Chapter 1: "Permanent Maxillary and Mandibular Incisors"\n Chapter 2: "The Permanent Maxillary and Mandibular Premolar Teeth"\n Chapter 3: "Dental Anatomical Features and Caries: A Relationship to be Investigated"\n Chapter 4: "Anatomy Applied to Block Anaesthesia"\n Chapter 5: "Treatment Considerations for Missing Teeth"\n Chapter 6: "Anatomical and Functional Restoration of the Compromised Occlusion: From Theory to Materials"\n Chapter 7: "Evaluation of the Anatomy of the Lower First Premolar"\n Chapter 8: "A Comparative Study of the Validity and Reproducibility of Mesiodistal Tooth Size and Dental Arch with the iTero Intraoral Scanner and the Traditional Method"\n Chapter 9: "Identification of Lower Central Incisors"\n The book is aimed toward dentists and can also be well used in education and research.',isbn:"978-1-78923-511-1",printIsbn:"978-1-78923-510-4",pdfIsbn:"978-1-83881-247-8",doi:"10.5772/65542",price:119,priceEur:129,priceUsd:155,slug:"dental-anatomy",numberOfPages:204,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"445cd419d97f339f2b6514c742e6b050",bookSignature:"Bağdagül Helvacioğlu Kivanç",publishedDate:"August 1st 2018",coverURL:"https://cdn.intechopen.com/books/images_new/5814.jpg",numberOfDownloads:13301,numberOfWosCitations:0,numberOfCrossrefCitations:4,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:8,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:12,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 4th 2016",dateEndSecondStepPublish:"October 25th 2016",dateEndThirdStepPublish:"July 16th 2017",dateEndFourthStepPublish:"August 16th 2017",dateEndFifthStepPublish:"October 16th 2017",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"178570",title:"Dr.",name:"Bağdagül",middleName:null,surname:"Helvacıoğlu Kıvanç",slug:"bagdagul-helvacioglu-kivanc",fullName:"Bağdagül Helvacıoğlu Kıvanç",profilePictureURL:"https://mts.intechopen.com/storage/users/178570/images/7646_n.jpg",biography:"Bağdagül Helvacıoğlu Kıvanç is a dentist, a teacher, a researcher and a scientist in the field of Endodontics. She was born in Zonguldak, Turkey, on February 14, 1974; she is married and has two children. She graduated in 1997 from the Ankara University, Faculty of Dentistry, Ankara, Turkey. She aquired her PhD in 2004 from the Gazi University, Faculty of Dentistry, Department of Endodontics, Ankara, Turkey, and she is still an associate professor at the same department. She has published numerous articles and a book chapter in the areas of Operative Dentistry, Esthetic Dentistry and Endodontics. She is a member of Turkish Endodontic Society and European Endodontic Society.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Gazi University",institutionURL:null,country:{name:"Turkey"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"174",title:"Dentistry",slug:"dentistry"}],chapters:[{id:"56461",title:"Permanent Maxillary and Mandibular Incisors",doi:"10.5772/intechopen.69542",slug:"permanent-maxillary-and-mandibular-incisors",totalDownloads:2552,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The permanent incisors are the front teeth that erupt between 6 and 8 years of age. They are eight in number, four upper and four lower, two centrals and two laterals. They have sharp biting surfaces designed for shearing and cutting of food materials into small chewable pieces. They are the teeth most visible to the others during eating, smiling and talking, and thus, they have high aesthetic value for the individuals. The unique characteristics, arch position, function, development and chronological age of each tooth will be highlighted. In addition, the different aspects with their geometric outlines, outlines and surface anatomy of these teeth will be described. A brief explanation about the pulp cavity, tooth socket and normal occlusion for each tooth will be included.",signatures:"Mohammed E. Grawish, Lamyaa M. Grawish and Hala M. Grawish",downloadPdfUrl:"/chapter/pdf-download/56461",previewPdfUrl:"/chapter/pdf-preview/56461",authors:[{id:"82989",title:"Prof.",name:"Mohammed",surname:"Grawish",slug:"mohammed-grawish",fullName:"Mohammed Grawish"}],corrections:null},{id:"62386",title:"The Permanent Maxillary and Mandibular Premolar Teeth",doi:"10.5772/intechopen.79464",slug:"the-permanent-maxillary-and-mandibular-premolar-teeth",totalDownloads:2732,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The permanent premolar teeth are placed between the anterior teeth and molars. Eight premolars are found in the permanent dentition, four per arch and two in each quadrant. The main function of premolars is to assist the canines in regard to tear and pierce the food and supplement the grinding of the molars during mastication. The other functions are to support the corners of the mouth reinforce esthetics during smiling and maintain the vertical dimension. Detailed morphology of the permanent premolar teeth is narrated in a pointwise and systematic manner in this chapter.",signatures:"Işıl Çekiç Nagaş, Ferhan Eğilmez and Bağdagül Helvacioğlu Kivanç",downloadPdfUrl:"/chapter/pdf-download/62386",previewPdfUrl:"/chapter/pdf-preview/62386",authors:[{id:"178570",title:"Dr.",name:"Bağdagül",surname:"Helvacıoğlu Kıvanç",slug:"bagdagul-helvacioglu-kivanc",fullName:"Bağdagül Helvacıoğlu Kıvanç"}],corrections:null},{id:"57546",title:"Dental Anatomical Features and Caries: A Relationship to be Investigated",doi:"10.5772/intechopen.71337",slug:"dental-anatomical-features-and-caries-a-relationship-to-be-investigated",totalDownloads:1671,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Dental caries is a multifactor disease affecting a significant number of people throughout the world. However, in recent decades the widespread availability of fluoride and other preventive measures have resulted in a decline in the prevalence of caries among children and young adults. Currently, it is accepted that most carious dental lesions are restricted to specific anatomical sites. The aim of this chapter is to review the influence of dental anatomy on dental caries development while taking into account recent findings in cariology. Occlusal fissures in the first permanent molar are generally the first sites in the permanent dentition to develop caries. An increased risk of caries is also found in proximal contacting surfaces between two adjacent teeth. Moreover, a partially erupted tooth, which does not participate in mastication, is also at risk for caries since it may provide a more favorable environment for bacterial accumulation than a fully erupted tooth. Bacterial biofilm on the tooth is frequently a high risk caries environment. Understanding anatomical dental features is of great importance for guiding oral health hygiene and preventive measures. Finally, the development of dental disorders plays an important role in dental caries risk.",signatures:"Marcel Alves Avelino de Paiva, Dayane Franco Barros Mangueira\nLeite, Isabela Albuquerque Passos Farias, Antônio de Pádua\nCavalcante Costa and Fábio Correia Sampaio",downloadPdfUrl:"/chapter/pdf-download/57546",previewPdfUrl:"/chapter/pdf-preview/57546",authors:[{id:"138852",title:"Prof.",name:"Fabio",surname:"Sampaio",slug:"fabio-sampaio",fullName:"Fabio Sampaio"},{id:"213662",title:"Prof.",name:"Isabela Albuquerque",surname:"Passos Farias",slug:"isabela-albuquerque-passos-farias",fullName:"Isabela Albuquerque Passos Farias"},{id:"213663",title:"Prof.",name:"Dayane Franco",surname:"Barros Mangueira Leite",slug:"dayane-franco-barros-mangueira-leite",fullName:"Dayane Franco Barros Mangueira Leite"},{id:"213664",title:"BSc.",name:"Marcel Alves",surname:"Avelino De Paiva",slug:"marcel-alves-avelino-de-paiva",fullName:"Marcel Alves Avelino De Paiva"},{id:"213666",title:"Prof.",name:"Antonio De Pádua",surname:"Cavalcante Da Costa",slug:"antonio-de-padua-cavalcante-da-costa",fullName:"Antonio De Pádua Cavalcante Da Costa"}],corrections:null},{id:"56119",title:"Anatomy Applied to Block Anesthesia for Maxillofacial Surgery",doi:"10.5772/intechopen.69545",slug:"anatomy-applied-to-block-anesthesia-for-maxillofacial-surgery",totalDownloads:1508,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Anatomy is a basic knowledge that every clinician must have; however, its full management is not always achieved and gaps remain in daily practice. The aim of this chapter is to emphasize the most relevant aspects of head and neck anatomy, specifically related to osteology and neurology for the application of regional anesthesia techniques. This chapter presents a clear and concise text, useful for both undergraduate and graduate students and for the dentist and maxillofacial surgeon. The most relevant aspects of the bone and sensory anatomy relevant for the realization of regional anesthetic techniques in the oral and maxillofacial area are reviewed, including complementary figures and tables. The anatomy related to the techniques directed to the three major branches of the trigeminal nerve (ophthalmic nerve, maxillary nerve, and to the branches of the mandibular nerve) will be approached separately.",signatures:"Alex Vargas, Paula Astorga and Tomas Rioseco",downloadPdfUrl:"/chapter/pdf-download/56119",previewPdfUrl:"/chapter/pdf-preview/56119",authors:[{id:"199400",title:"Dr.",name:"Alex",surname:"Vargas",slug:"alex-vargas",fullName:"Alex Vargas"},{id:"202023",title:"Dr.",name:"Paula",surname:"Astorga",slug:"paula-astorga",fullName:"Paula Astorga"},{id:"205059",title:"Dr.",name:"Tomas",surname:"Rioseco",slug:"tomas-rioseco",fullName:"Tomas Rioseco"}],corrections:null},{id:"55902",title:"Treatment Considerations for Missing Teeth",doi:"10.5772/intechopen.69543",slug:"treatment-considerations-for-missing-teeth",totalDownloads:971,totalCrossrefCites:2,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Specific terms are used to describe the nature of tooth agenesis. Hypodontia is most frequently used when describing the phenomenon of congenitally missing teeth. Many other terms to describe a reduction in the number of teeth appear in the literature: oligodontia, anodontia, aplasia of teeth, congenitally missing teeth, absence of teeth, agenesis of teeth and lack of teeth. The term hypodontia is used when one to six teeth, excluding third molars, are missing, and oligodontia when more than six teeth are absent (excluding the third molars). The long‐term management of hypodontia in the aesthetic zone is a particularly challenging situation. Although there are essentially two distinct approaches to manage this problem, that is space closure or opening for prosthetic replacements, implant or autotransplantation. These patients often manifest with many underlying skeletal and dental problems and a multidisciplinary approach for management of this condition is recommended. Two treatment approaches including space closure and space reopening are described in details in this chapter.",signatures:"Abdolreza Jamilian, Alireza Darnahal, Ludovica Nucci, Fabrizia\nD’Apuzzo and Letizia Perillo",downloadPdfUrl:"/chapter/pdf-download/55902",previewPdfUrl:"/chapter/pdf-preview/55902",authors:[{id:"171777",title:"Prof.",name:"Abdolreza",surname:"Jamilian",slug:"abdolreza-jamilian",fullName:"Abdolreza Jamilian"},{id:"171873",title:"Dr.",name:"Alireza",surname:"Darnahal",slug:"alireza-darnahal",fullName:"Alireza Darnahal"},{id:"173044",title:"Prof.",name:"Letizia",surname:"Perillo",slug:"letizia-perillo",fullName:"Letizia Perillo"},{id:"198961",title:"MSc.",name:"Fabrizia",surname:"D'Apuzzo",slug:"fabrizia-d'apuzzo",fullName:"Fabrizia D'Apuzzo"},{id:"206137",title:"Mrs.",name:"Ludovica",surname:"Nucci",slug:"ludovica-nucci",fullName:"Ludovica Nucci"}],corrections:null},{id:"55973",title:"Anatomical and Functional Restoration of the Compromised Occlusion: From Theory to Materials",doi:"10.5772/intechopen.69544",slug:"anatomical-and-functional-restoration-of-the-compromised-occlusion-from-theory-to-materials",totalDownloads:1253,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Many conditions can alter the occlusal interface, from tooth wear to tooth loss. The masticatory system is constituted by many components that can influence each other like muscles, joints, teeth and nervous system. This implies that (a) every change at occlusal level makes the other components to adapt and (b) an occlusal alteration may be the effect of an alteration occurred on muscles or joints. Keeping this in mind, traditional principles of occlusal rehabilitation are analysed, and the choice of the restorative materials is discussed.",signatures:"Nicola Mobilio and Santo Catapano",downloadPdfUrl:"/chapter/pdf-download/55973",previewPdfUrl:"/chapter/pdf-preview/55973",authors:[{id:"179565",title:"Dr.",name:"Nicola",surname:"Mobilio",slug:"nicola-mobilio",fullName:"Nicola Mobilio"},{id:"199397",title:"Prof.",name:"Santo",surname:"Catapano",slug:"santo-catapano",fullName:"Santo Catapano"}],corrections:null},{id:"57245",title:"Evaluation of the Anatomy of the Lower First Premolar",doi:"10.5772/intechopen.71038",slug:"evaluation-of-the-anatomy-of-the-lower-first-premolar",totalDownloads:881,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This chapter provides information about the lower first premolars. This tooth is considered to be one of the most complex teeth and the dentistry graduation students usually have difficulties in identifying it. The aim of this chapter is to present a detailed morphological study of