The BBA structure for multisource of
\r\n\tThe WHO classification in 2007; was based on the histogenesis and cell origin of the tumor. In the latest classification made in 2016; to better characterize the tumor and obtain better data on its prognosis; The combination of molecular and genetic biomarkers and histopathological features of the tumor was used. Despite all current treatment approaches, the median survival time is around 12 months in most GBM patients. Compared with the situation of some types of successfully treated cancers; the survival time of GBM patients is not at an acceptable level today. In the treatment of CNS tumors; surgery, chemotherapy, and radiation treatments (x-rays, gamma rays, electron and proton beams) are used. The therapeutic potential of chemotherapy; New strategies are needed to increase drug concentration at the diseased site, as this largely depends on the ability of the chemotherapeutic agent to achieve effective concentrations at tumor localization. Based on our better understanding of the genetic and molecular characteristics of CNS tumors; Targeted therapies, including vaccines, and treatment protocols such as immunotherapy are promising developments.
\r\n\r\n\tThis book supposes to be written by many authors who have an internationally honored place in their field to share their ideas about the treatment of CNS tumors. Surgery, Radiotherapy, Chemotherapy and Antiangiogenic Therapy Protocols, Immunotherapy, Molecular Therapy, Specific target-agents therapy with Nanoparticles and Gene Therapy for CNS tumors among the book chapters.
\r\n\tIn these sections; there are many practical pieces of information that can help the students who graduated from the Medicine Faculty and specialist doctors who are interested in Neurosurgery.
With widespreading of the concept and applications of performance-based earthquake engineering (PBEE) and performance-based seismic design (PBSD), the effective measures for assessing the performance state of structural components or entire structure have been deeply investigated in seismic engineering. In consistence with the different performance assessment criteria, the evaluation and measurements of damage states for structural components are divided into three main branches (e.g., displacement-based approach, energy-based measure and the combination of both). Due to the simplicity and convenience of observation and description for structural damage states, the displacement-based approach and corresponding damage index (e.g., inelastic displacement, maximum inter story drift ratio, and ductility demand, etc.) have been widely documented in building seismic evaluation and retrofit of existing building guidelines [1]. Notwithstanding the prevalent application of displacement method in damage assessment, the defect of lacking the influence of low cyclic fatigue of structural components is obvious. The hysteretic energy dissipation is considered as a more reasonable indicator for seismic structural damage, because it is a cumulative parameter involved cyclic-plastic deformations in a structure during earthquakes [2]. Despite the effectiveness of hysteretic energy, experimental observations demonstrate that the expression of energy would be significantly affected by the exceedance plastic deformation [3]. And the cumulative laboratory experimental data on structural members and structures indicate the fact that the structure is damaged by a combination of the excessive deformation and hysteretic energy. Park–Ang damage model [4], which takes into account the effects of both the first exceedance failure and cumulative damage failure in low-cycle-fatigue for a structural component during seismic load, is served as a baseline for many researches. Due to intrinsic simplicity as well as calibrations against a significant amount of observed seismic damages, the Park-Ang model and its modified version have been extensively implemented in seismic performance evaluation of structures [5–7].
\nAlthough the applicability and practicability of using the Park-Ang model and its modified versions have been supported by many researchers [8, 9], it should be noted that the Park-Ang-damage-index-based performance evaluation is still a challenging task due to the large uncertainties associated with the damage model parameters [10]. With the influence of these uncertainties [11, 12], the evaluation results of structural damage state are always represented with the empirical interval value (e.g., the minor damage state is represented by 0.25<
The traditional probability theory, based on the sufficient statistical information, is used to model the objective uncertainty (random), which is inherent in physical variability of materials and environment. Unfortunately, the limited number of experimental data set cannot support the strong assumption of probability theory, and the process of collecting data is always costly and time consuming. These shortcomings lead the assessment result of damage state of structures are not aleatory but epistemic. In the past decades, several alternative approaches have been developed to deal with epistemic uncertainty. Some of the potential uncertainty theories are the theory of fuzzy set [14], possibility theory [15], the theory of interval analysis [16], imprecise probability theory [17], and evidence theory [18, 19]. Among these promising uncertainty representation models, evidence theory with the ability of handling aleatory and epistemic uncertainty is used for UQ, risk assessment, and reliability analysis.
\nWith two complementary measures of uncertainty such as belief and plausibility, using evidence theory to UQ is flexible and effective. In comparison with the calculation of single probability density function (PDF) in probability theory, the computationally intensive problem involves computing the bound values over all possible discontinuous sets which is a main shackle of wide application for evidence theory. In order to break the computational barriers in the evidence theory-based UQ, the differential-evolution-based interval optimization is employed to enhance the computational efficiency as described by the authors [20].
\nTo effectively describe the damage state of structural components or entire structure, the original Park-Ang damage model and modified model were developed. The original Park-Ang damage model was presented here to access the uncertainty influence of the evaluation on the damage state of column components. There are various methods to estimate constants in Park-Ang damage model in different studies. In addition to diverse combination measures, the empirical estimation value and calibration value dispersed in a large range. Using the classification method proposed by Oberkampf and Helton [21], the aleatory and epistemic uncertainties involved in Park-Ang damage model are listed as:
\nThe random uncertainties rooted in experimental materials, e.g., the material composition of concrete and the strength test results in single compositional material.
The objective and subjective uncertainties of experimental condition. e.g., the environmental factor, the loading error of machine, and measurements error.
The subjective uncertainties of fitting measures of parameters in Park-Ang damage model and mathematical representation of model itself.
In consideration of these aleatory and epistemic uncertainties in Park-Ang damage model, the quantification influence of uncertainties is indispensable. To achieve this goal, a series of empirical expressions are summarized. Then, the Structural Performance Database of Pacific Earthquake Engineering Research Center (PEER) is used to construct the uncertain sources of parameters of damage assessment models. Using these calibration results of column set, the parameter uncertainties are represented by the fluctuation of ratio of empirical values and calibration values.
