candidature for being integrated with the several other photonic or electronic devices for on-chip signal processing and communication. Herein, we review optical gas sensors and discuss their basic principles, applications, recent advancement in the devices, etc. Gas concentrations can be easily detected and measured utilizing the characteristic optical absorption of gas species. This detection is crucial both for interpretation and observing of a wider range of phenomena extended from industrial practices to overall environmental change. Based on the findings, this review extends over a comprehensive overview of plethora of individual gas detection techniques, namely non-dispersive infrared, spectro-photometry, tunable diode laser spectroscopy and photo acoustic spectroscopy. This article focalizes over the discussion of the basic principle of the techniques introduced, their latest advancements and performance constraints, etc.

**Keywords:** sensors, optical gas sensors, spectrometers, infrared spectrometers, ultra-violet/visible spectrometers, fiber optic instruments, laser spectroscopy, optical and dielectric properties of gases


3.3.3. Changes in the SI of Root Length (SIRL)

About half of the breeding lines responded to 0.1 M D-mannitol with significant decreases in their root length, but the SIRL values of two breeding lines were significantly increased (Table 12, Figure S11a). One of them (C63) showed significantly a higher result than the C103 referent line, although the C103 referent line also responded with increased root length. However, as D-mannitol concentration increased, the SIRL values decreased, mostly significantly (Table 11, Figure S10a–c). When 0.2 M D-mannitol was applied, only the C103 referent line showed SIRL values higher than 100, but the SIRL values of C17 and C63 breeding lines did not differ significantly. Decreased SIRL values were found at this level of D-mannitol in almost all breeding lines. SIRL values of breeding lines C17 and C63 were significantly higher than those of the C107 referent line, and increased SIRL values were detected in more than half the of breeding lines. At the highest D-mannitol level (0.3 M), each genotype involved in the experiment showed decreased SIRL values, and we found significantly higher SIRL values in more than a third of the breeding lines compared to the C103 referent line.

**Table 12.** Root length SI (SIRL) values of potato genotypes under stress treatment induced by Dmannitol added to the medium at levels of 0.1 M, 0.2 M and 0.3 M.

**Table 12.** *Cont.*

Symbols mark significant differences from the C107 referent line (-), or from the C103 referent line () according to LSD test; the small letters indicate significantly different means between the breeding lines within a treatment, and the capital letters indicate significantly different means between treatment levels within a breeding line according to Tukey-B test.


**Author details**

Mohammed H. Al Aqad School of Education and Social Sciences, Management and Science University, Malaysia

\*Address all correspondence to<EMAIL_ADDRESS><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.


*3.3. Assessment of Antenna Cross-Talks Using an Iterative Method*

Van Zyl's Equations in Terms of *RR* for Azimuthally Symmetric Target

Under the assumption of an azimuthally symmetric target, the following equations are derived from [16] as a function of RR and the cross-talk estimate ∆1 and ∆ 0 2 [6]:

$$ \simeq \quad \Delta\_1 < |V\_{hh}|^2 > + \Delta\_2' \cdot < V\_{vv}V\_{hh}^\* > + \tag{7}$$

$$2\Delta\_2'^\* \cdot < |V\_{hw}'|^2 > \cdot (1 - \text{RR})$$

$$ \simeq \begin{aligned} \Delta\_1 \cdot &< V\_{hh}V\_{vv}^\* > + \Delta\_2' \\ &< |V\_{vv}|^2 > + 2\Delta\_1^\* \cdot < |V\_{hv}'|^2 > \cdot (1 - \text{RR}) \end{aligned} \tag{8}$$

The two unknowns ∆1 and ∆ 0 2 can be determined by solving the two equations above as a function of the voltages *Vhh*, *V* 0 *hv*, *Vvv*, and their cross-correlations measured over the azimuthally symmetrical reference target [6]. The two unknowns are determined using an analytical equation or an iterative method depending on the *RR* values:

The iterative method is applied as follows:

After the estimation of ∆1 and ∆ 0 2 , all the required calibration parameters ∆1, ∆2, *F*1, and *F*2 can be deduced using the additional equations provided by the corner reflector, as shown in [16].

