The cells of connective tissue.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\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:"9921",leadTitle:null,fullTitle:"Promising Techniques for Wastewater Treatment and Water Quality Assessment",title:"Promising Techniques for Wastewater Treatment and Water Quality Assessment",subtitle:null,reviewType:"peer-reviewed",abstract:"This book reviews the primary aspects of wastewater treatment processing techniques and designs, as well as water quality assessment. Chapters address microwave digestive techniques of wastewater treatment, advanced ozone oxidative and photo processes, and reactive distillation for various applications. The book is a useful resource for choosing applicable processing techniques and design parameters.",isbn:"978-1-83881-901-9",printIsbn:"978-1-83881-900-2",pdfIsbn:"978-1-83881-902-6",doi:"10.5772/intechopen.87732",price:139,priceEur:155,priceUsd:179,slug:"promising-techniques-for-wastewater-treatment-and-water-quality-assessment",numberOfPages:458,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"59ea12a5ea4a235dc8123537effc7cf1",bookSignature:"Iqbal Ahmed Moujdin and J. Kevin Summers",publishedDate:"December 1st 2021",coverURL:"https://cdn.intechopen.com/books/images_new/9921.jpg",numberOfDownloads:8445,numberOfWosCitations:0,numberOfCrossrefCitations:8,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:23,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:31,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 1st 2020",dateEndSecondStepPublish:"June 22nd 2020",dateEndThirdStepPublish:"August 21st 2020",dateEndFourthStepPublish:"November 9th 2020",dateEndFifthStepPublish:"January 8th 2021",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"197244",title:"Associate Prof.",name:"Iqbal",middleName:null,surname:"Ahmed",slug:"iqbal-ahmed",fullName:"Iqbal Ahmed",profilePictureURL:"https://mts.intechopen.com/storage/users/197244/images/system/197244.jpg",biography:"Iqbal Ahmed, Ph.D., is currently working at the Center of Excellence in Desalination Technology, Department of Mechanical Engineering, King Abdulaziz University, Jeddah, Saudi Arabia. He graduated from the Centre of Hydrogen Energy (ChE), University of Technology Malaysia and has more than sixteen years of research and teaching experience in the field of chemical engineering. Dr. Ahmed specializes in microwave synthesized materials and membrane technology (fabrication to application). He has authored more than sixty journal articles and fifty research papers in a variety of journals and international conferences. He has also contributed more than six book chapters.",institutionString:"King Abdulaziz University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"King Abdulaziz University",institutionURL:null,country:{name:"Saudi Arabia"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"197485",title:"Dr.",name:"J. Kevin",middleName:null,surname:"Summers",slug:"j.-kevin-summers",fullName:"J. Kevin Summers",profilePictureURL:"https://mts.intechopen.com/storage/users/197485/images/system/197485.jpg",biography:"J. Kevin Summers is a Senior Research Ecologist at the Environmental Protection Agency’s (EPA) Gulf Ecosystem Measurement and Modeling Division. He is currently working with colleagues in the Sustainable and Healthy Communities Program to develop an index of community resilience to natural hazards, an index of human well-being that can be linked to changes in the ecosystem, social and economic services, and a community sustainability tool for communities with populations under 40,000. He leads research efforts for indicator and indices development. Dr. Summers is a systems ecologist and began his career at the EPA in 1989 and has worked in various programs and capacities. This includes leading the National Coastal Assessment in collaboration with the Office of Water which culminated in the award-winning National Coastal Condition Report series (four volumes between 2001 and 2012), and which integrates water quality, sediment quality, habitat, and biological data to assess the ecosystem condition of the United States estuaries. He was acting National Program Director for Ecology for the EPA between 2004 and 2006. He has authored approximately 150 peer-reviewed journal articles, book chapters, and reports and has received many awards for technical accomplishments from the EPA and from outside of the agency. Dr. Summers holds a BA in Zoology and Psychology, an MA in Ecology, and Ph.D. in Systems Ecology/Biology.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Environmental Protection Agency",institutionURL:null,country:{name:"United States of America"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1354",title:"Wastewater Engineering",slug:"technology-environmental-engineering-wastewater-engineering"}],chapters:[{id:"73083",title:"Evaluation and Quantification of Anionic Surfactant in the Gomti River at Lucknow City, India",doi:"10.5772/intechopen.93517",slug:"evaluation-and-quantification-of-anionic-surfactant-in-the-gomti-river-at-lucknow-city-india",totalDownloads:176,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"In this paper, an attempt has been made to check the level of surfactants particularly in drinking water, which can lead to toxicity in human body system. In this study, a total of 10 locations were selected to enumerate the concentration of surfactants and other physicochemical parameters with metals in the flowing water of river during pre-monsoon 2019. Analyzed result showed that the concentration of surfactants was significantly high and other parameters were also high. It was also found that river at the vicinity of town areas or midstream in the Lucknow city contained high amount of an anionic surfactants due to the nonpoint sources generated by human activities, low concentration was found in upstream, and average concentration was found in downstream, showing natural degradation of surfactants. The values of other parameters were higher than the prescribed limit, which is the serious problem for human being.",signatures:"Vinay Kumar, Pokhraj Sahu, Pramod K. Singh, Nishi K. Shukla, Devendra P. Mishra and Markandeya",downloadPdfUrl:"/chapter/pdf-download/73083",previewPdfUrl:"/chapter/pdf-preview/73083",authors:[{id:"223774",title:"Dr.",name:"Markandeya",surname:null,slug:"markandeya",fullName:"Markandeya null"},{id:"325368",title:"Dr.",name:"Vinay",surname:"Kumar",slug:"vinay-kumar",fullName:"Vinay Kumar"},{id:"325369",title:"Dr.",name:"Pokhraj",surname:"Sahu",slug:"pokhraj-sahu",fullName:"Pokhraj Sahu"},{id:"325370",title:"Dr.",name:"Pramod",surname:"K. Singh",slug:"pramod-k.-singh",fullName:"Pramod K. Singh"},{id:"325371",title:"Dr.",name:"Nishi",surname:"K. Shukla",slug:"nishi-k.-shukla",fullName:"Nishi K. Shukla"},{id:"325372",title:"Dr.",name:"Devendra",surname:"P. Mishra",slug:"devendra-p.-mishra",fullName:"Devendra P. Mishra"}],corrections:null},{id:"73643",title:"Adsorption Processes in the Removal of Organic Dyes from Wastewaters: Very Recent Developments",doi:"10.5772/intechopen.94164",slug:"adsorption-processes-in-the-removal-of-organic-dyes-from-wastewaters-very-recent-developments",totalDownloads:616,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:1,abstract:"The problem of the treatment of contaminated wastewaters is of the upmost worldwide interest. This contamination occurs via the presence of inorganic or organic contaminants of different nature in relation with the industry they come from. In the case of organic dyes, their environmental impact, and thus, their toxicity come from the air (releasing of dust and particulate matter), solid (scrap of textile fabrics, sludges), though the great pollution, caused from dyes, comes from the discharge of untreated effluents into waters, contributing to increase the level of BOD and COD in these liquid streams; this discharge is normally accompanied by water coloration, which low the water quality, and caused a secondary issue in the wastewater treatment. Among separation technologies, adsorption processing is one of the most popular, due to its versatility, easiness of work, and possibility of scaling-up in the eve of the treatment of large wastewater volumes. Within a miriade of potential adsorbents for the removal of organic dyes, this work presented the most recent advances in the topic.",signatures:"Francisco Jose Alguacil and Felix A. Lopez",downloadPdfUrl:"/chapter/pdf-download/73643",previewPdfUrl:"/chapter/pdf-preview/73643",authors:[{id:"51571",title:"Dr.",name:"Francisco Jose",surname:"Alguacil",slug:"francisco-jose-alguacil",fullName:"Francisco Jose Alguacil"},{id:"225960",title:"Prof.",name:"Félix A.",surname:"López",slug:"felix-a.