Design variable for Co‐Blade.
\\n\\n
IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/132"}},components:[{type:"htmlEditorComponent",content:'With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
\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:"288",leadTitle:null,fullTitle:"Myocarditis",title:"Myocarditis",subtitle:null,reviewType:"peer-reviewed",abstract:"Myocarditis, the inflammation of the heart muscle, could be in some cases serious and potentially fatal disease. This book is a comprehensive compilation of studies from leading international experts on various aspects of myocarditis. The first section of the book provides a clinical perspective on the disease. It contains comprehensive reviews of the causes of myocarditis, its classification, diagnosis, and treatment. It also includes reviews of Perimyocarditis; Chagas' chronic myocarditis, and myocarditis in HIV-positive patients. The second section of the book focuses on the pathogenesis of myocarditis, discussing pathways and mechanisms activated during viral infection and host immune response during myocarditis. The third, and final, section discusses new findings in the pathogenesis that may lead to new directions for clinical diagnosis, including use of new biomarkers, and new treatments of myocarditis.",isbn:null,printIsbn:"978-953-307-289-0",pdfIsbn:"978-953-51-6529-3",doi:"10.5772/812",price:139,priceEur:155,priceUsd:179,slug:"myocarditis",numberOfPages:442,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"641982de7111f331fc849131901b3a68",bookSignature:"Daniela Cihakova",publishedDate:"October 21st 2011",coverURL:"https://cdn.intechopen.com/books/images_new/288.jpg",numberOfDownloads:78698,numberOfWosCitations:22,numberOfCrossrefCitations:5,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:23,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:50,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 2nd 2010",dateEndSecondStepPublish:"November 30th 2010",dateEndThirdStepPublish:"April 6th 2011",dateEndFourthStepPublish:"May 6th 2011",dateEndFifthStepPublish:"July 5th 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"61797",title:"Dr.",name:"Daniela",middleName:null,surname:"Cihakova",slug:"daniela-cihakova",fullName:"Daniela Cihakova",profilePictureURL:"https://mts.intechopen.com/storage/users/61797/images/304_n.jpg",biography:"Dr. Daniela Cihakova is an Assistant Professor and Director of Immunologic Disorders Laboratory at Department of Pathology, Johns Hopkins University in Baltimore, Maryland, United States. 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Among these alternatives, wind energy is a promising technology and recorded the fastest growing installed alternative‐energy production according to reference [1]. It is expected that by the year 2030, at least 20% of the United States energy will be supplied by onshore and offshore wind farms [1]. In the next decade and half, it is vital that authorities record a significant increase in wind turbine installations and operability. Nevertheless, the prime conflict will continue to be the ongoing challenge to maintain a profitable and competitive cost of energy with fossil‐fuel sources. Throughout the last 30 years, wind turbines have grown in size in order to reduce the cost of energy typically expressed in $/kWh. As a result, structural performance, durability requirements, safety hazards, transportation complications, noise and aesthetic pollutions all become issues that are more challenging for designers. Moreover, energy policies, international treaties, legislations and regulations set by governments have to be respected. For this reason, resolving the complex design problem of wind turbine design can be only achieved by
Number of published documents on wind turbine design in the last 40 years (reproduced from Scopus database).
The rapid growth in the number of research papers on wind turbine design and optimization (Figure 1) during the last two decades highlights the status of the field of wind turbine optimization. In the past, some authors have compared the impact of different optimization objectives on the quality of the solution, others have reviewed the optimization algorithms, energy policies, economics, environmental impacts of wind turbines, but numerous researchers have proposed different optimization methodologies and resolution strategies. According to a study conducted by Chehouri et al. [2] in 2014, it was identified that less than 25% of the surveyed wind turbine optimization problems were solved using a multi‐objective algorithm. In fact, solving such problems is not a straightforward task and often requires innovative techniques and algorithms. However, the main benefit is the ability to draw trade‐off curves that reveal weaknesses and anomalies in the wind turbine design. For instance, it is very useful to pursue both minimum cost of energy and maximum annual energy production in the early stage of the design process.
\n\nMany authors carried a multidisciplinary study [3–12], where many objectives are considered in the design of wind turbines. The most common technique to combine conflicting functions (such as annual energy production and cost of energy) is by means of an appropriate set of weights. The variations that exist among these contradictory functions are essential for designers and therefore pursue to sketch the Pareto fronts.
\nIn order to undertake the design of a horizontal wind turbine under multi‐objective optimization (MOOP), there are numerous issues to be considered. The motif of this chapter is to present the fundamental principles of multi‐objective optimization in the design of wind turbines. At the outset of this chapter in Section 2, we briefly discuss the fundamentals and terminology of multi‐objective optimization. Section 3 highlights the objective functions that are used by designers. The design constraints that are enforced by wind turbine designers are enumerated in Section 4. We list the most relevant multi‐objective optimization applied in wind turbine design in Section 5. The most common optimization algorithms used to solve multi‐objective wind turbine optimization problems are examined in Section 6. Finally, a numerical example that demonstrates the resolution of a multi‐objective design problem using a genetic algorithm (GA) is presented in Section 7.
\nThe term optimization refers to the finding of one or more feasible solutions, which correspond to extreme values of one or multiple objectives. Optimization methods are important in scientific experiments, particularly in engineering design and decision‐making. When the problem is to find the optimal solution of one objective, the task is called
In engineering optimization, the designers are sometimes interested in finding one or more optimum solutions when dealing with two or more objective functions. This is known as
Let us discuss the fundamental difference between single‐ and multi‐objective optimization by taking two conflicting objective functions as examples. Obviously, each objective function possesses a unique and different optimal solution. For instance, if one is interested in buying a house, the decision‐making has to take into consideration the cost and the comfort. If the buyer is willing to sacrifice comfort, they will get a house with the lowest price. However, if money is not an issue, then the buyer is able to afford a house with the best comfort. Between these two extremes, there exist many house choices at various costs and comfort. Now the big question is among these trade‐offs, which solution is the best with respect to both objectives? Ironically, no house among the trade‐off choices is the best with respect to both cost and comfort. Without any further information about these solutions (in our case example the houses), no solution from the set of trade‐off can be said to be better than any other. This is the fundamental difference between a multi‐objective and a single‐objective optimization problem. From a practical standpoint, after a set of trade‐off solutions are found, the user will use higher‐level information to determine the convenient solution.
\nNumber of published documents on multi‐objective evolutionary algorithms.
The classical way to solve multi‐objective problems is to scalarize multiples objectives with a relative preference vector. Since only a single optimized solution can be found in one simulation, evolutionary algorithms shined as interesting methods to solve MOOP. The main reason is, unlike classical methods, EAs use a population of solutions in each iteration and therefore the outcome of an EA is a population of solutions. This ability to find multiple solutions in one single run made EAs an ideal approach to solve multi‐objective optimization problems.
\nAccording to the available literature, the first real application of evolutionary algorithms in the determination of trade‐off solutions for a MOOP was proposed in the doctoral dissertation of David Schaffer [13]. He developed the vector‐evaluated genetic algorithm (VEGA), which demonstrated the ability of genetic algorithm to capture multiple trade‐off solutions. Not much attention was given until another half a decade when David E. Goldberg published his book in 1989 [14] on a multi‐objective evolutionary algorithm (MOEA) using the concept of dominance. From the latter derived many MOEAs such as Srinivas and Deb\'s non‐dominated sorting (NSGA) [15] and the niched Pareto GA by Horn et al. [16]. Other techniques different from the domination‐based MOEAs were proposed by Kursawe in 1990 (Kursawe\'s diploidy approach [17]) and Hajela and Lin\'s weighted‐based approach [18] just to name a few. It can be easily seen from Figure 2 that the number of studies conducted on the topic of MOEA has increased well over the last two decades. In less than 10 years, the number of year‐wise papers has tripled and it can be expected that the growth will continue as new studies, books, surveys, research papers and dissertations will be published.
