Chemical composition of ASTM F139 austenitic stainless-steel biomaterial – in percentage weight.
\\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:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{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"}]},book:{item:{type:"book",id:"6258",leadTitle:null,fullTitle:"Cognitive Behavioral Therapy and Clinical Applications",title:"Cognitive Behavioral Therapy and Clinical Applications",subtitle:null,reviewType:"peer-reviewed",abstract:"The main purpose of this book is to be useful in daily practice to clinicians, including less-discussed subjects that are frequently encountered in practice. For this, it was aimed to explain the formulation of the disorder in light of the basic CBT model in each chapter and then to present the treatment approach of the disorder with case examples. We believe that the case examples, which came from the authors' own practices, are the strength of the book.",isbn:"978-953-51-3928-7",printIsbn:"978-953-51-3927-0",pdfIsbn:"978-953-51-4073-3",doi:"10.5772/intechopen.68930",price:119,priceEur:129,priceUsd:155,slug:"cognitive-behavioral-therapy-and-clinical-applications",numberOfPages:248,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"c7d1f6f57a5c51471a114a2b97564adb",bookSignature:"Ömer Şenormancı and Güliz Şenormancı",publishedDate:"March 28th 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6258.jpg",numberOfDownloads:25777,numberOfWosCitations:20,numberOfCrossrefCitations:14,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:43,numberOfDimensionsCitationsByBook:1,hasAltmetrics:1,numberOfTotalCitations:77,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 7th 2017",dateEndSecondStepPublish:"June 28th 2017",dateEndThirdStepPublish:"September 24th 2017",dateEndFourthStepPublish:"December 23rd 2017",dateEndFifthStepPublish:"February 21st 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"66055",title:"Dr.",name:"Ömer",middleName:null,surname:"Şenormancı",slug:"omer-senormanci",fullName:"Ömer Şenormancı",profilePictureURL:"https://mts.intechopen.com/storage/users/66055/images/5318_n.jpg",biography:"Ömer Şenormancı, graduated from Istanbul University Cerrahpaşa Faculty of Medicine. After completing his psychiatry specialty training, he worked for as specialist in Bakırköy Research and Training Hospital for Psychiatry, Neurology and Neurosurgery for two years. Between 2013 and 2016 he held the position of assistant professor in Bülent Ecevit University Faculty of Medicine, Psychiatry Department. He has been working as an associative professor in University of Health Sciences Bursa Yüksek Ihtisas Training and Research Hospital. He gives lectures on CBT and he is a licensed therapist of Turkish Association of Cognitive and Behavioral Psychotherapies (accredited by the European Association for Behavioural and Cognitive Therapy, EABCT). He has published numerous research articles and written book chapters on CBT.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Bursa Yuksek Ihtisas Egitim Ve Arastirma Hastanesi",institutionURL:null,country:{name:"Turkey"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"201141",title:"Dr.",name:"Güliz",middleName:null,surname:"Şenormancı",slug:"guliz-senormanci",fullName:"Güliz Şenormancı",profilePictureURL:"https://mts.intechopen.com/storage/users/201141/images/5319_n.jpg",biography:"Güliz Şenormancı, graduated from Marmara University School of Medicine. She worked as psychiatry resident in Bakırköy Research and Training Hospital for Psychiatry, Neurology and Neurosurgery. She now works as a psychiatry specialist in University of Health Sciences Bursa Yüksek Ihtisas Training and Research Hospital. She has published numerous research articles on CBT.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1053",title:"Cognitive Psychology",slug:"cognitive-psychology"}],chapters:[{id:"58143",title:"Cognitive Behavioral Therapy Principles in Children: Treatment of Internalizing Disorders",doi:"10.5772/intechopen.71932",slug:"cognitive-behavioral-therapy-principles-in-children-treatment-of-internalizing-disorders",totalDownloads:1640,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Cognitive behavioral therapy (CBT) is an effectiveness-proven therapy method in the psychosocial treatment of childhood internalizing disorder. Considering the techniques included, even though anxiety and major depression are two different disorders, they are observed to occupy a quite common pool in terms of their similar nature, symptoms, etiologies, and high comorbidity rates. While these techniques are rationally similar to those in adult CBT, application ways, contents, session structures of the techniques, and styles of homework should be adapted to the developmental characteristics of children. In this book chapter, initially, several CBT programs for childhood internalizing disorders will be mentioned. After than, main points to take into consideration while adapting CBT, which was firstly designed for adults to children, will be emphasized. Lastly, information about main CBT techniques, whose effectiveness has been proven in the treatment of internalizing disorders, will be given.",signatures:"Emine Sevinç Sevi Tok",downloadPdfUrl:"/chapter/pdf-download/58143",previewPdfUrl:"/chapter/pdf-preview/58143",authors:[{id:"221358",title:"Ph.D.",name:"Emine Sevinç",surname:"Sevi Tok",slug:"emine-sevinc-sevi-tok",fullName:"Emine Sevinç Sevi Tok"}],corrections:null},{id:"56889",title:"Cognitive-Behavioral Therapy of Obsessive-Compulsive Disorder in Children and Adolescents",doi:"10.5772/intechopen.70612",slug:"cognitive-behavioral-therapy-of-obsessive-compulsive-disorder-in-children-and-adolescents",totalDownloads:1452,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"While obsessive-compulsive disorder (OCD) is present under the category of anxiety disorders in DSM-IV TR, it is classified under “Obsessive Compulsive Disorder and Related Disorders” in DSM 5. There is no different diagnostic system for children and adolescents. OCD has serious adverse effects on family, school, and social lives of children and adolescents, but adolescents with OCD often hide their symptoms and delay seeking help due to several reasons such as inability to recognize their symptoms as disease manifestations, embarrassment, fear of being stigmatized by other people, and believing that what the experience is transient. The age of onset has significance in terms of the disease progression. Therefore it is very important to detect OCD at its early stage, because the majority of the adult patients develop the disease during childhood or adolescence.",signatures:"Irem Damla Cimen",downloadPdfUrl:"/chapter/pdf-download/56889",previewPdfUrl:"/chapter/pdf-preview/56889",authors:[{id:"203625",title:"M.D.",name:"İrem",surname:"Çimen",slug:"irem-cimen",fullName:"İrem Çimen"}],corrections:null},{id:"59726",title:"Cognitive Behavioral Therapy for Social Anxiety Disorder: Intrapersonal and Interpersonal Aspects and Clinical Application",doi:"10.5772/intechopen.74302",slug:"cognitive-behavioral-therapy-for-social-anxiety-disorder-intrapersonal-and-interpersonal-aspects-and",totalDownloads:1647,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Research on social anxiety disorder (SAD), and its treatment, widely focuses on intrapersonal aspects. There also exists an increasing body of literature concentrating on its interpersonal dimensions. This chapter will present an overview about both intrapersonal and interpersonal approaches to SAD. This will be followed by a clinical application including dyadic and group session fostering the intra- and interpersonal perspective in cognitive behavioral therapy, in addition to the derivation of the patient’s individual model of SAD based on Clark and Wells model of treating SAD.",signatures:"Christina Hunger-Schoppe",downloadPdfUrl:"/chapter/pdf-download/59726",previewPdfUrl:"/chapter/pdf-preview/59726",authors:[{id:"210496",title:"Dr.",name:"Christina",surname:"Hunger",slug:"christina-hunger",fullName:"Christina Hunger"}],corrections:null},{id:"57252",title:"Video Feedback Techniques Used in Social Anxiety Disorders",doi:"10.5772/intechopen.71278",slug:"video-feedback-techniques-used-in-social-anxiety-disorders",totalDownloads:1108,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The effectiveness of video feedback in socially anxious individuals including the improvement of distorted self-perceptions has been reported. However, socially anxious individuals might overestimate their appearance on video as more negative or less positive. Such misjudgments might be caused by excessively high negative interpretations and lack of positive interpretations in patients with social anxiety disorder (SAD). The results of this study suggest that a person’s interpretations of his or her appearance on video interfere with the effectiveness of video feedback. The significance of these findings and techniques for improving cognitive interventions using video feedback are discussed.",signatures:"Kentaro Shirotsuki",downloadPdfUrl:"/chapter/pdf-download/57252",previewPdfUrl:"/chapter/pdf-preview/57252",authors:[{id:"211357",title:"Dr.",name:"Kentaro",surname:"Shirotsuki",slug:"kentaro-shirotsuki",fullName:"Kentaro Shirotsuki"}],corrections:null},{id:"57179",title:"Imagery Rehearsal Therapy (IRT) Combined with Cognitive Behavioral Therapy (CBT)",doi:"10.5772/intechopen.70899",slug:"imagery-rehearsal-therapy-irt-combined-with-cognitive-behavioral-therapy-cbt-",totalDownloads:1460,totalCrossrefCites:2,totalDimensionsCites:3,hasAltmetrics:0,abstract:"In cases of post-traumatic stress disorder (PTSD), nightmares can often persist, even after a cognitive behavioral therapy (CBT) for this disorder. Imagery rehearsal therapy (IRT) is a CBT that targets the treatment of nightmares directly. Objectives: the present study describes the feasibility and the efficacy of combining IRT with first-line, trauma-focused CBT for PTSD. Method: two individuals with PTSD took part in this experimental case study protocol. The efficacy of the combined treatment was evaluated using semi-structured interviews, self-report questionnaires, and daily self-monitoring diaries. Results: after three IRT sessions for Participant 1 and five IRT sessions for Participant 2, combined with CBT for PTSD, both participants experienced a slight decrease in sleep difficulties and in the intensity of their PTSD symptoms post-treatment. More particularly, one participant demonstrated a significant decrease in the level of distress associated with his post-traumatic nightmares (PTNM). Conclusions: these results demonstrate that it is possible and promising to combine IRT with CBT for PTSD.",signatures:"Katia Levrier, André Marchand, Valérie Billette, Stéphane Guay and\nGeneviève Belleville",downloadPdfUrl:"/chapter/pdf-download/57179",previewPdfUrl:"/chapter/pdf-preview/57179",authors:[{id:"175626",title:"Prof.",name:"André",surname:"Marchand",slug:"andre-marchand",fullName:"André Marchand"},{id:"184054",title:"Dr.",name:"Katia",surname:"Levrier",slug:"katia-levrier",fullName:"Katia Levrier"},{id:"184057",title:"Dr.",name:"Geneviève",surname:"Belleville",slug:"genevieve-belleville",fullName:"Geneviève Belleville"},{id:"194415",title:"Dr.",name:"Stephane",surname:"Guay",slug:"stephane-guay",fullName:"Stephane Guay"},{id:"210756",title:"Dr.",name:"Valérie",surname:"Billette",slug:"valerie-billette",fullName:"Valérie Billette"}],corrections:null},{id:"57215",title:"Dreams in Cognitive-Behavioral Therapy",doi:"10.5772/intechopen.70893",slug:"dreams-in-cognitive-behavioral-therapy",totalDownloads:1535,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:0,abstract:"In recent years, cognitive-behavioral-oriented therapists have found a new interest in work with dreams. Dream analysis within the framework of cognitive-behavioral therapy (CBT) seems to be fully justified if the cognitive processes involved in the dreaming process are considered. The aim of the chapter is to introduce three perspectives for working with dreams within the realm of CBT. The first perspective is dedicated to the historical view on the use of dreams in CBT. The second includes an analysis of the conceptual functions of working with dreams in CBT. The third presents practical issues related to dream analysis in CBT. To sum up, the chapter presents systematic and comprehensive information about the therapeutic work with dreams within CBT from a historical, functional, and processual perspective.",signatures:"Dagna Skrzypińska and Barbara Szmigielska",downloadPdfUrl:"/chapter/pdf-download/57215",previewPdfUrl:"/chapter/pdf-preview/57215",authors:[{id:"215223",title:"Ph.D. Student",name:"Dagna",surname:"Skrzypińska",slug:"dagna-skrzypinska",fullName:"Dagna Skrzypińska"},{id:"215224",title:"Prof.",name:"Barbara",surname:"Szmigielska",slug:"barbara-szmigielska",fullName:"Barbara Szmigielska"}],corrections:null},{id:"58891",title:"Cognitive-Behavioral Psychotherapy for Couples: An Insight into the Treatment of Couple Hardships and Struggles",doi:"10.5772/intechopen.72104",slug:"cognitive-behavioral-psychotherapy-for-couples-an-insight-into-the-treatment-of-couple-hardships-and",totalDownloads:7613,totalCrossrefCites:2,totalDimensionsCites:5,hasAltmetrics:1,abstract:"In this chapter, a comprehensive literature review of the theoretical underpinnings and clinical practices of cognitive-behavioral couple therapy (CBCT) will be presented. First, a description of the theory underlying CBCT and the role of the therapist will be reviewed. Different mandates and motives for couples to consult in CBCT will then be described, with attention given to specificities for diverse populations. The assessment process and main intervention techniques used by CBCT therapists will be presented, including communication training, problem and conflict resolution, cognitive restructuring, identification and expression of emotions, expression of affection and sexual problems as well as acceptance and tolerance of differences. The chapter will conclude with a critical analysis of CBCT and suggestions for future clinical developments.",signatures:"Caroline Dugal, Gaëlle Bakhos, Claude Bélanger and Natacha\nGodbout",downloadPdfUrl:"/chapter/pdf-download/58891",previewPdfUrl:"/chapter/pdf-preview/58891",authors:[{id:"57536",title:"Prof.",name:"Claude",surname:"Belanger",slug:"claude-belanger",fullName:"Claude Belanger"}],corrections:null},{id:"58462",title:"Cognitive-Behavioral Therapy: Current Paths in the Management of Obesity",doi:"10.5772/intechopen.72586",slug:"cognitive-behavioral-therapy-current-paths-in-the-management-of-obesity",totalDownloads:1213,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The treatment of obesity and its related chronic symptoms is one of the major issues that world healthcare systems are facing today. Cognitive-behavioral therapy (CBT) is one of the most effective therapies in the treatment of dysfunctional eating behaviors. In the first part of the chapter, the phenomenon of obesity will be introduced; subsequently, the role of CBT into obesity treatment will be underlined. CBT’s core strategies will be presented and analyzed: goal setting, self-monitoring, stimulus control, problem solving technique, and cognitive restructuring technique. The use of these strategies and related results is a major issue, emphasizing the need for further studies on the phenomenon of obesity, given the excellent results available in the short term, with significant weight loss, but the difficulties in keeping the results achieved in the long run. Since obesity is a chronic condition, CBT treatments must focus on different outcomes, considering weight loss as a consequence of a change in the individual’s eating style rather than as a major and only result to be pursued. Finally, we will take into account the topic of motivation in the psychological treatment of obesity since patient’s motivation assessment seems to be a major prerequisite for successful weight loss therapy.",signatures:"Alessandro Musetti, Roberto Cattivelli, Anna Guerrini, Anna Maria\nMirto, Francesco Vailati Riboni, Giorgia Varallo, Gianluca\nCastelnuovo and Enrico Molinari",downloadPdfUrl:"/chapter/pdf-download/58462",previewPdfUrl:"/chapter/pdf-preview/58462",authors:[{id:"155485",title:"Prof.",name:"Gianluca",surname:"Castelnuovo",slug:"gianluca-castelnuovo",fullName:"Gianluca Castelnuovo"},{id:"215922",title:"Ph.D.",name:"Alessandro",surname:"Musetti",slug:"alessandro-musetti",fullName:"Alessandro Musetti"},{id:"215966",title:"Dr.",name:"Roberto",surname:"Cattivelli",slug:"roberto-cattivelli",fullName:"Roberto Cattivelli"},{id:"216097",title:"Dr.",name:"Francesco",surname:"Vailati Riboni",slug:"francesco-vailati-riboni",fullName:"Francesco Vailati Riboni"},{id:"216098",title:"Dr.",name:"Anna",surname:"Guerini",slug:"anna-guerini",fullName:"Anna Guerini"},{id:"216376",title:"Dr.",name:"Giorgia",surname:"Varallo",slug:"giorgia-varallo",fullName:"Giorgia Varallo"}],corrections:null},{id:"58661",title:"Cognitive-Behavioral Therapy for Gambling Addiction",doi:"10.5772/intechopen.72671",slug:"cognitive-behavioral-therapy-for-gambling-addiction",totalDownloads:2013,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:0,abstract:"This chapter contains a brief history of gambling and a brief description of gambling disorders, followed by the risks that this behavior would become compulsive and the negative consequences that the gambler and the members of this family would experience. Thereafter, the psychological characteristics of the compulsive gambler will be specified, as results from the recent international researches, and we will also describe the psychological profile of the Romanian compulsive gambler. Next, we will approach the various methods of treatment of gambling addiction, focusing on the forms of psychotherapy that proved to be effective in treating this mental disorder, and listing of certain studies that have demonstrated their effectiveness.",signatures:"Steliana Rizeanu",downloadPdfUrl:"/chapter/pdf-download/58661",previewPdfUrl:"/chapter/pdf-preview/58661",authors:[{id:"214110",title:"Prof.",name:"Steliana",surname:"Rizeanu",slug:"steliana-rizeanu",fullName:"Steliana Rizeanu"}],corrections:null},{id:"57700",title:"Internet Addiction and Cognitive Behavioral Therapy",doi:"10.5772/intechopen.71277",slug:"internet-addiction-and-cognitive-behavioral-therapy",totalDownloads:2932,totalCrossrefCites:1,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Internet addiction has become a social and public health problem especially among adolescents and adults. The purpose of this chapter is to describe the Internet addiction and discuss the process of treating Internet addiction by using cognitive behavioral therapy for Internet addiction model (CBT-IA). Among the Internet addiction, I have elected to focus on the studies regarding definition, prevalence, risk factors, negatives consequences, and treatment modalities with focus on CBT-IA. In contrast, research on the CBT-IA is still in its early stages. Till now, there is no clear definition for Internet addiction, and these definitions are based on assessment tools that are developed by researchers. There was a variance in the prevalence of Internet