*3.4. WM Microstructure Alterations in Patients with Traumatic Brain Injury*

Compared with the healthy controls, TBI patients showed a significantly decreased FA value in widespread WM tracts, including the left inferior fronto-occipital fasciculus (IFOF), left superior longitudinal fasciculus (SLF), bilateral uncinate fasciculus (UF), forceps major, and forceps minor (Figure 2, Table 4). Mean diffusivity of WM regions showed significant difference in the forceps minor.

**Table 4.** Significant outcome of WM integrity between TBI patients and healthy controls.


**3. Model Description**

The purpose of the model developed in this paper was firstly to compare the performance and efficiency of various configurations and then to develop a control strategy in subsequent research. To check the system response according to the control design, the model has to be dynamic and not too complex [1,23]. In addition, the needs of dynamic models have been addressed by several authors because of the high operating temperature of SOFCs [24,25]. Therefore, a dynamic and lumped component model was developed in the SIMULINK environment. First, the steady-state characteristics of each system were examined to check the effectiveness of the systems. Dynamic characteristics and control strategies will be analyzed in future papers. In the following sections, the descriptions of each component model are discussed.

#### *3.1. A SOFC Stack*

In this model, a 1 kW class DIR stack has been adopted for an efficient SOFC system, and it is a planar SOFC stack with an anode-supported cell. The conditions inside a SOFC stack are appropriate for the SMR reaction because of its proper operating temperature and electrochemically generated steam. Additionally, internal reforming has several advantages, such as reducing the size of the external reformer and air blower power consumption by means of decreasing the stack air flow rate for stack cooling [23,26,27].

The SOFC stack is composed of reactant channels and the positive-electrolyte-negative (PEN) structure. The model accounts for mass balances, thermal balances and electrochemical reactions. Specifications of the stack are presented in Table 2. Fuel and air mixtures are assumed to follow the ideal gas law.

**Table 2.** A SOFC stack specifications.

#### 3.1.1. Mass Balance Model

In the mass transfer model, the species in the anode channel are considered as CH4, H2O, CO, H2 and CO2, and those in the cathode channel are only N2 and O2. Four reactions inside a stack are considered. All the reactions are presented in Table 3.

**Table 3.** Reactions considered inside a stack.

Mass balance dynamics for *i* species in fuel and air channels are given in Equations (1) and (2). In the fuel channel, (R1)—(R3) reactions described in Table 3 take place. (R4) reaction occurs in the air channel. *ri* is the molar rate of formation for *i* species, and is obtained from the equations below.

$$\frac{\partial \mathbb{C}\_{i,f}}{\partial t} = -\mu\_f \frac{\partial \mathbb{C}\_{i,f}}{\partial x} + r\_{i,f} \quad \text{(i} = \text{CH}\_4, \text{ H}\_2\text{O, CO, } \text{H}\_2, \text{ CO}\_2\text{)}\tag{1}$$

$$\frac{\partial \mathcal{C}\_{i,a}}{\partial t} = -u\_a \frac{\partial \mathcal{C}\_{i,a}}{\partial x} + r\_{i,a} \quad (i = N\_{2\prime} \ O\_2) \tag{2}$$

For calculating the reaction rates of (R1) and (R2), there are various relations accounting for the reaction rates of SMR and WGS [1,20,28]. Among the equations, Chinda et al. [29] model presented in Equations (3)–(8) has been used. Chinda et al. derived the reaction rate from Arhenius' curve fits using the data by Lehnert et al. [30]. *Rk* and *kk* represent the reaction rate and forward reaction rate constant of reaction *k*, respectively. Chinda et al. considered that the SMR reaction occurs at the surface of the anode and the WGS reaction takes place inside the void volume of the anode.

$$R\_{\rm R1} = k\_{\rm R1} \left( P\_{CH\_4} P\_{H\_2O} - \frac{P\_{H\_2}^3 P\_{CO}}{K\_{\rm R1}} \right) \left[ \text{mol } \text{m}^{-2} \text{ s}^{-1} \right] \tag{3}$$

$$R\_{\rm R2} = k\_{\rm R2} \left( P\_{H\_2O} P\_{CO} - \frac{P\_{H\_2} P\_{CO\_2}}{K\_{\rm R2}} \right) \left[ \text{mol} \,\text{m}^{-3} \,\text{s}^{-1} \right] \tag{4}$$

$$k\_{\rm R1} = 2395 \exp\left(-\frac{231266}{RT}\right) \left[\text{mol m}^{-2} \text{ Pa}^{-2} \text{ s}^{-1}\right] \tag{5}$$

$$k\_{\rm R2} = 0.0171 \exp\left(-\frac{103191}{RT}\right) \left[\text{mol m}^{-3} \text{ Pa}^{-2} \text{s}^{-1}\right] \tag{6}$$

*Kk* indicates the equilibrium constant of reaction *k* and can be calculated by Equations (7) and (8) [2], *Z* is defined as (1000/T(K)-1), The unit of *K*R1 is Pa2, and *K*R2 is a dimensionless constant.

$$K\_{\rm R1} = 1.0267 \times 10^{10} \exp\left(-0.2513 Z^4 + 0.3665 Z^3 + 0.5810 Z^2 - 27.13 Z + 3.2770\right) \tag{7}$$

$$K\_{\rm R2} = \exp\left(-0.2935Z^3 + 0.6351Z^2 + 4.1788Z + 0.3169\right) \tag{8}$$

The reaction rates of (R3) and (R4) are equal to the electrochemical reaction rate of the fuel cell and can be obtained by Faraday's law as shown in Equation (9).

$$R\_{\rm R3} = R\_{\rm R4} = R\_{\rm R5} = \frac{J}{2F} \left[ \text{mol s}^{-1} \right] \tag{9}$$

From Equations (3)–(9), the molar rate of formation per volume (*ri*) can be inferred. Nitrogen does not react, so *rN*2 is zero.

$$r\_{CH\_4} = -\frac{1}{t\_f} R\_{R1} \tag{10}$$

$$r\_{H\_2O} = -\frac{1}{t\_f} R\_{\mathbb{R}1} - \varepsilon\_a R\_{\mathbb{R}2} + \frac{1}{V\_a} R\_{\mathbb{R}3} \tag{11}$$

$$r\_{CO} = \frac{1}{t\_f} R\_{\mathbb{R}1} - \varepsilon\_a R\_{\mathbb{R}2} \tag{12}$$

$$r\_{H\_2} = 3\frac{1}{t\_f}R\_{\rm R1} + \varepsilon\_a R\_{\rm R2} - \frac{1}{V\_a}R\_{\rm R3} \tag{13}$$

$$r\_{\text{CO}\_2} = \varepsilon\_a R\_{\text{R2}} \tag{14}$$

$$
\sigma\_{O\_2} = -0.5 \frac{1}{V\_c} R\_{R3} \tag{15}
$$

The pressure decrease (Δ*p*) through the anode and cathode channels is calculated using Equation (16). Equations (17) and (18) represent the friction factor (*f*) for laminar and turbulent flow, respectively. *ϕ* is the aspect ratio of the channel, and *vg* is the gas velocity.

$$f = \frac{24}{\text{Re}[1 - 1.3553\rho + 1.9467\rho^2 - 1.7012\rho^3 + 0.9564\rho^4 - 0.2537\rho^5]} \text{ (Re} < 2000) \tag{16}$$

$$f = \frac{0.0791}{Re^{0.25}} \ (Re \ge 2000) \tag{17}$$

$$
\Delta p = f \frac{4L\_{ch}}{D\_h} \frac{1}{2} \rho\_\% u\_\%^2 \tag{18}
$$

3.1.2. Electrochemical Reaction Model

Cell potential can be computed by Equation (19). The thermodynamic reversible potential (*Vrev*) is determined by the Nernst equation in Equation (20) [31]. A voltage drop from the thermodynamic reversible potential exists because of overpotentials summarized in Table 4.

$$V\_{ccll} = V\_{rev} - \eta\_{act,a} - \eta\_{act,c} - \eta\_{ohm} - \eta\_{conc} \tag{19}$$

$$V\_{rev} = E\_0 - \frac{RT\_{PEN}}{2F} \ln \frac{P\_{H\_2O}}{P\_{H\_2} P\_{O\_2}^{0.5}} \tag{20}$$

$$E\_0 = 1.2723 - 2.7645 \times 10^{-4} T\_{PEN} \tag{21}$$

#### 3.1.3. Thermal Balance Model

For thermal energy calculations, the lumped capacitance method was used. Homogeneous temperature among the fuel cell components was assumed, so the temperature of the fuel cell was considered the same as *TPEN*. Heat absorption and release related to the reactions inside the fuel cell and convective heat transfer between the fuel cell and fuel/air bulk flows were considered in this model. Consequently, three temperatures were achieved from the thermal balance model: air, fuel bulk flow temperature and PEN temperature. The relevant equation for thermal balances in the PEN structure appears as follows.

$$\begin{split} \frac{\partial T\_{\rm PEN}}{\partial t} &= -\frac{q\_{\rm f\mathcal{E}}\boldsymbol{\mu}}{\rho\_{\rm PEN}\varepsilon\_{p,\rm PEN}L\_{\rm c}w\_{\rm c}t\_{\rm PEN}} - \frac{q\_{\rm f\mathcal{E}}}{\rho\_{\rm PEN}\varepsilon\_{p,\rm PEN}L\_{\rm c}w\_{\rm c}t\_{\rm PEN}}}{ + \frac{1}{\rho\_{\rm PEN}\varepsilon\_{p,\rm PEN}}\Delta H\_{\rm R1}R\_{\rm R1} - \Delta H\_{\rm R2}\varepsilon\_{\rm d}R\_{\rm R2} - \frac{1}{\rho\_{\rm PEN}\omega\_{\rm c}L\_{\rm c}}\Delta H\_{\rm R5}R\_{\rm R5}}} \\ \end{split} \tag{22}$$

For the calculation of heat transfer at gas channels, the Nusselt number (*Nu*) was considered to be a constant value of 3.39. Anode and cathode channels can act as fins of uniform rectangular cross-sectional areas, so relations for heat transfer at extended surfaces are used, as shown below, where *ηf* is the fin efficiency and the convective heat transfer coefficient *hch* is obtained from *Nu*.

$$q\_{\mathcal{g}} = \eta\_f h\_{\rm ch} \left( N\_{\rm ch} A\_f + A\_b \right) \left( T\_{\rm PEN} - T\_{\rm g} \right) \tag{23}$$

#### *3.2. Steam Methane Reformer*

The methane steam reforming process requires a large amount of heat; thus, exhaust gas from the system is generally used as a heat source for the ESR, namely, an allothermal reformer. The reference and AOGR #1 systems suggested in this paper adopted this type of ESR. However, DIR stacks can mitigate the demand for high-performance ESR [4]. ESR can operate at a lower temperature range than the normal operating temperature

(973.15–1073.15 K) [32]. Hence, an adiabatic reformer was also examined in the AOGR #2 system. A shell and tube type catalytic steam reformer was selected in this paper, and the suggested specifications of the reformer are detailed in Table 5. The model consists of mass and thermal balance models. Gas mixtures are considered ideal gases, and the porosity of the bed is constant.

