The crystallite and grain size obtained from the synthesis of nanoforsterite.
\\n\\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\\n\\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\\n\\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\\n\\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\\n\\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\\n\\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\nFeel free to share this news on social media and help us mark this memorable moment!
\\n\\n\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/237"}},components:[{type:"htmlEditorComponent",content:'
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\nIntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\n\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\nFeel free to share this news on social media and help us mark this memorable moment!
\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"6692",leadTitle:null,fullTitle:"Medical and Biological Image Analysis",title:"Medical and Biological Image Analysis",subtitle:null,reviewType:"peer-reviewed",abstract:"This book deals with medical image analysis methods. In particular, it contains two significant chapters on image segmentation as well as some selected examples of the application of image analysis and processing methods. Despite the significant development of information technology methods used in modern image analysis and processing algorithms, the segmentation process remains open. This is mainly due to intra-patient variability and/or scene diversity. Segmentation is equally difficult in the case of ultrasound imaging and depends on the location of the probe or the contact force. Regardless of the imaging method, segmentation must be tailored for a specific application in almost every case. These types of application areas for various imaging methods are included in this book.",isbn:"978-1-78923-331-5",printIsbn:"978-1-78923-330-8",pdfIsbn:"978-1-83881-648-3",doi:"10.5772/intechopen.72065",price:119,priceEur:129,priceUsd:155,slug:"medical-and-biological-image-analysis",numberOfPages:134,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"e75f234a0fc1988d9816a94e4c724deb",bookSignature:"Robert Koprowski",publishedDate:"July 4th 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6692.jpg",numberOfDownloads:10105,numberOfWosCitations:17,numberOfCrossrefCitations:28,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:46,numberOfDimensionsCitationsByBook:1,hasAltmetrics:1,numberOfTotalCitations:91,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 6th 2017",dateEndSecondStepPublish:"November 27th 2017",dateEndThirdStepPublish:"January 26th 2018",dateEndFourthStepPublish:"April 16th 2018",dateEndFifthStepPublish:"June 15th 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"50150",title:"Prof.",name:"Robert",middleName:null,surname:"Koprowski",slug:"robert-koprowski",fullName:"Robert Koprowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTYNQA4/Profile_Picture_1630478535317",biography:"Robert Koprowski, MD (1997), PhD (2003), Habilitation (2015), is an employee of the University of Silesia, Poland, Institute of Computer Science, Department of Biomedical Computer Systems. For 20 years, he has studied the analysis and processing of biomedical images, emphasizing the full automation of measurement for a large inter-individual variability of patients. Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"696",title:"Medical Imaging",slug:"medical-imaging"}],chapters:[{id:"60741",title:"Image Segmentation",doi:"10.5772/intechopen.76428",slug:"image-segmentation",totalDownloads:1272,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Image segmentation is one of the important and useful techniques in medical image processing. As the image segmentation technique results robust and high degree of accuracy, it is very much useful for the analysis of different image modalities, such as computerized tomography (CT) and magnetic resonance imaging (MRI) in the medical field. CT imaging gives more importance than MRI because of its wider availability, inexpensive and sensitiveness. In most cases, CT offers information needed to make decisions during urgent situations.",signatures:"Kumaravel Subramaniam Tamilselvan and Govindasamy\nMurugesan",downloadPdfUrl:"/chapter/pdf-download/60741",previewPdfUrl:"/chapter/pdf-preview/60741",authors:[{id:"232687",title:"Dr.",name:"K.S",surname:"Tamilselvan",slug:"k.s-tamilselvan",fullName:"K.S Tamilselvan"},{id:"245153",title:"Dr.",name:"G",surname:"Murugesan",slug:"g-murugesan",fullName:"G Murugesan"}],corrections:null},{id:"59741",title:"Active Contour Based Segmentation Techniques for Medical Image Analysis",doi:"10.5772/intechopen.74576",slug:"active-contour-based-segmentation-techniques-for-medical-image-analysis",totalDownloads:3470,totalCrossrefCites:25,totalDimensionsCites:43,hasAltmetrics:1,abstract:"Image processing is a technique which is used to derive information from the images. Segmentation is a section of image processing for the separation or segregation of information from the required target region of the image. There are different techniques used for segmentation of pixels of interest from the image. Active contour is one of the active models in segmentation techniques, which makes use of the energy constraints and forces in the image for separation of region of interest. Active contour defines a separate boundary or curvature for the regions of target object for segmentation. The contour depends on various constraints based on which they are classified into different types such as gradient vector flow, balloon and geometric models. Active contour models are used in various image processing applications specifically in medical image processing. In medical imaging, active contours are used in segmentation of regions from different medical images such as brain CT images, MRI images of different organs, cardiac images and different images of regions in the human body. Active contours can also be used in motion tracking and stereo tracking. Thus, the active contour segmentation is used for the separation of pixels of interest for different image processing.",signatures:"R.J. Hemalatha, T.R. Thamizhvani, A. Josephin Arockia Dhivya,\nJosline Elsa Joseph, Bincy Babu and R. Chandrasekaran",downloadPdfUrl:"/chapter/pdf-download/59741",previewPdfUrl:"/chapter/pdf-preview/59741",authors:[{id:"238868",title:"Prof.",name:"Hemalatha",surname:"R.J",slug:"hemalatha-r.j",fullName:"Hemalatha R.J"},{id:"242385",title:"Dr.",name:"Chandrasekaran",surname:"R",slug:"chandrasekaran-r",fullName:"Chandrasekaran R"},{id:"242386",title:"Ms.",name:"Thamizhvani",surname:"T.R",slug:"thamizhvani-t.r",fullName:"Thamizhvani T.R"},{id:"242388",title:"Dr.",name:"Josephin Arockia Dhivya",surname:"A",slug:"josephin-arockia-dhivya-a",fullName:"Josephin Arockia Dhivya A"},{id:"242389",title:"Ms.",name:"Josline Elsa",surname:"Joseph",slug:"josline-elsa-joseph",fullName:"Josline Elsa Joseph"},{id:"242390",title:"Ms.",name:"Bincy",surname:"Babu",slug:"bincy-babu",fullName:"Bincy Babu"}],corrections:null},{id:"60362",title:"Medical and Biological Image Analysis",doi:"10.5772/intechopen.75491",slug:"medical-and-biological-image-analysis",totalDownloads:993,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Today, technology and information communication are deeply embedded in our life. Information is present and used in many forms: electronic documents, audio, videos, photos, etc. Recent advances in technology, particularly in the computer industry and communication, have motivated organisations to replace their traditional manually stored and exchanged records with computer systems and digital documents for secure storage and smooth transmission. Medical and biological image processing is a numerical method and technique for modifying a digital image to improve or extract information. The main stages of image processing are:",signatures:"Abdelkader Moumen",downloadPdfUrl:"/chapter/pdf-download/60362",previewPdfUrl:"/chapter/pdf-preview/60362",authors:[{id:"233918",title:"Dr.",name:"Abdelkader",surname:"Moumen",slug:"abdelkader-moumen",fullName:"Abdelkader Moumen"}],corrections:null},{id:"60853",title:"Bioinformatics Solutions for Image Data Processing",doi:"10.5772/intechopen.76459",slug:"bioinformatics-solutions-for-image-data-processing",totalDownloads:1188,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"In recent years, the increasing use of medical devices has led to the generation of large amounts of data, including image data. Bioinformatics solutions provide an effective approach for image data processing in order to retrieve information of interest and to integrate several data sources for knowledge extraction; furthermore, images processing techniques support scientists and physicians in diagnosis and therapies. In addition, bioinformatics image analysis may be extended to support several scenarios, for instance, in cyber-security the biometric recognition systems are applied to unlock devices and restricted areas, as well as to access sensitive data. In medicine, computational platforms generate high amount of data from medical devices such as Computed Tomography (CT), and Magnetic Resonance Imaging (MRI); this chapter will survey on bioinformatics solutions and toolkits for medical imaging in order to suggest an overview of techniques and methods that can be applied for the imaging analysis in medicine.",signatures:"Pietro Cinaglia, Luciano Caroprese, Giuseppe Lucio Cascini,\nFrancesco Dattola, Pasquale Iaquinta, Miriam Iusi, Pierangelo Veltri\nand Ester Zumpano",downloadPdfUrl:"/chapter/pdf-download/60853",previewPdfUrl:"/chapter/pdf-preview/60853",authors:[{id:"236617",title:"Prof.",name:"Ester",surname:"Zumpano",slug:"ester-zumpano",fullName:"Ester Zumpano"},{id:"248312",title:"Dr.",name:"Pietro",surname:"Cinaglia",slug:"pietro-cinaglia",fullName:"Pietro Cinaglia"},{id:"248313",title:"Dr.",name:"Luciano",surname:"Caroprese",slug:"luciano-caroprese",fullName:"Luciano Caroprese"},{id:"248316",title:"Prof.",name:"Giueppe Lucio",surname:"Cascini",slug:"giueppe-lucio-cascini",fullName:"Giueppe Lucio Cascini"},{id:"248317",title:"Dr.",name:"Francesco",surname:"Dattola",slug:"francesco-dattola",fullName:"Francesco Dattola"},{id:"248319",title:"Dr.",name:"Pasquale",surname:"Iaquinta",slug:"pasquale-iaquinta",fullName:"Pasquale Iaquinta"},{id:"248320",title:"Dr.",name:"Miriam",surname:"Iusi",slug:"miriam-iusi",fullName:"Miriam Iusi"},{id:"248321",title:"Prof.",name:"Pierangelo",surname:"Veltri",slug:"pierangelo-veltri",fullName:"Pierangelo Veltri"}],corrections:null},{id:"60152",title:"Abnormal Tissue Zone Detection and Average Active Stress Estimation in Patients with LV Dysfunction",doi:"10.5772/intechopen.75202",slug:"abnormal-tissue-zone-detection-and-average-active-stress-estimation-in-patients-with-lv-dysfunction",totalDownloads:977,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Detection of regional ventricular dysfunction is a challenging problem. This study presents an efficient method based on ultrasound (US) imaging and finite element (FE) analysis, for detecting akinetic and dyskinetic regions in the left ventricle (LV). The underlying hypothesis is that the contraction of a healthy LV is approximately homogeneous. Therefore, any deviations between the image-based measured deformation and a homogeneous contraction FE model should correspond to a pathological region. The method was first successfully applied to synthetic data simulating an acute ischemia; it demonstrated that the pathological areas were revealed with a higher contrast than those observed directly in the deformation maps. The technique was then applied to a cohort of eight left bundle branch block (LBBB) patients. For this group, the heterogeneities were significantly less pronounced than those revealed for the synthetic cases but the method was still able to identify the abnormal regions of the LV. This study indicated the potential clinical utility of the method by its simplicity in a patient-specific context and its ability to quickly identify various heterogeneities in LV function. Further studies are required to determine the model accuracy in other pathologies and to investigate its robustness to noise and image artifacts.",signatures:"Sareh Behdadfar, Laurent Navarro, Joakim Sundnes, Molly\nMaleckar, Hans Henrik Odland and Stephane Avril",downloadPdfUrl:"/chapter/pdf-download/60152",previewPdfUrl:"/chapter/pdf-preview/60152",authors:[{id:"53776",title:"Dr.",name:"Laurent",surname:"Navarro",slug:"laurent-navarro",fullName:"Laurent Navarro"},{id:"233287",title:"Prof.",name:"Stephane",surname:"Avril",slug:"stephane-avril",fullName:"Stephane