\n\nA multi‐objective optimization problem is composed of a number of objective functions, which are to be maximized or minimized. Similar to single‐objective problems, the MOOP is subjected to a set of design constraints, which any optimal solution must satisfy. We can state the general form of a multi‐objective optimization problem as follows:\n
The solution
As stated earlier, the task in multi‐objective optimization problems is to find a set of solution called the Pareto‐optimal solution set, in which any two solutions must be non‐dominated with respect to each other. In addition, any solution in the search space must be dominated by at least one point in the Pareto set. Therefore, the ultimate goal in multi‐objection optimization is to find a set of solutions as close as possible to the Pareto‐optimal front and as diverse as possible. The concept of domination is used in most MOOP algorithms. Without going into deep details, a solution
The solution
The solution
To gain more knowledge on the procedures to find the non‐dominated set in a given set
The objective function that wind turbine engineers have used in their designs has evolved in the last few decades. In the early 1980s, the focus was towards the maximization of the power coefficient (
The annual energy is obtained by the integration of the wind turbine power curve with a wind‐speed distribution (e.g. Weibull) over the wind‐speed spectrum from the cut‐in to cut‐out speed [Eq. (2)]. One of the reasons why this metric is chosen by designers is due to the absence of a reliable structural and cost model. On the other hand, if the cost of energy is insignificant to the manufacturers and consumers, the maximum energy production is assumed as\n
Jureczko et al. [19] developed a numerical model of the wind turbine blade to perform a multi‐criteria discrete‐continuous optimization of wind turbine blades with the blade mass as the main objective function and the criteria\'s translated into constraints. Liao et al. [20] developed a multi‐criteria‐constrained design model with respect to minimum blade mass integrating a particle swarm optimization (PSO) algorithm using Federation Against Software Theft (FAST) [21]. Ning et al. [22] inspects the minimization of the turbine mass to AEP ratio as one of three examined objective functions. In a recent journal, Chen et al. [23] argue that a lighter blade mass will be beneficial to improve fatigue life based on requirements of the blade\'s strength and stiffness. Therefore, the minimum mass of the wind turbine blade was chosen as objective function.
\nMost engineering optimization problems include a set of equality and inequality constraints consisting of both linear and/or non‐linear types. Generally, solving constrained optimization problems is more challenging than unconstrained systems. We have identified the most relevant constraints imposed by wind turbine blade designers as follows:
\nA simple condition is set to prevent the collision of the blade with the ground.
\nA constraint for the maximum tip deflection was included to ensure the local and global stability of the blade.
\nSome designers include a feasibility condition on the shell thickness and the surface of the airfoil to guarantee a proper trailing‐edge separation.
\nIn order to control the aerodynamic behaviour of the airfoil near stall, various constraints can be applied. For example, the absolute value of the slope beyond the stall angle can be regulated. Similarly, the coefficient of moment
A major objection for wind turbines is the noise that it generates as the blades rotate. Most of the aerodynamic noise models are semi‐empirical and origin from the tip‐vortex/trailing‐edge interaction, turbulent inflow or the trailing‐edge thickness.
\nThe wind turbine is subjected to a large number of loads and therefore its components will be exposed to high stresses. To constrain these stresses, particularly on the wind blades and gearbox, the designers add inequality constraints that relate the generated stresses and the ultimate permissible stresses.
\nIn order to prevent the occurrence of resonance, the natural frequency of the blade must be separated from the rotor\'s rotation harmonics. Therefore, many designers limit some natural frequencies
Researchers began examining multi‐objective optimization algorithms in the design of wind turbines only two decades ago. In 1996, Selig and Coverstone‐Carroll [5] examined the maximization of the AEP with no or few constraints on the loads. A short year later, Giguère and Selig [3] presented a multidisciplinary optimization (MDO) for the blade geometry of horizontal axis wind turbines (HAWTs). A sharing function [26] is used to obtain the trade‐off curve between cost and energy. Only the structure of the blades is considered; however, the effects of the rotor on the wind turbine components are accounted in the cost model. The form of the cost model is indicated as follows (Eq. (3)):\n
where
Benini et al. [6] apply an MOEA for the design optimization of stall‐regulated wind HAWT with a trade‐off between the ratio of AEP per wind park area (AEPdensity to maximize) and the cost of energy (minimize). The idea behind using the first metric is that the number of turbines that can be installed in a given area is inversely proportional to the square of the turbine radius. The MOEA handles the design parameters and searches for the optimal solutions following a set of Pareto concepts and basic principles of genetic programming [27, 28]. The authors choose the tip‐speed, hub/tip ratio, and chord and twist distributions as their design variables. An assumption is made that the total turbine cost is reconstructed from the cost of the turbine blade alone, using the cost model from Eq. (4):\n
where
In 2010, Grujicic et al. [10] developed a two‐level optimization scheme to solve the MOOP. In the inner level, for a given aerodynamic design, the blade mass is minimized. In the outer level, a cost‐assessment analysis is performed. This procedure is repeated until minimums are found for both the outer‐ and inner‐level loops.
\nKusiak et al. [9] introduced a data‐driven approach to study the impact of turbine control on vibration. The authors developed a vibration prediction model using neural networks. Three objectives were included in the study (two vibrations and the power output), and a weighted sum of these objectives is minimized and set as follows (Eq. (5)):\n
where
In reference [29], Kusiak and Zheng present an approach to optimize the power factor and the power output of the wind turbine (bi‐objective problem established by weights), using data‐mining and evolutionary computation. The proposed approach generated optimized settings of the generator torque and the blade‐pitch angle.
\nIn the same year, Bottasso et al. [7] presented a thorough description of a multidisciplinary design optimization procedure. The authors assume that the weight is correlated to the cost. Since a reliable cost model is not offered to the public, no particular cost model was used. Instead of formulating a Pareto‐optimal design problem, a combined cost was defined as the ratio of the annual energy production to the total weight. A two‐stage sequential‐constrained optimization algorithm was used to solve the constrained problem.
\nA year later, Wang et al. [11] presented a multi‐objective algorithm. The power coefficient
Where
In the design of HAWTs, the most obvious feature is the rapid growth in the size of HAWT blades [30]; therefore, it is insufficient to perform an airfoil shape optimization by itself. Rather, the entire blade geometry must be taken into consideration. According to reference [12], a wind turbine blade airfoil should satisfy the following aerodynamic requirements:
High lift‐to‐drag ratio (
High lift coefficient
Good performance during the stochastic behaviour of the wind flow.
Low sensitivity to leading‐edge roughness.
The reader is referred to references [31–33] for the design of more efficient wind turbine blade airfoils. Ju et al. [12] developed a robust design optimization (RDO) for the design of a new series of wind turbine airfoils by maximizing both the
Multidisciplinary optimization was far and widely recognized of having the potential of becoming the cutting edge of the future [34, 35]. Kim [36, 37] and Michelena [38] apply maximum length sequence (MLS) algorithms called the target‐cascading methodology method, where multiple levels and interfacing between the levels are defined.
\nFor offshore wind turbines, the environmental conditions are more severe and more considerations have to be taken into account. Designers aim for a more proficient use of the capacity of the very expensive electrical cables, foundation, installation and erection costs.
\nThe selection of the appropriate optimization algorithm is a critical undertaking in any engineering optimization problem that relies on the attributes of the design space and on the nature of the problem. The final results depend on the algorithm in terms of accuracy and local minima sensitivity. Throughout the years, the algorithms used to solve wind turbine design problems have matured. At the outset, most of the methods derived directly from the blade‐element momentum (BEM) theory, typically from Wilson and Lissaman [39]. In the 1990s, Selig and Coverstone‐Carroll [5] were one of the originals to suggest a method based on GA for their wind turbine blade design tool. With the need to carry a multidisciplinary or multi‐objective optimization design, Wood [40] and Sale et al. [41] simplified the MOOP into a single‐objective question using a classical‐weighted method. The approaches for solving conventional multi‐objective design problems include:
objective‐weighted method
hierarchical optimization method
goal‐programming method.
It is important to highlight that all of the above algorithms convert the multi‐objective problem into a single‐objective problem. According to Ribeiro et al. [42], optimization algorithms can be categorized into two groups: gradient‐based approaches (GBAs) and heuristic algorithms, whereas Endo [43] separates the optimization methods between genetic and non‐genetic algorithms. In the last decades, in order to solve complicated optimization problems, evolutionary algorithms have been suggested such as:
\nMeta‐heuristics are algorithms often inspired from nature, designed to replace or assist conventional solvers. This is a growing research field since the last few decades as meta‐heuristics are now emerging as alternatives to the classical approaches.
