Ornstein-Uhlenbeck model: maximum likelihood estimates from 1000 simulations of model Eq. (14), using the exact and the approximated transition density.
\\n\\n
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"8303",leadTitle:null,fullTitle:"Gene Regulation",title:"Gene Regulation",subtitle:null,reviewType:"peer-reviewed",abstract:"Genes store life data and information in genetic language. 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The diversity of RNAs in different cells and in particular Eukaryotic cells explains the importance of RNAs in gene regulation.",isbn:"978-1-78985-684-2",printIsbn:"978-1-78985-683-5",pdfIsbn:"978-1-83962-079-9",doi:"10.5772/intechopen.78432",price:100,priceEur:109,priceUsd:129,slug:"gene-regulation",numberOfPages:78,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"717b32b5becef8d895adf106c5a3099d",bookSignature:"Payam Behzadi",publishedDate:"March 13th 2019",coverURL:"https://cdn.intechopen.com/books/images_new/8303.jpg",numberOfDownloads:4023,numberOfWosCitations:5,numberOfCrossrefCitations:1,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:4,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:10,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 14th 2018",dateEndSecondStepPublish:"September 4th 2018",dateEndThirdStepPublish:"November 3rd 2018",dateEndFourthStepPublish:"January 22nd 2019",dateEndFifthStepPublish:"March 23rd 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"45803",title:"Ph.D.",name:"Payam",middleName:null,surname:"Behzadi",slug:"payam-behzadi",fullName:"Payam Behzadi",profilePictureURL:"https://mts.intechopen.com/storage/users/45803/images/system/45803.jpg",biography:"Dr. Payam Behzadi was born in Tehran, Iran, in 1973. He began his collaboration with the Department of Microbiology, College of Basic Sciences, Shahr-e-Qods Branch, Islamic Azad University as a faculty member in 2004. He has a BSc and MSc in Microbiology and a Ph.D. in Molecular Biology and now continues his scientific activities in the position of assistant professor at Islamic Azad University. He has authored and edited more than twenty chapters and academic books and more than seventy original and review articles. His scientific research interests include urinary tract infections, antibiotics, bioinformatics, genetics, gene profiling, molecular biology, and cellular and molecular immunology. 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The founding member of this family, STAT1, is involved in a plethora of cellular processes, including interferon-dependent upregulation of various effector mechanisms in immune and non-immune cells to control bacterial, fungal and parasitic infections. In this chapter, we discuss the principles of STAT1-driven gene expression and focus on the clinical phenotypes of various human STAT1 mutations. 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\r\n\tPsychometrics generally refers to a specialized field of psychology concerning the techniques of assessment, measurement, and their respective theories. Psychometrics include objective measurement of cognitive functions (intelligence, memory, attention, reaction times, etc.), characteristics of personality (e.g. hypochondria, hysteria, paranoia, social introversion, psychotic traits), emotions (happiness, sadness, anger, fear, disgust, surprise), behavior, and socio-educational qualities. Their constructs are also applied to emotional and mental disorders (especially anxiety and depression). Psychometric measures are inferred through mathematical models based on observation and statistical analysis conducted on a sample of subjects in comparison with the general population that leads to the planning of mental tests, scales, and open or close-ended questionnaires. Psychometricians usually possess specific qualifications such as those of psychologists and other operators with experience and training in psychometrics and with knowledge in measurements theories.
\r\n\r\n\tThis book aims to provide an update on theoretical approaches, instruments, and procedures of modern psychometrics. Authors are encouraged to include examples of psychometric assessments in human experimental models and possible clinical applications in human disorders.
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This model contains both fixed and random effects to distinguish between individuals in a population, leading to a mixed effects model in which fixed effects represent fixed parameters for all individuals in the population and random effects account for individual differences. Moreover, mixed effects modeling has been shown to be useful in pharmacokinetic/pharmacodynamic studies; particularly for modeling total variation in and between individuals, the models used in pharmacokinetic/pharmacodynamic analysis are often presented by a system of deterministic differential equations (ordinary, partial, or delay). However, the pharmacological processes in reality are always exposed to incomprehensible effects that are difficult to model, and ignoring these effects can affect the parameter estimation results and their interpretations. So, the introduction of stochastic components to deterministic models is an important tool of analysis [1] and is more appropriate to model the intra-individual variations rather than ODEs. In addition, the extension of ODEs to the SDEs makes it possible to explain the differences between the observations and the predictions by two types of noise: dynamic noise that enters through the dynamics of the system and that can result from its random fluctuations or from the shortages of model and the measurement error which is added in the case of an indirectly observed process, which may be due to a test error or to the existence of a disturbance and represent the uncorrelated part of the residual variability. In the theory, there are rich and developed resources for mixed effects models whether deterministic [2, 3, 4, 5, 6] or stochastic and linear or nonlinear models (see many applications of stochastic NLME models in biomedical [7, 8, 9] and in pharmacokinetic [10, 11, 12] fields).
Parameter estimation in mixed effects models with SDEs, known by stochastic differential mixed effects models, is not an obvious procedure except in some cases simpler [14] because it is often difficult to write the likelihood function in its closed form. In this context, we propose a review on estimation methods of SDME models in [13, 15] and an example case that treats a generalized linear mixed models in [16] and also an example to approximate the likelihood function of an NLME with the likelihood of a linear mixed effects model in [17]. Moreover, to strengthen knowledge on estimation methods of SDME, we refer to [18, 19] that propose an example of stochastic mixed effects model with random effects log-normally distributed with a constant diffusion term.
In general, it is difficult to obtain an explicit likelihood function because the transition density of the stochastic process is often unknown or that the integral in the likelihood given the random effects cannot be computed analytically, and although the size of the random effects increases, the complexity of the problem increases also rapidly. Therefore, this requires a significant need for approximate methods to compute the transition density in an approximate closed form and for effective numerical integration methods to compute or approximate the integral in the likelihood function, using, for example, the Laplacian and Gaussian quadrature approximation [3, 8, 20] or other approaches [21, 22]. In the literature, several solutions have been proposed to approximate the transition density and have shown their effectiveness despite certain limitations. For example, the transition density could be approximated by the solution of the partial differential equations of Kolmogorov [23]; by the derivation of a Hermite expansion of closed form at the transition density [24, 25, 26] (this method has been reviewed and applied for many known stochastic processes for one-dimensional [8] and multidimensional [20] time-homogeneous SDME model); or by simulating the process to Monte-Carlo-integrate the transition density [27, 28, 29]. These techniques are very useful and can solve the problem, but unfortunately, they involve intense calculations which make the problem always complicated.
