Input data of the FunFEM algorithm.
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"5124",leadTitle:null,fullTitle:"Insulation Materials in Context of Sustainability",title:"Insulation Materials in Context of Sustainability",subtitle:null,reviewType:"peer-reviewed",abstract:"This book gives information and guidance on important subjects. It presents the major and efficient applications for efficient insulation materials. The book is divided into two parts. Part I discusses ecological insulation materials. In this part, the three sub-subjects are drafting, Unconventional insulation materials, Jute-Based Insulation Material, and Possible Applications of Corn Cob as a Raw Insulation Material. Part II: discusses Practical Applying and Performance of Insulation Materials (case studies), where three sub-subjects are drafting seismic aspects of the application of thermal insulation boards beneath the building’s foundations, flammability of bio-based rigid polyurethane foam thermal insulation, and the review of some commonly used methods and techniques to measure the thermal conductivity of insulation materials.",isbn:"978-953-51-2625-6",printIsbn:"978-953-51-2624-9",pdfIsbn:"978-953-51-6672-6",doi:"10.5772/61361",price:119,priceEur:129,priceUsd:155,slug:"insulation-materials-in-context-of-sustainability",numberOfPages:150,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"ca203e467e6f6fc2ece46fab2da10bbc",bookSignature:"Amjad Almusaed and Asaad Almssad",publishedDate:"August 31st 2016",coverURL:"https://cdn.intechopen.com/books/images_new/5124.jpg",numberOfDownloads:17455,numberOfWosCitations:47,numberOfCrossrefCitations:41,numberOfCrossrefCitationsByBook:2,numberOfDimensionsCitations:72,numberOfDimensionsCitationsByBook:3,hasAltmetrics:1,numberOfTotalCitations:160,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 8th 2015",dateEndSecondStepPublish:"September 29th 2015",dateEndThirdStepPublish:"December 26th 2015",dateEndFourthStepPublish:"January 25th 2016",dateEndFifthStepPublish:"April 12th 2016",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"110471",title:"Prof.",name:"Amjad",middleName:"Zaki",surname:"Almusaed",slug:"amjad-almusaed",fullName:"Amjad Almusaed",profilePictureURL:"https://mts.intechopen.com/storage/users/110471/images/system/110471.png",biography:"Prof. Amjad Almusaed has a Ph.D. in Architecture (Environmental Design) from Ion Mincu University, Bucharest, Romania. He completed postdoctoral research in 2004 on sustainable and bioclimatic houses at the School of Architecture, Aarhus, Denmark. His research expertise is sustainability in architecture and urban planning and design. He has carried out a great deal of research and technical survey work and has performed several studies in these areas. He has edited many international books and is an active member of many worldwide architectural associations. He has published more than 170 international academic works (papers, research, books, and book chapters) in different languages.",institutionString:"Jönköping University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"10",totalChapterViews:"0",totalEditedBooks:"10",institution:{name:"Jönköping University",institutionURL:null,country:{name:"Sweden"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"194040",title:"Prof.",name:"Asaad",middleName:null,surname:"Almssad",slug:"asaad-almssad",fullName:"Asaad Almssad",profilePictureURL:"https://mts.intechopen.com/storage/users/194040/images/system/194040.jpg",biography:"Associate Prof. Asaad Almssad has more than thirty years of experience in industry, academia, and research at Umeå University, Sweden; Karlstad University, Sweden; and various European and non-European institutions. His research focuses on building structures, materials, sustainable building, and energy efficiency in building systems. He has authored and co-authored more than fifty research papers and many books. Currently, he is employed as a docent at Karlstad University.",institutionString:"Karlstad University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Karlstad University",institutionURL:null,country:{name:"Sweden"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"936",title:"Biotechnology",slug:"metals-and-nonmetals-biotechnology"}],chapters:[{id:"50729",title:"Unconventional Insulation Materials",doi:"10.5772/63311",slug:"unconventional-insulation-materials",totalDownloads:2461,totalCrossrefCites:4,totalDimensionsCites:6,hasAltmetrics:0,abstract:"Materials obtained from petrochemicals (mainly polystyrene) or from natural sources processed with high-energy consumptions such as glass and rock wools are commonly utilized for the insulation of buildings. From the perspective of sustainable development, it is important to choose easily recyclable, renewable, locally available and environmentally friendly raw materials. Thermal performance of unconventional insulation materials such as pine apple leaves, wheat straw, rice straw, rice husk/hull, coconut fibre, bagasse, date palm fibre, cellulose fibre-forestry waste, corn cob and sheep wool were investigated for this study. In addition, an experiment was conducted to compare the thermal performance of different materials being used at the Eco-Center in Turkey. As a result, it can be said that the thermal conductivity of petroleum by-products (XPS, EPS, polyurethane foam) is slightly lower than that of plant/agricultural waste materials; however, preferring the latter over the former has many hidden advantages that have great long-term impacts.",signatures:"Neşe Dikmen and Soofia Tahira Elias Ozkan",downloadPdfUrl:"/chapter/pdf-download/50729",previewPdfUrl:"/chapter/pdf-preview/50729",authors:[{id:"178329",title:"Associate Prof.",name:"Nese",surname:"Dikmen",slug:"nese-dikmen",fullName:"Nese Dikmen"}],corrections:null},{id:"50068",title:"Possible Applications of Corncob as a Raw Insulation Material",doi:"10.5772/62339",slug:"possible-applications-of-corncob-as-a-raw-insulation-material",totalDownloads:2440,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Some alternative applications of corncob as a raw thermal insulation material are presented in this research work. Usually, corncob has been treated as an agricultural waste. Finding practical applications of this waste in product manufacturing may preserve the environment and may also allow using more green technologies. Therefore, a corncob particleboard, a lightweight concrete for nonstructural purposes, and a lightweight concrete masonry unit (CMU) are the granulated corncob-based products proposed. These products are studied in terms of thermal performance, and some thermal parameters are delivered. The results obtained through the experimental study allowed to estimate the thermal conductivity of the granulated corncob and of the granulated corncob particleboards. The values obtained were 0.058 and 0.101 W/m°C, respectively. A thermal transmission coefficient of 1.99 W/m2°C was obtained for the nonstructural corncob lightweight concrete, and it was concluded that the density and the thermal properties of this alternative solution are in accordance with the properties of the currently used expanded clay concrete. For the granulated corncob lightweight CMU, a value of 1.15 W/m2°C was estimated. This shows that this agricultural waste may have potential as a thermal insulation product.",signatures:"Jorge Pinto, Ana Briga Sá, Sandra Pereira, Isabel Bentes and\nAnabela Paiva",downloadPdfUrl:"/chapter/pdf-download/50068",previewPdfUrl:"/chapter/pdf-preview/50068",authors:[{id:"80460",title:"Prof.",name:"Isabel",surname:"Bentes",slug:"isabel-bentes",fullName:"Isabel Bentes"},{id:"179060",title:"Dr.",name:"Jorge Tiago",surname:"Pinto",slug:"jorge-tiago-pinto",fullName:"Jorge Tiago Pinto"},{id:"184190",title:"Prof.",name:"Ana",surname:"Briga Sá",slug:"ana-briga-sa",fullName:"Ana Briga Sá"},{id:"184191",title:"Prof.",name:"Sandra",surname:"Pereira",slug:"sandra-pereira",fullName:"Sandra Pereira"},{id:"184192",title:"Prof.",name:"Anabela",surname:"Paiva",slug:"anabela-paiva",fullName:"Anabela Paiva"}],corrections:null},{id:"50506",title:"Thermal Insulation Material Based on “Jute”",doi:"10.5772/63223",slug:"thermal-insulation-material-based-on-jute-",totalDownloads:2314,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Among