Avril"},{id:"243118",title:"Dr.",name:"Sareh",surname:"Behdadfar",slug:"sareh-behdadfar",fullName:"Sareh Behdadfar"},{id:"243119",title:"Dr.",name:"Mary M",surname:"Maleckar",slug:"mary-m-maleckar",fullName:"Mary M Maleckar"},{id:"243120",title:"Dr.",name:"Hans Henrik",surname:"Odland",slug:"hans-henrik-odland",fullName:"Hans Henrik Odland"},{id:"243121",title:"Prof.",name:"Joakim",surname:"Sundnes",slug:"joakim-sundnes",fullName:"Joakim Sundnes"}],corrections:null},{id:"58652",title:"Non-Conventional Radiotherapy for Total Body Irradiation: Antecedents, Current Research and Perspectives",doi:"10.5772/intechopen.73026",slug:"non-conventional-radiotherapy-for-total-body-irradiation-antecedents-current-research-and-perspectiv",totalDownloads:1028,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In addition to the conventional techniques used in radiotherapy, certain procedures, special-called for the treatment of both some cancer diseases and clinical application are usually required. Such practices typically manifest a technical problem with respect to the equipment used, which requires important adjusts that diverge significantly from the standard implemented in the common treatments. Total body irradiation is one of those special techniques, in which the radiation target is the entire patient body. In a broad sense, the concept covers all radiation processes with photon beam fields more wide than standard field size. Treatment with total body irradiation is usually applied with purpose of providing immunosuppression to prevent rejection in bone marrow transplantation procedure. Diseases such as aplastic anemia and a varied number of leukemia and lymphomas respond favorably to this treatment scheme. Beams of megavoltage photons, such as Cobalt sources and linear accelerators, are used for such purposes. In this chapter, the technique will be studied analyzing its definition and first applications. The chapter includes a description of the main treatment schemes on which it is based, covering the calibration process, ergonomic criteria as well as the main contributions in the clinical research field, opportunity fields and novel research perspectives.",signatures:"Francisco Mesa-Linares",downloadPdfUrl:"/chapter/pdf-download/58652",previewPdfUrl:"/chapter/pdf-preview/58652",authors:[{id:"233671",title:"Dr.",name:"Francisco",surname:"Mesa-Linares",slug:"francisco-mesa-linares",fullName:"Francisco Mesa-Linares"}],corrections:null},{id:"60729",title:"Automatic Image Analysis and Recognition for Ultrasound Diagnosis and Treatment in Cardiac, Obstetrics and Radiology",doi:"10.5772/intechopen.76284",slug:"automatic-image-analysis-and-recognition-for-ultrasound-diagnosis-and-treatment-in-cardiac-obstetric",totalDownloads:1179,totalCrossrefCites:1,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Ultrasound image analysis and recognition techniques for improving workflow in diagnosis and treatment are introduced. Fully automatic techniques for applications of cardiac plane extraction, foetal weight measurement and ultrasound-CT image registration for liver surgery navigation are included. For standard plane extraction in 3D cardiac ultrasound, multiple cardiac landmarks defined in ultrasound cardiac examination guidelines are detected and localized by a Hough-forest-based detector, and by six standard cardiac planes, cardiac diagnosis is extracted following the guideline. For automatic foetal weight measurement, biparietal diameter (BPD), femur length (FL) and abdominal circumference (AC) are estimated by segmenting corresponding organs and regions from foetal ultrasound images. For ultrasound-CT liver image registration, initial alignment is obtained by localizing a corresponding portal vein branch from an intraoperative ultrasound and preoperative CT image pair. Then portal vein regions of the ultrasound-CT image pair are extracted by a machine learning method and are used for image registration.",signatures:"Zisheng Li, Peifei Zhu, Takashi Toyomura and Yoshimi Noguchi",downloadPdfUrl:"/chapter/pdf-download/60729",previewPdfUrl:"/chapter/pdf-preview/60729",authors:[{id:"237293",title:"Ph.D.",name:"Zisheng",surname:"Li",slug:"zisheng-li",fullName:"Zisheng Li"},{id:"246858",title:"Ms.",name:"Peifei",surname:"Zhu",slug:"peifei-zhu",fullName:"Peifei Zhu"},{id:"246859",title:"Mr.",name:"Takashi",surname:"Toyomura",slug:"takashi-toyomura",fullName:"Takashi Toyomura"},{id:"246860",title:"Mr.",name:"Yoshimi",surname:"Noguchi",slug:"yoshimi-noguchi",fullName:"Yoshimi Noguchi"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:{id:"7",series:{id:"7",title:"Biomedical Engineering",issn:"2631-5343",editor:{id:"50150",title:"Prof.",name:"Robert",middleName:null,surname:"Koprowski",slug:"robert-koprowski",fullName:"Robert Koprowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTYNQA4/Profile_Picture_1630478535317",biography:"Robert Koprowski, MD (1997), PhD (2003), Habilitation (2015), is an employee of the University of Silesia, Poland, Institute of Computer Science, Department of Biomedical Computer Systems. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"69964",title:"Synthesis and Characterization of Forsterite (Mg2SiO4) Nanomaterials of Dunite from Sumatera",doi:"10.5772/intechopen.85082",slug:"synthesis-and-characterization-of-forsterite-mg-sub-2-sub-sio-sub-4-sub-nanomaterials-of-dunite-from",body:'\n
Forsterite is a magnesium silicate crystal with the chemical formula Mg2SiO4 derived from the olivine mineral group [1]. Olivine is a group of minerals composed of iron (Fe) and magnesium (Mg). The olivine mineral is green, with luster, formed at high temperatures. This mineral is commonly found in basalt and ultramafic rocks. Rocks whose overall minerals consist of olivine minerals are known as dunite rocks [2, 3].
\nForsterite is often used as an electronic component because it has a low coefficient of thermal expansion and electrical conductivity [4]. Besides that, forsterite has superior properties, namely chemical stability at high temperatures [5] and high electrical properties so that it can be used as a coating for the back, edges, and front of iron and steel [6, 7, 8, 9, 10, 11].
\nIron and steel are widely used in industry and property such as building materials, ships, electronic equipment, etc. But in its use, steel is easily corrosion. To overcome this corrosion problem, a lot of research continues. One of them is by soaking the steel with an anti-corrosion solution. But the results are not yet suitable for achieving commercial applications. The reason is that the particle size of the anti-corrosion powder used is relatively very large, giving rise to new problems, namely iron and steel which look uneven and homogeneous so that it disrupts appearance or esthetics.
\nNanoparticle technology brings fresh air in an effort to increase the resistance of steel to corrosion. With a very small particle size, the problem can be solved. Nanoparticles are very fine so they are homogeneous. The use of nanoparticles is widely applied in various fields, including industrial fields such as paint, health, etc. Nanometer-sized materials have chemical and physical properties that are superior to large (bulk) material [12]. Nanoparticles have several advantages, namely having a larger touch surface area so that the bond between one particle and another is easy to form, and the mechanical, optical, and chemical properties of the material can experience significant differences with the properties of the sized material micrometer [13].
\nThe advantage of forsterite made in nanometer size is that it is very strong, hard, and resilient at high temperatures, and is waterproof, corrosion resistant and has very active chemical properties that add durability when used for iron and steel coatings. Economical studies of nanoforsterite or nanomaterials are far more economical than conventional [14].
\nForsterite nanoparticles can be made chemically and mechanically. Chemical methods are co-precipitation, solvothermal, sol-gel, solid state, and others. The mechanical method is by using a ball mill [15]. The advantage of using mechanical milling is a simple and effective method for growing solid crystals (the size of crystal grains becomes small) without going through chemical reactions that require a long time in the process of nanoparticle synthesis [15].
\nForsterite nanoparticle synthesis has been carried out using synthetic samples with the milling method [16, 17, 18]. The crystallite and grain size obtained from the synthesis of nanoforsterite can be seen in Table 1.
\nThe crystallite and grain size obtained from the synthesis of nanoforsterite.
Table 1 shows the grain sizes of different forsterites resulting from several different research treatments. Size the forsterite nano single phase is formed after 40 h of milling and heating the temperature is 1000°C with a holding time of 1 h. The grain size of the Forster produced is a range between 0.1 and 2 μm [16]. The synthesis of forsterite nanoparticles was then continued using talc and magnesium oxide by the same method [17]. The variation in milling time is increased to 60 h with a heating temperature of 1200°C. The grain size obtained is less than 500 nm. Another study also uses a milling process with a variation of 5, 10, 20, and 30 h milling with temperature variations of 850–1100°C. Forsterite nanopowders were prepared from MgO and SiO2 mixtures by using a high energy ball milling method. The results found that the average particle size was reduced to 147.4 nm [18]. Characterization using XRD found that there was still Fe and Cr with a low content of 0.4256 and 0.5056%, respectively. Although many researchers have synthesized forsterite nanoparticles, the results obtained are still contaminated with other elements even at low levels. Contamination of samples with other elements can affect the nature of the final product.
\nThis chapter discusses the synthesis and characterization of forsterite nanoparticles using High Energy Milling Ellipse 3D Motion (HEM-E3D). HEM-E3D is a unique technique that uses collision energy between crush balls and chamber walls which are rotated and moved in a certain way. The advantages of High Energy Milling is that in a relatively short time it can make nanoparticles (it takes several hours, depending on the type of tool), nanoparticles are produced in relatively large quantities. Besides that, the milling technique is one technique for growing solid crystals without going through the vaporation phase or chemical reaction treatment as is usually needed in the synthesis process in general.
\nThe forsterite discussed in this chapter comes from dunite rocks originating from West Sumatra. West Sumatra is one of the regions in the Indonesian archipelago which has a fairly complex geological order. The West Sumatra region is traversed by the equator (zero degrees latitude), precisely located in the Bonjol sub-district of Pasaman district, because of the influence of this location, West Sumatra has a trophic climate with high temperatures and humidity. The land surface height between one area and another area varies greatly. However, physically, West Sumatra is a region that is largely photographed by the mountains and the Bukit Barisan plateau which stretches from the North West to the Southeast, 63% and the area is a dense forest with elevations up to 3000 m above sea level.
\nThe geographical condition of West Sumatra is quite unique which is partly located in the lowlands and partly in the highlands, marked there are many mountains, lakes, valleys/canyons, and rivers. This situation is due to its location at the confluence of three plates, namely the Eurasian plate to the north, the Australian Indian plate to the south and the Pacific plate to the east. The position of West Sumatra which is near the collision of two large plates namely the Australian Indian plate and the Eurasian Plate in addition to receiving negative consequences of natural disaster-prone areas also benefits, namely the emergence of sources of minerals containing economic minerals to the surface, one of which is dunite rock. Figure 1 is dunite rock from West Sumatra.
\nDunite rocks from West Sumatra.
Dunite is the main ingredient of the Earth’s mantle and is rarely found in continental rock. Dunite is found when mantle rock plates from the subduction zone have been elevated to the continental crust. Dunite formation takes place in conditions that are dense or almost dense (at high temperatures) in a magma solution and before it cools to this temperature, the rock is ready to unite to form a binding anhedral olivine mass [19].