addiction among adolescents and adults, which might be related to many factors including assessment instruments and cultural factors. There are many risk factors for Internet addiction that involve socio-demographic, social, psychological factors, and Internet use practices. Many negative consequences result from Internet addiction such as social withdrawal, lack of relationships with families and peers, and psychological problems including depression and anxiety. The CBT-IA is the most effective treatment for Internet addiction. The CBT-IA model is a comprehensive approach, which can be divided into three phases: behavior modification, cognitive restructuring, and harm reduction therapy (HRT).",signatures:"Malakeh Zuhdi Malak",downloadPdfUrl:"/chapter/pdf-download/57700",previewPdfUrl:"/chapter/pdf-preview/57700",authors:[{id:"214764",title:"Dr.",name:"Malakeh",surname:"Malak",slug:"malakeh-malak",fullName:"Malakeh Malak"}],corrections:null},{id:"58169",title:"The Internet and CBT: A New Clinical Application of an Effective Therapy",doi:"10.5772/intechopen.72146",slug:"the-internet-and-cbt-a-new-clinical-application-of-an-effective-therapy",totalDownloads:1797,totalCrossrefCites:2,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Mental disorders are disabling and common. Depression, for example, has greater global burden of disease than any physical disorder, and almost a third of people will experience some form of mental disorder in their lifetime. The effectiveness of psychological interventions is well established. Cognitive Behavioral Therapy is particularly effective for mood and anxiety disorders. But CBT is demanding of time and resources, partly explaining its limited availability, even in public systems. More and more people have access to the Internet and smartphones, even in the developing world. Internet therapies (including smart-phone apps) have been developed, offering CBT. Can technology help with access to CBT? In this chapter, we will look at the effectiveness of iCBT for several illnesses, based on new evidence from recent randomized controlled trials and meta-analyses, noting that while there is evidence for this therapy, not all programs have the same results. We consider iCBT in the real world, by looking at some popular apps and websites, including MoodGYM, and also present a case from The Scarborough Hospital (where we implemented a free-at-the-point-of-use iCBT program), demonstrating how it can be applied in an outpatient setting. We also present the current strengths and limitations associated with iCBT. Finally, we consider future directions for this field, considering chatbots and the possibilities with Artificial Intelligence.",signatures:"David Gratzer, Faiza Khalid-Khan and Shawnna Balasingham",downloadPdfUrl:"/chapter/pdf-download/58169",previewPdfUrl:"/chapter/pdf-preview/58169",authors:[{id:"174958",title:"Dr.",name:"David",surname:"Gratzer",slug:"david-gratzer",fullName:"David Gratzer"},{id:"224555",title:"Ms.",name:"Faiza",surname:"Khalid-Khan",slug:"faiza-khalid-khan",fullName:"Faiza Khalid-Khan"},{id:"224556",title:"Ms.",name:"Shawnna",surname:"Balasingham",slug:"shawnna-balasingham",fullName:"Shawnna Balasingham"}],corrections:null},{id:"57353",title:"Internet-Delivered Cognitive Behaviour Therapy",doi:"10.5772/intechopen.71412",slug:"internet-delivered-cognitive-behaviour-therapy",totalDownloads:1369,totalCrossrefCites:3,totalDimensionsCites:13,hasAltmetrics:0,abstract:"The delivery of cognitive behaviour therapy over the internet (iCBT) has developed in tandem with recent technological advancements. In this chapter, we briefly explore the background of iCBT and its ongoing evolution in the relatively short period that has been available. We summarise the empirical evidence that supports the efficacy and effectiveness of iCBT in different settings, and for different populations. We provide an overview on how an iCBT platform works for service users, and we offer some thoughts on the processes involved in repurposing an evidence-based treatment protocol into an online format. Using iCBT service, users can avail of the benefits of cognitive behaviour therapy in a flexible manner, with or without support. The case presentation provided an illustrative on some of these advantages and highlights opportunities for the individual. The service delivery examples describe the use of iCBT and its application in different contexts. 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\r\n\tPlatelets are disc-shaped cells. They are 2-4 µm in diameter and are produced from megakaryocytes. About a third of the platelets are in the spleen. Normal platelet count in blood is 150,000–450,000/μL. Platelet production is controlled by a growth factor called “Thrombopoietin (TPO).” The life span of platelets is 10–14 days. Their count and functional status, both are important to maintaining normal hemostasis. Thrombocytes adhere to subendothelial collagen tissue via von Willebrand factor after endothelial damage (adhesion) and they secrete ADP and thromboxane A2 which provide platelet aggregation (aggregation). In addition, other coagulation stages also take place on the platelet surface. Decreased platelet cell count (<150,000/μL) is called “Thrombocytopenia". It is a quite common finding in medical practice and is associated with a variety of diseases. Thrombocytopenia develops as a result of decreased production, increased destruction, and/or platelet distribution disorders. It can be the reason for a benign disorder or can be associated with malignant diseases like leukemia. This book will be aimed to provide readers with all updated information about thrombocytopenia.
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{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"}},{type:"book",id:"2270",title:"Fourier Transform",subtitle:"Materials Analysis",isOpenForSubmission:!1,hash:"5e094b066da527193e878e160b4772af",slug:"fourier-transform-materials-analysis",bookSignature:"Salih Mohammed Salih",coverURL:"https://cdn.intechopen.com/books/images_new/2270.jpg",editedByType:"Edited by",editors:[{id:"111691",title:"Dr.Ing.",name:"Salih",surname:"Salih",slug:"salih-salih",fullName:"Salih Salih"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"72278",title:"Biotribology of Mechanically and Laser Marked Biomaterial",doi:"10.5772/intechopen.92564",slug:"biotribology-of-mechanically-and-laser-marked-biomaterial",body:'The “ball-cratering wear test” has gained large acceptance at universities and research centers as it is widely used in studies focusing on the wear behavior of different materials [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]. Figure 1 presents a schematic diagram of the principle of wear test, where a rotating ball is forced against the tested specimen and liquid solution supplied between the specimen and the ball during the experiments.
“Ball-cratering wear test”: representative figure of its operating principle.
The aim of the “ball-cratering wear test” is to generate “wear craters” on the surface of the specimen. Figure 2 presents an image of such crater, together with an indication of the crater diameter (
Images of wear craters: (a) diameter –
In other line of research, the concept of “biotribology” has gained important spotlight in the area, including research works addressing the biotribological behavior of materials [23, 24, 25, 26, 27, 28, 29] used in the manufacturing of human body elements. Consequently, different laboratory techniques have been employed to reproduce conditions where there are friction and consequent wear of parts of the human mechanical structure with relative movement.
However, wear tests conducted under the “ball-cratering” technique present advantages in relation to other types of tribological procedures, as it favors the desired analysis of the tribological behavior. Therefore, considering the need of tribological characterization of biomaterials and the capacity that the “ball-cratering wear test” method presents to this goal, the purpose of this work is to study the biotribological behavior of mechanically and laser-marked ASTM F139 austenitic stainless-steel biomaterial.
Equipment with free-ball mechanical configuration (Figure 3a) was used for the sliding wear tests. Two load cells were used in the tribometer: one load cell to control the “normal force –
(a) “Ball-cratering” wear test equipment with “free-ball” mechanical configuration used for the sliding wear tests: (b) load cell mounted to control the normal force and (c) load cell positioned to measure the tangential force during the experiments.
“Normal” and “tangential” force load cells have a maximum capacity of 50 N and an accuracy of 0.001 N. The values of
The tested specimen was an ASTM F139 austenitic stainless-steel biomaterial, marked mechanically and with a nanosecond Q-switched Nd: YAG laser. Its chemical composition is presented in Table 1.
Chemical element | % (in weight) |
---|---|
C | 0.023 |
Si | 0.78 |
Mn | 2.09 |
P | 0.026 |
S | 0.0003 |
Cr | 18.32 |
Mo | 2.59 |
Ni | 14.33 |
Fe | Balance |
Chemical composition of ASTM F139 austenitic stainless-steel biomaterial – in percentage weight.
Balls of polypropylene and AISI 316 L austenitic stainless steel, with diameter of
To simulate the fluid present in the human body, a chemical liquid solution of PBS – Phosphate Buffered Solution – was inserted between the specimen and the ball. It was composed by the materials mentioned in Table 2.
Chemical element | (g/l) |
---|---|
NaCl | 8 |
KCl | 0.2 |
Na2HPO4 | 1.15 |
KH2PO4 | 0.2 |
Chemical composition of the PBS – phosphate buffered solution – in g/l.
