**5. Conclusions**

92 Reverse Engineering – Recent Advances and Applications

engineered models. We believe that this is a good measure of quality of reverse engineered models. If the reverse engineered models are 'good enough' to be used as inputs to code generation (onto another platform) that means we have made progress toward model reusability. Therefore, for our experiments we chose to put the reverse engineered models to test. The results are consistent with our hypothesis and show 40-50% of savings in development effort. The two platforms investigated are IBM WebSphere and SAP NetWeaver platforms. We tried our approach on 5 different platform independent models – either modeled or derived. On an average, we have noted that by using our transformations we can reduce the develop effort by 40%-50% in a 6 month development project (Table 2).

**Phase Low Medium High Very**

**Activity**

**Develop Back-end Data Objects** No Develop Data Dictionary Objects 4 578 No Develop Business Objects No **Develop Services** No **Develop Back-end Services (custom RFCs/BAPIs)** No Develop Custom RFC/BAPI(s) 8 16 32 40 No Expose RFC/BAPI(s) as Web Services using Web Service Creation Wizard 0.1 0.1 0.1 0.1 No Publish Web Services into Service Registry (UDDI Registry) 0.1 0.1 0.1 0.1 No Unit Testing of back-end Services 0.25 0.25 0.25 0.25 No

Development Local (CAF Layer) Business Objects with Attributes, Operations 0.5 0.75 1 1.5 Yes Development of Relationship amongst Business Objects 0.1 0.1 0.1 0.1 Yes Unit Testing of Local BOs 1 123 No

Import RFC/BAPI into CAF Core 0.1 0.1 0.1 0.1 Yes Import Enterprise Services into CAF Core 0.1 0.1 0.1 0.1 Yes Map External Services to Operations of Business Objects 1 111 No

Develop Application Services with Operations 1 246 Yes Map External Services to Operations of Application Services 1 122 Yes Implement Operations with Business Logic 8 16 24 36 No Expose Application Services as Web Services 0.1 0.1 0.1 0.1 Yes Publish Web Services into Service Registry (UDDI Registry) 0.1 0.1 0.1 0.1 No Unit Testing of Application Service Operations 0.5 1 2 4 No Deploy Entity Services into Web Application Server (WAS) No Deploy Application Services into Web Application Server (WAS) No Configure External Services after Deploying into Web Application Server (WAS) 1 122 No

Develop Visual Composer(VC) based User Interfaces 4 8 16 32 No Implement GP Callable Object Interface 8 12 20 24 No Develop WebDynpro (WD) Java based User Interfaces 16 32 48 64 Yes Develop Adobe Interactive Form (AIF) based User Interfaces 16 24 32 48 No

**With Model-driven Transformations** 18.9 36.15 55.4 73.9 **Total** 38.6 68.25 106.5 144 **Percentage Generated by Model-driven Transformations 48.96 52.97 52.02 51.32**

Table 2. Catalogs the development phase activities that our transformations help automate and the development effort reductions associated with them on SAP NetWeaver platform.

Our rationale for focusing on service abstractions in models is to keep the reverse transformations simple and practical. This allows developers to develop the forward and reverse transformations relatively quickly for new platforms and programming languages. In addition, one has to consider the capabilities of various vendor software middleware platforms as well in trying to decide how much of the modeling is to be done or to be extracted. For instance, software middleware platforms these days offer the capability to generate low level design using best-practice patterns and the corresponding code given a

**Develop & Deploy**

**Develop Entity Services (Local BOs)**

**Import External Services**

**Develop Application Services**

**SAP NetWeaver (Composite Core + Web DynPro)**

**Develop User Interfaces**

**Effort in hours**

0.1

**High**

**Model-driven Transformations** In this paper, we presented our approach to porting software solutions on multiple software middleware platforms. We propose the use of model-driven transformations to achieve cross-platform portability. We propose approaches for two scenarios. First, in cases where no software solution exists on any of the desired target middleware platforms, we advocate developing a platform independent model of the software solution in a formal modeling language such as UML and then applying model-driven transformations to generate implementation artifacts such as code and schemas from the models on the desired target platforms. Second, if a software solution already exists on one specific middleware platform, we propose applying reverse transformations to derive a platform independent model from the implementation artifacts and then applying forward transformations on the derived model to port that software solution on to a different target platform. We advance the traditional model-driven technique by presenting a service-oriented approach to deriving platform independent models from platform specific implementations.

The experiments we have conducted in deriving platform independent models from implementation artifacts have provided useful insights in a number of aspects and pointed us to future research topics in this area. The ability to leverage existing assets in a software environment depends significantly on the granularity of services modeled and exposed. Providing guidance on how granular the services should be for optimal reuse could be a topic for research. Rationalizing services that operate at different levels of granularity is another topic for further research.


**2.3. HIV-1-triggered damage of ECs**

**Figure 1.** HIV-1 capability to promote inflammation, dyslipidemia, and endothelial dysfunction through the activation

Monocytes, depending on the cytokine/chemokine stimulation, may differentiate into M1 macrophages, which promote inflammation or into M2 macrophages, which are inflammatory resolving cells [19]. In particular, IFN-γ and IL-1β drive monocytes to acquire an M1 profile, whereas IL-4 and IL-13 generate M2 macrophages. HIV-1, by infecting macrophages, polarizes these cells toward the M1 phenotype [20]. This leads to the imbalance of the M1/M2

Endothelin-1 (ET-1) is a potent vasoconstrictor that promotes migration and proliferation of smooth muscle cells. HIV-1-triggered secretion of ET-1 promotes a reduction of vascular nitric oxide (NO) production by ECs with the consequent proliferation and migration of smooth

Altogether, these findings suggest that HIV-1 produces a general inflammatory microenvironment that contributes to dyslipidemia, EC dysfunction, chemotaxis, and vascular smooth muscle cell proliferation and migration. All these conditions are likely to foster endothelial

ratio, a condition necessary for sustaining endothelial dysfunction [21].

muscle cells leading to arterial vasoconstriction.

350 Endothelial Dysfunction - Old Concepts and New Challenges

degeneration and atherosclerotic plaque formation (**Figure 1**).

of different immune cells such as T and B cells, macrophages, and natural killer cells (NK cells).

HIV-1 is not an endothelium-tropic virus. It displays a narrow tropism predominantly determined by the cell surface receptors required for HIV-1 infection. CD4 and co-receptors are usually essential for HIV-1 to infect cells efficiently. The chemokine (C-C motif) receptor type 5 (CCR5) is the main co-receptor used in vivo, but variants that use another co-receptor, namely, chemokine (C-X-C motif) receptor type 4 (CXCR4), evolve during disease. In vitro, more than a dozen different co-receptors have been identified that support infection of cell lines by different HIV-1 strains. Moreover, HIV-1 particles interact with a range of cell surface receptors via interactions of its envelope glycoprotein gp120 with glycolipid galactocerebroside (gal)-C and its sulfated derivative.

HIV-1 capability to infect ECs in vitro depends on the tissue source of ECs and on their functional status. Microvascular ECs from the brain, kidney glomeruli, hepatic sinusoid, and bone marrow may be infected by HIV-1 in the absence of cytolysis [22, 23]. HIV-1 infection of brain ECs has been largely studied for its relevance in neurological diseases. T cell tropic but not brain-derived macrophage tropic HIV-1 strains selectively infect the brain endothelium in vitro, suggesting that T cell tropism may be important for HIV-1 entry through the bloodbrain barrier [22] and spreading in the central nervous system [24]. However, it is important to underline that in vivo studies do not support the presence of replicating virus in ECs. Even if HIV-1 infection of ECs cannot be completely ruled out, this may suggest an indirect action of molecules released in the microenvironment by HIV-1-infected cells at the base of the mechanism for vascular dysfunction.

In the pathophysiology of cardiovascular disease, the damage of ECs assessed by responses to altered blood flow (e.g., flow-mediated dilatation) and differences in the levels of EC specific molecules released in the blood (e.g., von Willebrand factor) represent a hallmark. The equilibrium between the mechanisms of vascular damage and repair plays a crucial role during homeostasis of vascular integrity. Following a blood vessel injury, high levels of circulating ECs (cECs) and microvesicles are released from endothelium, and the reinstatement of the vascular integrity mainly implies activity of endothelial progenitor cells (EPCs), plaque neovascularization, and reverse cholesterol transport [25]. EPCs are key determinants of endothelial dysfunction and show a high predictive value of early vascular disease.  Decrease in the number of EPCs is attributed to HIV-1, which seems to be able to infect these cells because of their chemokine receptor CCR5 and CXCR4 expressions.

Along with reduced EPC levels, HIV+ individuals show high plasma levels of EC-derived microvesicles also known as microparticles that are small membranous structures released from ECs during apoptosis, which impair the restoration of physiological conditions and sustain endothelial dysfunction [27]. HIV+ patients also exhibit high plasma concentrations of high sensitivity C-reactive protein (hsCRP), IL-6, TNF-α, D-dimer, fibrinogen, soluble ICAM, and VCAM, suggesting endothelial activation and damage. These molecules are also responsible for an increased interaction of infected monocytes with ECs, thereby disrupting the integrity of the EC monolayer and promoting extravasation of HIV-1-infected cells into peripheral tissues and viral dissemination [28].


**2.2 Data description**

The variables were obtained directly from the questionnaire applied to the dataset of the sample that responded to the Complete Questionnaire and can be found on the website www.ibge.gov.br in the 2010 Census, sample, and microdata with more details about its description in Oliveira [1].

#### **2.3 Ordinal logistic regression**

A good number of the variables used in the social sciences and humanities are ordinal. Often, the dependent variable takes discrete values, or sortable categories, but the distance between them is neither known nor constant. For example, in epidemiological studies, the level of severity of visual, hearing, or physical is set out in the 2010 Demographic Census Sample Questionnaire, which can be classified as ¨*can not at all,*¨ ¨*he succeeds, but with great difficulty,*¨ ¨*can, but with a little difficulty*,¨ and, finally, "*no problem*" to hear, see, or get around. In the case of intellectual disability, it is divided into ¨*has*¨ or ¨*has not.*¨

Among possible adjustment models for ordinal logistic regression, the following ones stand out: proportional probability model, more suitable for interpretation when the response variable is continuous and has been categorized; continuous ratio model, suitable in situations where there is specific interest in a particular category of the response variable; partial proportional probability model that allows to moderate covariates with the assumption of proportional probabilities, and for other variables in which this assumption is not satisfied, specific parameters that vary for the different categories compared and an extension of the proportional probability model are included in the model; and finally, stereotype model, proposed by [5–7]) used in situations where the response variable is ordinal, which is not a discrete version of some continuous variable that was considered in this research.

For this work, we have response variables: visual, hearing, physical, and intellectual disabilities, which are ordinal variables. In view of this, we adopted the stereotyped model in this work.