the different natural fibres, jute is a less expensive fibre, annually renewable, and commercially available compared to other natural fibre crops. This jute is mostly cultivated in India and Bangladesh. More than a century, this fibre is well known as packaging (sacks), hessian, and carpet backing. Since 1950s, the synthetic fibres slowly took the market share of conventional jute textiles due to their low cost and high production speed. As far as suitability of the insulating material is concerned, it has high potential of using as three types of insulation (thermal, sound, and electrical). This present chapter gives emphasis on the basic methods of measuring jute based thermal insulation materials in different application areas. Apart from its evaluation methods, special attention has been made on the important factors affecting the thermal insulation behaviours of the jute-based textile materials. Focusing the needs of the industry, present chapter also covers the future aspects regarding the insulation application from jute-based materials.",signatures:"Sanjoy Debnath",downloadPdfUrl:"/chapter/pdf-download/50506",previewPdfUrl:"/chapter/pdf-preview/50506",authors:[{id:"23285",title:"Dr.",name:"Sanjoy",surname:"Debnath",slug:"sanjoy-debnath",fullName:"Sanjoy Debnath"}],corrections:null},{id:"50174",title:"Seismic Aspects of the Application of Thermal Insulation Boards Beneath the Foundations of Buildings",doi:"10.5772/62294",slug:"seismic-aspects-of-the-application-of-thermal-insulation-boards-beneath-the-foundations-of-buildings",totalDownloads:2072,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"In recent years, there has been a significant increase in the construction of energy-efficient buildings. These buildings are mainly characterized by their thermal envelope, which needs to follow the complete outer perimeter of the building without any interruptions, to avoid thermal bridges. It has been observed, however, that the specific new details which prevent the occurrence of thermal bridges can, in many cases, substantially affect the structural integrity of such buildings during earthquakes. This chapter deals with the seismic aspects of the application of thermal insulation (TI) boards beneath the foundations of buildings. For this purpose, the mechanical characteristics of the most commonly used material in practice (i.e., extruded polystyrene — XPS) were experimentally determined. Additionally, the shear behaviour of differently composed TI foundation sets was investigated and their friction capacity estimated. The authors have proposed a new solution for the foundation detail, which is based on controlling the sliding mechanism between the individual layers of TI boards in order to reduce the seismic forces induced on the superstructure. The proposed detail with a specially designed sliding layer surface is made of commonly used TI materials for modern passive houses, thus reducing the potential additional costs. The solution was verified by means of nonlinear dynamic analysis of several realistic building models and various friction coefficients between XPS boards. The selected results are presented in terms of fragility curves for the occurrence of sliding between the layers of XPS boards. Based on these curves, the desired seismic response scenario and level of protection of a building structure could be selected.",signatures:"David Koren, Vojko Kilar and Boris Azinović",downloadPdfUrl:"/chapter/pdf-download/50174",previewPdfUrl:"/chapter/pdf-preview/50174",authors:[{id:"178243",title:"Dr.",name:"David",surname:"Koren",slug:"david-koren",fullName:"David Koren"},{id:"178252",title:"Dr.",name:"Boris",surname:"Azinović",slug:"boris-azinovic",fullName:"Boris Azinović"},{id:"178253",title:"Prof.",name:"Vojko",surname:"Kilar",slug:"vojko-kilar",fullName:"Vojko Kilar"}],corrections:null},{id:"50924",title:"Flammability of Bio-Based Rigid Polyurethane Foam as Sustainable Thermal Insulation Material",doi:"10.5772/62539",slug:"flammability-of-bio-based-rigid-polyurethane-foam-as-sustainable-thermal-insulation-material",totalDownloads:2658,totalCrossrefCites:8,totalDimensionsCites:14,hasAltmetrics:0,abstract:"One of the biggest disadvantages of rigid polyurethane foams is its low thermal resistance, high flammability, and high smoke production when burning. Greatest advantage of this thermal insulation material is its low thermal conductivity, which at 20–25 mW/(m·K) is superior to other commercially available insulation materials. In recent years polyurethane materials from renewable resources have been widely studied. But their use on industrial scale was limited due to inconstant performance and relatively high price of raw materials. Different bio-based raw materials, such as rapeseed oil and tall oil, could provide abundant feedstock for PU foam production. Decrease of flammability of PU materials conventionally is achieved by addition of flame retardants, halogen-containing compounds, and phosphates. It can be considered that halogenated fire retardants could have several health hazards, such as volatile compound emission from materials and toxic gas release during burning process. Expandable graphite could be an answer to this flammability problem. This chapter describes development of bio-based rigid polyurethane foams and their flammability reduction using sustainable flame retardants. Different expandable graphite intumescent flame retardants provided significant flammability reduction while maintaining low thermal conductivity of insulation materials.",signatures:"Mikelis Kirpluks, Ugis Cabulis and Andris Avots",downloadPdfUrl:"/chapter/pdf-download/50924",previewPdfUrl:"/chapter/pdf-preview/50924",authors:[{id:"177961",title:"M.Sc.",name:"Mikelis",surname:"Kirpluks",slug:"mikelis-kirpluks",fullName:"Mikelis Kirpluks"},{id:"178278",title:"Dr.",name:"Ugis",surname:"Cabulis",slug:"ugis-cabulis",fullName:"Ugis Cabulis"},{id:"178280",title:"Dr.",name:"Maria",surname:"Kuranska",slug:"maria-kuranska",fullName:"Maria Kuranska"},{id:"178281",title:"Prof.",name:"Aleksander",surname:"Prociak",slug:"aleksander-prociak",fullName:"Aleksander Prociak"},{id:"178282",title:"BSc.",name:"Andris",surname:"Avots",slug:"andris-avots",fullName:"Andris Avots"}],corrections:null},{id:"51497",title:"The Review of Some Commonly Used Methods and Techniques to Measure the Thermal Conductivity of Insulation Materials",doi:"10.5772/64157",slug:"the-review-of-some-commonly-used-methods-and-techniques-to-measure-the-thermal-conductivity-of-insul",totalDownloads:5510,totalCrossrefCites:24,totalDimensionsCites:46,hasAltmetrics:1,abstract:"The use of insulation materials is considered as one of the most effective means of conserving energy in various fields. Thermal insulation materials enable systems to achieve energy efficiency. Many different thermal insulation materials have been developed to reduce heat flow by limiting conduction, convection, and/or radiation while performing one or more functions. These functions may vary in the context of thermal design, numerical simulations, and a wide range of engineering problems, such as determining the heat loss, temperature field, isolation, and cooling conservation, and in a variety of other technologies. One of the most effective ways to identify and determine performance is effective thermal conductivity. The thermal measurement performance is usually evaluated in a temperature and signal gradient for single or combined homogeneous/heterogenous materials. The two main categories of thermal conductivity measurement techniques are steady‐state methods and transient methods. The aim of this chapter is to present various measurement methods and to investigate their suitability for method purposes. This chapter presents new and accurate experimental techniques and methods for measuring the thermal conductivity of several most commonly used insulation materials. Some of these methods are commonly used in the field for measuring the thermal property of insulation materials. On the other hand, different insulation measurement practices are presented depending upon the overall structures. The analysis predicting the thermal conductivities of insulation materials is also discussed.",signatures:"Numan Yüksel",downloadPdfUrl:"/chapter/pdf-download/51497",previewPdfUrl:"/chapter/pdf-preview/51497",authors:[{id:"178245",title:"Dr.",name:"Numan",surname:"Yüksel",slug:"numan-yuksel",fullName:"Numan Yüksel"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"5289",title:"Landscape Ecology",subtitle:"The Influences of Land Use and Anthropogenic Impacts of Landscape Creation",isOpenForSubmission:!1,hash:"354db0cb765007d8e48728a1356f2b75",slug:"landscape-ecology-the-influences-of-land-use-and-anthropogenic-impacts-of-landscape-creation",bookSignature:"Amjad Almusaed",coverURL:"https://cdn.intechopen.com/books/images_new/5289.jpg",editedByType:"Edited 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Ganod",dateSubmitted:"February 14th 2021",dateReviewed:null,datePrePublished:"May 23rd 2021",datePublished:"March 2nd 2021",book:{id:"9076",title:"Recent Advances in Wound Healing",subtitle:null,fullTitle:"Recent Advances in Wound Healing",slug:"recent-advances-in-wound-healing",publishedDate:"March 2nd 2022",bookSignature:"Shahin Aghaei",coverURL:"https://cdn.intechopen.com/books/images_new/9076.