-lopez",fullName:"Félix A. López"}],corrections:null},{id:"75523",title:"Isolation and Identification of Carbazole Degrading Bacteria from Lake Water",doi:"10.5772/intechopen.96116",slug:"isolation-and-identification-of-carbazole-degrading-bacteria-from-lake-water",totalDownloads:392,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Heterocyclic hydrocarbon compounds have been identified as one of the major components of water pollution that occurs as a result of urbanization. It has been known that the presence of these compounds is hazardous and remain in the environment for a long period of time. This study was conducted to isolate and identify heterocyclic hydrocarbon degrading bacteria from lake water by genomic DNA extraction and sequencing as well as measure the degradation rate of the bacteria using Gas Chromatography Flame-Ionization Detector (GC-FID). The water sample was collected from west campus lake of Universiti Malaysia Sarawak where six strains of bacteria that has degrading ability was isolated using sub-culturing technique on MSM double layer agar plates. The genomic DNA of bacteria designated as strain IM1, IM2, IM3, IM4, IM5 and IM6 were extracted and amplified using Polymerase Chain Reaction (PCR). The isolates were then sequenced and were identified as Bradyrhizobium sp., Ochrobactrum sp., Pseudomonas aeruginosa sp. and Burkholderia sp. All six isolates possessed the ability to utilize carbazole as sole carbon and energy source as the degradation rate of carbazole was measured using GC-FID analysis. After 12 days of incubation, IM2 showed 96.37% degradation while the other five isolates were able to degrade 100% of the carbazole. Thus, bacteria isolated from this study may provide great benefit for bioremediation.",signatures:"Khairunnisa Binti Abdul Lateef Khan",downloadPdfUrl:"/chapter/pdf-download/75523",previewPdfUrl:"/chapter/pdf-preview/75523",authors:[{id:"335536",title:"M.Sc.",name:"Khairunnisa",surname:"Binti Abdul Lateef Khan",slug:"khairunnisa-binti-abdul-lateef-khan",fullName:"Khairunnisa Binti Abdul Lateef Khan"}],corrections:null},{id:"73429",title:"Emerging Trends in Wastewater Treatment Technologies: The Current Perspective",doi:"10.5772/intechopen.93898",slug:"emerging-trends-in-wastewater-treatment-technologies-the-current-perspective",totalDownloads:931,totalCrossrefCites:0,totalDimensionsCites:3,hasAltmetrics:0,abstract:"The quality of freshwater and its supply, particularly for domestic and industrial purposes are waning due to urbanization and inefficient conventional wastewater treatment (WWT) processes. For decades, conventional WWT processes have succeeded to some extent in treating effluents to meet standard discharge requirements. However, improvements in WWT are necessary to render treated wastewater for re-use in the industrial, agricultural, and domestic sectors. Three emerging technologies including membrane technology, microbial fuel cells and microalgae, as well as WWT strategies are discussed in this chapter. These applications are a promising alternative for manifold WWT processes and distribution systems in mitigating contaminants to meet acceptable limitations. The basic principles, types and applications, merits, and demerits of the aforementioned technologies are addressed in relation to their current limitations and future research needs. The development in WWT blueprints will augment the application of these emerging technologies for sustainable management and water conservation, with re-use strategies.",signatures:"Edward Kwaku Armah, Maggie Chetty, Jeremiah Adebisi Adedeji, Donald Tyoker Kukwa, Boldwin Mutsvene, Khaya Pearlman Shabangu and Babatunde Femi Bakare",downloadPdfUrl:"/chapter/pdf-download/73429",previewPdfUrl:"/chapter/pdf-preview/73429",authors:[{id:"237732",title:"Dr.",name:"Babatunde",surname:"Bakare",slug:"babatunde-bakare",fullName:"Babatunde Bakare"},{id:"281613",title:"Dr.",name:"Maggie",surname:"Chetty",slug:"maggie-chetty",fullName:"Maggie Chetty"},{id:"323336",title:"Dr.",name:"Donald",surname:"Tyoker Kukwa",slug:"donald-tyoker-kukwa",fullName:"Donald Tyoker Kukwa"},{id:"324641",title:"Dr.",name:"Edward Kwaku",surname:"Armah",slug:"edward-kwaku-armah",fullName:"Edward Kwaku Armah"},{id:"326294",title:"Dr.",name:"Jeremiah Adebisi",surname:"Adedeji",slug:"jeremiah-adebisi-adedeji",fullName:"Jeremiah Adebisi Adedeji"},{id:"326593",title:"Mr.",name:"Boldwin",surname:"Mutsvene",slug:"boldwin-mutsvene",fullName:"Boldwin Mutsvene"},{id:"326594",title:"Mr.",name:"Khaya Pearlman",surname:"Shabangu",slug:"khaya-pearlman-shabangu",fullName:"Khaya Pearlman Shabangu"}],corrections:null},{id:"73703",title:"A Review on AI Control of Reactive Distillation for Various Applications",doi:"10.5772/intechopen.94023",slug:"a-review-on-ai-control-of-reactive-distillation-for-various-applications",totalDownloads:521,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In this chapter, previous studies on reactive distillation process control including control using conventional as well as soft sensor control, membrane assisted reactive distillation design and simulation, estimation and control are discussed. The review of literature in different dimensions is carried out to explore the opportunities in the field of research work. The chapter is focused on dynamics and control of Reactive distillation, its control using Conventional Techniques, Model Predictive Control MPC), Reactive Distillation using Soft Sensors/Soft Controllers, Membrane assisted reactive distillation, Biodiesel in Reactive Divided Wall Column: Design and Control and Membrane reactive divided wall column. These control techniques are proposed and analyzed by many researchers. These techniques have potential use in process industries to have better soft sensor control of nonlinear processes.",signatures:"Vandana Sakhre",downloadPdfUrl:"/chapter/pdf-download/73703",previewPdfUrl:"/chapter/pdf-preview/73703",authors:[{id:"216887",title:"Dr.",name:"Vandana",surname:"Sakhre",slug:"vandana-sakhre",fullName:"Vandana Sakhre"}],corrections:null},{id:"73854",title:"Treatment of Wastewater by Nanofiltration",doi:"10.5772/intechopen.94329",slug:"treatment-of-wastewater-by-nanofiltration",totalDownloads:134,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In recent years, some countries have implemented regulations governing aqueous discharges. With a view to sustainable development, manufacturers are looking for wastewater treatment technologies to control their discharges. Nanofiltration seems particularly suitable for the separation characteristics that it allows with regard to the size of the target molecules. Pollution by rare earths and heavy metals affects groundwater and surface water. This changed the quality of the water and made it unsafe to use. Water pollution is a big problem, given the diversity of sources and characteristics of polluting species, the main ones being industrial, urban and agricultural discharges, generated by human activity. The great difficulty being that heavy metals are not biodegradable and tend to accumulate in living organisms (fish, mollusks, vegetables, etc.) consumed by humans. For these concerns, environmental laws have become more severe. For this, the treatment of aqueous effluents has become important. It can be concluded that separation and purification chemistry is an area of topical research. The discharges coming from the industry contain heavy metals (chromium, copper, zinc, nickel, iron, cobalt, cadmium, lead, …) which are harmful for the human health, the fauna and flora. It is necessary to be well controlled. This chapter presents a study of nanofiltration for industrial wastewater treatment.",signatures:"M. Amine Didi",downloadPdfUrl:"/chapter/pdf-download/73854",previewPdfUrl:"/chapter/pdf-preview/73854",authors:[{id:"324753",title:"Prof.",name:"M. Amine",surname:"Didi",slug:"m.-amine-didi",fullName:"M. Amine Didi"}],corrections:null},{id:"75397",title:"Principles of Membrane Surface Modification for Water Applications",doi:"10.5772/intechopen.96366",slug:"principles-of-membrane-surface-modification-for-water-applications",totalDownloads:360,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Membrane technologies offer efficient and reliable solutions to separate components from aqueous media. Among them, pressure driven membrane separation processes namely microfiltration (MF), ultrafiltration (UF), nanofiltration (NF) and reverse osmosis (RO) have been preferred in many industrial operations (food, pharmaceutical, chemical, drinking water, wastewater) due to the intrinsic advantages such as high selectivity, stability, ecocompatibility, scalability, flexibility, small footprint and low operational cost. This chapter will focus on the latest developments of surface modified polymeric membranes via the Layer-by-layer self-assembly approach and incorporation/decoration of nanomaterials. Variable parameters including size and charge of polyelectrolyte, ionic strength of the media, number of bilayers, and different types of nanomaterials on the bulk and surface property, water permeability, selectivity, antifouling, antibacterial, and adsorptive properties of the resultant composite membranes will be reviewed by comparison with the neat membranes. Membrane stability in terms of throughput and rejection characteristics during long-term filtrations will be addressed in this chapter.",signatures:"Yilmaz Yurekli",downloadPdfUrl:"/chapter/pdf-download/75397",previewPdfUrl:"/chapter/pdf-preview/75397",authors:[{id:"225038",title:"Dr.",name:"Yilmaz",surname:"Yurekli",slug:"yilmaz-yurekli",fullName:"Yilmaz Yurekli"}],corrections:null},{id:"72970",title:"A Comprehensive Method of Ion Exchange Resins Regeneration and Its Optimization for Water Treatment",doi:"10.5772/intechopen.93429",slug:"a-comprehensive-method-of-ion-exchange-resins-regeneration-and-its-optimization-for-water-treatment",totalDownloads:879,totalCrossrefCites:3,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Ion exchange membranes, specifically resin technology, lie at the heart of electrolytically conductive systems used in the treatment of wastewater. This chapter deals with ion exchange deionization and the effect of resin amount as well as the concentration of acid and base on the product conductivity. The strong acidic cation polymeric exchanger resin is commercially called MERCK 104765 cation exchanger IV with capacity greater than 3.2 mmol/ml, while the strong