#### *2.3.1 Stereotype model specification*

Imagine that the dependent variable consists of *J* categories (*m* = *1, … , J*) and consider *K* predictors (*J = 1, … ,K*). The stereotype ordinal model is defined at an early stage with the multinomial regression model to which the condition is added *βm J*j � *φmβ*^, where *J* is the reference category, that is, we have that the multinomial regression model is given by:

$$\text{Prob}\left(\mathbf{y} = \mathbf{m} | \mathbf{x}\right) = \frac{\exp\left(\boldsymbol{\beta}\_{m|\mathcal{J}}^{\prime}\mathbf{x}\right)}{\sum\_{j=1}^{J} \exp\left(\boldsymbol{\beta}\_{m|\mathcal{J}}^{\prime}\mathbf{x}\right)}, \text{with } m = 1, \ldots, J \tag{1}$$

Replacing *βm J*j ¼ *φ*m*β*~ in Eq. (1) results in the stereotype model that can be written mathematically.

$$\begin{aligned} \text{Prob}\left(|\mathbf{y} = \mathbf{m}|\mathbf{x}\right) &= \frac{\exp\left(\rho\_m \tilde{\rho}^\prime \mathbf{x}\right)}{\sum\_{j=1}^l \exp\left(\rho\_1 \tilde{\rho}^\prime \mathbf{x}\right)} = \frac{\exp\left(\rho\_m \tilde{\rho}\_0 + \rho\_m \tilde{\rho}\_1 \mathbf{x}\_1 + \dots + \rho\_m \tilde{\rho}\_k \mathbf{x}\_k\right)}{\sum\_{j=1}^l \exp\left(\rho\_m \tilde{\rho}\_0 + \rho\_m \tilde{\rho}\_1 \mathbf{x}\_1 + \dots + \rho\_m \tilde{\rho}\_k \mathbf{x}\_k\right)}, \\ \text{with } m = 1, \dots, l \end{aligned} \tag{2}$$

For some parameters of Eq. (2) that are not identifiable, we consider as constraints *φm*~*β*0 � *θm*ð Þ *m* ¼ 1, … , *J* , where *ϕJ = 0*; and *φm*~*βj* � �*θmβj* ð Þ *m* ¼ 1, … , *J* e *j* ¼ 1, … , *k* , where *φJ* ¼ 0 e *φ* ¼ 1*:* Thus, from Eq. (2), the stereotype model can be written as follows:

$$\text{Prob}\left(\mathbf{y} = \mathbf{m} | \mathbf{x}\right) = \frac{\exp\left(\theta\_m - \rho\_m \beta' \mathbf{x}\right)}{\sum\_{j=1}^{J} \exp\left(\rho\_1 \beta' \mathbf{x}\right)},\tag{3}$$

with *m* = 1, … , *J* where *θJ* = 0, *φJ* ¼ 0 where *φ* ¼ 1.

#### *2.3.2 Interpretation of estimated coefficients*

Applying logarithm in function (3) to any two categories, we get:

$$\log \left[ \frac{p(Y = q/\varkappa)}{p(Y = r/\varkappa)} \right] = \left(\theta\_q - \theta\_r\right) - \left(\rho\_q - \rho\_r\right)\beta'\infty. \tag{4}$$

Applying the exponential function to the exponential function to Eq. (4), it follows

$$\Omega\_{q/r} = \frac{p(Y = q/\varkappa)}{p(Y = r/\varkappa)} = \exp\left\{ (\theta\_q - \theta\_r) - \left(\wp\_q - \wp\_r\right) \beta' \varkappa \right\}.\tag{5}$$

Eq. (5) allows us to evaluate the odds ratio before and after we add a unit to the variable *xj*, that is,

*Logistic Regression: Risk Question for Disabled People DOI: http://dx.doi.org/10.5772/intechopen.106212*

$$\frac{\Omega\_{q/r}(\mathbf{x}, \mathbf{x}\_k + \mathbf{1})}{\Omega\_{q/r}(\mathbf{x}, \mathbf{x}\_k)} = \exp\left\{ \left(\boldsymbol{\rho}\_r - \boldsymbol{\rho}\_q\right) \boldsymbol{\beta}\_x \right\}.\tag{6}$$

The value obtained in expression (6) can be interpreted as adding a unit to the variable *xk*, the odds ratio of category *r* varies exp *φr* � *φq* � �*βk* n o, keeping all other variables constant.

## *2.3.3 Estimation of estimated coefficients*

The parameters of the stereotype model are estimated by the maximum likelihood method, in which the estimators are obtained by the system of equations given in (7) as follows:

$$\mathbf{p}\_{i} = \begin{cases} \text{Prob}(\mathbf{y}\_{i} = \mathbf{1} | \mathbf{x}\_{i}, \boldsymbol{\rho}, \boldsymbol{\theta}) & \text{if } \boldsymbol{y}\_{i} = \mathbf{1} \\ & \vdots \\ \text{Prob}(\mathbf{y}\_{i} = \mathbf{m} | \mathbf{x}\_{i}, \boldsymbol{\rho}, \boldsymbol{\theta}) & \text{if } \boldsymbol{y}\_{i} = \boldsymbol{m} \\ & \vdots \\ \text{Prob}(\mathbf{y}\_{i} = \mathbf{J} | \mathbf{x}\_{i}, \boldsymbol{\rho}, \boldsymbol{\theta}) & \text{if } \boldsymbol{y}\_{i} = \mathbf{J} \end{cases} \tag{7}$$

where *pi* is the probability of observing any value of *y*, and the Prob yi ¼ 1 xi j , *φ*, *θ* � � was defined in expression (3). Assuming that the sample is independent and identically distributed, the likelihood function is given by the following expression (8):

$$\mathcal{L}(\beta,\varphi,\theta|\mathbf{y},\mathbf{x}) = \prod\_{i=1}^{N} p\_i = \prod\_{m=1}^{J} \prod\_{\mathbf{y}=m} \text{Prob}\left(\mathbf{y} = \mathbf{m}|\mathbf{x},\boldsymbol{\varphi},\boldsymbol{\theta}\right) \tag{8}$$

on what Q *y*¼*j* indicates the multiplications over all cases where *y = m (m = 1, … ,J)*.

Applying logarithm to the likelihood function obtained in (8), we obtain the logarithm of the likelihood function given in (9) as follows:

$$\log\left(\mathcal{L}(\boldsymbol{\beta},\boldsymbol{\varphi},\boldsymbol{\theta}|\boldsymbol{y},\boldsymbol{\chi})\right) = \sum\_{m=1}^{J} \sum\_{\mathbf{y}=\mathbf{m}} \log\left[\text{Prob}\left(\mathbf{y}=\mathbf{m}|\boldsymbol{\chi},\boldsymbol{\varphi},\boldsymbol{\theta}\right)\right].\tag{9}$$

The parameters *ϕ*'s and *θ*'s of Eq. (9) are estimated by the Newton–Raphson method.

The odds ratio formed will have an upward trend, as the weights can be produced by sorting. Thus, the effect of covariates on the first odds ratio is smaller than the effect on the second, and so on.

These weights can be done a priori, being estimated by a pilot study or by a set of properly chosen values.

In the case of this work, the number of disabilities that a person may have can vary from 0 to 4, and there may be five response options.

In order to assess the goodness of fit for ordinal models, it can be done using tests such as Pearson's or deviation. These tests involve creating a contingency table in which the rows consist of all possible configurations of the model's covariates and the columns are the ordinal response categories [8]. The expected counts (*Elj*) from this

table are expressed by *Elj* ¼ P*NL l*¼1*p*^*ij*, on where *NL* is the total number of individuals classified in the row *l* and *p*^*ij* represents the probability of an individual in line l having the answer *j* calculated from the adopted model.

Pearson's test to assess the adequacy of fit compares these expected counts with those observed by the formula:

$$\chi^2 = \sum\_{l=1}^{L} \sum\_{j=1}^{k} \frac{\left(O\_{lj} - E\_{lj}\right)^2}{E\_{lj}} \tag{10}$$

The deviance stat also compares observed (*Olj*) and expected counts, but using the formula:

$$D^2 = 2\sum\_{l=1}^{L} \sum\_{j=1}^{k} \mathcal{O}\_{lj} \log \frac{O\_{lj}}{E\_{lj}} \tag{11}$$

The tests to assess the goodness of fit of the model are given by approximations of statistics (10) and (11) for chi-square distribution with (*L* – 1)(*k* – 1)*p* degrees of freedom, where *L* and *k* are as defined earlier and p is the number of model covariates. Significant differences lead to the conclusion that the model does not fit the data studied.

As an alternative, we will use the Wald test which is given by:

$$\mathcal{W} = \left(\hat{p} - \hat{p}\_0\right)' \hat{V}\_p^{-1} \left(\hat{p} - \hat{p}\_0\right) \tag{12}$$

on where *V*^ *p* is the consistent estimator of the variance-covariance matrix of the estimator *p*^ of the proportion vector *p*^. An estimator *V*^ *p* can be obtained by linearization method.

## *2.3.4 Significance test for the model*

The Wald test for the parameters considered individually can be obtained by comparing the estimate of maximum likelihood of a given coefficient *β*^*j* � � with the estimate of its standard error (based on the asymptotic distribution of the maximum likelihood estimators). Thus, the null hypothesis and the alternative hypothesis of the test are respectively:

$$H\_0: \hat{\boldsymbol{\beta}}\_{\mathbf{j}} = \boldsymbol{\beta}\_{\mathbf{j}}^\* \quad \text{vs} \quad H\_1: \hat{\boldsymbol{\beta}}\_{\mathbf{j}} \neq \boldsymbol{\beta}\_{\mathbf{j}}^\* \qquad \quad (\mathbf{j} = \mathbf{2}, \dots, \mathbf{k}), \tag{13}$$

the respective statistic under the null hypothesis:

$$T = \frac{\hat{\boldsymbol{\beta}}\_{j} - \boldsymbol{\beta}\_{j}^{\*}}{\sqrt{\text{var}\left(\hat{\boldsymbol{\beta}}\_{j}\right)}} \sim \mathbf{N}(\mathbf{0}, \mathbf{1}) \tag{14}$$

By rejecting H0, for a significance α, we conclude that the estimated parameter is statistically different from *β* ∗ *j* . Generally, use *β* ∗ *j* ¼ 0 which, under these conditions,

we conclude that the parameter is relevant to explain the behavior of the dependent variable.

### *2.3.5 Selection of variables*

Selecting variables means choosing a subset that retains the most important predictor variables in such a way that we seek to avoid problems such as multicollinearity and that this subset fits as well as the complete model and contains the most important predictor variables.

Among different procedures that can be used to select variables, we highlight forward stepwise and backward stepwise. Forward stepwise starts with the constant *β0* and sequentially adds the predictor *Xi* most correlated with Y to the model so that it improves the fit according to the evaluation of the *F* statistic and the introduction of variables when it fails to produce an F statistic greater than the 90th or 95th percentile of the distribution, *F1, N – k – 2*, where *N* is the sample size and *k* is the number of variables.