\nDunite is a greenish-black rock and has a mineral composition almost entirely of monomineralic olivine (generally magnesia olivine) [20, 21]. Its mineral accessories include chromites, magnetite, limonite, and spinel. Olivine mineral is an iron-magnesium silicate with its chemical formula (Mg, Fe)2 (SiO4), has an orthorhombic crystalline system, has no hemisphere, has a hardness of 6.5–7, specific gravity 3.27–4.37, has a gloss light, and the color of this mineral is yellowish green and grayish green. Olivine is one of the most common minerals on the surface of the earth and has also been identified on the surface of the Moon, Mars, and comets. Olivine minerals are generally as rock-forming minerals and also as accompanying minerals, in alkaline rocks such as gabbros, peridotite, etc. [21].
\nDunite rocks naturally contain magnesium (MgO) and have a very alkaline pH, which is around 7.5–9.5. These rocks can be used as basic fertilizer (natural) and compound fertilizer. MgO is one of the most important elements for plants for photosynthesis, while the pH level of rocks can neutralize acid soils such as peatlands. Because the magnesium content of dunite rocks is quite high, the fertilizer with rock material will be very beneficial for agricultural and plantation activities.
\nDunite contains 36–42% MgO and 36–39% SiO2. Olivine is a commercial source of a combination of magnesia and silica used in metallurgy. The content will increase if it is affected by an increase in temperature. The phase diagram of dunite consists of two phases namely forsterite and fayalite. The characteristics of the forsterite phase are melted at 1890°C and fayalite melts at a temperature of 1205°C [22]. Ultramafic frozen rock is an igneous rock which chemically contains less than 45% SiO2 from its composition. The mineral content is dominated by heavy minerals with elements such as Fe (iron) and Mg (magnesium) which are also called ultramafic minerals [23]. The mineral composition and structural characteristics of dunite mainly contain olivine minerals and always contain little brucite, spinel-chromite, magnetite, and pyroxene. For comparison, dunites from South Turkey have chemical compositions such as FeO: 7.05%, SiO2: 38.74%, MgO: 37.16%, CaO: 9.24%, Al2O3: 1.65% [24].
\nOlivine is the name of a group of rock-forming minerals found in mafic and ultramafic igneous rocks such as basalt, gabbro, dunite, diabas, and peridotite. Olivine is usually green and has a chemical composition ranging from Mg2SiO4 and Fe2SiO4. Olivine has a high crystallization temperature compared to other minerals. Olivine is the first mineral that crystallizes from magma. Olivine crystals are formed during the slow cooling process of magma and then settle in the bottom of the magma kitchen because of its relatively high density. This olivine accumulation can cause dunite-like rock formation at the bottom of the magma kitchen.
\nThe minerals in the olivine group crystallize in the orthorhombic system (Pbnm space group) with silicates, which means that olivine is nesosilicate. In an alternative view, the atomic structure can be described as hexagonal, near oxygen ions with half of the octahedral atoms bonded to magnesium or iron ions and one-eighth of tetrahedral occupied by silicon ions [12]. The shape of the olivine crystal structure can be seen in Figure 2.
\nCrystal structure of olivine [
Forsterite is a member of the olivine mineral prepared by Mg and Si. The general formula is Mg2SiO4. Figure 3 shows the shape of the forsterite crystal structure.
\nCrystal structure of forsterite [
Figure 3, Mg atoms are shown in purple, Mg2+ green, Si4+ white, and O2− pink. The radius of the Mg atom is 1.6 Ǻ, Mg2+ is 0.75 Ǻ, Si4+ is 0.4 Å, and O2 is 1.35 Ǻ. A common characteristic of forsterite is the orthorhombic crystal system with the dimensions of cells a ≠ b ≠ c, where a = 4.79 Å; b = 10.19 Å; c = 5.85 Å with α = β = γ = 90° and space group Pbnm and density (g cm−3) is 3.275 [26].
\nForsterite has a low electrical conductivity making it ideal for electronic materials. In addition, forsterite has a high melting point which is equal to 1890°C which indicates that forsterite is a refractory material and ceramic manufacturing application because it has good chemical stability and a low coefficient of thermal expansion [27].
\nThe nanoparticle technology is a material technology that deals with the creation or synthesis of small particles in nanometers (one per billion meters). The purpose of this technology is to use it for a more efficient future life. Nanoparticle synthesis can be done in two ways, namely top down and bottom up. Top down is the making of nanostructures by minimizing large material by mechanical activation, while bottom up is a way of assembling atoms or molecules and combining them through chemical reactions to form nanostructures. An example of a bottom up technology is using sol gel techniques, chemical precipitation, and phase agglomeration while top down method is grinding with a milling tool such as HEM-E3D [15].
\nThe process that occurs during milling can be explained below. Mechanical milling is a simple and effective method for growing solid crystals (the size of crystal grains becomes smaller) without going through the evaporation phase or chemical reaction, as is usually needed in other synthesis processes [25]. This smoothing machine is able to convert hard and easily broken samples into powder-shaped analytical samples [14].
\nHEM-E3D is one tool used for mechanical alloys that use hard balls made of carbon steel. HEM-E3D vibrates samples with engine shocks because milling is used for a collision of kinetic energy in the sample. The speed of the media causes a high amount of energy to form to collide with the sample. Various combinations of media milling are used in HEM. A ratio of balls to powder, or BPR (Ball Powder Ratio), usually also used to change milling parameters [28].
\nHEM-E3D is very good for reducing particle size. The nature of particle size reduction and subsequent growth is the same as oxide analysis. In the milling process, HEM-E3D works by destroying the powder mixture through the mechanism of colliding milled balls that move to follow the movement pattern of the three-dimensional elliptical container that allows the formation of nanometer scale powder particles due to the high frequency of collisions. The high frequency of collisions that occur between the mixture of powder and milled balls is caused by the container rotating at high speed, which reaches 500 rpm, and the shape of the spherical movement of the three-dimensional ellipse.
\nMilling with ball mill is used for pulverizing the powder into nanoparticles because there is a grinding of powder on the surface of the ball when it collides with another ball so that the impact size given by the ball is as big as the collision force of the surface area of the ball. There are two collision paths where the material is between two balls and pulverization where the material is exposed to the ball when it is close to the cylinder wall. The interaction between the balls in the chamber is shown in Figure 4.
\nInteraction between grinding balls in high energy milling [
Figure 4 shows the interaction between the balls in the chamber. The ball that has a smaller impact area, will give greater impact so that the destructive ability strengthens with the reduction of the touch area. Therefore, nanoparticle powder is easier to form by using smaller balls. Besides that, the collision frequency is the accelerating destruction factor.
\nThe formation of forsterite nanoparticles using HEM-E3D will be more helpful because this tool has several advantages including machines that can be used can be used to do mixing, homogenization (uniformity), chemical mechanic (making chemical-mechanical reactions), mechanical milling, mechanical alloys, and do an emulsion. So nanoparticles will be produced by smoothing the material up to the nanometer scale, high destruction rate, easy conditioning of the milling system so that the mechanism of amorphization and nanoparticle formation process is faster and more effective, becoming a tool for making nanopowder at low prices and increasing efficiency and electronic systems integrated system includes a motor controller, and a timer.
\nProcess parameters that must be considered in the grinding process include speed and time of grinding, comparison of ball weight to weight of powder, and empty space in the grinding container. In simple terms, increasing the rotation speed of the milling will increase the energy input to the powder. How fast the rotation of milling is affected by the design of the instrument. Speed also affects the increase in the temperature of the media milling. If the grinding speed is too high, the temperature of the components in the grinding process will increase. This increase in temperature can be advantageous for example when diffusion is needed to produce homogenizes and fusion of powder. The disadvantage is excessive friction or collision of the milling equipment that carries contamination.
\nThe rotation speed used is adjusted so that the milling process runs optimally. If the rotation speed is set too fast, the balls will only rotate on the cylinder wall due to centrifugal force. Conversely, when the rotation speed is very low, the impact energy to destroy the material is not enough so that the process will last long.
\nThe milling time needed also depends on the type of milling used, intensity of milling, ball-powder ratio, and milling temperature. Long milling times from the time needed will increase contamination and some unwanted phases will be formed. Therefore, milling powder did not require a long time for sample preparation.
\nIn addition, the size of the ball greatly affects the efficiency of milling, where generally large ball sizes (with high density) are more useful because greater weight can transfer greater impact and kinetic energy to powder particles. While small balls produce more friction, making it easier to form an amorphous phase. The resulting grain size is also smaller when the ball used is small. In practice, a combination of various sizes is often used. The use of the same size of balls can cause the ball to rotate along the bullet path and not hit the surface of the powder randomly. The larger the size of the ball used, the greater the energy when pounding, but when the material is small, the collision will rarely occur because space, where the collision occurs, becomes smaller and in the end, the process will not be optimal.
\nFor small scale or laboratory, generally, the ratio of balls to the weight of powder used is around 10:1, 10 g of balls and 1 g of powder, while for large scale or industrial scale, the ratio of ball weight to the weight of powder used can reach 100:1. The higher the ratio of the weight of the ball to the weight of the powder used, the shorter the frequency of impact.
\nThe grinding procedure is a forsterite powder inserted into a metal chamber with several steel balls in it that move continuously. The size of the milled ball used in this process is a small milled ball with a diameter of 5 mm with a weight of 0.5 g. The ratio of the weight of the milled ball to the weight of the powder in the grinding container used is 10:1. For example, the weight of the milled ball is 150 g while the amount of powder is 15 g. In the metal chamber, the balls will collide with each other. As a result of this ball collision, the homogeneous powder that is inserted into this tool will be crushed between the balls. This causes the particles to break. And so on until it reaches the desired size [30]. Figure 5 shows the concept of destruction of sample powder in the ball mill.
\nConcept of powder destruction at ball mill [
The synthesis of nanoparticles from forsterite discussed here comes from dunite rock samples taken from the West Sumatra region, precisely at coordinates: 00° 09′ 01.1″ LU and 99° 53′ 19.5″ BT. Samples were sieved with 2.1 mm sieves to obtain finer and homogeneous samples. The sample was then crushed for 3 min using bowl mill so that the samples were finer and homogeneous. Samples were tested by X-Ray Fluorescence (XRF) to see the composition of the compounds in the dunite. The results of XRF are known to dunite rocks found in West Sumatra, Indonesia has a composition of FeO: 11.12%, SiO2: 31.18%, MgO: 49.96%, CaO: 7.31%, Al2O3: 0.43%. MgO content in this region is quite high when compared to Southern Turkey [24]. To get forsterite minerals from dunite samples, calcination was carried out with temperature variations of 700, 800, 900, 1000, and 1100°C. Then the samples were carried out X-Ray Diffraction (XRD) test to see the phases which appear for each calcination temperature. From the results of XRD, it is known that the forsterite phase at the optimum calcination temperature.
\nSamples with optimum calcination temperature are then taken to be further synthesized into forsterite nanoparticles. Samples were weighed as much as 2 g and milling balls as much as 20 g for each milling time. The type of milling ball used is small carbon steel with 40 pieces which weigh 0.2 per fruit. Medium sized milling balls of 4 which weigh 0.5 per piece. While large milling balls as much as two pieces weighing 3.55 g per fruit. The samples that have been prepared are then processed using HEM-E3D with variations in milling time of 5, 10, 20, 40, and 60 h. The length of the milling process in each cycle is carried out for 30 seconds, and then the process is stopped for 1 min to avoid an increase in temperature and damage to the milling device due to rising motor temperatures that are too high.
\nThe characterization of forsterite nanoparticles was carried out using X-ray Diffraction (XRD) and Scanning electron microscopes (SEM). XRD is used to determine the structure and size of crystals of forsterite for each variation of milling time. The crystal size of forsterite is calculated using the Scherrer formula, D = 0.9 λ/β cosθ, where λ was the wavelength of X-ray radiation, β was the full width at half maximum (FWHM) of the peaks at the diffracting angle θ [31]. SEM is used to see the microstructure or morphology of forsterite and particle size. The results of the XRD test from dunite raw material for each calcination temperature variation can be seen in Table 2.
