**3.2 Wastewater surveillance system**

There is growing proof of presence of SARS-CoV-2 in the sewage [51, 52]. Multiple focused researches have been carried out to analyze the presence of virus in wastewater [53]. Many studies have proved wastewater based epidemiology as an eligible and effective tracking tool in detecting SARS-CoV-2 genome at ambient temperature of 45°C giving us a better understanding of the present spreading of the global pandemic. This has led to growing concern in public health authorities for the essential need of the analysis of sewage samples from the sewage treatment plants for computing the presence of viral genome of SARS-CoV-2. While this is a

**Figure 4.** *Analysis of disease prevalence using wastewater samples.*

grave concern, it has also presented as an opportunity for the utilization of waterbased surveillance system for monitoring and investigating the presence of virus in sewage. The detection of virus in the wastewater samples has created a further possibility that the wastewater containing the virus can also release the same virus into other water bodies such as sea, groundwater, etc. The samples can be collected from drains and sewage treatment plants to understand the load of virus being shed by a particular community [54, 55]. The samples once taken, can be analyzed for load of virus present in it, followed by data analytics to back calculate the disease prevalence in an area. **Figure 4** represent the analysis of wastewater samples for COVID-19 prevalence.

Through wastewater sample analysis, the disease prevalence calculation can serve as an early warning for spreading pandemic in an area. This may give the authorities to act in timely manner for management of proliferation of COVID-19 in the area considered for analysis. Wastewater surveillance comes along with many benefits such as it is an economical method and also acts as a early warning tool signaling the transmission of the disease by neglecting the other epidemiological indicators and gives successful evidences and results [55].


**OPTIMUM COMPOSITE STRUCTURES**

#### **Optimum Composite Structures**

http://dx.doi.org/10.5772/intechopen.74696 Edited by Karam Y. Maalawi

#### **Contributors**

Rashmi Aradhya, Nijagal M. Renukappa, Karam Youssef Maalawi, Bo Jin, Sergey Golushko, Ferhat Fedghouche, Sergey Shevtsov, Igor Zhilyaev, Natalia Snezhina, Jiing-Kae Wu, Sang Yoon Park, Won Jong Choi, Ruham Pablo Reis, Edson Botelho, Américo Scotti, Alberto Santos, Leonardo Sanches, Iaroslav Skhabovskyi

#### **© The Editor(s) and the Author(s) 2019**

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**The Roles of Classical Molecular Dynamics Simulation in Solid Oxide Fuel Cells**

Kah Chun Lau**1** and Brett I. Dunlap2 *1Department of Chemistry, George Washington University, Washington D.C. 2Code 6189, Chemistry Division, Naval Research Laboratory, Washington D.C. USA*


*3.4. Photovoltaic Response of Proposed Solar Cell*

Figure 5 shows the simulated current-voltage responses of the proposed solar cell both in the dark and in the presence of illumination, where the optimized thickness of PCBM (400 nm), NPB (100 nm), and Cs2TiBr6 (350 nm) as the electron-transport layer, hole-transport layer, and absorber layer were already incorporated in the simulation with interface defects states as discussed above. Both curves demonstrate the characteristics behavior of a photovoltaic diode response. In the dark current-voltage response, the proposed solar-cell shows a very low current passing-through the device especially at an earlier stage of the applied voltages. After the threshold voltage (approximately at 1.1 volts) both electrodes Ag and ITO started to inject relatively a large number of electrons and holes as the forward bias current [38,39]. Using the standard technique reported in the literature, the diode ideality factor was estimated as 1.3, which reflects the quality of the proposed diode as a solar cell [40,41].