basic anion polymeric exchanger resin is commercially called MERCK 104767 anion exchanger III with capacities greater than 1.0 mmol/ml. Water conductivity, as an indicator of regeneration efficiency, was monitored over time at the different conditions and scenario. In general, it was observed that the conductivity decreases with time until one point is reached and then starts to increase as a result of resin saturation. It was also noticed that the lowest conductivity is achieved when using 1-vol% NaOH and 5-vol% HCl in the cathodic and anodic resin tubes, respectively, and that water conductivity increases with the increase in the amount of water being used. The amount of resin significantly impacts the deionization efficiency; more ions are removed as the amount of resin increases.",signatures:"Sameer Al-Asheh and Ahmad Aidan",downloadPdfUrl:"/chapter/pdf-download/72970",previewPdfUrl:"/chapter/pdf-preview/72970",authors:[{id:"323279",title:"Prof.",name:"Sameer",surname:"Al-Asheh",slug:"sameer-al-asheh",fullName:"Sameer Al-Asheh"},{id:"323281",title:"Dr.",name:"Ahmad",surname:"Aidan",slug:"ahmad-aidan",fullName:"Ahmad Aidan"}],corrections:null},{id:"73485",title:"CFD Simulations in Mechanically Stirred Tank and Flow Field Analysis: Application to the Wastewater (Sugarcane Vinasse) Anaerobic Digestion",doi:"10.5772/intechopen.93926",slug:"cfd-simulations-in-mechanically-stirred-tank-and-flow-field-analysis-application-to-the-wastewater-s",totalDownloads:409,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Anaerobic digestion is a widely used process for waste treatment and energy production. This natural process takes place in a controlled environment, anaerobic digesters. Mixing is one of the main operating parameters. The understanding of the flows during the agitation of the medium is crucial for the optimization of the process yield. In fact, the mass and heat transfers are enhanced by the agitation. However, the complex biochemical reactions can be inhibited with overly vigorous agitation. A detailed and in-depth understanding of the phenomena occurring during agitation requires modeling studies. In this chapter, we propose a general approach, based on computational fluid mechanics (CFD), to analyze the mechanical mixing of an anaerobic reactor. We apply this work to the anaerobic digestion of the sugarcane vinasse, which is a liquid waste generated during the production of alcohol. The single-phase Reynolds-averaged Navier-Stokes (RANS) simulations of mechanical agitation of Newtonian fluids for different rotational speeds are presented. The equations system is closed with the standard k-epsilon turbulence model. The flow field is analyzed with the velocity profiles, the Q and Lambda2 fields, the pressure and the vorticity.",signatures:"Hélène Caillet, Alain Bastide and Laetitia Adelard",downloadPdfUrl:"/chapter/pdf-download/73485",previewPdfUrl:"/chapter/pdf-preview/73485",authors:[{id:"325919",title:"Ph.D.",name:"Hélène",surname:"Caillet",slug:"helene-caillet",fullName:"Hélène Caillet"},{id:"325922",title:"Dr.",name:"Laetitia",surname:"Adelard",slug:"laetitia-adelard",fullName:"Laetitia Adelard"},{id:"325923",title:"Prof.",name:"Alain",surname:"Bastide",slug:"alain-bastide",fullName:"Alain Bastide"}],corrections:null},{id:"74469",title:"Evaluation of the Use of Advanced Ozone Oxidative Process in Reducing the Danger of Environmental Toxicity by Endocrine Interferences of Magistral Pharmacy",doi:"10.5772/intechopen.95068",slug:"evaluation-of-the-use-of-advanced-ozone-oxidative-process-in-reducing-the-danger-of-environmental-to",totalDownloads:142,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The presence of emerging pollutants in the waters has been observed worldwide, resulting from improper domestic disposal, non-recommended veterinarian use, and product waste from pharmaceutical industries and magistral pharmacies. The contamination provoked, besides causing damage to the environment, remains in potable water even after passing through the treatment plants. The objective of this work was to verify the existence of environmental toxicity of raw effluents from gross pharmacy laboratories, as well as the same effluent treated with POA via ozone in the time of 1 hour, having as a risk parameter the changes that they cause in Euglena gracilis algae. Photosynthetic efficiency tests were conducted via PAM, and chlorophyll concentration and behavioral evaluation were checked via NGTOX. The results demonstrate that the hormone laboratory was considered the most impacted effluent treated, with lower production and significant chlorophyll reduction. It presented reduction in photosynthetic post-ozonation activity, due to the hormone decomposition, oxidative potential and ethylene formation. Effluents from psychotropic and solid laboratories presented different production demand, but similar follow-up, with impact on the behavior and algae’s photosynthetic activity, due to the presence of active substances on cellular action potentials. The treated effluent from dermocosmetics laboratory influenced the chlorophyll concentration, as well as the general speed and velocity of surface ascent. The behavioral differences between the laboratories and the pre and post-ozonation conditions demonstrate that the effluent treatment should be distinguished, according to the characteristics of the manipulated substances in each laboratory.",signatures:"Thais Francinne, Suellen Zucco Bez, Julia Carolina Soares, Sabrina Martins da Rosa, Aline Mirian Paszuck, Luciana Ferreira Karsten and Luciano Henrique Pinto",downloadPdfUrl:"/chapter/pdf-download/74469",previewPdfUrl:"/chapter/pdf-preview/74469",authors:[{id:"325493",title:"Dr.",name:"Luciano Henrique",surname:"Pinto",slug:"luciano-henrique-pinto",fullName:"Luciano Henrique Pinto"},{id:"325495",title:"Mrs.",name:"Suellen Zucco",surname:"Bez",slug:"suellen-zucco-bez",fullName:"Suellen Zucco Bez"},{id:"325496",title:"Mrs.",name:"Julia Carolina",surname:"Soares",slug:"julia-carolina-soares",fullName:"Julia Carolina Soares"},{id:"325497",title:"Mrs.",name:"Thais",surname:"Francinne",slug:"thais-francinne",fullName:"Thais Francinne"},{id:"325498",title:"Mrs.",name:"Sabrina Martins",surname:"Rosa",slug:"sabrina-martins-rosa",fullName:"Sabrina Martins Rosa"},{id:"325499",title:"Mrs.",name:"Aline Mirian",surname:"Paszuck",slug:"aline-mirian-paszuck",fullName:"Aline Mirian Paszuck"},{id:"325500",title:"Prof.",name:"Luciana Ferreira",surname:"Karsten",slug:"luciana-ferreira-karsten",fullName:"Luciana Ferreira Karsten"}],corrections:null},{id:"76352",title:"Water Quality Parameters and Monitoring Soft Surface Water Quality Using Statistical Approaches",doi:"10.5772/intechopen.97372",slug:"water-quality-parameters-and-monitoring-soft-surface-water-quality-using-statistical-approaches",totalDownloads:302,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Water is the matrix of life and is indispensable on Earth. Water has a multitude of applications and all known life forms depend on it. Therefore, water quality is important for all of us. Water quality can be represented by a set of physical, chemical, biological and bacteriological characteristics. These parameters allow water to be classified in multiple categories leading to its use for a specific purpose. This chapter establishes the connections between external causes and their effect on water quality parameters. In order to provide information on water quality, different Water Quality Index (WQI) models can be used. In order to study the association between water quality parameters, several correlation coefficients have been developed. For a coherent statistical approach, we have used Pearson and Spearman correlations. In order to exemplify the manner in which WQI can be calculated and interpreted, we used a series of data from our previous work, consisting of 13 parameters measured for water samples taken from the Danube River, from Galati City area, Romania.",signatures:"Romana Drasovean and Gabriel Murariu",downloadPdfUrl:"/chapter/pdf-download/76352",previewPdfUrl:"/chapter/pdf-preview/76352",authors:[{id:"302424",title:"Prof.",name:"Gabriel",surname:"Murariu",slug:"gabriel-murariu",fullName:"Gabriel Murariu"},{id:"314102",title:"Dr.",name:"Romana",surname:"Drasovean",slug:"romana-drasovean",fullName:"Romana Drasovean"}],corrections:null},{id:"75212",title:"Reliability and Problems of Wastewater Treatment Processes in the Algerian Sahara",doi:"10.5772/intechopen.96113",slug:"reliability-and-problems-of-wastewater-treatment-processes-in-the-algerian-sahara",totalDownloads:343,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This modest chapter deals more particularly with the reliability and the problems of the different processes used at the level of several treatment plants installed in the Algerian Sahara with the aim of eliminating the nuisances and the risks of contamination in the urbanized areas, protecting the receiving environment and water resources, the possibility of reusing treated effluents for irrigation. Through an evaluation of the performance of these stations after years of operation which confronted with climates such as the high temperature and evaporation and the impact of the sand winds on the efficiency of the basin, technical and anthropic problems such as the salinity and mismanagement of the waters on the other hand.",signatures:"Rachid Zegait, Saber Kouadri, Samir Kateb and Mohamed Azlaoui",downloadPdfUrl:"/chapter/pdf-download/75212",previewPdfUrl:"/chapter/pdf-preview/75212",authors:[{id:"325042",title:"Dr.",name:"Rachid",surname:"Zegait",slug:"rachid-zegait",fullName:"Rachid