\nThe Park-Ang damage model [4] combines the first exceedance failure and cumulative damage failure with a linear expression as:
\nwhere
In the last two decades of the twentieth century, a set of experimental results were conducted and some illuminate-, empirical-, or mechanical-based expression of
where
where \n
where
where
Conventionally, the damage index
In this work, the calibration set is selected from the structural performance database of PEER and the selection criteria are such as (1) the cross section of column is rectangle; (2) the column is loaded cyclically until failure and the corresponding failure model is dominated by flexure; (3) the longitude bars in column should not be spliced and the column should experience more than two hysteretic cycles. In conformity with these criteria, 185 specimens are selected. Using these column load-displacement data, the performance points on the backbone curve of column under cyclic load are calibrated.
\nSimilar to the most studies [23], the ultimate deformation under monotonic load
Performance point of backbone curve with obvious ultimate state point (a) and with the largest displacement point (b).
As shown in Figure 1, the column backbone curves are divided into two categories: one with obvious ultimate state point (the 80% maximum force) like in Figure 1a, the other with the largest displacement in backbone curve (e.g., Figure 1b). In order to yield the uncertainty distribution of empirical model, the attention is concentrated on the first category. Using the selected force-displacement data, the comparison of empirical model results and calibration results is given in Figure 2.
\nComparison of predicted results and experimental results of
As shown in Figure 2, the predicted and experimental values are scattered in a wide range, and this means the researchers should carefully handle the uncertainty derived from the empirical model in the process of evaluating damage state with Park-Ang model. Employing the parameter
Along with classical concept, probability theory plays a key role in the UQ of physical model, and the distribution type is determined by the hypothesis test and related parameter are calibrated by enough experimental data. However, the limited data of experimental set and large variation restricted the ability of probability theory. As a generalized UQ measure, evidence theory is compatible with both aleatory and epistemic uncertainties. So, the evidence theory is adopted in this work to handle the epistemic uncertainty rooted in parameters of Park-Ang damage model.
\nEvidence theory is a theoretical framework for reasoning with partial and unreliable information. It was proposed by Dempster [18] and further improved by Shafer [19]. Compared to the classical uncertain model theory, it offers the possibility to explicitly represent doubt and conflict. As the most basic concept of the evidence theory, the fame of discernment
An element A∈
Belief function (
Similar to the additive rule in probability, belief and plausibility measures of proposition A can be calculated from following formula:
\nwhere
where \n
For the purpose of UQ, the first step is the uncertainty representation of parameters using evidence theory, in which separate belief structures for each uncertain parameter should be constructed. In this work, we adopt a general methodology as described previously by Salehghaffari et al. [27] to obtain necessary information from available data and express the uncertain variables in the mathematical framework of evidence theory.
\nAccording to Salehghaffari et al. [27], two principle steps are involved in this methodology: (1) representation of uncertain parameters in several intervals through drawing bar charts by using all available data or directly from expert opinions and (2) identification of three relationships between all adjacent intervals and construction of the associated BBA structure. To further illustrate this, assuming that
Three relationships of uncertain intervals.
where
Employing this strategy, the uncertainty of Park-Ang model parameters can be properly represented with the evidence theory. In Figure 5, we use
Evidential uncertainty description of
Evidential uncertainty description of
In evidence theory community, uncertainty variable is usually expressed to be a series of focal element intervals based on limited information and the joint frame of discernment is composed of the Cartesian products of uncertain intervals, then, the BBA value of each element of joint frame of discernment is also the Cartesian product of BBA value assigned on the corresponding interval. Given two independent uncertain parameters
where the symbol ⊗ denotes the Cartesian products. Using Eqs. (15) and (16), the joint uncertainty input of system can be seemed as the multidimensional hypercube. Therefore, uncertainty propagation is a progress of finding the maximum and minimum of the system response value in each hypercube interval (proposition of the joint belief structure). To propagate the represented uncertainties of Park-Ang damage model constants, the damage index
Considering epistemic uncertainty of the system, the belief and plausibility functions of the response are obtained on the basis of the combined BBAs of the input parameters from different information sources using the evidence combination rules. For the prediction response process
where
Thus every element of the Cartesian set
As uncertain variable is represented by many discontinuous set instead of smooth and continuous explicit function, time consuming is inevitable in UQ with evidence theory. There are two main approaches to find the bounds of the system response: sampling and optimization. The accuracy of sampling approach is highly dependent on the number of samples and the number of hypercubes, and the process is costly. On the contrary, optimization methods have the potential to dramatically reduce the computational work. To alleviate this computational burden, based on authors’ previous work [11], the differential evolution (DE) [28] optimization approach is used to calculate the response bounds of each hypercube and compute the composite BBA of each hypercube, propagation of the represented uncertainty through Park-Ang damage model (Eq. (1)). The characteristics of derivative-free and capability of handling discrete belief structure make DE method to be a good choice for such an interval bound task.
\nDE is arguably one of the most powerful stochastic real-parameter optimization algorithms for solving complex and computational optimization problems in current use. As a novel evolutionary computation technique, differential evolution resembles the structure of an evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current generation population members with the scaled differences of randomly selected and distinct population members. The characteristics together with other factors of DE make it a fast and robust algorithm and as an alternative to EA. Since late 1990s, DE started to find several significant applications to the optimization problems arising from diverse domains of science and engineering. In a recently published article, Das and Suganthan [29] provided a comprehensive survey of the DE algorithm and its basic concepts, different structures and variants for solving various optimization problems, as well as applications of DE variants to practical optimization problems.
\nIn the context of DE, the individual trial solutions (which constitute a population) are called parameter vectors or genomes. Let S ∈ R
Pseudocode of DE.
Take the pseudocode of DE in mind, the illustration of DE-based computational strategy for finding the propagated belief structure by the example as shown in Figure 8 (only one uncertain parameter is considered).
\nUncertainty propagation of belief structure of system by DE.
The procedure of uncertainty propagation using the DE strategy is as follows:
\nCollect all uncertain information and construct corresponding BBA structure of each uncertain parameter, combine the BBA structures under the situation of evidences provided by different sources or experts using combination rules of evidence.
Use differential evolution algorithm to calculate the bound values of the system response within each joint interval and construct corresponding joint belief structures.
Given the complete BBA on the output response of interest damage index
Once the BBA structure of the Park-Ang damage index response is constructed, observed evidence on simulation responses is used in the determination of target propositions to estimate uncertainty measures, i.e., cumulative belief function (CBF) and cumulative plausibility function (CPF).
