2.2.3. Population Dataset

The population dataset was collected from WorldPop (https://www.worldpop.org/, accessed on 28 July 2021). It details the spatial distribution of the population with a spatial resolution of 100 m. Its units are number of people per pixel with country totals adjusted to match United Nations national population estimates. The format of this dataset is raster, where the digital value of every pixel reflects the total population within this grid. Considering that the samples in the earthquake case dataset have a long time series while population data of a single year have difficulty reflecting demographic changes, we collected population records in China's mainland every five years from 2000 to 2020 (2000, 2005, 2010, 2015 and 2020) to explore the change in population in a long time series.

A methodological flowchart of the investigation is shown in Figure 5.

**Figure 5.** Framework of the Z-SVR model.

Seismic fatality is a comprehensive result that is influenced by diverse factors, and whether a factor has a decisive impact on earthquake casualties is an essential question for feature selection of prediction models [33]. Therefore, before constructing a prediction model for earthquake casualties, it is crucial to establish a reasonable index system and analyze the importance of relevant indicators, which will serve as a reference for the prediction model to select more important features. Based on regional disaster system theory, this study established an evaluation index system for 14 major features that affect earthquake fatality. We used the earthquake case dataset and the random forest model to assess the importance weights of features, of which the ranking served as an important reference for feature selection of the prediction model.

Because of the variations among regions, there will be different numbers of casualties due to earthquakes with the same ground motion parameter. Therefore, in earthquake cases with the same seismicity, the diversity of disaster-formative environments and disasteraffected bodies reflects the difference among regions [34]. Due to the vast area of China's mainland, it is difficult to build a universal prediction model that is suitable for all regions. To enhance the accuracy of earthquake disaster assessment in emergency periods, it is effective to divide the study area into risk zones based on regional differences and construct a model that performs well for each risk zone. Based on the results of the importance assessment and feature selection, geological fault density and population density are the most important features of disaster-formative environments and disaster-affected bodies, respectively. Therefore, we chose these two features with relatively high importance weights as representative factors for developing a partition standard and dividing the study area into the defined grades of risk zones. The accuracy and applicability of the earthquake casualty prediction approach can be improved by building different submodels for areas with different regional characteristics.

As an extension of support vector machine (SVM) for solving regression problems support vector regression (SVR) has attracted much attention in the field of machine learning and displayed strong predictive ability in mortality evaluation. Compared with other machine learning algorithms, SVR can achieve the optimal solution with a small number of samples and avoid problems such as overfitting and local extremum as much as possible, which makes its generalization ability and performance stand out [35]. However, as a machine learning method that is based on historical statistics, it may be difficult for the SVR model to accurately predict casualties due to earthquakes occurring in different regions of the study area, especially those with vast acreage and diverse environments. Therefore, based on the characteristics of SVR and regional differences in the study area, we constructed a zoning SVR model (Z-SVR) for various regions in the study area; for which the optimal model parameters for all risk zones were identified using training samples from the earthquake case dataset.

### **3. Spatial Division**

#### *3.1. Importance Assessment*

According to regional disaster system theory, a seismic disaster is a complex mechanism that is a comprehensive result of interactions between disaster-inducing factors, disaster-affected bodies and disaster-formative environments [36]. Among them, disasterinducing factors, such as seismic magnitude and focal depth, are the sufficient conditions for disaster occurrence; disaster-affected bodies, such as population distribution and building destruction, represent the necessary conditions for disaster resilience; and disaster-formative environments, such as climatic condition and secondary disaster, provide a natural and human geological background that affects disaster-inducing factors and disaster-affected bodies [17]. The loss due to a disaster is attributed to the combined effects of these three factors; therefore, for screening the prediction indicators, we constructed an evaluation index system on the basis of regional disaster system theory, which is presented in Table 3.

**Table 3.** Evaluation index system of features that influence earthquake fatality.

Determining the importance weights of all features in the evaluation index system is a quantitative task in importance assessment. Although traditional linear models show good performance in the importance assessment of factors that affect earthquake fatality, the result can be easily disturbed by the uncertainty and fuzziness of input data [37]. An integrated ensemble model is an effective approach for mitigating the above problem and improving the accuracy and generalization performance of the evaluation method [38], which was demonstrated by previous studies [39]. Random forest (RF) is an effective integrated ensemble model with random binary decision trees for classification or regression [39]. As an expansion of the bagging method, this algorithm constructs multiple independent estimators that determine the output result by average or majority voting. This approach enhances the precision and stability of the prediction model, reduces the sensitivity of the model to noise and outliers, and avoids problems such as overfitting [40]. In contrast to other machine learning methods, the RF model can provide the quantified importance of prediction indicators by calculating their increases in predictive error by randomly permuting the values of a variable through out-of-bag observations of each tree.