In this work, we focus on two fundamental issues concerning the implementation of SDEs in NLME models. The first is how the transition density of an SDME model can be approximated when it is not known, and the second is about approximating methods of the likelihood function when the integral given the random effects has no analytic solution. Then, we propose an optimization algorithm to obtain maximum likelihood estimators in order to facilitate the estimation procedure for these models. Finally, the methodology is evaluated by simulation studies on the bidimensional Ornstein-Uhlenbeck model, and then it is implemented on the minimal model describing the glucose and insulin kinetics.
Consider an N-dimensional continuous and stochastic process
where
The joint density function of the vector
According to the model Eq. (1), the process Y is the same and follows the same form for each individual of the population, and the SDME model of type Itô expresses the dynamic of the individual i perturbed by the Brownian motion. So, the differences between the subjects are modeled on the one hand by the different realizations of the Brownian motion paths
The goal is to estimate the vector of fixed parameters
The likelihood function of an SDME model is expressed as follows:
with
where
In this work, we propose an approximate method to estimate the transition density for a time-inhomogeneous NLME model with SDEs in a closed approximate form. So, we suggest to derive the transition density by solving the motion equations of the process using the Fokker-Planck equation, and we deal with the use of its solution given in [34]. Then, using the expression obtained, we get a closed form approximation of the likelihood function that we maximize to obtain the approximated MLEs
For a multidimensional vector of random parameters, if the exact transition density or its closed form approximation is available, we can use the Laplace approximation method [16, 20, 35] to obtain an explicit expression of the likelihood function to maximize, despite the fact that the integral in Eq. (4) has no closed solution. So, for a q-dimensional random vector
where
and
Usually, a formal general solution of the stochastic differential equations as in Eq. (1) cannot be given, which makes the calculation of the transition density for this process more complicated. Moreover, this process has a lot of fluctuations that its exact position cannot be determined but can be known given a region by its probability density; with the FP equation, such a probability density can be determined. The FP equation is a differential equation for the distribution function describing a Brownian motion by which the probability density of the stochastic process can be calculated in a much simpler way by solving this equation. This motion equation is usually used for variables describing a macroscopic but small system, where the fluctuations are important as for some cases in physics, e.g., the position and the speed of the Brownian motion of a small particle. However, it can be also used for the larger system where, in spite of their small fluctuations, the stochastic description remains necessary when the deterministic equations may not be stable for this type of system.
So, under some assumptions, the transition density
where
In [34], H. Risken deals in this book with the derivation of the FP equation and its solution methods with some of its applications especially for problems of Brownian motion. So, here, we propose to characterize the transition density of the N-dimensional process
To test the effectiveness of this approach, we will guide our statistical methodology by simulation studies in order to examine the flexibility of its application to deduce its advantages and disadvantages. So, we substitute
For a nonlinear SDME model with Gaussian random effects using Eqs. (6), (7), and (11), we obtain the following approximated likelihood function:
The MLEs of
The genetic algorithm is a random search technique to look for an exact or approximated optimum points for optimization problems [36, 37, 38]. It is based on the concepts of natural genetic evolution which contains the following stages: the reproduction, the crossing, and the mutation of a constantly evolving population. It sets up the evolution of a random population of potential solutions of the N cardinal; then, the N simultaneous iterative trajectories interact with each other by following or imitating the biological evolution, for a convergence of certain elements of the population towards an optimal point of the fitness function.
The GA can search in multiple directions to explore all the search space by the possibility of jumping across them, so that the seeds spread uniformly over the whole search space. In this algorithm, we have a diversity of initial populations which gives the global optimum faster than other algorithms, where the initial value is very important and should be enough close to the global optimum. All of these features allows the GA to be regarded as a driving tool of evolution giving good results for optimization processes [37, 39]. In the literature, there were many works about the application of GA in optimizing problems specially for likelihood function [40, 41]. For the use of GA, we must first define some parameters of the algorithm: Population size N, EN, SR, CP, MP, fitness function, and convergence criteria. In the following we present the GA steps:
For
End For.
In the simulation studies paragraphs, the GA is implemented using the MATLAB software, where the function “ga,” to generate the genetic algorithm, requires inputs that are chosen according to the constraints of each example (see the help window in MATLAB). Moreover, the algorithm parameters are chosen according to what is early used in the literature [39],
To apply the proposed methodology and evaluate its effectiveness, we consider the two-dimensional OU process that is very useful in pharmacokinetic/pharmacodynamic studies and in biology [43], physics, engineering, finance, and neuroscience applications [14, 44]. Indeed, the choice of this process is due to the fact that it is one of the few known multivariate SDME models with known transition density. For this reason, we choose the OU process to evaluate the methodology presented above, and we perform a comparison study between the results obtained using the proposed transition density in Eq. (11) and those obtained using the exact density. The model is defined as follows:
With
We rewrite the system in matrix notation under the Itô formula; we denote by
Here, the random effects
with mean vector
We assume that the matrices
In Figure 1, we illustrate in (a) the simulation of the OU process using the Euler scheme [45] with the following set of parameters:
A sample path of the OU process in the third graph of (a) for the given parameters set with the initial condition:
In this simulation study, we generate 1000 artificial datasets of dimension
By plugging Eq. (16) in Eq. (4) and Eq. (18) in Eq. (6), we obtain a huge expression of the likelihood function but without a closed solution of integrals, so the exact estimators of
Table 1 shows that, for the given sample size, the results can be correctly identified using this estimated approach; even if some of parameters are overestimated or underestimated, the results remain acceptable because the results belong to the confidence interval. However, we believe that these results could be further proven by using other sample sizes and by adding alternative assumptions for the model that we did not consider in the methodology proposed in this chapter, which could further complicate the problem and be more time-consuming, as well as the present methodology suffers from some limitations. For the random parameters, the estimates can be provided using the optimization algorithm on Eq. (8) using the obtained estimate results of the parameters vector
True values | Mean and (Std) ( | ||||||
---|---|---|---|---|---|---|---|
2.8 | 2.5 | 1.8 | 2 | 3.10–3.25 | 2.48–2.56 | 1.72–1.65 | 2.11–2.37 |
(0.164)–(0.283) | (0.015)–(0.283) | (0.095)–(0.189) | (0.153)–(0.255) | ||||
0.8 | 1.5 | 0.3 | 0.5 | 1.06–1.13 | 1.57–1.64 | 0.28–0.37 | 0.56–0.45 |
(0.071)–(0.102) | (0.109)–(0.158) | (0.026)–(0.073) | (0.023)–(0.061) | ||||
45 | 100 | 100 | 25 | 44.75–52.43 | 100–112.75 | 102.35–89.64 | 24.72–31.02 |
(0.523)–(9.372) | (01.166)–(28.113) | (01.04)–(22.05) | (2.297)–(11.20) |
Ornstein-Uhlenbeck model: maximum likelihood estimates from 1000 simulations of model Eq. (14), using the exact and the approximated transition density.