Similarly, the proposed solar cell is also simulated for the photovoltaic response in the fourth quadrant operating between the open circuit and short circuit current, where a large amount of current flows when the solar-cell is illuminated, but this illuminated current is in the opposite direction compared to the dark current-voltage response, as shown in Figure 5. From the figure, it is clearly observed that the optimized device shows a reasonable photovoltaic response under the AM 1.5 illumination. As a result of this characterization, the photovoltaic parameters such as open-circuit voltage, short-circuit current. and fill-factor are extracted from the figure as 1.29 volts, 16.66 mA·cm<sup>−</sup>2, and 78.11%, respectively. All these photovoltaic parameters lead to the maximum powerconversion efficiency for the optimized proposed solar cell up to 16.85%. This efficiency is better than the previously reported power-conversion efficiency of a doped-free, lead-free perovskite solar cell [42].

**Figure 5.** The simulated dark and AM 1.5 photovoltaic current responses of the proposed Ag/BCP/PCBM/Cs2TiBr6/NPB/ITO solar cell as a function of applied voltage.


**3.1 Habitat preferences**

Szarvasi-1 energy grass prefers soil conditions similar to common cereals in terms of soil texture, nutrient and water content. However, on lighter soils (e.g. sandy, sandy-silt) it develops faster compared to medium or heavy soils. On sand and sandy soils it can develop seeds in the first year (after spring sowing) and reaches its maximal photosynthetic assimilation one phenophase earlier. The natural habitats of this indigenous plant mainly occur in the central part of Hungary, where the largest sandy areas are located, but it also has an exceptionally natural population on a more clay type soil in a salty marsh.

Considering only the habitats of the natural populations, tall wheatgrass seems to prefer rather alkaline soils where the pH is between 6.5 and 10. However, optimal growing potential and biomass production can be linked to a narrower pH range of 7.5-9. This means that energy grass, in spite of its alkaline origin, can show a more pronounced biomass production in a near neutral soil pH, similarly to the most common cereal cultivations. Slightly acidic soils do not hinder good biomass production, but soil pH below 5.5 negatively affects the yield.

The life span of Szarvasi-1 energy grass cultivation can be 10-15 years long, but the temporal change of biomass production during this time has not yet been monitored sufficiently. We have only one complete data series monitoring the yields of an energy grass field on solonec alkaline soil for more than 10 years. According to this study it takes two years for energy grass cultivation to reach maximal biomass production, which can then be maintained for at least 7 years. At around the tenth year energy grass cultivation starts decrease in yearly biomass production. In semi-arid climates without a ground water table serving as water source for cultivation the durability of the energy grass crops can be much shorter.

The flood tolerance of energy grass is relatively good, especially when the cultivation is at least two or three years old and the tussocks of the individuals are well developed. However, in the first year, the short and weak stems of the juveniles cannot tolerate permanent water cover and die out. Hence, the cultivated energy grass stand opens, the density of the stems declines and the establishment of the grass cultivation remains incomplete. In such a condition, weeds can gain multiple chances to invade and to establish.

High salt concentrations of the soil can be tolerated by Szarvasi-1 energy grass, but only in wet habitats, where a several weeks long seasonal high water table can occur every year. Because of the high salt resistance, Szarvasi-1 energy grass can be used as salt-tolerant forage and can play an important role in the recycling of saline drainage waters for irrigation.

Since *Elymus elongatus* subsp. *ponticus* is a native species of the continental and subcontinental climate in Eastern Europe, it tolerates well the summer high temperatures exceeding daily means of even 30-35 °C, and can also resist cold winter days when the temperature sinks below – 35 °C.

### **3.2 Gas exchange behaviour**

278 Sustainable Growth and Applications in Renewable Energy Sources

*E. hispidus* 4-8 truncate obtuse or acute keel with short

*E. elongatus* 5-11 truncate, glabrous obtuse, awnless two-keeled Szarvasi-1 7-15 truncate obtuse, awnless two-keeled

