**Appendix A**

In addition to the mean values of the analyses conducted within this study, the standard deviations of the results are illustrated in Figure A1. In comparison to the mean values, the deviations were acknowledged to be relatively minute.

**Figure A1.** Evaluations of the standard deviation of the analyses conducted at aligned and yawed flows. (**a**) Evaluation of the duct drag coefficient (*CD*) with free-stream velocity (*U*∞) within aligned flow conditions; (**b**) Evaluation of the duct axial velocity coefficient (*CU*,*x*) with free-stream velocity (*U*∞) within aligned flow conditions; (**c**) Evaluation of the duct static pressure coefficient (*CP*,*s*) with free-stream velocity (*U*∞) within aligned flow conditions; (**d**) Evaluation of the duct drag coefficient (*CD*) with angular bearing (*γ*) within yawed flow conditions; (**e**) Evaluation of the duct lift coefficient (*CL*) with angular bearing (*γ*) within yawed flow conditions; (**f**) Evaluation of the duct axial velocity coefficient (*CU*,*x*) with angular bearing (*γ*) within yawed flow conditions; (**g**) Evaluation of the duct static pressure coefficient (*CP*,*s*) with angular bearing (*γ*) within yawed flow conditions.


**11. Integral atherogenesis**

The complicated atherosclerotic plaque as integral atherogenesis is an overall principal par‐ ticipation in the progression of a lesion that is both pro-inflammatory and enlarging. Ad‐ vanced glycation end-products are implicated in the pathogenesis of diabetes-associated atherosclerosis by increasing smooth muscle cell susceptibility to insulin-like growth fac‐ tor-1 mitogenic effects [6].

In such manner, distributional effects integrate as quantitative formulations of an atheroscler‐ otic plaque that re-characterizes dimensions of phenotype switching and formulates roles for transforming cell types. Various nuclear receptors contribute to macrophage cholesterol me‐

Attributes of Hypoxic Preconditioning Determine the Complicating Atherogenesis of Plaques

http://dx.doi.org/10.5772/51127

245

Incremental involvement of the intima corresponds to a progression that spatially conforms to effects of a disturbed blood flow that interacts with the endothelium. Dysfunctionality of endothelial cells arises within contexts of such interface phenomena in the realization of the quantitative attributes of increasing hypoxic injury and as dictated by the an accumulation

Developmental parallels of involvement permit the destruction of the vascular intima that however promotes dimensions of aggregation of monocytes and the transformation to foam cells. In such manner, parameters of increasing involvement of the intima with atrophy of the tunica media progressively increases the abnormal flow of blood and promotes distur‐ bed reactivity of endothelial cells. It is such cyclical disturbance that increases also the sus‐

A conceptual realization of developmental events is suggestive of a series of parallel path‐ ways that coincidentally progress as overlapping systems of attempted reconstitution of pathways of possible recovery in the face of incremental destruction of the vascular wall. Monocyte recruitment into the vessel wall is a rate-limiting step in atherogenesis with a crit‐

In such events promotional permissiveness would prove a determining series of further pro‐ gression that calls into operative persistence the effects of hypoxia and of disturbed blood

Extension, both laterally and also deeply into the vascular wall, requires the activation of response elements such as the shear stress response element that augments the distribution‐ al disturbance of interaction of tonicity of the vascular wall with disturbed dynamics of

This phenomenon appears to augment further susceptibility to pro-inflammatory reactivity in the wake of an enlarging plaque that increasingly constitutes the phenotypic switching of such cell types as smooth muscle cells. Also, remodelling of the intima is both accumulative

Substantial reconstitution of the vessel wall, hence, is attempted in the form of replacement dynamics in the vascular wall in a manner that attempts the normalization, to some extent,

of proteoglycans and also modulatory in adapting to new blood flow dynamics.

ceptibility to progressive accumulation of lipoprotein within the vascular wall.

tabolism, which in turn keeps the arteries in a chronically inflamed state [26].

**13. Endothelial dysfunction**

of oxidized lipoprotein core.

**14. Permissiveness**

blood flow.

ical role played by reactive oxygen species [35].

flow that primarily target the endothelial cell bed.

The foam cells within the plaque are an expression of integrative participation within such schemes of complicating plaque formation as realized by such phenomena as lipid core accumulation, chemotaxis, paracrine secretion of growth factors and cytokines and the expres‐ sive effects of oxidized lipoproteins in particular. The distributional nature of disturbed blood flow is significant in the role of selective participation of injury to the overlying endothelial cells that become permeable to the inflow of lipoproteins within the vascular wall. Low density lipoproteins play a major role in initiating progressive atherosclerosis whereas high density lipoproteins suppress inflammation and thrombosis [1].

Hence, an overall series of dimensional agonists conform to and further establish the emer‐ gence of hypoxic influence in the quantitative formulation of the individual atherosclerotic plaque within a highly permissive micro-environment of vascular wall pathology. The sig‐ nificance of multi-component participation is symptomatic of the essential activation of en‐ dothelial cells that are at the interface with disturbed blood flow. Also, the accelerated or aggressive forms of atherogenesis seen in many forms of dyslipoproteinemias illustrate such phenomenon within parametric contexts of endothelial cell dysfunction.


*Article* **Short-Term Effect of Nitrogen Fertilization on Carbon Mineralization during Corn Residue Decomposition in Soil**

**Tanjila Jesmin \*, Dakota T. Mitchell and Richard L. Mulvaney**

Department of Natural Resources and Environmental Sciences, University of Illinois, Turner Hall, 1102 S. Goodwin Avenue, Urbana, IL 61801, USA<EMAIL_ADDRESS>(D.T.M<EMAIL_ADDRESS>(R.L.M.)

**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** The effect of N fertilization on residue decomposition has been studied extensively; however, contrasting results reflect differences in residue quality, the form of N applied, and the type of soil studied. A 60 d laboratory incubation experiment was conducted to ascertain the effect of synthetic N addition on the decomposition of two corn (*Zea mays* L.) stover mixtures differing in C:N ratio by continuous monitoring of CO2 emissions and periodic measurement of microbial biomass and enzyme activities involved in C and N cycling. Cumulative CO2 production was greater for the high than low N residue treatment, and was significantly increased by the addition of exogenous N. The latter effect was prominent during the first month of incubation, whereas N-treated soils produced less CO2 in the second month, as would be expected due to more rapid substrate depletion from microbial C utilization previously enhanced by greater N availability. The stimulatory effect of exogenous N was verified with respect to active biomass, microbial biomass C and N, and cellulase and protease activities, all of which were significantly correlated with cumulative CO2 production. Intensive N fertilization in modern corn production increases the input of residues but is not conducive to soil C sequestration.