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"64024",title:"Associate Prof.",name:"Shahin",middleName:null,surname:"Aghaei",slug:"shahin-aghaei",fullName:"Shahin Aghaei"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"311824",title:"M.Sc.",name:"Walid A.M.",middleName:null,surname:"Ganod",fullName:"Walid A.M. Ganod",slug:"walid-a.m.-ganod",email:"walid.ganod@gmail.com",position:null,institution:null}]}},chapter:{id:"76688",slug:"chronic-venous-ulcer",signatures:"Walid A.M. Ganod",dateSubmitted:"February 14th 2021",dateReviewed:null,datePrePublished:"May 23rd 2021",datePublished:"March 2nd 2021",book:{id:"9076",title:"Recent Advances in Wound Healing",subtitle:null,fullTitle:"Recent Advances in Wound Healing",slug:"recent-advances-in-wound-healing",publishedDate:"March 2nd 2022",bookSignature:"Shahin Aghaei",coverURL:"https://cdn.intechopen.com/books/images_new/9076.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"64024",title:"Associate Prof.",name:"Shahin",middleName:null,surname:"Aghaei",slug:"shahin-aghaei",fullName:"Shahin Aghaei"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"311824",title:"M.Sc.",name:"Walid A.M.",middleName:null,surname:"Ganod",fullName:"Walid A.M. Ganod",slug:"walid-a.m.-ganod",email:"walid.ganod@gmail.com",position:null,institution:null}]},book:{id:"9076",title:"Recent Advances in Wound Healing",subtitle:null,fullTitle:"Recent Advances in Wound Healing",slug:"recent-advances-in-wound-healing",publishedDate:"March 2nd 2022",bookSignature:"Shahin Aghaei",coverURL:"https://cdn.intechopen.com/books/images_new/9076.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"64024",title:"Associate Prof.",name:"Shahin",middleName:null,surname:"Aghaei",slug:"shahin-aghaei",fullName:"Shahin Aghaei"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},ofsBook:{item:{type:"book",id:"11277",leadTitle:null,title:"Macrophages -140 Years of Their Discovery",subtitle:null,reviewType:"peer-reviewed",abstract:"
\r\n\tMacrophages are the sentinel cells of the innate immune system, which along with fighting against foreign pathogens and molecules also regulate immune homeostasis. These cells also serve as potent antigen-presenting cells (APCs) to activate adaptive immune cells (T and B cells). Along with playing a crucial role in the host defense, macrophages are also crucial in human reproduction and embryonic development.
\r\n\r\n\tThe present book is intended to provide advanced information in the field of macrophages biology and their role in immune homeostasis, antigen presentation, adaptive immune and innate immune system regulation, their role in host defense against microbial infections, sterile inflammatory diseases, including autoimmune diseases, genetic immunological diseases originating due to their defective function (i.e. impaired phagocytosis), and their role in reproductive immunology along with embryonic development. The book will also target their role in viral diseases, including the current pandemic COVID-19 along with the HIV-1 infection (AIDS), sepsis, pneumonia (both, bacterial and viral ones).
\r\n\r\n\tThe book will prove beneficial to undergraduate, graduate, and advanced researchers interested in the macrophages biology and immune and non-immune functions.
",isbn:"978-1-80355-625-3",printIsbn:"978-1-80355-624-6",pdfIsbn:"978-1-80355-626-0",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,hash:"0a31e7d4dbdfd9164e16bc2fec80b9db",bookSignature:"Dr. Vijay Kumar",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11277.jpg",keywords:"Macrophage Development, Innate Immunity, Adaptive Immunity, Antigen Presenting Cells, Inflammation, Sepsis, Bacterial Infections, Viral Infections, COVID-19, Fungal Infections, Parasitic Infections, Embryonic Development",numberOfDownloads:289,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 29th 2021",dateEndSecondStepPublish:"December 7th 2021",dateEndThirdStepPublish:"February 5th 2022",dateEndFourthStepPublish:"April 26th 2022",dateEndFifthStepPublish:"June 25th 2022",remainingDaysToSecondStep:"5 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Dr. Kumar is working at the University of Tennessee Health Science Center, USA, and has more than sixteen years of research experience in the field of innate immunity, inflammation, and sepsis. He is the recipient of the prestigious 'Piero Periti Review Article Award”.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"63844",title:"Dr.",name:"Vijay",middleName:null,surname:"Kumar",slug:"vijay-kumar",fullName:"Vijay Kumar",profilePictureURL:"https://mts.intechopen.com/storage/users/63844/images/system/63844.jpg",biography:"Dr. Vijay Kumar Ph.D. has more than 16 years of research experience in the field of bacterial infections, including sepsis and pneumonia, innate immunity, immunopharmacology, immunomodulation, and inflammation. He obtained his Ph.D. in June 2009 from the Department of Microbiology, Panjab University, Chandigarh, India. Dr. Kumar is the recipient of the prestigious Piero Periti review article award for 2008, awarded by the Journal of Chemotherapy in the field of immunomodulation and antimicrobials for the article titled Innate Immunity in Sepsis Pathogenesis and Its Modulation: New Immunomodulatory Targets Revealed. He was the recipient of junior research and senior research fellowship (2004–2009) offered by the Indian Council of Medical Research (ICMR), New Delhi, India. He has been awarded 17 international travel awards to attend various international conferences in the field of infection and immunity. So far, he has published 70 publications in peer-reviewed international journals in this field. To date, he has achieved more than 2 100 citations and h-index 21 (Google scholar). He has contributed several articles on inflammation and immunity as invited contribution as well as in special issues of the journals, including the Journal of Leukocyte Biology, EXCLI Journal, and Frontiers in Immunology. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"65772",title:"Clustering of Time-Series Data",doi:"10.5772/intechopen.84490",slug:"clustering-of-time-series-data",body:'The rapid development of technology has led to the registration of many processes in an electronic environment, the storage of these records, and the accessibility of these records when requested. With the evolving technology such as cloud computing, big data, the accumulation of a large amount of data stored in databases, and the process of parsing and screening useful information made data mining necessary.
It is possible to examine the data which are kept in databases and reach to huge amounts of size every second, in two parts according to their changes in time: static and temporal. Data is called the static data when its feature values do not change with time, if the feature comprise values change with time then it is called the temporal or time-series data.
Today, with the increase in processor speed and the development of storage technologies, real-world applications can easily record changing data over time.