extracted lower first premolars. One hundred lower first premolars, belonging to the collection of the Laboratory of Anatomy of the Department of Morphology of the São Paulo State University (UNESP), School of Dentistry, Araraquara, SP, Brazil, were evaluated. Nine measurements were performed through direct observation without any instruments. Other 20 measurements were made by photographs and they were analyzed by the Image Tool 3.0 program. According to the results, it was concluded that most of the teeth presented the following features such as one lingual cusp; the distal occlusal pits were wider than the mesial occlusal pits; an enamel bridge linking the buccal and lingual cusps; the grooves in the lingual surface that emerged from the mesial and distal occlusal pits were absent, and where the grooves were present, they emerged from the mesial occlusal pit; one rectilinear root with no root grooves and where the root groove was present, it was observed in the mesial surface.",signatures:"Ticiana Sidorenko de Oliveira Capote, Suellen Tayenne Pedroso\nPinto, Marcelo Brito Conte, Juliana Álvares Duarte Bonini Campos\nand Marcela de Almeida Gonçalves",downloadPdfUrl:"/chapter/pdf-download/57245",previewPdfUrl:"/chapter/pdf-preview/57245",authors:[{id:"87871",title:"Prof.",name:"Ticiana",surname:"Capote",slug:"ticiana-capote",fullName:"Ticiana Capote"},{id:"199157",title:"Prof.",name:"Marcela",surname:"De Almeida Gonçalves",slug:"marcela-de-almeida-goncalves",fullName:"Marcela De Almeida Gonçalves"},{id:"199243",title:"BSc.",name:"Marcelo",surname:"Brito Conte",slug:"marcelo-brito-conte",fullName:"Marcelo Brito Conte"},{id:"199244",title:"Prof.",name:"Juliana",surname:"Álvares Duarte Bonini Campos",slug:"juliana-alvares-duarte-bonini-campos",fullName:"Juliana Álvares Duarte Bonini Campos"},{id:"217420",title:"Mrs.",name:"Suellen",surname:"Tayenne Pedroso Pinto",slug:"suellen-tayenne-pedroso-pinto",fullName:"Suellen Tayenne Pedroso Pinto"}],corrections:null},{id:"57752",title:"A Comparative Study of the Validity and Reproducibility of Mesiodistal Tooth Size and Dental Arch with iTeroTM Intraoral Scanner and the Traditional Method",doi:"10.5772/intechopen.70963",slug:"a-comparative-study-of-the-validity-and-reproducibility-of-mesiodistal-tooth-size-and-dental-arch-wi",totalDownloads:898,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Introduction: The introduction of intraoral scanning offers an alternative for measuring mesiodistal tooth sizes.",signatures:"Ignacio Faus-Matoses, Ana Mora, Carlos Bellot-Arcís, Jose Luis\nGandia-Franco and Vanessa Paredes-Gallardo",downloadPdfUrl:"/chapter/pdf-download/57752",previewPdfUrl:"/chapter/pdf-preview/57752",authors:[{id:"150456",title:"Prof.",name:"Vanessa",surname:"Paredes",slug:"vanessa-paredes",fullName:"Vanessa Paredes"},{id:"150458",title:"Prof.",name:"José-Luis",surname:"Gandia-Franco",slug:"jose-luis-gandia-franco",fullName:"José-Luis Gandia-Franco"},{id:"212242",title:"Prof.",name:"Ignacio",surname:"Faus",slug:"ignacio-faus",fullName:"Ignacio Faus"},{id:"212243",title:"Prof.",name:"Carlos",surname:"Bellot-Arcís",slug:"carlos-bellot-arcis",fullName:"Carlos Bellot-Arcís"},{id:"218390",title:"Prof.",name:"Ana",surname:"Mora",slug:"ana-mora",fullName:"Ana Mora"}],corrections:null},{id:"57378",title:"Identification of Lower Central Incisors",doi:"10.5772/intechopen.71341",slug:"identification-of-lower-central-incisors",totalDownloads:840,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Unlike the other teeth, the permanent lower central incisors have great symmetry between the proximal surfaces, being difficult to distinguish them. It was intended to facilitate the study of the anatomy of the lower central incisor for dentistry students, that this study searched for a better way to differentiate the third quadrant element (31) from the fourth quadrant element (41). The purpose of this chapter was to evaluate 100 permanent lower central incisors of the didactic collection of the Discipline of Anatomy of the Department of Morphology of the School of Dentistry of Araraquara - UNESP and to verify the presence of correlation between the some anatomical features. Besides, it was evaluated if there was difference between 31 and 41. It was verified that the systematic methodology used for the evaluation of the incisors in this study facilitated the identification of the teeth. There was no statistically significant difference between the measurements of 31 and 41. Distinguishing the right from the left central incisor is difficult, even for experienced practitioners. We could observe that the measurements do not facilitate the identification of teeth of different quadrants. Therefore, the anatomical features are relevant for the study of the dental anatomy in the identification of the lower central incisors.",signatures:"Marcela de Almeida Gonçalves, Bruno Luís Graciliano Silva, Marcelo\nBrito Conte, Juliana Álvares Duarte Bonini Campos and Ticiana\nSidorenko de Oliveira Capote",downloadPdfUrl:"/chapter/pdf-download/57378",previewPdfUrl:"/chapter/pdf-preview/57378",authors:[{id:"199157",title:"Prof.",name:"Marcela",surname:"De Almeida Gonçalves",slug:"marcela-de-almeida-goncalves",fullName:"Marcela De Almeida Gonçalves"},{id:"199243",title:"BSc.",name:"Marcelo",surname:"Brito Conte",slug:"marcelo-brito-conte",fullName:"Marcelo Brito Conte"},{id:"199244",title:"Prof.",name:"Juliana",surname:"Álvares Duarte Bonini Campos",slug:"juliana-alvares-duarte-bonini-campos",fullName:"Juliana Álvares Duarte Bonini Campos"},{id:"221435",title:"Mr.",name:"Bruno Luis Graciliano",surname:"Silva",slug:"bruno-luis-graciliano-silva",fullName:"Bruno Luis Graciliano Silva"},{id:"221438",title:"Prof.",name:"Ticiana Sidorenko De Oliveira",surname:"Capote",slug:"ticiana-sidorenko-de-oliveira-capote",fullName:"Ticiana Sidorenko De Oliveira Capote"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"7572",title:"Trauma in Dentistry",subtitle:null,isOpenForSubmission:!1,hash:"7cb94732cfb315f8d1e70ebf500eb8a9",slug:"trauma-in-dentistry",bookSignature:"Serdar Gözler",coverURL:"https://cdn.intechopen.com/books/images_new/7572.jpg",editedByType:"Edited by",editors:[{id:"204606",title:"Dr.",name:"Serdar",surname:"Gözler",slug:"serdar-gozler",fullName:"Serdar Gözler"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8837",title:"Human Teeth",subtitle:"Key Skills and Clinical Illustrations",isOpenForSubmission:!1,hash:"ac055c5801032970123e0a196c2e1d32",slug:"human-teeth-key-skills-and-clinical-illustrations",bookSignature:"Zühre Akarslan and Farid Bourzgui",coverURL:"https://cdn.intechopen.com/books/images_new/8837.jpg",editedByType:"Edited by",editors:[{id:"171887",title:"Prof.",name:"Zühre",surname:"Akarslan",slug:"zuhre-akarslan",fullName:"Zühre Akarslan"}],equalEditorOne:{id:"52177",title:"Prof.",name:"Farid",middleName:null,surname:"Bourzgui",slug:"farid-bourzgui",fullName:"Farid Bourzgui",profilePictureURL:"https://mts.intechopen.com/storage/users/52177/images/system/52177.png",biography:"Prof. Farid Bourzgui obtained his DMD and his DNSO option in Orthodontics at the School of Dental Medicine, Casablanca Hassan II University, Morocco, in 1995 and 2000, respectively. Currently, he is a professor of Orthodontics. He holds a Certificate of Advanced Study type A in Technology of Biomaterials used in Dentistry (1995); Certificate of Advanced Study type B in Dento-Facial Orthopaedics (1997) from the Faculty of Dental Surgery, University Denis Diderot-Paris VII, France; Diploma of Advanced Study (DESA) in Biocompatibility of Biomaterials from the Faculty of Medicine and Pharmacy of Casablanca (2002); Certificate of Clinical Occlusodontics from the Faculty of Dentistry of Casablanca (2004); University Diploma of Biostatistics and Perceptual Health Measurement from the Faculty of Medicine and Pharmacy of Casablanca (2011); and a University Diploma of Pedagogy of Odontological Sciences from the Faculty of Dentistry of Casablanca (2013). 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\r\n\tThis book will focus on arthropods, the largest phylum in the animal kingdom. They represent every animal habitat and bear members that live in aquatic, as well as terrestrial habitats. Arthropods are also known as invertebrates, animals lacking backbones. Characteristics of animals defined as arthropods are the presence of an exoskeleton, jointed appendages, and bilateral symmetry. They fall into the following subphyla: Hexapoda (e.g., flies, ants, termites, beetles, dragonflies, mosquitoes, cockroaches, butterflies, moths), Crustacea (e.g., crayfish, lobsters, crabs, shrimp, barnacles, woodlice), Myriapoda (e.g., centipedes, millipedes, sowbugs, pillbugs), Chelicerata (e.g., spiders, mites, scorpions, horseshoe crabs, sea spiders), and the extinct Trilobitomorpha (trilobites).
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1. Introduction
In this chapter, we focus on the novel Raman fiber laser (RFL)-based amplification techniques enabled by second-order pumping and fiber Bragg gratings (FBGs) at first-order pumping wavelengths [1], which is different from the conventional first-order or dual-order pumping schemes [2, 3, 4]. In first-order distributed Raman amplification, the signal is amplified by multiple first-order depolarized laser diodes to achieve flat gain profile. However, the signal gain can only occur near the fiber output, resulting in larger signal power profile and higher amplifier noise figure, which becomes the limiting factor of its performance in long-haul or unrepeatered transmission systems [2, 4]. In conventional dual-order Raman amplification, more uniform signal power distribution can be achieved, thanks to the Raman gain that occurs in the middle of the fiber. However, both first-order and second-order pump sources are also required. Particularly, to minimize the amplifier noise figure, the first-order pump power should be very small to enable higher second-order pump power, which requires multiple current and temperature controllers for the pump lasers [5]. Thus, the Raman fiber laser–based amplification reduces the high cost of dual-order pumping and improves the amplifier performance in comparison with first-order pumping, as it uses only second-order pumping with passive FBGs [1, 5].
In general, second-order RFL-based amplification is a distributed Raman amplification scheme, requiring depolarized second-order pumps (~1360 nm assuming the amplified signal is in C band, thanks to the two Stokes shift) and passive FBGs to generate first-order ultra-long Raman fiber laser when the transmission fiber is used as the gain medium. The induced Raman fiber laser together with the residual second-order pump is to amplify the signal in C and/or L band [6, 7]. However, due to different generation mechanisms of induced Raman fiber laser, there are two fiber laser regimes [8]. The first scheme is cavity fiber laser, in other words, Fabry-Perot cavity, where the transmission fiber between two end reflectors forms an ultra-long fiber laser cavity [9, 10]. This can be done with two high reflectivity FBGs or alternatively an FBG with weak Fresnel reflection [11, 12]. Random distributed feedback (DFB) Raman fiber laser is the other laser regime [13]. This is generated because the lasing threshold is overcome in the cavity formed by a distributed feedback (fiber Rayleigh scattering) and high reflective FBG [5, 14, 15].
RFL-based amplification schemes have different impacts on the coherent transmission systems, depending on the pumping schemes. Cavity fiber laser–based amplification can introduce a significant Q factor penalty, limiting the maximum reach to only 1500 km [2, 3, 8, 16], which means that that using forward (FW)-propagated pumping introduced a Q factor penalty, regardless of the reduction in the amplifier noise figure. However, random fiber laser–based amplification mitigates the signal relative intensity noise (RIN), reveals the benefit of the lower noise figure brought by FW-pumping, and effectively extends the maximum reach of the long-haul transmission system [5]. Such random fiber laser–based amplification technique can be applied in unrepeatered transmission systems and achieve a record transmission distance of over 350 km standard single mode fiber (SSMF) using 22 × 100 Gbits DP-QPSK WDM transmitter [17].
RFL-based amplification techniques are characterized from different perspectives, including signal/noise power distributions, relative intensity noise (RIN), and the mode structures of fiber laser. These results help give a better understanding of RFL-based amplification and also support the long-haul and unrepeatered coherent transmission performances demonstrated in this chapter.
2. Raman fiber laser–based amplification
2.1. Cavity Raman fiber laser amplification
2.1.1. Experimental setup
Figure 1 shows the schematic diagram of cavity fiber laser–based amplification using two FBGs. Two high reflectivity (>~95%) FBGs at 1455 nm with 3 dB bandwidths of ~0.5 nm were used at both ends of an 83 km SSMF. When the pump power of depolarized continuous wave second-order pumps at 1366 nm was high enough to overcome the lasing threshold, an ultra-long Fabry-Perot cavity (83 km was the cavity length) fiber laser was generated at 1455 nm [1, 5, 14, 15]. Therefore, the generated first-order fiber laser at 1455 nm and the second-order pump at 1366 nm amplified the signals in the C band [7].
