\r\n\tWe need such information of the environmental indicators day and night, from the crowded cities and the most remote locations. Therefore the study, development, and application of automated sensing systems have been booming during the last decades and the progress in this field is really fast.
\r\n\r\n\tThe current book intends to provide the reader with the most recent trends in the development of sensing technologies for environmental control and monitoring, application of these novel technologies for the detection and monitoring of different environmental indicators, but also identification of hazardous chemical compounds and pathogens, and to introduce various aspects of using the online sensing data for decision-making in different fields of social life.
\r\n\t
Cluster analysis is a critical step in exploratory data analysis intended to identify homogeneous subgroups among observations. Cluster analysis is also widely used for functional data in tasks such as the classification of electrocardiogram curves in the diagnosis of cardiovascular ischemic diseases [1] and the extraction of representative wind behavior [2, 3]. The various functional data clustering methods described in the literature can generally be categorized into two subgroups: distance-based methods and filtering-based methods.
The distance-based methods involve the construction of a distance matrix with a specific metric; the clustering results may be derived with hierarchical or centroid-based clustering tools [3, 4]. The filtering-based methods involve the approximation of the curves with linear combinations of finite basis functions, such as splines and functional principal components, and the cluster analysis is conducted based on the coefficients or scores of finite dimensions [5, 6, 7]. The focus of this study is on distance-based methods. In this paper, we propose a new family of clustering algorithms based on the chosen functional ordering. The dissimilarity matrix is constructed via the chosen functional ordering, which is applied to the set of differences of all pairs of the functional data under investigation. Various functional ordering can be chosen, but we concentrate on orderings with intrinsic graphical interpretation. But any ordering that treats the sources equally can be used, including the modified band depth [8] and the simplicial band depth [9]. The choice of functional ordering with intrinsic graphical interpretation allows us to show the resulting clusters and a central region that attains a natural interpretation. I.e., All functions contained in the central region do not leave the plot of the central region, and all functions not contained in the central region leave the plot of the central region in at least one point. It has to be mentioned that the classical functional orderings mentioned above do not satisfy this natural condition, and therefore we will concentrate on functional orderings defined in [10].
Functional data differ in various ways, such as in magnitude, shape, phase, and dependence structure, and hence they are difficult to analyze when clusters exist from multiple perspectives. The existing methods either focus on a single type of variation or pool the various sources of variation with weightings that rely on a delicate selection procedure. Without balancing, the clustering results could be dominated by the component with the greatest absolute variation. In order to achieve some balancing between the various sources, it is possible to standardize the curves before applying existing methods, such as
Since the proposed procedure applies functional ordering, such that every part of the function is treated equally, the different sources of variation are combined in an equal manner. For univariate cases, it may combine the raw curves and the derivatives equally to measure the magnitude and shape variation simultaneously. For multivariate cases, it may combine the marginal curves and the covariance functions equally to account for both marginal and joint variation among curves. Furthermore, the proposed method provides a reasonable graphical interpretation of the clustering result. Finally, it inherits the robustness of functional orderings and can stably recover the clusters when abnormal observations contaminate the data.
The remainder of this paper is organized as follows. In Section 2, we define the new proposed procedure with an arbitrary functional ordering. Further, we review several functional orderings already defined in [10] which satisfy the intrinsic graphical interpretation. Finally, we study the metric properties of derived dissimilarity. In Section 3, we describe the simulation studies we conducted to assess the performance of the proposed methods and compare them with some existing competitors in cases where the combination of the various sources is of interest. In Sections 4–6, we demonstrate the effectiveness of our method with three real-world examples. The proposed methods will be available soon in the R package GET.
Assume that the functions
To induce dissimilarity measure from functional ordering, we construct the set of functional differences:
We remark here that
After a dissimilarity matrix is established, the partitioning around medoids procedure can be used to determine the given number of clusters. This produces a family of clustering algorithms that depends on the choice of the functional ordering.