\nIn evidence theory framework, the plausibility function
Where \n
Procedure of UQ of Park-Ang damage model.
In order to investigate the effectiveness and feasibility of the proposed UQ measures, the column “zahn86u7” [31] is selected to compute the Park-Ang damage index in its load step. The backbone curve and load history are shown in Figure 10.
\nBackbone curve (a) and load path (b) of columns of column test.
As shown in Figure 10a, the ultimate cyclic displacement is calibrated by using the average value of 80% maximum force point on the force capacity reduction slope of positive and negative direction. The effective path in Figure 10b denotes the load path from initial state to ultimate state and the load path is the global displacement history. Using the properties of column, listed in the webpage of PEER, the nominal value of constants in Park-Ang damage model
Taking above uncertain information into the differential evolution-based uncertainty propagation framework, the evidential UQ results for each load step as shown in Figure 11.
\nThe evidential uncertainty propagation results of Park-Ang damage index.
\n | \n|||||
---|---|---|---|---|---|
Model A | \nModel B | \n\n | \n\n | \n||
Range | \nBBA | \nRange | \nBBA | \nRange | \nBBA | \n
[0.0345, 0.087] | \n0.301 | \n[0.0266, 0.067] | \n0.458 | \n[77.40, 133.19] | \n0.121 | \n
[0.0873, 0.139] | \n0.181 | \n[0.0672, 0.108] | \n0.325 | \n[105.22, 133.19] | \n0.422 | \n
[0.139, 0.192] | \n0.277 | \n[0.0672, 0.189] | \n0.181 | \n[133.19, 161.01] | \n0.26 | \n
[0.192, 0.244] | \n0.145 | \n[0.0672, 0.230] | \n0.036 | \n[161.01, 188.82] | \n0.139 | \n
[0.244, 0.296] | \n0.096 | \n\n | \n\n | \n[161.0, 216.63] | \n0.029 | \n
\n | \n\n | \n\n | \n\n | \n[161.01, 244.44] | \n0.017 | \n
\n | \n\n | \n\n | \n\n | \n[161.01, 272.42] | \n0.012 | \n
The BBA structure for multisource of
Model C | \nModel D | \nModel E | \n|||
---|---|---|---|---|---|
Range | \nBBA | \nRange | \nBBA | \nRange | \nBBA | \n
[0.034, 0.115] | \n0.568 | \n[0.043, 0.116] | \n0.649 | \n[0.0442, 0.104] | \n0.541 | \n
[0.115, 0.156] | \n0.207 | \n[0.116, 0.188] | \n0.351 | \n[0.104, 0.133] | \n0.180 | \n
[0.115, 0.196] | \n0.01 | \n\n | \n\n | \n[0.133, 0.193] | \n0.279 | \n
[0.196, 0.237] | \n0.125 | \n\n | \n\n | \n\n | \n\n | \n
The BBA structure for multisource of
To validate the generality of evidence theory, the variability of Park-Ang model parameters is also represented by probability theory. The goodness of fit test is applied to test the distribution type and determine the related distribution parameters. The uncertainty distribution information of model B for
Constants | \nDistribution type | \n||
---|---|---|---|
Normal | \n0.963 | \n0.529 | \n|
\n | \nLognormal | \n-0.171 | \n0.514 | \n
Normal | \n1.404 | \n0.697 | \n|
\n | \nLognormal | \n0.206 | \n0.537 | \n
Lognormal | \n-0.272 | \n0.225 | \n
The distribution information of Park-Ang constants.
From Table 3, the values of
Comparison of propagation results using evidence theory and probability theory. (a) The cumulative distribution of damage index in step 280 and (b) the cumulative distribution of damage index in step 412.
As illustrated in Figure 12, the probability theory based UQ results CDF1 and CDF2 are located in the range of curves CPF and CBF, this indicates that evidence theory is compatible to probability theory. The discrepancy of CDF1 and CDF2 demonstrates that probability theory may not be suitable to handle the epistemic uncertainty which is stemmed from limited experimental data. In other words, the probabilistic UQ result is ambiguous due to epistemic uncertainty and the choice of distribution type has a great impact on the quantification result. However, evidential UQ strategy demonstrates its power to quantify the epistemic uncertainty because of its two uncertain measures belief function and plausibility function. In order to further clarify the influence of epistemic uncertainty, the quantitative results of damage index in Figures 12a and b are reported in Table 4.
\nAs shown in Table 4, the belief interval of moderate damage state in steps 280 and 412 are [0.11, 0.447] and [0, 0.026], respectively. This means the exceeding probability of moderate damage state are [0.553, 0.89] and [0.974, 1] in steps 280 and 412, respectively. Table 5 also displays the cumulative distribution value for moderate damage state for probability-theory-based quantification results. Using the first probability strategy CDF1, the cumulative distribution for moderate damage state are 0.217 and 0 corresponding to steps 280 and step 412. This means the exceeding probabilities of moderate damage state are 0.783 and 1 in steps 280 and 412, respectively. Analogously, the cumulative distribution values of CDF2 for moderate damage state are 0.298 and 0 in steps 280 and 412, respectively. It is worth noting the divergence of the cumulative distribution values of CDF1 and CDF2 in step 280. Furthermore, the divergence of two kind probability-based quantification results provides the evidence that probability theory is not able to handle the epistemic uncertainty. Comparing the quantification results of collapse damage state, the similar conclusion can be obtained. Especially, the cumulative distribution value for collapse damage state is step 412, the evidence result is [0.094, 0.447], this means the value of damage index larger than 1 is located in the interval [0.453, 0.906]. While the cumulative probabilities of CDF1 and CDF2 are 0.178 and 0.233, respectively. This illustrates that the exceedance probability of collapse state is 0.822 for CDF1 and 0.767 for CDF2. From the view of risk assessment, the evidence theory will give decision maker a more robust UQ result, but the probability cannot.