On the other hand, the backward stepwise selection strategy starts with the model with all independent variables, and sequentially, excludes variables using the F statistic to choose the predictors to be eliminated. The predictor that has the smallest F statistic is eliminated, and the process stops when each predictor eliminated from the model has an F value greater than the 90th or 95th percentile of the distribution, *F1, N – k – 2*. For this work, forward backward and the Wald statistic were chosen.

In ordinal logistic regression, the TRV (likelihood ratio test) ensures the significance of the fit. Thus, at each stage of the process, the most important variable, in statistical terms, is one that produces the greatest change in the logarithm of the likelihood in relation to the model without the variable [9].

After estimating the parameters, the next step is to verify if the covariates used for modeling are statistically significant for the modeled event, for example, condition of an individual becoming a disability person.

To test the significance of the coefficient of a covariate, it is sufficient to compare the observed values of the response variable with the predicted values obtained by the models with and without the variable of interest [10].

The comparison between observed and predicted values is made using the likelihood ratio test, which is widely applicable by the maximum likelihood estimation. For test *H*0 : *θ* ∈ Θ0 versus *Ha* : *θ* ∈ Θ*c* 0, we calculated the statistics [11]:

$$\lambda(\mathbf{x}) = \frac{\sup\_{\theta\_0} L(\theta/\mathbf{x})}{\sup\_{\theta} L(\theta/\mathbf{x})}. \text{For } n \to \infty,\\ -2\ln \lambda(\mathbf{x}) \to \chi^2\_v. \tag{15}$$

where *ν* is obtained through the difference between the number of parameters existing in the tested model and the number of parameters existing in the saturated model [12].

To verify the quality of the adjusted model, it is sufficient to compare the observed and predicted values for the response variable (in this case, one of the different deficiencies already mentioned).

When choosing a particular model, it means that we must include as many independent variables as possible to improve the forecast; simultaneously, we want to include a smaller number of variables for reasons of cost and simplicity [10].

According to Draper and Smith [13], to select the best model is to reconcile two objectives (incorporating a certain number of variables that can improve the

predictability of the model, at the same time, discarding variables that are not significant as a way of simplifying the model to reduce costs). This selection involves a dose of subjectivity, and the result may be different if the procedure is used for selection changes.

### *2.3.6 Model selection*

Selecting a model means, after the formulation and adjustment of different plausible models, to select the model that ¨best¨ fits the data of a certain experiment according to a certain criterion adopted [14].

In statistics, there is a vast literature relevant to the selection of models [15–17]. An alternative for model selection is the use of methods based on the likelihood function that provides several statistical measures that help in the comparison between different models. The most common of these measures are as follows: Akaike Information Criterion (AIC) proposed by Paulino et al. [18] and Sakamoto et al. [19] with penalty given discounting the value of twice the difference between the number of parameters between the two models; Bayesian Information Criterion (BIC) discussed by Paulino et al. [18] and having as a penalty the value of double the number of parameters between the two models multiplied by the Naperian logarithm of the sample size; and, finally, Deviation Information Criterion (DIC) also discussed by Paulino et al. [18] and the penalty is given by the sum of the difference value between the number of parameters between the two models.

In this text, for each of the AIC, BIC, and DIC criteria, the model with the lowest value for each one of them is chosen.

### **2.4 Epidemiology**

According to the International Epidemiology Association (IEA), epidemiology is defined as the study of the different factors involved in the spread and propagation of diseases, frequency, their mode of distribution, their evolution, and the placement of the necessary means for their prevention in human communities.

According to Suser [20], epidemiology is essentially a population science, which is based on the social sciences for the understanding of social structure and dynamics, on mathematics for statistical, probability, inference, and estimation notions, and, on the biological sciences, the knowledge of the environment organic substrate where the observed manifestations will find individual expression.

A single and precise definition of epidemiology as a scientific field ends up not being possible due to the increasing complexity and scope of its current practice:

Science that studies the health-disease process in society, analyzing population distribution and determining factors of risk, diseases, injuries, and events associated with health, proposing specific measures for the prevention, control, or eradication of diseases, damages, or health and protection problems, promotion or recovery of individual and collective health, producing information and knowledge to support decision-making in the planning, administration, and evaluation of health systems, programs, services, and actions [21].

Epidemiology is a basic discipline of public health aimed at understanding the health-disease process within populations, an aspect that differentiates it from clinical practice, which aims to study this same process, but in individual terms and that studies the different factors that intervene in the spread and propagation of diseases,

their frequency, their mode of distribution, their evolution, and the placement of the necessary means for their prevention.

In scientific terms, epidemiology is based on causal reasoning; as a public health discipline, focusing on the development of a sequence of actions aimed at protecting and promoting the health of the community.

Epidemiology is also an important tool for policy development in the health sector. Its application in this case must be taken into account the available knowledge, adapting it to local realities.

Among the possibilities of applications of epidemiology, we highlight: the analysis of the health situation; identify profiles and risk factors; carry out epidemiological assessment of services; study and understand the causality of health problems; describe the clinical spectrum of diseases and their natural history; assess the performance of health services in responding to the problems and needs of populations; test the efficacy, effectiveness, and impact of intervention strategies, as well as the quality, access, and availability of health services to control, prevent, and treat health problems in the community; identify risk factors for a disease and groups of individuals who are at greater risk of being affected by a particular disease; define modes of transmission; identify and explain patterns of geographic distribution of diseases; establish methods and strategies to control health problems; establish preventive measures; assist in the planning and development of health services; and, finally, establish criteria for health surveillance.

In the discussion about disabilities, epidemiological views on social points of view, accessibility, assistive technology, among others, were used in these researches, and, physicians, from the perspective of prevention, treatment, and control.


**8. Results experimental**

This paper uses the following performance metrics.

Resource Management for Data Intensive Tasks on Grids 65

Fig. 5. Performance with SRPsp chosen at TRPS (a) Scheduling Algorithm Running time (b)

constraints associated with the LP formulation, we are allocating the resource with the highest precision and it this allocation is expected to be efficient. As constraint relaxation is applied at stage one of the algorithms in ATSRASSR, tms-nonSch increases. It further increases for ATSRABSR in which constraint relaxation is applied at both stage of the algorithm. Fig. 6(d) shows the total cost (tcost) for each of the three algorithms. ATSRAorg has the lowest tcost as we are allocating the resources with highest precision. This is followed by ATSRASSR and ATSRABSR. The overall makespan time, tms-total shown in Fig. 5(c) includes both the scheduling time and the execution time for the bag-of-tasks. It captures the tradeoff between tms-noSch and tsch presented in Fig. 5(b) and 5(a) respectively. For a very small number of nodes the scheduling overhead for the ATSRAorg is small and tms-nonsch is the lowest and as a result the best tms-total is achieved. For a large number of nodes, the scheduling overhead for ATSRAorg is very high and the benefit of using a better resource allocation is offset by the overhead and its performance deteriorates. ATSRABSR that exhibits the smallest scheduling overhead for a large number of nodes (see Fig. 5(a)) demonstrates the best tms-total (see Fig.

Makespan non-Scheduling time (c) Makespan Total (d) Total Cost

**Makespan total (tms-total):** The time in seconds required for completing all the tasks in the given bag-of-tasks, T.

**Makespan non-scheduling (tms-nonSch):** The time in seconds required for completing all the tasks in T, *excluding* the time taken by scheduling algorithm.

**Scheduling algorithm running time (tsch)**: Total time in seconds taken by the scheduling algorithm to schedule all the given resources.

**Total cost (tcost).** Sum of the time in seconds for which all the resources are utilized in performing the bag-of-tasks T.

To analyze the performance of the proposed RA algorithms, a simulation based investigation is performed. Various performance metrics described earlier were captured at the end of each simulation. Each experiment was repeated enough number of times to produce a confidence interval of ±5% for each performance metric at a confidence level of 95%. The workload chosen for these experiments is a bag-of-tasks consisting of 32 PBDTfixedpar tasks. Each of these tasks models the encoding of a raw multimedia file which is to be processed and delivered to a set of sink nodes. The choice of the raw data file is based on a typical animation movie described in [2]. The size of the raw data files of each of the tasks in the given bag-of-tasks is an important workload parameter. A detailed study of the characteristics of similar real-world tasks was carried out. The true representative probability distribution of the sizes of the raw or unprocessed data files used in similar tasks has been a subject of discussion over the years in the research community. Researchers seem to be split over characterizing it either with a Pareto or with Log-normal distribution. After careful analysis the Pareto distribution seems to be a better representative of PBDT multimedia workloads and is thus used. Another important parameter for the workload is the value of p, which is the number of partitions in which raw data files can be divided. This value is included in the metadata of each of the raw data file of the given tasks. The value of p depends on the structure of the raw data file and the type of processing required for it. For example, if a raw data file of multimedia animation contains 20 sub-sequences, each of which has to be processed as a single partition, then this task has a p of 20. The number of partitions (referred to as sub-sequences in the description of the movie rendering project presented in [1]) for each raw file varies from 1 to 30. We have used a uniform distribution [1-30] for modeling the number of partitions in each raw multimedia file. The mean of the raw data files is fixed at 650MB.

For performance analysis of the proposed algorithms total number of nodes of the Grid system is increased. Number of Grid nodes is directly related to the time-complexity of the deployed algorithm. All other parameters related to workload and system conditions are kept constant.

Fig. 5 shows the performance of three RA algorithms (ATSRAorg, ATSRASSR and ATSRABSR) with SRPsp deployed at the TRSP. Fig. 5(a) shows the time taken by each of the algorithm to run. It can be that for small number of nodes, there is not much difference in the scheduling time taken be these three algorithms. The time taken by the ATSRAorg algorithm rises sharply as number of nodes is increased more than 32. It can be observed that for ATSRABSR, tsch does not rise sharply. Fig. 6(b) shows the value of Makespan non-scheduling (tms-nonSch) for each of the proposed algorithms as the number of nodes is increased. It is clear that ATSRAorg has the lowest value of tms-nonSch for all values of n. This is expected as by using all

64 Grid Computing – Technology and Applications, Widespread Coverage and New Horizons

**Makespan total (tms-total):** The time in seconds required for completing all the tasks in the

**Makespan non-scheduling (tms-nonSch):** The time in seconds required for completing all the

**Scheduling algorithm running time (tsch)**: Total time in seconds taken by the scheduling

**Total cost (tcost).** Sum of the time in seconds for which all the resources are utilized in

To analyze the performance of the proposed RA algorithms, a simulation based investigation is performed. Various performance metrics described earlier were captured at the end of each simulation. Each experiment was repeated enough number of times to produce a confidence interval of ±5% for each performance metric at a confidence level of 95%. The workload chosen for these experiments is a bag-of-tasks consisting of 32 PBDTfixedpar tasks. Each of these tasks models the encoding of a raw multimedia file which is to be processed and delivered to a set of sink nodes. The choice of the raw data file is based on a typical animation movie described in [2]. The size of the raw data files of each of the tasks in the given bag-of-tasks is an important workload parameter. A detailed study of the characteristics of similar real-world tasks was carried out. The true representative probability distribution of the sizes of the raw or unprocessed data files used in similar tasks has been a subject of discussion over the years in the research community. Researchers seem to be split over characterizing it either with a Pareto or with Log-normal distribution. After careful analysis the Pareto distribution seems to be a better representative of PBDT multimedia workloads and is thus used. Another important parameter for the workload is the value of p, which is the number of partitions in which raw data files can be divided. This value is included in the metadata of each of the raw data file of the given tasks. The value of p depends on the structure of the raw data file and the type of processing required for it. For example, if a raw data file of multimedia animation contains 20 sub-sequences, each of which has to be processed as a single partition, then this task has a p of 20. The number of partitions (referred to as sub-sequences in the description of the movie rendering project presented in [1]) for each raw file varies from 1 to 30. We have used a uniform distribution [1-30] for modeling the number of partitions in each raw multimedia file. The mean of the