Figure 1.
Cavity fiber laser–based amplification with two FBGs.
Figure 2.
The pump power used in the cavity fiber laser–based amplification.
The FW pump and BW pump powers used in the experiment were demonstrated in (Figure 2) and only used to compensate the ~16.5 dB loss from the 83.32 km fiber. The FW pump power ratio means the percentage of the FW pump power out of total pump power.
2.1.2. Signal and noise power distributions along the fiber
Signal power distributions along the transmission fiber with different pump powers were measured at 1545.32 nm using a modified optical time-domain reflectometer (OTDR) setup [6]. The OTDR instrument was used to monitor the signal power traces along the fiber. The built-in pulsed Fabry-Perot semiconductor laser at 1550 nm was transferred into the RF pulses which modulated an externally tunable laser through an acoustic optical modulator (AOM). The pulsed tunable laser was then transmitted into the fiber under test (Raman amplified), and the reflections were fed into the OTDR instrument. In this way, the signal power profile along the Raman-amplified fiber span was acquired.
Figure 3 shows both the experimental (solid) and simulated (dotted) signal power profiles [8, 18, 19]. The simulations use a set of equations to describe the power evolution [1]. The signal power profiles were the mutual effect of second-order pump power profiles at 1366 nm and first-order Raman fiber laser power profiles at 1455 nm [8]. For different pump-power combinations, signal power variation (SPV) was calculated as the difference between the maximum and minimum power value along the span, which was used as a metric to compare different pumping schemes. The lowest SPV of ~1.6 dB (+/−0.8 dB) over 83 km SSMF was done by bidirectional pumping with the similar pump powers from both directions. The SPV was increased to ~5.6 dB using BW-pumping only. This means that the second-order FW-pumping reduced the power variation and increased the average signal power relatively, so the noise figure of a distributed Raman amplifier was reduced [4]. Figure 4 shows the simulated noise power profiles. Compared with BW-pumping only, the noise power was decreased up to ~4 dB using bidirectional pumping. Considering the ASE noise only, the more the FW pump power, the less the ASE noise (the lower the amplifier noise figure). However, when the distributed Raman amplification is evaluated in the long-haul transmission system, the optimum signal launch power depends on the best trade-off between the ASE noise and the nonlinearity (if the RIN-induced penalty from the forward propagated pump is not taken into account). This means that the flat signal power profiles (the smallest power variation) are the key to achieve the best transmission performance using high-order symmetric bidirectional pumping instead of FW-pumping only [5].
Figure 3.
Measured (solid) and simulated (dotted) signal power distributions.
Figure 4.
Simulated noise power distributions.
2.1.3. Relative intensity noise
Relative intensity noise (RIN) is essentially the intensity variations from the pump source [16]. As the process of the Raman gain is extremely fast, the noise from the pump can affect the signal. When the pump and the signal travel in the same directions (usually called FW-pumping or co-pumping), the pump noise is more likely to transfer to the signal [3, 16, 20]. When the pump and the signal travel in the reverse, as the pump noise is attenuated and averaged by the transmission fiber [3, 21], BW-pumping is more tolerant to the RIN. FW-pumping can improve the noise figure but increase the signal RIN at the same time. For long-haul transmission, as the RIN is accumulated over the number of spans, the RIN penalty would be very severe. The schematic design of the RIN measurement is illustrated in [8]. The setup for the RIN measurement was based on an ultra-low-noise receiver and an electrical spectrum analyzer (ESA) ranging from 1 up to 160 MHz. The measured RIN of the second-order pump at 1366 nm is ~−120 dB/Hz, which is likely to be the lowest on the market for fiber laser–based pumps. The RIN of the output signal at 1545.32 nm was measured after one span from a CW low RIN (~−145 dB/Hz RIN) tunable laser source, and the FW-propagated Fabry-Perot fiber laser through a 5% splitter was measured.
Figure 5 shows the measured signal RIN in cavity fiber laser–based amplifier using different pump power combinations. When the FW pump power ratio was increased to 46.4%, the signal RIN was 18 dB higher, compared with BW-pumping only. The RIN increase was 9 dB using a 27.6% FW-pumping ratio. Figure 6 shows the RIN for the first-order-induced fiber laser. The fiber laser RIN was similar for all the pump powers used, except that the fiber laser RIN with BW-pumping only was lower within the range of below 40 MHz. Overall, there were significant differences in RIN between the BW-pumping scheme and all the other schemes using FW-pumping.
Figure 5.
RIN of the output signal using different pump powers in cavity fiber laser–based amplification technique.
Figure 6.
RIN of the induced fiber laser using different pump powers in cavity fiber laser–based amplification technique.
2.1.4. Fiber laser mode structures
Figure 7 shows the measured intra-cavity electrical spectra of the FW-propagated fiber laser at 1455 nm for different FW pump powers. Note that the traces in Figure 7 are deliberately offset to aid the comparison. It indicates that there were two different fiber lasing regimes. Using FW-pumping, a ~1.2 kHz mode spacing was acquired which corresponded to an 83 km Fabry-Perot cavity. This mode spacing did not depend on the FW pump power used, because it was determined by the cavity length and the refractive index [10], as demonstrated in Eq. (1). Δv is the mode spacing, c is the speed of the light, n is the fiber refractive index, and L is the length of the cavity.
Δv=c2nLE1
Figure 7.
Mode structures of the induced fiber laser using different pump powers in cavity fiber laser–based amplification technique.
No mode structure (“modeless” fiber laser) can be seen in BW-pumping only. This was because although a high reflectivity FBG was placed on one side of the cavity, distributed fiber Rayleigh scattering formed on the other side of the cavity, which generated a half-open fiber laser cavity [15, 22]. Thus, the cavity length of this fiber laser was not fixed due to the randomly distributed Rayleigh backscattering from the fiber [22]. Overall, these results show that with closed cavity with two FBGs, a random DFB fiber laser can be still achieved, which is different from the usual Fabry-Perot fiber laser with bidirectional pumping.
Figure 7 shows that in the fiber span of 83 km, using bidirectional pumping forms a Fabry-Perot cavity fiber laser. On the other hand, using BW-pumping only forms a random DFB fiber laser. However, when the fiber in between FBGs is too long (i.e. >270 km) [10], even with bidirectional pumping, the induced fiber laser is still random DFB fiber laser. This is because due to the high fiber attenuation, the pumps are “isolated” from each other, not powerful enough to reach the FBG on the other side. Instead, similar to the BW-pumping only over 83 km, the fiber Rayleigh backscattering reflects the pump and therefore generates a random fiber laser. Using bidirectional pumping over a very long fiber span generates two separate random fiber lasers at the input and the output [17]. This can be used in unrepeatered transmission systems.
2.2. Random distributed feedback Raman fiber laser–based amplification
2.2.1. Experimental setup
The reflectivity near the input section was close to zero (measured result of 0.04%), achieved by replacing it with an angled connector instead. The schematic diagram of such an amplifier is illustrated in Figure 8. The FW pump and BW pump powers are demonstrated in Figure 9. Note that the pump powers were only to compensate the loss of 83 km SSMF.
Figure 8.
Random fiber laser–based amplification with one FBG.
Figure 9.
The pump power used in random fiber laser–based amplification.
2.2.2. Signal and noise power distributions along the fiber
The signal and noise power distributions along the fiber were shown experimentally (solid line) and theoretically (dotted line) in Figure 10 [5]. Using the BW-pumping only, the SPV was the highest (~6 dB). Using 45.6 or 39.7% FW-pumping ratios, the lowest SPV was reduced to just below 4 dB. However, for the FW pump power ratio of 45.6%, a rapid power increase was seen within the first 10 km near the input end. This was particularly undesired for long-haul transmission systems because this was limited to the maximum signal launch power in order to avoid the Kerr nonlinear impairment [23].
Figure 10.
Measured (solid) and simulated (dotted) signal (a) and noise power profiles (b) using random fiber laser–based amplification scheme.
In addition, random DFB fiber laser–based amplification is a good candidate for long-haul transmission system using mid-link optical phase conjugation (OPC) to combat the nonlinearity, given the good signal power symmetry of the link [24, 25]. As illustrated in [24], more than 97% symmetry level can be achieved using this amplification technique over 62 km SMF. There are several generation mechanisms of random DFB fiber lasers, but here only the half-opened mechanism is discussed due to the highest Raman gain efficiency [26].
2.2.3. Relative intensity noise
The RIN of the signal at the output end was shown in Figure 11. The signal RIN remained the same with the FW pump power over the whole frequency range. This means that the transmission performance can only depend on the balance between the ASE noise and nonlinearity without the RIN-induced penalty being considered [23].
Figure 11.
Signal RIN using different pump powers in random fiber laser–based amplification technique.
2.2.4. Fiber laser mode structure
Figure 12 shows the mode structure of induced fiber laser with different pump powers. No mode was observed using BW-pumping only or bidirectional pumping, which confirms that it was random DFB fiber laser [8, 15]. The fiber laser was generated due to the resonant mode overcoming the lasing threshold in a distributed cavity formed by the fiber Rayleigh scattering and an FBG.
Figure 12.
Mode structures of the induced fiber laser using different pump powers in random fiber laser–based amplification technique.
3. Raman fiber laser–based amplification in telecommunications
3.1. The application in long-haul coherent transmission system
To evaluate different RFL-based amplification schemes, a long-haul recirculating loop experiment was conducted using the setup demonstrated in Figure 13. The test signals consisted of 10,120 Gb/s DP-QPSK channels with 100 GHz spacing, while a 100 kHz linewidth tunable laser was used as the “channel under test.” The multiplexed signals were QPSK modulated with normal and inverse 231–1 PRBS patterns at 30 Gb/s with a relative delay of 18 bits between I (in-phase) and Q (quadrature). A polarization multiplexer with a delay of 300 bits between the two polarization states gave the resultant 10 × 120 Gb/s DP-QPSK signals [24]. The transmission span in the recirculating loop was 17.6 dB loss in total, including 16.5 dB from 83.32 km SSMF and 1.1 dB from pump-signal combiners. The loop specific loss was ~12 dB from the AOM, 3 dB coupler, gain flattening filter (GFF), and the passive components from the Raman amplified span. An EDFA was used to compensate the loop loss. The receiver was a standard coherent detection setup and digital signal processing (DSP) was used offline with standard algorithms. Q factors were calculated from bit-wise error rates.
Figure 13.
Schematic diagram of long-haul repeatered transmission systems.
As cavity fiber laser–based amplification in Figure 1 was used in the long-haul transmission, as shown in Figure 14(a); the Q factor at 1666 km was 13.1 dB (BW-pumping only) but decreased to 9 dB (symmetric bidirectional pumping). Using higher FW pump power reduced the Q factor due to the RIN penalty, regardless of the noise figure reduction [8]. The optimum launch power was reduced when the FW pump power was increased. This was because the flatter signal power profile resulted in a higher averaged signal power and therefore the optimum signal launch power was decreased to avoid the Kerr nonlinearity. The degradation in Q factor occurred in all the launch power levels, which indicates that it was not because of the nonlinearity. However, in random fiber laser–based amplification scheme (Figure 8) as the FBG near the input was removed, the Q factor at 3333 km (Figure 14(b)) with FW pump power ratio of 33% was 0.6 dB better than BW-pumping only, and even using ~50% FW pump power ratio had a similar Q factor to BW-pumping only. This means that using this scheme, the RIN penalty introduced by FW-pumping was minimized, which indicates that the uniform signal power distribution led to effective performance improvement in long-haul transmission systems.
Figure 14.
(a) Q factors versus signal launch power using cavity fiber laser–based amplification and (b) Q factors versus signal launch power using random fiber laser–based amplification.
Figure 15 shows the Q factors versus transmission distances using both the amplification schemes. Using random fiber laser–based amplification had similar or better transmission performance (up to 7915 km maximum reach) than the BW-pumping-only scheme, but using cavity fiber laser–based scheme had a significant penalty. An important application of this RIN penalty-free random fiber laser–based amplification scheme was in the nonlinearity mitigation using mid-link OPC because using the scheme had a very symmetrical signal power profile and could maximize the benefit of nonlinearity compensation using mid-link OPC. The details of this work can be found in [24, 25].
Figure 15.
Q factors versus signal launch power using BW-pumping only, cavity fiber laser–based amplification scheme, and random fiber laser–based amplification scheme.
3.2. The application in unrepeatered coherent transmission system
In unrepeatered transmissions, distributed Raman amplification offers enhanced noise performance leading to higher OSNR, compared with EDFA [16, 27]. By using higher order distributed Raman amplifications, the signal power variation can be reduced leading to highly uniform signal power profiles, better trade-offs between ASE noise and nonlinearity, and better transmission performance. Here, based on the RFL-based amplification, the transmission performance using 100G DP-QPSK WDM signals over 352.8 km SMF has been shown without using remote optically pumped amplifier (ROPA) or any specialty fiber [17].