**Table 5.** Reformer specifications.

For catalytic steam reformers, it is important to find the most appropriate catalyst because it directly affects the performance of the reformer. Among various active metals, nickel (Ni) is widely used because of its high reactivity and long durability [33,34]. Xu and Froment examined the kinetics of the SMR process with a Ni/MgAl2*O*4 catalyst under an operating range of approximately 675–1000 K. The results are the most widely used for SMR kinetcis [35]. The SMR model in this paper has been developed based on the Xu and Froment model. While various reactions take place in catalytic SMR, only the SMR reaction, the WGS reaction, and the direct steam reforming (DSR) reaction are considered. The SMR (R1) and WGS (R2) reactions are defined in Table 3, and the DSR (R6) reaction is presented below. It is assumed that the species in the reactant flow are CH4, H2O, CO, H2 and CO2. The SMR kinetics based on Xu and Froment model are organized in Table 6 [35].

Direct steam reforming reaction (DSR) *CH*4 + 2*H*2*O* → *CO*2 + 3*H*2 (R6) (24)

Mass and thermal balances in both the gas and solid phases are presented below. Equations (25) and (26) represent the mass balance in the gas and solid phases for each species *i*, respectively [28]. The mass transfer coefficient (*kg*,*i*) is presented in Equations (27) and (28) [36].

$$\varepsilon\_{b}\frac{\partial \mathbb{C}\_{\mathbb{S},i}}{\partial t} = -v\_{\mathbb{S}}\frac{\partial \mathbb{C}\_{\mathbb{S},i}}{\partial \boldsymbol{x}} - k\_{\mathbb{S},i}a\_{\mathbb{v}}\left(\mathbb{C}\_{\mathbb{S},i} - \mathbb{C}\_{\mathbb{s},i}\right) \tag{25}$$

$$\frac{d\mathbb{C}\_{\rm s,i}}{dt} = -k\_{\rm g,i}a\_{\rm v}(\mathbb{C}\_{\rm g,i} - \mathbb{C}\_{\rm s,i}) + (1 - \varepsilon\_{\rm b})\rho\_{\rm cut}r\_{\rm SMR,i} \tag{26}$$

$$k\_{\mathbb{S},i} = j\_{D,i} \text{Re} \text{Sc}\_i^{1/3} \frac{D\_i}{d\_p} \tag{27}$$

$$
\varepsilon\_b j\_{D,i} = 0.765 \text{Re}^{-0.82} + 0.365 \cdot \text{Sc}\_i^{-0.398} \tag{28}
$$

Equations (29) and (30) are the thermal balances in the gas and solid phases. The convective heat transfer coefficient (*hg*,*SMR*) in Equation (30) is known from the Chilton– Colburn j-factor (*jH*). For forced convection through a packed bed, Yoshida et al. [37] suggested empirical correlations of *jH*. Ψ is an empirical coefficient depending on the particle shape, and its value is 1 for a sphere.

$$\varepsilon\_b \frac{\partial T\_\%}{\partial t} = -\upsilon\_\% \frac{\partial T\_\%}{\partial \mathcal{X}} + \frac{h\_\text{\mathcal{g},SMR} a\_\text{\mathcal{v}}}{\rho\_\text{\mathcal{g}} c\_{p,\text{\mathcal{g}}}} \left(T\_\text{\mathcal{v}} - T\_\text{\mathcal{S}}\right) \tag{29}$$

$$\frac{\partial T\_{\sf s}}{\partial t} = -\frac{h\_{\sf S,SMR}a\_{\sf v}}{\rho\_{\sf bcd}c\_{p,\sf b}} \left(T\_{\sf s} - T\_{\sf S}\right) + \frac{(1-\varepsilon\_{\sf b})\rho\_{\sf cat}}{\rho\_{\sf b}c\_{p,\sf b}} \sum\_{k} (-\Delta H\_{k}\eta\_{k}R\_{k}) \left(k = \text{R1, R2, R6}\right) \tag{30}$$

$$h\_{\mathbb{S},SMR} = j\_H \frac{c\_{p,\mathbb{S}} \rho\_{\mathbb{S}} v\_{\mathbb{S}}}{Pr^{2/3}} \tag{31}$$

$$j\_H = 0.91 \text{Re}^{-0.51} \Psi \ 0.01 < Re < 50 \tag{32}$$

$$j\_H = 0.61 \text{Re}^{-0.41} \Psi \text{ 50} < \text{Re} < 7000 \tag{33}$$

#### *3.3. Catalytic Combustor*

To compute the temperature and species in the CC, a mathematical model including mass and thermal balances was developed. A Pt-catalyzed monolithic combustor is analyzed. Specifications of CC are given in Table 7. The oxidation reactions over Pt considered in this model are CO, CH4 and H2 oxidation. The rate expressions and reaction rate per Pt surface area are organized in Table 8 based on Ref. [38]. Chemical reactions are assumed to occur only on the external surface of the catalytic wall.

**Table 7.** Catalytic combustor specifications.

**Table 8.** Equations for the reaction rate of CC based on Ref. [38].

Homogeneous temperature, concentration and velocity within the channel are assumed for mass and thermal balance computations. Equations (34) and (35) describe the mass balances for the gas and solid phases for each species *i*, where *i* refers to CH4, H2O, H2, CO, CO2, O2 and N2. The corresponding equations for thermal balances for the gas and solid phases are presented in Equations (36) and (37). For the heat transfer coefficient (*hg*,*CC*) calculation, Nu is considered to have a constant value of 3.39.

$$
\varepsilon\_m \frac{\partial y\_{\mathcal{G},i}}{\partial t} = -v\_{\mathcal{G}} \frac{\partial y\_{\mathcal{G},i}}{\partial x} - k\_{m,i} S \left( y\_{\mathcal{G},i} - y\_{s,i} \right) \tag{34}
$$

$$(1 - \varepsilon\_m)\frac{dy\_{s,i}}{dt} = k\_{m,i}S(y\_{\mathcal{S},i} - y\_{s,i}) + \frac{RT\_{\text{CC}}}{P\_{\text{tot}}}aR\_{\text{CC},i} \tag{35}$$

$$
\varepsilon\_{\rm m} \rho\_{\rm \mathcal{S}} c\_{p, \rm \mathcal{S}} \frac{\partial T\_{\rm \mathcal{S}}}{\partial t} = h\_{\rm \mathcal{S}, \rm CC} S \left( T\_{\rm \mathcal{S}} - T\_{\rm \mathcal{S}} \right) - v\_{\rm \mathcal{S}} \rho\_{\rm \mathcal{S}} c\_{p, \rm \mathcal{S}} \frac{\partial T\_{\rm \mathcal{S}}}{\partial x} \tag{36}
$$

$$(1 - \varepsilon\_{\rm ff})\rho\_{\rm s}c\_{p,\rm s}\frac{\partial T\_{\rm s}}{\partial t} = h\_{\rm g,\rm C\mathcal{C}}S\left(T\_{\mathcal{S}} - T\_{\rm s}\right) + a\left(\sum\_{i} (-\Delta H\_{i})R\_{i}\right) \tag{37}$$

#### *3.4. Balance of Plant*

3.4.1. Air and Recirculation Blower

A model of air and recirculation blowers was employed to examine the outlet temperature and power consumption. The stack air flow rate was determined by the air utilization factor, and the CC air flow rate was controlled to maintain a CC temperature below 1123.15 K. In terms of the recirculation blower, the target recirculation ratio determines the flow rate. The blower outlet temperature and power consumption are obtained from Equations (38) and (39). In the computation, the values of *ηisen*, *ηmotor* and *ηmech* were 0.8, 0.9 and 0.9, respectively.

$$T\_{blower,o} = T\_{blower,i} - T\_{blower,i} \frac{1 - \left(p\_o/p\_i\right)^{\kappa - 1/\kappa}}{\eta\_{isen}} \tag{38}$$

$$P\_{blower} = \dot{m}\_{blower}c\_{p,air} \left(T\_{blower,o} - T\_{blower,i}\right) \eta\_{mator} \eta\_{mech} \tag{39}$$

### 3.4.2. Heat Exchanger

Heat exchangers are employed to recover heat from AOG, COG and exhaust gas. They act as fuel/air preheaters, a steam generator and a HR-HE. The outlet temperature of each gas and heat transfer rate are defined by the effectiveness-NTU method, as shown in

Equation (40) since the information is insufficient to use the LMTD method. *εHE* indicates the effectiveness of a heat exchanger and was set to 0.75.

$$q\_{HE} = \varepsilon\_{HE} \mathbb{C}\_{\min} (T\_{h,i} - T\_{c,i}) \quad \mathbb{C}\_{\min} = \min \{ \mathbb{C}\_{hot}, \mathbb{C}\_{cool} \} \tag{40}$$

### *3.5. Performance Factor*

*Uf uel* and *Uair* indicate the fuel and air utilization factors, respectively. Methane is only supplied as fuel in this model. If there are other hydrocarbons in fuel, they should be added to the denominator in Equation (41). The fuel and air flow rates of the stack are determined from the target utilization factor. Recirculated hydrogen, however, is not considered when the fuel flow rate is calculated.

