These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
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This collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
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To celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
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Initially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\n
These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\n
This collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\n
To celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\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:"7478",leadTitle:null,fullTitle:"Photocatalysts - Applications and Attributes",title:"Photocatalysts",subtitle:"Applications and Attributes",reviewType:"peer-reviewed",abstract:"This book enlightens the type, chemical structure, and application of photo-catalysts. It covers the recent developments in photo-catalysts and their applications, particularly in photo-catalytic degradation of different organic pollutants, hydrogen production, etc. It provides a concise but complete coverage and overview of photocatalysts and their recent advances for a broad audience: beginners, graduate students, and specialists in both academic and industrial sectors.",isbn:"978-1-78985-476-3",printIsbn:"978-1-78985-475-6",pdfIsbn:"978-1-83962-058-4",doi:"10.5772/intechopen.75848",price:119,priceEur:129,priceUsd:155,slug:"photocatalysts-applications-and-attributes",numberOfPages:156,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"26559479998a0a8d83546de0a220c87f",bookSignature:"Sher Bahadar Khan and Kalsoom Akhtar",publishedDate:"March 6th 2019",coverURL:"https://cdn.intechopen.com/books/images_new/7478.jpg",numberOfDownloads:13504,numberOfWosCitations:44,numberOfCrossrefCitations:42,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:92,numberOfDimensionsCitationsByBook:3,hasAltmetrics:1,numberOfTotalCitations:178,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 12th 2018",dateEndSecondStepPublish:"April 2nd 2018",dateEndThirdStepPublish:"June 1st 2018",dateEndFourthStepPublish:"August 20th 2018",dateEndFifthStepPublish:"October 19th 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"245468",title:"Dr.",name:"Sher Bahadar",middleName:null,surname:"Khan",slug:"sher-bahadar-khan",fullName:"Sher Bahadar Khan",profilePictureURL:"https://mts.intechopen.com/storage/users/245468/images/system/245468.jpg",biography:"Prof. Dr. Sher Bahadar Khan received his Ph.D from HEJ, Karachi University, Pakistan. After completion of his Ph.D, he started his post-doctoral career in nanochemistry and nanotechnology and continued to work as a post-doctoral research fellow to February 2010 at Yonsei University, South Korea. In March 2010, he joined the Center for Advanced Materials and Nano-engineering, Department of Chemistry, Najran University as an Assistant Professor and continued his work to 31 August 2011. He joined the Chemistry Department, King Abdulaziz University, Jeddah, Saudi Arabia as an Assistant Professor in September 2011. Currently he is Full Professor in the Chemistry Department, King Abdulaziz University and is doing research in nanochemistry and nanotechnology. He was honoured by receiving the top scientist award of KP Science & Technology in 2018. He was also honoured by the Deanship of Scientific Research awards at King Abdulaziz University for book, patent, and highly ranked scientific publication. He is the author of 320 research articles, twelve books, and six patents with almost 1000 ± 10 impact factor, 6665 citations, and 45 h-index.",institutionString:"King Abdulaziz University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"King Abdulaziz University",institutionURL:null,country:{name:"Saudi Arabia"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"245469",title:"Dr.",name:"Kalsoom",middleName:null,surname:"Akhtar",slug:"kalsoom-akhtar",fullName:"Kalsoom Akhtar",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Dr. Kalsoom Akhtar received her Ph.D from the Chemistry Department, Ewha Womans University, Seoul, Korea. Dr. K. Akhtar is an Assist. Professor in the Chemistry Department, King Abdulaziz University and is currently doing research in organic and nano-chemistry, which comprises photo-catalyst, organic synthesis, and metal oxide nanomaterials. She is the author of 2 books and 65 research papers.",institutionString:"King Abdulaziz University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"King Abdulaziz University",institutionURL:null,country:{name:"Saudi Arabia"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"509",title:"Photochemistry",slug:"photochemistry"}],chapters:[{id:"63788",title:"Disinfection Methods",doi:"10.5772/intechopen.80999",slug:"disinfection-methods",totalDownloads:3154,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Water must be made safe to drink, and an important step in ensuring water safety is disinfection. Disinfectants are added to water to kill disease-causing microorganisms. Ground water sources can be disinfected by “The Water Treatment Rule,” which requires public water systems for disinfection. Chlorination, ozone, ultraviolet light, and chloramines are primary methods for disinfection. However, potassium permanganate, photocatalytic disinfection, nanofiltration, and chlorine dioxide can also be used. Organic material is naturally present in water. Certain forms of chlorine can react with these organic materials and result in the formation of harmful by-products; the U.S. Environmental Protection Agency has anticipated maximum levels for these contaminants.",signatures:"Muhammad Saqib Ishaq, Zobia Afsheen, Amjad Khan and Amjad\nKhan",downloadPdfUrl:"/chapter/pdf-download/63788",previewPdfUrl:"/chapter/pdf-preview/63788",authors:[{id:"228353",title:"Dr.",name:"Muhammad Saqib",surname:"Ishaq",slug:"muhammad-saqib-ishaq",fullName:"Muhammad Saqib Ishaq"},{id:"246559",title:"Dr.",name:"Zobia",surname:"Afsheen",slug:"zobia-afsheen",fullName:"Zobia Afsheen"},{id:"246561",title:"Mr.",name:"Amjad",surname:"Khan",slug:"amjad-khan",fullName:"Amjad Khan"},{id:"271289",title:"Dr.",name:"Amjad",surname:"Khan",slug:"amjad-khan",fullName:"Amjad Khan"}],corrections:null},{id:"63016",title:"Selective Photodegradation Using Titanate Nanostructures",doi:"10.5772/intechopen.80311",slug:"selective-photodegradation-using-titanate-nanostructures",totalDownloads:988,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Adsorption and photocatalytic degradation are considered as the most important ways of treating water from organic compounds. It would be very useful if the adsorption and photocatalytic properties are combined in the same catalyst used in the treatment. Titania is one of the best well-known photocatalysts. However, due to its poor selectivity, it is unfavorable for photocatalytic removal of highly toxic low-level organic pollutants in wastewater in the presence of other less toxic high-level pollutants. Recent trials to introduce selectivity for titania have been achieved via controlling the catalyst morphology or by modifying the catalyst surface. This chapter summarizes the control of selectivity of titanate nanostructures toward adsorption and/or photocatalytic degradation of toxic organic dyes. In the first part, the effect of morphologies of titanites on selective photocatalytic degradation of three food dyes (color yellow sunset, red allura, and red carmoisine) was discussed. In changing the morphology of titanite, each dye is being preferably adsorbed by one morphology and decomposing more rapidly. In the second part, the selective adsorption and/or photocatalytic degradation of methylene blue dye from mixed dye solution using sodium titanate (NaTNT), cobalt-doped titanate nanotubes (co-doped TNT), and the decorated one with gold nanoparticles has been discussed.",signatures:"Ayman Hassan Zaki and Waleed Mohamed Ali. El Rouby",downloadPdfUrl:"/chapter/pdf-download/63016",previewPdfUrl:"/chapter/pdf-preview/63016",authors:[{id:"242465",title:"Dr.",name:"Ayman",surname:"Zaki",slug:"ayman-zaki",fullName:"Ayman Zaki"},{id:"261211",title:"Dr.",name:"Waleed",surname:"El Rouby",slug:"waleed-el-rouby",fullName:"Waleed El Rouby"}],corrections:null},{id:"62303",title:"Modified Titanium Dioxide for Photocatalytic Applications",doi:"10.5772/intechopen.79374",slug:"modified-titanium-dioxide-for-photocatalytic-applications",totalDownloads:3924,totalCrossrefCites:34,totalDimensionsCites:72,hasAltmetrics:1,abstract:"Titanium dioxide (TiO2) has been widely used as a photocatalyst in many environmental and energy applications due to its efficient photoactivity, high stability, low cost, and safety to the environment and humans. However, its large band gap energy, ca. 3.2 eV limits its absorption of solar radiation to the UV light range which accounts for only about 5% of the solar spectrum. Furthermore, the photocatalytic activity of TiO2 is also limited by the rapid recombination of the photogenerated electron-hole pairs. When used in water treatment applications, TiO2 has a poor affinity toward organic pollutants, especially hydrophobic organic pollutants. Several strategies have been employed to reduce its band gap energy, its electron-hole recombination rates as well as enhance its absorption of organic pollutants. In this chapter, we review some of the most recent works that have employed the doping, decoration, and structural modification of TiO2 particles for applications in photocatalysis. Additionally, we discuss the effectiveness of these dopants and/or modifiers in enhancing TiO2 photoactivity as well as some perspective on the future of TiO2 photocatalysis.",signatures:"John Moma and Jeffrey Baloyi",downloadPdfUrl:"/chapter/pdf-download/62303",previewPdfUrl:"/chapter/pdf-preview/62303",authors:[{id:"250026",title:"Dr.",name:"John",surname:"Moma",slug:"john-moma",fullName:"John Moma"},{id:"250963",title:"Mr.",name:"Jeffrey",surname:"Baloyi",slug:"jeffrey-baloyi",fullName:"Jeffrey Baloyi"}],corrections:null},{id:"64228",title:"Modified Metallic Oxides for Efficient Photocatalysis",doi:"10.5772/intechopen.80834",slug:"modified-metallic-oxides-for-efficient-photocatalysis",totalDownloads:1175,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The aim of the chapter is to present modified materials like alternatives for conventional photocatalyst such as titanium dioxide. Discussion about silver/graphene nanoparticles-modified zinc oxide for the degradation of pollutants like triclosan or bisphenol