Time-series analysis is a trend study subject because of its prevalence in various fields ranging from science, engineering, bioinformatics, finance, and government to health-care applications [1, 2, 3]. Data analysts are looking for the answers of such questions: Why does the data change this way? Are there any patterns? Which series show similar patterns? etc. Subsequence matching, indexing, anomaly detection, motif discovery, and clustering of the data are the answers of some questions [4]. Clustering, which is one of the most important concepts of data mining, defines its structure by separating unlabeled data sets into homogeneous groups. Many general-purpose clustering algorithms are used for the clustering of time-series data, either by directly or by evolving. Algorithm selection depends entirely on the purpose of the application and on the properties of the data such as sales data, exchange rates in finance, gene expression data, image data for face recognition, etc.
In the age of informatics, the analysis of multidimensional data that has emerged as part of the digital transformation in every field has gained considerable importance. These data can be from data received at different times from one or more sensors, stock data, or call records to a call center. This type of data, that is, observing the movement of a variable over time, where the results of the observation are distributed according to time, is called time-series data. Time-series analysis is used for many purposes such as future forecasts, anomaly detection, subsequence matching, clustering, motif discovery, indexing, etc. Within the scope of this study, the methods developed for the time-series data clustering which are important for every field of digital life in three main sections. In the first section, the proposed methods for the preparation of multidimensional data for clustering (dimension reduction) in the literature are categorized. In the second section, the similarity criteria to be used when deciding on the objects to be assigned to the related cluster are classified. In the third section, clustering algorithms of time-series data are examined under five main headings according to the method used. In the last part of the study, the use of time-series clustering in bioinformatics which is one of the favorite areas is included.
There are many different categorizations of time-series clustering approaches. Such as, time-series clustering approaches can be examined in three main sections according to the characteristics of the data used whether they process directly on raw data, indirectly with features extracted from the raw data, or indirectly with models built from the raw data [5]. Another category is according to the clustering method: shape-based, feature-based, and model-based [6]. But whatever the categorization is, for any time-series clustering approach, the main points to be considered are: how to measure the similarity between time series; how to compress the series or reduce dimension and what algorithm to use for cluster. Therefore, this chapter examines time-series clustering approaches according to three main building blocks: data representation methods, distance measurements, and clustering algorithms (Figure 1).
Time-series clustering.
Data representation is one of the main challenging issues for time-series clustering. Because, time-series data are much larger than memory size [7, 8] that increases the need for high processor power and time for the clustering process increases exponentially. In addition, the time-series data are multidimensional, which is a difficulty for many clustering algorithms to handle, and it slows down the calculation of the similarity measurement. Consequently, it is very important for time-series data to represent the data without slowing down the algorithm execution time and without a significant data loss. Therefore, some requirements can be listed for any data representation methods [9]:
Significantly reduce the data size/dimensionality,
Maintain the local and global shape characteristics of the time series,
Acceptable computational cost,
Reasonable level of reconstruction from the reduced representation,
Insensitivity to noise or implicit noise handling.
Dimension reduction is one of the most frequently used methods in the literature [7, 10, 11, 12] for the data representation.
The representation of a time series T with length n is a model T̅ with reduced dimensions, so that T approximates T [13]. Dimension reduction or feature extraction is a very useful method for reducing the number of variables/attributes or units in multivariate statistical analyzes so that the number of attributes can be reduced to a number that “can handle.”
Due to the noisy and high-dimensional features of many time-series data, data representations have been studied and generally examined in four main sections: data adaptive, nondata adaptive, model-based, and data dictated [6].
Many representation methods for time-series data are proposed and each of them offering different trade-offs between the aforementioned requirements. The correct selection of the representation method plays a major role in the effectiveness and usability of the application to be performed.
In particular, the similarity measure is the most essential ingredient of time-series clustering.
The similarity or distance for the time-series clustering is approximately calculated, not based on the exact match as in traditional clustering methods. It requires to use distance function to compare two time series. In other words, the similarity of the time series is not calculated, it is estimated. If the estimated distance is large, the similarity between the time series is less and vice versa.
Similarity between two “n” sized time series T = {t1,t2,….tn} and U = {u1,u2,….un} is the length of the path connecting pair of points [11]. This distance is the measure of similarity. D (T, U) is a function that takes two times series (T, U) as input and calculates their distance “d”.
Metrics to be used in clustering must cope with the problems caused by common features of time-series data such as noise, temporal drift, longitudinal scaling, offset translation, linear drift, discontinuities, and amplitude scaling. Various methods have been developed for similarity measure, and the method to choose is problem specific. These methods can be grouped under three main headings: similarity in time, similarity in shape, and similarity in change.
The similarity between the series is that they are highly time dependent. Such a measure is costly for the raw time series, so a preprocessing or transformation is required beforehand [34, 36].
The process of separating groups according to similarities of data is called “clustering.” There are two basic principles: the similarity within the cluster is the highest and the similarity between the clusters is the least. Clustering is done on the basis of the characteristics of the data and using multivariate statistical methods. When dividing data into clusters, the similarities/distances of the data to each other are measured according to the specification of the data (discrete, continuous, nominal, ordinal, etc.)
Han and Kamber [41] classify the general-purpose clustering algorithms which are actually designed for static data in five main sections: partition-based, hierarchical-based, density-based, grid-based, and model-based. Besides these, a wide variety of algorithms has been developed for time-series data. However, some of these algorithms (ignore minor differences) intend to directly use the methods developed for static data without changing the algorithm by transforming it into a static data form from temporal data. Some approaches apply a preprocessing step on the data to be clustered before using the clustering algorithm. This preprocessing step converts the raw-time-series data into feature vectors using dimension reduction techniques, or converts them into parameters of a specified model [42].
Given a dataset on n time series T = {t1, t2,…., tn}, time-series clustering is the process of partitioning of T into C = {C1,C2,….,Ck} according to certain similarity criterion. Ci is called “cluster” where,
In this section, previously developed clustering algorithms will be categorized. Some of these algorithms work directly with raw time-series data, while others use the data presentation techniques that are previously mentioned.
Clustering algorithms are generally classified as: partitioning, hierarchical, graph-based, model-based, and density-based clustering.
The K-means [43] algorithm is a typical partition-based clustering algorithm such that the data are divided into a number of predefined sets by optimizing the predefined criteria. The most important advantage is its simplicity and speed. So it can be applied to large data sets. However, the algorithm may not produce the same result in each run and cannot handle the outlier. Self-organizing map [44] is stronger than the noisy data clustering from K-means. The user is prompted to enter the cluster number and grid sets. It is difficult to determine the number of clusters for time-series data. Other examples of partition-based clustering are CLARANS [45] and K-medoids [46]. In addition, the partitioning approach is suitable for low-dimensional, well-separated data. However, time-series data are multidimensional and often contain intersections, embedded clusters.
In essence, these algorithms act as n-dimensional vectors to time-series data and applies distance or correlation functions to determine the amount of similarity between two series. Euclidean distance, Manhattan distance, and Pearson correlation coefficient are the most commonly used functions.
Contrary to the partitioning approach, which aims segmenting data that do not intersect, the hierarchical approach produces a hierarchical series of nested clusters that can be represented graphically (dendrogram, tree-like diagram). The branches of the dendrogram show the similarity between the clusters as well as the knowledge of the shaping of the clusters. Determined number of clusters can be obtained by cutting the dendrogram at a certain level.
Hierarchical clustering methods [47, 48, 49] are based on the separating clusters into subgroups that are processed step by step as a whole, or the stepwise integration of individual clusters into a cluster [50]. Hierarchical clustering methods are divided into two methods: agglomerative clustering methods and divisive hierarchical clustering methods according to the creation of the dendrogram.