We chose 7 indicators of disaster-inducing factors, 4 of disaster-affected bodies and 3 of disaster-formative environments as the input parameters of the RF model to evaluate their importance to earthquake fatality. The values of the input parameters were extracted from the earthquake case dataset. We utilized the machine learning package scikit-learn of the Python programming language to construct the RF model. The "feature\_importances\_" is an attribute of the RF model in the scikit-learn package. The importance of a feature is computed as the normalized total reduction of the criterion brought by that feature. The procedure is summarized as follows:

The ranking of all factors according to the importance weights from low to high is shown in Figure 6.

**Figure 6.** Importance weights of indicators on the index levels.

Based on the results of the importance assessment of influential features, magnitude, collapsed buildings, epicenter intensity, population density, geological fault density and GDP are major factors that affect seismic fatality. Magnitude and epicenter intensity are the two most important parameters to depict the severity of an earthquake and exert substantial influence on the seismic fatality; however, there is a strong correlation between these two features. To avoid information redundancy, we selected magnitude, which has greater importance weight, as the input parameter of the Z-SVR model. Building destruction is the direct cause of earthquake injuries and deaths [41], and the primary task of emergency rescue is to search for people who are buried in collapsed constructions. However, the aim of the proposed model in this study is to rapidly predict the possible casualties of an instantly occurring earthquake, which requires an extremely fast response speed. It will take some time to identify the situation of building destruction and count the number of collapsed buildings. Population density is the most important feature among the disasteraffecting bodies; since human beings are the major victims of earthquakes, it is significant to choose this feature as one of the prediction indicators. Geological fault is the most important factor under the level of disaster-formative environments, where the density of strata fault lines can be used to quantitively analyze regional differentiation and merits consideration. GDP is a comprehensive indicator that is mutually restricted with population density in terms of earthquake casualties; therefore, it is significant to introduce this factor as an input parameter and consider its comprehensive effect with population density to ensure the stability and accuracy of the prediction results. In conclusion, based on the result of the importance assessment and the principles of rapid evaluation and avoiding information redundancy, we finally selected magnitude, population density, geological fault density and GDP as the input parameters for the construction of the Z-SVR model, among which geological fault line density and population density were also applied to divide the study area into risk zones.


Preface

Chapter 8 **Opportunities of Scanning Probe Microscopy for Electrical,**

Caner Değer, Vahap Eldem and İzzet Paruğ Duru

Chapter 11 **A Comparative Study of Nanowire Arrays for Maximum Power**

Chapter 10 **Recent Trends in Plasmonic Nanowire Solar Cells 209**

Pavel Geydt, Mikhail S. Dunaevskiy and Erkki Lähderanta

**Nanowires 155**

**VI** Contents

A. Obayya

**Transmission 233**

Chapter 9 **Diluted Magnetic DNA Nanowires 189**

**Mechanical and Electromechanical Research of Semiconductor**

Mohamed Hussein, Mohamed Farhat Othman Hameed and Salah S.

Hasan Aykut Şatana, Barışcan Karaosmanoğlu and Özgür Ergül

This book describes understanding about the nanowires, their fabrication, characterization, and applications in the recent technology. The book is organized into eleven chapters in to‐ tal, which include fabrication of various metallic and oxide nanowires using different chemi‐ cal and electrochemical techniques and their physical characterizations. Some very important applications of the nanowires such as for dye-sensitized and plasmonic nanowire solar cells, field-effect transistors, DNA, power transmission, and photovoltaic applications are discussed and presented in this book. The writing of each chapter is generally clear and precise, and numerous illustrations provide an easier understanding of the phenomena de‐ scribed in the book. This book addresses not only researchers in specialized field but also PhD students, postdoctorate fellows, and experienced technical professionals.

All the contributors of the book are active researchers in their fields, and therefore, this book provides up-to-date information about the nanowires and their applications. The content of the book provides the fundamental preparation needed for further study of advanced topics in nanowires. At the end of each chapter, proper references have been included that can lead the readers to the best sources in the literature and help them to go into more details about the nanowires.

 I am very thankful to the Publishing Process Manager Ms. Mirena Calmic for her cooperative atti‐ tude during the entire reviewing and publishing processes. I hope that this book will pro‐ vide an opportunity to the readers to strengthen their knowledge and research capabilities in the field of materials science and nanotechnology with their specializations in one-dimen‐ sional nanostructures.