$$
\Delta I\_{fuel} = \frac{I}{8F\dot{n}\_{CH\_4}}\tag{41}
$$

$$\mathcal{U}\_{air} = \frac{J}{4Fy\_{O2}\dot{n}\_{air}}\tag{42}$$

The electrical, thermal and total efficiencies are shown in Equations (43)–(45). The electrical efficiency of the system is the ratio of the net generated energy of the system to the chemical energy of the supplied fuel. To estimate the thermal efficiency, the temperature of the final exhaust gas from the CC (*Tvent*) is assumed to become 393.15 K after the HR-HE.

$$
\eta\_{elc} = \frac{P\_{stack} - P\_{FCRlower} - P\_{CCBllower}}{\dot{m}\_{CH\_4} LHV\_{CH\_4}} \tag{43}
$$

$$\eta\_{\rm th} = \frac{\mathcal{C}\_{p, \rm COOG} \dot{m}\_{\rm COOG} (T\_{\rm COOG,O} - T\_{\rm rent})}{\dot{m}\_{\rm CH\_4} LHV\_{\rm CH\_4}} \tag{44}$$

$$\eta\_{\rm tot} = \frac{\eta\_{\rm elle} + \eta\_{\rm th}}{\dot{m}\_{\rm CH\_4} LHV\_{\rm CH\_4}} \tag{45}$$

### **4. Results and Discussion**


**4.1 Design-1 [SOMI using two open stub and three serial line sections]**

**Figure 3** presents the configuration of the design-1 SOMI, and **Table 2** displays the characteristic impedances of design-1 SOMI achieved with PSO, CSA and GSA. The algorithm steps have shown in section-3 by which these characteristic impedances are attained. The magnitude response, the AME, the phase response, the polezero plot, the fitness rate and the improvement graph are the parameters selected in **Figure 4(a)**–**(f)** for the frequency response analysis of the design-1 SOMI. **Table 3**

**Table 2.** *Optimized characteristic impedances of the design-1 SOMI.*

**Table 3.**

*Comparison summary of total magnitude and phase error for design-1 SOMI.*

records the overall magnitude error and the phase error. **Table 4** and **Table 5** summarize the statistical analysis and the qualitatively analyzed data of magnitude error of the design-1 SOMI. It is observed that GSA-based results have least values in all aspects with respect to PSO and CSA. The pole-zero plot of the Design-1 SOMI for stability analysis in which all the poles and zeros lie inside the unit circle is shown in **Figure 4(d)**. This plot asserts that the SOMI design-1 is stable. The convergence profile of all three algorithms in which GSA is clearly demonstrated to be faster than PSO and CSA in less execution time is shown in **Figure 4(e)**, which is also summarized in **Table 6**. The percentage improvement comparison in magnitude error for the design-1 SOMI as CSA to PSO, GSA to PSO, and GSA to CSA and the percentage improvement comparison in phase errors summarized in **Table 7** are shown in **Figure 4(f)**. The design-1 SOMI GSA-based magnitude response closely matches that of the ideal one in the 2.5–16 GHz frequency range. On the basis of these observations, compared to the PSO and CSA, GSA has the lowest magnitude error and the highest convergence speed.

**4.2 Design-2 [SOMI using one open stub, one short stub and three serial lines]**

*Percentage improvement in magnitude and phase error for design-1 SOMI.*

*Analysis of Wideband Second-Order Microwave Integrators*

*DOI: http://dx.doi.org/10.5772/intechopen.94843*

**Table 7.**

**Figure 5.**

**Table 8.**

**141**

*Configuration of the design-2 SOMI.*

*Optimized characteristic impedances of the design-2 SOMI.*

**Algorithm Magnitude PI (%) Phase PI (%)** PSO 4.1613 3.5758 CSA 13.4935 3.5855 GSA 9.7374 0.0099

The design-2 SOMI configuration is illustrated in **Figure 5**. The optimized characteristic impedances of design-2 SOMI obtained by PSO, CSA, and GSA. The algorithm steps have discussed in Section 3 by which these characteristic impedances are attained. **Table 8** offers the optimized characteristic impedances of the line elements of Design-2 SOMI. For the frequency response analysis of the design-2 SOMI, the same parameters are selected as the design-1 SOMI as shown in **Figure 6 (a)**–**(f)**. In **Table 9**, the total magnitude and phase errors are listed. **Table 10** and **Table 11** summarize the statistical analysis and the qualitatively analyzed data of different characteristics of magnitude error of the design-2 SOMI. It is observed that GSA-based results have the least values in all aspects as compared to the PSO and CSA. The pole-zero plot of the Design-2 SOMI for stability analysis in which all the poles and zeros lie inside the unit circle is shown in **Figure 6(d)**. This plot asserts that SOMI Design-2 is stable. The convergence profile of all three algorithms in which GSA is clearly demonstrated to be faster than PSO and CSA with less execution time is shown in **Figure 6(e)**, which is also summarized in **Table 12**. The percentage improvement comparison in magnitude error for the design-2 SOMI as CSA to PSO, GSA to PSO, and GSA to CSA and the percentage improvement comparison in process error, summarized in **Table 13**, are shown in **Figure 6(f)**.

**Characteristic impedance PSO CSA GSA** *ZOC* 116 26.87 141 *ZTL*1 30.5 142.6 33 *ZTL*2 145 84 138 *ZTL*3 9.5 19 10 *ZSC* 110 138.09 116.89

#### **Table 4.**

*Statistical data of magnitude error for design-1 SOMI.*

#### **Table 5.**

*Qualitatively data of magnitude error for design-1 SOMI.*

#### **Table 6.**

*Convergence profile for design-1 SOMI.*

*Analysis of Wideband Second-Order Microwave Integrators DOI: http://dx.doi.org/10.5772/intechopen.94843*

**Table 7.**

records the overall magnitude error and the phase error. **Table 4** and **Table 5** summarize the statistical analysis and the qualitatively analyzed data of magnitude error of the design-1 SOMI. It is observed that GSA-based results have least values in all aspects with respect to PSO and CSA. The pole-zero plot of the Design-1 SOMI for stability analysis in which all the poles and zeros lie inside the unit circle is shown in **Figure 4(d)**. This plot asserts that the SOMI design-1 is stable. The convergence profile of all three algorithms in which GSA is clearly demonstrated to be faster than PSO and CSA in less execution time is shown in **Figure 4(e)**, which is also summarized in **Table 6**. The percentage improvement comparison in magnitude error for the design-1 SOMI as CSA to PSO, GSA to PSO, and GSA to CSA and the percentage improvement comparison in phase errors summarized in **Table 7** are shown in **Figure 4(f)**. The design-1 SOMI GSA-based magnitude response closely matches that of the ideal one in the 2.5–16 GHz frequency range. On the basis of these observations, compared to the PSO and CSA, GSA has the lowest magnitude

**Algorithm Mean Variance Standard deviation** PSO 0.0410 0.0035 0.0588 CSA 0.0393 0.0036 0.0603 GSA 0.0354 0.0024 0.0494

**Algorithm Maximum Minimum Average** PSO 0.2642 4*:*8442 104 0.0410 CSA 0.3762 9*:*2306 104 0.0393 GSA 0.2384 4*:*2118 104 0.0354

PSO<PHONE_NUMBER> 0.6132 CSA<PHONE_NUMBER> 0.5547 GSA<PHONE_NUMBER> 0.4950

**per iteration**

**Execution time (s) per cycle**

*Comparison summary of total magnitude and phase error for design-1 SOMI.*

**Algorithm Total magnitude error Phase error** PSO 5.7746 1.0403 CSA 5.5353 1.0031 GSA 4.9954 1.0030

error and the highest convergence speed.

*Innovations in Ultra-WideBand Technologies*

*Statistical data of magnitude error for design-1 SOMI.*

*Qualitatively data of magnitude error for design-1 SOMI.*

**Algorithm Iteration cycle Execution time (s)**

**Table 3.**

**Table 4.**

**Table 5.**

**Table 6.**

**140**

*Convergence profile for design-1 SOMI.*

*Percentage improvement in magnitude and phase error for design-1 SOMI.*


*2.4. Symbiotic Organism Search (SOS)*

The SOS algorithm [56] is an evolutionary metaheuristic algorithm inspired by actual biological interactions in nature, such as mutualism, commensalism, and parasitism. Like other population-based algorithms (e.g., a GA and PSO), the SOS shares the following similar features: (1) Control parameters should be properly settled before operation; (2) it has operators to enhance or improve candidate solutions via the interaction of each solution; (3) it has a selection mechanism to determine the current optimal solution in the solution domain and preserve the current best solution during the process [56,57]. Furthermore, the SOS algorithm requires no algorithm-specific parameters. Only the initial ecosystem (population) size and the maximum number of iterations are needed.

In short, the organisms (solutions) in the ecosystem are guided toward the current best organism in mutualism and commensalism states, while the parasitism state is used to prevent the organisms trapping in a local optimal solution. These three states are repeated until the stopping criterion is achieved. Details about the SOS algorithm are given in the Supplementary Materials.

#### *2.5. Inverse Transient Analysis (ITA)*

The ITA introduced by Pudar and Liggett [58] was developed by minimizing the errors between the measured and calculated system state variables (i.e., pressure or flow rates). Various potential faults with unknown parameters (fault information) are tested in a numerical simulator until the measured state variable traces match the calculated ones [4]. A heuristic algorithm is a useful tool for the numerical simulators of ITA because it can explore global or near-global optimum solutions in the search space in an affordable time [28]. However, the ITA method relies on an accurate transient model of the system. A model consisting of transient and boundary conditions with correct system parameters is needed in ITA for obtaining a reliable transient response in the system [5]. The pressure measurements are theoretically more suitable than the volume measurements (i.e., flow rate) because the response of the pressure is more sensitive than that of the flow rate in the ITA [59]. Transient flow is not easy to precisely measure in practice with a very high sampling rate, when only the pressure can be measured. The objective function *F* in the proposed approach for fault detection is defined as

$$F = \text{Min} \sum\_{j=1}^{m} \sum\_{i=1}^{n} \left( H\_{ij}^{o} - H\_{ij}^{s} \right)^{2},\tag{14}$$

where *m* is the total number of observation points in the network; *n* is the total amount of data at an observation point; and *Ho ij* and *Hs ij* represent the *i*th observed and simulated heads at observation point *j*, respectively. Thus, an ITA model was set up for a pipe network, in which head specifications were computed as a function of unknown variables (fault information), e.g., *Lp*, *LL*, *CdLAL*, *Bp*, *BL*, *CdBAB*, *Dp*, *DL*, *LD*, *aD*, and *AD* (listed and defined in Table 1).