A, both considered as endocrine disruptors, which affect the hormonal development of humans, is presented. The best conditions to obtain the highest photodegradation degree are established. In addition, the bismuth oxychloride has gained attention during the last 5 years for photocatalysis. In accordance, the obtained results for phenol photodegradation, using such oxychloride, are also presented. In the chapter, the characterization of photocatalyst is reported along with the proposal for mechanisms of action for the modified ZnO photocatalyst and the bismuth oxychloride.",signatures:"Vladimir A. Escobar Barrios, Dalia Verónica Sánchez Rodríguez,\nNancy Ayerim Cervantes Rincón and Alma Berenice Jasso-Salcedo",downloadPdfUrl:"/chapter/pdf-download/64228",previewPdfUrl:"/chapter/pdf-preview/64228",authors:[{id:"12711",title:"Dr.",name:"Vladimir Alonso",surname:"Escobar Barrios",slug:"vladimir-alonso-escobar-barrios",fullName:"Vladimir Alonso Escobar Barrios"},{id:"252705",title:"MSc.",name:"Nancy",surname:"Cervantes-Rincón",slug:"nancy-cervantes-rincon",fullName:"Nancy Cervantes-Rincón"},{id:"252706",title:"Dr.",name:"Alma",surname:"Jasso-Salcedo",slug:"alma-jasso-salcedo",fullName:"Alma Jasso-Salcedo"},{id:"252707",title:"MSc.",name:"Dalia Verónica",surname:"Sánchez Rodríguez",slug:"dalia-veronica-sanchez-rodriguez",fullName:"Dalia Verónica Sánchez Rodríguez"}],corrections:null},{id:"63562",title:"Lanthanides Effects on TiO2 Photocatalysts",doi:"10.5772/intechopen.80906",slug:"lanthanides-effects-on-tio2-photocatalysts",totalDownloads:1073,totalCrossrefCites:2,totalDimensionsCites:4,hasAltmetrics:0,abstract:"Semiconductors have been evaluated to heterogeneous photocatalysis degradation of recalcitrant contaminants in aqueous media due to the capacity of mineralizing these compounds under UV or visible light irradiation. However, this process has the inherent feature of photogenerated charges recombination and the high bandgap energy of the electronic structure of some semiconductors that can reduce the formation of reactive oxygen species, which are responsible for the compound degradation. In this context, structural modifications in semiconductors have been proposed to enhance the photocatalytic activity, such as doping processes with elements that are capable of generating superficial defects that capture the formed electrons, avoiding the recombination, or increasing the density of –OH groups or water molecules on the surface of the catalyst, which can enhance the formation of hydroxyl radicals. Therefore, this brief review is proposed to show the role of lanthanides in the TiO2 doping and the synthesis method applied, as well as the results discussed in the literature.",signatures:"Gustavo Lopes Colpani, Micheli Zanetti, Rubieli Carla Frezza\nZeferino, Luciano Luiz Silva, Josiane Maria Muneron de Mello,\nHumberto Gracher Riella, Natan Padoin, Márcio Antônio Fiori and\nCíntia Soares",downloadPdfUrl:"/chapter/pdf-download/63562",previewPdfUrl:"/chapter/pdf-preview/63562",authors:[{id:"215607",title:"Dr.",name:"Josiane",surname:"Mello",slug:"josiane-mello",fullName:"Josiane Mello"},{id:"257258",title:"Dr.",name:"Gustavo",surname:"Colpani",slug:"gustavo-colpani",fullName:"Gustavo Colpani"},{id:"257927",title:"MSc.",name:"Micheli",surname:"Zanetti",slug:"micheli-zanetti",fullName:"Micheli Zanetti"},{id:"257928",title:"MSc.",name:"Rubieli Carla Frezza",surname:"Zeferino",slug:"rubieli-carla-frezza-zeferino",fullName:"Rubieli Carla Frezza Zeferino"},{id:"257929",title:"Dr.",name:"Luciano Luiz",surname:"Silva",slug:"luciano-luiz-silva",fullName:"Luciano Luiz Silva"},{id:"268440",title:"Dr.",name:"Cíntia",surname:"Soares",slug:"cintia-soares",fullName:"Cíntia Soares"},{id:"268441",title:"Dr.",name:"Natan",surname:"Padoin",slug:"natan-padoin",fullName:"Natan Padoin"},{id:"268442",title:"Dr.",name:"Humberto",surname:"Gracher Riella",slug:"humberto-gracher-riella",fullName:"Humberto Gracher Riella"},{id:"268443",title:"Dr.",name:"Márcio",surname:"Fiori",slug:"marcio-fiori",fullName:"Márcio Fiori"}],corrections:null},{id:"63323",title:"Carbon-/Zeolite-Supported TiO2 for Sorption/Photocatalysis Applications in Water Treatment",doi:"10.5772/intechopen.80803",slug:"carbon-zeolite-supported-tio2-for-sorption-photocatalysis-applications-in-water-treatment",totalDownloads:944,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The role of various carbon forms, i.e., activated carbon and carbon nanotubes/nanofibers as support for TiO2 in drinking water treatment, is discussed. Also, TiO2 supported onto zeolite that acts bifunctionally as a sorbent/photocatalyst for drinking water treatment is presented. The main contaminants of natural organic matter (NOM), arsenic species, and nitrogen compounds from drinking water sources by the type of groundwater and surface water can be removed/degraded by sorption/photocatalysis using TiO2 supported onto carbon and/or zeolite. TiO2 supported on powdered activated carbon (PAC-TiO2), granular activated carbon (GAC-TiO2), and zeolite (Z-TiO2), namely, supported TiO2, was synthesized through the sol-gel method, and TiO2 and multiwall carbon nanotubes/carbon nanofibers dispersed within epoxy matrix (CNT-TiO2-Epoxy, CNF-TiO2-Epoxy), namely, TiO2 composite, were obtained through the two-roll mill method. Kinetic study results through specific mathematic models allowed to elucidate some mechanistic aspects for sorption and photocatalysis for the application in drinking water. The intercalation of the carbon- and zeolite-supported TiO2 layers into a filtering system allows to develop a self-cleaning filtering system in drinking water.",signatures:"Florica Manea and Corina Orha",downloadPdfUrl:"/chapter/pdf-download/63323",previewPdfUrl:"/chapter/pdf-preview/63323",authors:[{id:"249975",title:"Prof.",name:"Florica",surname:"Manea",slug:"florica-manea",fullName:"Florica Manea"},{id:"251672",title:"Dr.",name:"Corina",surname:"Orha",slug:"corina-orha",fullName:"Corina Orha"}],corrections:null},{id:"63802",title:"Evaluation of the Role of Hydroxyapatite in TiO2/ Hydroxyapatite Photocatalytic Materials",doi:"10.5772/intechopen.81092",slug:"evaluation-of-the-role-of-hydroxyapatite-in-tio2-hydroxyapatite-photocatalytic-materials",totalDownloads:1079,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The TiO2/hydroxyapatite (HAp) composite has attracted much attention as a photocatalyst for pollution treatment in water or air because this composite can improve the properties of pure TiO2 including a low efficiency, narrow light response range, low adsorption capacity for hydrophobic contaminants, and difficult recovery of TiO2 particles after using in Aquarius environment. To obtain the best composite containing the two components including TiO2 and HAp, the role of HAp in TiO2/hydroxyapatite photocatalytic material should be analyzed and evaluated. This chapter will significantly present a review of the role of HAp in the TiO2/hydroxyapatite composite including the adsorption ability of contaminations and the promoted impacts of HAp component.",signatures:"Linh Nguyen Thi Truc, Seungbum Hong and Kwangsoo No",downloadPdfUrl:"/chapter/pdf-download/63802",previewPdfUrl:"/chapter/pdf-preview/63802",authors:[{id:"240618",title:"Prof.",name:"Seungbum",surname:"Hong",slug:"seungbum-hong",fullName:"Seungbum Hong"},{id:"250784",title:"Dr.",name:"Linh",surname:"Nguyen",slug:"linh-nguyen",fullName:"Linh Nguyen"},{id:"252169",title:"Prof.",name:"Kwangsoo",surname:"No",slug:"kwangsoo-no",fullName:"Kwangsoo No"}],corrections:null},{id:"63673",title:"Photoreduction Processes over TiO2 Photocatalyst",doi:"10.5772/intechopen.80914",slug:"photoreduction-processes-over-tio2-photocatalyst",totalDownloads:1170,totalCrossrefCites:3,totalDimensionsCites:8,hasAltmetrics:0,abstract:"This chapter presents the study of TiO2 photocatalyst for the photoreduction of several reducible chemicals. The photocatalytic reduction of several toxic metal ions, including Ag(I), Cu(II), Cr(VI), Hg(II), and U(VI) in the presence of TiO2, in order to decrease their toxicity, is described. Photodeposition of the noble metals, such as Ag(I), Au(III), Pt(IV), and Pd(II) for doping purposes by photocatalytic reduction over TiO2, is also addressed. Conversion of the greenhouse gas of CO2 into useful hydrocarbons and methanol by photocatalytic reduction using TiO2 photocatalyst is highlighted. Several operating parameters in photoreduction processes that are photocatalyst dose, time of the irradiation, pH of the solution, and the initial concentration of the substrates (the reducible chemicals) are also reviewed.",signatures:"Endang Tri Wahyuni and Nurul Hidayat Aprilita",downloadPdfUrl:"/chapter/pdf-download/63673",previewPdfUrl:"/chapter/pdf-preview/63673",authors:[{id:"225211",title:"Prof.",name:"Endang Tri",surname:"Wahyuni",slug:"endang-tri-wahyuni",fullName:"Endang Tri Wahyuni"},{id:"267675",title:"Dr.",name:"Nurul Hidayat",surname:"Aprilita",slug:"nurul-hidayat-aprilita",fullName:"Nurul Hidayat Aprilita"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"7358",title:"Cerium Oxide",subtitle:"Applications and Attributes",isOpenForSubmission:!1,hash:"7d1cd9a9ecf46270e344d15f94bc66ef",slug:"cerium-oxide-applications-and-attributes",bookSignature:"Sher Bahadar Khan and Kalsoom Akhtar",coverURL:"https://cdn.intechopen.com/books/images_new/7358.jpg",editedByType:"Edited by",editors:[{id:"245468",title:"Dr.",name:"Sher Bahadar",surname:"Khan",slug:"sher-bahadar-khan",fullName:"Sher Bahadar Khan"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8724",title:"Gas Sensors",subtitle:null,isOpenForSubmission:!1,hash:"bc4be4b954b559709aaace45f70adcd0",slug:"gas-sensors",bookSignature:"Sher Bahadar Khan, Abdullah M. 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\r\n\tOn-road and off-road vehicles constitute an important part of the land transportation sector. Even the economic and chip crises cannot completely stop the production of vehicles in a product range that varies according to customer demand. The use of automobiles in the world is increasing day by day, and vehicle users demand from the sector smarter, more environmentally friendly, and safer vehicles. Vehicle dynamics is one of the most important aspects that all vehicle manufacturers and related researchers should compute and pay attention to before the production of vehicles. Modeling and simulation of dynamic elements of vehicle parts such as tires, steering, brakes, the integrated driver helped systems, etc., is a crucial step before prototyping.