In agglomerative hierarchical clustering methods, each observation is initially treated as an independent cluster, and then repeatedly, until each individual observation obtains a single set of all observations, thereby forming a cluster with the closest observation.
In the divisive hierarchical clustering methods, initially all observations are evaluated as a single cluster and then repeatedly separated in such a way that each observation is separated from the farthest observation to form a new cluster. This process continues until all the observations create a single cluster.
Hierarchical clustering not only forms a group of similar series but also provides a graphical representation of the data. Graphical presentation allows the user to have an overall view of the data and an idea of data distribution. However, a small change in the data set leads to large changes in the hierarchical dendrogram. Another drawback is high computational complexity.
The density-based clustering approach is based on the concepts of density and attraction of objects. The idea is to create clusters of dense multi-dimensional areas where objects attract each other. In the core of dense areas, objects are very close together and crowded. The objects in the walls of the clusters were scattered less frequently than the core. In other words, density-based clustering determines dense areas of object space. The clusters are dense areas which are separated by rare dense areas. DBSCAN [51] and OPTICS [52] algorithms are the most known of density-based clustering examples.
The density-based approach is robust for noisy environments. The method also deals with outliers when defining embedded clusters. However, density-based clustering techniques cause difficulties due to high computational complexity and input parameter dependency when the dimensional index structure is not used.
The model-based approach [53, 54, 55] uses a statistical infrastructure to model the cluster structure of the time-series data. It is assumed that the underlying probability distributions of the data come from the final mixture. Model-based algorithms usually try to estimate the likelihood of the model parameters by applying some statistical techniques such as Expectation Maximization (EM). The EM algorithm iterates between an “E-step,” which computes a matrix z such that zik is an estimate of the conditional probability that observation i belongs to group k given the current parameter estimates, and an “M-step,” which computes maximum likelihood parameter estimates given z. Each data object is assigned to a cluster with the highest probability until the EM algorithm converges, so as to maximize likelihood for the entirety of the grant.
The most important advantage of the model-based approach is to estimate the probability that i. observation belongs to k. cluster. In some cases, the time series is likely to belong to more than one cluster. For such time-series data, the probability-giving function of the approach is the reason for preference. In this approach, it is assumed that the data set has a certain distribution but this assumption is not always correct.
In this approach, grids made up of square cells are used to examine the data space. It is independent of the number of objects in the database due to the used grid structure. The most typical example is STING [56], which uses various levels of quadrilateral cells at different levels of resolution. It precalculates and records statistical information about the properties of each cell. The query process usually begins with a high-level hierarchical structure. For each cell at the current level, the confidence interval, which reflects the cell’s query relation, is computed. Unrelated cells are exempt from the next steps. The query process continues for the corresponding cells in the lower level until reaching the lowest layer.
After analyzing the data set and obtaining the clustering solution, there is no guarantee of the significance and reliability of the results. The data will be clustered even if there is no natural grouping. Therefore, whether the clustering solution obtained is different from the random solution should be determined by applying some tests. Some methods developed to test the quality of clustering solutions are classified into two types: external index and internal index.
The external index is the most commonly used clustering evaluation method also known as external validation, external criterion. The ground truth is the goal clusters, usually created by experts. This index measures how well the target clusters and the resulting clusters overlap. Entropy, Adjusted Rand Index (ARI), F-measure, Jaccard Score, Fowlkes and Mallows Index (FM), and Cluster Similarity Measure (CSM) are the most known external indexes.
The internal indexes evaluate clustering results using the features of data sets and meta-data without any external information. These are often used in cases where the correct solutions are not known. Sum of squared error is one of the most used internal methods which the distance to the nearest cluster determines the error. So clusters with similar time series are expected to give lower error values. Distance between two clusters (CD) index, root-mean-square standard deviation (RMSSTD), Silhouette index, R-squared index, Hubert-Levin index, semi-partial R-squared (SPR) index, weighted inter-intra index, homogeneity index, and separation index are the common internal indexes.
The funFEM algorithm [55, 57] allows to cluster time series or, more generally, functional data. FunFem is based on a discriminative functional mixture model (DFM) which allows the clustering of the curves (data) in a functional subspace. If the observed curves are {
an E step in which posterior probabilities that observations belong to the K groups are computed,
an F step that estimates the orientation matrix U of the discriminative latent space conditionally to the posterior probabilities,
an M step in which parameters of the mixture model are estimated in the latent subspace by maximizing the conditional expectation of the complete likelihood.
Fisher-EM algorithm updates the parameters repeatedly until the Aitken criterion is provided. Aitken criterion estimates the asymptotic maximum of the log-likelihood in order to detect in advance the algorithm converge [57]. In model-based clustering, a model is defined by its number of component/cluster K and its parameterization. In model selection task, several models are reviewed while selecting the most appropriate model for the considered data.
FunFEM allows to choose between AIC (Akaike Information Criterion) [58], BIC (Bayesian information criteria) [59], and ICL (Integrated Completed Likelihood) [60] when deciding the number of clusters. The penalty terms are:
FunFem is implemented in R programming languages and serves as a function [61]. The algorithm is applied on a time series gene expression data in the following section. Input of the algorithm is gene expression data which is given in Table 1. The table shows the gene expression values measured as a result of the microarray experiment. The measurement was performed at six different times for each gene. The data were taken from the GEO database (GSE2241) [62]. FunFEM method is decided, and the best model is DkBk with
Gene Symbol | TP1 | TP2 | TP3 | TP4 | TP5 | TP6 |
---|---|---|---|---|---|---|
AADAC | 18.4 | 29.7 | 30 | 79.7 | 86.7 | 163.2 |
AAK1 | 253.2 | 141.8 | 49.2 | 118.7 | 145.2 | 126.7 |
AAMP | 490 | 340.9 | 109.1 | 198.4 | 210.5 | 212 |
AANAT | 5.6 | 1.4 | 3.7 | 3.1 | 1.6 | 4.9 |
AARS | 1770 | 793.6 | 226.5 | 1008.9 | 713.3 | 1253.7 |
AASDHPPT | 940.1 | 570.5 | 167.2 | 268.6 | 683 | 263.5 |
AASS | 10.9 | 1.9 | 1.5 | 4.1 | 19.7 | 25.5 |
AATF | 543.4 | 520.1 | 114.5 | 305.7 | 354.2 | 384.9 |
AATK | 124.5 | 74.5 | 17 | 25.6 | 64.6 | 13.6 |
. | . | . | . | . | . | . |
. | . | . | . | . | . | . |
ZP2 | 4.1 | 1.4 | 0.8 | 1.4 | 1.4 | 3 |
ZPBP | 23.4 | 13.7 | 7 | 7.8 | 22.3 | 26.9 |
ZW10 | 517.1 | 374.5 | 72.6 | 240.8 | 345.7 | 333.1 |
ZWINT | 1245.4 | 983.4 | 495.3 | 597.4 | 1074.3 | 620.7 |
ZYX | 721.6 | 554.9 | 135.5 | 631.5 | 330.9 | 706.8 |
ZZEF1 | 90.5 | 49.3 | 18.6 | 66.7 | 10.4 | 52.2 |
ZZZ3 | 457.3 | 317.1 | 93 | 243.2 | 657.5 | 443 |
Input data of the FunFEM algorithm.
Gene symbol | Cluster number |
---|---|
AADAC | 2 |
AAK1 | 3 |
AAMP | 3 |
AANAT | 1 |
AARS | 4 |
AASDHPPT | 3 |
AASS | 1 |
AATF | 3 |
AATK | 2 |
. | . |
. | . |
ZP2 | 1 |
ZPBP | 1 |
ZW10 | 3 |
ZWINT | 4 |
ZYX | 4 |
ZZEF1 | 2 |
ZZZ3 | 3 |
Output data of the FunFEM algorithm.
