These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
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This collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
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To celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
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Initially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
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These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\n
This collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\n
To celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"9894",leadTitle:null,fullTitle:"Advanced Ceramic Materials",title:"Advanced Ceramic Materials",subtitle:null,reviewType:"peer-reviewed",abstract:"This book examines exciting advancements in the field of ceramics, including nanotechnology, clean energy, and tribology as well as fundamental concepts like defects and structure. It is a comprehensive discussion on how today’s ceramics are processed and used in many of today’s critical technologies. It discusses current techniques for synthesizing durable and cost-effective ceramic components with biocompatibility, complexity, and high precision. This book is a comprehensive reference for researchers, engineers, dental clinicians, biologists, academics, and students interested in ceramics.",isbn:"978-1-83881-212-6",printIsbn:"978-1-83881-204-1",pdfIsbn:"978-1-83881-213-3",doi:"10.5772/intechopen.87703",price:119,priceEur:129,priceUsd:155,slug:"advanced-ceramic-materials",numberOfPages:296,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"9adbe58d10d5ca2b61e9ff2b6b138f40",bookSignature:"Mohsen Mhadhbi",publishedDate:"May 5th 2021",coverURL:"https://cdn.intechopen.com/books/images_new/9894.jpg",numberOfDownloads:6200,numberOfWosCitations:0,numberOfCrossrefCitations:11,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:14,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:25,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 29th 2020",dateEndSecondStepPublish:"August 5th 2020",dateEndThirdStepPublish:"October 4th 2020",dateEndFourthStepPublish:"December 23rd 2020",dateEndFifthStepPublish:"February 21st 2021",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"228366",title:"Dr.",name:"Mohsen",middleName:null,surname:"Mhadhbi",slug:"mohsen-mhadhbi",fullName:"Mohsen Mhadhbi",profilePictureURL:"https://mts.intechopen.com/storage/users/228366/images/system/228366.png",biography:"Dr. Mohsen Mhadhbi obtained his Ph.D. from the University of Sfax, Tunisia. He is currently an Assistant Professor of Chemistry at the National Institute of Research and Physicochemical Analysis, Tunisia. His research interests include nanomaterials, energy, powder technology, ceramics, composites, modeling, and simulations. Dr. Mhadhbi has published works in national and international journals and books. He is a teacher in Materials Science and has supervised several researchers in this field. He is also a member of various scientific associations and an editorial board member and reviewer of many reputable scientific journals.",institutionString:"National Institute of Research and Physicochemical Analysis",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Tunis El Manar University",institutionURL:null,country:{name:"Tunisia"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"155",title:"Ceramics",slug:"ceramics"}],chapters:[{id:"75967",title:"Recent Advances in Ceramic Materials for Dentistry",doi:"10.5772/intechopen.96890",slug:"recent-advances-in-ceramic-materials-for-dentistry",totalDownloads:774,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Dental ceramics constitute a heterogeneous group of materials with desirable optical and mechanical proprieties combined with chemical stability. They are inorganic non-metallic materials used in several applications. These materials are biocompatible to tissue, highly esthetic, with satisfying resistance to tensile and shear stress. Over the past years, several developments in new ceramic materials in dental restoration were achieved, including processing techniques and high mechanical properties. Thus, concepts on the structure and strengthening mechanisms of dental ceramic materials are also discussed. The dental practitioner requires best knowledge concerning indications, limitations, and correct use of started materials. The purpose of this book chapter is to overview advances in new ceramic materials and processes, which are used in dentistry. The properties of these materials are also discussed.",signatures:"Mohsen Mhadhbi, Faïçal Khlissa and Chaker Bouzidi",downloadPdfUrl:"/chapter/pdf-download/75967",previewPdfUrl:"/chapter/pdf-preview/75967",authors:[{id:"228366",title:"Dr.",name:"Mohsen",surname:"Mhadhbi",slug:"mohsen-mhadhbi",fullName:"Mohsen Mhadhbi"},{id:"324375",title:"Dr.",name:"Faïçal",surname:"Khlissa",slug:"faical-khlissa",fullName:"Faïçal Khlissa"},{id:"324535",title:"Dr.",name:"Chaker",surname:"Bouzidi",slug:"chaker-bouzidi",fullName:"Chaker Bouzidi"}],corrections:null},{id:"74215",title:"Ferroelectric Glass-Ceramic Systems for Energy Storage Applications",doi:"10.5772/intechopen.93855",slug:"ferroelectric-glass-ceramic-systems-for-energy-storage-applications",totalDownloads:521,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"An overview of ferroelectric glass ceramics, some literature review and some of the important previous studies were focused in this chapter. Nanocrystalline glass–ceramics containing ferroelectric perovskite-structured phases have been included. All modified glasses having ferroelectric ceramics which prepared by different methods are discussed, that producing nanocrystalline glass–ceramics. Then particular tested to their use as dielectric energy storage materials. These materials exhibit promising dielectric properties, indicating good potential for high energy density capacitors as a result of their nanocrystalline microstructures. The results of the analysis are summarised in this chapter to provide an overview of the energy storage characteristics of the different materials produced during the study.",signatures:"Abdulkarim Ziedan Khalf",downloadPdfUrl:"/chapter/pdf-download/74215",previewPdfUrl:"/chapter/pdf-preview/74215",authors:[{id:"323331",title:"Dr.",name:"Abdulkarim",surname:"Khalf",slug:"abdulkarim-khalf",fullName:"Abdulkarim Khalf"}],corrections:null},{id:"74064",title:"From the Laser Plume to the Laser Ceramics",doi:"10.5772/intechopen.94464",slug:"from-the-laser-plume-to-the-laser-ceramics",totalDownloads:591,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The main stages of preparation of ceramic active elements of solid-state lasers are considered. The physical principles of laser synthesis of nanopowders are described. The features and processes taking place during compaction and compacts sintering are specified. Also we report on the investigation of characteristics of highly transparent ceramics on the basis of nanopowders synthesized in laser plume. It is shown that this approach enables to increase the “orange peel” formation threshold in the ceramics with strongly disordered crystalline structure. It opens the road to relatively simple synthesis technology from oxide materials and application of this ceramics as the gain media with oscillation efficiency higher than 50% and also leads to simplification of the synthesis technology of magnetoactive ceramics and to production of highly transparent YAG samples without the use of sintering heterovalent additives.",signatures:"Vladimir Osipov, Vyacheslav Platonov, Vladislav Shitov and Vladimir Solomonov",downloadPdfUrl:"/chapter/pdf-download/74064",previewPdfUrl:"/chapter/pdf-preview/74064",authors:[{id:"97328",title:"Prof.",name:"Vladimir",surname:"Solomonov",slug:"vladimir-solomonov",fullName:"Vladimir Solomonov"},{id:"328400",title:"Prof.",name:"Vladimir",surname:"Osipov",slug:"vladimir-osipov",fullName:"Vladimir Osipov"},{id:"329374",title:"Dr.",name:"Vecheslav",surname:"Platonov",slug:"vecheslav-platonov",fullName:"Vecheslav Platonov"},{id:"329375",title:"MSc.",name:"Vladislav",surname:"Shitov",slug:"vladislav-shitov",fullName:"Vladislav Shitov"}],corrections:null},{id:"73127",title:"The Investigation on the Fabrication and Characterization of the Multicomponent Ceramics Based on PZT and the Relaxor PZN-PMnN Ferroelectric Materials",doi:"10.5772/intechopen.93534",slug:"the-investigation-on-the-fabrication-and-characterization-of-the-multicomponent-ceramics-based-on-pz",totalDownloads:485,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"This chapter presents the investigation of fabrication and the physical properties of the Pb(Zr1−xTix)O3-Pb(Zn1/3Nb2/3)O3-Pb(Mn1/3Nb2/3)O3 multicomponent ceramics. The multicomponent yPb(Zr1−xTix)O3-(0.925 − y)Pb(Zn1/3Nb2/3)O3-0.075Pb(Mn1/3Nb2/3)O3 (PZT-PZN-PMnN) ceramics were synthesized by conventional solid-state reaction method (MO) combined with the B-site oxide mixing technique (BO). Research results show that the electrical properties of PZT-PZN-PMnN ceramics are optimal at a PZT content of 0.8 mol and Zr/Ti ratio of 48/52. At these contents, the ceramics have the following optimal properties: electromechanical coupling factor, kp = 0.62 and kt = 0.51; piezoelectric constant (d31) of 130 pC/N; mechanical quality factor (Qm) of 1112; dielectric loss (tan δ) of 0.005; high remanent polarization (Pr) of 30.4 μC.cm−2; and low coercive field (EC) of 6.2 kV.cm−1. Investigation of the domain structure of the two ferroelectric phases (tetragonal and rhombohedral) in the ZnO-doped PZT-PZN-PMnN with compositions at near the morphotropic phase boundary is described as follows: the 90 and 180° domains exist in the tetragonal phase, while the 71, 109, and 90° domains are located in the rhombohedral phase, and the widths of these domains were about 100 nm. Besides, the ceramics exhibited excellent temperature stability, which makes them a promising material for high-intensity ultrasound applications.",signatures:"Le Dai Vuong, Vo Thanh Tung and Phan Dinh Gio",downloadPdfUrl:"/chapter/pdf-download/73127",previewPdfUrl:"/chapter/pdf-preview/73127",authors:[{id:"241214",title:"Dr.",name:"Le",surname:"Dai Vuong",slug:"le-dai-vuong",fullName:"Le Dai Vuong"},{id:"326085",title:"Prof.",name:"Vo",surname:"Thanh Tung",slug:"vo-thanh-tung",fullName:"Vo Thanh Tung"}],corrections:null},{id:"73186",title:"Self-Healing of Concrete through Ceramic Nanocontainers Loaded with Corrosion Inhibitors and Microorganisms",doi:"10.5772/intechopen.93514",slug:"self-healing-of-concrete-through-ceramic-nanocontainers-loaded-with-corrosion-inhibitors-and-microor",totalDownloads:400,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Self-healing was considered for repairing of destruction of reinforced concrete on bridges, houses, etc., that comes from corrosion of reinforcement metals as well as cracking in cement. The work was accomplished at producing and assessing through incorporate ceramic nanocontainers loaded with microorganisms. We produced various types of organic and inorganic nanocontainers that were incorporated into the cement that can act as carriers for the transport of bacteria. The microorganisms used in the work are Escherichia coli and Staphylococcus aureus. Precipitation of CaCO3 was observed by both bacteria. As microspheres do not affect the submersion of the mineral by the microorganism, additional studies were carried out to assess the interaction between transmission microsystems and bacterium. The mechanism of self-healing of building materials in this work was based on CaCO3 precipitation, through the ureolytic action of bacteria. When a crack appears in the cement, then the bacterium trapped in a nanocontainers is released and comes into contact with the water. In this way, the microorganism begins to metabolize and precipitate the mineral, in a way that eventually observes healing of the crack. CaCO3 microbial precipitation was based on the breakdown of urea (CO (NH2)2) into CO32− and NH3. Due to the high pK value of the NH3/NH4+ system (pKa = 9.2), the breakdown reaction led to an increase in pH, favoring the release of carbonated ions (CO32−), and in an environment rich in calcium ions (Ca2+), CaCO3 precipitation took place.",signatures:"George Kordas",downloadPdfUrl:"/chapter/pdf-download/73186",previewPdfUrl:"/chapter/pdf-preview/73186",authors:[{id:"306484",title:"Emeritus Prof.",name:"George",surname:"Kordas",slug:"george-kordas",fullName:"George Kordas"}],corrections:null},{id:"73538",title:"New Bismuth Sodium Titanate Based Ceramics and Their Applications",doi:"10.5772/intechopen.93921",slug:"new-bismuth-sodium-titanate-based-ceramics-and-their-applications",totalDownloads:536,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Ferroelectric materials are widely investigated due to their excellent properties and versatile applications. At present, the dominant materials are lead-containing materials, such as Pb (Zr,Ti)O3 solid solutions. However, the use of lead gives rise to environmental concerns, which is the driving force for the development of alternative lead-free ferroelectric materials. (Bi0.5Na0.5)TiO3-based ceramics are considered to be one of the most promising lead-free materials to replace lead-containing ferroelectric ceramics due to their excellent ferroelectric properties, relaxation characteristics, and high Curie point. After decades of efforts, great progress has been made in the phase structure characterization and properties improvement of BNT based ceramics. However, most of the studies on BNT system mainly focuses on its piezoelectric properties and application of piezoelectric sensors and strain actuators, little attention is paid to its ferroelectric properties and related applications. In this chapter, new BNT-based ceramics via composition modification and special focuses on the ferroelectric properties, phase transition behaviors under external fields and related applications, such as application in energy storage, pulsed power supply and pyroelectric detection were proposed.",signatures:"Hengchang Nie, Genshui Wang and Xianlin Dong",downloadPdfUrl:"/chapter/pdf-download/73538",previewPdfUrl:"/chapter/pdf-preview/73538",authors:[{id:"324929",title:"Dr.",name:"Hengchang",surname:"Nie",slug:"hengchang-nie",fullName:"Hengchang Nie"},{id:"332042",title:"Dr.",name:"Genshui",surname:"Wang",slug:"genshui-wang",fullName:"Genshui Wang"},{id:"332043",title:"Dr.",name:"Xianlin",surname:"Dong",slug:"xianlin-dong",fullName:"Xianlin Dong"}],corrections:null},{id:"74295",title:"Investigation of Structural, Magnetic and Electrical Properties of Chromium Substituted Nickel Ceramic Nanopowders",doi:"10.5772/intechopen.94941",slug:"investigation-of-structural-magnetic-and-electrical-properties-of-chromium-substituted-nickel-cerami",totalDownloads:309,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Nano-ceramic of NiCrxFe2-xO4 (0.1 ≤ x ≤ 1.0) ferrites were synthesized by citrate-gel auto combustion method. The structural parameter such as lattice parameter, X-ray density, bulk density and porosity variations with Cr doping were studied. The average crystallite size is in the range 8.5–10.5 nm. The surface morphology and elemental analysis was studied with SEM (EDAX) spectrum and the structural information analyzed with FTIR spectra. Magnetic properties were discussed with Cr3+ion concentration. Electrical parameters like dc resistivity and drift mobility were reported with function of temperature and dopent concentration from room temperature to well beyond Curie temperature and explained with hopping mechanism between Fe2+↔Fe3+ ions. The activation energies in ferri and para magnetic regions were investigated. Dielectric parameters like dielectric constant, dielectric loss and ac conductivity were investigated variation with frequency and composition.",signatures:"Rapolu Sridhar, D. Ravinder, J. Laxman Naik, K. Vijaya Kumar, N. Maramu and S. Katlakunta",downloadPdfUrl:"/chapter/pdf-download/74295",previewPdfUrl:"/chapter/pdf-preview/74295",authors:[{id:"289636",title:"Prof.",name:"Ravinder",surname:"Dachepalli",slug:"ravinder-dachepalli",fullName:"Ravinder Dachepalli"},{id:"346605",title:"Dr.",name:"Nyathani",surname:"Maramu",slug:"nyathani-maramu",fullName:"Nyathani Maramu"},{id:"346606",title:"Dr.",name:"Sadhana",surname:"Katlakunta",slug:"sadhana-katlakunta",fullName:"Sadhana Katlakunta"}],corrections:null},{id:"74406",title:"The Effect of Ceramic Wastes on Physical and Mechanical Properties of Eco-Friendly Flowable Sand Concrete",doi:"10.5772/intechopen.95041",slug:"the-effect-of-ceramic-wastes-on-physical-and-mechanical-properties-of-eco-friendly-flowable-sand-con",totalDownloads:359,totalCrossrefCites:4,totalDimensionsCites:5,hasAltmetrics:0,abstract:"This work aims to study the valorization and recycling of ceramic wastes (wall tiles) as a fine aggregate instead of sand in the manufacturing of flowable sand concrete (FSC). For this, the sand is substituted with the ceramic wastes at different dosages (0, 5, 10, 15, 20, and 25% by volume of the sand). The influence of the ceramic wastes addition on the physical (workability, density) and mechanical (compressive, flexural and elastic modulus) properties of FSC was studied. The results show that the use of ceramic waste as partial replacement of sand contributes to reduce the workability, bulk