2.6.1. Power

Power is the parameter that influences the most the photocoagulation effect. Selecting a certain power is relative, as it depends on individual factors: retinal pigmentation, edema, pre-retinal fibrosis, the presence of vitreous hemorrhage. Therefore, the key element in choosing a certain laser power is clinical (retinal reaction), not technical (mW). Usually, treatment is initiated with low powers (150–200 mW) in order to test retinal reaction, never on pigmented retina. If there is no visible mark, power is increased with 20–50 mW, until the obtainment of the desired effect: whitish lesion. When high powers are used, the lesion appears gray with well-defined margins and sometimes in its center a vapor stream is emitted. This effect can be identified when passing from a thicker to a thinner retina, for example from the posterior pole toward the periphery, without reducing the power accordingly. During retinal photocoagulation, power must be varied in order to obtain the appropriate tissue reaction [5].

### 2.6.2. Exposure time

Exposure time can vary between 50 ms and 1 s. At low exposure times, the effect in the tissue is cylindrical: the area of the lesion is the same at the superficial (RPE) and deep (choroid) levels. However, at low exposure times, high powers are required in order to obtain a clinical effect. This is associated with the risk of explosive effects. Usually, exposure times between 200 and 500 ms are preferred [5].


*2.2. Blood Sampling and Analysis*

Blood samples were collected from an antecubital vein by venipuncture at the time of race number collection which was 8 to 6 h before the start, after crossing the finishing line, 24 and 48 h post-race using BD Vacutainer PST II tubes. Samples were centrifuged at 3500 rpm for ten minutes and kept at 4 ◦C during transport to Vithas Rey Don Jaime Hospital (Castellon), where they were processed using the modular platform Roche/Hitachi clinical chemistry analyzer Cobas c311 (Roche Diagnostics, Penzberg, Germany), as previously published [1,19]. Lactate dehydrogenase (LDH) and creatin kinase (CK) were used to assess muscle membrane disruption, as a surrogate for muscle damage. C-reactive protein (CRP) as an indicator of acute inflammatory reaction [3] (supplementary Table S1).

The oxidative stress biomarkers used in the present investigation were GR, GPx, MDA and CG, which were analyzed as follows:

GPx activity, which catalyzes the oxidation by H2O2 of glutathione (GSH)to its disulfide (GSSG), was assayed spectrophotometrically as reported by Lawrence et al. [20] toward hydrogen peroxide, by monitoring the oxidation of nicotinamide adenine dinucleotide phosphate (NADPH) at 340 nm. The reaction mixture consisted of 240 mU/mL of GSH disulfide reductase, 1 mM GSH, 0.15 mM (NADPH) in 0.1 M potassium phosphate buffer, pH 7.0, containing 1 mM ethylethylenediaminetetraacetic acid (EDTA) and 1m sodiumazide; a 50 μL sample was added to this mixture and allowed to equilibrate at 37 ◦C for 3 min. Reaction was started by the addition of hydrogen peroxide to adjust the final volume of the assay mixture to 1 mL.

GR activity was determined spectrophotometrically using Smith proposed method [21]. Briefly, when the GR catalyzed reduction of GSSG to GSH is produced in presence of 5,5- dithiobis (2-nitrobenzoic acid) (DTNB), 2-nitrobenzoic acid is formed as a subproduct, which formation is monitored at 412 nm. The GSSG reduction was started by adding 25 μL of brain sample to a solution containing DTNB 3 mM prepared in 10 mM phosphate buffer, 2 mM NADPH, 10 mM MEDTA in 0.2 M pH 7.5 phosphate buffer.

MDA concentration was measured by liquid chromatography according to a modification of the method of Richard and coworkers [22], as previously reported [23]. Briefly, 0.1 mL of sample (or standard solutions prepared daily from 1,1,3,3-tetramethoxypropane) and 0.75 mL of working solution (thiobarbituric acid 0.37% and perchloric acid 6.4%; 2:1, *v/v*) were mixed and heated to 95 ◦C for 1 h. After cooling (10 min in ice water bath), the flocculent precipitate was removed by centrifugation at 3200× *g* for 10 min. The supernatant was neutralized and filtered (0.22 μm) prior to injection on an ODS 5 μm column (250 × 4.6 mm). Mobile phase consisted in 50 mM phosphate buffer (pH 6.0): methanol (58:42, *v/v*). Isocratic separation was performed with 1.0 mL/min flow and detection at 532 nm.

CG were determined to evaluate protein oxidation in milk samples. The CGs released during incubation with 2,4-dinitrophenylhydrazine were measured using the method reported by Levine et al. (1990) [24] with some modifications introduced by Tiana et al. (1998) [25]. Briefly, the samples were centrifuged at 13,000× *g* for 10 min. Then, 20 mL of brain homogenate was placed in a 1.5 mL Eppendorf tube, and 400 mL of 10 mM 2,4 dinitrophenylhydrazine/2.5 M hydrochloric acid (HCl) and 400 mL of 2.5 M HCl were added. This mixture was incubated for 1 h at room temperature. Protein precipitation was performed using 1 mL of 100% of TCA, washed twice with ethanol/ethyl acetate (1/1, *v/v*) and centrifuged at 12,600× *g* for 3 min. Finally, 1.5 mL of 6 N guanidine, pH 2.3, was added, and the samples were incubated in a 37 ◦C water bath for 30 min and were centrifuged at 12,600× *g* for 3 min. The carbonyl content was calculated from peak absorption (373 nm) using an absorption coefficient of 22,000 M−1cm−1 and was expressed as nmol/mg protein.

Biochemical results obtained immediately post-race were adjusted by employing the Dill and Costill method [26], using hematocrit and hemoglobin to determine the magnitude of plasma volume changes after the race in each participant.


*2.4. Animal Model*

The animal model chosen was 30 male Wistar rats (*Rattus novergicus*) aged 38 ± 2 days old and with a mean weight of 65 ± 10 g, provided by the Universidad Industrial de Santander (UIS) Health Faculty's Bioterium in Bucaramanga, Colombia. A period of eight days of acclimatization to their new environment in the animal testing laboratory at the University of Santander (UDES) was required before their usage in research. In the lab, they were housed with two animals per cage (of dimensions 24 × 37 × 24 cm), in an individually ventilated (IVC) Easy Flow cage system, with a bed of patula pine shavings, food (commercial rodent diet, Lab-Diet®) and sterilized water ad libitum. Variables such as humidity, temperature (21 ± 22 ◦C), ammonia and CO2 concentrations, and standardized dark/light cycles (lights on at 06:00 and off at 18:00) were controlled. For the experiments, animals were randomly assigned using the standard function = RAND in Microsoft Excel into six groups of six animals each. Three of these groups were controls: negative (not infected, not treated); hLIMOX-Control (not infected, treated with higher doses of LIMOX (EOs enriched in limonene and with added caryophyllene oxide)); and positive (infected, not treated). The three other groups were experimental: LIMOX (infected, treated with LIMOX); LIMOXBNZ (infected, treated with LIMOX + BNZ); and BNZ (infected, treated with BNZ). The positive control and experimental groups (LIMOX, LIMOXBNZ, and BNZ) were made up of animals infected with *T. cruzi*. The therapeutic schemes were administrated as described in Table 1 via daily, oral doses for 31 days continuously following the onset of the chronic disease phase as established through echocardiography and parasitemia absence. All experiments were carried out according to the NIH Guide for the Care and Use of Laboratory Animals to minimize animal pain and distress. The protocol used

was approved by the Bioethics Committee of the Universidad Industrial de Santander and the Ethics Committee of the Universidad de Santander (Agreement Number 010-VII of 15 and 16 May 2017).

**Table 1.** Therapeutic schemes administrated in *Trypanosoma cruzi*-infected Wistar rats.

Tryp: metacyclic trypomastigote of *Trypanosoma cruzi* clone 338Cl2 TcI; EO: Essential oil; *q. s. p*: quantity sufficient provided.

#### *2.5. Experimental Animal Model of Chronic Chagas Disease*

Animals from the positive control and experimental groups were infected using an inoculum prepared as described in the literature [10]. Briefly, 2.5 × 105 metacyclic trypomastigotes of *T. cruzi* clone 338Cl2 (isolated from a confirmed CCC patient), characterized as Discrete Typing Unit (DTU) TcI, were obtained from TAU3AAG media, resuspended in a final volume of 100 μL of phosphate buffer saline-glucose (PBS-G), and injected into the intraperitoneal cavity [10]. After infection, the rats' behavior and clinical parameters (weight, ocular perimeter, position of the vibrissae, physical condition, stool moisture, and parasitemia) were monitored every three days. Parasitemia was evaluated by observing parasites in peripheral blood obtained through puncture of the ventral coccygeal vein with a 27-gauge needle. The blood was collected in a microhematocrit tube and observed between a microscope slide and coverslip under a light optical microscope at one hundred microscopic fields (100× magnification objective) as described by Brener [12].

To determine the development and progression of CCC, the rats' heart silhouettes were evaluated via Two-Dimensional Ultrasound, measuring the Maximum Length (ML) and Maximum Diameter (MD) along the long axis of the heart with a convex transducer and a DP20 (Mindray, Madrid, Spain) ultrasound machine. This analysis was performed at three specific points during the experiment: (i) one day prior to infection to establish baseline measurements; (ii) between 60 and 70 days after infection (d.a.i) to determine the onset of the chronic phase of the infection (presence of cardiomegaly) and to define the start date for therapy; and (iii) at the end of the treatments to evaluate the effect of the therapies applied. Animals in the experimental groups (LIMOX, LIMOXBNZ, and BNZ) received doses of the assigned treatments (Table 1) in a daily, oral scheme for 31 consecutive days, and at a specific hour (7:00 a.m.).