Table 3 shows the hardness (
Material | Hardness – | |
---|---|---|
Specimen | ASTM F139 austenitic stainless steel | 180 HV |
Test ball | Polypropylene | 55 – Shore D |
AISI 316 L austenitic stainless steel | 318 HV |
Hardness of the materials used in this work.
Table 4 presents the test conditions defined for the sliding wear experiments conducted in this research.
Normal force – | Ball of polypropylene | 0.05 N |
Normal force – | Ball of AISI 316 L austenitic stainless steel | 0.40 N |
Test ball rotational speed – | 75 rpm | |
Tangential sliding velocity – | 0.1 m/s | |
Test time – | 10 min | |
Sliding distance – | 60 m |
Test parameters for the ball-cratering wear tests under conditions of sliding wear.
Following values of normal force (
All experiments were conducted without interruption, and the chemical liquid solution of PBS – Phosphate Buffered Solution – was fed between the specimen and the ball during the tests, under a frequency of 1 drop/10 s. Both the normal force (
Finally, the wear volume (
Figure 4 shows a scanning electron micrograph of the surface of a wear crater generated during the sliding wear tests.
Scanning electron micrograph of the surface of a wear crater generated during the sliding wear tests.
Occurrence of grooves, due to sliding movement between the ball and the specimen, was observed in the scanning electron micrograph. The result presented in Figure 4 is in qualitative agreement with the literature [30], where it is reported that the action of grooves on the surface of a material is characterized as a common tribological behavior of two metallic materials under relative movement.
Figure 5 presents the behavior of the specimen in terms of wear volume (
Wear volume (
In addition, Figure 5 shows a decrease in wear volume under laser marking process for both types of counterbodies. Decrease in wear volume is related to increase in local hardness of the specimen. Increase of hardness can be attributed to the action of laser on specimen surface. In relation to specimen marked mechanically, the possible increase of local hardness could have occurred due to local surface hardening.
However, the increase of local hardness caused by laser marking is higher than the local hardness caused by mechanical marking, justifying the results presented in Figure 5.
Figure 6 shows the behavior of the coefficient of friction (
Coefficient of friction behavior (
In the present tribological conditions, coefficient of friction was found lower for the wear tests conducted against polypropylene ball than AISI 316 L austenitic stainless-steel ball counterbodies.
The following conclusions can be drawn from the results obtained in this research, regarding to tribological behavior of ASTM F139 austenitic stainless steel:
tribological behavior was influenced by the type of the marking process – “mechanical” or “laser” – applied for the investigated biomaterial;
wear volume was found to be dependent on the normal force acting on the specimen, that is, they were dependent on the type of counterbody – ball of polypropylene or ball of AISI 316 L austenitic stainless steel; and
coefficient of friction was found dependent on the type of ball; the lower values of
diameter of the wear crater (mm) diameter of the test ball (mm) hardness (HV) test ball rotational speed (rpm) normal force (applied on the specimen) (N) radius of the test ball (mm) sliding distance (m) test time (min) tangential force (developed during the wear tests) (N) tangential sliding velocity of the test ball (m/s) wear volume of the wear crater (mm3) coefficient of friction density (g/cm3)
The established financial reporting system within an entity is the basic source of information on its financial position and results. The economic and financial globalization of the world market has emphasized the importance of high quality financial reporting. For the business decision-making process, financial and audit reports are the main source of information, as they contain information on financial position, business results, changes in equity, cash-flows and other reliable information [1]. Development of the capital market and the increase in the number of interested parties (investors) created even higher demand of reliable, on time and fair financial statements as the main results of financial reporting. The regulation of the relationship between the state and society, owners of capital and management, various stakeholders and society, and others; has been further improved by a quality financial reporting and audit process. However, in order to fulfill their main purpose for all interested parties, financial statements must provide information that is true, objective, comprehensible, comparable and uniform [2]. In the first place, financial statements have to be publicly available, which is usually regulated by law. For example, Law on Accounting of the Republic of Serbia prescribes that all business entities have to submit their financial reports to the competent institution which later publishes them on the official internet site [3]. Information contained in financial statements can be used for numerous purposes. For example, other business entities can use them in the process of making business, financial, investment and other decisions. Likewise, banks and financial institutions can use them in order to approve loans or assess investment risks related to the certain business entity. However, financial information contained in financial statements are not processed and represent a raw data that should be analyzed in order to assess the performance of a certain business entity. Aside Notes to financial statements, as one of the qualitative statements that business entities prepare and report, all other statements are quantitative in nature and offer hundreds of pieces of data. Therefore, it is of great importance to perform certain type of analysis on the collected data in order to gain a solid basis for business decision making process. Analysis of financial statements is one of the most common methods of assessing business performance. The main goal of conducting the analysis of financial statements is to obtain information on the performance of the observed company, i.e. liquidity, profitability and solvency. Measuring financial performance using compiled and disclosed financial statements is a quantitative analysis of the position of the observed company, including the way in which the company uses the capital invested in business. High quality analysis of the performance of the observed entity provides a comprehensive image of the business, including meeting the information needs of stakeholders. The authors [4] point out in their paper that the analysis of financial performance is crucial in determining the efficiency in terms of the use of available resources. Likewise, an entity owners will be able to assess management skills and decisions that have been made in previous, as well as in current reporting period, so that they could analyze entities strengths, weaknesses and therefore improve their overall performance [5, 6, 7].
Some pieces of data disclosed in financial statements have informational power to be used on their own, such as Total assets, Sales revenue, or Net result. However, informational power of data increases when they are put into relation with other pieces of data. Therefore, financial statements analysis using ratios has been one of the most commonly used methods of assessing business performance. Financial ratio is a relative magnitude of two (or more) selected numerical values taken from financial statements. For example, relation between Net result and Equity will provide information on how much dollars of profit an entity earns for each dollar invested in equity. Results of financial statements analysis can be used to compare performance of a certain entity over a period of time, or for comparison with other entities within the industry. However, since financial statements analysis takes time and there are numerous financial ratios that analysts could use (and the fact that most of these ratios are correlated), the number of ratios that are being calculated and assessed should be reduced so that an analyst could focus on several of them without losing data that could be relevant for the analysis [8]. One of the methods that can be used is Principal Component Analysis (PCA), which reduces number of observed variables for any further, regression, or any other type of analysis [9]. PCA analysis has found its numerous purposes in different industries, for example, in image compressing [9, 10, 11], as well as in biometrics or “bioimaging” where physical characteristics of a person are used for its identification with application on communication devices and security systems.
The significance of PCA results is reflected in the fact that they can be used for more effective and efficient analysis of performance of certain entity, or for all business entities within a certain industry, or if analyzed financial data is related to whole economy, than results could be used for the analysis of all entities within it. The main advantages of PCA are precision of results; reduction of time needed for the analysis and evaluation of results; as well as reduction of related costs and efforts of the analyst.
With the development of technology, we have gained the ability to generate massive amounts of data. The use of correct methodologies for data analysis has become essential when dealing with complex financial challenges. In this paper, we discuss the theory underlying PCA. This type of analysis is one of the most used statistical tools in the field of financial data analysis. To ensure that the proper method is used for the analysis, theoretical knowledge and an comprehension of statistical methods are essential.
PCA is primarily designed as a statistical technique that selectively reduces the dimensionality of data in complex data sets while preserving maximum variance. Since research in the financial sector involves both a large amount of data and a large number of variables simultaneously, it is difficult for us to perform analysis for this type of data.
Visualization techniques are only useful in two or three dimensional spaces, and single-variable analysis does not provide precise results due to overlapping variance. To achieve dimensionality reduction, it is necessary to generate principal components, i.e., a new set of variables containing a linear combination of the original variables. PCA can be used for a variety of tasks. A very small number of components are sufficient to cope with the variability of a data set. Since the number of components is reduced by using principal components, the complexity of the analysis itself is also reduced by avoiding analyzing a large number of output variables.
The standard PCA procedure takes as its starting point a data set in which
This equation is valid even when the eigenvectors are multiplied by −1. Here,
The
For the final outcome of the PCA assessment to be successful and significant, numerous conditions must be met. Initially, it is crucial that the data entered are uninterrupted and that variables should be measured on an interval or ratio scale. This condition must be met because PCA tests important correlation patterns for these variables.
Another crucial requirement is that the relationships between the individual pairs of variables are linear. If there are nonlinear relationships between the individual pairs of variables, appropriate data transformation techniques, such as logarithmic transformations, should be considered. Presumptions for PCA are filling missing values with not null values, outliers handling, and normalization scaling. All outliers should be filtered out prior to analysis, as they can bias the results by affecting the magnitude of the correlation.
To obtain more accurate estimates for the correlation population parameters, a large sample size is required. The data sets must be linear in order to be formed. The basic principle of PCA is that high variance must be taken into account, while variables with lower variance can be considered noise and are not taken into account. All variables must be processed at the same level of measurement.