density and improves the mechanical strengths of FSC according to the use of 25% of wall tiles waste.",signatures:"Mohamed Guendouz, Djamila Boukhelkhal, Alexandra Bourdot, Oussama Babachikh and Amine Hamadouche",downloadPdfUrl:"/chapter/pdf-download/74406",previewPdfUrl:"/chapter/pdf-preview/74406",authors:[{id:"323550",title:"Dr.",name:"Mohamed",surname:"Guendouz",slug:"mohamed-guendouz",fullName:"Mohamed Guendouz"},{id:"326866",title:"Dr.",name:"Alexandra",surname:"Bourdot",slug:"alexandra-bourdot",fullName:"Alexandra Bourdot"},{id:"326867",title:"Dr.",name:"Djamila",surname:"Boukhelkhal",slug:"djamila-boukhelkhal",fullName:"Djamila Boukhelkhal"},{id:"338169",title:"Mr.",name:"Oussama",surname:"Babachikh",slug:"oussama-babachikh",fullName:"Oussama Babachikh"},{id:"338170",title:"Mr.",name:"Amine",surname:"Hamadouche",slug:"amine-hamadouche",fullName:"Amine Hamadouche"}],corrections:null},{id:"73232",title:"Ceramics Coated Metallic Materials: Methods, Properties and Applications",doi:"10.5772/intechopen.93814",slug:"ceramics-coated-metallic-materials-methods-properties-and-applications",totalDownloads:685,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Surface coating can allow the bulk materials to remain unchanged, while the surface functionality is engineered to afford a more wanted characteristic. Ceramic coatings are considered as ideal coatings on metal which can significantly improve the surface properties of metal materials including anti-fouling, self-cleaning, corrosion resistance, wear resistance, oil/water separation and biocompatibility. Furthermore, various techniques have been utilized to fabricate a range of different ceramic coatings with more desirable properties on metal materials, which make the materials widely used in service environment. This chapter focus will be on the types, fabrication methods, surface properties and applications of ceramics coated metal materials.",signatures:"Dongmian Zang and Xiaowei Xun",downloadPdfUrl:"/chapter/pdf-download/73232",previewPdfUrl:"/chapter/pdf-preview/73232",authors:[{id:"324466",title:"M.D.",name:"Xiaowei",surname:"Xun",slug:"xiaowei-xun",fullName:"Xiaowei Xun"},{id:"325574",title:"Dr.",name:"Dongmian",surname:"Zang",slug:"dongmian-zang",fullName:"Dongmian Zang"}],corrections:null},{id:"73977",title:"Nanostructured Multilayer Composite Coatings for Cutting Tools",doi:"10.5772/intechopen.94363",slug:"nanostructured-multilayer-composite-coatings-for-cutting-tools",totalDownloads:424,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The chapter deals with the specific features concerning the application of wear-resistant coatings to improve the performance properties of ceramic cutting tools. The paper discusses the theoretical background associated with the specific operation conditions and wear of ceramic cutting tools and influencing the choice of the compositions and structures of wear-resistant coatings. The studies were focused on the application of the Ti-(Ti,Al)N-(Zr,Nb,Ti,Al)N multilayer composite coating with a nanostructured wear-resistant layer, as well as the (Cr,Al,Si)N–(DLC–Si)–DLC–(DLC–Si) and (Cr,Al,Si)N–DLC composite coatings in order to improve the cutting properties of ceramic tools. The chapter presents the results of the comparative cutting tests for the tools with the coatings under study, uncoated tools, and tools with the Ti-(Ti,Al)N commercial coating. The wear mechanisms typical for ceramic cutting tools with coatings of various compositions have been investigated.",signatures:"Sergey Grigoriev, Alexey Vereschaka, Marina Volosova, Caterina Sotova, Nikolay Sitnikov, Filipp Milovich and Nikolay Andreev",downloadPdfUrl:"/chapter/pdf-download/73977",previewPdfUrl:"/chapter/pdf-preview/73977",authors:[{id:"196459",title:"Dr.",name:"Alexey",surname:"Vereschaka",slug:"alexey-vereschaka",fullName:"Alexey Vereschaka"},{id:"207607",title:"Dr.",name:"Nikolay",surname:"Sitnikov",slug:"nikolay-sitnikov",fullName:"Nikolay Sitnikov"},{id:"264336",title:"Dr.",name:"Catherine",surname:"Sotova",slug:"catherine-sotova",fullName:"Catherine Sotova"},{id:"329434",title:"Prof.",name:"Sergey",surname:"Grigoriev",slug:"sergey-grigoriev",fullName:"Sergey Grigoriev"},{id:"329437",title:"Dr.",name:"Filipp",surname:"Milovich",slug:"filipp-milovich",fullName:"Filipp Milovich"},{id:"329438",title:"Dr.",name:"Nikolay",surname:"Andreev",slug:"nikolay-andreev",fullName:"Nikolay Andreev"},{id:"330235",title:"Dr.",name:"Marina",surname:"Volosova",slug:"marina-volosova",fullName:"Marina Volosova"}],corrections:null},{id:"74485",title:"Three-Dimensionally Ordered Macroporous-Mesoporous Bioactive Glass Ceramics for Drug Delivery Capacity and Evaluation of Drug Release",doi:"10.5772/intechopen.95290",slug:"three-dimensionally-ordered-macroporous-mesoporous-bioactive-glass-ceramics-for-drug-delivery-capaci",totalDownloads:418,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Bioactive glass ceramics (BGCs) have been used in orthopedic and dentistry due to having better osteoconductive and osteostimulative properties. This study aimed to evaluate and compare the drug release properties of two different BGCs; 45S5 and S53P4. The BGCs were composed with four phases of SiO2 – CaO – Na2O – P2O5 system, synthesized by sol–gel method using dual templates; a block-copolymer as mesoporous templates and polymer colloidal crystals as macroporous templates, called three-dimensionally ordered macroporous-mesoporous bioactive glass ceramics (3DOM-MBGCs). In vitro bioactivity test performed by soaking the 3DOM-MBGCs in simulated body fluid (SBF) at 37°C. The results indicated that, the 45S5 have the ability to grow hydroxyapatite-like layer on the surfaces faster than S53P4. Gentamicin drug was used to examine in vitro drug release properties in phosphate buffer solution (PBS). The amount of drug release was quantified through UV/Vis spectroscopy by using o-phthaldialdehyde reagent. S53P4 showed high drug loading content. The outcome of drug release in PBS showed that both S53P4 and 45S5 exhibited a slowly continuous gentamicin release. The resultant drug release profiles were fitted to the Peppas-Korsmeyer model to establish the predominant drug release mechanisms, which revealed that the kinetics of drug release from the glasses mostly dominated by Fickian diffusion mechanism.",signatures:"Reedwan Bin Zafar Auniq, Namon Hirun and Upsorn Boonyang",downloadPdfUrl:"/chapter/pdf-download/74485",previewPdfUrl:"/chapter/pdf-preview/74485",authors:[{id:"328197",title:"Assistant Prof.",name:"Upsorn",surname:"Boonyang",slug:"upsorn-boonyang",fullName:"Upsorn Boonyang"},{id:"338371",title:"Dr.",name:"Namon",surname:"Hirun",slug:"namon-hirun",fullName:"Namon Hirun"},{id:"338373",title:"Mrs.",name:"Reedwan",surname:"Bin-Zafar Auniq",slug:"reedwan-bin-zafar-auniq",fullName:"Reedwan Bin-Zafar Auniq"}],corrections:null},{id:"75213",title:"Challenges in Rietveld Refinement and Structure Visualization in Ceramics",doi:"10.5772/intechopen.96065",slug:"challenges-in-rietveld-refinement-and-structure-visualization-in-ceramics",totalDownloads:700,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:0,abstract:"The most common and basic characterization in the field of material science is the almighty X-ray diffraction (XRD). In every institute, every research report and every manuscript, concerning material properties, the X-ray diffraction pattern is essentially found. Although the basis of these works relies on the fact that X-ray diffraction pattern was found to be matching with some structure in a database, the in depth significance of the various characteristic diffraction manifestations of various physical characters are rarely discussed. Most of the researchers (especially beginners) are either not aware of the prowess of X-ray based characterizations, or have not been introduced to it properly or may be sometimes they are not interested in its results at all. The decreased interest (later) in the results from such studies might be for not being productive enough for time spending or non-effectiveness in justifying the motivation of the work. The former two are more related to the availability and accessibility of study material for the development of core concepts. Most of the institutes always do not have access to the span-wide scientific literature and the researchers joining these institutions are partly affected. In this context the effective open-access and free availability of intech-open, it is prudent to at least attempt to accumulate, assimilated and aggregate the concepts related to X-ray diffraction in a single package. 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\r\n\tBiodiversity conservation and sustainability are crucial, if not vital, for humans to be able to sustain their lives in a variety of ways. At the same time, biodiversity preservation and anthropogenic activities and development are frequently considered incompatible. A variety of research priorities are investigated that facilitate the move from conflict to mutual compatibility between conservation and sustainability goals. Biodiversity science has a long history, but its evolution as an interdisciplinary subject capable of addressing the scientific, political, and societal difficulties that we confront has not kept pace with those challenges. Also, reaching biodiversity sustainability goals has been hampered by a lack of interdisciplinary researchers working on solutions-based investigations.
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1. Introduction
‘Good nutrition allows children to survive, grow, develop, learn, play, participate and contribute—while malnutrition robs children of their futures and leaves young lives hanging in the balance’.
Adequate Nutrition is essential for human development. Malnutrition includes both undernutrition as well as over-nutrition and refers to deficiencies, excesses or imbalances in the intake of energy, protein and/or other nutrients. Benefits of good health are perceived not only at the individual level but also at the level of society and country level as well. Health of an individual is determined by interplay of various factors like social factors, economic factors, dietary factors, lifestyle related factors, environmental factors, government policies and political commitment, etc. [1]. Foundation of an individual’s health is laid in early phase of life. It is a well-known fact that in some developing nations, India being one of them, nearly half of children under 5 years of age succumb to death every year due to poor nutrition. It is quite difficult for the poor to bear the cost of treatment especially suddenly occurring out-of-pocket expenditures [2]. A dissimilar trend is observed among individuals of affluent society. Sedentary habits coupled with unhealthy food habits results in weight gain in them. Health experts refer these conditions as malnutrition. The irony is, India being the world’s second largest food producer and yet is also home to the large number of undernourished children in the world.
It is well acknowledged that investment in human resource development is a pre requisite for any nation to progress. In year 2012, while releasing HUNGaMA (Hunger and Malnutrition) Report-2011, the then prime minister of India, Dr. Manmohan Singh, expressed dismay at the ‘unacceptably high’ levels of malnutrition despite high and impressive GDP growth and said it was a matter of ‘national shame’. He, being renowned economist, also expressed that that ‘the health of our economy and society lies in the health of this generation [3]. We cannot hope for a healthy future for our country with a large number of malnourished children’.
India is home to 46.6 million stunted children, a third of world’s total as per Global Nutrition Report 2018. Nearly half of all under-5 child mortality in India is attributable to undernutrition. Children of today are citizens of tomorrow, and hence improving nutritional status of children becomes extremely important. Early childhood constitutes the most crucial period of life, when the foundations are laid for cognitive, social and emotional, language, physical/motor development and cumulative lifelong learning.
Recently Millennium Development Goals (MDGs) has been transformed into Sustainable Developmental Goals (SDGs) and maternal & child health (MCH) has received attention in the last two decades as never before. Adequate nutrition has always been a definitive tool for achieving the maternal and child heath targets. Nutrition is defined as the science of food and its relationship with health. Nutrition is a basic human need and a prerequisite for a healthy life. A proper diet is essential from the very early stages of life for growth, development and for a state of overall well-being. Food consumption, which largely depends on production and distribution, determines nutrition and health of the population. Apart from supplying nutrients, food provides other components (non-nutrient phytochemicals), which have a positive impact on health.
2. Methods for the literature review
We searched PubMed, Google search engine and other databases on the internet for relevant literature. We searched reference lists of all primary and review articles based on the key words ‘childhood malnutrition, determinants, diarrheal diseases, India, problem burden, intervention strategies and control program’. Apart from that database of government run nutritional programmes, critical review and analysis of these programmes and related published books were also studied. At few instances, stakeholders of nutritional programmes were also consulted. Relevant data was collected, summarized and analysed.
3. Meaning of malnutrition
Malnutrition is a term that refers to any deficiency, excess or imbalance in somebody’s intake of energy and/or nutrients. In simple words, malnutrition can either be due to inadequate intake or an excess intake of calories. The term malnutrition covers two broad groups of conditions namely undernutrition and overnutrition. One is ‘undernutrition’—which includes stunting (low height for age), wasting (low weight for height), underweight (low weight for age) and micronutrient deficiencies or insufficiencies (a lack of important vitamins and minerals). Another one is overweight, obesity and diet-related non-communicable diseases (such as heart disease, stroke, diabetes and cancer).
Stunting refers to a child who is too short for his or her age. These children can suffer severe irreversible physical and cognitive damage that accompanies stunted growth. The devastating effects of stunting can last a lifetime and even affect the next generation.
Wasting refers to a child who is too thin for his or her height. Wasting is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible.
Overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from energy intakes from food and beverages that exceed children’s energy requirements. Overweight increases the risk of diet-related non-communicable diseases later in life.
4. Why childhood malnutrition matters to us?
Malnutrition is a universal problem that has many forms. No country is untouched. It affects all geographies, all age groups, rich people and poor people and all sexes. All forms of malnutrition are associated with various forms of ill health and higher levels of mortality. Undernutrition explains around 45% of deaths among children under-5, mainly in low and middle-income countries.
As far as adverse effects of child malnutrition are concerned, growth failure and infections are quite important. Malnourished children do not attain their optimum potential in terms of growth and development, physical capacity to work and economic productivity in later phase of life. It is commonly observed that school absenteeism is much higher in such child that leads to poor performance in the class. Cognitive impairment resulting from malnutrition may result in diminished productivity. Apart from these, Undernutrition increases the risk of infectious diseases like diarrhoea, measles, malaria and pneumonia and chronic malnutrition can impair a young child’s physical and mental development. As per estimates of World Bank, childhood stunting may result in a loss of height among adults by 1%, which may further lead to a reduction in individuals economic productivity by 1.4% [4].
Micronutrient deficiencies can lead to poor health and development, particularly in children. Overweight and obesity can lead to diet-related noncommunicable diseases such as heart disease, high blood pressure (hypertension), stroke, diabetes and cancer.
Malnutrition is also a social and economic problem, holding back development across the world with unacceptable human consequences. Malnutrition costs billions of dollars a year and imposes high human capital costs—direct and indirect—on individuals, families and nations. Estimates suggest that malnutrition in all its forms could cost society up to US$3.5 trillion per year, with overweight and obesity alone costing US$500 billion per year [5]. The consequences of malnutrition are increases in childhood death and future adult disability, including diet-related non-communicable diseases (NCDs), as well as enormous economic and human capital costs [6]. According to UNICEF, one in three malnourished children in the world is Indian. It is estimated that reducing malnutrition could add some 3% to India’s GDP.