\nDamage index | \nCumulative distribution curve in step 280 | \nDamage index | \nCumulative distribution curve in step 412 | \n||||||
---|---|---|---|---|---|---|---|---|---|
CPF | \nCDF1 | \nCDF2 | \nCBF | \nCPF | \nCDF1 | \nCDF2 | \nCBF | \n||
0.25 | \n0.026 | \n0 | \n0 | \n0 | \n0.25 | \n0 | \n0 | \n0 | \n0 | \n
0.5 | \n0.447 | \n0.217 | \n0.298 | \n0.11 | \n0.5 | \n0.026 | \n0 | \n0 | \n0 | \n
0.75 | \n1 | \n0.522 | \n0.644 | \n0.354 | \n0.75 | \n0.244 | \n0.050 | \n0.053 | \n0.026 | \n
1 | \n1 | \n0.722 | \n0.818 | \n0.419 | \n1 | \n0.447 | \n0.178 | \n0.233 | \n0.094 | \n
The cumulative distribution value of Park-Ang constants in step 280 and 412.
Range | \nBBA | \nRange | \nBBA | \nRange | \nBBA | \n
---|---|---|---|---|---|
[0.035, 0.067] | \n0.297 | \n[0.044, 0.104] | \n0.568 | \n[77.40, 133.19] | \n0.121 | \n
[0.067, 0.087] | \n0.351 | \n[0.104, 0.115] | \n0.189 | \n[105.22, 133.19] | \n0.422 | \n
[0.087, 0.108] | \n0.127 | \n[0.115, 0.116] | \n0.102 | \n[133.19, 161.01] | \n0.26 | \n
[0.087, 0.139] | \n0.085 | \n[0.116, 0.133] | \n0.055 | \n[161.01, 188.82] | \n0.139 | \n
[0.139, 0.189] | \n0.108 | \n[0.133, 0.156] | \n0.058 | \n[161.01, 216.63] | \n0.029 | \n
[0.139, 0.192] | \n0.021 | \n[0.133, 0.188] | \n0.028 | \n[161.01, 244.44] | \n0.017 | \n
[0.192, 0.230] | \n0.011 | \n\n | \n\n | \n[161.01, 272.42] | \n0.012 | \n
The combined BBA structure for
With the incomplete knowledge of prediction model under the various operation conditions, different expert evidence conflicts are inevitable. To reconcile this task challenge, evidence combination rule is proposed to combine the evidences from multisource. Herein, the Dempster’s rule is applied to aggregate the different source of evidence for
Using the aggregated BBA structures of these three uncertain parameters, the system uncertain response CPF2 and CBF2 are shown in Figure 13. To clarify the effectiveness of combination rule, the uncertainty propagation results CPF1 and CBF1 from the model B of
Comparison of propagation results with uncombined and combined BBA input. (a) The cumulative distribution of damage index in step 280 and (b) the cumulative distribution of damage index in step 412.
As shown in Figure 13, the UQ results of Park-Ang damage index variate in a large range. The distance of CBF and CPF denotes the epistemic uncertainty that is derived from the limited experimental data and lack of knowledge for complicated composite materials (e.g., parameters model hypothesis, material properties) or incomplete knowledge of empirical model. In comparison with the distance of CPF1 and CBF1 for uncombined BBA, the distance of CPF2 and CBF2 for combined BBA is much narrower, and this can be explained as the high conflict information of multisources that are discarded by aggregating the multisources evidence. However, the aggregation rule is not established in probability theory. From this point of view, the evidence theory has great potential to quantify the uncertainty from multisources which are having great existence in civil engineering.
\nUQ of seismic damage model are important for PBSD and performance-based seismic assessment. In this chapter, the epistemic uncertainty of the constants of Park-Ang model is taken into account. The Park-Ang damage model constants are calibrated with column set, selected from PEER column performance database. To effectively represent the uncertainty inherent in Park-Ang model constants with limited experimental data, the UQ measurement that combines evidence theory and differential evolution is presented. In order to further investigate the feasibility and effectiveness of presented UQ measurement, the Monte-Carlo sampling method combined with classical probability distribution, which is fitted with given data, is used. Comparing the propagation results of evidence theory and classical probability theory, we can conclude that the evidence theory is flexible to handle the epistemic uncertainty, which is stemmed from lack of knowledge or sparse experimental data, whereas the classical probability theory may be limited by the selection of distribution type and the determination of value for the distribution parameters. Using the aggregation rules of evidence theory demonstrates that evidence theory is capable to handle the uncertainty from multisources.
\nThis study was supported by the Ministry of Science and Technology of China, Grant No. SLDRCE14-B-03 and the National Natural Science Foundation of China, Grant Nos. 51178337 and 51478356.
\nThis chapter stands as an introduction to the field of biometrics which is rising as an advanced layer to many user- and enterprise-centric security systems. In fact, conventional authentication methods, such as traditional passwords, have long been a weak point for security systems. Biometrics aims to answer this issue by linking proof-of-identity to our physiological traits and behavioral patterns. It is therefore important to present the concepts and primitives of performance metrics due to their impact on secure biometric systems. Thus, a brief overview is given to describe the main biometric traits along with their properties as well as the various biometric system operating modalities and the relatively known vulnerabilities. Finally, the criteria for performance evaluation have been defined to determine the system accuracy and security which are related to the applicability in real-world deployments.
Various biometric modalities have been developed over the years making the biometric technology landscape very vibrant. Prominent examples of physiological/biological and behavioral biometric characteristics, which have been the purpose of major real-world applications, are illustrated in Figure 1.
Examples of physiological/biological and behavioral traits applied in biometric recognition applications.
Biological biometrics make use of traits at a genetic and molecular level which may include features like DNA or blood, whilst physiological biometrics involve the individual physical traits like a fingerprint, iris, or the shape of the face. On the other hand, behavioral biometrics are based on patterns unique to each person, for example, how an individual walks, speaks, or even types on a keyboard. Some examples of biometric traits are briefly described below.
Fingerprint: Fingerprint recognition, which measures a finger’s unique pattern, is one of the oldest forms of biometric identification. This trait appears as a series of dark lines and white spaces when captured from the device and it consists of a set of ridges and valleys located on the surface tips of a human finger to uniquely distinguish individuals from each other. The fingerprint features are generally categorized into— (i) macroscopic ridge flow patterns (core and delta points), (ii) minutia features (which consists of the ridge bifurcations/trifurcation and the ridge endings), and (iii) pores and ridge contour attributes (incipient ridges, pore, shape, and width). Fingerprints of identical twins are different and so are the prints on each finger of the same person [1].