\nStructure of dunite raw material for each temperature variation of calcination.
Table 1 shows the appearance of the forsterite phase starting at the calcination temperature of 700°C. But at this temperature, other phases are still found, namely fayalite, lizardite, magnesium iron silicate, olivine and enstatite. For the synthesis of forsterite into nanoparticles, the sample taken is the result of calcination at a temperature of 1100°C. The reason for taking this condition is the forsterite phase, even though the olivine phase is still found. Through the HEM-E3D method with variations in milling time, it is expected that the olivine phase can decompose into forsterite.
\nThe XRD results for forsterite that have been milled for 5 h are shown in Figure 6.
\nXRD pattern from forsterite after being milled for 5 h.
Figure 6 is a plot of the 2θ diffraction angle on the intensity of the XRD pattern of forsterite after being processed for 5 h. The figure shows that the sample is dominated by the forsterite phase even though there are still other phases such as silicon which appear at scattering angles 2θ which are 26.63 and 47.2, and periclase with a scattering angle of 74.74 with a small degree.
\nFurthermore, for samples milled for 10 h, the XRD pattern is shown in Figure 7.
\nXRD pattern from forsterite after being milled for 10 h.
Figure 7 shows the sample is still contaminated with phases other than forsterite. Forsterite milled for 10 h there are several phases, namely forsterite, periclase, and silicon, but the phase concentration of the Periclase begins to decrease compared to samples which are milled for 5 h. The dominant phase is still forsterite.
\nFurthermore, for samples milled for 20 h, the XRD pattern is shown in Figure 8.
\nXRD pattern of forsterite after being milled for 20 h.
Figure 8 shows the sample is still contaminated with other phases other than forsterite. Forsterite milling for 20 h there are several phases, namely forsterite, periclase, and silicon. The dominant phase is still forsterite.
\nFurthermore, for samples milled for 40 h, the XRD pattern is shown in Figure 9.
\nXRD pattern from forsterite after being milled for 40 h.
Figure 9 shows no other phases such as periclase, and silicon that appear other than forsterite phase. This result is achieved after the sample has been milled for 40 h.
\nBased on XRD data analysis for each sample, it can be seen that variations in milling affect the phase of the sample. The phase that appears after being given the influence of milling variations for 5, 10, 20, and 40 h is shown in Figure 10.
\nEffect of milling time variation 5, 10, 20, and 40 h on the diffraction pattern of XRD results.
Figure 10 shows that at 5 h milling time, phases that appear forsterite, silicon, and periclase. The crystalline system at 5 h milling time is orthorhombic for the phosphoresce and cubic phases for the silicon and periclase phase. The crystallite size for the forsterite phase is 53.80 nm. While at the time of milling 10 h there was a clumping of grain size, so that the size of the crystallite became large i.e. 54.58 nm. Phases that appear at 10 h milling time are forsterite, silicon, and periclase. Crystalline size at 20 h milling time is 21.69 nm. While at the time of 40 h milling, the size of the crystallite is getting smaller to 18.78 nm. Changes in crystal size to milling time can be seen in Figure 11.
\nEffect of milling time on crystal size.
The milling time is very influential on the surface morphology of the sample, where the longer the process of milling is done, the smaller the particle size. Figure 12 shows the morphology of forsterite powder with a variation of 5 h milling time. 10, 20 and 40 h were taken using SEM.
\nDifferences in the form of morphology in each milling time variation with a magnification of 33,000×. (a) 5 h, (b) 10 h, (c) 20 h, (d) 40 h.
In Figure 12, there are differences in the form of forsterite morphology at each variation of milling time. At 5 h milling, the shape of the particles is round but not evenly distributed. Whereas at 10 h the particles clot back causing a large particle size. At 20 h the particle shrinks from the particle size to milling time of 10 h. The shape of the particles has seen a little round shape but has not been flat. At the time of milling 40 h the particle size has decreased and there are already smaller particles than before but the particles are not evenly sized. The grain size of forsterite particles for variations of milling time (a) 5 h, (b) 10 h, (c) 20 h, (d) 40 h are respectively 630, 717, 454, and 345 nm. The graph of changes in grain size to milling time can be seen in Figure 13.
\nEffect of milling time on grain size.
The milling time parameter does not affect the crystal structure of forsterite, but it affects the appearance of other phases with different crystal structures. HEM-E3D is used for mechanical activation in samples using a speed of 500 rpm so that collisions occur between milling balls so as to produce energy. The impact energy is used to stretch or break the bonds of atoms in the sample. The longer the grinding time, the higher the temperature on the collision of the milling balls. In the process, the faster the ball mill rotation, the greater the energy produced and produces higher temperatures [29].
\nHigh temperatures benefit in some cases that require a diffusion process to support the integration process in the powder and reduce internal stress or even eliminate it. However, in some cases, the temperature increase is very detrimental because it can produce an unstable phase so that it will form other structures during the milling process and the powder size will be larger. At high temperatures, the crystal structure can grow quickly, but more defects in the crystals formed. The lattice parameters of the crystal will also change due to crystal defects by the collision. The material that has been heated is then cooled slowly so that the atoms in the material can be arranged regularly to occupy the lattices to form a crystal. At certain times there will be a phase change.
\nThe number of crystalline phases has quite a combination of atoms or groups of atoms. The amorphous phase is relatively small because it does not have a long-range order and the atomic arrangement is not clear. The phase difference that occurs is inseparable from the influence of energy possessed by atoms for the diffusion process. At a certain level of energy, atoms can stay away from each other. If an atom has enough energy to break its bonds a diffusion process will occur [32].
\nMechanical activation is related to the formation of material which causes strain on the solid mixture. This is one form of mechanical alloying, involving two constituent powders with heterogeneous size distributions that will affect the material properties and mechanism of phase formation of a material. The process looks simple, where different types of metals can bind through exchanging short distances between atoms called atomic diffusion, this can happen if atoms have enough energy to release bonds with the surrounding atoms so that they can move from the original lattice position to the empty lattice position [33].
\nThe explanation of the reduction in crystal size due to the increase in milling time is that during the milling process of powder samples with variations in milling time there are four forces that occur in the material, namely impact (attrition), friction (shear), and compression (compression). With these four forces, the powder can be destroyed by a milling device in the formation of powder into nanoparticles. The size of the powder that has been crushed with a milling device becomes smaller than the size of the original powder.
\nThe results of characterization using XRD obtained a relationship of 2θ with intensity. Every variation in the milling time shows that peaks appear and disappear and also the intensity decreases. The peak that disappears is because atoms in other phases do not exist so there is no scattering of atoms by certain structures. The measured intensity at XRD is the result of scattering intensity by certain atomic structures. The magnitude of the relative intensity of the series of peaks depends on the number of atoms or ions present and the distribution in the unit cell of the material. The decrease in intensity is due to changes in the size of the crystal, this change also causes the crystal structure of the forming element to change. The crystal size obtained when the 40-h milling time is 18.78 nm. For comparison the size of forsterite crystals obtained from talc (Mg3Si4)10(OH)2 and magnesium carbonate (MgCO3) material is 33 nm [17].
\nWhen milling can measure the crystal grain size of the sample. This is because, during the milling process, the powder particles will experience repeated processes of destruction. When the milling balls collide with each other, the powder milled is between the collisions of the balls so that the powder will deform and the powder will disintegrate which will cause the grain size to be small and can also cause it to become large if the grain has clotted.
\nCompressive forces (compaction) that occur in particles other than destroying or breaking particles can also damage the pores on the surface of the particles, the pores are damaged due to the compressive force, especially small diameter pores that are very vulnerable to damage and disappear. In mills that are too long, particles can undergo agglomeration. After a long grinding and with very fine particles, the coupling forces become larger, and the presence of chemical bonds or Van Der Waals forces with bond strengths of 40–400 kJ/mol can make particles fuse or agglomerate. Or if there are particles trapped and then given an impact force, the particles can also be agglomerated. With the finer particles due to the long grinding time, the distance between particles will be smaller and more contact between particles will allow agglomeration to occur. Thus, on particles with porous surfaces, agglomeration allows for the formation of enlarged pore diameters due to “pore merging” due to agglomeration between particles.
\nThus it can be explained by the increasing milling time used, the grain size of powder particles will be smaller. This is what allows the formation of nanometer scale powder particles due to the high collision frequency and the length of time the milling is used.
\nThe grain size at the time of 10 h milling is 717 nm, there is a little clumping at 10 h milling time, while at 5 h 630 nm, when processed in 20 h the grain size decreases to 454 nm, at the time of milling 40 h the size of the crystal again decreased to 345 nm. The size of the grain increases during the milling time of 10 h because the particles agglomerate again after the particles are broken or broken by balls so that they are small and then group up to the milling time for 10 h this occurs due to the powder that has been solved and has a small effect. Unified powder causes large grain size, but at 20 h milling the grain size shrinks again because the sample experiences collision forces with balls that have high energy so the powder becomes eroded again and becomes small in size. In addition to the impact force, the powder also experiences other styles such as attrition, friction, and compression. So that at the time of milling 20 h forsterite has reached the fracture point and its size shrinks again.
\nThe milling time also affects the sample morphology. Each milling variation will produce different morphological forms. The collision between the milling balls and the powder repeatedly over time increases the milling time will make the particle size of the powder will be smaller. The SEM results also show that the grain size is getting smoother as the milling time increases. This happens because in HEM-E3D there are collisions of milling balls with powder. Assault particles are trapped between steel balls that are colliding. Furthermore, powders undergo microscopic deformation and fracture processes (breaking) and welding (joining).
\nThe forsterite particle size of the West Sumatra dunite has a grain size of 345 nm. The size obtained has not reached below 100 nm. The milling process carried out shows that forsterite powder has a tendency to agglomerate or clump, thus making forsterite under 100 nm not yet reached. This is due to the ratio of the ball to the powder used and also the speed of milling, and the length of time the milling is used. The higher the ball mill rotational speed will increase the collision and fineness of the powder, but if the speed has reached a certain level the fineness of the powder decreases. Likewise with the ratio of the weight of the milling ball to the powder, the variation in the number of pounding balls will also increase the collision contact area of the pounding and powder balls.
\nThe difference in forsterite morphology is also seen in every variation of milling time, this is because the granules have clumped between the grains with each other, and the shape of the granules is round and uneven. Each increase in the variable milling time looks more like the morphology of the mixture, it appears that between the grains with each other have merged with each other, and the fusion indicates the formation of a solid solution on the powder. The increase in grain size in one of the milling results is due to the agglomeration process. The agglomeration process is the process of joining small particles into a larger structure through a physical binding mechanism.
\nThe agglomeration process that occurs can also be caused by several things, namely sample powder contamination with crushing ball material and jar. Even though it has very high hardness, stainless steel on the crushing ball and jar will still give contamination to the ground powder sample. The high grinding speed and long grinding time cause contamination of the forming material of the crushing ball and the jar can be said to be almost unavoidable. Furthermore, which affects the grain size, namely the effect of the jar shape, the bottom edge design of HEM-E3D jar in the form of a curve can cause the formation of a dead zone which is an area where the powder is not grounded because the grinding media cannot reach it during milling.