In the following, we will discuss the possible choices of functional ordering. First, we assume functional orderings, which take different sources of the data variability equally. We call such ordering combined functional ordering. Such an approach is useful when the investigator wants to join different information about the data and combine them in one universal procedure. Second, we review several functional orderings which satisfy the intrinsic graphical interpretation.
Our proposed procedure then consists of the following steps:
Choose the appropriate data sources (e.g., raw data, derivative and second derivative)
Choose the functional ordering, which allows for intrinsic graphical interpretation and which gives the same weight to every chosen source (e.g., the studentized maximum ordering, the area rank ordering).
Compute the dissimilarity matrix
Apply partitioning around medoids
Plot the resulted clusters together with their central region with intrinsic graphical interpretation.
We consider now functions
We denote with
We can then apply to them the corresponding ordering and hence construct the dissimilarity matrix. Note that each of the orderings to be introduced considers each element equally by ranking or scaling, so the desired perspectives of ordering are considered and treated equally in such a combined ordering. To enhance the interpretability of the clustering results, we focus only on the notions that satisfy the intrinsic graphical interpretation.
The following definition specifies the properties of
Let us call
The definitions of all previously mentioned orderings are given in [10]. For the sake of completeness, we provide here a short list of these definitions.
Let
where
The division by
Let
The continuous rank measure is:
where
Here,
The
The area rank measure:
where.
Because we construct a symmetric set of functions to compute the dissimilarity matrix, here we use only the symmetric studentized ordering. The above orderings are based on the whole distributions of
The
where
In this section, we validate the dissimilarity matrix construction defined in Section 2.1 for studentized measure by showing that
where
Non-negativity:
Identity of indiscernibles:
Symmetry:
The fourth property of the metric, i.e.
Triangle inequality:
is not satisfied when
where
This section describes the intensive simulation studies we conducted to assess the empirical performance of the proposed clustering methods and compares this performance with those of the existing methods when the clusters demonstrate differences from various perspectives. For comparison, we also consider two clustering methods for functional data: the
Specifically, we consider the following five models on
Class 1:
Class 2:
Class 3:
Class 4:
Class 5:
Here,
In addition, to assess the robustness of the proposed methods, we also consider another situation by replacing
Top panel: Realizations of two settings. Bottom panel: Adjusted Rand index of four clustering methods with the two settings.
Under the first setting, data are generated from a Gaussian process. With regard to the adjusted Rand index, the four methods are quite comparable but the proposed methods are slightly better than the other two. However, our methods recover much better the characteristics of the true clusters; see Figure 2, which illustrates one clustering result for each of the four methods with both raw curves and normalized curves. In contrast, the
Clusters for setting 1 visualized on raw curves (top panel) and normalized curves (bottom panel). In each panel, from top to bottom: Area, studentized,
As for the second setting, in which the marginal distribution becomes heavy-tailed, our methods obtain more robust clustering results than the other two methods and reach higher adjusted Rand indexes (Figure 3). The model-based method relies heavily on the Gaussian assumption and thus shows less satisfactory behavior. Again, our methods still accurately recover the patterns of each cluster, whereas the other two methods completely fail to reveal reasonable group structures. Specifically, both
Clusters setting 2 visualized on raw curves (top panel) and normalized curves (bottom panel). In each panel, from top to bottom: Area, studentized,
Insurance consumption indicates the equilibrium of supply and demand of insurance products. For a given insurance market, the collection of total (Life and non-Life) yearly insurance consumption observations helps to explain the variation of insurance market development over time. A common measure of insurance consumption, and hence of insurance development, is insurance penetration (IP), defined as the ratio of insurance premiums on GDP. The pattern of the development variation is evident when one views the IP as a function of time, known as the IP curve.
