Author details

Vladimir Artemyev1 , Jury Rudenko1 and George Nistratov2 \*

\*Address all correspondence to<EMAIL_ADDRESS>
1 JSC "SUEK", Moscow, Russia

2 Scientific Research Institute of Applied Mathematics and Certification, Moscow, Russia


**2. Solar cell design technologies**

Two well-known solar cell design technologies employ the photovoltaic or photothermal mechanism for light harvesting. The first method directly converts the absorbed solar energy into electricity. In the second method, the absorbed energy is of the thermal type, and it is later converted into electrical energy. **Figure 1a** shows a photovoltaic solar cell, in which n and p-type doped semiconductors are used in the

*Photovoltaic and Photothermal Solar Cell Design Principles: Efficiency/Bandwidth… DOI: http://dx.doi.org/10.5772/intechopen.110093*

**Figure 1.** *(a) Photovoltaic solar cell and (b) electric energy production using photothermal solar cell [14].*

design. When the sun's photons hit this structure, they are absorbed and create energy carriers (electrons and holes) that contribute to producing electrical energy [7]. Moreover, **Figure 1b** shows the performance of a photothermal solar cell in which mirrors and lenses are used to focus solar energy. This thermal energy can run old steam turbines or Stirling engines to generate electricity. The critical point is that the heat energy, collected by the advanced thermal solar cells, can be stored and used to produce electrical power at the proper time [14].

It is interesting to mention that photovoltaic solar cells cannot absorb photons at wavelengths higher than their bandgap. Thus, a combination of two technologies can be a solution for designing effective solar cells [14]. On the other hand, a thermophotovoltaic system enhances absorption efficiency during the dark hours, and it consists of a heat source (sun), thermal absorber, and photovoltaic solar cell. The theoretical conversion efficiency of this pollution-free and portable system with a low-maintenance cost is 85.4% [15]. For instance, by using germanium selenide (GeSe) nano-pyramids with optimized heights in the active layer of the perovskite solar cell, the light at a wavelength higher than 800 nm can also be absorbed [16]. As another approach, the exploitation of the different III-V semiconductors separately generates electron-hole pairs in different parts of the incoming solar spectrum for wider absorption coverage [17].

There are three generations of photovoltaic solar cells in the market. The first generation is based on crystalline silicon and has a high conversion efficiency. The high cost of this generation, due to the required large material thickness, is its main drawback. The second generation aims to reduce the device cost without scarifying efficiency. In this category, thin-film cells have gained lots of interest due to the reduction in the utilized raw materials [7, 18, 19]. They usually include a metallic contact layer, an active region, and an indium tin oxide (ITO) top layer. Thin film solar cells collect the carriers effectively, but they suffer from a low absorption rate due to small optical path length. The third generation of solar cells aims to enhance the efficiency of the second generation, and it is an ongoing research topic [20, 21]. An essential parameter of photovoltaic solar cells is their quantum efficiency, which is calculated as the ratio of absorbed power to incident power. Thus [22],

$$QE(\lambda) = \frac{P\_{abs}(\lambda)}{P\_{in}(\lambda)}\tag{1}$$

The high quantum efficiency of the absorber shows that when the solar cell is exposed to a photon with an arbitrary wavelength, it can generate a significant current.

Photothermal solar cells exploit different patterns on top of the engineered substrates to absorb solar energy. **Figure 2a** shows an instance of this absorber category. The efficiency of the photo thermal solar cell is strongly dependent on the efficiency of the designed electromagnetic wave absorber [23, 24]. Apart from the high absorption rate, the operating bandwidth coverage (120–1000 THz) is a critical point to tarp the whole solar energy. To evaluate the performance of the solar cell, its absorption spectrum is compared with the AM1.5 solar radiation spectrum, as in **Figure 2b** [23]. The short circuit current density of the solar energy for AM1.5 is defined as [25]:

$$J\_{SC} = \frac{e}{hc} \int\_{\lambda\_{\rm min}}^{\lambda\_{\rm max}} \lambda \Phi\_{AM1.5}(\lambda) A(\lambda) \tag{2}$$

where *e* is the electron charge, *h* is the Planck's constant, *c* is the speed of light, *λ* is the wavelength, *ΦAM*1*:*5 is solar radiance at AM 1.5, and *A* is the absorption.


Regression Analysis

A polynomial regression model is established at all six upscaled resolutions (i.e., also called the low-resolution scales) using geospatial predictors to predict each individual precipitation variable. The equation deriving the relationship between geospatial predictors and precipitation variables at each low-resolution scale is as follows in Equation (4):

$$P\_{D\\_GPM\_{LR}} = p\_1 \cdot x\_{DEM\_{LR}}^{\beta} + p\_2 \cdot x\_{DEM\_{LR}}^{2} + p\_3 \cdot x\_{DEM\_{LR}} + p\_4 \tag{4}$$

where *PD*.*GPMLR* is the predicted precipitation for each GPM variable at each low-resolution scale, and *p*1, *p*2, *p*3 *and p*4 are polynomial coefficients, *xi DEMLR* is the low-resolution geospatial variable(s).

In addition, a linear regression model is established to evaluate the relationship between the EDBF-based weighted precipitation and geospatial predictor, i.e., the latitude, which is as follows in Equation (5):

$$P\_{\rm D.WTP\_{Rx}} = p\_1 \text{.x}\_{\rm latitude} + p\_2 \tag{5}$$

where *PD*.*WTPRes* is the predicted weighted precipitation at investigated resolution scale(s), e.g., the low-resolution (*PD*.*WTPLRes*) or the high-resolution (*PD*.*WTPHRes*), *p*1 *and p*2 are linear coefficients, and *xlatitude* is the geospatial predictor.