The glumes of *Elymus* species are indurate-coriaceous, obtuse or truncate, with 1-11 veins, possessing a short awn or no awn at all. The glume can reach half or two thirds of the spikelet in *A. pectiniforme*, two thirds of the spikelet in *E. repens*, and one third of the spikelet in *E. hispidus*, *E. elongatus* and Szarvasi-1 (Fig. 3.). The glumes are 1-3-veined in *A. pectiniforme*, and 3-7-veined in the other taxa. The lemma of *E. elongatus* is obtuse, glabrous, unawned and 5-veined; the palea is two-keeled (Melderis, 1980; Barkworth, 2011). Similarly to other representatives of the Poaceae family, the stigma is feather-like in the *Elymus* genus, where stigmatic secretion is absent even in the mature stage of the stigma, and the receptive surface is discontinuous (Heslop-Harrison Shivanna, 1977). The fruit is a caryopsis.

The evaluated anatomical features allow the differentiation of *E. elongatus* and Szarvasi-1 energy grass from the other investigated members of the *Agropyron*-*Elymus* complex. Szarvasi-1 shows several anatomical traits that enhance drought tolerance, such as a sclerenchymatized epidermis covered by a thick cuticle and dense coverage by nonglandular hairs. On the other hand, the mesomorphic position of stoma guard cells is characteristic of an intermediate water requirement. This dual nature of the habitat tolerance of *Elymus elongatus* cv. Szarvasi-1 has to be taken into account when the new cropfields of

Szarvasi-1 energy grass prefers soil conditions similar to common cereals in terms of soil texture, nutrient and water content. However, on lighter soils (e.g. sandy, sandy-silt) it develops faster compared to medium or heavy soils. On sand and sandy soils it can develop seeds in the first year (after spring sowing) and reaches its maximal photosynthetic assimilation one phenophase earlier. The natural habitats of this indigenous plant mainly occur in the central part of Hungary, where the largest sandy areas are located, but it also

Considering only the habitats of the natural populations, tall wheatgrass seems to prefer rather alkaline soils where the pH is between 6.5 and 10. However, optimal growing potential and biomass production can be linked to a narrower pH range of 7.5-9. This means that energy grass, in spite of its alkaline origin, can show a more pronounced biomass production in a near neutral soil pH, similarly to the most common cereal cultivations. Slightly acidic soils do not hinder good biomass production, but soil pH

has an exceptionally natural population on a more clay type soil in a salty marsh.

**florets/spikelet Glume Lemma Palea**

narrowing short-awned keel with short

trichomes

trichomes

**Taxon / Character**

this energy grass are planned.

**3. Ecological requirements**

below 5.5 negatively affects the yield.

**3.1 Habitat preferences**

**Number of**

*A. pectiniforme* 4-8 abruptly

*E. repens* 4-8 acute, tapering acute

Table 3. Reproductive features of wheatgrass (*Elymus* and *Agropyron*) taxa

Tall wheatgrass is classified as C3 plant with cool season characteristics and seasonally different water use efficiency in moderately saline habitats (Bleby et al., 1997; Johnson, 1991). Several cultivars have previously been developed based on adaptability to different environmental conditions in Europe and Asia, but not from ecophysiological perspective. Szarvasi-1 energy grass was developed from a native population of tall wheatgrass (*Elymus elongatus* subsp. *ponticus*) that was adapted to slightly salty habitats. Therefore it was expected that *E. elongatus* cv. Szarvasi-1 will be a good candidate for biomass crop status because it produces large amounts of organic matter with relatively broad tolerance spectra and a high adaptability to different environments. Here we review the current knowledge on environmental gas exchange responses of Szarvasi-1 energy grass under greenhouse and field conditions to different environmental parameters such as temperature, light, air humidity and carbon-dioxide.