In the following, the iterative method described above is adopted for the assessment of PALSAR2 system parameters using datasets collected over the Amazonian rainforest sites (with CRs), at free Faraday rotation conditions.

## **4. Assessment of PALSAR2 System Parameters at Low Faraday Rotation Conditions Using Amazonian Rainforests**

#### *4.1. Calibration Sites*

The Amazonian rainforest near the geomagnetic equator, at free Faraday rotation [15], has been used as the ideal site for the assessment and calibration of L-band ALOS-PALSAR and PALSAR2 [3,5,6,22]. CRs deployed at free Faraday rotation provide accurate estimation of antenna cross-talks in contrast to the biased measurements obtained with CRs deployed at significant Faraday rotation, as shown in [6]. In 2014, five CRs were deployed by JAXA in the Amazonian rainforests [23], and used in support of polarimetric PALSAR2 calibration. Six polarimetric images collected over the Amazonian rainforests (near Rio Branco, Brazil) at various incidence angles are used in the following to assess PALSAR-2 distortion matrix for five beams (FP6-3 to FP6-7) with incidence angle varying from 25◦ to 40◦ . The CR measurements are also used to confirm the very low Faraday rotation conditions during PALSAR2 data acquisition.

#### *4.2. Assessment of Polarimetric PALSAR2 Distortion Matrix*

Figures 1–4 present the response (in range) of a CR covered with the FP6-3 polarimetric mode on the 8th of August, 2014. According to Figures 3 and 4, there is no CR (HV and VH) return at the HH and VV peak location. This shows that the PALSAR2 cross-talk is very low, and the ALOS2 acquisition took place at very low Faraday rotation conditions. The various PALSAR2 modes considered herein, the dates of the acquisition, and the incidence angle (in degree) at the CR location are indicated in Table 1. Channel imbalance (*F*1 and *F*2) (magnitude and phase) are also given in Table 1. The following observations can be noted:

The extended Freeman–van Zyl iterative method is applied for the generation of PALSAR2 antenna cross-talks. ∆1 and ∆2 are given (in dB) in Table 2. The residual error *RR* is computed using Equation (5). The final error *RR f* computed at the last iteration corresponds to the residual error. All the cross-talks given in the table were obtained with a very low residual error (*RR f* lower than −43 dB).

**Mode Date CR Inc** |*f***1**| *φf* **1** |*f***2**| *φf* **2** FP6-3 8−08 28 1.06 −1.1 1.26 −28.0 FP6-4 8−22 31 1.06 −22.1 1.01 −25.6 FP6-5 9−05 35 1.03 −5.4 1.01 −28.2 FP6-6 9−19 37 1.06 −23.6 1.04 −27.4 FP6-7 8−13 39 1.05 −3.3 1.02 −25.7 FP6-7 8−27 39 1.04 −3.7 1.01 −25.7

**Table 1.** PALSAR2 channel imbalance (magnitude and phase (in degree)).

**Table 2.** PALSAR2 Antenna Cross-Talks (in dB) and Faraday Rotation (in degree).

**Figure 1.** Corner reflector reflector HH response (in range expressed in pixel numbers).

**Figure 2.** Corner reflector VV response (in range).

**Figure 3.** Corner reflector HV response (in range).

**Figure 4.** Corner reflector VH response (in range).

As can be noted in Table 2, the PALSAR2 antenna is highly isolated with cross-talk lower than −40 dB. The CR-measurements permit demonstrating the very high isolation of the PALSAR2 antenna: cross-talks lower than −40 dB [24]. Similar results were obtained in [3,22,25] using different calibration approaches.