Zegait"},{id:"334753",title:"Mr.",name:"Saber",surname:"Kouadri",slug:"saber-kouadri",fullName:"Saber Kouadri"},{id:"334754",title:"Dr.",name:"Samir",surname:"Kateb",slug:"samir-kateb",fullName:"Samir Kateb"},{id:"334755",title:"Dr.",name:"Mohamed",surname:"Azlaoui",slug:"mohamed-azlaoui",fullName:"Mohamed Azlaoui"}],corrections:null},{id:"73804",title:"Photo-Processes as Effective and Low-Cost Methods for Laundry Wastewater Treatment",doi:"10.5772/intechopen.94336",slug:"photo-processes-as-effective-and-low-cost-methods-for-laundry-wastewater-treatment",totalDownloads:272,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In this chapter, surfactants as cleansing agent in detergent used in laundry, are described. The negative effects of the laundry wastewater on the environment and human health are highlighted. Several methods examined for laundry wastewater treatment are also illustrated. Among the treatment methods, photo-process in the presence of TiO2 photocatalyst and Fenton reagents are described in more detail. Furthermore, the factors influencing the effectiveness of photo-process including reagent dose, reaction time, and pH are discussed. Additionally, modifications of the photo-process to improve its performance that is associated with effectiveness and operational cost are also demonstrated. The photo-methods discussed in this chapter offered low-cost due to simplicity and effective technique for treating the laundry wastewater.",signatures:"Endang Tri Wahyuni",downloadPdfUrl:"/chapter/pdf-download/73804",previewPdfUrl:"/chapter/pdf-preview/73804",authors:[{id:"225211",title:"Prof.",name:"Endang Tri",surname:"Wahyuni",slug:"endang-tri-wahyuni",fullName:"Endang Tri Wahyuni"}],corrections:null},{id:"77416",title:"Application of Water Quality Index for the Assessment of Water from Different Sources in Nigeria",doi:"10.5772/intechopen.98696",slug:"application-of-water-quality-index-for-the-assessment-of-water-from-different-sources-in-nigeria",totalDownloads:690,totalCrossrefCites:0,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Water quality index (WQI) provides a single number that expresses the overall water quality, at a certain location and time, based on several water quality parameters. The objective of WQI is to turn complex water quality data into information that is understandable and usable by the public. A number of indices have been developed to summarize water quality data in an easily expressible and easily understood format. The WQI is basically a mathematical means of calculating a single value from multiple test results. This chapter discusses, in detail, the application of a water quality index for the assessment of water quality to different several water sources in Nigeria.",signatures:"Ruth Olubukola Ajoke Adelagun, Emmanuel Edet Etim and Oko Emmanuel Godwin",downloadPdfUrl:"/chapter/pdf-download/77416",previewPdfUrl:"/chapter/pdf-preview/77416",authors:[{id:"256167",title:"Dr.",name:"Emmanuel",surname:"Edet Etim",slug:"emmanuel-edet-etim",fullName:"Emmanuel Edet Etim"},{id:"345734",title:"Mr.",name:"Oko",surname:"Emmanuel Godwin",slug:"oko-emmanuel-godwin",fullName:"Oko Emmanuel Godwin"},{id:"345735",title:"Dr.",name:"Ruth",surname:"Olubukola Ajoke Adelagun",slug:"ruth-olubukola-ajoke-adelagun",fullName:"Ruth Olubukola Ajoke Adelagun"}],corrections:null},{id:"76065",title:"The Effect of Wastewater Treatment Methods on the Retainment of Plastic Microparticles",doi:"10.5772/intechopen.97083",slug:"the-effect-of-wastewater-treatment-methods-on-the-retainment-of-plastic-microparticles",totalDownloads:287,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Microplastics as plastic pieces of ≤5 mm in size, are found in most ecosystems, both terrestrial and aquatic. Many of the microplastics find their way into the environment through the wastewater. For this reason, a knowledge of the microplastic retainment performance of wastewater treatment plants of various design is important. In this regard, several wastewater treatment processes have been studied, including new methods that are still at the development stage. This manuscript reviews the literature on such wastewater treatment methods and their ability to retain microplastics.",signatures:"Rana Zeeshan Habib, Ruwaya al Kindi and Thies Thiemann",downloadPdfUrl:"/chapter/pdf-download/76065",previewPdfUrl:"/chapter/pdf-preview/76065",authors:[{id:"338867",title:"Dr.",name:"Ruwaya",surname:"al Kindi",slug:"ruwaya-al-kindi",fullName:"Ruwaya al Kindi"},{id:"341063",title:"Prof.",name:"Thies",surname:"Thiemann",slug:"thies-thiemann",fullName:"Thies Thiemann"},{id:"341064",title:"Mr.",name:"Rana",surname:"Zeeshan Habib",slug:"rana-zeeshan-habib",fullName:"Rana Zeeshan Habib"}],corrections:null},{id:"76670",title:"Microwave Digestion of Hazardous Waste Sludge in Geothermal Hot Waters by Char/Fly Ash Granule Composts-Hazardous Sludges and Industrial Waste Water Treatment",doi:"10.5772/intechopen.97656",slug:"microwave-digestion-of-hazardous-waste-sludge-in-geothermal-hot-waters-by-char-fly-ash-granule-compo",totalDownloads:167,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Most of the previous were regarding characteristics of sludge from urban/municipal activities concerning environmental issues on industrial sludge discharges causing fatal disasters in the lakes and water streams. The washing treatment of mud was searched. This research study concentrated over oxidative heavy metal dissolution and sterilization washing of muddy sludge of chemical, steel and copper refinery plants. The hazardous Hg and Pd contents using washing dissolution provided recovery of metals and treated sludge as the feedstock for digestion process. The research used hazardous sludge which is the by-product of the heat treated steel manufacturing process of CN baths and sludge from pulp washing industries. However, there is a sterilization washing by microwave radiation was reported on various sludge metal contamination characteristics in wastewater treatment stage. The results of a limited number of bench-scale sludge washing experiments conducted in the tube reactor study confirmed high radiation trends for washing dissolution with H2O2 in soil samples obtained from different locations in the north lake area of discharge of at the Plant Site. In general the contaminants in waste pond soils partitioned preferentially to the fine fraction of the soil (<150 μm however, the sand fraction (−0,5 mm + 150 μm) still contained significant contamination. These tests also showed that the heavy metal contaminants were highly dissolved at 45–76% in the wash water, which will reduce washing toxicity and improve metal recovers.",signatures:"Yıldırım İsmail Tosun",downloadPdfUrl:"/chapter/pdf-download/76670",previewPdfUrl:"/chapter/pdf-preview/76670",authors:[{id:"200229",title:"Dr.",name:"Yıldırım",surname:"İsmail Tosun",slug:"yildirim-ismail-tosun",fullName:"Yıldırım İsmail Tosun"}],corrections:null},{id:"76538",title:"Treatment of Tannery Effluent of Unit Bovine Hides’ Unhairing Liming by the Precipitation",doi:"10.5772/intechopen.97657",slug:"treatment-of-tannery-effluent-of-unit-bovine-hides-unhairing-liming-by-the-precipitation",totalDownloads:269,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The tannery effluents are characterized by high toxic pollutants such as sulfides; used in the tanning of animal’s skin. The mean objective of this work is the evaluation of the pollution degree of various operating units, and the treatment of tannery effluent generated from unhairing-liming unit. According to physicochemical characterization, this effluent was largely basic and very loaded in sulfides, which have harmful effects on human health and the environment as well. Otherwise, the microbiological characterization showed an absence of pathogenic bacteria and a low concentration of mesophilic aerobic flora, because of this effluent toxicity. Thus, the treatment of this effluent is indispensable before its reject into the environment. In fact, chemical precipitation is a promising approach for the treatment of this effluent. In this regard, ferric chloride was used as chemical agent to reduce and removal sulphide ions from this effluent. As result, this treatment gave an excellent abatement rate of sulphide, which reached more than 90% using a pH of 8.5 and a ferric chloride concentration of 1.4 mol/L.",signatures:"Anass Omor, Karima Elkarrach, Redouane Ouafi, Zakia Rais, Fatima-Zahra ElMadani and Mustafa Taleb",downloadPdfUrl:"/chapter/pdf-download/76538",previewPdfUrl:"/chapter/pdf-preview/76538",authors:[{id:"248832",title:"Prof.",name:"Zakia",surname:"Rais",slug:"zakia-rais",fullName:"Zakia Rais"},{id:"324231",title:"Dr.",name:"Anass",surname:"Omor",slug:"anass-omor",fullName:"Anass Omor"},{id:"324620",title:"Dr.",name:"Karima",surname:"Elkarrach",slug:"karima-elkarrach",fullName:"Karima Elkarrach"},{id:"324621",title:"Dr.",name:"Fatima-Zahra",surname:"ElMadani",slug:"fatima-zahra-elmadani",fullName:"Fatima-Zahra ElMadani"},{id:"324626",title:"Dr.",name:"Redouane",surname:"Ouafi",slug:"redouane-ouafi",fullName:"Redouane Ouafi"},{id:"337882",title:"Prof.",name:"Mustafa",surname:"Taleb",slug:"mustafa-taleb",fullName:"Mustafa Taleb"}],corrections:null},{id:"73756",title:"Performance of Chitosan as Natural Coagulant in Oil Palm Mill Effluent Treatment",doi:"10.5772/intechopen.94330",slug:"performance-of-chitosan-as-natural-coagulant-in-oil-palm-mill-effluent-treatment",totalDownloads:364,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"This