> **Maaz Khan** Physics Division, PINSTECH, Islambad, Pakistan


*The New Landscape of Diagnostic Imaging with the Incorporation of Computer Vision DOI: http://dx.doi.org/10.5772/intechopen.110133*

the structures in the area will absorb the rays differentially, which will result in lights and shadows [31]. In the AI field of computer vision applied to X-ray, there is a preponderance of work in the area of the thoracic cavity [32]. Thus, we found work focused on the detection of pulmonary nodules with models trained on images from one pool of patients and tested in a different pool. We also found longitudinal work, where the model was trained and tested on images from the same patients, with images separated by a time window [32–34]. Another large part of the work focused on the detection of pneumonia. Several models were trained on datasets from different hospitals, which showed variations in various image features between hospitals. As expected, the models showed better metrics when trained and tested on data from the same hospital [32, 35–37]. With the advent of COVID-19, there was an explosion of research in the detection of this pathology in X-ray images. Thus, numerous models were created that attempted to distinguish COVID-19 pneumonia from viral or bacterial pneumonia. These developments were key since they allowed screening and managing patients automatically and to avoid spreading the contagion of COVID-19 patients [32, 38–41]. Work was also carried out to contribute to the detection of tuberculosis in chest images. These models demonstrated satisfactory performance in screening tuberculosis images with respect to normal lungs or other pulmonary pathologies. However, the models did not show the ability to distinguish between active and quiescent disease [32, 42, 43]. Additionally, part of the research was also directed to the detection of pneumothorax. This part of the development was of important value in patient triaging, especially in determining the size and position of the pneumothorax and its changes over time in the same patient. Several of these models have already received FDA clearance as assistive devices in the emergency unit [32, 44–46]. As a final part of this section, to a lesser extent than the previous ones, models were also built for the detection of other types of pulmonary involvement, such as consolidation, edema, emphysema, fibrosis, and pleural effusion [32, 47].


*2.3. Device Packaging*

The packaging of the electrochemical devices was performed by connecting three wires with one set of contact pads of the working, counter, and reference electrodes, respectively, using EPO-TEK® H20E conductive epoxy (Epoxy Technology, Billerica, MA, USA). After

mixing the resin part A and the hardener part B of the H20E conductive epoxy in a weight ratio of 1:1, the mixture was applied to each contact pad. Each device was then baked inside an oven at 80 ◦C for 3 h to solidify the conductive epoxy. The resistance of all three electrodes between the other set of contact pads and the wires was measured after the baking process. If the resistance of any electrode was 10% higher than the average value, that device was excluded from subsequent modification and experiment. This is due to the concern that a higher resistance value usually suggests bad connectivity of the Au layer, which can be caused by several factors, such as stripping of the metal connection line, a crack on the electrode or the contact pad, and peeling of the Au layer at certain locations. Thereafter, the EPO-TEK® H70E non-conductive epoxy mixture (resin part A and hardener part B in a weight ratio of 1:1) was drop-casted on the devices to cover all the contact pads (refer to Figure 1b) in order to provide electrical insulation. The non-conductive epoxy was cured by baking inside an oven at 80 ◦C for 1.5 h. Each packaged device was then placed into a 3D-printed polycarbonate mold for further modification. The detailed dimensions of the mold are presented in Figure S1.


**3. Conclusion**

Although the uptake of chlamydia screening and treatment is keeping in the high race, its incidence among young people remains high. With this regard to the knowledge disseminated in this chapter, there seems to be a need for establishing a multidisciplinary pedagogical guideline for teachers, health workers, and or other facilitators of SRH learning for adolescents, especially in middle and low-income countries.


**7.2 Methodology**

Two primary approaches, or paradigms to use Hein's [4] phrase, dominate methodological discourse around the capture of audience experience with interactive or indeed gamified systems. One approach is principally quantitative, focusing on measurable metrics, for example 'use time', participant progress or click count, and the other is principally qualitative, seeking to capture individual experience or subjective responses [48, 64, 65]. Much of the contemporary literature suggests that the purely quantitative is insufficient in capturing the nuances of engagement, with many advocating an ethnographic model, employing naturalistic methods as better equipped to offer experiential insight [66–72]. Moving away from methods derived from the 'behavioural and cognitive sciences', models founded in the 'cognitive and educational sciences' [68] are exemplified by the following quote: '*Observation, in some sense, of an interactive system in action is the only way to understand it*' [71]. This position is supported by many in the fields pertaining to audience studies in cultural spaces [73–75]. Refs. [76–78] highlight qualitative observation as being the best method of assessing complex behaviours or subjective emotions such as satisfaction or enjoyment.

