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Mosquera Cuesta",coverURL:"https://cdn.intechopen.com/books/images_new/1680.jpg",editedByType:"Edited by",editors:[{id:"15074",title:"Dr.",name:"Herman J.",surname:"Mosquera Cuesta",slug:"herman-j.-mosquera-cuesta",fullName:"Herman J. Mosquera Cuesta"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},onlineFirst:{chapter:{type:"chapter",id:"81745",title:"Hybrid Genetic Algorithms",doi:"10.5772/intechopen.104735",slug:"hybrid-genetic-algorithms",body:'Solving an optimization problem consists in exploring a search space in order to maximize (or minimize) a given objective function. The complexities (in size or structure) of the search space and the function to be optimized lead to the use of radically different resolution methods. However, the so-called global optimization methods are generally the best suited ones considering their main advantage: that of identifying the promised regions and converging towards a global optimum.
The zero-order optimization Genetic Algorithm (GA), initially laid down by Holland [1], remain the most recognized and practiced form of Evolutionary Algorithms (EA) which are global stochastic optimization techniques that mimic Darwin’s principles of natural selection and genetic dynamics. In fact, known advantages of the GA include its ability to combine both exploration and exploitation in an optimal way [1] in addition to its ability to be used in the case of discontinuous objective functions, within disjoined and/or non-convex design spaces, and together with discrete, continuous, or even mixed design variables. Besides, as a population-based method, GA, if initially well-tuned, minimizes the risk to converge to a local optimum thanks to the simultaneous processing of the whole candidate solutions instead of, for instance, gradient-based methods, giving thus the designer a multitude of options.
Nevertheless, like any optimization technique, GA has also its drawbacks starting with the numerous parameters sensitivity analysis to do to maximize efficiency and have a good initialization of the algorithm. In addition to the most notable drawback which is generally the great number of required iterations, and they slow convergence, especially in the neighborhood of the global optimum. Indeed, although GAs can rapidly locate the region in which the global optimum exists, they take a relatively long time to locate the exact local optimum in the region of convergence. Each generation of the algorithm consists of many objective function evaluations resulting in a lot of computational time in addition to being very expensive.
To overcome this main limitation and lessen function evaluation time, different alternatives have been proposed. But particularly the trend of hybridization has been observed in many works carried out on metaheuristics over the past 20 years, where it consists of incorporating a faster Local Search (LS) (or individual learning) optimization algorithm into a GA in order to improve the performance of the global resulting method, often known as Memetic Algorithm (MA). This latter was first introduced by Moscato [2] and is representing one of the recent growing areas of research in evolutionary computation [3] by offering the important challenge of the tradeoff between global searching and local searching in terms of time and computational effort. In fact, this integration aims to keep advantages of both optimization methods while offsetting their both disadvantages [4] As LS algorithms are actually capable to find the local optimum with high accuracy and fast convergence but suffer from the problem of foothills. Hence through their integration with the population-based method, the time needed to reach the global optimum can be further reduced since the local knowledge is used to accelerate finding the most promising search region in addition to locating the global optimum starting within its attraction basin.
These hybrid algorithms have also been used under the name of hybrid evolutionary algorithms, Baldwinian evolutionary algorithms, Lamarckian evolutionary algorithms, cultural algorithms, or genetic LS. They also have been successfully applied to hundreds of real-world problems in a wide variety of domains ranging from aircraft [5], aerospace [6], pattern recognition [7], control systems [8], vehicle routing [9], pharmaceutical industry [10] etc.
An example of a hybrid GA applied to the mechanical structures’ optimization is presented in this chapter. This test case reflects the relevance of this hybridization in the mathematical resolution of optimization problems related to the aerospace field.
The organization of the chapter is as follows: the second section is dedicated to the description of the different hybridization design including the combination between GAs and LS methods. General mechanisms of GAs are remained in the third section. Hybrid GAs are discussed in the fourth section then the test case with the problematic presentation and the obtained results is given in the fifth section. Finally, conclusions are given in the last section.