The approach to be taken depends on the application area and the characteristics of the data. For this reason, as a case study, the clustering of gene expression data, which is a special area of clustering of time-series data, will be examined in this section. Microarray is the technology which measures the expression levels of large numbers of genes simultaneously. DNA microarray technology overcomes traditional approaches in the identification of gene copies in a genome, in the identification of nucleotide polymorphisms and mutations, and in the discovery and development of new drugs. It is used as a diagnostic tool for diseases. DNA microarrays are widely used to classify gene expression changes in cancer cells.
The gene expression time series (gene profile) is a set of data generated by measuring expression levels at different cases/times in a single sample. Gene expression time series have two main characteristics, short and unevenly sampled. In The Stanford Microarray database, more than 80% of the time-series experiments contains less than 9 time points [63]. Observations below 50 are considered to be quite short for statistical analysis. Gene expression time-series data are separated from other time-series data by this characteristics (business, finance, etc.). In addition to these characteristics, three basic similarity requirements can be identified for the gene expression time series: scaling and shifting, unevenly distributed sampling points, and shape (internal structure) [64]. Scaling and shifting problems arise due to two reasons: (i) the expression of genes with a common sequence is similar, but in this case, the genes need not have the same level of expression at the same time. (ii) Microarray technology, which is often corrected by normalization. The scaling and shifting factor in the expression level may hide similar expressions and should not be taken into account when measuring the similarity between the two expression profiles. Sampling interval length is informative and cannot be ignored in similarity comparisons. In microarray experiments, the density change characterizes the shape of the expression profile rather than the density of the gene expression. The internal structure can be represented by deterministic function, symbols describing the series, or statistical models.
There are many popular clustering techniques for gene expression data. The common goal of all is to explain the different functional roles of the genes that play a key biological process. Genes expressed in a similar way may have a similar functional role in the process [65].
In addition to all these approaches, it is possible to examine the cluster of gene expression data in three different classes as gene-based clustering, sample-based clustering, and subspace clustering (Figure 2) [66]. In gene-based clustering, genes are treated as objects, instances (time-point/patient-intact) as features. Sample-based clustering is exactly the opposite: samples are treated as objects, genes as features. The distinction between these two clustering approaches is based on the basic characterization of the clustering process used for gene expression data. Some clustering algorithms, such as K-means and hierarchical approach, can be used to cluster both genes and fragments of samples. In the molecular biology, “any function in the cell is carried out with the participation of a small subset of genes, and the cellular function only occurs on a small sample subset.” With this idea, genes and samples are handled symmetrically in subspace clustering; gene or sample, object or features.
Gene expression data clustering approaches.
In
Also, among biologists who will use microarray data, the relationship between genes or clusters that are usually related to each other within the cluster, rather than the clusters of genes, is a more favorite subject. That is, it is also important for the algorithm to make graphical presentations not just clusters. K-means, self-organizing maps (SOM), hierarchical clustering, graph-theoretic approach, model-based clustering, and density-based approach (DHC) are the examples of gene-based clustering algorithms.
The goal of the
When
Subspace clustering was first described by Agrawal et al. in 1998 on general data mining [68]. In subspace clustering, two subspace sets may share the same objects and properties, while some objects may not belong to any subspace set. Subspace clustering methods usually define a model to determine the target block and then search in the gen-sample space. Some examples of subspatial cluster methods proposed for gene expression are biclustering [69], coupled two way clustering (CTWC) [70], and plaid model [71].
According to different clustering criteria, data can be clustered such as the co-expressing gene groups, the samples belonging to the same phenotype or genes from the same biological process. However, even if the same criteria are used in different clustering algorithms, the data can be clustered in different forms. For this reason, it is necessary to select more suitable algorithm for data distribution.
Clustering for time-series data is used as an effective method for data analysis of many areas from social media usage and financial data to bioinformatics. There are various methods introduced for time-series data. Which approach is chosen is specific to the application. The application is determined by the needs such as time, speed, reliability, storage, and so on. When determining the approach to clustering, three basic issues need to be decided: data representation, similarity measure, and clustering algorithm.
The data representation involves transforming the multi-dimensional and noisy structure of the time-series data into a less dimensional that best expresses the whole data. The most commonly used method for this purpose is dimension reduction or feature extraction.
It is challenging to measure the similarity of two time series. The chapter has been examined similarity measures in three sections as similarity in shape, similarity in time, and similarity in change.
For the time-series clustering algorithms, it is not wrong to say that the evolution of conventional clustering algorithms. Therefore, the classification of traditional clustering algorithms (developed for static data) has been included. It is classified as partitioning, hierarchical, model-based, grid-based, and density-based. Partition algorithms initially require prototypes. The accuracy of the algorithm depends on the defined prototype and updated method. However, they are successful in finding similar series and clustering time series with equal length. The fact that the number of clusters is not given as the initial parameter is a prominent and well-known feature of hierarchical algorithms. At the same time, works on time series that are not of equal length causes it to be one step ahead of other algorithms. However, hierarchical algorithms are not suitable for large data sets due to the complexity of the calculation and the scalability problem. Model-based algorithms suffer from problems such as initialization of parameters based on user predictions and slow processing time for large databases. Density-based algorithms are not generally preferred over time-series data due to their high working complexity. Each approach has pros and cons compared to each other, and the choice of algorithm for time-series clustering varies completely according to the characteristics of the data and the needs of the application. Therefore, in the last chapter, a study on the clustering of gene expression data, which is a specific field of application, has been mentioned.
In time-series data clustering, there is a need for algorithms that execute fast, accurate, and with less memory on large data sets that can meet today’s needs.
Defining the term “Human Factors” is somehow challenging. When we think about the factors which may impact humans, it is understandable that the list is long and diverse from one person to another. It includes the diversified societal, geographical, and environmental differences related to those people. It possibly is more suitable to predict the concept instead of becoming entangled in setting an all-inclusive definition [1]. Any factor that impacts a human’s performance anyhow then becomes of those beings that can be assessed to determine the impact upon the person. Trying to group these factors even into comprehensive groups can restrict our focus. Every person shares the majority of aspects to some extent. A reduced number of human factors may smear only to finite numbers of people or professions [2, 3].
Most of the factors which impact one group will influence the other with equivalent effect [4]. There are some features of the work for each group that makes them more inclined to affect by various factors and less so much from others. The communal thread, which relates these two groups, is concentration and focus. A deficiency can have calamitous consequences on the flying community [5, 6]. While air traffic controllers are at work at the moment of flight, it would make their requirement for focus and concentration more [7]. Still, when pondering the implication of a stick failure because of the lack of tightening a bolt or negligence of a moving part because of the lack of lubrication, it becomes evident that they were because of the lack of focus eventually the impact can have severe results. There are various kinds of work based on the common theme of aviation [8].
Moreover, the failure of a person to do efficiently has reflective impacts on protecting the National Air Space. Flight attendants have more awareness than the air traffic controllers than many aviation medical officers. However, some aviation medical officers perform physical tests on the air traffic controllers and understand the job responsibilities. As aviation officers do not have medical certifications, their importance and job function is not recognized by much of the aviation community, who do not directly contact them. A brief introduction to both kinds of work will be explained to understand the tasks of the environmental factors that may occur with either group [9]. Air Traffic Controllers can work commonly for five types of employers. Many of the civilian controllers are working under Federal Aviation Administration [10]. The other two nationally employed Air Traffic Controllers are those who are present on duty within the armed forces and those employed under Defense Department. Not all, but many smaller airfields had air traffic control services outsourced by the Federal Aviation Authority in some previous years. These Air Traffic Controllers are employed for private firms that manage the Federal Aviation Authority contract for that facility. Lastly are the private contract towers [11].