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Shilyaev and E.M. Khromova",authors:[{id:"18168",title:"Prof.",name:"Michail Ivanovich",middleName:null,surname:"Shilyaev",fullName:"Michail Ivanovich Shilyaev",slug:"michail-ivanovich-shilyaev"}]},{id:"13546",title:"Mass Transfer in Filtration Combustion Processes",slug:"mass-transfer-in-filtration-combustion-processes",signatures:"David Lempert, Sergei Glazov and Georgy Manelis",authors:[{id:"18042",title:"Dr.",name:"David",middleName:null,surname:"Lempert",fullName:"David Lempert",slug:"david-lempert"}]},{id:"13547",title:"Mass Transfer in Hollow Fiber Supported Liquid Membrane for As and Hg Removal from Produced Water in Upstream Petroleum Operation in the Gulf of Thailand",slug:"mass-transfer-in-hollow-fiber-supported-liquid-membrane-for-as-and-hg-removal-from-produced-water-in",signatures:"U. Pancharoen, A.W. Lothongkum and S. Chaturabul",authors:[{id:"16502",title:"Prof.",name:"Ura",middleName:null,surname:"Pancharoen",fullName:"Ura Pancharoen",slug:"ura-pancharoen"}]},{id:"13548",title:"Mass Transfer in Fluidized Bed Drying of Moist Particulate",slug:"mass-transfer-in-fluidized-bed-drying-of-moist-particulate",signatures:"Yassir T. Makkawi and Raffaella Ocone",authors:[{id:"16956",title:"Dr.",name:"Yassir",middleName:"Taha",surname:"Makkawi",fullName:"Yassir Makkawi",slug:"yassir-makkawi"},{id:"20080",title:"Prof.",name:"Raffaella",middleName:null,surname:"Ocone",fullName:"Raffaella Ocone",slug:"raffaella-ocone"}]},{id:"13549",title:"Simulation Studies on the Coupling Process of Heat/Mass Transfer in a Metal Hydride Reactor",slug:"simulation-studies-on-the-coupling-process-of-heat-mass-transfer-in-a-metal-hydride-reactor",signatures:"Fusheng Yang and Zaoxiao Zhang",authors:[{id:"17936",title:"Prof.",name:"Zaoxiao",middleName:null,surname:"Zhang",fullName:"Zaoxiao Zhang",slug:"zaoxiao-zhang"},{id:"20005",title:"Dr.",name:"Fusheng",middleName:null,surname:"Yang",fullName:"Fusheng Yang",slug:"fusheng-yang"}]},{id:"13550",title:"Mass Transfer around Active Particles in Fluidized Beds",slug:"mass-transfer-around-active-particles-in-fluidized-beds",signatures:"Fabrizio Scala",authors:[{id:"17940",title:"Dr.",name:"Fabrizio",middleName:null,surname:"Scala",fullName:"Fabrizio Scala",slug:"fabrizio-scala"}]},{id:"13551",title:"Mass Transfer Phenomena and Biological Membranes",slug:"mass-transfer-phenomena-and-biological-membranes",signatures:"Parvin Zakeri-Milani and Hadi Valizadeh",authors:[{id:"17166",title:"Prof.",name:"Parvin",middleName:null,surname:"Zakeri-Milani",fullName:"Parvin Zakeri-Milani",slug:"parvin-zakeri-milani"},{id:"19860",title:"Dr.",name:"Hadi",middleName:null,surname:"Valizadeh",fullName:"Hadi Valizadeh",slug:"hadi-valizadeh"}]},{id:"13552",title:"Heat and Mass Transfer in Packed Bed Drying of Shrinking Particles",slug:"heat-and-mass-transfer-in-packed-bed-drying-of-shrinking-particles",signatures:"Manoel Marcelo do Prado and Dermeval José Mazzini Sartori",authors:[{id:"19095",title:"Dr.",name:"Dermeval",middleName:null,surname:"Sartori",fullName:"Dermeval Sartori",slug:"dermeval-sartori"},{id:"19105",title:"Dr.",name:"Manoel Marcelo",middleName:null,surname:"Do Prado",fullName:"Manoel Marcelo Do Prado",slug:"manoel-marcelo-do-prado"}]},{id:"13553",title:"Impact of Mass Transfer on Modelling and Simulation of Reactive Distillation Columns",slug:"impact-of-mass-transfer-on-modelling-and-simulation-of-reactive-distillation-columns",signatures:"Zuzana Švandová, Jozef Markoš and Ľudovít Jelemenský",authors:[{id:"12119",title:"Dr.",name:"Jozef",middleName:null,surname:"Markoš",fullName:"Jozef Markoš",slug:"jozef-markos"}]},{id:"13554",title:"Mass Transfer through Catalytic Membrane Reactor",slug:"mass-transfer-through-catalytic-membrane-reactor",signatures:"Nagy Endre",authors:[{id:"19732",title:"Dr.",name:"Endre",middleName:null,surname:"Nagy",fullName:"Endre Nagy",slug:"endre-nagy"}]},{id:"13555",title:"Mass Transfer in Bioreactors",slug:"mass-transfer-in-bioreactors",signatures:"Ma. del Carmen Chávez, Linda V. 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1. Introduction
This chapter’s focus is on change detection by monitoring residuals arising from fitted models to time series data. The residual at time t is defined as rt=xt−x̂t, where x̂t is the predicted (estimated) value of the data xt. Large residuals or patterns in residuals could indicate that some type of change has occurred compared to usual behavior during the analysis period that was used to fit the model. For example, the number of positive test results for a disease could show a sharp rise or decline compared to the recent past, perhaps indicating that a signal of interest is present, such as a more infectious strain emerging. As another example, assembly line productions monitor product quality, such as the diameter of a machined part, which can drift due to measurement effects and/or machining effects. Diameter drifting can lead to detectable residual patterns, where the residual is the measured diameter—target diameter. Prior to diameter drifting, the time series should vary randomly around a mean value that is close to the target mean diameter. Therefore, time series fitting of the “in control” process data simply requires estimation of the mean and standard deviation of the measured diameter of each part. Other time series fitting options are less simple.
There are many types of time series, such as series of the unit or system failure times in reliability data, series of new disease cases or deaths, series of measured product quality, such as geometric dimensions in machined parts or salt content in bags of chips. This chapter does not consider predicting the next failure time, the time to the next spike in disease counts, or the time for the machined part mean dimension to shift. Instead, the chapter focuses on monitoring for possible changes in the meantime to failure, changes in the distribution of disease counts, or machined part dimensions. There are many applications in which a training period for model fitting is assumed to define normal behavior, and then the testing period monitors for various types of changes from the normal behavior, such as a shift to a different mean value.
Figure 1 is a time series of n = 50 values that have mean 0 and standard deviation 1 (independently and identically distributed normal random values, denoted iid N(0,1) in the figure caption) that exhibit no change in (a), a mean shift of 2 units on period 25 in (b) a mean shift of 2 units at time indices 26–30 in (c), and a mean shift at time indices 26–50 in (d). The human brain/eye is reasonably effective at spotting such changes but is vulnerable to being fooled by spurious patterns. Statistical methods, some very simple and some less simple have been developed to detect changes of interest, as this chapter explains.
Figure 1.
Time series, iid N(0,1).
Let x1,x2,…,xndenote a time series, which is a sequence of values at times 1, 2, …, n.Figure 2 plots an example simulated time series with n = 50.
Figure 2.
Time series, MA(1) generated from iid N(0,1).
An effective time series model leads to residuals that are approximately independently and identically distributed (iid) [1, 2]. The residual at time t is defined as rt=xt−x̂t, where x̂t is the predicted (estimated) value of xt. For example, f() could be linear in the x’s, resulting in the well-known auto-regressive (AR) model, f=ρ1xt−1+ρ2xt−2+…+ρlAxt−lA or f() might be linear in both the x’s and an underlying iid noise sequence e1,e2,…,en, resulting in the auto-regressive moving average (ARMA) model f=ρ1xt−1+ρ2xt−2+…+ρlAxt−lA+θ1xt−1+θ2xt−2+…+θlMxt−lM where lA is the AR lag, and lM is the moving average (MA) lag [1, 2]. The lag-one MA, AR, and ARMA models are given in (Eqs. (1)–(3)).
xt=θ1et−1+etE1
xt=ρ1xt−1+etE2
xt=ρ1xt−1+θ1et−1+etE3
Conditions on the magnitudes of θand ρ ensure stationarity (constant mean and variance over time). Often, time series can be transformed to stationarity by taking first differences, as is commonly done in stock market price series [1, 2]. Of course, f() might not be linear in prior x values or e values and in general could be an arbitrarily complicated function, f(xt−1,…,xt−lA,et−1,…,et−lM).
Figure 2 is simulated data from a lag one MA model, xt=θ1et−1+et . Figure 3 is the cumulative sum (cumsum, Ct=∑i=1txi) and Page’s cusum St with parameter k (see [3] and examples 2 and 3) defined as.
Figure 3.
IID N(0,1) time series, cumulative sum, and Page’s Cusum.
St=max(0,St−1+xi−kE4
for the data plotted in Figure 2 [3]. Because of the reset-to-0 feature of St if the sum goes negative and because of the parameter k, Page’s St does not have the large drift behavior that the cumulative sum does. Figure 4 is the estimated underlying residual sequence and the actual underlying simulated residual sequence. Figure 5 is the same as Figure 4 but plots the difference between the estimated and true residuals. All plots and analyses are performed in R [4]. Statistical tests for changes in the background due to signal are applied to estimated residuals, so alarm thresholds and signal detection probabilities should be estimated using estimated residuals obtained via simulation.
Figure 4.
Estimated residuals for MA(1) from N(0,1) data.
Figure 5.
Same as Figure 4, but plotting the difference, residual-estimated residual.
This chapter describes three change-detection examples. Example 1 monitors for patterns of large residuals, such as a consecutive string of three residuals exceeding a threshold. Example 2 monitors for excessive numbers of tweets in any of the 65 Florida counties. Example 3 monitors for nuclear material loss. Portions of Examples 1 and 3 have been published. Example 2 is entirely new.