Figure 16 shows the schematic diagram of the unrepeatered transmission system using DP-QPSK WDM signals and random fiber laser–based amplification technique. An important difference of RFL-based amplifier in unrepeatered and repeatered systems is that due to the fiber length of the unrepeatered system (i.e. 300 km), the generated Raman fiber laser at 1455 nm was actually two separate random DFB fiber lasers located near each side of the span and had no interaction with each other [5, 17]. Figure 17(a) shows the Q factor versus signal launch power per channel, and Figure 17(b) shows the Q factors of all the measured channels at 327.6 and 352.8 km. At 327.6 km, the maximum number of channels was limited to the number of lasers we had. At 352.8 km, 14 channels were transmitted at the FEC threshold of 6.4 dB. This was achieved without ROPA and low-loss fiber, which indicates that our proposed setup can be used to readily upgrade the existing SSMF legacy link without the installation of the new fiber. In addition, our proposed setup is compatible with ROPA by adding the seed pump at 1480 nm, which can simultaneously improve the transmission distance and the amplification bandwidth [28-30].
Figure 16.
A schematic diagram of random fiber laser–based amplification scheme in unrepeatered transmission.
Figure 17.
(a) Q factors versus launch power per channel at 327.6 and 352.8 km and (b) Q factors of all the measured channels at 327.6 and 352.8 km.
4. Conclusion
In conclusion, Raman fiber laser–based amplification techniques have been characterized as standalone amplifiers, and its performances have been analyzed and optimized in long-haul repeatered and unrepeatered coherent transmission systems. Based on random DFB fiber laser–based Raman amplification, the signal RIN can be successfully mitigated, and bidirectional second-order pumping would not suffer from the RIN penalty. Thus, it provides the best trade-off between ASE noise and nonlinearity and therefore offers the best transmission performance. The scheme is highly flexible and the signal power distributions can be adjusted to meet specific link requirements. This scheme is potentially to be highly effective to compensate the nonlinear impairment and enhance the transmission distance using different nonlinearity compensation techniques, that is, mid-link optical phase conjugation and nonlinear Fourier transform-based transmitter.
Acknowledgments
This work was funded by UK EPSRC Programme Grant PEACE (EP/L000091/1), UNLOC (EP/J017582/1), FP7 ITN programme ICONE (608099), and MSCA IF grant SIMFREE (748767). We would like to thank the support and contribution from Andrew Ellis, Paul Harper, Sergei Turitsyn, Paweł Rosa, Son Thai Le, Ian Phillips, Juan Diego Ania-Castanon, Md Asif Iqbal, and thank Changle Wang, Zhongyuan Sun, and Lin Zhang for providing the FBGs.
\n',keywords:"Raman amplification, Raman fiber laser, coherent transmission, random fiber laser, cavity fiber laser",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/59240.pdf",chapterXML:"https://mts.intechopen.com/source/xml/59240.xml",downloadPdfUrl:"/chapter/pdf-download/59240",previewPdfUrl:"/chapter/pdf-preview/59240",totalDownloads:933,totalViews:151,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:0,impactScore:0,impactScorePercentile:40,impactScoreQuartile:2,hasAltmetrics:0,dateSubmitted:"September 14th 2017",dateReviewed:"January 9th 2018",datePrePublished:null,datePublished:"July 25th 2018",dateFinished:"February 8th 2018",readingETA:"0",abstract:"The chapter demonstrates a detailed study of Raman fiber laser (RFL)-based amplification techniques and their applications in long-haul/unrepeatered coherent transmission systems. RFL-based amplification techniques are investigated from signal/noise power distributions, relative intensity noise (RIN), and fiber laser mode structures. RFL-based amplification techniques can be divided into two categories according to the fiber laser generation mechanism: cavity Raman fiber laser with two fiber Bragg gratings (FBGs) and random distributed feedback (DFB) Raman fiber laser using one FBG. In addition, in cavity fiber laser–based amplification, reducing the reflectivity near the input helps mitigate the signal RIN, thanks to the reduced efficiency of the Stokes shift from the second-order pump. To evaluate the transmission performance, different RFL-based amplifiers were optimized in long-haul coherent transmission systems. Cavity fiber laser–based amplifier introduces >4.15 dB Q factor penalty, because the signal RIN is transferred from the second-order pump. However, random DFB fiber laser–based amplifier prevents the RIN transfer and therefore enables bidirectional second-order pumping, which gives the longest transmission distance up to 7915 km. In addition, using random DFB laser-based amplification achieves the distance of >350 km single mode fiber in unrepeatered DP-QPSK transmission.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/59240",risUrl:"/chapter/ris/59240",book:{id:"6467",slug:"optical-amplifiers-a-few-different-dimensions"},signatures:"Mingming Tan",authors:[{id:"221733",title:"Dr.",name:"Mingming",middleName:null,surname:"Tan",fullName:"Mingming Tan",slug:"mingming-tan",email:"m.tan1@aston.ac.uk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Raman fiber laser–based amplification",level:"1"},{id:"sec_2_2",title:"2.1. Cavity Raman fiber laser amplification",level:"2"},{id:"sec_2_3",title:"2.1.1. Experimental setup",level:"3"},{id:"sec_3_3",title:"2.1.2. Signal and noise power distributions along the fiber",level:"3"},{id:"sec_4_3",title:"2.1.3. Relative intensity noise",level:"3"},{id:"sec_5_3",title:"2.1.4. Fiber laser mode structures",level:"3"},{id:"sec_7_2",title:"2.2. Random distributed feedback Raman fiber laser–based amplification",level:"2"},{id:"sec_7_3",title:"2.2.1. Experimental setup",level:"3"},{id:"sec_8_3",title:"2.2.2. Signal and noise power distributions along the fiber",level:"3"},{id:"sec_9_3",title:"2.2.3. Relative intensity noise",level:"3"},{id:"sec_10_3",title:"2.2.4. Fiber laser mode structure",level:"3"},{id:"sec_13",title:"3. Raman fiber laser–based amplification in telecommunications",level:"1"},{id:"sec_13_2",title:"3.1. The application in long-haul coherent transmission system",level:"2"},{id:"sec_14_2",title:"3.2. The application in unrepeatered coherent transmission system",level:"2"},{id:"sec_16",title:"4. Conclusion",level:"1"},{id:"sec_17",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Ania-Castañón JD. Quasi-lossless transmission using second-order Raman amplification and fiber Bragg gratings. Optics Express. 2004;12:4372-4377'},{id:"B2",body:'Bromage J. Raman amplification for fiber communications systems. Journal of Lightwave Technology. 2004;22:79-93'},{id:"B3",body:'Fludger CRS, Handerek V, Mears RJ. Pump to signal RIN transfer in Raman fiber amplifiers. Journal of Lightwave Technology. 2001;19:1140-1148'},{id:"B4",body:'Bouteiller J-C, Brar K, Headley C. Quasi-constant signal power transmission. In: ECOC. 2002. pp. 1-2'},{id:"B5",body:'Tan M, Rosa P, Le ST, Md A, Iqbal IDP, Harper P. Transmission performance improvement using random DFB laser based Raman amplification and bidirectional second-order pumping. 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Optics Express. 2015;23:28634-28639'},{id:"B19",body:'Rosa P, Le ST, Rizzelli G, Tan M, Ania-Castañón JD. Signal power asymmetry optimisation for optical phase conjugation using Raman amplification. Optics Express. 2015;23:31772-31778'},{id:"B20",body:'Ohki Y, Hayamizu N, Irino S, Shimizu H, Yoshida J, Tsukiji N. Pump laser module for co-propagating Raman amplifier. Furukawa Review. 2003;24:6-12'},{id:"B21",body:'Bromage J, Bouteiller J-C, Thiele HJ, Brar K, Nelson LE, Stulz S, Headley C, Boneck R, Kim J, Klein A, Baynham G, Jorgensen LV, Gruner-Nielsen L, Lingle RL, DiGiovanni DJ. WDM transmission over multiple long spans with bidirectional Raman pumping. Journal of Lightwave Technology. 2004;22(1):225-232'},{id:"B22",body:'Churkin DV, Babin SA, El-Taher AE, Harper P, Kablukov SI, Karalekas V, Ania-Castañón JD, Podivilov EV, Turitsyn SK. Raman fiber lasers with a random distributed feedback based on Rayleigh scattering. 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In: Optical Fiber Communication Conference, OSA Technical Digest (online) (Optical Society of America, 2014), Paper M3C.1'},{id:"B25",body:'Ellis AD, Tan M, Iqbal MA, Al-Khateeb MAZ, Gordienko V, Saavedra Mondaca G, Fabbri S, Stephens MFC, McCarthy ME, Perentos A, Phillips ID, Lavery D, Liga G, Maher R, Harper P, Doran N, Turitsyn SK, Sygletos S, Bayvel P. 4 Tb/s transmission reach enhancement using 10 × 400 Gb/s super-channels and polarization insensitive dual band optical phase conjugation. Journal of Lightwave Technology. 2016;34:1717-1723'},{id:"B26",body:'Churkin DV, Sugavanam S, Vatnik ID, Wang Z, Podivilov EV, Babin SA, Rao Y, Turitsyn SK. Recent advances in fundamentals and applications of random fiber lasers. Advances in Optics and Photonics. 2015;7:516-569'},{id:"B27",body:'Chang D, Pelouch WS, Burtesv S, Perrier P, Fevrier H. Unrepeatered high-speed transmission systems. In: Optical Fiber Communication Conference, OSA Technical Digest (Online) (Optical Society of America, 2015), Paper. W4E.3'},{id:"B28",body:'Rosa P. Quasi-lossless data tranmission with ultra-long Raman fibre laser based amplification [PhD thesis]. Aston University. 2013'},{id:"B29",body:'Cheng J, Tang M, Lau APT, Lu C, Wang L, Dong Z, Bilal SM, Fu S, Shum PP, Liu D. Pump RIN-induced impairments in unrepeatered transmission systems using distributed Raman amplifier. Optics Express. 2015;23(9):11838-11854'},{id:"B30",body:'Alcon-Camas M, Ania-Castañón JD. RIN transfer in 2nd-order distributed amplification with ultralong fiber lasers. Optics Express. 2010;18(23):23569-23575'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Mingming Tan",address:"m.tan1@aston.ac.uk",affiliation:'
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1. Introduction
The science of decision support is foundational for every type of policy, and this work offer a proposal to analyze its role in energy policy.
An example of application of a particular machine learning (ML) technique to an energy policy problem is presented. It is important to understand the role of ML in energy and environmental analysis, for two solid reasons.
The first concerns the need to process large volumes of data and to elaborate and model complex relationships, typical of the energy analysis and of the environmental analysis. In this context, the use of AI (Artificial Intelligence) and machine learning is almost mandatory.
The second concerns the need to a concerted effort to identify how these tools may best be applied to tackle major problems of recent years, like climate change [1]: about this, CO2 emissions is key variable that we must control to achieve the global objective of mitigating damage for humanity.
This work has a specific goal. Using known tools from the scientific literature on energy generation costs, we intend to show how the use of a machine learning technique (the support vector machines, SVM) can produce a more accurate modeling of these costs.
The link with CO2 emissions is provided by the possibility of using the cost model in a cost-effectiveness analysis (C-E A), in which the cost is represented by the Levelised Cost of Energy (LCOE) and the effectiveness is represented by the CO2 emissions of the technologies considered per unit of energy produced.
The CO2 estimation is then obtained by selecting the best generation options according to the C-E A results.
The meaning of this work is the following.
Imagine that you are an energy analyst, in the public or private sector, and you need to use only one or just few variable/s (such as a forecast on the cost of natural gas), to estimate the costs of an electricity generation technology.
This task can be accomplished using a cost model of electricity generation in which a single piece of information can vary, leaving everything else unchanged (or imposing a certain trend on it).
The metric used is the indicator LCOE (Levelised Cost of Energy) provided by IEA (International Energy Agency), using 2020 data.
Once you have obtained a certain level of accuracy in estimate of energy cost, it is possible to move into a context of cost-effectiveness analysis, in which the best energy option in terms of Incremental Cost-Effectiveness Ratio (ICER) was selected to produce energy and, finally, provide a certain level of CO2 emissions for the time horizon in which such a technology is still the “best option”.
In other words, the estimate of energy cost and the cost-effectiveness analysis, allow us to trace the scenarios for electricity generation mix and, finally, calculate a quantitative forecast of the CO2 emitted.
The proposed work just intends to show the application of one of the existing machine learning techniques to the estimation of the LCOE, starting from some explanatory variables.
A linear model (LM) and an SVM are compared in the prediction of the LCOE value for a combined cycle gas plant (CCGT) with a focus on the fuel cost, Operation and Maintenance (O&M) cost and CO2 price using IEA data for Italy in 2020.
The work carried out intends to highlight the possibilities of applying machine learning techniques not only in the purely engineering aspects of energy systems, but also in the statistical-economic ones at a higher level of abstraction.
Some words about why to focus on power generation systems.