As mentioned before, in an optimization problem solving approach, it can be extremely beneficial to combine two solving techniques, knowing that it is possible to hybridize all exact and metaheuristic techniques. Hence, the hybridization can take place in one or more components of a research method. It can also consist in assembling several hybridization methods to form a single hybrid method. Two strategies can be used depending on the optimization technique which drives the hybrid method:
Strategy 1: we can approach the global optimum by a stochastic technique and then refine the result by successively applying a local one. In this case, the result will be ameliorated but unfortunately time consuming. An optimal way to use hybridization in this case may be by applying a global optimization method and find the right time to switch to a local one.
Strategy 2: we can try to find a local minimum and keep it as a winning solution if it is the best of all the local (global) minimums. For instance, in the method of multiple initializations or (multi Start), a local optimization technique is used several times at different stress points; the solution of the optimization problem would be then the best obtained result.
According to the author in [11], a taxonomy for hybrid meta-heuristics can be proposed by classifying them into three categories depending on their architecture (see Figure 1):
Hybridization classification.
Sequential hybridization: it is the most popular type; it performs different research methods sequentially so that the result of the first one serves as an initial solution for the next one.
Synchronous parallel hybridization: this hybridization is carried out by incorporating a particular research method in an operator. It is more complex to implement than the previous one. The aim is to combine a LS with a global search for the purpose of improving convergence.
Asynchronous parallel hybridization: the hybrid methods belonging to this class are characterized by an architecture such that two algorithms A and B are involved simultaneously, and each one adjusts the other. The Algorithms A and B share and exchange information throughout the research process.
A global view of the different possible hybridization schemes is shown in Figure 2 where we can see hybridization between stochastic (particularly metaheuristics) and analytical optimization methods. Indeed, the common point between all these hybridizations is that they try to merge the strengths and eliminate the weaknesses of the different concepts. Therefore, the efficiency of the search solution space can still be improved, and new opportunities emerged which may lead to even more powerful and flexible research methods.
Possible hybridization schemes.
GAs use a vocabulary inspired from natural genetics. However, the natural processes to which they refer is more complex [12]. They are iterative optimization procedures that repeatedly apply GA operators (such as selection, crossover, and mutation) to a group of solutions until some criterion of convergence has been satisfied. Note that the essential element of the genetic vocabulary is the “individual” that belongs to a set of individuals called “population”. The individual is made up of a “chromosome” which is itself made up of “genes” which contain the inheritance characteristics of the individual. The principles of “selection”, “crossover” and “mutation” are the main genetic operators that are inspired by the same natural processes. First, the selection operator includes generally two variants; the parental selection devoted for reproduction and the replacement selection devoted to keeping the population size constant by individuals who will survive in the next generation. A selection favors the best individuals but has also to give a chance to the less good to avoid the problems of premature convergence. Note that the “fitness” or “evaluation” function characterizing how well adapted the solution is to its environment, is closely related to the selection process. Then, the crossover operator involves combining the information from two parents to create one or two new individuals (or “offspring”) making it possible to diversify the population and explore more the research space. At last, the mutation operator causes a small perturbation on an individual’s chromosome thus allowing to guarantee gene diversity so that the algorithm does not get stuck in local minima. This process continues, throughout the generations, until the stop criteria is reached. The latter can be in various forms such as for example several global iterations defined in advance or the difference between the average population’s fitness and the best one.
It is also important to underline that during the implementation of the algorithm it is necessary to take into account the encoding of the different parameters. In fact, one of the characteristics of GAs is their need to a genetic representation of the desired problem using an encoding. Therefore, the algorithm can evaluate the fitness of every individual and computes the problem’s objectives and constraints based only on the encoding without any further knowledge of real parameters.
Figure 3 shows the optimization flowchart offered by GA whereas Reference [13] provides further discussion on the theoretical properties of GAs and on standard genetic operators.
Flowchart of the optimization procedure based on a genetic algorithm.
The most natural hybridization in the literature is the use of a single solution meta-heuristic algorithm embedded in a population-based meta-heuristic algorithm like the unmissable GA in order to improve the qualities of the solutions. In fact, whereas population-based meta-heuristics tend to diversify the search space by exploring it different parts, LS meta-heuristics tend to intensify the search by exploiting only one part of the space.
LS methods are based on a neighborhood relation and on a procedure exploiting this neighborhood. They consist in moving from one solution to another closer in the space of the candidate solutions, until a solution considered as optimal is found, or the allowed time is exceeded. We can therefore summarize the main idea of a LS as follows:
Start from an initial solution,
Improve that solution,
Repeat the process several times.