For performance analysis of the proposed algorithms total number of nodes of the Grid system is increased. Number of Grid nodes is directly related to the time-complexity of the deployed algorithm. All other parameters related to workload and system conditions are kept constant. Fig. 5 shows the performance of three RA algorithms (ATSRAorg, ATSRASSR and ATSRABSR) with SRPsp deployed at the TRSP. Fig. 5(a) shows the time taken by each of the algorithm to run. It can be that for small number of nodes, there is not much difference in the scheduling time taken be these three algorithms. The time taken by the ATSRAorg algorithm rises sharply as number of nodes is increased more than 32. It can be observed that for ATSRABSR, tsch does not rise sharply. Fig. 6(b) shows the value of Makespan non-scheduling (tms-nonSch) for each of the proposed algorithms as the number of nodes is increased. It is clear that ATSRAorg has the lowest value of tms-nonSch for all values of n. This is expected as by using all

tasks in T, *excluding* the time taken by scheduling algorithm.

algorithm to schedule all the given resources.

performing the bag-of-tasks T.

raw data files is fixed at 650MB.

given bag-of-tasks, T.

Fig. 5. Performance with SRPsp chosen at TRPS (a) Scheduling Algorithm Running time (b) Makespan non-Scheduling time (c) Makespan Total (d) Total Cost

constraints associated with the LP formulation, we are allocating the resource with the highest precision and it this allocation is expected to be efficient. As constraint relaxation is applied at stage one of the algorithms in ATSRASSR, tms-nonSch increases. It further increases for ATSRABSR in which constraint relaxation is applied at both stage of the algorithm. Fig. 6(d) shows the total cost (tcost) for each of the three algorithms. ATSRAorg has the lowest tcost as we are allocating the resources with highest precision. This is followed by ATSRASSR and ATSRABSR. The overall makespan time, tms-total shown in Fig. 5(c) includes both the scheduling time and the execution time for the bag-of-tasks. It captures the tradeoff between tms-noSch and tsch presented in Fig. 5(b) and 5(a) respectively. For a very small number of nodes the scheduling overhead for the ATSRAorg is small and tms-nonsch is the lowest and as a result the best tms-total is achieved. For a large number of nodes, the scheduling overhead for ATSRAorg is very high and the benefit of using a better resource allocation is offset by the overhead and its performance deteriorates. ATSRABSR that exhibits the smallest scheduling overhead for a large number of nodes (see Fig. 5(a)) demonstrates the best tms-total (see Fig.

Resource Management for Data Intensive Tasks on Grids 67

Fig. 6. Performance with SRPsp+BF chosen at TRPS (a) Scheduling Algorithm Running time

In this chapter, by using BiLeG an allocation-plan is devised which reflects the overall resource allocation strategy comprising two parts; a policy used at the higher decision making module, TRPS, which has the responsibility to select a resource-pool for each of the tasks; and a resource allocation algorithm used at the lower decision making module, RA, which actually assigns resources from the resource-pool selected by TRPS for a particular PBDT task. Three RA algorithms and six TRPS policies have been proposed in this chapter forming different allocation-plans. The suitability of various allocation-plans under different

Detailed study of the various trade-offs, implicit in the use of different allocation-plans, is the focal points of this chapter. The most suitable allocation-plan not only depends on various workload and system parameters, it also depends on the user requirements and the hardware available. It can be seen that from the performance perspective various trade-offs exist among different allocation-plans and understanding these trade-offs in depth is the

(b) Makespan non-Scheduling time (c) Makespan Total (d) Total Cost

sets of system and workload parameters has been explored.

focus of the experiments conducted in this chapter.

**9. Summary and conclusion**

5(c)). It is interesting to see that ATSRASSR produces the best tms-total for a range of intermediate values of the number of Grid nodes. The accuracy of resource allocation for ATSRASSR lies between that achieved with ATSRAorg and ATSRABSR. For a small number of nodes, tsch of ATSRASSR is comparable to that of ATSRAorg; whereas the tms-nonSch achieved by ATSRASSR is inferior to that achieved by ATSRAorg. Thus if the number of nodes is small, ATSRASSR is inferior to that of ATSRAorg.

For a large number of nodes, although ATSRASSR gives rise to a lower scheduling overhead than ATSRABSR, the advantage is offset by the much lower execution time produced by ATSRABSR. The net effect is that tms-total achieved by ATSRASSR is inferior to that of ATSRABSR for a large number of nodes.

Fig. 6 shows the performance of ATSRA algorithms when SRPsp +BF is deployed at TRPS. As in the case of Fig. 5(c) the best tms-total is achieved by ATSRABSR for larger numbers of nodes; whereas ATSRAorg demonstrates the best performance for a lower number of nodes. ATSRASSR demsonstrates a slightly higher tms-total than ATSRAorg when the number of Grid nodes is small. Although the total makespan achieved by it is better than ATSRAorg at higher number of nodes, it is higher than that achieved by ATSRABSR. The relative performances of the three algorithms captured in Fig. 6(a) , Fig. 6(b) and Fig. 6(d) are the same as those displayed in Fig. 5(a), Fig. 5(b) and Fig. 5(d) respectively. ATSRAorg demonstrates the best in tms-nonSch and tcost followed by ATSRASSR and ATSRABSR; whereas the smallest scheduling overhead is achieved with ATSRABSR and ATSRAorg demonstrates the highest scheduling overhead. The rationale for such a behavior has been provided in the discussion presented earlier for Fig. 5(a) Fig. 5(b) and Fig. 5(d). Note that although the shapes of the graphs in Fig. 5(a) and Fig. 6(a) are similar, the value of tsh for a given number of nodes in Fig 6(a) is higher than the value of tsh for the same number of nodes in Fig. 5(a). This is because in SRPsp +BF backfilling is used which increases scheduling overheads. While the relative performance of ATSRAorg, ATSRASSR and ATSRABSR remains almost the same, this additional scheduling overhead has shifted the graphs upwards in Fig. 6(a) as compared to Fig. 5(a).

For ATSRAorg and ATSRASSR algorithms and any given number of nodes, the tms-nonSch achieved with SRPsp +BF is observed to be smaller than that achieved SRPsp (see Fig. 5 (b) and Fig. 6(b). This demonstrates the effectiveness of using backfilling that can increase the concurrency of task execution. Except for the case in which the number of Grid nodes is 128, a similar behavior is observed with ATSRABSR.

Comparing tms-total achieved with SRPsp (Fig. 5(c)) and SRPsp +BF (Fig. 6(c)), we observe that for any given ATSRA algorithm, the total makespan achieved by SRPsp +BF is superior to that achieved by SRPsp when the number of nodes is small. For higher number of nodes, SRPsp +BF demonstrates an inferior performance. This becauseat smaller number of nodes concurrent execution of tasks may be severely limited with SRPsp because many tasks may not be able to get all their resources at the same time. With the use of backfilling this problem is alleviated as RA is run for each waiting task with the set of unused resources as the resource pool. However, this problem with task concurrency is not that severe at higher number of nodes. Thus, SRPsp +BF that re-runs RA multiple times and incurs a higher scheduling overhead demonstrates an inferior performance as the potential performance benefit due to backfilling is offset by the overhead.

66 Grid Computing – Technology and Applications, Widespread Coverage and New Horizons

5(c)). It is interesting to see that ATSRASSR produces the best tms-total for a range of intermediate values of the number of Grid nodes. The accuracy of resource allocation for ATSRASSR lies between that achieved with ATSRAorg and ATSRABSR. For a small number of nodes, tsch of ATSRASSR is comparable to that of ATSRAorg; whereas the tms-nonSch achieved by ATSRASSR is inferior to that achieved by ATSRAorg. Thus if the number of nodes is small,

For a large number of nodes, although ATSRASSR gives rise to a lower scheduling overhead than ATSRABSR, the advantage is offset by the much lower execution time produced by ATSRABSR. The net effect is that tms-total achieved by ATSRASSR is inferior to that of ATSRABSR

Fig. 6 shows the performance of ATSRA algorithms when SRPsp +BF is deployed at TRPS. As in the case of Fig. 5(c) the best tms-total is achieved by ATSRABSR for larger numbers of nodes; whereas ATSRAorg demonstrates the best performance for a lower number of nodes. ATSRASSR demsonstrates a slightly higher tms-total than ATSRAorg when the number of Grid nodes is small. Although the total makespan achieved by it is better than ATSRAorg at higher number of nodes, it is higher than that achieved by ATSRABSR. The relative performances of the three algorithms captured in Fig. 6(a) , Fig. 6(b) and Fig. 6(d) are the same as those displayed in Fig. 5(a), Fig. 5(b) and Fig. 5(d) respectively. ATSRAorg demonstrates the best in tms-nonSch and tcost followed by ATSRASSR and ATSRABSR; whereas the smallest scheduling overhead is achieved with ATSRABSR and ATSRAorg demonstrates the highest scheduling overhead. The rationale for such a behavior has been provided in the discussion presented earlier for Fig. 5(a) Fig. 5(b) and Fig. 5(d). Note that although the shapes of the graphs in Fig. 5(a) and Fig. 6(a) are similar, the value of tsh for a given number of nodes in Fig 6(a) is higher than the value of tsh for the same number of nodes in Fig. 5(a). This is because in SRPsp +BF backfilling is used which increases scheduling overheads. While the relative performance of ATSRAorg, ATSRASSR and ATSRABSR remains almost the same, this additional scheduling

overhead has shifted the graphs upwards in Fig. 6(a) as compared to Fig. 5(a).

a similar behavior is observed with ATSRABSR.

benefit due to backfilling is offset by the overhead.