An example of this can be any airfield or Boeing field retaining air traffic controllers not assisting a federal contract. As the Federal Aviation Authority is involved with employee air traffic controllers, the remaining discussion will mainly be focused on this group. In this group, there are especially four sub-groups, sometimes stated as options. Three of them have direct responsibility and contact for guiding aircraft [12]. The fourth Autonomous Flight Safety System (AFSS) has a partial contract with aircraft in flight, based on the facility, first is the choice typically concepts of the public. Visual inspection is the pillar boosted by the usage of radar. As significant, complex fields radar is a virtual requirement [13]. They perform moving aircraft on the ground, aligning them for departure, and setting the preliminary headings instantly after departure [14]. They also have a responsibility for airplanes approaching the airport for landings. In three of the four options, the foremost task of the air traffic controllers is to help the pilot sustain the separation of their airplane from those around them [15]. The airport’s size, the number of runways, and the schedule of departures and landings impact the work’s complications; another option with a direct connection with the airplanes is the air route traffic control center (ARTCC). The kinds of facilities are for management of aircraft flying once they have recognized their air routes. They are not limited to commercial air transportation, though most of their traffic arrives from the higher altitude, lengthier distance type of flying. Radar is utilized for this control again. The facilities employ hundreds of controllers [16].
Automated Flight Service Stations (AFSS) are another type of air traffic control facility. They do not actively separate the aircraft; these stations get the flight schedules and provide weather details to the pilots for their flight route. They also help pilots in locating their position when they have no surety about their location. The usage of triangulation and radio does it [17, 18]. There are three types of work that fall under the aviation mechanic. The first one is the power plant mechanic. The second one is the airframe mechanic who performs the airframe and control services. As the name suggests, work performed has to do with the engine parts of the aircraft. The second type is the airframe mechanic, which works. Both power plants and airframe mechanics hold mechanical type certificates from the Federal Aviation Authority [19, 20].
A mechanic without a certificate may work on aircraft, though, requires to be under the direct regulation and control of another having a certificate. The third is the avionics personal integrated with the airplane’s communication, radio, and compass elements. Again these elements contribute to a secure and safe .flight of the aircraft and vital tolls for the pilot [17]. Now we will look into the human quality factors of these occupations after refreshing the basics of the aviation mechanics and air traffic controllers. Instead of covering each vocation distinctly concerning each general element, it seems less monotonous to cover most aspects and explain how they impact the profession [21].
Air traffic controllers in the Federal Aviation Authority have health standards and go through regular check-ups based on the age and controllers’ options. Except if employers cover them, mechanics do not possess a set of health standards [22]. The primary concern is those health conditions that can cause the person’s abrupt and or subtle exhaustion. Definitely, during a crucial time in the control of an airplane, a sudden breakdown could be distressing. The area gets much of the concentration when recognizing the health clearance [23, 24]. Though just as challenging and correspondingly as troublesome are the subtle exhaustions, those conditions, though they do not take one off the job, similarly do not let employees perform at 100 percent of their potential. It would be the case of mild or moderate breathing issues, cardiac arrest untreated, the disturbing headache, and the whole extent of other conditions which are both disrupting and physically draining for any reason. Aside from climbing stairs to access a tower, most of the work is air traffic control is inactive or walking and standing in a comparatively small area [5, 25, 26].
Though the same cannot be assumed for the mechanic. They function in troublesome places and large working areas, as the physical issue can significantly impact their work [27]. Instead of grabbing the appropriate tool or seeing the manual, utilizing a shortcut or presumption in this area could prove distressing. Insufficient strength in fastening and unfastening the nuts and bolts because of the neurological or musculoskeletal condition could eventually generate a problem [28, 29]. Also, states that impact the entire agility could transform into a less than required work effort. These health conditions affect human factors, which ultimately create an impact on job performance. Because of the public trust and work’s nature, these jobs differ from the typical professions [30, 31].
Keeping that recognition in mind is significant to avoid potentially adverse human factors [32]. Physical ability can have some impact on the job performance of both aviation mechanics and air traffic controllers. Height can act as an obstacle required to be handled by the air traffic controller [33]. The most common would be an individual height about the view from a tower or a piece of equipment. For the aviation mechanic, both height and body physique can generate issues. Because of the work’s nature, sometimes convenience into a limited work area becomes a need of the job. If they cannot access the job, a different individual will perceptibly have to do that job [34]. The grave concern is when they have to work hard to body physique to perform the task. The work quality can be compromised, which would not be tolerable. When all probable, spaces to alleviate the impact of body physique should be raised [35]. Moreover, some people have physical traits which, for any reason, make their work much easier for them in comparison to their colleagues [36].
Distinct thinking and sustaining concentration and focus on tasks at hand are symbols of excellent performance in air traffic controllers and airplane mechanics. Many professions need complete focus when working. It cannot be highlighted enough that these kinds of jobs, at times, do not endure errors [37, 38]. Numerous psychological conditions can impact workgroups, individual performance, and impact on other people’s environments. Situational stress and depression states might be the most common mental conditions that can impact employees [39]. The extent of the effect on a person and his colleagues is pervasive and versatile. Then again, some people essentially explore comfort and relief in the workplace if situational stress is an outcome of factors outside of their employment [9, 40].
Many of them work without issues during those times as work allows a genuine break from specific problems and thoughts. These people seem to be those who entrenched the capability to concentrate while performing delicate parts of the safety of their job. It effectively becomes automatic when starting these jobs [22]. Others are not as self-controlled, allowing thoughts and feelings to interrupt and hinder their concentration and focus [41]. Indeed, if the work environment creates the root of the depression, then arriving at the workplace would not be a seamless situation [42, 43]. Those impacted by stress, colleagues, and managers require to keep in mind the changing character of the human mentality. It is always assumed that 90 percent of the issues are caused by ten percent of the employees [44]. The group of people who might not have adequately identified personality disorders, though their societal behaviors and character make them very problematic to manage in the workplace. Their colleagues and managers mainly recognize their impact. So these employees can easily make their problem, the problem of their workplace [43]. The condition can profoundly affect the workplace and creates a troublesome situation where such an individual is working. Then there are also employees with a direct personality disorder that either has been identified or identified if the individual ever required professional aid [6, 45].
Possibly the worst of ten percent, their vital task in life is to make enemies with people around them. The causes for these actions are recognized, and they generate disruption and devastation in the staff, which is very factual. Again because of the several regulations, rights, and rules all distinct employees get, it is problematic to handle them. Lastly, come those employees who may be honestly psychotic [46]. When they beat the clinical level, still have not performed obvious actions to cause their displacement, they are very troublesome [12, 47].
Many other conditions involving anxiety and depression leave the individual with the information that they are distressed and may not be appropriate for specific jobs. Not always so with the psychotic person [48]. There is no requirement for discussing the issues related to shifting the acts and thoughts of a delusional and paranoid individual to the radar screen, safe repairing of a powerplant, and skies. Workload, scheduling, budget, and various other factors make it very troublesome to recognize some of these problems. Identifying the potential issue would be the primary step which can be and is frequently impractical at times. However, the psychological factors which can generate an unfortunate outcome may well be close to the top of the list concerning the rate of occurrence [24, 49].