\nAn interesting fact can be drawn from the progress of the field of meta‐heuristics and wind turbine optimization. In the recent years, wind energy showed an increase in the use of optimization methods such as linear programming, Lagrangian relaxation, quadratic programming and heuristic optimization (precisely genetic algorithm and particle swarm optimization) to name a few. However, it can be said that the gradient‐based approaches and genetic algorithm are the two most popular optimization algorithms that have been applied in wind turbine design. The reason is quite simple, in the case of blade‐geometry optimization, there is a large number of design variables, which are continuous (e.g. chord and twist distributions, blade pitch, etc.) and discrete (e.g. airfoil family, number of blades, etc.) at the same time. Moreover, some of these design variables are dependent from one another (e.g. chord and twist), as well as competing objectives within the definition of the objective function (e.g. cost of energy).
\nGAs are the most popular evolutionary algorithms because of their robustness and reliability in wind turbine design problems. A genetic algorithm is an optimization method that mimics Darwin\'s principle of ‘survival of the fittest’ over a population of solutions (individuals) that evolves from one generation to another. It was originally proposed by Holland in 1975 [48]. Individuals with a large ‘fitness’ value have a superior probability to ‘reproduce’ in forming the new generation. Similar to a DNA chain, each individual is coded in one string and uses reproduction, crossover and mutation operators to direct the search over the generations. The usefulness of a GA is due to its robustness in multimodal design spaces. Likewise, GA explores non‐linear, non‐derivable, non‐continuous domains and they are less sensitive to the initial domain.
\nIn this section, we will solve a numerical example for the design of a wind turbine blade using a GA multi‐objective optimization algorithm. The objective functions are the blade mass and the annual energy production. In order to calculate the mass, a structural model must be constructed. For the purpose of this study, a preliminary tool called Co‐Blade [49] is used. As for the annual energy, WT‐Perf [50] is introduced in the multi‐objective platform to generate the AEP.
\nCo‐Blade is a tool that helps designers to compute the structural properties of a wind turbine blade. It uses a combination of classical lamination theory (CLT) with an Euler‐Bernoulli theory, and a shear‐flow theory applied to composite beams is used to perform its analysis. This approach allows for a direct computation of the structural properties of a given blade, within several seconds of execution. The fitness function that Co‐Blade minimizes is the blade mass penalitized by the maximum stress, buckling, deflection and the natural frequency. The design variables are the chord‐wise width of the spar cap at the inboard and outboard locations, the thickness of the ‘blade‐root’ material and the thicknesses of the laminas within the leading‐edge panel (LEP), trailing‐edge panel (TEP), spar cap and shear webs along the length of the blade. They are listed in Table 1.
\nAt first, the blade is represented as a cantilever beam under flap‐wise and edge‐wise bendings, axial deflection and elastic twist. Additional coupling between bending, extension and torsion is accounted for, due to the offsets between the beam‐shear centre, tension centre and centre of mass from the blade‐pitch axis (Figure 3). The beam cross sections are assumed to be thin‐walled, closed and single‐ or multicellular, and the periphery of each beam cross section is discretized as a connection of flat composite laminates.
\nParameter | \nDESCRIPTION | \n
---|---|
w_cap_inb, w_cap_oub | \nWidth of the spar cap normalized by the chord length at the INB_STN and OUB_STN blade stations | \n
t_blade_root | \nThickness of the ‘blade‐root’ material at the INB_STN blade station | \n
t_blade_skin1 …t_blade_skinN | \nThickness of ‘blade‐shell’ material at control points 1 through NUM_CP. The control points are equally spaced along the blade between the TRAN_STN and OUB_STN blade stations | \n
t_cap_uni1 …t_cap_uniN | \nThickness of ‘spar‐uni’ material at control points 1 through NUM_CP | \n
t_cap_core1 …t_cap_coreN | \nThickness of ‘spar‐core’ material at control points 1 through NUM_CP | \n
t_lep_core1 …t_lep_coreN | \nThickness of ‘LEP‐core’ material at control points 1 through NUM_CP | \n
t_tep_core1 …t_tep_coreN | \nThickness of ‘TEP‐core’ material at control points 1 through NUM_CP | \n
t_web_skin1, t_web_skin2 | \nThickness of ‘web‐shell’ material at the two control points located at INB_STN and OUB_STN | \n
t_web_core1, t_web_core2 | \nThickness of ‘web‐core’ material at the two control points located at INB_STN and OUB_STN. | \n
w_cap_inb, w_cap_oub | \nWidth of the spar cap normalized by the chord length at the INB_STN and OUB_STN blade stations | \n
t_blade_root | \nThickness of the ‘blade‐root’ material at the INB_STN blade station | \n
t_blade_skin1 …t_blade_skinN | \nThickness of ‘blade‐shell’ material at control points 1 through NUM_CP. The control points are equally spaced along the blade between the TRAN_STN and OUB_STN blade stations | \n
t_cap_uni1 …t_cap_uniN | \nThickness of ‘spar‐uni’ material at control points 1 through NUM_CP | \n
t_cap_core1 …t_cap_coreN | \nThickness of ‘spar‐core’ material at control points 1 through NUM_CP | \n
t_lep_core1 …t_lep_coreN | \nThickness of ‘LEP‐core’ material at control points 1 through NUM_CP | \n
Design variable for Co‐Blade.
Orientation of the blade‐axe systems [
In regard to Euler‐Bernoulli beam theory, the beam cross sections are considered as heterogeneous and each of the material properties depends on the location in each cross section. The structural analysis at each discrete portion of the composite beam characterizes effective mechanical properties computed via classical lamination theory. Each discrete portion of the cross section then contributes to the global section properties of the composite beam (described further in references [51, 52]). Once the global cross‐sectional properties are calculated, the deflections and effective beam axial stress (
As mentioned earlier, Co‐Blade applies a penalized blade mass defined as the following (Eq. (7)):\n
Subject to
\nBefore we describe our fitness function, let us briefly discuss the second half of the multi‐objective algorithm, the aerodynamic tool that calculates the AEP.
\nWT‐Perf uses blade‐element momentum theory to predict the performance of wind turbines with good accuracy. Users must build an appropriate input file that consists of the following set of data (Table 2):
Model configuration
WT‐Perf algorithm configuration
Cavitation model
Turbine data
Aerodynamic data
Input/output settings
Analysis settings.
E11 | \nE22 | \nG12 | \nMaterial Name | \n||
---|---|---|---|---|---|
(Pa) | \n(Pa) | \n(Pa) | \n(‐) | \n(kg/m3) | \n(‐) | \n
2.80E + 10 | \n1.40E + 10 | \n7.00E + 09 | \n0.4 | \n1850 | \n(blade‐root) | \n
2.80E + 10 | \n1.40E + 10 | \n7.00E + 09 | \n0.4 | \n1850 | \n(blade‐shell) | \n
4.20E + 10 | \n1.40E + 10 | \n3.00E + 09 | \n0.28 | \n1920 | \n(spar‐uni) | \n
2.60E + 08 | \n2.60E + 08 | \n2.00E + 07 | \n0.3 | \n200 | \n(spar‐core) | \n
2.60E + 08 | \n2.60E + 08 | \n2.00E + 07 | \n0.3 | \n200 | \n(LEP‐core) | \n
2.60E + 08 | \n2.60E + 08 | \n2.00E + 07 | \n0.3 | \n200 | \n(TEP‐core) | \n
1.40E + 10 | \n1.40E + 10 | \n1.20E + 10 | \n0.5 | \n1780 | \n(web‐shell) | \n
2.60E + 08 | \n2.60E + 08 | \n2.00E + 07 | \n0.3 | \n200 | \n(web‐core) | \n
Design variables for Co‐Blade.
We have now defined two conflicting objective functions, the blade mass and the annual energy. Solving such MOOP can be achieved by the method of scalarizing. It consists of formulating a single‐objective optimization problem such that optimal solutions to the single‐objective optimization problem are Pareto‐optimal solutions to the MOOP. A general formulation for a scalarization of a multi‐objective optimization is given as (Eq. (8)):\n
where the weights of the objectives
We propose to use the following fitness function to minimize the mass and maximize the annual energy production (Eq. (9)):\n
For a value of alpha near zero, the mass ratio is eliminated and the fitness function becomes
Let us consider the following mechanical properties during the structural analysis. In our study, these properties are derived from Sandia 100‐m blade SNL‐100 [54]. Table 3 lists the mechanical properties utilized in the structural design of the blade. Likewise, in Table 2, we list the configurations (input, model, turbine data and algorithm) for the input file required by the WT‐Perf solver. The general flow chart of multi‐objective optimization algorithm can be summarized in Figure 5. The complete inputs for the multi‐objective optimization algorithm are listed in Table 4.