**Keywords:** aerobic incubation; CO2 production; microbial metabolism; enzyme activities; active biomass; gross mineralization/immobilization


**1. Introduction**

Designing the perfect home for its resident has been architects' ambition for centuries, aesthetic perfection, functionality, which responds to the consumer's market. Today, the residential sector is experiencing a paradigm shift due to the changes in needs of its inhabitants: the incorporation of technology in spaces [1], remote working trends [2,3], workforce intercity and intercountry relocation and fluidity. Additionally, an unaffordable urban housing market [4] that makes houses non-accessible for part of the population and an increase in loneliness [5–7] are factors that are driving people towards diverse rental typologies with more shared spaces and innovative plug-and-play solutions, like coliving[8].ThisisatrendthathasbeenacceleratedbytheCOVID-19pandemic[9,10].

Before COVID-19, Europeans spent around 55%–66% of their time at home—home indoors—[11]; these ratios have increased during the pandemic, when we spent a considerably larger amount of time at home due to restrictions. In 2021, a new normality has brought new routines and evolving requirements for residential spaces.

COVID-19 has led to a redefinition of the way we live, how we use our residential spaces, our behavior and home responsibilities [12]. Loneliness, once said to be the

**Citation:** Regodon, A.; Armand, M.; Lastres, C.; De Pedro, J.; García-Santos, A. Data-Driven Methodology for Coliving Spaces and Space Profiling Based on Post-Occupancy Evaluation through Digital Trail of Users. *Sustainability* **2021**, *13*, 12607. https://doi.org/ 10.3390/su132212607

Academic Editors: Pierfrancesco De Paola, Francesco Tajani, Marco Locurcio and Felicia Di Liddo

Received: 17 September 2021 Accepted: 4 November 2021 Published: 15 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

illness of the XXI century, has risen exponentially, partly due to the regulations imposed by governments in response to pandemic [5,6,13]. According to the United Nations, community actions to reinforce social cohesion and reduce loneliness are needed to reduce the mental health consequences of the pandemic [14].

Coliving is an emerging residential typology, a "top down, modern form of housing where residents share spaces, activities, values, and/or intentions" [1,8]. These shared living solutions have shown ways of fostering human relationships and close networks that improve daily lifestyle, without imposing sharing behavior or patterns, simply by enabling users to choose what spaces to occupy and the levels of camaraderie they want to engage with. Monitoring and evaluating the use of these spaces has become essential to improving the future of spaces and promoting sustainable housing, tracking the factors of environmental, social, and financial sustainability [15,16]. Post-COVID-19 housing resiliency is related to flexibility, adaptability, reducing risk infection and ensuring user well-being [17,18], and has turned even more human-centered.

HCD defines a design based on human needs and experience [1]. HCD puts the end-users, humans, at the center of the design [19]; psychology and technology are implicit in its initial planning [1,20,21]—the human factor of buildings. Research that connected architectural design and behavioral patterns [18] has grown exponentially thanks to smart technologies and sensors [22].

The level of digitalization of buildings is also growing exponentially [23]; as buildings become smarter and more connected, the Architects, Engineers and Constructors (AEC) industry must adapt [1]. Smart buildings are living entities capable of adapting to the changing needs of the users and reporting to practitioners to improve the future design of spaces.

This research aimed to generate a methodology of spatial analysis using Post-Occupancy Evaluation (POE) of the spaces' performance and user behavior patterns based on the available technology infrastructure.

There were two primary objectives: the first was to identify the data provided from existing data sources that will offer valuable information for HCD spaces, and the second was to generate data-driven Space Profiles (SP) based on the methodology generated that can help AEC experts improve the design of future coliving spaces based on datadriven techniques.

The innovation of the current research also relied on the methodology fully performed remotely due to COVID-19, in real time and relying on the existing IT infrastructure of Coliving without adding other sensors.


**4. Conclusions**

In the current work, an investigation into various LEP configuration cases have been undertaken and related with the rain erosion durability factors, in an effort to assess the response of changing material and processing parameters involved on its blade application.

Diverse cases are developed throughout the research work, in order to ponder the key issues on appropriate LEP system definition for its mechanical characterization, to avoid a lack of accuracy on erosion performance analysis. Viscoelastic material models are originally considered within a coating layer impedance characterization methodology, based on ultrasound measurements for the modelling input data in rain erosion lifetime applications. The computational tool has been used to define erosion performance analysis, depending on the relative acoustic impedance of liquid, coating and substrate materials. The proposed numerical procedures to predict wear surface erosion have been used to identify suitable LEP coating and composite substrate combinations. Experimental campaigns of LEP erosion performance at rain erosion accelerated rain erosion testing (RET) technique have been used as the validation key metric to assess the response of each combined material configuration.

**Author Contributions:** Conceptualization, F.S.; investigation, L.D., V.G.-P., A.Š. and D.P.F.; methodology, F.S.; resources, A.Š., D.P.F., E.S. and F.S.; software, L.D. and V.G.-P; supervision, E.S. and F.S.; validation, F.S.; writing – original draft, F.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research has been partially funded by the DEMOWIND-2 Project Offshore Demonstration Blade (ODB) Funded by MINECO with reference PCIN-069-2017, by the ESI-Group Chair at CEU-UCH and from the European Union's Horizon 2020 research and innovation program under grant agreement No 811473. Project "LEP4BLADES".

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


3. Performance evaluation parameters

After adjustment of classification or calibration models, it is important to have parameters to evaluate the performance through the results obtained. Some of the most widely used parameters and a few explanations on each of them will be provided in this section. These parameters can also be employed, for instance, to aid comparing and deciding among different methods applied to the same problem addressed.

#### 3.1. Evaluation parameters for classification

The first step to organize classification results for better visualization is to build a confusion matrix, like in the example from Table 2. The actual classes are represented in the columns, and the predicted classes, in the rows. The number of apple samples classified as apples is registered in cell AA; the number of apples classified as bananas is in cell AB and those

Table 2. 3 � 3 confusion matrix of results of fruits classification.