Eq. (2) associates the eigenvalue decomposition of the covariance matrix
Where
Here
Here
The variability associated with the set of retained principal components can be used to ensure the quality of any
The trace of
It is a common approach to use a pre-specified percentage of the total variance to determine how many principal components to keep, but graphical constraints often lead to keeping only the first two or three principal components. The percentage of total variance is a basic tool for measuring the quality of these low-dimensional graphical representations of the data set.
The biggest problem is the number of components needed to obtain a sufficient number of variances while achieving a reduction in dimensionality. There are several ways to determine the components, and one of them is to set a threshold.
The next very popular approach is the “Scree Plot” [14], where the components are arranged on the
The most popular method is parallel analysis [15], where PCA is performed with as many variables as the original data set includes. The average eigenvalues between the original data set and the simulated data set are measured. Any values from the original data that are lower than the data in the simulated set are discarded.
PCA has many advantages. In terms of maximizing variance in
Factor analysis is a method that is often combined with PCA and it inspires the concept of rotating principal components [16]. Assume that
Another method of simplifying the principal components is to limit the charges of the new variables. This is called adding a constraint. There are several variants of this strategy, one of which uses LASSO linear regression [17], that represents least absolute shrinkage and selection operator. In this approach, SCoTLASS components are discovered, solving the same optimization problem as PCA, but with the additional constraint
PCA is inherently sensitive to the occurrence of outliers and thus to large errors in data sets [19]. As a result, efforts have been made to define robust variants of PCA, and the terminology RPCA has been used to refer to several approaches to this problem. Huber’s early work focused on robust alternatives to covariance or correlation matrices and how they could be used to generate robust principal components [20]. The demand for methods to process very large data sets sparked renewed interest in robust PCA variants. This led to PCA research lines, especially in areas such as machine learning, image processing, web data analysis, and many others.
Wright et al. [21] defined RPCA as the sum of two
where
PCA was first introduced into mechanics by [22], as an analogue of the axis theorem. It was later named “PCA” by [23]. The range of applications in finance and economics is extensive. Take as an example [24], who used PCA to document three factor structures. Stock and Watson [25] used PCA to monitor economic development and activity, as well as the inflation index. Egloff et al. [26] used PCA as a way to analyze the dimensions of inconsistent dynamics. Volatility is a statistical measure that can be used to determine these inconsistencies using a two-factor volatility model. This includes long-term and short-term fluctuations in the volatility structure. Baker and Wurgler [27] used PCA to measure investors sentiment, i.e., their positive or negative view. This was done according to the principle of the number of sentiment proxies before Baker, [28] created the policy uncertainty index. This index represents potential risks in the near future.
The most important item in the construction of PCA is the estimation of the eigenvalues of the covariance matrix sample. Anderson and Weeks [29] and Anderson [30] showed that sample eigenvalues were consistent when dealing with asymptomatic sentiment proxy results. Waternaux [31] proved that similar results are obtained with simple eigenvalues as long as there is a fourth moment in the data. In addition to the discussions in the [32] book, [33] was able to establish the asymptotic distribution of eigenvectors using generalized assumptions.
However, this PCA approach to eigenvalues has some downsides. The first problem is certainly dimensionality, which can be noticed when the cross sectional dimension grows simultaneously with the sample in the same period. Then inconsistencies occur. Another problem arises from linear data types that do not include nonlinear patterns. A third problem [34] arises from the dependence of the asymptotic theory on fixed assumptions for the analysis. For these reasons, we have a problem when we use PCA for reimbursement data. Most of the time, we need years of data to make an assumption, which in turn leads to other problems, such as permanence and consistency of non-fixed parameters. This type of data has backlogs and volatility times often vary.
These problems stimulate the improvement in this field and motivate the development of tools for PCA methods. The approach to the problem, where the number of occurances grows in fixed time periods, touches all the listed downsides. Theoretically, it is known that as the frequency of the sample increases, the estimated variance and covariance increase. This is true until the microstructure of the market begins to take effect. Incidentally, this is not a serious problem if we choose a sampling frequency of minutes, which we use as opposed to the below one second time interval most often used for liquid stocks. A high frequency asymptotic analysis with the cross-sectional dimension is expected as the time interval increases sharply. This high frequency asymptotic framework allows us to perform non-parametric analysis as well as independent, non-static and analysis without underlying parameters as is the case with low frequency processes.
Asymptotic theory is very common in many contexts. Jacod et al. [13] and Jacod and Podolskij [35] also dealt with one problem that we deal with in this paper, where the cross sectional dimensions are invariant and the process is continuous. Mykland and Zhang [36] designed an alternative theory to the one put forward by [37], that discuss inference for volatility function dependence. It is based on the aggregation of local estimates and uses a finite number of blocks. Saha et al. [38] considered the expected values of the integrated covariance matrix under conditions where there is an error measure and the matrix is large containing high frequency data. Tao et al. [39] addressed work on the convergence rate. Jacod and Rosenbaum [40] analyzed estimators, composed of aggregating functions of estimates. They did so using integrated quarticity estimation. Heinrich and Podolskij [41] discussed empirical covariate matrices of Brownian integrals. Here is discussed the measurement of the leverage effect and its evaluation by the integrated correlation method [42].
PCA analysis can be used in analysis of financial data for different purposes. For example [43] used it to identify the type of impact on grouped impact factors, such as assessing the quality of accounting information and facilitating the process of financial analysis conducted by different users. On the other hand, [44] used PCA to assess the impact of the evolution of Finnish standards on IFRS (International Financial Reporting Standards). Finally [45] used PCA analysis to determine the macroeconomic impact on the profitability of Romanian listed companies, using data from 1997 to 2007, and identified following indicators: liquidity, solvency, and firm’s dimension.
When it comes to the use of PCA analysis in financial statements analysis, four papers that focus on Romanian listed companies will be reviewed first. All papers emphasize the importance of using PCA analysis in the analysis of key financial ratios. In the first paper author [46] analyzed the data of 16 initial variables which he grouped into 3 new variables (general efficiency indicator, indicator in correlation with historical debts of companies and development indicator (given long-term debt and deferred income). Those three variables where able to explain 96.72% of initial variability. In the second paper, [47] analyzed data for 2010 including initially seven indicators of standard financial analysis and they reduced them to only two (which explain 94% of initial variability). In third paper, [48] used data from the stock exchange in the period 2006–2011 to identify the main components of financial statements which explain 79.08% of initial variability. The same group of indicators has been used by [43] on research sample that consisted of 111 companies from Madrid stock exchange and 32 companies from Eurostoxx50 for reporting periods 2005–2007. Research results showed that those six indicators explained 87% of total variance, with the first two indicators at app 44% of total variance.
In order to provide an answer on defined research question, 3.013 medium and large business entities were selected by random and used as a research sample. Financial statements for 2019 reporting period have been downloaded manually from the official website of the Business Registers Agency (BRA). BRA is a state administrative body that collects financial statements and corresponding audit reports of business entities that operate within the territory of the Republic of Serbia. Information published by BRA is used for financial analysis of business entities and as a basis of decision-making process. Afterwards, data from the pdf files containing financial statements have been copied and recorded in pre-set up tables in Excel files. Namely, medium and large business entities in the Republic of Serbia have an obligation to prepare and disclose full set of financial statements, consisting of balance sheet, income statement, cash-flow statement, statement of changes in equity and notes to financial statements. Since all previously mentioned statement, except notes to financial statements, are quantitative in nature, they were used for this research. Values originally disclosed in RSD, as the reporting currency, were converted into euros by using the average exchange rate of euros on the balance sheet date (31st December). Values of each financial statement line is presented in thousands, and therefore they are presented as such in this research [49].
Financial statement item lines in official financial statements are marked by corresponding automatic data processing number (in Serbian: Automatska obrada podataka—AOP), that belongs to the national nomenclature system. These markings are used in order to perform control of mathematical calculations before each financial statement is accepted for publishing by BRA. They also serve as an instrument of connecting data and information regarding the same financial statement item presented in financial statements. Balance sheet items cover automatic data processing numbers from 0001 to 0465; income statement from 1001 to 1071; statement of cash-flows from 3001 to 3047; and statement of changes in equity from 4001 to 4252. Table 1 shows the formulas used for the calculation of the selected financial indicators that will be used in this research. Having in mind that these variables will be used in order to differentiate business entities to three major types of business activities, these variables have been selected by a common sense.
Variables | Derived from |
---|---|
Fixed assets in total assets | AOP2/AOP71 |
Percent sales of merchandise in total operating revenue | AOP1002/AOP1001 |
Percent sales of products and services in total operating revenue | AOP1009/AOP1018 |
Percent cost of merchandise sold in total operating expenses | AOP1019/AOP1018 |
Percent cost of material in total operating expenses | AOP1023/AOP1018 |
Percent fuel and energy cost in total operating expenses | AOP1024/AOP1018 |
Percent wage cost in total operating expenses | AOP1025/AOP1018 |
Percent productive service cost in total operating expenses | AOP1026/AOP1018 |
Percent depreciation cost in total operating expenses | AOP1027/AOP1018 |
Percent raw material in total assets | AOP45/AOP71 |
Percent WIP in total assets | AOP46/AOP71 |
Percent finished products in total assets | AOP47/AOP71 |
Percent WIP and finished products in total assets | (AOP46 + AOP47)/AOP71 |
Percent merchandise in total assets | AOP48/AOP71 |
Calculation of selected financial indicators.