\nIn summary, has been described the synthesis and characterization of nanoparticles from forsterite (Mg2SiO4) sourced from dunite rocks in West Sumatra. Forsterite is obtained from dunite powder by giving calcination temperature. HEM-E3D is used to obtain nanoparticles from forsterite. The results show that milling time affects crystal size, grain size and morphology of forsterite. The higher the milling time, the smaller the particle size of forsterite. The forsterite particle size obtained has not reached below 100 nm. However, this result is better than previous research using the same method. This is because forsterite powder has a tendency for agglomeration.
\nThe authors thank to RISTEK DIKTI for financial support through Hibah MP3EI 2016-2017 for this work.
\nThe global structure and productivity of ecosystems are deeply impacted by joined climate conditions and human drivers, causing general vegetation degradation [1]. The phenomenon has kept increasing in the last four decades and eventually affects whole ecosystem, soil productivity, biological systems, biotic diversity, and other environmental systems’ ability to support human needs in concerned areas. Main indicators are the decline in parameters, such as low biomass, less ecological production, fragmentation, or lower canopy cover [2, 3].
Inside the tropics, vegetation is globally sensitive to seasonal and inter-annual variation in precipitations and temperatures. Extremes seasonally, i.e., longer rainy season and shorter dry season in lowest latitudes, versus the reverse phenomenon towards medium latitudes, influence the vegetation distribution with several phenological and physiological adaptations, including cover and status changes [4, 5, 6]. Typically, forest colonizes wetter areas, while savannas cover drier areas, with a gradual species distribution such as dense forest, tree savannas, grassy/herbaceous savannas, and isolated desert shrubs or clumps of dry grasses known as steppes. However, transitions are not rigidly determined by climate [7]. There is an extensive overlap between forest and savanna creating a mosaic of landscapes, and most studies on the subject remain widely hypothesized and modeled with controversial results, supported by questionable evidences. Biases include the high species turnover around 1000 mm to 2500 mm rainfall, the (un)stable states of forest and savanna maintained by feedbacks between tree cover and disturbances, and for the satellite-based approaches, the structural (in)difference between trees or grasses layer [8].
These specificities are challenging to spatialize at a point that sub-Saharan African ecosystems have played a key role in the development of remote sensing of vegetation for decades [9, 10, 11]. Nowadays, several satellite-based models provide scalable spectral information relevant to vegetation distribution and changes, physiology, and phenology, in broad terms, to monitor and combat land degradation, especially in African drylands [12, 13, 14, 15]. As such, numerous spectral indices measure the vegetation parameters [16]. The Normalized Difference Vegetation Index, NDVI, especially, has purposely been widely used [17]. However, some limitations like sensitivity to soil background effects and atmospheric influence as well as values saturation under dense and multilayered canopy, usually alter the NDVI capacity to simultaneously predict senesced vegetation and efficiently discriminate individual anomalies, i.e., growth, vigor, leaf area index, biochemical components (anthocyanin, carotenoids, cellulose, etc.), water content or pigmentation [18, 19, 20] with accuracy. Then some previous studies focused on identifying or modeling the direction of change as well as underlying drivers of drylands vegetation [21]. Those models applied to two or more spatially close and interwoven vegetation species, require to implementation of specific processings. To the best of our knowledge, the recent progress in modeling sub-Saharan vegetation transition introduced the term of “bistability” around lower and upper transition boundaries between forest and savanna [8]. This model is based on paleo-ecological evidences (soil, topography) and climatic parameters change and oscillation (rainfall and temperature), that influence (for the firsts) and predispose (for the seconds) these two species to coexistence. With the support of a floristic survey, the ambiguity of mischaracterizing savanna as a degraded forest was clarified at some point, by identifying, the dense forest, the “bistable” forest, the “bistable” savanna, and the proper savanna.
This study approaches the forest-savanna transition study, by investigating its different spectral behaviors and their statistical meaning, in a context lacking field data, and from an open-source/open-data perspective. The triple aim is to assess the dynamics, discriminate species disturbance based on their empirical spatial distribution, and predict their extent and boundaries. As such, assuming a blurred boundary and an overlapping spatial gradient between the two species, some phenological and physiological characteristics are considered as separately as possible in terms of anomalies, and further integrated beneath the same model, so as to locate the spots requiring permanent monitoring or sustainable actions, without mischaracterizing punctual changes, factual distribution, and most accurate delineation.
The study was conducted on the sub-Saharan mixed ecoregion, highly dependent on varied annual precipitations (AP) and yearly medium to high average temperatures (T0), which both influence relative humidity (RH) changes. The area belongs to the medium Cameroon (central Africa), between latitudes 500’-805’N and longitudes 1000′-1505′E, a climatic transition between the agroecological zones of western highlands (AP = 1800-2500 mm; T0 = 19.5°C; RH = 75%), bi-modal rainforest (AP = 1700-2000 mm; T0 = 24°C; RH = 80%), and then Guinean high savanna (AP = 1500–1800 mm; T0 = 30°C; RH = 60%) to Sudano-Sahelian savanna (AP = 400–1200 mm; T0 = 28°C; RH = 50%) for the core area. Specifically, the vegetation density broadly reflects the climate gradient of dense moist broadleaf forest and highland forest to sparse extensive savannas featuring the co-dominance of woody shrubs, grassland in plains, and herbaceous steppe at the edge of Sahel (Figure 1) [22].
Location of the study: (A) Cameroon in the context of African ecoregions based on Olson et al. (2001); (B) distribution of ecoregions in Cameroon; (C) preview of the subset ecoregions; (D) Sentinel2-a median image of the subset for mid-November 2020 to end march 2021.
The study was conducted using European Spatial Agency, ESA, Sentinel2-A multispectral instrument (MSI) data, which represents a very valuable opportunity for the fine characterization and monitoring of vegetation types on large scales, but is poorly investigated for the tropical biome study [11, 23]. This sensor provides 13 varied spectral bands from 0.443 to 2.190 micrometers, a 10-day repeat cycle, and a spatial resolution up to 10 meters (Additional material 1).
The phenological dry season, globally from November to March, was selected because of its high temperatures and less rainfalls, assuming they are ideal conditions to observe the vegetation adaptations to extremes, for years 2015 to 2021. In the GEE cloud coding environment and using the JavaScript opensource simplified coding, the median reducer function was appended as the pixel-wise computation of all bi-annual collection images, based on a band per band processing [24]. Then, applying a date filter from November 15 to end of March 31, a boundary filter for the study area, and a cloud cover acceptance filter below 10%, one image of 13 bands was outputted per bi-annual periods 2015–2016 to 2020–2021, displayed a,nd converted from 16 bits to 8 bits before further processings. Offline tools, i.e., desktop software, were used to extract statistics and for some complementary processings using less memory, including final layouts. Namely, Erdas Imagine 2020, ArcGIS Pro 2020, and Microsoft Excel with extension Xlstat were specifically used.
In arid and semi-arid regions, common changes in density, spatial distribution, chlorophyll, pigmentation, water stress, anthocyanin, nitrogen, carotenoids, leaf structure, and browning or senescence differently impact the biomass [25]. Previous spectral indices-based applications investigated that, the visible (0.4–0.7 μm) wavelengths respond to photosynthetic and non-photosynthetic pigments, the NIR (0.7–1.4 μm) wavelengths respond to the cellular structure and exhibit solar-induced fluorescence (SIF) and the SWIR (1.4–3 μm) wavelengths respond to senescent non-photosynthetic vegetation. As such, the anisotropic behavior of vegetation at visible-SWIR wavelengths has been parameterized to describe vegetation structure [26].
We computed twelve spectral indices, whose three were selected as reference data according to their ability to better highlight the land cover targeted, i.e., second modified soil adjusted vegetation index, MSAVI2 [27], to assess the vegetation cover, normalized difference soil drought index NDSoDI [14], to highlight the dry bare soils, and new water index, NWI [28], to map the surface water. After testing dozens of other indices, two assumptions were emitted for an efficient last casting, such as
Name | Equation | Primary goal | Reference |
---|---|---|---|
MSAVI2 | Map vegetation cover while reducing soil background | [27] | |
NDSoDI | Map dry soils while reducing surroundings source of moisture | [14] | |
NWI | Extract surface water | [28] | |
PVR | Photosynthetic vigor for crop monitoring | [29] | |
GVMI | Vegetation water content and evapotranspiration | [30] | |
PBI | General biochemical reflectance | [31] | |
REIP1* | Canopy chlorophyll, nitrogen content and polluted soil dynamics | [32] | |
mCRIG | Chlorophyll, carotenoids and anthocyanin | [33] | |
LWCI | Water content, change and stress | [34] | |
MARI | Chlorophyll, carotenoids and anthocyanin | [35] | |
SRPI** | Nitrogen content, water and chlorophyll | [36] | |
PSRI | Pigmentation and vigor changes dues to vegetation senescence | [37] |
Spectral indices used.
Was inverted after first preview, to better highlight vegetation patterns instead of soil dynamics.
Was adapted to SENTINEL2-A covered wavelengths by using band 6.
These indices were stacked and previewed with, MSAVI2, NDSoDI and NWI seeking the following:
Relationships between each analytical spectral index and both referential ones. The bright green curve represents the dependent variable, while the two others are explanatory variables (MSAVI2 = dark green; NDSoDI = light brown). N = 500 for all variables and regression parameters. The following is noticed from the trends of curves: PVR, PBI and GVMI have a neat positive correlation with MSAVI2, but a sharp negative one with NDSoDI; conversely, MARI and PSRI curves describe the exact opposite trends (positive with NDSoDI and negative with MSAVI2); mCRIG and LWCI curves evolve in another direction cutting the MSAVI2 and NDSoDI curves, while IREIP1 curve shows no real relationship them.
This process creates a difference image between two or more bands in a multi-temporal image analysis, so to detect changes in the type or the conditions of surface features. Depending on the study, change vector analysis (CVA) can use calculation principles of the Mahalanobis or the Euclidean distance as in this study, according to the following expression [38]:
Where
Four classes of magnitude are represented for either degradation or re-growing [39] (Additional material 4). For each binarized image, a CVA process was performed, the magnitudes of increase and decrease patterns were used, while the stability magnitudes were ignored. A total of twelve difference images per index resulted, i.e., six per selected magnitude. Considering the need of complementarity among indices, only one CVA magnitude, i.e., increase or decrease, was selected per index for further processing. Criteria used to optimize the selection were the original goal of each index, as well as its spatial and statistical relationship with MSAVI2, NDSoDI and NWI (Table 2).
Index | Targeted asset | Compared trends to MSAVI2 / NDSoDI / NWI | CVA |
---|---|---|---|
PVR | Age & Cover | Same with a plus / Inverted / Inverted | Decrease |
GVMI | Water content & Cover | Same with a plus / Inverted / Inverted | Decrease |
PBI | Inner composition & Cover | Same with a plus / Inverted / Inverted | Decrease |
IREIP1 | Height, Cover & Health | Different / Inverted / Inverted | Decrease |
mCRIG | Health & Composition | Different / Inverted / Inverted | Increase |
LWCI | Water stress | Different / Inverted / Close | Decrease |
MARI | Health & Inner composition | Inverted / Close / Inverted | Increase |
SRPI | Health & Inner composition | Inverted / Close / Inverted | Increase |
PSRI | Age & Cover | Inverted / Close / Inverted | Increase |
CVA magnitude selected per spectral index.