In the work of Nadai et al., entitled "Inference of the biodiesel cetane number by multivariate techniques", a method consisting of successive application of principal components analysis (PCA), fuzzy clustering and ANN in a dataset composed by structural information from

The aim of that work was to obtain the cetane number of different types of complex mixtures from data of pure substances (esters). The authors pointed out two main characteristics that affect the cetane number values: the number of carbon-carbon double bonds and the structure

In 2014, with the research "Neural network prediction of biodiesel kinematic viscosity at 313 K" Meng et al. performed the prediction of the kinematic viscosity property of biodiesel by artificial neural networks. The authors used 105 samples of biodiesel collected from the literature and 19 types of fatty acid methyl esters (FAMEs) were set as inputs. The results obtained suggested ANNs as an option in predicting kinematic viscosity with a correlation coefficient of 0.9774 [25]. In the paper "Application of artificial neural networks to predict viscosity, iodine value and induction period of biodiesel focused on the study of oxidative stability", Barradas Filho and collaborators optimized ANN models to predict viscosity, iodine value and induction period

Also in the work of Barradas Filho et al., the ANNs optimization occurred by varying the activation functions, the number of neurons in the hidden layers and the convergence methods. The evaluation criteria of the models were MSE, RMSE, MAPE and r and R2 coefficients. After optimization, the ANN results were compared to other models and obtained the best perfor-

In 2017, the work "Attesting compliance of biodiesel quality using composition data and classification methods" of Lopes et al. compared four classification methods (decision tree classifier, Knearest neighbors, support vector machine and artificial neural networks) to evaluate the compliance of biodiesel samples concerning some quality parameters. This work aimed to obtain an alternative method with more accuracy when compared to other alternative methods [27].

After adjustment of classification or calibration models, it is important to have parameters to evaluate the performance through the results obtained. Some of the most widely used parameters and a few explanations on each of them will be provided in this section. These parameters can also be employed, for instance, to aid comparing and deciding among different methods

The first step to organize classification results for better visualization is to build a confusion matrix, like in the example from Table 2. The actual classes are represented in the columns, and the predicted classes, in the rows. The number of apple samples classified as apples is registered in cell AA; the number of apples classified as bananas is in cell AB and those

(oxidative stability) of 98 biodiesel samples by its fatty esters composition [26].

H NMR) of biodiesel fatty esters was implemented [24].

proton nuclear magnetic resonance (1

186 Advanced Applications for Artificial Neural Networks

mance [26].

of the alcohol moiety in each fatty ester [24].

3. Performance evaluation parameters

applied to the same problem addressed.

3.1. Evaluation parameters for classification

classified as coconuts are in cell AC. The same goes to the other fruit classes. The principal diagonal of the matrix represents the samples correctly classified (cells AA, BB and CC), and the other cells represent the misclassified ones. An ideal classifier would provide a confusion matrix in which all the cells out of the principal diagonal have zero value.

The evaluation parameters for classification methods are based on rates that can be obtained from the confusion matrix. These rates correspond to integer values as they are the numbers of samples classified and split according to some criteria, as will be explained below.

The example given in Table 2, and for banana class, the true positive rate (TP) corresponds to the number of bananas correctly classified as bananas (5 samples, cell BB) and the true negatives (TN) are the samples of the other classes (apple and coconut) classified in any class other than banana (21 samples, cells AA, AC, CA and CC). The false positive rate (FP) is the number of samples of other classes misclassified as bananas (2 samples, cells AB and CB) and the false negative rate (FN) corresponds to the banana samples not classified as bananas (4 samples, cells BA and BC).

For apple class, the TP, TN, FP and FN rates are 9, 19, 3 and 1, respectively, and for coconut class, these rates in the same sequence are 11, 17, 2 and 2. Once the TP, TN, FP and FN rates have been obtained for each class, their average values for all classes together can be used to calculate some global evaluation parameters within which the main ones will be briefly explained with the fruits example.

The accuracy (ACC), given by Eq. (1), reflects the global ability of correctly classification by the method. For the fruits example, ACC is 85.42%, which is the percentage of samples that were classified in its actual classes.

$$\text{ACC} = \frac{\text{TP} + \text{TN}}{\text{TP} + \text{TN} + \text{FP} + \text{FN}} \times 100\% \tag{1}$$

The sensitivity (SENS), also called "recall", can be considered as a global TP rate. The SENS of the fruits classification is 78.13%, calculated by Eq. (2).

$$\text{SENS} = \frac{\text{TP}}{\text{TP} + \text{FN}} \times 100\% \tag{2}$$

The specificity (SPEC) can be calculated by Eq. (3) and it is a global TN rate. For the fruits example, SPEC is 89.06%.

$$\text{SPEC} = \frac{\text{TN}}{\text{TN} + \text{FP}} \times 100\% \tag{3}$$

The false positive rate (FPR) can be interpreted as a global rate of FP for all the classes combined and it is the inversely proportional to the SPEC. In the example discussed here, FPR is 10.94%, calculated by Eq. (4).

$$\text{FPR} = \frac{\text{FP}}{\text{TN} + \text{FP}} \times 100\% = 100\% \text{ - SPEC} \tag{4}$$

Analogously, the false negative rate (FNR) is a global rate of FN (Eq. (5)). For the fruits classification, FNR is 21.87% and it is complementary to the SENS.

$$\text{FNR} = \frac{\text{FN}}{\text{TP} + \text{FN}} \times 100\% = 100\% \text{ - SENS} \tag{5}$$

The ACC, SENS, SPEC, FPR and FNR are some of the main evaluation parameters for classification. Here an example of three classes was presented, giving a 3 � 3 confusion matrix and, therefore, the evaluation parameters should be calculated by the average TP, TN, FP and FN rates.

Problems with only two classes are simpler and more widespread in the literature, usually involving samples that "have" or "do not have" a specific characteristic and giving a 2 � 2 confusion matrix. In this case, TP, TN, FP and FN rates are obtained only for the "positive class" and the evaluation parameters are directly calculated by these rates instead of by the averages.

A two class example, already cited in Section 2, is the classification of biodiesel samples according to their compliance to some quality parameters. For each criteria, the samples were split in "compliant" and "non-compliant" [27].

#### 3.2. Evaluation parameters for calibration

The evaluation parameters for calibration are quite different from those for classification. In calibration, these parameters are based on the difference between the actual response, that obtained experimentally by a reference method, and the predicted response, the one estimated by the calibration method.

The oxidative stability (h) of some biodiesel samples from the case study of Section 4 are show in Table 3 with the actual (y) and predicted (y0 ) responses given in hours. The residuals are the difference between the actual and the predicted responses. The other columns have values calculated to be used in equations of the evaluation parameters that will be explained.

The RMSE is an average deviation between the actual and the predicted values and it has the same unity from the responses. The example from Table 3, the RMSE calculated is 0.34 h, which means that the estimated responses differ, on average, in �0.34 h from the actual values. In papers, the RMSE is often abbreviated as RMSEC, RMSEP, RMSEV and RMSECV when

Table 3. Actual and predicted values of oxidative stability (h) of biodiesel samples.