Empirical distribution of estimates obtained using the exact and approximated transition density.
The minimal model describes the glucose-insulin kinetics and the dynamics of these processes illustrating the diabetes disease mechanisms. The diabetes is one of the most prevalent diseases in individuals and the nature and degree of its assignment changes from an individual to another and depends on certain individual characteristics, which implies that the concepts of stochastic modeling with random effects could be a good approach to modeling this disease. The Diabetes may be due to the insufficient insulin production (type 1 diabetes) or to the fact that the cells do not respond to the secreted insulin (type 2 diabetes) and the T2D patients tend to have substantially lower insulin sensitivity than healthy individuals, so that the T2D can be characterized by the level of insulin sensitivity for each individual. Therefore, to model the T2D, we observe how a person’s body responds to insulin in the process of transporting glucose to tissues by measuring his insulin sensitivity. So, we present in this section the estimation of the minimal model which represents a powerful model describing the glucose-insulin kinetics for an individual’s body in three differential equations, see the mathematical formulation of the model in [46, 47, 48, 49]. So, it is already clear that the model will contain both fixed and random effects, because the study of diabetes disease takes into account the response of each individual according to his own parameters and other common parameters that describe the process of glucose-insulin for the entire population. See the description of the glucose-insulin kinetics in Figure 3.
At first, glucose and insulin concentrations in the blood are described by two sets of differential equations (see [
From the mentioned literature and Figure 3, the glucose-insulin disposal can be represented, with respect to time, by the following nonlinear stochastic differential equations, perturbed by the stochastic terms
where
The insulin sensitivity is defined by combining the two rates
Finally, the stochastic minimal model under the Itô sense re-parameterized by
where
Random effects are assumed to be independent with a multinormal joint density function, with the mean
So, here we deal with a time-inhomogeneous NLME model with SDE describing the glucose-insulin kinetics (see [52] for the implementation of SDE time-inhomogeneous model) and [53] where the maximum likelihood estimation for a time-inhomogeneous stochastic differential model of glucose dynamics was treated. The measurements in the model Eq. (20) are assumed directly observed without measurement errors as well as in the theoretical approach presented above.
So, we wish to estimate
with
We have no closed form solution to this integral, so exact estimators of
We start this study with an application on artificial data generated on the intravenous glucose tolerance test principle (see [53] for a mathematical modeling of the test where glucose and insulin concentrations in plasma are subsequently sampled after an intravenous glucose injection). We generate 5000 sets of simulated artificial data of dimension
For each data from 5000 generated data sets, we estimate
True values | (M,m) | Mean and (Std) | ||||||
---|---|---|---|---|---|---|---|---|
0.074 | 0.10 | 0.0007 | 90 | (40,60) | 0.0737 (0.0014) | 0.100 (0.0013) | 0.00073 (2.02 × 10−4) | 89.08 (0.031) |
(40,10) | 0.0784 (0.0059) | 0.209 (0.0295) | 0.00068 (2.54 × 10−4) | 91.34 (0.712) | ||||
(10,20) | 0.0794 (0.0402) | 0.156 (0.046) | 0.00054 (0.0016) | 62.11 (1.13) | ||||
0.01 | 0.06 | 0.03 | 0.006 | (40,60) | 0.009 (0.0012) | 0.0616 (0.0011) | 0.0343 (0.0027) | 0.0061 (0.00010) |
(40,10) | 0.014 (0.0023) | 0.0708 (0.0060) | 0.0340 (0.0035) | 0.0073 (0.00030) | ||||
(10,20) | 0.015 (0.0031) | 0.0938 (1.0196) | 0.0480 (0.0108) | 0.0067 (0.0006) | ||||
0.000025 | 46 | 50 | 0.03 | (40,60) | 0.000021 (3 × 10−6) | 44.98 (1.25) | 45.96 (2.11) | 0.0315 (0.0007) |
(40,10) | 0.000029 (4.4 × 10−6) | 43.94 (1.75) | 45.16 (2.53) | 0.0349 (0.0036) | ||||
(10,20) | 0.000016 (0.8 × 10−5) | 41.09 (2.82) | 44.64 (3.07) | 0.0178 (0.0051) | ||||
0.0002 | 95 | 320 | (40,60) | 0.00021 (1.2 × 10−6) | 94.20 (1.12) | 321.2 (1.07) | ||
(40,10) | 0.00025 (1.6 × 10−6) | 92.05 (2.25) | 318.6 (3.11) | |||||
(10,20) | 0.00037 (1.35 × 10−5) | 122.51 (2.51) | 281.5 (4.88) |
In Table 2, we report the results obtained on large and small data by maximizing Eq. (22) using the GA. We notice that, for the case of the large data, the true values of the parameters are well identified with the exception of some in which, in all the simulations and samples, the true value does not belong to the estimated confidence interval, such as
SMM: empirical distribution of population parameter estimates obtained using
Finally, from this simulation study where we have considered two SDME models, we can conclude that the parameters values of the models appear to be correctly identified using the proposed approach based on the Risken approximation to approximate the transition density of a stochastic process.