5. The consequences of the problem
This inter-generational cycle of undernutrition transmitted from mothers to children greatly impacts on India’s present and future. Undernourished children are much more likely to suffer from infection and die from common childhood illnesses (diarrhoea, pneumonia, measles, malaria) than well-nourished children.
According to recent estimates, more than a third of all deaths in children aged 5 years or younger is attributable to undernutrition.
Undernutrition puts women at a greater risk of pregnancy-related complications and death (obstructed labour and hemorrhage).
Undernourished boys and girls do not perform as well in school as compared to their well-nourished peers, and as adults they are less productive and make lower wages.
Widespread child undernutrition greatly impedes India’s socio-economic development and potential to reduce poverty [7].
6. Measurement of malnutrition
Underweight is defined as weight that is 2 standard deviations below the WHO child growth standards for that particular age. In other words, child is underweight if Z-scores of child for a given weight for age is less than −2 SD from the median of the WHO/NCHS Child Growth Standards or References.
Wasting is defined as loss of body weight with reference to height. In other words, child is having wasting if Z-scores of child for a given weight for height is less than −2 SD from the median of the WHO/NCHS Child Growth Standards or References.
Wasting is also known as ‘acute malnutrition’ and is characterized by a rapid deterioration in nutritional status over a short period of time in children under 5 years of age. In children, it can be measured using the weight-for-height nutritional index or mid-upper arm circumference (MUAC). There are different levels of severity of acute malnutrition: moderate acute malnutrition (MAM) and severe acute malnutrition (SAM).
Stunting is defined as a height that is more than 2 standard deviations below the WHO child growth standards median. In other words, child is stunted if Z-scores of child for a given height for age is less than −2 SD from the median of the WHO/NCHS Child Growth Standards or References.
Stunting is also known as ‘chronic undernutrition’, although this is only one of its causes. Stunting is often associated with cognitive impairments such as delayed motor development, impaired brain function and poor school performance, as it often causes these negative impacts.
7. Magnitude of problem
In present era malnutrition is reflected as double burden, one aspect is undernutrition and other being overnutrition. But, in India and other low and middle-income countries (LMICs), basically malnutrition is synonymous with protein energy malnutrition or undernutrition, which signifies an imbalance between the supply of protein and energy and the body’s demand for them to ensure optimal growth and function.
7.1 Global scenario
Globally, approximately 149 million children under-5 suffer from stunting. In 2018, over 49 million children under-5 were wasted and nearly 17 million were severely wasted. There are now over 40 million overweight children globally, an increase of 10 million since 2000 [8]. (Figures 1–3) It is estimated that by 2050, 25 million more children than today will be malnourished [9].
Figure 1.
Global burden of malnourished under-5 children [8].
Figure 2.
Trends in global burden of malnourished under-5 children [8].
Figure 3.
Comparison of burden of malnourished under-5 Asian and African children [8].
7.2 Indian scenario
India is one among the many countries where child undernutrition is severe and also undernutrition is a major underlying cause of child mortality in India. Pattern of stunting prevalence among Indian districts is shown in Figure 4.
Figure 4.
Pattern of stunting prevalence among Indian districts (source: Menon et al. 2018).
The prevalence of underweight children under age 5 was an indicator to measure progress towards MDG 1, which aims to halve the proportion of people who suffer from hunger between 1990 and 2015. For India, this would imply a reduction in the child underweight rate from 54.8% in 1990 to 27.4% in 2015. Sustainable development Goals (SDG) 2 focuses on end hunger, achieves food security and improves nutrition and promotes sustainable agriculture. By 2030, end all forms of malnutrition, including achieving, by 2025, the internationally agreed targets on stunting and wasting in children under 5 years of age, and address the nutritional needs of adolescent girls, pregnant and lactating women and older persons and indicators are primarily prevalence of stunting, wasting and overweight among children under 5 years of age. In a recently released Global Nutrition Report 2018, revealed the prevalence of stunting, wasting and overweight at national level as 37.9, 20.8 and 2.4% respectively [10].
In India as per National Family Health Survey IV (2014–2015, recent in the series) 38.4, 21 and 35.7% of children below 5 years suffer from stunting, wasting and underweight respectively (corresponding figure for NFHS III, 2005–2006 were 47.9, 19.8 and 42.5% respectively). Prevalence of severe acute malnutrition (SAM) in India is 7.5% [11]. Trends in various nutritional indicator values and U5 mortality rate from 2000 to 2018 (India) are shown in Figure 5.
Figure 5.
Trends in various nutritional indicator values and U5 mortality rate from 2000 to 2018 (India).
In the 2018 Global Hunger Index, India ranks 103rd out of 119 qualifying countries [12]. With a score of 31.1, India suffers from a level of hunger that is serious. Figure 6 depicts dimensions and indicators of Hunger index and its relationship with child malnutrition.
Figure 6.
Diagrammatic representation of child undernutrition with Hunger Index.
8. Web of factors maintaining malnutrition in Indian communities
‘Asian enigma’ is a phenomenon of persistent and unusually high prevalence of child undernutrition in South Asia as compared to countries at similar levels of income or economic growth. In-depth analysis into why malnutrition is so resistant to improvement shows its complex aetiology. The immediate causes of undernutrition reflect a negative synergy between inadequate food intake and repeated infectious diseases. Underlying these causes is a constellation of factors particularly salient to India [13]. These include especially poor sanitation and high rates of open defecation that leads to various kinds of infestations, infections and environmental enteropathy; poor coverage of health services and half-hearted implementation of nutritional programs and policies; no political commitment and will, and economic, social determinants including economic growth and income distribution, deficiencies in governance and strategic leadership and the status of women [14, 15].
A new study from Harvard Chan School of Public Health has now pinpointed the five top risk factors responsible for more than two-thirds of the problem. Short maternal stature, extreme poverty, poor dietary diversity and mother’s lack of education are among the top five risk factors for malnutrition in children in India. Examining an array of 15 well-known risk factors for chronic undernutrition among children in India, the study found that the five top risk factors were essentially markers of poor socioeconomic conditions as well as poor and insecure nutritional environments in children’s households [16].
Economic conditions definitely play a crucial role. On the one hand, money is required to look after food, water and sanitary living conditions, whereas on the other hand, approximately 22% of the Indian population live below the poverty line. Rural population, a major chunk (especially agriculturists) is mostly dependent on rains for their income. They always live in a state of uncertainty of income. Apart of income, illiteracy plays a crucial role. Most of the people are not aware about their health, nutrition, balanced diet and breastfeeding practices. Without these, effective nutrition communication campaign cannot succeed in their purpose.
India ranked 97 among a list of 118 countries on hunger as per Global Hunger Index (GHI). It concludes that Indian population does not have access to sufficient and nutritious food. National Food Security Act is a great step in the direction of ensuring greater access to adequate quantity of quality food at affordable cost via Targeted Public Distribution System (PDS). Desired outcomes were not achieved due to corruption in PDS [17]. Wastage of food grains (theft, rotting) in Food Corporation of India (FCI) warehouses has also dented the access of food to common man. Greater efforts are needed to strengthen the existing initiatives to make them as corruption free and efficient institutions to get better results.
State of maternal health illiteracy is an important determinant of child nutritional status. The type of care a mother provides to her child depends to a large extent on her knowledge and understanding of some aspects of basic nutrition and health care [18].
Millions of beneficiaries have benefitted by ICDS Scheme however, problems are being observed in ensuring supply of quality food, and its uniform distribution. Anganwadi Workers (AWWs) and Anganwadi Helpers (AWHS) at Anganwadi centres are often dissatisfied by low wages. Thus they fail to play an effective role in tackling the problem of malnutrition.
8.1 Scam in ICDS project unearthed
Dibrugarh, Assam: two organizations have brought charges of rampant corruption in the Integrated Child Development Scheme (ICDS) amounting to more than Rs. 37 lakh in Panitola ICDS project of the district. While the officer-in-charge of the ICDS project in Panitola development block has drawn the money for 2007–2008 through two cheques (Nos. 107,895 and 017896) from UCO Bank, Dibrugarh after collecting the cheque from the district social welfare department, All India Youth Federation and All Assam Mottock Yuba Chatra Sanmilan unearthed through Right to Information (RTI) Act that the money has not been utilized till date. Suspecting misuse of the allotted money, the two organizations have demanded that the district administration institute an enquiry into the anomaly immediately. They have also demanded exemplary punishment on the erring officials (source: The Assam Tribune, 12 May 2008).
Village Health, Sanitation and Nutrition committee (VHSNC), one of the key elements of the National Rural Health Mission are non-functional in many of the states due to lack of funds. Similarly, Village Child Development Centres (VCDCs) were set up by state government of Maharashtra to provide malnourished children with medical care and nutritious meals. These centres are mostly non-functional due to lack of funds [19].
8.2 Toffees in the name of nutritious food
In Nigoha, the hot food scheme has stopped functioning due to lack of funds. The condition of Rampura AWC is also the same. The centre does not open on regular basis. The AWH, Sarvesh Kumari, distributes toffees instead of proper nutritional food to the limited number of children who come to the centre. Villagers are not even aware of the facilities provided to them by the AWC. Community participation is also lacking as parents do not sent their children to the centres (source: Dainik Jagran, Lucknow, 1 November 2009).
Social and cultural factors may also affect malnutrition. State government of Uttar Pradesh launched Hausla Poshan Yojana in 2016 to combat malnutrition among mothers and children by providing food cooked by Anganwadi Workers. Surprisingly beneficiaries refused to consume food because lower caste people prepared it [20]. Upper caste community considers lower caste as untouchables. Another cultural practice still prevalent in Indian communities is child marriage that is acting as limiting factor in improving health of children. 27% of girls in India are married before their 18th birthday and 7% are married before the age of 15. According to UNCIEF, India has the highest absolute number of child brides in the world [21]. A weak mother is likely to give birth to a weak child. This maintains the cycle of undernourishment.
As discussed earlier that poor sanitation is directly linked to malnourished children. The Census 2011 told us only 32% of India’s rural households had toilets. 59% of the 1.1 billion people in the world who practice open defecation live in India. On 2 October 2014, Swachh Bharat Mission was launched throughout country with an aim to achieve the vision of a ‘Clean and Open Defecation-Free India’ by 2 October 2019 [22]. These targets are difficult to achieve, as implementation is poor, as observed from the slow progress in meeting the targets, and the existence of several newly constructed but non-functional toilets [21, 23].
Diarrheal disease kills an estimated 300,000 children less than 5 years of age (13% deaths in this age-group) in India each year. Most mortality related to diarrhoea occurs in less developed countries, and the highest rates of diarrhoea occur among malnourished children under-1. The case fatality rate is highest among children aged 6–12 months because at this age the immune system is not yet fully mature, maternal antibodies are waning, and the foods introduced to complement breastfeeding may be contaminated. Among children who survive severe diarrhoea, chronic infections can contribute to malnutrition. In turn, malnutrition makes children vulnerable to diarrhoea infections. Better access to clean water and sanitation is the key, with fewer weak and malnourished children becoming infected [24, 25].
9. Commitments and targets to track progress to end malnutrition
Recognizing the seriousness of malnutrition for global health, in 2012 and 2013, the member states of the World Health Organization (WHO) adopted a series of targets to significantly reduce the burden of many of these forms of malnutrition by 2025 (Table 1).
Progress to tackle all forms of malnutrition remains unacceptably slow. The 2018 Global Nutrition Report [10] tracks country progress against the following global targets: child overweight, child wasting, child stunting, exclusive breastfeeding, diabetes among women, diabetes among men, anaemia in women of reproductive age, obesity among women and obesity among men. Data for 194 countries was analysed. As per this report, India is listed among those countries, which are on track for none (zero) of the nine targets. The key driver behind the goal to reach Zero Hunger and malnutrition is to ensure that no one is left behind in the pursuit of food and nutrition security. In the Indian context, this will also mean greatly improving the health of women and children.
10. Determinants of child malnutrition
The causes of malnutrition in India are several and multifaceted, from direct factors to underlying contributors. Malnutrition in children occurs as a complex interplay among various factors like socio-demographic, maternal, gender, home environment, dietary practices, hand washing and other hygiene practices, etc. Figure 7 depicts factors significantly associated with malnutrition among under-5 children in India.
Figure 7.
Factors significantly associated with malnutrition among under-5 children [36].
Socio-economic and demographic factors: literacy status of parents especially mother’s education, caste, birth order of child, gender of household head, residence, type of house, type of family (single/joint) lower socio-economic status, poverty, food insecurity, etc. are such important factors.
Gender: female gender is vulnerable to severe forms of malnutrition across all ages due to socio-cultural factors (responsible for child bearing and rearing, last one to consume food in the family). Undernourished girls grow up to become undernourished women who give birth to a new generation of undernourished children [26].
Maternal factors: short stature, mother’s nutrition, mother’s age, antenatal and natal care, infections, smoking and exposure to second hand smoke are important maternal factors.
Breastfeeding practices: inadequate, insufficient, inappropriate breastfeeding practices lay down foundation of malnutrition. Breastfed children are protected from infections in better way than who are not breastfed. Early initiation of breastfeeding and right timing of initiation of complementary feeding are also quite important [27].
Home environment: large family size, food insecurity, toilet facility, sanitation and hygiene practices, water storage and handling practices are extremely important factors.
Open air defecation: open defecation, the practice of people defecating out in the open wherever it is convenient, is one of the main factors leading to malnutrition. Approximately in the urban setting, 12% of the population open defecate and rural areas that number is 72%. Open defecation leads to polluted water; up to 75% of India’s surface water is polluted.
Poor hand hygiene: role of hand hygiene is quite important in prevention of infections and thereby malnutrition. Availability of soap and water is an important determinant. Hand washing before preparation, serving and eating meals and after going to toilets can prevent malnutrition to a great extent.
Diarrhoeal disease: diarrhoea is a leading cause of malnutrition in children under 5 years old. Poor sanitation, lack of access to clean water and inadequate personal hygiene are responsible for an estimated 88% of childhood diarrhoea in India. Based on current evidence, washing hands with soap can reduce the risk of diarrheal diseases by 42–47%. A survey conducted by UNICEF in 2005 on well-being of children and women had shown that only 47% of rural children in the age-group 5–14 wash hands after defecation [28].
Figure 8 depicts the underlying drivers of malnutrition. They are complex and multidimensional which include inter alia poverty, inequality and discrimination. Control of malnutrition will require a comprehensive approach targeting all these causes and contributors across sectors and stakeholders.
Figure 8.
Underlying drivers of malnutrition (source: Reproduced from the Global Nutrition Report 2016. International Food Policy Research Institute. 2016. Global Nutrition Report 2016: From promise to impact: Ending malnutrition by 2030. Washington, DC).
11. The life-course approach on malnutrition
The challenge of malnutrition calls for a multidisciplinary approach that targets multiple underlying factors. Crucial stages in people’s lives have particular relevance for their health, and the life-course approach recognizes the same. Taking a life-course perspective to tackle malnutrition emphasizes its intergenerational effects.
Intervening in the preconception period is fundamental to improve nutritional status and health behaviours in young people and adolescents and to prevent the transmission of risk to the next generation. Adopting a combination of top-down approaches through policy initiatives and bottom-up engagement of key stakeholders such as young people is recommended to prevent malnutrition over the first 1000 days of life. Targeting pregnancy and preconception periods increases nutrition awareness and influences dietary habits.
It is an established fact that preventing undernutrition during the first 1000 days of a child’s life, i.e. from conception to the second birthday is quite important. This time period is very precious because child may not be able to grow to her or his full potential in the future and even irreversible damage may occur, if foundation for good nutrition is not properly established during this time period. However it does not mean that there are no other entry points to improve nutrition. Moreover, even with coverage of 90% of direct nutrition interventions, only 20% of stunting deficits would be addressed [29]. It is essential that preconception services are incorporated into a continuum from childhood to antenatal care, involving both partners and linked to interventions to promote school attendance in young girls, and the planning of first and subsequent pregnancies [30].