#### Calculation of *r* Values

To execute EDBF algorithm for predicting the weighted precipitation from the multitemporal precipitation variables, the generation of initial weight vector for each contributing precipitation variable is a key process. In this regard, the *r* value is needed to formulate the initial weight vector for each contributing variable. The equation deriving *r* values at each low-resolution scale is given by Equation (6):

$$r = \frac{\text{Cov}\{Pr\_{i\prime}, Pr\_j\}}{\left(\sigma\_{Pr\_i} \times \sigma\_{Pr\_j}\right)}\tag{6}$$

where *Cov*-*Pri*, *Prj* is the covariance and σ*Pri* , σ*Prj* is the standard deviation of predictors (*i*, *j*) at *i*-th and *j*-th pixels, respectively. The equation deriving the covariance, the standard deviation and the mean for each investigated predictor is given by Equations (7)–(9), respectively:

$$Cov\left(Pr\_{i\prime}, Pr\_{j}\right) = \frac{\sum\_{ij=1}^{n} (Pr\_{i} - \mu\_{i}) \left(Pr\_{j} - \mu\_{j}\right)}{N} \tag{7}$$

$$\sigma = \left(\sum\_{i=1}^{n} (Pr\_i - \mu)^2 \Big| \mathcal{N}\right)^{\frac{1}{2}} \tag{8}$$

$$\mu = \frac{\sum\_{i=1}^{n} Pr\_i}{N} \tag{9}$$

where *Pri*, *Prj* are the two investigated predictors; μ*i*, μ*j* are the mean of investigated predictors, respectively, and *N* is the total number of observations.


The 21st Century has seen a resurgence of research of the gastrointestinal tract, especially since it was established that it plays a central role as an immune system organ and consequentially has a huge impact on causation, impact and transmission of most human ailments. New diseases such as the Acquired Immunodeficiency Syndrome, hepatitis and tumours of the gastrointestinal tract have emerged and they are currently subjects of intensive research and topics of scientific papers published worldwide. Old diseases like diarrhea have become extremely complex to diagnose with new and old pathogens, drugs, tumours and malabsorptive disorders accounting for the confusion. This book has set out algorithms on how to approach such conditions in a systematic way both to reach a diagnosis and to make patient management cheaper and more efficient. "Current Concepts in Colonic Disorders" attempts to put all the new information into proper perspective with emphasis on aetiopathogenesis and providing rational approach to management of various old and new diseases. As the book editor, I have found this first edition extremely interesting and easy to understand. Comments on how to improve the content and manner of presentation for future editions are extremely welcome.

Current Concepts in Colonic Disorders

Current Concepts in

Colonic Disorders

*Edited by Godfrey Lule*

Photo by wildpixel / iStock


**4.3** *CYP11B2* **genotypic variants and coronary artery disease**

Coronary artery disease (CAD) is a complex disorder comprised two major subsequent events: coronary atherosclerosis and myocardial infarction (MI). Despite the major progress in diagnosis of CAD, the pathogenesis and possible risk factors need further evaluation. The classical risk factors including positive family history, smoking, high body mass index, and disorders as hyperlipidemia, hypertension, and diabetes mellitus have been reported to be responsible for no more than 50% of total risk factors for CAD. Accordingly, genetic background seems also to participate in the predisposition for CAD [81].

Among the different polymorphisms described for *CYP11B2* gene, several studies have shown that the *CYP11B2* gene ˗344T>C polymorphism is associated with CAD in different ethnic groups with controversial results. Previous study on 201 CAD patients and 201 controls form Italian population have detected that *CYP11B2* polymorphism and CC genotype were associated with CAD risk in crude analysis with borderline significance which is lost by stratification to the confounding factors as smoking and family history [82]. A meta-analysis suggested that the −344T>C polymorphism in the *CYP11B2* gene might be associated with susceptibility to CAD in Caucasians and Asians [83]. A study on 609 Taiwanese male and female subjects who were unrelated and received coronary catheterization found that the C/C allele occurred more frequently in females who had CAD, and that it was associated with higher left ventricular mass (LVM) and left ventricular end diastolic diameter (LVEDD) [84]. Sharma et al. [85] also reported the association of the *CYP11B2* −344C>T polymorphism with the size of atherosclerotic plaque in the carotid artery. Neal et al. [86] suggested that −344C/T polymorphism is a cardiovascular risk factor due to its association with LV hypertrophy and decreased baroreflex sensitivity which predict the morbidity and mortality rates of MI. Others failed to find any significant association of *CYP11B2* with CAD in different populations as in an Indian population and other populations [87–89].

The underlying mechanism by which the *CYP11B2* ˗344T>C polymorphism can increase the risk for CAD remains unclear. However, it is related to its effect on increasing expression of *CYP11B2*, thereby increasing aldosterone secretion [83]. Also, the influence of gene-environment interaction has been involved as an etiological factor for CAD risk. Growing evidence has suggested the interaction between ˗344T>C polymorphism and positive smoking for enhanced CAD risk [90]. Also, Hautanen et al. [91] detected that smoking and dyslipidemia are associated risk factors for non-fatal MI in males who were carriers for *CYP11B2* −344C/T allele.