For ATSRAorg and ATSRASSR algorithms and any given number of nodes, the tms-nonSch achieved with SRPsp +BF is observed to be smaller than that achieved SRPsp (see Fig. 5 (b) and Fig. 6(b). This demonstrates the effectiveness of using backfilling that can increase the concurrency of task execution. Except for the case in which the number of Grid nodes is 128,

Comparing tms-total achieved with SRPsp (Fig. 5(c)) and SRPsp +BF (Fig. 6(c)), we observe that for any given ATSRA algorithm, the total makespan achieved by SRPsp +BF is superior to that achieved by SRPsp when the number of nodes is small. For higher number of nodes, SRPsp +BF demonstrates an inferior performance. This becauseat smaller number of nodes concurrent execution of tasks may be severely limited with SRPsp because many tasks may not be able to get all their resources at the same time. With the use of backfilling this problem is alleviated as RA is run for each waiting task with the set of unused resources as the resource pool. However, this problem with task concurrency is not that severe at higher number of nodes. Thus, SRPsp +BF that re-runs RA multiple times and incurs a higher scheduling overhead demonstrates an inferior performance as the potential performance

ATSRASSR is inferior to that of ATSRAorg.

for a large number of nodes.

Fig. 6. Performance with SRPsp+BF chosen at TRPS (a) Scheduling Algorithm Running time (b) Makespan non-Scheduling time (c) Makespan Total (d) Total Cost


**About the Editor**

#### **Antonio Guerrieri**

Researcher from June 1990 to October 1992 at the Faculty of Science, University of Bari, from November 1992 to now Antonio Guerrieri (Ph.D.) is Associate Professor in Analytical Chemistry at the Faculty of Science, now Department of Sciences, at University of Basilicata (Potenza, Italy). He teaches analytical chemistry in the chemistry and biotechnologies courses of his University, as well as in Ph.D. in Chemical Sciences.

Antonio Guerrieri was Head of Chemistry Courses, Head of Biotechnology Courses as well as Member of the Board of Governors and Vice Rector for Triennial Planning at University of Basilicata.

His research interests mainly deal with bioelectrochemistry and the application of biological electron transfer processes in bioelectronic devices, the interaction between electropolymerised films and biological systems, the development of amperometric biosensors based on electropolymerised thin polymer films and the study of protein/biomaterial interactions.

Antonio Guerrieri is co-author of about 100 scientific publications in international journals and congress meetings.


**Innovative Topologies and Algorithms for Neural Networks**

Editors

**Salvatore Graziani Maria Gabriella Xibilia**

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Maria Gabriella Xibilia University of Messina Italy

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This is a reprint of articles from the Special Issue published online in the open access journal *Future Internet* (ISSN 1999-5903) (available at: https://www.mdpi.com/journal/futureinternet/ special issues/Innovative topologies neural networks).

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*3.5. Secondary Screening of α-Amylase-Producing Bacteria and Measurement of Enzyme Activity*

The α-amylase enzyme-producing bacteria, which had produced a stronger halo in the primary screening stage, were selected for secondary screening and enzyme activity measurement. At this stage, bacteria were cultured in nutrient broth and incubated in a shaker incubator at 30 ◦C and 100 rpm. After 24 h, the culture medium containing the grown bacteria was centrifuged at 12,000 rpm for 20 min, and the supernatant was used as a solution containing the α-amylase enzyme in the next stages of the experiment [23]. The Bernfeld method was used to measure alpha-amylase enzyme activity [24]. For this purpose, 400 µL of phosphate buffer solution, 100 µL of α-amylase enzyme extract, and

500 µL of 1% starch were incubated at 60 ◦C for 20 min. Then, 1 mL of DNS solution was added to the test tube and incubated for 5 min in a boiling water bath. After cooling, 1 mL of distilled water was added to it. After stirring the contents of the absorption tube, the sample was read at a wavelength of 540 nm.

## *3.6. Identification of Potential α-Amylase Enzyme-Producing Bacteria*

In this study, the identification of potential bacteria producing the α-amylase enzyme was carried out by examining phenotypic and biochemical characteristics as well as using 16S rRNA gene analysis. In order to extract the bacterial DNA contents, the boiling method was used [25,26]. Polymerase chain reaction (PCR) was applied to amplify a 1500-bp fragment of the 16S rRNA gene using the extracted DNA of potential α-amylase-producing bacteria (HR13, HR16, HR15, and HR14) by means of forward (50 - AGAGTTTGATCCTGGCTCAG-30 ) and reverse (50 -AAGGAGGTGATCCAGCC-30 ) primers at a final volume of 50 µL, containing 2 µL of extracted bacterial strain DNA, 0.5 µL of forward primer, 0.5 µL of reverse primer, 25 µL of amplicon master mix solution, and 22 µL of distilled water. The thermal cycling was carried out as follows: an initial denaturation at 95 ◦C for 1 min, followed by 30 cycles of denaturation at 95 ◦C for 20 s, annealing at 63 ◦C for 30 s, extension at 72 ◦C for 1 min, and the final extension at 72 ◦C for 5 min.

After checking the quality and quantity of PCR products on 1% agarose gel electrophoresis, they were sent to FAZA Pajooh Co. for double-sided sequencing. The sequences were compared with the 16S rRNA gene sequences of bacteria registered in the NCBI database using the BLAST tool available on the NCBI website. Then, the closest strain was selected based on the S rRNA16 gene sequence and biochemical tests [27,28].

In order to analyze the phylogenetic relationships, the target sequences were assessed and compared in accordance with the NCBI gene bank. In the end, using MEGA4 software and the Neighbor-Joining (NJ) algorithm with bootstrapping, 1000 repetitions of the phylogenetic tree were drawn.

#### *3.7. Effect of pH and Temperature on α-Amylase Activity*

The α-amylase enzyme activity was evaluated at a temperature range of 20–90 ◦C in a 20 mM phosphate buffer (pH 7.4). In order to examine the enzyme activity at any temperature, both the substrate (1% starch) and the enzyme solution (buffer and crude enzyme extract) must reach equilibrium at that temperature before measuring enzyme activity. The relative enzyme activity was measured at different pH values of 2–12 at room temperature. For this purpose, a mixed buffer (containing 25 mM tris-base, glycine, sodium phosphate, and sodium acetate) was prepared and adjusted using NaOH and HCl solutions at different pH values from 2 to 12. In this experiment, a 1% starch solution was also prepared as a substrate, and the enzyme activity was evaluated in the above buffer at different pH values.

## *3.8. Effects of pH and Temperature on Enzyme Stability*

To measure the temperature stability of the enzyme, first, the crude enzyme extracts of both bacteria were placed at 80 and 90 ◦C for different time intervals (5, 10, 20, 30, 40, 50, and 60 min). Then, 500 µL of the substrate was added to each of the samples, and the resulting mixture was incubated in the assay conditions. Finally, α-amylase activity was stopped using dinitrosalicylic acid reagent and the absorbance of the reaction mixture was read at a wavelength of 540 nm.

In order to measure the pH stability of the enzyme, first, the crude enzyme extracts of both bacteria were exposed to pH values of 8 and 9 for different times (5, 10, 20, 30, 40, 50, and 60 min). Then, 500 µL of the substrate was added to each of the samples, and the resulting mixture was incubated in the assay conditions. Finally, α-amylase activity was measured using the dinitrosalicylic acid reagent and then reading the absorbance of the reaction mixture at a wavelength of 540 nm.


*3.5. Econometric Model*

We conducted a Hausman test to see whether the fixed effect or random effect model was suitable for regression. The test result yields a Chi-squared value of 391.42 with a 1% significance level and supports the use of the fixed-effect model. Accordingly, we ran the following fixed effect regression model.

$$\text{PERFORMAANCE} = \beta\_0 + \beta\_1 \text{SIZE} + \beta\_2 \text{AGE} + \beta\_3 \text{CFOP} + \beta\_4 \text{LEV} + \beta\_5 \text{FAM} + \beta\_6 \text{INS} + \epsilon$$

$$\beta\_7 \text{GOV} + \beta\_8 \text{FOR} + \beta\_9 \text{BO\\_SIZE} + \beta\_{10} \text{BO\\_MEET} + \beta\_{11} \text{BO\\_IND} + \beta\_{12} \text{FAM\\*IES} + \epsilon \tag{1}$$

$$\beta\_{13} \text{FAM\\*GOV} + \beta\_{14} \text{FAM\\*\ast FOR} + \beta\_{15} \text{BO\\_SIZESQ} + \beta\_{16} \text{BO\\_MEETSQ} + \epsilon$$

where PERFORMANCE represents the dependent variables: ROA and Tobin's Q. Variables such as SIZE, AGE, CFOP, and LEV are control variables defined in Table 2. Similarly, variables such as FAM, INS, GOV, FOR, BO-SIZE, BO\_MEET, and BO\_IND are the test variables defined in Table 2. We also include INS, GOV, and FOR as interaction with FAM to see their moderating effects on firm performance. Further, we square BO-SIZE and BO\_MEET to see the non-linear relationship. β0 is the unknown intercept for each firm, and ε is the between-entity error.

To ensure the consistency of our estimates, we also invoked the following random effect regression model.

$$\text{PERFORMANCE} = \beta\_0 + \beta\_1 \text{SIZE} + \beta\_2 \text{AGE} + \beta\_3 \text{CFOP} + \beta\_4 \text{LEV} + \beta\_5 \text{FAM} + \beta\_6 \text{INS} + \epsilon$$

$$\beta\_7 \text{GOV} + \beta\_8 \text{FOR} + \beta\_9 \text{BO\\_SIZE} + \beta\_{10} \text{BO\\_MEET} + \beta\_{11} \text{BO\\_IND} + \beta\_{12} \text{FAM4'INS} + \epsilon \tag{2}$$

$$\beta\_{13} \text{FAM4'GOV} + \beta\_{14} \text{FAM4'FOR} + \beta\_{15} \text{BO\\_SIZESQ} + \beta\_{16} \text{BO\\_MEETSQ} + u + \varepsilon$$

where *u* is the between-entity error, and ε is the within-entity error. All other variables are the same as defined in Equation (1).

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


*Article* **A Strategy for Variable-Scale InSAR Deformation Monitoring in a Wide Area: A Case Study in the Turpan–Hami Basin, China**

**Yuedong Wang 1,2, Guangcai Feng 1,2,\*, Zhiwei Li 1,2, Shuran Luo 1, Haiyan Wang 3, Zhiqiang Xiong 1, Jianjun Zhu 1,2 and Jun Hu 1,2**

**Abstract:** In recent years, increasing available synthetic aperture radar (SAR) satellite data and gradually developing interferometric SAR (InSAR) technology have provided the possibility for widescale ground-deformation monitoring using InSAR. Traditionally, the InSAR data are processed by the existing time-series InSAR (TS–InSAR) technology, which has inefficient calculation and redundant results. In this study, we propose a wide-area InSAR variable-scale deformation detection strategy (hereafter referred to as the *WAVS–InSAR strategy*). The strategy combines stacking technology for fast ground-deformation rate calculation and advanced TS–InSAR technology for obtaining fine deformation time series. It adopts an adaptive recognition algorithm to identify the spatial distribution and area of deformation regions (regions of interest, ROI) in the wide study area and uses a novel wide-area deformation product organization structure to generate variable-scale deformation products. The Turpan–Hami basin in western China is selected as the wide study area (277,000 km2) to verify the proposed WAVS–InSAR strategy. The results are as follows: (1) There are 32 deformation regions with an area of ≥1 km2 and a deformation magnitude of greater than ±2 cm/year in the Turpan–Hami basin. The deformation area accounts for 2.4‰ of the total monitoring area. (2) A large area of ground subsidence has occurred in the farmland areas of the ROI, which is caused by groundwater overexploitation. The popularization and application of facility agriculture in the ROI have increased the demand for irrigation water. Due to the influence of the tectonic fault, the water supply of the ROI is mainly dependent on groundwater. Huge water demand has led to a continuous net deficit in aquifers, leading to land subsidence. The WAVS–InSAR strategy will be helpful for InSAR deformation monitoring at a national/regional scale and promoting the engineering application of InSAR technology.