2. Example 1: monitoring for patterns of large residuals
The Stein-Chen (SC) method approximates the probability density function (pdf) that assigns probabilities to the number of times that a pattern such as It,It+1,It+2=101 occurs, starting at position t in a binary time series of length n. In example 1, the original time series that is converted to binary is assumed to consist of a sequence of independent iid residuals that result from fitting any type of time series model. Recently the SC method was shown to provide an accurate Poisson-based approximation and corresponding total variation distance bounds in a time series context [5]. The binary values.
I1,I2,I3,…,In are assumed to be independent and identically distributed with constant probability p = P(Ii = 1). The probability p is the probability that the original time series X exceeds a threshold, and the I notation denotes an indicator or binary variable. As an aside, the SC method can also be applied if p is not constant over time, but the independence assumption is difficult to avoid [5, 6, 7, 8, 9, 10]. Any type of time series model [1, 2] can be fit, and then the resulting residuals become the original series that is thresholded to convert to binary; therefore, the application is quite general. Figure 6 is the estimated residuals from Figure 4 but thresholded at 1.4 (values of 1.4 or larger are set to 1; values less than 1.4 are set to 0) to convert to binary.
Figure 6.
Binary version of the estimated residuals in Figure 3.
Note that if 101 is known to not occur, for example, starting at position t = 1, then this information impacts the probability that 101 occurs starting at position t = 2 or t = 3, because the trials to obtain 101are overlapping and thus not independent, so the Poisson distribution assumptions are not met. Nevertheless, Ref. [5] showed that Poisson-based approximation (that is strictly correct only for independent trials) can be remarkably accurate, and the SC method provides a bound on the total variation distance between the true and approximate pdf.
Consider scanning for {1 x 1} with x = 0 or 1, with p = P(Ii = 1) being quite small, such as 0.10 or less in a residual series of length n = 10. Then the probability of the pattern {1 x 1} is pp = p2, and there are n – 2 = 8 possible starting locations for the pattern in N = 10 trials. Because there are only 210 = 1024 possible patterns of 0s and 1s, all 1024 patterns could be listed, and the probabilities assigned to each set of 10 binary values that include {1 × 1} at least once could be summed to provide an exact calculation. For larger values of n, this exact calculation is unwieldy, so an approximate method is desired, provided the approximation is highly accurate with provable error bounds.
Start at index i = 1 and check whether {1 × 1} occurs in positions {1 2 3}, then start at index i = 2 and check whether {1 × 1} occurs starting at index 2 in positions {2 3 4}, then start at index 3, etc. Note, for example, that if {1 × 1} occurs starting at position i = 1, then the probability that {1 × 1} also occurs starting at index 3 is p. Clearly, there is a small neighborhood of dependence around each starting index, as just illustrated. This neighborhood of dependence violates the assumptions for a Poisson distribution (as a limit distribution for a sequence of N Bernoulli trials, each with a small probability of success), but Ref. [6] shows that provided the dependence neighborhood is modest, the Poisson distribution can still provide an excellent approximation to the pdf defined on the number of times {1 × 1} occurs in a series of length N.
2.1 Stein-Chen method
The Poisson pdf with mean parameter λ=N−2pp provides an approximation Y to the true pdf W for the number of times {1 × 1} occurs in a series of lengthn [5, 6]. The value n−2 is used instead of n because the length 3 pattern could only be found starting at index 1, 2, …, n−2.
The quality of the Poisson(λ) approximation can be measured by computingb1 and b2, where b1=∑i=1n∑j∈Nipipj, with Ni=i−2i−1ii+1i+2 being the dependent neighborhood Ni of index i, and b2=∑i=1n∑j,j′∈NiEIjIj′, where j≠j′. The term b3 is equal to 0 in Theorem 2 of Ref. [6] by the construction of Ni in this example. Then, the total variation distance (TVD) satisfies.
dTVDYW≤4b1+b2=4n−29pp2+3pppE5
The TVD is a general distance measure between two pdfs. The TVD is defined here as the maximum absolute difference between the probability assigned by Y and the probability assigned by W to any specified subset of possible integer values. In the current scanning context, the most important subset of possible values to consider is the single value {0}, which would imply that the pattern {1 × 1} never occurred (occurred 0 times) in the n−2 overlapping trials. Then, the SC method, in this context, uses the Poisson approximation to assign a value to P {0} and this method ensures that the Poisson approximation to P{0} is quite accurate, as shown by the numerical example below.
According to the Poisson approximation, P1x1never occurs = e−λ. For example, using n=1000 and p=0.01, λ=998pp=0.0998, then e−λ= 0.905 is the approximate probability that the pattern never occurs, with an SC-based bound of 4n−29pp2+3ppp=0.0123. Therefore, the maximum difference between the true probability defined by the Y random variable and the approximate probability assigned to any subset of the possible number of occurrences of {1 × 1} defined by the approximating W (Poisson random variable) is 0.01236. So, for example, if the probability that 1×1never occurs=e−λ=0.905, then the true probability of 0 occurrences of the pattern is between 0.89 and 0.92. Section 2.3 uses simulation to confirm the quality of the SC approximation in Eq. (5) in this context.
Simulation can be used to closely approximate the true probabilities, but only for moderate values of n. For example, in 106 repeated sets of n = 1000 Bernoulli trials with p=0.01, then λ=998pp=0.0998, and e−λ = 0.905, the simulation-based P(0 occurrences of 1x1) = 0.907. The Poisson-based approximation gives 0.905 with a SC-based TVD bound from Eq. (4) of 0.0123.
2.2 Summary
The SC method was described to approximate the pdf for the number of occurrences of an example pattern in an independent binary time series. In scanning for whether a pattern, such as {1 × 1}, occurs starting at index i, there are overlapping tries to achieve the pattern, resulting in many non-independent trials consisting of the values in three successive indices. As the time series length increases and the probability p = P(Ii = 1) decreases, the SC method shows that the Poisson approximation is excellent, with a small total variation distance bound.
The SC bound does not seem to be commonly used; however, related references are available [5, 6, 7, 8, 9, 10]. For example, Ref. [7] applies the SC method to calculate coincidence probabilities. References [8, 9] apply the SC method in different time series contexts than considered here. To simplify the calculation of bivariate Poisson moments, Ref. [10] applies the SC identity XfX=μE(fX+1), where X is a Poisson(μ) random variable, E denotes expected value, andf() is any bounded function defined on the nonnegative integers. The SC identity was used to develop the SC approximation method used in Ref. [5] and the example above. Reference [5] showed that the SC bound defends the use of the Poisson approximation in real applications (as opposed to unwieldy combinatorial calculations for long time series), and provides a small bound on the approximation error. Simulation and/or analytical approximation are needed to estimate p = P(Ii = 1) to apply the Poisson approximation and associated SC bound.
3. Example 2: monitoring for excessive numbers of tweets in 65 Florida counties
3.1 Introduction
Example 2 analyzes two types of daily tweet counts. The first type of counts is available from March 8, 2010, to December 31, 2015, in each of 65 Florida counties. In the available data from 65 counties (instead of the full 67), Lafayette is merged with Madison, and Liberty is merged with Gadsden; see Appendix 1. Such merging changes the spatial resolution available to detect spatial–temporal outbreaks for the four relevant counties. Reporting counts at the county (or merged county) level also changes the available spatial resolution, compared, for example, to geo-tagged counts. The second type of counts is time-tagged (to the nearest second) and geo-tagged (latitude and longitude) tweets, not aggregated to county-level counts.
One approach to monitor any numerically-valued time series has two main steps—(1) use training data to fit a model or models to the daily counts by county; and (2) monitor the corresponding residuals during training and testing to detect departures from the fitted model(s). For (1), effects such as day-of-week or time-of-year effects with multiple seasonal trends could be present, so the model building should be comprehensive, including assessment of simple exponentially weighted moving average (EWMA) models as well as models to fit trends and/or seasonal effects. For (2), the anomalies of interest could arise in neighboring counties (such as a reaction to a severe weather event such as a hurricane), and so could cluster in time and/or space.
3.2 Exploratory data analysis
Figure 7 is daily tweet counts from March 8 to December 31, 2010 (236 days) for (a) Brevard, (b) Broward, (c) Duval, and (d) Flagler counties (4 of 65 counties). This report addresses what types of models might fit these data, and options for monitoring residuals from the fitted model(s).
Figure 7.
The daily tweet counts for four of 65 Florida counties from march 8 to December 31, 2010.
Question 1: Is the distribution of daily counts stable or stationary? Stationary means that, for example, the mean and variance of the counts is constant over time [1, 2]. Informally, it appears that spiking occurs in Broward county and that a mean shift occurs in Duval county. If the counts are not stationary, sometimes, for example, the first differences in counts are at least approximately stationary, perhaps with occasional spikes (Figure 8) [1, 2].
Figure 8.
The first differences in daily tweet count for four of 65 Florida counties from March 8 to December 31, 2010.
Question 2: Is the background (“training”) data such as the 2010 counts adequately fit by a Poisson distribution [11, 12, 13]? Figure 9 plots the variance/mean ratio (which should be approximately 1 for Poisson data) for each of the 65 counties. Hillsborough, Miami-Dade, and Orange counties appear to be outliers. In view of Figure 9, question 2 is academic here, because it is not expected that a simple constant-mean Poisson model will be adequate [11, 12, 13]. Figure 9 suggests non-Poisson behavior.
Figure 9.
The variance/mean for each of 65 Florida counties in 2010. The three largest variance/mean ratios are Hillsborough, Miami-Dade, and Orange counties.
Figure 10 plots the daily number of tweets for Broward county and simulated counts assuming a constant mean (equal to the mean of the Broward county counts) Poisson model. Figure 10 also suggests non-Poisson behavior.
Figure 10.
The daily number of tweets in Broward county from March 8 to December 31, 2010, real (top), and simulated Poisson (bottom).
In Figure 10, the largest simulated count is 11 (the mean count is 3.4) but 7 of the 236 real counts exceed 11, and 2 of those exceed 20. More formally, the 99th percentile of the variance/mean ratio when sampling 236 observations from Poisson (3.4) is 1.23, but s2x¯=3.4 (Figure 8), where x¯=∑i=1nxin is the sample mean and s2=∑i=1236xi−x¯2235 is the sample variance. All analyses are performed using R [4]. Together, Figures 9 and 10 strongly suggest that a constant mean Poisson model is not adequate. Possibly, residuals around a fitted model could display approximate Poisson behavior, or perhaps another model such as the negative binomial for which variance/mean > 1 would be more appropriate [11].