Hence, through the hybridization, individuals attempt to identify promising areas of the solution space which are then explored in more detail by LS methods. The best known among the latters are the simulated annealing, the tabu search or simply descent methods (iterated local searches).
Concerning the hybridization adopting the global GA, as its decision-making mechanics are based on random principles, this detailed research can be difficult to carry out. Heuristics therefore serve to fill this weakness. In a general way, at all generations, an individual (or some individuals) are randomly selected among the best in the population. Then, a set of solutions defining its (their) neighborhood is constructed. Afterwards, each of these neighbors is evaluated in order to check whether there is an improvement in the evaluation function. GA then manipulates solutions that have already been improved locally. This has the effect of allowing even more advantageous solutions to be generated more quickly. However, as there are several ways to hybridize GAs while maintaining a fairly modular program structure, a sequential approach could be advantageous in such a way that it is enough to let the GA run to a substantial level of convergence, then let the local optimization procedure follow up the process, for example by taking the
Note that usually the individuals that undergo LS are chosen uniformly at random, but it would be more efficient to use a tuning technique which consists of a primary conducted experiment aiming to find the optimal part of the population that should perform LS. This part is commonly referred to as the LS probability mentioned above that is used to run the actual experiment and remains fixed during the algorithm execution. In the literature, several techniques aim to reduce unnecessary local optimization and therefore additional calculation time, such as distribution-based techniques [15], fitness-based techniques [15] and LS potential [16] that have been proposed to select the optimal individuals among the given population that should do a LS. In the following, some main LS methods are remained.
Simulated annealing is an optimization technique inspired by the simulation methods of Metropolis (1950s) in statistical mechanics. It is distinguished by the introduction of a temperature parameter which is adjusted during research [17]. Moreover, in optimization, this method considers a neighborhood exploitation procedure which makes it possible to go towards a neighboring solution of less good quality with a non-zero probability, thus allowing to escape the local optima.
At the beginning, a “temperature” parameter T is determined and decreases throughout the algorithm to tend towards 0. The probability of accepting deteriorated solutions depends on the value of this parameter (the more the temperature T decreases, the more this probability decreases). The interest of simulated annealing lies in the fact that there is a proof of its asymptotic convergence. Thus, when certain conditions are verified (decrease diagram of T), we are guaranteed to obtain the optimal solution. However, the parameterization recommended by theory is not very realistic and it takes a long time to get to parameterize these methods. Note that this method may also require a stop criterion, if the “optimal” setting has not been found. Hybridization between GA and SA algorithms has been used in several domains [18, 19, 20].
Tabu search was introduced as a new strategy to escape local optima using a notion of memory [21]. Exploitation of the neighborhood makes it possible to move from the current solution to its best neighbor (this one not necessarily having a better quality than the current solution). To avoid cycling between a local optimum and its best neighbor, the method prohibits moving to a recently visited solution. To do this, a tabu list containing the attributes of the last visited solutions is kept up to date. Each new solution considered removes the oldest visited solution from this list. Thus, the search for the following current solution is done in the neighborhood of the current solution without considering the solutions belonging to the tabu list. However, the size of this list is a method parameter that is difficult to adjust. In addition, this strategy requires the definition of a stopping criterion. We find the hybridization between GA and TS in many applications such as image processing [22], computational grids [23] and flow shop scheduling problem [24].
The descent methods are quick and simple to implement but generally do not lead to the best optima because they stop as soon as a local optimum is found [25]. In order not to get stuck on this local optimum, there are various strategies which allow the search to continue after having found an optimum. One strategy to overcome the abrupt stopping of the search for a local optimum is to iterate the descent method. The following steps are carried out from the found optimum:
Apply a perturbation on the current solution,
Apply a descent method on that solution.
Chose via an acceptance criterion if the new optimum becomes the current solution and go back to the first step until the stop criterion is reached. Common stop criteria are the execution time, the number of iterations and the number of total evaluations.
The disturbance can consist in restarting a solution taken randomly in the search space or in choosing a solution in a neighborhood far from the optimum or even in choosing a neighbor of the same quality as the optimum.