\nPareto front for the given numerical example in Section 7.
False | \nEcho: | \nEcho input parameters to ‘<rootname>.ech’? | \n
True | \nDimenInp: | \nTurbine parameters are dimensional? | \n
True | \nMetric: | \nTurbine parameters are Metric (MKS vs FPS)? | \n
1 | \nNumSect: | \nNumber of circumferential sectors. | \n
1000 | \nMaxIter: | \nMaximum number of iterations for induction factor. | \n
1.00E + 06 | \nATol: | \nError tolerance for induction iteration. | \n
1.00E + 06 | \nSWTol: | \nError tolerance for skewed‐wake iteration. | \n
True | \nTipLoss: | \nUse the Prandtl tip‐loss model? | \n
True | \nHubLoss: | \nUse the Prandtl hub‐loss model? | \n
True | \nSwirl: | \nInclude Swirl effects? | \n
True | \nSkewWake: | \nApply skewed‐wake correction? | \n
True | \nAdvBrake: | \nUse the advanced brake‐state model? | \n
True | \nIndProp: | \nUse PROP‐PC instead of PROPX induction algorithm? | \n
True | \nAIDrag: | \nUse the drag term in the axial induction calculation? | \n
True | \nTIDrag: | \nUse the drag term in the tangential induction calculation? | \n
3 | \nNumBlade: | \nNumber of blades. | \n
10 | \nRotorRad: | \nRotor radius (length). | \n
0.5 | \nHubRad: | \nHub radius (length or div by radius). | \n
0 | \nPreCone: | \nPrecone angle, positive downstream (deg). | \n
0 | \nTilt: | \nShaft tilt (deg). | \n
0 | \nYaw: | \nYaw error (deg). | \n
30 | \nHubHt: | \nHub height (length or div by radius). | \n
30 | \nNumSeg: | \nNumber of blade segments (entire rotor radius). | \n
Input file for WT‐Perf.
Flow chart of the multi‐objective optimization algorithm.
1000 | \nMaxIter: | \nMaximum number of iterations for induction factor. | \n
1.00E‐06 | \nATol: | \nError tolerance for induction iteration. | \n
1.00E‐06 | \nSWTol: | \nError tolerance for skewed‐wake iteration. | \n
True | \nTipLoss: | \nUse the Prandtl tip‐loss model? | \n
True | \nHubLoss: | \nUse the Prandtl hub‐loss model? | \n
True | \nSwirl: | \nInclude Swirl effects? | \n
True | \nSkewWake: | \nApply skewed‐wake correction? | \n
True | \nAdvBrake: | \nUse the advanced brake‐state model? | \n
True | \nIndProp: | \nUse PROP‐PC instead of PROPX induction algorithm? | \n
True | \nAIDrag: | \nUse the drag term in the axial induction calculation? | \n
True | \nTIDrag: | \nUse the drag term in the tangential induction calculation? | \n
3 | \nNumBlade: | \nNumber of blades. | \n
0 | \nYaw: | \nYaw error (deg). | \n
30 | \nHubHt: | \nHub height (length or div by radius). | \n
0.00001464 | \nKinVisc: | \nKinematic air viscosity | \n
0 | \nShearExp: | \nWind‐shear exponent (1/7 law = 0.143). | \n
False | \nUseCm: | \nAre Cm data included in the airfoil tables? | \n
True | \nTabDel: | \nMake output tab‐delimited (fixed‐width otherwise). | \n
True | \nKFact: | \nOutput dimensional parameters in | \n
True | \nWriteBED: | \nWrite out blade‐element data to ‘<rootname>.bed\'? | \n
True | \nInputTSR: | \nInput speeds as TSRs? | \n
\'mps\' | \nSpdUnits: | \nWind‐speed units (mps, fps, mph) | \n
0 | \nNumCases: | \nNumber of cases to run. Enter zero for parametric analysis. | \n
WS or TSR | \nRotSpd Pitch | \nRemove following block of lines if NumCases is zero. | \n
3 | \nParRow: | \nRow parameter (1‐rpm, 2‐pitch, 3‐tsr/speed). | \n
1 | \nParCol: | \nColumn parameter (1‐rpm, 2‐pitch, 3‐tsr/speed). | \n
2 | \nParTab: | \nTable parameter (1‐rpm, 2‐pitch, 3‐tsr/speed). | \n
True | \nOutPwr: | \nRequest output of rotor power? | \n
True | \nOutCp: | \nRequest output of Cp? | \n
True | \nOutTrq: | \nRequest output of shaft torque? | \n
True | \nOutFlp: | \nRequest output of flap‐bending moment? | \n
True | \nOutThr: | \nRequest output of rotor thrust? | \n
0.0 0.0 0.0 | \nPitSt, PitEnd, PitDel: | \nFirst, last, delta blade pitch (deg). | \n
80 80 0.00 | \nOmgSt, OmgEnd, OmgDel: | \nFirst, last, delta rotor speed (rpm). | \n
t | \nSELF_WEIGHT: | \nInclude self‐weight as a body force? | \n
t | \nBUOYANCY: | \nInclude buoyancy as a body force? | \n
true | \nCENTRIF: | \nInclude centrifugal force as a body force? | \n
true | \nDISP_CF: | \nApply correction factors to the beam displacements? | \n
0 | \nN_MODES: | \nNumber of modes to be computed | \n
50 | \nN_ELEMS: | \nNumber of blade finite elements to be used in the modal analysis | \n
t | \nOPTIMIZE: | \nPerform optimization of composite layup? | \n
GS | \nOPT_METHOD: | \nOptimization algorithm for the optimization of composite layup | \n
false | \nOPT_PITAXIS: | \nOptimize the pitch axis? | \n
0.375 | \nPITAXIS_VAL: | \nPitch axis value outboard of max chord (ignored if OPT_PITAXIS = false) | \n
3 | \nINB_STN: | \nInboard station where the leading‐ and trailing‐edge panels, spar caps and shear webs begin | \n
8 | \nTRAN_STN: | \nStation where the root transition ends | \n
28 | \nOUB_STN: | \nOutboard station where the leading‐ and trailing‐edge panels, spar caps and shear webs end | \n
4 | \nNUM_CP: | \nNumber of control points between INB_STN and OUB_STN | \n
false | \nREAD_INITX: | \nRead the initial values for the design variables from INITX_FILE? | \n
none | \nINITX_FILE: | \nInput file for the initial values of the design variables. | \n
false | \nWRITE_STR: | \nWrite structural input files at each function evaluation? | \n
f | \nWRIT E_F_ALL: | \nWrite the fitness value and penalty factors at each function evaluation? | \n
f | \nWRIT E_X_ALL: | \nWrite the design variables at each function evaluation? | \n
f | \nWRITE_X_ITER: | \nWrite the design variables at each iteration? | \n
100 | \nNumGens | \nMaximum number of generations for GA iterations | \n
100 | \nPopSize | \nNumber of individuals per generation | \n
1 | \nEliteCount | \nNumber of elite individuals per generation | \n
0.5 | \nCrossFrc | \nFraction of individuals created by crossover | \n
1.00E‐06 | \nGATol | \nError tolerance for the GA fitness value | \n
1.225 | \nFLUID_DEN: | \nFluid density (kg/m3) | \n
9.81 | \nGRAV: | \nGravitational acceleration (m/s2) | \n
6.03 | \nU_mean: | \nLong‐term mean flow (m/s) | \n
1.91 | \nWeib_k: | \nShape factor | \n
6.8 | \nWeib_c: | \nScale factor | \n
30 | \nNUM_SEC: | \nNumber of blade cross sections | \n
10 | \nBLD_LENGTH: | \nBlade length (m) | \n
0.5 | \nHUB_RAD: | \nHub radius (m) | \n
0 | \nSHAFT_TILT: | \nShaft tilt angle (deg) | \n
0 | \nPRE_CONE: | \nPrecone angle (deg) | \n
180 | \nAZIM: | \nAzimuth angle (deg) | \n
100 | \nMAX_ROT | \nMaximum rotational speed (rpm) | \n
10 | \nMIN_ROT | \nMinimum rotational speed (rpm) | \n
cosine | \nINTERP_AF: | \nInterpolate airfoil coordinates? (choose "none", "cosine", or "equal" with no quotation marks) | \n
1 | \nElmSpc | \nBlade‐element radial spacing (0 equal, 1 cosinus) | \n
60 | \nN_AF: | \nNumber of points in interpolated airfoil coordinates (ignored | \n
mats‐ Wind.inp | \nMATS_FILE: | \nInput file for material properties | \n
0.13 | \nRootTranSt | \nStart of root transition region | \n
3 | \nRootTranSt_index | \nIndex of start of root transition region | \n
0.288 | \nRootTranEnd | \nEnd of root transition region | \n
8 | \nRootTranEnd_index | \nIndex of end of root transition region | \n
3 9 19 26 30 | \nCP_Index | \nIndex of control points (chord and twist) | \n
Input file for the multi‐objective algorithm.