As countries work towards a low carbon world, it is crucial that policymakers, modelers, and experts have at their disposal reliable information on the cost of generation.
IEA [2] reports that the levelised costs of electricity generation of low-carbon generation technologies are more and more low the costs of conventional fossil fuel generation. Renewable energy costs continue their descent in recent years and their costs are now competitive with dispatchable fossil fuel-based electricity generation for many countries.
2. Methodology
This section presents the main tools used in this work: the LCOE methodology provided by IEA and the SVM, the used machine learning technique. Just before SVM presentations a very brief remind about ML and its use in energy systems and CO2 emissions estimates will be provided.
2.1 Levelised cost of energy
The Levelised Cost of Energy (LCOE) is the selected tool to measure the cost of an energy unit produced by the considered technologies. LCOE is a methodology described in the joint report by the International Energy Agency and the OECD (Organization for Economic Co-operation and Development) Nuclear Energy Agency (NEA) (now at the ninth edition in a series of studies on electricity generating costs) [1]. This report includes cost data on power generation from natural gas, coal, nuclear, and a broad range of renewable technologies.
The metric for plant-level cost chosen is the well-known levelised cost of electricity (LCOE) (IEA are now considering system effects and system costs with the help of the broader value-adjusted LCOE, or Levelised Cost of Value-Adjusted LCOE, VALCOE metric, here not considered).
The LCOE is widely considered as the principal tool for comparing the plant-level unit costs of different base load technologies over their operating lifetimes since indicates the economic costs of a technology family, not the financial costs of a certain projects in a certain market. Due to the equality between discounted average costs and the stable remuneration over lifetime electricity production LCOE recall the costs of electricity production in regulated electricity markets with stable tariffs than to the variable prices in deregulated markets.
Despite many limitations, LCOE has maintained its utility and appeal since it is a uniquely straightforward, transparent, comparable, and well understood metrics remaining a widely used tool for modeling, policy making and public debate.
The calculation of the LCOE is based on the equivalence of the present value of the sum of discounted revenues and the present value of the sum of discounted costs. Another way on the left-hand side one finds the discounted sum of benefits and on the right-hand side the discounted sum of costs:
PMWh The constant lifetime remuneration to the supplier for electricity;
MWh The amount of electricity produced annually in MWh;
1+r−t The real discount rate corresponding to the cost of capital;
Capitalt Total capital construction costs in year t;
O&Mt Operation and maintenance costs in year t;
Fuelt Fuel costs in year t;
Carbont Carbon costs in year t;
Dt Decommissioning and waste management costs in year t
PMWh is equal to levelised cost of electricity (LCOE).
Eq. (1) is the formula used here to calculate average lifetime levelized costs based on the costs for investment, operation and maintenance, fuel, carbon emissions and decommissioning and dismantling provided by OECD countries and selected non-member countries.
2.2 Machine learning
Machine learning (ML) is the field of artificial intelligence (AI) that provide methods to learn from data over time creating algorithms not being programmed to do so.
The literature about ML is relatively recent but is so vast that only some hint to review works can be made here, as an access point to this world1.
Machine learning approaches are normally categorized as in the follows.
Supervised machine learning, that trains itself on a labeled data set; unsupervised machine learning that uses unlabeled data with algorithms to extract the features required to label, sort, and classify the data in real-time, without human intervention; semi-supervised learning (SsL) namely a medium between supervised and unsupervised learning: SsL uses a smaller labeled data set during training and make classification and feature extraction from a larger, unlabeled data set; reinforcement machine learning is like supervised learning, but do not requires sample data for training (since using “trial and error” mode).
About the machine learning algorithms for use with labeled data the regression algorithms (as linear and logistic regression); decision trees (based on a set of decision rules to perform classification); instance-based algorithms: it uses classification to estimate how likely a data point is to be a member of one group, or another based on its proximity to other data points.
Methods based for use with on unlabeled data are: clustering algorithms: (like K-means, TwoStep, and Kohonen clustering); association algorithms: (that find patterns in data by identifying ‘if-then’ relationships namely association rules); neural networks: (that create a layered network of calculations featuring an input layer, when data in; one or more hidden layer, where calculations are performed; and an output layer. Where each conclusion is assigned a probability); deep neural network that uses multiple hidden layers, each of which successively refines the results of the previous layer. Deep learning models are typically unsupervised or semi-supervised. Certain types of deep learning models—including convolutional neural networks (CNNs) and recurrent neural networks (RNNs)—are driving progress in areas such as computer vision, natural language processing (including speech recognition), and self-driving cars.
In this work, the machine learning approach used is the SVM one.
SVMs2 are machine learning algorithms built on statistical learning theory for structural risk minimization. In pattern recognition, classification, and analysis of regression, SVMs outperform other methodologies. The significant range of SVM applications in the field of load forecasting is due to its ability to generalize (also, local minima lead to no problems in SVM).
SVM was chosen, in this work, for the sake of simplicity, since the performed Support Vector Regression (SVR) [5], extremely easy to understand in comparing a traditional statistical tool with a competing machine learning based one.
Often, the available applications of SVM in the energy sector are oriented on the engineering side3 while in this work the approach is oriented in support decisions for energy policy field.
Using one of the possibilities offered by SVMs, namely the SVR, the follows show how it is possible to obtain more accurate forecasts of costs per unit of energy produced, using LCOE as a metric.
The best available accuracy is then used in a context of cost-effectiveness analysis.
In the following, a method to select among competing options (options that can be differ even for slight changes in some significant LCOE parameters), the one characterized by the best Incremental Cost-Effectiveness Ratio (ICER) is presented.
The possibility of making this choice during the lifetime of the plant leads to the possibility of identifying the best technology available, year by year, to get the corresponding profile of the associated CO2 emissions.
2.2.1 Machine learning for energy systems and CO2 emission estimation
The growing utilization of data collectors in energy systems has resulted in a massive amount of data accumulated (an increasing mass of mart sensors are now extensively used in energy production and energy consumption) leading to a continuous production of big data and, consequently, to a massive number of opportunities and challenges in decision support science.
Today, ML models in energy systems are essential for predictive modeling of production, consumption, and demand analysis due to their accuracy, efficacy, and speed or to provide an understanding on energy system functionality in the context of complex human interactions.
[7] propose a comprehensive review of essential ML to present the state of the art of ML models in energy systems and discuss their likely future trends.
Machine learning was used for estimate CO2 emission from energy systems in several context, using different approach. It is possible to recall, among an increasing number of works in recent years:
[8] about flexibility of the electricity demand, a machine learning algorithm developed to forecast the CO2 emission intensities in European electrical power grids distinguishing between average and marginal emissions in Danish bidding zone DK2;
[9] an investigation on the causal relationship among solar and wind energy production, coal consumption, economic growth, and CO2 emissions for these three countries;
[10] on the linkage between energy resources and economic development the focus of that work is to develop and apply the machine learning approach to predict gross domestic product (GDP) based on the mix of energy resources with a higher predictive accuracy;
[11] about proposing a standardized framework for estimating the indirect building carbon emissions within the boundaries of various types of Local Climate Zones (LCZs using a random forest machine learning method);
[12] on the relationship among iron and steel industries, air pollution and economic growth in China (using a Long Short Term Memory, LSTM, approach);
[13] on the forecasting of energy consumption related carbon emissions for the Beijing-Tianjin-Hebei region.
[14] on the uses of gray relational analysis to identify the factors that have a strong correlation with carbon emissions for China to reduce carbon emissions by studying prediction of carbon emissions (using LSTM).
[15] on the creation of an automated, high-resolution forest carbon emission monitoring system that will track near real-time changes and will support actions to reduce the environmental impacts of gold mining and other destructive forest activities for the Peruvian Amazon (using deep learning models).
[16] on the use of a random forest machine learning regression workflow to map country of Peru by combining 6.7 million hectares of airborne LiDAR measurements of top-of-canopy height with thousands of Planet Dove satellite images into, to create a cost-effective and spatially explicit indicators of aboveground carbon stocks and emissions for tropical countries as a transformative tool to quantify the climate change mitigation services that forests provide.
[17] To determine whether China can achieve the commitment of reducing carbon emission intensity in 2030, through a general regression neural network (GRNN) forecasting model based on improved fireworks algorithm (IFWA) optimization is constructed to forecast total carbon emissions (TCE) and carbon emissions intensity (CEI) in 2016–2040.
2.3 Our methodology
The present work reports an experiment performed using a simple LCOE model, built according to basic methodology proposed by IEA. The performed experiment is simple and straightforward. Two energy scenarios were produced, one based on a certain hypothesis of change in the fuel cost, the other based on a hypothesis of change in fuel cost, O&M cost, and CO2 price, for the CCGT type plant, over a period of 30 years.
In each scenario, a certain LCOE profile is obtained for the time horizon considered. A simple regression analysis is then performed on this variable, using as explanatory variables, first the cost of fuel, and then the operating costs.
The analysis is carried out both using a LM and the SVM, with further manual tuning of the last to improve its performance. The manual tuning for SVR was used for the sake of simplicity since the main goal of the study is to suggest the application of this ML technique to gain forecasting accuracy to use in the following phase, the cost-effectiveness analysis.4
To evaluate the accuracy of the forecast, the Root Mean Square Error (RMSE), the Mean Average Error (MAE) and the Mean Average Percentage Error (MAPE) were used.5
This simple test was performed to show the accuracy of the fuel cost and O&M cost as a predictor of CCGT LCOE.
Once established the best technique, the data from the two scenarios in a third scenario are modified, under certain hypothesis explained in the follows, to made a C-E A between a technology represented by IEA data and another of the same type with little changes in O&M costs. Using ICER as a winning criterion, it is possible to select the best energy generation option and, finally, to trace the corresponding CO2 emission estimate trend over the plant’s lifetime.
First, a LCOE model based on IEA Eq. (1), with the following level of detail, was built.
The basic relationships of the model are:
PF=Power∗8760∗AVLF∗AAF100∗1−AuxPE2
ws=1−wdE3
ks=krft+EMRP∗BE4
i=wd∗kd+ws∗ksE5
d=i/1+iE6
dfi=∑j1/1+ijE7
icfinal=icfinal+icCnsT∗dfiE8
df=∑j1/1+djE9
icfinal=icfinal+icCnsT∗dfi1E10
dfi=∑j1/1+ijE11
Pro=Pro+PF∗dfE12
OM=FOM+VOM∗PF∗dfE13
Fue=CFue∗PF∗dfE14
CO2=PCO2∗PF∗dfE15
Cost=∑jOM+Fue+CO2E16
Decom=n∗Decom∗ProE17
LCOE=Power∗icfinal∗1000+Cost+Decom/ProE18
Where:
CC Cost of Capital (USD/MWh)
Power net capacity (MWe)
AVLFmin AVerage Load Factor min value (%)
AVLFmax AVerage Load Factor max value (%)
AAF Average Availability Factor (%)
AuxP Auxiliary Power (%)
Lifetime Time horizon of plant (years).
wdmin min weight of cost of debt on total cost (%)
wdmax max weight of cost of debt on total cost (%)
kdmin min value of debt rate (%)
kdmax max value of debt rate (%)
tmin min value of taxation (%)
tmax max value of taxation (%)
krftmin min value of free risk rate (%)
krftmax max value of free risk rate (%)
EMRPmin min value of Expected Market Risk Premium (%)
EMRPmax max value of Expected Market Risk Premium (%)
Bmin min value of Beta (%)
Bmax max value of Beta (%)
CnsTmin min value of Construction Time (years)
CnsTmax max value of Construction Time (years)
FOMmin Fixed Operation and Maintenance Costs min (USD*MWh)
FOMmax Fixed Operation and Maintenance Costs max (USD*MWh)
VOMmin Variable Operation and Maintenance Costs min (USD*MWh)
VOMmax Variable Operation and Maintenance Costs max (USD*MWh)
Cfuemin min value of Costs of Fuel (USD*MWh)
Cfuemax max value of Costs of Fuel (USD*MWh)
Effmin min value of Efficiency (%)
Effmax max value of Efficiency (%)
PCO2min min value of CO2 price (USD*MWh)
PCO2max max value of CO2 price (USD*MWh)
Decommin min value of Decommissioning (USD*MWh)
Decommax max value of Decommissioning (USD*MWh)
All other parameters are settled using the IEA values.
We have set two type of scenario, basing on the following assumptions about certain variables of the model. The basic hypothesis is a constant decreasing of 2% for every variable changed, except every 6 years (a totally arbitrary choice), simulating an increasing amplification of this cycle (every 6 years, the percentage variation of the cost respect to the previous value is double than it and then is multiplied for the number of the occurring, so the first time at year 6, this value is roughly 4, namely 2% multiplied by 2 and then multiplied per variation 1).
Table 1 describes the hypothesis used in this first step of the analysis.
Fuel Cost (baseline 45.5 USD/MWh)
O&MCost (baseline: 6.99 USD/MWh)
CO2 price (10.1 USD/MWh)
Scenario 1
Linear decreasing of 2% per year except every 6 years
constant
constant
Scenario 2
Linear decreasing of 2% per year except every 6 years
Linear decreasing of 2% per year except every 6 years
Linear decreasing of 2% per year except every 6 years
Table 1.