The life course approach underlines the dynamic nutritional needs at different stages of life, this holds true especially with women. It also explains that at each stage of life, nutrition can and should be addressed in order to break the cross-generational cycle of malnutrition [31].
Figure 9 depicts the life course approach which explains how the first 1000 days are critically important. Investments in nutrition must extend as per the changing needs and risks at later stages in life, such as adolescent girls and women of reproductive age. It also points towards underlying causes of malnutrition and the need to address them. Underlying causes can only be satisfactorily addressed with intersectoral co-ordination and involvement like health, agriculture, water and sanitation, social protection and education. These sectors should be involved taking into account the specific needs and roles of women in order to work towards sustainable and inclusive solutions.
Figure 9.
The life-course approach on malnutrition.
12. The fight against malnutrition
Massive and strategic investments have been made to combat malnutrition by governments of various countries, India being one of them. Recently (in April 2016), the United Nations General Assembly adopted a resolution proclaiming the UN Decade of Action on Nutrition from 2016 to 2025. The Decade aims to catalyse policy commitments that result in measurable action to address all forms of malnutrition. The aim is to ensure all people have access to healthier and more sustainable diets to eradicate all forms of malnutrition worldwide. Sustained and concrete results can only be achieved only if determinants of malnutrition are addressed with holistic approach [32].
Outcomes of these nutritional interventions are evident in the declining patterns in some of the India’s key health variables as reported by National Family Health Surveys NFHS-3 (2005–2006) and NFHS-4 (2015–2016) data.
12.1 Data on nutrition indicators as per the last available national survey (NFHS 4)
38% of children below 5 years (urban: 31%, rural: 41%) are stunted (low height for age).
21% (urban: 20%, rural: 22%) are wasted (low weight for height).
36% (urban: 29%, rural: 38%) are underweight (low weight for age).
More importantly, 7.5% of children are suffering from severe acute malnutrition, as per the last available national survey.
12.2 Related indicators
Only 41.6% newborns initiated on breastfeeding within 1 hour of birth while 54.9% children breastfed exclusively till 6 months of age.
Complementary feeding started for only 42.7% children on time (more than 6 months of age).
58.4% of children in age group 6–59 months are anaemic.
Figure 10 shows the comparison of nutrition indicators as per NFHS-3 and NFHS-4.
Figure 10.
Comparison of nutrition indicators as per NFHS-3 and NFHS-4 (source: NFHS-4, 2015–2016. Note: data on anaemia pertain to children aged 6–59 months).
12.3 Status of child mortality in India
The U5MR has declined at a faster pace in the period 2008–2016, registering a compound annual decline of 6.7% per year, compared to 3.3% compound annual decline observed over 1990–2007 [33].
As per latest Sample Registration System, 2016 Report; The U5MR in India is 39/1000 live births, IMR is 34/1000 live births and NMR is 24/1000 live births. This translates into an estimated 9.6 lakh under-5 child deaths annually.
Four States together contribute to 56% of all child deaths in the country, namely-Uttar Pradesh (2.45 lakhs), Bihar (1.2 lakhs), Madhya Pradesh (1.0 lakh) and Rajasthan (0.75 lakh).
About 46% of under-five deaths take place within the first 7 days of birth, 62% within first 1 month of birth.
The state of malnutrition in India is alarming and disturbing. A lot of work has been done, progress has been made but definitely pace of improvement is too slow. Following table shows the current status of important child health indicators and time bound targets to be achieved under National Health policy and Sustainable Development Goals (SDGs).
Sustainable Development Goals (SDGs) were released by the UN in 2016 (till 2030) showing unfinished agenda of Millennium Development Goals (MDGs) ended in 2015. SDG 2 calls to end hunger, achieve food security and improved nutrition and promote sustainable agriculture whereas SDG 3 calls to ensure healthy lives and promote well-being for all at all ages (Table 2).
13. Policy level nutritional interventions to fight against malnutrition
Based on understanding towards a wide range of factors responsible for malnutrition among children, the policy called for the adoption of a multi-sectoral approach along with multiple measures to achieve the goal of optimum nutrition for all. Important government led policy level interventions and programmes to combat malnutrition are as follows:
13.1 Direct policy measures
Inclusion of all vulnerable groups (children, adolescent girls, mothers, expectant women) under the safety cover of ICDS.
Fortification of essential food items with legal provisions (e.g. twin fortification of salt with both iodine and iron).
Popularize low cost nutritious food.
Control of micro-nutrient deficiencies with special focus on vulnerable groups.
13.2 Indirect policy measures
Guarantee of food security to citizens by increasing production of food grains.
Improve dietary pattern by promoting production and increasing per capita availability of nutritionally rich food.
Prevention of food adulteration by law.
Strengthening nutrition surveillance.
Improving purchasing power of landless, rural and urban poor.
Improving public distribution system (PDS).
The Government of India enacted the National Food Security Act (NFSA) in 2013 to enable food and nutritional security by ensuring access to adequate quantity of quality food at affordable prices to people to live a life with dignity. This legal provision has put the onus on the state to guarantee basic entitlements.
13.3 Plans, programmes and missions
Mid-day Meal Programme, 1962–1963
Goiter Control Programme, 1962 (now known as National Iodine Deficiency Disorders Control Programme)
Special Nutrition Programme, 1970–1971
Balwadi Nutrition Programme, 1970–1971
Nutritional Anaemia Prophylaxis Programme, 1970
Prophylaxis Programme against Blindness due to Vitamin A Deficiency, 1970
Integrated Child Development Services (ICDS), 1975
National Diarrhoeal Diseases Control Programme, 1981
National Health Mission, 2013 (subsumes former Rural and Urban Health Missions) National Iron+ Initiative, 2013
Promotion of Infant and Young Child Feeding Practices Guidelines, 2013
Weekly Iron and Folic Acid Supplementation, 2015
National Deworming Day, 2015
Sustainable Development Goals (2016–2030)
New National Health Policy, 2017
Establishment of: Nutritional Rehabilitation Centres; Village Health Sanitation & Nutrition Committee
Bi-annual Vitamin-A Supplementation
Village Health & Nutrition Days (at Anganwadi centres)
Reproductive, Maternal, Newborn, Child and Adolescent (RMNCH+A) Strategy
Figure 11 depicts multi ministerial involvement showing political commitment and intersectoral approach to end malnutrition.
Figure 11.
Multi ministerial involvement showing political commitment and intersectoral approach to end malnutrition.
14. Strategic nutrition related interventions rolled out by government of India
Various community nutritional programmes are running in India to combat child malnutrition and to get nutrition on track. These are based on strategic nutrition related interventions. A few of them are discussed below.
Promotion of Infant and Young Child feeding practices (IYCF): exclusive breastfeeding for first 6 months, complementary feeding beginning at 6 months and appropriate infant and young child feeding practices (IYCF) are being promoted. Mother’s Absolute Affection (MAA) programme was launched in 2016 to promote breastfeeding and infant feeding practices by building the capacity of frontline health workers and comprehensive IEC campaign.
Establishment of Nutritional Rehabilitation Centres (NRCs): NRCs have been set up at facility level to provide medical and nutritional care to Severe Acute Malnourished (SAM) children under 5 years of age who have medical complications. In addition, the mothers are also imparted skills on child care and feeding practices so that the child continues to receive adequate care at home.
Anaemia Mukt Bharat (AMB): to address anaemia, NIPI has been launched which includes provision of supervised bi-weekly Iron Folic Acid (IFA) supplementation by ASHA for all under-5 children, weekly IFA supplementation for 5–10 years old children and annual/biannual De-worming. The AMB strategy—Intensified Iron Plus Initiative—aims to strengthen the existing mechanisms and foster newer strategies of tackle anaemia, focused on six target beneficiary groups, through six interventions and six institutional mechanisms; to achieve the envisaged target under the POSHAN Abhiyaan. The strategy focuses on testing & treatment of anaemia in school going adolescents and pregnant women using newer technologies, establishing institutional mechanisms for advanced research in anaemia, and a comprehensive communication strategy including mass/mid media communication material.
National De-worming Day (NDD): recognising worm infestation as an important cause of anaemia, National Deworming Day (NDD) is being observed annually on 10th February targeting all children in the age group of 1–19 years (both school enrolled and non-enrolled).
Biannual Vitamin A Supplementation is being done for all children below 5 years of age.
Village Health and Nutrition Days (VHNDs) are also being organized for imparting nutritional counselling to mothers and to improve child care practices.
A few schemes and services rendered by them are tabulated (Table 3) below as per target group.
Target group
Schemes
Major services from schemes
Children (0–3 years)
ICDS
ICDS: supplementary nutrition, growth monitoring, counselling health education of mothers on child care, promotion of infant and young child feeding, home based counselling for early childhood stimulation, referral and follow up of undernourished and sick children.
RCH-II, NRHM
NRHM: home-based new born care, immunization, micronutrient supplementation, deworming, health check-up, management of childhood illness and severe under-nutrition, referral and cashless treatment for first month of life. Care of sick newborns, facility-based management of severe acute malnutrition and follow up.
Rajiv Gandhi National Creche Scheme
Rajiv Gandhi National Creche Scheme: support for the care of children of working mothers.
Children (3–6 years)
ICDS
ICDS: non-formal preschool education, growth monitoring, supplementary nutrition, referral, health education and counselling for care givers.
RCH-II, NRHM
NRHM: immunization micronutrient supplementation, deworming, health check-up, management of illnesses and severe undernutrition
Rajiv Gandhi National Creche Scheme
Rajiv Gandhi Creche Scheme: support for care of children of working mothers
Total Sanitation Campaign (TSC)/Nirmal Bharat Abhiyan (NBA)
TSC/NBA: household-level sanitation facilities
National Rural Drinking Water Programme (NRDWP)
NRDWP: availability of safe drinking water
School going children (6–14 years)
Mid-Day Meals (MDM),
Mid-day meal: hot cooked meal to children attending school.
Sarva Shiksha Abhiyan (SSA)
SSA: support knowledge dissemination on nutrition by inclusion of Nutrition related topics in syllabus and curriculums for formal education, school health check-up, mid-day meal.
Table 3.
Selected nutritional schemes and services rendered as per target group.
14.1 NGO’s working to combat malnutrition
Akshaya Patra—the world’s largest NGO-run mid-day meal programme serving wholesome school lunch to over 1.76 million children in 15,668 schools across 12 states in India.
Avantha Foundation Fighting malnutrition in Bihar
Nutrition CINI India
Salaam Baalak Trust Health and Nutrition
The Hunger Project India—The Hunger Project
Cry NGO in India to Support Child Rights
CARE India
Save the Children India
Feeding India works to eradicate hunger, malnutrition and food wastage in India
Yashoda Foundation
SNEHA—Society for nutrition
Freedom From hunger India trust
FMCH India—Foundation for Mother and Child Health
Real Medicine Foundation
Indian Impact—It offers individuals and corporations an easy way to help improve their nearest Anganwadi centre, and supports NGOs that are working to reduce malnutrition.
15. Case study
The following case study from Tamil Nadu, a southern state of India focuses on the complex challenges faced and the progress made so far as part of efforts towards combating malnutrition. It also demonstrates how lessons are being learned along the way.
15.1 The Tamil Nadu integrated nutrition project (TINP)
The Tamil Nadu Integrated Nutrition Project (TINP), a World Bank assisted intervention program in rural south India, offered nutrition and health services to children under-5 and pregnant and lactating women. TINP-I (1980–1989) eventually covered 174 blocks. It was a forerunner of the Bangladesh Integrated Nutrition Project (BINP). TINP-II (1991–1997) covered all non-ICDS blocks in the Tamil Nadu state. TINP-II was replaced by World Bank assisted ICDS III (WB-ICDS III) from 1998.
Since 1975, Indian government is providing a package of services to combat child hunger and malnutrition under Integrated Child Development Services (ICDS) program through Anganwadi centres (AWCs). Anganwadi means ‘courtyard shelter’ in local language.
15.2 TINP I (1980–1989)
Approximately 1.25–2.40% points per year (ppt/year) drop in underweight prevalence was noted among beneficiaries. On comparing drop in underweight prevalence between TINP areas and non-TINP areas, it was noticed that drop was approximately 0.83–1.12 ppt/year in TINP areas whereas reduction in underweight prevalence was approximately 0.26–1.12 ppt/year in non-TINP areas.
At the same duration, reduction in the underweight prevalence was estimated as 0.7 ppt/year for the whole of India. Therefore it can be stated that quarter to half of the reduction in underweight prevalence was attributable to the TINP project.
Having achieved a significant reduction in severe early childhood malnutrition, TINP-1 became inspiration for others as a ‘success story’ during the 1980s. Evaluations indicated a decrease in underweight prevalence of about 1.5% points per year in participating districts, twice the rate in non-participating districts. Several factors contributed in the success story of TINP I viz. selective feeding (the careful focus on supplementing the dietary intake of young children when their growth faltered and until their growth resumed), clarity in job responsibilities and description, positive worker-supervisor ratio and robust monitoring system.
15.3 TINP II (1990–1997)
TINP II was rolled out to move beyond reducing severe malnutrition and with a more ambitious objective to significantly reduce the burden of moderate malnutrition. In other words, it shifted towards a more preventive focus. Core strategies adopted in TINP II were regular growth monitoring, nutrition education, health check-ups, supplementary feeding of malnourished children and growth-faltering children, high-risk pregnant and lactating women.
Approximately 6.0 ppt/year drop in underweight prevalence was noted among TINP II beneficiaries. It was also noticed that drop was approximately 1.1 ppt/year in TINP areas. As per estimates of World Bank, the current underlying trend in the state was to be 5.0–7.0 ppt/year, which is most certainly an overestimate.
In the nutshell, TINP II achieved its objective to decrease severe malnutrition but failed to achieve its objective for moderate malnutrition.
A few lessons were learned from TINP II before planning a next phase nutritional intervention. For example, need to work on localized capacity building, improved home-based care by intensifying community mobilization and targeted interpersonal communications, and feeding of 6–24 months old children. Next phase of nutritional programme must incorporate improved service delivery, supportive counselling of caregivers, social mobilization and participatory learning.
Take home massage from TINP I was, interventions that are targeted using nutritional criteria, integrated within a broader health system and effectively supervised and managed can significantly reduce severe malnutrition. TINP II taught us that going further and preventing children from becoming moderately malnourished is in many ways a tougher task, and demands a significant shift in strategy [34, 35].
16. Conclusion
The facts and discussion presented above, highlights the worrying unacceptably high prevalence and universality of malnutrition in all its forms in Indian communities, but it is both preventable and treatable. Beyond health, malnutrition is also impacting the social and economic development. In Indian context, poverty, maternal health illiteracy, LBW, diseases like diarrhoea, home environment, dietary practices, hand washing and poor hygiene practices are few important factors responsible for very high prevalence of malnutrition. Government of India has rolled out various community nutritional programmes to combat malnutrition and to get nutrition on track. Despite enormous challenges, India has made considerable progress in tackling hunger and undernutrition in the past two decades, yet this pace of change has been unacceptably slow, uneven and many have been left behind. But with sustained prioritization, increased resource allocation, adopting comprehensive, coordinated and holistic approach with good governance and help of civil society, India has the potential to end malnutrition in all its forms and turn the ambition of the Sustainable Development Goals into a reality for everyone.