In this work, we have proposed a procedure to estimate the parameters of a mixed effects model containing stochastic differential equations, known by the SDME models, by proposing an approach to approximate its likelihood function to obtain the MLEs. This method has been evaluated by simulation studies on two SDME models in epidemiology: the two-dimensional Ornstein-Uhlenbeck process and stochastic minimal model. In fact, in models with SDEs instead of ODEs with random effects, the estimation of parameters is still not obvious even for one individual (one trajectory) because of the difficulties in deriving the transition densities, and difficulties become more interesting in using the population approach that treats the entire population simultaneously. The derivation of the exact density is not always possible for a stochastic and continuous process in an SDME model, so the search for an approximation of this density is an important step and requires an expensive calculation. This task is very interesting to give good results with good statistical properties of the estimators obtained by maximizing the likelihood function. In this work, the proposed estimation method has been applied to multidimensional and nonlinear SDME models with many random parameters normally distributed that can be extended to random parameters of any distribution. So, an approximation of the transition density
The classical inference of SDME models implies the problem of the numerical evaluation of the integral for the given random effects in the likelihood function, which becomes complicated especially when the model contains more than two random parameters. In the literature, several methods have been proposed and tested for the approximation of the integral (see references in the introduction) and the following examples: [8] which proposes the Gaussian quadrature method to solve the integrals for the case of an SDME models with a single random effect and [20] where the study was revised for a general case with several random parameters using the Laplace approximation to compute the integral in Eq. (4) and Eq. (6) numerically. For the mixed effects framework, see [3, 16, 55, 56]. In the case of using the Laplace method, as in this chapter, the calculation of the Hessian matrix can be done analytically when it is possible, as the examples in Section 2, or with the help of a symbolic calculus software or the automatic differentiation (AD) tools [57].
The results of simulation studies are satisfactory and can be obtained either by using moderate values for the number of experimental units M and of observations n taken for each experimental unit or by using a small sample size but with a number of measurements taken for each subject of 10 at least; this is relevant for applications where large sets of data are not available, such as biomedical applications where the mixed effect theory is widely applied.
The advantages of this approach, compared to those proposed in the literature for multidimensional SDME models with more than one random parameter [26], are that the computation of the approximate density is very easy and does not require a lot of time to calculate it or to program it in a software; the only task that can be time-consuming is in the optimization step to search for the optimum solution of the likelihood function, and also the proposed method is effective even with large data with a MATLAB program on a common PC (Intel Pentium IV 3.0 GHz with 512 MB of RAM). Nevertheless, the method suffers some limitations, for example, when the conditions to use Eq. (11) are not verified when, e.g., the inverse of the diffusion term does not exist and when, in certain cases, it is not obvious to derive the gradients and Hessians terms. Another limitation is that measurement errors are not considered in this work, and for a good stochastic version, it will be better to include noise on process increments and noise on observations that may be significant compared to system noise. These limitations may provide a perspective towards a more elaborate extension of the statistical study for SDME models, particularly in the field of epidemiology.
In conclusion, in this work, we proposed a method of parameter estimation for a mixed effects models with SDEs proposing an approximation method for the transition density in the case when it cannot be obtained in a closed form, with an approximation method of integral computation for the case of an SDME model with several random parameters. We have treated two examples to illustrate the effectiveness of this approach using computer tools. Indeed, we believe that this type of models is very interesting and provides a powerful and flexible modeling approach for repeated measurement studies such as biological and pharmacokinetic/pharmacodynamic and financial studies, as they combine the good characteristics of mixed effects and stochastic increments in intra-subject dynamics for a good modeling of a phenomenon.
Z.T., H.E., and F.B conceived the presented idea and contributed to the design and implementation of the research. F.B., H.E., Z.T., and H.A contributed to the analysis of the results. Both F.B. and H.E authors developed the theory and performed the computations and the numerical simulations and verified the analytic methods and calculations.
The author(s) declare no competing interests.
stochastic differential equations Markov Chain Monte Carlo Fokker-Planck equation maximum likelihood estimators ordinary differential equations nonlinear mixed effects stochastic differential mixed effects Ornstein-Uhlenbeck genetic algorithm elite number selection rate crossover probability mutation probability stochastic minimal model type 2 diabetes
As clean and renewable energy, wind energy has been widely used in the world and will be paid more and more attention [1]. A wind turbine is the most important equipment for the utilization of wind energy, and it is also a kind of rotating machinery, which can convert wind energy into mechanical energy, heat energy, and electrical energy [2]. According to the rotation mode of a wind turbine shaft, there are two key kinds of wind turbines, which are the horizontal axis wind turbine (HAWT) and the vertical axis wind turbine (VAWT). Currently, the HAWTs are used for the large-scale wind turbines mostly. In contrast, the VAWTs are often used for the small-scale wind energy utilization [3]. Thanks to years of researching, the performance of wind turbines has been greatly improved. The research focus also extends from the improvement of efficiency to how to ensure the safe, stable, and efficient operation of wind turbines. Meanwhile, global climate change intensifies and extreme weather occurs more frequently. Wind turbines are facing more severe weather conditions, such as typhoon, earthquake, lightning, blizzard, icing, etc. [4]. In the cold and moist regions, wind turbines will face the icing problem. When the environment meets a certain condition, supercooled water droplets in the air will accrete on the wind turbine surface and become icing [5]. An icing photo of a wind turbine taken by us in Northeast China in December 2019 is shown in Figure 1.
Icing on the blades of HAWT.
Icing event occurring on the wind turbine blade surface will greatly degrade the efficiency and safety of operation. The research contents of icing mainly include distribution and characteristics of icing on a blade [6, 7, 8], aerodynamic performance effect of icing on blade and rotor [9, 10, 11], anti-icing and de-icing methods and technologies [12, 13, 14, 15], icing detection technologies [16], etc. There are two main icing research methods, numerical simulation [17, 18] and icing wind tunnel test [19, 20]. In the present study, a small-scale icing wind tunnel was used to obtain the distribution characteristics of icing on a model of 2D blade with NACA0018 airfoil. Under different ambient temperatures and wind speeds, experiments on ice accretion on the 2D blade at two kinds of angel of attacks for 4 minutes were carried out. The icing distributions were recorded and analyzed. This research can provide a reference for researching icing mechanism and de- and anti-icing technologies of wind turbines.