**Keywords:** wide-area deformation; deformation detection; time-series InSAR; stacking; Turpan–Hami basin

#### **1. Introduction**

Advanced microwave remote sensing technology can precisely monitor deformation over wide areas, which helps geohazard surveys of phenomena such as underground fluid development, mineral mining, and landslide. In recent years, fast-developing interferometric synthetic aperture radar (InSAR) technology and abundant available synthetic aperture radar (SAR) data [1–4] has laid the foundation for high-precision and wide-scale InSAR ground-deformation monitoring. InSAR technology has been successfully used to monitor ground deformation at a regional [5–9] and national scale [10–13]. Large-scale geodetic technology, such as InSAR, usually describes the spatial characteristics of ground deformation by deformation rate, and shows deformation development over time using a time series of deformation. The deformation region usually accounts for a small part of the monitoring area [11], so the ground deformation we are interested in only accounts for a small part of

**Citation:** Wang, Y.; Feng, G.; Li, Z.; Luo, S.; Wang, H.; Xiong, Z.; Zhu, J.; Hu, J. A Strategy for Variable-Scale InSAR Deformation Monitoring in a Wide Area: A Case Study in the Turpan–Hami Basin, China. *Remote Sens.* **2022**, *14*, 3832. https://doi.org/ 10.3390/rs14153832

Academic Editors: Paolo Mazzanti and Saverio Romeo

Received: 11 July 2022 Accepted: 6 August 2022 Published: 8 August 2022

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

**Copyright:** © 2022 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/).

the wide-area monitoring results. At present, the engineering projects to obtain ground deformation in a wide study area (WSA) usually calculate the deformation time series using InSAR datasets covering the whole WSA, using time-series InSAR (TS–InSAR) technology. Even with multiple spatial resolutions, such schemes require a lot of computing resources and storage space, and even then require repeated calculations and provide redundant results, especially in the non-deformation region [14]. Therefore, it is necessary to develop a set of efficient monitoring methods and procedures for wide-area InSAR deformation and a more feasible multi-scale deformation product organization structure in the WSA.

One way to improve the computational efficiency of the TS–InSAR method is to introduce a parallel processing method, which can be realized using high-performance computers (HPC) [12,15–19]. However, the high cost of HPC equipment hinders the popularization of this strategy. Another way is to improve the TS–InSAR method itself, by introducing sequential adjustment theory for real-time InSAR data processing [20–22], introducing a geological model or time-series filtering algorithm for high-dimensional deformation calculation [23–26], or realizing a high-precision TS–InSAR deformation calculation using block solutions [27,28]. These strategies can improve the efficiency of the TS–InSAR solution to a certain extent. However, for wide-area InSAR deformation monitoring, high-precision independent calculation of all InSAR datasets in the WSA will provide many useless time-series results, especially in the non-deformation area. Therefore, it is necessary to develop a demand-oriented multiple spatio-temporal-scale deformation monitoring method, considering the universality of monitoring strategies, computing resources, measurement accuracy, and the efficiency of deformation calculation and interpretation.

The averaging of multiple interferograms (stacking) method was proposed by the authors in [29], which can obtain the ground-deformation rate by averaging the phase of the multitemporal differential InSAR (DInSAR) dataset. Compared with conventional TS– InSAR technologies, such as persistent scatterer (PS) [30], small-baseline subset (SBAS) [31], and interferometric point target analysis (IPTA) [32], stacking only obtains the deformation rate with lower technical requirements and higher computational efficiency. Stacking has been widely used for deformation identification [33–37]. A wide-area deformation monitoring project usually identifies deformation regions based on the ground-deformation rate [38]. For the deformation region, the corresponding deformation time series is extracted to analyze the spatio-temporal evolution of deformation. The deformation time series in stable zones has less information. Therefore, combining stacking and TS–InSAR may contribute to efficient variable-scale deformation monitoring.

In this study, we propose a wide-area InSAR variable-scale deformation detection strategy (WAVS–InSAR). WAVS–InSAR uses stacking technology to quickly calculate the low-spatial-resolution ground-deformation rate over the WSA. Then, an adaptive intelligent recognition algorithm is used to identify the location and area of the deformation regions and determine the regions of interest (ROI). Advanced TS–InSAR technologies are then used to obtain the high-spatio-temporal-resolution deformation time series in the ROI. Finally, the variable-scale InSAR deformation product in the WSA is obtained by a novel variable-scale deformation product organization structure. To verify the proposed WAVS– InSAR strategy, we applied it to the Turpan–Hami basin (about 277,000 km2) in Xinjiang, China. The Turpan–Hami basin is the driest place in China, and has the least rainfall in China. Many tectonic faults, as well as agricultural and mining areas, are scattered across the basin. It is of great significance to obtain the spatio-temporal distribution characteristics of ground subsidence and to investigate the surface deformation related to the active agricultural economy and mineral exploitation in the basin.

The remainder of the paper is organized as follows. We introduce the WAVS–InSAR strategy in Section 2. In Section 3, the general situation of the Turpan–Hami basin, InSAR data, and the data-processing details are briefly described. The variable-scale deformation product in the Turpan–Hami basin is shown in Section 4, followed by the discussion in Section 5. Section 6 presents the conclusions.

#### **2. Methodology**

We first collect all available InSAR datasets covering the WSA, and preprocess all datasets through registration and DInSAR, to generate the multitemporal DInSAR datasets with the same spatial reference data. Then, we apply the WAVS–InSAR strategy to process the multitemporal DInSAR data to obtain variable-scale deformation products in a wide area. The WAVS–InSAR includes the following four modules (Figure 1).


**Some Basic Issues and Applications of Switch-Mode Rectifiers on Motor Drives and Electric Vehicle Chargers**

C. M. Liaw and Y. C. Chang *National Tsing Hua University, National Chung Cheng University Taiwan*


**4. Conclusions**

In this paper, molecular dynamics simulations were used to study the stable occupancy of Xe/Kr clusters in defect-free configurations and configurations with five different defects. The results show that the system was energetically favourable when Xe/Kr clusters were trapped by vacancies, especially U vacancies, because they can provide a larger space, and the complex of Xe/Kr and vacancies were more stable. The diffusion of Xe/Kr atoms and small clusters at octahedral interstitial sites in bulk UO2 was also studied. The diffusion of Xe/Kr atoms in bulk UO2 was relatively complex. The activation energy of Xe/Kr small clusters is ~2 eV, and the Xe/Kr clusters are difficult to diffuse. The nucleation of Xe/Kr in systems containing different defect concentrations was also investigated. At 2500 K, the Xe/Kr atoms dispersed in the system gather into clusters after a 20 ns relaxation. A comparison of the different systems revealed that the vacancy-containing system is more likely to form large clusters than a system containing interstitial atoms.

**Author Contributions:** Conceptualization, W.H. and H.D.; methodology, L.W. (Li Wang), Z.W., Y.X., Y.C., Z.L., Q.W. and W.H.; validation, Y.X., Z.W., L.W. (Li Wang), Y.C., Z.L., Q.W. and L.W. (Lu Wu); formal analysis, L.W. (Li Wang), Z.W., Y.X., Y.C., Z.L. and Q.W.; investigation, L.W. (Li Wang); resources, Z.W., Z.L. and Q.W.; data curation, Y.C. and W.H.; writing—original draft, L.W. (Li Wang); writing—review & editing, L.W. (Li Wang), L.W. (Lu Wu) and H.D.; supervision, H.D. All authors have read and agreed to the published version of the manuscript.

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

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data sharing not applicable.

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

#### **References**


**1.3 Why covalent immobilization?**

There is not a universal method of immobilization and support for the enzymes and their uses in biotechnology. Several factors could drive the choice of a particular method, e.g., physicochemical properties of support or different features of substrates and products. As shown in **Figure 1**, all methods present advantages and disadvantages. However, why should the covalent bond be chosen? Covalent bonding is the most used strategy of the irreversible category. The removal of the covalent immobilized enzyme from the support without affecting its catalytic activity as an attempt to recover either the biomolecule or the support is meaningless due to hard process usually involved to disrupt the covalent bond. Moreover, better thermal and operational stability, as well as major resistance to pH, temperature, and solvent variations, are some of the well-known benefits of the immobilization approach. A good reason to choose this method is when a system with high stable protein coverage is recommended. Obtainment of the product of high purity, i.e., no contaminants including the enzyme, is another excuse to employ the covalent bond for immobilization.

### **2. About the particles used as support**

In order to prepare an immobilized derivative, at least the biomolecule, the support, and the method of immobilization are required. Choosing the support is an essential step in the immobilization process since the characteristics of the material can influence in the performance of the biocatalyst. Even today there is no general rule for selecting the ideal support to attach the biomolecule. However, materials with characteristics such as chemical inertia, hydrophilic character, low cost, mechanical resistance, and resistance to microbial attack are widely used. Often in the literature, terms as matrix and carrier are found as synonyms of support.

Magnetic particles as a carrier to biomolecule immobilization are desirable materials due to easy separation of the biocatalyst from the reaction medium by application of an external magnetic field. Among the particles with magnetic property, the iron oxides, in particular, magnetite (Fe3O4), are the materials with notable uses in the immobilization process. Maghemite (γ-Fe2O3) and hematite (α-Fe2O3) are iron oxides derived from magnetite and can be found together with it.