Face: Facial features use the location and shape (geometry) of the face, including the distance between the eyes, the distance from the chin to the forehead, or other measures that involve eyebrows, nose, lips, and jawline [2]. This kind of recognition is a nonintrusive method with reasonable authentication performance in commercially available systems. However, several constraints may be imposed by the systems on how the facial images are obtained to work properly, for example, controlled illumination and background. Moreover, its susceptibility to change due to factors such as aging or expression may present a challenge [3].
Hand geometry: This trait is based on the geometric characteristics of the hand such as the length and width of fingers, their curvature, and their relative position to other features of the hand. Though once a dominant method of biometric measurement due to the requirement of the low complexity in feature extraction and low-cost imaging, modern advances in biometrics have replaced its relevance in most applications [4]. Furthermore, such a biometric trait is not known to be very distinctive and hand geometry-based recognition systems cannot be scaled up for systems requiring the identification of an individual from a large population. In addition, hand-geometry features from both hands are expected to be similar, as their anatomy is quite similar [5].
Iris: Systems based on this trait are among the most accurate biometric systems available. This human characteristic refers to the colored part in the eye that consists of thick, thread-like muscles characterized by unique folds and patterns that can be used to identify and verify the identity of humans. Furthermore, this biometric trait is stable because iris patterns do not vary during the course of a person’s life and are not susceptible to loss, manipulation, or theft, making an iris recognition system robust to spoofing attacks. One interesting point worth noting is that even the two eyes in the same person have different patterns [6].
Ear acoustic: The main purpose of this kind of recognition system is to map one aspect within acoustic ear recognition, namely the performance of the ear characteristics bands and peaks. An ear signature is generated by probing the ear with inaudible sound waves which are reflected bouncing in different directions and picked up by a small microphone. The shape of the ear canal determines the acoustic transfer function which forms the basis of the signature. The recognition process is also possible, whilst the subject is on the move and caters to the protection of secrecy, which expands the applicability of this technology [7].
Vascular patterns: This biometric trait has been largely investigated for its advantages over other features. In fact, the vascular pattern of the human body is unique to every individual, even between identical twins [8], remains steady during the course of a person’s life, and lies underneath the human skin ensuring confidentiality and robustness to counterfeiting, as opposed to other intrinsic and extrinsic biometric traits that are more vulnerable to spoofing, thus leading to important security and privacy concerns [9]. To acquire the network structure of blood vessels underneath the human skin, a vascular-based recognition system uses near-infrared light to reflect or transmit images of blood vessels, since they are almost invisible in normal lighting conditions [10]. The most commonly used vascular biometric solutions use hand-oriented modalities, such as finger vein, palm vein, hand dorsal vein, and wrist vein recognition, as well as eye-oriented modalities, such as retina and sclera recognition [11].
Electrocardiogram (ECG): This trait considers the human heart and body anatomic features form the shape of the ECG signal typically acquired using a few electrodes, amplifiers, filters, and a data acquisition module, and which reports the strength and timing of the electrical activity of the heart [12]. However, scientific findings to date throw doubt on the specificities of real-world application scenarios and acceptability by the potential end users, which pose several constraints and questions.
Deoxyribonucleic acid (DNA): DNA matching is based on a common molecular biology method named short tandem repeat (STR)2 analysis, which is used to compare allele repeats at specific locations on a chromosome in DNA between two or more samples [14, 15]. DNA-based biometric recognition has been widely used in forensic science and scientific investigation due to its very high accuracy, despite the fact that identifications require tangible physical samples and cannot be done in real time.
Keystrokes, handwriting, gait, how a person uses a mouse, and other movements are some of the behavioral traits that a biometric system may analyze to assess the individual’s identity.
Gait: This characteristic may be changeable over a large time span due to various reasons, such as weight gain [16]. Thus, it can be used in low-security applications for massive crowd surveillance as it can quickly identify people from afar based on their walking style, even harnessing the potential of a large number of surveillance cameras installed in public locations into a biometric system. In fact, such a system does not require the individuals to be cooperative, nor that they wear any special device or equipment to be recognized [17].
Mobile interactions: It is based on the unique ways in which users swipe, tap, pinch-zoom, type, or apply pressure on the touchscreen of mobile devices like tablets and phones, thus providing characteristic patterns that may be used to identify people, even considering further features deriving from on-board sensors such as GPS, gyroscope, and accelerometers [18], which can also be configured to collect data in passive mode. Therefore, mobile interactions-based biometrics focuses not so much on the outcome of the user’s actions but rather on the way a user performs those actions.
Signature: Signature recognition is the most widely accepted method for documents authentication and it makes use of shorter handwriting probes compared to text-independent writer recognition methods, but it requires to write the same sign every time. A signature authentication scheme can be categorized into two methods—(i) off-line or static (the signature is digitized after the writing process) and (ii) online or dynamic (the signature is digitized during the writing process). Signature biometric features are extracted by analyzing curves, edges, spatial coordinates, inclination, the center of gravity, pen pressure, and pen stroke of the signature samples in both off-line and online applications. However, dynamic information like writing speed and stroke order is available only in online signatures [19].
Mouse dynamics: It makes use of patterns in mouse or trackpad cursor movement including clicks, trajectories, direction changes, tracking speed, and the relationships between them. Mouse-generated movement features are relatively stable for the same individual and different compared to other users, as such can be used to authenticate individuals [20]. These methods are most often used to continuously verify the user’s identity.
Keystrokes: Keystroke dynamics (also known as typing biometrics) include the tracking of the rhythm used to type on a keyboard. Two events constitute a keystroke event—key down and key up. The first one occurs when an individual presses a key, whilst the second one is associated with the event that occurs when the pressed key is released. Making use of these events, a set of inter-key and intra-key features known as delay times, hold times, and key down-key downtimes can be extracted. In general, keystroke recognition will work on the computer or virtual keyboards, mobile phones, smartwatches, and touchscreen panels, providing a low-cost authentication method that can be easily deployed in a variety of scenarios [21].