Figure 11 is the total counts overall 65 Florida counties for each year in 2010–2015. Figure 12 is the autocorrelation function ACFi=ρi=∑j=1nxi−x¯(xj−i−x¯∑j=1nxi−x¯2 (with a minor adjustment to avoid negative indices [1]) using indices 1–117, prior to the large drop at index 118 for the total Florida counts in 2015. The ACF is useful for selecting possible models, such as those included in the class of ARMA models [1]. Modern machine-learning (ML) methods can also be evaluated, typically using AR modeling [1, 2, 12, 13]. An example of linear AR model is the lag one model xt=μ+ρxt−1+et, where μ is the long-term mean and the condition a<1 ensures that the {xt}series is stationary. The noise term is denoted et. An example of a linear MA model is in Eq. (1); fitting an MA model requires estimating the noise sequence et [1, 2]. Figure 13 is (a) simulated MA(1) data and (b) estimated residuals versus true residuals as was also shown in Figures 4 and 5.
Figure 11.
The total counts overall 65 Florida counties for each year in 2010–2020,105.
Figure 12.
The ACF for total Florida counts in 2015 over indices 1–117 before the large drop.
Figure 13.
(a) Simulated MA(1) data and (b) estimated residuals versus true residuals.
3.3 Model selection and fitting
Exploratory data analysis (EDA) indicates no significant correlations in the residuals from the ARIMA(0,1,1) fit, so a simple MA(1) model could be competitive. The exponentially weighted moving average (EWMA) fit leads to the same fits as an MA(1) fit to the lag-one differences, which can be easily seen [1] as follows:
x̂t=1−λx̂t−1+λxt−1=xt−1+1−λxt−1−x̂t−1E6
xt=1−λet−1+etE7
x̂t=xt−1−1−λet−1E8
The forecast x̂t in Eq. (6) from EWMA is the same as the forecast x̂t in Eq. (7) from ARMA(0,1,1) which is an MA(1) after differencing [1, 2]. It should be pointed out that Poisson EWMA control charts as described in Ref. [11] are designed to monitor whether the Poisson mean μt is constant over time. In that case, μ̂t=1−λμ̂t−1+λxt−1 is monitored rather than monitoring residuals from a local fit to the time-varying mean as in the twitter count monitoring.
The EWMA is among the simplest and most effective methods to forecast a time series. However, anomalies that persist for more than one day will impact the EWMA forecast in a manner that leads to reduced detection probability (DP) to detect the anomaly. For example, suppose an anomaly persists for 5 days. The EWMA forecast will be quite effective for day 1 of the anomaly, but the day 2 EWMA forecast will tend to increase due to the elevated expected count on day 1; this increased forecast leads to a reduced residual, thus reducing the DP.
Smooth fits using wavelets [13], EWMA [1, 2], and iterative bias reduction (IBR) [14, 15, 16] have been compared on this data. Edge effects are present in the wavelet and EWMA smoothers at the beginning of the data. The RMSE for wavelets, EWMA, and IBR are 4.05, 3.33, and 3.58, respectively, so EWMA is the simplest and has the smallest RMSE in this example. In data regions that have peaks, EWMA also performs acceptably well. The smoothing parameter λ in Eq. (6), x̂t=1−λx̂t−1+λxt−1 can be chosen by using a grid search in Ref. [0,1] to minimize the RMSE in the training data. It is not unusual for EWMA to provide a competitive RMSE compared to other methods. As an example, Figure 14 is the daily counts and the EWMA fit for Bay county counts.
Figure 14.
Daily counts and a smooth fit for bay county. The green “P’s” are local maxima in the smooth fit.
The standard deviation of the residuals in Figure 15 is 3.43 while the standard deviation of the counts is 4.43. Figure 16 is the ACF of the residuals in Figure 15.
Figure 16.
ACF of the residuals in Figure 15.
3.4 Monitoring residuals
One effective option to monitor residuals from a fitted model is Page’s statistic applied in this case to residuals in a single county, or to sums of residuals from neighborhoods of counties [17, 18, 19, 20]. Reference [20] describes a scan statistic that spans temporally and spatially. Reference [21] illustrates that if the temporal and/or spatial spanning window is selected to provide maximum evidence of an anomaly, then the false alarm probability can be undesirably large unless the variable spanning is taken into account.
To monitor for positive mean shifts due to anomalously-large count(s), Page’s statistic applied to the residuals et=xt−x̂t is defined in Eq. (4) (St=max0St−1+et−k). The parameter k is chosen to have good DP for a specific mean shift, but monitoring whether St≥h for threshold h can be effective for a range of values of k. For the Poisson distribution, by using the ratio of the likelihood under the background mean μB to the shifted mean μS, the optimal value of k (leading to the largest DP) is given by k=μS−μBlnμSμB. A statistical test based on the maximum of Page’s statistic over an analysis window is equivalent to a statistical test based on a scan statistic defined as maxt,j∑i=0jet+i−k over the analysis window.
Because the mean clearly changes over time in the background/training counts, the approach taken here is to fit a model and monitor the residuals et that are scaled to have variance 1, so k=0.5 is chosen on the assumption that the residuals are symmetric around 0, and approximately normally distributed, so k=0.5 is optimal (leads to the largest DP) for a mean shift of one standard deviation. However, k=0.5 can lead to large DPs for other mean shift magnitudes. Figure 17 plots Page’s statistic applied to residuals from an EWMA fit for days 1–256 in 2012 in the first four counties (alphabetically: Alachua, Baker, Bay, and Bradford). Figure 18 is similar to Figure 17, but Page’s statistic is applied to the net residual in each county plus the residuals in all the nearest-neighbor of each respective county.
Figure 17.
Page’s statistic versus day for Alachua, baker, bay, and Bradford.
Figure 18.
Page’s statistic versus day for Alachua, baker, bay, and Bradford and their respective nearest neighbors.
To select a threshold (see Figure 19) for monitoring all 65 residuals (one from each county) and all 65 residuals (one from each county including the neighboring counties), the 0.95 or 0.99 quantiles of the distribution of the maximum of the 65 or 130 Page’s statistic values can be estimated (corresponding to a FAP or either 0.05 or 0.01 per analysis period (256 days in this example).
Figure 19.
The maximum value of Page’s statistic overall 65 counties (a) and overall 65 counties with residuals from the respective county’s neighbors (b).
Figure 20 plots the DP for one-county-at-a-time monitoring and one-county-plus-nearest-neighbors monitoring. The 65 Florida counties are mapped in Appendix 1, defining the neighborhood scheme. For example, the five neighbors of Brevard county are Indian River, Orange, Osceola, Seminole, and Volusia. The nearest-neighbor county DPs are slightly smaller than the one-county-at-a-time DPs because the injected anomalies only impacted one county, and the alarm thresholds are slightly larger when monitoring both individual counties and individual plus nearest-neighbor counties.
Figure 20.
The DP versus the mean shift occurring on days 1–10 in Brevard county. The nearest-neighbor monitoring includes both one-county-at-a-time and nearest neighbor monitoring.
If the injected anomaly impacts all five neighbors of Brevard county then the DPs are much larger than those in Figure 19. For example, the DPS are 0.36, 0.78, and 0.99 for a mean shift of 1, 2, or 3 standard deviations in each of the five neighbors.
Another Florida data set records each tweet to the nearest second by geo-location in latitude and longitude for each of 3 days during 2017 (January 16, June 13, and November 29). Figure 21 plots the log of the lag-one time differences,
Figure 21.
The probability density of log(Ti – Ti−1) for each of the 3 days.
log(Ti – Ti-1) for each of the 3 days (with the T values measured in seconds, but the time unit is ignored in applying the log function). If the inter-arrival times followed an exponential distribution with mean 1/γ, then Figure 20 would exhibit a single peak, and the number of counts in any given time interval of duration t would be distributed as Poisson(μ = γt). Clearly, the counts do not exhibit exponential inter-arrival times, and the lag-one time differences between the tweets show considerable similarity across the 3 days. The 3 days have a total of 8102, 6376, and 5839 counts, respectively.
The custom R function generate.data() can be extended to include seasonality such as a single dominant peak per year if appropriate; however, typically more than one local peak is present per year. For example, find peaks applied to the IBR-based smooth finds an average of 4.9, 5.0,3.9, 4.8, 4.1, and 4.7 peaks (across all 65 counties) in years 2010, 2011, …, 2015, respectively. Simulated data from generate.data() can be used to assess candidate fitting options and to estimate DPs when synthetic anomaly effects are added. However, DP estimates tend to be too optimistic if a data generator such as generate.data() does not include enough realistic effects [18, 19].
Model fitting includes both model selection and estimating parameters in the selected models [13, 18, 19]. Recall that ARMA models are linear models but more generally, for example, AR models such as xt=fxt−1xt−2…xt−p+et can be linear in the previous x values or not. Multivariate adaptive regression with splines (MARS) is a flexible nonlinear fitting option that was evaluated using the first 300 days to train and the next 300 days to gest for Brevard county counts. Although the RMSE from MARS fits was 2.48 in training (EWMA has an RMSE of 2.78 in training), the RMSE increased to 3.82 in testing (EWMA has an RMSE of 3.67 in testing), so EWMA remains competitive. Bayesian additive regression trees (BART) are another flexible option to fit AR models. The RMSE for BART (using gbart in R) was 5.07, which is larger than the standard deviation of the daily counts in the testing data (4.04).
Together with the time record of each tweet (to the nearest second), the latitude and longitude provide an option to monitor for spatial and/or temporal clustering. If there is no space–time clustering, then the event of being close in time is independent of the event of being close in time. It is, therefore, possible to check for independence by comparing the number of tweets that are close in space and time to the expected number assuming independence. Arbitrarily defining close in time to be the 0.1 quantiles of all the pairs of time gaps between tweets (and defining close in space to be the 0.1 quantiles of all the spatial distances between pairs of tweets), the 2-by-2 tallies for January 16 is in Table 1.
Close in time (0.1 quantile)
Not close in time
Close in space (0.1 quantile)
856,752
23,761,044
Not close in space
2,893,098
5,306,257
Table 1.
For the 8102 geo-located tweets on January 16, 2017, there are 8102*8101/2 = 32,817,151 comparisons (the sum of the four entries in Table) of time and space. The expected number in the “close in time and space” cell is 0.1 × 0.1 × 32,817,151 = 328171.51, while the observed counts are 856,752 and 856,752/32817151 = 0.026, which is statistically significantly larger than 0.01.