\n',keywords:"childhood malnutrition, determinants, diarrheal diseases, nutritional programmes, challenges",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/71300.pdf",chapterXML:"https://mts.intechopen.com/source/xml/71300.xml",downloadPdfUrl:"/chapter/pdf-download/71300",previewPdfUrl:"/chapter/pdf-preview/71300",totalDownloads:2881,totalViews:0,totalCrossrefCites:9,totalDimensionsCites:11,totalAltmetricsMentions:45,impactScore:7,impactScorePercentile:96,impactScoreQuartile:4,hasAltmetrics:1,dateSubmitted:"April 26th 2019",dateReviewed:"September 12th 2019",datePrePublished:"March 2nd 2020",datePublished:"May 6th 2020",dateFinished:"March 2nd 2020",readingETA:"0",abstract:"India is home to 46.6 million stunted children, a third of world’s total as per Global Nutrition Report 2018. Nearly half of all under-5 child mortality in India is attributable to undernutrition. Any country cannot aim to attain economic and social development goals without addressing the issue of malnutrition. Poor nutrition in the first 1000 days of a child’s life can also lead to stunted growth, which is associated with impaired cognitive ability and reduced school and work performance. Malnutrition in children occurs as a complex interplay among various factors like poverty, maternal health illiteracy, diseases like diarrhoea, home environment, dietary practices, hand washing and other hygiene practices, etc. Low birth weight, episode of diarrhoea within the last 6 months and the presence of developmental delay are often associated with malnutrition in most developing nations including India. This chapter is a small attempt to highlight the state of malnutrition in India and tries to get an insight to overcome the problem. This chapter also highlights the issues and challenges for not obtaining the desired nutritional outcomes. It also provides an insight that this issue can be addressed by adopting comprehensive, coordinated and holistic approach with good governance and help of civil society.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/71300",risUrl:"/chapter/ris/71300",book:{id:"7979",slug:"perspective-of-recent-advances-in-acute-diarrhea"},signatures:"Abhishek Singh",authors:[{id:"303173",title:"Associate Prof.",name:"Abhishek",middleName:null,surname:"Singh",fullName:"Abhishek Singh",slug:"abhishek-singh",email:"abhishekparleg@gmail.com",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. Methods for the literature review",level:"1"},{id:"sec_3",title:"3. Meaning of malnutrition",level:"1"},{id:"sec_4",title:"4. Why childhood malnutrition matters to us?",level:"1"},{id:"sec_5",title:"5. The consequences of the problem",level:"1"},{id:"sec_6",title:"6. Measurement of malnutrition",level:"1"},{id:"sec_7",title:"7. Magnitude of problem",level:"1"},{id:"sec_7_2",title:"7.1 Global scenario",level:"2"},{id:"sec_8_2",title:"7.2 Indian scenario",level:"2"},{id:"sec_10",title:"8. Web of factors maintaining malnutrition in Indian communities",level:"1"},{id:"sec_10_2",title:"8.1 Scam in ICDS project unearthed",level:"2"},{id:"sec_11_2",title:"8.2 Toffees in the name of nutritious food",level:"2"},{id:"sec_13",title:"9. Commitments and targets to track progress to end malnutrition",level:"1"},{id:"sec_14",title:"10. Determinants of child malnutrition",level:"1"},{id:"sec_15",title:"11. The life-course approach on malnutrition",level:"1"},{id:"sec_16",title:"12. The fight against malnutrition",level:"1"},{id:"sec_16_2",title:"12.1 Data on nutrition indicators as per the last available national survey (NFHS 4)",level:"2"},{id:"sec_17_2",title:"12.2 Related indicators",level:"2"},{id:"sec_18_2",title:"12.3 Status of child mortality in India",level:"2"},{id:"sec_20",title:"13. Policy level nutritional interventions to fight against malnutrition",level:"1"},{id:"sec_20_2",title:"13.1 Direct policy measures",level:"2"},{id:"sec_21_2",title:"13.2 Indirect policy measures",level:"2"},{id:"sec_22_2",title:"13.3 Plans, programmes and missions",level:"2"},{id:"sec_24",title:"14. Strategic nutrition related interventions rolled out by government of India",level:"1"},{id:"sec_24_2",title:"14.1 NGO’s working to combat malnutrition",level:"2"},{id:"sec_26",title:"15. Case study",level:"1"},{id:"sec_26_2",title:"15.1 The Tamil Nadu integrated nutrition project (TINP)",level:"2"},{id:"sec_27_2",title:"15.2 TINP I (1980–1989)",level:"2"},{id:"sec_28_2",title:"15.3 TINP II (1990–1997)",level:"2"},{id:"sec_30",title:"16. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Blössner M, de Onis M. Malnutrition: Quantifying the Health Impact at National and Local Levels. WHO Environmental Burden of Disease Series, No. 12. Geneva: World Health Organization; 2005'},{id:"B2",body:'Thang NM, Popkin BM. In an era of economic growth, is inequity holding back reductions in child malnutrition in Vietnam? Asia Pacific Journal of Clinical Nutrition. 2003;12:405-410'},{id:"B3",body:'HUNGaMA: Fighting hunger and malnutrition: The HUNGaMA Survey Report. 2011. Available from: https://www.eldis.org/document/A72128'},{id:"B4",body:'World Bank. India’s Undernourished Children: A Call for Reform and Action. 2019. 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The Lancet. 2016;387(10017):475-490'},{id:"B28",body:'Lakshminarayanan S, Jayalakshmy R. Diarrheal diseases among children in India: Current scenario and future perspectives. Journal of Natural Science, Biology, and Medicine. 2015;6(1):24'},{id:"B29",body:'Bhutta ZA, Das JK, Rizvi A, Gaffey MF, Walker N, Horton S, et al. Maternal and child nutrition study group. Evidence-based interventions for improvement of maternal and child nutrition: What can be done and at what cost? The Lancet. 2013;382(9890):452-477'},{id:"B30",body:'Kuruvilla S, Sadana R, Montesinos EV, et al. A life-course approach to health: Synergy with sustainable development goals. Bulletin of the World Health Organization. 2018;96(1):42-50'},{id:"B31",body:'Jacob CM, Hanson M. A life-course approach for influencing policies to prevent childhood malnutrition. Crisis and Opportunity of the Double Burden. 2018;2016(2025):18'},{id:"B32",body:'de Onis M, Blössner M. The World Health Organization global database on child growth and malnutrition: Methodology and applications. International Journal of Epidemiology. 2003;32:518-526'},{id:"B33",body:'SRS Statistical Report 2016. Office of the Registrar General and Census Commissioner, India. Ministry of Home Affairs, Government of India. 2016. Available from: http://www.censusindia.gov.in/vital_statistics/SRS_Reports__2016.html'},{id:"B34",body:'Gillespie S, Haddad L. The Double Burden of Malnutrition in Asia: Causes, Consequences, and Solutions. New Delhi: Sage Publications India; 2003'},{id:"B35",body:'Essential Nutrition Actions: Improving Maternal, Newborn, Infant and Young Child Health and Nutrition. Geneva: World Health Organization; 2013. Annex 3, Nutrition Programme Case Studies. Available from: https://www.ncbi.nlm.nih.gov/books/NBK258728/'},{id:"B36",body:'Mohseni M, Aryankhesal A, Kalantari N. Factors associated with malnutrition among under five-year-old children in Iran: A systematic review. Annals of Tropical Medicine and Public Health. 2017;10:1147-1158'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Abhishek Singh",address:"abhishekparleg@gmail.com",affiliation:'
Department of Community Medicine, SHKM Government Medical College, Haryana, India
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1. Introduction
Throughout the entire construction life cycle, quality assessment plays an important role in ensuring the safety, economy, and long-term viability of construction activities. Construction products that have been completely inspected and certificated by quality inspectors are more inclined to be chosen by developers and buyers. Typically, the structural work is considered as an essential aspect for quality assessment because structural problems directly influence the construction stability and integrity. Among the construction structural forms, concrete structures are adopted as the most common and basic construction structure. Therefore, exploring advanced technologies that enable effective concrete defect inspection can be deemed a worthwhile endeavor.
Normally, the types of concrete defects include blistering, delamination, dusting, etc. Among them, concrete cracks, usually caused by deformation, shrinkage, swelling, or hydraulic, appear most frequently in concrete components. Concrete cracking is considered the first sign of deterioration. As reported by the BRE Group [1], cracks up to 5 mm in width simply need to be re-decorated because they only affect the appearance of the concrete. However, cracks with a width of 5–25 mm have the possibility to trigger structural damage to concrete structures [2]. A 40-year-old oceanfront condo building collapsed on June 27, 2021, in Florida because of the neglect of cracks. Experienced engineers noticed the cracked or crumbling concrete, the interior cracks, and the cracks at the corners of windows and doors are the significant and earliest signs of this tragedy. Therefore, in order to prevent potential failures that may pose a loss to society, crack problems should be thoroughly examined and resolved.
In general, construction works are divided into two categories: new building works and existing building works. The new works refer to a building that will be constructed from scratch. The existing building works mean that a building has existed for many years and residents are living inside. In Hong Kong, quality assurance and control should be conducted by full-time quality managers on-site for both new and existing buildings. Normally, the quality managers visually inspect implied build quality and by appointing a score to the building’s quality in accordance to the Building Performance Assessment Scoring System (PASS) for new buildings, the Mandatory Building Inspection Scheme (MBIS), and the Mandatory Window Inspection Scheme (MWIS) for existing buildings. Meanwhile, to ensure a continuous and in-depth inspection, Non-destructive (NDT) methods e.g., eddy current testing, ultrasonic testing are also commonly applied in the quality inspection process.
Quality managers are commonly obliged to work 8 hours per day. Their salary ranges from HKD 30,000 to HKD 50,000 per month. In PASS, more than 300 quality assessment items are related to cracking-related problems. Cracks in all building components, including floors, internal and external walls, ceilings, and others are required to be strictly inspected during both structural and architecture engineering stages. Therefore, both manual and NDT inspections are considered time-consuming, costly, and dangerous, especially for large-scale and high-rise structures. To tackle this issue, computer-vision technique is increasingly introduced for automated crack inspection. For example, various convolutional neural network (CNN) architectures have been developed and implemented to increase the efficiency of manual crack inspection [3, 4].
Considering the aforementioned context, computer-vision-based automated crack inspection techniques were introduced by the authors in 2022. To achieve this, the theoretical background of CNN networks is firstly explained in the context of convolution, pooling, fully-connected, and benchmarking processes. AlexNet and VGG16 models were then implemented and tested to detail and illustrate the calculation steps. Meanwhile, a practical case study is used to compare the difference between manual and computer-vision-based crack inspection. The future directions of combining robotics and computer-vision for automated crack inspection are discussed. This study gives a comprehensive overview and solid foundation for a computer-vision-based automated crack inspection technique that contributes to high efficiency, cost-effectiveness, and low-risk quality assessment of buildings.
The term computer vision is defined as an interdisciplinary field that enables computers to recognize and interpret environments from digital images or videos [5]. Computer vision techniques are rapidly being used to detect, locate, and quantify concrete defects to reduce the limitations of manual visual inspection. By automatically processing images and videos, computer vision-based defect detection technologies enable efficient, accurate, and low-cost concrete quality inspection. Various techniques in the computer vision field, such as semantic segmentation and object detection, have been developed and applied to date [6]. Among them, image classification is considered the most basic computer vision technique and has been introduced most frequently to predict and target concrete defects.
The motivation of image classification is to identify the categories of input images. Different from human recognition, an image is first presented as a three-dimensional array of numbers to a computer. The value of each number ranges from 0 (black) to 255 (white). An example is shown in Figure 1. The crack image is 256 pixels wide, 256 pixels tall, and has three color channels RGB (Red, Green, and Blue). Therefore, this image generates 256 × 256 × 3 = 196,608 input numbers.
Figure 1.
An example of the input number array.
The input array is then computed using computer vision algorithms to transform the numbers to a specific label that belongs to an assigned set of categories. One of the computer vision algorithms is CNN, which has become dominant in image classification tasks [7]. CNN is a form of a deep learning model for computing grid-shaped data. The central idea of CNN is to identify the image classification by capturing its features using filters. The features are then output to a specific classification by a trained weight and biases matrix.
There are three main modules included in a CNN model: convolution, pooling, and fully connected layer. The convolution and pooling layers are used to extract image features. The fully connected layer is used to determine the weight and biases matrix and to map the extracted features into specific labels.
Convolution layer is the first processing block in CNN. During the convolution process, a set of convolution filters is used to compute the input array Α=aijm×n, m,n∈widthimageheightimage. After computing, a new image Α∗=a∗ijn×n, is output and passed to the next processing layers. The size of the output image can be calculated with Eq. (1). The values of output image pixels can be calculated with Eq. (2). The output images are known as convolution feature map.
n=m−f+2p/s+1E1
Here: n refers to the size of output image, m refers to the size of input image, f refers to the size of convolution filter, p refers to the number of pooling layer, s refers to the stride of convolution filter.
Αo∗=f∑kWo×Αo+boE2
Here: Αo∗ refers to the pixels of output image, f refers to an applied non-linear function, Wo refers to the values of convolution filter matrix, k refers to the number of convolution filters.Αo refers to the pixels of input image, and bo is an arbitrary real number.
An example of a convolution process is shown in Figure 2. In this example, both the width and height of the input image is 5. The pixels of the image are shown in Figure 2. The convolution filter is in a shape of 3 × 3. In this example, only one filter is used. The initial value of the convolution filter is set randomly. The filter matrix is adjusted and optimized in the following backpropagation process. In this example, the non-linear function, padding layer is not used, and the biases value bo is set as 0. The stride of convolution filter is set as 1. The convolution filter moves from left to right, and from top to bottom. The size and value of the output feature map can be computed using Eqs. (1) and (2). The detailed calculation process of the example feature maps value and size is shown in Table 1. Seen from Figure 2, the value of size of input image, size of filter is 5, 3, respectively. Suppose the number of the pooling layer, the convolution stride is 0, 1, respectively.
Detailed calculation process of feature map value and size.
A pooling layer is used to refine the feature maps. After pooling, the dimensions of the feature maps can be simplified. In doing so, the computation cost can be effectively decreased by reducing the number of learning parameters, whilst allowing only the essential information of feature maps to be presented. Usually, pooling layers follow behind convolution layers. Average pooling and maximum pooling are the main pooling operations. Similar to convolution layers, pooling filters are used to refine feature maps. For maximum pooling, the maximum value from the regions in feature map that is covered by pooling filters is extracted. For average pooling, the average value of the regions in feature maps covered by pooling filters is computed. The pooling filters slide in the feature map from top to bottom, and from left to right. The output of the pooling process is new feature maps that contain the most prominent features or average features. An example of maximum pooling and average pooling is shown in Figure 3.
Figure 3.
An example of max pooling and average pooling.
After extracting image features, the fully connected layers are applied to map these features with classification labels. The relationship between input feature maps and output classifications is calculated using an artificial neural network (ANN). The ANN is structured into input layers, hidden layers, and output layers. A group of neurons is included in the three layers. The neurons connect to one another in a processed weight matrix. The weights present the importance of input feature maps to classification labels. Therefore, the relationships between inputs and outputs can be obtained by calculating a weight matrix that connects image feature neurons and classification neurons.
To achieve this, the cube-shaped feature maps are first flattened into one-dimension vectors. The values of transformed vectors represent the values of input neurons. Then Eq. (3) is applied to calculate the value of new neurons that connect with input neurons. The initial weights and biases values are chosen at random.
yjx=f∑i=1nwjxi+bE3
Here: yj refers to the weights of output neurons, wj refers to the weights that connect different neurons, xi refers to the values of input neurons, b refers to the biases.