With this in mind, a methodology primarily employing narrative participant observation was selected as being best positioned to capture and understand the capacity of the TNAR application to elicit the forms of playful engagement that are our focus. These observations were undertaken by the author and describe the participant's engaging experience as a narrative, in which evidence, both verbal and physical, pertaining to the gaming experience, is captured. This employed, as far as

*Right Game, Wrong Place? A Case Study: Using a Gamified AR Application in a Heritage… DOI: http://dx.doi.org/10.5772/intechopen.107535*

possible, a total of one observer model [79, 80] in which interactions between participant and observer are limited. The observational narratives were augmented with post-engagement, semi-structured interviews and written feedback. This aligned to methods successfully applied by, that is Costello, Edmond or Muller [36, 71, 81], when seeking to understand the pleasure participants experience when engaging with interactive artworks.

These observations, and other evidence, including verbal and written feedback were subjected to systematic review [82] seeking interpretive commonalities, which were then categorised; these form the basis of the results and discussion to follow. This data can be viewed here.


*3.3. Validation and Application*

The validation of the prediction model is illustrated in Figure 13 as a comparison between the predicted and actual data from the validation dataset. The predicted power consumption, including the prediction interval, is the gray shaded area and the measured power consumption is the black line. The numbered red areas are the identified periods with operational disruption, and they include 14 datapoints out of a total of 85 in the validation dataset. The given operation disruptions have been identified as (A) uncontrolled water refill, (B,C) issues with the control system of the water temperature, (D) issues with controlling the indoor environment and water refill system, leading to a consecutive lockdown of the facility and (E) issues related to the control of the air handling unit and the air flow supply. The prediction model identifies all of the disruptions as illustrated. When the facility operates without flaws and faults, the facility performs within the operational baseline provided by the prediction model. Each of the operational disruptions are identified as major deviations from the baseline.

When excluding the data associated with operational disruptions, 14 datapoints in total (approximately 16% of the dataset), the predicted operation fits the actual performance well. Figure 14 illustrates the correlation between the predicted and measured power consumption exclusive of the operation disruptions. The Pearson correlation coefficient is 0.85. However, there are periods where the models seem to consistently over- or underpredict the performance model, and this may have to do with the lack of explanatory variables in the model. However, this deviation is within the prediction interval, which corresponds with no detection of operational disruption for the relevant period. Figures 15 and 16 present the range of the independent variables used in the prediction model. Even though the range of the training dataset was initially significantly reduced to only three months of data (29 datapoints), the dispersion of the variables within this dataset corresponds with the validation dataset.

**Figure 13.** Visual validation of the prediction model from September 2018 to June 2019. The prediction model includes the prediction interval in gray, measured power consumption in black and periods associated with operational disruptions in red (see Appendix A for higher resolution).

In the perspective of applying the presented method to industry, the combination of a short-term training dataset and the few predictors makes this method especially useful. This means that a facility can develop a model over a short period of time, with a minimum of sensors. However, the transferability with regard to the choice of independent variables must be further investigated in order to obtain a universal method for industry.

**Figure 14.** The predicted power consumption plotted against the measured power consumption for the validation dataset. The Pearson correlation coefficient is given as the R-coefficient.

**Figure 15.** The dispersion of the independent variables in the prediction model, for each dataset used in the analysis.

**Figure 16.** The dispersion of the independent variables in the prediction model, for each dataset used in the analysis.


**4. Feathers as source of proteins and amino acids**

An appealing alternating method to obtain amino acids and proteins is to use feathers that are relatively stable under natural conditions. Keratinophilic fungi used to hydrolyse keratin protein to obtain protein [33] and found maximum producer as *C. indicum*, *C. tropicum*, and *Malbranchea pulchella*. Parihar and Kushwaha [45] used *Verticillium tenuipes, Microsporum gypseum, Aphanoasus fulvescens*, *Chrysosporium keratinophilum* for study of protein release in hen feather degradation without rachis. *C. indicum* was used for degradation of human hair and estimated protein release 47.66 μg/ml and 112.66 μg/ml in 5 and 10 days respectively [46]. *Chrysosporium tropicum*, *Penicillium griseofulvum* and *Aphanoascus terreus* was analyzed for release of considerable amount of protein [47]. [48] observed protein release 409.6 μg/ml in case of *C. tropicum* in 12 days while *Malbranchea* sp. released 298.21 μg/ml in 4 days. [46] recorded 238 μg/ml in 25 days by *Alternaria tenuissima*.