The χ2test for independence strongly rejects the independence of space and time. Alternatively, the latitude and longitude values can be randomly reordered, breaking any true possible connection between space and time. The resulting test for independence is then not expected to be rejected; however, as an aside, while the random resorting of latitude and longitude reduced the 0.026–0.014, it turns out that 0.014 is large enough to 0.01 in this example, that some type of discretization phenomenon leads to this unexplained behavior. This same discretization phenomenon occurs for other arbitrary definitions of close, such as the 0.05 or 0.2 quantiles instead of the 0.1 quantiles.
Time-tagged Twitter counts with geo-locations are also available for Minnesota, Ohio, and Texas from January 01, 2016 to December 31, 2018 (1093 days with 3 missing days). Reference [19] provides plots of daily Twitter counts for Minnesota, Ohio, and Texas. Decreasing trends are obvious in all three states, and a t-test comparing the first 500 counts to the last 500 counts strongly rejects stationarity.
Reference [19] provides historgrams of the daily counts for Minnesota, Ohio, and Texas, respectively, along with simulated daily counts from a Poisson distribution having that respective state’s mean count rate. The real data are much more dispersed than a corresponding Poisson distribution.
3.5 Summary
Example 2 and Ref. [19] focused on monitoring residuals from simple EWMA fits. Other possible fitting options, such as MARS or BART for autoregressive modeling are described in Refs. [13, 18, 19]. If there were a more consistent seasonal peak, then model fitting could appropriately include seasonal peak fitting as in Ref. [22] for influenza forecasting. Another data source is Google’s flu query data (counts of google searches that seek information about influenza symptoms) as a real-time option to monitor for flu outbreaks [23]. It would be valuable to investigate why the google flu data monitoring option has not been effective. Also, in monitoring for spatial–temporal clustering, it was assumed that “close in space” and “close in time” were defined arbitrarily at the 0.1 quantiles of their respective distributions. If instead several possible quantile values were examined for statistical significance, and the quantile choice leading to the highest evidence of clustering is used, then a simulation-based method [21] could adjust for such maximal selection of statistical evidence of clustering.
4. Example 3: monitoring for nuclear material loss
In nuclear material accounting (NMA), the material balance (MB) is defined as MB = Ibegin + Tin − Tout − Iend, where Tin is transferred in; Tout is transferred out; Ibegin is beginning inventory; and Iend is ending inventory [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34]. All terms involve measured material, so the MB values should vary around 0 if there is no NM loss. The measurement error standard deviation of the MB is denoted σMB. Typically, many measurements are combined to estimate the terms Tin, Ibegin, Tout, and Iend in the MB; therefore, the central limit effect and years of experience suggests that MBs in most facilities will be approximately normally distributed with a mean equal to the true NM loss μ and standard deviation σMB, which is expressed as X ∼ N(μ,σMB), where X denotes the MB. If the MB at a given time (“balance period”) exceeds k σMB with k in the 2–3 range, then the NMA system “alarms.”
A sequence of n MBs is often assumed to have approximately a multivariate normal distribution X=X1,…,Xn∼NμΣ, where the n-by-n covariance matrix Σ=σ12σ122…σ1n2σ212σ22…σ2n2…σn12σn22…σn2. Estimating Σ is often one of the most tedious steps in frequent NMA (near real-time accounting, NRTA). A simplified example of estimating a component of Σ using a model of a generic electrochemical facility with one input stream, one output stream, and two inventory items is as follows. First, each individual measurement method is modeled with a measurement error model. A typical model for multiplicative errors is Mij=Ti1+Si+Rij with Si∼N0δS2 andRij∼N0δR2, where the jth measurement results (often j = 1) Mij of item i, Ti is the true value of item i,Rij is a random error of item i, Si is a short-term systematic error for item i. Then, the error variance for the two inventory items is σI2=T1+T22δS2+T12δR2+T22δR2, just as one example [26, 27, 28, 29, 34] of error modeling and variance propagation used to estimate a component of Σ.
In the early 1980s, some believed that a plant reporting an MB every 30 days would have a larger detection probability (DP) than that same plant reporting an MB every year (typically a facility is inventoried and cleanout out approximately once per year). However, Ref. [27] then showed that for optimal (from the diverter’s viewpoint, meaning that the DP is minimized) protracted diversion with the per-period loss being proportional to the row sums of the covariance matrix, Σ of the MB series, annual MB testing has larger DP than monthly mass balance testing. Reference [27] dampened hopes that frequent NMA, referred to as NRTA, would allow challenging diversion DP goals to be met. However, Ref. [27] conceded that NRTA has shorter detection times and higher DPs against abrupt diversion. Reference [27] showed that the best statistical test, the Neyman-Pearson (NP)-based matched filter for the worse case loss with μi∗=K∑j=1nΣi,j∑i=1n∑j=1bΣi,j, is based on the cumsum, Ct=∑i=1txi.
There are several reasons to apply statistical tests to a transformed sequence defined as Yi=Xi−EXiXi−1Xi−2…X1/σi∼ where E denotes the expectation and the standard deviation σi∼ of {Xi−E(Xi∣Xi−1,Xi−2,…,X1 is σi∼=σii2−fΣ−1fTwhere f=Σi,1i−1, the 1 to (i-1) entries in the ith row ofΣ [2, 7]. From properties of the MVN, each component Yi vector can be computed by calculating the conditional mean EXiXi−1Xi−2…X1=fΣi−1−1Xi−1 where Σi−1−1 is the inverse of the (i-1)-by-(i-1) matrix Σi−1 that corresponds to balance period 1 through period i-1. The Cholesky factorization Σ=LU leads to a more computationally efficient recursive approach that avoids matrix inversion [28]. The transformed sequence Y=L−1 X has ΣY=I, so Y is a residual time series.
This is a logistic advantage and a DP advantage to transform the X1,X2,…,Xn time series to the series of residuals Y1,Y2,…,Yn known in NMA as the SITMUF (standardized, independently transformed material unaccounted for sequence, here MUF is another term for the MB). The logistic advantage is that the SITMUF time series is iid N(0,1) if the loss μ=0, so alarm thresholds depend only on the sequence length n and the desired FAP. The DP advantage is a DP increase for many loss vectors arises because the variance of the SITMUF sequence decreases over time, so particularly if a diversion occurs late in the analysis period, the DP is larger for the Y sequence than for the X sequence. Note that one cannot claim higher DP for the Y sequence than for the X sequence in general, because the true loss scenario is never known, and the DP can be larger for X than for Y for some loss scenarios. Modern NRTA systems use a suite of several statistical tests, usually applied to the Y series.
Example DPs are plotted in Figures 22–24 (three different loss vectors for n = 16) for these five tests: (1) SITMUF test, (2) Page’s test applied to the SITMUF series, (3) CUMSUM test, (4) a combination of (1–3), and (5) the NP-based matched filter is also useful to compute the largest possible DP for a specified loss. The alarm threshold h is chosen so that the FAP per analysis period (usually one year) is 0.05 or whatever FAP is specified. In Figures 22–24, the covariance matrix Σ from Ref. [34] has 1 on the diagonal, −0.48 on the lag-one off diagonals, and 0.01 on all higher-lag off diagonals. The loss 1 vector (Figure 21) is 0 on periods 1 to 5, constant on periods 6–10, then 0 on periods 11–16 (the nonzero entries summing to the quantity plotted on the horizontal axis). The loss 2 vector (Figure 22) is all 0 except for an abrupt loss on period 6. The loss 3 vector (Figure 23) is constant for all 16 periods.
Figure 22.
DP versus the total mean shift (loss) for n = 16, for loss vector 1 (constant loss on periods 6 to 10).
Figure 23.
DP versus the total mean shift (loss) for n = 16, for loss vector 2 (abrupt loss on period 6).
Figure 24.
DP versus the total mean shift (loss) for n = 16, for loss vector 3 (constant loss).
This example is concluded with two remarks.
Remark 1. Reference [24] showed that assuming the model X1,…,Xn∼NμΣ leads to larger DPs than fitting an ARMA model on training data for which it would have to be assumed that the NM loss μ = 0 [31], provided Σ is well estimated. If Σ is not well estimated, a Bayesian updating scheme to improve the estimate of Σ could be used on training data for which the NM loss μ = 0 [35].
Remark 2. Figure 25 illustrates fixed-period testing and data-driven testing [29]. Some versions of NRTA use the most recent 1-year length sequences, so for n = 12 balance periods per year, the first evaluation period is months 1–12, the second evaluation period is months 2–13, etc. This scheme allows for a statistical decision to be made at every annual physical inventory, such as at months 12, 24, and 36. An alternate scheme consisting of a hybrid of period-driven and data-driven testing is described in Ref. [29], where it is pointed out that one should not simply truncate sequential statistical tests at the time of the annual physical inventory because the adversary could remove a portion of an SQ during year 1 and the remaining portion during year 2. The scan statistic has the highest DP (0.95 in this case, verified by simulation) if one knows that a loss will occur over a 12-month period with an unknown start period (such as month 7). The scan statistic computes a moving sum of months 1–12, 2–13, 3–14, etc.
Figure 25.
MB sequences over 36 months using fixed-period (annual) decision periods.
5. Summary
This chapter has described three change-detection examples. Example 1 monitored for patterns of large residuals, such as a consecutive string of three residuals exceeding a threshold. Example 2 monitored for excessive numbers of tweets in any of the 65 Florida counties. Example 3 monitored for nuclear material loss. Portions of examples 1 and 3 have been published as cited in Refs. [5, 34]. Example 2 is entirely new. Page’s statistic is generally recommended because of its reasonably large DP for a range of change patterns, such as any of those in Figure 1.
Figure A.1 is the 67 Florida counties, and this data merged Liberty into Gadsden and merged Lafayatte into Madison to reduce the 67 Florida counties to 65.
Figure A.1.
The 65 Florida countries. In the available data, liberty county is merged into Gadsden and Lafayatte county is merged into Madison to reduce the 67 Florida counties to 65. Therefore, there are 65 identified regions (counties) for which spatial and/or temporal residuals patterns can be monitored for change.