Figure 2 shows an icing wind tunnel experimental system in Northeast Agricultural University used in this study. It’s a low-speed and return-flow type wind tunnel. The refrigeration and spray devices were installed in a normal wind tunnel to provide low temperature and icing environment. The detailed information about this system can be found in the reference [21]. The test section is 250 mm × 250 mm. The range of wind speed is from 1 m/s to 20 m/s. The ambient temperature can be controlled lower to −20°C. By using different sprayers, the icing wind tunnel can supply different kinds of liquid water content (LWC) and medium volume diameter (MVD) of the supercooled water droplet. The LWC is from 0.1 g/m3 ~ 5 g/m3, and the MVD is from 20 μm ~ 100 μm.
Icing wind tunnel experimental system used in this study. (a) Schematic diagram of the icing wind tunnel experimental system. (b) Photo of the icing wind tunnel.
Figure 3 shows the test model of blade segment with airfoil profile used in wind turbine. To obtain the basic and normal icing characteristics of wind turbine blade, the symmetrical airfoil of NACA0018 was selected because it is often used for wind turbines and basic researches. The blade material was the aluminum alloy, which is usually selected for icing wind tunnel tests because of its good and stable thermal conductivity. According to the size of test section of wind tunnel, the test model has the chord length of 150 mm and the thickness or spanwise length of 20 mm. For this test model, the airfoil profile is consistent along spanwise, and its thickness is small. Therefore, it can be seen as a 2D blade airfoil in the present experimental research. The 3D effect of it can be negligible.
Test model of the blade with NACA0018 airfoil.
Based on the research object, several test parameters were selected for comparison. It included ambient temperatures, wind speeds, and icing times. To check the effect of an angle of attack (AOA) on the blade icing distribution, two kinds of angle of attacks (AOAs), such as 0°and 10°, were selected. The test conditions are listed in Table 1. Furthermore, the LWC in the icing wind tunnel used in the present study was in the range of 0.8 ~ 1.6 g/m3. The MVD was about 65 μm. In this study, the effects of LWC and MVD on icing of blade airfoil were not researched, which will be carried out deeply in the follow-up study. When the spray system begins to work, a high-speed camera (Phantom v5.1, with the revolution ratio of 1024 × 1024 pixels) begins to record the process of icing on blade surface and take photos at the moments of 2 minutes and the 4 minutes. By using the drawing software, the profile of icing shape can be obtained and analyzed.
Angel of attack (°) | Wind speed (m/s) | Temperature (°C) | Icing time (min) |
---|---|---|---|
0 | 5, 10 | −5, −10, −15 | 0, 2, 4 |
10 | 5, 10 | −5, −10, −15 | 0, 2, 4 |
Experimental conditions of the icing tests in the icing wind tunnel.
Figures 4 and 5 show the photos of icing distribution at the leading edge of blade under all test conditions. According to the figures, it can be seen that icing occurs on the leading edge of blade for all test conditions. It means that icing will appear when certain environmental conditions are met. However, the characteristics of icing distribution and ice accretion are obviously different under different test conditions.
Photos of icing distribution on blade at the wind speed of 5 m/s.
Photos of icing distribution on blade at the wind speed of 10 m/s.
For the effects of ambient temperature, the most typical characteristic is the change of icing type. When the temperature was −5°C, the type of icing on the blade surface was glaze ice with a transparent color. With the decrease in temperature, the type of rime ice, whose color was white, gradually began to appear. At the temperature of −10°C, there was a little rime ice, which only appeared on the upper surface away from the leading edge. This phenomenon was more obvious at the AOA of 0° than the one at 10°. When the temperature reached to −15°C, most of the ice transformed into the rime ice, while there was very little glaze ice, which only appeared at the front of the leading edge. The glaze ice was just formed by condensation of newly flowing supercooled water droplets in the ultimate period of photography. These results indicate that the icing type on the leading edge of blade is glaze at high temperatures, such as above −5°C, and transforms into mixed ice and rime ice with the decrease in temperature. Furthermore, another phenomenon should be mentioned. For the AOA of blade at 0°, when the temperature was −5°C, there was a short icicle appearing on the lower surface near the leading edge of the blade at the icing time of 4 minutes. The reason for an explanation of this result is that the heat transfer rate of water droplets decreased with the increase in icing time. At the initial icing stage, the water droplets impinged directly on the blade surface whose temperature was the same as the environmental temperature. In this case, the water droplets froze in a short time because of higher heat conductivity coefficient of aluminum material (237 W/m·K) and low blade surface temperature. That is why the type of rime ice is generated on the blade surface. After that, there was a layer of ice covering the blade surface. The consequent water droplets impinged on the ice layer. The water droplets could not freeze in a short time because of low heat conductivity of ice (2.22 W/m·K). Some super-cooled water droplets attaching on the ice layer surface had not enough time to freeze into ice completely due to the high temperature. These water droplets ran back along the blade surface to the trailing edge, and the icicle was generated. In this case, the type of icing was glaze ice due to high temperature of the icing blade. Under the action of wind speed and gravity, these incompletely frozen water droplets grow and form an ice icicle obliquely downward.
For the effects of wind speed, it was found that the wind speed does not play a key role in the change of icing type. When the temperature was constant, the type of icing was basically the same. When the wind speed increased from 5 m/s to 10 m/s, the icing amount decreases slightly. Meanwhile, the icing shape became smoother, especially at the temperature of −15°C. This may be caused by the constant flow rate of the spray nozzle in unit time. In the present study, the pressure of the pump is constant, which makes the flow rate of the nozzle constant in spite of increasing wind speed. Therefore, the water content passing through a cross-section is constant in unit time. In theory, the icing amounts are the same under two kinds of wind speeds. However, when the wind speed is higher, more water droplets with lightweight are blown away by high-speed wind, and the amount of water droplets impinging on the blade decreases. Additionally, the heat exchange of water droplets with air also accelerates and enhances because of the increase in wind speed. In this case, some small water droplets freeze before impinging on the blade surface, which also results in the decrease in the icing amount.