Scenarios used for the regression of LCOE on fuel cost and O&M cost
3. Results
Figure 1 shows the results obtained by performing a SVR about the data from IEA [1] for the first scenario considered (Figure 2).
Figure 1.
Comparison between LM and SVMBT in predicting LCOE of CCGT technology for Italy (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 1 - Y = LCOE (USD/MWh), X = fuel cost (USD/MWh).
Figure 2.
Comparison between LM and SVMAT in predicting LCOE of CCGT technology for Italy after tuning (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 1 - Y = LCOE (USD/MWh), X = fuel cost (USD/MWh).
The values of RMSE for the Linear Model (LM), the SVM Model Before Tuning (SVMBT) and the SVM Model After Tuning (SVMAT) are:
RMSE
MAE
MAPE
Linear Model
1,30E-14
8,39E-15
8,39E-17
SVM
5,25E-01
4,01E-01
4,01E-03
Tuned SVM
1,74E-03
1,54E-03
1,54E-05
with a clear improvement of performance of the SVM after tuning. The linear model since the strong relationships between the fuel cost and the LCOE is clearly preferable respect to the SVM (Figures 1 and 2).
Figure 3.
Comparison between LM and SVMBT in predicting LCOE of CCGT technology for Italy (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 2 - Y = LCOE, X = O&M Cost.
Figure 4.
Comparison between LM and SVMAT in predicting LCOE of CCGT technology for Italy after tuning (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 2 - Y = LCOE, X = O&M Cost.
The values of RMSE for the Linear Model (LM), the SVM Model Before Tuning (SVMBT) and the SVM Model After Tuning (SVMAT) are:
RMSE
MAE
MAPE
Linear Model
3.87E+00
2.70E+00
2.70E-02
SVM
2.77E+00
1.59E+00
1.59E-02
Tuned SVM
2.61E+00
1.45E+00
1.45E-02
Recalling that in the second case the O&M cost was used as a predictor, we can more appreciate the gain in terms of RMSE obtained by using the SVM.
The increasing accuracy of the SVR respect to the LM, can be used to perform a CO2 emission estimation in a cost-effectiveness analysis.
Let us look at a simple and plain experiment based on IEA data [2] for Italy, 2020 in the following scenario:
Fuel Cost (baseline 45.5 USD/MWh)
O&MCost (baseline: 6.99 USD/MWh)
CO2 price (10.1 USD/MWh)
Scenario 3
Decreasing of 15% at 15th year then linear decreasing of 1% until rest of the lifetime.
Decreasing of 15% at 15th year then linear decreasing of 1% until rest of the lifetime.
Decreasing of 15% at 15th year then linear decreasing of 1% until rest of the lifetime.
In scenario 3 we made a simulation basing on the hypothesis of a sudden shock for the three variables above reported in the 15th year, immediately followed by a linear decrease of them until end of the lifetime, starting from IEA 2020 data as a baseline value.
For scenario 3 the errors in predicting LCOE using O&M Cost over the considered time horizon are:
RMSE
MAE
MAPE
Linear Model
4.25878
3.49147
0.03491
SVM
2.70117
1.52912
0.01529
Tuned SVM
2.58541
1.52378
0.01524
In Cost-Effectiveness Analysis it is possible to calculate the Incremental Cost-Effectiveness Ratio (ICER), used as a measure of cost the LCOE and used as a measure of effectiveness through the quantity of CO2 emitted. The ICER can be used as a selection criterion between different options then, the winning options will be producing a certain level of emissions.
Now, let us imagine comparing two types of plants of the same technological family, in this case the CCGT. In this hypothetical exercise, the second type of plant is characterized by higher operating costs (+5% of the IEA base value).
In addition to this, let us imagine that the second type of plant has an average load factor of 94%.
Now, let us repeat the simulation performed for scenario 3 for the first type of CCGT plant (the real one), but only from the 20th year.
The meaning of this operation is as follows:
to use systems with different characteristics (in this case we have changed the O&M costs and the load factor of a single technology family);
to calculate the ICER corresponding to each plant in a defined time interval (in this case, from when the LCOE starts to vary);
to calculate the degree of uncertainty on the value of the ICER thanks to the MAPE of the SVR, defining the variation range for the ICER6;
to select the technology that has the lowest ICER and then we calculate the corresponding emissions over the time horizon considered;
finally, to calculate the emissions profile corresponding to the winning technology, year by year.
CO2 emissions from different kind of CCGT plants in scenario 3 (sources: IEA, 2020 + imaginary data).
Figure 5 illustrates what happens using the ICER criterion as a selector of the winning generation option. For the first 20 years, the first type of installation is selected, and the corresponding emissions are those of the blue line. From 20 years of age onwards, using the ICER as a criterion means choosing the second type of plant and the curve that shows the new profile of the emissions is the orange one.
4. Conclusions
ML can help in providing accurate forecasts of CO2 emissions from power generation, especially when we face simultaneous variation of major driver (like fuel cost, operating cost of the plant and so on); only a little piece of the possible comparisons between traditional techniques and a particular ML method was shown, focusing on the better performance of the ML one (SVM) respect to the traditional one (the LM).
In our case, the performed step was:
improving LCOE forecasting performance,
comparing multiple competing options by use of the ICER in Cost-Effectiveness Analysis;
consider the uncertainty about ICER using the MAPE (in this case, but is just an option) calculated by SVM;
choosing the best technology and calculating the CO2 emissions for it;
defining the trend of the CO2 emissions in the lifetime of the plant by step 4.
Recalling that a basic LCOE model can be brought to a great level of granularity, it is easy to imagine how this type of analysis could gain in depth and significance if the required data are available. Indeed, also in case of missing data, significant simulation can be provided by using each available piece of information on energy costs.
The experiment performed was conducted at the highest level of simplicity to better focus on the reasons that suggest ML integration not only about the engineering features of electricity generation field but also in support decision tools about energy policy.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"CO2 emissions, energy systems, machine learning, support vector machines, cost-effectiveness analysis, forecasting",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/76238.pdf",chapterXML:"https://mts.intechopen.com/source/xml/76238.xml",downloadPdfUrl:"/chapter/pdf-download/76238",previewPdfUrl:"/chapter/pdf-preview/76238",totalDownloads:146,totalViews:0,totalCrossrefCites:1,dateSubmitted:"November 12th 2020",dateReviewed:"March 26th 2021",datePrePublished:"April 12th 2021",datePublished:null,dateFinished:"April 12th 2021",readingETA:"0",abstract:"In the last decades, there has been an outstanding rise in the advancement and application of various types of Machine learning (ML) approaches and techniques in the modeling, design and prediction for energy systems. This work presents a simple but significant application of a ML approach, the Support Vector Machine (SVM) to the estimation of CO2 emission from electricity generation. The CO2 emission was estimate in a framework of Cost-Effectiveness Analysis between two competing technologies in electricity generation using data for Combined Cycle Gas Turbine Plant (CCGT) provided by IEA for Italy in 2020. Respect to other application of ML techniques, usually developed to address engineering issues in energy generation, this work is intended to provide useful insights in support decision for energy policy.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/76238",risUrl:"/chapter/ris/76238",signatures:"Marco Rao",book:{id:"10627",type:"book",title:"Engineering Problems - Uncertainties, Constraints and Optimization Techniques",subtitle:null,fullTitle:"Engineering Problems - Uncertainties, Constraints and Optimization Techniques",slug:null,publishedDate:null,bookSignature:"Dr. Marcos Sales Guerra Tsuzuki and Prof. Rehab O. O. Abdel Rahman",coverURL:"https://cdn.intechopen.com/books/images_new/10627.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83969-368-7",printIsbn:"978-1-83969-367-0",pdfIsbn:"978-1-83969-369-4",isAvailableForWebshopOrdering:!0,editors:[{id:"146384",title:"Dr.",name:"Marcos Sales Guerra",middleName:null,surname:"Tsuzuki",slug:"marcos-sales-guerra-tsuzuki",fullName:"Marcos Sales Guerra Tsuzuki"}],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. Methodology",level:"1"},{id:"sec_2_2",title:"2.1 Levelised cost of energy",level:"2"},{id:"sec_3_2",title:"2.2 Machine learning",level:"2"},{id:"sec_3_3",title:"2.2.1 Machine learning for energy systems and CO2 emission estimation",level:"3"},{id:"sec_5_2",title:"2.3 Our methodology",level:"2"},{id:"sec_7",title:"3. Results",level:"1"},{id:"sec_8",title:"4. Conclusions",level:"1"},{id:"sec_12",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Rolnick, D. and Donti, P. and H. Kaack, L.H., and Kochanski, K. and Lacoste, A. and Sankaran, K. and Ross, A. and Milojevic-Dupont, N., and Jaques, N., and Waldman-Brown, A., and Luccioni, A., and Maharaj, T., and Sherwin, E., and Mukkavilli, S.K., and Konrad P. K, and Carla Gomes, K., and Ng, A., and Hassabis, D., and C. Platt, J.C., and Creutzig, F., and Chayes, J. and Bengio, Y., Tackling Climate Change with Machine Learning, 2019'},{id:"B2",body:'IEA, Projected Cost of Generating Electricity 2020, IEA, 2020'},{id:"B3",body:'Saravanan and Sujatha, P., “A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification,” 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2018, pp. 945–949, doi: 10.1109/ICCONS.2018.8663155'},{id:"B4",body:'Wang, L. Support vector machines: theory and applications, Springer-Verlag, Berlin, 2005'},{id:"B5",body:'Awad M., Khanna R. (2015) Support Vector Regression. In: Efficient Learning Machines. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4302-5990-9_4'},{id:"B6",body:'Zendehboudi, A., Baseer, M.A., Saidur, R. Application of support vector machine models for forecasting solar and wind energy resources: A review, Journal of Cleaner Production, Volume 199, 2018, Pages 272-285, DOI: 10.1016/j.jclepro.2018.07.164'},{id:"B7",body:'Salimi, M., Mosavi, A., Faizollahzadeh, A.S., Amidpour, M., Rabczuk, T., and Shamshirband, S., State of the Art of Machine Learning Models in Energy Systems, a Systematic Review, Energies 2019, 12, 1301; doi:10.3390/en12071301'},{id:"B8",body:'Leerbeck, K., Bacher, P., Grønborg Junker, R., Goranović, G., Corradi, O., Ebrahimy, R., Tveit, A., Madsen, H., Short-term forecasting of CO2 emission intensity in power grids by machine learning, Applied Energy, Volume 277, 2020, DOI:10.1016/j.apenergy.2020.115527'},{id:"B9",body:'Magazzino, C., Mele, M., Schneider, N. A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions, Renewable Energy, Volume 167, 2021, Pages 99–115 DOI: 10.1016/j.renene.2020.11.050'},{id:"B10",body:'Cogoljević, D., Alizamir, M., Piljan, I., Piljan, T., Prljić, K., Zimonjić, S. A machine learning approach for predicting the relationship between energy resources and economic development, Physica A: Statistical Mechanics and its Applications, Volume 495, 2018, Pages 211-214, DOI: 10.1016/j.physa.2017.12.082'},{id:"B11",body:'Wu, Y., Sharifi, A., Yang, P., Borjigin, H., Murakami, D., Yamagata, Y. Mapping building carbon emissions within local climate zones in Shanghai, Energy Procedia, Volume 152, 2018, Pages 815-822, DOI: 10.1016/j.egypro.2018.09.195'},{id:"B12",body:'Mele, M., Magazzino, C. A Machine Learning analysis of the relationship among iron and steel industries, air pollution, and economic growth in China, Journal of Cleaner Production, Volume 277, 2020, 123293, DOI: 10.1016/j.jclepro.2020.123293'},{id:"B13",body:'Li, M.; Wang, W.; De, G.; Ji, X.; Tan, Z. Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm. Energies 2018, 11, 2475. https://doi.org/10.3390/en11092475'},{id:"B14",body:'Huang, Y., Shen, L., Liu, H. Grey relational analysis, principal component analysis and forecasting of carbon emissions based on long short-term memory in China, Journal of Cleaner Production, Volume 209, 2019, Pages 415-423, DOI: 10.1016/j.jclepro.2018.10.128'},{id:"B15",body:'Csillik, O. and Asner, G.P. 2020 Environ. Res. Lett. 15 014006'},{id:"B16",body:'Csillik, O., Kumar, P., Mascaro, J. et al. Monitoring tropical forest carbon stocks and emissions using Planet satellite data. Sci Rep 9, 17831 (2019). https://doi.org/10.1038/s41598-019-54386-6'},{id:"B17",body:'Niu, D. Wang, K., Wu, J., Sun, L., Yi Liang, Y., Xu, X., Yang, X. Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network, Journal of Cleaner Production, Volume 243, 2020, 118558, DOI: 10.1016/j.jclepro.2019.118558'},{id:"B18",body:'Korovkinas, K., Danènas, P., Garsva, G., Support vector machine parameter tuning based on particle swarm optimization metaheuristic, Nonlinear Analysis: Modelling and Control, Vol. 25, No. 2, 266–281, DOI=10.15388/namc.2020.25.16517'},{id:"B19",body:'Botchkarev, A. A New Typology Design of Performance Metrics to Measure Errors in Machine Learning Regression Algorithms, Interdisciplinary Journal of Information, Knowledge, and Management, Volume 14, pp. 045–076, 2019, DOI=10.28945/4184'}],footnotes:[{id:"fn1",explanation:"Here we just remind a recent review of the state of art in machine learning techniques [3]."},{id:"fn2",explanation:"For a good introduction to this topic see [4]."},{id:"fn3",explanation:"See, for example [6]."},{id:"fn4",explanation:"Indeed, manual tuning is often considered as one of the most significant choice [18]."},{id:"fn5",explanation:"See [19] for a complete discussion about the used metrics."},{id:"fn6",explanation:"Namely, ICER max/min = ICER +/− ICER*MAPE."}],contributors:[{corresp:"yes",contributorFullName:"Marco Rao",address:"marco.rao@enea.it",affiliation:'
ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Rome, Italy
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The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.