Heuristic Iterative Local Search (ILS) [26] is based on a simple idea: instead of repeatedly applying a LS procedure from randomly generated solutions, ILS generates the starting solution for the next iteration by applying a perturbation on the local optimum found at the current iteration. This is done in the hope that the disturbance mechanism provides a solution located in the attraction basin of a better local optimum. Therefore, the disruption mechanism is a key part of ILS. In addition, an acceptance criterion defines the conditions that the new local optimum must meet in order to replace the current local optimum (Figure 4). Thus, the acceptance criterion, combined with the disruption mechanism, helps to control the trade-off between intensification and diversification. However, many acceptance criteria which reconcile the two objectives can be applied [12]. Hybridization between GA and ILS is one of the simplest and common hybridizations that can be found in several works [27, 28, 29].
From a local minimum s, a disturbance of the latter generates a minimum s’ from which a LS is launched to arrive at a third local minimum s” potentially better than s.
The Variable Neighborhood Search (VNS) is a meta-heuristic algorithm [30], based on the principle of systematic neighborhood change during LS. Indeed, at the initialization step, a set of neighborhood structures must be defined. These neighborhoods can be chosen arbitrarily, but often a sequence ordered in size growing neighborhoods is used. The VNS procedure consists of three stages: (1) the disturbance (shaking), (2) the LS and (3) the displacement. Recently, a variety of problems have involved hybridization between GA and VNS [31, 32, 33].
We consider here the following optimization problem: search for the minimum mass of an inter-stage skirt made of composite sandwich with Carbon Fiber Reinforced Polymer (CFRP) skins and aluminum Nida core (composites are fabricated by attaching two thin but stiff skins to a lightweight but thick core) that are located between the first stage and the second stage situated on the upper part of a linear launcher (Figure 5).
Simplified model of the sandwich composite inter-stage skirt (in red) located on the upper part of the linear launcher.
The structure is subjected to pressure oscillations from upstream solid propellant engines to ensure “payload comfort”. The skirt therefore has a filtering role in order to limit the vibratory levels at the top of the launcher. To best dampen these oscillations, the structure must be mechanical sized according to a compromise between rigidity and flexibility linked to the need to hold in buckling, in addition to composite manufacturing constraints (mirror symmetry, balancing, grouping) [34]. Note that we need to optimize only one skin plus the core hence the final result is obtained by symmetry.
Mathematically speaking, we can write the optimization problem as follows:
Subject to:
To solve the above optimization problem, we have chosen the global GA. In fact, during the mechanical sizing of a composite structure, plies thicknesses are often predetermined, and plies orientations are usually restricted to a small set of angles due to manufacturing constraints and/or limited availability of experimental data. Most of the time, this gives rise mathematically to a discrete optimization problem. Hence GA is a suitable optimization method that we also decided to hybridize with a LS method to overcome the drawbacks of GA cited throughout this chapter.
First of all, an appropriate representation of the problem is recommended through the encoding of all the parameters. In fact, the initial population of the GA is composed of a set of skin plies (sandwich case) characterized by fiber orientations. Thus, an individual in the population is equivalent to a laminate (set of plies) and can simply be represented by an orientation chromosome (see Figure 6). Plies and core thicknesses of the structure are predefined based on composite design constraints [35] and are not considered herein as optimization variables. Therefore, an individual (a laminate) is represented by one chromosome regrouping one skin plies characterized by orientation angles (0°, 90°, ± 45°, ± 30°), encoded according to 5 possible values {0, 1, 2, 3, 4} called the chromosome domain where particularly add where 0° is encoded by 0, 90° is encoded by 1, ± 45° is encoded by 2, “no ply” is encoded by 3 and finally ± 30° is encoded by 4. “no ply” is encoded by 3.
The genetic representation of the design variables.
To handle constraints, concerning the composite manufacturing ones, a repair strategy is adopted during the decoding phase (Figure 7). More explanation about these constraints handling method could be seen in [36]. Whereas mechanical constraints (buckling and stiffness) are managed through the genetic selection operator.
Example of the decoding and repair strategy.