A Back-Propagation algorithm (BP) is commonly used to train and modify weights and biases. BP updates weights and biases by computing the gradient of loss function. In doing this, the optimal weights and biases matrix that enable the minimum loss between model outputs and actual value are identified. For now, various loss functions are developed and applied. For example, the mean square error (MSE), shown in Eq. (4), is one of the most frequently used loss functions to calculate loss value. Stochastic gradient descent (SGD) is then processed to determine updated weights and biases using the gradient of loss function, shown as Eq. (5).
Loss=1n∑i=1nyi−yi^2E4
Here: Loss refers to the loss value of output neuron and actual value, n refers to the number of neurons that connect to one specific output neuron, y refers to the actual value, y^ refers to the value of one output neuron.
w\'=w−η∂L∂wE5
b\'=b−η∂L∂b
Here: w\', b\' refers to updated weights and biases, η, η refers to former weights and biases, η refers to the learning rate, ∂L∂w, ∂L∂b refers to the partial score of the loss function for weights and biases, respectively.
An example of feature map updating using BP is explained. Figure 4 depicts an example of a fully connected process. The initial weights and biases in this process are determined randomly. Suppose the value of w11, w12, w21, w22, w5, w6 is 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, respectively. The value of x1, x2, actual output value is 5, 1, 0.24. The detailed calculation of the updated weights, biases, feature map is shown in Table 2.
Detailed calculation process of feature map updating using BP.
In conclusion, during the convolution and pooling processes in CNN, the features of the input image are extracted first. The pooled feature maps are then flattened and considered as input neurons in fully connected process. After several training periods, the appropriate weights and biases can be determined using BP. The classifications of input images can be predicted automatically and reliably using the optimal weights and biases.
A confusion matrix is a table structure that permits the viewing of CNN performance [8]. Each row of the matrix records the number of images from actual classes, while each column records the number of images from predicted classes. There are four type indicators in the matrix: (1) True positive (TP) represents the images that are predicted correctly as the actual class; (2) False positive (FP) represents the images that are wrongly predicted; (3) True negative (TN) represents the images that are correctly predicted as another actual class; (4) False negative (FN) represents the images that are wrongly predicted as another actual class. TP, FP, TN, FN can be expressed in a 2 × 2 confusion matrix, shown in Figure 5.
Figure 5.
An example of a fully connected process.
Based on TP, FP, FN, and TN, four typical CNN performance evaluation indexes: accuracy, precision, recall, and F1-score can be calculated using Eqs. (6)–(9). For the crack inspection problem, accuracy shows how many images can be predicted correctly. The percentage of actual cracked photos to all predicted cracked images is shown by precision. CNNs with a high precision score indicate a better inspection ability of cracked images. Recall shows the ratio of predicted cracked images to all actual cracked images. CNNs with a high recall score indicate a better distinguishing capacity between cracked and uncracked images. F1-score shows the comprehensive performance of precision and recall. A CNN with a high F1-score indicates stronger robustness.
Accuracy=TP+TNTP+TN+FP+FN×100E6
Precision=TPTP+FP×100E7
Recall=TPTP+FN×100E8
F1−score=2×Precision×RecallPrecision+Recall×100E9
For example, the prepared dataset contains 10,000 photos, with 32,000 and 7000 cracked surface images and uncracked surface images, respectively. After CNN processing, 2700 images are correctly predicted as cracked surfaces, 300 images out of the 3000 real cracked surfaces are wrongly predicted as uncracked surfaces. 6500 images are correctly predicted as uncracked surfaces, and 500 images out of the 7000 uncracked surfaces are wrongly predicted as cracked surfaces. Then, based on above-mentioned concepts, the values of TP, FN, FP, TN is 2700, 300, 500, 6500, respectively. Table 3 shows the details of the accuracy, precision, recall, and F1 score calculations.
Detailed calculation process of accuracy, precision, recall, and F1 score.
3. Example of concrete crack inspection using CNN
3.1 Textbook example of crack inspection using CNN
This chapter provides an example of how convolution, pooling, fully connected, and benchmarking can be demonstrated in real-world concrete crack inspection using CNN. The above-mentioned calculation was carried out using the Python programming language and the Pytorch package.
3.1.1 Dataset
In this example, the input images were gathered from Kaggle, the world’s most well-known data science community. Kaggle allows access to thousands of public datasets covering a wide range of topics, including medical, agriculture, and construction [9]. By searching “concrete crack” in Kaggle datasets module, 12 datasets were found. The “SDNET2018” dataset was chosen from among them since it comprises sufficient and clean concrete surface images with and without cracks [10]. In “SDNET2018”, 56,096 images were captured in the Utah State University Campus using a 16-megapixel Nikon digital camera, including 54 bridge decks, 72 walls, and 104 pavements. In this example, only images of walls and pavements were used to demonstrate the comparison analysis between manual inspection and CNN-based automatic inspection. Therefore, 42,472 images were used as training and testing dataset. Among them, 6459 cracked concrete surfaces are considered as positive class. The captured cracks are as narrow as 0.06 mm and as wide as 25 mm, while 36,013 uncracked concrete surfaces are considered as negative class. Images in this dataset contain a range of impediments, such as shadows, surface roughness, scaling, edges, and holes. The diverse photographing backgrounds contribute to ensuring the robustness of the designed CNN architecture. At a ratio of 80/20, the cracked and uncracked concrete photos were randomly separated into training and testing datasets. The input images’ pixels were standardized to 227 × 227 × 3 for AlexNet, and 224 × 224 × 3 for VGG16. Table 4 shows the details of the input images. Figure 6 shows the examples of the input images.
Total dataset
Training dataset
Testing dataset
Total images
42,472
33,978
8494
Cracked images
6227
4986
1238
Non-cracked images
36,245
28,992
7256
Image pixels
AlexNet: 227 × 227 × 3 VGG16: 224 × 224 × 3
Table 4.
Details of prepared dataset.
Figure 6.
Examples of cracked and non-cracked surface.
3.1.2 CNN architecture
In this section, two pre-trained CNN networks, AlexNet and VGG16, were introduced to illustrate CNN computation process. AlexNet was designed as an eight-layer architecture. VGG16 has a depth that is two twice that of AlexNet. According to [11, 12], the depth of CNN network has a significant impact on model performance. Therefore, by training and testing the prepared dataset with AlexNet and VGG16, the comparison of network depth to prediction performance and computation cost can be further highlighted.
AlexNet architecture
The AlexNet architecture, developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton in 2012, is considered one of the most influential CNN architectures [13]. AlexNet consists of five convolution layers and three fully-connected layers. The max-pooling layers follow the first, second, and fifth convolution layers. AlexNet was designed to predict 1000 object classifications. 1.2 million images with a pixel size of 2,242,243 were used as input images. As a result, 60 million parameters and 650,000 neurons are included in the computation process. The details of the AlexNet architecture are shown in Figure 7.
Figure 7.
Details of AlexNet architecture.
In the first convolution stage, 96 convolution filters with size of 11 × 11 were applied; they move with a stride with four pixels. The size of pooling filters is 3 × 3. The pooling filters move with a stride of two. It is worth noticing that the error rate can be reduced by applying overlapping pooling technique (the size of pooling filters is smaller than its stride). In the second convolution stage, the size of convolution filters becomes smaller from 11 × 11 to 5 × 5 while its number becomes larger from 96 to 256. The convolution filters in the third and the fourth convolution stage keep minimizing, from 5 × 5 to 3 × 3, while its number keeps increasing from 256 to 384. In the last convolution stage, the size of convolution filters remains same as 3 × 3, and its number turns back to 256. The size and stride of pooling filters also remain the same in the second and fifth convolution stage. Finally, 4096 neurons are included for both first and second fully-connected layers. The final fully-connected layer contains 1000 neurons to output the probabilities of 1000 classifications. The 1000 neurons are activated by softmax function.
The outputs of each convolution and fully-connected layer are activated by a non-linear function, namely the Rectified Linear Units (ReLU) [14]. It is proved in AlexNet that using ReLU instead of other activation functions effectively solves the overfitting problem and improves computation efficiency. Especially for the larger architectures trained on larger datasets. The local response normalization [15] technique (LRN) is also applied following ReLUs to reduce the error rate. Moreover, to avoid overfitting, drop-out techniques [16] are also applied in the first two fully-connected layers. The dropout criteria was set at 0.5.
AlexNet was computed using SGD. The batch size, momentum [17], and weight decay [18] were set as 128, 0.9, and 0.0005, respectively. The learning rate was set as 0.00001. AlexNet was computed for roughly 90 periods in NVIDIA GTX 580 3GB GPUs. As a result, the error rate of AlexNet on test set of top-1 and top-5 achieved 37.5% and 17.0%, which was 10% lower than the out-performed CNN architecture at that time.
2. VGG16 architecture
VGG16, designed by Karen Simonyan and Andrew Zisserman in 2015, was developed to investigate the influence of convolution network depth on prediction accuracy in larger datasets [19]. Therefore, VGG16 was designed as a deep architecture with 16 weight layers, including 13 convolution layers and three fully-connected layers. Convolution layers in VGG16 are presented as five convolution blocks. The details of the VGG16 architecture are shown in Figure 8.
Figure 8.
Details of VGG16 architecture.
As seen from Figure 8, there are two convolution layers in the first two convolution blocks, respectively, and three convolution layers in the following three convolution blocks, respectively. The size of all convolution filters is uniformly 3 × 3. All the convolution filters move with a stride of one. The number of convolution filters increases gradually from 64 to 128, 256, and 512 in the five convolution blocks. To preserve information about image boundaries as completely as possible, spatial padding is applied [20]. As with AlexNet, ReLU is applied as a non-linearity function for convolution and fully-connected outputs to avoid overfitting problems. However, unlike in AlexNet, LRN is not used in VGG16 because the authors stated that LRN has no influence on model performance and increases memory consumption and computation time.
Five max-pooling layers follow the last convolution layer in each block. The max-pooling filters are uniformed with a size of 2 × 2, and a stride of two. As with AlexNet, the first two fully-connected layers have 4096 neurons and 1000 output neurons. The output neurons are activated by softmax. To avoid overfitting problems, drop-out technique is also applied in the first two fully-connected layers. The dropout ratio is set at 0.5. It can be concluded that the most important novelty of VGG16 compared with AlexNet are: (1) the designed deep architecture; (2) the uniformed and small size convolution filters.
In the training process, the training batch size, momentum, weight decay, and learning rate were set as 256, 0.9, and 0.0005, 0.0001, respectively. As a result, the top-1 and top-5 errors of VGG16 achieved 24.4% and 7.2%, which is 13% and 9.8% lower than AlexNet. The result proved that the deep architecture and small convolution filters have positive influences on CNN performance.
3.1.3 Training and benchmarking
Finally, the prepared dataset mentioned in Section 3.3.1 was used to train and test AlexNet and VGG16, respectively. The training and testing process was conducted in Kaggle kernels [21]. Kaggle kernel, provided by Kaggle community, is a virtual environment equipped with NVIDIA Tesla K80, a dual GPU design, and 24GB of GDDR5 memory. This high computing performance enables 5–10 times faster training and testing processes than CPU-only devices. Both AlexNet and VGG16 were trained using SGD. Batch size was and learning rate set as 64, 0.0001, respectively. To avoid overfitting problem, dropout was applied at the fully-connected stage, dropout probability was set as 0.5.
Python was used to program the computing process. Pytorch library was imported. The whole computation time of AlexNet was roughly 2 h, and 4 h for VGG16. The model’s performance in the training and testing datasets is shown in Figures 9 and 10, respectively. The training and testing loss and accuracy values are represented on the vertical axis, while the processing epochs are represented on the horizontal axis. Since the loss and accuracy variation remained consistent after the 60th epoch overtraining the model could lead to an overfitting problem [22]. The training epoch was set to 60 epochs.
Figure 9.
Training loss and accuracy of AlexNet and VGG16.
Figure 10.
Testing loss and accuracy of AlexNet and VGG16.
As shown in Figure 9, both AlexNet and VGG16 converged successfully. The training loss for AlexNet reduced steadily from 0.43 to 0.05 in the 58th epoch and then remained constant in subsequent epochs. Similarly, at the 58th epoch, AlexNet’s training accuracy increased from 0.85 to 0.98. At the 35th epoch, the training loss for VGG16 dropped from 0.42 to 0.01 and subsequently stayed steady at approximately 0.008–0.01 in following epochs. At the 34th epoch, the training accuracy of VGG16 increased from 0.85 to 0.99 and then remained at 0.99. The results revealed that VGG16 performed better during the training procedure. VGG16’s convergence speed is roughly two times that of AlexNet. VGG16’s minimum training loss is 0.04 lower than AlexNet’s, while its maximum accuracy is 0.01 times higher. It is observed that deeper CNN designs assist in the faster processing of larger datasets, which contributes to producing more trustworthy weights and biased matrices. These results are in accordance with those proposed by [23].
Figure 10 shows the loss and accuracy variations of AlexNet and VGG16 in the testing dataset. The testing loss and accuracy consist of the fluctuation tendency of training loss and accuracy. It indicated that neither AlexNet nor VGG16 had overfitting or underfitting problems. VGG16 also out-performed AlexNet in the testing process. AlexNet and VGG16 have minimum testing losses of 0.01 and 0.00003, respectively. AlexNet’s maximum accuracy was 0.98, and VGG16’s was 0.99. In the testing dataset, VGG16 converges at the 34th epoch, which is nearly 2 times faster than AlexNet.
The confusion matrix of AlexNet and VGG16 is shown in Table 5. It can be shown that the accuracy scores of AlexNet and VGG16 are nearly identical, indicating that AlexNet and VGG16 have similar prediction abilities for cracked and uncracked concrete surfaces. VGG16 has a precision and recall of 96.5% and 89.6%, respectively, which is nearly 1% and 5% greater than AlexNet. The results show that VGG16 out-performs AlexNet for predicted positive variables (cracked surfaces). Meanwhile, more cracked images from actual datasets can be correctly identified by applying VGG16. AlexNet and VGG16 have F1-scores of 89.6% and 92.9%, respectively, indicating that the VGG16 model is more robust.
AlexNet
VGG16
TP
5242
5579
FN
985
648
TN
36,007
36,040
FP
238
205
Accuracy
0.971204558
0.97991618
Precision
0.956569343
0.9645574
Recall
0.84181789
0.895937048
F1-score
0.895532587
0.928981767
Table 5.
Confusion matrix of AlexNet and VGG16.
In conclusion, VGG16 demonstrates better performance. Since it is important to avoid ignoring any cracked surfaces, the model with the highest recall and F1-score is more worthwhile. Meanwhile, AlexNet is also a preferable option when the number of cracked and uncracked images is balanced because it shows a similar accuracy score as VGG16 and has a lower computation cost.
3.2 Comparison of CNN and manual inspection
During on-site construction quality management process, quality control managers (QCM) or registered inspectors (RI) are responsible for personally inspecting and reporting quality problems with forms, reports, and photocopies. According to the Mandatory Building Inspection Scheme (MBIS) and related contract regulations, QCMs and RIs are obliged to examine cracks and other defects in building components visually or with non-destructive equipment [24]. For example (1) cracks on the structural components, e.g., structural beam, column, (2) cracks on the external finishes, e.g., tiling, rendering, and cladding, (3) cracks on the fins, grilles, windows, curtain walls.
When using computer-vision-based inspection techniques, differently, there is no necessity for QCMs and RIs to conduct the aforementioned inspection tasks on-site. Instead, their primary responsibilities may switch to (1) taking photos or videos of building components, and (2) inputting the images and videos into pre-trained CNN models. To highlight the differences between manual and computer-vision-based crack inspection, an experiment was set up to calculate and compare inspection time and cost.