",metaTitle:"Our story",metaDescription:"The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.",metaKeywords:null,canonicalURL:"/page/our-story",contentRaw:'[{"type":"htmlEditorComponent","content":"
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
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In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\\n\\n
The IntechOpen timeline
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2004
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Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\\n\\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
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2005
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IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
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2006
\\n\\n
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IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\\n
\\n\\n
2008
\\n\\n
\\n\\t
Downloads milestone: 200,000 downloads reached
\\n
\\n\\n
2009
\\n\\n
\\n\\t
Publishing milestone: the first 100 Open Access STM books are published
\\n
\\n\\n
2010
\\n\\n
\\n\\t
Downloads milestone: one million downloads reached
\\n\\t
IntechOpen expands its book publishing into a new field: medicine.
\\n
\\n\\n
2011
\\n\\n
\\n\\t
Publishing milestone: More than five million downloads reached
\\n\\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\\n\\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\\n\\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\\n
\\n\\n
2012
\\n\\n
\\n\\t
Publishing milestone: 10 million downloads reached
\\n\\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\\n
\\n\\n
2013
\\n\\n
\\n\\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\\n
\\n\\n
2014
\\n\\n
\\n\\t
IntechOpen turns 10, with more than 30 million downloads to date.
\\n\\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\\n
\\n\\n
2015
\\n\\n
\\n\\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\\n\\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\\n\\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\\n\\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\\n
\\n\\n
2016
\\n\\n
\\n\\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\\n
\\n\\n
2017
\\n\\n
\\n\\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\\n\\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\n\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\n\n
The IntechOpen timeline
\n\n
2004
\n\n
\n\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\n\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\n
\n\n
2005
\n\n
\n\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\n
\n\n
2006
\n\n
\n\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\n
\n\n
2008
\n\n
\n\t
Downloads milestone: 200,000 downloads reached
\n
\n\n
2009
\n\n
\n\t
Publishing milestone: the first 100 Open Access STM books are published
\n
\n\n
2010
\n\n
\n\t
Downloads milestone: one million downloads reached
\n\t
IntechOpen expands its book publishing into a new field: medicine.
\n
\n\n
2011
\n\n
\n\t
Publishing milestone: More than five million downloads reached
\n\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\n\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\n\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\n
\n\n
2012
\n\n
\n\t
Publishing milestone: 10 million downloads reached
\n\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\n
\n\n
2013
\n\n
\n\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\n
\n\n
2014
\n\n
\n\t
IntechOpen turns 10, with more than 30 million downloads to date.
\n\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\n
\n\n
2015
\n\n
\n\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\n\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\n\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\n\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\n
\n\n
2016
\n\n
\n\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\n
\n\n
2017
\n\n
\n\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\n\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
\n
\n"}]},successStories:{items:[]},authorsAndEditors:{filterParams:{},profiles:[{id:"396",title:"Dr.",name:"Vedran",middleName:null,surname:"Kordic",slug:"vedran-kordic",fullName:"Vedran Kordic",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/396/images/7281_n.png",biography:"After obtaining his Master's degree in Mechanical Engineering he continued his education at the Vienna University of Technology where he obtained his PhD degree in 2004. He worked as a researcher at the Automation and Control Institute, Faculty of Electrical Engineering, Vienna University of Technology until 2008. 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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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He received his Ph.D. in Molecular Biology with his thesis “Genetic variability of the tick-borne encephalitis virus in natural foci of Novosibirsk city and its suburbs.” His primary field is molecular virology with research emphasis on vector-borne viruses, especially tick-borne encephalitis virus, Kemerovo virus and Omsk hemorrhagic fever virus, rabies virus, molecular genetics, biology, and epidemiology of virus pathogens.",institutionString:"Russian Academy of Sciences",institution:{name:"Russian Academy of Sciences",country:{name:"Russia"}}},{id:"310962",title:"Dr.",name:"Amlan",middleName:"Kumar",surname:"Patra",slug:"amlan-patra",fullName:"Amlan Patra",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/310962/images/system/310962.jpg",biography:"Amlan K. Patra, FRSB, obtained a Ph.D. in Animal Nutrition from Indian Veterinary Research Institute, India, in 2002. He is currently an associate professor at West Bengal University of Animal and Fishery Sciences. He has more than twenty years of research and teaching experience. He held previous positions at the American Institute for Goat Research, The Ohio State University, Columbus, USA, and Free University of Berlin, Germany. His research focuses on animal nutrition, particularly ruminants and poultry nutrition, gastrointestinal electrophysiology, meta-analysis and modeling in nutrition, and livestock–environment interaction. He has authored around 175 articles in journals, book chapters, and proceedings. Dr. Patra serves on the editorial boards of several reputed journals.",institutionString:null,institution:{name:"West Bengal University of Animal and Fishery Sciences",country:{name:"India"}}},{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.png",biography:"László Babinszky is Professor Emeritus, Department of Animal Nutrition Physiology, University of Debrecen, Hungary. He has also worked in the Department of Animal Nutrition, University of Wageningen, Netherlands; the Institute for Livestock Feeding and Nutrition (IVVO), Lelystad, Netherlands; the Agricultural University of Vienna (BOKU); the Institute for Animal Breeding and Nutrition, Austria; and the Oscar Kellner Research Institute for Animal Nutrition, Rostock, Germany. In 1992, Dr. Babinszky obtained a Ph.D. in Animal Nutrition from the University of Wageningen. His main research areas are swine and poultry nutrition. He has authored more than 300 publications (papers, book chapters) and edited four books and fourteen international conference proceedings.",institutionString:"University of Debrecen",institution:{name:"University of Debrecen",country:{name:"Hungary"}}},{id:"201830",title:"Dr.",name:"Fernando",middleName:"Sanchez",surname:"Davila",slug:"fernando-davila",fullName:"Fernando Davila",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/201830/images/5017_n.jpg",biography:"I am a professor at UANL since 1988. My research lines are the development of reproductive techniques in small ruminants. We also conducted research on sexual and social behavior in males.\nI am Mexican and study my professional career as an engineer in agriculture and animal science at UANL. Then take a masters degree in science in Germany (Animal breeding). Take a doctorate in animal science at the UANL.",institutionString:null,institution:{name:"Universidad Autónoma de Nuevo León",country:{name:"Mexico"}}},{id:"309250",title:"Dr.",name:"Miguel",middleName:null,surname:"Quaresma",slug:"miguel-quaresma",fullName:"Miguel Quaresma",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/309250/images/9059_n.jpg",biography:"Miguel Nuno Pinheiro Quaresma was born on May 26, 1974 in Dili, Timor Island. He is married with two children: a boy and a girl, and he is a resident in Vila Real, Portugal. He graduated in Veterinary Medicine in August 1998 and obtained his Ph.D. degree in Veterinary Sciences -Clinical Area in February 2015, both from the University of Trás-os-Montes e Alto Douro. He is currently enrolled in the Alternative Residency of the European College of Animal Reproduction. He works as a Senior Clinician at the Veterinary Teaching Hospital of UTAD (HVUTAD) with a role in clinical activity in the area of livestock and equine species as well as to support teaching and research in related areas. He teaches as an Invited Professor in Reproduction Medicine I and II of the Master\\'s in Veterinary Medicine degree at UTAD. Currently, he holds the position of Chairman of the Portuguese Buiatrics Association. He is a member of the Consultive Group on Production Animals of the OMV. He has 19 publications in indexed international journals (ISIS), as well as over 60 publications and oral presentations in both Portuguese and international journals and congresses.",institutionString:"University of Trás-os-Montes and Alto Douro",institution:{name:"University of Trás-os-Montes and Alto Douro",country:{name:"Portugal"}}},{id:"38652",title:"Prof.",name:"Rita",middleName:null,surname:"Payan-Carreira",slug:"rita-payan-carreira",fullName:"Rita Payan-Carreira",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRiFPQA0/Profile_Picture_1614601496313",biography:"Rita Payan Carreira earned her Veterinary Degree from the Faculty of Veterinary Medicine in Lisbon, Portugal, in 1985. She obtained her Ph.D. in Veterinary Sciences from the University of Trás-os-Montes e Alto Douro, Portugal. After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. She is also a frequent referee for various journals.",institutionString:null,institution:{name:"University of Évora",country:{name:"Portugal"}}},{id:"283019",title:"Dr.",name:"Oudessa",middleName:null,surname:"Kerro Dego",slug:"oudessa-kerro-dego",fullName:"Oudessa Kerro Dego",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/283019/images/system/283019.png",biography:"Dr. Kerro Dego is a veterinary microbiologist with training in veterinary medicine, microbiology, and anatomic pathology. Dr. Kerro Dego is an assistant professor of dairy health in the department of animal science, the University of Tennessee, Institute of Agriculture, Knoxville, Tennessee. He received his D.V.M. (1997), M.S. (2002), and Ph.D. (2008) degrees in Veterinary Medicine, Animal Pathology and Veterinary Microbiology from College of Veterinary Medicine, Addis Ababa University, Ethiopia; College of Veterinary Medicine, Utrecht University, the Netherlands and Western College of Veterinary Medicine, University of Saskatchewan, Canada respectively. He did his Postdoctoral training in microbial pathogenesis (2009 - 2015) in the Department of Animal Science, the University of Tennessee, Institute of Agriculture, Knoxville, Tennessee. Dr. Kerro Dego’s research focuses on the prevention and control of infectious diseases of farm animals, particularly mastitis, improving dairy food safety, and mitigation of antimicrobial resistance. Dr. Kerro Dego has extensive experience in studying the pathogenesis of bacterial infections, identification of virulence factors, and vaccine development and efficacy testing against major bacterial mastitis pathogens. Dr. Kerro Dego conducted numerous controlled experimental and field vaccine efficacy studies, vaccination, and evaluation of immunological responses in several species of animals, including rodents (mice) and large animals (bovine and ovine).",institutionString:"University of Tennessee at Knoxville",institution:{name:"University of Tennessee at Knoxville",country:{name:"United States of America"}}},{id:"251314",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Gardón",slug:"juan-carlos-gardon",fullName:"Juan Carlos Gardón",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/251314/images/system/251314.jpeg",biography:"Juan Carlos Gardón Poggi received University degree from the Faculty of Agrarian Science in Argentina, in 1983. Also he received Masters Degree and PhD from Córdoba University, Spain. He is currently a Professor at the Catholic University of Valencia San Vicente Mártir, at the Department of Medicine and Animal Surgery. He teaches diverse courses in the field of Animal Reproduction and he is the Director of the Veterinary Farm. He also participates in academic postgraduate activities at the Veterinary Faculty of Murcia University, Spain. His research areas include animal physiology, physiology and biotechnology of reproduction either in males or females, the study of gametes under in vitro conditions and the use of ultrasound as a complement to physiological studies and development of applied biotechnologies. Routinely, he supervises students preparing their doctoral, master thesis or final degree projects.",institutionString:"Catholic University of Valencia San Vicente Mártir, Spain",institution:null},{id:"125292",title:"Dr.",name:"Katy",middleName:null,surname:"Satué Ambrojo",slug:"katy-satue-ambrojo",fullName:"Katy Satué Ambrojo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/125292/images/system/125292.jpeg",biography:"Katy Satué Ambrojo received her Veterinary Medicine degree, Master degree in Equine Technology and doctorate in Veterinary Medicine from the Faculty of Veterinary, CEU-Cardenal Herrera University in Valencia, Spain. She is a Full Professor at the Department of Medicine and Animal Surgery at the same University. She developed her research activity in the field of Endocrinology, Hematology, Biochemistry and Immunology of horses. She is a scientific reviewer of several international journals : American Journal of Obstetrics and Gynecology, Comparative Clinical Pathology, Veterinary Clinical Pathology, Journal of Equine Veterinary Science, Reproduction in Domestic Animals, Research Veterinary Science, Brazilian Journal of Medical and Biological Research, Livestock Production Science and Theriogenology. Since 2014, she has been the Head of the Clinical Analysis Laboratory of the Hospital Clínico Veterinario from the Faculty of Veterinary, CEU-Cardenal Herrera University.",institutionString:"CEU-Cardenal Herrera University",institution:{name:"CEU Cardinal Herrera University",country:{name:"Spain"}}},{id:"309529",title:"Dr.",name:"Albert",middleName:null,surname:"Rizvanov",slug:"albert-rizvanov",fullName:"Albert Rizvanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/309529/images/9189_n.jpg",biography:'Albert A. Rizvanov is a Professor and Director of the Center for Precision and Regenerative Medicine at the Institute of Fundamental Medicine and Biology, Kazan Federal University (KFU), Russia. He is the Head of the Center of Excellence “Regenerative Medicine” and Vice-Director of Strategic Academic Unit \\"Translational 7P Medicine\\". Albert completed his Ph.D. at the University of Nevada, Reno, USA and Dr.Sci. at KFU. He is a corresponding member of the Tatarstan Academy of Sciences, Russian Federation. Albert is an author of more than 300 peer-reviewed journal articles and 22 patents. He has supervised 11 Ph.D. and 2 Dr.Sci. dissertations. Albert is the Head of the Dissertation Committee on Biochemistry, Microbiology, and Genetics at KFU.