#### **2.1 Iron oxides: Why use it as a support of biomolecules?**

Until now several magnetic particles obtained from pure iron oxide or a mixture of phases and composites materials have been presented as attractive support to enzyme immobilization or protein purification. To understand why these particles are so attractive, it might be useful to review the characteristics of the magnetite. The iron oxide is inorganic in nature, which presents a ratio of ferric/ferrous ions equal to 2:1, in stoichiometric magnetite. The ions positively charged (ferric,

**31**

*Magnetic Bio-Derivatives: Preparation and Their Uses in Biotechnology*

denoted as Fe3+, and ferrous, expressed as Fe2+) are distributed in two different sites with a crystal inverse spinel structure, formed by a cubic network face centered (fcc) of oxygen anions. Tetrahedral sites are filled by Fe3+ ions, while the octahedral

. Maghemite is also a ferrimagnetic mate-

), while the hematite is weakly ferromagnetic (*M*s minor

). It is important to mention that there is a correlation between the

magnetic properties of the material with their size. So, when the magnetite presents nanometer size (below 15 nm), the superparamagnetic behavior is observed [12]. In addition to physicochemical and magnetic properties of the magnetite, using these particles as a carrier of the biomolecules could avoid laborious procedures

At present, many synthesis methods to produce iron oxide particles have been developed. Evaluating the preparation method together with the features desires of the material (i.e., size, shape, size distribution, and surface chemistry) is very important once the magnetic property, as well as applications of material synthesized, depends on it. Moreover, the degree of structural defects (i.e., the presence of impurities) and distribution of the defects are also related to the synthesis methodology [13]. In general, the main preparation methods of the iron oxide particles can be classified as chemical, physical, and biological. Among these methodologies, the chemical route is the most used due to high yield along with low-cost production. Coprecipitation, hydrothermal, microemulsion, sonochemical, thermal decomposition, sol-gel synthesis, and electrochemical decomposition are some of the chemical methods [14]. A complete description about the most common preparation methods of the iron oxide particles together with their benefits and drawbacks can be

The "coprecipitation method" presents interesting characteristics that make it the chosen one among the chemical route category. For instance, this technique is simple and inexpensive and can be used for large-scale production. Briefly, iron oxide particles are produced by addition of a precipitating agent (e.g., ammonium hydroxide) to the solution containing a mixture of ferrous and ferric salts (e.g., FeCl2.4H2O and FeCl3.6H2O) at room temperature or high temperature. A molar ratio equal to 2:1 for Fe3+/Fe2+ ions is generally used. The obtainment of a black precipitate (Fe3O4) is a good indication that the synthesis was successful. The size, shape, composition and magnetic property of the particles can be modified depending on the iron precursors, iron concentrations, ferric/ferrous ratio, precipitating agent, pH, temperature, ionic strength, stirring velocity, surfactants addition, and

Processes of the nucleation and the growth of crystals are involved in this technique. Both processes should take place separately, i.e., first the nucleation and then the growth. According to Wu et al. [10], pH is an important parameter to address toward the nucleation (solution pH minor than 11) and the growth (solution pH major than 11) of crystals. Even though the coprecipitation method is widely used to produce iron oxide particles with high saturation magnetization, some limitations like broad and large size distribution, poor crystallinity,

According to the magnetic response, iron oxides can be classified as ferromagnetic, antiferromagnetic, paramagnetic, and ferrimagnetic. At room temperature, bulk magnetite is a ferrimagnetic material due to the combination of ferromagnetic and antiferromagnetic comportments that happens below the Curie temperature (850 K) [11]. This iron oxide presents a saturation magnetization

*DOI: http://dx.doi.org/10.5772/intechopen.85748*

sites are occupied by Fe3+ and Fe2+ ions [10].

(*M*s) value between 92 and 100 emu g−1

such as decantation, centrifugation, or filtration.

rial (*M*s = 60–80 emu g−1

**2.2 Synthesis of iron oxides**

found in the literature [1, 12, 14].

working under inert atmosphere [10, 15].

than 1 emu g−1

*Magnetic Bio-Derivatives: Preparation and Their Uses in Biotechnology DOI: http://dx.doi.org/10.5772/intechopen.85748*

denoted as Fe3+, and ferrous, expressed as Fe2+) are distributed in two different sites with a crystal inverse spinel structure, formed by a cubic network face centered (fcc) of oxygen anions. Tetrahedral sites are filled by Fe3+ ions, while the octahedral sites are occupied by Fe3+ and Fe2+ ions [10].

According to the magnetic response, iron oxides can be classified as ferromagnetic, antiferromagnetic, paramagnetic, and ferrimagnetic. At room temperature, bulk magnetite is a ferrimagnetic material due to the combination of ferromagnetic and antiferromagnetic comportments that happens below the Curie temperature (850 K) [11]. This iron oxide presents a saturation magnetization (*M*s) value between 92 and 100 emu g−1 . Maghemite is also a ferrimagnetic material (*M*s = 60–80 emu g−1 ), while the hematite is weakly ferromagnetic (*M*s minor than 1 emu g−1 ). It is important to mention that there is a correlation between the magnetic properties of the material with their size. So, when the magnetite presents nanometer size (below 15 nm), the superparamagnetic behavior is observed [12].

In addition to physicochemical and magnetic properties of the magnetite, using these particles as a carrier of the biomolecules could avoid laborious procedures such as decantation, centrifugation, or filtration.

#### **2.2 Synthesis of iron oxides**

*Applied Surface Science*

environments.

antibodies or lectins can be used.

**2. About the particles used as support**

**1.3 Why covalent immobilization?**

when the physical adsorption of the biocatalyst is carried out in hydrophobic

• **Affinity**. The immobilization by affinity interaction requires that the biocatalyst as well as the support present specific chemical groups on their surfaces, that is, the presence of complementary species, e.g., streptavidin-biotin interaction. As a consequence of the specific interaction, notable selectivity is a major benefit of this method. However, this procedure is expensive since

There is not a universal method of immobilization and support for the enzymes and their uses in biotechnology. Several factors could drive the choice of a particular method, e.g., physicochemical properties of support or different features of substrates and products. As shown in **Figure 1**, all methods present advantages and disadvantages. However, why should the covalent bond be chosen? Covalent bonding is the most used strategy of the irreversible category. The removal of the covalent immobilized enzyme from the support without affecting its catalytic activity as an attempt to recover either the biomolecule or the support is meaningless due to hard process usually involved to disrupt the covalent bond. Moreover, better thermal and operational stability, as well as major resistance to pH, temperature, and solvent variations, are some of the well-known benefits of the immobilization approach. A good reason to choose this method is when a system with high stable protein coverage is recommended. Obtainment of the product of high purity, i.e., no contaminants including the

enzyme, is another excuse to employ the covalent bond for immobilization.

In order to prepare an immobilized derivative, at least the biomolecule, the support, and the method of immobilization are required. Choosing the support is an essential step in the immobilization process since the characteristics of the material can influence in the performance of the biocatalyst. Even today there is no general rule for selecting the ideal support to attach the biomolecule. However, materials with characteristics such as chemical inertia, hydrophilic character, low cost, mechanical resistance, and resistance to microbial attack are widely used. Often in the literature, terms as matrix and carrier are found as synonyms of support.

Magnetic particles as a carrier to biomolecule immobilization are desirable materials due to easy separation of the biocatalyst from the reaction medium by application of an external magnetic field. Among the particles with magnetic property, the iron oxides, in particular, magnetite (Fe3O4), are the materials with notable uses in the immobilization process. Maghemite (γ-Fe2O3) and hematite (α-Fe2O3) are iron

Until now several magnetic particles obtained from pure iron oxide or a mixture of phases and composites materials have been presented as attractive support to enzyme immobilization or protein purification. To understand why these particles are so attractive, it might be useful to review the characteristics of the magnetite. The iron oxide is inorganic in nature, which presents a ratio of ferric/ferrous ions equal to 2:1, in stoichiometric magnetite. The ions positively charged (ferric,

oxides derived from magnetite and can be found together with it.

**2.1 Iron oxides: Why use it as a support of biomolecules?**

**30**

At present, many synthesis methods to produce iron oxide particles have been developed. Evaluating the preparation method together with the features desires of the material (i.e., size, shape, size distribution, and surface chemistry) is very important once the magnetic property, as well as applications of material synthesized, depends on it. Moreover, the degree of structural defects (i.e., the presence of impurities) and distribution of the defects are also related to the synthesis methodology [13]. In general, the main preparation methods of the iron oxide particles can be classified as chemical, physical, and biological. Among these methodologies, the chemical route is the most used due to high yield along with low-cost production. Coprecipitation, hydrothermal, microemulsion, sonochemical, thermal decomposition, sol-gel synthesis, and electrochemical decomposition are some of the chemical methods [14]. A complete description about the most common preparation methods of the iron oxide particles together with their benefits and drawbacks can be found in the literature [1, 12, 14].

The "coprecipitation method" presents interesting characteristics that make it the chosen one among the chemical route category. For instance, this technique is simple and inexpensive and can be used for large-scale production. Briefly, iron oxide particles are produced by addition of a precipitating agent (e.g., ammonium hydroxide) to the solution containing a mixture of ferrous and ferric salts (e.g., FeCl2.4H2O and FeCl3.6H2O) at room temperature or high temperature. A molar ratio equal to 2:1 for Fe3+/Fe2+ ions is generally used. The obtainment of a black precipitate (Fe3O4) is a good indication that the synthesis was successful. The size, shape, composition and magnetic property of the particles can be modified depending on the iron precursors, iron concentrations, ferric/ferrous ratio, precipitating agent, pH, temperature, ionic strength, stirring velocity, surfactants addition, and working under inert atmosphere [10, 15].

Processes of the nucleation and the growth of crystals are involved in this technique. Both processes should take place separately, i.e., first the nucleation and then the growth. According to Wu et al. [10], pH is an important parameter to address toward the nucleation (solution pH minor than 11) and the growth (solution pH major than 11) of crystals. Even though the coprecipitation method is widely used to produce iron oxide particles with high saturation magnetization, some limitations like broad and large size distribution, poor crystallinity,

aggregation, and tendency to oxidize can be cited. For instance, the formation of impurities (e.g., maghemite) depends on the initial and final solution pH as well as the reaction temperature [16]. Moreover, impurities are also observed in very small particles (minor than 20 nm) because of the high surface area/volume ratio, which allows a great number of surface atoms. This happening could lead to the formation of maghemite, for example, as a consequence of the oxidation of Fe2+ to Fe3+ ions. The maghemite can also be present when the magnetic sample is stored for an extensive period (6 months) as well as exposure to high temperatures (superior to 180°C) [16]. Since maghemite is also a ferrimagnetic material but with a slightly minor saturation magnetization than magnetite, the presence of maghemite in the magnetic sample may not be considered a disadvantage. However, the presence of hematite is an unwanted impurity since this oxide is weakly ferromagnetic and exhibits low saturation magnetization.

Our research group has used the coprecipitation method for the obtainment of magnetic particles to be used as a matrix for biomolecules immobilization [17, 18]. Among these materials, some magnetic composites were also synthesized. For this, materials (without magnetic property) can be added to the FeCl3.6H2O/FeCl2.4H2O mixture before the magnetization aiming different purposes: magnetic particle composite synthesis.

Maciel et al. [19] synthesized a magnetic levan; that is, under a polymer of fructose, the iron oxide particles were formed. The magnetic levan particles were treated with sodium periodate (NaIO4) and employed as a support for trypsin covalent immobilization.

Rêgo et al. [20] have reported a gum magnetic from *Parkia pendula* seeds as a matrix for concanavalin A (Con A) covalent immobilization. For this to be possible, seed gum was included in the solution containing the Fe3+ and Fe2+ ions. Afterward, it was functionalized with NaIO4 allowing the covalent immobilization of the lectin Con A.

Mercês et al. [21] described the process to convert Dacron to magnetic Dacron-hydrazide (mDAC). Heparin (HEP) was activated by carbodiimide and N-hydroxysuccinimide and covalently linked to mDAC (mDAC-HEP). Human antithrombin was then purified by affinity chromatography using the mDAC-HEP.