The specificity (SPEC) can be calculated by Eq. (3) and it is a global TN rate. For the fruits

TN þ FP

The false positive rate (FPR) can be interpreted as a global rate of FP for all the classes combined and it is the inversely proportional to the SPEC. In the example discussed here,

Analogously, the false negative rate (FNR) is a global rate of FN (Eq. (5)). For the fruits

The ACC, SENS, SPEC, FPR and FNR are some of the main evaluation parameters for classification. Here an example of three classes was presented, giving a 3 � 3 confusion matrix and, therefore, the evaluation parameters should be calculated by the average TP,

Problems with only two classes are simpler and more widespread in the literature, usually involving samples that "have" or "do not have" a specific characteristic and giving a 2 � 2 confusion matrix. In this case, TP, TN, FP and FN rates are obtained only for the "positive class" and the evaluation parameters are directly calculated by these rates instead of by the averages. A two class example, already cited in Section 2, is the classification of biodiesel samples according to their compliance to some quality parameters. For each criteria, the samples were

The evaluation parameters for calibration are quite different from those for classification. In calibration, these parameters are based on the difference between the actual response, that obtained experimentally by a reference method, and the predicted response, the one estimated

The oxidative stability (h) of some biodiesel samples from the case study of Section 4 are show

difference between the actual and the predicted responses. The other columns have values

The RMSE is an average deviation between the actual and the predicted values and it has the same unity from the responses. The example from Table 3, the RMSE calculated is 0.34 h, which means that the estimated responses differ, on average, in �0.34 h from the actual values. In papers, the RMSE is often abbreviated as RMSEC, RMSEP, RMSEV and RMSECV when

calculated to be used in equations of the evaluation parameters that will be explained.

� 100% (3)

� 100% ¼ 100% - SPEC (4)

� 100% ¼ 100% - SENS (5)

) responses given in hours. The residuals are the

SPEC ¼ TN

FPR ¼ FP

classification, FNR is 21.87% and it is complementary to the SENS.

FNR ¼ FN

TN þ FP

TP þ FN

example, SPEC is 89.06%.

TN, FP and FN rates.

split in "compliant" and "non-compliant" [27].

in Table 3 with the actual (y) and predicted (y0

3.2. Evaluation parameters for calibration

by the calibration method.

FPR is 10.94%, calculated by Eq. (4).

188 Advanced Applications for Artificial Neural Networks

calculated for the samples of calibration (training), prediction (test), validation and crossvalidation, respectively, and it can be given by Eq. (6), in which n is the number of samples (n = 8, in this example).

$$\text{RMSE} = \sqrt{\frac{1}{n} \sum\_{i=1}^{n} \left( y\_i - y\_i' \right)^2} \tag{6}$$

Another important error metric is the MAPE, which is a relative measure of the prediction accuracy, calculated by Eq. (7). From the example of oxidative stabilities of biodiesel, the MAPE is 2.48%, that is, on average, the predicted values deviate in 2.48% from their actual values.

$$\text{MAPE} = \frac{1}{n} \sum\_{i=1}^{n} \left| \frac{y\_i - y\_i'}{y\_i} \right| \times 100\% \tag{7}$$

The Pearson correlation coefficient (r, Eq. (8)) is a measure of the linear relationship between two variables and it is expressed in values from 0 to |1|. The closer to |1|, the more linearly correlated the variables are. In cases of calibration methods, r coefficient is used to compare the actual and the predicted values. Since y and y0 are expected to be equal, this represents a direct relationship and, then, the ideal r coefficient is +1.

$$r(y, y') = \frac{n \sum\_{i=1}^{n} y\_i y\_i' - \left(\sum\_{i=1}^{n} y\_i\right) \left(\sum\_{i=1}^{n} y\_i'\right)}{\sqrt{\left[n \sum\_{i=1}^{n} y\_i^2 - \left(\sum\_{i=1}^{n} y\_i\right)^2\right] \left[n \sum\_{i=1}^{n} y\_i'^2 - \left(\sum\_{i=1}^{n} y\_i'\right)^2\right]}} \tag{8}$$

For the oxidative stabilities, for example, r is 0.9977, which represents a high correlation between the actual and the predicted responses. However, it is important to perform the graphical analysis of the correlation by a scatter plot of the actual (y in x-axis) and the predicted (y0 in y-axis) values, because not all samples with a perfect correlation coefficient are well distributed along the line of the expected identity function for y=y0 .

Table 4. Evaluation parameters calculated from the results obtained by the ANN MLP 4-3-1-1 for the oxidative stability (h) of biodiesel samples.

Although the R2 coefficient can be obtained by taking the square of the correlation coefficient, they have different meanings. The R2 , calculated by Eq. (9) in which ym is the average of the actual values, expresses how much the calibration model explains from the total variance and it can range from 0 to +1. For example, from Table 4, the R2 obtained is 0.9954, which means that 99.54% of total data variance is explained by the regression, and the 0.46% remaining are attributed to residuals.

$$R^2 = r^2 = \frac{\sum\_{i=1}^n \left(y\_i' - y\_m\right)^2}{\sum\_{i=1}^n \left(y\_i - y\_m\right)^2} \tag{9}$$

Some of the main evaluation parameters for regression have been explained in this section. Besides the numerical parameters, it is also quite important to perform a graphical evaluation of the results by the correlation and residual plots. More details on this will be provided in the case study of the next section.

#### 4. Case study

Biodiesel, like any other fuel, needs to meet some parameters specifications so it can be marketed with quality and safety. These quality parameters are established by standards in each country or region, such as the standards EN 14214 (Europe), ASTM D6751 (USA) and RANP 45/2014 (Brazil) [27].

Among the parameters of biodiesel quality, there are general parameters, which are also applied to petroleum diesel, and there is a special group of parameters related to the chemical composition and purity of the vegetable oils. These parameters can be grouped into four sets: contaminants from the raw material, parameters related to the evaluation of the production process, properties inherent to molecular structures and parameters related to the storage process [7].

One of the main problems assigned to the quality of biodiesel is the possibility of its oxidation caused by the presence of unsaturations in its ester molecules, which is one of the most relevant differences between biodiesel and mineral diesel composition. The main products formed by the oxidation of biodiesel can cause formation of insoluble gums in the engine, filter clogging, injector cocking and corrosion of the metal parts of the engine. Therefore, the evaluation of the oxidative stability of biodiesel is considered by many researchers in the literature to be a very important analysis that should be done because it is directly related to the deterioration capacity (oxidation) and to the time in which the biofuel can be stored (induction period) [26].

The oxidative stability of biodiesel is measured by the method EN 14112, also called Rancimat method, which consists of a system composed of a reaction vessel connected to a cell monitored by an electrode. The sample is placed in the vessel in a heating block at 110C and a continuous stream of air is bubbled through. The increase in temperature and the presence of oxygen induce the accelerated oxidation of biodiesel. The primary products are formed, followed by secondary products of the oxidation among which are short chain volatile organic acids. These acids are carried to a cell containing deionized water and promote the increase in conductivity, which is measured by an electrode coupled to a device that records the conductivity as a function of time [28].