For the different AOAs, the amount of icing on the lower surface of blade increased because there was an upward angle of attack against wind. In contrast, the amount of icing on the upper surface had a decreasing trend. The key reason is that the windward state of the blade changes from symmetry to asymmetry due to the existence of AOA, which leads to the asymmetry of icing distribution on the leading edge of blade. Therefore, it was concluded that the AOA, or the windward state of blade, determines the location and distribution of icing on blade surface. Furthermore, a deep discussion can be made on how to use these results to the real wind turbine blade. As is known to all, the wind turbine blades always operate in a rotating state. Although the blade model in this research was in a static state, the research findings can be used for the analysis of rotating blades. Based upon the aerodynamics of wind turbine blade, the wind speed and the rotational speed will form a resultant speed at the local element of blade airfoil, which is known as relative wind speed. The AOA in the rotating condition should be decided by the relative wind speed. Therefore, the research findings obtained in the static condition can be used for the analysis of rotating blades.
In order to quantitatively analyze the distribution characteristics of icing, the captured icing photos were processed and the icing contours at the different moments were obtained, which are shown in Figures 6 and 7.
Ice accretion on blade at the wind speed of 5 m/s.
Ice accretion on blade at the wind speed of 10 m/s.
For comparatively analyzing with the results at the AOA of 0 degrees, the figures of the icing blade at the AOA of 10° are turned back 10 degrees and located at horizontal level for convenient observation and comparison.
Based on these pictures showing the profile of icing, the change of icing shape can be seen clearly. Furthermore, from these figures, the icing area (Ai) is calculated in the present study. The schematic diagram of icing area is shown in Figure 8.
Schematic diagram of icing area.
Figure 9 shows the changes in the icing area with the increase in icing time under all test conditions. Additionally, a dimensionless parameter, icing area ratio (
Variation of icing area with icing time.
The variations of icing area ratios with icing time, calculated from the data in Figure 9, are shown in Figure 10.
Variation of icing area ratio with icing time.
As shown in Figure 9, the icing area increased linearly with the increase in icing time in general. This characteristic can also be found in the result of the icing area ratio in Figure 10. Therefore, in this section, it only focuses on the analysis of icing area ratio in Figure 10. In the period of 4 minutes icing time, for all conditions in this research, the maximum icing area ratios reached to 6.79% and 8.31% at the AOAs of 0° and 10°, respectively. At the wind speed of 5 m/s, with the decrease in temperature, the growth rate of icing area ratio increased. This phenomenon was obvious, especially at the AOA of 10°. However, for the wind speed of 10 m/s, the effect of temperature on growth rate of icing area ratio became more obvious than that at a wind speed of 5 m/s. The reasons for this result are multifaceted and complex. The high wind speed gives high kinetic energy to the supercooled water droplets, which makes the impact course of water droplets on the blade surface complicated. Also, higher wind speed will affect the characteristics of heat transfer more obviously in comparison with that of the lower wind speed. When the temperature is low, a higher wind speed intensifies the heat transfer of water droplets, which enhances the growth rate of icing area. Deep researches on this issue will be carried out in our further tests.
More discussions were given on the effects of icing time and angle of attack. For the icing time, the icing time length of 4 minutes is selected in this study. Of course, with the increase in icing time, the icing area will increase. In nature, the process of icing on a wind turbine is a long course, which takes time from hours to days. In this research, the main purpose is to explore the initial stage of icing on the leading edge of blade surface. When the ice forms to a certain shape and thickness, the consequent icing process occurs between supercooled water droplets and ice formed on the blade surface, not the blade surface. For the angle of attack, the most important effect is to change the windward area of the blade. Based on the test results of AOA, it is concluded that the icing area will increase along with AOA. For the blade in a static condition, the range of AOA is wide. However, for the rotating blade of a real wind turbine, the relative angle of attack is controlled in a limited range. Therefore, the test results can be a useful reference to the icing research of a real wind turbine.
In this research, the icing tests of a blade model for a wind turbine were carried out in an icing wind tunnel. The distribution characteristics of icing on the leading edge of the blade were obtained and analyzed. Some key results in the present study are summarized and listed as follows:
In the present study, the icing on the leading edge of a blade with NACA0018 airfoil was explored and obtained. It is concluded that the ice type has good relationship with the ambient temperature. With the decrease in temperature, the ice type changes from glaze ice, mixed ice, to rime ice. Meanwhile, the phenomenon is more obvious when the wind speed increases. Additionally, with the increase in wind speed, the icing amount of the blade decreases slightly as the flow rate of spray nozzle is constant. Under low-temperature condition, high wind speed can accelerate the growth rate of icing.
When the environmental condition keeps constant, the icing area increases linearly along with the icing time. Additionally, the icing area is also affected by the angle of attack. With the increase in angle of attack, the windward area of the blade increases, which results in an increase in the icing area. Moreover, the area covered by ice on the lower surface increases with respect to the AOA. The maximum icing area ratios reach to 6.79% and 8.31% at the AOAs of 5° and 10°, respectively.
Based on the method of icing wind tunnel test in this research, the characteristics of icing on blade surface can be researched and obtained. This study can be a reference to the icing mechanism research and the anti- and de-icing research of wind turbines.
This work is supported by the National Natural Science Foundation of China (NSFC) [grant number 51976029]. The authors would like to thanks for the support.