The test subjects were euthanized one day after the end of treatment (99 d.a.i) using an anesthetic mixture of ketamine at 90 mg/kg and xylazine at 7.5 mg/kg intraperitoneally, providing hypnosis, analgesia, and muscle relaxation (the anesthetic triad). Once reflexes such as patellar, palpebral, and corneal had ceased, an additional anesthetic dose was applied for the induction of euthanasia by anesthetic overdose. Before any further procedures were effected, death was verified by establishing the absence of vital signs and reflexes (corneal, eyelash, and rhythmic breathing). Blood was then collected by cardiac puncture to measure biomarkers of liver (ALT and AST) and kidney (BUN and creatinine) function and to conduct a hemogram. Transaminases, BUN, and creatinine assays were carried out from serum samples using commercial Ccromatest kits purchased from Linear Chemicals S.L.U. (Montgat, Spain), in accordance with the manufacturer's instructions; and measurements were made using a URIT-8030 Automated Chemistry Analyzer. EDTA

whole blood samples were used for hematological analysis. Total blood cell counts were performed using established URIT-3000Vet hematology equipment. Blood cell morphology and differential leukocyte counts were made on slides stained with Wright.

During the animals' necropsies, photographic and weight records were collected from solid organs (spleen, liver, heart, and kidney), which were sectioned for subsequent immunohistochemical (heart tissue) and histopathological (kidney, liver, and spleen) analysis. These sections were placed into tubes containing 10% neutral formalin stabilized for 72 h, and then embedded in paraffin blocks. A fragment of cardiac tissue from the apical region of the left ventricle was collected in RNAlaterTM storage reagent (Sigma-Aldrich, St. Louis, MO, USA), for parasitemia determination by real-time polymerase chain reaction (qPCR). The remainder of the heart tissue was then divided by cross sectional cuts, with each half being randomly assigned for histologic or immunohistochemical analysis. To guarantee data blindness, all procedures (control of parasitemia, echocardiography, euthanasia, and sampling) were performed by independent veterinary personal unaware of which group the animal had been assigned to. The microscopic study was also performed blind by an expert pathologist. The animal remains were properly handled in accordance with the safety and environmental responsibility protocols established by the Universidad de Santander, and disposed of by incineration in accordance with the same.


**Graph Convolutional Neural Network for a Pharmacy Cross-Selling Recommender System**

**Franz Hell 1,\*,†, Yasser Taha 1,†, Gereon Hinz 1,\*, Sabine Heibei 2, Harald Müller 2 and Alois Knoll 3**

Received: 29 September 2020; Accepted: 8 November 2020; Published: 11 November 2020 -

**Abstract:** Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance in recommender system benchmarks. Adapting these methods to pharmacy product cross-selling recommendation tasks with a million products and hundreds of millions of sales remains a challenge, due to the intricate medical and legal properties of pharmaceutical data. To tackle this challenge, we developed a graph convolutional network (GCN) algorithm called PharmaSage, which uses graph convolutions to generate embeddings for pharmacy products, which are then used in a downstream recommendation task. In the underlying graph, we incorporate both cross-sales information from the sales transaction within the graph structure, as well as product information as node features. Via modifications to the sampling involved in the network optimization process, we address a common phenomenon in recommender systems, the so-called popularity bias: popular products are frequently recommended, while less popular items are often neglected and recommended seldomly or not at all. We deployed PharmaSage using real-world sales data and trained it on 700,000 articles represented as nodes in a graph with edges between nodes representing approximately 100 million sales transactions. By exploiting the pharmaceutical product properties, such as their indications, ingredients, and adverse effects, and combining these with large sales histories, we achieved better results than with a purely statistics based approach. To our knowledge, this is the first application of deep graph embeddings for pharmacy product cross-selling recommendation at this scale to date.

**Keywords:** graph convolutional neural network; recommender system; cross-sales; pharmacy; popularity bias

#### **1. Introduction**

Deep learning algorithms play an increasingly important role in recommender systems. In recent years, new deep learning architectures known as graph convolutional networks (GCNs) that can learn from graph-structured data [1–4] were introduced and applied to recommendation applications. The basic principle behind GCNs is to use neural networks to learn how to iteratively aggregate and transform feature information from a local graph neighborhood to obtain a final representation of a given node, called the "embedding". This way, GCNs can incorporate both feature information, as well as the graph structure. These methods can be leveraged to distill useful low-dimensional embeddings of input data such as images, text, molecules, or individual users [5–8]. These low-dimensional

embeddings then can be used in downstream tasks such as recommendation applications, where they can complement or even replace traditional recommendation algorithms, such as collaborative filtering. GCN based methods have set a new standard on countless recommender system benchmarks [1] and are now being used in real-world production environments such as Pinterest.

#### *1.1. Present Work*

The current state of cross-selling in pharmacies relies heavily on expert knowledge, which is typically provided by salespeople interacting directly with customers. The feedback loop for improving the knowledge about possible cross-sales is therefore very localized, with a very poor distribution of experience gained. Our recommender system has the goal of substantially improving the collection and distribution of general knowledge about possible cross-sales to pharmacy businesses, while taking into account the restrictions explained below. The task of our system is to recommend pharmaceutical products that are good cross-sales for a given pharmaceutical product. A good recommendation for a given query for example is an over-the-counter drug that is used for complementary therapy that the query product is intended for or helps with additional symptoms that the query product indications are associated with or a product that helps alleviate possible adverse effects of the query product. As sales of medical products are highly regulated in all countries, prescription medications can only be sold if prescribed by a physician, and hence are not legitimate cross-selling items.

The prescription pain killer medicine diclofenac is associated with the possible adverse reaction of increases in gastric acid, when taken for a long period of time. Pantoprazole, a proton-pump-inhibitor, can help alleviate this adverse effect by prohibiting excess production of stomach acid. Other good recommendations for diclofenac are additional gels for the treatment of blunt injuries such as contusions or sports injuries for which diclofenac is often prescribed, which mitigate swelling and additionally help in local pain therapy.

The setting for our recommendation system and experimental evaluation is the European Union, where the GDPR forbids, among other things, the processing of medical data for pure sales purposes if these data can be linked to specific persons, even if they are pseudonymized, as such information could allow for an individual data subject to be singled out and linked across different datasets. Traditional recommendation methods like collaborative filtering or content based approaches all require a user history (either user ratings or user-article interaction histories with information about the articles) for computing recommendations. A pharmacy cross-selling recommender system however is restricted from using the purchase history information. All our input data are therefore completely anonymized, and the only historical information that can be exploited is the co-occurrence of articles in sales transactions. The second data source we use is the description of indications, active ingredients, and adverse effects for each article.

This paper describes the main challenges when applying the idea of a recommender system based on graph convolutional networks to pharmaceutical product cross-selling recommendations, given the above-mentioned restrictions. The first challenge is to devise a recommendation system based on product feature information and sales data that solely represents which products have been sold together with no information about customer product relations. To address this challenge, we construct a graph representation of pharmacy sales and product data and employ a graph neural network to learn product embeddings, which can be used for a downstream recommendation task. We describe how we chose to encode this information into a graph structure by representing pharmaceutical products as nodes, their features as node features, and cross-sales-statistics as node-node edges.

We also describe the optimization goal of the graph neural network training process with respect to these data. We chose to use a semi-supervised training scheme by utilizing the triplet loss to optimize the network parameters. The definition of positive and negative samples is a key decision when using triplet loss in optimization. Here, we describe how we selected positive and negative samples based on cross-selling and feature information present in the graph.

A major problem when using sales statistics is the distribution of the sales data, which often shows that popular articles have a high probability of being sold, while the so-called long tail or distant tail [9] contains articles that have a low overall sales probability. This popularity bias also influences "cross-selling statistics", meaning that products that are sold often also dominate cross-selling statistics, even when they are not particularly related to the product with which they are sold. Here, we describe how we counteract this bias with a re-ranking approach based on probability theory and how we additionally use feature information in a hybrid sampling approach.

Finally, we describe how we evaluate the results of the algorithm by expert review of a selected segment of the generated cross-selling recommendations. In particular, we compare the results to purely statistics based cross-selling recommendations computed from the input dataset.

#### *1.2. Related Work*

#### 1.2.1. Pharmaceutical Product Cross-Sales Recommendations

A study by Rutter et al. [10] interviewed sixteen pharmacists and found that pharmacists relied mostly on personal judgment augmented by patient feedback to make product recommendations. Another study examining the factors affecting pharmacists' recommendations of complementary medicines [11] also reported that recommendations are made based on personal experience and education and concluded that in order to encourage the informed use of complementary medicines in pharmacies, there is a need for the development of accessible, quality resources. A study investigating the community pharmacists' recommendations for alternative natural products for stress in Melbourne reported that out of 94 pharmacies, twenty-five provided the customer with an inappropriate product and concluded that there is a need to develop guidelines for pharmacists to make evidence based decisions in recommending complementary and alternative medications [12]. A study investigating a recommender system for newly marketed pharmaceutical drugs developed an adverse drug reactions label prediction component, which emits, for a given prescription, a set of drugs that should be avoided as they will induce adverse drug reactions if taken together with the prescription [13]. Zhang et al. [14] proposed a cloud-assisted drug recommendation system for online pharmacies to recommend users the top-N related medicines according to the symptoms. In their system, they first clustered the drugs into several groups according to the functional description information and designed a basic personalized drug recommendation based on user collaborative filtering. In the collaborative filtering based part of their pipeline, the authors leveraged drug user ratings. In our approach, we do not have access to historical user data or user ratings, as explained above. We can base our modeling solely on raw sales data and product features. Therefore, we designed a unique approach to solve the pharmaceutical product-product recommendation task based on node embeddings generated by a graph neural network that can leverage sales data, as well as product features without the need for customer histories or user ratings.