Although the R2 coefficient can be obtained by taking the square of the correlation coefficient,

Table 4. Evaluation parameters calculated from the results obtained by the ANN MLP 4-3-1-1 for the oxidative stability

MAPE (%) 8.35 R2 0.9306 r 0.9647

MAPE (%) 5.51 R2 0.9733 r 0.9866

MAPE (%) 6.89 R2 0.9544 r 0.9769

actual values, expresses how much the calibration model explains from the total variance and it can range from 0 to +1. For example, from Table 4, the R2 obtained is 0.9954, which means that 99.54% of total data variance is explained by the regression, and the 0.46% remaining are

> Pn i¼1 y0 i � ym � �2

Pn i¼1

Some of the main evaluation parameters for regression have been explained in this section. Besides the numerical parameters, it is also quite important to perform a graphical evaluation of the results by the correlation and residual plots. More details on this will be provided in the

Biodiesel, like any other fuel, needs to meet some parameters specifications so it can be marketed with quality and safety. These quality parameters are established by standards in each country or region, such as the standards EN 14214 (Europe), ASTM D6751 (USA) and

yi � ym

R2 ¼ r

Step Parameter MLP 4-3-1-1

Training RMSEC (h) 1.31

Validation RMSEV (h) 0.43

Test RMSEP (h) 0.67

2 ¼

, calculated by Eq. (9) in which ym is the average of the

� �2 (9)

they have different meanings. The R2

190 Advanced Applications for Artificial Neural Networks

attributed to residuals.

(h) of biodiesel samples.

case study of the next section.

RANP 45/2014 (Brazil) [27].

4. Case study

The induction period used to evaluate the oxidative stability of biodiesel is the time at which the conductivity curve increases rapidly, corresponding to the emergence of the secondary products of the oxidation. The standards EN 14214 and RANP 45/2014 state that the minimum oxidative stability of biodiesel should be 8 h at 110C [29, 30], while ASTM D6751 specifies 3 h of oxidative stability [31].

Aiming to reduce the time, complexity and costs of analyzing biodiesel quality parameters, some papers in the literature report analytical methodologies alternative to official methods. In this context, the Rancimat method is a relevant case to be studied due to the long analysis time, since a sample of biodiesel that meets EN 14214 requirements will be under analysis for more than 8 h to obtain an oxidative stability result.

A case study of an application of ANN to predict oxidative stability of biodiesel will be presented below to better illustrate the main steps from data preprocessing and selection of sample sets (for training, validation and test) to the optimization of ANN configuration and application. Finally, some performance measures will be evaluated and discussed in the case study. All data handling, preprocessing, subset partitioning and ANN regression were carried out with software MATLAB® 2013a (MathWorks), PLS\_Toolbox (Eigenvector) and an algorithm implemented in MATLAB [32].


**6. Holographic optical elements**

PTR glass is a bulk material that is characterized by high homogeneity. Therefore, it is possible to manufacture gratings with high efficient thickness (say, about 1 mm and more). The use of high thickness of the material opens up a possibility to manufacture spatial and spectral filters with outstanding parameters. As known, the selectivity of the Bragg grating depends on its thickness; therefore, it is possible to create gratings with sub nm spectral selectivity and with angular selectivity of <1 angular min.

#### **6.1. Super narrow-band filters for laser diodes and their temperature stabilization of radiation**

The widespread use of semiconductor lasers is stimulated by a number of their advantages [33–35] such as the high efficiency (75–80%), small sizes, simplicity of operation, and relatively low cost. An important advantage of semiconductor lasers is also the possibility of fabricating emitters operating at different wavelengths in the visible, near-infrared, and mid-infrared spectral ranges. Apart from the above beneficial features, the semiconductor lasers have certain drawbacks: their emission is quasi-monochromatic and spectrally unstable. This is caused by a number of factors. The broadening of the lasing spectrum under an increase in the injection current stems from the fundamental aspects of charge-carrier transport and captures into the quantum-confined active region. The lasing spectrum is also affected by the multimode design of the laser cavity. A shift of the spectrum occurs as a result of heating the active region with an increase in the injection current, which causes a reduction in the band gap and, thus, the shift of the lasing spectrum to the longer wavelengths.

does not exceed 1 × 10−4, though this value differs from that obtained with absolute measure-

PTR glass is a bulk material that is characterized by high homogeneity. Therefore, it is possible to manufacture gratings with high efficient thickness (say, about 1 mm and more). The use of high thickness of the material opens up a possibility to manufacture spatial and spectral filters with outstanding parameters. As known, the selectivity of the Bragg grating depends on its thickness; therefore, it is possible to create gratings with sub nm spectral selectivity and with

**6.1. Super narrow-band filters for laser diodes and their temperature stabilization of**

The widespread use of semiconductor lasers is stimulated by a number of their advantages [33–35] such as the high efficiency (75–80%), small sizes, simplicity of operation, and relatively low cost. An important advantage of semiconductor lasers is also the possibility of fabricating emitters operating at different wavelengths in the visible, near-infrared, and mid-infrared spectral ranges. Apart from the above beneficial features, the semiconductor lasers have certain drawbacks: their emission is quasi-monochromatic and spectrally unstable. This is caused by a number of factors. The broadening of the lasing spectrum under an increase in the injection

**Figure 20.** Typical dependence of the RIMA on exposure for bromide PTR glass.

**6. Holographic optical elements**

angular selectivity of <1 angular min.

**radiation**

ments, ~8 × 10−4.

452 Holographic Materials and Optical Systems

This problem can be solved by means of VBG recorded in photo-thermo-refractive glass. Due to high spectral selectivity of recorded holograms, the implementation of such grating inside the external cavity of laser diode can significantly narrow the output spectra. This idea was used widely and proved its advantages. External cavity design based on the VBG can vary (see **Figure 21**), as well as both reflecting or transmitting Bragg grating can be used [26].

The simplest implementation of VBG as an external cavity element is shown in **Figure 21(a)**, where a radiation after being passed through the collimating lens falls normally on the VBG element. Unfortunately, due to a high divergence along the fast axis of the LD output radiation, it is impossible to create the reliable external cavity of LD without additional collimation optics. **Figure 21(b)** shows typical design of external cavity using transmission Bragg grating. The grating works backward and forward, and its diffraction efficiency has to be lower than 80% to couple output radiation efficiently. So there is a need for an additional mirror in the cavity setup to reduce the power loss through the nondiffracted radiation on backward cavity trip. The position of mirrors can be changed, but the number of output channels will remain the same. Also, the cavity designs for coupling the higher-order modes of the LD are also possible. Such designs require the high diffraction efficiency of the grating to provide the maximum output performance and are suitable for wide stripe emitting diodes. Stabilized by means of VBG, the laser diodes show a stable output in the temperature range from 15°C to 75°C [36].