The moving standard deviation index, MSDI, is a filter applied to satellite images multispectral or derivative channels using the moving standard deviation calculation, generally to assess degradation [40]. One common application is the vegetation and soil of semi-arid systems, where the variability of the MSDI is used to indicate levels of habitat degradation [41, 42, 43]. MSTDI has been proven efficient to operate well in complex regions [40, 42, 44].
Here, the five derivative images per selected CVA were used as entries. A standard deviation was computed for each entry. Difference between consecutive standard deviations were calculated, giving four new images. Then, the averaged MSDI (aMSDI) was conceived as follows:
For all
The global Moran’s I index was computed to assess the spatial autocorrelation of aMSDI outputs along the 1544 pixels transect. Its integration in spatial analysis is important to avoid incorrect statistical inference from inefficient or biased parameter estimates [45]. The formula is expressed such as follows:
Where
Values closer to
Ordinary Least Squares (OLS) regression was computed as the non-spatial measure of the spatial convergence/divergence of aMSDI. This method predicts or models a dependent variable relationship with a set of explanatory variables. The regression coefficients are usually estimated by using least-square techniques such as:
Where,
Here, each aMSDI of the nine indices was considered as dependent variable, whereas MSAVI2’s aMSDI of both CVA, were simultaneous explanators. Three OLS parameters were selected for interpretation, i.e., adjusted R-squared, corrected Akaike’s information criterion (AICc) which assesses the best-fit model between spatial and non-spatial Ordinary Least Squares (OLS) models, root mean squares error (RMSE), and three graphs, i.e., scatterplots of the relationship among variables, histograms of standard deviation probability and plots of residuals versus predicted.
At this point, the spatial correlation was assessed through the cross-correlation mapping process using only the finest
Further, the individual maps of anomalies were used to produce a single one. The first step consisted in stacking the aMSDI for each
Based on visual trends, binarized images were regrouped in three axes of three indices of vegetation each. A principal component analysis, PCA, was performed for each ax using the covariance reducer algorithm, that reduces some number of
Training:Testing | Total | ||||
---|---|---|---|---|---|
Vegetation1 | Vegetation2 | Vegetation3 | Soil/Built-up | Water | |
100:100 | 100:100 | 100:100 | 100:100 | 100:100 | 500:500 |
Samples distribution.
Three machine learning algorithms were performed, such as the Classification and Regression Trees (CART), Random Forest (RF) and Support vector machine, (SVM) [47, 48, 49]. The metrics used to assess general performance of each classifier were overall accuracy, OA, and the kappa coefficient, KC [50, 51]. Whereas, the thorough assessment of their efficiency on individual LULC classes was done using the error matrix to extract the producer accuracy (PA) and the user accuracy (UA). Eqs. (7) and (8) formulate the main quality measures of the learning:
with,
Where,
Overview of the study design.
Globally, each index showed a different interseason sensitivity to dynamics for the two main magnitudes of change in the vegetation distribution. The indices detecting vigor, biochemical composition, and water content, i.e., PVR, PBI and GVMI, that are highly correlated with MSAVI2, invaded the upper medium area for the increasing magnitude, while their increasing patterns were more concentrated on the south and lower medium area (Figure 4a and
a. Decrease trends of CVA per index. The background layers are from 2021 to 2022. b. Increase trends of CVA per index. The background layers are from 2021 to 2022.
Moreover, all magnitudes agreed to the same total areas for all indices, when adding the unchanged magnitudes. For the extreme cases, LWCI indicates a brutal decrease in between 2015 and 2016 and 2016–2017 (Change1, Figure 5a), from 61,609 km2 to 22,030 km2, i.e., 58% of the total change for the study period (Figure 5b). While, GVMI indicates an important increase between 2019 and 2020 and 2020–2021 (Change 5, Figure 5a), from 1439 km2 to 41,215 km2, i.e., 69.4% of the total change for the study period (Figure 5b). Then all changes were complementary between the two magnitudes, except for IREIP1 in the last period, 2020–2021/2021–2022, which the decrease was unsignificant (4km2) compared to the increase (Figure 5a&
Areas(a), percentages(b) and hierarchy of changes (c) based on the CVA decrease (row1) and increase (row2) magnitudes.
The outputs of the aMSDI were all obtained in the same interval,
From the spatial autocorrelation model synthesized in Table 4, the expected Moran’s I index and p-values are identical for all, −
aMSDI | Moran’s I | Expected Moran’s I | Variance | Z-Scores > +2.58 | P-Values |
---|---|---|---|---|---|
MSAVI2_Decr | 0.9 | −0.000648 | 0.000457 | 42.2 | 0.00 |
MSAVI2_Incr | 0.81 | −0.000648 | 0.000457 | 37.9 | 0.00 |
PVR | 0.95 | −0.000648 | 0.000458 | 44.3 | 0.00 |
PBI | 0.85 | −0.000648 | 0.000458 | 39.6 | 0.00 |
GVMI | 0.84 | −0.000648 | 0.000457 | 39.5 | 0.00 |
IREIP1 | 0.76 | −0.000648 | 0.000457 | 35.4 | 0.00 |
mCRIG | 0.86 | −0.000648 | 0.000457 | 40.3 | 0.00 |
LWCI | 0.91 | −0.000648 | 0.000457 | 42.5 | 0.00 |
MARI | 0.91 | −0.000648 | 0.000457 | 42.5 | 0.00 |
SRPI | 0.86 | −0.000648 | 0.000457 | 40.4 | 0.00 |
PSRI | 0.84 | −0.000648 | 0.000458 | 39.3 | 0.00 |
Spatial autocorrelation model report. N = 1544 for all variables and parameters.
First outputs of the cross-correlation mapping algorithm informed on the spatial relationship between each MSAVI2’s aMSDI and individual aMSDI of analytical indices. About the MSAVI2 decrease CVA, the highest positive correlation of aMSDI was with PVR (
Cross-correlation maps between aMSDIs of MSAVI2 and the selected aMSDI for each index.
The multi-regression performed between each analytical aMSDI and simultaneously both MSAVI2’s aMSDI on
OLS regression results synthesis (
Spatial synthesis of the cross-correlation mapping algorithm confirms the distribution of patterns and global trends. The combined correlation among the nine indices’ aMSDI were found strong at different degrees, for both highest and lowest values, then indicating convergences of significant or non-significant anomalies at the South-west and North-East of the area (Figure 8). For the three windows of calculation, they were clearly separated from predicted vegetation statuses, i.e., highly and lowly affected, soil, built-up and water features (Figure 8). From the cross-correlation map synthesis, the simple linear combination (SLC) outputs for each window discriminated positive from negative spatial trends. Then, the largest spots of extremely severe anomalies are located in the south-west, south-east and north-east areas, well separated on
Anomaly trends and spots. The synthesized correlation maps on the first row shows patchwork of high correlation in the medium area for the
The repeated sampling of vegetation gave spectral curves with at least three pairs of same trends for vegetation (Figure 9a). As spatial evidence, the patterns of ML were identical for the three algorithms, with three distinguished classes of vegetation, well separated from soil/built-up and water (Figure 9b). The three classifiers performed with a high and identical OA of
Spectral curves (A), spatial patterns (B) and statistics (C) of the three axes of vegetation on LULC. In details: (A) CART; (B) RF; (C) SVM: (D) stacked axes of vegetation with soil (NDSoDI) and water (NWI) features for 2021–2022; E) Sentinel2-a image of 2021–2022.
Concerning the vegetation types, discrimination, and extent, RF and SVM gave the exact areas for the three classes, CART agreed with them for the second and third axes, whereas the stacked derivative only agreed with all the algorithms concerning the third ax, i.e.,
The efficiency of the whole proposed methodology was assessed and discussed on selected aspects and the comparison with existing methods was basically empirical. At first, depending on their goals, previous MSDI-based studies analyzed only the standard deviation of the red and near infrared wavelengths, while those integrating vegetation indices were limited to three of them [40, 43, 52]. Because the goal of proposing aMSDI in this study was to assess consecutives dry season anomalies and discriminate them from empirical statuses of the forest-savanna specificities, we integrated nine spectral indices, selected on the basis of targeted phenological or physiological weaknesses, and whose computations basically integrate several wavelengths. Interestingly, although only one CVA magnitude was chosen per index, all individual models showed the expected visual convergence of similarity or dissimilarity trends.
Moreover, previous applications stated that the common calculation of MSDI on raw spectral index, gives outputs with a minimum value of zero and a maximum value determined by those of the pixels evaluated [43]. Consequently, outputs value cannot be directly compared. Here, by applying the averaging process to binarized CVA,
Besides, common attempts of mapping distribution, typology, and delineation of forest and savanna, have always been supported by fieldwork, based on climate parameters, as well as including paleo-ecological evidences and detailed floristic survey to be efficient [8]. The methodology presented in this paper has predicted three different axes of vegetation, resulting from the PCA processing and thresholding (Table 5). For each of the six study periods, the first ax in the south part is composed by a dense and potentially healthier vegetation, highly correlated with the referential data MSAVI2. Whereas the other two axes, more and more sparse towards north, are divergent with the first one and somehow each with another (Figure 10a&
Vegetation trends | Regrouping | PCA threshold |
---|---|---|
1st axe | PVR, PBI, GVMI | |
2nd axe | IREIP1, MARI, PRSI | |
3rd axe | mCRIG, LWCI, SRPI |
Vegetation axes, proposed groups and PCA thresholds used to binarize.
a. the three main axes of vegetation’s spatial distribution. b. Pixels’ value along the transect of the vegetation’s axes.
To answer the interrogations behind these ambiguities, a simple multicollinearity test was run, showing how independent one ax is from another. When the correlation between two independent variables is considerably high, it is a problem in the modeling process. The VIF (variance inflation factor) and tolerance were used for diagnosis. VIF is the reciprocal of tolerance, knowing that, tolerance is
Axe | VIF | Tolerance | Percentile for value 1 |
---|---|---|---|
AXE1 | 1.33 | 0.75 | [49.9–99.97] |
AXE2 | 1.12 | 0.89 | [62.5–99.97] |
AXE3 | 1.35 | 0.74 | [78.3–99.97] |
Multicollinearity test results.
Although from this study, we cannot properly use the qualifier of “bistable” forest or savanna, because it highly depends upon climate and paleo-ecological parameters, it is important to notice how ambiguous is the distribution and blurry are the boundaries. Thoroughly, on any ML output, three zooms distributed on three different latitudes helped to notice different types of transitions (Figure 11). Between the lower latitudes 5030’-6030’North, the transition is from the first (moist broadleaf forest) to third ax of vegetation (shrubland savanna), although the second ax (grassland savanna) would have been “expected.” Between the middle latitudes 6030’-7030’North, the transition mixes in the below area, the “-‘unexpected” third ax (shrubland savanna) with the ‘expected’ first ax (moist broadleaf forest) of vegetation, before the wide expansion of the ‘expected’ second ax (grassland savanna). At this point, the only “expected” transition was inside the upper latitudes 7030’-8030’North, where the second ax (grassland savanna) gradually gave way to third ax (shrubland savanna) of vegetation. These elements of analysis support the qualifier of “bistable” area, while still questioning the anisotropic distribution with latitudes, and encouraging the finest scale of analysis, i.e., spatial and spectral resolution.
Zooms on the transitions, a sign of anisotropic distribution with latitudes. Yellow square = lower latitudes (5030’-6030’N) transition; red square = middle latitudes (6030’-7030’N) transition; blue = upper latitudes (7030’-8030’N) transition.