chapter presents the study on pollutant removal in palm oil mill effluent using chitosan as natural coagulant. Up until today, palm oil mill effluent (POME) considered one of the significant sources of environmental pollution. The characteristics of POME include contaminating the source of drinking water, which also harmful to the aquatic ecosystem by creating a highly acidic environment or causing eutrophication. With increasing public awareness of environmental pollution, it creates the need to address this issue. Chitosan is non-polluting food-based anionic and biodegradable biopolymer that are environmentally friendly useful in wastewater treatment. The critical parameter to determine the effectiveness of pollutants removal is chemical oxygen demand, color, and total suspended solids. This chapter also presents and discusses some of the significant findings to provide proper understandings and implications in this topic.",signatures:"Man Djun Lee and Pui San Lee",downloadPdfUrl:"/chapter/pdf-download/73756",previewPdfUrl:"/chapter/pdf-preview/73756",authors:[{id:"327966",title:"Dr.",name:"Man Djun",surname:"Lee",slug:"man-djun-lee",fullName:"Man Djun Lee"},{id:"327968",title:"Ms.",name:"Pui San",surname:"Lee",slug:"pui-san-lee",fullName:"Pui San Lee"}],corrections:null},{id:"74084",title:"Tertiary Treatment for Safely Treated Wastewater Reuse",doi:"10.5772/intechopen.94872",slug:"tertiary-treatment-for-safely-treated-wastewater-reuse",totalDownloads:142,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The tertiary treatment of resulting water from a conventional biological treatment process is envisaged in the aim to obtain a high quality of water that can be reused for different purposes. This treatment is based on the integration of the membrane-based technologies in the total process of wastewater treatment. The experimental studies are carried out on a small pilot, equipped with different mineral membranes of micro and ultrafiltration. These membranes are used for the different tested processes (MF, MF-UF and cogulation-MF). The results obtained make it possible to attend a complete elimination of the total flora and an additional reduction of the other parameters such as turbidity, suspended matter, COD and BOD. Tests on a large scale are then carried out on a semi-industrial pilot, equipped with the same type of membranes. The optimization of the operating conditions made allow the obtaining under the conditions of transmembrane pressure 0.85 bar, a cross flow velocity of 2.25m/s and with ambient temperature a filtrate flux of about 200 L/hm 2. The coupling of a stage of coagulation in the membrane process allows the reduction of the effect of the membrane fouling and an improvement of 36% of the filtrate flux.",signatures:"Nebil Belaid",downloadPdfUrl:"/chapter/pdf-download/74084",previewPdfUrl:"/chapter/pdf-preview/74084",authors:[{id:"326087",title:"Assistant Prof.",name:"Nebil",surname:"Belaid",slug:"nebil-belaid",fullName:"Nebil Belaid"}],corrections:null},{id:"73774",title:"Immobilization of Powdered Coal Fly Ashes (CFAs) into CFA Beads and Column Studies on Color Removal from Pulp Mill Effluents Using These CFA Beads",doi:"10.5772/intechopen.94293",slug:"immobilization-of-powdered-coal-fly-ashes-cfas-into-cfa-beads-and-column-studies-on-color-removal-fr",totalDownloads:318,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In this study, immobilization process of the three (3) powder CFAs was studied. The major results on immobilization process were briefly presented. A total number of fifteen (15) column studies from the combination of the five (5) types of CFAs beads and the three (3) PMEs samples were performed. In each column study, a set of aggregate parameters of flow rate, empty bed contract time, operational time, and throughput volume was studied, and the data was fitted to existing modeling of breakthrough curves. The overall operational time was 12–24-hour, color removal efficiencies were 40–90%, and throughput volume of treated PMEs was 10–14 bed volume. For the column study, the correlation coefficient R2 value for each combination indicated that the Thomas model had a better fit with the observed data than the Adams-Bohart model, and the color adsorption capacities of CFA beads varied in a wide range of 0.31–28.23 mg/g.",signatures:"Musfiques Salahin and George Yuzhu Fu",downloadPdfUrl:"/chapter/pdf-download/73774",previewPdfUrl:"/chapter/pdf-preview/73774",authors:[{id:"323897",title:"Prof.",name:"George Yuzhu",surname:"Fu",slug:"george-yuzhu-fu",fullName:"George Yuzhu Fu"},{id:"323901",title:"MSc.",name:"Musfiques",surname:"Salahin",slug:"musfiques-salahin",fullName:"Musfiques Salahin"}],corrections:null},{id:"74120",title:"Downflow Hanging Sponge System: A Self-Sustaining Option for Wastewater Treatment",doi:"10.5772/intechopen.94287",slug:"downflow-hanging-sponge-system-a-self-sustaining-option-for-wastewater-treatment",totalDownloads:420,totalCrossrefCites:2,totalDimensionsCites:4,hasAltmetrics:0,abstract:"Need of self-sustaining wastewater treatment plants (WWTPs) has become critical to cope up with dynamics of the environmental regulations and rapid advancements in the contemporary technologies. At present there are limited number of self-sustaining WWTPs around the world. The aim of this chapter is to present state -of- art of Downflow Hanging Sponge (DHS) system which was developed as a post treatment unit of Upflow Anaerobic Sludge Blanket (UASB) from sustainability perspective. DHS system is a non-submerged fixed bed trickling filter (TF) that employs a core technology of polyurethane sponges as a media where the microorganisms thrive and major treatment processes take place. This chapter reviews the introduction of DHS system (UASB+DHS) summarizes the quantitative analysis of environmental, economic and social sustainability using indicators. Furthermore, self-sustaining prospects of DHS system are assessed and discussed by comparing with conventional TF (UASB+TF).",signatures:"Namita Maharjan, Choolaka Hewawasam, Masashi Hatamoto, Takashi Yamaguchi, Hideki Harada and Nobuo Araki",downloadPdfUrl:"/chapter/pdf-download/74120",previewPdfUrl:"/chapter/pdf-preview/74120",authors:[{id:"222602",title:"Dr.",name:"Namita",surname:"Maharjan",slug:"namita-maharjan",fullName:"Namita Maharjan"},{id:"329666",title:"Dr.",name:"Choolaka",surname:"Hewawasam",slug:"choolaka-hewawasam",fullName:"Choolaka Hewawasam"},{id:"329667",title:"Prof.",name:"Masashi",surname:"Hatamoto",slug:"masashi-hatamoto",fullName:"Masashi Hatamoto"},{id:"329668",title:"Prof.",name:"Takashi",surname:"Yamaguchi",slug:"takashi-yamaguchi",fullName:"Takashi Yamaguchi"},{id:"329669",title:"Prof.",name:"Nobuo",surname:"Araki",slug:"nobuo-araki",fullName:"Nobuo Araki"},{id:"333774",title:"Prof.",name:"Hideki",surname:"Harada",slug:"hideki-harada",fullName:"Hideki Harada"}],corrections:null},{id:"74103",title:"Experimental Investigation of Biomass Attachment to Wastewater Reactors",doi:"10.5772/intechopen.94426",slug:"experimental-investigation-of-biomass-attachment-to-wastewater-reactors",totalDownloads:313,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Attached mass bioreactors have extensively been adopted in the last decades when specific needs have suggested this choice. Benefits and advantages of this multi-faceted technology in wastewater treatment processing are well known, along with the kinetic and mass transfer aspects regarding their operation, essentially belonging to the mass transfer with chemical reaction theory applied to enzymatic catalysis, referred to as Languimur-Hinshelwood kinetics, notably Monod/Michaelis Menten equations. On the other hand, a consolidated literature has dealt with many aspects of the development of strain colonies forming a biofilm. However, a few works have been devoted to the systematic analysis of its physiology, within the framework of the wastewater management of complex substrates and high-loads effluents. This article presents the experimental findings of a research activity covering the junction area between microbiology and bioreactor engineering, against a multifaceted set of operating parameters directly affecting health and stability of the attached biomass. In this respect, important results have been obtained, providing guidance on the attached mass reactor start-up, steady- state operation, impact of xenobiotic substrates, role of nutrients, filaments and foam formation, as well as qualitative aspects of the post-treatment effluent.",signatures:"Renato Benintendi",downloadPdfUrl:"/chapter/pdf-download/74103",previewPdfUrl:"/chapter/pdf-preview/74103",authors:[{id:"323734",title:"Prof.",name:"Renato",surname:"Benintendi",slug:"renato-benintendi",fullName:"Renato Benintendi"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"9415",title:"Advanced Oxidation Processes",subtitle:"Applications, Trends, and Prospects",isOpenForSubmission:!1,hash:"60d177837fbb691b82c80922cd9bb295",slug:"advanced-oxidation-processes-applications-trends-and-prospects",bookSignature:"Ciro Bustillo-Lecompte",coverURL:"https://cdn.intechopen.com/books/images_new/9415.jpg",editedByType:"Edited by",editors:[{id:"189304",title:"Dr.",name:"Ciro",surname:"Bustillo-Lecompte",slug:"ciro-bustillo-lecompte",fullName:"Ciro Bustillo-Lecompte"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6539",title:"Wastewater and Water Quality",subtitle:null,isOpenForSubmission:!1,hash:"011810f6bbc0d25f6590e1169231962f",slug:"wastewater-and-water-quality",bookSignature:"Taner Yonar",coverURL:"https://cdn.intechopen.com/books/images_new/6539.jpg",editedByType:"Edited