**Figure 21.** Examples of design of external cavity of a diode laser based on VBGs (a) is an example of reflecting VBG implementation and (b) is an example of cavity based on transmitting VBG.

Recent studies of VBG-based external cavity LD show that the implementation of the grating inside cavity significantly increases its selective properties. For example, a grating used in our experiment [37] was recorded with estimated spectral selectivity as great as ~2 nm. We used a cavity shown in **Figure 21(b)** with the transmitting VBG. The emission spectra from such cavity show us two longitudinal modes with the separation of 100 pm and bandwidth of 4–8 pm (**Figure 22**).

**Figure 22.** Emission spectra of laser diode source. Left: spectra recorded with (1) and without (2) grating. Right: the detailed view of the emission line [36].

Similar to the conventional ways of LD stabilization such as Littrow scheme and Litman-Metcalf configurations, using the standard diffraction gratings based on the VBG in the external cavities can provide tuning of the output emission of the source. Merely by the rotation of the grating, we can achieve a tunability along all the gain spectra of the semiconductor crystal that can be really huge, up to 60 nm. An example of such tuning is shown in **Figure 23**.

**Figure 23.** Emission spectra of the external cavity laser diode with different angles of VBG.

#### **6.2. Laser beam combiners**

experiment [37] was recorded with estimated spectral selectivity as great as ~2 nm. We used a cavity shown in **Figure 21(b)** with the transmitting VBG. The emission spectra from such cavity show us two longitudinal modes with the separation of 100 pm and bandwidth of 4–8 pm

**Figure 22.** Emission spectra of laser diode source. Left: spectra recorded with (1) and without (2) grating. Right: the

Similar to the conventional ways of LD stabilization such as Littrow scheme and Litman-Metcalf configurations, using the standard diffraction gratings based on the VBG in the external cavities can provide tuning of the output emission of the source. Merely by the rotation of the grating, we can achieve a tunability along all the gain spectra of the semiconductor crystal that can be really huge, up to 60 nm. An example of such tuning is shown in **Figure 23**.

**Figure 23.** Emission spectra of the external cavity laser diode with different angles of VBG.

(**Figure 22**).

454 Holographic Materials and Optical Systems

detailed view of the emission line [36].

The diffraction efficiency directly depends on the thickness and RIMA, and as shown, this glass has quite a big inflicted refractive index change. Therefore, it is also possible to record multiple gratings inside the single volume of the glass (**Figure 24**).

**Figure 24.** Example of multiple gratings recorded in the single volume of the glass for spectral beam combining.

There is much interest in the use of spectral beam combining (SBC) to combine multiple highpower laser beams into a single high-power one with a narrow spectral linewidth and good beam quality [38]. This idea can be implemented by using several volumes of Bragg gratings for each channel multiplexed in the single volume of PTR glass. Recently, a two and four channel combiner based on the multiplexed reflective Bragg gratings was reported [39]. This approach allows one to develop a combining system with low complexity and better robustness.

#### **6.3. Collimator sights**

The holographic collimator sights are the development of the classical collimator sights. This new kind of design provides the greater transparency of the working aperture compared with the classical collimator and greater parallax suppression. This kind of sights has an open design, which means that the sight can be aimed with both eyes. So a shooter can use the peripheral vision and engage more effectively. Also, due to the properties of a hologram, such sight is very resistant to various injuries and pollution. The hologram is recorded over the entire area of the aperture, which is why the sight remains in the working condition even after a partial pollution and/or damage. Also, one of the main advantages over the conventional sights is the absence of a flare toward a target, which is crucial in a combat.

Basic elements of a holographic sight are shown in **Figure 25**. The operation principle of holographic collimator sights can be briefly described as follows. A radiation from the light source falls on the recorded hologram that creates an image of the recorded mark in the image plane. The high transparency of PTR glass in the visible range (above 90% without AR coating) opens up this field of applications.

**Figure 25.** Holographic sight (a) is the basic scheme of the sight and (b) is the observable image of a mark recorded on PTR glass.

**Figure 26.** Laser action of Nd3+ heavily doped (NNd = 2.5 × 1020) PTR glass measured for mirrors with the reflection of 1% (red curve) and 5% (black curve) [34].

The application of PTR glass can solve the problem of image stabilization, which is necessary due to the instability of laser diode source used in such sights. To date, this problem is solved by adding, into the optical scheme, the achromatizing diffraction elements such as thin gratings, complex two-cavity mirrors, or compound objectives. The wavelength shift caused by laser diode temperature changes can be nullified by spectral selectivity of thick hologram recorded on PTR glass. Because the diffraction efficiency of holograms on PTR glass can reach values of ~95%, an intensity required for the mark observation is rather low. In **Figure 25**, the observable image of holographic mark recorded on PTR glass is demonstrated.

#### **6.4. Distributed feedback (DFB) and distributed Bragg reflector (DBR) lasers**

The concept of DFB lasers was originally demonstrated in 1971 [40] when the laser output from a gelatin film on a glass substrate was obtained for the first time. Two years later [41], a generation from a similar structure on GaAs at nitrogen temperatures was demonstrated. Benefits of such a laser design are pretty obvious: Bragg grating acts as a selective mirror with very narrow reflection bandwidth and, thus, provides a narrow spectral emission output. Since then, DFB lasers had a lot of development, but yet there were no results in creating DFB solidstate lasers.

Doping PTR glass with rare earth elements provides an access to the construction of DFB and DBR lasers because such medium possesses both the laser and holography properties. Recently, first results on laser action on PTR glass were obtained [42, 43]. Later, a generation on heavily Nd-doped PTR glass was obtained in ITMO University [44]. Laser performance is shown in **Figure 26**. Our calculation shows that PTR glass itself is characterized, due to its outstanding homogeneity, by relatively low round trip loss estimated to be ~0.26%, which is comparable to that of commercially fabricated Nd:YAG crystals.

Further investigations of DFB/DBR effect on PTR glass showed that recording a grating inside PTR glass does not affect its lasing properties. For instance, the laser action in the DFB/DBR configuration on Nd- and Yb-doped PTR glasses was demonstrated [45]. In these experiments, the output radiation from both setups (DFB/DBR) and on both types of PTR glasses (Nd and Yb) was obtained. The emission spectra observed show a narrow line with 30 pm bandwidth.