Data preparation is a key process in data analysis. The basic preparation and cleaning procedures are:
Preparing a copy of the table
Adding new attributes
Conversion of column types
General data cleaning and adjustment
Specifically, the cleaning includes the following items:
Editing date variables—the most common formatting problems
Recoding of zeros/missing values
Decoding categorical variables using labels and hot encoding
Arranging outliers
Application of normalization/standardization/ log transformation
Calculating descriptive statistics—mean, median, mode, standard deviation, variance, rank, etc.
Calculating inferential statistics - distributions, t-value, p-value, frequencies, cross-tabulations, correlation, covariance, etc.
More advanced techniques include:
Coding:
Categorical variables are labeled as character variables and must be converted to a factor type for modeling purposes. Queues perform this task.
Outliers:
For numeric variables, we can identify deviations numerically by the value of the bias.
Normalization/logarithmic transformation:
One of the techniques to normalize the biased distribution is logarithmic transformation. First, a new variable is created, while later the value of the bias of this new variable is calculated and printed.
Standardization:
One of the standardization techniques is that all characteristics are centered around zero and have approximately the variance of one unit. Scaling is used so that the variable is converted. The result is that these variables are standardized with a mean of zero.
As part of the preparation for PCA, firstly missing values from the dataset were filled with zeros. After that, the data was scaled by using a standard scaler, which standardizes features by removing the mean and scaling to unit variance. The preprocessed dataset, was then used for:
PCA
Sparse PCA
Robust PCA
All three of the PCA methods were instanciated with the number of components set to 7. After PCA, the now transformed data went through several clustering methods for the purpose of comparing results. The clustering methods that were used for each PCA are:
K-means clustering
Agglomerative clustering
BIRCH clustering
Gaussian Mixture
Spectral clustering
Furthermore, each of the clustering methods were executed with just the preprocessed data, without PCA, also for the purpose of comparing results.
Data preparation:
Compute dot product matrix:
Eigenanalysis:
Compute eigenvectors:
Keep first 7 components:
Compute 7 features:
end procedure.
This chapter discusses the outcomes of PCA and cluster analysis. The initial variables that load on the principal components are studied. Correlations or covariances between the original variables and the principal components correlate with the loadings. The variable loadings are contained in a loading matrix, which is created by multiplying the eigenvector matrix by a diagonal matrix containing the square root of each eigenvalue. The entries are determined by the component extraction method used. Non-standardized loadings show the covariance between mean-centered variables and standardized component values, regardless of whether the extraction is based on the singular value decomposition of the matrix or the eigenvalue decomposition of the covariance matrix.
The eigenvalue decomposition of the correlation matrix results in the standardized charges. The correlations between the original variables and the component scores are represented by these loadings. Because they always vary between −1 and 1 and are independent of the scale used, standardized charges are easy to read. In most cases, a threshold is set and only variables with loadings above this threshold are examined.
The total variance presents sum of variances of principal components. The ratio between the variance of principal component and the total variance is the fraction of variance explained by a principal component.
Figure 1 shows total variance explained by using three methods of PCA. The steepest increase belongs to the PCA line, which cumulative explained variance is app. 87%. This line is almost parallel to the line from Sparse PCA which cumulative explained variance is 83%. However, when it comes to Robust PCA line it has been noticed that cumulative explained variance is only app. 26% and the increase of values is minimal.
Total variance explained.
PCA: The highest fraction of explained variance among these variables is 32%, and the lowest one is 5%. Cumulative explained variance is 86% (see Table 2).
Factors | Total | % of variance | Cumulative % |
---|---|---|---|
Factor 0 | 4.491515 | 32.082248 | 32.082248 |
Factor 1 | 2.540717 | 18.147978 | 50.230226 |
Factor 2 | 1.269778 | 9.069843 | 59.300069 |
Factor 3 | 1.243867 | 8.884762 | 68.184831 |
Factor 4 | 0.961330 | 6.866641 | 75.051473 |
Factor 5 | 0.867145 | 6.193891 | 81.245364 |
Factor 6 | 0.760536 | 5.432398 | 86.677761 |
PCA total variance explained.
Sparse PCA: The highest fraction of explained variance among these variables is 21%, and the lowest one is 5%. For instance, variables together explain 83% of the total variance (see Table 3).
Factors | Total | % of variance | Cumulative % |
---|---|---|---|
Factor 0 | 3.078591 | 21.989939 | 21.989939 |
Factor 1 | 2.186255 | 15.616108 | 37.606047 |
Factor 2 | 1.698036 | 12.128828 | 49.734874 |
Factor 3 | 1.757003 | 12.550022 | 62.284897 |
Factor 4 | 1.047037 | 7.478832 | 69.763729 |
Factor 5 | 1.062211 | 7.587224 | 77.350953 |
Factor 6 | 0.809469 | 5.781923 | 83.132875 |
Sparse PCA total variance explained.
Robust PCA: The highest fraction of explained variance among these variables is 21%, and the lowest one is 0%. For instance, variables together explain 25% of the total variance (see Table 4).
Factors | Total | % of variance | Cumulative % |
---|---|---|---|
Factor 0 | 3.035926 | 21.685184 | 21.685184 |
Factor 1 | 0.454951 | 3.249650 | 24.934834 |
Factor 2 | 0.108168 | 0.772628 | 25.707462 |
Factor 3 | 0.020284 | 0.144884 | 25.852346 |
Factor 4 | 0.006630 | 0.047355 | 25.899701 |
Factor 5 | 0.000018 | 0.000128 | 25.899829 |
Factor 6 | 0.000000 | 0.000000 | 25.899829 |
Robust PCA total variance explained.
PCA is the best approach for this kind of data, regarding number of features.
The amount of variance in each variable considered is represented by the communalities. The variance in each variable explained by all components or factors is estimated using the initial communalities.
The percent fuel and energy cost in total operating expenses is given here with 88% variance. The percent productive service cost in total operating expenses is given here with 75% variance. The percent finished products in total assets here is 75% of the estimated variance (see Table 5).
Columns | Communality |
---|---|
Percent merchandise in total assets | 0.159427 |
Percent sales of merchandise in total operating revenue | 0.222216 |
Percent cost of merchandise sold in total operating expenses | 0.224299 |
Percent sales of products and services in total operating revenue | 0.236318 |
Fixed assets in total assets | 0.347415 |
Percent cost of material in total operating expenses | 0.411423 |
Percent raw material in total assets | 0.426201 |
Percent WIP and finished products in total assets | 0.449704 |
Percent depreciation cost in total operating expenses | 0.683213 |
Percent wage cost in total operating expenses | 0.729997 |
Percent WIP in total assets | 0.731771 |
Percent finished products in total assets | 0.745349 |
Percent productive service cost in total operating expenses | 0.752027 |
Percent fuel and energy cost in total operating expenses | 0.880639 |
PCA communalities.
The percent fuel and energy cost in total operating expenses here is 91% variance. The percent finished products in total assets here is 80% of the estimated variance. The percent productive service cost in total operating expenses here is 74% variance (see Table 6).
Columns | Communality |
---|---|
Percent merchandise in total assets | 0.191833 |
Percent sales of products and services in total operating revenue | 0.227810 |
Percent sales of merchandise in total operating revenue | 0.260545 |
Percent cost of merchandise sold in total operating expenses | 0.263888 |
Fixed assets in total assets | 0.354743 |
Percent cost of material in total operating expenses | 0.407825 |
Percent raw material in total assets | 0.417451 |
Percent WIP and finished products in total assets | 0.451553 |
Percent depreciation cost in total operating expenses | 0.555661 |
Percent wage cost in total operating expenses | 0.695148 |
Percent WIP in total assets | 0.719447 |
Percent productive service cost in total operating expenses | 0.742714 |
Percent finished products in total assets | 0.800108 |
Percent fuel and energy cost in total operating expenses | 0.911274 |
Sparse PCA communalities.
The percent wage cost in total operating expenses here is 82% variance. The percent sales of merchandise in total operating revenue here is 79% of the estimated variance. The percent cost of merchandise sold in total operating expenses here is 74% variance (see Table 7).
Columns | Communality |
---|---|
Percent WIP in total assets | 0.200472 |
Percent merchandise in total assets | 0.317793 |
Percent finished products in total assets | 0.333984 |
Percent depreciation cost in total operating expenses | 0.345393 |
Percent fuel and energy cost in total operating expenses | 0.349862 |
Percent sales of products and services in total operating revenue | 0.365996 |
Percent raw material in total assets | 0.433737 |
Percent WIP and finished products in total assets | 0.444081 |
Percent cost of material in total operating expenses | 0.519423 |
Fixed assets in total assets | 0.651365 |
Percent productive service cost in total operating expenses | 0.680299 |
Percent cost of merchandise sold in total operating expenses | 0.745842 |
Percent sales of merchandise in total operating revenue | 0.789024 |
Percent wage cost in total operating expenses | 0.822730 |
Robust PCA communalities.