#### 1.2.2. Graph Convolutional Neural Networks and Recommender Systems

Conceptually, our approach is related to previous node embedding algorithms and contemporary advancements in the application of convolutional neural networks to graph-structured data. The core idea behind node embedding methods is to create useful low-dimensional vector representations of high-dimensional information about a node in the graph, including the node's (local) graph neighborhood. The use of low-dimensional vector embeddings as feature inputs for a wide variety of machine learning tasks such as classification, prediction, clustering, and recommendation tasks has been proven to be valuable [4,15,16]. The original work of [17], which introduced a variant of graph convolutions that were based on spectral graph theory, was followed by several authors, proposing improvements and extensions [1–4,7,15,18–21]. Early approaches to generating node embeddings, such as the GCN introduced by [2], were transductive and limited in their generalization to unseen nodes and in their scalability since those methods required operating on the Laplacian of the entire

graph [22]. Later approaches operated in an inductive fashion [23]. In contrast to transductive embedding methods that are based on matrix factorization, newer approaches leverage node features and the spatial structure of each node's local neighborhood, as well as the distribution of node features in the neighborhood [24]. Building on this, further improvements and new algorithmic features have recently been explored that ensure performance, scalability, and improved sampling [22,25,26]. These advancements led to new improved performance on benchmarks such as node classification, link prediction, or web-scale recommendation tasks, as well as the application of those methods to areas such as drug design [1,3,7,8,18].

#### 1.2.3. Popularity Bias

One obstacle to the effectiveness of recommender systems is the problem of popularity bias [27]. Collaborative filtering based recommenders typically emphasize popular articles (e.g., those with more sales, views, or ratings) over articles from the long tail [28] that may only be popular among a small group of customers or consumers. Although popular articles can be a good recommendation, they are also likely to be well known and are sometimes even bad recommendations, especially in the face of pharmaceutical cross-selling recommendations. Furthermore, delivering only popular articles will not enhance the discovery of newly introduced articles and will ignore good recommendations that are contained in the long tail. The idea of the long tail of article popularity and its impact on recommendation quality has been explored by some researchers [21,29–32]. In those works, the authors tried to improve the performance of the recommender system in terms of accuracy and precision, while others focused on reducing popularity bias by means of regularization [33] or re-ranking [9]. Substantial research has also been published on recommendation diversity, where the goal is to prevent recommending too many similar articles [30–32].

#### **2. Methodology**

#### *2.1. Data Representation in a Graph*

In order to harness the power of graph convolutional neural networks to learn product embeddings, we transform the pharmacy sales data and product information into a graph. We represent each unique pharmacy product as a node, which also contains the descriptive information of the corresponding product encoded into a multi-hot vector, which has about 15,000 entries, representing the medical indications, active ingredients, and adverse reactions of the products. The data were extracted from the main German commercial pharmaceutical database used in pharmaceutical and other software solutions for pharmacies, physicians, hospitals, and other health providers, which was provided by Pharmatechnik. Undirected weighted edges between two nodes then represent how often cross-selling occurred for each pair of products, where a "cross-selling" is defined as two products sold in the same transaction. The set of approximately 100 million transactions with information about which products were sold together was provided by Pharmatechnik, leveraging sales transactions that are documented via their pharmacy management system IXOS, which is currently being used in more than 5000 pharmacies. Before further processing, we limit the transactions on which we base our cross-selling numbers to those with two or three sold articles, which is roughly 25% of the complete set. We hypothesize that the fewer items are in one transaction, the more specifically related the sold products are to each other. These transactions together include about 700,000 different products, but also include many similar products that vary only slightly (or not at all from each) other. For example, there are multiple offerings of Aspirin 100 products from different manufacturers. For training, validation, and testing, we then randomly chose 60% of these preselected transactions to construct a training graph and 20% each to construct validation and test graphs. These graphs act as the input to the model training and validation stage. However, the final model that is used for inference is then trained on all the included sales transactions. In model training, we use the top ranked cross-selling articles (those with the highest edge weights with a given query) as candidates

for the aggregation and optimization part of the modeling process. See the details below. Popular articles unfortunately dominate this pool of candidates. To counterbalance this so-called popularity bias, we introduce a probability theory based approach that aims at updating the edge weights of all edges in the graphs.

#### 2.1.1. Probability Based Re-Ranking

In the probability based re-ranking approach (PBR), we aim to discern the residual cross-sales from the expected cross-sales. This is achieved by subtracting the expected cross-sales of one product relative to another from their actual raw cross-sales. The residual cross-sales is now the new edge weight for the directed edge between both products.

Given products *A* and *B*, we aim to compute the conditional expected cross-sales of *B* given *A*, assuming both are independent. We approximate the conditional expected cross-sales of *B* given *A*, *E*(*B* | *A*) to be:

$$E(B \mid A) = P(B, A \mid N(a)) \cdot Z \tag{1}$$

The joint probability of *A* and *B* is conditioned on N (*a*), the subset of nodes in the one hop neighborhood of *A*. Conditioning on N (*a*) quantifies how often *B* is sold with *A* relative to how often the neighbors of *A* are sold with *A*. Under the assumption of the independence of A and B, *P*(*B*, *A* | ·) in (1) can be reformulated as:

$$P(B, A \mid \mathcal{N}(a)) = P(B \mid \mathcal{N}(a)) \cdot P(A \mid \mathcal{N}(a)),\tag{2}$$

where we compute *P*(*B* | ·), *P*(*A* | ·), and Z as:

$$\sum\_{l \in \mathcal{N}(b)} \phi\_{bJ}(b^1, l^1)$$

$$P(B \mid \mathcal{N}(a)) = \frac{\sum\_{l \in \mathcal{N}(b)} \phi\_{bJ}(b^1, l^1)}{Z},\tag{3}$$

$$P(A \mid \mathcal{N}(a)) = \frac{\sum\_{u \in \mathcal{N}(a)} \phi\_{a,u}(a^1, u^1)}{Z},\tag{4}$$

$$Z = \sum\_{u \in \mathcal{N}(a)} \sum\_{k \in \mathcal{N}(u)} \phi\_{u,k}(u^1, k^1) \tag{5}$$

*φx*,*y*(*x*1, *y*1) is the factor representing the combination where *x* and *y* are sold together, and its value is the actual cross-selling amount between nodes *x* and *y*. To compute the conditional expected cross-sales, we first compute the cross-selling probability of *A*, *P*(*A* | ·), as the total cross-sales amount of *A* divided by Z, which is the total amount of cross-sales in the one hop neighborhood of *A*; that is, the total number of cross-sales of the neighbors of *A*, which includes all cross-sales of *A*'s neighbors with *A*. The cross-selling probability of *B* conditioned on the neighborhood of *A*, *P*(*B* | ·), is computed in a similar manner, by dividing the total cross-sales of *B* by the same factor, Z. We then compute the conditional expected cross-sales (1) of *B* given *A* by multiplying the conditioned expected cross-selling probability of the two products *P*(*B* | ·) and *P*(*A* | ·), their joint probability in the case of independence (3) , with Z. To compute the residual cross-selling amount of B with respect to A, the conditional expected cross-selling is deducted from the actual cross-selling of *A* and *B* (4). This difference is the new edge weight between *B* and *A*.

$$\mathcal{W}\_{residual}(B, A) = \mathcal{W}\_{raw}(B, A) - E(B \mid A) \tag{6}$$

*Wresidual* is the final residual cross-selling amount of *B* with *A*, and *Wraw*(*B*, *A*) is the cross-selling amount of nodes *B* and *A* from the input data. If *Wresidual* is positive, we assume that *B* is sold with *A* more often than expected; if *Wresidual* is negative, we assume that *B* is sold less often with *A* than expected. Note that *Wresidual*(*A*, *B*) is computed differently from *Wresidual*(*B*, *A*), as *P*(*B* | ·) and *P*(*A* | ·) are both conditioned on the one hop neighborhood of *A*. In the new graph, all nodes that were connected via undirected edges beforehand, therefore, now share two directed edges, expressing the residual cross-selling amount of both nodes relative to each other.

#### *2.2. Model Architecture*

We employ a graph convolutional neural network model (Figure 1) that uses localized convolutions on aggregated neighborhood vectors to produce embeddings of products represented by graph nodes, akin to the one introduced in [24]. The basic idea is that we transform the representations of the neighbors of a given node through a dense neural network and then apply an aggregator/pooling function (a weighted sum, shown as dark blue boxes in Figure 1, the "CONVOLVE" module) on the resulting set of vectors. This aggregation step provides a vector representation of a node's local neighborhood. We then concatenate this aggregated neighborhood vector (dark grey box) with the nodes' current representation (light grey box) and transform the concatenated vector through another dense neural network layer. The output of the algorithm is a representation of a node, called the node embedding, that incorporates information about itself and its local graph neighborhood. Details about the algorithm can be found in [22] (Algorithm 1); the only change we made is that we aggregate the neighborhood-node information only across the "top neighbors" of a given node. The top neighbor nodes of a given query node are those with the highest edge weight among all connected nodes, representing the products that are sold more often with the query than expected. We then compute the recommendations by using these final node embeddings, as described in Section 2.4.

**Figure 1.** The graph convolutional neural network uses localized convolutions on aggregated neighborhood vectors to learn product embeddings. Here, we show the two layer graph neural network that computes the final embeddings of nodes A and B using the previous layer representation of nodes A and B, respectively, and that of their respective neighborhoods. Different colors denote different nodes. The recommendation score between two products A, B is then computed via the utilization of the cosine similarity between the two final embedding vectors of nodes A and B.