Voice: Voice recognition technology falls under both the physiological and behavioral biometric categories. Voice biometric recognition allows to distinguish among humans’ voice for personal authentication as voice features include physical characteristics such as vocal tracts, nasal cavities, mouth, and larynx [22]. Behaviorally, the way a person speaks or says something, for example, tone, movement variations, accent, pace, and so on, is also considered unique to each individual. Using data from both physiological and behavioral biometrics creates, therefore, a precise vocal signature, though mismatches may occur due to illness or other factors.
The main requirements that should be satisfied before a trait can be characterized as suitable for its applicability in a biometric recognition system, are briefly discussed as follows [23].
Universality: Every individual or at least most of them, accessing the biometric application should possess the characteristic.
Distinctiveness (or uniqueness): The given trait should be sufficiently different across individuals comprising the user population. Otherwise, the proportion of times the biometric system grants access to unauthorized individuals would be unacceptably high.
Permanence: The biometric trait of an individual should be sufficiently invariant (with respect to the matching criterion) over a period of time. This implies that the given trait should not change significantly over time otherwise the proportion of times the biometric system denies access to authorized individuals would be unacceptably high.
Collectability: The biometric trait can be measured quantitatively with particular regard to the easiness of obtaining the biometric data using suitable devices that do not cause undue inconvenience to the user.
Even though any human characteristic can be used as a biometric trait as long as the previous requirements are satisfied, in real-world biometric recognition applications there are a number of other issues that should be considered, such as:
Performance: This is a property aimed at assessing the verification or identification accuracy, the computational time required for a single recognition, as well as the operational and environmental factors that may affect or not the recognition accuracy and speed.
Acceptability: It indicates the extent to which people are willing to accept the use of a specific biometric application as well as their willingness to provide their biometric data. Nowadays, this is a crucial aspect to be considered due to the current pandemic situation caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [24], raising questions about how safe using touch-based biometric systems really is as touching the sensors can potentially spread viruses. As a consequence, less-constrained biometrics will likely be the preferred modality, whilst there may be less demand for other solutions that rely on physical contact with a reader.
Circumvention: This property reflects how easily the system can be deceived through potential spoofing attacks. It refers to the ways in which an attacker can endeavor to bypass a biometric system and finally attack the weak spot of such a system in order to gain unauthorized access.
Real-life biometric recognition systems ought to meet the requirements of accuracy, speed, and resource constraints, be harmless to the users, be accepted by the intended population as well as sufficiently robust to various fraudulent methods and attacks to the system [25].
Table 1 is reported a comparison study of the most popular traits based on the characteristics of biometric entities [26].
Biometric trait | Universality | Uniqueness | Permanence | Collectability | Performance | Acceptability | Circumvention |
---|---|---|---|---|---|---|---|
Fingerprint | M | H | H | M | H | M | H |
Face | H | L | M | H | L | H | H |
Hand geometry | M | M | M | H | M | M | M |
Iris | H | H | H | M | H | L | L |
Ear | M | M | H | M | M | H | M |
Vascular patterns | H | H | M | M | H | M | L |
DNA | H | H | H | L | H | L | L |
Gait | M | L | L | H | L | H | M |
Signature | L | L | L | H | L | H | H |
Keystroke dynamics | L | L | L | M | L | M | M |
Voice | M | L | L | M | L | H | H |
Comparison study of the most common traits based on the characteristics of biometric entities.
H = High; M = Medium; L = Low.
A biometric system can provide two kinds of operating modes (identity management functionalities), namely,
Basic building blocks of a generic biometric recognition system.
Different operating modes of a biometric system—(a) enrollment mode, (b) authentication mode (the dashed line is an optional operation aimed at updating a specific user’s template), and (c) identification mode.
In the authentication mode, the purpose of the biometric system is to verify whether an individual’s claimed identity is genuine or not (binary classification). Thus, the captured biometric data (query) is compared only with the biometric template(s) stored in the system database and corresponding to the claimed identity (one-to-one or one-to-few comparison). Given a claimed identity
where
In the identification mode, the purpose of the biometric system is to recognize an individual’s identity by searching the templates of all the enrolled individuals in the system database for a match (one-to-many comparison) without the subject having to claim an identity.
This operating mode can be further split into negative and positive identification—in the negative identification (also known as
where
The identification mode is typically employed for screening4, where the aim is to prevent a single person from using multiple identities [28].
Biometric-based cybersecurity solutions ensuring tight access control are essential in preventing intrusions and unauthorized accesses. However, even though a biometric system enhances user convenience and security, does not necessarily mean that it is also exempt from security and privacy issues. Many security measures in biometric systems are designed to protect one or more facets of the CIA triad, which is a common framework that refers to confidentiality, integrity, and availability [31].
Confidentiality is roughly equivalent to privacy. Measures undertaken to ensure confidentiality are designed to prevent sensitive information from reaching unauthorized people. It is perhaps the most obvious aspect of the CIA triad when it comes to security; but correspondingly, it is also the one which is attacked most often. Confidentiality covers a wide spectrum of access controls and measures that protect data from getting misused by any unauthorized access. Cryptography and encryption methods are an example of an attempt to prevent illegitimate access ensuring the confidentiality of (sensitive) data.
Integrity of information refers to the ability to protect information from being modified or destroyed by unauthorized parties, thus ensuring nonrepudiation and authenticity of the information. Thus, integrity involves maintaining the consistency and trustworthiness of data. One type of security attack is to intercept some important data and make changes to it before sending it on to the intended receiver.
Availability of information refers to ensuring that only legitimate and authorized parties are able to access the information when needed. Problems affecting the information system could make it impossible to access information, thereby making the information unavailable. Some types of security attacks attempt to deny access to the appropriate user, either for the sake of inconveniencing them, or because there is some secondary effect.
Biometric recognition systems implicitly (and effectively) address the authentication problem included in the last issue of the CIA triad, which consists in guaranteeing access to data only to authorized users. The reason for this is because biometric traits are (generally) not susceptible to loss, manipulation, or theft, and therefore overcome the security issues affecting the conventional methods for personal authentication, such as knowledge-based and token-based systems. However, it must be kept in mind that a biometric-based security solution is composed of several different components and the recognition module, which is only capable of addressing the authentication aspect, is just one of them. Thus, a logical structure-based approach of biometric systems is used to describe the eight points of attacks illustrated in Figure 4.