In their effort to promote the European single insurance market through the integration process, European policymakers put emphasis on homogeneity and convergence aspects of development patterns of European insurance markets. That is equivalent to saying that they are interested in identifying a single group (cluster) of countries whose IP curves exhibit similarity in magnitude and shape. The clustering of European countries in terms of their IP curves provides a method for testing the magnitude and shape similarity of the insurance industry in Europe. In particular, functional clustering methods are appropriate for our data, given the time dependency in the observations.
IP curves (time-series data on IP) originated from the Swiss Re (2016) Database were analyzed by the proposed functional clustering (FC) method based on Area measure. The exploration concentrated on the IP curves of 34 European countries (EU and non-EU members) observed over 13 years between 2004 and 2016, that is, before, during, and post-financial and sovereign debt crises.
The FC method extracts the partitioning information from both the magnitude and the shape of IP curves. While the magnitude is captured in the IP curves, the shape is not straightforward to be detected. To this end, we performed two types of transformations on the raw IP curves to reveal their shape. First, the raw IP curves were centred relative to each country’s average IP rate to mitigate the widely different magnitudes in the IP data. After this, the resulting centred IP curves were then normalized with their L2 norms to a unit norm (to have a length of 1). These transformations are proposed to extract shape information by [13] By normalizing the centred IP curves in this manner, we eliminate their amplitude signal, while we are only left with the shape signal of the raw IP curves.
For the FC method to run properly, the most suitable number of clusters must be determined. We chose 6 clusters even if the median value of all methods presented in the NBclust library of the R software is 5. Our choice is justified as it better serves the analysis and the characterization of the produced clusters.
Given the IP curves of each cluster, the FC method also provides a graphical representation, through the central regions, of the deepest central IP curves within each cluster. We are interested in the so-called marginal plot style approach of the clustering solution. This means that the central regions are computed separately for magnitude and shape to better express each cluster component’s shape. Remark here that the proposed method also allows showing the central region with respect to the combined ordering with respect to the magnitude and shape together. The appearance of clusters is demonstrated by the deepest IP curve (solid curve) that corresponds to the medoid IP curve and the envelope of 50
Clustering results of the IP curves: Magnitude plot.
Clustering results of the IP curves: Shape plot.
Cluster 1: Developed insurance markets with middle-to-high IP levels and decreasing IP patterns in the whole period. This cluster includes Belgium, France, Ireland, Austria*, the UK, Portugal, Switzerland, Malta, Slovakia, and Germany. Cluster 2: Developing insurance markets with low-to-middle IP level and increasing IP pattern until 2010 and varying (decreasing) thereafter. This cluster of countries consists of Cyprus*, Turkey, Greece, and Luxemburg. Cluster 3: Developed insurance markets with middle-to-high IP levels and increasing IP patterns in the whole period. This cluster unites Finland*, Italy, Spain, Denmark, and the Netherlands. Cluster 4: Developing insurance markets with low-to-middle IP levels and increasing IP pattern until 2009 and decreasing thereafter. The within-cluster countries are Croatia*, Slovenia, Iceland, the Czech Republic, Sweden, and Romania. Cluster 5: Developing insurance markets with low-to-middle IP levels and almost quadratic IP pattern and vertex point in 2008. In this cluster, we see countries such as Russia*, Ukraine, and Norway. Cluster 6: Least Developed insurance markets with low IP level and increasing IP pattern followed by a decreasing one initiated in 2007, right on the start of the financial crisis. Members of this cluster are countries such as Lithuania*, Bulgaria, Hungary, Estonia, Serbia, and Poland. The * symbol denotes the medoid IP curve produced by the clustering for each cluster.