Within the last 20 years, wind energy conversion systems have reached maturity. The obvious growing worldwide wind energy market will culminate to further improvements. The continuous effort for the advancement in horizontal wind turbine performance strategies and techniques will result in additional cost reductions. The ultimate aim of any wind turbine manufacture is to design a wind turbine able to compete with fossil fuel. The number of research paper that applies optimization techniques in the attempt to reach an optimal blade design has demonstrated a significant increase in the recent decade alone. Despite the fact that a minimal cost of energy was chosen as the single main objective in most of the research papers, many have argued that it is more stimulating to evaluate the wind turbine design as an optimization problem consisting of more than one objective. Using multi‐objective optimization algorithms, the designers are able to identify a trade‐off curve called Pareto front that reveals the weaknesses, anomalies and rewards of certain targets. We can anticipate that future optimization problems will be set as multidisciplinary formulations. Consequently, solving such difficult optimization problem will require further developments in the optimization algorithm itself. Since traditional optimization techniques cannot overcome many of their drawbacks such as rapid divergence and sensitivity to the initial solution, population‐based and nature‐inspired algorithms will continue to emerge as worthy alternatives.
\n\nIn this chapter, we presented the fundamental principles of multi‐objective optimization in wind turbine design. We have identified the constraints and objective functions mostly targeted by designers. We briefly discussed the fundamentals and terminology of multi‐objective optimization. The most common optimization algorithms used to solve multi‐objective wind turbine optimization problems were presented. We highlighted the emergence of population‐based techniques, particularly genetic algorithms. Finally, we showed the steps to solve a classic multi‐objective wind turbine design problem using a genetic algorithm. The reader is referred to the following publications for further details [2, 55] concerning wind turbine optimization.
\nPositive airway pressure (PAP) is considered the gold standard treatment for patient with OSA [1]. Multiple studies showed the effectiveness of the CPAP therapy with reducing subjective symptoms of OSA, and cardiovascular and neurocognitive risks [2, 3].
The efficacy of CPAP treatment is limited due to patient’s compliance to therapy. Patient will achieve normal functioning with greater nightly CPAP durations [4]. Patient who fail or intolerant to CPAP therapy should consider alternative treatment options which surgery one of them.
Surgical procedures aim to improve airway patency by recognizing the location(s) of obstruction. Patients need to be selected in awareness of the individual underlying pathology, pathophysiology and anatomy, and severity of the disease and comorbidities.
The anatomical cause of OSA is generally heterogeneous, with multiple potential levels of airway obstruction; therefore, many different surgical procedures have been developed for the treatment of OSA [5].
Polysomnogram (PSG) and home sleep testing do not provide information about the location of the obstruction. Therefore, a complete history that include the chief complaint, other significant symptoms, past medical history and surgical history are helpful. Some symptoms can help identify potential surgical approaches.
The history should also include the patient past experience with continuous positive airway pressure (CPAP), an oral appliance, and/or weight loss.
Thorough physical exam to evaluate the structures that impact the upper airway. The nasal airway is evaluated in detail, checking for external deformity, nasal valve collapse, septal position, turbinate size, and nasal polyps.
Oral cavity and oropharynx examination provide information into the protentional upper airway surgery. It provides insight of the tongue size and position, dental health, and palate position.
Trans-nasal flexible laryngoscopy provides adequate evaluation of the lower pharyngeal and laryngeal airway. It gives great view of the entire upper airway while the tongue in native position.
Drug induce sleep endoscopy (DISE) using mild sedation (midazolam or propofol) required in some upper airway procedures like upper airway stimulation therapy [6]. It has been shown to be a valid assessment of the upper airway, with moderate-to-substantial test-retest reliability and moderate-to-substantial inter-rater reliability. It allows the evaluation of the airway in a situation as close to sleep as possible [7, 8].
VOTE (Velum, Oropharynx, Tongue base, and Epiglottis) system specifies grades for the degree of obstruction at the velum, oropharynx, tongue base, and epiglottis, as well as the type of collapse (Table 1).
Components | Classification |
---|---|
Structures | V–velum, including soft palate, uvula or lateral pharyngeal wall O–oropharyngeal walls (including palatine tonsils and lateral wall tissue) T–tongue base E–epiglottis |
Degree of obstruction | 0–No obstruction 1–Partial obstruction 2–Complete obstruction X–Not visualized |
Configuration of collapse | Anteroposterior Lateral Concentric |
The Friedman Palate Position is based on visualization of structures in the mouth with the mouth open widely without protrusion of the tongue. Palate grade I allows the observer to visualize the entire uvula and tonsils. Grade II allows visualization of the uvula but not the tonsils. Grade III allows visualization of the soft palate but not the uvula. Grade IV allows visualization of the hard palate only. Adapted from Ref. [
Several other diagnostic modalities have showed some value to supplement a physical examination, including lateral cephalogram, 3-dimensional cone beam computed tomographic scan, sleep endoscopy, or cine-magnetic resonance imaging (MRI) [10, 11].
A comprehensive counseling should be undertaken prior to the surgery, discussing potential site of the obstruction and non-surgical treatments options.
There are different surgical procedures used to treat OSA. American Academy of Sleep Medicine recommends that patient should be advised about potential surgical success rates and complications, the availability of alternative treatment options. The desired outcomes of treatment include resolution of the clinical signs and symptoms of obstructive sleep apnea and the normalization of sleep quality, the apnea-hypopnea index, and oxyhemoglobin saturation levels [12, 13].
Nasal obstruction has identified as an important target in the treatment of OSA. The main goal is to relive the obstruction as an adjunctive measure to improve the outcomes of continuous positive airway pressure (CPAP) by reducing CPAP pressure requirements, an oral appliance, or other surgery. Although nasal surgery in isolation does not have a consistent effect on the apnea-hypopnea index in OSA patients, it does have strong evidence on improving snoring, subjective sleep quality, daytime sleepiness, sleep-related quality of life measures, and other important OSA outcome measures [14, 15].
Turbinate reductions reduce the obstruction caused by inferior turbinate.
Septoplasty straightening a deformity of the nasal septum.
Nasal valve surgery improves the airflow in patient with nasal valve obstruction.
Rhinoplasty corrects any anatomical deformities that compromise the nasal airway.
The most common adverse outcomes for most of the intranasal procedures are postoperative temporary bleeding and temporary nasal congestions. More serious adverse effects could also occur but rare like cerebrospinal fluid leak.
The extent to which tonsillar hypertrophy contributes to OSA in adults remains unclear. Tonsillectomy with adenectomy is the first line treatment in pediatric patients with severe OSA and adenotonsillar hypertrophy. It also showed substantial improvement in AHI severity, oxyhemoglobin saturation and sleep quality in obese patient with OSA [16]. Patients who undergo tonsillectomy often experience significant reduction in the CPAP pressure required [17]. The most common postoperative complains include postoperative hemorrhage. Other risks such as pain, fever, and infection could also occur.
It represented as the first surgical procedure specifically designed to treat obstructive sleep apnea (OSA) and remains the most commonly performed surgical procedure to treat OSA.