We used the following photosynthetic parameters: *assimilation* as the measure of carbondioxide fixation, *transpiration* as the measure of water loss and *photosynthetic water use efficiency* as the ratio of carbon-dioxide input to water output. All of these parameters depend on stomatal regulation and the abiotic environment. In this section capacities and threshold limits of Szarvasi-1 energy grass gas exchange performance will be presented for a better knowledge of its abiotic environmental requirements (Fig. 10.). To define and to compare gas exchange capacities, growing pots were installed using three soil types (sandy soil, Alfisol-Mollisol, Aquic Mollisol) in the Botanic Garden of the University of Pécs with permanent irrigation. In addition, field experiments were established on three soil types (Alfisol, Alfisol-Mollisol, Aquic Mollisol) in South

Tall Wheatgrass Cultivar Szarvasi–1 (*Elymus elongatus* subsp. *ponticus* cv. Szarvasi–1)

This is presumably due to insufficient water and nutrient availability.

beneficial as an energy crop in mesic habitats with lighter soils.

**4. Propagation**

generations.

**5. Crop management and production**

as a Potential Energy Crop for Semi-Arid Lands of Eastern Europe 281

The assimilation and its capacity as the highest net photosynthetic rate in cultivated and natural C3 grasses vary greatly from slight to medium values (20-40 μmol m-2 sec-1) and in the case of C4 crops from medium to high (30-70 μmol m-2 sec-1) values under natural carbon-dioxide conditions, saturated light intensity, optimal temperature and adequate water supply (Larcher, 2003). Comparing Szarvasi-1 energy grass to other grasses or crop species, it has a low-medium assimilation capacity among C3 species with a range of 10.3 - 19.2 μmol m-2 sec-1 in greenhouse, and 10.6 – 20.3 μmol m-2 sec-1 under field conditions. Optimal water use efficiency was measured under moderate light intensity (500-1100 μmol m-2 sec-1) on all of the studied soil types under greenhouse conditions, while the maximum value was observed on sandy soil in spring (3.73 μmol/mmol). Time and phase shifting in plant growth, poor seasonal rates of assimilation and low water use efficiency detected on Aquic Mollisol under both irrigated and climatic drought conditions underline the negative effect of high clay content of soils on the optimal biomass production of energy grass crops.

Under non-stressed environmental conditions, optimum gas exchange in Szarvasi-1 energy grass occurred at the beginning of development in early or late spring, depending on irrigation and soil type. Climatic drought has a strong effect on gas exchange performance through the regulation of stomatal conductance, both for carbon-dioxide and water vapour. According to the studied ecophysiological parameters, Szarvasi-1 energy grass could be

Szarvasi-1 energy grass is propagated by seed. Since *E. elongatus* ssp. *ponticus* has evolved in regions of Europe that have long and severe winters, it germinates relatively late in the spring and by the time it develops its tussocks it is mid summer. This is why the suggested sowing time is in autumn, in the middle of September. Its germination needs no special circumstances. A period of only 7 consecutive days with approx. 16 hours dark each day and 18-20 °C air temperatures can maximize the germination success, up to 90 %. In different conditions the proportion of germinating seeds can vary between 52 and 90 %. Seedlings die rapidly without proper humidity conditions, but too much watering or a high water table are also poorly tolerated by Szarvasi-1 energy grass seedlings and juveniles. Similarly, strong competition of weeds can dramatically reduce seedling survival. The plant gets in full flower by the middle of June. The energy grass seeds belong to the transient seed bank-type, where the longevity of the seeds is shorter than a year. Hence, the seed bank of Szarvasi-1 energy grass fields contains seeds with the same age with no overlapping

The recommended sowing time is between the 1st and 20th of September. The soil must be prepared similarly to any other cereals (e.g. wheat, barley etc). The seeds should be sown at the depth of 2-2.5 cm with the sowing distance of 12-15 cm. The seed quantity for a hectare land is approx. 40 kg. The seedlings emerge in 14-18 days. Weed management is necessary in this phase of the development of energy grass plantations to avoid the weed species strengthening at the expense of energy grass individuals. In the early spring rolling on the plantation can be important to mitigate the negative effects of winter frost on the root

Hungary, under natural climatic conditions without any irrigation or fertilization. To evaluate threshold values of gas exchange parameters under different environmental regime, steady state and instantaneous field measurements by IRGA methods were executed.