> ctynbrs[5,] # Brevard county: neighbors of Brevard county
\n',keywords:"time series models, residuals, scan statistics, cusum, matched filter",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/81130.pdf",chapterXML:"https://mts.intechopen.com/source/xml/81130.xml",downloadPdfUrl:"/chapter/pdf-download/81130",previewPdfUrl:"/chapter/pdf-preview/81130",totalDownloads:15,totalViews:0,totalCrossrefCites:0,dateSubmitted:"January 21st 2022",dateReviewed:"February 8th 2022",datePrePublished:"April 7th 2022",datePublished:null,dateFinished:"April 7th 2022",readingETA:"0",abstract:"Change detection in time series can be approached by fitting a model to the no-change, ordinary background data and then monitoring time series of residuals, where a residual is defined as residual = data – fit. In many applications, models that fit time series data lead to residuals that exhibit no patterns unless the signal of interest is present. Therefore, an effective signal or change detection approach is to first fit a time series model to the background data without any signal and then monitor the time series of residuals for evidence of the signal. This chapter briefly reviews a few time series modeling options and then focuses on statistical tests for monitoring residuals, including Page’s cumulative sum (cusum, a type of scan statistic), the ordinary cumulative sum (cumsum), the matched filter (a version of the Neyman-Pearson test statistic), and pattern tests, such as those used in quality control. Simulation and analytical approximation methods are recommended for studying test behavior, as illustrated in three examples.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/81130",risUrl:"/chapter/ris/81130",signatures:"Tom Burr and Kim Kaufeld",book:{id:"11200",type:"book",title:"Mathematical Concepts of Forecasting",subtitle:null,fullTitle:"Mathematical Concepts of Forecasting",slug:null,publishedDate:null,bookSignature:"Dr. Andrey Kostogryzov and Dr. Nikolay Andreevich Makhutov",coverURL:"https://cdn.intechopen.com/books/images_new/11200.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-655-0",printIsbn:"978-1-80355-654-3",pdfIsbn:"978-1-80355-656-7",isAvailableForWebshopOrdering:!0,editors:[{id:"148322",title:"Dr.",name:"Andrey",middleName:null,surname:"Kostogryzov",slug:"andrey-kostogryzov",fullName:"Andrey Kostogryzov"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Example 1: monitoring for patterns of large residuals",level:"1"},{id:"sec_2_2",title:"2.1 Stein-Chen method",level:"2"},{id:"sec_3_2",title:"2.2 Summary",level:"2"},{id:"sec_5",title:"3. Example 2: monitoring for excessive numbers of tweets in 65 Florida counties",level:"1"},{id:"sec_5_2",title:"3.1 Introduction",level:"2"},{id:"sec_6_2",title:"3.2 Exploratory data analysis",level:"2"},{id:"sec_7_2",title:"3.3 Model selection and fitting",level:"2"},{id:"sec_8_2",title:"3.4 Monitoring residuals",level:"2"},{id:"sec_9_2",title:"3.5 Summary",level:"2"},{id:"sec_11",title:"4. Example 3: monitoring for nuclear material loss",level:"1"},{id:"sec_12",title:"5. Summary",level:"1"},{id:"sec_14",title:"Figure A.1.",level:"1"}],chapterReferences:[{id:"B1",body:'Chatfield C. The Analysis of Time Series: An Introduction. 6th ed. London, United Kingdom: Chapman and Hall; 2004'},{id:"B2",body:'Shumway R, Stoffer D. Time Series Analysis and its Applications with R Examples. 4th ed. Pittsburgh: Springer; 2016'},{id:"B3",body:'Lucas J. Counted data Cusums. Technometrics. 1985;27(2):129-144'},{id:"B4",body:'R Core Team. R. A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria. Available from: https://www.R-project.org/: R Foundation for Statistical Computing; 2017'},{id:"B5",body:'Burr T, Henderson B. Scanning for clusters of large values in time series: Application of the Stein-Chen method. Applied Mathematics. 2021;12:1031-1037'},{id:"B6",body:'Arratia R, Goldstein L, Gordon L. Poisson approximation and the Chen-Stein methods. Statistical Science. 1990;5(4):403-434'},{id:"B7",body:'Sahatsathatsana C. Applications of the Stein-Chen method for the problem of coincidences. International Journal of Pure and Applied Mathematics. 2017;116(1):49-59'},{id:"B8",body:'Kim S. A use of the Stein-Chen method in time series analysis. Journal of Applied Probability. 2000;37(4):1129-1136'},{id:"B9",body:'Aleksandrov B, Weis C, Jentsch C. Goodness-of-fit tests for Poisson count time series based of the Stein-Chen identity. Statistica Neerlandica. 2021;76:35-64'},{id:"B10",body:'Weis C, Aleksandrov B. Computing bivariate Poisson moments using stein-Chen identities. The American Statistician. 2022;76(1):10-15'},{id:"B11",body:'Borror C, Champ E, Rigdon S. Poisson EWMA control charts. Journal of Quality Technology. 2018;30(4):352-361. DOI: 10.1080/00224065.1998.11979871'},{id:"B12",body:'Venables W, Ripley B. Modern Applied Statistics with S-Plus. New York: Springer; 1999'},{id:"B13",body:'Hastie T, Tibshiranii R, Friedman J. Elements of Statistical Learning. New York: Springer; 2001'},{id:"B14",body:'Burr T, Hengartner N, Matzner-Løber E, Myers S, Rouviere L. Smoothing low resolution gamma spectra. IEEE Transactions on Nuclear Science. 2010;57:2831-2840'},{id:"B15",body:'Cornillon P, Hengartner N, Jegou N, Matzner-Løber E. Iterative bias reduction: A comparative study. Statistics and Computing. 2013;23(6):777-791'},{id:"B16",body:'Hengartner N, Matzner-Lober E, Rouviere L, Burr T. Multiplicative Bias Corrected Nonparametric Smoothers with Application to Nuclear Energy Spectrum Estimation, Nonparametric Statistics. 3rd ISNPS ed. Avignon, France: Springer; 2016 arXiv Preprint arXiv:0908.0128'},{id:"B17",body:'Mathes R, Lall R, Levin-Rector A, Sell J, Paladini M, Konty K, et al. Evaluating and implementing temporal, spatial, and spatio-temporal methods for outbreak detection in a local syndromic surveillance system. PLoS ONE. 2017;12(9):e0184419. DOI: 10.1371/journal.pone.0184419'},{id:"B18",body:'Burr T, Graves T, Klaman R, Michalek S, Picard R, Hengartner N. Accounting for seasonal patterns in syndromic surveillance data for outbreak detection, BioMedCentral. Medical Informatics and Decision Making. 2006;6:40'},{id:"B19",body:'Burr T, Kaufeld K. Statistical Evaluation of Daily Tweet Counts from Florida, Minnesota, Ohio, and Texas. New Mexico, United States: Los Alamos National Laboratory Report; 2021'},{id:"B20",body:'Kulldorff M. Prospective time periodic geographical disease surveillance using a scan statistic. Journal of the Royal Statistical Society. 2001;A164:61-72'},{id:"B21",body:'Burr T. Maximally selected measures of evidence of disease clustering. Statistics in Medicine. 2001;20:1443-1460'},{id:"B22",body:'Osthus D, Moran K. Multiscale influenza forecasting, nature communications 12. Art. 2021;2991'},{id:"B23",body:'Pervais F, Pervaiz M, Rehman N, Saif U. FluBreaks early epidemic detection for google flu trends. Journal of Medical Internet Research. 2012;14(5):e125. DOI: 10/2196jmir.2002'},{id:"B24",body:'Burr T, Hamada MS. Smoothing and time series modeling of nuclear material accounting data for protracted diversion detection. Nuclear Science and Engineering. 2014;177:307-320'},{id:"B25",body:'Burr T, Hamada MS. Statistical Challenges in Integrated Nuclear Safeguards, Nuclear Science in the Series Energy Science and Technology. Vol. 4(12). Vienna, Austria: IAEA; 2014'},{id:"B26",body:'Burr T, Hamada MS. Revisiting Statistical Aspects of Nuclear Material Accounting Science and Technology of Nuclear Installations. London, United Kingdom: Hindawi Publishing Corporation; 2013. pp. 1-15. DOI: 10.1155/2013/961360'},{id:"B27",body:'Avenhaus R, Jaech J. On subdividing material balances in time and/or space. Journal of Nuclear Materials Management. 1981;10:24-34'},{id:"B28",body:'Picard R. Sequential analysis of material balances. Journal of Nuclear Materials Management. 1987;15(2):38-42'},{id:"B29",body:'Burr T, Hamada MS, Ticknor L, Sprinkle J. Hybrid statistical testing for nuclear material accounting data and/or process monitoring data in nuclear safeguards. Energies. 2015;8:501-528'},{id:"B30",body:'Prasad S, Booth T, Hu M, Deligonul S. The detection of nuclear materials losses. Decision Sciences. 2007;26(2):265-281'},{id:"B31",body:'Speed T, Culpin D. The role of statistics in nuclear materials accounting: Issues and problems. Journal of the Royal Statistical Society A. 1986;149(4):281-313'},{id:"B32",body:'Downing D, Pike D, Morrison G. Analysis of MUF data using ARlMA models. Journal of Nuclear Material Management. 1978;7(4):80-86'},{id:"B33",body:'Bonner E, Burr T, Krieger T, Martin K, Norman C. Comprehensive Uncertainty Quantification in Nuclear Safeguards, Science and Technology of Nuclear Installations. London, United Kingdom: Hindawi Publishing Corporation; 2017. pp. 1-16. DOI: 10.1155/2017/2679243'},{id:"B34",body:'Burr T, Hamada MS. Bayesian updating of material balances covariance matrices using training data. International Journal of Prognostics and Health Monitoring. 2014;5(1):006-013'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Tom Burr",address:"tburr@lanl.gov",affiliation:'
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Since 2007, her laboratory has focused on all aspects of host-pathogen interactions in the context of Chlamydia infection, with a major emphasis on how Chlamydia co-opts the host cell to promote pathogenicity. \nDr. Paumet developed her expertise in cellular biology and biochemistry at the Pasteur Institute (Paris, France), where she received her PhD. During her post-doctoral training at the Memorial Sloan Kettering Cancer Center and Columbia University (NYC), she studied the molecular machinery involved in vesicular trafficking in eukaryotic cells. 