Anomalies versus LULC classes. (A) SVM classification map. (B) Two extreme classes of anomalies at
Finally, the display of anomalies with the LULC classes disambiguated the confusion of savanna and degraded forest. The observation was made by overlaying the highest and the lowest values of anomalies in the most concentrate area, on the SVM output. On three spots covered by grassland, shrubland and bare soil, the modeled extremely severe anomalies concern just a part of each class. Whereas, on two spots of lower to no-anomalies, savanna as well as bare soils are partially concerned (Figure 12).
This study has conducted an experimentation on the forest-savanna vegetation, with the goal of assessing dynamics, assuming anomalies and predicting boundaries. On Google Earth Engine platform and using Sentinel2-A satellite images of seven consecutive dry seasons, from 2015 to 2016 to 2021–2022, twelve spectral indices were selected according to their different phenological and physiological assessment of the vegetation, and other natural features to be discriminate. Using the processing of change vector analysis, CVA, it was successfully observed that each index brings a substantial information, to better assess increase or decrease patterns of the vegetation cover. Further, proposing the averaged moving standard deviation index, aMSDI, to face potential issues of simple MSDI, the scale of spatial trends appraisal was found identical between the same interval
To USGS for the free availability of satellite data. To all experts-developers of Google Earth Engine platform APIs. To StatsN’Maps Consulting Firm, for the logistic support. To our laboratories.
The authors declare no conflict of interest.
Conception, A.H.N.M.; study design, A.H.N.M.; acquisition of data, A.H.N.M., I.C.N.P., F.C.L.T., L.M.B., M.T. and J.V.M.M.; execution, A.H.N.M.; analysis and interpretation, A.H.N.M.; writing-original draft preparation, A.H.N.M.; writing-review and editing A.H.N.M., I.C.N.P., F.C.L.T., L.M.B., M.T. and J.V.M.M.; All authors have read and agreed to the published version of the manuscript.
Links to the code are available upon request.
Additional material 1. Characteristics of Sentinel-2A bands.
Sentinel-2A MSI | ||
---|---|---|
Name | Range (μm) | Bands (Resolution/m) |
Coastal aerosol | 0.421 − 0.457 | B1(60) |
Blue | 0.439 − 0.535 | B2(10) |
Green | 0.537 − 0.582 | B3(10) |
Red | 0.646 − 0.685 | B4(10) |
Red_edge1 | 0.694 − 0.714 | B5(10) |
Red_edge2 | 0.731 − 0.749 | B6(10) |
Red_edge3 | 0.768 − 0.796 | B7(10) |
NIR wide | 0.767 − 0.908 | B8(10) |
NIR narrow | 0.848 − 0.881 | B8A(20) |
Water vapor | 0.931 − 0.958 | B9(60) |
Cirrus | 1.338 − 1.414 | B10(60) |
SWIR1 | 1.539 − 1.681 | B11(20) |
SWIR2 | 2.072 − 2.312 | B12(20) |
Additional material 2. The spectral indices used. The first row is the supporting or reference data.
Additional material 3. Binarized indices and thresholds defined.
Additional material 4. Direction and magnitude of change as proposed by Kuzera et al. (2005).
Additional material 5a. Averaged MSDI patterns of MSAVI2.
Additional material 5b. Averaged MSDI patterns for PVR, PBI and GVMI.
Additional material 5c. Averaged MSDI patterns for IREIP1, mCRIG and LWCI.
Additional material 5d. Averaged MSDI patterns for MARI, SRPI and PSRI.
Additional material 6. Spatial autoregression sample plots of aMSDI for PVR (left) and MSAVI2 increase patterns (right). red square = spatial correlation targeted, for n = 1544 pixels.
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Singh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8660",title:"Tyrosine Kinases as Druggable Targets in Cancer",subtitle:null,isOpenForSubmission:!1,hash:"689f19fdd857c3a92227d533ac531196",slug:"tyrosine-kinases-as-druggable-targets-in-cancer",bookSignature:"Huan Ren",coverURL:"https://cdn.intechopen.com/books/images_new/8660.jpg",editedByType:"Edited by",editors:[{id:"237472",title:"Dr.",name:"Huan",middleName:null,surname:"Ren",slug:"huan-ren",fullName:"Huan Ren"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:2,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"66041",doi:"10.5772/intechopen.84873",title:"Non-receptor Tyrosine Kinases Role and Significance in Hematological Malignancies",slug:"non-receptor-tyrosine-kinases-role-and-significance-in-hematological-malignancies",totalDownloads:1288,totalCrossrefCites:4,totalDimensionsCites:6,abstract:"This chapter presents a review about non-receptor tyrosine kinases, their structure, mechanisms of action and physiopathology, and how they are regulated and interact with other molecules and other signaling pathways, contributing to the regulation of fundamental cellular functions such as cell division and differentiation, stress responses, apoptosis, survival, and proliferation, gene expression, immune response, inter alia. Special emphasis will be assigned to the JAK family, the processes whereby it can be mutated/regulated and aberrantly activated, clinical significance and association with hematological disease progression and malignancy, mainly in myeloproliferative neoplasms. Consideration of these mechanisms may have important implications for selection of anti-cancer targeted therapies.",book:{id:"8660",slug:"tyrosine-kinases-as-druggable-targets-in-cancer",title:"Tyrosine Kinases as Druggable Targets in Cancer",fullTitle:"Tyrosine Kinases as Druggable Targets in Cancer"},signatures:"Ana Azevedo, Susana Silva and José Rueff",authors:[{id:"282451",title:"Prof.",name:"Ana",middleName:null,surname:"Azevedo",slug:"ana-azevedo",fullName:"Ana Azevedo"},{id:"282845",title:"Prof.",name:"Susana",middleName:null,surname:"Silva",slug:"susana-silva",fullName:"Susana Silva"},{id:"282846",title:"Prof.",name:"José",middleName:null,surname:"Rueff",slug:"jose-rueff",fullName:"José Rueff"}]},{id:"65229",doi:"10.5772/intechopen.82513",title:"Cancer Management by Tyrosine Kinase Inhibitors: Efficacy, Limitation, and Future Strategies",slug:"cancer-management-by-tyrosine-kinase-inhibitors-efficacy-limitation-and-future-strategies",totalDownloads:1700,totalCrossrefCites:2,totalDimensionsCites:4,abstract:"Tyrosine kinase inhibitors are taking up an increasingly significant role in treating cancers. There are different types of TKIs currently used in clinical settings. However, TKI-associated limitations such as resistance and adverse effects are frequently reported. In this chapter, we would comprehensively review the clinical efficacy of current TKIs using the currently available clinical trial data. Significant limitations of TKIs on cancer treatment will be further summarized and discussed. The strategies on overcoming the limitations of TKIs to maximize their clinical effectiveness and efficiency, such as complementary use of Chinese medicine or development of novel TKIs, will be proposed. In conclusion, an overall picture of the clinical use and limitation of the current TKIs will be drawn and the prospective development in overcoming the limitations will be discussed. Evaluation of clinical efficacy of TKIs, evaluation of limitations of TKIs, strategies in overcoming the limitations of TKIs, and conclusion (including prospective development of TKIs) are discussed below.",book:{id:"8660",slug:"tyrosine-kinases-as-druggable-targets-in-cancer",title:"Tyrosine Kinases as Druggable Targets in Cancer",fullTitle:"Tyrosine Kinases as Druggable Targets in Cancer"},signatures:"Venice Wing Tung Ho, Hor Yue Tan, Ning Wang and Yibin Feng",authors:[{id:"14428",title:"Prof.",name:"Yibin",middleName:null,surname:"Feng",slug:"yibin-feng",fullName:"Yibin Feng"},{id:"175059",title:"Dr.",name:"Ning",middleName:null,surname:"Wang",slug:"ning-wang",fullName:"Ning Wang"},{id:"281068",title:"Ms.",name:"Wing Tung Venice",middleName:null,surname:"Ho",slug:"wing-tung-venice-ho",fullName:"Wing Tung Venice Ho"},{id:"281069",title:"Dr.",name:"Hor Yue",middleName:null,surname:"Tan",slug:"hor-yue-tan",fullName:"Hor Yue Tan"}]},{id:"77646",doi:"10.5772/intechopen.98552",title:"Importance of Protein Kinase and Its Inhibitor: A Review",slug:"importance-of-protein-kinase-and-its-inhibitor-a-review",totalDownloads:389,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Deregulation of a broad range of protein kinases has been linked to the development and growth of cancer cells. Protein kinases are intracellular enzymes that regulate cell growth and proliferation as well as the triggering and regulation of immune responses. Protein kinases are important therapeutic targets in cancer because of their critical role in signalling mechanisms that drive malignant cell characteristics. Intensive efforts in drug research have been made in this area over the last two decades. The current study delves into the catalytic domain of a protein kinase as well as information transfer from the cell’s membrane to internal targets. It also discusses the function of protein kinases in signal transduction and their cellular signalling pathways. Furthermore, it specifically outlines a systematic method to hybrid therapies to solve the issue of protein kinase resistance. The therapeutic use of nitric oxide, as well as other targets such as Phosphoinositide 3-kinases (PI3K), Protein Kinase B (Akt), serine/threonine protein kinase (mTOR), p38 mitogen-activated protein kinases (p38 MAPK), vascular endothelial growth factor receptors (VEGFR), epidermal growth factor receptors (EGFR), and anaplastic lymphoma (ALK) etc., According to the review article, selective therapy has shown high effectiveness in the treatment of advanced cancer, with protein kinase inhibitors being a main focus of the therapy. As a result, the latest review summarized that, the current state of science with the aim of identifying a novel protein kinase inhibitor that may be utilized in the treatment of advanced cancers.",book:{id:"8977",slug:"protein-kinases-promising-targets-for-anticancer-drug-research",title:"Protein Kinases",fullTitle:"Protein Kinases - Promising Targets for Anticancer Drug Research"},signatures:"Panneerselvam Theivendren, Selvaraj Kunjiappan, Yashoda Mariappa Hegde, Sivakumar Vellaichamy, Murugananthan Gopal, Senthil Rajan Dhramalingam and Sattanathan Kumar",authors:[{id:"347086",title:"Prof.",name:"Panneerselvam",middleName:null,surname:"Theivendren",slug:"panneerselvam-theivendren",fullName:"Panneerselvam Theivendren"},{id:"347087",title:"Dr.",name:"Selvaraj",middleName:null,surname:"Kunjiappan",slug:"selvaraj-kunjiappan",fullName:"Selvaraj Kunjiappan"},{id:"417750",title:"Dr.",name:"Murugananthan",middleName:null,surname:"Gopal",slug:"murugananthan-gopal",fullName:"Murugananthan Gopal"},{id:"418098",title:"Ms.",name:"Yashoda",middleName:null,surname:"Mariappa Hegde",slug:"yashoda-mariappa-hegde",fullName:"Yashoda Mariappa Hegde"},{id:"418121",title:"Dr.",name:"Senthil Rajan",middleName:null,surname:"Dhramalingam",slug:"senthil-rajan-dhramalingam",fullName:"Senthil Rajan Dhramalingam"},{id:"418122",title:"Dr.",name:"Sivakumar",middleName:null,surname:"Vellaichamy",slug:"sivakumar-vellaichamy",fullName:"Sivakumar Vellaichamy"},{id:"418123",title:"Mr.",name:"Sattanathan",middleName:null,surname:"Kumar",slug:"sattanathan-kumar",fullName:"Sattanathan Kumar"}]},{id:"78859",doi:"10.5772/intechopen.100315",title:"Introductory Chapter: Protein Kinases as Promising Targets for Drug Design