The results bring to surface first the difficulty of the European insurance industry to converge and to exhibit homogeneity among national insurance markets during the whole period. A fact that otherwise could lead to the building of single European insurance industry. Second and final, the differential behavior of European insurance markets under different phases of the macroeconomic environment. For instance, Least Developed non-EU insurance markets faced shrink challenges, especially during and after the financial and sovereign debt crises period. The same challenge with a time lag of approximately two years was obvious for some Developing insurance markets. Russia and Ukraine had their insurance markets running in parallel and separated from the other two Developing insurance markets to follow their own smile-shaped development pattern. A slight improvement in insurance activity was also observed for the remaining Developing insurance markets that lasted almost until the end of the sovereign debt crisis in 2011. However, this improvement was offset by their unstable development pattern thereafter. Over the past years, the overall development of Developed insurance markets has decreased, due to a contraction in life insurance business. However, few of them managed to succeed in an increasing pattern with varying IP rate changes over the years.
Over the last century, the world has seen rapid population growth. Particularly, the global population more than quadrupled. The magnitude of the population rate of change from one year to another is found by the fold change ratio (FCR). Fold change is calculated simply as the ratio of the year-end over the year-start population of a certain country. We refer to the evolution of FCR over the course of time as the population growth rate (PGF) curve. In this example, our objective is to find clusters of world countries in which their PGF curves share similar magnitude and shape properties. We use the output of the FC method based on Area measure for clustering world countries. This output will also give a hint towards the distribution of the world population and provide the trends or the dynamics that are defining our world, such that policymakers can set sustainable development goals for our societies.
Thus, we consider the world population data (United Nations 2016), which was analyzed by [21]. This dataset includes estimates of the total population (both sexes) in 233 countries, areas, or regions in July 1950–2015. Motivated by these estimates and the arguments needed for the execution of the FC method, we follow three steps. In the first step, we perform the preprocessing of the dataset by selecting those countries with populations of more than one million in July 1950. In total, 134 countries are included in our analysis. For each of these countries, we collect 65 data points that correspond to the FCR of each year interval and propose connecting them to make the PGF curve. In the second step, we derive the shape information from the L2 normalization of the shifted PGF curves towards their center. This particular step is the one that provides the set of PGF pattern (PGFP) curves. In the last step, we specify the input argument for the number of clusters which is required by FC method to start. The optimal number of clusters was arrived at by calculation of the median value of all methods presented in NBclust library of the R software. Based on the result of this calculation, the chosen number of clusters was three.
Figure 6 satisfies the marginal plot style approach followed in our case studies by presenting the output of the FC method in a two-panel display. The first panel is dedicated to magnitude clustering (it helps discern broad trends in PGF curves), and the second to the shape clustering (it helps identify patterns of pace for population rate of change). The first plot of each panel is the plot of the median curves of the clusters. Remark that the fraction of combinations of countries satisfying the triangular inequality with Area measure was 1 with respect to all combinations.
Clustering results for the population curves. Top panel: Magnitude plot; bottom panel: Shape plot.
Next, we present both the derived clusters and their characterization, which is based on the United Nations (UN) geographical region and classification of economies. For instance, we see that the population growth rates in Cluster 1 appear to follow an increasing trend or at least maintain a certain degree of stability because of a natural increase and migration. Most countries in this cluster have a developing economy and are mainly located in Sub-Saharan Africa. However, three European countries (Ireland, Norway and Spain) with developed economies are also members of this cluster of countries. In contrast, the other two characteristic population growth trends that are present in both Clusters 2 and 3 paint a picture of a stagnating or shrinking population in the future, the only difference being that the population in Cluster 3 has a faster speed of shrinkage than in Cluster 2. The most populated cluster (that is Cluster 2 with 64 curves) is mostly associated with another set of developing economies (such as those of Brazil, China and Singapore) located, this time, in Latin America and the Caribbean along with East Asia and Pacific. Additionally, the only developed economy that appears to reside in this cluster is that of the United States, while few economies in transition that belong to the Commonwealth of Independent States (such as those of Azerbaijan, Kazakhstan) make their presence visible for a first time.
Finally, Cluster 3 has united mostly the developed economies of Europe and East Asia and Pacific along with the economies in transition of South-Eastern Europe (Albania, Serbia and North Macedonia). Moreover, the population of few developing economies that are located, for example, at Cuba, Jamaica, Puerto Rico, Ghana and Mozambique, have distinguished themselves from the vast majority of developing economies in Cluster 1 or Cluster 2 by following the population behavior of developed economies.