The layout of the experiment is shown in Figure 11. Suppose this experiment case is a 15 m × 15 m × 2 m residential building that is located in San Bernardino. The inspection items include cracks on slab, internal walls, and external walls. According to Dohm, John Carl [25], the total manual inspection time for 1600–2600ft2 home in San Bernardino is around 13.65 h, including inspection items of building slab, shear walls, etc. The manual inspection service cost is around $85.9 per hour.
Figure 11.
Layout of the experiment case.
Referring to the computer-vision-based inspection process described above, the total inspection time includes the time of taking images or videos and CNN processing. Assume that the input videos are obtained with handheld camera devices while QCMs or RIs are by means of walking. Then, the time of taking videos can be considered as the time of walking.
Normally, the average walking speed between the age of 20–49 is around 1.42 m/s [26]. Considering the time delays of taking videos, the walking speed can be considered as 0.1 m/s. Suppose the walking path follows an S-curve, shown in Figure 12.
Figure 12.
Walking path of the inspectors.
According to [27], the universally accepted frame rate is 24 FPS per second. Suppose the inspector begins to record video while taking the first step. Then the time of captured video equals the time of walking. The number of the input images that converted from the captured video can be calculated as 2390 s × 24FPS = 57,360. According to the testing time of the textbook examples mentioned in Section 3.1, and the study outcomes of [28], the time of CNN processing is around 100 images per second. Then, the time of CNN processing can be calculated as 57,360/100 = 573.6 s. Therefore, the cost of computer-vision based crack inspection can be calculated as (2390 s + 573.6 s) × (85.9/3600) = $70.7.
Table 6 summarizes the calculation process of time and cost of manual and computer-vision-based crack inspection. It can be seen that using CNN-based technique can effectively reduce inspection time and cost. The inspection time decreases from 13.65 to 0.8 h in total, the inspection cost decreases from $1172.5 to $70.7.
Manual inspection
Computer-vision based inspection
Time
13.65 × 3600 = 49,140 s
Time
Taking video
(1/0.1 × 15) × 15) +1/0.1 × 14 = 2390 s
CNN processing
(2390 × 24)/100 = 573.6 s
Cost
(85.9/3600) × 49,140 = $1172.5
Cost
(573.6 + 2390) × (85.9/3600) = $70.7
Table 6.
Time and cost of manual and computer-vision based crack inspection.
4. Conclusion
To facilitate automatic building quality inspection and management, this study introduced a computer-vision-based automated concrete crack inspection technique. In order to demonstrate the computing and benchmarking process, the mathematical understanding of one of the most essential computer vision algorithms, convolution neural network, was first detailed.
The theoretical foundation was then explained using a textbook example. In this case, the input dataset “SDNET2018” was obtained from the Kaggle community. A digital camera was used to acquire the 56,096 photos from the Utah State University campus. To train the input images, the two most basic CNN architectures, AlexNet and VGG16, were chosen. The Pytorch library was used to carry out the training process in the Kaggle kernel. The model’s performance was evaluated using a confusion matrix. The results revealed that the prediction accuracy of AlexNet and VGG16 is nearly identical. However, VGG16’s precision and recall are higher than AlexNet’s, indicating that VGG16 has a stronger capacity to identify cracked surfaces. VGG16’s F1 score is also greater than AlexNet’s, signifying that VGG16 is more robust. VGG16 is deemed to have a better significance since it has higher precision, recall, and F1-score, which is crucial when distinguishing cracked and uncracked surfaces. When the ratio of cracked and uncracked images is almost the same, however, AlexNet is a feasible alternative because of its high accuracy score and low computation cost. It’s worth noting that, when compared to shallow CNN architectures, deeper and broader CNN architectures outperform shallow CNN architectures for larger datasets.
Next, an experimental case was designed to compare manual and computer-vision-based crack inspection in terms of time and cost. The results showed that the efficiency and cost-effectiveness can be effectively improved when adopting computer-vision-based techniques. The inspection time and cost or the designed case can nearly decrease from 13.65 to 0.8 h, and from $1172.5 to $70.7, respectively.
The findings help to demonstrate the computer-vision-based quality inspection technique in both theory and practice. Although the recently developed computer-vision-based technology improves the efficiency, cost-effectiveness, and safety of human quality inspection, it still relies primarily on the collected image quality. Some concrete surface images are difficult to capture in real-life situations, including among others high-rise buildings, component corners, and buildings in extremely harsh environments. To address this issue, robotics techniques are growing rapidly as a means of upgrading computer-vision-based quality inspection [29]. Previous research has begun to use mobile robots, such as UAVs in order to gather surface images [30, 31, 32]. Some studies have focused on exploring robotic inspection systems to raise the automatic level of quality inspection [33, 34]. Therefore, merging robotics and computer vision approaches may be considered as a worthwhile future research direction to improve the efficiency and accuracy of manual quality control and management.
Acknowledgments
The authors highly appreciate the full support funding of the full-time PhD research studentship under the auspice of the Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong. The authors would like to express their deepest gratitude to Prof. Heng Li for his guidance. Finally, the authors would like to acknowledge the research team members (Mr. King Chi Lo, Mr. Qi Kai) and anyone who provided help and comments to improve the content of this article.
Conflict of interest
All authors declare that they have no conflicts of interest.
List of abbreviations
PASS
Building Performance Assessment Scoring System
MBIS
Mandatory Building Inspection Scheme
MWIS
Mandatory Window Inspection Scheme
NDT
Non-Destructive
CNN
Convolutional Neural Network
RGB
Red, Green, and Blue
ANN
Artificial Neural Network
BP
Back-Propagation
MSE
Mean Square Error
SGD
Stochastic Gradient Descent
ReLU
Rectified Linear Units
LRN
Local Response Normalization
QCM
Quality Control Managers
RI
Registered Inspectors
\n',keywords:"building quality control, concrete crack, quality inspection, computer vision, artificial intelligence",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/81352.pdf",chapterXML:"https://mts.intechopen.com/source/xml/81352.xml",downloadPdfUrl:"/chapter/pdf-download/81352",previewPdfUrl:"/chapter/pdf-preview/81352",totalDownloads:37,totalViews:0,totalCrossrefCites:0,dateSubmitted:"February 26th 2022",dateReviewed:"March 8th 2022",datePrePublished:"April 21st 2022",datePublished:null,dateFinished:"April 17th 2022",readingETA:"0",abstract:"Quality performance of building construction is frequently assessed throughout the construction life cycle. In Hong Kong, quality management system must be established before commencing new building works. Regular building inspections are conducted in accordance with the code of practice of new building works. Quality managers are deployed in construction sites to inspect and record any building defects. The concrete cracks must be identified, which is usually followed by proposed rectifications, in order to protect the public and occupants from dangers. This chapter is structured as follows: Background information of concrete cracks is firstly given. Traditional technique of conducting regular manual inspection is introduced, in accordance with Hong Kong’s code of practice “Building Performance Assessment Scoring System (PASS)”. Then, an advanced technique of conducting crack inspection intelligently based on computer vision is introduced. The procedures of defining, training, and benchmarking the architecture of convolutional neural network models are presented. The calculation steps are detailed and illustrated using a simple textbook example. An experiment case study is used to compare the time, cost of inspecting concrete cracks using both manual and advanced technique. The study concludes with a presentation of the future vision of robot-human collaboration for inspecting concrete cracks in building construction.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/81352",risUrl:"/chapter/ris/81352",signatures:"Siwei Chang and Ming-Fung Francis Siu",book:{id:"11170",type:"book",title:"Quality Control",subtitle:null,fullTitle:"Quality Control",slug:null,publishedDate:null,bookSignature:"Dr. Leo Dimitrios Kounis",coverURL:"https://cdn.intechopen.com/books/images_new/11170.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-991-9",printIsbn:"978-1-80355-990-2",pdfIsbn:"978-1-80355-992-6",isAvailableForWebshopOrdering:!0,editors:[{id:"111582",title:"Dr.",name:"Leo",middleName:"Dimitrios",surname:"Kounis",slug:"leo-kounis",fullName:"Leo Kounis"}],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. Computer vision-based automated concrete crack inspection",level:"1"},{id:"sec_3",title:"3. Example of concrete crack inspection using CNN",level:"1"},{id:"sec_3_2",title:"3.1 Textbook example of crack inspection using CNN",level:"2"},{id:"sec_3_3",title:"Table 4.",level:"3"},{id:"sec_4_3",title:"3.1.2 CNN architecture",level:"3"},{id:"sec_5_3",title:"Table 5.",level:"3"},{id:"sec_7_2",title:"3.2 Comparison of CNN and manual inspection",level:"2"},{id:"sec_9",title:"4. Conclusion",level:"1"},{id:"sec_10",title:"Acknowledgments",level:"1"},{id:"sec_13",title:"Conflict of interest",level:"1"},{id:"sec_10",title:"List of abbreviations",level:"1"}],chapterReferences:[{id:"B1",body:'Driscoll R. Assessment of damage in low-rise buildings, with particular reference to progressive foundation movement. In: Digest. London: H.M.S.O.; 1981. p. 251'},{id:"B2",body:'Chitte CJ, Sonawane YN. Study on causes and prevention of cracks in building. 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The Hong Kong Polytechnic University, Hung Hom, Hong Kong
'},{corresp:"yes",contributorFullName:"Ming-Fung Francis Siu",address:"francis.siu@polyu.edu.hk",affiliation:'
The Hong Kong Polytechnic University, Hung Hom, Hong Kong
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Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274452/images/8324_n.jpg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"4",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:{name:"Majmaah University",institutionURL:null,country:{name:"Saudi Arabia"}}},booksEdited:[],chaptersAuthored:[{id:"67331",title:"Research in Medical Imaging Using Image Processing Techniques",slug:"research-in-medical-imaging-using-image-processing-techniques",abstract:"Medical imaging is the procedure used to attain images of the body parts for medical uses in order to identify or study diseases. There are millions of imaging procedures done every week worldwide. Medical imaging is developing rapidly due to developments in image processing techniques including image recognition, analysis, and enhancement. Image processing increases the percentage and amount of detected tissues. This chapter presents the application of both simple and sophisticated image analysis techniques in the medical imaging field. This chapter also summarizes how to exemplify image interpretation challenges using different image processing algorithms such as k-means, ROI-based segmentation, and watershed techniques.",signatures:"Yousif Mohamed Y. Abdallah and Tariq Alqahtani",authors:[{id:"274452",title:"Dr.",name:"Yousif",surname:"Abdallah",fullName:"Yousif Abdallah",slug:"yousif-abdallah",email:"y.yousif@mu.edu.sa"},{id:"294009",title:"Dr.",name:"Tariq",surname:"Alqahtani",fullName:"Tariq Alqahtani",slug:"tariq-alqahtani",email:"t.alqahtani@mu.edu.sa"}],book:{id:"8125",title:"Medical Imaging",slug:"medical-imaging-principles-and-applications",productType:{id:"1",title:"Edited Volume"}}},{id:"68016",title:"Application of Medical Imaging in Diagnosis and Assessment of Myocarditis and Pericarditis",slug:"application-of-medical-imaging-in-diagnosis-and-assessment-of-myocarditis-and-pericarditis",abstract:"Medical imaging is of one of the most essential means in the investigation of cardiac disorders. In patients, the detection of the whole heart and its adjacent tissues is a crucial procedure since it helps in myocarditis and pericarditis managements. Weekly, there are millions of cardiac imaging performed globally. Medical imaging and processing techniques such as image segmentation, augmentation, and scrutiny can detect cardiac lesions rapidly and precisely. This chapter discusses the application of medical imaging in the diagnosis of myocarditis and pericarditis. This chapter also summarized how to demonstrate image interpretation challenges using diverse image processing procedures and techniques.",signatures:"Yousif Mohamed Y. Abdallah and Nouf H. Abuhadi",authors:[{id:"274452",title:"Dr.",name:"Yousif",surname:"Abdallah",fullName:"Yousif Abdallah",slug:"yousif-abdallah",email:"y.yousif@mu.edu.sa"},{id:"306956",title:"Dr.",name:"Nouf",surname:"Abuhadi",fullName:"Nouf Abuhadi",slug:"nouf-abuhadi",email:"nabuhadi@jazanu.edu.sa"}],book:{id:"7889",title:"Inflammatory Heart Diseases",slug:"inflammatory-heart-diseases",productType:{id:"1",title:"Edited Volume"}}},{id:"69583",title:"The Role of Magnetic Resonance Imaging (MRI) in Autonomic Nervous System Monitoring",slug:"the-role-of-magnetic-resonance-imaging-mri-in-autonomic-nervous-system-monitoring",abstract:"Medical imaging of the nervous system is the methodology used to achieve pictures of parts of the nervous system for therapeutic uses to recognize the ailments. Magnetic resonance imaging (MRI) is a kind of medical imaging tool that utilizes solid magnetic fields and radio waves to deliver point-by-point pictures of the inside of the body. There are large number of imaging methodologies done each week around the world. Medical imaging is developing rapidly due to developments in image acquisition tools including functional MRI and hybrid imaging modalities. This chapter abridged the role of magnetic resonance imaging (MRI) in autonomic nervous system monitoring. This chapter also summarizes the image interpretation challenges in diagnosing autonomic nervous system disorders.",signatures:"Yousif Mohamed Y. Abdallah and Nouf H. Abuhadi",authors:[{id:"274452",title:"Dr.",name:"Yousif",surname:"Abdallah",fullName:"Yousif Abdallah",slug:"yousif-abdallah",email:"y.yousif@mu.edu.sa"},{id:"306956",title:"Dr.",name:"Nouf",surname:"Abuhadi",fullName:"Nouf Abuhadi",slug:"nouf-abuhadi",email:"nabuhadi@jazanu.edu.sa"}],book:{id:"8091",title:"Autonomic Nervous System Monitoring",slug:"autonomic-nervous-system-monitoring-heart-rate-variability",productType:{id:"1",title:"Edited Volume"}}},{id:"75401",title:"Characterization of Brain Stroke Using Image and Signal Processing Techniques",slug:"characterization-of-brain-stroke-using-image-and-signal-processing-techniques",abstract:"Cross-sectional imaging approaches play a key role in assessing bleeding brain injuries. Doctors commonly determine bleeding size and severity in CT and MRI. Separating and identifying artifacts is extremely important in processing medical images. Image and signal processing are used to classify tissues within images closely linked to edges. In CT images, a subjective process takes a stroke ‘s manual contour with less precision. This chapter presents the application of both image and signal processing techniques in the characterization of Brain Stroke field. 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Her research interest is radiology and neuroscience technology and application. She had been trained as an imaging scientist at several prestigious institutes including Columbia University, the University of Pennsylvania, and the National Institutes of Health (NIH). Her research focuses on multi-modal neuroimaging integration such as MRI/PET and EEG/MEG instrumentation to make the best use of multiple modalities for better interpretation of underlying disease mechanisms. She is the author and editor of more than twelve books for well-known publishers including IntechOpen and Nova Science. She has published more than 100 papers and abstracts in many reputed international journals and conferences and served as reviewer and editor for several academic associations.",institutionString:"University of Southern California",institution:{name:"University of Southern California",institutionURL:null,country:{name:"United States of America"}}},{id:"266012",title:"Prof.",name:"Herve",surname:"Yangni-Angate",slug:"herve-yangni-angate",fullName:"Herve Yangni-Angate",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"267693",title:"Dr.",name:"Ing Xiang",surname:"Soo",slug:"ing-xiang-soo",fullName:"Ing Xiang Soo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"278386",title:"Ph.D.",name:"Long",surname:"Chen",slug:"long-chen",fullName:"Long Chen",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"280160",title:"Prof.",name:"Yunchao",surname:"Huang",slug:"yunchao-huang",fullName:"Yunchao Huang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"280161",title:"Prof.",name:"Hua",surname:"Sun",slug:"hua-sun",fullName:"Hua Sun",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"281707",title:"Prof.",name:"Evangelos",surname:"Gazis",slug:"evangelos-gazis",fullName:"Evangelos Gazis",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"294009",title:"Dr.",name:"Tariq",surname:"Alqahtani",slug:"tariq-alqahtani",fullName:"Tariq Alqahtani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"302075",title:"Ph.D.",name:"Zhi",surname:"Dou",slug:"zhi-dou",fullName:"Zhi Dou",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"310181",title:"Dr.",name:"Liqiang",surname:"Yang",slug:"liqiang-yang",fullName:"Liqiang Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"OA-publishing-fees",title:"Open Access Publishing Fees",intro:"
The Open Access model is applied to all of our publications and is designed to eliminate subscriptions and pay-per-view fees. This approach ensures free, immediate access to full text versions of your research.