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They are considered as the biotechnologically valuable bacteria that are exploited for its secondary metabolite production. Approximately, 10,000 bioactive metabolites are produced by Actinobacteria, which is 45% of all bioactive microbial metabolites discovered. Especially Streptomyces species produce industrially important microorganisms as they are a rich source of several useful bioactive natural products with potential applications. Though it has various applications, some Actinobacteria have its own negative effect against plants, animals, and humans. On this context, this chapter summarizes the general characteristics of Actinobacteria, its habitat, systematic classification, various biotechnological applications, and negative impact on plants and animals.",book:{id:"5056",slug:"actinobacteria-basics-and-biotechnological-applications",title:"Actinobacteria",fullTitle:"Actinobacteria - Basics and Biotechnological Applications"},signatures:"Ranjani Anandan, Dhanasekaran Dharumadurai and Gopinath\nPonnusamy Manogaran",authors:[{id:"48914",title:"Dr.",name:"Dharumadurai",middleName:null,surname:"Dhanasekaran",slug:"dharumadurai-dhanasekaran",fullName:"Dharumadurai Dhanasekaran"}]},{id:"35104",title:"Restriction Fragment Length Polymorphism Analysis of PCR-Amplified Fragments (PCR-RFLP) and Gel Electrophoresis - Valuable Tool for Genotyping and Genetic Fingerprinting",slug:"restriction-fragment-length-polymorphism-analysis-of-pcr-amplified-fragments-pcr-rflp-and-related-te",totalDownloads:34054,totalCrossrefCites:6,totalDimensionsCites:26,abstract:null,book:{id:"1770",slug:"gel-electrophoresis-principles-and-basics",title:"Gel Electrophoresis",fullTitle:"Gel Electrophoresis - Principles and Basics"},signatures:"Henrik Berg Rasmussen",authors:[{id:"114068",title:"Dr.",name:"Henrik",middleName:null,surname:"Rasmussen",slug:"henrik-rasmussen",fullName:"Henrik Rasmussen"}]},{id:"50471",title:"Molecular Mechanisms of Skin Aging and Rejuvenation",slug:"molecular-mechanisms-of-skin-aging-and-rejuvenation",totalDownloads:5110,totalCrossrefCites:6,totalDimensionsCites:13,abstract:"The aging process in the skin is complex and influenced by more intrinsic and extrinsic factors than any other body organ. The effects of these two types of factors overlap for the most part. The combined effects of these two aging processes also affect dermal matrix alterations. The main clinical signs of skin aging include wrinkling and irregular pigmentation, which are influenced by a combination of intrinsic and extrinsic (e.g., UV radiation, heat, smoking, and pollutants) factors. Histologically, collagen decreases, and the dermis is replaced by abnormal elastic fibers as a cause of wrinkle formation through the loss of skin elasticity. There have been numerous studies of skin aging performed to elucidate the underlying molecular mechanisms and to develop various antiaging therapeutics and preventive strategies. We summarized the molecular mechanisms and treatments of skin aging. Mainly UV radiation induces ROS formation and DNA damage, leading to increased production of MMPs and decreased production of collagen in keratinocytes and fibroblasts, which reflect the central aspects of skin aging. Besides UV radiation exposure, extrinsic factors including tobacco smoking, exposure to environmental pollutants, infrared radiation, and heat contribute to premature skin aging. Like UV radiation, these factors cause ROS formation and increase expression of MMPs, thus accelerating skin aging by inducing extracellular matrix (ECM) degradation. Accumulated collagen fibrils inhibit the new collagen synthesis and account for the further degradation of the ECM through this positive feedback loop. Accumulating evidence for molecular mechanisms of skin aging should provide clinicians with an expanding spectrum of therapeutic targets in the treatment of skin aging.",book:{id:"5258",slug:"molecular-mechanisms-of-the-aging-process-and-rejuvenation",title:"Molecular Mechanisms of the Aging Process and Rejuvenation",fullTitle:"Molecular Mechanisms of the Aging Process and Rejuvenation"},signatures:"Miri Kim and Hyun Jeong Park",authors:[{id:"47695",title:"Prof.",name:"Hyun Jeong",middleName:null,surname:"Park",slug:"hyun-jeong-park",fullName:"Hyun Jeong Park"},{id:"185767",title:"Prof.",name:"Miri",middleName:null,surname:"Kim",slug:"miri-kim",fullName:"Miri Kim"}]},{id:"62731",title:"An Introductory Chapter: Secondary Metabolites",slug:"an-introductory-chapter-secondary-metabolites",totalDownloads:9738,totalCrossrefCites:33,totalDimensionsCites:52,abstract:null,book:{id:"6670",slug:"secondary-metabolites-sources-and-applications",title:"Secondary Metabolites",fullTitle:"Secondary Metabolites - Sources and Applications"},signatures:"Durairaj Thirumurugan, Alagappan Cholarajan, Suresh S.S. 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The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. 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He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. 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Saxena",hash:"d92a4085627bab25ddc7942fbf44cf05",volumeInSeries:2,fullTitle:"Current Perspectives in Human Papillomavirus",editors:[{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Bacterial Infectious Diseases",value:3,count:2},{group:"subseries",caption:"Parasitic Infectious Diseases",value:5,count:4},{group:"subseries",caption:"Viral Infectious Diseases",value:6,count:7}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:2},{group:"publicationYear",caption:"2021",value:2021,count:4},{group:"publicationYear",caption:"2020",value:2020,count:3},{group:"publicationYear",caption:"2019",value:2019,count:3},{group:"publicationYear",caption:"2018",value:2018,count:1}],authors:{paginationCount:249,paginationItems:[{id:"274452",title:"Dr.",name:"Yousif",middleName:"Mohamed",surname:"Abdallah",slug:"yousif-abdallah",fullName:"Yousif Abdallah",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274452/images/8324_n.jpg",biography:"I certainly enjoyed my experience in Radiotherapy and Nuclear Medicine, particularly it has been in different institutions and hospitals with different Medical Cultures and allocated resources. Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University, Kuwait. His research interests include optimization, computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, and intelligent systems. Prof. Sarfraz has been a keynote/invited speaker at various platforms around the globe. He has advised/supervised more than 110 students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He has authored and/or edited around seventy books. Prof. Sarfraz is a member of various professional societies. He is a chair and member of international advisory committees and organizing committees of numerous international conferences. He is also an editor and editor in chief for various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:null},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:"Beijing University of Technology",institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Lakhno Igor Victorovich was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPhD – 1999, Kharkiv National Medical Univesity.\nDSc – 2019, PL Shupik National Academy of Postgraduate Education \nLakhno Igor has been graduated from an international training courses on reproductive medicine and family planning held in Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor of the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s a professor of the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education . He’s an author of about 200 printed works and there are 17 of them in Scopus or Web of Science databases. Lakhno Igor is a rewiever of Journal of Obstetrics and Gynaecology (Taylor and Francis), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for DSc degree \\'Pre-eclampsia: prediction, prevention and treatment”. Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: obstetrics, women’s health, fetal medicine, cardiovascular medicine.",institutionString:"V.N. Karazin Kharkiv National University",institution:{name:"Kharkiv Medical Academy of Postgraduate Education",country:{name:"Ukraine"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"243698",title:"M.D.",name:"Xiaogang",middleName:null,surname:"Wang",slug:"xiaogang-wang",fullName:"Xiaogang Wang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243698/images/system/243698.png",biography:"Dr. Xiaogang Wang, a faculty member of Shanxi Eye Hospital specializing in the treatment of cataract and retinal disease and a tutor for postgraduate students of Shanxi Medical University, worked in the COOL Lab as an international visiting scholar under the supervision of Dr. David Huang and Yali Jia from October 2012 through November 2013. Dr. Wang earned an MD from Shanxi Medical University and a Ph.D. from Shanghai Jiao Tong University. Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:"Shanxi Eye Hospital",institution:{name:"Shanxi Eye Hospital",country:{name:"China"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRZkkQAG/Profile_Picture_2022-05-09T12:55:18.jpg",biography:null,institutionString:null,institution:null},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. RELACION DE PONENCIAS DE LA SOCIEDAD ESPAÑOLA DE OFTALMOLOGIA. 10/2014.",institutionString:null,institution:null},{id:"265335",title:"Mr.",name:"Stefan",middleName:"Radnev",surname:"Stefanov",slug:"stefan-stefanov",fullName:"Stefan Stefanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265335/images/7562_n.jpg",biography:null,institutionString:null,institution:null},{id:"318905",title:"Prof.",name:"Elvis",middleName:"Kwason",surname:"Tiburu",slug:"elvis-tiburu",fullName:"Elvis Tiburu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Ghana",country:{name:"Ghana"}}},{id:"336193",title:"Dr.",name:"Abdullah",middleName:null,surname:"Alamoudi",slug:"abdullah-alamoudi",fullName:"Abdullah Alamoudi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"318657",title:"MSc.",name:"Isabell",middleName:null,surname:"Steuding",slug:"isabell-steuding",fullName:"Isabell Steuding",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Harz University of Applied Sciences",country:{name:"Germany"}}},{id:"318656",title:"BSc.",name:"Peter",middleName:null,surname:"Kußmann",slug:"peter-kussmann",fullName:"Peter Kußmann",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Harz University of Applied Sciences",country:{name:"Germany"}}},{id:"338222",title:"Mrs.",name:"María José",middleName:null,surname:"Lucía Mudas",slug:"maria-jose-lucia-mudas",fullName:"María José Lucía Mudas",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Carlos III University of Madrid",country:{name:"Spain"}}},{id:"147824",title:"Mr.",name:"Pablo",middleName:null,surname:"Revuelta Sanz",slug:"pablo-revuelta-sanz",fullName:"Pablo Revuelta Sanz",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Carlos III University of Madrid",country:{name:"Spain"}}}]}},subseries:{item:{id:"27",type:"subseries",title:"Multi-Agent Systems",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. The area covers many techniques that offer solutions to emerging problems in robotics and enterprise-level software systems. Collaborative intelligence is highly and effectively achieved with multi-agent systems. Areas of application include swarms of robots, flocks of UAVs, collaborative software management. Given the level of technological enhancements, the popularity of machine learning in use has opened a new chapter in multi-agent studies alongside the practical challenges and long-lasting collaboration issues in the field. It has increased the urgency and the need for further studies in this field. We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",hasOnlineFirst:!1,hasPublishedBooks:!1,annualVolume:11423,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. 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