Figure 2 presents the amount of variance for each considered variable represented by the communalities. From the aspect of PCA and Sparse PCA it can be noticed that variable Percent fuel and energy cost in total operating expenses and variable Percent finished products in total assets have significant estimated variance. When it comes to Robust PCA, variance of 82% refers to the variable Percent wage cost in total operating expenses. From the economic point of view first two variables could be used to distinguish type of three major business activities. Mainly, the amount of fuel and energy cost will differ between business activities. It is expected that production entities will have higher values of fuel and energy costs because plant, machinery and equipment will require energy to operate. Also, merchandise entities will probably have higher values of fuel and energy costs compared to other services having in mind fuel spent for transportation of merchandise and energy needed for operation of their facilities. Second variable Percent finished products in total assets is also expected to be used for differentiation since only production entities will have this balance sheet line in their financial statements. Main surprise might be third variable Percent wage cost in total operating expenses, since most entities have very similar share of total wage costs in total operating expenses. Namely, although official state records showed that average wages differ across industries, management of companies usually plan operating expenses and their structure.
Amount of variance represented by the communalities.
The best approach for the PCA/Clustering combination regarding high level of Silhouette Index and Cluster Sizes are: K-means/Robust PCA and Spectral/Robust PCA. The Davies Bouldin Index implies that a smaller value gives better clustering. This produces the idea that no cluster has to be similar to another, and that object inside clusters are very uniformly distributed (see Table 8).
Clustering/PCA method | Cluster sizes | Silhouette index | Davies bouldin index |
---|---|---|---|
K-means/No PCA | (1345, 932, 733) | 0.30208710358306756 | 1.5444364169813884 |
K-means/PCA | (1353, 934, 723) | 0.3637346841903855 | 1.3405097768944103 |
K-means/Sparse PCA | (1356, 939, 715) | 0.36307616530243575 | 1.3418713066940657 |
K-means/Robust PCA | (1209, 944, 857) | 0.5193200382282146 | 0.7834359567299072 |
Agglomerative/no PCA | (1151, 935, 924) | 0.27839422485839554 | 1.7150687814273013 |
Agglomerative/ PCA | (1225, 962, 823) | 0.31642069773357084 | 1.4995739243069988 |
Agglomerative/sparse PCA | (1888, 893, 229) | 0.31642069773357084 | 1.4995739243069988 |
Agglomerative/robust PCA | (1311, 878, 821) | 0.4593880561940543 | 0.9274868826361716 |
Birch/no PCA | (1151, 935, 924) | 0.27839422485839554 | 1.7150687814273013 |
Birch/ PCA | (1225, 962, 823) | 0.31642069773357084 | 1.4995739243069988 |
Birch/sparse PCA | (1225, 962, 823) | 0.31642069773357084 | 1.4995739243069988 |
Birch/robust PCA | (1317, 867, 826) | 0.45631070311567473 | 0.9348852316431389 |
Gaussian mixture/no PCA | (1336, 992, 682) | 0.17495781525891207 | 2.1078218204567496 |
Gaussian mixture/ PCA | (1161, 1155, 694) | 0.2539355374019169 | 1.6227017939395394 |
Gaussian mixture/sparse PCA | (1161, 1155, 694) | 0.2539355374019169 | 1.6227017939395394 |
Gaussian mixture/robust PCA | (1467,784, 759) | 0.28455634384131373 | 1.1919962215015028 |
Spectral/no PCA | (2994, 8, 8) | 0.460433642421337 | 0.9718901349784725 |
Spectral/PCA | (3001, 7, 2) | 0.5399338738262545 | 0.6856986473871954 |
Spectral/sparse PCA | (3001, 7, 2) | 0.5399338738262545 | 0.6856986473871954 |
Spectral/robust PCA | (1346, 920, 744) | 0.5146721760042233 | 0.7917964357887189 |
PCA with different clustering methods.
Columns/factors | Factor 0 | Factor 1 | Factor 2 | Factor 3 | Factor 4 | Factor 5 | Factor 6 |
---|---|---|---|---|---|---|---|
Fixed assets in total assets | 0.178413 | −0.326641 | 0.415221 | −0.102354 | −0.025277 | −0.072754 | −0.141675 |
Percent sales of merchandise in total operating revenue | −0.436002 | 0.152729 | 0.080029 | −0.025182 | 0.028728 | 0.016652 | −0.025519 |
Percent sales of products and services in total operating revenue | 0.398117 | 0.022315 | −0.270570 | −0.046006 | 0.031509 | 0.012930 | −0.028959 |
Percent cost of merchandise sold in total operating expenses | −0.432559 | 0.162995 | 0.080296 | −0.035352 | 0.022542 | 0.026778 | −0.041260 |
Percent cost of material in total operating expenses | 0.269688 | 0.303749 | −0.078000 | −0.386050 | −0.243323 | −0.066637 | −0.166323 |
Percent fuel and energy cost in total operating expenses | 0.150958 | −0.217356 | 0.283494 | −0.008988 | 0.096350 | 0.822637 | −0.210100 |
Percent wage cost in total operating expenses | 0.210317 | −0.224488 | −0.145697 | 0.058149 | 0.719422 | −0.304476 | −0.022063 |
Percent productive service cost in total operating expenses | 0.137457 | −0.048360 | −0.397081 | 0.585499 | −0.374661 | 0.172006 | 0.245674 |
Percent depreciation cost in total operating expenses | 0.095815 | −0.269993 | 0.484683 | 0.000289 | −0.400868 | −0.359862 | 0.275725 |
Percent raw material in total assets | 0.190490 | 0.245296 | −0.165694 | −0.526444 | −0.137683 | 0.071830 | 0.032101 |
Percent WIP in total assets | 0.158335 | 0.359273 | 0.200936 | 0.383609 | −0.059087 | −0.175372 | −0.596528 |
Percent finished products in total assets | 0.174390 | 0.375283 | 0.278149 | 0.015943 | 0.252221 | 0.151402 | 0.640266 |
Percent WIP and finished products in total assets | 0.214830 | 0.474174 | 0.309621 | 0.255892 | 0.126328 | −0.013709 | 0.034896 |
Percent merchandise in total assets | −0.355975 | 0.151166 | −0.014079 | −0.013559 | 0.088827 | 0.039431 | 0.005508 |
PCA component matrix.
Columns/factors | Factor 0 | Factor 1 | Factor 2 | Factor 3 | Factor 4 | Factor 5 | Factor 6 |
---|---|---|---|---|---|---|---|
Fixed assets in total assets | 0.000000 | −0.020910 | −0.504987 | 0.000000 | −0.254639 | −0.185601 | −0.002365 |
Percent sales of merchandise in total operating revenue | 0.435472 | 0.000000 | 0.246576 | −0.085566 | 0.000000 | 0.052803 | 0.000000 |
Percent sales of products and services in total operating revenue | −0.433624 | 0.008108 | 0.000000 | 0.199284 | 0.000000 | 0.000000 | 0.000000 |
Percent cost of merchandise sold in total operating expenses | 0.438993 | 0.000000 | 0.254341 | −0.067395 | 0.000000 | 0.044065 | 0.000000 |
Percent cost of material in total operating expenses | −0.027509 | 0.085834 | 0.000000 | 0.630267 | 0.000000 | 0.045436 | −0.019978 |
Percent fuel and energy cost in total operating expenses | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | −0.954607 | 0.000000 |
Percent wage cost in total operating expenses | −0.453726 | 0.000000 | 0.000000 | −0.325539 | −0.594326 | 0.173439 | 0.000000 |
Percent productive service cost in total operating expenses | −0.333679 | 0.000000 | 0.000000 | −0.222344 | 0.762847 | 0.000000 | 0.000000 |
Percent depreciation cost in total operating expenses | 0.108694 | 0.000000 | −0.726249 | 0.000000 | 0.000000 | 0.128099 | 0.000000 |
Percent raw material in total assets | 0.000000 | −0.007293 | 0.083372 | 0.624407 | −0.000201 | 0.000000 | 0.143399 |
Percent WIP in total assets | 0.000000 | 0.460374 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | −0.712393 |
Percent finished products in total assets | 0.000000 | 0.573218 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.686680 |
Percent WIP and finished products in total assets | 0.000000 | 0.671977 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
Percent merchandise in total assets | 0.315974 | 0.000000 | 0.291738 | −0.076742 | 0.000000 | 0.031515 | 0.000000 |
Sparse PCA component matrix.