In conclusion, developing economies and economies in transition are split between two clusters, while the majority of the developed economies belong to one cluster. Based on the characterization of these clusters, it is understood that countries with developing economies experience population growth (or at least population stability). However, the more the economy of a country is developed, the more its population growth change decreases. This decrease, in certain cases, might have even a severe negative effect on a country’s future projected population size. Whereas, in some other cases, the effect of this decrease is smoother without forcing the population size to reach record lows.
The insurance industry generates a large volume of multivariate functional data from the simultaneously obtained measurements on variables related to life, non-life, and total insurance activities. In our case of interest, two main country-specific variables that include data on premiums are available. The first is the total IP (TIP) that represents the development of total activities. While the second is the R ratio of life IP to TIP that represents the development of the share of life premiums in total premiums.
Since the insurance industry of a country can be represented by the bivariate variables of TIP and R, it is important to take into account the dependence between them. We compute a variable that describes this dependence through the covariance function:
where
There is no doubt that the development of total activities is different from that of life share. Nevertheless, it may be assumed that a common development coordinates these differential developments of different insurance variables. Then, it is of great interest to identify groups of insurance markets with similar joint development patterns. With this consideration in mind, we aim to discover whether the European insurance market is homogeneous when national insurance developments are jointly differential by developing their total activities and their life share.
We obtained again insurance data from Swiss Re (2016) database and for the same European (EU and non-EU) countries as in univariate case. In particular, we employ a dataset of our main variables for 34 European countries sampled at annual frequency between 2004 and 2016. That is to say that the data for each variable can be viewed as curves. Yet, except for the curves related to TIP and R variables, we also included the computed curves for the Cov variable in our dataset and ended up with a set of three-dimensional vectors of curves.
Viewing the curves for each variable as a set of curves, a three-component list of curve sets is constructed to serve as an input for the FC method. This time, the optimal number of clusters is three and consistent with the median value of all methods presented in the NBclust library of the R software. Our proposed method concentrates on visualizing, in the marginal plot style approach, clusters of multivariate insurance functional data with regard to their magnitudes and covariance function (Figures 7–9).
Clustering results for bivariate curves of TIP and R: TIP plots.
Clustering results for bivariate curves of TIP and R: R plots.
Clustering results for bivariate curves of TIP and R: Covariance plots.
The clustering results are summarized as follows:
Cluster 1: Countries of high TIP and high R with no correlation whatsoever between the two variables throughout the study period.
Cluster 2: Countries of high TIP and high R with a positive correlation between the two variables throughout the study period.
Cluster 3: Countries of low TIP and low R with no correlation whatsoever between the two variables throughout the study period.
Additionally, the FC method suggests that the total and life share developments in Cluster 1 and Cluster 3 have independent paths since curves for Cov variable almost coincide with the
The functional cluster analysis revealed some differences in the dynamics of insurance markets in Europe. The clustering results clearly reject the hypothesis on the homogeneity of the European insurance market. Europe continues in a two-speed insurance market, with countries with high development and independent paths of total and life insurance business, and others with low. For both speed markets, detecting an increasing pattern in total insurance business does not guarantee that the life premiums will also follow at the same time the same pattern. Any similarity in their patterns could be explained by socio, economic, demographic, or other factors and not by the total business pattern itself. However, there is another high-speed market where the increase of total insurance business in the economy is an additional factor that accelerates the development of life business share.
In this study, we introduce a new class of functional cluster analysis methods based on functional orderings. We intended to work with methods that allow intrinsic graphical interpretation to obtain a natural interpretation of clusters via their central regions. Therefore, we propose the use of a studentized measure that forms a metric on the set of functional differences. Also, We suggest the use of the area measure, which orders the functions according to the area of the most extreme continuous rank and considers the entire distribution of the functions. This measure does not form a metric on any set of functions, but the simulation study results and the real data study suggest that it is a metric on any real data set of functions. The check for the satisfaction of the triangular inequality is provided for the given set of functions.