The proposed Hybrid Genetic Algorithm (HGA) herein is based on a strong and sequential cooperation between GA and a descent LS technique for the entire course of the algorithm where the LS optimization is integrated at the beginning of the GA previous to the genetic operators to intensify research only around selected promising solutions. Hence, the adopted scheme is as follows: for every new iteration, the best individual reached by the GA is selected as an initialization for the LS method. Then, a set of two neighbors is built following a well-defined structure. The latter aims to verify if there is any neighboring orientation that allows an improvement of the individual’s fitness by randomly selecting a gene related to the orientation and flipping it to either the previous or the next value in the concerned chromosome’s domain while of course respecting all manufacturing constraints via the repair cited repair strategy. For example, if the selected gene’s code is 2, the value of this gene will be replaced by 1 in the first neighbor and by 3 in the second one. However, if the randomly selected gene is the domain’s upper bound (4) or the lower one, then the two neighbors are built by flipping firstly the gene to the previous value in the domain (3) then to the lower bound (0) or conversely.
Afterwards, each neighbor is evaluated and if one’s fitness turns out to be better, then it is introduced in the population to replace the first individual and so onFigure 7 shows an example of the decoding and repair stategy where the steps decoding then reparing are explained, the randomly selected gene’s code is 2, corresponding to 90°. In order to respect the balancing composite rule, that gene should be replaced by -30° (repair) so the entire laminate respects the composite design constraints. The Figure 8 shows the adopted HGA flowchart.
Flowchart of the optimization procedure based on the hybrid genetic algorithm (HGA).
A comparative study between the classical GA and the proposed HGA is presented in the following. GA and HGA are implemented in a Python 3 environment and computed based on the same parameters (see Table 1). Note that at each iteration, the algorithms run through a database containing individuals already assessed with their respective fitness calculated through the finite element analysis program called NASTRAN [37] and look for the latter from the stacks previously evaluated. Besides, two criteria are implemented to stop the running of GA: the first stops the algorithm after a fixed number of generations have been created whereas the second stops after a given number of generations without improvement of the best individual in the population. This second criterion assures a certain quality of the best design, even if it is only relative to the previous best design. The comparison criterion is the fitness and the execution time associated with the number of iterations to achieve convergence. Concerning the HGA, the stop criteria is a prior fixed number of iterations.
Parameters | Values |
---|---|
Total number of iterations | 100 |
Number of individuals per generation | 30 |
Chromosomes’ size | 8 |
Mutation probability | 0,2 |
Mutant gene per individual | 1 |
Crossover probability | 1 |
Number of individuals chosen during the selection | 1 |
Number of iterations for the LS | 5 |
Number of neighbors per individual | 2 |
Initialization of the algorithms’ parameters.
Table 2 summarizes the optimization results. After 100 iterations, the best reached laminate is composed of 6 plies per skin against 8 initially and respects all the design constraints of composite structures thanks to the adopted decoding and repair strategy. Besides both stiffness and buckling constraints are satisfied with margins of +39.2% and + 5.9% for GA and HGA, respectively. Furthermore, the total mass is decreased by 8.4% compared to reference. This shows that generally all genetic processes perform well by offering a convincing solution. In fact, the program converges always towards the optimal solution. However, HGA provides a decrease from 26 to 18 in the number of needed iterations to converge with a gain of 24 minutes on the associated elapsed time. Thereby, HGA can outperform the standard GA on this sandwich composite structure optimization problem which highlight the hybridization mechanism and its capacity in the improvement of convergence.
Laminate (°) | S < 2e8 (N/m) | B | M (Kg) | Convergence (Iterations/Time CPU) | |
---|---|---|---|---|---|
NASTRAN | [45/0/30/0/−30/0/45/90] | 1.9e8 | 2.73 | 357 | — |
GA | [30/0/−30/30/−30/90] | 1.4e8 | 2.15 | 327 | 26/70 min |
HGA | [30/0/−30/30/−30/90] | 1.4e8 | 2.15 | 327 | 18/54 min |
Results of the comparative study between GA and HGA.
In this chapter we have underlined the importance of the process of hybridization in ameliorating the global genetic algorithm behavior and especially its convergence. In fact, although genetic algorithms are effective complete search algorithms able to combine both exploration and exploitation with crossover and mutation operators, they suffer from being computationally expensive and can hence be improved using local search methods, so they can be made competitive with other algorithms when the search space is too large to explore. More generally, the combination of metaheuristics based on population as well as on local search, may offer more effective and dynamic methods for the solution of several problems.