Columns/factors | Factor 0 | Factor 1 | Factor 2 | Factor 3 | Factor 4 | Factor 5 | Factor 6 |
---|---|---|---|---|---|---|---|
Fixed assets in total assets | −0.173467 | 0.275000 | 0.507855 | −0.499215 | 0.156525 | −0.088965 | 0.078114 |
Percent sales of merchandise in total operating revenue | 0.525938 | −0.128407 | 0.122190 | −0.093645 | −0.109748 | −0.078270 | 0.673835 |
Percent sales of products and services in total operating revenue | −0.444479 | −0.119035 | −0.307493 | −0.162452 | −0.114015 | −0.009416 | −0.142249 |
Percent cost of merchandise sold in total operating expenses | 0.510523 | −0.126489 | 0.118665 | −0.111772 | −0.087492 | 0.088045 | −0.653626 |
Percent cost of material in total operating expenses | −0.204519 | −0.558377 | 0.030438 | −0.282440 | −0.265091 | 0.084341 | 0.087885 |
Percent fuel and energy cost in total operating expenses | −0.119620 | 0.103472 | 0.453140 | 0.056594 | −0.245465 | 0.200560 | −0.125820 |
Percent wage cost in total operating expenses | −0.204794 | 0.179552 | 0.159958 | 0.368300 | −0.273464 | −0.715834 | −0.010912 |
Percent productive service cost in total operating expenses | −0.131802 | 0.032733 | 0.012123 | 0.533088 | −0.234838 | 0.537258 | 0.183658 |
Percent depreciation cost in total operating expenses | −0.098392 | 0.135731 | 0.478665 | 0.038015 | −0.137017 | 0.259630 | −0.023300 |
Percent raw material in total assets | −0.120240 | −0.430495 | 0.139923 | −0.141176 | −0.424059 | −0.117921 | 0.026698 |
Percent WIP in total assets | −0.048543 | −0.251299 | 0.164302 | 0.160983 | 0.282957 | −0.044589 | −0.000473 |
Percent finished products in total assets | −0.062609 | −0.324119 | 0.211913 | 0.207631 | 0.364950 | −0.057519 | 0.022279 |
Percent WIP and finished products in total assets | −0.071814 | −0.371772 | 0.243069 | 0.238158 | 0.418607 | −0.065970 | −0.072968 |
Percent merchandise in total assets | 0.289968 | −0.108389 | 0.082710 | 0.230665 | −0.308640 | −0.196863 | −0.167039 |
Robust PCA component matrix.
This chapter was focused on the use of Principle component analysis in financial data science. Research has been conducted that included 3013 medium and large business entities and their financial statements from 2019 reporting period. PCA has been used in order to differentiate between the three major types of business activities - merchandising, manufacturing, and service. Therefore, 14 financial ratios have been selected by common sense and further analyzed according to their significance in dimensionality reduction. Results of clustering gave 7 new variables: 1. cost of merchandise sold in total operating expenses, and cost of material in total operating expenses; 2. fuel and energy cost in total operating expenses, and sales of product and services in total operating revenue; 3. wage costs in total operating expenses, and sales on merchandise in total operating revenue; 4. productive service cost in total operating expanses, and fixed assets in total assets; 5. depreciation cost in total operating expenses, and merchandise in total assets; 6. raw material in total assets, and WIP and finished products in total assets; 7. finished products in total assets, and WIP in total assets. These groups of variables were able to explain 86.7% of initial variability. Compared to the results of authors previously mentioned in literature review, it can be concluded that percentage is within the range of reached results. When it comes to initial communalities which estimated the variance in each variable, three financial ratios that had the highest percentage were: fuel and energy cost in total operating expenses (original PCA—88%, sparse PCA—91%); productive service cost in total operating expenses (original PCA—75%, sparse PCA—74%); and finished products in total assets (original PCA 75%, sparse PCA—80%). Although these ratios showed the best results, it has to be mentioned that there is a correlation between all of financial ratios used in analysis and therefore results would be different when ratios are used.
We would like to express our gratitude to Prof. Nemanja Stanišić, Ph.D. from the Singidunum University for supporting this research through valuable suggestions, and assignment of a research database.
Authors declare no conflict of interest.
Author Stefana Janićijević contributed to the design and implementation of the research and analysis of the results. Authors Vule Mizdraković and Maja Kljajić prepared sections of the chapter that refers to the financial data science and financial reporting: introduction, related work, research methodology and analysis of discussion and result. All authors provided critical feedback and helped shape the research, analysis, and manuscript.
m | number of numerical variables |
n | individuals |
x | vector |
X | data matrix |
j | number of columns |
Xc | linear combinations |
c | vector of constants |
M | covariance matrix |
λ | lagrange multiplier |
U | matrix with orthonormal colums—eigenvectors |
A | matrix with singular vectors |
L | diagonal elements of the matrix |
L2 | diagnal matrix with one square of the singular values |
r | rank of the matrix |
q | dimensional subspace |
tr | trace of matrix |
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His present research includes organic synthesis, drug discovery and development, biochemistry, nanoscience, and nanotechnology.",institutionString:"Visiting Scientist at Lipid Nanostructures Laboratory, Centre for Smart Materials, School of Natural Sciences, University of Central Lancashire",institution:null},{id:"428125",title:"Dr.",name:"Vinayak",middleName:null,surname:"Adimule",slug:"vinayak-adimule",fullName:"Vinayak Adimule",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/428125/images/system/428125.jpg",biography:"Dr. Vinayak Adimule, MSc, Ph.D., is a professor and dean of R&D, Angadi Institute of Technology and Management, India. He has 15 years of research experience as a senior research scientist and associate research scientist in R&D organizations. He has published more than fifty research articles as well as several book chapters. He has two Indian patents and two international patents to his credit. Dr. Adimule has attended, chaired, and presented papers at national and international conferences. He is a guest editor for Topics in Catalysis and other journals. He is also an editorial board member, life member, and associate member for many international societies and research institutions. His research interests include nanoelectronics, material chemistry, artificial intelligence, sensors and actuators, bio-nanomaterials, and medicinal chemistry.",institutionString:"Angadi Institute of Technology and Management",institution:null},{id:"284317",title:"Prof.",name:"Kantharaju",middleName:null,surname:"Kamanna",slug:"kantharaju-kamanna",fullName:"Kantharaju Kamanna",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284317/images/21050_n.jpg",biography:"Prof. K. Kantharaju has received Bachelor of science (PCM), master of science (Organic Chemistry) and Doctor of Philosophy in Chemistry from Bangalore University. He worked as a Executive Research & Development @ Cadila Pharmaceuticals Ltd, Ahmedabad. He received DBT-postdoc fellow @ Molecular Biophysics Unit, Indian Institute of Science, Bangalore under the supervision of Prof. P. Balaram, later he moved to NIH-postdoc researcher at Drexel University College of Medicine, Philadelphia, USA, after his return from postdoc joined NITK-Surthakal as a Adhoc faculty at department of chemistry. Since from August 2013 working as a Associate Professor, and in 2016 promoted to Profeesor in the School of Basic Sciences: Department of Chemistry and having 20 years of teaching and research experiences.",institutionString:null,institution:{name:"Rani Channamma University, Belagavi",country:{name:"India"}}},{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/158492/images/system/158492.jpeg",biography:"Prof. Dr. Yusuf Tutar conducts his research at the Hamidiye Faculty of Pharmacy, Department of Basic Pharmaceutical Sciences, Division of Biochemistry, University of Health Sciences, Turkey. He is also a faculty member in the Molecular Oncology Program. He obtained his MSc and Ph.D. at Oregon State University and Texas Tech University, respectively. He pursued his postdoctoral studies at Rutgers University Medical School and the National Institutes of Health (NIH/NIDDK), USA. His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. Prof. Emeje was a national chairman of academic pharmacists in Nigeria and the 2021 winner of the May & Baker Nigeria Plc–sponsored prize for professional service in research and innovation.",institutionString:"National Institute for Pharmaceutical Research and Development",institution:{name:"National Institute for Pharmaceutical Research and Development",country:{name:"Nigeria"}}},{id:"436430",title:"Associate Prof.",name:"Mesut",middleName:null,surname:"Işık",slug:"mesut-isik",fullName:"Mesut Işık",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/436430/images/19686_n.jpg",biography:null,institutionString:null,institution:{name:"Bilecik University",country:{name:"Turkey"}}},{id:"268659",title:"Ms.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/268659/images/8143_n.jpg",biography:"Dr. Zhan received his undergraduate and graduate training in the fields of preventive medicine and epidemiology and statistics at the West China University of Medical Sciences in China during 1989 to 1999. He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a scientist and Principal Investigator at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering the lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via artificial intelligence-based analyses of exosomal Raman signatures. Dr. Paul also works on spatial multiplex immunofluorescence-based tissue mapping to understand the immune repertoire in lung cancer. Dr. Paul has published in more than sixty-five peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award and the 2022 AAISCR-R Vijayalaxmi Award for Innovative Cancer Research. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. 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