This study’s primary aim is to introduce methods that combine the various functional information sources equally. It is possible to study clustering while showing equal concern for both magnitude and shape, as shown in the first and second data examples. In other words, it is possible to study the clustering of multivariate functions when the marginal functions are taken equally. It is also possible to add to the study term, which summarizes the covariance between the marginals of the multivariate function, as shown in the third data example.
The simulation study suggests that the proposed method is robust and more powerful than studied alternatives that give equal treatment to various sources. The studied alternatives are the
Our proposed methods consider the covariance structure of the functional data via the ordering of the entire functional differences. Our proposed methods are also nonparametric and, as such, have no model requirement. Our simulation study also showed that our proposed methods are quite robust to heavy-tailed functions, which can be considered as a type of functional cluster outlier. The data studies show that our methods can cluster the functions with respect to magnitude and shape and that it provides a sensible graphical interpretation of the resulting clusters. The third example shows that the clusters can be also constructed with respect to the covariance of the marginals in the multivariate function. This study does not examine methods to choose the number of clusters in an optimal manner, and this problem is left to the user’s choice or further development.
Wenlin Dai has been financially supported by National Natural Science Foundation of China (Project No. 11901573). T. Mrkvička has been financially supported by the Grant Agency of Czech Republic (Project No. 19-04412S).
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. He has contributed in stochastic estimation of control area especially, in the Multiple Target Tracking and Interactive Multiple Model (IMM) research, Ball & Beam Control Problem, Robotics, Levitation Control. He has contributed in developing Algorithms for Fingerprint Matching, Computer Vision and Face Recognition. He has been supervising Pattern Recognition, Formal Languages and Distributed Processing projects for several years. He has reviewed many books on Management, Computer Science. Currently, he is an active and permanent reviewer for many international conferences and symposia and the program committee member for many international conferences.\nIn teaching he has taught the core computer science subjects like, Digital Design, Real Time Embedded System Programming, Operating Systems, Software Engineering, Data Structures, Databases, Compiler Construction. In the Engineering side, Digital Signal Processing, Computer Architecture, Electronics Devices, Digital Filtering and Engineering Management.\nApart from his Academic Interest and activities he loves sport especially, Cricket, Football, Snooker and Squash. He plays cricket for Esbjerg city in the second division team as an opener wicket keeper batsman. 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MMM could offer the physicochemical stability of a ceramic material while ensuring the desired morphology with higher permeability, selectivity, hydrophilicity, fouling resistance, as well as greater thermal, mechanical, and chemical strength over a wider temperature and pH range. Zeolites are fascinating and versatile materials, vital for a wide range of industries due to their unique structure, greater mechanical strength, and chemical properties. This chapter focused on zeolite-MMM and characterized various zeolite-reinforced polymeric membrane types and applications. Several key rules in the synthesis procedures have been comprehensively discussed for the optimum interfacial morphology between the zeolites and polymers. Furthermore, the influence of the zeolite filler incorporation has been discussed and explored for a range of applications. 