General society is just a reflection of several smaller social groups. There are people employed in and around the world of aviation from smaller groups, with fewer smaller groups confined within that world. Then there come the clicks which form in any work environment. As various flight specialists will recognize, an increased number of air traffic controller interviews, for any reason, make the declaration that they are the best in the system. The origin for this comes from a procedure in training future Air Traffic Controllers. It is perceived that they should have confidence and are told that they are the finest in the system. Now the reason here is deficient though the outcome of being inculcated in that way is fundamental. Not all air traffic controllers fall victim to that thought. However, many will repeat the phrase repeatedly as it is said that often saying something makes people feel that there can be any truth in the statement [50, 51]. The fundamental human factor issue comes from the lack of tolerance and competition that various air traffic controllers have. As they feel their plan is the only plan, it then follows that others must be doing it inaccurately. It can create unnecessary friction and discussion between the employees [52]. Moreover, if a person seriously thinks about it, there is a chance to obstruct the adapting or learning procedure. On the contrary, it could be said with rationality that competition is healthy. Societal characteristics, situations, and problems outside the workplace can influence anyone [22, 53].
As the total environment for a person can be segmented into various aspects, it is sometimes troublesome to keep these arbitrary divisions to the least. For example, working conditions will involve the physical setup of the workplace, equipment arrangement, accessibility of specific tools utilized in work performed [43, 54].
In the Air Traffic Controllers environment, the setting of equipment, towers, radios, displays, signal beacons, and other equipment segments can differ significantly from capacity to capacity. Research, study, time, and energy are invested in the settlement, arrangement of displays for computers and radar, display colors, chair types, and all the several equipment pieces required to perform their jobs [55]. Some management officials in the aviation industry feel there is an increased background noise during their connection with the flights. The noise comes from the air traffic controllers when they talk among themselves [56]. To avoid these, officials have placed carpeting in the trashcans to limit the sound of waste substances dropping to them [57, 58]. These are just utilized as instances of the thought procedures and considerations given to the alterations in the work environment of the air controllers. Another stimulating habit adopted by controllers, especially in the tower environment, is the rule recognized when approaching an air traffic controller on position the person wants to speak. Generally, the person will wait until the controller is talking on the radio before speaking to them. In the dynamic space, officials cannot understand if two persons speak at once but can talk to one and identify another person trying to speak to them [25, 26]. At a suitable time, the controller can identify the individual and start the conversation. The aviation mechanic is conditional to a more evolving environment based on the type of employer and work [59]. They can differ from working at the same place daily to working outside the airplane. The regulations and rules explain that they must have technical guides at hand when employed on an aircraft. The easy accessibility of these documents and manuals can influence their performance [10].
The settlements of the tools and equipment for them to carry out their tasks will make their jobs more accessible, and if they are not adequate for the job and cannot repair, it makes their work difficult [60]. The number of disruptions they may experience in their work and then the availability of the telephone will have an influence. When the mechanics are in the middle of servicing or assembling an airplane component, they require tracking the procedure and ensuring they finalize all the steps [61]. Not tightening a bolt or, in fact, over-tightening it can be destroyed. Working on some types of machinery can put the mechanic into a physical placement that can be uncomfortable and impact work performance [62].
The temperature and lighting are appropriately controlled and kept persistent for the air traffic controller. The air traffic controllers work in the weather condition from a tower, and the mechanic may work in the weather. The tower controller has to handle the weather and night and day lightening at the different airports [59]. Again, it does not take much of an idea to visualize how the various times of day, cloud cover, and sun positioning can affect the tower cab [63]. At the same time, the temperature can vary slightly due to the comparatively large amount of glass present. The aviation mechanic does not have the same benefit when it comes to weather conditions at the controller. Functioning in extreme heat and cold can affect the health of a person [64].
The inclusiveness and depth of training can differ from individual to individual and also between job types. There is an issue to permit access to training due to extensive contemplations and the demand on the other side when the academic procedure is completed. The all-inclusive and in-depth training is the better option. The air traffic controllers cannot understand if the training is too restricted even if the problem exists [36].
Various factors have been discussed in detail that allows the air traffic controllers, managers, quality management staff, quality control officers, auditors, employees, and people involved in the process improvement. However, the provided factors are a non-exhaustive list that could expand to other unknown or unknown factors. Everyone should play an important role in sustaining the high-quality levels and avoiding errors that could be fatal. Organizations need to measure their performance and monitor the error rate continuously to avoid any future accidents.
IntechOpen implements a robust policy to minimize and deal with instances of fraud or misconduct. As part of our general commitment to transparency and openness, and in order to maintain high scientific standards, we have a well-defined editorial policy regarding Retractions and Corrections.
",metaTitle:"Retraction and Correction Policy",metaDescription:"Retraction and Correction Policy",metaKeywords:null,canonicalURL:"/page/retraction-and-correction-policy",contentRaw:'[{"type":"htmlEditorComponent","content":"IntechOpen’s Retraction and Correction Policy has been developed in accordance with the Committee on Publication Ethics (COPE) publication guidelines relating to scientific misconduct and research ethics:
\\n\\n1. RETRACTIONS
\\n\\nA Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
\\n\\nA formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\\n\\nPublishing of a Retraction Notice will adhere to the following guidelines:
\\n\\n1.2. REMOVALS AND CANCELLATIONS
\\n\\n2. STATEMENTS OF CONCERN
\\n\\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\\n\\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\\n\\n3. CORRECTIONS
\\n\\nA Correction will be issued by the Academic Editor when:
\\n\\n3.1. ERRATUM
\\n\\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\\n\\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n3.2. CORRIGENDUM
\\n\\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n4. FINAL REMARKS
\\n\\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
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\\n\\nAny suggestions or comments on this Policy are welcome and may be sent to permissions@intechopen.com.
\\n\\nPolicy last updated: 2017-09-11
\\n"}]'},components:[{type:"htmlEditorComponent",content:'IntechOpen’s Retraction and Correction Policy has been developed in accordance with the Committee on Publication Ethics (COPE) publication guidelines relating to scientific misconduct and research ethics:
\n\n1. RETRACTIONS
\n\nA Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
\n\nA formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\n\nPublishing of a Retraction Notice will adhere to the following guidelines:
\n\n1.2. REMOVALS AND CANCELLATIONS
\n\n2. STATEMENTS OF CONCERN
\n\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\n\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\n\n3. CORRECTIONS
\n\nA Correction will be issued by the Academic Editor when:
\n\n3.1. ERRATUM
\n\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\n\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n3.2. CORRIGENDUM
\n\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n4. FINAL REMARKS
\n\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\n\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\n\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
\n\nAny suggestions or comments on this Policy are welcome and may be sent to permissions@intechopen.com.
\n\nPolicy last updated: 2017-09-11
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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"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. Osma",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDv7QAG/Profile_Picture_1626602531691",institutionString:null,institution:{name:"Universidad de Los Andes",institutionURL:null,country:{name:"Colombia"}}},{id:"69697",title:"Dr.",name:"Mani T.",middleName:null,surname:"Valarmathi",fullName:"Mani T. Valarmathi",profilePictureURL:"https://mts.intechopen.com/storage/users/69697/images/system/69697.jpg",institutionString:"Religen Inc. | A Life Science Company, United States of America",institution:null},{id:"205081",title:"Dr.",name:"Marco",middleName:"Vinícius",surname:"Chaud",fullName:"Marco Chaud",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDGeQAO/Profile_Picture_1622624307737",institutionString:null,institution:{name:"Universidade de Sorocaba",institutionURL:null,country:{name:"Brazil"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/348205",hash:"",query:{},params:{id:"348205"},fullPath:"/profiles/348205",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()