by",editors:[{id:"32956",title:"Dr.",name:"Taner",surname:"Yonar",slug:"taner-yonar",fullName:"Taner Yonar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6199",title:"Osmotically Driven Membrane Processes",subtitle:"Approach, Development and Current Status",isOpenForSubmission:!1,hash:"bbb718c2b4705962a3388b4cb551d87b",slug:"osmotically-driven-membrane-processes-approach-development-and-current-status",bookSignature:"Hongbo Du, Audie Thompson and Xinying Wang",coverURL:"https://cdn.intechopen.com/books/images_new/6199.jpg",editedByType:"Edited by",editors:[{id:"180165",title:"Dr.",name:"Hongbo",surname:"Du",slug:"hongbo-du",fullName:"Hongbo Du"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8915",title:"Advances in Membrane Technologies",subtitle:null,isOpenForSubmission:!1,hash:"19febde893f705494f2334d02977fd83",slug:"advances-in-membrane-technologies",bookSignature:"Amira Abdelrasoul",coverURL:"https://cdn.intechopen.com/books/images_new/8915.jpg",editedByType:"Edited by",editors:[{id:"151521",title:"Dr.",name:"Amira",surname:"Abdelrasoul",slug:"amira-abdelrasoul",fullName:"Amira Abdelrasoul"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6464",title:"Nanofiltration",subtitle:null,isOpenForSubmission:!1,hash:"ff27f3309a565c3d37afcac907cc7a2e",slug:"nanofiltration",bookSignature:"Muhammad Akhyar Farrukh",coverURL:"https://cdn.intechopen.com/books/images_new/6464.jpg",editedByType:"Edited by",editors:[{id:"63182",title:"Dr.",name:"Muhammad Akhyar",surname:"Farrukh",slug:"muhammad-akhyar-farrukh",fullName:"Muhammad Akhyar Farrukh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8804",title:"Water and Wastewater 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Frank",dateSubmitted:"September 1st 2021",dateReviewed:"September 8th 2021",datePrePublished:"October 15th 2021",datePublished:"April 20th 2022",book:{id:"11032",title:"Bats",subtitle:"Disease-Prone but Beneficial",fullTitle:"Bats - Disease-Prone but Beneficial",slug:"bats-disease-prone-but-beneficial",publishedDate:"April 20th 2022",bookSignature:"Heimo Mikkola",coverURL:"https://cdn.intechopen.com/books/images_new/11032.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"144330",title:"Dr.",name:"Heimo",middleName:"Juhani",surname:"Mikkola",slug:"heimo-mikkola",fullName:"Heimo Mikkola"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"423579",title:"Dr.",name:"Craig L.",middleName:"L.",surname:"Frank",fullName:"Craig L. 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Biodiesel has been recognised as a sustainable means of reducing reliance on fossil fuels and a solution to address the rising problem of global climate change policy. The monoalkyl esters of vegetable oils or animal fats are referred to as biodiesel. In comparison to petroleum-based diesel, biodiesel would provide benefits of non-toxicity, biodegradability, better air quality after combustion owing to reduced harmful emissions, energy security, and safety to handle, store, and transport, among others. The goal of this book is to explore the science in this field and educate the reader on a wide range of technologies for converting third and fourth-generation feedstocks to sustainable advanced biodiesels. The key sections address the overview of biodiesel production, technologies, innovative technologies for the latest generation of biodiesels, and socioeconomic and life cycle analysis of advanced biodiesel. In recent years, the advanced biorefinery idea has received a lot of interest as a model of decentralised production of advanced biodiesels, especially in rural regions. Such decentralised, small-to-medium scale biorefineries seem to have the most promise for expanding biodiesel production and hastening rural economic revival. Acquiring fresh scientific information and quickly integrating new knowledge and experience into plans and activities are critical in the renewable energy sector's dynamics. It is vital to equip experts, policymakers, and the general public with relevant and timely information so that they may make educated choices.
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His research interests stem from the PhD in the combustion of renewable fuels he completed at the University of New South Wales in 2019, where he focused on the reduction of emissions (particularly PM/soot) from diesel combustion. Dr IMR Fattah was named one of the Top 2% of Scientists in the World in 2021 by Elsevier BV and Stanford University. He was also featured as one of the 'Research Rising Stars' (Top 40 researchers who are less than 10 years into their careers) for his outstanding achievements in a September 2019 Special Report by 'The Australian'. He is actively engaged in the field by publishing over 80 articles and gaining over 5900 citations of his works. He is serving as an 'Associate Editor' at 'Frontiers in Energy Research' and as an editorial board member at ''Energies (MDPI)', ''Highlights of Sustainability (Highlights of Science)’ and ‘Advances in Environmental and Engineering Research (LIDSEN Publishing)’. He is managing/has managed ten topical/special issues as 'Guest Editor'. 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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"}},{type:"book",id:"117",title:"Artificial Neural Networks",subtitle:"Methodological Advances and Biomedical Applications",isOpenForSubmission:!1,hash:null,slug:"artificial-neural-networks-methodological-advances-and-biomedical-applications",bookSignature:"Kenji Suzuki",coverURL:"https://cdn.intechopen.com/books/images_new/117.jpg",editedByType:"Edited by",editors:[{id:"3095",title:"Prof.",name:"Kenji",surname:"Suzuki",slug:"kenji-suzuki",fullName:"Kenji Suzuki"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3828",title:"Application of Nanotechnology in Drug Delivery",subtitle:null,isOpenForSubmission:!1,hash:"51a27e7adbfafcfedb6e9683f209cba4",slug:"application-of-nanotechnology-in-drug-delivery",bookSignature:"Ali Demir Sezer",coverURL:"https://cdn.intechopen.com/books/images_new/3828.jpg",editedByType:"Edited by",editors:[{id:"62389",title:"PhD.",name:"Ali Demir",surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"872",title:"Organic Pollutants Ten Years After the Stockholm Convention",subtitle:"Environmental and Analytical Update",isOpenForSubmission:!1,hash:"f01dc7077e1d23f3d8f5454985cafa0a",slug:"organic-pollutants-ten-years-after-the-stockholm-convention-environmental-and-analytical-update",bookSignature:"Tomasz Puzyn and Aleksandra Mostrag-Szlichtyng",coverURL:"https://cdn.intechopen.com/books/images_new/872.jpg",editedByType:"Edited by",editors:[{id:"84887",title:"Dr.",name:"Tomasz",surname:"Puzyn",slug:"tomasz-puzyn",fullName:"Tomasz Puzyn"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"60312",title:"The Role of Extracellular Matrix in Tissue Regeneration",doi:"10.5772/intechopen.75728",slug:"the-role-of-extracellular-matrix-in-tissue-regeneration",body:'\nECM is a non-cellular structure that regulates almost all of the cellular functions. ECM is a highly dynamic structural network that continuously undergoes remodeling mediated by several matrix-degrading enzymes during normal and pathological conditions. Deregulation of ECM composition and structure has an association with the development and progression of several physiological and pathologic conditions. In this chapter, we describe the structure and function of ECM, also the role of ECM on the wound healing mechanism and neuronal regeneration in the central nervous system (CNS) and peripheral nervous system (PNS).
\nAn essential part of the holding capacity of tissues is the extracellular area. The extracellular region is primarily occupied by a complicated network of macromolecules constituent called as extracellular matrix (ECM). The composition of ECM is varied, depends on the species and also developing or ground molecules (Figures 1 and 2). Commonly, the ECM is composed of three major classes of biomolecules; there are glycosaminoglycans (GAGs), linked to a protein known as the proteoglycans, and also fibrous proteins, including collagen, elastin, fibronectin, vitronectin, and laminin.
\nThe structure of the extracellular matrix. The ECM mainly contained collagen fibers. There are also some glycoproteins as an adhesion molecule, such as integrin family fibronectin and laminin, which conduct cell attachments to the ECM by binding to collagen in the ECM and integrin. The intracellular part of integrin highly associated with the cytoskeleton thus may promote to anchoring the cell. In the end, there are various proteoglycans in the ECM that act as primary proteins and are profoundly modified by the addition of sugars.
The extracellular matrix of hyaline cartilage found in abundant collagen fibril and proteoglycan aggregates. The chemical analysis of the ground substance reveals that it contains a few glycoproteins and a high concentration of three types of glycosaminoglycans: hyaluronic acid, chondroitin sulfate, and keratan sulfate. Adapted from Crammer and Bakkum [
In addition, connective tissue (Figure 3) is also composed of the matrix of ECM. One of the essential components of connective tissue is fibroblasts and ground substance. Ground substance is a mixing complex between GAGs, proteoglycans, and glycoproteins (mainly laminin and fibronectin). In most connective tissues, the matrix constituents are secreted by fibroblasts, but in several certain specialized types of connective tissues, like cartilage and bone, these components are secreted by chondroblasts and osteoblasts (Table 1).