An attack on the biometric sensor consists of presenting a fake biometric trait (e.g., an artificial characteristic) to perform a spoofing attack aimed to either avoid detection (false negative) or masquerade as another (false positive). Methods used to prevent spoofing attacks include layered biometrics, liveness, and combining biometrics and conventional authentication methods such as passwords, tokens, or smart cards [32].
The connection between the biometric sensor and the subsequent modules of the system may be attacked to allow input of a stored digital biometric signal. This data can be obtained, for instance, by performing an eavesdropping (disclosure) attack [31].
Attacks on the feature extractor can be used either to create impostors or to evade detection. Hence, knowledge of the algorithms involved in this module5 may be used to forge features in presented samples to cause computation of incorrect features. To achieve this, an attacker can replace the feature extractor with a Trojan horse program that produces the desired feature sets.
An attack on the output of the previous module consists of spoofing the legitimate biometric feature set to replace it with a synthetic one.
Vulnerabilities of template database concern modifying the storage (modifying, removing, or adding templates), copying stored data for future use (identity theft or directly using the acquired information to gain access), or modifying the identity to which the biometric is assigned.
The channel between the template database and the matching module is similarly vulnerable to the previous one, however, the attack against data transmission may be easier than against the template storage, especially in the case of an adversary able to intercept any information communicated by the system by observing the data (passive eavesdropping). Encryption is crucial in this case, but may still be vulnerable to key discovery [33].
The matcher module is responsible for computing a similarity score between two biometric templates in order to confer the likelihood that they are from the same subject. Even though it may not be possible to do it easily, an attack against the matcher can be possible in specific cases. For instance, it is possible to replace the matcher module with a Trojan horse program that always outputs high scores thereby defying system security [34].
An attack on the final decision module means that if the final decision can be inserted or blocked by the attacker then the authentication system function will be overridden. If it is instead reviewed by a human operator, a DoS (denial of service) attack may be performed to mislead it or to force it to mistrust the output of the system [35].
Attack points of a general biometric system.
The reliability and validity of a biometric scheme as well as the selection of a certain biometric trait for an application are determined by specific measures that are used to evaluate the recognition accuracy and effectiveness as addressed in ISO/IEC Standards [36]. Accordingly, to evaluate the accuracy of the proposed method based on a single-sample approach for unimodal biometric systems, each sample in the database should undergo a one-to-one matching test against every single stored sample. Hence, a comparison between a subject with a real identity
where
Precisely, given a threshold
false acceptance rate (FAR), that is the probability of accepting the null hypothesis
false rejection rate (FRR), that is the probability of rejecting the null hypothesis
Let
The false acceptance and false rejection rates are functions of the system threshold
The genuine acceptance rate (GAR) is instead the probability of accepting the null hypothesis
Depending on the security level required by the final application (i.e., forensics, surveillance and homeland security, civilian, or high-security applications), the same biometric system may operate at different threshold values (
Examples of biometric system error rates: (a) FAR and FRR for a given threshold
Hence, in order to evaluate the biometric system performance as a function of the threshold
The receiver operating characteristic (ROC) is a graphical plot that illustrates the trade-off between false acceptance and false rejection rates when the threshold varies, whilst the intersection point for which rejection and acceptance errors are equal is named equal error rate (EER). The curve is generated by plotting the genuine acceptance rate against the false acceptance rate at various threshold settings,
The detection error trade-off (DET) is another graphical plot that illustrates the false rejection rate against the false acceptance rate at various threshold values. The two axes are scaled nonlinearly by their standard normal deviates6 or just by logarithmic transformation.
Furthermore, the above-mentioned ROC and DET curves are threshold-independent, allowing performance comparison of different biometric systems under similar conditions [23], as illustrated in Figure 6. Given a set of thresholds
Example of vascular-based biometric systems performance comparison [
where
Since biometric systems cannot jointly provide a false acceptance rate equal to zero and a perfect verification/identification rate, the system threshold must be adjusted for the given application considering the trade-off between accuracy and false positives. Once the threshold has been set, the system can be evaluated by means of common measures that are used to assess the classification accuracy and effectiveness. In this context, we are interested in confirming or denying the identity of a subject leading thus to a dichotomous binary classification problem, where the labels are
Predicted class | ||||
---|---|---|---|---|
Example of confusion matrix for a dichotomous binary classification problem.
which completely describes the outcome of the classification task. This contingency table may be expressed using raw counts of the number of records from class times each predicted label is associated with each actual class. As illustrated in Table 2, the confusion matrix reports:
true positive (TP), the probability of correctly accepting the null hypothesis;
true negative (TN), the probability of correctly rejecting the null hypothesis;
false positive (FP), the probability of falsely rejecting the null hypothesis;
false negative (FN), the probability of falsely accepting the null hypothesis.
Based on the entries in the confusion matrix, the total number of correct predictions carried out by the model is
where obviously
it represents the worst case (perfect misclassification).
Several measures have been defined to assess the quality of a prediction [40], aimed at conveying into a single figure the structure of
where the worst case (
it is a correlation coefficient between the actual and predicted binary classifications and it returns a value between −1 (worst case) and 1 (best case).
Accuracy and F-score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets [40]. Hence, among all the parameters described above, the Matthews correlation coefficient (MCC) is the only one that takes into account true and false positives and negatives and is generally regarded as a balanced measure that can be used even if the classes are of very different sizes [41].
Biometric-based technologies make use of unique behavioral (extrinsic) and/or physiological/biological (intrinsic) attributes to overcome the security issues affecting the conventional methods for identity authentication. Even though biometrics has been in use for decades, the advent of technology has expanded its application from primarily criminal identification to a wide range of everyday tasks, becoming a regular security process of our nowadays life. Accurate authentication or identification is fundamental to physical security, cyber security, military applications (e.g., biometric-driven lethal autonomous weapon systems), financial transactions, contracts and employment, public services, criminal justice, national security, and more. The approaches that have been proposed in literature depend on the type and the number of the underlying biometric traits, which, in general, cannot be easily transferred between people, and thereby represents a highly secure unique identifier. As a matter of fact, various biometric modalities have been developed over the years making the biometric technology landscape very vibrant. In this book chapter, we have provided an overview of the most commonly used biometric traits along with their properties, the various biometric system operating modalities as well as various limitations and weaknesses related to these systems. Indeed, biometric technologies have a number of vulnerabilities that underscore the concerns over their employment and may result in the failure of the technology to perform as anticipated. We have also discussed how the system threshold must be adjusted for the given application considering the trade-off between accuracy and false positives since biometric systems cannot jointly provide a FAR equal to zero and a perfect recognition rate. Finally, the criteria for performance evaluation have been defined to determine the system’s accuracy and security which are related to the applicability in real-world deployments, even though the existing evaluation metrics are more related to the data quality than the security aspects of the overall system. However, despite the risks, biometrics provide very compelling security solutions remaining a growing way to verify identity offering tons of promise for the future of cybersecurity.