As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 140,000 international scientists and researchers.
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XML Typesetting and pagination - web (PDF, HTML) and print files preparation
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Permanent and unrestricted online access to your work
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For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
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Indexing and listing across major repositories, see details ...
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Dissemination and Promotion
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Proven world leader in Open Access book publishing with over 10 years experience
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As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 140,000 international scientists and researchers.
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The Open Access Publishing Fee (OAPF) is payable only after your book chapter, monograph or journal article is accepted for publication.
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OAPF Publishing Options
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1,400 GBP Chapter - Edited Volume
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850 GBP Chapter - Book Series Topic (Annual Volume)
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10,000 GBP Monograph - Long Form
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4,000 GBP Compacts Monograph - Short Form
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850 GBP Journal Article (Across Portfolio)
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During the launching phase journals do not charge an APC, rather they will be funded by IntechOpen.
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*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
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Services included are:
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An online manuscript tracking system to facilitate your work
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Personal contact and support throughout the publishing process from your dedicated Author Service Manager
\n\t
Assurance that your manuscript meets the highest publishing standards
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English language copyediting and proofreading, including the correction of grammatical, spelling, and other common errors
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XML Typesetting and pagination - web (PDF, HTML) and print files preparation
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Discoverability - electronic citation and linking via DOI
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Permanent and unrestricted online access to your work
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What isn't covered by the Open Access Publishing Fee?
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If your manuscript:
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\n\t
Exceeds the number of pages defined by the publishing guidelines, an additional fee per page may be required
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If a manuscript requires Heavy Editing or Language Polishing, this will incur additional fees.
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Your Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
\n\n
Open Access Funding
\n\n
To explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at funders@intechopen.com for further details or assistance.
\n\n
For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
\n\n
Added Value of Publishing with IntechOpen
\n\n
Choosing to publish with IntechOpen ensures the following benefits:
\n\n
\n\t
Indexing and listing across major repositories, see details ...
\n\t
Long-term archiving
\n\t
Visibility on the world's strongest OA platform
\n\t
Live Performance Metrics to track readership and the impact of your chapter
\n\t
Dissemination and Promotion
\n
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Benefits of Publishing with IntechOpen
\n\n
\n\t
Proven world leader in Open Access book publishing with over 10 years experience
\n\t
+5,700 OA books published
\n\t
Most competitive prices in the market
\n\t
Fully compliant with OA funding requirements
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Optimized processes that assure your research is made available to the scientific community without delay
\n\t
Personal support during every step of the publication process
\n\t
+184,650 citations in Web of Science databases
\n\t
Currently strongest OA platform with over 175 million downloads
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. 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. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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Utilizing these technologies gives us an opportunity to perform research in screening new molecules using a software and database to establish natural products as a major source for drug discovery. It finally leads to lead structure discovery. Powerful new technologies are revolutionizing natural herbal drug discovery.",book:{id:"8290",slug:"pharmacognosy-medicinal-plants",title:"Pharmacognosy",fullTitle:"Pharmacognosy - Medicinal Plants"},signatures:"Akshada Amit Koparde, Rajendra Chandrashekar Doijad and Chandrakant Shripal Magdum",authors:[{id:"268668",title:"Dr.",name:"Akshada",middleName:"Amit",surname:"Koparde",slug:"akshada-koparde",fullName:"Akshada Koparde"}]},{id:"48805",title:"Biopharmaceutics and Pharmacokinetics",slug:"biopharmaceutics-and-pharmacokinetics",totalDownloads:26104,totalCrossrefCites:2,totalDimensionsCites:7,abstract:null,book:{id:"4491",slug:"basic-pharmacokinetic-concepts-and-some-clinical-applications",title:"Basic Pharmacokinetic Concepts and Some Clinical Applications",fullTitle:"Basic Pharmacokinetic Concepts and Some Clinical Applications"},signatures:"S. Lakshmana Prabu, T.N.K. Suriyaprakash, K. Ruckmani and R.\nThirumurugan",authors:[{id:"91590",title:"Dr.",name:"Sakthivel",middleName:null,surname:"Lakshmana Prabu",slug:"sakthivel-lakshmana-prabu",fullName:"Sakthivel Lakshmana Prabu"},{id:"128690",title:"Dr.",name:"Suriyaprakash",middleName:null,surname:"Tnk",slug:"suriyaprakash-tnk",fullName:"Suriyaprakash Tnk"}]}],onlineFirstChaptersFilter:{topicId:"219",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81238",title:"Artificial Intelligence in Healthcare: An Overview",slug:"artificial-intelligence-in-healthcare-an-overview",totalDownloads:30,totalDimensionsCites:0,doi:"10.5772/intechopen.102768",abstract:"The healthcare industry is advancing ahead swiftly. For many healthcare organizations, being able to forecast which treatment techniques are likely to be successful with patients based on their makeup and treatment framework is a big step forward. Artificial intelligence has the potential to help healthcare providers in a variety of ways, including patient care and administrative tasks. The technology aims to mimic human cognitive functions, as it offers numerous advantages over traditional analytics and other clinical decision-making tools. Data becomes more precise and accurate, allowing the healthcare industry to have more insights into the theranostic processes and patient outcomes. This chapter is an overview of the use of artificial intelligence in radiology, cardiology, ophthalmology, and drug discovery process.",book:{id:"10882",title:"Smart Drug Delivery",coverURL:"https://cdn.intechopen.com/books/images_new/10882.jpg"},signatures:"Syed Shahwar Anwar, Usama Ahmad, Mohd Muazzam Khan, Md. Faheem Haider and Juber Akhtar"},{id:"79411",title:"Phospholipid Based Nano Drug Delivery Systems of Phytoconstituents",slug:"phospholipid-based-nano-drug-delivery-systems-of-phytoconstituents",totalDownloads:120,totalDimensionsCites:0,doi:"10.5772/intechopen.101040",abstract:"The development of phytochemistry and phyto-pharmacology has enabled elucidation of composition and biological activities of several medicinal plant constituents. However phytoconstituents are poorly absorbed due to their low aqueous solubility, large molecular size and poor membrane permeability when taken orally. Nanotechnology based drug delivery systems can be used to improve the dissolution rate, permeability and stability of these phytoconstituents. The current chapter aims to present the extraction of phytoconstituents, their identifications, and development/utilization of phospholipid based nano drug delivery systems (PBNDDS). The content of the chapter also provides characteristic features, in-vitro, in-vivo evaluations and stability performance of PBNDDS. The results from the UHPLC and GC-MS showed different phytoconstituents in the extracted samples with quantitative value. Dynamic light scattering (DLS) data showed PBNDDS of different phytoconstituents in the range of 50–250 nm with PDI value of 0.02–0.5, which was also confirmed by the electron microscopic data. Phytoconstituents loading or entrapment for PBNDDS was in the range of 60–95%. PBNDDS exhibited better in-vitro and in-vivo performance with improved Physico-chemical stability.",book:{id:"10882",title:"Smart Drug Delivery",coverURL:"https://cdn.intechopen.com/books/images_new/10882.jpg"},signatures:"Mohammad Hossain Shariare and Mohsin Kazi"},{id:"79292",title:"Aliphatic Polyester Nanoparticles for Drug Delivery Systems",slug:"aliphatic-polyester-nanoparticles-for-drug-delivery-systems",totalDownloads:109,totalDimensionsCites:0,doi:"10.5772/intechopen.100977",abstract:"Drug delivery systems using aliphatic polyester nanoparticles are usually prepared via an emulsion process. These nanoparticles can control drug release and improve pharmacokinetics. Aliphatic polyesters are linear polymers containing ester linkages, showing sensitivity to hydrolytic degradation. The byproducts then promote autocatalytic degradation. These byproducts could enter the Krebs cycle and be eliminated from the body, resulting in the high biocompatibility of these nanoparticles. The properties of these polyesters are linked to the drug release rate due to biodegradation, i.e., polymer crystallinity, glass transition temperature, polymer hydrophobicity, and molecular weight (MW), all of which relatively influence hydrolysis. Mathematical equations have been used to study the factors and mechanisms that affect drug dissolution compared to experimental release data. The equations used as models for predicting the kinetics of drug release include the zero-order, first-order, Higuchi, Hixson-Crowell, and Korsmeyer-Peppas equations. Aliphatic polyester-based controlled drug delivery has surrounded much of the current activity in the estimation parameters of nanoparticles and stimulated additional research. Polymeric nanoparticles have potential in a wide range of applications, such as in biotechnology, vaccine systems, and the pharmaceutical industry. The main goal of this chapter is to discuss aliphatic polyester nanoparticles as drug carrier systems.",book:{id:"10882",title:"Smart Drug Delivery",coverURL:"https://cdn.intechopen.com/books/images_new/10882.jpg"},signatures:"Narumol Kreua-ongarjnukool, Nopparuj Soomherun, Saowapa Thumsing Niyomthai and Sorayouth Chumnanvej"},{id:"78844",title:"Targeted Nano-Drug Delivery System to Colon Cancer",slug:"targeted-nano-drug-delivery-system-to-colon-cancer",totalDownloads:122,totalDimensionsCites:0,doi:"10.5772/intechopen.100059",abstract:"Cancer has been considered as the most cause of death in world. Employing of nanocarriers as drug delivery systems provide a platform for delivering drugs with increasing the anti-cancer efficacy, enhancing bioavailability of drugs, reducing side effects, enhancing the circulation half-life of drugs, improving the distribution of drugs and overcoming drug resistance. A number of nanocarriers have been studied as drug delivery systems for improving the treatment of cancer including liposomes, micelle, polymeric nanoparticles, carbon nanotubes, dendrimers, solid lipid nanoparticle (SLN) and nanostructure lipid carrier (NLC). In order to enhance recognition and internalization of nanocarriers by the target tissues, their surfaces can be modified with targeting ligands such as integrins, transferrin, folic acid, polysaccharides and antibodies. In this chapter, we are going to introduce the targeted nanocarriers for improving the cytotoxic action of drugs with further attempt of decreasing dose to achieve higher anticancer activity. Targeted nanocarriers would provide a promising therapeutic approach for cancer.",book:{id:"10882",title:"Smart Drug Delivery",coverURL:"https://cdn.intechopen.com/books/images_new/10882.jpg"},signatures:"Eskandar Moghimipour and Somayeh Handali"},{id:"78619",title:"Strategies to Develop Cyclodextrin-Based Nanosponges for Smart Drug Delivery",slug:"strategies-to-develop-cyclodextrin-based-nanosponges-for-smart-drug-delivery",totalDownloads:124,totalDimensionsCites:1,doi:"10.5772/intechopen.100182",abstract:"In recent years, the development of various cyclodextrin (CD)-based nanosponges (NSs) has gained great importance in the controlled and-or targeted release of drugs due to their versatility and simple preparation. In this chapter, an introduction of different administration routes is explained. Further, different ways to obtain CD-NSs and their classification are shown with a brief explanation of the characterization of the inclusion complexes. Finally, illustrative examples in diverse processes or diseases will be reviewed and explained to demonstrate the potential of CD-NSs. Therefore, this division will serve to compile information on CD-NSs in recent years and to illustrate to readers how to generate and apply different derivatives of interest.",book:{id:"10882",title:"Smart Drug Delivery",coverURL:"https://cdn.intechopen.com/books/images_new/10882.jpg"},signatures:"Gjylije Hoti, Silvia Lucia Appleton, Alberto Rubin Pedrazzo, Claudio Cecone, Adrián Matencio, Francesco Trotta and Fabrizio Caldera"},{id:"78313",title:"Smart Drug-Delivery Systems in the Treatment of Rheumatoid Arthritis: Current, Future Perspectives",slug:"smart-drug-delivery-systems-in-the-treatment-of-rheumatoid-arthritis-current-future-perspectives",totalDownloads:201,totalDimensionsCites:0,doi:"10.5772/intechopen.99641",abstract:"Rheumatoid arthritis (RA) is a progressive autoimmune inflammatory disorder characterized by cellular infiltration in synovium causing joint destruction and bone erosion. The heterogeneous nature of the disease manifests in different clinical forms, hence treatment of RA still remains obscure. Treatments are limited owing to systemic toxicity by dose-escalation and lack of selectivity. To overcome these limitations, Smart drug delivery systems (SDDS) are under investigation to exploit the arthritic microenvironment either by passive targeting or active targeting to the inflamed joints via folate receptor, CD44, angiogenesis, integrins. This review comprehensively deliberates upon understanding the pathophysiology of RA and role of SDDSs, highlighting the emerging trends for RA nanotherapeutics.",book:{id:"10882",title:"Smart Drug Delivery",coverURL:"https://cdn.intechopen.com/books/images_new/10882.jpg"},signatures:"Largee Biswas, Vikas Shukla, Vijay Kumar and Anita Kamra Verma"}],onlineFirstChaptersTotal:7},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:31,numberOfPublishedChapters:314,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:17,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:14,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. 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\r\n\tIn general, the harsher the environmental conditions in an ecosystem, the lower the biodiversity. Changes in the environment caused by human activity accelerate the impoverishment of biodiversity.
\r\n
\r\n\tBiodiversity refers to “the variability of living organisms from any source, including terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; it includes diversity within each species, between species, and that of ecosystems”.
\r\n
\r\n\tBiodiversity provides food security and constitutes a gene pool for biotechnology, especially in the field of agriculture and medicine, and promotes the development of ecotourism.
\r\n
\r\n\tCurrently, biologists admit that we are witnessing the first phases of the seventh mass extinction caused by human intervention. It is estimated that the current rate of extinction is between a hundred and a thousand times faster than it was when man first appeared. The disappearance of species is caused not only by an accelerated rate of extinction, but also by a decrease in the rate of emergence of new species as human activities degrade the natural environment. The conservation of biological diversity is "a common concern of humanity" and an integral part of the development process. Its objectives are “the conservation of biological diversity, the sustainable use of its components, and the fair and equitable sharing of the benefits resulting from the use of genetic resources”.
\r\n
\r\n\tThe following are the main causes of biodiversity loss:
\r\n
\r\n\t• The destruction of natural habitats to expand urban and agricultural areas and to obtain timber, minerals and other natural resources.
\r\n
\r\n\t• The introduction of alien species into a habitat, whether intentionally or unintentionally which has an impact on the fauna and flora of the area, and as a result, they are reduced or become extinct.
\r\n
\r\n\t• Pollution from industrial and agricultural products, which devastate the fauna and flora, especially those in fresh water.
\r\n
\r\n\t• Global warming, which is seen as a threat to biological diversity, and will become increasingly important in the future.
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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine"},{id:"8",title:"Bioinspired Technology and Biomechanics",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation"},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"June 24th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:314,numberOfPublishedBooks:31,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",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.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/274452",hash:"",query:{},params:{id:"274452"},fullPath:"/profiles/274452",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()