**2. Soil erosion mechanism in earth structures**

Erosion in earth structures due to water flow occurs when the erosion resistant forces are less than the seepage forces that tend to produce it, in such a way that the soil particles are removed and carried with the water flow. The resistant forces depend on the cohesion, the interlocking effect, the weight of the soil particles and the kind of protection they have downstream, if any. Since the seepage through an earth structure is not uniform, the erosion phenomenon increases where there exists a concentration of seepage and water velocity; in places where this concentration emerges at the downstream side, the erosive forces on the soil particles might become very significant. This accentuates the subsequent concentration of seepage and erosive forces there.

This erosion process might occur at any crack that exists in the earth structure, due to differential settlements, seismic movements, tension stresses, or holes caused by dry roots or gnawing animals (rabbits, rats, etcetera). The existence of cracks is also due to shrinkage drying or swelling due to saturation. Favorable internal erosion conditions also exist in contacts between soils and rigid walls, concrete structures, interface with bedrock foundation, etcetera. Areas where ark effect is present are also very susceptible to internal erosion. In all the previous cases, if the vertical effective stresses are reduced by the effect of the water flow, then the existing crack might propagate in such a way that it will create the hydraulic fracture phenomenon.

The erosion starts at any point where the seepage water discharges and works toward the reservoir, gradually enlarging the seepage channel. Depending of the stage of this process, the occurred damage might be classified as a simple "incident", an accident, or a complete failure.

procedures and drainage blankets are also presented. A short section with some recommendations to protect river banks from the erosive attack of water (such as rockfill, *bolsacreto* or *colchacreto* system –concrete bags–, breakwaters, sheet pile walls, etc.) is also included. The construction of graduation filters to prevent piping and movement of erodible soils is also presented. Special emphasis is given in the actual filter design criterion that is recommended by the US Army Corps of Engineers (2000), U.S. Bureau of Reclamation (2000) and the U.S. Soil Conservation Service (1994). Together with these recommendations, those given for earth dams design by A. Casagrande (1968) for avoiding piping and internal

Several devices that have been developed to assess how resistant earth materials are to water flow are presented. Additionally, the main recommended laboratory and field tests to analyze soil dispersion or erosion are discussed. A description of laboratory tests to verify the best suitable material to use as a filter and protect a dam core against piping or internal

Two practical examples related to drainage failures caused by piping and by uncontrolled saturation and seepage forces are presented to illustrate the content of this chapter. In particular, analyses to assess how transient flow caused by rapid filling and drawdown affects soil erosion in typical levees constructed to protect urban areas exposed to flooding

Finally, several recommendations for preventing or solving problems related to soil erosion

Erosion in earth structures due to water flow occurs when the erosion resistant forces are less than the seepage forces that tend to produce it, in such a way that the soil particles are removed and carried with the water flow. The resistant forces depend on the cohesion, the interlocking effect, the weight of the soil particles and the kind of protection they have downstream, if any. Since the seepage through an earth structure is not uniform, the erosion phenomenon increases where there exists a concentration of seepage and water velocity; in places where this concentration emerges at the downstream side, the erosive forces on the soil particles might become very significant. This accentuates the subsequent concentration

This erosion process might occur at any crack that exists in the earth structure, due to differential settlements, seismic movements, tension stresses, or holes caused by dry roots or gnawing animals (rabbits, rats, etcetera). The existence of cracks is also due to shrinkage drying or swelling due to saturation. Favorable internal erosion conditions also exist in contacts between soils and rigid walls, concrete structures, interface with bedrock foundation, etcetera. Areas where ark effect is present are also very susceptible to internal erosion. In all the previous cases, if the vertical effective stresses are reduced by the effect of the water flow, then the existing crack might propagate in such a way that it will create the

The erosion starts at any point where the seepage water discharges and works toward the reservoir, gradually enlarging the seepage channel. Depending of the stage of this process, the occurred damage might be classified as a simple "incident", an accident, or a complete

are performed by numerical modeling based on finite element method (FEM).

are presented, together with the main conclusions of this chapter.

**2. Soil erosion mechanism in earth structures**

of seepage and erosive forces there.

hydraulic fracture phenomenon.

failure.

erosion in earth dams are given.

erosion is also given.

The first engineers that analyzed this problem were Blight (1910) and Lane (1935), as cited in Casagrande (1968), who defined the susceptibility to soil erosion through a percolation factor *C*, in terms of the horizontal and vertical paths of the water flow, the type of soil and the water head between the upstream and downstream water levels of a hydraulic structure. Figure 1 illustrates the definition of the percolation factor and Table 1 gives the minimum values of *C* recommended by these engineers to avoid soil erosion. Unfortunately, this criterion did not work well for all cases and its use is not recommended (Flores-Berrones, 2000).

Fig. 1. Dam example given by Blight (1910) to define the percolation factor *CB* (Casagrande, 1968)

Table 1. Minimum values of percolation factors to avoid piping, according to Blight and Lane criteria (Casagrande, 1968)

In 1967 Sherard et al. published a table which gives a rough empirical relationship between piping resistance in earth dam embankments and soil types. Such table indicates that soils with the greatest piping resistance are the well compacted high plasticity clays, the intermediate are the well graded coarse sand and sand gravel mixtures, and the least piping resistance are the uniform fine cohesionless sands.

The soil erosion in earth structures, particularly in earth dams and levees, might occur through the embankment, the foundation or from the embankment to foundation (Figs. 2a, 2b and 2c). This kind of erosion has the following phases: a) initiation and continuation of erosion, b) progression to form a pipe, and c) formation of a breach (Fell et al., 2003). The initiation of the soil erosion usually starts at the exit point of the seepage, and retrogressive erosion results in the formation of a "*pipe*". In fact, this is the reason why this erosion phenomenon is also called *piping* (see Fig. 2c). The removal of a small portion of the earth embankment or foundation by erosive action at any point, particularly at the exit part of the downstream slope, accentuates the subsequent concentration of seepage and erosive forces in that zone.

Internal Erosion Due to Water Flow Through Earth Dams and Earth Structures 287

to rapid filling and drawdown conditions due to these oscillations of river water level and also to the seepage forces generated by rain infiltration at the crown of the levees protecting the margins of rivers are observed. Instability problems in river banks commonly begin with erosion, which in some parts (depending on the type of soil) causes *piping* and might result

Landslide

(1)

in landslides as shown in Figure 3 (Auvinet & Lopez-Acosta, 2010).

Piping

**3. Factors affecting the earth erosion phenomenon**

Fig. 3. Evidences of instability in river banks caused by erosion (Auvinet & Lopez-Acosta,

Main factors affecting the erosion phenomenon are: a) the erodibility of the soil; b) the water velocity inside the soil mass or the water velocity on a river; c) geometry of the earth

*Erodibility* can be defined as the relationship between the velocity of the water flowing over the soil and the corresponding erosion rate experienced by the soil. This definition of erodibility presents some problems because water velocity is a vector quantity which varies everywhere in the flow and is theoretically zero at the soil-water interface. It is preferable to quantify the action of the water on soil by using the shear stress applied by the water on the soil at the water-soil interface. Thus, erodibility of a soil can be defined by the relationship between the erosion rate *Z* and the shear stress *τ* at the soil-water interface (Briaud, 2008):

> . *Z f*

Fig. 4. Proposed erosion categories for soils and rocks based on shear stress (Briaud, 2008)

Beginning of erosion

structure through its size and shape.