There are multiple approaches have been introducing to address the narrowing or collapse of the retropalatal region (Table 2). It traditionally involved removal of the uvula, a portion of the soft palate, tonsils and closure of the tonsillar pillars. All the new techniques involve resection or repositioning of the palatal tissues and pharyngeal walls to increase the dimension of the pharyngeal airway to reduce obstruction.
Relocation pharyngoplasty | Advancing the soft palate and splinting the lateral pharyngeal wall |
---|---|
Lateral pharyngoplasty | Microdissection of the superior pharyngeal constrictor muscle within the tonsillar fossa, sectioning of this muscle, and suturing of the created laterally based flap of that muscle to the same side palatoglossus muscle |
Zetapalatopharyngoplasty (Z-palatoplasty) | Widen the space between the palate and posterior pharyngeal wall, between the palate and tongue base, and either to maintain or even widen the lateral dimensions of the pharynx |
Expansion sphincter pharyngoplasty | Consist of tonsillectomy, expansion pharyngoplasty, rotation of the palatopharyngeal muscle, a partial uvulectomy, and closure of the anterior and posterior tonsillar pillars |
Palatal advancement | Soft palate is elevated by advancing it towards the hard palate. |
UPPP different procedure approaches.
Tonsil size is graded from 0 to 4. Tonsil size 0 denotes surgically removed tonsils. Size 1 implies tonsils hidden within the pillars. Tonsil size 2 implies the tonsils extending to the pillars. Size 3 tonsils are beyond the pillars but not to the midline. Tonsil size 4 implies tonsils extend to the midline. Adapted from Ref. [
To determine the likelihood for successful resolution of OSA after UPPP, a staging system was developed based on tonsil size, tongue-palate position, and BMI (Table 3) [18].
Patients with stage I found to have successful outcome of 80% when treated with UPPP. Stage II patients has success rate of 37.9% and only 8.1% for patients with stage III [18].
In a study where they used DISE to evaluate the site of the obstruction with the Friedman clinical staging system for patients selected for UPPP. There was a significant success rate. The result of the surgery as defined by 50% reduction in preoperative AHI with postoperative AHI < 20/h was seen to be 95.2%. There were significant changes in major presenting symptoms (e.g., snoring, excessive daytime sleepiness, disturbed sleep, morning headaches, dry mouth, and forgetfulness) documented 6 months after surgery. Postoperative change in AHI done after 6-month interval was seen to be statistically significant with P value <0.00 [19].
Most common adverse effects of UPPP are severe transient throat pain and chronic subjective dysphagia [20, 21]. Trouble with smell and taste, pharyngeal dryness, globus sensation, voice change, and pharyngonasal reflux were presented after UPPP [20]. The new technique used in UPPP like radiofrequency tissue volume reduction (RFTVR) is safer and less painful than resection technique [20].
For patients, who may still need a CPAP therapy after UPPP surgery, important considerations may include compromise CPAP therapy by increasing mouth air leak and reducing the maximal level of pressure that can be tolerated, especially in procedures with greater resection of soft palate [22, 23].
Multiple procedures were designed to improve the obstruction in the lower pharyngeal airway.
Multiple techniques to improve lower pharyngeal airway by decreasing the volume of the tongue tissues:
Radiofrequency tissue ablation: It is a minimal invasive procedure. Application of a temperature-controlled radiofrequency probe to multiple locations in the base of the tongue. It generates submucosal scar tissues that anticipated to reduce the tongue volume.
Lingual tonsillectomy: Improves airway by removing obstructing lingual tonsil tissue.
Partial midline glossectomy: Resection of the midline tongue base tissue.
Submucosal lingualplasty: Resection of submucosal lingual tissue of the tongue base.
Multiple procedures tend to improve lower pharyngeal airway by advance or stabilize the tongue base and pharyngeal muscular:
Tongue-base suspension: stabilize the tongue and prevent retrolingual collapse by placing a suture to the anterior mandible to create a tongue base sling.
Genioglossus advancement: Advancing the genial tubercle of the anterior mandible forward and create an osteotomy around it.
Hyoid suspension: Suspend the hyoid bone to the thyroid cartilage or mandible by using permanent suture. It helps stabilize the base of the tongue and lower pharynx.
Multiple studies showed the effectiveness of lower pharyngeal and laryngeal procedures. It demonstrates improvements in respiratory physiology during sleep, daytime somnolence and quality of life. Successful sleep study outcomes defined as a reduction in AHI of 50% or more and an AHI of less than 20, was achieved in 35–62% of patients [24].
Adverse effects reported were based on the surgical techniques that been used. Pain, hemorrhage, tongue infection airway complications, taste change and dysphagia seen in partial glossectomy, lingualplasty and lingual tonsillectomy [25].
Postoperative pain and submandibular edema were the two most common complications followed radiofrequency tissue ablation [26].
The maxilla and the mandible are advanced together with both upper and lower teeth to widen the retrolingual and the retropalatal segments of the upper airway. It is beneficial mainly for patients with craniofacial issues, but it is not limited for patients with this problem. The maxilla is moved by a Le fort I osteotomy and the mandible by a sagittal split osteotomy. It is a major operation but showed a significant increase in the pharyngeal airway dimensions and decrease AHI score below the threshold of 20.
New treatment for OSA by Implantable neurostimulator device was approved by US Food and Drug Administration in 2014. It keeps the lower pharyngeal airway open during sleep by activates the protrusion muscles of the tongue via the hypoglossal nerve.
Eligibility criteria include:
Age > = 21 years old
Moderate or severe OSA (AHI > 20 but less than 65 events per hour)
Predominantly obstructive events (central and mixed apneas <= 25 percent of AHI).
Unable to tolerate CPAP
DISE shows no concentric velopharyngeal collapse or any other anatomical findings.
BMI < 32 kg/m2
Hypoglossal Nerve Stimulation showed 68% decrease in AHI score, oxygen desaturation index score decreased by 70%, and improved quality of life [27].
Most common reported adverse outcomes are infection, hemorrhage, and tongue weakness. It is still unknown whether there are long term risks.
The most immediate, effective and definitive treatment for OSA is placing a permanent cannula in the neck to bypassing the upper pharyngeal airway. Patient will be able to breath, speak and eat by capping the tube during waking time and open the cannula during sleep. Tracheostomy significantly decreases apnea index, oxygen desaturation index, sleepiness, and mortality in OSA patients [28].
It requires a long-term care to reduce complications (e.g., pneumonia, mucus plugging, peristomal infections). Therefore, it is recommended primarily for patient with sever and life threating OSA who failed all the other treatment options and in morbid obese patients.
OSA is seen in about 45% of bariatric patients [28]. Surgically induced weight loss showed significantly improves obesity-related sleep apnea. It decreased the mean RDI to 15 ± 2 from 51 ± 4 (preoperatively). In addition, oxygen saturation, sleep efficiency, repaid eye movement latency and the requirement for continuous positive airway pressure [29].
Patient with OSA could have multiple locations of collapse in upper and lower pharyngeal tracts. Those patients would benefit from multilevel surgery. DISE is now a standard procedure during the presurgical evaluation which gives the surgeon personalized anatomical information. A combination of multilevel procedures improved the outcome compare to single-site procedure.
In a meta-analysis that used 49 multilevel surgery articles showed success rate of 66.4% for mixed multilevel surgeries (reduction in the AHI of 50% or more and an AHI of less than 20) [30].
Surgical follow up is based on the type of the surgery. It should include wound management and complications. Patient also needs a long-term follow up by a sleep specialist to evaluate the need for adjunctive use of positive airway pressure or other therapies.
There has been a significant improvement in the current surgical techniques for the treatment of the OSA. Surgical management is usually warranted in appropriately selected patients who could not or failed CPAP or other alternative therapies. Also, for patient with anatomical abnormalities that can be corrected. Currently DISE is very useful method and widely used to determine the levels of collapse.
A comprehensive discussion between surgeon and patient prior to the surgery is warranted, discussing realistic expectations of the treatment benefits and complications. Surgical treatments showed long-term benefits in appropriately selected patients but no complete elimination of OSA.
Patient who undergo any surgical procedure will require long term monitoring for recurrence or worsening of OSA.