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In this chapter, the advances, applications, and challenges of NGS are reviewed starting with a history of first-generation sequencing followed by the major NGS platforms, the bioinformatics issues confronting NGS data storage and analysis, and the impacts made in the fields of genetics, biology, agriculture, and medicine in the brave, new world of ”omics.”",book:{id:"5070",slug:"next-generation-sequencing-advances-applications-and-challenges",title:"Next Generation Sequencing",fullTitle:"Next Generation Sequencing - Advances, Applications and Challenges"},signatures:"Jerzy K. 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It provides a lot of useful information in this setting that transfers directly from laboratory to clinical management of patients. Lineage definition is the first goal leading to proper initial therapy. Some phenotypic patterns define specific subsets correlating with poor (mixed phenotype, dendritic cell neoplasm) or favorable (cortical T-lymphoblastic leukemia) outcome, thus guiding the application of treatment modalities. An advanced analysis of phenotypic data can address specific issues, such as the still debated role of multilineage dysplasia. The quality of response to chemotherapy is monitored by the detection of minimal residual disease and peripheral blast clearance during chemotherapy delivering. 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Moreover, aberrations caused by breaks play an important role in the evolution of a chromosome set and chromosome complement by decreasing or increasing the chromosome number. Therefore, both the aspects are discussed in detail in the present chapter. At present, the chapter will highlight the karyotype and its components, karyotype trends, evolution and its role in speciation, banding pattern and techniques, chromosome differentiation and linearization, banding applications and their uses, detection and analysis of chromosomal aberrations, chromosome and chromatid types of aberrations and mechanism of the formation of chromosome aberrations and breaks for karyotype evolutionary trends.",book:{id:"10566",slug:"cytogenetics-classical-and-molecular-strategies-for-analysing-heredity-material",title:"Cytogenetics",fullTitle:"Cytogenetics - Classical and Molecular Strategies for Analysing Heredity Material"},signatures:"Sanjay Kumar, Asikho Kiso and N. 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It receives special attention for genetic investigation because of its important role in the regulation of innate and adaptive immune responses and its strong association with numerous infectious and/or autoimmune diseases. Recently, genotyping of the polymorphisms of MHC genes using targeted next-generation sequencing (NGS) technologies was developed for humans and some nonhuman species. Most species have numerous highly homologous MHC loci so the NGS technologies are likely to replace traditional genotyping methods in the near future for the investigation of human and animal MHC genes in evolutionary biology, ecology, population genetics, and disease and transplantation studies. In this chapter, we provide a short review of the use of targeted NGS for MHC genotyping in humans and nonhuman species, particularly for the class I and class II regions of the Crab-eating Macaque MHC (Mafa).",book:{id:"5070",slug:"next-generation-sequencing-advances-applications-and-challenges",title:"Next Generation Sequencing",fullTitle:"Next Generation Sequencing - Advances, Applications and Challenges"},signatures:"Takashi Shiina, Shingo Suzuki and Jerzy K. 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Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. 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His research interests are focused on modern imaging methods used in medicine and pharmacy, including in particular hyperspectral imaging, dynamic thermovision analysis, high-resolution ultrasound, as well as other techniques such as EPR, NMR and hemispheric directional reflectance. Author of over 100 scientific works, patents and industrial designs. Expert of the Polish National Center for Research and Development, Member of the Investment Committee in the Bridge Alfa NCBiR program, expert of the Polish Ministry of Funds and Regional Policy, Polish Medical Research Agency. Editor-in-chief of the journal in the field of aesthetic medicine and dermatology - Aesthetica.",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",slug:"alexandros-tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",slug:"lulu-wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:"Shenzhen Technology University",institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda R.",middleName:"R.",surname:"Gharieb",slug:"reda-r.-gharieb",fullName:"Reda R. 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He completed a one-year Post-Doctoral Fellowship awarded by the DFAIT (Foreign Affairs and International Trade Canada) at the Institute of Biomedical Engineering of the University of New Brunswick (Canada) in 2010. Currently, he is Professor in the Faculty of Electrical Engineering (UFU). He has authored and co-authored more than 200 peer-reviewed publications in Biomedical Engineering. He has been a researcher of The National Council for Scientific and Technological Development (CNPq-Brazil) since 2009. He has served as an ad-hoc consultant for CNPq, CAPES (Coordination for the Improvement of Higher Education Personnel), FINEP (Brazilian Innovation Agency), and other funding bodies on several occasions. He was the Secretary of the Brazilian Society of Biomedical Engineering (SBEB) from 2015 to 2016, President of SBEB (2017-2018) and Vice-President of SBEB (2019-2020). He was the head of the undergraduate program in Biomedical Engineering of the Federal University of Uberlândia (2015 - June/2019) and the head of the Centre for Innovation and Technology Assessment in Health (NIATS/UFU) since 2010. He is the head of the Postgraduate Program in Biomedical Engineering (UFU, July/2019 - to date). He was the secretary of the Parkinson's Disease Association of Uberlândia (2018-2019). Dr. Andrade's primary area of research is focused towards getting information from the neuromuscular system to understand its strategies of organization, adaptation and controlling in the context of motor neuron diseases. His research interests include Biomedical Signal Processing and Modelling, Assistive Technology, Rehabilitation Engineering, Neuroengineering and Parkinson's Disease.",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",slug:"hitoshi-tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",slug:"marcus-vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",slug:"ramana-vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",slug:"luis-villarreal-gomez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",biography:"Dr. Luis Villarreal is a research professor from the Facultad de Ciencias de la Ingeniería y Tecnología, Universidad Autónoma de Baja California, Tijuana, Baja California, México. Dr. Villarreal is the editor in chief and founder of the Revista de Ciencias Tecnológicas (RECIT) (https://recit.uabc.mx/) and is a member of several editorial and reviewer boards for numerous international journals. He has published more than thirty international papers and reviewed more than ninety-two manuscripts. 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Waisundara",profilePictureURL:"https://mts.intechopen.com/storage/users/194281/images/system/194281.jpg",biography:"Dr. Viduranga Waisundara obtained her Ph.D. in Food Science\nand Technology from the Department of Chemistry, National\nUniversity of Singapore, in 2010. She was a lecturer at Temasek Polytechnic, Singapore from July 2009 to March 2013.