against Cancer",slug:"introductory-chapter-protein-kinases-as-promising-targets-for-drug-design-against-cancer",totalDownloads:108,totalCrossrefCites:0,totalDimensionsCites:1,abstract:null,book:{id:"8977",slug:"protein-kinases-promising-targets-for-anticancer-drug-research",title:"Protein Kinases",fullTitle:"Protein Kinases - Promising Targets for Anticancer Drug Research"},signatures:"Rohit Bhatia and Rajesh K. Singh",authors:[{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh"}]},{id:"76732",doi:"10.5772/intechopen.97726",title:"Time Series Analysis on the Conformational Change of c-Src Tyrosine Kinase",slug:"time-series-analysis-on-the-conformational-change-of-c-src-tyrosine-kinase",totalDownloads:183,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"c-Src tyrosine kinase plays an important role in signal transduction pathways, where its activity is regulated by phosphorylation of the two tyrosine residues. We performed targeted molecular dynamics simulation to obtain trajectory of conformational change from inactive to active form. To investigate the conformational change of c-Src tyrosine kinase, we applied network analysis to time series of correlation among residues. The time series of correlation between residues during the conformational change generated by targeted molecular dynamic simulation. With centrality measures such as betweenness centrality, degree centrality, and closeness centrality, we observed a few important residues that significantly contribute to the conformational change of c-Src tyrosine kinase for the different time steps.",book:{id:"8977",slug:"protein-kinases-promising-targets-for-anticancer-drug-research",title:"Protein Kinases",fullTitle:"Protein Kinases - Promising Targets for Anticancer Drug Research"},signatures:"Hyun Jung Yoon, Sungmin Lee, Suhyun Park and Sangwook Wu",authors:[{id:"343591",title:"Prof.",name:"Sangwook",middleName:null,surname:"Wu",slug:"sangwook-wu",fullName:"Sangwook Wu"},{id:"357504",title:"M.Sc.",name:"Hyun Jung",middleName:null,surname:"Yoon",slug:"hyun-jung-yoon",fullName:"Hyun Jung Yoon"},{id:"357506",title:"Dr.",name:"Sungmin",middleName:null,surname:"Lee",slug:"sungmin-lee",fullName:"Sungmin Lee"},{id:"357508",title:"MSc.",name:"Suhyun",middleName:null,surname:"Park",slug:"suhyun-park",fullName:"Suhyun Park"}]}],mostDownloadedChaptersLast30Days:[{id:"65229",title:"Cancer Management by Tyrosine Kinase Inhibitors: Efficacy, Limitation, and Future Strategies",slug:"cancer-management-by-tyrosine-kinase-inhibitors-efficacy-limitation-and-future-strategies",totalDownloads:1700,totalCrossrefCites:2,totalDimensionsCites:4,abstract:"Tyrosine kinase inhibitors are taking up an increasingly significant role in treating cancers. There are different types of TKIs currently used in clinical settings. However, TKI-associated limitations such as resistance and adverse effects are frequently reported. In this chapter, we would comprehensively review the clinical efficacy of current TKIs using the currently available clinical trial data. Significant limitations of TKIs on cancer treatment will be further summarized and discussed. The strategies on overcoming the limitations of TKIs to maximize their clinical effectiveness and efficiency, such as complementary use of Chinese medicine or development of novel TKIs, will be proposed. In conclusion, an overall picture of the clinical use and limitation of the current TKIs will be drawn and the prospective development in overcoming the limitations will be discussed. Evaluation of clinical efficacy of TKIs, evaluation of limitations of TKIs, strategies in overcoming the limitations of TKIs, and conclusion (including prospective development of TKIs) are discussed below.",book:{id:"8660",slug:"tyrosine-kinases-as-druggable-targets-in-cancer",title:"Tyrosine Kinases as Druggable Targets in Cancer",fullTitle:"Tyrosine Kinases as Druggable Targets in Cancer"},signatures:"Venice Wing Tung Ho, Hor Yue Tan, Ning Wang and Yibin Feng",authors:[{id:"14428",title:"Prof.",name:"Yibin",middleName:null,surname:"Feng",slug:"yibin-feng",fullName:"Yibin Feng"},{id:"175059",title:"Dr.",name:"Ning",middleName:null,surname:"Wang",slug:"ning-wang",fullName:"Ning Wang"},{id:"281068",title:"Ms.",name:"Wing Tung Venice",middleName:null,surname:"Ho",slug:"wing-tung-venice-ho",fullName:"Wing Tung Venice Ho"},{id:"281069",title:"Dr.",name:"Hor Yue",middleName:null,surname:"Tan",slug:"hor-yue-tan",fullName:"Hor Yue Tan"}]},{id:"65694",title:"JAK, an Oncokinase in Hematological Cancer",slug:"jak-an-oncokinase-in-hematological-cancer",totalDownloads:1136,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Janus kinases (JAKs) play an essential role in the regulation of cytokine signaling. They control cell survival, proliferation, differentiation, immune response, and hematopoiesis. Deregulation of JAK signaling has been associated to the pathogenesis of numerous immune-inflammatory diseases, hematological malignancies, and solid tumors. Thus, JAK proteins have emerged as attractive therapeutic targets in the last decade. The discovery of the gain-of-function JAK2 mutation (JAK2 V617F) as the main cause of polycythemia vera—a chronic myeloproliferative syndrome—led to the development of the JAK inhibitor ruxolitinib. This key finding opened the door to the search for new therapeutic agents able to suppress the constitutive activation of JAK signaling in hematological cancers and other tumors. However, given the conserved nature of the kinase domain among JAK family members, and the interrelated roles of JAK kinases in many physiological processes, including hematopoiesis and immunity, the broad usage of JAK inhibitors in hematology is challenged by their narrow therapeutic window. Novel therapies are, therefore, needed. This chapter focuses on the understanding of the complex signaling of JAK proteins in cancerous cells, the various JAK aberrations implicated in myeloproliferative neoplasms, leukemia, and lymphoma, and the clinically available JAK inhibitors in cancer therapy.",book:{id:"8660",slug:"tyrosine-kinases-as-druggable-targets-in-cancer",title:"Tyrosine Kinases as Druggable Targets in Cancer",fullTitle:"Tyrosine Kinases as Druggable Targets in Cancer"},signatures:"Carlota Recio, Haidée Aranda-Tavío, Miguel Guerra-Rodríguez, Mercedes de Mirecki-Garrido, Patricia Martín-Rodríguez, Borja Guerra and Leandro Fernández-Pérez",authors:[{id:"181749",title:"Dr.",name:"Leandro",middleName:null,surname:"Fernández-Pérez",slug:"leandro-fernandez-perez",fullName:"Leandro Fernández-Pérez"},{id:"185536",title:"Dr.",name:"Borja",middleName:null,surname:"Guerra",slug:"borja-guerra",fullName:"Borja Guerra"},{id:"271326",title:"Dr.",name:"Carlota",middleName:null,surname:"Recio",slug:"carlota-recio",fullName:"Carlota Recio"},{id:"271415",title:"Ms.",name:"Haidée",middleName:null,surname:"Aranda-Tavío",slug:"haidee-aranda-tavio",fullName:"Haidée Aranda-Tavío"},{id:"271416",title:"Dr.",name:"Mercedes",middleName:null,surname:"De Mirecki-Garrido",slug:"mercedes-de-mirecki-garrido",fullName:"Mercedes De Mirecki-Garrido"},{id:"271418",title:"Mr.",name:"Miguel",middleName:null,surname:"Guerra-Rodríguez",slug:"miguel-guerra-rodriguez",fullName:"Miguel Guerra-Rodríguez"},{id:"271419",title:"Dr.",name:"Patricia",middleName:null,surname:"Martín-Rodríguez",slug:"patricia-martin-rodriguez",fullName:"Patricia Martín-Rodríguez"}]},{id:"66041",title:"Non-receptor Tyrosine Kinases Role and Significance in Hematological Malignancies",slug:"non-receptor-tyrosine-kinases-role-and-significance-in-hematological-malignancies",totalDownloads:1288,totalCrossrefCites:4,totalDimensionsCites:6,abstract:"This chapter presents a review about non-receptor tyrosine kinases, their structure, mechanisms of action and physiopathology, and how they are regulated and interact with other molecules and other signaling pathways, contributing to the regulation of fundamental cellular functions such as cell division and differentiation, stress responses, apoptosis, survival, and proliferation, gene expression, immune response, inter alia. Special emphasis will be assigned to the JAK family, the processes whereby it can be mutated/regulated and aberrantly activated, clinical significance and association with hematological disease progression and malignancy, mainly in myeloproliferative neoplasms. Consideration of these mechanisms may have important implications for selection of anti-cancer targeted therapies.",book:{id:"8660",slug:"tyrosine-kinases-as-druggable-targets-in-cancer",title:"Tyrosine Kinases as Druggable Targets in Cancer",fullTitle:"Tyrosine Kinases as Druggable Targets in Cancer"},signatures:"Ana Azevedo, Susana Silva and José Rueff",authors:[{id:"282451",title:"Prof.",name:"Ana",middleName:null,surname:"Azevedo",slug:"ana-azevedo",fullName:"Ana Azevedo"},{id:"282845",title:"Prof.",name:"Susana",middleName:null,surname:"Silva",slug:"susana-silva",fullName:"Susana Silva"},{id:"282846",title:"Prof.",name:"José",middleName:null,surname:"Rueff",slug:"jose-rueff",fullName:"José Rueff"}]},{id:"67422",title:"Introductory Chapter: Tyrosine Kinases as Drug Targets in Cancer Treatment",slug:"introductory-chapter-tyrosine-kinases-as-drug-targets-in-cancer-treatment",totalDownloads:984,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"8660",slug:"tyrosine-kinases-as-druggable-targets-in-cancer",title:"Tyrosine Kinases as Druggable Targets in Cancer",fullTitle:"Tyrosine Kinases as Druggable Targets in Cancer"},signatures:"Huan Ren",authors:[{id:"237472",title:"Dr.",name:"Huan",middleName:null,surname:"Ren",slug:"huan-ren",fullName:"Huan Ren"}]},{id:"77646",title:"Importance of Protein Kinase and Its Inhibitor: A Review",slug:"importance-of-protein-kinase-and-its-inhibitor-a-review",totalDownloads:389,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Deregulation of a broad range of protein kinases has been linked to the development and growth of cancer cells. Protein kinases are intracellular enzymes that regulate cell growth and proliferation as well as the triggering and regulation of immune responses. Protein kinases are important therapeutic targets in cancer because of their critical role in signalling mechanisms that drive malignant cell characteristics. Intensive efforts in drug research have been made in this area over the last two decades. The current study delves into the catalytic domain of a protein kinase as well as information transfer from the cell’s membrane to internal targets. It also discusses the function of protein kinases in signal transduction and their cellular signalling pathways. Furthermore, it specifically outlines a systematic method to hybrid therapies to solve the issue of protein kinase resistance. The therapeutic use of nitric oxide, as well as other targets such as Phosphoinositide 3-kinases (PI3K), Protein Kinase B (Akt), serine/threonine protein kinase (mTOR), p38 mitogen-activated protein kinases (p38 MAPK), vascular endothelial growth factor receptors (VEGFR), epidermal growth factor receptors (EGFR), and anaplastic lymphoma (ALK) etc., According to the review article, selective therapy has shown high effectiveness in the treatment of advanced cancer, with protein kinase inhibitors being a main focus of the therapy. 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