#### *2.3. Model Training*

For the optimization of the network parameters, we utilize the triplet loss, shown in Equation (6), which is a distance based loss function that operates on the final embedding of three input nodes: the anchor *A*, the positive *P*, which is typically from the same class as the anchor or related by some other measure, and the negative *N*, which is typically from a different class than the anchor.

$$\min\_{\theta} L(A, P, N) \tag{7}$$

$$L(A, P, N) = \max\left(0, \text{sim}(A, N) - \text{sim}(A, P) + \Delta\right) \tag{8}$$

We use the embeddings of pharmacy products represented by nodes in the graph as inputs for the triplet loss. Each node can be an anchor node *A*, and each has positively related nodes *P* and negatively related nodes *N*. A given pair *A*, *P* is to be related by some measure, i.e., are often bought together or are similar in feature encoding. The pair *A*, *N* is thought to be related by some other measure, i.e., are never bought together or are bought together less than expected. The goal of the training phase is to optimize the parameters of PharmaSage so that the cosine similarity *sim*(*A*, *P*) of the anchor-positive pair is higher relative to the cosine similarity *sim*(*A*, *N*) of the anchor-negative pair by a margin of 0.5.

#### 2.3.1. Positive Sampling

We chose positives for a given anchor node A by randomly sampling nodes among the top neighbors of that anchor node with equal probability in each training iteration. The top ranked cross-sellers are those neighbors with the highest edge weight with the anchor, representing those products that have been sold more often with the anchor than expected. Together, they make up the positive sampling pool. To complement the edge-weight based positive sampling approach, we additionally utilize nodes that share encoding features with the anchor as possible positive samples. In this hybrid approach to positive sampling, we chose 50% of the positive samples based on the feature similarity of the anchor node relative to all other nodes and the other 50% based on edge weights representing re-ranked cross-sales. We only include products in the additional feature based positive sampling pool for a given anchor, if that product/node shares any feature with the anchor.

#### 2.3.2. Negative Sampling

We chose negative samples among nodes not connected to the anchor node, which are never sold together with the anchor. We additionally utilized nodes with negative edge weights with the anchor as possible negative samples, as their expected cross-selling amount with the anchor is higher than the actual cross-selling. Generally, we applied semi-hard negative mining among the pool of possible negatives, as introduced in FaceNet [34], which has been widely used ever since [35,36].

#### *2.4. Recommendation*

The final embeddings that are computed by our model after training are then used to calculate recommendation scores between all products. We obtained a recommendation score bounded within the interval [0, 1] with the utilization of the cosine similarity of two vectors, *sim*(*A*, *B*), commonly used in information retrieval and data mining techniques [37,38]. Given the similarity scores between one query product and all other products (except prescription medications, which can only be sold if prescribed by a physician, and hence are not legitimate cross-selling recommendations), the products with the highest similarity to the query are chosen as recommendations for the query article. A diagram illustrating the high-level architecture of how recommendations are computed between articles A and B is shown in Figure 1.

#### *2.5. System Setup, Runtime, and Validation*

For our software, we used the Python framework, the networkx library for creating the graph, and the PyTorch framework for the implementation of the GCN. The network is trained on a system with an Intel Core i7 8750H, an Nvidia GeForce GTX1070 GPU, and 32GB RAM. The training takes

approximately 48 h and is terminated once the triplet loss, which starts at the margin of 0.5, has reached a threshold of 0.05 for the training graph. The validation loss is at 0.092 and the test loss at 0.095 at this point. We found that, empirically, the quality of the results does not improve much beyond this point. We stopped the training of the model used for inference at the same threshold.

#### **3. Experiments**

#### *3.1. Popularity Bias in Sales Data*

Popularity bias is a phenomenon that is visible in many data sources, including retail and online sales data. Our analysis of pharmacy sales data shows the same bias, as 2% of all products are sold equally often as the remaining 98%. It is clear in Figure 2a that those products that have a high overall sales probability also are ranked among the top neighbors on average, while unpopular articles have a proportionally lower cross-selling rank. This indicates that top-sellers are products that are also top cross-sellers.

**Figure 2.** Popularity bias and re-ranking. (**a**) Depiction of overall sales probability and cross-sales (CS) probability (*y*1-axis) and the node degree (number of neighbors) (*y*3-axis) for all products. The average cross-sales rank (*y*2-axis) is shown for initial cross-sales statistics (solid black line) and the probability based re-ranking (PBR) approach (black dotted line). Note that the average rank is proportional to the popularity of the respective product in the initial cross-sales statistics, but this relation disappears in the PBR approach. (**b**) The probability density of edge weights (kernel density estimation (KDE)) shows the distribution of edge weights in the initial cross-sales based graph, as well as in the PBR based graph.

#### 3.1.1. The Effect of Re-Ranking on Cross-Selling Statistics

Re-ranking affects the ranking of cross-sellers. As shown in Figure 2a, the average cross-sales rank for all products was influenced by re-ranking. The analysis of the graph based on actual cross-sales shows that the average rank of products decreases proportionally to the overall sales probability of that product. In contrast, the overall rank of a product has no correlation to its overall sales probability in the PBR approach. The average rank of top sellers drops substantially based on the PBR, which is a result of two factors. First, top cross-sellers are being sold often with many other products, as reflected by the number of neighbors' curve (node degree). Second, it is likely that they are only partly cross-sold more often than average. The same seems to be true for all other products and is reflected in the edge weight probability density after re-ranking (Figure 2b). After applying the PBR approach, the average weight across all nodes drops to 0 with a standard deviation of 308, indicating that half of the pairs are sold less often together than expected, while the other half represents genuine cross-sales. To further examine the effect of re-ranking on the distribution of ranks, Figure 3a shows two articles before and after re-ranking. Based on the actual cross-sales statistics, the median rank of the example top-seller

is 7, but drops to 152 based on the PBR, respectively. The interquartile range based on the penalty re-ranking approach is 17.5 times larger than the PBR approach, which reflects a more dispersed ranking scheme. In contrast, the median rank of an example from the long tail is 562 and rises to 245 based on the PBR, with its interquartile range decreasing slightly.

**Figure 3.** (**a**) Cross-selling rank distribution for a top-selling product and a product from the long tail based on raw cross-sales and the PBR. (**b**) Average probability for products (grouped into 10 quantiles) to be among the top cross-selling articles that are being used for positive sampling in the triplet loss for both approaches. "New" denotes products that are not included in positive sampling in the approach based on raw cross-sales, but are relevant in the PBR approach, and shows their average positive sampling probability. These products show up in addition among top ranked cross-selling products when the PBR is applied. The PBR introduces around 75% more products with an average sampling probability 36.6%. Quantile selection of products for the first 10 product bins for both approaches is based on raw cross-selling statistics (red).

#### 3.1.2. The Effect of Re-Ranking on Positive Sampling

In order to assess the impact of the PBR on positive sampling involved in the network optimization, we evaluate how the positive sampling pool that is made up of the top neighbors of each node changes with the different approaches. The probability of being chosen among positive samples, as depicted in Figure 3b, shows that those products that dominate the positive sampling pool based on the raw cross-sales are used as positive samples significantly less often in the PBR approach. Sampling based on actual cross-sales statistics puts more emphasis on popular products, while the PBR puts less emphasis on those products, but adds more diversity to the sampling pool. Relative to the raw cross-selling statistics, the PBR introduces more diversity, with 36.6% additional products in the sampling pool.

#### *3.2. Recommendation Quality*

In order to evaluate the performance of our algorithm, we collected expert feedback on product-product recommendations for a set of 25 query products. For each recommendation generated by PharmaSage, the feedback given can have one of three values; 0 (no pharmaceutical relation to the query product), 1 (a pharmaceutical relation exists, and the product is a good recommendation for the query), or 2 (a pharmaceutical relation exists, and the product is a very good recommendation for the query). The mean across scores for the top 15 recommendations for 25 products is then used as a performance quantifier.

First, we compute recommendations based on the cross-selling data encoded in the graph, which can be thought of as a conventional recommendation approach akin to collaborative filtering. Products that are most often sold with the query product are chosen as recommendations. Second, we compute the recommendations based on the PBR graph. This result offers insight into what can be achieved without any additional learning. We then use the PBR graph to train the PharmaSage model. As can be seen in Figure 4, the quality of the evaluated recommendations is comparatively lower for simple cross-sales statistics based recommendations, and the quality increases in the PBR approach. The PharmaSage model that is then trained on the PBR approach introduces yet another increase in recommendation quality. Compared to approaches based solely on sales data, this model is able to learn from cross-sale information encoded in the graph edges, as well as leverage feature information encoded as node features.

**Figure 4.** Average recommendation quality among the top 15 recommended articles for 25 evaluated products. Recommendations are computed based on the graph incorporating raw cross-selling statistics and the graph, where the edge weights have been recomputed using the probability approach (PBR). PharmaSage is optimized based on the PBR approach as the input.

In Table 1, example recommendations computed by PharmaSage, their recommendation rank, and expert feedback are shown for prednisolone, a corticosteroid. Prednisolone is used to treat a wide range of health problems including allergies, skin diseases, infections, and certain autoimmune disorders. It helps by reducing inflammation and suppressing the immune system. PharmaSage recommends additional over-the-counter medications for allergies like hay fever (rank 1, 5, Table 1) for which Prednisolone is often prescribed and stomach acid blockers to reduce the adverse effect of the active ingredient in the query article (rank 2, 4, Table 1). Calcium and vitamin D3 can both help improve bone health, which can be affected by continuous therapy with corticosteroids (rank 6, 9, Table 1). The nonsteroidal anti-inflammatory drug helps with pain and induces additional anti-inflammatory effects (rank 8, Table 1). Furthermore, an antipruritic and anti-inflammatory gel (rank 10, Table 1) is recommended, which counters symptoms associated with the main indications of prednisolone, and a nasal spray is also for additional therapy, which frees up a clogged nose, reduces the swelling of the mucous membranes, and can help treat allergic reactions of the respiratory tract (rank 3, Table 1). ASS100 (rank 7, Table 1), an anticoagulant, should not be recommended without additional medical advice; hence, the expert feedback mark is 0.