Conversion of feathers into feather meals by applying physical and chemical methods results in the loss of nutritionally essential amino acids such as methionine, lysine and tryptophan. Therefore, currently the poultry feathers are converted into feather meal, a digestible dietary protein, for animal feed using keratinases. The microbial production of L-lysine is an expanding branch of manufacturing biotechnology. There are many reports are available worldwide. Indian researcher [37] studied the discharge of cysteine in the culture medium.

**37**

*Microbial Hydrolysed Feather Protein as a Source of Amino Acids and Protein in the Diets…*

The upgradation of feather meal through microbial or catalyst treatment has been defined earlier. Feather meal fermented with *Streptomyces fradiae* and supplemented with essential amino acid methionine bring about in the broilers growth rate comparable with those fed isolated soybean protein [49]. The application of feather-lysate from *B. licheniformis* with amino acid supplementation formed alike development rate in chickens when compared to chickens fed with a diet included with soybean meal [50]. Feather hydrolysates produced by microbial keratinases have been used as additives for animal feed [51]. The application of biotechnological approach using microbes for feather processing has nutritional significance. Culturing of the microorganisms and keratinase activity may result in modification of structure of feather keratin [4, 52–54]. This may alter its resistance to digestive enzymes of the consuming animals [55]. Fermentation of feathers involving microorganisms and microbial enzymes, not only it would retain the existing valuable amino acid content of keratin, but it would also add to it. Thus, the feather meal obtained after such microbial treatment would have enough nutritional value. Keratinase could play a significant role in enzymatic improvement of feather meal and amino acid production from high molecular weight substrate [56]. The microbial technology would significantly bring down the cost since it would not require hydrothermal treatment, however feather waste would be a cheap raw

Meat and feather meal protein gave equally as good results as soybean meal protein when supplied 3% protein in practical-type corn-soybean meal rations [58]. [59] observed growth of feather and composition in broiler chicken and found that that of threonine, isoleucine and valine increased with age while methionine content of feathers decreased with age. *B. licheniformis* produced crude keratinase enzyme augmented the total amino acid digestibility of raw feathers and commercial feather meal, could replace as much as 7% of the dietary protein for growing chicks [19]. [60] Studied dietary crude protein and lysine amino acid effect on growth of feather in chicks and found that crude protein has more influence on feather development than by levels of lysine. [61] Observed antioxidant potential property of protein hydrolysate developed by *Bacillus* sp. [62] observed replacement of fish meal with feather meal in broiler and found economic without any negative effect. Treated chicken feather meal used as a source of protein ton animal feed broiler chickens [63]. [64] Observed processed feather meal for their chemical composition and amino acid profile and found feather meal pre-soaked with wood ash for twentyfour hour boiled at 150°C for 1 hr. gave the best crude protein content. Using feather waste as a valuable resource can help the poultry industry to dispose of the waste feathers in an environmentally sustainable manner that also generates extra income for the industry [65]. [66] Improved digestibility of protein into feather meal by

[47] recorded cysteine produced by *Acremonium strictum* 32.00 μg/mL, *Chrysosporium tropicum* 25.00 μg/mL, *Chrysosporium indicum* 22.00 μg/mL, *Malbranchea aurantiaca* 21.00 μg/mL. [48] Studied the discharge of amino acids lysine, cysteine, methionine, valine by *C. tropicum* and *Malbranchea* sp. due to feather degradation. [6] Found *A. tenuissima* a potent feather degrading fungus and increased the nourishing value of the soil by adding proteins (238 μg/ml), cysteine (20.2 μg/ml), lysine (15.8 μg/ml), methionine (6.8 μg/ml) and valine

*DOI: http://dx.doi.org/10.5772/intechopen.96925*

(7.5 μg/ml) in 25 days.

**4.1 Animal feed**

material [57].

**4.2 Feather meal as Chick feed**

*Microbial Hydrolysed Feather Protein as a Source of Amino Acids and Protein in the Diets… DOI: http://dx.doi.org/10.5772/intechopen.96925*

[47] recorded cysteine produced by *Acremonium strictum* 32.00 μg/mL, *Chrysosporium tropicum* 25.00 μg/mL, *Chrysosporium indicum* 22.00 μg/mL, *Malbranchea aurantiaca* 21.00 μg/mL. [48] Studied the discharge of amino acids lysine, cysteine, methionine, valine by *C. tropicum* and *Malbranchea* sp. due to feather degradation. [6] Found *A. tenuissima* a potent feather degrading fungus and increased the nourishing value of the soil by adding proteins (238 μg/ml), cysteine (20.2 μg/ml), lysine (15.8 μg/ml), methionine (6.8 μg/ml) and valine (7.5 μg/ml) in 25 days.