The following important questions were asked: (1) when to apply local search optimization (2) to which individuals in the genetic algorithm should local searches be applied, and (3) what are the possible hybridizations that could improve the optimization results. Some answers to these questions have been provided particularly the reminder of the hybridization strategies and its different possible architectures in addition to the main local search methods known by their relevant coupling with the classical genetic algorithm. The latter being able to undergo different forms of hybridization thanks to its flexibility which is nevertheless one of its strongest points.
The application possibilities of these approaches are unlimited but in this chapter the test case that was presented is about an optimization algorithm combining traditional genetic operators with an iterated local search method specially designed to pursue the problem of mechanical sizing of a composite structure. The genetic algorithm was chosen as the global optimization tool because of its ability to deal with non-convex and discrete optimization problems, of which the design of laminated composites is an example. Besides, its flexible structure makes it possible to integrate the management of different mechanical and manufacturing constraints through genetic operators and the implementation of specific optimization processes such as the decoding and repair strategy. The developed hybrid algorithm was validated by comparing its results to those obtained via the classical genetic algorithm as well as a reference finite element analysis software. That test case showed hence that the developed hybrid genetic approach is able to obtain efficient results in a real-world problem related to the aerospace sector.
The author wishes to thank the French National Center for Space Studies that partially supported this work.
The authors declare no conflict of interest.
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\n\nOur books contain scientific content written by two Nobel Prize winners, two Breakthrough Prize winners and 73 authors who are in the top 1% Most Cited.
\n\nWith regular submission for coverage in the single most important database, the Book Citation Index in the Web of Science™ Core Collection (BKCI), and no rejected submissions to date, over 43% of all Open Access books indexed in the BKCI are IntechOpen published books.
\n\nIn addition to BKCI, IntechOpen covers a number of important discipline specific databases as well, such as Thomson Reuters’ BIOSIS Previews.
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\n\nThe need for up to date information available at the click of a mouse is one thing that sets IntechOpen apart. By developing our own technologies in order to streamline the publishing process, we are able to minimize the amount of time from initial submission of a manuscript to its final publication date, without compromising the rigor of the editorial and peer review process. This means that the research published stays relevant, and in this fast paced world, this is very important.
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\n\nThe utilization of CC licenses allow researchers to retain copyright to their work. Researchers are free to use, adapt and share all content they publish with us. You will never have to pay permission fees to reuse a part of an experiment that you worked so hard to complete and are free to build upon your own research and the research of others. The Edited Volume helps bring together research from all over the world and compiles that research into one book - accessible for all. The research presented in chapter one can inspire the author of chapter three to take his or her research to the next level. It is about sharing ideas, insights and knowledge.
\n\nCan collaboration be inspired by a publishing format? At IntechOpen, the answer is yes. The way the research is published, the way it is accessed, it’s all part of our mission to help academics make a greater impact by giving readers free access to all published work.
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\n\nNot sure if this is the right publishing option for you? Feel free to contact us at book.department@intechopen.com.
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Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Bacterial Infectious Diseases",value:3,count:2},{group:"subseries",caption:"Parasitic Infectious Diseases",value:5,count:4},{group:"subseries",caption:"Viral Infectious Diseases",value:6,count:7}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:2},{group:"publicationYear",caption:"2021",value:2021,count:4},{group:"publicationYear",caption:"2020",value:2020,count:3},{group:"publicationYear",caption:"2019",value:2019,count:3},{group:"publicationYear",caption:"2018",value:2018,count:1}],authors:{paginationCount:250,paginationItems:[{id:"274452",title:"Dr.",name:"Yousif",middleName:"Mohamed",surname:"Abdallah",slug:"yousif-abdallah",fullName:"Yousif Abdallah",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274452/images/8324_n.jpg",biography:"I certainly enjoyed my experience in Radiotherapy and Nuclear Medicine, particularly it has been in different institutions and hospitals with different Medical Cultures and allocated resources. Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University. 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:"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:"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:"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:"Igor Victorovich Lakhno 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.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in 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 been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), 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 a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). 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: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",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:"243698",title:"Dr.",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: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:"95",type:"subseries",title:"Urban Planning and Environmental Management",keywords:"Circular economy, Contingency planning and response to disasters, Ecosystem services, Integrated urban water management, Nature-based solutions, Sustainable urban development, Urban green spaces",scope:"