**Table 1.** Example recommendations for prednisolone, a corticosteroid.

#### **4. Conclusions**

We introduced PharmaSage, a graph convolutional network for pharmacy product cross-sale recommendations. PharmaSage is the first application of graph convolutional neural networks to pharmaceutical product-product recommendation, exploiting sales statistics and pharmaceutical product features like indications, ingredients, and adverse effects. In addition, we introduced a method based on probability theory that addresses the common popularity bias problem. We showed how popularity bias is present in the pharmacies' sales and cross-selling dataset and how it can be successfully addressed in order to increase both recommendation quality and diversity. We developed PharmaSage based on real-world pharmaceutical data and comprehensively evaluated the quality of the learned embeddings for a pharmacy product cross-sales recommendation task, demonstrating a substantial improvement in recommendation quality compared to traditional approaches that are based solely on cross-sales statistics. Our work demonstrates the positive impact that methods based on graph convolutional networks can have in pharmacy cross-selling recommender systems, and we believe that PharmaSage can be further extended to tackle other graph representation learning problems in the retail and online sales industry. A future point of interest will be to further evaluate the quality of recommendations given by PharmaSage by evaluating A/B tests against traditional recommendations given by pharmacists and how both impact cross-sales and evaluating customer feedback.

**Author Contributions:** Methodology, software, and validation: F.H., Y.T., G.H., and H.M.; data curation: S.H.; review, editing, and supervision: F.H., G.H., H.M., S.H. and A.K.; All authors read and agreed to the published version of the manuscript.

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

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

#### **References**

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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


4.1.4. Concentrated Oxidation Catalysts decomposed at 250 °C overthe NaX zeolite. The addition of Pt on the zeolite can significantly increase the catalytic activity. The main obstacle for the application of zeolite was the coke that formed during

The low concentration of VOCs can limit the catalytic efficiency of catalysts. Some porous materials were investigated to the concentration and decomposition of VOCs, such as zeolite, γ-Al2O3 (Table 8, Figure 13) [84,101,173–175]. R. Beauchet et al. tried to decompose the isopropanol and o-xylene mixture over the CsX, NaX, and HY zeolite [173]. O-xylene and isopropanol were totally decomposed at 250 ◦C over the NaX zeolite. The addition of Pt on the zeolite can significantly increase the catalytic activity. The main obstacle for the application of zeolite was the coke that formed during the reaction, which led to the short lifetime. Amir Ikhlaq et al. studied the mechanism and kinetics of decomposition of chlorinated VOCs by ozonation over the γ-Al2O3 and ZSM-5 zeolite [174]. The results suggest that ozone reacts with the absorbed VOCs on the surface of catalysts. However, the ozonation process will increase the cost in most of the industrial processes. Yuexin Peng et al. supported MnO2 on the Al-rich β-zeolite to degrade toluene [75]. The T90 is 285 ◦C, which is much lower than the MnO2 supported on γ-Al2O3 due to the lattice oxygen species in MnO2 and absorbed oxygen species on the zeolite. Cu and Co were also used to modify the β-zeolite for the destruction of toluene and trichloroethylene [76]. The modified zeolite showed good stability in the reaction. The catalytic activity is mainly from the supported metal oxides. The suitable acidity and strong oxidation stability can improve the CO2 selectivity. Dedong He et al. modified the HZSM-5 zeolite with a series of rare earth elements, including La, Ce, Pr, Nd, Sm, Y, and Er, for the catalytic decomposition of CH3SH [91]. Cu was also used to modify ZSM-5 for the combustion of acrylonitrile. The isolated Cu is the active center. The SiO2/Al2O3 ratio can affect the ion exchange capacity and the catalytic performance of the catalyst. When the ratio is 26, the catalyst shows the best catalytic activity [92]. SBA-15 was used to support MnOx for the combustion of toluene and showed good catalytic activity [176]. The La modified HZSM-5 zeolite showed much better activity and stability than HZSM-5 due to the tunable acidity, which can promote the adsorption and activation of the CH3SH molecule and inhibit the formation of coke deposit. The previous researches showed the potential application in the industry. However, to fulfill the requirements of the industry, the concentration-catalysis process to remove VOCs needs more research on lifetime, catalytic efficiency. the reaction, which led to the short lifetime. Amir Ikhlaq et al. studied the mechanism and kinetics of decomposition of chlorinated VOCs by ozonation over the γ‐Al2O3 and ZSM‐5 zeolite [174]. The results suggest that ozone reacts with the absorbed VOCs on the surface of catalysts. However, the ozonation process will increase the cost in most of the industrial processes. Yuexin Peng et al. supported MnO2 on the Al‐rich β‐zeolite to degrade toluene [75]. The T90 is 285 °C, which is much lower than the MnO2 supported on γ‐Al2O3 due to the lattice oxygen species in MnO2 and absorbed oxygen species on the zeolite. Cu and Co were also used to modify the β‐zeolite for the destruction of toluene and trichloroethylene [76]. The modified zeolite showed good stability in the reaction. The catalytic activity is mainly from the supported metal oxides. The suitable acidity and strong oxidation stability can improve the CO2 selectivity. Dedong He et al. modified the HZSM‐5 zeolite with a series of rare earth elements, including La, Ce, Pr, Nd, Sm, Y, and Er, for the catalytic decomposition of CH3SH [91]. Cu was also used to modify ZSM‐5 for the combustion of acrylonitrile. The isolated Cu is the active center. The SiO2/Al2O3 ratio can affect the ion exchange capacity and the catalytic performance of the catalyst. When the ratio is 26, the catalyst shows the best catalytic activity [92]. SBA‐15 was used to support MnOx for the combustion of toluene and showed good catalytic activity [176]. The La modified HZSM‐5 zeolite showed much better activity and stability than HZSM‐5 due to the tunable acidity, which can promote the adsorption and activation of the CH3SH molecule and inhibit the formation of coke deposit. The previous researches showed the potential application in the industry. However, to fulfill the requirements of the industry, the concentration‐catalysis process to remove VOCs needs more research on lifetime, catalytic efficiency.

*Catalysts* **2020**, *10*, 668 32 of 49

materials were investigated to the concentration and decomposition of VOCs, such as zeolite, γ‐Al2O3

The low concentration of VOCs can limit the catalytic efficiency of catalysts. Some porous

**Figure 13.** Scheme of adsorption/oxidation apparatus. Reprinted with permission from [1], 2009, Elsevier Ltd. **Figure 13.** Scheme of adsorption/oxidation apparatus. Reprinted with permission from [1], 2009, Elsevier Ltd.

**Table 8.** Concentrated oxidation catalysts for catalytic combustion of VOCs.


**Abstract**

Analog space missions in Poland include international scientific, technological, and business projects designed and realized by a private research company Analog Astronaut Training Center Ltd. (AATC) devoted to the future Moon and Mars exploration. Growing experience in educational aspect of the training as well as continuous development of the habitat and its professional space science laboratory equipment correspond to increased interest of educational organizations, universities, and individual students. We serve unique practical platform for space engineering, space master, and even space doctoral theses. In addition to a wide range of training courses offered for future astronauts, for example, diving, skydiving, rocket workshops, and stratospheric missions, AATC provides a private laboratory to simulate the space environment. It carries out scientific experiments focused on biology and space medicine, as well as addressing several multidisciplinary issues related to the Moon and Mars exploration, including space mining. The main goal of each our analog simulation is to get publishable results, what means that our analog astronauts obtain not only certification of completion of the training but also ability to continue studies and to perform it individually. This chapter summarizes methodology used by us, didactic tools, and obtained results for both educational and scientific analog simulations.

**Keywords:** analog missions, lunar and Martian science, space education


Meet the editor

Bryan Palaszewski has worked at the NASA Glenn Research Center at Lewis Field since 1989 and is currently directing research on high performance propellants and atmospheric entry. He is currently conducting analyses for the NASA Office of the Chief Technologist investigating nanometer-scale propellant additives for metallized gelled fuels for many space mission applications. Recently, he led work related to human Mars entry,

descent, and landing (EDL) where supersonic retro-propulsion (rocket deceleration) is planned for the final descent to the planet's surface. He is also investigating the mining of outer planet atmospheres and the challenges and benefits for future ambitious space missions. For six years, he led many studies of advanced space systems for orbital and interplanetary travel at the Jet Propulsion Laboratory, Pasadena, CA. He was also the lead propulsion subsystem engineer on the Ocean Topography Experiment (TOPEX) for three years, as well as being involved in other flight projects such as the Galileo Mission to Jupiter and the Cassini Mission to Saturn. He holds a Master of Science Degree in Mechanical Engineering from the Massachusetts Institute of Technology and a Bachelors Degree in Mechanical Engineering from the City College of New York. He has received the AIAA Sustained Service Award in 2004, and was chair of the AIAA Nuclear and Future Flight Propulsion Technical committee for three years beginning in 1997 and was also chair of this committee for the second time from 2008 to 2011.

Contents

**Section 1**

**Section 2**

Space Debris

**Section 3**

and Origins

*by Bryan Palaszewski*

*by Colin Sydney Coleman*

*by Ayodele Abiola Periola*

Introductory Chapter: Planetology

*by Bryan Palaszewski*

**Preface III**

Introduction **1**

**Chapter 1 3**

Planetary Exploration **7**

**Chapter 2 9**

**Chapter 3 35**

**Chapter 4 53**

**Chapter 5 65**

Computational Musings **105**

**Chapter 6 107**

Generic Computing-Assisted Geometric Search for Human Design

Technologies for Deviation of Asteroids and Cleaning of Earth Orbit by

*by Constantin Sandu, Cristian-Teodor Olariu and Radu-Constantin Sandu*

Solar System Exploration Augmented by In Situ Resource Utilization: System Analyses, Vehicles, and Moon Bases for Saturn Exploration

Space Access for Future Planetary Science Missions

*by Klaus Wilhelm and Bhola N. Dwivedi*

Impact Models of Gravitational and Electrostatic Forces


Recent Advances on Nitrification and Denitrification

*Edited by Ivan Zhu*

Published in London, United Kingdom

Recent Advances on Nitrification and Denitrification http://dx.doi.org/10.5772/intechopen.107611 Edited by Ivan Zhu

### Contributors

Reshalaiti Hailili, Zelong Li, Xu Lu, Xiaokaiti Reyimu, Wenqiang Wang, Dong Li, Shuai Li, Huiping Zeng, Jie Zhang, Ikhlass Marzouk Trifi, Beyram Trifi, Lasâad Dammak, Hui-Ping Chuang, Akiyoshi Ohashi, Hideki Harada, Ivan X Zhu

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