Among investigated abiotic environmental parameters photon flux density and air humidity turned out to have an essential role in gas exchange performance and regulation (Salamon-Albert & Molnár, 2009, 2010). Under non-stressed soil water conditions (P2, P3, P5) carbon fixation was the most favourable at the beginning of the growing period described by the assimilation capacity and light efficiency regulated by the air humidity (Fig. 10.A). After seasonal precipitation deficiency in late summer (P4), causing a decline in soil water content, hard reduction was detected in water use efficiency because of strong decrease in assimilation capacity and light efficiency, retaining a regular level of transpiration (Fig. 10.B,C). Effect of climatic air drought was significant for stomatal conductance, going shattered in seasonal response by a greater effectiveness to transpiration (Fig. 10.D). As for the other experimental soil types, overall and seasonal assimilation capacity and light efficiency was a little bit lower on Alfisol-Mollisol and significantly depressed on Aquic Mollisol.

Fig. 10. A) assimilation, B) transpiration, C) photosynthetic water use efficiency as a function of light and D) stomatal conductance for water vapour as a function of air humidity, derived from field measurement (instantaneous data, alfisol, unpublished). Fitted curves p<0.01, P2- P5 the vegetative phenophases.

The assimilation and its capacity as the highest net photosynthetic rate in cultivated and natural C3 grasses vary greatly from slight to medium values (20-40 μmol m-2 sec-1) and in the case of C4 crops from medium to high (30-70 μmol m-2 sec-1) values under natural carbon-dioxide conditions, saturated light intensity, optimal temperature and adequate water supply (Larcher, 2003). Comparing Szarvasi-1 energy grass to other grasses or crop species, it has a low-medium assimilation capacity among C3 species with a range of 10.3 - 19.2 μmol m-2 sec-1 in greenhouse, and 10.6 – 20.3 μmol m-2 sec-1 under field conditions. Optimal water use efficiency was measured under moderate light intensity (500-1100 μmol m-2 sec-1) on all of the studied soil types under greenhouse conditions, while the maximum value was observed on sandy soil in spring (3.73 μmol/mmol). Time and phase shifting in plant growth, poor seasonal rates of assimilation and low water use efficiency detected on Aquic Mollisol under both irrigated and climatic drought conditions underline the negative effect of high clay content of soils on the optimal biomass production of energy grass crops. This is presumably due to insufficient water and nutrient availability.

Under non-stressed environmental conditions, optimum gas exchange in Szarvasi-1 energy grass occurred at the beginning of development in early or late spring, depending on irrigation and soil type. Climatic drought has a strong effect on gas exchange performance through the regulation of stomatal conductance, both for carbon-dioxide and water vapour. According to the studied ecophysiological parameters, Szarvasi-1 energy grass could be beneficial as an energy crop in mesic habitats with lighter soils.


3.2.2. AM

It is not easy to match the features extracted from the FEN through convolutions because the point clouds from the LiDAR-stereo camera combination are generated differently. Even in the LiDAR-LiDAR combination, if the FOVs of the two LiDARs are significantly different, it is also not easy to match the features extracted from the FEN through convolutions. Moreover, because the deviation range of rotation and translation is set large to estimate calibration parameters, it becomes difficult to check the similarity between the point cloud of the target sensor and the point cloud of the reference sensor.

Inspired by the attention mechanism proposed by Vaswani et al. [28], we solve these problems: we design an AM that implements the attention mechanism, as shown in Figure 1. The AM calculates an attention value for each voxel of the reference sensor input using the following procedure.

The AM has four fully connected layers (FCs): FC1, FC2, FC3, and FC4. A feature is input into these FCs, and a transformed feature is output. We denote the outputs of FC1, FC2, FC3, and FC4 as matrices M1, M2, M3, and M4, respectively. Each FC has 1024 input nodes. Here, the number 1024 is the number of feature volumes extracted from the FEN. The FC1 and FC4 have G/2 output nodes, and the FC2 and FC3 have G output nodes. These FCs transform 1024 features to G or G/2 features. Here, G is a hyper-parameter. If the sum of the elements in a row of matrix F, which is the output of the FEN, is 0, the row vector is not input to FC. We apply layer normalization (LN) [40] and the ReLU function to the output of these FCs so that the final output becomes nonlinear. The output M2 of FC2 is a matrix of dimension *Vt* × G, and the output M3 of FC3 is a matrix of dimension *Vr* × G.