Huge thank you to all the staff at St. Vincent Hospital’s Sleep Laboratory for all their support.
The authors have no relevant conflicts of interest to disclose.
No industry funding to disclose.
IntechOpen has always supported new and evolving ideas in scholarly publishing. We understand the community we serve, but to provide an even better service for our IntechOpen Authors and Academic Editors, we have partnered with leading companies and associations in the scientific field and beyond.
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Contreras",coverURL:"https://cdn.intechopen.com/books/images_new/1592.jpg",editedByType:"Edited by",editors:[{id:"35182",title:"Dr.",name:"Carlos M.",middleName:null,surname:"Contreras",slug:"carlos-m.-contreras",fullName:"Carlos M. Contreras"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:2,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"32399",doi:"10.5772/36092",title:"Brain Energy Metabolism in Health and Disease",slug:"brain-energy-metabolism-in-health-and-disease",totalDownloads:9132,totalCrossrefCites:1,totalDimensionsCites:10,abstract:null,book:{id:"1592",slug:"neuroscience-dealing-with-frontiers",title:"Neuroscience",fullTitle:"Neuroscience - Dealing With Frontiers"},signatures:"Felipe A. Beltrán, Aníbal I. Acuña, María Paz Miró and Maite A. Castro",authors:[{id:"107041",title:"Dr.",name:"Maite A",middleName:null,surname:"Castro",slug:"maite-a-castro",fullName:"Maite A Castro"},{id:"109692",title:"Mr.",name:"Felipe A",middleName:null,surname:"Beltran",slug:"felipe-a-beltran",fullName:"Felipe A Beltran"},{id:"109695",title:"Mr.",name:"Aníbal",middleName:"I.",surname:"Acuña",slug:"anibal-acuna",fullName:"Aníbal Acuña"},{id:"109696",title:"Ms.",name:"Maria Paz",middleName:null,surname:"Miro",slug:"maria-paz-miro",fullName:"Maria Paz Miro"}]},{id:"54565",doi:"10.5772/67828",title:"The Role of the Amygdala in Regulating the Hypothalamic-Pituitary-Adrenal Axis",slug:"the-role-of-the-amygdala-in-regulating-the-hypothalamic-pituitary-adrenal-axis",totalDownloads:3554,totalCrossrefCites:6,totalDimensionsCites:9,abstract:"We investigated the regulatory role of the amygdala upon the function of the hypothalamic-pituitary-adrenal (HPA) axis as measured by median eminence corticotrophin releasing hormone (CRH) content and serum levels of adrenocorticotrophic hormone (ACTH) and corticosterone. Our findings showed that (1) lesions of the central amygdala inhibited the HPA axis responses to a variety of stressful stimuli. (2) Depletion of norepinephrine or serotonin in the amygdala and hypothalamus and local injections of norepinephrine and serotonin receptor antagonists into the central amygdala inhibited the HPA axis responses to neural stress. Norepinephrine and serotonin agonists injected into the amygdala caused an increase in HPA axis activity. The activation of the amygdala facilitated the in vivo release of serotonin from the paraventricular nucleus following electrical stimulation of the brainstem raphe nuclei. (3) Electrical stimulation of the amygdala impaired the glucocorticoid negative feedback action following neural stressful stimuli probably via a decrease in hippocampal corticosteroid receptors.",book:{id:"5485",slug:"the-amygdala-where-emotions-shape-perception-learning-and-memories",title:"The Amygdala",fullTitle:"The Amygdala - Where Emotions Shape Perception, Learning and Memories"},signatures:"Joseph Weidenfeld and Haim Ovadia",authors:[{id:"190851",title:"Ph.D.",name:"Haim",middleName:null,surname:"Ovadia",slug:"haim-ovadia",fullName:"Haim Ovadia"},{id:"192823",title:"Prof.",name:"Joseph",middleName:null,surname:"Weidenfeld",slug:"joseph-weidenfeld",fullName:"Joseph Weidenfeld"}]},{id:"32393",doi:"10.5772/34852",title:"The Neurochemical Anatomy of Trigeminal Primary Afferent Neurons",slug:"the-neurochemical-anatomy-of-trigeminal-primary-afferent-neurons",totalDownloads:4712,totalCrossrefCites:0,totalDimensionsCites:9,abstract:null,book:{id:"1592",slug:"neuroscience-dealing-with-frontiers",title:"Neuroscience",fullTitle:"Neuroscience - Dealing With Frontiers"},signatures:"Nikolai E. Lazarov",authors:[{id:"101891",title:"Prof.",name:"Nikolai",middleName:null,surname:"Lazarov",slug:"nikolai-lazarov",fullName:"Nikolai Lazarov"}]},{id:"54301",doi:"10.5772/67585",title:"Revisiting the Role of the Amygdala in Posttraumatic Stress Disorder",slug:"revisiting-the-role-of-the-amygdala-in-posttraumatic-stress-disorder",totalDownloads:2172,totalCrossrefCites:2,totalDimensionsCites:8,abstract:"Over the past 20 years, the reactivity of amygdala to emotive stimuli has been explored by emerging neuroimaging techniques in an effort to understand the role of amygdala in the pathophysiology of posttraumatic stress disorder (PTSD). A fear neurocircuitry model, whereby the amygdala is hyperactive due to poor top-down control from the anterior cingulate and ventromedial prefrontal cortices, has been supported by numerous experimental studies and meta-analyses. However, this model has not always been upheld by experimental data and clinical observations. In particular, many neuroimaging studies find that the amygdala fails to activate in response to negative stimuli in individuals with PTSD. Several technical and design issues may explain disparate results regarding amygdala reactivity in PTSD. However, biological and symptom-based factors emerge as possible mediators of amygdala function in PTSD, leading to the conclusion that symptoms of emotional disengagement and dissociation are associated with amygdala hyporeactivity, and symptoms of hypervigilance/hyperarousal and problems with fear conditioning and extinction are reflected by amygdala hyperactivity. Therefore, treatment of PTSD should take into account the nature of amygdala dysfunction in the individual to optimize treatment outcomes.",book:{id:"5485",slug:"the-amygdala-where-emotions-shape-perception-learning-and-memories",title:"The Amygdala",fullTitle:"The Amygdala - Where Emotions Shape Perception, Learning and Memories"},signatures:"Gina L. Forster, Raluca M. Simons and Lee A. Baugh",authors:[{id:"145620",title:"Dr.",name:"Gina",middleName:null,surname:"Forster",slug:"gina-forster",fullName:"Gina Forster"},{id:"195109",title:"Dr.",name:"Raluca",middleName:null,surname:"Simons",slug:"raluca-simons",fullName:"Raluca Simons"},{id:"195110",title:"Dr.",name:"Lee",middleName:null,surname:"Baugh",slug:"lee-baugh",fullName:"Lee Baugh"}]},{id:"55211",doi:"10.5772/intechopen.68618",title:"The Amygdala and Anxiety",slug:"the-amygdala-and-anxiety",totalDownloads:2984,totalCrossrefCites:4,totalDimensionsCites:8,abstract:"The amygdala has a central role in anxiety responses to stressful and arousing situations. Pharmacological and lesion studies of the basolateral, central, and medial subdivisions of the amygdala have shown that their activation induces anxiogenic effects, while their inactivation produces anxiolytic effects. Many neurotransmitters and stress mediators acting at these amygdalar nuclei can modulate the behavioral expression of anxiety. These mediators may be released from different brain regions in response to different types of stressors. The amygdala is in close relationship with several brain regions within the brain circuitry that orchestrates the expression of anxiety. Recent developments in optogenetics have begun to unveil details on how these areas interact.",book:{id:"5485",slug:"the-amygdala-where-emotions-shape-perception-learning-and-memories",title:"The Amygdala",fullTitle:"The Amygdala - Where Emotions Shape Perception, Learning and Memories"},signatures:"Sergio Linsambarth, Rodrigo Moraga-Amaro, Daisy Quintana-\nDonoso, Sebastian Rojas and Jimmy Stehberg",authors:[{id:"144923",title:"Dr.",name:"Jimmy",middleName:null,surname:"Stehberg",slug:"jimmy-stehberg",fullName:"Jimmy Stehberg"},{id:"194182",title:"Ph.D. Student",name:"Rodrigo",middleName:null,surname:"Moraga-Amaro",slug:"rodrigo-moraga-amaro",fullName:"Rodrigo Moraga-Amaro"},{id:"194183",title:"M.Sc.",name:"Sergio",middleName:null,surname:"Linsambarth",slug:"sergio-linsambarth",fullName:"Sergio Linsambarth"}]}],mostDownloadedChaptersLast30Days:[{id:"54675",title:"The Key Role of the Amygdala in Stress",slug:"the-key-role-of-the-amygdala-in-stress",totalDownloads:2940,totalCrossrefCites:3,totalDimensionsCites:4,abstract:"Several data highlighted that stress exposure is