#### *4.3. Measurement of the Actual Faraday Rotation during PALSAR2 Acquisition*

Many studies have shown that Faraday rotation is low near the equator [15,26]. These results are confirmed in the following for the five PALSAR2 images collected over the JAXA sites of calibration in the Amazonian rainforests. Several methods can be used to measure the Faraday rotation [26–28]. We have adopted herein the Bickel and Bates method [29] which calculates the Faraday rotation at the circular basis as follows:

$$
\begin{bmatrix} Z\_{11} & Z\_{12} \\ Z\_{21} & Z\_{22} \end{bmatrix}^T = \begin{bmatrix} 1 & j \\ j & 1 \end{bmatrix}^T [\mathbf{S}] \boldsymbol{\Omega} \begin{bmatrix} 1 & j \\ j & 1 \end{bmatrix} \tag{9}
$$

where [*S*]Ω is given as a function of the Faraday rotation angle Ω and the scattering matrix [S] of Equation (3) by:

$$[\mathbb{S}]\_{\Omega} = \begin{bmatrix} \cos \Omega & -\sin \Omega \\ \sin \Omega & \cos \Omega \end{bmatrix} [\mathbb{S}] \begin{bmatrix} \cos \Omega & -\sin \Omega \\ \sin \Omega & \cos \Omega \end{bmatrix} \tag{10}$$

Ω is given by [27,29]:

$$
\Omega = <\frac{1}{4} \cdot \arg(Z\_{12} Z\_{21}^\*)> \tag{11}
$$

where *arg*(*Z*) is the argument of the complex *Z*, and <> denotes spatial averaging.

Equation (11) is used to measure the Faraday rotation for the five PALSAR images. The measurements obtained at the CRs are given in Table 2. As expected, the Faraday rotation angle is very low (lower than 0.24◦ ) during the 5 PALSAR2 acquisitions. This confirms the accuracy of the PALSAR2 antenna distortion matrix measurements obtained above, under the assumption of negligible Faraday rotation.

#### **5. Impact of Significant Faraday Rotation on PALSAR2 Polarimetric Distortion Matrix Measurement**

The model of Equation (1) can be extended to take into account the Faraday rotation. Channel imbalances can also be separated from the polarimetric distortion matrices as done in [21]. This leads to the following equation [21,30]:

$$[V] = \begin{bmatrix} 1 & 0 \\ 0 & F\_1 \end{bmatrix} \begin{bmatrix} 1 & \delta\_2 \\ \delta\_1' & 1 \end{bmatrix} [\mathbf{S}]\_\Omega \begin{bmatrix} 1 & \delta\_3' \\ \delta\_4 & 1 \end{bmatrix} \begin{bmatrix} 1 & 0 \\ 0 & F\_2 \end{bmatrix} \tag{12}$$

with *δ* 0 1 = *δ*1/*F*1, *δ* 0 3 = *δ*3/*F*2, and [*S*]Ω is given by Equation (10) as a function of the scattering matrix [*S*] and the Faraday rotation angle Ω. It is worth noting that Equation (12), which is an extension of the van Zyl calibration model that takes into account the Faraday rotation, is expressed as a function of the conventional scattering matrix [*S*] in contrast to the Freeman model [30] expressed as a function of the transposes of [*S*] and [*V*] matrices.

The CR response combined with the channel imbalance phase difference, which can be derived using the Zebker method [31], permits the measurement of the channel imbalance *F*1 and *F*2 ([6,17,31]). After the channel imbalance correction, the measured voltages can be expressed (using Equation (12)) as a function of the antenna cross-talks and [*S*]Ω matrix elements in the following equation:

$$
\begin{bmatrix} V\_{hh} \\ V\_{hv} \\ V\_{vh} \\ V\_{vv} \end{bmatrix}\_{\begin{bmatrix} \delta\_{hh}^{\Omega} + \delta\_{4}\mathcal{S}\_{h\overline{v}}^{\Omega} + \delta\_{2}\mathcal{S}\_{v\overline{h}}^{\Omega} \\ \delta\_{3}'\mathcal{S}\_{h\overline{h}}^{\Omega} + \mathcal{S}\_{h\overline{v}}^{\Omega} + \delta\_{2}\mathcal{S}\_{v\overline{v}}^{\Omega} \\ \delta\_{1}'\mathcal{S}\_{h\overline{h}}^{\Omega} + \mathcal{S}\_{hv}^{\Omega} + \delta\_{4}\mathcal{S}\_{v\overline{v}}^{\Omega} \\ \delta\_{1}'\mathcal{S}\_{h\overline{v}}^{\Omega} + \delta\_{3}'\mathcal{S}\_{v\overline{h}}^{\Omega} + \mathcal{S}\_{v\overline{v}}^{\Omega} \end{bmatrix} \tag{13}
$$

where

$$
\begin{bmatrix}
\mathcal{S}\_{hh}^{\Omega} \\
\mathcal{S}\_{hv}^{\Omega} \\
\mathcal{S}\_{vh}^{\Omega} \\
\mathcal{S}\_{vv}^{\Omega}
\end{bmatrix} = \begin{bmatrix}
\cos^2\Omega \cdot \mathcal{S}\_{hh} - \sin^2\Omega \cdot \mathcal{S}\_{vv} \\
\mathcal{S}\_{hv} - \sin\Omega\cos\Omega \cdot (\mathcal{S}\_{hh} + \mathcal{S}\_{vv}) \\
\mathcal{S}\_{hv} + \sin\Omega\cos\Omega \cdot (\mathcal{S}\_{hh} + \mathcal{S}\_{vv}) \\
\end{bmatrix} \tag{14}
$$

For a CR, *Shh* = *Svv* = *K* and *Shv* = *Svh* = 0, and Equation (13) can be used to derive the CR-measured voltage vector as a function of the Faraday rotation angle Ω and the antenna cross-talks (*δ* 0 1 , *δ*2, *δ* 0 3 , *δ*4):

$$
\begin{bmatrix} V\_{hh}^{\text{CR}} \\ V\_{h\nu}^{\text{CR}} \\ V\_{v\nu}^{\text{CR}} \\ V\_{vv}^{\text{CR}} \end{bmatrix} = -K \begin{bmatrix} \cos 2\Omega + \sin 2\Omega (\delta\_2 - \delta\_4) \\ \cos 2\Omega (\delta\_2 + \delta\_3') - \sin 2\Omega \\ \cos 2\Omega (\delta\_1' + \delta\_4) + \sin 2\Omega \\ \cos 2\Omega - \sin 2\Omega (\delta\_1' - \delta\_3') \end{bmatrix} \tag{15}
$$

In 2014, several PALSAR2 images were collected over the CCRS calibration site in Ottawa. The PALSAR2 image collected on the 9th of September is used herein. Figures 5–8 present the polarimetric response of the CCRS (2.5 m CR) at HH, VV, HV, and VH. In contrast to the low return at cross-pol of the JAXA CR deployed in the Amazonian rainforests (Figures 3 and 4), the Ottawa CR presents a significant return at HV and VH polarization as seen in Figures 7 and 8.

**Figure 5.** Corner reflector HH response (in range expressed in pixel numbers).

**Figure 6.** Corner reflector VV response (Ottawa).

**Figure 7.** Corner reflector HV response (Ottawa).

**Figure 8.** Corner reflector VH response (Ottawa).

The results obtained in the previous section show that the PALSAR2 antenna is highly isolated with cross-talks lower than −40 dB. The analysis of the CR voltage Equation (15) with negligible cross-talks (cross-talks lower than −40 dB) leads to the following conclusions:

1. The ratios of the cross-like polarization voltage, *V CR hv* /*V CR hh* = −20.03 dB and *V CR vh* /*V CR vv* =−19.95 dB do not correspond to the actual antenna cross-talk contamination of the cross-pol (HV and VH) by the like polarization (HH and VV), as could be misinterpreted if the Faraday rotation contamination is ignored ([7]). In fact, these cross-like polarization voltage ratios lead to an estimation of the Faraday rotation angle Ω for a highly isolated antenna, as can be shown using Equation (15):

$$
\begin{bmatrix}
\frac{V\_{\text{lv}}^{\text{CR}}}{V\_{\text{v}\text{R}}^{\text{CR}}}\\\frac{V\_{\text{v}\text{R}}^{\text{CR}}}{V\_{\text{vv}}^{\text{CR}}}
\end{bmatrix}
\quad = \quad K \cdot \begin{bmatrix}
\end{bmatrix}
\tag{16}
$$

Equation (16) is used to estimate the Faraday rotation during PALSAR2 acquisitions. The results obtained are similar (within 0.2) with the ones obtained with the Bickel and Bates method (Equation (11)); Ω = 2.8◦ with *V CR hv* /*V CR hh* , Ω = 2.9◦ with *V CR vh* /*V CR vv* in comparison with Ω = 3.1◦ obtained over a forested area using Equation (11).

2. The sum of the CR cross-pol voltages cancels the Faraday rotation contamination.

  This can be shown using the following equation derived from (15):

$$((V\_{hv}^{\mathbb{C}R} + V\_{vh}^{\mathbb{C}R}) / \mathcal{K} = (\delta\_1' + \delta\_2 + \delta\_3' + \delta\_4) \cos 2\Omega \tag{17}$$

Since the cross-talks are negligible, (*V CR hv* + *V CR vh* ) should be close to zero. Figure 9 presents the CR response of the averaged PALSAR2 image (*hv* + *vh*)/2. The significant peaks that occur at HV and VH images of Figures 6 and 7 vanish in Figure 9. At the HH and VV peak location, the intensity of the averaged cross-pol return vanishes in the CR surrounding clutter of radar backscattering (−18.23 dB) which is much lower than HH and VV retro-diffusion (about 15 dB).

In summary, the analytical presentation of the CR voltage as a function of the antenna cross-talks and Faraday rotation leads to the conclusion that the HV and VH contaminations, which can be measured from SAR images collected with a highly isolated antenna, are mainly due to the actual Faraday rotation. In the case that the antenna is not highly isolated, Equation (13) obtained after the channel imbalance correction (using the CR) shows that additional reference point targets (of scattering response different from the CR) should be used for the measurement of the 5 unknowns; the 4 antenna cross-talks and the Faraday rotation.

**Figure 9.** Corner reflector (HV + VH)/2 response (Ottawa).

#### **6. Conclusions**

In summary, the PALSAR antenna is highly isolated. Both the antenna subarray and T/R are highly isolated, and as a result, the global antenna is highly isolated for all the modes considered. The excellent performances of the polarimetric PALSAR2 in terms of NESZ (better than −37 dB ([1,3]) combined with the high antenna isolation permit the demonstration of the unique L-band long-penetration SAR capabilities at low incidence angles (25◦ ) for subsurface peatland hydrology monitoring and discontinuous permafrost mapping [32,33]. The high isolation of the polarimetric ALOS2-PALSAR2 permits a simplification of the assessment and calibration of the ALOS2-Compact experimental mode, as discussed in [34].

**Author Contributions:** Concepts and Methodology, R.T. and M.S.; ALOS2-PALSAR2 raw data processing and Calibration, M.S. and T.M.; writing—original draft preparation, R.T. and M.S.; writing—review and editing, R.T., M.S. and T.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** The ALOS-2 original data are provided by JAXA under ALOS-2 Calibration Validation and Science Team (CVST) and the Research Announcement on the Earth Observations (EO-RA).

**Acknowledgments:** The authors would like to thank the anonymous reviewers for their helpful comments and suggestions. We would like to express our special thanks to JAXA for data observation and provisions. S. Nedelcu from CCRS is thanked for his help in PALSAR2 data processing.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**