2010)

This effect, due to concentrated water leaks, varies somehow from what is called *suffusion* or internal instability, which implies the internal movement of soil particles due to the adjustment of internally unstable soils; this is the case of gap graded or very broadly graded soils, such as coarse sands and gravels with small quantities of fine soils.

Fig. 2b. Piping in the foundation initiated by backward erosion (After Fell et al., 2003)

Fig. 2c. Piping from embankment to foundation initiated by backward erosion (After Fell et al., 2003)

The soil erosion problems also might occur in river banks. In tropical regions the intense rainfalls originate large and quick variations of the water surface of rivers. Problems related

This effect, due to concentrated water leaks, varies somehow from what is called *suffusion* or internal instability, which implies the internal movement of soil particles due to the adjustment of internally unstable soils; this is the case of gap graded or very broadly graded

INITIATION CONTINUATION PROGRESSION BREACH/FAILURE

concentrated leak

Backward erosion in progresses to form a pipe

Backward erosion in progresses to form a pipe

Breach mechanism forms

Breach mechanism forms

Breach mechanism forms

Continuation of erosion Enlargement of

Fig. 2a. Piping in the embankment initiated by concentrated leak (After Fell et al., 2003)

INITIATION CONTINUATION PROGRESSION BREACH/FAILURE

INITIATION CONTINUATION PROGRESSION BREACH/FAILURE

Fig. 2c. Piping from embankment to foundation initiated by backward erosion (After Fell et

The soil erosion problems also might occur in river banks. In tropical regions the intense rainfalls originate large and quick variations of the water surface of rivers. Problems related

Fig. 2b. Piping in the foundation initiated by backward erosion (After Fell et al., 2003)

Continuation of erosion

Continuation of erosion

soils, such as coarse sands and gravels with small quantities of fine soils.

Concentrated leak forms and erosion initiates along walls of crack

Leakage exits from the foundation and backward erosion initiations

Leakage exits from the foundation and backward erosion initiations

al., 2003)

to rapid filling and drawdown conditions due to these oscillations of river water level and also to the seepage forces generated by rain infiltration at the crown of the levees protecting the margins of rivers are observed. Instability problems in river banks commonly begin with erosion, which in some parts (depending on the type of soil) causes *piping* and might result in landslides as shown in Figure 3 (Auvinet & Lopez-Acosta, 2010).

Fig. 3. Evidences of instability in river banks caused by erosion (Auvinet & Lopez-Acosta, 2010)


**3.6 Verruca vulgaris (viral wart)**

Verruca vulgaris is an infection caused by human papilloma virus (HPV). There are more than 70 types of HPV which are identified by type numbers, and types that infect the human skin are different from those that infect the mucous of the oropharynx and genitalia. Therefore, doctors should inform the patients that the disease is not sexually transmitted. The pathogen is not included in the microbiota of AD skin, so it is a simple infection from other patients or the environment. The condition is frequent in healthy children and also in patients (children and adults) with AD. The clinical picture is different in patients with AD and the management strategy is also different, so it is discussed in this chapter.

The lesions are slightly elevated papules with obvious margins. Sometimes the lesions grow like cauliflower with a rough surface. The colour is more yellowish-white than the uninfected skin (Fig. 10). HPV promotes the growth of capillaries for their survival. Therefore, small black dots are often seen within a lesion. HPV is observed in piles within the lesion by electron microscopy.

Fig. 8. Molluscum contagiosum on the chest of a patient with AD.

Verruca vulgaris is an infection caused by human papilloma virus (HPV). There are more than 70 types of HPV which are identified by type numbers, and types that infect the human skin are different from those that infect the mucous of the oropharynx and genitalia. Therefore, doctors should inform the patients that the disease is not sexually transmitted. The pathogen is not included in the microbiota of AD skin, so it is a simple infection from other patients or the environment. The condition is frequent in healthy children and also in patients (children and adults) with AD. The clinical picture is different in patients with AD

The lesions are slightly elevated papules with obvious margins. Sometimes the lesions grow like cauliflower with a rough surface. The colour is more yellowish-white than the uninfected skin (Fig. 10). HPV promotes the growth of capillaries for their survival. Therefore, small black dots are often seen within a lesion. HPV is observed in piles within

and the management strategy is also different, so it is discussed in this chapter.

**3.6 Verruca vulgaris (viral wart)**

the lesion by electron microscopy.

When the skin of the face and the neck is affected in AD patients, the lesions often become itchy, and scratching releases HPV into the nearby skin. It results in an assembled manner of the lesions (Koebner phenomenon). The itching of the lesions is often ignored because the lesions are not itchy in patients without AD. Patients often apply topical anti-inflammatory drug to the lesion but it should be avoided because it promotes the proliferation of HPV. Therefore, the treatment for such HPV is often complex and difficult.

The first step of treatment should include cryotherapy using liquid nitrogen. When the lesion successfully drops off as a consequence, it is appropriate to apply topical antiinflammatory ointment immediately.

Fig. 9. Severe molluscum contagiosum in an infant with AD.

Microorganisms and Atopic Dermatitis 121

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Fig. 10. Multiple verruca vulgaris on the eyelid of an adult patient with AD.


**4. Case Study**

This section includes two parts. First, the processing of separating temperature-induced strain from the field measured data is given. Then, according to the above data, the phase subtraction method based on the Fourier fitting method is described in detail and its effectiveness is verified.

### *4.1. Separation of Temperature-Induced Strain*

The measured strain data includes the interaction of live load and temperature effects. Therefore, the separation of temperature strain is a prerequisite for thermal strain response studies. The measured data of the S6 strain measuring point of the bridge on 9 June 2017 is shown in Figure 6.

(**a**) Raw strain and temperature-induced strain.

**Figure 6.** *Cont*.

**Figure 6.** Strain time history of the raw data and the processed data.

In Figure 6, the separation of temperature effects is achieved by the wavelet decomposition and reconstruction method [20]. As shown in Figure 6, this method can separate temperature strain effectively. The raw strain data is finally decomposed into temperature-induced strain (Figure 6a, the red line) and live load strain (Figure 6b). It is worth noting that the live load-induced strain in Figure 6b is larger than that in Figure 2b. The reason is the location of sensor S6 is in the center of the bridge where the girder will withstand more vehicle loads.