\nThe components of connective tissue. In addition to the extracellular matrix, connective tissues are characterized by a lot of cells, mainly the fibroblasts, all of which are surrounded by the ground substance. Modified from Mescher [
Cell Type | \nFunction | \nDistribution | \nCharacteristics | \n
---|---|---|---|
Fibroblast | \nSynthesize and secrete collagen, elastic fibers, reticular fiber, and proteoglycan (among other molecules) Support ligaments, tendons, bone, skin, blood vessels, and basement membranes | \nThroughout all loose and dense connective tissue | \nFlat, stellate cells with dark, ovoid, staining nuclei, and one or more nucleoli Microscopically may appear to be of different shapes because of the plane of sectioning | \n
Chondroblast | \nSynthesize and secrete extracellular matrix of cartilage (collagen, elastic fiber and glycosaminoglycans) Support articular cartilage | \nPresent in hyaline cartilage of articulations and fibrocartilage of intravertebral discs Found also in elastic cartilage | \nMetabolically active with large vesicular nuclei and prominent nucleoli Cytoplasma pale and vacuolated because of high content of lipid and glycogen | \n
Osteoblast | \nSynthesize and secrete extracellular matrix of bone | \nIn bone | \nBasophilic cytoplasma resulting from presence of a large amount of rough endoplasmic reticulum that produce glycosaminoglycan and glycoprotein | \n
Myofibroblast | \nSynthesize and secrete components of extracellular matrix Capable of contractility | \nIn blood vessels and skin throughout the body | \nResemble fibroblast under light microscopy but ultrastructurally contain actin filaments for contraction | \n
In general, all the cells need to attach to the extracellular matrix to grow and multiply. Extracellular matrix provides support and anchorage for the shape of the cells, regulates and determines cells dynamic and behavior including cell survival, cell proliferation, cell polarity, cell differentiation, cell adhesion, and cell migration. Moreover, the ECM, also gives the mechanical support for tissues and is involved in the growth mechanism, regenerative, and healing processes.
\nGAGs are unbranched chains of polysaccharides; GAGs are composed of repeating disaccharide units and are heterogeneous groups in negatively charged polysaccharide chains that are covalently linked to proteins to form proteoglycan molecules. The name GAGs is because in this polysaccharide, one of the two sugars in a repetitive disaccharide is always an amino sugar such as N-acetylglucosamine or N-acetylgalactosamine [3]. The second sugar of GAGs usually is the uronic acid like glucuronic or iduronate. GAG molecules are negatively charged, because there are sulfate or carboxyl groups in most of the sugar. The five main groups of GAGs are differentiated based on the sugar type including (1) hyaluronan or hyaluronic acid, (2) chondroitin sulfate, (3) dermatan sulfate, (4) heparan sulfate, and (5) keratin sulfate. Hyaluronan is the simplest GAGs. Hyaluronan does not contain sulfate sugars; all disaccharides units are the same, and the chain length is extensively big (thousands of sugar monomers). The hyaluronan is not connected covalently to some core proteins. Proteoglycans are composed of GAG chains that are covalently linked to the core protein and considered to have a significant role in chemical signaling among cells (Figure 4).
\nThe structure of glycosaminoglycan (A) structure of a proteoglycan monomer. Several glycosaminoglycan chains (chondroitin sulfphate and keratan sulfate) attached to a core protein. The protein molecule can connect to a long hyaluronic acid molecule to help form a proteoglycan aggregate. (B) An example of an individual glycosaminoglycan chain, in this case, chondroitin 6-sulphate, and its attachment to the core protein. (C) The morphological of a proteoglycan monomer. (A and B) Adapted from courtesy Dino Juarez, National University of health sciences; (C) Adapted from Crammer and Bakkum [
Collagen is a major abundant fibrous protein in the extracellular matrix. Collagens, which constitute the primary structural element of the ECM, provide tensile strength, regulate cell adhesion, support chemotaxis and migration, and direct tissue development [4]. Recently, there have been already described 28 types of collagen. The main types of collagen found in connective tissues are types I, II, III, V, and XI.
\nCollagen polypeptide chains are synthesized on membrane-bound ribosomes and fed into the lumen of the endoplasmic reticulum as large precursors, called the pro-α chains. Each pro-α chain then joins the other two to form a hydrogen-bond, triple-stranded hydrogen molecule known as a procollagen. After secretion, the fibrillar procollagen molecule divides to become collagen molecules, which converge into fibrils [5].
\nFibronectin is an extracellular protein that makes cells adhere to the matrix. Fibronectin is considered as a large glycoprotein found in all vertebrates. Fibronectin usually exists as a dimer composed of two nearly identical ~250 kDa subunits linked covalently near the C- terminal by a pair of disulfide bonds at one end side. Fibronectin is a ligand member of the integrin receptor family. Integrins are structurally and functionally related to the cell surface as heterodimeric receptors that link the ECM with the intracellular cytoskeleton.
\nThe primary type of fibronectin is known as type III fibronectin replica (cylinder), which binds to integrins. This model has a length of about 90 amino acids. Fibronectin appears in a soluble and fibrillar form. There are two others fibronectin isoforms, which are fibronectin type I (hexagon) and fibronectin type II (square) [6]. Fibronectin is not only crucial for attaching cells to matrices but also to guiding cell migration in vertebrate embryos. Fibronectin has many functions, which allow it to interact with many extracellular substances, such as collagen, fibrin and heparin, and with specific membrane receptors in responsive cells.
\nExtracellular matrix is the primary factor required in the process of forming a new network and tissue. Along with the development found, many different factors can trigger the growth of ECM or used to create a synthetic ECM. Currently, ECM is involved in various mechanisms such as wound healing with or without the involvement of mesenchymal conditioned medium and neuronal regeneration capability associated with pathologic and/or neurodegenerative disease.
\nThe process of wound healing is strongly influenced by the role of migration and proliferation of fibroblasts in the injury site. Indeed fibroblast is one part of ECM. The proliferation of fibroblasts determines the outcome of wound healing. Fibroblasts will produce collagen that will link to the wound, and fibroblasts will also affect the process of reepithelialization that will close the wound. Fibroblasts will produce type III collagen during proliferation and facilitate wound closure. During proliferation stage, fibroblasts proliferation activity is higher due to the presence of TGF-stimulated fibroblasts to secrete bFGF. The higher number of fibroblasts also induces increasing of collagen synthesis. Collagen fiber is the major protein secreted by fibroblast, composed of extracellular matrix to replace wound tissue strength and function. Collagen fibers deposition was significant on 8–10 days after injury. The number of fibroblasts increases significantly, in correlation with the presence of an abundance of bFGF on 8–10 days after wounding.
\nMesenchymal stem cell conditioned medium (MSCM) can be defined as secreted factor that referred to as secretome, microvesicle, or exosome without the stem cells which may found in the medium where the stem cells are growing. The use of MSCM as cell-free therapy has more significant advantages in comparison to the use of stem cells, mainly to avoid the need of HLA matching between donor and recipient as a consequence to decrease the chance of transplant rejection. Additionally, MSCM is more easy to produce and save in large quantity. The presence of human umbilical mesenchymal conditioned medium (HU-MSCM), will accelerate curing of the acute and chronic incision and/or burn wound by increasing the number of myofibroblasts and encouraging the expression of VEGF, TGF, bFGF, and also PDGF to promote wound closure.
\nRecently, it has been mentioned that widespread neuronal cell death in the neocortex and hippocampus is an ineluctable concomitant of brain aging caused by diseases and injuries. However, recent studies suggest that neuron death also occurs in functional aging and it seems in related to an impairment of neocortical and hippocampal functions during aging processes. Data from WHO and Alzheimer report show increasing number of people suffering from dementia along with aging. Profoundly understanding the role of extracellular matrix (ECM) in influencing neurogenesis has presented novel strategies for tissue regeneration (Figure 5).
\nMicroscopic anatomy of the extracellular matrix within the central nervous system (CNS). The three major compartments of the extracellular matrix in the CNS are the basement membrane, perineuronal net, and neuronal interstitial matrix. The basement membrane is found surrounding cerebral blood vessels, the perineuronal net is a dense matrix immediately surrounding neuronal cell bodies and dendrites, and the neuronal interstitial matrix occupies the space between neurons and glial cells. Adapted from Lau et al. [
Central nervous system injury because of stroke vascular and amyloid plaque accumulation as the effect of Alzheimer’s diseases may cause the disturbance astrocytes, fibroblasts, and oligodendrocyte precursors cell proliferation which may form a glial scar [8, 9]. Within this glial scar, upregulated proteoglycans like CSPGs and changes in sulfation patterns within the ECM result in the building of regeneration inhibition [10].
\nTo solve the problem, some manipulation on the intrinsic extracellular matrix by using traditional herb such as
In addition, on the peripheral nerve injury, there is a chance to use scaffold by a chemical decellularization process, acellular nerve allografting that eliminates the antigens responsible for allograft rejection and maintains most of the ECM components, which can effectively guide and enhance nerve regeneration. In the field of tissue engineering by an in vivo model, a lot of successful carriers and matrices have been employed as a scaffold to promote direct axonal growth on peripheral nerve injury [16].
\nIn conclusion, the extracellular matrix is the primary factor required in the process of forming a new network and tissue. Along with the development found, many different factors that can trigger the growth of ECM are used to create a synthetic ECM. Recently, ECM is involved in various mechanisms such as wound healing with or without the involvement of mesenchymal conditioned medium and neuronal regeneration capability associated with pathologic and or neurodegenerative disease. In addition, on the peripheral nerve injury, there is a chance to use scaffold by a chemical decellularization process, acellular nerve allografting to eliminate the antigens responsible for allograft rejection and maintain most of the ECM components, which can effectively guide and enhance nerve regeneration. In the field of tissue engineering by an in vivo model, significant progress on matrices development have been utilized as a scaffold to promote direct axonal growth on peripheral nerve injury.