\nShe relocated to her motherland of Sri Lanka and spearheaded the Functional Food Product Development Project at the\nNational Institute of Fundamental Studies from April 2013 to\nOctober 2016. She was a senior lecturer on a temporary basis at the Department of\nFood Technology, Faculty of Technology, Rajarata University of Sri Lanka. She is\ncurrently Deputy Principal of the Australian College of Business and Technology –\nKandy Campus, Sri Lanka. 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His research interests include computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, intelligent systems, information technology, and information systems. Prof. Sarfraz has been a keynote/invited speaker on various platforms around the globe. He has advised various students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He is a member of various professional societies and a chair and member of the International Advisory Committees and Organizing Committees of various international conferences. Prof. Sarfraz is also an editor-in-chief and editor of various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:null},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Lakhno Igor Victorovich was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPhD – 1999, Kharkiv National Medical Univesity.\nDSc – 2019, PL Shupik National Academy of Postgraduate Education \nLakhno Igor has been graduated from an international training courses on reproductive medicine and family planning held in Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor of the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s a professor of the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education . He’s an author of about 200 printed works and there are 17 of them in Scopus or Web of Science databases. Lakhno Igor is a rewiever of Journal of Obstetrics and Gynaecology (Taylor and Francis), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for DSc degree \\'Pre-eclampsia: prediction, prevention and treatment”. Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: obstetrics, women’s health, fetal medicine, cardiovascular medicine.",institutionString:"V.N. Karazin Kharkiv National University",institution:{name:"Kharkiv Medical Academy of Postgraduate Education",country:{name:"Ukraine"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"243698",title:"M.D.",name:"Xiaogang",middleName:null,surname:"Wang",slug:"xiaogang-wang",fullName:"Xiaogang Wang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243698/images/system/243698.png",biography:"Dr. Xiaogang Wang, a faculty member of Shanxi Eye Hospital specializing in the treatment of cataract and retinal disease and a tutor for postgraduate students of Shanxi Medical University, worked in the COOL Lab as an international visiting scholar under the supervision of Dr. David Huang and Yali Jia from October 2012 through November 2013. Dr. Wang earned an MD from Shanxi Medical University and a Ph.D. from Shanghai Jiao Tong University. Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:"Shanxi Eye Hospital",institution:{name:"Shanxi Eye Hospital",country:{name:"China"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. RELACION DE PONENCIAS DE LA SOCIEDAD ESPAÑOLA DE OFTALMOLOGIA. 10/2014.",institutionString:null,institution:null},{id:"265335",title:"Mr.",name:"Stefan",middleName:"Radnev",surname:"Stefanov",slug:"stefan-stefanov",fullName:"Stefan Stefanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265335/images/7562_n.jpg",biography:null,institutionString:null,institution:null},{id:"7227",title:"Dr.",name:"Hiroaki",middleName:null,surname:"Matsui",slug:"hiroaki-matsui",fullName:"Hiroaki Matsui",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Tokyo",country:{name:"Japan"}}},{id:"318905",title:"Prof.",name:"Elvis",middleName:"Kwason",surname:"Tiburu",slug:"elvis-tiburu",fullName:"Elvis Tiburu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Ghana",country:{name:"Ghana"}}},{id:"336193",title:"Dr.",name:"Abdullah",middleName:null,surname:"Alamoudi",slug:"abdullah-alamoudi",fullName:"Abdullah Alamoudi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"318657",title:"MSc.",name:"Isabell",middleName:null,surname:"Steuding",slug:"isabell-steuding",fullName:"Isabell Steuding",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Harz University of Applied Sciences",country:{name:"Germany"}}},{id:"318656",title:"BSc.",name:"Peter",middleName:null,surname:"Kußmann",slug:"peter-kussmann",fullName:"Peter Kußmann",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Harz University of Applied Sciences",country:{name:"Germany"}}},{id:"338222",title:"Mrs.",name:"María José",middleName:null,surname:"Lucía Mudas",slug:"maria-jose-lucia-mudas",fullName:"María José Lucía Mudas",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Carlos III University of Madrid",country:{name:"Spain"}}}]}},subseries:{item:{id:"27",type:"subseries",title:"Multi-Agent Systems",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. The area covers many techniques that offer solutions to emerging problems in robotics and enterprise-level software systems. Collaborative intelligence is highly and effectively achieved with multi-agent systems. Areas of application include swarms of robots, flocks of UAVs, collaborative software management. Given the level of technological enhancements, the popularity of machine learning in use has opened a new chapter in multi-agent studies alongside the practical challenges and long-lasting collaboration issues in the field. It has increased the urgency and the need for further studies in this field. We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",hasOnlineFirst:!1,hasPublishedBooks:!1,annualVolume:11423,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. 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Adetoro and Olusegun Awotoye",slug:"assessment-of-diversity-growth-characteristics-and-aboveground-biomass-of-tree-species-in-selected-u",totalDownloads:4,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Forest Degradation Under Global Change",coverURL:"https://cdn.intechopen.com/books/images_new/11457.jpg",subseries:{id:"94",title:"Climate Change and Environmental Sustainability"}}}]},publishedBooks:{paginationCount:1,paginationItems:[{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/7726.jpg",slug:"swarm-intelligence-recent-advances-new-perspectives-and-applications",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Javier Del Ser, Esther Villar and Eneko Osaba",hash:"e7ea7e74ce7a7a8e5359629e07c68d31",volumeInSeries:2,fullTitle:"Swarm Intelligence - Recent Advances, New Perspectives and 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possibility to collaborate with more research groups interested in animal nutrition, leading to the development of new feeding strategies and food valuation while being more sustainable with the environment, allowing more readers to learn about the subject.",author:{id:"175967",name:"Manuel",surname:"Gonzalez Ronquillo",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/175967/images/system/175967.png",slug:"manuel-gonzalez-ronquillo",institution:{id:"6221",name:"Universidad Autónoma del Estado de México",country:{id:null,name:"Mexico"}}}},{id:"18",text:"It was great publishing with IntechOpen, the process was straightforward and I had support all along.",author:{id:"71579",name:"Berend",surname:"Olivier",institutionString:"Utrecht University",profilePictureURL:"https://mts.intechopen.com/storage/users/71579/images/system/71579.png",slug:"berend-olivier",institution:{id:"253",name:"Utrecht 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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine"},{id:"8",title:"Bioinspired Technology and Biomechanics",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation"},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:{title:"Biomedical Engineering",id:"7"},selectedSubseries:null},seriesLanding:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"June 24th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:314,numberOfPublishedBooks:31,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. 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