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This chapter aims to bring the reader a first-meeting introduction for quickly knowing about MREs, instead of a very deep understanding of MREs.",book:{id:"7685",slug:"smart-and-functional-soft-materials",title:"Smart and Functional Soft Materials",fullTitle:"Smart and Functional Soft Materials"},signatures:"Taixiang Liu and Yangguang Xu",authors:[{id:"283475",title:"Associate Prof.",name:"Yangguang",middleName:null,surname:"Xu",slug:"yangguang-xu",fullName:"Yangguang Xu"}]},{id:"67279",title:"Development, Characterization and Properties of Silk Fibre and Grafted Silk Fibre Reinforced Polymer Composite Films",slug:"development-characterization-and-properties-of-silk-fibre-and-grafted-silk-fibre-reinforced-polymer-",totalDownloads:1107,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"The use of natural fibres over synthetic fibres is gaining widespread importance due to its availability; renewability, low density and satisfactory mechanical properties making them an ecological alternative to synthetic fibres. The innumerable properties of silk fibre have made it superior to be used by researchers both in the plastic and biomedical sector. Silk fibre reinforced PVA (polyvinylalcohol) and PVA/PVP (polyvinyl pyrrolidone) films were prepared via solution casting technique. The effect of silk fibre concentration, on the structural, thermal, mechanical, bio-degradable and the morphological properties of the composite films was assessed. The results indicated that the addition of silk fibres improved the thermal, morphological, mechanical and biodegradable properties of the films. The extensive use of silk fibroin in the biomedical field, due to its robust properties has made it a promising material, suitable in tissue engineering applications. Keeping this in view, the current study also focuses on re-tailoring the properties of silk fibres by grafting a natural polysaccharide like chitosan and thereby fabricate composite films of PVA reinforced with this grafted fibre. The films were tested for their potential applications in tissue engineering, by subjecting them to in vitro biocompatibility tests. The films were also tested for their antibacterial properties. The results thus obtained indicated that the films were non-toxic in all concentrations and were found to be suitable for biomaterial applications.",book:{id:"8162",slug:"generation-development-and-modifications-of-natural-fibers",title:"Generation, Development and Modifications of Natural Fibers",fullTitle:"Generation, Development and Modifications of Natural Fibers"},signatures:"Sareen Sheik and Gundibasappa Karikannar Nagaraja",authors:[{id:"285152",title:"Prof.",name:"G.K.",middleName:null,surname:"Nagaraja",slug:"g.k.-nagaraja",fullName:"G.K. 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In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}},{id:"338856",title:"Mrs.",name:"Nur Alvira",middleName:null,surname:"Pascawati",slug:"nur-alvira-pascawati",fullName:"Nur Alvira Pascawati",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universitas Respati Yogyakarta",country:{name:"Indonesia"}}},{id:"441116",title:"Dr.",name:"Jovanka M.",middleName:null,surname:"Voyich",slug:"jovanka-m.-voyich",fullName:"Jovanka M. 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Dr. Chen\\'s research interests include bioactive compounds, chromatography techniques, in vitro culture, medicinal plants, phytochemicals, and plant biotechnology. He has published more than ninety scientific papers and serves as an editorial board member for Plant Methods, Biomolecules, and International Journal of Molecular Sciences.",institutionString:"National University of Kaohsiung",institution:{name:"National University of Kaohsiung",institutionURL:null,country:{name:"Taiwan"}}},editorTwo:null,editorThree:null,series:{id:"10",title:"Physiology",doi:"10.5772/intechopen.72796",issn:"2631-8261"},editorialBoard:[{id:"313856",title:"Dr.",name:"Christophe",middleName:"F.E.",surname:"Hano",slug:"christophe-hano",fullName:"Christophe Hano",profilePictureURL:"https://mts.intechopen.com/storage/users/313856/images/system/313856.png",institutionString:"University of Orléans",institution:{name:"University of Orléans",institutionURL:null,country:{name:"France"}}},{id:"33993",title:"Dr.",name:"Jose Carlos",middleName:null,surname:"Jimenez-Lopez",slug:"jose-carlos-jimenez-lopez",fullName:"Jose Carlos Jimenez-Lopez",profilePictureURL:"https://mts.intechopen.com/storage/users/33993/images/system/33993.jpg",institutionString:"Spanish National Research Council",institution:{name:"Spanish National Research Council",institutionURL:null,country:{name:"Spain"}}},{id:"191770",title:"Dr.",name:"Mohamed A.",middleName:null,surname:"El-Esawi",slug:"mohamed-a.-el-esawi",fullName:"Mohamed A. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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