\nThe authors declare there is no conflict of interest.
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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Examples of such applications are object detection, environment representation, scene understanding, human/pedestrian detection, activity recognition, semantic place classification, object modeling, among others. Robotic perception, in the scope of this chapter, encompasses the ML algorithms and techniques that empower robots to learn from sensory data and, based on learned models, to react and take decisions accordingly. The recent developments in machine learning, namely deep-learning approaches, are evident and, consequently, robotic perception systems are evolving in a way that new applications and tasks are becoming a reality. Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. 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In addition, arms, grippers, or tethers could be installed to UAVs so that they can assist in constructing, transporting, and carrying payloads. In this book chapter, the control laws of the attitude and position of a quadcopter UAV have been derived basically utilizing three methods including backstepping, sliding mode control, and feedback linearization incorporated with LQI optimal controller. The main contribution of this book chapter would be concluded in the strategy of deriving the control laws of the translational positions of a quadcopter UAV. The control laws for trajectory tracking using the proposed strategies have been validated by simulation using MATLAB®/Simulink and experimental results obtained from a quadcopter test bench. Simulation results show a comparison between the performances of each of the proposed techniques depending on the nonlinear model of the quadcopter system under investigation; the trajectory tracking has been achieved properly for different types of trajectories, i.e., spiral trajectory, in the presence of unknown disturbances. 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Current electromechanical artificial hands are either in the research stage, expensive, have patents, lack severely in function, and/or are driven by robotic/mechanical principles, which tend to ignore the biological requirements of such designs. In response to the challenges addressed above this chapter discusses the potential of current technology and methods used in design to bridge the chasm that exists between robot manipulators and the human hand. This chapter elucidates artificial anthropomorphic manipulator design by outlining biomechanical concepts that contribute to the function, esthetics and performance of artificial manipulators. This chapter addresses joint stabilization, tendon structures and tendon excursion in artificial anthropomorphic manipulators.",book:{id:"11455",title:"Recent Advances in Robot Manipulators",coverURL:"https://cdn.intechopen.com/books/images_new/11455.jpg"},signatures:"Mahonri William Owen and Chikit Au"},{id:"82056",title:"Learning Robotic Ultrasound Skills from Human Demonstrations",slug:"learning-robotic-ultrasound-skills-from-human-demonstrations",totalDownloads:14,totalDimensionsCites:0,doi:"10.5772/intechopen.105069",abstract:"Robotic ultrasound system plays a vital role in assisting or even replacing sonographers in some cases. However, modeling and learning ultrasound skills from professional sonographers are still challenging tasks that hinder the development of ultrasound systems’ autonomy. To solve these problems, we propose a learning-based framework to acquire ultrasound scanning skills from human demonstrations1. First, ultrasound scanning skills are encapsulated into a high-dimensional multi-modal model, which takes ultrasound images, probe pose, and contact force into account. The model’s parameters can be learned from clinical ultrasound data demonstrated by professional sonographers. Second, the target function of autonomous ultrasound examinations is proposed, which can be solved roughly by the sampling-based strategy. The sonographers’ ultrasound skills can be represented by approximating the limit of the target function. Finally, the robustness of the proposed framework is validated with the experiments on ground-true data from sonographers.",book:{id:"10823",title:"Cognitive Robotics",coverURL:"https://cdn.intechopen.com/books/images_new/10823.jpg"},signatures:"Miao Li and Xutian Deng"},{id:"82057",title:"An Episodic-Procedural Semantic Memory Model for Continuous Topological Sensorimotor Map Building",slug:"an-episodic-procedural-semantic-memory-model-for-continuous-topological-sensorimotor-map-building",totalDownloads:8,totalDimensionsCites:0,doi:"10.5772/intechopen.104818",abstract:"For humans to understand the world around them, learning and memory are two cognitive processes of the human brain that are deeply connected. Memory allows information to retain and forms an experiences reservoir. Computational models replicating those memory attributes can lead to the practical use of robots in everyday human living environments. However, constantly acquiring environmental information in real-world, dynamic environments has remained a challenge for many years. This article proposes an episodic-procedure semantic memory model to continuously generate topological sensorimotor maps for robot navigation. The proposed model consists of two memory networks: i) episodic-procedural memory network (EPMN) and ii) semantic memory network (SMN). The EPMN comprises an Incremental Recurrent Kernel Machines (I-RKM) that clusters incoming input vectors as nodes and learns the activation patterns of the nodes for spatiotemporal encoding. The SMN then takes neuronal activity trajectories from the EPMN and task-relevant signals to update the SMN and produce more compact representations of episodic experience. Thus, both memory networks prevent catastrophic forgetting by constantly generating nodes when the network meets new inputs or updating node weights when the incoming input is similar to previously learned knowledge. In addition, idle or outlier nodes will be removed to preserve memory space.",book:{id:"10823",title:"Cognitive Robotics",coverURL:"https://cdn.intechopen.com/books/images_new/10823.jpg"},signatures:"Wei Hong Chin, Naoyuki Kubota and Chu Kiong Loo"},{id:"81922",title:"Skill Acquisition for Resource-Constrained Mobile Robots through Continuous Exploration",slug:"skill-acquisition-for-resource-constrained-mobile-robots-through-continuous-exploration",totalDownloads:18,totalDimensionsCites:0,doi:"10.5772/intechopen.104996",abstract:"We present a cognitive mobile robot that acquires knowledge, and autonomously learns higher-level abstract capabilities based on play instincts, inspired by human behavior. To this end, we (i) model skills, (ii) model the robot’s sensor and actuator space based on elementary physical properties, and (iii) propose algorithms inspired by humans’ play instincts that allow the robot to autonomously learn the skills based on its sensor and actuator capabilities. We model general knowledge in the form of competencies (skills) of the mobile robot based on kinematic properties using physical quantities. Thus, by design, our approach has the potential to cover very generic application domains. To connect desired skills to the primitive capabilities of the robot’s sensors and actuators, it playfully explores the effects of its actions on its sensory input, thus autonomously learning relations and dependencies and eventually the desired skill. KnowRob is used for knowledge representation and reasoning, and the robot’s operation is based on ROS. In the experiments, we use a millirobot, sized 2 cm2, equipped with two wheels, motion, and distance sensors. We show that our cognitive mobile robot can successfully and autonomously learn elementary motion skills based on a playful exploration of its wheels and sensors.",book:{id:"10823",title:"Cognitive Robotics",coverURL:"https://cdn.intechopen.com/books/images_new/10823.jpg"},signatures:"Markus D. Kobelrausch and Axel Jantsch"},{id:"81693",title:"The Neo-Mechanistic Model of Human Cognitive Computation and Its Major Challenges",slug:"the-neo-mechanistic-model-of-human-cognitive-computation-and-its-major-challenges",totalDownloads:13,totalDimensionsCites:0,doi:"10.5772/intechopen.104995",abstract:"The neo-mechanistic theory of human cognition is currently one of the most accepted major theories in fields, such as cognitive science and cognitive neuroscience. This proposal offers an account of human cognitive computation, and it has been considered by its proponents as revolutionary and capable of integrating research concerning human cognition with new evidence provided by fields of biology and neuroscience. However, some complex cognitive capacities still present a challenge for explanations constructed by using this theoretical structure. In this chapter, I make a presentation of some of the central tenets of this framework and show in what dimensions it helps our understanding of human cognition concerning aspects of capacities, such as visual perception and memory consolidation. My central goal, however, is to show that to understand and explain some particular human cognitive capacities, such as self-consciousness and some conscious informal reasoning and decision making, the framework shows substantial limitations. I conclude the chapter by suggesting that to fully understand human cognition we will need much more than what the neo-mechanistic framework is actually able to provide.",book:{id:"10823",title:"Cognitive Robotics",coverURL:"https://cdn.intechopen.com/books/images_new/10823.jpg"},signatures:"Diego Azevedo Leite"},{id:"81719",title:"Service Robots in Healthcare Settings",slug:"service-robots-in-healthcare-settings",totalDownloads:21,totalDimensionsCites:0,doi:"10.5772/intechopen.104640",abstract:"Robots will play a part in all aspects of healthcare. The presence of service robots in healthcare demands special attention, whether it is in the automation of menial labour, prescription distribution, or offering comfort. In this chapter, we examine the several applications of healthcare-oriented robots in the acute, ambulatory and at-home settings. We discuss the role of robotics in reducing environmental dangers, as well as at the patient’s bedside and in the operating room, in the acute setting. We examine how robotics can protect and scale up healthcare services in the ambulatory setting. Finally, in the at-home scenario, we look at how robots can be employed for both rural/remote healthcare delivery and home-based care. In addition to assessing the current state of robotics at the interface of healthcare delivery, we describe critical problems for the future where such technology will be ubiquitous. Patients, health care workers, institutions, insurance companies, and governments will realize that service robots will deliver significant benefits in the future in terms of leverage and cost savings, while maintaining or improving access, equity, and high-quality health care.",book:{id:"10657",title:"Advances in Service Robots",coverURL:"https://cdn.intechopen.com/books/images_new/10657.jpg"},signatures:"Rohit Singla and Christopher Nguan"}],onlineFirstChaptersTotal:7},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:141,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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