Alves et al. [4] prepared a magnetic composite from azocasein and iron oxide particles (mAzo). The presence of azocasein, azo-dye-insoluble casein derivative, on the surface of the magnetic particles allowed trypsin to be purified by affinity binding. The enzyme forms complex with the modified substrate but does not hydrolyze it. Washing the enzyme-azocasein-magnetic particles removes unspecific proteins of the mixture. Afterward, the complex is disrupted by increasing the ionic strength, and the enzyme is collected in the supernatant.

Due to the inorganic nature of the iron oxides, the magnetic particles do not have chemical groups to enable biomolecules to bind covalently. Therefore, additional procedures should be performed for this purpose (functionalization). Two approaches can be carried out: (1) coating them with polymers containing these chemical groups and (2) adding materials encompassing these chemical arms during or after the magnetic particle synthesis (composites). In this context, Cabrera et al. [22] used the 3-aminopropyltriethoxysilane (APTES) as a silane agent to available amine groups on the surface of the magnetic diatomaceous earth (mDE). The treatment with APTES was carried out after the synthesis of the mDE particles. The composite material (mDE-APTES) showed efficiency as a matrix to immobilize invertase. Furthermore, Cabrera et al. [2] have also reported a simple, effective, and inexpensive synthesis methodology to obtain a magnetic composite made from mDE particles coated with polyaniline (mDE@PANI). The coating with PANI was carried out after preparation of the mDE particles. Three industrial enzymes (invertase, β-galactosidase, and trypsin) were successfully immobilized using glutaraldehyde as

**33**

**Figure 2.**

*Magnetic Bio-Derivatives: Preparation and Their Uses in Biotechnology*

a chemical arm on the mDE@PANI particles. All magnetic bio-derivatives displayed superior performance (related to catalytic activity and stability) compared to the free enzyme. Thus, the mDE@PANI particles showed potential as a matrix to immobilize other biomolecules. Tannase was also covalently immobilized onto the mDE@ PANI particles [3]. This enzyme catalyzes the hydrolysis of tannins present in several beverages. **Figure 2** displays the preparation of functionalized magnetic particles for

*Approaches to prepare functionalized magnetic particles. (A) Functionalized by coating process and*

Before proceeding with the immobilization process, essential aspects such as material and surface science, biomolecule chemistry, and reaction engineering should be evaluated. A satisfactory immobilization will be achieved when all these aspects are properly integrated. However, this is not very easy due to the multidisciplinary nature of problems along with the main effects occurring in different length

It is well known that an immobilized derivative presents, in the majority of times, minor retention of their catalytic activity when compared to its free counterpart. This happens for two reasons: (i) conformational changes of the biomolecule due to covalent bond with the support and (ii) problems arising of the catalytic reaction occur in a heterogeneous environment [24]. So, analyzing the behavior of biomolecule immobilized could help in choosing the most suitable particle size for

In the last times, the "nanoimmobilization" is widely used for several researchers to indicate nanostructures as support for the immobilization of biomolecules. The employment of NPs presents various benefits due to unique physical properties resulting from their nanometer size (below 100 nm). Higher surface area, significant biomolecule loading, superior mass transfer resistance, and minor diffusion

biomolecule immobilization or protein purification.

**2.3 Micro or nano iron oxide particles?**

*(B) functionalized by the addition of chemical groups.*

scales from nanometer to millimeter [23].

the material used as support.

*DOI: http://dx.doi.org/10.5772/intechopen.85748*

*Magnetic Bio-Derivatives: Preparation and Their Uses in Biotechnology DOI: http://dx.doi.org/10.5772/intechopen.85748*

**Figure 2.**

*Applied Surface Science*

composite synthesis.

covalent immobilization.

exhibits low saturation magnetization.

aggregation, and tendency to oxidize can be cited. For instance, the formation of impurities (e.g., maghemite) depends on the initial and final solution pH as well as the reaction temperature [16]. Moreover, impurities are also observed in very small particles (minor than 20 nm) because of the high surface area/volume ratio, which allows a great number of surface atoms. This happening could lead to the formation of maghemite, for example, as a consequence of the oxidation of Fe2+ to Fe3+ ions. The maghemite can also be present when the magnetic sample is stored for an extensive period (6 months) as well as exposure to high temperatures (superior to 180°C) [16]. Since maghemite is also a ferrimagnetic material but with a slightly minor saturation magnetization than magnetite, the presence of maghemite in the magnetic sample may not be considered a disadvantage. However, the presence of hematite is an unwanted impurity since this oxide is weakly ferromagnetic and

Our research group has used the coprecipitation method for the obtainment of magnetic particles to be used as a matrix for biomolecules immobilization [17, 18]. Among these materials, some magnetic composites were also synthesized. For this, materials (without magnetic property) can be added to the FeCl3.6H2O/FeCl2.4H2O mixture before the magnetization aiming different purposes: magnetic particle

Maciel et al. [19] synthesized a magnetic levan; that is, under a polymer of fructose, the iron oxide particles were formed. The magnetic levan particles were treated with sodium periodate (NaIO4) and employed as a support for trypsin

for concanavalin A (Con A) covalent immobilization. For this to be possible, seed gum was included in the solution containing the Fe3+ and Fe2+ ions. Afterward, it was functionalized with NaIO4 allowing the covalent immobilization of the lectin Con A. Mercês et al. [21] described the process to convert Dacron to magnetic Dacron-hydrazide (mDAC). Heparin (HEP) was activated by carbodiimide and N-hydroxysuccinimide and covalently linked to mDAC (mDAC-HEP). Human antithrombin was then purified by affinity chromatography using the mDAC-HEP. Alves et al. [4] prepared a magnetic composite from azocasein and iron oxide particles (mAzo). The presence of azocasein, azo-dye-insoluble casein derivative, on the surface of the magnetic particles allowed trypsin to be purified by affinity binding. The enzyme forms complex with the modified substrate but does not hydrolyze it. Washing the enzyme-azocasein-magnetic particles removes unspecific proteins of the mixture. Afterward, the complex is disrupted by increasing the ionic

Due to the inorganic nature of the iron oxides, the magnetic particles do not have chemical groups to enable biomolecules to bind covalently. Therefore, additional procedures should be performed for this purpose (functionalization). Two approaches can be carried out: (1) coating them with polymers containing these chemical groups and (2) adding materials encompassing these chemical arms during or after the magnetic particle synthesis (composites). In this context, Cabrera et al. [22] used the 3-aminopropyltriethoxysilane (APTES) as a silane agent to available amine groups on the surface of the magnetic diatomaceous earth (mDE). The treatment with APTES was carried out after the synthesis of the mDE particles. The composite material (mDE-APTES) showed efficiency as a matrix to immobilize invertase. Furthermore, Cabrera et al. [2] have also reported a simple, effective, and inexpensive synthesis methodology to obtain a magnetic composite made from mDE particles coated with polyaniline (mDE@PANI). The coating with PANI was carried out after preparation of the mDE particles. Three industrial enzymes (invertase, β-galactosidase, and trypsin) were successfully immobilized using glutaraldehyde as

strength, and the enzyme is collected in the supernatant.

Rêgo et al. [20] have reported a gum magnetic from *Parkia pendula* seeds as a matrix

**32**

*Approaches to prepare functionalized magnetic particles. (A) Functionalized by coating process and (B) functionalized by the addition of chemical groups.*

a chemical arm on the mDE@PANI particles. All magnetic bio-derivatives displayed superior performance (related to catalytic activity and stability) compared to the free enzyme. Thus, the mDE@PANI particles showed potential as a matrix to immobilize other biomolecules. Tannase was also covalently immobilized onto the mDE@ PANI particles [3]. This enzyme catalyzes the hydrolysis of tannins present in several beverages. **Figure 2** displays the preparation of functionalized magnetic particles for biomolecule immobilization or protein purification.

#### **2.3 Micro or nano iron oxide particles?**

Before proceeding with the immobilization process, essential aspects such as material and surface science, biomolecule chemistry, and reaction engineering should be evaluated. A satisfactory immobilization will be achieved when all these aspects are properly integrated. However, this is not very easy due to the multidisciplinary nature of problems along with the main effects occurring in different length scales from nanometer to millimeter [23].

It is well known that an immobilized derivative presents, in the majority of times, minor retention of their catalytic activity when compared to its free counterpart. This happens for two reasons: (i) conformational changes of the biomolecule due to covalent bond with the support and (ii) problems arising of the catalytic reaction occur in a heterogeneous environment [24]. So, analyzing the behavior of biomolecule immobilized could help in choosing the most suitable particle size for the material used as support.

In the last times, the "nanoimmobilization" is widely used for several researchers to indicate nanostructures as support for the immobilization of biomolecules. The employment of NPs presents various benefits due to unique physical properties resulting from their nanometer size (below 100 nm). Higher surface area, significant biomolecule loading, superior mass transfer resistance, and minor diffusion

problems are some of the advantages of the use of nanoparticles as a matrix. Also better stability and performance of biocatalyst immobilized along with a low protein unfolding were also reported [25, 26]. Some disadvantages for the NPs (e.g., large-scale application and price of material preparation) have been presented [26]. On the other hand, magnetic nanoparticles (MNPs) have been used as a support of several biological molecules as well as present superparamagnetism (i.e., magnetic response is only observed after application of magnetic field) and can be recovered by the use of a magnet [27]. So, the magnetic property as a plus feature to the nanoparticles would make them more attractive not only for potential uses in biotechnology [14] but also for a lot of biomedical applications including magnetic hyperthermia [28], drug delivery [29], contrast agent in magnetic resonance imaging (MRI) [30], and cellular therapy [31].

As above mentioned (Section 2.2), some factors influence the size (micro or nano) of iron oxide particles. For instance, Maciel et al. [17] assessed the effect of the temperature and the nature of the precipitating agent (strong base) to produce iron oxide nanoparticles. The authors employed sodium hydroxide (NaOH) as the precipitating agent and carried out the synthesis at low temperature (50°C). Small magnetic nanoparticles with a diameter near to 15 nm were obtained and used as a matrix to immobilize trypsin. The magnetic bio-derivative displayed about 90% retention of specific activity after five reuses. Despite Cabrera et al. [2] have reported mDE@PANI nanoparticles (~12 nm) as a promising matrix to immobilize trypsin, the immobilized derivative (mDE@PANI-TRYP) retained 75 and 60% of its initial activity after five and nine cycles of reusability, respectively. The decrease of catalytic activity could be attributed to the loss of the magnetic bio-derivative (mDE@PANI-TRYP) during the washing process after each reusability cycle. After coating with polyaniline, the mDE@PANI showed better stability in suspension. Thus, working with very small particles and good colloidal stability can lead to loss of the immobilized derivative.

Therefore, the choice of micro or nanoparticles as support will depend on several factors including colloidal stability of the particles, operating conditions, and application of the immobilized derivative, among others.