The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
DET | Detection error trade-off |
DNA | Deoxyribonucleic acid |
ECG | Electrocardiogram |
FAR | False acceptance related |
FN | False negative |
FRR | False rejection rate |
FP | False positive |
GAR | Genuine acceptance rate |
MCC | Matthews correlation coefficient |
NGI | Next-generation identification |
PPV | Positive predictive value |
ROC | Receiver operating characteristic |
SARS-CoV-2 | Severe acute respiratory syndrome coronavirus 2 |
STR | Short tandem repeat |
TN | True negative |
TP | True positive |
TPR | True positive rate |
"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
",metaTitle:"About Open Access",metaDescription:"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges.\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.",metaKeywords:null,canonicalURL:"about-open-access",contentRaw:'[{"type":"htmlEditorComponent","content":"The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
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\\n\\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
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\\n\\nDigital Archiving Policy
\\n\\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\\n\\nOpen Science is transparent and accessible knowledge that is shared and developed through collaborative networks.
\\n\\nOpen Science is about increased rigour, accountability, and reproducibility for research. It is based on the principles of inclusion, fairness, equity, and sharing, and ultimately seeks to change the way research is done, who is involved and how it is valued. It aims to make research more open to participation, review/refutation, improvement and (re)use for the world to benefit.
\\n\\nOpen Science refers to doing traditional science with more transparency involved at various stages, for example by openly sharing code and data. It implies a growing set of practices - within different disciplines - aiming at:
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\\n\\n\\n"}]'},components:[{type:"htmlEditorComponent",content:'
The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
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\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\n\nOpen Science is transparent and accessible knowledge that is shared and developed through collaborative networks.
\n\nOpen Science is about increased rigour, accountability, and reproducibility for research. It is based on the principles of inclusion, fairness, equity, and sharing, and ultimately seeks to change the way research is done, who is involved and how it is valued. It aims to make research more open to participation, review/refutation, improvement and (re)use for the world to benefit.
\n\nOpen Science refers to doing traditional science with more transparency involved at various stages, for example by openly sharing code and data. It implies a growing set of practices - within different disciplines - aiming at:
\n\nWe aim at improving the quality and availability of scholarly communication by promoting and practicing:
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Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Bacterial Infectious Diseases",value:3,count:2},{group:"subseries",caption:"Parasitic Infectious Diseases",value:5,count:4},{group:"subseries",caption:"Viral Infectious Diseases",value:6,count:7}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:2},{group:"publicationYear",caption:"2021",value:2021,count:4},{group:"publicationYear",caption:"2020",value:2020,count:3},{group:"publicationYear",caption:"2019",value:2019,count:3},{group:"publicationYear",caption:"2018",value:2018,count:1}],authors:{paginationCount:301,paginationItems:[{id:"116250",title:"Dr.",name:"Nima",middleName:null,surname:"Rezaei",slug:"nima-rezaei",fullName:"Nima Rezaei",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/116250/images/system/116250.jpg",biography:"Professor Nima Rezaei obtained an MD from Tehran University of Medical Sciences, Iran. He also obtained an MSc in Molecular and Genetic Medicine, and a Ph.D. in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also completed a short-term fellowship in Pediatric Clinical Immunology and Bone Marrow Transplantation at Newcastle General Hospital, England. Dr. Rezaei is a Full Professor of Immunology and Vice Dean of International Affairs and Research, at the School of Medicine, Tehran University of Medical Sciences, and the co-founder and head of the Research Center for Immunodeficiencies. He is also the founding president of the Universal Scientific Education and Research Network (USERN). Dr. Rezaei has directed more than 100 research projects and has designed and participated in several international collaborative projects. He is an editor, editorial assistant, or editorial board member of more than forty international journals. He has edited more than 50 international books, presented more than 500 lectures/posters in congresses/meetings, and published more than 1,100 scientific papers in international journals.",institutionString:"Tehran University of Medical Sciences",institution:{name:"Tehran University of Medical Sciences",country:{name:"Iran"}}},{id:"180733",title:"Dr.",name:"Jean",middleName:null,surname:"Engohang-Ndong",slug:"jean-engohang-ndong",fullName:"Jean Engohang-Ndong",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180733/images/system/180733.png",biography:"Dr. Jean Engohang-Ndong was born and raised in Gabon. After obtaining his Associate Degree of Science at the University of Science and Technology of Masuku, Gabon, he continued his education in France where he obtained his BS, MS, and Ph.D. in Medical Microbiology. He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. Recently, he expanded his research interest to epidemiology and biostatistics of chronic diseases in Gabon.",institutionString:"Kent State University",institution:{name:"Kent State University",country:{name:"United States of America"}}},{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",slug:"emmanuel-drouet",fullName:"Emmanuel Drouet",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",biography:"Emmanuel Drouet, PharmD, is a Professor of Virology at the Faculty of Pharmacy, the University Grenoble-Alpes, France. As a head scientist at the Institute of Structural Biology in Grenoble, Dr. Drouet’s research investigates persisting viruses in humans (RNA and DNA viruses) and the balance with our host immune system. He focuses on these viruses’ effects on humans (both their impact on pathology and their symbiotic relationships in humans). He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. 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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Animals need to receive a properly balanced diet. One of the new challenges we are now faced with is sustainable animal diets (STAND) that involve the 3 P’s (People, Planet, and Profitability). We must develop animal feed that does not compete with human food, use antibiotics, and explore new growth promoters options, such as plant extracts or compounds that promote feed efficiency (e.g., monensin, oils, enzymes, probiotics). 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