Here, *Vr* and *Vt* are the number of rows in which there is at least one element with a feature value among the elements in each row of *Fr* and *Ft*, respectively. Therefore, *Vr* and *Vt* can be different for each input. However, we fix the values of *Vr* and *Vt* because the number of input nodes of the multi-layer perceptron (MLP) of the IN following the AM cannot be changed every time. In order to fix the values of *Vr* and *Vt*, we input all the data to be used in the experiments into the network and set the values when they are the largest, but we make them a multiple of 8. This is because *Vr* and *Vt* are also hyper-parameters. If the actual *Vr* and *Vt* are less than the predetermined *Vr* and *Vt*, the elements of the output matrices of FCs will be filled with zeros. The output M1 of FC1 is a matrix of dimension *Vt* × G/2, and the output M4 of FC4 is a matrix of dimension *Vr* × G/2.

• Computation of attention score by dot product

An attention score is obtained from the dot product of a row vector of M3 and a column vector of MT2 . This score is the same as the cosine similarity. The matrix AS is obtained through the dot products of all row vectors of M3 and all column vectors of MT2 are called an attention score matrix. The dimension of the matrix AS (AS = M3·MT2 ) is *Vr* × *Vt*.

• Generation of attention distribution by softmax

We apply the softmax function to each row vector of AS and obtain the attention distribution. The softmax function calculates the probability of each element of the input vector. We call this probability an attention weight, and the matrix obtained by this process is the attention weight matrix AW of dimension *Vr* × *Vt*.

• Computation of attention value by dot product

An attention value is obtained from the dot product of a row vector of AW and a column vector of the matrix M1. A matrix AV obtained through the dot products of all row vectors of AW and all column vectors of M1 is called an attention value matrix. The dimension of the matrix AV (AV = AW·M1) is *Vr* × G/2.

Finally, we concatenate the attention value matrix AV and the matrix M4. The resulting matrix from this final process is denoted as AC (AC = [AV M4]) and has dimension *Vr* × G; this matrix becomes the input to the IN. The reason we set the output dimension of FC1 and FC4 to G/2 instead of G is to save memory and reduce processing time.


**1. Introduction**

The present chapter describes numerical simulation of sand geomaterial with the discrete element method (DEM), based on input data acquired from physical experiments. First of all, sandy soil is composed of different shape and size particles. That means, it is necessary to provide sieving test to determine granulometry curve of investigated sand. After this step, it is used scanning electronic microscope (SEM) to determine main sand particles morphology parame‐ ters, namely area, perimeter, form coefficient, angularity, roundness, circularity, and spherici‐ ty coefficients, respectively. Next physical experiments level—sand compression with oedometer. Having all this information, it is possible to start numerical DEM modelling of sand compression test.

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

Granulometric curve and separate particle shape discretization are one of the most relevant existing problems for numerical modelling via DEM. There are no direct recommendations for a single‐particle subscription with spheres (single‐particle subscription level based on spheres size and quantity). Subscribing soil particles with DEM, it is very important to choose size, shape, and physical properties of modelled particles. It is possible to model realistic size oedometer device without not scaled particles if quantity of modelled particles is small. Modelling of soil which is subscribed with a big quantity of particles is not possible because of the limitations of computer calculation capacity. All simplifications are accepted in order to decrees the calculation time. Another relevant problem, arising from all accepted simplifica‐ tions, is a very small quantity of the authors working on experimental and numerical testing validation. This fact only proves that there are still a lot of problems related to dispersive systems, which are modelled with DEM.