strongly associated with several psychiatric disorders. The amygdala, an area of the brain that contributes to emotional processing, has a pivotal role in psychiatric disorders and it has been demonstrated to be highly responsive to stressful events. Here we will review evidences indicating how the amygdala changes its functionality following exposure to stress and how this contributes to the onset of anxiety disorders.",book:{id:"5485",slug:"the-amygdala-where-emotions-shape-perception-learning-and-memories",title:"The Amygdala",fullTitle:"The Amygdala - Where Emotions Shape Perception, Learning and Memories"},signatures:"Diego Andolina and Antonella Borreca",authors:[{id:"190318",title:"Dr.",name:"Diego",middleName:null,surname:"Andolina",slug:"diego-andolina",fullName:"Diego Andolina"},{id:"192832",title:"Dr.",name:"Antonella",middleName:null,surname:"Borreca",slug:"antonella-borreca",fullName:"Antonella Borreca"}]},{id:"55211",title:"The Amygdala and Anxiety",slug:"the-amygdala-and-anxiety",totalDownloads:2985,totalCrossrefCites:4,totalDimensionsCites:8,abstract:"The amygdala has a central role in anxiety responses to stressful and arousing situations. Pharmacological and lesion studies of the basolateral, central, and medial subdivisions of the amygdala have shown that their activation induces anxiogenic effects, while their inactivation produces anxiolytic effects. Many neurotransmitters and stress mediators acting at these amygdalar nuclei can modulate the behavioral expression of anxiety. These mediators may be released from different brain regions in response to different types of stressors. The amygdala is in close relationship with several brain regions within the brain circuitry that orchestrates the expression of anxiety. Recent developments in optogenetics have begun to unveil details on how these areas interact.",book:{id:"5485",slug:"the-amygdala-where-emotions-shape-perception-learning-and-memories",title:"The Amygdala",fullTitle:"The Amygdala - Where Emotions Shape Perception, Learning and Memories"},signatures:"Sergio Linsambarth, Rodrigo Moraga-Amaro, Daisy Quintana-\nDonoso, Sebastian Rojas and Jimmy Stehberg",authors:[{id:"144923",title:"Dr.",name:"Jimmy",middleName:null,surname:"Stehberg",slug:"jimmy-stehberg",fullName:"Jimmy Stehberg"},{id:"194182",title:"Ph.D. Student",name:"Rodrigo",middleName:null,surname:"Moraga-Amaro",slug:"rodrigo-moraga-amaro",fullName:"Rodrigo Moraga-Amaro"},{id:"194183",title:"M.Sc.",name:"Sergio",middleName:null,surname:"Linsambarth",slug:"sergio-linsambarth",fullName:"Sergio Linsambarth"}]},{id:"32387",title:"The Mystery of P2X7 Ionotropic Receptor: From a Small Conductance Channel to a Large Conductance Channel",slug:"the-mystery-of-p2x7-receptor-from-a-small-channel-to-a-big-pore",totalDownloads:2420,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"1592",slug:"neuroscience-dealing-with-frontiers",title:"Neuroscience",fullTitle:"Neuroscience - Dealing With Frontiers"},signatures:"R.X. Faria, L.G.B. Ferreira and L.A. Alves",authors:[{id:"76663",title:"Prof.",name:"Luiz A.",middleName:null,surname:"Alves",slug:"luiz-a.-alves",fullName:"Luiz A. Alves"},{id:"76674",title:"Mr.",name:"Leonardo",middleName:null,surname:"Braga",slug:"leonardo-braga",fullName:"Leonardo Braga"},{id:"79615",title:"Dr.",name:"Robson",middleName:null,surname:"Faria",slug:"robson-faria",fullName:"Robson Faria"}]},{id:"32399",title:"Brain Energy Metabolism in Health and Disease",slug:"brain-energy-metabolism-in-health-and-disease",totalDownloads:9134,totalCrossrefCites:1,totalDimensionsCites:10,abstract:null,book:{id:"1592",slug:"neuroscience-dealing-with-frontiers",title:"Neuroscience",fullTitle:"Neuroscience - Dealing With Frontiers"},signatures:"Felipe A. Beltrán, Aníbal I. Acuña, María Paz Miró and Maite A. Castro",authors:[{id:"107041",title:"Dr.",name:"Maite A",middleName:null,surname:"Castro",slug:"maite-a-castro",fullName:"Maite A Castro"},{id:"109692",title:"Mr.",name:"Felipe A",middleName:null,surname:"Beltran",slug:"felipe-a-beltran",fullName:"Felipe A Beltran"},{id:"109695",title:"Mr.",name:"Aníbal",middleName:"I.",surname:"Acuña",slug:"anibal-acuna",fullName:"Aníbal Acuña"},{id:"109696",title:"Ms.",name:"Maria Paz",middleName:null,surname:"Miro",slug:"maria-paz-miro",fullName:"Maria Paz Miro"}]},{id:"54509",title:"The Contribution of the Amygdala to Reward-Related Learning and Extinction",slug:"the-contribution-of-the-amygdala-to-reward-related-learning-and-extinction",totalDownloads:1742,totalCrossrefCites:3,totalDimensionsCites:3,abstract:"There has been substantial research into the role of the amygdala in fear conditioning and extinction of conditioned fear. The role of the amygdala in appetitive conditioning is relatively less explored. Here, we will review research into the role of the amygdala in reward‐related learning. Research to date suggests that the basolateral and central amygdala are responsible for learning about distinct aspects of a reinforcing event. For example, the basolateral amygdala is essential for distinguishing and choosing between specific rewards based on the specific‐sensory properties of those rewards as well as updating the relative value of specific rewarding events. In contrast, the central amygdala is involved in encoding reinforcement more generally and for regulating motivational influences on responding. 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The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. 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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. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:3,paginationItems:[{id:"7",title:"Bioinformatics and Medical Informatics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",isOpenForSubmission:!0,editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",slug:"slawomir-wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",biography:"Professor Sławomir Wilczyński, Head of the Chair of Department of Basic Biomedical Sciences, Faculty of Pharmaceutical Sciences, Medical University of Silesia in Katowice, Poland. 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. 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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. 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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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His fields of interest are anterior segment disease, keratoconus, glaucoma, corneal dystrophies, and cataracts. His research topics include\nintraocular lens power calculation, eye modification induced by refractive surgery, glaucoma progression, and validation of new diagnostic devices in ophthalmology. \nHe has published more than 100 papers in international and Italian scientific journals, more than 60 in journals with impact factors, and chapters in international and Italian books. He has also edited two international books and authored more than 150 communications or posters for the most important international and Italian ophthalmology conferences.",institutionString:'University of Campania "Luigi Vanvitelli"',institution:{name:'University of Campania "Luigi Vanvitelli"',institutionURL:null,country:{name:"Italy"}}}]},{type:"book",id:"7560",title:"Non-Invasive Diagnostic Methods",subtitle:"Image Processing",coverURL:"https://cdn.intechopen.com/books/images_new/7560.jpg",slug:"non-invasive-diagnostic-methods-image-processing",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Mariusz Marzec and Robert Koprowski",hash:"d92fd8cf5a90a47f2b8a310837a5600e",volumeInSeries:3,fullTitle:"Non-Invasive Diagnostic Methods - Image Processing",editors:[{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",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}]},{type:"book",id:"6843",title:"Biomechanics",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6843.jpg",slug:"biomechanics",publishedDate:"January 30th 2019",editedByType:"Edited by",bookSignature:"Hadi Mohammadi",hash:"85132976010be1d7f3dbd88662b785e5",volumeInSeries:4,fullTitle:"Biomechanics",editors:[{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",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. 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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. 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