\nORCID https://orcid.org/0000-0002-9427-5739\nWebsite https://kpfu.ru/Albert.Rizvanov?p_lang=2',institutionString:"Kazan Federal University",institution:{name:"Kazan Federal University",country:{name:"Russia"}}},{id:"210551",title:"Dr.",name:"Arbab",middleName:null,surname:"Sikandar",slug:"arbab-sikandar",fullName:"Arbab Sikandar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210551/images/system/210551.jpg",biography:"Dr. Arbab Sikandar, PhD, M. Phil, DVM was born on April 05, 1981. He is currently working at the College of Veterinary & Animal Sciences as an Assistant Professor. He previously worked as a lecturer at the same University. \nHe is a Member/Secretory of Ethics committee (No. CVAS-9377 dated 18-04-18), Member of the QEC committee CVAS, Jhang (Regr/Gen/69/873, dated 26-10-2017), Member, Board of studies of Department of Basic Sciences (No. CVAS. 2851 Dated. 12-04-13, and No. CVAS, 9024 dated 20/11/17), Member of Academic Committee, CVAS, Jhang (No. CVAS/2004, Dated, 25-08-12), Member of the technical committee (No. CVAS/ 4085, dated 20,03, 2010 till 2016).\n\nDr. Arbab Sikandar contributed in five days hands-on-training on Histopathology at the Department of Pathology, UVAS from 12-16 June 2017. He received a Certificate of appreciation for contributions for Popularization of Science and Technology in the Society on 17-11-15. He was the resource person in the lecture series- ‘scientific writing’ at the Department of Anatomy and Histology, UVAS, Lahore on 29th October 2015. He won a full fellowship as a principal candidate for the year 2015 in the field of Agriculture, EICA, Egypt with ref. to the Notification No. 12(11) ACS/Egypt/2014 from 10 July 2015 to 25th September 2015.; he received a grant of Rs. 55000/- as research incentives from Director, Advanced Studies and Research, UVAS, Lahore upon publications of research papers in IF Journals (DR/215, dated 19-5-2014.. He obtained his PhD by winning a HEC Pakistan indigenous Scholarship, ‘Ph.D. fellowship for 5000 scholars – Phase II’ (2av1-147), 17-6/HEC/HRD/IS-II/12, November 15, 2012. \n\nDr. Sikandar is a member of numerous societies: Registered Veterinary Medical Practitioner (life member) and Registered Veterinary Medical Faculty of Pakistan Veterinary Medical Council. The Registration code of PVMC is RVMP/4298 and RVMF/ 0102.; Life member of the University of Veterinary and Animal Sciences, Lahore, Alumni Association with S# 664, dated: 6-4-12. ; Member 'Vets Care Organization Pakistan” with Reference No. VCO-605-149, dated 05-04-06. :Member 'Vet Crescent” (Society of Animal Health and Production), UVAS, Lahore.",institutionString:"University of Veterinary & Animal Science",institution:{name:"University of Veterinary and Animal Sciences",country:{name:"Pakistan"}}},{id:"311663",title:"Dr.",name:"Prasanna",middleName:null,surname:"Pal",slug:"prasanna-pal",fullName:"Prasanna Pal",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311663/images/13261_n.jpg",biography:null,institutionString:null,institution:{name:"National Dairy Research Institute",country:{name:"India"}}},{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",biography:"Catrin Rutland is an Associate Professor of Anatomy and Developmental Genetics at the University of Nottingham, UK. She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. Dr. Rutland has also written popular science books for the public. https://orcid.org/0000-0002-2009-4898. www.nottingham.ac.uk/vet/people/catrin.rutland",institutionString:null,institution:{name:"University of Nottingham",country:{name:"United Kingdom"}}},{id:"283315",title:"Prof.",name:"Samir",middleName:null,surname:"El-Gendy",slug:"samir-el-gendy",fullName:"Samir El-Gendy",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRduYQAS/Profile_Picture_1606215849748",biography:"Samir El-Gendy is a Professor of anatomy and embryology at the faculty of veterinary medicine, Alexandria University, Egypt. Samir obtained his PhD in veterinary science in 2007 from the faculty of veterinary medicine, Alexandria University and has been a professor since 2017. Samir is an author on 24 articles at Scopus and 12 articles within local journals and 2 books/book chapters. His research focuses on applied anatomy, imaging techniques and computed tomography. Samir worked as a member of different local projects on E-learning and he is a board member of the African Association of Veterinary Anatomists and of anatomy societies and as an associated author at local and international journals. Orcid: https://orcid.org/0000-0002-6180-389X",institutionString:null,institution:{name:"Alexandria University",country:{name:"Egypt"}}},{id:"246149",title:"Dr.",name:"Valentina",middleName:null,surname:"Kubale",slug:"valentina-kubale",fullName:"Valentina Kubale",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246149/images/system/246149.jpg",biography:"Valentina Kubale is Associate Professor of Veterinary Medicine at the Veterinary Faculty, University of Ljubljana, Slovenia. Since graduating from the Veterinary faculty she obtained her PhD in 2007, performed collaboration with the Department of Pharmacology, University of Copenhagen, Denmark. She continued as a post-doctoral fellow at the University of Copenhagen with a Lundbeck foundation fellowship. She is the editor of three books and author/coauthor of 23 articles in peer-reviewed scientific journals, 16 book chapters, and 68 communications at scientific congresses. Since 2008 she has been the Editor Assistant for the Slovenian Veterinary Research journal. She is a member of Slovenian Biochemical Society, The Endocrine Society, European Association of Veterinary Anatomists and Society for Laboratory Animals, where she is board member.",institutionString:"University of Ljubljana",institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"258334",title:"Dr.",name:"Carlos Eduardo",middleName:null,surname:"Fonseca-Alves",slug:"carlos-eduardo-fonseca-alves",fullName:"Carlos Eduardo Fonseca-Alves",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/258334/images/system/258334.jpg",biography:"Dr. Fonseca-Alves earned his DVM from Federal University of Goias – UFG in 2008. He completed an internship in small animal internal medicine at UPIS university in 2011, earned his MSc in 2013 and PhD in 2015 both in Veterinary Medicine at Sao Paulo State University – UNESP. Dr. Fonseca-Alves currently serves as an Assistant Professor at Paulista University – UNIP teaching small animal internal medicine.",institutionString:null,institution:{name:"Universidade Paulista",country:{name:"Brazil"}}},{id:"245306",title:"Dr.",name:"María Luz",middleName:null,surname:"Garcia Pardo",slug:"maria-luz-garcia-pardo",fullName:"María Luz Garcia Pardo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/245306/images/system/245306.png",biography:"María de la Luz García Pardo is an agricultural engineer from Universitat Politècnica de València, Spain. She has a Ph.D. in Animal Genetics. Currently, she is a lecturer at the Agrofood Technology Department of Miguel Hernández University, Spain. Her research is focused on genetics and reproduction in rabbits. The major goal of her research is the genetics of litter size through novel methods such as selection by the environmental sensibility of litter size, with forays into the field of animal welfare by analysing the impact on the susceptibility to diseases and stress of the does. Details of her publications can be found at https://orcid.org/0000-0001-9504-8290.",institutionString:null,institution:{name:"Miguel Hernandez University",country:{name:"Spain"}}},{id:"41319",title:"Prof.",name:"Lung-Kwang",middleName:null,surname:"Pan",slug:"lung-kwang-pan",fullName:"Lung-Kwang Pan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41319/images/84_n.jpg",biography:null,institutionString:null,institution:null},{id:"201721",title:"Dr.",name:"Beatrice",middleName:null,surname:"Funiciello",slug:"beatrice-funiciello",fullName:"Beatrice Funiciello",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/201721/images/11089_n.jpg",biography:"Graduated from the University of Milan in 2011, my post-graduate education included CertAVP modules mainly on equines (dermatology and internal medicine) and a few on small animal (dermatology and anaesthesia) at the University of Liverpool. After a general CertAVP (2015) I gained the designated Certificate in Veterinary Dermatology (2017) after taking the synoptic examination and then applied for the RCVS ADvanced Practitioner status. After that, I completed the Postgraduate Diploma in Veterinary Professional Studies at the University of Liverpool (2018). My main area of work is cross-species veterinary dermatology.",institutionString:null,institution:null},{id:"291226",title:"Dr.",name:"Monica",middleName:null,surname:"Cassel",slug:"monica-cassel",fullName:"Monica Cassel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/291226/images/8232_n.jpg",biography:'Degree in Biological Sciences at the Federal University of Mato Grosso with scholarship for Scientific Initiation by FAPEMAT (2008/1) and CNPq (2008/2-2009/2): Project \\"Histological evidence of reproductive activity in lizards of the Manso region, Chapada dos Guimarães, Mato Grosso, Brazil\\". Master\\\'s degree in Ecology and Biodiversity Conservation at Federal University of Mato Grosso with a scholarship by CAPES/REUNI program: Project \\"Reproductive biology of Melanorivulus punctatus\\". PhD\\\'s degree in Science (Cell and Tissue Biology Area) \n at University of Sao Paulo with scholarship granted by FAPESP; Project \\"Development of morphofunctional changes in ovary of Astyanax altiparanae Garutti & Britski, 2000 (Teleostei, Characidae)\\". She has experience in Reproduction of vertebrates and Morphology, with emphasis in Cellular Biology and Histology. She is currently a teacher in the medium / technical level courses at IFMT-Alta Floresta, as well as in the Bachelor\\\'s degree in Animal Science and in the Bachelor\\\'s degree in Business.',institutionString:null,institution:null},{id:"442807",title:"Dr.",name:"Busani",middleName:null,surname:"Moyo",slug:"busani-moyo",fullName:"Busani Moyo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Gwanda State University",country:{name:"Zimbabwe"}}},{id:"423023",title:"Dr.",name:"Yosra",middleName:null,surname:"Soltan",slug:"yosra-soltan",fullName:"Yosra Soltan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Alexandria University",country:{name:"Egypt"}}},{id:"349788",title:"Dr.",name:"Florencia Nery",middleName:null,surname:"Sompie",slug:"florencia-nery-sompie",fullName:"Florencia Nery Sompie",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sam Ratulangi University",country:{name:"Indonesia"}}},{id:"345713",title:"Dr.",name:"Csaba",middleName:null,surname:"Szabó",slug:"csaba-szabo",fullName:"Csaba Szabó",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Debrecen",country:{name:"Hungary"}}},{id:"345719",title:"Mrs.",name:"Márta",middleName:null,surname:"Horváth",slug:"marta-horvath",fullName:"Márta Horváth",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Debrecen",country:{name:"Hungary"}}},{id:"420151",title:"Prof.",name:"Novirman",middleName:null,surname:"Jamarun",slug:"novirman-jamarun",fullName:"Novirman Jamarun",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Andalas University",country:{name:"Indonesia"}}},{id:"420149",title:"Dr.",name:"Rusmana",middleName:"Wijaya Setia",surname:"Wijaya Setia Ningrat",slug:"rusmana-wijaya-setia-ningrat",fullName:"Rusmana Wijaya Setia Ningrat",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Andalas University",country:{name:"Indonesia"}}},{id:"339759",title:"Mr.",name:"Abu",middleName:null,surname:"Macavoray",slug:"abu-macavoray",fullName:"Abu Macavoray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Njala University",country:{name:"Sierra Leone"}}},{id:"339758",title:"Prof.",name:"Benjamin",middleName:null,surname:"Emikpe",slug:"benjamin-emikpe",fullName:"Benjamin Emikpe",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Ibadan",country:{name:"Nigeria"}}},{id:"339760",title:"Mr.",name:"Moinina Nelphson",middleName:null,surname:"Kallon",slug:"moinina-nelphson-kallon",fullName:"Moinina Nelphson Kallon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Njala University",country:{name:"Sierra Leone"}}}]}},subseries:{item:{id:"17",type:"subseries",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11413,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). 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. 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